fine-tuning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#%matplotlib notebook\n",
    "%matplotlib inline\n",
    "import torch as th\n",
    "import data_handler\n",
    "import dilated_model as DM\n",
    "import trainer as T\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "#import seaborn as sns\n",
    "import numpy as np\n",
    "from trainer import ema\n",
    "import os\n",
    "#sns.set_context('poster')\n",
    "#sns.set_style('whitegrid')\n",
    "plt.rcParams['figure.figsize'] = 14, 7\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = '0'\n",
    "#sns.set_palette(sns.color_palette(\"Paired\", n_colors=8, desat=.7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 3704896                             saved/RELU-16-quart/0001/trainer  MAX: 0.8226732015609741\n",
      " 3704894                             saved/SELU-16-quart/0001/trainer  MAX: 0.8264732956886292\n",
      " 3704900                        saved/ADAM-SELU-16-quart/0001/trainer  MAX: 0.8476480841636658\n",
      " 3704896                        saved/ADAM-SELU-16-quart/0003/trainer  MAX: 0.8526226878166199\n",
      " 3704894                        saved/ADAM-RELU-16-quart/0001/trainer  MAX: 0.8335200548171997\n",
      " 3704896                        saved/ADAM-RELU-16-quart/0003/trainer  MAX: 0.8002257943153381\n",
      " 2885849             saved/RESNET-quarter-K4N1-selu-adam/0001/trainer  MAX: 0.8011949062347412\n",
      "  644591           saved/FREQ-SKIP-FCN-quarter-selu-adam/0001/trainer  MAX: 0.9108186960220337\n",
      "  644591           saved/FREQ-SKIP-FCN-quarter-selu-adam/0002/trainer  MAX: 0.6692318916320801\n",
      " 2769684             saved/FREQ-ENCODE-quarter-selu-adam/0001/trainer  MAX: 1.0480226278305054\n",
      " 2769684             saved/FREQ-ENCODE-quarter-selu-adam/0002/trainer  MAX: 0.8072293400764465\n",
      " 2769684             saved/FREQ-ENCODE-quarter-selu-adam/0005/trainer  MAX: 0.8525879383087158\n",
      " 2769682             saved/FREQ-ENCODE-quarter-selu-adam/0006/trainer  MAX: 0.8055412173271179\n",
      " 2769684             saved/FREQ-ENCODE-quarter-selu-adam/0007/trainer  MAX: 0.8079320788383484\n",
      " 2769684             saved/FREQ-ENCODE-quarter-selu-adam/0008/trainer  MAX: 0.83958500623703\n",
      " 5047178                 saved/FREQ-ADAM-SELU-19-quarter/0001/trainer  MAX: 0.8838381767272949\n",
      " 5047178                 saved/FREQ-ADAM-SELU-19-quarter/0002/trainer  MAX: 0.7260318994522095\n",
      " 3712510                 saved/FREQ-ADAM-SELU-16-quarter/0001/trainer  MAX: 0.9467489123344421\n",
      " 3712510                 saved/FREQ-ADAM-SELU-16-quarter/0002/trainer  MAX: 0.7119004726409912\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<trainer.Trainer at 0x7f5cdc47d4e0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from glob import glob\n",
    "quart_trainers = []\n",
    "for t in glob('saved/*quart*/**/trainer'):\n",
    "    print('%8d'%os.path.getsize(t[:-7]+'/state_dict'), '%60s'%t, end='  MAX: ')\n",
    "    quart_trainers.append(th.load(t))\n",
    "    print(quart_trainers[-1].test_highscore)\n",
    "quart_trainers.sort(key=lambda x: x.test_highscore)\n",
    "quart_trainers.pop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 3712510                              /FREQ-ADAM-SELU-16-quarter/0001  MAX: 0.9467489123344421\n",
      "  644591                        /FREQ-SKIP-FCN-quarter-selu-adam/0001  MAX: 0.9108186960220337\n",
      " 5047178                              /FREQ-ADAM-SELU-19-quarter/0001  MAX: 0.8838381767272949\n",
      " 3704896                                     /ADAM-SELU-16-quart/0003  MAX: 0.8526226878166199\n",
      " 2769684                          /FREQ-ENCODE-quarter-selu-adam/0005  MAX: 0.8525879383087158\n",
      " 3704900                                     /ADAM-SELU-16-quart/0001  MAX: 0.8476480841636658\n",
      " 2769684                          /FREQ-ENCODE-quarter-selu-adam/0008  MAX: 0.83958500623703\n",
      " 3704894                                     /ADAM-RELU-16-quart/0001  MAX: 0.8335200548171997\n",
      " 3704894                                          /SELU-16-quart/0001  MAX: 0.8264732956886292\n",
      " 3704896                                          /RELU-16-quart/0001  MAX: 0.8226732015609741\n",
      " 2769684                          /FREQ-ENCODE-quarter-selu-adam/0007  MAX: 0.8079320788383484\n",
      " 2769684                          /FREQ-ENCODE-quarter-selu-adam/0002  MAX: 0.8072293400764465\n",
      " 2769682                          /FREQ-ENCODE-quarter-selu-adam/0006  MAX: 0.8055412173271179\n",
      " 2885849                          /RESNET-quarter-K4N1-selu-adam/0001  MAX: 0.8011949062347412\n",
      " 3704896                                     /ADAM-RELU-16-quart/0003  MAX: 0.8002257943153381\n",
      " 5047178                              /FREQ-ADAM-SELU-19-quarter/0002  MAX: 0.7260318994522095\n",
      " 3712510                              /FREQ-ADAM-SELU-16-quarter/0002  MAX: 0.7119004726409912\n",
      "  644591                        /FREQ-SKIP-FCN-quarter-selu-adam/0002  MAX: 0.6692318916320801\n"
     ]
    }
   ],
   "source": [
    "quart_trainers.reverse()\n",
    "for t in quart_trainers:\n",
    "    print('%8d'%os.path.getsize(t.path+'/state_dict'), '%60s'%t.path[5:], end='  MAX: ')\n",
    "    print(t.test_highscore)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "        return MozWebSocket;\n",
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       "        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",
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       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
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       "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",
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       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
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       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
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       "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",
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       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
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       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
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       "    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",
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       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
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       "\n",
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       "\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",
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       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#%matplotlib notebook\n",
    "ALPHA = 0.02\n",
    "#sns.set_palette(sns.color_palette(\"Set1\", n_colors=14, desat=.8))\n",
    "def add_plot(T, alpha=ALPHA):\n",
    "    plt.plot(ema(th.cat(T.test_F1)[:, -1], alpha), \n",
    "             label=T.path[6:-5]+' max: %.4f'%T.test_highscore)\n",
    "    \n",
    "plt.close()\n",
    "plt.title('TEST F1 (mean), ema=%.0e' % ALPHA)\n",
    "for t in quart_trainers:\n",
    "    add_plot(t)\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('Test F1 accuracy')\n",
    "plt.legend(loc='lower center', ncol=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  WINNER IS\n",
    "## A : = ADAM-SELU-16-quart/0003                            **(100 min)**\n",
    "## B : = FREQ-ENCODE-quarter-selu-adam/0005     **(65 min)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "A = quart_trainers[3]\n",
    "B = quart_trainers[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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9OupBGX5eJMqqwDYsKF8fLNm2foctMfFvfdW9vrjyADQlGGVFgC32KL45yk0hwFgti/If/4ZbwuYiPmqq3Q2AwboY3jato3/OXXooLerBmX5eJMqqwC4/vry8Vtska3PQXtc3HdfZa8/jgwATRlWaQGwWbPin6fQHEstpbMxAExXU6bEB8Arr9TbnH66zuaww+J/TiecoLerBmX5eJMqqwC43nry8Vtuma3PQXtc3H9/Za8/jgwATRlW5XkAqxEAe/XS22TpROoXADDOFvDll+tttNuyhx8e/3M67ji9XTUoy8ebVETMJ59c7lXkioh5nXXk47faKlufg3kAC2v//Q0ATZmVAWCpZADoNHlyugCohTIDQL2yfLxJlVUAXGst+fgWLbL1OdQ1AJw3j/mii1xBa6kMAE0ZVvUDYFqFoA0Anf74I/4WcNYB8Pjj9XbVoCwfb1IRMZ9ySrlXkSsi5jXXlI/fZptsfQ51DQC//daN13RhMgA0ZVgGgKXScccZADIz//57ugCohbJiYgANACtXcQDw66+TWQtExLzGGvLx222X7OcwaJCuhWNdA8Aff3Tj//pLbmMAaMqwDABLpWIA8Kab9DZZOpH6NWmSAWC1KWvH2/TpzF99pbPRAiB6Wo8apZtHu6bVV5ePTxoAtZ+zdnyll4GJA4Bt2hgAmjKr6gfAPn0MANOUAWC2NXcu87nn6uKYkj7eBg9mnjBBPv7yy/XrIWI+9VT5eBQ0Hz5cN492TauuKh9vAFhaxQXAqVPlNgaApgyr8gBw6aV1NgaA6WrixGwD4KGHxv+cqqEMzDffuNcyb57cJunjjciVOJHq1FOrBwAbNJCP1wJgTY0Dfs16kgTA//wn2eOofn3ms86Sj9euf/hwA0BTVam0AKg5icedwwAw2wA4YYIBYJY1eLB7LXPmyG3SAMANNpCPryYAXHll+XgtAGrbYFY6ABIxN2miGx8HAP/8U25jAGjKsAwAwzRzposB0ujYYw0AmZl/+82tbehQnV1cANRCWV0HwDiZjGkAoKYlWjUB4EorycdrAbBLl7oHgI0b68bH2QKeMkVus99++tc8aZJomAGgqVgZAIapVSv9PAaATgDAtDyABoA6DRniXsusWXIb7fH2zz86L4kWAE87LR4AnnaafHw1AGDXrnUPADfcUDc+DgBOniy30QLg7Nlu/NixBYcaAJqKlQFgmOrX19u0bx8fLG6+WW+TVQAcPz5dANT2502zF/Arr7iM1Szpu+/ca5kxQ26jPd6aNNElaxHpeuIaAMp0yinZAsB77tGNf/pp5p120q1HE0oQdwv4jz/kNloAnDXLjf/mm4JDDQBNxaryAHCZZXQ2t95qAJimxo3LtgcwTQAkYr7kEv1cSWroULcuzXc+aTAg0vXENQCUSbtVnjUA1PbR1R5HWQbAL74oONQA0FSsSguAzZsX/zyF5tB6AOMA4PLLGwDG1a+/urWl1Qou6wDYvbt+Lo0aNtQdPz/84NalqWWWBgCuu658/OmnGwBKpH2fsgaA2nANrSc57hbw77/LbVq31s0xc6YBoCk1GQCGyQAwvn75JV0AzPIWMBHz2Wfr59LO0bKlfHxWAXCddeTj4wLg6afLx8cBwDFjmBcu1K1JA4Dbblu3AFDbtpFIF0saFwCFSRrMrAfAGTPc+C+/LDjUANBUrEoLgFttVfzzFJpDuwXcu3f2AfCWW/Q2WQdATUspZgPAuNJ+7wCAmlpmaQDg2mvLx8cFwDPOkI+fOlUPgESu9IpmfF0CwLvvrmwAxBbwxIlyGy0ATp9uAGhKTQaAYTIAjK+xY93aBg/W2cUFwI4ddTaHHFK3AfD777MJgGl4ADUA+Oef8QDwggt045MEQG2spAFgfgEANV1rtACIFoQGgKYUZAAYpjgAeMwxBoDMbhuMSJTFlqNKAEDtXETM55yjn0s7h+Z7hySQSgbAM85IHgCnTIkHgJrPm0hXCLrSAfCuu+oeAO67r26Of/5x4wW9rg0ATcXKADBMBoDxNXp0ugDYqZPOJm0AzJoHEACordNXVwHwxx91c2gBMEkPYNaygCsdABED+NtvchstAP79twGgKTUZAIYpbQDs3Vtvk3UA/PprnV0leAC1sKkFwOnT3funncMAUDbHmWfKx0+eHM8DeO65uvEGgNGKA4Bp1AEcP15uowVAJB8ZAJpSUPkA8PzzXaFc7RzLLquzueUWA8A0NWqUW9ugQTq7tADw4IPTBUCNR0h7wcMcW28tHw8A1LSzyhoAnnlm8gD4xx+VvwWctULQWgA87LBsAuC4cXIbLQAi+Uhw/jQANBWr8gEgEXOzZvo5tB7AtACwXTsDQGbmkSPTBUAtlGUZAHfYIXkARCcQA8D8+v13A8BSj7/zzsoGwGHD9AC4zz66OZB8ZABoSkGlBUDNhYiIuWlT/RwGgNkGwBEjxFsYOUoLAA86KP7ndOKJepusAqCmn2kaAKgpBJ0GAE6alM4WcF0CwDvuqA4A/PVXuU1cABSE0BgAmopVeQFw0031c2i3gG++ObsAWFNTfQD4889ubYIyBjmKC4BaKKtGAGzRQj5+yJBsAmAaHsCzzpKPnzjRALDU4ysdAFFD85df5DbaLWAkHxkAmkqkfkRUQ0T7hvytvADYpIl+DgNAA0C/TdYBUAMEaXgAAYCafqZZA8CzzkoeACdMiAeA552nG28AGK04ANiokW685vlRQ3PsWLmN1gOI5CMDQFMJdCIRvU1EiyiLALjRRvo54gJgTY3cJi0AXLTI2dx6q84uywD4009ubYJeljkyADQAlCouAHbrJh8PANSWgUkSALfZRve6Tz65sgEwTi/gJAEQCVRjxshttACI5CNBGS0DQFM+bUBEv9T+m00PYOPG+jmqCQAXLqw+AEStrM8/19kZAKa7BaxpaF8XAfC33wwASz2+b9/kAXDDDXXjNc+P+NkkARDJRwaApiL1NhGdXPt/A8BFi+Q2cQDw6KP1NgsWVB8AolRCVgHwwAPrNgB++23lA2C3bskD4Pjx2dsC1gJg167ZBEDpzXicLeA0AFBTq1MLgEg+ErTSNAA0RekscgAIpQOAmgtRXABcbjmdDQBw4UK5jQGg0/PP6wCG2QPAgQN1dnEBsHNnnU0xAKidK4sAOHiws5k0STdHNQBg9+7y8ePGGQCWevztt+sAMGseQHjPR42S2+y9t24OJB+ZB9AUU02IaCIRNfL9Li8AduvWjXv06ME9evTg/v37yw9Wv4jcCUozPg0AvOkmPQDWr58OAM6f72z69NHZpQWAm2yinwelEj77TGdnABgfADXfOwNAmX791dkMG6abo0cP3fgkAfCkk7IJgNLdmKwBILznI0fKbbQAiNjTCADs37//4mt1t27dDABNS6gzEc0loslENKX2UUNEfxPRfYGxpfUAagFw4431c8QFwAUL5DZpAeC8edkGwKZN9fOgVEJaANili87GANDZTJyomyNpkNDUAUwDAH/5JR0AbNBAPt4AsPB6kgRAfHdGjJDbaAEQsae2BWyKqfpEtH7gUUNExxDRqoGxdQcA58+X2xgAOjVrpp8HpRI+/VRnlxYAHnBAugCo2RI0AJSpe/fkAXDsWGfzww+6Oc4/XzfePIDRigOAmsoS2vV/803yAIjYUwNAUwmVThmYLAJgr17ZBcC5c53Nbbfp7NICwC231M+DUgmffKKzqwQA1M6VRQDERSxrAJjGFvDZZ8vHAwC//143hwFgtG67zY2XhuNkFQB//llus9deujkQe/rttwWHGgCailXdAcB58+Q2cQDwqKP0NnPmVC8Afvyxzi4tANx/fwNAIhdrpJmjrgHgmDHOZuhQ3RxaAExyC7hLFwPAQuM1z//11278Tz/JbbQAiNhT8wCaUlDdAcC5c+U2cQDwyCP1NrNnVx8AolSCAWB6ALjttvLxuIgZAObX6NHO5rvvdHNccIFufF0CwD593HhpPHbbttkCwEGD3HhNbUgtACL21ADQlILKC4BxWsEZAGYbAFEq4aOPdHZxAfCkk3Q2Wd4C3nHH9ADwt990c2QJAOPGAGoAcNQoZzNkiG6OLAFg584GgIXGa57/q6+SB0CEHlgZGFMKKi0Aai5EcQFw+eV1NgDAOXPkNmkB4KxZzub223V2aQHgFlvo50GphKwCYDEeQO1cBoDy8WkA4DnnyMePHOlsBLFYOXMkCYAtWlQHAErjseNsAWtKi8UFQE1tyD331M2B0AMDQFMKqn4A7NkzuwA4c2a2ATBOFjAA8MMP5TY1NfEBsGtXnU01AuB228nHAwDHj9fNkTRIpJEFrCnJM2JE5QPgiSdWNgDG8QAmCYBffunGa0oDaQEQoQdff11wqAGgqVjVHQCcPVtuYwDoFAcAUWYkqwDYpk26AKipC5cGACKOyQAwv37+2dkIYrFy5jAAjNatt7rx0oS8rAHgF1+48ZrSQFoAROjBoEEFhxoAmopV5QFg/fo6mxtvzC4AzphRfQCILNMPPpDbLFpkAMicDgBiG2vcON0cdQ0Af/pJvBWXM8eFF+rGr7KKfLwWADt1MgAsNF7z/J9/7sZrSgNpARChBwaAphRUdwBw1iy5TZxewEccobeZPr18ADhpUuFg7M0318+DLcb335fbLFxYGQConSuLAIhtrKwBYBoxgFkEwCQ9gFkFQGlCXhwA1FSW0K5/4EA9AO6xh24OhB589VXBoQaApmJVXgDcZBP9HHEBcOZMuU1aADhtWvkAkIj57rvzj4kDgNhi1ADgggUGgMzpAuCvv+rmqAYA1MRj/vijsxHEYuXMcdFFuvEGgNHKKgBqakMaAJoyrNICoOZCZADoAWDfvjq7UgHgv/+df0ycXsAAwPfek9vMnx8fAE8+WWez3351GwARx1SXABAxphoAHD48HQCsS1vAvXu78dKEvEMOyRYAfvaZG6+pDakFQMSefvllwaEGgKZiVVoAbNlSN37TTfVzxAXAGTPkNsstlw4A/vNPeQAQF8Srrso/Lg4AIsZMA4DoiWwAGA8ANd87xDH98otujkoGQMSYagBw2DBnI4jFWqysAWDHjnUPADVhRdr1f/pp8gCI0IMvvig41ADQVKzKC4BxPIArrKCzueGGdADw8MP1Nn//XR4ARP3Ba6/NP26zzfTzYIvx3XflNsUA4Cmn6GzqOgBiG8sAML9++EG8FbdYcQAwyS3grAHgLbe48dKEvKwB4CefuPGa4uC7766bA6EHBoCmFFR3AHD6dLlNtQPglCnJASC2GAcMkNvMnVu9AKjpDZsGAGIba+xY3RxJg0SSWcBIMtLA+PffVz4AduhgAFhovOb5P/7YjdfUhtQCIEIPPv+84FADQFOxqrwt4DQAcNll0wHAv/5yNnfcobMrFgB//93ZF4oB3HTTdABwzpzKAEDtdnNaALj99vLxdREAkWSkAcChQ52NIBZrsYiYL75YN16zBbz11gaAhdZT6QCI0IOBAwsONQA0FatoAOzbV+eZyjoAaiA3LQCcOrU8ADhxorO/5JL84+IAIGLM3nlHboOeyGkAYOvW1QeAmu8d4pgqGQC7ddM9P5KMNJ9FNQDgCSdkCwBvvtmNl5bkigOAml0l7fo/+siN1xQH1wIgQg8MAE0pKBoA45wM0gDAf/1LZxMHAJdZJh0A/PPP8gDgb7/JLohxABAxZhoARExiXQfAHXZIHgARxzRmjG6OugaA333nbASxWIsluakKjk8yCaTSAfDgg7MFgB9+6MZrakPutptuDoQefPZZwaEGgKZiVV4A3Gwz+XjYxAXAf/6R21Q7AP76q7Pv1i3/uE020c+DLca335bboCVeHAA89VSdzb77Vh8AaraAsY2VNQBMMgkESUaaz2LIkOwBYKV7AG+6yY2XluTKGgB+8EHyAAjPswGgKQWVFgA1F6K0APD669MBwMMO09sgGePOO3V2xQLg2LEyoGnSRD8PthirEQC13sYsAiC2sUaP1s1RyQCIJCNNn95vv3U2gmD8xTIAzC8tAGZtC/kpK+4AACAASURBVPj99914TW1ILQDC8/zppwWHGgCailXdAcC//5bbLL10OgA4eXJ5ABANxzt2zD+uGADs319ug57IcQDwtNN0NlkGwO23jweAO+wgH49trLoEgEgy0gDg4MHORhCLtVhZA8Djj88mAEpLcmXNAwgA1NSG1AIgPM+ffFJwqAGgqVhVHgCuuKLOJssA+Mcf5QFAtBtq1y7/uDgAiBizt96S26AnsgFgegA4apRujqRBIsksYCQZaQDwm2/SAcBVV5WPNwAsvB5NXLl2/e+9pwfAXXfVzQHPswGgKQXVHQD86y+5TbUDIKrNt22bf9zGG+vnQYyZBgDREi/rAKjdbtZCRxoAiDgmA8D8+vprZyOIxVosIuZLL9WNr0sewF693HhpSa6sAeC777rxmtqQWgCE5/njjwsONQA0FavyAmDTpvLxsNEC4HXX6QFwqaXSAUDU40sbAFFrqk2b/OPiACBizN58U26DlnhpAOA++2QXAFu2TB4AsY01cqRujiwBoDYLGFnmF14ot6kGADzuuLoHgBqngnb9Awa48ZrSQFoAhOfZANCUgqrfAwgAnDpVbpMWAE6aVB4ARKmBPfbIP65x43QAEB1RDACTB0BsY1UDANbUyMYjyUgDgIMGORtBMP5iETFfdplufJJbwFkDwJ493XhpSa6seQDfeSd5AMSNx0cfFRxqAGgqVqUFQM2FKC4ArrSSziYtADz00PgAeNddOrtiARCBxjvumH9cHABEjNkbb8htigHA00/X2aQFgDU12QRAbGONGKGbI0sAiC3gRYtk45FkpAHAr75yNoJYrMXKGgAee6wBYKHxcQBQUxpol110c+DG48MPCw41ADQVq9ICYCGgCI6PswUcFwD//FNukxYAoiNH2gCIOJMWLfKP22gj/TyIMXv9dbkNWuJlHQA13sZFi/TQsd128QBQ873DNlYlAyA8gEkC4JdfZg8At9qqOgBQWpLroIP060lyC/jtt914TWkgLQDixsMA0JSCqh8Ar702uwA4YUJ5ABDbDIVOlnEAEDFmGgBES7xCvYmDigOAe+9tAEjE/PPPujmSBglNGRgA4MKFsvHIMr/oIvkc6GktiMVaLAPA/Kp0AOzfP3kAxI3HBx8UHGoAaCpW5QXAzTeXj4dNXACcMkVuEwcA27bV26AlW9oAiJPMeuvlH7fhhvEB8H//k9ugI4rGA4gt1qwC4MKFeuhIAwCxjVUNALhggWw8ssw1fXrR01oKgDgebQs4Wjfe6MZLS3LFAUCNU0G7/rfecuM1pYG0AIgbDwNAUwqqPABceWWdTRwArFcvXQC8+26dXbEAiItb/fr5x8UBQCQZaAAQHVE0AAgP2xln6Na3117pAOCCBXoA3HbbeAC4007y8djG+ukn3RxJg0ScLWApACLLXAOA6GktCMZnZu941BzDSXsA27c3ACw0PqsA+P77BYcaAJqKVd0BwMmT5TZpAeD48eUBwE8/ZV5+efccc+ZEj4sDgEgyeO01uQ06omgunvCwpQmAGm/j/PnZBEBsY1UDAM6fLxtfDAAKYrGY2QBQIvRllwLggQdmCwDffNON15QG0gIgbs7fe6/gUANAU7EyAAxTWgA4blx5APDjj5nXX989x8SJ0eMaNdLPgxizpAEQHrasAuC8edkEQHgxqgEA582TjUeWuQYAP/tMB4C4IckSAB5zTDYBUFqTNWsA+MYbbrymNFCrVro5cONhAGhKQXUHAP/4QzeP9kKsbVzOXD4A/PBD1+ZtpZWYf/ghelwxAPjqq3IbdETRJIHAw3bmmbr1pQWAc+fqAXCbbdIDwB9/1M2RJQBEGZi5c2XjkWWuadOGntaCWCxm9m5INMewAWB+ZQ0AX389eQDEjce77xYcagBoKlalBUDNhYiIuVkz+XjYaAHwmmuc3e+/6+ZJAwB//dXZpJ0E8t57rl5Wo0b5y1xssIF+HiQZvPKK3AYdUTTeE3jYsgqAc+bovU5pACC2sSoZAOEBlAIgssw1AIie1lIAxA2JFgBXW00+XguA7dplEwClNVnjAKDGqaBd///+58ZrSgNpARA3HgaAphRkABg1TxoA+Msv5QHAAQPcnXLz5vk7dsQBQCQZaAAQBbHTAMA999S/JmR4arabswqA2MYaPlw3RxYBMF/8ql9xABA9rQXB+MzsHY8GgNFCX3YpAB5wQDYBUFMaSAuAuPEYMKDgUANAU7EqDQDiAqndAk4TACdN0s2TBgCOHets0m4F178/8xZbMO+8M/Pzz0ePa9gwPgD26ye3QUFsDQBiizUNAIyTcTx7th46WrSIB4A77ywfj22sagDA2bNl41FmSPNZoKWhFABxPGZpC/joo7MJgNKarHEAUHNN0a7/tdeSB0DceLzzTsGhBoCmYlUaAMQFMg0PYIMGOhsDwCX15pvO+9e6NfNDD0WPiwOAyDLVACAKYmsAEB62s87SrS8OAMbJOJ41K5sACC/GsGG6OSoZAFFm6NJL5XOgpaEgGJ+ZvePRADBaaQBgkh7AV19146WlgZj1AIgbDwNAUwoqDQDiAplFALz6ameXL9s1bJ40AHDMGGdzxx06u2IB8PXXHWwccUT+uZEprBGSDF5+WW4TBwDhYcsqAM6cmR4AtmolHw8vRjUA4KxZsvHIMtcAIFoaCmKxmNkDwCuukM9BpNsCbt68OgBQWpM1awD4yituvDQznFkPgLjxePvtgkMNAE3FqjQAiAy4NLaAswqA2sblzMyjR5cHAF991ZUc6djRBWZHqRgAfOkluQ0KYmu8J/CwaQFwjz30rwnHt2a7Gf1nNQCo7fTArPcAAgDzZX+HzZFFAJw5UzY+DQDEDYkBYLTQlz1JAExyC7hfv+QBEMdd//4FhxoAmopVaQAQGXBaANxiC/l42MQFwAkTdPOkCYB9++rsigXAfv2YW7Z0QJPvorjeevp5kGWqAUAUxNZ4AOFh69ZNt744ABin5Az6z2YNALGNVZcAEGWGNACIloaCYHxm9m5IrrxSPocWALVbwEcdlS0A1NZk3X//bAKgNDOcWQ+AOO4MAE0pqDQAiAy4NABwlVV0NlkGwFGj0gPA33/3yma8+KL7rC66KD9Arbuufh5kmb74otwG9RA1AAgPmxYAd99d/5riZByj/6wGOuICoGYLGNtY33+vmyOLADhjhmw8ygxp+vSipaEUAHFDkiQAaj2AdREANdcU7fpfftmNlyYGMccHwLfeKjjUANBUrEoDgIh/0cYApgGAV13l7H77TTdPGgA4cqSzuf12nV2c9RG5CwKzy/zdeWd3Qu7cOdomLQBEPUTNBdoA0EkLgPBiVAMATp8uG48yQ5rjCy0NBcH4zOwdj1kCwCOPzCYASovyZw0AX3opeQDEjYcBoCkFlQYAEf9SlwFQ27icmXnEiHQBEFXyn32WedddmW+7zcUJRSkOAKLMyAsvyG3iACC2WNPYAo5Tcgb9ZzUAqN3iY9YDILwYQ4fq5qhkAIxTZggdbQTB+MzsHY8GgNGqdAB88UU3XpoZzuxutDVz4MYjX33WWhkAmopVaQAQ8S9ZBsDx43XzpAmAt92msysWAJ96ynnCHnjAnWSjtM46+nlQZkQDgCiIrYEleNi6d9etL44HEB5uDQCi/6wGauMC4C67yMfDi5E1AFxvPfl4AGDYeStMcQAQHW2kAIjj0QAwWtqi/G3aZAsAX3jBjZcmBjHrARA3Hm+8UXCoAaCpWJUGABH/ogXALbeUj4eNpm4Wc7YB8OefywOATzzhyqE8/bTzBEZp7bXjA2C+AtNBoR6iJmECHrY0toDjlJxB/9msAuB33+nmyKIH8J9/ZOPjlBlCQXNBMD4zewB41VXyOQwA86suAiBuPAwATSmoNACI+JcsA+C4cbp50gDAn35yNn366OziAiBqZD36KPM++7iEgG23jbaJA4AoM/Lcc3Ib1EPUtE2Dh03rAdxtt3QAEO3HNACovcAz6wEQ21jVAIB//y0bH6fMkBYAcUOSJQA84ohsASAS8qRF+eMAoOaaol3/88+78dLEIOb4APj66wWHGgCailVpABDxL9os4DQA8Morsw+At96qsyvWA/jww64LyBtvuItKlOIAIMqMaAAQ5XAuukhuAw/b2Wfr1hcHAOPUHET7sawBILwYQ4bo5qhkAESZIQ0AoqONIBifmb0bkquvls9BxLz66vLxBoCF15MkAD73nBsvTQxi1ieB4MbDANCUgkoDgNj+yCIAXnGFs/v1V908aQDgjz+WBwAffNCdXAcMYN5ss2ibYgDw2WflNiiHc+GFcps0ARAhDnEAULPtmAYAwotRDQD411+y8SgzpAFAFDSXAiCORwPAaFU6AD77rB4AtR5A3Hj8738FhxoAmopVaQAQ2x9pAKBmy4TZnfTTAMADD9TbDB/ubHr31tkVC4D33+/W++GHzI0bR9ustZZ+HtSZSxoAscWaJgBq4g3Rf1YDgFtumTwAwotRDQA4dapsfBwAREFzQTYmM3vHY5IAqD0+sgqA0q5M++2XTQCUJgYx6wEQNx6vvVZwqAGgqViVBgCx/aEFwHzbj1E2cQHwl19086QBgMOGlQcA773X1S387DPmhg2jbeIAIOrMPfOM3Ab1EC+4QG4DD1saABin5iDaj6UBgPkSeYICAH77rW6OSgZAlBnSfBaoZykIxmdm73jMEgAefni2ABDx2EkCoOaaol3/M8+48dK4UGYDQFOmVRoAxPZHFgHw8svTBcCaGrkNAPCWW3RzFQuAd9/NfOihzIMGuW3eKMXZAgYAPv203AblcM4/X24DD5sWAHfdVf+a4tQcRPsxjdcpDQCEF6MaAPDPP2XjUWZIA4CoZykFQByPWdoCzioASrsyZQ0An346eQCE5/nVVwsONQA0FavSACC2P7IIgJdd5uzGjtXNkwYA/vBDeQDwzjvdxWHIkPwxlXE8gCg0rAFAlMPp0UNuAw/bOefo1hcHAOPUHET7MQ0AbrFF8gAIL8bgwbo5kgaJOHUAp0yRjUeZIU1CDsoZCYLxmdkDwGuukc9R1zyASMirVAB86ik3XhoXyqwHQHieX3ml4FADQFOxKg0AYvsjDQDUnDCZ0wfARYvkNt9/72xuvlk3V7EA2LevqxE2bBjziitG28QBQNSZe+opuQ2yoc87T24DD1tWARDtx9IAwN12k4+HF6MuASDKDGkKjWsBEDckBoDRAgBKuzK1bq1fz1Zb6cZrnv/JJ5MHQHieDQBNKag0AIi73ywC4KWXOrsxY3TzpAmAN92km6tYAOzTh7ldOxd7t9xy0TZrrpkOACIbWgOA8LClAYBIctIAILpPZM0DCC/GN9/o5kgaJOJsAU+eLBuPMkMaAEQ9S0E2JjN7NyTXXiufQ3s+q/Qt4GoBQGliEHN8AOzXr+BQA0BTsSoNAOLud4cd5Ad6WgB4ySXpAuDChXKboUPLA4C9ezO3b+9io+rVi962jgOAKDT85JNyGwDguefKbdIEwDhFp+MAYLNmyQMgLmLVAIDSnrIAQE2nGZQzEgTjM7N3PCYJgFoP4GGHZRMApV2Z4gBgklvATzzhxkvjQpn1AAjP88svFxxqAGgqVqUBQNz9agFQc7cGGy0AXnyxsxs9WjeP9kJ8wAHOZsECuc133zmbXr10c8UFQHQCuekm5uOP91pkRa05zhYwCg1rABDlcDQwhy1WLQDusov+NcWpOYj39oor5DZpAuDXX+vmyBIAnnWWDgBRZkgDgChnJAVAHI9aAFxjDfn4SgdA1GRNEgCT9AA+/njyAAjPswGgKQWVBgBx8tNuAdd1ABwyJF0AhAewZ0/mDh28rfvZs8Nt4ngAAYBPPCG3QTa0BrDgYdN4DZnTA0C0H0sDADUxgPBiVAMASnvKosyQptUgstkF2ZjM7B2P5gGMFkpySbsy7btvtgDwscfceGlcKLMeAOF5fumlgkMNAE1h6kVEQ4loGhFNIKKniWiDiLGlAUCc/LQAuPXW8vGw0dwxM7v2YkTOC6CZR3shxhbw/PlyGwBgz566uYoFwBtuYO7UyYttC/v8meMBIDpNPP643AbZ0JotVnjY0gDAOEWn0X7syivlNptvnjwAwosxaJBujiwBILaApR0lUGZIA4DIZpcCII7HLAKgtDJBNQCg5pqiXf+jj7rx0rhQZg8ApZ8BAPDFFwsONQA0helGItqOiJYhd2A8RUTfRowtDQDi5JeGB1ALgBde6OxGjtTNE9cDqAHAb78tDwBeey1zly7O80cUnU0ZBwBRaFgDgEiG0dTZg4ctDQCMU3Qa3SeyBoDwYmQNADVZwPAASgsKo8yQptc0AFCQjcnM2QTAQw/NFgCiJqu0K1PWPIDFAKA0ORChBwaAphJpGyJaRESrhPytNACIi7EBIPO8eXKbwYOdzY036uYqFgCvvpq5a1eXsEIUXZerGAB87DG5DZJhNAAID5smc5g5HgDGKTqN7hOaLeA0ABAXsa++0s1RDQCoaTWIbHZBNiYze+fA666Tz6E9n8UFQCl8JP05oyRXpQLgI4+48dK4UGY9ACL04IUXCg41ADRJdDERjYn4W2kAEBfjLG4BX3CBsxsxQjdPXACcO1duAwC84QbdXMUC4BVXMJ96qvv/UktF10iMA4DoNBEHAM86S26DY07rAWzVKh0ARPcJTRZw06bxPtfdd5ePryYAlBYURp1JDQAim10KgDgetQCYhgcwKwCIklzSrkxZ2wJ++GE3XhoWwOwBoLQ6BDzPzz9fcKgBoKmQ9iOiGUTUJuLvpQFAbHdps4DjAOCaa+pszj8/uwD4zTflAcDLL2c+/XT3/xVWcB6SMBUDgI8+KrdBNvSZZ8ptcMzFBUBNxxaUOcoqAGo8gPBifPmlbo6kATBODKC0nhzKDGl6TSOZSZCNycze8ZgGAEqPXQCgFD6S/pxRkksKgPvsky0AfOih5AEQnmcDQFORaktEfxPRYXnGNCAi7tatG/fo0YN79OjB/dHnUPPlwHbX9tvLxuP50wDAHj2cXRTkRM2jvRDvv7+zmTNHbvP1187m+ut1cxULgJde6sHWKqs4D1yY1lhDPw9ajT3yiNwGyTBnnCG3wTGnLQOjDcpmjtd1BO3HNP1n0wBAeDEqGQDhAZQCIMoMaQAQyUxSAMTxmGQZGBQKlx67bdtmCwBRkUHalSlrAPjgg268NC6U2TvfSKtDAACfey70z/379198re7WrZsBoClUHcjB334FxpXGAwhvx3bbycbj+Vu0kI+HTTUB4KBB5QHAiy7yMm7XWiu6JEgcAESrMQ0AIhkGXkmJcMzFBUBNx5Y4AIj2YxoA3Gyz5AEQXoxqAEBpPTmUGTr/fPkcAEBBOQ5m9o7HNABQeuwCAKXwkRYASovy7723fj2aa0pcAJSGBTB75xtpciBCD559tuBQ8wCawtSdHPztJhhbGgCEt2PbbWXj8fxpeADPO8/Z/fSTbh7thRhbwFE19cIEANRsG8VdH5FXCPqCCzyY2WAD5oEDw22KAcCHH5bbIBZSA4A45jTbsszeFrCmY0ucriPoPnHZZXKbNAAQF7EvvtDNkUUAlJYTQZmhHj3kcyCZSQuA2l7AcZJApMdu1gAQJbmSBMAkPYD//a8eAHfaSQeACD0wADTFVA0RzSOi6bWPGbX/hgFhaQAQ3o5ttpGNx/PHAcC11tLZnHtuOgAID6AGAL/6qjwAeN55XvZskybMH3wQbhMHANFrVgOAiIU87TS5DY45Te1AZn1MDnO8riPoPqHpP6sFwJoaN16TBIKLWCUD4JlnJg+AiGUVlONgZu+GJEkAhAdQC4BS+Ej6c0ZFBmlR/qwB4AMPuPHSsABmDwCl1SHgeX7mmYJDDQBNxao0AAhvh9b9niYA/vijbp64ADhrltzmyy/120Zx10fkbQGfc44XD9WsGfPbb4fbxAFAtBp76CG5DQAQmckS4ZiLC4Caji0odK4BQHSfSBIAFy2KD4Cffy63SQMANVnAZ5zhbKTlRFBnUlMyCLGsgnIczOzdkFx9tXyOuAAoPXYBgFL4SPpzRkWGJAEwyS3g++/XeYWZvfONFgCffrrgUANAU7EqDQDC26G9+4oTA6gFwHPOSRcAZ86U23zxhd5rEHd9fgDs3t0rirvNNtF1rVZfPR0ARDLMKafIbQCAmtqBzN4dedIAiO4Tmv6zWgBEHUcNAMKLUQ0AKM0mRZkhDQAilEEKgDger7pKPkddA0BUZJB2Zdprr2wB4H33xQdAaXUIhB489VTBoQaApmJVGgCEt0NbhDMNADz7bGc3fLhunmoEQGwBn3mm55naccfoba44AIhesw8+KLdBLOTJJ8ttcNOhqR3IrI/JYfa6PGQNABcs0AMgvBhRcZ9hyhoAnn66DgBRZkhTMggAKCjHwcze8agFQE1MMwBQeuwCAKXwkRYASovyZxUApWEBzHoAROiBAaApBZUGAHGxa95cNh7PH2cLeO21dTbdu6cLgDNmyG0+/1y/bcRcPACefrqXnbrXXtFFm+MAIHrN/ve/chvEQnbtKrfBTYcWALVbMsxelwcNAKL7hKb/7KabJg+AuIhVMgCedpqzkZYTQZkhzeeHWFYpAOJ41LT+i+sBlB67hxySLQBERQYpAGILWNPKThuHrln/vffqvMLM3g2ntDoEQg+efLLgUAPA7OtUIlqx3IvIo9IAIC52W24pG4/nTxMAhw3TzZMGAA4cWB4APOUUr0XZCSdE9yJOCwARC6kBQNx0aIpHM+uDspnjdR1B94kkAXD+fD0A4iL22WdymzQAUJMEAgCUZpOizJAGABHKEFGPbQnheNS0/ksLAKXwkfTnjIoM0qL88ABqADBJD+B//hMfAKXJgQDAJ54oONQAMPv6mYimEdF/iKhFmdcSptIAICrtN2smG4/nTwMA0TUgLQCcPl1uAwDUbBvFXZ8fALt29aDzkkuiCzDHAcDHHnM2Dzwgt8FW+EknyW1w0xEXADUdWwCAGoDAdwKxlhJpAXDePD0A4iKWFQBEJrMGAE89NR4AakoGIZRBUI6DmeMDYJwtYOmxCwCUwkfSAIiEPC0AalrZJQmA99yj8wozezsO0s8AoQcGgFWj1kT0HLnSLJ8TURciql/OBflUGgBEpX0AhkTaLytstACImmE//KCbRws+bdo4m7D3MkqffZYuAALQu3TxMo/vuIP58MPDbVZbTT8Pes3GAcAuXeQ28LBpuocwu5hHLQCizZcGAPGd0PSf3WQT3fs9d258APz0U7lNkmCATGYNAJ5yirORZpOizqQGAOHJlgIgbkg0rf/iAqDUo5c1AERCnrQo/557uvGaTiZJbgEDAKVeYWbvhlNaHQKhB48/XnCoAWBlaS0iuoSIRhHRX0TUl4g2LuuKSgWAqLSPLFOJ4gLgOuvobLIMgJ9+6mw0cUNx1+cHwE6dvP7DDz7IvN9+4TbFAOD998ttEAvZubPcBh42TfFoZg8ANR1b4rSdw3ciSQCcM0cPgLiIZQUAkcmsiQE8+WRnI80mRZkhTckgAKCgHhszezckms4vWgBs1iweAErhI2kAREKetCZr1gDw7ruTB0CEHkTFZftkAFhZakZEtxPRVCIaSkSvENEcIjq7jGsqDQCi0Opmm8nG4/nTAEAUjf3+e908cQHwn3/kNp98ot82irs+IudBYGbu0MGL+3vmGeZddgm3iQOAjzzibO67T26DrfATT5TbwMOmBUBtTA6zB4AaDxIAUNN/VguAs2e78XvsIbfBReyTT+Q2SYIBElk0ANi1q7ORJhOgzJAGABHKIAVA3JCkAYDSYxcAKK1MkDUA3GMPN17TySRJALzrLp1XmNk730g/A4QeGABWhZYj15f3Y3Kw9yTlduTYkVzbtnKpNACIQqubbiobj+dPAwBRM8wA0APA445jvukm9//XXos+acYBwIcf1gMgtsI1AAjA0nQPYfY8gBoARJsvDQDipkgDgE2a6N7vWbP0AIiLWFYAEIksGgA86SQdAKLMkKZmJDzZgoK8zOzdkEhb/yH2Mc4WsNSblDUAREKetCarBgDxfiYJgHfeGR8ApcmBAMBHHy041AAw+5pKRKOJ6FIiWjNizFfpLWcJlQYAUWh1k01k4/H8aQAgaoYNHaqbJy4A/v233Objj52NJm4o7vqIvCzt9u2Ze/d2/x8wINpzWwwA3nuv3AZb4Z06yW0AWJruIcweAGo6tqDNl8aDhJui88+X28QFQM0WMC5iH38st0kSDJDIogHALl2cjTSZAACoKRkET7YUAHFDIu38gtjHOB5ALQBK4SNpAERCnhQAd9/djZfUPUwDAO+4Q+cVZtYDIEIPHnmk4FADwOzrECKqV+5F5FFpABCFVps0kY3H82sAEF/wrALgfvvpAfCjj8oDgO3aMffp462hceNwmzgA+NBDegCEJ7RjR7lNsQCoKdiNNl8aDxJuijT9Z7UAOHNm5QMgElk0ANi5s7ORJhOgzqQGAHEjIyjIy8ze8SgFQMQ+agrbAwClxy4AUFqZIGkARDy2tCarBgAB1NtuK19PXACU3hQwewAo/QwQemAAWBXakojWD/yuIRFtUYa1hKk0AIhCq1EgESYtAMbJFmT2aoZ9951ubXEB8K+/5DYAQE3cUNz1+QHwyCOZ+/Z1/x84kHn99cNtVl1VP8+DDzqb//xHbgMA7NBBbgMPm6Z9HDPzDjukA4C4KUoSAGfMcOM1W8C4iGUFAJHIogHAE0/UASDqTGpKBgEABQV5mdkDQGnnFwBgHA+g9Ng9+OBsASDisaUluQCAkrqHeD+TBMC+fZMHQIQePPxwwaEGgNnX10TUPPC75kQ0qAxrCVNpABB1tjbaSDYez58GAKJmWNIA2Lq1s5k6VW7z4YflAcDDD3eeIGYXIB/lhUgLALEVfsIJcht42OICoKZgN/q8ajxIuCnS9J/deGPd+z19uh4AcRH76CO5TRoAqPled+rkf4oVJQAAIABJREFUbKTJBCgzpAFAeLKlAIgbEikAIvklDgBKj10AoLQyQSUDIN7PJAHw9tt1XmFmDwClnwEAUNBL3QAw+5oW8rt6VN7ED79KA4Cos7XhhrLxeH4NAOIOLy4ADhmiW1saAPjBB85GGjgOFQuAbdu6bFBmB8arrBJuEwcA//tfZ3PPPXIbeEI1AAgPm6Z/MHM8AESfVw1A4KYoSQCcNk0PgLiIZQUAkcms+V537OhspLFkKDOkqRmJGxlBQV5m9m5IpJ1fkPwSBwCl3iRsAUsT05IGQCTkSUtyAQAlNTvxfm63nXw92vXfdpvupoBZD4CIPTUArAr9RkSrBn63GhFNKsNawlQaAESdrUaNZOPx/BoAxB2eFgBRNPbbb3Vr04LPvvs6mz//lNu8/76zkcYNFbM+Iq9X88EHezF6w4cz/+tf4TZxAPCBB5wNAFMieEKPP15uAw+bpn0csweAmo4t6POqAUDcFGnax2kB8J9/9ACIi9iHH8ptkgQDJLJotoA7dNABIMoMaUoG4UZGCoC4IZF2fkHyS5wYQOmxCw9gVgAQ8dhSANxtNzdeUvcQ72eSHsA+feIDoPQzQOjBgw8WHGoAmH09Q64N3FK1Py9FRHcT0QtlW1GuSgOAqLO1wQay8Xh+PwBOmeIumlGKUy6C2SsaawDoAeCBB3qFmkeNYl522XCbtAAQntDjjpPbwMMWFwA1BbvR5kvjQcJNkQYAGzfWvd9//60HQFzEsgKASGTRfK9POMHZSJMJ4gAgjmNBRwZm9m5ItAAYxwMoPXYBgNLEtKQBEPHY0pJcWQPAW2/V3RQwewAo/QwQeiDopW4AmH1tRERjiGgCEX1S++9YImpcxjX5VRoARJmFhg1l4/H8fgDcddf888UFQBSNzQeXYWuLC4BTpsht3nvP2UjjhopZnx8A27TxTjAochzWcH2VVfTz3H+/s7nrLrkNAPDYY+U28LBp+gczM2+/vR4A0eZLA4C4KdJ0D9EC4F9/ufF77im3AQB+8IHcJkkwQCKL5nt9/PHORhpLhjqTmpqROI4FBXmZ2bshkXZ+QfZzHACUepMAgNLEtKQBEOE40ooMuCZIanbi/UxyC/iWW3Q3BcwGgCZakYg6EtEVtf+uWN7l5Kg0AIgsu6hs0jAFAXCddfLPF6deGHN6ALjPPs5m8mS5zbvvpguAW23l/t+6tZdlNmmS+1tYqYU4AHjffXoAhCe0fXu5DTxsmv7BzB4Aagp2o82XxoOEmyJN8WgAYBiMhwkAqPEAwouRFQBEIovme33ccToARJ3JJAEQNyTSwt9IftFsAW++uQFglPB+JgmAN98cHwClnwFiTwW91A0ATcWqNACIO2zpSTysaOd66+WfD19wDWQye10DvvlGbpM2AEoDx4tZHxHz1lu7/++9t1dp/s8/3d/CSksUA4DIMpYIntBjjpHbwMOWJgBqAAK15zQAuNFGOgCcOlUPgL17O5v335fbJAkGSGSJA4DSWDKUGdLUjMRxLOjIwMzeDYkUAJH8EgcApd4kAKA0MS1pAEQ8trQiwy67uPGSwtd4P5MEwJtu0t0UMOsBELGngl7qBoCVoU2I6GQiuoyILvc9sqDSACDKeEiLNAMA/R7AtdbKPx++4JptZmava0DSALj33s7mjz/kNgMGlAcA99zTi2OBBybsohIHAO+919nccYfcBu+DBgDhYevcWbc+AKCmYDf6vGoAEF5xTfcQAOCiRbLxgHcNAGIbKysAiEQWDQAee6yzkcaS4fykKRmE41gKgLghkXZ+iQOA2AKWwgQAUBqXnDQAYjcmCQBEMlHLlvL1aNffq5fumGD2Cs9LIRyxpwaAVaHDiWguEf1ERPNr/11ARAPLuSifSgOAiOFae23ZeNT08wNgoa4T+IJrEk2YPQD8+mu5TVoA+M47zkYaOF7M+vwAuPvuXi0reFbDPJdxAPA//9EDIN6Hdu3kNvCwxQVATcFu9HnVeJBQey4OAC5cKBs/ZYobr4kBxDbWe+/JbbIGgO3bOxvpVmIcAMRxLOjIwMze8Sgt/I3zWRwPYKUCIHZjpCW5WrWK3p0ICu9nkh7Anj3jA6D0MwAACnqpGwBmX4OJ6Iza/6P234Xk4gGzoOIB8KOPmA84QHcyQ00/PwCuvHL++RAsrik1w+y1jUoLAH//XW7z9tvORho4Xsz6/AC4yy5eP0t8Fr/9tqRNgwbxARCdRiTC+3D00XIbeNhOPFG3vpYt9QCIPq8agEDtOU33kA031AHg5Ml6AMQ2VlYAEJnMGgA85hgdAKLOpKZm5D33OBtBRwZm9o5HKQAi+zkOAEq9SQBAaWJa0gCIm3FpRQYNAOL9TBIAb7xRd1PA7G0BSwEQsaeCVpoGgNnXNCJauvb//9T+uzwRjS/PcpZQ8QDYvLk3VprRhpp+fgBcYYX882GrUlNsmtlrGzVokNwmDmDttZezmTRJblMuANx5Z+bnn/f+ttRSzGPHLmkTBwBx4dQA4FtvOZujjpLb4ILbqZNufQBATcFutPnSACBKj2i6hzRq5GwWLJCN/+OP+AD47rtymyTBAIksGgBs187ZSLcSUWcySQCEx1da+Bs3tGkAoDQuOWkAxM24FAB33tmNlxRtx/uZ5BbwDTfojglmzwMohXDEngo6KRkAZl9/ENEKtf8fQ0TrEVF9IppRthXlqngA3GILb+waa8gOcpR08SeBLLNM/vmwVaRpN8fstY3KIgD27+9spIHjUFwABHDvsAPziy96f6tfP7yvahwAvPtuZ3P77XKbN990NkceKbfBBTcNAESbLw1AIPM0DgCGZWSH6fff9QCIOKasACASWTQAePTRzka6lYgQFU3NSBzHgo4MzOx5fNMAQKk36aCDsgWAuBmXVmQAAEoKX8NBkCQAXn998gCI2FMDwKrQG0R0aO3/HyaiAUT0KhF9WrYV5ap4AMRJqV495tVXlx3kKOniB8BC82GrKC4AfvWV3CYOYO25p7OZOFFuA89X2gDYsiVzv37e31ZeOTyjshgAvO02uc3rr+sBEBfcjh116wMAagp2o82XBiCQearpHrLBBskDIOKYBgyQ2yQJBkhk0QDgUUfpABBlhjSf31136QAQHl9p4W8ASxwAlMIEAFAal5w0AOJcnAQAIps8SQC87jrdMcHsAaAUwhFCI2ilaQCYfTUiVwyaiGgNInqMiPoR0eZlW1GuigfATTf1xq62muwgBwD6t4ALzQdPQePGsjkg9A3NIgDC8yXNHCxmff73e9ttmV991fvb6quHb8vEAUBcODUA+L//OZsjjpDb4ILboYNufQBATcFutPnSAAQSD+IA4Lx5svGo4agBQMQxZQUAkciiAcAjj3Q20q1ElBnSFA2/805nI2jJxcxeKSxp4W8AS5IAiC1gaVxy0gCIc7G0IgMAUFK0HTtESQLgtdfqjgnm+AAo6KRkAJhtLUNE55Db8s2qigdABK4TudZhzIXrmKFqexgARtnCU7DxxoXX5Bf6hn75pdwmDmDtsYezmTBBbvPGG+kCIDyuW2/toAtad93w9ycOAOLC2aeP3Oa115zN4YfLbXDB1QLgdtvpARBtvjQAgcQDTfeQhg2dzdy5svETJ+oBEHFMWQFAJLJoAPCII3SeJACgpmbkHXfoLvbY8pcCIIBFWjmB2QNAKUzAA5gVAMS5WJqQlzUAvPpqPQAiCUTqhUXsqQFgVWhauRdQQMUBYE0N8/LLe2MbNPC2pfK57VF6BACIuoD5Ln7wFDRpUvCLkaMsAyA8X9LMwWLWR+T1yWze3HkfoUaN3AUsqEKZ2WGKA4CvvOJsDjtMboML7gkn6NYHANQU7AYAagACiQcaAFx/fR0ATpgQHwDfeUdukyQYIJFFA4CHH64DQBRcjwOAgpZczOxt+UsLfyOkJY4HUAoTAEBpXHLSAIgezlIABDxJirbj/UwSAK+6SndMMHseQCmEI4RG0EnJADD7eoeIWpZ7EXlUHADCK4fHyit79bDybbmiCCo8UqjhFATHWbO87FRcKDbZpOAXI0c46XzxhdymGAAMK6cSJXi+pIHjxazPD4DNmrkEFGiTTcILA8cBQFw4b71VbtOvn7M59FC5DS64aQAg+rxqAAKJB5r2cQDAOXNk43/7TQ+ACGTPCgDihlEDgIcd5mykW4koNK6pGdm3r7MRtORiZs/jK637iOznOB5ALQBKw1KSBkD0cJYm5GkAEO/n9tvLntvvdJDqyit1xwSzHgARQiPopGQAmH1dQUTjyOsDfILvkQUVB4Dff+9BAhHzSit5Xq233oq2A/ABALENRJQbnI+WT8zehWLTTQt+MXKEk07SALj77s5m/Hi5DTxfaQNg06a5ANCsmStJE1QcAMSFUwOAL73kbNq2ldsgxu7443XrAwBqCnajz6sGIJB4oOkegnaIkt6nzO5Y0wIgAtnDPu8oZQ0ADz1U50lCoXHN53f77bqLPTy+UgBETLO0exKzB4BSmMgaAOJ8Lo3HBjxJuvbg/ZR6ANGMQLP+K66ID4BSL6wBYFVpUsRjYjkX5VNxAPjuuw7ILr/c9RhdcUWvmTUKDYcJRTsBgL/84s3nP1mhuDKzF++02WYFvxg5wknn88/lNmkBIDxf0szBYtbnB8BNN80tBLzNNrlJIVAxANi7t9zmxRf1AAiPy3HH6dYHANQU7EafV03RacSdaQBw3XV1ADhunB4Ar7kmWwCIRBYNALZtqwNA1NvUfH633eZsBC25mNnz+EoLf2P3JA4ASmECACgNS0kaANHCLwkAxPu5ww6y50YBfM36//1v3THBrAdAhNAIOikZAJqKlQgAv//eZb7X1LgatTNn1v7/ycd59k4ted78hXz+bYfzzGWJPzrtQGYi7nXChjxrXsSFLAiAw4cz/+tfrpTML7944w45xPuCIt6paVPZFwlKCwB3283ZjBsnt4HnK0kArKlx2+hEXpX8Jk1yt3xbtWJ+9tklbeMAIDwnt9wit3nhBWdzyCFyG3hctAC47bZ6AESfVw1AIO5M0z4OACjpfcrs3ThpegEjkN0fAlBISYIBbuw0AIjzgnQrEQCoqRnZp4/uYg+Pr7TuI2Ka111XvqamTeMBoDQsJWkARAs/aTz2Dju48ZKuPdhF2nFH2XOjFq1m/ZdfHh8ApV5YhNAICukbAJqK1WIAhBOmXj1XKQJfDlrlV6bTWjKtP4ip035M19DixzkHEr/czP2/aXfiWcsQ993Z2V3YhpjO3Dr8yEURVADg11+7YOj69ZlHjPDGYauH2Yt30gIg7joHDpTbxAHAXXfVAyDedGnmYJz1AUT8ALjxxg6goH32CW9vtNJK+vcBnhMNAD73nLM5+GC5DTwuxx6rWx8AUFOwe9AgPUAg7kwDgOus42wkra+YPbDffXf5HAhkzwoA4sZOA4AobyL1JKHgehwAFPRkZWbP4ysFwDjJLyi5JYWJrAEgWvglAYB4P6UAiFJkmvVfdpnumGD2AFDqhcUOigFgVeiBPI8sqAER8VFHTVv8XSBipmVnegB4TfTjhj2I793e/X+z7sSz6y3LzzZ3dlfuTXz2ZREXWRRBBQB+/LEr8NygQW5BYmT7MXvbXZtvLvsiQbjrTBoAd9nF2fz6q9wGni8NAGqDlwFXfgDcaCP3nkMHHxxeeb4YALz5ZrnNs886m4MOktsAANu3160vDgAisUkDEIg707SPW3ttHQCOHq0HQMQx5YvRDSpJMMCNnQaEADZSkEDBdU3R8FtvdTaCnqzM7N1oSes+xol93GSTeAAoDUtJGgDRwUUaj7399m68pGsPQgl22kn23ChFpln/pZfqjgnm+AAo6KRkAJh9PRN4fEREc4nouXIuyqcGRMREPgDc6xqmazwajIK/sX+P5cd3WI4/7LQnj546hmcM/c6VhNlrL7d1eMkl+Y7cXADs39+1lFtzzdzSDqj3tXChA6tiAPCzz+Q2cQAQjcv9W9iFBDiTlo5g1seuYJvZD4DBsi/t2oWXbYmzBQzPiQYAn37a2Rx4oNwGW25xAVBTsBt9hzUAgW1HTfs4AKCk9ykz88iRbvxuu8nnQBxTVgAQiSwaEDrwQB0AouC65vPr3Vt3sYfHV1r2J07sY5MmOpjA+5QVAEQHF2k4DgBQ0rUHoQQ77yx7blSi0Kz/kkviA6DUC4sQGkEhfQPAylQHIrqt3Iuo1WIAHDyYedw/4xYDHr4cX/7mTrLj/hnHf83+i6fPnc6z59fG9h16qFevaNQo5mWXdRmlLVrkhxoU7QQAvvyy+7Kvv37u3SEq/qMcTBwAxLZDHAAsVNDaLxQt1QAgPF/SzEFmfezKq69645Eh17Bhrke0UydXHy6oOB5AeE5uuklu89RTegDEltsxx3i/W7SosB0AUFOvEX2HNUWnse2oAcC11nI2ktZXzC5cgsiFH0iFbSx/HchCShIM4NnXgNABB+g8SSi4rvn8brnF2Qh6sjKzB/xSAASwrL++fE0AQClM4H2ShqUkDYA4nycBgPAkt2ole25/6TGpLrpId0ww6wEQOygGgFWrpYhoSrkXUavFADhvHud4+ERfjp12cl4sZrcdtcwyrh3c4Yfnv/AFAfDJJ10ge+PGuVuTuGP880/mMWPc/7UxgO3aObuwQsdRigOAqFmFuoUSwfOlAUDt1gUufkRejaz11su9eJ52mvMMBQUA1LwPAMBeveQ2TzzhbA44QG6DLTcAIDyjw4blt9tmGz0Aou+wBiDgddK0j1tzTR0A/vSTG7/LLvI5sI2VFQCEZ18DgPvvrwMJ9JrWfH4336y72AP4pXUfASwNG8rXtPHG8QBQEpYSpy6edjx2dKThOJq2jfAkSwFw5kz9+i+80I0X9OldLACg1AuLHRRBIX0DwMrUjkQ0udyLqFUDIuL27afxnAVzFsNfm8fbyL4c663nfZkBaESurl2++myo2g4AfOABd7Jq2tRd2CHEjIwf7zyMcQAQzxEHACUeJQhfdA0AwvMlLR3B7G1dLLWUbDwuTH4AXGed3O2zc88Nb0cHANS8D9g60wDg4487m/33l9tgy61dO/czvGGFvEItWuguosweAGqKTgO84wCgpPUVs8ue1wLgxRc7mzfekNskCQbIZNZkw7ZpEw8A/Z/fiBH5Y8sAgNKLPeIMpQAIYNEAYOPGOpgAKEsAME5ZFO14xHQnAYC4kZACIBIRNeu/4ILkARA30II6qgaA2dcAct1A8PiMXAxgz3IuyqfFWcCN+zZmuoZ4yKQhvHCR4GQATxQ8KdiirVfPxTHk6+uKqu0AwL593fbAVlvleiZQ8X/UKC/eSVsHEAD4ySdyG7z2hQvlNshYGzNGbgPPlwYAcee69NKy8bgw+QFw7bVzS2hceml48HocAMTWWc+ecpvHHnM2bdrIbZBkcfTR7meUavF7kMO09da5J+TvvivsDRw40Nloik4DOjT9g9dYw9lIOh8wO2+n5qLH7G1jZQUAcd7Q1MPbbz8dSKA4vf/zI2Led99om5tu0l3s4fGV1n3E1rcGADfayNlIt3QBypKwlAULlvzcDjvM1QCLkva4wPlcGo6j6dqD40gaA4g4dM36zz/fjRf06V0sXBekn5kBYFWpV+BxORHtX9YV5WoxAML7V4PtvkJfjpEjXcwf4AB38mut5YoG5ruYo2o7ALBnT7c907KlK0eCOz5sYQwb5nl4tACIbeRCYOBXHABEvMro0XIbeL6kpSMuvtgV29YAIEDED4BrrpnbRuvaa8O7JAAANe9DHAB85BFns99+chvEXAEA0RquUIHjrbbKBUAil7iUT599pgdAQIcGAFdfXQeA33+vu+gxe9tYr78ut0kSAJHJrGmJ1rq1DiTQcjEIgEiKClOvXrqLPTy+0rI/OF9qAHDDDXUwAVCW7EqExRYThYeG+P+uOS5Q1ku6G6MBQOxASb8LCEPSrL9HDzde0Kd3sQCA0uoQ2EERFNI3ADQVqyUAcLHyfTkWLnRbby1aeL+DC36rrVxsW77MRFRtBwBecYU7ce6zj/t9o0YOAvfay/08eLAX76RtBYfA4zgAuGCB3AbbFRoAfPRRHQBusYW3tmWXldn4k0BQJX/11V1/W+iWW8KzaQGbGgDE1tmNN8ptHn7Y2bRuLbfB1vZRR7mfsSX8yiv57Zo3z72IErmttXz69FM3TlN0GtDh7x9cqMAzAFDS+YDZeS+J5KUvmD0vxv/+J7dJEgDh2V9rLfnz77uvDgDxHfB/fkT5Qw569tRd7HGjJQVAeKw0SSAbbKCDCQ0AhsUWE+Wv5hAcf8MN+UMe0MFFC4CSto0IEZJ+FxCGpDmuzztPD4BwDEg/M9xAC+qoGgBmX8cSUYvA77YhomPKsJYwNSAivufje5iuIX7iuye8oyvflwMxUbj4MntbGq1bu4twvrtrVMFHa7Lzz3e18FC0Ofi4+GKvaHJcAPzoI7kN5p0/X26Dk9WoUXIbeL6ktcOaNfPWttxyMpuXX14SAFdd1cEDdNdd7u48KACgBoSxdaYBQLQP1AAgtraPPNL9DOB6+un8doBoPwAW8sLAu6gpOg3ogGcVW2z5ttRWW82NkRS+ZWYeMsSNlxa/Zfa8GK+9Vnjs2Wd7IRT+c8Fff+VPDAqOnzHDFd4NEzz7a64pWz+zd6MoBQn03AYAIo42X2/gG2/UXezh8ZXWfYTHSgOADRvqYAKeUklYypw54QB44YXRNmHj8wEVOrhIw3E0AIgbCel3AbtQ9ep5v5s5M38bxnPPdTaCPr2LBceAtDoEbqAFZbQMALOvkUS0fuB36xPRiDKsJUwuC/hS5/2bMc9Xfyzfl/nVV90WpP9Chay244938Vn5yrUAAOEB7N7dQR62CMIeSy/t/t1kE8G3yCdkniUNgCgvogFAeL6kpSPQCorIdU2RCMWm/QDYoEEuiDz4YPj2KwBQ8z4AAMPKykTpv/91NvlisoJCzBUA8Pnn3c8PPZTfDv1UcRElKrz9+PHHSwLgoYcy//xztA2gA5CBjhf5PNGrrqoDwMGD9QCIi1hY7+eggt9B/+/zXQTDxkdl4MKz7wfAjh3z1/hDj3AJSPzzjxdri8/vxx/dz/lKVQEApRd7AL+07A+2vjXZz+utlwsTo0c78IkSPKWSXYmwsihEzusVJS0AooMLPre33sofM6lp2/jzz7rvAnah/AC47rr5KxGcc46zQZ/emprC8cMGgHVa05W/T1uLAXCNm9fIPbryfZkffdSdhP1CYsepp7o780aNoo9c9G0EAKIMyeuvexfBqAcAEDEcw4fn/5Yg8Njf+qyQMFeU1yJMKC+S74QcFDxfAMCamtxWeEGhFRQR8woryOZArUE/AK68cm7HlaeeCt+yjwOAiJ26/nq5zQMPOJt99pHbIObqiCPcz4inLBSzBYjGCVniffroIzcO2+TImIxq19Srl0seIPL6Bw8d6n4eMCB6Hhz7yE799FMHtlFC4gs+V4lwESu0Vc7s3XSFXegRexmmsPFRmcrIZF5jjdzx+cIiNADoXz8+P3iP88HHDTfoABDALwVAeKw02c/oFY0tXaL8HkR4SgGA48ZF34CElUUpBMlSAETRa3gksQbUNYxSsGvPkCHR32/cSPi/C23aRJc7ghPCX02BiHnLLaPXc/bZuQCIKg5hWrTI7VrBi4nP7I47mK+8MnoO3EAL6qgaAGZfI4lok8DvNiGiMWVYS5gaEBHXv6I+D5k0JPfoygeAt9/ueV78BzxO3IMH557Qg0IbJGwBd+niEhGYvS9y1KNJEzfuww/dzw8+mP9bgriTOAA4d67cBuVFNAAIzxdKR+DCFCWcMImY//Uv2RxPPrkkAK64Yi44v/yyVyTaLwCgBoQRO6UBwPvuczbBm4p8QswVAPD++93PhYKnAdG//OJty66+en4bHGuoOYhWhmGZevgeoPwDABCZxP36Rc+zyipuDArfFurEgh7FSO6RqHv3XAD84otob+2yy0YDYD5vrX883g98TkEhk9n/Gfi/E2FCbDBAYsyYaGAMA0DccOCzCdP11+de7PNp5EjPy+iPgcvX0g9b334AnD07f7wtOsX4ATDf8QEAxK4EEhLCFCyLgpucsPCUgQOZr7lGDoC4+cF3Arsx8GhGCTfVAEBsIYcJNxL+7wKRVyYqKPQO9ifTEYWfByF8d3Djh5uEMCFxDOcbbMPXq5f/NeMGWlBGywAw+7qZiD4hoi2IaOnafz8iot7lXJRPi5NAllC+k8tVV4Xf6RIxX365g7h8HircESJO8IQTvAMeJx7Eu9SvHw6AKANSqEwDThoffOB+njzZrS+fNAA4YYKDYZQXyefB82vYMO9uDxe7hx5yP8+ZE26DOmBEDs4kQokVnICZ3Wfjfw/eesvFxgUFANSAMADwuuvczzNnFm6ddO+9zqZQNq5fAECUG7rjDvczbiSihH6qY8d6cUD5blaYvb7DAEAcv2GZzvAsYPsN/YORpPL449HzNGiQC4B4/6OEFnX5LlpBdeuWC6KAhDCFASAKBueL8fWPx/sRBVvIZF5tNfczvv/+5Jmg9twzFyRQ2zAof2kT/+eHfqv52ghed13uxT6f/HMg6xtxo1HClqW//M1yy+X3IKJTDGCiEADCU4qbUiSRhClYFgUxgWFeUjxv2PiVVlpy/DffuL/hHInPDXAUJQAg2jYiqSVMuJEAAOI4jQo9gBMCAIgblbDjGs+FECX06b3qquj14KYRkIvPrFB3JZw/DQCrQv8ioheIqIaIFtU+XiSiFcu5KJ/iAWD37uHBwQ895O6skBASVT8O8VC4cAV70Q4c6CrwEy25Jbzxxm4MYm4KxZqhIToAEB078glzAcR6944uzotuHjiZIS7s55/zJyT4XxNOsvBiRcW8NGrk2fhPtIMHR9sgzpDIy5BbfvlcUP3ww/BM2H/9Sw+AiJ0CAGJrNp8XEdulAMCamsK9ahF0f9hh7mfA9KWXho9/9VUqM07CAAAgAElEQVQHvuimMHasl7m+6qr55wIAwpuA+K2wYw8Xot12c/+i/2y/fu7n++6LngcAiDJIhS7wSMbSAOBZZzmbl192P+P7EaYwAEQChb8CQFD+8fDMRPXhRSYzPgN4osIAsKbGle/ZYw83Bl59vKag0G4tCIDwsuDYCdO11xYHgLghifICYqfDH3/qP7+FCXUisaVb6PiApxQAiCSjoC6+2IND/B0hNmFAivJc/vF4r8NiGhFDi/ALfG7IyI9SEADxuYcJNxL4Lsybl/+4w00cABAxkNiV8suftOgHQHTVCUuIwjkD32l8Zriehenzz71+w7i5nDHDtYULmcMAsHLUkIh2J6INyr2QgOIBYIcO+bM8EWAbdfJDwgi+rIcfvmS2HYL64QUJAuBzz7mf82WpMXsnq/ffdz8vs0z+k46/JRIywvJduLFOnJwBgIVAMwwAcdGI8lDCK0rktgf9zxW1DYhtZj8ALrtsbrLKl1+GJ0IAAAHCF1xQuEcltkXgiQPU5guWvvtuN2bPPd3PgIZ81fOR9YuLOLakzj03fDxKOKCY7pgxXhzW0kvnz2p9/303DnFv2NK64oolxyJeENnGuABhKz7fliK2fFH3LN938PXXPS94Pm9cUGec4Wxeesn9jCSpsNcfBoCI3w2bc8IEb3sc45GoErUFjEzmVVZxP2NrLixDFyESu++eCxIdOoS/T8FwEgD81Ve7n/OVgQEA4mKfT/45AK533pn/uMcxHgTADTcMHz9/vgdwo0fLWrfBU4qbPXyeQcFLhQdA2w+0fiGu2j9/voLeSNhCHUN8bqjJGbXtjbAavIcoqRJ2rOJGAsclADaqew+cEMss434G5IXd2OC5sYWOcyBKKoXdIKNMFeJoAYAo9RQm/3uK6yuucyGhRQaA2dfaRLRK4HcNiGitMqwlTHIAvP56d+Jkdtlc+XpkoqgoYjeCgocQAHjQQQ4U/ELMVBAA4anC1ma+DNoZM7xq+ADAfDEYU6fmJk34ATCqLhNif7BVDXjbbLPoefwnbyKvdhiKgEZlQPoBEBdMrC9qGxPxdX4AXHrp3NIQQ4fmAiW0wgq5AIjXmU8AwGuucT/fdZf7eejQ8PFHH+15NvfYw/0OpWv8tQqDQtA9ytfgzjkqsB8na8w1erTnrSPK33/3vfdyAfCLL9zPYTcfWDs+K2xB4XPIF6OI7aE//shtzRV2wSNiPvBA9y+8FnPnugtHvizi0093Ni++6H5G6aWwmzXcLPnPBShfggSu4JqCZWPgpYwqDA9AbNDA/QyQCLtw47u2yy7uX3j1EeYRfJ+++ip3/fj8LrnEzYcbjjDhhkILgABX9HT98cdwGxx7/vqHROGtLlH0HN6jUaPCCzcHBQDETWnU8RQGgABUf6kvCP3V/fMDtrGV7xduktdZJ/dzw5ZwVOmVIAAioSIsKQ03EgBAeCSjSjfBCYF6qtgNCDuu4c3DerFbhZvKsOunv/4qPjNmz1EQpjAA/Pe/3c/B5LFRo3jaqacaAGZcn5Lr/evXTkT0cRnWEqbCABjWGWTnnR0o5VMwzswvfNnwZW3d2tXEC+r3373MsSAA4oKKGKugcAeGuJH33nO/X2656C/g5ZfnzjVrlneiBfwGhe1LgCVec74vur8IKZFXOwzbp1HdLPIBYFQxY6yPyKuSv9RSucVh0dUlKACgH4QLZR8jeB4AiO22qKK9/vdh993d7+AJzVe6B6DVtq37GeVNorZ8kMGHOKrRox1grrqq++zylWl4993ciyE8gt27LzkWHlds/QAAAQT5Qhb8AOjfvgzbPvcf27howfv42GPRc5x6qhvzwgvu506d3M9hvZHDABDezzBQIWLeddfc8YiFisoCRiYzbkAARmEeQ2Rd4kKMmzp4+YP1KgHuQQA891znCY4qGjx5suclLOTxxusOAiC+y199FW7zww/u78hAx3kmDEAuuyz3mBo5MjxrNyhsmQYBMAhQQQBctMhLMApL9oHHNey4CItNRmwzABafGwAvqvA5PIQ4NrEFG3azhhsJ3AwhTCOYrAjBw4nzHgA2LBYa5xqUkAoCYFidQkBvEABx/glCeNApgPPEiSe6n4PhRP/+N09z8GcAmGH9RURLBX63NBFNLcNawlQYABHH5/+yr7tu4Ubs66wTffJDGyQA4B57uJN7mCZOzK1lt9FG7vcI5A67Q2X2KqojyB0AiG3NMJ1wQu6XcOZMLzj6ggvCbQA4eOCOH67/MOHkjzEAQFx0nnsu3M4PgPCY4MQRFQcGmPIDIFFuMVncDQe3YvBeoYMFUeEC1AieBzBfcYX7OSymDzcCeKAUDfrV5itWjGMCAHj66Q5Oo8qTYOsT26yjRrnjc911l+yMEhQSOHCsIQElrOgvYhHxgCcLW4r5SkDA2/37794xQuQ82X4hvgnebWxbobNMPq/4KafkAiA8gP6yQFAYAMIzH1bmCaDnHw/v1dZbh68HoAEAxM9hHkN4ALGVCJDATWLQk4S4Szzw+Z12mgOtsO0+bHGfeWbuxT6f/HMg2eXKK3PPO0EBmACASEoI6+vcubP7Gz6PkSO9BKawc8zkye6GEHGoP/2UCxhBby/Ky+CxcKEH7mFb/V26LHlc4HPDlqpfOAfhfILPDTF+UYWet9wyFwDzjUeiCQAa35+w4+iMM7wtegAgADKsziziqJGEh+x/lFQKa82HYxUPbOEikzvYFchfiJvIq6LQvr37ORg6ctllBoAVoN9pyYSPFYloShnWEqbCALhwYe7JA8GyhYpzbraZ85yECdtIuFvbeWfvghQmAKMfAHGhjSrciS0cxI1gLSi1EZagsv/+SwIgTsxRZSlwd44Hyqvg57DtO1wU8UCgNZ4ruB0OhQEgvIlRXTRuuy0XAPFZ+uPrcDEJggY8gH4A9NfNChPedwAg7pLD6tn5u5TUq+e8R8xeUH++GnjYqj/kEPfziSe6mmgHHxw+vmvX3IvoqFGuzt6GG7qTftSFmtmLtYM3AfAZ5m286CLPG+wHQGSqRrXWmjfPA8BJkxyc4mfUBYRQcxPgA5BBNnXYd2LBAtcFBB4/vLeI5wqrqRcGgHgvgjGjOK4Q+4rxiNVE9r5fl13mlWlCUhO8mGF1KZEYBk8YPjMkOwTPY/4MeD8Anniiy2QN6ysOkEFsJABw2LDom8AwAMRNTLDsD262cIwjdAOezzCvJBJ18BgxItdDHDzHwEONLdMff/RuGsKOp6AHcMECL24vbGcBXmT//Ch7EnZuhTcUN7z43OABDALUlCkOjJE0gnNVsI0j9OWXnpcRAIjPEbsKfvmPIQAg2j2G3djcfrv7G0AZYRwIpwir/IAar/7PjNnbBg/2Nw7uCgEAkXkc3IG6+GIDwArQ60R0ReB3lxNR/zKsJUyFAXDBgtzA7h9+cHdy+YLmmR14IdMwKLjn8WXdbrv8hWlxwSPygqSvvtrFoyFuLCicgFH2AwCIrdmwmCfEzOAxY4YXkxQVSwIXfRQAhvV/9Wfm+gEQcWpRcUdhSSCoJxbWyo3ZiysEACK2zB+cjszO4EkJAIj3yn9hjxJKI1x1lfsZHqeweo24C8eJGAAImyiv8Ouve55XAGD79m7LKKqYdLDN4MiR7kK56abOO/X669GvCcCObUmARViNsa5dvUxjItcZh9m74Id1VkC8EDwkkyY5EMLnjSxICBCBchzwrt1+u8vwbt58yTkQ9N6qlfsXXmZ4EcM8tGGFoF9+2XmBcQMCwYMB6MD45593Nw1hSUb+58bWIUI3wrIxARJ44DsNT5cfbN5+20H3mmt64wHw7du7/4dd7OE1RBkfeHsuvDD8s/DH4hF5ISnwDgXL/gTjElH/EJmyW2215JpQP88PE/6b4uCWLrx38Fj9+GNumZfga/BnAOOc/8IL7vwaliF/0km54xctct8leNeDSRHIlsUDAAgPX7B7EtYPbxmAD+fyYEKEv1QYboYAdGFF0v0ACI8lvPxhSSzY1YDzAAAIz+yQIUva+ENv/ACI4zHYng9ZyXigikKbNu47HbxxvOACA8AK0NZE9DcRfUVETxDRl7U/B/sDl0uFAXD+fA+CiJznJewCE9Ruu0WXQUH2Jb6szZtHV2xnzq3nBQC85BJ3gYza+sSXE184BNHiDiwsQQV3mHhMn+4FQ0cFjCPOBo9hw3ID+MO2K/xFVIm84rEoNBpWA2rhQg/IiDyPCe68ozxf/i3JVq3CE3TgvQneWWM+eAYlAIgtXwDg8ce7n8Og9uKL3cmNyM2FODF8do8+Gj6H/73D6z70UPdZYJs7KH9sGk7I/fu7i9COO3pJEWEClAAAEX+K7We/2rbNjVtF/1m8prCCxYBmvN8TJ7o5t9giPD7R3/bKDw29erlj2B8fCsHrjosuYnhx/CIr2C+/JxOf++OPO4/R8svnjkUWpf87xOy2wtZaKzw2zP/cKGwOr3BYjCFukIIAuPPO7mf/rgTG4PX6AfDQQx3E+BMwINycYSsbAAgPcjA8I1hqBl5hhBwE65TCgxoEQIBn2BZkENB+/tk7BvzfTwg3LDj3DR/ubW0TLXk8ITPef85/7DG3lnr1cj16/sQpPwC+9ZbzwOO86RfOa8HPDaWzhg3LHY8uP4jXxnkJnspgP23/c+OagrjdsGsVjjcirwzMq6+6G5WwJBY4E3CuQkIgbirD4pv9Oy/4zJi9eYOvAd9PPFBFYffdnecxWN3gvPMMACtE6xPRv4noQXLewIYpzXstEU0gohlE9CERNQ8ZUxgA581z8RG4+7nssmhvk1+tW7uTaZjgtcKXNd92cXA9uGs/5xxnH9VzGNu58GIAAP1JAEEBDv0AiNiSsC0s5iVPzsOG5Xosw+bp0iXXMwEAPO20XHjyCz0igwCIC0dUSQu/16RVK28rKAimyy+/ZG/bOACIrDXEumELIyz5oXNnDxhWXtmLfwI0/ve/S9rAW4nHQQe537dp47Yzw4LomZf8nEaMcNuT227rIOjJJ8PtOnb0iiej6DQSOsL6J7dokXvBAwAeeaR7jWF11bA9jDIdEyY4ENp++yVrNjJ78UrNmuVe5K6+2kuICMZz4kYG8XPPPON+j9IWTzyx5LqCF/qaGheLhGB8/y4AbhL9tSqZnecX6wxuDfqfG8lFTz7pvDINGy65nmD2Kb7TCPPwhzVgDLaHiTyAb9PGbYeHZb4jax3xZvD24H0NtuhCiZAgAAIYgzdzqIuJB4DjwQf/z96Zx9lU/3/8c+/MGDPGOnYytiyRIku2siQpJWkjpc32VUlJSGmxFS2ERELf4ktCEROyJbuUJZFdoZ+sCQ0z798fb6/5fM6559577lzXzDTv5+NxHu5c555z7j3nfM7r8175N7CXgUlL8y3H88svLCDgokfhcPDFF/o3xZiEygu5cvne52aHIYz5Y8fqycHJk7zekSM6pMNcLl5kAYUkCdSxBPaYQZw3CE97/O2kSdb1Ea+MJJING6zrm+vCGg4RaR+3MTmHmENIy//+x+fCaaKCeFAs6NOL8ImFC30/g4LOpgA0rcVr1ljXx/1pF4C1a/NE0F7d4JlnRAAKfnlBKbVfKXWNUipWKTVEKfWb4sLUJsEF4PnzPMMpVYoHj1at/NdaM7njDv8dIJBxhZs1Kcl/j0rzeOLi+EFOxDdE06b6bzvIFMSCmxSmf/sMLDXV1+V16pR2JcTG+rq9L1zgGbJZrHrrVu2iy5uXHxB2br7Z6m5GrS0MlH36+H4GLkQsGKgwWPpzfSL5QCm2asBVZ3f3Fijg68rAA+T0aXd1x4h0HCNq5DVtyttxKtD84IPajVmokLbe4UHvVGrIPlOGAGzYkH9HpwnB33/zeTJj2nbsYKtf3bosCCZOdP4+NWroMiOoOfjSSyzAzfiitDT+TaOieLvYD0IHmjRhq5ZT3CAEJpbff2f3d6NGfL3iWk1NZWGCBCjEJEEA9umjLY32UjAQjaglB+s8BLhTnUsnAdi3r46PM7OTIYRMsULEFjBYX01LlT3rEeWFxo9nS4+TJcbuCsU9DYFputWwjikaIeBvvJEf0E6Z77CYo4wTBGCNGmzh6tfPuj5ch1iQ9d2xI//dv79eNzXVmpSllHaxvvkmixV7b2Dcr6b1/5dfWAQhnMVuuUcyEJZt29jrAte9fezDdzXH/OHDedKilLas2q8HUwDOmKGFuL32oVkayBSAsOjZxRBcrnYBCIug3eJmrgtrOCzJ9sLUcIVjrIcAnDSJXeZO1wTOpV0Awsjg1N4RVkPznMEKW6KEb2tS3J9YUEWhenW+f+yddHr0EAGYTcivlGqulGqvlOpgLJFkj1LqKePvKKXU/ymlHrKtF1wAnjvHdZDKl+dBuUAB/w9Lk3vu8R/Ltn279WYtUcJ3ELBTtCgLBgi+Rx7hwd3pQUHElj7zYYSHBQYRe4YyEiHMOLtTp3iwgmXQ7tpAggjKFUBY7trF+y5VirMmTU6e5Fk+XERK6eKxyEJ+5hnf74MyJ3YBiMB4p1hIeyus+vV1Eo/dalCihG9mN2JrTp+2ZqkFiv9EPT4IwLp1eWB1airftq1+OBQtqgPgMbN26sJgBpsrxbXwiDgU4PnnnQvp4nzAQgEBOHUqi6w77/TfUrB4cS1SIQC7dmXRYcYXmQW3TTcZBGCNGiz6ndqP2S0kv/3G22vRgi3FGzfyerjOYI3CtYwG9t27+49VQ/YuzikEYPnyvA+ncif2B31qKl+3uHbN+2HBAi0uTQH4zjvaCmy6aFHwGQtcyu+9x7+VU7Z50aL6O5v3NO5Z07KFdUzrDQRg1ao6acBuKUX2LrYJd1+FCmz5tMdwojQNFiT93HcfTzrMUkGwCjoJwD59eBJj70sN0WC2gdy+nc9n6dI8DthdqCisjmXrVh6X8ublcTGQBQ1j/muv8XWJklEYN5yWCxfYgtyoEQsru9fD3jkE5w0C1j75t/9O+/Zpy118vDVhyz7G4ZkybRpP0uwxjIiFxeLx8Ptjx2qrr91S3aaN9TOwAmMS7+Q9QAypec527ODr/JprfGNucX9iQdJHhQo8AbWPG127igDMBjRSXPLlb6XUxUv/XlBKHYrgPvMpbj1Xz/b+N0qpEQ7rBhaAZ8/qeCS4d5DGHwizv68dmLthuUhM5BlQMGCJJOIBtls35wcFTO3IMlOK42JgdYiO9p2B4eY0b/aTJ9lNWKOGcyzW5s08ozbjy7Zs4QE3b14WHKZ7AMJXKWsCBATgvffyTB+FoU3s5WYQMzViBP/tFPtmloqAAESrLbuFqHx5XaAVQCycOmXdlr9exZ98omuEvfQSv3fNNSzs4OY2ueMOLSZKltQCEFmPTkWT4d7CgozXqlXZzeyUbLBpEz9YYaFQimfkkyfzQG1vRQjS0vhageUJoQ/t2rH71wzYh9t39GjtblNKD9xXXcWvIUJMYFHDcvAgXx9t2vDvsnq17lWqlHbBYkHtsocf5t/AXueRSGeGYkGCTYkSfJ0i65CIRYc9uQFi6eGHtZsfrr60NLaKQOiZAnDoUB4L7N1n7MkQEIBDhuiHp1nXD65/c7KFepmw6ptCCOugMLlSWsCXKqVbCdqTtBBnCDELa0/p0my5797duv577+lkBVMAtm7NkwezownCPsxqA4jXfOIJvg7tvXR37+Zr0DznP/+sJ+XFi/tOnu1jxZYtLPqKFOEJ0sqVel27EMf93acPW6bj43ncQjFkfwJw4kQO+8mf31r0/c8/rZnhpgBE0ggsgvv3sxcCpbtMAYhxq2RJa7y4PQYTz5TJk/k822NVzdhJ8zp9+229X/s1Ya9FCwGI72W3nuPaNi3W27dztnLx4jxZtVsN7fcnBGCpUjym2sOunnxSBGA2YJVSarDiWoAnLv37jlKqcwT3WVqxAKxse/9/SqnxtveCC8C//+b4juuv164WfwWeTR5/3H/xZNRoguUib17nOmR2Vq3SAvC223TdPHsB2IMHWbDdd5/1YXH+PL8uUkTPwGDNWrmSB/kjR/hGVYpT86dPZ5dRwYK+sSqLFnEgs9kcffNmttgUKeKbCY3AZKV0Wyul9EPirrvY2uhU3PqVV/ghYReAb7zBg65TvS4zU1ApFqpwgSCuB1xzjW8iDgTgyZPara2U/7pd5r4gAMuU4cHLzKLes4d/91tu0YWjS5TgAZNID7joh2liCmdTACYl8Szenp1KxCVOkpKslojt29ndeNtt7OJxam2IWE60dUPSx803s2ivWJH/XrWKLTTIdDZLOqD/bEICW0HhsjZBFqspAIcNY4t3uXI8WYEVs08fHUiOpUoV3k7btixInIqwm9ebUtpqUaAA7x+uTVh64Uq2C8B77tGTjt9+Y2GKeKoRI/RrPFhffZVd8wUL6hCD1FRdWgMLJnIDBujaZ+a4tGoVTxTR/UQpnpimpbHgVUpvH/e5UtaYOwjAAgV0hxJ7SRSU9sC1DwFYpAhfx+ZEZuVKDjUxH/TI+m7enCeOZp1ShIqYlQNwvbZty4LLnND26MHjWMGC1vqKP/+sJ+VXX+3bJcJe0H7LFj7Wq67iawUCDOfA9HooxWK7Rw92YyYm8rhnxrRVqmQV4ikpLIJatfL1ejgJRgj32Fhevv6a/0bMoWntVIonM/C2XHutdUzdtMm6Lp4p776rz4tp0UPhcbsAHDRIPy/M8JjUVF8BC8NGjRp8/Jg8rlnD5+SRR3hbZrkcnLMqVfh+QwwuEd/bqC2KBXHghQrx5NJez7BTJxGA2YATimPwlFLq5KV/E5RSuyO4z5AtgD169KBevXpRr169KDk52XrjnjnD8R316ulg4UBts0D37v5rnqFxN25Wp0B3J1at4hkgEbsbUPds1ixr3Mm6dTxgm3Fzycm6P+TVV7MlCcH8RDwjg4jCbPPECZ5JNm2qH8QmH3/M/4dSGhCA33+vLQYoA7FggT5epXSdKlMA3nYbD65O5UWefdYagI2g+X79WGQ5lY+wz3YbNNDixH4Ob7jBNxPUFIC7drElIjrat2wDMPeF2KfERC7rAvEEV87ChexCgSsuLk67VBs3ZrGDOBiT55/X9cGU0skvRYtyILdTDM+CBWwZQA08CMDRo1kUPPmkc4FmxKrC8o3vUL06fz+EI2CbcE2Zbqn77tPWtMGDnTsrmN9HKbYgDhzIYgNWZFgPzGsIC+IeEctYoIBv7Kndcoqkj9hY/g0QdoA2d0WLWh/ySvH3atmS61TC1WeWr9i505psQ8TXZ7du/P733+tkkU6d2BKiFAs4nLfnntP3rVn8tmxZ3rdpLV2wQLsmvV6+p4isGa9z5vB9WLiwtqLExupzi3Fj40YWg0hAwgJrT758bN1DjJ953s04QyT9NGjAExwkCpmVAXr10q+RiNKypc6gt3dfUsoaM7xtm56U16rlW24LItYckxYv5nHPLLmFCRFi/bD8/TeL9ldf1YLu6ad1lu+nn1qtmCkp2mJtej3MhC0zThrPGI+HxwccPxL0lLJeR3v36glQgwZW8dS/vzUsAM+U/v1ZfOP7gEWLfNuLEvGEFUl4CCUwz5mZIIiJ6dVX8++Dki3m+Dx+vDUZ7OefeXyqX9+aIHnyJB+/3QuA8Sg+nl9fijdOTk7mZ3WlStRDBGCW56hSKubS6/1KqUSlVLRS6kyE9+sUA/iHykgM4F9/8cPippu0C8MNvXo51zwj0gHjcF15vYFbcYHVq7UAvO46ayHhSpX0zG3qVBYT5kw4OVm7O+rV4++ENP5hw3h7EBOmm/SDD3hmi4Hzt9/0ADpwID/ITAvgTz+xEKhY0doz2f7QRi1EpXSAb9OmfNz28iK//MLvmcHREIDPPsuzYqeyGfaSDQ0aaKuWvQ5io0baKpSSwi55CMATJ/gY4uKcLaFEvkH9sCjFxvLgiiQVfO9p0/g8oNm5UixCidjimpjonDjSrp0eLIsV0+csb15tYbXXRYMVF/FxGJDfeYcf3k89xdYOO999x+vCHYiag0lJbGFAFwds0/xdTHGA627iRN8Cx2lpvha9/fv5eHr04Ad8//58jZYvry0F+IzHowXgjTfydy1e3LeftD0x4JNPtFv5scd0V5Np0/Q69kD2Cxf4PE6ezILop590RwtYP+11AJ97juNXK1RgNxdKlBQpou/PmBhdj617d2tx9fXrdbLAwoXWeo4LFmjLUOHC2vK0a5deZ8kS/p5jxvA9hIf6b7/xfnfv1m3VHnuMBbppxYQAzJWLhSwsumZIhHldQQDWrMmCEaEZZlwe4gxNAdi0qU5A+ecf32x30w25dasWEzffrFv/ffUV/36o2GCOSfPm8ThhlueCxcue2XvmDFthhw/X5+3hh7UIXbFCiysc74gRvD3T62G6jc1M4+RkfR6SkrSggyWyZUurG3jPHrbuFiyorz8iff2aYhTPlC5d9PcyrbzTpllDg5TiexDXqXntmiE7qJ+ZN68WgFddxduCocM8rzNmWK+Lbdv4WXL77TyOIObYvN/MCdeAAXxcHg+PU5gcX7zIz7nWrcUCmA1YopRqeun1dKXUJ0qpMUqpTRHeb2+l1D7FpV/iFLuhD6qMZAGfPs2DOwpS4uYIRt++/ltSoXF3lSrOhYn9sXq1zuoqX54HJnP2pxTPRDt04BmrWQR5wQLdeuzWW3mGBjcXBlfE7+CBcOwY33z33KMHHrin4Ip85RVtycBDcf58vpkxiNrb/MTEaGukUloA1q3Lgx+6epw9ay0pM2YMFwieNk1nTXbpwhYzp4r99hIVDRpwTI5SvnEuLVrohzhcQxCAx4/rIPIyZZwzts1i4UrxQxyWsJEjdXwfyjOMHcsWjDlz9GdQ07FaNRY19gkELJoQCNddp10jUVH6+x4/zscDQTBiBAsxtAdUigXgsGF8zvv1c+70ggkGsodRc7BwYZ5AIF4L2zSzMfHevfeyeM6dm4vrws0NUD/PLGa7f78Wpabb+tprrRMHCB+I/xo1WAQ4ZdXbW9RNmaKtZ1zhUdoAACAASURBVL17a9fl+PF6HbtoTEnRJXNw/yEMApgPYyIWsX368DldsMDa1hEljKKidD22Tp10WIBS2iqDbE3Tijt/vo6RM7+zmVGJhIexY1kAYnJ37JgOPUFXmYQEtuqYtQyHDtWTmz59tBt5wgTduxmJWErpUIeqVfn6hUUKyVFKWd2puIYaNtTbOX3a2gpQKWs3kK1b2YLUvDl/p9GjrdecffnpJ85Mr1PHep83acITCXsJkjNneH/jxvE9Ons2f2/89nv3Ws/D+fP8nR56yOr1MAWROTFYsEAL3Nq1tRiCSBw82Orq372bz22ZMvw+Yu4wTuEewjOFiK2a8PCgldyUKbwNZPVjSU1l4WwWrL5wwVpBAbHNlSvrcJHChXnCgNqe5vjy9dfaoqsUC8AhQ3i86dRJezdatNCub4xrxYrxZ1Gya9Ik7eHBc6dJExGA2YDrlVLXXXpdTnE9vk1KqfpXYN+vKqUOK7Y2LlMZrQN46hTfoOZN44YBA5x7pRLpAbpyZT0Q2GtHObFmjRaARYuydcAsj2AuU6daMzPnz2cXVWysjpVCjAYsm3DF4sF47Bjf7A89pD9jj9caMULP3BDnhBpuiIOElQRLnjzWoH7E/FWqxLFlsBLZyyfgAYeEFSL+bOvWznXTUMMQS8OG2j1mT+S46y6ugUakCxND8B8/rhMpqlXTMTsm9njDvn21yJ0+XQdnmzX0lLK2xYMLHu5z+wQCVgH02WzZkrcDFysy/A4e1IM3AuJ797bWF9u2jdfp1IkHbnuW3YQJvpYRCMDcufl6gtXKfHACvNeunZ64IKHIZP16tnbCRYZko8cf5/NgPmhRJ/HUKW3drVxZtzSrXJkFWaVKvnFh9qzEKVP0ZGDYMJ2cYq5nhikoxQ8kWBlhvR0/3loPEQ9KjBNPPKEnSbNnW7vgHDyoX0Pg3XefvkbwnW+5RbsV0SUmOpqvw59/5vsJ7fzslmh4FsaO5THMvP6LFeNxokULdoEjmcS0Mg4ZomMKBw3SMacPPqiTYWApxmeJeEI2dKgOE0BnEKV0GR+MBUQshJBR/H//x9+lZEn+nrlyaeuU18uTMVg027fXMWnm9zatyj/+yKK9cWM+zyNH8u9UpIieJJmf/esv9gh89hkLLiT3vPUWT15TUqxu5vPn+fh69uR1R43Sxf7t7TWVYgGIsaFTJ/YWEelyNCNH6koASrEAnDWLr6F77tExdxUq8LkzQy4gABs35nEiVy7dOQRJJ/b41tRUHndfe01XjsC5xYLQgDvv1DVNExJ4kobx2pwILFtmTUDaulUn1vTvzxObtDS21i5cyN4BdAS69lreP+K158zRBcKN60gEoBAu7gTgu+9aTf5uGDhQ17ezA2FSubIeCJyOwc6aNbpOVlwcm+jNbgW7d2uhevKkntkrxQ9sZO126MADu73AJ7p9mKVSIGQff5xntPaaf2fP6gw9uEenTuVB4ZlnOGZtxgxrjUGUJsDfqA1XrBjPAuEKRdKNx2Mt3QAhS8QC4+GHfctHELEbsGhRLWQaNtQuM7OGGxF/PyRuwDUHAXjsGMdVFi2qB1Y79jpWffvqxJGlS7WF0t5HdPlyFl/t2mkBmJDAD1JkRwMIQFgC772XxQHccadP84B/112+dSBHj7ZaG7du5e/ZrZsuuWKC9czaga1aaYs1EoXMh49ZHgcPknvu4WuvWjUWu3DXrl3L1oiZM/nBhjhSZPC2b8/CzBShZvwgzmOdOtoCl5TEYgSt7dLS+CH+4Ye8Loo+K8XWPQiwCRP4d7SLJ1jCsfzzD2/jiy+05alvX2uRWjPulkhbwevX5/vRFD9mjBXKcaB+6P792pVpFonH9vPnZwGIiUmVKuxahIVPKbY24px88AFvGxOVtDRrYsXixXpSYh7jkCH6Qfz++2w1I+LzNWECiwdzMoeJRLFi/FBHlq8p5M0+sRCAsN4qxZbkzz5ji31aGv9OaD8YG8sCcPhwFstduuh4W/NcYexQin8jeHGQKb5xI1/baNtmfvb0aX08Zts3s1+7OXaeO8fCf9Agvr8feICF0W23+SZpYCxG7OiIETrkBWWaPvrImsC3axdP3po14/uhZ08OS4mO1tnuWBf3V/XqfH2Y9U2RFAIvjnkdPvAAC9wffuBx/P77rRZAiOBHHtECMCqKr7lcuVgUm27+9eutHqgtW7Q1fPRonkweOMDbOHuWt4cuMbfcwucU9/iyZdbkx0vbFAEohEtwAXjyJD+IzOBoN7zxhnM2K5G2LCBuD4NIMBAIb8bx4Jhg5v/qKx1QjobmSvFgsH49ixgE/ZszwfLlddsn0yr5/PPsTh48WLcusv8OeLAWKsQD3qRJ7J6Ba/GNN3iAGTqUXaGIN7MLwNhYnv3CbYQHlL3W4c6dOluwVSu2bjmVw0EyCkpbNGyoyybYM6eHD9cZi4j9gnv92DGdgX3PPdaacRcv8mD87bfW3+bFF3XcIEpQEPEgDJc5hBQRi7Prr9eCqmdPHU8F6tTR+x4xggfS5s15gEc2pT3+Ccu8ebpor9fLAvC55/iBAvcYEZ8LdObAdYHXt92mXd2IMzMFh8mJE/yguOcetr40asRCuFw5/n/TwtC8OU82NmzgB8LevRznOHKkdpMqZRWpmKQ0bKgFYLFifI1DpOH+uP56/j3N5INJk3Rx4GnTWGyYgg+13Mzf8Px5drtDGAwcyA9KZMoS6ckDLHqtWrH4atqURYwZx2rfPpFej0hbgdBfm0gLj5Il+ZzCK3DttVzaxbQqmiD+ypw8mVamI0f4O0+aZHV9Dx6sx6jJk3W7wtq1re0DsT5iBPPl4+PxelnEmUkcphsc2fxVqvAEAQlx77yjW9cRaSGVkMAT2Rdf5MkLxif7b2mKnE2b2GJ4551ahPTrp62Z9s+eOsXX6dKl1jhqM0kMHVMwdiOuzYzfHDVKr2+6Q2G5jY9na2DlyrxPjK9mOSnca6NG8fkaOZK/x/vvW63pWBfhEBUr8piUlMSTTCK+l8uXZ0/KQw9pMXXhAm971Ci+9zweHSaE7SIU45FHeDzHOHXgAJ/jAwfYUIKM9O3brb/Rli0sjgcMYEtfjRo8WTP7UaM00UMP8e++bx8fyw8/6Ak+4o9FAAqXgeAC8MQJXRT0o490vEkwEO/gBOokXX21fkjZC7I6sXYtP+RgNTTj6JxAg3UMOrjhnnmGH/z+rJqI2Tt6lC0OffrobUEQmAkKzz+vBeAPP+iHDdzHjzyiW/uYYN8PPaT3OX26bl8Ey6K9vRke3ERskcBM027VW7GCLW84vkaNtJvUXux03jwdQA2hYLaaWr6ctwXXz5gxfO4Q52M+1CAAUQ5n+3YWgshANkUAZudffsnfE9a8IUOsNfPS0tiFY3Yr+fhjtgp89ZXuKlOokN52jx56EN66lfffsqXuhoC4n8WLtYiyC0cz9uq227TbFP8iwcOpFd+77/JD/P33+aG1erW2YJsiE787EQvAPXt4e2iFhwkPXMD4PZRiVzncQ/nz8/dq2JAtSGi5iIe3GeP08ccsJPLn1789rGPIprX/HufO6b7dffroTiSmZQjxbRCAiBm87Ta2RPbuzSIJbjm7AKxXjy3mRDo71RyfkFl59dX8wFyxgh+KyGJHhQH7PY0yJajRScQTDKxrWm9N0T14sBaVX3yhrfNwOdt/JwjAmBhtJf7rL56IwcKHf5XSyVyow4k6ehB4JseO8f//9JMOEXj1VW0pxzbnz7e6nH/4QSdpvPQSf7ZTJ+uYZJ6Hkyc5LGbDBquIMTPL09L09XX2LE8gpk611iA0e3mbMcJff63LbiGcxdz/lCnWyfmvv+oYw7lz2brXtq21JuO+fTyRgQC86irdxalAAR7vYmL0dUekJ8MpKdqiax7n//7H9+yGDTo0olMnHr+w3okTfM9i0oyM/gMHrCFImzdr8bhxI49T8AyAGTN4XYxLGDe3b2exjAnapT7up6pUEQEohEVwAXj8OM9GnILkA/Hmm9babyYYGCtW5IcdAsCDsW4dC0BTNAYSgKb7Yd48/aAfP54fnHD52EuomO3SYOnAA/fpp9myZVosU1PZOoKC1nCZjxunS8g4dU8xBSCE2fLlOs6xTBke+OyV/n/9VVs569bV7iF7d48lS/j7IhutUSP9MLN380AwfUoKf0dzQD56VGc2m4Hd5mJvcdWnjxaNZ8+y5QLJHWasDGK0EB+H6+GTT6yt1jBYm8krcAtNmaJdc2aJh+HD+XctV067WYj0QxalLjZu1OUo7N/LtJq1bKldVwjQxu/hdA+99x4/qF5/nQf/bdt00L/Z/i0pSX8mOprPhb0/sVLaMmy+d8cdesIA6xEK8ppxWuvW8XdViv9v4kRtUV+8WFtO7f1x7QIQ8YWmewudSoh02AVcutdfzwLz7rvZwgIriNP2ibT7mkgnZ5nAml2jBosBxDwiZs2MxzOBAPzhBz7XRNYsT5Nly/T7gwbp++3rr/VYUaCA/6xvjEsHDvDv8OuvOrazalUevyB6IABRbqVYMT5X7dvr8iImKPGDmF0IOzM5aMkSa9LJDz/oUJaZM/m3MysU4PjRmePkSX0tIW7WXi+RSE+Czp7l7zV/Pk8MsV/TYojxs3BhPr+4380yQqZwNF3Pv/6qvSnbtrEYuuEGqwWWiK8FxMOi4wm2gWcGXN5Eet8pKdYqCKaQBuvW8bXw6KN8XjDxsyf43XMPH39qqjXDd/NmnnC8+aa2KJcsaZ3U/fMPGwAQOgSP1d69PKHauJHP/6WxWyyAQrgEF4DHjvHF6NSeLBBvv+1cz45IF2GtUEG7Cd2AODTMjMzjdMIsBzFvHi/Vq7P7ITqa3S4vvGB1MRHpoO8//uCbFqUgli51HggB2nYhu9QcgOxFlvE7PPssr7tpk44hhMvXqc0TvhcEIB6YXq+1FyoRx5TgOyrFliFkQtu5eJGTG7Zvt7apgwBE4Vl7IWYsjzxiDTx/4QWdDU2kk2d++YV/w0GDrFbfuXP5u8DdsXKlNbFl6VKrUCLih1PTpmyVRo03M2Zv/Hjn81SgAA/IiLOz9xfGg1kpa1xPy5b697PHyzm1x3vvPRY+KImEDiEXL1prr8EtTKRLk9jrMirlm+izYAGfl3Ll9PEcOMAPd/v3OXyY3YpKsRD/6CMWOuXK+bahMjHfP3tWW6pQvxHuMJCayv8PAQhLGWKs7rvP2uFl+nRdozAtTZcdIeKHsv14zAzwr77S5U1uuYWPCW40e8HtDz9kK6SZSGZ+PxMze37QILYaJiToWnqICTTHAqzfrp21ikBiIguB2Fir1R2CG9n8ECwVK/I5LVHCueMSYtpQD2/sWN/zvXy5FvsQ6CjsvH8/jxXXXWe13G7erMcrCJTDh3XCld1jQGStKIBWkqb7HAWfTWrU4HM2ZQpPwu1xpkrxOG+2g9u5kyelvXvr0AePx7cDimnJh5Uf20AXIBNzMmfeb4i7tfccJtLJIv7Ggbvv1uuaYTE//qgTCc3PIKTABM/bb7/l+wGxgJMmWdY/tXOnCEAhLIILwD//9F8nLRB4+DmRlKQFINxQbsCMCEKQSCeBOGG2OZo7V7twzKBtFCU1gXXnjz901pwb4DZB94O0NB3XYu8JDCAWcbPDQgcrmVPXDRRlJtIPWLiGTCDA4G5s0EDPJp1AbUWz4wgeCHjQmrPazp35oVutGlunzDpWL7zALg3McNesca7rB7B9xMlhcIbl7p13fOsjTp7Mlj+0GyOyHvf//ue8L1hR2rblc2t27lCKXVOPPMIC2iyRceutVusr3vfX83rkSL4H4G4zQxfMOEhT5KNlWlKStfC4Us59hGFlNa3W9sK+eIDDTd+sGQvABQv4vNnLBZmY7rmzZ7VrDRMBp9ANc5KB+xW/AWKrTHDNp6Za+4IjC94ELkZYFpFxj6SU//6XhYUddH2ByxgsXerbAxvHD4G7cSPf2wghQTKZKfqvvZa/a7t2uizJ2bM88WnfXicnABQWRjwirNIdO2pLnGmtApgkwhI7ZYq1E0nlynw9v/mmfm/DBvYyDBnC5wsJSvbWj/b41jNnWHCZsXwmCNc4c4aF7C+/WDtamC3nACy3CI8wx2IIJSLrGLRzpxZeRDrMw146DF4KIm3BtN8HJhBW589bOyEh3GfLFt/jh9fgxx91Mp85KTbHKFhl8+fnZ9cdd/jWhbWXhSLicbJ7d+0FwbjRs6clMe7UqVMiALMhMUqpnzP7IC4RXAAePcoPemSIugVxT3bM2U/58jw4oaBuMNav55kyxBKRrk7vhJmhOXcuPxwaN7Z+PydhBgF45AgHbyMoPRiYxeMGxvdt1cp/9xQk2JjuJdPtaE/WIOKBBW5z9AItXdp3wP3qK35QIvarfn3t6nXiwQc55snMWIQInT2bRUtyshaTeACifzKC6hMSeLb+8cfWEiGBgHV29mw+5rQ0tsLBOnv//dZ+tUS6tEXv3jxJIbIet73hOihYkEWPmclpfu74ce3iN5MKWrTQZUewL6fWbmDkSP5tEGAO16C97poJylbExuqOBEQcR2YvcE2kBSAeEqdPW2PbzH2kpPD6+N4QT+Z3tB/Pd9/pmLgzZ/Q5OXnSORucSFs6iXQM07BhLLyKF9cxfsAM6YB1loj3sX27dd3jx/m816zJAhAJLPfdx+5QJAvYGT+eLbiw4gXCjC1+/XWdAIXY5TFjfMv5EOmaofj+aWlsTYqJYfFpgokhBCAE1K5dLApQSN0OEs0Qbzxzpi5vYp47U5Rs2MBWwg8+4P+rUIHft7ffRELTDz/o5JVAoEapGQ+bmqqTK5z6u8Ny+/LLuq2eee3BiwELYHQ03wft2unyL6jEYJ984LmA+/nAAXa7jxjB94i9C4/pxkXSC5FOUnSqTfv44ywAzevILJdkP88nTuguOLBSm9/5+ut99/Hyy+yuf/VVnojieaSUpbuWCMDsSaxSKjWzD+ISwQXg//2fDnwNBSRC2DEzd+F+QnePYCCbdPhwXRn95595YPbH9Ok8u/vqK/0QINLNt516EKOu3OHDLFJRgywYRYrwYNCrl65tFYw33+QHNh7GwOlhDOCy3LRJuzqqVvUVPLNmWTui1K9vTSCxM2gQu+rM1lYY2D//nAP04dYwxQi2D9dJ8eIsyt5/35rIEYivv2ZL4pgxWlSVLcuDMoLm7bXt0KLwoYd0cVZYN5RyduEQ6XqN5cvrbf7zjy7sa2IW4m7RQnckIOKHi72nsgnEiFk6J39+dvGUKaN/K5PYWP1ACbRtAIsWrBkpKWxFCmT5gBXus8/YwpCWxtaF335z7siDBzxquwXL2Ddd3UqxwDTd7PbrFPGvFy7wtemv1aAJOvOgVSPKm7zwgi7MazJhAt/7uM4CYU4cX3+dr8Hy5a2WUieRCavWjh3atYvWfZ07W9fds4ePHfciyv8EA8IPsaJmpQMzRMBMQFi/nu99dNxAOIbduwB37LJlzj217eDegKUNSWhIHHHqGX/99Xw/P/UUjxFE1uv0r7/4PUxCIYxbttSWY8T52lmyhM+T6YIPhFmBAhNpIu0Kd7rOUdt12jTtjjVbbjpNeNFG9Kab9DiA+9+phefrr/MzFwWjTaMJfjMSAZiVGR9gmaiyugA0LzjEwZmlHtwwfrxzZiTi/5TiB7xpzQvGhg08AObL5+wO8wdmnQjKJ9IPJKdBFw+An35iF7M9ucIfKE7dtav/Gbydt97i3xe1AwFm9U7g2BHTdewYJ4PYLStwwaLkyI038mCNh5OdJUv4IWJ2n1CKxQXEQmqqDtIHcAvDbV2hAoucAQPYleOG+fNZqD/3nLbm1a/Pv0udOnwe7N1L5sxhIdCsme6tiWK+/sQ9EV9DELLmuYXrzcScfd9yiw5DcMP777NQQE0yIt1YfvBgFvD26y82ltfNlSu4BYaILXRlyrBwg9XG7Ct6772cQGOCfr6oDRcMuPqSk50LjtuBRdF0eRPp+8oe24YEH7ix7cfrBApLw7XbuTM//G+91RpjCCZM4P8zH9yBwO/32mvaVW4+6FF7zwTji+keRHtCp77WCCcIpRtSYqJOovv9d7boOon8Tz/V769bp+skEunizHbvAoTT3LnuzjMsht9/r8vZEOkJgNP3geu+Y0dt0XeaqMACGBfHAhBZxkTOEzUiLdTdlhbDemfPaks1Ed9Dc+c6fwaFzc24Y7MtoNMzD+56M8MdkwkUrjZBSEvjxtr7hO0bmc8iALMuF5VSc5VS0xyW6SqrC0DTJXbkiA5eDYWJE3VLMxMEaSvFlgu4/txgdrYwZkJBue46HnRQSoCIB93hw51jmPD933tPlxdxA+KdHnjAvWB+6y1eHxmtIJBre+9e/r8SJbTVsHlz3eIJTJ3Kog01uOrV48HUHKxNzpxhEYEYTSyHD+sHqBPodPLzz+xu6dKFhdyDD+ouBcFYsICtmHffrWv9PfCAzpY9eND3MyijYFo/+/XTvWPN1mwmiYl8bsuUsb6/aZNzTCh+h+bNdW1FNyAMAhmeRBzDE8g6kTs3Xwtu97FyJcelmefVrDnXo4fvZ9BOy1+Yhh084MaM0cXSAwErv2kJBIcP+wpbxHtiPydOBN8Hes5CZJvlR8zkBgCxi2SQYJgCEJZms9sN+oGbIO4ZJU6IdDyhU/ksJHOh7ih6mQeicGGdpXz8uLZW2cdnJNZAAJr9oZE0YgcJFp9+ai1N5A94SpTyzR73J+Lhujfridqtl0S6dSGEGWIHifxPjhE/jPp5wSZQiNWEhdtNNypkscNKZ+5bKWevF0onwWoNlHLuloXM7tKleYKHdZWyxBuLAMy6/KyUau7n/3KrrC4AzfIqhw9b40fcggB9O2Y7rqQk5/6o/kAPYaUssRBBwaxzwABfV4w/EhJYwPgrZu1EsWI8yDoFuvtj+HAWOnaX+bffOlsyiKzuNAwQnTrxzNTkk0/4HKBUSd261hg2J6pWtYo/XAOm9dTOxYssKJAp2LMnC8DatX2tkv5AlnGNGnqQfPFFHuz9xWwhDses9k+k3aFwJ9kpXJhd9GaZmUC0acOioXlzfvii7EowRo9mdzZq+xHxQ8lsGWcnLo4nD+iKEozvv2cBaLqmzfqWTpZoFGd2aoHnBFx9vXr57+5jAgG4dav/yYYJBCAEo72epRO1a7MAxMMSbmSlrF1zwMSJLAAx4QoGtvXqq9qbYZYsOX7c9zOI+URRcyI9mfQnAKOjQ+uGVLgwT3Zg4ULPYLvbHD23leIxCUkRRLpbkh0I0bFjde/uQJieokBjiglEEHoNE/Hn7ffUhQvs6o6PZwEINyoRi3Gn41+2jL1KmzZpC2wgIPx++YUFo5tatN268T317LO+vcpfeUWLbBP0PEc/bHDmjHOM98iR/Pt4PNqKevIkL0Y2tgjArMsUpVRPP/8Xo5RafQWPJRDOAtCc2R06xA8+uNjcgu4HdsyyGmXKcEyEG6sCEd+o+Kw9cy8QuAGffpqFiRtKl+bjd7Kg+ANxJEWL6r69wcADLJDAsoNYLKX0LNcsCAuQhIHevnXrWgvhOtGhg68APHSIXZboWBKMZ59lsVCggHMguBPJyewOQacDIl2Cwl8NyhUrdDblkSPW/3PKoARJSSy27Z1GAgEL7cKFvhmd/hg9mgf96Gjngd6JuDh2n/uzttpBWROU+iHiBwvq9KGEkQkmdC+/7M5FjyzpNm3cxQKbYsyfFdsEgn37dhbLblzfZu9cVBtA5qiTOJs4kV1q/qwudt56i4XGwIEcQvHEE9Z4UKfzOWoU/0YffWSNBZs50zd8gUgnc4XSDalIEV2vMi2Nr/Nnn/UVL5gsx8ZqiyEsjLNmOU/O4YIfPNhdaACR/j3s3Yr8Adf9zTdr92Z8vH+PTnw8T1pRYYGIr0cn9zLuhS+/dOdVgsV55UrnVppOoCKGv+L+TtSpw4YOdCcJxtixPLG1lw6yIQJQCBdnAWjGPf3+uy6yGgr+Ym0utbEhpdhyYSZmBMNM6w8FzDrbttXt3oJRvTrPJkOxNJYowaVHoqICCxCTkSP5uIYNcy9InArYOiXd4LdFTbA6dYKX3Rk82FkAhlIMvFcvHiDdWjWIuGYYyjvAQoY6Xv4mH2vW8P9HRQUcKH2oXp0tCqGUNkKyAcrVuGHMGHbpuo1xJWJLSseO7sX2Dz/ww9F+PSBb2yz2C1AE2MmK4QQEYJ06zgXN7YQqAJGNuWFD4MmJSZ06eizBBBLZ8058/LGO8XRy0zmBgP8OHTjBBC5Sf/tA0k+vXu7uFRQ9x+/lxgJVpAiPxf7ieO3bL1lSZ3E7ZZGboP7pU0+593zg93AbF3vDDXw8des6u+rt5MnDAjB3bmtWvBMoz/PSSzz+BAMC8L//9S2y7g94NxBG4YaGDTkcp0wZ59I4dpDAE2SiKQJQCBdnAYiBQCl257i9WU1mzHB2I7z1lt526dLabeIGs9p9KNSqxTExlSrpYPxgNGrElht76ZFAlCnDVh837gcwZgzXjkLtQDeYZUSA2aYKjB3L7h40Na9dm4OPA83WP/yQ182d2zoJwMDnhl69eF9us7uJdCP0YsX0excu8LXhFP9HxOJHqdD2Q6R7LLut70jE1oqmTXU5HDcgEN+tNYWIH/CNGrnPIt+61Zr4AlDLzOwmAmrV4nP5+ONsBQwGXJRlyvgmADmBpA63lnpYwJYutRZpDkTduvq6xkTzzz/9Z35/8ok+JrcPe7Rbq1+fRZcZG+0E4hGvv95/DUqTPXt09q+/2px2ihZlgeDW4layJItfdKAJxMWLLEjr1XM/7uH3cJM0QsSiZupUnoS5uZby5NEJE8GSg1DiqkULdyFLmNi0auXrPfHHc8/xWFirFltS3dCkif6dAlWsACimHeS5KAIw65Js+/veTDmK4DgLQMSCKMUPX2TRhoK9rAmAhalQIRaAgwaFFmeXEQHYoAHfVF4vB2W7oXVr3g8Cld1QsSIPnKEIEmQxvviiXXuCAgAAIABJREFUc/kKJ5xa4NkbixNplxR6795wA8fHoBWWExh8UKYAk4Ann3QnFoh4kPR4fAs3BwLZkm5ijwDin5yus0AgC9LstBEMtJubNs2arR0I9CLt0sX9fsqX5+tnyBB36+/cyRnDeJABlJJxymbEeW3WzJ1FDwIQJWqCgVi5smXdfQcE48+cGbxEC6hbV7eFq1cv+Ppz5ujv7dShwglkfBYvrpN4LlywtoAzef99Fu9er29IghNIKsF17IZixXji4naMKVWKJ932hCd/IMHCqQuJE/hN7V16gq1fvry7fSQk6Exns52jE7juqlb1n8VrAkNHyZLuxdwLL3BYUKlS/icbdlq00N/bXqzfCYQ2mL2OHRABmHU5bfv7eKYcRXCcBaDp6jhwgGdtbmvhATSYtzNwIFu6UFzVLOLrhowIwObNuRtG7tzuXYVduvB+Jk1yv59rruH4rWCFZk0+/piPLxQLG5Hv7+Dk2n33XS6g+sYbvG6tWpxJXaSI/+2i2K2ZDPLbb1ys2m1GLx7M9qSUQMC979Y9R6STYUL5DJFOknAK2PbHJ59w3JLb0ilE2rXnVALEH9ddx5+xZ3T7w8xMNePMtm3j95Yv9/0M1ndrhUHrM6XcTaDwIEbR9WAgGH/UKOcuHk7Uq6dj/txMGpCl6dbSQ8TbRxcdN6VpYP23T8T8AUspzrkbihXTpYTcULo0W5PdTqxwnt24o831ncb6QOubHV8CkZDAVveYmOCxobhOExLcibO0NN060o1rlkj3JI6J4VhsN7Rqpb+3vbC5EzNm8LqBOieRCMCsjF0AnsiUowiOswA0+zPu388zeacHSSD8lXfp35/FFQpA25vDByMjArB1a46zq1TJ/WdgqQzF8okixP4KLTuB1lVduoTWbcX+O8D9YQ6SyDBGnb5atdzXsbv2WqsVGM3n3fD886GJGCJt1QzFAojWVf56TvsDpViCxRSZoA4i2ru5AS7DCRPc7wf9b7/80t36EBH2ftooFeRkrcJ5NYvfBgK/s1LuEhWQ1IH2fMFALFbjxu4ngzfeqFvFuSm5hBJSbgP3idjq/Z//uLM+EXE4h1LuqxqY9ePcjmnFi4e2funS/Du5DbP58Uf/5ZOcwHjnxrKF9UOxhuXNy+Oim6x4s4C3vb+7P9CT2e1YMGCALpTvr7uTnTvv1Me1e3fw9VEqLciEWwRg1iV7WwDNAX/fPmsNKbegrIcdmNBXr+ZZoL05fDAyIgDvvZcfLm5jt4h0/JZbM795bKEc37RpOjh90KDQ9wUQSG8mnwwdynUPU1PZ1VezJp9He/cJJ8y+vgcOsPvTbUs8ZB27FTHmdwpFpKMMRSjZvET6of7bb+4/8/nnLE7xm7rl6FH31hQibS1wI8yItPXM7hI0M2vt4Lyim0QwzPHADdi32wSqlBTtxnbjYiZiN/xNNwVuxWeCzHm31zARC8DHHuPPufEcjBvH67oNfTA9LW6tSaGOMVddxRM+t9b7UFEqtPvBPH433xmT6j59Qtv+4cPu1ofL203tSSI9duTO7S5bnchamN1fPLMJJitB4hhFAGZdziul+hvL37a/+2feoVlwFoCmy2fvXt06KxQWLXJ2hSLzcM0aFiK33hqahSQjAvDhh9k969YlRaRbxbkdmM1jC+X4Pv9cf2bgwND3BWC1NctPvPGGLlb65ZccnL56tbv4ocqVrQIQ5RvcgOxMpz7LgVDKfYA+qFaNY8dC4emnQxv0iXTXkVBDFkKlbdvQxCnEmf1eQxyvk5XCvE7dZGmj44Pb6xqTEXRucEO5cu4tbUQcNnH11Rya4Ab8TosXuz+mzp15chET4259lC1y2/3GjOV1KyawfqAwDpPSpXn9UCfwblHKffIa1sfipvMJ1nX7/IFL100tSSI98XD7+0OQuo15JNKWaqXcFftGTGyQ56IIwKzLGsW1/vwtqzLv0Cw4C0CzGfqePdY2OW5BX0Y7PXrwbA6WKLM9jhuqVQstaYSIB/KiRd11AABHjvBnQrHe4Dd76y33nzGD093WDjT3BcwMRRzzwIH6YfTVVxyns2qVuy4TZhLI/v38sHVTw4pIJ5Kg8Kxb1q1z1w81XNCzOFhZDJP589kq2qGD7jscCWABdFs3EIHsbpMniPR5ddtuLqMC0G0NSCJdNsgtrVuz9cZt4hSR/9aA/ujSha03bkvToPuGU5s4f4Q6YcT6bgUO1nd7PYXKt986110Mdjx16ri79pRy15cYxMeH9nuihJJbPB59/G656y79vd1MOuHZePvtgKuJABTCxVkAmgVPd+9236jcZPly51kSYt3WruWA5sqV3WflZRQUyW3XLrL7wW+GQsZumDfPlbnfcV/R0c77R3uvl17S9cjmzmUBuHKlu4xAJIF4PCwAixVzV8KASAtAtzUArzRIUgmFRYvYPV2rVuglkUKhefPQjg3C323nECJ9nbjNJIV12Y37ikiXdQlFGBw7Flpx9/vu43247bmdEbp04Tqabq1tqL0YSnkhpdwLTKyfEcGYVcDxuO2UpJT7GoNEnIQTyvetVSu09b1eXj+UCgfIAg5lQtyoESdyBUAEoBAuzgLQDE5G9w03WXAm33/vXBvqscc4K3LdOr6xMxJfGCojR/J3cFMcNBxCdecQsfhVit1Hoe7LXgwW+4fAe/FF3clk7lxus7ZihTv3Rb9+fP48Hhb/uXK5H8AOHIj8bx0OZtcMtyxbxm7K/Pn9lwG5HKBbRSgo5a4Uirm+Uu4zN4k41jAUQl0/VGAxdNtzOyN07cqxhm7L2Xz3HR9TKBMEpdxnDWP9f4MAnD7d/fpuS9gQsacilO+7f39oHqgKFXj7Zh/gYCCxK5TnggtEAArh4iwAEViO2Aul/Dev9wdi/Ox07MgutPXreWadOzeLzEgC10yQukphk5HB9ttv+TNu4+vMfdmLu2L/6O7w/PNcXoZId7CAkAnGxYscN+X1spj3eNx3N8nq9O0b+nlatYqL0rp142SUG2/MmAAMJb4V10nz5qHtJysBK+6HH0ZuH88+y2EsboUyCha7LSlCxOuHkvgUqssyqwlAxN8mJ7tbP9Tf59df2VofKVB6CuOqG264ISLnQASgEC7OAhAuHKXYLaOUcy/LQEDg2bn/fo6R27RJZ2CFal0MFbQUC+WmzQgZGWzRsSHUQUsp344j8fHWuKiePVkEEnEHlOrVOTYzlNZkUVFsnXRb6T87sG5daDFzRLqwstuEgIwycGBoLkEiPq5mzUJbXykuEZRdgVXfrSUpIwwbFpq4Pn+eM8XdBPoDpdxbGInYWhjKGLN4sfsix1cCZEq7zXJXij0XWQX0Sw6lnNA114gAFLIkzgIQQdwejy6gGkoyBBELPKcG223bcpYoRJlS7uspZRTESYWSBJIRMiIAV63SQjvUfdkFdload+tAs/sePXQpjvnzWfR8+21ohaqjoriTSCgWpn8jsO5kJWsKCPXaxveIZDZzpEHylFtLUkZAKSi3PbAzwqxZoVVYKFUqa16Dbpk4kY/fbVJhqPGtkQYJGm57yhPpDPfLjAjArE9bP+/ffUWPwj/OAhB1vKKjWTjYkw3cgMbcdlq35or5iC3MiLjMCM2b834jSUYEAixLW7aEvi8nF7vZWq9rV11cGgJw0aKgTcYtREdzvEtWjum7Emzfzr/5xx9n9pH4ohTXxHMLAuVD6VCS1di0KTRLUkbAOZ88OXL7CJWSJf8dAtBtnT6lnBsKZCb33htaUiSKd19mRABmfewFoUFWKQztLADRXSBXLm5q7rbxuMm2bRwzZadlS47b2b2b9xEfn7GrPyuyaVNoGcD4jFIcWxIKSjkX2h4+nN3sRLqXKRHRggXsivjmG+fP+QN1r555JrTj+7exa1fGXPVXgvffD00IwZoZSv3NrEZGSs2ESloaJ0OFGv8cSb75JrT+5FmN0aP5vJ054259pTh2OTsTamayS0QAZn3+cngvv1Lqzyt9IH5wFoDoYZo7N6frh5KFBXbs8M1SJeJYpY8/5uwrJzdmTgOu8D/+CO1zGzY4l+UYNUq3ferUid23RLozC4SgW2DVjKSlJTtw4EDoAf5ZlZMnuTSFm76+WZXUVJ6YZiVxJgTn7bdDy4jNajGAGSE2VgRgDuNXpdROpdTFS/+ayyml1MzMOzQLvgLQ7AMcH88WpYyY4HftcnYdN27MSQWwMoZSAuHfyN9/szBzW9g1GOPG6fZYHTsSDRnCr7/5hqhKFZ0M4hZcC5e5hEG2A2ERbmshCoLgy5AhoYmhL75w39c3q/LUU0RNm172zYoAzLp0VUp1U0qdu/QaS2el1J1KqZjMOzQLvgJw7Vr90E9I4KSC+vVDvzr37uUkEjs33siZe2h3E0pCghCcSZN0RuiDD+quJAsXsgBEPUC3wBKc00FtzEjWABSEfzvIAhbCRgRg1qd5Zh9AEHwF4IQJWgDmy0fUqxe7i0IFLjN7E/VatTjz7cSJf0d8R1Zj6lQuXkvEwcqIF1q4kJM/vvqKewK7RSnuApLTQXvEUJN1BEHQnD8vk6jLhAjArE8dpdRVl14nKqUmKKU+UEoVyLQjsuIrAF9+WQvA/Pm5jy5iykIBLl57v9Vrr2UrFFzNtWuHdxcIVr74Qv+md9+t21KhldmcOSzC3SJueiY1lQsDXy5XvSAIQhiIAMz6/KiUqnzp9YdKqZVKqSVKqf9m2hFZ8RWAvXppAVigAFGHDryECrL0zp2zvl+lCick/PMP/3+jRuHdBYIV08XbujXR2LH8evFidrfPmhWa6FaKqGLFy3+cgiAIQoYRAZj1OWG8PqyUKqXY+nckcw7HB18B2KULC76OHTnL7q67dGHhUEA7OXu6f4UKXIwYxZmzczuqrAhcvURccgdN11EAeuZMojp13G9PqdCyhgVBEISIIwIw63NMKRWllKqiOAMYOJWHyQx8BWCHDkRDh/LrQoVYoGWkBhxipuwlZsqU4fZnRPz/t9+esatfcGb5ct1aqnlzTgohYgFYsSI3Pq9Xz/32lAotZlAQBEGIOCIAsz7JSqlRSqnPlVIjL71XVim1P7MOyIavALzrLi4sS0SUmMhZu/36hX51/vUXiwd7na4SJXTbM6WI7rknY1e/4MyaNfwbExHdfDOX3CHSPYCnTw8tq1sp7m8qCIIgZBlEAGZ9yimu+TdVKVX40nvtlVJvZ9oRWdEC8Oef2W3brJm2GhUuzDXj3ngj9Kvz7FkWD/bG6PnzcycCIv7/9u3DugkEGz/8wMKdiLOBp03j10uXEpUvz38jS9gNEqcpCIKQ5RABKISLFoBKEd16K7sHZ8zgK6xIEW5knZHWQykpzj0fo6O5SDQR//+jj4Z3FwhWtm3j+o1EbLn7/HN+vWwZn8vPPuNi3G5RStcVFARBELIEIgCzBzcodgOj+8d1Sql6mXc4FqwCsEED3S6MiKhoURaB48aFfnUiycNsVwZReOgQ/60UUbdu4d8JgmbXLu7fS8TlXubM4dfLl7MA/O9/2TXsFqWIbrvtsh+mIAiCkHFEAGZ92ipO+PhEKXX60nt1lVKLM+2IrLAA3LyZH/SPPMJFf9eu5SusWDFuB4c4slBRimjfPv03ij8j5lAprq0mXD5Qf/Gff7gczLx5/D6SQ6ZMIWrSxP32lOK4UEEQBCHLIAIw6/OTUurmS69REiZWKfVH5hyODywAp03jB/1jj7H16Ndf+QorXpzf/+KLjF2hUVFEu3frv3//nbd34QL/rRTRK6+EdxcIVmBl/e03tuYmJ/P7K1YQJSVZW8W5QSnuKCIIgiBkGUQAZn1OGq+PX/rXY7zObFgAvvmmLsmiFJdwISIqWZL//uabjF2hsbFEO3bov3fuJMqVS/89YwbR6dMZvwMEZxITORnk6qu5ADQR0XffcQmejz8muuUW99tSKmOFwAVBEISIIQIw67NZKVXj0muIvppKqR8y53B8YAFYqJDuyxsdTZSWxldYhQr8/qpVGbtC4+M5uxj8+CMXlxYiC+I4y5Vj1y8R0cqVLAA/+oiTfdwiiTqCIAhZDhGAWZ8nlFK7lFIdlVKnlFL3KqW2KqU6ZeZBGbAAhPs3f36O+wPXX88CYPPmjF2hefMSbdmi//7+e6LSpcO76oXgNGnCsX5XXcW/ORELwKuu4s4goSR1KMX9oAVBEIQsgwjA7EE3pdQOpdRFpdTvSqk+mXs4FlgA3nAD0aef8sO+WjV9hTVuzO/t2ZOxK7RgQbb6gYULiSpVCu+qF4Jz//1Eb73FMZxI6IH4HjeOqFUr99tSKmOtAAVBEISIIQIw69LQ4T3PFT+K4LAAvOsuolmz+GFvlghp1ozfO3o0Y1dokSJE69bpv+fMIapZM6yLXnDB008T9e7Nhbw3buT3Vq0iKlWKaPTo0LJ6lSJ64onIHKcgCIKQIUQAZl1OB18lS8ACsHt3zhZViqhtW32FNW/O7507l7Er9OqriRYt0n9/9pl0lbgSvPEGl/QpUEC771ev5qSe994Lrf2eUkQ9e0bmOAVBEIQMIQIw6/JXZh+AS1gADh7MZUJQCxBAACIpJFTq1tVdRYiIJkwILQFByBgffshxfgkJOgkHAnDECHYRu2XFCu7rLAiCIGQZRABmXbKXBXDSJHYVKkX0n//oK6xVK34vo9x2G9EHH+i/33vPamEUIsO0adzVJTaWS+8QEa1ZQ1SiBNGwYVLWRRAEIZsjAjDrclEptSTIkhVgAbh4MdH27Sz2+vTRV9h994UnAB9/nGjAAP334MFEHTtmfHuCO77+mkv6REXpBJ61azkpZNAgq5VXEARByHaIAMy6/KOUeiPIkhVgAbh3L9GBAyz2XntNX2HffBNeH9jXXiN6+GH998svS0bplQBFn81ezOvWcYmfgQNZmAuCIAjZFhGAWZfs5QI+dYrozz9ZMAwefPmu0MmTiW66Sf/dty9R9+6Xb/uCMz/+yDUYleLewERE69ezAHzpJaIuXTL3+ARBEISwEAGYdcl+AvDsWRYMQ4Zcvit06VLuPwt69+YSJUJk2bOHz6VSRCdO8Hvr1xMVLUr0wgtETz2VuccnCIIghIUIwKxL9soCPnWKM32V4iSBy8Wvv1pbyz37LFGvXpdv+4IzR49qAfjPP/zehg1cl1HOgSAIQrZHBKAQLloAEl1+F/CxY7zNM2f476eeYguUEFnOn9cCEOJ740YuDN2jhzXRRxAEQch2iAAU7DyslFqplDqmlDqqONu4QYD1rQLwww9ZtF0u/vmHRciRI/x3t25E/ftfvu0L/oEABD/8wAKwSxeOAxQEQRCyLSIABTvdlVItlFJ5lFJRSqmeiuMRS/pZ3yoALzdwKx84wH8/+STRK69EZl+CFScBmJhI9OijRK++mnnHJQiCIISNCEDBDSeUUm38/F9kBSARUUwM0a5d/PrRR4lefz1y+xI0dgG4aRNRoUJEDz10ed38giAIwhVHBKAQjHpKqRSlVFk//x95AWi2I+vY8fJmGQv+sQvAH38kKliQ28ANH555xyUIgiCEjQjAnMMkpVSaUir10r/2xamzyFVKqb1KqdcCbDfyArBQIRYfREQPPkj01luR25egsQvAn34iKlCAW/G9917mHZcgCIIQNiIAcw7xSqlCAZa8tvUrKqX2KKWGBtluPqUU9ejRg3r16kW9evWi5OTky3uVFi/OXSiIiO69l+iddy7v9gVn7AJw82YWgHfcQTRmTOYdlyAIgpAhkpOT05/VPXr0EAEo+FBDKXVIKdXfxbqRtwCWKcOtyYiI2rQhGjUqcvsSNPnyceFnsHkzUf78RLfeSjR+fOYdlyAIghA2YgEU7DRQXALmGZfrR14AVqxI9O23/PqOO4jGjo3cvgTNxo3s9gVbtrAobNyY6JNPMu+4BEEQhLARASjYWaKUuqi49Mtfl5bTSqm+ftaPvACsVo1owQJ+3bKlWJ8yi61buT/w9dcTzZmT2UcjCIIghIEIQCFcIi8Aa9Yk+vJLft28OdHHH0duX4J/tm1jAVihgrbICoIgCNkSEYBCuEReANarR/T55/z65pvF/ZhZ/PILUVwcxwUiKUcQBEHIlogAFMIl8gKwcWOiTz/l1w0bEk2dGrl9Cf7Zs4coKoood24Wg4IgCEK2RQSgEC6RF4Cm27dePaIZMyK3L8E/v/+uS8McOpTZRyMIgiCEgQhAIVwiLwBvv51o3Dh+fcMNRLNmRW5fgn+OHtUC8MKFzD4aQRAEIQxEAArhEnkBePfduvbfddcRffVV5PYl+Of0aRZ/Xm9mH4kgCIIQJiIAhXCJvAC8/36iESP4tVkSRriynD/PAjAmJrOPRBAEQQgTEYBCuEReAHbsSDR4ML+uVIlo0aLI7UvwT1oaC8BcuTL7SARBEIQwEQEohEvkBeDjjxMNHMivy5cnWro0cvsSAqMUkceT2UchCIIghIkIQCFcIi8Au3Uj6tePX5t9gYUrD5JABEEQhGyNCEAhXCIvAJ9+mqh3b35dsiTR6tWR25cQGBGAgiAI/wpEAArhEnkB+NxzRD178uuiRYnWr4/cvoTAiAAUBEH4VyACUAiXyAvAF18k+s9/+HWhQkSbNkVuX0JgRAAKgiD8KxABKIRL5AXgyy8TPfkkv86Xj2jz5sjtSwiMCEBBEIR/BSIAhXCJvAB8/XWiTp34tfShzVxEAAqCIPwrEAEohEvkBeDQoUQdOvBrr5do797I7UsIjAhAQRCEfwUiAIVwibwAHDGC6L77uP+sUkSHDkVuX0JgChXiRBxBEAQhWyMCUAiXyAvAkSO5H/Dff7MA/PPPyO1LCMzx47wIgiAI2RoRgEK4RF4Ajh1LdMcdREePsgD8++/I7UsQBEEQcgAiAIVwibwAnDCB6NZbiXbtIoqO5p60giAIgiBkGBGAQrhEXgBOmULUpAnRxo1EiYmR248gCIIg5BBEAArhEnkBOHUqUcOGRN9+S1ShQuT2IwiCIAg5BBGAQrhEXgB+/jlR3bpEs2YR1aoVuf0IgiAIQg5BBKAQLpEXgHPmENWsSTRpElHTppHbjyAIgiDkEEQACuESeQH49ddE1asTjR5N1Lp15PYjCIIgCDkEEYBCuEReAC5cSFS5MtHw4VwQWhAEQRCEsBABKIRL5AXgkiWc/GH2BBYEQRAEIcOIABTCJfICcMUKorJlifr2JerWLXL7EQRBEIQcgghAIVwiLwC//56odGminj2JnnsucvsRBEEQhByCCEAhXCIvANesISpRgqhLF6KXXorcfgRBEAQhhyACUAiXyAvA9euJihYlevRRotdei9x+BEEQBCGHIAJQCJfIC8BNm4gKFSLq2JFo8ODI7UcQBEEQcggiAIVwibwA3LyZKH9+ogcfJHrrrcjtRxAEQRByCCIAhXCJvADcto0oIYGoXTuid96J3H4EQRAEIYcgAlAIl8gLwB07iOLiiNq0IRo1KnL7EQRBEIQcgghAIVwiLwB37SKKiSG64w6iDz6I3H4EQRAEIYcgAlAIl8gLwL17ibxeoltvJZowIXL7EQRBEIQcgghAIVwiLwAPHiRSiqhpU6JJkyK3H0EQBEHIIYgAFMIl8gLw0CEWgA0aEH36aeT2IwiCIAg5BBGAQrhEXgD+8QcLwBtuIPrf/yK3H0EQBEHIIYgAFMIl8gLwzz9ZAFarRjRzZuT2IwiCIAg5BBGAQrhEXgCePMkCsGJFoi+/jNx+BEEQBCGHIAJQCJfIC8C//mIBWLo00ddfR24/giAIgpBDEAEohEvkBeDZsywACxcm+uabyO1HEARBEHIIIgCFcIm8AExJYQGYNy/RkiWR248gCIIg5BBEAArhEnkBmJrKAjAmhmjFisjtRxAEQRByCCIAhXCJvAAkYgGoFNGqVZHdjyAIgiDkAEQACuFyZQRgVBQLwPXrI7sfQRAEQcgBiAAUwuXKCMDYWBaAmzZFdj+CIAiCkAMQASiEy5URgPHxLAC3bInsfgRBEAQhByACUAiXKyMA8+ZlAfjLL5HdjyAIgiDkAEQACuFyZQRgwYIsAHftiux+BEEQBCEHIAJQCJcrIwALF2YBuG9fZPcjCIIgCDkAEYBCuFwZAVikCAvA336L7H4EQRAEIQcgAlAIlytrAfzjj8juRxAEQRByACIAhUA8q5RKU0q9HmCdKyMAExNZAB47Ftn9CIIgCEIOQASg4I/KSqndSqkfVVYQgIUKsQCM9H4EQRAEIQcgAlBwwquUWqOUuksptVRlBQGILOCzZyO7H0EQBEHIAYgAFJwYoJT676XXWUMAFijAAjAlJbL7EQRBEIQcgAjAnMMkxfF8qZf+tS9LLq13nVJqn1KqwKW/s4YAzJ+fBWBaWmT3IwiCIAg5ABGAOYd4pVShAEtepVS0UuonpdTdxudcCcAePXpQr169qFevXpScnHz5r9R8+VgACoIgCIKQIZKTk9Of1T169BABKKSTpNhC+H9KqaOXlhSl1N9KqS1+PnNlW8EJgiAIghA2YgEUTDxKqZK2ZZVS6l2lVDE/n7kyAjAhQQSgIAiCIFwmRAAKwViiskIMYHy8CEBBEARBuEyIABTC5coIwLg4HwF47tw5OnXqlCyyyCKLLLJk6+XcuXORfYY6cOqUCEAhPK6MAMyd2yIAz507R8WLF8fFK4ssssgiiyzZdilevPgVF4EiAIVwuTICMDbWIgBx4R48eDDTZ26yyCKLLLLIktHl4MGDdEWeozZOnRIBKITHlRGAuXI5CsArfcMIgiAIwuUks55nIgCFcLkyAjAmRgSgIAiC8K9DBKCQXbkyAjA6WgSgIAiC8K9DBKCQXbkyAjAqSgSgIAiC8K9DBKCQXbkyAtDrFQEoCIIg/OsQAShkV66MAPR4cpQAfOKJJ8jj8dDzzz/v+P+TJ08mj8eTvuTJk4fKli1Lbdu2pRkzZlBaWprfba9cuZI8Hg8VL16cUlNTHdfBdl966SXH/y9Xrhx5PB56+OGHXX18K8pHAAAgAElEQVSf2bNn00033URFixaluLg4SkpKorvvvtvSN3rZsmWW72QuXq83/Vzv27ePPB4PTZw40e/+Bg4cSB6Px/H7ufk8mDJlCrVr146SkpLI4/HQY4895nfd1NRUevfdd6l69eqUO3duSkxMpBYtWtCRI0eC7icr8t5779GsWbMy+zAizuzZs6lmzZqUO3duSkpKokGDBvm9L+zMnTuXGjVqRIUKFaKCBQtSw4YN6csvv7Ssg+st0DVNRLRhwwbq0qULValSheLj46lMmTL00EMP0d69ex33/fvvv9Njjz1GxYsXp9jYWCpXrhz1798/w7+DkHmIABSyK1dGACqVYwTguXPnKH/+/OT1ev2KtMmTJ5PX66VZs2bR2rVracWKFfTpp59S+/btKSoqim655RY6f/684/Y7d+6c/gCaN2+e4zoej4fy589PZcuW9fm/FStWkNfrpbx587oSgCNHjiSPx0OdO3emefPm0dKlS+mjjz6itm3b0osvvpi+3rJly8jr9dKYMWNo7dq1PgtErRsB9+qrr5LX6w1bALZo0YJq1qxJnTt3pgIFCgQUgO3bt6fChQvTu+++SytWrKC5c+dS7969ad++fUH3kxUpW7asa4GfXUlOTqaoqCjq1q0bLVu2jN59913KnTs39e3bN+hnFyxYQF6vl+6//35KTk6m5ORkateuHXm9Xpo/f376erjeBgwY4PeaJiLq3bs3NWrUiD744ANasWIFTZs2japWrUqJiYn022+/Wfa9b98+KlWqFDVu3Jg+//xzWrFiBX3yySf0yiuvXL4fR7hiiAAUsisiAC8zn332GXk8HmrdujV5vV76+uuvfdaBANy9e7fP/82aNYu8Xi8988wzPv937tw5KlCgADVr1owSEhLovvvuczwGj8dDjz76KEVHR9Py5cst//fkk09Ss2bNqFy5cq4EQpkyZahdu3ZB14MF8Ntvvw243pUUgCalS5f2KwCnTZtGMTExtGnTppC2mRX5559/iChnCMCaNWtS06ZNLe+9/vrrFBsbS3/88UfAz3bo0IHKlCljeS81NZVKly5NHTp0SH/P7fX2559/+ry3f/9+8nq9NHDgQMv7LVu2pHr16rm2VApZGxGAQnZFBOBl5tZbb6XExET6888/KT4+nh544AGfdQIJQCKitm3bUlxcnE9l+alTp5LX66WZM2dSx44dKS4ujk6ePOnzeY/HQy+//DI1b96cnnzyyfT3z58/TwUKFKDJkye7FggJCQn0n//8J+h62VkA3nTTTXTLLbeEtD2Ts2fPUvfu3SkxMZESEhKoTZs26a76KVOmpK938803+wgWIqKkpCTLsR09epS6du1KlSpVovj4eLrqqquoQ4cO9Pvvv1s+B1f51q1b6dZbb03fd9myZX1cloGsn5MmTSKPx0OrVq2i+++/n/LmzUvFihWjoUOHEhFby2rWrEl58uShOnXq0MaNGy2fX7hwId1+++1UokQJio+Pp+rVq9Pbb79tOX+LFi0ir9dLo0aNsny2Q4cOjlayYBw8eNDxOti7dy95PB6aPHlywM/fe++9VLVqVZ/3q1SpQg8++GD63xm93kCxYsUs9+Du3bvJ4/HQZ599lqHtCVkPEYBCdkUE4GXk0KFDFBUVRT169CAifrg5ibRgAnDChAnk9Xrpu+++s7zfsmVLKlSoEKWkpNDChQvJ4/HQuHHjfD4PATh58mTKnz9/ujt52rRplJCQQGfOnHEtAJs1a0bx8fE0fPhw2rlzp9/1IAAXLVpEFy9etCymEMhqAvDChQsUGxtLPXv2pD59+lDhwoUpJiaG6tWrR0uWLHG17Y4dO1JsbCwNHTqUFi1aRH369KEyZcqQ1+u1CMAmTZo4CsCyZctajm3Hjh307LPP0uzZs+m7776j6dOnU926dalcuXLpFj4i/p08Hg9VrFiRhg4dSkuXLqXly5fTjz/+SCVKlKBWrVrRunXraO3atbRnzx6/x4+Y1EqVKtGgQYPo22+/pW7dupHH46EXX3yRatSoQTNmzKCvv/6arrnmGipTpgxduHAh/fPjxo2jd955h5KTk2nZsmU0YsQIypcvH/Xr18+ynz59+lDu3Lnpp59+IiIWnl6vl2bPnm1ZL5hgJWL3r9frpTVr1vj8X548eahPnz4BP79w4UKKiYmhIUOG0J9//klHjx6l1157jeLi4uj7779PXw/XW9GiRSk6Opry589Pd911F23ZsiXg9omIfv75Z/J4PPTOO++kv/fJJ5+kT+JatGhBsbGxVLBgQXrkkUfo2LFjQbcpZD1EAArZlSwvANPSiE6ditwSIN8iZIYNG0Zer5fWrl1LRETffPMNeTwe+vDDDy3rBROA+NyMGTPS3zt06BBFR0dT9+7dL/0uaVS6dGmqX7++z+chAM+cOUN58uSh6dOnExHR7bffni763ArAnTt30nXXXUder5c8Hg8VLlyY2rdvTwsXLrSsBwGI9czl2muvTV8vqwnAI0eOkMfjoXz58lHNmjXpyy+/pAULFlCzZs0oV65cPtYuOzt27KCoqCh66623LO937949wwLQTmpqarrFa86cOenv43d6//33Hbfp1gUMATho0KD09y5evEhFixalXLly0f79+9Pf/+qrr8jr9dKKFSv8bu/ixYs0ePBgKlSokOX9CxcuUJ06deiaa66hH3/8kfLmzZt+PZvExMRQ586dAx4zrOE7duzw+b/SpUtbrG7+mD9/PhUsWDD9Os2fPz8tWLDAss7hw4epe/fuNHv2bFq5ciV99NFHVK5cOcqXLx/98ssvfrd98eJFuummm6hYsWKWCeCwYcPS9/XUU0/R0qVLacKECZSYmEh16tQJesxC1kMEoJBduTICMDExwwLw1CmtHyOxXM6vXq1aNapSpUr636mpqVSqVClq2LChZb1gAjA5OZk8Hg99/vnn6e+9+eab5PV6afXq1env9e3bl7xer49lDgKQiK1TrVu3piNHjlB0dDQtWrSIiHwFgt1qZ5KWlkbfffcdvfzyy9SiRQuKi4sjj8dDgwcPTl8HAnDcuHG0ceNGy/Lzzz+nr3e5BWBaWppfa6OJPwF46NAh8ng8FB8fb8n4PXPmDBUtWtTiDnTaDyw69mzP5cuX+7iAQxGAY8eOpeuuu44SEhIsmadvvvmmz+908OBBx22GIgC9Xi9t2LDB8n6DBg2oevXqlvd27tzp48I8fPgwdenShZKSkigmJsZyvPZYvF27dlHevHkpd+7cVK1aNZ8wB7d89tlnjtc+EVGpUqWCCsDVq1dToUKF6OGHH6ZvvvmGFi5cSB06dKA8efLQsmXLAn724MGDlC9fvoC/b9euXSlXrly0ePFiy/tDhgwhj8dDd999t+X96dOnk9frtWTWC9kDEYBCduXKCMD9+4m2bfO5cP9NFsB169aRx+Ohfv360cmTJ+nkyZN04sQJevrpp8nr9dKvv/6avq5bF/DKlSvT36tevTqVLVs2fdsnT56kVatWpWcompgCEK6uF198kUqVKpWeuWgXCKb1zuv1+iSPmBw+fJhq1KhBuXLlSrduXM4YwEGDBpHX66WUlBSf/0MMFYQV3KBYnAQWkX8BeO7cOfJ6vVS7dm2f/2vTpg1VrlyZiHxL95QrV46ItDA/e/as5bO//PJLhgXgqFGjyOPx0AsvvECLFi2iDRs2pF9fr732Wvp6EIB2wY5thioA7ddjkyZNqHHjxpb3nAR47dq1qXTp0jRx4kRauXIlbdy4kQYMGEBer9diPQQtW7Ykr9dLI0eOdHV8TiCLN6Mu4Nq1a9NNN93k836jRo2oVq1aQfd/++23WyZ7Jn379qWoqCjHOL8PP/yQPB4PjR492vL+iRMnyOPxpMddCtkHEYBCduXKCEA/F+6/KQawR48eji5Qr9dLXq83XZARBReAbdq0ofj4+HTryIYNG/y6V71eLyUlJVk+bwrAtLQ0KlWqFEVHR1vKttgFgt1qd+bMmYDfd9SoUeT1emn9+vVEdHkF4Pjx48nr9TqWYPnuu+/I4/GkW0oOHz5sOW5/cYqBkkAqVqzo6H6766670hMFjh8/btnP1q1biSg0C+Btt93mYw0mIsqXL5/l2Bo2bEgtWrSwrIPkBicB6GT1vFIC8NdffyWPx0NTp061rPfKK684CsCJEyeSx+OhOnXqUMGCBenAgQOujtHOgQMHHK8jHF+wJJDcuXM71uns1asXxcXFBd1/q1atHJNIMHkZM2aM4+eQHGT/fwhA08IrZA9EAArZFRGAl4GUlBQqXLgw1a9fn5YvX+6z1KxZ01KTL5AA/OKLL8jr9VoeTk899RRFRUXRnDlzfLbdr18/8nq9tHTp0vT1TQFIxFaHtm3bWmKW3AqEw4cPO76PGDe4H1EH8HIIQLgZ3377bZ//69mzJ8XHxztmPwcikAB84YUXKC4uzpJle/r0aSpSpAh16tQp4HYRA2h/cHfr1s0nBrBr165UpEgRSwIFhKJ5bDVr1qTbb7/dsr2XX36ZvF6vawFYuXJluvfeewMeOwhHAP70008+8aopKSlUoUIFHwG4Y8cOSkhIoKeffpr++usvKleuHN10000BC58H4vrrr6dmzZpZ3nvjjTdclYEpW7asowWwQYMGVLFixYCf3b9/v49oJ9I1M4cNG+b3sxcvXqQSJUrQnXfeaXkfMY1uE4+ErIMIQCG7IgLwMvDFF1+Qx+Oh//73v47/P27cOPJ4POmxRXAnzpw5k9asWZNeCPrBBx+kqKgoatWqVXq254ULF6hIkSLUvHlzx22fPHmS4uLi6NFHH01/zy4AnXArABMTE+mBBx6gKVOm0IoVK2jevHnp4q99+/bp68EC+P7779OaNWt8lr///puItIDo3r07zZw502eBOOrcuTPlypWL+vTpQwsWLKC5c+dSt27dKCoqit54442gx03EWZgzZ86kzz//nBITE6lZs2bp+zl69Gj6en/88QeVKFGCatSoQTNnzqQvv/ySGjduTAkJCZb4RX88/PDDFBsbS0OGDEnPAk5KSvIRgEuXLiWv10sdO3akxYsX0/jx46lq1apUsGBBi5jo168fRUVF0ZAhQ2jx4sXUv39/qlSpUkgCsG3btlSsWDGaN28ebdiwIWBB63AEYEpKCpUtW5auvvpqmjlzJs2ZM4eaNGlCV199tUUApqSk0A033EA1atRIv7ZXr15NMTExlu9ERBQVFeU6iSMqKoq6du1Ky5Yto3feeYdy585tsXQTEb322msUHR1tsTa+/fbb5PV6qUOHDjR//nyaP38+PfDAA+T1ei3u2eeff5569epFM2bMoKVLl9IHH3xASUlJVKhQIYu1edq0aeT1eun222/3ufbt19CUKVPI6/VSt27daOHChTRmzBgqWLCg33tcyNqIABSyKyIALwNt2rShAgUK+A1oP3XqFOXJkyf9IY8HLpb4+HgqW7Ys3XPPPfTFF19YPjt79mzyer0B64Y99NBDlDdv3nSR5fV6g3YVKFeuHD3yyCNBv9uHH36YXlsuLi6OEhL+v70zD4+iSP/4t3tyX4QkJIGQcIQkrisQWAV+QjiyguCBIghyLASUBTw4VlFXIiAr6i6KoiC3InKIKJgAagxBElYlYFgP3FW8wQNYbuRYcnx/f9T0MD09k0wCkUx8P89TTzLV1dVVXV3d337rreowtm/fnk8++aTJkmVYAD0FYzbtd999V2k656UwZs+ezTZt2jA4ONixBt2LL75YZZkNDIHkLrj6OH755Zfs27cvIyIiGBYWxl69enHXrl1eHefMmTO88847GR0dzfDwcN58880O/0xnAUiq4W1jfb/OnTtz165dbNGiBUeNGmXJLzY2lhEREezbt6/jvM2YMcNUP5vN5lYAfv755+zatStDQ0Op63qls4wrE4CuVjKjHM4W3I8//pgZGRkMDQ1lYmIip02bxqVLl5oE4H333ceQkBCLGHr00Ufp7+/P999/3xGn67rpfFTG+vXrmZ6ebvoUnKtF0ThPrsPRq1atYqdOnRgVFcWoqCh26tTJMWPe4IUXXmCHDh0YFRVFf39/Nm7cmMOGDbO4GmRlZXm81tz5fa5YsYKtW7dmUFAQmzRpwgkTJjj6r+BbiAAUfBURgIJQCxiWMlcBKAhC/UIEoOCriAAUhFpABKAg/DYQASj4KiIABaEWMIZKRQAKQv1GBKDgq4gAFARBEIQaIgJQ8FVEAAqCIAhCDREBKPgqIgAFQRAEoYaIABR8FRGAgiAIglBDRAAKvooIQEEQBEGoISIABV9FBKAgCIIg1BARgIKvIgJQEARBEGqICEDBVxEBKAiCIAg1RASg4KuIALxILFu2jJqmWYKu6ywoKCB5/usQRrDZbIyPj+fQoUO5b98+S57u8jPyzMnJMaU9evQoH3zwQaalpTEoKIhRUVHs3bs38/Pzva5DVlaWx2P269fPkW769OnUNI2tWrViWVmZKY+vvvrK4xcw3n//fQ4cOJBNmjRhQEAAo6Oj2bNnT7700kuW79nu2LGD/fv3Z1xcHAMDA9m8eXPeeeed/PHHHy35duvWzXROGzZsyPT0dN5zzz387LPPLOk9tZWmaWzYsKHX56su8cYbb3D27NmXuhg1onPnzuzZs+elLoaDoUOHslWrVpe6GD7P/PnzmZaWxsDAQKalpXHx4sVe7/vCCy+wffv2DA8PZ2xsLHv16sX33nvPlGbz5s1u+3CjRo1M6fLz8zl06FAmJyczODiYycnJvOuuu/jf//7X7bE/++wz9u/fn9HR0QwODmZaWhrnzZvnsawiAAVfRQTgRWLZsmXUdZ3r1q1jcXGxKZw8eZLkeQGYnZ3N4uJibtu2jbNmzWJoaCj/8Ic/WMSUpmm8/fbbLfkVFxfz2LFjjnT79u1jcnIy4+Li+PTTT7OwsJA5OTkcMGAANU3jrFmzvKpDVlYW4+LiuGPHDsvxvvzyS0c6QwDqus6FCxea8vAkAJ9++mnqus6ePXtyxYoV3LZtG3Nzc3n33XczNDSUubm5jrTLly+nn58fe/TowTVr1nDbtm1ctGgRk5OTGRsby08//dSUd/fu3Zmenu4od35+Pp9++mm2bt2a/v7+nD9/vtdtVVJS4tW5qmtkZWUxMTHxUhejRnTp0qVOCcBhw4YxJSXlUhfDp3n++eep6zqnTZvGwsJCTpkyhbquc8mSJVXuO2/ePGqaxvHjx3Pz5s3MyclhRkYGg4ODTX1/8+bN1HWdCxYsMPXhXbt2mfLr168fr7/+er700kssKirikiVL2KRJE6ampvL06dOmtMXFxQwPD2e/fv2Ym5vLrVu3ctGiRZwzZ47H8ooAFHwVEYAXCUNUfP311x7TGAJw6dKlpviZM2dS13UWFxeb4jVN48MPP1zlsbt168aYmBh+//33lm2TJk2iruuWt2d3eCsiDAHYu3dvJiYm8n//+59jmzsBWFhYSF3XOXHiRLf5ffPNN44b++eff86goCAOHDjQku7IkSNs1aoV09LSTGK5e/fuzMjIsKQvKyvjgAED6Ofnxw8//NAR701b+QrGua8NAejcrrWJCMD6xblz5xgdHc3Ro0eb4ocPH874+HiLtd+VDh06sFu3bqa448eP08/Pj1OnTnXEGQKwsLCw0vwOHTpkiduyZQs1TePLL7/siKuoqGBaWhoHDRpUaX6uiAAUfBURgBeJCxGAb731FjVN49q1a03x3gjA4uJiaprGp556yu32s2fPMjo6mjfddFOVdaiOANR1nSUlJdR13TT06E4A9unTh40aNfJKUIwdO5YBAQE8cOCA2+2vvvoqNU3jq6++6ojzJABJ8uDBgwwMDOTw4cMdcRdDAD7zzDNs3rw5g4KCeNVVV3Hbtm1s3rw5R44c6Ugzbdo0appm2XfEiBFs3ry5KW7atGls3749GzRowJiYGGZmZnL79u2mNFu3bqWmaVy3bh1Hjx7NRo0asWHDho6he13XHcNgLVq0cOx36NAhjh07lgkJCQwMDORll13GRYsWmfI2hsWLiop46623MjIyku3atav0HCxfvpzp6ekMDQ1lgwYN2KZNG8u1vWXLFmZmZjIsLIxhYWHs06cP//3vf5vSuArAxYsXU9M0y3D/lClT6OfnV2mZSPLMmTOcMGECf//73zM0NJSNGzdm3759+cUXX1jS5uXlMT09nYGBgUxJSeHixYvdCsDs7Gy2a9eOERERjImJ4R//+Efu2LHDlMYYktywYQNHjx7NqKgoNmzYkPfeey8rKiq4fft2Xn311QwNDeUVV1zhlXvGlClTqGka9+zZw549ezIkJITNmjXj8uXLSap2S0tLY1hYGDMzM/ntt9+a9l+5ciW7d+/ORo0aMTw8nO3bt+eKFStMaRYsWEBN07hp0yZHXHl5Oa+++mqmpqby1KlTVZbTmXfffZe6rnPr1q2m+Pz8fOq6zn/+85+V7p+ens4bb7zRFFdaWsrg4GBmZ2c74ozzXZUAdMfJkyepaRqfeOIJR1xeXp7bF/GqEAEo+CoiAC8ShqjYs2cPy8rKHMH5bdeTADSGS1yHLozhYuf8jGDw2GOPOY7ridtuu40RERFV1sEQgJUdjzwvAMvLyzlo0CDGxsbyl19+IWkVgOXl5QwJCeHQoUOrPD5Jpqam8uqrr/a4/dSpU7TZbBw7dqwjrjIBSCofs+TkZMdvT21VVlbGioqKKsu4ZMkSx/B8Xl4e582bx6ZNmzIyMtIkAI3z5EpWVpZJoJHk6NGjuXLlSm7dupWbNm3i4MGDGRgYaBryMgRg06ZNOXr0aObl5TEnJ4fffPMNr7/+etPw/UcffUSSPHHiBFNTU9msWTMuXbqUBQUFvP/++2mz2Th37lzTOdE0jUlJSXzggQdYUFDAvLw8j+dg69at1HWd9913H7ds2cL8/HzOmTOHTz75pCPNG2+8QT8/Pw4YMIAbNmxgTk4OO3XqxOjoaP7000+OdK4CcMmSJdR13SIAs7Oz6e/v77FMBkeOHOGYMWO4du1aFhUVcf369bzmmmsYFRVl8vv69NNPGRAQwG7dujE3N5dr1qxhWloaExMTLQLw9ttv56pVq1hYWMhNmzZx4MCBDAoKMolZQ5C0bNmSkydP5ubNmx0CbsKECbz88su5fPly5uXlsXPnzoyIiODRo0crrUt2djY1TWPbtm05d+5cbt68mTfffDN1XedDDz3EjIwMbtiwga+++iobN27MLl26mPZ/9NFHOX/+fObn57OgoIAPP/ww/f39LfegW265hbGxsdy/fz9JJTwDAwNN9ySjb8+cObPSMs+dO5e6rlssbz/99BM1TbO8fLiyaNEiBgQEcNmyZTx27Bh/+OEHh6D+5ptvHOmM8x0fH0+bzcaYmBgOGzbMrT+1Kxs2bKCmaSZf6qlTpzp8tjt06EB/f3/GxcVx4sSJPHv2rMe8RAAKvkqdF4AVFRU8fvZ4rQVvHvje4GligbMwMQTg4sWLWVZWxtOnT7OgoIBNmzZ1O+TpatVxngRy+PBhkuS4ceOo6zrPnTvnsWwPPvig2xuyK54mgei6brIwOgvAPXv20M/Pj3/7299IWgXggQMHqGkaH3roIa/OY3BwMIcMGVJpmvj4eF5//fWO31UJwMGDBzMkJMTxu7JJIK6WB1cqKiqYmJjI6667zhS/Zs0aappWYwHoTHl5OcvKypiWlmYaNjcEYP/+/d3m6c56O2PGDAYHB1usnYYF0XhBMc7Jvffe67FczjzxxBOMi4urNE3z5s3Zp08fU9zx48cZFRXFyZMnO+IutgB0pby8nKdOnWJISIhJ9A4cOJBxcXGmh/t3331Hf3//SoeAy8vLWVpayuTkZN53332OeEOQOL+ckGSbNm2o67rJYrhr1y5qmsZVq1ZVWvbs7Gzqus5XXnnFEXfo0CHqus7Y2FiTdW727NnUdd0krp2pqKhgWVkZR44cySuvvNK07ciRI0xMTGSvXr24ZcsW2mw2y6jC119/TX9/f5PVzB0zZsxw3B+cOXv2rMXq5oklS5YwKCjI0S8TEhIsL8gffvgh77//fm7cuJFFRUV85pln2KhRIyYlJTnuj+44ceIEU1JS2LZtW9P9/4477qCmaYyOjuaMGTNYWFjIWbNmMTg42O392UAEoOCr1HkBePzscWI6ai0cP3tx6m5YlXJzc1lSUuIIzpY5QwC6irorr7zS7fCopmm84447TPkZwbi5jh07tkoB+MADD1DXdR45coQkPVr3srKyGB8fz127dlmOd/DgQUc6ZwFIKutIZGQkjx49+qsIwLi4uGoJwNtuu42hoaGO357aqqSkxCSU3J2nvXv3UtM0vvjii6ZjlJWV0d/fv8YCMD8/nz169GB0dLRJeDsLKEMAOvstOefpTgB27tyZ3bt3t9Rl7dq11DTNYWE0zsm2bdssebizaBcUFFDXdQ4fPpybNm2y9OX//Oc/jrI6719aWso+ffqwY8eOjrQ1FYCGUHZnpV69ejU7dOjABg0amM7nPffc40iTlJTE22+/3VLfjIwMiwDMy8tj9+7dLe3j/MJgCMA33njDtO/AgQMZGRlpijt9+jQ1TePf//53y/Fd66zrumnSF0nGxsby5ptvNsW9+eabliHML774ggMHDmRCQgJtNpuj7OHh4ZZjFRYW0mazMSgoiNdee22l5aqMRx55xK0APHPmjFcC8PXXX2dwcLDDurxx40Zec801jIuL4+eff17pvjt37qTNZuMjjzzidntpaSl79+7NyMhIiyvCqFGjHFZtZwwfbeeJcM6IABR8lTovAH3JAuitD+C0adNYUlLCbdu2cfz48Y7hRFe88QE0bk5VDQEHBweTPC8iDBHqLFCq6wNo3OD37t3LoKAgPvjggxYBWFZWVq0h4JSUlFoZAnZe1qM6beV8nr7//nuHz+Wbb75p2Sc+Pr5GAnDXrl0MCAjgTTfdxJycHO7YsYMlJSVMT5MIf9EAABUiSURBVE9njx49HOmMttu8ebPbPN21XUpKilsrslEnw0/LOCdfffWVaX9juNsIzsJo7dq17NatGwMCAujv789evXpx9+7dJJWYqMyC7dweNRWAXbp0MeVpDE2uW7fO8fL01ltvcefOnSwpKbFMTAgICHD7YjJgwABTPXfu3El/f3/HzFCjfVq3bm0qt6dJCcOGDbMI/rKyMmqa5lGoONfZ3TXUtGlT07Xm7vgnTpxgYmIiW7duzVWrVvGDDz5gSUkJs7Ky3FpSy8rKeNlll1HXdZM/YHV57rnnajwEXF5ezkaNGlnuh2fPnmWzZs0qtcQZpKam8oYbbrDEV1RUcNCgQQwJCWFRUZFl++TJk6nrOt9++21T/M6dO936aBuIABR8lTovAH2FC5kEkpWVRV3XuXPnTlO8NwJw+/bt1DTN5HvljDEJxLAknTx50mL1ci5HTQQgSU6YMIFhYWHctm2bZRLIddddx9jY2EqtlAbGJBDDF8kVY6jV+WZcmQA8cOAAAwMDTQ9Lb9rq3LlzlvNUWlpaLQvgE088QV3XWVpaakp74403mgTBlClTGBoaarGYNGvWzK0ANNaVdMZT23Xq1IldunRxa9UtKSlx+G56OieHDx82pXe3ruKpU6e4YcMG/u53v2NSUhJJcvfu3Y7r0t1xDaFIWgXgihUrqOu6ZULDuHHjTMJlz549pjyNa2bQoEG8/PLLTfuePXuWNpvNJAC9tQA+8MADjIiIsLwsJiQk1GkB+NZbb1mGnklyyJAhbgXgX//6VwYFBbFt27ZMTk52XBvVxZhh63oejPJVNgnkhx9+oKZplqWbSNVvWrduXeXxU1JS3LpyjBw5kgEBAR7FrdEHXH1fd+zYQU3T+Prrr7vdTwSg4KuIALxIXIgA3L9/P0NCQixvrd4IQJLs2rUrY2JiLA9Mkpw4caJjuLMqLkQAHjx4kGFhYezduzd1XTcJwKKiItpsNo4fP95tft9++y0/+eQTkmroMDAwkLfeeqvlgXv48GEmJyd7vQxMaWkp+/fvTz8/P5P/0IXMAq6oqGBSUpLFt+2VV16x+ACuXr2auq7zX//6lyPu6NGjjI6ONgmCSZMmMSIiwnQ+CwoKqGmaRQA6LyzuzJgxYxgTE2OJnz59OmNiYjwuemtwMWZGGz5oR48edZynUaNGVbmfqwA0XiI2bNjgiCstLWWrVq288gG88cYb2aZNG1PcwoULqWmaSQAOGjSIcXFxPHPmjCPu22+/tfgA3nPPPYyKijJdj3l5edQ0rU4LwNdff90yuezQoUOMiIiwnMd3332XNpuNc+bM4ffff88GDRqYZs5Xh3PnzjEqKop//vOfTfEjRoxgXFxcpcvAnD59mgEBAZa6nTlzhklJSezdu3elx96+fTttNptlosr48ePp5+fHNWvWeNzXWDFg0qRJpvgZM2bQZrO5XWaLFAEo+C4iAC8SFyIAyfPDD843a03TOGrUKG7fvt0Sfv75Z0e6vXv3Mjk5mfHx8XzqqaccC0Hfcsst1HWdd999t1d1MBaCdnc8Q6CR7gUgeX7JClcBSKplU2w2G3v27MmVK1c6FoIeP368ZSHoZcuW0d/f37EQdFFRERcuXMhWrVqxUaNG/Pjjj015GwtBG2XNz8/n7Nmz2bp1awYGBlq+QGBMeHjttdfc1rWqdcqWLl1KXdc5cuRI5uXlce7cuW5nAR87doyRkZG88soruXHjRr722mvs2LEjmzVrZhIExvITw4YNY0FBAZ9//nk2adKEiYmJXlsA58yZQ13XOX/+fO7cudPh23f8+HFefvnlTEtL44IFC/juu+9y48aNfPLJJ01LA1VXAE6ZMoVjx451tM/LL7/Mli1bmnz7cnNz6e/vz8GDB3PdunUsLCzkmjVrOGHCBD777LOOdK4CsLS0lC1atGBqairXrVvHnJwc9urVi82bN/dKAM6bN8/hy1VQUMDHH3+ciYmJjIyMNAnA3bt3MyAggBkZGczJyeHq1at52WWXMSkpySQAN23a5PB3LCgo4Ny5c9m4cWM2bdrUIgDdWb5+TQHofPz9+/czPDycHTt25JtvvslXXnmFV1xxBVNSUkzn8fDhw0xISDD51a5evZqapnHlypWOuK+//po2m42PP/54pWUm1UxgPz8/x0LQDz30EG02m6UvDh8+nEFBQaa4cePG0WazceLEiXznnXe4fv16ZmZm0mazmVwvBg8ezKlTp3L9+vXcsmULZ82axejoaLZs2dLh70yqmdCapnHMmDGWvu48q5ikY5Z0dnY2N2/ezJkzZzIoKMgiZp0RASj4KiIALxLeCkBd190KwMOHD7NBgwYmx25d1z0G1xl6rp+CM9K5fqmjMoyhaHfBeehl+vTptNlsFqF07NgxRkdH02azuf0U3AcffGD5FNy1117rdiZkcXGxY2kK50/B/fDDD5a03bt3d5TTZrM51rAbP368xdGbPN9WnkJlMwgNnn32WTZv3pzBwcG86qqr+N5771nWASTJ9957jx06dGBoaCjT0tK4cuVKjhw5ki1btjSlmzt3Llu2bMmQkBB26NCBBQUF7NGjBzMzMx1pKrMAnjp1ikOGDGFUVBR1XTcJjmPHjvEvf/kLW7ZsycDAQMbFxbFr166mrxtUVwDm5uby2muvZZMmTRgUFMSkpCSOGTPGsn7j+++/zxtuuIFRUVEMDg5my5YtOWTIENNEhS5durBXr16m/Xbv3s1u3boxPDycLVq04LPPPuv1LODy8nJOmTKFCQkJDA0NZWZmJj/55BMmJiZaHuTvvPMO27Vrx6CgILZq1YpLly7ln/70J8skkDlz5rBFixYMCQlhx44duXXrVmZkZJjKXZkF0LW9y8rKqOs6Z8yYUWldsrOzabPZLPGJiYkW66q74xcUFLBdu3YMDg5mSkoK582bZzmP/fr1Y+PGjU0TvUh1P4iMjOR3331HUs3w13Wdjz32WKVlNpg/fz5TU1MZFBTk8VNww4YNY0BAgCmurKyMzz33HNPT0xkREcHY2FhmZmZyy5YtpnQzZ85k27ZtGRkZyYCAADZr1ozjxo2zXINdunTx2NddF6smyaeeeoqtWrViYGAgW7RowRkzZlT6UigCUPBVRADWUz766CNGRERw4MCBF22ii1A57gSgIAj1GxGAgq8iArAes2nTJvr7+/Ouu+661EX5TSACUBB+e4gAFHwVEYCCcJFo0aKFV5MeBEGoP4gAFHwVEYCCIAiCUENEAAq+ighAQRAEQaghIgAFX0UEoCAIgiDUEBGAgq8iAlAQBEEQaogIQMFXEQEoCIIgCDVEBKDgq1xSAbhv3z4eP35cggQJEiRI8Mmwb98+EYCCT3JJBOCZM2cYHx9vXLwSJEiQIEGCz4b4+HjTN6V/DUQAChfKJRGApBKBl/rNTYIECRIkSLjQ8GuLP1IEoHDhXDIBKAiCIAhCzRABKLgjAcAKAIcAnADwCYArPKQVASgIgiAIPoYIQMGVhgC+BfAPAJH2uGQAMR7S12sB+Pbbb1/qItQaUjffpD7Xjazf9ZO6+Sb1tW4iAAVX/gZgZzXS12sBOGnSpEtdhFpD6uab1Oe6kfW7flI336S+1k0EoODKBwBeAbAOwGEA/wGQDUD3kF4EoI8idfNN6nPdyPpdP6mbb1Jf6yYC8LfDiwAqAJTb/7qGLfZ0XwIoAzAYgA1AGwB7AUz2kG8EUH/X47vrrrsueRmkblK330rd6nv9pG6+Gepr3Yz1ByECsN4TAiCqkhBuT1cCZQV05kE3cQYJqANrKEmQIEGCBAkSahQSIAgAFgN43yWuMgGoQV08ERIkSJAgQYIEnwoJUM9xQUB7AGcBDITy+/s9gO8ATLyEZRIEQRAEQRBqmeuh1v47CeUT6Mn/TxAEQRAEQRAEQRAEQRCE+s4jAH6EshZuhRoyrmsMAlAE4DjULGjXJW3aACgE8AuAHwBMc5NHVfX0Jo+LzeNQltrj9rKtAtDUJU0igA1QX3Q5COA5AH4uae6CWvz7FwAfAsioQR4Xm6kAvgJwzH7MtwC0dUnjq+3mynqomfiZTnHdoSZknQLwNYCxLvsEAJgH4L9Q7Z8La9tXlUdtMQ1qJYETUOf9BICVTtvrQ7v9H4ACqLodBfDPapatLtZvN1R9jHAK6rq8qRplqov1MoiFukfuB3AEwHsAujpt7w7f7XORABZCndMTAPIApDlt99XngFCHmQzgewCXAwgE8BjUBRhyKQvlhp5QInAkrAIwDMBPAB6F6uBXANgHYIJTmqrq6U0etcFMAO2gOmEE1EP2X07bNSiB+CKAUKgO/DGAp53S3Ar1AOtiz+dOqJt3QjXyqA1SADSw/+8H4C9QN27DWdmX282Z4VA363KcF4DNoG7CY6Hq3hVKCN/ktN88AB9BPYDCALwEYJfT9iQv8qgtpkG9cLmjPrTb/0H1mWH28ukArqpG2ep6/QzugXrQB3hZprper9ehxGcU1H3kL1BiJhK+3+dyALwJ9SWvAKj7814AwfDt54BQh/kGwN1Ov21QN4yhl6Y4VdINVgE4AkpYOMeNh/J9NKiqnt7k8WvQFqp+hnDqBuB/UDcFg75QHdvf/nsLgKdc8tkFYEo18qhtAqEmIZUDiLbH1Yd2awo1waopzBbAqVBWBGdmA8i3/x8IZWG4wWl7NIBzADp7mUdtUpkArA/tVgRglodt9aF+Bp9BiThvy1TX6/URlKg1CIXqd1fBt/tcMJTF/SqnuEAApVDr+XZF/XgOCHWICKjO09ElPg/Ak79+cbzCnQCcDTW86Mz/2dOFwbt6VpXHr8X9UDdhg/FQX3JxpjFUfa6w/z4CZR11ZiGA16qRR21xHdRbaQXUDc75oVsf2i0PwO32/50F4DoA813SDgZwyP6/IfTjXNJ8gfMP4KryqE2mQT0YDkANKa0E0Ny+zdfbzXjY/h1AMdT53AngFi/LVtfrZ5AJJSCSvCyTL9RrMJTQiYcSLQ9A9ZlA+HafC4Uqm/O5N67TWfD954BQBzGsFmku8a8AWPTrF8cr3AnAJQBWu6S7zJ6uCbyrZ1V5/BpcA/XQ7ekUlw3r+o1BUPW52v67DMC1LmmeAPBONfKobSKhhon6O8X5ervdCfVwNKgA0MP+/2Yo/05nekNZGwA1TFMO9eByZjuAh7zMoza5HGqICFAPiRVQVp4Q+H67JUCV72eoJbN0AP2grCOdvChbXa+fwVoofy+D+lCvJKhh0gqofrAfSoACvt/n8uwhFkoQPgd1b1+I+vMcEOoQYgGsO2+2N0BZyvq6xFf21mY4Z/vKm58G5U/T2v7bl9utJZQvVKJTXHUsgG1Qd60R7ggAcAbqJcWX2w04X77HXOLfhnr4+3r9ANW/z0GJFwNfr5cGNSljKZSLjA51vzwGZd3z9T4XA+Wftw9qEs50qCH8mahfzwGhDuHO5+MAfMsHcDhq5ttyAMCQauRRWwyFEn/XuNnWFWpRb3d+GwH231tgFewlOO/74U0evwZ+UD44xlCbL7fbCKhzehBqRuF/oW6kRwEsAPAwqu+PFANlhTLexqdCzeTzlMevSQCA01DWaV9uN4Mv4VkA1of6TYcSS874er2ioPqY60oCJVCTV2riA1iX+1wjqD7XA/XrOSDUIe6DcmL/PZTPwUyoN5C6NgtYh+rAvaAEYIj9twb15vkjgL9BmbSvgJrJ5jwzrap6epNHbXA3lGjo7GG7BuX4/IK9jElQs4SdZ24NgHr76wLlFzMOamZcQjXyqA3GQw1nAOpmtsheTuMN3JfbLQhqyMs5VEDNxIvE+dmEY6DaJAOqnZ1nE86FctJOhPqO90swP8C8yaO2uBXnJ+vEAVgOJQ5C4dvtZjAeyoLbFqp/9IV62F7pZdnqcv1sUDN373OJ9/V6Acoitgiqv2hQYu4MlEjy9T6XCnWfBIBWUEO3ufbfvvwcEOo406H8YX5B3V0HcATUA7bcHoz/jTWgroCa2XcK6sb+sJs8pqPyenqTx8WmAuoN1Fi3y1hzzVkQJgLYaN/2XwBzYJ21dSfUjfsU1BtsF5ft3uRxsdkAdb5PQj003oDyuXLGV9vNHc7LwADq2twFVa5voB4qzgRA+fkcgmrzDbB+0L2qPGqLHCjLzy9QAmAl1LC3QX1otwegltk4DtVnnC1Dvly/W6DEbJSbbb5cLwBIhlpz8wDU0O+nOD8JC/DtPjcSqq/9AiWqH4fZMuerzwFBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARB+G0yAupLBIIgCIIgCMJvhBFQn0YTBEEQBEEQfiOIABQEQRAEQbgEBAJ4DMBXAA4D2Aog3b5tGoBt9u0HAPwE4B8AbE77XwbgTagPve8FsABAhNP2IACPAvgCwAn7cf5k32YIwD8D+BbAUQCvAghz2n8G1DDxcXvaRy+otoIgCIIgCAKWAXgHQGMAOoA7AewH0ABKAJ4DMB2AP4BUAF8D+Kt93zAAP0CJwkAAcVACcr1T/isAbLfvCwDxOC8wRwAoBfAUgAD7/nvsxwWAa6DEX4L9dySAjhdWXUEQBEEQhN82UQAqALRyid8DYAiUEPsZgOa0bSyAL+3/D4ayDOpO29PtecYCiLb/387D8UcAOO2y/z8AbLL/3xXAQQC9oSyJgiAIgiAIwgXSAUqgHXEKRwH8AuB+KAFY7LLPtQDO2v+fDGCHy/Ywe55X2kM5gFAPx3fnAzgNQJHT7ywoq+JxAO9CWQUFQRAEQRCEGpIEJdCaetjuzgI4DsqPDwBugxoudrUAlkNZAGPs/1dmAaxKABr4A5gE4Aw8C0pBEARBEATBC14HkAMlBgEgHGrINR5KjP3P/jcAQBqU+HvIKe2PAJ6AGqKNB7AFVh/A92H2ATQEYVUC8CoAGTg//DsawCnIcLAgCIIgCMIFEQhgKtQs3eNQkzpew3kBWARgJpQv3s+wzgL+HYC3ARyCmrCxAGoCiUEwgMehJo+chBKQQ+3bqhKAPQDstJfrKNRwdM8LqKsgCIIgCIJQBZ6GYwVBEARBEIR6ighAQRAEQRCE3xgiAAVBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBqBb/D/5YSF8bQ61yAAAAAElFTkSuQmCC\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#%matplotlib notebook\n",
    "ALPHA = 0.5\n",
    "#sns.set_palette(sns.color_palette(\"Set1\", n_colors=14, desat=.8))\n",
    "def add_plot(T, alpha=ALPHA):\n",
    "    plt.plot(ema(th.cat(T.test_F1)[:, -1], alpha), \n",
    "             label=T.path[6:-5]+' max: %.4f'%T.test_highscore)\n",
    "    \n",
    "plt.close()\n",
    "plt.title('TEST F1 (mean), ema=%.0e' % ALPHA)\n",
    "for t in A, B:\n",
    "    add_plot(t)\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('Test F1 accuracy')\n",
    "plt.legend(loc='lower center', ncol=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Restore trained models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EncodeWideResNet (\n",
      "  (nonlin): SELU (\n",
      "  )\n",
      "  (encoder): Sequential (\n",
      "    (0): Conv1d(8, 8, kernel_size=(7,), stride=(1,), padding=(3,))\n",
      "    (1): BatchNorm1d(8, eps=1e-05, momentum=0.1, affine=True)\n",
      "    (2): SELU (\n",
      "    )\n",
      "    (3): MaxPool1d (size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (4): Conv1d(8, 16, kernel_size=(7,), stride=(1,), padding=(3,))\n",
      "    (5): BatchNorm1d(16, eps=1e-05, momentum=0.1, affine=True)\n",
      "    (6): SELU (\n",
      "    )\n",
      "    (7): MaxPool1d (size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (8): Conv1d(16, 32, kernel_size=(7,), stride=(1,), padding=(3,))\n",
      "    (9): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True)\n",
      "    (10): SELU (\n",
      "    )\n",
      "    (11): MaxPool1d (size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (12): Conv1d(32, 64, kernel_size=(7,), stride=(1,), padding=(3,))\n",
      "    (13): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True)\n",
      "    (14): SELU (\n",
      "    )\n",
      "    (15): MaxPool1d (size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (resnet): Sequential (\n",
      "    (0): ConvModule (\n",
      "      (residuals): Sequential (\n",
      "        (0): DilatedBlock (\n",
      "          (nonlin): SELU (\n",
      "          )\n",
      "          (block): Sequential (\n",
      "            (0): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True)\n",
      "            (1): Conv1d(64, 64, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)\n",
      "            (2): SELU (\n",
      "            )\n",
      "            (3): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True)\n",
      "            (4): Dropout (p = 0.5, inplace)\n",
      "            (5): Conv1d(64, 64, kernel_size=(9,), stride=(1,), padding=(8,), dilation=(2,), bias=False)\n",
      "            (6): SELU (\n",
      "            )\n",
      "          )\n",
      "        )\n",
      "        (1): DilatedBlock (\n",
      "          (nonlin): SELU (\n",
      "          )\n",
      "          (block): Sequential (\n",
      "            (0): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True)\n",
      "            (1): Conv1d(64, 64, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)\n",
      "            (2): SELU (\n",
      "            )\n",
      "            (3): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True)\n",
      "            (4): Dropout (p = 0.5, inplace)\n",
      "            (5): Conv1d(64, 64, kernel_size=(9,), stride=(1,), padding=(8,), dilation=(2,), bias=False)\n",
      "            (6): SELU (\n",
      "            )\n",
      "          )\n",
      "        )\n",
      "        (2): DilatedBlock (\n",
      "          (nonlin): SELU (\n",
      "          )\n",
      "          (block): Sequential (\n",
      "            (0): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True)\n",
      "            (1): Conv1d(64, 64, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)\n",
      "            (2): SELU (\n",
      "            )\n",
      "            (3): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True)\n",
      "            (4): Dropout (p = 0.5, inplace)\n",
      "            (5): Conv1d(64, 64, kernel_size=(9,), stride=(1,), padding=(8,), dilation=(2,), bias=False)\n",
      "            (6): SELU (\n",
      "            )\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (1): ConvModule (\n",
      "      (residuals): Sequential (\n",
      "        (0): DilatedBlock (\n",
      "          (nonlin): SELU (\n",
      "          )\n",
      "          (block): Sequential (\n",
      "            (0): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True)\n",
      "            (1): Conv1d(64, 64, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)\n",
      "            (2): SELU (\n",
      "            )\n",
      "            (3): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True)\n",
      "            (4): Dropout (p = 0.5, inplace)\n",
      "            (5): Conv1d(64, 64, kernel_size=(9,), stride=(1,), padding=(8,), dilation=(2,), bias=False)\n",
      "            (6): SELU (\n",
      "            )\n",
      "          )\n",
      "        )\n",
      "        (1): DilatedBlock (\n",
      "          (nonlin): SELU (\n",
      "          )\n",
      "          (block): Sequential (\n",
      "            (0): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True)\n",
      "            (1): Conv1d(64, 64, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)\n",
      "            (2): SELU (\n",
      "            )\n",
      "            (3): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True)\n",
      "            (4): Dropout (p = 0.5, inplace)\n",
      "            (5): Conv1d(64, 64, kernel_size=(9,), stride=(1,), padding=(8,), dilation=(2,), bias=False)\n",
      "            (6): SELU (\n",
      "            )\n",
      "          )\n",
      "        )\n",
      "        (2): DilatedBlock (\n",
      "          (nonlin): SELU (\n",
      "          )\n",
      "          (block): Sequential (\n",
      "            (0): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True)\n",
      "            (1): Conv1d(64, 64, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)\n",
      "            (2): SELU (\n",
      "            )\n",
      "            (3): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True)\n",
      "            (4): Dropout (p = 0.5, inplace)\n",
      "            (5): Conv1d(64, 64, kernel_size=(9,), stride=(1,), padding=(8,), dilation=(2,), bias=False)\n",
      "            (6): SELU (\n",
      "            )\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (2): ConvModule (\n",
      "      (residuals): Sequential (\n",
      "        (0): DilatedBlock (\n",
      "          (nonlin): SELU (\n",
      "          )\n",
      "          (block): Sequential (\n",
      "            (0): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True)\n",
      "            (1): Conv1d(64, 64, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)\n",
      "            (2): SELU (\n",
      "            )\n",
      "            (3): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True)\n",
      "            (4): Dropout (p = 0.5, inplace)\n",
      "            (5): Conv1d(64, 64, kernel_size=(9,), stride=(1,), padding=(8,), dilation=(2,), bias=False)\n",
      "            (6): SELU (\n",
      "            )\n",
      "          )\n",
      "        )\n",
      "        (1): DilatedBlock (\n",
      "          (nonlin): SELU (\n",
      "          )\n",
      "          (block): Sequential (\n",
      "            (0): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True)\n",
      "            (1): Conv1d(64, 64, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)\n",
      "            (2): SELU (\n",
      "            )\n",
      "            (3): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True)\n",
      "            (4): Dropout (p = 0.5, inplace)\n",
      "            (5): Conv1d(64, 64, kernel_size=(9,), stride=(1,), padding=(8,), dilation=(2,), bias=False)\n",
      "            (6): SELU (\n",
      "            )\n",
      "          )\n",
      "        )\n",
      "        (2): DilatedBlock (\n",
      "          (nonlin): SELU (\n",
      "          )\n",
      "          (block): Sequential (\n",
      "            (0): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True)\n",
      "            (1): Conv1d(64, 64, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)\n",
      "            (2): SELU (\n",
      "            )\n",
      "            (3): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True)\n",
      "            (4): Dropout (p = 0.5, inplace)\n",
      "            (5): Conv1d(64, 64, kernel_size=(9,), stride=(1,), padding=(8,), dilation=(2,), bias=False)\n",
      "            (6): SELU (\n",
      "            )\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (logit): Conv1d(64, 3, kernel_size=(1,), stride=(1,))\n",
      ")\n",
      "torch.Size([256, 3])\n",
      "Created empty directory at: /home/csbotos/af_challenge_itk/dry/finetune/FREQ-ENCODE-quarter-selu-adam/0005/0005\n"
     ]
    }
   ],
   "source": [
    "th.multiprocessing.set_sharing_strategy('file_system')\n",
    "\n",
    "transformations = [\n",
    "    data_handler.Crop(8000),\n",
    "    data_handler.RandomMultiplier(-1),\n",
    "    data_handler.Spectogram(15),\n",
    "    data_handler.Logarithm()\n",
    "]\n",
    "dataset = data_handler.DataSet(\n",
    "    'data/raw/training2017/REFERENCE.csv', data_handler.load_composed,\n",
    "    transformations=transformations,\n",
    "    path='data/raw/training2017/',\n",
    "    remove_noise=True, tokens='NAO')\n",
    "train_set, eval_set = dataset.disjunct_split(.9)\n",
    "train_producer = th.utils.data.DataLoader(\n",
    "        dataset=train_set, batch_size=256, shuffle=True,\n",
    "        num_workers=4, collate_fn=data_handler.batchify)\n",
    "test_producer = th.utils.data.DataLoader(\n",
    "        dataset=eval_set, batch_size=256, shuffle=True,\n",
    "        num_workers=4, collate_fn=data_handler.batchify)\n",
    "\n",
    "net = DM.EncodeWideResNet(in_channel=8, init_channel=8,\n",
    "    num_enc_layer=4, N_res_in_block=1, use_selu=True, bias=True)\n",
    "\n",
    "D1 = net.state_dict()\n",
    "D2 = th.load('saved/FREQ-ENCODE-quarter-selu-adam/0005/state_dict_highscore')\n",
    "net.load_state_dict(D2)\n",
    "print(net(next(iter(train_producer))['x']).size())\n",
    "\n",
    "\n",
    "trainer = T.Trainer('finetune/FREQ-ENCODE-quarter-selu-adam/0005', dryrun=True)\n",
    "#trainer(net, train_producer, test_producer, gpu_id=0, useAdam=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              encoder.0.weight          torch.Size([8, 8, 7])               encoder.0.weight          torch.Size([8, 8, 7])\n",
      "                encoder.0.bias                torch.Size([8])                 encoder.0.bias                torch.Size([8])\n",
      "              encoder.1.weight                torch.Size([8])               encoder.1.weight                torch.Size([8])\n",
      "                encoder.1.bias                torch.Size([8])                 encoder.1.bias                torch.Size([8])\n",
      "        encoder.1.running_mean                torch.Size([8])         encoder.1.running_mean                torch.Size([8])\n",
      "         encoder.1.running_var                torch.Size([8])          encoder.1.running_var                torch.Size([8])\n",
      "              encoder.4.weight         torch.Size([16, 8, 7])               encoder.4.weight         torch.Size([16, 8, 7])\n",
      "                encoder.4.bias               torch.Size([16])                 encoder.4.bias               torch.Size([16])\n",
      "              encoder.5.weight               torch.Size([16])               encoder.5.weight               torch.Size([16])\n",
      "                encoder.5.bias               torch.Size([16])                 encoder.5.bias               torch.Size([16])\n",
      "        encoder.5.running_mean               torch.Size([16])         encoder.5.running_mean               torch.Size([16])\n",
      "         encoder.5.running_var               torch.Size([16])          encoder.5.running_var               torch.Size([16])\n",
      "              encoder.8.weight        torch.Size([32, 16, 7])               encoder.8.weight        torch.Size([32, 16, 7])\n",
      "                encoder.8.bias               torch.Size([32])                 encoder.8.bias               torch.Size([32])\n",
      "              encoder.9.weight               torch.Size([32])               encoder.9.weight               torch.Size([32])\n",
      "                encoder.9.bias               torch.Size([32])                 encoder.9.bias               torch.Size([32])\n",
      "        encoder.9.running_mean               torch.Size([32])         encoder.9.running_mean               torch.Size([32])\n",
      "         encoder.9.running_var               torch.Size([32])          encoder.9.running_var               torch.Size([32])\n",
      "             encoder.12.weight        torch.Size([64, 32, 7])              encoder.12.weight        torch.Size([64, 32, 7])\n",
      "               encoder.12.bias               torch.Size([64])                encoder.12.bias               torch.Size([64])\n",
      "             encoder.13.weight               torch.Size([64])              encoder.13.weight               torch.Size([64])\n",
      "               encoder.13.bias               torch.Size([64])                encoder.13.bias               torch.Size([64])\n",
      "       encoder.13.running_mean               torch.Size([64])        encoder.13.running_mean               torch.Size([64])\n",
      "        encoder.13.running_var               torch.Size([64])         encoder.13.running_var               torch.Size([64])\n",
      "resnet.0.residuals.0.block.0.weight               torch.Size([64]) resnet.0.residuals.0.block.0.weight               torch.Size([64])\n",
      "resnet.0.residuals.0.block.0.bias               torch.Size([64]) resnet.0.residuals.0.block.0.bias               torch.Size([64])\n",
      "resnet.0.residuals.0.block.0.running_mean               torch.Size([64]) resnet.0.residuals.0.block.0.running_mean               torch.Size([64])\n",
      "resnet.0.residuals.0.block.0.running_var               torch.Size([64]) resnet.0.residuals.0.block.0.running_var               torch.Size([64])\n",
      "resnet.0.residuals.0.block.1.weight        torch.Size([64, 64, 9]) resnet.0.residuals.0.block.1.weight        torch.Size([64, 64, 9])\n",
      "resnet.0.residuals.0.block.3.weight               torch.Size([64]) resnet.0.residuals.0.block.3.weight               torch.Size([64])\n",
      "resnet.0.residuals.0.block.3.bias               torch.Size([64]) resnet.0.residuals.0.block.3.bias               torch.Size([64])\n",
      "resnet.0.residuals.0.block.3.running_mean               torch.Size([64]) resnet.0.residuals.0.block.3.running_mean               torch.Size([64])\n",
      "resnet.0.residuals.0.block.3.running_var               torch.Size([64]) resnet.0.residuals.0.block.3.running_var               torch.Size([64])\n",
      "resnet.0.residuals.0.block.5.weight        torch.Size([64, 64, 9]) resnet.0.residuals.0.block.5.weight        torch.Size([64, 64, 9])\n",
      "resnet.0.residuals.1.block.0.weight               torch.Size([64]) resnet.0.residuals.1.block.0.weight               torch.Size([64])\n",
      "resnet.0.residuals.1.block.0.bias               torch.Size([64]) resnet.0.residuals.1.block.0.bias               torch.Size([64])\n",
      "resnet.0.residuals.1.block.0.running_mean               torch.Size([64]) resnet.0.residuals.1.block.0.running_mean               torch.Size([64])\n",
      "resnet.0.residuals.1.block.0.running_var               torch.Size([64]) resnet.0.residuals.1.block.0.running_var               torch.Size([64])\n",
      "resnet.0.residuals.1.block.1.weight        torch.Size([64, 64, 9]) resnet.0.residuals.1.block.1.weight        torch.Size([64, 64, 9])\n",
      "resnet.0.residuals.1.block.3.weight               torch.Size([64]) resnet.0.residuals.1.block.3.weight               torch.Size([64])\n",
      "resnet.0.residuals.1.block.3.bias               torch.Size([64]) resnet.0.residuals.1.block.3.bias               torch.Size([64])\n",
      "resnet.0.residuals.1.block.3.running_mean               torch.Size([64]) resnet.0.residuals.1.block.3.running_mean               torch.Size([64])\n",
      "resnet.0.residuals.1.block.3.running_var               torch.Size([64]) resnet.0.residuals.1.block.3.running_var               torch.Size([64])\n",
      "resnet.0.residuals.1.block.5.weight        torch.Size([64, 64, 9]) resnet.0.residuals.1.block.5.weight        torch.Size([64, 64, 9])\n",
      "resnet.0.residuals.2.block.0.weight               torch.Size([64]) resnet.0.residuals.2.block.0.weight               torch.Size([64])\n",
      "resnet.0.residuals.2.block.0.bias               torch.Size([64]) resnet.0.residuals.2.block.0.bias               torch.Size([64])\n",
      "resnet.0.residuals.2.block.0.running_mean               torch.Size([64]) resnet.0.residuals.2.block.0.running_mean               torch.Size([64])\n",
      "resnet.0.residuals.2.block.0.running_var               torch.Size([64]) resnet.0.residuals.2.block.0.running_var               torch.Size([64])\n",
      "resnet.0.residuals.2.block.1.weight        torch.Size([64, 64, 9]) resnet.0.residuals.2.block.1.weight        torch.Size([64, 64, 9])\n",
      "resnet.0.residuals.2.block.3.weight               torch.Size([64]) resnet.0.residuals.2.block.3.weight               torch.Size([64])\n",
      "resnet.0.residuals.2.block.3.bias               torch.Size([64]) resnet.0.residuals.2.block.3.bias               torch.Size([64])\n",
      "resnet.0.residuals.2.block.3.running_mean               torch.Size([64]) resnet.0.residuals.2.block.3.running_mean               torch.Size([64])\n",
      "resnet.0.residuals.2.block.3.running_var               torch.Size([64]) resnet.0.residuals.2.block.3.running_var               torch.Size([64])\n",
      "resnet.0.residuals.2.block.5.weight        torch.Size([64, 64, 9]) resnet.0.residuals.2.block.5.weight        torch.Size([64, 64, 9])\n",
      "resnet.1.residuals.0.block.0.weight               torch.Size([64]) resnet.1.residuals.0.block.0.weight               torch.Size([64])\n",
      "resnet.1.residuals.0.block.0.bias               torch.Size([64]) resnet.1.residuals.0.block.0.bias               torch.Size([64])\n",
      "resnet.1.residuals.0.block.0.running_mean               torch.Size([64]) resnet.1.residuals.0.block.0.running_mean               torch.Size([64])\n",
      "resnet.1.residuals.0.block.0.running_var               torch.Size([64]) resnet.1.residuals.0.block.0.running_var               torch.Size([64])\n",
      "resnet.1.residuals.0.block.1.weight        torch.Size([64, 64, 9]) resnet.1.residuals.0.block.1.weight        torch.Size([64, 64, 9])\n",
      "resnet.1.residuals.0.block.3.weight               torch.Size([64]) resnet.1.residuals.0.block.3.weight               torch.Size([64])\n",
      "resnet.1.residuals.0.block.3.bias               torch.Size([64]) resnet.1.residuals.0.block.3.bias               torch.Size([64])\n",
      "resnet.1.residuals.0.block.3.running_mean               torch.Size([64]) resnet.1.residuals.0.block.3.running_mean               torch.Size([64])\n",
      "resnet.1.residuals.0.block.3.running_var               torch.Size([64]) resnet.1.residuals.0.block.3.running_var               torch.Size([64])\n",
      "resnet.1.residuals.0.block.5.weight        torch.Size([64, 64, 9]) resnet.1.residuals.0.block.5.weight        torch.Size([64, 64, 9])\n",
      "resnet.1.residuals.1.block.0.weight               torch.Size([64]) resnet.1.residuals.1.block.0.weight               torch.Size([64])\n",
      "resnet.1.residuals.1.block.0.bias               torch.Size([64]) resnet.1.residuals.1.block.0.bias               torch.Size([64])\n",
      "resnet.1.residuals.1.block.0.running_mean               torch.Size([64]) resnet.1.residuals.1.block.0.running_mean               torch.Size([64])\n",
      "resnet.1.residuals.1.block.0.running_var               torch.Size([64]) resnet.1.residuals.1.block.0.running_var               torch.Size([64])\n",
      "resnet.1.residuals.1.block.1.weight        torch.Size([64, 64, 9]) resnet.1.residuals.1.block.1.weight        torch.Size([64, 64, 9])\n",
      "resnet.1.residuals.1.block.3.weight               torch.Size([64]) resnet.1.residuals.1.block.3.weight               torch.Size([64])\n",
      "resnet.1.residuals.1.block.3.bias               torch.Size([64]) resnet.1.residuals.1.block.3.bias               torch.Size([64])\n",
      "resnet.1.residuals.1.block.3.running_mean               torch.Size([64]) resnet.1.residuals.1.block.3.running_mean               torch.Size([64])\n",
      "resnet.1.residuals.1.block.3.running_var               torch.Size([64]) resnet.1.residuals.1.block.3.running_var               torch.Size([64])\n",
      "resnet.1.residuals.1.block.5.weight        torch.Size([64, 64, 9]) resnet.1.residuals.1.block.5.weight        torch.Size([64, 64, 9])\n",
      "resnet.1.residuals.2.block.0.weight               torch.Size([64]) resnet.1.residuals.2.block.0.weight               torch.Size([64])\n",
      "resnet.1.residuals.2.block.0.bias               torch.Size([64]) resnet.1.residuals.2.block.0.bias               torch.Size([64])\n",
      "resnet.1.residuals.2.block.0.running_mean               torch.Size([64]) resnet.1.residuals.2.block.0.running_mean               torch.Size([64])\n",
      "resnet.1.residuals.2.block.0.running_var               torch.Size([64]) resnet.1.residuals.2.block.0.running_var               torch.Size([64])\n",
      "resnet.1.residuals.2.block.1.weight        torch.Size([64, 64, 9]) resnet.1.residuals.2.block.1.weight        torch.Size([64, 64, 9])\n",
      "resnet.1.residuals.2.block.3.weight               torch.Size([64]) resnet.1.residuals.2.block.3.weight               torch.Size([64])\n",
      "resnet.1.residuals.2.block.3.bias               torch.Size([64]) resnet.1.residuals.2.block.3.bias               torch.Size([64])\n",
      "resnet.1.residuals.2.block.3.running_mean               torch.Size([64]) resnet.1.residuals.2.block.3.running_mean               torch.Size([64])\n",
      "resnet.1.residuals.2.block.3.running_var               torch.Size([64]) resnet.1.residuals.2.block.3.running_var               torch.Size([64])\n",
      "resnet.1.residuals.2.block.5.weight        torch.Size([64, 64, 9]) resnet.1.residuals.2.block.5.weight        torch.Size([64, 64, 9])\n",
      "resnet.2.residuals.0.block.0.weight               torch.Size([64]) resnet.2.residuals.0.block.0.weight               torch.Size([64])\n",
      "resnet.2.residuals.0.block.0.bias               torch.Size([64]) resnet.2.residuals.0.block.0.bias               torch.Size([64])\n",
      "resnet.2.residuals.0.block.0.running_mean               torch.Size([64]) resnet.2.residuals.0.block.0.running_mean               torch.Size([64])\n",
      "resnet.2.residuals.0.block.0.running_var               torch.Size([64]) resnet.2.residuals.0.block.0.running_var               torch.Size([64])\n",
      "resnet.2.residuals.0.block.1.weight        torch.Size([64, 64, 9]) resnet.2.residuals.0.block.1.weight        torch.Size([64, 64, 9])\n",
      "resnet.2.residuals.0.block.3.weight               torch.Size([64]) resnet.2.residuals.0.block.3.weight               torch.Size([64])\n",
      "resnet.2.residuals.0.block.3.bias               torch.Size([64]) resnet.2.residuals.0.block.3.bias               torch.Size([64])\n",
      "resnet.2.residuals.0.block.3.running_mean               torch.Size([64]) resnet.2.residuals.0.block.3.running_mean               torch.Size([64])\n",
      "resnet.2.residuals.0.block.3.running_var               torch.Size([64]) resnet.2.residuals.0.block.3.running_var               torch.Size([64])\n",
      "resnet.2.residuals.0.block.5.weight        torch.Size([64, 64, 9]) resnet.2.residuals.0.block.5.weight        torch.Size([64, 64, 9])\n",
      "resnet.2.residuals.1.block.0.weight               torch.Size([64]) resnet.2.residuals.1.block.0.weight               torch.Size([64])\n",
      "resnet.2.residuals.1.block.0.bias               torch.Size([64]) resnet.2.residuals.1.block.0.bias               torch.Size([64])\n",
      "resnet.2.residuals.1.block.0.running_mean               torch.Size([64]) resnet.2.residuals.1.block.0.running_mean               torch.Size([64])\n",
      "resnet.2.residuals.1.block.0.running_var               torch.Size([64]) resnet.2.residuals.1.block.0.running_var               torch.Size([64])\n",
      "resnet.2.residuals.1.block.1.weight        torch.Size([64, 64, 9]) resnet.2.residuals.1.block.1.weight        torch.Size([64, 64, 9])\n",
      "resnet.2.residuals.1.block.3.weight               torch.Size([64]) resnet.2.residuals.1.block.3.weight               torch.Size([64])\n",
      "resnet.2.residuals.1.block.3.bias               torch.Size([64]) resnet.2.residuals.1.block.3.bias               torch.Size([64])\n",
      "resnet.2.residuals.1.block.3.running_mean               torch.Size([64]) resnet.2.residuals.1.block.3.running_mean               torch.Size([64])\n",
      "resnet.2.residuals.1.block.3.running_var               torch.Size([64]) resnet.2.residuals.1.block.3.running_var               torch.Size([64])\n",
      "resnet.2.residuals.1.block.5.weight        torch.Size([64, 64, 9]) resnet.2.residuals.1.block.5.weight        torch.Size([64, 64, 9])\n",
      "resnet.2.residuals.2.block.0.weight               torch.Size([64]) resnet.2.residuals.2.block.0.weight               torch.Size([64])\n",
      "resnet.2.residuals.2.block.0.bias               torch.Size([64]) resnet.2.residuals.2.block.0.bias               torch.Size([64])\n",
      "resnet.2.residuals.2.block.0.running_mean               torch.Size([64]) resnet.2.residuals.2.block.0.running_mean               torch.Size([64])\n",
      "resnet.2.residuals.2.block.0.running_var               torch.Size([64]) resnet.2.residuals.2.block.0.running_var               torch.Size([64])\n",
      "resnet.2.residuals.2.block.1.weight        torch.Size([64, 64, 9]) resnet.2.residuals.2.block.1.weight        torch.Size([64, 64, 9])\n",
      "resnet.2.residuals.2.block.3.weight               torch.Size([64]) resnet.2.residuals.2.block.3.weight               torch.Size([64])\n",
      "resnet.2.residuals.2.block.3.bias               torch.Size([64]) resnet.2.residuals.2.block.3.bias               torch.Size([64])\n",
      "resnet.2.residuals.2.block.3.running_mean               torch.Size([64]) resnet.2.residuals.2.block.3.running_mean               torch.Size([64])\n",
      "resnet.2.residuals.2.block.3.running_var               torch.Size([64]) resnet.2.residuals.2.block.3.running_var               torch.Size([64])\n",
      "resnet.2.residuals.2.block.5.weight        torch.Size([64, 64, 9]) resnet.2.residuals.2.block.5.weight        torch.Size([64, 64, 9])\n",
      "                  logit.weight         torch.Size([3, 64, 1])                   logit.weight         torch.Size([3, 64, 1])\n",
      "                    logit.bias                torch.Size([3])                     logit.bias                torch.Size([3])\n"
     ]
    }
   ],
   "source": [
    "for u, v in zip(D1.items(), D2.items()):\n",
    "    print('%30s'%str(u[0]), '%30s'%str(v[1].size()), '%30s'%str(u[0]), '%30s'%str(u[1].size()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "inspect_dataset = data_handler.DataSet(\n",
    "    'data/raw/training2017/REFERENCE.csv', data_handler.load_composed,\n",
    "    path='data/raw/training2017/',\n",
    "    remove_noise=False, tokens='NAO~')\n",
    "data_iter = iter(inspect_dataset)\n",
    "data_sample = next(data_iter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 'NAO~')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(inspect_dataset.class_lists), inspect_dataset.tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "N_idx = 0\n",
    "A_idx = 0\n",
    "O_idx = 0\n",
    "\n",
    "def plot_normal(d=0, transformations=[], dd=None):\n",
    "    global N_idx\n",
    "    N_idx += d\n",
    "    if dd is not None:\n",
    "        N_idx = dd\n",
    "    data = data_handler.load_composed(\n",
    "        inspect_dataset.path + inspect_dataset.class_lists[0][N_idx], \n",
    "        transformations=transformations)\n",
    "    label, sample, length = data['y'], data['x'], data['len']\n",
    "    plt.figure()\n",
    "    plt.title(inspect_dataset.class_lists[0][N_idx])\n",
    "    plt.plot(sample.numpy().squeeze())\n",
    "    \n",
    "def plot_af(d=0, transformations=[], dd=None):\n",
    "    global A_idx\n",
    "    A_idx += d\n",
    "    if dd is not None:\n",
    "        A_idx = dd\n",
    "    data = data_handler.load_composed(\n",
    "        inspect_dataset.path + inspect_dataset.class_lists[1][N_idx], \n",
    "        transformations=transformations)\n",
    "    label, sample, length = data['y'], data['x'], data['len']\n",
    "    plt.figure()\n",
    "    plt.title(inspect_dataset.class_lists[1][A_idx])\n",
    "    plt.plot(sample.numpy().squeeze())\n",
    "    \n",
    "def plot_other(d=0, transformations=[], dd=None):\n",
    "    global O_idx\n",
    "    O_idx += d\n",
    "    if dd is not None:\n",
    "        O_idx = dd\n",
    "    data = data_handler.load_composed(\n",
    "        inspect_dataset.path + inspect_dataset.class_lists[2][N_idx], \n",
    "        transformations=transformations)\n",
    "    label, sample, length = data['y'], data['x'], data['len']\n",
    "    plt.figure()\n",
    "    plt.title(inspect_dataset.class_lists[2][O_idx])\n",
    "    plt.plot(sample.numpy().squeeze())    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vavEps0yqzkfT8F1mVXdsdakL3VZ369Z+Vl2PbbG9Z9P96MJnzpzusPiENGHG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\nh8YCVB9sJ+CyhFsn7yb9vWRJ9vF52jH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BATlW73rho1t8AFlhHnlEuhv19koRYKKnJz5n\n+nT5sutWn/e8Jx7c2hSoydXN5oWiRdKmY3t75ayY3tgkDWZvvhn45jfNjY1LMIQFC9yFj2lQZ7L4\njI3ZRQlRr//b37amoUKdoTqIUF3ddIuPjzU+k5MyfVMZLF7c7ncfRcDznx/nsacn2eJju8bns59t\nXfg7MCBng7LW+ESRdAlNcjFTr08zvkuWxB1HWroA8Ic/JB+jvjNZFh/1WPo7aX+KvBYfmrGy6aT6\n+tIHgCYLsG2jTTOdSRYfU9pJVkW1YyLIKkmDC1/ChwYHGzbIZzV3rt1s77598ni1TqoWHxtRYYPJ\n4uOyeJwGFq4WH8qjWtf7+mSY+KzofIAUPkcd1XqvNrP7QOv7TO3Vv/+7eV8uuiaJiCiSYdmf9CS7\nek9bRpiET5a1SRUudGxS4AZdrBaxxkd/x9IGmGpaFCxkzpzkuqi6KlNfaKpv110HHHts63euwQ1c\nLD62Fm/CZoJXF5C2bl+0CXdae6xHcBweluU+a5a90KDtC2ysIFkWFv34sTGZx9mz7e6ZtqCYPdtu\n7Kjmx/SM9TzqwVjS8h5F8vyeHvtgMgMDcrzjusbHNNbcsKE1QuWBAyx8AMQ3qc7u9/TITmHJkvRz\nP/Wp1llsU5Suz34WuPVW+bfP4AY02Eo7Vu0gSVWbKqP+4LOEDw3Y1Y5pZMRe+Kidl6kjU10T6P5e\n/3qZ/i9/aU6T7kHtNNIaWNNeTWqdoBfTdY2Pmh99rY1J+CSF0V2yxCx8VBe0445zEz5JL7guvm0s\nPlS2SeF39ee6b598X0z7ZpkGVRSyV6evr/Wa6hoQ0/NR75kaRNP+FM96FnDjjeZr6lx8MfAf/xGn\nnzXDqOZl0yY5WJyYMD8Ptc2wWfSpYrteI22gTYNEU/2hctMtPnlc3WbPlpY+E/Qc/+7vpMuxjbii\nNmjx4tZ3wtbVbXgYOProOK00VLc/wiW4y969UvxSW9Db62bxoedLFp/Vq+3W+OzYIf3ddYvP3Llu\n+4XQ32RF1svLFN54+3Y74QPIerxsWet90qdJkKuormqUT2rr9f7YVfjYrPGxdXVTrwvEwiet3aX3\njyaS9DQIUwCdrGBIahlnWXz274/LlN47l+AGQ0PZE2smi7qtxScrkI5ed2iyz2YzZhr30Not0/EH\nDsRrlIFYaKSlrwaOOXhQCjGbdo8moWjvPdPzHR6Ox6RAtiA3vSeUl6T3Tv1+YiJZ+ND11OdLwsdW\nCKdZfPRzsqyFetq+qFz4qPufUKc4d65cBJUlfGbObI32lhalC7CbQdatMlkz4kCs2ufPN798eidM\nlSspnLCeT9X0a8oL3bPqykD7bdgO0uha+gB5927g3nvj/9M9kOA84wxzelQGpgpuugdy+1AjK6lr\nfMj9TJ0BdxGyv/qVFMqqxWfBgtYobBMTyQs8589vXxwYRXHHfPrpMhKcSfiYghsAyb7ulOfx8biR\nNq3x2bkzFgdUbuvXy3CxadBAe8EC2SDqs9Mm4ZP0Xp14ojRnE9To6/tx6fcWRTJYwuzZ5tnxO+6I\nO4eshnDzZjmDrebBJmIdCZ/Vq5MtEPrMN2DvApBm8Tn//Nb7pvLV06ZoPiYXU4qSRIOL+fPzh7Me\nHW2vNybf8Cc/OX0AoM8M64ugVdfVtM5vcFDW7X/6p+y8Dw+3l7Gr8Fm1Km4LzjsvnuTwJXwobwsX\n5nN1U6F3lFy/9WtTvVWt6eTqarq/446T1iM6Nk340PlJ4l4VLtS2JkXIMq3xKTKcNQ0wk0S32m/v\n2iWFj2nWno5TRd/WrcARR5jbBtXSRoyOysG0TTCTgwfT68Q558jtPugaWWG7X/EK4LLL4v/T2MVl\njY9tZEdqmxYsaL8vQv8uywKyfbvcNJOgepIkNB57THrR0HtCQS6ShM/evXINLr3flL7tpFKW8PnM\nZ4A1a+TfNmt89DyOjcn8JHm97N0b5121+CSJMKB9bJo1ia8LYTWNJD7zGbuJna4QPnSTBw+2Wnz2\n7s0WPjqLFrX6Q+qVYnJSLp63iV5CDeWCBfbmyqSZefq/2jmk7aOiVir67O+XnY+pwlCFI+FDjd/8\n+fabnlEjpr9kb30r8K53yc78rLPi9I46Su7WmzTrYLNw+YYb4uuZhI/q6kbQ8a6ubrfcIj9V4UOB\nNIg0i4+6v5SaNnVqM2cmh1Q3BTcAsjdHHB1Nd3W76CLZidGxAPDGNwLPe156ulSfkiw++vqB8XF5\njMka2Nsbiw4aUOlh6fVrE8PD8h1IcguiZ5/VEPb3t0bG2r8/PfIYsWePvKeFC5MH8/rMd5I769CQ\nDLKiMjAg0zUJn69+NV60GkXJUbY2bpTvnnpNqvc0mBAijsSTNSsZRcmbY+riXA/zDGRbfPQBki58\nbCw+Q0Py3GOPlc8xj8VHH0Sn0d8vLT7qc6KJHZc1PoAUTEkTIED7TP78+cUJH3oH9EkKen7qYGh0\nNNkq8NBDwM9/Hv9/YEAu7FbPp08S90kTIyZXt6RoqT6CG9Dv550XW3ySZtbVvk+1+Oh1yOS5sGWL\ntFCa6gulq9aJsbH0Nbh6PUlzddPXdWVt1HrjjcDXvhanbxPZUp/hz5q1J1QLSNbAXndFS3pO//AP\ncSAKIWR+0iwyVO8ofbUemMqUylNdazZnjv2kkip8TM9Xn/BUBbkpP+p3QsQTwkn5WbwYePvb4/+n\nubqZrDVZ3kg//GF7fZg+3a6tZeEzhSp81DU+QD7hozbA1MCqaladGTahR16z2dyQjqUBqj4wovPV\njoPMv6bKYrL47NmTvBaB0lB91G3W+KjpJ5ne6fs/+zPg+OPj3/fuleuW5s0zr/UZHW0P10zPk8TS\nK18ZW5NoE0Dd4tOp8CFuu01+qgJEFSoPPihnhZJEMfkbqy9sFMXrLCgioa2rm8l1Dmi9/sGD6fUq\nabBh2hxMb1DTLD66+BsZkY1VFJnLXA8Du2CBWVAB7QPjJUuSZ8dt10X197da47JCLhMPPihnaYFs\n4aO6AJiEz223AR/7mNlFSK8TVIZUl0j4mKzLGze2ulBGUTzBoa57Uy2Dafd9550yPRNJwkftqLMs\nPgQ9X70Nslnjs327XFietN5Oh9b46DPqQPs17rpLulHSsRMT7RYNIBY+aZNeusVnYkKet3KlXZji\nsTFZPup1swatpnRU4bNyZfuECr1f6ia5NFGT9AzUhfuDg1L4mCw++/enr2VQ229q26g+JVl8XFzd\nsgZJLq5ualo7d8o6Ybo307iChI/NrDoQC88ighuo6aoWH9tw8SR8irL4qJDFZ9685Hde3xydJpGT\nhIneR6uTg6by1AV1ltAgK7seVt12/WSWxUetk/R+ubi6DQ/L49Py8/jjcfp5LD5J/dz27XLPNLXv\ncvEu+sxn7Fx5fVO58NGjugFxY7dwoVtauvAhv1d1ME0V3TSA278/n8UHaH35svzNVeFjs8ZneFjm\nN6mxoXTVgbTNGh/d7UJviF/84njzwvHx1hd5715Z3kuXtrrYqfesh4ReulR+jo3Fz4TcSx57THba\nSa5uhCp8XPbxoXLThQ8NXt77XukykGTxEaJ9MK9afGbNshc+p50GnHCCOa6+ev2hobjTmzOnXUgk\nuZeQJUBFFRdk8SHhs25da6QltWOhGSabhl+1JJkEFdA+EFm6NNnik7V+gNi7Ny7LHTvke7x0afZ7\n+9BDcWCFToWPSfwfOGAWPvQsdFe3FSva096yRU44qINOmiRQw1mTxSdr7QxZVk3o76vpudhafCh/\nCxaY1/jo1lYVcjMypWliaEjWK/W+kxaP/+QnrdG7aH8O/f3atUu+7y7CZ2xMCp8jjrCbyR8ba7f4\n9PXJOulq8SERcsQR7e2KLnxo8Xjac1QHNmmubgcOyHctaQKPLO2UTzU/+rX1DUwnJooLbkDXTXJ1\n0wfTaRYfU9CjbdukF4TN4HLvXnnvacJHf4/ShI/evtgIH/W3LIvP/v2tZZC1xkdHFT5pUcuovcla\n46N+N2OGFPpLliTXZyofOo+EQ5LQoHGUKnzofbF1dZs3L3lCQHdjJNc7G4sPEAu3tPdXnTCiSei0\nslGfb5qrG7UtNG4j4ZO0r5M+cTU0FFu0Te/ueee1Lq3wReXCx2TxocqVNAhNQg9uQNYdeqEmJ+MH\nZxp0LlgQDyqoUbeZOQbSZ+bVF4hIW0Cmu7rt2SMrYtKLR+nSTJ+rqxtZfKix1N3DAOnSNHNmq3hb\nuDB5AEOui2oochI+o6NxXmlmdeNGuV5EnV1Oc3VzXeNDJFl8qI6kuUHq7m6qxYeivlB6//3f8T1S\nVBj1vCRXMPX6e/fKawohG1K9zqpunSomS5BaruS6duihMv13v1tukmbKAxBPSthEqVGFT16LDx2j\nWyBWr5bBSkzXJOFx0UWtUYTSBkY7d0qxAdi7uunuqfS8VVc79Xh1plz9Xv0ki4/Jovvd77bvYbZ5\ns1yYrgsfG4uPaV3ZlVfKTz3IhEn42K7xoYG97s5Dz1SddNBRhc8Xv2jevNqUTz06mvpJ6G4vVF9n\nzWotm76+dJc1/XqAvM+9e6W1ymZAS+WjC66ktSKm+6C/h4flc1m0qP0Z60I7yzUIaL1+kqvbxIS8\nbtIgaffu1h3ns4SPaRCWZvV1DW5Am5KaBrxqm3f00enBDfT8q2MFG+Fzww1SfCdtErltm9xLTk17\n4cJk4aNPxtlEXVPfA9ozJ2lQ/4IXSHd3QNahK6+0i6JG11H3ojNdgwbRVD97e9PXvKjfkfBZvDjZ\nwqK7ftPgO0lY6XuqZbm6XXONXLNJ9+ti8RkZyWfxoYjGSc9MXRM9NpYciTPN4mM6nvo5dW30wYPJ\nFh/TeGratGSPnW98wz6oUScUInyEEGcJIR4UQvxRCPEhl3PpxfnjH+NBbt5wrIsWAffdF8/okS+7\nuqiTBoVJC8tV60VaYzY2FqehuprYCJ+DB+VDT2qcdLczmp1PevFMFp+0ymgi6+XevbtV+ADxgMF0\nDRosqwN+suCNjcWiYOdO4He/kxafpz2tdTBYhKubrfChfKaJbZPwoRnJ665rtfi8/vXxAmHV+vWq\nVwHnnptsHdKFD23uaRI++/a1DwxOOkmWrz7YVsXFNdfI60+bFruVqr+bLD6zZiX7LBO6xcdW+OgD\nbH2Gjs7ZvNm8n5Du6kYzejY+2cuWyc88Fp/775flSL+pkOuVbvEA4rLWhc/y5a152LpVvneqICGL\nz7HHtgofcg/JEj7qJBBBZWpj8Vm+3N7lY2xM5kmtM1S3siw+9Fzuvjv+/qGHWn3XCapnNsJHf1/I\ncj17dqvVbtcuuZ1CWkQ+XbDv3Cnf00MOcbP4qPm2FT4qtJ3C3LmyvPW6qEcXJItPmnVQt/iYXN0O\nHJD3mrRIX/9OdXUzhbR13cvKdR+fb30reZNftd2fnIyFj2ndpu7qNjoqr5M1q0739Y//KD+TLIov\neYl0Laf803rnKDLfr56//fuT92gj1HSyLD733BOPqb7znViYJPXBuihXA6+kWXyoXHfvTnd1U6+p\nWnyyItOp90tCw5Q+PS+6jyxXt699Ta7ZJGyFD03eqhafrLVnQsjyWbw4vX9T+/C77062LuqTDeTp\nlLSWVRc+WZPsJuEzOZne9pjWQxdNx8JHCDENwGcBvBTA0wD8uRAiwYu8HbUS6xYfV5YulRXwWc+S\n/7/9dvlA6IWanIyFj95p6T7h5DqQ5Npy/vnxQP6KK9yEjzoDYmN+zNqcUBc+VBnJ1e3WW9sbzH/7\ntzgyCll8kiKjPO1p0j1r1qzWdEj4JOVp9uzWDuVP/1R+qsLnF7+QjfzBg3Iwp1qtinB101HTo0HQ\n+Hj8gqatKUiz+ACxmNm6Vf5fXdtFx11/vQwKkfQs1YZiz5540JskfBYvjv//xjfKNQx6kA+gdTC0\nfXt8H5S+mpe8Fp+tW+2FDxDPFutrp/QNE9X8rVrVnp4ufKhjSwtFq68jtJmdU10Aoih2G9u2rdVF\nE4g7e9PeTnQdtQ0i4aPm4fLL5ed557Wev2kT8JSntB6ruj+oZfn4462R0UwTPkce2X6vgHw+ulAQ\nws3iowsfer/SLD60vkLn1luBq65qb8uoLpNrMyCvOW2anfChdkytQ3v2SAGSZvFRZz0B+V4tXZoc\n5EQIOXAkTK5uW7emrxEyoU5OmISPHtzgU5/Ktvio3w8Oml3d+vvTB3i6cCYLDfVN+rV1QZHl8WEj\nfPSxhDpZp27FoF5rZCRex5Jm8aHv9+1LX2Oirq0C4rb54EHz8fqeaSTObcI7T07K9A87zG4vLNWl\nP23cRf0mecTQWqm0YAL0N7l+JR2vlgcg3z0XV7ddu2JXNxvRnBXcgMqN2sMDB9KjuqmRhWfNyhY+\ndE2aNFVdMG1c3bZtk21E2vurWny+9z3ZptpYfOhekyYzTJs9m9ZyEqZ3eGJCpp20kbHt2t5OKMLi\n8ywAD0dRtCmKojEA3wJwtu3JasPVqcVn9er4702bgC9/GXjRi1otPvrsAkHHqAMY8l825efuu1sr\npKvwSZudVaOJALHFx1b4UH4oROWaNe3uT+97H/D3fx/fq76OQ03r9tvlPim6CCHxZmvxufBC2agN\nDcURrR57TH5OmyZ922kwSQMnnxYfIB6kUEOh7gulYxI+JH57euLBHIVhpmupwkfNh+lZ6jOPacKn\nvz+2CFH+Zsww18GBAXODQgJAbdBdLT7U0V19dXq0OKDd4tPTI49V30faM0h1rVFdUPX70vfgoXTT\nZgCprSDhYxqsfvjD0jpH1x0YaJ25pHxu2tS+Dw91fibLHh2jWnxMwUvWr5fvjFp3aCH1Mce0Lgam\nGUx9cPGZz8g0KA+mQBKTk+ZIYkNDrQLkd7+Tn7ZrfEZHZb1V718VPjZrfK64Iha7tGA3a63bvn0y\nfP28ee3vAb1f1HaQ+5NJ+KxcmT547O9vDSSybZvsL9IE9/r18pPaXF343HFHexQ/E2o5T5sWTzSZ\nhI++ePymm+yFD7lCLVnS7urW25u8Dka9rppPcjMybU45ONg6GM2a+bURPnoejjxSXnvjRuCFL4y/\nV/tyEnRCpAc3UNe7pu21pgufgQG5KfsLXpAtcMnis3hxsiuUCrlbrljRHvRAHQNQuX3uc/FWGWmu\nwfp9JQmByUngK1+J/6+6uiW1x/okHXk62GzoOW2aHDOsWuVu8UmyOujPiyYgk4Sb+h1Zjm2ED9Vv\nmugxPd9LLmlv37Zvl2OltPeX+nVqs5MCmeiT7Dt2xHk3HU/9nBpIa/du2Vbq93r99eb2k9rev/5r\nc97rInyOALBF+f8TU99Zob5oVBGKED7nnCM/Tz65dZOmJIuP7n4CxGFxTZXX1GhnRXWjCpxl8aFK\nSMerrm5p4az1nceXLEmPsa6vVVAtPmeeGf9G96V3REK4WXymT5eDjMWLZXSpU0+NXVmGh+XLrDZ0\naa5uRazxAeKBqU30qEMOae1I1fDXExOx+w7N5NALnCR8TM9St7YkCR96xv/8z3FIa2rs9GdCs6zq\nzJSevoperiSKbdb40MaoScENdIEyd648Xp09J5GjNpokiPU0+/tby+zNb45dC/V3Uf2bghrQczEN\nxD/3udaN7CiAAKVLwmfLlrhdUdf8zJ9vfs7qAIjKRHV1GxkBnvMc6dr12teiDVpsbrKk6C5kJOTJ\nVcVk8UmKJKYKn/e8J7bYJrVb4+Ot7ommNT5qPtPW+NB1V62SFmdARuED2gNJ6HXik5+Unybho7fF\nNJtOM7Xqvajtp4l9+2SQEmLPnnhRc9KAlvKTFNwAaF2/ZYuN8CFI6LoIH/J8mJyM2wdV+JjaBb2P\n1fcs0a9NLjZElvCxCW6g3/sRR5itjfrifWorbYIb7NkTtwtps+p0/MQE8JrXJLu66fedZvGhvFEb\nODQk288nP7k10uEPfiCfIf1ftWaQm3uaRUkvA3JX0u9Xr3u68DHdb39/3HcBrRYfm31tSPi4WHxs\nXN3UCeW0YAJqXZo9O7b2py0DAOLn/PjjMjCGLvQWLZKRQlWEiIVPmrsYlee//Zv8NFm/gfa6uWlT\n7M5sOp76afocHJR1wRSh+OwE84ca0MxELVzdAJiGjMbmaO3atf/zb926dQCKdXVThQ+5TKkzllEk\nXQmmT88WPuTKctRRyZ28fn7SrI8uZMj0m/Ui0efevXYWH/U+SfiYFv4S6iBFFT4DA3KtFNC6sNhU\nIW3X+Kh7nCxbJiN3HHdcnPc3v7nVjSnJ1Y3qi2s4a0IXFuSSlRbUgNAtPqowGx+PB3Mf/7j8bsYM\n4A1vkANjtWGn37Jc3eiaQLvwod22zz1Xzuirx+qNFs2ymp4f5UufqSd0i0+WO5gaLY7quxBy/6x9\n+1oHfmSZWby4NQTvXXe1uoipbmX6QGbvXjnbRIyNyedALllqftV3fvZsKS5OPln+3zQQ14WmLny2\nbZPPa/PmOP+68DE9Z93VbXJSlg25uj36qLSy7t8vXdqA2LJH5ab7YP/t35rv48EH5cwyRWc0WXzS\nhA9ZXtR6mdThPvWpwAUXxP8fG5PPUa0fWRaf+++XPvPUbqvv+QMPyE9d+OjCgfJmEur0f6pHUKpr\nEQAAIABJREFUqqubOuhYvNjspkh85SvSmvaMZ8TfDQzIayaJfvX6QLLwyVqfAZgnJ5Jc3YaH211O\nli5NHzjprm6HHhoLBvqtt1daFpImRPS6du+96RGpVMEB2Fl8svoAk9Vp1iy5JhOQz9zUDlM+bCw+\nNFC3tfgAya64W7a05ocmRRYtMltAaPKFymH9evn3ihWt7R+tXda9T4C47ze1VdQv6PWE9qbTv9e9\nEtavzxYC+/bFE1FAbEGzcXW77jrZDh9+uJvFx8bVjZ7bj38c9ydpwaWAdotP2oQzvZMkfHShp1rC\n1Pdixw7pymhj8SGSgnRRADDKJwmftGcFxG2MuhZOfR/UwFg6VOaqtwoA/OQn6wCsxaOPrgWwNjmB\nAijCqPQEgCcp/18FwLhbztq1a9u+M7m66QVii3repk1yzYM6oz05Kb8/4YRsV7ehIflwV62ys/j0\n9SU3frr4sHV1o+NpMVvSZogUyIAGBDQQU8Oims6jQciWLbFVYtYsuWkpnfeyl8XHmzaTS3N1mz1b\nioAzzogHZoDs2H//e7nQH5C/f+UrrVaXpOAGVF9cw1kTumWnE+FDi4QBWbdmz5bmXVprMH163MHq\nlhWbKGJA3ODpwocEhn4vlLb6TNTNLU33pF/XJHzSNmhVB6cU1pvK9c1vlt/fdx/w3OdKIUgkCZ87\n7pBryqgcyUW1p6f9ve3vl+8oCaOBgVbLl14OVO4zZ8ZuR0D6LD1Bi7lV4fPsZ8v3jSwrasjaJCut\n7up24IB8DrTJHA1mgLh+qdHvaGZczS9FflPDMkeRtBr967/G9dDUDiSFvx0ejgWIbrkF5Punfq9G\n8CKLBtUbfcFwksWH1jXRQIjq1thYHABFX1ujD/SpLpjCC6vugUCrq5tKb2/yOjUAeNvb5OfTnx5/\nR/7xPT2tAQdM0DrMhQvb8z9jhrxnvXxV1LbjO9+R17nvPhn8Qd/HZ2hI3qPqxpsW/hdonXQgK01P\nj/wkC1xvr3z3Bgft1vjQQu4kN6Px8dbyKtrV7bTT5OfEhIy6Cch8U7RG1b07y+KjWmvI4pPUF5qE\nT9JYQd/bTRU+poF6X1/rNh733SeDIwBxm6Z6XlCbqtY5OsY0sE9z9TQJJVO0XJroTbP4HH983H6Q\nkExyddNd48g1Ns3ic9JJcZhkdYPUtKhu1KfdcIP8fO97071b6DPL1U1P/7HH5KR92kTEsmUy3dtu\nk0sXklzd9HaW+OhH48kvlXe/O74HQIqwlSvTXd3U946E0uzZcbv86U+3rit95jNjlztA5vuII1pd\nTQHglFPWAFiDI46gJRAXmYqiEIqw+NwB4BghxGohxCwAbwJwve3JasNFL+cNN8iZvzxQBw/IzklV\nvgcPygf71Ke2N8rUEdLn5s3SHzjJN1LvEDdsiF2/9MquRtMB7C0+dPzu3bJhTvNJVfe9WLdOBnhQ\n3avSIm7cdFNrAAVanA+07qOgzyIA6a5us2YBp5zSKnoAOdu4Z088WJs7V+ZFiNZoNibh42rxUevX\nz34Wd37qPQ0N2bm66cKHZrJWrJDPZ86c1gXWVL5nntku5pNmj5I2fdWFDz0vIM57kqsbCR/TIMpk\n8dHrCq21SmrI1bKjhfI9PbJeXX1163VUMWMSPrt3y52h16xpfceGhsx7/uzbF4dJB2IRAcgyVzv4\nq66KXWn0epUWZYzyq/qSk/A59VTZSPf2yuPomdICVNM7q8+6Urh6em6q8CHoHqnc9MWk9K6rgmLH\nDvn+rlkjF0z39ZnvkcLf6oOA97wnrrf6u5blVkJWZBLCRJbFh54PbSxL7/nevTKPy5a1W+K2bo0t\nd4Bsu3/+c3N0NN3aplp8dJLEmfquHHZY/DdZfISQz8u0XlBd49bfL9sONb2Xvzy5H9HvQxUGX/+6\n/ExyddM3A1+6NF34qO+eKnyAePH9jh3pa3yuvbb9uzSLz9hYaz6zfP1thI8qkmm9nrqP0+hoLH5V\nqG1NCm6gbmxua/FR75fKQD/eVN+ofTAN1Pfsaa2Dw8PSzQ1onXCgtv/hh6WVUm/vkyw+JPKAOPQ/\nIPt10zPcv1/24epzTBMCZO1W085ydduypfX/anADPT8TE7Ke/K//FX9HfWeSsDI9g1NPTR+vqeOk\nLOFDfSBdh97PpPwArX1cmsVH37MLkPsxZkWKpHRsXN2ojweAiy9ut/jo+2+edVbr/9/9brnGTb/X\npDD3PuhY+ERRNAHg3QBuBvAHAN+KougB2/PVDpX+PvJIOQOQh9e9LvZFj6LWGaR3vEMOthctSrb4\nUKfx6KNShSdVAJPLyLOelW7xoQ6bdiY3Vdwf/Qj4r/+Sf9OL0deXPkM3ONjacNx+u1TTasfR2yvd\njW67LTZDqp0VNQb6Wh71hSZ/9rPPlhUXyLb4mKBZ5CdN2QnVfJJrD82I6mtj1FCQrlHdTj+9PT1a\nhJ/H4kO+y3ffLWeT9LSpfG++uT2tJFe3fftarWzUIS9Y0DrTRSE8gXhAQnU9SfiYNjalc/ToZYQQ\n8QasSS4tqvAhd8Xp06X1S7+Omq89e2QjuXix3CfiX/8V+MQnpFXo6KNbLRdJwke3fKkBH1asiAXJ\n6Kh0CaSOVC+LpChj6g72GzfKQYW6xufss+WmmH/8YzxYB6RIOuKIdOGjlgPt00XpnndeHPZWze/o\nqLTi0GalBNU91T3rwQelO+nq1VIMmDZTBcwWn+Fh+Y8mQfSJnjQ3MEJ1kSLUcNam82mmnTp6Ej6U\nlh40Y+dO+bw//OH4O5q0mjUreS0HfW7darb4AO3vOyCjUKpthVqPSPhQ/vv6ksuI2hyyjhI0u5xl\ngRwbM2/wneTqprqBA3F909sgkwsd7fGmt29PPJHuFvONb8jPP//z+DuamDBN/IyPy2Pp/dUjOOrY\nrPFRrbq6Ww8gy6qvLy5LmnhSXd3U4//iL+TeWmrZ26zx0T0UyNXNNHA9/fT4/9QeHHFEsvBR3cSA\nuB1S31ESPi9+sRQtKvv3xxYrkws/RQ6ld+T886UFxdS2UZ+ovvO0Zi8p6E5PD/Db38bfbdyYLPQm\nJ80TQzNnmuszCW114vFzn5N5NwmrRx6R1kB9knfdunRXNyqbyUkpTJYtS36HSbhR23L00bIPTQtP\nrS7ZoMiPpvI3LW2gtaamiQYd1dUtKRDFkxT/LtPkh3rPO3fGFmKCgpskuZC6rm/MQyH7+ERR9KMo\nip4aRdGxURRd5nZu/Hee8MQmyKym7lMCyHj0p58uX4KkNT70uWGDdGPImsUhXv3q2H84SfgQjz2W\nvBjvN79pv0aW8Bkaap312blTbgaqDoz+6Z+kq9Hzny8bv+nTW/cs2rYtrsAqJuHzta/FM4xJ5XPn\nnckWGRrU0KD+u9+Nf7vyytiNhBZdqlCatrsoZ3WMuqub2lHqJAmfww6TnY+eV/KtN1mTkgYdAwOt\n1jvqiFetks+I7p82bQNarSyA2cXr0EOlBUYdIKrnqOjuCmNjcbQ4tVHSN9oF4vU2+rtMHa+6aHrT\nJjkgW7RI1tkLL5QWoo9/vN3NKGnPn82bWzv+J56I/3/YYfHsGs1S04BGDQMOJFt86H0YGZFpr14d\nl8O2bbGFUojWQXBvr7x+mqsbQa4sVCf6+6Vgufji+Bh6Dy+6SJZtT0/rs6PfVUHx0EPSuq1a+kz3\naBI++ppHvdxNA2wVdU2U2oZkWXz27ZMLcul9JOFDaekRAElgqmVBA0XdhdJ0/w8/LMteXSdGmFzd\n9PZh4UI5UHvb22Qeqb4sXSrbMtP7BcRiF5ARNnXSLD533y37J7W+nXMO8O1vtz+X3buB738/tgIQ\nSYOhwcHW57V7t8ynEK33smxZq5uLmtd7722N7KUGEKFNmU2DvLExabmj/L/+9eb7J7LW+Nx1l3nS\n6e6743bwyCOlJYiEDHkhqK5u6r1985tyfKGWfZbFh/b/Ukmy+JB1jbjvvnij8CThQ5OeVM6UN5ok\nGB2NJyqPPVauTVN57DF530kWH93qS5NBpvqzdWsscgg1gIhJ+MybF7vq0dKCY44x3y/t8WMiSUge\ncki7y+lxx5nTp75PtwDOnJksTNQJ2t//XvZJq1aZ2zjyHJg5M742TWAuXGjefPxzn2sVGxMTyWu+\nqZ1W270kN0O9bVuyRD5jsvgktc+qxWfPntb9rnp75ftx+eXyesuWxRMYT31qfJ5pEtV1H69OKET4\ndII6aMqzWD2Jk0+WwoUq/KpVsmBXrDALH32Nz+OPSyWeNvOmDmhJeJgqmL4Y97774ugyasUln3iC\nBjCbNsVx25M2AKPKRY3U8uWt+fv1r1vPOeaY1lmB9evleoWZM1sFkdoJzpoln5cawjXJPHvxxdJl\nyQTNXpgsED09sftHmsUnyxWEiCLgTW9qjc6lX492Pf/4x+WC9ySShA+hW7iGhpL91E3Pcv/+9kZa\nHdAuXBgP5FXhQ500NWR62VDjv3Jl65oEuieg1QWHrCSArEMXXigHC3pjRddTnz+JDr1O0HlqcIPN\nm2PhQ+zeLa29+loVk8XnnHOkVYT27QLku0DCcdmy2N2IFsbTO6F3hEkWH+rAH344buBnzZLPgdbl\n3HxzvCkssWGDvH6SxUePYqS6upnWhuj1iK5NqK5uVG5k8VHvN0n4UDhbQnfP1Z+nqQ3VISuNi6ub\nWq+BeKZcFT5qHaAF9uo15s6V/1efP6G3GbRG4kUvap1VfcMbzEEK9LZ8wQI5aFy4UNY9el+XLWt1\nqdKjaW3eHNf7Sy6R6xxVTOWzcaMsC7Kkqu0nuY3qwueSS9onB4B4XxW9bvb3t7rVkJs10FrfKALh\nihXtEyJPf3o8efX978dlMnu2TD/J1Y0WzNN13/9+pJLl6vbBD7b+n+ryM54BXHqpfGYUHn7RIjlo\n/eIX5TGm4AaqpUG1+PT1mbd2ePRR6dZD+3+pJJXBwECr8Lnzzthap1sotm2Tm2TT2IOeG/VJVH/V\n9vx1r2sX42TJNuWH3LmB+No0kWLqw379aznBqi9hSJoU/tGPYtdQIL7W8ceb80MRzUyYhOThh8tJ\nNbU9Pfpo6f5rEj50P6Z+O2lNzeBg+4Qxufjqfcru3bL8VVdAOlf1UFA55pjWiYsjjkjeT80UVEWN\nLqo+F7KAEhQdlvq5wUEZNValv7/deqxafV/3OrnchCbqARlcZ/fu1on4Rlh8OkEVO0VZfADZiB1+\nePySUyexdKmdxWfXLjlQnDdPNkZqPqnyqA0IzbqYHmh/vwwYQL8//ri0yOgdhure8prXxN/39Ukh\nlzTjsGOHnMkB4kGf3tDq6DOct98uB/2zZrX6RWetfUkyd6dBAxuTqwZdM0n4UKOb5FduYt484KUv\nNf/2/7d35vF2FVW+/9Wdknsz3CQ3yU0IEBLGpgEJ2omfNE1CVEQEaVQEhwZEWhA+Ng8eBgiitoKi\nrYDSDQqNtMNT0oAgz35CmNIOH8OchCHNIAQSAgkkITeEEEJS7491llW7zt777HPPPuP9fT+f+znD\nPefs2rVrV9VvrVWrVPhs21YcAhASTrp0x2XFL+v06dKWkuLU466lxpr7AurAA93z3Xd3qZ7Xri3e\n5FGtReEAoPtSAMWDnr5+8EFp8+vXF3fiDz0kA04oDjZvLg4RTPL4hPfXOee4EAStt82bpdzt7cWT\nzjjhc9tt8ujHbwPRcCmd+PoDyt13u0WdSpLHRzvvJ5+UAROQ9rRihTvXOXNEhPnXbfVqJ3ziBks/\nNNX3+Lz9drzwCdtRd3e0vemx40LdAFmvAxRP3IH4rG5azzohCdvEyJHpwsf3+Pj3SKnkBqHw0bT1\nOiEM78F164qFj7bJCROiRhxAjul7VDXMqbPTbag3bZqsT4kTPv7vtbe7SVh/v1xzvQ5Tp4rBStE2\noI/nnuv6P2Nc21L8caS3V/YG23NPWaemWRz9evWFz+bNEpL3oQ8VW5XVcjxqlPyF7UHXDyg/+Ul8\nG9A6TAt1AyQSQr+nwkc3v4zzhGo7Hzeu9NhTSvi8+abbUw2ITpjb2qLtYMwYGWN1rZjfX2rKXjWe\n6OeVgQG5RqFYXbJE9r/TjdB9RowonrhaC5xySnEfrRPN0Mh4zTVyj2tf4q8HBdy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dB2WW7W0zSMkVDMrON4lnvcn0vcdpvzrmvWyDQmTJD+/5JLokl8kvi7v4s3/qb9PpDdw+WzaVNp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\nCCGk5aHwIYQQQgghhLQ8/x9X15F94xb2YwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ca4c50518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_other(dd=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hhBBSOI2IGAoeYgMKHUIIIYQQUgkocIhNKHQIIYQQQkjhNJJ/Q8FDbEChQwghhBBCKgEF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\nO48+KuVTNVkKH1x/fZj3k0TUoxME0paOGR42rNZ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zJXaDQNTx8ceHql4nj5oXW4fUaJop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\nO/4/cOVxFqxAUQkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ca4c76e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_normal(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ph375MGJdE/P6lcrBg3dD5+qrlReHPTrpTpRYCfWcQzwh6UruY5Ca0IZOTuT4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ca4c45e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_af(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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