Ex 5.4 Q-Learning Agent.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DAT257x: Reinforcement Learning Explained\n",
    "\n",
    "## Lab 5: Temporal Difference Learning\n",
    "\n",
    "### Exercise 5.4: Q-Learning Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import sys\n",
    "\n",
    "if \"../\" not in sys.path:\n",
    "    sys.path.append(\"../\") \n",
    "    \n",
    "from lib.envs.simple_rooms import SimpleRoomsEnv\n",
    "from lib.envs.windy_gridworld import WindyGridworldEnv\n",
    "from lib.envs.cliff_walking import CliffWalkingEnv\n",
    "from lib.simulation import Experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Agent(object):  \n",
    "        \n",
    "    def __init__(self, actions):\n",
    "        self.actions = actions\n",
    "        self.num_actions = len(actions)\n",
    "\n",
    "    def act(self, state):\n",
    "        raise NotImplementedError"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class QLearningAgent(Agent):\n",
    "    \n",
    "    def __init__(self, actions, epsilon=0.01, alpha=0.5, gamma=1):\n",
    "        super(QLearningAgent, self).__init__(actions)\n",
    "        ## TODO 1\n",
    "        ## Initialize empty dictionary here\n",
    "        ## In addition, initialize the value of epsilon, alpha and gamma\n",
    "        self.epsilon = epsilon\n",
    "        self.alpha = alpha\n",
    "        self.gamma = gamma\n",
    "        self.qsa = {}\n",
    "        \n",
    "        \n",
    "    def stateToString(self, state):\n",
    "        mystring = \"\"\n",
    "        if np.isscalar(state):\n",
    "            mystring = str(state)\n",
    "        else:\n",
    "            for digit in state:\n",
    "                mystring += str(digit)\n",
    "        return mystring    \n",
    "    \n",
    "    \n",
    "    def act(self, state):\n",
    "        stateStr = self.stateToString(state)      \n",
    "        action = np.random.randint(0, self.num_actions) \n",
    "        if stateStr not in self.qsa:\n",
    "            self.qsa[stateStr] = np.zeros(self.num_actions)\n",
    "            \n",
    "        ## TODO 2\n",
    "        ## Implement epsilon greedy policy here\n",
    "        if np.random.binomial(1, self.epsilon) == 0:\n",
    "            idx = np.where(self.qsa[stateStr] == np.max(self.qsa[stateStr]))\n",
    "            action = np.random.choice(idx[0])\n",
    "        return action\n",
    "    \n",
    "    \n",
    "    def learn(self, state1, action1, reward, state2, done):\n",
    "        state1Str = self.stateToString(state1)\n",
    "        state2Str = self.stateToString(state2)\n",
    "        \n",
    "        ra2 = 0\n",
    "        if state2Str in self.qsa:\n",
    "            ra2 = max(self.qsa[state2Str])\n",
    "        ## TODO 3\n",
    "        ## Implement the q-learning update here\n",
    "        self.qsa[state1Str][action1] += self.alpha * (reward + self.gamma * ra2 - self.qsa[state1Str][action1])\n",
    "        \"\"\"\n",
    "        Q-learning Update:\n",
    "        Q(s,a) <- Q(s,a) + alpha * (reward + gamma * max(Q(s') - Q(s,a))\n",
    "        or\n",
    "        Q(s,a) <- Q(s,a) + alpha * (td_target - Q(s,a))\n",
    "        or\n",
    "        Q(s,a) <- Q(s,a) + alpha * td_delta\n",
    "        \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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6HL1igJ0+aZnWblQd0D//Lobs/Mxn8v/fewDctDDmw7uRYBArQICdK2TsS8CBAgQIECAAAECwxMQoDuamQBdDi1Al1v1sVKALlcXoP/U6vPf+lmed9LSfPT5u2XnLZobsL0IEKhVQICudTL2RYAAAQIECBAgQGB4AgJ0RzMToMuhBehyqz5WCtDl6gL0n1odf8ZleccXL883Xrd3br/OWuWQVhIg0LmAAN05uQsSIECAAAECBAgQmFgBAbqj0QrQ5dACdLlVHysF6HJ1AfpPrf76xHPzs1/9Np/5m93LEa0kQKAXAQG6F3YXJUCAAAECBAgQIDCRAgJ0R2MVoMuhBehyqz5WCtDl6gL0H62mp6ez4+s/m3222SzHPWX7ckQrCRDoRUCA7oXdRQkQIECAAAECBAhMpIAA3dFYBehyaAG63KqPlQJ0uboA/UerH/zihuz+xi/m75+8XQ7cdUk5opUECPQiIED3wu6iBAgQIECAAAECBCZSQIDuaKwCdDm0AF1u1cdKAbpcXYD+o9UnL/pJXvyhC/KfL35Yttn8juWIVhIg0IuAAN0Lu4sSIECAAAECBAgQmEgBAbqjsQrQ5dACdLlVHysF6HJ1AfqPVq//1KX54NeW//4LCNde8zbliFYSINCLgADdC7uLEiBAgAABAgQIEJhIAQG6o7EK0OXQAnS5VR8rBehydQH6j1b7v/uruWU6+dgLHlIOaCUBAr0JCNC90bswAQIECBAgQIAAgYkTEKA7GqkAXQ4tQJdb9bFSgC5XF6D/YHXTzbdk26NOz4G73DOvffzW5YBWEiDQm4AA3Ru9CxMgQIAAAQIECBCYOAEBuqORCtDl0AJ0uVUfKwXocnUB+g9Wl/7kV9n3bV/OW5+2Q564w93KAa0kQKA3AQG6N3oXJkCAAAECBAgQIDBxAgJ0RyMVoMuhBehyqz5WCtDl6gL0H6w+fO4PcvjHL8mZr9gjW2yyXjmglQQI9CYgQPdG78IECBAgQIAAAQIEJk5AgO5opAJ0ObQAXW7Vx0oBulxdgP6D1REfvzinXfLTXPjavbLGGv62U/4JspJAfwICdH/2rkyAAAECBAgQIEBg0gSUgI4mKkCXQwvQ5VZ9rBSgy9UF6D9Y7fvWL+dOd1gn//q8XcvxrCRAoFcBAbpXfhcnQIAAAQIECBAgMFECAnRH4xSgy6EF6HKrPlYK0OXqAnTym9/d/PsvIDxkj/vk5Y/eqhzPSgIEehUQoHvld3ECBAgQIECAAAECEyUgQHc0TgG6HFqALrfqY6UAXa4uQCdLr7gm+737nPzLs3bKXlvfpRzPSgIEehUQoHvld3ECBAgQIECAAAECEyUgQHc0TgG6HFqALrfqY6UAXa5eQYBeN8mXktw2yVpJTklyZJJ7Nd8NmGTjJOcneWaS363sZFNTU9NLly4tP/iMle89+/t5/acuzbmv3jN33qDZjhcBAkMQmCNAb5nklUnu2f515dbjPHII57JHAgQIECBAgAABAgS6FxCgOzIXoMuhBehyqz5WCtDl6hUE6Oav8esluT7J2knOTvKSJC9L8vE2Qr87yUVJ3jWOAP3iD12Q8664JuccsWc5nJUECPQuMEeAbv560fx1Y1mSm2dstPnPXgQIECBAgAABAgQIEPgzAQG6ow+FAF0OLUCXW/WxUoAuV68gQM/c7O3bAP2CJP+ZZLMkNyXZLclRSfYeR4De441fzP032yDvfuZUOZyVBAj0LjBHgG5Cs/9B9z4hGyBAgAABAgQIECAwHAEBuqNZCdDl0AJ0uVUfKwXocvVKAvSa7Z2K903yT0nemORrSZr/3LzukeTTSbZd6AB97Q2/yw5HfzaH7XP/vGCP+5TDWUmAQO8CswTo5pE9zevFSa5K8h9Jbpyx0Wt637QNECBAgAABAgQIECBQpYAA3dFYBOhyaAG63KqPlQJ0uXolAfrWDW/YBqPXJnnfCgH6tCTbrXCyg5M0v7JkyZKp5cuXlx+8XXnWd36evz7x3Pz7QbvmIffdZOSf9wMECPQnMEuA/n6S6SSz/bNj8+fv3d9uXZkAAQIECBAgQIAAgZoFBOiOpiNAl0ML0OVWfawUoMvVKwvQzcabLyC8IclhXTyC4+2f/68c/7nv5KIjH50N1m0eQe1FgMBQBOZ4BEfzbaK/XeEcs/25oRzVPgkQIECAAAECBAgQGLOAAD1m4FvfXoAuhxagy636WClAl6tXEKA3TfI/Sa5NcrskZyT5hyR/neRjM76E8OIk71zZyaampqaXLl1afvB25UEnnZfvX/3rfP7le4z8s36AAIF+BeYI0OcnedAKu5vtz/V7AFcnQIAAAQIECBAgQKAaAQG6o1EI0OXQAnS5VR8rBehy9QoC9PZJTkrSPAf6NklOTnJ0+6/KfzhJ80zXC5I8Y4Vnuf7JIVcnQE9PT2fnYz6f3bfcJMc/dYdyNCsJEKhCYJYA3Xxx6d2SfDDJgTMexbFBkncnuX8VG7cJAgQIECBAgAABAgSqExCgOxqJAF0OLUCXW/WxUoAuV68gQJdvdo6VqxOgf3Ltb/KQ476Qo5+4TZ612xYLsg9vQoBAdwKzBOjm35x4dpKdksz8VyKuS/L+JB/vbneuRIAAAQIECBAgQIDAkAQE6I6mJUCXQwvQ5VZ9rBSgy9UXc4D+9CVX5gX/dn4+8cKHZod7NN9/6EWAwJAE5ngEx1PaR/gM6Tj2SoAAAQIECBAgQIBAjwICdEf4AnQ5tABdbtXHSgG6XH0xB+jjPv3tvPfs7+Ubr9s7t12reQKIFwECQxKYI0C/bJZz/HeSZUkuHNIZ7ZUAAQIECBAgQIAAgW4EBOhunCNAl0ML0OVWfawUoMvVF3OAPuA9X8uvf3dTPnnow8rBrCRAoBqBOQL0v7eP4fh/7WYfm+S89hnQH03yhmoOYSMECBAgQIAAAQIECFQhIEB3NAYBuhxagC636mOlAF2uvlgD9C23TGf7152RJ+94t7z+SduWg1lJgEA1AnME6NOTNI/huL7d7B2SnJLkye1d0FtXcwgbIUCAAAECBAgQIECgCgEBuqMxCNDl0AJ0uVUfKwXocvXFGqAvv+q6POr4L+VN+z8w+03dvRzMSgIEqhGYI0B/K8kDk/yu3ext20dvPCDJBUl2rOYQNkKAAAECBAgQIECAQBUCAnRHYxCgy6EF6HKrPlYK0OXqizVAn7LsR3nFRy/KZ1+6e+53l/XLwawkQKAagTkC9Gvau51PbTf7+CSfTPKPSd6T5OnVHMJGCBAgQIAAAQIECBCoQkCA7mgMAnQ5tABdbtXHSgG6XH2xBujXnvqNfPz8H+eiIx+dNW/jbzPlnxgrCdQjMEeAbja5U5KHJmn+B352kqX17NxOCBAgQIAAAQIECBCoTUAZ6GgiAnQ5tABdbtXHSgG6XH2xBugnvuPs3G6dNfPhg3crx7KSAIGqBFYRoNdMcpcka83Y9A+qOoDNECBAgAABAgQIECBQjYAA3dEoBOhyaAG63KqPlQJ0ufpiDNA33nRztj3y9Dz3YffKEY9pHgnrRYDAEAXmCNAvSnJkkp8lubm9C3o6yfZDPKc9EyBAgAABAgQIECAwfgEBevzGv7+CAF0OLUCXW/WxUoAuV1+MAfqiH16bJ/7TV/Kupz8oj9nuruVYVhIgUJXAHAH68iS7JvlFVRu2GQIECBAgQIAAAQIEqhUQoDsajQBdDi1Al1v1sVKALldfjAH6A+dckdee+s185fBH5m4b3q4cy0oCBKoSmCNAfzHJXkluqmrDNkOAAAECBAgQIECAQLUCAnRHoxGgy6EF6HKrPlYK0OXqizFAv/zki3LWd36e8/52z6yxhr/FlH9arCRQl8AcAfq9SbZK8p9Jbpyx6+PrOoHdECBAgAABAgQIECBQi4A60NEkBOhyaAG63KqPlQJ0ufpiDNCPOv6s3HPj2+e9z965HMpKAgSqE5gjQDfPf57t9brqDmFDBAgQIECAAAECBAhUISBAdzSG+QToy9/84I526TKrI3Dfl35tdX7s9z9jtqtN19kPzme+iy1AX/fb/8n2rzsjL33UlnnxnvfrbEYuRIDAwgvMEaBvvdh6SX698Ff2jgQIECBAgAABAgQITJqAAN3RRAXojqB7uMx8AqUA3cPARrzkfOa72AL0V797dQ78l6/npOfukr/YctMRpS0nQKAmgTkC9G5Jmsdw3CHJkiQPTPL/JTmkpv3bCwECBAgQIECAAAEC9QgI0B3NQoDuCLqHy8wnUArQPQxsxEvOZ749B+h7JPlAks2S3JLkPUnemmTjJB9JskWSK5I8Nckv52KZmpqaXrp06Srl3nXmd/MPn/l2LnjNXtlovXVWud4CAgTqFZgjQH89yX5JPplkx/YE30iybb2nsTMCBAgQIECAAAECBPoUEKA70hegO4Lu4TLzCZQCdA8DG/GS85lvzwH6rkmaX+cnWT/JsiRPSvLsJNckOS7J4Uk2SnLYQgToF3xwWS698lc565WPGFHZcgIEahNYRYDeNckFMwL0Re2d0LUdw34IECBAgAABAgQIEKhAQIDuaAgCdEfQPVxmPoFSgO5hYCNecj7z7TlAr3jSU5O8o/21R5Ir20B9ZpKtFiJAP+TYz2dqi43z9gNuvSlyRGzLCRCoRmCOAH1KkuPbv5Y0X1Lx4iQ7JXlaNZu3EQIECBAgQIAAAQIEqhIQoDsahwDdEXQPl5lPoBSgexjYiJecz3wrCtDN4za+1P4r8j9IsuEMhubxG81d0Ct9lTyC46rrfptdjvl8/u9jH5CDHn7vEZUtJ0CgNoE5AvQm7eN8HpWk+efIM9oI3fybFV4ECBAgQIAAAQIECBD4MwEBuqMPhQDdEXQPl5lPoBSgexjYiJecz3wrCdDNF4WdleSYJB9Pcm1hgD44SfMrS5YsmVq+fPmccp+79Gc56ANLc8rzd8tOWzSPmfYiQGDIAnME6NmO9TdJ3jLk89o7AQIECBAgQIAAAQLjExCgx2f7J+8sQHcE3cNl5hMoBegeBjbiJecz3woC9NpJPpXk9PZfmW9Of1mSBX8Exz+ecVneeeZ3842j9s7t1llzRGXLCRCoTWDEAN38mxVLajuD/RAgQIAAAQIECBAgUIeAAN3RHATojqB7uMx8AqUA3cPARrzkfObbc4Bu/vp+UvuFg83dibe+3pjkFzO+hLC5XflVc7GUPILjWSeem59fd2M+/ZKHjyhsOQECNQqMGKB/mOQeNZ7DnggQIECAAAECBAgQ6F9AgO5oBgJ0R9A9XGY+gVKA7mFgI15yPvPtOUA/LMmXk1yS5Jb22K9O8vUkJ7d3KzZ3Le7fRuqVyqwqQE9PT2eHoz+bx2y7WY57yvYjCltOgECNAiMGaHdA1zhEeyJAgAABAgQIECBQiYAA3dEgBOiOoHu4zHwCpQDdw8BGvOR85ttzgB7xpCtfvqoAfcXVv84ebzozx/7ldjlgF/8W/oLBeyMCPQrMEqCvSzI9y5aaf5a8XZK1etyuSxMgQIAAAQIECBAgULGAAN3RcATojqB7uMx8AqUA3cPARrzkfOa7WAL0qRf+OC/58IU57cUPz9abbzCisOUECNQoMOId0DUewZ4IECBAgAABAgQIEKhEQIDuaBACdEfQPVxmPoFSgO5hYCNecj7zXSwB+uj/d2n+/dzlv/8CwrXWvM2IwpYTIFCjgABd41TsiQABAgQIECBAgMAwBQTojuYmQHcE3cNl5hMoBegeBjbiJecz38USoJ/yrq+m+ZvJKS94yIi6lhMgUKuAAF3rZOyLAAECBAgQIECAwPAEBOiOZiZAdwTdw2XmEygF6B4GNuIl5zPfxRCg/+fmW7LdUafnwF3umdc+fusRdS0nQKBWAQG61snYFwECBAgQIECAAIHhCQjQHc1sPgH69J9c2NEuF+4y9/nI81f7zTb81rA+lpu855zVPuvVB++22j/b1w9e+4DZvoOqbDdDm21zqvnMdzEE6G/+5L/z2Lednbc+bYc8cYe7lX0QrCJAoHoBAUR/FjAAACAASURBVLr6EdkgAQIECBAgQIAAgcEIDKv0DYb1zzcqQJcPb2iRcj6BUoAu/1z0tXI+810MAfpD5/4gR3z8kpz1yj1yzzut19eYXJcAgQUWEKAXGNTbESBAgAABAgQIEFjEAgJ0R8MXoMuhBehyqz5WugO6XH0xBOjDP3ZxPvPNn+aC1+yVNdbwt5TyT4eVBOoWEKDrno/dESBAgAABAgQIEBiSgFrQ0bQE6HJoAbrcqo+VAnS5+mII0Pu85Uu58wbr5gPP3aUcxkoCBKoXEKCrH5ENEiBAgAABAgQIEBiMgADd0agE6HJoAbrcqo+VAnS5+qQH6Bt+d1O2O+qMHLLHffLyR29VDmMlAQLVCwjQ1Y/IBgkQIECAAAECBAgMRkCA7mhUAnQ5tABdbtXHSgG6XH3SA/R5V1yT/d99Tk541k551NZ3KYexkgCB6gUE6OpHZIMECBAgQIAAAQIEBiMgQHc0KgG6HFqALrfqY6UAXa4+6QH6hC9/L3/3n9/KuX+7Z+68/rrlMFYSIFC9gABd/YhskAABAgQIECBAgMBgBATojkYlQJdDC9DlVn2sFKDL1Sc9QL/oQxdk2RXX5KtH7FmOYiUBAoMQEKAHMSabJECAAAECBAgQIDAIAQG6ozEJ0OXQAnS5VR8rBehy9UkP0Lu/4YvZZvMN8q5nTJWjWEmAwCAEBOhBjMkmCRAgQIAAAQIECAxCQIDuaEwCdDm0AF1u1cdKAbpcfZID9C9//bvs+PrP5rB97p8X7HGfchQrCRAYhIAAPYgx2SQBAgQIECBAgACBQQgI0B2NSYAuhxagy636WClAl6tPcoA+87Kr8uz3nZd//z+75iH32aQcxUoCBAYhIEAPYkw2SYAAAQIECBAgQGAQAgJ0R2MSoMuhBehyqz5WCtDl6pMcoN/2+f/Kmz/3nVx85KOz/rprl6NYSYDAIAQE6EGMySYJECBAgAABAgQIDEJAgO5oTAJ0ObQAXW7Vx0oBuly9ggB9YpLHJbkqybbtzjdO8pEkWyS5IslTk/xyrlNNTU1NL1269E+WPO/952X5NTfkcy/7i3IQKwkQGIyAAD2YUdkoAQIECBAgQIAAgeoFBOiORiRAl0ML0OVWfawUoMvVKwjQuye5PskHZgToNyS5JslxSQ5PslGSw0YJ0NPT09n5mM/lL7a8c/7xqQ8sB7GSAIHBCAjQgxmVjRIgQIAAAQIECBCoXkCA7mhEAnQ5tABdbtXHSgG6XL2CAN1strnT+VMzAvRlSfZIcmWSuyY5M8lWowToH1/7mzz0uC/k6Cduk2ft1ry9FwECkyYgQE/aRJ2HAAECBAgQIECAQH8CAnRH9gJ0ObQAXW7Vx0oBuly90gB9bZINZ5yiefxGcxf0Sl8rPoLjtEuuzCH/dn5OfeFD88B7zHyrchsrCRCoW0CArns+dkeAAAECBAgQIEBgSAICdEfTEqDLoQXocqs+VgrQ5eoDD9AHJ2l+ZcmSJVPLly//34Mfe9q38r6vXJFLXvfo3HatNctBrCRAYDACAvRgRmWjBAgQIECAAAECBKoXEKA7GpEAXQ4tQJdb9bFSgC5XrzRAz/sRHE97zzn5ze9uzqmHPqwcw0oCBAYlIEAPalw2S4AAAQIECBAgQKBqAQG6o/EI0OXQAnS5VR8rBehy9UoD9BuT/GLGlxBunORVc51q5iM4br5lOtsfdXqeMnX3HP3EbcsxrCRAYFACAvSgxmWzBAgQIECAAAECBKoWEKA7Go8AXQ4tQJdb9bFSgC5XryBAf6j9wsFNkvwsyZFJPpHk5ObJGkl+kGT/JNeUBuj/+tl12evNX8qb9n9g9pu6ezmGlQQIDEpAgB7UuGyWAAECBAgQIECAQNUCAnRH4xGgy6EF6HKrPlYK0OXqFQTo8s3OsXLmHdAfXfrDvPKUi/O5l+2e+955/QV5f29CgEB9AgJ0fTOxIwIECBAgQIAAAQJDFRCgO5qcAF0OLUCXW/WxUoAuV5/EAP1/P3FJPnHBT3LxkY/ObW7jbyHlnwYrCQxLQIAe1rzslgABAgQIECBAgEDNAupBR9MRoMuhBehyqz5WCtDl6pMYoJ/wjrOz3jpr5UMHP7gcwkoCBAYnIEAPbmQ2TIAAAQIECBAgQKBaAQG6o9EI0OXQAnS5VR8rBehy9UkL0DfedHO2PfL0PO9h987hj7l/OYSVBAgMTkCAHtzIbJgAAQIECBAgQIBAtQICdEejEaDLoQXocqs+VgrQ5eqTFqAv/OG1edI/fSXvevqD8pjt7loOYSUBAoMTEKAHNzIbJkCAAAECBAgQIFCtgADd0WgE6HJoAbrcqo+VAnS5+qQF6JO+ekWO/OQ389XDH5nNN7xdOYSVBAgMTkCAHtzIbJgAAQIECBAgQIBAtQICdEejEaDLoQXocqs+VgrQ5eqTFqBf9pEL8+XLr865r94za6zhbx/lnwQrCQxPQIAe3szsmAABAgQIECBAgECtAgpCR5MRoMuhBehyqz5WCtDl6pMWoPf8xzNzr03Wywl/vXM5gpUECAxSQIAe5NhsmgABAgQIECBAgECVAgJ0R2MRoMuhBehyqz5WCtDl6pMUoL9w9jnZ/qgz8vK9tsyL9rxfOYKVBAgMUkCAHuTYbJoAAQIECBAgQIBAlQICdEdjEaDLoQXocqs+VgrQ5eqTFKDf9uHP5MATvp6TnrtL/mLLTcsRrCRAYJACAvQgx2bTBAgQIECAAAECBKoUEKA7GosAXQ4tQJdb9bFSgC5Xn6QA/dw3fThv+MxlufC1e2XD269TjmAlAQKDFBCgBzk2myZAgAABAgQIECBQpYAA3dFYBOhyaAG63KqPlQJ0ufokBeipl7w7l/30upz5ykeUA1hJgMBgBQTowY7OxgkQIECAAAECBAhUJyBAdzQSAbocWoAut+pjpQBdrj5JAXrtp/xDdt5i47ztgB3LAawkQGCwAgL0YEdn4wQIECBAgAABAgSqExCgOxqJAF0OLUCXW/WxUoAuV5+UAP3AHR40fe3eR+c1j9s6z3vYvcoBrCRAYLACAvRgR2fjBAgQIECAAAECBKoTEKA7GokAXQ4tQJdb9bFSgC5Xn5QAfb9ttp/+3eP+Pqc8f7fstMXG5QBWEiAwWAEBerCjs3ECBAgQIECAAAEC1QkI0B2NRIAuhxagy636WClAl6tPSoC++/22mV5nvzfkG0ftnduts2Y5gJUECAxWQIAe7OhsnAABAgQIECBAgEB1AgJ0RyMRoMuhBehyqz5WCtDl6pUH6H2SvDVJU5RPSHLcyk628T3vP73rS9+T017y8PLDW0mAwKAFBOhBj8/mCRAgQIAAAQIECFQlIEB3NA4BuhxagC636mOlAF2uXnGAbqLzd5LsleRHSc5LckCSS2c73e3uer/pl7zjlBz7l9uXH95KAgQGLSBAD3p8Nk+AAAECBAgQIECgKgEBuqNxCNDl0AJ0uVUfKwXocvWKA/RuSY5Ksnd7miPaPx472+nW2ey+0yed+vk8bZcl5Ye3kgCBQQsI0IMen80TIECAAAECBAgQqEpAgO5oHAJ0ObQAXW7Vx0oBuly94gC9X5LmERwHtad5ZpJdkxy6sgB9wbJl2XrzDcoPbyUBAoMWEKAHPT6bJ0CAAAECBAgQIFCVgADd0TgE6HJoAbrcqo+VAnS5esUBev/27ueZAXqXJC+acbqDkzS/mte2Sb5RfvKqV26S5Oqqd1i+OWcpt+pq5STNZKsk63cF5zoECBAgQIAAAQIECEyugADd0WwF6HJoAbrcqo+VAnS5esUBeqRHcCRZmmSn8pNXvdJZ6hzPpMxlUs7RfEom6Sx1furtigABAgQIECBAgMAiERCgOxq0AF0OLUCXW/WxUoAuV684QK/Vfgnhnkl+3H4J4YFJvrmS001SiHKW8o9wlysnZS6Tcg4BustPv2sRIECAAAECBAgQmHABAbqjAQvQ5dACdLlVHysF6HL1igN0c4h9k7wlyZpJTkxyzBwnE9XKx97lSnPpUrvsWmZS5mQVAQIECBAgQIAAAQKLSECA7mjYAnQ5tABdbtXHSgG6XL3yAF1+kD88C/o9o/xAxWudpc7hTMpcJuUczadkks5S56ferggQIECAAAECBAgsEgEBuqNBzydAX/7mB3e0y4W7zH1f+rWFezPvNDECi+2zPEEBemI+gw5CgAABAgQIECBAgAABAgQIdCsgQHfkLUB3BO0yVQsI0FWPx+YIECBAgAABAgQIECBAgAABAgsuIEAvOOnsbyhAdwTtMlULCNBVj2dlm9snyVvbZ0WfkOS4QZ4iuUeSDyTZLMkt7SNFmnMN9dU8u7t53nDzJZKPG+ohkmyYpPlcbZtkOslzk5wz0PO8NMlB7TkuSfKcJL8dyFma58A3n6Or2lk02944yUeSbJHkiiRPTfLLgZzHNgkQIECAAAECBAgQqEhAgO5oGAJ0R9AuU7WAAF31eGbbXBM5v5NkryQ/SnJekgOSXDq4kyR3zR9+nZ9k/STLkjxpoGdp+F+WZKckGww8QJ+U5MtthF4nye2TXDvAz9fdkpydZOskv0lycpLTkrx/IGfZPcn17W/SNL8Z0LzekOSa9jedDk+yUZLDBnIe2yRAgAABAgQIECBAoCIBAbqjYQjQHUG7TNUCAnTV45ltc7slOSrJ3u1/eUT7x2MHd5I/3/CpSd6R5LMDPMvdkzTh9pg2RA/1Dugmnl+U5N7tXcMDHMX/brkJ0M2XHzwwya+SfCLJ25KcMaBDNXc6f2rGHdCXJdkjyZXtb96cmWSrAZ3HVgkQIECAAAECBAgQqERAgO5oEAJ0R9AuU7WAAF31eGbb3H5JmkdwNI8VaF7PTLJrkkMHd5I/3XAT2r7UhrYmFg7tdUqS5jcBmju5XzHgO6B3aB+F0txR34Tb5q70lyT59dAG0u632XvzmwLNHdBNeH76wM6xYoBu7kRvHpFy66t5/EZzF7QXAQIECBAgQIAAAQIERhIQoEfiWv3FAvTq2/nJyREQoAc3y/3bu59nBuhdkrxocCf544bvkOSsNhR+fIDnaO523jfJIe3dqUMO0M0jRJq7hh+a5Ovts8ab3xB4zQDn0oTZjyX5q/YRIh9N0vxGwQcHdBYBekDDslUCBAgQIECAAAECQxIQoDualgDdEbTLVC0gQFc9ntk2N2mP4Fi7fcTA6UmOH9w0/rDh5s7n5k70m5Ks2z4DugnpzxjgeZovhGwCdBM+m9fDkzTPGn7sAM/S/GZN828LPK/d+7OSPLj9jYKhHMcjOIYyKfskQIAAAQIECBAgMDABAbqjgQnQHUG7TNUCAnTV45ltc2u1X0K4Z5Ift19CeGCSbw7uJEnz97vmucnNl6r9zQD3P9uWm+fzDvkO6OZMzRcQNnfYN88bbp43vl6SVw5wPs2jaU5MsnP7CI7myweXJnn7gM6yYoB+Y5JfzPgSwo2TvGpA57FVAgQIECBAgAABAgQqERCgOxqEAN0RtMtULSBAVz2elW2uedzDW5Ks2Qa25hm3Q3w9rI2dlyS5pT3Aq5OcNsTDtHuehADdPAf6hCTrJPlekuckaZ41PMTX69pHcDR3p1/QhvUbB3KQD7WPdNkkyc+SHNl+keLJSZYk+UGS5i7v5jdwvAgQIECAAAECBAgQIDCSgAA9EtfqLxagV9/OT06OgAA9ObN0EgIECBAgQIAAAQIECBAgQIBAiYAAXaK0AGsE6AVA9BaDFxCgBz9CByBAgAABAgQIECBAgAABAgQIjCQgQI/EtfqLBejVt/OTkyMgQE/OLJ2EAAECBAgQIECAAAECBAgQIFAiIECXKC3AGgF6ARC9xeAFBOjBj9ABCBAgQIAAAQIECBAgQIAAAQIjCQjQI3Gt/uL5BOirD95t9S/c009e+4Dp1b7yht8a1sdyk/ecs9pnNdvVpuvsB+cz389Nn7IsyU6dbdaFCBAgQIAAAQIECBAgQIAAAQKVCQyr9FWGN8p2BOhyLQG63KqPlYvpNxca3wEG6BOTPC7JVUm2neUz0vx1/61J9k1yQ5JnJzm/j8+SaxIgQIAAAQIECBAgQIAAAQKTLyBAdzRjAbocWoAut+pjpQBdrt7THdC7J7k+yQdWEqCb8PyiNkDv2sbo5o9eBAgQIECAAAECBAgQIECAAIEFFxCgF5x09jcUoMuhBehyqz5WCtDl6j0F6GaDWyT51EoC9D8nOTPJh9qTXJZkjyRXlp/MSgIECBAgQIAAAQIECBAgQIBAmYAAXeY071UCdDmhAF1u1cdKAbpcvdIA3YTp45Kc3Z7k80kOS7K0/GRWEiBAgAABAgQIECBAgAABAgTKBAToMqd5rxKgywkF6HKrPlYK0OXqlQbo/0xy7AoB+lVJmi9MnPk6OEnzK+utt97U/e9///KDW0mAwOAFli1bdnWSTQd/EAcgQIAAAQIECBAgQKB3AQG6oxEI0OXQAnS5VR8rBehy9UoD9MiP4JiamppeutQN0uWTt5LA8AXWWGON5jeldhr+SZyAAAECBAgQIECAAIG+BQTojiYgQJdDC9DlVn2sFKDL1SsN0I9NcuiMLyF8W5Jd5jqVAF0+cysJTIqAAD0pk3QOAgQIECBAgAABAv0LCNAdzUCALocWoMut+lgpQJer9xSgmy8XbL5UcJMkP0tyZJK1212/O0nz1/13JNknyQ1JnrOq5z8L0OUzt5LApAgI0JMySecgQIAAAQIECBAg0L+AAN3RDATocmgButyqj5UCdLl6TwG6fIOFKwXoQijLCEyQgAA9QcN0FAIECBAgQIAAAQI9CwjQHQ1AgC6HFqDLrfpYKUCXqwvQ5VZWEiBQl4AAXdc87IYAAQIECBAgQIDAkAUE6I6mJ0CXQwvQ5VZ9rBSgy9UF6HIrKwkQqEtAgK5rHnZDgAABAgQIECBAYMgCAnRH0xOgy6EF6HKrPlYK0OXqAnS5lZUECNQlIEDXNQ+7IUCAAAECBAgQIDBkAQG6o+kJ0OXQAnS5VR8rBehydQG63MpKAgTqEhCg65qH3RAgQIAAAQIECBAYsoAA3dH0BOhyaAG63KqPlQJ0uboAXW5lJQECdQkI0HXNw24IECBAgAABAgQIDFlAgO5oegJ0ObQAXW7Vx0oBulxdgC63spIAgboEBOi65mE3BAgQIECAAAECBIYsIEB3ND0BuhxagC636mOlAF2uLkCXW1lJgEBdAgJ0XfOwGwIECBAgQIAAAQJDFhCgO5qeAF0OLUCXW/WxUoAuVxegy62sJECgLgEBuq552A0BAgQIECBAgACBIQsI0B1NT4Auhxagy636WClAl6sL0OVWVhIgUJeAAF3XPOyGAAECBAgQIECAwJAFBOiOpidAl0ML0OVWfawUoMvVBehyKysJEKhLQICuax52Q4AAAQIECBAgQGDIAgJ0R9MToMuhBehyqz5WCtDl6gJ0uZWVBAjUJSBA1zUPuyFAgAABAgQIECAwZAEBuqPpCdDl0AJ0uVUfKwXocnUButzKSgIE6hIQoOuah90QIECAAAECBAgQGLKAAN3R9ATocmgButyqj5UCdLm6AF1uZSUBAnUJCNB1zcNuCBAgQIAAAQIECAxZQIDuaHoCdDm0AF1u1cdKAbpcXYAut7KSAIG6BATouuZhNwQIECBAgAABAgSGLCBAdzQ9AbocWoAut+pjpQBdri5Al1tZSYBAXQICdF3zsBsCBAgQIECAAAECQxYQoDuangBdDi1Al1v1sVKALlcXoMutrCRAoC4BAbquedgNAQIECBAgQIAAgSELCNAdTU+ALocWoMut+lgpQJerC9DlVlYSIFCXgABd1zzshgABAgQIECBAgMCQBQTojqYnQJdDC9DlVn2sFKDL1QXocisrCRCoS0CArmsedkOAAAECBAgQIEBgyAICdEfTE6DLoQXocqs+VgrQ5eoCdLmVlQQI1CUgQNc1D7shQIAAAQIECBAgMGQBAbqj6QnQ5dACdLlVHysF6HJ1AbrcykoCBOoSEKDrmofdECBAgAABAgQIEBiygADd0fQE6HJoAbrcqo+VAnS5ugBdbmUlAQJ1CQjQdc3DbggQIECAAAECBAgMWUCA7mh6AnQ5tABdbtXHSgG6XF2ALreykgCBugQE6LrmYTcECBAgQIAAAQIEhiwgQHc0PQG6HFqALrfqY6UAXa7eU4DeJ8lbk6yZ5IQkx62w42cneWOSH7d//h3tupUebGpqanrp0qXlB7eSAIHBCwjQgx+hAxAgQIAAAQIECBCoRkCA7mgUAnQ5tABdbtXHSgG6XL2HAN1E5+8k2SvJj5Kcl+SAJJfO2HUToHdKcmjpSQToUinrCEyOgAA9ObN0EgIECBAgQIAAAQJ9CwjQHU1AgC6HFqDLrfpYKUCXq/cQoHdLclSSvdtdHtH+8VgBunxuVhIgkAjQPgUECBAgQIAAAQIECCyUgAC9UJKreB8BuhxagC636mOlAF2u3kOA3i9J8wiOg9pdPjPJrivc7dzcAd0E6Z+3d0u/NMkP5zqVO6DLZ24lgUkREKAnZZLOQYAAAQIECBAgQKB/AQG6oxkI0OXQAnS5VR8rBehy9R4C9P7t3c8zA/QuSV40Y9d3SnJ9khuTPD/JU5M8cpZTHZyk+ZUlS5ZMLV++vPzgVhIgMHgBAXrwI3QAAgQIECBAgAABAtUICNAdjUKALocWoMut+lgpQJer9xCgSx7BMfMAzTOjr0lyx7lO5Q7o8plbSWBSBAToSZmkcxAgQIAAAQIECBDoX0CA7mgGAnQ5tABdbtXHSgG6XL2HAL1W+1iNPZP8uP0SwgOTfHPGru+a5Mr2Pz85yWFJHixAl8/VSgKLQUCAXgxTdkYCBAgQIECAAAEC3QgI0N04R4Auhxagy636WClAl6v3EKCbze2b5C1JmrubT0xyTJKjkyxN8sn2+c9PSHJTe/fzC5J8W4Aun6uVBBaDgAC9GKbsjAQIECBAgAABAgS6ERCgu3EWoEdwFqBHwOphqQBdjt5TgC7fYOFKj+AohLKMwAQJCNATNExHIUCAAAECBAgQINCzgADd0QDcAV0OLUCXW/WxUoAuVxegy62sJECgLgEBuq552A0BAgQIECBAgACBIQsI0B1NT4Auhxagy636WClAl6sL0OVWVhIgUJeAAF3XPOyGAAECBAgQIECAwJAFBOiOpidAl0ML0OVWfawUoMvVBehyKysJEKhLQICuax52Q4AAAQIECBAgQGDIAgJ0R9MToMuhBehyqz5WCtDl6gJ0uZWVBAjUJSBA1zUPuyFAgAABAgQIECAwZAEBuqPpzSdAX/7mB3e0S5dZHYH7vvRrq/Njv/8Zs11tus5+cD7zFaA7G5MLESCwwAIC9AKDejsCBAgQIECAAAECi1hAgO5o+AJ0R9A9XGY+gVKA7mFgI15yPvMVoEfEtpwAgWoEBOhqRmEjBAgQIECAAAECBAYvIEB3NEIBuiPoHi4zn0ApQPcwsBEvOZ/5CtAjYltOgEA1AgJ0NaOwEQIECBAgQIAAAQKDFxCgOxqhAN0RdA+XmU+gFKB7GNiIl5zPfAXoEbEtJ0CgGgEBuppR2AgBAgQIECBAgACBwQsI0B2NUIDuCLqHy8wnUArQPQxsxEvOZ74C9IjYlhMgUI2AAF3NKGyEAAECBAgQIECAwOAFBOiORihAdwTdw2XmEygF6B4GNuIl5zNfAXpEbMsJEKhGQICuZhQ2QoAAAQIECBAgQGDwAgJ0RyMUoDuC7uEy8wmUAnQPAxvxkvOZrwA9IrblBAhUIzBLgL4kyfQcG9y+ms3bCAECBAgQIECAAAECVQkI0B2NQ4DuCLqHy8wnUArQPQxsxEvOZ74C9IjYlhMgUI3ALAH6nu3mXtj+8V/bPz49yQ1Jjq5m8zZCgAABAgQIECBAgEBVAgJ0R+MQoDuC7uEy8wmUAnQPAxvxkvOZrwA9IrblBAhUIzDHIzi+kuShK2x0tj9XzVlshAABAgQIECBAgACBfgUE6I78BeiOoHu4zHwCpQDdw8BGvOR85itAj4htOQEC1QjMEaAvTHJokrPbzT4kyTuT7FDN5m2EAAECBAgQIECAAIGqBATojsYhQHcE3cNl5hMoBegeBjbiJeczXwF6RGzLCRCoRmCOAP2gJO9Lcsf2mdD/neS5Sc6vZvM2QoAAAQIECBAgQIBAVQICdEfjEKA7gu7hMvMJlAJ0DwMb8ZLzma8APSK25QQIVCOwkgB9myT7JTk5yQZJmn+ObAK0FwECBAgQIECAAAECBFYqIEB39OEQoDuC7uEy8wmUAnQPAxvxkvOZrwA9IrblBAhUIzDHHdBfSrJ7NRu1EQIECBAgQIAAAQIEqhcQoDsakQDdEXQPl5lPoBSgexjYiJecz3wF6BGxLSdAoBqBOQL0a5L8JslHkvx6xoavqWbzNkKAAAECBAgQIECAQFUCAnRH4xCgO4Lu4TLzCZQCdA8DG/GS85mvAD0ituUECFQjMEeA/v4sm5xOcu9qNm8jBAgQIECAAAECBAhUJSBAdzSO+QToqw/eraNdLtxlrn1A8/+Lrt5rw28N62O5mM7aTHQ+5910q6tX70PR40/dcd/LV/vqAvRq0/lBAgR6FpgjQPe8M5cnQIAAAQIECBAgQGBoAsMqfUPTnbFfAbp8eAJ0uVUfKwXocnUButzKSgIE6hJYRYDeNsnWSdadsesP1HUCuyFAgAABAgQIECBAoBYBAbqjSQjQ5dACdLlVHysF6HJ1AbrcykoCBOoSmCNAH5lkjzZAn5bkMUnOTrJfXSewGwIECBAgQIAAAQIEahEQoDuahABdDi1Al1v1sVKALlfvKUDvk+StSdZMckKS41bY8W2TNHcqTiX5RZK/SnLFXKeampqaXrp0afnBrSRAYPACcwToS5I8MMkF7R/v0v615vGDP7QDECBAgAABAgQIECAwFgEBeiysf/6mAnQ5tABdbtXHSgG6XL2HAN1Ed+WvqAAAIABJREFU5+8k2SvJj5Kcl+SAJJfO2PUhSbZP8vwkT0vy5DZCr/RgAnT5zK0kMCkCcwToc5PskmRZkkckuS7JN5JsMylndw4CBAgQIECAAAECBBZWQIBeWM+VvpsAXQ4tQJdb9bFSgC5X7yFAN99YelSSvdtdHtH+8dgZuz69XXNOkrWS/DTJpklW+s2hAnT5zK0kMCkCcwTodyZ5dfsbWC9Pcn2SC5M8Z1LO7hwECBAgQIAAAQIECCysgAC9sJ4CdCswn0gpQHf0oVzNy8xntptudfVqXrW/H7vjvpev9sV7CNDNM1ibR3Ac1G76mUl2TXLojEM0dyo2a5o7pJvXd9s1Kx3ORkvuP73Xq09cbYfSH9x68w1y5OPdRFnqZR2BcQqs4ksIb730Fkk2SHLxOPfivQkQIECAAAECBAgQGLaAAN3R/NwBXQ4tQJdb9bFSgC5X7yFA79/e/TwzQDf/qvyLZuz6m+2amQG6WdM8D3rm6+Akza/c4a73mdr3yH8tP/hqrhSgVxPOjxEYg8AcAbp5hvyX21/fHsOlvSUBAgQIECBAgAABAhMmIEB3NFABuhxagC636mOlAF2u3kOA9giO8vFYSYDAHAJzBOhHJnlYkocnuXf7+I0vtV9+ypQAAQIECBAgQIAAAQJ/JiBAd/ShEKDLoQXocqs+VgrQ5eo9BOjmmc7NlxDumeTH7ZcQHpikuev51tcLk2w340sI/zLJU+c6lWdAl8/cSgKTIrCKR3A0X3i6c/slhM0Xmv4myf0n5ezOQYAAAQIECBAgQIDAwgoI0AvrudJ3E6DLoQXocqs+VgrQ5eo9BOhmc/smeUuSJhA1D24+JsnRSZYm+WSSdZM0z9PYMck17ReJfU+ALp+rlQQWg8AcAfrzSdZL0nyRafMojrOTXLUYTJyRAAECBAgQIECAAIHVExCgV89t5J8SoMvJBOhyqz5WCtDl6j0F6PINFq50B3QhlGUEJkhgjgD95iRTSW5M8pUkzeM3mhjd3AXtRYAAAQIECBAgQIAAgT8TEKA7+lAI0OXQAnS5VR8rBehydQG63MpKAgTqEljFIziazd4hyXOSvCLJZkluW9cJ7IYAAQIECBAgQIAAgVoEBOiOJiFAl0ML0OVWfawUoMvVBehyKysJEKhLYI4AfWj7BYTNXdDL2zugm0dxfKGuE9gNAQIECBAgQIAAAQK1CAjQHU1CgC6HFqDLrfpYKUCXqwvQ5VZWEiBQl8AcAfqVbXReluSmunZtNwQIECBAgAABAgQI1CggQHc0FQG6HFqALrfqY6UAXa4uQJdbWUmAQF0Cq3gEx8OS3C/J+5Js2j6O4/t1ncBuCBAgQIAAAQIECBCoRUCA7mgSAnQ5tABdbtXHSgG6XF2ALreykgCBugTmCNBHJtkpyVZJtkyyeZKPJnloXSewGwIECBAgQIAAAQIEahEQoDuahABdDi1Al1v1sVKALlcXoMutrCRAoC6BOQL0hUl2THJ++8dm4xcn2b6uE9gNAQIECBAgQIAAAQK1CAjQHU1CgC6HFqDLrfpYKUCXqwvQ5VZWEiBQl8AcAfrcJLu0AfpBSdZLco4AXdf87IYAAQIECBAgQIBATQICdEfTEKDLoQXocqs+VgrQ5eoCdLmVlQQI1CUwR4B+Rfv8572SHJvkuUk+lORtdZ3AbggQIECAAAECBAgQqEVAgO5oEgJ0ObQAXW7Vx0oBulxdgC63spIAgboEVvElhE18fnSS5p8jT0/y2bp2bzcECBAgQIAAAQIECNQkIEB3NA0BuhxagC636mOlAF2uLkCXW1lJgEBdAqsI0DM3u2aSpyX5t7pOYDcECBAgQIAAAQIECNQiIEB3NAkBuhxagC636mOlAF2uLkCXW1lJgEBdArME6A2SvDDJ3ZJ8sr3rufnPr0zSfDHhE+s6gd0QIECAAAECBAgQIFCLgADd0SQE6HJoAbrcqo+VAnS5ugBdbmUlAQJ1CcwSoE9N8sv2Cwf3TLJRknWSvKQN0HUdwG4IECBAgAABAgQIEKhGQIDuaBQCdDm0AF1u1cdKAbpcXYAut7KSAIG6BGYJ0Jck2a7dZfPYjauTLElyXV07txsCBAgQIECAAAECBGoTEKA7mogAXQ4tQJdb9bFSgC5XF6DLrawkQKAugVkC9PlJHjRjlyv+57oOYDcECBAgQIAAAQIECFQjIEB3NAoBuhxagC636mOlAF2uLkCXW1lJgEBdArME6JuT/LrdZfPPj7dLckOS5v+eTtI8I9qLAAECBAgQIECAAAECfyYgQHf0oRCgy6EF6HKrPlYK0OXqAnS5lZUECNQlMEuArmuDdkOAAAECBAgQIECAwGAEBOiORiVAl0ML0OVWfawUoMvVBehyKysJEKhLQICuax52Q4AAAQIECBAgQGDIAgJ0R9MToMuhBehyqz5WCtDl6gJ0uZWVBAjUJSBA1zUPuyFAgAABAgQIECAwZAEBuqPpCdDl0AJ0uVUfKwXocnUButzKSgIE6hIQoOuah90QIECAAAECBAgQGLKAAN3R9ATocmgButyqj5UCdLm6AF1uZSUBAnUJCNB1zcNuCBAgQIAAAQIECAxZQIDuaHoCdDm0AF1u1cdKAbpcXYAut7KSAIG6BATouuZhNwQIECBAgAABAgSGLCBAdzQ9AbocWoAut+pjpQBdri5Al1tZSYBAXQICdF3zsBsCBAgQIECAAAECQxYQoDuangBdDi1Al1v1sVKALlfvOEBvnOQjSbZIckWSpyb55Sy7vTnJJe2f/0GSJ6zqRFNTU9NLly5d1TL/PQECEyQgQE/QMB2FAAECBAgQIECAQM8CAnRHAxCgy6EF6HKrPlYK0OXqHQfoNyS5JslxSQ5PslGSw2bZ7fVJ7lB+ikSAHkXLWgKTISBAT8YcnYIAAQIECBAgQIBADQICdEdTEKDLoQXocqs+VgrQ5eodB+jLkuyR5Mokd01yZpKtBOjyeVlJgMAfBQRonwYCBAgQIECAAAECBBZKQIBeKMlVvI8AXQ4tQJdb9bFSgC5X7zhAX5tkwxm7ax6/0dwFveLrpiQXJmn+2Nwt/YlVncgd0KsS8t8TmDwBAXryZupEBAgQIECAAAECBPoSEKA7khegy6EF6HKrPlYK0OXqYwjQn0uy2Sw7+NskJxUG6M2T/CTJvZN8IcmeSb47y3senKT5lSVLlkwtX768/OBWEiAweAEBevAjdAACBAgQIECAAAEC1QgI0B2NQoAuhxagy636WClAl6uPIUDPdfHSR3DMfI/3J/lUklPmemN3QJfP3EoCkyIgQE/KJJ2DAAECBAgQIECAQP8CAnRHMxCgy6EF6HKrPlYK0OXqHQfoNyb5xYwvIdw4yatW2G3zSI4bktyYZJMk5yR5YpJLBejyuVpJYDEICNCLYcrOSIAAAQIECBAgQKAbAQG6G+cI0OXQAnS5VR8rBehy9Y4D9J2SnNw8MSPJD5Lsn+SaJDsleX6Sg5I8JMk/J7klyW2SvCXJe1d1IndAr0rIf09g8gQE6MmbqRMRIECAAAECBAgQ6EtAgO5IXoAuhxagy636WClAl6t3HKDLNzbiSgF6RDDLCUyAgAA9AUN0BAIECBAgQIAAAQKVCAjQHQ1CgC6HFqDLrfpYKUCXqwvQ5VZWEiBQl4AAXdc87IYAAQIECBAgQIDAkAUE6I6mJ0CXQwvQ5VZ9rBSgy9UF6HIrKwkQqEtAgK5rHnZDgAABAgQIECBAYMgCAnRH05tPgO5oiy5DgMACCwjQCwzq7QgQ6ExAgO6M2oUIECBAgAABAgQITLyAAN3RiAXojqBdhkBFAgJ0RcOwFQIERhIQoEfispgAAQIECBAgQIAAgTkEBOiOPh4CdEfQLkOgIgEBuqJh2AoBAiMJCNAjcVlMgAABAgQIECBAgIAA3f9nQIDufwZ2QKBrAQG6a3HXI0BgoQQE6IWS9D4ECBAgQIAAAQIECLgDuqPPgADdEbTLEKhIQICuaBi2QoDASAIC9EhcFhMgQIAAAQIECBAgMIeAAN3Rx0OA7gjaZQhUJCBAVzQMWyFAYCQBAXokLosJECBAgAABAgQIEBCg+/8MCND9z8AOCHQtIEB3Le56BAgslIAAvVCS3ocAAQIECBAgQIAAAXdAd/QZEKA7gnYZAhUJCNAVDcNWCBAYSUCAHonLYgIECBAgQIAAAQIE5hAQoDv6eAjQHUG7DIGKBAToioZhKwQIjCQgQI/EZTEBAgQIECBAgAABAgJ0/58BAbr/GdgBga4FBOiuxV2PAIGFEhCgF0rS+xAgQIAAAQIECBAg4A7ojj4DAnRH0C5DoCIBAbqiYdgKAQIjCQjQI3FZTIAAAQIECBAgQIDAHAICdEcfDwG6I2iXIVCRgABd0TBshQCBkQQE6JG4LCZAgAABAgQIECBAQIDu/zMgQPc/Azsg0LWAAN21uOsRILBQAgL0Qkl6HwIECBAgQIAAAQIE3AHd0WdAgO4I2mUIVCQgQFc0DFshQGAkAQF6JC6LCRAgQIAAAQIECBCYQ0CA7ujjIUB3BO0yBCoSEKArGoatECAwkoAAPRKXxQQIECBAgAABAgQICNBVfAZ+nmR5FTuxCQIEuhK4Z5JNu7rYuK4zNTU1vXTp0nG9vfclQKBCAQG6wqHYEgECBAgQIECAAIGBCrgDeqCDs20CBAh0JSBAdyXtOgTqERCg65mFnRAgQIAAAQIECBAYuoAAPfQJ2j8BAgTGLCBAjxnY2xOoUECArnAotkSAAAECBAgQIEBgoAIC9EAHZ9sECBDoSkCA7kradQjUIyBA1zMLOyFAgAABAgQIECAwdAEBeugTtH8CBAiMWUCAHjOwtydQoYAAXeFQbIkAAQIECBAgQIDAQAUE6IEOzrYJECCwgsD+SY5K8oAkuyRZ2bcG7pPkrUnWTHJCkuNWJSlAr0rIf09g8gQE6MmbqRMRIECAAAECBAgQ6EtAgO5L3nUJECCwsAJNeL4lyT8necVKAnQTnb+TZK8kP0pyXpIDklw611YE6IUdlHcjMAQBAXoIU7JHAgQIECBAgAABAsMQEKCHMSe7JECAQKnAmXME6N3au6T3bt/siPaPxwrQpbzWEVgcAgL04pizUxIgQIAAAQIECBDoQkCA7kLZNQgQINCdwFwBer8kzSM4Dmq388wkuyY5VIDubkCuRGAIAgL0EKZkjwQIECBAgAABAgSGISBAD2NOdkmAAIFG4HNJNpuF4m+TnNr++bkCdPOc6Obu55kBunle9Itmec+DkzS/mte2Sb4xISPYJMnVzlKdwKTMZVLO0XxAtkqyfnWfFBsiQIAAAQIECBAgQGBwAgL04EZmwwQIEJhTYMEfwdE+T3qnCXFvvpzRWeob5qTMZVLO0XxCJuks9X3i7YgAAQIECBAgQIDAIhIQoBfRsB2VAIFFITBXgF6r/RLCPZP8uP0SwgOTfHMVMpMUopylzv8ZTMpcJuUcAnSd/zuxKwIECBAgQIAAAQKDFBCgBzk2myZAgMCfCTw5yduTbJrk2iQXto/b2DzJCUn2bX+i+eNbkqyZ5MQkxxRYimoFSD0sMZce0P1mTX3odkSAAAECBAgQIECAQN0CAnTd87E7AgQI1CDQPAv6PTVsZAH24CwLgDiGt5iUuUzKOZoRT9JZxvCR9ZYECBAgQIAAAQIECJQKCNClUtYRIECAAAECBAgQIECAAAECBAgQIECAwEgCAvRIXBYTIECAAAECBAgQIECAAAECBAgQIECAQKmAAF0qZR0BAgQWp8A+Sd7aPjO6eZb0cQNluEeSDyTZLMkt7SNFmnMN9dU8w7t5BnTzZZKPG+ohkmzYPqN82yTTSZ6b5JyBnuelSQ5qz3FJkuck+e1AztI8D775HF2VpJlF89o4yUeSbJHkiiRPTfLLgZzHNgkQIECAAAECBAgQqEhAgK5oGLZCgACBygSayPmdJHsl+VGS85IckOTSyvZZsp27Jml+nZ9k/STLkjxpoGdpzvuyJDsl2WDgAfqkJF9uI/Q6SW7ffolmyUxrWnO3JGcn2TrJb5KcnOS0JO+vaZNz7GX3JNe3v0lza4B+Q5Jr2t90OjzJRkkOG8h5bJMAAQIECBAgQIAAgYoEBOiKhmErBAgQqExgtyRHJdm73dcR7R+PrWyfq7OdU5O8I8lnV+eHe/6Zuydpwu0xbYge6h3QTTy/KMm927uGe2ad1+WbAP21JA9M8qskn0jytiRnzOtdu/3h5k7nT824A/qyJHskubL9zZszk2zV7ZZcjQABAgQIECBAgACBSRAQoCdhis5AgACB8Qjsl6R5BEfzWIHm9cwkuyY5dDyX6+xdm9D2pTa0NbFwaK9TkjS/CdDcyf2KAd8BvUP7KJTmjvom3DZ3pb8kya+HNpB2v83em98UaO6AbsLz0wd2jhUD9LXtI1JuPUbz+I3mLmgvAgQIECBAgAABAgQIjCQgQI/EZTEBAgQWlcD+7d3PMwP0LkleNGCFOyQ5qw2FHx/gOZq7nfdNckh7d+qQA3TzCJHmruGHJvl6+6zx5jcEXjPAuTRh9mNJ/qp9hMhHkzS/UfDBAZ1FgB7QsGyVAAECBAgQIECAwJAEBOghTcteCRAg0K3ApD2CY+32EQOnJzm+W8oFu1pz53NzJ/pNSdZtnwHdhPRnLNgVunuj5gshmwDdhM/m9fAkzbOGH9vdFhbsSs1v1jT/tsDz2nd8VpIHt79RsGAXGfMbeQTHmIG9PQECBAgQIECAAIHFKiBAL9bJOzcBAgRWLbBW+yWEeyb5cfslhAcm+eaqf7S6Fc3f75rnJjdfqvY31e1u9TbUPJ93yHdAN6duvoCwucO+ed5w87zx9ZK8cvU4ev2p5tE0JybZuX0ER/Plg0uTvL3XXY128RUD9BuT/GLGlxBunORVo72l1QQIECBAgAABAgQIEEgEaJ8CAgQIEJhLoHncw1uSrNkGtuYZt0N8PayNnZckuaU9wKuTnDbEw7R7noQA3TwH+oQk6yT5XpLnJGmeNTzE1+vaR3A0d6df0Ib1GwdykA+1j3TZJMnPkhzZfpHiyUmWJPlBkuYu7+Y3cLwIECBAgAABAgQIECAwkoAAPRKXxQQIECBAgAABAgQIECBAgAABAgQIECBQKiBAl0pZR4AAAQIECBAgQIAAAQIECBAgQIAAAQIjCQjQI3FZTIAAAQIECBAgQIAAAQIECBAgQIAAAQKlAgJ0qZR1BAgQIECAAAECBAgQIECAAAECBAgQIDCSgAA9EpfFBAgQIECAAAECBAgQIECAAAECBAgQIFAqIECXSllHgAABAgQIECBAgAABAgQIECBAgAABAiMJCNAjcVlMgAABAgQGIXBzkktm7PTDSY6bY+fPT3JDkg/M83RXJNkpydXzfB8/ToAAAQIECBAgQIAAAQITIiBAT8ggHYMAAQIECMwQuD7JHXoQEaB7QHdJAgQIECBAgAABAgQI1CwgQNc8HXsjQIAAAQKrJ7CyAN0E4o8keUT7tgcmuTzJUUman3lTkhcnae6IvinJpUmelmTjJCcmuXd7p/TBSS5OcqckH0qyaZJzk+yTZKq9A/oZ7Xutk+TrSQ5J0tyZ7UWAAAECBAgQIECAAAECi0hAgF5Ew3ZUAgQIEFg0Ais+guPYNjw3AfpfkhyT5FlJnprkcSsE6J8kuVeSG5NsmOTaJG9vo/LrkjwyyfFJdkjytvbPH53ksUk+1cboJki/IclfJvmfJO9M8rUFeMTHohmggxIgQIAAAQIECBAgQGBSBAToSZmkcxAgQIAAgT8KzHUHdBOQv5dk7SQ/be9innkH9Gfau6E/kaT51bzXBUme0v5cc5UfJtk2yVltZG7er3ldk2TL9q7pVye5qv3zt2vvlG6u40WAAAECBAgQIECAAAECi0hAgF5Ew3ZUAgQIEFg0AnMF6ObxG99vA/SVSTZZ4Q7oNZPsnuQJSfZNsk2SpSuE5iZAN3/+S0me3L7fzAB9QJLNkxyxaMQdlAABAgQIECBAgAABAgRmFRCgfTAIECBAgMDkCcwVoN+d5LgkzTOa/yrJ42cE6ObRGkuSNI/qaO6Q/lGSrZI0j9j4eZLXJ9kjyZuT7Ng+gqO5y/nvkjwmyWntIzjunOTUJA9t74JuniG9fpLlk0ftRAQIECBAgAABAgQIECAwl4AA7fNBgAABAgQmT2DFZ0A3j9U4vA3L72vvbL5NkuZO5ZlfQvjWJF9McsckzT8jfLCN1U1Abn6ueTb0DUlW/BLC5i7qWx/HceuXEDZxu7kDurlO8xzoF7bPgZ48bSciQIAAAQIECBAgQIAAgZUKCNA+HAQIECBAYPEINHc279R+ceDiObWTEiBAgAABAgQIECBAgEBvAgJ0b/QuTIAAAQIEOhcQoDsnd0ECBAgQIECAAAECBAgsbgEBenHP3+kJECBAgAABAgQIECBAgAABAgQIECAwNgEBemy03pgAAQIECBAgQIAAAQIECBAgQIAAAQKLW0CAXtzzd3oCBAgQIECAAAECBAgQIECAAAECBAiMTUCAHhutNyZAgAABAgQIECBAgAABAgQIECBAgMDiFhCgF/f8nZ4AAQIECBAgQIAAAQIECBAgQIAAAQJjExCgx0brjQkQIECAAAECBAgQIECAAAECBAgQILC4BQToxT1/pydAgAABAgQIECBAgAABAgQIECBAgMDYBATosdF6YwIECBAgQIAAAQIECBAgQIAAAQIECCxuAQF6cc/f6QkQIECAAAECBAgQIECAAAECBAgQIDA2AQF6bLTemAABAgQIECBAgAABAgQIECBAgAABAotbQIBe3PN3egIECBAgQIAAAQIECBAgQIAAAQIECIxNQIAeG603JkCAAAECBAgQIECAAAECBAgQIECAwOIWEKAX9/ydngABAgQIECBAgAABAgQIECBAgAABAmMTEKDHRuuNCRAgQIAAAQIECBAgQIAAAQIECBAgsLgFBOjFPX+nJ0CAAAECBAgQIECAAAECBAgQIECAwNgE/v927JgGAACGYRh/1kWRqyZQad4XATqjNUyAAAECBAgQIECAAAECBAgQIECAAIFvAQH6+/+uJ0CAAAECBAgQIECAAAECBAgQIECAQCYgQGe0hgkQIECAAAECBAgQIECAAAECBAgQIPAtIEB//9/1BAgQIECAAAECBAgQIECAAAECBAgQyAQE6IzWMAECBAgQIECAAAECBAgQIECAAAECBL4FBOjv/7ueAAECBAgQIECAAAECBAgQIECAAAECmYAAndEaJkCAAAECBAgQIECAAAECBAgQIECAwLeAAP39f9cTIECAAAECBAgQIECAAAECBAgQIEAgExCgM1rDBAgQIECAAAECBAgQIECAAAECBAgQ+BYQoL//73oCBAgQIECAAAECBAgQIECAAAECBAhkAgJ0RmuYAAECBAgQIECAAAECBAgQIECAAAEC3wIC9Pf/XU+AAAECBAgQIECAAAECBAgQIECAAIFMQIDOaA0TIECAAAECBAgQIECAAAECBAgQIEDgW0CA/v6/6wkQIECAAAECBAgQIECAAAECBAgQIJAJCNAZrWECBAgQIECAAAECBAgQIECAAAECBAh8CwjQ3/93PQECBAgQIECAAAECBAgQIECAAAECBDIBATqjNUyAAAECBAgQIECAAAECBAgQIECAAIFvAQH6+/+uJ0CAAAECBAgQIECAAAECBAgQIECAQCYgQGe0hgkQIECAAAECBAgQIECAAAECBAgQIPAtIEB//9/1BAgQIECAAAECBAgQIECAAAECBAgQyAQE6IzWMAECBAgQIECAAAECBAgQIECAAAECBL4FBOjv/7ueAAECBAgQIECAAAECBAgQIECAAAECmYAAndEaJkCAAAECBAgQIECAAAECBAgQIECAwLeAAP39f9cTIECAAAECBAgQIECAAAECBAgQIEAgExCgM1rDBAgQIECAAAECBAgQIECAAAECBAgQ+BYQoL//73oCBAgQIECAAAECBAgQIECAAAECBAhkAgJ0RmuYAAECBAgQIECAAAECBAgQIECAAAEC3wIC9Pf/XU+AAAECBAgQIECAAAECBAgQIECAAIFMQIDOaA0TIECAAAECBAgQIECAAAECBAgQIEDgW0CA/v6/6wkQIECAAAECBAgQIECAAAECBAgQIJAJCNAZrWECBAgQIECAAAECBAgQIECAAAECBAh8CwjQ3/93PQECBAgQIECAAAECBAgQIECAAAECBDIBATqjNUyAAAECBAgQIECAAAECBAgQIECAAIFvAQH6+/+uJ0CAAAECBAgQIECAAAECBAgQIECAQCYgQGe0hgkQIECAAAECBAgQIECAAAECBAgQIPAtIEB//9/1BAgQIECAAAECBAgQIECAAAECBAgQyAQE6IzWMAECBAgQIECAAAECBAgQIECAAAECBL4FBOjv/7ueAAECBAgQIECAAAECBAgQIECAAAECmYAAndEaJkCAAAECBAgQIECAAAECBAgQIECAwLeAAP39f9cTIECAAAECBAgQIECAAAECBAgQIEAgExCgM1rDBAgQIECAAAECBAgQIECAAAECBAgQ+BYQoL//73oCBAgQIECAAAECBAgQIECAAAECBAhkAgJ0RmuYAAECBAgQIECAAAECBAgQIECAAAEC3wIC9Pf/XU+AAAECBAgQIECAAAECBAgQIECAAIFMQIDOaA0TIECAAAECBAgQIECAAAECBAgQIEDgW0CA/v6/6wkQIECAAAECBAgQIECAAAECBAgQIJAJCNAZrWECBAgQIECAAAECBAgQIECAAAECBAh8CwjQ3/93PQECBAgQIECAAAECBAgQIECAAAECBDIBATqjNUyAAAECBAgQIECAAAECBAgQIECAAIFvAQH6+/+uJ0CAAAECBAgQIECAAAECBAgQIECAQCYgQGe0hgkQIECAAAECBAgQIECAAAECBAgQIPAtAWBFAAAgAElEQVQtIEB//9/1BAgQIECAAAECBAgQIECAAAECBAgQyAQE6IzWMAECBAgQIECAAAECBAgQIECAAAECBL4FBOjv/7ueAAECBAgQIECAAAECBAgQIECAAAECmYAAndEaJkCAAAECBAgQIECAAAECBAgQIECAwLeAAP39f9cTIECAAAECBAgQIECAAAECBAgQIEAgExCgM1rDBAgQIECAAAECBAgQIECAAAECBAgQ+BYQoL//73oCBAgQIECAAAECBAgQIECAAAECBAhkAgJ0RmuYAAECBAgQIECAAAECBAgQIECAAAEC3wIC9Pf/XU+AAAECBAgQIECAAAECBAgQIECAAIFMQIDOaA0TIECAAAECBAgQIECAAAECBAgQIEDgW0CA/v6/6wkQIECAAAECBAgQIECAAAECBAgQIJAJCNAZrWECBAgQIECAAAECBAgQIECAAAECBAh8CwjQ3/93PQECBAgQIECAAAECBAgQIECAAAECBDIBATqjNUyAAAECBAgQIECAAAECBAgQIECAAIFvAQH6+/+uJ0CAAAECBAgQIECAAAECBAgQIECAQCYgQGe0hgkQIECAAAECBAgQIECAAAECBAgQIPAtIEB//9/1BAgQIECAAAECBAgQIECAAAECBAgQyAQE6IzWMAECBAgQIECAAAECBAgQIECAAAECBL4FBOjv/7ueAAECBAgQIECAAAECBAgQIECAAAECmYAAndEaJkCAAAECBAgQIECAAAECBAgQIECAwLeAAP39f9cTIECAAAECBAgQIECAAAECBAgQIEAgExCgM1rDBAgQIECAAAECBAgQIECAAAECBAgQ+BYQoL//73oCBAgQIECAAAECBAgQIECAAAECBAhkAgJ0RmuYAAECBAgQIECAAAECBAgQIECAAAEC3wIC9Pf/XU+AAAECBAgQIECAAAECBAgQIECAAIFMQIDOaA0TIECAAAECBAgQIECAAAECBAgQIEDgW0CA/v6/6wkQIECAAAECBAgQIECAAAECBAgQIJAJCNAZrWECBAgQIECAAAECBAgQIECAAAECBAh8CwjQ3/93PQECBAgQIECAAAECBAgQIECAAAECBDIBATqjNUyAAAECBAgQIECAAAECBAgQIECAAIFvAQH6+/+uJ0CAAAECBAgQIECAAAECBAgQIECAQCYgQGe0hgkQIECAAAECBAgQIECAAAECBAgQIPAtIEB//9/1BAgQIECAAAECBAgQIECAAAECBAgQyAQE6IzWMAECBAgQIECAAAECBAgQIECAAAECBL4FBOjv/7ueAAECBAgQIECAAAECBAgQIECAAAECmYAAndEaJkCAAAECBAgQIECAAAECBAgQIECAwLeAAP39f9cTIECAAAECBAgQIECAAAECBAgQIEAgExCgM1rDBAgQIECAAAECBAgQIECAAAECBAgQ+BYQoL//73oCBAgQIECAAAECBAgQIECAAAECBAhkAgJ0RmuYAAECBAgQIECAAAECBAgQIECAAAEC3wIC9Pf/XU+AAAECBAgQIECAAAECBAgQIECAAIFMQIDOaA0TIECAAAECBAgQIECAAAECBAgQIEDgW0CA/v6/6wkQIECAAAECBAgQIECAAAECBAgQIJAJCNAZrWECBAgQIECAAAECBAgQIECAAAECBAh8CwjQ3/93PQECBAgQIECAAAECBAgQIECAAAECBDIBATqjNUyAAAECBAgQIECAAAECBAgQIECAAIFvAQH6+/+uJ0CAAAECBAgQIECAAAECBAgQIECAQCYgQGe0hgkQIECAAAECBAgQIECAAAECBAgQIPAtIEB//9/1BAgQIECAAAECBAgQIECAAAECBAgQyAQE6IzWMAECBAgQIECAAAECBAgQIECAAAECBL4FBOjv/7ueAAECBAgQIECAAAECBAgQIECAAAECmYAAndEaJkCAAAECBAgQIECAAAECBAgQIECAwLeAAP39f9cTIECAAAECBAgQIECAAAECBAgQIEAgExCgM1rDBAgQIECAAAECBAgQIECAAAECBAgQ+BYQoL//73oCBAgQIECAAAECBAgQIECAAAECBAhkAgJ0RmuYAAECBAgQIECAAAECBAgQIECAAAEC3wIC9Pf/XU+AAAECBAgQIECAAAECBAgQIECAAIFMQIDOaA0TIECAAAECBAgQIECAAAECBAgQIEDgW0CA/v6/6wkQIECAAAECBAgQIECAAAECBAgQIJAJCNAZrWECBAgQIECAAAECBAgQIECAAAECBAh8CwjQ3/93PQECBAgQIECAAAECBAgQIECAAAECBDIBATqjNUyAAAECBAgQIECAAAECBAgQIECAAIFvAQH6+/+uJ0CAAAECBAgQIECAAAECBAgQIECAQCYgQGe0hgkQIECAAAECBAgQIECAAAECBAgQIPAtIEB//9/1BAgQIECAAAECBAgQIECAAAECBAgQyAQE6IzWMAECBAgQIECAAAECBAgQIECAAAECBL4FBOjv/7ueAAECBAgQIECAAAECBAgQIECAAAECmYAAndEaJkCAAAECBAgQIECAAAECBAgQIECAwLeAAP39f9cTIECAAAECBAgQIECAAAECBAgQIEAgExCgM1rDBAgQIECAAAECBAgQIECAAAECBAgQ+BYQoL//73oCBAgQIECAAAECBAgQIECAAAECBAhkAgJ0RmuYAAECBAgQIECAAAECBAgQIECAAAEC3wIC9Pf/XU+AAAECBAgQIECAAAECBAgQIECAAIFMQIDOaA0TIECAAAECBAgQIECAAAECBAgQIEDgW0CA/v6/6wkQIECAAAECBAgQIECAAAECBAgQIJAJCNAZrWECBAgQIECAAAECBAgQIECAAAECBAh8CwjQ3/93PQECBAgQIECAAAECBAgQIECAAAECBDIBATqjNUyAAAECBAgQIECAAAECBAgQIECAAIFvAQH6+/+uJ0CAAAECBAgQIECAAAECBAgQIECAQCYgQGe0hgkQIECAAAECBAgQIECAAAECBAgQIPAtIEB//9/1BAgQIECAAAECBAgQIECAAAECBAgQyAQE6IzWMAECBAgQIECAAAECBAgQIECAAAECBL4FBOjv/7ueAAECBAgQIECAAAECBAgQIECAAAECmYAAndEaJkCAAAECBAgQIECAAAECBAgQIECAwLeAAP39f9cTIECAAAECBAgQIECAAAECBAgQIEAgExCgM1rDBAgQIECAAAECBAgQIECAAAECBAgQ+BYQoL//73oCBAgQIECAAAECBAgQIECAAAECBAhkAgJ0RmuYAAECBAgQIECAAAECBAgQIECAAAEC3wIC9Pf/XU+AAAECBAgQIECAAAECBAgQIECAAIFMQIDOaA0TIECAAAECBAgQIECAAAECBAgQIEDgW0CA/v6/6wkQIECAAAECBAgQIECAAAECBAgQIJAJCNAZrWECBAgQIECAAAECBAgQIECAAAECBAh8CwjQ3/93PQECBAgQIECAAAECBAgQIECAAAECBDIBATqjNUyAAAECBAgQIECAAAECBAgQIECAAIFvAQH6+/+uJ0CAAAECBAgQIECAAAECBAgQIECAQCYgQGe0hgkQIECAAAECBAgQIECAAAECBAgQIPAtIEB//9/1BAgQIECAAAECBAgQIECAAAECBAgQyAQE6IzWMAECBAgQIECAAAECBAgQIECAAAECBL4FBOjv/7ueAAECBAgQIECAAAECBAgQIECAAAECmYAAndEaJkCAAAECBAgQIECAAAECBAgQIECAwLeAAP39f9cTIECAAAECBAgQIECAAAECBAgQIEAgExCgM1rDBAgQIECAAAECBAgQIECAAAECBAgQ+BYQoL//73oCBAgQIECAAAECBAgQIECAAAECBAhkAgJ0RmuYAAECBAgQIECAAAECBAgQIECAAAEC3wIC9Pf/XU+AAAECBAgQIECAAAECBAgQIECAAIFMQIDOaA0TIECAAAECBAgQIECAAAECBAgQIEDgW0CA/v6/6wkQIECAAAECBAgQIECAAAECBAgQIJAJCNAZrWECBAgQIECAAAECBAgQIECAAAECBAh8CwjQ3/93PQECBAgQIECAAAECBAgQIECAAAECBDIBATqjNUyAAAECBAgQIECAAAECBAgQIECAAIFvAQH6+/+uJ0CAAAECBAgQIECAAAECBAgQIECAQCYgQGe0hgkQIECAAAECBAgQIECAAAECBAgQIPAtIEB//9/1BAgQIECAAAECBAgQIECAAAECBAgQyAQE6IzWMAECBAgQIECAAAECBAgQIECAAAECBL4FBOjv/7ueAAECBAgQIECAAAECBAgQIECAAAECmYAAndEaJkCAAAECBAgQIECAAAECBAgQIECAwLeAAP39f9cTIECAAAECBAgQIECAAAECBAgQIEAgExCgM1rDBAgQIECAAAECBAgQIECAAAECBAgQ+BYQoL//73oCBAgQIECAAAECBAgQIECAAAECBAhkAgJ0RmuYAAECBAgQIECAAAECBAgQIECAAAEC3wIC9Pf/XU+AAAECBAgQIECAAAECBAgQIECAAIFMQIDOaA0TIECAAAECBAgQIECAAAECBAgQIEDgW0CA/v6/6wkQIECAAAECBAgQIECAAAECBAgQIJAJCNAZrWECBAgQIECAAAECBAgQIECAAAECBAh8CwjQ3/93PQECBAgQIECAAAECBAgQIECAAAECBDIBATqjNUyAAAECBAgQIECAAAECBAgQIECAAIFvAQH6+/+uJ0CAAAECBAgQIECAAAECBAgQIECAQCYgQGe0hgkQIECAAAECBAgQIECAAAECBAgQIPAtIEB//9/1BAgQIECAAAECBAgQIECAAAECBAgQyAQE6IzWMAECBAgQIECAAAECBAgQIECAAAECBL4FBOjv/7ueAAECBAgQIECAAAECBAgQIECAAAECmYAAndEaJkCAAAECBAgQIECAAAECBAgQIECAwLeAAP39f9cTIECAAAECBAgQIECAAAECBAgQIEAgExCgM1rDBAgQIECAAAECBAgQIECAAAECBAgQ+BYQoL//73oCBAgQIECAAAECBAgQIECAAAECBAhkAgJ0RmuYAAECBAgQIECAAAECBAgQIECAAAEC3wIC9Pf/XU+AAAECBAgQIECAAAECBAgQIECAAIFMQIDOaA0TIECAAAECBAgQIECAAAECBAgQIEDgW0CA/v6/6wkQIECAAAECBAgQIECAAAECBAgQIJAJCNAZrWECBAgQIECAAAECBAgQIECAAAECBAh8CwjQ3/93PQECBAgQIECAAAECBAgQIECAAAECBDIBATqjNUyAAAECBAgQIECAAAECBAgQIECAAIFvAQH6+/+uJ0CAAAECBAgQIECAAAECBAgQIECAQCYgQGe0hgkQIECAAAECBAgQIECAAAECBAgQIPAtIEB//9/1BAgQIECAAAECBAgQIECAAAECBAgQyAQE6IzWMAECBAgQIECAAAECBAgQIECAAAECBL4FBOjv/7ueAAECBAgQIECAAAECBAgQIECAAAECmYAAndEaJkCAAAECBAgQIECAAAECBAgQIECAwLeAAP39f9cTIECAAAECBAgQIECAAAECBAgQIEAgExCgM1rDBAgQIECAAAECBAgQIECAAAECBAgQ+BYQoL//73oCBAgQIECAAAECBAgQIECAAAECBAhkAgJ0RmuYAAECBAgQIECAAAECBAgQIECAAAEC3wIC9Pf/XU+AAAECBAgQIECAAAECBAgQIECAAIFMQIDOaA0TIECAAAECBAgQIECAAAECBAgQIEDgW0CA/v6/6wkQIECAAAECBAgQIECAAAECBAgQIJAJCNAZrWECBAgQIECAAAECBAgQIECAAAECBAh8CwjQ3/93PQECBAgQIECAAAECBAgQIECAAAECBDIBATqjNUyAAAECBAgQIECAAAECBAgQIECAAIFvAQH6+/+uJ0CAAAECBAgQIECAAAECBAgQIECAQCYgQGe0hgkQIECAAAECBAgQIECAAAECBAgQIPAtIEB//9/1BAgQIECAAAECBAgQIECAAAECBAgQyAQE6IzWMAECBAgQIECAAAECBAgQIECAAAECBL4FBOjv/7ueAAECBAgQIECAAAECBAgQIECAAAECmYAAndEaJkCAAAECBAgQIECAAAECBAgQIECAwLeAAP39f9cTIECAAAECBAgQIECAAAECBAgQIEAgExCgM1rDBAgQIECAAAECBAgQIECAAAECBAgQ+BYQoL//73oCBAgQIECAAAECBAgQIECAAAECBAhkAgJ0RmuYAAECBAgQIECAAAECBAgQIECAAAEC3wIC9Pf/XU+AAAECBAgQIECAAAECBAgQIECAAIFMQIDOaA0TIECAAAECBAgQIECAAAECBAgQIEDgW0CA/v6/6wkQIECAAAECBAgQIECAAAECBAgQIJAJCNAZrWECBAgQIECAAAECBAgQIECAAAECBAh8CwjQ3/93PQECBAgQIECAAAECBAgQIECAAAECBDIBATqjNUyAAAECBAgQIECAAAECBAgQIECAAIFvAQH6+/+uJ0CAAAECBAgQIECAAAECBAgQIECAQCYgQGe0hgkQIECAAAECBAgQIECAAAECBAgQIPAtIEB//9/1BAgQIECAAAECBAgQIECAAAECBAgQyAQE6IzWMAECBAgQIECAAAECBAgQIECAAAECBL4FBOjv/7ueAAECBAgQIECAAAECBAgQIECAAAECmYAAndEaJkCAAAECBAgQIECAAAECBAgQIECAwLeAAP39f9cTIECAAAECBAgQIECAAAECBAgQIEAgExCgM1rDBAgQIECAAAECBAgQIECAAAECBAgQ+BYQoL//73oCBAgQIECAAAECBAgQIECAAAECBAhkAgJ0RmuYAAECBAgQIECAAAECBAgQIECAAAEC3wIC9Pf/XU+AAAECBAgQIECAAAECBAgQIECAAIFMQIDOaA0TIECAAAECBAgQIECAAAECBAgQIEDgW0CA/v6/6wkQIECAAAECBAgQIECAAAECBAgQIJAJCNAZrWECBAgQIECAAAECBAgQIECAAAECBAh8CwjQ3/93PQECBAgQIECAAAECBAgQIECAAAECBDIBATqjNUyAAAECBAgQIECAAAECBAgQIECAAIFvAQH6+/+uJ0CAAAECBAgQIECAAAECBAgQIECAQCYgQGe0hgkQIECAAAECBAgQIECAAAECBAgQIPAtIEB//9/1BAgQIECAAAECBAgQIECAAAECBAgQyAQE6IzWMAECBAgQIECAAAECBAgQIECAAAECBL4FBOjv/7ueAAECBAgQIECAAAECBAgQIECAAAECmYAAndEaJkCAAAECBAgQIECAAAECBAgQIECAwLeAAP39f9cTIECAAAECBAgQIECAAAECBAgQIEAgExCgM1rDBAgQIECAAAECBAgQIECAAAECBAgQ+BYQoL//73oCBAgQIECAAAECBAgQIECAAAECBAhkAgJ0RmuYAAECBAgQIECAAAECBAgQIECAAAEC3wIC9Pf/XU+AAAECBAgQIECAAAECBAgQIECAAIFMQIDOaA0TIECAAAECBAgQIECAAAECBAgQIEDgW0CA/v6/6wkQIECAAAECBAgQIECAAAECBAgQIJAJCNAZrWECBAgQIECAAAECBAgQIECAAAECBAh8CwjQ3/93PQECBAgQIECAAAECBAgQIECAAAECBDIBATqjNUyAAAECBAgQIECAAAECBAgQIECAAIFvAQH6+/+uJ0CAAAECBAgQIECAAAECBAgQIECAQCYgQGe0hgkQIECAAAECBAgQIECAAAECBAgQIPAtIEB//9/1BAgQIECAAAECBAgQIECAAAECBAgQyAQE6IzWMAECBAgQIECAAAECBAgQIECAAAECBL4FBOjv/7ueAAECBAgQIECAAAECBAgQIECAAAECmYAAndEaJkCAAAECBAgQIECAAAECBAgQIECAwLeAAP39f9cTIECAAAECBAgQIECAAAECBAgQIEAgExCgM1rDBAgQIECAAAECBAgQIECAAAECBAgQ+BYQoL//73oCBAgQIECAAAECBAgQIECAAAECBAhkAgJ0RmuYAAECBAgQIECAAAECBAgQIECAAAEC3wIC9Pf/XU+AAAECBAgQIECAAAECBAgQIECAAIFMQIDOaA0TIECAAAECBAgQIECAAAECBAgQIEDgW0CA/v6/6wkQIECAAAECBAgQIECAAAECBAgQIJAJCNAZrWECBAgQIECAAAECBAgQIECAAAECBAh8CwjQ3/93PQECBAgQIECAAAECBAgQIECAAAECBDIBATqjNUyAAAECBAgQIECAAAECBAgQIECAAIFvAQH6+/+uJ0CAAAECBAgQIECAAAECBAgQIECAQCYgQGe0hgkQIECAAAECBAgQIECAAAECBAgQIPAtIEB//9/1BAgQIECAAAECBAgQIECAAAECBAgQyAQE6IzWMAECBAgQIECAAAECBAgQIECAAAECBL4FBOjv/7ueAAECBAgQIECAAAECBAgQIECAAAECmYAAndEaJkCAAAECBAgQIECAAAECBAgQIECAwLeAAP39f9cTIECAAAECBAgQIECAAAECBAgQIEAgExCgM1rDBAgQIECAAAECBAgQIECAAAECBAgQ+BYQoL//73oCBAgQIECAAAECBAgQIECAAAECBAhkAgJ0RmuYAAECBAgQIECAAAECBAgQIECAAAEC3wIC9Pf/XU+AAAECBAgQIECAAAECBAgQIECAAIFMQIDOaA0TIECAAAECBAgQIECAAAECBAgQIEDgW0CA/v6/6wkQIECAAAECBAgQIECAAAECBAgQIJAJCNAZrWECBAgQIECAAAECBAgQIECAAAECBAh8CwjQ3/93PQECBAgQIECAAAECBAgQIECAAAECBDIBATqjNUyAAAECBAgQIECAAAECBAgQIECAAIFvAQH6+/+uJ0CAAAECBAgQIECAAAECBAgQIECAQCYgQGe0hgkQIECAAAECBAgQIECAAAECBAgQIPAtIEB//9/1BAgQIECAAAECBAgQIECAAAECBAgQyAQE6IzWMAECBAgQIECAAAECBAgQIECAAAECBL4FBOjv/7ueAAECBAgQIECAAAECBAgQIECAAAECmYAAndEaJkCAAAECBAgQIECAAAECBAgQIECAwLeAAP39f9cTIECAAAECBAgQIECAAAECBAgQIEAgExCgM1rDBAgQIECAAAECBAgQIECAAAECBAgQ+BYQoL//73oCBAgQIECAAAECBAgQIECAAAECBAhkAgJ0RmuYAAECBAgQIECAAAECBAgQIECAAAEC3wIC9Pf/XU+AAAECBAgQIECAAAECBAgQIECAAIFMQIDOaA0TIECAAAECBAgQIECAAAECBAgQIEDgW0CA/v6/6wkQIECAAAECBAgQIECAAAECBAgQIJAJCNAZrWECBAgQIECAAAECBAgQIECAAAECBAh8CwjQ3/93PQECBAgQIECAAAECBAgQIECAAAECBDIBATqjNUyAAAECBAgQIECAAAECBAgQIECAAIFvAQH6+/+uJ0CAAAECBAgQIECAAAECBAgQIECAQCYgQGe0hgkQIECAAAECBAgQIECAAAECBAgQIPAtIEB//9/1BAgQIECAAAECBAgQIECAAAECBAgQyAQE6IzWMAECBAgQIECAAAECBAgQIECAAAECBL4FBOjv/7ueAAECBAgQIECAAAECBAgQIECAAAECmYAAndEaJkCAAAECBAgQIECAAAECBAgQIECAwLeAAP39f9cTIECAAAECBAgQIECAAAECBAgQIEAgExCgM1rDBAgQIECAAAECBAgQIECAAAECBAgQ+BYQoL//73oCBAgQIECAAAECBAgQIECAAAECBAhkAgJ0RmuYAAECBAgQIECAAAECBAgQIECAAAEC3wIC9Pf/XU+AAAECBAgQIECAAAECBAgQIECAAIFMQIDOaA0TIECAAAECBAgQIECAAAECBAgQIEDgW0CA/v6/6wkQIECAAAECBAgQIECAAAECBAgQIJAJCNAZrWECBAgQIECAAAECBAgQIECAAAECBAh8CwjQ3/93PQECBAgQIECAAAECBAgQIECAAAECBDIBATqjNUyAAAECBAgQIECAAAECBAgQIECAAIFvAQH6+/+uJ0CAAAECBAgQIECAAAECBAgQIECAQCYgQGe0hgkQIECAAAECBAgQIECAAAECBAgQIPAtIEB//9/1BAgQIECAAAECBAgQIECAAAECBAgQyAQE6IzWMAECBAgQIECAAAECBAgQIECAAAECBL4FBOjv/7ueAAECBAgQIECAAAECBAgQIECAAAECmYAAndEaJkCAAAECBAgQIECAAAECBAgQIECAwLeAAP39f9cTIECAAAECBAgQIECAAAECBAgQIEAgExCgM1rDBAgQIECAAAECBAgQIECAAAECBAgQ+BYQoL//73oCBAgQIECAAAECBAgQIECAAAECBAhkAgJ0RmuYAAECBAgQIECAAAECBAgQIECAAAEC3wIC9Pf/XU+AAAECBAgQIECAAAECBAgQIECAAIFMQIDOaA0TIECAAAECBAgQIECAAAECBAgQIEDgW0CA/v6/6wkQIECAAAECBAgQIECAAAECBAgQIJAJCNAZrWECBAgQIECAAAECBAgQIECAAAECBAh8CwjQ3/93PQECBAgQIECAAAECBAgQIECAAAECBDIBATqjNUyAAAECBAgQIECAAAECBAgQIECAAIFvAQH6+/+uJ0CAAAECBAgQIECAAAECBAgQIECAQCYgQGe0hgkQIECAAAECBAgQIECAAAECBAgQIPAtIEB//9/1BAgQIECAAAECBAgQIECAAAECBAgQyAQE6IzWMAECBAExD68AAAIXSURBVAgQIECAAAECBAgQIECAAAECBL4FBOjv/7ueAAECBAgQIECAAAECBAgQIECAAAECmYAAndEaJkCAAAECBAgQIECAAAECBAgQIECAwLeAAP39f9cTIECAAAECBAgQIECAAAECBAgQIEAgExCgM1rDBAgQIECAAAECBAgQIECAAAECBAgQ+BYQoL//73oCBAgQIECAAAECBAgQIECAAAECBAhkAgJ0RmuYAAECBAgQIECAAAECBAgQIECAAAEC3wIC9Pf/XU+AAAECBAgQIECAAAECBAgQIECAAIFMQIDOaA0TIECAAAECBAgQIECAAAECBAgQIEDgW0CA/v6/6wkQIECAAAECBAgQIECAAAECBAgQIJAJCNAZrWECBAgQIECAAAECBAgQIECAAAECBAh8CwjQ3/93PQECBAgQIECAAAECBAgQIECAAAECBDIBATqjNUyAAAECBAgQIECAAAECBAgQIECAAIFvAQH6+/+uJ0CAAAECBAgQIECAAAECBAgQIECAQCYgQGe0hgkQIECAAAECBAgQIECAAAECBAgQIPAtIEB//9/1BAgQIECAAAECBAgQIECAAAECBAgQyAQE6IzWMAECBAgQIECAAAECBAgQIECAAAECBL4FBOjv/7ueAAECBAgQIECAAAECBAgQIECAAAECmYAAndEaJkCAAAECBAgQIECAAAECBAgQIECAwLfAACPp60hS7GW/AAAAAElFTkSuQmCC\" width=\"720\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-757769fa6500>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0magent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mQLearningAgent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maction_space\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mexperiment\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mExperiment\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0magent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mexperiment\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_qlearning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minteractive\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/library/LabFiles/lib/simulation.py\u001b[0m in \u001b[0;36mrun_qlearning\u001b[0;34m(self, max_number_of_episodes, interactive, display_frequency)\u001b[0m\n\u001b[1;32m    193\u001b[0m                 \u001b[0;31m# if interactive display, show update for each step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    194\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0minteractive\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 195\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_display_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    196\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    197\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepisode_length\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepisode_length\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# keep episode length - for display\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/library/LabFiles/lib/simulation.py\u001b[0m in \u001b[0;36mupdate_display_step\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     58\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimgplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'rgb_array'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mupdate_display_episode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3_420/lib/python3.5/site-packages/matplotlib/backends/backend_webagg_core.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    159\u001b[0m         \u001b[0mbackend_agg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRendererAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    160\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 161\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    162\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m             \u001b[0mbackend_agg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRendererAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3_420/lib/python3.5/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3_420/lib/python3.5/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1298\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1299\u001b[0;31m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1301\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3_420/lib/python3.5/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3_420/lib/python3.5/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3_420/lib/python3.5/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2435\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2436\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2437\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2439\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3_420/lib/python3.5/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3_420/lib/python3.5/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3_420/lib/python3.5/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1147\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1149\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_offset_text_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mticklabelBoxes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mticklabelBoxes2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1150\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moffsetText\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_offset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1151\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moffsetText\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3_420/lib/python3.5/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_update_offset_text_position\u001b[0;34m(self, bboxes, bboxes2)\u001b[0m\n\u001b[1;32m   1879\u001b[0m             \u001b[0mbottom\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mymin\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1880\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1881\u001b[0;31m             \u001b[0mbbox\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmtransforms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbboxes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1882\u001b[0m             \u001b[0mbottom\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1883\u001b[0m         self.offsetText.set_position(\n",
      "\u001b[0;32m~/anaconda3_420/lib/python3.5/site-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36munion\u001b[0;34m(bboxes)\u001b[0m\n\u001b[1;32m    725\u001b[0m         \u001b[0mx0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxmin\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mbbox\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbboxes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    726\u001b[0m         \u001b[0mx1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxmax\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mbbox\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbboxes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 727\u001b[0;31m         \u001b[0my0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mymin\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mbbox\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbboxes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    728\u001b[0m         \u001b[0my1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mymax\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mbbox\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbboxes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    729\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mBbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3_420/lib/python3.5/site-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    725\u001b[0m         \u001b[0mx0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxmin\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mbbox\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbboxes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    726\u001b[0m         \u001b[0mx1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxmax\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mbbox\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbboxes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 727\u001b[0;31m         \u001b[0my0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mymin\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mbbox\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbboxes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    728\u001b[0m         \u001b[0my1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mymax\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mbbox\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbboxes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    729\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mBbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3_420/lib/python3.5/site-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36mymin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    353\u001b[0m         \u001b[0;34m(\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0mattr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mymin\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mbottom\u001b[0m \u001b[0medge\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mbounding\u001b[0m \u001b[0mbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    354\u001b[0m         \"\"\"\n\u001b[0;32m--> 355\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_points\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    356\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    357\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3_420/lib/python3.5/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36mamin\u001b[0;34m(a, axis, out, keepdims)\u001b[0m\n\u001b[1;32m   2395\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2396\u001b[0m         return _methods._amin(a, axis=axis,\n\u001b[0;32m-> 2397\u001b[0;31m                             out=out, **kwargs)\n\u001b[0m\u001b[1;32m   2398\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2399\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3_420/lib/python3.5/site-packages/numpy/core/_methods.py\u001b[0m in \u001b[0;36m_amin\u001b[0;34m(a, axis, out, keepdims)\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_amin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mumr_minimum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_sum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "interactive = True\n",
    "%matplotlib nbagg\n",
    "env = SimpleRoomsEnv()\n",
    "agent = QLearningAgent(range(env.action_space.n))\n",
    "experiment = Experiment(env, agent)\n",
    "experiment.run_qlearning(10, interactive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7303d86278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7303dfff98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7303d5d7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interactive = False\n",
    "%matplotlib inline\n",
    "env = SimpleRoomsEnv()\n",
    "agent = QLearningAgent(range(env.action_space.n))\n",
    "experiment = Experiment(env, agent)\n",
    "experiment.run_qlearning(50, interactive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"720\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interactive = True\n",
    "%matplotlib nbagg\n",
    "env = CliffWalkingEnv()\n",
    "agent = QLearningAgent(range(env.action_space.n))\n",
    "experiment = Experiment(env, agent)\n",
    "experiment.run_qlearning(10, interactive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73041311d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7304131a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7304131128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interactive = False\n",
    "%matplotlib inline\n",
    "env = CliffWalkingEnv()\n",
    "agent = QLearningAgent(range(env.action_space.n))\n",
    "experiment = Experiment(env, agent)\n",
    "experiment.run_qlearning(1000, interactive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f730398c7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7303dd8470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7303db0e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interactive = False\n",
    "%matplotlib inline\n",
    "env = WindyGridworldEnv()\n",
    "agent = QLearningAgent(range(env.action_space.n))\n",
    "experiment = Experiment(env, agent)\n",
    "experiment.run_qlearning(50, interactive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}