Geo_Data_Parsing.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Atlan.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mgvB0L32qVT0",
        "colab_type": "text"
      },
      "source": [
        "## Required Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jYKNrvCuVeio",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import requests\n",
        "import json\n",
        "from sklearn.cluster import KMeans, AgglomerativeClustering\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J5sR_gqVqe0_",
        "colab_type": "text"
      },
      "source": [
        "## **Query URL**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SR828L0qV58F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "overpass_url = \"http://overpass-api.de/api/interpreter\"\n",
        "overpass_query = \"\"\"\n",
        "[out:json];\n",
        "(\n",
        "  node[\"amenity\"=\"restaurant\"](28.52089106940537,77.17071533203125,28.57148207754995,77.26006507873535);\n",
        "  way[\"amenity\"=\"restaurant\"](28.52089106940537,77.17071533203125,28.57148207754995,77.26006507873535);\n",
        "  relation[\"amenity\"=\"restaurant\"](28.52089106940537,77.17071533203125,28.57148207754995,77.26006507873535);\n",
        "  \n",
        "  node[\"amenity\"=\"college\"](28.52089106940537,77.17071533203125,28.57148207754995,77.26006507873535);\n",
        "  way[\"amenity\"=\"college\"](28.52089106940537,77.17071533203125,28.57148207754995,77.26006507873535);\n",
        "  relation[\"amenity\"=\"college\"](28.52089106940537,77.17071533203125,28.57148207754995,77.26006507873535);\n",
        "  \n",
        "  node[\"amenity\"=\"university\"](28.52089106940537,77.17071533203125,28.57148207754995,77.26006507873535);\n",
        "  way[\"amenity\"=\"university\"](28.52089106940537,77.17071533203125,28.57148207754995,77.26006507873535);\n",
        "  relation[\"amenity\"=\"university\"](28.52089106940537,77.17071533203125,28.57148207754995,77.26006507873535);\n",
        "  \n",
        "  node[\"amenity\"=\"fuel\"](28.52089106940537,77.17071533203125,28.57148207754995,77.26006507873535);\n",
        "  way[\"amenity\"=\"fuel\"](28.52089106940537,77.17071533203125,28.57148207754995,77.26006507873535);\n",
        "  relation[\"amenity\"=\"fuel\"](28.52089106940537,77.17071533203125,28.57148207754995,77.26006507873535);\n",
        "  \n",
        "  node[\"amenity\"=\"bank\"](28.52089106940537,77.17071533203125,28.57148207754995,77.26006507873535);\n",
        "  way[\"amenity\"=\"bank\"](28.52089106940537,77.17071533203125,28.57148207754995,77.26006507873535);\n",
        "  relation[\"amenity\"=\"bank\"](28.52089106940537,77.17071533203125,28.57148207754995,77.26006507873535);\n",
        "  \n",
        "  node[\"amenity\"=\"hospital\"](28.52089106940537,77.17071533203125,28.57148207754995,77.26006507873535);\n",
        "  way[\"amenity\"=\"hospital\"](28.52089106940537,77.17071533203125,28.57148207754995,77.26006507873535);\n",
        "  relation[\"amenity\"=\"hospital\"](28.52089106940537,77.17071533203125,28.57148207754995,77.26006507873535);\n",
        "  \n",
        "  node[\"amenity\"=\"cinema\"](28.52089106940537,77.17071533203125,28.57148207754995,77.26006507873535);\n",
        "  way[\"amenity\"=\"cinema\"](28.52089106940537,77.17071533203125,28.57148207754995,77.26006507873535);\n",
        "  relation[\"amenity\"=\"cinema\"](28.52089106940537,77.17071533203125,28.57148207754995,77.26006507873535);\n",
        "  \n",
        "  node[\"amenity\"=\"marketplace\"](28.52089106940537,77.17071533203125,28.57148207754995,77.26006507873535);\n",
        "  way[\"amenity\"=\"marketplace\"](28.52089106940537,77.17071533203125,28.57148207754995,77.26006507873535);\n",
        "  relation[\"amenity\"=\"marketplace\"](28.52089106940537,77.17071533203125,28.57148207754995,77.26006507873535);\n",
        ");\n",
        "out body;\n",
        "\"\"\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y3Ofhdr_qlg5",
        "colab_type": "text"
      },
      "source": [
        "## **Network Response**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x-9Y80fCV78T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "response = requests.get(overpass_url, \n",
        "                        params={'data': overpass_query})\n",
        "data = response.json()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LtMp0aPhqsap",
        "colab_type": "text"
      },
      "source": [
        "### **Parsing JSON**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6jgdCY4ZV-RC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Collect coords into list\n",
        "coords = []\n",
        "for element in data['elements']:\n",
        "    if element['type'] == 'node':\n",
        "        lon = element['lon']\n",
        "        lat = element['lat']\n",
        "        coords.append((lon, lat))\n",
        "    elif 'center' in element:\n",
        "        lon = element['center']['lon']\n",
        "        lat = element['center']['lat']\n",
        "        coords.append((lon, lat))\n",
        "\n",
        "# Convert coordinates into numpy array"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_bi80ovhqxta",
        "colab_type": "text"
      },
      "source": [
        "## **Creating Elbow Curve for Kmeans Clustering of Spatial Data inorder to identify the key POIs** "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8j5tEozCWBvv",
        "colab_type": "code",
        "outputId": "e3f1c395-56d6-4da2-b3c4-8e7a26e8d565",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "sse = {}\n",
        "for k in range(1, 100):\n",
        "    kmeans = KMeans(n_clusters=k, max_iter=10000).fit(coords)\n",
        "    data[\"clusters\"] = kmeans.labels_\n",
        "    sse[k] = kmeans.inertia_ # Inertia: Sum of distances of samples to their closest cluster center\n",
        "plt.figure()\n",
        "plt.plot(list(sse.keys()), list(sse.values()))\n",
        "plt.xlabel(\"Number of cluster\")\n",
        "plt.ylabel(\"SSE\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jnf+WrnO/pCtqzF9dCUpJyyT9PB1+i6T70p97JU0FrgDenE775L7uh7U3/+vB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\nGnJYmJlZQ/8fbLwbcfkV1mkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qRRChmSyrJYF",
        "colab_type": "text"
      },
      "source": [
        "## **Clusting Co-ordinates and finding cluster centers**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9i8cPOpCXz3P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def doKmeans(X, nclust=2):\n",
        "    model = KMeans(nclust)\n",
        "    model.fit(X)\n",
        "    clust_labels = model.predict(X)\n",
        "    cent = model.cluster_centers_\n",
        "    return (clust_labels, cent)\n",
        "\n",
        "elbow_val = 50\n",
        "kmeans = doKmeans(coords, elbow_val)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jki9PhdHrVhU",
        "colab_type": "text"
      },
      "source": [
        "## **Plotting Points based on overfitted cluster**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3xx1-_htaYht",
        "colab_type": "code",
        "outputId": "8b5ff557-6df0-44e8-dd26-4f9ad87b2cef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        }
      },
      "source": [
        "target = kmeans[0]\n",
        "X = np.array(coords)\n",
        "fig = plt.figure(figsize=(20, 10))\n",
        "ax = fig.add_subplot(111)\n",
        "scatter = ax.scatter(X[:,0],X[:,1],c=target,s=50)\n",
        "ax.set_title('Public Places in Hauz Khas, Delhi')\n",
        "ax.set_xlabel('Longitude')\n",
        "ax.set_ylabel('Latitude')\n",
        "plt.axis('equal')\n",
        "plt.colorbar(scatter)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.colorbar.Colorbar at 0x7fcced89e7f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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e1m4f6+MVm9i8uarRByqA+vooj/93jpIRssd6Zw0g5qLtPo5zYM308gn4gnx9\n6E3c/cnNxN322TSyMIMvD/5BhzWvXPThOn5w06PEPY/6+ihmNPlzA+D3GYs/Wq9khEiXYsSbbce4\n91IyQkQ6VEnPfDZurmpyXTTqUVyYm+KIRGRPOeeY8doS/vPQLDZurKSkpIB43KNscxXhcKIC6sOl\n63nw4Vn8+Y9foXfvHrt1/C1bqvH7my87LStr+neJSFv0DPViaN4oVtQsajSVZ6LHettKJvICBZSE\nmu/pU5q7LzeM/BsLK2exqX4NPUO9OLhoEtn+jmkmGQ5H+eGPH6Wm9rMGnM0lIiDRUDkvN9Qh524v\n5xxlde+wYtuj1Me2UJIzjn0Lv0RuoE+6QxORDKBkhIh0qHOnjudXf5lOfXiXOdzN2G9ICf16a4oz\nkb3FXX99lWnPvU99feLucs2qLY22CYdjRKNxfnP78/zmlvN26/iDB5cQjTYe1gXg8xn779d394MW\naeCCwVfzz09+wYa6VTg8fObHOY+J+0xh1pYXW5xhAyBoIc4s/WarvUuCvizG95zckaHv8NKMRdTV\nN90ksykOmDA+/RVFzjneK/sFq6ufJ+7qANgaXsKKykeY1P9P7JM9Ns0Riuw91DNCRKQNvvC5Ebz9\n3ie89s4ywuEoDsjJCpKTHeRnV56S7vBEpI3Wrq3g6Wnzm+wBsyvPc7y/cDXbttXRo0fbh2H17VPI\n2IMH8978lUSjOzcADAb9nHvOobsdd9nmKhYsXE0oGGDCIUPIycmMO8SSHjn+PL49/Fesrl3Omtpl\n5PjzGdljAln+HIK+LN4oewqAuIsRsBAhXxbheD0ecQbm7sdJ/S5kSN7ItMUficb4+31vEY+3XsXh\n8xnBoJ/rrzqJrFD63+Jvqpu1UyICwCOC5yLM2vADpg6e3uzwFxHJfGbmB+YAa51zp5jZUOAhYB9g\nLnCRc67FTGr6f1OJpEB9XYRpD7zNC4/Mpr42wsETh/Gly45l4LDe6Q6tyzEzbvr2iZx23GienfEB\nVTVhJo4dwglHjSQ3u+0fChZ8uJb/vryA8soaxo0cwOlfGENRj8wb4rEtWsXDq57mrc2ziboYw/IG\nc/6gMxhVuH+6QxNplzfe+gjP89q8vd/vo6q6freSEQA33XAaN9z0GEuXbdhxHM9zXP/Dkxk6pFeb\njxOPe9x+x3RefmURgYAfLLHsO5cfxylTE3dgy8q2UbG1lv79e+5RjwvZew3MHc7A3OE7LTuh75c4\nrPg4FlbOIupF2Df/QAbnjsiIGVw2bNrGG//7iPWbKqmqrmt2OzPo27sQv9/HiOF9OP+cw9lvWGYM\ngVhR+chOiYiG4l4tW+rnU5IrkchjAAAgAElEQVQzPsVRiey9MrBnxJXAEmD7GM1fA79zzj1kZn8B\nvg78uaUDKBkhXV59XYSrz72LtSs3E0mWGr/2zHz+9+IH/PKeSxk1fkh6A+yCzIyxIwcwdmTjzuOt\ncc7xu3tmMG3GQsKRGM7B+0vWcv9T73LnT89lxNDMeJMFUB7ZyrXzf0ZtvHbHyOOl1Sv41Yd38r39\nvsahxSpBlb1XJBIjHm97MsJnRq+Sgt0+T35eFn+4/UKWf7yJJR+uo0dBNocfNozs7OBuHeef/3qD\nV2YsJhKNE2lQZXHnn18hFArw9LT5fLR0A+DwPMfoA0v5+c1nUZCvhrrdWVGoF0f1OjXdYexQWxvh\nom/dzeby6jZt7xwcNKqUG6/NvMrD+vjmFtb6CMe3piwWEelYZjYAOBn4P+BqS2RxjwUuSG5yL/BT\nWklGqDZKurxnH5i1UyICEiXF4boot/3wYVxLXaAk5d5duJJpMz6gPhzb0aArHIlRUxfh+t88nTGv\n19ZIJVe/91NqGiQitot4Ef6+4gE81/YPciKZZvy4IWRltS0hkJUV4JyzJhBqR2n48GG9OfXksUz+\n/AG7nYiIRGI8+dS8HU01GwqHY9z62+dZtHgtsVicWMxLDitZw9nn/YlNm7btccwiHe28r/21zYmI\n7V5/6yM2bKrspIj23D7Z4zCa/ln2iFCUNSLFEYnsvZwzPOdL2RdQYmZzGnx9c5eQfg/8ENj+Zncf\nYKtzO7oFrwFKW7suJSOky3vh0dk7JSIaKt9UxdpPWsrcS2erD0d5493lvPjWEjZu3sZjL8ynPtz0\n61VZXcfi5RtSHGHT7lp+L3Ve43nltwvHw6yuXZfCiEQ61kEHljJ8WG9CQf9Oy/1+wwxycoLk5oYI\nBf2ceMJovnrRkWmKtPVZN5qr8IhG49x08xOdEZLIbpu/YBXbqpofktEsM+a892mHx9Newwu/hM8a\nJyh9hOidczh5wVY/p4hI+mx2zk1o8PW37SvM7BRgk3NubntPomEa0uWF65rvm+IP+KirbbmTtnSe\n515bxG3/eBmfzwfOEYt7ZLdwJ9bnM8ora1IYYdO2RatZtG1pq9t5qDJC9l5mxq2/PJc//OllXn1t\nCX6/EY95fP6oEVz+zWP5aOl6IpEYo0cPpLhnx0xhuKcKCrKJ7caQkoY+XrGJTZu27fa0pCId7bmX\nF+7RfvG4l/g7mmFyg/2Z1O9O3tnwA+LJWUs8YvTKOZTD+tyS5uhEpB0mAaeZ2VQgm0TPiDuAIjML\nJKsjBgBrWzuQkhHS5Y09YjivPDUPr4lO1J7nGDQ8c3oQdCfzl6zhN3e/THiXTv2RXTrqNxSNxhk2\nqO0N7TrLtug2AuYn1mje+s/4zc/AHN31kb1bTk6I666dyve+/QW2lFdT3DOP3NxE48cjJg5vZe/U\n6dEjh4NHD2Te/JV43s6/630+a7SsoYDfT3lFjZIRknZ5e9hU1fM8Jk7Yt4Oj6RglOeOZOuRFNte/\nRyReQVHWSPKCu99PSkQgniFTezrnrgeuBzCzo4FrnXMXmtmjwDkkZtT4KvBUa8fKjCsS6UTnfusY\nQqHGd9uzcoJ88RuTydrNscnSMe557O1GiYiWmMG4UQPp37uwE6Nqm5Ks4hb7QRjGBYPPJODzN7uN\nyN4kJyfEgNLiHYmITPTDa6fSs2feTv0mcnKCDB1Sgs/XfAfyuOfRr19RKkLcK4SjMZ6eu5jrH3qe\nX/73VRauzoyhcd3B+Wcfvkf7DRqwT9qrk1pi5qdXzgRK849XIkKka7uORDPL5SR6SPyjtR1UGSFd\n3oChvfjlvZfy2x8+wuYNlfgDiWnjzv3m0Xzp8mPTHV63tezTst3e59tf/nwnRLL7sv3ZTO51BK+X\nvU3ENe5vcWr/4/lCn6PSEJlI99WrpID7/nEpL768iLdmLiM7K8CJU8Yw8bB9uea6h1iwcE2jffx+\nY/JRIyjczelIu6qNldVceOeDbKsLUxuJ4jPjiXcXccq4kfzk7OMyYsrLrqx3SQGTDh/O/95Z3uZ9\nAn7j2u+e0IlRiUgmcICXeVN74px7DXgt+XgFcNju7K9khHQLI8cO5u/Tr2Xdyi3U1YYZNKw3oTZ2\niZfOUViQw9bdaNQVDPiZv2QNwwenf5gGwFeHfpGK6FYWVC4Bl6iGiDuPCwadwcmlX0h3eLIbNtZV\n8tL6D6iK1TGm5yCOKBmOz1Q4uDfKzc3ijNPGc8Zp43dafvut5/Ob259n+ksfAIlKq0DAz5jRA7nm\n+yemI9SM9MMHnqOsqoZ4cliL5xz10RjPzv+QI/YfxJQx+6c5wq7vlz8+i+9f/yDvLVjd6rahoJ/z\nzj6MMQcOTEFkIiIdT8kI6TbMjNIhJekOQ5K+OHUcd/77deqbmIqvKc6x4w1yJgj6gvzggCtYW7eB\nxduWEvKFOKRoNPnBzC2Vlcb+veIt/rT0ZXCOiIuT6w/RO7sHd0+8lOKs/HSHJx3E7/fxox+czHcu\nP46Zs5ZTWxthzOiB7Ds0M5KbmWDD1io+WL2hyd+zdZEo974+V8mIFPnxD0/jvK/9lUgLQxkDfh/X\nfncKU447KIWRiUj6WMb0jOhISkaISFqcdtwY3nz3YxZ8tJa6ZqZebcjMmDh2SOcHtptKc/pSmtM3\n3WHIHphb/gl/XvYKEe+zN/y18Qhrasv50XsP87eJX09jdNIZ8vOzOeEL+vDWlLJtNQQDfsKxppsI\nb9xWneKIuq/innlMGDuYmbM/bnabUChAj4LUDy+qja6jKvop2f7eFGZlThNbEdk7KRkhImkR8Pv4\n7fVnMXPeCqbN+ICNm7ex7NNNNFX8EAr6mXzYcAb3L059oLLXinlx1tVVkJ2sdtjVv1e8RX28cSIs\n5jwWbl3NuroK+uf0TEWoImk3cJ9CIs0kIgCG9dknhdF0b7V1Ed6Z+0mL20SiMYbt2ztFEUEkXsXs\njdexuX4uPgvhXIy8QCkT+/6W/NDglMUh0l05wHOZ1zOivZSMEJG08fmMIycM48gJw4DEdJ9/uHcG\nH67YBCTGdedkhzj/lEP46lkT0xmq7GUeWfkOf176MhEvTtx5DM4r4Sejz2RU0WfTrX5avbnZ/YO+\nAGtrlYyQ7qMoL4djRw3jlUXLicZ3ni0oKxDgG8fsVk8yaYeXZizC85qfsQlg1Ij+9C4pSFFEMHP9\nt9ka/hCPKJ6LALAtuoLX1l7CiYOnEfDlpiwWEek6lIwQkYwxduQA/nnLRcTiHnHPIxKJkZeT1eK0\nfCK7evjTWdzx0fSdqh6WVW3gG+/8gwcmXcHg/ETvmIF5+7CqdkuTx4h6MfrnaLpHyRzReJxHZy3k\ngZnzqayt54D+vbj8CxMZP7S09Z3baMrB+zN94dJGy81gcIl+HlLl7XdX4FpokeTzGb/66dkpi6e8\nfhGVkeV47FpJ5oi7elZVPce+heekLB6R7ipO1+sZ0fWuSET2egG/j6xggIK87L0mEVEeruCVjW/y\n0oY32FTf/B136VxRL85dS19ucvhFOB7lqjn3c9P8x5i25j3OHzKRbH/jWXX8+BhZWEpproYFSWaI\nex6X/+NJbn/uTT4tq6Cipo63l63im3c/wTPzlnTIOZxz/OaZ15v8EByLe9w9490OOY+0Ljcn1OL6\nc047hPzcrBRFAxXhhTjX9BCeuKujrE7fGyKyZ1QZISLSDs457l/5BNM3zMBnPpxzOByTSg7jW8O+\nrCkiU2xVzWbirunyZg/Hp7Wb+bR2M69uXExBMJvzBh3OQytn4TmPqPPI9Ycozsrn1vFfSnHkIs17\nddHHvL9qA/XRnWdXqI/G+PkTr3D86P3IDrbvLd2a8krKa5qebjnmeby4YCk3nHFMu84hbXPS8aOZ\n+c7yJps7B4N+LrlwUkrjCfp64LPAjuEZO/OR5ddwNpHO5jD1jBARkZ3N2DSTlza+TtTFEt2FkmZu\nmUOf7BLOGjA1fcF1QyFfgHhL9c1JdfEIES/G+1tX8djnv8cL6xZQFa1nbPFgjuy1PwGfPwXRirTN\nk+8uoi7S9KxDZsbs5av5/Mih7TpHaz82HpkztXJXN2HsYA4ZN4Q58z7ZafrrrKwA3/3GceSmsCoC\noF/eZN4r+3mT6/wWYkiPM1Iaj4h0HUpGiIi0w5NrnyPsNb5bFPEiTFv3MmeUnqjqiBQakFtMn+we\nzfaCaCjuPBZXrsNnPr4+/OjOD05kD9U2k4hIcNRHW58euTUDigvpkZPVqPoCwO8zjh2laRzbIh6L\ns37lZkLZIXqX7lnFgJnxs+tP54WXP+DRp+awtbKWfYf04qLzjmDcmEEdHHHrgr48xvf6CfPKbibu\nIkCi+sxvOQztcQ5FWQekPCaR7sjrgh0WlIwQEWmHsnB5s+vCXpj6eJjcQOrngu+uzIyfjDmTb797\nL5F4rNW7uSGfn7L6KvqpWaVksMkjh/LB6sbDNACicY+xg/u3+xw+n3H96cdw/UMv7HQeM8gNhfjm\ncZpNozXP/vt/3PubZ4lGYnieo+/AYr7/m/MZOX7Ibh/L7/dx8pQxnDxlTMcHugcGFpxIQWgoy7be\nS2V4KTnBvgwvvJA+uUekOzQR2YspGSEi0g75gTyqYtVNrvOZjyx/y43IpOONKx7CfZ+7jL8ue5U5\nW1ZQF48S8Rp/iAOIeHEG5Gq8s2S2sw49iHtem0MkFsdrMJ4iOxhg6tgR9C7M75DzHD96P0IBP799\n9k1WllWAGUfsN4gfnXY0/Xv26JBzdFXP3v8//v6LpwjXfVYpt2rZRq4//y7ueOZqBu/fN43RdYyi\nrBEc2ueX6Q5DpFtyDuLqGSEiIg1N6TuZp9e9SMTbuUw6YAGOKpmI39R7IB2GF/ThN+PPB2Be+ad8\nZ/a91O/yGoV8fo7qPYLirI75ICfSWQpzs3ngu+dzw0Mv8MGajQT9fuKex3lHjOGqk47q0HNNHrkv\nk0fuS10kSsDnIxjQ77DWxGNx7r312Z0SEdtFw1Ee+P0LXH/XxakPTEQkwykZISLSDmeUnsjibUtZ\nUb2Kei8MQLYvi77ZvbhoSOrmgZfmjS8ewlUjT+T2Jc/jMx9xF8dnPkb16M9Px5yV7vBE2mRAcSH3\nXXEem6tq2FpTR2lxITmhxlPTdpTOPHZXs37VFqKRpquvPM8x/3/LUhzRZ+JxjzdmLmXGmx8C8PlJ\n+3P0pBEElGQSkQygZISISDsEfUFuGnUVCyqXMHPzHOIuzsR9xjO+52hVRWSQLw4+nBP6jWbGxiXU\nxsKMLR7MqMLSdIclsttKCvIoKchLdxjSQCgriBdvekphgGBW+95uR6Nxqqrr6VGQvVtJhDXrKrji\nmvup3PbZlK2v/28pv89/mT/f/mUGlha3Ky4RSS1N7SkiIo34zMfYogMZW3RgukORFhSGcjlj4CHp\nDkNEupjepT3pO2gfVi3b2GhdMBTg+HP2rPlnfX2UP909g+mvfIDD4fP5OHXKGL55yWRCwZbfwsfj\nHlde9+BOiYjtqqrrueqGh3jknsvx+brehxsR2Xt0vflBRKTLWbepkvcWr2HTlqp0hyIiItLIVbed\nT3ZOaKcP98FQgOI+PTj7W8fs9vGcc1z9/x7mhZcXEo7EiETi1NdHefr597nxF0+2uv/c+SvZuq22\n2fUVW2uZv3DVbsclIunhMDznS9lXqqgyQkQyVll5FTfePo2PPtlIMBggGo0xbtRAbr5yKj3yNV2m\niIhkhgPGDeGOaVfz4B9eZN6bHxHKCnL8OYdy1jePIb8wd7eP996CVaz4tIxINL7T8nAkxvyFq1m6\nfAP7D29+ho5Va7YQb2HoiOc51qyrYPzBg3c7NhGRjqJkhIhkpGgszrdufJCy8hrinrfjDdncD1bx\nvZ89xj2//jJmKi8VERF4f+Yy/vP7F/hk8Tryi3I55StHctrFR1FfG+ahO1/i1SfnUl8TpndpT758\n1YlMmnowPl/H3v0btF9frvvjVzrkWDNnf0xdfbTJdbFYnHfmftJiMqJXSQF+v49YrOmEhM+Mfn0K\nOyRWEUmNOF3vfa+SESKSkd6YvZzK6nri3s5vpGJxj9XrK5i/ZA3jRg1MU3QiIpIpXnzkHe666THC\ndYkP79Xb6vj3bc/xxjPvsXr5Rupqwju2XbVsI7/6zn0cOfVgrv/TVzM2qR0M+DED5xqv85kRCLSc\nSDnisGGEQgFiscbTjQLk52erKkJE0k49I0QkI81bvLrZu0LhaIyFS9elOCIREck09XUR/vzjx3ck\nIrYL10dZtnD1TomI7ZzneHv6At6dsSRVYe62yUfuT1ao6XuG5vNx1MT9Wtw/FAxw683nEAo2nn0j\nOyvA7b88D79fHwNE9haOxGwaqfpKFVVGiEhG6pGfjd9vxOONbwsFA37ycrLSEJWISPfw6qKP+esr\n77BycwW9CvL46ucP4axDD8q42Rfe/9+yZmNyXhNlBUmxqMfz/5nJYceO6qzQ2uWA/fpxxKHDePvd\nj6kPx3Ysz84KcsKxBzKgDdNyjh41gMf/fQWPPz2Xme8sx+/3ccxRIznj5LFkZQU7M3wRkTZRMkJE\nMtKUo0by0LS5xOOxRuuccxxzeMt3hUREZM/85eVZ3P3qbOpjiV491fURfvXUDN5ZvopbL5iaUUMb\nIuEozaccWlZZXt2hsXS0m354Kk9Om8cjT85hS0U1vUsKuOCLEzllypg2H6NHQQ6XXHgkl1x4ZCdG\nKiKdz1I6y0WqKBkhIhlpSOk+nDt1PI8+/x714UT5rQFZoQCXX3gUxUV56Q1QRKQLKttWzV9eeYfY\nLjMxhGNxXlq4jPdXrWfs4P5piq6xAw/dl9guM060hRmMnbR/J0TUcfx+H+ecPoFzTp+Q7lBERDqF\nkhEikrEuv+Aoxo0awIPT5rBu4zaGDCjmwtMOZezIAekOTUSkS3p10ceNEhHbxTzHva/PZexXMicZ\nUdy7B1845zBefeLdnfpG+PxGMBQkUh9psglkMDvIKRdNSmGkIiLt42k2DRGR1Jo4digTxw5Ndxgi\nIt3Cik3lLa5fsm5TiiJpu2//4hwKinJ5+p43wCAe9Rh16FCuvOU8Hvrji7z02Ls79Y8oLM7jF/++\njGJNbSkiklZKRoiIiIgIAH2LClpcH/A1np0h3fx+H5dcdwoXXjmFsvVbyS/MobA4H4CrbruAi687\nldmvLKKirIqDj9yPA8YOzqi+FyIirXEO4imc5SJVlIwQEREREQCOGjGE3z33Fl5TYxuAorzsFEfU\ndqHsIKVDezVa3rNXAVO+NDENEYmISEu6XktOEREREdkjw/uWcOCAPs2uX7K2jOkLlqYwIhERAfCc\nL2VfqaJkhIiIiIjscOfFp+H3NV0OHI7FuHP6zBRHJCIiXZGSESIiIiKyQ1YwgLXQtX3l5q0pjEZE\nRLoq9YwQERGRbiPqhXm/4iXe3/oSUS/C/gWHceg+p1MQLE53aBkjKxhoscFjblYwhdGIiIjD8NTA\nUkRERGTvFInX8a9PrqUisoGYCwMwu3wD71VM5+J9b2OfrAFpjjAzBP1+jh89nBcXLCPmeTutC/n9\nnHXoQWmKTEREuhIN0xAREZEuxzlHeXgdZfUr8VwcgFlbnqQisn5HIgIg7qLUezU8s/b36Qo1I11/\n+jH0KcwnJ/jZfaucUIDBvYr49vFHpDEyEZHuycNS9pUqqowQERGRLuXTmgU8u/YPVMcqMDP8FuDY\n3hczv2I6MRdpYg/Hhvrl1MQqyQsUpjzeTNQzL4cnr/kKz8xdzAvvL8Xv83Hq+AM48eARZAX19rEt\n3ntrKY/c9TKrl2+kd2lPzv7WsXxuyugWh8CIiHQn+msiIiIiXcb6uuU8svJmoturHxxEgRc3/D3x\nn2b48BPx6shDyYjtckNBzjviYM474uB0h7LXefyvM/j37c8RrosCsGVDJbd9/36mnDeRy24+K83R\nicjexkGX7BmhYRoiIiLSZbyx6T9Em6h+iLkwnvOa2CPBZwF6BEs6M7ROF4t7vLHkEx6ZtYA5K9bg\nXPPJF+k85Zu2ce9tz+5IRGxXXxvh+Qff5pMP16UpMhGRzKLKCBEREekyVtV+QHMVEIYRsFCjoRpB\ny2JSr/Pw2977tmjh6g1c8c//EonFiXsePjNKeuTx90vPorRY1R6pNPOFBfiaGYoRi8Z59Yk5fP2G\n01IclYjs7TzX9eoIut4ViYiISLcVsFDzK81xUv/vkOvvQciXQ5Yvl6BlMbHkLCbuc2bqguxg2+rq\n+cbfH6eipo6acIT6aIzaSJQ1Wyr52t8ew/NUIZFKdTVhYrF4k+u8uEfNtroURyQikpn23lsAIiIi\nIrs4qOgY5m6ZRpxYo3V9svdlTNGxHFQ4mfV1y4m5CP2yhxPy56Qh0o7zzNwlxOONh6B4zlFRU8fb\ny1cxaf/BaYisezrw0H0JhgLEY42HC+XkZTHuyBFpiEpE9mrO1DNCREREJJMdWXIeecGe+C24Y5nh\nI+TLYWr/7wDgMz+luSMYnDd6r09EAHywZgN10cbJF4BoLM7yDZtTHFH3NvKQIQw5oB/B0M73/PwB\nH4XF+RwxZXSaIssscS/Mlrr5lNcvwHNNf/+KSNemyggRkT3guShGQFO0iWSYnEABlw77I+9sfpIP\nKmcQc1GG509gUq9z6Rnql+7wOkW/oh4E/T6iTVRHBAN+ehXkpSGq7svM+MW/L+f2ax7g3RmLCYYC\nRCMxDpwwlB/ccRGBoD/dIabd8q0Psrj8TiDxN9TMz9iSHzGw4KT0BiaSoRzg0fXecyoZISLSBp6L\nsGHbPazfdhee2wY4fJZLn/yL6V90JT7LSneIIt1CbWwb71W8wIrqeWT78xnXcwrD8g/B7LNizxx/\nPkf3uYij+1yUxkhT56zDDuLeN+YBTcwW4uCYA4elPKbuLq8gm5v+9jW2bqlm4+otlPQtYp++aiQK\nsHLbMywq/wNxV//ZQgfzyn5GyF9En9wj0heciKSUkhEiIq1wLsZHm75MdXgODd/se66WDVV/pzoy\nnxG9/6MqCZFOtjm8mntX/ICYi+yYEeOT6vnsmz+eswf+aKeERHcyoLiQH502mVuefp245xHzPEIB\nP36f8fuvnEpOKNj6QaRTFO2TT9E++ekOI2M451hUfufOiYikuKtnUfmdSkaINKMr9oxQMkJEpBUV\ntS9QE15AU3cdHVFqIvOpCr9Dj+yJqQ9OpBt5YvUt1Hs1NJy6M+rqWVE9l0WVr3NQ0THpCy7Nvjhx\nDIcOG8gjsxawestWRvbvzTmHj6Z3oT4IS+aIetWE4+XNrq8Mf5TCaEQk3ZSMEBFpRVnNIzga38XZ\nznO1VNQ+r2SESCfaEl5LRWQDDRMR20VdmHfLn+nWyQiAIb168sNTJ6c7DJFm+VuaehfwW3aKIhHZ\nuzi6ZmVE96xnFBHZDV4T5aQiklq18Ur81nzjv9pYZQqjEZE94fdl0Sf3c1gTH0F8BBhYMDUNUYlI\nuigZISLSip45J2A036DSZzn0zD0xhRGJdD/7hAYQd9Em1xlGv5z9UhyRiOyJsSU/IugrxMdnVRI+\nyyI70JtRxVekMTIRSTUlI0REWlGSfy5+X0Eza/3khcZSkKUhGiKdKTfQg1GFkwk0UebttxCfKzk3\nDVGJyO7KDfbj+EGPsX/Pr1IQHEqP0HBG9ryM4wY+TJa/KN3hiWQsz1nKvlJFPSNERFoR8PVgVN+n\nWLH5Wqoj77C9kaURok/B1ygtukozaYikwEn9riDi1bGsajZ+C2AYDsdppVfTN2ffdIcnIm2U5S9m\nVPEVqoQQ6eaUjBARaYOsQCkj+z5INF5OLL6VYKAXfstXEkIkhQK+EGcPvJ6tkY2sq/uIkC+XIXkH\nE/Bp6koREem6HKmtWEgVJSNERHZD0F9M0F+c7jBEurWiUB+KQn3SHYaIiIi0g5IRIiIiIiIiIhnM\nQ5URIiIinaK+Jswrj8zijSffxef3cdx5R/D5MycQylIJvoiIiEhXo2SEiIikXeWWKq487v/YWraN\n+toIAEtmf8wTf5rOb5//ETn52WmOUERERCRNHF2yZ0SnTe1pZgPNbIaZLTazRWZ2ZXL5WDObZWbz\nzWyOmR3WzP7x5DbzzezpBsvNzP7PzJaa2RIz+15nXYOIiKTGX370IGXrKnYkIgDqa8OsXraB+3/9\ndAt7ioiIiMjeqDMrI2LANc65eWZWAMw1s5eAW4GbnXPPm9nU5P+PbmL/Oufc2CaWXwwMBA5wznlm\n1vv/s3ff8VVX9x/HX+fubMLeewsiQ1ARVNwbZ611W62ttdW2dtj+6qh2aKutWrVWq7ZuwVHqQEEE\nEUWWKArI3iEQyM6d3/P7IwEJuQkJ5N6b8X76uA/Dd76vhuTezz3ncxITX0REkiESjjJ32mJikVjN\nfaEo0/8zl+t+d3EKkomIiIiknqVljoxIWDHCWrsN2Fb1dYkxZjnQjcr/ltlVh+UAWxt46e8Dl1pr\nnapr5zdOYhGR5Is5FcScQjzudriML9VxUiJUEcY6ttb95SUVSUwj0vyVhsJMWfYlby5fiTFwzpDB\nnD/sMNJ96r8iIiJNR1J6RhhjegMjgfnAzcB0Y8yfqZwmckwtpwWMMQupHGHxR2vt61Xb+wHfMsac\nB+wAfmStXRXnntcD1wP07Nmz8Z6MiEgjiDrFbNx1O7vK38IYF2DokHkp3dv8vNUVJTKy08hsk07h\njpK4+7v175zkRCLNV0F5Oef9+3l2l1dQEY0CsHz7Dp5etISpl3+bnID6r4iINEctcWREwnpG7GGM\nyQSmAjdba4upHNlwi7W2B3AL8GQtp/ay1o4BLgX+aozpV7XdDwSr9v0T+Fe8k621j1trx1hrx3To\n0KERn5GIyKGxNsaK7Rezq/xNLCEcW4Fjy9lR8ixrdv4w1fGSzhjDt392Fv70mkUYf5qPK247NwWp\nRJqn378/m/zSsr2FCICKaJQtRcXcP+ejFCYTERGpLqHFCGOMl8pCxHPW2lerNl8J7Pn6FSBuA0tr\n7Zaqf68FPqByZAXA5mUa7zAAACAASURBVH3Ofw04vNGDi4gkUGHFTELRTVjC1bY7BCkOzqEiUmOw\nV4t3znWTmPy9E/H6PaRnBUjPCuBP83LFbecy4dwxqY4n0izEHIe3V64i6jg19kUch1e//CoFqURE\n5FBZDI5N3iNZEjZNwxhjqBz1sNxae/8+u7YCx1FZYJgExJtikQuUW2tDxpj2wHgqG10CvA6cAKyr\nus7XiXoO0jh2h3fz5rY3Wbh7IQBjcsdwZpczyfXlpjiZSGoUVszEsWVx91nrUFQxhzTvgCSnSi1j\nDFf/9gIuvOlUls5didvtYsSEIaRnaUi5SH2FYzFicQoRe1REojjW4jItb6iviIg0P4nsGTEeuBz4\nwhjzWdW224DrgL8ZYzxAkKq+DsaYMcAN1trvAkOAfxhjHCpHb/zRWrunnP9H4DljzC1AKfDdBD4H\nOUQ7Qju488s7CcaCxKjslP9B/gd8UvAJdxx2B+397VOcUCT5KntCGCr7+e7HuFpdz4h9ZeVmcuzZ\no1MdQ6RZCng8dMzMYFtJadz9fdrmqhAhItJM2RbYMyKRq2nMpfLVdjw1XmlaaxdSVViw1s4Dhtdy\n3ULgzEaKKQn24sYXKY+VY/d50xUjRkWsgpc2vcSN/W9MYTqR1GibfjY7y6bi2PKaO62lTdpJyQ8l\nIs2eMYYfH3s0d743q1rPCIA0j4efTKitZ3jLFXMcFu7cxO5QBcNyO9M9s02qI4mISJWkrKYhrZNj\nHT4r/KxaIWLvPhyW7F6CtRajT2mklcn0H0l2YDxFwblY+82ylS6TRsfMK/F5uqQwnYg0ZxcOH0ZB\nWTkPzZuPx1XZGizmOPx04nhOHzQwxemS69P8jdw491VCscrCTMSJMaFLX/56zLmke1rvCDQRkaZC\nxQhJGFv1T22cqn/cuJOYSiT1jDH0b/8o20ueYXvJv4g4Bfjd3emacxNt089JdTwRaea+d9RYLht1\nBAs3b8EYw5Hdu5Hm9aY6VlJtKi3k6g9eoiIWqbb9w21ruWXef/nHxAtTlExE5OA4tU46aL5UjJCE\ncRs33dK6sblic9z93dO64zYqREjrZIyHztnX0jn72lRHaXa2rsvnuT9NY/70pQCMP3sU37n1bDr2\naJfiZCJNR4bPx3F9+6Q6Rso8tfJTok6sxvaQE2POtjVsKSuiW0ZOCpKJiMgeCV3aU+TiHhfjc9Uc\nCukzPi7ucXEKEolIc7bp62388Li7mPXKJ5QWllNaWM6MF+bxg4l3krdhR6rjiUgTsXDHZiI2/soi\nPreHFYX5SU4kInLwrKVFLu2pYoQk1PCc4Vzf53raeNvgc/nwuXzkenO5vu/1DMsZlup4ItLMPPrL\nF6goDeI4+zTFjTqUF1fw5B1TU5hMRJqSdoGMWvc51tLGl5bENCIiEo+maUjCjW47mlG5o9ge2g5A\nJ38nNa0UkQaLhKN8NmcFNk4rGsexfPymmuKKSKXLB4xmwY6NlEcjNfZlen2MbN8tBalERA5eS1za\nUyMjJCmMMXQOdKZzoLPeKIjIQXFi8Ydc7xE7wH4RaT1O6NqP03oMJt3zTeNOn8tNusfL3489H5de\ni4iIpJxGRoiISLPgT/PRc1AX1n+1Je7+IWP6qtgpIkDlhyD3jTuLs3sO5bnViykIljG2Y0+uGDiG\nLunZqY4nItJAye3lkCwqRoiISJO1dV0+T//uVea/8znWsfQe1h3jMlin+lwNj9fNtXdqqT4R+YYx\nhuO69uO4rv1SHUVEROJQMUJERJqkrWu3c9MJd1drWPn1onVxjzXG4HJr5qGIiIi0TOoZISIikiT/\nunMq5futnFGbaCTKC39+MwmpRERERKQxaGSEiIg0iOM4/O/JD5jy4DsU5BXRtlMOF9x0CudcNwmX\nq/Fq3HumZtSHtbB66YZGu7eIiIhIU2FBPSNEREQeuOlp5ry2kFBFGIAdW3bx1J2vsnLhOn7xz+sO\n+fqlReXMnvopkUi0Qedlt8s85HuLtGSloTBf79xJps/HgPbt1PBVRERSSsUIERGpt/VfbWH2qwsI\nByPVtocqwsx7czFrv9hE3+E9Dvr6n777Ofdc/VjlRwD1GxQBgD/dx7nfO/Gg7yvSksUch/tmz+XZ\nJUvxuFzErKVdehr3n3U6o7p1TXU8ERE5EFs5CrSlUc8IERGpt4/fWkIsGou7LxKOMnfaooO6bt6G\nHSx6fxn3XPUYofLw3lEX9RHI8DPsqAF06NaWhTOWUVZccVAZRFqq+2bP5bklSwlGo5SGw1REImwu\nKuaKl6by6abNqY4nIiKtlEZGiIhIvcWisVobSjqOrbVQUZv1y7fwp+v+yZY123Ech2i4/ue73C6G\njOtH38O6M+P5eSxfsAaAaCTGxTefznd+fraGoUurVxoK82xVIWJ/wWiUy16cwmGdOnL/WafTp21u\nChKKiEh9OLS81zQaGSEiIvU25qTh+APeuPsCaX7GnnJ4va+1a3sRPz3tj6z/ajPhYKRBhQgAf5qP\n879/Mu8++xEVZSHKS4KUlwQJByO88uA7/O/JWQ26nkhL9PXOnXjrWPbWsZZledu58NkX2VWuUUUi\nIpI8KkaIiEi9DRrdhyFj++PbryDhC3gZOLIXQ8f1r/e1pj3xPuFQ5KDmQPrTfVz5m8k8f9+0uFM6\nQuVhnrt3Go7jNPziIi1Ihs9H7ACr0lggGI3w/GdLkxNKREQEFSOkldtZXMb0z1Yya9kaykORA58g\n0soZY7jzxZs48+rjCaT78fo9+NN9nHbFRO6eckuDpkUseG8ZkVD9V8wwBlweF740LxnZafz38fdZ\nu6z2+e5lRRWU7C6r9/VFWqKB7dvRNj3tgMeFojFmrVmXhEQiItJQFrDWJO2RLOoZIa1SzHH4/dRZ\nvLHgy6rhq4aY4/Dzycdx4dH1H2Yu0hr5/F6+9/tvce2dF1BaVEFmThoeb8N/naRnBep/z4AXf5qP\nsuIKwhURdlUUHfAcay2BdH+Dc4m0JMYY/nLmaVz1yquEIlHqGiuU6fMlLZeIiIhGRkir9Nj0T5i2\n8CvC0RhloQhloTDBSJR7X5/NvJUbUh1PpFnweD20aZ91UIUIgNOvnEggo37FgnAwQsnuMpxY/aZd\nuNwujjx5OP40vbkSGd29G9OuvIzzDhtaa/uzdK+Xi0cMS2ouERGpL4Njk/dIFhUjpNWJRGM8O2cx\nwUiczuKRKI9N/zgFqaQlKigoZcvmXcSi6lsQz8TJYxg0ug/+9LoLBm6vG7en/r+ufGle2rTP4od/\nvuxQI4q0GL3b5vKnM0/l4XPPIuDxVCtKpHk8HN6lM6cOHJCyfCIi0vpomoa0OtuLSqirl9eqbTuT\nF0ZapDWrt3Pfn95k/foduN0uvF43V10zkXMnj9ZSk/twe9zcM+Vmpj87l2lPzKKksJyeA7uwdW0+\nhTuL8XjchEMRuvfvzLova+8NAeD1e+jcsz3pWWlMmDyG066YQGZOepKeSetQES1ic/liLJYe6aNJ\n8+SkOpIchFMHDaBzdhZ/nzefz7flkRMIcPmoEXxrxHA8Ln1GJSLSVB1Mw++mTsUIaXWy0wNE6xjq\nnZ1e/3nsIvvbtq2QH//oP1SUV67wEInECAYj/PMfs4hFHS64aGyKEx6aXduLeOc/H7Ju2Sa69OnI\nGVdNpHOvDgd9PY/Xw5lXH8+ZVx9fbfvGlVspLiil5+CuzH51AU/89pW4q2bs6w+v/5T2XXMPOovE\nZ61l/s6nWLLrZVy4AYNDlCPaXsRR7a9Rga0ZGtGlM49fcG6qY6SUrXpVr+9fEZHUUTFCWp3stABj\nB3Tnk5Ubie1XYgx4PVwy/ogUJZOW4IXn5hGOszJLMBjhmac/5JzJo/F63SlIdugWz/qSOy/7O07M\nEglF8HjdvP7YDH70wBWcdMnRjXqvnoO67v36+AuO5InfvlzrsV6/h/FnjVIhIkG+KnqLz3ZNIWbD\nxPbZvnTXVLI9nTgs96yUZRNpqBWF+fxhyUzmba/sD3VMp178auSJDG7TMcXJRETqlsxVLpJF4/Gk\nVbrj4lNom5VOYJ/Ge2k+L0N7dOKyiSNTmEyau0/nryEWiz+OzlrL+vU7kpyocQTLQvzu8kcIlYeJ\nVBVbopEY4WCEB2/5Nzu37k7YvbNyM7n1H9/Fn+bDvV8hx+V2cfwFY/nJ369O2P1buwU7/0PUBmts\nj9ognxb8OwWJRA7O8t35XPjeM3yYt46YdYhZhw/z1nHhe8+wfHd+quOJiLQ6GhkhrVKnNpn895dX\n8caCr3j/i9UEvB7OHXsYJwzrh8etGp0cPK+v9h+rjmPx17G/Kfvof4tr3Wet5b3nP+LbP0vcJ+TH\nnj2aASN68+ZTH7BhxVZy2mUy8vghjDrhMHLaZSXsvq1dzEYpjdZeQCuLFhCzEdzGm8RUzc+X2/OZ\nv3EzAY+bkwf0p0NmRqojtUr3LJlBebTmyLXyaITfL5nBfyZdmoJUIiIHZm3LHBnRPF8VizSCjICP\nSyccwaUTNC1DGs9ppx3Os//5iHC45motOW3S6dGzXQpSHbqCbYVxp58AREJR8jYmvvFrp57tuOb2\nCxJ+H/mGCzce4487MgLAY3y49FKiVhWRCDe8+l8WbdlKzHFwu1zc/f5sbjrmKL5/dPPuH9PcxByH\nT/JrX7r74/wNe/8fiYhIcugnrohIIzr3vNFkZQfYvyea3+/l1l+c2WybpfUc1AVfIP4SnIF0H/0P\n75nkRJIMxhiG5JyGK87IB7fxMjjn1Gb7PZ0Md8x4n4WbtxCMRok4DsFolHAsxiOfzGfWmrWpjtf6\n1NWJvgV2qReRlsWxJmmPZFExQkTkIEQiMeZ+uJJXpy5gwadricUcrLU8/OC7lJaGqi2/5HIZfnbr\nGYwc2TtleQ/VkScPJz0rEPeNp8vtZtLFjdvAUpqOozt8l1xfdzzmm5WGvCZAjrc7x3S4LoXJmraS\nUIj/LV9JKBarsa8iEuWRjz9NQarWy+1yMbpD91r3j+7QXaMiRESSTGMrRUQa6Ksvt3DbL18iFnOI\nRGJ4vG4yM/x8+zvH8OGclYSC1aczOI7loQenc8KJQ5vtp8huj5t7p93KLyf/hdLCMmKRGG6vB4/P\nzd0v/5iM7LRUR5QE8bnTubj3Y6wpmcPXRTOxWAZmn0T/rAm4XfFHywhsKynB43ITomYxAmD97sQ1\nfZX4bht5IpfOfJaKWPVpdGluD7eNPDFFqURE6se2wBFcKkaIiDRAaUmQX9z6AuXl4b3bIpEYwYow\nj/59BpFI/DcexcVB/v3Mh1x51cRkRW1U1lp25RUx6eKj2LFlFx26tGXwkX058uRheLz6VdLSuY2X\ngdknMjBbb9jqq0NGBpE4oyL26JiZmcQ0AjCiXVeeP/EyfrfoPZbu2lq5rW1X/m/0yYxo1/UAZ4uI\nSGPTK0gRkQZ4990vcJyapWlrIRqt/Y0HwKtTF3L5FRNwuZrX6IhgeYhfX/BX1nyxkWBZCJfbhdfn\nYWL+kYw77fBUxxNpknLT0ji2d0/mrNtA1HGq7UvzevjukaNTlKx1G9GuK1NOuZJQ1egIv1svhUWk\neWiJq2locpyISAOsXrWdYDD+qhIHGj4XDkXZkV+cgFSJ9c//e5mvl6wnWBYCwIk5hCrCzHl9Ae/8\n+8MUpxNJna+253PNK68x9C8PMuz+h7hl2ltsKizau/+Pp59K95xsMryVDUBdxpDm8XDawIFMPmxI\nqmILlUUIFSJERFJLP4VFRBqgS9c2eL3uWqdj1MVai8/fvH7shoMRZrzwMZE4y3qGysO88uB0zrjq\nuBQkE0mtpdvyuOzFVwhGonsXYnhzxdfMXrue/171Hbrn5NA2PY23r7mCGavXMHvtetK9XiYfNoTD\nu3ROaXYREZGmoHm9KhYRSbHTTjuc55796KDO7d2nA7m5GY2cKLGKd5XWub9gq5rwSet0+7szqYhU\nb4ToWEtpOMwDH87jL2edDoDX7eb0QQM5fdDAVMQUEZEWwGI0TUNEpLXr0DGbq69p2EgAl8uQlubl\nJz87I0GpEie7Xd1N9tp3zU1SEpGmozgYZMWOnXH3Odby3qo1SU4kIiLS/KgYISLSQCNH9iItzVvn\nMW63ITc3g5ycNCadeBiP/uMaBg5sfkOzfX4vJ116DL5AzefrT/dx0Y9PS0EqkdSKWUtdn085LXH9\nNRERSSmbxEeyaJqGiEgD9ezVnjgLalTjcrt45tkbyMjwJydUAl3/u4vZuGIrq5ZsIFgRwu1y4fa6\nOeHCcZx2xYRUxxNJuty0NLq3yWHdrprTlAxwTK+eyQ8lddpWXkxeeQk9MtvQPtC8psuJiDQlxpgA\nMAfwU1lPmGKtvd0Y0wd4EWgHLAIut9aG67qWihEiIg0UCHg566wjmDplQa3HHDGyV4soRAD403zc\nO+1Wln+6hoXvL8Pj9TD+rFH0Gtw11dFEUub/TjyeH7w2jWC0et+IgNfDTyeOT1Eq2V9+RSk/nvc6\nn+3cis/tJhSLckLX/tx71FlkeVvGz2gRaQVsk1raMwRMstaWGmO8wFxjzNvAT4AHrLUvGmMeA64F\nHq3rQipGiIg00OdLN/Lxx6vrPKZ3r/ZJSpMcxhiGjuvP0HH9Ux2lWamIFvJV0TsUhNaQ4+3G0DZn\nkOXtmOpY0ggm9unNI+edzT0zZ7OhsBCAwzp15I6TTmBQh5b197+5CsdiXPDeM+SVFxOzlpBTWTia\ntXU1V816kSknX4ExTebFvYhIs2CttcCeDufeqocFJgGXVm1/BrgDFSNERBrPu9O/4K/3v00oFK3z\nuHC44Ut/SsuyuWwJ/9v8aywOMRvGhZfFu17kxM4/Z2DOpFTHk0YwsU9vJn63N0XBIG7jItPvS3Uk\n2cf0zSsoDFUQ26+HR9iJsaIwn88KtjKyfbcUpRMRaaDktiNqb4xZuM+fH7fWPr7nD8YYN5VTMfoD\nfwfWAIXW2j0vkDcDB/wBq2KESBOyYcdupi34il1lFYzq042TRwzA79Vf06YiFIrwtwfeOWAhIhDw\nMuIIzRlvzSJOkDe3/B9RG9y7zSECFt7Pu4+u6YeT6dWn5y1FTiCQ6ggSx7y8DZRF409XjjgxFu7Y\nVGsxIuo4zNq6mrc3rcAAZ/YcynFd+uJ2qfe7iLQKO621Y2rbaa2NAUcYY9oArwGDD+Ymepcj0kT8\n871Pefy9T4g5lqjj8NaiFdw/bQ7//tEldG+Xk+p4AixetB6X68BDej0eN+OPHZiERNJUrS35EGpZ\nUcFiWVE0nTHtv5PkVCKtS5bPjwuDE+fjRI/LTbon/kiW8miYS2Y8y9qSAsqjEQCmb17JkDadeHbS\npfjdevksIsnXhHpG7GWtLTTGzAKOBtoYYzxVoyO6A1sOdL7KuyJNwOK1W/jnjPmEojGijgNAeTjC\nrtIKfvyv/6Y4newRDtc9ImKPs84ZicfjTnAaacpKozuI1tJAOmbDFEe2JTmRSOtzXu9h+NzxfxZb\n63Bqj0Fx99372Sy+LtqxtxABUB6NsGxXHg8u+zAhWUVEmgtjTIeqEREYY9KAk4HlwCzgwqrDrgTe\nONC1VIwQaQKenb2YUKTmG13HWjbtLGTVtp0pSCX7Gza8B5FI3b0g/H4vxx0/JEmJmr9YNMbSD1fw\n8VufsWt7UarjNJpcXy88rvifunpMgPZ+NQIVSbQhuZ24uO8I0j3eats9xsWNhx0bd4lPx1peWfs5\nYafmz/qQE+W5VUsSlldEpC7WJu9xAF2AWcaYz4EFwHvW2v8BvwB+YoxZTeXynk8e6EIaZybSBGwq\nKKq1J43H7WLb7mIGdNH88lRr1y6Tk04ZxvszvyQUrFk88vrcHD6iBwMHdk5BuuZn/vTPue+GJ3Fi\nMTCGSCjCCReO40cPXI6nmfdK6Z15FF6TToQg+3ecMsbFoJyTUhNMpJW5ffQplEZDvLZu2d6/iS5j\neHzFx0zo0ocR7aovURyMRQg7tY+CK44EcazFpVU4RKSVstZ+DoyMs30tMLYh19LICJEmoH/ndrW+\nsInEHHq2z01yIqnNLbeczuTJYwgEvLjdlT9CjQGfz8M554zirrsvPMAVBGDtF5v4/dWPUVpYRnlJ\nkPLiCiKhKLNfXcA/bnsp1fEOmcu4Oa/nX8jwtMVr0nDjxetKx+/K4twe9+J3Z6Y6okirsLwwn7c2\nrqhWEgw7MUojYb47+2ViVVMj90hze8nx1d6QtEt6lgoRIpJ0lsqeEcl6JEvz/uhJpIW44vjRzPxi\nNcH9pmp4XC6GdOtA744qRjQVbo+L62+YxJVXT2B7XlFlUcLjJjs7Da9XfSLq68UH3iQcitTYHqoI\nM/3ZuVz1m/PIyElPQbLGk+vvyZX9XmRj2QIKw5vI9HSiT9bRuI33wCeLSKN4dtUiInGmXACUREL8\nYO5UJnUbwFk9h5Lh9WGM4XtDjuavX8yhIlb9d3Ka28uNQ8cnI7aISKugkREiTcCQ7h351fkn4Pe4\nSfN5cLsM6X4vPTu04YGrz051PInD7/fSs1d7OnbKoV27TBUiGmj5grVYJ/7kJI/Pw6ZVeUlOlBgu\n46Z35lEc0fYi+mdPVCFCJMk2lxURq2UCdNiJ8d6WVfxu8Xsc88ZDfF5Q2Vj22sHjmNx7GH6Xm4Db\nQ5rbi9/l5pJ+R/Dt/jVGJouIJJ4FrEneI0k0MkKkiThv3DBOGNaPGZ+voqg8yGE9OjNuQA+MhoNK\nC5Sdm8GOzbvi7otFYmS3rdlYTkSkoYbndubT/I1xG1LusWfVjKs+eIFPJv8Yn9vNPWPP4AeHjWfO\ntrUY4Liu/eiSnp2k1CIirYNGRog0IW0y0rjw6MO59sSxHDWwpwoR0mKdc/2J+NNrrjZhDHTt05Gu\nfTulIJWItDTfGTAat6nfy92I4/D+1lV7/9wtI4dv9x/JJf1HqhAhIpIAKkaIiEjSnXTJ0Qw/eiCB\nDP/ebT6/h/TsNH7xxHUpTCYiLUnXjGwem3AhmR4fGR4fLmov8oedGJvLWs4SwyLSsjShpT0bjaZp\niIhI0rk9bu586UfM+99i3n56DqXF5Yw5aThnX3s8uR1zUh1PRFqQCV36sOD8m5m5ZRVvrP+SD7at\nidvU0udy0ztTDaNFRJJFxQgREUkJt9vFhHPHMOHcMamOIiItnN/t4YyeQxjbsSfH/fcRItQsRgTc\nHo7v2j8F6URE6iGJIxaSRdM0RERERKRVaB/I4LEJF5Lu8ZLuqVzdJsPjo60/nWcnXYrHpZfGIiLJ\nopERIiLS6lhrKYpswVqHNr7umHo2uBNpisrCYWZvXE95JMyYLt3o3UZTDeoyoUsfPpn8I97auIK8\nimL6Zbfj5G6D8Lm1RPOBFO4o5u2nZjH/zc/YnV/ZX8Of7scY6NSrPWdddyJHnjpCDbhFGp3BJnHJ\nzWRRMUJERFqV9aWf8EHeAwRjJQD4XGlM6PRDBmSfkOJkIg336oov+c2sGbiMwQIxx+G4Xn148NQz\n8Xv0Mq82mV4/F/cbkeoYzcrqz9Zz66m/J1QeJhatOc1l/ZebWTp7OePPGc2tT96ggoSIHJA+ChIR\nkVZjS/lS3tlyJ6XRHURtkKgNUh7bzcxt97KuZF6q44k0yKJtW/jNrBlURKOURSKURyKEYjHmbFjP\nb2fPTHU8aUGstdz5rb9SXlwRtxCxR7AsxEf/XcT8tz9LYjqRVsIm8ZEkKkaIiEirMS//caI2VGN7\n1Ib4KP+xFCQSOXgPL5hPRTRaY3swFuWNlcspDgVTkEpaopUL1lBcUFqvY4NlIaY9NiPBiUSkJdD4\nPRERaTXygytr3VcU2UbEqcDrSktiIpGD99XO/Fr3+dxuNhQVMbxj4KCu/UX+dmauWwPAiX36Mbxj\np4O6jrQMu/OLcbnqP+1iTz8JEWkkFvWMEBERac5cxkPMhuvcL9JcdEjLIL+sLO6+cCxGu7SGF9ai\njsONb0/jw43rCUVjgOXxxQs4tkcv/n762XjV5LFV6n1Yd0LBSL2O9XjdDBs/KMGJRKQl0DQNERFp\nNfplTcTE/dVn6JExGrfxJj2TyMG6+ohRpMVpUukyhqEdOtI1K7vB13x00XzmbFxPRTSKg8UBKqJR\nPty0gUcWzm+E1NIcdenTkc69O9TrWI/Pw/k3nZrgRCKtkHpGiIiINF/HdLiOgDsb1z4DA1248bky\nmNjphylMJtJw5w0eygm9+5Lu+aaIlubx0DaQxt9OOfOgrvmvzxYTjNeHIhrlqaVLsDaJr1KlSbnu\n95dg6piq4XIZctpn8bvXfkrn3h2TmExEmiuNRxURkVYj09uBS/o8weKCF1hd8gHWWvpmjWd0u0vJ\n8mpOvDQvLmN4+LSz+GjTRl5ZvoziUIgTevfh/MGHkenzNfh6kViMomDtTS9LwiHCTgy/Wy8fW6Nx\nZ4ykW79ObFmTh3Wq7/P4PNz+8o8ZfdLhuN36rFMkMdQzQkREpFnL8LRlQqcbmdDpxlRHETlkxhiO\n7dmLY3v2qtfxRcEgW0tL6JSRQdu09Gr7PC4XOf4AhbWswpHp8+FzqWdEU1JWXMH/Hp/BjOc/IhaN\nMf6cMZz3w1Np27lNo9/L5XLxp7d/xf+d/xe2rM7D5XaBhfTsNC748Rm43W6cmKNihIjUm4oRIiIi\nIi1cSTjEr2a+y3vr1uBzuQk7MSb06M2fTz6VNoHKRpfGGK4+YhSPLvq0xlSNgMfD1SNGYUzL+2Su\nuSreVcpN43/LrrxCwlXNJV99+B3efuoDHpp7J136NP5Uifbd2vLo/HtYtWQdc6Z+yjtPz6Zg626e\n/M2LeH0e3B43P//XDRx1xshGv7dIq9cCZ8mpdCkiIiLSgllrueL1Kby3bg3hWIzSSJhwLMacjeu4\neOpLxJxvxtz/YMw4JvTsRZrHg4vKF4ppHg/je/TkxjHjalxXPSRS59l7XmPn1t17CxEA0VCUssIy\nHvzRU412H2stxQUl5K3P5/0XPmLmCx/x5hOzmPLXtyguKAEgFokRLAtRVlTO7y97mHXLNjXa/UWk\n5dLICBEREZEWOgXV+wAAIABJREFUbMHWLXy9q4BwLFZte8Rx2FpSzJyN6zmhd1+gcqrG42dO5vPt\necxctwaAE/v04/BOnfeetyx/O7//aDbzt2zGAMf27MVt449jYLv2SXtOAjOfn0s0XLPZqONYPp+9\nnGBZkEBG4KCvb63lmbum8NpD0wmWhQAwLvB4PURCNe+7RzgU4aU/T+OXT//goO8tIq1DwooRxpge\nwL+BTlQOKnncWvs3Y8wRwGNAAIgCP7DWfhrn/BjwRdUfN1prz6na/jRwHFBUte8qa+1niXoeIiIi\nIs3Z/K2bCUYicfeVRSLM3bRhbzFij8M7da5WgNhj6fY8vv3qS1TsM41j9ob1zN6wnjFduvLzoydw\nZLfujfsEJK5QRbjWfcZlCJaHG1yMsNayfeNOgmVB7rnsYTYu31p9v0OdhYjKYywrFqxp0H1FpB5a\n4EC0RI6MiAI/tdYuNsZkAYuMMe8B9wJ3WmvfNsacUfXn4+OcX2GtPaKWa99qrZ2SkNQiIiIiLUia\nx4PH7a4xMgLAbQwZ3vqvvHHXnPerFSL2tXDbVi5+9SUO79iJJ846jw4ZGQedWQ6s/xG9WT5/ddx9\nWbmZ5LTPatD1vpi7kge+/wQ7NhfgxByikZrfL/XV0HuLSOuUsJ4R1tpt1trFVV+XAMuBblTWdLKr\nDssBtsa/goiIiIgcqtP6Dax1QTiv2805AwfX6zqhaJSl2/MOeNwX+dv51qsvVutFIY3vqjsvwp9W\ns5DkT/dx5R0XNqjZ6JqlG/j1ufeyZXUe4WDkkAoRvoCXc79/ykGfLyJxWMCa5D2SJCkNLI0xvYGR\nwHzgZuA+Y8wm4M/Ar2o5LWCMWWiM+cQYM3m/ffcYYz43xjxgjPHXcs/rq85fuGPHjsZ5IiIiIiLN\nTPfsbL436kjSPN5q29M8Xi4ZOpz+bdvV+1r1GSVsgfyyMuZsXN+gnNIwRxw3lFuf/B7Z7TJJywqQ\nnpVGWlaAq+68iNOuPK5B1/r3XVMJ1zHto76MyzDmlMM5/uKjDvlaItLyJbyBpTEmE5gK3GytLTbG\n3A3cYq2daoy5GHgSOCnOqb2stVuMMX2B940xX1hr11BZvMgDfMDjwC+Au/Y/2Vr7eNV+xowZ0wJn\n2IiIiIjUzy1HjWdYx048tuhTNhQV0jWrskBxRv+B9b6G3+NhWIdOfJ5/4NERZZEIi7ZtrdGLQhrX\nhPPGcsw5Y1i9ZD3RSJQBo/rg83sPfOJ+Pv9wBQezMIrb48LldhENx8hul8kP/nI5Ey8ch8ulBftE\nGltLXLwoocUIY4yXykLEc9baV6s2Xwn8uOrrV4An4p1rrd1S9e+1xpgPqBxZscZau63qkJAx5ing\nZwmKLyIiItJinNy3Pyf37X9I1/jtxOO57PUpBGvpG7GHz+0mx3/wKzlI/bndLgaNObSij9fvgZL6\nHx/I8NO5dwe+/5fLCZYG6TW0O136dDykDCLS+iRyNQ1D5aiH5dba+/fZtZXK1TA+ACYBq+KcmwuU\nW2tDxpj2wHgqG11ijOlird1Wdf3JwLJEPQcRSazdFQtYW/goJeEV+Ny59Mi6jG7ZF+EyWnVYRKQp\nGt2lG89Nvoifz5jOmsJdtR5ngLMGDkpeMDlowfIQbTu3oWjngasR/nQfw48ZxMlXTGT8uWPw+vT7\nWiRpNDKiQcYDlwNfGGP2LL15G3Ad8DdjjAcIAtcDGGPGADdYa78LDAH+YYxxqOxr8Udr7VdV13jO\nGNOByt9znwE3JPA5iEiCbCl5jRUFd+HYIAARZzdf776XHRWzGNnpMYzREE8RkaZoVJeuzLj8aqav\nWcUvZk6nKBSqtj/N4+GWccfQJVMrKjR1juPwyzP+yKZV22ruNOBP83H61SfQrksbxp5+BL2HatlW\nEWk8CStGWGvnQq3Nm0fHOX4h8N2qr+cBw2u57qTGyigiqRFzyllR8Lu9hYg9HBukMLiInRWz6ZB+\nQorSiYhIfZzabwCn9O3P26tX8cKypWwtLaFvbluuHzWGI7vqTWtz8NkHX7Huy01EQzWn3bhcLn71\nzI0cfdaoFCQTkRqSuMpFsmhslYgkXUHFPEwti/nEbDlbSl5TMUJEpBkwxnDGgIGcMaBmI8xQNMqb\nq7/mg/XryPB6OW/wUI7s2q1BS05KYs1/cwnB0lDcfcZl2LRya9xixLplm3jj0XfZtHIbvYd249wb\nT6XnoK6JjisiLYyKESKSdDEboq6JbzGnLHlhRESk0e0oK+O8V55nd7CC8kgEA/z36xWM79GT8T16\nURQKMqJTZyb07I1LxYmUcXtcGGOwcdr0G5fB7XHX2P7Wk+/z2K3PEQlHcWIOy+ev4r1n53LzI9cw\n6ZLxyYgt0ioZ9YwQETl0bQIjsTZ+J3aXCdA+/fjkBhIRkUb10xlvs720hGjVm1wLlEcjvLduDR9s\nWEfUcUj3eumYkcGL53+LjhmZqQ3cSk04fxxvPjGLYHnN0REuYzj67OqjInZs3sWjP3uWcDCyd1ss\n6hCLhnng+08y5uTDyW6nXiEiUj/qECfSyllr2R1cxOpdD7Gu8B+Uhdcl/J5pnq50zDgVl9l/2TcX\nHlcG3bLOS3gGERFJjIKKcuZv2by3ELG/iONggbJIhI1FRVz/5hvJDSh7DR7bj1EnDcOf7qu23Z/u\n55TLJ9C1b6dq299/8aO4oyigcsrO7CnzE5ZVpFWzSX4kiUZGiLRiMaeC+Vsuoiy6lj0/eVbvfpCu\nmecztP1dCZ3Xe1iHe/AWtGFL6SsY3FgbIdt/OMM6/BGPS5+QiYg0VzvLy/G63IRjsQMeG7OWlQU7\nWbOrgH5t2yUhnezLGMNvnruJ1x6ezqsPvUNhfjEdurflWz87i9Ovqdm7adf2IiJxml0ChCrCFO0s\nTnRkEWlBVIwQacUWbruGsuia/bY6bC2dSo5/BN2zL0zYvV3Gy+D2tzGg7c1URDfjdeXi93RI2P1E\nRCQ5umVlE3UOXIjYw+tysb6oUMWIFHF73Fx48xlcePMZBzx20Oi+pGUGqCgN1tiXlhmg7+G9EhGx\nTiWRYr4u+RKXcTEoaxjpnoykZxBJPKPVNESk5QhFd1Ec/qyWvZY1hQ8mtBixh9uVTqavZhd2ERFp\nnjJ9PiYPGsobK78iWI/REVHHoXt2ThKSyaE69rwjefyXzxMsC1WbruFyGbLaZjDu9CPqPH93uIBP\nCmZTEN5Bj7Q+jG13LGnu9AbnCDshlhd9wccFH7Ci+As8rsq3NI6NcVqX8zil87kNvqaIJF+9ihGm\ncqz2d4C+1tq7jDE9gc7W2k8Tmk5EEmZH+ft17g/HCpKUREREWpo7j5tEflkpH2/ZtPdNayhOYcJl\nDL3b5DKoXftkR5QGCJYFKcgrok2HbP4y4zf8ZvKf2ZVXiHEZrGPp1LM9v3vtZ3FX39hjfsEcXtr4\nLyyWqI2yxDWf/219mR8O+BW9MvodMEPMxviqaCmLds1jadECrIUYlVNGYs43U0em571BO19HRrc9\n+tCfuIgkVH1HRjwCOMAk4C6gBJgKHJmgXCKSYB5X3Z9EuPAmKYmIiLQ0fo+Hf51zPqt2FfDJ5k2k\neT1sKS7m0UWf4lhLxHHI8HrJ9Pl5/Ex9it1UhYNhHvnZs8x8bi4ut4tYNMZRZ4zib3PuYMuqPPLW\n76Bb/04MGNWnzj5TO0LbeWnjv4jYb1bhCDuVK3g8uvo+7jn8Ydym9rclW8o38vfVfyAYCxKx4boz\nOyHe2jZVxQhpeVrx0p7jrLWjjDFLAKy1u40xvgOdJCJNV7u0Y6hcUMeJu79D+slJzSMiIi3PgLbt\nGLBPL4gLhhzGayuWsytYzujOXTml3wB87to/TZfUuuOiv/L5h8urNa2c+/oCVi1Zx7++uI8h4/rX\n6zrzdr6PY+O/3ojaCF8Vfc7wNqPi7o84YR5adQ9lsdJ6594Ryqv3sSKSOvVd2jNijHFTVY8xxnSg\ntncwItIseN1t6Jl9JVDzkwy3SWdI+9uTH0pERFq07tk53DT2KG6fOImzBg5u0YUIa0M4TnGtS2E2\ndas/W88XH62osXqGtZa89Tt46vaX632tHcHtxIjfP8SxDoWRXbWeu7RwIVEbfwWP2gTcaQ06XqRZ\naIFLe9a3GPEg8BrQ0RhzDzAX+H3CUolIUgxseysDc3+Bx2QBbsBFu8B4xveYjtet5TVFREQaKhrd\nTMHOq9i2dSB524azffuRlJdNTXUsrLV88tYSfnXWn7hu5C+499rHWLdsU63HL5n1JdFI7Q1IX3to\nOquXbmD+25+xceXWOu/dLb0XHhN/+qfLuOjo71zruXnBLYScmqt31MZjvIxvP6nex4tI6tRrmoa1\n9jljzCLgRCo/Rp1srV2e0GQiknDGGHq1uZKeOZcTcYpwm3TcLn+qY4mIiDRLsVgBO3ecjuMUQtVI\nACe2laKin+PYYjIzr05JLmstD/3oaWa+8BHBsspeDZtX5TH3tQX84qnvM/7cMTXO8QV8ONHaB0JH\nozFunngH3oCXWCRGr6HduePlm2nXNbfGsce0P54Z26fV+MTVYMjwZDEga2it98n1tcPn8u/tMVEX\nv8tPB38XTut83gGPFWl2mucgqzrVOTLCGNN2zwPIB14Ange2V20TkRbAGBc+d64KESIiIoegrOwJ\nHKcE9puSYG0FJcV/wNoDv6FOhK8+XsWM578pRAA4MYdQRZg/Xf0IoYqaTSHbxykqVGMhEo5SXlxB\nqCLMmqXr+elJdxOL1Sxg5Hhzua7vLfhdAfyuAC7c+F0Bcry5/HDAr3CZ2t+SjGwzrs4YLty4jYcB\nmUP5ds/r+OmgO/G7A3VnF5Em4UAjIxZRWYMxQE9gd9XXbYCNQJ+EphMRERERaSYqKt4E4q/2YG2M\nSPhzfP7kL0b39lOzCMcpOACEKiI89duXueG+y6ptn/7M7AbdIxZ1KNxRzILpSznqjJE19g/OHs49\nw//O54ULKYoW0jnQjaHZI+osRACkezK4ps+PeHLt36qWBY3gwo3FoXOgO8e0P55x7SaS5q57lTCR\nZs0CtvYVa5qrOosR1to+AMaYfwKvWWvfqvrz6cDkxMcTEREREWkeDHU15Kwg5hQkLcu+inaW1NlI\nc9o/ZnDFby8gPeubxo/rv9rc4PtUlAZZuWBN3GIEgN8d4Mh2xzb4uoflHMEdwx7g450fsC24hU6B\nrhzT7nhyfAcYvSEiTVp9G1getacQAWCtfRs4JjGRRERERESaH3/glDr3h4IfJSlJdUeccBgud+0v\n+91eN5+8uaTatg7d29VydO28AS9ZbRPTADvb24ZTu0zmqj43cnqX81SIkFbH2OQ9kqW+xYitxpjf\nGGN6Vz1+DdTdNldERBrMWsvcLev57cfvcfsnM5ift6nZLgsnItLaeL3DiLdk9h7h8CfJC7OPU6+Y\niMtVey7rWMqKyqttu+BHpxHIqNlLyuV2YUz8axng+IuOOqSsItJ61LcY8W2gA5XLe74GdKzaJiIi\njSQYjXDRWy9w3czXeGb5Ep75ajFXvTuFK959hXCs9uXVRESkafC4uwBpte53uVPT/z2zTQbfuvVs\naqkhYFyGwWP7V9t29NmjOfmyCfjTfZiqQkYg009upxwuve3cyu1VFzQugz/Nx/V/upS2ndsk9LmI\ntFo2iY8kqe/SnruAHyc4i4hIq3bfog/5fOc2QlWFBwuURyN8mreZRz7/hJtHjk9tQBERqZPXNwqX\nKwvHKa+xz5h0MjKuTEGqShfefAb/e3wmxQWl1UbceXwe+o3oxYCRvasdb4zhh3+9klOvmMh7z35I\naWE5I08cxnEXjMUX8DH2lBFMffBtNn+9jZ6Du3H+j05n0Ji+SX5WItKc1asYYYyZRZwaibV2UqMn\nEhFphRxreWHl0r2FiH0FY1Ee/+JTbhxxFF5XXc3RREQklYxxkdv2MXYVfAdrI0Ckans6fv9xBAKn\npSxbelYaD8z6LXdd8je2rcvH43UTDkY54oSh/OrpH9R63oBRfRgwquYCeoPH9ufXz96UyMgi0sLV\nqxgB/GyfrwPABUC08eOIiLROFdFI3ELEHmXRCOdPe45Xzvw2AY83iclERKQh/P5xdOg4k9LSfxIO\nfYzL1ZaMzCsJBM7AHGAZy0Tr1r8z/1j4BzYs30LBtt10H9CFjj0a3qhSRKQx1HeaxqL9Nn1kjPk0\nAXlERFqldI+XDK+XonCo1mNWFu7g4aWf8LPRE5KYTEREGsrj6UWbNnenOkateg3pRq8h3VIdQ0Qa\nIJmrXCRLvcqzxpi2+zzaG2NOBXISnE1EpNUwxnDtsCPx1zENIxSL8fzKpUlMJSIiIiKSGPWdprGI\nyp4RhsrpGeuAaxMVSkSkNbrx8KNYtjOPdzeurvWY4jpGToiIiIhIC2VrX563uarvxLUh1tq+1to+\n1toB1tpTgAWJDCYi0tp4XC4emzSZLG/Ndd336N8mNcvCiYiIiIg0pvoWI+bF2fZxYwYRERFwu1zc\nOOIo0jw1B66leTzcMvLYFKQSEREREWlcdU7TMMZ0BroBacaYkVRO0wDIBtITnE1EpFW6YfhYdlSU\n8Z/lS/C5K3tIRB2HW0dN5NReA1KcTkRERESSylY9WpgD9Yw4FbgK6A7cv8/2EuC2BGUSEWnVjDH8\ndtwkbjz8KD7J24jH5WZ8155k1jF9Q0RERESkOamzGGGtfQZ4xhhzgbV2apIyiYgI0C4tnTP7DE51\nDBERERFJtdY2MsIYc5m19lmgtzHmJ/vvt9beH+c0EREREREREZFaHWiaRkbVvzPj7GuBtRkRERER\nERGRpsW0wHffB5qm8Y+qL2dYaz/ad58xZnzCUomIiIiIiIhIi3WgkRF7PASMqsc2EZFGY61l8Sdr\neOu1RZQUlXPE2L6cef4YcnIzDnyyiIiIiEhL0dpGRhhjjgaOATrs1zMiG3AnMpiItG7WWv70m6l8\nPHsFwYoIAMu/2MyU/8zj/ieuoXf/TilO2DrEHIfVRQW4jIv+OW0xxhz4JBERaTGstczOW83L6xaz\nvHA7aR4v5/Uawbf7jtYqTyJySA40MsJHZb8ID5C1z/Zi4MJEhRIRmfv+8mqFCIBwKEokHOXuX7zM\nE1NvSmG61mHKqi+4+9MPCMeiOEAbX4Dfjz+FST36pTqaiIgkwddF+Xx37gvkVRRX2/7gl7N5ed0S\npk66lmxfIEXpRFqZ1jYywlo7G5htjHnaWrshSZlERHjjhU+qFSL2sBby84pYvyaf3v06piBZ6zBt\n7XJ+Pe89grHo3m0V0Qjff/8N/nPqRYzt3COF6UREJNGKw0Eu/eAZiiPBGvtCTpQtZYU8svxDfjni\n5BSkE5GWoL49I8qNMfcBhwF7y5/W2kkJSSUird6ugtJa93k8Lgp3lYE+oE8Iay1/WDC7WiFij2As\nyr2L5jDlzO+kIFnjKakI8frcZcxY9DUet5uzjh7CGeOG4PfW99eiSGLEYg6z3l3GlOc/Jj+vCLfH\nzYBBnbnosmMYOaZPquNJK/LqhqWE4vwe2CNqHZ5eNZ/vDR5Prj89iclEWh9jW+FqGvt4DngJOAu4\nAbgS2JGoUCIig4d3Z9vmXThOzZ+84XCMXn07JOzeH2/byEOfzWP57h10SMvgmsPGcFH/YbhdroTd\nsykpCgfJr6i9GPTZjm1JTNP4dhSVctk9z1NSESIYrnyhvXzjdp6fuYSnf3EJGQFfihNKa1VcVMEN\nl/+DnTtKqm1fOH8tSxdv4OLLjuHK649PTThpdRbs3EDIqb0YAeBg+dXCaTw2/ltJSiUiLUl9X1m3\ns9Y+CUSstbOttdcAGhUhIglz8RXj8fpq1kt9Pg/jjx9MbrvMhNz3+RVLufrdKXy0bSO7ghWs3L2T\nOz6ZyfdnvYG1LbAkHYfP5a5zWqLP3bxHD/zx+ffZVVK+txABEAxH2ZRfyBNvzW/Ue+XtKuHpdxbw\n1ylzmLl4FZFYrFGvLy3L3b+ZUqMQsUckEuOV5z9m4/qdSU4lrVUHfyb1aVn8Yd5qCsMVCc8j0upZ\nk7xHktS3GLFn4vY2Y8yZxpiRQNsEZRIRoXf/Ttz2h4tIz/CTnuEnLd2H1+dh9NH9+MntkxNyz5Jw\niDvnz6Riv2GpFdEIH25Zz0dbW0frnHSvj3GdesR9EeoxLs7pMzjpmRpLMBxl7hfriMUbcRON8frc\nZY12r+dnLua83z7Fo9Pm8e/3FnH709M59zdPkbcr/pvN+thdWsGKjfnsLilvtJwtmY1twSm6Eyf/\neJwdp+CUPoJ1ah/1k0qFu8v4fHHdP2OikRjvvb00SYmktbuoz0j89Sg+Gww7g03z75WING31/Xjr\nbmNMDvBT4CEql/a8OWGpRESAoyYO4uWZP2fJ/LWUlYYYPKwbXbonrg76wea1lVMx4nx4XR6N8Mrq\nZRzbrXfC7t+U3H3MyZw77T+URyJErQNUjpjIDaTx09ETUpzu4FWEI3UuT1oWDDfKfb5Yt42HX/+I\nUOSbb6byUIRgJMotj7zBC7+5rEHXKykPcvvT7zLvy/V4PW4i0RhHDe3FnVedSk6GOtnHYyOrsAUX\nAxVA5fcwpQ9jy6dA+9cxruxUxquhYEdJ1fdm7eOSHMdStFuFKEmOw3K7cEX/sTz19SdEqn4PxBOx\nMTqnNa2/TyItUgscoFuvkRHW2v9Za4ustcustSdYa0ej1nEikgRer4exxw7khNOGJ7QQAVARjdY5\nFaM0HEro/ZuSvjltmT75ar4zeASd0zPplpnNdcOO5J3JV9EhLSPV8Q5aTnqA9Dp6QvTt0q5R7vPc\njMWEIjXnWjuOZcP23azZWv+h9tZarr9/Ch99uY5wNEZZMEw4GuPjL9fz3T+/HLevioAt+jlQxt5C\nBABRcLZgSx9JUaradeiUfcCpYB6vm21bdvO7X0/h7f8uIRisueKQSGP62fAT+eHQ43DVMWGjvT+D\nTK8/ialEpKU4lG5sP2m0FCIiTcC4zt2J1fJmIN3jZVKP1lWD7ZqZzV1Hn8z8S37AvItv4OdjJtI2\n0Lw7prtchuvOHEcgTj+SgM/DD849plHus2H7bmp7X+lxu9iys7je1/p0xSY25hcSiVb/ZDISc9hW\nUMzHXyVu+tCmoiI+WLeO5Tt2NKueKdbZBdGvatsL5S8mNU99ZOekM3pc3zqPiUZifL5kAx++v5xH\n/zqdqy58mPy8oiQllNbq8v5H4nW54+5zYfjh0OOSnEhEWopDKUYkr7OFiEgS9MrOZVL3vgT2myPr\nNoZMr4/z+g1NUTJpTJeccASXnzwav9dNRsBHRsBHmt/LTy46jomH1/1msL76dG6Lq5bpINGoQ/f2\nOfW+1uKvN1MRiv8JeHkowsKvNx1UxroUBYNcMXUKpzzzND9+600ufPEFTnnmGdbu2tXo90oEG9tB\n3eNZm2azvdvuOp+u3XPj7tvz7bSnJhSsiFC4u4x7/m9qktJJa5Xp9XPT0ONIc3urbfcYF32y2nF+\n7xEpSibSuuxZ3jMZj2Q5lJbozecjEhGRenrw+LO5c/5MpqxahsflIuzEGNWhKw9MPJN0r5Z8bAmM\nMXz/nGO47OTRfLZ6C26Xi1EDuscdLXGwLjtpFB8sXVNtxQ4At8vQt2s7+nat/3SQtIAXr9tFJFZz\nzrbb7SLd3/jfl1e/9ipf5ucTcRxCVSuArN29i4teepHZ136XTF9T/7twoCHjTXOZ3ozMAE+9fCOf\nzlvNW28sprQ0SP8BnVm6eD1rV+fXON5xLGu+3s72bYV06tImBYmltbh+8DF0Tsviwa9ms7FsN+lu\nL+f3HsEtw06oV5NLEZF46vzpYYwpIX7RwQBpCUkkIpJCPrebe445hV8deRybS4ppG0ijY3pilhGV\n1MpK8zNheOOMhNjf0N6dueXCifzl5dlgIByJke73kpuVzv3fP6dB1zpp1AAem/Zx3MaqjuPQNrtx\nfx0vzctj5c4CIk714ocFQrEYbyz/iu+MOKJR79nYjKcrFh9QS0NSd2L+vzcGYwzjxg9g3PgBe7dd\nNvlvtR7v8brZVVCqYoQk3Dm9hnNOr+E41tY68ktEEqgFDgWosxhhrc1KVhARkaYk0+tncNsOqY4h\nzdhFx43ghCP6M33BSorKggzr05nxw3pXrtjSAN07tOHyk0bz3MzFNUZaWAv3vzyHjdsLueXCiY2S\ne2leHk4tr3jKIxE+3rS56RcjjA+bfiWUP803q5Pv4cNk35qCVAevd7+O7MgvjtuHJBKO0vX/2bvv\n+CrL+//jr+s+M4sRCBD23sgUByIOELc4qrVqba1aZ7WOfrt+Vru0jtZuZ+u2ap3FrYgggrJlL0FW\nIARCCEnOvK/fHwEEc04SIDkn4/30cR4m577Pfb8PkJxzPvd1fa56bu4rsj8VIkSkrmhclYiISD1p\n2zKLS8aPOOzjXD9pDN3zc7nj3+9U+UBaEYny4tSFnHnMQPp0anvY52oZDOA1hkRrxzjG0CazcQyM\nNDk/xrpFEHqTymkZBnAh5zZM4IT0hjtIF3/3OBbO/YrwN3qH+P0ejjm+Hy1bNe7GsiIiUoMU93JI\nlYY5aVJEREQOEI7ECPoSX0OIxeNMnpls9YiDc3LPXrhJVs7wezz0a9uWi196kZH//AcTnnyCZxcu\nJO5W7WeRbsZ4cVr9AZP3HqblXZiWd2PafYqTdXm6ox20QUO7cO2PT8Ef8JKR6ScQ8BIIeBk0tCu3\n/PysdMcTERE5JBoZISIi0giUhSIJm1gCxF1LSVmoTs6T7fdz7ykTuf29d4nG4/uWu83wehnZsSO/\n/fhjQrHK6SLFoRC/n/YxH61dyyPnnHPQw7cLd+/m0w3rMcYwtlt3Wgf9EC8AJxPj1M3UA+PpCBnn\n1smx0un0c0Yw7uSBzPxkJRXlEQYf0YUevdunO5aIiKRKExwZoWKEiIhII3BEz3x8Hg+xBAW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NjCA+9OZ966TXgchxMH9OSWicfRJbfVIR0vLyeLX5x5Ir9/cyrReJy4a/E4Bp/Hw8/POIG8nMRL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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CJvXtBDSrdL7",
        "colab_type": "text"
      },
      "source": [
        "## **Preparing to remove clusters formed due to lesser number of points than mean number of points per cluster**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tjevbTcIZI25",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = pd.DataFrame(data=X)\n",
        "data[2] = kmeans[0]\n",
        "data['count'] = data.groupby(by=[2])[2].agg('count').reset_index(drop=True)\n",
        "mean_val = data['count'].mean()\n",
        "new_data = data[data['count'] > mean_val]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bpaWgigwrtvK",
        "colab_type": "text"
      },
      "source": [
        "## **Visualizing after Removal**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e8DNYvaEh2ud",
        "colab_type": "code",
        "outputId": "c4500065-213d-47cd-a7da-7dd755ee5865",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        }
      },
      "source": [
        "fig = plt.figure(figsize=(20, 10))\n",
        "ax = fig.add_subplot(111)\n",
        "scatter = ax.scatter(new_data[0],new_data[1],c=new_data[2],s=50)\n",
        "ax.set_title('Reduced Public Places in Hauz Khas, Delhi')\n",
        "ax.set_xlabel('Longitude')\n",
        "ax.set_ylabel('Latitude')\n",
        "plt.axis('equal')\n",
        "plt.colorbar(scatter)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.colorbar.Colorbar at 0x7fcced8236a0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kqY71pJURkiRJkiRJw2JlhCRJkiRJdcqeEZIkSZIkSSPAyghJkiRJkupUEpQasI6g8e5I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\nYII=\n",
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-6lhhom5igCa",
        "colab_type": "code",
        "outputId": "c86a334d-a286-4c72-ff44-c0568a52110d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "dump = new_data[[0,1]]\n",
        "\n",
        "dump = dump.rename(columns={0: \"longitude\", 1: \"latitude\"})\n",
        "dump.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>longitude</th>\n",
              "      <th>latitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>77.222783</td>\n",
              "      <td>28.561444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>77.189300</td>\n",
              "      <td>28.544418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>77.217240</td>\n",
              "      <td>28.524793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>77.234057</td>\n",
              "      <td>28.566347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>77.184788</td>\n",
              "      <td>28.540346</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   longitude   latitude\n",
              "0  77.222783  28.561444\n",
              "1  77.189300  28.544418\n",
              "2  77.217240  28.524793\n",
              "3  77.234057  28.566347\n",
              "9  77.184788  28.540346"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p0A3E3zRr1MX",
        "colab_type": "text"
      },
      "source": [
        "## **Getting Data Ready for conversion to Shape format**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n9dVgugImf6D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dump.to_excel(\"output.xlsx\", index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0giD4qAXm2hB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import files\n",
        "files.download('output.xlsx')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rp0ojTrn-rwy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}