code/Lab7_WebScraping.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#Web Scraping"
      ],
      "metadata": {
        "id": "PRbYAGuQExd8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The first thing we'll need to do to scrape a web page is to download the page. This can be done using the Python requests library"
      ],
      "metadata": {
        "id": "ArG9wb98E366"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "page  = requests.get(\"https://stolkarjen.github.io/human-interaction/\")\n",
        "\n",
        "page"
      ],
      "metadata": {
        "id": "xGjyQlgRIN84"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "A 200 response means that the page downloaded successfully. Codes starting with a 4 or 5 indicate an error"
      ],
      "metadata": {
        "id": "TmgXhGecIiFR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's print out the HTML content"
      ],
      "metadata": {
        "id": "EnoaO8WfItyB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "page.content"
      ],
      "metadata": {
        "id": "7LwAkLE5Iwvt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hopefully, you'll be able to recognize some of the following structure in the above content. Probably, there's simply too much content to parse manually\n",
        "\n",
        "![Screen Shot 2022-02-15 at 3.47.59 PM.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA2oAAAG0CAYAAABDgNR9AAAK4WlDQ1BJQ0MgUHJvZmlsZQAASImVlwdUk8kWgOf/00NCS6hSQm+CdAJICT0U6VVUQhJIKCEmBBW7sriCq4iICCoKrogouLoCsoqIBQsiWMCCLsiioKyLBVFR2R94hN1957133s2ZzHfuf+eWOTPn3AGAHMgSCtNgeQDSBZmiMF8PWkxsHA33HEBAARCAAXBgscVCRkhIIEBkdv67fOhGrBG5az7l69+//1dR5HDFbACgeIQTOWJ2OsItyBhlC0WZAKBOInq9FZnCKb6HMFWEJIjw0BQnz/CXKU6cZrT8tE1EmCfC+gDgSSyWKBkAkiWip2WxkxE/pBCELQUcvgDhDQi7snksDsJIXDA/PT1jikcQNkbshQCQqQjTE//iM/lv/hOl/lmsZCnP1DUteC++WJjGWvV/bs3/lvQ0yWwMQ2SQeCK/MGRWRfbvQWpGgJQFiYuCZ5nPmbafZp7EL3KW2WLPuFnmsLwCpGvTFgXOchLfhyn1k8mMmGWu2Dt8lkUZYdJYSSJPxiyzRHNxJamRUj2Py5T6z+ZFRM9yFj9q0SyLU8MD5mw8pXqRJEyaP1fg6zEX10dae7r4L/XymdK1mbwIP2ntrLn8uQLGnE9xjDQ3DtfLe84mUmovzPSQxhKmhUjtuWm+Ur04K1y6NhM5nHNrQ6R7mMLyD5ll4AW8QSDyo4FIYA0ckGEJQkFQJndl5lQxnhnCVSJ+Mi+TxkBuHJfGFLAt5tOsLa2tAZi6vzNH4l3o9L2ElNvndBkHkaM8htyZgjld4h4AGrYgoR/O6fTLAZDLAaD+ElsiyprRoaf+MIAI5AAVqAEtoAeMgTmSmz1wBu5Ixv4gGESAWLAUsAEPpAMRWAHWgI0gF+SDArAblIJyUAmOghPgFGgA58BFcBXcBJ3gPngM+sAgeAVGwQcwAUEQDiJDFEgN0oYMIDPIGqJDrpA3FAiFQbFQApQMCSAJtAbaDOVDhVApdAiqhn6CzkIXoetQF/QQ6oeGobfQZxgFk2AqrAkbwgtgOsyAA+AIeAmcDC+Hs+EceDtcAlfAx+F6+CJ8E74P98Gv4DEUQMmglFE6KHMUHeWJCkbFoZJQItQ6VB6qGFWBqkU1odpQd1F9qBHUJzQWTUHT0OZoZ7QfOhLNRi9Hr0NvQ5eij6Lr0ZfRd9H96FH0NwwZo4ExwzhhmJgYTDJmBSYXU4w5gjmDuYK5jxnEfMBiscpYI6wD1g8bi03BrsZuw+7H1mFbsF3YAewYDodTw5nhXHDBOBYuE5eL24s7jruAu4MbxH3Ey+C18dZ4H3wcXoDfhC/GH8M34+/gX+AnCPIEA4ITIZjAIawi7CAcJjQRbhMGCRNEBaIR0YUYQUwhbiSWEGuJV4i9xHcyMjK6Mo4yoTJ8mQ0yJTInZa7J9Mt8IimSTEmepHiShLSdVEVqIT0kvSOTyYZkd3IcOZO8nVxNvkR+Sv4oS5G1kGXKcmTXy5bJ1svekX0tR5AzkGPILZXLliuWOy13W25EniBvKO8pz5JfJ18mf1a+R35MgaJgpRCskK6wTeGYwnWFIUWcoqGityJHMUexUvGS4gAFRdGjeFLYlM2Uw5QrlEEqlmpEZVJTqPnUE9QO6qiSopKtUpTSSqUypfNKfcooZUNlpnKa8g7lU8rdyp9VNFUYKlyVrSq1KndUxlXnqbqrclXzVOtU76t+VqOpeaulqu1Ua1B7oo5WN1UPVV+hfkD9ivrIPOo853nseXnzTs17pAFrmGqEaazWqNRo1xjT1NL01RRq7tW8pDmipazlrpWiVaTVrDWsTdF21eZrF2lf0H5JU6IxaGm0Etpl2qiOho6fjkTnkE6HzoSukW6k7ibdOt0nekQ9ul6SXpFeq96ovrZ+kP4a/Rr9RwYEA7oBz2CPQZvBuKGRYbThFsMGwyEjVSOmUbZRjVGvMdnYzXi5cYXxPROsCd0k1WS/SacpbGpnyjMtM71tBpvZm/HN9pt1zcfMd5wvmF8xv8ecZM4wzzKvMe+3ULYItNhk0WDxeoH+grgFOxe0LfhmaWeZZnnY8rGVopW/1SarJqu31qbWbOsy63s2ZBsfm/U2jTZvbM1subYHbB/YUeyC7LbYtdp9tXewF9nX2g876DskOOxz6KFT6SH0bfRrjhhHD8f1juccPznZO2U6nXL6w9ncOdX5mPPQQqOF3IWHFw646LqwXA659LnSXBNcD7r2uem4sdwq3J6567lz3I+4v2CYMFIYxxmvPSw9RB5nPMY9nTzXerZ4obx8vfK8OrwVvSO9S72f+uj6JPvU+Iz62vmu9m3xw/gF+O3062FqMtnMauaov4P/Wv/LAaSA8IDSgGeBpoGiwKYgOMg/aFdQ7yKDRYJFDcEgmBm8K/hJiFHI8pBfQrGhIaFloc/DrMLWhLWFU8KXhR8L/xDhEbEj4nGkcaQksjVKLio+qjpqPNorujC6L2ZBzNqYm7HqsfzYxjhcXFTckbixxd6Ldy8ejLeLz43vXmK0ZOWS60vVl6YtPb9Mbhlr2ekETEJ0wrGEL6xgVgVrLJGZuC9xlO3J3sN+xXHnFHGGuS7cQu6LJJekwqShZJfkXcnDPDdeMW+E78kv5b9J8UspTxlPDU6tSp1Mi06rS8enJ6SfFSgKUgWXM7QyVmZ0Cc2EucK+5U7Ldy8fFQWIjogh8RJxYyYVaZTaJcaS7yT9Wa5ZZVkfV0StOL1SYaVgZfsq01VbV73I9sn+cTV6NXt16xqdNRvX9K9lrD20DlqXuK51vd76nPWDG3w3HN1I3Ji68dYmy02Fm95vjt7clKOZsyFn4Dvf72pyZXNFuT1bnLeUf4/+nv99x1abrXu3fsvj5N3It8wvzv+yjb3txg9WP5T8MLk9aXvHDvsdBwqwBYKC7p1uO48WKhRmFw7sCtpVX0Qryit6v3vZ7uvFtsXle4h7JHv6SgJLGvfq7y3Y+6WUV3q/zKOsbp/Gvq37xvdz9t854H6gtlyzPL/880H+wQeHfA/VVxhWFFdiK7Mqnx+OOtz2I/3H6iPqR/KPfK0SVPUdDTt6udqhuvqYxrEdNXCNpGb4ePzxzhNeJxprzWsP1SnX5Z8EJyUnX/6U8FP3qYBTrafpp2t/Nvh53xnKmbx6qH5V/WgDr6GvMbax66z/2dYm56Yzv1j8UnVO51zZeaXzO5qJzTnNkxeyL4y1CFtGLiZfHGhd1vr4Usyle5dDL3dcCbhy7arP1UttjLYL11yunbvudP3sDfqNhpv2N+vb7drP3LK7dabDvqP+tsPtxk7HzqauhV3Nd9zuXLzrdffqPea9m/cX3e/qjux+0BPf0/eA82DoYdrDN4+yHk083tCL6c17Iv+k+KnG04pfTX6t67PvO9/v1d/+LPzZ4wH2wKvfxL99Gcx5Tn5e/EL7RfWQ9dC5YZ/hzpeLXw6+Er6aGMn9XeH3fa+NX//8h/sf7aMxo4NvRG8m3257p/au6r3t+9axkLGnH9I/TIznfVT7ePQT/VPb5+jPLyZWfMF9Kflq8rXpW8C33sn0yUkhS8SabgVQyICTkgB4W4X0x7EAUDoBIC6e6a+nBZp5E0wT+E8804NPiz0AlT0ARKwGIPAWAHtLkZYW8S+HvAtCyIjeEcA2NtLxLxEn2VjP+CIhvR/m6eTkO6QPxu0C4GvB5ORExeTk10ok2V4AWgQzff2UyB9HXjLNHjZWgb1VzRvAP2Sm5/9Ljf+cwVQGtuCf85+NeB2dqNS5AAAAAIplWElmTU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUAAAABAAAARgEoAAMAAAABAAIAAIdpAAQAAAABAAAATgAAAAAAAACQAAAAAQAAAJAAAAABAAOShgAHAAAAEgAAAHigAgAEAAAAAQAAA2qgAwAEAAAAAQAAAbQAAAAAQVNDSUkAAABTY3JlZW5zaG90xZDIYwAAAAlwSFlzAAAWJQAAFiUBSVIk8AAAAdZpVFh0WE1MOmNvbS5hZG9iZS54bXAAAAAAADx4OnhtcG1ldGEgeG1sbnM6eD0iYWRvYmU6bnM6bWV0YS8iIHg6eG1wdGs9IlhNUCBDb3JlIDYuMC4wIj4KICAgPHJkZjpSREYgeG1sbnM6cmRmPSJodHRwOi8vd3d3LnczLm9yZy8xOTk5LzAyLzIyLXJkZi1zeW50YXgtbnMjIj4KICAgICAgPHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9IiIKICAgICAgICAgICAgeG1sbnM6ZXhpZj0iaHR0cDovL25zLmFkb2JlLmNvbS9leGlmLzEuMC8iPgogICAgICAgICA8ZXhpZjpQaXhlbFlEaW1lbnNpb24+NDM2PC9leGlmOlBpeGVsWURpbWVuc2lvbj4KICAgICAgICAgPGV4aWY6UGl4ZWxYRGltZW5zaW9uPjg3NDwvZXhpZjpQaXhlbFhEaW1lbnNpb24+CiAgICAgICAgIDxleGlmOlVzZXJDb21tZW50PlNjcmVlbnNob3Q8L2V4aWY6VXNlckNvbW1lbnQ+CiAgICAgIDwvcmRmOkRlc2NyaXB0aW9uPgogICA8L3JkZjpSREY+CjwveDp4bXBtZXRhPgqzqwMqAAAAHGlET1QAAAACAAAAAAAAANoAAAAoAAAA2gAAANoAAE3uubMNRQAAQABJREFUeAHsnQe4FcX5/wd7wQI2VBR+ioIgokZFNCoqVlTswYpEUbHXf4wltsRo7AVjB2PFXqNERUXsFcRQLIjGQrBgF4nOf76bzD5z9u5p95S7957PPM+9u2d3dspnvmd33zMz77Qzxlj3R4AABCAAAQhAAAIQgAAEIACBjBBo58qBoZaRxqAYEIAABCAAAQhAAAIQgAAERABDDR1AAAIQgAAEIAABCEAAAhDIGAEMtYw1CMWBAAQgAAEIQAACEIAABCCAoYYGIAABCEAAAhCAAAQgAAEIZIwAhlrGGoTiQAACEIAABCAAAQhAAAIQwFBDAxCAAAQgAAEIQAACEIAABDJGAEMtYw1CcSAAAQhAAAIQgAAEIAABCGCooQEIQAACEIAABCAAAQhAAAIZI4ChlrEGoTgQgAAEIAABCEAAAhCAAAQw1NAABCAAAQhAAAIQgAAEIACBjBHAUMtYg1AcCEAAAhCAAAQgAAEIQAACGGpoAAIQgAAEIAABCEAAAhCAQMYIYKhlrEEoDgQgAAEIQAACEIAABCAAAQw1NAABCEAAAhCAAAQgAAEIQCBjBDDUMtYgFAcCEIAABCAAAQhAAAIQgACGGhqAAAQgAAEIQAACEIAABCCQMQIYahlrEIoDAQhAAAIQgAAEIAABCEAAQw0NQAACEIAABCAAAQhAAAIQyBgBDLWMNQjFgQAEIAABCEAAAhCAAAQggKGGBiAAAQhAAAIQgAAEIAABCGSMAIZaxhqE4kAAAhCAAAQgAAEIQAACEMBQQwMQgAAEIAABCEAAAhCAAAQyRgBDLWMNQnEgAAEIQAACEIAABCAAAQhgqKGBhiGw0UYbmfbt25uZM2eaCRMmtOp6b7755maFFVZoUofZs2ebhx9+uMlxDkAAAhCAAAQgAAEItC4CmTbU5p13XnPwwQdHRD/66CPzwAMPFKS7xRZbmO7du8dx5s6da9577z0zdepUo+vLDfPPP7/ZeOONzYYbbhj9/fDDD+bFF180L7zwQvRXanqLLLKIWW+99aK/X/3qV2a55ZYzH374YVSu2267zcyYMSNOarXVVjMDBgyIP5e7c/fdd5sOHToYsVCYOHGiefbZZ4sms8MOO5iVVlopinfvvfeaTz/9NNrv2rWr2W677Zpc/8svv5iPP/7YTJ8+Pfr77rvvmsTxB/bYYw+z9NJL+49Ft4888oh5//33i8YrN8Jbb71levbsaVS/XXfdtdzLMxVfjLbddtsmZVId11xzzSbHOQABCEAAAhCAAAQg0PoIWFfkTP4ttNBC1ocnnniiaBlHjRrlozfZfvPNN/bpp5+26667btF0xMMZV9a9DDdJxx+45ppr7DzzzFM0LffSbN955x1/WZPtzz//bP/85z/H6eyzzz5N4pRzYIMNNrCLL764dYZWdNknn3xiF1100Tj9tLZeddVV7U8//RTFf+2113LqtdNOOxXNfs6cOfayyy6zyy67bGo+SrOcoDzTylnpMWfERMW45557Kk7fGZ529OjR0Z8ziitOr9y6HXPMMfa6666L/5xhG9Vt0qRJdS9LuWUnfjbvt7QL7YIG0AAaQANoIHMayFyB4hfNSgw1vbjq7z//+U+OjeB62eyf/vQnu+CCC8b5JEUpQ+e5556Lr5syZYq9/PLL7fXXX28/+OCD+Pidd95p55tvvrzp7LbbblYGooIMsjfffNNee+21kWH297//3f773/+OzikdXwZd44bmpf65XqwovuqUL443RH/7299GcfXvjDPOiNP3+YRb5e/DJptskhM3NNQ+++wz+/bbb0d/7777rv3xxx/9ZdHWDbuzq6yySs71yscbaip/vnKHx7fZZpsmaYTlbe5+NQ21lVdeOa77QQcdVJPyllNP10sYlQdDLbv3s3Lak7i0IxpAA2gADaABNOA0kF0IlRhqbghh9PLshi/a1Vdf3Q4dOtTOmjUrfrkeOXJk3pfr//f//l8c79Zbb82JJwPPDSWMz7shdDnnPU83xC4yzhTRDQu0bphak3iq36mnnmrVE+ivK7T96quvonxLeRlXb98rr7wSxf/222/t8ssvn5qHG9YZxdE/NwyzSZzQUDvhhBNyziuPvn372ueffz5OY/z48Tk9cqqPN9RUjkL1q/U5DLXsftdr3fakT9ujATSABtAAGkADrVAD2W20ahhqYYNoaJ43KtQrtcYaa6QaDupBU3DzyKzKEKahfaWj4X4KDz30UJPzinPLLbdE5/Vv0003TY3j011ggQUKnvfxyjHUdI2bXxeXQT15Pp1wK8NKQcakm6PWJE4hQ82ns9hii0W9lz4z5evPaYuhVvvvGD1qtWccapp9eKMBNIAG0AAaQAN10EDLQO7Vq5fVML9CFay2oaa8ttpqK29P2LvuuqtJ/s4zYHw+n3GjdJ566qkongy+ZG+VevP8kEvNiytUx3LOlWuoKW1vMKo8Yh7mp95AH/7whz/knPPxSjHUFPeUU07xSVnnACYnrawaatJX//79o6GhN9xwg9W8L83X83UPt0suuaTdeeed479hw4bF9R0xYkR8PIyTNPJ79OgRxdPwUhm3hx9+eDTH7MADD4w+K78+ffpE5bnqqqvs4MGDU8sSlsvvl2OoDRkyxHbp0qXktH0ebFvmXgl3uKMBNIAG0AAaaFgN1K/ictChIYh+/lfaULtQiLUw1JT+5MmTo5dszZlKOsA488wz4xfwPffcM+/LbGiY7LfffjnxLrjggjiN/fffP+dcWL9y95tjqHXu3DnqLVOBnNv2uCwaEjpt2rSonM5zY2rPocpXqqHmPDvGdb7wwgvjfJRGFg21Tp06Wc2zS4avv/7aDho0KKf8qsP666+fjFr084orrpiTzllnnRVd8+qrr9onn3wy53oZWuuss07cVv7k6aefnpNGPs2UY6i9/PLL0bBcOcuRsS4t5EuX4/W7P8Ia1mgADaABNIAG0EBCA7UHohfQK6+80npDw7+EFpubVStDTWXxQXOsQiBXX321P2XXWmutnHNhPPUG+nDiiSfmxLv//vv9Ketc8eecC9Mod9/zK2WOWpj2aaedFpdnyy23jMpzxBFHxMd23333vGUs1VBT75APmuMX5p81Q00eRN944w375ZdfRr2qF110kR07dqwvfqRTOZQJ6yAnKTfffHP85w0jXeSWa4iPh3HcMgk5aXhDTde4ZRPsH//4R+uWONDHKMjxjNKSsxs5blGQ4ViKIeXLU4o2wjmWykMeQs8991zbrVu3nPKG9We/9vdJGMMYDaABNIAG0AAaSGigNkA0tEtD4LxDC70QKmhul4YcDhw40Lp10gq+GNbKUFMvhQ+/+c1vcspw3333+VMFh4fJ4PFBPWghVBkBChpuWIoL//DaQvvNNdTE0btvl9GkYXze46QMlEJ5lmqoqbfOB7cmW06a3lCTx82//vWvBf/CpQoKlas557wzEZVTvapujbiccp500km+CvbYY4/NOZfMrzleH0NDzXvHdAtw2++//z7K163TZ/VZeYXpJz1xJsuiz+UYatLDXnvtZceMGRM7vFEB1MMsI1ZDLgt5RU3Ln2O1uY/CFa5oAA2gATSABhpaA9WtvHqotL6TPPyFQcO91Iuz1FJLFXwBDsWol0UfHnvssaLXheuoea+PYXp+/9BDD/XJ2mTvj3o0fJBB469Jbt0C1j6avemmm3LiyU29gtYwS15XyefmGmrKU71mPqgtFGRI9u7du2AZixlqMkTVo+iDeqmWWWaZnDS9oebjFNq6hclzrq2EV/La0FBL88IpvfmerGLDckNDqlT3/N5Qk5Ecls0toh4h0TY8Lv0olDJ8thxDLcxDQ2NPPvnkeBhslKH7Jw7qaczncCdMg/3q3kPhCU80gAbQABpAA2jgfxqoHIQMGhlhEyZM8O950VZrY+llr5gxUEiMfq2uNMcfyetKNdRCJxpXXHFFzsvxe++9F5VdvQuFesNkCPqgnglfFg2Z80E8/PFqbCsx1JR/cl6UnGAUK1doqGntt9///vfRn+boaZiohtqFYd99922SpjfUZBjK4C70d/vttze5vlgZSz3vDbVCBvQzzzwTVWfcuHEFy1GJofbPf/4zJ+1HH300yjPpQVTxFPTdKlbH5hpqYbry1ikHOl5nUebun5jIWFx44YWLliNMj/3K760whCEaQANoAA2ggYbWQOWV9z0CerH76aefomFYesEvtBh0qaLzc3gKeWD0aZVqqO29997+HdSed955OS+f/uVYEQq5zZcHRR8eeOCBOA1do8WtFaZOnRof92WsZOtfoEuZh5SWjzwKygBVUM9Xx44di5YvNNSiC/P8Uy9RPoPCG2pZWUdNSzSk8dEx/SCgIEcr+eLoeCWGmno0w7S9kXXHHXfkHPc9nzKKw/hp+z6N5mojTFNOf2RwP/7447FexETz78J47Fd+74QhDNEAGkADaAANoIECGqgcjrzI+XDnnXdGiyAXyLCslz3fC5I0qNLSL9VQ0/wjHw477LCc8oS9TskhfGGeoRv/a665JicNDd9T0BDI8JpK9ys11JS/76FMGgv5yhYaalpXTm2tPxnnms938cUXWw0l1ZzEfGlkzVBTufOVVT16CupZzRdHxysx1OT1NEzb55kcQuvX/JPTkTB+2n41DTWlr95kLWXhl6EQk2LDQdPKxbHK768whCEaQANoAA2ggYbVQOUV14t60tW5nDXIOUPSRXm5QrvssssiBwfqBSt2bamGmow+H+TUJEzXvzTrfKF5btttt51Pwp599tk5afjlBxRBjhvC9CvZb2lD7YQTTmhWXbJmqN1zzz156+Hbv5aGmrwuhjrweWbBUOvevbs955xzosXeY4G7HTnI2WWXXXLKHdaB/crvozCEIRpAA2gADaABNJDQQHWAtGvXzsoT4ujRoyPPjv4lT8MANYdLhlat57iUaqipZ8CH5CLQl156qT9VsGdwn332ieMlh/yF6VfTGQOGWmVa9b2zGGq5HDXH9JBDDrG+B88LW8sFqLdYa8glbhp8dr2OMIEBGkADaAANoAE0UGMNVB/w0ksvbY8//ng7ZcoU/84XbWVoaK6ZnBbUolKlGGp6KfUeKT///PMmxuMxxxwTl7mQi3Y54vBh++23z6nP0Ucf7U9Fa2VVq64YapVptVaG2rBhw3LaP197e6+PtehR0/w2BX3n8uUfHtfSGOoVVm+elgUIw0svvWRVJ79UQHgd+5VpEH7wQwNoAA2gATSABsrQQG1hbbrpppH7+uTL4Ntvvx05LCijoEVfQEsx1GRA+vC73/2uSZorrLBC5LZeceTlMF/55GxCQU40kgsSL7roonbWrFnRec3rKrZeXL48kscx1CrTajUNNc3J86HUIaG1NNS0lp9CKfMitZyCd9Lj66Dr9OODHM4kdcfnynQHP/ihATSABtAAGkADzdRAfcB16NDBHnnkkXbixIn+3bCocwINufLu4LUt5qWwkKGmoZnK/7vvvovy14uqvNulQfOLN8u4THtxVQ+aD1p+IC0NrU3lw+WXX26Vf1o8GXmlLGisazHUKtNqNQ01tccXX3wRNXGp3hBraagNHz7cy83269cvVWtef6Hzn/Hjx9shQ4Y06Vn2cdlWpjn4wQ8NoAE0gAbQABqoQAP1h7fhhhva66+/PloYu1DBQ++MegtdffXVC76AhobaAQccYGVQHX744faSSy6xGs7lgwywPfbYI29agwYN8lHtBx98EA3V1FIDcgyyww472K+//jo6r3XBknPcfH20ntqMGTPidDRvbYMNNohd/nfq1MnutttuVk5X5CnTX1do29oNNXmcVJ2L/aUZx4W4lHqu2oaa1j1TmDt3bqQzOc5R76k8JqatwVdLQ00/YviF1t955x0rJzldu3aNvFPKQ6UW8/actG6bvHX27NkzPubPsa3//RDmMEcDaAANoAE0gAbyaKDlwOTrZfIFrcRQiy2kxI4W7y1m8Cn/4447LmcNKRlJ4fBN7cug82VN28q9f+juX0XRdRouGYZGMdTCOhfal2GdxrPSY9U21LSQ+5w5c/JWpXPnzjn1qKWhJjZyCOLX8EsWKpwXWux7Vylnrm+5eyrsYY8G0AAaQANooE1pILuVqdRQU0+HFp3WgtTnn3++HTx4cN4hiGmi3n///e306dNz3nm1YLRe+Pv375/zEp52vY6pJ+7cc89tsnyBElVajz32WLReVb7rw+OtvUctB2SBD63FUFPbrLLKKnbkyJF2woQJ0SLiYbXqbaipPJtvvrlVj1lyDlpoqIWaYj+79z/ahrZBA2gADaABNNDYGmj3PwG4DSEfgWWXXdb07dvXuKF7xg2hNM5gyhe14HE3T8+su+66Ruk5JyPGOVQxM2fOLHgNJyEAAQhAAAIQgAAEIACBxiOAodZ4bU6NIQABCEAAAhCAAAQgAIGME8BQy3gDUTwIQAACEIAABCAAAQhAoPEIYKg1XptTYwhAAAIQgAAEIAABCEAg4wQw1DLeQBQPAhCAAAQgAAEIQAACEGg8Ahhqjdfm1BgCEIAABCAAAQhAAAIQyDgBDLWMNxDFgwAEIAABCEAAAhCAAAQajwCGWuO1OTWGAAQgAAEIQAACEIAABDJOAEMt4w1E8SAAAQhAAAIQgAAEIACBxiOAodZ4bU6NIQABCEAAAhCAAAQgAIGME8BQy3gDUTwIQAACEIAABCAAAQhAoPEIYKg1XptTYwhAAAIQgAAEIAABCEAg4wQw1DLeQBQPAhCAAAQgAAEIQAACEGg8Ahhqjdfm1BgCEIAABCAAAQhAAAIQyDgBDLWMNxDFgwAEIAABCEAAAhCAAAQajwCGWuO1OTWGAAQgAAEIQAACEIAABDJOAEMt4w1E8SAAAQhAAAIQgAAEIACBxiOAodZ4bU6NIQABCEAAAhCAAAQgAIGME8BQy3gDUTwIQAACEIAABCAAAQhAoPEIYKg1XptTYwhAAAIQgAAEIAABCEAg4wQw1DLeQBQPAhCAAAQgAAEIQAACEGg8Ahhqjdfm1BgCEIAABCAAAQhAAAIQyDgBDLWMNxDFgwAEIAABCEAAAhCAAAQajwCGWuO1OTWGAAQgAAEIQAACEIAABDJOIFOG2kYbbWTat29vZs6caSZMmJBxdIWLt/nmm5sVVlihSaTZs2ebhx9+uMlxDkAAAhCAAAQgAAEIQAACEPAEMmWovfXWW6Znz57m3nvvNbvuuqsvY6vcPvLII2bbbbdtUnbVcc0112xynAMQgAAEIAABCEAAAhCAAAQ8gTZtqB166KFGPVtffPGFGT58uK9zXbbHHHNMjkE2YMAA06VLF4OhVhf8ZAIBCEAAAhCAAAQgAIFWT8C6GmTizxkxVuGee+6pSnluuOGGKL1//etfVUmvEk6ulzAqy6RJk1q8LJXUg2uz8V2hHWgHNIAG0AAaQANoAA20eQ1kp4IYatlpC774tAUaQANoAA2gATSABtAAGmhRDbRo5jm9Sxhq2WkLvpS0BRpAA2gADaABNIAG0AAaaDkNtNgctV69epkePXqYu+++27X/f4Pmb4XORBZaaCGz4YYbmv79+5uVV17ZTJw40Tz44IPm3Xff9ZfkbBW3U6dO8bEjjzzSbLHFFtEctQMPPDA+7nfef/9988Ybb/iP0XbnnXeOts8++6xZbbXVzO67725+/PFHc99995mXXnrJqEx77LGH2WyzzcyMGTPMzTffbKZPn56TRtoHOUhR2qpjMWciQ4YMMU899VSUflpaHIMABCAAAQhAAAIQgAAE2j6BnF4tV92afV5kkUXs0KFD7XPPPRfN17rtttty8gp71JzBZZ1BFsUL/3399dd20KBBOdf5Mju392HUovvXXnttTjrzzDNPfM0ll1xi586dG3/+6aef7AYbbGBvvfXW+Jh2PvzwQ7viiivmpOPLE27LmaP28ssv259//tk6z5HWeb+0888/f9H0w7zYr52GYQtbNIAG0AAaQANoAA2ggXpooC49auuss44ZNmyY2Weffcziiy/u6vXfcOONN5oDDjjAf4x6m9SjNnbsWLPUUktFXhKfeOIJ88EHH5i111478uCoyM5YMyuttFK0jS92OyeccEIUzx/r16+fWWWVVcwPP/xgnIMSfzjeqtfquuuuiz87Q804Ayn67Gwwc+WVV5rlllsu6lXTwW+//dYssMAC5oorroh623bcccco7kknnWTOO++8aD/fv3J61NSbpzXlfNC6cqNGjYrK+s477/jDbCEAAQhAAAIQgAAEIACBNkygJr01iy22mD344IPtK6+8ktMDNWfOHHvXXXfZgQMH2nnnnTcnb9+jpgsmT55su3btmnPeGURxWscee2zOOdc+TT6X6/Ux7FH729/+Fqd31llnxfmeffbZ8XGfvjMm42Np5dCxcnrU3PBKu9dee9kxY8ZEPWs+819++cUqr8GDB9sFF1ywaJ75ysLxplqBCUzQABpAA2gADaABNIAGMqaB6jZI3759reulsq73ydsX0fbVV1+1RxxxhHU9ZXkNjNBQc4tFN4kn4+Szzz6L0ksOm0yD6g2pUt3zh4baYYcdFue/3XbbxXXRvs/rkEMOiY6/99578TF/Lrktx1ALr+3cubM9+eST7bRp0+IyaEccLrroIrvGGmsUzTtMj/3q6h2e8EQDaAANoAE0gAbQABqokQYqB7vkkktGRtiECRNyjAk3ZC8yJnr37l2SMeENtU8++SRv/GeeeSbKY9y4cXnjeFCVGGp77rlnnL5zUhLXa7311ouPK46CjCafZ75tcw21ML2NN97Yal7dV199FZdHO2Ky//7724UXXrhoOcL02K9c+zCEIRpAA2gADaABNIAG0ECNNFA52BdffDE2HOR0Q0bJTjvtZOebb76yDAdvqD3//PN5r9OwSQX1MBUDUomh5uafxen36dMnrp+bQxcfVxwF1blYWaphqPk85JRl3333tY8//rjVcEgfnAfKouXwabCtXPcwhCEaQANoAA2gATSABtBADTVQOVx5KfThzjvvtBr+2JwCe0PNucLPe/3tt98eZVXKcMNKDLWtt946LoNbRsBXz6666qrx8W222SY+XsworaahJrYaprnVVltZ5xAlLkMpw0Gb0y5cU/l3BIYwRANoAA2gATSABtAAGihTA5UDO/TQQ5u40pczEDn/KMV1vS+wN9Sch8bYGPLn/LZehpqMIJ9nlgy17t2723POOSdaFiC20NyOWw/O7rLLLnGZfdnZVq5vGMIQDaABNIAG0AAaQANooAU0UB3o7dq1s1tuuaUdPXq0lWdHH7QemLwX7r333kXnUGGopbeF5gDKcYmGhIbhm2++sddcc41df/31MdBSvH62wJeJdqAd0AAaQANoAA2gATSABqqlgXTjoJKX3KWXXtoef/zxdsqUKaFdETnBkDMMOcVIS78tG2p33HFHxEJM0uqePKalC+RhUj2Ibh24HI4vvfSSdevS2fbt25eUVjJtPldf8zCFKRpAA2gADaABNIAG0ECVNVBboJtuuqm96aabmhgbb7/9duQQI6xMtQ21Cy+8MDJwtFRAmE++/dA9f7WHPl5wwQVRWWbPnl20LCeeeKL9+OOPc4wzXTdixAgrxyb5ys/x2moZvvBFA2gADaABNIAG0AAaqKMG6gO7Q4cO9sgjj7QTJ06MDZCk84tqG2pHHXVUnFe3bt2KGji1NNSGDx8el6Vfv34FyxI6Zxk/frwdMmRI0WGjdRRMwbJTjvp8n+AMZzSABtAAGkADaAANtHkN1L+CWpfs+uuvjxbGDgVWbUNNc7d80FpjWgNNwwVlkOlP8+rC/GtpqHXs2NGqV0zhnXfesQMHDrRdu3a1K6+8cvSnxbx9WR599FF78cUX23ApAH+Obf31CnOYowE0gAbQABpAA2gADbSABloOetJQqrahJpjqtcsXrrvuutg4UtxaGmpKXw5B5FwlLYTz9pJcWkAUOVzIv+W+I7CHPRpAA2gADaABNIAGGlYD2al4LQw1OeUYOnSoHTdunH3//fft3LlzYzup3oaavmSbb765VY9Zcg5aaKjxZcyOJmkL2gINoAE0gAbQABpAA2igJTTQ7n+Zug0BAhCAAAQgAAEIQAACEIAABLJAAEMtC61AGSAAAQhAAAIQgAAEIAABCAQEMNQCGOxCAAIQgAAEIAABCEAAAhDIAgEMtSy0AmWAAAQgAAEIQAACEIAABCAQEMBQC2CwCwEIQAACEIAABCAAAQhAIAsEMNSy0AqUAQIQgAAEIAABCEAAAhCAQEAAQy2AwS4EIAABCEAAAhCAAAQgAIEsEMBQy0IrUAYIQAACEIAABCAAAQhAAAIBAQy1AAa7EIAABCAAAQhAAAIQgAAEskAAQy0LrUAZIAABCEAAAhCAAAQgAAEIBAQw1AIY7EIAAhCAAAQgAAEIQAACEMgCAQy1LLQCZYAABCAAAQhAAAIQgAAEIBAQwFALYLALAQhAAAIQgAAEIAABCEAgCwQw1LLQCpQBAhCAAAQgAAEIQAACEIBAQABDLYDBLgQgAAEIQAACEIAABCAAgSwQwFDLQitQBghAAAIQgAAEIAABCEAAAgEBDLUABrsQgAAEIAABCEAAAhCAAASyQABDLQutQBkgAAEIQAACEIAABCAAAQgEBDDUAhjsQgACEIAABCAAAQhAAAIQyAIBDLUstAJlgAAEIAABCEAAAhCAAAQgEBDAUAtgsAsBCEAAAhCAAAQgAAEIQCALBDDUstAKlAECEIAABCAAAQhAAAIQgEBAAEMtgMEuBCAAAQhAAAIQgAAEIACBLBDAUMtCK1AGCEAAAhCAAAQgAAEIQAACAYFMGWobbbSRad++vZk5c6aZMGFCUMy2t9uxY0ez3nrrRRV7+eWXzZdfftn2KkmNIAABCEAAAhCAAAQgAIFmEciUofbWW2+Znj17mnvvvdfsuuuuzapQa7lowIAB5rHHHouKu9lmm5lx48a1lqJTTghAAAIQgAAEIAABCECgxgQw1GoMOF/yGGr5yHAcAhCAAAQgAAEIQAACEMBQayENYKi1EHiyhQAEIAABCEAAAhCAQCsggKHWQo2EodZC4MkWAhCAAAQgAAEIQAACrYAAhloLNRKGWguBJ1sIQAACEIAABCAAAQi0AgItZqj16tXL9OjRw9x9990xpqQzkYUWWshsuOGGpn///mbllVc2EydONA8++KB5991342sK7ay00kqmb9++pk+fPtH1U6ZMMa+//roZO3as+emnnwpdauaZZx7Tr1+/6K9z585mmWWWMTNmzDCTJk2KyqFtKWGDDTaI0lh77bXN1KlTI6chL7zwgtliiy3yOhMZMmSIeeqpp6L8SsmDOBCAAAQgAAEIQAACEIBA2yNgXZXq8rfIIovYoUOH2ueee84q3HbbbTn5OkMtOn7PPffYTp06WWeQRZ/Df19//bUdNGhQznXJ8rdr186eeOKJds6cOeGl8f4rr7xiV1111bxpHHTQQXbWrFlx/LSd6667zjpDMm8a8847rx0xYkTapfbGG2+0W2+9dXxu0003zUnHueu3P//8s33kkUes835p559//pzzyfryuT76hTOc0QAaQANoAA2gATSABuqogdrDXmeddeyVV15pv/rqq9g40c6oUaNyDBBvqD3xxBP2jTfesG5tMXvXXXfZiy66yLpesPhapbP44ovnXOuBzTfffHbMmDFxXNeLZa+66ip7xhln2DvuuMP++OOP0Tml0b1799Q0Lr744ijOd999Z5988kl77bXX2lNPPTUqx/jx4+O0X3vtNbvYYoulpqFy+zB58mR72WWX2UsuucT6Or766qv+tE0aas8++2x8TjuffvqpPffcc223bt1S8/J1Z1t7LcMYxmgADaABNIAG0AAaQAN10kBtQMuAOfjgg616r8KgXi4ZMQMHDrTqdQor6Y0YxZdx07Vr15zzJ510UpzUsccem3POp3PEEUfEcS644AK7wAIL5MTr3bu3/fDDD6M4Muj8deH2mGOOsccff7zt0KFD6vkDDzwwzuOoo45qEscN14zP33///TllUO+YDMYwuHXUctJQT91ee+0VGZzqWfPhl19+sTJiBw8ebBdccMGca8Lys18bTcMVrmgADaABNIAG0AAaQAN11EB1Ybs5YVbDAr/99ltvX0Rb9SDJiFpqqaXyGhihobbttts2iSfj5LPPPovSSw6bFLCOHTvazz//PDovAykfxJ122iku25Zbbpk3Xr7rddwboG6+WZPrlbeCjNK0+sqIDfkkDbUwXzc/zp588sl22rRpcZm1Iw7qaVxjjTWa5B9ez3519Q1PeKIBNIAG0AAaQANoAA3USQOVg15yySUjI2zChAk5xsTMmTMjY0K9WKVUxhtqn3zySd74zzzzTJTHuHHjmsTR3DIfNtlkkybnwzKobApnnnlmwXjhNeH+DTfcEF2vdMLjmh/n58Zprl14LtzXsE8fChlq4TUbb7xxNAwzOYRUTPbff3+78MIL580vTIf9yjUPQxiiATSABtAAGkADaAAN1FIDVfH6+OKLLxp5N1SYO3euefjhh83IkSPN3//+d/Of//wnOl7KP2eomZ49exp5RZTHxbTghk2a3Xbbzbz99ttm9dVXz4nyl7/8xTgnItExN7wwKouP4Awov2u073rSzLLLLht5kXQ9bPG5cMf10Jkddtgh8hgpD5JuKGR8Wp4klb+bx2bat28fH19hhRXMRx99FH12RqBxc+Pic+GOG7ppXI9YdEheLZ9++unwdMF955TFOCcj5oADDoi8R/q63XLLLWbfffcteC0nIQABCEAAAhCAAAQgAIHWQaDiXhh5KfThzjvvtBr+6Kpe9p/vUbvvvvvyXnv77bdHWb333ntN4ui6coNz198kHc0R07DCcHhivnSdm/+c68P5acOHD885FzLRHDQfSu1RC693ywfYrbbayjo3/j6ZJl40w/jsl69HmMEMDaABNIAG0AAaQANooKU0UJUetUMPPTTqyVpllVVcPf4btGaZc0NvbrrppriHyZ/Lt/U9avfee2/UY5QWzxlq5je/+Y2ZPn26CfNT3Oeffz5ad+37778vuWdp9uzZxnl2zMnKDUs0WstMQfVQnh988IFx89+Ms4qi486hiHHLBES9ds5hSXRM/9zcOuPc6kef1bulXq604JypmIceeig6VU6PmvNUGZVtv/32M1rfzQc37NSoB0/sCBCAAAQgAAEIQAACEIBA6yeQt9fHVa3kc5qbJccco0ePjudoqatHXgvlXXHvvfcuOofK96gVmttVqEfNGUVR75K8IzZ37bEBAwZEaejfFVdckbf+zgCN4iV71DQfz4ejjz467/Vu2KKPZov1qGkO4CGHHGKdIRpfo51vvvnGXnPNNXb99dfPm085bUjc0vUOK1ihATSABtAAGkADaAAN1FgD1Qe89NJLR+7tXW9UjmEhJxhak0xOMdIqVamhdvrpp8f5rbjiiql5pOUbHjvllFOiNNxcu4KGpV/rLGmoLbHEEnEZ/vznP+ctQ7jUQJqhpqULtttuOyvD9IcffojT1M5LL71khw0bZt3cuLzph3Viv/oahylM0QAaQANoAA2gATSABmqsgdoC1mLO6n1KGhvOGYh1QwNzDI1KDbXdd989NmjUY9UccH/729+iNGbMmJH3+uWWW8664ZVRvKShpjz9EgJprvt9mR5//PG4rElDzTlEsR9//HF8XjtuiKYdMWKEdU5M8pbLp822tpqGL3zRABpAA2gADaABNIAG6qCB+kDW4tFHHnmknThxYmyAJNdCq9RQm2+++eykSZOi9N9//30rpyCFAGpIYXLh6LPPPju6XkM209ZAU3oaEulDmqF23nnn+dM2bWmC1VZbzWp4pg9JQy10zjJ+/Hjr5ssV7N0rVEfO1UffcIYzGkADaAANoAE0gAbQQJU1UH+g8ox4/fXXRwtjh5Wp1FBTWltssYW3f+ybb75pnZv/HGNNxpzmdGlRbuda3zq3+znntdC2D255AbvuuuvG52X4eSOtUI/a8ssvb3/88ccoGRmMzgFInEa3bt3su+++67OItklD7dFHH7UXX3yxdUsVxNeFnNivv2ZhDnM0gAbQABpAA2gADaCBOmug5YDLAUlY2WoYakrvuOOOiw0lWUKaG6d5XZMnT7bqAQtD0lDT9XfffXcYJTKs5MbfX6t5Y5deemkUJ61HTWkcdthhkSMVn5CGek6dOjX66NaWs1dffbU/ZZOGWpJLyIj9ltMr7GGPBtAAGkADaAANoAE0UEcNZAd2tQw1wVtjjTXsuHHjrIyiZJCjEM0RkzGVHPqoa+Wk48ILL7SKFwbNE5OBJUcfF1xwQXQqn6GmdHbccUc7a9asMAn7xRdf2H322ceG3iU1j6+ODU5erm3gDQM0gAbQABpAA2gADaCBLGugKuuouQpmNjhDzGjtMWe4mTlz5kTrobmhh8b1shUts/NeaXr16mXcUEbj5tYZ1yMXr6NW9OIggpuTZtZee20zbdo044ZjGjc/LTjLLgQgAAEIQAACEIAABCAAgVwCbd5Qy60unyAAAQhAAAIQgAAEIAABCGSfAIZa9tuIEkIAAhCAAAQgAAEIQAACDUYAQ63BGpzqQgACEIAABCAAAQhAAALZJ4Chlv02ooQQgAAEIAABCEAAAhCAQIMRwFBrsAanuhCAAAQgAAEIQAACEIBA9glgqGW/jSghBCAAAQhAAAIQgAAEINBgBDDUGqzBqS4EIAABCEAAAhCAAAQgkH0CGGrZbyNKCAEIQAACEIAABCAAAQg0GAEMtQZrcKoLAQhAAAIQgAAEIAABCGSfAIZa9tuIEkIAAhCAAAQgAAEIQAACDUYAQ63BGpzqQgACEIAABCAAAQhAAALZJ4Chlv02ooQQgAAEIAABCEAAAhCAQIMRwFBrsAanuhCAAAQgAAEIQAACEIBA9glgqGW/jSghBCAAAQhAAAIQgAAEINBgBDDUGqzBqS4EIAABCEAAAhCAAAQgkH0CGGrZbyNKCAEIQAACEIAABCAAAQg0GAEMtQZrcKoLAQhAAAIQgAAEIAABCGSfAIZa9tuIEkIAAhCAAAQgAAEIQAACDUYAQ63BGpzqQgACEIAABCAAAQhAAALZJ4Chlv02ooQQgAAEIAABCEAAAhCAQIMRwFBrsAanuhCAAAQgAAEIQAACEIBA9glgqGW/jSghBCAAAQhAAAIQgAAEINBgBDDUGqzBqS4EIAABCEAAAhCAAAQgkH0CDWGoLbbYYqZ79+5Ra0ydOtV88803NW+ZzTff3KywwgpN8pk9e7Z5+OGHmxznQMsQaAltVKOm6KsaFNtmGmijbbZrW65VS9yH+Z60ZUVRNwi0LQLWVadN/7kbsvVhiy22qEtdH3nkEZ9lznbSpEl1yb9Wbdq1a1c7fPjw+K9Dhw6tuj4toY1qtE1b1Vc12DR6GmijbT/PaqnvtdZaK763L7nkknW7t7fEfZjvCd+TWn6XSBt9VUsDme1RO/TQQ41+8friiy+MMwxcfZsflM7YsWOjBLbccst4v5QUm1uOY445xqy55ppxFgMGDDBdunQxb731Vs7xOEIr2TnnnHPM73//+7i0Bx54oLnhhhviz61tpxJttGRd26q+WpJpW8kbbbSVlqx/PZ577jnTr18/M3HiRNOnT5+CBWjuszEt0ebeh5deemkzYsSIKMmrr766rGc735O0luAYBCCQRQJ1+9XMVb7kvNzLf9QT9a9//avka/Klv9lmm9kffvgh+uvfv39Z6VWrHPfee29Un9beo+YMzZwewvvuu68snvnaqKWOV6KNlipzWr5tRV9pdeNY6ffNNFZoozJ+aUzb4rFNNtkkvrfvs88+Re/r1Xo2imVz78Mrr7xyXOaDDjqoaJkLtRvfE74nhfTBOfTRghrIJvxqPgQqgVutcrSFh0C3bt3ih+Jjjz0W7X/33Xd24YUXrugBWUn7cO1/v79tQV+0ZW3uxWijNlzbml7d3Ononj59+nQ733zzFb2nV+vZWAlHDDW0XYl+uBb9tBINZLOhsvAQUANWqxxt4WXp2GOPjR7kP/74o11vvfVio23HHXcs+lBvJV+GVluPtqAvNFKbezHaqA3XtqRXzU3z4YgjjijpPlitZ2MlHDHU0HYl+uFa9NMaNNBic9R69eplevToYe6++27HyZgNN9zQdOrUKdrXvyOPPNI4xx/RHDXNg0qG999/37zxxhvJw9HnRRdd1PTt2zf13Ouvv26+/PLL1HM6WM1yhJm4lyWz8847N2uO2vzzzx/VZ+211zbugWrcME6jejz77LPm7bffDrOJ94cMGWKeeuopM2PGjPhYpTtKzw1RMa43zWy99dbmgw8+MCuttJK57rrrzLBhwypNvuTr3RAds/HGGxvn2MQsvvjiZubMmVFZVC43tLRgOs3VhrSqv88//zzS3f7772/WWWcd8/zzz5s77rgj8iSqOR277LJLpGOxuv3225uURWVWOyr84x//MP/5z3/Mr3/964irvIS+8sor5plnnjH//Oc/m1xb6EC99VWoLM09Jy3peyuO7gXMTJkyJdK55pf+9NNPqcm6oczGOT2IOMqbqnvZTI3Xvn17o3miCh999JF5+eWXm8RbdtllzTbbbGNWW20107lzZ/PLL79EepKmFP+rr75qco0OtGVtNKdNUiE14+C2225rFlpooei7/dprr0VzfPVM0Hf/448/Ni+++GJ0j3O9+gVTb267JhOVNpV37969zeTJk6PvqcqwxBJLRPcCxVc55dk3X2jOvTxfWoWOJ5+vheLq3M0332zccEcza9asiLOeMclQzWdjc+/D+q7rO+/DMsssY6655pro45VXXhk9m/w5v3300UeN+3HRf8y7rfc9tBbP6LyV4wQEINCqCZT065mrYcXxFllkETt06FDrJixHP97ddtttcZp+2IX/Va/Y9tprr42vTZbNPUjzXl7M62M1yxGWq7m/aruXRuteElPr415e7QknnGDbtWvXhIWu+fnnn608W+26667WvSA0iROWr9h+x44drTMqonIcf/zxUVpqA4VPPvkktQzF0iz3vHt5tk8//XSUZ75/7qFcsJ7N1cZZZ50VZfnqq6/aJ598Mid7ta0z2qyGgYbh9NNPb1KWgw8+OI7iXvDt448/Hn/2O2o3z7hURvXWV6nlKiWe9HviiSfaOXPmeAQ5W2e82lVXXbUJS6UtbfugNPLld9NNN0XRlMevfvWrnHgauvvCCy9YZ5j5pJpspfFNN9005zqfV1vURiVt4rlUunXGWNQOo0aNsttvv32qPt58802rXpW0vCptV5/mvPPOa53Diiaa0AFnJNitttoqPpdPI0qrufdyX45i20LP10LXypPv3LlzozqcdtppqSx1fTWfjc29D6+//vox61J3Vlxxxbx1CrnU+x5a7Wd0WBf2K39fhSEMM6SB2jeGXmLdr13W/SKdc2/VA9iDkMHhftWL/959990o7vfffx8fC88XmjisB88DDzwQ/3nDUAkWM9SqWQ5fN22b8xDYbrvt7Lfffhtx0INUjjvOPvtse/nll1vnlSs6rn/O21XM0efpetvi89r59NNP7bnnnms1z8zHKWe77777xuk5b5ZRGrvvvnt8zP3a2qx0Sy2DDE3XexXlJ0NmzJgx9swzz4wM1SuuuMI+8cQTkWEqQ6pQms3Vhn8ZVwHE/o9//KP1L5I65tbmi172//SnP9nPPvtMh+zXX3/dxEAODTUZIAoyEs4777zopc/1UkbH9E95FqpLeK7e+grzrmRfc2HUlj64dQ7tVVddZc844wzreiqthtkq6N7h1kJM5XHZZZdFcfTDxQYbbNAkzn777Red1z/XU9/kvOsRic9PmzbNul7+qD1UhpEjR1rXQxKd13fwgAMOaHJ9W9NGNdqkEk34a/33Sw6Y5AzKeQC2t9xyS3QP1A8y3rCWEZ32Ml5pu/pyjB49OtbHO++8Y//617/a888/37res+j4uHHj4vP5DLVK7uW+HPm2pTxf812r47p/Kugeph/k8sWt5rOxuffhVVZZJed9wN/3VH7dR8N3BL9f6hIyPq1yHH5V0q7VfkbnazeO1/4dF8YwrrEGagPYLWBp9VLqX0Z1I1XQL9p33XWXHThwoNUvlfkqV83x7871738zd/+LGWrJ8lSrHOU+BPTrqH9p1+TuddddN4eV2F144YVRvfTCstFGG+Wcd0OG7F577RW9BMuw8UFxZdQMHjzYLrjggjnXJOsefr7zzjujJEIvnFpnx/eyObf9JacVplvqvltWwVfBHnbYYal56eHvXEannsuXT6naCF/G9bKg9NxwOqsfEhT0IqnPOh7Om5AntTDv0FDTdTKew/N6qfDfGaXthvbknA/jhvv11leYdyX7mg/jwwUXXGAXWGCBnPrql/cPP/wwiiKDLi0vXeOZ6QceNxw2jqcfJvQCqiADLO163av0Mp5sKx9XbTJhwoQoDRkFyTK2NW1Uo008u0q23lATeGlAPVJhejKa/b1N2gnPab/SdlUaMvx9kHGo+7LPZ5555onnMPs4bmh4fN7Hq/Re7tMJt5U+X31aur/4e9hFF13UpOw+Xtq2Ws9GpV3qfThZjvBeW+jH2+R1aZ/rfQ+t9jM6rU4cq837LVzhWmcNVBe4G8dv3ZyluCfIP8DU06EXgKWWWqqkh0EWHgJqiGqVo9yHgH7NV9Cv+Gm9BCqbXhT8sEg3rykvVw0ZPPnkk616C8Kgnh89nNdYY4281yofvZiqd0jh+uuvz4nrfxUs51fI5ghcQwF9cGvn5JShOen5a0p9QfAv4//+979z8nZzVKJiaevT1FYv9ApuLlvO8dBQc/MLc87560NHLcrXHy+0bUl9FSpXoXP69d7N+Ys43X///XnrudNOO0Vx9E8Ge1qaMp59z5cfUh0acO+9956tZAHfHXbYIS6DmzuVU4a2pI1qtklaO5VzLDTU8rmLf+ihh6J20f2pOe1bqF1VVo1iUFBv7XLLLZfT7jqvl2035zmKo39phlo17+XVer76dtAIDQXVT88Jf7yUbbWejcqr1PtwslwtaahVs10rfUYnufC5uu+18IRnC2ug8gbQA1JGmP/VObrzu3/OyUNkCOhX8XIrmYWHgMpcrXKU+yKtnisF9X4VYqfeJQU9aJO/9Kdd5ybDW80tSw5DlaEnoyLN1b5zsBDloX977rlnTnn+8Ic/xOfyzSNKK0e5x9zipHE+e++9d04Zyk0rjF/qC4J/GXdOPnLy1q/sCnphDNNVPAV9L8LjoaF2yimn5JwL4/n16pzTmLxxwvhZ0VdYpmL7+gXch3y9WT4N3UsUNNzVH0tuw6G4zgGRVS+LgnrxNbclGb+cz+EL4fDhw3PSakvaqHablMM4GdcbajLC0u5Lir/HHntEbax/gwYNymmXZHppnwu1q34I8/Mm77nnnrxpa+i5D2mGWqX38lo8X8VCIwA0nFRBQ3zT+BQ6Vq1no/Io9T6cLE/YfvXuUau0XZN18Z+b84z217Kt/H0WhjDMoAYqbxTfq6AbvgwGvTTqV/BS1mLJByQLDwGVrVrlKOdF2nnEEsooOO9iVo4Q/Hh7bTVPQ3+33nprjjOK5PDIfGx1XMNxNO9Mziz8XA9lqPST12l+oYKGOSbnMOgXXh/kvj95bbU+h0OQ9IA8/PDDbZcuXSrOr9QXBP8ynpwD59tV86nCuiqeQtIYCw01/ZofXhPuO4+R0fXq9QyP59v35SilZ7Me+spXzvD4X/7yl6iO+qehh2ka9zr3hprmnoZpJPf9fBsNRfW6PvroowteE6Yhg/Goo46K5iApb7Wr/vSi7oPm6oTXtCVt1KJNQlbl7HtDTcNa810nhzw+JH8UCa9pTrtq3psP6j0J0wv3Ne/Rh6ShVo3vWi2eryr/cccdFxVb35NioyrC+vr9aj0blV6p92Gft9+2lKFWjXb1dci3LecZnS8Njlf+fgtDGGZAA5U3gh9+p7u+5jLp5b3SimXhIaA6VKsc5bxIO/ft/rlf1rbcX5T1i7E8ljlX8nE+fthY2H5+jpCceYTHta80/PA1eUNMnq/mZ/8SHhfW7Wgen+Ye6dfUUofVhmUq9QXBv4zLMU14vTeoZEyHx73jEzkdCY+HhpqGOIbnwv1LL700rqZeCsJzaftZ1FdaOcNjflhZXNESdor1MGrepTeSlZy4hHnm25cOXnrppRJKYKNhxGE6bUkbtWiTkFU5+95QS/ZWh2lo/qAPMjLDc9qvpF0179eHQw45pEnaPi+NMvAhaahV415ei+erRl/4HiG1ua9LOdtqPRt9O3mG5cwjbylDrRrtWgrrUp/RpaRFnMrfdWEIwxbSQOXg5cDBe2n0N1v1BJ100kmp3rhKqWgWHgIqZ7XKUc6LdDjUUHPC3NpcJf0tv/zyJT1w5T1Pzj+8AebbzK1LF+UTto966XzQC7CcXyT/3Jp2URTNp0v2uIVpVWNfw9vUC6ie22RQ71Ny/lCxPMs11DQnL0yzEkNt9dVXz0krTNfPHVEd9TISnkvbz5K+0sqXdswbs1rWoFSNq73S0vLH5B007IFI9nT6eOFWXky9d1V5mZTRrV4SDavTYu76C1/GNd8zvN4bam1BG7Vok5BVOfveUNPIgXzXyamSD7pXh/EqbdfwPizPoWHa4X44zy1pqIVpNPdeXovn629/+1uPzTbXY2+1no1iWep9OOSu/ZYy1KrRrsm6hJ/LeUaH17Ff+fssDGGYQQ1Up1G07o4m+msIkx/XryeBd6WueUX55hmkQcnCQ0DlqlY5ynmRlnczH5I9MmmsSjmmeQ76Vdi/iPn05RFPawHlm8OjOUHlBA2nLKU8lcaR1zPpTe7wwx8JNPcubdJ/vvxKfUGoxcu45iLkK5dcgCtoWFIpcw9bWl/56lHouIYW+jpWus6fz+eSSy6J0vTeSPVBw2T9+bTt+PHjo2vUM5xvCQDNv/ShHoZaS2mjFm2SxryUY95QcwvZ520/fdd9SN4rK21XDQf0odAafZoP6UPSUKvWvbyaz1f10uiHVAWtS1lKW6TFqdazUWmXeh9OlqOlDLVqtWtYn+Y+o8M02K/O+ywc4ZgxDVS/QeSVT176pkyZ4p9f0VYv0XJkUeglxMPJwkNAZalWOfTLvoKY+Drm22pun+8xEq988Yod16/NWudFvT6asxMGDfMaNmxY7FI+X1oaaqYgz2aap5Pvz7vJLqUHI19ezT0uXuFQwXzu+9PSL/UFoRaG2m677Za3bf2cKL2sppU7eawl9JUsQ7mfT3cLgvuQtg5WuempV84HzSOT91kF/XCUXOTapy3t+O9G8kXfx9E2XNS4HoZaS2mj2m0SMix33xtqWtQ637VrrbWWb/JoORgfrxrtqjlCPuj+4tNObkOHSklDrVr38jDPSp+v4fdEz4cw7XL2q/VsVJ6l3oeT5QsNNT3PkufL+dwS99BqPKPLqSNxq/++C1OY1kEDtYWsBUA1lMi/DPkHn1yTF+p98WuEaUhSpRCa+xBQvtUqh/dAJxfipdRHTiEUtMCqHvalXBPG0S/A/kXHM1feI0aMsH369CkpvfAhqJfeMP3kvu+pk4e2UnqAktdX+lkvVd55hBaPLjW9UrVRC0NNTlrSyqneJS0DoKBegbQ4yWP11lcy/+Z8Dr00pi0kXU6a//d//xe755cnTvVASBPe+6Z6XbUAcjJNufX3YejQoU3O+/j+PqC49TDUWkob1WwTz665W3//Uu/oCiuskNo2chTjw4ABA+I41WhXlVv3XwXNhdVLdVpdwjmRSUNN8Su9l6fl6Y815/nqhwZrqLtPpzlb/51oyWe0Rlb4kHTyU26d6n0PrcYzutw6Er+277vwhW+NNFAfsJr0rXkfEydO9PdVm+a4wldSntd80KK1/jE0tUQAAA2uSURBVHhztqW+jKelXa1yyKW3D/369StaHy1W7UMpizh36tQpJ81wArpe9ocMGVLW0FOx0JAxHzRfJ42PPxb+qqzx+/54tbbFFucOh6aV88tqqdqohaGmXkq9aCQZqTfFh1LrUm99JcvcnM/6AcK/xGqeo9akKpSOhgal6UA/DPgFrz/99NOcoa/qcfE/EqUteK30/DBJDZtMy3+llVaK01C71MNQayltVKtN0jiWe8wbamJ+2mmnNWkbDeHzS8JIP+EPWtVoV5U3vAcm10TU+f79+6t4cUgz1Cq9l5fCrdTna1jeSpc5qdazUfUr9T6cxsIvMZDmsTgtfr5j9b6HVuMZna8uHK/Pey2c4VwnDdQftCYva2J1oV4azZnyQWt8yUOe1n3Rw1l/+sW8VECVPASqVQ452fAL8upX2oEDB9quXbtGk6HVc5X2Aqr5Awpy0iHnH0nvf/JyqHlnellJLkStXoWLL77Y9uzZs2ROSZ7/+Mc/ovz1IqsXgeT58HPoPl+9duG5auzL46PqtPPOO+f02OnlTHqSRhRU1nJcTZeqjVoYaiqvvEiGC3jLjbjXyUcffZRT10Ic662vQmUp55w8vPmgIW7JHzHUvvoO6l4hpyMympLpX3755VES6lFN+5EgfNlOc9X/wgsvRNdriKR61cKeN3n+U2/K999/74tZF0NNmbWUNqrRJsk2as7n0FDTPVAOPfx9X0a9Xsx9SHPNX2m7qszKxy9erzLIpb16b3XvlQGmEQSzZs3yxbBphprSqeReXi67Qs9Xv+6jFoAPDdty81D8aj0blVap9+G0cvpFz9U++q5rGLV6P/27Qto1acfqfQ+txjM6rR4cq/87LcxhXmMNtBxg/9DNV0H1uOUL+Yy8X//615EHNw3H8H/+F3WlpX1/3G+LLbbbnHKk1UlGlZ/LlaxX2rw9vRD4lw3F17UawqVhhuHLgc4lDbVibNPKFx5bfPHF43lyyi88l7avh6Ivk15s0+JUcsyv5aa6yhgTh3HjxjVZuLvQpP9KtFELQ83XSe2qnubQC6cMA3kbLIdZPfVVTrmKxdXLr7wt+qC5rJpDKYcHfq6mP5c01MIFj88///y8vLzbeRlj+lEhLJM8m+olzwfFkQdH9c4p6GU89PJWjx61ltZGJW0Ssq1k3xtqY8aMiYcgyuGLek/VRj7oB6U0R1WVtqsvu9LxZfF5+u20adPs4MGD/UeroYj+unBbyb08TKec/eQzYJ111onLKYOmnLTyxW3Os7GS+3BaOXr37p2jh7iS/9vp3LlzyXWt5z002T5pdeNYy72fwh72GdJAdhtDv4rpF269kGtoS/gylc9Q04Oy3JDvV1DfSM0ph782udUvh/olLfngTzPUdK3y/t3vfhevVZasm17u1aOgl4lkXpV8Dt2RF1rsNczDe4xTGatdHhkteiELezZCFpqLpF+4w/Ik9yvRRi0MNfWoasioeorCMH369LxeOJN1Sn5uLfpKllu9oPqe+2GIIQ9977Ukg5zEhD3PGhIto05Bw4gKeY7Ur+XeEBbfZA+xhoT5+Ww+b/XQ6Zh6+dSb70M9DLUsaKM5bZJs10o++3vkqFGjotEH6nENg4x49d4X6hmqpF3DsusHAvXgSTsKeh6pXNLg9ttvHx3TPw21Da8L91vqXu7L4JcS0fzXNMPWxytn25xnYyX34Xxl05zEkSNHRqNLNGw4DOUYakq/td5D87HheHbfcWkb2qYUDbT7XyS3IWSdgFsnzfTq1cs4l9TGvcQY13Nl3BAW4x5KWS961crnXtQjBmLhXr6NG5Zk3EtTxMG9WFctn1ol5Ba8NldffXWUvPuVPSq7MzCM80ponMME43oLonatVf6F0s2CvtS+zkW+cUaCcb0mEQvXe2qcQVao6FU55174o7x79Ohh3LIVxvXqGTcMtSppl5JIVrXRUm2ie5w0eeONNxrnbCZCqM9uOGr0vX/ttdeM64ktirba7eqMduNGY8T5HnTQQcZ5540+uzmnOefiSImden/X3BxeM3XqVOMMK+Pm+xnn4TRRIj5Wg0C927UaZSYNCEAg+wTy/gLois45GKCBKmrAvYzHP/aq14TvGPcYrwG0kauFsEfNM8riVnOBFWbOnJnZ77Nfl1HrZiZ7k7PIlDLlfhfgAQ800NAaaOjKZ/bBypeybeqSl/G22a7V+L6ijVxtZMVQk7fRfO0ro8cPv62FE6V8+ZZzXMMT5dH0qquuslqcu5xriZurSXjAAw2ggRbQANBbADoPyyr2UrWm9uNlnPtNPr2ijVxtZMVQc8MardZKk2dJOeTQfEU3xM3usMMOVs5EFGSsLbPMMtzXG/S+nu87zfHc7zQ84IEGmqWBZl3EA4kHEhpohgZ4Ged+k+9BhTZytZElQy2yxvL8k3dQORTJ164cz21XeMADDaABNFC6BnAm4tRCgEC9COy+++7GeXqMstt2220jpzD1ypt8sk0AbeS2z9ixY41bY9A88MAD5tRTT809WcdPbg1P45aBMG4ZF9OlSxfjes6MW2TZuOUjjOtpM26Ny+hzHYtEVhCAAAQg0CAEMNQapKGpJgQgAAEIQAACEIAABCDQeghgqLWetqKkEIAABCAAAQhAAAIQgECDEMBQa5CGppoQgAAEIAABCEAAAhCAQOshgKHWetqKkkIAAhCAAAQgAAEIQAACDUIAQ61BGppqQgACEIAABCAAAQhAAAKthwCGWutpK0oKAQhAAAIQgAAEIAABCDQIAQy1BmloqgkBCEAAAhCAAAQgAAEItB4CGGqtp60oKQQgAAEIQAACEIAABCDQIAQw1BqkoakmBCAAAQhAAAIQgAAEINB6CGCotZ62oqQQgAAEIAABCEAAAhCAQIMQwFBrkIammhCAAAQgAAEIQAACEIBA6yGAodZ62oqSQgACEIAABCAAAQhAAAINQgBDrUEammpCAAIQgAAEIAABCEAAAq2HAIZa62krSgoBCEAAAhCAAAQgAAEINAgBDLUGaWiqCQEIQAACEIAABCAAAQi0HgIYaq2nrSgpBCAAAQhAAAIQgAAEINAgBDDUGqShqSYEIAABCEAAAhCAAAQg0HoIYKi1nraipBCAAAQgAAEIQAACEIBAgxBoc4baRhttZNq3b29mzpxpJkyY0KabsWPHjma99daL6vjyyy+bL7/8sk3Xl8pBAAIQgAAEIAABCECgUQi0OkNtrbXWMhtvvHHUPrfddpuZPXt2Tlu99dZbpmfPnubee+81u+66a865tvZhwIAB5rHHHouqtdlmm5lx48a1tSpSHwhAAAIQgAAEIAABCDQsAetq3mr+nnvuOavgestSy+wMtej8Pffck3q+NdW1WFmdoRbVVf823XTTNl/fYjw433q+x7QVbYUG0AAaQANoAA2ggaIaKBohMwbAJptsEhsm++yzT2q5MNRaT3vy5aSt0AAaQANoAA2gATSABtBAXg3kPZFqCLUkyIcffjgy1KZPn27nm2++1PJhqLWe9mxJLZE3OkEDaAANoAE0gAbQABrIuAZaRwO5uWlxb9oRRxyRaqQJNIZa62jPjH8p8uqLcqMvNIAG0AAaQANoAA2ggXpooMWcifTq1cv06NHD3H333a6excPNN99s3HBHM2vWLNOlSxfzww8/pF7kDLUcZyILLbSQ2XDDDU3//v3NyiuvbCZOnGgefPBB8+6776Zenzy40kormb59+5o+ffpE10+ZMsW8/vrrZuzYseann35KRs/5vOyyy5ptttnGrLbaaqZz587ml19+MZMmTYr+5KXxq6++yomf78MGG2xg+vXrZ9Zee20zderUyGnICy+8YLbYYou8zkSGDBlinnrqKTNjxox8yXIcAhCAAAQgAAEIQAACEMgwgbr1HiyyyCJ26NCh1jsEcV4bS8q7a9eudu7cuVGP2mmnnVbwmrBHrVOnTtYZZHFPnN/5+uuv7aBBgwqm065dO3viiSfaOXPm+Mtytq+88opdddVVU9NYeOGFrTOkrDPMcq4JP3zyySdFHYDMO++8dsSIEeFl8f6NN95ot9566/hz0pmIMwTtzz//bB955BHrvF/a+eefP7WsTpcchwEaQANoAA2gATSABtAAGsieBmr/or7OOuvYK6+80roepNiw0M6oUaNKEsQVV1wRXffNN99Yt3ZYwWu8ofbEE0/YN954w7q1xexdd91lL7roIut6weL8VZbFF188NS3NfxszZkwc1/Vi2auuusqeccYZ9o477rA//vhjdE5pdO/evUkaSyyxRHzttGnTrOs1tOedd150/ciRI61bUiA6L+PzgAMOaHK9N55Ubh8mT55sL7vsMnvJJZfEwztfffVVf7qJ0ffss8/G57Tz6aef2nPPPdd269Ytb34+X7a1/07AGMZoAA2gATSABtAAGkADRTRQG0CLLbaYPfjgg616nsKgHioZIAMHDrTqMSpSOLvMMsvY77//PkpCxlax+N5Q0wUybtQbF15z0kknxcU59thjc875eJoD58MFF1xgF1hggZx4vXv3th9++GEURQadv85vVffRo0dbean0x8Jthw4douUFlIB61pLpK64brumLYO+///6cOOodk8EYBreOWk5ebsin3WuvvSKDUz1rPqiXT0bs4MGD7YILLphzTVhG9mvzvYArXNEAGkADaAANoAE0gAZK0cD/BwAA///2EPu9AABAAElEQVTtvQncHtPd/z/22IrgsQeJEpRIS4nYl6JUS7UvW5PGrmhrex5Ffna101YoEuKhFVtotcVjj30tidiXWCpiiSD25fzPZ/6+8zrX3DPXOtd9z53rfV6v+565Zs45c877fGb5zjnnO1EURa7Iv3XXXdeNHj3azZw504Xh0UcfdQceeKBbdNFFGzreCSecEGfz+eefu2WXXbZm2smTJyeH3XrrrbvEn2eeedw777wTx7niiiu67O/bt69799134/1/+9vfuuw3Vttvv31ynM033zw3nsVPL7fbbrskfVY5dWyFzz77LJPZggsuWMF44403zi2DuB155JHuueeeS46pFXE466yz3KqrrpqbNl1ufhd7vsATnmgADaABNIAG0AAaQAM5GmgdzMILLxwbYU888USFITBt2rTYEFhjjTWaMgQWWGABN3369DjPSy65pK48zFCbOnVqbvy77747znPChAld4uy1115JHTbccMMu+0OIqp/CcccdVzVemMbW+/Xrlxxn//33r0g/22yzxQaaIowfP75in6XXcuzYsUke1Qy1MM3QoUPdRRdd5N5///0krVbEZNiwYW7eeefNPV6YD+utnzcwhCEaQANoAA2gATSABtBAFQ20DufBBx9MHvrV83Xdddc59TjNOeecLT30H3LIIXG+X3/9dd29Pmao3X///bnHvuaaa+J81cOUBnPaaacldbnyyivd5Zdfnvz95S9/cfb317/+1Zmh9ve//71LPpavjL1f//rX7vTTT4/TXnXVVU5/MsAsHHbYYRXpl156advljj322Ip9lq+WBx98cBKvXkPN0s8333xu9913d7feeqsTXwuqr8Vh2fq5AUMYogE0gAbQABpAA2gADTSpgdbBPfzww/ac766++mqn4Y9NFiZJN/fcc7vXX389zvf6669PttfK1wy1amnGjRsX5/vSSy91yVfpGg3//ve/u+Sz6aabuoceeqiurDQsMazXeuutl6RL97aF8XbZZZckXqOGmvKZffbZ3ZZbbunuvPPOJJ+s4aDhMVlv/XyBIQzRABpAA2gADaABNIAG6tBA65D2228/9+KLLyYP+1p5+umn3RFHHOGWWWaZCiOkjgLF8ffYY48kPxku9aYzQ63akMFqhpp64hQ++ugjt8MOO9T1J6MsLN93vvOdZP7Yp59+6i677DJ30EEHuZ/97GfuRz/6Ufz385//PKlf2lDTnDULu+22W0Xe4XG23XZbi+YaMdRWWWUVd/LJJ7vXXnstSa+Vxx9/PK5veAzWWz8/YAhDNIAG0AAaQANoAA2ggSY0UAw0zauSUw0NF5QDDAtfffWVu/nmm92uu+5a9/wn9fTI0FO46667cg2VrMq2aqhpaKOChgPONddcDR3bynPPPffEecgpiYwi2x4uBwwYEMfRv7Shpjl9Fn7zm99kpldev/zlLy1aTUNN8wj33XdfZ4aoJfzwww/dhRde6NZZZ53c44TlZr2Y8wWOcEQDaAANoAE0gAbQABqooYHiAS222GLu0EMPdc8884zZA/FSDizkyEIOLaoVSj1ZFrbZZpuqcdP5tGqoHXPMMXbopnoDNS/vk08+ifM48cQTc8uuIYcW0obaQgstZLvc73//+9w81GNpIatHbY455nDipx5EK5PF17DMvffe28lhS5ohv4s/J2AKUzSABtAAGkADaAANoIEGNdBeYBtttFE89C9tKDz//POxM4uswppzEg3Fy9pfbVurhtpOO+1ktkzcY1XtWFn7+vfvn6QfMWJEbvnPPPPMJF7aUFO+9gmBBx54IDcPOQKxkDbUDj/8cPfGG2/Y7ng5Y8YMN2rUKDdo0KDcPLPqxLb2niPwhS8aQANoAA2gATSABtBAhga6B8oiiywSz9OaOHFiYjxkOa7YZJNNkv0aLplR4KrbWjXU1CP25JNPxmWYMmWK69OnT9XjaUihvs1m5dT6l19+Gac/55xzku22X8vllluuoocry1A79dRTEw5Znzf49re/XeGtMW2ohQ5eNBRz+PDhdQ89DcvKevecH3CGMxpAA2gADaABNIAG0EBKA90PRM5BxowZE38YO1UYd9NNN8UGijwyNuPev1VDTeXZbLPNEiNp0qRJbsiQIRUGl8qlOV36sLecjsjwCuuhXjAFzdVTr5qGMtr+9ddf37366qvu448/To6RZagttdRSTo5IFGQwhnPdVlpppS7OW9KGmjieffbZbrXVVkuObWVg2f2ahznM0QAaQANoAA2gATSABhrUQM8BkwOSsLCDBw9OjJcDDjigYl8Yr9p6EYaa8tc33MxQUqE0v07zuuTkRN+KC0PaUPvud7/rvvjiiySKDLZ7773Xvfnmm/G2Dz74wG211VbJ/ixDTWX41a9+5eSMxYKGiz777LPxT/XaXXDBBbbLpQ21NNtqzNjXc+cA7GGPBtAAGkADaAANoAE0kKOB8oAxt/lvvfVW08P0ijLUBGvVVVd1EyZMSIYyJlaRX5EhpjliMqbCoY8GWUM4n3rqqTBJPFRR29RDJyceFvIMNeUld/5vv/22RY2X06dPd3Lbv8UWWyTbNRfQjs2yPJqmLWgLNIAG0AAaQANoAA2ggWY0MNs3ifyiZ4N3Vx/53qLIeyqMRo4cGXmPiT1boODo3hCL/NDDyBtuke8di/zQxch/Ny7yvWxBrK6rfohknG7gwIGRd4Mf+R65yDv06Bqxji1+Tlq01lprRc8991zkh2NG/vMBdaQiCgQgAAEIQAACEIAABCDQGwmUxlA7//zzI//h7GjmzJlRv379ovfee6838qTMEIAABCAAAQhAAAIQgAAEWiZQCkNNvWjeXX3kPSxG3lth5B2NtFwxMoAABCAAAQhAAAIQgAAEINBbCZTCUOut8Cg3BCAAAQhAAAIQgAAEIACBdhDAUGsHVfKEAAQgAAEIQAACEIAABCDQAgEMtRbgkRQCEIAABCAAAQhAAAIQgEA7CGCotYMqeUIAAhCAAAQgAAEIQAACEGiBAIZaC/BICgEIQAACEIAABCAAAQhAoB0EMNTaQZU8IQABCEAAAhCAAAQgAAEItEAAQ60FeCSFAAQgAAEIQAACEIAABCDQDgIYau2gSp4QgAAEIAABCEAAAhCAAARaIICh1gI8kkIAAhCAAAQgAAEIQAACEGgHAQy1dlAlTwhAAAIQgAAEIAABCEAAAi0QwFBrAR5JIQABCEAAAhCAAAQgAAEItIMAhlo7qJInBCAAAQhAAAIQgAAEIACBFghgqLUAj6QQgAAEIAABCEAAAhCAAATaQQBDrR1UyRMCEIAABCAAAQhAAAIQgEALBDDUWoBHUghAAAIQgAAEIAABCEAAAu0ggKHWDqrkCQEIQAACEIAABCAAAQhAoAUCGGotwCMpBCAAAQhAAAIQgAAEIACBdhDAUGsHVfKEAAQgAAEIQAACEIAABCDQAgEMtRbgkRQCEIAABCAAAQhAAAIQgEA7CGCotYMqeUIAAhCAAAQgAAEIQAACEGiBAIZaC/BICgEIQAACEIAABCAAAQhAoB0ESmWobbDBBtF8880XTZ06NZo0aVI76ltYnuuss060yCKLRNOnT48eeeSRwvIlIwhAAAIQgAAEIAABCEAAAqUy1F544YVowIAB0bhx46Jddtml1K1zzz33REOHDo3uuOOOaLPNNit1WSkcBCAAAQhAAAIQgAAEINC7CGCoNdleGGpNgiMZBCAAAQhAAAIQgAAEIFCTAIZaTUTZETDUsrmwFQIQgAAEIAABCEAAAhBonQCGWpMMMdSaBEcyCEAAAhCAAAQgAAEIQKAmAQy1moiyI2CoZXNhKwQgAAEIQAACEIAABCDQOoEeM9RWX331aODAgdG1116b1CLtTGS55ZaLNt1000jeIN95553owQcfjJ13fPDBB0mavJXZZpstWn/99aO11lorGjRoUPTZZ59FTzzxROyh8fHHH89LVrG9b9++saOQddddN5p//vmju+++Oz7+m2++GeUZaquuumq0yiqrxPnccsst0UcffVSRZ/rHdtttF80555yR8nzggQfi3cOHD4/uvPPO6JVXXklH5zcEIAABCEAAAhCAAAQg0CEEnK9nt/x51/tuxIgR7r777nMKV1xxRcVxvaGWbN9yyy3dp59+Gv8O/z333HNu5ZVXrkiXLr83sNwNN9wQJqtYP//8890888xTNY/vfe977j//+U9FOv2YMWOG++53v+u8oRbvu/322yvy8R4gkzR77713xb50Ob2L/yTuIYccksR9+OGH3VdffeVuvPFGt+OOO7q55por2ZfOg9/do104wxkNoAE0gAbQABpAA2igmzXQfuCDBw925513nnv//fcTw0QrY8eOrTBAzFB76qmn3Mcff+zee++92Jg74YQT3L/+9a/YeFE6/+0yt9JKK1WkNWgLL7ywmzJliqLFwfdSudNPP92de+65bvLkybbZafvss8+emYfvQXO+JyyO+8knn7jrrrvOHX/88W78+PFxuXzvnvO9XfH+tKHme/Kc1UMGqZUra6kyKfjePrf44osnce+99954u/3zvW3ulFNOya1zVt5sa7+uYQxjNIAG0AAaQANoAA2ggTZqoD1wF1xwQbfPPvs4/zFoszfipYySa665xm277bZujjnmSIwTVdAMHEV84403nB9GWLHff1vNffnll3E+Y8aMqdhngI499th4v/4dd9xxFXH8EEN36aWXJvt32223iv2Wx0033RTHkbGmHjLbruWGG27o/NDLJA//HbWK/Yrzu9/9Ltnvh0F22a84c889t3v33XfjeFdffXVFnD59+jjV9eabb06MU0X8+uuv3W233eZ23nnnmj2CYZlZb4/G4QpXNIAG0AAaQANoAA2ggTZqoFi46o0aPXq0mzlzZmKsaOXRRx91Bx54oFt00UUrjJKwYqGhtscee2TGk1GjIINvmWWWqYiz0EILxUMTtV8GYpi3rctAeuuttxTFPfPMM1161TSs0cKZZ56ZmYcMQAtZhtpSSy3lvvjiiziKesLs2OHypz/9qWXhtt5668w4ir/sssu6I4880mnIZxjUq3fWWWd1MWbDY7BerLbhCU80gAbQABpAA2gADaCBbtRA67A13FBGmHfWEdoSbtq0abExscYaa+QaImFFzVBTT9YCCyyQmcY730iOMWzYsIo4W221VbJvv/32q9gXHueMM85I4vXv378i3lFHHZXsW3PNNSv2WR4rrLBCEifLUFM8DZdU0Dy3dM+h9tsculdffbWLsWjHSS+HDh3qLrrooi5DSL2TEycW8847b2Z50/nwu3XNwxCGaAANoAE0gAbQABpAA23WQOuAvTfGxHD5/PPPYyNl++23dxpq2EjhzVCbNGlSbrp+/folxzr66KMr4mmopQX17OUde/fdd7doznuVrIh34YUXxvvkzCPLwLI8bb5dnqGmoZ0WfvjDH1YcY4kllkh63NLDMy3/aks5ZVEdbr311ng4pB3n8ssvrzhOtTzY17ruYQhDNIAG0AAaQANoAA2ggTZqoHW48lJoQUMTqxlJ1Spihpp3a59rcMhbowX1LoX5nXTSSbbLLb/88hX7wnibb755Ek9eKMN9Nj9NvYHh9vT6s88+G+eRZ6jJyHvttdfiOOk5aPLwqKA5Z+qdS+ddz285QpFnTO/GP85L/9JeNOvJhzit6x+GMEQDaAANoAE0gAbQABpogwZah6phhi+++GJiMGjl6aefdkcccUSXeWTVKmCGmv+2WlXjRfPTFNIG0J///Od4u/5pOGbesUK3+IceemhFPHmDVHj++ecrtqfzMuM0z1BTfHmKVFB5w7l5NkS0mkGaPp79lnOSk08+OTEC4wP4f/7bcG6HHXaoWmbLg2XrmochDNEAGkADaAANoAE0gAbarIFiAMstvXqqrrzyytgwMQNCQwjlvXDXXXetOYfKDLUJEybkGhzyJmnh7LPProinoZAWqn1rTUMRLciDYghYRqKCPg0Qbk+v2ycAqhlq6tVT/RUOOuigOD99qsBC+tjpY9hvGZ377ruvu//++y1pvPzwww+dhmrK8LS4LIvRMxzhiAbQABpAA2gADaABNNDDGii+ARZbbDGnnip5VQyD5nVpuKKcYmRV2gw1eTjM2q9t+n6ahYMPPrgi3i9+8Qvb5TbeeOOKfWF+e+65ZxJvyJAhFfFk/Fmo9lFsfV9NoZqhpmPKSFV47LHH4uOcc8458W+55q+Wv4ZObrPNNm7cuHHOjhUn9P8eeughp49p5zlcCevKevH6hilM0QAaQANoAA2gATSABrpBA+2FvNFGG7nLLrusi7GhoYVyiBFW0Aw1zd3Km2MWOgyRw5Iw/QYbbGC2jDvxxBMr9oXx1OtnYckll6yIJ++VFvLc5ofHqWWo7bTTTpadW3vttZNPA/zhD3+oOG5YvsMPPzz+jlyS0K/MmDHDjRo1yg0aNCg3XZgH6+3VNXzhiwbQABpAA2gADaABNNBmDXQP4EUWWSQe/jdx4sTE/kg7vzBDTRGyDC0NrzQPk3J7r2+ihXDUC2VOPvTB7Cx39UsvvXQyNFOOQ8L0WldvoD4PoJA3V06Gp4Vahtpcc82VGGeTJ0+2ZC7P9b/KYPPfFPmee+5xw4cPz6xLuuz87h4twxnOaAANoAE0gAbQABpAA92gge6HvN5667kxY8bEH8YOKxgaal9++aXTEEUZZ4qjYYIXX3xxYugcdthhXYwsxQuHP2qumwwvO8aAAQMqhmOmhz1avD/+8Y/JcU444YTkMwP63MDIkSOTfVqpZagpz9NPP70ijT7+bcfKWsqA1BDM1VZbrWq8rLRs6349wxzmaAANoAE0gAbQABpAA23QQM9BNSPMKmWG2m233RZ7jZR1M336dPfII4+4Tz/9NDF2br/9djf//PNnGjHqVZM3SAsaRvnkk0928Up52mmnZaZXWfSdM3lRtDBz5sy4l0vOOxTUy2X76zHU5KkxDOZYxOqdXqa5pPfzu+c0C3vYowE0gAbQABpAA2gADXSTBsoD2gw1DYnUEEX1PIVBH9OWl0MNJ6wFR3PN7KPUYR5Tp06ty429HHWMHz8+8dqoPGT06SPT3/rWt2JjTdtkNNYqi/ab238ZnKGr/nrSEqc8GqUtaAs0gAbQABpAA2gADaCB7tDAbN8cxC/KGXzvVuS9REb+A9SR95wYeQ+IdRfU90xFK664YuQdcET+W2aR/35Z5Oe21Z1eEb3BFnn39/HS96RF3m1/Q+kVuU+fPvFx+/btG3lHJpF3y99wHiSAAAQgAAEIQAACEIAABDqHQOkNtVmhKYYNGxZdeumlcVV+8IMfRP5D17NCtagDBCAAAQhAAAIQgAAEINAmAhhqbQJr2XrPk5GfIxf1798/8t+HiwYOHBj5EZO2myUEIAABCEAAAhCAAAQgAIEuBDDUuiBpfYOMspVXXjnyLvijESNGxMaZctX62LFjWz8AOUAAAhCAAAQgAAEIQAACszQBDLU2NO9RRx0V+e/AVeR80kknRUcffXTFNn5AAAIQgAAEIAABCEAAAhDIIoChlkWlxW377LNP5L1ORt5LZeQ/Xh3PSfMeJFvMleQQgAAEIAABCEAAAhCAQKcQwFDrlJamnhCAAAQgAAEIQAACEIBAryGAodZrmoqCQgACEIAABCAAAQhAAAKdQgBDrVNamnpCAAIQgAAEIAABCEAAAr2GAIZar2kqCgoBCEAAAhCAAAQgAAEIdAoBDLVOaWnqCQEIQAACEIAABCAAAQj0GgIYar2mqSgoBCAAAQhAAAIQgAAEINApBDDUOqWlqScEIAABCEAAAhCAAAQg0GsIYKj1mqaioBCAAAQgAAEIQAACEIBApxDAUOuUlqaeEIAABCAAAQhAAAIQgECvIYCh1muaioJCAAIQgAAEIAABCEAAAp1CAEOtU1qaekIAAhCAAAQgAAEIQAACvYYAhlqvaSoKCgEIQAACEIAABCAAAQh0CgEMtU5paeoJAQhAAAIQgAAEIAABCPQaAhhqvaapKCgEIAABCEAAAhCAAAQg0CkEMNQ6paWpJwQgAAEIQAACEIAABCDQawhgqPWapqKgEIAABCAAAQhAAAIQgECnEMBQ65SWpp4QgAAEIAABCEAAAhCAQK8hgKHWa5qKgkIAAhCAAAQgAAEIQAACnUIAQ61TWrpk9VxwwQWjVVZZJS7Vs88+G3344YclKyHF6c0Eequ+Nt1002jppZfugn7GjBnRP//5zy7b2QCBWYnAQgstFK200kpR3759o9lm0+NJFN16663R119/PStVs9C6rL/++tECCywQTZs2LXriiScKy7u3XkMLA0BGECgRAefLwt83DDbbbDO3//77x3/+ppHLpV+/fkm8733ve7nxYJutLf9A6iyIOZyyOcGlOS69VV833nijnRYVyyeffJJzhPvULKuBgQMHuttuu61C8/ajT58+s2y9i7i+T548OUY1fvz4Qjn11mtoEUzJo7n7bivc9txzz/iZeosttihUx62UqURpu79BSlT5LoIYO3as3R/ct7/97S77rew//OEPk3hHHXVUbjyLz7JSZ9wEKnmgj2J59FZ9/fa3v3WjR49O/qZMmRJfZzDUitVHM+fbYost5q688sr4r7e/XNpvv/3iepx//vk9fu+ad9553auvvprcT9944w03ceJE53uH4r955pmnx8vYjF4aTdOsvjDUev7a0GhbE7+yzbbccsvk/N9xxx074nxvUAOVwBpMPMsBxVDrHj1svPHG7pNPPon/Ntlkk1lOR51+HvV0/WcVfV133XUYaiXpSdMoCgt77bVXr75mXXzxxXFVXn/99R6vx4EHHmhY3Q477NDj5empa1ez+mqXoTarXEN7qj05bv3Pktab/swzz7jZZ5+9Y68BVTRTP8wqmcwyYDHU0EMn6Jw69g6dY6iVp52afZAu47lWJkPtggsuiA21F198cZZ5jmimzZvVV7sMtWbqQJryXK96S1uss846yYsaDX/sLeXu5nIirBA4hhp6CPXAOnroSQ1gqJVHf80+SPekfvKOXSZD7aabboof1O67776OfkhrVl8YauW5RuSdb2zPb6Nrr702Pv//85//uLnnnrujrwF5Oukxr4+rr7565CcQR76RfNn+/7D11ltHfuJw5MerR4899li0/PLLR34uQDR06NDIj1uPHnzwwejOO++MPvroI0uSLJWfvEXJM9qXX36ZbG90xRtq0fDhw+NkK6+8cvT8889nZuHnqCVe2I4++ujopJNOyow311xzReuuu2601lprRWuuuWbkh/tF//73v6N77703N+8woznnnDPabrvt4k1PPfVU9Nxzz8XesH70ox9FG220UewhTmyU39/+9rdc71itliMsUzPr888/f8whK614vPfee1m7MrdtuOGGsSZWWGGF6Fvf+lbs7UqaueWWWyI/lyczTdEbF1544ehnP/tZNGDAgGi55ZaLPv7442jq1Kmx162bb745mjlzZs1DLrnkktEGG2wQDRo0KNa6vF/Ka9fdd98dvf/++5npdc7o7913340ef/zxaNiwYdHgwYOj+++/P7rqqqti75nKzw8hipS/zpdx48Zl5mUbe1obVo5W2rVZfRXFU1rUOa7wf//3f/E1SG3rhw/F5+gjjzwSt6vO4UaCN9Sin/zkJ5F/GIu+853vNJI0Kku7qtBLLbVU5N+cRnbdl5dX34MS3XDDDdELL7xQtV7y/ifPduIrbX/22WfxeSKmOgfygrxnfv/7349333777dEHH3wQrbjiinGb6J6ic0h56B7knxS6ZKNz3A/LTrYvvvji0YUXXhj/Pu+88+LrTbLzmxVvdESffvppenPyu9k2abUu6623Xnw9sIIcdNBB8b11+vTpkX+LbZuTpZ8bWZVtErGJFd3Tdd22cMIJJ8Ta9sOeot/97ne2OVn+/e9/z72vNXMNTTL2K0XdX8M8610vSl+6Nqy22mqRrhV+fk/8DKX2lna98Rf5+X7xeabzrVpo9hqalWcr1/Ks/OrdVuR1+L/+67+irbbaKvK+CqJll1021qCeL/T38MMP596j02XV85+uN2ussUb09NNPx/cBPcvKw6nu3Qp63pVn37ygZwzlo+uf2lTnip6bdF37/PPP85J1+3Y/3zR+LtIzkLyQ1gry/K17oh/uGB1++OHRGWeckZmk1XbVM72ehV555ZXM/HvDxm6zYOebbz43YsQIpzdnCldccUXFsTWJWEG9WnLW4W/I8e/w36RJk5zePHmwFX/bbrttHE15eKPJ+Rtyxf50/LzfRfaoyRmJP6HD4ifr/uRyhx12mPMPIVXL6d3uJmmOPPJIp4nVxi/Z8c2KN1Iz8yqiHHm86t3uL1Lp4ia/652Y7y+W7q677krSZa3o7Wy9ZWo2nn/Icf6imnX4eJt/UHMa318t/5122sl5Yywzj9dee835C3tm+uOPPz5O8+ijj7o77rijIr16X/yF3/kXGRXbjznmmMy8VL4yaKOIdm1WX0Xx3GeffRLm3vhz3qV48ttWvvrqK3fooYfmtkWWXprtUStDu6o+mm8gByn+RYZhqFh6t+tOni6z6q5t3k2788ZcRZrwh5xh5Dmb8C9Skqj+JZn77//+7+R3uPKPf/zDeVfkXcoQDskJ41dbX2aZZbrkY3VrpU1arYvuDY2Eiy66KLceVp9ml9YDVG958rw+NnsNDctdxP01zK+R9aL0ZTzl9dEbrk5DSNPBv6RwP/7xj6u2abPX0LDORVzLw/waXS/iOiznNg888IDTtSkv+Jeyzr8or8pzjjnmcKNGjcrMwr/wcaEDjby89HzoDZjMZ2Fl7F80Of+yuGo5GmXYTHxdX//0pz85/8I9rq83wOoqkxxnKShd1jXYytJqu+o5XPdf3WvkrMS/MKurfHb8EiwrDZ52FEgPj/4NZJcHUxlF4fHMUJOHMzma8G/73F/+8hfn37g5PYDbiaOTJH1DDEWvhldc/2bb6QbXSHdqUYbaNtts43yviorivvjiC3f99dfH9ZCY5dHKgsbnhwzS6+kbiU5wBT2M+7cycb4yXnWMhx56qEteRZUjXa5Gf/s3Is6/GU3+QmOzHkNNJ5bvNYrrrhPOv7Fxxx13XGzsnnvuubFrZ22XAdNo2RqJv/3221foUMfWA6Ae+P/6178m3st8j2duOQ4++OC4HvonjeuFheoijdgDrdoz68ZqhoXSSkcnnniis/NG23xPRXyT0cuKd955R5ucbtJZF6YyaKOodm1WX0XxDG8kunkq6GZ/6qmnOp2zoVc7HbNezTVjqJWhXVU/te0dwcuEN998011zzTWx1vUAM2HChPi6JVZZL6x8j4Mzr5fG8/TTT3c65+zh1LZnTUAPjZuzzz5bUeMHG6W/5JJLnMpj4dhjj+3SJv3793eXX3558mdtoTRq23CfrS+yyCJd8hGLVtuk1bropaCVUUt7mNf1Jtxu6+10lqJzwo6jpbXDW2+9VbHd4mRdu1q5hobnXqv31zCvRteL0pedC3LI4HuY44denWdnnXWW8z0uJvH4+cv3ZGbqU2Vv9hpq9S7qWm75NbMs4jqszzJZ8COYnIbmSbO6Rui6YS9pdY/+5S9/mctTHmIt+FEDTi+VdP3yvWfxZl3/LGQZar63N37OsTh+xI3785//HJfDj55xeiGsoBe+9RpGzTDNS6NzR9cJPYeGQc9h9RiPfpRAYoDqeSXvONrearv6EWdhEeNrzimnnOL8KLyqx61Wpm7e1x5DTdax4NpDi1FSL5kuIuoB0xuHsLLhA6d6FfQGMtyvk0IiUPBdpBX7FG/VVVeNT6gwH8XVDUAniB/K2CVNmL/WQ0NND9A6ubL+wjeUaff86jm0B7OXX37Zffe73604rup95plnqmjxQ78f1lOxPyxTeCORu2IZoEqbNj7F6v/9v/9XkU+R5QjLVMR6o+7TN99885iX/v3qV7+qqKeVRzcauZ223+1Y+mGJcTl0Ac/6zp7aVg9VfohCZjn08GkXej0spS9ofsiK80Oy4mNkeUAKDQvd6FVHacQMPL3g0G9tD+c8+KEoFeUpizba1a716qsonuGNRI2nm0CoPz3A27VQbeWH0VXsD+OG62Yc6OVVuD1vvSztqvKp99CCHnak/XS5dZ7obWeWoaYHIwt6kRGm1UPMpZdearvdbrvtVrFfcUPjRhH/53/+pyKOeh90fVbQPSJ9TQ2Pp/XwfGrEkCmiTYquS5nmqJkxcc8991S0T5q//W71Gmr5aNnK/TXMp4j1ZvVlhpp07IfWxQZXWJ4jjjhCu+IgAzfcV2293muo5dGua7nlX8+yiOuwnl1lZKXvmXZ8Xcv1LKagToOs64Yfch3v1z91MugaYOn1UsnOP4uUNQIn9Iaq5930cdQDqudkBb24tvzbvVx77bWdOhj0AjgMfpqQ07OwH6ZZV1n0TK6gZ5YllliiappW21W98rvsskvMyWwIHVvP03rBsfPOO+eOzGg3zzrzL9ZQ8+No428AWW+SYCiop0PCW3TRRXMbJDSwsm68qpCGqShIJFk3fsXRw7KGToZvHeJE/p8fpxrf1POGU4SGmqWptUwbavaAoTcuOmGzGkInqw2L1MN/VhxtC28kKkf6ATAvnbYXWY5qx2lmX6M3gfChT9+baeaYrabRw6RdnPRWvpn8rE3Uln7uYWYe4dvi9HlghoUeLMPj21stLcPtupEo+LlsFdutHEVoNDxeo+vtatd69VUUz/BGohtWFgfd4CzouFlx0tsaNdTK0q4yguxc0UNNliFmdU2/sNN2vQSxFxoycC1uuNSDi84DhayXGqFxox6wMK2th/qr9Va62QfpItqk6LrYg2IZ3PM3aqgZT7V7M9dQa3stW7m/hvkUsd6svkJDzc/z76JzDQ220RXp6SbVyl3vNdTyCM+lnrpHt+s6bHW0pXRnIYu5RscoaIpLlhGi508bJqh4aUNNQ77tha33PdClTa0cGuFjQYaybS96qevx/vvv7/y8ODtcvNQ1fsyYMc7Px27o2Hp2t/uDRtvVKm+R7arhuZpGpJftYdA5ol5odfjUKk8P7G/dUBN0GWH2lsEq7ycTxhWX5V9PxcxQUwNqnHBWmvCGlTU0LJ1Ggj/ggAMSo8jKppPgnHPOcX5ye8VxQkNN3aXeQUXmXyjYtKGmm5+CLPV0ecLf6hlS0Mmcflti8cIbiR5c8obWWPxwWWQ5wnyLWG/0JqB5LhZ23XXXqlyLKF9eHvbwqGEI1R4+89Lb8E15OMp6QFU6vcyw+Zn/+7//W1FXMyz8BNyK7Xprp6AXGeGxFU9B52e4vSzaaFe71quvoniGN5L09SDkbg9Vun6E2/PWGzXUytKu6tm24J2h1FXXkIGfxG/Jq/aS602zBethtnzCe0VeT3vYC1BrCHazD9JFtEnRdenNhlqr11DTh5at3F/DfIpYb1Zfdk3RS7m8cthIEA23y4uT3l7vNdTStetabvnXs2zXdTh97LCtZMCE+/US3u7fmjcY7gvX7bMUun6lDTX12FvI69mzvPScrZAedWD7W1lqtJeGe4bz3tULpZcrv/jFLyp6Chs5ju6RCt7xn0tft7PyaVe7yheA5uOm/QXofNHL7Tw7JKuMbd7WuqFmb/MFXkaHHi5k6Wt4SiOFN0Mt7w2q8tJEfQvph89ax/Je0+IhkzYm3vIJh1iGhlq4PZ23euwshA9m3nOSbY6HIVx22WUV4+41505/ms8UOhxID4+044U3kmqT7i2+LYsuh+Vb1LLRm0A4lEAPPjK+vVfQhvRVRNn/9a9/Je2rt5O6wOY5M8g6nmm8VltqTLuCnKeE+ZhhkZ6LZw/06kUO4yueQjs1Gh6v0fV2tWu9+iqCp+oc3kjy3vIrnvfAGbeH3t7Vw8ratZ6hj2U65/Vm0kK1SeJ5DEKeGqWRF2/33Xe3wzi1eRgvNG5k+IX7bF33BAu1XgCFD2f1Dn0sqk2KrktvNtRavYZa22vZ7P01zKOo9Wb0pWOboSYDNq8smm6ioF6EvDjp7fVeQy1du67lln89y/C6UcR1WEbSr3/963jqjJ7bdH/VnwwwC5r/GZZN/hMsqPc33BeuyymZhbShdtppp9mueBimzdfU0p4h7TnSDDX5AAjzb3Vd82rD8NJLL8XTazTFpJW8ZfxYmevt4S26XdPl19BU3Uv0TC5D1IJ4p+P20O/WDTUbwqfKXX311a7ajbVaJe0CnO4VCNOoR8mCxBzuq2fdu4l23gVwxRuCcMhLq4aadyFtxWtomdc7GN5INFeunjoqTtHlqPe49cZr9CagfDXcMB00F1DzX/TgVG1Ybb3lqhVPLwrefvvtimJoUq+co2juYJ63RuUrg84uAnpLVe1Y9gbUu5OtiGeGhY4XpjcDQC8Gwu329llOR2x72bTRjnatV19F8BTX8EaiIY7GOr38wx/+kGhHD/Hp/enfjRhqZWpXG6KukQvpOtXzW5PLLVR7IRP2iMmjcJh3aNyITbjP1sMXf+nhwRbHls08SBfVJkXXpbcaakVcQ609tWz2/hrmUdR6M/rSsc1Q03C7vLLY/UEP23lx0tvrvYaG6dpxLQ/zr7Ve1HVYdZdztnqChtGF5VIvlIV99923Yl8Y7+c//7lFi1/4hvts6GQSoY6VekdphMeptm6e1HVoDTHXqIS86UbV8knvs5FkyjfvupxOU1S7pvMNf6snVE4JNT3KQr2GZJhPm9ZbN9TUgOZFyiqoSa2axJr2zlitEmaoqbcpL56Gi1nQzSYvXrhdF3edFHIAoq5WC5pHp/G1oVOIVg21cMiO8vbfs6rrTwZkWGZbD28k/nttmXEsbrgsuhxh3kWsN3MT0HHlkllvPdRzmw7qpcgaL15EecM8/PdVYle05jAmXQ69cQsnD1tazduxoGG3tj1rac5q5MUx3G+GhYblhtvtRlyPoVZGbRTdrvXqqwieaofwRlLNaZE82FrQg1nYhlnrjRhqZWpXe3nXyBv8sP7ybmah2sNB6OJcc2TCPELjRu6jw3223m5Drag2KbouvdVQK+Iaam2vZbP31zCPotZbNdSqDbOz+0O7DTWxKPpa3gjfIq7D6mU3Hwt6Cat7qnq/dA7Km7P+QiMrbaiF57yGB+aVP5znlu5RsxesGnJY7zOk7nl5x2pmu16QqSc2fNaS4w9pSc9ZeVM3qh1LaaRBhUY+pVREu+aVSx01J598cuKYxe478qAq9nnpunl764aaCqz5Onq7KW85Nj5XFZaHFXmk0bCSWuM9zVDTvLA8CJqYaSHsJciKr5499UKFkzaVVsPBZFxmuapt1VDTcEkLtcqXVeb0tvBGkr4gpOOGv4suR5h3Eev1PkjnHUvDqaQ3vXkPXxJorHHW5N28fFrdrpN8jz32iC9o4UuALKcvemNjF71qLyNUJnubJycJYRmLMCzKrI2i2rVefRXBU+0T3kiq9arqeqSgntW8ealhezdiqJWpXc01tR40wvrUu66XUhaqGb7hEHR57grzL9q4aeZBuqg2KbouvdVQK+IaGmqk2ftrmEdR683oS8e2HrWyGGrGo6hrueVXz7KI67C8jypoNEA42io8vrw1W0g/l8kZhQV9Ay1MF677D81btC49ahrWqKD7RNbnKcJ82r2uF9P6BFHa+Ybm2euzBf5j67l1TJdNnhctNGJYFtGuYVn08k+9nWYQW5n0Ylyf09ELwDB+CdaLMdTCisjjj95u6iEzDHqI1sS9vAcZM9T0XbAwv3Bdb0YtqPHCfVqXRxf15GUdWw9JeXPBLJ9WDTXNy7OH8SI+GtrsjaTochifopb1PkjXczzVNRxSlue+v568WomjN3E29jrtldHyNaNSk3FtW9Yyz+1uEYZF2bVhPFpp13r1VQRPlTe8kfz0pz/NbVub26BrndWz2lK9swppgz0rTZnaNew5VC9IVnmrbdObaAvpt81huvBhZ8iQIRXHKdq4CR+k995774pjhWUK14tqk6Lr0lsNNbFt9Roatk+z99cwj6LWm9GXjl1WQy3k0sq1PMyn1nqr12GVU71GCtVetIff7U0bahpNY0HPJXll1ueULKSvccccc4ztamhUWt6xdA1Wr1H4p57PvPhZ29UZo/uqXjLbN9yskHqxLL8Bct6Xlda2qZdKIe2d2vbnLVttV+Wr3jzNu1OPoLVxWH5d03U9yCtDD28v3lALK6QP+anrOA1GLqw1eS+Ma4aaeib0Mbxwn63/5je/MbZuiy22SOLow3X6wHX4jQRFlMWsHo965oPoGK0aaspDE/8V5BBCJ76VvZllKzeSIsvRTNmrpan3QbpaHuE+XRxt/pfe8oT7unNdx7aQ1YMsT6AK6mkIh9yGZQzfyGkIWLivKMOizNoI69tsu9arr6J4hjeSPHfDejNq7uTr/WaUeTWUt9GQS956Wdo1fHOa/n5ZXtnD7XL3bKHaA5P13Clu2iAs2rhRD4GFtAOBsOzp9SLapOi62Hc8NcQrXd7u/t2oe/5Wr6Fh/Vq5v4b5FLHerL56g6EmPs1eyxth2+p1WB4ILaTnvIblsPNHcdOGmuKZMzBNj8gbImiOvpRH2lCTEWWh2ke1wzJVWw87OCxffYeyWppq++QPQF4+TXuWp30nOctgC52T7Ljjjg0du9V2Vc+m2RdWVt1TR40alfvN22r174F97TXUrEJyAqJxvhMnTjROLj1RLwQ5cuTILg2pYQ/2CYApU6ZUGEHhxMfp06e7P/7xj67ezwJYGbUswlALH1Ly3EKHx0w/YIT7WrmRFFmOsExFrNf7IG3HquVZMRyKUO/bbsu73qUuuLUMb72UUNCwgKx8w54CvXTIihNOyNYHsMM4RRkWZdFGu9q1Xn0VxTO8kWiotR66wnbTunraLNSrUbl+tpDuMUrnr99laVcN69QnLBQ0d1TDZ7LKq21ZH0jVuWbpdV/Ieumhl3k2zD5rvkPRxo3KqnuLQiPewIpok6LrIk92FvSSM69tumN7o4Zaq9fQsE6t3F/DfIpab0Zf9rDc00Mf23Utb4Rtq9dh1cGmMOTNI9f1Kux4yDLU1LtkIctJ0SabbGK742XaUNNzhr3g0bNu3nd/jY2G8lXjX7ShZsfVUiPk9Nz88ccfJ3XKGjJqTjo0OkTP8mEetdZbbVebM60C6iXp8OHDM+8ptcrRg/u7x1ALK6iHTznaGD16dEVjhYaaPsSrC7J9q0pC1c3RQto1vww1uTJXL10tUYdlSa8XYagpT5VFQfVQd3O6R09vJDRGVoanWKTLYb9bvZEUVQ4rT1HLeh+k7XgyXvQwpm8yhXN7dEGTnsxLoi6y7fpgoR42pVG94U9/+0PGti7OdpHXGHMre7jUA6iN9dYQWX3Q2jSuumjYroWsh8+iDAuVqQzaaFe71quvoniGNxK1n7xyhh99lZtn+wafjPhQw6E+0ut6M2np9JZW1zm5R9YwKf1l3ZzL0K6qhyacW9AnNTSnNLxB65qoYcr6bqadA2H9wwdyff8p5KkXM+Hw9iwjtmjjRmUzb5a6rut8l7MsndOqV1i3sB5ab7VNiq5L6IRF1055KtW9xuqR1R7pOhX1u1FDrdVraFjuVu+vYV5FrDejr7IYau26ljfCtYjr8AMPPBBftvQSSL1q4cgXeXRUL1lolGQZanoG1XftFHStOOSQQ9yKK64Ye6bWixtd80Lv0WlDTXXWdx0taDpQ+hqn5wWdx3qO1gidrBdexq6dhpodQ5x0TdTwxrShpmc0CxqubmnqXbbarnqWOvvssxuaT1dv2bopXvcbalax9M3ADDU5H7GuY03o1HfV7M2pGltDHNNvWNN52TEaXRZlqOmktBNeZdaQTI2t11DM8ATVvnYaakWVo1GOYXwNY9IQm/AvfCOl9XCf1tMfedRwMgsyhsRSD2/pDxVWm7wblqmZdRlqYVDPicZm25sv26c3YOFDZfpYepsmXVtQPnrjo4uthZdffjnzolKUYaEylUEbRbRrK/oqimd4I7E66ZzXCAKbb6i21Q1eXsPSmqj2Wy900kO6TSdZ833L0K5WH714CK/dOrd1zsjzlw1VVl2yrt96INfnXiwovs41m6Nk2/M+01K0caM6aZRGWB8rgy01R9rqHi5bbZN21EUjWvJC+iVqWJei1xs11HT8Vq6hYfnLZqg1o68iDLVWrqHG06570lRP3aOLuA7Lj4GMKws63+Vl2b6/KyMr9OyYZaiJifKxZ1rLy5Z6WSvnRxY0Rcg4hksZeOF8MD3v6Popr+rmC8Hy6GlDLSx3+npunxvQC7t6X1KG+bXarunyhHn3kvWeM9TSgEzUMpb01lhvEcIgYWpMqd4kpNMW9bsoQ03l0YOG5meED+VhffQA96c//amqg5MibiRFlKMVvroINRrSb5j0cCsDPXyTFeb51FNPxcO+WilnrbQaY685Y/YSITy+1qXPCy64oOqbLTuGekP0lj18WFUeusHJJW6eS/KiDAsrR09ro4h2bUVfRfEMbyS6dmmieGh4q21lfDfrTUo9hHoraNdI5aeQZaipbXu6XU1fWurhUz2MWcamHljyhgBbHho9kX4ho7rrjXU198ntMG5UJvWmX+K/g6jREGmPwnmGmtK10ibtqIvKox4DvfDSy6Xw4bTshpp4NnsNNV1pWcT9NcyviPVG9VWEodbKNdTqXMS13PJqdlnUdVgvAvRMEQbdq7VNPVv16kbGk0aC6dqvoPNMz5gabhx6rFWPV16dNUJI56iN1okz+uafzll9rkgjE7JGV1ie3dGjZsdKL1V+e85pZG5vmE9R7Rrm2ZvWZ/umsH7R88E/hET+e2KRn+QY+QmUcYH023c3R/6mHD322GORf7vQ8wVtogSqx+qrrx551/GR6um7zyP/Vjny51sTuTWfpCzlaLYG/mIUc1Q9/LCwWBf+4hez9BeDZrNtOJ2/AEf+hhrr1V9AI5XBG3CRH6bWUF7+gh/5i2jkv1kS+YfWyN90e0zjPamNsrRrQ40XRPY3ksgb6fEW33sS68E7D4m+973vRX4uVeRHBcTnfJCk21Z7sl3DSvrhQJH/bln8599Kx+esn4dW1zXQvxGNxHXQoEGRf8MdeSMp8kNIw+x71XpZ2qRXQcspbJmuoTlF7KjNPXktL/I67DsEIj+EL75eebftke/Javj+Hja8dOpHFCSb9tprr8h7Bo9/+znNFfuSSMGKuKo83vCJr4F6hvSjCyL/EiuIVb5V/1Irfp7Xs5F/uRKJZaOhyHZt9NhliZ9ryfsCdus+e1usNw7dfWyO171tDW94z0oaSL/xm5XqRl04V9EAGugNGuhN12HNmVLQJ316A9tmyqgeRRuiqW/eNpOH0vSmdm22jjXSlecChKFWnraoIZqmTzjypY1nRQ1wI0HXs6KuqRO67k0aKNN1OG/6gnjKC7oN59Z0nt7EuJGyai6fpovor5rn31p5lqlda5W1TfvLcyHCUCtPW7RJbLPsBQlena1dbiSd3f6c/7Q/Guh5DZTpOuyHNTp9K01zbAcPHhzPa/NDnt12222XeH6Wsbb44ovzXFRj9F6Z2rWHzvOeP7ms4hhq5WkLaxOWtAkaqK0BbiS1GaEjGKEBNNBODZTpOixDrVqQF0k5FGknj1kl7zK1aw8xLc+FQ2565c5a36nqIRgct8abDdqlPOcLbVGetthpp53ia5euX/oQM21TnrahLWgLNNAZGijTdVjfJjz11FNjj7f6dqbmask4u+OOO9wZZ5zh9I1MdFmfLsvUrj3RZqXy+ugBECAAAQhAAAIQgAAEIAABCHQ8AQy1jpcAACAAAQhAAAIQgAAEIACBshHAUCtbi1AeCEAAAhCAAAQgAAEIQKDjCWCodbwEAAABCEAAAhCAAAQgAAEIlI0AhlrZWoTyQAACEIAABCAAAQhAAAIdTwBDreMlAAAIQAACEIAABCAAAQhAoGwEMNTK1iKUBwIQgAAEIAABCEAAAhDoeAIYah0vAQBAAAIQgAAEIAABCEAAAmUjgKFWthahPBCAAAQgAAEIQAACEIBAxxPAUOt4CQAAAhCAAAQgAAEIQAACECgbAQy1srUI5YEABCAAAQhAAAIQgAAEOp4AhlrHSwAAEIAABCAAAQhAAAIQgEDZCGCola1FKA8EIAABCEAAAhCAAAQg0PEEMNQ6XgIAgAAEIAABCEAAAhCAAATKRgBDrWwtQnkgAAEIQAACEIAABCAAgY4ngKHW8RIAAAQgAAEIQAACEIAABCBQNgIYamVrEcoDAQhAAAIQgAAEIAABCHQ8AQy1jpcAACAAAQhAAAIQgAAEIACBshGY5Qy1DTbYIJpvvvmiqVOnRpMmTSob76Q8ffv2jdZee+3498MPPxy99957yT5WIAABCEAAAhCAAAQgAIHOJtDrDLU111wzGjp0aNxqV1xxRTRjxoyKFnzhhReiAQMGROPGjYt22WWXin1l+rHFFltEt9xyS1ykjTfeOJowYUKZikdZIAABCEAAAhCAAAQgAIEeJuD88XvN33333ecUnnjiicwye0Mt3u+NuMz9ZamrN9TicurfRhttVOqyloUZ5eg95yltRVuhATSABtAAGkADaKBlDbScQbcZGRtuuGFi3Oy2226Zx8VQ6z3tyclLW6EBNIAG0AAaQANoAA2ggVwN5O7INIR6EuQ///nP2FB7+eWX3ZxzzplZPgy13tOePakljo1O0AAaQANoAA2gATSABkqugd7RQH5uWtKbduCBB2YaaQKNodY72rPkJ0Wuvig3+kIDaAANoAE0gAbQABroDg30mDOR1VdfPRo4cGB07bXX+nrWDpdffnnkhztGb7/9drT88stHn3zySWYib6hVOBNZbrnlok033TSSN8h33nknevDBB6M77rgj+uCDDzLThxtnm222aP3114/WWmutaNCgQdFnn30W+blx0SOPPBI9/vjjYdTc9e9///vRkCFD4jyeffbZ2GnIAw88EG222Wa5zkRWXXXVaJVVVonzlMORjz76KDd/7dhuu+0i38MYvfnmm5HyVhg+fHh05513Rq+88kr8m38QgAAEIAABCEAAAhCAQO8i0G29B95tvhsxYoQzhyD1OvxYYYUV3BdffBH3qI0cObJqecMetS233NJ9+umnSU+crTz33HNu5ZVXrpqPd5/vbrjhBkvSZXn++ee7eeaZJzePOeaYw40aNapLOm249NJL3Q9+8INkX9qZiDfikn1777137jG8zNw666yTxD3kkEOSuN7lv/vqq6/cjTfe6HbccUc311xzJfuUjj8YoAE0gAbQABpAA2gADaCBUmug/YUbPHiwO++889z777+fGBVaGTt2bF0Gw7nnnhun+/DDD50MqGqCMkPtqaeech9//LHz3ydzMghPOOEE969//Ss2XpTZ9OnT3UorrZSZ18ILL+ymTJkSH1P/fC+VO/30053KMXny5Irts88+e2Ye11xzTRLv6aefdn/84x/dOeeck6R/9NFHk/1pQ8335CVDOGXUVquvsfG9fW7xxRdP4t57771J/lrxvW3ulFNOya1ztWOwr/3nCIxhjAbQABpAA2gADaABNJDSQHuALLjggm6fffZxfphghcEgg0JGzLbbbuvU65QqTJffMj5kcCmcddZZXfan05uhpvhvvPGG88MIK9L4b6u5L7/8UrvdmDFjKvZZXscee2y8X/+OO+64ijhyYqIeMQtZ3ifXW2892+3+9re/ubnnnjvJQz1bV111VbJfK/47asl+K8Pvfve7JI4fBtllv+Ip33fffTeOd/XVV1fE6dOnj1Ndb7755sQ4VcSvv/7a3XbbbW7nnXeu2iNo5WDZnvMDrnBFA2gADaABNIAG0AAaqKGBYgGtu+66bvTo0W7mzJmJoaEV9SDJCciiiy5aYVDUKFzcE6b0n3/+uVt22WVrpg0NtT322CMzvowaLYbzAgAAFfVJREFUBRmNyyyzTEWchRZayPmPaMf7ZWRmlU8G0ltvvRXHeeaZZ1y6V03GmYLyz6qvjNiQT5ahttRSSyXDPdUTllWOn/70p/Fx9G/rrbfOjKN04nbkkUc6DfkMg5+zFxu/aWM261hsK/Y8gSc80QAaQANoAA2gATSABmpooHVAGiooI0wfoQ7DtGnTYkNgjTXWyDUiqhVugQUWiIcoKs9LLrmkrjzMUPMOOJzSZ+XvnW8kxRw2bFhFnK222irZt99++1XsC/M644wzknj9+/dP4mnYogw0hfHjxyfbw7Ra17BPC1mGmuJcd911cZT//Oc/mb2PNofu1Vdf7WIspo9nv4cOHeouuuiiLsNQ7777bicW8847b26ZLQ+WrZ8zMIQhGkADaAANoAE0gAbQQA0NtA7Ie1I0myPu+ZKBsf322+d+66xGgRJDQc4xFDRcr95eHzPUJk2alOSTPl6/fv2S8h599NEV8TRc04J6B9Np7ffuu+9u0Zz3KpnEW3rppZPtGkJp8dPLgw8+OImXZ6hpeKiFH/7whxV5LbHEEkmPW3p4ZvpYWb/l2EV1uPXWW2O+dhzvXbPiOFlp2db6OQNDGKIBNIAG0AAaQANoAA3U0EDrgORh0IKGFVYzcGoUJjESNLzw9ddfj7O9/vrrk+210puh5t3a56aRt0YL6l0K8zzppJNsl/OfAajYF8bbfPPNk3jyZGn7wvlp+++/f7Ld9ttS88cs5BlqmsP32muvxdHSc9BCI1ZeMS3fRpYasinPmN6NvxUldrzSSB7Ebf38gSEM0QAaQANoAA2gATSABjI00DoUDRF88cUXk4d9rcjT4RFHHNFlDlhGATKNDM0vsyDjp950Zqj577NVTWPDE9MG0J///Gc7rNOQzrzjhm7xDz300CSe5opZyHI0YvmFvWV5hpriHn/88XF26fluNsy0mkFqx0ov5Zzk5JNPToxAK6//NpzbYYcdkrqk0/G79XMFhjBEA2gADaABNIAG0AAaqFMDxYDS3Cz1Ml155ZXJHC0ZAPqWlzwP7rrrrnXPf1JPjww9hbvuuqshw8EMtQkTJuSmkzMPC2effXZFPA2FtFDtW2saimhBHhQNtubjWfjNb36TbLf9tvzlL39p0Vw1Q029emKocNBBB8X56XMHFsJjW95ZSxmd++67r7v//vstabzUJw8uvPDC+HtsWenYVsz5AUc4ogE0gAbQABpAA2gADTSogeKBLbbYYk69TPKIGAZ9R01DDeXQoloh1atjYZtttqkaN52PGWrycJjeZ7/1/TQLmitm27X8xS9+YbuqGlB77rlnEm/IkCFJHvIaaeH3v/99sj08htbV22ihmqGmuDJ0FR577LE4P32PTUGu+Wt9dFv8xo0b5z755JM4jf176KGHnD6mnedwJV1efhd/nsAUpmgADaABNIAG0AAaQANVNNBeOPqY82WXXdbFUHj++edjZxZZBTPnJBqKl7W/2jYz1OSAJG+OWegwRE5Pwvw22GADs2XciSeeWLEvjKeeQwtLLrlkRTy5vVfQh7LDNOG6nHhYqGWo7bTTThbVrb322smnAf7whz/k5n/44YfH35FLEvoVfXZg1KhRbtCgQbnpwjKy3t5zA77wRQNoAA2gATSABtAAGqiige6Bs8gii8RD9yZOnJjYDldccUUXg2GTTTZJ9mu4ZJWCZ+4zQ02ZZBlaGqJphqDc3ocfo9ax5MDj2WefjcugD2ZnuauXZ0eb43bTTTd1Kcepp56a1CHr0wTf/va3Kzwt1jLU9JFs+27b5MmTk7zXXHPNLsc2XqGDl3vuuccNHz48sy4Wn2X3nAdwhjMaQANoAA2gATSABtBAnRroflByDjJmzJj4w9jpQsrwUXjppZeacu8fGmpffvml0xBFGWc6joYJXnzxxYmhc9hhh2UaOuHwR81101BOK+eAAQMqhnSGwx4tjj5W/emnn8bHmTJlipPzDtunYZdpxyu1DDWlPf3005Nya0UfELc8s5biqPl3q622WtV4WWnZ1v3nBMxhjgbQABpAA2gADaABNJDSQM8BMQPKChQ6yTjggAOaMjDMULvtttsShyTTp093jzzySGI8ydC5/fbb3fzzz595DPWqyRukBQ2jfPLJJ7sYWKeddlpmetXnV7/6VeIERPloqKf11MmAvOCCCyx7V4+hJmMvDOZYxNill2m26f387jndwx72aAANoAE0gAbQABpAA3VooDyQ5PRCQcP8soYc1lEZZ4aahlVqiKJ6nsLw+eefx14ONZywVn4HHnigkwOUdJg6dWpdbux/9KMfubfffrsiuYxGue3fYostku2ax1erLNqvOW8K6q1bdNFF60pTT77EKc85QFvQFmgADaABNIAG0AAaQAPSwGzfCMEvejb4IYWR73GKfG9WNHLkyMjPLyusQEsssUTkPU1G06ZNi7znxMh7QKw7b98zFa244oqRd8AR+Xlpkf9+WeTnttWdXhH9nLRorbXWirwnymjSpEmR76FrKL0i9+nTJz5u3759I+/IJPJu+RvOgwQQgAAEIAABCEAAAhCAQO8hUIqemfPPPz/uLdJ3veR4xOPjL2AwbNiwmI/+bbnllrAJ2KAVzhU0gAbQABpAA2gADaCBWU0DpehRUy/amWeeGfcaeW+FkXc04jkTjIAfBhr5OXJR//794165gQMHRt5es90sIQABCEAAAhCAAAQgAIFZjEApDLVZjGkh1ZFRtvLKK0feBX80YsSISMaZgtbHjh0br/MPAhCAAAQgAAEIQAACEJg1CWColbRdjzrqqC7z9E466aTo6KOPLmmJKRYEIAABCEAAAhCAAAQgUBQBDLWiSBaczz777BN5r5OR91IZaTjoLbfcEo0fP77go5AdBCAAAQhAAAIQgAAEIFBGAhhqZWwVygQBCEAAAhCAAAQgAAEIdDQBDLWObn4qDwEIQAACEIAABCAAAQiUkQCGWhlbhTJBAAIQgAAEIAABCEAAAh1NAEOto5ufykMAAhCAAAQgAAEIQAACZSSAoVbGVqFMEIAABCAAAQhAAAIQgEBHE8BQ6+jmp/IQgAAEIAABCEAAAhCAQBkJYKiVsVUoEwQgAAEIQAACEIAABCDQ0QQw1Dq6+ak8BCAAAQhAAAIQgAAEIFBGAhhqZWwVygQBCEAAAhCAAAQgAAEIdDQBDLWObn4qDwEIQAACEIAABCAAAQiUkQCGWhlbhTJBAAIQgAAEIAABCEAAAh1NAEOto5ufykMAAhCAAAQgAAEIQAACZSSAoVbGVqFMEIAABCAAAQhAAAIQgEBHE8BQ6+jmp/IQgAAEIAABCEAAAhCAQBkJYKiVsVUoEwQgAAEIQAACEIAABCDQ0QQw1Dq6+ak8BCAAAQhAAAIQgAAEIFBGAhhqZWwVygQBCEAAAhCAAAQgAAEIdDQBDLWObn4qDwEIQAACEIAABCAAAQiUkcAsZ6itv/760QILLBBNmzYteuKJJ8rIvO4ybbrpptHSSy/dJf6MGTOif/7zn122swECEIAABCAAAQhAAAIQmDUI9DpDbc0114yGDh0a07/iiisiGS1hmDx5crTaaqtF1113XbTjjjuGu3rd+o033hhtvfXWXcqtOn7nO9/psp0NEIAABCAAAQhAAAIQgMCsQaDXGWr33XdfNGTIkGjixInRoEGDurRC0YbafvvtF6lna/r06dH+++/f5Xjt3PDb3/62wiDbYostouWXXz7CUGsndfKGAAQgAAEIQAACEIBAOQg4X4xe8bfhhhs6C7vttltmmb0RE0cZP3585v5G63rxxRfH+b3++uuF5Nfo8cP4vpcwLsuTTz7Z42UJy8V67zh/aCfaCQ2gATSABtAAGkADvUoDvaewfl5WbKi8/PLLbs4558w0VjDUek97cqGgrdAAGkADaAANoAE0gAbQQK4GcndkGkI9BdLPTYuNNP078MADc8uGodY72rOndMRx0QcaQANoAA2gATSABtBAb9BAj81RW3311aOBAwdG1157redUO1x++eWRH+4Yvf322/E8rU8++SQzkeZvhc5E+vTpE6233nrRJptsEvXr1y+e23bDDTdEL774YmZ6xV1yySWTfQcddFC02WabxXPU9txzz2S7rUyZMiV6/PHH7WdcJ9Xr3XffjbcPGzYsGjx4cHT//fdHV111VfThhx/Gc+t22GGH+Dh33nlnNG7cuCR9tRU5SPnJT35S1xy14cOHR8r7lVdeqZYl+yAAAQhAAAIQgAAEIACBkhLI7Z3y5S1033zzzedGjBjhvEOQuHfMe22sK/8VVljBffHFF3GakSNHVk0T9qh5g8t5gyxOF/774IMP3I9//OPMfGx4ZRi/2vpFF11Ukc/xxx8fR3/00UfdHXfcUZFUc8y80eY++uijiu3HHHNMRR553BuZo/bwww+7r776ynnPkc57v3RzzTVXXcfIOzbbiz0X4AlPNIAG0AAaQANoAA2ggRoaaD8gGSfnnXeee//99ysMlLFjx9ZlPJx77rlxOt8b5fr27Vs1jRlqt912m/M9Xe69995z11xzjTvrrLPc7bffnhxfZfnWt77VJa/DDjvM+d675M8MvY8//jjZFu7fa6+9KvIwQ00H8p4p3YknnujeeOON5LiqwwMPPOBOOukk984778TbZTjWY0g1Yqjde++9yTG18uabb7pTTjnFrbTSShXlrSEO4hb8sgLe7b/ewBjGaAANoAE0gAbQwCyigfY05IILLuj22Wcf98gjj1QYDJ999llsOG277bZujjnmqGkILL744k5GkoKMrVrQzVBT/KefftqpNy5Mc8QRR2hXHA4++OCKfWE8W2/U62NoqPXv3z/O33+AO6mDH7Lp9Fv5+6GYVhQnj5Z2zLxlI4aaH/LpdtllF3fzzTfHPWt2oK+//trJiN15553dPPPMU/OYeWVhe3vOG7jCFQ2gATSABtAAGkADaOAbDRQLYt1113WjR492M2fONNsgXmoooJyALLroog0ZByeccEKc/vPPP3fLLrtszbShoeY/Ft0lvowT68mqZ+hls4baW2+9VXHsBx98MK6HluHJN3Xq1Hi7n8tWsT2MY+uNGGqWRktxO/LII91zzz0XH8v+iYOM31VXXbXmscP8WC/2nIEnPNEAGkADaAANoAE0gAYyNNA6lIUXXjg2wp544gmzAeLltGnTYkNgjTXWaMoQUM+T/9B0nNcll1xSVx5mqMkAyqhsvO3uu++O85wwYUJuHEvbrKH21FNPVeR90003xcf8xz/+UbFd8RSqebK0sjRrqFl6LYcOHeo0ry49DFVMZCzOO++8FeUL07Le+rkCQxiiATSABtAAGkADaAAN1KmB1kFZb5EMDvV8yaDYfvvtc791VmfB3CGHHKIsnYbr1dvrY4aa97KYa3BozpqCephqlaVZQ009iGHeZmR5z48V2xVP4aijjqrYHqa1dcujiA9ey7HL7rvv7m699daYb1wI/0/z7+x4LFs/N2AIQzSABtAAGkADaAANoIEmNdA6OHkYtHD11Vc7DX9ssjBJurnnntu9/vrrcbbXX399sr1WvmaoVUvj3eHH+b700ks1823WUJNny7CsdszLLrusYrsMSgU5HQnjZ60Xaagp/9lnn91tueWWzrvxj8ugf/UMB80qG9taP49gCEM0gAbQABpAA2gADaCBQAOtw9hvv/26uMGXIw857lhmmWVqGiBBYZK4e+yxR2I8+G+bJduz4obbzFAbP358bhozmtppqMnrYlguO2YZDLVVVlnFnXzyye61115LGGtFXjL9990qyh3WgfXWzxUYwhANoAE0gAbQABpAA2igTg0UA2q22WZzm2++ubvyyiudPDta0Le85Hlw1113rXv+k3p6ZOgp3HXXXQ0ZDhhq2e2peYT77ruvsx48ax99LuDCCy9066yzTkOc6xQXefqeS1jBAA2gATSABtAAGkADaKAJDRQPbbHFFnOHHnqoe+aZZ8weiJdyYCFHFnJoUa2g6tWxsM0221SNm85nVjbUNL9NQVzT9c76rc8fiJ968/RZgDA89NBDbu+9904+FZCVnm3FnxswhSkaQANoAA2gATSABtBAnRpoL6iNNtrIabhf2lB4/vnnY2cWWYU05yQaipe1v9q2og21M888M7Zv9LmBase1ffYdtXYMfTzjjDPissyYMaNmWQ4//PCKD20rodKNGjXKDRo0qGZ6qw/L9p4f8IUvGkADaAANoAE0gAbQQI4GugfMIoss4g466CA3ceLE2NjQvyzHFZtsskmyX8Mlcwqdu71oQ+3Xv/51Up6VVlop97hWznYaavvvv39SliFDhlQtS+jg5Z577nHDhw+ve+ip1YVl95wbcIYzGkADaAANoAE0gAbQQIYGuh+KnIOMGTMm/jB2ukD2vTE5+phzzjmrGiPptPpdtKGmuVsW9K2xtddeOx4uqHl0+tPcvLAc7TTU+vbtG/eKqTwvvPCC23bbbd0KK6zg+vXrF//pY95WFnE8++yz3WqrrZZss30su1/zMIc5GkADaAANoAE0gAbQQIMa6DlgaSNn8ODBZhO5Aw44oCkDo2hDTTDV85cXRo8eXVHOdhpqKoscgshBS1YI5/6l2TYoioo6kbbnzhHYwx4NoAE0gAbQABpAAx2rgfJU3FzYv/XWW00P02uHoSanHCNGjHATJkxwU6ZMcV988UViJ3W3oaYTddNNN3XqMXvjjTeScmglNNQ4ocuja9qCtkADaAANoAE0gAbQABpoVAOzfZPAL3o2DBgwIHr22WcjbxRFI0eOjPwHoHu2QBwdAhCAAAQgAAEIQAACEIBADxEojaF2/vnnR/7D2ZH3rhj5OVfRe++910NIOCwEIAABCEAAAhCAAAQgAIGeJVAKQ029aN4NftSnT5/IeyuMvKORnqXC0SEAAQhAAAIQgAAEIAABCPQggVIYaj1Yfw4NAQhAAAIQgAAEIAABCECgdAQw1ErXJBQIAhCAAAQgAAEIQAACEOh0Ahhqna4A6g8BCEAAAhCAAAQgAAEIlI4AhlrpmoQCQQACEIAABCAAAQhAAAKdTgBDrdMVQP0hAAEIQAACEIAABCAAgdIRwFArXZNQIAhAAAIQgAAEIAABCECg0wlgqHW6Aqg/BCAAAQhAAAIQgAAEIFA6AhhqpWsSCgQBCEAAAhCAAAQgAAEIdDoBDLVOVwD1hwAEIAABCEAAAhCAAARKRwBDrXRNQoEgAAEIQAACEIAABCAAgU4ngKHW6Qqg/hCAAAQgAAEIQAACEIBA6QhgqJWuSSgQBCAAAQhAAAIQgAAEINDpBDDUOl0B1B8CEIAABCAAAQhAAAIQKB0BDLXSNQkFggAEIAABCEAAAhCAAAQ6nQCGWqcrgPpDAAIQgAAEIAABCEAAAqUjgKFWuiahQBCAAAQgAAEIQAACEIBApxPAUOt0BVB/CEAAAhCAAAQgAAEIQKB0BDDUStckFAgCEIAABCAAAQhAAAIQ6HQCGGqdrgDqDwEIQAACEIAABCAAAQiUjsD/BxIDcbmiCa90AAAAAElFTkSuQmCC)"
      ],
      "metadata": {
        "id": "TmnHx_uPI6Z5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To more effectively parse a web page's content, we can use the BeautifulSoup library"
      ],
      "metadata": {
        "id": "b76sE8K1J2NA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from bs4 import BeautifulSoup\n",
        "soup = BeautifulSoup(page.content, 'html.parser')\n",
        "\n",
        "print(soup.prettify())"
      ],
      "metadata": {
        "id": "2UfUq6otKD41"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The result is quite comparable, if not identical, to the source code at https://github.com/StolkArjen/human-interaction/blob/main/index.html"
      ],
      "metadata": {
        "id": "RUyo8iXGKV9z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally, let's look for any tag that has the lecture-title class"
      ],
      "metadata": {
        "id": "60sEGxPvK52b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "soup.find_all(class_=\"lecture-title\")"
      ],
      "metadata": {
        "id": "mDR7DI79LF02"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Scraping Twitter (needs a paid account as of late 2023)"
      ],
      "metadata": {
        "id": "Bp1371f3cNr1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To use the Twitter API, a Twitter Developer account is required. Please see here on how to obtain one for research purposes: https://developer.twitter.com/en/portal/petition/academic/is-it-right-for-you"
      ],
      "metadata": {
        "id": "MAGIb1jdHynE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "After obtaining an account, let's install twarc2, version 2 of the API for accessing the TWitter ARChive. In this archive, each tweet is represented as a JSON object"
      ],
      "metadata": {
        "id": "FdrIpWvCchzF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install twarc --upgrade"
      ],
      "metadata": {
        "id": "pxNufk3vcg6j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, configure the account to provide you with archive access. This requires the API password/code you should have received through the above application procedure"
      ],
      "metadata": {
        "id": "P-iW1eRzc2L2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!twarc2 configure"
      ],
      "metadata": {
        "id": "bJ-3atDrdLMa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can now use the API to perform a search, the outcome of which is returned in json line (JSONL) format. The archive flag in our call leads the search to consider Twitter's entire historical archive. Without it, the search is limited to tweets archived during the past week only"
      ],
      "metadata": {
        "id": "F05GBI9rAGbZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!twarc2 search \"sensorimotor alpha and beta rhythms\" --archive results.jsonl"
      ],
      "metadata": {
        "id": "ltlaUqJUAS8N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "To make our data a little easier to work with, we convert the JSONL-style output to a CSV file with the twarc-csv plugin."
      ],
      "metadata": {
        "id": "aS2aTvgDHLxk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install twarc-csv"
      ],
      "metadata": {
        "id": "ilP_u1hLHmoC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!twarc2 csv results.jsonl results.csv\n"
      ],
      "metadata": {
        "id": "VmKcK--kHuDy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we can read in the CSV file with pandas for further analysis"
      ],
      "metadata": {
        "id": "5J0anVGNINJe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "tweets = pd.read_csv('results.csv', parse_dates = ['created_at'])\n",
        "\n",
        "tweets.columns"
      ],
      "metadata": {
        "id": "QVtkEB7TIQoY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's look at who has tweeted about this, and from what country"
      ],
      "metadata": {
        "id": "wRkcsSWyJNfN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(tweets['author.username'])"
      ],
      "metadata": {
        "id": "GFsIMMmCJmwT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can even reconstruct the URLs of the tweets"
      ],
      "metadata": {
        "id": "avch2RCTKWOw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make Tweet URL\n",
        "def make_tweet_url(tweets):\n",
        "    # Get username\n",
        "    username = tweets[0]\n",
        "    # Get tweet ID\n",
        "    tweet_id = tweets[1]\n",
        "    # Make tweet URL\n",
        "    tweet_url = f\"https://twitter.com/{username}/status/{tweet_id}\"\n",
        "    return tweet_url\n",
        "\n",
        "tweets['tweet_url'] = tweets[['author.username', 'id']].apply(make_tweet_url, axis='columns')\n",
        "\n",
        "for t in range(len(tweets['tweet_url'])):\n",
        "  print(tweets['tweet_url'][t])"
      ],
      "metadata": {
        "id": "70DwzOE4KayO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Rename the columns for readability purposes"
      ],
      "metadata": {
        "id": "mbh7unl1L7CG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tweets.rename(columns={'created_at': 'date',\n",
        "                          'public_metrics.retweet_count': 'retweets',\n",
        "                          'author.username': 'username',\n",
        "                          'author.name': 'name',\n",
        "                          'author.verified': 'verified',\n",
        "                          'public_metrics.like_count': 'likes',\n",
        "                          'public_metrics.quote_count': 'quotes',\n",
        "                          'public_metrics.reply_count': 'replies',\n",
        "                           'author.description': 'user_bio'},\n",
        "                            inplace=True)"
      ],
      "metadata": {
        "id": "hMyNv3DuMVBu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tweets = tweets[['date', 'username', 'name', 'verified', 'text', 'retweets', 'likes', 'replies',  'quotes', 'tweet_url', 'user_bio']]"
      ],
      "metadata": {
        "id": "Hgv8Evs5Qsfs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sort by the number of retweets"
      ],
      "metadata": {
        "id": "B1JXN3RJLtNi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tweets.sort_values(by='retweets', ascending=False)[:5]"
      ],
      "metadata": {
        "id": "N0eV2FO1Lx-0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's plot the tweets over time"
      ],
      "metadata": {
        "id": "5lcfCcBqOTh1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tweets = tweets.assign(count=1)\n",
        "tweets = tweets.set_index('date')"
      ],
      "metadata": {
        "id": "N1QnVWlLOamW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tweets['count'].resample('M').sum()\\\n",
        ".plot(title='tweets over time');"
      ],
      "metadata": {
        "id": "d1pf90kcOzEc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "More examples can be found at https://melaniewalsh.github.io/Intro-Cultural-Analytics/04-Data-Collection/12-Twitter-Data.html"
      ],
      "metadata": {
        "id": "H7JUen1q-9Zq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Scraping Reddit (needs a paid or time-limited free account as of late 2023)"
      ],
      "metadata": {
        "id": "bHK6NgF9Pcjr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We'll be using the Pushshift API to collect Reddit data. Let's install the API"
      ],
      "metadata": {
        "id": "8av0B_9DRp0W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install psaw"
      ],
      "metadata": {
        "id": "upBsTtGIPdwx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import pandas and the API"
      ],
      "metadata": {
        "id": "fkh_vTYSS9UU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "pd.set_option('max_colwidth', 500)\n",
        "\n",
        "from psaw import PushshiftAPI\n",
        "api = PushshiftAPI()"
      ],
      "metadata": {
        "id": "QUNgYyeDTDLe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Collect posts in a subreddit with an upvote score of at least 2,000 or more"
      ],
      "metadata": {
        "id": "zA46NvMkTIRW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "api_request_generator = api.search_submissions(subreddit='AmITheAsshole')"
      ],
      "metadata": {
        "id": "GKgW9gajTS6y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Turn the resulting list into a Pandas Dataframe"
      ],
      "metadata": {
        "id": "HKNuA4zm7W5r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "aita_submissions = pd.DataFrame([submission.d_ for submission in api_request_generator])\n"
      ],
      "metadata": {
        "id": "LxvVu9TYTV_b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "How many posts did we collect, and what columns/metadata exist in this data?"
      ],
      "metadata": {
        "id": "_0Ma8fiI7rir"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(aita_submissions.shape)\n",
        "print(aita_submissions.columns)"
      ],
      "metadata": {
        "id": "H87P3KybTaxQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's look at 10 random rows of data and inspect their title and upvote score"
      ],
      "metadata": {
        "id": "RVMWP8YI8EJL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "aita_submissions[['title', 'score']].sample(10)"
      ],
      "metadata": {
        "id": "esVAr-3_Tdmd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Okay, let's scrape a different subreddit. What about the jokes' subreddit? Here's a start. You can get the full post by looking at the 'selftext' column"
      ],
      "metadata": {
        "id": "8BTBmdb38k1Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "api_request_generator = api.search_submissions(subreddit='jokes', score = \">2000\")"
      ],
      "metadata": {
        "id": "uBTnwjZ78kS2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "More examples can be found at https://melaniewalsh.github.io/Intro-Cultural-Analytics/04-Data-Collection/14-Reddit-Data.html"
      ],
      "metadata": {
        "id": "_kWSvp0S_Gid"
      }
    }
  ]
}