{
  "nbformat": 4,
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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
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            "_view_name": "OutputView",
            "layout": "IPY_MODEL_50eb14f7560b48078070277e1aa2cc1d",
            "msg_id": "",
            "outputs": [
              {
                "output_type": "stream",
                "name": "stdout",
                "text": [
                  "Coeficiente de correlación simulado: 0.90\n"
                ]
              },
              {
                "output_type": "display_data",
                "data": {
                  "text/plain": "<Figure size 1000x600 with 1 Axes>",
                  "image/png": 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w559/8s477wBmR8zk5ORKT708noMHDwIct3D+97//TYMGDTjzzDNp3749o0ePPuZau/bt25f5+pRTTsFqtbJ9+3bA/Nk0DIP27duX+56mpqaW+362aNGi3NTKXr16MXjwYCZNmkR4eDgDBw5k/vz5x1wrCOb3OT8/v8Lvl9PpxO12l1ujdfS/08aNGwNU6+8lEalZWiMlIl4pMDCQM844gzPOOIMOHTpw88038+abb/7jmo1jbVBbUdz4W6OGY40M/NNifTALpN69e9OxY0dmzJhBy5YtCQwM5KOPPuKJJ56o9b10jvUaGEc1pjhRJc/n8ccf51//+leF55SMRFSkY8eOpKSk8Pvvv5crcE9WUFDQMdvi/30ECY48jxtuuIHExMQKH9O1a9dj3mvIkCF8++233H333fzrX/+iQYMGuN1uLrnkEq/5nldGbGwsp59+Oi+//DIJCQm8/PLLBAYGMmTIkJPOa+PGjQDExMQc8xyn08mWLVv44IMP+Pjjj3n77beZM2cOSUlJTJo06bjXP/rfrdvtxmKxsGzZsgpfk6N/Lo/+mSi55ltvvcWaNWt4//33Wb58OcOGDWP69OmsWbPmuD/bVVHT/05FpOapkBIRr1cyJWrXrl01do+ST4NzcnLKxCszzeb999+nsLCQpUuXlvmU+ehpRCXT7TZu3HjcN5ZHa926NZ9++ikHDhwo88n+zz//XHq8OrRu3ZqNGzdiGEaZN6hbtmwpc17J87Db7SfUjn3AgAG8+uqrvPzyy9x3333/mFNJDhdeeGGZY1u2bDmp5x4REUHDhg05fPhwlZ/H/v37+eyzz5g0aVKZ1uQlo3V/v4fdbi8tKCqrJp93RRISEhg/fjy7du3ilVde4bLLLiv9N3EyXnrpJSwWCxdddNFxzwsNDeWaa67hmmuu4dChQ1x55ZU8/PDD3HfffWWmNm7durXMyGJaWhput7u0y+Upp5yCYRi0bdv2mM0tKuvss8/m7LPP5uGHH+aVV17h+uuv57XXXuOWW24pd25ERAQhISHl/q2A+e/UarVW+4cGIuJ5mtonIl7j888/r/DT2JL1DyfTivmftG7dGj8/P7788ssy8Tlz5vzjY0s+Wf577i6Xi/nz55c57+KLL6Zhw4Y8+uij5Vo2H+9T6H79+nH48GGeeuqpMvEnnngCi8XCpZde+o85Vka/fv3IzMzkrbfeKo3l5+czd+7cMuedfvrpnHLKKUybNq106tbf/VML56uuuoouXbrw8MMPs3r16nLHDxw4wH/+8x/ALKKbNm3Ks88+W2Zq1bJly0hNTeWyyy6r0nP8Oz8/PwYPHszbb79dYaFzvOdR0fccYObMmWW+tlqtDBo0iPfff59169aVu86xvu81+bwrcu2112KxWLjzzjv57bffyu0LdSL+97//8cknn3DNNdeUm5L3d0e3iw8MDCQ2NhbDMMqtUXr66afLfD179myA0n8DV155JX5+fkyaNKnca2sYRoWt6Y+2f//+co8tGXk91vQ+Pz8/Lr74Yt57773SaYYAe/bs4ZVXXqFHjx5lNiUWkbpBI1Ii4jXGjBlDfn4+V1xxBR07duTQoUN8++23vP7667Rp06bcovDq5HA4uPrqq5k9ezYWi4VTTjmFDz744LhrZEpcfPHFBAYGMmDAAG699VYOHjzIvHnzaNq0aZlRNLvdzhNPPMEtt9zCGWecwXXXXUfjxo1Zv349+fn5LFy4sMLrDxgwgAsuuID//Oc/bN++nbi4OD755BPee+89xo0bV6axxMkYMWIETz31FAkJCSQnJxMVFcVLL71UboNSq9XK888/z6WXXkqnTp24+eabadGiBTt37uTzzz/Hbrfz/vvvH/M+AQEBLFmyhD59+tCzZ0+GDBnCueeeS0BAAJs2beKVV16hcePGPPzwwwQEBPDYY49x880306tXL6699trSNuBt2rThrrvuOqnn/L///Y/PP/+cs846ixEjRhAbG0t2djY//PADn376KdnZ2RU+zm6307NnT6ZOnUpRUREtWrTgk08+Ydu2beXOfeSRR/jkk0/o1asXI0eOxOl0smvXLt58802+/vrrCvdNq+nnfbSIiAguueQS3nzzTRo1alSlQq24uJiXX34ZMJtupKens3TpUjZs2MAFF1xQrhA/2sUXX0xkZCTnnnsuzZo1IzU1laeeeorLLrus3Nqqbdu2cfnll3PJJZewevVqXn75Za677jri4uIAc0RqypQp3HfffWzfvp1BgwbRsGFDtm3bxjvvvMPIkSOZOHHicfNZuHAhc+bM4YorruCUU07hwIEDzJs3D7vdTr9+/Y75uClTprBixQp69OjBqFGj8Pf357nnnqOwsJCpU6dW5qUUEV9Ty10CRUSOadmyZcawYcOMjh07Gg0aNDACAwONmJgYY8yYMcaePXvKnHus9udHtywuaZu8b9++MvGK2jbv27fPGDx4sBESEmI0btzYuPXWW42NGzdWqv350qVLja5duxo2m81o06aN8dhjjxkvvviiARjbtm0rd+4555xjBAcHG3a73TjzzDONV199tUxuf29/bhiGceDAAeOuu+4ymjdvbgQEBBjt27c3Hn/88TLtkw3DbMk8evTocq/t0a/XsaSnpxuXX365ERISYoSHhxt33nmn8fHHH1fYGj4lJcW48sorjSZNmhhBQUFG69atjSFDhhifffbZP97HMAxj//79RlJSktGlSxcjJCTEsNlsRufOnY377rvP2LVrV5lzX3/9dSM+Pt4ICgoywsLCjOuvv97IyMgoc87xWnEf63UxDMPYs2ePMXr0aKNly5ZGQECAERkZafTu3duYO3du6TkVtT/PyMgwrrjiCqNRo0aGw+Ewrr76aiMzM7NcW2zDMF/XhIQEIyIiwggKCjLatWtnjB492igsLDQM49jt90/meVf0c3o8b7zxhgEYI0eOrPRjSlqvl/wJCQkx2rRpYwwePNh46623jMOHD5d7zNHtz5977jmjZ8+epT9Hp5xyinH33XcbLper3HPZvHmzcdVVVxkNGzY0GjdubNxxxx3lthIwDMN4++23jR49ehihoaFGaGio0bFjR2P06NHGli1byuRRUdv1H374wbj22muNVq1aGUFBQUbTpk2N/v37G+vWrStzXkXf5x9++MHo27ev0aBBAyMkJMS44IILjG+//bbMOcf6XXWsnwER8V4Ww9CqRhERkfruvffeY9CgQXz55Zecd955nk6njIceeohJkyaxb98+wsPDPZ2OiAigNVIiIiICzJs3j3bt2tGjRw9PpyIi4hO0RkpERKQee+2119iwYQMffvghTz755HE3hxYRkSNUSImIiNRj1157LQ0aNGD48OGMGjXK0+mIiPgMrZESERERERGpIq2REhERERERqSIVUiIiIiIiIlWkQkpERERERKSK1GwCcLvdZGZm0rBhQ3UrEhERERGpxwzD4MCBAzRv3hyr9djjTiqkgMzMTFq2bOnpNERERERExEv8/vvvREdHH/O4CimgYcOGgPli2e12D2cjIiIiIiKekpubS8uWLUtrhGNRIQWl0/nsdrsKKRERERER+cclP2o2ISIiIiIiUkUeLaS+/PJLBgwYQPPmzbFYLLz77rtljhuGQVJSElFRUQQHB9OnTx+2bt1a5pzs7Gyuv/567HY7jRo1Yvjw4Rw8eLAWn4WIiIiIiNQ3Hi2k8vLyiIuL4+mnn67w+NSpU5k1axbPPvssa9euJTQ0lL59+1JQUFB6zvXXX8+mTZtYsWIFH3zwAV9++SUjR46sracgIiIiIiL1kMUwDMPTSYA5B/Gdd95h0KBBgDka1bx5cyZMmMDEiRMBcLlcNGvWjAULFjB06FBSU1OJjY3l+++/p1u3bgB8/PHH9OvXj4yMDJo3b16pe+fm5uJwOHC5XMdcI3X48GGKiopO/omKeJifnx/+/v5q9S8iIiJSgcrUBuDFzSa2bdvG7t276dOnT2nM4XBw1llnsXr1aoYOHcrq1atp1KhRaREF0KdPH6xWK2vXruWKK66o8NqFhYUUFhaWfp2bm3vcXA4ePEhGRgZeUnOKnLSQkBCioqIIDAz0dCoiIiIiPslrC6ndu3cD0KxZszLxZs2alR7bvXs3TZs2LXPc39+fsLCw0nMq8uijjzJp0qRK5XH48GEyMjIICQkhIiJCn+KLTzMMg0OHDrFv3z62bdtG+/btj7vRnIiIiIhUzGsLqZp03333MX78+NKvS3rFV6SoqAjDMIiIiCA4OLi2UhSpMcHBwQQEBJCens6hQ4ew2WyeTklERETE53jtR9GRkZEA7Nmzp0x8z549pcciIyPZu3dvmePFxcVkZ2eXnlORoKCg0j2jKrt3lEaipC7RKJSIiIjIyfHad1Nt27YlMjKSzz77rDSWm5vL2rVr6d69OwDdu3cnJyeH5OTk0nNWrlyJ2+3mrLPOqvWcRURERESkfvBoIXXw4EF+/PFHfvzxR8BsMPHjjz+yY8cOLBYL48aNY8qUKSxdupSffvqJhIQEmjdvXtrZz+l0cskllzBixAi+++47vvnmG+644w6GDh1a6Y594hvOP/98xo0bV+P3uemmm0p/vk7U9u3bsVgspT/XIiIiIlL3eLSQWrduHfHx8cTHxwMwfvx44uPjSUpKAuCee+5hzJgxjBw5kjPOOIODBw/y8ccfl1nTsXjxYjp27Ejv3r3p168fPXr0YO7cuR55Pt7kpptuwmKxYLFYCAgIoFmzZlx00UW8+OKLuN3uKl1rwYIFNGrUqGYSraQlS5bw3//+16M5iIiIiIiU8GizifPPP/+4LcUtFguTJ09m8uTJxzwnLCyMV155pSbS83mXXHIJ8+fP5/Dhw+zZs4ePP/6YO++8k7feeoulS5fi7+87vUbCwsI8nYKIiIiISCmvXSNV17gNN7/88Qvf7/yeX/74BbdRtVGhExEUFERkZCQtWrTgtNNO4//+7/947733WLZsGQsWLCg9b8aMGXTp0oXQ0FBatmzJqFGjOHjwIABffPEFN998My6Xq3SE66GHHgJg//79JCQk0LhxY0JCQrj00kvZunVr6XXT09MZMGAAjRs3JjQ0lE6dOvHRRx8dM985c+bQvn17bDYbzZo146qrrio9dvTUvjZt2jBlyhQSEhJo0KABrVu3ZunSpezbt4+BAwfSoEEDunbtyrp160of89BDD/Gvf/2rzD1nzpxJmzZtjpnTxx9/TI8ePWjUqBFNmjShf//+/Prrr2XO+e6774iPj8dms9GtWzdSUlLKXWfVqlWceeaZBAUFERUVxb333ktxcXHp8bfeeosuXboQHBxMkyZN6NOnD3l5ecfMS0REREQ8S4VULUjZlcL45eMZ89EYJn4ykTEfjWH88vGk7Cr/hrumXXjhhcTFxbFkyZLSmNVqZdasWWzatImFCxeycuVK7rnnHgDOOeccZs6cid1uZ9euXezatYuJEycC5vTBdevWsXTpUlavXo1hGPTr14+ioiIARo8eTWFhIV9++SU//fQTjz32GA0aNKgwr3Xr1jF27FgmT57Mli1b+Pjjj+nZs+dxn8sTTzzBueeeS0pKCpdddhk33ngjCQkJ3HDDDfzwww+ccsopJCQknNRGynl5eYwfP55169bx2WefYbVaueKKK0qnRx48eJD+/fsTGxtLcnIyDz30UOnrU2Lnzp3069ePM844g/Xr1/PMM8/wwgsvMGXKFAB27drFtddey7Bhw0hNTeWLL77gyiuv1AbQIiIi4jM8MWjgab4zt8tHpexKYfKqyWTlZxFtjyY0IJS8ojySM5NJz0knqVcS8VHxtZpTx44d2bBhQ+nXFY303HbbbcyZM4fAwEAcDgcWi6VMS/mtW7eydOlSvvnmG8455xzAXK/WsmVL3n33Xa6++mp27NjB4MGD6dKlCwDt2rU7Zk47duwgNDSU/v3707BhQ1q3bl26du5Y+vXrx6233gpAUlISzzzzDGeccQZXX301AP/+97/p3r17mZb5VTV48OAyX7/44otERESwefNmOnfuzCuvvILb7eaFF17AZrPRqVMnMjIyuP3220sfM2fOHFq2bMlTTz2FxWKhY8eOZGZm8u9//5ukpCR27dpFcXExV155Ja1btwYofc1EREREvF3KrhQWrl9I6r5UCooLsPnbcEY4SYxLrPX3ubVJI1I1yG24Wbh+IVn5WTjDndiD7PhZ/bAH2XGGO8nKz2LRhkW1XrEbhlFmX6xPP/2U3r1706JFCxo2bMiNN97IH3/8QX5+/jGvkZqair+/f5k2802aNOHUU08lNTUVgLFjxzJlyhTOPfdcHnzwwTLF29EuuugiWrduTbt27bjxxhtZvHjxce8P0LVr19K/N2vWDChbgJTEjt5rrCq2bt3KtddeS7t27bDb7aXTAHfs2AGYr0PXrl3LNEApac9fIjU1le7du5d5zc8991wOHjxIRkYGcXFx9O7dmy5dunD11Vczb9489u/ff8I5i4iIiNSWkkGD5MxkwoLDaB/WnrDgMJIzk5m8arJHZmDVFhVSNSgtO43UfalE26PLbehrsViItkezee9m0rLTajWv1NRU2rZtC5ituvv370/Xrl15++23SU5O5umnnwbg0KFDJ3WfW265hd9++40bb7yRn376iW7dujF79uwKz23YsCE//PADr776KlFRUSQlJREXF0dOTs4xrx8QEFD695LXt6JYyTQ8q9VabrpcyTTEYxkwYADZ2dnMmzePtWvXsnbtWuDkX5u/8/PzY8WKFSxbtozY2Fhmz57NqaeeyrZt26rtHiIiIiLVzVsHDWqLCqka5CpwUVBcQGhAaIXHQwJCKCguwFXgqrWcVq5cyU8//VQ6ZS05ORm328306dM5++yz6dChA5mZmWUeExgYyOHDh8vEnE4nxcXFpYUFwB9//MGWLVuIjY0tjbVs2ZLbbruNJUuWMGHCBObNm3fM3Pz9/enTpw9Tp05lw4YNbN++nZUrV1bH0wYgIiKC3bt3lymmjrfXU8nzuf/+++nduzdOp7PcSJHT6WTDhg0UFBSUxtasWVPunJI1ZCW++eYbGjZsSHR0NGAWfeeeey6TJk0iJSWFwMBA3nnnnZN5uiIiIiI1ylsHDWqLCqka5LA5sPnbyCuquPtaflE+Nn8bDpujRu5fWFjI7t272blzJz/88AOPPPIIAwcOpH///iQkJAAQExNDUVERs2fP5rfffuOll17i2WefLXOdNm3acPDgQT777DOysrLIz8+nffv2DBw4kBEjRvD111+zfv16brjhBlq0aMHAgQMBc+3V8uXL2bZtGz/88AOff/45Tqezwlw/+OADZs2axY8//kh6ejqLFi3C7XZz6qmnVtvrcf7557Nv3z6mTp3Kr7/+ytNPP82yZcuOeX7jxo1p0qQJc+fOJS0tjZUrVzJ+/Pgy51x33XVYLBZGjBjB5s2b+eijj5g2bVqZc0aNGsXvv//OmDFj+Pnnn3nvvfd48MEHGT9+PFarlbVr1/LII4+wbt06duzYwZIlS9i3b98xXysRERERb+CNgwa1SYVUDYoJi8EZ4SQjN6PclDLDMMjIzSC2aSwxYTE1cv+PP/6YqKgo2rRpwyWXXMLnn3/OrFmzeO+99/Dz8wMgLi6OGTNm8Nhjj9G5c2cWL17Mo48+WuY655xzDrfddhvXXHMNERERTJ06FYD58+dz+umn079/f7p3745hGHz00Uel0+sOHz7M6NGjcTqdXHLJJXTo0IE5c+ZUmGujRo1YsmQJF154IU6nk2effZZXX32VTp06Vdvr4XQ6mTNnDk8//TRxcXF899135Trs/Z3VauW1114jOTmZzp07c9ddd/H444+XOadBgwa8//77/PTTT8THx/Of//yHxx57rMw5LVq04KOPPuK7774jLi6O2267jeHDh3P//fcDYLfb+fLLL+nXrx8dOnTg/vvvZ/r06Vx66aXV9txFREREqpunBw08zWKoxzK5ubk4HA5cLhd2u73MsYKCArZt20bbtm3LNBSorKO79oUEhJBflE9GbgbhoeEk9az9rn0iJ/tzLSIiIuI23IxfPp7kzGSc4c4y0/sMwyA1K5VuLbox/eLpWC2+M35zvNrg73znGfmo+Kh4knolcXrz08n+M5u07DSy/8ymW4tuKqJERERExGdZLVYS4xIJDwknNSuV3MJcit3F5BbmkpqVSnhoOAldE3yqiKoK7SNVC+Kj4omLjCMtOw1XgQuHzUFMWEyd/aESERERkfqhZNCgZB+pzAOZ2PxtdGvRjYSuCXV60ECFVC2xWqx0aNLB02mIiIiIiFSr+jpooEJKREREREROSn0cNKjbZaKIiIiIiEgNUCElIiIiIiJSRSqkREREREREqkiFlIiIiIiISBWpkKrHli9fzvz58z2dhoiIiIiIz1EhVU+tX7+eW265hbPPPtvTqZTTpk0bZs6c6ek0Km3BggU0atSoVu950003MWjQoFq9p4iIiIgcoUKqjjreG+39+/dz/fXX89prr+F0Oms3sVqwfft2LBYLP/74o6dTqTFPPvkkCxYs8HQaIiIiIvWW9pGqhxo3bszGjRs9nYacBIfD4ekUREREROo1jUjVUxaLhXfffRc4MoKzZMkSLrjgAkJCQoiLi2P16tVlHvP1119z3nnnERwcTMuWLRk7dix5eXmlx1966SW6detGw4YNiYyM5LrrrmPv3r3HzWPv3r0MGDCA4OBg2rZty+LFi8udk5OTwy233EJERAR2u50LL7yQ9evXH/Oabdu2BSA+Ph6LxcL5559feuz555/H6XRis9no2LEjc+bMKT1W2ddhwYIFtGrVipCQEK644gr++OOPcjm89957nHbaadhsNtq1a8ekSZMoLi4uPW6xWHj++ee54oorCAkJoX379ixdurTMNTZt2kT//v2x2+00bNiQ8847j19//RUoP+L48ccf06NHDxo1akSTJk3o379/6bkiIiIiUv1USFWVYUBenmf+GEaNPrX//Oc/TJw4kR9//JEOHTpw7bXXlr75//XXX7nkkksYPHgwGzZs4PXXX+frr7/mjjvuKH18UVER//3vf1m/fj3vvvsu27dv56abbjruPW+66SZ+//13Pv/8c9566y3mzJlTrvi6+uqr2bt3L8uWLSM5OZnTTjuN3r17k52dXeE1v/vuOwA+/fRTdu3axZIlSwBYvHgxSUlJPPzww6SmpvLII4/wwAMPsHDhwkq/DmvXrmX48OHccccd/Pjjj1xwwQVMmTKlzOO/+uorEhISuPPOO9m8eTPPPfccCxYs4OGHHy5z3qRJkxgyZAgbNmygX79+XH/99aXPaefOnfTs2ZOgoCBWrlxJcnIyw4YNK1OM/V1eXh7jx49n3bp1fPbZZ1itVq644grcbvdxX38REREROUGGGC6XywAMl8tV7tiff/5pbN682fjzzz/NwMGDhmGWNLX/5+DBSj+nxMREY+DAgcc8DhjvvPOOYRiGsW3bNgMwnn/++dLjmzZtMgAjNTXVMAzDGD58uDFy5Mgy1/jqq68Mq9V65LU5yvfff28AxoEDByo8vmXLFgMwvvvuu9JYamqqARhPPPFE6T3sdrtRUFBQ5rGnnHKK8dxzz1V43ZLnk5KSUu4xr7zySpnYf//7X6N79+6Vfh2uvfZao1+/fmWucc011xgOh6P06969exuPPPJImXNeeuklIyoqqvRrwLj//vtLvz548KABGMuWLTMMwzDuu+8+o23btsahQ4cqfI7/9P3dt2+fARg//fRThcfL/VyLiIiIiGEYx68N/k4jUlKqa9eupX+PiooCKB0dWr9+PQsWLKBBgwalf/r27Yvb7Wbbtm0AJCcnM2DAAFq1akXDhg3p1asXADt27Kjwfqmpqfj7+3P66aeXxjp27FimA9769es5ePAgTZo0KXPvbdu2VWnqWl5eHr/++ivDhw8vc50pU6aUu87xXofU1FTOOuusMud37969zNfr169n8uTJZe4zYsQIdu3aRX5+foX3CQ0NxW63l97nxx9/5LzzziMgIKBSz2/r1q1ce+21tGvXDrvdTps2bYBjv/YiIiIicnLUbKKqQkLg4EHP3bsG/f1Nu8ViASidGnbw4EFuvfVWxo4dW+5xrVq1Ii8vj759+9K3b18WL15MREQEO3bsoG/fvhw6dOiEczp48CBRUVF88cUX5Y5VpeX4wb++Z/PmzStXCPn5+ZX5+nivQ2XvNWnSJK688spyx2w2W4X3KblXyX2Cg4MrfT+AAQMG0Lp1a+bNm0fz5s1xu9107tz5pF57ERERETk2FVJVZbFAaKins6h1p512Gps3byYmJqbC4z/99BN//PEH//vf/2jZsiUA69atO+41O3bsSHFxMcnJyZxxxhkAbNmyhZycnDL33b17N/7+/qWjLP8kMDAQgMOHD5fGmjVrRvPmzfntt9+4/vrrK3WdijidTtauXVsmtmbNmjJfn3baaWzZsuWYr1VldO3alYULF1JUVPSPo1J//PEHW7ZsYd68eZx33nmA2RhERERERGqOCqk6zOVyldtLqUmTJqWFTlX8+9//5uyzz+aOO+7glltuITQ0lM2bN7NixQqeeuopWrVqRWBgILNnz+a2225j48aN/Pe//z3uNU899VQuueQSbr31Vp555hn8/f0ZN25cmdGYPn360L17dwYNGsTUqVPp0KEDmZmZfPjhh1xxxRV069at3HWbNm1KcHAwH3/8MdHR0dhsNhwOB5MmTWLs2LE4HA4uueQSCgsLWbduHfv372f8+PGVeh3Gjh3Lueeey7Rp0xg4cCDLly/n448/LnNOUlIS/fv3p1WrVlx11VVYrVbWr1/Pxo0byzWmOJY77riD2bNnM3ToUO677z4cDgdr1qzhzDPP5NRTTy1zbuPGjWnSpAlz584lKiqKHTt2cO+991bqPiIiIiJyYrRGqg774osviI+PL/Nn0qRJJ3Strl27smrVKn755RfOO+884uPjSUpKonnz5gBERESwYMEC3nzzTWJjY/nf//7HtGnT/vG68+fPp3nz5vTq1Ysrr7ySkSNH0rRp09LjFouFjz76iJ49e3LzzTfToUMHhg4dSnp6Os2aNavwmv7+/syaNYvnnnuO5s2bM3DgQABuueUWnn/+eebPn0+XLl3o1asXCxYsKG2XXhlnn3028+bN48knnyQuLo5PPvmE+++/v8w5ffv25YMPPuCTTz7hjDPO4Oyzz+aJJ56gdevWlb5PkyZNWLlyJQcPHqRXr16cfvrpzJs3r8LRKavVymuvvUZycjKdO3fmrrvu4vHHH6/0vURERMQ7uQ03v/zxC9/v/J5f/vgFt6FuvN7EYhg13FPbB+Tm5uJwOHC5XNjt9jLHCgoK2LZtG23bti2zvkXEl+nnWkRExLul7Eph4fqFpO5LpaC4AJu/DWeEk8S4ROKj4j2dXp12vNrg7zS1T0RERETEi6TsSmHyqslk5WcRbY8mNCCUvKI8kjOTSc9JJ6lXkoopL6CpfSIiIiIiXsJtuFm4fiFZ+Vk4w53Yg+z4Wf2wB9lxhjvJys9i0YZFmubnBVRIiYiIiIh4ibTsNFL3pRJtjy7dhqWExWIh2h7N5r2bSctO81CGUkKFlIiIiIiIl3AVuCgoLiA0oOLtdkICQigoLsBV4KrlzORoKqRERERERLyEw+bA5m8jryivwuP5RfnY/G04bI5azkyOpkKqktTcUOoS/TyLiIh4p5iwGJwRTjJyM8r9/9owDDJyM4htGktMWIyHMpQSKqT+gZ+fHwCHDh3ycCYi1Sc/Px+gwn2pRERExHOsFiuJcYmEh4STmpVKbmEuxe5icgtzSc1KJTw0nISuCVgtehvvaWp//g/8/f0JCQlh3759BAQEYLXqh1Z8l2EY5Ofns3fvXho1alT6QYGIiIh4j/ioeJJ6JZXuI5V5IBObv41uLbqR0DVBrc+9hDbk5Z833Tp06BDbtm3D7VabSakbGjVqRGRkZLluQCIiIuI93IabtOw0XAUuHDYHMWExGomqBdqQtxoFBgbSvn17Te+TOiEgIEAjUSIiIj7AarHSoUkHT6chx6BCqpKsVis2m83TaYiIiIiIiBfQ2KCIiIiIiEgVqZASERERERGpIhVSIiIiIiIiVaRCSkREREREpIpUSImIiIiIiFSRCikREREREZEqUiElIiIiIiJSRSqkREREREREqkiFlIiIiIiISBWpkBIREREREakiFVIiIiIiIiJV5O/pBEREREREpH5yG27SstNwFbhw2BzEhMVgtfjGWI8KKRERERERqXUpu1JYuH4hqftSKSguwOZvwxnhJDEukfioeE+n949USImIiIiISK1K2ZXC5FWTycrPItoeTWhAKHlFeSRnJpOek05SrySvL6Z8Y9xMRERERETqBLfhZuH6hWTlZ+EMd2IPsuNn9cMeZMcZ7iQrP4tFGxbhNtyeTvW4VEiJiIiIiEitSctOI3VfKtH2aCwWS5ljFouFaHs0m/duJi07zUMZVo4KKRERERERqTWuAhcFxQWEBoRWeDwkIISC4gJcBa5azqxqVEiJiIiIiEitcdgc2Pxt5BXlVXg8vygfm78Nh81Ry5lVjQopERERERGpNTFhMTgjnGTkZmAYRpljhmGQkZtBbNNYYsJiPJRh5ahrn4iIiIgX8uX9dUSOx2qxkhiXSHpOOqlZ5lqpkIAQ8ovyycjNIDw0nISuCV7/824xji4D66Hc3FwcDgculwu73e7pdERERKSe8/X9dUQqo6Kf89imsSR0TfDoz3llawONSImIiIhHaMSlYnVhfx2RyoiPiicuMs5nfw+okBIREZFapxGXih29v05Ja+iS/XVSs1JZtGERcZFxPvNmU+R4rBYrHZp08HQaJ0T/AkVERKRWlYy4JGcmExYcRvuw9oQFh5GcmczkVZNJ2ZXi6RQ9pq7sryNSH6iQEhERkVpz9IiLPciOn9WvdMQlKz+LRRsW4Tbcnk7VI+rK/joi9YEKKREREak1GnE5vrqyv474Lrfh5pc/fuH7nd/zyx+/1NsPNSpDa6RERESk1lRmxCXzQGa9HXEp2V8nOTO5zBopOLK/TrcW3bx+fx3xTR5bu1hUBG+9BQMHQkhIzd2nmnn1iNThw4d54IEHaNu2LcHBwZxyyin897//LbNxl2EYJCUlERUVRXBwMH369GHr1q0ezFpERESORSMux1eyv054SDipWankFuZS7C4mtzCX1KxUn9lfR3yPR9YuHjwIM2dCTAxcdx3Mn1/996hBXv2v8LHHHuOZZ57hqaeeIjU1lccee4ypU6cye/bs0nOmTp3KrFmzePbZZ1m7di2hoaH07duXgoICD2YuIiIiFSkZccnIzeDorSxLRlxim8bW6xGX+Kh4knolcXrz08n+M5u07DSy/8ymW4tuJPVU63OpfrW+dnH3bvjPf6BlS7jrLtixA5o2hYCA6rl+LfHqqX3ffvstAwcO5LLLLgOgTZs2vPrqq3z33XeA+Qt35syZ3H///QwcOBCARYsW0axZM959912GDh3qsdxFRESkvJIRl/ScdFKzzLVSIQEh5Bflk5GboRGXv/j6/jriW6qydvGkWpVv2QLTpsGiRXDokBnr0AEmToQbbwSb7SSeRe3z6n+N55xzDp999hm//PILAOvXr+frr7/m0ksvBWDbtm3s3r2bPn36lD7G4XBw1llnsXr16mNet7CwkNzc3DJ/REREpHZoxKVySvbXOaPFGXRo0kFFlNSYGu8W+c03MGgQOJ3w/PNmEXXOOfDOO5CaCiNG+FwRBV4+InXvvfeSm5tLx44d8fPz4/Dhwzz88MNcf/31AOzevRuAZs2alXlcs2bNSo9V5NFHH2XSpEk1l7iIiIgcl0ZcRLzH39cu2oPs5Y6f0NpFtxuWLoXHH4dvvz0SHzgQ7r4bzj23GjL3LK8upN544w0WL17MK6+8QqdOnfjxxx8ZN24czZs3JzEx8YSve9999zF+/PjSr3Nzc2nZsmV1pCwiIiKVVDLiIiKeVa3dIgsK4KWXzCl8f80qIzAQEhJgwgTo2LGGnkXt8+pC6u677+bee+8tXevUpUsX0tPTefTRR0lMTCQyMhKAPXv2EBUVVfq4PXv28K9//euY1w0KCiIoKKhGcxcRERER8QXVsnYxOxueeQZmz4Y9e8xYo0Zw++0wZgz87b16XeHV4+f5+flYrWVT9PPzw+02O4a0bduWyMhIPvvss9Ljubm5rF27lu7du9dqriIiIiIivuqE1y6mp8O4cdCqFdx/v1lEtWwJM2aY3fgeeaROFlHg5SNSAwYM4OGHH6ZVq1Z06tSJlJQUZsyYwbBhwwCzi8i4ceOYMmUK7du3p23btjzwwAM0b96cQYMGeTZ5ERERkXrCbbi13q0OqNLaxR9/NNc/vf46HD5sxrp2Ndc/XXONz7UyPxFeXUjNnj2bBx54gFGjRrF3716aN2/OrbfeSlJSUuk599xzD3l5eYwcOZKcnBx69OjBxx9/jM0HO3+IiIiI+JqUXSksXL+Q1H2pFBQXYPO34YxwkhiXqA6MPui4axcNA1asMAuoTz89Eu/TxyygLroIjmqfXpdZjKN3w6uHcnNzcTgcuFwu7PbynUpEREREpLyUXSlMXjWZrPwsou3RhAaEkleUZ66rCQknqZfa2dcJRUXwxhtmAbV+vRnz84MhQ8wCKr5ufY8rWxt49YiUiIiIiHgnt+Fm4fqFZOVnlen0Zg+y4wx3kpqVyqINi4iLjNM0P1918KC579MTT5jrnQBCQuCWW+Cuu6BNG4+m52kqpERERESkytKy00jdZ3Z4sxw1nctisRBtj2bz3s2kZaepzb2v2b0bZs0yu/Dl5Jixpk1h7FizC19YmEfT8xYqpERERESkylwFLgqKCwgNCK3weEhACJkHMnEVuGo5MzlhW7aY+z8tWgSHDpmxDh3M/Z8SEkA9CMpQISUiIiIiVeawObD528grysMeVH4dSX5RPjZ/Gw6bwwPZSZV88425/mnpUrOhBED37ub6p8svN9dDSTmasCoiIiIiVRYTFoMzwklGbgZH9y4zDIOM3Axim8YSExbjoQzluNxuePddOPdc6NED3nvPLKIuvxy+/hq+/RauuEJF1HFoREpEREREqsxqsZIYl0h6TjqpWeZaqZCAEPKL8s2ufaHhJHRNUKMJb1NQAC+9BNOnm1P5AAIDzal7EyZAx46ezc+HqP05an8uIiIicqKb6la0j1Rs01gSuiao9bk32b/fbB4xaxbs2WPGHA4YNQrGjIGoKM/m50XU/lxEREREKuVkNtWNj4onLjLuhIowqQXp6TBzJsybB3l5ZqxlS7N9+S23QMOGHk3Pl6mQEhEREanHjrWpbnJmMuk56ZXaVNdqsarFubf58UezA99rr8Hhw2asa1ezgcQ110BAgEfTqwv0UYGIiIhIPXX0prr2IDt+Vr/STXWz8rNYtGERbsPt6VSlMgwDVqyAiy+G+HhYvNgsonr3huXLzeLqhhtURFUTjUiJiIiI1FPaVLeOKCqCN980W5j/+KMZs1phyBBzBOq00zyaXl2lQkpERESkntKmuj7u4EF44QWYMQN27DBjISEwfLi5BqptW8/mV8epkBIRERGpp7Spro/avRtmzza78O3fb8aaNjW7791+OzRp4tn86gkVUiIiIiL1VMmmusmZyTjDnWWm95VsqtutRTdtqusttmwx939atAgKC81Y+/bm/k8JCRAc7Nn86hkVUiIiIiL1lDbV9RHffmuuf3rvPbOhBMDZZ8M998Dll4Ofn2fzq6e0IS/akFdERETqN22q64Xcbnj/fZg61SykSlx+udlA4txz4agGIVI9tCGviIiIiFSKNtX1IgUF8NJL5hS+LVvMWGAg3HijOYXP6fRsflJKhZSIiIiIaFNdT9u/32weMWsW7NljxhwOs3nE2LEQFeXZ/KQcFVIiIiIiIp6yYwc88QTMmwd5eWYsOtpsXz5iBDRs6Nn85JhUSImIiIiI1Lb1680GEq+9BocPm7EuXcwGEtdcAwEBns1P/pEKKRERERGR2mAY8NlnZgH1ySdH4hdeaBZQF1+sBhI+RIWUiIiIiEhNKi6GN980O/D9+KMZs1phyBCYOBFOP92j6cmJUSElIiIiIlITDh6EF14w10Clp5uxkBAYPtxcA9W2rWfzk5OiQkpEREREpDrt2QOzZ8OcOWY3PoDwcLP73qhR0KSJZ/OTaqFCSkRERESkOvzyi7n/08KFUFhoxmJizP2fEhMhONiz+Um1UiElIiIiInIyVq821z+9957ZUALgrLPg7rth0CDw8/NoelIzVEiJiIiIiFSV2w3vv2924PvmmyPxAQPMAqpHj9IOfG7DTVp2Gq4CFw6bg5iwGKwWq4cSl+qiQkpEREREpLIKCuDll2HaNNiyxYwFBMCNN5pT+GJjy5yesiuFhesXkrovlYLiAmz+NpwRThLjEomPivfAE5DqokJKREREROSf7N8Pzz4Ls2bB7t1mzOGA224zm0g0b17uISm7Upi8ajJZ+VlE26MJDQglryiP5Mxk0nPSSeqVpGLKh6mQEhERERE5lh07zPbl8+ZBXp4Zi44225ffcgvY7RU+zG24Wbh+IVn5WTjDnVj+muZnD7LjDHeSmpXKog2LiIuM0zQ/H6VCSkRERETkaOvXm+ufXnsNDh82Y126mBvoDh0KgYHHfXhadhqp+1KJtkeXFlElLBYL0fZoNu/dTFp2Gh2adKipZyE1SIWUiIiIiAiYHfdWrjQ78H3yyZH4hReaDST69i1tIPFPXAUuCooLCA0IrfB4SEAImQcycRW4qiNz8QAVUiIiIiJSvxUXw1tvmQVUSooZs1rh6qvNAur006t8SYfNgc3fRl5RHvag8tP/8ovysfnbcNgcJ5u9eIgmZIqIiIhI/ZSXZzaPaN8err3WLKJCQmDMGEhLM6f1nUARBRATFoMzwklGbgZGyd5SfzEMg4zcDGKbxhITFlMdz0Q8QCNSIiIiIlK/7N0Ls2fDnDmQnW3GIiLMAmrUKGjS5KRvYbVYSYxLJD0nndQsc61USEAI+UX5ZORmEB4aTkLXBDWa8GEW4+gSuR7Kzc3F4XDgcrmwH6PzioiIiIj4uK1bYfp0WLjQ3A8KICbG3P8pMRGCg6v9lhXtIxXbNJaErglqfe6lKlsbaERKREREROq2NWvM9U/vvms2lAA46yy45x4YOBD8/Grs1vFR8cRFxpGWnYarwIXD5iAmLEYjUXWACikRERERqXvcbvjwQ7OA+vrrI/EBA8wGEj16VLoD38myWqxqcV4HqZASERERkbqjsBBefhmmTYOffzZjAQFw443mFL7YWM/mJ3WGCikRERER8X05OfDss/Dkk7B7txlzOOC222DsWGje3KPpSd2jQkpEREREfNfvv8PMmTB3Lhw8aMaio2HcOBgxAtRITGqICikRERER8T0bNsDjj5t7PRUXm7HOnc31T0OHQmCgZ/OTOk+FlIiIiIj4BsOAlSvNAmr58iPxCy4wO/D17VtrDSREVEiJiIiIiHcrLoa33jILqB9+MGNWK1x1lTkC1a2bZ/OTekmFlIiIiIh4p7w8eOEFeOIJ2L7djAUHw/DhcNdd0K5dlS/pNtza00mqhQopEREREfEue/fC7NkwZw5kZ5ux8HAYMwZGjTL/fgJSdqWwcP1CUvelUlBcgM3fhjPCSWJcIvFR8dX4BDxLxWLtUCElIiIiIt5h61aYPh0WLDD3gwI45RSYOBESE83RqBOUsiuFyasmk5WfRbQ9mtCAUPKK8kjOTCY9J52kXkl1opiqL8WiN1AhJSIiIiKetWYNTJ0K775rNpQAOPNMs4HEoEHg53dSl3cbbhauX0hWfhbOcCeWvxpS2IPsOMOdpGalsmjDIuIi43x65Ka+FIvewnd/UkRERETEd7nd8P77cN550L07vPOOWUT17w+rVpnF1eDBJ11EAaRlp5G6L5Voe3RpEVXCYrEQbY9m897NpGWnnfS9POXoYtEeZMfP6ldaLGblZ7FowyLchtvTqdYZKqREREREpPYUFpoNJDp1gssvh6+/hoAAuPlm2LTJLK569qzWNuauAhcFxQWEBoRWeDwkIISC4gJcBa5qu2dtqw/ForfR1D4RERERqXk5OfDss/Dkk7B7txmz2+G22+DOO6F58xq7tcPmwOZvI68oD3uQvdzx/KJ8bP42HDZHjeVQ0ypTLGYeyPTpYtHbqJASERERkZrz++8wcybMnQsHD5qxFi3M9uUjRpjFVBVVtStdTFgMzggnyZnJZdZIARiGQUZuBt1adCMmLKbKuXiL+lAsehsVUiIiIiJS/TZsgGnT4NVXzQ11ATp3NjfQHToUAgNP6LIn0pXOarGSGJdIek46qVnm9LeQgBDyi/LJyM0gPDSchK4JPt1ooj4Ui95GhZSIiIiIVA/DgM8/NzvwLV9+JH7BBWYBdcklJ7X26WS60sVHxZPUK6m0CMs8kInN30a3Ft1I6Jrg893s6kOx6G0shlHSY7L+ys3NxeFw4HK5sJ/A8LKIiIhIvVZcDG+/bRZQP/xgxqxWuOoqs4Dq1u2kb+E23IxfPv6YIy6pWal0a9GN6RdPP26xUNc3q61oxC62aWydKBZrS2VrA41IiYiIiMiJycuDF1+EGTNg+3YzFhwMw4bB+PHQrl213aoqXek6NOlwzOtYLdbjHvd18VHxxEXG1eli0VuokBIRERGRqtm7F556Cp5+GrKzzVh4ONxxB4webf69mqkrXeXV9WLRW6iQEhEREZHK2boVpk+HhQuhoMCMtWsHEydCYiKEhNTYrdWVTryNxvhERERE5PjWroXBg+HUU+G558wi6owz4M034Zdf4Pbba7SIgiNd6TJyMzh6iX9JV7rYprHqSie1RiNSIiIiIlKe2w0ffgiPPw5ffXUkftllZgOJnj1PqgNfVakrnXgbde1DXftEREREShUWwuLF5h5QqalmLCAArr/enMLXqZNH01NXOqlp6tonIiIiIpWXk2NO23vySdi1y4zZ7XDbbTB2LLRo4dH0SqgrnXgLFVIiIiIi9dnvv8PMmTB3Lhw8aMZatIBx42DkSLOY8jLqSifeQIWUiIiISH3000/m+qdXXzU31AXo3NmcvnfttRAY6Nn8RLycCikRERGR+sIw4PPPzQLq44+PxC+4wGwgcckltdpAQsSXqZASERERqeuKi+Htt80CKjnZjFmtcNVVZgHVrZtn8xPxQSqkREREROqqvDyYPx9mzIBt28xYcDAMGwbjx5ub6YrICVEhJSIiIlLX7NsHTz1l/snONmPh4TBmDIwaZf5dRE6K1/eJ3LlzJzfccANNmjQhODiYLl26sG7dutLjhmGQlJREVFQUwcHB9OnTh61bt3owYxEREfFGbsPNL3/8wvc7v+eXP37Bbbg9nVL1S0uD22+HVq1g8mSziDrlFJgzB9LTISlJRZRINfHqEan9+/dz7rnncsEFF7Bs2TIiIiLYunUrjRs3Lj1n6tSpzJo1i4ULF9K2bVseeOAB+vbty+bNm7HZbB7MXkRERLxFRZu4OiOcJMYl1o1NXNeuNdc/LVliNpQAOOMMuOceuOIK8PPzbH4idZDFMEr+tXmfe++9l2+++YavvvqqwuOGYdC8eXMmTJjAxIkTAXC5XDRr1owFCxYwdOjQSt2nsrsXi4iIiO9J2ZXC5FWTycrPItoeTWhAKHlFeWTkZhAeEk5SryTfLKbcbvjoI7OA+vLLI/HLLjMbSPTsqQ58IiegsrWBV0/tW7p0Kd26dePqq6+madOmxMfHM2/evNLj27ZtY/fu3fTp06c05nA4OOuss1i9erUnUhYREREv4jbcLFy/kKz8LJzhTuxBdvysftiD7DjDnWTlZ7FowyLfmuZXWAgvvmju+TRggFlEBQTATTfBxo3wwQfQq5eKKJEa5tWF1G+//cYzzzxD+/btWb58Obfffjtjx45l4cKFAOzevRuAZs2alXlcs2bNSo9VpLCwkNzc3DJ/REREpO5Jy04jdV8q0fZoLEcVFhaLhWh7NJv3biYtO81DGVZBTg489hi0bQvDh0NqKtjt5vS9bdvM7nydOnk6S5F6w6vXSLndbrp168YjjzwCQHx8PBs3buTZZ58lMTHxhK/76KOPMmnSpOpKU0RERLyUq8BFQXEBoQGhFR4PCQgh80AmrgJXLWdWBRkZMHMmzJ0LBw6YsebN4a67YMQIcDg8mp5IfeXVI1JRUVHExsaWiTmdTnbs2AFAZGQkAHv27Clzzp49e0qPVeS+++7D5XKV/vn999+rOXMRERHxBg6bA5u/jbyivAqP5xflY/O34bB5YTHy00+QmGiOQE2fbhZRnTrBggXmCNTEiSqiRDzIqwupc889ly1btpSJ/fLLL7Ru3RqAtm3bEhkZyWeffVZ6PDc3l7Vr19K9e/djXjcoKAi73V7mj4iIiNQ9MWExOCOcZORmcHR/LcMwyMjNILZpLDFhMR7K8CiGAZ9/DpdeCl27wqJFUFwM558PH354pLgKDPR0piL1nldP7bvrrrs455xzeOSRRxgyZAjfffcdc+fOZe7cuYA5t3ncuHFMmTKF9u3bl7Y/b968OYMGDfJs8iIiIuJxVouVxLhE0nPSSc0y10qFBISQX5Rvdu0LDSehawJWi4c/Wy4uhrffNjvwJSf/lbwVBg82O/CdcYZn8xORcry6/TnABx98wH333cfWrVtp27Yt48ePZ8SIEaXHDcPgwQcfZO7cueTk5NCjRw/mzJlDhw4dKn0PtT8XERGp2yraRyq2aSwJXRM82/o8L89sEjFjhjldDyA4GG6+GcaPNzfTFZFaVdnawOsLqdqgQkpERKTucxtu0rLTcBW4cNgcxITFeG4kau9eeOopePppyM42Y02awJgxMHo0hId7Ji85Jq/6+ZEaVdnawKun9omIiIhUF6vFSocmlZ+xUiPS0szGEQsWQEGBGWvXDiZMMPeBCgnxZHZyDBWNaDojnCTGJfrmZs5SLVRIiYiIiNS0tWvN9U9LlpgNJcBc93T33XDlleDn59n85JhSdqUwedVksvKziLZHExoQSl5RHsmZyaTnpJPUK0nFVD2l8UgRERGRmuB2wwcfQK9ecPbZZjMJw4B+/czOfGvXwtVXq4jyYm7DzcL1C8nKz8IZ7sQeZMfP6oc9yI4z3ElWfhaLNizCbbg9nap4gEakRERERKpTYSEsXgzTpkFqqhkLCIDrrzf3furUybP5SaWlZaeRus/s9mixWMocs1gsRNuj2bx3M2nZaZ6fNiq1ToWUiIiISHXIyYHnnoMnn4Rdu8xYw4Zw661w550QHe3R9KTqXAUuCooLCA0IrfB4SEAImQcycRW4ajkz8QYqpERERERORkYGzJwJc+fCgQNmrHlzGDcORo4Eh8OT2clJcNgc2Pxt5BXlYQ8q370tvygfm78Nh03f4/pIhZSIiIjIifjpJ3P63iuvmBvqgjltb+JEuO46CAz0bH5y0mLCYnBGOEnOTMYZ7iwzvc8wDDJyM+jWohsxYTEezFI8RYWUiIiISGUZBnzxhdmBb9myI/FevcwOfJdeClb18qorrBYriXGJpOekk5plrpUKCQghvyifjNwMwkPDSeiaoP2k6iltyIs25BUREZF/UFxsti5//HFYt86MWa1m6/K774Yzz/RsflKjKtpHKrZpLAldE9T6vA7ShrwiIiIiJys/H+bPNzfR3bbNjNlscPPNMH48xGhKV30QHxVPXGQcadlpuApcOGwOYsJiNBJVz6mQEhERETnavn3w1FPw9NPwxx9mrEkTGD0a7rgDIiI8m99xuA233vDXAKvFqhbnUoYKKREREZESaWkwY4Y5ClVQYMbatoUJE8xRqJAQz+b3DyqaguaMcJIYl+iVU9BU9IkvUyElIiIi8t135vqnJUvA7TZj3bqZ65+uvBL8vf8tU8quFCavmkxWfhbR9mhCA0LJK8ojOTOZ9Jx0knoleVUx5WtFn8jRvP+3goiIiEhNcLvNzntTp8KXXx6JX3op3HOP2Ynvb+2uvZnbcLNw/UKy8rPKtOm2B9lxhjtJzUpl0YZFxEXGecWIj68VfSIV8fy/JBEREZHaVFhoTt3r0gX69zeLKH9/SEw094b66CM4/3yfKaIA0rLTSN1ntue2HJW3xWIh2h7N5r2bSctO81CGRxxd9NmD7PhZ/UqLvqz8LBZtWITbcHs6VZHj0oiUiIiI1A8uFzz3HDz5JGRmmrGGDeHWW+HOOyE62rP5nQRXgYuC4gJCA0IrPB4SEELmgUxcBa5azqy8qhR9au4g3kyFlIiIiNRtGRlm8fTcc3DggBlr3twsnm69FRwOz+ZXDRw2BzZ/G3lFediDyu97k1+Uj83fhsPm+efqS0WfyPFoap+IiIjUTRs3wk03Qbt2MG2aWUTFxsKLL8Jvv5nroOpAEQUQExaDM8JJRm4GhmGUOWYYBhm5GcQ2jSUmzPP7Xv296KuINxV9IsejQkpERETqDsOAL76Ayy4z10AtXAhFRWbjiA8+MNdA3XwzBAV5OtNqZbVYSYxLJDwknNSsVHILcyl2F5NbmEtqVirhoeEkdE3wikYTvlT0iRyPpvaJiIiI7zt82GxdPnUqrFtnxqxWs3X53XfDmWd6Nr9aEB8VT1KvpNKW4pkHMrH52+jWohsJXRO8pgteSdGXnpNOapa5ViokIIT8onwycjO8qugTOR6LcfRHAfVQbm4uDocDl8uF3V5+XrGIiIh4qfx8WLAApk83p+sB2GzmqNP48RBT/0Y1fGWT24r2kYptGutVRZ/UT5WtDTQiJSIiIj7Fbbj5bet3BD/3Is0WvoX/H/vNA02awB13wOjREBHh2SQ9yGqx+kS3u/ioeOIi43yi6BOpiAopERER8RmbVi8l+5EHOOOTjdgOmfsMZUU6+HPMbbQclwQhIR7OUKrCV4o+kYpUupDKy8sjNLTiNpUiIiIiNer779n/3//Q8YMV+P21KGFbTDhLB3TgHaeFxg23kOTaQnyId00J85VpdiJSdZUupLp27crChQvp0aNHTeYjIiIiYjIMWLbMbCCxahWN/wr/dHpLPrkyjl86R4HFQgfDIDUrlUUbFhEXGec1hUpFa4CcEU4S4xK1BkikDqh0ITV48GAuvPBC7rzzTh5++GECAwNrMi8RERGprw4dgldeMfd+2rQJAMPfnxVnN2XFFV3Jbd+qzOkWi4VoezSb924mLTvNK6aKpexKYfKqyWTlZxFtjyY0IJS8ojySM5NJz0knqVeSiikRH1fpj2ymTp3Kl19+yYcffshpp51GSkpKTeYlIiIi9Y3LBY8/bm6ge/PNZhHVsCFMnMj6b9/h4WEx7D+lRYUPDQkIoaC4AFeBq5aTLs9tuFm4fiFZ+Vk4w53Yg+z4Wf2wB9lxhjvJys9i0YZFuA23p1MVkZNQpWYTZ599NikpKdx///2cc845XHTRRfj7l73EkiVLqjVBERERqeN27oQnn4TnnoPcXDMWFQXjxsGtt4LDQcgfv2D72UZeUR72oPLtiPOL8rH523DYHLWbewXSstNI3Wfuj2SxWMoc88bRMxE5MVXu2ldYWMjevXuxWCw4HI5yhZSIiIhIpWzaZE7fW7wYiorMWGwsTJwI110HQUGlp8aExeCMcJKcmYwz3FmmQDEMg4zcDLq16EZMmOf3jXIVuCgoLiA0oOImXSEBIWQeyPSK0TMROXFVqoJWrFjBsGHDiIqKIjk5GafTWVN5iYiISF1kGLBqlTmF76OPjsR79oR77oFLLwVr+ZUHVouVxLhE0nPSSc0yR3tCAkLIL8onIzeD8NBwEromeEWjCYfNgc3fN0bPROTEVfq3za233sqAAQMYMWIEq1evVhElIiIilXf4MLz5Jpx1FlxwgVlEWSwweDCsWWMWV5ddVmERVSI+Kp6kXkmc3vx0sv/MJi07jew/s+nWohtJPb2neUPJ6FlGbgaGYZQ5VjJ6Fts01itGz0TkxFV6ROqbb77h22+/5bTTTqvJfERERKQuyc+HBQtg+nT47TczZrPBTTfB+PHQvn2VLhcfFU9cZJxX783kS6NnInLiLMbRH5Ucw6FDh+psy/Pc3FwcDgculwu7vfwQvIiIiFTRvn3w9NPmn6wsM9akCYwebf5p2tSz+dWCivaRim0aS0LXBK8ZPROR8ipbG1R6RKquFlEiIiJSjX79FWbMgPnz4c8/zVjbtubo0803Q2jFDRjqIl8YPRORE6eWeyIiInLyvv/ebCDx9tvg/mt/pNNPNxtIXHkl1NMuv1aLVS3OReqo+vlbTURERE6eYcCyZTB1qtksosSll8Ldd8P555sNJURE6iAVUiIiIlI1hw7BK6+Ye0Bt2mTG/P3NvZ8mToQuXTybn4hILTihSbpfffUVN9xwA927d2fnzp0AvPTSS3z99dfVmpyIiIh4EZfLnL7Xrp253mnTJmjYECZMMDvyLVyoIkpE6o0qF1Jvv/02ffv2JTg4mJSUFAoLCwFwuVw88sgj1Z6giIiIeNjOneZap1atzP/u3AlRUfC//8GOHebIVMuWns6yRrkNN7/88Qvf7/yeX/74Bbfh9nRKIuJhVZ7aN2XKFJ599lkSEhJ47bXXSuPnnnsuU6ZMqdbkRERExIM2bTKLpMWLoajIjDmd5vqn666DoCDP5ldLKmpj7oxwkhiXqDbmIvVYlQupLVu20LNnz3Jxh8NBTk5OdeQkIiIinmIY8OWX5hS+Dz88Eu/Z0yyg+vUDa/1p352yK4XJqyaTlZ9FtD2a0IBQ8orySM5MJj0nnaReSSqmROqpKv8mjIyMJC0trVz866+/pl27dtWSlIiIiNSyw4fhrbfgrLPMbnsffmh23Bs8GNasMbvy9e9fr4oot+Fm4fqFZOVn4Qx3Yg+y42f1wx5kxxnuJCs/i0UbFmman0g9VeXfhiNGjODOO+9k7dq1WCwWMjMzWbx4MRMnTuT222+viRxFRESkpuTnw5w5cOqpcPXV5n5QQUFw222wZcuR4qoeSstOI3VfKtH2aCxHtXG3WCxE26PZvHczadnlP2AWkbqvylP77r33XtxuN7179yY/P5+ePXsSFBTExIkTGTNmTE3kKCIiItUtKwuefhqeesr8O0BYGIweDXfcAU2bejY/L+AqcFFQXEBoQGiFx0MCQsg8kImrwFXLmYmIN6hyIWWxWPjPf/7D3XffTVpaGgcPHiQ2NpYGDRrURH4iIiJSnX77DWbMgBdfhD//NGNt2sD48TBsGIRWXDTURw6bA5u/jbyiPOxB9nLH84vysfnbcNgcHshORDzthDfkDQwMJDY2tjpzERERkZry/fdmA4m33wb3X2t6TjvNbGc+eLC5oa6UERMWgzPCSXJmMs5wZ5npfYZhkJGbQbcW3YgJi/FgliLiKZX6rXnllVdW+oJLliw54WRERESkGhkGLFtmFlBffHEkfsklZge+Cy4wG0pIhawWK4lxiaTnpJOaZa6VCgkIIb8on4zcDMJDw0nomoDVUn8acIjIEZUqpBwODVmLiIj4jEOH4NVXzT2gNm40Y/7+cO21MHEidO3q2fx8SHxUPEm9kkr3kco8kInN30a3Ft1I6Jqg1uci9ZjFMAzD00l4Wm5uLg6HA5fLhd1efg60iIiIT8jNhblzYeZM2LnTjDVoALfeCnfeCS1bejQ9X+Y23KRlp+EqcOGwOYgJi9FIlEgdVdna4IQnRO/du5ctW7YAcOqpp9JU3X1EREQ8IzMTnnwSnn3WLKYAIiNh3DiziGrUyJPZ1QlWi5UOTTp4Og0R8SJVLqRyc3MZPXo0r732GocPHwbAz8+Pa665hqefflrTAEVERGrLpk3m9L3Fi6GoyIw5neb0veuvN/eDEhGRGnFCG/KuXbuWDz74gJycHHJycvjggw9Yt24dt956a03kKCIiIiUMA1atgv79oXNnWLDALKLOOw/ef99cEzVsmIooEZEaVuU1UqGhoSxfvpwePXqUiX/11Vdccskl5OXlVWuCtUFrpERExOsdPgzvvGN24PvuOzNmscCVV5od+M46y7P5iYjUETW2RqpJkyYVTt9zOBw0bty4qpcTERGR4/nzT3PUafp0+PVXM2azwU03mZvotm/vyexEROqtKk/tu//++xk/fjy7d+8uje3evZu7776bBx54oFqTExERqbf++AMmT4bWrWHUKLOICguDBx6A9HR45hkVUSIiHlSpEan4+Pgyu3lv3bqVVq1a0apVKwB27NhBUFAQ+/bt0zopERGRk7FtG8yYAS+8YI5GAbRpAxMmwM03Q2ioR9MTERFTpQqpQYMG1XAaIiIi9dy6deb6p7feArfbjJ12GtxzDwwebG6oKyIiXkMb8qJmEyIicmw1uhGrYcDHH5sF1OefH4lfconZQOKCC8yGEiIiUmtqfENeERGRui5lVwoL1y8kdV8qBcUF2PxtOCOcJMYlEh8Vf+IXPnQIXnvNLKA2bjRj/v5w7bXmHlBdu1bPExARkRpT5ULq8OHDPPHEE7zxxhvs2LGDQ4cOlTmenZ1dbcmJiIh4SsquFCavmkxWfhbR9mhCA0LJK8ojOTOZ9Jx0knolVb2Yys2FefPgiSdg504z1qABjBwJ48ZBy5bV/jxERKRmVHluwqRJk5gxYwbXXHMNLpeL8ePHc+WVV2K1WnnooYdqIEUREZHa5TbcLFy/kKz8LJzhTuxBdvysftiD7DjDnWTlZ7FowyLchrtyF8zMhH//2yyUJk40i6jISHj0Ufj9d7O1uYooERGfUuVCavHixcybN48JEybg7+/Ptddey/PPP09SUhJr1qypiRxFRERqVVp2Gqn7Uom2R5fpWgtgsViItkezee9m0rLTjn+hzZth2DCz697UqeaIVMeOZke+7dvh3nuhUaOaehoiIlKDqlxI7d69my5dugDQoEEDXC4XAP379+fDDz+s3uxEREQ8wFXgoqC4gNCAiluNhwSEUFBcgKvAVf6gYcCXX0L//tCpE8yfD0VF0KMHLF0KmzaZxVVQUA0/CxERqUlVLqSio6PZtWsXAKeccgqffPIJAN9//z1B+p+CiIjUAQ6bA5u/jbyivAqP5xflY/O34bA5jgQPHzZbl599NvTqBR9+aHbcu+IK+PZb+OorGDAArNXU8U9ERDyqyr/Nr7jiCj777DMAxowZwwMPPED79u1JSEhg2LBh1Z6giIhIbYsJi8EZ4SQjN4OjdwkxDIOM3Axim8YSExZjbpr7zDNw6qlw9dXw3XfmaNOtt8LPP8OSJdC9u4eeiYiI1JST3kdq9erVrF69mvbt2zNgwIDqyqtWaR8pERE52tFd+0ICQsgvyicjN4Pw0HAmdRpD1yVfw+zZkJVlPqhxYxg9Gu64A5o18+wTEBGRE1LZ2kAb8qJCSkREKlbRPlLnHW7BravyiHj9fXM0CqB1a5gwwVz7FFrxuioREfEN1boh79KlS7n00ksJCAhg6dKlxz338ssvr1qmIiIiXio+Kp64yDjSstMoWruaVnNfp8H7C7G4/2p7Hh8P99wDV11lbqgrIiL1RqVGpKxWK7t376Zp06ZYj7NI1mKxcPjw4WpNsDZoREpERCpkGPDxx/D44/D550fiffvC3XfDhReaDSVERKTOqGxtUKlmE263m6ZNm5b+/Vh/arqI+t///ofFYmHcuHGlsYKCAkaPHk2TJk1o0KABgwcPZs+ePTWah4iI1HGHDsGiRRAXB/36mUWUvz/ccAP8+KNZXPXurSJKRKQeq1LXvqKiInr37s3WrVtrKp9j+v7773nuuefo2rVrmfhdd93F+++/z5tvvsmqVavIzMzkyiuvrPX8RESkDsjNhenT4ZRTIDERfvoJGjSAu+6CX3+Fl14yi6tKchtufvnjF77f+T2//PELbsNdg8mLiEhtqtKE7oCAADZs2FBTuRzTwYMHuf7665k3bx5TpkwpjbtcLl544QVeeeUVLrzwQgDmz5+P0+lkzZo1nH322bWeq4iI+KDMTJg1C559Fv7aaJ7ISLjzTrONeePGVb5kRY0qnBFOEuMSiY+Kr+YnICIita3K+0jdcMMNvPDCCzWRyzGNHj2ayy67jD59+pSJJycnU1RUVCbesWNHWrVqxerVq495vcLCQnJzc8v8ERGR2uM1IzWbN5ud9tq0gcceM4uojh3h+edh+3a4994TLqImr5pMcmYyYcFhtA9rT1hwGMmZyUxeNZmUXSnV/lRERKR2VbnFUHFxMS+++CKffvopp59+OqFHtXmdMWNGtSUH8Nprr/HDDz/w/ffflzu2e/duAgMDadSoUZl4s2bN2L179zGv+eijjzJp0qRqzVNERCrH4yM1hgFffw1Tp8IHHxyJ9+hhNpDo3x+O01jpn7gNNwvXLyQrPwtnuBPLX+uo7EF2nOFOUrNSWbRhEXGRcVgtJ34fERHxrCoXUhs3buS0004D4JdffilzzFLNi25///137rzzTlasWIHNZqu26953332MHz++9Ovc3FxatmxZbdcXEZGKHb3JbWhAKHlFeSRnJpOek05Sr6SaK6YOH4b33jMLqLVrzZjFAoMGmQVU9+7Vcpu07DRS96USbY8u9/9Fi8VCtD2azXs3k5adRocmHarlniIiUvuqXEh9/vf2rzUsOTmZvXv3lhZuAIcPH+bLL7/kqaeeYvny5Rw6dIicnJwyo1J79uwhMjLymNcNCgoiKCioJlMXEZGjeGyk5s8/YeFCs4lEWpoZCwoym0lMmAAdqreYcRW4KCguIDSg4o15QwJCyDyQiavAVa33FRGR2uXVuwf27t2bn376qUzs5ptvpmPHjvz73/+mZcuWBAQE8NlnnzF48GAAtmzZwo4dO+heTZ8siohI9aj1kZo//oA5c2D2bNi3z4w1bgyjRsGYMdCs2cnfowIOmwObv428ojzsQeX3H8kvysfmb8Nhc1T7vd2Gm7TsNFwFLhw2BzFhMZo+KCJSQ06okFq3bh1vvPEGO3bs4NChQ2WOLVmypFoSA2jYsCGdO3cuEwsNDaVJkyal8eHDhzN+/HjCwsKw2+2MGTOG7t27q2OfiIiXqbWRmm3bYMYMePFFyM83Y61bw/jxZmOJBg1O7vr/ICYsBmeEk+TM5DIjbwCGYZCRm0G3Ft2ICYup1vt6fO2ZiEg9U+WPqV577TXOOeccUlNTeeeddygqKmLTpk2sXLkSh6P6P137J0888QT9+/dn8ODB9OzZk8jIyGot5kREpHr8faSmIic9UpOcDEOHQkwMPPWUWUTFx8Mrr5hT+saOrfEiCsBqsZIYl0h4SDipWankFuZS7C4mtzCX1KxUwkPDSeiaUK0jReoSKCJS+yyGYRhVeUDXrl259dZbGT16NA0bNmT9+vW0bduWW2+9laioKJ/shpebm4vD4cDlcmG3l5+GISIiJ89tuBm/fPwxR2pSs1Lp1qIb0y+eXvkiwzBg+XJ4/HFYufJI/OKLzQYSvXubDSU8oKIRotimsSR0TajWEaIaeV1FROqxytYGVZ7a9+uvv3LZZZcBEBgYSF5eHhaLhbvuuosLL7zQJwspERGpeSUjNek56aRmmWulQgJCyC/KJyM3o2ojNUVF8NprZgFVspbWz88ckbr7boiLq9knUwnxUfHERcbV+JoldQkUEfGMKhdSjRs35sCBAwC0aNGCjRs30qVLF3JycsgvmYsuIiJSgfioeJJ6JZWO1GQeyMTmb6Nbi26VG6k5cADmzYMnnoCMDDMWGgojR8K4cdCqVY0/h6qwWqw1XryoS6CIiGdUuZDq2bMnK1asoEuXLlx99dXceeedrFy5khUrVtC7d++ayFFEROqQExqp2bULnnwSnn0WXH8VBM2awZ13wm23md346ilPdgkUEanPKl1Ibdy4kc6dO/PUU09RUFAAwH/+8x8CAgL49ttvGTx4MPfff3+NJSoiInVHpUdqUlNh2jR4+WUo6RJ76qkwcSLccANU42btvspTXQJFROq7ShdSXbt25YwzzuCWW25h6NChAFitVu69994aS05EROohw4CvvzbXP73//pH4uefCPfdA//5grfo6o7q6x1K1rj0TEZFKq3TXvq+++or58+fz1ltv4Xa7GTx4MLfccgvnnXdeTedY49S1T6Tu86U30b6Ua7U6fBjee88soNasMWMWCwwcaDaQOOecE750fdhjqba6BIqI1HWVrQ2q3P48Ly+PN954gwULFvDVV18RExPD8OHDSUxMJDIy8qQT9wQVUiJ1my+9ifalXKvNn3/CokUwfTps3WrGgoIgMdHcRPfUU0/q8iV7LGXlZxFtjyY0IJS8ojxztCYknKReSXXmta23RbiISDWqsULq79LS0pg/fz4vvfQSu3fv5pJLLmHp0qUnejmPUSElUnf50ptoX8q1WmRnw5w5MGsW7Ntnxho3hlGjYMwYs5nESdIeSyIiUlWVrQ1O6v8aMTEx/N///R/3338/DRs25MMPPzyZy4mIVCu34Wbh+oVk5WfhDHdiD7LjZ/XDHmTHGe4kKz+LRRsW4Tbcnk7Vp3I9adu3w9ix0LIlPPCAWUS1agUzZ8KOHTBlSrUUUVC1PZZERESqosrtz0t8+eWXvPjii7z99ttYrVaGDBnC8OHDqzM3EZGTUh0bldbWVKl6sanqDz+Y65/efNNcDwXwr3+Z65+uvhoCAqr9ltpjSUREakqVCqnMzEwWLFjAggULSEtL45xzzmHWrFkMGTKE0NCK/yclIuIpJ/smujbXK9XZN/yGAZ98YhZQn312JH7RRWYB1aeP2VCihmiPJRERqSmVLqQuvfRSPv30U8LDw0lISGDYsGGcepILgEVEatLJvIk+1nql5Mxk0nPSq329Up17w19UBK+/bhZQGzaYMT8/GDrU3APqX/+qlTS0x5KIiNSUSs9PCQgI4K233iIjI4PHHntMRZSIeL2SN9EZuRkc3Ven5E10bNPYcm+iPbFe6URz9ToHDsCMGXDKKXDjjWYRFRoK48bBr7+aG+vWUhEFR/ZYCg8JJzUrldzCXIrdxeQW5pKalao9lkRE5IRVekTKF7vxiUj9dqIblXpivZLPb6q6a5fZfe+ZZ8D11/TDZs3MphK332524/OQ+Kh4knollU7TzDyQic3fRrcW3bTHkoiInLATbjYhIuILTuRNtKfWK/nkG/6ff4Zp0+Cll+DQITPWoYO5/umGG8Bm82x+f4mPiicuMk57LImISLVRISUidV5V30R7cr2ST7zhNwz45huYOhXef/9I/NxzzQJqwACwelG+f7FarL7b8VBERLyOCikRqReq8iba0w0KvPYN/+HD8N57ZgOJNWvMmMUCAweaBdQ553g2PxERkVqkQkpE5Cg+v16puv35JyxaBNOnw9atZiwoCBISYMIEUPMhERGphyzG0e2h6qHc3FwcDgculwu7vfw0HhGpnyraRyq2aaz3rleqbtnZMGcOzJ4Ne/eascaNYdQoGDPGbCYhIiJSx1S2NtCIlIjIMfjEeqWasH07PPEEvPAC5OWZsVatYPx4GD4cGjTwaHoiIiLeQIWUiMhxeO16pZqQkmKuf3rjDXM9FJh7Pt19N1x9NQQEeDQ9ERERb6JCSkSkPjMMWLHCLKA+/fRI/KKL4J57oHdvs6GEiIiIlKFCSkSkjnAb7spPQywqgtdfN/eAWr/ejPn5wdChMHGiORIlIiIix6RCSkSkDqioMYYzwkliXGLZxhgHDsDzz5troH7/3YyFhsKIETBuHLRu7ZH8RUREfI0KKRERH5eyK4XJqyaTlZ9FtD2a0IBQ8orySM5MJj0nnaReScRbomDWLHjmGcjJMR/YrBmMHQu332524xMREZFKUyElIuLD3IabhesXkpWfVWbzYHuQHWe4k7yfkilccCPG51uxHDpkPqhDB3P63o03gs3mwexFRER8lwopEREflpadRuo+c9Ngy9+aQpyyeTcXL1nPv75LP3LyOeeYDSQGDABrHW/hLiIiUsNUSImI+DBXgYuC4gJCA0KxHHYT9106F7+zgVN+3gOA2wLf/KsJ4Q8+hnPg8Gq/f5UaXIiIiNQhKqRERHyYw+bA7g7g7I/WM+CDrTTLdAFQ5G9lzYUdeKdfOzaFFTO7x3nVfu9KN7gQERGpg1RIiYj4quxsYua8ziszvqVhzp8A5IUGsqpfLJ/374yrUTCpWal0a9qNmLCYar11pRpcqJgSEZE6TIWUiIiv2b7dbF/+wgtY8/JoCOwND+a1PpGk9DsNq91BflE+GVmphIeGk9A1oVqn2/1Tg4vUrFQWbVhEXGScpvmJiEidpUJKRMRXpKTA44/DG2/A4cNmLC4O7r6bnee157fNr5C5L5WC7H3Y/G10a9GNhK4J1T4ydKwGFwAWi4VoezSb924mLTuNDk06VOu9RUREvIUKKRERb2YYsGIFTJ0Kn312JH7RRXD33dCnD1gsxANxLbvVSuOHvze4qEhIQAiZBzJxFbiq/d4iIiLeQoWUiBdRBzQpVVQEr78O06bB+vVmzM8PrrnG3AMqvvwok9VirZURIIfNgc3fRl5RHvYge7nj+UX52PxtOGyOGs9FRETEU1RIiXgJdUATAA4cgOefN9dA/f67GQsNhVtugbvugtatPZsfEBMWgzPCSXJmcpk1UgCGYZCRm0G3FtXf4EJERMSbqJAS8QLqgCbs3g2zZsEzz0BOjhlr2hTuvBNuuw3Cwjya3t9ZLVYS4xJJz0knNctcKxUSEGI2uMjNqJEGFyIiIt5GhZSIh6kDWj33888wfTosWgSHDpmxDh3M6Xs33gg2m2fzO4b4qHiSeiWVjqJmHsis0QYXIiIi3kaFlIiHqQNaPfXNN2YDiaVLj8TOOcdsIHH55WCtvaL5RNfmxUfFExcZp3V9IiJSL6mQklqlZgrlqQNaPeJ2w3vvmS3MV68+Eh840Cygzj231lM62bV5tdXg4u/0e0RERLyBCimpNWqmUDF1QKsHCgrMqXvTp8Mvv5ixwEBISIAJE6BjR4+k5Ytr8/R7REREvIUKKakVvviGrbZ4ugOaL32670u5ApCdbTaPmD0b9uwxY40awe23w5gxEBXlsdR8cW2efo+IiIg3USElNc4X37DVJk92QPOlT/d9KVfS08325c8/D3l5ZqxlS7N9+S23QMOGns0P31ubp98jIiLibVRISY3ztTdsnuCJDmi+9Om+z+SakmKuf3rjDTh82Ix17Wquf7rmGggI8Gx+f+Nra/P0e0RERLyNCimpcb72hs1TarMDmi99uu/1uRoGrFhhFlCffnok3Ls3O2+7jl1nd8YR3IgYfz+8aZzE19bm6feI9/K5KbciItVEhZTUOF97w+ZJtdUBzZc+3ffaXIuKzJGnxx+H9evNmJ8fDBnCz4n9edb4jtR9r1OwYqFXTkP09Nq8qtLvEe/kU1NuRUSqmT4ykhpX8oYtIzcDwzDKHCt5wxbbNNZr3rDVB5X5dL+guMArPt33ulwPHoSZMyEmBm64wSyiQkJg7FhISyNl+t3ct/9NkjOTCQsOo31Ye8KCw0jOTGbyqsmk7EqpnTz/QcnavPCQcFKzUsktzKXYXUxuYS6pWak1ujbvROj3iPcpmXLr7T/rIiI1xTv+Dyl1mq+9YasP/v7pfkW86dN9r8l19274v/870jRixw5o2hT++1/z708+ibt1qzLTEO1BdvysfqXTELPys1i0YRFuw12zuVZSydq805ufTvaf2aRlp5H9ZzbdWnQjqaeXrDv7i36PeJejp9x6+8+6iEhN0NQ+qRWeaKYgx+ZL07o8nuuWLTBtmrkP1KFDZqx9e5g40dwHymYrPdVrpyEeR22uzTtZ9en3iLevO/LFn3URkeqmQkpqjS+9YavrPNly3Wdy/eYbc/3T0qVmQwmA7t3NDnyXX26uhzqKrzZEqK21edWhPvwe8YV1R776sy4iUp1USEmt8qU3bHWdL326X2u5ut1m4fT44/Dtt0fil19uFlA9ehz34WqIUDvq8u8RX2n1r591EREVUiL1mi99ul+juRYUwEsvwfTp5lQ+gMBAuPFGmDABnM5KXcbj0xDFp3l9q/+/0c+6iIgKKZF6z5c+3a/2XPfvh2eegVmzYM8eM9aoEdx+O4wZA1FRVc7PV6ZMivfxpXVH+lkXEVEhJSL1UXq62cJ83jzI+6sbYEk3vltugYYNT/jSvjRlUryLr6070s+6iNR3KqREpP5Yv95c//Taa3D4sBnr2hXuuQeGDIGAgGq5jS9NmRTv4YvrjvSzLiL1mQopEanbDAM++wymToUVK47E+/QxG0hcdBEcNY2qOvjSlEnxDr667kg/6yJSX6mQEpFaVyt75BQXwxtvmCNQP/5oxvz8zJGniRPhtNOq934iJ0nrjkREfIvFMEo2SKm/cnNzcTgcuFwu7Pby0ylEpPrU+B45Bw/CCy/AE0+Ya6EAQkLMtU933QVt2pz8PURqUEX/RmKbxmrdkYhILalsbaARKRGpNTW6R86ePTB7NsyZY3bjA2ja1Oy+d/vt0KRJ9T0RkRqkdUciIr5BhZSI1Ioa2yNnyxZz/6dFi6Cw0Iy1b29O37vxRggOroFnI1KztO5IRMT7qZASkVpR7XvkfPutuf7pvffMhhIAZ59tduC7/HJzPZSIiIhIDVEhJSK1olr2yHG74f33zQLqm2+OxC+/3OzAd+65NdKBT0RERORoKqREpFac1B45BQXw0kvmFL4tW8xYYKA5dW/CBHA6azh7ERERkbJUSIlIrTihPXL274dnnoFZs8xmEgAOh9k8YuxYiIqq5WchIiIiYlIhJSK1okp75KSnw8yZMG8e5OWZF4iONtuXjxgBDRt69LmIiIiIaB8ptI+USG067h45eyzm+qfXX4fDh80HdOliNpC45hoICPBs8iIiIlLnaR8pEfFK5fbICbIT88N2rIn/hhUrjpzYu7fZQOLii9VAQkRERLyOCikRqXVWi5UOjnaw/A1zBOrHH/86YIUhQ8wC6rTTPJqjiIiIyPGokBKR2nXwILzwAjzxhLkWCiAkBIYPN9dAtW3r2fxE6im34T4yUmxzEBMWU7XNsUVE6hmvLqQeffRRlixZws8//0xwcDDnnHMOjz32GKeeemrpOQUFBUyYMIHXXnuNwsJC+vbty5w5c2jWrJkHMxeRcvbsgdmzYc4csxsfQESE2X3v9tuhSRPP5idSj1W0dtEZ4SQxLpH4qHhPpyci4pW8+qOmVatWMXr0aNasWcOKFSsoKiri4osvJq+kixdw11138f777/Pmm2+yatUqMjMzufLKKz2YtYiUsWULjBwJrVvDww+bRVT79vDss+aI1P33q4gS8aCUXSlMXjWZ5MxkwoLDaB/WnrDgMJIzk5m8ajIpu1I8naKIiFfyqa59+/bto2nTpqxatYqePXvicrmIiIjglVde4aqrrgLg559/xul0snr1as4+++xKXVdd+0RqwLffmuuf3nsPSn7NnH222YHv8svBz8+z+YkIbsPN+OXjj7m/W2pWKt1adGP6xdM1zU9E6o3K1gY+9VvR5XIBEBYWBkBycjJFRUX06dOn9JyOHTvSqlUrVq9efczrFBYWkpubW+aPiFQDt9ssnHr0gHPPhXffNYuoAQPgq6/M4uqKK1REiXiJtOw0UveZ+7pZjuqOabFYiLZHs3nvZtKy0zyUoYiI9/KZQsrtdjNu3DjOPfdcOnfuDMDu3bsJDAykUaNGZc5t1qwZu3fvPua1Hn30URwOR+mfli1b1mTqInVfQQE8/zzExsKgQfDNNxAYCMOGwebNsHSpWVypjbmIV3EVuCgoLiA0ILTC4yEBIRQUF+AqcNVyZiIi3s+rm0383ejRo9m4cSNff/31SV/rvvvuY/z48aVf5+bmqpgSORH795trnWbNgpIPLxwOs3nE2LEQFeXZ/ETkuBw2BzZ/G3lFediDyk9fyS/Kx+Zvw2FzeCA7ERHv5hOF1B133MEHH3zAl19+SXR0dGk8MjKSQ4cOkZOTU2ZUas+ePURGRh7zekFBQQQFBdVkyiJ1244dMHMmzJtntjMHiI4225ePGAENG3o0PRGpnJiwGJwRzmOukcrIzaBbi27EhMV4MEsREe/k1VP7DMPgjjvu4J133mHlypW0PWp/mdNPP52AgAA+++yz0tiWLVvYsWMH3bt3r+10Req+9evhhhugXTtzH6iDB6FLF1i0CH77DcaPVxEl4kOsFiuJcYmEh4STmpVKbmEuxe5icgtzSc1KJTw0nISuCWo0ISJSAa/u2jdq1CheeeUV3nvvvTJ7RzkcDoKDgwG4/fbb+eijj1iwYAF2u50xY8YA8O2331b6PuraV7dpk8mTZBjw2WdmB75PPjkSv/BCuPtu6NtXa59EfFxF+0jFNo0loWuC9pESkXqnsrWBVxdSR3cQKjF//nxuuukm4MiGvK+++mqZDXmPN7XvaCqk6i5tMnkSiovhzTfNAirlr31krFYYMgQmToTTT/dsfiJSrfShk4iIqU4UUrVFhVTdVLLJZFZ+FtH2aEIDQskryiMjN4PwkHCSeiWpmKrIwYPw4oswY4a5YS5ASAgMH26ugTpqiq2IiIhIXVLZ2sAnmk2IVJXbcLNw/UKy8rPKLKC2B9lxhjtJzUpl0YZFxEXG6RPXEnv2wOzZMGeO2Y0PICICxoyBUaOgSRPP5iciIiLiRVRISZ1UlU0mOzTp4KEsvcQvv8D06bBwIRQWmrGYGHP6XkIC/LUeUURERESOUCEldVJlNpnMPJBZvzeZXL3aXP/07rtmQwmAs86Ce+6BgQPBz8+j6YmIiIh4MxVSUidpk8ljcLvhgw9g6lT45psj8QEDzA58PXqoA5+IiIhIJWhxiNRJJZtMZuRmcHQ/lZJNJmObxtafTSYLC+GFF6BTJ3O06ZtvIDAQhg2DTZtg6VI47zwVUSIiIiKVpBEpqZNKNplMz0knNctcKxUSEEJ+Ub7Zta++bDK5fz88+yzMmgW7d5sxhwNuvx3GjoWoKM/mJyIiIuKj1P4ctT+vy+rtJpM7dsDMmTBvntnOHCA62mxfPmIENGzo0fREREREvJXan4sA8VHxxEXG1Z9NJjdsMBtIvPaauaEuQJcu5vqnoUMhIMCz+YmIiIjUESqkpM6zWqx1u8W5YcDKlWYBtXz5kfiFF5od+C6+WGufRERERKqZCikRX1VcDG+9ZRZQP/xgxqxWuPpqcwTq9NM9m5+IiIhIHaZCSsTX5OWZHfieeAK2bzdjISEwfLi5Bqpt21pJw22468+USREREZGjqJAS8RV798Ls2fD002Y3PoCICBgzBkaNgiZNai2Vipp4OCOcJMYl1u0mHiIiIiJ/USEl4u22boXp02HBAnM/KICYGJgwARITITi4VtNJ2ZXC5FWTycrPItoeTWhAKHlFeSRnJpOek05SryQVUyIiIlLnqZAS8VZr1sDUqfDuu2ZDCYCzzjIbSAwcCH5+tZ6S23CzcP1CsvKzcIY7sfzVxMIeZMcZ7iQ1K5VFGxYRFxmnaX4iIiJSp6mQEvEmbjd88IHZQOLrr4/E+/c3C6gePTzagS8tO43UfeYGx5aj8rBYLETbo9m8dzNp2Wl1u1OiiIiI1HsqpES8QWEhvPwyTJsGP/9sxgIC4IYbYOJEiI31bH5/cRW4KCguIDQgtMLjIQEhZB7IxFXgquXMRERERGqXCikRT9q/H559FmbNgt27zZjdDrfdBnfeCc2beza/ozhsDmz+NvKK8rAHld/pO78oH5u/DYfN4YHsRERERGqPCikRT9ixA2bOhHnz4OBBMxYdDePGwYgRZjHlhWLCYnBGOEnOTC6zRgrAMAwycjPo1qIbMWExHsxSREREpOapkBKpTRs2mOufXnvN3FAXoHNncwPdoUMhMNCz+f0Dq8VKYlwi6TnppGaZa6VCAkLIL8onIzeD8NBwEromqNGEiIiI1HkWwyhpB1Z/5ebm4nA4cLlc2L10JEB8mGHAypVmB75PPjkSv+ACs4FE374ebSBxIiraRyq2aSwJXRPU+lxERER8WmVrA41IidSU4mJ46y2zgEpJMWNWK1x9tdlAols3z+Z3EuKj4omLjCMtOw1XgQuHzUFMWIxGokRERKTeUCElUt3y8uCFF+CJJ2D7djMWHAzDh8Ndd0G7dh5Nr7pYLVa1OBcREZF6S4WUSHXZuxdmz4annza78QGEh8OYMTBqlPl3EREREakTVEiJnKytW2H6dFiwwNwPCuCUU2DCBEhMhJAQj6YnIiIiItVPhZTIiVqzxlz/9O67ZkMJgDPPNBtIDBoEfn6ezE5EREREapAKKZGqcLvhww/NAurrr4/EL7vMLKDOO8/nOvCJiIiISNWpkBKpjMJCWLzY3APq55/NWEAAXH+92YGvUyfP5iciIiIitUqFlMjx5OTAs8/CrFmwa5cZs9vhtttg7Fho0cKj6YmIiIiIZ6iQEqnI77/DzJkwdy4cPGjGWrSAceNg5EizmBIRERGRekuFlMjfbdgA06bBq6+aG+qCOW3v7rvh2mshMNCz+YmIiIiIV1AhJWIY8PnnZgOJ5cuPxM8/H/fECaSd1R5XYS6OA9uJCYvBarF6LlcRERER8QoqpKT+Ki6Gt982C6gffjBjVitcdRXcfTcpLfxYuH4hqctmU1BcgM3fhjPCSWJcIvFR8Z7NXUREREQ8SoWU1D95efDiizBjBmzfbsaCg2HYMBg/Htq1I2VXCpNXTSYrP4toezShAaHkFeWRnJlMek46Sb2SVEyJiIiI1GMqpKT+2LsXnnoKnn4asrPNWHg43HEHjB5t/h1wG24Wrl9IVn4WznAnlr/2hbIH2XGGO0nNSmXRhkXERcZpmp+IiIhIPaVCSuq+tDSYPh0WLICCAjPWrp25/1NiIoSElD09O43UfalE26NLi6gSFouFaHs0m/duJi07jQ5NOtTSkxARERERb6JCSuqutWvN9U/vvGM2lAA44wy45x644grw86vwYa4CFwXFBYQGhFZ4PCQghMwDmbgKXDWVuYiIiIh4ORVSUre43fDRR2YB9dVXR+KXXWa2MO/ZE44aZTqaw+bA5m8jrygPe1D5/aLyi/Kx+dtw2BzVnb2IiIiI+Agt8JC6obDQbCDRuTMMGGAWUQEBcNNNsHEjfPAB9Or1j0UUQExYDM4IJxm5GRglI1l/MQyDjNwMYpvGEhMWU0NPRkRERES8nUakxLfl5MBzz8GTT8KuXWbMbofbboOxY6FFiypf0mqxkhiXSHpOOqlZ5lqpkIAQ8ovyycjNIDw0nISuCWo0ISIiIlKPWYyjP3Kvh3Jzc3E4HLhcLuz28lO5xAtlZMDMmTB3Lhw4YMZatIBx42DkSLOYOkkpu1LMfaT2pZbuIxXbNJaErglqfS4iIiJSR1W2NtCIlPiWn36CadPglVfMDXXBnM43cSJcey0EBlbbreKj4omLjCMtOw1XgQuHzUFMWIxGokREREREhZT4AMOAL76Axx+HZcuOxM8/3+zAd8kllVr7dCKsFqtanIuIiIhIOSqkxHsVF8Pbb5sFVHKyGbNaYfBgs4Dq1s2z+R2H23BrJEtERESkDlMhJd4nLw/mz4cZM2DbNjMWHAzDhsH48eZmul6sorVVzggniXGJWlslIiIiUkeokKoD6szox7598NRT5p/sbDMWHg533AGjR5t/93Ipu1KYvGoyWflZRNujCQ0IJa8oj+TMZNJz0knqlaRiSkRERKQOUCHl4+rE6EdaGkyfDgsWQEGBGWvXDiZMMPeBCgnxZHaV5jbcLFy/kKz8LJzhTix/rduyB9lxhjtJzUpl0YZFxEXG+WahKyIiIiKlVEj5MJ8f/Vi71lz/tGSJ2VAC4IwzzPVPV1wBfn6eza+K0rLTSN1n7jtlOar5hcViIdoezea9m0nLTquRBhZ1ZmRSRERExAeokPJRPjv64XbDRx+ZBdSXXx6J9+tnFlA9e9ZYB76a5ipwUVBcQGhAaIXHQwJCyDyQiavAVe33rhMjkyIiIiI+RIWUj/L06EeVFRbC4sXmHlCpqWYsIACuv97cA6pTJ8/mVw0cNgc2fxt5RXnYg8pv3pZflI/N34bD5qjW+/r8yKSIiIiID/KioQqpisqMfhQUF9TI6EeV5OTAY49B27YwfLhZRNntcPfdZke++fPrRBEFEBMWgzPCSUZuBkbJVMW/GIZBRm4GsU1jiQmLqbZ7Hj0yaQ+y42f1Kx2ZzMrPYtGGRbgNd7XdU0REREQ0IuWzPDX6UWkZGTBzJsydCwcOmLHmzWHcOBg5EhweyqsGWS1WEuP+v717D46qvv8//tolIUsCG4RAAgQUCUoQiDEBJuIIM1IDotXiiOUbJCAtVoNyqXgt2HqLClIRqaitXFoRRIVWqHQQEC/cQxAwEUylYLiHmN2QsOSyn98f+2N1JUAWMWeTPB8zO0M+57Ob9wnvmeQ1n3M+J1P7SvYpv8i3WhgZHqnyynIVugsVExWjkb1GXtRLLevdyiQAAEADwYpUPXWxVj+8xqs9x/doy4Et2nN8z09fudi5U8rM9K1AvfiiL0RddZVv5WnvXt9KVAMMUaclt0vW1P5TldI+RcUni1VQXKDik8VK7ZCqqddf/Evs6s3KJAAAQAPDilQ9dTFWPy7aBgXGSB9/LL3wgrRy5ffjAwb4gtPgwfV2A4kLkdwuWUlxSXWyg17Ir0wCAAA0UASpeuz06sfpMHSw9KAcYQ6ldkjVyF4jzxmGLsoGBVVV0nvv+Xbgy8nxjdnt0u23+wJU794X8WzrF7vNXieX0p1emcw5mBOwe6P0/cpkaofUi3pfFgAAAAhS9d6FrH785K3Ty8p8l+rNmOG7XE+SmjWTRo+WJk2SunT5OU4VNbDiviwAAAAQpBqEYFc/LniDgmPHpFde8b2Ki31jrVtL998vZWVJMTEX43QQpJ+yMgkAAIALQ5BqhIJ+cGxBgW/1ae5cyePxjV1+ufT730ujRkmRkXVTOM6qLu/LAgAAAEGqUartBgWxX/5PGv+89P77vg0lJN99T5MnS0OHSk2a1G3hOKe6ui8LAAAABKlG6VwbFKjaq06f7tBTa13qtHPY9+M33eQLUP37N6od+AAAAICaEKQaoZo2KHAqQslr85W+bJc6Hyz3TQwPl/7v/6QHH5R69LC2aAAAACCEEKQaqdMbFCxe/7oSlqzWrf/ZpzYlFZKk6uZRavK7e6Xx46X4eIsrBQAAAEIPQaqxKixU8syFuvq1t2QrLZUkVcW1lX3i79XknnukaB7gCgAAAJwNQaqx2blTmj5dWrhQqqqSTZK6d5cefFBhGRlS06ZWVwgAAACEPIJUY2CM9PHH0rRp0ocffj/ev79vA4nBgyU722QDAAAAtUWQasiqqnxbl0+bJm3d6huz231bl0+eLPXpY219AAAAQD1FkGqIyst9D8998UVp717fmMMh3X23NGmS1KWLtfUBAAAA9RxBqiE5dkx65RVp9mzp+HHfWOvW0rhxUlaW1KaNtfUBAAAADQRBqiEoKJBmzPCtQnk8vrHLL/etPo0eLUVGWlsfAAAA0MA0mB0GZs+ercsuu0wOh0N9+/bV5s2brS7p57d5s3THHdKVV0qvvuoLUamp0uLF0u7dvlUoQhQAAABw0TWIILV48WJNmjRJTzzxhLZt26akpCSlp6fr6NGjVpd28Xm90ooVvh33+vaV3n3XNzZ4sLRmjS9cDRsmhTW8xUav8WrP8T3acmCL9hzfI6/xWl0SAAAAGimbMcZYXcRP1bdvX/Xu3VuvvPKKJMnr9apjx466//779cgjj5z3/W63W9HR0XK5XHI6nT93uRemosL37Kdp06S8PN9YWJiUkSE9+KDUo4e19f3Mcg/lav4X85V/LF+eKo8cYQ4ltklUZlKmktslW10eAAAAGojaZoN6v2xRUVGhnJwcPfroo/4xu92ugQMHasOGDRZWdpG4XNLrr0svvSQdPOgba9FCuuceafx4KT7e0vLqQu6hXD257kkVlRcp3hmvqPAolVWWKedgjvaV7NPU/lMJUwAAAKhT9T5IFRUVqbq6WrGxsQHjsbGx+uqrr2p8z6lTp3Tq1Cn/1263+2et8YIUFkozZ0qvvSaVlvrG2rf3had77pGio62tr454jVfzv5ivovIiJcYkymazSZKcEU4lxiQqvyhfC3YsUFJckuy2BnGlKgAAAOqBRvmXZ3Z2tqKjo/2vjh07Wl3S93btkkaN8u26N326L0R17y69+ab0zTfSQw81mhAlSQXFBco/lq94Z7w/RJ1ms9kU74xX3tE8FRQXWFQhAAAAGqN6H6RiYmLUpEkTHTlyJGD8yJEjiouLq/E9jz76qFwul//17bff1kWp57d8udSzpzR/vlRZKV1/vW9s507fNuYREVZXWOdcHpc8VR5FhUfVeDwyPFKeKo9cHlcdVwYAAIDGrN4HqaZNmyolJUWrV6/2j3m9Xq1evVppaWk1viciIkJOpzPgFRJuuEGKjZVuv13auFFat04aMkSy1/v/pgsW7YiWI8yhssqyGo+XV5bLEeZQtKPxrNIBAADAevX+HilJmjRpkjIzM5Wamqo+ffropZdeUllZmUaPHm11acFp1kz6+mvfZhKQJCW0SlBim0TlHMwJuEdKkowxKnQXKrVDqhJaJVhYJQAAABqbBhGk7rzzTh07dkxTp07V4cOHdfXVV2vlypVnbEBRLxCiAthtdmUmZWpfyT7lF/nulYoMj1R5ZbkK3YWKiYrRyF4j2WgCAAAAdapBPEfqp6oXz5Fq5Gp6jlT3tt01stdItj4HAADARdNoniOFxiG5XbKS4pJUUFwgl8elaEe0ElolsBIFAAAASxCkUG/YbXZd0foKq8sAAAAA6v+ufQAAAABQ1whSAAAAABAkghQAAAAABIkgBQAAAABBIkgBAAAAQJAIUgAAAAAQJIIUAAAAAASJIAUAAAAAQSJIAQAAAECQwqwuAPWT13hVUFwgl8elaEe0ElolyG4jlwMAAKBxIEghaLmHcjX/i/nKP5YvT5VHjjCHEtskKjMpU8ntkq0uDwAAAPjZEaQQlNxDuXpy3ZMqKi9SvDNeUeFRKqssU87BHO0r2aep/acSpgAAANDgcS0Was1rvJr/xXwVlRcpMSZRzginmtibyBnhVGJMoorKi7RgxwJ5jdfqUgEAAICfFUEKtVZQXKD8Y/mKd8bLZrMFHLPZbIp3xivvaJ4KigssqhAAAACoGwQp1JrL45KnyqOo8Kgaj0eGR8pT5ZHL46rjygAAAIC6RZBCrUU7ouUIc6issqzG4+WV5XKEORTtiK7jygAAAIC6RZBCrSW0SlBim0QVugtljAk4ZoxRobtQ3dt2V0KrBIsqBAAAAOoGQQq1ZrfZlZmUqZjIGOUX5ct9yq0qb5Xcp9zKL8pXTFSMRvYayfOkAAAA0ODxFy+CktwuWVP7T1VK+xQVnyxWQXGBik8WK7VDqqZez9bnAAAAaBx4jhSCltwuWUlxSSooLpDL41K0I1oJrRJYiQIAAECjQZDCBbHb7Lqi9RVWlwEAAABYgiUEAAAAAAgSQQoAAAAAgsSlfSHEa7zcdwQAAADUAwSpEJF7KFfzv5iv/GP58lR55AhzKLFNojKTMtkJDwAAAAgxBKkQkHsoV0+ue1JF5UWKd8YrKjxKZZVlyjmYo30l+zS1P9uKAwAAAKGE68Ys5jVezf9ivorKi5QYkyhnhFNN7E3kjHAqMSZRReVFWrBjgbzGa3WpAAAAAP4/gpTFCooLlH8sX/HOeNlstoBjNptN8c545R3NU0FxgUUVAgAAAPgxgpTFXB6XPFUeRYVH1Xg8MjxSniqPXB5XHVcGAAAA4GwIUhaLdkTLEeZQWWVZjcfLK8vlCHMo2hFdx5UBAAAAOBuClMUSWiUosU2iCt2FMsYEHDPGqNBdqO5tuyuhVYJFFQIAAAD4MYKUxew2uzKTMhUTGaP8ony5T7lV5a2S+5Rb+UX5iomK0cheI3meFAAAABBC+Os8BCS3S9bU/lOV0j5FxSeLVVBcoOKTxUrtkKqp17P1OQAAABBqeI5UiEhul6ykuCQVFBfI5XEp2hGthFYJrEQBAAAAIYggFULsNruuaH2F1WUAAAAAOA+WOwAAAAAgSAQpAAAAAAgSQQoAAAAAgkSQAgAAAIAgEaQAAAAAIEgEKQAAAAAIEkEKAAAAAIJEkAIAAACAIBGkAAAAACBIBCkAAAAACBJBCgAAAACCRJACAAAAgCARpAAAAAAgSGFWFxAKjDGSJLfbbXElAAAAAKx0OhOczghnQ5CSVFpaKknq2LGjxZUAAAAACAWlpaWKjo4+63GbOV/UagS8Xq8OHjyoFi1ayGazWVqL2+1Wx44d9e2338rpdFpaC+oHegbBomcQLHoGwaJnEIxQ6xdjjEpLS9W+fXvZ7We/E4oVKUl2u13x8fFWlxHA6XSGRCOh/qBnECx6BsGiZxAsegbBCKV+OddK1GlsNgEAAAAAQSJIAQAAAECQCFIhJiIiQk888YQiIiKsLgX1BD2DYNEzCBY9g2DRMwhGfe0XNpsAAAAAgCCxIgUAAAAAQSJIAQAAAECQCFIAAAAAECSCFAAAAAAEiSAVQmbPnq3LLrtMDodDffv21ebNm60uCSEiOztbvXv3VosWLdS2bVvddttt2r17d8Acj8ejrKwstW7dWs2bN9ftt9+uI0eOWFQxQs1zzz0nm82mCRMm+MfoGfzYgQMHNGLECLVu3VrNmjVTz549tXXrVv9xY4ymTp2qdu3aqVmzZho4cKC+/vprCyuGlaqrqzVlyhR17txZzZo1U5cuXfTUU0/ph/uY0TON2yeffKJbbrlF7du3l81m07JlywKO16Y/iouLlZGRIafTqZYtW2rMmDE6ceJEHZ7F2RGkQsTixYs1adIkPfHEE9q2bZuSkpKUnp6uo0ePWl0aQsC6deuUlZWljRs3atWqVaqsrNSNN96osrIy/5yJEyfqgw8+0JIlS7Ru3TodPHhQQ4cOtbBqhIotW7botddeU69evQLG6Rn80Hfffad+/fopPDxcH374ofLy8vTiiy/qkksu8c954YUX9PLLL2vOnDnatGmToqKilJ6eLo/HY2HlsMrzzz+vV199Va+88ory8/P1/PPP64UXXtCsWbP8c+iZxq2srExJSUmaPXt2jcdr0x8ZGRn68ssvtWrVKi1fvlyffPKJxo4dW1encG4GIaFPnz4mKyvL/3V1dbVp3769yc7OtrAqhKqjR48aSWbdunXGGGNKSkpMeHi4WbJkiX9Ofn6+kWQ2bNhgVZkIAaWlpaZr165m1apVpn///mb8+PHGGHoGZ3r44YfNddddd9bjXq/XxMXFmWnTpvnHSkpKTEREhHn77bfrokSEmCFDhpi77747YGzo0KEmIyPDGEPPIJAks3TpUv/XtemPvLw8I8ls2bLFP+fDDz80NpvNHDhwoM5qPxtWpEJARUWFcnJyNHDgQP+Y3W7XwIEDtWHDBgsrQ6hyuVySpFatWkmScnJyVFlZGdBD3bp1U6dOneihRi4rK0tDhgwJ6A2JnsGZ/vWvfyk1NVV33HGH2rZtq+TkZL3xxhv+43v37tXhw4cDeiY6Olp9+/alZxqpa6+9VqtXr9aePXskSV988YU+++wzDR48WBI9g3OrTX9s2LBBLVu2VGpqqn/OwIEDZbfbtWnTpjqv+cfCrC4AUlFRkaqrqxUbGxswHhsbq6+++sqiqhCqvF6vJkyYoH79+qlHjx6SpMOHD6tp06Zq2bJlwNzY2FgdPnzYgioRChYtWqRt27Zpy5YtZxyjZ/Bj33zzjV599VVNmjRJjz32mLZs2aIHHnhATZs2VWZmpr8vavpdRc80To888ojcbre6deumJk2aqLq6Ws8884wyMjIkiZ7BOdWmPw4fPqy2bdsGHA8LC1OrVq1CoocIUkA9k5WVpV27dumzzz6zuhSEsG+//Vbjx4/XqlWr5HA4rC4H9YDX61VqaqqeffZZSVJycrJ27dqlOXPmKDMz0+LqEIreeecdvfXWW1q4cKGuuuoqbd++XRMmTFD79u3pGTQKXNoXAmJiYtSkSZMzdss6cuSI4uLiLKoKoWjcuHFavny51q5dq/j4eP94XFycKioqVFJSEjCfHmq8cnJydPToUV1zzTUKCwtTWFiY1q1bp5dffllhYWGKjY2lZxCgXbt26t69e8BYYmKi9u/fL0n+vuB3FU6bPHmyHnnkEf36179Wz549ddddd2nixInKzs6WRM/g3GrTH3FxcWdsvFZVVaXi4uKQ6CGCVAho2rSpUlJStHr1av+Y1+vV6tWrlZaWZmFlCBXGGI0bN05Lly7VmjVr1Llz54DjKSkpCg8PD+ih3bt3a//+/fRQI3XDDTdo586d2r59u/+VmpqqjIwM/7/pGfxQv379zniswp49e3TppZdKkjp37qy4uLiAnnG73dq0aRM900iVl5fLbg/8U7JJkybyer2S6BmcW236Iy0tTSUlJcrJyfHPWbNmjbxer/r27VvnNZ/B6t0u4LNo0SITERFh5s2bZ/Ly8szYsWNNy5YtzeHDh60uDSHg3nvvNdHR0ebjjz82hw4d8r/Ky8v9c373u9+ZTp06mTVr1pitW7eatLQ0k5aWZmHVCDU/3LXPGHoGgTZv3mzCwsLMM888Y77++mvz1ltvmcjISPOPf/zDP+e5554zLVu2NP/85z/Njh07zK233mo6d+5sTp48aWHlsEpmZqbp0KGDWb58udm7d695//33TUxMjHnooYf8c+iZxq20tNTk5uaa3NxcI8nMmDHD5Obmmn379hljatcfgwYNMsnJyWbTpk3ms88+M127djXDhw+36pQCEKRCyKxZs0ynTp1M06ZNTZ8+fczGjRutLgkhQlKNr7lz5/rnnDx50tx3333mkksuMZGRkeZXv/qVOXTokHVFI+T8OEjRM/ixDz74wPTo0cNERESYbt26mddffz3guNfrNVOmTDGxsbEmIiLC3HDDDWb37t0WVQurud1uM378eNOpUyfjcDjM5Zdfbh5//HFz6tQp/xx6pnFbu3ZtjX+/ZGZmGmNq1x/Hjx83w4cPN82bNzdOp9OMHj3alJaWWnA2Z7IZ84PHTwMAAAAAzot7pAAAAAAgSAQpAAAAAAgSQQoAAAAAgkSQAgAAAIAgEaQAAAAAIEgEKQAAAAAIEkEKAAAAAIJEkAIANGg2m03Lli2r9fw//vGPuvrqq885Z9SoUbrtttt+Ul0AgPqNIAUAsNQtt9yiQYMG1Xjs008/lc1m044dOy748w8dOqTBgwdf8Pt/DmVlZerSpYsmTZoUMP6///1PTqdTb7zxhkWVAQBqiyAFALDUmDFjtGrVKhUWFp5xbO7cuUpNTVWvXr2C/tyKigpJUlxcnCIiIn5ynRdTVFSU5s6dq1mzZunTTz+VJBljNHr0aPXr10+//e1vLa4QAHA+BCkAgKVuvvlmtWnTRvPmzQsYP3HihJYsWaIxY8bo+PHjGj58uDp06KDIyEj17NlTb7/9dsD8AQMGaNy4cZowYYJiYmKUnp4u6cxL+x5++GFdccUVioyM1OWXX64pU6aosrLyjLpee+01dezYUZGRkRo2bJhcLtdZz8Hr9So7O1udO3dWs2bNlJSUpHffffec53399dfr/vvv1+jRo1VWVqaZM2dq+/bt+utf/3qenxgAIBQQpAAAlgoLC9PIkSM1b948GWP840uWLFF1dbWGDx8uj8ejlJQUrVixQrt27dLYsWN11113afPmzQGfNX/+fDVt2lSff/655syZU+P3a9GihebNm6e8vDzNnDlTb7zxhv785z8HzCkoKNA777yjDz74QCtXrlRubq7uu+++s55Ddna2FixYoDlz5ujLL7/UxIkTNWLECK1bt+6c5/7MM88oLCxMI0aM0GOPPaZZs2apQ4cO5/uRAQBCgM388LcWAAAW+Oqrr5SYmKi1a9dqwIABknwrNpdeeqn+/ve/1/iem2++Wd26ddP06dMl+Vak3G63tm3bFjDPZrNp6dKlZ90cYvr06Vq0aJG2bt0qybfZxNNPP619+/b5Q83KlSs1ZMgQHThwQHFxcRo1apRKSkq0bNkynTp1Sq1atdJHH32ktLQ0/+f+5je/UXl5uRYuXHjOc//Pf/6jQYMGafDgwfr3v/993p8VACA0hFldAAAA3bp107XXXqs333xTAwYMUEFBgT799FM9+eSTkqTq6mo9++yzeuedd3TgwAFVVFTo1KlTioyMDPiclJSU836vxYsX6+WXX9Z///tfnThxQlVVVXI6nQFzOnXqFLAylJaWJq/Xq927dysuLi5gbkFBgcrLy/WLX/wiYLyiokLJycnnredvf/ubIiMjtXPnTrlcLkVHR5/3PQAA63FpHwAgJIwZM0bvvfeeSktLNXfuXHXp0kX9+/eXJE2bNk0zZ87Uww8/rLVr12r79u1KT0/3byhxWlRU1Dm/x4YNG5SRkaGbbrpJy5cvV25urh5//PEzPicYJ06ckCStWLFC27dv97/y8vLOe5/U4sWLtXz5cq1fv14tWrTQxIkTL7gOAEDdYkUKABAShg0bpvHjx2vhwoVasGCB7r33XtlsNknS559/rltvvVUjRoyQ5NvcYc+ePerevXtQ32P9+vW69NJL9fjjj/vH9u3bd8a8/fv36+DBg2rfvr0kaePGjbLb7bryyivPmNu9e3dFRERo//79/uBXG0eOHFFWVpaefvppJSUlad68ebr22mt1xx13hNx27QCAMxGkAAAhoXnz5rrzzjv16KOPyu12a9SoUf5jXbt21bvvvqv169frkksu0YwZM3TkyJGgg1TXrl21f/9+LVq0SL1799aKFSu0dOnSM+Y5HA5lZmZq+vTpcrvdeuCBBzRs2LAzLuuTfJtXPPjgg5o4caK8Xq+uu+46uVwuff7553I6ncrMzKyxlrFjxyoxMVETJkyQJPXp00eTJ0/W2LFjtWvXLi7xA4AQx6V9AICQMWbMGH333XdKT0/3rwZJ0h/+8Addc801Sk9P14ABAxQXF3fWzSPO5Ze//KUmTpyocePG6eqrr9b69es1ZcqUM+YlJCRo6NChuummm3TjjTeqV69e+stf/nLWz33qqac0ZcoUZWdnKzExUYMGDdKKFSvUuXPnGucvWLBAH330kebOnSu7/ftfxX/605/UsmVLLvEDgHqAXfsAAAAAIEisSAEAAABAkAhSAAAAABAkghQAAAAABIkgBQAAAABBIkgBAAAAQJAIUgAAAAAQJIIUAAAAAASJIAUAAAAAQSJIAQAAAECQCFIAAAAAECSCFAAAAAAEiSAFAAAAAEH6f1LpFS+TIHF7AAAAAElFTkSuQmCC\n"
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                  "\n",
                  "💡 Aplicación financiera:\n",
                  "- Analizar relaciones entre variables financieras, como riesgo vs. retorno.\n",
                  "- Observar cómo el ruido y la dispersión afectan la fuerza de la relación.\n",
                  "- Útil para entender tendencias y tomar decisiones basadas en correlación.\n"
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              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>simulador_correlacion</b><br/>def simulador_correlacion(n=50, correlacion=0.8, ruido=10)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/tmp/ipykernel_1627/1792502760.py</a>Simula datos para análisis de correlación y dispersión.\n",
              "\n",
              "Parámetros:\n",
              "- n: número de observaciones\n",
              "- correlacion: fuerza de la relación entre -1 y 1\n",
              "- ruido: desviación estándar del ruido aleatorio</pre></div>"
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      ],
      "source": [
        "# 📦 Librerías necesarias\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from ipywidgets import interact, FloatSlider, IntSlider\n",
        "\n",
        "# 🔹 Función para simular análisis de correlación y dispersión\n",
        "def simulador_correlacion(n=50, correlacion=0.8, ruido=10):\n",
        "    \"\"\"\n",
        "    Simula datos para análisis de correlación y dispersión.\n",
        "\n",
        "    Parámetros:\n",
        "    - n: número de observaciones\n",
        "    - correlacion: fuerza de la relación entre -1 y 1\n",
        "    - ruido: desviación estándar del ruido aleatorio\n",
        "    \"\"\"\n",
        "\n",
        "    # 🔹 Generar variable independiente X\n",
        "    X = np.linspace(0, 100, n)\n",
        "\n",
        "    # 🔹 Generar variable dependiente Y basada en correlación y ruido\n",
        "    Y = correlacion*X + np.random.normal(0, ruido, n)\n",
        "\n",
        "    # 🔹 Calcular coeficiente de correlación real\n",
        "    r = np.corrcoef(X, Y)[0,1]\n",
        "\n",
        "    # 🔹 Mostrar resultados\n",
        "    print(f\"Coeficiente de correlación simulado: {r:.2f}\")\n",
        "\n",
        "    # 🔹 Gráfica de dispersión y línea de tendencia\n",
        "    plt.figure(figsize=(10,6))\n",
        "    plt.scatter(X, Y, color='green', alpha=0.6, label='Datos simulados')\n",
        "    # Recta de tendencia ajustada por mínimos cuadrados\n",
        "    pendiente, intercepto = np.polyfit(X, Y, 1)\n",
        "    plt.plot(X, pendiente*X + intercepto, color='red', label='Línea de tendencia')\n",
        "    plt.xlabel('Variable X')\n",
        "    plt.ylabel('Variable Y')\n",
        "    plt.title('Simulación de Correlación y Dispersión')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "    print(\"\\n💡 Aplicación financiera:\")\n",
        "    print(\"- Analizar relaciones entre variables financieras, como riesgo vs. retorno.\")\n",
        "    print(\"- Observar cómo el ruido y la dispersión afectan la fuerza de la relación.\")\n",
        "    print(\"- Útil para entender tendencias y tomar decisiones basadas en correlación.\")\n",
        "\n",
        "# 🔹 Interactividad\n",
        "interact(\n",
        "    simulador_correlacion,\n",
        "    n=IntSlider(value=50, min=10, max=200, step=5, description='N° Observaciones'),\n",
        "    correlacion=FloatSlider(value=0.8, min=-1, max=1, step=0.05, description='Correlación'),\n",
        "    ruido=FloatSlider(value=10, min=0, max=50, step=1, description='Ruido')\n",
        ")"
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}