Cipri_workspace.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# interactive in the notebook\n",
    "# %matplotlib notebook\n",
    "\n",
    "# inlined but not interactive\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from kohonen import load_data, som_step, standardize, de_standardize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your digits:  [2 3 5 7]\n",
      "Loading from binary...\n"
     ]
    }
   ],
   "source": [
    "data, labels, target_digits = load_data()  # default name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Standardise the data:\n",
    "data, data_mean, data_std = standardize(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Kohonen algorithm hyper-parameters\n",
    "size_k = 6        # size of the Kohonen map (size_k, size_k)\n",
    "sigma  = 3        # width of the gaussian neighborhood\n",
    "eta    = 0.1      # learning rate\n",
    "tmax   = 10000  # max iteration count; substitutes convergence criterion\n",
    "\n",
    "# initialise the centers randomly\n",
    "dim = data.shape[1]     # 28*28 = 784\n",
    "data_range = 255.0\n",
    "centers = np.random.rand(size_k**2, dim) * data_range\n",
    "\n",
    "# build a neighborhood matrix\n",
    "neighbor = np.arange(size_k**2).reshape((size_k, size_k))\n",
    "\n",
    "# set the random order in which the datapoints should be presented\n",
    "idxs_random = np.arange(tmax) % data.shape[0]\n",
    "np.random.shuffle(idxs_random)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training and checking SOM difference at each epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0, 5.0, 10.0, 15.0, 20.0, 25.0, 30.0, 35.0, 40.0, 45.0, 50.0, 55.00000000000001, 60.0, 65.0, 70.0, 75.0, 80.0, 85.0, 90.0, 95.0, "
     ]
    }
   ],
   "source": [
    "maps = []\n",
    "epoch = 500 # how often we check for convergence\n",
    "\n",
    "for step, example_idx in enumerate(idxs_random):\n",
    "    mov, win = som_step(centers, data[example_idx,:],neighbor,eta,sigma)\n",
    "    if step % epoch == 0:\n",
    "        maps.append(centers.copy())\n",
    "        print(step/tmax * 100, end=', ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "map_diffs = []\n",
    "for i in range(len(maps) - 1):\n",
    "    map_diffs.append(np.sum(np.abs(maps[i+1] - maps[i])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        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": {
      "text/html": [
       "<img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA8AAAAGQCAYAAABhxwlFAAAgAElEQVR4nOzdd5xU1d3H8a/YAAGxC2osyWOhqDE2jL08SQxq7Ik+itEYo6iEPNhjQDH2WKIxSuyKGHuiIpgYisbYjV2DIgpLUzosZdn5PX/8Zh6GYWF3Yc7cc2Y/79frvmBm7t75zezdO/O959xzJAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACIXS9JufzyrQYe37fo8QMrWFdTbCmpv6StVmEbOUmXN2G9EZL+UXR7v/zP7ruCdcplRa/zc0l3B3jOrPWSVC/pG1kXkpFdJD0naYKk+ZImSXpW0p6Bn7ccf1OhrS3pOkkTJdVKelnSPk382ZMlPSZpnPzvtxr/dgAAALAChQA8U9JlDTx+V/6xesUXgAshdFXqamoA3j6/FD93vZYOwKXrlMuKXudOkrYO8JxZ20DS7pLWzLqQjBwo6feSjpOHu6Ml/VPSQkm7BnzecvxNhTZY0nRJp0o6QNLj8iC8YxN+9nlJb0kaJGmGCMAAAAAtTiEA3y3ps5LHWkuaJQ/BMQbg/bXqdTU1AJdqKACHsr/ifP9RWe0kLZB0c8Dn2F9x72s7yf9mTy66b3VJH0t6qpnbGi8CMAAAQItT6Gp6QP7f7xY9doKk2ZIO1bKtQrtKelT+JbJW/gX0t/LQXGykpBclHS7pPfkX+I8kHduE2jaRdJ+kmvzPTZT0V0kbaklLVb2WdNEuDqTHS3pB0lRJc+StPsVfmgtykgZKurjotYySf9EufR2lXaBLA3DpOutIukXSF/n6p8hboLYtWqe3vAvnNHmL1L/k73fx86zodY7Tsl/id5f09/zrnpv//24l69ybf707SxotaZ6k/0g6Q5Wxm/y9+Fr+nn8m6daix0+Rv9biLtBtJP0x/zNz5C1/PbRsILpX/tq+I281Leyfhff1V/Ku47PkoWmDktoa+51kpZW85t+t5M+vLuki+d/fAvnf1fXyLsVS+f6mQrpUXnvpcWaAvKt4c3oMEIABAABaoEIA3kZ+DevtRY89Jw+gDXWLPFr+ZbSnvIvmL+QB9aGS7Y+QX7/4ufzL8g/kIbY+v90V+Zs8uPxY0t7557xNHoraSTozv52z5KFv9/z9kgfasyX9d77uAfLuoz8veY6cpC+1JKQfm3/OryR1LHkdjQXg0nX+lH/tp+TrP0LStfk6C66TdHq+xkPk3V7r83WrCa+z9BrgHeWB73VJR+aX1/L3dS9a7x55mPog//wHSXow/3409ntZVevIw+Wz8mC5r3zfKN73GroG+EF5yDk/X++Vksbm1ysOYoXX9r78vf9vecifLw98f5Hvh6fk13u4pL7Gficr0koeNBtbmmo1SWvI34fb5JcjrGw3+4flwfUS+WvrLQ/4j+Yfb6/y/E0t73WU430ZIg/wpY7N171DE7ZRQAAGAABogYoD8E/lwWQtSZ0k1cm/6DblusDVJZ0oabGk9YruH5HffnELZCv5l9hRjdQ2R/6Fe3maer1i4cv3IElvlzyWk7doFbcobSlpkZa+JnplAvB78sDVVIU6h0t6suS5lvc6SwPwY/LrI9sX3dde/nt9rOi+exqofy1562pxEA3hO/nn7raCdUoD8Lb52/9bst7NajgAl/Zm6C5/Dz+Sv88Fv5OHuOL7ii3vd7I8I7Sk9XR5S3O6zj9a9HOTJO3VxJ8rtU9+GyeW3H9Cvp7C9bPl+JtqyD1q2vvSWIvycHnrfKmDtOzvvDEEYAAAgBaoOAC3k3eZPVoeNL7Mr9PQl+L2kq6R9Kk8QBR/iS1u4Rwh76Zb6nJ5i9yK/EPeffhcNRyWVvRl/Vvy1qIJ8lBeqK+2ZL2cvMtsqdHyLroFKxOA75YHyovkoa9VA8/zHUnPSJqspbuefljyXE0NwFMk3d/AevfIW7WLb89pYL2XJQ1t4P5iy2u1a2prXgd5IH9ZHsg2b2Cd0gB8Uv72liXrFYJdaQCeXbLemvn1bim5//T8djsX3deU38ny/Jd8BOcVLd+Wt4I3xVb5en4k7xExI7+N5rpC/vfWWkv/jjaSv7bCiaZy/E015Btq/H3ZRUufPGsIARgAAACrpDgASz7C6l8kvSPpqvx9DX0pflz+ZfxseQjZRUu6T5aGwlcbeN7e+XU3WkFtG8oDy/j8ujXybtcFy/uyvo48dL8vb+HaM1/fnfntFMvJg3ypR+Xdg4tfR3MDcFv5ddH/ya/7taQb5NeySh78Zkh6SdIx8hMHu8gD6NgmvE5p2QBct5zXc5U8tBTcoyUnOIo1ZSqn4utEC+9B6fWjpe9zqR3l7/HM/PrvSTqq6PHSAHx+/nYbLW1bNRyAG3ptOS074Fnp/t/U38nylLsLdLE1JX2ixk9QNGSQVtzy2j+/Xjn+phpSri7QD4su0AAAAFgFpQHge/KgVPxlsvRL8dryoHVRybZ+qoZD4bgGnnegGm8BLvZf8lasnJYM1FQIoaVf1g/O39+j5P771HAAvreB5ytHC3CxLST1lXetLpxYKLQ+dipZd6SWDcDLG5m3oRbg+xpYr6EW4JUNwKWtduvkl9L7m6KVPGQ+Kd+nuuTvX9UW4JUNwE39nSxPubtAl3pUfkKlua6SD3T2bTXc8rppfr1y/E01pFxdoBkECwAAAKukNAC0krcCF4/IW/qluIP8C+t5JdsapYZDYb2kPYruK1wDPHIl6p2mJd1Y98zX8cOSdQ7Xstcdrydv2WsoAE/V0i2LW8mD6oCi+1Y1ABe8Kenp/P/P1bKt4NvKg2Bx2Fre65SWDcCPyINucRfb9vLW50eK7luVABxC4Rrdo/O3SwPwdvnb/Up+7hY1fA3wygbgpv5OlqcpXaALJw2aq638970yLcCF/fWARtYrx99UQ8rVBXrnfH0nFd23urx7OtMgAQAAoFGlAaAhDXWLfFl+jeRJ8hF1Cy1TDYXCifIv7r3ko/4+I29lXlErWAf56MV95K3SB2rJaLw98+usLw+qj8sHB/qOPFhsKO9a+1r++Y6Td+ku1FcsJ7/O+EX5KM3Hy0eBnqrGR4HONfBai9d5WdKF8jCxn7ybaZ2WXG/ZJV//MPlow73k79OnWjpsLe91SssG4O7ylr5X5V2Kj8r/f56WHQU6qwD8Q3k3+5/K553tKW9tn6kl1+I2NAr0A/JWvgvkLZJXyl9/vaT/KVpvVQJwU38nod0uf31Hyfexk+S/x/ladiCscWra72yw/ATSr+UjOR8sb/F+Qn59r1Sev6nQhshfx2ny48Jj8uuQS6cu+1R+3XSxHeQnWY6RnxR6IX/7aC07HRYAAACqUFMDcGm3yG/Ip7GZJQ/CN8uDcEMBeLQ85Lwn/wL/kfwL6IqsJZ/z9T35gEYz5QHg+JL1Tpd/0V1U8tz7y1tb50kaIw+d/bXsl/V6eSi6UEvmAR6ppcNi4XW8UHR7eS3Axetcla9hhnzAqXfk1z4XO0beelWbf63HyQPcZ018nWMl3VWybmGO3dn5531eHmSK3SMP/qVKX0MI28pDzGfy1z1FflKkuHWxoQDcWtIf5MFltrzb9A/kwfawovWW99rqtfTI3sXPU7z/N/V3EtJP5dchf5WvY4x8GqiuDaw7VR5um+Ic+ajNtfL98m1JV2vpUcNX9W8qtLXlo6tPlL+Of8m7wpcaq2X35UK9DS0r2y0dAAAA+H+FAAyE0E/em6ChkaRbgsIgYKUnOAAAQJlsLOkheYvPdEn/1NJnTXeUXwc4Vz5VQv/SDcjPwNfIWyZGatkz2k3ZBoA0EIBRLj+Ud3/+nrx78kB5C2BTWz+r0c/k3bUBAEAgj8vD6fryaQ1+Je+K1lE+h+hE+Uita8nn7Rwvv4av4Dx517Qu8m5MV8pDbtv8403ZBoB0jJAfM4BVta+8u+t0+dzTY+WfIWtlWRQAAKhu/5ZfQ1Swjrz71W7yUTgny0d4LThXfq1QwVgtGQBG8tEbp0g6MX+7VxO2AQAAAABAcD+Rjza5qXy+vQskfSJvzb1B0nMl6/eQDy7RTkumTtmjZJ3h8gEt1IRtAAAAAABQEd+Qz0GYk48QOVkeUCXpTvnInsW2l4fXzvJBSnLy+RyLPSxpUBO3UWo1SZvJwzULCwsLCwsLCwsLS/bLZvLv6UDSVpNPA3GXpHXl3ZQPl09PsqOyaQHeTJKxsLCwsLCwsLCwsES1bCYgcevLA2zpZPNvyge3auga4D5q/BrgqZJOyN9uyjaKdZBk48ePt1mzZiWzfPTRLJNm2Vln9c68lpVZevdOs+6Ua0+17pRrT7XulGtPte6Ua0+17pRrT7XulGtPte5Uax8/fnwhAHdoYsYAova+vLtye3mLcE9J8yUdIG+hrZFPTdFaUndJX2rpEZz7SRonn/qojaSr5KM8F48C3dg2inWQZLNmzbKUTJhgJpn98pd9sy5lpfTtm2bdZunWnmrdZunWnmrdZunWnmrdZunWnmrdZunWnmrdZunWnmrdZmnWPmvWLAIwqso3JT0pH7l5pqT3JJ1W9Hg3+Zyf8+TTGV3awDYGSJokn+d3pJadB7gp2yggAGcgxYNxQaq1p1q3Wbq1p1q3Wbq1p1q3Wbq1p1q3Wbq1p1q3Wbq1p1q3WZq1E4CBsJIOwM89NyzrUlbKsGFp1m2Wbu2p1m2Wbu2p1m2Wbu2p1m2Wbu2p1m2Wbu2p1m2Wbu2p1m2WZu0EYCCspANwLpd1JQAAAED5EICBsAjAAAAAQCQIwEBYBGAAAAAgEgRgICwCMAAAABAJAjAQFgEYAAAAiAQBGAiLAAwAAABEggAMhEUABgAAACJBAAbCIgADAAAAkSAAA2ERgAEAAIBIEICBsAjAAAAAQCQIwEBYBGAAAAAgEgRgICwCMAAAABAJAjAQFgEYAAAAiAQBGAgr6QBcX591JQAAAED5EICBsJIMwDU1BGAAAABUHwIwEBYBGAAAAIgEARgIiwAMAAAARIIADIRFAAYAAAAiQQAGwiIAAwAAAJEgAANhEYABAACASBCAgbAIwAAAAEAkCMBAWARgAAAAIBIEYCAsAjAAAAAQCQIwEBYBGAAAAIgEARgIiwAMAAAARIIADIRFAAYAAAAiQQAGwiIAAwAAAJEgAANhEYABAACASBCAgbAIwAAAAEAkCMBAWARgAAAAIBIEYCAsAjAAAAAQCQIwEFbSAXjx4qwrAQAAAMqHAAyElWQAnjiRAAwAAIDqQwAGwiIAAwAAAJEgAANhEYABAACASBCAgbAIwAAAAEAkCMBAWARgAAAAIBIEYCAsAjAAAAAQCQIwEBYBGAAAAIgEARgIiwAMAAAARIIADIRFAAYAAAAiQQAGwiIAAwAAAJEgAANhEYABAACASBCAgbAIwAAAAEAkCMBAWARgAAAAIBIEYCAsAjAAAAAQCQIwEBYBGAAAAIgEARgIiwAMAAAARIIADIRFAAYAAAAiQQAGwko6ANfVZV0JAAAAUD4EYCCsJAPwpEkEYAAAAFQfAjAQFgEYAAAAiAQBGAiLAAwAAABEggAMhEUABgAAACJBAEa1eV/S7KJlnqScpCPyj+8oaZSkuZImSOrfwDYuk1QjaY6kkZK6ljzelG0UEIABAACASBCAUe3OkTRV0lqS2kmaKOmK/O1uksZL6lO0/nmSvpDURdLakq6Uh9y2+cebso1iBGAAAAAgEgRgVLsP5SFWknpJmiypVdHj50oaU3R7rKSzi26vLmmKpBObsY1iBGAAAAAgEgRgVLMDJdVJ+kb+9g2SnitZp4ekennLbgd5d+k9StYZLun6Jm6jFAEYAAAAiAQBGNXsUUlPF92+U9KQknW2l4fXzpI2lwfg7UrWeVjSoCZuoxQBGAAAAIgEARjVqpOkRZK+X3RfZi3AvXv3tr59+1rfvn1t2LBhWf/dN4oADAAAgGoxbNiw//8u3rt3bwIwqtIASZ+W3Heylr1+t48avwZ4qqQTmrGNYrQAAwAAAJGgBRjVaHX5yM39Su5vJ5/eaKCk1pK6S/pSS4/g3E/SOPnUR20kXSUf5bltM7ZRjAAMAAAARIIAjGp0lKRaSes38Fg3SaPl8wNPlHRpA+sMkDRJPs/vSC07D3BTtlFAAAYAAAAiQQAGwiIAAwAAAJEgAANhEYABAACASBCAgbAIwAAAAEAkCMBAWARgAAAAIBIEYCCspAPwokVZVwIAAACUDwEYCCvJADx5MgEYAAAA1YcADIRFAAYAAAAiQQAGwiIAAwAAAJEgAANhEYABAACASBCAgbAIwAAAAEAkCMBAWARgAAAAIBIEYCAsAjAAAAAQCQIwEBYBGAAAAIgEARgIiwAMAAAARIIADIRFAAYAAAAiQQAGwiIAAwAAAJEgAANhEYABAACASBCAgbAIwAAAAEAkCMBAWARgAAAAIBIEYCAsAjAAAAAQCQIwEBYBGAAAAIgEARgIiwAMAAAARIIADISVdABeuDDrSgAAAIDyIQADYSUZgKdMIQADAACg+hCAgbAIwAAAAEAkCMBAWARgAAAAIBIEYCAsAjAAAAAQCQIwEBYBGAAAAIgEARgIiwAMAAAARIIADIRFAAYAAAAiQQAGwiIAAwAAAJEgAANhEYABAACASBCAgbAIwAAAAEAkCMBAWARgAAAAIBIEYCAsAjAAAAAQCQIwEBYBGAAAAIgEARgIiwAMAAAARIIADIRFAAYAAAAiQQAGwiIAAwAAAJEgAANhEYABAACASBCAgbCSDsALFmRdCQAAAFA+BGAgrCQD8NSpBGAAAABUHwIwEBYBGAAAAIgEARgIiwAMAAAARIIADIRFAAYAAAAiQQAGwiIAAwAAAJEgAANhEYABAACASBCAgbAIwAAAAEAkCMBAWARgAAAAIBIEYCAsAjAAAAAQCQIwEBYBGAAAAIgEARgIiwAMAAAARIIADIRFAAYAAAAiQQAGwiIAAwAAAJEgAANhEYABAACASBCAUY16SHpB0mxJMyS9VPTYjpJGSZoraYKk/g38/GWSaiTNkTRSUteSx5uyjQICMAAAABAJAjCqTQ956D1R0tqSWknaLf9YO0kTJV0haS1J3SSNl9Sn6OfPk/SFpC75n79SHnLbNmMbxQjAAAAAQCQIwKg2oyVdt5zHekmaLA/FBedKGlN0e6yks4tury5pijxQN3UbxQjAAAAAQCQIwKgmbSQtlnSNpFclfS3pdUlH5R+/QdJzJT/TQ1K9vGW3g6ScpD1K1hku6fombqNU0gF4/vysKwEAAADKhwCMarKZPMBOkrSLvJX2SEkLJe0p6U5JQ0p+Znt5eO0safP8z29Xss7Dkgbl/9/YNkolGYC/+ooADAAAgOpDAEY1KbTgXlly/zBJVynDFuDevXtb3759rW/fvjZs2LCs/+4bRQAGAABAtRg2bNj/fxfv3bs3ARhVZYyWH4BP1rLX7/ZR49cAT5V0Qv52U7ZRjBZgAAAAIBK0AKPanCsfpXknSatJOlxSraRd5S20NZIGSmotqbukL7X0CM79JI2TT33URh6cx2vpUaAb20YxAjAAAAAQCQIwqtEF8lA6S9IbknoWPdZNPlL0PHlQvrSBnx8gv454rhqeB7gp2yggAAMAAACRIAADYRGAAQAAgEgQgIGwCMAAAABAJAjAQFgEYAAAACASBGAgLAIwAAAAEAkCMBAWARgAAACIBAEYCIsADAAAAESCAAyERQAGAAAAIkEABsIiAAMAAACRIAADYRGAAQAAgEgQgIGwCMAAAABAJAjAQFgEYAAAACASBGDEqLOkPbMuokwIwAAAAEAkCMCIyYaSnpeUkzQvf99xkm7JrKJVl3QArq3NuhIAAACgfAjAiMlgSfdJ2kzSjPx9G0v6T2YVrbokA/DXXxOAAQAAUH0IwIjJZElt8/+fXnT/zAxqKRcCMAAAABAJAjBiMkFS6/z/CwG4g6QvsimnLAjAAAAAQCQIwIjJYEnX5P9fCMCXS7o3k2rKgwAMAAAARIIAjJh0lvSRpPGS6iSNkTROUqcMa1pVBGAAAAAgEgRgxGYtSUdKOl/SyZLaZVvOKiMAAwAAAJEgACMWa0l6VkuuAa4WBGAAAAAgEgRgxGSqpNWzLqLMCMAAAABAJAjAiMltknplXUSZEYABAACASBCAEZM/S1oo6Z+SHpR0f9GSKgIwAAAAEAkCMGJyzwqWVBGAAQAAgEgQgIGwCMAAAABAJAjAiM1qknaXdIyk3fK3U0YABgAAACJBAEZMOkl6XVK9pK/z/74uqXOWRa0iAjAAAAAQCQIwYvKwfCCs9fO315c0JH9fqgjAAAAAQCQIwIjJJC27I64raXIGtZQLARgAAACIBAEYMZksqV3Jfe0lTcmglnIhAAMAAACRIAAjJn+Wz/9b2BnXlfSApEcyq2jVJR2A583LuhIAAACgfAjAiElnSW9LWixpqqS6/O3NsixqFSUZgKdNIwADAACg+hCAEZtWkvaUdGz+31bZlrPKCMAAAABAJAjAiMnaktYsuW/N/P2pIgADAAAAkSAAIyYvSNq35L59Jf0tg1rKhQAMAAAARIIAjJh8LWmNkvvWyN+fKgIwAAAAEAkCMGLylaTWJfe1ljQtg1rKhQAMAAAARIIAjJi8IOkXJff9XNLIypdSNgRgAAAAIBIEYMSkh6RaSX+VdF3+31pJe2VZ1CoiAAMAAACRIAAjNl0l3SLpmfy/XbMtZ5URgAEAAIBIEIARg00kbVx0ey1J/SU9JalvJhWVDwEYAAAAiAQBGDF4RtJJRbevlTRD0mPyAbD6ZVFUmRCAAQAAgEgQgBGDCZI2yv9/NXnoPTx/+yBJ72ZRVJkQgAEAAIBIEIARg9lF/+8uH/hqzfztVpKmV7yi8iEAAwAAAJEgACMGNZLWzf//FEmvFD22lqRZlS6ojAjAAAAAQCQIwIjBQ5L+KGlnSa9KGlD0WHdJH2VQU7kQgAEAAIBIEIARg60kfSgpJ+nfktYreuwqSX/KoKZyIQADAAAAkSAAIybrN3BfR0ltK11IGRGAAQAAgEgQgIGwkg7Ac+dmXQkAAABQPgRgIKwkA/D06QRgAAAAVB8CMBAWARgAAACIBAEYCIsADAAAAESCAAyERQAGAAAAIkEABsIiAAMAAACRIAADYRGAAQAAgEgQgFFt+ktaLGm2pDn5fwcXPb6jpFGS5kqakF+/1GWSavI/P1JS15LHm7KNAgIwAAAAEAkCMKpNf0mjl/NYO0kTJV0haS1J3SSNl9SnaJ3zJH0hqYuktSVdKQ+5bZuxjWIEYAAAACASBGBUmxUF4F6SJktqVXTfuZLGFN0eK+nsoturS5oi6cRmbKMYARgAAACIBAEY1aa/vOvyFEmfy7s/b5V/7AZJz5Ws30NSvbxlt4OknKQ9StYZLun6Jm6jFAEYAAAAiAQBGNWmi6Qt8v/vJOlBeetsW0l3ShpSsv728vDaWdLm8gC8Xck6D0salP9/Y9soRQAGAAAAIkEARrVbS9J8SQcrwxbg3r17W9++fa1v3742bNiwrP/uG0UABgAAQLUYNmzY/38X7927NwEYVa0QgA+RdLKWvX63jxq/BniqpBPyt5uyjWK0AAMAAACRoAUY1eZYSRvk/7+JpPvloXYdeQttjaSBklpL6i7pSy09gnM/SePkUx+1kXSVfJTn4lGgG9tGMQIwAAAAEAkCMKrNX+QDYM2VB9fBkrYperybfJToefLpjC5tYBsDJE3Kb2Oklp0HuCnbKCAAAwAAAJEgAANhEYABAACASBCAgbAIwAAAAEAkCMBAWEkH4Dlzsq4EAAAAKB8CMBBWkgF4xgwCMABUgwULzAYMMJs8OetKACAOBGAgLAIwACAzv/udWZs2ZpttZvavf2VdDQBkjwAMhEUABgBkYvp0s/XWM3vuObObbjJr29bsD38wy+WyrgwAskMABsIiAAMAMtGvn9nBBy8JvKNHm226qdnJJ5vNm5dtbQCQFQIwEBYBGABQcZ9/bta6tdlbby19/8SJZt/9rtlOO5l99lkmpQFApgjAQFgEYABAxZ14otlJJzX82KJFZuee692jn322snUBQNYIwEBYBGAAQEW9+aYPfPXFFyteb/Bgs3btzPr3N6uvr0hpAJA5AjAQFgEYAFAxuZzZgQeanX9+09Z/912zb33L7Ac/MJs2LWxtABADAjAQFgEYAFAxQ4eabbCBH8ebasYMs8MOM9t662WvGQaAakMABsIiAAMAKmLxYrNu3XzKo+aqrze74gqzddYxu/fe8tcGALEgAANhEYABABVx551m22xjtnDhym9j+HCz9dc3O/NMswULylcbAMSCAAyERQAGAAQ3d65Z585mf/7zqm/r88/NvvMdsz32MBs/ftW3BwAxIQADYRGAAQDBDRxotvvuPghWOcyfb3bqqWYbbWT2j3+UZ5sAEAMCMBAWARgAENSUKT6d0ahR5d/2oEFmbduaXXtt+cI1AGSJAAyERQAGAAR11llmhx8ebvuvvWa2xRZmRx9tNnt2uOcBgEogAANhEYABAMF8/LFZ69ZmH30U9nmmTjU76CCz7bc3+/DDsM8FACERgIGwCMAAgGCOPNLsjDMq81yLF5tddJFZ+/Zmjz5amecEgHIjAANhJR2A6eoGAPF66SWft3fSpMo+7xNPmHXoYHbeeWZ1dZV9bgBYVQRgIKwkA/DMmQRgAIhZLmfWo4fZgAHZPP8nn5h16WK2//4+CBcApIIADIRFAAYAlN1jj5ltumm2l6rMmWN2/PFmm21m9q9/ZVcHADQHARgIiwAMACirhQvNvvUtn6Ioa7mc2Y03+lRJt93GVEkA4kcABsIiAAMAyur3vzfbYYe4rr8dNcpsk03MevUyq63NuhoAWD4CMBAWARgAUDYzZ5ptuKHZ009nXcmyanfCtywAACAASURBVGrM9trLbOedzT77LOtqAKBhBGAgLAIwAKBsLrrIbL/94u1qvHCh2TnnmK23ntnQoVlXAwDLIgADYRGAAQBlMX68WZs2Zq+9lnUljXvgAZ+iacAAs/r6rKsBgCUIwEBYBGAAQFmccorZj3+cdRVN9847Zt/8ptmhh5pNn551NQDgCMBAWARgAMAqe+cds9atzcaOzbqS5pkxw6xnT7NttjF7++2sqwEAAjAQGgEYALDKvvc9s759s65i5dTXmw0c6F2i778/62oAtHQEYCAsAjAAYJU8/7xZx45m06ZlXcmqGTbMbP31zc46ywfLAoAsEICBsAjAAICVVl/v0wpde23WlZTH55+b7bKL2Z57mk2YkHU1AFoiAjAQFgEYALDS7r/fbMstzebPz7qS8qmtNfvpT8023thsxIisqwHQ0hCAgbAIwACAlVJba7bFFmYPPph1JeWXy5kNGmTWtq3Z9dfHO68xgOpDAAbCIgADAFbK1Vd7d+Fqnkf31Vc95B97LJ85ACqDAAyERQAGADTbV1+Zrbuu2QsvZF1JeFOnmh10kNkOO5h99FHW1QCodgRgICwCMACg2fr0MfvBD7KuonLq6swuvNCsQwezxx/PuhoA1YwADISVZACeN89s9dXN9t3X5258+WX/cgIACO/TT81atzZ7772sK6m8J57wEHz++XzuAAiDAAyElWQANjMbO9bs9tvNjjnGbIMNzNq3N+vZ0+zGG83efZcBSwAglOOOMzv11KyryM7HH5t16WJ24IHePRoAyokADISVbAAuVl9v9tZbZtddZ/b97/uonRtvbPbjH5v96U8elgEAq+6VV/wY29LnyJ0zx+yoo3wQsNrarKtpORYt8uvPgWpGAAbCqooAXGrhQrPRo8369zfbe2+zNdYw23prs5/9zGzIELMpU7KuEADSk8uZ7bOP2SWXZF1JHBYuNOvRw6xXL3odVcKCBWZ77uljgHTu7NegX3ih2cMP++BkixdnXSFQHgRgIKyqDMCl5swxGzrU7H//12znnc1atTLr3t3sl780e/ppBtMCgKb4y1/MNtrIrMo/MpqlpsZs003Nbr0160qqWy7n3e53281PYr/4otktt/iJ7d1282vS27Qx2313s9NP99/HSy/x+Y40EYCBsFpEAC711VdmjzxidsYZZt/6lg+o1aOH2a9/bTZihJ9lBgAsUVdntv32BL2GvPiidwt/8cWsK6let9xitskmZuPHN/x4XZ3Zhx+aPfSQ2QUX+OVQm27qrcXf/KZ3V7/sMj+JM24cLfaIGwEYCKtFBuBSX3xhdvfdZiee6B+YbdqYHXKI2dVXm73+Ot2qAOD228223davwcSybr3VPz9qarKupPqMGOEnGP75z+b/7OTJZsOHm117rX/Gd+3qJ707djTbbz+zc8/1z/833+TkN+JBAAbCIgCXyOXMPvjA7Pe/NzviCLN11zVbbz2zI4/0LzgffcSZYwAty+zZ3vr2xBNZVxKvXM6vBe7Rw68NRnmMG2e24YZmgwaVb5vz55u98YbZXXd5AN53X/+sX2MNs27dPChfd53Z888zZgiyQQAGwiIAN6KuzuzVV82uvNLsoIP8OqPOnc1OOsns3nuX3x0LAKrFb35j9t3vcvKvMbW1Pir0L36RdSXVYd48s29/uzLvZy7nYfupp7yr9FFHmW2zjXeh7tTJu1RfcIEPpPnhh/QMQ1gEYCAsAnAzzZ9v9sILZhdfbLbHHt6Vatttzc480+yxx8ymTcu6QgAon4kTzdZZx+zll7OuJA2FFsu77sq6krTlcj6V4d57Z9uiPmuWX9t9660+uFbxgFu77eaDcN16q6/DVymUCwEYCIsAvIpmzvRBNc49168tatXKWwDOO89s2DCzuXOzrrBlqq83mzHD7LPPvKvb9OlZVwSk6fTTzY4+Ousq0vLCC37N6muvZV1Juq65xmzzzf0a3tgUBtwaMsSnYfr+972VWPJW4yOP9Fbkp54y+/xzek6g+QjAQFgE4DKbNMls8GCfrmHLLc3WXNOvL7rsMp+SgQFkmmfhQv8C9OGH/v49/bTZffeZ3Xij2aWXmp19ttkJJ/gXkN1391G9N9jAT0RI3kK/3nr+Reqtt7J+NUBaPvjAW7v+85+sK0nPddf5cYdrSJvvuef8BMIbb2RdSfNMmeLXDV93nV9H3K2bfwatu65/Dzj/fO8mDzSGAAyERQAOKJcz+/RTszvuMDvuOO8W166dtxDvs4/Z977nZ4pPPNFbWfr08bPJl19udv31Zrfd5tcZP/KI2TPPmP3jH2avvGL27ru+3YkTvQU69lCdy/lczF98Yfb229468uijPqjJ1Vf7l4Kf/cxbmQ44wGynncy22MLfK8mXtm39y+SOO5rtv7+/b6ed5i3tV13lI9Q+8ojZ3//uQXfcOO+Olsv5ctVVvr0nn8z63QDS0bOnn2RC8+VyZscf78erurqsq0nHmDE+QvMDD2RdSXnMn+8jTN99t5+kPeww9gc0jgAMhEUArqD6erN//9tHUn3wQQ+AN93kA2z9+tdmv/qVD/Zx8slmxxxjduih/uVp993Nunf3uQw7dfKzyWuuuSQcSj56ZYcOPg3HNtv4mefdd/efP/RQ397JJ/v2f/Urf74rr/TnHzTI63niCe+2PXq0n3n/8EMPklOnelfuRYt8DuX//MeD+NCh/nO33OIt3H36+OBgP/yh2V57+ZyhG2/stUlmq61mtv76/jp2281PAPzkJ2ZnneX13HCDB/6//tWvp/rgA29RL9fUFE884SH46qvpkgY0ZsQIs/bt/e8fK2fuXD8W9+2bdSVpmD3brEsX/4yqRtOn+/5w6ql8BmHFCMBAWATghC1a5K2ckyb5ta7vvecjVo8YYfbss97Ket99Zn/8o9nvfmc2cKDZRRd5UD39dG95PvJID6L77GP2ne+Y7bCDd93eaCMf+Ga11ZYO2pLZ2mt7EO/a1X/u8MPNTjnFv7RccYXZH/7g10YNH+7zKH/6qX/w19dn/Y556/Bmm/l0Jcz5CDSsvt5s113NfvvbrCtJX6FFc/DgrCuJW3292Y9+ZHbIIdXdQjphgn/GXnRR1pUgZgRgICwCMFYol/MuXNOmeZfrefPSP3NdU+Nf7vfem9YtoCFDhviJonnzsq6kOgwd6r1P/v3vrCuJ14AB3nupJcyk8PHHfknUTTdlXQliRQAGwiIAo0WaN8+vy956a7P338+6GiAeCxaYbbWV2T33ZF1JdRk40I83LSHgNdeTT/oJgnffzbqSynn1VX/N9AxAQwjAqGZPSspJOrDovh0ljZI0V9IESf0b+LnLJNVImiNppKSuJY83ZRsFBGC0WLmcWf/+fu300KFZVwPE4YYbfMyBxYuzrqS61NebHXGEX3LCe7vE++/7teaPPZZ1JZU3fLgP8DhsWNaVIDYEYFSrkyUNk1SvJQG4naSJkq6QtJakbpLGS+pT9HPnSfpCUhdJa0u6Uh5y2zZjG8UIwGjxhgzx651vuin97t3Aqpg+3acN4wt5GLNmmW23ndnFF2ddSRymT/ep637966wryc7gwd4S/OqrWVeCmBCAUY02lzQu/29xC3AvSZMltSpa91xJY4puj5V0dtHt1SVNkXRiM7ZRjAAMmI9qvckmZmecEf+0UkAo/fqZHXwwJ4JC+vBD73Xy+ONZV5KtxYt9/vaePeMYIDFLN93k89d//HHWlSAWBGBUo+GSTsv/vzgA3yDpuZJ1e8hbidvJ/whykvZoYHvXN3EbpQjAQN4XX/gcxAcd5C0TQEvy+edmrVv7SOkI64knvNvvBx9kXUl2zj/fW8Nnzsy6kjhcdJHZN77ho0QDBGBUm7PkgbWgOADfKWlIyfrby8NrZy1pMd6uZJ2HJQ1q4jZKEYCBInPm+LRO225r9sknWVcDVM6JJ/o83qiMiy/240xLDIAPPeTz2dPiuUQu5/MDd+vGCVgQgFFdtpFfn7tF0X1RtAD37t3b+vbta3379rVhXPyFFm7xYm+dWG89sxdeyLoaILw33zRr08Z7QaAyFi/2AbEOP7xldQF+6y0fc+GZZ7KuJD51db4/fPe7TEHWEg0bNuz/v4v37t2bAIyq0UvSAklTJX2VX3KSZki6XT4w1hQtff1uHzV+DfBUSSfkb5+sZa8BLt1GMVqAgeW45x4fofOOO7KuBAgnlzM78EA/6YPKmjbN5769/PKsK6mMqVO9m+9vf5t1JfGqrfU56g87zAMxWiZagFFNWsu7IRcvOUnHSuoob6GtkTQwv253SV9q6RGc+8kH0OoqqY2kq+SjPBePAt3YNooRgIEVGD3aByfp04epS1Cdhg41W399sxkzsq6kZfr3v71F9Nlns64krEWLzPbbz+zYYxlkrTHTp3tX6FNP5b1qqQjAqHbF0yBJPm3RaEnz5N2lL23gZwZImiSf53eklp0HuCnbKCAAA4347DOzHXYw+8EPfBoToFosXuxftG+8MetKWraHHjLr2NFszJisKwnn7LPNdtzRbO7crCtJQ02N2ZZbml14YdaVIAsEYCAsAjDQBDNn+vV6XbuajR2bdTXxWrzY7MEHfXCf7bYze/RRWjBidtdd3gV34cKsK0Hfvn4yYs6crCspv7vu8l4GHDub5+OPzTbckBNULREBGAiLAAw0UV2d2Tnn+BeSF1/Mupq41Nd72O3SxVst7rzT7PbbzTp1Mtt1V7O//z3rClFq7lyzzp3N/vznrCuBmR9f9t/f7Ljjquuk0b/+5WMpcAxYOa+95lNmPfhg1pWgkgjAQFgEYKCZbrvNv9Ddd1/WlWQvlzP7y198/uTOnf29KW5NnDfP7OqrvXvnIYeYvfFGdrViaVdcYbb77tUVtlI3ZYrZ5pubXXdd1pWUR02NnwSjBXPVDB/unzlM0tFyEICBsAjAwEr429881F14YcuawqQglzN77jmz3XYz23hj/4JbW7v89adPN7vgAv8Sd+yxzLGctSlTvFVp1KisK0Gp116rjhbTBQvM9tzTrFcvTrKUw0MPmbVrZ/bqq1lXgkogAANhEYCBlfTxx2bf+pbZkUe2rIFd/vEPn6dygw3Mrrmmea99wgSzn//cbO21/d8JE8LVieU76yyfbxRxuusu//saNy7rSlZOLmd22ml+gmz+/KyrqR433eT7xUcfZV0JQiMAA2ERgIFV8PXXft3ezjubjR+fdTVhvfSS2QEHeMv3wIFms2ev/LY++cSvdWzb1luGp08vX51YsY8/Nmvdmi/RsTvzTLNddllxz4pY3Xqr2SabVP8xMQsXXeRzKfPeNm7SJH+/LrnELyv405/MHnvMe1e88YbZp5/6Z3iM8y0TgIGwCMDAKlq40OxnPzPbdNPq7J722ms+Anb79maXXlre+WJff92vDe7Y0eyqq/yaYYR15JFmZ5yRdRVozMKFZnvtlV4X4pEj/cTWSy9lXUl1KrSud+1qNm1a1tXEKZczGzzYRx4/7DDvbXTssf5Zs9tuZv/1X2YbbWS2xhpmki/t25ttsYVZ9+5m++zjPWROPtns3HPNfvMbsxtuMLvnHrMnnzQbMcLs7bfNPv/cPw9DXAZFAAbCIgADZZDL+QfkOuuYPfxw1tWUx9tv+5eHtm3Nzj/f7Kuvwj3X3//uX0w6dfLRoxctCvdcLdlLL/k+OmlS1pWgKWpq/MTarbdmXUnTfPGFj5J/xx1ZV1Ld6urMjjjCL0XhpOHSJk/2k3wbb2z2xBMrXjeX80t4Jkwwe/99Pz4+/bTZAw+Y3XKL93T63//1Ew5HHWV24IHeK2ObbTxct2rl4Xm11fwk7lZbeW+w/ff3Gn76U5/e7LLLzG6+2ez++83++lez0aPN3n3X7Msvfdqzhk5wEYCBsAjAQBk984yfSR4wIK1Wm2Lvv292zDFmbdqY/fKX/oWiEnI575623XZ+hv7Pf26ZA4yFksuZ9ejh+ybS8dJLfhIq9qnX5s0z+/a36V1QKbW1ZnvvbdazZ5xdeCstl/OTzxtsYHb88WFP2BbU15vNmuUnft55xwcVfOops3vv9eu1Bwzwz9BevfyExX77+YwJW25p1qHDktbn1Vf3ur/5TbPvfMfs4IPNjjiCAAyERAAGyuy99/wD7sc/Tuv6vU8+MTvhBL8+9Kyzshugqq7Or9XabDM/2z58eLonE2Ly2GPemjhnTtaVoLn+8Af/3dXUZF1Jw3I5s5/8xANZ8TRoCGv6dLNu3bylsSUfI6dMMTv6aO998OijWVfTdIsX++9w7Fizt97yASYff9wHwbviCgIwEBIBGAhgyhS/fm+PPeLvbjp2rNkpp/jIzKedFs/Is7W1Ztdea7beej74VjVeX10pCxf6iOV0TU1TLud/oz16+PRCsbn2Wp+/uFK9RbBETY2fcL3ggqwrycYjj3jwPeYY/9ytFnSBBsIiAAOBzJ9v9j//4wNrvP121tUs68svl0xJdNJJZmPGZF1Rw2bMMLv4Yu8GetRRjF68Mm65xWyHHegqmbL58717ZGxdjIcN87/N11/PupKW65NPPATecEPWlVTO1Kk+sNUGG/jlMtWGAAyERQAGAsrlzH77W7N27fzaoBhMnGh2zjne1fn449MJlBMn+tQwrVt7S/WXX2ZdURpmzvQvx08/nXUlWFWFQabuvDPrStyYMd5D4/77s64Er73m4088+GDWlYT32GM+ivORR1ZvrwMCMBAWARiogMcf9xB8zTXZXas1daqPaNm2rdmPfuSDdqRozBi/vrpNG7N+/XweRyzfxRf74Cst+RrBavLCC/43nPUlAbNnm3Xp4qPcIg7PP+/7xnPPZV1JGF9/7cf+9dc3e+ih6j6mEYCBsAjAQIW8+aYP7HTKKZW9jm/aNA9B66xjduihZm+8UbnnDumtt8y+/30fTfOKK3w6Cyxt/Hj/Qvzaa1lXgnK6/nq/5jarax7r6/0k2sEH060+Ng895CdbX3kl60rK68knzTbZxEdTjn1cjXIgAANhEYCBCqqpMdt1V7N99gk/TcPMmT4NQ4cO/kX15ZfDPl9WRozwwcY22cRHy2UU2iVOOcVbTFBdcjn/ve63XzZzZl92mdnWW9P7IlY33+zXxqZyecuKTJtmduKJ3tX+gQequ9W3GAEYCIsADFTYvHk+eMfWW5t98EH5tz9njtmVV/oXhn32MRs5svzPEZtczuyJJ3ygp2228VaQlj6H8Dvv+PXSY8dmXQlCmDvXrHt3n2e0kp56ylsYU72EoqW4+GIfgHH8+KwrWXl/+YtP/9WzZ7xTgIVCAAbCIgADGaivN7v0Um+dLdf1WrW1Zr/7nQ8Osscefj1YSzlbXlBXZ3b33f7Fb+ed/b1tae9Bwfe+x/WZ1e7TT806djQbPLgyz/fBBz7QUkpzrbZUuZwPFtili7eipmT6dJ+ZoGNHs/vua5nHcAIwEBYBGMjQ4MF+jebNN6/8h/yCBT7NTadOZt/+ttkzz7TMLwzF5s/3kwHrr+/dRKu1+/fyPP+8f3lM7Ysvmm/oUL++P/RUa9On+1zSl1wS9nlQPnV1fs3sXnt5z6MUPPOMWefOPl7FhAlZV5MdAjAQFgEYyNi//uXXr/7iF827nm/RIrM77vDWzm7dvAtwSw++pWbONPv1rz0gHHFEmC7nsamv99bva6/NuhJUysCBfklFqBMeixf7gHM9e3JpQWpqa/1SmJ49s7levKlmzPAxC9Zd1+yee/gsIwADYRGAgQh88YXZjjuaHXSQt7SsSF2d2b33+hfe7bYzGzKEL6WNmTzZ7Oyz/ZrYU07x97ta3X+/2Te+4a3gaBnq6/0Ez3//t4fVcrvgAj/WzJxZ/m0jvBkz/HrxU06JM1gOHeozJHz/+2lfs1xOBGAgLAIwEInZs80OO8xs223N/vOfZR+vr/fBnbbd1gd6uu8+piBprs8+8xFFW7f262NDj8RdabW13iPggQeyrgSVNmuWh9SLLirvdocM8Va5jz8u73ZRWTU1ZlttZXb++VlXssTMmWannupjYdx5Z5zhPCsEYCAsAjAQkcWLzc47z0dw/sc//L76erPHHjPr2tVb9v70p7i7sqXg3//2a8zat/cpXebMybqi8rjmGrNddqFHQEv10UceJh5/vDzbe/ttv3zgmWfKsz1k65NPzDbc0MdHyNrw4T6X9SGHVHePnJVFAAbCIgADEbrrLh8c6+KLfWCrTp18jtsFC7KurLqMGuUDxGy8sdnvf5/2HMJff+0tdS+8kHUlyNKTT/qJnVW93n3qVLMttzS74oqylIVIvPaa7x9Z9RKZNcvs9NO9hjvuoNV3eQjAQFgEYCBSI0ea9ehhdsMN3rUVYeRyPt9k167eRfCOO8z++lcPEo8+avbww2YPPuhdzu+6y2zQILPbbvORt2+80ez6673l9be/Nbv8crPf/MZHyr3gArN+/Xye1nPOMTvzTLOf/9y7/PXq5V2xf/xjs2OOMTvySO/+fuihfh3nQQeZ7b+/2d57+z6w227esrvjjl7ndtv5iLxbbeVdnjt18vB76KFZv5uIwSWX+KUSK3vN7qJFvv8dcwwBpRo9/7yfYB06tLLP+7e/eS+mgw4yGzeuss+dGgIwEBYBGADMu5/fd5+HzZ128sC5++4eQPfZxwPBwQf7/Lo//KHZ4YebHXWU2bHHmv3kJ2b/8z8ebE87zYPuWWd58O3b14PwhRd6MOnf34PylVd6cP7d78xuusns1lvN/vhH7+J+990+mNXgwWZ//rN3gX/qKbOnn/a5jZ9/3rvIjxpl9s9/mr3yitnrr3OiBK4wavPhh69cd/hzzvFBk6rl0gAsa8gQs3bt/NgR2uzZZmec4c/3xz9yUqUpCMBAWARgAACqzPTpPlje5Zc37+fuvtvnz/7sszB1IR6//73/rj/8MNxzvPCCd6U/4ACzsWPDPU+1IQADYRGAAQCoQu+8461uzz7btPVfecW7xv7tb2HrQjwuucQvo/jyy/Jud84c7wWzzjreu4WB+ZqHAAyERQAGAKBKDRli1rGj2ZgxK15v4kSzzp19zAG0HLmc2c9+Ztali9m0aeXZ5ogRPk/9vvuaffppebbZ0hCAgbAIwAAAVLFf/coHT1veNb0LFpjtuafZSSdxfWZLVFdn9qMf+Yj48+at/HbmzjU7+2xv9b35Zlp9VwUBGAiLAAwAQBWrq/NrMI87btmAm8v5wG277sogai1Zba0P9vfDH67cPPOjRvk153vv3XhvAzSOAAyERQAGAKDKTZ3q13pee+3S9//hDz4PdrmvAUV6Zszw0b979Wp6T4B588z69PFW3xtvpNW3XAjAQFgEYAAAWoDXX196kKtRo/z2iy9mWxfiUVPj84ufd17j6774os9HvtdeZp98Er62loQADIRFAAYAoIW4+26zDTYwGz3abKONzG6/PeuKEJtPPjHbcEOz669v+PHaWr+uvG1bn8d88eLK1tcSEICBsAjAAAC0IGeeaSaZnXFG1pUgVq+/bta+vdn99y99/8svm227rQ+a9vHH2dTWEhCAgbAIwAAAtCALF5r98Y/+L7A8f/ubt/IOHeqtvv36+e1rr6XVNzQCMBAWARgAAADLePhhs3btvNV3993NPvww64paBgIwEBYBGAAAAA26916/HriuLutKWg4CMBAWARgAAACIBAEYCIsADAAAAESCAAyERQAGAAAAIkEABsIiAAMAAACRIAADYRGAAQAAgEgQgIGwCMAAAABAJAjAQFgEYAAAACASBGAgLAIwAAAAEAkCMBAWARgAAACIBAEYCIsADAAAAESCAAyERQAGAAAAIkEABsIiAAMAAACRIAADYRGAAQAAgEgQgIGwCMAAAABAJAjAQFgEYAAAACASBGAgLAIwAAAAEAkCMBAWARgAAACIBAEY1eY3kj6VNFPSVEnPSdqpZJ0dJY2SNFfSBEn9G9jOZZJqJM2RNFJS15XYhkQABgAAAKJBAEa1+S9J6+b/v4akX0maLGm1/H3tJE2UdIWktSR1kzReUp+ibZwn6QtJXSStLelKecht24xtFCQdgIcNG5Z1CSsl1brN0q091brN0q091brN0q091brN0q091brN0q091brN0q091brN0qydAIxqtrakX0qql7RB/r5e8kDcqmi9cyWNKbo9VtLZRbdXlzRF0onN2EZB0gG4b9++WZewUlKt2yzd2lOt2yzd2lOt2yzd2lOt2yzd2lOt2yzd2lOt2yzd2lOt2yzN2gnAqEaHSpohKSdpsaTrih67Qd4tulgPeUhuJ/9DyEnao2Sd4ZKub+I2ihGAM5Bq3Wbp1p5q3Wbp1p5q3Wbp1p5q3Wbp1p5q3Wbp1p5q3Wbp1p5q3WZp1k4ARjXrKO+WfHTRfXdKGlKy3vby8NpZ0ubyALxdyToPSxrUxG0U6yDJxo8fb7NmzUpu6d27d+Y1tKS6U6491bpTrj3VulOuPdW6U6491bpTrj3VulOuPdW6U619/PjxBGBUtdXkA2J1z9+udAvwZvI/MBYWFhYWFhYWFhaWeJbNBFShNSTNk3RU/vbJWvb63T5q/BrgqZJOaMY2ClaT/3F1YGFhYWFhYWFhYWGJYtlMSwbJBZJ2rqSN8//fSN5tebqkTfL3tZNPbzRQUmt5y/CXWnoE536SxsmnPmoj6Sr5KM9tm7ENAAAAAACCelrSJPn8vTWSnpK0S8k63SSNlrcMT5R0aQPbGZDfzlw1PA9wU7YBAAAAAAAAAAAAAAAAAAAAAJFrK+lE+YjRg/L/nqgl1w8D1aSdfOqv0tHPU0DtlUfdlZdy7aislPeVlGtHZbGvAGXWVdIESVPk0yY9nP93qnwQrdLriFF+qR7YUqu7MOp5fdEyRmkMBEftlUfdlZdy7QWpHRcLUqs75X0l5doLUttfClKruxr2FSBKIyVdKZ82qdjq8pGkR1a4nuZKufU61QNbinX3l5/QOVc+//X2+X/7yEdDj3lAquSOlwAACN9JREFUOGqvPOquvJRrl9I8Lkpp1p3yvpJy7VKa+4uUZt2p7ytA1OZIWns5j7WRjygdq5Rbr1M9sKVa9wT5KOgN6S4fFT1W1F551F15Kdee6nEx1bpT3ldSrj3V/SXVulPeV4Do1UjqspzHusqnVYrVSKXbep3qgS3VuudKWms5j7WWTw0WK2qvPOquvJRrT/W4mGrdKe8rKdee6v6Sat0p7ytA9C6T9IWksyXtIWm7/L9nSxonn1s4Vim3Xqd6YEu17lGSrpDUquT+VpIGyufHjhW1Vx51V17Ktad6XEy17pT3lZRrT3V/SbXulPcVIAnnSRorKVe0jJXUL8uimiDl1utUD2yp1r2j/CzvZEnPSRqS/3eyfD9a3tnhGFB75VF35aVce6rHxVTrTnlfSbn2VPeXVOtOeV8BktJB0ub5f1OQcut1qge2VOuWfNTHXpJukHRn/t+TlcZokNReedRdeanWnupxMdW6pXT3FSnd2lPdX1KtW0p3XwEQWKqt11K6B7ZU6waAUFI9LqZaN7KR6v6Sat0AsEKptV6j8hqaMusEpTFlFrVXHnVXXsq1o7JS3ldSrh2Vxb4CYIVSm9xcSvfAlmLdKU+ZRe2VR92Vl3LtUprHRSnNulPeV1KuXUpzf5HSrDv1fQVAQClObi6le2BLte6RSnfKrJGi9kobKequtJFKt/ZUj4up1j1S6e4rI5Vu7anuL6nWPVLp7isAAkp1cnMp3QPbSKVZd8pTZlF75VF35aVc+0ileVwcqTTrTnlfSbn2kUpzfxmpNOtOeV8BEFCqk5tL6R7YUq075SmzqL3yqLvyUq491eNiqnWnvK+kXHuq+0uqdae8rwAIKNXJzaV0D2yp1p3ylFnUXnnUXXkp157qcTHVulPeV1KuPdX9JdW6U95XAASU6uTmUroHtlTrltKeMovaK4+6Ky/V2lM9LqZat5TuviKlW3uq+0uqdUvp7isAAkp5cnMp3QNbqnUXpDxlFrVXHnVXXoq1p3pcTLXughT3lYIUa091f0m17oIU9xUAAVXD5OapHthSrTvFKbOqAe97ZfF+ZyPV42KqdbOfZyPV/SXVutnPASxXW0kHSzpE0joZ19IcHNgqI+Ups4qdIeklSf+U1Lvy5TRbiu97yu95iu+3lPZ7XozjeWWwn2eL/bwyUt3PAQQ0uOj/W8kPCvPzy1hJ22RQU3OkeGBL9cM75SmzZhf9/2eSpkv6TX6ZJun0LIpqolTf91Tf81Tfbynd97yA43nlsJ9nh/28clLezwEEVPxBMljSg/JRodeSdL+WDsixSfXAluqHd+pTZhW8IekHRbf/W9LblS2nWVJ931N9z1N9v6V033OJ43mlsZ9ng/28slLezwEEVPxB8rmkTkW3N5EfqGOV6oEt1Q/vlKfMKv7w/krSGkW3V5d/mMcq1fc91fc81fdbSvc9lzieVxr7eTbYzysr5f0cQEDFHyRTGng81snNpXQPbKl+eKc8ZVatpAMkHSj/grF20WNraunfSWxSfd9Tfc9Tfb+ldN9zieN5pbGfZ4P9vLJS3s8BBLRYfgAYLT/Dt0XRYxuo4VAci1QPbKl+eKc8ZVauZDmg6LFdJH2YRVFNlOr7nup7vrz3e4rifr+ldN9zieN5paV6XJHYz7PAfg6gqvQvWboUPXaspL9kUVQTpXpgS/nDuxqmzCr1bUk9sy6iEdX2vsf+nlfb+y3F/55zPK889vPKYz+vvHby/bqa9nMALVy1fYDH/uENtFS3S+qYdRErKZXaOZ5nZ09JV+WXXTOupbn20JLad8u4lqZgP6+sjvL9otBqvaOk87R0iAeAqrCapH2zLmIlpFD3usu5/7sVrWLlpFr7avJLEgq6SzpKPlBdzFKs+8DlLDPkvWIOzK60RqVce6lU56RPpe6XtCQsHiVpoaRh+aVWcQealGs/Qsu/BjhmR8i7OqdmX3n37JykT+Qjbs+U9KZ8us/TsisNAMpvbfnceqmJue7N5SM91svniD6k5PFYrwGS0q59J3n3uHpJ10k6Wv4BPk8+8Mgu2ZW2QqnWnZPXXNrlL1f0WKxSrj3VOelL6/5UadQ9Q0uuQ31d0pFFjx0uH+k3VinXnpM0VdKVkr6RcS3NUVz3lhnX0hz/lPQr+cmoC+SfSYfnH+sp6b2M6gKAldZqBUtb+QE7RqnW/ZD8y95Oks6XD5p2eNHjcxr6oUikXPtz8g/wX8oHresjb1ldQ9IgSU9lV9oKpVr3k/KWpM1L7v9KUufKl9MsKdee6pz0Kddd6BL6tZYelGk1SbMqXlHTpVz7PEnnyK+ZrZP0tPR/7Z2xSxxREIe/M0K0SAohh6I2SmprSRGQQ0KwP+SqtBKvEuxCGhXxPwjYpglpAylicV06CXaeEEJCJEWCnQqCxdxye+teXK+4nZ/MBwu395pvYZfdmffmDS9KNSqGqvdf7J4Am8G+pHu/VLCkbBAEgRRXBQ6PqHr/Ah6nzmtYJr7WOfc8i6rs/gcLGh9iyZF0LecT7No8ouoNVo/3nd7lcQpBJOi6q/akV/X+AjQ6v7/SuyJjAfgxdKPiKLun3zVLwAcsKGsDG8BEGVIFUPU+pes2ib2LklKoR/judBIEQZDLGfAKeJ5zLOM3kFT2Hs38t4IFks/wHUQqu6cz1P9yxr3OXqt6J8wAnzvHLBbQew8iExTdVXvSq3ovYs/lHrAL/MaWt25juxRvlqd2K8ruee+aKeAt8BOrYfaIqvd7rPXUOnAAfAL2sUTJO2zVTBAEgRQtoN5nLJl18oiq9zdst8csDWyZ0cVwde6Esnubbsb6ZWasin38eUTVO8sa9lF9jv8gMouSu2pPelVvsECyhV1DUid+DDTLlCqIqvv/kq0PsE29PKLqXQU+AofAKlZ3fYLdL0fAfHlqQRAEg1Hn5mZGCSNYmwGPqHrvAFt9xl7jN3AHbfc3wNM+Y02sFssjqt55zGHP5VjZIgOg4q7ak17VO804ME1vmYgKau5HZQsMiKp3HhX8LtkOgiAIgiAIgiAIguC+cA0sx9wieKPBuQAAAABJRU5ErkJggg==\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "plt.figure(figsize=(12,5))\n",
    "x = np.arange(tmax-epoch, step=epoch)\n",
    "plt.plot(x, map_diffs)\n",
    "plt.xticks(x, rotation='vertical')\n",
    "plt.ylim(3000, 8000)\n",
    "plt.xlim(xmax=8000)\n",
    "plt.xlabel('Number of iterations')\n",
    "plt.ylabel('Score')\n",
    "plt.title('Map stabilisation -- sigma = 3, eta = 0.1 ')\n",
    "# plt.savefig('Map stabilisation1.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training and checking total movement -- Doesn't work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "movs = [] # movements created at each step\n",
    "all_cs = []\n",
    "\n",
    "for example_idx in idxs_random:\n",
    "    mov, win = som_step(centers, data[example_idx,:],neighbor,eta,sigma)\n",
    "    movs.append(mov)\n",
    "    all_cs.append(centers.copy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7145611080>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtYAAAFwCAYAAACcvTyoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XucVXW9//H3h4t3QFCB5KKYgpD3C2Za7mOFigXWMdPM\nLlp2kn569HdOgr9TTHUq7ZyOZqWWt6CjIWqlJioSjnkHAwUFZFRAZoxBLgloyjDz+f3xWdu9Z5hh\nxpnvzOyB1/Px2I9Z+7u/a63vWuu71n7Pmu/eY+4uAAAAAG3TrbMbAAAAAGwPCNYAAABAAgRrAAAA\nIAGCNQAAAJAAwRoAAABIgGANAAAAJNBssDazm82s2swWFJUdbmZPmdl8M5tjZscUvTbJzCrMbLGZ\njSkqP8rMFpjZUjO7Jv2mAAAAAJ2nJXesb5V0SoOyn0ia7O5HSpos6b8kycxGSTpL0khJp0m6zsws\nm+d6SRe4+3BJw82s4TIBAACALqvZYO3uj0ta36C4TlKfbHpPSVXZ9DhJ09x9i7svl1QhabSZDZTU\ny93nZvWmSjqjjW0HAAAASkaPVs53qaSHzOynkkzSR7LyQZKeKqpXlZVtkVRZVF6ZlQMAAADbhdZ+\nePGbki5x96GKkH1LuiYBAAAAXU9r71h/2d0vkSR3v8vMbsrKqyQNKao3OCtrqrxRZuatbBcAAADQ\nYu5uzddqmZYGa8seeVVmdpK7P2pmH1eMpZakeyXdZmZXK4Z6HChpjru7mb1pZqMlzZX0JUnXbmuF\n7mRr1FdWVqaysrLObgZKDP0CDdEn0Bj6BRpT+I6NNJoN1mZ2u6ScpL3M7DXFt4B8XdK1ZtZd0juS\nLpQkd19kZtMlLZJUI+kiLyTkCZJ+I2kXSTPc/cGkWwIAAAB0omaDtbt/oYmXjmms0N1/LOnHjZT/\nVdKh76t1AAAAQBfBf15El5HL5Tq7CShB9As0RJ9AY+gX6AhWimOZzcxLsV0AAADYfphZ0g8vcsca\nAAAASIBgDQAAACRAsAYAAAASIFgDAAAACRCsAQAAgAQI1gAAAEACBGsAAAAgAYI1AAAAkADBGgAA\nAEiAYA0AAAAkQLAGAAAAEiBYAwAAAAkQrAEAAIAECNYAAABAAgRrAAAAIAGCNQAAAJAAwRoAAABI\ngGANAAAAJECwBgAAABIgWAMAAAAJEKwBAACABAjWAAAAQAIEawAAACABgjUAAACQAMEaAAAASIBg\nDQAAACTQbLA2s5vNrNrMFjQo/z9mttjMFprZlUXlk8ysInttTFH5UWa2wMyWmtk1aTcDAAAA6Fwt\nuWN9q6RTigvMLCfp05IOdfdDJf13Vj5S0lmSRko6TdJ1ZmbZbNdLusDdh0sabmb1lgkAAAB0Zc0G\na3d/XNL6BsXflHSlu2/J6qzJysdLmubuW9x9uaQKSaPNbKCkXu4+N6s3VdIZCdoPAAAAlITWjrEe\nLuljZva0mT1iZkdn5YMkrSyqV5WVDZJUWVRemZUBAAAA24UebZivr7t/2MyOlXSnpAPSNQsAAADo\nWlobrFdK+r0kuftcM6s1s70Ud6iHFtUbnJVVSRrSSHmTysrK3pvO5XLK5XKtbCoAAAAglZeXq7y8\nvN2Wb+7efCWz/SXdl31QUWZ2oaRB7j7ZzIZLetjd9zOzUZJuk3ScYqjHw5IOcnc3s6clXSxprqT7\nJV3r7g82sT5vSbsAAACA1jIzubs1X7Nlmr1jbWa3S8pJ2svMXpM0WdItkm41s4WS3pX0JUly90Vm\nNl3SIkk1ki4qSsgTJP1G0i6SZjQVqgEAAICuqEV3rDsad6wBAADQ3lLfseY/LwIAAAAJEKwBAACA\nBAjWAAAAQAIEawAAACABgjUAAACQAMEaAAAASIBgDQAAACRAsAYAAAASIFgDAAAACRCsAQAAgAQI\n1gAAAEACBGsAAAAgAYI1AAAAkADBGgAAAEiAYA0AAAAkQLAGAAAAEiBYAwAAAAkQrAEAAIAECNYA\nAABAAgRrAAAAIAGCNQAAAJAAwRoAAABIgGANAAAAJECwBgAAABIgWAMAAAAJEKwBAACABAjWAAAA\nQAIEawAAACABgjUAAACQQLPB2sxuNrNqM1vQyGv/18zqzKxfUdkkM6sws8VmNqao/CgzW2BmS83s\nmnSbAAAAAHS+ltyxvlXSKQ0LzWywpE9KWlFUNlLSWZJGSjpN0nVmZtnL10u6wN2HSxpuZlstEwAA\nAOiqmg3W7v64pPWNvHS1pH9vUDZe0jR33+LuyyVVSBptZgMl9XL3uVm9qZLOaHWrAQAAgBLTqjHW\nZjZO0kp3X9jgpUGSVhY9r8rKBkmqLCqvzMoAAACA7UKP9zuDme0q6QrFMJB2U1ZW9t50LpdTLpdr\nz9UBAABgO1deXq7y8vJ2W765e/OVzPaTdJ+7H2Zmh0iaJeltSSZpsOLO9GhJ50uSu1+ZzfegpMmK\ncdiPuPvIrPxsSSe5+zebWJ+3pF0AAABAa5mZ3N2ar9kyLR0KYtlD7v6Cuw909wPcfZhiWMeR7r5a\n0r2SPm9mO5nZMEkHSprj7qskvWlmo7MPM35J0j2pNgIAAADobC35ur3bJT2p+CaP18zsqw2quAqh\ne5Gk6ZIWSZoh6aKiW88TJN0saamkCnd/MM0mAAAAAJ2vRUNBOhpDQQAAANDeOmsoCAAAAIBtIFgD\nAAAACRCsAQAAgAQI1gAAAEACBGsAAAAgAYI1AAAAkADBGgAAAEiAYA0AAAAkQLAGAAAAEiBYAwAA\nAAkQrAEAAIAECNYAAABAAgRrAAAAIAGCNQAAAJAAwRoAAABIgGANAAAAJECwBgAAABIgWAMAAAAJ\nEKwBAACABAjWAAAAQAIEawAAACABgjUAAACQAMEaAAAASIBgDQAAACRAsAYAAAASIFgDAAAACRCs\nAQAAgAQI1gAAAEACzQZrM7vZzKrNbEFR2U/MbLGZPWdmd5tZ76LXJplZRfb6mKLyo8xsgZktNbNr\n0m8KAAAA0Hlacsf6VkmnNCibKelD7n6EpApJkyTJzEZJOkvSSEmnSbrOzCyb53pJF7j7cEnDzazh\nMgEAAIAuq9lg7e6PS1rfoGyWu9dlT5+WNDibHidpmrtvcfflitA92swGSurl7nOzelMlnZGg/QAA\nAEBJSDHG+nxJM7LpQZJWFr1WlZUNklRZVF6ZlQEAAADbhTYFazP7f5Jq3P13idoDAAAAdEk9Wjuj\nmX1F0lhJJxcVV0kaUvR8cFbWVHmTysrK3pvO5XLK5XKtbSoAAACg8vJylZeXt9vyzd2br2S2v6T7\n3P3Q7Pmpkn4q6WPuvrao3ihJt0k6TjHU42FJB7m7m9nTki6WNFfS/ZKudfcHm1ift6RdAAAAQGuZ\nmdzdmq/ZMs3esTaz2yXlJO1lZq9JmizpCkk7SXo4+9KPp939IndfZGbTJS2SVCPpoqKEPEHSbyTt\nImlGU6EaAAAA6IpadMe6o3HHGgAAAO0t9R1r/vMiAAAAkADBGgAAAEiAYA0AAAAkQLAGAAAAEiBY\nAwAAAAkQrAEAAIAECNYAAABAAgRrAAAAIAGCNQAAAJAAwRoAAABIgGANAAAAJECwBgAAABIgWAMA\nAAAJEKwBAACABAjWAAAAQAIEawAAACABgjUAAACQAMEaAAAASIBgDQAAACRAsAYAAAASIFgDAAAA\nCRCsAQAAgAQI1gAAAEACBGsAAAAgAYI1AAAAkADBGgAAAEiAYA0AAAAkQLAGAAAAEmg2WJvZzWZW\nbWYLisr6mtlMM3vJzB4ysz5Fr00yswozW2xmY4rKjzKzBWa21MyuSb8pAAAAQOdpyR3rWyWd0qBs\noqRZ7j5C0mxJkyTJzEZJOkvSSEmnSbrOzCyb53pJF7j7cEnDzazhMgEAAIAuq9lg7e6PS1rfoHi8\npCnZ9BRJZ2TT4yRNc/ct7r5cUoWk0WY2UFIvd5+b1ZtaNA8AAADQ5bV2jHV/d6+WJHdfJal/Vj5I\n0sqielVZ2SBJlUXllVkZAAAAsF1I9eFFT7QcAAAAoEvq0cr5qs1sgLtXZ8M8VmflVZKGFNUbnJU1\nVd6ksrKy96ZzuZxyuVwrmwoAAABI5eXlKi8vb7flm3vzN5vNbH9J97n7odnzqyStc/erzOxySX3d\nfWL24cXbJB2nGOrxsKSD3N3N7GlJF0uaK+l+Sde6+4NNrM9b0i4AAACgtcxM7m7N12yZZu9Ym9nt\nknKS9jKz1yRNlnSlpDvN7HxJKxTfBCJ3X2Rm0yUtklQj6aKihDxB0m8k7SJpRlOhGgAAAOiKWnTH\nuqNxxxoAAADtLfUda/7zIgAAAJAAwRoAAABIgGANAAAAJECwBgAAABIgWAMAAAAJEKwBAACABAjW\nAAAAQAIEawAAACABgjUAAACQAMEaAAAASIBgDQAAACRAsAYAAAASIFgDAAAACRCsAQAAgAQI1gAA\nAEACBGsAAAAgAYI1AAAAkADBGgAAAEiAYA0AAAAkQLAGAAAAEiBYAwAAAAkQrAEAAIAECNYAAABA\nAgRrAAAAIAGCNQAAAJAAwRoAAABIgGANAAAAJECwBgAAABJoU7A2s0vN7AUzW2Bmt5nZTmbW18xm\nmtlLZvaQmfUpqj/JzCrMbLGZjWl78wEAAIDSYO7euhnN9pX0uKSD3X2zmd0haYakUZLWuvtPzOxy\nSX3dfaKZjZJ0m6RjJQ2WNEvSQd5IA8yssWIAAAAgGTOTu1uq5bV1KEh3SbubWQ9Ju0qqkjRe0pTs\n9SmSzsimx0ma5u5b3H25pApJo9u4fgAAAKAktDpYu/vrkn4q6TVFoH7T3WdJGuDu1VmdVZL6Z7MM\nkrSyaBFVWRkAAADQ5bU6WJvZnoq70/tJ2ldx5/pcSQ3HcDCmAwAAANu9Hm2Y9xOSXnX3dZJkZn+Q\n9BFJ1WY2wN2rzWygpNVZ/SpJQ4rmH5yVNaqsrOy96Vwup1wu14amAgAAYEdXXl6u8vLydlt+Wz68\nOFrSzYoPI74r6VZJcyUNlbTO3a9q4sOLxymGgDwsPrwIAACATpL6w4utvmPt7nPM7C5J8yXVZD9/\nLamXpOlmdr6kFZLOyuovMrPpkhZl9S8iPQMAAGB70eo71u2JO9YAAABob6X2dXsAAAAARLAGAAAA\nkiBYAwAAAAkQrAEAAIAECNYAAABAAgRrAAAAIAGCNQAAAJAAwRoAAABIgGANAAAAJECwBgAAABIg\nWAMAAAAJEKwBAACABAjWAAAAQAIEawAAACABgjUAAACQAMEaAAAASIBgDQAAACRAsAYAAAASIFgD\nAAAACRCsAQAAgAQI1gAAAEACBGsAAAAgAYI1AAAAkADBGgAAAEiAYA0AAAAkQLAGAAAAEiBYAwAA\nAAkQrAEAAIAE2hSszayPmd1pZovN7EUzO87M+prZTDN7ycweMrM+RfUnmVlFVn9M25sPAAAAlIa2\n3rH+maQZ7j5S0uGSlkiaKGmWu4+QNFvSJEkys1GSzpI0UtJpkq4zM2vj+gEAAICS0OpgbWa9JX3U\n3W+VJHff4u5vShovaUpWbYqkM7LpcZKmZfWWS6qQNLq16wcAAABKSVvuWA+TtMbMbjWzeWb2azPb\nTdIAd6+WJHdfJal/Vn+QpJVF81dlZQAAAECX15Zg3UPSUZJ+6e5HSXpLMQzEG9Rr+BwAAADY7vRo\nw7yVkla6+7PZ87sVwbrazAa4e7WZDZS0Onu9StKQovkHZ2WNKisre286l8spl8u1oakAAADY0ZWX\nl6u8vLzdlm/urb+hbGaPSvq6uy81s8mSdsteWufuV5nZ5ZL6uvvE7MOLt0k6TjEE5GFJB3kjDTCz\nxooBAACAZMxM7p7syzTacsdaki6WdJuZ9ZT0qqSvSuouabqZnS9pheKbQOTui8xsuqRFkmokXUR6\nBgAAwPaiTXes2wt3rAEAANDeUt+x5j8vAgAAAAkQrAEAAIAECNYAAABAAgRrAAAAIAGCNQAAAJAA\nwRoAAABIgGANAAAAJECwBgAAABIgWAMAAAAJEKwBAACABAjWAAAAQAIEawAAACABgjUAAACQAMEa\nAAAASIBgDQAAACRAsAYAAAASIFgDAAAACRCsAQAAgAQI1gAAAEACBGsAAAAgAYI1AAAAkADBGgAA\nAEiAYA0AAAAkQLAGAAAAEiBYAwAAAAkQrAEAAIAECNYAAABAAgRrAAAAIAGCNQAAAJBAm4O1mXUz\ns3lmdm/2vK+ZzTSzl8zsITPrU1R3kplVmNliMxvT1nUDAAAApSLFHetLJC0qej5R0ix3HyFptqRJ\nkmRmoySdJWmkpNMkXWdmlmD9AAAAQKdrU7A2s8GSxkq6qah4vKQp2fQUSWdk0+MkTXP3Le6+XFKF\npNFtWT8AAABQKtp6x/pqSf8uyYvKBrh7tSS5+ypJ/bPyQZJWFtWrysoAAACALq9Ha2c0s9MlVbv7\nc2aW20ZV38ZrTSorK3tvOpfLKZfb1ioAAACAbSsvL1d5eXm7Ld/cW5V7ZWY/kvRFSVsk7Sqpl6Q/\nSDpGUs7dq81soKRH3H2kmU2U5O5+VTb/g5Imu/szjSzbW9suAAAAoCXMTO6e7DN/rR4K4u5XuPtQ\ndz9A0tmSZrv7eZLuk/SVrNqXJd2TTd8r6Wwz28nMhkk6UNKcVrccAAAAKCGtHgqyDVdKmm5m50ta\nofgmELn7IjObrvgGkRpJF3FbGgAAANuLVg8FaU8MBQEAAEB7K5mhIAAAAAAKCNYAAABAAgRrAAAA\nIAGCNQAAAJAAwRoAAABIgGANAAAAJECwBgAAABIgWAMAAAAJEKwBAACABAjWAAAAQAIEawAAACCB\nkg3W7p3dAgAAAKDlSjZY19V1dgsAAACAlivZYF1b29ktAAAAAFquZIM1d6wBAADQlZRssOaONQAA\nALqSkg3W3LEGAABAV1KywZo71gAAAOhKSjZYc8caAAAAXUnJBmvuWAMAAKArIVgDAAAACZRssGYo\nCAAAALqSkg3WGzd2dgsAAACAlivZYH3wwdJ993V2KwAAAICWKdlgLUmVlZ3dAgAAAKBlSjpYAwAA\nAF1FSQdr985uAQAAANAyJR2sAQAAgK6i1cHazAab2Wwze9HMFprZxVl5XzObaWYvmdlDZtanaJ5J\nZlZhZovNbExz6+CONQAAALqKttyx3iLpMnf/kKTjJU0ws4MlTZQ0y91HSJotaZIkmdkoSWdJGinp\nNEnXmZm1pfEAAABAqWh1sHb3Ve7+XDa9SdJiSYMljZc0Jas2RdIZ2fQ4SdPcfYu7L5dUIWl0a9cP\nAAAAlJIkY6zNbH9JR0h6WtIAd6+WInxL6p9VGyRpZdFsVVkZAAAA0OW1OVib2R6S7pJ0SXbnuuHI\naEZKAwAAYLvXoy0zm1kPRaj+rbvfkxVXm9kAd682s4GSVmflVZKGFM0+OCtrQpnuv1964w0pl8sp\nl8u1pakAAADYwZWXl6u8vLzdlm/ehq/eMLOpkta4+2VFZVdJWufuV5nZ5ZL6uvvE7MOLt0k6TjEE\n5GFJB3kjDTAzl1w/+IH0H//R6uYBAAAATTIzuXuyL9NodbA2sxMk/UXSQsVwD5d0haQ5kqYr7k6v\nkHSWu/89m2eSpAsk1SiGjsxsYtmeH0FSUyP1aNN9dQAAAGBrJROs21NxsN68WerZs5MbBAAAgO1O\n6mDNf14EAAAAEij5YF2CN9QBAACArZR8sK6r6+wWAAAAAM0r+WDNHWsAAAB0BQRrAAAAIAGCNQAA\nAJAAwRoAAABIgGANAAAAJECwBgAAABIgWAMAAAAJlHywrq3t7BYAAAAAzTMvwVvCZuZSoV0l2EQA\nAAB0cWYmd7dUyyv5O9YAAABAV0CwBgAAABIgWAMAAAAJEKwBAACABLpEsH7xxc5uAQAAALBtXeJb\nQSS+GQQAAABp8a0gAAAAQAkiWAMAAAAJEKwBAACABAjWAAAAQAIEawAAACCBLhOsX3mls1sAAAAA\nNK3LBOsjj5TWrOnsVux4vvUt6V/+pbNbgR3FAQdIf/5zZ7cCkNau5WteAbx/XSZYb9woHXtsZ7ei\n7daulVas6OxWtNx110m/+lXL6q5ZwxuRJD3wgFRX19mt6JqWLZMefbSzWwFIe+8tTZ3auW144gnp\njjs6dp01NdKGDR27zrZ6/XXJkn0LMdA2JRusf/vbrcsqKzu+HamNHy/tv39nt6Ll3k9Q3mcfadq0\n9mtLVzF2rDR3bme3Atujd96RXn21/Za/fHn88l/qbrxRWrSo/dfzt7+1/zq25Wtfk84+u2PXeeml\nUp8+6Zb3k5+0/1+bq6vbd/mlaOZM6eabO2ZdZtLs2R2zru1ByQbrceO2LtuyRZoxI6Z//GNp8OC2\nrWPBAmn+/LYt4/0qftN64432u7O5cWNsX15dnVRbu3U9M+kvf2nZMs2kefPql82eXX+oSMML3Dvv\ntP7uR21t+9/5nTpVWrgw/XLfzy8kjz3W+LFJobJSuvXW+mXf/KZ0ww3ts7689eulCy9s3bz81aNp\n3/++9MEPxvQrr8R+luIu41tvbXvemprmlz9sWPzy39EeeCCu7y114YXSj37Ufu3pampqpCVL0iyr\noiLNcvIuv1y6++60y9yWefOkCRPadx0vvfT++mtbNbauCRPiF6/2NH++dMYZMV2cJ9riySelf/xj\n6/Lnn5eqqtKso7N1eLA2s1PNbImZLTWzy5uq17t34+Wnnx4/y8tbdxCefFL6+tdj+qij4tHQunXS\n22+3bHmvvBL1i23cWAgH+Te+vOLQ0L+/1L27tGlTy9bV0LvvRmitq5P22EN67bXCa5deKh1+eOH5\n+edLBx1Uf/6bboqfixe3fJ0N78TeeGMMFVm2bOu6S5dKZ54pDRnS/HI3b9667JBDpHPPbXnbzjtP\nuvPOlteXpC9/WbriisZfq6ysf6fFTPr737euN2lS3OlraPNmac6c5tvwsY9JPXrE9KZN9Y9jQ3V1\njW/jli2N32n82c/i2D/0UKHshhuka6+tX6+2Nn4JasyKFY3/mXXJkq37d94zz0TfyG/LypUt/1Pt\nf/5nYXrNmgiMY8fWHyLiHm9uzbnnnvicQEMPPSRNnFi/7OWXtx1OR4yQ/vu/6/eJJ5+Upk9vvh0t\naWdL7urlj/HixdKBB0rnnBPPv/pVacCAuB6tXFmonz+uy5ZJO+207WU/+2z8bHiX9p134lFX1/gv\nPU8+2fZx8WPHSo88su06s2bF9W716njes2fb1tkS7nHOb+uXvbPPlq6+OqYrK7fdh97PndWqqsbD\ncmMh6/rrpZEjW77s1pg7d+ttW7lS+q//arz+7NnSKafEdGPX96qqwjXBPYa95N/TX3yx5e1au7b+\njYkpU2II4/nnNz1Pw/768suFm3a77irdf3/j8735pvTUU9LBB0u33NLyNjb0i1/E+TR1asv+OtCz\nZ7Tv5JNbdiOourrxv/C7b/3+Ulcn3Xtv48t58MG4NklSt27v75eJ555rvPyEE6Sf/jTyQbEjjpA+\n97mYrqx8/0N7/vGPpt/DOpy7d9hDEeRflrSfpJ6SnpN0cCP13N09usHWj698xX3MmJhessT9pZf8\nPaef7r5smfuWLV7PM8+4P/CA+wUXxHzFy3/33cLzPfaIn8OGRdkf/+i+aZN7ba37mjVRtn69e1WV\n+333Rd1TTims5/XXo6xbN/cnn4zpTZvitRUrCussXv9Pf1qYv67O/eijo/11dVFWWen+ve+5v/NO\nlJWVuX/xi+6f/7z7TjsVlvPUU7EtNTXuPXsWyt96y/2gg2L6oIPcv//92Gf513/969i+xjS2//Ne\ne839jDOirG/f+Hn11e6rV7vPn7/1PM8+W9imjRvdZ86MbZ89u/5y8/shP+8XvuC+YYP7Qw894lu2\nuE+d6v7441Fn0aLY5zU1UffUU93/7d/c7703+kFd3dZ9Ie/qq2OeT32qUHb33e5DhtRve/5YSO6L\nF8f+K26v5P7tb7v/8pexH/PHwiymv/EN98cei7rvvhvb0tQ+/sxnGt8X+f22cmW8nn/ecP45c9z/\n/OfCa9/+duG1fv3cly8vPL/7bve//z3q/fM/R9nSpfXXPWKE+x13xGuXXRZ9sLh/jRsX21RbG8t4\n442Y7/776x/7v/wlpl9+Ofb3vfe6//Wv7ps3u//iFzH/kiWFeTZurH++SO4nnFA41/PLd3e//PJH\nfPPmxo9xft5Nmwrnobv72LGF+evqYhsk9wkTYh+uXx/H76ijtm6H5P7EE+6/+lWhr+S3u6LCfdas\nWM7mze6//737n/7kvm6d+/PPF47b738f/b6yMs7n/HLfeSeW4R775PXX3e+6K649a9e6f/3r9dvx\noQ9F3cMOq1/+rW/Fui65pH75VVe5/+Y37p/8pPvZZxf6VPH5ln8MGRLH+uij3Y87zn3AgDi3Lr00\nXn/00Zi/V694vnp1tHn69Oh3Dc+7V17xeseprs79vPMK6/7zn9179456TR3LL39563Z26xavn3tu\nXHPr6tx/8YtH/Kmn3G+/Pepce23U2bAh2lddXX/Zt98efXTmzMK+l9xPPnnr615j7TrkkEI/ye/X\nTZvieN54Yyzvqafq77eGZs92/9GPYjqXc//hD+tfA3fbrfD8H/+IPrd6ddT/wQ+abuMrr8Rrf/1r\n468vWRL9ffLk2Ib8e+s3v+n+pS/FufKv/xplkyYVrr3Dh7tfcUVhvZWV9a+X559faO/Qoe533uk+\nY0ZhvU88UbiW/exnMf2JTxT2/YoVce689Zb7Cy/EeXD88e6/+12cT+vXu69aFXXPPDN+1ta6d+9e\nWO///m/UzfvjHx/xW26J1/Lv35dfXv/8kwrble/D+fO2uN9973uF8z5vy5ZYxrbkt2/dusbfX996\nq7C+DRsia0jx/iK5n3VW/fqVle6vvhr7w72QcXbeOX7OmBHXy/HjIwMV95OJEwvL2bDB/Z573H/7\nW/cHH3S/4YY4b/Kv569Ty5a5T5vmft11cZ0ufi/Ky7e5eJvz+7JhLqitjUyXP49+/OM4R/LvqzU1\nUe+OOwrvo8Vuv939mmvcP/CBOG+qqtznzat/vXd3v+km9732ijovvVQ/82SZU6keyRbUopVJH5b0\nQNHziZKyRbxEAAAL7UlEQVQub6Teexv88Y833vkaPs4+271Hj8LzgQPjjaCp+p/9bP3n+bBU/PjO\ndwrLam79xxwTYbwlbW3qccABbZs/xeOyy+Lnscdu/WbdXo/+/Vtad3Kb17XHHoVtbPi44YaO28+7\n796yepMnF+p+7nOF8kMOiTeuQw9tet7zzougUlzWr9/W9YrfiPKPz362ELYbPkaO7Lj91Njje98r\nTA8dGv3i6KOLn8fjE594/8veZ5/Wt+vnP29Zve9+tzBd/Atw8eOQQ9q+n/LhoD0f27rGnntu65d7\n+umF6XPOKQTHlj8mN1vnkEPiOnfOOfXLhwwp3IgofhxwQATAq68uBPamHvl+mD++F14Yv9DkX580\nKd7g88+Lf7kaMaL1+y1/HkjR1nzIyj9Gj45fkvLPW9vf86Et/2h4TTj++Kbn/fSnW3Y899679fuh\nLf2i+HHqqS2r9/GPu992W+F5vv/kM8E11xR+Ac0/vva1bS9z1Kj22P54jB+/9TFs66M4LxxzTON1\nim/05B/tmXvy53afPlu/ttdesR/iuZIGa8uCbIcws3+WdIq7X5g9/6Kk0e5+cYN6XtyulSuloUM7\nrJkoWWXZAyhWJvoF6isTfQJbKxP9AlszuXuy75XpkWpB7WnIkOx3CsVYqpoaaeedYxzOqlVS374x\nXnnnnWNczpIlMWa1f//4sM8++8RY5pEj4xP1o0bF+MSVKwtjpXK5GIdYWyvttpv0kY/EOOs774x1\nnHlmjC967bUYE/vcczH2bt9949PpCxZIe+4Z43xXr46xWIMGxbjMp5+O17p3l/r1izFcW7ZE+/v2\njXqzZ0snnRRfG3TQQTFO+8EHo12bN8fYwmOPjXHVa9fGOMgPfjA+iFZTI3360zFWql+/2PaPfCSm\nH3ss1uEery9dGuPYPvOZWMaIETFGcvToWPYVV8SYud69pV12iQ+yDBsW6+/bN9a1dGnhwwd/+IP0\nxS/GOLGKihj/lR+Hd+SR8ZVVhxwSY+K7d48xUPvsE9uw554xxv2JJ+JYLFwY48IHDIht69Ej1v36\n67Gvfv7zmOekk+IDl+PGxXH81a9ijPlll8WxGjgwXneP8WHnnBN9YuPG2F8LFkT9XXaJD5Aefngc\nj6eeiuO2997SiSfG8X/77VjmsGExzm3+fKlXr9gfvXvHMvfdN47hq69GWX45Y8fGNr/xRiz/xRej\nH23aFP3y0ktj3OuCBbGPamtjucOGxba/+GKMVV6yJMYJ77lnHLOFC6V/+qf4kM4HPhAf/Hr+eemj\nH43jOnRojO3efffoA336xFi+wYPj2J98chy/4cNjbPicOXFuHHus9Kc/Rfntt8f8n/tctKNPnzgu\nr78eY66PPDLOjbFjpWOOif0wf34cp/32i77Sr1/sl4qK2OY5c2Jf9OgR9U48MfrSRz8a7enePfrC\needFP6uoiG0ZOTLGdy5dGsd7xIhYX9++0gsvxLYdcUR8/qJXr2jr66/H44EHYiz1vHkx/vjMM2M5\n/frFdWXt2qhTXh7HsX9/acyYaPePfhR9Y9y46E9jxkQf+OEPow+eeKL0y1/GMbvgghhTvnlzjEX8\n/OdjHcuXx/mwfn201yzGR48aJR12WIyrfvTR2Cfl5dJnPxttra2VTj019sno0fE5joUL47zYuDHO\n5Zkzo18sWxbXmDFjoq/utlv0ldpa6de/juMtxXVr3rzY5l12kT784einGzYUrqnDhsV0t25xzP76\n17i+HXpo9NOBA6VPfCLGcD7zTFxnFi2KY3LAAdGuxx6LNnbvHte6RYuk44+Pc2DVqthGs7gWrFkT\n41uPOSbmGz48rnsnnRTbXFsbYzLXr4/nb74ZfWjkyPh2pbfeiu3ZsCEeQ4bEsr/7Xem446RPfSqW\nW14e+/OFF6JP9+oVx/Kuu+KcOPPMKDOLsdwTJsR1cdSo2A9vvhnXrd13j/ovvyxdeWWcx4cfLv3x\nj9GeF1+Mz/CsWyc9/nhcUw87LPrMsmXSxRfHPnvllZjvpJPiHBo5MvZPv35x3Xr77Zj3xBPjmL/x\nRlwreveO7Vy/Pt4namqin994Y1yHe/aMc2vo0OjXJ58c9UeOjPnfflu67bY4zmvWxAfT8tfeYcPi\nnNtjj3hfXLo01r9kScy/dm2UDRkSx/QLX4h2dOsW45qPOCKO/4c/HJ872Hvv2L5nn43r1N/+FsfX\nLB7u8T7au7e0115x/ayri+3Jnyv77Sf94AdxfrnHmO3586Ots2ZFv3z++ehnFRXR7484IvrM734X\n+6F371j/q69K//M/ccyWLo35jjgi+u6BB8b8fftGP6ioiPNozhzp6KPjeC5fHvXXrSu8j/XoEete\nvDj6qllcy2tq4nq5dGns327doq994xtxnj37bPTJsrK4Lv/97/WzwXPPxX7YY494j6yujnUcdlh8\ny8oFF0TfzH+2ad99Y/7Vq+NY9uoV9fPna7fs03Tz5kX7H300zodHHol+ccwxsY4TTojz9tZbo29U\nVMQ51KNH9MH+/ePc3n336BdLlsT+2Wuv2M6ePeM68OqrsR377BNt2rQp2rR6dfSd/v1jXRs3xrW/\nR484p3bdNZa9bFkci549Y7m1tdHfvvOdOF79+8e5tv/+Me8JJ8S+f+GFOG4nnBDfLNenT/Tl+++P\n62OfPtGOt96Ka+Ddd8e+nzQpbWbt6DvWH5ZU5u6nZs8nKm7BX9WgXsc1CgAAADuslHesOzpYd5f0\nkqSPS/qbpDmSznH39/G9FAAAAEDp6dChIO5ea2bfkjRT8Q0hNxOqAQAAsD3o0DvWAAAAwPaqpP7z\nYkv/eQy2D2Z2s5lVm9mCorK+ZjbTzF4ys4fMrE/Ra5PMrMLMFpvZmKLyo8xsQdZvruno7UBaZjbY\nzGab2YtmttDMLs7K6Rs7KDPb2cyeMbP5WZ+YnJXTJyAz62Zm88zs3uw5/WIHZ2bLzez57JoxJyvr\nkH5RMsHazLpJ+oWkUyR9SNI5ZnZw57YK7exWxfEuNlHSLHcfIWm2pEmSZGajJJ0laaSk0yRdZ/be\n/2a6XtIF7j5c0nAza7hMdC1bJF3m7h+SdLykCdm1gL6xg3L3dyX9k7sfKekISaeZ2WjRJxAukbSo\n6Dn9AnWScu5+pLuPzso6pF+UTLCWNFpShbuvcPcaSdMkje/kNqEdufvjkhr+U+zxkqZk01MknZFN\nj5M0zd23uPtySRWSRpvZQEm93D3/z9anFs2DLsjdV7n7c9n0JkmLJQ0WfWOH5u5vZ5M7Kz4f5KJP\n7PDMbLCksZJuKiqmX8C0dcbtkH5RSsF6kKSVRc8rszLsWPq7e7UUAUtS/6y8Yf+oysoGKfpKHv1m\nO2Jm+yvuUD4taQB9Y8eV/bl/vqRVkh7O3uzoE7ha0r8rftHKo1/AJT1sZnPN7GtZWYf0iy7xD2Kw\nQ+PTtTsoM9tD0l2SLnH3TY18vz19Ywfi7nWSjjSz3pL+YGYf0tZ9gD6xAzGz0yVVu/tzZpbbRlX6\nxY7nBHf/m5ntI2mmmb2kDrpelNId6ypJxf+4fHBWhh1LtZkNkKTszzCrs/IqSUOK6uX7R1Pl6MLM\nrIciVP/W3e/JiukbkLtvkFQu6VTRJ3Z0J0gaZ2avSvqdpJPN7LeSVtEvdmzu/rfs5xuS/qgYbtwh\n14tSCtZzJR1oZvuZ2U6SzpZ0bye3Ce3PskfevZK+kk1/WdI9ReVnm9lOZjZM0oGS5mR/znnTzEZn\nHzb4UtE86LpukbTI3X9WVEbf2EGZ2d75T/Cb2a6SPqkYe0+f2IG5+xXuPtTdD1Bkhtnufp6k+0S/\n2GGZ2W7ZXzxlZrtLGiNpoTroelEyQ0H45zE7HjO7XVJO0l5m9pqkyZKulHSnmZ0vaYXik7py90Vm\nNl3xye8aSRd54UvYJ0j6jaRdJM1w9wc7cjuQlpmdIOlcSQuzMbUu6QpJV0maTt/YIX1A0pTs26O6\nSbrD3WeY2dOiT2BrV4p+sSMboBgu5oqce5u7zzSzZ9UB/YJ/EAMAAAAkUEpDQQAAAIAui2ANAAAA\nJECwBgAAABIgWAMAAAAJEKwBAACABAjWAAAAQAIEawAAACABgjUAAACQwP8HLDUVdXd/+lAAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f71456eb6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.figure(figsize=(12,6))\n",
    "# plt.plot(np.convolve(movs, np.ones(2000)/2000, mode='valid'))\n",
    "# plt.plot(np.convolve(np.array(movs[::10]), np.ones(10)/2000, mode='valid'))\n",
    "plt.plot(movs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7145fae2b0>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6gAAAFwCAYAAAC1s1ZDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7145b86f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "win_size = 500\n",
    "smoothed1 = np.convolve(movs, np.ones(win_size)/win_size, mode='valid')\n",
    "plt.figure(figsize=(16,6))\n",
    "plt.plot(smoothed1)\n",
    "# plt.plot(np.convolve(np.array(movs[::10]), np.ones(10)/2000, mode='valid'))\n",
    "# plt.plot(movs[::10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The double smoothing is used for when there is a lot of variation in the signal, usually for a very small learning rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        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": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "smoothed2 = np.convolve(smoothed1, np.ones(win_size)/win_size, mode='valid')\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(smoothed2)\n",
    "plt.show()\n",
    "# plt.plot(np.convolve(np.array(movs[::10]), np.ones(10)/2000, mode='valid'))\n",
    "# plt.plot(movs[::10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show the clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWEAAAEACAYAAABiV8coAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkf8CLoEqLl67PAMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7145008630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "for j in range(size_k ** 2):\n",
    "    plt.subplot(size_k, size_k, j + 1)\n",
    "    cs = de_standardize(all_cs[5000-1][j, :], data_mean, data_std)\n",
    "    plt.imshow(np.reshape(cs, [28, 28]), cmap='Greys')\n",
    "    plt.axis('off')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}