VDP_with_sweep_parameters.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### モデルのパラメータを変えたシミュレーションを行う\n",
    "[JModelica User Guild - Version 2.10](https://jmodelica.org/downloads/UsersGuide-2.10.pdf)の5.4.2 を行います。\n",
    "\n",
    "5.2 のVDPモデルについて、パラメータである初期値 $(x_1(0), x_2(0))$ を$(-3,0)$ から $(3,0)$ まで等間隔に変えた11ケースのシミュレーションを行い、各ケースの結果を１個の図にプロットします。横軸 $x_1$、縦軸 $x_2$ の図にすると、各ケースの結果はそれぞれ始点の異なる軌跡となります。 \n",
    "\n",
    "必要となるモジュールをインポートします。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import numpy as N\n",
    "import pylab as P\n",
    "from pymodelica import compile_fmu\n",
    "from pyfmi import load_fmu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "VDP モデルをコンパイルし FMU を作成します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define model file name and class name\n",
    "model_name = 'VDP'\n",
    "mofile = 'VDP.mo'\n",
    "# Compile model\n",
    "fmu_name = compile_fmu(model_name,mofile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "初期値 $(x_1(0), x_2(0))$ の配列を作ります。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define initial conditions\n",
    "N_points = 11\n",
    "x1_0 = N.linspace(-3.,3.,N_points)\n",
    "x2_0 = N.zeros(N_points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "シミュレーションを実行します。 for ループ内が各ケースについての処理を表し、モデルのロード、初期値の設定、シミュレーションの実行、結果の取得、プロットを行っています。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "        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",
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       "\n",
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       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
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       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
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       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
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       "        return fig.key_event(event, event['data']);\n",
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       "\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",
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       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
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       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
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       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
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       "\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",
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       "    this._resize_canvas(600, 600);\n",
       "\n",
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       "        return false;\n",
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       "\n",
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       "        canvas_div.focus();\n",
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       "\n",
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       "\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",
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       "        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"
      ],
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\" 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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Run Statistics: --- \n",
      "\n",
      " Number of steps                                 : 286\n",
      " Number of function evaluations                  : 432\n",
      " Number of Jacobian evaluations                  : 6\n",
      " Number of function eval. due to Jacobian eval.  : 12\n",
      " Number of error test failures                   : 32\n",
      " Number of nonlinear iterations                  : 428\n",
      " Number of nonlinear convergence failures        : 0\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)    : 1e-06\n",
      " Tolerances (relative)    : 0.0001\n",
      "\n",
      "Simulation interval    : 0.0 - 20.0 seconds.\n",
      "Elapsed simulation time: 0.0162968635559 seconds.\n",
      "Final Run Statistics: --- \n",
      "\n",
      " Number of steps                                 : 299\n",
      " Number of function evaluations                  : 428\n",
      " Number of Jacobian evaluations                  : 6\n",
      " Number of function eval. due to Jacobian eval.  : 12\n",
      " Number of error test failures                   : 25\n",
      " Number of nonlinear iterations                  : 424\n",
      " Number of nonlinear convergence failures        : 0\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)    : 1e-06\n",
      " Tolerances (relative)    : 0.0001\n",
      "\n",
      "Simulation interval    : 0.0 - 20.0 seconds.\n",
      "Elapsed simulation time: 0.0186381340027 seconds.\n",
      "Final Run Statistics: --- \n",
      "\n",
      " Number of steps                                 : 288\n",
      " Number of function evaluations                  : 403\n",
      " Number of Jacobian evaluations                  : 6\n",
      " Number of function eval. due to Jacobian eval.  : 12\n",
      " Number of error test failures                   : 24\n",
      " Number of nonlinear iterations                  : 399\n",
      " Number of nonlinear convergence failures        : 0\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)    : 1e-06\n",
      " Tolerances (relative)    : 0.0001\n",
      "\n",
      "Simulation interval    : 0.0 - 20.0 seconds.\n",
      "Elapsed simulation time: 0.0163948535919 seconds.\n",
      "Final Run Statistics: --- \n",
      "\n",
      " Number of steps                                 : 322\n",
      " Number of function evaluations                  : 445\n",
      " Number of Jacobian evaluations                  : 6\n",
      " Number of function eval. due to Jacobian eval.  : 12\n",
      " Number of error test failures                   : 25\n",
      " Number of nonlinear iterations                  : 441\n",
      " Number of nonlinear convergence failures        : 0\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)    : 1e-06\n",
      " Tolerances (relative)    : 0.0001\n",
      "\n",
      "Simulation interval    : 0.0 - 20.0 seconds.\n",
      "Elapsed simulation time: 0.0183138847351 seconds.\n",
      "Final Run Statistics: --- \n",
      "\n",
      " Number of steps                                 : 297\n",
      " Number of function evaluations                  : 432\n",
      " Number of Jacobian evaluations                  : 6\n",
      " Number of function eval. due to Jacobian eval.  : 12\n",
      " Number of error test failures                   : 29\n",
      " Number of nonlinear iterations                  : 428\n",
      " Number of nonlinear convergence failures        : 0\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)    : 1e-06\n",
      " Tolerances (relative)    : 0.0001\n",
      "\n",
      "Simulation interval    : 0.0 - 20.0 seconds.\n",
      "Elapsed simulation time: 0.0169498920441 seconds.\n",
      "Final Run Statistics: --- \n",
      "\n",
      " Number of steps                                 : 3\n",
      " Number of function evaluations                  : 7\n",
      " Number of Jacobian evaluations                  : 1\n",
      " Number of function eval. due to Jacobian eval.  : 2\n",
      " Number of error test failures                   : 0\n",
      " Number of nonlinear iterations                  : 3\n",
      " Number of nonlinear convergence failures        : 0\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)    : 1e-06\n",
      " Tolerances (relative)    : 0.0001\n",
      "\n",
      "Simulation interval    : 0.0 - 20.0 seconds.\n",
      "Elapsed simulation time: 0.00062108039856 seconds.\n",
      "Final Run Statistics: --- \n",
      "\n",
      " Number of steps                                 : 308\n",
      " Number of function evaluations                  : 425\n",
      " Number of Jacobian evaluations                  : 6\n",
      " Number of function eval. due to Jacobian eval.  : 12\n",
      " Number of error test failures                   : 23\n",
      " Number of nonlinear iterations                  : 421\n",
      " Number of nonlinear convergence failures        : 0\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)    : 1e-06\n",
      " Tolerances (relative)    : 0.0001\n",
      "\n",
      "Simulation interval    : 0.0 - 20.0 seconds.\n",
      "Elapsed simulation time: 0.0177299976349 seconds.\n",
      "Final Run Statistics: --- \n",
      "\n",
      " Number of steps                                 : 321\n",
      " Number of function evaluations                  : 443\n",
      " Number of Jacobian evaluations                  : 6\n",
      " Number of function eval. due to Jacobian eval.  : 12\n",
      " Number of error test failures                   : 25\n",
      " Number of nonlinear iterations                  : 439\n",
      " Number of nonlinear convergence failures        : 0\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)    : 1e-06\n",
      " Tolerances (relative)    : 0.0001\n",
      "\n",
      "Simulation interval    : 0.0 - 20.0 seconds.\n",
      "Elapsed simulation time: 0.0185670852661 seconds.\n",
      "Final Run Statistics: --- \n",
      "\n",
      " Number of steps                                 : 288\n",
      " Number of function evaluations                  : 403\n",
      " Number of Jacobian evaluations                  : 6\n",
      " Number of function eval. due to Jacobian eval.  : 12\n",
      " Number of error test failures                   : 24\n",
      " Number of nonlinear iterations                  : 399\n",
      " Number of nonlinear convergence failures        : 0\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)    : 1e-06\n",
      " Tolerances (relative)    : 0.0001\n",
      "\n",
      "Simulation interval    : 0.0 - 20.0 seconds.\n",
      "Elapsed simulation time: 0.0167670249939 seconds.\n",
      "Final Run Statistics: --- \n",
      "\n",
      " Number of steps                                 : 301\n",
      " Number of function evaluations                  : 424\n",
      " Number of Jacobian evaluations                  : 6\n",
      " Number of function eval. due to Jacobian eval.  : 12\n",
      " Number of error test failures                   : 24\n",
      " Number of nonlinear iterations                  : 420\n",
      " Number of nonlinear convergence failures        : 0\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)    : 1e-06\n",
      " Tolerances (relative)    : 0.0001\n",
      "\n",
      "Simulation interval    : 0.0 - 20.0 seconds.\n",
      "Elapsed simulation time: 0.0201840400696 seconds.\n",
      "Final Run Statistics: --- \n",
      "\n",
      " Number of steps                                 : 289\n",
      " Number of function evaluations                  : 427\n",
      " Number of Jacobian evaluations                  : 6\n",
      " Number of function eval. due to Jacobian eval.  : 12\n",
      " Number of error test failures                   : 30\n",
      " Number of nonlinear iterations                  : 423\n",
      " Number of nonlinear convergence failures        : 0\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)    : 1e-06\n",
      " Tolerances (relative)    : 0.0001\n",
      "\n",
      "Simulation interval    : 0.0 - 20.0 seconds.\n",
      "Elapsed simulation time: 0.0179991722107 seconds.\n"
     ]
    }
   ],
   "source": [
    "fig = P.figure()\n",
    "P.clf()\n",
    "# P.hold(True)\n",
    "P.xlabel('x1')\n",
    "P.ylabel('x2')\n",
    "\n",
    "for i in range(N_points):\n",
    "      # Load model\n",
    "      vdp = load_fmu(fmu_name)\n",
    "      # Set initial conditions in model\n",
    "      vdp.set('x1_0',x1_0[i])\n",
    "      vdp.set('x2_0',x2_0[i])\n",
    "      # Simulate\n",
    "      res = vdp.simulate(final_time=20)\n",
    "      # Get simulation result\n",
    "      x1=res['x1']\n",
    "      x2=res['x2']\n",
    "      # Plot simulation result in phase plane plot\n",
    "      P.plot(x1, x2,'b')\n",
    "P.grid()\n",
    "P.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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JModelica.org 使い方メモ