mimic_keras_pyopencl_gpu.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mimicking Keras using PyOpenCL for convolutions etc\n",
    "\n",
    "Same goal as mimic_keras_predict but this time we're going to try to push convolution and pooling onto a GPU/FPGA using PyOpenCL."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#System imports\n",
    "from __future__ import print_function\n",
    "\n",
    "#External imports \n",
    "from keras import models, layers\n",
    "from keras import backend as K\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "#Local imports \n",
    "from naive_prediction.data_utils import read_keras_model_from_file\n",
    "#from forward_pass import predict_with_keras_model\n",
    "from data import generate_straight_tracks\n",
    "from drawing import draw_2d_event, draw_2d_input_and_pred\n",
    "#import tests\n",
    "\n",
    "#For consistent printing\n",
    "import numpy as np\n",
    "import pyopencl as cl\n",
    "from pyopencl import array\n",
    "np.set_printoptions(suppress=True)\n",
    "\n",
    "#to test performance\n",
    "import time\n",
    "\n",
    "#for colors\n",
    "from termcolor import colored\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib notebook\n",
    "\n",
    "#pyopencl magic\n",
    "%load_ext pyopencl.ipython_ext"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Now we load our trained model and see how Keras predicts with it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "input_3 (InputLayer)             (None, 64, 64)        0                                            \n",
      "____________________________________________________________________________________________________\n",
      "reshape_5 (Reshape)              (None, 64, 64, 1)     0           input_3[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_27 (Convolution2D) (None, 64, 64, 8)     80          reshape_5[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_28 (Convolution2D) (None, 64, 64, 8)     584         convolution2d_27[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "maxpooling2d_7 (MaxPooling2D)    (None, 32, 32, 8)     0           convolution2d_28[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_29 (Convolution2D) (None, 32, 32, 8)     584         maxpooling2d_7[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "maxpooling2d_8 (MaxPooling2D)    (None, 16, 16, 8)     0           convolution2d_29[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_30 (Convolution2D) (None, 16, 16, 16)    1168        maxpooling2d_8[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "maxpooling2d_9 (MaxPooling2D)    (None, 8, 8, 16)      0           convolution2d_30[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_31 (Convolution2D) (None, 8, 8, 16)      2320        maxpooling2d_9[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_32 (Convolution2D) (None, 8, 8, 16)      2320        convolution2d_31[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_33 (Convolution2D) (None, 8, 8, 16)      2320        convolution2d_32[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_34 (Convolution2D) (None, 8, 8, 16)      2320        convolution2d_33[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "upsampling2d_7 (UpSampling2D)    (None, 16, 16, 16)    0           convolution2d_34[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_35 (Convolution2D) (None, 16, 16, 16)    2320        upsampling2d_7[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "upsampling2d_8 (UpSampling2D)    (None, 32, 32, 16)    0           convolution2d_35[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_36 (Convolution2D) (None, 32, 32, 8)     1160        upsampling2d_8[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "upsampling2d_9 (UpSampling2D)    (None, 64, 64, 8)     0           convolution2d_36[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_37 (Convolution2D) (None, 64, 64, 8)     584         upsampling2d_9[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_38 (Convolution2D) (None, 64, 64, 8)     584         convolution2d_37[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_39 (Convolution2D) (None, 64, 64, 1)     73          convolution2d_38[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "reshape_6 (Reshape)              (None, 64, 64)        0           convolution2d_39[0][0]           \n",
      "====================================================================================================\n",
      "Total params: 16,417\n",
      "Trainable params: 16,417\n",
      "Non-trainable params: 0\n",
      "____________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#load model, summary\n",
    "model = read_keras_model_from_file('/home/boser/opencl-cnn/model1.h5')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Batch generator function, from heptrkx\n",
    "def batch_generator_convae(num_event, det_shape, num_tracks, binary=True):\n",
    "    \"\"\"\n",
    "    Generator function for batches of toy data for training.\n",
    "    This function generates a fixed number of tracks in a batch.\n",
    "    \"\"\"\n",
    "    while True:\n",
    "        events = sum(generate_straight_tracks(num_event, det_shape)\n",
    "                     for i in range(num_tracks))\n",
    "        if binary:\n",
    "            events[events > 1] = 1\n",
    "        # Input and target are the same here\n",
    "        yield events, events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": 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",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // 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": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"900\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_test = 1\n",
    "det_shape = (64, 64)\n",
    "num_tracks = 8\n",
    "test_events, test_target = batch_generator_convae(num_test, det_shape, num_tracks).next()\n",
    "test_pred = model.predict(test_events)\n",
    "draw_2d_input_and_pred(test_events[0], test_pred[0], cmap='gray_r');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## The prediction looks good\n",
    "## Now let's set up PyOpenCL!\n",
    "We're going to be running everything on a GPU now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<pyopencl.Platform 'NVIDIA CUDA' at 0x56279df0c190>]\n"
     ]
    }
   ],
   "source": [
    "platforms = cl.get_platforms()\n",
    "print(platforms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<pyopencl.Device 'GeForce GTX 980 Ti' on 'NVIDIA CUDA' at 0x56279df19a00>]\n"
     ]
    }
   ],
   "source": [
    "devices = platforms[0].get_devices()\n",
    "print(devices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#I'll use my CPU for this example.\n",
    "ctx = cl.Context([devices[0]])\n",
    "queue = cl.CommandQueue(ctx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We need to create Kernels which can be run on GPUs/FPGAs\n",
    "Overall very similar to C code, these kernels allow our code to be parallelized. I've created 3 kernels for various activation functions in order to minimize number of conditionals. The kernel code currently contains only the convolution operation and none of the preparation (i.e padding). Will need to look into whether it makes sense to push the preparation onto an FPGA as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%cl_kernel -o \"-cl-fast-relaxed-math\"\n",
    "\n",
    "__kernel void convolute_2d(__global float *c,\n",
    "__global const float *a, __global float *b, __constant float *d, int filter_size,\n",
    "int output_width_height, int num_filters, int num_input_channels, int padded_matrix_dim)\n",
    "{\n",
    "    size_t j = get_global_id(0); //height\n",
    "    size_t i = get_global_id(1); //width\n",
    "    size_t k = get_global_id(2); //num filters (output depth)\n",
    "    size_t h;\n",
    "    size_t l;\n",
    "    size_t s;\n",
    "    float conv_result = 0;\n",
    "    for(h = 0; h < num_input_channels; h++) {\n",
    "        for(l = 0; l < filter_size; l++) {\n",
    "            if(num_input_channels == 1) {\n",
    "                for(s = 0; s < filter_size; s++) {\n",
    "                    conv_result += b[k + s*num_filters + l*num_filters*filter_size]*a[j+s + (i+l)*padded_matrix_dim];\n",
    "                }\n",
    "            } else {\n",
    "                for(s = 0; s < filter_size; s++) {\n",
    "                    conv_result += b[k + h*num_filters + s*num_filters*num_input_channels + l*num_filters*num_input_channels*filter_size]*a[h + (j+s)*num_input_channels + (i+l)*num_input_channels*padded_matrix_dim];\n",
    "                }\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "    conv_result += d[k];\n",
    "    c[k + j*num_filters + i*num_filters*output_width_height] = conv_result;\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%cl_kernel -o \"-cl-fast-relaxed-math\"\n",
    "\n",
    "__kernel void convolute_2d_relu(__global float *c,\n",
    "__global const float *a, __global float *b, __constant float *d, int filter_size,\n",
    "int output_width_height, int num_filters, int num_input_channels, int padded_matrix_dim)\n",
    "{\n",
    "    size_t j = get_global_id(0); //height\n",
    "    size_t i = get_global_id(1); //width\n",
    "    size_t k = get_global_id(2); //num filters (output depth)\n",
    "    size_t h;\n",
    "    size_t l;\n",
    "    size_t s;\n",
    "    float conv_result = 0;\n",
    "    for(h = 0; h < num_input_channels; h++) {\n",
    "        for(l = 0; l < filter_size; l++) {\n",
    "            if(num_input_channels == 1) {\n",
    "                for(s = 0; s < filter_size; s++) {\n",
    "                    conv_result += b[k + s*num_filters + l*num_filters*filter_size]*a[j+s + (i+l)*padded_matrix_dim];\n",
    "                }\n",
    "            } else {\n",
    "                for(s = 0; s < filter_size; s++) {\n",
    "                    conv_result += b[k + h*num_filters + s*num_filters*num_input_channels + l*num_filters*num_input_channels*filter_size]*a[h + (j+s)*num_input_channels + (i+l)*num_input_channels*padded_matrix_dim];\n",
    "                }\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "    conv_result += d[k];\n",
    "    if (conv_result < 0) {\n",
    "        conv_result = 0;\n",
    "    }\n",
    "    c[k + j*num_filters + i*num_filters*output_width_height] = conv_result;\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%cl_kernel -o \"-cl-fast-relaxed-math\"\n",
    "\n",
    "__kernel void convolute_2d_sigmoidal(__global float *c,\n",
    "__global const float *a, __global float *b, __constant float *d, int filter_size,\n",
    "int output_width_height, int num_filters, int num_input_channels, int padded_matrix_dim)\n",
    "{\n",
    "    size_t j = get_global_id(0); //height\n",
    "    size_t i = get_global_id(1); //width\n",
    "    size_t k = get_global_id(2); //num filters (output depth)\n",
    "    size_t h;\n",
    "    size_t l;\n",
    "    size_t s;\n",
    "    float conv_result = 0;\n",
    "    for(h = 0; h < num_input_channels; h++) {\n",
    "        for(l = 0; l < filter_size; l++) {\n",
    "            if(num_input_channels == 1) {\n",
    "                for(s = 0; s < filter_size; s++) {\n",
    "                    conv_result += b[k + s*num_filters + l*num_filters*filter_size]*a[j+s + (i+l)*padded_matrix_dim];\n",
    "                }\n",
    "            } else {\n",
    "                for(s = 0; s < filter_size; s++) {\n",
    "                    conv_result += b[k + h*num_filters + s*num_filters*num_input_channels + l*num_filters*num_input_channels*filter_size]*a[h + (j+s)*num_input_channels + (i+l)*num_input_channels*padded_matrix_dim];\n",
    "                }\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "    conv_result += d[k];\n",
    "    //S(x) = 1 / 1 + e^-x\n",
    "    conv_result = 1 / (1 + exp(-conv_result));\n",
    "    c[k + j*num_filters + i*num_filters*output_width_height] = conv_result;\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wrapping code for convolution\n",
    "Padding, turns ndarrays into arrays that can be used in openCL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def zero_pad_matrix(input_matrix, num_zeros):\n",
    "    \"\"\"\n",
    "    Pad the 3d (nxmxz) input matrix with p zeros\n",
    "    Assumes 'reshaped' matrix. Need to look at this more closely.\n",
    "    \"\"\"\n",
    "    num_zeros = int(num_zeros)\n",
    "    return np.pad(input_matrix, ((0, 0), (num_zeros, num_zeros),\n",
    "                                 (num_zeros, num_zeros), (0, 0)), 'constant')\n",
    "\n",
    "def keras_single_layer_output(model, layer_number, tinput):\n",
    "    \"\"\"\n",
    "    To test other functions\n",
    "    \"\"\"\n",
    "    get_layer_output = K.function([model.layers[layer_number-1].input],\n",
    "                                   [model.layers[layer_number].output])\n",
    "    return get_layer_output([tinput][0])\n",
    "\n",
    "\n",
    "def convolution_2d(input_matrix, filter_weights, stride, activation='relu', padding='same'):\n",
    "    weights_matrix = filter_weights[0]\n",
    "    bias_vector = filter_weights[1]\n",
    "\n",
    "    #pad matrix to maintain input dimensions\n",
    "    filter_dim = len(weights_matrix)\n",
    "    if padding == 'same':\n",
    "        num_zeros_padding = (filter_dim - 1) / 2\n",
    "        padded_matrix = zero_pad_matrix(input_matrix, num_zeros_padding)\n",
    "    else:\n",
    "        padded_matrix = input_matrix\n",
    "        \n",
    "    padded_matrix_dim = len(padded_matrix[0][0])\n",
    "    num_filters = len(weights_matrix.T)\n",
    "\n",
    "    try:\n",
    "        num_input_channels = len(input_matrix[0][0][0])\n",
    "    except:\n",
    "        num_input_channels = 1\n",
    "    output_matrix_width_height = padded_matrix_dim - filter_dim + 1\n",
    "    \n",
    "    a = cl.array.to_device(queue, padded_matrix)\n",
    "    b = cl.array.to_device(queue, weights_matrix)\n",
    "    c = cl.array.zeros(queue, shape=(1, output_matrix_width_height,\n",
    "                                     output_matrix_width_height, num_filters),\n",
    "                      dtype=np.float32)\n",
    "    d = cl.array.to_device(queue, bias_vector)\n",
    "    \n",
    "    if activation=='relu':\n",
    "        convolute_2d_relu(queue, (output_matrix_width_height, \n",
    "                             output_matrix_width_height,\n",
    "                             num_filters,), None, c.data, a.data, b.data, d.data, np.int32(filter_dim),\n",
    "                    np.int32(output_matrix_width_height), np.int32(num_filters), np.int32(num_input_channels),\n",
    "                    np.int32(padded_matrix_dim))\n",
    "    elif activation=='sigmoid':\n",
    "        convolute_2d_sigmoidal(queue, (output_matrix_width_height, \n",
    "                             output_matrix_width_height,\n",
    "                             num_filters,), None, c.data, a.data, b.data, d.data, np.int32(filter_dim),\n",
    "                    np.int32(output_matrix_width_height), np.int32(num_filters), np.int32(num_input_channels),\n",
    "                    np.int32(padded_matrix_dim))\n",
    "    else:\n",
    "        convolute_2d(queue, (output_matrix_width_height, \n",
    "                             output_matrix_width_height,\n",
    "                             num_filters,), None, c.data, a.data, b.data, d.data, np.int32(filter_dim),\n",
    "                    np.int32(output_matrix_width_height), np.int32(num_filters), np.int32(num_input_channels),\n",
    "                    np.int32(padded_matrix_dim))\n",
    "    \n",
    "    return c.get()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks good. Now let's test this convolution operation against Keras and make sure we're getting the same result!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def keras_get_layer_output(model, layer, test_input):\n",
    "    \"\"\"\n",
    "    Helper method, gives the output matrix from a Keras layer\n",
    "    \"\"\"\n",
    "    get_layer_output = K.function([model.layers[0].input],\n",
    "                                  [layer.output])\n",
    "    return get_layer_output([test_input])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_layer = model.layers[1]\n",
    "output_layer = model.layers[2]\n",
    "test_input_matrix = keras_get_layer_output(model, input_layer, test_events)\n",
    "test_correct_output = keras_get_layer_output(model, output_layer, test_events)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "our_test_output = convolution_2d(test_input_matrix, output_layer.get_weights(), 1, activation='relu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def compare_ndarrays(keras_ndarray, comparison_ndarray):\n",
    "    \"\"\"\n",
    "    Returns total difference between two ndarrays.\n",
    "    \"\"\"\n",
    "    if keras_ndarray.shape != comparison_ndarray.shape:\n",
    "        print(\"Unable to compare ndarrays with different shape.\")\n",
    "        print(\"Keras ndarray shape:\", keras_ndarray.shape)\n",
    "        print(\"Your ndarray shape:\", comparison_ndarray.shape)\n",
    "        return -1\n",
    "\n",
    "    if np.array_equal(keras_ndarray, comparison_ndarray):\n",
    "        print(\"Keras ndarray and comparison ndarray are the same.\")\n",
    "        return 0\n",
    "    \n",
    "    if np.allclose(keras_ndarray, comparison_ndarray, atol=0.001):\n",
    "        print(\"Every item in Keras ndarray and comparison ndarray within 0.001 of eachother.\")\n",
    "    print(\"Keras ndarray and comparison ndarray are NOT the same.\")\n",
    "    print(\"A total of\", np.sum(keras_ndarray == comparison_ndarray),\" out of\",\n",
    "                               keras_ndarray.size ,\" elements are the same.\")\n",
    "    return abs(np.sum(keras_ndarray - comparison_ndarray))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keras ndarray and comparison ndarray are the same.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_ndarrays(test_correct_output, our_test_output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perfect, we're getting an exact match! Looks like our convolution operation is perfect!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Max pooling\n",
    "Well convolution works, now let's try and do the same for max pooling. Should be straightforward. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%cl_kernel -o \"-cl-fast-relaxed-math\"\n",
    "\n",
    "__kernel void max_pool_2d(__global float *c,\n",
    "__global const float *a, int kernel_size, int stride,\n",
    "int output_width_height, int num_filters, int input_w_h)\n",
    "{\n",
    "    size_t j = get_global_id(0); //height\n",
    "    size_t i = get_global_id(1); //width\n",
    "    size_t k = get_global_id(2); //num filters (output depth)\n",
    "    int l;\n",
    "    int s;\n",
    "    int i_i = i*2;\n",
    "    int j_j = j*2;\n",
    "    float curr_max = 0;\n",
    "    for (l = 0; l < kernel_size; l++) {\n",
    "        for (s = 0; s < kernel_size; s++) {\n",
    "            if (curr_max < a[k + (j_j+s)*num_filters + (i_i+l)*num_filters*input_w_h]) {\n",
    "                curr_max = a[k + (j_j+s)*num_filters + (i_i+l)*num_filters*input_w_h];\n",
    "            }\n",
    "        }\n",
    "    } \n",
    "\n",
    "    c[k + j*num_filters + i*num_filters*output_width_height] = curr_max;\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def maxpooling_2d(input_matrix, kernel_size, stride):\n",
    "    input_matrix_width_height = len(input_matrix[0])\n",
    "    input_matrix_depth = len(input_matrix[0][0][0])\n",
    "    output_matrix_width_height = int(((input_matrix_width_height - kernel_size) / stride) + 1)\n",
    "\n",
    "    c = cl.array.zeros(queue, shape=(1, output_matrix_width_height,\n",
    "                                     output_matrix_width_height, input_matrix_depth),\n",
    "                      dtype=np.float32)\n",
    "    a = cl.array.to_device(queue, input_matrix)\n",
    "    \n",
    "    max_pool_2d(queue, (output_matrix_width_height, \n",
    "                         output_matrix_width_height,\n",
    "                         input_matrix_depth,), None, c.data, a.data, np.int32(kernel_size),\n",
    "                np.int32(stride), np.int32(output_matrix_width_height), np.int32(input_matrix_depth),\n",
    "                np.int32(input_matrix_width_height))\n",
    "    \n",
    "    return c.get()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "input_layer_pool = model.layers[3]\n",
    "output_layer_pool = model.layers[4]\n",
    "test_input_matrix_pool = keras_get_layer_output(model, input_layer_pool, test_events)\n",
    "test_correct_output_pool = keras_get_layer_output(model, output_layer_pool, test_events)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "our_test_output_pool = maxpooling_2d(test_input_matrix_pool, 2, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keras ndarray and comparison ndarray are the same.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now we check to make sure pooling works consistently with keras' pooling\n",
    "compare_ndarrays(test_correct_output_pool, our_test_output_pool)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! Max pooling works using our parallelized opencl kernel.\n",
    "### Upsampling, reshaping arrays\n",
    "Does it make sense to upsample on the FPGA? Maybe not for now, values are simply being mapped to one another (assigning one value in the input array to multiple in the output array). Numpy will probably do this more efficiently on a CPU than we will on an FPGA. Will want to look into this however.\n",
    "\n",
    "Sample applies to reshape."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def upsampling_2d(input_matrix, kernel_size):\n",
    "    \"\"\"\n",
    "    Increase width and height of input matrix.\n",
    "    \"\"\"\n",
    "    output_matrix = np.repeat(np.repeat(input_matrix, kernel_size, axis=1), kernel_size, axis=2)\n",
    "    return output_matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 64, 64, 1)\n",
      "(1, 64, 64, 1)\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "def reshape(inputs, target_shape):\n",
    "    return np.reshape(inputs, (-1,) + target_shape)\n",
    "\n",
    "#def reshape_tf(inputs, target_shape):\n",
    "#    return tf.reshape(inputs, (1,) + target_shape).eval()\n",
    "\n",
    "#def reshape_keras(inputs, target_shape):\n",
    "#    return K.reshape(inputs, (1,) + target_shape)\n",
    "\n",
    "print(reshape(test_events, (64, 64, 1)).shape)\n",
    "#print(reshape_tf(test_events, (64, 64, 1)))\n",
    "#print(reshape_keras(test_events, (64, 64, 1)))\n",
    "print(test_input_matrix.shape)\n",
    "print(np.array_equal(test_input_matrix, reshape(test_events, (64, 64, 1))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making a full prediction using parallelized convolution and pooling\n",
    "Let's time it to see how we compare to Keras! This is still all being done on a CPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_layer_type(keras_layer):\n",
    "    \"\"\"\n",
    "    return string describing the type of a layer\n",
    "    \"\"\"\n",
    "    config = keras_layer.get_config()\n",
    "    return config['name'].split('_', 1)[0]\n",
    "\n",
    "def predict_with_keras_model(keras_model, event):\n",
    "    prediction = event\n",
    "    for layer in keras_model.layers:\n",
    "\n",
    "        #get layer information via keras\n",
    "        layer_type = get_layer_type(layer)\n",
    "        layer_config = layer.get_config()\n",
    "\n",
    "        #perform appropriate operation\n",
    "        if layer_type == 'convolution2d':\n",
    "            prediction = convolution_2d(prediction, layer.get_weights(), 1, activation=layer_config['activation'], padding='same')\n",
    "        if layer_type == 'maxpooling2d':\n",
    "            prediction = maxpooling_2d(prediction, layer_config['pool_size'][0], layer_config['strides'][0])\n",
    "        #not parallelized\n",
    "        if layer_type == 'reshape':\n",
    "            if(layer_config['target_shape'] != (64, 64, 1)):\n",
    "                prediction = reshape(prediction, layer_config['target_shape'])\n",
    "        if layer_type == 'upsampling2d':\n",
    "            prediction = upsampling_2d(prediction, layer_config['size'][0])\n",
    "    return prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A single prediction took 0.0469059944153  seconds.\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "our_prediction = predict_with_keras_model(model, test_input_matrix)\n",
    "end = time.time()\n",
    "print(\"A single prediction took\", end-start, \" seconds.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our prediction\n"
     ]
    },
    {
     "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",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // 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": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"900\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keras prediction\n"
     ]
    },
    {
     "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",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // 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": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"900\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3kAAAGGCAYAAADGq0gwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ncOApPVdAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7142a8b5d0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Our prediction\")\n",
    "draw_2d_input_and_pred(test_events[0], our_prediction[0], cmap='gray_r');\n",
    "print(\"Keras prediction\")\n",
    "draw_2d_input_and_pred(test_events[0], test_pred[0], cmap='gray_r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There was some issue with numpy's reshape module so I've been forced to use Keras' reshaped input. \n",
    "Oh well, aside from that it works! Now we've just got to run this on an FPGA!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def full_run_comparison(keras_model, test_input, cheaters_input):\n",
    "    \"\"\"\n",
    "    From start to finish compare the output of a Keras prediction to\n",
    "    a prediction done using forward_pass.py\n",
    "    \"\"\"\n",
    "    previous_layer_output_keras = test_input\n",
    "    previous_layer_output_self = cheaters_input\n",
    "\n",
    "    for layer in keras_model.layers:\n",
    "\n",
    "        #get layer information via keras\n",
    "        layer_type = get_layer_type(layer)\n",
    "        layer_config = layer.get_config()\n",
    "\n",
    "        #perform appropriate operation\n",
    "        if layer_type == 'convolution2d':\n",
    "            previous_layer_output_self = convolution_2d(previous_layer_output_self, layer.get_weights(), 1, activation=layer_config['activation'], padding='same')\n",
    "            self_out_with_keras_input = convolution_2d(previous_layer_output_keras, layer.get_weights(), 1, activation=layer_config['activation'], padding='same')\n",
    "        elif layer_type == 'maxpooling2d':\n",
    "            previous_layer_output_self = maxpooling_2d(previous_layer_output_self, layer_config['pool_size'][0], layer_config['strides'][0])\n",
    "            self_out_with_keras_input = maxpooling_2d(previous_layer_output_keras, layer_config['pool_size'][0], layer_config['strides'][0])\n",
    "        elif layer_type == 'reshape':\n",
    "            if layer_config['target_shape'] != (64, 64, 1):\n",
    "                previous_layer_output_self = reshape(previous_layer_output_self, layer_config['target_shape'])\n",
    "                self_out_with_keras_input = reshape(previous_layer_output_keras, layer_config['target_shape'])\n",
    "            else:\n",
    "                self_out_with_keras_input = previous_layer_output_self\n",
    "        elif layer_type == 'upsampling2d':\n",
    "            previous_layer_output_self = upsampling_2d(previous_layer_output_self, layer_config['size'][0])\n",
    "            self_out_with_keras_input = upsampling_2d(previous_layer_output_keras, layer_config['size'][0])\n",
    "        else:\n",
    "            continue\n",
    "\n",
    "        #perform keras prediction for this layer\n",
    "        previous_layer_output_keras = keras_get_layer_output(keras_model, layer, test_input)\n",
    "\n",
    "        #now let's see how we compare to keras\n",
    "        print(\"\\n===========================================================================\")\n",
    "        print(\"Forward pass on a\", layer_config['name'],\"layer.\")\n",
    "        if np.array_equal(self_out_with_keras_input, previous_layer_output_keras):\n",
    "            print(colored(\"PASS: This layer successfully produces the same results as Keras.\", 'green'))\n",
    "        else:\n",
    "            print(colored(\"FAIL: This layer does no perform in the same way as Keras.\", 'red'))\n",
    "        if previous_layer_output_keras.shape != self_out_with_keras_input.shape:\n",
    "            print(colored(\"CRITICAL ERROR: This layer returns the wrong output shape.\", 'red'))\n",
    "        print(\"Using self output as input\", np.sum(previous_layer_output_self == previous_layer_output_keras),\n",
    "                                                   \" of\", previous_layer_output_keras.size, \" correctly predicted\")\n",
    "        print(\"Using keras output as input\", np.sum(self_out_with_keras_input == previous_layer_output_keras),\n",
    "                                                    \" of\", previous_layer_output_keras.size, \" correctly predicted\")\n",
    "        if np.allclose(previous_layer_output_self, previous_layer_output_keras, atol=0.001):\n",
    "            print(colored(\"SUCCESS: All output values within 0.001 of eachother.\", 'green'))\n",
    "        else:\n",
    "            print(colored(\"FAIL: All output values NOT within 0.001 of eachother.\", 'red'))\n",
    "        print(\"===========================================================================\")\n",
    "    return previous_layer_output_self, previous_layer_output_keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===========================================================================\n",
      "Forward pass on a reshape_5 layer.\n",
      "\u001b[32mPASS: This layer successfully produces the same results as Keras.\u001b[0m\n",
      "Using self output as input 4096  of 4096  correctly predicted\n",
      "Using keras output as input 4096  of 4096  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a convolution2d_27 layer.\n",
      "\u001b[32mPASS: This layer successfully produces the same results as Keras.\u001b[0m\n",
      "Using self output as input 32768  of 32768  correctly predicted\n",
      "Using keras output as input 32768  of 32768  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a convolution2d_28 layer.\n",
      "\u001b[31mFAIL: This layer does no perform in the same way as Keras.\u001b[0m\n",
      "Using self output as input 27808  of 32768  correctly predicted\n",
      "Using keras output as input 27808  of 32768  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a maxpooling2d_7 layer.\n",
      "\u001b[32mPASS: This layer successfully produces the same results as Keras.\u001b[0m\n",
      "Using self output as input 6300  of 8192  correctly predicted\n",
      "Using keras output as input 8192  of 8192  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a convolution2d_29 layer.\n",
      "\u001b[31mFAIL: This layer does no perform in the same way as Keras.\u001b[0m\n",
      "Using self output as input 4345  of 8192  correctly predicted\n",
      "Using keras output as input 4816  of 8192  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a maxpooling2d_8 layer.\n",
      "\u001b[32mPASS: This layer successfully produces the same results as Keras.\u001b[0m\n",
      "Using self output as input 947  of 2048  correctly predicted\n",
      "Using keras output as input 2048  of 2048  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a convolution2d_30 layer.\n",
      "\u001b[31mFAIL: This layer does no perform in the same way as Keras.\u001b[0m\n",
      "Using self output as input 2089  of 4096  correctly predicted\n",
      "Using keras output as input 2180  of 4096  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a maxpooling2d_9 layer.\n",
      "\u001b[32mPASS: This layer successfully produces the same results as Keras.\u001b[0m\n",
      "Using self output as input 401  of 1024  correctly predicted\n",
      "Using keras output as input 1024  of 1024  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a convolution2d_31 layer.\n",
      "\u001b[31mFAIL: This layer does no perform in the same way as Keras.\u001b[0m\n",
      "Using self output as input 293  of 1024  correctly predicted\n",
      "Using keras output as input 335  of 1024  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a convolution2d_32 layer.\n",
      "\u001b[31mFAIL: This layer does no perform in the same way as Keras.\u001b[0m\n",
      "Using self output as input 424  of 1024  correctly predicted\n",
      "Using keras output as input 476  of 1024  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a convolution2d_33 layer.\n",
      "\u001b[31mFAIL: This layer does no perform in the same way as Keras.\u001b[0m\n",
      "Using self output as input 262  of 1024  correctly predicted\n",
      "Using keras output as input 337  of 1024  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a convolution2d_34 layer.\n",
      "\u001b[31mFAIL: This layer does no perform in the same way as Keras.\u001b[0m\n",
      "Using self output as input 296  of 1024  correctly predicted\n",
      "Using keras output as input 384  of 1024  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a upsampling2d_7 layer.\n",
      "\u001b[32mPASS: This layer successfully produces the same results as Keras.\u001b[0m\n",
      "Using self output as input 1184  of 4096  correctly predicted\n",
      "Using keras output as input 4096  of 4096  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a convolution2d_35 layer.\n",
      "\u001b[31mFAIL: This layer does no perform in the same way as Keras.\u001b[0m\n",
      "Using self output as input 2224  of 4096  correctly predicted\n",
      "Using keras output as input 2364  of 4096  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a upsampling2d_8 layer.\n",
      "\u001b[32mPASS: This layer successfully produces the same results as Keras.\u001b[0m\n",
      "Using self output as input 8896  of 16384  correctly predicted\n",
      "Using keras output as input 16384  of 16384  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a convolution2d_36 layer.\n",
      "\u001b[31mFAIL: This layer does no perform in the same way as Keras.\u001b[0m\n",
      "Using self output as input 2832  of 8192  correctly predicted\n",
      "Using keras output as input 3648  of 8192  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a upsampling2d_9 layer.\n",
      "\u001b[32mPASS: This layer successfully produces the same results as Keras.\u001b[0m\n",
      "Using self output as input 11328  of 32768  correctly predicted\n",
      "Using keras output as input 32768  of 32768  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a convolution2d_37 layer.\n",
      "\u001b[31mFAIL: This layer does no perform in the same way as Keras.\u001b[0m\n",
      "Using self output as input 16035  of 32768  correctly predicted\n",
      "Using keras output as input 19075  of 32768  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a convolution2d_38 layer.\n",
      "\u001b[31mFAIL: This layer does no perform in the same way as Keras.\u001b[0m\n",
      "Using self output as input 18631  of 32768  correctly predicted\n",
      "Using keras output as input 21342  of 32768  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a convolution2d_39 layer.\n",
      "\u001b[31mFAIL: This layer does no perform in the same way as Keras.\u001b[0m\n",
      "Using self output as input 387  of 4096  correctly predicted\n",
      "Using keras output as input 774  of 4096  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a reshape_6 layer.\n",
      "\u001b[32mPASS: This layer successfully produces the same results as Keras.\u001b[0m\n",
      "Using self output as input 387  of 4096  correctly predicted\n",
      "Using keras output as input 4096  of 4096  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.001 of eachother.\u001b[0m\n",
      "===========================================================================\n"
     ]
    }
   ],
   "source": [
    "#This function tells us layer by layer how our layers are performing in comparison to Keras'.\n",
    "ourout, kerasout = full_run_comparison(model, test_events, test_input_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keras prediction vs our prediction\n"
     ]
    },
    {
     "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",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // 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": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"900\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3kAAAGGCAYAAADGq0gwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzs3XmcXFWZ//Hvk86+dVaSkISENYCEZcJikE2RAVQkjmgc\nRAzMT3YjjMoIyLCIA4OahE0YGREUBWUZEAbBCSiEHUIgIBACZiEkhKzdna2z9Pn9cW9rddVzk65O\ndd2q25/361WvTn3vqepzu6vryal7z7kWQhAAAAAAIBs6pd0BAAAAAEDpMMgDAAAAgAxhkAcAAAAA\nGcIgDwAAAAAyhEEeAAAAAGQIgzwAAAAAyBAGeQAAAACQIQzyAAAAACBDGOQBAAAAQIYwyAMAAEAi\nMwtmdnkbHjc6fuyk0veq9Mzsz2b255z7Je+/mc03s9tL9XxAEgZ5QAUws0lxITmwAvrS08wuN7Oj\n0u4LACCSUyeCmR3mbDczez/e/nAafUTEzA6N62i/tPuCjqtz2h0AUHF6Sros/vefU+wHAKDQBkkn\nS3o6Lz9S0ghJjWXvUXYtkNRD0qYiH3eoojp6u6TVedvGSGra7p4B28CRPAAAgOrxiKQvmVn+B/Un\nS5op6cPydyk98RHMHu3x3CGyIYSwpYTP2RhCKHbQCBSNQR5QgczsdjNbY2bDzeyB+N/LzOzHZlaT\n0655vsB3zOwCM1tgZuvN7Ekz2yfvOVvMNcj7XvObn0/SsnjTZTmnBl3eTrsKACjOXZIGSjqmOTCz\nrpJOkvQb7wFm1svMfhKfztloZnPiumF57bqZ2dS43jSY2e/NbETCcw43s9vMbGn8nH8xs9PbskM5\np6IeYWb/ZWYrzKzezH5pZv3z2s43s4fN7Fgze1nSekln5mw/xcxmxrVwpZndbWYjne95hpm9F7d7\n0cwOd9q4c/LMbE8z+138c1of/zx/GG+7XNKP4qbzcuro6Jz+3573fLuY2T1xf9eZ2fNm9tm8NkfF\nz/NlM7vEzBaZ2QYze9zMdmvljxodCKdrApWrRtJjkl6Q9B1Jn5b0bUnvSbo5r+2pkvpIuklSd0nf\nkvSEmY0NISwt4nsuk3R2/Pz/I+n+OJ/dxn0AAJTWfEnPSfpnSX+Is+Ml1Uq6W9Lk3MbxQO73kj4p\n6eeSXpV0rKKByHBJF+Q0/29JpygaLD4r6VOS/je/A2Y2RNLzkoKkGxXVjuMl/dzM+oYQprVx325U\ndHrj5YpOazxb0igzOyqEEHLajVE02P0vSbdKmhP36xJJP5D0u3hfBkv6pqSnzOyAEMLquN2/xI99\nVtI0SbvEP6OVkt7fWgfNbF9JMxSdwvkzRb+PXSWdIOkSRXVzD0W/nwskLY8fuiz/ueLnGxL3o6ek\n6yWtkPR1Sb83s5NCCP+T95DvKTrd88eKfucXSvq1pEO21m90QCEEbty4pXyTNElRsTwwvn97fP/S\nvHavSHo55/7ouN06ScNz8oPjfEpO9mdJf3a+9+2S5ufcHxQ/9vK0fy7cuHHjxi265dYJSedKqpfU\nI972O0lPxP+eL+nhnMedGD/ukrznu0fRYGHX+P5+cbub8tr9Or8mKBpALZY0MK/tXYoGac39aq5R\nk1q5by9L6pKTfzfOP5+TzY+zY/OeY5SkzZIuzsv3UTQguzi+30XSUkmzJHXNafeN+Hn/nJMV9F/S\nk/HPfqe872M5//5O/LjRzr7Ol3R7zv2pcdvDcrLekv4qaZ6kTnF2VNzuzbx+T47zfdJ+jXKrrBun\nawKV7Za8+zMUfeKY74EQwgfNd0IILyo6AviZduwbACAdv1O0IMjnzKyPpM8p4VRNRXVgi6KjRLl+\nIskUHYFrbienXYujcvGRwS9Keii+O6j5pujsk1pJ/1D0HkV+FlrOV7tZ0cAtv5bNCyE8lpf9k6Jp\nSL/L69OHkuYqOpIpRYPkHSTdEkLYmPP42yXVba1zZjZY0hGSbgshLMzdFkII/qO26TOSXgwh/G0h\nnRDCGkVHCUdL2juv/S/y+j0j/ur93wAdGKdrApVrQwgh//SOVZL6O23nOtk7kr5c8l4BAFIVQlhm\nZtMVLbbSU9Hp/fcmNB8laXEIoSEvfytne/PXJkVTAnLNybs/WFI/SWfEN88OW92BZC1qWQhhjZkt\nUTTYyTXPeezuigatXj2U/r5CZvP+5n+vTWb21230r3kg9cY22hVjlKIPZfPl/n5yv9/CvHar4q/e\n/w3QgTHIAypXyVbzigVFBTBfjZMBACrbbxTNRxsq6Q8hnm9WBs1ngd0p6Y6ENu09j3u9k3VSVOeO\nl18/17Rrj8on6f8GXn1HB8YgD8iG3Z1sD0Xn/jdbJf90jlF599t6ygkAoHz+R9HiIR+XNHEr7RZI\n+rSZ9ck7mrdnzvbmr50ULSKSe/RuTN7zLZPUIKkmhDC9jX1PsrukPzXfMbPekoYpumzEtrynaKAz\nL4TwzlbaNe/v7pKeyPleXSTtLOm1rTy2+UjfPltpIxVXRxeo8GcsFf5+gKIwJw/IhglmNrz5jpkd\nrGilrT/ktHlP0p7xnILmdvtJ+kTec62Lv/Zrp74CALZTPG/rbEUrUT60laaPKDpj47y8/AJFg5Hm\nOtH8dXJeu/Pzvu8WSfdJ+mL+pXqkv81ba6sz4sFWs7MVHZD4Q0L7XPcrOsp1mXNpCDOzgfHdlxUN\nVM+KLz3RbJK2UffiKRRPSTrdzHbK/x45d9fGX1tTRx+RdLCZjc95rl6KToWdr2ihFaBoHMkDsuFd\nSU+b2c2SuikqyiskXZvT5jZJ/yrpMTP7uaI5E2dJ+oukvs2NQgjrzexNSRPN7B1FS0q/EUIo5RwE\nAMB2CiEknS6Z6yFFR8d+GF+r7TVJ/6ho1c1pIYT34ud61czuknSOmdUqWtb/aEneNdi+p2ghkxfM\n7FZFA5EBihZc+XT877boKulxM/udoqNb50h6WtHlDbYqhPCemX1f0tWSRpvZA4qOOO4s6QuKFjL5\ncTz37vuKjoI+YWa/jducpr8fqduayXGfXjGznymaHzha0mcl7R+3mRl//aGZ3a1oPuBDIYS1KnSN\n4sthmNn1imru1+M+fTGE0NSKPgEFGOQB2fBLRRPmz1c0eHtR0nkhhCXNDUIIb5nZqZKulDRFUVH+\nmqKJ+0flPd//k3SDoqWdu0q6QqWdaA4AKIMQQpOZfV7Re/9ERYOZ+YouT/CTvOanKzrK9VVJExSd\nzvhZ5V07LoSwND5j5N8VrWp5jqIPFv8i6d+2o7vnxd/7SkWXOrhL0uTWrlwZQrgm/nDyAkmXxfH7\nkv6onIFiCOFnZlaj6GfwI0mvS/q8omvsbet7vGZmH4/bnq3o2rQLFK142tzmJTO7VNEHqccpOnNu\nZ/39CF/u8y01s0Ml/aeia/p1VzSn8YQQQsE1CoHWsrav+AogbfGnsvMkfTeE8ON0ewMAQPHMbJKk\nX0g6KITwcsrdATKBOXkAAAAAkCEM8gAAAAAgQxjkAQAAAECGMCcPAAAAADKEI3kAAAAAkCEM8gAA\nAAAgQzJ/nTwzM0k7KrogJgAge/pIWtzaa2llDXUOADKv6DqX+UGeosK3KO1OAADa1QhJH6TdiZRQ\n5wAg+4qqcx1hkNcgSQsWLFDfvn1bbOjUibNVAaCabN68ucX9+vp67bzzzlLHPopFnQOAjChVnesI\ngzxJUt++fSl+AFDl8osf/o46BwDVr1R1jnd/AAAAAMgQBnkAAAAAkCEd5nTNTp06cdoKAFS5zp07\nb/V+R0adA4DqV6o6RzUAAAAAgAxhkAcAAAAAGcIgDwAAAAAyhEEeAAAAAGQIgzwAAAAAyBAGeQAA\nAACQIakP8sxsuJndaWYrzGy9mb1uZgfmbDczu9LMlsTbp5vZ7mn2GQCA1qLOAQDKLdVBnpn1l/SM\npE2Sjpe0t6RvS1qV0+xCSZMlnSXpEElrJT1mZt3L21sAAIpDnQMApCHtq8j+m6T3Qwin5WTzmv9h\nZibpfElXhRAejLNTJS2VNEHS3WXsKwAgZStXrmxxv76+PqWetBp1DgDQaqWqc2mfrvl5SS+b2T1m\n9pGZzTKzb+Rs31nSUEnTm4MQQp2kFySNL29XAQAoGnUOAFB2aQ/ydpF0tqS5ko6VdLOk683s6/H2\nofHXpXmPW5qzrQUz62ZmfZtvkvqUvtsAALQKdQ4AUHZpn67ZSdLLIYSL4/uzzGwfRfMS7mjjc14k\n6bJSdA4AgO1EnQMAlF3aR/KWSHozL3tL0k7xvz+Mvw7JazMkZ1u+qyXV5txGbH83AQBoE+ocAKDs\n0j6S94ykMXnZHpIWxP+ep6jIHS3pVUmKT005RNEpLwVCCI2SGpvvR3PaAQBpyp9I3uzee+9189tu\nu83N8yegb9myZfs61v6ocwDQAVRanUt7kDdV0rNmdrGk30k6WNIZ8U0hhGBm0yR938zmKiqGP5C0\nWNID6XQZAIBWo84BAMou1UFeCOElM/uColNP/l1RcTs/hPDrnGbXSuol6WeS+kl6WtJxIYQN5e4v\nAADFoM4BANKQ9pE8hRAelvTwVrYHRYXx38vWKQAASoQ6BwAot7QXXgEAAAAAlFDqR/LK5Z577lHP\nnj1bZCeddJLbtkuXLuXoEgBUlKTJ3SeccIKbP/roo61+7vz332aHHnqom1955ZVuvuOOO7a4v2bN\nGo0fzzXDJeocAGxLR6pzHMkDAAAAgAxhkAcAAAAAGcIgDwAAAAAyhEEeAAAAAGSIRSs3Z5eZ9ZVU\nd8opp6hr164ttr3++uvuY2699VY332+//UrdPQBotaamJjf/5Cc/6eYzZsxw86T3/e7du7v5s88+\n6+b7779/QWZmbttiJfUxf9J8fX29Bg4cKEm1IYT6knzzKkOdA5AV1LnS1TmO5AEAAABAhjDIAwAA\nAIAMYZAHAAAAABnSYebkzZo1S3369GmxrbGx0X3M2Wef7ebz58938wceeMDNDzjggFb3E0B2LV68\n2M0PPvhgN//ggw/cvFevXm7+3HPPufk+++zj5qWaT5CGTZs2tbhfX1+vQYMGSczJo84BSA11rnRK\nVec4kgcAAAAAGcIgDwAAAAAyhEEeAAAAAGQIgzwAAAAAyJDOaXegXCZPnqzOnVvu7ujRo922J5xw\ngpv37dvXzSdOnOjmH330UUF25plnum0vvPBCNx8wYICbV/OEUqASJV2AdcqUKW5+6aWXtvq599hj\nDzd/5pln3HzHHXd085qaGjdPej+ohveJ1l4Mtln+QiJJC4t0RNQ5AFtDnUtHWnWOI3kAAAAAkCEM\n8gAAAAAgQxjkAQAAAECGMMgDAAAAgAyxpMmAWWFmfSXVPfroo+rVq1eLbV26dHEfM3PmTDe/9957\n3fy9995z89WrVxdkGzZscNsm/R569Ojh5kmTZ/Mn3W9L0vdNmuS5cePGovpTSfr16+fmX/7yl938\nnHPOcfOxY8e6eadOfGbSnpL+dmbMmOHmZ5xxhpsvWrTIzbt16+bmkydPdvNTTjmlIKutrXXbrlix\nws2T/m6SFr9ImqS9atWqor7vnnvu6eZJE+GLeV9Jek/ZvHmzm69cudLN33//fTd/7bXXWtxfv369\nzj//fEmqDSHUt7qjGUKd2zrqHHWuWlDnqHNS6eocf60AAAAAkCEM8gAAAAAgQxjkAQAAAECGMMgD\nAAAAgAzpMAuvrFy5smCSZ01NTUm+RzFXsvcmqUvSDTfc4OY/+9nP3Lx79+5u/o1vfMPN//Ef/9HN\nR40a5ea9e/d2865du7p5qX6W7SlpMu8777zj5meddZabP//8826eNFm/GGbm5kk/36QFC5LaJ02a\nL3YScdKCBZs2bWr1c5dK0t/CwIED3XzEiBFu3rNnTzdP+hkUs699+vQpqi9JedLf39KlS9182bJl\nbv7WW2+5ubdPkjRs2DA39/6m6uv9OeFr1qxx86TfX9J707hx41rcb2xs1H/+539KLLxCnaPOUedE\nnZOocxJ1TuJIHgAAAABkCoM8AAAAAMgQBnkAAAAAkCEdZk5eXV1d4oUXK0HS7yHp/Pd77rnHzb/z\nne+4edJ52JdeeqmbH3nkkW6edM61dwHWpIvwdiRJv9eki4MmzadIOs/7zTffdPNXX33Vzd9++203\nT7pQZ9I56occcoib77bbbgXZ8OHD3bZJf49J5+O390V4i30v9NonzTVJkvQ6KDZP6nuxc1CS5ios\nWLCg1c+TNH8maf5F0lyFpPkw+Re+bWho0AEHHCAxJ486R51LBXWOOrc11Ln06hxH8gAAAAAgQxjk\nAQAAAECGMMgDAAAAgAxhkAcAAAAAGcLCKxmTNGF11qxZbp50IdSkidGnn366m3sTlL3JyZI/eV0q\nfjJvWpL+ZrzJvMuXL3fb1tXVufmcOXPcfObMma3+npK09957u/n48ePdPGniedJk4WIuClwtv1cU\nrxST8ouV/95UX1+vAQMGSCy8Qp2jzpUMdY46h0g11zmO5AEAAABAhjDIAwAAAIAMSXWQZ2aXm1nI\nu72ds727md1kZivMbI2Z3WdmQ9LsMwAArUWdAwCkoRKO5P1F0rCc22E526ZKOkHSlyQdKWlHSfeX\nu4MAAGwH6hwAoKxSXXjFzC6XNCGEsL+zrVbSMkknhxDujbM9Jb0laXwI4flWfo8ONSG9WEkT2H/z\nm9+4+TXXXOPm3gTlcePGuW0nT57s5nvuuaebJ02KLpWkv4E1a9a4+b333uvmc+fOLcg6d+7stt1h\nhx3cfI899nDzpJ9NbW2tm3fr1s3NO3XyP9dJypMmnjPJHO0t6e8yP6+vr1f//v2lCl14hTqXPuoc\ndW5rOXUOaWnvOlcJR/J2N7PFZvZXM/u1me0U5+MkdZE0vblhCOFtSQsl+UsnAQBQeahzAICy8j9+\nKZ8XJE2SNEfRKSyXSZphZvtIGippYwhhdd5jlsbbXGbWTVLuxzt9StlhAACKQJ0DAJRdqoO8EMIf\ncu7ONrMXJC2Q9GVJ69v4tBcpKqIAAKSKOgcASEMlnK75N/Gnme9I2k3Sh5K6mln+FUWHxNuSXC2p\nNuc2oh26CgBA0ahzAIBySPt0zRbMrLekXSX9StJMSZskHS3pvnj7GEk7SXou6TlCCI2SGnOesx17\nXP2SJiKfcsopbv75z3/ezWfPnl2Q/f73v3fbnn322W6eNDn+yiuvdPPDDjvMzZMmUa9bt87NX3nl\nFTe/5557inqefffdtyDr0aOH2zZJPLG2QNIE86R9LVaaCzBVK+9ntmXLFrdt0mtm4cKFbv7WW2+5\n+YsvvujmM2fOdPNZs2a5eX29P2876W+wFIp9L056be+6664t7if9zCsVda78qHPUOYk61xbUueJU\nWp1LdZBnZj+W9JCiU1d2lHSFpC2S7goh1JnZzyVNMbOVkuol3SDpudauOAYAQJqocwCANKR9JG+E\npLskDVS0jPTTkj4eQlgWb79AUpOiTzi7SXpM0jkp9BMAgLagzgEAyi7thVe+so3tGySdG98AAKgq\n1DkAQBpSvRh6OXCR2NJKer2sXp2/Arj09ttvu21nzJjh5nPmzHHzpLkEXbp0cfOePXu6+fLly928\na9eubr7//gXXLpYkfexjH3Nz79zqpHPUGxsb3TxpTkLSz2bDhg2t7oskjRo1ys2TfjZJP+Mjjzyy\n1c//7rvvum1vueUWN/fmvUhSQ0ODm1eDpIsFJ71Whw8f7uYHHXSQmx9zzDFF5YMGDXLzpLlLlTTn\nK/9vp76+vvmiyxV5MfRyoM6VFnWOOidR54pFnSudUtW5ilpdEwAAAACwfRjkAQAAAECGMMgDAAAA\ngAxhkAcAAAAAGdJhFl5ZtWpVwYT0pMmXSL5YZNJFLa+55pqCbP78+W7bpNdc0oIBgwcPdvPFixe7\nedJk76QLtm7cuNHNkybtDhkyxM29C7wmfc+kPi5btszN169f7+ZJff/www/dfPPmzW4+YsQIN+/e\nvbubL1myxM29hQnGjBnjtj322GPd/BOf+ISb9+vXz835O+7Y6uvrVVtbK7HwCnWuSNQ56pxEnUPl\na2ud41UDAAAAABnCIA8AAAAAMoRBHgAAAABkCIM8AAAAAMgQ//L0GTR16tSCybWnn3662za+qnwB\nM9vufiRNxt6wYYObJ00A/+1vf+vmzz33nJvPmjXLzevq6tw8qZ9Jk3+HDh1akI0bN85tu88++7j5\nmjVr3Ly+3p9jmjTZO56cWmD48OFuvttuu7n52rVr3Xz58uVuPmzYsIJs7733dtuOGjXKzZMm5Xfp\n0sXNk16TxeYAqh91jjpHnaPOAc04kgcAAAAAGcIgDwAAAAAyhEEeAAAAAGQIgzwAAAAAyJAOs/DK\nwoUL1bVr1xbZiSee6LbdsmWLm48fP97NkyZGT58+vSCbO3eu2zZpAnivXr3cfOzYsW6eNAn8u9/9\nrpvvtddebt6/f383z/8ZNmvPic5JP5ukPGki+a233urmSZP7Dz/8cDf/+te/7ub9+vUryPr06eO2\nTZpgDgBtRZ2jzlHnADTjSB4AAAAAZAiDPAAAAADIEAZ5AAAAAJAhDPIAAAAAIEMsaVJvVphZX0l1\n3/zmN9WtW7cW2xoaGtzHrFixws0XLlzo5j179nTzz372swVZ0mTmgQMHunmnTozD21vSAgRPPvmk\nm0+aNMnNd95554LslltucduOGTPGzfl9A8Wpr69XbW2tJNWGEOrT7k8aqHPYFuocUL3aWuf4SwMA\nAACADGGQBwAAAAAZwiAPAAAAADKkw1wMvU+fPurevXuLrHNnf/fz2zVLuqBqUn7kkUcWZAMGDHDb\nco56empqatz8U5/6lJsvWLDAze+7776CzHsNSMlzFZ544gk356KyALaFOock1Dmg4+EdFwAAAAAy\nhEEeAAAAAGQIgzwAAAAAyBAGeQAAAACQIR3mYuiXXnppwUTzXr16uY/ZtGmTm69cubKo77127dqC\nbPTo0W7b4447zs132203N2eCcnVI+vu6++673fycc85x82OOOaao52GBA3QUXAydOod0UeeA9sXF\n0AEAAAAADPIAAAAAIEsY5AEAAABAhjDIAwAAAIAM6TALr3z1q19V165dW2zbvHmz+5ikn8nXvvY1\nN1+1apWbz5s3ryCbM2eO2/bNN99081122cXNr7jiCjffY4893BzVIem1N23aNDe/8MIL3XzYsGFu\n7r0mJammpqYVvQMqDwuvUOdQXahzQHFYeAUAAAAAwCAPAAAAALKkYgZ5ZvY9MwtmNi0n625mN5nZ\nCjNbY2b3mdmQNPsJAEBbUesAAOVQEYM8MztI0pmSZudtmirpBElfknSkpB0l3V/e3gEAsP2odQCA\nckl94RUz6y3pFUnnSPq+pFdDCOebWa2kZZJODiHcG7fdU9JbksaHEJ5v5fP3lVT37rvvqk+fPi22\ndevWzX3M1KlT3fyGG25w8+HDh7v59ddfX5CNHTvWbfv++++7+U033eTmDz/8sJsfeOCBbv7LX/7S\nzfv37+/mqG6vv/66m48bN87NBwwY4OaLFy92806dKuLzIaBqFl5pz1pHnYtQ5zoW6hw6impeeOUm\nSf8bQpiel4+T1EXS3/IQwtuSFkoan/RkZtbNzPo23yT1SWoLAECZlKzWUecAANvSOc1vbmZfkfQP\nkg5yNg+VtDGEsDovXxpvS3KRpMtK00MAALZPO9Q66hwAYKtSO5JnZiMlXSfpqyGEDSV86qsl1ebc\nRpTwuQEAaLV2qnXUOQDAVqU2J8/MJkj6H0lbcuIaSUFSk6RjFZ2+0j/3E04zWyBpWgjBn1BQ+H36\nSqpbtWqV+vbt22Jbqc63Trrw5lFHHVWQjRw50m2bNPegV69ebt7Y2Ojmp556qps/88wzbn7GGWe4\n+SWXXOLm3bt3d3NUt9Wr8w8iRIYO9Q8kTJ482c2vvfbakvUJaI1Kn5NXjlpHnYtQ57A11DlUq7bW\nuTRP13xcUv7s7F9IelvSf0p6X9ImSUdLuk+SzGyMpJ0kPVe+bgIA0GbUOgBA2aU2yAshNEh6Izcz\ns7WSVoQQ3ojv/1zSFDNbKale0g2SnmvtypoAAKSJWgcASEOqC6+0wgWKTme5T1I3SY8pWn4aAICs\noNYBAEqqogZ5IYSj8u5vkHRufAMAoOpR6wAA7S31i6G3t+YJ6XV1dQUT0tPwxhtvuHnSxV2T2u+6\n665Ffd+kSfMTJkxw86TJ+ldddZWbf/azny3IzKyVvUO1+ad/+ic3f+ihhwqypMnuSYstAMWo9IVX\nyoE6F6HOoZSoc6gU1XwxdAAAAABAiTDIAwAAAIAMYZAHAAAAABnCIA8AAAAAMoSFVypEU1OTm8cT\nLQucdtppbn7dddeV5PsuWbLEzS+88MJWt7/rrrvctkOGDHFzJrBXP2/y+Q477OC2TXptL1u2rKR9\nQrax8Ap1rq3flzqHtqDOodxYeAUAAAAAwCAPAAAAALKEQR4AAAAAZAiDPAAAAADIkM5pdwCRTp38\n8XZDQ4ObX3XVVW6+9957u/mrr77q5jU1NW6eNGn81ltvdfNZs2YVZKeffrrbdtGiRW4+ZcoUNz/i\niCPcvGvXrm6O9PTr168g27hxo9v20UcfdfOkhQmSFmG47bbbWtk7AGmizlHnsoA6h2rBkTwAAAAA\nyBAGeQAAAACQIQzyAAAAACBDuBh6xsybN8/N99prLzefPn26mx900EFunvR6Wb58eUG2bt06t+2W\nLVvcPOmc8//+7/9286FDh7r5nXfe6eb777+/myfN10Blufjii9386quvdvOlS5e6edJFa1GduBg6\nda4ZdY46V+2oc/BwMXQAAAAAAIM8AAAAAMgSBnkAAAAAkCEM8gAAAAAgQ1h4pYNoampy8+HDh7v5\nmDFj3Pzhhx928169erW6L0mvuaQ8aQL7woUL3fwLX/iCmy9YsMDNJ06c6Ob/8R//4eaDBw92c1SW\npAsdf/TRR27e2Njo5lyMuLKx8Ap1rhl1jjrX0VDnOgYWXgEAAAAAqHNrG5rZlNa2DSH8a9u6AwBA\neqh1AIAsaPUgT9IBrWyX7fM/AQBZRq0DAFS9Vg/yQgifbM+OAACQNmodACALijmSV8DMdpO0q6Sn\nQgjrzcw1lOcZAAAgAElEQVRC1ldyqVKdOvnTL5csWeLmM2fOdPP99tvPza+++uqC7MQTT3TbdunS\nxc3NzM1ramrcfPTo0W6e1PeGhgY3nzZtmpsfdthhbj5ixAg3/8UvflGQjRw50m2btK8onaVLlxbV\nPmnBiqTXzebNmwuypNcqqhu1rjpQ56hzHQ11DlvTpoVXzGygmT0u6R1Jj0gaFm/6uZn9pFSdAwAg\nLdQ6AEC1auvqmlMlbZK0k6R1OflvJR23vZ0CAKACUOsAAFWpradr/qOkY0MIi/IOx8+VNGq7ewUA\nQPqodQCAqtTWI3m91PJTzWYDJPlXWgQAoLpQ6wAAVcnaMnfczB6RNDOEcKmZNUjaV9ICSXdL6hRC\nOKm03Ww7M+srqa6uri5xwilab8uWLW5+9tlnF2R/+tOf3La/+tWv3HzcuHFu3rmzf8A5qS9JkiaB\nexOLJWnNmjVuftttt7n5T35SOEUn6TX3ox/9yM0/85nPuHnSJH6kp3///gXZ6tWr3bZJr9WkhSLQ\nevX19aqtrZWk2hBCfSmfu1pqHXWutKhz1DlEqHOVoa11rq2na14o6XEzO1BSV0nXSvqYok83P9HG\n5wQAoJJQ6wAAValNw+sQwhuS9pD0tKQHFZ3Scr+kA0II75WuewAApINaBwCoVm2+Tl4IoU7SD0vY\nFwAAKgq1DgBQjdp6nbzLzazgsWZWa2Z3bX+3AABIF7UOAFCt2rrwyvuS3pd0Sgjhr3F2lKRfSvow\nhHBwKTu5PZiQnp4NGza4+eTJk938ySefdPOf/vSnbn7EEUe4eU1NTSt613ZJE9s9dXV1bv61r33N\nzR9//HE3P+SQQ9z8zjvvdPPhw4e3oncolwEDBrj5qlWr3LypqcnNi3ntdRTtvPBKVdQ66lx6qHPU\nOUSoc+2nrXWurUve7CtpkaRXzewbZvYjSX+U9CtJh7bxOQEAqCTUOgBAVWrrwiurQghflnSjpP+S\n9C1Jx4cQLgkh+Ov0OszsbDObbWb18e05Mzs+Z3t3M7vJzFaY2Rozu8/MhrSlzwAAFKMUtY46BwBI\nQ5svXmFm31RU8O6S9FdJ15vZfkU+zSJJ35M0TtKBkp6Q9KCZfSzePlXSCZK+JOlISTsqWtkMAIB2\nV4JaR50DAJRdW+fkPaqoWJ0VQrjXzHpImiJpkqTLQgjXtrlDZislfVfSvZKWSTo5hHBvvG1PSW9J\nGh9CeL6Vz8dchSrR0NDg5qeccoqb9+jRw82nTZvm5oMHD3bz9p7bUAqvvfaam3/qU59y86SLyl5z\nzTVufuqpp7o5FzFNR9LvLylft25de3anorXznLx2qXXUuY6LOpeMOtexUOdar9xz8mok7dtclEII\n60MIZ0s6SdIFbXlCM6sxs68oug7Rc4o+9ewiaXpzmxDC25IWShrfxn4DANBaJa111DkAQLm06Tp5\nIYRjEvL/NbOxxTxX3P45Sd0lrZH0hRDCm2a2v6SNIYTVeQ9ZKmnoVp6vm6RuOVGfYvoDAIBUulpH\nnQMAlFvJj1GHEJYX+ZA5kvaXdIikmyXdYWZ7b0cXLpJUl3NbtB3PBQBAgSJrHXUOAFBWrT6SF88h\n2COEsNzMVklKnMwXQvAvluG33Sjp3fjuTDM7SNEk999K6mpm/fI+5Rwi6cOtPOXViuZMNOsjCiAA\noBXao9ZR5wAA5VbM6ZoXSGqeMXx+O/SlWSdFp6HMlLRJ0tGS7pMkMxsjaSdFp724QgiNkhqb73NR\nxerRp49/xtGDDz7o5gsWLHDzE0880c332GMPN7/xxhvdPJ7kWhH2289fzG/FihVunnTx0U9/+tNu\nfuaZZ7r5GWec4eZTpkxx86QJ0yjOpk2b3Pzdd99186T3ucsuu8zNL7/88jb1q4MoR62jznVQ1Llk\n1LmOhTrX/lo9yAsh3BFPGv83SZ+X1FXS45KuCCGsb8s3N7OrJf1B0STzPpJOlnSUpGNDCHVm9nNJ\nU+JPVusl3SDpudauOAYAQDFKXeuocwCANBS78MrFki5XtBLYekWnm+wg6fQ2fv8dJP1S0jBF8wpm\nKyp8/xdvv0BSk6JPOLtJekzSOW38XgAAtEYpax11DgBQdsUO8k6VdE4I4b8kycw+Lel/zez/hRCa\niv3mIYR/2cb2DZLOjW8AAJRDyWoddQ4AkIZiV9fcSdIjzXdCCNMVTUrfsZSdAgAgRdQ6AEBVsxAS\nFw4rbGy2RdLQEMKynKxB0cVi57VD/7abmfWVVFdXV6e+ffum3R2UwcaNG938Bz/4gZs/9NBDbv6d\n73zHzb/yla+4eefObbrsZEVIeh+46KKL3PynP/2pm48bN64gu+eee9y2gwYNamXv0FaHH364mz/9\n9NNuvmzZMjev5N9VfX198+IRtSGE+lI8Z7XVOupcx0OdKx51Lpuoc8mK/Ws1SbebWWNO1l3SLWa2\ntjkIIfxTkc8LAECloNYBAKpasYO8O5zszlJ0BACACkGtAwBUtaIGeSGE09qrIwAAVAJqHQCg2hW7\n8AoAAAAAoIIVtfBKNWqekL5kyZKCCek9e/ZMp1OoKM8++6ybf/Ob33TzTp38z0Yuu+yyguyYY45x\n23br1q2VvatMmzZtcvM77yw8oy1pYn/SAhGPPPKIm++5555ubmZujmRNTf5VAAYOHOjm69f71wD3\n8nL/Ptpj4ZVqQ53DtlDnikedq27UOY7kAQAAAECmMMgDAAAAgAxhkAcAAAAAGdJh5uT16tWr4Bza\nzZs3Jz3Gzc8880w3T7r4aO/evVvfUVScpPPxH3jgATe/4oorCrLPfe5zbtuzzjrLzYcMGeLm3bt3\nd/NqPk9/0aJFbj527Fg3r6urc/Ndd93Vzf/0pz+5+YgRI1rRO+RatWqVm3uv16Tfx1/+8hc3T5r7\n01rMyaPOoe2oc+2LOlc9sljnOJIHAAAAABnCIA8AAAAAMoRBHgAAAABkCIM8AAAAAMiQDrPwygcf\nfFBwUcoePXq4j6mv9+c0XnfddW4+depUN29oaCjIiv15Dx061M3vu+8+Nz/kkEPcvKampqjvi2RJ\nF9hcsmRJQZY08bxfv35uPmbMGDc/7LDD3HyvvfZy8wEDBrh50usgaWJ7JU14T/rbefvtt938iCOO\ncPPGxkY3T5ogn3QxW/iuvvpqN7/kkkvc/OKLL3Zz76LLUuGFaevr6zVy5EiJhVeoc9S5kqHOpYM6\nVx2qpc5xJA8AAAAAMoRBHgAAAABkCIM8AAAAAMgQBnkAAAAAkCEdZuGVurq6ip5YmvR7mDdvnpt/\n8YtfdPM33njDzTdv3uzmXbt2dfOkCdC33Xabm++0004FWSVNZi6lYv5mNm7c6OZvvvmmm7/44otu\nvmnTJjffbbfd3Hz8+PFuXltb6+YdyerVq9182LBhbp60eID3tzZw4MC2dyzjtmzZ4uaf/OQn3fyj\njz5y80MPPbTF/Y0bN+rXv/61xMIr1DnqXMlQ56obdS4dlVbnOJIHAAAAABnCIA8AAAAAMoRBHgAA\nAABkCIM8AAAAAMiQzml3AJGkydu77LKLm8+aNauo50+aRJ006fPBBx9088MPP9zNlyxZUpAlTYJP\nMmjQIDe/5ZZb3HzChAluXlNTU9T3LVbS78r7GXfq5H+OMnr0aDdP6vvs2bPdvHfv3m6eNBEeyRPM\n169f7+YrV6508913370gS/p9v/POO27ev39/N8+ipNf2k08+6eZPP/20m7/wwgst7m/YsGH7Ooay\noc5R5yTqXDlQ59JRaXWOI3kAAAAAkCEM8gAAAAAgQxjkAQAAAECGcDF0tJuk19batWvd/OGHH3bz\nc889181XrVrl5knnf0+bNs3NTzrpJDdPuoBu0lyFpqamgizpZ5CUNzY2uvmCBQvc/KmnnnLzpJ/x\nqaee6uZDhgxxcxRn+fLlbp405yhpPs9bb73l5qNGjWpbxzJk/vz5Le43NDRo3333lbgYOnUuBdQ5\n6lxHQ51rf6WqcxzJAwAAAIAMYZAHAAAAABnCIA8AAAAAMoRBHgAAAABkCAuvoGolvXbnzp3r5pMm\nTXLzV1991c2TLvjpXRxUkk4++eSC7MQTT3Tb7rDDDm7evXt3N0+6wOa6devcPOmipEmT/pN+NkkX\ns02alI/iJF3gdNiwYUW1X7RokZsPGDDAzbP0+6uvr1dtba3EwivUuQyizlHnqh11bvu1tc5xJA8A\nAAAAMoRBHgAAAABkSKqDPDO7yMxeMrMGM/vIzB4wszF5bbqb2U1mtsLM1pjZfWbGxU4AABWPOgcA\nSEPaR/KOlHSTpI9LOkZSF0l/NLNeOW2mSjpB0pfi9jtKur/M/QQAoC2ocwCAsquohVfMbLCkjyQd\nGUJ4ysxqJS2TdHII4d64zZ6S3pI0PoTwfCuekwnpaJOmpiY3X7t2rZvPnz+/IHv88cfdtk899ZSb\nL1682M2TJhAfcsghbj5y5Eg3f+2119x8xowZbn7ccce5+dSpU908aUI9SiNpQvqoUaPcPJ6oXeC2\n225z83333dfNe/fu7eZJizaUU7UtvEKdQyWhzlHnKg11rlBWFl5p/k2tjL+OU/Sp5/TmBiGEtyUt\nlDTeewIz62ZmfZtvkvq0Y38BACgGdQ4A0O4qZpBnZp0kTZP0TAjhjTgeKmljCGF1XvOl8TbPRZLq\ncm7+mqsAAJQRdQ4AUC4VM8hTNGdhH0lf2c7nuVrRJ6XNtxHb+XwAAJQCdQ4AUBad0+6AJJnZjZI+\nJ+mIEELuJ5IfSupqZv3yPuUcEm8rEEJolNSY89zt0GMAAFqPOgcAKKdUF16xqDLdIOkLko4KIczN\n2948If2fQwj3xdkYSW+LCemoYlu2bHHzTZs2ufmHH7r/19PcuXPd/Pnn/T+NjRs3unm3bt2Kev51\n69a5+Q9+8IOCbMyYMU5L/mNaDnPmzHHzSZMmubm3qIIkDRs2zM1PO+20gmzixIlu28GDB7t5sa+D\n/IUi6uvr1b9/f6lCF16hzqGjos5R58qBOpcs7SN5N0k6WdKJkhrMrHn+QV0IYX0Ioc7Mfi5pipmt\nlFSvqFg+15rCBwBAyqhzAICyS3uQd3b89c95+WmSbo//fYGkJkn3Seom6TFJ55ShbwAAbC/qHACg\n7FId5IUQtnn8MoSwQdK58Q0AgKpBnQMApKGiLobeHprnKnzwwQcFcxWSLmjZuXPaBziB1kn6+02a\nk9DQ0ODmL730kpsnXVR21qxZbj579mw39xx//PFufvrpp7v5nnvu6eb8vRYvaU5M0usjaQ7DHXfc\nUZC98sorbtsJEya4edLvu0uXLm6er76+XsOHD5cqdE5eOVDnkGXUOf5e24I6V1mXUAAAAAAAbCcG\neQAAAACQIQzyAAAAACBDGOQBAAAAQIZ0mIVXunbtWnAxwsbGxnQ6lYKuXbu6edKk4BtuuMHNR4wY\n4eZc8DMdSX+/+RfS3Fa+YcMGN1+8eLGb33///W7+f//3fwVZ0gVuk/7+ki5Am/QaO+KII9z829/+\ntpvvvffebp50odxOnfzPwpLyalDsQgb19f48b29ie9IiBtdff72bJ732xo8f7+a77757i/vr16/X\nueeeK7HwCnWOOpdJ1DnqXFtQ5ziSBwAAAACZwiAPAAAAADKEQR4AAAAAZAiDPAAAAADIkM5pd6Bc\nli5dqr59+7bIqnlCabGSJpRee+21bj5mzBg3X79+fcn6hMrXubP/FpGUe3r06OHmY8eOdfOjjjrK\nzRcuXOjmb7/9tpsfffTRbu5Nopak7t27u/ngwYPd/PDDDy/IJk6c6Lbdb7/93Lx///5unvTeVOzC\nD8VOPF+1alVRufe7HTlypNv2/PPPd/PZs2e7edJ7zerVq1vcT5rQ3hFR56hzKB51jjq3tbya61zH\nefcHAAAAgA6AQR4AAAAAZAiDPAAAAADIEAZ5AAAAAJAhljRhMSvMrK+kurq6uoIJ6QBKa8uWLQVZ\n0sTiGTNmuPmVV17p5nPmzHHzj3/8427+pS99yc3nzp3r5suXL3fzpMnbixcvLsg++ugjt+2KFSvc\nfM2aNW7epUsXN6+trXXzYcOGufmgQYPcPOm9MGkifNJk/T59+hRk3bp1c9v27t3bzRsaGtw86Wcz\ncODAFvc3bNigH/7wh5JUG0LwV97IOOocUD7UOepctdQ5juQBAAAAQIYwyAMAAACADGGQBwAAAAAZ\nwiAPAAAAADKEhVcAVLWnn37azU888UQ3P/XUU9383HPPdfP6en+O86pVqwqypMnVPXr0cPOmpiY3\nT5rEn7Svb7zxhpsn9b1Xr15u3rNnTzcfMWKEm3v7lTSZPqnWdO7c2c333XdfN8+f2L5u3TpNnDhR\nYuEV6hyQUdQ56lxb6hxH8gAAAAAgQxjkAQAAAECGMMgDAAAAgAzxTxIFgCpx2GGHuXnShVmvvfZa\nNz/88MPd/KWXXnLzMWPGFGRJcw9qamrcPEljY6ObH3300W7uXZxXSr7wbdIFd1977TU3/+CDD9z8\nueeeK8iSLpS7efNmN//EJz7h5kkXle3Xr1+L+0nzOgAgK6hz1Lm24EgeAAAAAGQIgzwAAAAAyBAG\neQAAAACQIQzyAAAAACBDuBg6ACj5Iqb5E6Cb7bLLLgVZ0uT1pAuhpiVp4nyx7c2sIEuaHJ80Cf5b\n3/qWm7/zzjtu/tWvfrXF/cbGRl133XUSF0OnzgHYKupc8e2ruc5xJA8AAAAAMoRBHgAAAABkCIM8\nAAAAAMgQBnkAAAAAkCGVNUsSAFLiTa6WpLq6OjdfunRpQdatWze37U477eTmSZOuu3Tp4ual0qlT\ncZ/vFdO+pqbGzceOHevmTzzxhJvX1/tzy2+++eYW9zds2NDqvgFAR0adK037aqlzHMkDAAAAgAxJ\ndZBnZkeY2UNmttjMgplNyNtuZnalmS0xs/VmNt3Mdk+rvwAAFItaBwAot7SP5PWS9JqkcxO2Xyhp\nsqSzJB0iaa2kx8yse3m6BwDAdqPWAQDKKtU5eSGEP0j6g1R4nrBFwfmSrgohPBhnp0paKmmCpLvL\n2lkAANqAWgcAKDcLIaTdB0mSmQVJXwghPBDf30XSe5IOCCG8mtPuSUmvhhD8y8cXPm9fSXV1dXXq\n27dvO/QcALbuoYcecvMTTzzRzW+88UY3P+ecc0rWp6yor69XbW2tJNWGEPxZ7BWkPWoddQ5A2qhz\n7aetda6SV9ccGn/NX9pnac62AmbWTVLu0j99StwvAABKpehaR50DAGxL2nPy2sNFkupybovS7Q4A\nACVFnQMAbFUlD/I+jL8OycuH5GzzXC2pNuc2ovRdAwCgJNpS66hzAICtquTTNecpKnBHS3pV+tu8\ng0Mk3Zz0oBBCo6TG5vtJF34EgHI54YQT3LypqcnNDzjgADc/77zz3HzNmjVu3rNnz1b0DikrutZR\n5wBUGupc5Ul1kGdmvSXtlhPtbGb7S1oZQlhoZtMkfd/M5ioqhD+QtFjSA+XvLQAAxaPWAQDKLe0j\neQdK+lPO/Snx1zskTZJ0raLrC/1MUj9JT0s6LoSwoYx9BABge1DrAABllfZ18v4sKfE8kxBd3+Hf\n4xsAAFWHWgcAKLdKXngFAAAAAFCktE/XBADkmTVrlptHB3wKJU08HzIkf8HGyPz589vULwAASoE6\n1/44kgcAAAAAGcIgDwAAAAAyhEEeAAAAAGQIgzwAAAAAyBAWXgGAKmHmr8K/fv16N1+8eHFRz7Ny\n5Uo379+/fyt6BwDA9qHOlQ5H8gAAAAAgQxjkAQAAAECGMMgDAAAAgAxhkAcAAAAAGcLCKwCQUTvu\nuKObhxDcfIcddnDzQYMGufmbb77Zto4BAFAC1LlkHMkDAAAAgAxhkAcAAAAAGcIgDwAAAAAyhDl5\nAABJ0kcffeTmjzzyiJt7F5ttampqdVsAAMqpI9U5juQBAAAAQIYwyAMAAACADGGQBwAAAAAZwiAP\nAAAAADKEhVcAAFv1mc98xs29i81269bNbfvd737Xza+66qq2dwwAgBLIYp3jSB4AAAAAZAiDPAAA\nAADIEAZ5AAAAAJAhDPIAAAAAIENYeAUAUDKNjY1uftFFF7l5ly5d3Hzx4sVuPmDAgBb3t2zZUkTv\nAADYPtVS5ziSBwAAAAAZwiAPAAAAADKEQR4AAAAAZAiDPAAAAADIEPOu5J4lZtZXUl1dXZ369u2b\ndncAADmSJpT379/fzQ877LAW9zdt2qTp06dLUm0Iob7E3asK1DkAqFxp1TmO5AEAAABAhjDIAwAA\nAIAMYZAHAAAAABnCxdABAKmpqalx8/p6f9rBeeed1+L+xo0bS94nAABKJa06x5E8AAAAAMgQBnkA\nAAAAkCFVMcgzs3PNbL6ZbTCzF8zs4LT7BABAKVHrAAClUvGDPDObKGmKpCsk/YOk1yQ9ZmY7pNox\nAABKhFoHACilir8Yupm9IOmlEMJ58f1Okt6XdEMI4ZpWPJ6LxAJARtXX16u2tlaq8ouhb0+to84B\nQHa1tc5V9JE8M+sqaZyk6c1ZCKEpvj8+4THdzKxv801Sn7J0FgCANii21lHnAADbUtGDPEmDJNVI\nWpqXL5U0NOExF0mqy7ktarfeAQCw/YqtddQ5AMBWVfogry2ullSbcxuRbncAACgp6hwAYKsq/WLo\nyyVtkTQkLx8i6UPvASGERkmNzffNTFLyBQcBANUrI+/tRdU66hwAdBxtfW+v6EFeCGGjmc2UdLSk\nB6S/TUY/WtKNrXyaPpI0cuTIdukjAKAi9JFUlaOcEtQ66hwAZF9Rda6iB3mxKZLuMLOXJb0o6XxJ\nvST9opWPX6zoVJYGRT+cRTn3s4x9zSb2NXs6yn5K7bevfRS911ez7al11Dn2NUvY12xiX7f/OYuq\ncxU/yAsh/NbMBku6UtEE9FclHRdCyJ+gnvT4IOkD6e+ntEhqqOaltluDfc0m9jV7Osp+Su26r1X/\nc9ueWkedY1+zhH3NJvZ1uxX9PBU/yJOkEMKNav3pmQAAVB1qHQCgVLK4uiYAAAAAdFgdbZDXKOkK\n5axKlmHsazaxr9nTUfZT6lj7mpaO9DNmX7OJfc0m9rXMLDqVHwAAAACQBR3tSB4AAAAAZBqDPAAA\nAADIEAZ5AAAAAJAhHWaQZ2bnmtl8M9tgZi+Y2cFp96kUzOwIM3vIzBabWTCzCXnbzcyuNLMlZrbe\nzKab2e5p9betzOwiM3vJzBrM7CMze8DMxuS16W5mN5nZCjNbY2b3mdmQtPrcVmZ2tpnNNrP6+Pac\nmR2fsz0T+5nPzL4Xv4an5WSZ2Vczuzzev9zb2znbM7OvkmRmw83sznh/1pvZ62Z2YM72TLw3VZos\n1jrqXIs2mXif6Kh1Tsp2raPOVVad6xCDPDObKGmKopVu/kHSa5IeM7MdUu1YafRStD/nJmy/UNJk\nSWdJOkTSWkX73r083SuZIyXdJOnjko6R1EXSH82sV06bqZJOkPSluP2Oku4vcz9LYZGk70kaJ+lA\nSU9IetDMPhZvz8p+/o2ZHSTpTEmz8zZlbV//ImlYzu2wnG2Z2Vcz6y/pGUmbJB0vaW9J35a0KqdZ\nVt6bKkaGax117u+y8j7R4eqc1GFqHXXu79J9bwohZP4m6QVJN+bc7yTpA0nfS7tvJd7PIGlCzn2T\ntETSd3KyWkkbJH0l7f5u574Ojvf3iJz92ijppJw2e8ZtPp52f0uwvysl/UsW91NSb0nvSPq0pD9L\nmpbF36mkyyW9mrAta/t6jaQZW9me2femlH/uma911LnsvE84+5vZOhfvQ+ZrHXWuxfbU35syfyTP\nzLoq+qRoenMWQmiK749Pq19lsrOkoWq573WK/iNQ7fteG39dGX8dp+hTz9x9fVvSQlXxvppZjZl9\nRdEn2c8pm/t5k6T/DSFMz8uzuK+7x6ec/dXMfm1mO8V51vb185JeNrN74tPOZpnZN3K2Z/m9KRUd\nuNZl+bVEnYtlYT/VcWoddS6S+ntT5gd5kgZJqpG0NC9fquiHn2XN+5epfTezTpKmSXomhPBGHA+V\ntDGEsDqveVXuq5mNNbM1ii6keYukL4QQ3lT29vMrik4ru8jZnKl9VfTGPknScZLOVlQAZphZH2Vv\nX3dRtI9zJR0r6WZJ15vZ1+PtmXxvSllHrXWZfC1R57Kzn1KHqnXUuQqqc53L8U2AErtJ0j5qeZ53\n1syRtL+iT3JPknSHmR2ZbpdKy8xGSrpO0jEhhA1p96e9hRD+kHN3tpm9IGmBpC9LWp9Or9pNJ0kv\nhxAuju/PMrN9FM1LuCO9bgFVgzqXER2p1lHnKqvOdYQjecslbZGUv3rPEEkflr87ZdW8f5nZdzO7\nUdLnJH0yhLAoZ9OHkrqaWb+8h1TlvoYQNoYQ3g0hzAwhXKRo0YFvKVv7OU7SDpJeMbPNZrZZ0UTs\nyfG/lyo7+1og/jTzHUm7KVu/Vymah/BmXvaWpObTdjL33lQBOmqty9xriTqXrf1UB6511DlJKb43\nZX6QF0LYKGmmpKObs/g0iKMVnfudZfMUvZBy972vohV+qmrf42Vob5T0BUmfCiHMy2syU9EKR7n7\nOkbRH1tV7WuCTpK6KVv7+biksYo+yW2+vSzp1zn/zsq+FjCz3pJ2VVQosvR7laIVx8bkZXso+kRX\nytB7U6XowLUuM68l6lwm65zUgWsddS7l96a0V6cpx03SREWr2Xxd0l6S/kvREqdD0u5bCfatt/7+\nphEkXRD/e6d4+7/F+/p5RW8yD0j6q6Tuafe9yP38qaTVij79Gppz65HT5mZFf1yfVPTJ2bOSnk27\n723Y16slHSFpdPw7u1pSk6JTPTKznwn7/mfFK45lbV8l/Th+/Y6WdKik/5O0TNLgDO7rQYqK+cWK\nPsE9WdHS0V/NaZOJ96ZKumW11lHnqHPVup9b2f9M1jrqXGXVudR/SGX8ZZwXv7AaFU0MPSTtPpVo\nv46Ki17+7fZ4u0m6UtGnCRsUrfKzR9r9bsN+evsYJE3KadNd0TyGlfEf2v2Shqbd9zbs688lzY9f\nq9fZRfkAAAOcSURBVB/Fv7NjsrafCfueX/gys6+S7pa0OP69Lorv75rFfY3353OSXo/fd96S9I28\n7Zl4b6q0WxZrHXWOOlet+7mV/c9kraPOVVads7gTAAAAAIAMyPycPAAAAADoSBjkAQAAAECGMMgD\nAAAAgAxhkAcAAAAAGcIgDwAAAAAyhEEeAAAAAGQIgzwAAAAAyBAGeQAAAACQIQzyAAAAACBDGOQB\nFcLMbjezB9LuBwAA7YVaB5QHgzwALjPrmnYfAABoT9Q6ZBWDPKAKmNm/mtnrZrbWzN43s5+aWe94\nWy8zqzezk/IeMyFu3ye+P9LMfmdmq81spZk9aGajc9rfbmYPmNklZrZY0pwy7iIAoIOj1gGlwyAP\nqA5NkiZL+pikr0v6lKRrJSmEsFbS3ZJOy3vMaZLuDSE0mFkXSY9JapB0uKRPSFoj6dG8TzGPljRG\n0jGSPtduewMAQCFqHVAiFkJIuw8AFH26KKlfCGFCK9qeJOmWEMKg+P7Bkp6VNDKEsMTMdpD0gaRP\nhxCeNLNTJH1f0l4h/qOPC95qSRNCCH+Mv/9xknYKIWws/R4CADo6ah1QHhzJA6qAmX3azB43sw/M\nrEHSryQNNLOekhRCeFHSXxR98ilJp0haIOmp+P5+knaT1GBma8xsjaSVkrpL2jXnW71O0QMApIFa\nB5QOgzygwsVzCR6WNFvSFyWNk3RuvDn39JP/ljQp/vdpkn4R/n6ovrekmZL2z7vtIek3Oc+xttT9\nBwBgW6h1QGl1TrsDALZpnKIPZL4dQmiSJDP7stPuTknXmtlkSXtLuiNn2yuSJkr6KIRQ3879BQCg\nWNQ6oIQ4kgdUlloz2z/3Jmm5pC6Svmlmu5jZ1ySdlf/AEMIqSfdL+pGkP4YQFuVs/nX8PA+a2eFm\ntrPZ/2/n7k0iCAIwDL+T2YJiLXJ9iE0Y+FOBoWBmoC2ZCBobWYHpGdwhIoiXrMrwPLDJMgu70cc3\nOzNjNca4GWMcLv9ZAPBB1sHClDz4X1bVw5frpDqtzqvH6ri6/Ob5uzbLWu4/31yv12/VUfXSJhyf\ntmP3KrOdAPymVbIOFuV0TZjIdubzujqwqRyAGck6+Jk9eTCB7clj+9VFdSv0AJiNrIPdWa4Jczir\nnqvX6uqP3wUAliDrYEeWawIAAEzEnzwAAICJKHkAAAATUfIAAAAmouQBAABMRMkDAACYiJIHAAAw\nESUPAABgIkoeAADARJQ8AACAibwDTKL9CNVhOFQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7142827e50>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Keras prediction vs our prediction\")\n",
    "draw_2d_input_and_pred(kerasout[0], ourout[0], cmap='gray_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12+"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}