examples/aggregation_optiinput.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# tsam - 2. Example\n",
    "Usage of the time series aggregation module (tsam) to output the relevant parameters for a design optimization.\n",
    "<br>Date: 23.03.2018\n",
    "\n",
    "Author: Leander Kotzur"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import pandas and the relevant time series aggregation class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import tsam.timeseriesaggregation as tsam\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Input data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in time series from testdata.csv with pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "raw = pd.read_csv('testdata.csv', index_col = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transform the index to a datetime index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw.index = pd.to_datetime(raw.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/MQLw9AEatANuHwbWTwZm7QGCh9h/zczbdBRfnGV5jOiBO8bOXFsiclxgAOaO\n0ONOWZZn3WHP/QSUCOwThPBnEmJsmQXc2g+8zenUXVzNn8+ulj+fRWhr+fkB6bLCGUarYUDb0ebt\nskLAvZa5M2YZ9xWwT8Es9/ImYORi8/apb+j/bXMA32bAG9ctz9n8hOU+XRlVQU34Buj9jHhdHze0\nb4apUi6oM4n7HQ1Hv/7vS8655m8jgFXGpHFRW+l/sc7YGtxIYHYUL6a+0BSbR93s6J2r6tEKXI0P\nLjK7r64dL153/Fk6qhUiJoRYLm+m/2/tp/+5Mwbud2nKmYkAQJ3GwOvXqa2ES1I4NfqybRWq2eRg\nPacyoukM47Nm1CPLvTa/nKevsuuFfkUFdeRGIOMW/1h+kvhA7O5Jy32JxmSce143OySUFSprA5d9\nbwNrHrT9PBWbqfkziZoy4q8Isu8AmTFA/TbOuV5BGjVWAtKjb72OdvAurny9PUBVNHqB6kFMSJzn\nGEPzkwFNET9YbNMMy3Punbac2dQLMquarv5DR95C5GYRaVFA7Anzdvga8+fYY/TeevkCyYJ8S3OP\nAn7NgCZdaTkWg44m4QtoA7QcYFmfq6fl/WG58BtwLwxIv8bfz3Xbbd6PzviUwgpqMdZPBd68ad6W\nUo1xZxdnf6KG7B0i/in73qZCZNQyIGiQ5fHzVXKlzxqJOpO4nxEGgxl0wI+9gOsiUcd3Q6kayRa4\nI1+pqF5tMfBJI+BLEcEk1knrrBhC100Afh4CbJxmvZyQKb+bP1/4VTxmQujZZAs/9gIOi3gW+Rkz\n0gxbJH7e+snA5qcs90/6n3x9QgEh5JmDVGhJMXOL9DEhQjvCoQ/Fy3HtU6yAiw+zLHf+FyDxArBW\nxMFA6KJckGae4SVckBZQKnahCon7GSnPEzHVwboJVI10c6/8NbmdANfwev4X8fiDFc3p/9Jcaj/i\n4iriKmyt47MFblvbjqHul1516TYxADdFor3jz8pfMzeev+3dkL/NDWIDwHPyc69FZw1isDYXLrbM\nAsRgGL66qecs8+chbwJtbVDnaAqpAP33ZaAoy1KtJYaLG525idmZ5OAKzJw4YGU7OpNJjqTu0t/3\noGo2FaegColyZunSpVXX/VVqdG+QCU7bNFP6mEEPfB4kff2obfLtOScw6AoNzuF/ANskjJ32UMTp\nnJr15P+X4t8X5Y/veoW/3c3KjGbuUf42O3tpL2Er4WLQAzM2WS8nxptGuwLX7XnYQsDDh7Zp5BLb\nXF/1Gvq90smVAAAgAElEQVT7Rq6ncRgFRo+nAa8AL4UBfUXiWBhXYNer1q+dzlFj6TTmYEMA+K4b\n/R8XSo3oLKv68n9fIcmXLF2M//uABv2t7Gi9TfcRqpCoahBScS6A1/4R329rBC/L+qn87fQb/G12\nxKg055FQyOxZYP7s64SkwQnnLOua+L3j1+Xi4gbZfFBCoTRpNR3RP/6n+KyixUDg4R+AsV8A7cfS\nP3tw9zL+rwUMWgDM2g34NgXeS7Q/oI39fS6uM0ejj/kEaNRJPObDxRWiAYNC9r5h/rxrvnQ5oZrp\ny1bi6sm069R9l02sCAC5CeYgxoJky3PuY1QhUdX4/SHqAmiLj7m97HldfL8whYQUOg1wfAX1Tsm+\na6kSEXpLFWfS/0LDrRiEUD2zh4RK5bFfgfkOLLgDADtfNn9mhU7d5srOnS1Qu41eLl5u6NvSWVRf\njbTc16gz8PD3gKsbMGe/5fH4M0DPp4F+Lygb6T93lAoAIVx10OhlQPBQ69dyBDEDu0EHJImkXBGS\nFQMsqwd83MjsCKGUIpEIdTYlyqlvqHAAgG+78Muw3nERa83ODFySI83ebDWcGiskiNK8N46iLbHL\nmCnZvgSjzttWPa2z0SiICI78mwbinfoG+L679fIXfgPSrokbpFk2zqDCR6+l8Q+DXwO6iqhsXD2k\nU2Bw6f2s9TItBykrx6VJN+ANzkypu0AN5+lHff09vIFadcWv4W8l3beoe6qMYHg1kqYZZ3n9Oo17\nCR4KBArWsRZ6kklhrzoLAK+twsedcQWubOa7YANA/3nmz6w9pzCNqh65M8tmMp5WXMQGW+wsCqDC\n4XsRFWNZAXDvDLD7NarSEs6EfnkA2MFRoa0ZA0SsU9amyuLqP8Dfk5XnBDNSI4WEl5cXsrKyKkZQ\nZNykIx0hhNAHVKQN7HoSXl5elud51KH/NUVUF3s31MkNVgjXd/3Mj/xjujLgmy7mmchFKy/Hw5yZ\nyeqBQKpM3sbofTTOQGf0VnGrBTwm4u6opJNr3h+Y8DXw2mXzvg9zgbFfCsr1BVw4r8JCKxHZAODm\nRdUzC+NpZ+wm/C05v7uYEVdsFmFRh6flPjGPow5GTy3/YOrx9OIp4I2bZq8pAKjTyHp9YrQfCyxK\nAt5PA6Zv4B9bYCX/5uTfzJ9HLeUfE8ssO30j4MNpJzfFipCkcKBuC/n6AXO0PRehujNbJDHjpb9p\nBD/LkY/Er39zHxUgCWeB3QrsK45QkEZtJneOWi8rxrY5QMxhm4OLa16cxCdNEMh4IHHGcYitNeF0\nco3T2TyB/r00n3rseDfkj1yMsCvTWcB69GiKgN+N3iWvX+e/8I6QfpMGibXoJ19OW0xjGFzdaGoL\nLrvm81NbFFm5z0GDaSfKjgRZne/AV4EzIjYAvZa6PwLmILSOD/MXBGLv08ytwAaBLYRloFF/XS8I\n8G9NOwOGockA+z1PXzjA0iPJy5d2WML4ipEfmpPjsULKy4/+CUeaXSabP9f2B3LvAe3GAo+soqoX\n36bibebCMECvOUCrB2gn1ekRoJ2Ix9H09fzBSOMQyzIP/2D21lpiY5oUT+PAhauSeu4oVc21Gs6P\n7WB5JZxvFK/TgO6Ti7XoMI4/Gn/4B+mMtC+E0kzAJ7/g7w/sY352APEULErySR0UuCNf+4eq5VgY\nFzq72TSDvp8Vwcp29P/p74HWI2w7lxsE+vMQmyLaa56Q0BbDHcWOrfh24D2azG0Ix2BGCM08WphO\n9dFuxk5qaX/jf8FNX/84cPsg8PhftIOzRnE2HTmyaSC4RtVvOin/UcsKaAd6ZTPQaZKlH/z/rAgH\nlqQIqkLqJxKF3bwvvb5S3GoBc4+Zk/qxHVqrYUC36XR2wUVTCGw3qn9u7QcGvEwzuHJhhQQ39QSX\ndmOBNiPN288fA0py+WVm76UR1837W54vTADY70WaGO/IMvF0Ety0GwAwnpMfaepa4Pyv1G7hYuPk\nfeK39H/nR+XLWbNP1PansyhCbG8Di7vR88q7oTl9y8zNtAPSldLveO80TagoliyyflvgrRjqnbTn\nDX7Kk2cP0f/crMBevpadPkuTrvQ7n/wCaDOKjpABatBn1/12q2WZc2qTSKoQJRRnmwdNAN/z7ptO\ntl8v6SJ9pr38lNmWNJwgzpy7ttd3YQ1/W+hUIkPNEhLOUi+dXUX/c4XEMo5e+ZvOwJvR/JfNYOBv\ns6NrJZ5C8efMswaWQ4v52yU50usds9wLo4noWHbNB965S7O8jlhs2ZGxPHuY+pdzOW6MVBe6pQJ0\nJCPHpNU00Rw7Y3DzBBp0MB/njsYbdaY++dykc5m36TKeN3YDQ4zuw9yYCb8WdHYAWL5gQ98Ghr5j\nFuIs7IifS9BgOiIVG3kD1Dhey5+OoF3daV1KhPXzx/n3ul4Q9fKpbBjGsYyuLi60M+d6Xbl5mtVi\nIxeLn8elTgMq8FoMNI+MAbNwaNiRuud6GxcTm7lFeqnWxiHACyeBRiHmFOeu7tTg7+kLbJ1FV9e7\n+Bcw9Q8g7Ecg9rj5/DGfUgN0t+lA467AVyLeZA9+QmfSmkJgeQDNx+UdIP39Tn0LDF4gfRyg/RQr\nyMZ8RgdB1mBzZQGAn0LnCi6sUJj4HbWzSLm/i1AzbBKrB1HVATdU316BwQ3CYTtD4bWK0mmGUS6s\nDr2sgKZfYD14rCWDA4DUK+L7XT2BesYX5NeR/GOZAjuIUECwfBFME9V91gzYON3y+OA3gOZ9gMFG\n+wLrTSSXuTX3Hn975BLz53rBQLcZ/GhlTx/x0SubGXX4+3SmwRL2IxB9AAhoS1UtANCAs8znqA/N\nIzouS/OAER9YCgg52BGpGF0fB9qOotdT0rlOXkM7LWFupppE8750VuIorBAALFOD+zQyPy+1/enz\nAZiF/FSOSqpJN1qWMQrlskJqy2jchQp4gBrH10/hCwiAlpv8K51xst/JxQ14jfM+1qpLZyosyRfl\n+5bDEpHmXD5pbP58cJHti2bFhcrHgIjh5gHUrg/UbUm3hfnMZKgZQiLNaEDb/4553w9WgqKkOPap\n+TM7mhfzXtrxAj8/kKaYGnQ/C6TpF0z7ZbyElvrRCFWpLJy1/YFes+nn7DvAtR30Ab11kNax1A+4\nYkygJyYghAhfkkYh5g5+1FLayT5tTGpni8dWy8HmzwYd7VC5RlcpIzO738WVxgsszjJHPBu0fFtB\nl8nm2YhwFPTMf1RHXtmETKGdlop1uIOGTo/Il33gHfpsvh1Ln5HOIsvGTv2D/uequTRWEgdy06m7\nuNKB0oxNQL2W5v1X/wFmbDarGDNvWc8bdUTgDq3XmfsBg8Hy+f2mi/WFo1jbSwNjoN9tG9c1L8qg\nKnTWiUJJf2GkSgoJhmEeYhgmmmGYGIZhFlo/Q4TsWEs9nBKEhjJCzP79AFWNsPzLmSZqi8WncFIP\n6mnjymeR66XbYtDxUy9snU0F14bHzfv+eY4/FZXK/yNGw46WI2TWPVXMrz1YIlV1A47aINBomGTV\nLXL5f4TxA65u/PvLHWEyjFmgNe7KP69FP+ekOFepWGxd8c7VTXwGCVBBszSPdoQs6VYMysJElqOW\nmm1crEt0+7G0zvHGVZYPvmcejI77ijpOCAn9is709Vqa9faf54BPjY4KYurbwlQg9GvL/SxcW9pT\nO+j/f18CPuOonXLj6aDxhkhMDEBnHt4NRJ1orFHlhATDMK4AVgEYC6AT6JKm0pYhuala6Ndmv3sl\nFKRa7ltWF8jiRHIOe8+c44br1aEt5o8GOk6kU2CpNMiHlojv51KUwc+nA4gHuh1eavzA0E52UZJ5\nqi2Hn4h3lZzdQ2wEx57zfip1vXzEmHRuxBLaBq4b46uCwClG5PEbOB948GP6UrYXjHY6jAfeS6Eq\nIpXqz9sx1GZWXkz4RvrY5DXyz/qEr4EFUUDfuXRbTN2oLTarFrtOp7Yylh97AX8/Bvw2kmoAADo7\nl+qvxNZxZ2HT7E/8DvBtYt5flm/uc6KNgZebnxTv74oygNoB0otRyVDlhASAvgBiCCGxhBANgE0A\npOejwtH7I/8ze5bkJ1K/eymdP8B3DVtp1HsLk7IdWUr/T/ubjipGiugd488CWRx/69HL6UMoXLTG\nFuYeo/Wx6y1b46kdVIXjWUc+YI3lgXcs9/k0ttwHAE17UndMLi+FmTt+91rUkOhhnM62HQW8e5c/\nExKm0hBLrcEwVFD0kQhu81CQOE6lelCrrnPsG1J0k8kzpqReYRzG88f52/1fpob4549TL7QAgQee\ncD2NzwJlbBYESLlCY4+4nXzSRXN69UZGBwuuA8Z/RpU4161XbLGn4kw6k7Aj5U5VFBLNAHCdmxON\n+8QRBrJ1fZy6KtbhdHa/jaSLpeg0NMS+yKg+SrlMjUhHP6ZqpXbG4Jn54TT1AQurZ2aTrnkHAB8I\nwv33LDB7CI37igY21aoLXN1BVUussevav2b/fClmbqU6djavj3A2IQU3qjbIyipwTXuKCxIpA61v\nU3qMDfYDqJeL0DVVDmFWVzkvERUVR3H3oiqo1gKnj5CptscZAHyHhHfumm1qTXvQd2ny79QF1x6i\n9wH73wXCf6cLRpXmA58Gmr2gAHM/9FIY0P1J+vnsKmDjTL797vZ/1IOJTTmi11JHFO/6NC9XCEdd\nrYCqKCSswjDM8wzDhDMME65jjB3P07to58z+cIUC1dG/L1IXt++6AV+2pqP+JKMH0skvjWqpEmO6\naD8ayDPaGGV5ZYvZi4LFzZNOWceL6BJZj6SsGKAsj6qWWH/trQo6/HYP8nXsDAM8YMU049/KHPQE\nUPc+qZxB7DWlEIsbYP3Cn+DoYJWmdhCrs89ztp2romIvT/3Dj/iu306qpHVGfggESnh4eQfQnFpj\nPlN+vXmcGJD4M/T/ngXAF634KUtajzTbY/yaAT04s4XovdSYznWaiFhrtl2yKfq969NrTP6VZudV\nSFUUEkkAuI7AgcZ9JgghvxBCehNCersRDfV6afWAWX8oRSnHx/2Hnvx0Cbf2U++fuhzPBlZnqdcA\nPhxdIEvIFGr8FcIaz7hqq1V9LMtx6WTU9/tIROIOX2SeKTwq4l0hdMvzDqB2AikGyqQQeOaA5b4H\nP6b/Ww40CxF7fO5fPE1HYdxgMxWV8mbw69QzavRymzpIC4a8ATx3SL7MgJf58TQDRTLXPvMf0PcF\n8aDD4izLdP3CCH1/kbxlnR4xD1ABargvzTPHbNXmGPXF+jMJqqKQuACgLcMwwQzDeACYDkBkqTQu\nIp3VyCV0WjXsPWkXO26CLhau+5sXJ4BOalQutjBOHaNwEBpq5XyTRy6hD9abMpGQM7fQ6F3uGgVs\nnn6xDtvVnboNvhBK/fe5SBmh2WstzaOGO4AG73CT6T31j/mYrTTuUr56aBUVKVzdgEGvVpxda/pG\n6m3ITbrYtAfw7j3qkTfuC/quiXlICeGmeQHMfQxLp0k0OeJrgpxgRz8xq+S5NsCBygVllYu4JoTo\nGIZ5BcBBAK4AfieESC9H5ubFD+Zi4bpSXtshvqi9GNyZRGNO+mBhSgcWMZULGyjkWYemQmCjtf8W\n/ND+rajgKEgWSRAnQm1/y/QMPZ+W99v2DqB/XC+rUUut1wUAvoFAj6csjcge3uJLe6qoqJjpMM6c\n3sW9NvWGmvi9ZVbgdg9StVOmMdKcm31hYQJ1/hAOArnbdRoDj3MCDOsFm1N3nP/ZvAZ8UwWZmkWo\nckICAAgh+wDsU1S4YUfLPDtC2oymahypxUS4HTnXG8e/FU2pkBMHhEwWO9MyCMbLjy84uD8md0Ut\ngKaoDpkCHP+c79+thIUJnNkNYz0+wrMOHdk07qIseyZAbTCP/Gi9nIqKijzvp8gfb9DOHG/08lng\nf0aVrtwa5CzcteQB82yC6yDTsLPtNkQjVVHd5Hw86wDPykQoMgztQN28zGmXWdhZgdTiN9yVxYIf\noKmjhYgZtwGgeT+a5O6Z/eJpoeXw8qXTZo/awNJc6tVljQ7jlAsIFRWVyqFhR2ocn/STfDk2KWIn\nCdUx1zlklhWNvQxVciZRLtRtQdcRuHWAv4IamwemwzjggzTL81iDsJx6ZcArNN+Q2LoHAFXXpF6h\nHlVxxvUhWg6yXKhGRUVFBbBuHAdoHq15F6TTzo9fSWM5tMW2ayo43D9CAqBrCHSfQbOurjeqj6xl\nVmU9A4RTOi6jllFvIR+ZhV0mfkdd0diMll5+0llZVVRUVJTQwIo7r5LVG61wf6ibuHj60GjgVsYg\nFW7EtRhP76RBdmJLaLK4uskLCJba/ua1JZrZmYBQRUVFpQJhKmwt6HKid+/eJDw83PYTM2No9tVp\nf/GN1RWBttSuRFsqKioqzoJhmAhCiNXFwu8vdROX+m2ApyWWRixvVAGhoqJSTaj2MwmGYQoARFst\n6Bh+ALhLktUHkClR1pk4Wo+w3eVdn61I1Wdrux2tz1lU1nPiaH323u/K/n7l9ZxI1ecM5Npc0fez\nJSGkgdVShJBq/QcgvALq+KWi63RGPcJ2V4V7qaQ+W9tdVb5fZT0njtZn7/2u7O9XXs9JeX4/uTZX\n9P1U+nf/Ga7tQ2IljyqP2u6KRW13xVId213t2qwKCQUQQqrdDwuo7a5o1HZXLNWx3dWxzTVBSFhZ\ncLZa11nR302tT61Pre/+qU8R1d5wraKioqJSftSEmYSKioqKSjmhCgkVFRUVFUlUIaGioqKiIkm1\nj7iuX78+CQoKKvd6yvRl8HS1MZ23ioqKShUlIiIikygIpqv2QiIoKAh25W6ygT2xe7AodBG+GfkN\nhgQOKde6VFRUVCoChmHuKSmnqpsUcCOLrjsdmxdbyS1RUVFRsZ8CTQG+ifgGWoNW8TmqkKjClOhK\n8Nm5z1CoKbReWEVFRcUKP1z6Ab9f/R177uxRfE6lCQmGYX5nGCadYZirnH3+DMMcYhjmtvG/lRWB\n+FzNvIqQdSG4lnXNqW09Ek9XskstSnXqda2xJXoLNtzcgDVX11RovSoq1ZUzSWcQsi4E9/IVaVLu\nOzbe3AgA0BGd4nMq0yaxFsCPAP7k7FsI4AghZAXDMAuN2+8qveCxhGMAgJOJJ9E5oLPTGppUmAQA\nuJVzy2nXVIKBGAAAWr3yqaGKJVqtFomJiSgtLa3splQYXl5eCAwMhLu7e2U3pULZe3cvACAyPRIt\nfVtWcmtqBpUmJAghJxmGCRLsfgTAMOPndQCOwwYhwYBxQstUahqJiYnw8fFBUFAQGKbmPyOEEGRl\nZSExMRHBwcGV3RyVKkhemfIM61XNJtGIEJJi/JwKwOqaoNeyruGHSz8gMj0SP1/5GQDwv8j/2Vxx\nmb4Mzx18DtHZ0ktTuLkol6ms6os1etvK2qtr8XXE1wCA+IJ4u64hxbKwZdgTq1wnWd0pLS1FQEDA\nfSEgAIBhGAQEBNxXMycWNc2QOIfvHcbi04tN27ZoJ6qakDBB6K8t+oszDPM8wzDhDMOEA8AvV37B\nOyffcai+qIwonEs9h0/PfWpxbGjgUADAQ0EPKb7ejL0zAABP7X/KrvasjFhp+nwn945d15Bi261t\nWBS6yKnXrOrcLwKC5X77vizE2GXcr99fitePv45/Y8wrcdb1qqv43KomJNIYhmkCAMb/6WKFCCG/\nEEJ6E876rC5M+X0VXw9fAICri6vN5+oMyg1EUjT3ae7wNVQqj9dffx3ffvutaXvMmDF47rnnTNtv\nvvkmPv30U0yZMsWm665duxavvPKK09qpcv/Qyq+V4rJVTUjsAjDL+HkWgJ1KTyzSFske33hzI9KK\n0iSPsyOQi+kXMWjjIJ4nE2vrOBR3SGlzTOiJnrdtIAb8fvV3m9xaezTsIbr/csZlHIs/ZnObVCqW\nQYMG4cyZMwAAg8GAzMxMXLtm9sA7c+YMRowYgW3btjm97ryyPKy9uva+UcMkFyYDAGJzY/Hz5Z9N\n3zsmJwaT/p2EEwknELIuBMmFycgozsCjOx9FSmGK3CWrDOtvrEdGcYZTrsU6xSihMl1gNwIIA9Ce\nYZhEhmGeBbACwGiGYW4DGGXcVkRuWa7ksbSiNHx67lO8clR61MV9ifI1+Ri9bTS3rQCA44nHlTYH\nboy4/SI0MRTfRHyDzy98rvhaUjy570m8euxVh6+jUr4MHDgQYWFhAIBr166hS5cu8PHxQU5ODsrK\nynDjxg34+/ujS5cuAOgM4bHHHsNDDz2Etm3b4p13zKrUP/74A+3atUPfvn1x+vRpq3UvC1uGlREr\nEZ5WvlkJqgoX0y8CANZcXYMfI3/EjWxqE3x016O4k3fH1AeM2T4Gzxx8BjG5MRj3z7hKa69SEgoS\nsOL8Ciw4tsAp17Nl0FCZ3k0zJA6NdHZd7GhezqJvi9+wEhiGEbWoaAwaADTyUSlE3DSjYgefn/8c\nN7NvOvWaHfw74N2+0k54TZs2hZubG+Lj43HmzBkMGDAASUlJCAsLg5+fH0JCQuDh4cE7JzIyEpcu\nXYKnpyfat2+P+fPnw83NDR9++CEiIiLg5+eH4cOHo0cP8VkmS74mHwBsirCtSbxx/A0cmHxA9FhW\nSRYA2999rV6LHyJ/wNyQufDx8HG4jUpgf8c8jXKvJDmEGg45qpq6qVxJKUrB1ltbRe0EeoOym/bn\ntT9xKf2S1XKujLj9wsV4y4/EH4FGr8GppFM2SfWLaRcr9YW/nnWdJ+Bic2ORWZLp8HUj0iJMv4FW\nr8WppFO4m3e3wgMYpeAsVm8XAwcOxJkzZ0xCYsCAAabtQYMGWZQfOXIk/Pz84OXlhU6dOuHevXs4\nd+4chg0bhgYNGsDDwwPTpk2TrE9r0NLn3Njk3FLpmba93My+aZMrZWXAdq5iGKBc5cJlT+we/HH1\nD7u8KO3lnRN0NinllXQn945N76EtA89qn+CPxcfDR9Ho/KOwj5BUkIQFvfjTNi83L8lzuPEXX4Z/\nCQDYNnEb2vu3lzxHypDO9br44sIX2By9Gb8++Cv6N+lvte3Xs65j1oFZmN15Nt7s/abV8kIi0yMR\n7Ge/3zwhBNP2TEPXBl2xftx6AMAjOx8BAETNirL7uhFpEZh9YDZe7vYyXur+Erbd3sbzMnPk2kLk\nRvxyXMukNoTO9e0L0mTtElFRUejSpQuaN2+OlStXwtfXF3PmzLEo7+lpzjjs6uoKnc620e7tnNtI\nLzb7fbwb+i7GtXKuWmXq7qloU7cNdjyyw6nXdSZygz97hX6pnroWO3OwFp0dDT9PPzT2bix6nHWD\nl+rjJu2cBG93b5ydeVZRfdXCJuFMArwCMLntZMXlY3JjLPbJxUCIudPllOXI1iE5k+AIj/BUqie2\nNgJgpX5OKa3TnshvAzHgqf1P4aXDL9l8Lgs7Rb2SccXua4jBGuPY38XZ6qCqwMCBA7Fnzx74+/vD\n1dUV/v7+yM3NRVhYGAYOHKjoGv369cOJEyeQlZUFrVaLrVu3SpZlO8DyVlWKvUuVwcW0i/g24luL\n/XKdob33hr2mMz0qp+yewrODSiEnmKw573CxRd1UI2YSGoMGhVq+t1Cxthi13WsDsLyxWoMWJboS\neLl6mQSAmApKa9DC3cVd9ObLjUKKtcWSftrcB4u1T0gJFO71DMSAMn0ZAKpLtTVVB/v97A3u416D\nxWkeM4JbpdFrRItp9Bq4MC7I1+Sjrmddu15SvUFvlyuzIxiIAV26dEFmZiZmzpxp2h8SEoLCwkLU\nr18fhYXWvd2aNGmCpUuXYsCAAahbty66d+9uUzsc+e46gw4GYoCHq4fVstml2fD38rerHq1eCzCA\nu4tt6URmHZglul+uMyzRlZg+l+pKLbQJeoMeOqJDibbEFFfA9S5y1vMvd5304nSeIC7Vl0Kr18Ld\n1bF0K3tj9youWyOERIGmANtu8d0H+23oh12TdiHYLxgTdkzgHTuTfAZ91/fFe/3ew4wO1H4u9jCN\n3T4Wh6cexqF7lq6vUqOQyPRI2QA6ruoqoSABAPBf3H8YGzxW8py119YiuzQbu+7sAgBE50Sj5989\nTccLNAVWDWgrw2lwniMGeu492nF7R7l5zEiNTnv93Qv+Xv7ILs3GEx2fwMK+C226bpG2CHF5cWjp\n2xJ1POo4o6lW0eg1uJ1zG028myA/n68fX7t2relzUFAQrl6luS5nz56N2bNnm47t2WOOjp8zZ46o\nekoKrmDv/ld3u1V3U3dPRUxujOl8qRH6b1G/4buL3+HJjk/apdrr+XdPNKrdCIenHrarnUKa1mmq\nqFyf9X1w8cmLvM53YehCHIijRu/tD2/HrZxbvCBUZ83SVkWukjz24uEXcTvnNm9fz797OqyCFevT\npKj26qbabrUlj1mbCnNvlJjuMq1YJq5CQvpbU8WIjX5PJ1t3ZWQFhBis8VBObSUUovbA7XAO3Tsk\n2yZr7Li9w8Loyb50bPCiGNml2QCoYLWVYm0xANum5Y7CzorkDKjlibP05sJ3SWqE/lvUbwCAv2/8\nbXddcu+drUxsNVFxWeG9YgUEAKQUpmBXDP95d9ZMQu49EgoIFqnZdnlQ7YWEs8LvbdHRAdKjCLHp\nPHfUJdZeRxMTEhBcy7qG4VuGY8dtcSOiLYYqgAqcny//zHtxuPfIkUjyO7l3sOTMErx36j0Alt9f\nSVtdGBdEZUThVNIpu9txP+CMiH8xpH6j8qrPXlxdXGWDaLnIzbLFbJb2ekcJsef977u+r+zx9TfW\nY96RefY2iUe1FxKsnl4MQgge2/WY7HGWsOQw0TJvnXhLdP9Lh1/CgmML0H9Df557oViepW5/djN9\nPpl40uJ4sa4YIetCELIuBFuit5hyviuFEILpe6YDAJacWSI6ehR7AUZtHYV9sfss9u+6swvDtwzH\nj5E/oudfPTFxx0RsvbWVN9sKS+Hfr4EbB/J0vFLsvrMbk3ZOAmC+F+yo6NC9Q/jqwleK1FhpxWmY\nuW+mTYZ41m5lq8BUSnZptuTIz1nE58fb5BbMBpMpYemZpfj+4veKykp5Dcm9jyz77+5HyLoQXEq/\nhCS1guEAACAASURBVHlH5iFkXQhKdcqTEbLvSm5pLibsmICfLv8kWbZYW4xR20Ypuq6cJ5SYkHDW\nTIKdHQM0Yrzv+r6IzY2VdbHVEz0GbhiIfE0+ItMjLY6vOL9CtK+xh2ovJORGLgYYFL+0UtPjg3EH\nJc85En8ERdoiXM64bNq39Za0xwlAJbwcy88uF00yKIfQmKjUdz2tOA0fnf3IYv+HZz7kbcflx+Gj\nsI9k73WBpgBJBUlW6/wozLI+7kuy7vo6q9ewB0KISd3EOgw4m5TCFAs1gLMTzhVoCkxBYHIQQmzW\nmW+/vR2/Rv2qqKw1QSvnLcgm41xyeompI2PXbLGFiPQI3Mu/J6vTVyK0WOSebzHnEnsHG4WaQpM9\nEjC71AI0W2uJrgRbb23F6surZa9ToC3AqUQaTySHo8Ks2gsJWazcG6U/crM6zWSPV3bGSaGqTGlg\nICB+D6Q8S5wRlV4Z98rLywtZWVmml6WmrztCCIGmQIOEkgTrhe3EVvWsGFwhZk+Hq0S1JbyunCeh\nnuiRUpiCFedXWHgPij239t6Dp/Y/JZkKxNOVxsdwBYccy88u59k5xWZkjv5WNcK7SQprI6mL6RcR\nsi4EG8fLq3es3WQGDNZdW4ceDXvAlXEVLR+yLgThT5aPN5DBwH8RskqzoDFosPj0YvRp3Acjmo+Q\nPLdEV4KE/AQ096WZZrNLsyXVRo4sC5tZkonfr/5ucW2tQWsydtpLfH48Wvi2MG2HJoYiqzQLk9pQ\ntVZgYCASExORmpkKBgxy3XJR5CVtvDYQAwo0BfD18DV1DqmFVMVTmlwKF8bF5Brt4+FjUUabqoXe\noEdt99oo1ZUiuzQbDMOg2LvY5u+mNWhRrC2Gr6cvGDCmOlwyzB2D3qBHgZa2l+0wLmdcxq/xlrOC\nhPwEjNsxDtPbT8f7/d9HZHokrmRcQf1a9SXbwF1jZUv0FkxtNxWDNw027UsvTse6a+vwRq83TPu4\nHXiRtgiDNg7C9yO+x4CmA0z7uUuMCt/VU0mnMLjZYMhxLuWc7HEAilSgLGdTziIyPRLbb2/HoKb8\nKHhCiIWg0Bl0uJt3F3ti9+CV7q8oGgAtOLbA5ASQWZJpcd8zS6nziVJHkxJdCa5nXee1icvVzKto\nU7eNomtJUbOFhMJp1oy9MzC9/XRsit4kelzYCQvx8fDBV+FfAQAW9FyAby9aBvUAENX/OwOhUPr4\n7MeIyqQuchFpEbI6WwB45egr2DmJJtxlXWXFWHxqseQxaywLW4bjCcct9ocmhsomZ1TCvCPzsPvR\n3abtl4+8DACY1GYSCCFwd3dHcHAwHj75MABgbNBYfNHjC8nrLTm9BDtiduCzIZ9hQivqPv34usd5\nZQY2HYgzyWew5sE16Nukr2iZqFlROJ5wHAuOLjBt28qs/bNwMf0iDkw+gGZ1mpnq4F7r24hvsebq\nGizuvxiPt6fHHz//uOj1xu2gI9hN0ZvwXr/3FK13svTMUtPn5WeXo2/jvhbHQ5NCMbjZYIwJGmOh\nol0TtQZ6ose8I/OwqK/4OibCEf9Lh1+yer+sqXYBau/j8uuDv+KZg8+Ill18ejGGNx8OwNJ7iIBg\ndMvROJN8htfmFw69gJSiFMzoMENW0LIciT9i+vxR2Ef4fgTfBmTtXRXCgMGFtAumbWFfMGPvDITN\nELe3KqVGq5ts0cnKqZSszSS4D3hdT+nFPMpL1SJ8wWzRwwJ8l1A5wap0CiyGlCumEkFubaYhde23\nTryFrn92tazTynPBjsbk1HbsMWseLo4ayVkjtdx9Yn8/W91dlXoiJRYm8raF94+18RAQ0SA6rppS\n6l1SqhKxVb8uVBv1adzHpvO59dZyq8XbpzPoTO+aPSpMZ7ixurq4ol/jfqZtsfvoqMdZjRYSW6K3\nKC4r95BmlcobCtn0xIC8N8nZFGV5VWwlIj2Ct21r2o604jSTH39yUbLd7dgduxu77+xGXlkentj7\nBPbG7kWprhS9/+6N00nisSAnk6x7YGyNlh8xShk92RHtvth92H3HPNM4EHcAX1z4AgfiDpiMwOGp\n4dhwYwPyNfkmYf7B6Q+QUJCAXn/1srj2uVSq6th+azuKtcX44+ofom3gZgLYemur6Z7klOZg151d\n+OXKL4jJoeqHk4kneZ5yZ5LPmH4PNiULlx8v/YjI9EiT8dfWCHSlqyYK3w1h1D6r9tl8czPPu4/1\nGOTemy8uiM/glpxeYmGAfebgM4jNjcX+u/sRmhiK7NJsXj4qJQi98KzBqtaEgjC7NNviN9ARnWkQ\ncDr5NPQGPTbc2IAXD79ocrs1EAP2xe7D4XuHLTwoucZrexnUdBD+u2eOGeI+5yw3cxxLc8NU98VI\nagXXIm2WOqZzA4D5Pebjh0s/OKFF1RM2aWDIuhCHr9WrUS9EpFHB1axOM7s8V7gwYHBl1hXZtnFV\nE7Z8BzZBHXvOwKYDUb9WfZsCBYP9gkU9TKJmRWHjzY0W3mqPt3sc0TnRPK+4sBlhGLBxAC95Ivd7\n+Lj74MzMM6Z9h6cctnDtfL/f+5jeYbrFubYiVPP039Df7gDEv8b+ZfcSvkLa12uP6BzpNeitMaXd\nFHw44ENF9+brYV/jjeNv8Pa92etN3rLCQ5oNQWRGpCnp3qqRq3ixCVGzovBvzL+8taWFRM2Kwqit\no5waQBg1K0rRd7w6+2oEd3VPKWrcTKJTQCe7zrPFI6gmwjV+OQp3NOmogADKN0ldXH4cbzs+P95m\n1YGcC6KLyCuWXJTMM9oCZpVAXF6cRXmAujtyEXPjLa8lfB1Rmdmyboo1YvNiHTp/XnfHgsssvAiJ\nntdviLmeK3FX7trAUiXqCGK2Py6/j/ndpuvVOMO1rYnBWIQqm/sNZ64L4KghWoz4/HjZ4ztu78CI\nFiNsXt5RZ9DxvHcSCxMtdPD2kleWJ+o2nFSYZHGPUoroEpr5mnzklOZY6L/Z81jEVBVF2iLE58cj\nNCnUoXbH5sZif9x+BHgFoFejXjZ5CAk5mnDUobZwcTTFiC19g1iAmvCeZ5Vk8Qzj++/u5x3fdFPc\nEUaIs9eHmX90vuxxWwdBNU7d9HL3lyt0MZCaQm232jj3xDmnqJtUzLzd+23TGiQqlcvZmWfh7e5d\noc941/pdcSVTOp9b1KwovHj4RUmbXXmw77F9GPfPuPtH3ST0AX6h6wuy5cVGaEpY+YBZF/nd8O/w\nWs/XFJ03vf10u+pjGd9qvEPnK4VNDcLF3nulYsYZQWeO8Fhb6bQ09xOjW46Gt7u3xf65IXOxdaJ1\nV1p7kRMQLDqDDq38Wpm263nWEy33Ujf714IBqHDY/9h+NPdpjoOTpTNJCKn2QkI4dbKmlxX6eMvB\nXSWqnhf94drVa4cRLUagfT3pVem4dG9oW85/IR4u1vP3lxdd61vqSoN8gyq+IdWYyl5bmhu8dj8z\np7N4evXWdVujg38HNKzVsIJbRFl7dS10Bh1vLQupBc10Bh1OTT+Ft3u/bVddzX2aI9AnEIDyFOpA\nTRASNsYeSC0PKAa3g2bTVwfWoTfZ11M6nTUXubTX1ujWoBvmhsxFSH3zCH9qu6mS5YVRoo7CjWJm\nae7T3Kl11HQq22OuSFNxadGrMlK5pNhRuy2dpjNZGbES8fnx8HKVXj6ZJas0C36efnbFWzmiFaj2\nhmt3F3c81uEx+Hr4mn7oFUNWYGGo5YI0b/Z6E9M7TEfvxr1RpivDB6c/EL3m7km7TQn/4qOpwbS9\nf3t8O/xb00yka/2uWD5ouah7W5BvkMlrZnCzwejaoCtvnYmXu7+MLdFbkFmSiW+Hf4sFxxZYXAMA\n/h5H2/Dn2D9NRrHxrcajtltt0UR4q0etRk5ZDh7Y/IDo9Wzh8yGfY3TL0Wjp2xLBfsFo5dcKqUWp\naOffDoM2OlcYqZQfCQUJ8PP0c6pjgj209G1p4dHlCLsn7cbEf5WvFcEVEnsf3YsV51fA38vfNNNa\nMmCJbMbo8iSjJAPt6rVTXL6Ou+WCWT0b9uTFawnhqsttpdrPJADgvX7v4ZUer5j0r1w9ftSsKExr\nPw0A4OXmBS83LzwU9BAeafOI5PWC/ILwQf8PLFRXI1uMNK0AxzAMJrWZhM+HfG5x/lOdzH7hDMOY\n/N5ZHmz5II49fgxRs6IwssVIq9/PzcUNE1tPxMTWE+HCuIjqVtm65JaN5LbLGuNajYO7qztmdZ6F\noYFDEegTiN6Nezs0M1KK2EugYh8EBB3qdVBUdljgsHJrx5BmQ5x6vSC/IJvKc4VEC98W+N+o/+Hj\nwR+bRuXOfubmhsy1qbySZWFZV2ThMqsAUMtdfqbg5+lnU3u41AghYQ0286Otrl/W1p4GgN6NLZ0D\n2EyOUihxxWtQq4HkMXsXO7HXPbiiqS7trA64u7grXtdaGIvhTCo7U7Jc6nLA+e2r7S69YqYYbPAp\nAAxqJj5TZwetYnZXTxfLPqejf0fTZ0cyH1dJIcEwzEMMw0QzDBPDMIxtCxmLMK/HPMzoMAOT2k7i\n7f9u+He8kXFT76b4ZPAnpu1XerwCANg0XtrfuUGtBhZ5nwgIFvVdxAtaeaS1eeYiNWpYPcqcP15M\n+LDM6jQL9WvVx3Mhz5n2cWdPodNCsXXiVizsuxAPtnzQtH9uyFy81vM1tPBpgVmdxBeOB4AP+omr\n4VhYNZgtDGgyAP2b9OdNe7m2Fi7Wgn2mtJtic/0sXJsKm8xNKUqMm892edamay4ftFz2+Pwe8/HR\nQMs1OJQyuuVofDTwI0UOBwFeAbLHlwxYgka1G5m2hzUfZvq8etRqPNnxSQDU7bdfE3M+ISkPveY+\nzTE3ZK5ppq8U1gj9fr/3FZ9jzQGE+72UwB0Itq3XlnesU0AnzOgwg7fPWiAfd3nbHg16WByf2WEm\nXu/5OgBgRIsRmNlhJu+42MzqmS7mRIad63eWrV+OKmeTYBjGFcAqAKMBJAK4wDDMLkKI3SHBvh6+\neK/fexb7R7QYgREtRphcPw9O4buFebt7W81EyTAM5veYz7OBBHgFmNJUs3w48EPsvEMzrQpHyrZm\nB63jUQfHHj+G1KJUU/K7zwZ/Zjpe16su6nrVRQf/Dnii4xO8c58Lec4kXN7qQ1fd6/5nd56r5rQO\n8i9ttwbdeKH/bPvZ7dZ+rfHvpH9Fz+VGdm8Yv0HUZ71NPfk0KwObDuSlUmZjY+Z0mSOZQ4nbTpbc\n0lwM2WypBtn5yE48stMs1JcMWIKPwj7CkMAheKbLMxi/g3Z6vz34G68zBGgA3Zqra3j7nu3yrMW+\nWm61cP6J8wCox92Y7WNMx7ZN3IYpu6cgyDcIz3d9HgB9Ft888abo9+Hew6hZUbiVcwuTd00GQONf\nGnk3MmXJFd5vNh3N3JC58HLzwn/3/sPckLl4teerFmWntpuKMUFjTDapvo374njCcTzR8QkMbjYY\ng5sNxrt93wUATGw9EUM3DwVAB0js7z6nyxxeSnGWzdGbAQAHJx/kGZG5zxj7+Y3e9PzpHaYjvThd\n0UJJ1mZTDMMg4skI9PrbMk+XGOxo/tzMc/jpyk+8xc02T9jMS0QY+VQkXF1c8WyXZ9Hz754AqMFc\nyotJrK2L+pmz57q7uGNRv0XYcHODuc7ozXBhXHjR8Vy1tCPR+FVxJtEXQAwhJJYQogGwCYC0AaGa\nwFVdKZmKCkcKYrAeC+4u7g5NlyvSl98ZKTYKNYXWCylA6SJKbGoNPdGjhW8L9G/SH4Bj38WNMY/P\nhPefva41FYkU7eq1M82YrLWRddF1d1Wm4vNx9zF9ZtsnlmWUO9K25T7Zek+dmYrE3vstlnqF+z6y\nnT73+jM6zrA4xxFc4GLRZzhrvfGqKCSaAeDGvyca95lgGOZ5hmHCGYYJz8gQT8PwTJdnMLDpQEUV\nPtnxSZtVD1yEo8kO/paGQu7DLOfu9nDrh/Fw64cVxVf4ePigpW9LqyoLa3DVGa39Wis+r1+TfjwD\nXVNvOgJ8vdfrkucE+wYDALoEdAFAR+ly9Tar08xCVTKo2SD0a9wPjb0bo2GthpgQPAFuLm6Y1HqS\naf0HFva3ZVUhXMSMeX6efmhap6mpzomtJmJo4FDUcqtlegmf7/o8gv2CRT1S6rjXQaPajXjBUQ+3\neRjT2k/D052eNu374gFzNlSuqqOpd1O08GmBup51eSPuHg17INgvGA+3pmtidG9gfj66NTCvoc7y\nVu+3UNezLhp589Uoz3R5hucqPT54PLxcvTAheALGBo2Fh4sHJraW9hridn7Dmw+Hl6uXqFs217ja\nOaAzxgSNgYeLBya1nmRRlovQFte/SX9ZFd7E1hPh4eKBdvXaYVr7aXix24vo2bAnHmz5IJ7u9LTp\n3igx3LowLqIu3n6efqbndGjgUPRt3BdLBixBw9oN4enqyQtYlFOBMQyDFj4t8OGADzGxFf8eT2w1\nEaNbjgYAjAkyzyrb1muLd/q8Y7Xtywct56mfXRlXUx9ir/AztbuqpeVgGGYKgIcIIc8Zt58C0I8Q\n8opY+d69e5Pw8PJZ8U1FRUWlpsIwjKK0HFXOJgEgCQBXnAca94kSERFRyDCM/fmDleEHgOtoXh/A\n/9s77/goiv6Pv+culVBDAiEEBGmJIEWxiw1sIKCiiCjFBuLP8ogi6mMXRcSKj+hjRRQfO6LYH2zo\nIypYgQQCoZeQhBJIz938/ti9y97tziWX3F3afl6ve93u7Ox8ZnZn5zvzne98Jz/MnKHg8c93uPmC\nhYov2HzXlS9UqK96Ule+2j7v+i5fuOqJii8UCJTnSD/Pw2oUS0rZoH5ogisH6A7EAH8CfQPEXxmB\nPL0Qac5Q8PjnO9x8oSpfsPluKOWrr3pSV77aPu/6Ll+46kk4yxcoz5F+njX9NbiRhJSyUghxPfAF\n4ARekVKuqedsmbd7ahyw8x1Z2PmOLBpjvhtdnhuckACQUn4KfFrf+fBAStnoXizY+Y407HxHFo0x\n340xzw3RuilYvNCEOSNdNpvP5rP5mg9fjdDgrJts2LBhw0bDQVMYSdiwYcOGjTDBFhI2bNiwYUMJ\nW0jYsGHDhg0lGqR1UzBISkqS3bp1qx9yKbWfw5a1NmzYaFxYtWpVvpRSvSeBjkYvJLp160Z9ueXY\ncMZQKnbuJP3vv5Dl5TgSqrwuSimpzM0lOqXm26XasGHDRqQghKjRVoFh7wILIdoKId4TQmQJITKF\nECcIIRKFEF8JIbL1/3aG+Hfo+0isE0KcHSjtusBdXIy7vNwnTEqJrKxk/ZAhZKZnUL5tmzc8/4UX\nqdy71yd+xc6dAGy98irWHT0YWVnldXH/O++y4bTT2fP009gWZDZs2GisiISe5GngcyllOjAAyARu\nB5ZJKXsBy/RzhBBHAOOAvsA5wHx9f4lawXXgAHsefxxZUeENy3v2WTLTM1h31NGs6z+Ayn1VPt13\n3XEnWf2OxJWnuU/ZeKa2YU/OyJHkPfEE2SeeRN68Z3zSAyj+9VcAtl5RtcnHvv/8B4CC555n72vm\n/aht2LBhozEgrEJCCNEGOAV4GUBKWS6l3I+2P4Sn5XwN8PgPHg28JaUsk1JuAjag7S8RNKSUrD/u\neApefIkDS5Z4w/Of+ZdPvOwTqtyJH/jQvFGOu6iI8g0bq+6fP5+sI/vjOmD20VX866/sfUPbz1pE\nVWny9r/3nimuDRs2bKhQlp1N4Rdf1nc2gPCPJLoDecCrQojfhRAvCSESgI5Syl16nN2Ax+l9tXtJ\nQM32k9g0Zoz3OG/eMwEzeeiHH8l/0Xp3K38VkzfNp+dZhufOmgVA6erV3jArgWLDhg0bKuSMHMWO\nm27yUWHXF8ItJKKAo4DnpJSDgCJ01ZIHUlPYB6W0l1K+IKUcLKUcnJxsnpwvy86mbG2m97xyzx42\nDDtTmV7uww+T9/gT1lxlZZbhRT//rEyv5K+/fM4TjjteGdeGjaYCgzfTRglXYSGVBQX1nQ0fbDhj\naH1nIexCYjuwXUrpaVHfQxMauUKITgD6/x79elB7SahQsGCBKaxi+3Zch4os45fn5CjTki63Zbhw\nqLcLPfTttz7nhUuXmuYxbNhoasjKOIJdd6p3Zgs33OXlFCxYYOp977zzn2yZOKna+zecMZTsk04O\nV/ZMcO3fj9uiEyrdVW1O5Z49puuRRliFhJRyN7BNCNFHDxoKrAU+AjxvbRLgmTT4CBgnhIgVQnQH\negG/BMtbsW27dX5KS5T3OBMTrS+4Ffs/B9pYPcpsWbx/8WJl9LLsbNzFxer0AkBWVOAuLa3VvYFQ\nmZdH2aZN3vPS9esbXC/LRsPDAYt6vvWaKWQe0TesvNLtJvvEk9jzyBz2vfW2b54++IDiX6pvRtyH\nQrN3ek1Qum49648/gXUDBlK8ahXSpbUzlXl5ZFk8q4rcXIpWrGD7DTdS8Mqr3k5n2caNlK4L755r\nkbBuugFYJIT4CxgIPAw8ApwphMgGhunn6PtGvIMmSD4H/k9KqWil1WhxrPVct/QzefXGHzyY6E6d\nrO9RjCRwqh9ddGqqKWz3Pfd6j3fcOoO9CxeyZeIkCr/6ipyRo9h1193K9AJhy+UTWDdwkPJ66br1\npjB3UZHJ/NeIA0s/IXvIKeScO9wbtmnUaHLOP18TaH73luVsojgCa1Vchw4p32EoId1uSv74wxRe\nlp1Nxe7dYedvaihavhzciu8oRCj57TdvI+/aW31nxnWoiJ0zZ/pYN3qw58mnfM4rdu4k+7TTyT7j\nDIp/+12ZZmV+vs8oxl1aSt78+RT99BN7Fy70mtTnzZvHptGjvfG2XHY5efOewVVYSPaQU0zpSinZ\ncOppbJ18BQe/+oo9jz5K3jxtTjRnxHlsGh143/C6IuxCQkr5hz5/0F9Keb6Ucp+UskBKOVRK2UtK\nOUxKudcQ/yEpZQ8pZR8p5Wc15SnfviNgwwdYDu0Aort2BWGtPirLzrYMb332OUoeR0yMNX+JNpIp\nXLqU3IdnU/zLL+y44UYt7NNPyUzPYMeM28js20+ZthGlmZmU/PmnT9pGFH72GZtGj6bwiy9xFxV5\nedYdPZjNF5k3rweo3LePnbfeannNlZdPzshR5M56CICKXbvIm/cMOcOHs+XyCeTOnVujfFcHlV57\n/eBj2H7jTSHhCIS9C15j87hLKfrZt/eZM3IUG047nTI/9WTRip/JGTXasv4dWr7cHoHp2PPYY7jL\nyth55z/ZPeshXAcPUrRCPbcXDIr+95P32NOxkxUV3nrvjwMfLeHAko9M1o4ABf/+NyWr13gFSOFn\nn1O5ezeVO3exZfx4svoPMKm0StetJ/vkIWybMgWA8m3bWDdwEPnznmHrFVeS+/BsNp55lrbmav5z\nlpx7nrCeF7XqsBS8+JKP+rxSYcATCjQJfxKyvJyNw4axrv8ALUAxXaDshQqUQmLXHXdYhjtatVTn\nR2GRcPDrr00NjD8KP/4YXC62Tb3Wazq7/4PFlPz1F9LtpjQzk5yRI3EVFrLpggu995X8/jul69ZR\nYdBhlvytWVjtuOkm1h09GHdJCTum3wJA2XrzCANg9/0PmML8e8/733mHspwcds68nfz5873he19+\nxTLNspwctkycpPxgPXAdKmLb9deTlXGEj6qrfPt2yrdrKsRD335rMgyozMtjx4zb2HLFFQHTN0KW\nl1O+bRsVubm+aeXns+fRRzXeLZu94cZnkDN8RFU6UpL70CzK1q+ncOknPh+rrKxk2zVT2Dx+vEnw\nyVr2rEvWrFF2XBoaKvPzKf7tN+95wUsvU/jZZxz44AP2vfEG6485lq2TJ5OZnoGUkvKtW70LVA9+\n+61lL98KroMHfeqhrKxAlpez6cILWXf04Kp4hYUAlPz9N7kPPKjxfPUVW664wjRnuPmii9g8bpyW\nnt81WV5Orl5H3EVFFK1Y4R0ZeISVqnevaoNaDx/O/netTeVL1661DM9/psrCMnvIKWSmZyitMeuC\nRu+WA2DP0097jyvz1fuIq17Qgfc/CJrTU8msUL5lqzV/aZlPAxMIh777jkPffUeb889n1513AhA/\n+GhKVq4CoOjHH33ib73yKu9xRlYmpWvXsvcV30bb85F4ULZhAzE9eiB0ASml5ODnn/vmWUo2nHa6\nKX8FL7xo2XspXb+euN69keXl7LzznxQuXVpVpv/9j1annsrWqVNxHyikw62awIo/6ihc+/ax4fQz\nqtJZs5bY7t0B2OhnmbZ57CWkr1mNcDpxHTjgM0TfOHwEPT79hEPLlxOVnMym8y8g9fHHaDPC97lv\nungsZbouN3HyZDrePlPL43ffVUVyaw377gdnsW/RIlM53YeK2DJ+vDfM854ysjLZ/eAsSlb/DUDF\nlq3sfeUV2l91FRV79rBx6DBvw9PqzGGkPVNloi2lpOiHH9kxfTrugwfp+e03RKekULxqFQUvvuQ1\niuj98wqcbdqYnn84ID0+yoTw1hUPXIcOISsqOPjlVz7xhRBknzzElJYjNtaao6yMjWdpDha6LniV\n7ddOI65vX7q/rzWcsrxcEzq//kobg6oGIHfOHJ/z/e+9j/tQEWXZG3zCy7dswdkukc0Xj/WGVebl\nUZmXR8UOs31Mhf4dizhznvctfJ2OM2f6CCEPSv78k6iUFEuDGFUb5EhoAS5rzXpM18MswyvzzSPU\nnFGj6f3Dcsv4tUWTGEkYe7AbzhiqHBUUKHq6oUb+s89ahsd07xZ0WsZejEdAQIBRkY5NF44xhVXm\n+QrQnPNGsuvuqrmQva8uMPMrJtSly2X5nDeN0j7g0rVrfQQEgHA42Hz5BIp/WkHp2rVsvfIqtl55\nFdumTKV8yxa/uGrrMYD8Z7Weo/9cjufD3HbNFDadfwEABc//u+q+558nMz3DKyAA9urWcO7iYnb9\n8y5DIbXevr+AAK2nGGgydN+iRZT+WTXi2TP3MYp++ond997n804PfvVfCr/UFk3tfnAWWRlHsO2a\na3AfPAhA0YoVyIoKtlx2uY/VXM6oqoayaMXPbDjzLEurvkM//kipxaixOms7d2kp+c//G3dJjeuY\ngQAAIABJREFUCfnPzifriL5kZRzBxhHnsff1N7S5qZIS1g8+huwTTmT3vVVzbiW//8HuB2dZpiui\no635DOrSrZO1EWHpmjWUrlvPoe+/J6v/ADacMZSdM2/HZZhg3n7zzRx4733ftA4cYP/bvpPXAJsv\nHov70EFr/gDfU1T79pbhh5ZbN8abLxlHwvHHWV6TCiOTgCNLaX3N2batKcwVoJNcWzQJIWFEoMbT\nv5ccadRmYYyr0LpSF69cSXRn0zpDAOXcQO4js01hxg+saMVPpusulYrIVYlwWlt4uQ4VsXncpeYL\nUlLqpyoCKP7lF1PjUbZhA3sXvm7NjdZ4ApbqO5Nqx/CR5T31tH90LwpeesnnfPf9D+Dav986spQI\nVa9Y8Z73LnzdcmHlDn2exUoYyfJytl9/gym8MjcXd3ExWUf2Z+vkyVRs28aeR+ZQsnoNoFnDFLz8\nMtuuuppNo0Z7w6XbzY7p08k6sr/JKmbPE0+SN28eO2bcxrqBg8h76in2Lljg0+CWb9xI7kMPkTNy\nlHINkWv/fsuygFpIqBrP3FmzKPzEd7t7d1FVx+XgZ8F90yr+oh//p7xHKnr4rn2KuqExWYaq5kWR\nEKPwZq2qT3FHWs9dVjc3GyyanJAAvGqCBodaCAlZYt2T3//ue8oRk2puwH0wsImfsJhwV80jyEqX\npakvQGWetW230lIM88ebP/85ch9+WPmBeMKFVR78P+rK6g3kyjZssJxQLM3KUt5jyY26oyIrKxGx\n1kYNyg6E2+2rAjOgeOVK04jA44Jmw6mnsWfuY97wzRddBGijxcJPNXuQTaPPx1VY6G0EC154gfz5\nz2nzYjoq8/LUwlBhYCBdAeq50/qZWRlegPZc/NWknm9C1eC2PO00Nb+ix573tLrzgKLexhxmrQYC\nlN+mSrDW5h7P/I0/No8xaxHqgiYqJIK2mo0IajOSUPY8HA5lpVKhrBp7aivVkrHX5hPX7VKOJFCt\nug3UeCjucZdal9/TOFo11P5Dd5/nruhJ+i+A9MARF2cZDgF6xaqenKsSR4yiwVUJFkUvFvBxTe/B\nvtdfV66bcRcVmYwQ1h97HFkBrOlkRaVSSKh06AE7QwrViWrRmKysNNWNrVdfw8ZzhytHH0hJy2HW\nK5VVzzO2Vy9FhrW6bolA9VnxbSrXNOlzPlbIfWSOZXi+wt1QWfYGin4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IMeyDDVqjBNDx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\nxP8DOJ8pA+OZo7cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22ac9d50780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = raw.plot(sharex = True, subplots = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggregate the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize an aggregation to typical weeks which integrates the day with the minimal temperature and the day with the maximal load as periods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "aggregation = tsam.TimeSeriesAggregation(raw, noTypicalPeriods = 5, hoursPerPeriod = 24*7, \n",
    "                                        clusterMethod = 'hierarchical', \n",
    "                                        extremePeriodMethod = 'new_cluster_center',\n",
    "                                       addPeakMin = ['T'], addPeakMax = ['Load'] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the typical periods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "typPeriods = aggregation.createTypicalPeriods()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show the resulting order  of aggregated periods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculates how the original index is represented by the old index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "indexMatching = aggregation.indexMatching()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the appearance of the 5+2 aggregated periods in the original timeframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "visDF = pd.DataFrame(0, index = indexMatching.index,\n",
    "                     columns = aggregation.clusterPeriodIdx)\n",
    "for col in visDF.columns:\n",
    "    visDF.loc[indexMatching['PeriodNum']==col,col] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22ac82bae80>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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B1uVsfCuIzvrZyyL9iU7/Wwjs7+4XdXt/qvGIDhbuRfRL7Xh3/0YP8YqOAxtP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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22ac87284a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visDF.plot(kind = 'area', cmap = 'viridis', lw = 0, ylim = [0,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get input for potential energy system optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**i. clusterPeriodNoOccur** <br>The number of occurance of each typical period to evaluate it correctly e.g. in the objective function\n",
    "<br>Note: Period three is only partially evaluated since its appearence in the end of the year exceeds the original time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 10, 1: 17, 2: 7, 3: 4.1428571428571423, 4: 8, 5: 3, 6: 3}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aggregation.clusterPeriodNoOccur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x22ac9a02f28>,\n",
       " <matplotlib.text.Text at 0x22ac9d82c18>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QpJEzCCRp5AwCSRo5g0CSRs4gkKSRMwgkaeQGnUcgSbMt99Vfl2v9nhFI0sgZBJI0cgaB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\n7AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22ac97e1748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = pd.Series(aggregation.clusterPeriodNoOccur).plot(kind='bar')\n",
    "ax.set(ylabel = 'Number of occurence', xlabel = 'Period index')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ii. clusterPeriodDict**\n",
    "<br>The dictionary which describes the aggregated time series by an index touple (period number, time step number) the resulting aggregated time series value for each time series, e.g. 'GHI' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "132.86908868164946"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aggregation.clusterPeriodDict['GHI'][(aggregation.clusterPeriodIdx[3], aggregation.stepIdx[12])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively this is given as data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>GHI</th>\n",
       "      <th>T</th>\n",
       "      <th>Wind</th>\n",
       "      <th>Load</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>TimeStep</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.220495</td>\n",
       "      <td>0.307872</td>\n",
       "      <td>416.965693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.008529</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>409.436001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.203436</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>403.636735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.097453</td>\n",
       "      <td>0.205248</td>\n",
       "      <td>404.487067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.203436</td>\n",
       "      <td>0.307872</td>\n",
       "      <td>410.103512</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            GHI         T      Wind        Load\n",
       "  TimeStep                                     \n",
       "0 0         0.0  0.220495  0.307872  416.965693\n",
       "  1         0.0  0.008529  0.000000  409.436001\n",
       "  2         0.0 -0.203436  0.000000  403.636735\n",
       "  3         0.0 -0.097453  0.205248  404.487067\n",
       "  4         0.0 -0.203436  0.307872  410.103512"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aggregation.typicalPeriods.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**iii. clusterOrder**\n",
    "<br> The order of the typical periods to represent the original time series e.g. to model seasonal storage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 2, 5, 6, 6, 2, 5, 5, 3, 0, 0, 0, 4, 0, 0, 4, 1, 4, 4, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 1, 4, 4, 0, 0, 3, 0, 0, 0, 2,\n",
       "       2, 2, 2, 6, 2, 3, 3], dtype=int64)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aggregation.clusterOrder"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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