notebooks/SwissHouseRSlaTankDHW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# SwissHouseRSlaTankDHW notebook example"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "In this example, the usage of the model \"SwissHouseRad-v0\" is demonstrated."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "First, we import Energym and create the simulation environment by specifying the model, a weather file and the number of simulation days."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "import energym\r\n",
    "\r\n",
    "weather = \"CH_BS_Basel\"\r\n",
    "env = energym.make(\"SwissHouseRSlaTankDhw-v0\", weather=weather, simulation_days=20)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "the initial variables are {'uFlowDHW': 0, 'uHP': 0, 'uRSla': 0, 'uValveDHW': 0}\n"
     ]
    }
   ],
   "metadata": {
    "scrolled": true
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "The control inputs can be inspected using the `get_inputs_names()` method."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "inputs = env.get_inputs_names()\r\n",
    "outputs = env.get_outputs_names()\r\n",
    "print(\"inputs:\", inputs)\r\n",
    "print(\"outputs:\", outputs)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "inputs: ['uFlowDHW', 'uHP', 'uRSla', 'uValveDHW']\n",
      "outputs: ['TOut.T', 'heaPum.COP', 'heaPum.COPCar', 'heaPum.P', 'heaPum.QCon_flow', 'heaPum.QEva_flow', 'heaPum.TConAct', 'heaPum.TEvaAct', 'preHea.Q_flow', 'sla.QTot', 'sla.heatPortEmb[1].T', 'sla.m_flow', 'temRoo.T', 'weaBus.HDifHor', 'weaBus.HDirNor', 'weaBus.HGloHor', 'weaBus.HHorIR']\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "To run the simulation, a number of steps is specified (here 288 steps per day for 10 days), a control input is specified and passed to the simulation model with the `step()` method. To generate some plots later on, we save all the outputs in lists."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "source": [
    "from scipy import signal\r\n",
    "import math\r\n",
    "\r\n",
    "steps = 288*5\r\n",
    "out_list = []\r\n",
    "outputs = env.get_output()\r\n",
    "controls = []\r\n",
    "hour = 0\r\n",
    "for i in range(steps):\r\n",
    "    control = {}\r\n",
    "    control['uHP'] = [0.5*(signal.square(0.1*i)+1.0)]\r\n",
    "    control['uRSla'] = [0.5*(math.cos(0.01*i)+1.0)]\r\n",
    "    control['uFlowDHW'] = [0.1]\r\n",
    "    control['uValveDHW'] = [0.0]\r\n",
    "    controls +=[ {p:control[p][0] for p in control} ]\r\n",
    "    outputs = env.step(control)\r\n",
    "    _,hour,_,_ = env.get_date()\r\n",
    "    out_list.append(outputs)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Since the outputs are given as dictionaries and are collected in lists, we can simply load them as a pandas.DataFrame."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "source": [
    "import pandas as pd\n",
    "out_df = pd.DataFrame(out_list)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "source": [
    "out_df"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          TOut.T  heaPum.COP  heaPum.COPCar  heaPum.P  heaPum.QCon_flow  \\\n",
       "0     279.157205    3.762732      11.960393    1000.0      3.762732e+03   \n",
       "1     279.136250    3.742857      11.897217    1000.0      3.742857e+03   \n",
       "2     279.108281    3.725294      11.841389    1000.0      3.725294e+03   \n",
       "3     279.074444    3.709089      11.789879    1000.0      3.709089e+03   \n",
       "4     279.035885    3.693992      11.741891    1000.0      3.693992e+03   \n",
       "...          ...         ...            ...       ...               ...   \n",
       "1435  273.586667    3.431754      10.908329       0.0      2.407412e-35   \n",
       "1436  273.587500    3.431857      10.908658       0.0      2.407412e-35   \n",
       "1437  273.588333    3.431961      10.908987       0.0      2.407412e-35   \n",
       "1438  273.589167    3.432064      10.909317       0.0      2.407412e-35   \n",
       "1439  273.590000    3.432168      10.909646       0.0      2.407412e-35   \n",
       "\n",
       "      heaPum.QEva_flow  heaPum.TConAct  heaPum.TEvaAct  preHea.Q_flow  \\\n",
       "0        -2.762732e+03      294.117931      269.526938            0.0   \n",
       "1        -2.742857e+03      294.313846      269.575803            0.0   \n",
       "2        -2.725294e+03      294.477898      269.609372            0.0   \n",
       "3        -2.709089e+03      294.621649      269.632279            0.0   \n",
       "4        -2.693992e+03      294.748883      269.646548            0.0   \n",
       "...                ...             ...             ...            ...   \n",
       "1435     -2.407412e-35      301.198417      273.586634            0.0   \n",
       "1436     -2.407412e-35      301.198417      273.587467            0.0   \n",
       "1437     -2.407412e-35      301.198417      273.588300            0.0   \n",
       "1438     -2.407412e-35      301.198417      273.589134            0.0   \n",
       "1439     -2.407412e-35      301.198417      273.589967            0.0   \n",
       "\n",
       "         sla.QTot  sla.heatPortEmb[1].T  sla.m_flow    temRoo.T  \\\n",
       "0      760.630230            287.739587    1.000000  287.549429   \n",
       "1     1173.085349            287.843677    0.999975  287.550406   \n",
       "2     1521.605382            287.934538    0.999900  287.554136   \n",
       "3     1823.762646            288.016156    0.999775  287.560216   \n",
       "4     2087.899803            288.090315    0.999600  287.568340   \n",
       "...           ...                   ...         ...         ...   \n",
       "1435   613.410568            296.240877    0.394385  296.087524   \n",
       "1436   583.743623            296.221339    0.389503  296.075403   \n",
       "1437   556.537503            296.202218    0.384632  296.063084   \n",
       "1438   531.578525            296.183478    0.379773  296.050583   \n",
       "1439   508.671759            296.165085    0.374926  296.037917   \n",
       "\n",
       "      weaBus.HDifHor  weaBus.HDirNor  weaBus.HGloHor  weaBus.HHorIR       time  \n",
       "0                0.0             0.0             0.0     282.725000  1296300.0  \n",
       "1                0.0             0.0             0.0     282.900000  1296600.0  \n",
       "2                0.0             0.0             0.0     283.075000  1296900.0  \n",
       "3                0.0             0.0             0.0     283.250000  1297200.0  \n",
       "4                0.0             0.0             0.0     283.425000  1297500.0  \n",
       "...              ...             ...             ...            ...        ...  \n",
       "1435             0.0             0.0             0.0     202.990744  1726800.0  \n",
       "1436             0.0             0.0             0.0     202.887893  1727100.0  \n",
       "1437             0.0             0.0             0.0     202.802802  1727400.0  \n",
       "1438             0.0             0.0             0.0     202.738996  1727700.0  \n",
       "1439             0.0             0.0             0.0     202.700000  1728000.0  \n",
       "\n",
       "[1440 rows x 18 columns]"
      ],
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TOut.T</th>\n",
       "      <th>heaPum.COP</th>\n",
       "      <th>heaPum.COPCar</th>\n",
       "      <th>heaPum.P</th>\n",
       "      <th>heaPum.QCon_flow</th>\n",
       "      <th>heaPum.QEva_flow</th>\n",
       "      <th>heaPum.TConAct</th>\n",
       "      <th>heaPum.TEvaAct</th>\n",
       "      <th>preHea.Q_flow</th>\n",
       "      <th>sla.QTot</th>\n",
       "      <th>sla.heatPortEmb[1].T</th>\n",
       "      <th>sla.m_flow</th>\n",
       "      <th>temRoo.T</th>\n",
       "      <th>weaBus.HDifHor</th>\n",
       "      <th>weaBus.HDirNor</th>\n",
       "      <th>weaBus.HGloHor</th>\n",
       "      <th>weaBus.HHorIR</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>279.157205</td>\n",
       "      <td>3.762732</td>\n",
       "      <td>11.960393</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.762732e+03</td>\n",
       "      <td>-2.762732e+03</td>\n",
       "      <td>294.117931</td>\n",
       "      <td>269.526938</td>\n",
       "      <td>0.0</td>\n",
       "      <td>760.630230</td>\n",
       "      <td>287.739587</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>287.549429</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>282.725000</td>\n",
       "      <td>1296300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>279.136250</td>\n",
       "      <td>3.742857</td>\n",
       "      <td>11.897217</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.742857e+03</td>\n",
       "      <td>-2.742857e+03</td>\n",
       "      <td>294.313846</td>\n",
       "      <td>269.575803</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1173.085349</td>\n",
       "      <td>287.843677</td>\n",
       "      <td>0.999975</td>\n",
       "      <td>287.550406</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>282.900000</td>\n",
       "      <td>1296600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>279.108281</td>\n",
       "      <td>3.725294</td>\n",
       "      <td>11.841389</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.725294e+03</td>\n",
       "      <td>-2.725294e+03</td>\n",
       "      <td>294.477898</td>\n",
       "      <td>269.609372</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1521.605382</td>\n",
       "      <td>287.934538</td>\n",
       "      <td>0.999900</td>\n",
       "      <td>287.554136</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>283.075000</td>\n",
       "      <td>1296900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>279.074444</td>\n",
       "      <td>3.709089</td>\n",
       "      <td>11.789879</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.709089e+03</td>\n",
       "      <td>-2.709089e+03</td>\n",
       "      <td>294.621649</td>\n",
       "      <td>269.632279</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1823.762646</td>\n",
       "      <td>288.016156</td>\n",
       "      <td>0.999775</td>\n",
       "      <td>287.560216</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>283.250000</td>\n",
       "      <td>1297200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>279.035885</td>\n",
       "      <td>3.693992</td>\n",
       "      <td>11.741891</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.693992e+03</td>\n",
       "      <td>-2.693992e+03</td>\n",
       "      <td>294.748883</td>\n",
       "      <td>269.646548</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2087.899803</td>\n",
       "      <td>288.090315</td>\n",
       "      <td>0.999600</td>\n",
       "      <td>287.568340</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>283.425000</td>\n",
       "      <td>1297500.0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1435</th>\n",
       "      <td>273.586667</td>\n",
       "      <td>3.431754</td>\n",
       "      <td>10.908329</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.407412e-35</td>\n",
       "      <td>-2.407412e-35</td>\n",
       "      <td>301.198417</td>\n",
       "      <td>273.586634</td>\n",
       "      <td>0.0</td>\n",
       "      <td>613.410568</td>\n",
       "      <td>296.240877</td>\n",
       "      <td>0.394385</td>\n",
       "      <td>296.087524</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>202.990744</td>\n",
       "      <td>1726800.0</td>\n",
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       "    <tr>\n",
       "      <th>1436</th>\n",
       "      <td>273.587500</td>\n",
       "      <td>3.431857</td>\n",
       "      <td>10.908658</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.407412e-35</td>\n",
       "      <td>-2.407412e-35</td>\n",
       "      <td>301.198417</td>\n",
       "      <td>273.587467</td>\n",
       "      <td>0.0</td>\n",
       "      <td>583.743623</td>\n",
       "      <td>296.221339</td>\n",
       "      <td>0.389503</td>\n",
       "      <td>296.075403</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>202.887893</td>\n",
       "      <td>1727100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1437</th>\n",
       "      <td>273.588333</td>\n",
       "      <td>3.431961</td>\n",
       "      <td>10.908987</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.407412e-35</td>\n",
       "      <td>-2.407412e-35</td>\n",
       "      <td>301.198417</td>\n",
       "      <td>273.588300</td>\n",
       "      <td>0.0</td>\n",
       "      <td>556.537503</td>\n",
       "      <td>296.202218</td>\n",
       "      <td>0.384632</td>\n",
       "      <td>296.063084</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>202.802802</td>\n",
       "      <td>1727400.0</td>\n",
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       "    <tr>\n",
       "      <th>1438</th>\n",
       "      <td>273.589167</td>\n",
       "      <td>3.432064</td>\n",
       "      <td>10.909317</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.407412e-35</td>\n",
       "      <td>-2.407412e-35</td>\n",
       "      <td>301.198417</td>\n",
       "      <td>273.589134</td>\n",
       "      <td>0.0</td>\n",
       "      <td>531.578525</td>\n",
       "      <td>296.183478</td>\n",
       "      <td>0.379773</td>\n",
       "      <td>296.050583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>202.738996</td>\n",
       "      <td>1727700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1439</th>\n",
       "      <td>273.590000</td>\n",
       "      <td>3.432168</td>\n",
       "      <td>10.909646</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.407412e-35</td>\n",
       "      <td>-2.407412e-35</td>\n",
       "      <td>301.198417</td>\n",
       "      <td>273.589967</td>\n",
       "      <td>0.0</td>\n",
       "      <td>508.671759</td>\n",
       "      <td>296.165085</td>\n",
       "      <td>0.374926</td>\n",
       "      <td>296.037917</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>202.700000</td>\n",
       "      <td>1728000.0</td>\n",
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       "<p>1440 rows × 18 columns</p>\n",
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      ]
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "To generate plots, we can directly get the data from the DataFrames, by using the key names. Displayed are the room temperature, the supply temperature and the return temperature, as well as the external temperature, and the heat pump energy."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib notebook\n",
    "\n",
    "f, (ax1,ax2,ax3) = plt.subplots(3,figsize=(10,15))#\n",
    "\n",
    "\n",
    "ax1.plot(out_df['temRoo.T']-273.15, 'r')\n",
    "ax1.plot(out_df['sla.heatPortEmb[1].T']-273.15, 'b--')\n",
    "ax1.plot(out_df['heaPum.TEvaAct']-273.15, 'orange')\n",
    "ax1.set_ylabel('Temp')\n",
    "ax1.set_xlabel('Steps')\n",
    "\n",
    "ax2.plot(out_df['TOut.T']-273.15, 'r')\n",
    "ax2.set_ylabel('Temp')\n",
    "ax2.set_xlabel('Steps')\n",
    "\n",
    "ax3.plot(out_df['heaPum.QCon_flow'], 'g')\n",
    "ax3.set_ylabel('Energy')\n",
    "ax3.set_xlabel('Steps')\n",
    "\n",
    "plt.subplots_adjust(hspace=0.4)\n",
    "\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ],
      "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.get_websocket_type = function() {\n    if (typeof(WebSocket) !== 'undefined') {\n        return WebSocket;\n    } else if (typeof(MozWebSocket) !== 'undefined') {\n        return MozWebSocket;\n    } else {\n        alert('Your browser does not have WebSocket support. ' +\n              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n              'Firefox 4 and 5 are also supported but you ' +\n              'have to enable WebSockets in about:config.');\n    };\n}\n\nmpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = (this.ws.binaryType != undefined);\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById(\"mpl-warnings\");\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent = (\n                \"This browser does not support binary websocket messages. \" +\n                    \"Performance may be slow.\");\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = $('<div/>');\n    this._root_extra_style(this.root)\n    this.root.attr('style', 'display: inline-block');\n\n    $(parent_element).append(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen =  function () {\n            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n            fig.send_message(\"send_image_mode\", {});\n            if (mpl.ratio != 1) {\n                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n            }\n            fig.send_message(\"refresh\", {});\n        }\n\n    this.imageObj.onload = function() {\n            if (fig.image_mode == 'full') {\n                // Full images could contain transparency (where diff images\n                // almost always do), so we need to clear the canvas so that\n                // there is no ghosting.\n                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n            }\n            fig.context.drawImage(fig.imageObj, 0, 0);\n        };\n\n    this.imageObj.onunload = function() {\n        fig.ws.close();\n    }\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n    var titlebar = $(\n        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n        'ui-helper-clearfix\"/>');\n    var titletext = $(\n        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n        'text-align: center; padding: 3px;\"/>');\n    titlebar.append(titletext)\n    this.root.append(titlebar);\n    this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n    var fig = this;\n\n    var canvas_div = $('<div/>');\n\n    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n\n    function canvas_keyboard_event(event) {\n        return fig.key_event(event, event['data']);\n    }\n\n    canvas_div.keydown('key_press', canvas_keyboard_event);\n    canvas_div.keyup('key_release', canvas_keyboard_event);\n    this.canvas_div = canvas_div\n    this._canvas_extra_style(canvas_div)\n    this.root.append(canvas_div);\n\n    var canvas = $('<canvas/>');\n    canvas.addClass('mpl-canvas');\n    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n\n    this.canvas = canvas[0];\n    this.context = canvas[0].getContext(\"2d\");\n\n    var backingStore = this.context.backingStorePixelRatio ||\n\tthis.context.webkitBackingStorePixelRatio ||\n\tthis.context.mozBackingStorePixelRatio ||\n\tthis.context.msBackingStorePixelRatio ||\n\tthis.context.oBackingStorePixelRatio ||\n\tthis.context.backingStorePixelRatio || 1;\n\n    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband = $('<canvas/>');\n    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n\n    var pass_mouse_events = true;\n\n    canvas_div.resizable({\n        start: function(event, ui) {\n            pass_mouse_events = false;\n        },\n        resize: function(event, ui) {\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n        stop: function(event, ui) {\n            pass_mouse_events = true;\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n    });\n\n    function mouse_event_fn(event) {\n        if (pass_mouse_events)\n            return fig.mouse_event(event, event['data']);\n    }\n\n    rubberband.mousedown('button_press', mouse_event_fn);\n    rubberband.mouseup('button_release', mouse_event_fn);\n    // Throttle sequential mouse events to 1 every 20ms.\n    rubberband.mousemove('motion_notify', mouse_event_fn);\n\n    rubberband.mouseenter('figure_enter', mouse_event_fn);\n    rubberband.mouseleave('figure_leave', mouse_event_fn);\n\n    canvas_div.on(\"wheel\", function (event) {\n        event = event.originalEvent;\n        event['data'] = 'scroll'\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        mouse_event_fn(event);\n    });\n\n    canvas_div.append(canvas);\n    canvas_div.append(rubberband);\n\n    this.rubberband = rubberband;\n    this.rubberband_canvas = rubberband[0];\n    this.rubberband_context = rubberband[0].getContext(\"2d\");\n    this.rubberband_context.strokeStyle = \"#000000\";\n\n    this._resize_canvas = function(width, height) {\n        // Keep the size of the canvas, canvas container, and rubber band\n        // canvas in synch.\n        canvas_div.css('width', width)\n        canvas_div.css('height', height)\n\n        canvas.attr('width', width * mpl.ratio);\n        canvas.attr('height', height * mpl.ratio);\n        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n\n        rubberband.attr('width', width);\n        rubberband.attr('height', height);\n    }\n\n    // Set the figure to an initial 600x600px, this will subsequently be updated\n    // upon first draw.\n    this._resize_canvas(600, 600);\n\n    // Disable right mouse context menu.\n    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n        return false;\n    });\n\n    function set_focus () {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>');\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            // put a spacer in here.\n            continue;\n        }\n        var button = $('<button/>');\n        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n                        'ui-button-icon-only');\n        button.attr('role', 'button');\n        button.attr('aria-disabled', 'false');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n\n        var icon_img = $('<span/>');\n        icon_img.addClass('ui-button-icon-primary ui-icon');\n        icon_img.addClass(image);\n        icon_img.addClass('ui-corner-all');\n\n        var tooltip_span = $('<span/>');\n        tooltip_span.addClass('ui-button-text');\n        tooltip_span.html(tooltip);\n\n        button.append(icon_img);\n        button.append(tooltip_span);\n\n        nav_element.append(button);\n    }\n\n    var fmt_picker_span = $('<span/>');\n\n    var fmt_picker = $('<select/>');\n    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n    fmt_picker_span.append(fmt_picker);\n    nav_element.append(fmt_picker_span);\n    this.format_dropdown = fmt_picker[0];\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = $(\n            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n        fmt_picker.append(option);\n    }\n\n    // Add hover states to the ui-buttons\n    $( \".ui-button\" ).hover(\n        function() { $(this).addClass(\"ui-state-hover\");},\n        function() { $(this).removeClass(\"ui-state-hover\");}\n    );\n\n    var status_bar = $('<span class=\"mpl-message\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n}\n\nmpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n}\n\nmpl.figure.prototype.send_message = function(type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n}\n\nmpl.figure.prototype.send_draw_message = function() {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n    }\n}\n\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n}\n\n\nmpl.figure.prototype.handle_resize = function(fig, msg) {\n    var size = msg['size'];\n    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1]);\n        fig.send_message(\"refresh\", {});\n    };\n}\n\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'] / mpl.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n    var x1 = msg['x1'] / mpl.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n}\n\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.figure.prototype.handle_cursor = function(fig, msg) {\n    var cursor = msg['cursor'];\n    switch(cursor)\n    {\n    case 0:\n        cursor = 'pointer';\n        break;\n    case 1:\n        cursor = 'default';\n        break;\n    case 2:\n        cursor = 'crosshair';\n        break;\n    case 3:\n        cursor = 'move';\n        break;\n    }\n    fig.rubberband_canvas.style.cursor = cursor;\n}\n\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Called whenever the canvas gets updated.\n    this.send_message(\"ack\", {});\n}\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function(fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            /* FIXME: We get \"Resource interpreted as Image but\n             * transferred with MIME type text/plain:\" errors on\n             * Chrome.  But how to set the MIME type?  It doesn't seem\n             * to be part of the websocket stream */\n            evt.data.type = \"image/png\";\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src);\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                evt.data);\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig[\"handle_\" + msg_type];\n        } catch (e) {\n            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n            }\n        }\n    };\n}\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function(e) {\n    //this section is from http://www.quirksmode.org/js/events_properties.html\n    var targ;\n    if (!e)\n        e = window.event;\n    if (e.target)\n        targ = e.target;\n    else if (e.srcElement)\n        targ = e.srcElement;\n    if (targ.nodeType == 3) // defeat Safari bug\n        targ = targ.parentNode;\n\n    // jQuery normalizes the pageX and pageY\n    // pageX,Y are the mouse positions relative to the document\n    // offset() returns the position of the element relative to the document\n    var x = e.pageX - $(targ).offset().left;\n    var y = e.pageY - $(targ).offset().top;\n\n    return {\"x\": x, \"y\": y};\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys (original) {\n  return Object.keys(original).reduce(function (obj, key) {\n    if (typeof original[key] !== 'object')\n        obj[key] = original[key]\n    return obj;\n  }, {});\n}\n\nmpl.figure.prototype.mouse_event = function(event, name) {\n    var canvas_pos = mpl.findpos(event)\n\n    if (name === 'button_press')\n    {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    var x = canvas_pos.x * mpl.ratio;\n    var y = canvas_pos.y * mpl.ratio;\n\n    this.send_message(name, {x: x, y: y, button: event.button,\n                             step: event.step,\n                             guiEvent: simpleKeys(event)});\n\n    /* This prevents the web browser from automatically changing to\n     * the text insertion cursor when the button is pressed.  We want\n     * to control all of the cursor setting manually through the\n     * 'cursor' event from matplotlib */\n    event.preventDefault();\n    return false;\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.figure.prototype.key_event = function(event, name) {\n\n    // Prevent repeat events\n    if (name == 'key_press')\n    {\n        if (event.which === this._key)\n            return;\n        else\n            this._key = event.which;\n    }\n    if (name == 'key_release')\n        this._key = null;\n\n    var value = '';\n    if (event.ctrlKey && event.which != 17)\n        value += \"ctrl+\";\n    if (event.altKey && event.which != 18)\n        value += \"alt+\";\n    if (event.shiftKey && event.which != 16)\n        value += \"shift+\";\n\n    value += 'k';\n    value += event.which.toString();\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, {key: value,\n                             guiEvent: simpleKeys(event)});\n    return false;\n}\n\nmpl.figure.prototype.toolbar_button_onclick = function(name) {\n    if (name == 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message(\"toolbar_button\", {name: name});\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.close = function() {\n        comm.close()\n    };\n    ws.send = function(m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function(msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        // Pass the mpl event to the overridden (by mpl) onmessage function.\n        ws.onmessage(msg['content']['data'])\n    });\n    return ws;\n}\n\nmpl.mpl_figure_comm = function(comm, msg) {\n    // This is the function which gets called when the mpl process\n    // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n    var id = msg.content.data.id;\n    // Get hold of the div created by the display call when the Comm\n    // socket was opened in Python.\n    var element = $(\"#\" + id);\n    var ws_proxy = comm_websocket_adapter(comm)\n\n    function ondownload(figure, format) {\n        window.open(figure.imageObj.src);\n    }\n\n    var fig = new mpl.figure(id, ws_proxy,\n                           ondownload,\n                           element.get(0));\n\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n    // web socket which is closed, not our websocket->open comm proxy.\n    ws_proxy.onopen();\n\n    fig.parent_element = element.get(0);\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error(\"Failed to find cell for figure\", id, fig);\n        return;\n    }\n\n    var output_index = fig.cell_info[2]\n    var cell = fig.cell_info[0];\n\n};\n\nmpl.figure.prototype.handle_close = function(fig, msg) {\n    var width = fig.canvas.width/mpl.ratio\n    fig.root.unbind('remove')\n\n    // Update the output cell to use the data from the current canvas.\n    fig.push_to_output();\n    var dataURL = fig.canvas.toDataURL();\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\n    // the notebook keyboard shortcuts fail.\n    IPython.keyboard_manager.enable()\n    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var width = this.canvas.width/mpl.ratio\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Tell IPython that the notebook contents must change.\n    IPython.notebook.set_dirty(true);\n    this.send_message(\"ack\", {});\n    var fig = this;\n    // Wait a second, then push the new image to the DOM so\n    // that it is saved nicely (might be nice to debounce this).\n    setTimeout(function () { fig.push_to_output() }, 1000);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>');\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items){\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) { continue; };\n\n        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n        nav_element.append(button);\n    }\n\n    // Add the status bar.\n    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n\n    // Add the close button to the window.\n    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n    button.click(function (evt) { fig.handle_close(fig, {}); } );\n    button.mouseover('Stop Interaction', toolbar_mouse_event);\n    buttongrp.append(button);\n    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n    titlebar.prepend(buttongrp);\n}\n\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.figure.prototype._canvas_extra_style = function(el){\n    // this is important to make the div 'focusable\n    el.attr('tabindex', 0)\n    // reach out to IPython and tell the keyboard manager to turn it's self\n    // off when our div gets focus\n\n    // location in version 3\n    if (IPython.notebook.keyboard_manager) {\n        IPython.notebook.keyboard_manager.register_events(el);\n    }\n    else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    var manager = IPython.notebook.keyboard_manager;\n    if (!manager)\n        manager = IPython.keyboard_manager;\n\n    // Check for shift+enter\n    if (event.shiftKey && event.which == 13) {\n        this.canvas_div.blur();\n        // select the cell after this one\n        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n        IPython.notebook.select(index + 1);\n    }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.find_output_cell = function(html_output) {\n    // Return the cell and output element which can be found *uniquely* in the notebook.\n    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n    // IPython event is triggered only after the cells have been serialised, which for\n    // our purposes (turning an active figure into a static one), is too late.\n    var cells = IPython.notebook.get_cells();\n    var ncells = cells.length;\n    for (var i=0; i<ncells; i++) {\n        var cell = cells[i];\n        if (cell.cell_type === 'code'){\n            for (var j=0; j<cell.output_area.outputs.length; j++) {\n                var data = cell.output_area.outputs[j];\n                if (data.data) {\n                    // IPython >= 3 moved mimebundle to data attribute of output\n                    data = data.data;\n                }\n                if (data['text/html'] == html_output) {\n                    return [cell, data, j];\n                }\n            }\n        }\n    }\n}\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel != null) {\n    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n"
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\" width=\"999.1666666666667\">"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "To end the simulation, the `close()` method is called. It deletes files that were produced during the simulation and stores some information about the simulation in the *energym_runs* folder."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "env.close()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}