Epicycle-Drawer-svg-input.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math, numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib notebook \n",
    "\n",
    "import sys\n",
    "sys.path.insert(1, '_helperFuncs')\n",
    "# self-written helper functions\n",
    "import fourier_funcs as ff\n",
    "import animation_funcs as af\n",
    "import drawing_funcs as df\n",
    "\n",
    "#for saving animations\n",
    "import matplotlib.animation as animation   \n",
    "Writer = animation.writers['ffmpeg']\n",
    "writer = Writer(metadata=dict(artist='Ahish'), bitrate=1800)\n",
    "\n",
    "import svgpathtools as spt\n",
    "from svgpathtools import Path, Line, CubicBezier, svg2paths\n",
    "import scipy.interpolate as interpolate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# settings\n",
    "sample_percent = 1 #percentage of original no. of points to be sampled\n",
    "save_anim = 0 #if animation is to be saved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gets the drawing input\n",
    "# df.drawingBoard().run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "fname = \"img/input/d4.svg\"\n",
    "paths, attributes = svg2paths(fname)\n",
    "\n",
    "print(len(paths))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_SAMPLES = 400\n",
    "\n",
    "myPath = []\n",
    "\n",
    "for j in range(len(paths)):\n",
    "    for i in range(NUM_SAMPLES):\n",
    "    #     myPath.append((paths.point(i/(NUM_SAMPLES-1)).real, paths.point(i/(NUM_SAMPLES-1)).imag))\n",
    "        myPath.append(paths[j].point(i/(NUM_SAMPLES-1)))\n",
    "\n",
    "\n",
    "\n",
    "sampled_points = np.asarray(myPath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate complex points sampled numpy array\n",
    "# sampled_points = np.asarray(df.points_touched)[::int(1/sample_percent)]\n",
    "\n",
    "#calculates DFT\n",
    "N = len(sampled_points)\n",
    "points_fft = np.fft.fft(sampled_points, N)\n",
    "\n",
    "#calculates the inverse fourier to verify\n",
    "generated_function = ff.inv(points_fft, N)\n",
    "\n",
    "#gets real and imaginary part of inverse function\n",
    "real_pts = np.real(generated_function)\n",
    "imag_pts = np.imag(generated_function)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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 overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_dft_op = plt.figure(\"Output of 3D Sinusoidal Signal Generated\")\n",
    "plt.title(\"Output of Fourier Transform\")\n",
    "plt.plot(real_pts, imag_pts)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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 overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_gen_pts = plt.figure(\"Generated Points\")\n",
    "plt.scatter(real_pts, imag_pts, color='red')\n",
    "plt.title(\"Scatterplot of Output of Transform\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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 overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"900\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_cmp = plt.figure(\n",
    "                     \"Comparison of Generated Signal vs \\\n",
    "                     Original Drawing\", figsize=(9,5)\n",
    "                    )\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.scatter(real_pts, imag_pts, color='red')\n",
    "plt.title(\"Generated Points\")\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.scatter(np.real(sampled_points), np.imag(sampled_points), color='blue')\n",
    "plt.title(\"Samples from Drawing\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# animation & drawing\n",
    "abs_fft = np.absolute(points_fft)/N\n",
    "phase_fft = np.angle(points_fft)\n",
    "n_circles = len(points_fft)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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 overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Epicycle 2D Drawing Animation\n",
    "fig_epic = plt.figure(\"Epicyclic Drawing of Generated Function\")\n",
    "circles = af.get_init_vals(n_circles, abs_fft, phase_fft)\n",
    "ax = plt.axes()\n",
    "\n",
    "def init_epic():\n",
    "    \n",
    "    #initial position of circles\n",
    "    init_cs_pos = af.pos_at(0, abs_fft, phase_fft)\n",
    "    for i in range(1, n_circles):\n",
    "        circles[i].center = init_cs_pos[i]\n",
    "        ax.add_patch(circles[i])\n",
    "    \n",
    "    #shows current location of each circle and final circle\n",
    "    global dot_sc\n",
    "    global sc\n",
    "    dot_sc = ax.scatter(\n",
    "                        [x[0] for x in init_cs_pos[1:]], \n",
    "                        [x[1] for x in init_cs_pos[1:]], color='g'\n",
    "                        )  \n",
    "    sc = ax.scatter(real_pts[0], imag_pts[0], lw=5, color='g')\n",
    "    \n",
    "    return circles\n",
    "\n",
    "def animate_epic(curr_frame):\n",
    "\n",
    "    #updating circles\n",
    "    new_cs_pos = af.pos_at(curr_frame, abs_fft, phase_fft)\n",
    "    for m in range(1, n_circles):\n",
    "        circles[m].center = new_cs_pos[m]\n",
    "\n",
    "    # plotting generated function\n",
    "    ax.plot(real_pts[:curr_frame+1], imag_pts[:curr_frame+1], color='r')\n",
    "    ax.axis(\"scaled\")\n",
    "\n",
    "    #removing & changing location of scattered points\n",
    "    global dot_sc\n",
    "    global sc\n",
    "    dot_sc.remove()\n",
    "    sc.remove()\n",
    "    dot_sc = ax.scatter(\n",
    "                [x[0] for x in new_cs_pos[1:]], \n",
    "                [x[1] for x in new_cs_pos[1:]], \n",
    "                color='black', lw=0.01, alpha=0.75\n",
    "                )\n",
    "    sc = ax.scatter(real_pts[curr_frame], imag_pts[curr_frame], lw=5, color='g')\n",
    "\n",
    "    #shows frame number\n",
    "    plt.title(\"t = \"+str(curr_frame))\n",
    "\n",
    "    return circles\n",
    "\n",
    "anim_epic = animation.FuncAnimation(\n",
    "                        fig_epic, animate_epic, \n",
    "                        init_func=init_epic, frames=N, \n",
    "                        interval=20, blit=True, repeat=False\n",
    "                        )\n",
    "\n",
    "plt.show()\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "CalledProcessError",
     "evalue": "Command '['ffmpeg', '-f', 'rawvideo', '-vcodec', 'rawvideo', '-s', '1280x960', '-pix_fmt', 'rgba', '-r', '5', '-loglevel', 'error', '-i', 'pipe:', '-vcodec', 'h264', '-pix_fmt', 'yuv420p', '-b', '1800k', '-metadata', 'artist=Ahish', '-y', 'current.mp4']' died with <Signals.SIGABRT: 6>.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mBrokenPipeError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[0;32m~/epicircle/lib/python3.6/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36msaving\u001b[0;34m(self, fig, outfile, dpi, *args, **kwargs)\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 230\u001b[0;31m             \u001b[0;32myield\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    231\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/epicircle/lib/python3.6/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, filename, writer, fps, dpi, codec, bitrate, extra_args, metadata, extra_anim, savefig_kwargs, progress_callback)\u001b[0m\n\u001b[1;32m   1155\u001b[0m                             \u001b[0mframe_number\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1156\u001b[0;31m                     \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrab_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0msavefig_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1157\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/epicircle/lib/python3.6/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36mgrab_frame\u001b[0;34m(self, **savefig_kwargs)\u001b[0m\n\u001b[1;32m    383\u001b[0m         self.fig.savefig(self._frame_sink(), format=self.frame_format,\n\u001b[0;32m--> 384\u001b[0;31m                          dpi=self.dpi, **savefig_kwargs)\n\u001b[0m\u001b[1;32m    385\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/epicircle/lib/python3.6/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[1;32m   2179\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2180\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/epicircle/lib/python3.6/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m   2088\u001b[0m                     \u001b[0mbbox_inches_restore\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_bbox_inches_restore\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2089\u001b[0;31m                     **kwargs)\n\u001b[0m\u001b[1;32m   2090\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/epicircle/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_raw\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    445\u001b[0m                 \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_file_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"wb\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfh\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 446\u001b[0;31m             \u001b[0mfh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_renderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    447\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mBrokenPipeError\u001b[0m: [Errno 32] Broken pipe",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mCalledProcessError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-cd149b44ac13>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0msave_anim\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0manim_epic\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"current.mp4\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwriter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwriter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/epicircle/lib/python3.6/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, filename, writer, fps, dpi, codec, bitrate, extra_args, metadata, extra_anim, savefig_kwargs, progress_callback)\u001b[0m\n\u001b[1;32m   1154\u001b[0m                             \u001b[0mprogress_callback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe_number\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_frames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1155\u001b[0m                             \u001b[0mframe_number\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1156\u001b[0;31m                     \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrab_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0msavefig_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1157\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1158\u001b[0m         \u001b[0;31m# Reconnect signal for first draw if necessary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/contextlib.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, type, value, traceback)\u001b[0m\n\u001b[1;32m     97\u001b[0m                 \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 99\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraceback\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    100\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m                 \u001b[0;31m# Suppress StopIteration *unless* it's the same exception that\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/epicircle/lib/python3.6/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36msaving\u001b[0;34m(self, fig, outfile, dpi, *args, **kwargs)\u001b[0m\n\u001b[1;32m    230\u001b[0m             \u001b[0;32myield\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 232\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfinish\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    233\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/epicircle/lib/python3.6/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36mfinish\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    365\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfinish\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    366\u001b[0m         \u001b[0;34m'''Finish any processing for writing the movie.'''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 367\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcleanup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    368\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    369\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mgrab_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msavefig_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/epicircle/lib/python3.6/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36mcleanup\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    409\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_proc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturncode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    410\u001b[0m             raise subprocess.CalledProcessError(\n\u001b[0;32m--> 411\u001b[0;31m                 self._proc.returncode, self._proc.args, out, err)\n\u001b[0m\u001b[1;32m    412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    413\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mCalledProcessError\u001b[0m: Command '['ffmpeg', '-f', 'rawvideo', '-vcodec', 'rawvideo', '-s', '1280x960', '-pix_fmt', 'rgba', '-r', '5', '-loglevel', 'error', '-i', 'pipe:', '-vcodec', 'h264', '-pix_fmt', 'yuv420p', '-b', '1800k', '-metadata', 'artist=Ahish', '-y', 'current.mp4']' died with <Signals.SIGABRT: 6>."
     ]
    }
   ],
   "source": [
    "# override\n",
    "save_anim = 1\n",
    "\n",
    "if save_anim:\n",
    "    anim_epic.save(\"current.mp4\", writer=writer)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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 overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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width=\"500\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3D animation\n",
    "\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import mpl_toolkits.mplot3d.art3d as art3d\n",
    "\n",
    "fig_3d = plt.figure(\"3D Plot\", figsize=(10,5))\n",
    "ax_1 = fig_3d.add_subplot(131, projection='3d')\n",
    "ax_2 = fig_3d.add_subplot(132, projection='3d')\n",
    "ax_3 = fig_3d.add_subplot(133, projection='3d')\n",
    "\n",
    "#first subplot\n",
    "ax_1.view_init(8,-55)\n",
    "ax_1.set_xlabel('t')\n",
    "ax_1.set_ylim3d([0, 600])\n",
    "ax_1.set_ylabel('X')\n",
    "ax_1.set_zlim3d([0, 600])\n",
    "ax_1.set_zlabel('Y')\n",
    "ax_1.invert_xaxis()\n",
    "ax_1.title.set_text(\"3D-View\")\n",
    "\n",
    "#second subplot\n",
    "ax_2.view_init(0, 0)\n",
    "ax_2.set_xlabel('t')\n",
    "ax_2.set_ylim3d([0, 600])\n",
    "ax_2.set_ylabel('X')\n",
    "ax_2.set_zlim3d([0, 600])\n",
    "ax_2.set_zlabel('Y')\n",
    "ax_2.invert_xaxis()\n",
    "ax_2.title.set_text(\"2D Projection\\nIgnoring time\")\n",
    "\n",
    "#third subplot\n",
    "ax_3.view_init(0,-90)\n",
    "ax_3.set_xlabel('t')\n",
    "ax_3.set_ylim3d([0, 600])\n",
    "ax_3.set_ylabel('X')\n",
    "ax_3.set_zlim3d([0, 600])\n",
    "ax_3.set_zlabel('Y')\n",
    "ax_3.invert_xaxis()\n",
    "ax_3.title.set_text(\"2D Projection\\nIgnoring Complex Part of DFT\")\n",
    "\n",
    "def animate_3d(curr_frame, circles1, circles2, circles3):\n",
    "    \n",
    "    #updates the circles\n",
    "    new_cs_pos = af.pos_at(curr_frame, abs_fft, phase_fft)\n",
    "    for m, c1, c2, c3, new_c_pos in zip(\n",
    "                                        range(1, n_circles), circles1, \n",
    "                                        circles2, circles3, \n",
    "                                        new_cs_pos[1:]\n",
    "                                        ):\n",
    "        \n",
    "        ym, zm = af.get_circle(abs_fft[m], new_c_pos)\n",
    "        c1.set_data_3d([curr_frame for k in range(len(ym))], ym, zm)\n",
    "        c2.set_data_3d([curr_frame for k in range(len(ym))], ym, zm)\n",
    "        c3.set_data_3d([curr_frame for k in range(len(ym))], ym, zm)\n",
    "    \n",
    "    #updates the drawing\n",
    "    ax_1.plot(\n",
    "            range(curr_frame+1), real_pts[:curr_frame+1], \n",
    "            imag_pts[:curr_frame+1], color='b'\n",
    "            ) \n",
    "    ax_2.plot(\n",
    "            range(curr_frame+1), real_pts[:curr_frame+1], \n",
    "            imag_pts[:curr_frame+1], color='b'\n",
    "            )\n",
    "    ax_3.plot(\n",
    "            range(curr_frame+1), real_pts[:curr_frame+1], \n",
    "            imag_pts[:curr_frame+1], color='b'\n",
    "            )\n",
    "    \n",
    "    #extending x-axis limits to include current frame\n",
    "    ax_2.set_xlim3d([0.0, 1.5*curr_frame])\n",
    "    ax_1.set_xlim3d([0.0, curr_frame])\n",
    "    ax_3.set_xlim3d([0.0, 1.5*curr_frame])\n",
    "    \n",
    "    return circles1\n",
    "\n",
    "#initializing anim values\n",
    "circles1 = []\n",
    "circles2 = []\n",
    "circles3 = []\n",
    "init_cs_pos = af.pos_at(0, abs_fft, phase_fft)\n",
    "for m in range(1, n_circles):\n",
    "    ym, zm = af.get_circle(abs_fft[m], init_cs_pos[m])\n",
    "    circles1 += ax_1.plot(\n",
    "                          [0 for k in range(len(ym))], \n",
    "                          ym, zm, color='black', alpha = 0.7\n",
    "                         )\n",
    "    circles2 += ax_2.plot(\n",
    "                          [0 for k in range(len(ym))], \n",
    "                          ym, zm, color='black', alpha = 0.4\n",
    "                         )\n",
    "    circles3 += ax_3.plot(\n",
    "                          [0 for k in range(len(ym))], \n",
    "                          ym, zm, color='black'\n",
    "                         )\n",
    "    \n",
    "anim_3d = animation.FuncAnimation(\n",
    "                        fig_3d, animate_3d, frames=N, \n",
    "                        fargs=(circles1, circles2, circles3), \n",
    "                        interval=20, blit=True, repeat=False\n",
    "                        )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "CalledProcessError",
     "evalue": "Command '['ffmpeg', '-f', 'rawvideo', '-vcodec', 'rawvideo', '-s', '2000x1000', '-pix_fmt', 'rgba', '-r', '5', '-loglevel', 'error', '-i', 'pipe:', '-vcodec', 'h264', '-pix_fmt', 'yuv420p', '-b', '1800k', '-metadata', 'artist=Ahish', '-y', 'current-3D.mp4']' died with <Signals.SIGABRT: 6>.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mBrokenPipeError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[0;32m~/epicircle/lib/python3.6/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36msaving\u001b[0;34m(self, fig, outfile, dpi, *args, **kwargs)\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 230\u001b[0;31m             \u001b[0;32myield\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    231\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/epicircle/lib/python3.6/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, filename, writer, fps, dpi, codec, bitrate, extra_args, metadata, extra_anim, savefig_kwargs, progress_callback)\u001b[0m\n\u001b[1;32m   1155\u001b[0m                             \u001b[0mframe_number\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1156\u001b[0;31m                     \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrab_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0msavefig_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1157\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/epicircle/lib/python3.6/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36mgrab_frame\u001b[0;34m(self, **savefig_kwargs)\u001b[0m\n\u001b[1;32m    383\u001b[0m         self.fig.savefig(self._frame_sink(), format=self.frame_format,\n\u001b[0;32m--> 384\u001b[0;31m                          dpi=self.dpi, **savefig_kwargs)\n\u001b[0m\u001b[1;32m    385\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/epicircle/lib/python3.6/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[1;32m   2179\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2180\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/epicircle/lib/python3.6/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m   2088\u001b[0m                     \u001b[0mbbox_inches_restore\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_bbox_inches_restore\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2089\u001b[0;31m                     **kwargs)\n\u001b[0m\u001b[1;32m   2090\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/epicircle/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_raw\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    445\u001b[0m                 \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_file_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"wb\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfh\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 446\u001b[0;31m             \u001b[0mfh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_renderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    447\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mBrokenPipeError\u001b[0m: [Errno 32] Broken pipe",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mCalledProcessError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-1e8dc923946e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0msave_anim\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0manim_3d\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"current-3D.mp4\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwriter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwriter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/epicircle/lib/python3.6/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, filename, writer, fps, dpi, codec, bitrate, extra_args, metadata, extra_anim, savefig_kwargs, progress_callback)\u001b[0m\n\u001b[1;32m   1154\u001b[0m                             \u001b[0mprogress_callback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe_number\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_frames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1155\u001b[0m                             \u001b[0mframe_number\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1156\u001b[0;31m                     \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrab_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0msavefig_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1157\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1158\u001b[0m         \u001b[0;31m# Reconnect signal for first draw if necessary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/contextlib.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, type, value, traceback)\u001b[0m\n\u001b[1;32m     97\u001b[0m                 \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 99\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraceback\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    100\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m                 \u001b[0;31m# Suppress StopIteration *unless* it's the same exception that\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/epicircle/lib/python3.6/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36msaving\u001b[0;34m(self, fig, outfile, dpi, *args, **kwargs)\u001b[0m\n\u001b[1;32m    230\u001b[0m             \u001b[0;32myield\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 232\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfinish\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    233\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/epicircle/lib/python3.6/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36mfinish\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    365\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfinish\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    366\u001b[0m         \u001b[0;34m'''Finish any processing for writing the movie.'''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 367\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcleanup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    368\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    369\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mgrab_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msavefig_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/epicircle/lib/python3.6/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36mcleanup\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    409\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_proc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturncode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    410\u001b[0m             raise subprocess.CalledProcessError(\n\u001b[0;32m--> 411\u001b[0;31m                 self._proc.returncode, self._proc.args, out, err)\n\u001b[0m\u001b[1;32m    412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    413\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mCalledProcessError\u001b[0m: Command '['ffmpeg', '-f', 'rawvideo', '-vcodec', 'rawvideo', '-s', '2000x1000', '-pix_fmt', 'rgba', '-r', '5', '-loglevel', 'error', '-i', 'pipe:', '-vcodec', 'h264', '-pix_fmt', 'yuv420p', '-b', '1800k', '-metadata', 'artist=Ahish', '-y', 'current-3D.mp4']' died with <Signals.SIGABRT: 6>."
     ]
    }
   ],
   "source": [
    "# override\n",
    "save_anim = 1\n",
    "\n",
    "if save_anim:\n",
    "    anim_3d.save(\"current-3D.mp4\", writer=writer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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