correlate-v2.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction of emission reduction using multivariate correlation\n",
    "\n",
    "This file presents a worked example of multivariate correlation analysis as implemented in the `brsflufight_nerc2` package.\n",
    "The example in this file predicts the change $CO_2$ emissions due to the COVID-19 lockdown for the Energy sector in the UK. \n",
    "\n",
    "While this file shows in depth information about the intermediate steps and their motivation a minimal working example of the correlation framework is shown in [`minimal-correlation-example`](minimal-correlation-example.ipynb). This minimal example serves to illustrate the extensibility of this framework to other data sources.\n",
    "The datasets used in this notebook are available in directory [`data/processed`](data/processed), and are generated by the notebooks in folder [`data/`](data/); in addition the notebook [`data_viz`](data_viz.ipynb)\n",
    "provides visualisation of those datasets. \n",
    "\n",
    "The correlation framework follows 3 steps:\n",
    "\n",
    "+ data preparation (extraction and combination)\n",
    "+ correlation;\n",
    "+ and prediction.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import display, Markdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import brsflufight_nerc2  as bff2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "WARNING: \"example_air_quality_file.csv\" not found in dictionary `file_read_functions`.\nA loading function must be defined in `data_access.default_file_read_functions`.\nWARNING: \"no2_by_pua.csv\" not found in dictionary `file_read_functions`.\nA loading function must be defined in `data_access.default_file_read_functions`.\nWARNING: \"o3_by_pua.csv\" not found in dictionary `file_read_functions`.\nA loading function must be defined in `data_access.default_file_read_functions`.\nWARNING: \"pm10_by_pua.csv\" not found in dictionary `file_read_functions`.\nA loading function must be defined in `data_access.default_file_read_functions`.\nWARNING: \"PublicData_Countries_v6.xlsx\" not found in dictionary `file_read_functions`.\nA loading function must be defined in `data_access.default_file_read_functions`.\nWARNING: \"so2_by_pua.csv\" not found in dictionary `file_read_functions`.\nA loading function must be defined in `data_access.default_file_read_functions`.\n"
    }
   ],
   "source": [
    "data_sets = bff2.load_data_files()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data is loaded into a `DataGroup` which is simply a dictionary which exposes the method `get_country`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Success: historical_GHG_Sectors_GCP\nSuccess: historical_GHG_Sectors_PIK\nSuccess: historical_GHG_Sectors_UNFCCC\nSuccess: mobility_apple\nFailed: United Kingdom not in mobility_citymapper\nSuccess: mobility_google\nSuccess: uk_energy_daily\nSuccess: uk_energy_demand_reduction\n"
    }
   ],
   "source": [
    "country = 'United Kingdom'\n",
    "\n",
    "d_uk = data_sets.get_country(country)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare yearly energy data\n",
    "\n",
    "For the energy we get the data for which we have full years of data and get the average power. We drop years with incomplete data (i.e. 2011  and 2020)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "d_uk['uk_energy_yearly'] = bff2.summarise_to_freq(\n",
    "    d_uk['uk_energy_daily'],\n",
    "    freq='1Y', \n",
    "    fun_test_data=lambda gr: gr.count()[\"dayofyear\"]>350,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare yearly energy forecast for 2020\n",
    "\n",
    "In order to predict GHG emissions, the energy use is forecasted for 2020 on the basis of daily consumptions and a \n",
    "seasonality model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x1e1c2f90f88>"
     },
     "metadata": {},
     "execution_count": 8
    },
    {
     "output_type": "display_data",
     "data": {
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dS7dh0ZeoALI3SMxdTnW2h2gyw3zXJtyazvOJqbK9fLxQRX1byeCiorKWujJ/kWewbEc/MxtKqQh5pfW0psHJX0GvmMiy7f1mYNkoSNM1F693wbmHNeL3uIgTgHQU1/YX8+ecq28gMirPICqUGQhX6cCBg/1D72aheYYNoa0SVTJJqWMqnyDLXWskkzHSKRk+IFlA4QZpNqf4/PN+BHWzJK6ZikiXBLfPDA6X1EqmYTKSVwZPrjGppFgqY2u13xdNSSWy5nI8A7xBGHc05DJoqx6gVa/mzHlGURlBSA3T2tVLX9rD0+s687GcLd0SKM7HDIBhfx01uR6Gk1mmaFL9d+9WCRQrbZ9rW06/XsqYiiB1pQG6C2IG69qHmDOmnPGVITos7bDf2NHPB371Mi8pz8H4wiS8Fei4OH1WHQGvW5RBKkp2QPoTUVLL7My64phB+8riMvRU1Gx2dwCCSQcUf3ivuMUOHLxTMVI2TvNT9uXBnSKo21dKtuJZN0pAF2TOuWplr1A+VorHhtrEUFtoeAdVk+V1+8uiMFTre3VstBtSw0T1AD9fZE5gbBuI0x9L55d7oilwuYWaOuRjBp4ATDweXXPjGW7jxexcjhgvlEskF4BkhIGhIRK6jweXtuQP29ItaalhvznoLRpooFHrpT+aototgvyNbp03dvRLh0BAa19Jr15GY0WQ2lI/kWQmX7XcM5ykO5JkVmMpIZ/bVrOwrl16Jm3sNHonTTsLqqfyTOAsJlaHmFxTYngGPkjFcEfbiLgrYNLJTEmsycc4AGlMdcfZxWlr6Zi4m6EacU3fKcgkpbJy87N73teBg7cD/dvgB03w+Nfs2T/NTwu/77IMhOzZKMPoPQFpeDnRiA10rjUKWy1tacrGimcw1CZZhEroK2XQv9XeuUClmQ93QSrK2t4siXSWH18qiS8t/XH6Y2Zjur5h47011rAfOLiVgTcIgTK2+yUb6IHsqTSWBykNeIhpogyiw0PE8LN4o/mwNhvKIOQz/8nJUAMNWh9dkQRVnji6r4RQwM8dL27Nt4zQcmnDMwhQVyqUjIobqMZysxrLCPncttYWyhPZ3mukrs69hO5rX+bz3Rdy/txGNE3D73UT0wPo6RjlyU5igXoYfywVmW7CCUvWQW+zZA51FoyJNtzKwdAEOrau3d8ne+CggmROHMPBOxGZlPQxSw5KKrlKzU7HZdLYjPNh1vtg9oWyvvUNSSmdc7HQO4Fy8fS3vQDoZg8ygPJx0oV4qNVsKwGiYBSqp5jvlTKIijLoSXmZUhvmuCmSHdk2kKA/KgqgsTxAbzRpHnfIewYGR35n7ESezC7gDX0ajeUBQj4PUUJkEhH0VJwEst+CicLNbTaEs9UzSIUaqNYiDAxFqHAn0PzlXDBvDM+u70JXLSOAPsKMKQ9SVxYAyMcN7MrAY+uAuqVHlM/WXjMY9MjKNnI6XDxfviR+j4th3YeGTpPWQa50DIyXEdOzMmvNeEOXIegVf6mQjoE3xKMdlYT7Vkvzq3cCVJCsb1txW3EHDt5O6Dr8eDr8+9vgNyhh1dBy24uQScC0M+HSP8AH7hR+/82/CN8/+yLzPLXTJesIij2DTELiC6q+CMDjM98f+WHzvaKJIh2QidOV9DCuMkhdqR+3S6N1IJaniabWhem1egaHtDLQXOCS298y4VI+mb4B0Ah43YT9biJ6gFwiQkhL4vZLitYHF46n1O9hMJ7GpUHAa378bNj4Z0XaKNfiEChjal2YWCpLfzybTz/dpjfQUB6gLCCKZDiRoXMowW8Xb2VmQylVJT6Ce/IMkDjC+Kog0+qlYC3gdTOsi4KZ5OqUL1L9YaRdAa51P0F6nZHJoHqRDLUyNNhnKolUjJw3xH3ZMwhrCXKv33WAHvRe4tVfSZWlauOhui+mIgd8TJ8DB/uFVNSMvR1xpdBBqi5g6/NibKqUdLdHZIAal1s7wzxP7Szy6eNWZaDaT6cili7GBsYukPR3lYIKQvGiybAsoCPuZlxlCI/bRUNZgNb+OAOxFC4NJtWUmMHkklrxJvbT2DqIlYE5bzidleCP6uUT8nmI6AFcqQgBkoyvr+b/XTyXDxw5jvpyEbglPg+a5Rw5I6jrj7ZTRhQC5YyrlEZxLf0x+ODd/G7yz7jbfxUBr5ugT4LP8XSWh5bupGc4yW1XzDeu784XtiUzWVr6Y/jcLlr64/l7jaWyVARNC8HvcTGcNZf1srHg9jAcHMORrmZ8D14hG7rX5/f50C33cO+SHfIlSMfoSXpYozfxcnY2+ht37+8T3jc88XVpw926TJYVTQRO3yQH7yykjJY2k0+F0/9baB1DEBPpEAHuDZr7104HdFESVqpn/ELzvTWAbO1XZqWJAK59FL6+3T433e2R7CCDUh3I+hhXKdcfXxVkR1+MvmiKipCPmrCfoUSGVCYncYdMYr/ZgINYGZi3PpzMcNqMWp764smAZAkNZf24MzFCJPEFSrjymAm4XRoNBr1TYqGIADRDi1dlewkTB38ZYyvkH9HSH4f6ObyQmU19haFwvHJ8PJVlIJYm5HMz3bDyQz5P3jPY3BUlp8NxU6rJ5nS29Yh3EE1mCPnMbKaA181wzlQGmuFWbmm60v65u9ZB/VwAprtaeGZdp9FyQ2en8d3+d24B7v4t9pbebxVUzrTKxLAqAydu4OA/ice/Bot/NPr91TCsI66WZpIVE03PINZnzgxQqJ0pr9VTJYtHYfwuhlzVzxHrH8zfhYI3aOs99Pib7ZLyXlKbN5qieiCvDCbXhtnaE2UglqYi5KU6LLKiL5oSJQbmQJx9xEGrDHQ0rrnzNaLJDMPJDGVBb97SD/k8DOYCaOhUaRE8AfOhNxieQUXIazufu1yEb6PWR1gfhkA5YyuVMjDaSg/EaTSOD/jk0cXTWaKpjE25hHxuUtkcmWyOVS0ywPqTJ09G0+DRVSIcY6ms7Ri/x0Uka96Tq1oyDtpnXMX/pS+Tlem40C5NJ5L2hJmvNeNzu/IFZ60xuacXcqIs2LzIeFi6DNz4T3P22YzZW2WToQyG2uWHoLkkE8OBg/8UlvwaFn1fCkRXPSgN3OLGAPmRvvvGIKl8b7HKiaZnEO8zp4kpKGqoZqp9fblp9d+1pI2HVxip4d4AfHE1HPdZmHTyLm+7fTDOp//0BgtveppMsCZvNMUI5NmJyTUl9MfSbO4epjLko7pE4qA9w0mz3kGN5N1HHLTKIJ2D5zd2s2LnAFE1qMZA2O+hPyMPq1SL47Uog+tOnMQNZ03np5cfYTtfMFzGgF5Co9ZLUI9CoIzyoJfSgIfWfun70z2cpN7wLIJesQwS6SzDyazt+srij6WzrGwZoDzo5bgp1Zw4tYaHlu4km9OJpgo9AxeDFs/APc6knPoRb6S/bbNkEpU20F29gGNda/F73fmBOF1xFwGvi836GGLBRimjz+Xg/qtk4MZT39mPJz4K9G+V/OvyCdJuIzksnkHlJKibbVJHDhwcaFiF/aNfhL99XBq4bXxCJh3eWFE8QF55BqrFfMVEaRmRHN69Z2D0MrOh6SQIVnHjI2u5/v4V5vrSBjj7Jmk7PQJyOZ01reaI27ZMqVA+QJRAnp2YVCMybH1HhMqQj1ojm7FjMGFWPb9xj71lhsJwN/z6pD0Wox60yiCrixcQSaSJJDKEA3Zh3J8xBas3YHb/m9VYxufPmMbMhjLb+RrLA7TrVaIMssP5KWXjKkO09MfRdZ2heJryoFjvAUMZxFNZhhNpmzLIxxNSWVbsHGTe+Ao0TeOcwxpoG0zQPhgnlsyaFdCA3+NmKGN6BqFg0PgsHvp1sVzu/LsRRA7XsaPsKCa7OvjBpvflS+Pb427mjasANLpLZkD3RgnabviXuK8v3wYtS/f2UY8eKp4x/yppz92+QjyS8rEyY6Jl2QFptevAQRGsfLm1WCzSAcvvkfcqU2jR/wqdpNrg5z2DJnkd3CmB5ULPoHoaHHUtzHl/8fU//DCxL5gZfo+sbCOSSBfvV4AFNz3Nx/5o/ib7Kc+/j+OnLCgyQikDgMqQl9mNZfjcLl7f1ieBZ7dffuf/+lKxF9SxSv72YIwdtMoghyiD1oEEyUyOUoswLvELTaSgeUN7PF9pwEuPq4YmrRO3nsmnmo2tCNI6EGc4mSGnk//neN0uvG5NaKJk1lbNrCz+vmiKjZ0R5o2Tf7AKGA8nM+IZ+O2eQSwrn2l5bioBjzt/rgHDM6iIifuYDdawPih9k0K5iFg+QGtUY/aYMjwujS7vGLHU1Vi8U78h2RJqNvR/AipGMfdSeV12t3gGk06WSvHk4K6pop2vibLY1Xmfu8VJTT1U0bm22KovhMpUazhcXsvGyhyR4U5zvvhQq1BIi38odFLeMzCUgVFcSu9moZCsMwRAArzv+xnUzy6+vsvNjn6z68Dn/ryc7z1ir/dZ0zbIdx5ene8okM3pwvkjxmhZwEO3bhqpui+cp77HV5kyrLE8QNDnZv6ECp5Z38W6zmF74Lowa09Rt0Y/tF3hoFUGuqEMVDWxlX8v8bmlHYWCNSNgNxjy1THNZfB9hmdQXeKjP5ZiyOgPpDwDEO8gns6a85QNqGK21v442Zyed/WUwoga8woKPYM12Qm8VH8V1+s34HKp+IebAV2UwbiM8JlbEyVs1CZwYfJ7bA4eZhS8wEDGy7jKEFUlPnZqY8TdVMVpFRMkjW2k6UsHCokhQBNaqLIJ3nxQ1k8/J18zQctrxcfpOvzlOpkbMdK4wBV/hudu3u8AmYODENkM3Hm2WPO7gxJ4hxszTppOlAKwSId0CgYxjl77rXlMXhkY2ysNZdBu0DyFNJEFuZzO6lZ79s62HntTude22RXYLxY188dXtvOxu19H1/V8jzQQw7G+LCA0kbqGz1QMXreLS44ax/vnj+W6EyWeeNyUapq7hjn3Zy+QU58fiukgVZ38blUGyjPY2lNcQBbye/I5+8ColUEsYMkEMJRBWdDDUDzDUFxcvrKAqQyCXrcRMygOIAP5/kRlhgIpNais3uEU2Zxe5BnkcPGXqk8w7KuznMtDv6EMJuREGC7t8dAfTbFSn8orwdPM+9f9jK0IUB32syVnfBY1+SxYCVNOh8437X3UH7ke/vTBA2N1JyNiZblcpncw/hixWqqmiIU20iS2jjdhcIc0BFv55+LtanjRHr7MDt6F6NkgVrrqEqrrMrNDfV+3vQiv3G56wBNPkFG0Cz8ufX+GO2WQDEiWztqHzXMrC1oNkSmpBW8I2pbLciFNZMFjq9t5789fZNl2U+CrOqJl3z6Tb543k+29MeH0AV3XpZMx8OqWPp7f2E3boDl46zOnTaWuzM+KTBNoLv5V+WESAftc9R9dOo+fXHYE5UbyywXzzEI23SrK+7bYb1Y1sdtDYdpBqwx0XIwpD+QLukoDVs/AQ9TmGeyZJgJIhyzpX4a1UB70Ek9n89V+ZRbPQBWXFQawlTLoNJSBujelMLoKu6YingFAfyyVD06rcymaaIouyuCldi3vXi7ymlkKEnAKUV3iY33a+CKpGEGoCqaeKe9VllGkA5bfK7MaHv7s/tcBKGUA0rr7v5bAB/8oyy6XzIfYOYIy2PAYoMmPcSTPRfVasioxB4cGlGDuXCtCfc3f4a5zTKPhuVukglhRlCU1cM7NkvuvPANVWLbxSUkdHWu0pu9tltRPj9HtV9PEg1bXDFby7IYunl3fxcfufp17XtmWv60lW0QJ/GtVR752aHtfjMqQl+qwn+OnSNeCV7b0kMnmuOfV7bQPJvjOe2fTWB7g9y9spX1A5MOjnzuRz58xjfrSAEvi4+A7fdwfvtoWBx0Jk2vDPH79SQC8etKdcNq3JWuvsFFlniZ6lyqDHBrTG0rz1nfYbwm++t15axqwl4LvDtZCkrxnIOdV6aWFnsFINFHQqEFQVoE6Rgl/pQwKs4kA+mPpfABa9vGQxEfaFcCnZejTw7y6fSjfsKorE2RD/fkADOhhxlYGCfs97EyXSzMtNV4zWAUNcyWQ3Py0rFt+L+Qysm7FvXt2xfeEVMQ2ApS6mfZGXOMWSpC5sDhmy3Mw9kjJ0ijkhnXddHuH90MZbHtJ+GIHBwe2vyzc/sr7ZTmblACwsua3viC05I5XQM/C+n/J+i8Q11gAACAASURBVJDZOkY8gy5TGSgL+chr5LVng3xfrYVfFRPz18gFqvjGX9/khgdX8PS6Lp6wtJNeul3OeedLWznu5mdIZrLs6I0xoVoCvTMbSvG4NJq7hnlmfRffeVjo2lNm1HLOYQ0s295P24B4BuON9NHaMj/dkSQ6FMmUXUFlN270zIBTviJ91HZJE71LlQFo+Zx/wKZFw34PA5RyUfJ73DLlXph8yqjOeP77LmFNqTG/1MgdVoK8xUgvVQFkkJhBJJEhmcntliZSnoH653YZ6+11BoZnEE3ZlITP48Lj0oi6xVMZclfSHUmysVNiJbFklsemfIdTkz/m+guPp6rER8jvJprWpTtiLi2BY/Wln3K6eAa5rGQYVE+DTzwnP5z9LQpLRuxzWwsxfiGgwy0TTEsum4a2FUInhaqKg1+RdrNSdH/6r9x/JSz+v30/3sFbixd+LMbJthdM7r5jlUkPvfkQ3DJejBmQBnPeEPgsLEBpvRgog63muoUfE8MDRLlYjRcw4wbA+iEPHUOJfD+gde0RdF0nkkizoWOI+RMkMN0znGJLd5S2wThjDJnkcbtorAiwsy+eLzT9zYeOYkptmKl1YeLpLG/s6Cfkc+dlSl1pgFQ2x0AszXBidMqgMuTF69boVO30qyaZhXMK7/aYAZqLulKLMhghm2eFPpVE+eRRn7K8JMCcLz0OX9+Zr+pT/6hdeQaqP4g9ZiHXVzOSlXdRSBON7BnYaSK1XxLJRNJVZ0MD0VSGwUSWXv94PnxcU37/WCprtsoNVpnWT9NJYin1bRH3MVwnntO0s+yWcy4r1tnexBKSkeIflxVNJ8P0c+W9on4610jtxNijIFRdrAxUOiDsO02UislworejItuBWKqrHty7YxJDUH8YnPk/0ijO7ZdhMOr7kTPiAJVNMMYQ7oFy+znChleaikjh11e3ymxiVQ2cSYC/lFxO5+EVrfJbrjK7iD65NWU7XV80RXckyeKNPeR0+PJ7ZuS7HqzvGKJzMJG31AHGVYRo6Y/ROhCnLODh7DlyP1NqxWB6cVMPDeWBfMZQfZnQVZ2RBNGCOOSuoGmajOBV81NCNaZ3vfiHsOQ35u9mD73B9ny1dyg0zcXkWjP31koTWbl4K8c/agTMKH550O4ZWGMTQZ+bns6Ucf3ibKJCz8DnceFzu/Jtr0fyDCIJezGaOl/USJXtnXYpTbkQ23pjlPjcRJNZBi31D+rzx1IZUxlYA2HK8hlsEWWgqiorJogbnU5I5eSLPxHL7OhPCNV02Z8gbFdERUhGisvurXB74MzvwsbHTaqo1YhpjFsgHVljvaKAlPJacZ/QXaGafQ8gK3rgYKSJMknplmmlMg423PdBUf7Tzy4W2LtCYkAKFU/8oixXT4GeZrPACuCE6+Gs78Haf8KDH7K3PoHiWQHqdxCqllhBLg3+Up7f1M3196/ApcGSr3yQ2r7NkIzwyLohjp9SzfqOCAljtvna9iHueHELE6tDHDu5Gl3XjXz/fqKpbL7DAUg/oWc3dFMZ8jG20vRYlDKIJDPMKzeL0ZRx2zWUJDJKmgiwT10MVoqxl8vZaV9fqSjF3WCPnoGmaQFN017TNG2lpmlrNE270Vg/SdO0JZqmbdI07QFN03zGer+x3Gxsb7Kc6xvG+g2app1tWX+Osa5Z07Svj+oJuFycNdvM/rHSRFYhW7aHIMyeoDyBnf0xwn4PHrf5yIJedz6QW2KLGYgwH4yn8XtceUGv7lN5DFah77d0UA0WKQM33/d+nouTN9I35f0886VTeeGrp3HN8U1EUxkGYimbMgj63CTSOXLKyrGmyKn4yVCbZGAoT0PFSwZbxJJ4/oey/NpvpVPjo18wz5FNi+dQiGTETNPbFdT2hFF12b5S7q9iovxI9aypKLrWwaoH4JhPSfxhNJ5B51pYdJO9uE3RS4MtB1fRW7QXfjTNLJp6p2PN30VgF0KlcHastq/PJOHBa2D1X4uPiQ/YFUfNNMkqU1bv2KPE2geYfYHU0ZzzA/s5VM0B2GsGNC1vtDQPaXzzb28CkNPh1Z0JOO+HNJ/wQ7b0xDj3sAZ+etkR/PZDCwB47M123tgxwDXHNeF2aXjcLqbVh3l+g1AxDVbPoDJEdyTJ5u7hfHo5QE3YLIg9ZbppYKkZKZ1DiaKklN2hvjSQT1YhWCG/n8SAfafyscUHFmA0NFESOF3X9XnAEcA5mqYdC/wAuFXX9WlAP3Cdsf91QL+u61OBW4390DRtNnA5MAc4B7hd0zS3pmlu4JfAucBs4Apj391C01yEfB5mqOZwFmrFWgB27OTqUXzEXUN5Fp1DySLFEvDahbyC26Xh98ijLQ3YPZMSv3u32URgBqAVQn43S+MNLNenEfJ5cLs0xleFKA140XW5N2uvJXXeZFmTcQKLMlAzWgd2yA9LzWowBvgwsF2EcDZptOY1tq1/VAaBANx13sitLfZEE4H5A1d9Yfq3idWnaaIMQDyWRf8L//5vcHvh+M+baYJWZJLw07kihECUxa+Ok9iANf6hjsul9y8IDfLMCgcL/aew8j75YS+/9z93DV3fvyyyaI9w8roOD10Lv1hQvI8yNDpW2dd3roG1/4C/fFQCwtZ7SgzYWzhUT5P7HO6U9s8fX2QvtDr163Dsp+znL7H89gsLyIw4YnfvAO2DCc6cJedS/P6Ta+Q7c9bsBk6eXsuJ02oYUx7gX0ZvMatcmdlQRqsRDLbSROOrRAFs643lG84Btm7JVx5jJq3UGTTRjr4YOb24meauUFfmtyiDSkA3KVE1alOleu8Ge1QGusCI4OE1/nTgdOAvxvq7ATXt4UJjGWP7GZp8+guB+3VdT+q6vhVoBo42/pp1Xd+i63oKuN/Yd/c3bswy+MdnTuDJL5ycL9ICscwrQ14uWzCew8aO0i3dBawxgkLKKegzH581ZgHmP9IacAa7AiisM8ivL/QMvB4iRtGb1WtQSq9tMF7kGQBESwxKyPpD8BqUS+dqQB/BM9hpWtJn/g/MuwKO+4wsD3eIZd2+wky/U9D10SkDXwlobtP6H9hhVn4qZbDzVeE7m5+SdNiSamknPNwFT30Xbj9OGuFtekqOf+b7cpx1tKY1o8KqRAZ27P7+rMhlYc0/JOag8MTX4VfHF3/+/wTe+COgiWd2IAruhtrNNEOFrYvhtiNGblWw7A9mGvJI6NsCP5wCf7wg3ywx39ffCmMIFU98HV77nbneGg9a8zfzfSYB2ZRZOQySaaZn5T53k/9fBNXErVAZGPRTWJP7/uo5M2koC7DNGEClrHkr7TOrsYxoKovbpdko6qMmmue27j/OQg1ZPQOARz57In/99HEFiSceSv2efLr8nlJLFerLAgwlMkINq8+plMHUM+HrO+DEG4Qq2g1GFUA2LPgVQBfwFLAZGNB1XY3zagGUHzIW2AlgbB8Eqq3rC47Z1frd35OhDII+NzMa7B9S0zSWfvssfnDJ4SMdulcIeIXnB7tiAGyB3kItPqZCvhSFnoHV9duVZ1CkDEYIjluPH4gVxAyM/Yd9teIJVI/QZVGlnCoBXNooQnpghyk8Jx4HF//aPH6oXfj3bMrs7qiQigK62fRrV9A0URiJIRG2gy2mIlI/cmv/JEUFVE4SYbDsDxJb+NMl8MBVsk01DrO6xtY50NYspL2JGzxyPTx0jVlJDWZmxqNfHP159gVDbdK646hrZflAVI7/ZCb8pMDpVsVczc+I0lNCRNfl899zsVm0pRDrk5z9p2+U5d7m3beLUKmdAI9/Vag8ENpHc8O0s2HDE2ayguo0avUMVKfQdGyXlcH/98R67nqpwMuZeZ68WtpFA1A9hY5z7+Tz6c/x08uOYHp9KROrQ2wzCsf6oimqSny2Q2Y1CsXZVB2ysQInTDW9BCtNNNMil8ZW2pXB3HHlHDWx+HPUlfnzY3kLDcxdQXkdO/viFmVgPIdAhXjjLhc07l4ejkoZ6Lqe1XX9CGAcYsnPGmk343WkSJe+D+uLoGnaJzRNW6pp2tJkMjXSLnm4XQcm4KZpWt66L7TybcrAZ982y2iEV0gtjRRbALtnEBghm2ik91Y6zFYMZ9BM0XSWFRc9Re/hH7d/qLKx+Xa3m6LGl9ftkfUDO0UZeAImv6+CwpE207IeajVpIyju87I7BMrFMxhqk9TAygLPQAWVv9wMTcakKaWQEgOw4Do480a5RzADxEro+MuKlYGyiqwZRbE+GWM4EobaixucAfmva9tyu5DbExbdBEv3YvpcqyGk510u6br7S02pGE+2oN2HGp+6dbFY+D+bJ0LZKtwLOf3HvypB4Q2Pmeusz7UwAy0xIN7o0Z+U/4MKbPZskv/97Avlu6VoJKXUrTGD2pnmYPpdeAa3P7eZGx9Zy5buYX63eAvf/seb9Bz/32SveIjO0mLmuXPMGWzVG/MJHpNqSvI0Ue/wrpVBofE5wdI3yOq5lwa8LP7KaVy+cPyo6eq60gBb8l0VRpf8MtGobdjRFzO9KVWFbCjUXE6HxiNGOjyPvUot1XV9AHgOOBao0DRNSbZxgDHfkBZgPICxvRzos64vOGZX60e6/m91XV+g6/qC8tI9WKAHED1G9fHRk+xfwoDlH6/4PgX1xUmm7QFL5fqFfG4btbU7z6DcMhGtsCDN3KfYM4ilslx592p++1IBNWKZuvRf/9jJs+sNYVoxQYR9tFv4WMVtWoPOeZpFhyELdVHY52V3CJRJzEDlQ+c9A+MH0/GmCA3L7Gmbd1M/G078AnyjBQ6/XAQ3iEXpLzeCjQXKoGKCUAaWSXE89hX463XQvkpopbjFs+heZ3lvOSYxINk9MHI19UhoXSZxjEXfL7ayo72w7lERoPEBMye+7Q0Rfo3zoG7W/iuDwtxzBTVGdedrZquQeL+9inXHK/Zj8jUiKZj5XnmvPE11vBXxAVFq5/0fHPMJUSJ9W0QZ1Ew3e/3vWGLuD3aayFdiBoRHUAa5nKmArr9/BTc9to57X93Bs5v6uW1HE8f8v2fy6ZepjMwaGTK6iirvvammhN5oiqFEmr5oiuoiZSBKYFpdMRNx2YLxHDOp+L4mVIe45QOHFymWXaGuzC/Ty7Abe7uDUkY/enIDv1xipI/2bZPXYCVbe6JM/uZjrGXSbs8zmmyiWk3TKoz3QeBMYB3wLHCJsds1gGr68U9jGWP7Il0G9f4TuNzINpoETANeA14HphnZST4kyPzPPd2XJzRyf/D/JN5/5DjbsrLsJ9WU2IQ5wOwxIhRVfYJC2BDgMwusi9KCFtxWjK0IWLaNnDZrHaGpju8aShJLZekeKrAGVateoFcv49anjU6iFeONmEGnfWJTsFKs8KE2+wANK1W0V56BkfGgFIuKGfjCpqCtarKnU4aqTOGgaCG3Vyiv4U6xfBMDECwXxbH1eRFwYHyeOpk81bYcXviJCCwVt/jrx+DnR4plrKAs5smnQpdFGcT7hYfV3BLbsGJgJ9z7geJ6hpduk/1jvfD9GtPSjg/AXecK3bXuEfjxTLjVsGBb3xAl4A3KfXeu3r/+UdbPoM6TywkVNXaB3WOItJsxF0/QVLYKQxZbTQ10tyoD63ckk4J01PzfLbhOpoS9foconOqpUtMTbjCVUWIEmghMmkMrFluqIh/gTUsDuY7BBM9vFGpvc3cUXdc54QeLuOau1/JxOOXxNxkW9raeKL3RZJEAn1RTwnffN5srjp5AIX5wyeE88MnjitbvLawB6NJRegaVIS+lfg8bOiP84Q3js1toonXtkqzxnY0Td3EGwWg8g0bgWU3TViGC+yld1x8FvgbcoGlaMxITuMPY/w6g2lh/A/B1AF3X1wAPAmuBJ4DPGPRTBvgs8CSiZB409t093sK866+cPYOPnzSJmrDd+vcasYTp9cVeivIMrKmoIJPRAE6faW9CFfC6ufb4JqCYI7PyjVZqaXZjcT2E7GN0TR0QRdQXK6DU5l+VfztAON93iYoJhsBvsSsDlYoXaReBp36MVmuzcGrU7uAvk5hB31ZAM8f2aZq0rIDimbGaZnoH1dPM9WVjJJag2g4EK80OqX+6VATfcJd8nvo5IoCeuVGGgJQan7HHEPztK81gcfcGEWBNJ4kHpFJh4wNyzYa5YsluXmQKzmdvklYfhaMX+zaLUlEzcY2W42xeJNcOVsLDn5HiO4WudaYlXDdHBGRhHv1o8dwtcP8V5rL6Xw3uFA7+yA+Z/XpArtO3Rf7PE46xXzfaKxTh/A/BBb8wj7MpA4vHWCjYyxqlfmDHKxIoLh8v/9vxCyU2s+LPRZ7BzY+v49kNXRJbsKy3Qnnv7zHSzeeMKaO6xEfHUIJKI9Nue2+UFzb10B1J8lJzb37egPIM1MyAde1DJNI5qsJ2ZaBpGh85YZItSHygodJLYfSegaZp+bjiIEZsZKhVFLk3wKDRZHNp2+7nK4wmm2iVruvzdV0/XNf1w3Rd/56xfouu60fruj5V1/VLdV1PGusTxvJUY/sWy7lu0nV9iq7rM3Rdf9yy/jFd16cb224a1RN4C/GZ06byrfOLOUdViDajoZgaKQ96ufn9c7nrIwtt63f0ibA5ZXpd0THfOn8W37twjq0bIcDYCpOTtMZCfB5XUXts63s1oa0/WqAMgpU8dcxd/CR9CafNbDCtqvLxgC4US7jg/srGiIU4sEN+zC6PDM9RUC0jRuUZGDRRy2sioD0WJXvKV+XV5Sk+rma60EDWYiKVKhtpM3LTKySd7pSviSAa7rR7BgqeoF3rKm9D0SbdG4SnrjP+73+9ToahJIxrTDhW6J8/XwHPfE+ezcr7hedeeb+9BcJwtwjBT78o99ayVEaYqsD2e281BTRI2+Z4n5nppfrnd1moq72BNYMHzIyiIeMeKybCh/8J7zf2i3SIAisfL9us9R0dhtCfe4kokVC1PEulUMHuGeWDwZZsnnCd+VlUIeNYIyX1H58yY0aBCvqjKX7z/Bb+9Op2CQZ/9EkzqI708GkbiOfbQV9y1DgCXhdnzKqnoTxAx2AiT61u7Yny+xfFYnZp5IWk8sonVsvvbJnRd6imxG78vRWos3gGe6N0VDuKFF6ybsN4NBRwVyEzsAscvO0o3gE4b64IpUsK6COFK46ekK82VPjfiw7joydM4rCxxQrE63bx4eOaqAjZLZLCTAQrPn+GWMtjLKlr6suvcp/7YimW7+hn7nefZH2HCJ2XMzP4vftS5k+oIJbKksxk7dWdVs8ARBn0bxUuu36OWGkr7hWh98/PmxkihSl8IyFQLpTJztdh4vH2bZNOEYvznJuLjzvtG3Dl/XavsMwIbg+123PTJxhDyluWCgUSrpf2BgqFhTkLPiqvKojZswFqp0s++mGXwKZ/G03Rcob3cYxY8pmEXKN/G6BLYFvPwWNfllTXXE5iMCV1ctz0c+R+tr8s6zU3zLoQrrjfjJlE2oWPz1vThpe0r+04agxPSgnc9Y/aG7gFKyULbPaF5rPsbZb6j9JGuU8V61DUoPLOVKdPkN5A1VPF67ptvjQgVNewWvMldWYqaolhdMy/2ow/bHjCOKY8L5hX7BxA13U2Bw9j2n8/ycZOoSVv/OcaLvjFi/k8+8m1YZ764il85rQpNJQFaB9M5IX+su39vNTcQ8jnJqfD9t4YmmZStwGvm8byQP6ao+X5DyTOmlXPt86bxWvfOsNGCe8JHz/JjAfEPYZsMZ656niwJzjKYD9w+LgKtt1yPhOqR9ciG+CwseV8532zbYUne0J96a4tlMsWTmDZt89ker1pkavMppa8Z5Dms/ctJ5LM5FvvDsUzVAS9ecUzEEvbu7ZarW+AyaeJkIr1ikA79WsiUO+/Et64G169XSzEUrtXMyL8ZSIM0lEZuGOFponFWVHMy1IxoVh55D2DdrFClTJSwmr7S/IarhOhqKpU43327qlzLhavo+NNoYpivWIV+0rgpBtkHxXEDVaYygaERlIDUcYtFKt5w2Nwz0UyP0LPmp7WxOPF69n2omRBldRI2t+Mc+GcW2QfFbxVn0Up5n0tmIv1wawL4L0/keWnviP/N6syAPHQglVCH3VvEK+otAHQzXRjNTfAGtyvM5ILg1WiVPu2yN8j15v3bOX/rccq76ekBi67V84x1ELOG+aZjb0s3iR8f89witaBOIvWdZHO6vzh5W2kMjmeXNNBz3CKRUYSRG2pn/FVIfwet3gGQwn6ogZNsr2fbE7nYyeK4NzQESHs99gSOZqqS9hs5PkX0kRvBYI+Nx8/ebKt79po8M3zZrHppnOpCfsZ0gxZYPxfuyNJ2yTIXcFRBgcBCuMOhaguiGWouIKiiYaTmbyX0GtQRpFEmtKAl0pDGfTHUkIL1M+FGeeLNWzF7AslwOvySgC1cZ7wxUoIJgbEknSN4itlTRksFO57i5JaEa5DbSLcAhZr2hsSoQtmdtSxn5JmZDFDGdTOggtvF6FXP8fsj6TODWZqbZfF+ykbI7UPah/VQrm0QbyDeQZHv+xu+7l8JeKhtC4V+qjEQsep/Pl8WqAhpH0hUaCREXozpeP2FF+QWMaPZpjnifdJAN7a5LB/W7EyUJ91+8vi8dQfZmaSKa8k2iv3YqX2lGJMx+CIK2HMfFEK/dukMBDsnoGVgrS+17T80Pl1nplcd/dS/vjK9vz3ecXOgXwK9r/XdHL8Lc/kJxD+6812fG6XLZW7sTxAXzRFh2WIzFETKznTiCts6IwU1Q41WWYNF2YTvZOhaRpet4sptSW86DZiZgZd1xVJMmvMnrP8HGXwLoTLpRH0uokkM0XbVA/1SCJDWdCTD671R9OSnfPpF+GK+4qLx/xhOPnLIkxVI7/ZF9n3qZ7CSGgdiEt1pIIKQE97T7EHsrdwuSQTpW+ztJtQFqjLJfejaB8r7RWqMj2DxsPNgHplk9Gmo8D6DVaK16OoMCXYrnxAOGyXR1ota24R6GWNUqxXMx2WGbUFVqE3bqFkCw132Jv/KaFcqAzU/Rd6Btk03NQohXFWLP6h7Lv6b0bKar/cl7XXf8108aQ0tz3OU9ZoTparn2P+f1QQOdZTnNo5/hh5jffJPX/iOeknBfnYwMMboub+SgG6PMXB4GM/BZNO4QclX8bj0rjymAn84JLD8bldvNk6mDdmeoaT9AynqAn78skaZUGvzeNuKBfqtD+W5qpjJvDQp47jwU8eR6OxPpLI2LL4AKZYKovfDppof1FfFuBu3gfAJt9MfvP8Zlr644yrCNpa1owERxkcJPj3F0/m0c+dOOr9C9NTQdJZlTIYMjwDRRMNxndfxAdICf97LJ0Q51wkKadK0FqzfCy4+Jcv8fNFlrz/mefBEVebAcv9RVnjyDEL1VsJClJlq0zPwOqlqGwqZQUr4alpco0uC00E0vG1eorZ9jhcZ/eMZpwr8QOwewDjFkrAvW253VpX5+0z0wI3dw9L3nlpg+kZJIakIdyyPwC6vfirbbm08UATbyUZkcK+UBV4LMItlzWURIU9BmNp4UztDJOG2/maUYzWa1cqIJlVhVDPu3M1KTx84ZEdDKhEBaVkQzXFnuTsC+Gaf9KSDHL2nAb+38VzuWDeGCbXlrCxI5JvDAnw6VOn8OLXTucLZ8r3TrWTVxhjCcBOqAqxsKkKt0ujusSHzzNyV4Erjp7Ah46dyCVHjRt1o7h3EipDXlpiHrI3bOSC1qu5+fH19AwnqS3zUxvefUDcUQYHCabXl+5Vn6XQCGlp0+pLbZ5BacCTtxY+de8b3PL4+qJjdouKCfClDdJKGMxApQWJdJauSJJNncPmysomuOiXxXnk+4pSizVrtTStFJR1fchQBsmhYmWAXtyqA4R2UoK9MEiuPndh0L3JHElqE/oq9bVwfYFnsCPh5z23LuZXz222ewaLvg+/OQle+pkse0tEUGcz8M/Pyb7Hf06C/T1GxpeioI4wvKDEgJmKa8XJX5FXt0+ooJIa8eBevk2UTrTHzvmDeJQX/4a7Z/46n9NPwJi0l0nQTTU6Lla2GDEaw0vKhmo54ZZFfO0vq8hk7cWZA7G0zZKd2VDKxs5heodTuF0a581t4FOnTCHgdXP2nAZuuvgwfna5vcJ27jjzf2u18l0uLZ+FV+gZlPg9fP+iw/jRpfP2Kq73TkFliY/BeJperZw4pjL0uDRqdxN7BEcZvGsRMmoNrPUHYyoCtA0kyOV0hhJpyiwxA4BfP7+56Dx7RLBChK7bZ2arWKDSVlXM4j8C61hTq3BrOsl8b7VAjSAleq5AGRjZVKoJnbXrpWVOw60vdtkqXvNpqYX0yYRjRr6vqkmm4rAqg0AFoOWVwR+XD5LN6Ty4dCd6uF48A12X+oR0TAK9jUdIID7aI4V2HW/Ce24yP7tqQKeuf9HtUigW74d4P1l/BT99eiMJo/6FcC18fjl88gWyOZ2cjsyy0NzyXGK9NiX59+Ut/HVZC7m5l/HdFWVcc6dR6Kdp+c/Y75PXN7b3S9aa8Zkj7gpaB+I8sHQnf1tupuLmcjoDsZTtuzm9oZTWgTjb+6IcOaGC2686yvbdvuqYiVx4hL02pTTgzTs9hZTPxfNl3+buYd5NUJ9zW49ka33zvJnMG1/B2XMaHGVwqOKoJvnxqx+D1y3WUCqbo2c4mfcMrO0t9rmd05j58M02s5mYBcqtby2oxD6gsA7UsVIWu4hhWAVzwlPKyp1GiqnKYGpbDi4Pw1qYb/39TfGmDAG42Tudny1u5bZFln5FakBQukDhWbn4QjpE9YmxDFLC5RLlmkmgu7zct6KXxvKACMFUmaSy9mw0Wm1o4hEc/zk5dmA7rH1Ygvyz3mcqNhUzCVVx10tb+euyFnMASryfrkyQnz69iX+vtQSnqyZD3UzOv+0Frrv7daGXKozZutGe/LO4b8kOvvjASr700Mp8+qYNRryh2yWexM+e2cSFv3iJlL8K0OhFFLHf4+KRlWZVcySZIadj8wxUq/rVrUNU70X+vzqusN/XdUZG0bmH7WYYaZPZiwAAIABJREFU00EIRftuMZTczIYyHv7MCRw+rsKhiQ5V/Pf5szlpWg1fOGMaR0+q4rcfWsAYI3DW3D1MNqcXteSeZMmk2Gu4Rw5OKWUwlMjke8EccCjPwBe2W+eaBhf9Ci7+rX3/kKkMfvBsOxf+8iXS2ZxQQZoLhjsZ1Mq46vdL+NOSHfxpyfZ8oPfR6msBuOOFrSa1oXh2a6qqwhX3w7n/x+rWwfwPFDBz+gs7cBqKKuMrJ5bKccNZ09E0WD9s1JG8+ZC8fuD3cPVfzKK4vi1SPzD9HGlTrmoT2lflr3PjI2v50kMr5RrZFETa6clIWvRz67t4YnU7X3xAssMS6SzrOyI8u6FbalOqpojXkU2yIeJj4U1P89vFpifZOVIuu+EZtGRNT2J9R4Tfv7wDJp/KSvdsyoNePnriJF7e3Jv/rqjYgrXextocbm9SPv/f++cypbakiGIt8XtY//1z+No5M0Z9roMBVcYzU51Prd6V4xkcogj63Nxz3TFce8IkHvzkcZw2sy5fvLa+XQp2CvnSbG4/et/sAtaAn0p1PeBQVI+ia6w44kqYd5l9nUUAbxg0Zk9HU6LQjIBpW7okz3G3DSTg2P+C657mReYDYr2uaTOqhmuN6x79ieLrzziXrtnXcPlvX+W//vQGuuoLdMSVcN1TZGdekC+Y0nUd3bi3YZcIv1Nm1DK5poTVQ4Yy2GpkLc25WOg55c1seV4onMmnGs+kTJ6L4Rn06hZFr+Inw520JoVXfn5jN5+69w3+vryVeCqbb+UM8NDSFvEWjLjMo81puiNJtvXG8pb3my2mIoynDMrJ8Ay2pSu5fOF4Vv3PezhzVh2/fm4zQx98iPvSpzGjvpRz5jSQzem82CxZXGoAfaXFMxhbEcy3aq7ZiyyfIydU8syXTrVRSvnH4HUflHGB3aGyRD6nmolg9a4cZeAgD1WlrKqQVSbFHz6ykKl14XzjrgOJt0QZjD9a6iNUUdWeYAn0DuliGaveNornHybIpJoSptSWsKZtUBrGjV9IXzTF0U0isF/ebNQjBMrhfwYZnHM1tz610cyaMfCzpzcxnMywviPCWbcuZsmWXtA0ko1H8fF7lnHczc+wbHs/1929lKe7hDZyJ/uZWB2irjTA3LHlLO01BGDHm0bWkkF7+MPCwW/6tywbefqAOb0OWD9goUksNNm2qI/6Mn8+ZRNkWFJzl+nFtPTHzHnaQB+mlf6RE5oAWK6oNizNGY3nvDldQX1ZgLKAl+vPmM5QIsO9r25nQ2eE6Q1h5owpI+z3yHPBjDNZPQNN0zhygtx36CDM8nmroGIGqg22NVayJ0rMUQaHEMoCHsJ+D+s77J7BqTPqOHNWPZFExrRcDxCsymBrT3Q3e+4HgpVSHzFm/uj2n3AsnPcjlpWfzSZdWon0Ro20xPlXA3CUtokfXXo4589tpLlrOG/tDsTSTKsPM70+zMube3h1Sy87jX5TP3xyPT97ZhM/fXqT7XKrWgZZ2FRJic9Nc9cwv3xO6JVn13ezaH0XPo+Lz973BovWd/HtyPsB6MyVc8R4seAPG1vO2ojhGaSjxVlLjUeYMx1qLd6R0QAwEmjkqjvNSWb5dgVAZzrAp0+ZQr2lBXvbgCgDTZO27e2DCZsyeHWgnGMnV/GRE5o4zWi4uHyHqQxU/y11/Z252ryVP3dcOTPqS1m0rotIIkNTdQket4sFTZXc//pOHlnZxqDhGRTmxZ86Q6g6zwGaVfJuhKKFtvZE8bldthTzwtnqhXCUwSEETZMgsqkMzB9bacBDKpsjmTmwA+P7oikqQ17mji3nF88251Nb31a43HD0x/lC+lNMqBMrP9+5ddIpZF0+bsteTEN5kDljy8np8JE/vEYkkabfyHI5fkoNr2/r49q7XuOWx9fTOZTgviU7KA14+NOS7XkFATLgfHJNmH99/iQumDeGV7f0EktlaDcqY//3orkicIFOqsh8egkfS30l36hszphyhigh5zKsvMJCPVX0FW6wZ0dl5TM9kDnFtntfzqSM2vQaptTJvX3rPKnLaB9I0Nw1zLjKIJNrSuR/Nv5omHwq60+4lc36WD5z2lS++7451JX6CXrd+TbJIBRFMpOFORfTdt7dbNbHUmmxUBvKA6w19leN2Y6ZVE02p/O5Py9n6XZpmVIZKs4Auv2qI/nQcbtvxXwoI+B15yu2K0u8e0WDOcrgEMOYikB+eEa5ZXKbKuM/0EHevmiK6rCfn11+BIPxNI+uGnFu0VuOlv4YO/vinH+4uM55msTl5rZjX+Rn2UuoK/VzyvRaLjlqHK9u6ePVLX35LJfjplSTSOdIpHMs2drL5u5hcjp8931z0DSN8297gd8t3kI2p9MznKSuzE9TTQmXLRxPKpPjhU09dEWSeN0aHzhyLH/4yMI8J97mmcD2bFU+GCiD1TVifiMNtdAzUHULtQUxkwlSZ/FquYx+PHGqZPXcucwcPvNcbh5VJT5qwn6uOb4JTROaaFPnMNPqShlTEaRnOEXSVw4ffpjXS88AyPfC0jQtP1wl5HMzs6GU3yzewrwb/83fVnXTVie1FlbBXlfqJ2Z4Wqrv1uULx/N+I93zsTelnqKQ59c0jfPmNhbNDnFgh6KGCpXpnuAog0MM1u6mds9A3qu4wZItvZz+4+f2uz6gL5qiKuRjcm2Y2lI/GzvfGXndq4yA5+kz6/C4NHot1asdQ0lqw368bhcBr5tPnyrZQmpoSlWJj2MnVefTdnuGU7xqxA+OGF/Otcc3MZTIcNNj61i8sZucblrAC5uqCHrdvNzcQ+dQgrrSAJqmceqMOr52jvD9+UwQ40ct+8CQxwh8F3oGY4+SoLIRL8jldF7f1od+4hfgy800J8p47+GNfPu9Yvnft1rouq6qo0jiywsPn8dFTdjPlu4om7oiHDamjEbDO+kwPJeeSFJGS1gs/XnjxRupDPn4xMmT6RlOkkjn+O7Da9jUVZzVYp0KqJ5LZYmPn1x2BGMrgvRFU5QFPAdsdO2hBhUoLswW3BMcZXCIwTqw22p5qWlPkUSGSCLNZb99lS3dUXMk5j7COlh8Wl04Lxz2hJ19Me56aWt+AMmBhopl1JcFqA77TJoIaB9K5IUgkK9WXWMog8qQj/KQlxOn1uSt7YeNPPmG8iBfes90fn31kbg0+N0LUkCmhpb4PC6OmljJkq19dEeStgwPJWBV8FZ5BkpI92kFXUyBJ9d0sKQ1CVc9JO1CgLtf2calv36FFzYPQLiWXmOEo+qEGSfAxwI/4W9zfp7/PApjKoIsWt9FTpdYhTIe2gYMZTCcpDLkszVPPH6KPIPOoQTvmzeGr5w9gx9ecjiRZIYXjK6jVv7fOs2rriDDRcVJrj7WoYL2FSqov3mUvzUFRxkcYjhqYiWlfg93XLPAVohjegZp7n/NHFu4dFuf7fgt3cPs6B19AdlAPJ1Pd5teX0pzZ2SPQeq+aIqzf7qYGx9Zyy+sPY32A81dEY6/+Rn+YVS6qiKp8qCX6hK/GUAGOgbjtsEiAa+b6hIfq9sMZWAI7XuuO4Z7rjuaipCX7b0xwn4J0Ps9bs45rJFTZ9TlM46sAvCYSVWs74iwsTNiE4aVBcpAPTcQJd6RM+IBhmfQ3BXhk/cs44rfvQpTz4CyMcRTWW43AtT/WNFKKpMjkshQVeKnwqL84zVz6IlLQ0Pr92BMeYBho8Hh3HFWZSAeYnckWVS8dNwUqSPI5HS8bhefOW0qJxhKUnlT1WE7TQQQ9nsoKcgM+s77ZnPbFfP5ytnvrvz/txIXzBvDFUdP4L/fWzyQa3dwlMEhhuOn1rDqf97DGbPsvLPKLOqPpfnDy9s4ZlIV581t4PVt9uHmp//4eU7+4bOjvt5gPJ1PYZ1aFyaaytI2uPthG29s7yeWylIT9vHHV7bbMpL2FR//4zLaBhP5/vhD8TQ+j9BA1WGfLbWyK5Is6ic/tjKYnyZlzX/XNC2fa2/NyAFzBCPYLeBjJovw7BxK2igT5RmMVDDUUB6gNSPK4OmdGh+6Ywk3PrI2fw8KNz22lu5IksPGlvHk6o48vVMV9tn69g/G0/TH0kVtGqwtHRrKAjSWBygNeLj58fVs6R6m5/+zd97xUVXp/3/faZmZZNIbJIHQJJSQEKrSBAQsYEX5ujZsrOC6iqvr/nb9KrqyNtQVV0VxF+UrKir2sq5KlUUl9BJ6SyjpfSaZdn5/3Lk301IoIoH7fr3yyuTWM3cm53Oe5zzneeoaSbQFnpMSbcZi1HPtgKYiT0m2CCQJCiscJESaAgq1JPmebbBVoFzr8pyOZ138/+lEkiSevDqbK/untX6w/3mnOpTwdDFw4ECRn5//azfjrOFwlYNhTy3l9uFd+OcP+3l2cj/qGt089vl21vy/MWra38w/yXn7v/r9CHqkRPGvH/ZzVV5a2GIcDS4PWf/7bx6c0JO7R3fnp33lTHn9R968dRDDuieqNaSDefabHby2Yh/vTRvK5HlrmHNtDpMHhK8mB+ByuSgqKqKhIbzIeLxCjdaxGHUkREVQaXfS4PKqOe/tTg82s4Fos5EjVQ75td9IurzOqdav7hhrRufXWVXZndQ1ejAbdCT6dXBur1A747RYs9rBCSG3xytk95wilkIIDlc1oJfAI+RRutKBV9mdeJ0O4qUaynSJKEtCdBJ4hdwmj1dQXNOIzWzApNdRXu8kPtJIRb2LhEgTFpMet8dLtcOFyyMw6iU8XhFQahHk+hdCNA0QnG4vJbWNxFgM1Dd6MBl0bUrvfLS6AY9XYDLoAjp+t9fLsepGIgy6VhdCaZwYZrOZ9PR0jMaQSfh1QojQJGKAtnpDA2j6x1cWpHVNiqTRJUcd7S2pp0OMJcC988G6QkwGHa+t2IfL4+V3Y0Izltb4uWKgqXDIOz8dYvrb61l05xB1IZE/GwuryOpgI69THIlRJlbvKWtRDIqKirDZbGRmZoYdUdY4XHjL69FJEmajnu7JURwsr6fB5aVnqo2KeidFlXb0OonzUmy4j9bQMcYS0LEfqXJQVteISa+jZ6ot4D7ldY0crnIQGWEIKXMqiuT4+97pgRlaI0rrqG90kxZrCShOJI7IyekkoHdajHqfktoGjlU30C3FirvYjgAkJDrEmjlS5aBrchRVdhdSvZNeqTZcHi+7S+pIiDRhrHfSNSlKTcl8tMpBeb0Ts1GPTpJLRbZGwdEaoiIMVDtka8I/EKE5jCW12J0eYi1GOiU0hbN6vQJxpJpYi+m4qgRqtA0hBOXl5RQVFdGlS5fWT/ChuYk0ALkOrCRBgS9VRVqsVe289/tSE9Q4mlYov/PTIeavlCdHnZ4mkVi2o4Q73spnV3Gt6pdXRthJURGYDDqW7ijB4fIwc/FG6oMK8Hi9gk2F1eRmxKLTSZzfLZEf9pS1OM/Q0NBAQkJCs64FZURvMxvkHETI1oKyeCk+0kSSLQKvaErJERzJovydGmMOuY/icw+XziM9zhJ29KsIZHP30et0Afcx+hLdVTq8COQqXulxFrXEaaPbS6XDhS3CgEGvw+jL16+EcPov1NLrJbxC4PJ4W62ipxBh0OFwevAKgUHfNheOYvkpbVHQ6WRRbm0RlMaJIUkSCQkJzVrKzaGJgQYg/4Mm2yKoqHdi1MuvU6PNmI06DvhWDpf5JlnvH3ceHq8gMzESnURAWOa/Vu/nu4Ji7lyYHzBJq9wjPc6C2yuwmvQcqrDz2OfbAjr68nondY1ueiTLfvgLuiWoeXAAPlpfJOf3D0LpOL1hRMPh9BBh0BNh0OP2CIQQeLwioCM26HQIIXD6xCK4k06MiqBLYmQzOW506jHBxEdGqC42fxIiTWGvZ/B1+sH3N/o64NoGNxISiVERxPkVaaltcOP2eFXhNeh06HWSKoQBYuB77fJ427yaN8Kop8GtXKtt3YYiBiZD6PHnpdg0F9EvyInMuWhuIg2V7LQYimtK6BhrUX3VmQmRTWJQK3f6/TvF8sndw+gYa2HKa2sormlk8dpDbD1cw2pfsrGD5XaO+RKw+Xd4GXFW9pXW079TLLkZsby8bC+HKuyMOi+Z6Rd2U5O2KZOxnX1uhKNVDtJiLcz+soBqh4vuyVF0S4oMcHFU2Z0UVTronhyljtaFEOp8gFEvIRC4vbIYmI3+YiC/VlZghxux28zh47b1Oh390o+vUI8khb+e1aTH7nTLK3j9UDpWh8uDUS+p/+x6nVz7VlksaDE2dbwmvQ6H1xPyfvw787bG8kf4dehttQyU40xttD40fl20T0lDJTtN7tD8E4RlJkSqbiIlmVtiVAR902KIjzSREGXiu4JiHlqyhf/78SBeAZf0lUMflbBCfzFQVqt2SYzkD+N6MvWCTH7cV6EW1mkSA3lSU1kXcaymgf9sP0Z5vRO3V3DnwnzuXNgUQOD1KpOyQk10BrKbxO31YjMbVJeIy+MNtQx8HZcyT/Jr5b9JiZYnp+ODVo8qYiCECJl4jzTp1XkG/9W5yojcYmrKzjlr1ixefvEF9Zi2vk+znxhYjG1z71h8WUGDawkoZGZmUlZW1qZrhePSSy+lqqqKqqoqXnnlFXX78uXLmThx4glf91xFEwMNlX6+MoHVfp1pZmIkhRV23L6iOBDoDvGf/FQWYE0ZJGfL3Fwoi0G0X6psObUCdEmMQqeTmHV5Hx6c0JNqh4u6RrdqTShx/oooFNc0smJnKfGRJjW0s7CiaXV0vdONy+PFqNdRWe9SXU+KWyUqwqC6WlwegUcEioFetQxCR9KnE71OonfHaFJsgVaDTiepbTIGjcyVWH2TQR8QPqpMYQSvC/D3ILTVbx8ZYaBjrIWsVFuzUWDBREUY6N2h7ccfL1999RWxsbEhYnC6cLtPfZbfXxNNDDRUlAIg/oVEuiVF4vIIDlbYKatrRCcFpsVVcstHmw0svG0wa/7fGPr7IoQUyyA6yE0E0CWxKYok0xdpcqCsnuIaOd2B0oFFRhiwRRgormmguLaR9DgLX/x+BLcP74LT46XU57py+3q+OKsRt9er5l+qd7qxmPTypKriavFNquqlUNdJc26i1nj77bcZPHgwubm5/Pa3v8Xj8RAVFcVf/vIXcnJyGDp0KMXFcjWx0tJSrrnmGgYNGsSgQYNYvXo1II/ap02bxsUTJnDLLbdgt9u57rrr6NevH1OmTOE3E8eybdMGPnhnITNnzlTvvfjtN3n2sb8EuIhAdrXFR5r4x/PP0LNnTy666CJ27typhsUWHtjP1ZdPZMCAAYwYMYIdO+Qa2FOnTmX69OmMHj2arl27smLFCm6//XZGDMpl2h23q9efPn06AwcOpE+fPjz66KNNn2dmJo8++igDBgwgNydHvW55eTnjx4+nf//+/Pa3v20xKOCZZ55h7ty5AMycOZMxY8YA8P3333PjjTeq9ykrK+NPf/oTe/fuJTc3lwcflGs419XVMXnyZLKysrjhhhtavNfatWu54IILyMnJYfDgwdTW1tLQ0MCtt95KdnY2/fv3Z9kyeW3Nm2++ybXXXsukSZMYP348QggefPBB+vbtS3Z2NosXLwZk6+TCCy8M24bHH3+cQYMG0bdvX6ZNm3bKMwWfKNqcgYZKki2CxdOG0qtjU4rjrFT59c5jtZTVyakl/DtKxTJQ5hmUydL4SBMV9U4iTfqAkeGonkncO7aHmsIAINMnDAfK6ymubiAxKiIgyiUlxsyx6gZKaxtJizWTFmvhol4p/POH/Ww/WkMy4PZFNL20dA+bCqswG/XqBKpOanKfOFweuYiMkCdF/d0kSmSTJBGwSKp3x2gendSn2edWUFDA4sWLWb16NUajkRkzZrBo0SLq6+sZOnQos2fP5o9//CPz58/n4Ycf5t5772XmzJkMHz6cQ4cOMWHCBAoKCgBYt24dP/zwAxaLhTlz5hAXF8fmzZvZunUrublyqcxrrp3C2GGDeOaZZzAajby98C0effoFYoJSPltNBgq2bGLx4sVs2LABt9tNXl4eeXl5xEea+P3//oH5r79Gjx49+Omnn5gxYwZLly4FoLKykqVLl/LZZ58xadIkVq9ezRtvvMGgQYPYuHEjubm5zJ49m/j4eDweD2PHjmXz5s3069cPgMTERNavX88rr7zCnDlzeOONN3jssccYPnw4jzzyCF9++SWvvx5Ugc6PkSNH8txzz/H73/+e/Px8Ghsbcblc/PDDD4wYMSLg2KeeeoqtW7eycaNcpW358uVs2LCBbdu20bFjR4YNG8bq1asZPnx4yH2cTidTpkxh8eLFDBo0iJqaGiwWCy+++CIAW7ZsYceOHYwfP55du3YBsGbNGjZv3kx8fDxLlixh48aNbNq0ibKyMgYNGsTIkXJyvuba8Lvf/Y5HHnkEgJtuuokvvviCSZMmNfssTheaGGgEoKyOVeiREoVOkssVHqlyhETMKGkGgpNidU6wUlHvDImWsZoMzBwXmF3T3zI4VtMQkD8J5HmD4toGSmsbyPUlRevdQRapbUeqSU6RFzJJSKrvXx5tSci/AieKnW5ZOILH/pIk15sP3dMy33//PevWrWPQoEEAOBwOkpOTMZlMqu96wIABfPvttwB89913bN++XT2/pqaG2lo5pPfyyy/HYpEF9YcffuDee+8FoG/fvvTq0xeAuOgoxowZwxdffEGvXr1wuVxMGDEkbNtWrVrFVVddhdVqVa8vSRKxRi8//biGa6+9Vj22sbEpKmzSpElIkkR2djYpKSlkZ8u1pfv06cOBAwfIzc3l/fff5/XXX8ftdnP06FG2b9+uisHVV1+tvu+PPvoIgJUrV6qvL7vsMuLiQteYKAwYMIB169ZRW1tLREQEeXl55Ofns2rVKtViaInBgweTni6vTcnNzeXAgQNhxWDnzp106NBB/eyio+Xv1Q8//MA998j1pbOysujcubMqBuPGjSM+Pl497vrrr0ev15OSksKoUaNYu3Yt0dHRzbZh2bJlPPPMM9jtdioqKujTp48mBhpnPmajnsyESDYcquTn/RVcP7hTwH4lIsYWlGNmcJd4NhyqosLeeiqJyAgDybYI9pfZKa5pID0ucCFScnQEP+yupbzeqaYyiLEaSY+zsP1IDaNTLHg8cvz7rEl92HakRl0YtfVwdcAiKbfHy76yerxC0DUxEpPfhOuOYzU43V4sJr0a2toWhBDccsstPPnkkwHb58yZ0xT1o9erPmav18uaNWvUTj/gWUQ2Lc4Kdh9IPpEy6HXccccd/O1vfyMrK4tbb721xfaFCzP0er3Exsaqo+lgIiJk0dfpdOpr5W+3283+/fuZM2cOa9euJS4ujqlTpwbEtSvn+L/v5toSDqPRSGZmJgsWLOCCCy6gX79+LFu2jL1799KrV69Wz/dvc3Ab/BFChG1TS66blj6j1trQ0NDAjBkzyM/PJyMjg1mzZh33eoBfCm3OQKNVeqbaWLW7jEa3V83/r6D8G6XEBI7mbx8ur3xscLWtWE7XpEj2lNZxpMpBakyg9ZEabaakthEhAvPZ9OkYzXZfHWK3LzpIkiRMeh1OtxevEHhFcNSQjvNSbGSlRgcIAfjF+B9njPbYsWP58MMPKSmRM7xWVFRw8ODBZo8fP348//jHP9S/m+uQhw8fzvvvvw/A9u3bKdi+VX5/Bh1DhgyhsLCQd955h+uvv77Ze40cOZKPP/4Yh8NBbW0tn3/+OSCPgLt06cIHH3wAyJ3apk2b2vyea2pqiIyMJCYmhuLiYr7++utWzxk5ciSLFi0C4Ouvv6aysrLV4+fMmcPIkSMZMWIE8+bNIzc3N6TzttlsqmV1vGRlZXHkyBHWrl0LQG1tLW63O6Ctu3bt4tChQ/TsGZo8b+TIkSxevBiPx0NpaSkrV65k8ODBzd5P6fgTExOpq6vjww8/PKF2/xJoYqDRKkppw4x4CwOC0kdM6JPK9Au78dCErIDtyTYz//hNfxZMHdSme2SlRrP1cDU1DW418ZuCv6XgLwa9O8Swv1we5bv9VhSbDDqcHi/eZlYTN0dydARWk6HZ9QTN0bt3b5544gnGjx9Pv379GDduHEePHm32+Llz55Kfn0+/fv3o3bs38+bNC3vcjBkzKC0tpV+/fjz99NP069ePfl07qHMw1113HcOGDWvR3ZKXl8eUKVPIzc3lmmuuCfC3L1q0iH/+85/k5OTQp08fPv300za/55ycHPr370+fPn247bbbGDZsWKvnPProo6xcuZK8vDz+85//0KlTpxaPHzFiBEePHuX8888nJSUFs9kcMl8AkJCQwLBhw+jbt686gdxWTCYTixcv5p577iEnJ4dx48apo3ePx0N2djZTpkzhzTffDBjpK1x11VX069ePnJwcxowZwzPPPENqamqYO8nExsZy5513kp2dzZVXXqm6p84EtER1Gm2itsGFADWp2qnm/bWF/HHJZgA+mnFBQM6ivaV1jH1uBQCf3D1MzXn/3fZi7liYz6c3ZBKRlIHVaKBTgpUjVQ4q6p30SI5iZ3EtGXHWgLKL7QWPx4PL5cJsNrN3717Gjh3Lrl27MJnk9zJx4kRmzpzJ2LFjf+WWapyJFBQUhLjUtER1GifN8Y6Wj5esDrI1oJOgV2p0wL6uiU0+Wv8UBn3S5ONKahtJifMSbW6yDLxC0NhMaon2gt1uZ/To0bhc8rqJV199FZPJRFVVFYMHDyYnJ0cTAo1ThiYGGmcE56XY0EnQLSkqZCGUJEnYIgzUNroDFlClRpt9cxOyH1Z1EwWvJ2inYmCz2Qhn/cbGxqqRLQrl5eVhheH7778nISEhZPuZwuls91VXXcX+/fsDtj399NNMmDDhlN6nvdKqGEiSlAEsBFIBL/C6EOJFSZLigcVAJnAAuE4IUSnJszsvApcCdmCqEGK971q3AA/7Lv2EEOIt3/YBwJuABfgKuFe0V/+VxglhNurJ6xSnLnwL5qt7R7CxsCog6ZkkSfzvxN6szpcnPpUvjHJMg6t9i8HxkJCQ0Oxp4t51AAAgAElEQVRE9JnM6Wz3xx9/fFru015pi2XgBv4ghFgvSZINWCdJ0rfAVOB7IcRTkiT9CfgT8BBwCdDD9zMEeBUY4hOPR4GByP+36yRJ+kwIUek7ZhrwI7IYXAy0Hp6gcVbx3rShzYYeZsRbyYgPn/teqcWg5OsPtgx0xxkdpKFxLtJqNJEQ4qgyshdC1AIFQBpwBfCW77C3gCt9r68AFgqZH4FYSZI6ABOAb4UQFT4B+Ba42LcvWgixxmcNLPS7lsY5hEGvO6FRvFGvIzstRs3Ro/Nl8mwuHbWGhkYoxxVaKklSJtAf+AlIEUIcBVkwgGTfYWlAod9pRb5tLW0vCrNdQ6PNBFsUinUgIaFpgYZG67RZDCRJigKWAPcJIWpaOjTMNnEC28O1YZokSfmSJOWXlpa21mSNc5jguQUNDY2WaZMYSJJkRBaCRUKIj3ybi30uHny/S3zbi4AMv9PTgSOtbE8Psz0EIcTrQoiBQoiBSUlJbWm6xjmKkllVhB9XnLPMmjWLOXPm/NrNANpHPYPly5fz3//+95Rc60ynVTHwRQf9EygQQjzvt+sz4Bbf61uAT/223yzJDAWqfW6kb4DxkiTFSZIUB4wHvvHtq5UkaajvXjf7XUtD44SIjDDQOcFKelzrhdvPRM62XPm/BKejnsEvKQZCCLzetqVrOR20xTIYBtwEjJEkaaPv51LgKWCcJEm7gXG+v0GOBtoH7AHmAzMAhBAVwF+Btb6fx33bAKYDb/jO2YsWSaRxCoixmIiPPD11dk9lPYPx48dz8803h9QzGDJkCPn5+fzzn/8MqGcwf/587r///mbbNnv27IB6Bgp79+7l4osvblM9g9tuu41evXoxdepU9fzW6hnk5eWRnZ19RtUz+P777+nfvz/Z2dncdtttaqZWfyslPz+fCy+8kAMHDjBv3jxeeOEFcnNzWbVqVdi2FhcXc9VVV5GTk0NOTo4qHs8//zx9+/alb9++/P3vfwfgwIED9OrVixkzZpCXl0dhYSHvvvsu2dnZ9O3bl4ceeki9bnPfn88//5whQ4bQv39/LrroInX7SSPndm9/PwMGDBAaGkIIsX379qY/vnpIiH9demp/vnqo1ftPnDhROJ1OIYQQ06dPF2+99ZYAxGeffSaEEOLBBx8Uf/3rX4UQQlx//fVi1apVQgghDh48KLKysoQQQjz66KMiLy9P2O12IYQQzz77rJg2bZoQQogtW7YIvV4v1q5dK+rq6kTXrl3V+51//vli8+bNYduWn58v+vbtK+rr60V1dbXo1q2bePbZZ4UQQowZM0bs2rVLCCHEjz/+KEaPHi2EEOKWW24RU6ZMEV6vV3zyySfCZrOJzZs3C4/HI/Ly8sSGDRuEEEKUl5cLIYRwu91i1KhRYtOmTUIIITp37izmzp0rhBDi5ZdfFrfffrsQQoh77rlHPPbYY0IIIb744gsBiNLS0rDtXrNmjZg8ebIQQojhw4eLQYMGCafTKWbNmiXmzZun3qe0tFTs379f9OnTRz132bJlIjo6WhQWFgqPxyOGDh0qVq1aJRwOh0hPTxc7d+4UQghx0003iRdeeCHgWkIIsXbtWjFq1Cj1M1GeV3Ncd9116nXcbreoqqpSn3tdXZ2ora0VvXv3FuvXrxf79+8XkiSJNWvWCCGEOHz4sMjIyBAlJSXC5XKJ0aNHi48//lgIIZr9/lRUVAiv1yuEEGL+/Pni/vvvD9uugP8LH0C+aKZP1VYga2icJKernoFSKyAyMjKknoFSbyCYcPUMQB45//e//z2n6hnYbDa6dOnCeefJ9TRuueUWXn75Ze67775Wr9cSS5cuZeHChYCcqjomJoYffviBq666Sk13ffXVV7Nq1Souv/xyOnfuzNChQwG5ytqFF16IMgd6ww03sHLlSq688spmvz9FRUVMmTKFo0eP4nQ66dKly0m1X0ETA42zi0ueav2YU4w4TfUM/NHqGbRMuFoCLT1Pg8Gg+u9PRX2Blu7V1s/YaDSG/f7cc8893H///Vx++eUsX76cWbNmnXR7QUthraFx0pyuegZbtmxR92n1DJpoaz2DrKwsDhw4wJ49ewD4v//7P0aNGgXIcwbr1q0DYMmSJcd17bFjx/Lqq68CcqbZmpoaRo4cySeffILdbqe+vp6PP/44bPrtIUOGsGLFCsrKyvB4PLz77rtqm5qjurqatDR5KdZbb73V4rHHgyYGGhonyemsZxAT05S7SatnINPWegZms5kFCxZw7bXXkp2djU6n46677lLbdu+99zJixAj0+qZEiZMmTeLjjz9ucQL5xRdfZNmyZWRnZzNgwAC2bdtGXl4eU6dOZfDgwQwZMoQ77riD/v37h5zboUMHnnzySUaPHk1OTg55eXlcccUVLT6XWbNmce211zJixAgSExNbPPZ40OoZaLR7wuVtPxvQ6hlonAxaPQMNjbMErZ6BxulEEwMNjTMUrZ7BmdXu2bNnq3MsCtdeey1/+ctffqUWnVo0N5FGu+dsdRNpaJwMx+sm0iaQNTQ0NDQ0MdDQ0NDQ0MRAQ0NDQwNNDDQ0NDQ00MRAQ+Oc4myqZxCOk6ll8MknnwTkjHrkkUf47rvvTlXTzng0MdDQOAPR6hk0IU5T3v9gMXj88ce56KKLfvH7nilo6ww0ziqe/vlpdlTsOKXXzIrP4qHBD7V4zNtvv83cuXNxOp0MGTKEV155hZiYGO69916++OILLBYLn376KSkpKZSWlnLXXXdx6NAhAP7+978zbNgwZs2axZEjRzhw4ACJiYm88cYbTJ06lR07dtCrVy8OHDjAyy+/zKZNm9i6dSsvvPACINczKCgo4Pnnnw/bttmzZ7Nw4UIyMjJISkpiwIABgFzP4O6776a0tBSr1cr8+fPJyspi6tSpWCwWduzYwcGDB1mwYAFvvfUWa9asYciQIbz55puAXM9g7dq1OBwOJk+ezGOPPQbII/5bbrmFzz//HJfLxQcffEBWVhbl5eVcf/31lJaWMnjw4BaTtB04cIBLLrmE0aNHs2bNGj755BN27tzJo48+SmNjI926dWPBggVERUXx73//m/vuu4/ExETy8vLUa8yaNYuoqCgeeOABQM78+sUXX5CZmcnChQvVRIL9+vVj+vTpfPbZZ6xYsYInnniCJUuW8Ne//pWJEycyefJkvv/+ex544AHcbjeDBg3i1VdfJSIiotn32h7RLAMNjZOkoKCAxYsXs3r1ajZu3Iher2fRokXU19czdOhQNm3axMiRI5k/fz4A9957LzNnzmTt2rUsWbKEO+64Q73WunXr+PTTT3nnnXd45ZVXiIuLY/Pmzfzv//6vmkjtf/7nf/jss89wuVwALFiwoNnMpevWreO9995jw4YNfPTRR6xdu1bdN23aNF566SXWrVvHnDlzmDFjhrqvsrKSpUuX8sILLzBp0iRmzpzJtm3b2LJli5pYb/bs2eTn57N582ZWrFjB5s2b1fMTExNZv34906dPV91Sjz32GMOHD2fDhg1cfvnlqhg2x86dO7n55pvZsGEDkZGRPPHEE3z33XesX7+egQMH8vzzz9PQ0MCdd97J559/zqpVqzh27Firn9e2bduYPXs2S5cuZdOmTbz44otccMEFXH755Tz77LNs3LiRbt26qcc3NDQwdepUFi9ezJYtW3C73Wpiuubea3tEsww0zipaG8H/Emj1DE59PQMgIO//jz/+yPbt29WEeE6nk/PPP58dO3bQpUsXevToAcCNN97I66+/3uJ1ly5dyuTJk9Ukb/Hx8S0ev3PnzhbrIIR7r+0RTQw0NE4SrZ7Bqa9nAKHPYty4cbz77rsBx2zcuLHZa/rXKICmOgVCiONqR2tZGpp7r+0NzU2koXGSaPUMfpl6Bv4MHTqU1atXq7UI7HY7u3btIisri/3797N3716AALHIzMxk/fr1AKxfv579+/cD8uf1/vvvU15eDsifFzRfu6ClOghnE5oYaGicJFo9g1+mnoE/SUlJvPnmm1x//fX069ePoUOHsmPHDsxmM6+//jqXXXYZw4cPp3Pnzuo511xzDRUVFeTm5vLqq6+qbp4+ffrwl7/8hVGjRpGTk8P9998PyHMxzz77LP3791fFBVqug3A2oSWq02j3nK2J6rR6Bhong1bPQEPjLEGrZ6BxOtHEQEPjDEWrZ3DmtvtsRBMDDY2zgISEhGYnos9k2mu7z0a0CWSNs4L2OvelofFLcCL/D5oYaLR7zGYz5eXlmiBoaCALQXl5OWaz+bjO09xEGu2e9PR0ioqKKC0t/bWboqFxRmA2m0lPTz+uczQx0Gj3GI1GunTp8ms3Q0OjXaO5iTQ0NDQ0NDHQ0NDQ0NDEQENDQ0MDTQw0NDQ0NNDEQOMMYtZ/Z/HBrg9+7WZoaJyTaGKgcUZQ2VDJkt1LeHzN4792UzQ0zklaFQNJkv4lSVKJJElb/bbFS5L0rSRJu32/43zbJUmS5kqStEeSpM2SJOX5nXOL7/jdkiTd4rd9gCRJW3znzJWOp+qExlnDD4d/ACDKGBWy76t9X5H9Vjb1rvrT3SwNjXOGtlgGbwIXB237E/C9EKIH8L3vb4BLgB6+n2nAqyCLB/AoMAQYDDyqCIjvmGl+5wXfS6Od4hXeNq8KXlG0AoAMW0bIvje3vQnAnqo9p6xtGhoagbQqBkKIlUBF0OYrgLd8r98CrvTbvlDI/AjESpLUAZgAfCuEqBBCVALfAhf79kULIdYIuddY6HctjXaMV3i5/JPLmbVmVpuO31slFxMJN/pPsMjZKw/XHj5l7dPQ0AjkROcMUoQQRwF8v5N929OAQr/jinzbWtpeFGa7Rjvl0z2fcvs3t/Phrg85WHOQj3a3rUB4qUNOJVHnqgvZFxchG5EHag6csnZqaGgEcqrTUYTz94sT2B7+4pI0DdmldFwl8zROD26vm5c3vszR+qNsLt0MQGpkaqvnOT1OqhurgfCWQaOnEYD91ftPYWs1NDT8OVHLoNjn4sH3u8S3vQjwd/qmA0da2Z4eZntYhBCvCyEGCiEGJiUlnWDTNX4pVhSu4Gi9XPu3wdNAh8gOVDVUtTpvUOYoAyAtKo1GTyMujytgv2ItHI9l4PF6cHqcx9F6DY1zmxMVg88AJSLoFuBTv+03+6KKhgLVPjfSN8B4SZLifBPH44FvfPtqJUka6osiutnvWhrtjH3V+wC4rOtlpFhTmHzeZBo8DTjcjhbPU1xEmTGZQKh1UOusBeBgzcEAYfEKL3d9excri1aGXPO1za8x+fPJJ/xeNDTONdoSWvousAboKUlSkSRJtwNPAeMkSdoNjPP9DfAVsA/YA8wHZgAIISqAvwJrfT+P+7YBTAfe8J2zF/j61Ly1X57C2kKm/nsqNc6akH1e4cUrvL9Cq3496l31GCQDj13wGEsuX0KyVZ5KqmgIjj8IpNQui0GXaDnzaK2rNmC/IgYOt0N1GQGUO8pZfWQ1/z3y35Brbi3byv7q/arVoaGh0TJtiSa6XgjRQQhhFEKkCyH+KYQoF0KMFUL08P2u8B0rhBB3CyG6CSGyhRD5ftf5lxCiu+9ngd/2fCFEX985vxPtqELJ65tfZ13xOr498G3IvgFvD+C+Zff9Cq069awsWsmj/3201ePsbjtWo5UIfQQxETHEm+MBeUFZS6iWQXQmEGoZ+Iut/wTzkXrZo1hcXxxyzaI6OS6hoLyg1XZraGhoK5BPCmWBVLgIGLfXzbLCZae7Sb8Id39/Nx/t/qhVd0+9q55IY6T6txIF5G8ZFNUWMeWLKZQ7ytVtpfZS9JJeXWNQ5wx8nnXOOlKsKUCTlQBwpE4WgxJ7ScDxXuFV9+2o2NG2N6mhcY6jicFJoHR8wWLg8jZNgAZ3VO0Nf0Ottfdid9kDxCDeIlsG/mLwxpY32F6+nW8PNllTZY4yEswJREdEA4GWQaOnEafXSceojkCgUByuk9cdFNsDLYMyR5nqTtLEQEOjbWhicBJE6COA8CNZhZ+P/Rywz+F2qBOtweyu3B3gEz8T2F/TFM4Zzh3jT72rHqvBqv4dzjJQXseaY9VtB2sO0iGqQ1hxVSyBDpEd5L/95hOO1smRS2WOMjxej7q9qFZ2EVkN1maftYaGRiCaGJwEitskeALZ35Wxu3J3wL4Pdn7AlM+nhIQ92l12pnwxhU/3nFnBVBtLNqqvg0fgwdS767Eam8TAarRiMVgC5gwUMVAsDpfHxbbybeQk5WAz2YBAMVWerSIGAZZBvWwZeISH8oYmt5NiMfSM7xnwWWhoaDSPJgYnQYOnAZCjWuqcdRTWyous/TugYD97maOMBk9DyKRqjbMGl9cV0KmdCRyqOaS+bk0Mgt1EAPHmeHXtATSJgfJcCioKaPQ0kpucG9YyUDp/1U3kt+9o3VHMejPQ5MIqrClk/pb5GHQGusd2x+6yH8e71dA4d9HE4CRQOrRSRym//fa3XPrRpQghAiyFYDFQ/OFVjVUB2+1uudM60zqvEnsJHSI7EG2K5lj9sRaPDScGg1MHs/rIavZW7cXtdYeIgWJ55CTlYNab0Uv6gDmDEDeR72+nx0lhbSG5yblAk1C9s+MdDtce5vlRzxMbEUu9u77NyfI0NM5lNDE4CRrcsmVQ5ihjc5mcfqGioSLAMlCOUVBGtsFi4HDJneOZJgbF9mJSrCmkRKa0zU3kN2cAcEmXS6h31XPlp1cyb9M8taNXxGBn5U6SrckkW5ORJIkoU1SAmCrPSXUTuerwCi87Knbg8rq4qNNFAKpQ/Xj0RwakDGB0p9FYjVa8wqtacBoaGs2jicFJoHRo/i6fwtpCtcOPjYgNsQyUzr6yMdBNpFgG9e4zK2d/ib2ElMgUUqwpbZtANgaKweDUwZwXdx4A/9z6T3W78hyO1R+jY2RHdXu8OT5gwnlv1V457DQ6g0hjJHXOOu7+/m5u+OoGAEZljMJqsFJYW0hxfTF7qvZwfsfzgaZor3pXPR6vByEETo+Tp39+OiC0VUNDQxODk0IZ9Qu/3HqFtYWqZZBkTQoRA9UyaAhyE7nOPDeREIJiezHJ1mQ6RnbkcN3hEJeL2+vmjS1vUNVQhdvrDnET6XV6Ppz0IfcPuB+3161uV57LsfpjAcnsgsVgd9VuMqMzidBHEGWM4udjP6uFcABSrCl0ju7MwZqDbCyVXU6DUwcDTWJgd9mZ/PlkXt/8Ol/t/4q3C97mtc2vnYpHpKFx1nDOiUFFQwW7Kncd1zmf7/08bJRPuEVYRbVF1Dhr0Ek6Es2JIW6iVucM3GeOGNQ4a3C4HaRYU+gR14MaZ03IWoPt5dt5cf2LfHPgG4AQMQCQJIlRGaMAGNd5HAnmBBxuhyo2/mKQYE4IGLXvrtxNj7geANhMNnZV7lJdUX0T+iJJEhm2DAprC9XzlOtFGuS2FNUVsadqDwUVBepiNIvBcvIPSEPjLOKcE4MbvryBaz675rjOeW/He7xd8HbIdofbofqyFRTLIMoYhdVoxe62s6l0kzoqblYMTpNlcLjuMLN/nB2wMK45lI4/JTJF7ZB3Ve7i5q9v5umfnwZQU08rYZ7BcwYKXWO68u5l7/L0iKexGCw43A4qGytp9DSqq4sh0DKoc9ZxuO6w6mZShGZ42nC+nfwtr170KgCdoztzuPawGokVExEjt8XnslLSaRfXF6tioJf0IW38f6v+Hw+tfKjV56KhcTZyzomBkrPmeOrpVjurwyY8c7gddInpErBNEQObyYbFYGFP1R5u/OpGXt74csB9g0NLT3U0kVd48Xg9eLwebvjqBpYeWgrIVs57O98LWxvgus+v440tb6h/KxPGimUAshhsKNmgiqMy2atUIQtnGSj0TeyLUW/EYpTFQJn0DXATWeLlMFuPi91V8hoN5d6K8AxOHUxqZKq6cK1TdCfcws328u3YTDYMOkNAWzaVblLfj1I6U7mWP/nF+eqxGhrnGuecGCi0NhnqT1VjFZUNlQGrXEEWgyRLkupyMOlMHKk7Qq2zlmhTNGaDWT12XfE6oEkMgjsjRQRO1QTy+A/HM+WLKdQ6a9lcupl7l90LwLbybQCU2QPFrd5VT0FFAS+ufxGQ6wF8tvczNWdQtCmaDpEd1PNBnlNQ5keUhV7BE8jhUCwD5TMIdhOBPMG+tWwrAL0TegNN9QwGpQ4KuF4nm1zoaEvZFnXVM4SKQamjlJ0VO4Hwltmx+mMcqz8WMLehoXGu0K7FoLqxmu8Ofhd2X4O7ocUU0q3FzCt4hZeaxho8whPSgTR4GrAYLGpnlpWQRVlDGVWNVaploFDvqsftdathjs1FE50qy6DYXszOyp0hYZXby7YDUOII9P37P4+fj/7MiMUj+Hr/18zInUGiJRGA7rHdA9JrFNuLmyyDutYtAwVFDI7Z5Xv6u4kUMVhfvJ5NpZtIsaaoqbDHdR4HEGKNpUXJlVKrG6sD0lwobfEP9XULt3qsP4dq5cV1HuFp83dDQ+Nsol2LwdM/P83M5TPV0Z6Cx+vhko8u4cNdHzZ7bmsx8wq1zlo1WijYVeRwObAYLWpnlhWXhVd42Ve1j2hTdIgY+LumWoomOpWLpPxj9kvsJaoIKDUEFPxXCc/fMp9aZy239rmVO7LvULdn2DICOtG9VXupaZSvrwhlc3MG/lgMFhrcDRypO4JRZ1QL3kNTcrsHVz7INwe+oV9SP3Xf0yOeZs31a5DrIDWRZE1SXUOxEU1i4G+l+M/tnBd3XoiwH6g+oL5WhE1D41yiXYuB8g8d7Oetc9VR5ihTE5b5o6QvaOvoz7/z849y8Xg9OL1OLPpAywDkZGoZtowAMbC77GqHH2WMotoZ5CbyWQZu4W7T5G4wjZ7GgA5NwV8o/SuC+UcF3fbNbUz/brr69/ri9QDc1PsmdFLTV0RJCaGwp2pPSCEa/864ORTLYHflbrrFdgu4h1IDQaFbbDf1tVFvJMoUFXI9naQj1Zoacn9/YfJ3LfWK7xUgBnXOOj7b+5n6tyYGGuci7VoMFJ98fnF+wPbmiqt7vB7VbdJWyyBADPzyBinXsRgsdI7uTIQ+gqy4LHV/5+jOAWJQ56pT1xgkWZNCLAD/MNUTcRV9tPsjJn8+GYfbETC3sb18u/r60z2fopN0pEWlqQVlANYeWwvInWpWfBZOrxODzhAwYocmdwyAhER+cb5qGYDsllFcOi1hNVhxuB3sqtylRgop+IvBsLRhTOw6sdXrQZNQ+c8ZKNYCwMCUgerr2IjYgM913qZ5rDq8ihRrCnpJH3YQoaFxttOuxUAZ3a47ti5gu/KPHhyz7+87bqtl4D+C9HcTKZ232WDmN1m/4b3L3iPdlq7uDxYDl9elumKSLckIRIA/318ATmQSudxRTqOnkXJHecD79heDjaUb6RbbjU62TiFuIpDnRzpHdwYg1ZoaMGKHQMvgmvOuYc2RNQGi2iW6S4gLJxwWg4VSRymljtIQMYgyRnFXzl18MOkD5l00T21PayhuIP85A396J/Tm1j63sujSRcSaY2nwNKhrQBTX2YtjXiQ1MlWzDDTOSc4KMShxlAREgCgumOARtr9rps2Wgd854cTAYrBgNVrpHted2IhYTDoTIBd3D17YpKSzTrQmhrTPvwM/EcvAPzWGv0WkhFIqk6l9EvqQZE1SO8DgRXHK6D/YJQSoaSNsJhuXZF5Co6eRLWVbmvaHOScc/s8lWAwkSeLu3LvJis8KPq1FlHs356bKsGVw/8D76ZfUT12HoAh9nbOO3gm96ZPQh3hzfNiwUw2Ns512LQal9lK1wIy/m0V1EwWNsBWXRlxEXJvXGSjXshltAWKgdKL+HZskSSRbk4k0RpJgTlDnJxSU8NIkSxIQKgCKkAS3Lf9YPh/v/rjFdirtqWioCDhfmUBW3C19EvqQbE2mzF6GV3gD1jt0ju6sWjf+4Z4KMRExWA1WEi2J5KXkhUwWx5njQs4JR0ticKIoloG/m8gf/8lkRTCUz1ZZFwKyZaLVQNA4E3B5XeQfy+fljS/zTsE7CCEQQvD+zvd5Pv/5Ux71Zmj9kDMTt9eNR3joGt2VXZW7sLvs6j+08k+uZAJVUEb5qZGpajH11lCulW5LDxgx+ruJ/EmLSiPOHIckSViMcqcnIZFkSWLV4VWAnxgEWQZJ1iQO1x0OcW/d+s2tAFzR/YoQ101weyoaKsK6Sq7qfhVVjVWMzhjNfw7+B7dwU+usVVf7PjjwQSZ1m6SWiQxeWQ2y2KXZ0oiNiMWgM5CdmM1Px35S9/uHiLaE8swybBkh8xInSs/4nkhIZERntHqsIgaKZVDrrCXJKn8mUaYoNeRVQ+N42ViyEa/wkmHLIM4cFzBvJYTA5XVh0pvUbYq7dlnhMow6I5d0uQSzwUyNs4YZ380ICI4pqisiMzqTv/74V3XObv74+VgN1ja5Z1uj3YqBy+tCh47M6Ex2Ve4KtAyc4ecMlM68Q2QH1X3SGsqagThzXMCIURlxR5uiA45/eOjD6voGZQQcoY9gUIdBfLnvS3rE9aBrbFcgdNI4zZbG4brDISKmUFRbRKfoTny460Nyk3LpHtdd3afMP1Q0VJDilDvlSGOkaiWkRKYwZ9QcQB79gmyBKJPiOck5xJnj6BLTBZ2kIzMmM2wb/jz4z+qXuW9iX3469hPX9LgGvaTnN71+0/yD9EOxRoanDW/T8W2hd0JvVkxZEWKdfDv525Bjlc9M+TyV9CHKvuAyphpnL3XOOho9jWwr34bNZCM3KRdJkhBCUFBRwJf7vsSgM1DnrKNTdCdu6HUD+6r38fX+r1lRtIJe8b1IMCdwW9/b+PrA1zz505NqKHpaVBqPnP8IHSI78MaWN9hTtYft5dtJsiQxrvM4hqcN54EVDwT0U0sLl/L3C//O3PVz2Va2jVnnz2Jk+kjmb5nP/23/PwDykvP4Ta/f8MCKBxj27jBiI2J5eezL9Ensc1LPot2Kwb7qfXSnuzrB6N+xKu6gYHeLWkIxqiyCUbUAACAASURBVAMurwuXx4VRb2zxPlWNVcRGxGIz2dS8NtCk6MqCLAX/TlRxExl1Ri7tcilf7vuSBwY+oLpXgi2DRLN8rWD3VpQxijpXHTsqdtAhqgOPrXkMnaRj3Y3reKfgHWIiYgLSXCjnJ1uT1bQT/i4r/4piimWgRPGkRqayZNKSkIVdCgNTm6JylLDP6sZqXhj9Qtjjw6EUpLnuvOvafE5bCOemCufuUsSgoqGCQzWHqHWFdxMdqz9GgiUBo67l74jGL8uuyl3Em+PV/7UGd4NqXbo8LjnE22Dh6Z+f5kj9EYakDuFAzQGcHid3Zt8JEnyx7wtu6nUTTq+TNUfW0Dm6Mx2jOjLx44kBg7wJmRN4/ILHeWnDS7xd8DZGnRGv8KKTdLi8LhYVLFIDQfol9WP14dVUN1bzwa4PqHPVMTpjNGM7jaXGWcOSXUu4+7u7iTDIruzO0Z25M/tODtUe4t0d7/LOjnfIsGUwtdtUcpNy2Ve9j6d+forHf3ycr/Z9xaRuk7jmPDmP2p+H/JnhacM5Vn+MCZkTiImIIS0qjW8Pfsu/9/+bGd/PYFq/adhMNiZ2nRjgQahz1rGoYBE39LqhxefcbsUAZIXsn9wfCLQCmosmUjo+xZ1hd9uJ0ce0eI8jdUfUSl/+C7iU0EzFvRAOpdM36o2MTB/JqimriDXHqrH/SvuK64txuB10i+3G0sKlIRPIHaM6sqtyFzsqdqiLsLzCy9ayrTyb/2zAvSobK9WRrb8YKF9ICLQMlGeirPwFAiyOlrig4wUA6he2rYxMH8nGmzai14UmizsdKB3/ezvf44kfn0AgmsTAFEWDp4EyRxkTP55In4Q+vDD6BVUsS+2lbCjZQK+EXmTYWndJKdhddgw6Q4CLoCWEEKwrXsfPx34mMzqTizpf1OZzm6O4vhiT3hQgmg63gw3FG+iX1A+T3oTD7VAn2A/VHMLuttMzrmeAG0KpWCchsbl0M52iO9EjrgffHfyOzOjMkO/Pz0d/5usDXzMmYwxRpiiy4rOwGCzYXXbe3/k+IIcRKzmoAOaun8uS3Usw6AyU2EuwGCzc2OtGMmwZzFozi5ykHGoaayh1lGJ328mKy2Jr+VZSrCksL1yuWuU/Hf2JSFMkuyt3s2DrAhrcDQgEBp1BnTv8Xe7vyEnOYUvpFl7a8BKbSzdztP4o1/S4hpkDZqqunZ+P/szcDXO5te+t3NjrRjWMelflLl7b9Bp6Sc8Tw59QP6crul/BqxtfpaKhgrty7goYYG3uvZnC2kKGpw1Xn/f5Hc/nUM0h3tnxDkadkZt73xzwHEemjwz4u29iX/om9uWK7lfw229/y1M/PwXAqqJVPDXiKf6+/u8crDnItrJtlDhKAqIdw9GuxeCxCx5TXUJh3URBneqGkg30iOuhPny7y66+bo7C2kJGZ4zGZrJR46xBCIEkSZTYS4iJiFEnsMOhdHbq6lifL1+ZzFTEQInIGdphKPO3zA+xaBS3046KHQEhof71gJVrlTeUq+cnW5LV+/uPbiNNPsvAWUeFowKz3nxCKZ0TLAlsuWVL6weG4dcSApAtI52kY0/lHtWkV8RA+b21bCuNnkbWl6znus+vY2iHodzT/x7uW3YfW8u3IiFxYcaFmA1mOkR24IpuVzB3w1x2VOyga0xXusd1J8oYxfjO43lt82t8ue9LzAYzNpON7MRsrjvvOox6I4+sfoSqxirSbel4hZc4cxyvjH2FPyz/A8uLlqtt7rypM3PHzKVrTFc8Xg8OtwOT3kRBRQEdIzuqgxK7y87Oyp0cqTvCoNRBJFuTWVSwiK/3f01BeQFJ1iT+p+f/MCxtGDaTjdu+uY3C2kKMOiMmvQkhBM+MfIZt5dt4ffPreISHMRlj+MvQv5BsTWZf1T7uX34/e6v3qm0zSAau7nE17+96H4vBwnXnXUejp5HO0Z25uMvF/HHlHylvKFczAuglPdf0uIafjv3EwZqDADy37jn6JPQhwZJA34S+zN8yn8Gpg0mxptAzvifby7czf8t8QJ5rqnPJbpvspGysBivritdxc++beWDgAxyoOUCyNZkDNQe4b9l97K7czV05d1HVUEWiJZFBqYN4cf2LrC9ZzxXdruC3Ob8F5P+/3gm9eWvbWwzpMIQ/D/lzgABf1PkiLup8Ucj36by483juwudCtkebonlocPgsuP2S+gWsrld4aPBDXNb1MhIsCQHrelqia0xXFk9czL6qfaw9tpZXNr1CrauW1YdX0zm6sxo5+MmeT1q8TrsWgyhTFE6vEwjs+BXLwN8V1OBuYEPxBqZkTQnpjJtDGTmn29KRkNTcQhaDhTJHmToR3BxJliR6xPXg/gH3B2xXRvG1zlpe2vAS7+54F6POSE5yDjpJF9IuRejKHGUBi8XCrTj2Dy1VRi7BUU3BlkG8Of6UTEC1FyRJwmayBQQEBIuBsj5j9vDZfLT7I7458A3/OfgfHG4Hfxr8JyoaKli8czESEjXOGv619V+Y9WZGpo9kb9Ve/nvkv3iFl5c2vATAlJ5TMOgMVDdWs/rwar4/9D0gJ9mb1G2Smu32p6M/ccNXN7CjYgf39L+HG3rdwLridTyy+hGm/Wca03OmM2/zPI7VH8OoM+LyuogyRvH7vN+z+vBqVh9ereZfMugM3NT7JhZuW0iHyA5cmHEhywuX89y65/hg1wd0jOpImaOMvw3/G7urdlPrrCX/WD6/W/o7AC7rehldorvwj43/YGnhUsZ3Hs+uyl3UOGu4N+9eXF4X53c4n/lb5vP+rvfpFtONlMgUFhUsIsIQQb2rnpc2vITT4+S1i17DqDdS56zjm4PfqMLxrwn/oktMFz7b+xnLDi1jd+VuVhatpEdcD/4x9h8Bg5Rb+tzCx7s/ZmrfqS12lMoIvE9CH7666isKawvVeTqFty55izJHWchgcFjaMIalDTvOb9SpQyfpwopEa8Sb44lPjad/cn82lm5k9eHVDE8bzitjXwHgwvcv5KejP7V4jfYtBsYotaMMF1oKTa6gDSUbcHqdDO0wVN3XWnhpYW0hEJiTp9ZZKy+aspe2KgZGvZGPLv8oZLsiRopZp7yXCH0EVoM1xKJR3pvdbQ+wDJS6y/4ooaUR+gj1ix4c8aSIQZ2rjkO1h9q0avhsI9oUHfA9UZ6J8nt7+XYkJC7OvJjLu13OxpKNLN65mNykXK7reR2SJDEjZwaSJLGnag8vb3iZKT2ncEHaBeo1i+uL+WLfF0ToI7ix943qdqfHybcHv2X14dXMyJ0RYL7PWTuHxTsXM77zeO7MvhNJkhiZPpLXxr3Grd/cyqw1s+gR14Mru19Jo7uRXgm9WLhtIX/76W/YTDZu6n0TeSl5JFmTeC7/ORZsXUDXmK68fenb2Ew2dlbsZEfFDh5e/TCHag/x8JCHmdRtknr/GmcNa46sIcWaos7tDO4wmBWFK3hr21uYDWbmjpkbkN7j5bEvs6NiB4mWRBItiXi8HvQ6PYsKFvHqpld5csSTAc9lVMYoOkd3pn9yf/U6t/W9jdv63qa6oBLMCSHWY++E3moG27Zi1BtDhEAheL7vbECv0zPvonkU24vVqEaA9Kj0gAqC4Wi3YiAhqZ0nNHWYxfXFFNuL1fw3iivo7YK3iTJGMTBloDrqa80y8BcDxZ1Q01hDsjWZUkdpsxE3rWHWm9FJOrzCy4CUAXi8HtX8tBqszVoGDpcjwDLwX12sUNlQSa2rlkhjpDpRHGIZ+PL7HKk7wpayLQHJ6M4VgqPAgi2DgvICUiNTVTdBbnKu2jkqKJ3VeXHn8eKYF0PukRKZwu3Zt4dsN+lNXNb1Mi7relnIvgcGPcAfBv4hxFLrGd+TeRfN4/2d7/OHgX8I8PtPyJzAlrItpEelB4Tqzh83nyP1R0iLSlMnFHvG96RnfE8yYzKJNkWHBApEm6KZkDkhYFv/5P70T+7PXTl3YdAZAsIlFfwXCSrP5YZeN/CbrN+EvBedpGN6znTCYdAZzsnByalEkqSQwIk0W1rYwaM/7VYM9JJejuX3mZFKB/rEj08AcGvfW3ll4yscrT/K3376GyuLVnL/gPuxGq1NbqJWVvr6i4GSZbTWVYtXeCl1tG4ZNIckSVgNVupcdXSP7c7DQx9W91mNgZaBV3gDrJ9SRynx5ngqGypDFp3oJT2NnkbK7GVqpTUItQwsBgsSEt8f+h6v8J7SEM/2gtLpKyjioGwvcZSotZRPN8257JrzM+skHTlJOSHb9Tp9s5Pc4Y5vjeDvUVs4l9yPZzLpUS1PHkM7XoGs08lNV76gSoe5rmQdE7vKUSAAPx/7meVFy7m6x9VqaJUyYm7NTbS3ai/x5nhsJhvREXJnUdNYQ1WjXPy9pUii1pCQ/0mCFdxqtAa0q9HTCMiTdHa3nRJ7CSnWFCKNkTR6GpGQVNeGcq1DtYdky8AQ3jLQSToijZHsq96HzShPaJ5rBFsGirXkLxLHEy2koXEm01okEbRjMVBq2OoknRqmVt1YTa2zlk62Tqr7SEkh8Zus36gmf7BrqTnWFa8jLzkPaOokfrf0d1z72bXAyaVSUFI/B6/aDXYTKW2MN8fjER6O1B0hyZqkdl4Wg0Xt2JT8PIdqDhFviVePCTeiUwSxa2zXsGb/2Y4i7gr+6wwU/EMdNTTaMwNTBrY639JuxcB/UYUyP6Bkm0y3pasuEkUM/HPTtMVNdKTuCIfrDquLrPxHjCWOEh45/5GQ8osnQrBlEGmMDGiXKga+oi9FtUUkWhLVTstqtKqdvpJIzi3cpFhTmnUTQVOn19bwtbONYDeRYkX5i8GYjDGntU0aGr8UnaI7sXji4haPabdDQsUyAFkMiu3Fah6PtKi0UDHwS6qmzDM8t+45Npdt5vkLnw+5vlIjQcmD7995rJyyss1J2VojOAdQiGXgS02hLApzep3EmGLU9lgN1rAde4o1pVk3ETQJxLkqBoo19eSIJxmZPlKd9PSPYOkQFZqfSUPjbKXdioG/ZWA1WllZtFKt5JVuS1ezeIazDHSSTo3R/vZgaO4akBd4mfVm1VXgv2jrVAkBhHETBU0g+7uJFGwmW5MYGK0hbiJAzZ4K4S0DZcFaW3yJZyPKM0u0JIbMH9yVcxd9E/r+Gs3S0PjVOGPEQJKki4EXAT3whhDiqZaOD7YM/LGZbKofvMRegoQUMjoOLi0phGDxzsVc1vUybCYbhTWFZERnBIjOuM7j6Jt4ajuJ4NxIwRPIihj4hwxGm6Kb3ESGJjdRnDlOdZkpk8wQXgyUjJ3nqmWgiHC47Kx35959upujofGrc0aIgSRJeuBlYBxQBKyVJOkzIURoIL0P/046XAF5s96MQWfA5XURaYxsMcRNCMH2iu3M/mk2ywuXM2/cPA7VHgqJwQ7nTjpRFl26KKAOsYJSElJJjhXOMoiOiFYtA4vRogqDxWAh3hzP4brDJFuTsRgs6CRdWDeRsuDqXBWDEekj+HDSh22upKahcbZzpkwgDwb2CCH2CSGcwHvAFS2d4G8ZKBPH1513Ha9d9Bogxzcr5n9wEZZgal211Dvl0fjqI6vxeD0U1hbSydbpBN9O6/RL6hc2z4nVaJVLYvrcXA5PeDeRv2WgCINZb1aLu6RGpiJJElf3uFpNKOePMiIOl9XzXEAn6egZ3/PXboaGxhnDGWEZAGlAod/fRcCQlk7wtwyUZda3Z98e4De3mWxUNFQEzBeEo6S+JCAj6fLC5bi8rl/Fn65M+trddqxGqzqBHGAZmKIDQktVK8FgId4ST4Q+QhXCR89/NOx9Fly8gN2Vu8/JsFINDY1QzhTLIJwPJ8T3I0nSNEmS8iVJyq+va/KrZ0ZnAqH+X5uxKeImmLcvfZuZA2YCcqiovxisL1kPyOFYpxtFuB5e/TAN7oawcwY2ky3gvSmVu6JMUfSM60mfhD6trvxMi0rjwowLf4F3oKGh0R45U4aFRYD/cs90IKQupRDideB1gH79+6liseDiBZQ7ykM6QP/RczA5STnER8TzwroXKLWXBiQt21W5S25EG5Zwn2oU4Vp9eDXriteFnzMwRQdEE03InIDFaCE1MpXf5/3+tLdZQ0Oj/XOmiMFaoIckSV2Aw8D/AC3WUPTPM65kSwzGv8MMR6JVPqfUURoQwaNUMvLvgE8X/m6b8oZyHG4HElJAOKu/m8hqkHMtXZx58Wlvq4aGxtnDGSEGQgi3JEm/A75BDi39lxBi28let7UJZMXffqz+GELI1Y/cXrcajnoiBV9Olq4xTel2j9Ufw+F2YDaYMelM6CU9Jr0Jo97YqtBpaGhoHA9nypwBQoivhBDnCSG6CSFmn4prtqXD7GTrxKGaQ1Q7q0m1ypE1DreDKGPUr5JxMSM6g003byLeHM/R+qPUOGuINkWrmU6Dc+i0FimloaGh0RbOGDH4JWhLh9kl5v+3d+5RVlX3Hf98GRjeMwR8gCCPGNAGiqgJLC1LMbVNNGYZoilSVpSE2hibUJPax0rSJE1WogtjMERTg0ZjXC0+SmwgyyjUMCVKa0DCMz4LpioEperwUiL46x9735nD5d7LnDP3zr1z5/dZ6657zt7nnO/Z5+yzf/u9x7F9z3b2HNzD0H5D2/rk56phqkEv9WL4wOHs3L+T1oOtbYvUJCelG9M0hgvHXliW+ZEcx3Hq2hjkctG5kbiFGNc8jt/t/x27DuxicN/BbaWIUud0BSMGjmDX/l1tJQM4cuqJxoZGFpy3oCo9nhzHqT96hDEoVU2U65a6rXUbTY1NbaWI5OyV1WD4wOHs2LfjiJLB8IHDe+xcQo7jVJaaaECuFLlcdKmG4OSUE02NTW0lgoGN1S8ZHDh0gJf3vdy2utXCGQuPGGznOI5TLuraGCSneS7G6KbR9FZvDtkhmvs2t5UiaqFkAKExu7kxlAyq2Y7hOE59U9fZzBMGnIBQyQW2+zb05YqJVwBhKcpaMQbJ0dT5q3I5juOUm7ouGYwaPIrlM5cfc8K5+WfMZ1i/YXxw7AfZvnY7UBsNyDny59t3HMcpN3VtDIAOTVHc0KuhrXTQVjKocpXMsP7D2gbB5RqQHcdxKkVdVxNlIVciqHY1US/1aluAxY2B4ziVxo1BHrXStRTaq4pyDciO4ziVwo1BHrUy6AzajYE3IDuOU2ncGORRK9VE0N691EsGjuNUmrpvQE5Lrpqo2oPOAC6dcCknDjix6o3ZjuPUP14yyOM9Q97D4MbBNbFQ/MhBI5l12qxq34bjOD0ALxnkMfG4iayZvabat+E4jtOleMnAcRzHcWPgOI7juDFwHMdxcGPgOI7j4MbAcRzHwY2B4ziOgxsDx3EcBzcGjuM4DiAzq/Y9ZELSXuCZhFMz0Frk8GJ+adyPA3ZnuFYW/eOAt1Oe0xn9XNg6o5NG/1jPslw6+XrluFZHzsnXq6e42Qz0Id3z7Kx+R59nufRLfX/l1Mm5F3qelYqbp5rZ4IJHmFm3/AHr8vYXlzi2oF8a95xe2mtl1F9Xjnvu6Dn5z7Jcz6yY37GeZQXCeVT4Kqlf53Fzcdrn2Vn9jj7PcumX+v4qEc40319n9Yu9OzOrq2qi5Rn80rrX8jk9Xf9YftXUr6fnXG39LOdUWz/LOV3+bXTnaqJ1Zva+etSr57C5nuu5XvX0Sml155LB4jrWq+ewuZ7ruV719IpqdduSgeM4jlM+unPJwHEcxykTbgwcx3Gc2jYGkvZ1odZhSRsSv7Eljp0h6WcZdUzSPYn93pJezXq9FLozo/ZpFdSoStgSel0WXzqqKalFUqcaB7vi3RXQ/JKkrZI2xe9hWoX1Rkn6qaTnJP2PpO9Kaixx/LWSBmTUMkk3Jfavk/S1LNfqgFYuXdkqaaOkL0iqyXS3Jm+qSrxpZlMSvxcqpLMfmCSpf9z/E+DlNBeQlGWFutnAY8DlKbUaUhze6bA5Bcn07rIi6WzgYuBMM5sMXAC8WEE9AT8B/t3MxgMTgEHAN0ucdi2QyRgAB4GPSTou4/lpyKUrEwnfw0XAV7tANzU1bwwkDZL0qKT1kjZLuiS6j5X0lKTbo9VdkUiEyqXdIOlGSWtjDunTCe8mSQ9K+o2k21Ja+58DH47bs4ElCc2pktZI+nX8PzW6z5X0gKTlwIqU4RgE/BEwj5igxNLN6kJhkLRP0tclPQGcnUYrY9h+KWlK4rjHJU1OqZs794hSm6RbJM2N2y9I+qdEXCpLTruUZhmuXezdFQvjRZKelvSYpEUZS2UjgN1mdhDAzHab2Q5JZ0n6T0lPSnpE0oio2SLp5vhOt0iamlLvA8BbZnZX1DsMfB74lKSBkr4d39cmSZ+TNB84CVglaVWG8B0i9Kr5fL6HpDExvdkU/0dLao5xJ/d9DJD0oqQ+aUTN7BXgL4HPKlA0fZH0dzHMGyXdkCGMqal5YwC8Bcw0szOB84GbYk4CYDxwa7S6bwCXdkKnv9qriB6MbvOAVjN7P/B+4CpJ46LfVOBvgD8ETgE+lkLrXuBySf2AycATCb+ngXPN7AzgK8C3En5nA1ea2QdShu2jwMNm9izwmqQzjxGGgcAWM5tmZo+l1MoStjuAuQCSJgB9zWxTSt2OsjvGpX8GrquQRjkp9u6OIj7zHwAXmtl04PiMmiuAkyU9K+n7ks6LCd/3gMvM7CzgTo7MuQ80s3OAa6JfGiYCTyYdzGwP8L/AXwDjgDNiKeVfzGwRsAM438zOzxA+gFuBOZKa89xvAX6c0wIWmVkrsBE4Lx7zEeARM3s7raiZbSOkuydQJH2RdCHhvU8zs9OBBemDl57uYAwEfEvSJuA/gJHAidFvu5ltiNtPAmM7oZOsJpoZ3f4UuELSBkKiNoxggAB+ZWbbYi5mCTC9o0IxoRtLyDk/lOfdDDwgaQuwkPCh5FhpZq+lDBdR5964fW/ch+JhOAwszaCTNWwPABfHBOdTwI+yaHeQn8T/zsaXrqLYuyvEacA2M9se95eUOLYoZrYPOIuQi30VuA/4NDAJWBm/hy8DoxKnLYnnriaUmoekkBRQqI+7gHOB28zsULx+lvh/FNHY/BiYn+d1NvCvcfse2r+J+4BZcfvyuJ+VXGa2WPpyAXCXmR2I91qWMB+LLHXPXc0cQg7nLDN7W9ILQL/odzBx3GGgrNVEhJf2OTN75AhHaQZHR960AzaWAd8GZhAiQY5vAKvMbKZCI3ZLwm9/Sg0kDSMUwydJMqAh3utDBe45t/9WNBBZSRU2MzsgaSVwCfBnQGcaXA9xZCanX55/Ls4cpnzx/1iamSjx7pYV0RNlIr7/FqBF0mbgr4CtZlas2rAz38NW8kr1kpqAk4FtKa+VhpuB9cBdJY7JaS8Drpc0lGAof5FFUNK7CXHvFYqnLx+icmEuSncoGTQDr0RDcD4wpgu1HwE+k6sblDRB0sDoNzUW6XoRcgxpq1PuBL5uZpvz3Jtpb3Sdm+22j+AyQrF3jJmNNbOTge2EHE9nw1CMLGG7A1gErO1kTui3wHsl9Y1VAH/ciWtVW7PYu6OI3tPAu9XeE24WGZB0qqTxCacpwFPA8QqNy0jqIylZap0V3acTqj6KzZ5ZiEeBAZKuiNdoAG4ilBBXAFcrdpqIiTHAXqDw7JsdJMaz+wnVNTnW0N5QP4f4TcTS0q+A7wI/y5JZknQ8cBtwi4XRvsXSlxWE9pIB0X1osWuWk5otGcSXf5BQb7dc0jpgAyHCdxV3EKoS1sd2ilcJdXkA/wXcQKhvXw08WOgCxTCzlwgRK58FwN2SvkDG3Eceswn3mWQp8Bk6GYZiZAmbmT0paQ+lc2lFycUXM3tR0v3AJuA54NdZrlcjmsXe3Z8TErEj9MzsTUnXAA9L2k1IvLIwCPherOo5BDxPqDJaDCyKBqg3IWe9NZ7zuqQ1QBOhqq/DmJlJmgl8X9I/EjKpDwFfJOSiJwCbJL0N3E6o118M/FzSzk60G0AwOp9N7M8H7pT0t4Tv/ZMJv/sIVZozUly/f6wG6kN4lvcA34l+BdMXM3tYoUPFOkm/p/1ZVJSanY5C0unA7WaWtmeC0wFiVdd1ZnZxte8FQNJJhGqJ08zsnQznd3l8qcU4KmmQme2LicutwHNmtrDCmi2EuLSukjpOZanJaiJJVxMapL5c7XtxKk+sHngC+FJGQ9Dl8aWG4+hVMSe6lVAt94Mq34/TTajZkoHjOI7TddRMyUDSyZJWKQwk2yrpr6P7UEkrFYapr5T0rug+R2GgxiaFwS6nJ671IUnPSHpe0j9UK0yO4zjdhZopGSiMZhxhZuslDSb0A/8oodfJa2Z2Q0zY32Vmfy/pHOApM3s9DtL4mplNiz0RniUM/X4JWAvMNrPfVCNcjuM43YGaKRmY2U4zWx+39xK6so0k9D2/Ox52N7E3j5mtMbPXo/t/0z4AZirwfBxM9XvCQJ1LuiYUjuM43ZOaMQZJYj/pMwiNiiea2U4IBoMwjDufeYQ5cSAYkOSkWi9FN8dxHKcINTfOQGFirqXAtWa2Ryo9qDIORJtH+7DxQifURl2Y4zhOjVJTJYM4Em8pYTKq3Bwyu9Q+O+IIwjDu3PGTCQM3LjGz/4vOLxGGsecYRZjUynEcxylCzRiDOEjmh4RG4e8kvJYBV8btK4GfxuNHEyYd+0Sc0THHWmB8nGahkTC0fFml799xHKc7U0u9iaYDvwQ2A7mBR18ktBvcD4wmTGn7cTN7TdIdhMmtfhuPPWRm74vXuogwVL4BuNPMSi2S4TiO0+OpGWPgOI7jVI+aqSZyHMdxqocbA8dxHMeNgeM4juPGwHEcx8GNgeM4joMbA6eHIGlIXAUMSSdJ+rcKak2J3Zsdp9vgxsDpKQwBrgEwsx1mdlkFtaYAbgycboWPM3B6BJJys9c+Q1g3+A/MbJKkuYSZcBuASYQ1cRuBTxDW4L4oDnI8hbCM5PHAAeAqM3ta0seBrxLW6m0FLiCsGdwfeBm4nrCI/c3R7U3gk2b2TArtFsL631OJawybWdb1AmpgbQAAAZdJREFUjR2nMGbmP//V/Y+w8PiWAttzCYn3YEJC3wpcHf0WEiZMBHgUGB+3pwG/iNubgZFxe0jimrcktJuA3nH7AmBpSu0WwlrLAOfm7t1//ivnr+ZmLXWcKrDKwhoaeyW1Asuj+2ZgcpxJ9xzggcQsun3j/+PAjyTdT5grqxDNwN2SxhNm0O3TUe3EcUsAzGy1pCZJQ8zsjYzhdZyjcGPgOKFKJsc7if13CN9IL+ANM5uSf6KZXS1pGvBhYIOko44BvkFI9GfGtTpaUmi3SeVLlwiP46TGG5CdnsJeQnVMasxsD7A9tg+gwOlx+xQze8LMvgLsJkyfnq/VTGg/gFA1lIVZUW860GpmrRmv4zgFcWPg9AgsrHfxuKQtwI0ZLjEHmCdpI7CV9qVUb5S0OV53NbARWAW8V9IGSbOABcD1kh4nNBZn4XVJa4DbCIs5OU5Z8d5EjlPjxN5E15nZumrfi1O/eMnAcRzH8ZKB4ziO4yUDx3EcBzcGjuM4Dm4MHMdxHNwYOI7jOLgxcBzHcXBj4DiO4wD/DwND2zCJeNEpAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "d_uk['uk_energy_demand_reduction'].plot(y=data_sets['uk_energy_demand_reduction'].data_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the modelling this data needs to be averaged for the year:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_uk['uk_energy_demand_reduction_yearly'] = bff2.summarise_to_freq(\n",
    "    d_uk['uk_energy_demand_reduction'], freq='1Y', fun_test_data=None\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "            energy_demand_with_corona  energy_demand_without_corona  \\\ntimestamp                                                             \n2020-01-01               28061.809075                  31803.739168   \n\n            energy_demand_reduction  \ntimestamp                            \n2020-01-01              3741.930094  ",
      "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>energy_demand_with_corona</th>\n      <th>energy_demand_without_corona</th>\n      <th>energy_demand_reduction</th>\n    </tr>\n    <tr>\n      <th>timestamp</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2020-01-01</th>\n      <td>28061.809075</td>\n      <td>31803.739168</td>\n      <td>3741.930094</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "d_uk['uk_energy_demand_reduction_yearly'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare combined column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_uk['historical_GHG_Sectors_GCP']['Gas and Coal (CO2)'] = \\\n",
    "    d_uk['historical_GHG_Sectors_GCP']['Gas (CO2)'] + d_uk['historical_GHG_Sectors_GCP']['Coal (CO2)']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlating datasets\n",
    "\n",
    "### Choice of datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we define the datasets and the columns we want to correlate. Implicitely we are emitting the hypothesis:\n",
    "\n",
    "*Column C in dataset X is correlated to column D in dataset Y.*\n",
    "\n",
    "for all columns and datasets in the dictionary `to_correlate`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_correlate = {\n",
    "    'uk_energy_yearly': ['oil', 'ccgt', 'coal', 'demand'],  # Carbon intensive energy sources\n",
    "    'historical_GHG_Sectors_GCP': [  # Select only CO2 data not other gases\n",
    "        s for s in d_uk['historical_GHG_Sectors_GCP'].columns if f\"(CO2)\" in s\n",
    "    ],\n",
    "    'historical_GHG_Sectors_PIK': [  # Select Energy data not other sectors\n",
    "        'Energy (KYOTOGHG)', 'Energy (CO2)', 'Energy (N2O)', 'Energy (CH4)'\n",
    "    ],\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this work we are correlating three datasets:\n",
    "\n",
    "+ `'uk_energy_yearly'` The average yearly power consomption of the UK by type of power station.\n",
    "+ `'historical_GHG_Sectors_GCP'` the global carbon project's breakdown of yearly carbon emissions by fossil fuel.\n",
    "+ `'historical_GHG_Sectors_PIK'` The Potsdam Institute for climate change impact research (PIK) breakdown of yearly carbon emissions by sector of the economy.\n",
    "\n",
    "The first 3 lines are displayed in the subsections below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.Markdown object>",
      "text/markdown": "#### dataset `'uk_energy_yearly'`'"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "                  demand         coal      nuclear          ccgt         wind  \\\ntimestamp                                                                       \n2017-01-01  31858.397066  2357.162886  7484.045780  13678.825019  3689.535314   \n2018-01-01  31458.583256  1764.089781  6923.616315  13205.261560  4498.223694   \n2019-01-01  30606.290673   679.510313  6022.150777  13080.728889  5302.395747   \n\n                pumped       hydro      biomass       oil        solar  \\\ntimestamp                                                                \n2017-01-01  316.498433  457.617018  1627.341894  0.000000  1189.161055   \n2018-01-01  271.923046  366.609060  1840.524415  0.004410  1248.855466   \n2019-01-01  195.267497  408.815841  1965.136313  0.011887  1230.144531   \n\n                ocgt      other  dayofyear  \ntimestamp                                   \n2017-01-01  1.890384  14.902725      183.0  \n2018-01-01  2.098168  81.660209      183.0  \n2019-01-01  7.983079  88.507691      183.0  ",
      "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>demand</th>\n      <th>coal</th>\n      <th>nuclear</th>\n      <th>ccgt</th>\n      <th>wind</th>\n      <th>pumped</th>\n      <th>hydro</th>\n      <th>biomass</th>\n      <th>oil</th>\n      <th>solar</th>\n      <th>ocgt</th>\n      <th>other</th>\n      <th>dayofyear</th>\n    </tr>\n    <tr>\n      <th>timestamp</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>2017-01-01</th>\n      <td>31858.397066</td>\n      <td>2357.162886</td>\n      <td>7484.045780</td>\n      <td>13678.825019</td>\n      <td>3689.535314</td>\n      <td>316.498433</td>\n      <td>457.617018</td>\n      <td>1627.341894</td>\n      <td>0.000000</td>\n      <td>1189.161055</td>\n      <td>1.890384</td>\n      <td>14.902725</td>\n      <td>183.0</td>\n    </tr>\n    <tr>\n      <th>2018-01-01</th>\n      <td>31458.583256</td>\n      <td>1764.089781</td>\n      <td>6923.616315</td>\n      <td>13205.261560</td>\n      <td>4498.223694</td>\n      <td>271.923046</td>\n      <td>366.609060</td>\n      <td>1840.524415</td>\n      <td>0.004410</td>\n      <td>1248.855466</td>\n      <td>2.098168</td>\n      <td>81.660209</td>\n      <td>183.0</td>\n    </tr>\n    <tr>\n      <th>2019-01-01</th>\n      <td>30606.290673</td>\n      <td>679.510313</td>\n      <td>6022.150777</td>\n      <td>13080.728889</td>\n      <td>5302.395747</td>\n      <td>195.267497</td>\n      <td>408.815841</td>\n      <td>1965.136313</td>\n      <td>0.011887</td>\n      <td>1230.144531</td>\n      <td>7.983079</td>\n      <td>88.507691</td>\n      <td>183.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.Markdown object>",
      "text/markdown": "#### dataset `'historical_GHG_Sectors_GCP'`'"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "                   Country       date Data source    Unit  Bunkers (CO2)  \\\ndate                                                                       \n2016-01-01  United Kingdom 2016-01-01         GCP  MtCO₂e            NaN   \n2017-01-01  United Kingdom 2017-01-01         GCP  MtCO₂e            NaN   \n2018-01-01  United Kingdom 2018-01-01         GCP  MtCO₂e            NaN   \n\n            Cement (CO2)  Coal (CO2)  Gas (CO2)  Gas flaring (CO2)  Oil (CO2)  \\\ndate                                                                            \n2016-01-01          4.55       47.10     165.77               4.52     175.19   \n2017-01-01          4.41       38.44     161.79               4.59     175.37   \n2018-01-01          4.41       32.07     161.92               4.59     173.26   \n\n            Total fossil fuels and cement (CO2)  Gas and Coal (CO2)  \ndate                                                                 \n2016-01-01                               399.84              212.87  \n2017-01-01                               387.39              200.23  \n2018-01-01                               379.04              193.99  ",
      "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>Country</th>\n      <th>date</th>\n      <th>Data source</th>\n      <th>Unit</th>\n      <th>Bunkers (CO2)</th>\n      <th>Cement (CO2)</th>\n      <th>Coal (CO2)</th>\n      <th>Gas (CO2)</th>\n      <th>Gas flaring (CO2)</th>\n      <th>Oil (CO2)</th>\n      <th>Total fossil fuels and cement (CO2)</th>\n      <th>Gas and Coal (CO2)</th>\n    </tr>\n    <tr>\n      <th>date</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>2016-01-01</th>\n      <td>United Kingdom</td>\n      <td>2016-01-01</td>\n      <td>GCP</td>\n      <td>MtCO₂e</td>\n      <td>NaN</td>\n      <td>4.55</td>\n      <td>47.10</td>\n      <td>165.77</td>\n      <td>4.52</td>\n      <td>175.19</td>\n      <td>399.84</td>\n      <td>212.87</td>\n    </tr>\n    <tr>\n      <th>2017-01-01</th>\n      <td>United Kingdom</td>\n      <td>2017-01-01</td>\n      <td>GCP</td>\n      <td>MtCO₂e</td>\n      <td>NaN</td>\n      <td>4.41</td>\n      <td>38.44</td>\n      <td>161.79</td>\n      <td>4.59</td>\n      <td>175.37</td>\n      <td>387.39</td>\n      <td>200.23</td>\n    </tr>\n    <tr>\n      <th>2018-01-01</th>\n      <td>United Kingdom</td>\n      <td>2018-01-01</td>\n      <td>GCP</td>\n      <td>MtCO₂e</td>\n      <td>NaN</td>\n      <td>4.41</td>\n      <td>32.07</td>\n      <td>161.92</td>\n      <td>4.59</td>\n      <td>173.26</td>\n      <td>379.04</td>\n      <td>193.99</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.Markdown object>",
      "text/markdown": "#### dataset `'historical_GHG_Sectors_PIK'`'"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "                   Country       date Data source    Unit  Agriculture (CH4)  \\\ndate                                                                           \n2015-01-01  United Kingdom 2015-01-01         PIK  MtCO₂e              25.75   \n2016-01-01  United Kingdom 2016-01-01         PIK  MtCO₂e              25.75   \n2017-01-01  United Kingdom 2017-01-01         PIK  MtCO₂e              26.00   \n\n            Energy (CH4)  Industrial Processes and Product Use (CH4)  \\\ndate                                                                   \n2015-01-01          7.90                                        0.08   \n2016-01-01          6.98                                        0.09   \n2017-01-01          7.00                                        0.08   \n\n            Other (CH4)  Total excluding LULUCF (CH4)  Waste (CH4)  ...  \\\ndate                                                                ...   \n2015-01-01          NaN                          53.0        19.33  ...   \n2016-01-01          NaN                          51.5        18.65  ...   \n2017-01-01          NaN                          52.0        18.90  ...   \n\n            Total excluding LULUCF (KYOTOGHG)  Waste (KYOTOGHG)  \\\ndate                                                              \n2015-01-01                              520.0              21.0   \n2016-01-01                              495.0              20.4   \n2017-01-01                              482.0              20.7   \n\n            Agriculture (N2O)  Energy (N2O)  \\\ndate                                          \n2015-01-01              14.10          2.62   \n2016-01-01              14.10          2.49   \n2017-01-01              14.36          2.51   \n\n            Industrial Processes and Product Use (N2O)  Other (N2O)  \\\ndate                                                                  \n2015-01-01                                        0.85         1.18   \n2016-01-01                                        0.86         1.12   \n2017-01-01                                        0.89         1.07   \n\n            Total excluding LULUCF (N2O)  Waste (N2O)  \\\ndate                                                    \n2015-01-01                         20.17         1.42   \n2016-01-01                         20.03         1.46   \n2017-01-01                         20.35         1.49   \n\n            Industrial Processes and Product Use (F-Gas)  \\\ndate                                                       \n2015-01-01                                          16.0   \n2016-01-01                                          15.2   \n2017-01-01                                          14.2   \n\n            Total excluding LULUCF (F-Gas)  \ndate                                        \n2015-01-01                            16.0  \n2016-01-01                            15.2  \n2017-01-01                            14.2  \n\n[3 rows x 30 columns]",
      "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>Country</th>\n      <th>date</th>\n      <th>Data source</th>\n      <th>Unit</th>\n      <th>Agriculture (CH4)</th>\n      <th>Energy (CH4)</th>\n      <th>Industrial Processes and Product Use (CH4)</th>\n      <th>Other (CH4)</th>\n      <th>Total excluding LULUCF (CH4)</th>\n      <th>Waste (CH4)</th>\n      <th>...</th>\n      <th>Total excluding LULUCF (KYOTOGHG)</th>\n      <th>Waste (KYOTOGHG)</th>\n      <th>Agriculture (N2O)</th>\n      <th>Energy (N2O)</th>\n      <th>Industrial Processes and Product Use (N2O)</th>\n      <th>Other (N2O)</th>\n      <th>Total excluding LULUCF (N2O)</th>\n      <th>Waste (N2O)</th>\n      <th>Industrial Processes and Product Use (F-Gas)</th>\n      <th>Total excluding LULUCF (F-Gas)</th>\n    </tr>\n    <tr>\n      <th>date</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>2015-01-01</th>\n      <td>United Kingdom</td>\n      <td>2015-01-01</td>\n      <td>PIK</td>\n      <td>MtCO₂e</td>\n      <td>25.75</td>\n      <td>7.90</td>\n      <td>0.08</td>\n      <td>NaN</td>\n      <td>53.0</td>\n      <td>19.33</td>\n      <td>...</td>\n      <td>520.0</td>\n      <td>21.0</td>\n      <td>14.10</td>\n      <td>2.62</td>\n      <td>0.85</td>\n      <td>1.18</td>\n      <td>20.17</td>\n      <td>1.42</td>\n      <td>16.0</td>\n      <td>16.0</td>\n    </tr>\n    <tr>\n      <th>2016-01-01</th>\n      <td>United Kingdom</td>\n      <td>2016-01-01</td>\n      <td>PIK</td>\n      <td>MtCO₂e</td>\n      <td>25.75</td>\n      <td>6.98</td>\n      <td>0.09</td>\n      <td>NaN</td>\n      <td>51.5</td>\n      <td>18.65</td>\n      <td>...</td>\n      <td>495.0</td>\n      <td>20.4</td>\n      <td>14.10</td>\n      <td>2.49</td>\n      <td>0.86</td>\n      <td>1.12</td>\n      <td>20.03</td>\n      <td>1.46</td>\n      <td>15.2</td>\n      <td>15.2</td>\n    </tr>\n    <tr>\n      <th>2017-01-01</th>\n      <td>United Kingdom</td>\n      <td>2017-01-01</td>\n      <td>PIK</td>\n      <td>MtCO₂e</td>\n      <td>26.00</td>\n      <td>7.00</td>\n      <td>0.08</td>\n      <td>NaN</td>\n      <td>52.0</td>\n      <td>18.90</td>\n      <td>...</td>\n      <td>482.0</td>\n      <td>20.7</td>\n      <td>14.36</td>\n      <td>2.51</td>\n      <td>0.89</td>\n      <td>1.07</td>\n      <td>20.35</td>\n      <td>1.49</td>\n      <td>14.2</td>\n      <td>14.2</td>\n    </tr>\n  </tbody>\n</table>\n<p>3 rows × 30 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ],
   "source": [
    "for d in to_correlate:\n",
    "    display(Markdown(f\"#### dataset `'{d}'`'\"))\n",
    "    display(d_uk[d].tail(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating correlations\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once chosen we pass it to function [`correlate`](brsflufight_nerc2/model_predict.py) \n",
    "which calculates and returns correlations between columns of the datasets in `selector` and the dataset defined by `main_compare`. Linear fits for each correlation are also computed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Linear regression failed for: 'Bunkers (CO2)' fit to  'oil':\n  Message: Input contains NaN, infinity or a value too large for dtype('float64').\nLinear regression failed for: 'Bunkers (CO2)' fit to  'ccgt':\n  Message: Input contains NaN, infinity or a value too large for dtype('float64').\nLinear regression failed for: 'Bunkers (CO2)' fit to  'coal':\n  Message: Input contains NaN, infinity or a value too large for dtype('float64').\nLinear regression failed for: 'Bunkers (CO2)' fit to  'demand':\n  Message: Input contains NaN, infinity or a value too large for dtype('float64').\n"
    }
   ],
   "source": [
    "correlation_dict = bff2.correlate(\n",
    "    selector=to_correlate,  # datasets defined above\n",
    "    data_sets=d_uk,  # UK data\n",
    "    main_compare='uk_energy_yearly',  # reference dataset, the other are correlated to it\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`bff2.correlate` returns a dictionary of dictionaries corresponding to the datasets that were correlated to `main_compare` (in this case `'uk_energy_yearly'`).\n",
    "For each dataset 3 items are returned:\n",
    "\n",
    "  + a `'correlation'`: the pearson correlation coefficient between datasets;\n",
    "  + the merged `'data'` used for the correlation;\n",
    "  + a linear `'fit'` between the datasets done through `scikit-learn` `LinearRegression`;\n",
    " \n",
    "We can visualise the correlations to identify relationships of interest, we are looking for numbers close to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "=====================================================================\nPearson correlation coefficients in dataset 'historical_GHG_Sectors_GCP'\n\t on data from 2012 to 2018 (inclusive)\n_________________________________________________________\n"
    },
    {
     "output_type": "display_data",
     "data": {
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{\n            background-color:  #f7f0f7;\n            color:  #000000;\n        }    #T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row2_col6 {\n            background-color:  #023858;\n            color:  #f1f1f1;\n        }    #T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row2_col7 {\n            background-color:  #023858;\n            color:  #f1f1f1;\n        }    #T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row3_col0 {\n            background-color:  #fff7fb;\n            color:  #000000;\n        }    #T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row3_col1 {\n            background-color:  #f1ebf5;\n            color:  #000000;\n        }    #T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row3_col2 {\n            background-color:  #023858;\n            color:  #f1f1f1;\n        }    #T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row3_col3 {\n            background-color:  #faf3f9;\n            color:  #000000;\n        }    #T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row3_col4 {\n            background-color:  #fff7fb;\n            color:  #000000;\n        }    #T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row3_col5 {\n            background-color:  #f7f0f7;\n            color:  #000000;\n        }    #T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row3_col6 {\n            background-color:  #023858;\n            color:  #f1f1f1;\n        }    #T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row3_col7 {\n            background-color:  #023858;\n            color:  #f1f1f1;\n        }</style><table id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Bunkers (CO2)</th>        <th class=\"col_heading level0 col1\" >Cement (CO2)</th>        <th class=\"col_heading level0 col2\" >Coal (CO2)</th>        <th class=\"col_heading level0 col3\" >Gas (CO2)</th>        <th class=\"col_heading level0 col4\" >Gas flaring (CO2)</th>        <th class=\"col_heading level0 col5\" >Oil (CO2)</th>        <th class=\"col_heading level0 col6\" >Total fossil fuels and cement (CO2)</th>        <th class=\"col_heading level0 col7\" >Gas and Coal (CO2)</th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4level0_row0\" class=\"row_heading level0 row0\" >oil</th>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row0_col0\" class=\"data row0 col0\" >nan</td>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row0_col1\" class=\"data row0 col1\" >-0.845</td>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row0_col2\" class=\"data row0 col2\" >0.883</td>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row0_col3\" class=\"data row0 col3\" >-0.613</td>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row0_col4\" class=\"data row0 col4\" >-0.700</td>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row0_col5\" class=\"data row0 col5\" >-0.883</td>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row0_col6\" class=\"data row0 col6\" >0.883</td>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row0_col7\" class=\"data row0 col7\" >0.883</td>\n            </tr>\n            <tr>\n                        <th id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4level0_row1\" class=\"row_heading level0 row1\" >ccgt</th>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row1_col0\" class=\"data row1 col0\" >nan</td>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row1_col1\" class=\"data row1 col1\" >0.739</td>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row1_col2\" class=\"data row1 col2\" >-0.786</td>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row1_col3\" class=\"data row1 col3\" >0.643</td>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row1_col4\" class=\"data row1 col4\" >0.487</td>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row1_col5\" class=\"data row1 col5\" >0.857</td>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row1_col6\" class=\"data row1 col6\" >-0.786</td>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row1_col7\" class=\"data row1 col7\" >-0.786</td>\n            </tr>\n            <tr>\n                        <th id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4level0_row2\" class=\"row_heading level0 row2\" >coal</th>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row2_col0\" class=\"data row2 col0\" >nan</td>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row2_col1\" class=\"data row2 col1\" >-0.703</td>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row2_col2\" class=\"data row2 col2\" >1.000</td>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row2_col3\" class=\"data row2 col3\" >-0.571</td>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row2_col4\" class=\"data row2 col4\" >-0.775</td>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row2_col5\" class=\"data row2 col5\" >-0.786</td>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row2_col6\" class=\"data row2 col6\" >1.000</td>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row2_col7\" class=\"data row2 col7\" >1.000</td>\n            </tr>\n            <tr>\n                        <th id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4level0_row3\" class=\"row_heading level0 row3\" >demand</th>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row3_col0\" class=\"data row3 col0\" >nan</td>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row3_col1\" class=\"data row3 col1\" >-0.703</td>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row3_col2\" class=\"data row3 col2\" >1.000</td>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row3_col3\" class=\"data row3 col3\" >-0.571</td>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row3_col4\" class=\"data row3 col4\" >-0.775</td>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row3_col5\" class=\"data row3 col5\" >-0.786</td>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row3_col6\" class=\"data row3 col6\" >1.000</td>\n                        <td id=\"T_37105568_b78d_11ea_80c8_f8e4e3d5f2f4row3_col7\" class=\"data row3 col7\" >1.000</td>\n            </tr>\n    </tbody></table>"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "=====================================================================\n\n=====================================================================\nPearson correlation coefficients in dataset 'historical_GHG_Sectors_PIK'\n\t on data from 2012 to 2017 (inclusive)\n_________________________________________________________\n"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<pandas.io.formats.style.Styler at 0x1e1c0d99e08>",
      "text/html": "<style  type=\"text/css\" >\n    #T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row0_col0 {\n            background-color:  #023a5b;\n            color:  #f1f1f1;\n        }    #T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row0_col1 {\n            background-color:  #023a5b;\n            color:  #f1f1f1;\n        }    #T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row0_col2 {\n            background-color:  #023858;\n            color:  #f1f1f1;\n        }    #T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row0_col3 {\n            background-color:  #023858;\n            color:  #f1f1f1;\n        }    #T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row1_col0 {\n            background-color:  #fff7fb;\n            color:  #000000;\n        }    #T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row1_col1 {\n            background-color:  #fff7fb;\n            color:  #000000;\n        }    #T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row1_col2 {\n            background-color:  #fff7fb;\n            color:  #000000;\n        }    #T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row1_col3 {\n            background-color:  #fff7fb;\n            color:  #000000;\n        }    #T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row2_col0 {\n            background-color:  #023858;\n            color:  #f1f1f1;\n        }    #T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row2_col1 {\n            background-color:  #023858;\n            color:  #f1f1f1;\n        }    #T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row2_col2 {\n            background-color:  #023e62;\n            color:  #f1f1f1;\n        }    #T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row2_col3 {\n            background-color:  #023d60;\n            color:  #f1f1f1;\n        }    #T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row3_col0 {\n            background-color:  #023858;\n            color:  #f1f1f1;\n        }    #T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row3_col1 {\n            background-color:  #023858;\n            color:  #f1f1f1;\n        }    #T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row3_col2 {\n            background-color:  #023e62;\n            color:  #f1f1f1;\n        }    #T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row3_col3 {\n            background-color:  #023d60;\n            color:  #f1f1f1;\n        }</style><table id=\"T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Energy (KYOTOGHG)</th>        <th class=\"col_heading level0 col1\" >Energy (CO2)</th>        <th class=\"col_heading level0 col2\" >Energy (N2O)</th>        <th class=\"col_heading level0 col3\" >Energy (CH4)</th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4level0_row0\" class=\"row_heading level0 row0\" >oil</th>\n                        <td id=\"T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row0_col0\" class=\"data row0 col0\" >0.986</td>\n                        <td id=\"T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row0_col1\" class=\"data row0 col1\" >0.986</td>\n                        <td id=\"T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row0_col2\" class=\"data row0 col2\" >0.812</td>\n                        <td id=\"T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row0_col3\" class=\"data row0 col3\" >0.986</td>\n            </tr>\n            <tr>\n                        <th id=\"T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4level0_row1\" class=\"row_heading level0 row1\" >ccgt</th>\n                        <td id=\"T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row1_col0\" class=\"data row1 col0\" >-0.829</td>\n                        <td id=\"T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row1_col1\" class=\"data row1 col1\" >-0.829</td>\n                        <td id=\"T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row1_col2\" class=\"data row1 col2\" >-0.829</td>\n                        <td id=\"T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row1_col3\" class=\"data row1 col3\" >-0.886</td>\n            </tr>\n            <tr>\n                        <th id=\"T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4level0_row2\" class=\"row_heading level0 row2\" >coal</th>\n                        <td id=\"T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row2_col0\" class=\"data row2 col0\" >1.000</td>\n                        <td id=\"T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row2_col1\" class=\"data row2 col1\" >1.000</td>\n                        <td id=\"T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row2_col2\" class=\"data row2 col2\" >0.771</td>\n                        <td id=\"T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row2_col3\" class=\"data row2 col3\" >0.943</td>\n            </tr>\n            <tr>\n                        <th id=\"T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4level0_row3\" class=\"row_heading level0 row3\" >demand</th>\n                        <td id=\"T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row3_col0\" class=\"data row3 col0\" >1.000</td>\n                        <td id=\"T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row3_col1\" class=\"data row3 col1\" >1.000</td>\n                        <td id=\"T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row3_col2\" class=\"data row3 col2\" >0.771</td>\n                        <td id=\"T_371536f4_b78d_11ea_8d22_f8e4e3d5f2f4row3_col3\" class=\"data row3 col3\" >0.943</td>\n            </tr>\n    </tbody></table>"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "=====================================================================\n\n"
    }
   ],
   "source": [
    "bff2.display_correlations(correlation_dict, display_fun=display)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before predicting the emissions based on GHG let us check what the data from which we predict is like"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Refining dataset and column selections\n",
    "\n",
    "Not all these relationships will enable prediction of GHG emissions. In the subsections below\n",
    "correlations between GHG emissions and: closed cycle gas turbine power stations (`'ccgt'`), coal power stations (`'coal'`) and, fuel power stations (`'fuel'`); are discussed.\n",
    "\n",
    "#### Rejecting `'ccgt'`\n",
    "\n",
    "'Combine cycle gas turbine' used is inversely correlated with GHG emissions. This is because\n",
    "there are two drivers of the long term trend in GHG emissions reduction due to the energy sector:\n",
    "\n",
    "+ Transition to cleaner fossil fuel sources of power ('ccgt' rather than 'coal');\n",
    "+ Reduction in 'demand';\n",
    "+ And increase in renewables.\n",
    "\n",
    "A possibly exploitable correlation can be seen with the global carbon project dataset, specifically gas use. For the purpose of this prototype this was not done, and the power produced by `'ccgt'` are not used. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Rejecting `'oil'`\n",
    "\n",
    "Plotting the '`oil`' data, shows that despite high correlation it is 4 orders of magnitude lower than the other power generation methods. This suggests that the correlation is not significant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "            oil          ccgt          coal        demand\ncount  8.000000      8.000000      8.000000      8.000000\nmean   0.508933  11579.530732   7192.968170  33331.404544\nstd    0.796658   2229.139008   5982.333526   2221.087664\nmin    0.000000   9148.029085    679.510313  30606.290673\n25%    0.003308   9603.858167   2208.894609  31758.443613\n50%    0.154770  11489.689029   5852.376027  32712.718536\n75%    0.650764  13323.652425  11866.890665  34907.808907\nmax    2.309853  14506.012432  15641.439384  36581.013455",
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     "execution_count": 16
    },
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "dsource = 'uk_energy_yearly'\n",
    "d_uk[dsource].plot(y=to_correlate[dsource])\n",
    "d_uk[dsource][to_correlate[dsource]].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Rejecting `'coal'`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because 'coal' plants are stopped preferentially data is truncated from mid-April in 2020 as they have not been in use. This implies that some of the lockdown impact will be missed at least when using the PIK GHG emission dataset. \n",
    "\n",
    "Indeed the global carbon project (GCP) includes data broken down by fuel. While not all those emissions will be the energy sector strong correlations can still be observed. These metrics can also be predicted using the same framework.\n",
    "\n",
    "Because of this truncation `'coal'` is not used for predictions.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x1e1c49ba948>"
     },
     "metadata": {},
     "execution_count": 17
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "d_uk['uk_energy_daily'].loc[d_uk['uk_energy_daily'].index >= \"2019\", to_correlate[dsource]].plot(y='coal')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting GHG emissions from the Energy sector from energy demand\n",
    "\n",
    "From the correlations emissions are predicted based on the \n",
    "data from the Potsdam institute for climate impact research (PIK) ([Gütschow et al., 2016,](http://doi.org/10.5194/essd-8-571-2016)) (called '`historical_GHG_Sectors_PIK`' in our framework, [data download](https://dataservices.gfz-potsdam.de/pik/showshort.php?id=escidoc:4736895)) using specifically:\n",
    "\n",
    "+ 'demand' : The average yearly power demand;\n",
    "\n",
    "In order to predict emissions, this quantity needs to be \n",
    "These values are predicted in [`predict.ipynb`]()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_data_set = correlation_dict['historical_GHG_Sectors_GCP']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example prediction on the avilable data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                  demand          ccgt          coal\ntimestamp                                           \n2012-01-01  36581.013455   9470.393216  15641.439384\n2013-01-01  36266.654436   9148.029085  14361.405081\n2014-01-01  34454.860398   9898.649169  11035.385859\n2015-01-01  33048.731071   9648.346483   8508.842895\n2016-01-01  32376.706000  14506.012432   3195.909159\n2017-01-01  31858.397066  13678.825019   2357.162886\n2018-01-01  31458.583256  13205.261560   1764.089781\n2019-01-01  30606.290673  13080.728889    679.510313",
      "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>demand</th>\n      <th>ccgt</th>\n      <th>coal</th>\n    </tr>\n    <tr>\n      <th>timestamp</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2012-01-01</th>\n      <td>36581.013455</td>\n      <td>9470.393216</td>\n      <td>15641.439384</td>\n    </tr>\n    <tr>\n      <th>2013-01-01</th>\n      <td>36266.654436</td>\n      <td>9148.029085</td>\n      <td>14361.405081</td>\n    </tr>\n    <tr>\n      <th>2014-01-01</th>\n      <td>34454.860398</td>\n      <td>9898.649169</td>\n      <td>11035.385859</td>\n    </tr>\n    <tr>\n      <th>2015-01-01</th>\n      <td>33048.731071</td>\n      <td>9648.346483</td>\n      <td>8508.842895</td>\n    </tr>\n    <tr>\n      <th>2016-01-01</th>\n      <td>32376.706000</td>\n      <td>14506.012432</td>\n      <td>3195.909159</td>\n    </tr>\n    <tr>\n      <th>2017-01-01</th>\n      <td>31858.397066</td>\n      <td>13678.825019</td>\n      <td>2357.162886</td>\n    </tr>\n    <tr>\n      <th>2018-01-01</th>\n      <td>31458.583256</td>\n      <td>13205.261560</td>\n      <td>1764.089781</td>\n    </tr>\n    <tr>\n      <th>2019-01-01</th>\n      <td>30606.290673</td>\n      <td>13080.728889</td>\n      <td>679.510313</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 43
    }
   ],
   "source": [
    "df = d_uk['uk_energy_yearly']\n",
    "pred_x = df.loc[\n",
    "    (df.index >= \"2010\") & (df.index < \"2020\"), [\"demand\", \"ccgt\", \"coal\"]\n",
    "]\n",
    "pred_x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using function `bff2.apply_prediction` applies the linear prediction to each correlated column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                      predictor_value  Bunkers (CO2)  Cement (CO2)  \\\npredictor date                                                       \ndemand    2012-01-01     36581.013455            NaN      3.891558   \n          2013-01-01     36266.654436            NaN      3.931423   \n          2014-01-01     34454.860398            NaN      4.161184   \n          2015-01-01     33048.731071            NaN      4.339502   \n          2016-01-01     32376.706000            NaN      4.424724   \n          2017-01-01     31858.397066            NaN      4.490453   \n          2018-01-01     31458.583256            NaN      4.541156   \n          2019-01-01     30606.290673            NaN      4.649239   \nccgt      2012-01-01      9470.393216            NaN      4.088739   \n          2013-01-01      9148.029085            NaN      4.060572   \n          2014-01-01      9898.649169            NaN      4.126157   \n          2015-01-01      9648.346483            NaN      4.104287   \n          2016-01-01     14506.012432            NaN      4.528724   \n          2017-01-01     13678.825019            NaN      4.456449   \n          2018-01-01     13205.261560            NaN      4.415072   \n          2019-01-01     13080.728889            NaN      4.404191   \ncoal      2012-01-01     15641.439384            NaN      3.926277   \n          2013-01-01     14361.405081            NaN      3.982125   \n          2014-01-01     11035.385859            NaN      4.127239   \n          2015-01-01      8508.842895            NaN      4.237472   \n          2016-01-01      3195.909159            NaN      4.469274   \n          2017-01-01      2357.162886            NaN      4.505869   \n          2018-01-01      1764.089781            NaN      4.531744   \n          2019-01-01       679.510313            NaN      4.579065   \n\n                      Coal (CO2)   Gas (CO2)  Gas flaring (CO2)   Oil (CO2)  \\\npredictor date                                                                \ndemand    2012-01-01  154.049802  152.786774           3.941956  167.952562   \n          2013-01-01  146.782504  153.188278           3.987587  168.341183   \n          2014-01-01  104.897758  155.502328           4.250577  170.580985   \n          2015-01-01   72.391100  157.298257           4.454683  172.319291   \n          2016-01-01   56.855339  158.156577           4.552231  173.150071   \n          2017-01-01   44.873162  158.818569           4.627466  173.790822   \n          2018-01-01   35.630334  159.329217           4.685500  174.285086   \n          2019-01-01   15.927180  160.417777           4.809214  175.338720   \nccgt      2012-01-01  124.842713  151.572785           4.193127  169.056218   \n          2013-01-01  131.123833  150.744668           4.165221  168.642373   \n          2014-01-01  116.498339  152.672925           4.230200  169.606004   \n          2015-01-01  121.375374  152.029926           4.208532  169.284671   \n          2016-01-01   26.725941  164.508708           4.629043  175.520847   \n          2017-01-01   42.843315  162.383759           4.557436  174.458920   \n          2018-01-01   52.070486  161.167229           4.516442  173.850968   \n          2019-01-01   54.496949  160.847319           4.505661  173.691096   \ncoal      2012-01-01  151.790260  151.233853           3.991772  167.913881   \n          2013-01-01  140.916483  152.120268           4.053981  168.522519   \n          2014-01-01  112.662246  154.423513           4.215624  170.103993   \n          2015-01-01   91.199489  156.173127           4.338413  171.305328   \n          2016-01-01   46.066591  159.852296           4.596620  173.831552   \n          2017-01-01   38.941517  160.433122           4.637383  174.230364   \n          2018-01-01   33.903413  160.843821           4.666206  174.512362   \n          2019-01-01   24.690008  161.594885           4.718916  175.028064   \n\n                      Total fossil fuels and cement (CO2)  Gas and Coal (CO2)  \npredictor date                                                                 \ndemand    2012-01-01                           486.115835          306.836576  \n          2013-01-01                           479.680475          299.970782  \n          2014-01-01                           442.590565          260.400086  \n          2015-01-01                           413.805169          229.689357  \n          2016-01-01                           400.047894          215.011916  \n          2017-01-01                           389.437399          203.691731  \n          2018-01-01                           381.252662          194.959551  \n          2019-01-01                           363.805064          176.344957  \nccgt      2012-01-01                           457.099865          276.415498  \n          2013-01-01                           462.125584          281.868501  \n          2014-01-01                           450.423272          269.171263  \n          2015-01-01                           454.325539          273.405300  \n          2016-01-01                           378.593590          191.234649  \n          2017-01-01                           391.489602          205.227074  \n          2018-01-01                           398.872547          213.237715  \n          2019-01-01                           400.814036          215.344268  \ncoal      2012-01-01                           482.352651          303.024113  \n          2013-01-01                           473.023726          293.036751  \n          2014-01-01                           448.783608          267.085759  \n          2015-01-01                           430.370094          247.372616  \n          2016-01-01                           391.649288          205.918887  \n          2017-01-01                           385.536483          199.374639  \n          2018-01-01                           381.214150          194.747235  \n          2019-01-01                           373.309705          186.284892  ",
      "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></th>\n      <th>predictor_value</th>\n      <th>Bunkers (CO2)</th>\n      <th>Cement (CO2)</th>\n      <th>Coal (CO2)</th>\n      <th>Gas (CO2)</th>\n      <th>Gas flaring (CO2)</th>\n      <th>Oil (CO2)</th>\n      <th>Total fossil fuels and cement (CO2)</th>\n      <th>Gas and Coal (CO2)</th>\n    </tr>\n    <tr>\n      <th>predictor</th>\n      <th>date</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 rowspan=\"8\" valign=\"top\">demand</th>\n      <th>2012-01-01</th>\n      <td>36581.013455</td>\n      <td>NaN</td>\n      <td>3.891558</td>\n      <td>154.049802</td>\n      <td>152.786774</td>\n      <td>3.941956</td>\n      <td>167.952562</td>\n      <td>486.115835</td>\n      <td>306.836576</td>\n    </tr>\n    <tr>\n      <th>2013-01-01</th>\n      <td>36266.654436</td>\n      <td>NaN</td>\n      <td>3.931423</td>\n      <td>146.782504</td>\n      <td>153.188278</td>\n      <td>3.987587</td>\n      <td>168.341183</td>\n      <td>479.680475</td>\n      <td>299.970782</td>\n    </tr>\n    <tr>\n      <th>2014-01-01</th>\n      <td>34454.860398</td>\n      <td>NaN</td>\n      <td>4.161184</td>\n      <td>104.897758</td>\n      <td>155.502328</td>\n      <td>4.250577</td>\n      <td>170.580985</td>\n      <td>442.590565</td>\n      <td>260.400086</td>\n    </tr>\n    <tr>\n      <th>2015-01-01</th>\n      <td>33048.731071</td>\n      <td>NaN</td>\n      <td>4.339502</td>\n      <td>72.391100</td>\n      <td>157.298257</td>\n      <td>4.454683</td>\n      <td>172.319291</td>\n      <td>413.805169</td>\n      <td>229.689357</td>\n    </tr>\n    <tr>\n      <th>2016-01-01</th>\n      <td>32376.706000</td>\n      <td>NaN</td>\n      <td>4.424724</td>\n      <td>56.855339</td>\n      <td>158.156577</td>\n      <td>4.552231</td>\n      <td>173.150071</td>\n      <td>400.047894</td>\n      <td>215.011916</td>\n    </tr>\n    <tr>\n      <th>2017-01-01</th>\n      <td>31858.397066</td>\n      <td>NaN</td>\n      <td>4.490453</td>\n      <td>44.873162</td>\n      <td>158.818569</td>\n      <td>4.627466</td>\n      <td>173.790822</td>\n      <td>389.437399</td>\n      <td>203.691731</td>\n    </tr>\n    <tr>\n      <th>2018-01-01</th>\n      <td>31458.583256</td>\n      <td>NaN</td>\n      <td>4.541156</td>\n      <td>35.630334</td>\n      <td>159.329217</td>\n      <td>4.685500</td>\n      <td>174.285086</td>\n      <td>381.252662</td>\n      <td>194.959551</td>\n    </tr>\n    <tr>\n      <th>2019-01-01</th>\n      <td>30606.290673</td>\n      <td>NaN</td>\n      <td>4.649239</td>\n      <td>15.927180</td>\n      <td>160.417777</td>\n      <td>4.809214</td>\n      <td>175.338720</td>\n      <td>363.805064</td>\n      <td>176.344957</td>\n    </tr>\n    <tr>\n      <th rowspan=\"8\" valign=\"top\">ccgt</th>\n      <th>2012-01-01</th>\n      <td>9470.393216</td>\n      <td>NaN</td>\n      <td>4.088739</td>\n      <td>124.842713</td>\n      <td>151.572785</td>\n      <td>4.193127</td>\n      <td>169.056218</td>\n      <td>457.099865</td>\n      <td>276.415498</td>\n    </tr>\n    <tr>\n      <th>2013-01-01</th>\n      <td>9148.029085</td>\n      <td>NaN</td>\n      <td>4.060572</td>\n      <td>131.123833</td>\n      <td>150.744668</td>\n      <td>4.165221</td>\n      <td>168.642373</td>\n      <td>462.125584</td>\n      <td>281.868501</td>\n    </tr>\n    <tr>\n      <th>2014-01-01</th>\n      <td>9898.649169</td>\n      <td>NaN</td>\n      <td>4.126157</td>\n      <td>116.498339</td>\n      <td>152.672925</td>\n      <td>4.230200</td>\n      <td>169.606004</td>\n      <td>450.423272</td>\n      <td>269.171263</td>\n    </tr>\n    <tr>\n      <th>2015-01-01</th>\n      <td>9648.346483</td>\n      <td>NaN</td>\n      <td>4.104287</td>\n      <td>121.375374</td>\n      <td>152.029926</td>\n      <td>4.208532</td>\n      <td>169.284671</td>\n      <td>454.325539</td>\n      <td>273.405300</td>\n    </tr>\n    <tr>\n      <th>2016-01-01</th>\n      <td>14506.012432</td>\n      <td>NaN</td>\n      <td>4.528724</td>\n      <td>26.725941</td>\n      <td>164.508708</td>\n      <td>4.629043</td>\n      <td>175.520847</td>\n      <td>378.593590</td>\n      <td>191.234649</td>\n    </tr>\n    <tr>\n      <th>2017-01-01</th>\n      <td>13678.825019</td>\n      <td>NaN</td>\n      <td>4.456449</td>\n      <td>42.843315</td>\n      <td>162.383759</td>\n      <td>4.557436</td>\n      <td>174.458920</td>\n      <td>391.489602</td>\n      <td>205.227074</td>\n    </tr>\n    <tr>\n      <th>2018-01-01</th>\n      <td>13205.261560</td>\n      <td>NaN</td>\n      <td>4.415072</td>\n      <td>52.070486</td>\n      <td>161.167229</td>\n      <td>4.516442</td>\n      <td>173.850968</td>\n      <td>398.872547</td>\n      <td>213.237715</td>\n    </tr>\n    <tr>\n      <th>2019-01-01</th>\n      <td>13080.728889</td>\n      <td>NaN</td>\n      <td>4.404191</td>\n      <td>54.496949</td>\n      <td>160.847319</td>\n      <td>4.505661</td>\n      <td>173.691096</td>\n      <td>400.814036</td>\n      <td>215.344268</td>\n    </tr>\n    <tr>\n      <th rowspan=\"8\" valign=\"top\">coal</th>\n      <th>2012-01-01</th>\n      <td>15641.439384</td>\n      <td>NaN</td>\n      <td>3.926277</td>\n      <td>151.790260</td>\n      <td>151.233853</td>\n      <td>3.991772</td>\n      <td>167.913881</td>\n      <td>482.352651</td>\n      <td>303.024113</td>\n    </tr>\n    <tr>\n      <th>2013-01-01</th>\n      <td>14361.405081</td>\n      <td>NaN</td>\n      <td>3.982125</td>\n      <td>140.916483</td>\n      <td>152.120268</td>\n      <td>4.053981</td>\n      <td>168.522519</td>\n      <td>473.023726</td>\n      <td>293.036751</td>\n    </tr>\n    <tr>\n      <th>2014-01-01</th>\n      <td>11035.385859</td>\n      <td>NaN</td>\n      <td>4.127239</td>\n      <td>112.662246</td>\n      <td>154.423513</td>\n      <td>4.215624</td>\n      <td>170.103993</td>\n      <td>448.783608</td>\n      <td>267.085759</td>\n    </tr>\n    <tr>\n      <th>2015-01-01</th>\n      <td>8508.842895</td>\n      <td>NaN</td>\n      <td>4.237472</td>\n      <td>91.199489</td>\n      <td>156.173127</td>\n      <td>4.338413</td>\n      <td>171.305328</td>\n      <td>430.370094</td>\n      <td>247.372616</td>\n    </tr>\n    <tr>\n      <th>2016-01-01</th>\n      <td>3195.909159</td>\n      <td>NaN</td>\n      <td>4.469274</td>\n      <td>46.066591</td>\n      <td>159.852296</td>\n      <td>4.596620</td>\n      <td>173.831552</td>\n      <td>391.649288</td>\n      <td>205.918887</td>\n    </tr>\n    <tr>\n      <th>2017-01-01</th>\n      <td>2357.162886</td>\n      <td>NaN</td>\n      <td>4.505869</td>\n      <td>38.941517</td>\n      <td>160.433122</td>\n      <td>4.637383</td>\n      <td>174.230364</td>\n      <td>385.536483</td>\n      <td>199.374639</td>\n    </tr>\n    <tr>\n      <th>2018-01-01</th>\n      <td>1764.089781</td>\n      <td>NaN</td>\n      <td>4.531744</td>\n      <td>33.903413</td>\n      <td>160.843821</td>\n      <td>4.666206</td>\n      <td>174.512362</td>\n      <td>381.214150</td>\n      <td>194.747235</td>\n    </tr>\n    <tr>\n      <th>2019-01-01</th>\n      <td>679.510313</td>\n      <td>NaN</td>\n      <td>4.579065</td>\n      <td>24.690008</td>\n      <td>161.594885</td>\n      <td>4.718916</td>\n      <td>175.028064</td>\n      <td>373.309705</td>\n      <td>186.284892</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 44
    }
   ],
   "source": [
    "pred = bff2.apply_prediction(prediction_data_set['fit'], pred_x)\n",
    "pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is useful to check here what the correlations between predictors (rows) and predicted values are (columns) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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{\n            background-color:  #fff7fb;\n            color:  #000000;\n        }    #T_61cafcd8_b78f_11ea_95a9_f8e4e3d5f2f4row2_col6 {\n            background-color:  #023858;\n            color:  #f1f1f1;\n        }    #T_61cafcd8_b78f_11ea_95a9_f8e4e3d5f2f4row2_col7 {\n            background-color:  #023858;\n            color:  #f1f1f1;\n        }</style><table id=\"T_61cafcd8_b78f_11ea_95a9_f8e4e3d5f2f4\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Bunkers (CO2)</th>        <th class=\"col_heading level0 col1\" >Cement (CO2)</th>        <th class=\"col_heading level0 col2\" >Coal (CO2)</th>        <th class=\"col_heading level0 col3\" >Gas (CO2)</th>        <th class=\"col_heading level0 col4\" >Gas flaring (CO2)</th>        <th class=\"col_heading level0 col5\" >Oil (CO2)</th>        <th class=\"col_heading level0 col6\" >Total fossil fuels and cement (CO2)</th>        <th class=\"col_heading level0 col7\" >Gas and Coal (CO2)</th>    </tr>    <tr>        <th class=\"index_name level0\" >predictor</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_61cafcd8_b78f_11ea_95a9_f8e4e3d5f2f4level0_row0\" class=\"row_heading level0 row0\" >demand</th>\n                        <td id=\"T_61cafcd8_b78f_11ea_95a9_f8e4e3d5f2f4row0_col0\" class=\"data row0 col0\" >nan</td>\n                        <td id=\"T_61cafcd8_b78f_11ea_95a9_f8e4e3d5f2f4row0_col1\" class=\"data row0 col1\" >-0.703</td>\n                        <td id=\"T_61cafcd8_b78f_11ea_95a9_f8e4e3d5f2f4row0_col2\" class=\"data row0 col2\" >1.000</td>\n                        <td id=\"T_61cafcd8_b78f_11ea_95a9_f8e4e3d5f2f4row0_col3\" class=\"data row0 col3\" >-0.571</td>\n                        <td id=\"T_61cafcd8_b78f_11ea_95a9_f8e4e3d5f2f4row0_col4\" class=\"data row0 col4\" >-0.775</td>\n                        <td id=\"T_61cafcd8_b78f_11ea_95a9_f8e4e3d5f2f4row0_col5\" class=\"data row0 col5\" >-0.786</td>\n                        <td id=\"T_61cafcd8_b78f_11ea_95a9_f8e4e3d5f2f4row0_col6\" class=\"data row0 col6\" >1.000</td>\n                        <td id=\"T_61cafcd8_b78f_11ea_95a9_f8e4e3d5f2f4row0_col7\" class=\"data row0 col7\" >1.000</td>\n            </tr>\n            <tr>\n                        <th id=\"T_61cafcd8_b78f_11ea_95a9_f8e4e3d5f2f4level0_row1\" class=\"row_heading level0 row1\" >ccgt</th>\n                        <td id=\"T_61cafcd8_b78f_11ea_95a9_f8e4e3d5f2f4row1_col0\" class=\"data row1 col0\" >nan</td>\n                        <td id=\"T_61cafcd8_b78f_11ea_95a9_f8e4e3d5f2f4row1_col1\" class=\"data row1 col1\" >0.739</td>\n                        <td id=\"T_61cafcd8_b78f_11ea_95a9_f8e4e3d5f2f4row1_col2\" class=\"data row1 col2\" >-0.786</td>\n                        <td id=\"T_61cafcd8_b78f_11ea_95a9_f8e4e3d5f2f4row1_col3\" class=\"data row1 col3\" >0.643</td>\n                        <td id=\"T_61cafcd8_b78f_11ea_95a9_f8e4e3d5f2f4row1_col4\" class=\"data row1 col4\" >0.487</td>\n                        <td id=\"T_61cafcd8_b78f_11ea_95a9_f8e4e3d5f2f4row1_col5\" class=\"data row1 col5\" >0.857</td>\n                        <td id=\"T_61cafcd8_b78f_11ea_95a9_f8e4e3d5f2f4row1_col6\" class=\"data row1 col6\" >-0.786</td>\n                        <td id=\"T_61cafcd8_b78f_11ea_95a9_f8e4e3d5f2f4row1_col7\" class=\"data row1 col7\" >-0.786</td>\n            </tr>\n            <tr>\n                        <th id=\"T_61cafcd8_b78f_11ea_95a9_f8e4e3d5f2f4level0_row2\" class=\"row_heading level0 row2\" >coal</th>\n                        <td id=\"T_61cafcd8_b78f_11ea_95a9_f8e4e3d5f2f4row2_col0\" class=\"data row2 col0\" >nan</td>\n                        <td id=\"T_61cafcd8_b78f_11ea_95a9_f8e4e3d5f2f4row2_col1\" class=\"data row2 col1\" >-0.703</td>\n                        <td id=\"T_61cafcd8_b78f_11ea_95a9_f8e4e3d5f2f4row2_col2\" class=\"data row2 col2\" >1.000</td>\n                        <td id=\"T_61cafcd8_b78f_11ea_95a9_f8e4e3d5f2f4row2_col3\" class=\"data row2 col3\" >-0.571</td>\n                        <td id=\"T_61cafcd8_b78f_11ea_95a9_f8e4e3d5f2f4row2_col4\" class=\"data row2 col4\" >-0.775</td>\n                        <td id=\"T_61cafcd8_b78f_11ea_95a9_f8e4e3d5f2f4row2_col5\" class=\"data row2 col5\" >-0.786</td>\n                        <td id=\"T_61cafcd8_b78f_11ea_95a9_f8e4e3d5f2f4row2_col6\" class=\"data row2 col6\" >1.000</td>\n                        <td id=\"T_61cafcd8_b78f_11ea_95a9_f8e4e3d5f2f4row2_col7\" class=\"data row2 col7\" >1.000</td>\n            </tr>\n    </tbody></table>"
     },
     "metadata": {}
    }
   ],
   "source": [
    "display(\n",
    "    prediction_data_set['correlation'].loc[pred.index.get_level_values(0).unique()]\n",
    "    .style.background_gradient().set_precision(3)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us check the prediction on the 'training set'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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<use xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-53\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_13\">\r\n     <g id=\"line2d_24\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"483.049659\" xlink:href=\"#m5d7177f0cc\" y=\"361.9155\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_23\">\r\n      <!-- 2016 -->\r\n      <g transform=\"translate(470.324659 376.513937)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-54\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_14\">\r\n     <g id=\"line2d_25\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"518.217726\" xlink:href=\"#m5d7177f0cc\" y=\"361.9155\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_24\">\r\n      <!-- 2017 -->\r\n      <g transform=\"translate(505.492726 376.513937)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-55\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_15\">\r\n     <g id=\"line2d_26\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"553.385793\" xlink:href=\"#m5d7177f0cc\" y=\"361.9155\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_25\">\r\n      <!-- 2018 -->\r\n      <g transform=\"translate(540.660793 376.513937)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-56\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_16\">\r\n     <g id=\"line2d_27\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"588.55386\" xlink:href=\"#m5d7177f0cc\" y=\"361.9155\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_26\">\r\n      <!-- 2019 -->\r\n      <g transform=\"translate(575.82886 376.513937)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-57\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"text_27\">\r\n     <!-- date -->\r\n     <g transform=\"translate(454.190625 390.192062)scale(0.1 -0.1)\">\r\n      <use xlink:href=\"#DejaVuSans-100\"/>\r\n      <use x=\"63.476562\" xlink:href=\"#DejaVuSans-97\"/>\r\n      <use x=\"124.755859\" xlink:href=\"#DejaVuSans-116\"/>\r\n      <use x=\"163.964844\" xlink:href=\"#DejaVuSans-101\"/>\r\n     </g>\r\n    </g>\r\n   </g>\r\n   <g id=\"matplotlib.axis_4\">\r\n    <g id=\"ytick_6\">\r\n     <g id=\"line2d_28\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"342.37739\" xlink:href=\"#m1493f7e35c\" y=\"358.03125\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_28\">\r\n      <!-- 20 -->\r\n      <g transform=\"translate(322.65239 361.830469)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_7\">\r\n     <g id=\"line2d_29\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"342.37739\" xlink:href=\"#m1493f7e35c\" y=\"311.373736\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_29\">\r\n      <!-- 40 -->\r\n      <g transform=\"translate(322.65239 315.172955)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-52\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_8\">\r\n     <g id=\"line2d_30\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"342.37739\" xlink:href=\"#m1493f7e35c\" y=\"264.716222\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_30\">\r\n      <!-- 60 -->\r\n      <g transform=\"translate(322.65239 268.515441)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-54\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_9\">\r\n     <g id=\"line2d_31\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"342.37739\" xlink:href=\"#m1493f7e35c\" y=\"218.058708\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_31\">\r\n      <!-- 80 -->\r\n      <g 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xlink:href=\"#DejaVuSans-104\"/>\r\n     </g>\r\n    </g>\r\n   </g>\r\n  </g>\r\n  <g id=\"axes_3\">\r\n   <g id=\"patch_14\">\r\n    <path d=\"M 637.789154 361.9155 \r\nL 883.965625 361.9155 \r\nL 883.965625 35.7555 \r\nL 637.789154 35.7555 \r\nz\r\n\" style=\"fill:#ffffff;\"/>\r\n   </g>\r\n   <g id=\"matplotlib.axis_5\">\r\n    <g id=\"xtick_17\">\r\n     <g id=\"line2d_41\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"637.789154\" xlink:href=\"#m5d7177f0cc\" y=\"361.9155\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_39\">\r\n      <!-- 2012 -->\r\n      <g transform=\"translate(625.064154 376.513937)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-50\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_18\">\r\n     <g id=\"line2d_42\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"672.957222\" xlink:href=\"#m5d7177f0cc\" y=\"361.9155\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_40\">\r\n      <!-- 2013 -->\r\n      <g transform=\"translate(660.232222 376.513937)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-51\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_19\">\r\n     <g id=\"line2d_43\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"708.125289\" xlink:href=\"#m5d7177f0cc\" y=\"361.9155\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_41\">\r\n      <!-- 2014 -->\r\n      <g transform=\"translate(695.400289 376.513937)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-52\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_20\">\r\n     <g id=\"line2d_44\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"743.293356\" xlink:href=\"#m5d7177f0cc\" y=\"361.9155\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_42\">\r\n      <!-- 2015 -->\r\n      <g transform=\"translate(730.568356 376.513937)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-53\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_21\">\r\n     <g id=\"line2d_45\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"778.461423\" xlink:href=\"#m5d7177f0cc\" y=\"361.9155\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_43\">\r\n      <!-- 2016 -->\r\n      <g transform=\"translate(765.736423 376.513937)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-54\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_22\">\r\n     <g id=\"line2d_46\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"813.629491\" xlink:href=\"#m5d7177f0cc\" y=\"361.9155\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_44\">\r\n      <!-- 2017 -->\r\n      <g transform=\"translate(800.904491 376.513937)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-55\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_23\">\r\n     <g id=\"line2d_47\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"848.797558\" xlink:href=\"#m5d7177f0cc\" y=\"361.9155\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_45\">\r\n      <!-- 2018 -->\r\n      <g transform=\"translate(836.072558 376.513937)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-56\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_24\">\r\n     <g id=\"line2d_48\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"883.965625\" xlink:href=\"#m5d7177f0cc\" y=\"361.9155\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_46\">\r\n      <!-- 2019 -->\r\n      <g transform=\"translate(871.240625 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<g id=\"line2d_53\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"637.789154\" xlink:href=\"#m1493f7e35c\" y=\"157.005189\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_52\">\r\n      <!-- 260 -->\r\n      <g transform=\"translate(611.701654 160.804408)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-54\"/>\r\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_18\">\r\n     <g id=\"line2d_54\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"637.789154\" xlink:href=\"#m1493f7e35c\" y=\"111.560264\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_53\">\r\n      <!-- 280 -->\r\n      <g transform=\"translate(611.701654 115.359483)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-56\"/>\r\n       <use 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "analysis_cols = ['Gas (CO2)', 'Coal (CO2)', 'Gas and Coal (CO2)']\n",
    "indep_idx = [\"ccgt\", \"coal\", \"demand\"]\n",
    "\n",
    "fig, axs = plt.subplots(1, 3)\n",
    "fig.set_size_inches(15, 6)\n",
    "axs = axs.flatten()\n",
    "\n",
    "i = 0\n",
    "preds = pd.DataFrame()\n",
    "truths = pd.DataFrame()\n",
    "for idx, analysis_col in zip(indep_idx, analysis_cols):\n",
    "    mask_pred = (pred.loc[idx].index <= prediction_data_set['data'][analysis_col].index.max())\n",
    "    truth = truth.loc[pred.loc[idx].index[mask_pred], analysis_cols]\n",
    "    current_cols = [analysis_col]\n",
    "    axs[i].set_title(f\"'{analysis_col}' from '{idx}' \\n(correlation: {prediction_data_set['correlation'].loc[idx, analysis_col]:.2})\")\n",
    "    axs[i].set_ylabel('CO2 emissions [MtCO2e]')\n",
    "    pred.loc[idx, current_cols].plot(\n",
    "        y=current_cols,\n",
    "        ax=axs[i], label=[f\"{c} - prediction\" for c in current_cols],\n",
    "        linestyle='--', marker='+'\n",
    "    )\n",
    "    truth[current_cols].plot(\n",
    "        y=current_cols,\n",
    "        ax=axs[i], label=[f\"{c} - truth\" for c in current_cols],\n",
    "    )\n",
    "    preds = pd.concat([preds, pred.loc[idx, current_cols].rename({c:f\"{c} from {idx}\" for c in current_cols}, axis='columns')])\n",
    "    truths = pd.concat([truths, truth[current_cols].rename({c:f\"{c} from {idx}\" for c in current_cols}, axis='columns')])\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected for the data sets with the highest correlation the fit is very good. as indicated by the graphs above and the percentage errors within the range $\\pm 4$% for the KYOTOGHG and CO2 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = preds.groupby(level=0).first()\n",
    "truths = truths.groupby(level=0).first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Percentage errors between prediction and truth\n"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<pandas.io.formats.style.Styler at 0x1e1cda50fc8>",
      "text/html": "<style  type=\"text/css\" >\n</style><table id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Gas (CO2) from ccgt</th>        <th class=\"col_heading level0 col1\" >Coal (CO2) from coal</th>        <th class=\"col_heading level0 col2\" >Gas and Coal (CO2) from demand</th>    </tr>    <tr>        <th class=\"index_name level0\" >date</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4level0_row0\" class=\"row_heading level0 row0\" >2012-01-01 00:00:00</th>\n                        <td id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4row0_col0\" class=\"data row0 col0\" >-3.82%</td>\n                        <td id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4row0_col1\" class=\"data row0 col1\" >2.59%</td>\n                        <td id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4row0_col2\" class=\"data row0 col2\" >0.42%</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4level0_row1\" class=\"row_heading level0 row1\" >2013-01-01 00:00:00</th>\n                        <td id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4row1_col0\" class=\"data row1 col0\" >-3.98%</td>\n                        <td id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4row1_col1\" class=\"data row1 col1\" >-1.19%</td>\n                        <td id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4row1_col2\" class=\"data row1 col2\" >0.12%</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4level0_row2\" class=\"row_heading level0 row2\" >2014-01-01 00:00:00</th>\n                        <td id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4row2_col0\" class=\"data row2 col0\" >6.16%</td>\n                        <td id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4row2_col1\" class=\"data row2 col1\" >-2.55%</td>\n                        <td id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4row2_col2\" class=\"data row2 col2\" >0.37%</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4level0_row3\" class=\"row_heading level0 row3\" >2015-01-01 00:00:00</th>\n                        <td id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4row3_col0\" class=\"data row3 col0\" >3.28%</td>\n                        <td id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4row3_col1\" class=\"data row3 col1\" >-0.53%</td>\n                        <td id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4row3_col2\" class=\"data row3 col2\" >-3.85%</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4level0_row4\" class=\"row_heading level0 row4\" >2016-01-01 00:00:00</th>\n                        <td id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4row4_col0\" class=\"data row4 col0\" >-0.76%</td>\n                        <td id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4row4_col1\" class=\"data row4 col1\" >-2.19%</td>\n                        <td id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4row4_col2\" class=\"data row4 col2\" >1.01%</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4level0_row5\" class=\"row_heading level0 row5\" >2017-01-01 00:00:00</th>\n                        <td id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4row5_col0\" class=\"data row5 col0\" >0.37%</td>\n                        <td id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4row5_col1\" class=\"data row5 col1\" >1.30%</td>\n                        <td id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4row5_col2\" class=\"data row5 col2\" >1.73%</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4level0_row6\" class=\"row_heading level0 row6\" >2018-01-01 00:00:00</th>\n                        <td id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4row6_col0\" class=\"data row6 col0\" >-0.46%</td>\n                        <td id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4row6_col1\" class=\"data row6 col1\" >5.72%</td>\n                        <td id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4row6_col2\" class=\"data row6 col2\" >0.50%</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4level0_row7\" class=\"row_heading level0 row7\" >2019-01-01 00:00:00</th>\n                        <td id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4row7_col0\" class=\"data row7 col0\" >nan%</td>\n                        <td id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4row7_col1\" class=\"data row7 col1\" >nan%</td>\n                        <td id=\"T_9d6af1dc_b793_11ea_bebb_f8e4e3d5f2f4row7_col2\" class=\"data row7 col2\" >nan%</td>\n            </tr>\n    </tbody></table>"
     },
     "metadata": {}
    }
   ],
   "source": [
    "err = (((preds - truths)/truths))\n",
    "print(\"Percentage errors between prediction and truth\")\n",
    "display(err.style.format('{:,.2%}'.format))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicting 2020 with and without lockdowns\n",
    "\n",
    "Because 2020 is not finished we must model the expected average energy consumption with and without lockdowns.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "            energy_demand_with_corona  energy_demand_without_corona  \\\ntimestamp                                                             \n2020-01-01               28061.809075                  31803.739168   \n\n            energy_demand_reduction  \ntimestamp                            \n2020-01-01              3741.930094  ",
      "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>energy_demand_with_corona</th>\n      <th>energy_demand_without_corona</th>\n      <th>energy_demand_reduction</th>\n    </tr>\n    <tr>\n      <th>timestamp</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2020-01-01</th>\n      <td>28061.809075</td>\n      <td>31803.739168</td>\n      <td>3741.930094</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "source": [
    "pred_corona = d_uk['uk_energy_demand_reduction_yearly']\n",
    "pred_corona"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_corona.drop([\"energy_demand_reduction\"], axis='columns', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "AttributeError",
     "evalue": "'numpy.float64' object has no attribute 'predict'",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-26-9a8ab486f140>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mprediction_data_set\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'fit'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mpred_corona\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[1;33m{\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'demand'\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mpred_corona\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m )\n",
      "\u001b[1;32mc:\\Users\\alexa\\Documents\\code\\covid19\\BristolFluFighters\\Covid_Hackathon_2\\brsflufight_nerc2\\model_predict.py\u001b[0m in \u001b[0;36mapply_prediction\u001b[1;34m(fit_df, x_values, x_col_map)\u001b[0m\n\u001b[0;32m    101\u001b[0m         \u001b[0mdf_temp\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"predictor_value\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx_values\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol_pred\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    102\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0my_col\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfit_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 103\u001b[1;33m             df_temp[y_col] = fit_df.loc[x_col_map[col_pred], y_col].predict(\n\u001b[0m\u001b[0;32m    104\u001b[0m                 \u001b[0mx_values\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol_pred\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    105\u001b[0m             )\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'numpy.float64' object has no attribute 'predict'"
     ]
    }
   ],
   "source": [
    "pred = bff2.apply_prediction(\n",
    "    prediction_data_set['fit'],\n",
    "    pred_corona,\n",
    "    {c: 'demand' for c in pred_corona}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "NameError",
     "evalue": "name 'pred' is not defined",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-27-e7a89b9b4ecd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdifference\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpred\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"energy_demand_with_corona\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mpred\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"energy_demand_without_corona\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mratio\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdifference\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mpred\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"energy_demand_without_corona\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mdifference\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"quantity\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"absolute difference\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mratio\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"quantity\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"relative difference\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'pred' is not defined"
     ]
    }
   ],
   "source": [
    "difference = pred.loc[\"energy_demand_with_corona\"]-pred.loc[\"energy_demand_without_corona\"]\n",
    "ratio = (difference)/pred.loc[\"energy_demand_without_corona\"]\n",
    "\n",
    "difference[\"quantity\"] = \"absolute difference\"\n",
    "ratio[\"quantity\"] = \"relative difference\"\n",
    "\n",
    "change_corona = pd.concat([difference,ratio])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Predicted GHG emissions based on energy demand:\n___________________________________________________\n"
    },
    {
     "output_type": "error",
     "ename": "NameError",
     "evalue": "name 'pred' is not defined",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-28-1149a6fcbcd7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Predicted GHG emissions based on energy demand:\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"___________________________________________________\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mdisplay\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'pred' is not defined"
     ]
    }
   ],
   "source": [
    "print(\"Predicted GHG emissions based on energy demand:\")\n",
    "print(\"___________________________________________________\")\n",
    "display(pred)\n",
    "print(\"\")\n",
    "print(\"\")\n",
    "print(\"Predicted change in GHG emissions based on energy demand:\")\n",
    "print(\"_____________________________________________________________\")\n",
    "display(change_corona)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outcomes\n",
    "\n",
    "From this framework the predicted reduction in GHG emissions for the energy sector is:\n",
    "\n",
    "+ from column `'Energy (KYOTOGHG)'` above: $77\\ [MtCO_2e]$ (Megatons of $CO_2$ equivalent);\n",
    "+ it corresponds to a 20% reduction of emissions for the sector due to a fall in demand of 12%.\n",
    "\n",
    "This observation suggests that demand reduction will provide an effective route to de-carbonisation: for each\n",
    "point of demand lost, almost two points of emissions are lost for the sector.\n",
    "\n",
    "### Correlation framework\n",
    "\n",
    "While this file provided a worked example of correlating two datasets a minimal working example\n",
    "of the correlation framework is presented in [`minimal-correlation-example`](minimal-correlation-example.ipynb).\n",
    "In this file the process is summarised to 4 key actions:\n",
    "\n",
    "+ data extraction;\n",
    "+ data combination;\n",
    "+ correlation;\n",
    "+ and prediction."
   ]
  }
 ],
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