ClassExample9.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelica.Media を使って室温の変化を調べる\n",
    "\n",
    "Modelica.Media ライブラリを使用して、圧力を一定に保った部屋を加熱するモデルです。部屋には通気孔があり、膨張した空気が流出します。\n",
    "これは [1. Modelicaのクラスの概要](https://www.amane.to/wp-content/uploads/2018/12/8ec4f21241c98ee8413280240090c942.pdf)の ClassExample9 として作成したもので、Modelica.Mediaライブラリの BaseProperties モデルの使い方を示す例題です。\n",
    "\n",
    "\n",
    "部屋の容積を $V = 22.0$ m3 (4畳半程度)、圧力を $p_{amb} = 101325$ Pa (1気圧)、初期温度を $T_{amb} = 293.15$ K、加熱量を $Q_{flow} = 100$ Wとします。\n",
    "\n",
    "空気の密度を $\\rho$、比内部エネルギーを $u$ とすると、室内の空気の質量 $M$ と内部エネルギー $U$ は、 \n",
    "$$ M = \\rho V $$\n",
    "と\n",
    "$$ U = M u $$\n",
    "となります。質量保存式とエネルギー保存式は、\n",
    "$$ \\frac{dM}{dt} = m_{flow} $$\n",
    "と\n",
    "$$ \\frac{dU}{dt} = Q_{flow} + h \\cdot m_{flow} $$\n",
    "となります。圧力は、\n",
    "$$ p = p_{amb}$$\n",
    "となります。\n",
    "\n",
    "これらの方程式を実装したモデルを作成します。$m_{flow}$は質量流量で流出する場合は負になります。$h$ は比エンタルピです。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting ClassExample9_1.mo\n"
     ]
    }
   ],
   "source": [
    "%%writefile ClassExample9_1.mo\n",
    "package ClassExample9_1\n",
    "  import Modelica.Media;\n",
    "  import SI = Modelica.SIunits;\n",
    "  \n",
    "  model RoomA\n",
    "    package Medium = Media.Air.DryAirNasa;\n",
    "    parameter SI.Temperature T_amb = 293.15;\n",
    "    parameter SI.Pressure p_amb = 101325;\n",
    "    parameter SI.Volume V = 22.0;\n",
    "    parameter SI.HeatFlowRate Q_flow = 100;\n",
    "    \n",
    "    Medium.BaseProperties medium;\n",
    "    SI.MassFlowRate m_flow(start = 0.0);\n",
    "    SI.Mass M;\n",
    "    SI.Energy U;\n",
    "  equation\n",
    "    M = medium.d * V;\n",
    "    U = medium.u * M;\n",
    "    der(M) = m_flow;\n",
    "    der(U) = Q_flow + medium.h * m_flow;\n",
    "    medium.p = p_amb;\n",
    "  initial equation\n",
    "     medium.T = T_amb;\n",
    "  end RoomA;\n",
    "\n",
    "end ClassExample9_1;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "モデルをコンパイルして FMU を作成します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymodelica import compile_fmu\n",
    "fmu1 = compile_fmu('ClassExample9_1.RoomA', 'ClassExample9_1.mo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FMU をロードして、シミュレーションを実行します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Run Statistics: --- \n",
      "\n",
      " Number of steps                                 : 3\n",
      " Number of function evaluations                  : 9\n",
      " Number of Jacobian evaluations                  : 1\n",
      " Number of function eval. due to Jacobian eval.  : 1\n",
      " Number of error test failures                   : 0\n",
      " Number of nonlinear iterations                  : 5\n",
      " Number of nonlinear convergence failures        : 0\n",
      " Number of state function evaluations            : 4\n",
      "\n",
      "Solver options:\n",
      "\n",
      " Solver                   : CVode\n",
      " Linear multistep method  : BDF\n",
      " Nonlinear solver         : Newton\n",
      " Linear solver type       : DENSE\n",
      " Maximal order            : 5\n",
      " Tolerances (absolute)    : 0.0005\n",
      " Tolerances (relative)    : 0.0001\n",
      "\n",
      "Simulation interval    : 0.0 - 2000.0 seconds.\n",
      "Elapsed simulation time: 0.000372886657715 seconds.\n"
     ]
    }
   ],
   "source": [
    "from pyfmi import load_fmu\n",
    "model1 = load_fmu(fmu1)\n",
    "res1 = model1.simulate(final_time = 2000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "シミュレーション結果の部屋の温度と流出する空気の質量流量をプロットします。流出流量を正にするためにマイナス符号を付けています。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.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",
       "\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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "t1 = res1['time']\n",
    "T1 = res1['medium.T']\n",
    "m_flow1 = res1['m_flow']\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(1)\n",
    "plt.subplot(2,1,1)\n",
    "plt.xlim(0,2000)\n",
    "plt.plot(t1,T1)\n",
    "plt.legend(['Temperature [K]'], loc='lower right')\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.xlim(0,2000)\n",
    "plt.plot(t1,-m_flow1)\n",
    "plt.legend(['Mass Flow [kg/s]'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RoomA の熱や流体の出入り口をコネクタで表現したモデル RoomB を作成します。熱の出入りを HeatPort_a で表し、通気孔に流体の出入りを FluidPort_b で表します。そして、RoomB と熱源を表す FixedHeatFlow および圧力境界を表す Boundary_pT を接続したテストモデル RoomBTest を作成します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting ClassExample9_2.mo\n"
     ]
    }
   ],
   "source": [
    "%%writefile ClassExample9_2.mo\n",
    "package ClassExample9_2\n",
    "  import Modelica.Media;\n",
    "  import SI = Modelica.SIunits;\n",
    "\n",
    "  model RoomB\n",
    "    replaceable package Medium = Media.Air.DryAirNasa;\n",
    "    parameter SI.Volume V = 22.0;\n",
    "    Modelica.Fluid.Interfaces.FluidPort_b port_b(redeclare package Medium = Medium);\n",
    "    Modelica.Thermal.HeatTransfer.Interfaces.HeatPort_a port_a;\n",
    "    Medium.BaseProperties medium;\n",
    "    SI.Mass M;\n",
    "    SI.Energy U;\n",
    "  equation\n",
    "    M = medium.d * V;\n",
    "    U = medium.u * M;\n",
    "    der(M) = port_b.m_flow;\n",
    "    der(U) = port_a.Q_flow + actualStream(port_b.h_outflow) * port_b.m_flow;\n",
    "    port_b.p = medium.p;\n",
    "    port_b.h_outflow = medium.h;\n",
    "    port_a.T = medium.T;\n",
    "    initial equation\n",
    "    medium.T = 293.15;\n",
    "  end RoomB;\n",
    "  \n",
    "  model RoomBTest\n",
    "    replaceable package Medium = Media.Air.DryAirNasa;\n",
    "    RoomB roomB1(redeclare package Medium = Medium);\n",
    "    Modelica.Thermal.HeatTransfer.Sources.FixedHeatFlow fixedHeatFlow1(Q_flow = 100);\n",
    "    Modelica.Fluid.Sources.Boundary_pT boundary(\n",
    "      redeclare package Medium = Medium,\n",
    "      T = 293.15, nPorts = 1, p = 101325);\n",
    "  equation\n",
    "    connect(roomB1.port_b, boundary.ports[1]);\n",
    "    connect(fixedHeatFlow1.port, roomB1.port_a);\n",
    "  end RoomBTest;\n",
    "  \n",
    "end ClassExample9_2;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "fmu2 = compile_fmu('ClassExample9_2.RoomBTest', 'ClassExample9_2.mo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Run Statistics: --- \n",
      "\n",
      " Number of steps                                 : 3\n",
      " Number of function evaluations                  : 9\n",
      " Number of Jacobian evaluations                  : 1\n",
      " Number of function eval. due to Jacobian eval.  : 1\n",
      " Number of error test failures                   : 0\n",
      " Number of nonlinear iterations                  : 5\n",
      " Number of nonlinear convergence failures        : 0\n",
      " Number of state function evaluations            : 4\n",
      "\n",
      "Solver options:\n",
      "\n",
      " Solver                   : CVode\n",
      " Linear multistep method  : BDF\n",
      " Nonlinear solver         : Newton\n",
      " Linear solver type       : DENSE\n",
      " Maximal order            : 5\n",
      " Tolerances (absolute)    : 0.0005\n",
      " Tolerances (relative)    : 0.0001\n",
      "\n",
      "Simulation interval    : 0.0 - 2000.0 seconds.\n",
      "Elapsed simulation time: 0.000428915023804 seconds.\n"
     ]
    }
   ],
   "source": [
    "from pyfmi import load_fmu\n",
    "model2 = load_fmu(fmu2)\n",
    "res2 = model2.simulate(final_time = 2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.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",
       "\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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t2 = res2['time']\n",
    "T2 = res2['roomB1.medium.T']\n",
    "m_flow2 = res2['boundary.ports[1].m_flow']\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(2)\n",
    "plt.subplot(2,1,1)\n",
    "plt.xlim(0,2000)\n",
    "plt.plot(t2,T2)\n",
    "plt.legend(['Temperature [K]'], loc='lower right')\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.xlim(0,2000)\n",
    "plt.plot(t2,m_flow2)\n",
    "plt.legend(['Mass Flow [kg/s]'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

JModelica.org 使い方メモ