deep_weather.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "deep_weather.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "f2mLHDbBrC8v",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Weather Prediction with Deep Learning (30 minutes)\n",
        "Denis Akhiyarov\n",
        "    * Senior Consultant at Wood\n",
        "    * Python.NET (Python for .NET) core developer\n",
        "    * Faculty at North American University\n",
        "    \n",
        "https://notebooks.azure.com/denfromufa/libraries/pmlc\n"
      ]
    },
    {
      "metadata": {
        "id": "5RS9Ep6iWfcd",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Economics of weather forecasting?\n",
        "\n",
        "<table>\n",
        "<tr>\n",
        "   <td>\n",
        "       <!---->\n",
        "     <ul>\n",
        "        <h3>1. Insurance and reinsurance</h3>\n",
        "        <h3>2. Transportation (air, taxi, boats)</h3>\n",
        "        <h3>3. Agriculture and fishing</h3>\n",
        "        <h3>4. Input for energy industry</h3>\n",
        "        <h3>5. Climate change and extreme events</h3>     \n",
        "     </ul> \n",
        " </td>\n",
        " <td> \n",
        "   <p>\n",
        "     <img src=\"https://rstorage.filemobile.com/storage/23092306/1085\" \n",
        "          alt>\n",
        "     <!-- width=\"600\" height=\"400\" --> \n",
        "   </p>\n",
        "   <p>\n",
        "     <em>https://www.theweathernetwork.com</em>\n",
        "   </p>\n",
        "  </td>\n",
        "</tr>\n",
        "</table>\n"
      ]
    },
    {
      "metadata": {
        "id": "tYgLDUo6F7pF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Yandex Weather\n",
        "\n",
        "https://yandex.com/company/blog/winter-is-coming-yandex-weather-nowcasting-helps-users-plan-for-precipitation"
      ]
    },
    {
      "metadata": {
        "id": "RVKkCI4UClGU",
        "colab_type": "code",
        "outputId": "1511af54-9db6-4d9f-a2e5-e75d6c65a28d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        }
      },
      "cell_type": "code",
      "source": [
        "from IPython.display import YouTubeVideo\n",
        "YouTubeVideo('aR5p7_g8qXQ', width=800, height=600)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "        <iframe\n",
              "            width=\"800\"\n",
              "            height=\"600\"\n",
              "            src=\"https://www.youtube.com/embed/aR5p7_g8qXQ\"\n",
              "            frameborder=\"0\"\n",
              "            allowfullscreen\n",
              "        ></iframe>\n",
              "        "
            ],
            "text/plain": [
              "<IPython.lib.display.YouTubeVideo at 0x7f60bd06dba8>"
            ],
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iyZUpiAWi2/Rbysx1Iru9ldphwh9tgt2JZLZXmsMwgOOkDXzsupgMdmADjpZdpydTbtx8\ndzls+m/CNJaWv0XRoU2gQNl5vtHGOzhrCcvIXS7IxZezxK48mO9N5cWUm9OnUYFic6HdFsLgs9Sm\nCuziXmCtlEzuoIACgGylRn7TworMgleZOGcwmxtvC9aHXvosfalem1padSscmO8W8LquA55DSLFp\nIJHXkHZZKpl2sBOmJPWyU8z5DT/S8ePt7rqYrUWB1jABP9/lRjqDGOAY6UmpU/xA3UU2ZiQDou96\nj5vNlvJZhNgbt36fSltNrrAqxAEtIvFj+5Cmn4SI+Vny/bnNkVhvyqsEmFujMdAVQ0Q4hzWwBYjq\nn00TTYCI9Fd2AdsJB0T6WHifCequ0EWvB4XK5/pXLc0j0WhrYZ+m/KY9ozWAF0nMZ3ldN7RcOINw\nNFdwblzZgD/1g/BVKZPpv/CtlBaBv5rpx270rDUAOirRpkkkLa2nGgBJ5uhggr1b6UhtEl0bqriO\nNFoqmHX/AFBZ51URambxC00gRN/70WRlitDv06/KgdJPWdlnJAJj0RRqHMATZVDTmM7ILV3EwBoE\noiApeVSo+VBGZTKodFamLjzQFRhsYMHRUWuo8xlvA2WYBQVaNlZry02Q4GVUlUamV7SP17EfykPe\nSoaXRYJtTNla0x4bjm6glzTEpF54K3ZdLqH0wbldJVZ6dTLe89PlWZWcOCEmpqVajBMFAqoU6gzM\n2ZVxhs+rgAN1FNoaeeeFdiwJYIiZ0PC0huX9QvGyGvaWiwkO+FUv8d4v8LIYx0lUqvy6n0VXPA0I\nnlUvEQDJ1CqNNDtC5zAkQr0v/pULiLcE7cLnk6gBaKJkgaEXSkaMT+rjm8pMWV6hJiRdWZrJ9V8/\nK97ChLdLj9kd44jgeapVxRNgLdbyqCs6Ymx2SY7Z21UoEE/tPqm5spi43090qiz/AOYg7C26u07l\npI2jdTk/tW25roIYXTmt5aRZHcBsg6hLpvHhd1sD05C0YluZoqaA6jaf9rGOW9YWOvHy5cf9WOsC\nLDzv+yq3FOaPC6J2VnVXOBDAJm3skvDSAT4SV1+PV/Gvdc+Tx3lHoMJ2gcrQ4GCLHnmF0swLZC8Z\nReQ8FsyPdd7B9rMLPFY8LrhyfVeXPGXuOpZUi11npY1h3stgiF23LOnGyxRlIkSsXa+HBZMXGhW7\nvJMBOq0w9sFMpvHS4X8nhp5EyT/CXiHxJgDyXS7XwppPGUalcquZtFl5Mcfy7erly1jtnbrf1Tmu\nAfNOQOqvQw4cNYOyZh8FUqN8LZC1buvl27qW0s19/wBlQiB57rojsuqGzl+lir0w3mdwucl21JRQ\nb4mxc73/AGWpwkgAQDrxI5WOjTkwSG2Ik3+F2aeHYcO47si+mY6K2ePeV01rthqVxTdDSXRpFut1\nmq1M28WuEyo1rrAkQNRoszGjm8QBGqY+M9e1DJzN3/uqqQBYj0V20+TCo8knXRb86qQ4AiP2/laa\nsQ4wJ5F/dZCbT8qO9d4j0/lXG25RE58pIUVqgPiHCTUdJnoqAXMXC9ejQ1Mq+iANCtNGk3KHOQIc\n6RAHqh7fEIT3szCZ8h/tVYCBwoFCGmT7Kz6wItqfhUrNOYyd9kymwR4hMIKGnfaEvITdPJGmypUb\nlME66eqgQXkp7YgJTnKzn8IHC4vJKr3bbQYI5S21LXQ9s391BWrJcSdzsrUKYcROhVQYkRroqAmw\nmyoe5jGk5SDx0VHuJm6plVgFBoovINym1Hm5O6xZ93Xlam1A7m63pWcidk+hgJuXNaOphWe1rWXE\nnX/Sz95Jlx9FQ7EQ0FoIBBixmesiyy0ySVesIG91SiYMXQb8zGtDQRMSQlVnbkT1Wd4gly0UmFzb\naKCMwJnlSHQ5pG5gqr6RG26uBJRBXALiRaTdOw7IkbrLVqcFI7x2YmbyscnrTrx4efTsuph1ogrK\n50AAanfyVG4w5dbpZeZk/wB5Xk+Oz/jPJhcLqgtmbQQpgNHJ2UT5z0V20iQSrLpykaqDWjfiQtVL\nLEBwEaTdY6TeQYTGmJLZA4lcuSbyrotxrH91TGYokGRIhY8STAIhWw9bPaIyj+ldJPGPo/w+CZXy\nyOw7m1HZP0nrxqtGIwTmmR+kDYhYACK7HDY39V3sY9opeHXgLWunvztmcn05LgYuYHt6KtKg3I1r\nXgvdNth6qlZ5Il0CNLR8cq1KGski+x4STy9vJz8Uw7x/8QwuaYBvpb9l2cH2qGsyu2XIa3KTtIsp\noNnMTod1Zn43Tz/HMsdvRU8W0iQtTcTJXkYc14EmJXpMMxzgCu+OdvVOXgxxkyxvVM7RoB7JOuy8\njiGycuhB916TH46CGDVZMR2c3LmdxKmV76TDjxvH+f36cKmz/tofRem7MxbW0w0CXECB6LhuJJvE\nNtOqmk99NwcJ9Vzv4fk8XhqvRONZ1jABXNxHZNQg7+SbQ7XIAzi3K6uH7QovEyFrDKZfavON7Iqt\nExedOn9hBq1csO0mTsLafK735uzvO7tHKfVwVOpBIlc8uKZXpl40U6rpIB8gLK1SiWQS0hexFNrR\nAaEh2AbU/U1b+EeQIi/uoa7e3qvSYrsOkbiy8vjaIY8tBkLF47PYae7tnv5KlTTKILQDHuLlLawk\nT8lNo0tSTspjNUZXG6vSmS5aG05MHZaYEERbyj2K9iszaEjN/SrWylrRJ4lXBI8hsFBcCdv7qpTT\nOK2W1vMKRiAbkXlKrC5KXCiHVKgEWEozkgG6XTcD+qTwmE9IQDrXISnvJIOsJ5dLSICQwJsSajTt\nCpMlS+xVQUEkdIUzClVdEeqCXQUsBWlQEDACeFdtMnZSCDGg5TyeoWbQsYJp/wAojlXZRytMQbak\nqv6vJXLYbuum2lHXaQd1n7qB1TDUg8fKHglJUpIPhugVYv8ACZTba/sofSHKdVKYwgwdJTS8tsDo\nkZhaPRDXIQzF4guAAsAh9SKYsZO6TUgBQXkiCSQqqWtJuAquYS53mU/DkaLTa4M20XLlrvwZzG9u\ndkPCc1pgC3SVWIOt08y4NJK43K4vd4YcsVLTrDfQpgfGtreqXlU12Bpi8wJB9/2UmX+mP8PD2szE\nFkA3aVuZQa7oYkLmCDZaKVSo2mTNhO1+qZYefc6rzcnDcctRGKaC4kG39hFFkETvrylhriJkgkWg\nCEynUBOWpAOxAsenQq3Hfp9Lg5Jx4yZTX+zg0MdEA3mT+y6OFxrHSHNg7QsFWllixHn02XR7MwtN\n857Ss4y71Hq5rhMPKsNSoxzy97i0dAJPrNlmqVS6w0/v+lq7e7Ma14dTcCDaDss9NsANt6a++63n\nj1qPBx8st3mim+DcEtOy2MYIkHn09EilROogEHcLTRa5rYIvBJ6/2yxJvrJnl7suHutODotc4EkX\n2/ldvEPZTpm8WXnwBGanLYO5v5LecSDTBd4nLtMtOPx7m9+mSvR/zkSdOVR9bMxwc68SPpGKraEj\ncCEjENtIG8H+LLnc92zH28+WeXjokgDzGlx/ZUVaji4OJuOOEPcSbaK2IeGsnewC4Y5WXtyIc+CQ\nbbJlMCdZ/v8AtIbcXm+p9VakctuFrKavSmssSLk7dV6DsntEOpAPMQYnledq9JiJEfKsA40nRqCN\nFry1ZlPti9PYBwJ1VqlcMaSTC8ZQxVdogZk2sMS9nimOF6fPatfaHbMOLWmRGy4TnZtdZlMOHI1V\nAFytl91m2GMENBmyYx7QDba6WyIgSnsAzeYWPLvpnzS2qRNw47qlZveXaY44Psl1DlzAb68+SZRf\nYD+/3Vex0UIIy3m0EcpTneifjHgQ0ArMVEVcVQhMhTkvCgWDYCyu52YyFQs3Cu1h1CChqGCDqoaC\nd4Uub5qwAiECy07qfRNLbKpagoQoLbSruUnMgSQiEzuzE8IA6K7Esgeyux2a2yplKcwANWKlQbCw\nkqO9J1B6q9VxMGAAENaIJOq6RpRrp81ZpVXeUKWvUqWocEoOyunhaSBqs+UkncSkJ2ZUYXGVbu25\nTe4I9UB3T1S6r+NFe1VrnNEKALKIspAVF2GLckJ5qeGARKo/DuYGOOWHXEOBPqBp6qkXXDmnpUwN\n000zHTY+iUGzCc07G4/ui4731XXj5Lheiw7L+k+ys5suBM3uVJZAkEHZUNSdbH91ZhdvpY8+Nx69\nr02gExE/2/RVrOOUifDHuqNdtoFd7Zatf6jrMPjl5eS/8QyrAbpbcgfzZMc4ON2jS8QJ9NB6LBlc\nTAk9FqptygTck6ceqXDX24/5eN9w2oHNDfES06A3jyPCfnLQfFEb+aRUlzCODI+lYw5mVpkC8n2h\nWd9uePN+NwvUvparWy6mZH9Cq1hPitom06QqFraYB2J1aI46ruDD0qTL3PW5KzMfuryfy5h+OPbN\ngKzWtDSPEdP76JQohz3OMiTMfC39n4IAkxaZA46K+MomfDFvJdvGeLz8fJcs94zW3PZSLHHL4hMG\n3z+yf+GcRmtA4/2kV6paQGmTGgXZwLmvoEOLQdSJ0Uxk0fyLltwn6E5SeZ/ZQ82JMDnp/StH4Z5e\n6CCNuNU+jhmiQ/W3Oy5Y4Zb+pHgvyb7ct9BziMo13j3U1sHUYJcJb/pejbSayIATnAOFwr8HTUeQ\nLCABHr+yGsg+UL1b8OzLEABcHG0m5zl16crHJhq7tbnZP4Z0X9E+jRiYiZFvTdWw+GqHU/K62CwQ\nYJNymM31Iup9l4Ci6fEwQt9fI1viAhZq/aTGSJuFy8b2uH0yIuV3mUnTNhPajqc+DcbLlOa31Vi5\nxsB1Q5oBkzB0gLyW9uSgbpwE6kfGBGl9L6Kj3EuAAtEabJ1MlpLm26rWONpIz1i0OOUne8ILIcIi\nCPdQ+cxIG6GAyZML3OgqtLnCLpTmkbLdZrSQJssfeTwiKtEHSyHBGZW10UFXU42Q0kcwnCq55uZQ\nQIh2h4VRmcVFNu8jyT+7GyjKOL9FAkuQdtv5TpsVBceB7JpSS1MBurGqAZInoqktdoI81BEiVBdv\n+ylkHbzVZugrncW5dplMY4xBEhSac3lXLctpWdhZqkgmNEkvJ3WoN8JjT90qpTa0RIldYpLTfqms\nABGYeaS7VSaiB9TJFieqQfhXY4WUuAdoiKOeSLbqGWVw0AKGt4RTA0SDNlD2W6JrWHUK7hz7qDLT\nEWT3C/mJVqYHqtDKQcBY20garly/12rPRkzZMaPK3K1DDOaLMcAeonzS3tjQa9F4vLdUmNYFuCUi\nrS3BWkCZlTkJcBF+IXfj5LLoZGNMiQFvZRlhkap1TBOpC8gO4UsyRAknpf0U5OW43UhlyZZe6MHh\nGGk7QOG6xi1gNOflPxOGfSp5nSATEfawAguhxMDRXL8sduvDZvVaajySd+vVSymwz4BJ4Wij2dUf\nTLmtJb8rK3D1ASCxw9FjDc9PRn41oBIsDAGy1GuQMxMnayp2fhH1LZbhdKh2NUJGbUG0aN6jqnxZ\nW9+nimNtbOy3F1PQSpq0Mri7UldDB4HumwTJSMaYXrx7nbpb438XnqlMtqF0a2E7Eq/eZActhvC6\nha1wSfy0ExsVyy475Sz01jy7u8nLw1ctJykncLrU2io0OLYPCXUFKgNJISKHaoDiHiBtC3h11Wv5\nOeOeW8XTZTzeSc2lAWDDdrMdUyxA5XaD2TAInhb6ryS7Y69MEQsJ7PbGl12n4ca7pDmKXGZe2pdM\nLKd7q2JqtayZWiv4WE9F5PFVapcSZ1iFnK66WM9Y53kg76JXcmfEco5RUJE2Eyl1RN/hcJjZ3Vzz\nxhjqrBYS6PcqjsS4zlAaOizwm0ydOVbZO5Hnt76XeMzkymS4OkmwiPMqtIDN1V2gNY47E7eazjbc\npDeyXuMmD8/siY1jzTGtDpg6m0pdZmpXtdVvxEAC0cJTjJsllRmKMpJVmutCiJ81Lad7qDRQa5ua\nbHLayGgOZfXRTTrloPJ0WYiT0RWtz2hpjcfss7TLtVSsTGllNLaNUQ90G2hSib+Sa0EXnfRKgl2i\nKHQqZt4UO10VwLKCKNjNir1XF0Nt0AS8qHPywbeSlQhxM3VsxEGEyQ4k6FLqdVFOe/YGVnDuVAJ3\nVSF2aSVR5RN1LhIRDaTZEpw4mOmyXTECysYO6grUIjqppVIAsqdE1rYE+iBrnwNFIfInrosxcrUW\nlxgC6g06mVs7PrAGI287rEBAiPdMawtINvKVLJZodLEYvwC3iaNDssVeq513Gf46KKlUTdsTreyj\nLb5Xg+PHH7a2G0XOBIGguVp7MqNa8F3okte4NMO12jZJcSTEQVrHKS9QepxXdOaHuIiLLgYiuGuB\nbp01KWys7KWuuB1tfRUAzOFrLXJljvdjjnn40/GY81y2mRAK6TexWECOFysNhg+uItHK9cAAweS7\ncd852vHnb2Z2fTFNgaNFTGUXVTDYbHTVVZUcDfRMqVoC6a0672bh2Bjdp3T6bwuc3EEpzaqtXGt7\n3SsNenmKmniJB5VjonpLdswoyn0qUJW9kytXyMJ3U3pCO0aNPI5zhdeaqYlgDhllxWrGYwv8OpJX\nNr0HAEkbyZ42Xkxz3lcv0mV1Bh3kGf8AwdVqGLeHyHeLSZ/hYwyYj3VzqbSeUyl8ty6ea4ZXLcej\n7O7WLvC832XRqVoE6LyFJ2R4MEnjjldDF4x5p6WO4/ldceaa1vt6tXXae1u1fDlYdVyH+MtDRGkm\ndTuZVajJIJMIpmdrLzZW27ov3Mi/ulZCCSJTzXhuXXhIzn2Vxzt6ySzakNkyPZS5p5t+yYy9t0GA\n7T0V1L6rn4a9KMgAnfZVqT3ZEalaHAEDnkKK5sA3/wBWuPGzOdJJdslJxBtPom5WuaBpG6q5hHsq\n0mEn15XtdCst01uHbrMrQWtAiDm8xHslvYLC2qIUWN2V2Uy7SPcBWoi+kkGw5VySToC46gbLFv7C\nHNgwUsVAPJMefRKiTdWBrneEddFSlU2NuvHVQ8Cxm6q4WQNdUucriRsYhSIaAd9kgGLlDqiBrYLp\nO6mnWNN0g6fIU90MgdnEn/GNEst3RQ50m8yluZPmrOKV3hmyiG02wwzAPVD2EgEqGhznTFv2T9dV\ni02Q6kkvZey2AEoxDGkRAB3I/uq6ytsYpWk/CW5NzkDLNpmOqqLn4W0Th3RPCknhO7oBkf5ZjbaE\nmL6+yghoM39VqYwOjYJZbPQJlI5TZQKe2HWTaGbNIEfyrOpuJJ1T2Mhs29EtFHTIB9eU1rwIJHp7\nqH125RDIO5OpUF1hHMi0LNotVh5jQnZUxBAAZod1pZUbnDoHkTKy4gCoXEEarhyYf/RKsGw0AQRO\npTqVO0zHRLw4LWw4SNQJWhrM+vhgWnfgTyvJa0VllxBEkj9l0KHZFV7C7QLO+sAWgQCDBI3/ANL0\nrarm0rXXfHGZTv6cs8d9vP4fC93Ua1x8R+rr0FJogXXnsTXL62aIIXdwglgWuHe7+mOOH5R6qjmk\n66JhEBXIlene3Zl7uSrtZK0ZBCU4RZWKRkINk7vFGQqH6RClonPCxY3FlzSBbZaKr8jZMrj4mp+o\n9fa4XPPOY6n7axx3toGADAHZiXHQRN1zcTnJgyuhUrgtMkW0XNrvzPBeTB63XmvNu610zcWeo6wy\n2Gn/AKr0YBiY49lNTBOc0OYPC2MzpsMxtKp4mmbHrCzln5TUWTTo4ejJIMB0EttrG10YfE5H+MHK\nbHL9FYO/Nt/Vae8bltuvPcbPbWy8S6/haACVn29U6q+bSYAEKguJ4Xoy/rKzUUm3MiwCoWxwrVHE\nWFieUo1FcePK9ps2nIk9FD6mYSQJVBWAEXVmjMIbtf8A8UuFndEtbcRPXlNqi4NrLOHEO3F/3sg1\nRfUytY7k2xndQVC4AHzSxWdynOfAtokReys5MnnnKu2qTqJTab51A9kneApV+TL9usuzqkyBbTYD\ndEQJGsahVLiBEXUDEQDDR82WLllkbqalAuGYEaT7brOfE2wupq1H3uYOwJhMEZQBrC9s26MwYQrg\njKQQZ+FYWE6qRU2Coy92TqrGmrOqCb6qprHZAQg3jhFASYV6gynWyGlTokAEOTY3Cq4zqEAKt9yr\nNqOBMHWyCALkQUFki2pWQ/vg1p4tNlV9UFvhKRUrSMsRdRTZ+0rpppWJOnmnACLBVDdOEwCSqioG\nvCgNuU1zQLbqNdkAw30lTA6pjKZF5v8A2EkkyZQOY2bkwEyg60dUlmIjTRWNQRrdZqiqZcf5S3ON\nhdWn50SwDmujJtOf1R6apmF8RzZZk6AKhdlZA0N1OBBLgG7mNY16pe5oamVXZwQLt0MKcQ52Y3kn\nUqcQ4hxAaG5REB033k8pVUkjY+S+bljrJtowNDNUadV6fu5yk7LidjCdtN10MZUdksYP8L08escf\nKpWTGhr6hAac3P8AC7HZrMtK4grBgKzSJIl3K7dGnLdIWse8vKemYzPaoD7LY6kslemAF1kVQut1\nUd5OqQX2UsEjW61sae8SKr5WapiWhwZPiWtrFJZRAcHCHBc6vhfEQAYMx5rpPak4iu2mJcuXLhMn\nXDLXTiVKTmRmt1WNpbJJPl15XX7VqB7AZiFxiw6yuGcwmVY3TKzxP+XlpykuOunpomVAZvcwoiDB\n0WZnqfiINOw0kqzCQ0/sprvBI4iw9VAuFccvKfkireNlVrT/AOpoZIKOeRKfJJNQLqEujbhLq0+J\nPMpwaYKbQolwdxEJObX/AA0wZhwrMq5RpqnVMOWkgj1CzVWQYXpmeOcRLatxmNpQaUOibKDR6hVc\n4gyNwFNTLciWbjWG+GQbDUbqlpsNUUTnEAeamo8AkN02Xk9XThqbWNPeR1UBmXU6aKBI1kSrDg3U\nN6QSPNULvFGkyrVWu2AgaAKjaRdBsI2Wpr2vks68CEt9rg67JxgDMGwOVmdfRe6Xcdt7S6TbVVqs\nyiZvOibSdl1E2Sc/iJNwVQmCVCa5sm2iXlQQDdSXklVKgC6BplVJgjhS79KhroiRMILtbsYPVNaQ\nq96CIiJ3VmMAEzM7rFGbLCmk1xdEwExzDKbSIaLtldlWqFumkqmUWgq1nQT4R+6u2nA2hQLYwHeV\nSq8tNgE+wMCZ0UVmRf0lUR3skS20KHMzAmVS0T7CUsPIGXMYn2QRltwqmoQrscZiQprNuYQJDzOq\nbmk3VGUyCtDKJ1iyULLjfdWpu20/lS4SUxuHvcgdFNmlqLrrQ0gjcE6WVMrR4QbnoqVnGw+f4XDl\nwl/Kk6es7NoAUxAUYqkLbErFge05oRu1VxOO7wiBErjycuNx8U263Z2Ga3W67LIiy83hMcxsAkxG\n66OH7TY6QDELpwZS46V0HuCzVWhyRXqgmQ5Rn0Oy7eU9N+PS1SkA2ywvqZXRyuoHghcXtis1rDIk\n7Jb10misPRms5x9F1WtMLymGxBcdYK6mH7ZAs/ZYxymM7R2yuN2xiGWAuQmP7XYRZcevUIdMA5tJ\n8/8ASuWUs6WXRNZ5HklNJJtNrJ9WoTNoHRKMt/VqP5Xju/sS18E2UZnOsAbJQK3YV2gaLnQ9VbPC\nb12jKaTomLKaZlae+LAqaG0GVJyddw0s0gD+Upx8SsW+gUupwR5LlFGXiSm0aobAJkHkWCSSNlEw\nT0SzYZUrC5AtoL/MLHW0B9E97iQAdyoxNMZLahax6GNziVV8ZR7IU06RfIaJNl6uLrJDMHWy544s\ntdXDuLQ4QbbbcrLRZDo0IN7cJtF7g774WOfHWW45ZddqiXEe39Cl1PKNbnlayYA0gm0arNipFoib\nrhMt1wnJMroCpAltubIYxxEghJa68jUKW1DM2C14/pq4/paq9zRqCDrb2SnG0/CmpVndUZUEmeF7\neOWY9u+PpcAmDFikus43jor5rWNv2VGGLm66KJQRCpmBMxC0Z2EIM9QbpSfXcDpoltYIUFcx3uFa\nxUhtrqMqAdH+MqGvIVspCqWwoNdVzZsPVLLhxI4Sy4qH0zZ2xW1S6pNogJtGob7hIsBfVUbW9lRr\nIEbyOqpUxTogi3yjvbbFJJsgDUuDupBkaeqVmT2ddFQcQPNMmBopycIMKCoddankROnmszaS1MAI\nKlC/L3+lAaNRZWey4kx1481Lme/REVa8AR7BWu4OJ2vKz1G+KVVx8MGZlNbGqhUcGug20IW3BMJB\n4Giw4eHMN4I18tluwzDl/UeYANx1Xh5eKyp9m1mXvad5nTZVrUiQMrovAjfn9lBxGgcBlHTT1TaV\ndpvPS1o9/RZ7mOuN34bjL+Rfeupt1JdsuvgarnUwXLguqZicwtyNQtNPEOY0McTG8KYZay3Xpym5\n09RhwCF53tyk9r8zpyjT3XXwFQHKGPB5krT2jhGVWw74XqmPnN157l4vCufJnQnjRMbBMklacZhA\nKuVvuklhaDYGbLlyTd6eeZxTPI4EzC0sfnDcxgcwsZYRcixW3DMFg4atMbclZuNxxrVWxAJAEzaQ\nQNVkLy4devAWsmBpEfCyy0kkWnXposcV+vpCy2AFrotmCJnUQlAg2Puo8TLtII/t/NayymfV6WVe\noCXGbkqodBV6dcSS8meqUXCVjwynVi7aHVh52S6jpynol5xa6kv8OuinhZ9GwP3Q92s67pRf/wBf\ncpbwdSZXScf7qXJppXM8J4c2bgQs+HqANA9U5z2xt7rlnjrLTRlEMvIaSeUPdTFQQMo6cpObN4RM\nTt/Co+kM0SS3ny1hd+Di78qlWxWIB3JaJj4lJZiN+qMQWzrHRYnuGYlogL2XGZTVZs37dSniKYnU\nki+nss7at7iR12WcERmPslioV5v8ftz+OSunkEAQCT6fPCyVqRaSE3DYjYnyTazZYbyQu2OEx9N6\nYCYKhjgCruG+yrK2qzAOYSy66rKlBACqUwaKpVENbJAJgSn1KLRBBt1SFLnrIu4yFVruVXvLXCAU\nDnOhV71vEKHGb/CWSsi510VyY4UmDcJcroqlSEpzYPRMqGSoZBMQtCWW3RmlNNPwhUYzooK0x4pW\ngEOkzqoNLeVFQxcACdkGhtMDXhLeQ0zqOAlU62gKnEMAjne6DRRId4gT7bqC7xWn1KyMeQNU6nVk\nSRqlRrdX8OUNbfeL+6rTO0Sf7/pJqGLqe+sToYtB091Awth11Wph5uLc3UYUktv4r9ZW5+DmiH3E\n6A73vCEZ8IO7YXiCZyxwrvYQSTILrj1TMHhahBysMSmPdkJa+JiQDfVcuXHynXsuO+2buIGYuEdN\nVRjzMNAvsU1jy5pEwdElggyRyvFNxk+lTkxewvKlryD4vb+ArulgynU6njosz5zGD8rUzmX9o6Y8\nlx9UVqxY8ZJaRrdMf2rWtD5WTFCHneQD8JK6261r08vLnbk3DtB7j4iFZuV0l7oAHv5LnjWFqFMg\neQkrll240x758I/TFvPqrjNDf7rKQzWdFpLjBuIkWjWEnUrrx72u7U2josrqsyIHRaDUE3N4MSL+\niy06ha8uaBN9RzZZ4vb0gP6JgqQEtvlqraLWcm0q8NIP7cJQY2d1OhkKHN62WsLZL2l3oxrQePZQ\n2kINrwqTZSx0EFZ8sv2zNrAFzbWi/ol1RxpCbUBEOkQTHVVqsGeGX81nG9myWuhOpEgSIE78JWS8\nGxWhjZBAIEcr0/H5Zb+nXajTNhYqr6kW46Qm0DB5Ik6cAlZ3PzQSJPwvSIqFuWTJdPNoSxTsZI8t\n1cuP0gsJEGeiDPmUN1UuaQYKZSaPVKgYyD+ycXkfpJg6hRCq47LIkk+iWRurvA4VC1BAZe6h4EmN\nFaTEKkHhUS0gA8qqtlE30VSLwDKCIQUxrLLRSw4InUzEQmhihQE1zIkcJeW4QNpSbBKLYVwSDIsQ\nrggmSAPJZGdvRMYwnk+SsWZfCiLLorOd0+geQlup7rRh2wJIJQPqC+UaJTm3i5TDM9Cpr0S0XBCh\n9s+bLtZVY7MZKs49FDeQqFlkOKcWtykqSGkEk/8AqzgGCgMqtTYTtZXJHHrv7KrSUGkubljj9+ZS\n208wk6Ksz58KaTgT1UGmi3wuDbJlJuR5kz/dlelQlsyIWWs4zGw0Ug6LMaWHKLg62S8TDpfaRAif\neyUaUmRAsJ6JOJkGJmVRpbhzkznfTaY3UYeqJnYJWEa7xDVv90U1mH/EX/deTn4+9s6+1sfepI0g\nJTKR1NlHfeG4urNc7kwNVwkutJpTFj9IGoF/76rIStJG+6zu8oXo8p6ZykWpETcJs6zqopshhNpl\nWMkXus241nwjRhqhEyZHVNpkzETNkgVQ2nlg63VqdRp30WcsPx6axwmJTmkVINoCpTaTKmq7M65g\nHdOZSaBeVmXx0sv0miG5SCT6Jcdf6FSqyP4SRcwbK3HvbWu2l4iBKt+pmmn9+0p2gI1FimYdwkzM\nH+lXFS5sEHZSHZZAF1Z1dxaBa+kBZu2UADV1lehUDXEi8DXZItq4zypYAbGwXTDj8vaaFV5fJ2Cl\n1QSCGnrP+lA8JOkRul5jwvXJqajae9E6GYsqZiJt8KSDKsAYlGtFj5TGviJklVc0qpINjZVBUfOy\nimYKo4qJRDTUvAUVKloF+qoDuoCmg0VLRqVdrv8AEhLptB9FZp8Wsoq5p/3hEWBlNZUgGRM+6W+A\nBYX+FdBb2gpWRwKaplNi4sAE2niYsRoqPvcWSw66DU5rH3BgrK8KwUvAPuqM56KolN7sTqrupzpr\nwnSK1HSbqkrnntF8/pb7H7UfjncN+ftFdIXstNKlca+kaLjN7ReP8W/P2nDturEZWaRoftQdUubn\nGuX+Ftr1W5YEmdh0Xmm9r1B/iz2P2rM7ZqD/ABYTyQftTVRvcyRMGCbHZVyECVzn9qVXRMW8/tW/\nN6uTJDY9b/KqtIdFwnFkgwuUca7hvz9pg7UqDZvsftUdgU+f4S8Q4GGgQRquYe1akgw0RxP2lu7Q\neTJDZPn9qDeRdOpCYO/7rlfj38N+ftSO0H8N+ftUdh1biyllQF0kW36/6XGPaD5mG+x+1YdqVBs3\n2P2oPS4VzSIETI10V6mDY8fqOb48l5hvatQaBvz9pru3axIPhEcAifO6aHbqVe5cAIMW6KjMR3kg\nAgxrK4NTtSo7UN9j9qre0HjSPn7Szc1R14g3UtBaPNck9qVI0b7H7UjtSpEQ2LbHb1XGcVl2jrCN\nCf8AaRWEO1lc13aLzs32P2qnHP4Hz9rPw3bNxrpsTWi4Erks7ReDIDfZXb2o8f4sPmP9peKkxdfF\nNynLpuop0yQD1XJqdq1HGS1nsftS3taoBAaz2NvlPjy1NJljb6egotbMOjQzOl7bJVSsRYRF1w2d\nq1AIAb7H7VfzJ8RDfY/afFl96JMo61WpPol5hxdc09ovOzfY/ar+Pfw35+1q8dsbdZlS8GwlNmCD\nNtRHHC4n49/Dfn7Vm9pPGzfY/az8NHdNKST0Wd5OUk8/2FzfzepEQ32P2qVO0nu1a30B+1qcPe6m\nnQbdMFlym9ovGgb7H7U/mT+G+x+121+ldZz5EFLXN/Mn8N9j9o/Mn8N9j9oOnCZVgHw8XXI/Mn8N\n9j9o/MX8N9j9pYroE8zCgbwuee0HnZvsftQMc/hvz9ppNdN7aZIlDGSb+foFib2i8bN9j9qr8e91\nzB91Rud00QsH413DflH413DflTSt4F4VssLnfjXcN+VP5g/hvz9oOtTdOvqqYg8adFzR2g8GYb8/\nas7tR5/xYPIH7VHQpNjVXC5Q7Rfw32P2pHaT+G+x+1B04RmC5n5k/hvsftVdj3kzYeSDrNVg6ZB9\nFyfzJ/DfY/akdpvGzfY/ao6eUqwdBXK/M38N+ftR+ZP4b7H7QY0IQgEIQgEIQgEIQgEIQgEIQgEI\nQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEI\nQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEI\nQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEI\nQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEI\nQgEIQgEIQgEIQgEIQgEIQgEIQgEIQg//2Q==\n"
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "metadata": {
        "id": "8gZNsDS_E9PO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Deep Learning at 15 PFlops Enables Training for Extreme Weather Identification at Scale\n",
        "National Energy Research Scientific Computing Center\n",
        "\n",
        "![](https://6lli539m39y3hpkelqsm3c2fg-wpengine.netdna-ssl.com/wp-content/uploads/2018/03/Intel-NERSC-feature-graphic-weather-675x380.png)"
      ]
    },
    {
      "metadata": {
        "id": "CKPX2EbKR9HB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Package imports"
      ]
    },
    {
      "metadata": {
        "id": "rWRy8e9OrC81",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from numpy import exp\n",
        "from IPython.display import HTML, Latex, Math, display\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib\n",
        "import seaborn as sns\n",
        "from sympy.printing import latex\n",
        "from sympy.core.sympify import sympify\n",
        "%matplotlib inline\n",
        "pd.options.display.max_columns=50\n",
        "pd.options.display.max_colwidth=100\n",
        "matplotlib.rcParams['savefig.dpi'] = 300\n",
        "matplotlib.rcParams['figure.dpi'] = 300\n",
        "matplotlib.rcParams['figure.figsize'] = (3, 2)\n",
        "fontsize=3\n",
        "matplotlib.rcParams['figure.titlesize'] = fontsize\n",
        "matplotlib.rcParams['axes.labelsize'] = fontsize\n",
        "matplotlib.rcParams['font.size'] = fontsize\n",
        "matplotlib.rcParams['axes.titlesize'] = fontsize\n",
        "matplotlib.rcParams['legend.fontsize'] = fontsize\n",
        "matplotlib.rcParams['xtick.labelsize'] = fontsize\n",
        "matplotlib.rcParams['ytick.labelsize'] = fontsize\n",
        "matplotlib.rcParams['lines.markersize'] = 2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1fuv6YIMSJi-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## How to get weather data?!\n",
        "\n",
        "Even Google Datasets does not explain well, multiple commercial offerings online\n",
        "\n",
        "\n",
        "https://www.kaggle.com/selfishgene/historical-hourly-weather-data"
      ]
    },
    {
      "metadata": {
        "id": "MWCjFEitrC88",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Historic data of weather in Houston requested and retrieved from Department of Commerce > NOAA > NESDIS > NCEI\n",
        "\n",
        "https://www.ncdc.noaa.gov/"
      ]
    },
    {
      "metadata": {
        "id": "BeN1rggjDhJU",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "![alt text](https://raw.githubusercontent.com/denfromufa/deepweather/master/Capture.PNG)"
      ]
    },
    {
      "metadata": {
        "id": "aO6HehcFrC88",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Data Exploration\n",
        "\n",
        "Let's read the head of the table and look at the data using pandas"
      ]
    },
    {
      "metadata": {
        "id": "KfKspF_XrC89",
        "colab_type": "code",
        "outputId": "a412d14c-2581-419c-e0ee-b1ece55838d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        }
      },
      "cell_type": "code",
      "source": [
        "dfhtown=pd.read_csv(\"https://raw.githubusercontent.com/denfromufa/deepweather/master/9693687323818dat.txt\", delim_whitespace=True)\n",
        "dfhtown.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>USAF</th>\n",
              "      <th>WBAN</th>\n",
              "      <th>YR--MODAHRMN</th>\n",
              "      <th>DIR</th>\n",
              "      <th>SPD</th>\n",
              "      <th>GUS</th>\n",
              "      <th>CLG</th>\n",
              "      <th>SKC</th>\n",
              "      <th>L</th>\n",
              "      <th>M</th>\n",
              "      <th>H</th>\n",
              "      <th>VSB</th>\n",
              "      <th>MW</th>\n",
              "      <th>MW.1</th>\n",
              "      <th>MW.2</th>\n",
              "      <th>MW.3</th>\n",
              "      <th>AW</th>\n",
              "      <th>AW.1</th>\n",
              "      <th>AW.2</th>\n",
              "      <th>AW.3</th>\n",
              "      <th>W</th>\n",
              "      <th>TEMP</th>\n",
              "      <th>DEWP</th>\n",
              "      <th>SLP</th>\n",
              "      <th>ALT</th>\n",
              "      <th>STP</th>\n",
              "      <th>MAX</th>\n",
              "      <th>MIN</th>\n",
              "      <th>PCP01</th>\n",
              "      <th>PCP06</th>\n",
              "      <th>PCP24</th>\n",
              "      <th>PCPXX</th>\n",
              "      <th>SD</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>720617</td>\n",
              "      <td>208</td>\n",
              "      <td>201604010015</td>\n",
              "      <td>***</td>\n",
              "      <td>0</td>\n",
              "      <td>***</td>\n",
              "      <td>722</td>\n",
              "      <td>CLR</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>10.0</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>*</td>\n",
              "      <td>79</td>\n",
              "      <td>66</td>\n",
              "      <td>******</td>\n",
              "      <td>29.67</td>\n",
              "      <td>******</td>\n",
              "      <td>***</td>\n",
              "      <td>***</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>**</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>720617</td>\n",
              "      <td>208</td>\n",
              "      <td>201604010035</td>\n",
              "      <td>330</td>\n",
              "      <td>6</td>\n",
              "      <td>***</td>\n",
              "      <td>722</td>\n",
              "      <td>CLR</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>9.1</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>*</td>\n",
              "      <td>75</td>\n",
              "      <td>66</td>\n",
              "      <td>******</td>\n",
              "      <td>29.66</td>\n",
              "      <td>******</td>\n",
              "      <td>***</td>\n",
              "      <td>***</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>**</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>720617</td>\n",
              "      <td>208</td>\n",
              "      <td>201604010055</td>\n",
              "      <td>330</td>\n",
              "      <td>3</td>\n",
              "      <td>***</td>\n",
              "      <td>722</td>\n",
              "      <td>CLR</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>10.0</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>*</td>\n",
              "      <td>73</td>\n",
              "      <td>66</td>\n",
              "      <td>******</td>\n",
              "      <td>29.67</td>\n",
              "      <td>******</td>\n",
              "      <td>***</td>\n",
              "      <td>***</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>**</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>720617</td>\n",
              "      <td>208</td>\n",
              "      <td>201604010115</td>\n",
              "      <td>330</td>\n",
              "      <td>6</td>\n",
              "      <td>***</td>\n",
              "      <td>722</td>\n",
              "      <td>CLR</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>10.0</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>*</td>\n",
              "      <td>73</td>\n",
              "      <td>66</td>\n",
              "      <td>******</td>\n",
              "      <td>29.68</td>\n",
              "      <td>******</td>\n",
              "      <td>***</td>\n",
              "      <td>***</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>**</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>720617</td>\n",
              "      <td>208</td>\n",
              "      <td>201604010135</td>\n",
              "      <td>340</td>\n",
              "      <td>7</td>\n",
              "      <td>***</td>\n",
              "      <td>722</td>\n",
              "      <td>CLR</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>10.0</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>*</td>\n",
              "      <td>72</td>\n",
              "      <td>64</td>\n",
              "      <td>******</td>\n",
              "      <td>29.70</td>\n",
              "      <td>******</td>\n",
              "      <td>***</td>\n",
              "      <td>***</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>**</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     USAF  WBAN  YR--MODAHRMN  DIR SPD  GUS  CLG  SKC  L  M  H   VSB  MW MW.1  \\\n",
              "0  720617   208  201604010015  ***   0  ***  722  CLR  *  *  *  10.0  **   **   \n",
              "1  720617   208  201604010035  330   6  ***  722  CLR  *  *  *   9.1  **   **   \n",
              "2  720617   208  201604010055  330   3  ***  722  CLR  *  *  *  10.0  **   **   \n",
              "3  720617   208  201604010115  330   6  ***  722  CLR  *  *  *  10.0  **   **   \n",
              "4  720617   208  201604010135  340   7  ***  722  CLR  *  *  *  10.0  **   **   \n",
              "\n",
              "  MW.2 MW.3  AW AW.1 AW.2 AW.3  W TEMP DEWP     SLP    ALT     STP  MAX  MIN  \\\n",
              "0   **   **  **   **   **   **  *   79   66  ******  29.67  ******  ***  ***   \n",
              "1   **   **  **   **   **   **  *   75   66  ******  29.66  ******  ***  ***   \n",
              "2   **   **  **   **   **   **  *   73   66  ******  29.67  ******  ***  ***   \n",
              "3   **   **  **   **   **   **  *   73   66  ******  29.68  ******  ***  ***   \n",
              "4   **   **  **   **   **   **  *   72   64  ******  29.70  ******  ***  ***   \n",
              "\n",
              "   PCP01  PCP06  PCP24  PCPXX  SD  \n",
              "0  *****  *****  *****  *****  **  \n",
              "1  *****  *****  *****  *****  **  \n",
              "2  *****  *****  *****  *****  **  \n",
              "3  *****  *****  *****  *****  **  \n",
              "4  *****  *****  *****  *****  **  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "metadata": {
        "id": "Kpv7aZ-drC9H",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The data is messy, the names of columns not clear, so let's look at html version. Again using pandas!"
      ]
    },
    {
      "metadata": {
        "id": "qixn7in5zD0m",
        "colab_type": "code",
        "outputId": "8911f8cb-2bfa-499e-db3e-0da17fb6d508",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        }
      },
      "cell_type": "code",
      "source": [
        "import sys\n",
        "!pip install lxml html5lib"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting lxml\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/03/a4/9eea8035fc7c7670e5eab97f34ff2ef0ddd78a491bf96df5accedb0e63f5/lxml-4.2.5-cp36-cp36m-manylinux1_x86_64.whl (5.8MB)\n",
            "\u001b[K    100% |████████████████████████████████| 5.8MB 5.4MB/s \n",
            "\u001b[?25hCollecting html5lib\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a5/62/bbd2be0e7943ec8504b517e62bab011b4946e1258842bc159e5dfde15b96/html5lib-1.0.1-py2.py3-none-any.whl (117kB)\n",
            "\u001b[K    100% |████████████████████████████████| 122kB 26.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: webencodings in /usr/local/lib/python3.6/dist-packages (from html5lib) (0.5.1)\n",
            "Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.6/dist-packages (from html5lib) (1.11.0)\n",
            "Installing collected packages: lxml, html5lib\n",
            "Successfully installed html5lib-1.0.1 lxml-4.2.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "g0w6EEU0rC9I",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "dfheader=pd.read_html(\"https://raw.githubusercontent.com/denfromufa/deepweather/master/72061700208-2016-04_9693687323818dat.html\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "TlJ3yTgnrC9N",
        "colab_type": "code",
        "outputId": "7573a019-4ba2-4ed4-cc15-42d54fcf4db8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 65
        }
      },
      "cell_type": "code",
      "source": [
        "def pd2html(df, header=False, index=False):\n",
        "    return HTML(df.to_html(index=False, header=False))\n",
        "pd2html(dfheader[0])"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>AWS ID 720617</td>\n",
              "      <td>WBAN ID 00208</td>\n",
              "      <td>Name HOUSTON EXECUTIVE AIRPORT</td>\n",
              "      <td>Country UNITED STATES</td>\n",
              "      <td>State TEXAS</td>\n",
              "      <td>Latitude +29.800</td>\n",
              "      <td>Longitude -095.900</td>\n",
              "      <td>Elevation +0051.2 (meters)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "id": "QZouBM2BrC9U",
        "colab_type": "code",
        "outputId": "7cdd46d7-47bb-4d12-934e-e594836650ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116
        }
      },
      "cell_type": "code",
      "source": [
        "pd2html(dfheader[1][:2])"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>USAF</td>\n",
              "      <td>WBAN</td>\n",
              "      <td>YR--MODAHRMN</td>\n",
              "      <td>DIR</td>\n",
              "      <td>SPD</td>\n",
              "      <td>GUS</td>\n",
              "      <td>CLG</td>\n",
              "      <td>SKC</td>\n",
              "      <td>L</td>\n",
              "      <td>M</td>\n",
              "      <td>H</td>\n",
              "      <td>VSB</td>\n",
              "      <td>MW</td>\n",
              "      <td>MW</td>\n",
              "      <td>MW</td>\n",
              "      <td>MW</td>\n",
              "      <td>AW</td>\n",
              "      <td>AW</td>\n",
              "      <td>AW</td>\n",
              "      <td>AW</td>\n",
              "      <td>W</td>\n",
              "      <td>TEMP</td>\n",
              "      <td>DEWP</td>\n",
              "      <td>SLP</td>\n",
              "      <td>ALT</td>\n",
              "      <td>STP</td>\n",
              "      <td>MAX</td>\n",
              "      <td>MIN</td>\n",
              "      <td>PCP01</td>\n",
              "      <td>PCP06</td>\n",
              "      <td>PCP24</td>\n",
              "      <td>PCPXX</td>\n",
              "      <td>SD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>GMT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MPH</td>\n",
              "      <td>MPH</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Miles</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>F</td>\n",
              "      <td>F</td>\n",
              "      <td>Mb</td>\n",
              "      <td>inches</td>\n",
              "      <td>Mb</td>\n",
              "      <td>F</td>\n",
              "      <td>F</td>\n",
              "      <td>inches</td>\n",
              "      <td>inches</td>\n",
              "      <td>inches</td>\n",
              "      <td>inches</td>\n",
              "      <td>inches</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "id": "HbGs8zYUrC9b",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Data cleaning\n",
        "\n",
        "Let's set the index of dataframe (table) to dates"
      ]
    },
    {
      "metadata": {
        "id": "OnFYVrRArC9c",
        "colab_type": "code",
        "outputId": "dcb0a02a-6813-4134-d8ae-77659f16da32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        }
      },
      "cell_type": "code",
      "source": [
        "dfhtown.set_index(dfhtown.columns[2], inplace=True)\n",
        "dfhtown.head(3)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>USAF</th>\n",
              "      <th>WBAN</th>\n",
              "      <th>DIR</th>\n",
              "      <th>SPD</th>\n",
              "      <th>GUS</th>\n",
              "      <th>CLG</th>\n",
              "      <th>SKC</th>\n",
              "      <th>L</th>\n",
              "      <th>M</th>\n",
              "      <th>H</th>\n",
              "      <th>VSB</th>\n",
              "      <th>MW</th>\n",
              "      <th>MW.1</th>\n",
              "      <th>MW.2</th>\n",
              "      <th>MW.3</th>\n",
              "      <th>AW</th>\n",
              "      <th>AW.1</th>\n",
              "      <th>AW.2</th>\n",
              "      <th>AW.3</th>\n",
              "      <th>W</th>\n",
              "      <th>TEMP</th>\n",
              "      <th>DEWP</th>\n",
              "      <th>SLP</th>\n",
              "      <th>ALT</th>\n",
              "      <th>STP</th>\n",
              "      <th>MAX</th>\n",
              "      <th>MIN</th>\n",
              "      <th>PCP01</th>\n",
              "      <th>PCP06</th>\n",
              "      <th>PCP24</th>\n",
              "      <th>PCPXX</th>\n",
              "      <th>SD</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YR--MODAHRMN</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>201604010015</th>\n",
              "      <td>720617</td>\n",
              "      <td>208</td>\n",
              "      <td>***</td>\n",
              "      <td>0</td>\n",
              "      <td>***</td>\n",
              "      <td>722</td>\n",
              "      <td>CLR</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>10.0</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>*</td>\n",
              "      <td>79</td>\n",
              "      <td>66</td>\n",
              "      <td>******</td>\n",
              "      <td>29.67</td>\n",
              "      <td>******</td>\n",
              "      <td>***</td>\n",
              "      <td>***</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>**</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>201604010035</th>\n",
              "      <td>720617</td>\n",
              "      <td>208</td>\n",
              "      <td>330</td>\n",
              "      <td>6</td>\n",
              "      <td>***</td>\n",
              "      <td>722</td>\n",
              "      <td>CLR</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>9.1</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>*</td>\n",
              "      <td>75</td>\n",
              "      <td>66</td>\n",
              "      <td>******</td>\n",
              "      <td>29.66</td>\n",
              "      <td>******</td>\n",
              "      <td>***</td>\n",
              "      <td>***</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>**</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>201604010055</th>\n",
              "      <td>720617</td>\n",
              "      <td>208</td>\n",
              "      <td>330</td>\n",
              "      <td>3</td>\n",
              "      <td>***</td>\n",
              "      <td>722</td>\n",
              "      <td>CLR</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>10.0</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>*</td>\n",
              "      <td>73</td>\n",
              "      <td>66</td>\n",
              "      <td>******</td>\n",
              "      <td>29.67</td>\n",
              "      <td>******</td>\n",
              "      <td>***</td>\n",
              "      <td>***</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>**</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                USAF  WBAN  DIR SPD  GUS  CLG  SKC  L  M  H   VSB  MW MW.1  \\\n",
              "YR--MODAHRMN                                                                 \n",
              "201604010015  720617   208  ***   0  ***  722  CLR  *  *  *  10.0  **   **   \n",
              "201604010035  720617   208  330   6  ***  722  CLR  *  *  *   9.1  **   **   \n",
              "201604010055  720617   208  330   3  ***  722  CLR  *  *  *  10.0  **   **   \n",
              "\n",
              "             MW.2 MW.3  AW AW.1 AW.2 AW.3  W TEMP DEWP     SLP    ALT     STP  \\\n",
              "YR--MODAHRMN                                                                    \n",
              "201604010015   **   **  **   **   **   **  *   79   66  ******  29.67  ******   \n",
              "201604010035   **   **  **   **   **   **  *   75   66  ******  29.66  ******   \n",
              "201604010055   **   **  **   **   **   **  *   73   66  ******  29.67  ******   \n",
              "\n",
              "              MAX  MIN  PCP01  PCP06  PCP24  PCPXX  SD  \n",
              "YR--MODAHRMN                                            \n",
              "201604010015  ***  ***  *****  *****  *****  *****  **  \n",
              "201604010035  ***  ***  *****  *****  *****  *****  **  \n",
              "201604010055  ***  ***  *****  *****  *****  *****  **  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "qEqJ_M4SrC9j",
        "colab_type": "code",
        "outputId": "b781555d-f0c1-4dfd-d882-df6b12417193",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        }
      },
      "cell_type": "code",
      "source": [
        "dfhtown.index=pd.to_datetime(dfhtown.index, format=\"%Y%m%d%H%M\")\n",
        "dfhtown.index.name='datetime'\n",
        "dfhtown.replace(to_replace='\\*+', value=np.nan, regex=True, inplace=True)\n",
        "dfhtown.dropna(axis=1, inplace=True, how='all')\n",
        "dfhtown.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>USAF</th>\n",
              "      <th>WBAN</th>\n",
              "      <th>DIR</th>\n",
              "      <th>SPD</th>\n",
              "      <th>GUS</th>\n",
              "      <th>CLG</th>\n",
              "      <th>SKC</th>\n",
              "      <th>VSB</th>\n",
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              "      <th>TEMP</th>\n",
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              "      <th>datetime</th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2016-04-01 00:15:00</th>\n",
              "      <td>720617</td>\n",
              "      <td>208</td>\n",
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              "      <td>66</td>\n",
              "      <td>29.67</td>\n",
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              "    <tr>\n",
              "      <th>2016-04-01 00:35:00</th>\n",
              "      <td>720617</td>\n",
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              "      <td>9.1</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>66</td>\n",
              "      <td>29.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-04-01 00:55:00</th>\n",
              "      <td>720617</td>\n",
              "      <td>208</td>\n",
              "      <td>330</td>\n",
              "      <td>3</td>\n",
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              "      <td>CLR</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>73</td>\n",
              "      <td>66</td>\n",
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              "    <tr>\n",
              "      <th>2016-04-01 01:15:00</th>\n",
              "      <td>720617</td>\n",
              "      <td>208</td>\n",
              "      <td>330</td>\n",
              "      <td>6</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>CLR</td>\n",
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              "      <td>73</td>\n",
              "      <td>66</td>\n",
              "      <td>29.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-04-01 01:35:00</th>\n",
              "      <td>720617</td>\n",
              "      <td>208</td>\n",
              "      <td>340</td>\n",
              "      <td>7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>722</td>\n",
              "      <td>CLR</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>72</td>\n",
              "      <td>64</td>\n",
              "      <td>29.70</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       USAF  WBAN  DIR SPD  GUS  CLG  SKC   VSB   AW TEMP  \\\n",
              "datetime                                                                    \n",
              "2016-04-01 00:15:00  720617   208  NaN   0  NaN  722  CLR  10.0  NaN   79   \n",
              "2016-04-01 00:35:00  720617   208  330   6  NaN  722  CLR   9.1  NaN   75   \n",
              "2016-04-01 00:55:00  720617   208  330   3  NaN  722  CLR  10.0  NaN   73   \n",
              "2016-04-01 01:15:00  720617   208  330   6  NaN  722  CLR  10.0  NaN   73   \n",
              "2016-04-01 01:35:00  720617   208  340   7  NaN  722  CLR  10.0  NaN   72   \n",
              "\n",
              "                    DEWP    ALT  \n",
              "datetime                         \n",
              "2016-04-01 00:15:00   66  29.67  \n",
              "2016-04-01 00:35:00   66  29.66  \n",
              "2016-04-01 00:55:00   66  29.67  \n",
              "2016-04-01 01:15:00   66  29.68  \n",
              "2016-04-01 01:35:00   64  29.70  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "metadata": {
        "id": "lA_PaG8nrC9u",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Let's look at subset of columns with physical units from HTML version of the table"
      ]
    },
    {
      "metadata": {
        "id": "rjgsvHCErC9w",
        "colab_type": "code",
        "outputId": "51a50171-793c-427c-fa1c-6443a03dc8e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        }
      },
      "cell_type": "code",
      "source": [
        "dfunits=dfheader[1][:2]\n",
        "dfunits"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
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              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
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              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "      <th>31</th>\n",
              "      <th>32</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>USAF</td>\n",
              "      <td>WBAN</td>\n",
              "      <td>YR--MODAHRMN</td>\n",
              "      <td>DIR</td>\n",
              "      <td>SPD</td>\n",
              "      <td>GUS</td>\n",
              "      <td>CLG</td>\n",
              "      <td>SKC</td>\n",
              "      <td>L</td>\n",
              "      <td>M</td>\n",
              "      <td>H</td>\n",
              "      <td>VSB</td>\n",
              "      <td>MW</td>\n",
              "      <td>MW</td>\n",
              "      <td>MW</td>\n",
              "      <td>MW</td>\n",
              "      <td>AW</td>\n",
              "      <td>AW</td>\n",
              "      <td>AW</td>\n",
              "      <td>AW</td>\n",
              "      <td>W</td>\n",
              "      <td>TEMP</td>\n",
              "      <td>DEWP</td>\n",
              "      <td>SLP</td>\n",
              "      <td>ALT</td>\n",
              "      <td>STP</td>\n",
              "      <td>MAX</td>\n",
              "      <td>MIN</td>\n",
              "      <td>PCP01</td>\n",
              "      <td>PCP06</td>\n",
              "      <td>PCP24</td>\n",
              "      <td>PCPXX</td>\n",
              "      <td>SD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>GMT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MPH</td>\n",
              "      <td>MPH</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Miles</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>F</td>\n",
              "      <td>F</td>\n",
              "      <td>Mb</td>\n",
              "      <td>inches</td>\n",
              "      <td>Mb</td>\n",
              "      <td>F</td>\n",
              "      <td>F</td>\n",
              "      <td>inches</td>\n",
              "      <td>inches</td>\n",
              "      <td>inches</td>\n",
              "      <td>inches</td>\n",
              "      <td>inches</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     0     1             2    3    4    5    6    7    8    9    10     11  \\\n",
              "0  USAF  WBAN  YR--MODAHRMN  DIR  SPD  GUS  CLG  SKC    L    M    H    VSB   \n",
              "1   NaN   NaN           GMT  NaN  MPH  MPH  NaN  NaN  NaN  NaN  NaN  Miles   \n",
              "\n",
              "    12   13   14   15   16   17   18   19   20    21    22   23      24   25  \\\n",
              "0   MW   MW   MW   MW   AW   AW   AW   AW    W  TEMP  DEWP  SLP     ALT  STP   \n",
              "1  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     F     F   Mb  inches   Mb   \n",
              "\n",
              "    26   27      28      29      30      31      32  \n",
              "0  MAX  MIN   PCP01   PCP06   PCP24   PCPXX      SD  \n",
              "1    F    F  inches  inches  inches  inches  inches  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "metadata": {
        "id": "bO1fAHcLrC92",
        "colab_type": "code",
        "outputId": "718ee7c8-cfd2-4e78-b753-411b7ceb08a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        }
      },
      "cell_type": "code",
      "source": [
        "dfunits.rename(columns=dfunits.iloc[0]).iloc[1].loc[dfhtown.columns].dropna().to_frame()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>SPD</th>\n",
              "      <td>MPH</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GUS</th>\n",
              "      <td>MPH</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VSB</th>\n",
              "      <td>Miles</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TEMP</th>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DEWP</th>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ALT</th>\n",
              "      <td>inches</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           1\n",
              "SPD      MPH\n",
              "GUS      MPH\n",
              "VSB    Miles\n",
              "TEMP       F\n",
              "DEWP       F\n",
              "ALT   inches"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "metadata": {
        "id": "ox7sRfjIrC97",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "* So SPD and GUS are some velocity measurements for wind\n",
        "* VSB??\n",
        "* TEMP - temperature\n",
        "* DEWP - temperature dewpoint (used for humidity)\n",
        "* ALT - perhaps the pressure in inches of liquid, such as mercury"
      ]
    },
    {
      "metadata": {
        "id": "tQC5QeafrC98",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The types of columns are very generic (object), so let's improve this!"
      ]
    },
    {
      "metadata": {
        "id": "wwszOkDhrC9_",
        "colab_type": "code",
        "outputId": "efc68ca7-8053-43ee-e5cb-a32448187f6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        }
      },
      "cell_type": "code",
      "source": [
        "dfhtown.dtypes"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "USAF     int64\n",
              "WBAN     int64\n",
              "DIR     object\n",
              "SPD     object\n",
              "GUS     object\n",
              "CLG     object\n",
              "SKC     object\n",
              "VSB     object\n",
              "AW      object\n",
              "TEMP    object\n",
              "DEWP    object\n",
              "ALT     object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "id": "B1Mz005GrC-F",
        "colab_type": "code",
        "outputId": "c7749d92-2547-4b28-d31d-d533437b608c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "cell_type": "code",
      "source": [
        "dfhtown = dfhtown[['DIR','SPD','VSB','TEMP','DEWP','ALT']].astype('float64',raise_on_error=False)\n",
        "dfhtown.head()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: FutureWarning: the raise_on_error=False keyword is deprecated, use errors='ignore' instead\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DIR</th>\n",
              "      <th>SPD</th>\n",
              "      <th>VSB</th>\n",
              "      <th>TEMP</th>\n",
              "      <th>DEWP</th>\n",
              "      <th>ALT</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>datetime</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2016-04-01 00:15:00</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>29.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-04-01 00:35:00</th>\n",
              "      <td>330.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>9.1</td>\n",
              "      <td>75.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>29.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-04-01 00:55:00</th>\n",
              "      <td>330.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>29.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-04-01 01:15:00</th>\n",
              "      <td>330.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>29.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-04-01 01:35:00</th>\n",
              "      <td>340.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>29.70</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       DIR  SPD   VSB  TEMP  DEWP    ALT\n",
              "datetime                                                \n",
              "2016-04-01 00:15:00    NaN  0.0  10.0  79.0  66.0  29.67\n",
              "2016-04-01 00:35:00  330.0  6.0   9.1  75.0  66.0  29.66\n",
              "2016-04-01 00:55:00  330.0  3.0  10.0  73.0  66.0  29.67\n",
              "2016-04-01 01:15:00  330.0  6.0  10.0  73.0  66.0  29.68\n",
              "2016-04-01 01:35:00  340.0  7.0  10.0  72.0  64.0  29.70"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "metadata": {
        "id": "JVrehrIjrC-M",
        "colab_type": "code",
        "outputId": "ea43d1a7-a722-48e0-ecae-3e2562c1fd30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "cell_type": "code",
      "source": [
        "dfhtown.dtypes"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DIR     float64\n",
              "SPD     float64\n",
              "VSB     float64\n",
              "TEMP    float64\n",
              "DEWP    float64\n",
              "ALT     float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "id": "QCfK7om9rC-T",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The relative humidity (RH) is not given in table, so let's calculate using measured temperature and dewpoint temperature"
      ]
    },
    {
      "metadata": {
        "id": "Er-whQnPrC-T",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "rel_hum_eqn = \"100*(exp((17.625*TD)/(243.04+TD))/exp((17.625*T)/(243.04+T)))\"\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UF_1tquWrC-Y",
        "colab_type": "code",
        "outputId": "ea60afe5-a71b-4d3f-d017-ad0a6ed97045",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        }
      },
      "cell_type": "code",
      "source": [
        "dfhtown['T']=(dfhtown['TEMP']-32)/1.8\n",
        "dfhtown['TD']=(dfhtown['DEWP']-32)/1.8\n",
        "dfhtown['RH']=dfhtown.eval(rel_hum_eqn)\n",
        "dfhtown[['RH']].rolling(window=24*7).mean().plot(style='.');\n",
        "plt.ylabel('Relative Humidity [%]');"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1kKoZ0VxrjxuSJfXgxRcR4UIBDUQikWXIqpY4nJ+/5hwQLatRT74A8/gAjGWdoWb56skX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\nRK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f52a71338d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "G7ROJga4rC-e",
        "colab_type": "code",
        "outputId": "2f53fc38-e48f-48bb-a3ee-cb8da1b61155",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        }
      },
      "cell_type": "code",
      "source": [
        "dfhtown.describe()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DIR</th>\n",
              "      <th>SPD</th>\n",
              "      <th>VSB</th>\n",
              "      <th>TEMP</th>\n",
              "      <th>DEWP</th>\n",
              "      <th>ALT</th>\n",
              "      <th>T</th>\n",
              "      <th>TD</th>\n",
              "      <th>RH</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>20087.000000</td>\n",
              "      <td>25833.000000</td>\n",
              "      <td>23997.000000</td>\n",
              "      <td>25844.000000</td>\n",
              "      <td>25844.000000</td>\n",
              "      <td>25853.000000</td>\n",
              "      <td>25844.000000</td>\n",
              "      <td>25844.000000</td>\n",
              "      <td>25844.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>168.682232</td>\n",
              "      <td>6.866527</td>\n",
              "      <td>8.109147</td>\n",
              "      <td>71.196835</td>\n",
              "      <td>63.041751</td>\n",
              "      <td>30.041237</td>\n",
              "      <td>21.776019</td>\n",
              "      <td>17.245417</td>\n",
              "      <td>78.277455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>108.760045</td>\n",
              "      <td>5.283873</td>\n",
              "      <td>2.605837</td>\n",
              "      <td>12.736647</td>\n",
              "      <td>13.240728</td>\n",
              "      <td>0.162329</td>\n",
              "      <td>7.075915</td>\n",
              "      <td>7.355960</td>\n",
              "      <td>19.340430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>29.450000</td>\n",
              "      <td>-7.222222</td>\n",
              "      <td>-12.222222</td>\n",
              "      <td>17.552120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>110.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>29.950000</td>\n",
              "      <td>17.777778</td>\n",
              "      <td>13.888889</td>\n",
              "      <td>64.138382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>170.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>9.100000</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>30.020000</td>\n",
              "      <td>22.777778</td>\n",
              "      <td>18.888889</td>\n",
              "      <td>83.472995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>200.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>30.130000</td>\n",
              "      <td>27.222222</td>\n",
              "      <td>22.777778</td>\n",
              "      <td>93.564177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>990.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>30.790000</td>\n",
              "      <td>37.222222</td>\n",
              "      <td>27.222222</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                DIR           SPD           VSB          TEMP          DEWP  \\\n",
              "count  20087.000000  25833.000000  23997.000000  25844.000000  25844.000000   \n",
              "mean     168.682232      6.866527      8.109147     71.196835     63.041751   \n",
              "std      108.760045      5.283873      2.605837     12.736647     13.240728   \n",
              "min       10.000000      0.000000      0.000000     19.000000     10.000000   \n",
              "25%      110.000000      3.000000      7.000000     64.000000     57.000000   \n",
              "50%      170.000000      7.000000      9.100000     73.000000     66.000000   \n",
              "75%      200.000000     10.000000     10.000000     81.000000     73.000000   \n",
              "max      990.000000     36.000000     10.000000     99.000000     81.000000   \n",
              "\n",
              "                ALT             T            TD            RH  \n",
              "count  25853.000000  25844.000000  25844.000000  25844.000000  \n",
              "mean      30.041237     21.776019     17.245417     78.277455  \n",
              "std        0.162329      7.075915      7.355960     19.340430  \n",
              "min       29.450000     -7.222222    -12.222222     17.552120  \n",
              "25%       29.950000     17.777778     13.888889     64.138382  \n",
              "50%       30.020000     22.777778     18.888889     83.472995  \n",
              "75%       30.130000     27.222222     22.777778     93.564177  \n",
              "max       30.790000     37.222222     27.222222    100.000000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {
        "id": "fv46Co1JrC-i",
        "colab_type": "code",
        "outputId": "3dc7ac24-197d-4172-ffc7-77f2ff649f0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        }
      },
      "cell_type": "code",
      "source": [
        "dfhtown.rolling(window=72).mean()[['TEMP']].plot();"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fDgtt19nnS8rxCYIgiIkB2fASRIoJJaFuIcrek22KdRF3/e97eOadGhw83Y7I8DYH3G2I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\nS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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f52a712c6a0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "pSHfNi2TrDAi",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "The vapor pressure answers will be in units of millibars (mb) or hectoPascals (hPa).\n",
        "To convert the saturated vapor pressure and/or the actual vapor pressure, the\n",
        "temperature values must be converted to degrees Celsius (°C) ."
      ]
    },
    {
      "metadata": {
        "id": "KYxZ5bYurDAj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "vp='6.11*(10**((7.5*{T})/(237.3+{T})))'\n",
        "dfhtown['e']=0.1*dfhtown.eval(vp.format(T='TD')) #converted from hPa to kPa\n",
        "dfhtown['es']=0.1*dfhtown.eval(vp.format(T='T')) #converted from hPa to kPa"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "leCiV7pdrDAp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Let's verify calculation"
      ]
    },
    {
      "metadata": {
        "id": "hrA_wSgQrDAq",
        "colab_type": "code",
        "outputId": "43c7659a-9d71-4e01-b6cd-99d746297ff5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        }
      },
      "cell_type": "code",
      "source": [
        "ax1=dfhtown[['RH']].rolling(window=24*7).mean().plot(style='.')\n",
        "(dfhtown.e/dfhtown.es*100.).rolling(window=24*7*2).mean().plot(style='.',ax=ax1)\n",
        "plt.ylabel('Relative Humidity')\n",
        "plt.title('Comparison of calculated humidity with various methods');"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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E4Tz1i+CA6FmNtgwOQb58EOK4BTjrlIGGtWHL4BBuunPr9PbmwSF89J1nNPSa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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f52a2d51080>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "GmN-JjJerDAy",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Back to apparent temperature (AT)!"
      ]
    },
    {
      "metadata": {
        "id": "iFIPSyadrDAy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "AT = \"-2.7 + 1.04*T + 2.0*e -0.65*v\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2g_g2j1_rDA1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "dfhtown['v']=dfhtown.SPD*0.44704 #MPH to m/s"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "tLqV7GWdrDA5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "dfhtown['AT']=dfhtown.eval(AT)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YfnEmu04rDBB",
        "colab_type": "code",
        "outputId": "7edb8a0d-af73-42a7-8daa-98d6aac3271b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        }
      },
      "cell_type": "code",
      "source": [
        "plt.figure()\n",
        "dfhtown.eval('AT-T').rolling(window=7*24).mean().plot()\n",
        "plt.title('Different between apparent temperature and measured temperature')\n",
        "plt.ylabel('Delta T [deg. C]')"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0,0.5,'Delta T [deg. C]')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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oQH8NxjCrZ9Psm+w3MKrK7YExN+Q5yf5SknP/NQD/HX89vwDg4wCuEMYQwh8A+FF82w4Y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\nkiSp0yx6JEmSJHWaRY8kSZKkTvsP329BTe6eOIEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f52a2938748>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "2JX8Wpi6rDBG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Comfortable apparent temperature **22-27** deg. C\n",
        "\n",
        "https://en.wikipedia.org/wiki/Thermal_comfort"
      ]
    },
    {
      "metadata": {
        "id": "g8mr3xBMrDBH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "ixat = (dfhtown.AT>22) & (dfhtown.AT<27)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "FMtCNNM9rDBL",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Comfortable hours to spend time outdoors are between 7am and 8pm"
      ]
    },
    {
      "metadata": {
        "id": "DzUmdXx4rDBO",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "dfhtown['hour']=dfhtown.index.map(lambda x: x.hour)\n",
        "ixhr = (dfhtown.AT>7) & (dfhtown.hour<20)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9CtbLkafrDBR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "When humidity is above 95% - very high chances of rain! So not good for outdoors..."
      ]
    },
    {
      "metadata": {
        "id": "XACuMuM8rDBS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "ixrh = dfhtown.RH<95"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "TEtrUOf-rDBV",
        "colab_type": "code",
        "outputId": "f3864cef-0405-4d55-a1d9-acab8c6078a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        }
      },
      "cell_type": "code",
      "source": [
        "dfhtown[ixat & ixhr & ixrh].resample(\"H\").sum().resample('M').AT.count()[:-1]\n",
        "#.plot()\n",
        "#plt.ylabel('Total hours comfortable outdoors in a month');"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime\n",
              "2016-04-30    720\n",
              "2016-05-31    744\n",
              "2016-06-30    720\n",
              "2016-07-31    744\n",
              "2016-08-31    744\n",
              "2016-09-30    720\n",
              "2016-10-31    744\n",
              "2016-11-30    720\n",
              "2016-12-31    744\n",
              "2017-01-31    744\n",
              "2017-02-28    672\n",
              "2017-03-31    744\n",
              "Freq: M, Name: AT, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "metadata": {
        "id": "2XvmnoXsJNBH",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Deep learning approach\n",
        "\n",
        "This part is based on the deep learning book \"Deep Learning with Python\" by Keras Lead François Chollet"
      ]
    },
    {
      "metadata": {
        "id": "RWj4ltX3wN0H",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "https://github.com/fchollet/deep-learning-with-python-notebooks/blob/master/6.3-advanced-usage-of-recurrent-neural-networks.ipynb"
      ]
    },
    {
      "metadata": {
        "id": "HSnRL9SM7YK4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "9dfdefd1-bfd9-420e-b14d-ee656c695eef"
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "print(\"tensorflow version = {}\".format(tf.__version__))\n",
        "\n",
        "import keras\n",
        "print(\"keras version = {}\".format(keras.__version__))\n"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensorflow version = 1.12.0\n",
            "keras version = 2.2.4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "r2N5W5KQ7-zc",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Transforming the data\n",
        "\n",
        "\n",
        "The exact formulation of our problem will be the following: given data going as far back as `lookback` timesteps (a timestep is 10 minutes) \n",
        "and sampled every `steps` timesteps, can we predict the temperature in `delay` timesteps?\n",
        "\n",
        "We will use the following parameter values:\n",
        "\n",
        "* `lookback = 360`, i.e. our observations will go back 5 days.\n",
        "* `steps = 3`, i.e. our observations will be sampled at one data point per hour.\n",
        "* `delay = 72`, i.e. our targets will be 24 hours in the future.\n",
        "\n",
        "To get started, we need to do two things:\n",
        "\n",
        "* Preprocess the data to a format a neural network can ingest. This is easy: the data is already numerical, so we don't need to do any \n",
        "vectorization. However each timeseries in the data is on a different scale (e.g. temperature is typically between -20 and +30, but \n",
        "pressure, measured in mbar, is around 1000). So we will normalize each timeseries independently so that they all take small values on a \n",
        "similar scale.\n",
        "* Write a Python generator that takes our current array of float data and yields batches of data from the recent past, alongside with a \n",
        "target temperature in the future. Since the samples in our dataset are highly redundant (e.g. sample `N` and sample `N + 1` will have most \n",
        "of their timesteps in common), it would be very wasteful to explicitly allocate every sample. Instead, we will generate the samples on the \n",
        "fly using the original data.\n",
        "\n",
        "We preprocess the data by subtracting the mean of each timeseries and dividing by the standard deviation. We plan on using the first \n",
        "200,000 timesteps as training data, so we compute the mean and standard deviation only on this fraction of the data:"
      ]
    },
    {
      "metadata": {
        "id": "eKtzRgoN6TsB",
        "colab_type": "code",
        "outputId": "4a4c95dc-019b-420c-de5f-4517d5870c12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158
        }
      },
      "cell_type": "code",
      "source": [
        "usedcols = 'DIR\tTEMP\tSPD\tVSB\tDEWP\tALT'.split('\\t')\n",
        "float_data = dfhtown[usedcols].dropna()\n",
        "display(float_data.head(2))\n",
        "float_data = float_data.values\n",
        "float_data.shape"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DIR</th>\n",
              "      <th>TEMP</th>\n",
              "      <th>SPD</th>\n",
              "      <th>VSB</th>\n",
              "      <th>DEWP</th>\n",
              "      <th>ALT</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>datetime</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2016-04-01 00:35:00</th>\n",
              "      <td>330.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>9.1</td>\n",
              "      <td>66.0</td>\n",
              "      <td>29.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-04-01 00:55:00</th>\n",
              "      <td>330.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>29.67</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       DIR  TEMP  SPD   VSB  DEWP    ALT\n",
              "datetime                                                \n",
              "2016-04-01 00:35:00  330.0  75.0  6.0   9.1  66.0  29.66\n",
              "2016-04-01 00:55:00  330.0  73.0  3.0  10.0  66.0  29.67"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(18771, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "metadata": {
        "id": "rVZhWVBx2ubT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "mean = float_data.mean(axis=0)\n",
        "float_data -= mean\n",
        "std = float_data.std(axis=0)\n",
        "float_data /= std"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "dIjS__Qd2wku",
        "colab_type": "code",
        "outputId": "4886c999-cfe7-4bc0-b1fd-fc1df08aeaf0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        }
      },
      "cell_type": "code",
      "source": [
        "pd.DataFrame(float_data, columns = usedcols).hist()\n",
        "plt.xlim([-2,2])\n",
        "plt.legend()"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f52ab0ba8d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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M1Va3/vd+wzz0+9nvD7d3kX1qv9L6o3GU9v1ubfCZ5tV6po2rxHRbE+9l3zNzm6ZjMzjL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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f52ab2a9b38>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "-Z0eHEwJx4Sg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "outputId": "d6fa52be-79d5-4750-c47e-ce957d228deb"
      },
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "#plt.matshow(pd.DataFrame(float_data, columns = usedcols).corr())\n",
        "\n",
        "sns.heatmap(pd.DataFrame(float_data, columns = usedcols).corr(),\n",
        "            xticklabels=usedcols,\n",
        "            yticklabels=usedcols)\n"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f52aaf80a20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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k47tYCwAAU9m3b+ceTGbkT3P/5exf35UqAABggUbOETi1qj54iH1rSa63m8UAADANy4fO\nw8hG4IyBrw0AAIs2rBHo7tePem0AAHaQRGAWhjUCVXVykucm+aYklyY5O6vlRC8eVRMAACzFyHbt\nl5N8JMk3JPmfSa6b1b0DAACYMzcUm4WRcwRuk6Q2lgpNVT0kyVsG1gMAAIsxdPnQg01AknT3fya5\n6sB6AACYgkRgFkYmAtvdJ8C9AwAAZs7yofMwshG4flU95XDbuvtxu1wTAAAswshG4A+T3HDLtlcm\nuUFWNxQDAGCOJAKzMOwsdfdDk7wmyfd093cl+f4kFyb5b0nemeS7R9UGAAB73bBGoKp+NKvlQr9g\nY9OTktwnyR9ntZzoYweVBgDA0Vhb27kHkxmZ2zwkyR26+6NVtS/JQ5M8srufnlVD8J0DawMAgD1t\nZCOw1t0f2Pj4q5KcmNVQoXT3x5IcN6owAACOguVDZ2HkZOHNzkjyl9196ehCAAA4OpYPnYeRjcDf\nV9Vjk7w5yf9K8rMHd1TVPZP886jCAABgrxvZCPxIknOS/ESSVyR5dpJU1RlJfivJXcaVBgDAEdsn\nEZiDYY1Ad78/yW232fW2JP9jYz8AALADjpU5Apfp7o8m+ejoOgAAOELmCMyCswQAAAt0zCUCAADM\nnERgFjQCAABMSyMwC84SAAAskEQAAIBJuaHYPDhLAACwQBIBAACmJRGYBWcJAAAWSCIAAMC01tZG\nV8AVIBEAAIAFkggAADCtPThHoKq+MclZSa6d5BNJHtndf77lmGsleX6S2yQ5kOS3u/und7vWK2rv\nnSUAAIZaX9u3Y48RquoaSc5O8pjuvnmSxyQ5u6quvuXQpya5JMmpSb46yf2q6p67WuyVoBEAAIDD\nOz3Jhd19bpJ093lJPp7ktC3HfUeSZ3T3ge7+VJLfSvKAXa30StAIAAAwrbV9O/cY40uTvGfLtguS\n1MEnVXW9JNfdctznHXOs0QgAAMDhXSOrIT+bXZzkxC3HZMtxW485ppgsfBjrV9k67IuddpX3v210\nCYtz1qfePrqExfnha91qdAmL8+RPvmN0CYtz3fWLRpewQNccXcBl1vfe8qEX5XMX+gedkOTTW47J\nxnEXH+KYY4pEAAAADu+dWQ0P2qySXPYuQ3d/LMmFW477vGOONRoBAAAmtb6+c49Bzktyzaq6R5JU\n1X2SXD3J67Yc98IkP1JV+6rq5CQP39h2TNIIAAAwqQPr6zv2GKG7P5PkXkkeX1Xvzmr50Ht396VV\ndV5V3X7j0J/Iao7Au5L8eZJnd/cfDyn6CjBHAAAALkd3vzXJbbfZfvqmj/8zyYN2s66joREAAGBS\n40bwcGUYGgQAAAskEQAAYFIHRAKzIBEAAIAFkggAADCp9YHrfHLFaQQAAJiUoUHzYGgQAAAskEQA\nAIBJCQTmQSIAAAALJBEAAGBS5gjMg0QAAAAWSCIAAMCkLB86DxIBAABYIIkAAACTOjC6AK4QjQAA\nAJMyMmgeDA0CAIAFkggAADApy4fOg0QAAAAWSCIAAMCkLB86DxIBAABYIIkAAACTsnzoPGgEAACY\nlJFB82BoEAAALJBEAACASR0QCcyCRAAAABZIIgAAwKTkAfMgEQAAgAWSCAAAMKkDIoFZ0AgAADAp\nc4XnwdAgAABYoGMmEaiqGyW5U5Lzu/tvR9cDAMCROWC68CwMaQSq6qZJnp3kpknOSfKcJG9J8oEk\nN6+qh3f32SNqAwCAJRg1NOiZSc5PcmaSL07ysiQP7O6vSXL7JI8bVBcAAEdpfX3nHkxn1NCgU7v7\nW5Kkqs5L8u/dfV6SdPc/VNUJg+oCAIBFGJUI7D/4QXd/JskHt+zX7wEAzNSB9Z17MJ1RjcDW0+i0\nAgDALho2NKiqNqcAX7jp+VqS6w2oCQCACRjLPw+jGoEzBr0uAAA7zPKh8zCkEeju12/dVlX7kly/\nuz80oCQAAFiUIXMEquqGVfXqqvqCjee3TPL+JP9aVe+oqpuMqAsAgKNn+dB5GDVZ+BlJ3pPkMxvP\nfz7JG7KaG/DcJE8ZVBcAACzCqDkCt05S3b2/qq6d5E5Jvqq7P15VZyW5YFBdAAAcpQPeup+FUYnA\npd198F4Ct0/yse5+R5J093qSSwfVBQAAizCqEThQVdfa+PieSV57cEdVnZhxdQEAcJT2H9i5B9MZ\nNTTopUnOrarzkzwoyWlJUlXXSfJLSc4dVBcAAEfJ0KB5GNUIPC/JR5N8eZK7dfcbN7bfNsn1k3z7\noLoAAGARRjUC70vyx0l+LclrDm7s7lcnefWgmgAAmMB+icAsjBqL/2VJ/jarZUTfX1VPrqpTBtUC\nAACLM+rOwv+U5AlJnlBVd0rykCRvq6q/SvL8JGd392cO8yUAADhGmSMwD8NX5+nu13b3Q5PcMMkL\nknxvkg8OLQoAAPa4UXMEPk9VHZ/kW7KaJHzLJH8wtiIAAI6UZT7nYWgjUFVfm+ShSe6f5D1ZDQt6\nQHd/cmRdAAAcOUOD5mFII1BVj8lqXsANkrw4yZ26++0jagEAgCUalQjcJcmTk5xjUjAAwN5i+dB5\nGLVq0OkjXhcAAFg5JiYLAwCwdxwQCMzC8OVDAQCA3ScRAABgUvtFArMgEQAAgAWSCAAAMCn3EZgH\njQAAAJParw+YBUODAABggSQCAABMytCgeZAIAADAAkkEAACYlOVD50EiAAAACyQRAABgUuYIzING\nAACASVk+dB4MDQIAgAWSCAAAMClDg+ZBI3AYH/7s8aNLWJy/u/OZo0tYnK/5x7eMLmFxnvzJd4wu\nYXF+/NpfMbqExbnohWePLmFxXvig648ugZnRCAAAMKkDlg+dBXMEAABggSQCAABMyqpB86ARAABg\nUiYLz4OhQQAAsEASAQAAJrVfIjALEgEAAFggiQAAAJOyfOg8SAQAAGCBJAIAAEzK8qHzoBEAAIAj\nVFU3SvKCJKck2Z/kad393Mv5nB9O8vTuXtuFEg/J0CAAACZ1YH19xx7HoOcmeWt33zzJHZM8sapu\nfaiDq+qmSR6+S7UdlkYAAIBJ7V9f37HHsaSqTkpy1yRnJUl3/1uSlyX5jsN82nOT/MTOV3f5NAIA\nAHBkTkny6e7+yKZtFySp7Q6uqocl+UR3v3w3irs85ggAADCp/Xts+dCqun+SZ26z6+1JLtmy7eIk\nJ27zNW6Y5MeT3G7yAo+QRgAAAA6ju1+S5CVbt1fVVyd5/ZbNJyT59DZf5leTPKm7PzR9hUdGIwAA\nwKT2WiJwGBckOaGqbrgxPyBZDQt6x+aDquraWU0kvnVV/eSm7e9Lcrfu/vtdqXYLjQAAAByB7v5k\nVb0iyaOT/O+qOiXJvbO66P+845JcZ/O2qlrv7pvsUqnb0ggAADCpBSUCSfKIJC+oqguSXJrkUd39\nD0lSVU9NcmF3/+LIAg9FIwAAwKSW1Ah094VJvvUQ+37sMJ839GZiieVDAQBgkSQCAABMakmJwJxJ\nBAAAYIEkAgAATEoiMA8SAQAAWCCJAAAAk5IIzINGAACASWkE5sHQIAAAWCCJAAAAk5IIzINEAAAA\nFkgiAADApCQC8yARAACABZIIAAAwqc9KBGZBIgAAAAskEQAAYFLmCMzD0Eagqu6W5C5JLk3ye939\n5pH1AABw9DQC8zBsaFBVnZnkmVk1AccneXlV3WdUPQAAsCQjE4HvTXK77v7XJKmqZyV5TpJzBtYE\nAMBR2r8uEZiDkZOFjzvYBCRJd5+f5OSB9QAAwGKMTAT2b7PtwK5XAQDApMwRmIeRjcDxVXVqkrVD\nbevudw2pDAAA9riRjcDNkvQ22w9uW09y3O6VAwDAFCQC8zCsEehuNzMDANiDNALzMKwRqKp3Jfnj\nJK9K8truvmRULQAAsDQjhwY9PskZSX41yRdV1Ruzagpe1d3vGFgXAABHYf8B67/MwcihQS9N8tIk\n2ZggfHpWjcFjq+qSrBqC7xtVHwAA7GUjE4HLdPe7k7y7qt6c5I5JvjPJdyfRCAAAzIw5AvMwtBGo\nqhsn+aaNx+lJ/j3JeUmemOR14yoDAIC9beRk4U7yBUl+P8krkjyquz88qh4AAKYhEZiHkYnAOVnN\nCbh3kpOTnFRV53b3ewbWBADAUfqsRmAWRk4WflySx1XVdZKcltXwoEdX1VWzGh50bne/eFR9AACw\nlw2fLNzd/5FVOnBOVV09yf2SPCrJQ5JoBAAAZsbQoHkY3ghU1a2ySgPunOT2ST6Q5E+yus8AAACw\nA0ZOFn5RVkOCTkzy+qwmDH+/OQIAAPMmEZiHkYnAB5M8KMmfdfelA+sAAIDF2Tfwtd/f3ecdbAI2\n7ilwmap64ZiyAAA4GvsPrO/Yg+mMbAS+f8vzV295fpvdKgQAAJZm5NCgtct5DgDADHnnfh5GNgJb\n/4b4GwMAsAdoBOZh5NAgAABgkJGJwPFVdWo+NyTovzwfUxYAAEdjXSIwCyMbgZsleWc+f25Ab/rY\n3yAAANghwxqB7jYsCQBgDzogEZiFkYlAkqSqTk5y+yQnJflQVjcYu2hsVQAAsLcNfVe+qn4qyb8k\neXKShyV5VpIPVtUPjqwLAIAjt76+vmMPpjMsEaiqByW5b5Jbdve7N20/Lcnzquoj3f27o+oDAODI\nmCw8DyMTgUckedjmJiBJuvtPkzwgyaOHVAUAAAswshG4SXf/xXY7NrbfYJfrAQBgAgcOrO/Yg+mM\nbAQ+dZT7AQCAIzRy1aCrbrmB2H/Zv5vFAAAwjfUDoyvgijjWbii2mewHAAB2iBuKAQAwKct8zsPw\nG4oBALC3mNQ7D96VBwCABZIIAAAwKTcUmweNwGFc+3iByW57y/NfOrqExbnlfr+sd9t11y8aXcLi\nXPTCs0eXsDgnPvjbRpewPA963+gKmBmNAAAAk5IIzIO3vAEAYIEkAgAATOqA5UNnQSIAAAALJBEA\nAGBS5gjMg0YAAIBJaQTmwdAgAABYIIkAAACTOiARmAWJAAAALJBEAACASa1bPnQWJAIAALBAEgEA\nACa1fmB0BVwRGgEAACZlsvA8GBoEAAALJBEAAGBSbig2DxIBAABYIIkAAACTkgjMg0QAAAAWSCIA\nAMCkDrih2CxoBAAAmJShQfNgaBAAACyQRAAAgElJBOZBIgAAAAskEQAAYFIHJAKzoBEAAIAjVFU3\nSvKCJKck2Z/kad393G2Ou2qSX0py5yQHkvxlkh/q7k/sYrmfx9AgAAAmtb6+vmOPY9Bzk7y1u2+e\n5I5JnlhVt97muB9Kcqskt0jyFVm9If/E3SpyOxoBAAA4AlV1UpK7JjkrSbr735K8LMl3bHP4LZK8\nsbs/090HkrwuyVfuUqnbMjQIAIBJLWjVoFOSfLq7P7Jp2wVJ7rDNsa9O8tiq+pkkFyf51iR/svMl\nHppGAACASe21ycJVdf8kz9xm19uTXLJl28VJTtzm2N9NcvckH07y2SRvO8TX3DUaAQAAOIzufkmS\nl2zdXlVfneT1WzafkOTT23yZn8nq2vukJP9v4/nvJLnnpMVeCeYIAAAwqfUD+3fscYy5IMkJVXXD\nTdsqyTu2OfZbkvxOd1/S3fuTvDjJGbtQ4yFpBAAA4Ah09yeTvCLJo5Okqk5Jcu+sLvK3Oj/J3arq\n4PX3tyb5h92o81AMDQIAYFLH4Dv3O+kRSV5QVRckuTTJo7r7H5Kkqp6a5MLu/sUkj8pqTsC7qmp/\nkn9O8p2Dak6iEQAAgCPW3Rdm9e7+dvt+bNPHH8wqLThmDBkaVFU/PeJ1AQDYeQuaIzBroxKB+yb5\nqUGvDQDADlrf74J9DkwWBgCABRqVCPy3qtpuNvVluvsBu1UMAADTMYRnHkY1Apcm6UGvDQAAizeq\nEbiwu00YBgDYgyQC8zBqjsDaoNcFAAAyLhH42UGvCwDADpMIzMOQRKC7f+Nw+6vqNbtUCgAAE3Mf\ngXk4VpcPvfHoAgAAYC8bNTTo8qyPLgAAgCPjnft5OFYTAQAAYAcNSQSq6imH2b2W5At3qxYAAKZ1\nQCIwC6OGBt3wcva/cleqAACAhRrSCHT3d23dVlXHJ7lPku9O8g0bfwIAMDPmCMzD8MnCVXXrJA9L\ncv8kb0jym0m+bWhRAACwx42aI3CdJA9O8l1JLs7q4v+B3X3vEfUAADAdicA8jEoE/jHJ85Lcv7s7\nSarqqYNqAQBgQuv7NQJzMGr50DdkNR/ggVV1yqAaAABgsYY0At19vyTfmORjSV5eVX+e5PiqutaI\negAAmM76gf079mA6wyYLd/ePPKzKAAAKF0lEQVRHkzw9ydOr6uuSfE+Sf6qqtyY5u7t/fVRtAACw\n1x0Tdxbu7r/o7ocn+ZIk58TSoQAAsyURmIfhy4du1t0XJXn+xgMAANghx1QjAADA/Hnnfh40AgAA\nTGr9wIHRJXAFHBNzBAAAgN0lEQAAYFKGBs2DRAAAABZIIgAAwKQkAvMgEQAAgAWSCAAAMKkDEoFZ\n0AgAADCp9f0agTkwNAgAABZIIgAAwKRMFp4HiQAAACyQRAAAgElJBOZBIgAAAAskEQAAYFISgXmQ\nCAAAwAJJBAAAmJREYB7W1tfXR9cAAADsMkODAABggTQCAACwQBoBAABYII0AAAAskEYAAAAWSCMA\nAAALpBEAAIAF0ggAAMACaQQAAGCBNAIAALBAGgEAAFggjQAAACyQRgAAABZIIwAAAAukEQAAgAXS\nCAAAwAJpBAAAYIE0AgAAsEAaAQAAWCCNAAAALJBGAAAAFkgjAAAAC6QRAACABdIIAADAAmkEAABg\ngTQCAACwQBoBAABYII0AAAAs0FVGF8CVU1XrSf4pyaVJrp3kfUme1d0v3HTM+5I8ortfVVW/keRu\nSS7c2L2W5JIkP9/dv71rhR/jqupZSe608fSLk3wyycc3nv9Kkp9L8s/bfOqds2qo/ynJs7v7+7d8\n3e9L8uwkd+ru11XV65J82cbXXktyXJJzk/yf7v70lN/THFXVlyR5WpJbbNr8G9391G3+Ll8lyV8n\n+V/d/e9VdcesfpYXZHVOTtzY/9TufvPufAfzUlV/muTV3f0zW7bfNEknuWmShyX59qz+rh6f5G1Z\n/cw/uOVnnqzOybuT/GB3v3dXvolj2BX8fb35mM2eleS/J7lRdz9g0/FPSfJt3f2lm7bdL8lTuvvm\nW77eviQHkjynu39p+u9wnqrqpUnumOTLu/ujG9t+I8mHuvtHtxz7XUkeu/H05Kz+DXxw4/nvdfdP\n7EbNsFM0AvP0zd39ziSpqv8vya9V1Snd/YRDHP+8zb/cqup/JHlTVf19d79958s99m2+gN+4WH9J\ndz974/lDk7y9u79+u8+tqpsk+fckd6+qM7v7M5t2PzjJv2z5lCds+tonJDk7yf9N8qhJvpl5OyfJ\n7ye5b3evV9V/T/JnVXXwZ3jZ3+WqOi6rJus5Se61sf8j3f1lG/v3JXlAkldW1f27+9zd/EZm4vlJ\nHp/kZ7Zsf2iSV2X1c71bkm/o7o9X1VWT/HKS30pyxsaxm3/ma0l+Mavzcucdr34ersjv68uO2ayq\nzkjyoqpa6+71jc3flOSkqvri7n7/pm2vOsRr3jir3/fv7e5XTPutzU9VnZzVGw3PS/KgJGcd7vju\nfkGSF2x87hOSfFl333+Hy4RdY2jQzG2803mvJD9aVde/gp/zD1m923eLyzuWK+ziJH+T5O4HN1TV\nKUmum+SQ74x298VJnpvkLjtd4Ex8eZI3Hbzo6e5/SXLbJL+79cDu3p/kmUnuvHEBunX/gY3U68eS\n/PyOVj1f5yS5QVVd1uRu/Cy/M8mvZXU+zu/ujydJd/+/JP87yX23+2Ib5+3cJDfb4bpn6Qh+X/9Z\nVsnWVyVJVV03q8Ty97O6+D9oayOw+TU/kNV59jtm5cFZ/TxemOS7BtcCw2kE9oDufldWF5u3vyLH\nV9WdsnHBtZN1LdDvJHnIpucP3th2eY7Pfx0WsFSvSPLbVfXYqrpNVR3X3RduXIBu5/gkl256t/RQ\nX/OrNi6i2KS7L0nyoqwSgIPumORqSf4oySuTPLCqnldV31JVX9Dd/9nd/7Hd16uqqyV5YFYXqmzj\nyvy+3kgXX5fPXfSfllVz8PpsJDJVdWqSGyR57WG+lN8xn/OwJL+1kZhcUlW3GV0QjGRo0N7xsSQn\nHWLf91TVPTc+/qKsxvPewxjeK+VWVbU1uv/r7n7gpucvS/LMqvqi7v5wVhdEd05y+qG+6MbF6Q9n\nNTyI1QXpI5LcL8kTk3y6qn4zyY9vPbCqrpHV2N1zLudrfmzjz5M2fczn/FqS124Ma7skq3Pwm939\n2SSvqqrTk/xgVsMjrrsxr+DRm4YVnrzp38aNshqf/vhd/Q7mZ+vv6z+qqq0X6nfeGPrzqiT3zGqe\n0jclOS+ri/6zNtKbM5K8sbsv2u6Fquors/r3dK/t9i/JRvL1yY1mLEl+I8l3ZzWXCBZJI7B33DjJ\nhw+xb/O46scnuUN3n7drle0Nh5wjcFB3X1xVL8/qHdS3Jvlwd/9TVW099AlVdebGx5dk9e7p1jHa\ni9TdlyZ5RpJnVNXVs7rweUaS/RuHbG5qP5vkNbn8i84bZzVh8sLLOW6Ruvtvq+q9Se5ZVX+Y5N5J\nvnrT/jckeUNy2fyiH0/yx1X1xRuHXDZHYOOYM5K8uapuszEshf9q6+/rbecIbHhVkl/YmE90RpKn\ndfeHqurCJLfa2LZ1WNDBxmJfko9kNXn7jZN+B/P08KzSwYMLQawl2V9VPzKwJhhKI7AHVNVpSa6X\nVWR8eX4xyQ9U1d27+w92trJF+q2sxqOfktUY1O1cNlmYz9mYxHfr7n5NctmwlVdW1c2T3DWrlTo+\nb+L7FfTdWb1j+qlJC95bfi2roWzHZ5V0vTtJquouSf6yuz+WrOYXVdUPZPWO9rZj3Lv73Kr6QFar\ncB3q38BiXcnf1+nuCzZ+nvdLcvymd7PPy2oY1x2S/NSWTztcY7FIVXXNrFKRGx2c87Kx/eVZNb+w\nSOYIzFxVfV1W/4n/eHd/8vKO7+7/zOo/jadV1fE7Xd8CvT6r/+TvnuSlg2uZmxOT/H5VXfaf8sbQ\nqXvk8OOft1VV+6rqwUl+KMmjJ6tyb3pxkm/I6h3T52/afmaSp29cRB1ciekhSbq7P/hfvkouSw1O\nTXL+jlY8Q1f29/Umr8pqVbHNSe6fZjWp+5KNBSA4vG/Pqqn9+Jbt52T1ZgEskkRgng7GvtfKKl5+\nfHe/+Ep8/q9n9Z/KmVmNO+XybTdHIFkNS/mrg082lrx8UZKvPNSESrbX3f9cVf8zq6FTP7uxeX9W\n7yr/XD7/AvVQNo9Xv05Wa97fpbv/cvKC95Du/kRV/UFWDezm+Sr3T/LUJH+zsT798Un+Isk3bzrm\n5C3/Ni5J8j3d/Tc7XPZcXJHf19vNEdg8B+lVWc3TeNqm/a/L6iL2BdOXvCc9LNv/rF6Z1VKiL8nn\nDz1Mkn/t7kPO8YK9YG19/XCLbQAAAHuRoUEAALBAGgEAAFggjQAAACyQRgAAABZIIwAAAAukEQAA\ngAXSCAAAwAJpBAAAYIE0AgAAsEAaAQAAWCCNAAAALJBGAAAAFkgjAAAAC6QRAACABdIIAADAAmkE\nAABggTQCAACwQBoBAABYII0AAAAskEYAAAAWSCMAAAALpBEAAIAF0ggAAMACaQQAAGCBNAIAALBA\nGgEAAFggjQAAACyQRgAAABZIIwAAAAukEQAAgAXSCAAAwAJpBAAAYIH+fxmtlzqmwcvrAAAAAElF\nTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f52aafc4860>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "oG00rtx4AY11",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "Now here is the data generator that we will use. It yields a tuple `(samples, targets)` where `samples` is one batch of input data and \n",
        "`targets` is the corresponding array of target temperatures. It takes the following arguments:\n",
        "\n",
        "* `data`: The original array of floating point data, which we just normalized in the code snippet above.\n",
        "* `lookback`: How many timesteps back should our input data go.\n",
        "* `delay`: How many timesteps in the future should our target be.\n",
        "* `min_index` and `max_index`: Indices in the `data` array that delimit which timesteps to draw from. This is useful for keeping a segment \n",
        "of the data for validation and another one for testing.\n",
        "* `shuffle`: Whether to shuffle our samples or draw them in chronological order.\n",
        "* `batch_size`: The number of samples per batch.\n",
        "* `step`: The period, in timesteps, at which we sample data. We will set it 6 in order to draw one data point every hour."
      ]
    },
    {
      "metadata": {
        "id": "VbAi8DrUB1JM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def generator(data, lookback, delay, min_index, max_index,\n",
        "              shuffle=False, batch_size=128, step=6):\n",
        "    if max_index is None:\n",
        "        max_index = len(data) - delay - 1\n",
        "    i = min_index + lookback\n",
        "    while True:\n",
        "        if shuffle:\n",
        "            rows = np.random.randint(\n",
        "                min_index + lookback, max_index, size=batch_size)\n",
        "        else:\n",
        "            if i + batch_size >= max_index:\n",
        "                i = min_index + lookback\n",
        "            rows = np.arange(i, min(i + batch_size, max_index))\n",
        "            i += len(rows)\n",
        "\n",
        "        samples = np.zeros((len(rows),\n",
        "                           lookback // step,\n",
        "                           data.shape[-1]))\n",
        "        targets = np.zeros((len(rows),))\n",
        "        for j, row in enumerate(rows):\n",
        "            indices = range(rows[j] - lookback, rows[j], step)\n",
        "            samples[j] = data[indices]\n",
        "            targets[j] = data[rows[j] + delay][1]\n",
        "        yield samples, targets"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pQxUJQp4CMlR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Now let's use our abstract generator function to instantiate three generators, one for training, one for validation and one for testing. Each will look at different temporal segments of the original data: the training generator looks at the first 200,000 timesteps, the validation generator looks at the following 100,000, and the test generator looks at the remainder."
      ]
    },
    {
      "metadata": {
        "id": "4h2QqCUHIrs9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e1fc0184-f1a8-42ab-f860-2e40d6721d0e"
      },
      "cell_type": "code",
      "source": [
        "float_data.shape"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(18771, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "metadata": {
        "id": "8cdWa9V7CSM0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "lookback = 360\n",
        "step = 3\n",
        "delay = 72\n",
        "batch_size = 64\n",
        "min_index_train = 0\n",
        "max_index_train = 12000\n",
        "\n",
        "min_index_valid = 12001\n",
        "max_index_valid = 18000\n",
        "\n",
        "def gen_train_val_test():\n",
        "  train_gen = generator(float_data,\n",
        "                        lookback=lookback,\n",
        "                        delay=delay,\n",
        "                        min_index=min_index_train,\n",
        "                        max_index=max_index_train,\n",
        "                        shuffle=True,\n",
        "                        step=step, \n",
        "                        batch_size=batch_size)\n",
        "  val_gen = generator(float_data,\n",
        "                      lookback=lookback,\n",
        "                      delay=delay,\n",
        "                      min_index=min_index_valid,\n",
        "                      max_index=max_index_valid,\n",
        "                      step=step,\n",
        "                      batch_size=batch_size)\n",
        "  test_gen = generator(float_data,\n",
        "                       lookback=lookback,\n",
        "                       delay=delay,\n",
        "                       min_index=max_index_valid,\n",
        "                       max_index=None,\n",
        "                       step=step,\n",
        "                       batch_size=batch_size)\n",
        "  return train_gen, val_gen, test_gen\n",
        "\n",
        "train_gen, val_gen, test_gen = gen_train_val_test()\n",
        "\n",
        "\n",
        "# This is how many steps to draw from `val_gen`\n",
        "# in order to see the whole validation set:\n",
        "val_steps = (max_index_valid - min_index_valid - lookback) // batch_size\n",
        "\n",
        "# This is how many steps to draw from `test_gen`\n",
        "# in order to see the whole test set:\n",
        "test_steps = (len(float_data) - max_index_valid - lookback) // batch_size"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "lvUcL6RFCcJP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## A common sense, non-machine learning baseline\n",
        "\n",
        "\n",
        "In our case, the temperature timeseries can safely be assumed to be continuous (the temperatures tomorrow are likely to be close to the temperatures today) as well as periodical with a daily period. Thus a common sense approach would be to always predict that the temperature 24 hours from now will be equal to the temperature right now. Let's evaluate this approach, using the Mean Absolute Error metric (MAE). Mean Absolute Error is simply equal to:"
      ]
    },
    {
      "metadata": {
        "id": "q5Dpqcb_8JY1",
        "colab_type": "code",
        "outputId": "fe610ef3-c73d-4cbe-e8eb-e457dc314a71",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        }
      },
      "cell_type": "code",
      "source": [
        "batch_maes = []\n",
        "predsi = []\n",
        "targetsi = []\n",
        "for step in range(val_steps):\n",
        "    samples, targets = next(val_gen)\n",
        "    preds = samples[:, -1, 1]\n",
        "    predslast = samples[:, -2, 1]\n",
        "    preds = preds + preds - predslast\n",
        "    mae = np.mean(np.abs(preds - targets))\n",
        "    batch_maes.append(mae)\n",
        "    predsi.extend(preds)\n",
        "    targetsi.extend(targets)\n",
        "print(np.mean(batch_maes))\n",
        "plt.plot(predsi[:1000], 'b', linewidth=1, label='prediction');\n",
        "plt.plot(targetsi[:1000], 'g', linewidth=1, label='target');\n",
        "plt.legend();"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.649737159589518\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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qFAaTWm/yZleJioBfbQFxSiV0VdFBQBYmoZDWV6iQELtkus+zKfppl5T4SB5f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\nQwACAMA8ZAYgI9lhTeaLpUG23dF7WxclMq5SgsqpZk/w67EJn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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f52aaf192e8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "8Wsi2Wi6YUwn",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## A basic machine learning approach\n",
        "\n",
        "In the same way that it is useful to establish a common sense baseline before trying machine learning approaches, it is useful to try simple and cheap machine learning models (such as small densely-connected networks) before looking into complicated and computationally expensive models such as RNNs. This is the best way to make sure that any further complexity we throw at the problem later on is legitimate and delivers real benefits.\n",
        "\n",
        "Here is a simply fully-connected model in which we start by flattening the data, then run it through two Dense layers. Note the lack of activation function on the last Dense layer, which is typical for a regression problem. We use MAE as the loss. Since we are evaluating on the exact same data and with the exact same metric as with our common sense approach, the results will be directly comparable."
      ]
    },
    {
      "metadata": {
        "id": "YnybxFwqfyY_",
        "colab_type": "code",
        "outputId": "a80d858b-569c-42d1-b9fe-dface7da974a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "lookback // step, float_data.shape[-1]"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "metadata": {
        "id": "DaB1qA9FYVlf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# from keras.models import Sequential\n",
        "# from keras import layers\n",
        "# from keras.optimizers import RMSprop\n",
        "\n",
        "# model = Sequential()\n",
        "# #model.add(layers.Flatten(input_shape=(lookback // step, float_data.shape[-1])))\n",
        "# model.add(layers.Flatten(input_shape=(None, float_data.shape[-1])))\n",
        "# model.add(layers.Dense(32, activation='relu'))\n",
        "# model.add(layers.Dense(1))\n",
        "\n",
        "# model.compile(optimizer=RMSprop(), loss='mae')\n",
        "# history = model.fit_generator(train_gen,\n",
        "#                               steps_per_epoch=500,\n",
        "#                               epochs=20,\n",
        "#                               #validation_data=val_gen,\n",
        "#                               #validation_steps=val_steps\n",
        "#                              )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "oAJbFzeqfe3L",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# import matplotlib.pyplot as plt\n",
        "\n",
        "# loss = history.history['loss']\n",
        "# val_loss = history.history['val_loss']\n",
        "\n",
        "# epochs = range(len(loss))\n",
        "\n",
        "# plt.figure()\n",
        "\n",
        "# plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "# plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "# plt.title('Training and validation loss')\n",
        "# plt.legend()\n",
        "\n",
        "# plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "AfaXvXXZsChu",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Using Recurrent Neural Networks\n",
        "### Gated Recurrent Unit - GRU cells\n",
        "### Long Short-Term Memory - LSTM cells"
      ]
    },
    {
      "metadata": {
        "id": "j5rtUahprpyI",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "![](https://upload.wikimedia.org/wikipedia/commons/thumb/6/63/Long_Short-Term_Memory.svg/1594px-Long_Short-Term_Memory.svg.png)"
      ]
    },
    {
      "metadata": {
        "id": "fwUNOW6Ziq72",
        "colab_type": "code",
        "outputId": "0dd5784b-8caf-45d0-f7d3-fb9ce9730017",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        }
      },
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras import layers\n",
        "from keras.optimizers import RMSprop\n",
        "\n",
        "train_gen, val_gen, test_gen = gen_train_val_test()\n",
        "\n",
        "model = Sequential()\n",
        "#model.add(layers.GRU(16, implementation=2, input_shape=(None, float_data.shape[-1])))\n",
        "model.add(layers.CuDNNGRU(16, input_shape=(None, float_data.shape[-1])))\n",
        "model.add(layers.Dense(1))\n",
        "\n",
        "model.compile(optimizer=RMSprop(), loss='mae')\n",
        "history = model.fit_generator(train_gen,\n",
        "                              steps_per_epoch=80,\n",
        "                              epochs=20,\n",
        "                              validation_data=val_gen,\n",
        "                              validation_steps=val_steps)"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "80/80 [==============================] - 4s 53ms/step - loss: 0.5599 - val_loss: 0.6423\n",
            "Epoch 2/20\n",
            "80/80 [==============================] - 3s 34ms/step - loss: 0.5089 - val_loss: 0.6319\n",
            "Epoch 3/20\n",
            "80/80 [==============================] - 3s 33ms/step - loss: 0.4931 - val_loss: 0.6019\n",
            "Epoch 4/20\n",
            "80/80 [==============================] - 3s 33ms/step - loss: 0.4921 - val_loss: 0.6151\n",
            "Epoch 5/20\n",
            "80/80 [==============================] - 3s 33ms/step - loss: 0.4785 - val_loss: 0.5905\n",
            "Epoch 6/20\n",
            "80/80 [==============================] - 3s 33ms/step - loss: 0.4696 - val_loss: 0.5925\n",
            "Epoch 7/20\n",
            "80/80 [==============================] - 3s 33ms/step - loss: 0.4587 - val_loss: 0.5977\n",
            "Epoch 8/20\n",
            "80/80 [==============================] - 3s 33ms/step - loss: 0.4596 - val_loss: 0.6064\n",
            "Epoch 9/20\n",
            "80/80 [==============================] - 3s 33ms/step - loss: 0.4414 - val_loss: 0.6027\n",
            "Epoch 10/20\n",
            "80/80 [==============================] - 3s 33ms/step - loss: 0.4455 - val_loss: 0.5918\n",
            "Epoch 11/20\n",
            "80/80 [==============================] - 3s 33ms/step - loss: 0.4400 - val_loss: 0.6055\n",
            "Epoch 12/20\n",
            "80/80 [==============================] - 3s 34ms/step - loss: 0.4353 - val_loss: 0.6077\n",
            "Epoch 13/20\n",
            "80/80 [==============================] - 3s 34ms/step - loss: 0.4438 - val_loss: 0.5976\n",
            "Epoch 14/20\n",
            "80/80 [==============================] - 3s 33ms/step - loss: 0.4410 - val_loss: 0.6175\n",
            "Epoch 15/20\n",
            "80/80 [==============================] - 3s 34ms/step - loss: 0.4300 - val_loss: 0.6130\n",
            "Epoch 16/20\n",
            "80/80 [==============================] - 3s 33ms/step - loss: 0.4284 - val_loss: 0.6249\n",
            "Epoch 17/20\n",
            "80/80 [==============================] - 3s 33ms/step - loss: 0.4225 - val_loss: 0.6348\n",
            "Epoch 18/20\n",
            "80/80 [==============================] - 3s 33ms/step - loss: 0.4215 - val_loss: 0.6311\n",
            "Epoch 19/20\n",
            "80/80 [==============================] - 3s 33ms/step - loss: 0.4149 - val_loss: 0.6473\n",
            "Epoch 20/20\n",
            "80/80 [==============================] - 3s 33ms/step - loss: 0.4138 - val_loss: 0.6512\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "JIYUmkqQi_eG",
        "colab_type": "code",
        "outputId": "464031d8-f85a-480a-9c5d-e470d3431a12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 605
        }
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs = range(len(loss))\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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f77ZFRY7GjQurX79oDlqGmrK31AFy5rHHpKOOck8UNGSIZEx+1gMHAADwuvff9+lvfytL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\nmU/UMvMIK00EYSV3rruuWBM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J4OFb7+cffrD+CCjpDtXJlrIyR3/9qxtODjkkrp13Luyh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\nACcRVgAAAAA4ibACAAAAwEn/B4NpABlc+FouAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f52610baba8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "cXE_RmuKnWhp",
        "colab_type": "code",
        "outputId": "da17fda1-ece7-4f89-8dd2-5a0fd2cd761d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1404
        }
      },
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras import layers\n",
        "from keras.optimizers import RMSprop\n",
        "\n",
        "train_gen, val_gen, test_gen = gen_train_val_test()\n",
        "\n",
        "model = Sequential()\n",
        "model.add(layers.GRU(64,\n",
        "                     #dropout=0.1,\n",
        "                     recurrent_dropout=0.2,\n",
        "                     input_shape=(None, float_data.shape[-1])))\n",
        "model.add(layers.Dense(1))\n",
        "\n",
        "model.compile(optimizer=RMSprop(), loss='mae')\n",
        "history = model.fit_generator(train_gen,\n",
        "                              steps_per_epoch=20,\n",
        "                              epochs=40,\n",
        "                              validation_data=val_gen,\n",
        "                              validation_steps=val_steps\n",
        "                             )"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/40\n",
            "20/20 [==============================] - 14s 681ms/step - loss: 0.5384 - val_loss: 0.6619\n",
            "Epoch 2/40\n",
            "20/20 [==============================] - 12s 580ms/step - loss: 0.5094 - val_loss: 0.6319\n",
            "Epoch 3/40\n",
            "20/20 [==============================] - 12s 579ms/step - loss: 0.4975 - val_loss: 0.6321\n",
            "Epoch 4/40\n",
            "20/20 [==============================] - 12s 583ms/step - loss: 0.5255 - val_loss: 0.6550\n",
            "Epoch 5/40\n",
            "20/20 [==============================] - 11s 565ms/step - loss: 0.4929 - val_loss: 0.6330\n",
            "Epoch 6/40\n",
            "20/20 [==============================] - 11s 568ms/step - loss: 0.4793 - val_loss: 0.6046\n",
            "Epoch 7/40\n",
            "20/20 [==============================] - 11s 568ms/step - loss: 0.5135 - val_loss: 0.5886\n",
            "Epoch 8/40\n",
            "20/20 [==============================] - 11s 568ms/step - loss: 0.4978 - val_loss: 0.5772\n",
            "Epoch 9/40\n",
            "20/20 [==============================] - 11s 569ms/step - loss: 0.4998 - val_loss: 0.5809\n",
            "Epoch 10/40\n",
            "20/20 [==============================] - 12s 578ms/step - loss: 0.4775 - val_loss: 0.5757\n",
            "Epoch 11/40\n",
            "20/20 [==============================] - 12s 589ms/step - loss: 0.4688 - val_loss: 0.5736\n",
            "Epoch 12/40\n",
            "20/20 [==============================] - 12s 575ms/step - loss: 0.4839 - val_loss: 0.5967\n",
            "Epoch 13/40\n",
            "20/20 [==============================] - 11s 575ms/step - loss: 0.4776 - val_loss: 0.5867\n",
            "Epoch 14/40\n",
            "20/20 [==============================] - 12s 576ms/step - loss: 0.4865 - val_loss: 0.5834\n",
            "Epoch 15/40\n",
            "20/20 [==============================] - 12s 579ms/step - loss: 0.4770 - val_loss: 0.5748\n",
            "Epoch 16/40\n",
            "20/20 [==============================] - 12s 584ms/step - loss: 0.4607 - val_loss: 0.5719\n",
            "Epoch 17/40\n",
            "20/20 [==============================] - 12s 576ms/step - loss: 0.4675 - val_loss: 0.5597\n",
            "Epoch 18/40\n",
            "20/20 [==============================] - 12s 581ms/step - loss: 0.4635 - val_loss: 0.5591\n",
            "Epoch 19/40\n",
            "20/20 [==============================] - 12s 579ms/step - loss: 0.4852 - val_loss: 0.5771\n",
            "Epoch 20/40\n",
            "20/20 [==============================] - 12s 581ms/step - loss: 0.4883 - val_loss: 0.5506\n",
            "Epoch 21/40\n",
            "20/20 [==============================] - 12s 576ms/step - loss: 0.4676 - val_loss: 0.5601\n",
            "Epoch 22/40\n",
            "20/20 [==============================] - 12s 576ms/step - loss: 0.4618 - val_loss: 0.5478\n",
            "Epoch 23/40\n",
            "20/20 [==============================] - 12s 577ms/step - loss: 0.4768 - val_loss: 0.5490\n",
            "Epoch 24/40\n",
            "20/20 [==============================] - 12s 575ms/step - loss: 0.4565 - val_loss: 0.5515\n",
            "Epoch 25/40\n",
            "20/20 [==============================] - 12s 575ms/step - loss: 0.4495 - val_loss: 0.5472\n",
            "Epoch 26/40\n",
            "20/20 [==============================] - 12s 579ms/step - loss: 0.4497 - val_loss: 0.5440\n",
            "Epoch 27/40\n",
            "20/20 [==============================] - 12s 579ms/step - loss: 0.4610 - val_loss: 0.5578\n",
            "Epoch 28/40\n",
            "20/20 [==============================] - 12s 576ms/step - loss: 0.4596 - val_loss: 0.5565\n",
            "Epoch 29/40\n",
            "20/20 [==============================] - 12s 579ms/step - loss: 0.4537 - val_loss: 0.5612\n",
            "Epoch 30/40\n",
            "20/20 [==============================] - 12s 584ms/step - loss: 0.4424 - val_loss: 0.5560\n",
            "Epoch 31/40\n",
            "20/20 [==============================] - 11s 574ms/step - loss: 0.4567 - val_loss: 0.5604\n",
            "Epoch 32/40\n",
            "20/20 [==============================] - 12s 575ms/step - loss: 0.4421 - val_loss: 0.5516\n",
            "Epoch 33/40\n",
            "20/20 [==============================] - 12s 576ms/step - loss: 0.4481 - val_loss: 0.5545\n",
            "Epoch 34/40\n",
            "20/20 [==============================] - 12s 577ms/step - loss: 0.4515 - val_loss: 0.5609\n",
            "Epoch 35/40\n",
            "20/20 [==============================] - 11s 573ms/step - loss: 0.4476 - val_loss: 0.5535\n",
            "Epoch 36/40\n",
            "20/20 [==============================] - 11s 575ms/step - loss: 0.4398 - val_loss: 0.5546\n",
            "Epoch 37/40\n",
            "20/20 [==============================] - 12s 585ms/step - loss: 0.4577 - val_loss: 0.5578\n",
            "Epoch 38/40\n",
            "20/20 [==============================] - 12s 576ms/step - loss: 0.4358 - val_loss: 0.5625\n",
            "Epoch 39/40\n",
            "20/20 [==============================] - 11s 567ms/step - loss: 0.4330 - val_loss: 0.5700\n",
            "Epoch 40/40\n",
            "20/20 [==============================] - 11s 568ms/step - loss: 0.4429 - val_loss: 0.5629\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ska6vOVwoGiU",
        "colab_type": "code",
        "outputId": "add91033-d630-4557-93fd-e678b2c44fb2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 605
        }
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs = range(len(loss))\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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x74t6nw+uucZNXJyP9PTsL/qXLHHSooW7sAt3gtsN771n/+NWvryX5s1dxMZC\no0YePv44+303bXKwbZvBRRcFe7737OuxKGfLizyqw4JTHRac6jA4VI8FpzosONVhcER2PRZ1ywqW\nZX0O1M3m+UYnfe0B+mX9O3W/zcDVhVnGSJSYCNdc42H58uy/xcuXO/B6/V3GQmHlSge//WZ/sttv\ndxMb6/+6R49M27ACMGlSNC+/nBHsIoqIiIhImCnqMStSiJo2tW85SUmJYsuW0H37c1qxvmvXv1ep\nbdTIQ5Uq9i0nM2dGk7Vwq4iIiIgUYworxVigsAKwbFloGtZ+/92wbeEBqF/fzfnn/x1OHA7o3t1l\nu39qqsHMmYHDj4iIiIhEPoWVYuyMM3xcfLH9IPqlS0MTVt57LxqPx37R05NbVY7r3NlFdLR9X8p3\n343WADsRERGRYk5hpZhr1sy+dWXHDgd79tiHiGDwemHaNPtWkDJlfLRq9e8yVqzo4+abA5d940ZH\nUMooIiIiIuFJYaWYCxRWoPC7gq1Z42DPHvsfs/btXcRnP8MyPXrYdwUD/0B7ERERESm+FFaKudq1\nvZxxhv1g9cLuCpaXgfWnql/fwwUX2Hdjmz/fyf79hdsyJCIiIiJFR2GlmDMMaNLEvnVl3TpHoc2s\nlZJisHixfRiqU8cTcL0Uw4A77rAPMy6XwXvvqXVFREREpLhSWCkBAnUFc7kMVq0qnNaV99934nLl\nbWD9qW6/3UVCgv1I+smTo/HYN76IiIiISARTWCkBGjb0BLzgL4yuYD5f4IH1iYk+2rTJOayUKgXt\n2tnvt2dPFCtXaqC9iIiISHGksFICxMfDtdfat658/LEj6K0TGzY42LnTPkTcequLpKTcHSvngfYx\neSmaiIiIiEQIhZUSolkz+zRy4EAUmzYFt3ViypT8D6w/Ve3aXq64wr78H39c+FMwi4iIiEjoKayU\nEI0b5zSFcfDCyl9/wYIF9l3LLrrIw6WX2g+sz06PHpm223w+I8dwJCIiIiKRR2GlhKhUyUedOvat\nE8Fcb2XWrGjS0wMPrDfy2BDSurWb5GT7gDNtWjQZGXk7poiIiIiEN4WVEiTQrGDffefghx8K3pXK\n5wvcBSw+3kf79rnvAnZcXBx06mRf/pSUKBYuLNw1Y0REREQktBRWSpCmTQt/Nfsvv4xixw77LmWt\nWrkpUyZ/x+7e3b4rGGhFexEREZHiRmGlBLnwQi+VK9t3pQpGWCnIivU5OfdcHzfcYB+4NmxwsmOH\nfqRFREREigtd2ZUghhG4dWXDBgeHDuX/+EePwocf2oeVGjU81KtXsDmSc57GWK0rIiIiIsWFwkoJ\nEyisuN0GK1fmv3Xlo4+iSU2+0gqoAAAgAElEQVS1H/fSpUveB9afqkkTN2eead86NHNmNEePFuwc\nIiIiIhIeFFZKmAYNPCQmFs5q9oG6gMXE+Lj99sBjZnLD6YTu3e1bV44eNZg9W60rIiIiIsWBwkoJ\nExtLwHEfK1Y4ceVjWMm2bVF8+aX9wPqbbnJTvrx9SMqLLl1cOJ32x3rnnWh8wTmViIiIiBQhhZUS\nKFBXsEOHDD7/PO8LRBbmwPpTVark46ab7N/D9u0ONm3Sj7aIiIhIpNMVXQnUuLEHwwheV7DUVP9C\nkHbOPttLw4YFG1h/qpwH2scE9XwiIiIiEnoKKyVQhQo+rrgieFMYz5/v5PDhwAPro4L8k9aggYfz\nz7cPQPPmOfnzz4IvcikiIiIiRUdLfpdQzZq52bQp++5eP/wQxdtvR5OY6MPlMnC5wO0m6/+/H/uf\nMwKGG4fDR6dOwesCdpxh+FtXnnwy+/eQmWnw3ntO+vQJ/rlFREREJDQUVkqoZs3cDB0aa7t94MC4\noJynSRM3lSoVzmj322938fzzsbbTJc+YEa2wIiIiIhLB1A2shKpRw8vZZ9t3BQuWbt0KLyyUKQNt\n29of/7vvHOzcqa5gIiIiIpFKYaWEMgx/60phOvNMLzfeGNyB9ae6447AYWjRIq25IiIiIhKpFFZK\nsEBTGAdDp04uHHmfBTlPLrnES9Wq9i1Eixapp6OIiIhIpFJYKcGuusrD6acXTlewhARfoXYBO84w\nCLjmypYtDvbuVVcwERERkUiksFKCRUfDoEEZQT+uw+Fj2LB0zjwzNMvIt2wZuIVo8WK1roiIiIhE\nIl3FlXDt27spXTqVV1+NZceOKNxucDr9/6KjfVn/g8Phfxwdzb+eO/44OtrHeef5aNvWxcUXF/7g\n/ePq1vVQsaKX/fuzz94LFzrp2VOzgomIiIhEGoUVoWlTD02bphZ1MfItKgqaN3czZUr2q9avX+/g\nwAFITg5xwURERESkQNQNTIqFQF3BPJ7AC1eKiIiISHhSWJFioWFDD6VL24+RWbhQUxiLiIiIRBqF\nFSkWYmKgSRP71pVVqxwcPRrCAomIiIhIgSmsSLERaArjjAyDlSvVFUxEREQkkiisSLFx441u4uLs\nu4JpgUgRERGRyKKwIsVGYiJcf71968ry5U4ygr+sjIiIiIgUEoUVKVYCdQU7csTgs88cISyNiIiI\niBSEwooUK82auXE41BVMREREpDhQWJFipVw5uPpqj+32xYudeOw3i4iIiEgYUViRYidQV7CUlCg2\nbVJXMBEREZFIoLAixU6gsAKwcKG6gomIiIhEAoUVKXbOOMPH5Zfb9/VatMiJz35Yi4iIiIiECYUV\nKZYCta78/HMU27bpR19EREQk3OmKTYqlli1dAberK5iIiIhI+FNYkWKpWjUfNWsG7gomIiIiIuFN\nYUWKrRYt7LuCffutg127jBCWRkRERETySmFFiq2WLXOaFSw6RCURERERkfxQWJFiq1YtL1Wrem23\nL16srmAiIiIi4UxhRYotwwjcFWzzZge//aauYCIiIiLhSmFFirWcuoJpoL2IiIhI+FJYkWKtbl0P\nFSrYdwVTWBEREREJXworUqw5HIG7gq1b5+DgwRAWSERERERyTWFFir1AXcE8HoOlS9W6IiIiIhKO\nFFak2GvY0EOpUj7b7eoKJiIiIhKeFFak2IuJgSZN7FtXVq1ycuxYCAskIiIiIrmisCIlQqCuYOnp\nBitXOkJYGhERERHJDYUVKRFuuMFNbKy6gomIiIhEEoUVKRGSkvyBxc6yZU4yM0NYIBERERHJkcKK\nlBg33WQfVg4fNvjkkxAWRkRERERypLAiJUbTpm4cDvuuYB9+GMLCiIiIiEiOFFakxEhOhquv9thu\nnzsXPPabRURERCTEFFakRAnUFWzfPtiwIYSFEREREZGAFFakRAkUVkBdwURERETCicKKlChnnOGj\nTh37vl4ffQQ++2EtIiIiIhJCCitS4gRqXfnxR/jqqxAWRkRERERsKaxIidOypSvgdnUFExEREQkP\nCitS4px3no8LLgjcFUxEREREip7CipRIgbqCff017NwZwsKIiIiISLYUVqREymlWMLWuiIiIiBQ9\nhRUpkWrX9lKlitd2u8KKiIiISNFTWJESyTACt66sXw+//26EsEQiIiIiciqFFSmxWrYM3BVs8WJH\niEoiIiIiItlRWJESq25dDxUq2HcFW7jQGcLSiIiIiMipFFakxHI4oHlz+9aVTz91sH27fkVERERE\nioquxKREC9QVzOs1GDQoFp8vhAUSERERkRMUVqREa9jQQ5ky9mlkzRonH3+ssSsiIiIiRUFhRUq0\n2Fjo2TMz4D7PPBOLyxWiAomIiIjICQorUuL16ZPJaafZD7TfudPBu+9Gh7BEIiIiIgJQpNMdmaZ5\nDfBfoDRwCHjIsqy12ez3BHAf4AFWA70sy8owTbMU8DZwOeAFplqWNSRU5ZfiISkJBg7M5OGH42z3\nefnlWNq3d1G2bAgLJiIiIlLCFVnLimmaCcAs4FHLsqoDjwKzTNOMO2W/jkBn4BKgBhAHXJ+1eRiQ\nDpwP1AFuN02zTUjegBQrnTq5ufRS++0HDxqMHBkbugKJiIiISJG2rDQC9luW9TGAZVkrTNP8C7gR\nWHTSfncDIy3LOpj1uONJ2zoBzSzL8gJHTNOcjD/YzCn00tswjNCuep7d6fzPafX1vHA6YeRIaNTI\nfp+JE6O5804X552n6cGyo5/FglMdFpzqMDhUjwWnOiw41WFwRHo9FmVYqQHsOuW5nYDJP8PKpcAy\n0zTXAhXwB5Gn8HcdSz7lGDvxh5UiU6FCUlGeHoDy5Yu+DJHoxhuhdWuYNy/77S6XwbBhicwpsigc\nefSzWHCqw4JTHQaH6rHgVIcFpzoMjkiqx6IcYJ+AvwvXydKAxFOeKwvUB5oADYGWQO+s13PKMbJ7\nvUiuvfyyv5XFzty58MknoSuPiIiISElWlGHlGH8HjuPigaOnPHcQmGxZVqplWfuBiUDTrNdzyjGy\ne71IrtWoAX36BN6nf3/weEJTHhEREZGSrCjDyrf4u4KdzAS2n/LcLvxdvo7zAG7Lsg4A+085Rnav\nF8mTQYMgOdl++9at8O67oSuPiIiISElVlGNWVgBJpmneYlnWXNM02+Gf6WvVKfu9A/Q2TXN21uOu\n+FtXAKYAA0zTvB3/+JW78XcRKzIpKaFt2DGMf/c7/PPPo/g0BjxPTq7HcuVg8GB46CH7/QcO9HLj\njakkRU6Xz0JX0J/Fgwdh9OgY3nvPSXQ01KvnYejQTE47reT8MOv3ueBUh8Gheiw41WHBqQ6DI1j1\nWFTjsossrGStk9IWeMM0zRHAAeBWy7IyTdNcATxjWdZnwASgJv6WmDRgHjA+6zBPA+OA7/C3uIyx\nLGtxiN/KP/hC/hv075kcfL6iKEek+2c93ncfjBrlZefO7Bsf//gjilGjohk4MDMUhYsQ+f9Z9Hrh\n3nvjWbXq7z9JH30UxZo1DubOTaNGDftFO4sX/T4XnOowOFSPBac6LDjVYXBEdj0akVLQSLF//5GQ\nVqhhGP9KuikpRyPmBzBcZFeP06en0aVLvO1r4uJ8rFt3jMqVVddQsJ/FGTOcPPRQ9nV91lleFi1K\n5Ywzin896/e54FSHwaF6LDjVYcGpDoMjWPVYsWKpIpnruCjHrIiEtSZNPFx7rdt2e3q6wdChWigy\nGCZPjrHd9uuvUXTsGM+hQyEskIiIiIQFhRURG4YBzz6bQVSU/Z2HDz+MZvNm/RoVxLffRvHFF46A\n++zY4aB793jST53sXERERIo1XWWJBHDhhV66dHEF3Ofpp+M02K8Apk2LztV+69c7uf/+OE0bLSIi\nUoIorIjk4LHHMklKsk8jX3zhYM6copxYL3JlZMDMmbmvu4ULo3n88ViFQxERkRJCYUUkB6ed5qNf\nv8Czfj33XCxpaSEqUDGyZImTAwfy9mfo3XdjeOUV+zEuIiIiUnworIjkwr33ZlKliv30ub/8EsW4\ncbqAzqupU3PXBexUL70Uy5Qp+XutiIiIRA6FFZFciIuDp5/OCLjPa6/FsG9fkczqF5F++slg9er8\nd5/7z39iWbxY3e9ERESKM4UVkVy65RY3devaj+4+dszgpZfUupJb770XuGXkoosCj6T3eg169Ypj\n48bAM4mJiIhI5FJYEcklw4Dnngs8d+60adFs26Zfq5x4PIHDSoUKXhYuTKVxY/t1bsC/1k23bvF8\n+63qXEREpDjSJ7xIHtSp46VdO/upjH0+g2ee0WxVOfnkEwe//Wb/56dDBzcJCTB+fBqXXx64heWv\nvww6dozn11/VBU9ERKS4UVgRyaMnn8wgLs4+jXz6qZNly9Q1KZCcBtZ36eKffS0xEaZOTaN69cCB\nZe9e/yr3Bw8GrYgiIiISBhRWRPKocmUfDzwQeCrjwYPjyAy8S4m1b5/BsmX2A+Pr13dTvfrfYbB8\neR/vv59GpUr2s7EBWJaDbt3iNYW0iIhIMaKwIpIPffpkBrx43rUrikmTNLVudj74IBq3277LVpcu\n/+5mV6WKjxkz0ihdOnD/us8/d9KrVxzuwENdREREJEIorIjkQ1ISPPFE4KmMhw+P5ccfNY7iZD6f\nfxICO6VK+WjVKvukcdFFXiZPTiM2NnBgWbIkmsce07ghERGR4kBhRSSfOnRwU7u2/ViKw4cN7rwz\nnmPHQlioMLdhg4MffrD/s3PrrS4SEuxff/XVHt58Mx3DCJxEpkyJYfhwTSMtIiIS6RRWRPIpKgqe\nfTZw68r27Q4GDIjTXf4sOQ2s79rVfqa141q1cjNsWOB6Bxg5MpYXX4whJUWtWyIiIpFKYUWkABo0\n8NCiReAL7A8/jGbcOI1fOXQI5s+3H1hfq5aHiy8OPIj+uLvuctG/f86B5ZVXYrn44kS6dYtn/nwn\n6YGXyREREZEwo7AiUkBDh2ZQpkzgppPBg2NZu7ZkT2c8e3Y06emBB9YbeWgEeeyxTLp2zXnKNbfb\nYOlSJz17xlO7dhKPPBLL559HqbVLREQkAiisiBRQlSo+xoxJCziOwuMxuOeeOPbuLbldkqZPt29d\niovzBVxsMzuGAcOHZ9C8ee5fd+iQweTJMdx8cyL16iUyYkQMu3eX3O+JiIhIuFNYEQmCRo08PPZY\n4Lv8KSlR3HVXPBk5914qdr76KoqvvrJvWbr5Zjdly+b9uE4njBmTTt26gReNzM7u3VEMHx7LlVcm\n0apVPFOmRHPoUN7LICIiIoVHYUUkSPr1y8zxLv+WLQ4GDowNUYnCR6DpiiF3A+vtJCTA1Kmp1KiR\n98By3MaNTgYMiKNWrSTuuSeO5csduPJfJBEREQkShRWRIImKgtGj0znvvMCDxKdOjWHKlJIz4D41\n1T9exc6553q56qr8Bw2AcuXgo4/ScpzsICcZGQZz50bTpUsCl16ayOTJ0XhzN+ZfRERECoHCikgQ\nlS4NkyalkZgYePT2wIGxbN5cMn79Fixwcvhw8AbW26lY0cekSenMm5dKt26ZOa52n5P9+6N45JE4\nHnggDnf261SKiIhIISsZV0siIWSaXkaNCjxHbmamwV13xfPHH8V/cHegLmAOh48OHYLX38owoH59\nDyNHZvD110cZPz6NJk3cOBz5Dy4ffhhNz55xJXKskYiISFFTWBEpBK1auXnoocBXt7/9FsU998QV\n67ERu3YZrF9vv7ZK06ZuKlUqnDmE4+PhllvcTJuWxv/+d4znnkundu38dTdbvDia7t3jSU0NciFF\nREQkIIUVkUIycGAm110XuP/Q+vVOhgwJzoD7ffsMPvrIyahRMWzY4MBTsGEgQVGYA+vz4rTTfPTq\n5WLFilRWrz5Gnz4ZnH563gajfPKJk06d4jlypJAKKSIiIv+isCJSSBwOGDs2jSpVAl8UjxsXw6xZ\n9q0Pgfh88NlnDu6+O47LLkukV694hg6NpXXrBG68MYHVq4tuIUqXC2bMsH9fZ5zh5YYbQp+oatb0\nMmhQJl9+eYwPPkilfXsXCQm5a91Zv97JbbclcPBgIRdSREREAIUVkUKVnOwfcB8XF/hieMCAOL7+\nOve/jocOwbhx0TRsmMCttyYwb140bvc/x7/s2OHgttsS6No1np07Qz82ZsEC/yB1O506uXDmL6MF\nhcMB11/v4c0309m27SijRqXlar2WLVsctG2bUCLGG4mIiBQ1hRWRQla7tpcRIwIPuE9LM7jzzvgc\n79hv3RpFv36xXHxxEk89Fcf33+fccrJsmZNrr03k6adj+euvvJS8YCZMCLy9U6fwGayTlAQdO7qZ\nNy+Vrl0DL+4JsH27gzZt4tm7V4FFRESkMCmsiITA7be76dkz8EXwnj1R3Hdf/L/GmqSmwvTpTpo2\nTaBp00SmT48hLS1vF8lut8HYsTHUq5fEhAnRhT6o/+efYckS++3XXuvm7LMLZ2B9QTgcMHJkBvfe\nm3Ng2bnTQevWCezercAiIiJSWBRWREJkyJAM6tULPOD+k0+cvPRSDADffx/FU0/FcsklSfTrF8/W\nrQUff3LwoMETT8Rx/fUJfPyxA18h5YVJkwi4mGKoBtbnh2HAc89l8PDDOc9VvGdPFK1bJ/D99/pT\nKiIiUhj0CSsSIjExMGFCOpUqBR5w/9prsbRokUCDBomMGxfDoUPBv3P//fcOOndOoEOHeL79Nrh/\nBrxeePtt++3lyvlo0SK8V1k0DP9sbk8+mXNg+f33KG65JT5PY45EREQkd/TpKhJClSr5mDgxjejo\nwE0amzeHZhavVaucXH99Ao8+GktKSnBC0YoV8NNP9ttvv91FbHBmay50fftm8sILgccbAaSkRHHr\nrQl88YX+pKanw3vvOenRI47eveNYtqzoZqQTEZHIp09WkRCrW9fL888Hfzn0WrU8jBiRzrBh6ZQr\nl/v+XV6vwaRJMdSvn8gbb0QXeKX2nAbWd+4cvl3AsnP33S5eey0Nwwhcp4cOGdx2WwJr15bci3O3\nG3r0iKdv33gWLYpm5sxounZN4OmnIySdiohI2FFYESkCd9zhCspsWHFxPjp0cLF48TFWrEile3cX\nPXu62LjxKL16ZeJ05j60HD5sMGRIHNdck8jChc6AY07spKTARx/Zb7/8cg81a+bjwEWsc2c3Y8ak\n51ifx44ZdOoUz4oVJTOwjB4dw8qV/56PeuzYGDZt0seNiIjknT49RIqAYcBLL6Vz6aX5WxSxWjUv\nQ4ak87//HeX119O5/HIvxkm9uMqW9Q8S//TTYzRvnrdQtHt3FHfeGc8FFyTRrVs8b7wRzZYtUbma\nQWzKFALu16VLZLWqnKxtWzcTJ6YRExM4sKSnG3TvHs+CBUW4iEwROHwY3ngjxnb75Mn220REROwo\nrIgUkbg4mDgxjfLlc9fS4HD4uOkmFx98kMq6dce4/34X5coFfs155/mYPDmdWbNSqVkzb8Hor78M\nli51MmRIHM2bJ3L++Um0bx/PyJExrF3rIC3tn/v7fIG7gCUk+GjTJnLDCkDz5h6mTUvLccV7l8vg\nnnvimDHDWWgzroWb8eMDTwaxaJGT9JyH/4iIiPxDybr1JxJmKlf2MW5cOrfdFo/Xm/2F3umne+na\n1UW3bi7OOCN/V77XXuth5cpUpk+PZtiwGFJS8n6fIjXVYM0aJ2vW+P9sREf7uOwyD/Xre7jqKg9R\nUQbbt9u/vm1bF0lJ+Sp+WLnuOg8zZqTRpUs8R47YX5x7PAYPPRTPyJFeWrRwc9NNburW9eAohj3E\nDh+GMWMCt5wcOWLwySfOsJ8JTkREwotaVkSK2DXXeHjnnfR/3a2/9lp/t6PNm4/x6KOZ+Q4qxzkc\n0K2bi40bj/Hggxk5dmfKictl8PnnTkaNiqVTJ/80yIFEchewU9Wv72H27NRcTWTw009RjBkTQ+vW\nCdSunUj//rEsX+4oVq0MuZ1ie+5c3R8TEZG8MXwlpY9CiOzffySkFWoYBhUq/PN2dUrKUfR9zZtw\nqMcDB2DtWv/F3CWXeKhatXDPvXu3wXPPxTJ/fnShngfgggs8rF6d+o9xNcXBjh1RtG8fz/79eb/v\nk5joo1Ejf4tL48ZuSpcOj5/DvDp0CK64IilXYSUhwcf27UdJSCi88kRiHYYj1WPBqQ4LTnUYHMGq\nx4oVSxXJp7haVkTCRHIytGrlplUrd6EHFYBzzvHx9tvpzJ2bysUX52+gf2516eIqdkEFoGZNL/Pn\np3LWWXmf4ezYMYN586K57754atZMokOHeCZNcvLbb4VQ0EKUl4VLU1MNVqxQ64qIiOSewopICXfV\nVR6WLUvl9dfTqFUr+KElJsZH+/bFd5xCtWo+5s1L5Zxz8j8ls8vlH8/xn//EceaZcNVVMHw4/Pxz\nEAtaCA4d8k9LnBdz5iisiIhI7imsiAhRUdChg5uVK1P5+uujvP12GnffnUmtWp4cF0PMyU03uSlf\nvng32Vep4mP+/FQuuig4YW/DBnjsMTjvPBg9uvC76eXXuHExHD6ctyaz5cudHD1aSAUSEZFiR7e4\nROQfKlXyneiOBv67559/7mDDBgfr1zv53/+icLlyf4Hao0fxGVgfSKVKPpYtS2XixGimTInmu+8K\nPu2XywVDhsRSvryXjh3Dq3UqP60q4F+HZulSJ+3ahdf7ERGR8KSwIiIBlSkDTZp4aNLEA2SSmgpb\nthwPLw42b3aQmpp9eLnjDhdXX12442HCSXQ09OrlolcvFzt3GixaFM3ixU42by5YcHn88TguvzyV\n88/Pf1ezYBs7Nu+tKsfNnauwIiIiuaPZwIJMs4FFJtVj/rlc8NVXUWzY4GTz5lh++cV/0X7vvdCy\n5VGcTtXh778bLFniZNEiJ5995sDtzvtFfs2aHpYsSSU+8AzRIXHoEFx+eZJtWDEMH7Vre/nqq+xD\nWkyMj2++OUqZMsEvm36Xg0P1WHCqw4IrDnWYkQFTp0bz4YfReDzQuLGbu+/OpGzZ0JUh0mcDU8uK\niBRIdDRcfrmXK65wUaFC7D+2paRQYlZwD+T003306OGiRw8Xhw7Bxx/7g8uKFU7bVqlT7djhYNCg\nWF5+OaOQS5uzMWMCt6q0aeOfkvmee7JPVpmZBosXO8Oua5uISDD9+qvBHXfE/+PGzZYtDqZOjWbi\nxDTq1Amf1vJwpgH2IiIhVKYMtGvn5u230/n226NMnZpKly6ZVKiQ84fWu+/GMG9e0d5j+usv/8B6\nO4bho3//TBo3dv9rodOTzZkTvhMHiIgU1KZNUTRtmpBtC/PevVG0bp3AlCn6O5gbCisiIkUkLg6a\nNvXw6qsZfP31MebMSeW00wK/5uGH49i9u+gWrRkzJoYjR+zP37atG9P0kpgITZvat5ysWePgwIHC\nKKGISNGaMcNJ27YJARcMzsw0GDAgjocfjiU9PYSFi0AKKyIiYcDhgAYNvEydSsAFNI8cMbj33ngy\nM0NXtuMOHoTx43NuVTmuTRv7sOJ2GyxcqLuKIlJ8eDwwaFAsDz0UT2Zm7m4qTZsWQ+vWCfzySzFc\nOTlIFFZERMJIkybw+OOB99m61cHQobGBdyoEY8fm3KpSo8bf3dluvNFNqVKBuoJp2KSIFA+HDkGX\nLvGMGZP3Kd23bnXQuHECq1cXfMr74khhRUQkzDz7LDRoEHifMWNiWLYsdB9sBw/mPFZlwIB/NvfE\nxUHz5vatK2vXOvjjD91NFJHItmuXQYsWCaxcmf8bMAcORNGhQzyjRsVoYppTKKyIiIQZpxOmT4dy\n5QLv9+CD8ezdG5qL/bFjYzh6NHCrSnbrwLRpY78oqNdrsGCBWldEJHKtXOmgWbNEdu4s+M0jr9dg\n6NBY7rorjiNHglC4YkJhRUQkDFWtCpMmBd7n4EGD++6Lw13IMwDn1KoSFfXvVpXjrrvOQ9my6gom\nIsWLzwdjxkTTuXN8rhbIrVzZS8OGuftjvXBhNM2bJ/Ddd7pMB4UVEZGw1bo19OoVeCT9hg1ORozI\nex/pvBgzJn+tKgAxMdCypX3rysaNDn77TV3BRCRyZGRA375xDBoUh9eb89+vevXcLF2aysyZafTt\nm7u1sr7/3kGzZgnMn68bOgorIiJh7OmnM7nkEk/AfV59NYY1awpn/MqBA4FnAPO3qgT+8L3lFvu7\niT6fUeRrx4iI5Na+fQZt2yYwY0buZjPs2jWT2bPTqFjRh8MBTz6ZyTvvpJGUlPPAlGPHDHr2jGfI\nkNhCb0EPZworIiJhLDYWxo0L/MHm8xncf39coQxWz6lV5dZb3VSvHvhDt2FDT8BFL7VApIhEgq++\niqJZswS++CLnm0MOh48XXkhn5MgMYk6539OypZtly45Ro0bgG1HHvfFGDB06xJOSUjJboRVWRETC\n3Lnn+njllcCrhu3fH0Xv3nF47TNBnuWmVaV//5y7NDid/g9nO5s3O9izp2R+CItIZJg710mrVgns\n3ZvzpXPZsj5mzEjj7rtdtutmVa/uY8mSVFq1su8me7JPP3XSpEkCX35Z8i7dS947FhGJQG3auOnW\nLfD4ldWrnbz+evDGr4wZE8OxYwVrVTmubdvAfRjmzlXrioiEH68XXnwxhnvuiSctLeebKjVqeFiy\n5BjXXZdzq0lSEkyYkM4zz6QTFZXz39Jff42iVasEZswoWV1nFVZERCLE0KEZ1KwZ+APwxRdj2Lix\n4ONX/vzTKPBYlZPVq+ehUiX7Zp+5c0vWh6+IhL916xzcfHMCr7ySu0V4mzRxs2hRKtWq5X6hFMOA\n3r1dzJqVFrC77HGZmQb9+sWxalXJWUBSYUVEJELEx8O4cekkJNh/EHo8/umMDx4s2LnGjIkO2KrS\nrp2b887L/QeywwGtW9u3rnz1lYMfflBXMAkujwd27zbw5G5ogAgAX38dRceO8bRpk7vxKQB9+mQw\neXIapUvn75wNG3pYvjyVOnVy/mH1eg1GjSrcWSDDicKKiEgEMU0vw4YFHr/y669R9O0bl+9VkP/8\n02DChOC1qhx3yy2B+6n+90AAACAASURBVGarK5gEy+7dBg8+GEeNGklceWUSF16YxKOPxpbYAcr5\n5fHAsmUOnnkmlhEjYor92LIffjC49944GjVKzPVq9LGxPt54I41BgzJxFLCx46yzfMydm5pjl1+A\nb75Ry4qIiISpjh3dtG8f+MJ/yZJoxo/P38X/W28FblVp396dp24Ox11xhZezzgo0K1jwuoL5fHD4\nMPkObBKZfv/d4NFHY7n66kTefz+aI0f8P8cHDxpMmhRD/fqJjBsXjSt3Y5oLxOuFRYugXz8YMACW\nLSv8cwbT99/7Z77q2jWBt96KYfjwWK66KpFRo2KK3e/Vb78ZDBgQS4MGiXmanbBSJS9z56Zy223B\nm1c4NhZGjszgtdfSiI21r+jzzy85zYUKKyIiEcYwYPjwdKpVC9y/eciQWN5/38mGDQ527Iji998N\n0tICHzunVhWHI3czgGUnKirwmis7djiwrIJ9LLnd8NJLUKEClCkD1atT4gajlkR//mkweHAsV16Z\nyKRJMbjd2Yftw4cNnnoqjhtvTCi0tYk8Hpg928k11yTQsiX897/wyivQrBn06RMbkqBUED4fvPtu\nNI0bJ/DVV/+sI5fLYOjQWHr3jiM9cANvRDhwwP93sl69RKZMicHjyX3L0WWXeVi2LJU6dYI4BeNJ\nOnd2M39+KpUrZ3/8Xr3C/AcpiPQXXEQkAiUlwfjxadx0UwIZGdl/wLpcBg8+GP+v52NjfZQp46Ns\nWR9lypD1v//xjz9GkZoa/FaV49q0cfHmm/ZhaM4cJ489lnMXiOy43XDffbHMm/f3cz/8AA8+GMeR\nI3D33SXnw72kOHLEP2vdW28FXg/oVJbloH37BFq2dDFkSAZVqxa8qcD1//buPT6uus7/+HuSmWSS\nlnIrchEypCBfQRAXRBq0SFtBsBRowCUQ1qLUBSlgGi+VFtmVhQr9YVolCCJYELKEUujiBVhuLXdF\ndFHB5etqwhQVtAUFmuvM5Pz+OAnNdTLJTOacM/N6Ph48QmYmk28/801y3vO9JdyQsnZtudraRg/d\nd90V0VtvSd//frfKM1uznVdvvBHSsmXlevDB9KMLGzZE1N5eoltv7dKeewZvmGX7dummm8p0/fVl\n746+TcTppyfU1NStipG/XnPqQx/q08MPd+rKK8u0fn1EiURI73lPn5Yv79XChcVzSmTIKbSxPI9t\n3fpOXgsaCoU0c+b0Ibdt27ZdvK4TQx2zRw2zN5ka3nJLRJdeGp3qpklyR1Wefrojq7DiONJHPjJN\n8fjoF3MHHpjS0093jnk2wVj6+qQvfjGqu+4a/SIrHHb0k59M3bughcbvP89dXdIPfhDRddeV6c03\nsxuNi0YdLV3aq4sv7lVl5cS/vrdXWr8+orVry7RlS2ZtmT8/qR/8oGvKL3YnYvPmUl18cVR//Wvm\n9dx77z798IddOvzwqfm5ynU/7OmRbr89oqamMm3bNvF+U1XVp5Ure3TaackJ/47K1ttvS11dIe2y\nizPhoJurOu6xx06eLFpiGhgABNjnPpfQggX5GTHIdlRFcqewnXba2O39wx9K9dJLE/vT5DjSpZeW\njxlUJCmZDOlf/7VCb789oadGlvr63Iv5XEkkpFtvjejoo6fpG9+IZh1UJKm7O6Rvfctdr/CjH4Uz\nXo/R0yOtWxfR7NnT1NgYzTioSNKjj4Z1zjkV6uiYZKNzqKdHuvzycv3zP1dOKKhI0muvleiUUyr1\nox/5e6JOKiXddVdYH/3oNK1YEZ1wUNljD3djk2ee6dCiRfkPKpI0Y4a0554TDyqFgLACAAEWCklr\n13Zrv/2mdsSgtNTRsmWTW6syXLp1K9LEzlxxHOk//qNM69aNv43nli0lWrZs8rukIXNtbSGdc06F\n9t57uvbddycddtg0nXpqhRoby3XddWX66U/DevnlkozXPaRS0t13h3XMMdP01a9G9frrub98+fOf\nS7RkSYUWLapIG5i7uqTvfz+io46apuXLo/rTnybXliefDKuurkLvvDPZFmfP2hKdeGKlbrxx8tvg\ndnWFtGRJha65pkx9Phu43Lo1pFtuiWju3EpdfHHFhAKlJM2Y4WjFih4991yHzjsvobLi2S3YV5gG\nlmNMAwsm6pg9api9bGr4/PPuO5xjLSzOVl1dQt/5Tm5W1DqO9LGPVer//m/0Bc6xWJ+ee64jo3cv\nm5rKdPXVE3urcfXqbp17LutX0smmL77+ekgnnlipv/xl/AvDUMjRvvs6qq7u0wEH9GnWrB0f99vP\nUTgs3X9/WNdcU6aXX574gvjyckcf+lBKP//5xN7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fkVRqrV06FW3OACMrADAJiYR7of0/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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f52536da208>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "R7Deh2zlqjcD",
        "colab_type": "code",
        "outputId": "83e30ffb-367f-4769-82bd-28c988b9589b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        }
      },
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "gru_10 (GRU)                 (None, 64)                13632     \n",
            "_________________________________________________________________\n",
            "dense_17 (Dense)             (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 13,697\n",
            "Trainable params: 13,697\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "lb-4QibZq7Gv",
        "colab_type": "code",
        "outputId": "8fbad7cf-76b1-47a5-84a1-0144b8742386",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        }
      },
      "cell_type": "code",
      "source": [
        "train_gen, val_gen, test_gen = gen_train_val_test()\n",
        "\n",
        "batch_maes = []\n",
        "predsi = []\n",
        "targetsi = []\n",
        "for step in range(val_steps):\n",
        "    samples, targets = next(val_gen)\n",
        "    preds = model.predict(samples)\n",
        "    mae = np.mean(np.abs(preds - targets))\n",
        "    #print(mae)\n",
        "    batch_maes.append(mae)\n",
        "    predsi.extend(preds)\n",
        "    targetsi.extend(targets)\n",
        "print(np.mean(batch_maes))\n",
        "plt.plot(predsi[:1000], 'b', linewidth=1, label='prediction');\n",
        "plt.plot(targetsi[:1000], 'g', linewidth=1, label='target');\n",
        "plt.legend();"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.6658328132574057\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QS6wAYaJxX7LP789Qg3TCu2DZTHb847/+C/jgD8DeWTlqlTbCnCyuCGEhumBRDEgeU18P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\noDR963D9HhIgRMbRW4gwXwWIw2G8Evrvftc7cV2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+NfuaEexOxbtqlRlXX12ELVvMaGw0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\neIYAAgAAAMAzBBAAAAAAnvk/XBvy6kB7mU0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f5256839d30>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "C9qNblUlw8l6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}