path: root/gm_platform/fw/Scope.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib notebook\n",
    "import numpy as np\n",
    "import struct\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_data(offx=0, end=-1, signed=False, channels=1):\n",
    "    with open('/tmp/scope_dump.bin', 'rb') as f:\n",
    "        raw_data = f.read()\n",
    "        data = struct.unpack(f'<{len(raw_data)//2}{\"h\" if signed else \"H\"}', raw_data)\n",
    "    \n",
    "    fig, axs = plt.subplots(channels*2, 1, squeeze=False, sharex=True, figsize=(10, 5))\n",
    "    axs = axs.flatten()\n",
    "    for i, (ax_t, ax_f) in enumerate(zip(axs[0::2], axs[1::2])):\n",
    "        le_slice = data[offx:][:end][i::channels]\n",
    "        ax_t.plot(np.linspace(0, len(le_slice)/1000, len(le_slice)),\n",
    "            [math.nan if x==-255 else x for x in le_slice])\n",
    "        ax_t.grid()   \n",
    "        \n",
    "        ax_f.specgram(le_slice, Fs=1000)\n",
    "        ax_f.grid()\n",
    "                             \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
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     "output_type": "display_data"
    },
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IYEfhnQs3/lA0O1WjzSJnJqAJzpqMj6zJsDnXd6sIPL/fr6TLkXt4XxfK1niBgofStDrGbL0EJ6PBD2N5HzPkUA6nMbZfDYjTI6bo/1HZRXbAayCnsbZnIOThUubRgctUeg6JaiK3v+evHedOzxX16fP1wpXJjhujt6bJkt+cGWC4+Zo4uo9ovOGLAj3CBfvlT07LZgnQBo3fm46Je0Jt/vk0yg9PltRas5rN0nWu75q15HQ503fIHce59VKabLjJyyR47jxaos4gp7meT/thQn6431lTttrSsMEvfFnst71z1eECXoatQV/XqVqC6Lw9IQ0EwZqQtAvZEZcaXwtuLncsh2y5MM0ZlY5aumu2L+z18nbo1en/l3A7z4K+telyY5Lmew/q7B9LdKsk5qAtwqkHYtYE1SwVpbr20yL/JvznXQpag/n+1rJEjdQ+FCCXs+QpYfQ8zwaMmocvfrJcnpqwOJUo6E27TtGLwxZRk8PXEzjV8mIFhHRVawSl4bE8kBF2utZejhcZZQGb7yP0nFztOEL2b3is6J/8b48EOYYzlyQHVfey95mYjAqSprEsCuV1fXp85ffhi/ClXCpIZotJa4ugOewXbaX7zwsOu/lT5abc9K48fMRWJ0myyrvtnz/tx8uFJ1XUVEZIgtSQzQ+Ks1xc1QgNOrbeTD8TEiVHpxQVhfAV0fQ15TKJOC2fP/BnnNF5620khZSQ7Qxy0pD50knRXCFjv/8yO6B7Ta/eb/sHtj7UnWeEYBdhZX6WthJkinCvnyuKKhu/7LXCU+mpZnxzsfcOW6O9pbJkqk3tZtmzpm3Wa4+Gb9qDz09cDG9MGSZ2OSSiOij2VvpqQGL6bURK+jLY7K2kh0HT4R+2+pSmc/E0dMevTlqFT01YLFYkUMUvgdpPDRW7DxMfx68lP40aEmqiR5cdRTXAhNFuj6ev53+OmwZvTVqldiYsbKykjpOLqanBiymf4xeRSfLZUoLxT8Xm/cfoxeHLqOW49eJ9i8iP/n99ujV9NIny6n3zPgY1V4r+KynBy6mcSvlbUt1hSxxA4UPJej1DFl6CD3Pozf7jjcvyQsayWV0/GV+W8cZ4s/k5mJ/6CfvleaBSsvxsmq4LQOWSldt8iOVD3PzLceVV945eBXeceWSzSZMtnxTu+hqhA1uoue4ORq2KL6XnL/8uDt6mmBuKOsJd9wcDV6wXXReqwnrQudJA+o/9l9kznksheyfj8B6e7Ss8r7PUhVIxwjayQ6pIZpd9ZUGIHayQ9o3/ZsPF4TOi2uHsIMkPlv51eGy6QL2yDrpKKzlOw+HzpMaoo1ingOOK2+H+BPr2XXcHI1dLmuHaDR2deg8KfHi5zhu9S7pQIVluCf1tbBVMtK9k7u4O26OVsX0odvrhD9zjisfWccTk46bo50HZVVm3l8vTaYRhWX/TYQKjdPeudA4Mem+t7o0nHCSTqIYbyVCdwtJbLPPg3f6pU3k6q2XWCI0zfjUEmb+eXun6BgiiqDz3/b7frJ7ZycmpQknxT8XPG6UJmzfGLkydO/i/DDstfLOhGBvSOPnU1fIEjdQ+FCCXs+QpYfQ8zx6rff40OZ3QGgaxYPMNP2l93SbZc67r/ts8Xm8j/KlYbL+XrsCIVUI8DFbacgPl0Y7brrZ28AHRZtD/4bUEO2NT4OXmNTYziaV1c3g5i+/+VvCUvW3hIZogy2putQQjUsvHTdH09bL5Nx/6BcQ9IYpWiP4CKznPpaRPO7E7rg5uqebjFTaUvW/DpMZotlV35FCNUKnycWh86RqhCf6hgl6HLG3g6Tr20xNTYSOWtLeH3eQJQC5o7rjyg3RRi4JE3RpO0zDd2eGzqsuwcVhB5xSJZFN0CV79VnLL0Lqa2GvZ6mqoGBN2C8iLrlpr5N3WYuO48ol1ny0pePK1Qh8eoVUoUEUrrxLVTm2VF16LVdYCSfpJJLR1t4gnfDAk7yOK1NoEBH9eXDgAZBm8ghP9MYlle11YitC4qTxNjbvD08CGSOcPKL45+LFoUF71SLhvve49e7ZF1P4sNcKf49f2SLd1KC6QJa4gcKHEvR6hiw9hJ7n0d8sgl7daBwO3jOYplf67q6zUr9cicJ9lL/5UNaTuu1AmKBLe6Wnrg8T9IHC0VfcqM1x083eBjoXhgmUtCLE+4ovaTJJdI4dhFenTOAvv3lWL7nUEI2b6DlujroJ5Zd2EC5tqeC9qWmSSNws6VGhKeHuI+FrGVcRsmFXfaWGaHbVd6jQSb99QbhSuWCLMEjqEw6S4vwK7CDpR60Dgn5vN1lCruxkmND8SDiresn2MEGXGqKNsNpKpH4Fv+o9P3Se1K+AG6I5bo6KNspkwTZBl+zV5WfDBF26V9vr+Z0JMsM929BxakwyzV4nvK3EcXO0tRrDSg4ehDuuXBFy/3sBQf+ZUKFBRHRD24CgS8047RGMXQpl0wWW7QjvDdLpAiOtvUFqxtlobPhaStV0z30cKEluTVGp5GMw4xJH9jqxRzBK91l7VKd0v1T8c8HJ9pwSWXzETSAdN0fbYwwr7bXCzVAvajwp1TjLukCWuIHChxL0eoYsPYSe59GrvcIEXdpX99aoVaHzpL3SvOp0UeNJ4iw9/yxpdXLLl+H+RGmvdKHVRykd12RXv9OMawK4LMtx5YZoTw1YHDpP4jxvB+HVSZD5y4+PgnPcHD0rNESz5ftSQzQ7CB+5RFYttmXZUjd+PgJLOh7Mbqe4vo2MVNpV3yeEhmh21XeA0NjMlhJLjc0es8ho3Hl2kHTNO1PYNZEl5A5bjvhxDs82FlmtF1JDtE8WhQn630bIpPicmDhu9QoUjleHhwn6JKF/gE3QJXu13Usu3avt9fya8JrYPhNx6iN7nXS1KuhSot3YqvpKFSFc4i5VaBCFFSFSrw97BKM02bHUSjhJR4V+uiS8nqVeH+6YcOuFdAQjb/VIM3mEj8H8QfPJkX9jrxN7gog06WdPEPlotiyZpvjnghdopEo4uyVo/Z7omMheK/Z5tTWZoKbIEjdQ+FCCXs+QpYfQ8zx6xSLoUjkcrzI6rrxXmvegO271s7eBqrI2GfmxZW3SXunJa8NVIKkbdI/pJaHz0rhBA/ZLRRpw2hXOo6eTnUztILw6TwD+8rPNqqTSZW6i57hyQzS7sjNIqGiwpXFST4Bn2Aisq4TtAnaPY1zAaWPxtnAQLpXb2maC0naB1lbPblyF08ajvcImcXGGaHaQxMezSRNyBy1HfMeVyW0XbAkTdKnc1vZG+LNQuvymlaTsKDQU5D27jpuj0UK5rX1NJHv1qfKqBF2yV9vtQX/sL/MLsUfWfbokmlTa66RLYZigL61mogSHvV9K2wXu7RYQ9OuECg2isCJE2kpkj2BsNFa279kJJ+n7xN4b+gr3hn+MDq9nqWs8Hxl4WQo1HR+DeXHjaNWXvU5sY1Npz/v6PWGC/r4wmab454I/P9LEZEerLWvZjuiYqLrJEI6bo7KT2XZ3zxI3UPhQgl7PkKWH0PM8eumDMEGXSl5f/zTcRyntlb7TGtdU3extwJa1SQN9W9Ym7ZW2+yhfGCLrC+YBh+Omm70N2PLX+UJDtF/2mBu+HwIyagfhP69mPBt/+dkmeg8LgyTbRO/vI2X3w67sVOfkymFXfaVu/Ha/dfnZ5Mr7dqud4oJGOZGkzjbck8pthy0Kk0ppwGkb7uVWy4Ik28U9bj68HSTxMX6OK0vI2eaFjpsTuS7bY/ykclt7jJ+UjLbNhZMdUvNKrtBw3BwNEcpt7Wsi2atPWIZ7jpsTuYjb7UHSqRS2eWFce5C9TmxvBGl7kE0qpef9jFXyvt9MlkwjIvphqymhz5Oocuz1LFUj2AmnnjNkz7i9N7QXKpXshJN0wsOTlnpLqqZrYbUuRb3T7XViK0KkyeG1u8OTEzoUpH83K74azlVUhswSpQpDu3VQ6mthr+faGB34VZAlbqDwoQS9niFLD6HnefRXi6BL+9X+ZhFJ6Txl7oLruNXP3jbf05K1Oa7M5deWtUl7pe0+ysf7yGTHdh9lmtnbwF8Gh/utpDKwuyzDqrg+LQ47CL+xbbwEmb/8bBM9qczQNtGTGqLZQXhXYcuBXfWVur8/2DOc7IgzpeHYmm+n4AHIaS9ZUmcb7knltkOsqq+0r9U23Isj2jYwfxtmjXGGaHaQxGdpO64sIWc7iDuuzBBtbsmB0HeUym0/nr89dJ60t9iu+kod/7lfhOPKzSvtayLZq7lBGX6fxNfCbg+SuiCPXR42KIsbmWavkw4FYYJeKHTZtoPwmu6X0v5UPibUcWWJUHs9S6cL2Akn6chHO+H0pjAxbZsXxjnw2+BmnI4rmy5AFB6DGbdf2uvEVoRIXdxtR3xpMk3xz4OtjJK2qtkEXeprYa9nqa9FXSFL3EDhQwl6PUOWHkLP8+jFnmGCLq2ovWL1UUp7pX/cwSfoGL8kcaa1jY4cV+bWa8vapL3Sdh+lVCqLPkr8tjSztwE+GsxxczRBaIiG64pDUi22Zwhf0iTeSIW//GwTvTs6yfq00aMPEvuM0BDNDsLb5mQ9nHbVV2oux6trjitz4weh4RVjSRIJpBLXRCq3BanEIe3n56OUHDdegmwDCg1UEOMM0ewg6fvNJoeeicXbkhNymC5wceNJdHkzf0a8xBBt9qYvQ99RKrcdmDcvxHn3V6Mk4bCrvtIxcnC9xj2Xmlfae6Bkr+YGZWg32Lg3ee8ssdRHlzYpEJFYe7pAnH+HvU7aW94I0uqaHYRLZbO2A7/UEO1KSxEi2Wf3loUJulShYXt9SKcL2HuDdJ9949OaTRfgZpyOK1NoEBH99sPweVHjs+x1YitCpCaeK61pGf8YLUtaAF+UnYrtfa6PKNl3TGwoDNiKRqlyyPb2kfpa2CpPqa9FXSFL3EDhQwl6PUOWHkLP8+iFnhNCm5jUUMbuo5T2St/Sfjo5bo5ubuf/VzJfl8varm8zTRzor9sTlrVJ5XDoo8RnXSusxMGBHb9N6iLN8es+YVm2NMvMe7scN75PiwNB+A9YlTOud52//GCih+qo1Pyr54ySEImVGqIhCMfnSecPo+oLkvfx/O2i827rGE52xEnqOOB3cO07U+jSpv7nSeYPg1TimkjltgMtR3xpRcgepTREOJMZrtdIBMUZotlBEq4FxlNJEnKYLnBpkwKzriWGaEV5bwSerJLIbeGNgPOk5l921Vc6kg/mcrjnUvNKm6BL9mpuuHdrfu9dIdirN+6tujdIetft6QKtJ0Yn0+x1Ys8zlxqi2UG4lNjbXihSQzRcD+wpkhakPZYZ52NChcasTWGC7o6RKTSwN1yWf/akfdpVpgsUy6YL2GacNZlFH0fs7XVij6yTjmq1p2W8LFTTAThP2pf/rwye9EvjjF5TTwXb22dETMxorxVb5SlRa9YlssQNFD6UoNczZOkh9DyPnu8RJuiDhQG73Ucp7ZVGoI4KhsTwi8va7ujk97BLRnTYfWfSQAV9lPgsqeEXzEzw29LM1wVQ9b2kyaRUBArSS8hY529Jvh8Iwq9rPdVU8+Ikm/zlBxO96/LkSWqk9n7+RYtRRdLedQThIGvS6geqvnBelhqpIcGCqu/0DcmyWVQHrms91VTZJG0GMNxDMujCRjnRd+xvOeJL2ylswz3paDAE01jbcW0GdpCEdYzzJAQK0wUua1pg7sWa0uTqx4zifaHPclxZZfSj2T5Bh+xZ2rtuV32fEs5dh+s11qXU1Rv7EQ7JqCguK8V1kVRGN3xxNPQdpSTWni4QZ7BprxN7eoU0eLeDcGlC0/ZCkRIvEHPsRRISW3o4bCAp7ecvssw4pdMFsDfgO0pJbE2nC9hmnBKFBlFVFUNUQtNeJ/bIOumUDXtaxnMfy9R0RGEPHGnS4l8ZW1n7yzGBGS1gK0KkXjK2t4/U18JWedZk7G1tIkvcQOFDCXo9Q5YeQs/z6C8WQZc6vtp9lNLeP5AzBPyScSdc1oYxHZL+S7vvTNorjT5KfJZ0rjhmTOO3SQMjDvQ/g3BLCXWqlXgAACAASURBVBSqy6jKzRTMVkYLwA1tp5lqS5xsjb/8ML8WFUeplBg9+vAhkEqJEYSDrL3+6UrReb94P1z1lRqpYY2iWhbnWM4Bv4Pr20wzo8UkJksgldyb4azAeAqkEscbI2XXxHa2jesRtgHXa6ztOJMlO0hCkgMVeAmBwnSB7zebbEjpsh3J1Y9p+dYLPuNa4tzbN++NgPUinRIBkzisy999JFPoYCQi1mWzz2XTJfD3WCuS0XowKLugkW8CKQ1Ukdy8se00cw/3liX3W9sz5eOeVXud2N4IUkM0OwiXymZtLxSpIRoUIbjnkl55e1pGQ6FCw/b6kE4XgCIE3/EuYYuW3RMunS5gq74kCg2iqiqGbREJTXud2CPrqvNN4VhijayTPqtE4SSXJPH9rw6uCJF6DxFVVYRIW3tsb58PimS+FrbKU6LeqktkiRsofChBr2fI0kPoeR49ZxH0blM3ic61zcykvdKoyDyUrxRLCAKXtSGIlgRGdt/Z7Z1kBlyj832U+CzHlUm5MGMav+1uoSM3xwM95hjSnIZAodcXldgpguuDIPymdtNMQiDOxIu//Cau9nv0UQGROpajR/+ebrNSXR8E4ah6SeWJICSojEqN1LBGr80TbcnaRrLjxrbTzL2T9CyCVPK+d4ljeV/LcE96Td6y+vm7T5M97/h+WNtthNJlKDNg2CchUBhZd0XzySZJJgmMp+RbL7j3gGS6RO+Z/rX8Vd71X6oIwcg6rEupmSR8JhrmCYpU/QCJOu6FRBECg7KLGk8y906STF1T6u8Nt7SfnsoHwJ4pHzcBw14ntqO3dAKGHYRLK++2Z8fqUpkhGhQhuOcSHwB7WobUcM/2+pD2rmNaBr7jncIq80vDwtdSmuz4lTUtQ0pibRVDVOXdXif2yDrps2rLqx8R9q4Thf0YZmW8Clsb4AknaWsjUVVFiPQZR9yAQ+prYas8pe0vdYUscQOFDyXo9QxZegg9z6Nn3w8TdKnZ1HMfhwl62l5pzO2WVDW5rO2RfKAvCYzsvjNpZWxkvo/yEeYCLqlqoo8Sv00qv+MAqUTlUEqgUNlBZWji6mRSiSD81vbTDSmNMz3iLz/06PMEhmTcEFyv0RsuNZdD4AhiIh17h2ovvqd0bYOYowIlcTpHsuPmdtMNkZIE/SCV3DleMooMpBKH1JDw7yPDBF3qDI0kB9Z2XM87XyeVlYE0FEZSEgKFkXVXtSg0906iCJm8dq8h2lCESKTLMCGCG7VUEYKRdViX0qAfn4P1KXXZxnrEWpHsnTAou6TJJHPvJNLlVfnk5o87zDCqnKgKpw17pvyTMbJ/O5i2zQulhmh2EC6VzeIZxSE1RIOaAPc8zrCKw56W8cNWMk8TeH3gkE4XwLQMfEfpZIgXh9ZsusAj1rQMSUsQUVWvj6g2FnudHLCcwOPmp9uwR9alUbfxUZhZr8LWBpBAdVy5cpKoqiJEavZqT8uQ+lrYKk+pr0VdIUvcQOFDCXotoXfv3nT11VfTN7/5TfrmN79Jt9xyCxUUhAOxBQsWUMOGDekb3/gGffvb36af/OQndOpU0BflOA41aNAgdLium+p7ZOkh9DyP/mQRdKnc8tl8HyUOSS85ERn5L4LUuGwoB5e1YUa1JDCy+86uEPaSf7pkZ+izHFc2Mgt9lPht0sCIA1Ji/FdKoNB7jur02OXJpHIlC8IRsMaN1uEvP/To88qJZLQOevRxXW9uJ6skIXAEWZP2D+Ja4HtKjdTgdo0kyUiBGRdPdgSy7OTqAvr5f9V7Pl2Y4APAAVIJwvC00KkZ/fw4L64SbgPVXqztOKM+vk4qWO8m+q4lBAo9jle3LDTBv0QRUpBvvfh1n/lGESIZrQPzQpi3SRUhkGVD9fLLHjLPCbhXI1ElnYsNZQZ6fiWKEMhRL21aYO6dpJKE5OZtHWeYhJVEBm6P+IpTFdjBNMwLsY9JVQXPDwkniqWyWSQ7cBQJEkBEgSIEe5HEqM8eWXepMAGE9YxD2hKEsVRYl1IZuH0tpcnhh6xpGZLkMFFVFUNUG4u9TvYfqzqCUZIcnmeNrJOqCojItHQ5rnws5b8ytrOEk7QwQ1RVEdJinCzW7GhNy5D6WtgqT6m6pq6QJW6g8KEEvZYwYcIEmjRpEm3atIk2bdpETZo0oX//93+ndev8oH3BggX0rW99izp06EDr1q2jkpISGj16NJ05ExjjOI5DrVu3pr1795rj+PF0sxWz9BB6nkdPvxcm6H8fKavmPD1wceg8aeUC5AcEP66XlYPL2p7M929KAiP0ncG0Sxp4D8/3UeKzHDfe3ZwDATt+mzQw4pBKiW2A3CHoj3M65UAQfkenIvpJXm4Y53TKX37o0f99v2BMjmSkGHr00YP7I+FIMQSOkEnHVeVsoOqL79lorMz9HRVD3Iu4md8cvOJ4VwozLgR/j/dZkEpKDGdbrG1pTyX6+eFZ0FwYJGF9YG3HGfXxdXL2XDAeEZVOCYHC+L9r3plikjmSoD+3+gtDYKEIkYx/hHkhl0tLgn5UfZEAklbl8JtQ0X5pmKw9Ab8J+5JEEQKDssubFZgkiSSoRnLzzs5FdGM+MSAZN4gRX1iXD/SIJpV2MA3zQoy6kxqiYWQdFBNS2Sx+E76nRFXAFSHYiyRTSOyRdY4rmy6A9YxkmnS6AJJ3WJfSKSS4llemnC7A1T+OK0toEgUTXXBESePtdbLvaFWCHjWezYZtUHZDinfzEKYKyXoVtjbATeKkcR9RVUWINAmHuAGH1NcCCVccUl+LukKWuIHChxL0OsR//ud/Uv/+/YmI6Oabb6ZmzZpV+/eO41D37t2/0mdm6SH0PI+eyhN0vJSlwSKCRAT6ca7ONkB+UBWVEFAua4O0XhIYoe8MwZjjyirhwxbtCH2W4+bokICAoo8Sv01KQDnQ143RNRICxQNHBP8SN34E4T/pXGQq9vNi3Pj5yw89+k8NWGyCx32Cqi969EG4r2wh6x9E4IjKo5SMouqLF7XUSA3mcHCBl4xnW5FPdtzeaYaRLkvGs6Gf/4m+C0zyKs4HgAPOtiBt0tFN6OeHA790dBNUAVjbbwiCpPKzAUHHqDwJgQKh+VHrqaZfW6IImbBqj3l20rQZdJ/mmxBx+b9EEYKqL9al1IwLxBwVbWl7AtqDUBmSKELQL/qD5pON5FPSW4zk5k+7zDSVzjh1DQdGfGHPbRhzTexg2h3jmxfis6SGaHi2ocySymbh1YHvKTFE427euOeSKRuYlnEFG1kncb/GeoYa5KZ2MlKJhBO+41XCfRbXEtdEmryDmgC/TzoJBp+D5HJUG4u9TjCC8ZImk1KZF8KgDN9R+u4hCruIS01b/5WBBGqa542oqiJE+j5G3IB79/yQ6L3BXitISCK2lSbv6gpZ4gYKH0rQ6wDnzp2jESNG0Ne+9jVav3497d+/nxo0aEA9evSgW2+9lf77v/+b7rzzTpo7NyxZdByH/ud//oe+853v0DXXXENt27al8vLqiduZM2fo6NGj5igtLaUGDRrQwYMHyfO8Oj1OnjxJT3b3CfpN+ZflUwMWic79fb6nFC/Z1hPWis7DJvvKJ36w2Oyz1Ynn7DnkvxAuaJSj5wf7m+6guVsSz5u7aZ8hoHgp7C87kXjeoHlb84HwEhM87D50PPG8JmNXh37blS0KU98TfNen8sTkH6NWJp5z+gxTGPT370vfmSWJ580v8U1bftq5iB7IB1lT1+2JXSvjxo2jkydP0vBFfpXs6QGL6Pv5qu/W/WWJn9dqvJ/AeDVfqbykySTRNflTXq2B3/ZY73mi8+7Mk8qX8qTypaHLROehko0q2YezNiees3iLHwDe0WkG/eJ9n6BPi7mW/Bi7zFdr/KbvfEO21+w6lHhel8l+0HJbh+n5ZMIc0W97MZ8c+XH+vNeGLxedh8/B2n5pWPS15Ovk+Mmg2uWOWSXeJ9aVHjJJBNz7YQu2ia/l7z5cYO79ws37xdey0ZiAoEv2ibdHrwqtyzs6zRBdS6yrZwYuMs+R5LwftioM3YO3R69KPGfzPr/14ooWk+nlvIpBsp6xdzbsUkR35H0tFm35MvG8D2dtNnuK4/qGaEnrxPM8ejOfwPlZ17zq5aOFomvy9IBFhrxK3yee59GPWk8Jfc9B87YmnnPydJAoxj2XXMs1u4L1DIn8HsH7ZMxSfz3fnP9tV7eUvU+6FhaHvuPlzQpSXUtck7+PXCE6DwlJxAIfzNgkOg/7HRKiBat3J66THV/6ZpyXNS0wxKtk75HEz5q2bk/oO17UeBKVl5cnnofYA0f3qRtFv2359gN0Q5updHvHGVR68JjoHM/zaPyKXXRNXkXScdJ68XlnzpTTbz9cQFe0mEwjFm0Xn1eTY/3usLfP5r3J7378Nn6e9H38Tj5uwL37Y7/ovcFeK4ihENu2GLfm//S6fNXj4MGDStAzBiXotYg1a9bQf/zHf9D/+3//j7797W/TpEm+wcjChQupQYMG9J3vfIcGDhxIK1asoNdff52+9rWvUUlJIIvp1q0bzZo1i1avXk39+vWj7373u/Tcc89V+5ktW7as0rfeoEEDGj58OI0bN67Ojz/mCfoNLSf6AVK7CaLz7mnvn3dj/rzfd5Odd0lj/++feNc//3eC8waPGudn2t2J9Ghn/7zX+4xPPK/z4PF+kNhqIl3YyP/cj0cmf8c3+/rnPdxpAl2cP2+g4LzfdZsQ+m2XNJ6Y+n5c18L/vIc6+f/Gr7skX5+xn40zL70H8+e93Cv5+rw72D/vplYT6adt/c99Z0Dyee6H/vV5oOME+kET/7wPRyT/tqfya+23XYO2is8/Tz7v/g7+3/+yo//fO9vIriuu5ePvBudLzruoUfAsOG6OXhFcy25Dxpnn6M42/vltBiaf16yffy3vaT+Brm3un/f+sOTviPGIN7eaaP4r+W1YVzflz3u4k+yaXNM8/Nw+KDhv5NhgXaKV5o/dk8/r9Yl/3tXNJtLD+e/7Zt/ka9k0fy3vbT/BXJfOg5PPw7V8svsEutD1zxs8KvmaYB1jXV7TXHYPftImuPb4vpLzvp9/1n6T/9zH300+76MR/rX8fpOJ5jmQ7A1d8nvnza0mmrXSRXAtX+vtn3dba/+cK5rKrgm+G56dhm1l5/08vzdcn38P/bar7Fpe0XRi6Hu+1jv5t41h+yzu+auCa/kBW8+X5t9/Az5N/o5Yz7j+FzeSXZPne4bX5YVuumt5e/6aPNJZdi1vecf/++vz++0LPZOvybhx4+jKpsEacdwcteyffN6AT8eZd+tVzfzzen2S/FntBo0PrRPHzdHYz5LPe2fA+BCpfK6H7Jq83Cs4r7ngd+Hgn+W4OfF5g0YG5z4gfM/V9Og9PPw93xLszePGjaMW/cPXUvo+hsoT9+4u4d5wX/vweb8T7g11dQwfPlwJesagBL0WUV5eTps3b6alS5dSo0aN6Lvf/S6tX7+e5s+fTw0aNKDGjRuH/v7qq6+mRo0axf57Y8aMMdXwOGS9gv6HPKm8L2+o9WgvWXXyN339/jZUC6WVOLiNo9r85sjkCvHOA75E8JImk+j1EX71tVdRcpZ+ZrHf83Rvt1kmS795X3K2d8Ac3wX3r0OX0hUt8ucJssRvj14Z+m0XN5ZViPnBK5v4Dknn8Erl3/LnvVu4IfG8ORsx4mumuZ+fLdsZu1bGjfOz00MX+DLW5wYtphvyVZDVO5Orvs0+9yXB+K/j5uj4qTOJ5yET/pIxi5stupao+jYe61c6n+ovq1RCOvlUvqIkqZpwNcJjvf0K6fgVuxLPG7XEb6f4Q7+FRj2xQFD17TjJNySE58BtHWXV2z/nZawP9pxjfqPkPFQosbb/NHBx4jo5fDyYl4vv+/bo5Od91c6gNQVqC4kiZORi/1r+sd/CVCqGDvnv1nLcmlSKEFR9sS5vaDNVdC1x7fHbnug7X3Qe9iI8P3EqBn4U7/G9Ea5pNcWoGLoWFieeNyu/d97TdZZ5NxRt2Jt4Xu+iEvMecVy/cp+0TjzPM/vd4/mZ2g8KFSF/zPtL3Jv/jq+PkL2HoEbA9+wxLfkZP8b2Wdzz9wXnrdzhr+eb2k0zn1u8J7nqi/V8/3uzzedKqr6d8j27LzFXdsl5uJa4B88MlO0NUD3gvx0LZJVfVIrRjjJqyY7EdbJ1v68I+UHzyea8ZdsOJH7W5DW7Q+vEcXN05PipxPMmrCwNkcrmn8uqsO9NDZzH+81OVlngsAm69LyNe4KRsn+IqTD/sw4oQnB0m5K8n3heoAjBIX0fN8/vd7h3D/WcG/l39lqByvOevJeMVBFSV4dW0LMHJeh1iLvvvpuef/552rZtGzVo0ICGDh0a+r8/8cQT9Pvf/z72/N27d1ODBg1o0SLZfFKibPWZeJ5Hv88TdMwOljoRo9cZ/31xqGz0FebIYoZwnOEHB3cifnu0TxA+KEqeDz4733f28/fmmB5FiRMx+ihfGb7CnLd5f/J5+G74bY4rM6XjQNAB0yRJL+bJ8rPm8xrlCVRHgfv73BLf2fa+7rONcVtcL6bnBf1d6NF/fshS0zO6UtCfClOtDgWBK6ukFxN+B+hjlppx4bth/J20dx0yVPTsdp2a7GYMv4O73p1JGCkmmTQwKj/S76kBixN9ADhwDdHHLB0hCF8FGOc90Vc2u9u0suSv5VOC8VlHTwcBJ0ztJH2HfGTdP/Iycsnzjmv59MDFhngVCtzfcS3bTFxvTMo270/2AXgzP1Me61JqxgVvA+wXUv+A7zfzCTpMk+J6MTl4Pz9MLCWTIfjeie9bVJzsdN43P+ILz8ClTaIdy/k6ISJ6LW9eiH1I+ozDowCmmi9/IvNQgd8DnoNugmec77O455LzVpceMUQUTvzr9yS//zHu8zE2LaP8bLJ5Yee8xB3fUXoeriV6d3/7oWy/vDtPgOAaLzWXwz2A70qUsam9TjBT/soWhfTTvMdInLEpB8ZZcsd5ibEpjPpwSD074APguLJJNYBN0KXAnplmT68p+Gc5rnw6ymcrwskO6fsY3j64d3HTDOy1gmcb+5ck1qxLZIkbKHwoQa9D3HXXXfT0009TZWUlfe9736tiEnfttddWqapzTJw4kRo0aEA7d8qdPbP0EHqeR7/LyyVhSnZvN1lgBKMjvMzjAnYbqE5idIYkoOJOxDBmkoyAmbnRr2r+4v05hmCs3Z3sRNx/rk/Q/zZihTETipsPzvFWPmDnY0EkgREHKpUY2Sa5rsfPBIEjZjPHzQrlQBB+/3tzjBFbnGM5f/lhlNKLQ5cZI7bF25KDJNy7blM3me978PiZxPNAQpF8kJpxwSUYc1QlRIgb7sHxXEJoMGf3Z11nmYTCGIHx1Mglu8xzlIYItbcM96QjBOHCDqOxh3rKEnJ4DrC2f98vOrji66TsZEDQP5rt+zq8JHje+ci6pp8HayYJ/FpiP5uwKtn9Hdey3aQNqQgUCBDWpdSMC6ZazVngKQGcyvH8PCMYrQeDsuvbTDW/U2LMyffONKPues/cEnovOG50ktIOpl/NmxdiPceZy9ngiSasawlgVornQZLQ5PssEqgS4yk+zjJNQhPjPrH/OW6OjqcYZ4l16bg5OlmefB4+5+W8skO6LkGwkRSTvHuIgntw/3v+8xBlLmevE8yUv6plYaqE5uS1e807II2x6fi8UR+OV4fLpgvAeNJx48eCRcEm6NIEP0wdHTdHj/SS3beaAvszjleE1wTmsjikiUkk9vGM3911VuTf2WsFJomYZiBN3tUVssQNFD6UoNcSGjduTHPmzKHt27fTmjVrqEmTJvRv//ZvNHWqX3nq3r07fetb36LRo0fT5s2bqVmzZvT1r3+dtmzxx0gsWLCAunXrRitXrqRt27bRyJEj6Xvf+x499NBDqb5Hlh5Cz/NMHyXGpklHuSCrD1fox4VZW1Qn8QJ7YUhy5Z07EaMKJAmMior3mwzqbR39wGj5zuTZ1P3mbDUZ1zRu0HCB5i9nSWDEASKEKoikilF2KiBCnfLBmWSePYLwB3rMMSOmBsbMs+cvP4xSemnY8lSO5Qhqe0wvMUqKL8pOJZ6Hahzu/R2dZDNskezAnOsHBWSUjwZD9VbiVDs/P2f33m6zjRuyZNTdiPxIv+c+XmICiclrk4lQ25yfwAFJlI4QfCb/nL/xKUy5ooMdG3B9x9qWzLc+zMYjIqkjUYTwkXVIVEkIFL+WaZIkuJbtCzakIlCYKY91eZlwvvXP84QEyg7pfOtLm/gEvU++Sh2XJOHY8IVvqnVD22n07pSNJjGQBL53Yu56bnXyKDKM+MJYPceNnpxhB9MghUiK3dZxRuJnEZFJxKDy/rQgaUFExqz0pWHLxaSSK0Jwz9sLRt0tZxMe0iQ0h7P1jM+VTBOBwgLf0XFzVHYyWamEfRaO+tJkPSrZIETS+dZoO8O+F+WQbq8TzJT/YaspqRKafJwllCiScZbjVu4OkUrpdIGuLAktiXEAKHhwnDmbPHWGKHCpd1w/qfZ/CezPOCT7EFGgCMEheR8TETXOJ/bxjMe9/+21AkKPdZnmPtQFssQNFD6UoNcSnn32WXIch772ta/Rf/3Xf9Hdd99tyDnQoUMH+t///V/6xje+QbfeemvIxX358uV0880307e//W36+te/Tpdffjm1bNmSTp5M3uQ5svQQep5nTJ8g5/1xB1lghGw5XuYSaTyvTvaaudkEH0mArO2K5pODYFoQGM0o9nusH+o512T5JbOpP5y9xZAYjJdatiOZ2IP04Lc5rmxGKweIECTBjwqy4ZwIQVoXN6eaA9fnwZ5zzXfvOyt6ril/+aEF4OVPlpsZuNM37Ev8PN6egAB5+4ETiechCMe4lVvbT088hyhIdiDhIpHN8tFgyNxLJHy8XSDNOCu0C/x58FITSEik8SCuqMLGESEbIK74bdLn/dp8qwfWdlyVhq+Tg2w84kgm5U8CH1kH+fk7E5IJ1CeLdpprmWYcI2T7HScXpyJQIJOoTF/UeFLiOURkKn9Iwt3TTZYkQeVvUP75+3Wf5ArUuj1+teumdtPMniKR6U5bn987P5hnqtSSdYlkGPYTx83R0Yg2FjuYBlHGHnGjcE41kgd45qSybJA0JLgkCU2uCMFeJFmXfJxlmoTm0IVBK9HFKUaKtWOKEHzfAwKlEvZZ7C23d5LtDXfmvTOQ5G80VlYxxrQMVO6j2ljsdbJ5v68IufadKakSmnycZZo2FluW/Yd+snZGJMOkzynAJ844bo6OnExOyBARTV4bSPGlSdeaAvszDmk7ChQhuO/S86AEgbrm5nbR7397rUDliXX57KDkWLMukSVuoPChBL2eIUsPoed5xj0X1aDr28gCI/QDoQoUJzviqGBzZEGanhQE7EbW1qLQyPckgRGCzIc/mGcCo9mbvkw8DxWqv49cZYj9IgGxRx8lfps0MOJAzzv+jQd6JGfDORFCcuG1EcmyM359kGjpMb0k8m/5yw8tAK8OX2GUFAVrkqtraAHoPXOLIXybBJ4ACMIhVb9BGLwj2TEkH+g2FKhDTnvnzLXE2m76eXLAydsFXk5QI3Dgu70wZJmpEIxauivxPFTHeD+/pKcSfaY477rWst51BLVYl3EJOb5OvjwWrEtUoiQEatkOPwC8s3ORqURJ7gEnNCB8gwT3AG0hnQuLzT4xpyR5n8B95sG4RMXws3zPLpJpknVJFKiPoBSQVKDQL3pL++mhxGMS0LP7SK95if4UHPhNXF4dtQfawfSLeUMzrOsftpL18/86b2iG99evhLJZtAtA1fP26OSkxSGWCMU9lxD7xdsOmfucJqEJ1clfhy0z1eYdB5MTmti3OhQUpyL22Ge75n+bNBa4PT+GD20zb41KTg4TBYoQKASivD7sdbIp76lwXeupqRKakKr/9sOFpo1l3Z7kdjdbli1JmBMFPgCOK2/XICLjBYRDojAjCicS7uwsU5jVFNifcUjfx0igIsaR7ntI2iG+ivP6sNcKEjgg9pJYsy6RJW6g8KEEvZ4hSw+h53n06y4TQgHVVS1lfZQINNAPKanEcfkwgg+JUQhkbVe3LDTBgyQwQpD5aK95xu166vrkwAhVwrdGrTLVLknFA32UA+ZuSxUYcVyVN84B2ZBU1/Yf892FL2iUM/Lzvw5LlnMVsuuDSmycoQ1/+YGkvTZiRSpDNFSr+s7aYuTnEk8ABOGoAErNuJAEQJAlWaMnWJ8pgixJ8M7bBUAWPppdVbJpYzALwpN8ADhwv7pO2WgC3T1HkoM5VENR6fx+s3DvemVlJfWfu43mW32dWJd4buOqH3yd7D/qr8sLG+VMhUdCoJbm+yl/2mWmkUxLFCGc0OAefDg7WhHCAVL47pSNqQgUkgA9mCGUxHPirnzSD4lASaWSq4+Q7JBUoGBQ9uMOM0zlXeIDwO8XCJREjYAWiKafrzFtLFHr0g6mEURDnm2vyzggCMf7S2pyimcGyRlJQvMAS4T2YImIJCxkBpJpEpr8fqUxOUUFvNPkYlOtlMi5sc/C6E/qa4G2ECSspGZceEfivA4FVdtY7HVSvPeoSR6kSWjimflDv0Xm+64QtLtB+YO1/HNhOwr3oblG+L4iIlPtxbHly+QqP1GgxHLc+ApzFIqK91Pjz9ZQu0kbaP8xWayC/Rlr6+LGk0SJScQ08IaRqreQ2EcRIW5d2msF3hlow5Sa0tUVssQNFD6UoNczZOkh9DyPfpUn6Gn7KOHYiqqMhDRx+TAMneJ6WTkga7vmnSmpAiNuDAO5U5o+yrdHrza/s2hjcp8bAo1B87al6nPjuDJvnANS+RNBNnxv2WnzouR9i0lAEP5Y7/mm6hLXOsBffqYSN3KlCShGCoIkTlxRdZG0DiAIx+deKTTjQtUX8kZJpv8Y6zN9P4XzOHp2H+w5N5Xz+EAWhCf5AHBw87Q0kk3IWPG5jhuu+uJ3OG7YQRjrEs9tnFEfXyd8XaKdQkKgTMXx3ZnGdo1p5wAAIABJREFUXE5CoIw3wifLTdWl54xoRQiHSXZM3ZSKQKHqi3XpuDk6ITDxQs8u7oEkoD7H1EfY1yQVKBiU3dZxRqgFIAm8Zxe/c0iEiZcNKB6aj1tr1kxUG4sdTCM5hYSvtF0AQTjeX9K+6Ysts1JJQhOJ0Asb5YwZ3puCavH8LX77y8+6zkqV0BwwN5gmksbkFNeiS+FGk1jbKiB62Geh6rlQ6GsBwgUPFkkCiIjowrwiBNMQolRx9jpZv8cn6De2nZYqoYkK8x/7LzJtLBJVHGTZ2GOlHj1INOE4e05mFsv9BqT3m4hCqr0fCVVRREEbGJ49CbA/I9HhuNFtLDaQQMX+J1VoYF1hXV/SJHpvsNcKVJ5YX2laDeoCWeIGCh9K0OsZsvQQep5Hj3b2CTokr1KzKTivInN7ebNkYs/lw8hoPyxwit3ERgWlCYwKWJCJPrfPViTLNHkSAJIzSeUdxkiDF2xPFRhxgNhjvIuk39qMoWtSQGOWBYFIEkwQ3neBMZeL67fmLz9U/t4ctcrIGocK+q3Rs9t/7jZTRVywJTlIQhCOipI0iYR7ACm/RDbL+0xRSZI4907fEPTswtTmvWnJ5JC3CyT5AHDgM96fXkI3t5suDuYgY+WGPafKg951PF82Qce6xHMbJ6Pk62Q3W5fo0ZcQKFQc7+46K5UihBMa9Nh3FYw44pMh0hAoVH0HsWSHxIwLPbsgzJIReR5TH0GtIalAQY56R6cik/ST+ADwnt1XmDIoCZB+txy/jn6UbzGJamOxg2m4qfP1d64i+T2EIBzvLymBAjl8b5q/10sSmvuOBgknEKK/CRJH85iBZJqEJlcqIaEpMTnl6prq7oEN7LNcLi3xtYAaCu0CkvF/XBGCZGNUG4u9TvgIxjQJzTFs7adpY8EzClm8tC8fXjk4vjwma3Xj0w8cVzZCjohCKh5pApsoMOqr7t1vgytC0vw+7JO4/lcL1Zp4L/J2roqIvcFeK1BD4V0piTXrElniBgofStDrGbL0EHqeR4/kCTp3HvcE2V5ssp+v2F3tpsnB58iCPEt6rANZ29RQ0JIEkNwn+i4wL75PlyTLNBG0Nf5sjZH9SSpqMCoaunBHqsCI49J8byQInyR4h8v95c0KaMKqILBOAv72Nx8uMPc/biQMf/mhBeAfo1cZ1YAkeOe92XCyniXwBEAQjmBJWl1DBQ/kUJJE4n2mqHBKyOHUfBLgkV7zzNxWyfxbXiFO8gHgwN/2nFGSytgMFWIQMMcNj7rj7sMc6NnFcxtHDvk64esSVRcJgULF8Z5us1IpQjhpgvGbZCYzny6QhkDxCh6umcRzAhMlQJgliSOe3FyUD5AlihDeLjCePe9J4J4Bb6RoF0BbSKsJ66pNHNnBNKYLcGUHTxzFAUE49i+pbBafgSScJKH5RVmQcOKTLJIwp+RLQ0zSJDS5Z8BdKUxOubrmxhT91thn0frkuDJfC5BXVDglZlzcjwZkNqqNxV4nfARjmoTmqHxC8pmBi027m6SNBbJsVIulBqXYf3Bs3CuLAyDbxyHxzCEik2DH+pQCMvW46x8FTCy5p9ss0yqyW9BehQQqEkGS9zFR4O3DY1TJZAioH7EuJbFmXSJL3EDhQwl6PUOWHkLP8+jhPEFHVdRxZTJN9GaDmDhu8kgxPkcWBFTSRwlZ2w1tp6UKjCYwYxjjrC2QaXZjxlRpKmoIvj5ZtNNUFSSBEQd63fASlHgC7DwYjKErZOZOSUAQ/ruPFhrSHadM4C8/9C+7Y1abvvI+giAJPbsfz99ugkGJMgHjdMYww56kZBBRUB0ASbmwUS7xHN5nioTAcx8nV4Rw3X/F2wUE5BAE4Y1PVyb6AHBwGf39KZIdmL4wee1eQ7pLDwdtGNz5mVcwEYhBqh5HDvk64euSz4JOAiqO93WfnUoRgmTH65+uNNLlVhOSq0JIdnxQtDkVgeLj9NKMDUSwj/3pB4Je31PlAUHHmCMJseftApDGS4yukHj9Q79FoekLScB1bz1xfbUTMOxgGsQECRnHlakREITj/SWRzfLqLd4nkoQmFCGXNS0wSRnJ3PVZEQaSkoQmNytFQlNC2LiCJ83YQCQ7ior3p/K1uL6Nn4zG3iEx4+J+NFBdRCXd7XXCRzCmSWiiNefZQUtSJd0HW7JsqSEavA1w2J4ecYCJJw6JQz0RhUbqOa58fjoKAo4rn2fOJ5YgCb5NMI0FCVSY+0nex0SBtw+PUSWTIfBexJQNqWt8XSFL3EDhQwl6PUOWHkLP8+ihTj5B5zJNSdYcTsQIph03uXrE58hik5c4nELWdlO7aakCIxDQ3/dbaDb5qFmrNngfZZp5yhjtNGLxzlSBEQekl5idK5Fzc5f7orz8VTILFUH4H/svMi/PODk3f/kFTs1rjDnT+4IgyfSyLtyRyhPgF+/PCREax5XNh0V1AOvHcZN7AWFsdkGjnKm6SGYro5//133mGymeZLYyb9lI8gHgQF9dn1lbTFV88trka8lbNq6NMJ5C9c1xw4k69OziuZU46W7Pr8srWxSaJJuEQKHi+PP35qRShJhkx8iVJsnWJEYRwoFkR6+Zm1MRKKhyRi7ZlcpzAj27MLGUVLy4eSFafiRGarxdAP4CkkrS2OVBYoS3ACQBvbdtc+vp7vw7IqqNxQ6mQUx4Ek5iWoUgHO8viWyW9/Ojx1iS0IQi5PvNJhvC94xgbyhiBpJpEppcqYSE5jRBQhN7co/pJWZs11KBVBrJjpkb96fytYBaDERbMqnhzNkg4QTPlyilkr1O+AjGNAnNQImzNNXYQKwrFCSk/d02YZ6wao/oPLTi4fh8RfJ3JAqSjGnej0RBQcC/NrIxZJhY8vP35ph7L1EIQBHyDDPCk/Tm81aG6mJNe61A5Qlj4TRu+nWBLHEDhQ8l6PUMWXoIPc+jB/MEfdiiHYYc7juaHBhxyZ3UKZb396Kyc0enZBM0yNpuaT/dBEZ/EsjoOAEFoek1M7kKxPson00hjX+WBexpAiMOXB8QGoknwOb9vsv9D1tNMZV3ySxUBMNPDlhsDFxeGBIt5+YvP0jNmny2xsi5uxQmB0l/YZLgNJ4AqB5x6aVE5YHqAEwGHTdZ5cGNzXgVMQm8nx/rp8W45EkD3KU8yQeAg8uOQW4k15K7lN+aJ4qrS4Mk0utsfjXvK8TegOf2qpg+R75OtuanL1zVstCsUQmBQsXxF+/PSaUI4ckOXFfJyCee7EhDoCCHH72sNJXnBGTH+J0XCCpJPLkJZYKk1YO3C8xj0tQk8OQUNx1LAqqo7SdtqFbZYQfT2A8+X7HbPLdc2REHBOEYPSeRzfJ+fiRxJQlNXPcrmk8OmY4lAaqTh3rOTZXQ5EolJDQnCaq+XPFQXZLExv2sSp/G1wIO8/i+EjMu3rIBH44ogmivE+6pkCahyRP7z6RoY4EsGyouad80kgc4Pp6/XXQeFHs4PlmUHHcQBVVmHMcEpm1ERBc1Dgi61OWcTyzBOllTmrxOUAGHV4/kfUwU9vapTtlhrxUkVbAuJbFmXSJL3EDhQwl6PUOWHkLP8+iXHX2CPnzxzlQjWXjfq7Tf+jDr74XkVdLTxWVtvLKTBP63jVNUgXgfJYyghggkr3jxj1q6K1VgBPC+PFTFHTfZE6AkX1G79p0pRs4tmYXKg3BUkuISH/zlB4VBs8/Xmv7BdoIgCQqD4Yt3pvIEQBCOSpTjyuSvqPqi8uW4OTpysnp1CDc2Q5/244LqLTfV4h4GSeCGhEk+ABzoy+s3Z2so8ZEEPpUgqq+V90DyfcB+buOUHXyd8MQR70dPAq84plGE8GRHGhMvkPIPZ29JRaCgrhm7vDSV5wSckxdsOWiua5IhGk9uQuUhOY+3CyzJ7w2SyRA8EZqGCPH2juoMNu1gmrcSXdVCnuy4p9ssQ+wdVyab5dVbyP4lCc3tTKk0IUU/P0wqH/5gXqqEJlcq8QRGEjCWqvfMLWbvlEjjuXlaGl+Lq/PJKVRIJWZc3I8G5DnqnV6VoAdrOE1CE870LwxZZt7pkjYW7CNoDZKOnuNKJMeNnvEeBUzZAHGWKP6IgncrjoMCLwwiogsaBedIkqBE4YklMLxctiN5nUAR8gZLAie9j4kCb58hC3ekmgwBlSfWpdQ/oK6QJW6g8KEEvZ4hSw+h53n0izxB/3TJTvOilcja7mQVYqmc+yDr742TvB44foaW7TgcIgd8VFCawIi7FqMK1GlyeNaqd66iSjaW91G+HOEUW1lZGZnEeIrJ4VH1tZ1ij5wsjx1Jwis7qOQ6brhafOD4mSpZZ26ih7nHt1gvo4qKqt+Z9+WhImRXi71zFfRF2anQy49XiJHMsIOkk+Vnq8jQuMIgyhPAO1dBK3cdqZL9RxDO2yls19i9ZaeryPq4IgSByP4EdQgnknxOPAfuP1c2cL8DBCJ29Xbrl8dp2Y7DVHYq+H2czEf5AFRWVtL6PUerfO9XmbM2J+vA2XMVtGrXkdBnEVGoqhk1QhCVIscNZIu8Z3fDF0dNAMlx5GQ5lZ3yQuuEJ472sZnoSdeSB4BxipBzFZVVKqyolDQau9r0jr44NKwIOXP2XBWFEFQD/eZsjSVQx057VVp/OGmC54Sk4nhdnsyjjcVxw6ZHRyM+iyc3eTXdNlKzryVvF1gVkxSN2htGMHM+7Id8DFZlZSWV7DtW5R7AHKvj5GIzMSCqjcUOpkFMJq7eY65P8d7wO/LQiXI6bilnkAgF0XbcqrLZPUdO0aZ9x8x14dVbVAMlCc0tXwYqED4nPgl8H0mT0OQz5Xk7RRIwlqrvrC1GMTOjOJwkKTvpVXkPgaDP23wgUv1wqvxcpFs3Eiro549Kpu0+ciqUTOLjLKHkimpjsdcJkkwNu8yMTWieq6isYlqG/eCvw5almkoAcof1ealwgggKAjiiHOqjgGcGsZhkTCRRVWm8xAuD7+uOK+/R5hNLQILnb0nuscf+/Pbo1ebdnPQ+Jgor79JMhmiYT0BjXUrHutUVssQNFD6UoNczZOkh9DyP7u/gE/RRS3cZN9b1e5K/Gx/7Iq0Wf3nMJ+gXNMqFZpsDx057JkPquIFsCoHsHZ2KQrO7kzCSObfCKKSN1RcMoyduZMT7KPnsbgA9xnb1l0uNowIjBIaXNS2IlK3zys6Rk0FAfigfrMNh3HZ2X7fHbwG4se002rg3GEnHgWoMl0jyvjxItG15In7T0m0HzMuPk/L3Y+bS/+L9OXRF88khgvg0Uxj8LYJUIhliB8t8fUWZce04eIIubFRVIsnJvFQdsuOgXyW7ovlkI019sGd4djdI9SCWtOF+B9ysDODV/5vbTTckoiszJOzPxoQB+LcuaTIp1JOLvrxB87ZFVn3xv93YdlqIsPAqWZQPAJ9ti4Qb79kFSXHcoPUC6/aSJpPo9Jlys06wFq9rPTW0nrkiBL+ZB6I8AETFzJYnQsbLq7NYi40/W2MUIbajNHp5t7DqLB//B4k2J1CVlZXGcJATYl71TeM5AUkwnlvHDSSplZWVZq1y0s7NC8vPBok8riRBwo2bE/J2ASTyrrP2BpBq3iLBJcHo4WzOWjaw3h033H8Kc6zOhcVm7xi7vGrrhR1Mg5hMWvOF6dHnrRcgdHZrBYLwmez54gnM7UyJNDrvI8IN9+CIbyc0o4B31rXvTAnWqLU3RIG/s+ISmlHg151XEZPwBntnofJbuC4wG+Prh5NmJELnbzkQ6WuBpPzesjCpuiL/bOB9Yo9SRM8y9zcpOxUQdLx7oiq49jpZxEZ8xRmb4nnm/froJX/5k+UmgSFpY+nDXP4dVz5BBPsv1DKSSSBEZAzs8AzYBYU4YIY9jh0Hk03b+L4e9d6NA59YAn+YmSzJGweuCEmj1uTePmkmQ8DYD8nGa2J8U7KCLHEDhQ8l6PUMWXoIPc+jn+cJ+phlpamCTP63qLrZWXobqKJd1HhSyEAK4GZePGDksjbIBR8SyOh4kP5uRJBJFJA4Top4H+VbEb3rOMc2aeLmM1GBESqKdmAN8MDxxJmzhhTAIRWEkZMjft1ubjfdzES/uPGk0N9gJFTnwuCFjyD8+SFLTaXN7k/F570+Yrl5+XGFQdQosnMVlaZizYNsLgmGBJAb/CDpYwfZCMIXbzsUacaFAJgH2bw6cOhEuUk+JVU4Td90i8KQGU7UNXHcoBrM/Q4GsmAQQCUGB6R9XI2AhBJ3QUawicAZQF/ekAXbTUKJVzghL3bcsNmWCcI3HzCBD5fG87E7+Dwe0PN2gfKzPtHm/9vew8fNOuEqGa4O4cqKqGvJA0AYotmO5TjnfnZv+PQFnjCJOo+3unCVTBQZ5RVrXtVFVW3Cqj2pPCcCpVKwH6BizqXs3Lxv/7HAvLCystJIYLkaAM8Gv5ZQI/yyx1zTvnFJk/DegHN44o9LgnlgDXC3fy67hlLp3SkbjWJmxOKqbSx2MM0J4Z0R1xLEzHHDExwQhC9k/3cum8VYQOznRGHDPagK7KRFFLAWr2s9NeTMngTjT9EnaH/h1zIOXQoDLxQoZvoLqr48+RmlYsC4OMfNhRKoSIQu3HowMrmCc+zENPYMVMJtMy58B74uecIO77Woyru9TtAW8rOus2KNTfHv8iQ+eslfHb7CjFWUtLGgbQaJfMeVOaTDeBLrU2I0ShQQbbQY2AWFOGB0XNTeEQe+Jzuu3KGeTyzByLQp65Ld5rkiBHvgFkEbC1fepZkMgX0ErY5p5sPXBbLEDRQ+lKDXM2TpIfQ8j+5r7xP0z1aUpuo74wZT1UkZORAYXNJkEpUe9oN6LhlD0IMDxIHL2tIERrxCjF5fd0y40ovP4gQdfZQdCopDI2vsc+zxSBgfMn7VHnoi4ppwSWtUfyUfQ3faO2fmJaO/C4ZPjhsm+LxHn8s3+d/gxc8r3QjCXxy6zFTz7Oo8/q3nBy8xLz+uMAAR4vJEXu3j8mkEfZ+v2G0qx1yeiJev44YJOIKcpdsPRb7YkWi4lCkTeHXgyMlyUzlOGkUG0vTDVlNMMHh31+ikBQ84EQQ8OWCxmZ37/JBg0gCf4eq4gckNr6ihKsddtvnYnYmrAydgVNSGLtxhyD8fU4QWC8cNk0quRkAVGmOKeF+o4wZzgvmaAlF03KD1AiaOjpuj9aWHzTpB4ujGfOAHZ2guT4y6ljwA5GuJV97xv/F9gJPrKRHmcjxpw59nPv6PG80BvArLey35vpdG6nkV66O0Wy+2MoXC8p1BEMqTm/zf4OONkNDj15JXekN7AyNm+N/4s88lwVHXBFVIxw2TRphjdZ26yUxtGBwx2tIOpnlC01xLNpqKK1B4y0/QA3s4UjaLZ9Fxfc8MorC8GiqPONNDjqCVaFoqM07uT4Fr+feRyeaFnVhrwZusrzwJXMKNpDFPovCEGr9WPBEa5WuBc3i7F1Fgxok2H1vt8kQEQeftbtVdS3ud4G/v7TY71tgU/y7vaQeZf23EilSmh5Blc2MzifM47hfeO1IDNiRWcZ5EaUEUeIvgkLTacMWe9BkgCpJev+4z39xb/m6KA9+foSzY8EVyHCz19rHXChL+eP4kE3HqElniBgofStDrGbL0EHqeR/fkCfq4lbvNS2FuSXKQyaVGXLpcHfgc2f0RPanog8YB+TOXtc1P4USMOdZ/Gbw0NG8a4KZsnKC/M8En6J0mF5ueVF7pxTl2BZ2T8ihTn2lsZvySiEobr9SVn60wclxkp2ezBAYnqMvZ6BkiMm0CPAkAksvH0/GZ8pwA8AoVPu/J/gvNyw99lO0nbTB94Ty42sSUAnw8HZcED44IrvDSdtywioNnza+LGOuC5Ivj5kyPKq8OlJ3yTKCa5HSO7/6j1lON4Z5tqsXXKAC/A264x+XV7VnF0XEDnwe0S7wzYX2kceL9jGhzJ2AoIj5ZtNN8Ng9IUdF1XL+nFEASbtHWg0YJgSCQB+486OLKDi5NRQKIy4vnbtpn1gmfvkAUEAAeXEVdSz6yjqsxuBIA53CCziuOUITwvkpeneYVdD7+D1LYl4YF6gc4RztuWBHD5ymnmVMNIr3z4ElDbtAzizXnuGGJ7h5mXkhEke1IURV0rkbgn82JPc7hBJ2rQDi5AfioJK7KgTKm+7RNkW0sgB1Mc0O5KG8EBNmOG/afQBC+YufhSNns+2xvACnmaxgtLZLeYihCbmw7zSSNf9plZuJ545k/xYCINpY4IBHaZuL6yERxHHj7S9S7me/P3GyLJ0JtXwu+n35QFJ6EgrYjJMXsdoGoCjpvd4NCLkpiba8TPoM7bqILPou/69Aq9ManKyPbWOKA9cOVTLz1JA4wnkSFWdKSRxRUwvFfyZhIovB7wnHDyb048ISd4/rvfok6ILc6mFhSXRuLDb4/Q4G5SqDW5N4+aSZD4DOwB0rnrtcVssQNFD6UoNczZOkh9DyPftbOJ+jjV+2JNZSJAkYFrdtTVm2lhIPPkT3ETI9ACBEI4kBlj1cyMeapoSAwGspkmibwZrJjLrPjcmRk2DsXFpv+zA4FQRCKc2yCjp7egjVfRAZGIG6O65sa2eDf5+y5CmMMBIkoHzPGCb4d4IDQcokoxkBxUyME4a8MXxGSMXODKvxvj/WeZ15+/JrANIxLRHml/8PZQcWHG0EZh/Q+QeWd+w/wNQglwYqdhyPNuPjMWfTe8erAsdOeqSolueLi91zfZqohzD/uMCP0N3yNApCn/2nQktD4OsAeu4PvD+LeNrfePB/cowC/13HDTsCQXH66ZGek0zk/bzybwcul2LZEFL8XB8YQcUkwJ+uQqvPZ1eOW7zLrZJV1/UBouaok6lrykXVEwZxlKAF4Yo0TdFQcW01YFxn08/55PqMers7DFu2IDPqnsOduOJNr/8rIsvem2jtBJEsPnzT9u1i33OyMS4mhOEIVKMo9OYqgczUCUUBo+Xk4hxP0/kwSHLV3PsgktVyVBCL5/vQSI/O1CR1R1WD6ITb+L6o9CAk9xw0TcB7oR6lrWrDnDkkXvs/uY474SeSEtxJhHvdtHWdUew5R2J8iSl0TB95qxd9JSeBjqfjkDGAVe855BZOrEfgsdaJw8tiuPKPdAuo224yLE3QYee5nCWE7kcdhr5PZTEEXNQaTK6f4BAf0kv995KoQUUwC2mZQEXfcXBWjwijAuwZJadvHJA5oF8Re+fbo1cknUeAtgoNP5oiDrZji96c6cKNevIei2lhscEUIkkFRhQob3NsnzWQIqDy5MrMiYepFXSJL3EDhQwl6PUOWHkLP8+juPEGfuHpPpDFMHLhECdnivgmmK3yOLH/h46WAih6OjnmDFF41t6vF1WEIk2mimv5nllXnAftTjEzxqjmvYhCFAwCboD/GAvaowAimNo4bPd+Uy/4qKytNUIAAFwEJPgOwkxZ4iSHA5eSbV3wQhCOQucr0xgYBLs574P055uUHI6hOk4tDSggYDvFqFzes4kZQIPF35fsVKysrjUTVccOJDR6Eg6zzCvsrbAYsKge8OnDizNlYF38bXJaN//dN7YKA03a+BZB8ee7jJaZixmWNPMBz3KCNhKsROBE+ceYsVVZWmuqU44adgI2r89Jdkc79uJeOGzaz42oE27mfexwgwCcKS4LPnD1nxtfBqA/VKcfN0cC5W8w6sUmMGVnIEnlR1xLrB5Ma7rYkz9zRnPsDQI3QeuJ607LB+yqXsOo0N/CDGmH44p3m3vEpETAZ4s8iEZnq2NT1+wxZLxT0YqJqvufIKSP7xzOHfcpxw74XPLlJFDb7AzhBx7PI1QhEVEWVwxNZnKDzqnkUqeRmgnxPBbHrOaPEVNO7RYyYsoNpEJOi4v2h3n4ARMlxw+oZmGmtKS0zqgJOOuEv4Li+WSgRhZLDnKzDUyEOIJK3tp8eIutJ4DPTuXFpEnirFU/kJYG3v0Qlz/l4P97Ly9UIUJHhHcwTGTaxhcIF/65txoWEi+MGBnO83Y339tuw1wmfwc17+wH+DuXklk/WiPJUiANk2dyVXTLiExV37NO2cV4cYLoGBR5X/FUHeIvgkCh5eEtdmt+GhNPvPloYSgYlgcdSmFHO1V1x4N4+aSZDQOXJ17skAVFXyBI3UPhQgl7PkKWH0PM8uqvtRHJcnzRxCXISuNS4yWfxgRgHnyPLCRQy0uiJtQMBLmtLExgZCfcny0Mj1wAesP+yR5DhbvZ50EfJTbyIwv3V9kznR5lhStSLCwEXAlgbXPZHFFRXUe0bzogCJ/h2rzRMVZAc4MHV1S2DPjPbbRzVVZBHLmts2GWmefnxHj7+N5i9yqtdfNQYTwCh9/PafDBn98PxZA8PwqMy73iBO24gC+bVgVPl50xQZnsQ2ODVHC53B+ygBlU33Js/D14a6tEDXmJEwXEDiR4C4Q4FxVRZWUmXNQ1kujyJ5bhhIz7I/kYvKw38HJoElXdO7Luy9gwQqxU7D5vKAkjuqDx5wAGXYy4J9s5VVDHqAzF23By9W7jBrBNIw9GTCnMmyMttF2FIR+0EB4IyEDbMV3fcsLqBk5htbK8BeHWaqxu4GiHKhBJGUY4bNuID2Z2+YV+qXkwkOPaWna7Si4meVwSygG2qGWXOhLXjuIG6wVYj2KocPs6RG/GZlqCRKyNVBTwZwFU52MM/KNpcheRx2ME0d4N+kklaAezJWLsAV9REyWbxTnPcIOnC9/Co91AcuCKETyhIwhj27okzL4wCT4R2td5D1eEvrP0lylyOJ+I4QeLXz3ab38Z8GLg8nycskTy3zbjQVuO4vuKOKNzutj3iWQXsdcJHMEY9q9yIlZNbPMNvj15t1rbEB4BX2+33XHVA0hhxgKQVgijwDsFzKmmFIAqSmPZ7sDrY7xfHlY1n44aoUSM+48AVITwhlwTu7cOr6TbstQKVJ/f+OSFQP9QVssQNFD6UoNczZOkh9DyPGuYJ+uS10bLsOFybHxVUsu9YqApYHbayObJnGbGD6y6qzggUkAHnbtp85ncSuISb9wECPGBThCpeAAAgAElEQVTnfb+8j5LPqSYK9+9xUkEUVK6nrd8XGRhh/I3j+jJcG7YRFAIJEGhUvB03kB4SUZW+fEhLkQRA7yQOmG3ZgQoSDFBQcGJ2S/vp5uVnjKDyxA/SUjjH8moX78PmCQyejDhXURmqZNlriQfhUWZcXN6HOcG86nvaO2eqgLzaFwUuazeO7iypARUIDlQcuCM+et4eZoEjAi4cuMa2dJUTaG5O5rhhIz4eqHDZ+bHTXhV3Xt7HyM0dbYUA1gMOSOr5vTlXUWmq8/A4wHpz3Bw1GbvarBO7hx/JLph18WqX4wZ92CAxqOyjCogefChGHDesYjH7UMGG0LOEpAWvTnMjPq5GmBfhcdGaJdZ4HzZk3kXF+yM9J+JwIeup58knooCUOW6YYNhr0U6m2v2kMOKz1Qi2KoePenPcwACLG8PBABEeB3y9OW6YfLzFzMy4osGGHUyDmMwp+TJSfQQjP/vZ5734DSOSd3xvwEg07ojP2yUOWbPnbaxg6i0+Ez0Jo1jVPCp5FwfuiI/EjT3OMgp8LFWUug09xI4blgrfytYiJ7RE4XcITy7w64f9xO7nh0oE95corAgx/goRPgD2OuEjGKOmbHA3/xeHBgnNHmwcKI8LktCRybIh5ZfM7kZCFu98u00qDlivUEHw31Ad4O+Bg49TjQP35IA3RZR5rQ1uiFpdG4sNrgjh7UFJ4MlPnky1Ya8VJD/5dCA+4SFryBI3UPhQgl7PkKWH0PM8+kkbn6AXrttrMu+O61cG1+85Sg/2nEu3d5pBD/acG5p1eTWTQ8ORG4F3UfF+uq/7bLrr3Zmh3iRUvq55Z0oo8w7THxA4VMzQGzuT9djy6hle9h0Kiqlhl5nUsMtM+svgpSbI5BJuTsZL9h0LBRY4QJp4HyWvnh0+UR6SSzlu4MZNFPRRzijeZwIjx/VJDZfT89/GYRtBjbLGbvHvwl82tiEWAmMkAaBAwIEAA0E4qtx/Zve/oqIyVHm/skWhefnZ0lUExgg6ebWLk1SewDhrjd3io38cN1zd4EE4dydHvy8y5SAGROHkQvnZChMYo9pXWVlJr3+6khp2mUm/7jPfXBNUfW/vNCNkmobPepqZYzlu0O/KHfG54zQqRqgC4EAFwDYhRGVh+oZ9IXMyxw27yYMQgqChornj4IkqlRFuxMfNHfdYY7dsIzuYKHFJcGVlpekJBwnkI4gcN0cDPvXXid16MdAyYONO4I4bkFQuCSYio9BBQghEAgdG8oFId5xcXOX+2woNnpDDPR29rLRKCw2vMPPvRBSu+uLfgH/A6lJ//GTDLjPpvu6zQ5VKvu4hK0Yii5tRYX8lCu+d/Jpjf7XvHYz4bDUC/g5KANv9GUZ8nKDh38B6h2IDByepuKd9Z20x7wXHzdHR00EP8X3dZ1PDLjPprb7jTTANYjJv84GQqz4A2TueDeD6NoE/Af838Fn8e8LI0k6EQtGAhFRlZSW9NmIFNewykx7vs8BcE+5rgEQdn+Tx7pSN1LDLTHqw59yQeR83jbTnp1dWVtLfRqygOzsX0c+6zgqN9+OO+JjU8MZIP2lz/MxZ+kO/Rf57792ZIa8PPpaKKxqIqErSj6vl+N5gm9nht+NAKxHfx3n1Golp7k/BPw/mfFc0nxxSNCCZ9kHRZmrYZSb9tHMR3dd+Ah076d8Dbnq4kJnHAvxdwNVyPNHOk6nAql3+NBp7xCyfWGIbOlYHJBWhGrL78uMAqTr2gec+XpJ8EgXqN7wHJIlCvq9zMpsEbohaXRuLDa4I4VVxYFZ+b3hl+IpQrzi8fSat+SI0XpTIn6zwUE9/n7232yxqNyjYU/h7Cr+TG0x2KfSf1+c+TvaDqA1kiRsofChBr2fI0kPoeR7dmSfo09bvC/WA7z5yysjq+AHwMT/26Bg+koRXokosyTAnFURkqqOQh2Hj5LN8eWUU1SxktnFg9jb6KF//dGVok+4xvaRKoOK4ARHmfZTo4XRc3/yNG0bZLzQj29q4P0SmN+07VqU6yYMHwDaCsufDQnaIA5JfJDBgmAXpOnrLUZHEgeDRrpLwyvfWL4+HZI2Om6Mxn/nEiycwiMKjwIjC1S4+csc20romr8LYuPdYlQQG79FEEL5x77GQVPyFIcuq9GmjR9Ou+mISAKq5diUcRAdV3zs7F4UqhQiU+JxwHqhC1v/SsOWhtYUefMihsVYxvggBDsgBpOsjl+4KuaM7brif2g5wUHlfvvNwKLHiuOEezRuYuSNvAzhx5qwJJvEdUa3jATT/LEiNuQmU4+botd5+kGQH0FiHqObivuJAAnCM1Y6Cdd9i3NoQ8cYBqT0CwM6FxWEX/5NelWQHN+KDpHrs8tKQSSBRYP6Hg5s9ced2yLkd1ye5NmFGQs6u2MKV+Io80bOVFnDtttst+CgtosDgCwcqU7YaAb24fx+5KkSucMCIj1cceQWq54wSo2LinhFQ5aBi+NHsraF9B5Vavjdc33KiCaZBTOZvOVClys//73zfIwqC8JJ9x4zsHz4AdrIDXgi8/5koIOhYp/a7AaocPu6TV+HR789bDLipKB/3aXtv2Oomxw2k9khMdZ+2ySQAQSrtvYETQD6WyvbeQGIPB69E3sgSofw7E1VN8kJqz/1NQLr5XgFyhQPr1bSgtCyssg8RBS10OGZv9O8pTA8f7TXPeG+g3c1WYfF9rxsrItjeG0RBld+W2XNZNp++kAT4bWBPkigtiAKpOpQoUXFCFOAtAmUj1mx14Pt6lOlkHLghKm/RSgJXhPAWLQBJDcfN0eb9gc8E9/ZBAhCJeHt86YOdJpg9BfHF5v3HTYwACX9FRaV5z0mMHmsDWeIGCh9K0OsZsvQQep5Hd7T2CfqM4n2hntCtXx43rpv2S5eIQu7DfFwXEYUq8VyKbsvTOakgIrorL9MCUYCUzq46oDID+Rf/LMcNxvPYVQdUFroUbgxV4hH8oAJsVx0gsf58xe5QJd5xw+M++AgQHohzyaAJHphcGbCrMuh5hPkXJwGOG1SYeAKDKCA4qPahcokDY/R6WGY5/DuX7DtWRf768UifoPMEBlHgyOu4fuWdV7t4PyKqdSBiuN/ztxwIkQB+r4nCQXj52QoTvDz38dIqhA332q764n5DIsz70vi9BpFH1ReyaIypurSJH4TjvzAuG8TGUhEFKgyoE2CKA4KMnj17fBIITp9ZW8x9RTKMB49PWGY5SH5MW7+vCsHgFSZeceT3+8DxM+SOWW0CXscNxhTZIxF5lZ8ocGfH8VwPP0iy5xsjyEfSDsEkgmP0HI+yjLTw7Pxj9KrIqg96jm01Ag/KuCweB8gAl6eDZOAZ5L4P/LcQUWgsJd87dxw8Yar5uN4gA2etxAE+7+J8NddWaIAI2XunLS2FeSJIMypotjydj5HjqgIQIqxnu7UHxLrVhHWGVF7fZlqV2eO8J7WystL8flRO+Uzs7zcJCDr2goVbD1bx3uDr1nGD6QJE4SAcqgJI42EQB8k3+sV3W5JqrEsE6na1GGSAj/uM8t7gicJ/jA4UQKjY/mXw0ireG/w9hAMVe7wHe0wvMV4CeEfhnYjrwsdj8rFUtvcGEoU4+ExzPE/Fe49W6ZW3E9PY13hrha02IQrIFRKbaOVBQvaHrXw1nT0iD/cVpHjyGn/9IGH+WO/5Vbw37JYZ7ivDe/ht7w2iaLNKIgpNLLnaau2pDlCjoehh+9XEAVJ1JGckXgVEQSyFlpkhC3cknoMk04WNcpGtY3EYsTgwROUtAEng3j58TCjAkzmLmAs9b41rZ5kl4r4iHr2vfUDQ+f2ykyvcaNRWTdQVssQNFD6UoNczZOkh9DyPbs8TdJBaHsBz8yf75cWNomAihIAaARE2fwQOCOxQCeSkgiiQSqOX9NE8KbJ7erlkkEvlQQAR6Pexeqz5/FNezbfHVPE+SiLWo7pkl5FK4+DzP21nZZ6VRg8hyA8PHgDbLAcS60vz1T4QYxyoutpmOUVWRR1GNzgQLCMI5z3K/P7bgeoHn/gE3SYHtpz4Z5ZhDfr077dmReOFPHH1npBhn+OGs9q20zU3rdpqVd5RcbCrvvyFXH62wqwpHCADtuEevheUAKi24T5i7dqSUAQxMNaClBlrBB4C9jgj9Om1m7TBBOH4LN5PzUf6EQVVs5FLdlWpyl3LXJVteTp/jnFfQcAxuxeBN0gkr/ITBQEUzvttVz9IAvGBg3FA7KaG7iue249mb61yf/m1ffmT5aH7Cok5jIZ4AEhEoV55Xs0HGUCwxvu5eWW0srLSkCvcA95Laqq+eVKLZ2fDF0dN5QzXBGSAVxyPnvZCfaDlZytMNR/XBAkfPoObiEJGjURBgI7vicDXNsfkezUnV/g8VKe7WW1LXJXDjTrxm6HKsb03nrVM6Wwzq+OnfHKLvX/xtkNVvDcqKsITHrj0nQfh9r6EpAKeKSRdsK+CNCH5AQJfaJHRNtbegCQN1i+8N7iSi3tvDGHjPm3vDVul5LiBogwJsw+KNpv12zCfbMP+xVsUMB6T+1PY3ht2AohPCuDGr7xSTVRVhYWRe7z6zZNn2HexN+BZhSoH71/sTVi/IEu4rzflE5qfLfPXTwFzbre9N3BtcfDEJDd7i5rOYX9vgKtyeKI4CUgWIUEincENqTqS8VGJ/Cig7QfPkcS0DUqrixtPCvZS1sYYBz4RB/sEnzASB64IgRKOTxjhffQFrIeet8b1mRUuuuC+Yt9r2DZI+iGxvf3AiSojGDfv99efVNlQG8gSN1D4UIJez5Clh9DzPLotT9BRCeYmUu0smSZ/eUHOV3r4ZGguJlEQlPEXNxFVcWB/mpEKooDQQraEnuopVrDAJWq8IoV/D/14fLQKUfglzStSi6zgh/dREgVja4Ys2B4aIWa/CFElRcDOs9Igw3gJ8X5iwDYe4sHP8TNnjWQT2WIQawRTqPLCiRwBCCoxOFA1sb0DiMLz7dHbjqPjx750+U0rgcHHg532zlWRJ6JPn1cc+XUdvGC7qWrgt/HeTtuUjJOOpRaxR+KDVweIwpK2fUdPh2bSO27QLmGbhOGeQAkAooCKHwiNPbIOaxZJExAZOM7D2domFWgXeWPkSvNv3MUCF4yCsk12sGZ7zdxsEiu4lhc0Csy/gmSHH2TyoBz/Bsg+VAS2iROv8hMFVTmc90BHn6DbJk68El/BzObw3EImyUfWEYUrNvzfABGBTJIHgERhc0FIi+/pNquKRB+Kj4mr91QxF8SoRtwD7toNsol+byRhlu88bAg0rgmIte0azgl72SnPVPNxTWBqae+dSJ6icoXKGb4n9iUuRyUKzzDmzy0+D+TXnl7BVTmQFt/afrpJUuCZhvQeewyq2Pj//8jaG3Yd9NchiMnS7YeqeG/YxK8PMzzjQbh9TbC/gEQg6QKCjj3GVi7ZqgmY9UERgoQTyBBmTvNzuPfGYDbu0/besFt7HDdoM0CiuNfMzYbQwoPg/7P3psF6VdW6P3VvXevWuaXWOXXv/XDrVi0bUFEQRFE4Ko2oh6OCiqJHAVEQUDwiHL2uJCRAQt8EJAESIA0JkBACZKfvIAnpe9IS0pCGkI70ffZKssf/w1q/OZ853nfv7Pv/IPtW9qyadTzsrHetNddsxnjGM56h9e19eUwtS+W1N24VodIsj2wT/Tartu6r0TUZ7MYEAGK/Kw2p/+b48aawNzC/YOXAJACsA8gi/ekL1Xe9qJoXL84q5yUCdwhmapqcT+1RME0jvV57w387bUrLrlfGr7kGqA+LL8vjHtxSg6rO83Kmn6jhoAKCK/DSXCPV47ROY2rSQ1pqWnZR2U0nasoI4QxR7QRYE1meMgA0NY797NoqGMQ4YVd9vVt00D8vUXM958wiG6a16vp/j9aWfIP2VrZ2B/0ka21pERZFYf/ctXTQifpiKM1dtzMpC0Yn1xCK76bdh2oo6B6lxxnQOrJmFoTUcPQwcMkdJ18YWjkRPQ7pK3vNTAxcHEfyfn2O9eOSV6nRfF9HWvMozWJU6Nmp7yaCSRyktG87g11RaYxEDpt6qq4+qmBmSbSP1AGMYij+SvszixRODBDy4aBgYpRhhGtdW1WV9rTGzs+WDrpXBlanY+/hIjix3I88fc0zNYv07kcnrAzfWHNNDzWWkfcviBFuloqIYahyL8ZVowM0jfZpTXp+yywKS2GYaoRT3xNBQMAl1TswizRZxMYwFnBgGHMPdhDhvLbfnDAm3CvLo2q81uA2i+yQu0cuD5FrjVYijuPBDo1cMe8BDSi7BZWUiKNG+c2iAcV1F95dGkmTHTNFacE7DzSGMcERwcjTknVmad1djeZDp2YeYgCijaBOn5Zq9BR9cuhHL9mcOFC7DjSG9B2+garGYxRSlpC5O3319hDN591Yi778X1NTBI627DkcovlchzOoTrGZ1dRyBljjOUk/8WBHYNv0TPU8uB9CfL7MlupcaKUDwA1YOUTGcPT/Ivnkx443hZrZ9EUbyr0Sx2T++l1BewOlfaJd9O4iSKXUVaL8gG3sl/x31jBpBYBunjXh05FgH+gcMov07dFLNidMrixPtTd8+guOyMqt+xJhNTopXwoUK3376LHjiRq8L4+JPsWIRZtqHBEtF8Z+QdN0AV+Boe+0NE0K4EgFKb0Q44EjR8PewDzgjPJsOlg5VJDhu36/OkP7TC3X+AhXjUXT5LzQqJ6jWoPbn1dmzTvoysrxFRdaathAWnIUMcuWGqkq2Av1mHb1GuABjAVAypaalrr7mStl2VIDtPzd8/PDuafVLZprmjoIOwTmmNfDIOXLLKbGTXpnW7A1GRe+K/PsXNG1UHaYCiCaWQ1w1RZaW/IN2lvZ2h30k6y1pUVYFIWdVznoKN+qkelznrM85sdpLV9f5guaJp18Iq0jaxbLIpFPxGGLgU7USWltZpHed/kT05PDFjSViLkKHZlZQo/SiBQGIA6Jr+35Z4lkeKofzr9ZzKPkfRWVJlcK4+lLdern+qiCjsnCDbtCxBB0HvpbACyq/98bIOQI823JK0VjQEu+qcPm3/XWXqWDfqsDMNTx0vxnHETYGZpnapaW3VJWBM72xl0lBdnnj6nDxlzh3XAiNTpA02gfTiagCk6kdyqbmprCXMeg4d5ZHh0Rr3cAdfXzXcZaU1N0TDCQmTce7MCBuqzntGTe63ozixUDcDLD3H4pRt5/+MT0RIjPrBbsUJYH8161J4pjx2sijio0ZhaBKJw6xL+8NoKZVn/YFwwo7ocT6VWWxwtDRKP57E+U5MPowzlVETeAl3/929Qaij7rgyoOgI/v7z4U9Bu0njZRt4uqCOq8qqwX0cIJy7eGaL4Kb+45VCQRR1I/VHATh5frEP/CKSb1wzuj5A/znIiCebBD92pNTeF+sHJ8iTTA1a/d+3rQb/jmg5MCjZwIuVdZVl0AdTKJPk96u4zY4Zgs3LArvCtgxOx308oZnBdmluQue+0NUq2YT1leAkMqUGaWRoEPNR4L84q1wfzVOWQWAYCBs9YnGgRZnmpv4NwiFMg+OHPNjrDna4eBpfusghvb9h1O9nxfHlP1KTyVV4VAszyeBbo23/1gf9Af4QwGtEA3AoaAApjeydq+/0hwrkKkswIKPM3cs3L4rj/rXa7Nnq+XDqfPjdc0Od2fszyKrZql6W1mETBdu/1ADbiiCuKdRCOEAALMm5YaNhCAa5a3rsQX5y/jDRh0ouYZWg+OPbFom5a6w7Z4ZX5tfXHfVHNI2SEnasoI6eL0Qnx6wh1SzUC1fSh1iA3Jd8WuOqtLdNA/I6r7ur+YWavLrv49W1vyDdpb2dod9JOstaVFWBSFfe2u0kEnqhnQyhXbatD2LI/0OxUHYtPEePRKseQTaR1ZM6HzVlFHEGqi1BhQntamIi+ah4YhBP3OCx1pLqtGpLxo0B+dQnJHoc4iukTXEh2+Dq+i0jgvGI31RGN8VMEsFeQiYsPhBk1+uEsxMItO7brtB0KkD2Max6denWKNZHla428r8a9bHIChYmOIcZ1557hgLFBSTPNMzdKyW5rXBtqNGr8XECLN4Ge9ZwZDRtWhDzYeTaIDNI32YRDiUMCeIHqnTiXRCQAm/Y5EvnGQ/1yBQ+r86P8m8omxjlMM2LFADBB9T5w4It+hYkCVfw3171d95yQAhgrxmcWoAmCH0gf/XZgi6gz4iKPXnCB1gUgh4l+eWWMWndrZ7+4IZYtYl9A5tWSdWVpGUPOH+faMORF86J2aBoAuw/d7TK1xBvh30Ds1DYAIkepqwOyIGhMpVX74ok2JtoA6A16vQefXsk17grYAY4L4Fyr0RGafd6re0JN5TkA3D3YskL1adRm4H2N+nxNjUlaORleJMGJoQyt/vqKoEuHqNnJ5iBZ/8a7x9m/VWL0yr8wtxjFZvHF3+MYo7XvdD8131dJXXntDy41pSpYKlJlZ4lzvPNAY1iR7Cs7AZJlDZlE/4m8TVyUgJR0AxkcZVXujnoo7uhZ+n+Ubr9iyN+z5//b0rASI0N8fs2Rzkh7QePR4OBPpMH7M0nSBtU4PhfQwQFeYHTqHfOnS93YeDM4VKUCMuWfTeVYO3/U3VST6obHlfx+2sNzbAGE0TU7LYuoz6e/DrsPZnr9+ZwIyZHnqSOs69oBcS011AD7hhBRbathAzBnNo2+pMTeY88qMaK6R2vH5LmNrBBZbalpHXtOPTtSUERK+R/W9PVAF28Qs1fbhmT2bizVw+u3RQQdo3bznUPh22GZePLEttLbkG7S3srU76CdZa0uLsCgK+2rloBPVjGj8luAIau8+YWVNDXMvsqPRJjXWMDIvqGhzGPpQCMkZxUnC8PK0NsR6vvXI5CTvTennew4WNQImGLU3DJiX0DTViDl2vCnkTSJgoqVqhrq6rtzXLFJqKVWiqDQRhD9J6R8iyyMXb7J+09eGPFnyTM1SQS6MHc1rGzxnQ8jZVMVXEOP563eFKCrjgRLwfS6qYGbJQea/Y5aPsikrttSUeDKzYITgVF708OQAdJzTbYItfX9Pzfho2S0MklsGLwzv+fOnZ9p7Ow/W1J9VemeIoo55Oxjhd41YFgAYpSRrtI9x1YjS87PWh+e/XHJIMRAmirAc3/GsruPttYUba3LxtPybquEzB7O8NOC9qBbO8Oc6j02McCjMT0xabW+9tztErZlDSv1TowljHVaJOipmEUQauXhTItxDzt6Que/VRBx1HPYcLIL2AkBalo+y5Rt31Yg7mkVneMySzWF8WN84n1qyrtw3olCf5gsrgHSw8WhguuB4qzo7e8plPacF5s453SbY8eNNNekCAERLNu5JxNG4F6rdPioDs+WluRsSOmcEvXbWlP8zS8UkAeF0L9t9sLGGcqz1pQ81HgtrnHl5WqcxtmXP4RqwQ/dqjWjr/fYeLsIYYUArK4exvPjhyUEv5LROY6w4djxElRGpU5B0llRIuLkqqfT0lBJMwTFZ+v6eZE/ff+Rosmbo5POqUr8vu6XlxiJ9e1+IKisF+jRZF+zb7A2f7VwCBTqHzGIaQJeGpTX07iwvHfddBxoju6ZyhlV7w1evYD2Y1ebz4xzPWL09iSTDHCG1Q8tSqcDe1r2HayLoNw2MkU/N2fXCkCpaxrWL3tudzCEfiV65dV/YO5WNVRw7npy/ZrWsHL7rHyoAtduIMqIKQI4+AQykJyevDnuDplEgXsjzk5IG22X8si01QqNaG17XMeM/SxTGm2sqPMmeC8DcUsMGgkaOrXSiBqjIGddFItDNNRWlBahWAcbmmuqt+CoRZqU+whOTVtsTk1aHdA2zlBECa+qsrqWKP7YP/RfPRFuG82X66u0J2+Vg49HwXdmfT+0QHXTVnIENA3jryw+2hdaWfIP2VrZ2B/0ka21pERZFYefeWTroRDU1CkSNZt81ar1j/5EakR1+A0QewwEjk3w4pfOaxUiSqof//oX5NbQ2HP1vPPhGkkepgmGPjH8niKBxWKnAiNI01fjcd7gIh9yAiqap+Wv8hhc7Wvr+nmCIcygpKs1vQJmjQznN8ogCa346h1rvKWtCBHrs0i2JEULJEj0kiYyqWjmOEGJ4PqpgFumXalBpfd/Tbh8THBFVYIWCDf3tx09OT0QGL3xoUiKiZRYjo9999M2gcv/XoYuTWtDXPze3poapKgzfJuOjtaAxgj8vYnOh5vj4dwJFfOTiTeHZuR+/TYM+p8Jy/jsSuVBEnvnMPD+10+igF5DlpfN7sxPR0pxO7nfVs7Nr6lz7igEaeQ/MhBcXBCOftcpYItwX1N/nvZfQk5kHn+wwqibiqE5F9wkro+MgRtblPafVKEGXayJG+zCgANyI0PucXWWWqFE5SWpB9522NhjrCA/hLL4we334Bj98YnoAg7K8pL9jrJMucLGkYvSWShAe3CDqS5k3/ZZK5/yhOAPeoTFLI0RaUoj9c80H+2uELBXEfHH2hkBP1nn59QfeqAE7tIycKoprFYWeb6yqcWjMIvsCQPBbj0xOQJIxSzaH74tqOwDHrS+9Feb+FU/NsM6vlY7PA6PLSB+OybJNe5KyWxt2HAx7g+55sB3UCA9MqKrslpYb029FTXkV/MOBX7V1X2CnKGhx4MjRmmoZsA5ufnFBcobg6LJ2fTURZWQRSdaOrgXnEE6TMrJUhwNnB70AnUNmqfYGqVZ0PTM0Z9fnlqPvwL5K1zlkluZyv/Xe7uBcTV31QRJJXuDYdJ6Vw3f9a8WK6fBKOXYA5IiEqfbGOkkr4hnIMcYho8ybVhfAntDvR9OKLl7ktKWGDTRi0aag+7GmFeXZODPY++tp1dRr3IModWsiwypK65lcLTXVW1E9ILOoV0I/S0AwZYSwJrK8tNtGO5aMUvs1DaupqSkB0/iuHWXfO3ykZEAoCwzQP8tH1TxLW2ltyTdob2Vrd9BPstaWFmFRFPblykEnqknu1ND5G8OhfO49E5ODHYc8y8vojhfZIWqHQ4nhAP0ZIxODHrxFa/EAACAASURBVKq2Km1ygF7Za2YNrU3z1zxllFzMPw1eWCOCptRfjUhpjvC2fYftd1V0ByVR3r1Lw9JgkF7Xf24iNDZmyeYQUaNUjKLSmm+vB1FvOahwFLTEmCL4SqlVZ5HrYCKYxSjKCDEyeWcOzXpGOHnaKgq0fNNeu3fUsvD/89vkmZrFyCzv8/OnZ9rmPYfCd/x8l7E1pXTU6dCSb8s37Q0R4x8+MT0Ya1AE1VDWKNN0ATuIihL19e+rJd+YX1keyxb9RMRjMPRV8AfAxV+HaJdZjLDyDT7XeWxiyD8y/p2ayIX+nftd3Wd2MA7pGC1oR+iaUEqtCmwtfX9PIkimc3TAzHVBTPD5WesTB5DfJuKoDlSXhqVJ6sLdI8t5cv59r9eIF5qlTge/r7Wgjxw9VpOzSwT/C3eMS4xKzXm9a8SyAGZhdEF57/nGqgQs0Cj2C7PXBzCLdAEcnDFLNieVIM4WJ87MakSjVKtCtQXUGdBcS5pqVVwuJYUuEMFOX/5PxTGfmLQ6aAv4eenBDt2riZJCo8ex+evQxTUOjVl0Yolof7v7lCSi9fSbaxJauVlMV7hx4LyECdV9/IrqXmVU2Stkq/YGUV9VIOcbs29/sO9IDdCqNGPW+6R3tkkFj5hKdJ5UL1HA5DSJfvpqIpwn//b0rET8z4NpvpqIshPekrQZTRUxs3gOVfus7hVayYL1TolHLUule/q0VdtrSm5C19c9/P3dh5KqF1v2HA7nEA4rHVr5p6pI+2QBzWas2Z4IpwJqv705lvCEEaLihfpduw4vWXx/GlTOX1+VQLU33pW9gfJosHLQQkAEUcULvcbBaZIWpesY1tLkVpQiu1J0AHwpwpYa/xYA5lxJd2upsf4B6G6T1IXmGmfDWV3H11QTaan1Fi0fAgyUHuSsUNAbYWE9q7Vc5pglm8NaggmgaX5e20eFTvmualft2n8oYXPs2H8kCVQcO95UE4RpC60t+QbtrWztDvpJ1trSIiyKws65o3TQY9Q3UhS1DqtZVApWxLme8BEOHIcUxqE3Mj3NTQ1gBKa+32NqDY1K1c49ZXSoCAVBQSQfE2r0T56aUQMWkLO9fseBmjxKIjj5K4uTXGkzC9Hr52etT0rUmaX5xd1GRudQjQEMiSwfFaINSmvrKCI1nlILPZrrMFrMYrRFc+aJnmV5SRH1tZTNYjQPWiYGQlEUdmqHkeGb6PiYRXokwnywHSiL9YkOo2ocGoy0Cx6aVFPyTVWjeWaUyFV8zEeZoAZilBL1NUuFgnw0hLGA4q/1Z3E6NPLKe9K5TmvKQ+mHjkxkhnft+NqSWMKvGkt1Ornfr/rOCU4jHQeGigFaNrC3qxXLu05Z+UECRJnFvMBeU9aEufzi7A0JRZ9ItUYclbqsaumrt5YG2mc7j6mJruic1bHcvv9IMOg27zlUk7OLIv+nOo6uqXTAPL1l8MKadIEHRAQRsID0DhyOHq+vqjG8dU2TupC/sjjJkzZLy7iZWSJ8pHROdQa2ybvQtCyWagL8SMS/fIkvs5gf+/jrq8L+5edlHwd26F4N8IGQIvnq1z83t8ahMYvOAwwNnkXTTJRWbhap+NeINsIvn51l/aeX3/i3Ve6qL4Ok2hta8o11+sDYFTVGuFmqvUGKQ8Nb7yfpRvW0PpQ1ofXt2bPeem93TTURZQDpuJLGQffVRHSvUyaXr+gAYEZuuTKAcFR/3W9OcObYcy+XOWSWam8QbeeMJgfcLM3ZNUu1EZjDXpeG5z+1U5zPOofVuVJQMZy/FZtOGUD6XR8ZV+7ZNw4o54mvSqDaGwB9Z3UdH5x1WDloJTxaVQAAyOo6YnmwS7Q8KOknCvp5pk1LDeHJMUs2B7sAYLqlBohBRFnTMFpqsB+wM26WHO7mmjJJwtyacGL1d7SD/vzyopr0Rhgh7BVZXtqIZrWMEF2TgOSwsQANzWq1fRijlVv3JSr73G/zrv2JHsKuA40J63P/kaM1525baG3JN2hvZWt30E+y1pYWYVEU9qXKQefwYBPtP31tjegShv44Kb+173BRI7KDM8FvkU/kjUwOCBTNQU/XfLA/5Ct+65HJSbTALNI0P99lbJI/3tTUFJy37z0+NRjoiKCRp3tZz2nh9wELOFBWbNlbk0epOYREj8jd0/w/X8pDUWkccUADOs4ARpoaLWZpmRcPAGA0EbFRwTpy4JSCuu9wkUScutQxwnFWMCoRTyuKwj7XaWT4Jjo+ZhHVhm0AmKIpCETgGB8VF9Qa9WaRXaE1wDHCp4iasj9omSs4U2rgqCie0ubMojOHUYregVl02JSW18eVHQK80pryGBsoaRPNJ3p808D54bcxwtU45X6/6T83OHF+LIkqqOPqI3bMBQVrtldjiWH26ISVgd7/0tzyu+KAYgSrQ6OOq6Yu7NwXlZShrap44a0i0Ma/232wMYz70vdj3jdRIGXJEKHBIFRAztfcxtH/46CFweAFeHlAaMGovZMuoGu6h5Qz81UIeGYiY/cJ3VYplOoMUF1AHRpNhQH8mvzOtvDfX5i9vqYutf92MBr8vPTl/3SvZiwRUgTE+NGT05PfprG/AQDxLMyFv7y8KKGVm1kY95/2mpEArcMXlnPjiioa7dkJqr2h1Sb4Hn8durjGCDdLtTdITRm+aFP4Hs+8+W6NgrhZWhJTvzPf440VW2uqiaAt8eW7JyaUcBxruq8mog6ORpLDXl/tx4DlCHfhhHR6bUlwVK/rPzfsh3wPLUtllmpvsAeybyLEZxbTlLZWTCXO+zdXfhAiyT4CD6tAo85Xiq6FOleAxsMXbUrOd7NUe0O/61OTyrG7pk+5H/uqBKq9sUrsCRV+O3L0WAJ0m8Uz/U+DFwbmFedvlpdOnFmkQveZtrZGTLKlpgKVvgxeSw0biPdCpO9EDfYD36c16uTKJAFku3/MiXOyVW8FRs4nO5SCfFqp51NuPnlGiDK22BsAkzgXzFKNDrMYKFr6/p5EZf9z1R64ZuueBOjec7Co0U36rbPx2kJrS75Beytbu4P+d2pPPfWUnXnmmfbRj37UPvrRj9p5551nY8akStozZ860iy++2P7hH/7BPv7xj9uFF15ohw4dCn/ftWuXXX311faxj33MPvaxj9nVV19tu3efGBXV1pYWYVEUdnaX0unC6SOn9+k31yTRNbMouqRlaw5UB5nmBYE0g7ZjOPg6sqrIbJaWG1okpXY0WmBmiZHrKaNa0xzDGCVivb8vDadOhs+j7C85vb6siOb/nesMdkWltdyQGjgaIeZ6VW5VqqkHADicuO4GOZRBpwcKLftwcSwYwiu27A3PpzVTiX5AK4XdUBSFndF5ZPhvOj5m0cBmzMkR1IOSiCw0VtD28+57PYxj1xHlt4Klwb2yPBrh+h1vcA4uUVscWI36KgjgnS1UXYnAqeAehoVGJzXFIMtj3qOWh8HAZc6QWoAQ3ZW9ZgYnTMcSUSfud/1zcxMVcR1LFIWJGJ1x57iE8WEWqYUqLshYKqtA89HNYpkyHH51aNTJ0PzexsZG+3TFtMCQU8EfooQ6lnsOFYkz0Nvl7KpCNoY99GRV7vYRGoy+q56dHejVgAXqvHNv0gV0TSOC1um1JVE5v3J8APWI+vJvO762JAEL1BmoV11ARRe1NBwAX4/XVyWgFI095eFx74Top5+XvvyfWdyrGUuEFMlDv+ChSTUOjVmknfPteBZN+1FauX6f7/eYmgCt01aWDggU56icX+bpqtK+Amsxyj8vmRd7DpZRustFe0MFEDXKr/sOjYjn6CWbk5SNa6Q+t68moikCqmbutVs0fccsLVOlkWSvnO/TBbjud8/PT5hcAA4AaJq+Y2aJ0j7rnf+W5dEZ/aRj1wDsDVv4fgDWNK2LfVfnkFlUVn953nuJcwVFv78IonL+KpCrYMeAGeU6hfniqxJo2S1lEWl5zM17DiVq7GYpsKdMCM8w0nWstPUTNRWe1BSDEzWAbp6pXrWXeo31z96v6W7NNZgk594zMawxzt+Wmp4vugZ3HmhMKvWoPWdWywhRhqFqC0DXJ2ffa9doqpx+1y91K/eQZRt3JilA1LrXCib1zt0Pu7Ul36C9la3dQf87tREjRtjo0aNt5cqVtnLlSuvUqZP9l//yX2zZsjJiN3PmTPvYxz5m999/vy1btsxWrVplQ4cOtSNHjoTfuPTSS+2MM86wmTNn2syZM+2MM86wH/zgB/9Xz9GWFmFRFHZW5aDj9Gkt4dtcvWsopFqqhZJDqpKLk8dGjuHg68hqHpLmQK7fcSCJCGq0wMwSoSWMpE9WIjWIlJx2+5gaJWI1hjxYoPXfvWGkxpA662ZR0fTfBy0M+e+UooNudv+YFQnFTg0cxJ2yfFQ4nDFa/G/4iB1Rea5DqdksGkkaUWs8ejyW3Vq9PRjhiPiZxfxFIv2I+CiYg2PC+JhFZwVD+Dqh22M80HFotNauCvGZxVy2hCpXGeEYd9/uPqXmoIVBwT01z1Sppb7silKuMdxoiH+pDgNOtu8wAMxiVI7fhimi7IDfuLlmFhWMud9vB8xLxN60U+JLWSW+vCAgj4r2MZbdpdLBr0R/wiyCPHxTdWg0yq2pC7qnMD+vejaOJdRyHcv9R44GZ+C1hRuD8w8DwCxG94gsARaocrfXjiBN4tK/Ta0RmoQWfNWzsyOluWJTaE6vCk2irTG6KhsJ1ZKoL8DCrS+9lYAF6gxo7WE/Jk9MWp2kXmiUH0dXy/+ppgLj4+elL/9nVhulgwZMfv8Zd4wLhi815c1iJItvR/4yUdQfPjE9oZWbWVRuf2RyYFT8pv9ce/v9XWH/M4uOCca8lt1SEAma+Y+fnJ4op2OEAzK9NHdDUm5Mo/yLXGqVzvWh8zcmTqWKdPpqInp/VQL3pcwYL9g1Og7K5HpUIuT6LqQLcP8re89MyuxtqkCfT3ccbU1NTTXpO49KSs3dcp6oEJ9ZFHiDXYNz2mfa2oQRou/Gu6qwmupaaO1wgItHxr9Tc/42NTWFPVvH8pV55dn7/cfLuTbQMdi07JY6nGZppFXXmJklTDsFv04XJXszS3KVVZn9RE3p8AB7pBy01HBOsVU0FaalRrSa76MMsOYaZ+zX7n09OQdO1B4XVpFZ3E9Wbd2XVOrx9opnhKBx9PjrqxJtAdXeMLMa7RpNqdLv+vX7y3No7rsfJHoUAFCaRuPB6LbQ2pJv0N7K1u6gf4jtH//xH61Pnz5mZva1r33NOnfu3Oy/ffvtt+2UU06x2bOjsTlr1iw75ZRT7J133mn2Ot/a0iIsisK+WEVFl21K8yi7j38nIJwI8mDoa4SAWq8qsgPFDscaw8HXkT3gSmZovWuthTsolI0qUXNF23FMoIxqrhFGJirlmvPuwQIVZ/KGkUZ+vIAVuVO/eGZWOABWVgb7AxIVViqWGjjaccyUxqpRFZ+niVOLYYNSs1mkzSqV+NjxphAFGLFoUwLG0KDmYRQT4SqKwr5SCQpiRLwshxt0aOaGUuw4wOk4NNQ+PafbhBowhfQH7pXl0QhXcTl/0M5ygj8qsqOGhdZ7N7OaUk4agcA4JcrwiQ5pyTXt6B2YxWgezgFgAe99VtfxwSnWsWQdKB0VQ8R39AgApj5z+5iafH7e+zYR2WIsVWHaRz95b4A5dWg0t/lcyVUtisK+3q2cJxj4CnYACvFurH3+7bNT360pWWcWHTiUtQELeO9TO42uYVMQKf3qvRNrhCaVFuzLJ+G8/+KZWYnQpKdvR1CyjPTAVrlp4PwELMC5/t7jUxMghaZaEJp6oVH+ejXlWTP3jn47RD9V2T7LY8RTx5K9mrEk9UL1PHBMVTSKPG2+Hc8CTfsbD75R48QAoJx/3+vJPr5194Fwr+LY8bDO1+8oHXQtu6Ug5VwR96xnhMP6enLy6qTc2CCJ8mtqDY3o3sBZ6xOtj24CFHiQxyyyvnCosjxlLbEOszyya5TNoZFkrx1xjZtvsL4u6T4l3ON3z89PxkHLHs6oGCEDZF4C6jw4dkUixFcvnx+GxkPjVoT12delUPBuSsVWYTVl16j2hj9/zWIKhY7l2CXlmMMq8ww2zf1nblBmT1k5WlXBzAJIc959ryd6N+dIrXkzS9Yx3+PVBRvtRE2FJyPd/cSRdwACFQ8kBaGlRuSfc+wKEeZsrmkt+np7bnNNWUVmMUd81rs7kko9nppO4IMUKl3Xqi3wIyn1a2Y1qX1aNURV9i+p9qep72xN7MCDjeXeoOvar6220NqSb9DeytbuoH8I7dixYzZ48GD7yEc+YsuXL7dt27bZKaecYj169LDzzz/f/uf//J92wQUX2LRp0Rjq27evffzjH6/5rY9//OPWr1+/Vt+7LS3CoijszMpBh3aspbd8vWuojVpqpfFoqdB5sYjBQNPEqMzyEpHmIKSsmuaabd9/JKl3rVFyUHMo3Bq5wNnRHDioTDipqJSrONAkBxaQ+/Tqgo01hrjWo/YCVm+KoX+2sAjMLKnDrlQsNXDqdTValPrt8zR9yRxUfM2iwan3a2pqCgbH02+uqdEYMLMQdcJpIB++KAo7765yrhCt08ONQxRHUuvrYnjRoa6pqI8e1maxRquqwWKEqwK/j/ouEsNGjTUzS8SavOCe5mdneSq4h8HJM36q4+iQj+o7egdmUQ2ceQBYoGkZmh9Ow1nhfr9/YX6NOBMdowUmyamdRtfk82O04YBneUxNUYNXxcrMIkODXF51aAJN98UFiX5DURT2L/eNsCyP+YQKdgAA8m5ZXoJ8OAMPjl1RQ9E3iwAQjhZggRpi3I8IDXTyUzuNDiAb9ZOJHp17z8Swd5Ejqs675j+zdrpPWGlNTU2B/fJuNZ/1HgoW4KR+5vYxNTXlzVIgT1MvmJOX9ZxWt6a80lIZAy2XluWjEq0AGu/LWCKkqMrdzBUEy8wiy4hvx7OslfJWWl6qXOMRjIKefMOAeXbkSKN9Ii/3k617DwfHhFrRRJmv7TcnzMMHx64IUf4sH5Uop2OEA2h2G7k8KTeGbspFD08OUT4Uvs2iNsLTb6ZOJeP3H0MW1YA8ZjGaB/39Ex1GBcYVnXnDelSwRSPJON0/fnK6NR49Hr7BsIXleuSsO7vr+BomF3vG6m37E+V0s5j289NeMxIwCCfypbkbEm0C0l8Akzu8urhu9YUsj7bAmTKfVb/gfGHXqPaGB+vNIuOI583yUSEV4tMdR9vBxqPJvmNWRt7Jv0bvAOE79rPXFm6sSdcLTLtOYxIFec4F6NS6jmE7qTjq/iNHbevew0Gp3L/L5He2hf2zNZF3wFlslSyPNpZZafscOXosuUbBFfYgZdrotdqUSeLtGrNSr2Lr3sO2de/h5Bl8+VqAsNFLNifiuwAkiOr5wIeeU6otgN2GlohP7VPghe/ae8oau6wKDI1e9H4S/CGIpOCnB6PbQmtLvkF7K1u7g/53bEuWLLH/9t/+m/3n//yf7eMf/7iNHl1GXYmE/9M//ZP169fPFi5caLfeeqt95CMfsVWryujivffea6eddlrNb5522ml23333NXvPI0eO2N69e0PfuHGjnXLKKbZjxw4riuJD7QcPHrQv3F5F0DfutKIo7P7RpfF1R8MS+/3z5eHUd+oaK4oi5IOpovShw0esKAr73uPlpjlx2Sb7bmXITVmxJYmA/q5yGi/rOTU8AwfS2m17QwT5vR37bO+Bw+E6hGJuGDDXiqKwxsYY6Zm/tjRwPtt5TPjNf66oTpf1LDfsB0Yvt6IobMWm3ZVBOs7GLy2d7st6lM9ybd/ovPysd3ngDJ273oqisFGLNob35lluHbzAiqKwt9aXEduv3D3BvnhX+a7vbNptRVFYj4nlAfTnIW/ZX14uDUD+W0v9+4/H8XmwipJ3fm1xcAj4/b9NSH/rjy8uCNfdVI31Q2PfDoZjURTW4ZVYJudX1SH1xBsrw3W8e8dXy3/3rUcmh7nyjSoyGoyBanyKogiGzS2DFoRvzd8ucmWHVm0pn3/VlvJ7fOGOcdalMur4Vhu276sZlz0HDllRFDbn3dKB+voDb9gvKweXZ1n+flrT9rz7Xg/P8cQblGtaGKKIC9Ztt6IobORbqYN+Xf854br/GFICVbdXtZtP7TTaGha8V/fbdRuxNFxHXut9VaTza/dOtKIo7NDh6KCzVobO2xCu4ztzv5ufnx9+w/e31pf7yKad+8N35t/e2bDEiqKwEdW7/Ut1rywfZXsPHLaiKGzQ7HVhLvy8+vbDFryXzC8iE9944I3wjKhw3zBgbgCOlm/cZQcPHrQrHh4R5k6Wj7Jf9Z0druM7825ZPsoOHDpij1Zlt/7Py2/ZY9V9/zp0Ubjugorx0GvyqvDt+RviQEG8cNZaK4rC9h2Me8jTU1Ynz8L8+nTH0eG3Z63eVrOmKS/YdfhS+1M1t1lrn7+jdCpXbymZAwif/eSp6XZd/9IYfX7mWlv3wd7kOvYgnv9RKTnGWpm5epu9vjw6KjdXtOEfPzk9XPdAtVfrWM5Z80EyPx6u1r+O5b/+7c1kLM/uOj78DbCFufLUpFXhb5dWhnGnCmz80RPTrCgK27E3lt2ENTJ8YTmHePfTbh9j/aeVEeIbB8xNzp7vPjolnAPrPihBHiKnP+gxNdRMf3DM2/bBnnivvw6NjJB9B8v5/OSklWEf4lnGLnnfZq2OzAL24gsenBTejXrb3cetCGDQW+t32Asz14Z5M2ROBXQ9Oytc96Mnynv0nVq+26c6jraX5qRO7BXVecl6fPOdEiz4dvfJ9sbb5Te+9LE3bfDseN3X73/dfvJUed2r88u9YfOuuMZ7V9/u5hfmW1EU9o1q//3y3RNCRHP6qq1WFEVwci9+eFIYy4fGvh32/ywfZbmcC9v3HrSiKOy5ao1f33+O3TigdJr4hv7dzrorzqFuI5aGvVDHkj3zJ09ND+fvD3rEs+6qyqFmLLN8lM1ZHQVpP9t5TJh7vHdRFGEv71c92z/fX+75rJmnp6wOa5f5rPbF0Lnrw9z9dvdy7N58Z4sVRRHW8Qsz19r11f/O8lG2Zdd+W7huRwgqXPjQJDtY2UJFETU13nh7s13TpxJsnb3uhDYZv4etkuXx3GOfYGy55vCRaA+xB32n+5Tkdycs22Sf7jjaBs5YG/7bXDlHGbubqnO7sbHRvv941Mf56r0Tw3MEm2RYOZ9/WznU/ae/G+batx6ZbD+s1kaWj7LFG3ba1dX3HTJnfY19hG352+fm2p+HxHW9bc+BwNBatKE8635QOeLjl24K37XX5FX206eiXar7xP5D5XfBgR+3dJP9GwGZ+RtO+E3+Xn3Hjh3tDnoba+0O+t+xNTY22urVq23evHnWoUMH++///b/b8uXLbcaMGXbKKadYx44dk39/5plnWocOHcysdNA/85nP1Pzmqaeeavfff3+z97zzzjvtlFNOqemDBg2yhoaGD71/vjKSeg8q//+beg63LB9lv3x0hF3+YGlo/7n3cGtoaLA7+pR/+9pd0VEbNqy87uJ7qrzTvsPD3x8eMNyuenRE+LenV/e68O6RNffvNaghRFQGvNxgrw1rCNfd2LP8jR8+OCJc96lKiOqRgeW/O61j/E0cSRTqb+hRXtf3pfhv7+5XvssF1bPkTw8P9+O6zs+W731v//Jv3+g20v74ZPm/r3yk/M3nhpS/+ckOI+2zlcr5M4PL5/jTU+W/veLhEXblI+U7/PuTw+2vcq96/Zvd4rvo9+D3n61+/5an0t/56cNxfH78UHm/6x4fEZ6voaHB7u8fr/lCxZ645anh4brvEP3sXv7f8+6Kz3LRPamDzvg0NDTYlyv6+4+q+/74ofgsX3eOfZ+XGmq+x9WPldfdWH2r54c21IzLy6+W1z3+Qvm3s7uMtG/fW17XpU/5LP2GpNd96Y74/HyPnzw8IqR2PPFiQ824ZPko+8ED8fn/rRqLX1T/99QOI+3lVxvs/K4ja57xN4/H63i2a/82ouZZPlN9S/K1eX5dE9zvxw+NsEcGxP+uned/QcbrN9X9fv238llYI3zvLB9lQ18rr+v8bPne37lvhF1yb/n3O/uWz8I85b5fvjM+P3/7wQMj7PTqXZ6u5uXV1ZqHnfM9GUvGgnfL8lH26rAG+0vv+Hu/kznPdbA3fv9E+bcvy1jyLblfh2fiWH6yQ3odz/LSq3G8uP6x58trnh1c/u2znUaGsfzN30bYXX3jHDm7y0g7rePIZD7fU+0p3+w20r73wIjwLLqX8Q0+f3t8ft1TvnxHfJZBr9Red/E98Tr2RR3LF4c22AV3x+/MeOtYXnh3OiZf6Bx/89w703n5J9kb2FdZDxdVzzJM3o/r76rm0MCX49/+o9fwsEc0NDSEMTq908ggLNh/SHmvhwfEs+aX1Xy6qecIGzasIVSTYM/J8lH2SjWfmUeXPTjCvlk97739h9uQV2uf8VyZz9dU+8/1PUaEefTkiw3hnPhmt5HW8Znyf//r/XEsYYv8ofqGn+ow0oa82mDnyd7AeXhttR7/9nxD2A/Yd77ebaT1eakhjLt+gzuqveFleQe+3RXVnv9LOWcpndp9YPmMPav98qwuI8N5fFPPEdbhmTifvypn+uBX0r3hu/eNsB9U3+ovvYfbDT3ivfgGOp/5+zWPpWPJN/3qXSPDWrlAbAHsDcYyy0fZS682JMAwc/sKOesY33+vrjun2hv0mwIa6nzG3mDOXHj3yDBn7uk3PJmjHZ4ZHp4vy0fZrb1qz3H2gYaG+P0ffG64XfZgHLsT2WPYNZyNWT7KBlXfQ++V5aPCNa++Fv92TzWfviJzW99Vr3usmodfvmNksH++X+2Pr7xWez/Omuur7/urx8p/q7aNrlvOP9bMd6u1gt1wq+wHf66+weUPjgjvkOWj7KEBw4M47VMvpvtXt37Dk+96W694Xa9B8flfrWxUzrc7+gyPZ12fE3+Tv1cfNGhQu4Pexlq7g/4htksuucRuvPFGW7t2rZ1yyin2Wf8/QQAAIABJREFU/PPPJ3//2c9+Zr/85S/N7P8/xb2tR9AxronK9ny9jED8x5CFARl9bvq7VhSFjVsa8z/Z/PgtRUdB8KetLBH852emeWsaBYICRyQ8y8s6lkURI2Ndh5eI/O+fj1FZoldvVFGmz98xNvztV1U0PJQeGfu2FUVhG3eUUbNPdRxtoxeVERqiQEURo8Bc91qFrk5ZESMeivoWRWH7Dx1J3i3LR9nqrWVETVHpWweXSO+Tk8po9eINO2uuo/9QnilGE98KNNA11e/3mZpGM257aWG4jkjdnQ1lxPLUTqPD334hpUyyvIww+CgGz/svj04Jc0UP3CyPkdaiKMI3J8qg0fwrXA3vtdvKKNl6ia4RsX9kXPmtdu47WDMuByoknAjnV++dGCL+PMsWyW3N8jTq+8ybZRT15hfm21eqSOHiDSVzZMaqNHf3xoqtURSRdXDbS2Uk/XPC1hg4I53b945aVjOWRH006sv9ya1ukLEkisn9dCyJxtCXvlc+/3aJYhKlvmdk+SwajaET7SGq9bPeM0LEbsRbG60oInOEftFDMeL44qwy2ndt39mBJbNy8247ePBgAIUQfVQ2wt0jlyXvluWj7MiRxhDl//GT00Pt406vLg7XEb2FEXKhPAs0Y+730pwYqWItE/XhWTTqxF4y990PrCgKW7ttb/jO7D2M5fKNJUPj7K7jE/ZPURQh4n3pY2+G6CRRs8krtiRjeU63CeEZie7/8cUFYQ+aUz1LX7fGf9prRriuexVR07Hcue+gNTY2hpx0IsM6ljA7HhhTRuC/cnd8FphQsDh0byAixt7wk6fiPs5e/dUq2jVqUTmHNJLVS6K+Bw8eDGDaqZ1GJ+ypoihsZhXxvuihSSG62338CiuKGPnju2V5ZHIR8b6u/5wQAZy4bFOy/onIXfzw5PD8GvUlD3npezsDI+Hr978e5vyv+0VGCGcN3+K02+PecGWvWGuc3y6Kwpa+tzPMgXqRZO7PN4CNcFCYN7An/jSo3BuUlcO4zF7zQXLWnHvPxLCXMZZEPBHYyvJRtmt/GSkdVu0NP+89I2GEFEVkHHEvnc+6ftnLlr6305Zt3BnWzpjFtefvzaSQSKR4z779JbBX7XuwZG4ZFPdE2B5EmL/5YLnPKivnD1U0/Zk343xmzjEvfvLU9JrzRNcxjMIsH2UvzloXor509o+iKJJIPGexslGa66SYYKtkeRmtL4oiuVeWR9trvzCFYGecL8wxfy3/TdfYyxWL4KqKHaLsI+bGG29vtqIoAqvoruHlfL5dWBnsc999dIr9pl9kHNw0cF6wO4has1Z/029O2Odg3rFHDVvwXlBnX75xV/K3hgXvJd9VWTmMQ5aX54uu1UGz14WzjrXVFnp7BL3ttXYH/UNs3/rWt+zaa6+1pqYm+1//63/ViMSdffbZIaqOSNycOTGfcvbs2f/Pi8QRjUA9l1ykWwYvDLUiyelE0AbRL5TTzVKRnYudQrbWTcfIpAVV8TXRQd9Z5cChkkz+tuZY8zdUfTWnk7wkOmXEtBSO5uXREIqjj1xc5lGqMBH5sdS0NbOExp/lMY9y8JwofEKOI4J7W/ceTq7R/hMReFGRp89IKTszq8l1/OvQ+EyMASI+Wq7lj04RHI0Bs6j+jv7A9yr13KIo7NL7Uwddy82Qn8r1WtZJc5+zvNQY0DH4dMfRiaKrmSXCR/SjVZ6fagl4ZW3NSeab0ci3vvmFBTWCewjm0MlxNItiSYimoXptFkUC6Q+OjXVkybkjL1+fBQEdDDKe3yzm3HE/HUvyWekIEqpYEkJsPIvOe/qx46Xw0BipU+3r/Pp8Zi3/94oIJ2r1haIoQgScruX/yOnm3bK83EMQ/rvo4clBW0FL1pEvizCiPouWKszyNJ+fZyM3mfJMZrF2MH1RpRKsZRxVVMssLQ2pmhlmUcTr292n1IgQaU4pc5em+cSa/2wWBcXoqs5MfrSOJdoC7EmsdR1LcnPvCakXUaeB9cS87D897g0YxuwNlBsziwrJ3Hd8NYd0XrJ3/mlwCSS+WIcls62qmbxAyjV5MUuUu7Ufr+bzy/Ni7jrzYkpVboz9mGf8dvdYLUNL66nWB1oYp3cZK/t5nEMoU1P1QvdZ1j/nJWKlPP/pXcaG/GfVFkD5HP0NxMU01xitGEoRmkXRQjp51CulPjj7LOXGGGcqkGR5zOfXvcELfKmQm5/PWvddhVPR3vhEh3j+6lmHKC1jmeWj7PCRRmtoaAjOO2CH7omI0pLTfFGlm4L2xm8HzAuVOHQ+o1WDUOaVvWdG0dGqqoYKw5GDnuXl8w90wqLkWptFIbOZa3aEMdeShc01xN4+2HckfP8te8o14ec8Tc88/Z7a6l03W2rRU/2BNa36DlR2QQvBlwPUCixo8lz6t6lBWJB56vUpEF38xTOzrF+1B/6hOnc5556bsS6pEGQWy/0OW/h+uEf/6SVwBJuC387yKLJHdYHnZqxL6tS3ldaWfIP2VrZ2B/3v1Dp27GhTp061devW2ZIlS6xTp072n/7Tf7IJE8rSR4899ph97GMfs6FDh9rq1autc+fO9l//63+1NWuiSM6ll15qX/ziF23WrFk2a9YsO/PMM/+fL7MG1Rb1XFWH9Ycy6rccHJ+WEiC3Skm2C5165zxn6KthRwmSCaKKvftg6aBjrGIQ3iICJjgxbMQIHZlZUk4qy6PTp8IhOBhXyrPoAZzl0WnSmuwoYlPCyiw6W3QMdkSeru4zOxjKfSpnWOt0+n5lr/hMquyLWvvmPeXvIwhDV9AAATiM23qq0XQ1WvjmiP8g6FcURaA50seIU4kwIKJnWiILoR06z69iaTwnqtEqIEjHCFf195+4g9Zfh8idmSWlibygH4J1dAWDEKnigFcwCICI/vC4CNYBcLE2LhankhxFuir8Mu+5n5bIUiV2fX410lC2fWT8O2FMPiVie2q0UCLrx09OT2r3mllS7ijL0/J/ABNXPTs7qb5QFEVN6oWW/0MVnXcD5ENM7Iw7x9WI3JnFusJ8C3WuMLboGJJmFqKQXKfP4h0aRIi27SuBo090GJWIapmlpSE/5QxonJaLHp5cI/DlATmtKa+lo7RknVl0+umqIE45OsYyy2PZSxwu9jStLgCIBvB0vpTPwyGhD5i5LvwNsSvAWAULmLM4GBOrOaSCnnz72yr2kVK26R/sKxXEVf3di1mq0+/nMwr8V/eZHdbY1FWlg65Ge5aXda9pWmYKh3rNB/uTmtxULlDAiX0SsOP0OuXGiNQC8uj8Gifrj/Yd0YrI8giYmcUSjIgt6j4L2E0H5EFY78w7x9WUG0Ms8QwBmRHVYm/40ZPTw5yhWobfLxXkAVT+y8uLEufqqNTMBizV8xdQ+R7R22hsLB10dEBYz7onAqxz7rPPjl1aggBXPDUjKf1G450503/+9MxEDM/MknWs+8yoxZvDO9C1ZJeK5nYT4d2WmlfSB0AA8PdznqZ2DUCgivb5a2kz1+wIe6l+a7N0jXGOYbv4iiuq6j5ZKlboWdV52NJQQQa7Su/J2sLGo+pN9wkrwzdHXPZaEZvT71oUhV1Ssfz4vU9IEEmFAv1Z1xZaW/IN2lvZ2h30v1O77rrrLMsy+8hHPmL/43/8D7vkkkuCc067//777X//7/9t//AP/2Dnn39+ouJuZrZz50676qqr7KMf/ah99KMftauuusp27979f/UcbWkRFkUR8ig5BLTmuC9hhWNEP61TjBYoSkwEAAQflV+6GnZEbDQSuedQWQIKFBqn8daXImpOmSyiGmd3jQ66lhbL8hh5KcRAwFn7+dPRQMCxoeM0vb25PPS+fPeERJmd9iNH4cYBVVQapLefOMPQm33XZ1J1VRyCrVWUabiLrnUS0IDvgQK5lsHxSvIDxWjhMEZJ9fIqslMURcgvj+MTDUeiGETZVDX6T4PTiD1RMo3sankkGsYoHSNclaF/JErNNAybLE8jrRoB84c+it90VbPFICFapmAQEQN69/HRQScKQPTmEnEq/ZzR52ducz8tfYPhQkdBXAEfIqqPTlgZrtN68mq0qKI0olpEgvYcTKmRWv5Pa0IDHL2/u6TGktNJ1/J/GHO8GyDfTpkLjHfXEVERn30ChWh1rjxDY3gVoTGLTBvGTZ/lfEnVyfJYalKfhfvxXTWy5OfzAinf5cuNeUBOnWKdl4hqARb4PVcVxKlFz1hmeXSumEM/q4xirS7AvwcU0/J5WqEjy2NNeR1nzgUFC1BIplPzWQEzHKg/v1wK1g0b1hAcejrsKa1p7RW4Vbnbz2etuEG5selVubHxjsml85k9MX9lcXDc1m4/YE1NTWEfQm1fK1SwtgFylF3jmVyAPOr8ABpoJNkzuSaKE8HehqOt+yxrhA7IQ8T+813G1pQbW1VF13WvRe37jRXl3nBZz2k1jBDPytH5zLy87aW3avZZHHZYMnrW8U4KIBdFUebDVzRqggO6JwLSch37LIDZhQ9NCvNa5zPPAnD0y2dnhW9G9QJdx5fIHH9t4cbAfKEzP83MLqpYhPPW7QyAo7JY6jVV0t99sDHYB+zxft+hKYgEqKpMDn8tDfDvu4++mTjW/jexJR4aVwJMvuLK02+uCd9bK/Uo26vriOXBNsCu0goG2DnYeAqY6Xo0S+up63ctisJ+WNkorFWtIw+bsPuElTVnXVtobck3aG9la3fQT7LWlhZhURR2auWgQ5vWCIQvN+aphZ+R0ma6aXua5p5DqaGvhh33oARPlsdyWtBaMTKV1sZhySF5TrcItgyZlypsEy1oamoKBiEHgj4LVC06kQsiEGfcOa4u/RZnlo4DrQgxyLwi+BzivuszKfWLvxNl8tHbLvJMOBawD9Sp1LHO8rRsDAceBhmRnaIo7KcPpw66OpUYlRx8HeuABf75dV7wnL3FyIGenOWpEf6ufI96By20yixPI63Mi1/3mxNoruuqQ98bnDrXmBfM1S/JXPPsEHWK+WaAHupUYvjR1QhnXnA/ZUbc49TceX417ogqwBwxS6NyarRouSPYLJPe2WZm5XrRMneXSvk/Shpd2WtmQsUsisI6PZM66H+QdAEcId6N8ojHjzeFvGkiL3eLU0n0FiNen+W3jqFBaoqZhXxjorDKjLjYRRwpNanGKfcjTaYes4P5TDTya/e+XkPnNIvR/CxPneKXZV5+pXpenkVZJowbjZrUv5KoN+WeiB4DnEFHNYvAEeNcr9wY/cXZG8LfiFxBMVWwgL2azhwys/Bdidj/deji4HjpGs/yyJ6Cln1OtwkJO4um4IrO5zEyL325samrUjBNy1kC6v7HkEVB62PDjvJMBDTBuaUGt1kEH0krqVdujA7Io2uVvVgjycx1OmCHWQR1uW+HV+M+68EVQB7Ax8/cPiYpS2Vmtn7HgZr5zBzSWuW+LJVn5eh8Zl7+cdDC4FyxTxHl50z4xTPxrANEYyw/2SE66OQ90xWYwD7gevZZovxn3DkuKZdGg2XC/n51n9nRiau+Fet45OJNYS/J8pJV6GvC3zs6RshjDfBdIRVF2Q71mgYQ9hwqArhIGpP/TgDWCqSSnvOJDmn9dP6urEd1kEkxBNzQ38Tm4RzSsodmKesyAL5PRNYF35TqP9hV89eXZ+cFD02KTn517lJO7/cvzE9SqMzi/jVg5rrkuxZFEUQQWaundorvC0B47+i3w76o+9SH3dqSb9DeytbuoJ9krS0twqIognrupoqWTW7Ylb1n1hzK3lj8XOdI53tEHNfzHU1ToxDesNO63PydHDgoUTgOSmsjvxDDSnPgtP56lqe1fIm8cGjqs+gzZHmkP5F3+pnbx4T8OKXfYvzTt+0rHXRFpesh+J6aW298OPzU+CLKBFpN12cimsB16lRC/aOrEQ6QgJFIjn5RFEG9ma5OJYcvVPfOwyJYoHWas7yk75ml1Dyekxx9Mws09CxPjXCMys93GVvjVJpFuq0aa2ZpyoGvu+xz3nWu8c1xqr98dxxLnDK65hliRAN2qFPpQR01wgGfuJ+CHRhLdJwIpUfy2wBTZqnRr0YLTsu/PPZmTc6umQWHkXlMg5p7xVMzAui1bV/poKvaeZaXhjoN+ivvppEeovzsO2rw8t9w1vRZbnEMDc3nZy/iW2iaDEYaHT0CjZJzPwU7oPT7+bxiSxn1PafbhBo6p1kKyH3jwejQKCjqtRGUFpzlpRNPw4glpzzLo7YAeaPogdwnY0m6DWtdtRE6OobG4Dlxb2Be8W4KFuAk0XUOsecSse/w6pLgeCmYluWRPaW0bL5vH9HK0G+n81lB0W8JzdgsOgR0rcENYPvHQQvD9wW0Zl3AHFB9ChzeP1cOwVldm0+1UvAO/QLOHI0k+1Qr3dsYL0A4ZU35b7d8U2ljkF7xqY6jAygD2IFDp505RIT1O49OqWGEmKWsHJ3PUL/VuWKfIspPFFTPOgCcP4tzxTxR4TjmEI35j1MGM0JBV0CsQTKfSZPD4bym75ya/Gpdx8raGDhzXZgzdNUDQOxx4YZdwZFXoLJe03SQfYeLAAjA7DnbpeTAltH31P8N0KK/q0w6BWBYG4B1+jt8T1I7fNrPUNEjIVXxR09OD2PHPsqehN2gWjLYcZy7MLSu7D2zhuqv6YLMo8FzSuG5m6rKFqzV0ySIpOxH1vSbsk992K0t+QbtrWztDvpJ1trSIiyKIpT1II9ygojWcPBxKB9wuX+a16wosadpmlmCPl8jhh0GgzoeHDwgsBiZSmtjs4cyqzmdb72XqlZrVJYIBLQzfRaf001UVnMG7x9TGgpKv+W/0bdXBrui0vUQfB/5U2OCRoTlOjHaiDJNd/mp+kw4xRh76lR68S80BswsRLKhoxHZKYoilM+iq1OJ4YVRrGABY+2fX51i3k+NcOYRxhpNhbrqOZVKt1UaK3TSXz47q0ZwT9kVWZ4K7jE3GRMFg3yedg9x5DA0cY7Vqfx3J9Q3WYxwqLncT9MpyDum8/xmFiJaOLMKTN0sDAxlvjBHv/PolJqcXbM0V/4HPWLKD+JWl0vkdPv+Ukn7PleyTtMF0FTg3TRnlyg/II/mbLIX4FTqsyAiSdd8ftJtcGg0TcZTgldVkSo1aLkfaTJmkTZP31UBZkTszrxznGgjxGdRQE6dYqVlq0AZTXPlr39ubvjvpOnoexA1w6Fi34WOamYh7QWHW3UaEB+ja14tIBp7r4IFGi3zcwi2CmBHp9eig44jQ993uHTQFYSrlx6kufJqhCst24uVeqE+9DXM0ogdKRuA1qwnwEBlYcCwAERQIBSHgK7gHXnuUHk1kqxrNctTJ4I9kW+nrKlHHHi3YktpY6huAs8JELpjfwq66xxCROziRybXZYSwT2V5GQWlcWbdMGBejXPl55Cev5xZPCOK+A0NDaECAF2BCeYCIBzMiHraG3rWAeQSef91vznJN3t26rvJOtbfeXbquzWA/rWyHpRFSArLdf3Ltbvmg/1248B5dnWf2Xb9c/OCnaTn4YEjR8NvsP9f7Bh37Dv6fVWLBMFIUg6yPE0FVPYUIqloCehvwpa6okrDQP+gewU4KZtKgVvWS5aXADwsG+wGUh+/cMe4RAzXLIJDl3SfUnNWs3/1nrIm2A1D5pZq7JRrA8BTALiX6Plwrk1btd3aSmtLvkF7K1u7g36Stba0CIuiCPUxyaOcIqgqjgWH8jFH71Q0VlFiT9M0Sw19PchA/buJYdh4tER+MVRA6tVpAp3FOT5PcuBw4OhKjeS3uJ8amT4izUGidC+uU8roy45ST4RbUWlFemnesaf/pn80wtEE0DzbvZUR6x1tfSaiN1x3rjiVa5xTqUY4Rg6OF3oBRVGEmsp0jexgwJH7qmCBdyqJkimlj+dUwTqcK4w12qaKsnlapzF1D9ofSg6n0lhVEwBGB3oBZpYIJWmE5m8TS+MQGr8KIm1z4l8atSZiiGOmDoFnXagRDujA/TSdYqATJsKJMItROaKZT78ZHXSMqixPmS9qhGNwz1gdx1Kp+JfL80+qjDtVUN91oCxn88iAVPzrNnGKiTrxbrqHMIfYPx4QRXwcEkCty0X1mggYXUW1iFqz/jTK5QXRVm8rcz11n+N+qo2gDI0sH2V7DqZO5eldxtbQOf031/dWA9fn7JqlVPzfikDZoMrwZ75rGgjPTTRYqwvgVLLmVBvBO3lD50dFfPbj71Xly66TfUqF6rI85n2bRYCBf9OlYWlwvChHRcepUFp2PYEvpeKrEa7nlxcr9VooOp9xKn87YF6N1gcOo1ZXoHF+8YwKhHotFAXv+C3OE40k/9lpoejextzj2ykQ6nOioUbr+QXgROWOfYfT9LMsj3NIKciAQFq5Q1k5FwnIM2Tue2F+fMZVOmC8eA+1BTizGMvPdo4Oev/pabRaGVo4abybMiM0yp/l6VkHDZ3I+3X95yYg/Sc7REdv/LItIcLPfsD3ZX7r/q5ij6pWblZ77mPXKHPnUOOx4JADYv7QaRMwphpAOH68KVGCN7MEBNO1ovni6LqQoqG/qRU2zGIqAmk//M4PekwLQMZPe81Izqpr+s6poZVv2ROruBDgIXUBNtKXuk2oAczYv3q+sSpEyl+eVzrod1bsLdaXAsAKwgHiz1jT7qC3t+Zbu4N+krW2tAi1PmaI+gqtDSdYD2WleWleM9GcGwbMC4ciNE0zS2iY6oBCl1J6HuW0yAejay6uGigc+DQf6VfaNJRXDhk1Mhe5yDsHiaLSXKeU0aPHjgfnKMtjhFhR6d8I0ks71HjMxi7dHA5AukbJcP5/LrXLMWL98+ozYQTgrKlTuftgmm+tRjjoNFHMq56dHebK9T1SB12dSvJSORgVLABkoBMl03xe3k8F61SVWJ1KpWwSdVWnUimiGmnVskHeCDeLwlp8ZxrloXBMVBDJi3+pI8dcIZKgpZS8kr4a4fx7nF81wgEZ6DBfzKKAFAJ0Ou9hmmR5ynxRI/wSl7NrZon2gT4/9EgVTtpzqKzn2uOF1EFXpxhNBcZSc3YxsOmIEpnFyBtU1R+J6rWWZcryVJkXQ4zrNA/U5+y+K04xRi7XqQDUJU4QDcAM4OjUTqNr6JxmaW57lkdHSJX0vSCSWSxvluVpmTg0FRhLTQPBiKVrdQHmJfu7qvMTyaJryTocVdaclhvzgmhq+JLriyN15/BlwfG6rGeaZoAKvZZgrJcepICuzmcVvvJipV5JX+fzECnPpikbZhEAYs9QwAmnDQBI2TVe5VvBO/YavoVGkhVM83sboAMOs6rze9FQKjzUU+QGCFW2SJanpVM5X/75/jdCJFkrd2ik/+JmSjD6yiMAQJyXev5S6o6xPL3L2DBPhsxJdVOUOQA7jXdTR9kr4r8iZx3nC6D09c/Ns6PHjgcWRpbH82zi8q125OixAJY9OmFlWCvsB2qDKIuQ9c18gykAeIZwpX6nw8WxUDUHcNoLiwIo6loxi1R4ABoPKFINJbCgnpieMCmKY8eTM9Y7774k6iwp1xbAxt4z7djxprA+ruw1s4ZWrlF6QHCAcQUIWI+c1Wg7PDphZaKTVBSFPTxgeLJWFQgdJGVv+faz3o1n3Yfd2pJv0N7K1u6gn2StLS3CxsboqBH1JSp70cOTg2Goh7LSLZUupQZOPZqmUnrVASXnC8dQDxBPEdRcXCJq0Fu//kA8HL3Kr9KmMXAizTMamT7yDm1aI2pcp5RRs9QgJkKMAvOXuk1ISoP4Vrg8Uy3jQ36q0mMxYlGXp2vEEVo5kTw1Hjz1D40Bs6hkj2NDZKfM70qpy+pUXiMCalme0pO1hF6WR40Bs+gI8X4qWHepRGfVCFcthHoHrUagNEqm+an8nSiDWRqhUAMQBgDGno6lWarGr7RyjBMi01pKyUdR1KHhOfgGykaAkk7HiTCLDAAiFUoJ1miGlolbKMrjF7ucXX0H//w4QlpicN/h0kHvPSh10NUp5jkYS83Z9YCcOpU4A0T0rxDVawAUugoGwrBQkTmaF0RbJ04xDAvup2wEpW5meRS1xKjM8lE1dE6aXkfDIbi857QaQSQzC2XNsjwVKMMRYiw1DQQVY7pWFwAcgsWgaSD9XBRWc47Z+2Al6D7lBdEU5AEUBcTrOmJ5cLx+1jvV4SC9SY13HC/VyiDiluWpEa61nXFM3mpGrFTnM/usAsloC/j5pYATzhaOgqZaebFSBe9w6gA2NJJ8n8td172NPYFvp0KKPuUJFoZSp3lO2AiqXZHlKcijoof1GCEKJCgLQ1OJPu2AUD+/9PwllYhn/MId48I8Gb4wHUsFLYm4c52yaxTYzvL0rGNfBhhU8AsWD8/POgaseWDsijAHuYeCyFy/fNPeRIzNLK4/0k8IWCiA13j0eFJN4tjxphp1fzR+lFFmFoEH1qCvVsH5q8DgMYm8b9t3OGgTnNZpTPIcxbHjgRlG2o+WoSV3HE2FSUKj92w3XY+ArADj3ibiuXT8Hhq3ItFJKorCermzRwFg9sur+8wOexjpL22htSXfoL2Vrd1BP8laW1qEh4/EjZeorxrs9Q5l3exVOV1pXFpHlqYRQ6Vpgqhr9IzmIxCai6s5pVmeKhH751RHBWO23qHsSyhpHiUOLddpdM8sVYAnQqw5VhwkGpGi+fxnLePDuKpTQBkcT1XXZyK3EWdNRXzMYlQry1MjnMMXIAPDsSgK+8OTqYNeL2qNk6iU2vnrdyXXYYSbWTCAeD8V8VGDRI1wNRhwEPWgVQNXo2SIB+pYAkyZWZLnrwYguYYc6n6uXShOquodYLxzHYJ7ZhZq79LVCAes4DplI/iyWzBfzGJeK3NcKcGIP2Z5ynyB5XHefa8npYHqPaeWgiJqouvsYONRK4rC+g1JjSR1iqESc69zWsjZVaeS6C2RvCt7RVGtPk5NedKKmHrB/GdMNXXBi2ohZGUWheC4n7IRrnSCaABmKqyEMTrZqQTrdTSlZWtN+XrPqQJl7A2MpWoLeAErFSijEgArOBnBAAAgAElEQVQAmKaBeLaLlqyDoo/jq/uUF0RTkIdoMVHfe0ZFB/3avmmJPNKblOWD46X5w/qcaoQrI4S5uXhj6aB7o1/nM46FshU4E1+cnY6JAk68NwCQplp5tovuDUQTOU80kqxslywfZXPWxvXIN2N+qZCiB2wBnPS9AZyUjUCUO8tTkAea8ZfvnhD2YmWn6HOqGKemEn3SsRF8LXoFeYiiMpZn3Bkd9InL0rFU5gDgP++m7Bqfz69nHd+AiP3vno/zWVPydB1zzncbuTxUpNDgA3sBLMIVW/bWCLABbEBhx5nVOX/02PGEubf/yNGaSgnMi427ooitWdTWoJoFLAA6wBNVYFgHgOtvb96bpJhohY1tew+HfQBGCKX6zu46vobOP0tSqDzbTe0t9iRNXTjLieJx1mkddkC/hrfet6Io7Pmh6dmjADBr/Ge9Z4Y9af76XdZWWlvyDdpb2dod9JOstaVFePBwjFIQ9VXU/Id1DmXNh/zy3TFaAH34J0/NqEvT1Nrbatjx33HwlGLnDRyNakJLQwVeRZfMUpXf52asq/nv3E8PZbO0hrbmUfLfceIeEefBLM2zBqF+X5BtDpJhC9+3ek0j/lqrGcdKDQbUWX3EXx0aHCuiBBe48dHfUyPc5wiC7hdFYX96KnXQNUqGkQN1Ucdnncv9BGDQ9+Z51AhX8Ss1wjWnEiOcPFOzKESW5WmUDFEcfXeMcLPIHsjyNGqNA8iY+Ln2Y4nIq94BQAHXaSklFe5RY8ssOoBcp6kLvpKCAgwoPOOwKRthlqRRKPNl2aZyvZ97z8S6RgupK/75KS+nQM/h4pgVRWEvOCNJnWIcK95N9xCfX69OJdFbLb1I8w6UCgbiWADKtKR6rYJ7CJtxv77CwvmVy10HcFKKKsa3VwlWtgVtutCyT3M5u2ZpXrgKlGFwMpYawaNiAV1V6Imus5drzq53oLRkHePFN9dofncHrihgxn0Yy/tGvx0cr98/nwploiCuY4njpfnDyspRI/wtoWXjmKhYqZYNVJAHZ0WdIM7Eca5+ugJOjCUAkLJrfDUR3Rs4WzlPNJLsxccUMOP5gjq/MJW8Hka9Cg8ATipWylzP8lTrA2rzWV3Hh/sqO0XXq7IwGEtlfeFc+Si/nr9EpLnui3eND/Nk+qp0LLsJcwChWd5N2TU+XUDPOt4J9peeu74MJuuYKH/nYUsDoNDxtSUB5GAP0RJpqlZuVpv6RMRfgefjx5sS4P6DfUdqqk4AGmAHkG9NSggArQr0ZnkE/7QkoVlkdUxfvT38JnvKOeK8w1CDLaYAwbCF5f4B827xxhhd5/cBozU9DKaWAuO+DCZnHWvurhHLApgzfNEmK4rCXhvWkAQ7VLRxnDDoVGW/rbS25Bu0t7K1O+gnWWtLi3D/oWjsE/VNUPM6h7IeEufWKW12WTM0Ta29rYYdzhQHotbpnLkmzc2uV9pMld61qRGtec04U9xPD2WzNF9LacdEJ7muuzgPZmlEB4NdnSkOEkXwtanhrkY4oit6WJEC4A0yRFvMorGDIefHRw0Q1RiAZsyhjuGoCql0jZJhFHA4qnPlhYjQGDCzUO6M99MUAEANjMR6vweVUA9aFfrRKBklxXQsyR82S2nnGrVGfIkx8WOpavwaaSWnEocGwT2zKFhDV4CBuVJPgduX3UKgzCzSKpnDykYgysHaphGRP6fbhLpGC0ZclqeloBZUTBvWRZaX0c+iKOzlV1MHXZ1iNBV4N91DNMqf5anqNSkkSlmnYRTSlfmC0c51GqG5zVHcVXCP9+I6FS8EFNT3NkupxHwDrxKsKQE0pWXXEy9UWrAq4vNtGEtNAwGMoqsKPc40z6g5uz4dRdObcHYYG43me0E0nc+MIWDhA2NXBMfrtpfSagYoiOtY8g01f1hZOWqEK8Dsy1SZpfusgjycX6pMTurCPCfGqVoogCc4e5pq5dNR6rEw2Ic1kuyBqgWyHhUIyPKUqeQZAsrCgAHGc+reoFUJVEQMBtgZd4yrWzd6tOwNysKoV+EB58pH+fX8ZQ7xjGd3jQ76gnXpWOr+DLDKdcpU8mwEBZxIdWK/1XP3Dy7yzjom3eGvQxcHxk/nYUsDGARbg+jv6m37A0D9hTvSHG6o8WhAaFoH7XQpU6eCnLo2/e8DAHAGe6E8ctPZb1kHzMnhizbZhh0Hkz2F6Pf01dsTFXWztBIAZy+aCgryYIsARitwBHNPmRHKZsnyCKZ3l3FHY2fk4k1hrmg5Oj3rJknZW8+uaQutLfkG7a1s7Q76Sdba0iLcdzA6eAiPaf3ZeoeyRjVVeEzrKfvSKmZp7W017DA+cZwVwV+5NaXz6uZNziOH3LdEpMYsNb41ksi/5356KJtZEkFR2jHOD9ep82CWRlmIEGuOFdepgaBND1FVCcZgVkVzmhd70ygZxg5Ol+YImqUGiBrhpCJwyJECUBSF5U+nDrpGybzAlz6Lz3MEYDCLBjPvpykAKvamRrhS/zCE9KBVNX6NkmEw61gy783SqLZGrQdWUWTGxI8lxkWWp5FWoi0ocyO4p9+HrhF00iG4n0+nAADL8hRgQDmYuaqChGpE6ViqAYXRr2OpAoZaCorIyOnyLMeON1lRFDZsWOqgq1NMri/vppRgD8ip6jXRW8AVHUsf4VTmCwYc1ynI50XiVHCP9ch1CvJ5xx7ASbUqAGW8SrCmbdCgwF740KSESkrTqLYKlPHejKVqC5C6QFeBMlhLzBNNA/ERzrFLY3oT+bd8c61v7wEndSo5R2B2PDzunWBM3/5aKmZHU2eTSKOyj5SVo0Z4BJgnhm9AuTGzNJqoIA9pBpquAl3Zl1JULRSiqIAQylTy5T51b2CNcy5oJNmX+ySH3izOZ95NdRrMLAHM3q9T4YHn1L1BKdCqeq1R1EvrlLNUVo6yMCbVYSoBJHpQWWuDk5bBded0mxDmydvvp2lSuj+jQ8N1Crz4CiKj5azDlgH00HP3Di/UV61j2A23vvRW2N/vaFganGfSazjX3v1gfyK41tTUFM4LdHQAddCwUBYhkesVW/aG92OP4KxkfrL+NcpvVlsWclE1n0ZIeUezmDbYf/rasL5ILcNuanjr/ZBWCCNEz2PSEkmN0+g6a0sZIZ8W4cUsT5kRqr2R5ZHRwv7V4dXF4RuOXrI5zBUVl1XRRnRTvt19St1SwB92a0u+QXsrW7uDfpK1trQIRy2KhgDGCMjp6V3G1q0xraWJVM06lGt6eHJdmuactTEKoU4xkXUMAEXwFVH2mzf5vVAFVYnYLK3nq+JCPD/300PZLK3nq04TzgvXqfPg3w8KukaBuE4NBG2ay6tGOAJSGFF6ePuceTXCcTY5iPz4qAGiGgOMK2kKGI5FUdjtz6YOukbJ/ujqeuuzmEXRrSwflfx3HGzeT41wdaDUCFflYZxVPWhV3V6NNeaoGqTMe7MoIpPlqeAeFGqooH4sHxRBLo20YrwDWGkpJZ9vPV6+gYokZXltOoWyPBRg4L9jpKs6vzqPKgSlUbJ6lGAc+CxPnWIilUoZbmpqCkbS5zrHlA0tE4ezybspJdgDcjqHiN4COOlYoihPV6cY0S+uU5DPU9XVQWfdcJ2CfJ0cNV4BJ4xnDHSvEqz3pCktm7+peCEiU1meCpQR9WUsVVuA9Bq6ihfitAAcaRqI14vQeUnuJ99co/leEE2dSvZoAKTu46ODfvfIZeEa3dsU1MMxUfbRXseioSngdI7QjGnqgP/y2eigA96dJ/swTChNqcnyVAslMJUqp0DLjXm9CN0bcM40F53mmSSIgZlFVhHzyzO5tFa2sjAAVb5Vh6mk1SuIwppZEAo7tdPo8A2UnaKsHGVhANZ/qw5TyUf59fyFqcRYfuWeiWGerPsgjbwrqwiwnvsp8OId9LFLa8vEAQDdKuducxohPOPNLywIQqx3Dl9WU/aONblu+4Fkrh45eixE/NHV4N95NXYzS1hNfAPO5it7z7Rt+w4nc94/o1ltLjdpZCrmp+PYffw7wekntQwA/pbBC8NzI76r1VhQtic1Tmn77G8K3gUl+ypgoMwImAZ0WJ580/8Yskjq1G8Jc0Uj7yraCBPmgocmJSJ+baW1Jd+gvZWt3UE/yVpbWoQYoWqMnKjGtIqu1FOD/uf73wjOmBq8WoP2tiHxIITy6mt+mtXWXdfNGzoyz6g5cGZpuSCtPU6uNPdTZ9jMEjV5RXr590SBvAOq+eDkUerBxfUakdKmpZuoBWoWIzsYmyric8ypsWuOI3XpQc/9+KgysYpqYYSTGw7boSgK69YvddD1oPVRRXUIzFLDWBuRSt5PcwTVgFEjXGnegEF60Oq3UFo2kcpzJKKg+fDq6ClFn7q+GoXSpoJcKsyG8c43UqVmT2NVlgp5qdzPG+GqQq+Ce9CnMcZVsdgsCpSpEa75i/UowcrS0OcnUkmnBjdGkkYqVWka54N3U/FCNeYw9mhEb9mz9FnYe+qtWwA5nkefxSuPK8UdY5L7KSWYNUKHlm0WI5X8XwX5zFIwhwbYofvxDhH/U0dP8/mhsXMvZUYcLo4luZhKr2a+ssaVErzKgSQqcueV4XXv9HohysLAYGYvemziyjBP/jYh5teryJ1ZpGWzdyr7qKmpKZwzaoQr4HR2oBlHB10rQyjIQ/USjTYCtHoGkGqhwISBlaPlxjY5kET3BqKVvJuC1h5wUgYAADLfXNOazGJKR5anJSQBY3DGNV3g23L26Jmupa7qlbNUVo4qp8OEUcd/vwCJygBSpximEmP51Xujg759b6q3otR+WF/cT4EXGDt0BaNxqrEhFPxS7Y0sj2yxWKprXlgPXUcsT86/bfsOJ9R0Pa92HWgMyv1eA+N9p8au32bG6u1JFRGuubL3zABssv6Hixiafnsd96amppp8cajjnV5bUuP0q8gvLEMV3wWEBrSgUo8GKQAKFLwDbMAuU2YEv0UHjGb/umXwwsBAGb8sOug3DojMO2Vowfo6777Xgw2gpYA/7NaWfIP2VrZ2B/0ka21pEf6L1Aglj1LzpusdygucMUxTIRQMK6VpNjU12f1jVtj1z80NFCuzeJgQQVa1brO0PJtu3hwmOMyXSg6cWZoTrnQ+AAbup4eyWWp8q1gWhzgRFu+AmpXUK3WSzSKFi/tpRErb6CWb7Zq+c+z65+YlEUyiZ0RMvBHbd9pau7rPbLv5xQWJgwHVjAPwe4+n4/P+7kN28wsL7C8vL0qiyAAfRAKhsRZFYUNcbrEetFraLMvTslRmJRPgV33nJFEks0g55f00H14FftQIV4MZJ0QPWmUWKH0XR07zUDUfvvFoWULmd8/PT/QToJwyJv/q5poagaqO3NvR2FWp+cjRY9bh1SV28wsL7M7hy5IoLDnt3M+nU0xbtd2u7Tcnqc1tFkV1mHNe7+CV+Rvtmr5zkv8ejMLbx9SlBJuVQMOv+81JRAE1ss49zaKDPmTOeru6z2y7YcC8pNwiqS68mxcvVAq4vh/rEqfrNzKWx443WedhS+3qPrOtw6tLgmNlFtMkuE5Vr3cdaLS/vLzIbho4vwYEwdjnOhUv1BJfn+iQAk5qBJd7SOqgH2o8Zn8dujgBZMjL1dxJFS9samqyu0cut+ufm5eAJ0QqGUtlmZiVhuzVfWbbH15ckERToaOzdlT8r6mpybqNXG5X95lt/2foogQAUrE6v3dqlD/LUxYGedZEFXu8viqJjN78wgK7us/sxIE1i2AHDskYxz4C0FAjXGnZOCZaTYSoaZantcfZG/T76ZpUAUlNk8BZgH3i01+IgPq9AZFTzgWNJB85Ws6Rq/vMbnZvoD/umFw3yN+1BCPAA8+p4N33Ho+ghbIwFDADSNA9QEtzqXI6YMd5rsIDTSue6BzC+eUZz7vv9TBPiqJI9AGU2g9bjusUeDl67Hiit6KCt6pfk+WpOr/XtWAdvyI51rzHPaOWB5CNf+urMTD33/1gf3DmfeUDBDv1jFcdIED8Z6e+KyKbE5K0DrOYpvLtai5iA2jQYu/hIghJsg6Uvu+dfgXuWCMqvstaY5+mUo8veZvlKXjHvARIVWZEX1edAzuF/evmFxYEds7E5VvDXOnwSlyrytBSvZV64N2H3dqSb9DeytbuoJ9krS0twp9LDVqivhoxu6jOoWxWv1TQ6m37wwHP35Sm2VxD8ZUNU9W6zdLIu27eGMkcyt4BVbEjpfNxOHI/PZTN0txgFctS4ZosT0vmtNRA0rlu4vKtJ75IGtEIDnxVam6pEQHgcPxBj2knvsgs1COlQ2Pl8HtMIl5KvdQ8bIyI1jQMVN5PjXAV+NF0CjNLooNZPipxAs3iHFXFdfJyPyv0azV+m2veWNN8S7PIcsjyVB3Zl7pSpeaWmhch8+kUzTWNhGV583oH2pRW+aU6lODmms/LJeqjBnW95kXILnKCe7AcsjwFeYhWfSFEXOZaaxoOC0alql631AA7uJ8qiONIZHmaLmBWSydd0AqVYMAOVdMm37Kl5kXIlGXSUlPBzixPWSYtNS+4pXsnAC1dgQSMbyLeT0xafcJ5Yhb3Tuionn0EPVyNcK3fXK+aiDLAlIWBg6WpOMqMUFaOpklAJyYi59NfVKxP9wZETllztzomV3ONyDvdM7nIx87ytAQjY8hzKkinjByt8KDUbIAGzwjh+VU5HRBfmUoK9Gg5Qk2TgE3Hdf98/xvJPNG9QyuW4CifI86eNk33oJ65WWTT8W7KXPO6FqxjrfONg03gAGdx/LItNWl+AH7z1u0MzDBSoOicI3rGozkwYtGmsCfNWbsz5PJ/quPoMHcR3ATwo4Slivbyv9dtPxDGm3UAg+HGgfNqnH6zyDigqy4HTDhAdS1f6+uwK3gHQE+OuzIjfEUJ2G7sXzcMmBcAjDdWRAf9obER+FCGlrJr6oF3H3ZrS75Beytbu4N+krW2tAhvEhEOjBEtbwNK6w9lpVXTMGDU+dESUM01BL2gSKmBYJYKrWkOOjnWGMTeAdVIv9L5cCS5nx7KZmkupUb6oQ7yfq11QHk+7qd08tY0ctuJkn3BMQyaa4MrJwJHVimILbXuLkpGOgKH34AZ0enUg9bT9fq5SHlzDeOI91MK4kChGaoRbpaWS6p30PLfNSJEhOKT4ty3psHyoF/eM51rGj1RKnT/6WkEQI2Wlpqv3euN8Oaar93rI471mjJmVHn4RA2tCjraESdyvNBUoHtxRwXkdI0RvcXwVdXrlhoODWWQ1ABsqcEe4jrdQ9R4VzqqmSVl57I8ZZk013hnrUetlODmmgr4ZXkq2tlSI2WDrpTglpqmxWR5Wm5sqxP/entzPN+uE7HHLC/ZR61x0GG6APpOcOAmDoMa4UrLrldNRFk5ysLw+eKfdMwIZeWoijt7FGCAT39RsT5NtSK9gnPBM7maa17rQ9NAzFJa8C45f4lmM6aaSoQTmOXRqfPvjCOl6SNmERRUMU6oxMpU0lQiTf36tYAkMJEYy68/kDroytLQFCRAZe6nwItZCggoQI7GCe+m6SM+zQBbgLX/4yenh8g9gQPm+eA5G2qqMeC8j1u2JbACPYBLdF6rMfy6ivIPmfdeCJjMW7czyeWHScP6x3n/ZIcSgFbRXtIH5q/fFfYBmF2qwA7gplU2/jQ4nXuqy0GghGobKnroy8NpOho2JiCRMiM8KwdmFM/9635zgn7ApHe2hbnSd2oMsihDS9liqhHQVlpb8g3aW9naHfSTrLWlRZgLFYimOUOgov5QVsVxmjfQsjylaTbXvGqwGghmKZ1Zc9ehOeEwe6dp/Y5o6CudDwMNx1UPZbO0dq1GiH1d1D7TWueAQsPkfprT2ZrmRZs8w6C55kWbVAiqpdZclIzDb+Rbkc6tB62vN+vpqs01zU33BpSW8VEj3CzSX+n+oNW/0bwR7qOfzTV9jixPafNmMWqHcUbzeYxaXrCldoszhOqlU9RrSlXFGDxRU+P1DKFhnqihzkvHqDyR4zXFGb7fdpRgBeQUmPDz8qaBrRvLf3cOjVe9bq5d6koa6R6iUX6fcnKeixbpHtJc0+gsXSnBzTV9jiyvZZk013xerlKCW2q+8oA6qlrTOMvTlBOvlv/0m61z0InOEknX6KdZXCdqhCstG/aQVhPRqKWyMNY4RohqfdDqzT3YFNzLp1pp9FbTJBDj5FzwTK7mGvnLdJ/morRgLcGIY8Z5qewajYxqxFTFOAHvNO3LLDr3KsaJc6dgvaad6FjqXgoQylh+88FJyTzRs1DTfhDc5H4KvJilNo2mUAGE8m5aDtKX1mMdo1D//R5TwzcE9CMN4olJq0MQAx0AnPdBczaE+3oAl5QoTfNDnO25GeuCxgjfgCgwVHVAbHXedx5oDOO5cdfBkEI0ftmWAOLD7OKc+2mvGUm5QhrvS1fxXYB2FN5V9PAaJ8apKVS8E2wYTTXyrBxYnuxfV/eZHc68KSs/CHNl2IJo+yhDS8HoepWGPuzWlnyD9la2dgf9JGttaRE+MCZuuDQVZsNg94eyCprRdjmBpyxvHU1zrjsI1UCg8Tc9eJVm6g96s7RWttL5VN3dH8pmqeCURohV3T3LWx8h1pJeWT7K3hRF/Na0Ra5Uz1mOYdBc49Cmaz3wllqP1+tHyTj8ZqyKlE09aFVEJstTdL2lpkq/WZ4a4VrGx+cqc8DSN+xID1r9G400DHo9I7xe83RHD3aoIatR38Fujmp5wZaaChViuLWmad3hLG9dOoU6xBhyrYkqbNmTAnIYlSdyvDwg991H04ijAnIqCumjt60FO2514oVKjW2pEZmpt4dolN+nnKgwlt9DmmteTCzLU0pwc82L43mWSXNthGOEeEpwc81H/Dq6vVMjpppy4mtKPzv13VY56NCyYctMcuAmwoFqhOt8himzcVfcG5SV81thYXiQ5DQHvJjFPUVz9gFCcbR9qpU6h1pmDa0Pv8+eqPlUIq95orRgLcFI9JUx0WoiKMpneRoxVVsANoJnhMBQUeV08peVqXTMpRLV29dxEBnLCx9KHXStoa5pP5xZ3M+nv+ieomU8AXh4NxX/U1aUrmNKdX3n0SnhzAP0g2rfdcTy8A7oAJDS8MSk1SGFyQuFYpsoCA8g89TkNcGWIPWOb0ruOOXazGK5vVVb9wVmzqbdh4Imx+A5G0J1EthIsBm/32NqYH4o6KfaG/yGn18ACip66AVkdW/AluT/quihD/rA8tQ0A60uwFx58514Xqtoo+4NACi6N3zYrS35Bu2tbO0O+knW2tIifHpK3HC1cdARpfSHspaxoB0Qajy9NTRN74CqGBiNv6kQzSuuVqx3mpqamsLBpHS+B50SsR7KZil1VyPEviZnayPE3gHVGs2tad5Q8AyD5prP31KjsqXmD2EYBhx+KzbF76W5yh74UKp3S+07j6Z508owUGEan6us+br1Dlr9G00dqyyvjX421ya6vGlVvfb305rlLzsWg9aNbqn9xQnutTadQnNJs7x16RS6bln3rYkqQCWmk0pwIsfL1zr3EUezOJZ/lqiij97+oZVghx9Lr3rdXPNjOUL2EDX0Pu1YGBe79d6aMj5+LLM8ina21Hyt828+OOmE15hFoT76r/u1zkHv5yJ+Wm7MLGXDaJqEB0n6TlvbKgfdsxE8uAloo0a4Lz2Z5Wm5MWXDaMqJsmCyvL7WB3/TPHPPRrisZ63WB39TcKi7K7PomVzNNV95wO8N0J2zPD1/L3H6FJrPr3ntPk0CR5OzVAW+9Hk0TcKzEbI8zefXMdEUpPEOCL344cnJPFkn+3fPN6KDruJnWV4//YW/adQX55d3U/E/r7FBygaR9QsfmhRKlfJdcZSVAUU1BgCZu0YsC7aEZ1hxnab5dR5W3WPCykAHh25P/XaE2XT9I3w3690dwRndsudwEHN9YtLqcH8YIQDiFz8yOdhlCvr5oIiK73KOA/ao6KG3DXRvwMEG2FTRQ8/KobF/XfHUjCAcOH319jBXlr4Xgz4q2qhgWb294cNubck3aG9la3fQT7LWlhbhkDnrazY/s1r6sD+UVUmW5uubZnnraJqKimd5qshL428q+OWjQPWcJvKi1Lj2VFk9lM3SHHzNo/RU2YGtjBB7qqzWaG5N8+PTWiEoX/aotUJQ3hHCCOfw27YnGkkqBHWfU6TVw7ul5sdH6+xqGR/vfJx5Z+qgq4K9WerA03yUrLWCe5NE6CnL64Md/E2jit54b60QlAo9ZXnr0ykUOMvykvZ3olbPaGlNVMGXREPt90SOl2fM+IijWRxLjYT56G1rwQ4/ll71urnmx1Kpsb7sljZVms7y1pXx8WOZ5Wl1geYamgr19seWmgecruvfOsE9H/G7w4GbPxZQQzUhPEjSf3rrHHRVQM/ytNynmYUIoBrhymaha7kxZeUoVV3pr1me5gHT6u3BNfoUdbQ++FvnYXG8PBDqmVzNNc9U6uv2Bp0Tev56fQqtJqK5xf78VdG8LK9lhAA0aJqEppdleW2lAx0TXT9en+KS7lOSebJNIqqa/oJjXO+7+vvpeJEvTVfxP88QYh2TX3/+fa8H55l8eHK4f/lsTIdDBwDNnFsGLwz2E/OXznUKwiNEd8+o5UFsDbo9vwMApiA2mjmjl2xOovmc011HLA/rGTaSliGDnaOpZZ5JpuK7UNRJl9CzzgMoKtoIU4lUQAVempsnpCFe3nNa0ECYuWZHmCubdkZwRTVOjruyvX5v+LBbW/IN2lvZ2h30k6y1pUU4fummms3PrLZUkD+UoWvpdU1NTTXK2q2haXq0vR5Nk78pvdsfFv8fe+8eHlV1/f/PVx9oa3/IU7Wftrbf71grapVLERUEIYQECBjwVkQL1VYFrUWtH7WD4RK5X0IAb3hDrVQDqMgRAZFwEZSL3FQiKvdLQEQDhEskiSHr98fJnllnZSZZe+LM2WfOej3Penwehz2ZTNbeZ7/3Xpdookk9NPDmmj4s8ENZ/R7qgAJvrmnuH67IWxe4bUApWIMAACAASURBVFQwZJ9o67CV9CWOFmEQjYUkb5pbCEptJJSpCAP18Csvj4gJfNhAIxPe3FAc60c4oAVkcIQBDrGkxQP/RKpl0wct3twr9pNQYm7BPVowKNphh3oN597RzTuuoVAXOXEW3MPFnoKh2oImGtEO1uhhRzRKyyodY1SLr/qEF62pEK27gHoNp2XQ21tc/bku6I0j3QDGQlUVVkYL7kXbOALU9mfaXSAa+EZeGae7AK2pkE6iTGJB6wBwuwtQQUEPN1VVbLoJpwUkZ6zezRLoKmw22noDEOkAgm+0q6JswnG7MRyVg9MkqD83j1LrQ72Gw99pfYobotT6UK/hQyW6zmLxXhe0NRdudQXgFJb4+Uv9Eqe/4Oc5ff7iPPJgqHZEiKoFg9Mk6DobrdZHtPlD/bLrZKdAx1XlcSQMbccVLf1FvYbreVC/HIWK0FJ/UPM4Ut18cXidVvnwKkQcH9KpOgAq4q//9LXhfHTcChaPw2l+Kr9+yNzN4QsHtR9T6Q5KFONIEtW9Aqd0fHei3NH6Ux32qNQrNTdajXg/vE7jFASal4/TBVR6lXoG4Wcd/T1x0UZ1qKee57QgKvY/6ic9pq4MRyiu3RkR6GWnIodtdL9Ei8tyOg0lC5O0gWAjAt1nmDQJj508BS2Gvgt9n3PePtNWQfShvLekDJoNWVgrxJH2u+SEadJbTVoMDMC+ubnwsQWOHER6qxlNNI1Z8AVcOvQ9R0gVvYnDD2XFna+sgytHF8JJFCI4eI7zYT6TGcLd51nnRn8dKbhXH/QAg1sIioYLcgtBTSebHbUJxxulPs+thqvHFDr6p9MeybioVl3QAwza0k/dKtAcTVyoMNqDdu3OEvj9YGf+Ns1p4xbcW0XypqMddoxZ8AU0G7LQkZdPD0m4haCGx1lwD9/cBEO8aI14bxVOkJQWlbtan/CiKS3RbhynFm6Di3IWOFqU0dtb7mEHvXGkVa9j0e9FZ0shWnBP5VXSitHXE3/mVMSPlh7Egfaipz24Y0H9mdtdgBaeHDHPuXaqQ5R2Y5c4xCH159fW7mEJdBqWvZYcbh4sPQWXD1/k6MRBoxuUMFGcqqwKH97h/G36N4hW66Pg473w+8HOavL0oPimKLU+Rs/fAhcPWeg48FXClq6z9UEPQmeQtaHqdDV0nfwB9Ji60hFWTtdZXOsDV/UfRCJTaCoRXt8A7Bv7ZkMWOqqq03U2Wq0P65P98PvBztoOtD5F9ykrHH6CDxPHo24Mr5K1IVqtj/te3wiXDF3oOKyhhU3HLIi0YKTh1SoiRD2Pm+cuCkfnqHx4dQPdGrWXU3UA1PP4+qc/clRmV3UUgqFImzhcB0BdKDz8xqfhavNqP6aq16voCDz/1YUCjtQ4fLLC0d1GHQiov7kq/NlsyMKwGMe38jRFbO6myN9O3dir9Q+nJ9HWmvj50qdG0KtLIVoQVR2C4AsYFYXVccKycCj/ut2HHb6iDlhp6sgfyeUTp9NQsjBJGwg2ItB9hkmTsLKyEua8bd+KYmirIPpQBrBDCWleWXMSdswJ0zxEHua0GBj+eRi6ybztheg3xHQc3ejjh7Kiurq61uEC3ehzQ7jpRn/DHj2Bvps8FLmFoHA1+mBoPtzOLARFK8uqTTh++EX7fmjqAN541cWNJNeXbsJPn66GQ8dP1fI1tVlRVnKi9kk4/dvTMFZuwb01pJ1VrMMO+vPoIQm3EBTetAVD/HQK2qeWfpexoJEveBMbi+8rnKHEKjS2PuFFaypEu3EEqP1d0tvbh5mHHTT/kVa9jsXtpPIwbfFVXR3dL2lo/C5GwT2aZkBbfMWCbphpD+5YqNaNynBLpLqghSejHW4eOVlRa20YGaU+BUeg07DsaIeb0Z5D9JaMbsIrfjhd6//R0PjWMWp9UL+k62y0VKto42g+L43kigXNXY9WjLPqdHWtZ6/KV1ZGC+4dLauAQ8dq+7MqNqZsa5SIEPq7fXfCuc5GK7gXbRxdZ7OmrqzlJ9G+L9UXWxk9ZACw5yv9eXSdHbvwC8e/x+1k1TxWFwqXDn0vHCKvbn1pZ4tgKFIHABdgU2uLijKjzwlcB0BdKDw4c1N4X6ZS79RhuhL2uOCmul2fjPxFddVRheJUTrvKF8cpZapWSKwia/QZrwSxilp89M3I+kyjpvD6oA6V1ZylBVGrq6vh2+PljogidUhy+fBF4boXG/Y4Bfrp0/Y46s/08onTaShZmKQNBBsR6D7DpEkYa5NEi/NEeyhHg95qcsI0j5Y58y9pMbBY0HCrfi/ybohpuBV+KNcFDS3E+Vd18XfSAxjfCnKgEQbcQlDLSYQBbT0TC3qAoSpp17ehptXfcRufuqARBrSlXyyoj3IetLTTALfgHvU1btVrmlPJLQSliv4o46ZT/I10GuB+l1TQfBflsINCb5jCLX7q8RNaUyHajWM06O0t3gDWhaqsrOz5FTyBjitbB0PzYckX9VfEBwC49XlnFAMO54wFDcumhediQdcG2oM7FrT6O07LqAtaeDLa4WY0aMXy2ev2sQQ6bRtIu4nEgoZlc9YGGkkSrZtINGhYNrcYJy1yOjLKYUc0aDcDbjHOPiT9hVOfAqD2M50TEUKf6ZcM5RXjpG0Dez4RW6DjtZQ+0x9gpr/QZ/oEdCsPAOEuNngeq2KCF+UsCNdWULe+9NAyGIrUAVA9vbtPWRE+SFVRZrTYI46SUwfm972+MSzEVSTG3E32fFQHrHj+q/z48SjiQnXVUQcLSqyqfHH8+VWdCtwGs7q62lGTANf2Ub+TinrBUS24JgGNWqMHobQrQTSwf6mw/417j7DWFHqwjzsduI1J2kCwEYHuM0yahLEWNNoqiPNQBoBwERNlHGjVXVzUoy7oQ40bwk0Ld9GHcizyFjlDuHH+VV3QHsCfkor49UFbMHEPMHA132CIn2dKbyPUAUZ9Dz+a209zdmNBc325Bxjq9F/nQUtvALgF9zYSQcOtek037zQlJBZUVHLTKaio5H6XNDXlCCPsj4pKlZpSn5/Qmgp9nuUJGnp7i3tw14UKA1XGrYhPuzbQG8dY0J6/nIr4NCw71o0jheb69ohSET8aNIoh2o1jNOZ/5kzZ4B5u0vSXNzcUszbTNCybdhOJBQ3L5rT7BADHjSm31gcNy+YW46RFTrmHHbSIJ+6tXhc0/QUX46wLVbxL2c5v698L0PSXP0YpuBeNT0j6S/aTH8YU6DjFhR52cItxjifpAqpdmgKLOTWPcRSWaoeJb31p+0+V6qGiA7pMWh7+W6gbaDqPcZScqrQ+cMb6cOi8yoentVHw/FfRATh65XjNM1JFAyqx/b+z7cNOvA6pwzjaBhN/J7i2jzocVq3g8AEKbnlLixDiuhXc9fn06erwobL67yf7jsZVePIko9NQsjBJGwg2ItB9hkmTMNaCRlsFcR7KAM42O9wwzR9IkapMZh4lLZDEDeGmm0z6UI7FE+SGGOdf1cX9pPq7qsDKhaYApDMPMFbtcG4co7WeiQbt3a1y/ep7+D1HNo40ZzcWNAWAuwmnh0icln40z5S7CVe5hcq4Va9pGgZt6RcLKiq56RRUVNLuC7GgeXmqsFFdUFGpUlPq8xNaU+EW5o0jvb2lPbhjQUOCadXrWKh+vsq4N453koiZ/YyCewDOKAbujSPN9b3uSZ5AV4WulHFvHGm+9Xjm4ebUQufaOWcjT6DTsGyuP9Ow7OPMWzJ8UBWtm0g0aFh2rFQrCg1r5h520Boqs5mRXPTgiNvukx6E7o4jZYNbjJMeHPV+KrZAx1EfdG3g1qegB0e4DR4AQCe0n1HzGBePU892fOtLvy8VVq+iAzpOWBaOslHtX+n3hevwqOiAv7+yLnzjrS5M6PeFC26q6ABcJFOJUXowgWujqAMGlVJEo3Jw2gk+hFdF6ZSAx4fR+FlB55XqCa+MWxD1SnIT/lkxT6DTvS2uo+M2JmkDwUYEus8waRLGWtBoqyDOQxnA2dOaG6YJEOm7HgzVPrGNBc2/5N5q0oIl9KEcC3pzwc2xfpi0GKIV8euD5vNxDzDWko0jN8+UhhKrA4z6Hn5048gNCaYbR+4BBq3wzHnQ0o0Qt+Ae3QhxoxHo34BbCCqfbBy56RS4n7GOr9HaEdywP3zjqCqI1+cntKbCrc/zBA09WONGI9CDtVeYG0B6sMa9caSHJNw+u1gccm8caU2FaD24o7H9kPOQhHvjSPOtJy7iCXTaUsz6ZD9rM03Dsrn+fMVIZ1g295YM37xza33Em2pFU5C4hx20uNxbzG4ZNNWK2+6THoRyIkJoZ4hoFfGjQdNfrn/6o5gCHbdgpC1FufUpaN0UXBkewClG1TzGh7xqruNb31411cyVqVoAuD2b8mt8A4075+B2iSpyqP/0teHWourCRIXbK+uN5r865MUV+tUzkq6JuDaKmjuqywCNysGRGO8VRQ7h1YGmOkSgh9FqDBXotH0t7UoQC1qfomh/aVx1LWhdAjcxSRsINiLQfYZJkzDWgkZz/zgPZQBnKxdumCaAM2cwixmmScPCuLeaNOyYPpRjQVu5cHOsaSsX3FudA83n4+aZ0ny+aJVto0HDBfNrqvPW9/CjbbC4IcF044h7q9dFF3ISznnQ0o1jh/G8TTi9ceRWvaZ/A26eabQbRw50s8P1NVo4hytomiFRqSoI1+cnNG+aK2jo7S3twR0LmnpBq17HQoWvhgUN88bxn687b94PMfvsYnHYnHnjSGsqXB+lIn409pY4/wYqxLU+qKjkHm7SlmLzPj3A2kzf9oIzLJvrzzQsm9PuE8DZupFb64OGZXPrU9AIJ24kFy2WyO2WoXpmK+MWkKQ3jsVH9FM2uMU4aVeCG5+JLdDxuvFekfPwjluMkx4cPbHE2YLxBlTAVM1jXExQhXXjqJw7SB0QVYdnywH7GXLl6MJwIUl8A61Cw4MhZ7tE1arz1ufXhHPi1YUJLWyI578qDod73Kt5QA/TcbqQigBQhxc0Kgc/Y95HUXIP1PwcFY1FD6PVGCrQ6TrLXZ/p2vD5AZ5A7x3jAMUETNIGgo0IdJ9h0iSMtaDRVkGchzKAsyI3N0wTAMInw8GQXRiGA709upsZwq2qk8Z6KMfiv2ucudkLmDnWtPo7brXDAfd+DYb4Bxi0EBQ3z1QVnlGmDjDqe/jR4nIrmCHBNAeNuwnH0RrB0HyoZDxoaSEo7iZ8G8mb5kYj0M27KrhXH7QQFDedAm/GgqH4iztyw/5wvqWqIF6fn9CaCtzaEfT2lhuN8PwKZ+RLtKrX0cA3T8FQ7fZ/sfjXLOeGk9tnF4dlc9v/0ZoKN8aoiE+hfwNu+z9V6EoZbq1VF/Twbv5nX7M20zS6hrt2xgozrg8cNpsWo5sIhUbX0LZ7saCHd9zDDhrhxI3kotE13HafdJ09wEzZwNE1sSriU2hU3J+fXRVToONq+YVbaDFOnkCnUXFPLXXuBfBtsZrH+BmiXsdROaq9mTJVRVw9Q/404v3wPgmngeF6C7gOj2rVefO0VeFb9r0lkf0YLmSHC26qZwgOIVfPSLqXwelCmTVF3lShUhqVg/czhaizhfq91d+ddiVQY6hAVwXrdNdnehD65cFjrDWFdtngFDJOFiZpA8FGBLrPMGkSxlrQ6CLGfSjjCsbcME0A58YI51HVBd2ccm81acEv+lCOxRtkY/ReEU+g0wrG25iiSUGL6HHzTGkRvQeZeabvkOJFqsdrfQ8/erPDzXGkRfS4opKGqnEftLilGLfg3k6SN82tek0379w802nL40unoJtDbnFHWtmWe+OIN4cqsqM+P6E1Fbi1I+jtLe3BHYvpJPLl9bW8olqD5zgjX7g3jlTYR2v/Fw0cls29caQ1FW5mVsSnh5vcG0eabz21ML7DzfeKeAKdRtdsP8RbG2hYNufwDsAp7Lm1PmhYNrdbBl2fuYcd9AAVV9KuCxpdw233mTXVGU33DTMiBEfXtBnFE+g0uqbPc6tr+YmaJ/hgfRlZG7j1KehN8jPLtztex4dtOKpIiVDV+/tVdOtLw+YV6vDh8uGLwhcguHUj9nWcxqYOH3o//VH4QBRHNOI8eXxooQ4f8PtW1TwjacFNnC6kbpjV+tebROXg9RT7kOoJr4xGi6n2a2NJMcQcEmHIXZ9pD/ut3xxnrSm05o1JmKQNBBsR6D7DpEkYa0GjrYK4D2X8MOCGaQI4N1T0gRALmk/MvdWkGyr6UI4FzXN7n1kEjd7g7WAW3FPQ37M3M8/08wPODSC3cA7N9VU9Xut7+NHq+NwbR1qMiysqaRoGF1yMi9sxALeJCYb40Qi0IBq3YwD1GW46BRWHnB7cALW7L3AFDb71VZEv9fkJranAFTT09pYbjUBzdrlVr1WbImXclnXDycaR22cXH5Jw2//RtYHb4otW2ea2//uURIQ8yYw+omkz739+kLWZpmHZ3GKl6aQ+RRXz8A4XOeXW+qDRNTg3ui5okVNuJNd7Rc5UD5zHXBc0soPb4SH7SWdI8KHjvL0ALjzJ7ZZBIzv6Pl9boH9z7BTM2VjsiIqgHUu49SloZMezpAf3wdJT8PyKHfDhNudhs6oXob6bGejWl14cKFSP9IuHLAyLYFyn5RFUqwbX4VEHkz2fWBn+ubjwJA7Dx/NfCWksSNVtPk0JwPnit9Tkx6uQ9RtIVM7J8h/g1dW7Yc7GYkePcbpe0vX52+Pl8Mb6fbUOf2mEIbdjCb342H6IJ9BxxN6FGnWSkoFJ2kCwEYHuM0yahLEWNBpayH0o34fCjrhhmgDO21D6QIgFDVfm3mpSsUUfyrGgPa1xeFddvEk2p1zRpKBV7rl5pnQDyC2cQx/eqsdrfQ8/unH8eBdP0NBiXNxNOC7Gw+0YAOAsxsXdhKvNlTJu1WtaxIebZ0pvfbnpFPTWF4dC1gVtPcMVNPjWV0W+1OcntKYCV9DQ21t6ExML2jaQW/VatSlSxu3BTTeO3BZf+PaW24Ob1lTgtviiLfJymDeOVIyqw7v6UGG6eO3kbKZphed4ipViYaIzLt5ipdxUKzqOG8lFW2tx21nSYqXcbhlYAAZD8+E7ZkQILjzJ7ZZx6Lgzuua2F9aw/ITm83O7ZcwgkR3Pr+DtBVS9CFVMl9764kMNheq4cOFjC8K1enCdFtxaE9eZUW38uk7+AJrl2M8uXHgSt9bEBTdfrUk56/Ps6lqfhUYj4XQhlVev3vcmZlQOXS+567MKpQ+vz8yOJbROy45vT7B8Be83LsoRgS7UjQh0n2HSJIy1oNFWQdwwTbwJ+BMzTBPAmfPOfSAAOG9D/8ksgkZDbHGLlLqgm4ClX/IE+rIvnQ9DrmhS0II73O+HVmrmhrHS9j9q01Lfw48W3+OGUNKbnT0lvE049hmdBy2+2eEW3KNC+yFm1WsqRvOZYayvkJsdbjoFvcXg1o7AN4c6gganpqjIl/r8hNZU4Lb/o9WyuVWvVZsiZdyq13TjyBU0dOPIbfGFo4iuYt440kPKv7zIq4gP4AxB5goaGoLMjT6iIcjLvjzE2kzTsGxusVIals0FFznl1vqg3wk31YoWOeV+l+vIPOC2s6QhyNyWdTTd7chJXkRIa3R4x21Zd5gUPez34lqWn9DvhFufooC0FOX04AaIFBNUnUTorW//6Wtr+V4JihxSFxK4dSOOmsKV01UnkPS85eH9Do5oxFFTeP6rlDN1W48PsT/e5fy+cLqQqlXQ93lb2Pd5lnfoN3bBF473HLeQtz7Tlm/cjiW08OSu706yfAXnvF+sUcg4GZikDQQbEehJYtq0adCiRQto0qQJNGnSBNq1awcLF0YmaFpaGgQCAYf17dvX8R7BYLDWvwmFQlqfw6RJGGtBo62CuA9lnBfEDdMEcFbk5D4QAJxi635m2PFxIhC4D2Va8Gs5s0o5DQvlbjIxuA0d9/uhNzTcwjm0GJf6fup7+NFN0iZmCCW92eF+P7hHsk7HAHyzw92E05sdbtVrWmWXm7NLb3a46RQ0XJDb4gtXxL9AIxqhHQqNVwXK6vMTWlPhnhm81BRa9JDb4otGsHCrXtObcK6goRvHsgpeRXwcls0VNADOmgrcgnsAzsr9XEFTQtITuNFHtN3g8q94Ap0e3nEPnPANpk50DV5TuMVKaVg2N9WK1gHgfpe0rsViZiQX7ocdDPFb1imhpqy0jHfghA/vuC3raHh4/+k8gU7rytACZbGgBfdwNXbO76ZSg+itL76lVeCDSVUZH7duxOsUrjOjfrdrJywN7wNwRCNep/D8VzVz1GEAbntL6x+MQvniqo6JmkO3MNNmVFs3Zdx0LtpSlHuASuta7CnhCXT8jLx0KL9OUjIwSRsINiLQk8S8efNgwYIFsHXrVti6dSvk5ORAo0aN4PPP7cU8LS0NBgwYAAcPHgxbaanzIRYMBmHkyJGOf3PihF5OsUmTMNaCdh+pkMl9KONTVG6YJoAzTIv7QABwbjK5RdBoyDj3oUxDxrlVyml49H5mwT3MRTkLtL8ferPDDWOlt13q+6nv4Uc3jlxBQ6u4cr8fHLqn86DFPsMtuEeFCbfqdXV1taOaMTdnlxbc46ZTjHzXeevLbfGFQ3t18vJwWLYqUFafn9C86X+8xhM0dFM5iVn1mhbV4hbcy1vk3DhyBQ09XOEW3FPVk3UEDYBzbeC2+AJw1h3gChp6uMINCaZ/u5XbvmVtpunhHffACUfX/EHDn3HtFW5PeRqRxU21omKU+13SgpU4j7ku6OEdt50lLarFjQjBh3fcdpa06OHtL33M8hPVY1wZt50lLZbG7cGt5o7qfkFvfXEdCsX3FVWO7yMYcrZuxJF22PfU3MHfJ04zwAeCuOCmWvfUYUCznMghNo2uG4PC0dVBjhqHw+brQrV1012faU92bscSWtdi3+Eylq9MQDf2l2vUSUoGJmkDwUYEuov84he/gOnTpwOALdAffPDBOv99MBiEKVOmNOhnmjQJYy1o9OaC+1DGFUy5eWcAzpYZt73AD9PEhZW4YccAEM7l0nkoU8HLrVKOH8zB0Hw4WMoTTRjcJ577/dAQSm4YK81xVN9PfQ8/WhCNK2hon3ju94NvdnQetLilGLfgXmmZczMdeosXjQDg7G/Nzdmdvc55s7PsS160xhgSZsjNF8UhwXgjVx+4grAqUFSfn9ADMm5qiuojrIxb9ZoWd+RWvaYbzi0HeOs13ThyW3zhOhzXTuALdFxT4W/MFl8Azv7Wo5iChobUc6OPaH/rVdu/Y22mQ285D++4B0434+gaDX++HdVe4db6oId3XH+mB1Xc75Km2yxjRnLRwztuO8vbST0aXM28LnBdC247S/qd/O1lnkCnBVG5BSStT5yHd9we3GrdU9FY9NYXrx0KXC9C3cDjQqo40g77nipqi59bOOUQrzd4/qt1Tx2i4nBuujfA3UXUM+TqMfZn5KbN0Nag3HSueDuW0LoWxUd4Av1JdCCgUycpGZikDQQbEeguUFVVBTNnzoTGjRvDli32Yp6WlgbnnXcenHvuuXDZZZfBww8/DMePO9u6BINB+PWvfw3nnHMOtGrVCkaPHg0VFbzwb4VJkzDWgvYIubngPpSfQ31FdcI0cR5Vvxf5YZp4E8AtggbgrD7NfSjTkMRVO3gCHcDZL5q7ycRchkL5ud/PN+RmhxvGqorS0O+nvocf3ThyN4A0b5r7/eAetc01HrT4UIe7CadpEdyq1wDOUE9VcK8+aFg2N52C5vNxU1Nw7q1OXh4Oy1Y3LfX5Ca2pwE1NoQXKuOkCtOght+o13XB+eZDnzwvIxvEHZkV8HJbdidmDG8C5NtzJrIgP4Pybj2EWdAJwrmXc6CMqCFbvKGFtpmlYNrenPD68u2Qo359x1XhuT3l6E84tIEkPO7jfJT0s/IAZyYWLkAVD/HaWOLotGJoP31fwDpw6o7WB286SFi+86z/rWH6ylawN3HaWdK5ye3CrlCBVx4GmzeAaIgq87rWoEfa4dSOOtMOFctVtN07Nwus6LviGC24uqqkl02aU/fzBUWZHSK4/ruehDhfUwTI3beY50lN+SiFPoNNWd9wDVBrp93Xp9yxfwT+P284yWZikDQQbEehJZPPmzfDzn/8czjzzTGjatCksWBAJf3vhhRegsLAQioqKYObMmXDBBRdAZmamY/zkyZPhgw8+gM8++wxefPFFOO+88+Cuu+6q82eWl5fDsWPHwlZcXAyBQABKSuxNiptWVlYGlmVBWVmZ4///m7RrOnbyFOv9XvkwskhfM3YJ+3MMfTtyU9LvxTXscel5kRu8h2d/wh6nToeDofnwn492ssYcPeHcZH649Rv2z2s/LhKe9vWRE9p/pxbo4dyf+f0cPHrS8Xlzrc2scR9udeagv7pqZ52+ouzbUmeEwZbiI6yfN4xswg8yv5/+SKBfNvw99ndJ86Y5Y46ddB52DH7rU/bPuxYdIk1bto015s31zhD3pV98zRo38T3nJrzkWPS/FbVeT0XE2qVDF7J/twx0C3tbTUuk+vyksrLSUVPh/tc3sn7W1q+dYaxTFn/FGreoiNygf1LMGvfUEnKDzvTnws+dP6+8vII1rjfqSpA2cRn7b4AjNO585WP2uJunRcLAR7/7OXvcn1CKyPSVO1hjviFr0apth1h+MpSsDd8cPcn6ebe9EBHofxzGXxv+8d+IQL95Gm9toM+FBwp4/lxZWekocsr9Lk987zwoXvbFQda48QvIDfp+nj/fTQT6ie/LWeNwXYv0PJ4/V1SQDg+vfMzyk68OONeGsfO3sH7egk+dN+j/Xb2LNa4b6RLw1vq9Md8X/3+VjqIi4tZsPxR+DT9jej25Mvz/t39jRwfgSJnv0Lq+aXeJ4/tS///9mnVP7R3wM/JEmfN5Nn5B5Pt6eqm97qnaFtz9xvMfbHe85+T3v2SNm77SKezf2bSPNe6jbc59SnHJcZavvLoqItBbj3yf9bOSZSUlJSLQDUMEehKpqKiA7Vb7XAAAIABJREFU7du3w/r162Hw4MFw3nnnhW/QKRs2bIBAIAAbN8YOWXvrrbfCYjsWubm5tQrLBQIBKCgoAMuyjLQ7n5jnfAC9zRuX88I74TFXDH83rp+XNW4ee9w1I94Nj7s1nz/uqtzIuH8//w5rzNtzLcd3kj+D/322ezzy8157U//v0Xp4ZHwP5vczd64FFz8WGfe3qbxxT7/u/D1DzO/nrbed456fyfvdBj7p9LXXmd/P4y+94xgXz9+iy2iej86da8GFgyPj+k/m+1o75KMPTuN9l6Nfcf5uE17ljXuG/O1mzeF9xrRRkc94cQ5/3uJx3cfyv5M/5kTG/XkSb9yMN5y/2z+f4X0nM99yjhv5Mm/cI885/wZcf540w4rLL7uPjcyDqx/n/w2uGx8Z12sC/2+Af95dT/LH4bXo4ed43+Ubc5zfyZT/8n7WALI2FLzFG9d7wry4/PnPeZFxGWN4496ea8HvQ/r+bFkWNHtM/7ucS37eRObacP8z8flzn0nOv8Ec5l4Arw06/nwpWhu4/kzXhruZ/pz3qvM74T7rrh35rmPc8OnOcW/PteD6ifPgH087/7/6e19Q8/ej8+COqfOg7ePvwpAXI+Nenu383YKh+TATzQP8Ov6+xv/H/t0uqvmZl5J50B49lwag7+thsu5x92N03H1P877L0PPOcY+/xBv35GvO72XGGzz/GvJi5Oe1GMr3y2RYQUGBCHTDEIHuIhkZGTBw4MCor1VXV0OjRo1g1qxZMcfv378fAoEArF0bOwzIizfo05Y5wzvLTvFOzd/ZFMmd7ThhKftzTH4/Epp7x0tr2eN6oTDNf7/Jv9XsMTWS7/n6Gt6peWVlpeMUe+2Ob9njstHn/LaUd6sZa/zfXuZ/P/M+iYRKj2Leku39zhku+Pqa3XX6irKKigrHzei2g0dZP4+eoH/HvPWtrHSGenLH9H0ukp/a59lV7HE4NH7oXF40QmVlJVz/dORm9IUV21lj1u101gFY+RU/WgPfHnIjX/BtavPcReyfdecrkfzUv05fw/KTyspK6Ixy1x+atYn1s74/5bw5fGrJVvbnHPXu5+Fx7322nzUGz51gaD5s/Zrnzxt3R1JEfj+Y75f3ozocGZOWs8d9XhzpnjDw1XXscQNejdyMjl/Iu3GsrHTejL7CjD6qqKhwVJv/eOe3LD95cYXzVu7wcd7aMPitT+Py50ffiNRe6fscf23A+cFcf66srIRWKNWKG8lVWemMmuBGchWs3e34LrnrM547wdB8OMWMCPn7y5G1ITOf78+dULvBga+uY/lJGVkbJiz8gvWz8NwJhuZDwdrdrHF4TQ+G+Le+OEw9GJoP63bWv4fYf9hZ2yUYmg9HTnwffv04ug2/Z8b68P9f+ZV9w6zmXcvHnfPgkz2Rm/e89yLfF/UT7n7sv6udrUGfLORFOM3Z4IwWm/8pL8Lpi/3Oyv0Hj5xg+cr8TyPr+tWjC9l+mQyTG3TzEIHuIl26dIE77rgj6mtFRUUQCARgxYrYvZLfffddCAQCsHfv3pj/hmJSnkllZfScHZoDW3Wa1xcZVyLl5p0BOCuR4jyq+sA9WrlVygGcRYRmM/tuAoAjvHMjs40YgLPieOn3vIJ7mDvQZuduZt9oAGeeFrdvNG0N9mZNAZxYvoLBG0duv/d3PnWGBHMLEgKAYxyX++IsSIiLeHGrXgM4WwhyCxLuLXGmC6zZGTtCh4J/P26BMpyz2/Jxfl4ersD/95r8Z46f4Pn36Jv82hG4Av9zzLZUAM6iTdyq17Q12O7vTrLG4YJoOhXER8yLhCB3mxz7mUPBvsKtIA7gLASaz6y4DADQC4Xiv7aWl7ML4GyJ+em+oyw/oUW8uLVQcOsmHX/GbUJ1aqF0m7wiLn/GqVavr+XvIXBdi7XMtYG2z9xTwvNnmltcXc3bC+C6Mt2n8P35xmc+cvgzx08AnHVluAXKaF0Zbosv/DwPhubDws28uhZXoN7wwRCv0wnNFw+GarduVHPrPlSgcD1pe/onkm+Na8bggpu0qOZd/+HtN94gLeu49Vber8mVV8ZtKUrrWhw+WcHylQ9QEdxrNOokJQOTtIFgIwI9STz22GOwcuVK2L17N2zevBlycnLgjDPOgMWLF8OOHTtgxIgRsH79eti9ezcsWLAALr30UmjdujVUVdmb3NWrV8PkyZPhk08+gV27dsHs2bPh/PPPh969e2t9DpMmYawFDQttnYfy6h2RTW2XScvZnwP3tBygIUBxC5ihc3lVygEA+k+PjOM+lAGcrYk+3cdrI0Z/3gnmJhPz0OzIZprbNxrAKTLyFvE34VhoqwI4nIefKkgTDPF7FtOidNy+0QDxCXS8CdfpG42L0nGrXgM4iyzNYBYhOkHaDa3bfZj983JQVXxugTI8j1qPXMz+WbgondrIcfxkACrGNXgOvyI+rjzOrXoN4BQZ3KrXtPL4vsM8f8bFnpppFNx7elnktjhrKq/9H4BTZHAriAM4uydwCzoBAPR5LiJOCj7mi0p8y1y0v5TlJx9uc64N3JZ1uEgXFSZ1MQpVOtdpWYfbs+l0eLh2QqQ+xax1/O8Sd09Yz1wbNpFe4Vx/xsLrAo2e8rg/t44/3/Wf9Q5/5gp0XLCS68+0owQt9hYL/CwIhubDe0U8UYkPZNQ8qA9aoDTaPFDFcgehgpuf7HPm5V9B1nV8EI+fZ0u/dB7kcPdjtJ0l9wCVznFuS9HD5ODiaBlPoK9DBxfc9n/JwiRtINiIQE8Sd955JwSDQWjcuDH88pe/hIyMDFi82F609u3bB506dYJzzjkHGjduDH/4wx/ggQcegMOHIw+/jRs3Qtu2baFp06bw05/+FC655BLIzc2FsjLeg05h0iSMtaDhlh86D2U8LjP/A/Y43CpER4D+/ZWI+BnObCMG4BQI3IcyADjCOzcX89qIAThFmo4AVYyeH9k4cvtGAzj7w+rcknVEYYaq7Qnn4YfHcXsWqzYyuptwgPgEOm4FqLMJH1SwKTxOp+o1biHIvXGsrq4OVwgOhubDhj18gT5xUWRjzD1Y+ytqpdRmFF+g44q4aiPH8ZPBcyI3749pRL7giJnpzKrXAACvro6EbXKrXtMNIPfACY/TafFV8HEk1LPnE3xBgw9zdA43cdutJ5bwKuIDOKN5dEQl7rjx+QGeQKet9bgRIfNQVA4VJnWBeyTfodGyDs9xnQ4P+HmiE8mFo3k27OFFctGoHK4/48P6CzUiQnBUnI4/49Z69xdsYgt0HJWj4884Kofb4ou2nuPe+qqWZ3ge1AdtPRdtHqgCkw+iDgJF+52t59qMqt32Vr32v7MjUR+rdjgFM3c/Rm/eX1jBO0DdsMd5cMRtKUq/l9LvK1m+gr8Xbvu/ZGGSNhBsRKD7DJMmYawFDff81nko43YnOmGauKeljgC9Z8aG8DhuGzEAgAdnRsQW96EM4GxNxO3zDQBw32uRDZyOAFXgXqE6t2T4FlDnluz6pyNhhqrtCefhh0M9ue3SDpF2cBU/8G59AeIT6DOQWNPpG537zufhcdw2PgDOUM+ZGjeOuNq8TjoF3hhzwQddV46uvZGLxdubIgdr9/7XnrccP8GHCEPm8gXNwBmRg7WXP+ILdHwLuHIbT6CfPl3tqKlw4CjvwOn7iqq4/HIRCvXMfvJD9jjcmup2jQOnSSgM/KmlfEHzj9cia66OqOyKKl9/efAYy09oq0huRMgqFJUTTZjEAh/e6bSsG46icnRSrbKmRp4nb2pEct2AwsA3MdcGGpXD9Wd86K5z4IQP3XX8GR+SPDiTL9DxobuOP+OoHG6LL9p6jnvrm4YiH9Q8qA/aei4Ymg+VZB6o9fuhWZ+E/x9tPXdVlHU9mq/Tm3fufmwRCVXnHqDSQzhuS1HarvP4KZ5A3/FtJKdfJw0zGZikDQQbEeg+w6RJGGtBK6twPsy5YGGvE9aGe3nqCND70a3miHn8sOPBcyLhndyHMoAz94zb5xsA4CGU76kjQBWzkcDg9tkFcOZpPalxq4A3IAtq8us4Dz8s7L87wetZXEnCDLn1DgDA0aaIC+59q1Pv4MklkcKJE5j5/ABOYa8jaPBhkE46BRajXHBYadsx/Lw8nM+n8h85fvLSh5EQZJ3IFxyW/erq3exx8z+L/M1Xbf+OPQ6HZX/DPHCiG0cuG/ZEQi97P83rwa1Q4/o+v5o95pnlkZD6Z5ZvZ4/D6TY6orI3Whu2fXOc5ScVPzjXBm5EyJcHI5v+aMIkFvg70an1MbUwsjbopFrh70QnkguHWHPymAFqR+UcLOX5M36m66Rs4PonvZ/iC3QclfPQrE/YAh1H5Ty9jO/POCpn/me8XHJ8MRAMzYelX/IEema+sz0btxc9LrAY7Rn58Bv2ITC+Cd/5rbO4XLR1Xb12I+q7/tVBp7C/j7kfo6Hx3APU3d+ddIxbwYxwAnB+LyfLf2D5Cs6910nDTAYmaQPBRgS6zzBpEta1oMWzyfzuRCQfUkeg41zp+wv4AlQ9mIKh+TB6Pl+g44JM3IcygDMU+KuDvIcrgPNAgHsLhMEFfv6FTsnrA4fb6oT94Vvf94r4Av0WlJ965GQF++fFswkHALgMFZ7isgb5ms4m/LW1kToJOvn8OBdTR9BgX9NJp1i85Rvt7wRvONtpFM7B4YIq/1G3+JdO5Au+9eXm8wM4N4+rd/AL7uFb30PHeYIGIL61cxfaqN7wTHwC/XoNYf8yytN+VqPg3hDUm1xHVOJChNsPnWALr3i+y0PHIzfvOgIdi8OBM/hrwwxUQ0XHn/GBr04kFz5A1YnkwlE5XH+O97Aep1fp+DOOynn4jU/ZfoKjcnT8GUflcIu94XSnYIhf1wJHTNjzgLeHaJaz0DGOPiPHLvgCgiFngUJ8sBIMRS+Ipl5LRzfJdNwg5n5sxVZn5xFuQVQaJfPhNv4BquonHwzZkYkcXzlaVhH19zYBk7SBYCMC3WeYNAl/bIGO84I6TeTn93x+ILLRf1DjhhjfqI3VyAvOR1WduQ9lAGeo+jbm6TeA8xb1tMYNsQIX+MGn5PWB/x7xikqVX8d5+Knf84qRi2uF4dUFbj+jA66qz2UbCv3TqXfwXlHkFlYnnx8X/9IRNLjKts4mXG1AdIpj4bBSne/y+KlKuHSofUiiKgFz/GTltshmTifyBYtKnQriuHglt+o1gPPAiRsRAhDf2ln6faQY1HVP8g838c/TORTFkRbPr+ALGlwPQ09URoTQru9OsoVXPIdwOCrn8uGL2ONw+otOqhXOv9Xp8ICLM76jEcmFU7Q4ecwKLBAT7c9Y6N00bRV7HM55f/RNvkDHhys6/oxz3hcxc8lxRFwwxK9rgTsgBEPzYee3J1jjcAeEaH8DVXMBV07HN8XBUPSCaOriYO6myDzGlyzBED9ibx2pGj+DGeF0jBTB04lwwvUDyn/gCXS8Nlw9hn94lwxM0gaCjQh0n2HSJKxrQWsRp2hSY3Ta2xwsjZyiPqRxQ4yF77iF/LDj/6yKbMS4D2UAgKFzI7mGO5gPVwCAsQu/iOu7VOACPzptfHC4rU6EAc5jVm2pOA+/qtPV8FnxUSgt02slh6sZ69B2zBLtcSVoA6JTVOvjXZENiE4+Py7+pSNocBEvnXQKADtygtuSCsDOt45nEw5gb+iK9peGb3U4foIP5HT8Et+86+Tz47xKbtVrAGdkgU5ECM5d54Lnqm51YTUuXSNkE0cV6FTEx4ebOqISt+TbW1LGFl64sJYOasxFOfwaKrPWReaqTqoV/i5HanR4wLUfdCK5cAQYJ49ZgVuYHY4zwokLFno6Ah1HOIXe+oztJzjCScefH58X2UMsZuaS4wizYIhf1+ImVMguGOK3bsQt5H4fo2hv8ZEyx806FdrRCqKdPl0N+0ktgpOkVgE3Ym8HCan/LzPCiaa46UQ44ed/ZdVp7aicizVSNpKBSdpAsBGB7jNMmoR1LWi4lYsO8TzMccsPnbBjFdoVDOnlBeMqv9yHMoCzn/Iu5sOVjosHXOBHJwUAIPL3GKaR64sL/KiqqtyHXzzgmwUd4vFRXHTnluf4ObvbD0U2IDrpArh4jk69A3zzrpNOES/xCnQKx0/wgZyOQMcteWav4+fz44JJ3KrXAM68Vp1DJ9zvWAc1RufWF49rP44v7PGhxUsaFfFxwcp3P+P7M27Jt+8wX6D3fGJlg75LnXE4vFpnncWVr3X8GR8A6URy4bWBm8cM4GwHd7QssQIdC71eGjnoODR+8JzNbD+ZszHyt9Pp8ICjh9RhdH3gVpbBEP/WF0fkBEP2QRUHXAuDW7S3tMx5M53GjGikh7UPzeYJdBw6HgzNh9fX8g9QL8qJ1JLRiXDCz/+q09UJTZtJBiZpA8FGBLrPMGkS1rWg4QI2OsS7+Kkxf36Wf9qOc1J1QrhxlV/uQxnA2a5pTwlfoOPiQ/GAb9d0encDRL7Xf7/J78+LC/yo8L1ECnTcJ14HnCOsgxqj02ngCMrnV+HcHNaj0L8FGpvwmejmXSedIl6aD48vYobC8RN8IKfTlgpX/H1Do+AeDrflVr0GcIrKY6f4fo/bZ+nQ0LVTp2I5/k5e0aiIj6OPdPwZhyAfOPo9ez3BBdF0iOe7xMUEdWp94MMOnVQrXOSU20sbwBmVw81jBgD4G2qRV/o935/j+S6x0Os6md9yFXceyXmbL9BxCpJOh4enlkYK/HFzyXHkXjDEv/XFKQ3BEL/VHb4p5kaE0JtwnXzri1ExwUfe4EXsnT5dDReioq06EU44WnOdRoQT7hpTXS0CXfjxEYHuM0yahHUtaLjfrQ4N3WRmaPRPx5W1dfKCcZVf7kMZwJlruO8w7+EKADAdVa2OFzVe5zYCj9PJ7d9cHAlBVuF7iRToeKOqQ/aT8d28qzE6OWh4w6lTCApX01UF9zjgYm866RTxEm+aAUV3k6Tas3HALfl0Cu7hcM9PNCriP78icluskzKAQ1l1aOja2TyXf/OON/A6RbVmr9sXlz/jG86DpafYfnIf6jGuQzzfJZ5z3JtDAGfla51UK1zkVCeSC0flcPOYAZw39sc1Dpzi6ZYBEPkb6NSjKT4SOTgaOreI7Se4Ewy3QBmA8+CIm0s+CqUfBUP8W1+8pwqG+K3u8NrMraRPOyDoVCzHtV10DvbbjCoMj9OJcLp6TGTchj18gU4j77i+ckEcKUjJwCRtINiIQPcZJk3CuhY0XKRKhz/GUdQHAN8CLWaPee6DyAZa51YTV/lVIdwccLEpmrtVF/9FVX7jRY2PlkvGGadT9Ai3Z1Phe4kU6LiPsA43N1AI6eagqXE6re5wqKFOSPCGPZHCgDrpFPESb5oBRVeg67QGw/mKOmHZuBL1x7v4G0Dc3vD7iir2OFxlW4eGCnSdPtU4KmfsQv6tL04P0qnfgQXUoeN8gY6rxusQz3eJ2wZybw4BnNEIOqlWuMipTiQXjsrh5jEDOIvL6Rw4xVOoDyC+g1DceWTIXP4NOq4Rwi1QBgDw+tpINAI3l3w8KarJvfW9G/VqD4b4re7S8yIROZcM5c1x2u4xU+Pi4xpU7X/wHL5AxzfaOgeondHvpxPhhLsgAPCfPa3jTEFKNCZpA8FGBLrPMGkS1rWg4eIpOjS0qM8fmDlWAM6qzlML+XnBeKOvU7gL57J+XcoX6G+inO54UeN1bsnwuL+/wu/5jau/q5udRAr0/Dhz9JMZ/orH3f7Sx+wxeKPELZwD4OwPq5NOES/xphlQdAV69yn8NAM8Ll/jQA5HP3A34QDOG9VTlXyBjm9GdYi3m0FD/Tn0Fn8Tjls+6tz64tzikhPlbD/JW/RV0r4TfACr8518ezwSoTFxEV+g41BpnYNiHJWjE8mFC/XpHDjFK2jUmFYaHSW+r4g8e3SquOOoL511Ftcd4OaS56PUumCIf+v7j9ec/dO5re5wKtcfh73HGgMAjpBznXQunKLz2Nv8FCRc42DORr5A74G6C3yqEeFEn1lcX0mPMwUp0ZikDQQbEeg+w6RJWNeCNrVwW1yLWDKL+uDKrU9qFO7CP08nHA6HynLD0wCct07x0tBN+F9eXBPXuNk1ub6JFOj4oEUHXAVZh4Z+l73jTDN4ainfR3H7GZ0803iJN82AoivQ247h913H43SKHuJxhRqiErcO0mkbiFuR6RBvmkFD/Vmn53e89TvwLfPhkxVxtc/SIZ4wVhy1olMbAacLjNHIQcdFTrnh1QDOqByd9JdhKFKp/Ae+QMf903VQYy4dyheV+EDzgZmb2H6Ci3gWaOQ/v1cUSRfg5pI/gVLrgqH5sJF563s/6Z/ObXWHBaxOAUmcS65zEIpTx4bM5c8DnI6CW7fVB04J2lzMbxtII5W4vhJvClKiMUkbCDYi0H2GSZOwrgUNt47RId6WYjfUtIDRET+42vjTy7Zr/Tw1TiePElcA555+Azhvj+JF9bDto1F5HCDye+qEseJxKkc/kQId31zpgGsQ6KBu3nWKFwFEqs3q3CoARL5LnT7oeKOqc0sWL7gHdEPg+slVo+28Q53UC4DId6kT4o7H6eSg417CVaer6x9QA65VoYNq46gTRQQQWRtu1mhnBRD5TiZp1O/YtPdIrbWBA04zKP2+ku0n+EBAh0uGLtQeh0WeTjV2HKExXOPgCFdj12n/F+/aMAYdCOgcOKU1sKPLBTFag9U37p4ZG9h+cgClZc1axxfoK1BaAzf95VmUWqezpuDIGnVQxQEX7NWJoMOFP3s+sZI9DoeO6xyE4gMgnchEfBNetJ8v0Gl9Cq6v4OLCJmGSNhBsRKD7DJMmYV0LWnV1NUz/cBc77EtxqrIKnlyyTas/K4AdJjilcKtW6DgOt5y2nF/oCMDO43vugx1wWmPjDWCfuM/WqCCt+O+aPbBcY0NL+ebYKZhSuBW+OcY/GAAA2PbNcXhyyTYoq+DnHAIArN1ZAi+s2KnV37oh/GfVblihcYsEYFcDf3rZdq2HOoCd5zilcCu7iq5i13cn4Ykl27QqIAMAbNx7BKYt36El8gAACrd8o1UNtyFUna6GZ5Zv18oBjAbXT4qPlMHUwm1a/ZgBAIr2l8LTy7Zr3QACAKzc9i28qpGbqpizsVirRzWAvXa+sGKnVssgAFvEPrFkm3bV/m+OnYKphdvgkOba8NXB4/DUUr21ARfY1Ln1BbCrpKtDKp315JWPdmmlJgA4e0dzqa6uhpc+3AUj392iVWMEIL5uGd+dKIexC76Ap5dt114bFm/5RkuIAjiFic7P6z5lRVyCBovReMbd8fLHbD/BXTZ0btBxlAz3kAR32AiG5sNnxTyBTovLcVs39n0+IphbPs5PF7hydKT4WvaT/IuP21+KFLPTKYiKc/N1Wori3PwtB/j7xodmO+skcX2l/Ad7j6rzs5KBSdpAsBGB7jNMmoSJFl2JBt/mPKdRiVjQx+u+IiQH8ZPUZm9JJFRdVzRjEu0nuDJ0MlA/S6dbRrLBN/bq4JVD7zgLSDZUoN/6/Bq2n+C6KTptA4v2R3LXN+zhHU6+j6ro69z64narwRC/dSNuj/cnjXz+jhMikQ86kYm42v/j8/gCHYf+6xxqPoCKF+pc7OAiiwDef/aYpA0EGxHoPsOkSej1BQ1vFl9YsdPtj5PSeN1XhOQgfpLa4A4YH27Ti67CJNpPsDhJBjgs21Tizeen1bK5NFSg3/DMR2w/wWH/Om0Dd6CCe9xQ9Q17Djt+N+5N7Kx1zpv3E8xK+ve9FgnlvmIkv8sNjny4/umP2OMeQh18Rr3LT/XA/rVwM1+gD54TKV64VSN6iBYy9vqzxyRtINiIQPcZJk1Cry9ouDiPbpE4QQ+v+4qQHMRPUpsTaM3VqTxOSbSf4MrXyQCHZZvKjDjbfcbb4aGhAj1r6kotP1HjdDq64DoT3FD1XajDhs6tL+4KEQzxK+nj3PU2o/gt61Rdn2BoPtz4DF+g56CbaZ2ih7id7HtF/BaMWGjrFETFIfUA3n/2mKQNBBsR6D7DpEno9QUNn5rrhGIJ+njdV4TkIH6S2uBCmTq3ZJRE+0mvOMOy4wWHZZvKG+v3xfWd0GrZXBoq0NMnLY9LoI/X6EWPK+Jz62+Ufl/p+N24t764S0AwxG/dqIpHBkPz4arRfIF+2wuRtmc6BSRxrrxOcdk5GyNFe9//nC/QJy6KCG2drgS005DXnz0maQPBRgS6zzBpEnp9QQOIPJT/NesTtz9KSpMKviIkHvGT1EetuTpdCSiJ9pM/P5vcVko4LNtU3omz3ScOsdZBdb2IV6C3H7c0LoGuc1hf/kMkd30Ns6hjdXW143fj3vruJjfvFT/wKunjdnw6bSnxwUqfZ/ndX3Cfd53DjoWbvw6P02nB+NTSiNDe/d1J9jhcTR/A+88ek7SBYCMC3WeYNAm9vqABRB7KfzM4tDAVSAVfERKP+Enqo9bchnQYSLSf9HsxvrDseFE/q8dUfjurZLMIFTfTgVbL5nI5avOlQySce3FcAl2nDSaOwtPpSoCLEHJvfY+fct68cyvpT168NTym3Vi+QP8nakN2i0Z7VlzMbuIivkBf/tWh8Did9JfpH+4Kj9tbwu+s8vJHuxz+5fVnj0naQLARge4zTJqEXl/QACIP5b++JAI9kaSCrwiJR/wk9VFr7utrzRXo/3htQ1ziMF7Uz+qlUS072azc9m1c38lw1N9ah/bjljZIoLcbuyQugZ6jIdDxuKVf8m99s6auDI/bxbz1xYcBwRC/kv5z6Ka4/bil7M/46JuR3HWd1ItXkPCd9P5X7HEf74oUz9NpKfv62kjxvH2H+QJ9NknZ8PqzxyRtINiIQPcZJk1Cry9oAHZeXaeJy7R7Bwt6pIKvCIlH/CT1GbfwS+g+ZYVW/3RKov1k/9HvoXPecq2WWw1h7qb90HHCMvjia/ef67H4oeo03DxtFTz65qda4747UQ7pk5bDM8u3a40r2l8KHScsg3c/4/fEBrBv+q+Gz1oqAAAgAElEQVSdsBQ27T2i5ScvrtwJ6XnL4WDpKa2fN2TuZuj91IfskHMAgFuei1S231PCD8vGAp3LjNW7w2M6jOcLdHyw0u/Ftexxs9dFhG/+4q3scZuLIy3rVmhEI8zdtD88bv/R79njTlVWwXVProQR8+xK815/9pikDQQbEeg+w6RJ6PUFTUge4isCB/ETgYP4icDBVD+5A/Um17n1bZazUFug4+J+HScsY48btzBSfK3/dL5Ax7UKphTyBfr2Q5GWdTotGHHqxdelfIFOMdVXuJikDQQbEeg+w6RJ6PUFTUge4isCB/ETgYP4icDBVD/BKRTFR/gCvXmufl7+/M8ixdfSJvIF+hNLIsXXbtdIASxE7eCe0Ghfu/9opGXdqu18gb5iayT14ptjetEPGFN9hYtJ2kCwEYHuM0yahF5f0ITkIb4icBA/ETiInwgcTPUTXDhP59a3zahCbYG+9MuIYE7PW84e98KKnXEV0f1o+3fhcU8v46c1HDlZEdcN+vrdkdz1Q8dFoJugDQQbEeg+w6RJ6PUFTUge4isCB/ETgYP4icDBVD8ZMndzXLe+8RTOW72jJDymy6Tl7HH/XbMnPO7OV9axx23cG+nXrlN34FRlpGWdTpu1ov2R3PXvTpSzx1FM9RUuJmkDwUYEus8waRJ6fUETkof4isBB/ETgIH4icDDVT8ag3uQ6t75dJi3XFuif7jsaHpOZ/wF73FsbisPj7vrPeva4L74+Fh737Ac72ONwlfoFm79mj8O560dOVrDHUUz1FS4maQPBRgS6zzBpEnp9QROSh/iKwEH8ROAgfiJwMNVP8lFvcp1b3x6oPRuXrd8cD4/pNnkFe9zCzZHc9QGv8gX6npKT4XHPr+ALdIBIlfq3NxWzxxQfKQuPKy2L/+9sqq9wMUkbCDYi0H2GSZPQ6wuakDzEVwQO4icCB/ETgYOpfjJteaQ3+WGNW98bnvlIW6DvOxwRsN2n8AX6sq8OhcfdM2MDe9yh46fC415cuZM9DiAi0As+3sse8+3x8vC4EglxN0IbCDYi0H2GSZPQ6wuakDzEVwQO4icCB/ETgYOpfvLSh7vCovJoGV+g931+tbZA/+5EeVwCfe3OSO76P17jC/TjpyrD46Z/uIs9DiAi0L86eJw9puKH0+FxpyqrtH4exlRf4WKSNhBsRKD7DJMmodcXNCF5iK8IHMRPBA7iJwIHU/3k9bV7I2HZ3/M/2+0vfawt0MsqfoirivtnxZHc9X++vpE97oeqiGB++SM9gf7t8XIo2l+qNQbADqvf+e0J7XEYU32Fi0naQLARge4zTJqEXl/QhOQhviJwED8ROIifCBxM9RNcgO34Kf5nG/Dqem2Bfvp0pPjaNWOXsMdtPxTJXb+/YBN7HABAs5yFEAzNh/+s2q01zk1M9RUuJmkDwUYEus8waRJ6fUETkof4isBB/ETgIH4icDDVT9797EBY/J4s/4E97v6CTdoCHSASOt565GL2mP1Hvw+Pe3CmnkBv+fj7EAzNhxmrd2uNcxNTfYWLSdpAsBGBniSmTZsGLVq0gCZNmkCTJk2gXbt2sHDhwvDraWlpEAgEHNa3b1/Hexw5cgT69+8PZ599Npx99tnQv39/OHr0qNbnMGkSen1BE5KH+IrAQfxE4CB+InAw1U8Kt3wTV970I2982iCB/sdh77HHHD5ZER73gKZAbzd2CQRD8+G1tXu0xrmJqb7CxSRtINiIQE8S8+bNgwULFsDWrVth69atkJOTA40aNYLPP/8cAGyBPmDAADh48GDYSkuduTRZWVnQvHlzWL16NaxevRqaN28O2dnZWp/DpEno9QVNSB7iKwIH8ROBg/iJwMFUP1m57duw+C3/gS/Qh8zd3CCBfuFjC9hjTlVWxVXFHQAgPW+5djV2tzHVV7iYpA0EGxHoLvKLX/wCpk+fDgC2QH/wwQdj/tsvvvgCAoEArF27Nvz/1qxZA4FAAL766iv2zzRpEnp9QROSh/iKwEH8ROAgfiJwMNVP1u0+HBa/lVWn2eNGvrulQQJdZ1x1dSR3/Y6XP9b6eT2fsPu1z1onAj1ZmKQNBBsR6C5QVVUFM2fOhMaNG8OWLVsAwBbo5513Hpx77rlw2WWXwcMPPwzHj0daRbz00kvQtGnTWu/VtGlTePnll9k/26RJ6PUFTUge4isCB/ETgYP4icDBVD/BFdKrTlezx01478ukCXQ87tbn12iNu3naKgiG5sPs9fu0xrmJqb7CxSRtINiIQE8imzdvhp///Odw5plnQtOmTWHBgki40AsvvACFhYVQVFQEM2fOhAsuuAAyMzPDr48ZMwaaNWtW6z2bNWsGY8eOjfkzy8vL4dixY2ErLi6GQCAAJSUlUFlZ6aqVlZWBZVlQVlbm+mcRM9vEV8Q4Jn4ixjHxEzGOmeonuw4di4S4l1ewx722OtI/XefnYYEez7jrn/5Qa9ztL62FYGg+vLV+r+vftdd9hWslJSUi0A1DBHoSqaiogO3bt8P69eth8ODBcN5554Vv0CkbNmyAQCAAGzfa/SPHjBkDF198ca1/d9FFF8G4ceNi/szc3NxaxecCgQAUFBSAZVliYmJiYmJiYmIesvuefgf+99l3tMbMmWvBX6fMgxEv6Y3DAj2ece1HvKs1Ln+GBbfmz4OCt9z/nv1iBQUFItANQwS6i2RkZMDAgQOjvlZdXQ2NGjWCWbNmAUD8Ie5ygy6WCia+IsYx8RMxjomfiHFM/MS2ht6gp+ctd/13SLR53VfkBt08RKC7SJcuXeCOO+6I+lpRUREEAgFYsWIFAESKxH38caTYxtq1a6VInOALxFcEDuInAgfxE4GD+InNhY8taFAOevtxSxP0yczB675ikjYQbESgJ4nHHnsMVq5cCbt374bNmzdDTk4OnHHGGbB48WLYsWMHjBgxAtavXw+7d++GBQsWwKWXXgqtW7eGqqpIC42srCxo2bIlrFmzBtasWQMtWrSQNmuCLxBfETiInwgcxE8EDuInNpcNe69BAr3NqMIEfTJz8LqvmKQNBBsR6EnizjvvhGAwCI0bN4Zf/vKXkJGRAYsXLwYAgH379kGnTp3gnHPOgcaNG8Mf/vAHeOCBB+Dw4cOO9zh8+DD069cPmjRpAk2aNIF+/frB0aNHtT6HSZPQ6wuakDzEVwQO4icCB/ETgYP4iU3rkYsbJNCbD1+UoE9mDl73FZO0gWAjAt1nmDQJvb6gCclDfEXgIH4icBA/ETiIn9hcM3ZJgwR6s5yFCfpk5uB1XzFJGwg2ItB9hkmT0OsLmpA8xFcEDuInAgfxE4GD+IlN57zlSe2f7kW87ismaQPBRgS6zzBpEnp9QROSh/iKwEH8ROAgfiJwED+xyZq6UgR6PXjdV0zSBoKNCHSfYdIk9PqCJiQP8RWBg/iJwEH8ROAgfmKzce8R+MNjC2Da8h1a4+Zu2g8XPrYA3iv6OkGfzBy87ismaQPBRgS6zzBpEnp9QROSh/iKwEH8ROAgfiJwED+JUFl1OqnjvIbXfcUkbSDYiED3GSZNQq8vaELyEF8ROIifCBzETwQO4icCF6/7iknaQLARge4zTJqEXl/QhOQhviJwED8ROIifCBzETwQuXvcVk7SBYCMC3WeYNAm9vqAJyUN8ReAgfiJwED8ROIifCFy87ismaQPBRgS6zzBpEnp9QROSh/iKwEH8ROAgfiJwED8RuHjdV0zSBoKNCHSfYdIk9PqCJiQP8RWBg/iJwEH8ROAgfiJw8bqvmKQNBBsR6D6jtLQUAoEAFBcXw7Fjx1y1kpISKCgogJKSEtc/i5jZJr4ixjHxEzGOiZ+IcUz8RIxrXveV4uJiCAQCUFpa6rZMEWoQge4z1CQUExMTExMTExMTExMLBOzLO8EMRKD7jNOnT0NxcTGUlpYac2Jnwm2+mNkmviLGMfETMY6Jn4hxTPxEjGte95XS0lIoLi6G06f90bfeC4hAF1zj2DHJeRF4iK8IHMRPBA7iJwIH8ROBi/iK8GMjAl1wDVnQBC7iKwIH8ROBg/iJwEH8ROAiviL82IhAF1xDFjSBi/iKwEH8ROAgfiJwED8RuIivCD82ItAF1ygvL4fc3FwoLy93+6MIhiO+InAQPxE4iJ8IHMRPBC7iK8KPjQh0QRAEQRAEQRAEQTAAEeiCIAiCIAiCIAiCYAAi0AVBEARBEARBEATBAESgC4IgCIIgCIIgCIIBiEAXBEEQBEEQBEEQBAMQgS4IgiAIgiAIgiAIBiACXRAEQRAEQRAEQRAMQAS6IAiCIAiCIAiCIBiACHRBEARBEARBEARBMAAR6IIgCIIgCIIgCIJgACLQBUEQBEEQBEEQBMEARKALgiAIgiAIgiAIggGIQBcEQRAEQRAEQRAEAxCBLgiCIAiCIAiCIAgGIAJdEARBEARBEARBEAxABLogCIIgCIIgCIIgGIAIdEEQBEEQBEEQBEEwABHogiAIgiAIgiAIgmAAItAFQRAEQRAEQRAEwQBEoAuCIAiCIAiCIAiCAYhAFwRBEARBEARBEAQDEIEuCIIgCIIgCIIgCAYgAl0QBEEQBEEQBEEQDEAEuiAIgiAIgiAIgiAYgAh0QRAEQRAEQRAEQTAAEehJIjc3FwKBgMN+9atfhV+vrq6G3Nxc+M1vfgM//elPIS0tDT7//HPHexw5cgT69+8PZ599Npx99tnQv39/OHr0aLJ/FUEQBEEQBEEQBCEBiEBPErm5uXD55ZfDwYMHw/btt9+GXx8/fjw0adIE5syZA0VFRdC3b1/4zW9+A8ePHw//m6ysLGjevDmsXr0aVq9eDc2bN4fs7Gw3fh1BEARBEARBEAThR0YEepLIzc2FVq1aRX2turoafv3rX8P48ePD/6+8vByaNm0Kzz33HAAAfPHFFxAIBGDt2rXhf7NmzRoIBALw1VdfJfbDC4IgCIIgCIIgCAlHBHqSyM3NhbPOOgt+85vfwAUXXAB9+/aFnTt3AgDAzp07IRAIwKZNmxxjevfuDbfffjsAALz00kvQtGnTWu/btGlTePnll2P+3PLycjh27FjYjh49Cjt37oTS0lLH/xcTExMTExMTExMT85eVlpZCcXExnD59+kdUPkJDEIGeJBYuXAhvvfUWbN68GQoLCyEtLQ1+9atfQUlJCaxatQoCgQAcOHDAMWbAgAHQrVs3AAAYM2YMNGvWrNb7NmvWDMaOHRvz50bLfRcTExMTExMTExMTE1NWXFz844ofIW5EoLvEyZMn4Ve/+hXk5+eHBfrXX3/t+Dd33303dO/eHQBsgX7xxRfXep+LLroIxo0bF/Pn0Bv0ffv2QSAQgN27d0NJSYmrdvDgQSgoKICDBw+6/lm49qcHxokl2Fo+XNvaPDoeCgoKoM2j46O+Lsa35o+mrrUJ1fhJaLyrn6OFWMItFfzEaPu3WJvBNX4yeHxC3t/t/YzYj2de3M9i2717NwQCASgtLf1xxY4QNyLQXSQzMxPuvffehIa4U44dOwaBQACOHTvWsA//I1BZWQmWZUFlZaXbH4VN8/+dLJZguyxU21rnTAHLsqB1zpSor4vx7dKc1LVWQ20/aTV0iquf44+PiSXaUsFPjLYhYq2G1fjJsCkJeX8hdfDifhZjkjYQbESgu0R5eTn89re/hREjRoSLxE2YMCH8ekVFRdQicR9//HH436xduxYCAb0icSZNQi8uaB2zJ4gl2NK7jK1l3bPGg2VZ0D1rfNTXxfjWrcXQlLXrrhwOlmXBdVcOd/VzdL16hFiC7Zo+eXFb2m35YFkWpN2W36D3SWXLbDvC99aj4yiwLAt6dByVkPcXUgcv7mcxJmkDwUYEepJ4+OGH4YMPPoBdu3bB2rVrITs7G5o0aQJ79uwBALvNWtOmTeHtt9+GoqIiuO2226K2WWvZsiWsWbMG1qxZAy1atNBus2bSJPTigtbhholiCbYuncbUsqzMcWBZFmRljov6uhjful71eMpazw4jwbIs6NlhpKufo8ONE8USbA05BMy4MQ8sy4KMG/NcP5A01a7uN8n3du0d9kHOtXfkJ+T9hdTBi/tZjEnaQLARgZ4kVF/zRo0awfnnnw833XQTbNmyJfx6dXU15Obmwq9//Wv4yU9+Ap06dYKioiLHexw+fBj69esHTZo0gSZNmkC/fv3g6NGjWp/DpEnoxQWt65W5Ygm2a3tNrGVdbpoElmVBl5smRX1djG9Zv//flLVelz4KlmVBr0sfdfVztL85TyzRdlP81rmvLbw6981v0Puksl3110m+tw5/t/2kw9/zE/L+Qurgxf0sxiRtINiIQPcZJk1CLy5obotXP5gIdBHoItDFRKC7a26LYxNMBLrAxYv7WYxJ2kCwEYHuM0yahF5c0LL+74NiCTYR6Im1jA6jUtay0sfYqRDpY1z9HJ0zxokl2ESgJ9Y69pzge8u4viYV4vrEfB9C6uDF/SzGJG0g2IhA9xkmTUIvLmhu5w/7wTplja9lmb0mgmVZkNlrYtTXxfjmdu5wIi39FvsgJ/2WSa5+ji6dx4gl2NK6j4/bumbb60nX7IkNep9Utra3TvK9dexvH+R07J+fkPcXUgcv7mcxJmkDwUYEus8waRJ6cUHr1GOCWIIt49rRtSyry1j7ZrTL2Kivi/HN7Rv8RJopkRZuiys/WEPWmMze9s1oZu8819c7U81tcWyCiUAXuHhxP4sxSRsINiLQfYZJk9CLC5rbmyY/mAh0Eegi0MVEoLtrbotjE0wEusDFi/tZjEnaQLARge4zTJqEXlzQ3O5h7QfreN2EWpZxQ00u4A15UV8X41v3S0Ipa9ktc8CyLMhumePq50jrOk4swZYKqRAmm9uHbSZYog/8hNTBi/tZjEnaQLARge4zTJqEXlzQXK9c7APL+sMjtazXZSG7Ovdloaivi/Gt69UjUtZ6XjvK7oN+7ShXP8c1ffLEEmwmF/9KBXO7VoYJlujaJ0Lq4MX9LMYkbSDYiED3GSZNQi8uaG6LVz+YCHQR6CLQxUSgi0B320SgC1y8uJ/FmKQNBBsR6D7DpEnoxQXN7Zw4P1i0zXjabXYuYNpt+a4LA6+b263Q/NBmze055AdrSKeIrMxxtp9kjnO9a4WplnnNSN9bj7TRYFkW9EgbnZD3F1IHL+5nMSZpA8FGBLrPMGkSenFBc/t22Q/mxk2Gn8ztHPhEmim1CtzuYe0HE4GeWOvWaqjv7bqrh4NlWXDd1cMT8v5C6uDF/SzGJG0g2IhA9xkmTUIvLmhZv3tALMEmIamJtav/Mill7drb7UiLa2/Pd/VzuC1e/WBdOo6O27IyarpCZIxt0PuksrldLNQE6541HizLgu5Z4xPy/kLq4MX9LMYkbSDYiED3GSZNQi8uaG6LVz+YCHQR6CLQxUSgi0B320SgC1y8uJ/FmKQNBBsR6D7DpEnoxQWtXd9JYi5Yp3628OrUL9/1z+J1u/KO/JS19ndNBsuyoP1dk139HNf2niiWYBOBnlhz++9rgnW5uabN2s2TEvL+Qurgxf0sxiRtINiIQPcZJk1CLy5obt8Q+sE6Z4yrZd2yJoBlWdAta0LU18X45nZ/5USaKf2tu//yHrEEW0N6qHftaa8nXXtOcL2fu6nmdg9yE0z6oAtcvLifxZikDQQbEeg+w6RJ6MUFze0K3H4wEehi8ZoxfpIplmjLuuChuK3XJY/YbRsveaRB75PK5vYBgQmW6IMcIXXw4n4WY5I2EGxEoPsMkyahFxc0t8WrH8xo4SVmtBnjJwYI2FQ3Eegi0EWgC6bgxf0sxiRtINiIQPcZJk1CLy5obucP+8Ey2o+qZVmda/pbdx4T9XUxvrndCs0PbdbcDs31g3VvPiRuy24zDCzLguw2wxr0PqlsXa963PfWs8NIsCwLenYYmZD3F1IHL+5nMSZpA8FGBLrPMGkSenFBczvH1g+Wce3oWpbVpaaoU5exUV8X41v3Zo+mrGU3H2wLr+aDXf0c1/w5TyzB1jF7QtyWcWPNQc6NDXufVDa3D2BMMMlBF7h4cT+LMUkbCDYi0H2GSZPQiwua2xsGP5jRocspYG4fECTSTDnIcbv6tB+s7W2T4raOf7W7QnT8a36D3ieVze1IKhMs0d1DhNTBi/tZjEnaQLARge4zTJqEXlzQ3BavfjAR6CLQRaCLiUAXge62iUAXuHhxP4sxSRsINiLQfYZJk9CLC1pa9/FiCTYR6Ik1t/++ibSu2RPtok7ZE139HJ2yxBJtHW6YGLel96lpx9dnUoPeJ5XtyjvyfW/t75oMlmVB+7smJ+T9hdTBi/tZjEnaQLARge4zTJqEXlzQ2t+cJ5Zgi3Zb1uXmmlzAmye5fnPndXNbRPtBoHfpNEYswdbulry4rdNfam5G/5LfoPdJZWt76yTfW8f+NZEW/fMT8v5C6uDF/SzGJG0g2IhA9xkmTUIvLmht/pYvlmDr1GNCLcvsbRd1yuydF/V1Mb65XXwqkWZM8S8DKtqLxTZTqv2bbFnnD/K99brwQbsd34UPJuT9hdTBi/tZjEnaQLARge4zTJqEXlzQ3BavfjAR6CLQRaCLJdJEoNdvbotjE0wEusDFi/tZjEnaQLARge4SY8eOhUAgAA8++GD4/5WXl8OgQYPg3HPPhbPOOgt69eoFxcXFjnF79+6F7OxsOOuss+Dcc8+F+++/HyoqKtg/16RJ6MUF7Yq78sUSbN3+NKyWXdc2FyzLguva5kZ9XYxvXa/MTVnr2X6E3be4/QhXP0fmNSPFEmwNmQOyntRvbrdMNMES3bZRSB28uJ/FmKQNBBsR6C6wbt06uOCCC6Bly5YOgX7vvffCb3/7WygsLIRNmzZBeno6tGrVCqqqqgAAoKqqCpo3bw7p6emwadMmKCwshPPPPx8GDeKfxJo0Cb24oF1xZ75Ygs3om9EUMLeLeyXSMnvZOeiZvSa6+jk6Z44TS7DJDXpirfvlOb637CuG2gL9iqEJeX8hdfDifhZjkjYQbESgJ5kTJ05As2bNoLCwENLS0sICvbS0FBo1agSzZs0K/9sDBw7AGWecAYsWLQIAgIULF8IZZ5wBBw4cCP+bmTNnwk9+8hP2pDJpEnpxQXNbvPrBRKAn1twW0SLQxUSgm29ui2MTTAS6wMWL+1mMSdpAsBGBnmRuv/12+Ne//gUA4BDoS5cuhUAgAEeOHHH8+5YtW8Lw4cMBAGDYsGHQsmVLx+tHjhyBQCAAy5YtY/18kyahFxe0Pz42WSzBdtVfJ9WyDn+3q+l2+Ht+1NfF+HbNn/NS1tJus/0k7bZ81z+LmLkmflK/ud3izASTNmsCFy/uZzEmaQPBRgR6Epk5cyY0b94cTp06BQBOgf76669D48aNa43p2rUrDBw4EAAABgwYAF27dq31bxo3bgwFBQVRf2Z5eTkcO3YsbMXFxRAIBKCkpAQqKytdtbKyMrAsC8rKylz/LFxrPmayWIKtxYgptezKUVPBsiy4ctTUqK+Ladio1LUrx9T4yZiprn6O5qPFEm2p4Ccmm9vPAROszbgpYFkWtBmXmO/D7f2M2I9nXtzPYispKRGBbhgi0JPEvn374H/+53/g008/Df8/jkDPzMyEe+65BwBsgd6tW7da/6ZRo0Ywc+bMqD83NzcXAoFALSsoKADLssTExMTExMTExMTEfGoFBQUi0A1DBHqSmDt3LgQCATjzzDPDFggE4P/8n/8DZ555JixZsiQhIe5yg/7j2hX/niKWYJMbrwTfjBlw+5koazPW9pM2Y6e6+jkuHzdZLNE2Nn67Yrx9M3rF+CkNep9UtssmiLXOs/2kdd6UhLy/2/sZsR/PvLifxSY36OYhAj1JHD9+HIqKihx25ZVXQv/+/aGoqChcJG727NnhMV9//XXUInFff/11+N/MmjVLisQlkcsfmSyWYGt9d34ta3uvnQvY9t7or4vx7crbU9fa31mTM3rnZFc/x5/uFUu43RO/tf1nzXryz8kNep9Utlb3iV19v+0nV98/OSHvL6QOXtzPYkzSBoKNCHQXwSHuAHabtd/97newZMkS2LRpE3Tp0iVqm7WMjAzYtGkTLFmyBH73u99Jm7Uk4vqm1AfmRjVdP1mHGyamrKX3mQSWZUF6n0mufo4uHUeLJdg69ZgQt2X2tqu4Z/bOa9D7pLJlXfCQ763XJY+AZVnQ65JHEvL+Qurgxf0sxiRtINiIQHcRKtBPnToFgwYNgnPOOQd+9rOfQXZ2Nuzbt88xZu/evXDdddfBz372MzjnnHNg0KBBUF5ezv6ZJk1CLy5oblfg9oNJm7XE2jV98lLWHNW5Xfwc6eljxRJsXTqNiduyMseBZVmQlTmuQe+Tytbt/7vD93bdL+8Cy7Lgul/elZD3F1IHL+5nMSZpA8FGBLrPMGkSenFBc1u8+sFEoItAF4EuJgJdBLrbJgJd4OLF/SzGJG0g2IhA9xkmTUIvLmhui1c/WMeeE2pZxvU1Av36vKivi/Gt/U15KWud+9oCvXPffFc/h9uHMH6w7peE4rbsljl2ykzLnAa9TypbZtsRvrceHUeBZVnQo+OohLy/kDp4cT+LMUkbCDYi0H2GSZPQiwva1f0miSXYujcfUsuy2wyzN9RthkV9XYxvnTPHpax16zEBLMuCbj0muPo5Ol43QSzBlnnNyLitR9poW3iljW7Q+6SydT9voO8t+3f32c+d392XkPcXUgcv7mcxJmkDwUYEus8waRJ6cUG7+i+TxBJsGe1H1bKszmPskNTOY6K+LsY3t0OTE2ndu48Hy7Kge/fxrn6OjGtHiyXY0rqPj9u6Zk8Ey7Kga/bEBr1PKpvbRQBNsKyMsfZzJ2NsQt5fSB28uJ/FmKQNBBsR6D7DpEnoxQXNbfHqBxOBLgJdBLqYCHQR6G6bCHSBixf3sxiTtIFgIwLdZ5g0Cb24oLW9dZJYgq1L5zG1LKtbTVGnbuOivi7Gt+6XDk5Zy241xA5JbYbXVIEAACAASURBVDXE1c/hdu6sHywVahWYbG6LYxNMBLrAxYv7WYxJ2kCwEYHuM0yahF5c0Ny+XfaDZf3hkVrW67KQ3Y/2slDU18U07MKHU9Z6/fHftp/88d/ufpb/+6BYgq1T1vi4LbOXfYOe2Wtig94nlc3tGgMmWMYNNcVJb8hLyPsLqYMX97MYk7SBYCMC3WeYNAm9uKC5LV79YBkdRtWyrPSaEPf0MVFfF+Ob27fcfrhBT+s6TizBZnJ17lSwDjdO9L2l3zIJLMuC9FsmJeT9hdTBi/tZjEnaQLARge4zTJqEXlzQ3BavfjAR6CLQRaCLiUAXge62iUAXuHhxP4sxSRsINiLQfYZJk9CLC5rb+dl+MAlJTaxlXfBQylqvSx6xQ9wvecTVz3Ft74liCbaG1GGQmhb1W4cbJvre0vvUCPQ+kxLy/kLq4MX9LMYkbSDYiED3GSZNQi8uaG3+li+WYIsm2jv2t4s6dewf/XUxvrm96fXyhlrMHGvXd1Lc1qmfvZ506pffoPdJZXP79toEkxt0gYsX97MYk7SBYCMC3WeYNAm9uKC5LV79YCLQRaDHayLQ/WMi0EWgi0AXTMGL+1mMSdpAsBGB7jNMmoReXNDa35wnlmBr+c/JteyqB6aAZVlw1QNTor4uJtbyn+b4SesB+WIJtjZ/j9+uGTAZLMuCawZMbtD7pLQZcFjrtl1zd42f3D05Ie8vpA5e3M9iTNIGgo0IdJ9h0iT04oLW7pY8sQRb64H5taztffZGqe19k6O+LsY3t0W0HwT6FXfmiyXYWt8dv7W9t2Y9uXdyg94nlc3tSB8TLNGRW0Lq4MX9LMYkbSDYiED3GSZNQi8uaG1vmySWYItWsblrzwlgWRZ07TnB9erRXrcrb89PWWt/py282t852dXPcU2fPLEE29X9JsVt195hC69r78hv0PuksrldpM4ES3QxQSF18OJ+FmOSNhBsRKD7DJMmoRcXNLfFqx9MBLoIdBHoYiLQRaC7bSLQBS5e3M9iTNIGgo0IdJ9h0iT04oLW7U/DxBJsHa+bUMsybsgDy7Ig44a8qK+L8a39TXkpa5372sKrc998Vz/H1X+ZJJZg69JpTNyWlVkjvDLHNeh9Utm6N3vU95bdfDBYlgXZzQcn5P2F1MGL+1mMSdpAsBGB7jNMmoReXNDc7v3rBzNZeKWCub3xT6SZIrzcrsDtB2vIHJD1pH5LTx/re+vefTxYlgXdu49PyPsLqYMX97MYk7SBYCMC3WeYNAm9uKC5fWvkB5MNdWIt6zf/TFnr9fsHwLIs6PX7B1z9HJ2yxosl2BrSKaLzrTXrya35rnetMNXcjqQywa5rmwuWZcF1bXMT8v5C6uDF/SzGJG0g2IhA9xkmTUIvLmhui1c/mAj0xJrbIloEupgIdPPNbXFsgolAF7h4cT+LMUkbCDYi0H2GSZPQiwtahxsmiiXYMtqPqmVZncfYocudx0R9XYxv3S8dnLKW3WqInTPaaoirnyO9y1ixBFsqpEKYbG4XqTPBEl1MUEgdvLifxZikDQQbEeg+w6RJ6MUFze38bD+YG9V0/WQZHUalrGWl1xzkpLv7e3a4caJYgq0hhRKl6GT95vZzwATrcvMksCwLutw8KSHvL6QOXtzPYkzSBoKNCHSfYdIk9OKC1rHnBLEEW+eMcbWsW5bdZq1bVvTXxfjm9t83kZZxfY3wuj7P9c8illgzWXilgnW/PMf3ln3FUDsi54qhCXl/IXXw4n4WY5I2EGxEoPsMkyahFxc0tzelfjAR6CLQ4zUR6P4xEegi0EWgC6bgxf0sxiRtINiIQPcZJk1CLy5obod1+sG6/88/aln2/xtkb5T+36Cor4vxze0DgkSaKQc5budn+8HSuo6L27r2tP2ka88JDXqfVLbMa0b63nqkjQbLsqBH2uiEvL+QOnhxP4sxSRsINiLQfYZJk9CLC9pVf50klmDrnDmulnXrUSO8ekyI+roY39ze+CfSTBFeXa96XCzBltl2RNzWo+MoW3h1HNWg9xFLbUu0nwipgxf3sxiTtIFgIwI9SUybNg1atGgBTZo0gSZNmkC7du1g4cKF4dfLy8th0KBBcO6558JZZ50FvXr1guLiYsd77N27F7Kzs+Gss86Cc889F+6//36oqKjQ+hwmTUIvLmitB+SLJdg6Zk+oZRk31oQu35gX9XUxvqV1H5+y1jV7oi3Qsye6+jncvl32gzVE3PfsMBIsy4KeHUa6ftBgqrkdYm+CSZE4gYsX97MYk7SBYCMCPUnMmzcPFixYAFu3boWtW7dCTk4ONGrUCD7//HMAALj33nvht7/9LRQWFsKmTZsgPT0dWrVqBVVVVQAAUFVVBc2bN4f09HTYtGkTFBYWwvnnnw+DBg3S+hwmTUIvLmhui1c/mAh0Eegi0MVEoItAd9tEoAtcvLifxZikDQQbEegu8otf/AKmT58OpaWl0KhRI5g1a1b4tQMHDsAZZ5wBixYtAgCAhQsXwhlnnAEHDhwI/5uZM2fCT37yE60JZdIk9OKC1vrufLEE2xV31rZ2AyeDZVnQbuDkqK+L8c3tv28ire29tp+0vXeyu5/FgIOuVLeW/5wct131wBSwLAuuemBKg94nle2Ku/J9b+3uqXnu3JOY70NIHby4n8WYpA0EGxHoLlBVVQUzZ86Exo0bw5YtW2Dp0qUQCATgyJEjjn/XsmVLGD58OAAADBs2DFq2bOl4/ciRIxAIBGDZsmUxf1Z5eTkcO3YsbMXFxRAIBKCkpAQqKytdtbKyMrAsC8rKylz/LFxrNXSKmAt29fCpYFkWXD18quufRcxcM8VPWuaKJdpajIzfrhxt+8mVo6c26H1S2kaIXTmqxk9GTU3I+7u9nxH78cyL+1lsJSUlItANQwR6Etm8eTP8/Oc/hzPPPBOaNm0KCxYsAACA119/HRo3blzr33ft2hUGDhwIAAADBgyArl271vo3jRs3hoKCgpg/Mzc3FwKBQC0rKCgAy7LExMTExMTExMTExHxqBQUFItANQwR6EqmoqIDt27fD+vXrYfDgwXDeeefBli1bYgr0zMxMuOeeewDAFujdunWr9W8aNWoEM2fOjPkz5Qb9xzW3bwj9YG0erW3XDJ4KlmXBNYOnRn1dTKzNo+b4SescsUTbn4bGb21rIi3aDp/aoPdJaRsi1nZYjZ8Mm5qQ93d7PyP245kX97PY5AbdPESgu0hGRgYMHDgwoSHuFJPyTCorvZez0+Zv+WIJti4dR9eyrIyxYFkWZGWMjfq6GN/a35SXsta57//f3rlHR1Wd/X8Uk4AIsbi0oCAIEpJyUSImQO4zSWaSTLjKTW7eKBdTRF/rC0oJl9znAm/fUttS6mK1BFwVPGu1ZbVKq1gBXe0CKy60F+VW4F0tFdRSAkWe3x97zO+MM9aTGfbs2/ez1uefcyabDey983znnLNPiCzLotIZIaH9EL25lQkWVbcmrGdiZNPJiYGk2tFZ0a9MlEHer20E+qBiPWtHpmwAGAjoAnG73TR//vzOTeKef/75znOnTp2Ku0ncqVOnOj+zfft2bBKXYkQXpSbozXoqRv/IFWRZFvlHroh7Hjo3f2ZQW4vmsIBeNCcktB+idzg3QXdpY8L6KpvZF36VzUm1o7PFVa3GWz6BfZFTPiHApX2gDyrWs3ZkygaAgYCeIlasWEGvvfYaHTlyhN5++216+umn6dprr6WXXnqJiNhr1vr370+7d++mAwcOkNvtjvuaNY/HQwcOHKDdu3dT//798Zq1FCM6vJogAjoCOgI6REBHQBctAjpwior1rB2ZsgFgIKCniIceeogGDhxI6enpdPPNN5PH4+kM50REFy5coLq6OurTpw/16NGD/H4/HT9+PKqNY8eOUU1NDfXo0YP69OlDdXV11NHR0aV+yDQJVVzQRN+ebIK+QY/HWDvsSbIsi2qHPRn3PHSu6NvQTbjFXXR4NcGimtaE9UyK3OI+KZBUOzpb4m0x3gp/G7vF3d/GpX2gDyrWs3ZkygaAgYBuGDJNQhUXtFJPM+RsvGLc62shy7LI62sRHgxUt8jfqq2eyZHgNTkgtB/u4kbI2WJfS8KW17LgVV7bllQ7Oiv6TioZdE8NkmVZ5J4a5NI+0AcV61k7MmUDwEBANwyZJqGKC5rocGWCBZPbYiybzgqlsunBuOehcz3j12mrr7SR3bpc2ii0H6I3+DJB3OLO19LyZuOtrGKbxFVWtXJpH+iDivWsHZmyAWAgoBuGTJNQxQVNdHg1QQR0BHQEdIiAjoAuWgR04BQV61k7MmUDwEBANwyZJqGKC5ro176YIAI6X8vKmrTV6408CuFtEdoP0c/OmqCnsCFhfe7IaxvdTUm1o7Pl+WuMt6poHVmWRVVF67i0D/RBxXrWjkzZADAQ0A1Dpkmo4oImumgywcrRq2KsyV9NlmVRTf7quOehc8uvuU9bq66fxQrq62cJ7YfoOWSCyexzUemLXBn1tQrfc0NWRT//LYN4Bh04RcV61o5M2QAwENANQ6ZJqOKCJnqHcxPELu58HXdfQFtLZrFd3EtmhYT2w1OwDnI2mbt0KqpZQK+obhV+x5CsjpsWMN6o9YRD+0AfVKxn7ciUDQADAd0wZJqEKi5oosOrCSKgI6AjoEMEdAR00SKgA6eoWM/akSkbAAYCumHINAlVXNDyZwUhZ0U8C2iSootelQtqp4p+3ZwJ6rBXgcyK/rJNBnl/4Qf0QcV61o5M2QAwENANQ6ZJqOKCJjqAmKD3lsUx+m+vI8uyyH97Xdzz0Lmir3zy1FcW2cW9rFFoP0SHVxP0Dn86Yf25K9l6krsyqXZ0tmBSm/GWTYtsTjotyKV9oA8q1rN2ZMoGgIGAbhgyTUIVFzTRtx2a4PipgRhLZ7IrGaUzQ3HPQ+cW1bRqq2dSgCzLIs8ksX/P/JlByNlk5gDWEwdOgaUzIuNkRohL+0AfVKxn7ciUDQADAd0wZJqEKi5oosOrCaKg5qvoEI2ADhHQFVCCgCxaBHTgFBXrWTsyZQPAQEA3DJkmoYoLmujbv01w9NdDMeYvCZNlWZS/JBz3PHTuPQ/o67hH2DgZ90hYaD9yH4K8HTM/ccc/zMbJ+IfDSbWjs6MfgfmLIr93FoW5tA/0QcV61o5M2QAwENANQ6ZJqOKC5s16CnI23sZxRXPZlYyiuSHhm9hBeZVlnNw7B/J27PRAwhbfz8ZJ8f2hpNrRWdFzWQZ5rydAH1SsZ+3IlA0AAwHdMGSahCouaKLDqwnKHLyg3MoyTkSHVxNEQEdAV309AfqgYj1rR6ZsABgI6IYh0yRUcUErmNwGOesubYzRV9nMdueubI57Hjq3zN2krV5f5PVZvhah/RC9+7QRJrHGlE2P7M49PSh8vZNV0c9/yyCeQQdOUbGetSNTNgAMBHTDkGkSqrigCd+4xwBl3vxLB0s9zdpa6Wsly7Ko0if271k4oQ3ytjZx3VNYQHdPCSbVjs6K3otEBqPeg86hfaAPKtazdmTKBoCBgG4YMk1CFRc00Vc1TLB83NoYq0oayLIsqippiHseOlf0/y9PZbkyOu6+AORsMm+KqKhmX+RUVLcKf2uFrIq+vVwGcYs7cIqK9awdmbIBYCCgG4ZMk1DFBU10ADFBBHQE9ERFQDdHBHQEdAR0IAsq1rN2ZMoGgIGAbhgyTUIVF7SxM4KQsxV5a2KsLlxHlmVRdeG6uOehc0U/A89TWfYqEH17sgnq8EWOzIp+1EkGS2dGnkGfGeLSPtAHFetZOzJlA8BAQDcMmSahigua6J11TbCsrClGrzey+Ze3Je556FzRV+Z4KsuVUdGb5Zmgu7gxYX3lkS9yypuTakdni30txlte20aWZVF5bRuX9oE+qFjP2pEpGwAGArphyDQJVVzQiqpbIWfdRQ0x+jxNrKD2NMU9D50r+gsYnsry+qyKMfWQszJfGdVBz/h1xusrbWS/d0obubQP9EHFetaOTNkAMBDQDUOmSajigiY6vJogAjoCOgI6REBHQBctAjpwior1rB2ZsgFgIKAbhkyTUMUFTXTRZIKl5c0xVlZFXp9V1Rr3PHSu6NvQTbjFXXSwMMG8+4MJWziPBfTCeaGk2tFZ0a9MlEHer20E+qBiPWtHpmwAGAjohiHTJFRxQRszLwQ5K+JKhkl6b16orf4Bj5JlWeQf8KjQfoj+osIEZX6/tQ6KvtNHBnnfuQX0QcV61o5M2QAwENBTRFNTE40ZM4ZuuOEGuvnmm2nixIn03nvvRX2mo6OD6urq6KabbqLrr7+eamtr6cSJE1GfOXbsGPn9frr++uvppptuom984xt08eJFx/2QaRKquKDd80AIcha7uPPV22eBtvpvXcwC+q2LhfZD9G72Jlg4oS1h3VPZLu7uqcGk2tFZ0a+DlEHer/cE+qBiPWtHpmwAGAjoKcLr9dJzzz1H77zzDr311ltUU1NDt99+O/3zn//s/MyiRYvotttuo5dffpkOHDhAZWVldNddd9Hly5eJiOjy5cs0YsQIKisrowMHDtDLL79Mt956K9XV1Tnuh0yTUMUFTXR4NUEEdAR0BHSIgI6ALloEdOAUFetZOzJlA8BAQBfE3/72N3K5XLRnzx4iIjp37hylpaXR9u3bOz9z8uRJuvbaa+mXv/wlERHt2rWLrr32Wjp58mTnZ7Zt20YZGRmOJ5VMk1DFBS1nRRhyNm92MMbC+ZFnRueH4p6Hzr3nwZC2jlsQJsuyaNyCsNB+iH6HtQmOnxJI2NIZkU3iZoSSagfqLe9xAvRBxXrWjkzZADAQ0AXx5z//mVwuFx06dIiIiH7961+Ty+WiDz/8MOpzo0aNolWrVhER0be+9S0aNWpU1PkPP/yQXC4X/eY3v3H058o0CVVc0AatD0LODg6EYswJseCVEwrHPQ/h4IA842RIK+Tt4CTMCUTGSSCcVDta2wZzgpFxEgxzaR/og4r1rB2ZsgFgIKAL4MqVK1RbW0uFhYWdx7Zu3Urp6ekxn62oqKCvf/3rRES0YMECqqioiPlMeno6tbe3x/2zOjo66KOPPur0xIkT5HK56MyZM3Tp0iWhnj9/nizLovPnzwvvi1NzgmHI2ayNoRhHfpcVSiO/G/887IL/q68jvxMZJ98JC+3HsG9D3mZ9J3FHboyMk43hpNqBest7nIiuZ+DVU8V61u6ZM2cQ0CUDAV0AS5YsoYEDB0ZtAPdFAb28vJwWLlxIRCygV1ZWxnwmLS2Ntm3bFvfPqq+vJ5fLFWN7eztZlgUhhBBCCCE01Pb2dgR0yUBATzF1dXXUv39/+uCDD6KO87rFHVfQr675i8OQsyPXro9xTMMGsiyLxjRsiHseOveulfqat4qNk7xVG4T3BfJ1VH3i3ruGjZN712xIqh2dFb1OySDv3zui6xl49VSxnrWLK+jygYCeIq5cuUKPPvoo3XrrrfSnP/0p5vxnm8Q9//zzncdOnToVd5O4U6dOdX5m+/bt2CQuhXztqTDkbMWY+hirx68hy7KoevyauOehcwtr27TVPSWyO/eUoNB+jJ0RhJzVYZzIrKdgnfH6yhrJsizylTVyaR/og4r1rB2ZsgFgIKCniMWLF1NmZia9+uqrdPr06U7/9a9/dX5m0aJF1L9/f9q9ezcdOHCA3G533NeseTweOnDgAO3evZv69++P16ylENHh1QQR0BHQVQ9eosOrCeowTmRWdDiWQQR04BQV61k7MmUDwEBATxHxngN3uVz03HPPdX7mwoULVFdXR3369KEePXqQ3++n48ePR7Vz7NgxqqmpoR49elCfPn2orq6OOjo6HPdDpkmo4oJ215IQ5GyppznGSl8rWZZFlb7WuOehc71feVhb/f0WkmVZ5O+3UGg/ivytkLMVaTMTtrr3HPaFX+85SbWjs+OnBoy3dGbkNWszQ1zaB/qgYj1rR6ZsABgI6IYh0yRUcUG7e2EIchZXvDhfGSts0Fafu4ld8XI3ie3H7csgZz3j1yWsrzRyZbS0Mal2dLaoptV4PZMCZFkWeSYFuLQP9EHFetaOTNkAMBDQDUOmSajignb3ohDkbP6sYIxFc9mVjKK58c9D54ou/HkqS/AqczdBzrqLGhLW54l8keNJrh2dLfa1GG95bRtZlkXltW1c2gf6oGI9a0embAAYCOiGIdMkVHFBEx1eTRABHQEdAR0ioCOgixYBHThFxXrWjkzZADAQ0A1Dpkmo4oI2+ushyNkSb0uMFX5WKFX42+Keh84V/Vynys+MOrWsrAnyNolw7/W1kGVZ5PW1CP+iQVZF314ug7jFHThFxXrWjkzZADAQ0A1Dpkmo4oKWNzsIORtvQyjP5EihNDkgfHMq1b13TlBbCx5gAb3ggZDQfogOFiY47r5AwpbMYuOkZFYoqXZ01nfHE8Zbm/1NsiyLarO/yaV9oA8q1rN2ZMoGgIGAbhgyTUIVF7T8mUHI2fFTAjGWzohcGZ0RinseOlf0FwQ8leWLHNFXP00wmcc88MjMlyv6bRMyyPvtIUAfVKxn7ciUDQADAd0wZJqEKi5oosOrCSKgI6AjoEMEdLGKDscyiIAOnKJiPWtHpmwAGAjohiHTJFRxQRN9C68JFle1xlg+gQWv8gmBuOehcytHr9LWmvzVZFkW1eSvFtoP0a+bM0Ed9iqQWe/wp43Xn7uSLMsif+5KLu0DfVCxnrUjUzYADAR0w5BpEqq4oIkOrybo/crDMfr7LWSFUr+Fcc9D54r+goCnsnyRI/r5bBOsvGtlwtbkrWJf5OStSqodnfX1X2q8tXc+zp5Bv/NxLu0DfVCxnrUjUzYADAR0w5BpEqq4oI1+JAQ5WzGmPsbq8WvIsiyqHr8m7nnoXNG3jap8S6pTRX9RYYJF1YnrmRh5FGJiIKl2dLZgUpvxlk0LkmVZVDYtyKV9oA8q1rN2ZMoGgIGAbhgyTUIVFzTR4dUEEdAR0BHQIQI6ArpoEdCBU1SsZ+3IlA0AAwHdMGSahCouaHcvCkHO4rVIfBW9SR1PZdlM0F3aCDk7Zn4oYcc/HCbLsmj8w+Gk2tFZ0a/blMHC+Ww9KeT07wH0QcV61o5M2QAwENANQ6ZJqOKCds+DIcjZeFfLZHm2WAdF7zHAU1negy76TQgmmPtwKGHHLmQBfezCcFLt6Kzoq9cyiCvowCkq1rN2ZMoGgIGAbhgyTUIVFzTRr74xwcIJbTG6p7JCyT01/nnoXNG7Q/NUlt25Rc8hE0xmDmA9+XIrR6403poxkc0Ex6zi0j7QBxXrWTsyZQPAQEA3DJkmoYoLmuii1ARRUPNVdIhGQIe81gmnYj35ckWHYxlEQAdOUbGetSNTNgAMBHTDkGkSqrig3b0wBDlbWt4cY2VVZPOvqta456Fzy8qatNXrbSHLssjrbRHaj8LaNsjZintXJ2x1wVq26WTB2qTa0VnfoMeNt3bYk+w1a8Oe5NI+0AcV61k7MmUDwEBANwyZJqGKC9rIpWHIWc/4dTH6ShvJsizylTbGPQ+d6/3aCm31j15JlmWRf/RK4X2BfC3ytyasZ3JkF/fJgaTa0Vl3UYPx+jxN7PeOp4lL+0AfVKxn7ciUDQADAd0wZJqEKi5oosOrCSKgI6AnKgK6OSKgI6AjoANZULGetSNTNgAMBHTDkGkSqrigjfpGGHI23u2s7imRZ0anBIXfWqu6vgGPaWvt0CfYLalDnxDaD09hA+RsweS2hC2bHtmde3owqXZ0tqSi2XgrqtmjVRXVrVzaB/qgYj1rR6ZsABgI6IYh0yRUcUEbVReGnC31NMdY6Ys8g+5rjXseOlf0+6t56qtsZle8KsX+PUU/i2+CMgcvHSz2tRhveW0be71nbRuX9oE+qFjP2pEpGwAGArphyDQJVVzQRIdXEyyqbo3RMzFyS+rEQNzz0Lnj7gtoa8kstot7yayQ0H6Uj1sLOVs5elXC1uSvZrtz569Oqh2dHTctYLxR6wmH9oE+qFjP2pEpGwAGArphyDQJVVzQRIdXE0RAR0BHQIcI6AjookVAB05RsZ61I1M2AAwEdMOQaRKquKCNeDwMOVsxpj7G6vFr2GuRxq+Jex469965QW0teJAV1AUPhoT2o/Lub0HOJvOYBx6Z+XJF32Ivg3gGHThFxXrWjkzZADAQ0A1Dpkmo4oKWszwMOYuCmq9jZwS1tXg2C+jFs0NC+yH6+WwTTOZNBngrxJdbXNVqvOUT2J1b5RMCXNoH+qBiPWtHpmwAGAjohiHTJFRxQRu2Kgw5K+JKhkmKfn0TT2V5fZbocGWCMm/+pYOiX6Mng7xf2wj0QcV61o5M2QAwENANQ6ZJqOKCJjq8miACOgI6AjpEQEdAFy0COnCKivWsHZmyAWAgoKeIPXv2kN/vp379+pHL5aIXX3wx6vyVK1eovr6e+vXrR927d6eSkhJ65513oj7z4Ycf0pw5c6h3797Uu3dvmjNnDp09e7ZL/ZBpEqq4oA1pDUHO5s8Kxlg0l926XDQ3/nno3Lz79bVwHhsnhfNCYvsyG/J2/JRAwpbOYOOkdEYoqXZ0VvSXbTLI+ws/oA8q1rN2ZMoGgIGAniJ27dpFzzzzDO3YsSNuQG9paaFevXrRjh076NChQzRjxgzq168fffzxx52f8fl8NGLECNq3bx/t27ePRowYQX6/v0v9kGkSqrigDfxuAHL2zsZQjMObw2RZFg1vDsc9D+GdjfKMk6HrIG91GCdQbnmPE6APKtazdmTKBoCBgC6Azwf0K1euUN++famlpaXzWEdHB2VmZtL3vvc9IiI6fPgwuVwueuONNzo/s3//fnK5XPTee+85/rNlmoQqLmhZ9WHI2YFbmmPM2sJucc/a0hr3PIQDt2CcQGdinEAn8h4nQB9UrGftyJQNAAMBXQCfD+jvv/8+uVwuOnDgQNTnJkyYQPPmzSMios2bN1NmZmZMW5mZmfSjH/3oC/+sjo4O+uijjzo9ceIE7V5GYQAAFPtJREFUuVwuOnPmDF26dEmo58+fJ8uy6Pz588L74tSRa9ZDzmZtaY1x5BZ2q+HILYG45yHM2oJxAp2JcQKdyHuciK5n4NVTxXrW7pkzZxDQJQMBXQCfD+h79+4ll8tFJ0+ejPrcggULqLKykoiIGhsbaejQoTFtDR06lJqamr7wz6qvryeXyxVje3s7WZYFIYQQQgghNNT29nYEdMlAQBfAFwX0U6dORX3ukUceIa/XS0QsoGdlZcW0deedd1Jz8xffKoUr6FfXrO8HIWdHr1gfY/7KDWRZFuWv3BD3POyCT+tr1DgR2ZflkLd3r0zc/FWRcbJqQ1Lt6OxdkPIi4yRv1QYu7YuuZ+DVU8V61i6uoMsHAroAUnmL++eR6TmTS5fUe2ZH9KY1Jlg4oS1G99QgWZZF7qnBuOehcwsm6WvZNDZOyqYFhfbjngdCkLOl5c0JW1nFni2urGpNqh2dLapuNV7PxMgu7hMDXNoH+qBiPWtHpmwAGAjoAviiTeJaW///gn3x4sW4m8S9+eabnZ954403sElcihEdXk0QAR0BHQEdIqAjoIsWAR04RcV61o5M2QAwENBTxCeffEIHDx6kgwcPksvlonA4TAcPHqRjx44REXvNWmZmJu3cuZMOHTpEs2bNivuatVGjRtH+/ftp//79NHLkSLxmLcVkrQlDzuI96Hz1jF+nrb7SRrIsi3yljUL7UeJtgZxN5j31hfPYelI4L3R13nuvod7s5cbrv+sZsiyL/Hc9w6V9oA8q1rN2ZMoGgIGAniJeeeWVuJu1zZ8/n4jYVfT6+nrq27cvZWRkUHFxMR06dCiqjX/84x80e/Zs6tWrF/Xq1Ytmz55NZ8+e7VI/ZJqEKi5o2c+EIWeLfS0xlte2kWVZVF7bFvc8dG5RTau2eiZFrnhNCgjtR1lZE+Rs+bi1CVtV0kCWZVFVSUNS7eis6Lksg7zXE6APKtazdmTKBoCBgG4YMk1CFRc00eHVBBHQEdBlLaidKjq8miACOgK66usJ0AcV61k7MmUDwEBANwyZJqGKC1rO8jDkrMy3Lutg5d3f0taa/HqyLItq8uuF9kP0s7MmWFLRnLAV1ewZ9Iok24F6y3ucAH1QsZ61I1M2AAwEdMOQaRKquKBlPx2GnC2Y3BZj2fTI5l/Tg3HPQ+d6h/23tvpHPc2eGR31tNB+jJ0RhJytHLkyYWvGrGJf5IxZlVQ7OjtuWsB4S2axvQpKZoW4tA/0QcV61o5M2QAwENANQ6ZJqOKCJvrqsgniFne+ir7KbcIVdNEbfJkgdnHnvIu7v9V4PZMjt7hPDnBpH+iDivWsHZmyAWAgoBuGTJNQxQVNdHg1QQR0BHQEdIiAjoAuWgR04BQV61k7MmUDwEBANwyZJqGKC5ro8GqCKKg5F94SPD/MS97vLXaq6Nu/TTCZxzzwyMyXW1gL3VPYOHFPCXJpH+iDivWsHZmyAWAgoBuGTJNQxQXta/8dhpx1lzbG6KtsZpvEVTbHPQ+dK/q5TpWfGXWq6DsJTLBwQlvCuqdGgtfUYFLt6GzBJFg2LfJFzrQgl/aBPqhYz9qRKRsABgK6Ycg0CVVc0LLWhCFny/PXxFhVtI69FqloXdzz0Ln3zglqa8EDLKAXPBAS2w8JwoXujrsvkLBRX+Qk0Y7Ojp8CS2ewcVI6I8SlfaAPKtazdmTKBoCBgG4YMk1CFRc00eHVBBHQEdAR0CECOgK6aBHQgVNUrGftyJQNAAMB3TBkmoQqLmiin882wTsbQjEObwqTZVk0vCn+eejcoWvD2jqiYT1ZlkUjGtaL7cu6EOStDuNEZkX//0rgiEb2e2dEI59/D6APKtazdmTKBoCBgG4YMk1CFRe0O0JByNnbv98WY9YP2LOAWT+Ifx52we/pa9b3I+Pk+0GxfRH9f2yCOowTKLW8xwnQBxXrWTsyZQPAQEA3DJkmoYoL2sCNAcjZQeuDMQ7bwG41HLYhFPc87IIb9HXY/0TGyf+EhPcFcjaJOYD1BDqR9zgB+qBiPWtHpmwAGAjohiHTJFRxQRMdXk1QRKFklKKDFUcR0A1S4uAF9RABHThFxXrWjkzZADAQ0A1Dpkmo4oKWtToMORtvQyjer7sxybHTA9pafD8rqIvvDwntx71zg1BiCx6MbCb4YEh4X2S1qKbVeD2TAmRZFnkmBbi0D/RBxXrWjkzZADAQ0A1Dpkmo4oJ296IQ5GzB5LYYy6ZHAvr0YNzz0LmiQ7QJAb3U0ww5W95tRsJW3TCbvRXihtlJtaOz3uFPG68/dyVZlkX+3JVc2gf6oGI9a0embAAYCOiGIdMkVHFBu+fBEORsRW59jNVj15BlWVQ9dk3c89C5xb4WbS2vbSPLsqi8tk1oP8ZPDUDOlpY3J2xlVStZlkWVVa1JtaOznoJ1xusrayTLsshX1silfaAPKtazdmTKBoCBgG4YMk1CFRc00eHVBBHQEdAR0CECOgK6aBHQgVNUrGftyJQNAAMB3TBkmoQqLmiinws0wcIJbTG6p7Jb3N1T45+HXbBWX91TIuNkSlBoP8rHrYWcLfG2JGyFn32RU+FvS6odnRX9CIMMVvoiX+T4Wrm0D/RBxXrWjkzZADAQ0A1Dpkmo4oIm+hlbE/RmPRWjf+QK9izgyBVxz0PnlrmbtNXrayHLssjraxHaD9FXl03QO/SbCesfsZytJyOWJ9WOzubNDhpv4Xy2p0Xh/BCX9oE+qFjP2pEpGwAGArphyDQJVVzQRIdXE0RAR0BHQIcI6AjookVAB05RsZ61I1M2AAwEdMOQaRKquKCJvoXXBPPuD8ZYOC9SKM0LxT0PnSv6NW88leV1fKKDhQmOuy+QsCWz2HpSMiuUVDs6W1LRbLwV1ewW94rqVi7tA31QsZ61I1M2AAwEdMOQaRKquKDlPhyCnEVBzdf8WUFtLZrLxknR3JDQfrhLGyFnx84IJmzx7Mjr+GaHkmpHZ0V/USuDvPe0APqgYj1rR6ZsABgI6IYh0yRUcUETffXTBO95IBTjuEfCZFkWjXskHPc8dO7oR/Q1fxEbJ/mLwkL7cfdCKLP5j0bGyaNh4X2R1ZFLw8Y75vH1ZFkWjXl8PZf2gT6oWM/akSkbAAYCumHINAlVXNBEh1cTREBHQEdAhzxFQP9yRYdjGURAB05RsZ61I1M2AAwEdAXZuHEjDRo0iDIyMig3N5dee+01xz8r0yRUcUEbsSwMBXjPf7FC6Z7/Wi+8L6o76hv6eu8yNk7uXbZeaD9GPA55i4DO1699M2y8o5ez9WT08vVc2gf6oGI9a0embAAYCOiKsX37dkpLS6NNmzbR4cOH6bHHHqOePXvSsWPHHP28TJNQxQVt6NowFOCIBlYojWhYL7wvyrsupK0jGlnwGtEo+O8p+v8Y/kexnny5dzaGjHd4M1tPhjfz+fcA+qBiPWtHpmwAGAjoipGXl0eLFi2KOpadnU3Lly939PMyTUIVF7Qzf70VCvD/jg0my7Lo/44NFt4XKK8YJ9CJGCfQibzHCdAHFetZOzJlA8BAQFeIixcvUrdu3Wjnzp1Rx5cuXUrFxcWO2pBpEqq4oIkuGEwVBTV0IsYJdCLGCXQiAjpwior1rB2ZsgFgIKArxMmTJ8nlctHevXujjjc2NlJWVlbcn+no6KCPPvqo0+PHj5PL5aIjR47QmTNnhHr69Glqb2+n06dPC++LU/92OBsK8NShu6i9vZ1OHbpLeF+gvGKcQCdinEAn8h4nousZePVUsZ61e+TIEXK5XHTu3LlUxBngAAR0hfgsoO/bty/qeENDAw0bNizuz9TX15PL5YIQQgghhBDCuJ44cSIVcQY4AAFdIRK5xf3zV9DPnj1L77//Pp07dy7quAhPnDjRuSCI7guUW4wV6ESME+hEjBPoRIwT6FTVx8q5c+foxIkT9Omnn6YizgAHIKArRl5eHi1evDjqWE5OjuNN4mTio4/wzAtwBsYKcALGCXACxglwAsYJcArGCrjaIKArxmevWdu8eTMdPnyYli1bRj179qSjR4+K7lqXwYIGnIKxApyAcQKcgHECnIBxApyCsQKuNgjoCrJx40YaOHAgpaenU25uLu3Zs0d0lxICCxpwCsYKcALGCXACxglwAsYJcArGCrjaIKADYXR0dFB9fT11dHSI7gqQHIwV4ASME+AEjBPgBIwT4BSMFXC1QUAHAAAAAAAAAAAkAAEdAAAAAAAAAACQAAR0AAAAAAAAAABAAhDQAQAAAAAAAAAACUBABwAAAAAAAAAAJAABHXBl48aNNGjQIMrIyKDc3Fx67bXX/uPnX3jhBcrJyaH09HTKycmhnTt3pqinQCRdGSc/+MEPqLCwkG688Ua68cYbyePx0JtvvpnC3gKRdHVN+Yxt27aRy+WiiRMncu4hkIGujpOzZ8/SkiVLqG/fvpSRkUHZ2dn0i1/8IkW9BaLo6jhZv349ZWVlUffu3al///60bNkyunDhQop6C0SwZ88e8vv91K9fP3K5XPTiiy9+6c+8+uqrlJubSxkZGXTHHXfQs88+m4KeAp1AQAfc2L59O6WlpdGmTZvo8OHD9Nhjj1HPnj3p2LFjcT+/b98+6tatGzU1NdG7775L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width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = plot_data(offx=4, signed=True, channels=1)\n",
    "#print(''.join(str(x) for x in data[4:][3::4]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/tmp/foo'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-32-d8e3fa510bf1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0mplotdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mdelta\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mdelta\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdelta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplotdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mplot_avg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-32-d8e3fa510bf1>\u001b[0m in \u001b[0;36mplot_avg\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mplot_avg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/tmp/foo'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m         \u001b[0mvals\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrombuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'uint16'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/tmp/foo'"
     ]
    }
   ],
   "source": [
    "import random, struct, numpy as np\n",
    "\n",
    "def plot_avg():\n",
    "    with open('/tmp/foo', 'rb') as f:\n",
    "        vals = np.frombuffer(f.read(), dtype='uint16')\n",
    "    \n",
    "    vals = vals.copy()\n",
    "    idx = 1\n",
    "    vals &= 1<<idx\n",
    "    vals >>= idx\n",
    "    \n",
    "    delta = 10000\n",
    "    plotdata = [sum(vals[i:i+delta])/delta for i in range(0, len(vals), delta)]\n",
    "    plt.plot(plotdata)\n",
    "plot_avg()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}