python_c_modules.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performance difference between Python and C\n",
    "Python is a interpreted language and therefore slower for most applications. However, correct use of numeric libraries yields a significant speedup\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64 float64 float64\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import time\n",
    "N = 1000000\n",
    "a = np.random.rand(N)\n",
    "out1 = np.zeros(N)\n",
    "out2 = np.zeros(N)\n",
    "print a.dtype, out1.dtype, out2.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.744857\n",
      "0.411321\n",
      "0.398875\n"
     ]
    }
   ],
   "source": [
    "#slow elementwise exponential function\n",
    "import math\n",
    "for k in xrange(3):\n",
    "    t0 = time.clock()\n",
    "    \n",
    "    for i in xrange(a.size): \n",
    "        out1[i] = math.exp(a[i])\n",
    "    \n",
    "    print time.clock() - t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.02135\n",
      "0.019969\n",
      "0.017721\n"
     ]
    }
   ],
   "source": [
    "#correct use of numeric libraries\n",
    "for k in xrange(3):\n",
    "    t0 = time.clock()\n",
    "    \n",
    "    out2 = np.exp(a)\n",
    "    \n",
    "    print time.clock() - t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "# both ways of elementwise exponentiation have identical results.\n",
    "print np.max(np.abs(out1-out2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Numeric solution of differential equations\n",
    "Both ways use dopri5 solver with equal precissions. Due to the chaotic nature of the differential equation they die not agree on long term behaviour. The pure C-Module is significantly faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  6.04252857,  -1.50134487,  32.60628488, 100.        ])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This uses a numeric library for solving the ode, but since the function is defined on Python level, \n",
    "#it is way slower than the C-Module\n",
    "from scipy.integrate import ode\n",
    "\n",
    "y0 = [10.,1.,1.,0.]\n",
    "\n",
    "def f(t, x, fargs):\n",
    "    sigma, R, b = 10., 28., 8./3.\n",
    "    \n",
    "    return [sigma * ( x[1] - x[0] ),\n",
    "            R * x[0] - x[1] - x[0] * x[2],\n",
    "           -b * x[2] + x[0] * x[1],\n",
    "           1.]\n",
    "r = ode(f)\n",
    "r.set_integrator('dopri5', atol=1E-12, rtol=1E-12, nsteps=100000000)\n",
    "r.set_f_params(None)\n",
    "r.set_initial_value(y0,0)\n",
    "r.integrate(100.)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -14.37071006  -17.55357795   31.45138625  100.        ]\n"
     ]
    }
   ],
   "source": [
    "# this uses dopri5 with equal precission, but the precission is defined in the C-code\n",
    "%matplotlib notebook\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import lorentz\n",
    "\n",
    "pars= {'sigma' : 10.,\n",
    "       'R' : 28.,\n",
    "       'b' : 8./3.,\n",
    "       'dt': 0.001,\n",
    "       'numsteps' : 100000}\n",
    "\n",
    "outarray = np.zeros((pars['numsteps']+ 1, 4))\n",
    "outarray[0,:] = np.array([10.,1.,1.,0.])\n",
    "\n",
    "lorentz.solve(outarray, pars)\n",
    "print outarray[-1,:]\n",
    "#plt.plot(outarray[:,1],outarray[:,2])\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\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",
       "            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",
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       "                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",
       "        this.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 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);\n",
       "        canvas.attr('height', height);\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'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\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;\n",
       "    var y = canvas_pos.y;\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",
       "    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 + '\">');\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 dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#The way I wrote it, the C-Module also outputs the full trajectory. It can be plottet using matplotlib\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "fig = plt.figure()\n",
    "ax = fig.gca(projection='3d')\n",
    "ax.plot(outarray[:,0],outarray[:,1],outarray[:,2], label='parametric curve')\n",
    "ax.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}