osm2gmns_quickstart_Gila_County_AZ.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "KbmkUUHXWpXS"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/asu-trans-ai-lab/OSM2GMNS/blob/master/osm2gmns_quickstart_Gila_County_AZ.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# __OSM2GMNS Quickstart Notebook__\n",
        "\n",
        "OSM2GMNS is a Python package that helps generate high-fidelity mesoscopic transportation network models from OpenStreetMap data. This enables even the smallest transportation authorities to begin analysis with a high quality network model for future analysis. \n"
      ],
      "metadata": {
        "id": "KbmkUUHXWpXS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. Data Collection\n",
        "We rely on a few open-source projects to gather the raw data to start our analysis. Export data from your area of interest from the following sources.\n",
        " * __OpenStreetMap base map__: https://www.openstreetmap.org/export\n",
        "   * __Geofabrik__: Alternative for non-rectangular Areas of Interest: http://download.geofabrik.de/\n",
        " * __Bike Transport Info__ (Optional):  https://extract.bbbike.org\n",
        "\n",
        "For this demonstration, we'll use sample data from the ASU Transit AI Lab Github\n",
        "\n",
        "osm2gmns also enables users to download OSM data within the region of interest using a built-in function. A region can be a state, city, or even university. On OpenStreetMap homepage, search the region name to get its unique relation id. The following example shows how to download Tempe city OSM data using function downloadOSMData.\n",
        "\n",
        "https://www.openstreetmap.org/#map=18/33.42141/-111.93472&layers=DG "
      ],
      "metadata": {
        "id": "9URrMF7xXE-g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. Install the osm2gmns package"
      ],
      "metadata": {
        "id": "nqUaVegsbTO_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install osm2gmns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0L0kgefhbZaB",
        "outputId": "75bceebb-c838-4b11-d28c-8c50372d90d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting osm2gmns\n",
            "  Downloading osm2gmns-0.7.2-py3-none-any.whl (58 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.8/58.8 KB\u001b[0m \u001b[31m974.6 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from osm2gmns) (1.21.6)\n",
            "Requirement already satisfied: shapely>=1.7 in /usr/local/lib/python3.8/dist-packages (from osm2gmns) (2.0.0)\n",
            "Collecting osmium>=3.1.3\n",
            "  Downloading osmium-3.6.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m28.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from osmium>=3.1.3->osm2gmns) (2.25.1)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->osmium>=3.1.3->osm2gmns) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->osmium>=3.1.3->osm2gmns) (2.10)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->osmium>=3.1.3->osm2gmns) (4.0.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->osmium>=3.1.3->osm2gmns) (2022.12.7)\n",
            "Installing collected packages: osmium, osm2gmns\n",
            "Successfully installed osm2gmns-0.7.2 osmium-3.6.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. Create a network model from OpenStreetMap exports"
      ],
      "metadata": {
        "id": "WobI3HSQbqsw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import osm2gmns with the alias as \"og\" (easier to type)\n",
        "import osm2gmns as og\n",
        "# relation id for Manhattan  is 8398124\n",
        "# relation id for City of Tempe is 110833.\n",
        "# relation id for Gila County is 1832190\n",
        "og.downloadOSMData(1832190, 'map.osm')\n",
        "# Generate a net from the Tempe, AZ OpenStreetMap export and include Points of Interest (POI=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ila8lUreb0TB",
        "outputId": "92faf96a-66dc-49ac-9628-53cf8bbc17e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "osm2gmns, 0.7.2\n",
            "valid reponses got from API server.\n",
            "receving data...\n",
            "map data has been written to map.osm\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_file = 'map.osm'\n",
        "net = og.getNetFromFile(input_file, link_types=('motorway','trunk','primary','secondary')) \n",
        "\n",
        "# Use og.show(net) to visualize the network model\n",
        "og.show(net)"
      ],
      "metadata": {
        "id": "O4RJqNGWVtxk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "bf111819-eafe-4393-99a9-26f6cdfe80eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "arguments used for network parsing:\n",
            "  filename: map.osm\n",
            "  network_types: ('auto',)\n",
            "  link_types: ('motorway', 'trunk', 'primary', 'secondary')\n",
            "  POI: False\n",
            "  POI_sampling_ratio: 1.0\n",
            "  strict_mode: True\n",
            "  offset: no\n",
            "  min_nodes: 1\n",
            "  combine: False\n",
            "  bbox: None\n",
            "  default_lanes: False\n",
            "  default_speed: False\n",
            "  default_capacity: False\n",
            "  start_node_id: 0\n",
            "  start_link_id: 0\n",
            "\n",
            "Building Network from OSM file\n",
            "  reading osm file\n",
            "  parsing osm network\n",
            "    generating nodes and links\n",
            "  number of nodes: 659, number of links: 1164, number of pois: 0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x1322.3 with 1 Axes>"
            ],
            "image/png": 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fgM3t0Qjj3FGHPnbg+wu6/6dmHPz1FPjHTXDzTBjpX0hJkiQg2ohTOW7OyGhXy64QfrA17jTKGKUVPW4O3jjjsU0qis4iPeZmPQuXcPjrYWFQwHffsIS5o+Bfy2HLBfDEORBcemvPUnzprQff/aupkB/Ap9ek4zs4hpbG6AzXW/Kjty2NtHTAF9bBu1fAt16GZHIQ80iSJB3Bqao66MP1cNc2aLoIhufFnUaxa2mEuy4h3Lvl4F3BIK1xjNvPX4EP1cMnJsE3Z6Z2s5x9nXDFimjqbVcIY/PhxbpqJuytJwiTJEmwubiS6ZV1jMmHSYXQsD8qs5+ZDH8xGTa1wRnFkNgVnftKc0NU9BfURkf6SJIk9ZNrHPW6Opsb2XDHfGa0r41GY0pmRiM4XojmtL9dD7e8DA+cAReOiTvN4PnpVvjd43fy8w3XAKnbHGjaE7CnC26bHW3o84Ot8K+/zCefroOP6SSPH72vk49N6r6dhL9eH+2GvDcZZRmZB9vWVFPUEhXOg8qqDhXIFoulJEk6MRZHvb7eNkMZNRVu2BBTIMWpPRntvPu9LfBHJbDkjNw7puLI3VcDgOvX9bt8faQe/vNV2HYhlBQc9oHbq6G5HsIkBAkorYRr6475PMkkvOl5+P2DPQtnFPawz7+9mrC5u1gex/NKkiQdqzi6xlGH9LYZyu6NcSRRzJbtgplPwne3wMcnwm9Oz73SCFFRPHwDnRCiEbwT8NhOePvzcNJj8NNX4Z7qI0ojRCOBpZUQ5EVvF9T2+ZyJBCw9G5JjKwiDI/6Mh0mSzQ0kk5Bsbjg0Ghkmo5FHSZKkfrA46pAjNkNR7ukK4XNr4eJno+mRS8+Eb8yCghz+S3H4nIwATqh8LdoGlzwLW9rh/SfB1gvgneN6eWBJeTQS+LnO6O1xjmgWvruWoLTnuSFJEtQXVZD3CNQXVZA88Gc+SPjfuCRJ6rccvhzUUY41ynHYTo/KXptb4fSn4dub4c8mwvoL4IKSuFPF7PI7Do00Al3AjlHHV77ak/Cx1fCucfDCufDN2TCuMMX5DhTO69dFaxuDPBJllVS+v5bG86Hy/bUkyo5/JFOSJOlYXOOonn4wjXDXxh4XywdnKCYK4Lp6N9fIMm1J+N5m+KtGGFsAvz4N5o6OO1UG6d5gJmxuYM2wCt49s5Y5xfCzhhqGtTTAmOnR43auh9IKNl9ey43by/nldhiVBxve4HmQkiRp6HBzHB2fwy6SCbuOXvNYVuXmGkPcy63ww63wYAus3AvNnZAHXDsBvpvj01JfT1sy2tn07b+qZnZrPXkke7zA0kWC+mGVvP2cOm6cDJ+dFK1HlCRJGiqOVRx9HVw9dU99CwBu7mU3FDfXGLK2tcMVL8JTu6OjHOaMgOtOidbcnTcaCi04r6soATeeCmFbAwHRpjOH/1eSR5KqtgZeuiCefJIkSelicdSxlcyElrUHb4ZAy6gKxsaXSCeqpRHuuoRw7xZOAh4HVl96B5XzBnYWYa4LSisOHZ/R4wOJ6GOSJElZxjEGHdvCJVF57NZUPJN5U2r50roYM+nELK4h3Lvl4LESAVD58DUxh8oChx+fUTIz+ucGNJIkKYs54qhjKymH69ccvDkOuGkrfLQBHmqBB8+EEf4GZbYjzubMwaMY0+PAbqaSJEk5whFHnZDrJsCL50JjK4x7HD69Olo7p8zUObaC49/+SpIkSeqdxVEnbE4xbLkAPjkJ/msbnPI4vO356Nw6ZZbPn1bLloKJPcvj5XfEFUeSJElDlBMN1S/5Cfj6jOjfIzvgnXVQ/geoP88z62Kz8k64/9D6xRAIJt3KrxZu5s8mxRdLkiRJQ58jjhqwPxoLG98AnSHMeBIa9sadKEfdf81R01Jv3XwjH58YSxpJkiRlEYujUmJ0Pqw9H04tgjlPw6wn4b+3xZ0qC7Q0wg+mRWdq3hzALYWwaWmPh3Qk4b7XukcYD7v/4E6q7ogjSZKkAXJSoVJmZD4smwfP7Yab1sP7VsKLe+Dvy+NONnSFi+bDro0HC2EYdtB+73xmX9DG9o7olZ99yaggdsSYU5IkSdnNEUel3Fmj4NdnwG2z4WubYFdn3ImGpjX7gJa1R40iFobtzBkBVcVwwRj4/mzYfQkExb3MSb301kFKK0mSpGzmiKPS5oaJ8Odr4DdN8P6T404ztGxshUueha29fCxIFPLAmb184H2PwuIaaG6A0oroIPoSh3slSZI0cBZHpVUigC4PEjw+LY0k76uBHQ3sL6rgpFm17Bw5k5I9a3s+buGS3j/fQ+klSZKUJk5VVVp1hVCcF3eKzLerE1777xrC5noSYRezWut56qUaSq5aAmVVEORFb69fB1MuizuuJEmScowjjkqflkaeWVFD1XKnTvaqpZHwvhrCHQ1sLqpgdmsDeSQByCPJ8J0NjiJKkiQpIzjiqPTYtBR+NIOq1pUEYRc0rYS7Lok7VeZoaST8yRxoXkki7GJ26yrCRB5h0P2fZJCIyrYkSZKUASyOSo9FbzvqXEH2bokpTGZp6YDNd9dAsv3gzyePkPxkJ0FpZTQttbQyGqGVJEmSMoBTVZUeyQ6G+rnzySS0hzAsRWs0d3XC9fXwi+3Qtqfh6J9PWaXTUiVJkpSRLI4S0NoFP3kFftUE9XvhlXZoPWw32IIgGp5PBFCSD9XF8OYSeP94mDq87+fe1wk3rIa7tkFZAfy0EvI3VEBTPXSvaSRR6AijJEmSMpbFUekxairh7o09R9UuvyOuND21NB4873DX6Aquqajlt13lFCZgzgh481i4rATeURqVxHWtsGYf7E/C3i5Yvgce3wX/tAm+tB4KA5hcBBeMhs+dCueMir5Mczt8bh3csQ1G58H3K+D6Cd0ZFtR65qIkSZKGjCAMj/+QvXnz5oXLli1LYxxljU1LSS6aT5BsJ0gURmcPZsoxErdX09VUTx5JukiwaUQla6+q462lJ/5UrV3w6yaobYKHW2BTG+QF0SsyrSGMK4CvTIVPTU75dyFJkiSlXBAEy8MwnHfk/Y44Kj0WXxGVRoBkOyy+Av58V7yZNi2FRfMJk+0cWLaYR5Lp+xuY3o/SCNH6x/eMj/5BVCQfaonWM14wBqYMS0VwSZIkKV4WR6VHx+6e01Q7dseVJNLSCPe88eBOrwd3fE3xsRfD8uDyspQ9nSRJkpQRPI5DOSF5X02P40EOllqPvZAkSZJelyOOylp1e+A7W+CBZljd3HD0L3ui0OMvJEmSpONgcVR65BcTdu49NLKXX5yer9O9Q2rY3MCOURXcdGYtTyXKWdm9C+opBfDGEmgrqSC/ZRXRJNVuC5ekJ5MkSZKUZZyqqvQYOaHv2wPQ2gX3boObN0Hjf9XQ1VRPEHYxZlc9n1tew7gCuHkG7L0Ytl4Ed1ZD8cJaKJsDQR6UVcH16zJnl1dJkiQpwzniqPTYub7n5jg71w/o6V5uhe9shvu2w+r9UBDA8AR8dl8DeSSBaIfUmfsbeODMXp6gpNxpqZIkSVI/WRyVHmOmE7asPVQex0w//s89bPrpK8UVvGNGLc/ll1OaD5eMgR9WwMUl3Y9dVwHN9RAmU75DqiRJkqSIxVHp0bGv79vHsKcTOu+tYdTOevJIMn5PPfc31pB/bR3jCnv5hAW1sLgGmhui0ugOqZIkSVLKWRyVHnu39JyqunfL0Y/pHlmkuYGdoyv4kxm1/F9QTsfOntNPT9nTAL2VRnAKqiRJkjQI3BxHg+fmIPr3H9OgpZHkfdHGNoRdjNxZzx0NNdSfC/llFdG0U3D6qSRJkpQBLI4aFOHh7+/eyKo7akg29xxZnLKvgYpioummpZXRDqillU4/lSRJkmLmVFUNiuCI9yvaGugcWwEtvWxs4/RTSZIkKaM44qhYJEorKHy3I4uSJEnSUOCIowbfqKlRSXRkUZIkSRoSLI4aPNevi8qiJEmSpCHFqapKj5KZPTbEoWSmpVGSJEkaoiyOSr2VdxK2rAW6d1MtnggLl8QaSZIkSVL/OVVVqbXyTsL7rwEO20l17xZHGyVJkqQhzBFHpdRRpVGSJEnSkOeIowZu01JY9DbCZAdgaZQkSZKyjcVRA3fv2wjDjoOFMeSI8nj5HYOfSZIkSVLKOFVVA3dYaYReSmPV1YMcSJIkSVIqOeKo9Cirgmvr4k4hSZIkKQUccVR6LKiNO4EkSZKkFLE4Kj08fkOSJEnKGhZHSZIkSVKfLI6SJEmSpD5ZHDVw+SMIj7gtSZIkKXtYHDVwH3qRV0dW0UletJvqh16MO5EkSZKkFPI4Dg1cSTm3va2Ob26G5ovjDiNJkiQp1RxxVEqMyoOO8PUfJ0mSJGnosTgqJcbmQ6fFUZIkScpKFkelRGmBxVGSJEnKVhZHpURpPnSFEFoeJUmSpKxjcVRKlBVCCLQl404iSZIkKdUsjkqJsvzol+m1jriTSJIkSUo1i6NS4uRCSATwUEvcSSRJkiSlmsVRKREEcHoxfHdz3EkkSZIkpZrFUSnz5Snw9G5oao87iSRJkqRUsjgqZd5d0MiLddWM/XY+3F4NLY1xR5IkSZKUAhZHpcbKOwl+PIM5rStJhF3QtBIW18SdSpIkSVIKWByVGvdfQwgEh9/X3BBTGEmSJEmpZHHUwK28EziiNAKUVgx6FEmSJEmpZ3HUwHWPNh5lQe1gJ5EkSZKUBhZHpcRRo42X3wEl5XFEkSRJkpRiFkcNzLJv9D7aWHX1YCeRJEmSlCYWR/VfSyM8fOPR9xdPHPwskiRJktLG4qj+W1xz9E6qAO97NIYwkiRJktLF4qj+a244ujSCaxslSZKkLGNxVL+FYyvoOvLOS2+NI4okSZKkNMqPO4CGqJV3QvNKEhBNVy2eGE1RdbRRkiRJyjoWR/XP/dcAh61v3LvF0ihJkiRlKaeqqt96Xd8oSZIkKetYHCVJkiRJfbI4qn+KJxJ2vxt235YkSZKUnSyO6p/3PUqytIpO8tg5psqzGyVJkqQs5uY46p+ScvKuq+NPXoDn9sDLJXEHkiRJkpQujjhqQL43G7a2w73b4k4iSZIkKV0ccdSAnDoM3jcerl4FBS8v5YqH5xMk2yFRCAuXwJTL4o4oSZIkaYAccdSA/XwOXHcKzF86H5LtAITJdsJF82NOJkmSJCkVLI4asCCA71dAUdh+8GzHACDZzv6uGINJkiRJSgmLo1ImSBQefD8E2oNCrq+HMDz250iSJEnKfBZHpc7CJdHaRoBEIVdVLuH5vfCNl+ONJUmSJGlg3BxH/fbf2+A7m2FMPpxaBKcWXcaUq9s4tQhebYf/Ww0PzoG3vgDzRsElHtkhSZIkDUkWR/XLC3vg02vghxXQFcJLbbCpDZ7bHr1fEMDd1XDGKPhpJVy1Ep46ByYPizu5JEmSpBNlcdTr6grhp6/A+la4ogzOHglf2wifnQw1417/899eBh+fCJ9cA788Pf15JUmSJKWWaxzVpzCED66CH2+FzhCuWQXDH4XXOuDPJh7/83xxCjy6E15pS19WSZIkSenhiKP6tKQZntsDz86DogR8bTq0JWFY3ok9T1ECphTB5nY4pSg9WSVJkiSlhyOO6tPvdsCfnhwVP4jObDzR0gjQkYzWPk4qfP3HSpIkScosFkf1KQEkgoE/z7c3w1kjHW2UJEmShiKnqqpPpw6D1fv6//lhCP+0KTq249GzU5dLkiRJ0uCxOKpP+7pgRD+mpgLs74KPNMDa/fDUXJjkaKMkSZI0JDlVVX1atjs6fuNEbWqFS56FEHjoLEujJEmSNJRZHHVMYQiP74ILR5/Y5z2/By58Bq4aD3fM6f+IpSRJkqTM4FRVHdPGVgiAqcOO/3OWNMMHVsG3Z0XFUZIkSdLQZ3HUMT2yMxptDI5zV9XdnfDBVbD4NLhoTHqzSZIkSRo8TlXVMd29Dd510vE//kdb4Y0llkZJkiQp21gc1atX26P1jQvGHd/jO5Pwzc1w4+T05pIkSZI0+CyO6tXd26CmDIqPc2ObXzXBKYXwBkcbJUmSpKxjcVSvfvYqfPDk43/8N1+GT01KXx5JkiRJ8bE46ihr9sHLbfCmscf3+EdaYFMbXHkC6yElSZIkDR0WRx3lF9vhXeMg7zh2U93VCR9tgH8phwJ/myRJkqSs5KW+jq3DwywAACAASURBVPKbJnjncWyKs78L3r0C3jwWrvTMRkmSJClrWRx1lPWtUDmi78es3QeXPgcTiuBbswYnlyRJkqR4WBx1lKYOGFfQ+8dau+AfNsAbnoGrx8N/Vh7flFZJkiRJQ1d+3AGUWdqT0BHCiF5eUtjWDm95HsqHwbK5MG344OeTJEmSNPgsjuphZyeMyYPgiFHE1i545wq4ogz+fvrRH5ckSZKUvZyqqh52dsKYI15OCEP4+GqYXGRplCRJknKRI47qYXQ+NHdGZfFAQfzmZnhuDzx2jqVRkiRJykWOOKqH8YXRxjjP7oluP7QD/mkjLD4NivPizSZJkiQpHo446igLx8E9r0FZAVy9Cn4+x41wJEmSpFxmcdRR3jse3lsHT++CGyfDW0rjTiRJkiQpThZHHeXszkZ++0wN5fsbyCutgHfVQkl53LEkSZIkxcQ1jjpKcO+bmLV/Jfl0ETSvhHveFHckSZIkSTGyOOpouzYSHHFbkiRJUu6yOEqSJEmS+mRxlCRJkiT1yeKoXoVxB5AkSZKUMSyO6lXw+g+RJEmSlCMsjuqVI46SJEmSDrA4qleOOEqSJEk6wOKooyUKe444JgrjSiJJkiQpA1gcdbSFS+g8UB4ThbBwSdyJJEmSJMUoP+4AykBTLuNLNW387FV49aK4w0iSJEmKmyOO6lVn6DpHSZIkSRGLo3r1SjsM97dDkiRJEhZHHamlEX40i/96IKDx8QB+NCu6T5IkSVLOsjiqp0XzCVvWEtA9VbVlLSyaH3MoSZIkSXGyOKqn7tJ45H2SJEmScpfFUZIkSZLUJ4ujeiqZGZ3feMR9kiRJknKXxVE9LVxCY9Fh5bFkJixcEmciSZIkSTHLjzuAMktLcTkVp63hZ3Pg/SfHnUaSJElSJnDEUT08tQsSAVw0Ju4kkiRJkjKFxVGHrLyTt94e0LYs4NTvBLDyzrgTSZIkScoAFkcdcv81AIfOcOy+LUmSJCm3WRx1UAhHn+EoSZIkKedZHCVJkiRJfbI46qC9wyb2PMNx5MS4okiSJEnKIBZHHfT5eY+yengVBHlQVgVXPRp3JEmSJEkZwHMcdVBtVznNf1THPafFnUSSJElSJrE4Cloa6bqvhk3NDeweXQGTa6GkPO5UkiRJkjKEU1UFd19Conkl+XRRsmsl3H1J3IkkSZIkZRCLo2DPloPHcATdtyVJkpQBWhrh9mq4JT9629IYdyLlKItjrlv2jbgTSJIk6VgW15BsqoewC5rrYXFN3ImUoyyOue7hG3sewSFJkqSMkWxuIEEyuhEmobkh3kDKWRZHHZymetDld8QRQ5IkSd2a2qH6KVhVVEHywCV7kIDSiniDKWdZHHNc2P2vh6qrY0giSZIkgBV7YMofYE8XlCysJVFWGZ2zXVoJC2rjjqcc5XEcOe7m6bfy+fU3Hrrj0lvjCyNJkpTjVuyBucvh/FGw9CxIJMrh2rq4Y0kWx5y1aSnJRfP5fLKdMFFIsHAJTLks7lSSJEk5a28nXPAsnDcKHjkn7jRST05VzUUtjXDPGwmS7QRAItkOi+bHnUqSJCmnzX8BhieikUYp0zjimGtaGuEncwg5YlOcZHtMgSRJkvTzV+CJXfDMPMhzaEcZyF/LXLO4BrpHGntIFMaRRpIkKeft7YSProYPT4AzR8adRuqdxTHXHOvsn4VLBjeHJEmSALhiBYzMg+/PijuJdGwWx1xTWkHX4eONiUK4fp0b40iSJMXgvtfgoRb41WmQ8MpcGcxfz1yzoJaNI+bQRR6UVcF1q6CkPO5UkiRJOWdvJ3xgFVwzHi4YE3caqW9ujpNrSsr5y0vqWL4b1l8QdxhJkqTcsbcT7ngVVuyDDa3wcAuMyoOfVMadTHp9FsccdNO+Oznj8WsIH+/eWfXyO6Dq6rhjSZIkZaVlu+Av1sH/7YSCAMoK4KQC+ODJ8G8zIN85gBoCLI456MxHrwEOO47j/mvgfz8JH1zutFVJkqQUamqH856B6hHRyOIHT4k7kdQ/vr6RgwI4+jiO9hb40QzYtHTwA0mSJGWpggQUJeClNpg2LO40Uv9ZHHVQCHDPG+H2amhpjDuOJEnSkDc6H3ZcBDOGw4fq404j9Z/FMSfl9XrvgVHIsLkeFtcMXhxJkqQsNiwPPn8qbGmLO4nUfxbHXHTl74HuEcbD3h4QhEm6mhtIJgc1lSRJUta6cDS0hRAeeeElDREWx1w05TK48iGCROFRpREgSYKGogpKHoMfbhnscJIkSdmnyKtuDXH+CueqKZfBjW3cdlVI4byQ5j9dB2VVEOSRKKtk5vtrec9J8LHVMO0JeG533IElSZKGrvp90WKh4KgdCqWhweKY466bEP0R++dd5XBtHXyuE66to7C0nB9XwtYLYVIRnLMcPrMm7rSSJElD07O7obj3bSakIcHimOOG5cFV4+G2LdDWy5rG8YXw2DnwsznwvS0w8w+wpXXwc0qSJA1lK/bBuIK4U0j9Z3EUX5kGrUn4jz7WM15zMrx8AeQHMPVJ+MnWQYsnSZI05NXthame46ghzOIoyofD3JHw1Q3Q3sdOquMLof58+PNJcH0DvP35vh8vSZKkaCfV5/fAu8fFnUTqP4ujALhyPIzIg//a9vqPvXkmPHE2/GEXnPw4PL0z/fkkSZKGqv/eBp0hfGxi3Emk/rM4CoBpw+Ckgmit4/E4fwxsuwjmjYLzn4UvrktvPkmSpKHqX1+CN4yGAq+8NYT56ysA3jYWNrTCir3H/zmFCfifM+E/ZsMtL8Ocp2Bbe/oySpIkDTX7O+G5PfA3U+NOIg2MxVEAjMyHC0bDqH5sE/2RibD+/GhX1lOfiKZjSJIkCT65JjqGY35Z3EmkgcmPO4Ayx9r90dTT/pg8DBrfAB9vgPethF+8BnfOgYQvTUiSpBzSnoQHmuH+Jli6Exr2wd1VcaeSBs7Leh20qQ2uGOCrYbdVwP2nwwubG1lzWzXhLflwezW0NKYmpCRJUob69XYofhQW1sGvm2H6MHhhXrQJoTTUOeKog4oT8GBLNPV0IOaXwYuba2B/PQFJwqaVBD+aAWVVsKAWSspTE1iSJCmDfKQB3lEKvzw97iRS6jniqIO+Nj1an/iLFKxRzNvRQB7RIY9B931hcz0srhn4k0uSJGWY19rg1Q7495lxJ5HSw+Kogz46CWrK4MMN0UG1A1JaAUHPX68gTBI2NwzwiSVJkjLP1zZBST5MGx53Eik9LI7q4d9mwO4ueGWgx2osqIXSyh53dZHg1eKKAT6xJElSBmlppPVH1Xz9V/k0rHRfB2Uvi6N62NxdGEf041iOHkrK4do6uH5dtLYxyGP7qErePqN2wBklSZIyxc57aihoqSefLsbvcVmOspeb4+igrhD+dBVUF8OYVP1mHCiQQFsrPP+HFD2vJEnSIPjfZvh/G2DFXigrgFtnwjvHwa5OWLACfrfr0L4OhElwWY6ylMVRB318NWxth8fOTs/zD3jdpCRJ0iDY3h5dF/1vC+zshLmj4KapsLQF3rUCCgNoD+HkAugoqSB/Zz2ESbpIEIytcEqfspLFUQD8Zjv8eCv8YDZMGpaer7G0BYb7l1SSJGWwu1+FP62PRhc/MRG+cCqUFEQf+8KUaKTx/iaYNQLOGQVU18LiGsLmBtYUVfA3s2u5J9bvQEoPi2OOS4bwxXVwy8tw9Xj48ADPcOzL0p0woTB9zy9JkjQQP38lKo0fnwjfnd37Y0bnw1UnH3ZH97KcAOjYA/ctg6+sh69OH4zE0uCxOOaorhD+ch385JVoF9VvzIRPT07v13xmN5xWnN6vIUmS1B/tSbhhNdww4dil8fWcPjK6pvrMWjhnJLzzpNRmlOLkxMEctXAFfGszXDMetl6Q/tLIpqU8+WgRi38XwK1FsGlpmr+gJEnS8fvzNVAQwHdnDex5PjUZPjoB3l0H33o5NdmkTOCIYw56Yif8ugnuqYZ3DdIrYeGi+RSF7QQAyXZYNB9ubBucLy5JkvQ6lu2GeaMgkYJhldsqYEpRNPL47G748ZyBP6cUN4tjFqvbA59aC5taYfowmFAE2zvg4RaoGAELxg1eliDZ3vOOI29LkiTF6LOTo/WNdXugeuTAn++vp8GZI+GdK2B8IfzzjIE/pxQnp6pmqfq9cNZyeLkVLhodrWN8ahe81g7XT4BlcyEIBjFQopDwiNuSJEmZ4gOnwEkF8NNXU/ecfzwu2pX1e1tS95xSXBxxzFJ/sS7awXT1+YNcEI9l4RKS976NRNgBQFA8AVoao53IJEmS4tbSyBMv1DDlDw1QVgELalNynfKXU+CfX4KVe6AqBSOZUlwcccxSW9ph9vAMKY0AUy4jr3QWSRLROsc9L8HimrhTSZIkRRbNZ/q+leTTRdi0MtqPIQXGFsDkQvj6Syl5Oik2FscsNbkIXs60vWeaG8gjGb0fJqG5Id48kiRJB7Ss5cDr7UH37VT5kzJ4oDllTyfFwqmqWaqpA/IyZbTxgNIKkk31JEhCkIDSirgTSZKkLLdpP3xiDTS2wrmj4JYZMCwPbn0pui8/gF2d8F9Aui6dPjsZbtsafZ3RXn1riPJXNwvdvAn+sAseOD3uJEdYUMumu2qYvLeB/NLutQOSJElp8Fp7tEvqkmaYOgzOGxW9f1L35jfFeXByAXSGMCwBawtnMrP90KgjJTNTlqWiGPKAp3fBm0tT9rTSoLI4Zpn2JPz1eviLU+GtZXGn6enJoJwLKuu4pwoWjo87jSRJylY3roFvboaJhfDL06DmsCPIXtwTXS/NHX3EJ1UsifZfaG6IZkWl8AXu/3wFksC5R35NaQixOGaZbe3RK2c3TIw7SU/7OuHNz8P8UkujJElKj5YOuOhZWL0f/mM2XN/L9dDpx9rZtKQcrq1LeaadHXBDA3xiotNUNbS5OU6WmVgUzdX/xWtxJ+nW0gi3V1P07/ksW1FN7eTGuBNJkqQstOg1OOVx2NUFjef3Xhrj8McvQmkBfGtW3EmkgbE4ZplEABeNhh9ujTtJt8U1JJvqyaOLitZ68n/lERySJCm1frsdrqyDq0+GjefDqcPiTnTI8j1w09QMOiJN6ieLYxb66ERYux/CMO4kEDY3RLuoAoFHcEiSpDT4/Dp4Uwn8uBISGXZ1e+4ouGk97O+KO4k0MBn2n5ZSYcUeKAhifmWre4oqYRcH+6tHcEiSpBR7Yic07Id/mxF3kt79zxnREWnnLs+MF/Wl/rI4ZotNS+GWQsKbA/5hccDmumlReYvLovmETSt7nodUWukRHJIkKWU6k/COF+AtY+GsUXGn6V1RHiw/B9bsh6tWxp1G6j+LY7a4922EYQcB0eG1pfs3wj1vii9Py6FzkA6Wx2vroh3LJEmSUuA9ddAF1J4Wd5K+TRkOD5wRbeDz7ZfjTiP1j8UxW3SXxgMCgF0bYwoDzsSQJEnp9MvX4FdNsPi0aFQv071xbHQkx5fXx51E6h+Lo9Ji+4iZPctjycy4okiSpCyzYg9cuRI+fAq8aWzcaY7fP5XD7i54cEfcSaQTZ3HMEslRU48e5QsK4ohCMglvnLGEbaOqIMiDsipYuCSWLJIkKbu83ArnPQMXjoYfVsad5sSMzIczi+FvN8SdRDpx+XEH0MB1JmHBnAf59nNvYmr7xmiaaqIAFv4uljzf2QKri8op+0idL01IkqSU2dkBZyyD6cPgwTPjTtM/X5wCH1gVXb/le52kIcTiOESFIbzaDo2t8A8b4ffJcnZft4FgZNzJ4Adbot3N/GMoSZJSpb0Lqp+G4Ql4Zm7mndd4vK4aD9c1wN9thL+bHnca6fhZHIeY3Z1w41q4/RVIEm2Ckx/Ad2fB6RlQGgE2t8N7xsedQpIkZYtkCOcsh71JWH/+0NgM51iCAL48JZqu+pFToh1XpaHA4jgEPLwDvtgIz+6B9hBG58E3ZsLVJ0NRAoqCzBrdK86Dl9riTiFJkrLF21+IZlnVnwsl8WzhkFI3TYM7t8GbX4A158edRjo+GVQ31Jvfboc3Ph+dUXTbbNh1Mey8BD41GUoLopKWSaUR4JIx8PvmuFNIkqRscH8T/H4HPHp2do3O/e+ZsKEV/nJd3Emk45NhlUNHumF1tF7w6blw3QQYNQTGiD8xCTa2RYu+JUmSBuL7W6BqBMwdFXeS1JpQFC01uvkleGpn3Gmk12dxzFDNHfCm5+CVdvjpENtq+qIxUBjAz16NO4kkSRrqtrbDhMK4U6THRyfCO0rh4uecraXMZ3HMMB1J+LMGGP8YNOyDh86KXpEaak4rhp++EncKSZI01M0YHs1kyla1Z8B7T4L5L8D3NsedRjo2i2OGSIZwUyOMehR+vg1umQmbL4RLSuJO1j/zS6FuX9wpJEnSUHfRaNiSxcUR4OdV8NVp8Mk18Nk1caeRemdxzACP7ICTH4evvwSfPxV2XgR/PjnuVAPzgZOhqQOedM6+JEkagAvGwP4c2DfhpmlwVxV8ezMseDE6s1vKJBbHNGrrgr9phBvq4e5tsK/z0Mc6uuC+1+D85XDZ83DOSGi6CP6xfOgeaHu4OcXwzjK48Fm4coUb5UiSpP6ZWBidXZ0LReq94+GRs+C3zfDG53Lje9bQMQT26Bya9nfBpCegPQnjCuCnr0ZnMBYGEABtIeQHcHoxPHzW0J2S2pf7ToefvwKfWAPnLodnz407kSRJGmrau8tTSHQNle0uLIHlc6Nrpzc8A0+cnR2DChr6LI5p8rWN0UY3Oy6Ggu7/2Le1waM7o1fNzhoJM4dDkOV/AT9wSrTLavmTsKUVJg6LO5EkSRpKPrQKJhdBIsuvmQ53+kh48Vw4axmctRyemZt553Yr9/grmCZNnTAy/1BpBBhfBAvHw5XjYdaI7C+NB0wfDsMS8GBL3EkkSdJQ8kATPLwT/rsq7iSDb9YIWHUubGiF6qejWWxSnCyOafKpifBqO6zfH3eSDNDSyLMrqrn6zny4vRpaGuNOJEmSMlxXEt6/Mtoz4YIxcaeJx5ThsPo82NYOs5+MlkJJcbE4pknVSDi5AL66Ie4kGWDRfCr2ryQRdkHTSlg0P+5EkiQpw31sdbQnxF05ONp4uFOKYN35sDcJM5+E3R1xJ1Kusjim0Z+UOT0TgJa1PRezt6yNK4kkSRoCXtgDP3kFvj8bivLiThO/0kJoPD/aIGjGU9DcHnci5SKLYxrt6ITh/oQlSZKOWxjCO16AuSPhg6fEnSZzjMqHtedH15Yzn4JX2uJOpFxjrUmj3++Ad58Ud4oMUDKTA8cQhd23JUmSevPx1bCtA35zRtxJMs+IvGjNY1kBzH4KNrqXhgaRxTFNnt4Fu7rgi6fGnSQDLFxCUFZFJ3m8XFwFC5fEnUiSJGWAh3bAqU/AzD/At16GLzfCD7bCnXPgpMK402WmojxYeS5MLYKqp2HNvrgTKVd4jmOa/N0GmDEMSgriTpIBSsrh2jpOfwpOGwH3lMQdSJIkxWVXJ9y1DR7bCT9/Fd46FvID+MtGCIBbZsB7xsedMrMVJOC5c+H85XDG01B3LpSPiDuVsp3FMQ32dMKSHXDzjLiTZI72JKzeB/9SHncSSZIUh6U74MMN0bmEwxIwNh++OQs+OSnuZENTXgBPzYXTnoaaFVB3XtyJlO0sjqmyaSnc+1bCsJNiYA8FFEz7HXBZvLkyxL9uihZzXzEu7iSSJCkOH6qHUXmwfC6cPSruNNkhEcB3ZsJbXog2FQqC1/8cqb9c4zhQLY3Rofb3vJEw7CQgmmZRQAfBLzyv8IDbtkTHk0iSpNy0tR2+Ot3SmGqXjY3ePrQj3hzKfhbHgWhphB9XEjatBOhxVmEAkPSQHVoaaf1xNRueyOc/n6yOfmaSJCmnNHVARwg1pXEnyT5BAJOK4M5tcSdRtrM4DsTiGsKwg2POCki4HRiLayjYUU8+XRS21MPimrgTSZKkQdawL7roLMyLO0l2On8UPLoz7hTKdhbHgWhu6KM0FnjsBEBzA3kko/fDJDQ3xJtHkiQNqvYuuLYeJhfFnSR7XVISTQWW0snNcQaitAK6p6keVDITrl8TT55MVFpBV1N9VB6DRPQzkyRJOWFfF8x5KjrbeuW5cafJXkUBJMO4UyjbOeI4EAtqo6J4QMlMRxmPtKCWHaMr6SSP9pLK6GcmSZJywsXPwL4krDsfJjjimDYXj4l+zrdtjjuJspkjjgNRUu7o4uspKWfcR+uYuyxaGL+hJO5AkiRpsKxthb+eAqUFcSfJbtUj4e+mwSfWwLYO+H/T4k6kbOSIowZF7WmwsQ1W7Y07iSRJGixnjYRF2+NOkRtumgbfnw1/uwH+dWPcaZSNLI4aFBOHQcVwuGF13EkkSdJg+dREeHY3dCXjTpIbPjoRvjIVbtoAbf7MlWIWRw2aH1bAYzvh/7N35/F11wW+/1/fk60LbdOk0LKVNk1JmwICllVEloHKjNFqRR1hFAZHZ9EZWZwZx/He3/3pZWZUQB1c761UHUTFYjXMaHDUKpuFlr1p0qaBlq3QJk33Zjnne//4pkuaLjTL+Zxz8no+Hnkk55tT8i4PODnv72d7bnvoJJIkKRveeyykgbW7QicZOf7HNBiVgptbQidRobE4KmsuKoezjoH3rgydRJIkZcPmnuTzKaPD5hhJogg+MxW++Qr81WpotbRriFgclVX3zYHVu+BRD6mVJKngPb4NSiIo8x1nVv3DKfD3J8NPN8KMZVDxENy9IXQq5Tv/N1ZWnTIaZo2BW9eHTiJJkobbH7bC+KLQKUamW2fAhrfAprfAOyvhz5rg39w0R4PgcRzKusmlsLUndApJkjTcVmyHkzy/MajKElg0G+aOg79tgaIIbpkaOpXykSOOyrqeOHnRkiRJhe3BDrhiYugUAvj4SXDbDPj7VmjvDp1G+cjiqKzbkYaxTluRJKmg/eA12JaG/zktdBLtcePJMKEIvvRi6CTKRxZHZd3mbji+NHQKSZI0nD61Ft45CY5xYVROuWZUKx/65Rzi24th0RzoaA0dSXnC4qis256BEyyOkiQVrDtehA1d8O2ZoZPoQF95eh41uxqJ4jS0NcLieaEjKU94D0hZVxbBRufWS5JUkB7ugFvWwv8/DY51Y5ycU7Slpe+FjpaDP1E6gCOOyro5Y5PtuSVJUmG593V421PwnmPhM9NCp5E0lCyOyrozj4FXOkOnkCRJQ2XdLjh3Bby/Ef7yBLh3TuhEOpSeCdXE+18orw4VRXnG4qisa9yZnCkkSZLy244euHolVC2DzT3wxJvhzlNDp9LhfHB2A2tG10JUBJW1sKAhdCTlCdc4Kuse3pLcjZQkSfnrng1wfXNyxNYPZsP7J4dOpCP5RRv8pLuKv792JYwPnUb5xuKorFq/O7kj+cmTQieRJEkDkcnAB1fBjzcmN4LvrIaUc9hyXiYD166Cd1XCXEujBsDiqKz61itQUQzHeRyHJEl556XdcMET0NYDDafDFZWhE+mNunkt7EzDPbWhkyhfeX9IWdW6y9IoSVI+atwB1ctgXDG8fIGlMW90tNJz1xy++PNiXl49h1HbWkMnUp6yOCqrylKQjo/8PEmSlDt29CQjjWePg8ZzYaKb3OWPxfMoam+kmDQVWxth8bzQiZSnLI7KquLI4ihJUr656Knk5u/vzgydREeto4XogMfSQLjGUVnVuBOO9S6lJEl5429Ww3M7YM25UOKQgzRi+b+/sqOjlZ0L5/D73xbz30/OgQ7n10uSlOt+/Bp84xW4ZzZMGx06jQakvHrvl/EBj6WjYXHU8Fu/lHjhDEZ3JPPrj9nSCEvqQqeSJEmH8cIuuKYJ/uoEeO9xodNowBY0QGUtcVTEqlG13H5uQ+hEylNOVdXw+8mVAH3n17c3B4kiSZLemD95Fk4dDV87NXQSDUp5FVy3kgj4+Tr47Avw0R44xhago+SIo4Zf3N23NAJU1IRIIkmS3oCfb4RVO2HJnNBJNJT+8RQoieDu10InUT6yOCqM+fWhE0iSpINYuR0+sAreeyzMHBs6jYZaOoZKNyrUAFgcNfzGn0J8wGPKq0KlkSRJh/C5F+CM5XD2MfDD2aHTaKit3QldMbyjMnQS5SOLo4bfW24lhn3l8S23BgwjSZIO1N4FZz4O/+sF+NIMeOhsSPkuseD8eCNMKIJRRaGTKB/5kqDh98vriNhvc5xfXhcuiyRJ6uMnr8MJj0JbN6w+D248OXQiDZdfb4aZY0KnUL6yOGr4Hbg5TtwdKokkSerVnYZ3Pwvva4RrJsP6C6DKsxoL2ppdMPeY0CmUr9yIV5IkaYRp3glveQJ2Z+BXb4LLJ4ZOpGzoiT2GQwPnfzqSJEkjSDoD565IRhcfOhPG+m5wxIiAnkzoFMpXTlXV8Evt2/M5PuCxJEnKruuakpGnZWdZGkeaVAT2Rg2UxVHDb8EDkColBjqjUn5z2QOhE0mSNCK9tBvufh2+OwtK3VlzxImATHzEp0kH5X0mDb+pl8CNnUTAHc+08s4H64h/3UxUUQPz6z3TUZKkLKl7FqpGwXuPC51EIUQko83SQDjiqKz69OPzqN3dSBSnoa0RFs8LHUmSpBFhyUZ4Zgf81xmhkyiUVATp0CGUtyyOyq6Olr5Hc3S0hEoiSdKI8pWX4E3HwKme4zdiFUew0+aoAXKqqiRJUoGr3wS/2wI/rg2dRCGdMRYe3BI6hfKVI46SJEkFbHsPLFgJ109xbeNId3s1vNwJf9kcOonykcVR2TXuFOIDHkuSpOHzf16FkggWzgqdRKFNHQX3nZb8N3Gzq4V0lCyOkiRJBez+NqhxXaN6vXMS/GA23PESfLY1dBrlE9c4Kru2reu7Oc62daGSSJJU8JZvhaUd8L3ZoZMol7x/MuzOwPXNUJaCf54WOpHygcVR4XW0epajJElDLJOBK5+Bt5XDNZNDp1Gu+fDxSXn8qzVJefzU1NCJlOucqqrsKq/uf+2u2Ul5lCRJQ+au12BbGu4/xzWErgAAIABJREFUPXQS5aqPnQhfqYZ/aE2Oa5EOx+Ko7FrQ0P9apguW1GU/iyRJBWzxRqgdA2OKQidRLvvESfDFKrixBe56NXQa5TKnqiq7yqugshbaGvteb3dfaEmShkJ7N9yyFhra4UszQqdRPrh5KnT0wA3NMCaVrIGUDuSIo7Jvfj2kSvcey5EhBRU1QSNJkpTv4hg+vRaOezjZSfVfq+DGk0OnUr74XBXcfDJ8cBX8dGPoNMpFjjgq+8qr4PpVREvqyLQ301RWQ/U76ykNnUuSpDz1w9fgxrXQ1g1fmAE3WRg1AF+cAaO3tnLqj+vIdDaTqqhJbvi7iaGAKI7jIz+r19y5c+Ply5cPYxyNNJkMlD8MH54M/35q6DSSJOWHXWn4rza453X4r3boysAVE+GeWigvCZ1OeW3RHNJtTRSRIUOKVOUsuG5l6FTKoiiKVsRxPPfA6444KqhUCj43sZUrflVHfH8zkXe2JEk6rJ9vhPeshFQEU8vgs6fALSdDiQuQNBTamykiA0CKDHF7c98zuDVi+RKj4P72D/OYvbuRKE4nm+Ysnhc6kiRJOSeO4fMvJKXxz6dA19ug5Xz49CmWRg2hihqIkv+g0qRYVVbDhs7AmZQTfJlRcNGWlr53sjpaQkWRJCmnbOqCjzbDBStg/EPwuXXJQe3fnhU6mQrW/HqomAVREamKWXzitHrevAJ6MqGDKTSnqio3LZrjlFVJ0og3dwVs6YGzx8EnT4RPT4UxvnvTcCqv2rumMQJ+2gMnPwpveRKWvTlsNIXliKPCK6/e++XerZram2BJXZA4kiTlik29u6T++szkuARLo7JtfDE8djY8uR0+vCp0GoVkcVR4CxqgshZg35TVOJOsd+xoDRZLkqRQnt8FH2yEHRk43vOqFFjNWPjZafD91+C29aHTKBSLo8LbMyWispZ+h8M46ihJGkHu3wTTH4WqZfBgB3yxCt4xKXQqCa6qhC/NgE+1wv96PjlSTSOLxVG5Y359/+2e25tDJJEkKet+9Bq88zmYNRZazoMXL4RbpoZOJe1z08nJzYxb18OoB+HCJ2D51tCplC0WR+WO8iqorCXT+59lHKWSLaElSRoBft4GM0bDL85IPku56OapsOutcNcs2J2B856Axa+HTqVssDgqt8yvJ1U5izRFNJfNYltdfehEkiRlxXGlsCsdOoV0ZKkUXDMZnpgLf3MCvK8xGTFXYXNvLuWW3vWO3Wm4bBnQDC3nuoucJKnwdWYg1W/NhpTbvnoqlBXBn66C7hiunRI6kYaLI47KSaOKYPW50BPD254OnUaSpOG3tAPOGBs6hXT0vjgD/nEqfKgJ7no1dBoNF8dxlLOOKYb/PA3OezLZuSvlbQ5JUgFbsws+Ny10Cmlgbq2C0ghuaIYxKXj/5NCJNNQsjsppJ4+i/xEdkiQVoBTJVD8pX/1/02FzD1zbBBdMgKmjQifSUHIMRzlt+TYoiRxtlCQVth09EAFP7widRBqcr8yEWaPhoic967HQ+HZcOe3ZHXBMUegUkiQNr5rHYHwx3HRi6CTS4D14Fmzshvc3hk6ioWRxVE6bUgrb07CzJ3QSSZKGz4YuuKcWji0LnUQavPIS+M/T4b5N8Km1odNoqFgcldM+PBkqi+HYR+CqZ2CVU3gkSQXg2kaY8jCMfxDe/jSkgTe5o6oKyGUT4Xuz4fYX4ZInYbuDAHnP4qiclkrBugvg5pPg+V1w+uOwfGvoVJIkHVkmAxs6+1777PNQ/iD88HX40BS4+WRY1wmXTIBJpWFySsPlmsmw7GxYtRMqH4Z/WOu6x3wWxfEb375r7ty58fLly4cxjnR4Vz4Nj2yB9edDhb9gJUk57C1PwCNb4Wsz4a971y7WPga7MvDAGTBzTNh8UrZkMvD59fAv66E4gs9Ph787KXQqHUoURSviOJ574HVHHJVXfnk6TCqB6cvgvc/B36+Fju7QqSRJ6u/xbTBjVDLK+L6VcNzDycjL3HGWRo0sqRT8j2mw7SL4s8lwy1oY9yBc/Rw8tz10Or1RFkfllVQKms6F9xwLTTvh/7wKxz8Cz2wLnUySNJLtTvd9HMfQE8PCWTCxGB7fCh84Du6thXtmh8kohVacgq+fmhTIW06CFdvh9OVwyqPw6JbQ6XQkTlVV3rv0qeTF5tlzvIMrSRpeL25o5YRf1FG0uZl4Yg2fOqOeb+6o4rhdrTzzUh3HbGmGihoev6ye81ur6LoYirxNLx3Shk744CpY2gF1lfCjWhjlUWxBOVVVBevXZ8CcsXDBEy64liQNn5XboefHl5Fqb4Q4De2N/M3Dl/HpU+CB1jpGdzRBnCZub2Lif9YxbZSlUTqSKWXwmzPhF6fDg1ug4mG469XQqXQwvpwp76VS8PBZsC0NX345dBpJUqGqLIFp3euIeh9HJI9f3A0zdjVTRHL3MoozTNvZzK/fFCyqlHfmVcKmC+FDk+EjzXDGY7B+d+hU2l9x6ADSUBhVBBeXw/c3wE0nh04jSSpEU8rgYAt8vvUqfGF8DeO3NkGcIU2KjvE1TBud9YhSXkul4Js18KmT4U+ehel/SHZf/VJV8r2DeWU3/KYj2cF41U7ozCTrio8tgeNKYUopnFgKJ5bB1FFwQmmy1jLrOlphSR20J9PZmV8P5VUBggycxVEF49rj4GOrQ6eQJBWyPcUx2u/r51fNYdzOJkgVA2mKKmqYNL8+TECpAMwYA03nwZ0vwc1r4e7XYPEcuKg8+f4v2uDf1sMftkJnDKNSSVGcWgZjUvBaF6zZBdt6kuNvOmPozsCePawioCiC0ij5s2OL4JgiGF8E5cVQUZzs4j+5LCmaJ5UlH6eUwZiBtqcldcTtTURxBtqbkhJ53coh+LeVPRZHFYw/nQzXN8NT2+DMcaHTSJIKU0TUWxn3lMeTdzYRkYG4Bypm5d2bQSlXffwk+NAUeM9zcPFTUJaCdJx8nDEW7pyZfL/0KEYQd6dh3W5Y3wkvdcIrXfBaJ7zeDZt7ko91u2F7BnamYXcGunp/5p6bRUVASQrKIhjTWzrHFcGE/UrnsSUwuRSO7y2e57Y3J6URIM4kI495xuKoglG6tZWWlfOYvqIluVBeDQsa8m4agCQpd6UqZ+8dNUiTIkVm79rGfH0zKOWy8cXw32fCpi5o2JyUtKsqjq4s7m9UEdSMTT6OViaTFM0XdsOLvcVzQxds6ob2HtjcDat3woo07Mj0ls4MdMfwTFkNs3Y3Ja8XUSqZrppnLI4qHEvqmL67Ze+mBXS05OU0AElSDptfT9u9dZRvbealsTWMjrs4dmcrKTJkSJHKwzeDUj6YVArXTA6bIZWCk0YlH0ftzPr+axzzjMVRhaO9eV9p3O+aJElDpryKT1+8kt9shrXnAx2txD+to6e9meZRNey4tJ5zQ2eUlHvKq/J+MMPiqMJRUQNtjXsfxkDknV9J0hCb3dXKjSvqiB9pJqqoIXp3PT/qrOITa+CBiaHTSdLw8BxHFY759VBeTUxSGjePrc7LaQCSpNz2yeV11OxuIorTe3dHvGYytF8Ec8eHTidJw8MRRxWO8iq4YQ0RMPVRqKuEr5WHDiVJKjSpzc3ghjiSRhhHHFWQdqbhuJLQKSRJBamihsyet1B5ujuiJB0ti6MKUobkrB9JkoZa5p31tI6ZRZqi5NxGl0VIGgF8a62CNLEYGneETiFJKjgdrbz84zqm7WymZ2LvlvqeFyxpBLA4qiBdMTE5JFaSpCG1pI4TdjRRTJqyjmRjHEkaCSyOKkifnw4bu+HhLaGTSJIKSdzeTJEb40gagSyOKkiTSuHMY+BTa0MnkSQVivteh9Wjaki7MY6kEcjiqIL1+WmwbCv0ZEInkSTlu4ufhKsb4fNn1ZOZOAsiN8aRNLJ4jqMK1h9PgqIIFm+E908OnUaSlK8at8ODW2D1uTBzTBVcsDJ0JEnKOkccVdCmj4KfbgqdQpKUz17ohJIIZo4JnUSSwrE4qqC9dQI8sjV0CklSPkvHEIUOIUmBWRxV0D41vpVfPDGH+PZiWDQHOlpDR5Ik5ZmeOHQCSQrP4qiCVvNAHbN2NxLFaeK2Rlg4A9YvDR1LkpRHNnVDsUOOkkY4i6MKW3szRb1f7v2dv3heoDCSpHz0g9dg5ujQKSQpLIujCtvBztfKdGU/hyQpL/Vk4KEtcONJoZNIUlgWRxW2A87XioE4VRomiyQp79z5cnK004emhE4iSWFZHFXYyqvg6t9CqpQY6IxK+ezZDaFTSZLyxJ0vw7wKiFzjKGmEKw4dQBp2Uy+BGzuJgAfb4dZnYMUz8PWZMN01K5KkQ2jZCWt3w5LTQieRpPAccdSIckUF/OfpsGoHzFgGFz4B7S55lCQdII7hymfg9LFw2jGh00hSeI44asS5qhJeuACWb4V3r4TjH4VFx7fypw/Og46W5Enl1bCgIZnqKkkacW5ohpc7Yd35oZNIUm5wxFEj1tzx8OIF8PET4Yxf1RHvKY2QFMgldeHCSZKCuW8jLNoAP66FKWWh00hSbrA4asS7rRpqO5vpt+9Be3OIOJKkgF7dDX/aCB+ZAu86NnQaScodFkcJiA447zEG4okHOQNSklSw4hgueBKmjYJvzwqdRpJyi8VRguS8x/JqICmNa0uruW5W/eH/jCSpoLy/EV7rhkfODp1EknKPm+NIkGyCc8MaACJg8xb4wVOw9aFWFq+pI7W5GSpqegumG+ZIUqGI42Q94/9eBy/shoY3QWVJ6FSSlHsccZQO4pwJ8Nxc+NJT84jaGyFOQ1sjLJwBy78cOp4kaYjUPAZ/0QxVo6H5XLh8YuhEkpSbHHGUDqFmLMSdLf03zfndjTD3kyEiSZKG0NPboWUXvH4hTCoNnUaScpsjjtJh9CuNJGsgJUn5b0pJ8po+3tvoknREFkfpcHo3zNnD0ihJhWNyGYxOwW0vhk4iSbnP4igdzoIGGH1cn0ufPPEOFr8eKI8kaUi9oxK+8UroFJKU+5ycIR1OeRX89Wt7H0ZAvBquboQxTTC6CK6cCHfNglJvw0hS3vm3KqhaBo07oHZs6DSSlLt8qysdpa+eCmvPgy9Xw0emQH0bvGl56FSSpIGYPhpmjIJbWkInkaTcZnGUBmD6aPjICfAvM2D1ubB6J/y6PXQqSdJAfHYaPLAZdvSETiJJucviKA3SlDKYUAyPbg2dRJI0ENceBxmS4zkkSQfnGkdpkDLrlvLKY/MoW9YFqdJkQ52pl4SOJUl6g77+ChRFcM740EkkKXc54igNUnTfPMriruTMx0wXLJ4XOpIk6Q3qycA/Pw8fmgwlviuSpENyxFEapCjT1ffCgY8lSTnrrU8mZ/TeOTN0EknKbd5bkwYrVUrc+2W85+O2CO4og/VLw+WSJB3WnzfBiu3w+NnJ8UqSpEOzOEqDtaCBKFXa59Leaas/uTJIJEnS4W3uhkUb4N5aqPH8Rkk6IqeqSoM19RK4sROA6Lao7/fi7uznkSQd0e0vwvgieNexoZNIUn5wxFGSJI04P9wIl00MnUKS8ocjjtJQioqJ4x6i/R5LknLL7jSs3QXfnRU6iSTlD0ccpaG0f2nsfSxJyi3feAVGp+DCCaGTSFL+sDhKw23hTOhoDZ1CktTruxvgwvGhU0hSfrE4SkOpvLr/tY4WWFKX/SySpH7SGXhuB3z8xNBJJCm/WBylobSg4eDX2xoddZSkHPDd1yAVwTsnhU4iSfnF4igNpfIqqKw9+Pfumm15lKTAvvEKnDsOoujIz5Uk7WNxlIba/PqDT1nNdDllVZICemk3rNgG/3RK6CSSlH8sjtJQK6+CG9YcfOSxvTn7eSRJAFy9EqpGwR9Xhk4iSfnH4igNl/n1kCol7n2YJuL1Y2rIZIKmkqQR6b6NsGwb/OgQqwkkSYdncZSGS3kVXL+KqLKWOCpi47jZvG1aPXP+u5Vt/3cO3F4Mi+a47lGShtmqHfCBRrhuCrzZYzgkaUCiOI6P/Kxec+fOjZcvXz6McaTClsnAhm/N5PidLezdl6G8OpnaKkkacut2Qe3jcNYx8NDZodNIUu6LomhFHMdzD7zuiKOURakUfUsjJOc8SpKG3P2bYOZjMGsM/P7M0GkkKb9ZHKUscwd4SRpeu9Pw7mfhnc/BtZNhxdzkxp0kaeCKQweQRpzyauKOZNQxBroppuS2KCmU5dWwoCFZHylJOio7e+BvW+B7r8G4IvjP0+Eqd1CVpCHh/Tcp2xY0EFXWQlREJlVKCT37RiE7WjzrUZKO0pZu+GAjTHgI7tsEX6iCjRdaGiVpKDniKGVbeRVctxKAotv7/y8Ytzc7nVWS3oCXd8PHW+Dnm6CyBO48FT52QuhUklSYLI5SSBU10Na492EMNJbVULwDasaGiyVJuaY7A1c+Aw9tSR5HQHcMJ5XCd2fBtVOCxpOkgmdxlEKaXw+L5+3dWTUqr+ajVfXUvAjfmRU4myTlkOua4PGt8KNaKC+GnWk4fzxMKg2dTJJGBoujFFJ5Vb8zHF98FM509bEk9dHQDn9zIrzn2NBJJGlk8u2plGMqiuH3HZDJhE4iSbmhoxvaeuAvXb8oScFYHKUcc08ttOxK1vJIkmDRBhibgumjQyeRpJHL4ijlmNljYdnZ8JsOWL41dBpJCu+nm+A0NwyTpKAsjlIOOmMcXDgeLnsalm4OnUaSwnpqO7xrUugUkjSyWRylHPWbM+GC8XDDY6288M05xLcXw6I50NEaOpokZc3GTtiahj8/PnQSSRrZLI5SjipNQcOb4NmX6jh5RxNRnCZub4IldaGjSVLW3LsRxqRgssduSFJQHsch5bgxW5qBZIvVKM4QtzcThY0kSVmzbBscb2mUpOAccZRyXUUNRMn/qmki4jhNfFsEd5TB+qVhs0lSFqS8WyZJwVkcpVw3vx4qZkFURBExERABcaYL7r00WfPY0QoLZ8JtUfKxcKZrISUVhHFFsNtzbSUpOKeqSrmuvAquW5l8fVu0d5pqBMRAtHAG8X7XAOKOFtI/raP4+pVZjSpJQ62i2OIoSbnAEUcpn6T6LvSJDvN1UXuju7BKynuTSqDT4ihJwVkcpXyyoOGglw9c/hPvudbWCHfNtjxKyluTSqE7PvLzJEnDy6mqUj6ZegncsDY5kqOt8ZBP279IxpkuooUz+j5h7AnwgQeTabCSlMMml1gcJSkXOOIo5Zs9ax5vWAuVtUAqmcIaFSWPy6v7PH3vusf9L+54xZFISXlhcimkLY6SFJwjjlK+2n/TnP11tCalMNPV53K/6ayZLqIldQf/Z0hSjphSmtz4imOIPJZDkoJxxFEqNOVVcP2q3tHIQ4uAnrZmvtvUSte3p+07yuOOUs+HlJQzyntvce9Ih80hSSOdxVEqRP2ms/YXE7FuTA3n/rqOkm3r9n0j0w2L52UpqCQdXnHvO5XXug7/PEnS8HKqqlTIDpzO2tGabKzT3kxUUcOM+fXwnVP7/bE409VvaqskhZICNnTDjCM+U5I0XCyO0khysHWRFTV9dmiNga6olGe3wtzx2Y0nSQdTHMHLnaFTSNLI5lRVaaSbXw/jTtn3OFXCP57VwLlPwM0t4WJJ0h7jiuDJ7aFTSNLI5oijNNKVV8FHX9j7MALuAE57Ff5qNTz5cisNa+so6WhORifn13v+o6SsuqoCvv0K3DrdnVUlKRRHHCUd1A3Hw/rz4Vsr60htboQ4nUxpXTjDXVclZdWdp8LWNNz2YugkkjRyWRwlHdKUMpi5u5mi/a7F4K6rkrJqQjF8eDJ8yeIoScFYHCUdXkVNn4cRya6rkpQtu3qgeSeU+a5FkoLxJVjS4c2v7/MwBjqjUj7/4FLiO8rgtgjuKHP6qqQh9+Ju+KOnYNxDyeY4i2aFTiRJI5fFUdLhlVfB1b+FVCkAUaqUJ65s4JbH58GekcdMF9x7aXJOpCQNge4MzH4M1uyCu2bB1rfCpRNDp5KkkctdVSUd2dRL4MZ9h6hdCMQNXfTb3HBJXf9zIiVpAO7bBF0xPH8epLzNLUnB+VIsaUCi3hHI/cVtjXB7MSya4+ijpEE5sRR6YtieCZ1EkgQWR0kDtaChz8N47xce2yFp8C6ilZUr5zD2q96MkqRcYHGUNDBTL4Eb1kJlLURFRNBn6qrHdkgalCV1nLq7iaI4De1NyVR4SVIwFkdJA1delaxpvKknKZD78dgOSYPS3kwRvfNU4wy0N4fNI0kjnMVR0tA4xLEdf9kMGdcoSXqD1u2Ch7fA86NrSO95mxKl+p0pK0nKLoujpKFxkGM7Hrm8gUUbYNoyWLszcD5JOW3tTpjwYPJ6cfGTcP3serrKZ0FUBBWz+t2ckiRlVxTH8ZGf1Wvu3Lnx8uXLhzGOpELT3gWXPw3P7IALJ8BZY6FuElxe7hb7kva5+EnY0AWN50Cxrw2SFEwURSviOJ574HVfmiUNq4pSePIc+E4NxDEs3gRvfwaOfxS29oROJylXbE/DiWWWRknKVb48S8qKDx8PD50NL18IO94KRRGc90ToVJJyxT9Ohd93QI9roiUpJ1kcJWXdqCJYfjas2QlfXB86jaRc8L7jks9PbgubQ5J0cBZHSUGcMAo+PRU+83yyDlLSyPbMNsgAM0aHTiJJOhiLo6RgPlcFk0uh7rnQSSSFsmYn/N9X4MIn4aIJybpoSVLuKQ4dQNLIVn8anL0CGtpgXmXoNJKy5T82wMdWw84MjErBlRNh8ZzQqSRJh2JxlBTUmePgTyrhmlXw+oUe0SEVskwGvvwyfPUlWN8JHzse/n2mO6lKUj7wpVpScD+aDTvS8HctoZNIGi7P74Ipj8KnW2HuOFhzLnyjxtIoSfnCEUdJwY0phq+fCh9phg9MhrdMCJ1I0lDqyiRT0k8qhRfOS/6flyTlF+/zScoJ1x8P75oElz4FD61rhUVz4Pbi5HNHa+h4kgbh42ugJ4YVcy2NkpSvLI6ScsZ9p8HVx8LE++tItzVCnIa2Rlg4A9YvTQqkhVLKK+1dsGgDfGYqlPquQ5LyVhTH8Rt+8ty5c+Ply5cPYxxJgsztxaTi9N7HMdATlbJt7FQmbm8h2vONVAlc3wTlVSFiSjqC7T1Q8xiURNB6nptfSVI+iKJoRRzHcw+87ku4pJyTqqjp8zgCiuOuvqURiDPdrP9hHf97HSzbkuzYuFdHKyycCbdFycfCmY5QSln0ehdMXwaZGJ45x9IoSfnOl3FJuWd+fb9LUaq0T2mEpFCesKOZ215MDg8v+j0c83uYuQzW/bCOuGO/bVo7WvZNeZU0rO55Dab/AcYXwfPnw3jXNUpS3vOlXFLuKa+Cq38Li+dBpgtSpbCgAX71F0kB3E9xZQ3tFyVfr90JD2yGR7fAiTua+xXNGIjuvRRuWOv0VmmQdqehaSc8sx0Wb4IV22BHJjlaJx3D+46Fu2c70ihJhcI1jpLyR0drUib3lMfy6qRQHqwELpqTbKxzgBiIKmvhupXDm1UqUN96GT7zPLT1JKP+ZSmYPgouL4dpo6BqNNRVej6jJOWrQ61xdMRRUv4or4Ib1ryx586v71sye0VApr3ZefrSAFzfBN/bANdPgVur4LjS0IkkSdnieydJhWlPybz6t30uZ4Auiog90kM6Kp9em5TGX54B/3eWpVGSRhqLo6TCNvWSZE1jZS1ERaRSpZTEPURxmri9CZbUhU4o5bzFr8O/vQh31cAVFaHTSJJCsDhKKnzlVcmaxpt6IE5TRHJuRxRnknWQ7rQqHVImA9c1w7XHwYeOD51GkhSKxVHSyHLAGZExJGshJR3UV1+Gzgx8Z1boJJKkkCyOkkaWA86IjIA40wWueZT62dkD//w8fHiKu6RK0kjnrqqSRpbyquRcyEwX0DviCBCnidsaiRbO6H3eYY76kEaAngycsRzGFsE3ZoZOI0kKzfuHkkaeBQ1JeSQZcYx6L0f7P6ejBe6a5QikRqSuDNQ8Bpu64em5jjZKkiyOkkaiqZfAjZ1wc5zstnoomW53XdWIszsNpy6DzT2w+jyYUhY6kSQpF1gcJY1s8+v3jj4eTKa9mUwmi3mkgHoyMOdx2J6GlnM9q1GStI/FUdLIVl4F169K1jQeIAZaS6ez5ptziN08RwUuk4G5K2BjNzSdCxWWRknSfiyOklReBTesgRvW9imQUXk1VaOgelcTUZwmbm9y6qoK1sVPwepd8OxcmGRplCQdwF1VJWmPPQVyP6nbi4FkrmoUZ4jbm/tuoiPlq47W5AzTjhZi4K6yaqIFDZwy2p2EJUn9OeIoSYdTUQNR8lKZJsULo2sCB5KGyJK6ZPdgkh2FqztbqG5wRF2SdHAWR0k6nPn1UDELoiK6J87iqhn1XPwkbpij/Nfe3OdhdJBrkiTt4VRVSTqc8iq4biUAo4CfbIfzn4ATH4WGM+CMcWHjSQMVT6yB9sa+U68rHFGXJB2cI46SdBROOwZeuRCmj4YzV8BlT8HzG1ph4Uy4LUo+Fs5091XlvH8/p56W0mriPRfKq5MRdkmSDsIRR0k6SuOL4ZGz4Vft8DdrYOfiOuLdLftGbjpakvVjvSOVUi76wtYqHr98Dd+vDZ1EkpQPHHGUpAG6ogJWnwe1nf13Wk23NZG+aw54/qNy0PYeeLkLPntK6CSSpHxhcZSkQYoOWBcWAz1RMbQ3QZxOPnv+o3LI9zbA6BScOjZ0EklSvrA4StJgza9P1of1isqrKSVNUe/5j/Se/yjlii+8CBeOD51CkpRPXOMoSYNVXgU3rOlzKVo0p3fEMUOaFK2japgZKJ60x640XPQkvNoFj5wVOo0kKZ844ihJw2G/8x8zE2fxjup6/n5t6FAayRq3J8fIrNsNz50DJ4wKnUiSlE8ccZSk4bDf+Y8lwG1PLuWPfjabOO4iSpXCggaYeknQiBo57noV/qIZzh0Pv30TlBWFTiRJyjeOOEpSFrxj6TzK4q5k99VMF9x7qTutathlMnBNI9zQDLecnBwjY2mVn2hbAAAgAElEQVSUJA2ExVGSsiHT1e/IDnda1XB6rQtmLIPFG+G/Tod/nRE6kSQpn1kcJSkbUqX9r7U1ZT+HRoSfb4Kpj0JxBC9dAG+vDJ1IkpTvLI6SlA0LGoDkjMd9fAnW0HqtCy58AuY/Bx+cDKvPg0kHuWchSdLRcnMcScqG3o1w+k5X7QkQRIUojuFTa+HLL8FJZfD42fBmz2mUJA0hi6MkhdTRmuzAKg3CO56FX22G26rh704KnUaSVIicJyVJ2ZIqOWCqKrB4XogkKiAPdcAv2uHhsyyNkqThY3GUpGxZ8ED/nVU7WkIkUQH54oswawyc49RUSdIwsjhKUrb0rnOUhtKT2+GiCaFTSJIKncVRkrKpvHrvl/EBj6WB2NAFf1IROoUkqdBZHCUpmxY0QGUtmaiIVaNq9x7TIQ3EM9ugO4a3WxwlScPMXVUlKZvKq+C6lXR0wZxHID3eO3gamEwG3tsIp42BsqLQaSRJhc73K5IUQMWGpex6oozojgjuKIP1S0NHUp7oycDXX4bjH4VXOqHhjNCJJEkjgcVRkkJYPI+yuCvZZTXT5bEcOqKfbYTzV8Co38ONLTB3HGy4EE4YFTqZJGkkcKqqJIWQ6ep7NEemK1QS5bjuDJy9Ahp3JGXx7lp437EQ9TvbRZKk4WNxlKQQUqV7y2IMpFOlviDroK5dBc/vghfOh5MdXZQkBeJUVUkKYUFDUh5JSuMV1Q3MfzbZ8ETa3/1t8PnplkZJUlje4JakEKZeAjd2AskL8ee3wOVPwRXPwK/PDJpMOWZ3Bs4bHzqFJGmkc8RRknLAWybAY2fD0g743quh0yiXRCTTmSVJCsniKEk54oxx8NHj4WNrYGdP6DTKJT1OYZYkBWZxlKQc8rWZcEwRXPyU6x2ViCJIhw4hSRrxXOMoSTkklYKHzoQzV8DbftfKr9bMY9S2luSb5dXJpjrlVWFDKut6nKsqSQrMEUdJyjE1Y2Hd+fDdpjrK9pRGIO5oIf3TuoDJFEKExVGSFJ7FUZJy0HGlULWrmf3PeI+AuL2ZyofgE6udyjpSREDa4ihJCsziKEm5qqKm36XMxBqumwILN8BJf4B1uwLkUlb1xFBREjqFJGmkszhKUq6aX5+sa9yjvJrS99RzWzVsuBCOLYGZj8HvN4eLqOH1wi7IAG8+JnQSSdJI5+Y4kpSryqvghjUH/db4Ynj6HJj/LLz9Wei4CEq9FVhwbn8RKouhrCh0EknSSOfbDEnKY/fNSdbAfe6F0Ek01DIZuOs1+PCU0EkkSXLEUZLyWioF/zShlff91zzizpZ9m+nsd3THzzdByy64ciKc5pTHvPGZ56ErA7dOD51EkiSLoyTlvX96og72L40kR3d03FvHrNqVtHXD2CK4ZS3M6GzlgdY6pu1qJqqo6V1H6bmQuWbVDvjCi/DlaqepSpJyg1NVJSnPRZv7HtsByfTVcVubuWA8tF8EW94KPRfDihfrmLqziShOQ3sTLPFcyFyTieGPnoazx8EnTgqdRpKkhCOOkpTvKmqgrbHPpRgorqxhyen7rqVSMH5rM8k+nUCcgfbmg/8zO1ph8TzoaEke7zf1VcPrphbY1A3Pzg2dRJKkfRxxlKR8d+CxHUBUXp1cP1BFDXGUvPSnSbF9fA2ZTLIRyxfWwcxlMO5BaLy7jnhPaYSkQN41OymUGjadGbjzZbhtBlSUhk4jSdI+jjhKUr47zLEd/cyvJ1pSR9zezPOja7jq5HrW/h6KouRO4jsr4dITYdbj/ae/kulKprZet3KI/wLa4/sboCSCjztFVZKUYyyOkjSSlFfBdSuJgGpgDbB8K7zSBX9cAcV75qEcZPorcOiprRoSD26BE8tCp5AkqT+nqkrSCDd3PLxz0n6lEfpMf417L8UAUZHTVYdROoZUv6FeSZLCszhKkvrbM/31hrVEqRJikp1ayXQlm+ZoWNSOSTbGkSQp11gcJUmHVl4Fme6+6x333zRHQ+qt5bClJ3QKSZL6szhKkpQjyqJ9U4MlScolFkdJ0mFlJlT3LTMHHP2hofHSbnj7s3DmMaGTSJLUn8VRknRYmfc00DiqljgqgspaWNAQOlLBWbEtOUNzcik8fFboNJIk9edxHJKkwyquqOLKs1ZyeTl8rzZ0msLTmYa3PgkXToD/fhNE7qoqScpBjjhKko5o7jh4ZkfoFIXpxrVQHMGvzrA0SpJyl8VRknREnRko8TfGkOtKw8JX4Z+mQsp/v5KkHOavKUnSEV0wHtbsDJ2i8HyqFUpT8A9TQyeRJOnwLI6SpCP6xImwJW15HErdGfjWK/Cpk52iKknKfRZHSdIRVZTClBK4/cXQSQrHp1shFcFnHG2UJOUBi6Mk6Q2ZVwH3t4dOURh+/Drc8RJ8fjoU+ZtYkpQH/HUlSXpDPnMKvNwJ3301dJL8Fcfw4VXwgUb4+Ilw08mhE0mS9MZYHCVJb8jMMXDjSfDR1ZDJhE6Tn97fCPe8DvefBl+ZGTqNJElvnMVRkvSGfbEKMkC9U1aP2rrd8JONSWn840mh00iSdHQsjpKkNyyVgurR8JWXQifJP3+9Gk4pgysrQyeRJOnoWRwlSUflGzPhdx3wmdbQSfLH7jQ0tMPnpodOIknSwFgcJUlH5ZKJcFcN/Mt6uNORxzfkn5+HMUVw7ZTQSSRJGpji0AEkSfnnQ8fDhi742xaoGQNXVIROlNu+/xpcOzl0CkmSBs4RR0nSgPz9KfDHFfDhptBJctvuNLzeDX93YugkkiQNnMVRkjRg35kFr3bBul2hk+Su32yG4ghqxoZOIknSwFkcJUkDdlwpjErBQ1tDJ8ld/90Bk1wYIknKcxZHSdKgpICeTOgUuevRrTBzTOgUkiQNjsVRkjQoGaDU3yYHlcnA8m3wEXdTlSTlOX/VS5IGrSgKnSA3fWdD8ovWHVUlSfnO4ihJGriOVlY8O4f3/kcxLJoDHa2hE+WUJ7fDcSWQ8retJCnP+atMkjRwi+cxe3cjqTgNbY2weF7oRDnlsnJ4rTt0CkmSBs/iKEkauI4WogMeO+q4z7smQU8MT7jrrCQpz1kcJUlDa0ld6AQ5oziVTFX9/uuhk0iSNDgWR0nSwJVX97/W3pz9HDns9LHw8JbQKSRJGhyLoyRp4BY0QKqUuPdhTAoqaoJGyjUnlEFHT+gUkiQNjsVRkjRw5VVw/SqoqKWHInqiYmhrcofV/Uwshp3p0CkkSRoci6MkaXDKq4iuX8nOY6ZTHHcBGXdY3U9lCezOhE4hSdLgFIcOIEkqDOO3t/S90NFy8CeOMMeWQGd85OdJkpTLHHGUJGkYHVcC3Y44SpLynMVRkjQ09tthNQZeGn2QHVdHoCmlyVmOkiTlM4ujJGloLGiAylqIiugsr+XSGQ18+5XQocI7vgzcG0eSlO9c4yhJGhrlVXDdSgBGAcc/Cd/bAB89IWys0I4vTT6nM1Dk7VpJUp7yV5gkaVhcVQFPbIeeEb6+b1RR8vn1rrA5JEkaDIujJGlY3HwyFEdQtQyad4ROE1YEbOoOnUKSpIGzOEqShkVpClrPgzN7WulZNIfM7cWwaA50tIaOllVxnGwWNLEkdBJJkgbO4ihJGjaTSuHnq+dRu7uRVJyGtkZYPC90rKxauyv5fGJZ2BySJA2GxVGSNLw6WogOeDyS3N8GxxRBFB35uZIk5SqLoyRJw+h3W2Cao42SpDxncZQkZd8dZbB+aegUWfHMdjhrXOgUkiQNjsVRkjS8yqv3fhnv+ZzpgnsvHREF8pUuuGJi6BSSJA2OxVGSNLwWNEBlLTHsXeu453Oc6SrozXK2dMPuDLyjMnQSSZIGx+IoSRpe5VVw3UqiVGm/b0UAma6sR8qW+jYYFXkUhyQp/1kcJUnZsaABDiiPMfS7Vkh+tRlOcGMcSVIBsDhKkrJj6iVwYydc/VtIlRID3VFpUigL1JPb4fSxoVNIkjR4xaEDSJJGmN4CeXML/Mdr8PrU0IGGzwu74c+nhE4hSdLgOeIoSQrikyfBxm54vUCXOO7sgW1pmD8pdBJJkgbP4ihJCmLqKBhfBN95NXSS4fHtV2FMCqaNDp1EkqTBszhKkoI5fSzc3xY6xfBYtAHOGRc6hSRJQ8PiKEkK5h2V8MyO0CmG3gu7kr/XrVWhk0iSNDQsjpKkYP78+GQdYHuBrXP8UBNMHwUXTgidRJKkoWFxlCQFc1wpjE3BD18PnWTofH8DPLQF/mN26CSSJA0di6MkKajKEnhuZ+gUQ+P1LrihGT5xIlzgaKMkqYB4jqMkKZz1S1n96DxK4y5IlcKChuScR4COVvjxZbBtXd8/U1YJ1z4G5bm3gPCSp+DkMvjKzNBJJEkaWo44SpLCWZyUxggg0wWL5yWFceFMWDiD+MDSCNDZBkvqsp30iD7TCmt2we/ODJ1EkqShZ3GUJGXf/dfCbRFxprc09oozXTx/Tx1xRwtAn+/10dYI65cOc8g37plt8K/r4avVcNKo0GkkSRp6FkdJUnatXwrNdxPTtxjGvR9TdzYfujDu/9zF84Yp4NHJZODyZ+AtE+CvTgydRpKk4eEaR0nS8Fu/FBbPI87sO3fjwHIYAREpqKxJRhQPIyIZnXzpm3M4cWczUUUNzK8Psu7xfY2wKw2/PD3rP1qSpKxxxFGSNOwyvaVxT1k85Ihi5azeAli971p5NdywNtk8p1fc+/n4HU1EcZq4vSnIusefbYL7NsHPToMx3oqVJBUwf81JkoZN8w74s1Ww7I2UxlTJvlHDG9b0//6ChmTznEwXUaoUMt0UkUn+mXEG2puH469wSFt74AON8KHJcHlFVn+0JElZZ3GUJA1ORyvcexlsXUcMdFPCx05/gEcnXMLqXTB7DMSpUqL9pqn2U16dFMPDTTWdegnc2Lnv8aI50N4EcYY0KYoqaobqb/SGXPE0VBTDd7L7YyVJCsLiKEkauMYfwC+u2bvRTQSU0M23npvHn83r5Gsze0fjpuwbLex3XuNAza+HJXXE7c2k4xSptkai2yKISuC9Dwz+n38Yv9kMj2+DZ8+BlIs+JEkjQBTH8ZGf1Wvu3Lnx8uXLhzGOJClvrF8K917ab3fUvW5+479fBmXRHOK2xr4ZomK4qXvYfuSMP8DJZbD0rGH7EZIkBRFF0Yo4juceeN0RR0nSgGQWz9s7ytjPfhvZDLv2gxzfEfcM24/7RRs8vxt+86Zh+xGSJOUcJ9hIko7ad1+FaL8Nb/qISpKpqNmS5bWNf9EMfzQRThmd1R8rSVJQjjhKko5s/VJYfCVxJpn+eXHpKaSjEorjA6aDXnU31H4wu9nm18PCGYeeMjuEPrEaXu+Gp2cP8w+SJCnHOOIoSTqiePE84kz33qmp07rWUXzMCfumpKZK4erfZr80wt6dWPuVxjvKksI7RG5dB197Bb43CyqzOBNXkqRc4IijJOmw1u+Gkw+YlhoBbH8Jbhq+tYSDlulKdnLd/wiPAdjWDe9eCb/tgDur4QOThyifJEl5xBFHSdIhffsVmLEMuqJS+u2RmuW1hQOS6YJvT0vOmtxfR2tyDuTtxcnnA7/f644XYdIj8OwOeOhM+OuThj+yJEm5yBFHSdI+65fC4nnEmS66o1LumdnALbWXUDa9ARZfCb1rHBl3SrK2MFdMOhs2PXHw721bBz+8GEZNgPZmustr6OjqomJHC0UAbY2wcEYy1Xb81OTv39ECwJ+UVtN9SQOfOq2KaLgXUEqSlMM8x1GSlDjgXMYYiFOlpAY51TNrbjt8s9vz90qTIkXmoBvp7PmNGO3/Zypr4bqVQxZTkqRcdqhzHJ2qKklKLL6yz86kEZDKdAUMdJTKqw/77T1/r6JDlMYDn7f36/bmweWSJKkAWBwlaaTrXe+3Z9fUPlJ5tH1o79mRR5xHE6UOWTL3jLT2kQ9rOSVJGmYWR0ka4drurSPd1nTwUbjeMpYXyqvgqruPfJZjxazk73X1bw/67T5/vrw6t9ZySpIUiJvjSNIIdlMLfGFrM0Vk+n9z3Ckw9ZKsZxqU2g/Co/8Teje36WPsCfCBB/ee+0h5VVIe771071NiIBOVUHRTHk3RlSQpCxxxlKSRpvEHcFtEfFvEl34W0Tl6SjJ9c3+VtfC+34TJN1gLGvpORS2vhhvWwl++vK807jH1kqQ89k7JTadK+aOZD/DzTdmLK0lSPnDEUZJGkvVL4RfX9NkEZ+yul5Oi2N6crOebX9+/YOWT8iq4Yc0bf/7US6B359hiYPZqePdz8MAZcHnFsCSUJCnvWBwlaQSJF88D+u6cCnjcxH6+fips74Ern4F7T2zlPQ/XFU6pliRpgJyqKkmFpHeHVG4vTj53tO791t2vAZmuI28eI75XCzedDDUP1JFuWwVxGtoaYeGMZNRWkqQRxuIoSYVkSR2ZtiaI06Tbmlj9gzre/Syc/hj82SroiUr7Hzdx1d0hkua8L86A2s5mig78N3bvpck6UUmSRhCLoyQVgj1nMbY1kurdIbXo/7V39/FV3/Xdx1+/cw5JuG0IFAq2UAJtAqm9G9La1lp7qeiuZsP1Zl3Vrlq36pzb6s32sNVLLx/zWndTOuec060WOze1SkXjpujast7Y1tI7FEgoHAql5aZwONwmhOT3u/44BJIQDkk4yS/nnNfz8eABOZwkbyh68s7ne0PI7LYW0m1wVhU8eRGMum45QdfdjImK3MEw826KMfjIFvRxh2ME8JP3OnmUJJWVIIpOelXyUfPnz49Wrlw5hHEkSYMR3tdAlGkmSXjs4Jsgkbuz0P2Lg5dN55an9nL07/jWDe55lCSVlCAIno2iaH7vx504SlKRu38rhJljdzEe3cNYU+/l9aeq667HXgKOTB7vnQ13B7C4wgmkJKmkWRwlqUjt7YCLV8IHWmDHuDqirrsYg0Tueo1bVjsNK4QZV/W5D7T7IUNRdDi39/HuwP2PkqSSZHGUpCL0r6/B1F/AtnZYtwCm/24TQU09BEknjUNh3k19Th67dJXIo/sfJUkqMe5xlKSRbPMKWLoQwnZIVLD/Pcu57eBVfHsHfGQ6fHkOJPwW4PDJpmFZY+5qjhOIgNfHzuH0A+uPTSUTo+Dan+Wmlxqcrr9779SUpCF1oj2OFkdJGim6lcQoUcGSy5fzvscXkoraj+6pOxRUMG3BIb5ZD781Oea85SybhvvqITzc4+Hur6hBr8cDgOo5cO1yC083YQjf3A7//Fpugt4Z5f6+IiCKOLJzF375wjnMaOtWxqvnwK0vxZJZkkqZxVGSRrjwnkqCsGdJrDxSGrtEQPCJ/v//toZQNg3fqCeKDvf4b5TP0QI5aV5ZTsw6QrhzI/xkF+zphNYQMochGcAlE+CNY6EigFQi9/eUBBJH/nL/8gfBcf9buPf3Ih7J5t7v5qkwvSr3e9nDcOXzsOYgjE/B186FG6YM759VkorViYpjKo4wklRqfrwT/mUrdETwyiE4LZX7YvZjb4C5Y0/+/m2dUBkeK4kBUBXllqcSth993tE7GBW/6log7Fkag2RuGeUJlrJ2PTfc1UxiWWPxXpXS17JR6HMpaUcIL+yH+7bBvVshFcA7JsL0Sjh9FCwYD++qGdyS6z9+CWZWwY93wac3wtgEzKiCdCtMqYCfXQD3b4PfWwOVCfjt7lN6l75K0oA4cZSkU/Tx9fD3W+C8I9OSsyoh0wEbWmFrOzxydporH1oI2fW5d+hjueLdm+Gj36/sOWFMVOSe122PI9cud5/cSLKkgc5dufszj96buagp99+s67/3CXSSZO/YWVQf6LX8sgiWsnZ8o4Fgd+7P3UmC7ePqCYAp+489tn50Pec3rKY9yp3EVzMK/ng6fHbmIPfl3ntOj7/TsHoOiW5LVbOHc0teH8/ChePh02cd+zwfXQdffQ0mj4Iwgk7gFy82cG7bsbwtVfVcev5qZlVBVQJaDsL+TqhOwV218KHpp/I3JknFw6WqkjQEXmuDM5+C78zreynce9fAnY80MLdtTY+TN1uq5nHnW1fzvXm5L24XvgjTtq9gyWpLYlHJptnxQCM1+1pITepjapVN91kioyBBB6mj+1f7NEJL5EMZeOt9KVJ0Hn2sgyRAj8fCIMkP39/BmyfAGZUF+MSnOCF8PAvLM7nJY2UCPvH9FImoZ95/uL6D/94NbSG8fSJcNgHu356blp5ZCWdUwJ4O2NuZe87YJCycCJ+eAbPHFODPKEkjgMVRkobAO16EtQdgy2Unfk7n4hTJbl+gQu6L1AnzO6hKwPQK+PVBWNoA7zl9iAOr4B7eDe98ETquOskTexefXc0cO/rlJGI+lbUjhD9P55Z9ZjpgU3MDZx5sJoi6TVoBMs3Q/bGRvBR3SUO/825ug09uyJXG00fB1AqYOgpeaoP/3JVbWTB1FNxwOtwxs0BFWZJi4h5HSSqwj7TkSsOjF+V/XrKPPW+Jmjpeuwz+bD3saIe/PweunjiEYTVkrjgtt/RxR3tuX90JVdf2LCZLGvJe69FdFB4mWLoQbj90SlkHKgzhcy/D323JLTf9w2nwqbNg+oVN/dvjOJItOsGfoQ8zquCBhhN/qNfa4POb4Duvw5dfgzEJGJ2AMcncvsuxSZhWmZtiXj/52CE+klRMnDhK0iDctxVubYFl5/XjWozeyxVH6BJEDd6YR+Ef5gxwH9wJlrGeSARMXBAxpQLOroK5Y+A3xsHlp8HsnStg6TuPXQ8yfibc8PCg/411hPB3r8AXN8PhEG4/E744yztD+2PDQVixB149BDvbYVcHZDtyh2ZtbMvtm6wIYFoFvGk8fGFW/w7QkqTh4lJVSTpV3b7Qj4Bto+cw7SYLoKD+aTh/XP6p1An1o0BGQJSo4O5rD/HifnipNVdMMh25Ky1an+t5sFIEdEycx6gPrj7xNy72boalC4nCdtqDCm6oX85D466iPYLDEVQGcOs0+NKc3PUYKoyDHdC0C5bvhv/J5srk5afBd+c6iZQ0MlgcJelU9VpaGAHBpHkjex+XhsXvr4XH9kD60gJ8sL6KZJ49jmEIwT3BcYfsdJBkwps6WLW6gdkH1/R5CE/XvZIR0Jmo4Js3HmJ6BdSPgVmjC/Bn0Uk9sQduXgsvt8F7p8C/1kOFRV1SjNzjKEmnKtPS482gj8dUnn7ndPj2jgJ9sOpa6HbNxMkkEhx33ydAUFPHPbNh1jMtJzy5tfu9oamwnVu/U5ErqJOuGkRwDcblp8GGS+H+rfCx9fD9x3ste3apu6QRwu9pSVJ/1dT17zGVnXfX5JZ3rtoXU4Brl+emkl3GzyT5niZuewMkJ/X/32gUHobvvQ3uDnI/Fidzk/ZseghCq7ubp8Huy+HmqXDbOqh9Cr76Kuz5fiNR9+lzdn3uUB9JGmYuVZWk/vI7/8pj7tMwKoBVC+JO0ssgDuEJev0M9Jxa+m9/SG07BDeugWf2wZ5net6ZCUCQhI93xBNOUslzj6MkSUNoYyuc8zT8zWz4+FlxpzmBbBq+dzXs3dTnb3eVxQGzSA6dvq5tcW+1pCF0ouLoUlVJkgpg1mj40DT4Yt+dbGSoroU/eBk+EcH1j/Rc3sogSyPkppn3zs4tb/362S5tLaRFTTB+5tHJL4lRcPVX4kwkqUxZHCVJKpC7amF3Bzy6O+4k/TDjKri9PVciu35c/0juoJ1TsW+Te/AKqboWKsYSHvmSLYo64eGPxhxKUjmyOEqSVCDVo+D8sfCZl+NOMkgzroLbDx0rkrduyC1DHaCOXS3ctQnaOk/+XPVDpoUkIQBBFHqas6RYeB2HJEkF9Bcz4ObmuFMUSF9Xg2xeAUvfCeHhPt8lAl4dW8dfboI7NkLDGPhMdZobHltI4MFSg1NTB5lmiEI6SZD0NGdJMXDiKElSAV1/OnRG8EJcV3MMta4lrieYRgbjZzLzxib2XwkrLoApFfDGnzf2PNXVKyUGZlET1NTTGSTZMLo+97YkDTMnjpIkFVAqAeOT8HAWLhwfd5oh1Nc0spcrJ8JDEyF6uOW4g3c6My2sOwBzxw5dxJJRXcuT71nNFc/n9tF+qjruQJLKkRNHSZIKrDIB2b5XcpaloNfSygjYUFXHvGdg6hNw+0uQaY8nW7Fo/BW8fSJ8akbcSSSVK4ujJEkF1hrCGad4OGlJWdTUY1lrUD2Hc29q4tVL4bcnw/3bYfIv4Nyn4e7N0B7GmHUEeiiTO633Bw1xJ5FUzoIoik7+rCPmz58frVy5cgjjSJJU3NpDqHwUmt8EdS7D7Lfn9sH/fRl+vhsOhfAb4+HPz4LrpsSdLH43roaV+2D9pXEnkVQOgiB4Noqi+b0fd+IoSVIBfe01qEpYGgfq4vHwwzfCwSvhwfMgFcCNa2D0/8BvroJn9sSdMD6rD8AF4+JOIancWRwlSSqgv30ltxdNg/fbk+EXF0PblfA3syHdCpc8DxMfhw80w+a2uBMOr0SQ2xcqSXGyOEqSVCD3boVXD8G/nBt3ktKQSsDHzoTmSyBzOXxoGvx0F8x8CmY8CZ9NQ9vLK+CeSrg7yP28eUXcsQtuXBL2dMSdQlK5szhKklQAYQgfewlumw5nVMadpvRUj4K/nQ1bL4d1C+CKCfClV4EHFxKFuSNZo7Adli6MN+gQuGAcPLsP2jrjTiKpnFkcJUkqgCf35Q51+cc5J3+uTs05Y+A/GmDvW6Ayaj96R2RArjw+lIkzXeH9XW3uipczfgHbDsWdRlK5sjhKklQAs6vAWySGX5A4du9JBBwOKnjHKhj/GHzu0RWEJbCMdUwKXn0zjE3Ch9fFnUZSubI4SpJUABvK7MCWEePa5XCkPAaJCiquW87+K+CTZ8IdKxcSdFvGGn3vbbkSee85kE3HmXrAUoncybOvOHGUFJNU3AEkSSoF16+Gq6oh4bdkh9eMq+D2nm1qDPC5WUDUfvSxoNvvR9n1RA9cTeLAVgjbc8Xz2uW5jzWCjU7k7gmVpDhYHCVJOkWfTcPOw7CmIe4k6iFRkSNydkYAABihSURBVCuGvQQA+zYRdf06bIfvve3Y+4zQEvnaodwhQZIUB78vKknSKdjZDn+1Gb44yy/qR5xuy1j70mMK2fWLEXoy67ZD8NRe+OAZcSeRVK4sjpIknYL3roXplfCpGXEn0XG6lrHeugGqux13Wz2HIDjW8o9OHrveDtvZuX7FiLkfctshmPsMnDsGPjAtthiSypxLVSVJOgX/k4WvnBt3CuVVXQu3vtTzsc0rcpPFsP24yWN7UMG4Hy0kOnLVRxS2EyxdeNxeyuHyzhdhYgpemB/Lp5ckwImjJEmDtnIvtEfw/qlxJ9GAdU0jPxHB9Y/0OJm18rrlfd4Ped9WCGM4nGbTIfiTN0CFX7VJipETR0mSBumjL0HdaL+gL3p9nMwadDtYp+t+yNvWwW3r4IrT4DMz4eqJwxOvMgHZjuH5XJJ0Ir7USZI0CF/aAiv3wfc8SbU09XE/ZNtb4Kvn5k7QffuLMPZRuPoF+O72oZ1E9t6DKUlxcOIoSdIAZdrhkxvgzhlw3ri402hI9DGFTAC3Tsv9aOuEf3oNvrUd3tecOySpfgxcfzr86ZmFPWG3LYQpJz4cVpKGhRNHSZIG6MMvwaQUfKE27iSKS1USPn4WPDcfDr0FHjwPZlTB4i0w8QmY9gTcshZW7Tv1z9URwiSvepEUMyeOkiQNQBhC0y74wtlxJ9FIkUjAb03O/QB46SAsfgX+MwP3b4cxCbh0AtwRrOBtD7+TIDyce+L4mXDDw7lTX/M4HMEZFkdJMXPiKEnSAPzHDuiI4PYz406ikeqcMfDVOtj8Zth7BXzubMh0wGUPLYSu0ghE+zbBssaTfrxO4KyqocsrSf1hcZQkaQDu2gxXngYpX0HVD+NS8KkZuSWt3a/4gNyBNx27WvhgM/x6fx/vnE1z4N4GDj+b4uzvNEA2PUypJel4vuxJktRPmXZYcxD+36y4k6gYBYmeJ9xEwPZxdfwsA+evhHGPwjtfhKU7INy0guje2YzJriFFJ0FmDTxwdTzBJQmLoyRJ/fZXr0B1Ci45Le4kKkrXLofEsc2KwfiZvOF3m9hy2bElrdvb4ca10L50Ye453d9/3ya4O4B7KmHziuFMLkkEURT1+8nz58+PVq5cOYRxJEkauc5/BmZUwo/PjzuJSlkYQnBPcNzdjT3uc0xUHHddiCQVQhAEz0ZRNL/3404cJUnqp5fb4OqJcadQqUskjl/WCr2mj2H7sOWRJLA4SpLUL8/tg/2d8L6pcSdRWbh2OQR5bk3ro1hK0lCyOEqS1A8fXgfnj4Upfr2u4TDjKvj4Ybh1A0yaB0G3L9kSFbliKUnDKM+3siRJEuQudF+5Dx67KO4kKjvVtXDL6rhTSJITR0mSTuaTG6C2Ci73NFVJUpmyOEqSdBLP7oN318SdQpKk+FgcJUnKY38HvNYO158edxJJkuJjcZQkKY8/aIGaFFzpNRySpDLm4TiSJJ3AznZ44HX4Zn3cSSRJipcTR0mSTuDmZphWAe87I+4kkiTFy+IoSVJv2TSHv9HAj36eYs2aBsim404kSVKsLI6SJPW2rJHE7mZSdDJhbzMsa4w7kSRJsXKPoyRJ3YQhkGkhSZh7IAoh0xJrJkmS4ubEUZKkIzYchNOegObKOsKul8ggATV18QaTJClmFkdJkrJpWNLAzK+meP5XDZx7zVdITKqHIAk19bCoKe6EkiTFyqWqkiQtayTc1UyKkNmtzQSPfBRuWR13KkmSRgwnjpIkZVpIHNnTGOCeRkmSerM4SpLK3o5xdXS6p1GSpBOyOEqSytbiV2DCY/DmmU3smeCeRkmSTsQ9jpKksrR6P3xyA3ziLLhzRi3Vo9zTKEnSiVgcJUnlI5uGZY1EmRZGj67jyrlN/O3s2rhTSZI04rlUVZJUNjp+0EjnrmaCqJOZB5v5yYbGuCNJklQUnDhKkspDNk0ys4bgyJtJQkbv8fRUSZL6w4mjJKk8LOtjuujpqZIk9YvFUZJUFqJMy9Fp41GenipJUr9YHCVJZaH1tF53NU6aB9UejCNJUn9YHCVJZeHGc5vYOMa7GiVJGgwPx5EklbwfvA4/7qzlr29eDWPjTiNJUvFx4ihJKmlhCB9sgetOh7mWRkmSBsXiKEkqaV/YBK2d8K25cSeRJKl4WRwlSSXt61vhfVOhwlc8SZIGzZdRSVLJag9hazt86qy4k0iSVNwsjpKkkvXIbkgFUOfeRkmSTonFUZJUsl45BJVB3CkkSSp+FkdJUmnKpmn8cQPZZ1KwpAGy6bgTSZJUtCyOkqTStKyRyfuaSdEJmWZY1hh3IkmSipbFUZJUmjItJAlzv45CyLTEm0eSpCJmcZQklaRwYh2dXS9zQQJq6uINJElSEbM4SpJK0tcWNNFSVQ9BEmrqYVFT3JEkSSpaqbgDSJI0FP56by0/f+tqHjwv7iSSJBU/i6MkqeT8nzRsOQSPXhR3EkmSSoPFUZJUUja2wl9uhn8+F2ZUxZ1GkqTS4B5HSVJJWfwKTB0Ffzg97iSSJJUOi6MkqaS8cgimVsSdQpKk0mJxlCSVjmyaux9v4NlHU7CkAbLpuBNJklQSLI6SpNKxrJGzDzaTpBMyzbCsMe5EkiSVBIujJKlkhJkWkoS5N6IQMi3xBpIkqURYHCVJxS+bhiUNBFEnUddjQQJq6uJMJUlSybA4SpKK37JGOnc1E3R/rKYeFjXFlUiSpJLiPY6SpKLXfYlqABAk4ZbVsWaSJKmUOHGUJBW1FbthbWUdnV0vaS5RlSSp4CyOkqSidnMz/M3FTSQn1ecmjS5RlSSp4FyqKkkqWpta4ZVD8PlLauHNLk2VJGmoOHGUJBWtb26HiSmYNTruJJIklTaLoySpaK09AJNHxZ1CkqTSZ3GUJBWlL2+BB16Hm6bEnUSSpNLnHkdJUtHJtMOfrocvnA2fOTvuNJIklT4njpKkonPnRjgtZWmUJGm4OHGUJBWVja3w9a3wT+fEnUSSpPLhxFGSVFQ+uQFmVsFtb4g7iSRJ5cPiKEkqKiv3wf+aGHcKSZLKi8VRklRUdnfA+WPjTiFJUnmxOEqSikplAna0x51CkqTyYnGUJBWNTa2w6zBcMynuJJIklReLoySpaCzeAlNHwSWnxZ1EkqTyYnGUJBWNKIKUr1ySJA07X34lSUXjYAjJuENIklSGLI6SpKLw9dfg/u3wv93fKEnSsLM4SpJGvB/thA+vg49Mhy/PiTuNJEnlJxV3AEmS8sqmmfdAI4dbW0i+XAenN0F1bdypJEkqK04cJUkjVzZNeN9cZreuIUkn7FoLyxrjTiVJUtmxOEqSRq5ljQRhO8HRByLItMQYSJKk8mRxlCSNXJmWbqXxiJq6OJJIklTWLI6SpJGrpo7O7i9ViQpY1BRfHkmSypTFUZI0ci1qIj26ns4gCZPmwQfWejCOJEkx8FRVSdLIVV3LbZet5lAIT1wcdxhJksqXE0dJ0ohWk4K9HXGnkCSpvFkcJUkj2oXj4KVWy6MkSXGyOEqSRrQ7ZkDNKLjy+biTSJJUviyOkqQRLZGAJy6CVQdg5d6400iSVJ4sjpKkEW/WaHjTePjNX8F+l6xKkjTsLI6SpKLw0AUQRnDjmriTSJJUfiyOkqSiMC4FN02BF/bHnUSSpPJjcZQkFY2zquBAGHcKSZLKj8VRklQ0ZlVBa2fcKSRJKj8WR0lS0bhoHByKoM3yKEnSsLI4SpKKxuwxkArgsT1xJ5EkqbxYHCVJRWNnO3REcGZl3EkkSSovFkdJUtH4VBqmjoK5Y+NOIklSebE4SpKKxtbtaZ75VQMsTsGSBsim444kSVJZsDhKkorGP69tZPqBZog6IdMMyxrjjiRJUllIxR1AkqT+mnmwhYAjFzlGIWRa4g0kSVKZcOIoSSoaQU0d4ZGXrihIQE1dzIkkSSoPFkdJUvFY1ERQU08HSdKj62FRU9yJJEkqCy5VlSQVj+pagg+sZtNBqPsl/MlOWFwddyhJkkqfE0dJUtGZPQb+bS58aQu840XoCONOJElSabM4SpKK0u9Nhacvhl/uhbOfgr0dcSeSJKl0WRwlSUVr/gR45c0QAXOehkx73IkkSSpNFkdJUlGbkIKXFkBVAt7+aJqO+xpgcQqWNEA2HXc8SZJKgsVRklT0xqRg3QL4zrpGgkwzRJ2QaYZljXFHkySpJHiqqiSpJFQl4Zy2FgKOnJQThZBpiTeUJEklwomjJKlkBDV1REHupa2TBIer62JOJElSabA4SpJKx6Imgpp6oiDJxjH1XHxWE1va4g4lSVLxc6mqJKl0VNfCLasJgLNDYCW869E0z21ppCLbAjV1sKgp9zxJktRvThwlSSUplYAX58MPNzSS3O2BOZIknQonjpKkkpVIQG2rB+ZIknSqnDhKkkpa7wNzOiZ6YI4kSQNlcZQklbZuB+ZsGF1PY21T3IkkSSo6LlWVJJW2bgfmVLbCz56G7++A66bEHUySpOLhxFGSVDZmjoaFNfAX6biTSJJUXCyOkqSycvdsSLfBa97vKElSv1kcJUllZe5YOGMUfObluJNIklQ8LI6SpLJzyxnw4Otxp5AkqXhYHCVJZefOmbC3E1bsjjuJJEnFweIoSSo741Jw/lj4/MtxJ5EkqThYHCVJZemOmfDYHth2KO4kkiSNfBZHSVJZumEKnDMa5j4DP8/EnUaSpJHN4ihJKlsvzIcF42HhKjj7SfjhzrgTSZI0MlkcJUllqyoJyy+AzZdC/Rh4z6/hzF/Ad7fHnUySpJHF4ihJKntnVsFPL4Btl8H88XDTWpj2BNy3Ne5kkiSNDBZHSZKOmFIBy94Ir18GV5wGf9ACkx6Hf9wSdzJJkuJlcZQkqZeaCvjeeZC9Aq6ZBLdvgAmPwV9tgjCThiUNsDiV+zmbjjuuJElDzuIoSdIJjEvBN+fCgbfA+6fm7n1s+XYjnbuaIeokyjTDssa4Y0qSNOQsjpIknURFAr5yLux7C9QdaiFJCEAQhXTsauEdL8Ize2IOKUnSELI4SpLUTxUJSNTUQZB7+YyCBLvG17H9EFzyPFz765gDSpI0RCyOkiQNxKImqKmHIElQU8/UG5pYtQAevRB+vMvyKEkqTam4A0iSVFSqa+GW1cc9fEU1PHQB3PJ0mp2PL2TygfVHnj8Hrl2eez9JkoqUE0dJkgrkimpY+Uojk7pKI0B2PSxdGF8oSZIKwImjJEkFVL235fgHs+uPf0ySpCLixFGSpEKqqTvuoQj4s5VpWu/1/kdJUnGyOEqSVEiLmiAYdfTNCNg5dg5/9MtGKrLe/yhJKk4WR0mSCqm6Fj7YDJPm5U5enTSP029czrltPe9/JNPHklZJkkYo9zhKklRofZ28WlMHmWaIQjpJkOxjSaskSSOVE0dJkobDkfsfO0jy+rj63NuSJBUJJ46SJA2H6lp+/lurWbgKMpcDo076HpIkjRhOHCVJGiafexkuGgfVlkZJUpFx4ihJ0jDY1ApP74Ufnhd3EkmSBs6JoyRJQ2xTK9Q/AxePh2smx51GkqSBszhKkjTE3vw8nDManr4o7iSSJA2OS1UlSRpCG1thazs0L4CE366VJBUpX8IkSRpC6VZIAhP8Vq0kqYhZHCVJGiIr98L1a+CCcXEnkSTp1FgcJUkaAmsPwCXPwXlj4bEL404jSdKpceGMJElD4MuvwrQKeNQDcSRJJcCJoyRJQ2BcEg5HcaeQJKkwLI6SJA2BD49J8/ALDUSLU7CkAbLpuCNJkjRoFkdJkoZA7fJG6tuaCaJOyDTDssa4I0mSNGgWR0mShkKmhSRh7tdRCJmWePNIknQKLI6SJA2FmjqiIPcyG5KAmrqYA0mSNHgWR0mShsKiJoKaesIgydqqep65uinuRJIkDZrXcUiSNBSqa+GW1SSAz/0afrQRXp4C06viDiZJ0sA5cZQkaYh9/zyYMgo+sSHuJJIkDY7FUZKkYfA7k+G/s3GnkCRpcCyOkiQNg/9zNuw6DCv3xp1EkqSBszhKkjQMJlfAzEr4hy1xJ5EkaeAsjpIkDZMzKmHb4bhTSJI0cBZHSZKGyYEOGJ+MO4UkSQNncZQkaZhsPwwzvY5DklSELI6SJA2DuzbB64fhY2+IO4kkSQOXijuAJEml6sk98JF1sPYgdEaweDbMGh13KkmSBs7iKEnSEPh5Bt61Ci6ZAPfWwQ1ToMJ1PpKkImVxlCSpwJ7cA+9eBe+fCkvmxp1GkqRT5/c+JUkqoB3tcNULcM0kS6MkqXRYHCVJKqA70jAxBcveGHcSSZIKx+IoSVIB7emAajeCSJJKjMVRkqQCumkqbGiLO4UkSYVlcZQkqYDeNB46orhTSJJUWBZHSZIKaFu7L66SpNLja5skSQX0ta0weVTcKSRJKiyLoyRJBRKG8K1t8NE3xJ1EkqTCsjhKklQgf7gOCOAzM+JOIklSYXlguCRJp6AjhI+8BEtfh70d8N15kPDbspKkEmNxlCRpoLJpWNZIlGlhQ1UdT53TxK21tXz6LKipiDucJEmFZ3GUJGmgljUS7momQcic1mZe2NJI8h2r404lSdKQsThKkjQAHSEEu1pIEgLkft7dEnMqSZKGlrswJEkagHetgnVVdYRdL6FBAmrq4g0lSdIQszhKkoZfNg1LGmBxKvdzNh13on452AEPZyFzTROJSfUQJKGmHhY1xR1NkqQh5VJVSdLw67ZHMMo0EyxrhFtG/h7Bj66H8Um4fGZtUeSVJKlQLI6SpOGXaSFxZI9gEIWEmZYRvwTmA81w/7bcdRuSJJWbkf46LUkqRTV1ub2BQEiCtZV1zH4KPtwCS3fAxlYIQ3itDe7aBN/alns7Ltesgm9thx+/Ea6bEl8OSZLi4sRRkjT8FjXBssbc5LGmjjHvauLCnfBfGbhvG7RHx546IQmtIXywBd5aDZ8/Gy4/bfii3pmGn2bgqYth/oTh+7ySJI0kFkdJ0vCr7rlHcBaw9IyeT9nfkft5XCo3bfzGNrhnC7zleahOwYemwV2zIDGEa2dW74e7NsOX51gaJUnlzaWqkqQRaVwq9wNy5fBD02H1AsheAb8/Fb60Bd6xqrCfsz2EvR2501P/cQtc8hxcOgH+6MzCfh5JkoqNE0dJUlGZkIJ7zoEPnAEXPwvn/xIePCvNnOW5pa/U1OWWwlbX9vtjhiG86Tl4bv+xxyoDeM9k+Le5Q/CHkCSpyFgcJUlF6fzxsPpNcP1qOPRgI51tzSQHeb3HX2yEtQdh/QKYPWYIQ0uSVKRcqipJKlp1Y2HVAph3qIVkt+s9ol1r4O4g92PNf5z04yzbCddMsjRKknQiFkdJUtELul3vEfX+zZ+8N+/7/mgnbGiFO2YMTTZJkkqBxVGSVPwWNUFNPQRJAIJ+vltHCDetgRtPhwvHD108SZKKncVRklT8uq73+HgHAcemjsdNH3v57Mbcc+73ABxJkvKyOEqSSsu7//1oeYyA1oX/fsKnfnsHXDsZUr4aSpKUl6eqSpJKy7ybYN5NRCGc8SRc3Ak/7et52TT/9Vwj9Yda4JcDv8JDkqRy4vdYJUklKZGAB+bBS9vS7Ph6AyxOwZIGyKZzT1i6kLlta0hEnbBrDSxdGG9gSZJGMIujJKlkXTURVm5uZNK+Zog6IdMMyxpzv5ld3/MQnez6OCJKklQUXKoqSSppE/e1wJE7HonC3HTx7v6euypJksCJoySp1PW64/FkJ61KkqTjWRwlSaVtkHc8SpKkYyyOkqTS1v2Ox0RF3GkkSSpKFkdJUvm4djkkRsWdQpKkomNxlCSVjxlXwe3t8IkIquf0/L3eb0uSpKMsjpKk8nTtcpg0L7f3cdK83NuSJKlPXschSSpPXXsfJUnSSTlxlCRJkiTlZXGUJEmSJOVlcZQkSZIk5WVxlCRJkiTlZXGUJEmSJOVlcZQkSZIk5WVxlCRJkiTlZXGUJEmSJOVlcZQkSZIk5WVxlCRJkiTlZXGUJEmSJOVlcZQkSZIk5WVxlCRJkiTlZXGUJEmSJOVlcZQkSZIk5WVxlCRJkiTlZXGUJEmSJOVlcZQkSZIk5WVxlCRJkiTlZXGUJEmSJOVlcZQkSZIk5WVxlCRJkiTlZXGUJEmSJOVlcZQkSZIk5WVxlCRJkiTlZXGUJEmSJOVlcZQkSZIk5WVxlCRJkiTlZXGUJEmSJOVlcZQkSZIk5WVxlCRJkiTlZXGUJEmSJOVlcZQkSZIk5WVxlCRJkiTlZXGUJEmSJOVlcZQkSZIk5WVxlCRJkiTlZXGUJEmSJOVlcZQkSZIk5WVxlCRJkiTlZXGUJEmSJOUVRFHU/ycHwevApqGLI0mSJEmK0cwoik7v/eCAiqMkSZIkqfy4VFWSJEmSlJfFUZIkSZKUl8VRkiRJkpSXxVGSJEmSlJfFUZIkSZKUl8VRkiRJkpSXxVGSJEmSlJfFUZIkSZKUl8VRkiRJkpTX/wfz6c+a46Fr8AAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#4. Modify the network model "
      ],
      "metadata": {
        "id": "CShNFfHWdHT2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Example: Consolidate Complex Intersections \n",
        "Intersections can have lots of nodes representing different turn lanes, crosswalks, etc. \n",
        "\n",
        "Using og.consolidateComplexIntersections(net), we can combine nodes in intersections into a single node to make analysis faster and simpler"
      ],
      "metadata": {
        "id": "p1xUAFM1TV4y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "og.consolidateComplexIntersections(net)\n",
        "og.show(net)"
      ],
      "metadata": {
        "id": "cIVcv2OZTZVB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5e5a0d5e-e56a-4f79-973b-2bb697ac5fd3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Consolidating Complex Intersections\n",
            "    0 intersections have been consolidated\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x1322.3 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Example: Points of Interest\n",
        "\n",
        "We can connect Points of Interest (POIs) to the network model. Nodes representing the center (centroids) of POIs are generated. These nodes are connected with a link to the nearest node on the rest of the network"
      ],
      "metadata": {
        "id": "FUCd9l0ydMfk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "net = og.getNetFromFile('map.osm', link_types=('motorway','trunk','primary','secondary'), POI=True, POI_sampling_ratio=0.05)\n",
        "og.connectPOIWithNet(net)\n",
        "og.show(net)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 757
        },
        "id": "HO5sgUZtdG5R",
        "outputId": "77bbaa90-02f7-401d-c601-799d589158dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "arguments used for network parsing:\n",
            "  filename: map.osm\n",
            "  network_types: ('auto',)\n",
            "  link_types: ('motorway', 'trunk', 'primary', 'secondary')\n",
            "  POI: True\n",
            "  POI_sampling_ratio: 0.05\n",
            "  strict_mode: True\n",
            "  offset: no\n",
            "  min_nodes: 1\n",
            "  combine: False\n",
            "  bbox: None\n",
            "  default_lanes: False\n",
            "  default_speed: False\n",
            "  default_capacity: False\n",
            "  start_node_id: 0\n",
            "  start_link_id: 0\n",
            "\n",
            "Building Network from OSM file\n",
            "  reading osm file\n",
            "  parsing osm network\n",
            "    generating nodes and links\n",
            "    generating POIs\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-a795bf09c339>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnet\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetNetFromFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'map.osm'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlink_types\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'motorway'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'trunk'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'primary'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'secondary'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPOI\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPOI_sampling_ratio\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.05\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnectPOIWithNet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnet\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnet\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/osm2gmns/osmnet/build_net.py\u001b[0m in \u001b[0;36mgetNetFromFile\u001b[0;34m(filename, network_types, link_types, POI, POI_sampling_ratio, strict_mode, offset, min_nodes, combine, bbox, default_lanes, default_speed, default_capacity, start_node_id, start_link_id)\u001b[0m\n\u001b[1;32m    470\u001b[0m     \u001b[0mosmnetwork\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreadOSMFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPOI_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstrict_mode_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbbox_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    471\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 472\u001b[0;31m     network = _buildNet(osmnetwork, network_types_, link_types_, POI_, POI_sampling_ratio_, offset_, min_nodes_, combine_,\n\u001b[0m\u001b[1;32m    473\u001b[0m                         default_lanes_, default_speed_, default_capacity_, start_node_id_, start_link_id_)\n\u001b[1;32m    474\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/osm2gmns/osmnet/build_net.py\u001b[0m in \u001b[0;36m_buildNet\u001b[0;34m(osmnetwork, network_types, link_types, POI, POI_percentage, offset, min_nodes, combine, default_lanes, default_speed, default_capacity, start_node_id, start_link_id)\u001b[0m\n\u001b[1;32m    375\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    376\u001b[0m     \u001b[0;31m# create node, link, poi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 377\u001b[0;31m     \u001b[0m_createNLPs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mosmnetwork\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnetwork\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlink_types\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnetwork_types\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPOI\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPOI_percentage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    378\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    379\u001b[0m     \u001b[0;31m# remove isolated nodes and links\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/osm2gmns/osmnet/build_net.py\u001b[0m in \u001b[0;36m_createNLPs\u001b[0;34m(osmnetwork, network, link_types, network_types, POI, POI_percentage)\u001b[0m\n\u001b[1;32m    356\u001b[0m     \u001b[0m_addSignalFromLink\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnetwork\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    357\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 358\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0mPOI\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mgeneratePOIs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mosmnetwork\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPOI_way_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mosmnetwork\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mosm_relation_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnetwork\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPOI_percentage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    359\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    360\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/osm2gmns/osmnet/pois.py\u001b[0m in \u001b[0;36mgeneratePOIs\u001b[0;34m(POI_way_list, osm_relation_list, network, POI_percentage)\u001b[0m\n\u001b[1;32m    176\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'    generating POIs'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m     \u001b[0mPOI_list1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_POIFromWay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPOI_way_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnetwork\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbounds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m     \u001b[0mPOI_list2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_POIFromRelation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mosm_relation_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnetwork\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbounds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/osm2gmns/osmnet/pois.py\u001b[0m in \u001b[0;36m_POIFromWay\u001b[0;34m(POI_way_list, net_bound)\u001b[0m\n\u001b[1;32m     55\u001b[0m     \u001b[0mPOI_list1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mway\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mPOI_way_list\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m         \u001b[0mpoly\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpoly_xy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetPolygonFromNodes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mway\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mref_node_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     58\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpoly\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpoly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisjoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnet_bound\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/osm2gmns/utils/util_geo.py\u001b[0m in \u001b[0;36mgetPolygonFromNodes\u001b[0;34m(node_list)\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_list\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[0mpoint_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeometry\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mnode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnode_list\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m     \u001b[0mpoly\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgeometry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPolygon\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpoint_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m     \u001b[0mpoint_list_xy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeometry_xy\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mnode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnode_list\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0mpoly_xy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgeometry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPolygon\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpoint_list_xy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/shapely/geometry/polygon.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(self, shell, holes)\u001b[0m\n\u001b[1;32m    249\u001b[0m                 \u001b[0;31m# conversion of coords to 2D array failed, this might be due\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    250\u001b[0m                 \u001b[0;31m# to inconsistent coordinate dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 251\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Inconsistent coordinate dimensionality\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    252\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshell\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLinearRing\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m             \u001b[0mshell\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLinearRing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Inconsistent coordinate dimensionality"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#5. Export transportation network model\n",
        "\n",
        "We can export our transportation network model to a CSV format for ease of use in further analysis with other systems. \n",
        "\n",
        "og.outputNetToCSV(...) will generate two files: node.csv representing nodes, and link.csv representing links (edges) between nodes."
      ],
      "metadata": {
        "id": "1JebB_n6f5Co"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "og.outputNetToCSV(net)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8QtMOShGgB2Q",
        "outputId": "15cabd9e-5fdf-4c19-ba09-4e8e40563d7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Outputting Network Files\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "Take a peek at the format of the CSVs using the Pandas library\n",
        "Loading the CSV files gives us a DataFrame object which is very heavily used in \n",
        "data science and analysis. \n",
        "\n",
        "For more information on how this data is organized, check out the GMNS specification\n",
        "\n",
        "https://github.com/zephyr-data-specs/GMNS/pull/54\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "node_dataframe = pd.read_csv(\"node.csv\")\n",
        "node_dataframe.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "SNYnbMiFgW0h",
        "outputId": "d09756a0-105b-401b-d0dd-d44fcfbc1605"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  name  node_id   osm_node_id      osm_highway  zone_id ctrl_type  node_type  \\\n",
              "0  NaN        0  4.205830e+09              NaN      NaN       NaN        NaN   \n",
              "1  NaN        1  4.205830e+09              NaN      NaN       NaN        NaN   \n",
              "2  NaN        2  4.242209e+07              NaN      NaN       NaN        NaN   \n",
              "3  NaN        3  7.925259e+09  traffic_signals      NaN    signal        NaN   \n",
              "4  NaN        4  4.242211e+07              NaN      NaN       NaN        NaN   \n",
              "\n",
              "   activity_type  is_boundary    x_coord    y_coord  intersection_id  poi_id  \\\n",
              "0            NaN          NaN -73.960423  40.798207              NaN     NaN   \n",
              "1            NaN          NaN -73.961106  40.798494              NaN     NaN   \n",
              "2            NaN          NaN -73.933387  40.801644              NaN     NaN   \n",
              "3            NaN          NaN -73.933812  40.802004              NaN     NaN   \n",
              "4            NaN          NaN -73.933771  40.802147              NaN     NaN   \n",
              "\n",
              "   notes  \n",
              "0    NaN  \n",
              "1    NaN  \n",
              "2    NaN  \n",
              "3    NaN  \n",
              "4    NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-03707b2d-ee3a-4aa1-8d10-062b8f5f0c9b\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>node_id</th>\n",
              "      <th>osm_node_id</th>\n",
              "      <th>osm_highway</th>\n",
              "      <th>zone_id</th>\n",
              "      <th>ctrl_type</th>\n",
              "      <th>node_type</th>\n",
              "      <th>activity_type</th>\n",
              "      <th>is_boundary</th>\n",
              "      <th>x_coord</th>\n",
              "      <th>y_coord</th>\n",
              "      <th>intersection_id</th>\n",
              "      <th>poi_id</th>\n",
              "      <th>notes</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-73.960423</td>\n",
              "      <td>40.798207</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>4.205830e+09</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-73.961106</td>\n",
              "      <td>40.798494</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>4.242209e+07</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-73.933387</td>\n",
              "      <td>40.801644</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>7.925259e+09</td>\n",
              "      <td>traffic_signals</td>\n",
              "      <td>NaN</td>\n",
              "      <td>signal</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-73.933812</td>\n",
              "      <td>40.802004</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>4.242211e+07</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-73.933771</td>\n",
              "      <td>40.802147</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-03707b2d-ee3a-4aa1-8d10-062b8f5f0c9b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-03707b2d-ee3a-4aa1-8d10-062b8f5f0c9b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "link_dataframe = pd.read_csv(\"link.csv\")\n",
        "link_dataframe.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "DEoWxS5DhxrN",
        "outputId": "6020a5ad-aab1-46cd-ffcf-84ffed0b9f48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                name  link_id  osm_way_id  from_node_id  to_node_id  dir_flag  \\\n",
              "0  West 106th Street        0   5668966.0             0           1         1   \n",
              "1  West 106th Street        1   5668966.0             1           0         1   \n",
              "2                NaN        2   5669025.0             2           3         1   \n",
              "3                NaN        3   5669025.0             3           4         1   \n",
              "4                NaN        4   5669067.0             5           6         1   \n",
              "\n",
              "   length  lanes  free_speed  capacity link_type_name  link_type  \\\n",
              "0   65.84    NaN         NaN       NaN      secondary          4   \n",
              "1   65.84    NaN         NaN       NaN      secondary          4   \n",
              "2   55.94    1.0         NaN       NaN       motorway          1   \n",
              "3   16.43    1.0         NaN       NaN       motorway          1   \n",
              "4  311.56    1.0         NaN       NaN       motorway          1   \n",
              "\n",
              "                                            geometry allowed_uses  from_biway  \\\n",
              "0  LINESTRING (-73.9604229 40.7982075, -73.960708...         auto           1   \n",
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              "4  LINESTRING (-73.9291580 40.7972731, -73.929207...         auto           0   \n",
              "\n",
              "   is_link  VDF_fftt1  VDF_cap1  \n",
              "0        0        NaN       NaN  \n",
              "1        0        NaN       NaN  \n",
              "2        1        NaN       NaN  \n",
              "3        1        NaN       NaN  \n",
              "4        1        NaN       NaN  "
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              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-595422a3-3b74-43eb-84c7-433588c481b6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-595422a3-3b74-43eb-84c7-433588c481b6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-595422a3-3b74-43eb-84c7-433588c481b6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "link_dataframe = pd.read_csv(\"poi.csv\")\n",
        "link_dataframe.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "_pcNV8TCYOWV",
        "outputId": "2c9cdad5-1062-498d-9013-e66cedda9709"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  name  poi_id   osm_way_id  osm_relation_id    building amenity leisure  way  \\\n",
              "0  NaN       0  264643810.0              NaN         yes     NaN     NaN  NaN   \n",
              "1  NaN       1  249657791.0              NaN  apartments     NaN     NaN  NaN   \n",
              "2  NaN       2  264650593.0              NaN         yes     NaN     NaN  NaN   \n",
              "3  NaN       3  241822236.0              NaN  apartments     NaN     NaN  NaN   \n",
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          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#6. Basic analysis with Pandas\n",
        "\n",
        "The node and link files are in CSV format, which can be used with a wide variety of programming languages, such as R, and programs, such as Excel.\n",
        "\n",
        "Here, we show a quick demonstration of some simple analysis using Python"
      ],
      "metadata": {
        "id": "17tTpV4Ah0-O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# From the previous cells, we have pandas DataFrame objects representing the node and link data for our model\n",
        "print(node_dataframe.head(), link_dataframe.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z52bpRrVhzM_",
        "outputId": "dbd21c8b-5e0c-45ab-a76e-0e1fdf10f94e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  name  node_id   osm_node_id      osm_highway  zone_id ctrl_type  node_type  \\\n",
            "0  NaN        0  4.205830e+09              NaN      NaN       NaN        NaN   \n",
            "1  NaN        1  4.205830e+09              NaN      NaN       NaN        NaN   \n",
            "2  NaN        2  4.242209e+07              NaN      NaN       NaN        NaN   \n",
            "3  NaN        3  7.925259e+09  traffic_signals      NaN    signal        NaN   \n",
            "4  NaN        4  4.242211e+07              NaN      NaN       NaN        NaN   \n",
            "\n",
            "   activity_type  is_boundary    x_coord    y_coord  intersection_id  poi_id  \\\n",
            "0            NaN          NaN -73.960423  40.798207              NaN     NaN   \n",
            "1            NaN          NaN -73.961106  40.798494              NaN     NaN   \n",
            "2            NaN          NaN -73.933387  40.801644              NaN     NaN   \n",
            "3            NaN          NaN -73.933812  40.802004              NaN     NaN   \n",
            "4            NaN          NaN -73.933771  40.802147              NaN     NaN   \n",
            "\n",
            "   notes  \n",
            "0    NaN  \n",
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            "3    NaN  \n",
            "4    NaN     name  poi_id   osm_way_id  osm_relation_id    building amenity leisure  way  \\\n",
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            "                         centroid   area  area_ft2  \n",
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