HeatPump_ThermalBuffer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tools\n",
    "-> Pandas https://pandas.pydata.org/\n",
    "\n",
    "-> Numpy http://www.numpy.org/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Models\n",
    "Models are imported from a file `models.py`.\n",
    "\n",
    "\n",
    "#### Models based on ODE (using odeint from scipy.integrate)\n",
    "`ThermalBuffer` is a 3 meshes constant volume model of a stratified thermal storage \n",
    "\n",
    "`HeatPump` is a dynamic model based on a ratio of the theoretical COP of Carnot for heat pumps\n",
    "\n",
    "#### Simple models\n",
    "`Profile` is a model that can interpolate any time value from a given profile\n",
    "\n",
    "`Hysteresis` is a simple hysteresis model that return a boolean\n",
    "\n",
    "#### Methods\n",
    "`dtok` transforms °C into K\n",
    "\n",
    "`ktod` transforms K into °C\n",
    "\n",
    "`t_out_from_m_dot_and_p` compute the dT given a mass flow and a heating power and return the output temperature based on the input temperature\n",
    "\n",
    "`m_dot_from_p_and_t_in_and_t_set` compute a mass flow to ensure a given output temperature based on heating power and an input temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from models import ThermalBuffer, HeatPump, Profile, Hysteresis\n",
    "from models import dtok, ktod, t_out_from_m_dot_and_p, m_dot_from_p_and_t_in_and_t_set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "Creation of random synthetic consumption profiles (in kW) with Numpy and Pandas "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <th>2011-01-01 00:00:00</th>\n",
       "      <td>15.591771</td>\n",
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       "      <th>2011-01-01 03:00:00</th>\n",
       "      <td>21.020610</td>\n",
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       "                             a          b          c          d          e  \\\n",
       "2011-01-01 00:00:00  15.591771  11.244383  17.666318  23.815414  16.860878   \n",
       "2011-01-01 01:00:00  16.830781  19.418583  13.951260  18.728109  20.924631   \n",
       "2011-01-01 02:00:00  13.827462  22.998669  17.396562  12.352724  18.811957   \n",
       "2011-01-01 03:00:00  21.020610  24.643619  12.953975  13.435997  15.824736   \n",
       "2011-01-01 04:00:00  17.097919  14.547021  13.085306  18.754166  21.917787   \n",
       "\n",
       "                             f          g          h          i          j  \n",
       "2011-01-01 00:00:00  20.426764  16.842997  23.554703  11.778452  17.185936  \n",
       "2011-01-01 01:00:00  10.957010  17.627015  15.539137  15.407723  11.904494  \n",
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   ],
   "source": [
    "data = np.random.uniform(low=10.0, high=25.0, size=(24*365, 10))\n",
    "rng = pd.date_range('1/1/2011', periods=24*365, freq='H')\n",
    "\n",
    "profiles = pd.DataFrame(data, columns=[x for x in \"abcdefghij\"], index=rng)\n",
    "profiles.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Full model\n",
    "\n",
    "#### Schema of the full model\n",
    "\n",
    "<img src=\"./figures/model_AEE-Intec.png\" alt=\"Model\" style=\"width: 500px;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Parameters\n",
    "`m_dot_pri` is the massflow rate of the primary side, between the heat pump and the thermal storage\n",
    "\n",
    "`t_set_ret_sec` is the set point temperature for return of the consumer on the secondary side"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_dot_pri = 0.15  # kg/s\n",
    "\n",
    "t_set_ret_sec = 40.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "hp = HeatPump(25.0, dtok(80.0), dtok(25.0))\n",
    "hp.io = True  # Initial value -> ON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tb = ThermalBuffer(ex_surf=0.5, mesh_h=2.0/3)\n",
    "tb.t_snk = dtok(t_set_ret_sec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "hy = Hysteresis(x_min=0.25, x_max=0.75)\n",
    "pr = Profile(profiles[\"a\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Full model algorithm\n",
    "\n",
    "STEPS:\n",
    "\n",
    "1. Hysteresis\n",
    "2. Heat pump\n",
    "3. Thermal storage\n",
    "4. Consumer (Profile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def full_model_step(heat_pump, thermal_buffer, hysteresis, profile, step, unit=\"seconds\"):\n",
    "    hysteresis.x = thermal_buffer.soc\n",
    "    hysteresis.make_step()\n",
    "    \n",
    "    heat_pump.io = hysteresis.y\n",
    "    heat_pump.make_step(step, unit)\n",
    "    \n",
    "    if hysteresis.y == 1.0:\n",
    "        thermal_buffer.m_dot_src = m_dot_pri\n",
    "    else:\n",
    "        thermal_buffer.m_dot_src = 0.0\n",
    "        \n",
    "    thermal_buffer.t_src = t_out_from_m_dot_and_p(thermal_buffer.t_bot, m_dot_pri, heat_pump.p_sink)\n",
    "    heat_pump.t_cond = thermal_buffer.t_src \n",
    "    \n",
    "    thermal_buffer.m_dot_snk = m_dot_from_p_and_t_in_and_t_set(profile.value, thermal_buffer.t_top, t_set_ret_sec)\n",
    "    thermal_buffer.make_step(step, unit)\n",
    "    \n",
    "    profile.make_step(step, unit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "end = 12*24*2\n",
    "results = np.array([[0.0, 0.0, 0.0, 0.0]] * end)\n",
    "start = tb.time\n",
    "\n",
    "for i in range(end):\n",
    "    results[i] = [\n",
    "        round(tb.soc, 2), \n",
    "        round(ktod(tb.t_top), 1), \n",
    "        round(ktod(tb.t_mid), 1), \n",
    "        round(ktod(tb.t_bot), 1)\n",
    "    ]\n",
    "    full_model_step(hp, tb, hy, pr, 5, \"minutes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>SoC_%</th>\n",
       "      <th>T_top</th>\n",
       "      <th>T_mid</th>\n",
       "      <th>T_bot</th>\n",
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       "      <th>2000-01-01 00:00:00</th>\n",
       "      <td>0.50</td>\n",
       "      <td>80.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>40.0</td>\n",
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       "      <th>2000-01-01 00:02:00</th>\n",
       "      <td>0.50</td>\n",
       "      <td>79.8</td>\n",
       "      <td>59.8</td>\n",
       "      <td>40.0</td>\n",
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       "      <th>2000-01-01 00:04:00</th>\n",
       "      <td>0.49</td>\n",
       "      <td>79.6</td>\n",
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       "      <th>2000-01-01 00:06:00</th>\n",
       "      <td>0.49</td>\n",
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       "      <td>40.0</td>\n",
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       "      <th>2000-01-01 00:08:00</th>\n",
       "      <td>0.49</td>\n",
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       "                     SoC_%  T_top  T_mid  T_bot\n",
       "2000-01-01 00:00:00   0.50   80.0   60.0   40.0\n",
       "2000-01-01 00:02:00   0.50   79.8   59.8   40.0\n",
       "2000-01-01 00:04:00   0.49   79.6   59.6   40.0\n",
       "2000-01-01 00:06:00   0.49   79.3   59.4   40.0\n",
       "2000-01-01 00:08:00   0.49   79.1   59.2   40.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range(start=start, freq=\"2Min\", periods=len(results)) \n",
    "df = pd.DataFrame(results.tolist(), columns=[\"SoC_%\", \"T_top\", \"T_mid\", \"T_bot\"], index=rng)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
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    "import matplotlib.pyplot as plt"
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       "            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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.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.\n",
       "mpl.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\n",
       "mpl.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",
       " */\n",
       "function 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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.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",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.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 overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.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.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"900\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(9,6))\n",
    "\n",
    "ax1.plot(df.index, df.T_top, color=\"red\")\n",
    "ax1.plot(df.index, df.T_mid, color=\"orange\")\n",
    "ax1.plot(df.index, df.T_bot, color=\"yellow\")\n",
    "\n",
    "ax1.set_xlabel(\"Time (MM-DD HH)\")\n",
    "ax1.set_ylabel(\"Mesh temperature [$^\\circ$C]\")\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(df.index, df[\"SoC_%\"], ':b', alpha=0.5)\n",
    "ax2.set_ylabel(\"SoC\", color='b')\n",
    "ax2.tick_params('y', colors='b')"
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}