ClassExample1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Package 内の Class のシミュレーションを実行する\n",
    "[Modelicaのクラスの概要](https://www.amane.to/wp-content/uploads/2018/12/8ec4f21241c98ee8413280240090c942.pdf) の ClassExample1.mo の中のクラス MassA　のシミュレーションを実行します。\n",
    "\n",
    "このモデルは、質点の放物運動を表すモデルで、運動方程式\n",
    "$$\n",
    "v=\\frac{dx}{dt}, \\ f=m \\frac{dv}{dt} \n",
    "$$\n",
    "を解きます。パラメータを、\n",
    "$$\n",
    "m = 1.0, \\  f=mg, \\ g=-9.8\n",
    "$$\n",
    "とし、初期条件を\n",
    "$$\n",
    "x_0 = 0.0, v_0 = 5.0 \n",
    "$$\n",
    "とします。\n",
    "\n",
    "Modelica のコードをMagicコマンド %%writefile で出力します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile ClassExample1.mo\n",
    "package ClassExample1 // package\n",
    "  constant Acceleration g = -9.8;\n",
    "\n",
    "  // type\n",
    "  type Mass = Real(quantity = \"Mass\", unit = \"kg\");\n",
    "  type Force = Real(quantity = \"Force\", unit = \"N\");\n",
    "  type Velocity = Real(quantity = \"Velocity\", unit = \"m/s\");\n",
    "  type Position = Real(quantity = \"Length\", unit = \"m\");\n",
    "  type Acceleration = Real(quantity = \"Acceleration\", unit = \"m/s2\");\n",
    "  \n",
    "  // class\n",
    "  class MassA\n",
    "    parameter Mass m = 1.0;\n",
    "    parameter Force f = m*g;\n",
    "    parameter Velocity v0 = 5.0;\n",
    "    parameter Position x0 = 0.0;\n",
    "    Position x(start = x0);\n",
    "    Velocity v(start = v0);\n",
    "  equation\n",
    "      v = der(x);\n",
    "      f = m*der(v);\n",
    "  end MassA;\n",
    "\n",
    "end ClassExample1;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "クラス MassA をコンパイルして FMU を作成します。compile_fmu の引数はクラス名とファイル名です。クラス名はパッケージ名から辿ったフルネームです。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the function for compilation of models and the load_fmu method\n",
    "from pymodelica import compile_fmu\n",
    "from pyfmi import load_fmu\n",
    "# Compile model\n",
    "fmu_name = compile_fmu(\"ClassExample1.MassA\",\"ClassExample1.mo\")\n",
    "# Load model\n",
    "model = load_fmu(fmu_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "シミュレーションを実行します。オプション ncp は Number of communication points で、ソルバーとFMUが通信する回数を表しています。これを 0 にするとソルバー内部の時間ステップになります。オプションの詳細は [JModelica User Guide - Version 2.10](https://jmodelica.org/downloads/UsersGuide-2.10.pdf) P.26-2９ を参照してください。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Run Statistics: --- \n",
      "\n",
      " Number of steps                                 : 15\n",
      " Number of function evaluations                  : 19\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                  : 15\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 - 1.0 seconds.\n",
      "Elapsed simulation time: 0.0149250030518 seconds.\n"
     ]
    }
   ],
   "source": [
    "opts = model.simulate_options()\n",
    "opts['ncp']=500\n",
    "res = model.simulate(final_time=1.,options=opts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "シミュレーション結果をプロットします。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
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       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
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       "\n",
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       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
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       "}\n",
       "\n",
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       "\n",
       "\n",
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       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
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       "    };\n",
       "}\n",
       "\n",
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       "\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",
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       "        break;\n",
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       "        break;\n",
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       "}\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",
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       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
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       "    // 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": {
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width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "# Import the plotting library\n",
    "import matplotlib.pyplot as plt \n",
    "x = res['x']\n",
    "v = res['v']\n",
    "t = res['time']\n",
    "\n",
    "plt.figure(1)\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(t, x)\n",
    "plt.ylabel('Position (m)')\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(t,v)\n",
    "plt.ylabel('velocity (m/s)')\n",
    "plt.xlabel('Time (s)')\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