machine-learning/06-弹性网络回归算法/弹性网络回归算法.ipynb · Saisimon/AIMindMap

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弹性网络回归算法.ipynb 630.36 KB

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "创建演示数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# 坐标轴起始点\n",
    "start = 0\n",
    "# 坐标轴结束点\n",
    "end = 6\n",
    "X = np.array([[1, 1], [1, 2], [1, 3], [1, 4], [1, 5]])\n",
    "y = np.array([1598, 3898, 6220, 7799, 10510])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用 scikit-learn 拟合："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-278.17313921, 2099.04700612])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import ElasticNet\n",
    "\n",
    "# 初始化弹性网络回归器\n",
    "reg = ElasticNet(alpha=0.1, l1_ratio=0.5, fit_intercept=False)\n",
    "# 拟合线性模型\n",
    "reg.fit(X, y)\n",
    "# 权重系数\n",
    "W = reg.coef_\n",
    "W"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可视化："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
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       "        return MozWebSocket;\n",
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       "        alert(\n",
       "            '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",
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       "\n",
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       "    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",
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       "\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",
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       "    this.image_mode = 'full';\n",
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       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
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       "    this._init_toolbar(this);\n",
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       "    var fig = this;\n",
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       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
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       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
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       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\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, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"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\";/* global mpl */\n",
       "\n",
       "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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib notebook\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['PingFang HK']  # 选择一个本地的支持中文的字体\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_facecolor('#f8f9fa')\n",
    "\n",
    "x1 = X[:][:, 1]\n",
    "y1 = y\n",
    "ax.scatter(x1, y1, marker='o', s=20, c='#e63946')\n",
    "x2 = np.linspace(start, end, 100)\n",
    "y2 = W[1] * x2 + W[0]\n",
    "ax.plot(x2, y2, '#457b9d', label='弹性网络回归')\n",
    "\n",
    "for i in range(len(x1)):\n",
    "    ax.plot(np.linspace(x1[i], x1[i], 100), np.linspace(y1[i], W[1] * x1[i] + W[0], 100), '#219ebc', linestyle='--')\n",
    "ax.set_title('弹性网络回归', color='#264653')\n",
    "ax.set_xlabel('X', color='#264653')\n",
    "ax.set_ylabel('Y', color='#264653')\n",
    "ax.tick_params(labelcolor='#264653')\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用坐标下降法实现弹性网络回归："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def elasticNet(X, y, lambdas=0.1, rhos=0.5, max_iter=1000, tol=1e-4):\n",
    "    \"\"\"\n",
    "    弹性网络回归，使用坐标下降法（coordinate descent）\n",
    "    args:\n",
    "        X - 训练数据集\n",
    "        y - 目标标签值\n",
    "        lambdas - 惩罚项系数\n",
    "        rhos - 混合参数，取值范围[0,1]\n",
    "        max_iter - 最大迭代次数\n",
    "        tol - 变化量容忍值\n",
    "    return:\n",
    "        W - 权重系数\n",
    "    \"\"\"\n",
    "    # 初始化 W 为零向量\n",
    "    W = np.zeros(X.shape[1])\n",
    "    for it in range(max_iter):\n",
    "        done = True\n",
    "        # 遍历所有自变量\n",
    "        for i in range(0, len(W)):\n",
    "            # 记录上一轮系数\n",
    "            w = W[i]\n",
    "            # 求出当前条件下的最佳系数\n",
    "            W[i] = down(X, y, W, i, lambdas, rhos)\n",
    "            # 当其中一个系数变化量未到达其容忍值，继续循环\n",
    "            if (np.abs(w - W[i]) > tol):\n",
    "                done = False\n",
    "        # 所有系数都变化不大时，结束循环\n",
    "        if (done):\n",
    "            break\n",
    "    return W\n",
    "\n",
    "def down(X, y, W, index, lambdas=0.1, rhos=0.5):\n",
    "    \"\"\"\n",
    "    cost(W) = (x1 * w1 + x2 * w2 + ... - y)^2 / 2n + ... + λ * ρ * (|w1| + |w2| + ...) + [λ * (1 - ρ) / 2] * (w1^2 + w2^2 + ...)\n",
    "    假设 w1 是变量，这时其他的值均为常数，带入上式后，其代价函数是关于 w1 的一元二次函数，可以写成下式：\n",
    "    cost(w1) = (a * w1 + b)^2 / 2n + ... + λρ|w1| + [λ(1 - ρ)/2] * w1^2 + c (a,b,c,λ 均为常数)\n",
    "    => 展开后\n",
    "    cost(w1) = [aa / 2n + λ(1 - ρ)/2] * w1^2 + (ab / n) * w1 + λρ|w1| + c (aa,ab,c,λ 均为常数)\n",
    "    \"\"\"\n",
    "    # 展开后的二次项的系数之和\n",
    "    aa = 0\n",
    "    # 展开后的一次项的系数之和\n",
    "    ab = 0\n",
    "    for i in range(X.shape[0]):\n",
    "        # 括号内一次项的系数\n",
    "        a = X[i][index]\n",
    "        # 括号内常数项的系数\n",
    "        b = X[i][:].dot(W) - a * W[index] - y[i]\n",
    "        # 可以很容易的得到展开后的二次项的系数为括号内一次项的系数平方的和\n",
    "        aa = aa + a * a\n",
    "        # 可以很容易的得到展开后的一次项的系数为括号内一次项的系数乘以括号内常数项的和\n",
    "        ab = ab + a * b\n",
    "    # 由于是一元二次函数，当导数为零是，函数值最小值，只需要关注二次项系数、一次项系数和 λ\n",
    "    return det(aa, ab, X.shape[0], lambdas, rhos)\n",
    "\n",
    "def det(aa, ab, n, lambdas=0.1, rhos=0.5):\n",
    "    \"\"\"\n",
    "    通过代价函数的导数求 w，当 w = 0 时，不可导\n",
    "    det(w) = [aa / n + λ(1 - ρ)] * w + ab / n + λρ = 0 (w > 0)\n",
    "    => w = - (ab / n + λρ) / [aa / n  + λ(1 - ρ)]\n",
    "    \n",
    "    det(w) = [aa / n + λ(1 - ρ)] * w + ab / n  - λρ = 0 (w < 0)\n",
    "    => w = - (ab / n - λρ) / [aa / n  + λ(1 - ρ)]\n",
    "    \n",
    "    det(w) = NaN (w = 0)\n",
    "    => w = 0\n",
    "    \"\"\"\n",
    "    w = - (ab / n + lambdas * rhos) / (aa / n + lambdas * (1 - rhos))\n",
    "    if w < 0:\n",
    "        w = - (ab / n - lambdas * rhos) / (aa / n + lambdas * (1 - rhos))\n",
    "        if w > 0:\n",
    "            w = 0\n",
    "    return w"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "计算权重系数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-278.21198187, 2099.05755164])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W = elasticNet(X, y, lambdas=0.1, rhos=0.5)\n",
    "W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import sklearn.datasets\n",
    "\n",
    "diabetes_train_splitsize = 1.0\n",
    "\n",
    "beta_path = None\n",
    "sum_abs_coeff = None\n",
    "\n",
    "def fetch_diabetes():\n",
    "    diabetes = sklearn.datasets.load_diabetes()\n",
    "    X_all = diabetes.data\n",
    "    y_all = diabetes.target\n",
    "\n",
    "    total_N = len(y_all)\n",
    "    train_N = int(total_N * diabetes_train_splitsize)\n",
    "    test_N = total_N - train_N\n",
    "    rand = np.random.mtrand.RandomState(seed=123)\n",
    "    train_idx = set(rand.choice(total_N, size=(train_N,), replace=False))\n",
    "\n",
    "    train_X = X_all[list(train_idx)]\n",
    "    train_y = y_all[list(train_idx)]\n",
    "\n",
    "    test_idx = np.zeros((test_N,), dtype=np.int32)\n",
    "    test_n = 0\n",
    "    for n in range(total_N):\n",
    "        if n not in train_idx:\n",
    "            test_idx[test_n] = n\n",
    "            test_n += 1\n",
    "    test_X = X_all[test_idx]\n",
    "    test_y = y_all[test_idx]\n",
    "\n",
    "    def get_add_mul(X):\n",
    "        add = - np.average(X, 0)\n",
    "        X1 = X + add\n",
    "        mul = 1 / np.sqrt((X1 * X1).sum(0))\n",
    "        return add, mul\n",
    "\n",
    "    X_add, X_mul = get_add_mul(train_X)\n",
    "    y_add = - np.average(train_y)\n",
    "\n",
    "    train_X = (train_X + X_add) * X_mul\n",
    "    train_y = train_y + y_add\n",
    "    if len(test_X) > 0:\n",
    "        test_X = (test_X + X_add) * X_mul\n",
    "        test_y = test_y + y_add\n",
    "    return train_X, train_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  33.14936862,  -35.24321699,  211.02705534,  144.56065518,\n",
       "         21.93051897,    0.        , -115.61926092,  100.65748468,\n",
       "        185.32559078,   96.25686133])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X, train_y = fetch_diabetes()\n",
    "\n",
    "elasticNet(train_X, train_y, lambdas=0.01, rhos=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  33.14720152,  -35.24560914,  211.02392962,  144.56011492,\n",
       "         21.93153316,    0.        , -115.6200168 ,  100.65883785,\n",
       "        185.32633378,   96.25721354])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import ElasticNet\n",
    "\n",
    "reg = ElasticNet(alpha=0.01, l1_ratio=0.5, fit_intercept=False)\n",
    "reg.fit(train_X, train_y)\n",
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 758354.8717697824, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 776587.2876085138, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 796593.3244981075, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 818319.0728340697, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 841661.6409969445, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 866468.9288357574, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 892541.2969745502, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 919634.77971131, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 947465.69894387, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 975716.9249908995, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1004046.3621805666, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1032098.2444435438, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1059517.3623900537, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1085965.4970589462, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1111138.4212501126, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1134781.2441552852, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1156699.8972567315, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1176767.2094890801, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1194923.0624379646, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1211169.1926081914, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1225560.003737103, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1238191.1122671796, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1249187.2915362113, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1258691.1373097932, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1266853.3136422003, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1273824.7889305442, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1279751.1169128327, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1284768.5827683257, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1289001.9109823906, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1292563.1926194166, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1295551.7052201754, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1298054.3432343325, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1300146.432387179, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1301892.7562583715, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1303348.6716347584, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1304561.2285815603, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1305570.2415387654, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1306409.2800948594, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1307106.5638478287, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1307685.7563994536, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1308166.660350033, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1308565.8192424753, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1308897.0345860585, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1309171.8069996727, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1309399.7106079042, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1309588.7094217716, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1309745.4237594055, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1309875.3539563916, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1309983.067774836, tolerance: 262.1009124434389\n",
      "  positive)\n",
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1310072.357101662, tolerance: 262.1009124434389\n",
      "  positive)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import ElasticNet\n",
    "\n",
    "train_X, train_y = fetch_diabetes()\n",
    "\n",
    "n_ratios = 100\n",
    "ratios = np.linspace(0, 1, n_ratios)\n",
    "n_alphas = 50\n",
    "alphas = np.logspace(-3, 1, n_alphas)\n",
    "\n",
    "coefs = []\n",
    "for r in ratios:\n",
    "    coef = []\n",
    "    for a in alphas:\n",
    "        reg = ElasticNet(alpha=a, l1_ratio=r, fit_intercept=False)\n",
    "        reg.fit(train_X, train_y)\n",
    "        coef.append(reg.coef_)\n",
    "#         coef.append(elasticNet(train_X, train_y, lambdas=a, rhos=r))\n",
    "    coefs.append(coef)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ρ 对弹性网络回归的影响："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            '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",
       "\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 = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(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 (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.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 = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\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, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"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\";/* global mpl */\n",
       "\n",
       "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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as animation\n",
    "\n",
    "%matplotlib notebook\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "lines = []\n",
    "text = plt.text(9, 700, '', fontsize=12)\n",
    "coefs = np.array(coefs)\n",
    "for i in range(0, 10):\n",
    "    line, = ax.plot(alphas, coefs[0][:,i])\n",
    "    lines.append(line)\n",
    "\n",
    "def init():\n",
    "    ax.set_xlim(alphas[0] , alphas[len(alphas) - 1])\n",
    "    ax.set_ylim(-800, 800)\n",
    "    ax.set_xscale('log')\n",
    "    ax.set_xlim(ax.get_xlim()[::-1])  # reverse axis\n",
    "    return line\n",
    "\n",
    "def update(i):\n",
    "    for j in range(0, 10):\n",
    "        lines[j].set_data(alphas, coefs[i][:,j])\n",
    "    text.set_text(\"ρ = %.4f\"%(ratios[i]))\n",
    "    return lines\n",
    "\n",
    "ani = animation.FuncAnimation(fig, update, frames = range(len(ratios)), init_func=init, interval=100, blit=True, repeat=False)\n",
    "# ani.save('弹性网络变化.gif')\n",
    "plt.title('弹性网络回归', color='#264653')\n",
    "plt.axis('tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "弹性网络回归与Lasso对比："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            '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",
       "\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 = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(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 (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.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 = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\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, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"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\";/* global mpl */\n",
       "\n",
       "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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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8gF788P5dtxRte2Vh+9L28ek56fbg8+84ALB+/p0sw53jbCFPy62Qw45xMoeXtK1zLyq6bk2sk+3dMe1Zv1Gu7Rzb9N4j60uvtCG9OyYqV9uB4A//LrMrur+z5iHAzrdZDdddQkBoK8rnpURQAABBBBAINECBICJFmV7CCCAAAIIIJCtAivXrLXHXnj1oH2otdlbH33qfh86BqDXBfilh0bYCUf/bwZeLXdKixtdyBQuYPO6n2YMAF955wO7c9wku/mqS61/pxvTBYBznp1oHe8eYR998Y3d0/0WO+/Uhtbkhs7umO7scrOddXL6SSy03H2Tn3GvPz1qUMTWeeVKl7LiRYseVOfQFoDtW15uT7z8unW5rmW6LrUPPP2CPfrCdNctecp999i+/ftt1Zp1Vq1KpXTbGzD2EXv13bl27mkN7dEh/0ymEksA+OvylXZZl9tNXaOfGnF3WtdndSk+/8autuXPbXZ7uzamcf9+XPKbax03tGdHO7ZOTbum10A3O/K9vbtYy4v+aeGo8fE0ZqJaLiqMVAtBjdmYWcntAFDHdvt9D9obcz+xwocdZrMeG+daN3qFFoDZektg4wgggAACCCAQgwABYAxILIIAAggggAACySMw78uF1uGuezM9oOwMAB9/6XUb8+RzduuNra3LdS0OCgBfnv2+3fXgo3bOKSfaeac1tMEPP+4CuQUvPWUliqUP8RI1C7BCSk3C8fFXi6xls/Pt3tv+mXVYxQsk27W8zG67qbWbfEOB2fSHR1m50oenLXdlt74uoOt+/dXW44Zr3e9jCQB7Dhtj73zymavrpP8Gh1r3sf971cZNed4qly9r7zz5sN376BR7afb7NrJ3V7uiaRO3fQWTCigPzZfPxg28zY3117bvIDeTcMlixdxkInWqHxH14sssAFSX7et63xV1G6ELhGuVGG0SkI1btlrz9r3c2IanNahvz4z6Z+IXFQLAuPhZGAEEEEAAAQSyQYAAMBtQ2SQCCCCAAAIIZJ/A6+99aHeMmWAFCxSwZuecHnZHPdtea0dU/KcFVqJbAN7/xFR78uUZroVfm0ubHRQAKgg6q3UHF/qdWK+uaRbgjIGQd9CJDABrHlHV1BVXE5Qo6OvcuoX7/tSWN7tQ6v5+Pe3800+xFt372fJVq91kHVPvH+JmJVZrvUYtbnRj+E0cfIdbTiVaAKjWkHc98Og/Ad6A22zP3r22dOV/bNnvq+yTb761HTt3uW7S6i69Z89e++zbf7tg1Cs6vj73PWRvfPiJFSiQ302CsnL1Wit0WEF7auQ9EWf9zXjSkyEA1DF54a++l/fl559DAJh9twK2jAACCCCAAAJxCBAAxoHFoggggAACCCCQ+wIK3xTCVa9a2d55cnzUA0r0LMAa/0/jAHqTXYROAqIuwCr3PPSY+/fFt977J0gLMy6cfp/IALD79dfY+GdftEemvey62U4eNtB1oVXLNRVvdlyNR3jtrQNt91977IbLm9tdXdvbR19+Yx3vGuHG5vv4+clpLQOjBYBnXNveFHhGKkdVP9Jenzg6bcbkcMspNLym1wD7aen/xkl8+O4+duFZp0U9t94CsQaAn708JdNtDhr/mJt1OCstALVhBZqy/fbnX13Lx7efHG+HFSxIC8CYzyQLIoAAAggggEB2CRAAZpcs20UAAQQQQACBbBHwWuA1Or6eTR09NOo+Eh0AXnBTN9dK7Z2nHrbqVSod1ALQO6AJz71sD0990bVmmzv10bDj2CU6AFQA1WPYaDd5SanixVx3YM1AfGSlivbe0xPSrKa/M8cGjvsnrFSQqYk5tNwxtaq7Mfe8Ei0A7DPqIZs152M37l3NI6pYrWpVrUTRovbczNluE6FjAoY7Ues2brZRjz/jxs4LLXVrVHNjJp56Qv2o51cLxBoALn7nlUy31/f+8Tbzg3lZDgC1cbX41BiGKhr7sOO1VxIAxnQWWQgBBBBAAAEEslOAADA7ddk2AggggAACCCRcoP+YCfbaex/axU3OdN1Oo5VEdgHWRCMnX9nWCuTPb4tmPOdatoVrAahjuqzz7bb4txV23aUX2aDuHcIeZqIDQO1k+46d1qJnf1v+nz/S9nntv5ra0F6d0h3D7SMfdF1vFRSWKlnCLa+wSqGVV6IFgKvXbbB9B/Zb1QrlXetBFW+dc0892R4dOiBsvTURybSZb7sWi9t37nTLaPw/dZme8uos27t3n/tds7NPt34d2lqVCuUyPc3JFADqQNsPHG6ffL3IqlWu6ILiKdNn2ajHn3VjQH45/Z9JXzT5zCdfLYp2+aZ7/Yvvf7QX3njH/W7+/z1hZQ8vFdf6LIwAAggggAACqStAAJi6556aI4AAAgggkCcFNAGIJgK56apLXNfaaCWRAaAm2lAXz6NrVrcZk/5pKRcpAFyxarW1GzjMpoy8x46sXDHsYWZHAKgd/bL8d7um5wDb9ddfbr8P3tnbmp9zRrpj2Lptuwsp12zYmPb7qaOHWKPjj037OVoAmLFS3y1e4rrz5s9/qM2cNNa1CgwtGmvwtffm2tTX37IVf6xxLykU69v+eru6+QUuRFyyYqXd/dBj9s0PP7vX1YX2wrNOtSsuaOxmMlb35owl2QLAH35dZg8/95KbjKVMqZI2+cXXbOxT01zY+vkrT7vD/3npcru8a59ol2/E1wkAs0zHiggggAACCKSkAAFgSp52Ko0AAggggEDeFbiqWz/7Ycky63vLDXbL1ZdHrUgiA0BNnqGZa4sWLmx3d2vv9h0pANRrCryKFSkc8RizKwDUDr3ZivW9ZufVLL0Zy4KF39nNA4a5seuKFilsn788xbVu9Eo8AeD+/futZc/+bibhrte1tF43tkrbzu9/rHHdgqe/MzetxZ/CPrXwU1ff0NmItZKO54U33rUHnn7etZTzSvkype2y8862G6+8xMqX+d8MxskWAGZ0vnfSFHv29Tet9pFV7c3HH3QvEwBG/a/LAggggAACCCCQQAECwARisikEEEAAAQQQyH6Bxm06uVZr6i5ao0rlsDvU7MCNG53kXvMCwOfGDLUT6tZJt/wZrdrbth07bXCPDtbiwvPSvdb7vgfcWHo9brjGNMFGpJJZAKh1NGvx3xFW1mQRXpfOOzq0tcNLljhoSY0hmLH1nrfQeW272qq168IeY8/hY9yEFiqaDEQt8kqVKH7Q9pvf0suWrVzlxvF7Y/IDVrVi+bRl4gkA0yZnqVLJZj461rXc88q0WW/b0AlPuB8VMF5y7lnWruVlpklCMiu7dv9lb3+8wF6a/X5ai0Ad34xJY9MFq8keAPYcNsbe+eQzO+vkE+zJEXdn+T+Jur6rC7wKLQCzzMiKCCCAAAIIpKQAAWBKnnYqjQACCCCAQN4VqH9Jq7Qx4iLVot8tN1j7/7YOTPQkIBn3GS0APPZf15rGvMtqqVi2jH007Z9ZhTOWSAHg+k2b7dwbutjeffvs0Hz5bP+BA3bR2afZ+LvSdzlVN9vWve9K2+zZDRvYE/f+7+dYA0CNddiyR39Xz2dGDUrXjVjdkdVlW12iixUtYi0uPNcKFigQN8efO3bYG3M+sQvObGQN6x+Tbv1kDwDVLVphryZlUbfgrBYCwKzKsR4CCCCAAAIIEAByDSCAAAIIIIBAnhFQoKWJIyKVV9+daxs2b7H+HW+0m1tc6hZLxQBw0vPT7cFnXrAihQvZiN5d7dZ7xzmL0Xf0tMvOOyeNT7+fPe9TK1akiGmCE3W9Hdv/VtdCTyWWAHDbjh12dc8B9tt//rDOrVtYx2uvcLMKf7bo3/bep1+4yUVCJ7+YNnO2DX3kybivOQWTCijDlVgDwO9nvZDpfgeMfcRNjHJV03NtZJ9u6Za9tFNvN7ZitBah4XZwTpuOtnbDJut+/dXW44Zr4667twIBYJbpWBEBBBBAAIGUFyAATPlLAAAEEEAAAQSCI3BF1z7209LlNrDzzXbjlRdHrdgpLW50Y8wNv62LXd3s/KjLh1sgWgvAzDaaHWMAHjhwwC64qZutWrvebri8ud3Vtb0NHDfRpr8zx01CoTHoNHusZvDVcmq1p5l/Nb6hltGkFW8/Od6FdtECQIV+fUY9ZP/+ZambwKNerRpu5uPQFo/azkVnneaMVXIzAIz1BCcyAPxj3XpTS02Fq/f27mItL8radaZjJwCM9QyyHAIIIIAAAghkFCAA5JpAAAEEEEAAgcAIXNq5t/3y2+92T7f21uay5lHrFcQAUC361LJPgZyCvOpVKrmQ8+KOt9m6jZvswjNPtYfv6WtjnnzOTRRSvGgR+3Dqo7Zn715r1r6Xbd2+3a679CIb1L1D1ABw1pyPXQCYsWjMwfNPP8WannGqnXJ8vXQTi3gBYJUK5ey9Kf+MZxep/LV3r514+fXu5US0AIx6Qfx3gUQGgN4MwJq9+MPnHrUKZUrHehgHLUcAmGU6VkQAAQQQQCDlBQgAU/4SAAABBBBAAIHgCDRr39N1RVVLq2ZnnW7rNm3OtHKatXbHzl1uxlrNSBupKEyrUTX8hCPJ1ALwrz177F8dbrX/rFln5556sj06dEBaleZ89pV1GXSfXXFBY7u93fX2rw693AQonVtdZbfdfJ1bThOSDH74cVNY9fL4kTZtxtv26ntz3Zh+U0cPOYhny5/b7Ny2XdxkHvXr1LLjjqpl9Y+qZbWOrOoCyHDlfwFgeZvz7MRMz8+ePXvtuEtb+woAE3V1Z7UL8GWdb3etIiMZxnN8BIDxaLEsAggggAACCIQKEAByPSCAAAIIIIBAYASaXN/ZVq/f4Max03iB3oypfiuosfQWvv5c2M0kUwA48flX7KFn/s8dp2Y9PuW4eumO+fX3P7LLzjvb+o4a78a60wzDc5+dZKVLlXTLqfvw1b0GuC69Tc9sZMWLFM00APTWUWAYa0mlAPDX5Svtkk63ORrNNN36kotiZQq7HAGgLz5WRgABBBBAIKUFCABT+vRTeQQQQAABBIIl0PCqtq5V28TBd7hur6kUAGoMQ3Xh3bl7t5vEQyFouPLpN9/ZzQOGupdCW/95y363eIn1vX+8PT3yHjfhSmYtAOO5ejQuYP5DD00bA7BKheC3AOx49wj76ItvXL0/fn5yWtAaj1vosgSAWZVjPQQQQAABBBAgAOQaQAABBBBAAIFACKj76/GX/tOV9flxw+3kY4+OWq8gjQFYsngxGz7xKTem3+wnHrJypQ8/qP7qUntpl9vdzLzVq1a2mZPG2GEFCx603O6//rJChx0WdQzAaMBrN26y9+d/bm9/vMDq1qxud3VplzIB4POz3rEhEx53RN5kLNG8or1OABhNiNcRQAABBBBAIJIAASDXBgIIIIAAAggEQuDnZcvt8i59XF00+UWkMftCKxukALBL6xbWfuBwu+CMRnb95eEnQFm2cpVd0bWvm/DjudFDrGGGLsIZL4RoswBnXH7//v2mbq+ffftve/eTz+ybHxe72W9Vrr+sud3drX1KBIAah/LKrn1t119/WbXKFW3GpLFWuNBhaVzdhtxv73/6ha//d/P/7wk3mzMFAQQQQAABBBCIRYAAMBYllkEAAQQQQACBpBfQ2HcaA0/j0X07Y5oVLFgg6jEHKQDsfv01rttz0cKF7NBDD41Y95lz5tnCHxe7WX6jlV7Dx7rWe5EmsNi4Zat9+/OvtujHxbbo51/t+8VLXBfk0FKqRHE779SG1uqSpnbC0UelBYCaKfjp+wZleggKKr1QN9ZZgKPVye/rPW64xmQdqfz+xxrrdM9IU9iqa1GTpzSsf0y6xQkA/Z4F1kcAAQQQQACBeAUIAOMVY3kEEEAAAQQQyFUBhUJf//Cz6+paIH9+27Bpi72/4Av7vzffc5NYxDPbanYGgOe17Wqr1q5LmJWCJNUttHj7iBZKZTwItdTLGBIquFuxarUVLFDQTQ6y9Pf/WO+RD7gxFTVD8kN33X5QXbyZcTO+UKVCOTvv9FOs6RmNXPgVui9vEpB4YfJCADj/62/tthEP2Nbt2131Ol17pfVu16y2ZJoAACAASURBVOagqq7dsNF27EoflMbioVaDY5+a5halBWAsYiyDAAIIIIAAAp4AASDXAgIIIIAAAgjkKQF1KT2rdQfbsHnLQcddolhRe/Leu+z4o+vEVKdUDQDD4WzfuctOu+Zm27t330EvR5rBtuvgUfbxV4vs2No17IRjjrIG+jr6KKtUvmxE/+wOADX+Y8WyZWI6//Es1G7gMDd2YqSwVTMsDxz7iO0/cMAOOeQQ633zddbx2ivj2UXUZRkDMCoRCyCAAAIIIIBABAECQC4NBBBAAAEEEMhzAhqb7vUPPnIt/goUyG+aUbZJo5PcOHNHVKoQc32yMwBcuXqt7d13cJgW88FlWLBy+bJuYo7QktUWgJGO4aY7htiCRd+nvaxuupeee7YLs8J1K9YkH4eXKG4FC0Tvbu1t1AsAgzYLsCza9h1kazZstPv79rSLzj4tq6c64nofffmNPfbCq+51zXSt7tUUBBBAAAEEEEAgFgECwFiUWAYBBBBAAAEEklJg127NVlvQtbhKxaKupPv2H7ASxYpY8aJFU5EgqeqsEHDdxk123FG1k+q4OBgEEEAAAQQQQIAAkGsAAQQQQAABBBBAAAEEEEAAAQQQQACBAAsQAAb45FI1BBBAAAEEEEAAAQQQQAABBBBAAAEECAC5BhBAAAEEEEAAAQQQQAABBBBAAAEEEAiwAAFgLp7cVatW2ejRo23hwoW2bNky27hxo1WvXt1q1qxpl19+ud18882WP3/+iEe4bt06mzBhgr322mu2YsUKt2ytWrWsVatW1qlTJytWrFjU2n3yySf2yCOP2IIFC2zNmjVWrlw5O+mkk6xz587WvHnzqOuzAAIIIIAAAggggAACCCCAAAIIIIBAcgsQAObS+VHwN2jQINu1a5c7ggIFCljx4sVt06ZNaUdUp04de/HFF+3EE0886Cg///xzu/LKK2316tXuNYV9+/bts927d7ufjznmGJs1a5YLBCOVIUOGmL7+/vtvt0jp0qVt8+bNaT8rRJw4caLly5cvl5TYLQIIIIAAAggggAACCCCAAAIIIICAXwECQL+CWVj/hRdesOuuu87NWNixY0fr3bu3C+oOPfRQ27Fjh3300UcuHPzqq6/syCOPdP+qZZ5XFPo1aNDA1ALwoosusjFjxlj9+vVdcDd//nzr0aOHLVq0yOrVq2dffPGFFQ0zK+BTTz1l7du3d/scOnSodevWzUqWLGk7d+60KVOmWJ8+fVyYOHjwYHcsFAQQQAABBBBAAAEEEEAAAQQQQACBvClAAJgL5+3oo4+2xYsX27333msDBw4MewQK4k477TT7/vvv7f7777e+ffumLaeWeZMnT7ZzzjnH3n//fdd6MLSoFV/Dhg1dt+IRI0bYgAED0r2uVoeVK1e2LVu22GOPPeZCyIxl5syZrhuyAsLly5db1apVc0GKXSKAAAIIIIAAAggggAACCCCAAAII+BUgAPQrGOf6W7dutcMPP9y11lu7dq2VL18+4hbuu+8+F95dddVVNn36dLecWghWqlTJtm3bZl9//bUbry9cUddhjQVYu3Zt++WXX1xrQ69MnTrV2rZtayeccIIbfzD0tdBtaQzAt99+24YNG2Z33XVXnDVlcQQQQAABBBBAAAEEEEAAAQQQQACBZBAgAMzhs6ButUWKFHEB4H/+8x+rUqVKxCNQ8HbPPfe47sLTpk1zy6l7brt27VyX4SVLlkRcV638NKaf9vfhhx9a48aN05bV9/PmzYsa7D399NNuIpIaNWrY0qVLIwaFOUzI7hBAAAEEEEAAAQQQQAABBBBAAAEE4hAgAIwDK1GLnn/++TZnzhzX/VfdgMMVtfRT6z613nvmmWdciz2VNm3a2PPPP+9m6Z00aVKmh6TxAd9991278847bfjw4W7ZPXv2WKFChVwA+dlnn9mpp54acRt//PFHWkD566+/utaEFAQQQAABBBBAAAEEEEAAAQQQQACBvCVAAJgL5+unn36yc889103ice2117oJOBSulSpVyrUKVDCnwE7jBGocPnX/1Vh8KlpPLfomTJjg1susaHKRBx54wLXi06QfKitWrLDq1au777dv3x52gpDQbXozA2tiEo05SEEAAQQQQAABBBBAAAEEEEAAAQQQyFsCBIC5dL5WrlzpZt994oknwh6BgjdN4KGZevPnz5+2TN26dV2rQLUCbN26daZHr9aFGrtPLQE1lp/KggUL7IwzznATh6g1YLRSp04d19VYMxdrTMFYi4LMzIq6Jv/8889WoUIFN8NxaB1j3QfLIYAAAggggAACCCCAAAIIIIBA5gL79u2z9evXu4WOO+441yuQknoCBIC5dM4ffPBB0yQfmghERS38SpQoYZrB1ysK6saPH28nn3xy2u+KFy/uWu7Nnj3bmjVrlunRq4tw165d3X/w7777zi2r1oQtW7Z0wduaNWui1l5dhL/44gsbO3asqUVhrCXSxCKxrs9yCCCAAAIIIIAAAggggAACCCCQWAE9359yyimJ3ShbyxMCBIC5cJr69+9vo0aNcqFfx44drVevXlazZk3XKk+zBH/55ZdufED9q8BP4/iddtpp7ki9AFAt+tSyL7OSWQBYsWJFW716ddTaEwBGJWIBBBBAAAEEEEAAAQQQQAABBPKEAAFgnjhN2XKQBIDZwhp5o9988401bNjQTcLx0ksv2dVXXx12Yb3eokULe+211+yEE04wrZcvXz6LpwuwxhG8++67fXUB1tiEmgE40V2A1QVaLRxVdAOqVKlSDp8JdocAAggggAACCCCAAAIIIIBA8AXU+KdRo0auor/99lvavADBrzk1DBUgAMzh6+Gmm25ys/peeumlNnPmzEz3rklC1FJPYaAm/mjcuHFgJgHRGIFHHHGEq7/CwKpVq+bwmWB3CCCAAAIIIIAAAggggAACCARfgOfv4J/jWGpIABiLUgKXUV/7r776ykaOHGnqChytHHXUUfbrr7/aI4884sbza9OmjZsApHPnzqYuvpmVCy+80N577z2788473azCKn/99ZcVLlzYhYqabVhdfCOVVatWpQVzOga1BkxU4QaUKEm2gwACCCCAAAIIIIAAAggggEBkAZ6/uTokQACYw9eBxvL7/PPP7f7777e+fftG3Xu9evXsp59+cmGfQr8pU6ZYu3btrFatWm523khl165dppmENdvu3LlzrUmTJmmLqiXhvHnzbNiwYW6W4Ejl6aeftptvvtk1D162bJklcmIPbkBRTz0LIIAAAggggAACCCCAAAIIIOBbgOdv34SB2AABYA6fRoV4jz32mBvf75VXXsl075s2bbJy5crZgQMH0lrr7dixw42Xt23bNvv666/tpJNOCruNF1980Vq1auVa7f3yyy/pwrupU6da27Zt3diCCxcujBjsNW/e3DTZSLSgMCuE3ICyosY6CCCAAAIIIIAAAggggAACCMQnwPN3fF5BXZoAMIfP7PTp061ly5YudHvzzTdNIVukogBPQV6FChXcQJ3quqvSqVMnmzx5sp199tn2wQcfuNmDQ8vmzZvdRCNqtTdixAgbMGBAutfVOrBy5cq2ZcuWtJaFGY9hxowZdsUVV7iZipcvX57wMfq4AeXwhcfuEEAAAQQQQAABBBBAAAEEUlKA5++UPO0HVZoAMBeug+uuu87NqluwYEHr0aOHdenSxapVq2b58+dPa9mnrrnz5893QaFa4Wk8P69oBp8GDRqYJglp2rSpjRs3zurXr+/G9dM63bt3t2+//dbUfVgz7BYtWvSgWj711FPWvn17F/ANGTLEHUeJEiVs586d9uSTT1q/fv1c9+HBgwfboEGDEq7EDSjhpGwQAQQQQAABBBBAAAEEEEAAgYMEeP7mopAAAWAuXAfqvtutWzebNm2a696rovBPAZy6/XpF3X/vu+8+N+ZfxqJxBNVCb82aNe6lYsWK2b59+1xop3LMMcfYrFmz3FiBkYrCvaFDh7rgUEGjxgzU/vWziloaTpw40fLly5dwJW5ACSdlgwgggAACCCCAAAIIIIAAAggcJMDzNxeFBAgAc/E6WLRokZvdV2P0LV261IVvmnBDoZ0mC+nZs6cVL1484hGqBeDDDz9sr732mq1YscK15tO6rVu3dhOGKBSMVj755BObMGGCLViwwNauXWtlypSxk08+2c043KxZs2irZ/l1bkBZpmNFBBBAAAEEEEAAAQQQQAABBGIW4Pk7ZqpAL0gAGOjTm7yV4waUvOeGI0MAAQQQQAABBBBAAAEEEAiOAM/fwTmXfmpCAOhHj3WzLMANKMt0rIgAAggggAACCCCAAAIIIIBAzAI8f8dMFegFCQADfXqTt3LcgJL33HBkCCCAAAIIIIAAAggggAACwRHg+Ts459JPTQgA/eixbpYFEnUD0qQnW7ZscbMX79+/P8vHw4oIIJD3BTQOapEiRaxUqVJWqFChvF8haoAAAggggAACCCCAQAIEEvX8nYBDYRO5KEAAmIv4qbxrvzcgzVS8evVq27p1ayozUncEEIggULJkSatUqZKb4ZyCAAIIIIAAAggggEAqC/h9/k5luyDVnQAwSGczD9XF7w1ow4YNtn79+nQ1zp8/fx4S4FARQCDRAvv27Uu3yXLlylnZsmUTvRu2hwACCCCAAAIIIIBAnhLw+/ydpyrLwUYUIADk4sgVAT83oD179tjSpUvTjrt8+fKuy5+6/1EQQCB1BTQMgIYEWLduXRpCrVq1rGDBgqmLQs0RQAABBBBAAAEEUl7Az/N3yuMFCIAAMEAnMy9Vxc8NaNOmTbZ27VpX3TJlypgCQAoCCCDgCSgA3Lhxo/uxQoUKVrp0aXAQQAABBBBAAAEEEEhZAT/P3ymLFsCKEwAG8KTmhSr5uQGtXLnStm/f7qpZo0YNBvvPCyecY0QgBwU0OdBvv/3m9lisWDE74ogjcnDv7AoBBBBAAAEEEEAAgeQS8PP8nVw14Wj8CBAA+tFj3SwL+LkBqfuvugFrcP+6desyyH+WzwIrIhBMAU0StHjxYtO/6v6rbsAUBBBAAAEEEEAAAQRSVcDP83eqmgWx3gSAQTyreaBOfm5Av/76q2mwf036UadOnTxQWw4RAQRyWoD7RE6Lsz8EEEAAAQQQQACBZBXw8/ydrHXiuOIXIACM34w1EiDg5wbEg30CTgCbQCDgAtwnAn6CqR4CCCCAAAIIIIBAzAJ+nr9j3gkLJr0AAWDSn6JgHqCfGxAP9nn3mti7d6/rvl20aNG8WwmOPGYBnWuN2aluuDk9Dh/3iZhPEwsigAACCCCAAAIIBFzAz/N3wGlSqnoEgCl1upOnsn5uQDzYJ895zHgkEyZMsLFjx9ojjzxi//rXvw460BdeeMFuueUW69Wrl40YMSJiRXbs2GGffPKJFS5c2M4555yYKvzLL7+4sKlmzZpucphkL2+++abrwn7UUUdFPNRly5bZW2+9ZWeccYaddNJJyV6lg47v22+/tQYNGrivhQsX5ujxc5/IUW52hgACCCCAAAIIIJDEAn6ev5O4WhxanAIEgHGCsXhiBPzcgHiwT8w5iGUrGzZssLPPPjviogr8zj///LTX58yZ436uV6+efffdd3booYemW1dh3scff2wPPvigCwEjFU3gcPTRR1v16tXTZnONdry33Xab2+7gwYNt0KBB0RZ3r1977bWmY050ufvuu61nz54RN6uWcVWqVDH5qq6RQsA+ffq4QPWOO+6w++67L9GHme3bIwDMdmJ2gAACCCCAAAIIIIBAVAE/z99RN84CeUaAADDPnKpgHaifGxABYM5dC2vXrrWKFStG3OHrr79ul19+ebrXW7ZsadOnT7dHH33UOnXqlPbaDz/8YPXr13et+latWmWHH354lgLAv/76y4Vrp59+ut10001p28hKANi8eXN7++23Ew6qsE6hXaTy4osvWqtWrVwdPv3007CL7dq1y6pVq2br1693Yepxxx0X13F+9NFH1qRJk7jWiXVhza4bS8kYACoYVWgcTyldurRp5u94C/eJeMVYHgEEEEAAAQQQQCCoAn6ev4Nqkor1IgBMxbOeBHX2cwPiwT73T+DNN99sTz/9tIULABctWmQnnniiVahQwZYvX26FChVyB9yjRw8X/iioGzduXKaVyKwFoPeawjt1j/WKnwDwww8/tMaNG/uGHTJkiGuBGC0AVCtJtTycOnWqXX/99WH3620r2kH179/fRo4cedBiXgCo8RYTNf6eulkfOHDAshoA9u3b18aMGROtSuleL1u2rAtB4y3cJ+IVY3kEEEAAAQQQQACBoAr4ef4Oqkkq1osAMBXPehLU2c8NiAf73D+BmQWAOrp+/frZJZdckjZ+386dO61y5cq2b98+16W3XLlyWQ4AZ8+e7cYXbN++vT3xxBPZGgDqePPnzx8zeCwBoFqzaew/dQHWGH8FChQ4aPta5vjjjze5qXtwvnz5DlpG/4e2b99uo0ePNnUVzli8AFDnYdasWTHXIbMF1Wpzy5YtWQ4A4zkIGdSuXdsIAONRY1kEEEAAAQQQQAABBA4W8PP8jWdwBAgAg3Mu81RN/NyACABz/1RnDADfeecda9asWdwHpgBPQZ6K16rskEMOcePieWMAzpw50wVYAwcOdMtpgpHu3bu7bsAKGr2icf+efPJJ6927t/vKWIoVK2YlS5ZM92uvC3CkFoDdunVz+37++eftrLPOOmibb7zxhttXly5dXMvGWALAzp0722OPPeZaQWqdjEUToKhr8Pfff+9aJerYMhb5aFKQMmXK2E8//RR2VuWcDACXLFliaok4ceJEK1++fNrh+hkDkAAw7v9OrIAAAggggAACCCCAQFgBP8/fkAZHgAAwOOcyT9XEzw2IADD3T3WkAFCttfTllY0bN7rum2rxp7DKK5s3bzaNLxgaAGrWXwVemin4hBNOSAsANVmIgrL58+e72XA1ruDkyZPjRrj11lvtgQceiCsAPO200+zzzz+POFHHXXfdZffee29al99oAaBa/CnYlJFCsyJFipjGNLzhhhvssssuM42f2KJFi7SuzZpERQFgaPi4d+9e03F98803bqzFq666KqxFTgaACkoV/mlMxilTphAAxn11sgICCCCAAAIIIIAAAtkn4Of5O/uOii3ntAABYE6Lsz8n4OcGRACYsxeRAihN2hFa1IJNs/l6YwB6LQAzzsA7atQo1zIs45h4Xiu+0ADw1VdfdeGXJuXQ7L9eC0AFWZoMQy0FH3/8catatar98ccfrntoaPdcBYoKFjOGkN5xt23b1gYMGBAxAFR33xo1aljNmjXdMvq5ePHirvvttm3bXHdchW+hk5dceOGF9t5775nqr++jBYA6Bo37p7BMrQZVHnroIVM42aFDB9MYe6qvZlGeNGmSXXnlla51n0LISpUq2f79+03bUIvESy+91NQ6MlLJyQBQsxmrW/PWrVtt3rx5aYElLQBz9v8qe0MAAQQQQAABBBBAIJyAn+dvRIMjQAAYnHOZp2ri5wZEAJizp1qtz9T6Tq3VMo5Fp9Z6GmMuEQGgZg1WKPbll1+64M0LADVm4KmnnurGDlR3XLV+UyCon9Vd2Ct+JgHRhCba95FHHmkLFixwIZ9m3lVLRO1bE3Y0bdrU1I1YE4+oZZ66LGuGWo2Jp/BRXV8zCwB//PFHN5OvZkL++uuvXXipUFGBo/5Vl1d1a9b1rRBUQeb777/vulYr7Hz33Xddd2O1+tMx6TUdT7wBoFosquViLEX70IQlXslsDMAHH3zQtdTU2IULFy501woBYCzKLIMAAggggAACCCCAQPYK+Hn+zt4jY+s5KUAAmJPa7CtNwM8NiAAwZy8ktexSd1WFVIULFw67cy8A9NMFWGP4DR061E2MsWfPnnQB4P3332933HGHnXfeeS6M0/dqVRha/ASAc+fOtWeeecbNbKzAS2Gbfm7Xrp3rcqwWewri1NrP27euQ03Qock8dD2rRAoA1RVaoZ1CPnXZVcCpcfw0fp9azQ0bNsyFchr/T638SpQokVY1L1g77LDDXHdhBYhqZRfaEjHcSYnUAtALANWiMOOYiN52Vq9e7Y4rngBQrSMVcKpe06ZNs+uuu44AMGf/q7I3BBBAAAEEEEAAAQQOEtAzxO+//+6eXVRWrlzpelVRUk+AADD1znlS1Di7A8ADB/62zTv3JEVdc+ogDi9S0PLl+1+LuETtV2GUWnOppVukkohJQNT9V5NqKARTUBbaAlDXi17TpB/qjqsJMhSEJSoAVDfnRo0auTEGFy1a5MI4dSdWV2V1t23durWtW7fOGjRoYGvWrHEBoVr9XX/99daqVSs3bmFmAeDu3btda8Fdu3a55RSkqsXgihUrXKvDn3/+OWK4+sorr7gxArUNFXVjVtAYbvbgUI9oAaDCzhtvvDHsKfXGeIwnANSG5KDgr27duqYWjzpPMtOXWgXGU5gEJB4tlkUAAQQQQAABBBBIJYEDBw64hgP6EF5DF6mxgL7CFTVc0BBKTZo0cS8TAKbSlZK+rgSAqXvuc7Xm2R0Abtz+l508/P1crWNO7/zruy6wMsXC3/SzeiwKrNT195hjjnGBTqSSiC7AGn9PLdIUwIXOAqyuviqa4feWW25xLejUDTZj8dMC0JsFWPs6+eSTXdip1oyawETjH1auXNntTi0F1UKwYsWK7g+oAi9NeqHJL1Qy6wL84osvpuvarNaFamX48ssvu8k/MhaFZ5oARftUV+crrrjCPvjgA/vzzz/dp3ejR492E4dEKrkRAOpNiK4VvclQK0CNZXjiiScSAGb1PyDrIYAAAggggAACCKScgIYaUqjnhXsZv9fP+gotmnRRDQ7CFT3jqAUgAWDKXUoHVZgAkGsgVwQIABPPnh0BoP5YaIw6db1V+BSp+A0AN23a5GYJVku3Z599NmwAqJZ/en348OEHtf7TcSUiANR23nzzTTfBhv7wqiWbWueFFoVy48ePT/uVrmV1A1aJNgmIt5JmPD7nnHPs3HPPPchV3XsnTJhgmhRFgZrGIdT4iBr7UF1zVU+FiSqnnHKKmxTk2muvdTMth5bcCAC1f7Us1LErsFVorDcaqqe6bnvH/NVXX8X8H8ALYmNe4b8LMlRAvGIsjwACCCCAAAIIIJCbAmqZp6GQMoZ7sRyThgdSD6NwReGfnusIAGORDPYyBIDBPr9JWzsCwMSfmuwIAD/77DM7/fTT3ayuffr0OeigNTnFscce63sSEP2h0/h6mtSicePGYQPAaGKJCgC1n65du7pZeM8880xTWBda1CpSXVo1Y6/q/u9//zvt5VgCQI3BoVBPf4g10YgMVTTxh7o4q+Wfisbl0M86Fk06ElrUBVlBqGZiVtGEIgotX3rppbSZkXMrAFR3BG+yGLUCVDdpL9jVsSr4jKU7sLajcScJAKNd+byOAAIIIIAAAgggkEwCeh/rtdpTK77Qr1KlSkUch1tjoWvZrBSNMe71Wsq4vrr/angdvQ9XoQtwVoSDsQ4BYDDOY56rBQFg4k9ZdgSAM2bMcF1PIxUFVKNGjUoLAP1MAhK6j3BdgL3XFYxpUo2MRX8s1WJOYZi+MpYffvjBtWYMLc2bN3fBm9cF2HtNn44pQFN57rnnrE2bNunW69+/v6u3PmXTseoPuUosAaAXVGpik759+6Zt94svvrCLL77Y1BVay1x99dVh6xF6IJqcRd2I9aXJSbp165b2cm4FgKHHJyNZadzCESNGxHXRMwZgXFwsjAACCCCAAAIIIJCDAt74e3oGUWOG0H8za8GXWUs9BXP6ADyeoqGC9OxTtGhRq1ChQthV1bNJwxodccQR7nUCwHiEg7UsAWCwzmeeqU12B4BMApKYS0EtANWKK2NR6ze1QvOCnURMAhK6j8wCQHV13bBhQ9wVVFhWq1atdOuFCwBVN3X91Rga+gOsMRAVHmrcPxX9cdfEHZoMREWzBKuLrkq0APC1114zTXaiLrHyU/fit956y41pqMBL9dZ+FUBqkpFYSocOHezWW29Na3XnrZMMAWCPHj1cd2bNotylS5dYqpO2DAFgXFwsjAACCCCAAAIIIJBDAsuXLzf16slKUY8nb/igjOtruB+N9+0VL9zT5H9eI4fQ7yM1fAh3XH6ev7NST9ZJTgECwOQ8L4E/Kj83IMb2yv3Lw+vaeeedd7quqJGK1wLsvvvucy3UYi2ZBYCRtpGoLsBq1agJNgYOHOgm7VDIefnll9vrr7/udq1ZgdUiUJNbKKTTH+pPP/3UjdGXWQColoQKCxUqqmuxgkZNMqKimZbV0lIt+VT06Zz+j8RS1MJu5MiRBy0aLQCsVKlSxO4HqtPWrVst3lmAMx6EZnLWuZw/f76bYTmeQgAYjxbLIoAAAggggAACCGRFwJtwQ634Qr8U1Km1XriioXw0LFBWimbqrV69ethVtU21HgwN+bKyj3Dr+Hn+TtQxsJ3cFyAAzP1zkJJH4OcGRACY+5fM1KlT3eQTgwYNssGDB0c8oLwWAOqTPLXuUxdjDZSrkKxRo0ZuzDqFntddd50bE1EtI1955RX3JkG/O+mkk+zLL7+0YcOGOY9wgadawHktBfVpnWbIveCCC1y337PPPtv9ofeKFwDu3r3b9CYhXPFmRc5qABjLVeQnAPzmm2/cjMqqy4oVK9xMxvEUAsB4tFgWAQQQQAABBBBAIDMBBX2hAZ/e93s/67WMJbMx9dQTSB+WRysaF1vv8UO/ChYs6Lrr5nTx8/yd08fK/rJPgAAw+2zZciYCfm5ABIC5f2k98cQTpq6n0Vr25bUA0DteddNVwKeyaNEiN9uuZtpVCz4NnlunTh3XfVd/1L3xAh977DHXGjBSAKgQUSGhwsLjjjsuYrCnfWZnAOjn6tGnoFu2bHEzJEcrGt9wzJgxdvvtt7t/4y0EgPGKsTwCCCCAAAIIIIBAqIDet6r3jcI+DeMTuGpWRwAAIABJREFUy3tYb/3MWuqpsYA3JJE+2Feo54V8od9nnMgvN8+On+fv3Dxu9p1YAQLAxHqytRgF/NyACABjRM7GxcaPH2+9evWyBx980AVCmbUCjHYYt9xyiz3++OPpFsuNLsDHHHOMC/Y07oZm/lU3Xa8oDPzXv/7lWv9p5l6FfR07dnQvaxZgdQfWrFs33nijawUYLRiNZOLNoJvXA0AFofXr17dNmza50LNhw4bRLoODXicAjJuMFRBAAAEEEEAAgZQSUHdZvX9W6BauaPbbbdu2ZclEvVeOOuqosOsqTNR+FfqpQUBeKH6ev/NC/TjG2AQIAGNzYqkEC/i5AREAJvhkZGFzQ4cOdd1/p0yZ4v6oKhAMVzZv3uy605YpUybiGBqa7TbjDLG5EQCqi6+CyFatWtkLL7xwUHUUdmqcQYWE33//fboWfHfffbc1bdrU5s6dG7EFYCRmtTCcNWuWzZw502644Qbr2bNnnm8BqLB09uzZrnt0uElkYrnkCABjUWIZBBBAAAEEEEAg+AIK29RdVy35Qr80E68m7PNmt80ooVZ6ehaJpSjIU5DofSncUzfgeIexiWVfubGMn+fv3Dhe9pk9AgSA2ePKVqMI+LkBEQDm/uXVuXNn1wpOE2NogoxIJS91AVbLvc8//9x17c04M5cm+9AngGodOGfOHDeLb7gSbRZgdUFQ4PfVV1+5LwWG3mQf6j6gSUAUmuXlFoCTJk2yrl27ulmTNXuyZjVW2bFjR9pMyrFcwXqjJ6+yZcumTZYSy3reMtwn4tFiWQQQQAABBBBAIDkE9B7QC/k0Hrb3faTuu+pmW7t27bAHr4YKagUYWrS8uvcq6PP+1fd6Lx7k4uf5O8guqVY3AsBUO+NJUl8/NyAe7HP/JGrGW4Vl0bp35qUAUJNyvPXWW64bb8aiNx+aqOP44483Tb4RqagrtELAjF2At2/f7mbB/fHHH02fVHpFnyiqq3Hr1q1NLSHLlSvnXvICQIWOkboVqOu1BiCONgmIBhmO9KlovFeSZi7Wm7JIb8AeeOAB69Onj1tmxowZdtlll6XtQgGgZlOLtxAAxivG8ggggAACCCCAQN4S0PtEfdDuhX3xHn3NmjXTTajnra9Wg3rP7LXqU+CXTOPyxVtPP8v7ef72s1/WTS4BAsDkOh8pczR+bkAEgLl3mWi8i8mTJ1v37t2tUKFCtm7dOtc0PlJJZADota7zU/u3337bLrroorRNNG/e3PS7Dz/80Bo3bpzppletWmWFCxdOa9GWcWGFXmoN+cYbb9i4ceNcd+HQonUVJKp1oSYS0f60f806nLF4AWAsdY0WAMayjXiXyRgA6s2VWoWqS7jeWD311FOuJaOfQhdgP3qsiwACCCCAAAII5B0BhXTqcZOVog/Uq1at6roCUyIL+Hn+xjU4AgSAwTmXeaomfm5ABIA5e6oV4k2YMMF9cqY/zPqETqVfv36m1zIriQwAtS21rPNTNJnH+eefn7aJeALAcPv9+uuv7eKLL3ahl1r5adILlffffz/dfvS76dOnW4MGDaxWrVpRq5DXugArAGzfvr0b90/dws8666yodYy2gK41tSZUq0F1K463cJ+IV4zlEUAAAQQQQACBxAmo4YA+/NbXrl273Bh+aqkXrneLXv/999+j7lzddNUIQe+9vS+N1ReUcfqiAvhYwM/zt4/dsmqSCRAAJtkJSZXD8XMD4sE+Z68SzYh73nnnmf6I6w+uZnft0KFD2iy4ORUAZket/QaA6s6rLqr61FJFk53cdNNNNmbMGF+Hm9cCQK+yahFavnx5X3VP1MrcJxIlyXYQQAABBBBAAIHMBdQ7RN13FeTpS6Gfnh0ylmrVqrnniYxFPWn03i20eGP0eYGf/k3V7ruJuP78PH8nYv9sIzkECACT4zyk3FH4uQHxYJ87l4smZFA31iB9wqaZwfRmRWPv6U0GJTgC3CeCcy6pCQIIIIAAAggkl4A+BPda9nmBn0K8aKVChQpWqlSpsIutX7/eTcThtewj7IumGd/rfp6/49sTSyezAAFgMp+dAB+bnxsQD/YBvjCoGgIJEuA+kSBINoMAAggggAACCPxXQK36NC62PkDPSilZsqRVrFgxK6uyjk8BP8/fPnfN6kkkQACYRCcjlQ7Fzw2IB/tUulKoKwJZE+A+kTU31kIAAQQQQAABBCIJqJXfkiVLLOOEcJGW13h/6rrrfak3kVr5UXJewM/zd84fLXvMLgECwOySZbuZCvi5AfFgz8WFAALRBLhPRBPidQQQQAABBBBA4B8BdelVV14N+aMvddON1FV35cqVbplwRRNyKORT4Kd/1Z03SMMH5eXrxc/zd16uN8eeXoAAkCsiVwT83IB4sM+VU8ZOEchTAtwn8tTp4mARQAABBBBAIAcF1IIvNPDT96GlePHiVrly5bBHtHHjRtM42gr2FPAp6PNCPwWAlOQU8PP8nZw14qiyIkAAmBU11vEt4OcGxIO9b342gEDgBbhPBP4UU0EEEEAAAQQQiENA4/Z5Lfz0b2aTdmgCjtq1a4fdusYB3LNnjwv91MWXkjcE/Dx/540acpSxCBAAxqLEMgkX8HMD4sE+4aeDDSIQOAHuE4E7pVQIAQQQQAABBOIQUKu+rVu3mhfY7du3L461zapXr+5a+FGCIeDn+TsYAtRCAgSAXAe5IuDnBsSDfa6cMnaKQJ4S4D6Rp04XB4sAAggggAACEQTUUk9j9HlfGX8uX7582DW3bdtmf/zxR8yu6tKrsfuKFCnivvQ9Lfxi5kv6Bf08fyd95TjAmAUIAGOmYsFECvi5AfFgn8gzwbYQCKYA94lgnldqhQACCCCAQNAE1J1WYZ0CPrXSCw379H20GXdr1aoVdmbd3bt324oVKzLlKliwoBUtWjQt9CPwC9rV9b/6+Hn+Dq5K6tWMADD1znlS1NjPDYgH+6Q4hRwEAkktwH0iqU8PB4cAAggggEBgBLwWeV54Fxrihf6uWrVqYVvU7dixw/RslNVy5JFHuvH4MhaFh0uWLEn36/z586eFfWrlx6QdWVXPe+v5ef7Oe7XliCMJEABybeSKgJ8bEA/2uXLK2CkCeUqA+0SeOl0cLAIIIIAAAkkvoEk0NKZeaKin7zObTCO0UjVq1DC1uMtYYmmplxlOpUqVrESJEmEXWbt2rWsdqKBP4/kxpl/SX2bZdoB+nr+z7aDYcI4LEADmODk7lICfGxAP9lxDCCAQTYD7RDQhXkcAAQQQQCA1BdSlVhNjKLwL91W1atUcbamnY1m2bFlMJ0NddDVDb+hXyZIlXas+CgKZCfh5/kY2OAIEgME5l3mqJn5uQDzY56lTzcEikCsC3CdyhZ2dIoAAAgggkHQC6gqrGXHV0s77N7NWe9nVUq9y5cpWvHjxg3x0LJqswwv11GIvXNCn1zVRBwWBrAj4ef7Oyv5YJzkFCACT87wE/qj83IB4sA/85UEFfQrojeRvv/3m3jzqTWwqFu4TqXjWqTMCCCCAAALmWvdpXD2FffrSz/GUI444ImyLOrUWXLp0acRNKZwLDfH0vcI871+N08eYe/GcCZZNpICf5+9EHgfbyl0BAsDc9U/Zvfu5AfFgn7KXDRWPUUDj05QqVcp9bd68Oca1grUY94lgnU9qgwACCCCAQKwCfifViDSmnroOr1mzJi3QCw33vO9jPUaWQyCnBfw8f+f0sbK/7BMgAMw+W7aciYCfGxAP9lxaCGQuQABoxn2C/yUIIIAAAggEV0BhXKTusOFmvw0noZ4SCu4yfhUtWjTsZB3B1aRmqSDg5/k7FXxSpY4EgKlyppOsnn5uQDzYJ9nJ5HDSCaxYscKqV6+eLSrLly+3atWqRd12xgDw8ccft379+kVdL+MC+n+qN8F5sXCfyItnjWNGAAEEEEAgcwHNxPvnn3+6rypVqlihQoXCrqChUPbs2ZP2msJCLasvdcXVv3TH5WpLJQE/z9+p5BT0uhIABv0MJ2n9/NyAeLDP2ZPqfbr64YcfWuPGjXN253lwb14AWLBgQatZs2ZCaqA3sXrDm9UA8JFHHrHu3bvHfSzbt28nAIxbjRUQQAABBBBAIJECGnvPC/30fsgrhx9+uJUvXz7srjZs2OACQC/sO+yww8LO7JvI42RbCCSzgJ/n72SuF8cWnwABYHxeLJ0gAT83IALABJ2EGDdDABgj1H8X8wLA+vXr2/fffx/fyhGWPvHEE23RokVZDgDjOQh1m1FXGBUCwHjkWBYBBBBAAAEEEiWgLr4ay0+9GvR+JFzR5Bq1atViZtxEobOdQAv4ef4ONEyKVY4AMMVOeLJU188NiAAwZ88iAWB83jkZAGog6j59+tiAAQPs2GOPTTtQP2MAEgDGd75ZGgEEEEAAAQQSJ6BWe3ofoxZ/avkXrUSasTfaeryOQKoJ+Hn+TjWrINeXADDIZzeJ6+bnBkQAmLMnlgAwPu+cDABHjx7txvZr0qSJzZ07lwAw5FRxn4jvumVpBBBAAAEEckvgwIEDrpWfgr+dO3dGPQz1VChevLiVKFEi4hiAUTfCAgikmICf5+8Uowp0dQkAA316k7dyfm5APNjn7HklAIzPOycDQI2DU69ePVu2bJlNmzbNrrvuOnewtABkFuD4rlqWRgABBBBAIHcE1M33jz/+MIWAmRW9H/VCvyJFitDtN3dOF3vNwwJ+nr/zcLU59AwCBIBcErki4OcGRACYs6csUQHgjz/+aJqN9qOPPrJVq1a5cV1q165tdevWtW7dutk555wTsWKzZ8+2iRMn2s8//2y6djTo85FHHmkXXXSR9ezZ08qUKRN23ayup40tWbLExo8fb++++67bp4q6mVx44YVunxpzJlyJFAAqoLv++utjOnlPPPGEtW/fPm3ZzMYAfP311+3KK6+0SpUq2eLFi92bYwJAAsCYLjQWQgABBBBAIJcF1M1XH2RqzL9wRbP1lixZ0r2/0Zh/FAQQyJqAn+fvrO2RtZJRgAAwGc9KChyTnxsQAWDOXiCJCAAfffRR69q1a9qbu6JFi7o3cRrfRUX76NGjhz300EPpKqfx6Fq1amWvvPKK+32+fPmsdOnSLuDau3ev+526f3z88cd2/PHHp62b1fW8Deh4e/Xq5WaPU9HMcXpj6v2sGX4VDnbq1OmgkxEtACxbtqzpK1zRjHX6iicA1HYuuOAC++CDD+zee++1gQMHEgAaAWDO3iXYGwIIIIAAAlkXUAvAbdu2pW1A7/cU+tHFN+umrIlARgE/z99oBkeAADA45zJP1cTPDSjuAHDfPyFOlkq+Q830Fa5k13b37zWL8Clo2OPIXzBLVYt1Jb8B4IwZM+yKK65wu1MI2LdvX6tevbr7ef369fbwww/b8OHDXcA2ffp0u+qqq9IObdiwYXbPPfeYAsMJEyZY69at08K4hQsXugkwNPadWgPqulAwp5LV9bTuq6++ai1atHDb0bFogg2Fizq+b7/91m37jTfecK9rWbW+Cy3RAsDBgwfboEGDwvIPGTLE9Hq8AeCCBQvsjDPOcOHo8uXLXTeaUqVKua/NmzfHeqrdckwCEhcXCyOAAAIIIIBAJgK7du1y70X0Hi3SB6DqFaJnA3XtVfBXrFgx96EvBQEEEifg5/k7cUfBlnJbgAAwt89Aiu7fzw0o7gBw8eysK5evZ3Z4tfDrL3nfTGFdVkqZ2mZl64Rf87ePzfZsj32rdZvHvmwWlvQbADZu3NjmzZvnWvip1Vy4oi7A6uLbsGFD+/LLL9MWOfPMM+3TTz81BWMKAjMWDRhds2ZNFyS+8847rnuuSlbX05vUY445xhTi6ZgUTnr19/atcE2tAxVIVqtWzXVLVvcUr+RGAKh9n3/++TZnzhzXClBBq7pJEwDuMw0UXqdOhP9rWfj/wCoIIIAAAgggkLmA3iupRZ+CP41XrKKeH3rPFi7Y04es6tnhfZCLLwIIJF7Az/N34o+GLeaWAAFgbsmn+H793IAIADNcPEkcAOoN3VtvveW6tTZr1swqVKgQ9sp///33rWnTpu6TX4V6XuimQO+9996zDh062OTJk8OuO2vWLPvll19cC7jTTz/dLZPV9bxZdcuVK2e//faba3kYrmiGOrViVPCoddQSMbcDwA8//NB1/33uuefcm2wdX40aNdy4OipXX311WlfqWG8/OheRDGLdRm4tF/d9IrcOlP0igAACCCAQEAGN57dlyxb3pR4FGUvFihVdCz8KAgjkvICf5++cP1r2mF0CBIDZJct2MxXwcwOK+8GeFoC+rka/LQBj2bnO6VFHHeUWXb16tekNosqUKVOsXbt27nvNcKvJNxo1ahR15resrqfuvJpU44477rD77rsv00NXV+YxY8a4LsDqCuyV3GoBqP3rE3d9sj5//nw766yz7Oyzz3atL1VuuOEGV7doRaGtuuKoEABG0+J1BBBAAAEEENAYyWrtpzGaI03mIaXChQu7YVsoCCCQ8wJ+nr9z/mjZY3YJEABmlyzbzVTAzw2IADADbRK3AAw9UoVSasXnzeS7Zs0aF1hlLBrDTl1rvaKutv37908LpdSKULP/Xnzxxa6ln7q5hitZWU/df3V8ChBvuummTK/hJ5980m655RbXZVgzHHslNwNA7xhefPFFN3mKxkx8/vnn47obMQZgXFwsjAACCCCAQMoKeOP7hU7gEQ6jQIEC7v2aJvXQ0BwUBBDIeQE/z985f7TsMbsECACzS5btZirg5wYUdwCYXZN1ZNd2AzYJiFr0XXPNNfbJJ5+kXRN686eBoL03geoyokBQJWMAqN9t2rTJpk2b5roTq6vr7t273bJaX9vWxBnhxnmLdz11Qdab2dmzZ7suy5mVt99+25o3b+66LXst5rR8MgSAY8eOdd2S+/XrZ6NGjYrrbkQAGBcXCyOAAAIIIJBSAl5PAb3H0numzIreI2lMYg0nknFM5ZRCo7IIJIGAn+fvJDh8DiFBAgSACYJkM/EJ+LkBxR0AxndoLJ1BwE8XYLXwO/XUU+2rr76yMmXKuDHqFNhVqVIl3RvBpUuXWu3atSMGgKGHpPBPYaLCQLVuW7t2rZUvX94++OADq1+/fsTzF8t6Rx99tC1evDimFoBPPfWUtW/f3rTOTz/9lLbfaAGggs9Is+BprER9xTsLcMZKq4Xku+++60JTdZ2OpxAAxqPFsggggAACCKSWgD6wVVffzIpa+in4C50kLbWUqC0CySfg5/k7+WrDEWVVgAAwq3Ks50vAzw2IANAXfdwr+wkAv//+ezv++OPdPr/44gs75ZRTwu5f4d0FF1wQUwAYugF9+nzJJZfYggULXJfgN954I6b6RVrviiuusBkzZrguxyNHjsx0W94YgFrntddeS1s2WgAYywH6CQDXrVtnlStXNnW30ffFixePZZdpyxAAxsXFwggggAACCKSUgHo96H18xqIxiDXBh4I/vQehIIBAcgn4ef5OrppwNH4ECAD96LFulgX83IAIALPMnqUV/QSAjz/+uHXs2NHq1q3rxtaLVLp06WKPPvqoe9nrAvzHH3/Yscce62a0VQvBSLPGeePdaWwZDUCd1fW07/vvv99NAKIWhZoFWF1XwpXQWYDVxVZdbb0SKQDMEv5/VzrxxBNt0aJFYbtHZ9zuI488Yt27d7cWLVrEPeuvtkUA6OdMsS4CCCCAAALBF9B7tb/++stVVMOx6D2YvvSejYIAAskp4Of5OzlrxFFlRYAAMCtqrONbwM8NiADQN39cG/ATAKolW4cOHdyMbwrU9OlwxuJ1pfV+HzoGoGYG1vmeNGmSde7cOexxjx492gVwxx13nH333Xdumayup7FsNKmHQrxu3brZww8/fNCYNerWfOutt7rXjjjiCBdshgaFuRkA6lP5Bg0a2JIlS+zll1+2li1bxnWuCQDj5mIFBBBAAAEEAiWg9zl//vmnFSxYMOIHoXp948aNVrp0adfTINz7u0ChUBkEAiDg5/k7ANWnCv8VIADM5UtBrXrGjx/vJjZQy6Vy5cq5UKFp06auFY9+jlTUvU8znar7oUIHfQJXq1YtN/tnp06drFixYlFrp7HU1GJIXSg1pof2d9JJJ7mwRRMcZFfxcwMiAMyusxJ+u34CQIVjasWnN5Pt2rVz3WrVuk6fGiusGzFihL3++ututtoXXnjBHUBoAKj/G7169XJvQBW46dr2wjaNP6N1evfu7Qah1r+a/EIlq+tpXR3PlVde6bajVnQat1Dhoga91jEPGTIkrauxWh9qTMPQkpsBoNeS8swzz7R58+Zl6Q05LQBz9v8Xe0MAAQQQQCAZBLzgT8GeJmcrXLiw+wA3XNF7IhUm9kiGM8cxIBCbgJ/n79j2wFJ5QYAAMBfPkkKK22+/3f2R1Sdn+hRty5Yt7mcV/eGdOXNm2thooYf6+eefu5BCM6yqKOzTet7sqGrFNGvWLBcIRioKMvTl/RHX/tWF0vtZIeLEiROzFCJEY/VzAyIAjKab2Ne9N3cKhw877LCYNn766afbSy+95JZVyHfnnXemrafrTOGdgiYVhVYPPvhg2rYzzgKs4HDKlCnp1te1rk+fvXLGGWfYnDlz0h1fVtfTNtVyUQG8173Fq3fozw899JAL2jOW3AoANSmKxkFUQPrtt9+mTaqi46tevbr7pD6W4s3up2W3b9/uZu7Li4X7RF48axwzAggggEBOC2QM/kL3r0YJkYZDyenjZH8IIOBPwM/zt789s3YyCRAA5tLZUMslzc6pkE8BiWYTVRN6/RH+8ssvXWCiiREU7H399deuS6NXFPqpm59aAGq2zzFjxrjZT/XgPn/+fOvRo4cbL6xevXpu4oVwD/Bet0uN1TF06FDX3VFjrGlsM4Utffr0cWHi4MGDbdCgQQlX8nMD4sE+4acj0w1m5dPdJk2a2Ny5c9O2+84775i66mq2XIXMekOpVmpdu3a1hg0buuW8/WQMAPWaWqoqcFOLQgVsCsw10UXNmjXt5ptvNk3EEW7cmayup30uW7bM7VPH7g12rePW/zm1SqxRo0ZYNy8AVNcZHV8iirpPK3wMZ6Pt636iwFP/Z/XBgu4BoUXhrWYXjrcQAMYrxvIIIIAAAgjkHQF9mKr3B3v37g170Ar/9N6HggACeV/Az/N33q89NfAECABz4VpQyFa1alXXCkqtdhQoZCx79uwxtaL65ptvTLONanICr6jV0eTJk+2cc86x999//6CZthSwKFRRgKFwccCAAek2r+6SCk/U2vCxxx5zkzRkLGp5ePnll7tQRaGDjjeRxc8NiAAwkWeCbSVawAsAE71dbS9jAKjQ/5577rHhw4e7AFUTkuh+4afQBdiPHusigAACCCCQ/AIaM1jBn9dzKNwRq5FCmTJl8mxPgOQ/CxwhAjkr4Of5O2ePlL1lpwABYHbqRti2NzGCxg7TGGKRisYFnDFjhlWqVCltllH9wdbP27Ztcy0DNV5fuOLNjFq7dm375Zdf0o3RMXXqVGvbtq2dcMIJtnDhwojjd2gMwLffftuGDRtmd911V0Kl/NyACAATeirYWIIFcrILsALA/v37u/ERn3vuObvqqqt810atkG+88Ua3Hd2rYu327XvHCd4A94kEg7I5BBBAAIE8L6DAb/369a7HT6RC8JfnTzMVQCCsgJ/nb0iDI0AAmAvnUt13NUaXxvFr1KhRXEeg7rnq6qex/TTTZ6SiVn4aa01/6BUkNm7cOG1Rfa8JAqIFe08//bTrXqmujkuXLk3oQL9+bkA82Md1ybBwDgvkZADoVW3t2rVWoUKFHK5pcu+O+0Rynx+ODgEEEEAg5wTUs0gt/tSAIFIpVKiQlS1blhZ/OXda2BMCOSrg5/k7Rw+UnWWrAAFgtvIevPEff/zRzYqqLrirVq1yC6iVnVraLF682I0JqIBQ3XvbtGlzUOim3z3//PNult5JkyZlevTqWvzuu++68QTVRVBFbwD0B14thz777DM79dRTI25DsxJXqVLFva6HabUmTFTxcwPiwT5RZ4HtIBBcAe4TwT231AwBBBBAIHYBhX4aP9yb5C/jmgUKFDCNFayxyCkIIBBcAT/P38FVSb2aEQDm8Dl/88037ZJLLnHj+2mSD3W1e/nll8MehbrgqsVfaMuec88917XomzBhgpu4I7PSu3dve+CBB1wrPk36oRI6PlksA/x7MwN/9NFHLpRMVPFzA+LBPlFnge0gEFwB7hPBPbfUDAEEEEAgdoF9+/aZJhPTEB+hJX/+/G6MvxIlSrjJ1SgIIBBsAT/P38GWSa3aEQDm8PnWpBtqvafx/zS21rRp09yg/fpZM/1q4o5Zs2a5mXnVWu/EE0+0r776Ku0Pc926dd2YfmoF2Lp160yP/t5773Vj96kloFoZqixYsMDOOOMMN3GIth+t1KlTx3U11iyjrVq1irZ42uverKmRVtAnkV7355UrV8Y1yQgP9jGfBhZEIGUFuE+k7Kmn4ggggAACGQQ2btzougCrKOzTB/yHH344wR9XCgIpJEAAmEInO5OqEgDm8HVw9913u+64mlVXXYA1C3CzZs0OOgp1zz377LNNn9qpq69CQxU1z1fLvdmzZ4ddL3RDWq9r16523HHH2Xfffedemj59urVs2dK1KlyzZk3U2quL8BdffGFjx441tSiMtWhG0lgLAWCsUiyHAAKxChAAxirFcggggAACQRBQC79ILfn02vLly61YsWIu/FPrPwoCCKSWAAFgap3vSLUlAMzh66BTp042efJkt9fQrrnhDsPrwqux9/Qwq+IFgGrRp5Z9mZXMAsCKFSu68UCiFQLAaEK8jgACyShAAJiMZ4VjQgABBBBItMD+/ftNLfw01l/16tXt0EMPDbuLzALCRB8T20MAgeQTIABMvnOSG0dEAJjD6uqSq665KtHG1dMswaeddpr7NG/Hjh1u8o54ugCrpaFaHPrpAqzwUTMA0wU4hy8UdocAAr4dDHB8AAAgAElEQVQECAB98bEyAggggECSC2hSjz///NPWr19vCgFV1K23fPnySX7kHB4CCOSGAAFgbqgn3z4JAHP4nHit8rRbdcENneAj46Fs2bLF/SFX+fbbb+344483JgH5Z0ZidY1W9wWNUUhBAAEEMgpwn+CaQAABBBAIqsCuXbts3bp1tnv37oOqqFaAGmecggACCIQKEAByPUiAADCHrwNN8HHZZZe5va5duzbTT+n0h90LCDXxh8KuNm3auAlANCagwsTMyoUXXmjvvfee3XnnnW7cQZW//vrLChcubPrUUOMMqotvpKIxCjVWoYoeptUaMFHFzw2IB/tEnQW2g0BwBbhPBPfcUjMEEEAgVQXU0k8t/rZu3RqWQGNw69mhZMmSqUpEvRFAIIKAn+dvUIMjQACYw+dSD6Wa7Vdl3rx5bqKPSOWTTz5xr6vrryb+0JgeU6ZMsXbt2lmtWrXc7LyRij4Z1CC/+mRw7ty51qRJk7RFGzdu7PY9bNgwN0twpPL000+7cQr1SaJmJ45nYo9orH5uQDzYR9PldQQQ4D7BNYAAAgggECQBdfdV4wCvu2/GuhUtWtQ1LChYsGCQqk1dEEAgQQJ+nr8TdAhsJgkECABz4SRccMEF9sEHH1iHDh3SJgQJdxjehCGnnHKKm4lXRWMBVqpUyQ30+/XXX9tJJ50UtgYvvviitWrVyrXaU+vB0PBu6tSp1rZtWzvhhBNs4cKFEYO95s2bmyYbiRYUZoXQzw1IYxLu2bPHHbfGRExkMJmVurAOAggkl4BaOC9evNi1dNaDkD4woSCAAAIIIJAXBfbu3et6DekZIFwpUKCAC/40wy8FAQQQiCTg5/kb1eAIEADmwrl844037NJLL3WTe6h13jnnnHPQUah77llnneU+5XvrrbdMYZxXvGBQrQMVJOoPf2jZvHmzNWzY0LXaGzFihA0YMCDd62odWLlyZdMYg+pGrO7EGcuMGTPsiiuucK0Oly9fntYVOFFcfm5AK1eudC0iVWrUqOFaSFIQQAABT0Atn3/77Tf3ox6IjjjiCHAQQAABBBDIUwL6EEvv6Tds2OA+0MpY9AF42bJlrVSpUu6ZgoIAAghkJuDn+RvZ4AgQAObCudQfcYV+6uKr1imjR4+2a665xipWrOg+4Xv55Zetb9++rvuufq/WfKFl9erV1qBBA9cNoGnTpjZu3DirX7++e3Mwf/586969u5s0pF69eq7loLoEZCxPPfWUtW/f3gV8Q4YMsR49eliJEiVs586d9uSTT1q/fv3c/gcPHmyDBg1KuJKfG9CmTZuck0qZMmWY7SzhZ4cNIpC3BXRv3Lhxo6uExkLScAgUBBBAAAEE8oqA3oNrskCN3R2u0N03r5xJjhOB5BHw8/ydPLXgSPwKEAD6Fczi+vpET5OBKAT0igbsDR3Ut0uXLjZmzBgrUqTIQXv5/PPPXQs9vTlQUSsXzYzrzQZ2zDHHmCYcyazrm8K9oUOHuuBQnyLqIVnhmvcpo1oaTpw4MVs+VfRzA1L3X3UD9oq6PejTT4WZFAQQSF0BtZhWy2YFgF7RPZDxkFL3mqDmCCCAQF4UUHdfvVfOWPReV+979aE9BQEEEIhHwM/zdzz7YdnkFiAAzMXzc+DAAXvuuefcrL7fffeda+KvIKtRo0auRd5FF12U6dHpIffhhx+21157zVasWOECMD3stm7d2nXrjWUsEAWQEyZMsAULFrhWdWpRd/LJJ1vXrl2tWbNm2abj9wYkK82CFlpUf8YDzLZTxoYRSGoBfXCRcWD0cuXKue5RFAQQQAABBPKagHr8aOIPr6ihgP6u8YF3XjuTHC8CySHg9/k7OWrBUfgVIAD0K8j6WRLwewPSw77eGIW2mMzSgbASAggEUkAPSpowiQ8FAnl6qRQCCCAQeAH17NE43Ar8NJxFuB5BgUegggggkDABv8/fCTsQNpSrAgSAucqfujtP1A1IXZ7V5U9jF2Zs/ZO6utQcgdQU0EOSHpDUkprJgVLzGqDWCCCAQF4S0Bh/hx12WMRD1vtcDWPBJB956axyrAgkp0Cinr+Ts3YcVawCBICxSrFcQgW4ASWUk40hgAACCCCAAAII5BEBDQOkyao09rZaqzOmXx45cRwmAnlYgOfvPHzyEnjoBIAJxGRTsQtwA4rdiiURQAABBBBAAAEEgiGgVn0axkaT2qmodV/16tWtQIECwaggtUAAgaQU4Pk7KU9Ljh8UAWCOk7NDCXAD4jpAAAEEEEAAAQQQSBUBtfpTiz+1/MtY/p+994yRLL3Lvq/KOXVO09OT087u2utdB7AfBxAWD7wG63kFr0hGRgSRwyuSXr4gEYRE/GDAFkkgGxAIxAckDA4YG5v1ptmZndg90zmnyrle/e9Tp6eq+lRX6OqudN2jo0p3/N2nz9S56h88Hg+mpqb6BQXXSQIk0AYCvP9uA/QOHJICYAduSj9MiRegfthlrpEESIAESIAESIAESEBi/a2trUGs/yqLJKsaHBzEwMAAE1fxVCEBEjgxArz/PjG0XdUxBcCu2q7emSwvQL2zl1wJCZAACZAACZAACZCAMQFJVrexsYFCoXCogiQAGRsbY+IqnjwkQAInToD33yeOuCsGoADYFdvUe5PkBaj39pQrIgESIAESIAESIAES0Ajkcjll9ReNRg2R6FZ/zPDLM4YESOA0CPD++zQod/4YFAA7f496coa8APXktnJRJEACJEACJEACJND3BBKJhEr0kclkDrGw2+3K6s/lcvU9JwIgARI4PQK8/z491p08EgXATt6dHp4bL0A9vLlcGgmQAAmQAAmQAAn0IQFx893d3cXm5qbh6oPBIIaHh1XmXxYSIAESOE0CvP8+TdqdOxYFwM7dm56eGS9APb29XBwJkAAJkAAJkAAJ9B0Bcfnd398/tG4R/MTqz+fz9R0TLpgESKAzCPD+uzP2od2zoADY7h3o0/F5AerTjeeySYAESIAESIAESKBHCcTjcSwuLpatTlx9x8fHYbPZenTVXBYJkEA3EOD9dzfs0snPkQLgyTPmCAYEeAHiaUECJEACJEACJEACJNBrBLa3t7G1taWWNTAwgKGhIZhMpl5bJtdDAiTQZQR4/91lG3ZC06UAeEJg2e3RBHgB4hlCAiRAAiRAAiRAAiTQawQkDqC4Avv9fng8nl5bHtdDAiTQpQR4/92lG9fiaVMAbDFQdlcfAV6A6uPEWiRAAiRAAiRAAiRAAp1FIBKJKHGPyTw6a184GxIggeoEeP/Ns0MIUADkedAWArwAtQU7ByUBEiABEiABEiABEmiSQDabxfr6OqLRKAYHB5V7LwsJkAAJdAMB3n93wy6d/BwpAJ48Y45gQIAXIJ4WJEACJEACJEACJEAC3UJArP5E/MvlcgdTnp6ehiT5YCEBEiCBTifA++9O36HTmR8FwNPhzFEqCPACxFOCBEiABEiABEiABEig0wmI4CfCnwiAlcVut+Ps2bN0Be70TeT8SIAEwPtvngRCgAIgz4O2EOAFqC3YOSgJkAAJkAAJkAAJkECdBMTVV8Q/cf2tLBaLBaOjo/D5fHX2xmokQAIk0D4CvP9uH/tOGpkCYCftRh/NhRegPtpsLpUESIAESIAESIAEuoCAZPCVI5/PY2trC/v7+4az9nq9SvyzWq1dsCpOkQRIgARAC0CeBIoABUCeCG0hQAGwLdg5KAmQAAmQAAmQAAm0nYAIbLrYVvkolnU2m81wjuFwWFnjVWt71PuStbda0o7l5WWV2KNWkay/IyMjCAQCtarycxIgARLoKAK8/+6o7WjbZCgAtg19fw/MC1B/7z9XTwIkQAIkQAIkcPoERCCDHCYTTCaTmoCIcbogl8/lkMmkkc/lUZD3lTVcDoW8ZhWXLzyt63a64HQ5tf4gDwU43B5YrFaIoJbJZA6EOuk3EYkgD+lXs7IrKzItyHsFBPx+DIYGnvaLAjyBIOwuNxYWFpBIJMqaRra2VDs1h+Jj2UNxLLfLhZGhwYO5yhPf4BDc/oCab6UAuL+xpjgoTibA5XRheGgQNqsVJrNZ7ChgMhc5Kp5mVVfek7k6PV7DDU4nE4qB6tZkBqSPg77Maiy9r9M/QzgiCZBArxLg/Xev7mxj66IA2Bgv1m4RAV6AWgSS3ZAACZAACZAACRybgAhUuWxG6UiFglinaY9PXxcFpnxeE5DEgk19qNXzD40Yiz2JOCI72wf1pGPVU9HVVO9Le0sT5572W8Do+YtKUJNEFLpQJ4/JWBQbTx6rsXMyFxHodHFOPS8UBbw8vC4XHHb7gUgmY01duwmH242HDx+qfvWSTaexufC4Lp4Brxd+r6es7vilq0pQe/z4MdLp9MFn+VwW649n6+rX63Yh5PeX1R05dwG+gSEsLi4iHo+Xfbb66EHZ2qoN4nI6MBQMln08OHUWwdExrKysHErysfFkTp0TZpMJAZ8PMq96S3B0HINT04bVl+7eRioeq6srJRDqgiAA78AghqZnDNtuLy0iFRcrxqIwqTUsCpjaoyZmljxXdQC7y6X4GpVENIJsKqXNo6RPvS9pc9BvsT9NXDbBbDHD5nAa9ivCsvpjqOi3LjCsRAIk0DAB3n83jKwnG1AA7Mlt7fxF8QLU+XvEGZIACZAACZCAEDgQpoqiVakVmbWKq6ZYOYmYJEKZ6qMomIlQpalhWr/lopf22myxKlHGqER3dxDd2dZEumJfyqJMOlSWZU/Fu/J551W/M8++zbDf/Y11bC0+aXrDz7/9pQOLOj2GnIh2Iv6tP36kCXJKlCuKc2JZp+YqlnXaZz6PuyjUPZ3G2Ztvg9lqVUJdaRG+20sLdc036PfB53aX1dUFwEePHilxUS+NCIB+jwcBX7mVmy4APnnyBCkRjYpFBFbhUE/xuFwYCBgLgPL9MRYrF8/WZh9qYm2N4nI4MBQyFgBXV1ch7sWlRQRAm8WMkM8Hq9VSq/uyz4NjExicPGPYZvGtN5FOlIuY9XbuGxzGyMx5w+orD+4hETGOWVirf3cgiPGLVwyriXAb3REry8aLw+PF1NUbhg23FuchVpbGRRcrS0RHqVgUMG12ByavXjdsGt7aRHhzXRM6DwRL1VjVPxBCi/3plrBS32yxYLiKwCqibWxvVxNYlb75tH99atpnmqha+lxqekIDB9eI0omLyJwWq9aiRa4256KoejDnkvH0pRTHt9hshv1q19e8xuDpBA8YNL6bbNHtBHj/3e072Jr5UwBsDUf20iABXoAaBMbqJEACJEACPUlACWPFGzW56xNrL6OSjEaRzaSfCl/FNpo7ZanFmmb5dRALLZ+H3IRXE9RERIrt7RX7KFqg6ZZoB1ZqFe6axQk6Pb6qN+FbC0+wLzfhTRSxGJp+5jnDlrury9hZWWqiV8BstuDc295h2Da8tYHNebGo011dnyaD0KzpNOFORDuP03VIEDr/theVJd7c3FyZe6tY6smc6ymDgQDc4lJbUqZvPg8ROx48eFDWbzqZxPbSfD3dKpFOxLrSMnXtGeWuK/MVV129yDkmHOopIlgGKzLgiogkYtL8/DySyeRBN2JluD5XLmIajSHiidvpPCwAzlxQ7rqbm5uqX13EkceV+28VLQA1YUbJMkURRhdTzPK3ZTHD6XCUDTs4OY3g2LiyVpQ5lva7dOdN5HNP2dTDRK8TGp/EwMSUYZPFO7cgAm4zRSxNh8+eM2y68uAuEpFyEbPeMTyBEMYuXjasvj73CNHd7Xq7KqvXjmvEzsoydlebvEZYrDj3/AvG14jNjbqtY406kGuE5jpeXkRUXJsVK9bmivxIYFUWvuVFLDe1v41apSgQ6pacxepT124oV/bKkkkmsXTvzsHbT1sXBVr9k6KgefCyKESOnL8Al/dw5uxcNovlkn61dmW96xrp0ymVjCEWt2J9bFTE6vbphCv6rBijsl5gZBReCUlgUORvQ+ZdrRyIy5UVTJo1bzWrW/n/U36MOVQqmD5F/XRNLr+/qkX67Vdexs13vKSaiTX11JTxNarWGcPPu5sABcDu3r+unT0FwK7dOk6cBEiABHqOgIp1ptwoi8eBK2VRSFOfa3HQpI4IJ06vcXwvsWrJpJIHdTVxrijKHYh1ukhXbrV0tHXPCd3cH8e6x+2BCElGRYTFvfXVps4Vq92BszefN2y7u7aCneXFI/vVRTxJ2FB6AyY333ITLoKXZHjV3WrlMbq7i921ZSXy1SpiRSbWZKXl3PPvUBY8lZZ6qVgMO3WKEeL2WulmKkKoCKKVlnqNCIDipivuuqVl8uoNFaOu0lVXBEC5+dQMnJ66ih48F7dO4Wo2KQaeomWhZvBkgrjqyk24xNMTrtJO6svfwbq46hb7NJfEy1N1lKurPp7qTE1X37/A6FjVm3ux1JM9r7zZLnNRLc6v/IbZBHcgAJev3NpQryPWaXJzf8gCtsSqVeqWWrfqYr5/aLjqTbhmASgCYO1zrfJcDAyPVnUBXr73FpKxSK3T1/Bzb2hQuZsbFbGwjO3tNNWv0+vH5JVrhm03F55olnpNFJvDhelnnjVsKT8Q1Cu6V3ZgESvhKgJgK62ES8cVq+Z6xPFqmM4++3YYWWKLGCyicLNFtxKubC/Wq3ION1t0K+HK9mIJ+eSNV5vtFqPnLipRzajMvvK1pvsdOnMWgRFji/Qnr7+CXK66AHjUoCf2I8HwaFUr1pe/+Hm89P4PqmlRAGz6lOj6hhQAu34Lu3MBFAC7c984axIgARI4bQKl4pwmwuUh7oRl71e8LuS1eGlyM1DNIkCscDLp1IGLaiPrCo5OYHDK2L1v6a3bSCXqi+9VOaZ3YAij5y4YTmX14X3Ew3uNTPOgrtsfxPglY/c+EU8i25tN9etweTB1vZoAuIi99ZWm+rXY7AeuurKPIiTpx+7aKraWF4vx7orWekUBV0tYoVnpSRkdHIC9zEXZhAsvvKQEQLF8Ky1ys1wuWJbGSXsa40zaDA2E4HG5tQQQRXe9icvXlPvg/fv3y/rNpFKI7e6UuPSJqiX2aOWxz+SNgdAAQuL6WuJaKFZvYhU6Ozurst+qZmIFUiggk0wocU0/ROAsfS0Wj2YR6txuuGW+JW6LYjEk9fU4fbpQp/d94DVYGkeuqd1ko2oEjFzrlStz0aW90sVdrnmyb3ancSxCEZJyykpYj19ZdDtXbxi8VxQvZX61rIQT4bBysz+Yk96n1nExdmVp3EzN1V/EVfnbMCpiaSqWt80UYXDmxgkIgFYbZp57u+GURBCWH3iaLRdeeKdhUwlpUK97vFEHMl+L9XDW7OMKgGeu3zS0ADwpATCbyWD+VicKgDMQK0CjQgGw2b8GtmsnAQqA7aTfx2NTAOzjzefSSYAEepaALsopoU6SFuR0yzlNtFNHiYinC3n+4ZGqVjhzr75cV3wvI6gDk2cQGpsw5D3/5uvIpp/GKGtkU8QaQKwCjMqJWeE8eoDYvsSfary04yZcLPXEYkbPblqemEDTDLJFy0qH3QGL1XIQr0sEL3FzFFdPcSUtLWL5JrG4dAs1PSaYbv1Vmuhgcnwcbo+naMmmWa2J9aaIipWWepoVl2Z3ZuSmVzqH0dFRBCuSSeifVybVkPfLRblKke7pa7fbDZfLWNwR8a9MpGv8NGALEjh1AkaWmfokxLpSSwRSFCiLYqJmhKvHBy1mdi5JmiOfyt+okRupfCbXh5RKFPM01qjWXzH5TpVxpI70W811O76/h8j2lmGyHqP+teGfWnmKoGZURLjdkvADatlPkwBpL4sxU4/YuZnnXjAMHREP72P14b2m9/ykBEARhI2sbikAPt2qY4UJoAVg0+d8vzSkANgvO91h66QA2GEbwumQAAl0HAFNTNNEswNhrfhc/6zyfT3DqPxabWQlIn2JxVdlplFt8RU3SMWbkdKbGLkx8gRDh1jJPJ+8/mrTQp2IPdWyqD5+/RVIBtFmyom52LQhDpe4ZcoNnSZwmQGxPitzo9TcXXXxSh71ujanoypfuVkWK7XDGUI1waxMuCtavJW+L1ZmpUVuWEWsKj3E4q7yPe3GVitnz56F03k4U6iRpV4j58H4+Dj8Fdlk9faVlnpG/eqCmwh4FotY02liXSAQgLeKC7gen67MMq9K3KZG1sK6JEAC/U3gIPmS9r/1QSIlsTw2ijUn/y/nJL5n8VJ7IEaWXHsPRzwoZjsvFFRm6Mrru+yA9KsE1orGpdf0QztVUtfh8RhaLMr3Gfk/TvvqUeEeXzrnKq7zTq9PxUs1KpIYpqrLvYEnfun40q9kTDfsd3OjLIt6cfKGdUvFYKkg/VYTscObG1pWek0TrlKMP5AfuYy+p0knd2+9juvPaYmw6ALcv9cTCoD9u/dtXTkFwLbi5+AkQAInREDFHtOt3HQLuFz24D1lEae/zuUgFmpGsXtElCkLWt3gfKvH2MniyRuvNNjb0+ojxUD8lR3Iuude/Z+m+x2cOls1ScWTW68pt7ZmSnB0HBIY3KgIX+FcrShxTcU604Q0LZ6c9todDFa1LBQLEbEsNJm1mzKtj1Kx7mmf0p/0q4t5SjSyNJZttBkurW4j2V7X19eVa63uptrIGBKI3FORpELaG1nqNdLvyMgIQqHDgrX0ITEA9X3Vxb3KR7U3LCRAAiRAAiTQwQTENT+dzCIVzyKVyCJdfJTX6YS8n1HvLy8t4f/5+Q+plVAA7OANPeGpUQA8YcDs3pgABUCeGSRAAp1MQP1yns2qX8/z2Sxc/oDhL+yS4VPiGOWzmmtro5Zq1YJsS5a9hTtvNI1Iz8RZ2YGIk49f/3rT/Q6fPQ8Jbm9UHr/2dc2dq4kSGp/CwMSkYcuF27eQST3NmFkqzGkxzp4KdfpzZREhLmI+f9VfwkX8ky/Netw0XezT+2hiGT3RRGLtiZBXeYiwNzExAbtBtkmJIyfJJJotY2NjyqrOqJRmvxWBrtahW+uVWuw1Oy+2IwESIAESIIHTIiAxbEWsS0YzSMaySMa050rAi2uvlcgn7xefJ+MZJfjVkb8Ku9FN/H9/891qORQAT2tXO28cCoCdtyd9MSMKgH2xzVwkCXQsARHCUok4JEOnBLQWkS+bzRwIfpVCVrUg28loFMv37zS9zokr1w1dQLLpNObffK3pfscuXK7qqitCXbPlKFddI0s9TVyzKKu20udPX5thsliUUFctWYck6tDiyGnWcrXiszW7tn5pJ1Z1IuaJaGck9Mnn1cpJWOqJUDc0NFQ1pp7MUxf9+mWPuE4SIAESIIHuJiA/MIrVnQh4iUgaCRH15Hm0+DwizzUhT4l5ciSyzSQHrxsUBcC6UfV0RQqAPb29nbs4CoCduzecGQn0IgGxqIvt7yEVi6rYNaUWZfWst2ow7GQCEqy52dKIpZ4SwJQLqghqutWb9lxENt1VVZIiSDw9o5g14qq7u7qiZQOVUpJt9GmYMi37qZ6m9CC2kAlwur2wGcRqk66Eq7i6lgp+RnGJmmXFdscnIK66T548abqjo5JflGap1U4tE6xW66HDZrMdvKdb6TU9ITYkARIgARIggVMikEnnkAinERdBTwS8g+fp4vNysU/Lmt05hQJg5+xFO2dCAbCd9Pt4bAqAfbz5XDoJtIFAdGcb648fNT1y1ax1NSz1lDCnkgdYYLYWH+V18RB3WrvLOLh0OpmgmNb0jvV+QxFzxYJPLOT0Q38tyS8ko2xlOW5MvcHBQWWtZ1TC4bAS/XSBT8Q9CsC9fx5yhSRAAiTQzQQkNrOIefFwGrH9lHpUx748Fl+r52lkUs2FOTlNPirbvcsKu9uqHh3FR3m9H9/CR3/0fWo6dAE+zV3prLEoAHbWfvTNbCgA9s1Wc6EkcGIElACSTEDi8IkrrzyKUGexWg+NmUklsXC7mZh6JlhsNozMnDd0UZWsddG9naLAZz0k9FEAObHt75uOJSZfqchXKvZVy7p4VEy9Sku9aiDFek/EvNJDMvU6HMZZFvtmQ7hQEiABEiCBjicg1nfiYhvbSylhT3ssinz6872Usuarkli4rWu0Oixwuq1wem1wemxwuG1weKxwVjzK+06PCH1Sxwqbo/qPb7z/buuWdszgFAA7Ziv6ayK8APXXfnO1JNAKArmsxE+JakcsinQ8dijpRLXstzL+49dfqUjSYYLd5YLD7YHV7lDZeM1WqxL8RES0WLVHFhI4bQJiqbeysgJx2W0mq+7AwACGh42TtSwsLCCRSKi4epUCn/5axD9mwD3tXed4JEACJEAC9RAQqz2xyIvuptQh4l50N4moCHu7KUR2k4jvpSFJNTqhiJjn8trU4fTatUefJuyp557ioYt9HiusNkvLp87775Yj7coOKQB25bZ1/6R5Aer+PeQKSOCkCYgLbDIaKYp+EYgVX60yMDGF0LhxNtnNhScQiz2nxwu7263EP3HNZSGB0yQg4p5Y8Ym4d5RF3cOHD3FUQo6j5uzz+VTGXqMibsJ6ptzTXDfHIgESIAESIIF6CEjW28hOCtGdJCLFQ3uuCX0i+NWT9baesZqpY7aa4PbZ4fLZ4fbLo63kuSbwyWdivSfPrfbO+K7J++9mdrv32lAA7L097YoV8QLUFdvESZLAqROQ7Lvrc4+QikaRy2UbHt8TCGHs4uWG27EBCZwEARHbROgrPUT804vE05O4ekZFt9SrNS8R8+x2e9khbrryHgsJkAAJkAAJdBIBCV2RimcR3kogsp1EeCuJyHYCYRH4tpNK9EsnTz/WnsTOc/tscAccStQ7OALy/Ol7Lr8ddmd3xrjl/Xcn/SW0by4UANvHvq9H5gWor7efiyeBIwnM33od2UyqbkpWmwNOr1e58jp9PmXhx0ICp0lAbmhE2Esmk0rs0x9rWfAdZam3traG/f39g2WIe26l0CevxV2XhQRIgARIgAQ6hUA2nVPC3v5WAuHNBMIi8CmhL6meZ05R4DOZoMQ8T9ChxD159AS01x71WhP4xFJPRMBeLPfTLJIAACAASURBVBIPMbudwOOX7+PK/367WiKTgPTiTte3JgqA9XFirRYToADYYqDsjgQ6nIC43ibjMSQjYSQiYbgDIQRHxwxnLRaA0d1tw89MZrMS+Bwe78GjxO5jIYF2EZBYfdFoFNUSchw1L7HUm5mZMawSj8dV/D9d9GNMvnbtMMclARIgARKoJJCMZbAv4t5mQj3qYp88Fxfd0yjiiusNOuANOZWg5w0Vj6ATHnkedEAs9sw9KuxVY5xPZJFejCC9EEZqIaKeFxJZrIY38NIn/g8FwNM4OTt4DAqAHbw5vTw1CoC9vLtcGwlAiSHpRBzx8D4S4TCSsYiKv6cXly+AictXDVHtb6xja/GJ+sxik1gqPmXh5/T6YHe5wcy6PMNOi4Bu2SePEq/PqCwvLysBsNEiFn0iAE5OGsesbLQ/1icBEiABEiCBVhJIJ7PY30hgbyOOvfX40+cbcaRijYdpaXRu7oAdvgGnOrzqURP71OuQeH/Y+v47oVj3ZdbjSuxLK7EvjOxGwhA1BcBGz8DerE8BsDf3teNXRQGw47eIEySBhglk0+mi4Cei3/6RMfzEku/ccy9AHitLJp1SyT9E8LPZHQ3Pgw1IoFkCErNP3HflkEy54s4rbrwejwdTU1OG3W5vb2Nra6vqkGK5J0Jf6SFWfZKFl4UESIAESIAE2klAMuqGt5PYW4tjV471mCb0rcuPuE9j1rZ6juKaq8S8QSf8Q3K4ysQ+sd6z2A5/R2z1PLqtv1w4rUQ+TeyLIL0UQSH99Af2o9ZDAbDbdvtk5ksB8GS4stcaBCgA8hQhge4nIAk7kpHIgegnWXsbKRNXrivrPhYSaAcBEfZE4BOhTz9yOePA4yLWXbx40XCaYv0nVoBSJB6fntlXBD95LpZ+LCRAAiRAAiTQTgJizSeinoh86nE1ht31uLLuy2cLJzI1yY4bGHYpcU8JfCL2KcHPpVx0LRYKfEeBL2RySC9Hi+68muCXO4Z79Vp+Dy/+zv+lhmQMwBM55buiUwqAXbFNvTdJCoC9t6dcUf8R2Ftfw/bSfEMLFxdeEf1c/gBcPj/MtIJqiB8rN09A4unpQp9u5ddI3L5z584ZZtYV0VD6E8GPCTma3x+2JAESIAESOD4BEfp2V+PYWY1iZyWGneLz6E7r4/JJ0gxxy1Ui37AbgSFX8bkIfk7YnUxSVe+O6ok6Diz7FiPIrMaAfJPirMUE+6QX9mk/7NM+9bga2cD09DQFwHo3pUfrUQDs0Y3t9GVRAOz0HeL8SEAjIHH7jNx05TOJ8bf41ptHorI5nAdin8vng8VKayieW+0hsLOzg83NzaYGF2FvfHwcbre7qfZsRAIkQAIkQAKtJJDN5JTQt7UUxY5Y863GlOAX2Um2chjABOWaGxxxITjiRkAdLgRH3cqij1Z8zeHOxTIHiTqUK+9iFIVk83EVLQNO2M+I0OeDY9oP27gHJmu5hSXvv5vbq15rRQGw13a0S9bDC1CXbBSn2ZcEspkM4vt76pBYfpNXr6vkG0blya3XkMs8jRFjtliVZZ9bLPz8fogAyEICp0EgLTEo43EEg0HD4cT6b2FhoeZUJGafuO7K4XK51CMt+2piYwUSIAESIIETICCW6pHtJLaXo9hejhUfo8qNt9CkcZjRNJ0eG0LjboRG3QiMupXYJ4d/2AmrjTFrj7O1ypV3JaYJfsUjdwyh1uSwaGKffkz7YPHaa06R9981EfVFBQqAfbHNnbdIXoA6b084o/4mkEklEdvdRXRvB6lYeUbTgckzCI1NGALaeDKHTDIJd0BcegNweDx9n5Gtv8+k01u9xO8TwU+EPXnU4/fNzMwod9zKIjH/Hj16pDJUlxZJyCFCn37IaxYSIAESIAESOG0C2XROiXybixFl2bct1n0rUaSTxvFpG52fJN7wDbkQGtOEvtCYB0F5PibhWfh/X6M8jeorV96tRJnYdyxXXhNgG3VrrrxFCz/rsBvift1o4f13o8R6sz4FwN7c145fFS9AHb9FnGAfEBAX3ujuLmJ7O8qdt1pxev2YvHKtD4hwiZ1MQDL0itCnHxLTz6iMjIwgFAoZfib/94gAqFv2ySOz8XbyrnNuJEACJNCbBJKxjCb0LUSxtRTB5mIUe2uxllj1idAnrroi7A1MeLRj3IvgqIvWfC0+nXIRycr71LJPZeU9hmBr9tkPhD4l+E15YXa0JpYi779bvPld2h0FwC7duG6fNi9A3b6DnH+3EkjGosrST0Q/sfqrr5hw7vkXmLCjPlis1SICYrGni32xWAzi4ltP8fl8mJgwtlitpz3rkAAJkAAJkEArCcTDaWzMh7G5EMHGvFj3RdCSpBwmqIy6g0rgKwp9Ex4Vn49uu63cQa2vfCqHzLIWr0+EPknYkdtvPrmKyWaGTSXq0N15/bAE7CfmScP779afE93YIwXAbty1HpgzL0A9sIlcQlcRkIy9e+urZfH6jlqAJP5w+wJwB4Nw+4Ow0i2yq/a72ye7uLioXHsbydIra5ZYfV6vF6Ojo92OgPMnARIgARLoQgKJaBqb85rQp4t+0d3mRSIdgcNtxeCkF4NTXiX4yaOIfsy0ezInSSFXQGY9poS+jAh+i2Fk1uNAs3EXTYB1xF0Wu8826oHJ0rgrb7Mr5v13s+R6qx0FwN7az65ZDS9AXbNVnGiPENhZWcbu6tKRq7FYrHAHQ/CGBlQij2rZf3sECZfRwQQkWYcIgLWKCH7ixivZeeVg/L5axPg5CZAACZBAqwhkUjkl8q0/DmPjSRgbCxGVsONYxQSVfGP4jCb2DU35MDjpgSfoODHLsGPNtwcay4+Nud2UZtVXdOfNLEdRyOSbXp3Fb4etNFFHC115m50U77+bJddb7SgA9tZ+ds1qeAHqmq3iRLuIQDadrmqpJ4k6Fu68cWg1Fpsd3mAIntAAnF4fv1x20X5361QleYe49JpMpqqx+ra3t7G1tXVoiZKhVxf7PB4PBb9uPQk4bxIgARLoMgL5fAG7qzGsP9EEPzkkQcdxMvFarGYl7g2d8WFoyovhaRH7vLA5mHX3JE+PXCyDjC72LWnuvPlopukhTXaLitWnXHmnNHdeS+BwMrKmB2hRQ95/twhkl3dDAbDLN7Bbp88LULfuHOfdaQSymQxiu9uI7Gyr7L1nbjwLu9NlOM2lu7eRisdgczjhCQ7AEwrB6fF22pI4nx4jILH8xJovGo0q4U+SeUix2Ww4f/684WqlvlgBStEt/ETwczqdFKl77PzgckiABEigEwmIK+/aXBhrc/uahd98GJljJHew2s1K4Ds4zvhUBl6LxdyJy++ZOeXTOWQkk7Iet28pgtxxrDTNgG3MU+bK22xW3tOGzPvv0ybemeNRAOzMfen5WfEC1PNbzAWeIIFCPo/Y/h4i25uI7++jNCBJaHwSAxNTxqJKNKIyntpd7hOcHbsmASCXy6kEHpFIRIl+IgIalZmZGTgch38lF3ccaccsvTybSIAESIAETppAQaz71uJK7Fud28fa7D72JN5bk8ViMysX3uFpP0bO+jB81ofQmAdm8+nFe2ty6l3dTPYxuxEvy8orcfzQvCcvLANOTexTln1e2Ca8MNu700KT999dfXq3bPIUAFuGkh01QoAXoEZosS4JaATEei+ytYnozjZyuawhFrHum37mOSIjgVMnkM1mlWgnop+If/Uk8BgeHsbAwMCpz5UDkgAJkAAJ9C+BTDqnrPpE6BPRT45U3Ph7VS1KJrNJufGOzvgxIsdZPwbG3TDTsq8WumN9Xha3r+jOq+L2pZtX+8xuqxL7bEU3XnHrtXjtx5pnJzXm/Xcn7Ub75kIBsH3s+3pkXoD6evu5+AYI5LIZRLa3lbVfOlH712hJ5DF1/Saz9jbAmFUPExCLPflyrR8Sr08SbhiVcDiMvb29upJ26O2lL3Hp9fv9KqYfCwmQAAmQAAmcFIFUPIPV2X2sPNxTh2TplZh+zRTvgAOjMwEl+I2e8yvrPluXWoQ1s/52tcnHM0hLvL6FsPa4GEE+1nzcPljNsE96tdh9xWQdYu0n33d6tfD+u1d3trF1UQBsjBdrt4gAL0AtAsluepKAiC5xcfHd2lSuvqUuvkYLlmy9EtPPNzDI7L09eUaUL0rEOXGxLT3E+k5eS4w8n89nSGF9fR2SgEN3x9UfS4U+/XllByLUjY+PG/Yr13Ox/KtVZG5er1cJf/KchQRIgARIgAROgkA8nNbEvkea4Le9HK31VcpwGhK3Twl9558Kfp4OTO5wEgzb2adk302vitgXUQk6MosRZI8Tt88E2EbdTy37xMpv1A1Tn1lp8v67nWd154xNAbBz9qKvZsILUF9tNxfbIIHd1WXsrCzVbOX0+OAbGoY3NACzpTvjkdRcZB9WkGQZIuiVHqViX7V4eoIqGAxidHTUkJok1ZDkGs0UEe4mJycNm66srCi3X6Mi1n3SVg5J+sFCAiRAAiRAAq0mIILf8v1dLD3YxcqDvabj93lDDoxdCGDsfADjFwIYFBfQPhOJWr03tfpTcfu2EuVx+1Ylbl9zFpoyniXkKInb59Pi9jGzMnj/Xets7I/PKQD2xz533Cp5Aeq4LeGEOohAJpXEwu03DGdktTngHRyEf3AYNlpRddCuVZ+KiHeS+bZU0BNhrJrr6+zsrKrbTAkEAhgbGzNsuri4qGLzNVPEam9qyji5zOrqKsQNWIq4zkhdXfSTpDMsJEACJEACJNBKApKhV4Q+Jfrd31UJPBotErtvaMqrhD5d9PMN0Dq9UY6N1s9F02Vin7jyFo6RXVni9h3E7FPJOnorbl+jfI+qz/vvVtLs3r4oAHbv3nX1zHkB6urt4+RbQCBXFHgsVeKqrT68j3hY3H9FVBEX35Cy9nP5/D0dn6QFaE+1CxH3RKzTBb5SoU9/bpQMY3BwEENDQ4ZznZ+fRzKZbGodrXDVNRpYxMozZ84YzmlnZ0e5FuvuvWazuam5sxEJkAAJkAAJGBFIJbJYebCL5ft7SvBTLr0NFovVrGL2TVwKYvyiZuVndxrHtm2wa1avQqCQzSO9UnTlXYwo4S+309z3GzVEady+aS0zb6/H7WvlycX771bS7N6+KAB279519cx5Aerq7ePkj0FAMvnub6yrTL7BsXEMTBhbVcX2diGuwP6hEXhCA6gmFB5jKmxaBwER76oFhJ6bm1PCXzPlKEu95eVliBtwPUUs7EoPEepCoZBhU921WNbTzEFhr54dYR0SIAESIIHjEshl8ypL7+LdHXVsPAmj0KBHqM1pUdZ94xeDSvQbPeuHxcYfqI67N9XaH2TlXQxrsfvkWIkCuQY3Th/ABFhH3ErksxfFPttY/8Xta+V+8f67lTS7ty8KgN27d109c16Aunr7OPkmCCSjUSXo6VZ90oXFZsfZZ56DJPFgaQ8B+cIqIl7pkU6nD14PDAxArPWMynEs9Y6yqNvc3FSuupIpVw4R+PTHSsGvl7PVteeM4KgkQAIkQAKnTUD+L95ZjWHp7i4W7+1g+cEesqlcQ9MQwU+EvsnLIUxeDir3XjPj9zXEsJHK+VROJegQqz5N8AsjH23uR1EZ1+y3H2TjVVl5Zf8ctNBsZE9q1eX9dy1C/fE5BcD+2OeOWyUvQB23JZzQCRFIRCPYXVlGIrJvOMLo+UsqiQfLyRMQd11xVa08jFx09dm0ylJPCb5FIU/EPJfLVVVYPHkSHIEESIAESIAE2ksgEUlj4a0dLMpxbwfx/XRDE5IMvWLdJ2Lf1JUBDE9T8GsIYAOV5XuSZOFNz4t1n2bhl1mLNZVZWYY12cywTXlhP+PXRL9pH6zMrtzAjjRXlfffzXHrtVYUAHttR7tkPbwAdclGcZpNE4iH97G7uoJkVEuOYFQktl9ofAKhcePsqk0PzoZlBCRJhVjUNZNY4yhLvY2NDezu7qqxRNyTLLci7umPugWf/kgXWp6YJEACJEAC/Uogn8tj/UkEC3e21bGxEGlIQJIYfmMX/MrCb+pKCCMzfsh7LK0noKz7lGWf7s4bRj7eXHIymZ11xKWJfeLKe8YH26gHJoup9RNnj0cS4P03TxAhQAGQ50FbCPAC1BbsHPQUCCjhb2UZyVik6mgWqw2BkVH4hkZgtdlOYVa9O0Q+n4e47EpxVsmKLNebWCzWFAQR7y5cuGDYVtyG5VdxqUNxrym8bEQCJEACJNDDBGJ7KSy8tY352ztYureDVIMi0tAZL85cHcDUtZCy9rPZmVm+1aeLit23k0RKWfdFlJXfcaz7JCuvZtVXFPymfDC76Mrb6n1rpj/efzdDrffaUADsvT3tihXxAtQV28RJNkBAhL+dlSWkYtWTN4jwFxybgH94GGYzv8Q2gFdVFbFP3HclQ67+KOKffHmVDLSTk8aWlBJTTzLVHlVEwBPLPTnsdvvBc/11o3NlfRIgARIgARLoNwL5fAHrc/t48qaIftsNZ+v1hhw4c21AHZNXQnD77f2G8MTXW8jkkF6OKqEvNX/M2H1mE2zjHs2yb9oPxxkfLIPOqsnTTnxxHOBIArz/5gkiBCgA8jxoCwFegNqCnYOeEIGNJ3OIbG9W7V2SfYTGxuEbovBX7xboyTkSiYQS/HTRr1q8PhHqzp8/b9h9OByGuAGr//RMJiXwORyOskOs+FhIgARIgARIgAQaI5CMZVQcvydvbmH+zjZSsfpdRa0Oi3Lnnb6uiX6BERfFo8bw16ydC6c0674nRQu/Y2TmNXttmtB3VhP8bJNemGmVWXMPOqUC7787ZSfaOw8KgO3l37ej8wLUt1vfkwuX7L5i/VdZrDaHivHnGxxipt86dl7Evmg0eiD4icVfI+XixYsqFl9lEVdd6VtEPxEK6a7bCFXWJQESIAESIIGnBOSHuN21uCb4vbmN1dl9FPKFuhENTnowfX0Q0zcGMH4hCIuNcfzqhlejouxDZj2O9Py+EvxE+MvtpprrXqz7Jjxw6K68035YQg4KtM3R7IhWvP/uiG1o+yQoALZ9C/pzArwA9ee+9+qq5cvw8r07SMW1OHNWe1H4G6Dw18ieb29vY2trq5EmB3VF2BMXYBH5WEiABEiABEiABFpHQBJ4rDzax+M3NvHk1hbCW8m6O7e7rDhzLYTpG4PK0s8bctbdlhWPJqAl69Cs+/QYfoVUrilsldZ99ikvTDaGq2kKZoc24v13h27MKU+LAuApA+dwGgFegHgm9BqBdCKOlQf3MDA5BR+Fv7LtFUs+ceEVKzyJ3TcxMWG4/ZKoQ64NtYruwitJP+QQ0c/I8q9WP/ycBEiABEiABEjAmEAmlVMJPB6/voUnt7cacu0VK7+zN4dw9plBjJ3zw2yhlV8rzrNcJI2UuPI+2VeCX0bceRtzltCmYQJsYx7Yz2qJOhxn/bAMMHZfK/aok/vg/Xcn787pzY0C4Omx5kglBHgB4unQbQRy2SyS0Qg8wVDVqRfyebr6AsjlckrskyMejyvxr7ScO3dOxeGrLNLu0aNHZW+LsOdyuZTQpx8U+7rtr4fzJQESIAES6AYC8XBaWfiJpd/i3V3ksvWpSxarGVNXQ5i5OaiEP98ArfyOu9/iXZLdSmjWfUXRL7tdv+Vl6fgmh0WJfQ5J1iGi3xkfzE7GPj7uHnVF+1wWSO4ByX0szd3HmZf+t5r24uIipqamumIJnGRrCVAAbC1P9lYnAQqAdYJitY4gIBl+N588RjaTxtS1G3C4PR0xr06ZRC3Br3KeY2NjCAQChtOXZB2SkEMX+8S1l4UESIAESIAESOBkCIS3Eph9dVOJfqtz+0Cd4fw8QceB4CeJPGwOuoseZ4cKuQIyq1El9qUeazH88rFMU12KNZ9Y9Smx76wftlE3TGZTU32xUZcT2HkMbN5Ti1ha28KZ938/BcAu39LjTp8C4HEJnkD7j3/84/izP/szzMzM4PHjx1VHiEQi+OM//mN85jOfwdzcnLK6kTYf/ehH8eM//uMYHh6uObtbt27hj/7oj/D5z38ey8vLCAaDeOaZZyBz+K7v+q4TC/RKAbDm1rBCBxDI53PYXlxAeGvjYDZ2pwtT157pa0s/cekVyz459Cy9jWyXiH8iArKQAAmQAAmQAAmcPoGd1RjmXtvA7Gub2FqM1j2BoTNenH9+GDM3hyDPTSaKSnXDq6hYyOSRXoogJe68j8NIz4fRVPw+PVmHiH0zkqE3AIv/sJdFs/Nkuw4mkMsACc26D4MXAKO/x8QusPBVCoAdvI2nPTUKgKdNvMZ4f/qnf4of+ZEfUbWOEgAfPnyIb//2b8f9+/dVXXGRE7c4yaApRWJs/dM//RNefPHFqiN+8pOfxE/8xE8gnU6rOqFQCOFwWAmJUj7ykY/g05/+tOq71YUCYKuJsr9WE0jF41ife4RMKnGo6+DYBAYnz7R6yK7pz8hVt57JS6w+uZ54vV54PLSirIcZ65AACZAACZDAcQmIO+nWUhRzr21i9tUNlcW3nmI2mzBxOYhzzw3j3HN07a2HWbU6KmHHvGbdpyz8liJAtk5zy5JOTXZx5/XBMRNQgp9y57XT+vI4e9M1bTNJILEDiKgnRyrydOoz7wUc3sNLyeeBR58FCnlaAHbNRp/sRCkAnizfhnr/2te+hve9733IZDKQ/6irCYASKF+Evbt37+Id73iHsuB75zvfqX6Fe+ONN/BzP/dz+NznPqcsbF577TVDS5t///d/x7d8y7dALHl+9md/Fr/0S7+EkZERNfbf//3f4yd/8iexs7ODH/iBH8Bf/MVfNLSOeipTAKyHEuu0i8D+xhq2lxZRKByOfePy+TE8cx42e29nm9V/GDCK1Sf7Mj8/fyi2X+l+yfVI3HhF8HO73eo5Y/e164zmuCRAAiRAAv1GQO4lNp5ElOA3+9pG3Zl7xZVXMvaK4CdJPJwehuJo5tzJJ7OaO+/cPtIi+C1HmkrYYfbZ4Tgnln1i4RdQyTtMFlpeNrMnXdcmFdUEv3hR9MseEQNy9AYQnDZeolgAFgpY2kngzNs/pOowBmDXnQ0tmzAFwJahPF5HGxsbeOGFF7C9va0sAH//93+/qgD4m7/5m/iVX/kVXLlyBf/zP/8Dv99fNrhk2fzABz6A//7v/8YP//AP40/+5E8OTe7q1avKevCXf/mX8Ru/8RuHPn/llVfwrne9C9lsFl/5ylfw7ne/+3gLrGhNAbClONlZiwjkshkV6y+2v3uoR5PZjMHJafiHR3rS5UV365UfGOSQHwMkJMDo6Kgh3c3NTfUjgV4qBT8R/sxmZv1r0anJbkiABEiABEigJgER/TYXInj0yoY6InUmjXB6bTj/3BDOv20EEs/PYuP/3zVhV1TIxTJadt65fSX8qQy9jRv4wTrk0lx5zwXgmGF23kb3oavrp+NAfFs7RPjLpupfjn8CGH/uyPq8/64fZy/XpADYAbsrIts3f/M34wtf+AL+6q/+SlnlfexjHzMUAOU/9kuXLmF2dhb/+I//iO/8zu80XIFYE4qAJ+KgBNUXCxy9fOlLX1KWhnJjLzEGq7n4/tiP/ZiKMSjxAD/1qU+1lBQvQC3Fyc5aQCARCWP98SxyGc0lvrRI0o/Rcxdhc/ZWVjsR+UTsk9ABEs9Pri+lRRJwnD9/3pCu1N/a2lLXFrmGUPBrwUnILkiABEiABEigQQLyf/f2chQPv66JfuHNw6FLjLr0BOxK8LvwtmGMXwzAbKHo1wj6fDyjYvel5vaQmt1HZi3WSHOtrgnKok/EPrtY+c0EYPExfl/jIHugRYmrbkOrMZkBZwDwjgID545syvvvhsj2bGUKgB2wtT//8z+P3/3d31Xx+MSd9y//8i+rCoCSrOODH/yguukW6xuJqVWtiAuxuOmJC6+48upFnovQWEvYE0FSLAklXpeIiPLYqsILUKtIsp/jEpAvzrury+owKsHRcQxMTPVE0g9ZqyTt0K38xFq4VhEBkJl4a1Hi5yRAAiRAAiRwugS2V6J4VBT99tbri+nnG3Qqwe/C20cwOuNnZtgGtiyfyGrx+8TCb24PmdVY4xZ+ZhPsU96i4BdQbr1ml7WBWbBqVxPIZYF8FrBVMShY+Jpm+XdUMdsAVwhwBbVHZxCo0+OG999dffa0bPIUAFuGsrmO/vZv/xbf/d3fjfe85z3KAlButI8SAH/1V39Vuex++MMfxr/+678eOai4EktSke/5nu/BX//1Xx/UnZycxMrKisoeLJl+qxWxDhoYGFDWQZ/97GfxTd/0Tc0t0qAVL0AtQ8mOjkEgk05hY24WyVhJEN1ifxaLFSPnLsAdCB5jhPY3FYtiEfwka7g8yut6i8TsGx8fZ8KOeoGxHgmQAAmQAAmcIIHwVgIPXl7Hw5fXsbNSn8VZYNiFiy+MKNGPmXvr35x8SgS/Egu/Zlx6rSaVpEO5854PwD7tZ8KO+reg+2uKZ41k6BWX3timlrE3MAmM3TRe29YjYPth+WdWB+AaANwDmuBn9xpn+62DFu+/64DUB1UoALZxk2/fvq3cdMWy7tVXX1WZe6UcJQD+4A/+oLLo+4Vf+AX8zu/8zpGz/8M//EP89E//tLLik6QgUiR7p1gNyqOMf+PGjSP7ePvb364Sicicvv/7v79ltHgBahlKdtQkgUI+j4Xbt5DNHLaCc/kCSvyz2ro/8PX+/j7W1tbqpiSuvJKhVw65VkhsPxYSIAESIAESIIH2EIiH03j0iib6rc2F65qEf8iJiy+M4uI7RjA05eX/5XVQK2TySC2EkZrVXHrTi5K0o7EgfiabGfbpEsHvjB/yHksfEZBMvfEtTfAT4S+XqRD0nMCFDxgDkWQfK68C7sGnh93TMni8/24Zyq7uiAJgm7ZPbsolk6/E4BNx7r3vfe/BTI4SACVz77/9278pK0BJ4HFU+Zu/+Rt87/d+r0oWcu/ePVVVXHl1oVGsAMW656gisQklY7AkHpFMwfUWucAcVWQeL730kqrCLET1UmW9VhMIb21i84i1/QAAIABJREFUc36upFsTBianIG6/vSJ8SYxRiRlarUiiDhH75IcICS1gtdIVpdXnGfsjARIgARIggUYIpBNZzL2+qaz9lu7uSALPmsU3IKLfiBL9hqd9PfM9pubCm6xQyOWRXooWBb89pObDQLYO0KXjWc0qUYey8LsQgH3KB5OVgl+TW9KdzXQrv+gGENsAUoe9ig4tbOa9gMMgtJb0dYI/vFMA7M5TrNWzpgDYaqJ19CdxuD7ykY/gX/7lX/B7v/d7+Jmf+ZmyVkcJgDdv3lSWe5/4xCfwoz/6o0eOJi7C3/qt3wqfz4dwWPvFULL7vuMd71DPJRaYs0ZSA3ER/ru/+zv85E/+JMSisN7SiHhCAbBeqqx3EgTW5x4hursNq92hEn04Wxjr8iTmW9qnuOmLa68cQ0NDVV115W9MknboRSz7dCs/sfhr5O/1pNfE/kmABEiABEigHwnkMnnM397Gg5fX8OTWNnLZ2iE7vCEHLojo90Ixpt8Jigfdvidy/5VdjyP5aA8pOeb2UUjnGluW1QTHtF+58zouBJV7LwW/xhD2RO18DoiJld8GIMJf7nACwarrlKQdkq3XN3bqKCgAnjryjhyQAmAbtuXXf/3X8Wu/9msq9t+nP/3pQzOoRwCU7LwS4++ooguAYtkjAoEUCoBt2HAO2dEE8rkcdlaWEBqfhKULrN/Eok8X/UTE10swGFSZvY3K3t6eaiPXAjmY1KOjT0lOjgRIgARIoE8IiCi1/jiMe19dw6OvryMVz9ZcudNjU4LfpZdGMX4+wEQeRxDL7qeQeiiC3y6Ss3vIRyrcMWvRthRj+F0IaqLfNF16ayHri88ja8DKa/UvVdx4PcOAe0iL5We21N+2hTUpALYQZhd3RQHwlDdPRLlv+7Zvw/Xr1/HVr37V0GKnVS7Akvjj+77v+47lAiyJP/7jP/6DLsCnfJ5wuNYSSMXjcLjdre30FHuTmJ266FdqyVc6BXHdlYy9tOY7xY3hUCRAAiRAAiTQBIH9zQTuf20ND762Bnleq1gdFpx/bgiXXhzFmesDsFjoZmrETGXqnds7sPLL1sG2rB8TYJv0wimCn1j4zTBpR61zsy8/l2y+s/8BFKpY6UqmXhH6RPTzDAE2V0dgogDYEdvQ9klQADzFLVhYWMDzzz+vsnC+/PLLuHTpkuHoTAJyipvCoXqagCT62Fqch8T6G790BW5/oGvWK6KfZO0V930R/cRKoFaZnp6GuPSykAAJkAAJkAAJdBaBZCyDR69sKNFvdXa/5uTMZhOmnxnE5RdHMfPsEGyO9lgN1ZxoGysUcgWklyJIPthF6uGueo7antNlM7aOujXB72JQxfIzuxgLuY1b2v6hJYZfZB2IrgFDVwDvsPGcFl/Wkn3oRVn5jQDeES1bbwe641MAbP/p1QkzoAB4irvwz//8z/iO7/gOdYM+ODhYdWS56d/d3YXFYilL0nHnzh389m//tkoA8uEPfxhiTXhU+eEf/mF88pOfxPd8z/dArAH1Mjk5CUkA8pnPfAYS469aSafTGBgYUCLEZz/7WYg1YKsKL0CtIsl+qhHIptNYm32AVDymqpgtVkxduwGbw9mx0ETkE7FPRD+x+KtH9JMkHhLnUw65tshrFhIgARIgARIggfYTyOfyWHhrB/f+exWPb20hX0eSifGLAVx+aQwX3z4Cp9fW/kV02Ayy2wkkH+4i+UCy9e6hkGosjp8l6FBin/OSuPUGYfHZO2yFnM6pE0juPxX90tp9gyqBKWDspvF09hYAcQUWKz8R/VqYrfek1s/775Mi2139UgA8xf3SBcBmh5Q4Xq+++io++MEPqmydOzs7kGD+1crMzAzm5+fx53/+5/jYxz52UO0HfuAH8Fd/9Vf4+Mc/jk996lNV23/hC1/ABz7wAeWmvLa2pmKHtarwAtQqkuzHiEAiEoYk98hly2O9OFweTF670bFuspIdXP7WahVx85W/R7/fr64FFP1qEePnJEACJEACJHB6BHZWYkr0EzffeLh2goDAiAtX3zWmhD//EC35S3cqn8yqpB1K9Hu4h9xOsqGNNLmscF4IwHExBOfFICyDzo79HtjQwlj5eATE0i+8CkRWgEwVN3yLDbjwoY605mtm8bz/boZa77WhANiBe/rFL34R73//+yEC3uPHj8tmKBZB4jo8OzuLf/iHf8BHP/pRwxV87Wtfw7ve9S5lFbS6uloWa/BLX/oS3ve+96mEAdJ/NZfBH/uxH4MkG6klFDaDkBegZqixTT0E9tbXsL20AKDcZdZkNmP47Dn4Bobq6aYtdcTtV/62jSz/RPQTMV5EP3mk6NeWLeKgJEACJEACJGBIQHfxvfuVVWw8CdekJMk8Lr1jBJffNYbRGT9FqSKxQr6AzEpUufXKkV4IN+bWK5l6z/rhuKQJfrYJLxOl1Dwb+6SCCH3hFSCyCogAWE+ZegnwVPfcq6eLTqnD++9O2Yn2zoMCYHv5G45+lAAoDX7zN38Tv/Irv4LLly+rWIIiCJSWVCqlBERJMiJuwH/yJ39yaJyrV6/i/v37+MVf/EX81m/91qHPv/71r+Pd7343JOPoV77yFfW8lYUXoFbSZF9CQOL9bS48QWR78xAQq92BsQuXOyIRiLjUS8KOata74p6vZ+2WhYjYJ0K+WPxJWAAWEiABEiABEiCBziCQzxewdFdz8Z17fQu57NEB6MxWE87dHMLld47h7DODsFgZtkN2MhdJKwu/lIh+D/eQjzWWrdc25tYEv0shJu7ojD+NzplFNq0JfnIkduubl9UBeMcA31jHxvOrbyHltXj/3Qy13mtDAbAD97SWACgCwosvvoi7d+/ihRdewB/90R8paz+xEHrjjTfwMz/zMxD33bGxMbz22mvqsbJITD+JIygJSX76p39aCYojIyPIZDL4u7/7O/zUT/2UcjEWd+G/+Iu/aDklXoBajrSvO8xls1ifewhx/a0sbn8QI+cuwGJtX1BnsegTUU/+pkSgDwQChn+XMvdoNIqtrS0l7MshYiELCZAACZAACZBA5xAIbyUgln4i/EV3UzUnNjLjx7X3jOPiCyMQy79+Lyp5x2IYyfualV9mOdoQErPXpsQ+x6UgnBdDsPgZx68hgP1UWdx8V1+vvWLJ1KtEv1HAGewZt9/ShfP+u/Zp0A81KAB24C7XEgBlyg8fPsS3fdu34cGDB2oF4sYr1kEiHkiZmJjAP/3TPymhsFqRBCE/8RM/AUn2IUUSfkgMMnFDlPKRj3wEn/70p08kqygvQB144nXplDKpJNYePUA6eTh+R2h8EnKION6OIgK7/E1JUh8R1/Ui7rsXLlygG287NoVjkgAJkAAJkEATBHKZPObe2MTdL69g8d5uZaSRQz26/XZceecYrr57HAMTniZG7K0myspPLPzu76gEHoVktv4FWkwqQ68S/S6HIBZ/7fpuV/+kWbMjCORzwOzngLzB+SaWfr4JwD8OOAMdMd2TnATvv0+Sbvf0TQGwA/eqHgFQpi0WRZ/4xCdUNt+5uTkl3EncQIkLKMLe8HCVtOUla37zzTfxB3/wB/jc5z6nMgMHg0E888wz+KEf+iGVIfik/nPlBagDT7wunFIyGlWZfiuTfUi8v5GZC/CGBtqyKvlblKQ9IvzpgnrlRMbHxw+577dlshyUBEiABEiABEigKoHt5SjufllL6CFx/o4qZosJ554bUqLf9PUBmC396+IrsfzSixFN8LvfuJWfdcgF52VN8HOcD8BsZxgU/pkaEMimgPCy5qorh1FZfUOL/SfFbNOs/PyTPeXeW8+5wfvveij1fh0KgL2/xx25Ql6AOnJbumpS0d0dbDyWhBnl8XYsVhvGLl6G09O6rNX1ghErPxH9RPwzSuSh92Oz2ZRAL7H9WEiABEiABEiABDqLQDqZxaOvb+CtL69g/XHthB7D0z4l+l1+cRROb/+6+ObjGWXll7i7g9TDXeTj9Vv5mewWOC4Glegnh3XA2VknBWfTOQTyeSC2CYSXgKjE/i4A/glg/DnjOca2gf0FzdrPMwyY+1OY5/1355zC7ZwJBcB20u/jsXkB6uPNb8HS04k4Ft9681BPdqcLY5euwGZ3tGCU+ruQZDkSt0/cfY8qkvhDXO1F+Dsp69r6Z82aJEACJEACJEACpQQ2FyK4/aVlPPyfdWRSWkicasXhtuLyS2O49g3jGD7Tnz/oyY+d2fU4Evd2kLy7o2XsLdR/TtnGPHBeCanDPu2HiUlR6ofXjzXTMWBvUbP4y2khrA6KyQJc+ABg6V8BvtYpwfvvWoT643MKgP2xzx23Sl6AOm5Lum5C28uL2FsrmvNLHExfAKPnL556sg9xxV9fX6/q6itg3W63Ev4koy8LCZAACZAACZBA5xAQa7+HL6/jzpdWIAJgrTJ5OYhr3zCBC28bhrUP3VILmRySs/tIiuh3bwe5vdpJUHSmJocFTrHyuzIAx5UQrIHT/cG21t7y8w4kUCgA0Q1gbwGIbx09wdEbQHC6AxfRGVPi/Xdn7EO7Z0EBsN070Kfj8wLUpxvfwmXLr87iAhzd3YZvcBjD0zOQ2H+nWUT4E3ffakUs/UT4czrpxnKa+8KxSIAESIAESKAWARH77nxpGQ/qsPaThB5X3zOuMvkGR9y1uu65z3PhNBL3tpWVX+rRHgqZ8vArRy3YOupWgp9Y+TnO0sqv506Ok1qQxPbbX9KEv2zy6FHE+s83BoTO9kUyj2aR8/67WXK91Y4CYG/tZ9eshhegrtmqjp5oIZ+HxAL0DQ61ZZ7i8ru2tlY2trj2BgIBhEIh2O32tsyLg5IACZAACZAACRwmoMf2E+FvY/5oaz+TCTj7zCCuf+OEeuynhB7yI2tmNaYEv8TdbWSWonWfTiabGY4LQTivaaKfNcgfQeuGx4pAfEcT/aLrQEWc70N4nEEgMAX4xgGLlfRqEOD9N08RIUABkOdBWwjwAtQW7Bz0BAgsLy8jGtW+GIuL7+joKCTJBwsJkAAJkAAJkEBnENheieLOF5dx72tryCSPju3nDTmU6CfWft5Q/4hXhWweqdk9lcBDhL/cfv2uvZagA86rA5rodz4Ak40ZezvjzO+yWeSywOzngMIRf6OSxVdEPzkcp5/wr8uIlk2X99/dvHutmzsFwNaxZE8NEOAFqAFYfVxVfoHeWVlCcHT81GP71YtdEoAsLCxgcHBQWf6xkAAJkAAJkAAJtJ9ALpvH3GubePOLS1h9dHSSLmXtd3MIN947gekbgzCbTe1fwCnMQLL2qgQeb20j+WAPhfTR4ujBlEyA/axfiX6uqwMQN18mNzuFDeuHIdZuA/uLh1fqDGjx/cTaz0yBuZlTgfffzVDrvTYUAHtvT7tiRbwAdcU2tX2SmwtPEN5ch83hxPjFK7C1IZZeLpdTFn5HiXsiVPKLb9tPF06ABEiABEiABBDeSuDOf63g7pdXkIhkjiQi1n6S0OP6N/SPtV92J4mECH5vbSP1ZB+oM5yfSuBxJQTXtUE4Lodg8dDbgX9uTRLIpgFrlTA5yTAw/2WtY5NZE/xE+HMFmxyMzXQCvP/muaD+rApy58pCAqdMgBegUwbehcPtra9he2n+YOYWixWjFy/D5fWd2moSiQRWV1eRyWQwOTkJr5euBqcGnwORAAmQAAmQQJ0E8vkCFu5s485/LuPJ7W3giLsbPbbfjfdOYlpi+/W4tZ+K57cSOxD9JLZfvcUy4IRL3HqvDcAxE4DJerrJ1uqdJ+t1CYHYFrDzGEjuA+ffXz1u3+obgMOvuflaKDS3and5/90qkt3dDwXA7t6/rp09L0Bdu3WnMvHY3i7WZh+UjWUymTF++eqpCIDK9XhnB1tbWyUCpAUzMzOwWhlk+FROAg5CAiRAAiRAAjUIJGMZ3P3yKm7/5xLCW0dnCnX57crST4Q/30Bvx/Yr5ArKui95ZxuJO9v1x/MT194zPjivDyrhzzpC117+ER6TQD4PRFaA3SdAqiTxzvBVYODcMTtn80YI8P67EVq9W5cCYO/ubUevjBegjt6etk4uFY9h+f5bkAy/pWX03EV4BwZPfG4S00+s/uLx+KGx/H4/xsfHT3wOHIAESIAESIAESKA6gc2FCN78whIevLyOXOZoH9aJS0E8878mcf75YVh62IKtkMkh+XBPCX7Ju9vIx7P1nUJWM5yXgnBdH1SWfhZvFdfM+npjLRLQCEhCj/0FTfjLGiSUsbmAc/8LEJNcllMhwPvvU8Hc8YNQAOz4LerNCfIC1Jv7etxVZdIpLN97C7lMuqyrgYkphMYnj9t9zfYS629tbQ0S96+yuFwujI2NwW7nF+OaIFmBBEiABEiABFpMQJJ6zL62gTc/v4y1uaOTetidFlx51zhuvG8CgxO9G74jn8wieW9HE/3u76CQri+gn9lthfPaIFzXB+C4FILZzqQKLT5d+7e7XAbYW9BcffNHxOAU194z72Im31M8U3j/fYqwO3goCoAdvDm9PDVegHp5d5tbWz6XU5Z/6US55Z1vcBgjM+eb67TOVvl8Xrn77u7uGraQDL9yMNFHnUBZjQRIgARIgARaRCC6m8Kd/1rGnS+tIBEu/4GwcoihM148875JXHpxFHZnb4bsyEXTSvCTIzW7B+TqC+eu4vmJa+/1QZXB12Sh5VWLTlF2IwRE+BNrv935o4U/uwcInQP8E8zme8pnDu+/Txl4hw5HAbBDN6bXp8ULUK/vcGPrk5h7a48eIB7eK2vo8vlV9l+T+eSCTqfTaeXym0wejh0k8f7E5dftdje2INYmARIgARIgARJomoB8L1id3cetzy1h7vVNFPLVRS6zxYQLbx/BzfdPYey8vyd/rMvtp5C4vYX47W2kJXNvfZofbBMeuG4MwXVjENZRxvNr+oRkw+oExL1XhD+x+ssf4XbuGtBi/nmG6fbbpvOJ999tAt9hw1IA7LAN6Zfp8ALULztd3zo3F54gvLleVtnmcGHy6nVYTjDpRjgcxvr6OsQCsLJ4PB7l8sukH/XtIWuRAAmQAAmQwHEJZDM5PHx5Hbc+v4StxeiR3XkCdtx43ySuf+MEPAHHcYfuuPbZnaQS/eRIL5QkTzhqppLEY8aviX7XB2Ht8WQnHbdp/TahVBSY/wpQOBw65wCFdwQYuAC4gv1Gp+PWy/vvjtuStkyIAmBbsHNQXoB4DugE9tbXsL00XwbEYrFi8toN2Bwnk6VPBL+NjQ3s7x+OISRuvsPDwwiFQtwkEiABEiABEiCBUyAQ2Uni9n8u463/WkEyekTcMACS1EOs/c49PwSL5eQ8BE5h2YeGyGzGi6LfNjLLRwugB40tJjgvBpXo57zOJB7t2Le+HlMEwKRBTE7vKDB4EXD6+xpPJy2e99+dtBvtmwsFwPax7+uReQHq6+0/WHxsbxdrsw/KYJhMZoxfvgqX13dikBYWFpBIJA71b7PZMDExAafzZITHE1sQOyYBEiABEiCBLiOg3Hwf7ePW5xcx9/rWkW6+VrsZV945poS/wcneSuqR2Ygj8eYWEm9uIrNWHge52paa7GY4rwzA9cygejT3aLzDLjul+3O6kXVg5dXi2k2AbwwYvAA4Tu57fH+CPv6qef99fIa90AMFwF7YxS5cAy9AXbhpLZ5yJpXE0t07yOfK44WMnrsI78Bgi0cr7y4SiWBlZaXsTb/fj5GREVgszIR3ovDZOQmQAAmQQF8TaMTN1z/kVKLf1XePw+mx9Qw3Jfrd2kT8zS1k1+sU/ZwWuCRzr4h+l0Mw2fh9pWdOiE5eSD4HxDY1Yc+oFAqaG7AIfiL8SZIPlo4kwPvvjtyWU58UBcBTR84BhQAvQDwPMsmksv5LJ59a4g1MTCE0PnkqcCTr7/b2NsxmsxL+AoHAqYzLQUiABEiABEigHwnE9lO4/UXJ5ruMRORoN98z10K4+YEzOPvMIMzm3shWm1mPIX5LLP22kN2oT/Qzu61wSubem0NwXgjCZO0tl+d+/DvomjWLsLe/BGw/BCTRx/S7AFeV8DgSS/sEE/Z1DbMOnyjvvzt8g05pehQATwk0hyknwAsQzwghkM/nsDn/BNGdLXhDgxg9f/HUwIjrkcQBDAaDcDh6L3j4qYHkQCRAAiRAAiRwBIGN+TDe+NwiHn19A/lc9fS1VocFV4tuvgMTvWFFdGDpd6sB0c9n05J4PDMEx7kATJbeEED5R9IlBET4i6wBWw+ATIlQLVl8p9/ZJYvgNI0I8P6b54UQoADI86AtBHgBagv2jh00srMFTyAEc4vdb7PZLLP4duyuc2IkQAIkQAK9SiCfy6u4frc+t4jVWYMEASUL1918r71nHA5397v5ZrcSiN/aROLWFjJrsbq22OyzK9de981hlcXX1CNWj3UtnpU6h0BsC9i8D6TCxnOaehHwDHXOfDmThgjw/rshXD1bmQJgz25tZy+MF6DO3p9emF00GsXq6irde3thM7kGEiABEiCBriCQjGXw1pdX8OYXlhDdSR0556mrITz7wd5w881uJ1Q8P4nrl1mpU/Tz2+F+ZgiuZ4dgn6bo1xUneK9OMh0DNu4BsY3qK3QPAsNXmdW3i88B3n938ea1cOoUAFsIk13VT4AXoPpZsWZjBMS1V2L7ySHFZDJhenqamX0bw8jaJEACJEACJFA3gb31uLL2u/vfq8im81XbWWxaNt9nP9D92XyzeyktkYeIfkvRulhZ/HYVz08Oin51IWOlkySQywDbs8DePFCo8nfrDABDl2n5d5L7cEp98/77lEB3+DAUADt8g3p1erwA9erOVl9XZFvi/A3AdIJBgnO5HNbW1iDWf6XFarXi7NmzdAfuv9OOKyYBEiABEjghAvKD28rDPbz+74t48uYWUD28HzwBO555/xRuvHcCLq/9hGZ08t3mImkkbm8h/sYm0k+quElWTEPce90i+tHS7+Q3iCPUR0Al+FjU4vyJCGhUJJvv0GUUvKOQv3W91PNc6tpsxu78Ep5HDinV+qr8rPS11+s1nG46nUYymTzU71F9la5JYoJLYsDKIv1GIpG6+jViNDAwYHj/kclkDowVKscs5WLUp7w3NDQEu/3wtVTYrq+vG+6XeEa99NJL6rPFxUVMTU3Vd76wVk8RoADYU9vZPYuhANg9e9WKmYY3N7C58BgOj1cl+rDZW590Q/4jXV5eRip12OXI5/NhbGzM8D/2VqyPfZAACZAACZBAvxDIZfN49MoGXv/3BWwtHm35NjLjx3MfmsKFt4/AYunODLb5eAaJO9vK0i/1aO9IoVM/B8xem0ri4X6WMf1O6u9CvveJUKKLJZXPS4Wf0jryvohTTqfTcGoi9oiIUkvoMvpcksoFAgHDfnd2dg6+o9bqu3Tu8lz6HRkZMexXPF50gaqyXaWQJK/NqT3Ydh/BnIkpDsNDFTH9LHYl/G2mHdjZ3W1q++SH9wsXLlTlsLm52VS/4tVz+fJlw7b7+/vKCKDZcvHiRVgMYpHHYjHIfWuz5dy5c4ZCnYiV8/PzzXaLM2fOwO12H2ovfxdzc3OG/Qqf97///eozCoBNo+/6hhQAu34Lu3MBFAC7c9+amXUqHsPyvbdQKLoWWCxWjJy/CLff+AtSM2PIf6Ii/um/KJb2MTw8DPn1jYUESIAESIAESKB5AhLf786XlvHm55cQ209X7UgSWFx42zCe+9AZjJ1v3f/1zc+88Zb5VA7Ju9vK0i/5YBc4Inux3rvZbVWin+vZ4Y7K3qsLY/IoAoqRlZN8lkgkDgS10jaVApvRZ/JDq8vlMgQt38/y+XxZ31KxtJ/S16XPxcppcHDQsF8ROUTsaKaEQqGqgpqIMrolWaN9C4eJiYmqHCo9VOrtX4QeEXyMiog6In7VW+x7s7DFVlR1m82K0ZFRranJDATPAoMXAYsVItKJaNlMOSkBUOZy5coVwymFw2EV+7vZQgGwWXJs120EKAB22471yHwpAPbIRtZYRi6bxdLd28imy63yxi5chicYagkE+TK1srJS9kutdCy/4o2Pj8Pj8bRkHHZCAiRAAiRAAv1IQOL7vfEfi7j31aPj+9ldVlz/xgkV3883YGxd1cn8Ctm8Evvir28geXcHhUz1WIb6OkxOy4Gln+NCAKYKK0dd+KoUwCpfixhW+p4ISUYulLp7X6UIV/m6tK9S5pOTkzByoZT6Dx8+bHp7RkdHIS6URkX6lf6bKSclAMpcZc5GZWFhQYmhzZRuEADF5de9/nWYClnlmjomHLwjWoIPcfstluMIgPIdXAQ1o7K7u4uNjSOSjdQAf1ICoFgsCo/KclwLwJmZGWXBWVloAdjMXxjbtIIABcBWUGQfDROgANgwsq5ssPboAWL75e4DgZExDJ0525L1VPsSITEx5EuuUWyMlgzMTkiABEiABEighwmIoLT6aA+vfbZ2fD//kFNl8732nnHYnYdvoE8TUzVhTMQ0sX6rLJl0BuF7G8jc2UX2fhhI5mpP12oCzrlRuOTB6IszsLs64+a+9sShrNNEpKoswu3Bgwf1dGFYR9xTxarOqDx69AgSo7mZItZ/IgIalcePH0PiszVTuk0AFOtKSWhnVCTe297envaRuPhmIrAkd1Gw2JH1jBu2sUWXYd+fg8Xlx/izHwI8h60st7a2qsaoq8W8HgGw9O+x2nMZp/Kz8+fPGw4vBgEy58pyVN+l/cvfhpELsAh1emLB0r6NridG78n5ayTmi/VqpYWlUftKBvocxNXc6D5H/tYOzocKGGIw8eyzz6p36QJc6yzu3c8pAPbu3nb0yigAdvT2tGRye2ur2F5eKOvL6fFh4vLVYycCkS+q8sukCICVRb4kifhn9J94SxbGTkiABEiABEigRwnkc3nMvrqp4vttzGuB76uV8YsBPP9N05h5dghmc7m4Vmn5VmqRVu25/N9ezd1TrHDkZrnSUk4X/PQ+q8211LpH2mSWo4i/vonYGxsoRGq7kRYsQG7Sgex5B3JTDsAb4b0VAAAgAElEQVSmrbeadY/EI37y5EnTZ4kE5zfyYBALwNnZ2ab7Fc8Iv99v2P7+/ftN93tUuJXjCIASwkX6NirCtzLusy6glD5Wvid9CYNqwqJYp5X2a9S+mqgkcQWrCaHipiv91iN8VQpBIiAZCbeylkRkD5n9NZgT2zDFt2DKawk24PAhd+bd6umhMQt5mKJrMPkn4TKIIydt5FzThdtG51zN1bzpE4wNDQko9/xsHvl0HoVMTlktF+R5Vh6Lr+U9deSwtLyMa/83k4D0++lEAbDfz4A2rZ8CYJvAn9KwiWgEK/fvyk+RByNK7L+p6zdhNchY1ci05Eu+xPgwiqMiX+jEpcMovk0jY7AuCZAACZAACfQyAfm/VG7udeEsGU/j/ldXcfu/lhHbSwEm+f+7oP5pCoL2WsKEnbk2iG/41psYOXtYSDque58E+DeygpEkB2K90mxRVkN7GSX6Jd7YRHartotnwQTkJuzInXcgO+0A7IeTmJw9e9YwmYRYpomFWrOlmquu7JkIas0WSYhWLUlFqbAoe1Dt0AWl0s9FnKoWckWskfT4g6VtK/sx+kxcMo3cMqWu7lasz6NZJl3TLp8D0lEgJUek+DwCZLXMt4bl/AcAW/e543fNntQx0UK+UBTmciikcsiLMKeOPCTWqCbUaa/lufZ5UcBTr0vFveJnRUFPhSk4Ivt65fRWwxt46RP/R71NC8A6Nq9Hq1AA7NGN7fRlUQDs9B1qfn4q7t9bt5HNlMf9G7909diJP+QLpMRmMQrOfJSbSPOrYUsSIAESIAESaC8BXagTwaP00K3hKt/XX6v4XmNjhpPX3fsSkTSe3NrCwls7yB4R885qt2D6xgDO3RyE2+/EpUuXDPsVsUdcEpst0q/Rj3jyo58kk2i0mOI5WB6n4FkuILscq6t5btSmLP2yM07AeXTmYnHLNEp+cVQmzkphTdZb+Z5Ypxn1K3su7oh6faO2R4l34h3BH0nrOg06o1IyDGw/1ES/TLzxOY3dBAJTjbfr4xaaYJdDIZnTBDoR7ZJZ7bEo2OnvV33vQOTTBLtOKRQAO2Un2jsPCoDt5d+3o1MA7N2tX388i+hOeQyO0PgkBiZa8wWk0rpAvuiK1V+1X7R7lzRXRgIkQAIk0MkEdCGuUsCrfC1CXbVs9WXxvRpcrLgNVouX9ej2Ir7+hXsqzp94kVUrLp8N554dxplrIYgIKEX+3xVLPaPSEZk4M3lY51OwzKZgWU1rxos1SmHQhsJlD0wXvTAFtHiBtQ4R0iShhlF8LxHqxAqwmlBXaz78vE8I5DKaNZ8zCJgNxObkPjD/lcZh2L2AZxjwTwBOY5fvxjvt3BYHrrC6aJfMIp8U8S77VMQT8U59XiLm6aJeqcCXai5eZefSeTozCoDdsEsnP0cKgCfPmCMYEKAA2JunRWRnCxuPy2PTOL1+Le6fQfDtZilIfBYRAuXLt7jJuKvEL2m2f7YjARIgARIgASGgi3XyKP+PGWVzlHpilSVWapWutfVQlD4llpxRaWUmTmVF/9YOXvu3BSw8WkXWWt0iLjjqxvnnhjF63mdoMdaMq26lGFZpvSavxWLRKIavWNQJXyNRTlm05QvIzEaQfnMbqft7QB0ZfC0DTrifH4b7uWHYRp9mP61nz1iHBBomkEkAIuiJVZ+IfqnwU/fds+8BnIHDXYrb78N/qz2UyaIl8hDRTw6bq3abDqohMevyCRHtsupRCXVKxMuikCh5Xvp+qdgnol2uDqW/g9Z8YlMxm2CymbXDboHJKo9m9bga3cTz/+83q6HpAnxiO9DxHVMA7Pgt6s0JUgDsvX3NpFPK9TefKwYfBmBWcf+egc1+OEPecQjITYyIgJLFrdrN2HH6Z1sSIAESIIHeJCBWWSImiaBXKu7pryvfL6UgPzadOXPGEMza2hokyH8z5ShLPREWjTJb1jOObqmXy+bx4H/WVWKPnRVN9MuZU8jaogfdmCTgncmE8QtBXHrHGIanfEpsE3FNF+pKHyXRgZErqfATvkZt6plzI3XEVS+9EEb8tQ0k3txCPv70+0e1fsxeG9zPDsP1/DDsZ7Q1spBAywmUin0i+qX2AbH2q1aOctWd+2K5+6+Iew4fYPcBDi8g1n5yGFkQtnxhR3corrO5eBb5WEb9PebjxcdERhP45L3iY0Eei+93kpvsiSOTS60Icw4LzPKoC3V2s/ZaHUXxzi51RMwreU+Je9rrg/b65/KZpXrYAt5/n/judsUAFAC7Ypt6b5K8APXWnoogt/rwHhKRcNnCRs5dgG9gqLcWy9WQAAmQAAm0lYAe+07PUlkp3tntdvUDkVGROHJGSaTqWZBk+JSkD0allZZ6pf0bhb3QRblKca7y/Wwqj+U347j1uUXE9tNl09aSe8hhgs1uwbX3TOC5D00hMOyuB0Vb62TWYoi/vqESeuQkYUmNIjfKrhtDytrPcTEEk4Wi3//P3psHyZKV5cNP7Uvv+959++537jr7DAIywyLhTz9CwuULAdFAUQIE3BXD8B8VjV+EBoKBCBEioQFiYPCHhsoAM3wjM8Dcmbvvt+/tfaveu2uvrPriOVlVXdVdS1Z1ddf2niHJ7KqTJ895zum8nU8+7/vkw0y+LxABvvxeeaAr/PKRfZmabjsEdJ/KfNH1af1zknwk/sx6KP5+FxpMaCTwSOYpQi+M6FY4heBLfL5N9ClTilorVjPMzhTCjsSdI/6zw6qTdeozq9rrxJ7+Gcm8JKkXJ/xg1dMLlKPI83c5UK+8awoBWHlzUhc9khtQbU3z2sI8lqcn0gbV2NaBnsNHix5owpkwUyhQ0Y3KiYKAICAICAJVgQBJOho+pZJ7qYRfrkHQjXRwMHPe2XIp9VLVdAkjhgRhx5+7u7szDikx/lRyL98Ebq4EcOXbU7j5/VmEc+SzcjXbce65QZx56wCcDbZ8zZb1+8haQHfwvewBCcC8xWyC83ibIv2cj3Soh3ApgsC+IcBEmvdeAGJF5I+jtTZz9VEFuI9Fhdl6w9C2dOJOI6m3FdJJvZ2feSPKkbZqi8UUJ+2s22SdUyfotsk7knXx750pn++ol0tRV234yPN3tc3Y/vRXCMD9wVVazYOA3IBqa4kw/Hfx4QMEtnQFoNXmUKG/Fqu16IHOzc3B7/erhziqOaQIAoKAICAIVD4CO9V4uX6my2k2l9rZ2Vlsbm4WNeD9UurlCtUlWcmNRF2C4NtJ9BU1mAJOWpreVPn97l1cBMNjs5W2XjcuvHMYx5/qgdVWucQYCQr/9SWl9gs9TI8wyDY2+0izIv1c57pgqXBSs4CplarlQiDkAwJrgH9N3zcPAG2ZVcCY/CHgX8ndU7NNz/VHJR/NObi3NRQdvktSTyOJtxmGtpkg87b32qZO8nEfC+QPkS8XzKnXVSGxJOScVrWZXNzrP5vUZ/Fjknnqs3hdKu4S31lzu3dXwjjL0Qd5/i4H6pV3TSEAK29O6qJHcgOqvWlmSNb6wjxWZqfRd+wEXE3Fu44x3xHzHrHwAaq/v1+MPmpvyciIBAFBoAoQIIGXuBdn6i7zsfp8PiTUeYUMab9y6tFV98iRIxm7srKyAm78tyWVqEv8vPOzVEKvXGFbuTDlv73Tt1dx6YVJTN3MTT70H2vFo+8axsjpDpjM5QlBy7c+mPTff2MZviseBO+vAgYiCq3dLrgvdKvN2u7Mdwn5XhDIjABVfDTm8K9ub5EdIeZNfUD/hczne+7oYcCJkiD7SPQp0q8ZsBsLsadTrbYRUlt0UyfwFMGnjvWNx0byXpZluk2AmcSd2wazO77nz+qzOKnH71I+U8ck9IS827cpk+fvfYO2qhoWArCqpqt2Ois3oNqZy50jiYTDsNqKDyXa2NgA1X+phQ9ko6OjGZ0BaxdJGZkgIAgIAvuDAFMsJAi7TPvUz0gwNTU1qRcxmcpecuqVwv02QdClEngkALu6uvYHvAppNapFcf+NRaX4W5raNvPY2T2mmjryeDcuvGMYPYeKfzG3n8OOhjQEbq0o0i9wZ8WQm6el2a6MPEj62foaypZTaz9xkbb3GQE67FLZlyD8qPCL5lHJ0YDj8Nsyd8y3Ang9Ock+pdgjsUcSbyMIbT1O5vGz9WD88xBiOUL39xmV3c2TzCNZ12CFucEGizqObwmCjz/HiT4LCT4SeRX6kuHA8augC8rzdwVNRhm7IgRgGcGv50vLDaieZz/72Kki4drgA2dqGRgYQGNjo4AmCAgCgoAgUCQCVOp5vV5F/JEALKTkUuotLCxgbW2tkOaSdXOF1PJlEDeSeankHo9TP8vkRFtUZ6rkpFAggluvzKkcf8z1l61Y7Wac+rF+XHj7EJo7XRU3OhIhgTur8F31IHBzGUbMA0gquM92KuLPMdoiBEPFzWqVdIik38oYsOWJG+EU2O8jbwesu1PTKAfc9RAiJPIShB6PSfKpfVDl2yt7IaEXJ/AsjTaYG+0qXJ4O2YrgSzlWxJ9LyLyyz1mJOiDP3yUCssqbEQKwyiewWrsvN6Bqnbn963coFMLk5KRK+J5amBi9ra1t/y4sLQsCgoAgUAUIJFR74XBYkXipW+IzvijJllNvv5R6qSkbUmFkuOxO4i7TzzTskJIfAd9GCNdemlZb0JddpeRqssWNPQbhbCxejZ+/R4XXiGkxBB+s6WYeN5YQC+Q3GWA+MOfJdrjPd6m9hAcWjrucsQOB8e/rob5Gi8WGmKMV0WgDIkEXNL8L2npYOVBH1nRij8c01ihbsZiQJPMSpF5TnNzb8bMQemWbpbJfWJ6/yz4FFdEBIQArYhrqrxNyA6ruOd/wLKKxvQNmS2mSh5P0m5iYAB9iU0trayt6enqqGyzpvSAgCAgCBSBAlV4wGFQEH++JCXJv58uRTE3ul1IvV049Gl+wvwlyL6HOqzdlXgFTXFDVtUWfUvvdenUOWji7crOl26XCfE8+0wtrBTneUtEfmtpU7r1U+xlSQFniDr4k/U51KNdOKYJAyRDYnAdmL6U1F9OiKp8e3XC1gAURvxuaz4nwhgORDbNS8SGHsU7J+rajIbrWMtxdqfSa7bA06ZuZx4225Gc0yqjEHKX7hYu0WxwC8vxdHG61dpYQgLU2o1UyHrkBVclEZejm5vISFsfHYLU70D16BK7Gpj0NhqoWrgc6/qYWqkIY+it/0OwJXjlZEBAEKgSBVEKPZFk25dtelHp0TGe+1EyFxkpU66WWhEqPpF2CuMu2Z5+lHBwCC+MbKr/fg0uL2JEVI60TPaPNythj9HwXzBWUcys871VKP5J+Wo5Q5eRgzIDjSKtS+rke6VA5x6QIAkUjwF8aJsCMF7piU6kXWQlAW/YjNvG/iK2tQfNGEPHaEPE6EY02IQr+TXsAa89qhqUlTuiRzGt26GRecnMoks9cQWR+0XMhJ1YMAvL8XTFTUdaOCAFYVvjr9+JyA6rOuQ8HA5i+eR1RJk5WxYSOgSG09vYVPSAafjDPU2phYvihoSEx/SgaVTlREBAEDhoBvsxIKPaY0iBxnNin5jYl+Tc4OJixi3vJqUfV3bFjxzK2m1DqJci+BNF30DjJ9bIjwDUyeXMFl741gZk7ufMqHjrbgUffNYK+oy0V86KM5AqNPHyXFxFZ8Bmaavtos076nemEpXF3XjVDjUglQQBA1BdGZNkPbWYC0bm7CIeHEd5wIrIagLYaTFPwmbEKk8kPLdYNwFpS/JRqr8WhE3xqHz8mycdjEns0ykghKEvaAWlMEMiCgDx/y9JQT++xndn2BRdB4AAQkBvQAYBc4kvwVjF79zYCW+lkXc/hY2hsay/qapkUKXwoHR4eBpPDSxEEBAFBoFIR4IsLGhclyD4q/IyWXEq9lZUVeDxMTp+5kORLkHi8T6YSejx2Op1GuyH1KgQBjY6+F+noO4HlGW/2ubeYcPzpXjz6jmG091dG7kTNG4b/Kkk/D0ITxvKq2QYaddLvfBesLY4KmQXpRqUjwL9Do5shRJYD8c2vFH2K9FsJIObbhM10H2aTTj5HYy0IxU6WdljMtdfqUOuWe7W1OGBNOTY7S0solnYA0lo9IyDP3/U8+9tjFwJQ1kFZEJAbUFlg39NFmffPM/kwrY2mji50HzpcVLt8cJ6amko7l29DSf7JA2xRkMpJgoAgUAIE+JBJ1R6JPZZsDuTz8/NYX18v6oq81x0/fjzjuVtbWyAJmCD3du4lFLcoyCvyJOXo+/05XP7OJLZWgln7aHNacOYtAzj3/BAa28pPmEVDmnLu9V1aRODemqHcaNYuV5L0s3W5K3I+pFPlR0CRfFthRJb8+rYc36vjQFa3aDNWYDc/AJBuLBOMnkYMjYYHRvWetc0BS5tTJ/q4byPBp/9Ml1xTBYXaGx6YVBQEAJVyiRFWLHwGyxaJIGDVNgJCANb2/Fbs6OQGVLFTk7FjkVAIUzeupoT+AlabA0OnzxZlBEKlzPj4+C7HX+b8y/awXV2ISW8FAUGg0hFgyC5JPhpYcJ/YSP4lgiOYjuDQoUMZh5JPqZdt/CT0uPX390uag0pfJPvUP6OOvu4WO84/P4TTbx2Aw1VeVRFNEkj2Mbw3cGM5KxGTChlDHV0XuuA+3w1bf4OEPO7TeqrGZqOBCCIeP8Ik9jy+ONkXUPtYML879PaYo7CapmA1zWf++zXWg0hs+x5uspthbSep59zexwk/qvjETKMaV5P02SgC8vxtFKnaricEYG3Pb8WOTm5AFTs1GTs2d/8OfOvp+Yj6jp2Eu7mlqIFkyvvX2dmJjo6OotqTkwQBQUAQyIYA3XOzEX35UMuVU29zcxOzs7O7mqBCjwQfw3wTZF9izxBdyfuUD/Xa/X7d48PlF/I7+rb2uJWxx4mnemGxmcsGSMLBl0o/hvlGvfnD3JnbzHW2U5F+9kPNopYq2+yV/8LKeGM1gLCHJB+VfD6d9PP4VSjv3ksIdhXyu7ndlAkwN9AltwXoOg5z1wCsHST7XErJpxR8kntv79BLC1WJgDx/V+W0lbzTQgCWHFJp0AgCcgMyglJl1NlcWcLiw7G0zuwl9JcN8YGcJKDXq+c6Esffyphr6YUgUIsIMJ8e1XrFlsOHD2fMSUpSMRGqm0r2SYhusUjX7nmLE7qj79gbuR19ew/T0XcEo+c6y0qchT0+3cH38iK05UDeiTHZzHCeaof7Qjecx9tgspaPtMzbWalQcgRi4aiu5Fv0IbxIks+H8IJPhe8iEiv59WA1w9YahLNhHJYmEyxNdNO1wdzkgKXBClPbENBzGjCLc3npwZcWqxkBef6u5tkrXd+FACwdltJSAQjIDagAsMpYVYuEMXX9KjRt+62/xWrD0OlzsFj3Fo5EZQEfnplDi3n/qIyRIggIAoKAEQQYpsvQ3cTGlwqJvDY7z6dZB184FFqo/iOx19vbC4YCSxEECkGA/8ZN3aKj7ySmb6/mPPXQuU489q5h9B1tLeQSJa2rbYXgv+KB97IH4akURVW2q5gBx9E2uC90wXW6A2aH/Bte0gmpwMaY+5HuziT5FNGX2FYCQKl5PqtJqfasnYnNCWuHC7YOJ8yRGZiW72HXRU1moPsRoFXPcSZFEBAE0hGQ529ZEURACEBZB2VBQG5AZYG94IsuPBzD1spS2nl7cf3N1AHm4eKDthRBQBAQBHYikDDkCAQCSbKPxyT8dpajR49mzKnH+hMTE1nBTYTrkuxL3eSlhKzHYhCI0tH3DTr6TmJpaitrE2Y6+j7Vg0ffOVI2R990M49VIJp/xPbhJt3M41yXUl5JqT0EYmEN4UW/TvLNe5Waj8d02i1pMQGWdidsSZJvm/Cjs+4usw0tDMxfBbYWd3fD5gL6HwWcxaWmKem4pDFBoEIRkOfvCp2YA+6WEIAHDLhcTkdAbkCVvxKY84+5/1JLQ0sbeo9mdq6s/BFJDwUBQaCSEVD5xkIhJMi+xJ4vCYwUKgDd7t3uojz//v37ybx8JPmo6EuQffICwgi6UicfAuGghluvzOHytyexmSNs1uaw4PRb+nH+7XT0deZrtuTfMy9baHwd3tcX4b++ZMhwgSosKv3cj3YrFZaU2kAgpsVUmG6YJJ/afIgseBEpsaLP5LTC1h0n97rcsHW5QFdoriXD4eKBDWD2DSDs3w1+QxfQdx6w2GpjYmQUgsA+ISDP3/sEbJU1KwRglU1YrXRXbkCVPZNRTcPUjWuIhIPJjpotVgw9chZWu7zxr+zZk94JAtWJwNbWFmZmZorufHd3N9ra2jKeT3JREr8XDa2cmAMB/1YI116cxrWXZhDwhrPWdDXT0XcQZ+jo6z54ooI52nxvLMD3xiK0te1/27N1mGYJVPqR9LMNNsrvT5X/FmiboW2iby6u6lvwljRHn6XFDmu3G7Zut77v4t5VGuON2cvAZoZUDp3HgfbDgBh7VPkKle4fBALy/H0QKFf+NYQArPw5qskeyg2osqfVMzmODc9CWie7RkbR3NldcMeZf4t5/vr6+iTPX8HoyQmCQG0gEIlElLKPW2tra8Z7AeuMjaUbDuUaPZV7CSUf9zQToqpPiiBwEAhsLPlx+dtTuPX9WUTC2VWqLd0uPPrOYZx4phdW28GaEkR9YfiuLiniLzSZP68fzTyYz4+kH/P7mSymg4BSrlFCBGJaVDnthkjyzW4hzP2cF9Ec5HRBlzcB1nZnOtGnCD/X/uaBjGrA5A+A4IbeXar9+i4ADZ0FdV8qCwL1jIA8f9fz7G+PXQhAWQdlQUBuQGWB3dBFqZSh6+/W6nKyvqupGf3HTxk6P7USE/RPTk6CIXjMp0USMFOIXsENywmCgCBQsQgkQnn9fj+4kfRjaG+i9Pf3o6mpKWP/SQCSCNxZ6KxLks/pdCb3zN0nqr6KXQY12zHP5CYuvTCJ+68vguG02UrPaDMee9cIDp3vhNl8cEQawzoD91bhe30B/pvLgJbHncFEM49WuB/rgesRmnkcLElZswvlAAZGglcRfcltS+XryzvnBvum8vNRydfjhrWH+wYVumu2l2mNhAPA5CuAlfn+LgDM+ydFEBAEDCMgz9+GoarpikIA1vT0Vu7g5AZUuXOT6Jl3bRVUAka1iAr9tTkKy1VE0o+J91Mf/Nn2oUOHxFGz8qdfeigIGEaAhhwk+RJkH/e58va1t7ejq6srY/sMAWZbqUQfiT+SfVIEgXIhQFKbTr6XvjWBqVu5HX1HznYkHX0PkqAOe3zwXVyA941FRDe3CfdsmJHQaXisR+X2o+GClMpFgOuPcxqa2UJ41osQlX0zW4ZCuY2Mytxkh63XDVtvgyL5FOHX7a5MMjjk1QlAMY8zMrVSRxBIQ0Cev2VBEAEhAGUdlAUBuQGVBfaCL6pFIgj6vHA3F+6qNjc3B4b/ppbm5malApQiCAgC1Y0A8/X5fD61UelbSKEKmIYdmYq4gheCpNTdbwTo6Dv2hgdvfGsit6Ov2YRjytF3GB0DjfvdrWT70UAE/qtL8F6cNxTiq/L60czjsR7Y+htEQXtgM2X8QiT76Lark3xxsm92C9Gt7PkljbbOEG+rIvniZF9fgyL9LA0V9IKFZh9Wh75JEQQEgZIiIM/fJYWzahsTArBqp666Oy43oOqev3y9Z86/+fn5tGpU8QwPD0McN/OhJ98LApWPwIMHDxAOF/ZASjUUlX0kADs7JW9T5c9y/fYwHNJw6/v5HX2tdPR9s+7o29RemEq+WHQZdhx8uK7UfsrFN0f+QXUNi0mF9rof64bzOPP6mYu9tJxXYgSSZN/MFkLTJPw2lcovFtD2fCVLqwM2Enxqa4S9rwEM6TUdYDh6wYPYXADmrgCOJmDoaVH5FQygnCAI5EZAnr9lhRABIQBlHZQFAbkBlQX2A7koQ37Hx8fBP2wThaTfyMiIJOg/kBmQiwgCe0OAKjwq+1gaGzOrmUjwk+jPVZj30+VyqS0R0isvAPY2N3L2/iKgHH1fmlGuvjkdfZtsOPf8kHL0dR6QeiqyGlB5/RjiS4VYvkLn3oYneuA+1wVzGVyH8/Wv3r5PI/sYyhsn/WKB3TlPC8LGYtIVfX2NScKPZF/VzfnyGLB0d3voTX16nj8pgoAgUDIE5Pm7ZFBWdUNCAFb19FVv5+UGVL1zl6vn/AOXph/M4ZVaGPbL8F8pgoAgUHkI8PeWv7Mk/bxerzrmZyTuqNrNVHaqfKnuo8o3QfZxL3n7Km+upUeZEVhb8OHKd6Zw69U5aLkcfbtcuPDOYZx89mAcfWNhDf7ry/C+voDg2BqQx8/D3GhTDr4Nj/eo0E4p5UNAY86+qU2EprltITy9iahvb2SfyWFRJJ99oBG2/vjW7apuVWc0CixcBzZmdk9W71mgZbB8kyhXFgRqDAF5/q6xCS1yOEIAFgmcnLY3BOQGtDf8Snl2OBTE3N076BgcQkNr256aXlpawvLytnswG2tpaUFvb++e2pWTBQFBoLQIUKmbIPy4z2TaQVLv6NGjGcP2Gf7LPJ8M5+VGhZ+o+0o7R9La/iMw/2BdOfo+uOzJSa51jzThsZ8YweiFrn139FUu2lObSu3nu+LJHw5qNsF5sl2Rfs6TEuK7/6tm9xWYi5Ghu5w3En2hqS1o64XlRt3ZKvM1Mk9jguyz9zdWfghvoeBrYWDmdcCfwVineQDoOSNhwIViKvUFgRwIyPO3LA8iIASgrIOyICA3oLLAnvGi82P34F1bUd+5mlrQNXwINmfhuYzo/En1X2qhAoiuv0IMVM58S0/qEwESfPwdpXkHVX5G8/cNDg6ioUGURPW5ampz1NFoDONXl3DpW5MgAZirDJ/uwGM/MYz+Y637bphBxZjv0iK8FxcQWdRD8HMV5eLLEN8L3bA02fNVl+9LhEBMiyG84NXVfZMk+zYR8fjyqjNzXZ5kn32wEbaBRp3wG2iCpcW+72uuRJAU10w4AEy/BoS2dp/feRzoOFJcu3KWICAIZEVAnr9lcRABIQBlHZQFAbkBlQX2XXFZP9gAACAASURBVBf1baxj7t7ttM+bu3oUCVhI0TQNExMTu0gFhg8yFFCKICAIlAcBhvNSlUvSLzUvp5He8He3o6NDCEAjYEmdikcgEtJw+wfzuPztSawv+rP210xH3yd7VKhv5+D+OvrGtCgCt1cU6Re4swJEc8NocloU4adCfAcba5sgqpAVpW0EFdEXjBN+VPjlNV7J0Xdzg1URfCT6FNk3WAdk3048gls6+RfZkcvSZAH6zgNNPRUy+9INQaC2EJDn79qaz2JHIwRgscjJeXtCQG5Ae4KvJCfHolFM3byOcHD7QchisWLozHlYrNaCrpHJEIDEgTh9FgSjVBYESo5AMBhUpjxGit1uV+G8VPyR/LNYLEZOkzqCQEUj4N8M4dr3ZnD9e9Pwb2Z3rrY7LTj9lgGce34QjW2Fq+ALASE871WkHxV/UW8eN20T4Djaqkg/1+kOmGzye1kI1oXUjUWiCM1uITRBdd+G2rT1UCFNpNU12S26qm+oSSn87CT72hz1Tdwy3Hf6dSC6Y91bHcDAE4BT8kUXveDkREEgDwLy/C1LhAgIASjroCwIyA2oLLCnXXR1fhYrM1Npn3WNjKK5s7ugzm1ubmJ2djbtHOYDo/qPOcSkCAKCwP4hQFUfQ3tpwJGNsBsbG0Mksjv5POsncviR9BPTjv2bJ2n54BFYnffi8nemcOcH8zmNPRrbHMrR9/Sb+2F3Ffbyq5BRRX1hldOPhh7h6Qxhjzsas7Q7Fennfrwb1tb9JSQLGUct1aW6L5gk+zYRmtkEInmcVrIBQDdeGnQMkuxrgn2oEdYuN0xm+TsoCdmWB5i9BMS0dBTtDcDgk4BNIkZq6fdLxlJ5CMjzd+XNSTl6JARgOVCXa0JuQOVdBJFQCJM3roAqwERxuBswcPJ0waTd1NSUMhNIFOb7GxkZAdVEUgQBQWB/EGBo78bGBkjAk9zr7u5GW1tmE5+FhQWsra2pjpCcb2xsVCo/koZC0u/P/Eir5UGAhPjc/TVcemFK5fnLVToGG/HoO4dx9IluWCzmfelwLBpD8P6aIv38N5bykksmmxmus50qt5/9UIuQRyWcFZW7b96L0Pg6gszdN7EBba14ow4StErZN0yyrwn2vkZw/qRkQWB9Bpi/hl3JEp2twMDjgFX+ZpS1IwjsNwLy/L3fCFdH+0IAVsc81Vwv5QZU3ildeHAfW6vpbr0k/5wNhec7orkA3X9XV3UXNzr+0vlXiiAgCJQWATr3Jkg/HqcWhuxSdZupkCxkKDBJP2uB4f2lHYG0JgjsDwJRLYqxSx5cfmESixObOS8y9Ei7Iv4G6Zi7Tyr1yJJfkX6+NxYMhZDaR5oV6ec61wmzY/9UiPuDfmW2Gg1G9Nx94xuK7ONxLLRDeWaw6yaHRSf5Ugg/S6MQVgbhA9an4+TfjjMauoD+RwGzhLUbxlIqCgJ7QECev/cAXg2dKgRgDU1mNQ1FbkDlm61Mxh9NHV3oPnR4T52iyQAdRnt6JHnznoCUkwWBFASo7kuQfiTycpXR0VFR3srqqSsEQoEIbn1/Dle+M4XNley/H2aLCcef7MH5d+yfsUc0qMF/bQne1+cReriRdx7MzXY0PNYNNw09utx560uF3AhE1oK6uo9k3/iGUvuhyGhea7cbjpFmXd033CShvHtdfHT8nfoBEE4x32nuB3rOAmZRTe4VXjlfEDCKgDx/G0WqtusJAVjb81uxo5MbUHmmJpPxh9lixfCZc7BYbeXplFxVEBAE0hBgGCMJ9fX1dUWqGykM5yX5Lq7bRtCSOtWOAMm+qy9O4+bLMwgFsqu6HG6rbuzx3CAaWh0lHzZ/V0k20dDDf82DWCiPja/FBNcjHYr0cx5rg8ki+eGKmRSGVkc8Pl3d93Bd7YsN51XqPkX0Neuk31ATzPuYC7KY8dbEOSEvMPkqoIWBtlGg6wSwTwrcmsBLBiEI7AMC8vy9D6BWYZNCAFbhpNVCl+UGVJ5ZXJufw/LMZNrFu4ZH0dxVmPFHeXovVxUEahuBcDiscvVR8ZfJtGPn6Gna0dTUhObmZpXPT4ogUOsILE5s4PK3p3D/9UWQBMpWmjudOP/2IZx8tg92Z+lDaiPrQRXe63t9EQz3zVdoDqFCfC90w9IgL9vy4bXz+6Q778MNBMfXVUhv1Lfb2MhIu9YOJxhyzY2EH9V+YtRhBLkS1PGvAXQBbh8tQWPShCAgCBSKgDx/F4pYbdYXArA257XiRyU3oIOfIhp/TN24imh0Wy3hcDVg4FThxh8H33u5oiBQ+wjQzXdyMp2g3zlqOvcmSD9R+9X+mpARAtFoTBl6XP72JObur+eEpGe0GRfeMYzDFzphLrGxRywchf/mklL70dgjX3ip2W2F+0I33DT06C88v249z300pCmSL6HwC01tgvgXXCwm2Acak2QfST9Lk+TuKxhHOUEQEARqAgF5/q6JadzzIIQA3DOE0kAxCMgNqBjU9nZORuOPE6fhbDT+YEK3X4/Hg76+Psk1trfpkLMFgYwIPHz4EDsNPmhUQOdeKv3cbjfotC1FEKh1BMJBDbdf1fP7rXtyqOxMwOi5TmXs0XukpaTGHgzxDU9vwXtxHr4rS4gF8qjOTIDzRLsK8XWdaofJKr+rRtZp1BdWZB/VfcGHGwjPbAE5FJ7Z2jS5rHoYb1zdZx+iM68YTBiZg5LU0SLAxgzQNlKS5qQRQUAQKC0C8vxdWjyrtTUhAMs8c//xH/+Bf//3f8fVq1dx+/Zt9Pf34+TJk/j5n/95vO9978v5h+zm5ib+/u//Hl/72tfw4MEDaJqGQ4cO4b3vfS8++tGPoqurK+/oeN3PfvazePHFFzEzM4PW1lacOXMGH/rQh/ALv/ALJf1DOrUzcgPKOzUlreDf3MDs3VtpbRZq/EG33/HxcTBMkYREd3e3Wi9SBAFBwBgC/B1iTj+q+OjIm6msrKwokp3F6XQqR20q/niOFEGgHhDYWg3i2kvTuPG/Mwh6sxNuVrsZp97Uj/NvH0RLiU00tM0QfJcWldovsujLC7u1y6VIP5p6WJolHD8fYNpGCMGHa4rsCz5cR2QhP8aZ2rS0OGAfbYbjUAschyScNx/u+/o9yb+Zi3qIL3P8dZ/c18tJ44KAIFA4AvL8XThmtXiGEIBlmlW6Ob7//e/HN77xjWQP2traVP4nvnFmedOb3oR//dd/xeDg4K5e3rt3Dz/90z+NO3fuqO8YCsYHxETCeBKJ3/zmN/Hkk09mHeEXv/hFfOxjH0uqTXh95p4ikcjynve8B1/96lf3Jam83IAOduH5tzbhGX+IcFBXUdD4Y+j0OVhtxnMRLS4uYnV1Na3jVAJSlSRFEBAEsiOQyO1HUw/eX0nsjYxkVkgw9x9JQP5esZ4UQaBeEEjk9xt7fVGF/WYrDS12nHt+CI+8uR/OEubTY565wO0VRfoF7q4AeSJOaR7hPtelh/gON+3bC9NamH869JLoCz2gwm/dUN7ETOO29rgV0UfCj8SftVXukRWxPkj+Tb8GBNa2u9N+WDf6kCIICAIVg4A8f1fMVJS1I0IAlgl+Em9/93d/p8i1v/3bv8XP/MzPoKOjAwyx/Pa3v43f+73fw927d/GWt7xFqfNS1R90hySxd+vWLTzxxBNKwff000+rPz6vXLmC3/7t38Z3v/td9Pb24tKlS2q/s/AaP/ETPwEqUn7rt34Lf/iHf6gUXXxQ/bd/+zf85m/+pnoI/eAHP4gvf/nLJUdJbkAlhzRvg3QAXlucx+rcDDoGhtDSvXtdZGskU24yrt2hoSF56MmLvFSoVwR4r+ZLnUxOviQAheCr15Uh404gQKLv4RWPCvPNl9+vc6hR5fc7+ng3LCUKrVUhvrNe+F5fgO/yoiFjCcfRVj3E93QHzHZR5u5czcRUWw0iGCf7SPhpK4HCF705nr8vReFndht/aVn4BeWMohCgq+/0xXTyjw1ZbMDImwGbkLRF4SonCQL7gIA8f+8DqFXYpBCAZZi0N954QxF4JN++973v4a1vfeuuXjAcl6G4fHj8whe+gA9/+MPJOp/+9KfxqU99CidOnMCPfvSjXQqsYDCI5557Dq+++qo6j+fvLAwzpnrwj/7oj/AXf/EXu75//fXX8cwzzygnyldeeQXPPvtsSZGSG1BJ4SyoMZqBWGw2w8Qd1ymNCbiuEoVkMwkMcR4tCHqpXAcIUOFHJTXv3Ttz+aUOnwo/KmilCAL1iEDIH8GtV+Zw9cUpbCzlJocOne3A+XcMY+B4q+F/t/JhqkJ8Ly8q4i88nz/81NLuVOG9JP6sbUJo7MQ3shJA8MGaTvo9WIe2tv33Qr65SH5vNcMx3AT7aAsco82wDzcLwWoYvDJVVOQflX87zHlI/g09DTiaytQxuawgIAhkQkCev2VdEAEhAMuwDn72Z39Whf5yT7VdtkIV3uc+9zl84AMfwFe+8hVVjW9Wjx07hrGxMZU7kMrBTOWHP/yhIvD4kDk3N6cSxyfKyy+/rEjHnp4eMOF8NifJj3zkIyrHIPMBfulLXyopUnIDKimc+9rY8vIylpaW0q7R2dmpFKtSBAFBQEeA6mmGyDPMl6R5rsJ7LlMuMLefFEGgnhDYWPLj6kvTuPW/swgFth3pd2JgtZlx4tk+nH9+EG29mfNlFopbMsT39QUE7hgI8bWZ4TrbiQaG+B5qgclsKvSSNVs/skrCbx3BMZ30K4bwYwg1w3l1wq9FufWKaUoVLZlISCf/ghvpnbbYgaGnhPyroqmUrtYPAvL8XT9znWukQgCWYR1Qucfw3n/5l3/BL/7iL2btAUN7P/7xj+P8+fO4fPmyqsdw4Oeff14RegzRzaXAoiHIxMSECuFlKG+i8JiEYj5i76WXXlJKQrpPkkTkvlRFbkClQnJ/26Hqj2sokZeSV+OaGx4eFifS/YVeWq8SBJjPlcQfVX85/7E1mZShB41zRDlbJZMr3SwJAvz3Y/beGq5+d1qF+8bTHGdsm/n9zj43iNNvHoCzce/hnirEd2YL3tcX4L/iMRTiy9xyDQzxPdsJs8NaEgyqvZHIelAn+8b0HH7FhPQqh17m7zusE362/kYhVat1YSjy70dAcDN9BFYHMEjyr3TPC9UKkfRbEKhEBOT5uxJn5eD7JATgAWPOkDC6PzK0liG6VOllK7/zO7+Dv/7rv1a5+v77v/9bVfvjP/5jFbL77ne/G//1X/+Vs/e//uu/jn/4h39QbsL//M//nKw7MDCA2dlZ5R5Mp99shYqW9vZ2lb/qhRdewDve8Y6SoSU3oJJBuW8N8cGJob8kOFKL5C7bN8il4SpDYH5+Xin+chW73a5IP6qxxcm3yiZYursnBCJhDfdeW8SV705heXorZ1tdw004//ahkuX30zaCuovv64uGXHwtrY6ki6+1w7WncdfCydpWaFvhN1acaYe5waqIPqXuO9wKW49bCL9aWBy5yD+G/dpLo9itBahkDIJApSEgz9+VNiPl6Y8QgAeMOwnA1157TV318ccfz5oEnuTLo48+qkw9SPr92Z/9mTrnV37lV5Si73d/93fxf//v/83Ze5qLfOITn1AqPpqCsDA/FdUn3F+/fh2nT5/O2cZjjz2mjET+6Z/+Cb/0S79UMrTkBlQyKDM25Ftfw7pnQZl92F3b4d+FXJWqJjr/phYSwl1dXYU0I3UFgZpFgCpsj8eTcXxUTJP44wsfKYJAPSHgXQ/i+vdmcOPlGfg3w9mHbgIOn+9SxF/f0ZY95/eLhTX4byzD+8YigvdWgexGwqpPpniIL/P6kaSq5xDfqC+slH1U+AXG1hBZyJ8XcefEKsLvcKuu8DvcAmu3e89zWk+/N1UxVpJ/Uz8EQjsIfSr/hPyriimUTtY3AvL8Xd/znxi9EIAVug4+85nP4JOf/KRSjFy8eBEXLlxQPaUa8Fvf+pZSAdLAI1dhiPH73/9+ZRZy+/ZtVZWhvP39/eqYKsB8Sejf+c53KldiGo/QKdho4Q0mV2E/nnrqKVVlamoKg4ODRpuWenkQIHk8ffMaQgE/H3HQ1NGJ9v5BWO12w9iRqB4fH08L/bXZbGBYudlsNtyOVBQEahkBvkh58OBBMucfzXGo9CNRTuWfFEGgnhBYnNhQar/7FxcR1bKzbzanBafe1Idzzw2hpWtvajv+exea2IDv9UX4rnoQC2bPK5iYCwnxBaIhDaHxDUX2Be+vITy7lZcw3bmWze64wo+E35FWnfCTPIm1+ytPw48phv3uSHdhdcbJv+JeNtcuYDIyQaDyEBACsPLmpBw9EgKwHKjnueZ//ud/4ud+7ufg9/sV6UbyLVHOnj2rlHuf//zn8Ru/8Rs5W2KI8E/+5E+qRPOJ/FR0933iiSfUeWzf6cztZscQ4a9//eugIQkVhUYLH4SNFiEAjSJlrN7G0iI8Ew/TKneNHEZzp3HlHufE50tXAAwNDaWZyRjrjdQSBKoXAaZBoJsvc/dlI/SokuX9lWo/blar5Ayr3hmXnheKgKZF8eCSR+X3m3+QOxy+ucuFc88N4tSzfbC79vZ7Eln26yG+lxahLed2EeaYLG0OuB/rUU6+9RjiG9NiCE1vKrJP5fKb2ABykLSZ1oHJSYUfyT4q/CSkt9DflaquH9V0ww//avowbC49559dyL+qnl/pfN0gIARg3Ux1zoEKAVhB64DOkX/1V3+FP/mTP1EhunTq/Z//+Z80ki5BANKdlzn+cpUEAchQtM1NPVGvEIAVNOH70JVoVMPk9avQwqFk6wwBHjx1xnAoDnOaMbdZaiEB0tvbuw89liYFgcpDgOY3DO/lfZMKIxJ7dE3PVHiv5gsPUcZW3jxKj/YPAf9mCDdensX1703Du779702mKw6ebMO554cwcqYD5j0oxKKBiFL5+d5YVOq1fMVkt+guvo93152LL+9bDOMNkPDj9nDdkDoyFVPi5xhtVuo+bra+BlH45Vt0tfq9dwmYvog0mSiVf8PPACQBpQgCgkBVICAEYFVM0753UgjAfYfY2AVIuHzgAx9Q4bYszLf3xS9+cZfqpJAQYBp/sM29hADT+OM73/mOhAAbm8ay11qdm8HKbHr4dd+xk3A3txjqG81pHj58mAxp5ElUNDH0VwwMDEEolaoYAapeSfx5vd60UZDgO3z4sKj7qnhupeulQcAzuYmrL04pcw8tEs3aqMVmxomnehTx1zFQvCMolWuBe6vwvbEA/81lIJIvsR8UWcW8fq7THTDbLaUZeBW0QuOTwD2d8CPxF93MTczuGpLVrLv0xhV+9sFGmCyS8qMKpv5guri5AMxdBmJRwGLXyT8x/DgY7OUqgkCJEBACsERAVnkzQgBWwASS9GOuvoWFBZUw/m/+5m/wa7/2axl7JiYgFTBhFdqFSDiMqetXQBVgoriaWtB//KThHlM1QPOPpaWlZP4/ukZTRSpFEKhFBLjmqfTjut/peJ063o6ODnR2dtYiBDImQSAnAokw32svTmNuLHeYb0OLHWfeNojTb+mHq7G4PJj8nQzPelWIr+/yIqJbOYxE4j23drrgfrwb7kd7YG111MWMRoOabtxxb1URfgUbd5gB+2ATHEd1hZ9juFkZo0gRBLIi4F0G5q8CA48DzmYBShAQBKoMASEAq2zC9qm7QgDuE7BGm/3TP/1T5fDL8N+nn34aVO0dPXo06+l0BKYByLvf/W4wxDdX+fCHP6xUhO973/tUu4lCQocGIF/72tfAHH/ZCo0gmMyeapgXXngBVAOWqsgNqFRIbrfjmRzHhmchreHBU2fhcBeem4VzT1Uq1X8J05jS91haFATKgwDvt7yvcdva2lIpF7IVMfYozxzJVcuPgG8jhJv/O4vr/98MvGvBnB3qO9KCs88N4vCjXbAUqRqLrAd10u/SoiEyiyYUrvNdaHisBzaq1QrIPVx+dAvvQSwaz+N3jwq/VYQmNwvO42ftccOZIPwOt8Ds3FsuxsJHIWdUPQLRKCBmcFU/jTKA+kRAnr/rc953jloIwDKtAz6A0sSDBB3L7//+7+PP//zP84aYvfjii3j++eeVGQND1RyO7G+6GbY5MTGBf/zHf8Qv//IvJ0f6wQ9+EF/5ylfwoQ99CF/60peyIvDSSy/hueeeU6pEkkGlVIHJDai0C4+Ov1M3rqXlZ2nq6EL3ocNFX4gqDG6S26xoCOXECkKAa5lmHST8SPzx51yF676trU2MPSpoDqUrB4PAwsMNXH1pCvdfX0Q0R8it2WrC8Sd6FPHXPVKcGigajMB/bRm+SwsI0kQkT4QvLCY4T7YrMw/niXaYrLWtWIusBFQItK7yW0csECloEVhaHUrhlyD9LE3FqTILuqhUFgQEAUFAEKhIBOT5uyKn5cA7JQTggUOuX/ATn/iEctVlXjWScKkEXa4u8aH12LFjGBsbwze+8Q28973vzVj9hz/8IZ555hnlADw3N6dIvER5+eWXlcEIk9oz35vLlTmB70c+8hHQbCQfUVgMhHIDKga17OfM378L7/q2O5vJZMbwmfOw2uWP/dIiLa1VMwK831HdmqvYbDZF/NH4Rsjvap5t6XshCETCmiL8GOa7OKGbhmUr7hY7zv74AB558wDczYX/G6Py+t1nXr9FBG4uIxbOnksw0Qf7UBPcj3XDda4LlgZbIUOrqro0Oknk8CPxZ8ThOHWAJodFhfM6j7XCcawN1g5nzSsjq2qCq6Gza5OA1QU0dlVDb6WPgoAgUAAC8vxdAFg1XFUIwDJMLsNp3/Wud6krf+5zn8NHP/rRgnrx6U9/Gp/61Kdw/PhxvPbaa2huTn/zTgfLt73tbfjBD34AhgF/4Qtf2NX+yZMncefOHfzBH/wB/vIv/3LX9xcvXsSzzz4LmkK88sor6riURW5ApUPTv7WJ2Ts30xps7e1Hx8BQ6S4iLQkCVYIA1dXZiDuPx6OU05mK0+lUxB9fmtR6KGGVTKV08wAQ2FwJqBBfhvoG8uTa6z3cjHPPDelhvgUq71Rev5ktPcT3isdQXj+q19yPdiviz9ZVeCqLA4Bvz5cgGRqa3tQVfvfWEJraAPLzodvXNZtgH26C81gbHMdaYR9ogsli2nO/pIE6RWBjFpi7AsAE9J0HmvvqFAgZtiBQmwjI83dtzmuhoxICsFDESlCfYbUMr33zm98MqvEKLQxfe/LJJ3Hr1i08/vjj+OxnP6vUfnxovXLlCj75yU+q9nt7e3Hp0iW131lIQjKPIB+WqUYkodjd3Y1wOIyvf/3r+PjHP64elBku/OUvf7nQLuatLzegvBAZrjB96zqCvm3XUovFiqEz52Gx5s/tox7KwuFdbtOGLy4VBYEKQICqPob2MsSX5N/w8HDGXvn9fkxOTia/o/qZqQ242UUtWwEzKV04CAR435+5s4pr35vBw8se5IqGJ9F37InuosN8GcKaMPOIePx5h0cFm+tspwrxtR9qgclce2RWZDWAwN3iw3qt3e6kws8x2gKzo36cjvMuIKlQPAJbi8DMG2mpZNBzGmjN/O9p8ReSMwUBQaBcCMjzd7mQr6zrCgF4wPNBwo0KE5/Pp/YMMzNSpqam0qrdu3cPP/VTP4W7d++qz/kgy3BiPgSz0Ljhm9/8piIKsxXmH/zYxz6WDImj4cf6+noyIf573vMefPWrX80aImyk39nqyA1oL+htn7u1soyFh/fTGuscGkFL927SN9MV19bWsLi4qJRPnH+uISmCQDUgQOKa7r3cdrr3Hj58GAzl3VlIfDCfKXOoMi0CTW6kCAL1gkDQF8btV+eV4m9twZdz2I1tDpxhmO+P9cNVYN44zRuG/9qSIv5CExv54TWb4Dzepof4nmqHyVZb/w4pt94HawjSvOPuKiJL+YnQVNDMDTY9j19c5WdtqQ+X4/wLR2qUDAHfCjD9GhDbIT/tOAZ0ZjcmLNn1pSFBQBA4EATk+ftAYK74iwgBeMBTxLBbht8WWjIlrOeD7+c//3nl5vvgwQNF3NH4g3kBSex1deXP33Ht2jV85jOfwXe/+13lDNza2oozZ87gV3/1V5VD8H6FwskNqNAVsLt+LBrF5I2riIS23RltDieGHjkLkwGHNoZ3MycaSWkWkiHMC1lKs5e9j1JaEAS2EeCaTZB+VPNlK7z3kdCWIggIAoBnchPXvzeNu68tIBLKHV86eLINZ982iENnO2AuwM03Ftbgv7WiSD+SXNDyuXlAOfc2PNqtnHwtjYXnEqzUuaVbb3jOm1T5BUmCGsAjOR6rCY5DLbrK72gbbH0NNamErNT5q7t+hbzAxKtANJw+9LZDQPepuoNDBiwI1DIC8vxdy7NrfGxCABrHSmqWEAG5Ae0dzMDWFmbv3QKJwETpOXwMjW3GiA8qoaj4TC2Dg4NphjF776W0IAjsDQES1Ex7kHDwNdIaFX5DQ5ID0whWUqc2EaCpxxhNPb43A7r65ipWhwUnn+nF2R8fRHv/tmFYPmSYv47KNpJ+/hvLiAW1fKfA0u6E+0KXyu1XS3n9tM1QSljvmqEch6lgWXsY1tumSD87w3rttaWCzLswpEJ5EIiEgMlXgfAORXDLINB7tjx9kqsKAoLAviEgz9/7Bm1VNSwEYFVNV+10Vm5ApZnLSCiE1bkZbCx54GxoxMDJRww1vDMXGk9iSDpDx6UIApWAANMkkPSj4i+hUs3VL+b+4xrmRgJwv9TLlYCN9EEQyIbAusePGy/P4Nb35xDw7lD07DiprdetwnxPPNMHh8tYOLzKGzu9Bd9l42YeZrdVufeS9KNhRS38bsYiUQTH15VxR/DuqlL8FVLMDVal7kuQfhYJ6y0EPqlbCgT48nj6R4B/Nb21hm5g4DHAVHv5N0sBm7QhCFQzAvL8Xc2zV7q+CwFYOiylpQIQkBtQAWAZqBoK+MGwI4c7v1MiH+BohJCaN40PZKOjoxnzphm4vFQRBEqOANMaMM9fPtKPIesJ0i+b+2/JOycNCgIVhICmRTF+dQk3Xp7FxHSD+AAAIABJREFU1M3MLteJ7prNJoxe6MLZHx9A//FWw2RceMmvK/2ueIzlsLOa4XqkHe4L3Sq/n6lA1+AKgld1hf9uMncfyT5F+o2tIRYuwK6Xbr0jzQoLqvxs/Y0S1ltpk1xP/aHzz9xlYHM+fdSOZmDoacBi7IVAPUEmYxUEagEBef6uhVnc+xiEANw7htJCEQjIDagI0Ep0Co0/FhYW0lrr7OxER0dHia4gzQgCe0dgeXkZS0tLuxoiWU3Sr7m5WSn9hPTbO9bSQnUisLkSwM3/ncXN78/Ctx7KOYiGFjtOv1U39WhoNWYioW2E4LvqUWo/qv7yFhPgONyilH6uM50wO6ubRIgGIgjeX0Pg3qoK79VWt/Pt5sWCeXU7nHAowq8NjiN0661uPIyMWepUCQKeO8DKg/TOWp3A8LOAzVklg5BuCgKCQKEIyPN3oYjVZn0hAGtzXit+VHIDKs8U0SiGxh/cJwrdUmkeI0RKeeakXq8aDAaVGzodqDOVUCik1mqi0OmcpB/VfuJWXa+rRsYdjcYweX0Z11+eUXsKeXIVmnowzPfQuU5YDJh6RH1h+K8vw3dlEcEH60B+Lw/YBhr1vH7nulDNoazKvGN2S5F93EKTm0DUAADxCTA5LHAcaU2q/KwdLlmwgkDlIbA2BSxcT++X2aor/5zNlddf6ZEgIAjsCYFoVENUiyKmaZicmsTR4ydUe1NTU2Dudyn1h4AQgPU35xUxYiEAyzMNVP5RAZhaxPijPHNRj1dlLj/m9OMaTISgM/Tcbs/sADo3N6e+I/FHolqKIFCvCGytBnDrlTml+NvKo0RzuK048Uwvzrx1AG29+U09oiENATr4XvEgcGfFkGNt0szjQjds3flTT1TqvFHlmFD4Be+vIuqNGO+qCYr81MN62/T8hgZIVuMXkJqCQIkR8C4B0xcZ1J7SsAkYfAJo6CzxxaQ5QUAQKBaBBGkX1SKIaiTwNGX6qEUiiEU12JwuuJtbMja/8OA+/FubivBjO6llbmERb/k//48QgMVOTI2cJwRgjUxktQ1DCMDiZsy3sa7MPsyWwh0CSbhMTEykXZihlAMDA8V1Rs4SBAwiwLVHx2maeuw09Ghvb0dXV5fBlqSaIFA/CPCN/cT1ZUX6cZ9P7dd7uAWn39qPo491w5rHRTamRVUuO//lRfhvLiMWyp/PztxoUyo/14Uu2Ieq08wjad5xN27eMV+geUeTTTfuONGmTDwsDfJion5+I6t8pCEfMPF9ILqD5O45A7QOVfngpPuCQOUgwJyxOmkXgclsgTXLC+zl6SmEA34VlUWyTtMicdIuilgs97/JTR1d6D50OOOg5+7dgW8jXeyRqCgEYOWsk3L2RAjAcqJfx9cWArDwyQ+Hgpi6flWF6rb09qGluwdmszEiUIw/CsdbztgbAiT6tra2sLq6mmY4s7NVhvMePnxYQtD3BrecXUMI0Mn31vdncevVuby5/exOC0483avy+3UMNOZEgSGuDOv1X/XAf30JUV9+tRvDWl2nO+A+36UIL5OlupxBE+YdDOmlgQfHX5B5h8UEx2iLnsfveBtsveIwXkO/avU1FL5BWLqbnvuv/TDQpYcDShEEBIHMCNBokcq7aCQCLRJGNKKTdYrkU5/xWP9ZP6bqTlfZtvb0o2MwM8E+fes6gr7CXkIletjY1oGew0czdpgKwK3V5czkoCgAZZkDEAJQlkFZEBACsHDYPRMPsbG0mDzRanNg+Mw5mMzmvI1RfTU/n+72JsYfeWGTCkUgEIlEVIgvt9Rck5maIvnX0tICqgAlr18RYMspNYOAFo7iwRWPUvtN317NO67ukSZF+h17ogc2R/YXQSTAmMuO7r2+ax5EN3M7a6sLW0xwnWxXSj/uTTZjL5rydvqAKiTNO+K5/LS1As07Ol0qrJeEH01NzHnUlAc0LLmMIFAaBNYmgYWbQFMP0HcBMFUXqV8aEKSVekOA/xbqJF5Y7ROEXiRMQi8Md0ur2jKViWuXEQkV9u9Iop3mzm50jYxmbHf27m34N9eLmgp3cyv6jmUm7xfHH2Bz2SMEYFHI1sdJQgDWxzxX3CiFACxsSsLBACavX03L29LS1YPO4UN5GxLjj7wQSYUSIMAwX6r9mOOPf2jlKg0NDYr4417MZ0oAvjRRtQiszHpx85VZ3PnBPAJbuck5En3HnuzB6bf0o3ske7J+/v6FZ73KwZfEnyECjA6+R1uV0s91uhNmV/U41irzjpkU846pDSB/RHNyzSjzjqMJ8442WNvFBbVqf6Gk48YQ8K0AzhbAYBSJsUalliBQXgQokoiEQtDCcZJP7fVjKvRyldbefnQMVI5SL7WvJpNZ/a1stlpV5JfJYoGzoQEdg8MZhxT0+dS4KRBhfbPFrFJH8XhmdhZDQ/o4xQSkvOu1nFcXArCc6NfxtYUALGzyd77N4T8Gw2fOw5rFPCG19cXFRUXMpBbm/WP+PymCQCkQ4PriOstVrFarIv24iaFHKVCXNqoVgZA/gnsXF5Spx8LDjbzD6BltxiNv7sfRx7thd2Ym5lSo64JPJ/2uLiGy5M/bLiswlx+VfsrBtymzGY+hhg64krYeTDHvWDMUzpzsYqp5x/E2PZ+hmHcc8AzK5QQBQUAQ2I0ATStI4FGZp4g8dZwg9cJoau9EY3tHRugmrl5GJFxZSj1XUwv6j5/M2N+tlWWEAgGdnLNYYLFaFWnHvSLurDppZyTSy+hakudvo0jVdj0hAGt7fit2dHIDMj414UAAkzd2qP+6e9E5NGKokXA4DI/Ho5RZLGL8YQg2qVQAAlxjDx48yHiGy+VCW1ubWncmCTUqAFWpWksIkKCbvbemSL+x1xcRCeeWqCkn36d7FfGXK7dfeNGncvqR+IssGiP9bH0NcJ3XSb9qUbvFwhqCDzfAXH507SXZWUgxN9njbr2tcBwT845CsJO6goAgIAiUEgHv2qoKqY2EEuReSB1r4dAu19qd190vpV5Dazt6jxzLOEzm1POtrykFnsVi1fck6UjcJT8jgWeLk3msY9HrFmHaWEqsd7Ylz9/7iW71tC0EYPXMVU31VG5Axqdzl/rPTPXfhayuUtla9nq9WFpaQl9fH+wGlIPGeyg1BQFgZmZGmX6wkOhrbm5Ga2srnE4Jp5P1Ub8IbK0GcPvVeWXoseHJT9D1H2tVpN+RR7uyOvmGPST9luC/5kF43hgRZu1y6eG957pg63ZX/IQoReOiTyf8aN5BpWSkgLheqwmOQy066Xe8DdYeMe+o+EmXDpYOgcAGsHAd6D0HOGo/2oPhjgz91LegUo/RNp33EZWSJLFnIp2ofh9xNbegubMrI+ZMu8PzdJLHKi8v86xMpdoLhUGzQqXYCwXhbGqGq7Ep45njVy8psq+YktP99v4dRdTlK5xTNbdWK6wk7axWOBsa0dzVnfFUroVaeYEtz9/5Vkd9fC8EYH3Mc8WNUm5AxqYkk/qvtacva94HY61KLUGgMASCwaAKIydxTMOOTMXn82Fubk6RfgzzZcivFEGgHhGIhDU8vLKE26/OYermCp89cxZ3sx0nnunFIz/Wj9aezOQcQ3p915aU2i88Z8w10NLm2Cb9+hoq/gFG84YRvL+mE373VqFtFPaASJIzad4xKuYd9fi7J2MGoIWBie8DYT9gtgJ954HGzMRGteM1deOqIp0SpF4h42nu6kFXljzaO11UUwmjBCmYtqfay2pTpBL3RtLzFNLXctcloaor9uIkazCQRrjSEXdnaesbQHv/YMau78X9lkYdfUczm1+szs0g6PXCYrPpW3xOdMVe/DNLaUNqyz03hV5fnr8LRaw26wsBWJvzWvGjkhuQsSna+UcI80CMnL2g/iGTIgjsNwJ+vx8rKytJZR9JvdHR0azGHdFoVEw99ntSpP2KRIAKgfkHG7j9gzncv7gI5vnLVcxmE0bOduDUj/Vj5HS7StK9syil33WSfkvGSb8WO1xnqfTr1HPbVXDYfUyLKofiRFgvjTyQhyxNxcjktMB5tFW59SqVX6uojSvyl0M6dXAI8G3D9EXAt5R+zYHHK5YEJHlHYolEXiQYTO4Tn7X3DWRVZlWiO2uunG8bnkUEfV6lKkwNEdVzwOmfpeZ+O7iFk/lKM7dvIuj3FkWw5lLqzd+/C+96brd7Pu9YbfZt4s5mU5FPdpcbDa1t5Yamaq8vz99VO3Ul7bgQgCWFUxozioDcgPIjFQr4wbebqaW1px8dg5ldqvK3KDUEAWMIMFycxB9VfTtLT0+PUvlJEQQEAWBzJYA7P5jD7R/MY91ADr62Xrci/Zjfj8q/jKSfCu9dQnjemNKP+e3cZztVXj9F+plNFTs1kWV/3LxjDcGxNcSCmvG+mnTTEubwI+FnH6R5R+WO1fjApKYgUCIEPHeBlbH0xpytwNDTgHn3S4YSXTVvMwzRDZPco3JM7fWNx/lMG3L93Ttz5xYCW/mNlDJ1MBdBNX3zuiK+iimNbR3oOXw046nzY/fgXVsx1OxO51dnYyM6sygWSSoSz9SS7eUPlXwJ7Em6EofWnt6MfdqLUi8XEboyOwP/xrpSSlKpp4g+u75XxzY9l56U0iMgz9+lx7QaWxQCsBpnrQb6LDeg/JO4KwTBbMHw2fN51X9UYbHQMl6KIFAIAszhxzyRDPnNVhwOBw4dOlRIs1JXEKgpBEKBCB5c9qjcfjN3V/Oq1mxOC4492YNTb+pDz6HmXaq8pJHHtSXD5hbmRhtcZzvhPtsFO9usUNIvGojoYb33aN6xBm0lUNBasLTYk4Qf1X5mt6jfCwJQKtcPApvzwOyl9PFa7MDIjwG2/VfH5sqTtpecb7kItfS/k006mWTXSaQEgUQiTJFhiT14DJXzLZuSbPL6VYSD+XO2ZlpcuUKLZ+/egn+zOMLS3dyKvmOZQ1935govZNHnVOoVQFjqzx2WOKlnBwnLbCHAhfRP6pYWAXn+Li2e1dqaEIDVOnNV3m+5AeWewKDPh+lb19Iq5XK+Sq1IAmd9fR2dnZ3KiKGSQ8CqfBnXTPdJ/C0vLyMQyP5wTkKZyj86+kp+v5qZehmIQQSi0Rhmbq/izo/mMXbJg0g+5ZoJGDjehpPP9uLIo92wObbVDHxQZh4/Fd57fcmwe68i/U53KCMPx2hLRZJ+MS2G0PSmyuFHwi80tQEU4N1hspnhONySJP2Y10/+DTO4SKVa/SIQ3AQmXgViqYpaEzD0FODOnLe3GLBo9sDc1IxQCQeCCHMfDChFWUt3L9r7BzI2y1DSgHezmEvC0dCIwZOnM57Lv5XZJ0X6WW1g2GipCkOTtUgkvoURjUQQ1TRQzajvI+qzRJ2oph+39vahYyBzpE4lKAt34uNqakb/8VMZYVuamsD64nz8O5Ou0HM4dLztdtjUsUOF5nIvqr1Srb79a0eev/cP22pqWQjAapqtGuqr3IByT+bOMAG+VRtWuf9yGyuEw2E8fPhQdz0DQLUWQzZdLlcNrR4ZSqkQSDhD5yL+SPaR9KOxh0VCMkoFvbRTBQjwPro0taVIv3uvLcC3nt+UoqXbhZPP9ClTj6b2bdVNLKoTYzrpt2xYCadIvzOdSu1XqaRfZCWgFH6K9Lu/jlggd/7DnVNv62vQ8/gda1XOvSZr6R7iq2CZSRcFgb0hoEw/XgHCO1J2dD8CtI0U1TZJrpCfJJ9O9iWOc4Xr5lTqPRzD1sqOvIQ7esYceDaSSXGCKUEucc+8b9VSSBxmIyLX5udUjsMkmaiRRNQUiUkCMZeZSXNnN7pGRjPCMHv3Nvyb60VBROKOucUzFc49Sc2EqlJexhQFcUWdJM/fFTUdZeuMEIBlg76+Lyw3oOzzn0n9l8tNK7Wl+fl5pf5LLQzXJBEoRRBIIMDcflSK0uQjW7HZbMrxlypSCSeXtVNPCDCv390fzePODxewasBx1+6y4tgT3Tj5bB96RrdV1yT9gg/XFekXuLFs2NXW3KSTfszrZychVmHhvcmw3nhor7ZcWFgvSc2kecexNliadudCrKf1JmMVBIpGgC97Z14HvJ70JloGgd6zBTe78HAM/vU1ZHJ1zdeYw92AwVNnMlZjzjc6tOqqMadSjnFPsk/tmQsuzwvufNevhe9JACoyME4KKpWhRnJQg83phKuxKeMwGQrNZwe9ZHZSSggDiPM29jr+VAFKqQ8E5Pm7PuY53yiFAMyHkHy/LwjIDSg7rEzm65kcR9C7pSrxrejwGeb+y63+Y9628fHxtIap2urtzZzcd18mVhqtCgQ2NjYwNzeXsa8k/jo6OiR8vCpmUjpZKgQC3jAeXPLgzg/nMXtvLW+zTCk19EiHCvEdPd8Jq00P8Y2FowiMremk360VRL3hvG2p+3yzHe640s8+Ulk5/RJhvXTrZT6/QsN6YTUpZZ/zWBscx1ph622oOFLT0CRJJUGg0hDIaPrRAgw9k2b6QSJJD9v1q78pG9syhwUXYlKxEwq2O3rh8YwIkdSiqYUoyCptAUl/6g0Bef6utxnPPF4hAGUdlAUBuQHlh923sY6VmSmVpJgKwHyFmDKkM1H4h9bo6ChI6EgRBFIR4JtgksWh0HZIoxB/skbqDYFwUMP41SXcfW0BkzeWEdUyKydScekablIOvkef6EZDi66spiIucHsF/pvLCNxeRSxkzNnW0ubQw3vPdFaUey/vD5Elf9y8owi3XgDWHrci/JRb76FmmO3i6Fhvv18y3n1GIIvpR7j3CYQiUfBlcsjnU8qw1NDdXDnfdKXedN6OW20OpUizO51qr6v69GMpgoAgULkIyPN35c7NQfZMCMCDRFuulURAbkDGF0OufCKJVkj8EdPUQhUXjUCkCAKZENjc3MTs7KwiiCXUV9ZIvSCgRaKYurmiSL+HV5fym3kAKpff8ad6cPwpJrlvUFBpmyFF+PlvLCM4tgYYIA95Hk0tEqSfrb+hYhQx2lZIjYPGHVT5aWvZncAzrRVzg02p+5xH9Vx+ljg5Wi/rSsYpCBwoAsEtxMZfQdDvQygYUlswEEao+RCittz58ixWGw6dfyxjd7dWV7Dw4J76joo9neRz6SSfOnbD5nQot1cpgoAgUH0IyPN39c3ZfvRYCMD9QFXazIuA3IDyQmS4AtUaExMTYAhwotCsgeo/MW0wDGNNVdQ0Tbn6ct/X15dxbFw3JAEbGxslx19Nzb4MZicCdPBlWC+NPMYuLSLozW9S4XBbceTxbpx4qhd9R/Q8fGGPD4GbutIvNLmRLdXSrgmgyYVO+nXA1qMTiOUuDFUOjq8jcH9NmXeEZ7fV44b6lgzrbVWOvRLWawg1qSQI7B2BqIaVi/+B1flZIG74phptHQIauw21P3LuMeXcurPQ/COwtaVIP+bnk5BdQ3BKJUGgahCQ5++qmap97agQgPsKrzSeDQG5AZVubdD0g+YfqYXOv62traW7iLRUFQhEo1Gsra0p8o/HLMPDw+ICXRWzJ50sJQI04Jh/sI77byxi7PVFeA04+FqsZoyc6VAhvtybLSaEpjb10N6by4h4spvmpPXdBDCPn+uRDrhOd8DaUX4XduIRnt3SCT9u4xtARL9HGC22Xrci+xjaK2G9RlGTeoJAYQgw6oPKvkgolDlXnxbG+o3vYenh3e2G3R1A+6E8FzKpkF066rYPDinHXSmCgCBQXwjI83d9zXe20QoBKOugLAjIDag0sJPkefjwISKRbUWL3W4HnX/lzW1pMK6GVhJqPjr7hsPppgMul0uRgFIEgVpHgCTXwvgG7l9cVMSf10AYK5V9gyfbcOyJHhx+tAs2i0kRZHo+vxVEt4yZeMBiUs62ztMdcJ3qqAhn28iyf5vwG1tD1Jdf+Zi6RsxNdhXOq0i/I62wNItbb63/Dsn4Dh6BcDCAoNeLgHdLbczbF4tFVQguTTVMZvOuTgW2NjFz8UVgfQawOYGuk2mmHzTkcLjdiuyjOy/3JP8ytXXwI5YrCgKCQLkQkOfvciFfWdcVArCy5qNueiM3oPSp3vAsqvwqTM5cSKHSi6RPahkYGFBhnVLqAwGfz4fFxcW0EPDUkZMIHhkZgcMhb/vrY0XU1yhJfivS73Vd6be1aix3HcN6jz3ZgyOPdcNhgm7icWtFhcMyPNZIMTkscJ5og+t0p9qbnbmd2o20uZc6iTx+wTE9tFdbCRTUnMlmhuNwS1zl1wprt1teJBWEoFQuJwK8F9DIhxtzfSaOo5p+nOkzpgdQmxYD3a71n7fP5UsF9V0shlgU6nv1WXzPY0bh8mceUHiv19U/B+uqr+LnaRq0sB+RkA9aOKD2UU0n5mNgRXWQLK5WpnLRjTXYRqKwj96Vu4hFIzBb7DDb3Kqe2eqA2eqE2bw7vJfO5SzJl8MmQH20/X9gHVXPZIrv9fqqilnfp36vvuOH/E6dm7Inb2kywWzWL8S9qqP28c0cP8fMevHv4t/v/tkEU7yOaiu+JY71vd4Gj6ngznSsPrPodUyJfQKcci5gubYgcAAIyPP3AYBcBZcQArAKJqkWuyg3oO1ZZc6ViWuXwbAPR0Mj2nr74W5pzfvgRdUf1X+JUE+2KGqvWvxtyTwmKv08Ho/K45etNDU1KSMYqkKlCAK1gkBC6Td2yaNIv02DRFfHYCOOk/R7vAuuYDRO+i0jPL1lGBpzk00p/JyPdCjFn8m6W51juLE9VowGIwg+WNdDesfWEJ73FdaiCbANNOpuvcdaYR9uLut4Cuu81N4PBBSJFokhEolCC0cRCZOw4l7/WW0k1CI6ocZjLUySLAot/lniOEG87STiEoRb4nudUIP6G0jf64SbIt4SBJsi9ng9fa/ajKQcx8m7/cBkL23GYhoQ8yMWCwBR7qkozu82nrim2doFkznzi+FYNACY7EopKGVvCChiMk4GphKDaccWcxqhmCQRUwjFJOnItizmbSLSYoIlQTimfK7aiBOVlkT78c8S7e9sJ9Pnqe3o35t1kjOxpRCiEh20t7VS7WfL83e1z2Bp+i8EYGlwlFYKREBuQNuArcxOY3VuJgVBE0bOXoA1D2lD1dfq6moa8pLvrcCFWIXVSfhy3qn+TFUEpA6FRHBXV5fk/qvC+ZUuZ0aApMHcvTWMXfbgwSWPofBettTW14Cjj3fjyPlOuL1hBG4tI3BrBdpGyDDU1h63ns/vkQ5FmFFBUo4Si0SV+YjK4ze2jtDUBmBMrJjsrrXTBQdDlY+2KrWf2b1bKVSOsck1i0OAhFzQH0GIWyCyfaw+09Rn4aCm3K65D4fie34WiuqfqWMtSfIV1xM5KxMCsVgQ0fB0ceCYbDBbWrMSgMU1KmfVOwKpCsjdxGGcuEySknEVZUIpqYjEOLmYUGDuUFNmJ1C3FZkJZWaaGtMcV4km1J1x9Whm1ee2clSpSRPq0fixrjaNf67228pUqlEzfcd1kVpvp9K1GteNUh7HFcj8W4E/T01N4/BRPV/o1NQUBgcHq3Fo0uc9IiAE4B4BlNOLQ0AIQB23qKYp9V8iBISfNXV0ofvQ4ZzAhkIhjI+PpxFAVHv19/cXNyFyVlUg4PV6sbCwsCvPX6LzVPpR8ce1IEUQqHYEqDCaubMKKv0eXvHAv2ksH19br1uRfqPHW+FaCyqlH1VyRkN7+YBgP9QC1yPtivQrl4kHQxKVcceYrvALjW8YH0N88s2Ntm3C72grrK16OKGUykCAD2Qk4ALesHKnDvjCCPkiCPrSj4O+sCL3+LnaSPD5IkqFtz8lBpg1mMwRmJL7+LEpCpg0mNQ+CpOZ+eo0/ZifqZ/ZrxhgisEU32f+Od77pCguQa6bkkI5tqBKTAWgqr36LP5zLOVz9V3MDMTMUJ+rvWXHz/HvoxbEolb9ex6zblSvG4+LzQmt/gIuhFjUD5OZ4fK7lfa6onKcssbc02SywGRyACZnfE8HXsv+TK20KggIAgUjkAhx1+Pg+T89tD1JFLLFeFj9rpeEifD7xL0sXndXJ1LEwSolQKLED9Uth6Seuh/G0wLEMwfoGQSYjyCeciCL0Hh1y4M/+Zf/V7UsBGDBy6BmThACsGamsroGIgSgPl9rC/NYnp5Im7yhR86qhM25SiAQUM6/waCe74pvs2j8IaGe1fV7YLS3fIiYnZ3F1lbmUEWGe5D4a2lpUaEfUgSBakWASqTJmytK5Td+bUmRHUZKa48bRx/twmi/G/YlPwJ3VhFZMB4Sq/L5HW+D82S72iwNB6+MU069816l7iPhF3y4jlhQMzL8ZB2T3QLHaLNO+h1rA9WLEvJVEIRFV6ZKNegNw78VRiC++bdCitxTP5PU2wqDZF6AZJ8i/cIqnNV4iSlSzmzzw2wNxrcATCnHZhs/D+jfWYIwWcIwW0Jqn3q8/VkIZgvJvQgQJ/z4cz0XnQgkOWhVe32zxQlCEockC01x4tAKs7kRFksjTOB9wwHESAbaYYIdYZ8PWkQDorZ4O3aYLY2wWppgsTbDYmuBleeaHDDBpcJ6zbDoefiiEWBtgtIkmJp7ARsdxRPJ++Lkw46JSkkVuP1NnDFI/S6NTGDNJMkQz1+YSjAkSAeSC/wvnqswXWG0rTZKqI7IeyZzIu7Ij5jMpcjrxMO+E+Hgej5FPe9iMn9iMu/i7lyM6bkZ9fakCAKCwG4EhACUVaF4g1i2GDLBRxDYRwSEANTz3UxcvwItvB2K1tDSht6jxw0hz1/djY0NZQJCxVd3d7eh86RSdSJA5d/a2tquzpP0I/lntZbXgKA6UZVeVwICJEnGry4rld/UzRUVimikkPQ7drodw20O2BZ9CNDAI2CcMLO0O+E6pRN+jtGWA89/x3t4xOPXyT7m8ivCqZfuw/bhJuXSS9LPPtQEk0VeAhhZP/nqML9dYJOEXgj+jfh+MwzfZgiBzRB8m3FiT5F+IZ2szsc7mKKKvLPYvDDb9b3F7oPZ7oPF7o1/R3IvsH1Mss9GQo/1/TBZjK/xfGOU7ysTAaoJLRYXLOo/KywkBU0OWBxtsDi3LAgYAAAgAElEQVQ79O8sbljM3DfAYqUJSIP6zMrP48eqTvK4AWYzcwaWJ4XBQSKdJCaTxi4JE5Z0ExeVSzJh7JLpOHE+804mDGFSjGMSeSqTeS4TBjJsM25Ik8x5qT6LG9LE81tu58PU81kmTGd25slMu3Zau9v9ynvvOcgJkGtVLAJCAFbs1Bxox4QAPFC45WIJBIQABDaWPPBMPEhbFAMnTsNZoIMvc8Lxjx2LRcJFavk3TNM0ZfrCPYvT6VSkL/P9SREEqg2BdY9fEX4PryxhjuG5+YiT+AA7Bxtw4nALeh0WmGa3EJ4xbuChQntHmnXS71QHrF2uA30YVoTfkj9J9pH0i24ZC2tOzi+NO/oa4DjapvL42Q81w2yXe3+u9c+HaobMKhUe1XhU3lGNF1fgUZlHJR5Vebo6Tyf2cqtPY4qkszi2YLFvweIgkZeyt3vj3+mfm/mzIvn81farKv2tIQRMJqsiBK2KNCRhuPO4ERYr1Yn8nvtGWFU97nWVI/fczOaDvX/W0DTsy1DSycNtYjBBTvJ7vtRI1kuSmzpZmfwuTnSmkZsp5GemNhTZSZVoKuGa0n7CSTtp7JPipL2bgNVVn9smQNtq0DT37bhBUMIoSI1LSU/3Bd6aaVQIwJqZyj0NRAjAPcEnJxeLQL0TgCoR641rCAe3Hwacjc0YOHGqWEjlvDpAgApAKj4T4b718Ca/Dqa1LobIe55nclMRfg8ue7Ay6zU87qGRRhztbUB7NAptarMglZ/JZd0O7T3edqChvRyzthxA4EFc4UfCrwDzkQRA1m4XHEdalcrPPtpyoGMwPEkHWJF575gP0q9UeCG158/JUNstXZG3TfhFDIUEmixBWJ0bsDg2YHVuwuLQN6vab8Hi3ICV+/jn9R4mq+eoY+66xGZNOTbDBJWZX4Ws6v9WcZ/Ir2feRb5vByTF40vVmtKf5mMqh57+cB9TzjcMN018xqDU7c/ovKvcd8GXozzmniHNwgyU9tfUrJOBihRkOHMqSdiU4bsmFfasE4jcN8HCMGiLo7TdktbqGoHUsPRUclClx2NoeDy0fTtMXScY9c/1e04y9DwtDH47tDxxr0qGwsfvTfq9Kp6bb/v2lXIfi09NItx+Z7bRRK7ANJFuMoGgnkGQqQFSUqWm5SJMGplsm54ogxSVwFDfz85N45HzeqSZ5ACs318VIQDrd+7LOvJ6JwC9a6uYH7ubNgd9R0/A3dJa1nmRi5cXgfX1dfUPdHNzc8aOqLei0aioPcs7TXJ1gwjQ3GD69grGry1j4toSvOvGnHctJuDYcCOGWx1o2AohuhwweEW9mq2/Ac4TemivCok9INfepMLv4TpCDOl9uA7N4JhTB8jQZBXSe6QFjsOtsDTvNhcoCJAqqExSz7cRgm89BN9GUB1zvfg3dIJPJ/p00s9oXkg+yDHcVpF6znVY1UaCb3PHXif8zFZj67Oy4DSlhXvqai09FDSxmc1OWMwOmC3cO8Gf9WNH8tjMY4aZmm0wmW0w04FWhYtyv31MBZm+kfSrzFBSmquF/H6EAj59r479iISCsLucGDx1ShGD0WgEsVhYkYP8ed0zi5W5SQAaYtAAkoeI6BvrgSHeEZitJlidFljtFljsxIFtBRGNhtReiwbiP29/FtX0z/TvAohqPPZD0wKIxapx3ZXut4DhzglVodXanCQHdbIwfbPEf7bF66mfhUQs3WRISzWPQL0/f9f8BBscoBCABoGSaqVFoN5vQNO3riPo21bA0PSD5h9S6hMBujozx5/P51MmHjR0sdkO3oSgPtGXUZcSgc2VgCL7SPpN31mFZjCfX5vdjGMDDeiymWFdCQAFuJsqA49jrTrpd6INluaDUZQowm/Rp4g+lcPv4TqiBp2K0wi/ZrtS+CUIP2t77Tj1cv6960FF5nnXgtsbST5F9umkH9V7xktMhdVaXeuwulZhda3p5B5/3kH2VTKpRyKNhIeNJhDWFuikhk6A6GoqkhsJtRRDM7d/ToRpksyrVCLO+HwWV1P9/gWDCPpJ9PnU31Qk+0j0ZStUII4++kRGzHwb65i7d3vXqXanC86mZrgam9TeWuJ/m0k+kggkMahpfkRnL0LzLUCLhRCNhaDFgtAaO6A5G6FFfaqOpun7aHyvaV5E1DE3b3yvH9dDIVlNNSF/Z1LJwfTjVDIxhWikEYuFIc2SR7ke1kq9j7Hen7/rff4T4xcCUFZCWRCo5xuQf3MDs3dvpeHePXoETe2dOedieXkZDQ0NKveblNpAgA8wq6urKqw31Y+psbERAwMDtTFIGUVNI8DQmcWJDYxf1Um/5WljOfkcJmCgyYaRNgeaghGYCjDvIKAqD97/z96bxciSpXWef9t9i329ce+Nu+RelZVZVRS1UVW9VEPzMNKIUUtU84BAPPRII5CG0bwgjdQPg3hkECDeYBgk4HUkHqahm6ZroTqpqiSXyj3z5t3vjc0jwjdz20f/c8x8ibCIcPfwiPBwtyOZjpm52bFj3zE3d/vZ//s+Zu19fg7WjelzSeCRZOl1CfzEVEHYF7iSl4I6ReBHdd8MclT4LVxOiOO5AWrlJmq7Dgh+Wdd3mwL21WLYRzfcfoqiehLqFXZhJHCPgC+ZcpzfF1lrR6VQJWcYs/E011HH8zrrGPKJWkK/LIbaYCNID4q9p08E9AvD/hOiXP/sKyDUO1h8z8P2vU9h5PIwcjmY+bzYTj3P+MrlO8DWB91dKy4BV38mPeXvCSak+zPBou8TCsopmfeDGoJ4vc/P/BpELbaptWuxnss1oXIc1yISqCQKxBZ47wSFBPPdgFFC+ikYBiFiMXZxH1cLZec1DhaY5OfvcRi/YZ1DBgCHZcmsnb4sMMk3oMcfvg+7ut+yl25aWH/51WPf4tu2jfv36ZoCEA4tLCxkILCvK270NnYcB0+fPkWzme7euL6+niX4GL1hy3oECDfM+++Wce+nO3jwXlnEWjupMFrYgq7g2rSBFUuDafcHcNSCDuu5OeQ4PU+32LNX+UV+CPdxDRL4VeDcrSBq9tdvAfxKhoB9QuV3ewb64ugHz+cLCY5rZbuJyo4tAV/ZQW232ZrvT7XHcHCeBHuFHQH5jALVe+1agL9cbwD5pOvtNJ9TTWQYCzDNBZjGPAxzHqaxACNe5nrDmIdpzotaPviPpjvsaexwUfsS6lHJlyuWUrtQ2y1j485HA3WPCsDVZ59HYXpmoP3PdCenCtz7RwYaax9Gt4Ab3wD00QgDQAAoAWFVgEQJETkfQ8KOeblNNQaL8Tbx5+Pp9izd8Q+6LbeWD7g0SxVvpypRJlnJlIhn+i2b+MYn+fl74ge/wwAZAMyuhguxwKTegJxGAw/fe7vL5ovXb2JmeeXYcWCgVrqHJoUZf2/fvi3cRbNyuSzAGH7lcllMnaq/5Cx0XcfKyooAvVnJLDAKFkhUfgR+93+6g8371RPj6ROHzGkKlgwFa1MGpvwQSj8x+Jmx9/qUVPg9Pwfz2tnH8gvdAO79Kty7UuHH+ahHF+bOcVKnCPxmYd2SKr/zzjbc6zXj2r6AewLybduo7DRRjWvO+04/6qpIxNYj3DMK5TboK5TFsliXr/TataFvxwdry1qCaXJabAM9Qj0xyXWc57YZ0Bv6EKQ2KGBfw4bTqIH/j5x6Da54KRbh5qs/A00/7JbpNZu4/86bJ3RQgWHFKr5YyWcWCjBMC8oo/m8KQ+D+DwHnwHfk2peB4sL5DMY5HiUIHPgB4aCEhHKqyTroXJfMVzq2qyIIqmOrRpSxO9uJUg4mWUkSqRwME9DO2kyQmKkRz/FyvlSHmtTn70s1SOfQ2QwAnoORs0MctsCk3oA27nyM2u5OB8jTsf7K56Gq1MekF4I/AsDOsrS0hPn5+ezSumQWoNrvyZMnYMy/tDIzMwOOLQFvVjILXKQFGJuN6j6h8nu33FOMtmkVIobfsqViQVOg9QP8mE90zhIKP4vx/J6ZhVo42ziYQc2Fe08q+1xOj2tA0Gen2e8ZU8A+kyq/W6Oj8CO4pWKvBfi2bOwT8G1J6Nefgi8UWXCNIgHfjqyL2xLuxfX5x9tTBLizrGWY5jIswj1rCZbJZc4vxvOL0LTDLp8X+f2axGPzhZfnNNGs1QToaxL22faR2XmvPPdiqlKP7dx94yct919N02EWirAKBTCesphyudEEfUcN/PbHwM4BVePcTWD5pUm8VHo65zSI6PkVARVZtxWIEiLKzzohYyXOFt3T4S7dRhIiSlVhkq05iR2a1J2fH/xM7lfMQhVcupE/vsOT+vw9ZsN46tPJAOCpTZg1MIgFJvUGVH78CPubTxEG0o1sfu0a5q4cH+stU/8NcoWN1j58YKHij3Ec01R/TPixurqKQqEwWh3PejMxFvC9AE8+3hfQj9P2g5NdMUsqsKirWNQVLJkq+nVSY/IOusWKBB7PzZ1pLDx+74KdpoB9zt19Af78LcKH/guz9Fo3p6Vb760ZcPmiFGPMtEz13j7hHsFeB+Sj227YB9BUjQaM4hZMAr3StgR7xa243jnXuHt8+MzlrsAyV2BZnJZhWivIWSuilpBvUWSozcpoWiAKQ9i1ahfwS/779NLj+avXMbe6lrppZXtLJOMg7NPNfu88vRz9HLdp7gP3ftgNQs2idP0dRbXiOZrmLA/F34QwtDvgYBsUtlWJhxWIiWpRAEWRZKX/l0ZneV7Db1sVGcUlLCxCZ5bxONs41YmcF+uoXBTbFOJlmZFcZiaXqkZZMwRG5r00/HHqrcVJff7uzTqTs1UGACdnrEfqTCf5BhQGfGDbQmVrE1df/Eyqi0syWGnqv+XlZczNzY3UeGadOdoCVPsx1h/jOKYVKjkZ0zFz586uovO0gIDSj+sS+L1bxqOP9k7M2FsUwE+R0M9QkOs37pkK4corYvklbr3a2cROa8Xvu1dpqfzCHmIVpo2BvlKQwI8qPyr8Zs4+/mBnP6jU298k4Gt0g74tW2TR7bkogVTslQj5tkTdCfw0sx1mouc2B9iQMbEsaxU564qordwacqzFPNevijhaWbncFmBSjXtvvT7gSSgiNMri9RsD7n9JdqPr770fAG7nCxcFWP8qkJ+9JCcxud1kkpV2TMQ01+V0l+aW63NARSLHviPu4wSYU4JAThISSmgYL7eAYQdYjKFja5sWcCxCU/MZVOzjmpnk5+8+zDT2m2YAcOyHeDRPMLsB9TYuTPzRCY4YH+7WrVsZLOrNfBe+VbVaFfCPcf8OFsuyhOovy+p84cM0MR1gltZHH5Tx4P1dAf4a+8fDo0In8NMV5NX+YZ2xWoT17KzMentrBmrucEyvYQxAUI3deRnDj9DvURXwB1BmqICxVoJ1U/bXvDkNrXi2KjPCWLvqxXCvEcM+G/ubEvg5jd4Tj6h6E0ZpMwZ8VPJxnvWWcN1V1LN90FQUXcI9AfWuSLgXz4s6t5bBvWFc8BfcBq9Zuu82axXkSlOwCsXUHt17+w34rnNCbxXhsmsVS8KN1yqUYBbyx4ZGueDTH97h6Q2y9R6w/7Dd5sKzwOJzwztG1tJIW0Co05lYRcDAJC5i7KosYiIy0UriuiyBoQCIMTxM1IpUM05yIURkZnWpUCzEYJBwkYAwAY4FqK3ljvUCIvKzHDQ1J/ZhW5qWg6pyOtv/AOc5btnz93lae3SPlQHA0R2bse5ZdgM6eXjr9Tpop86Sqf9OttsobdGZvbmzX5nqb5RGaXz7QuXYow928ZDT+7vY2zhe4TWlMlOvKrL1choE+NEdNieAn4R+Wmn47nlREMF7Wof7gOq+Kpx7FQTl9GzaJ42uYqgwb0yLiSo/c30KqjV8SNmCfJsN7CVqvk0bezHk85q9JtyIoJk1qeIrbR6q9Vz1pFM+1eeKYgrX3HzuGnK5q3LKX4uX14S7rqJkMUxPZeQR3JnuvM1GHc1qRbj0NmtMwiCv2dnVNSxcvZ7a64Nxj7mRZpgiwy8nqySh33FxkEfQHMPvUm0TePo2oOeA9a9lrr/Dt/DYtxiGPoKggSAGiW1wmGRoTiBiPc7MXIvVi+1awMWghijq/aXT2BsWAF9sEQRKMMhagkHCQkJDVbXan8XrxGeq1f68c9u0z7htsl41zyysSPb8PQlX7MnnmAHAk22UbXEGFshuQCcbNVP/nWyjy7DF9va2iP3HksX6uwwjdnn76DZ9PPlkX8C+h++Xsf2wdmx4ohktBn6aBH7WAAo/ddpEjrHwBPCbhT6fG7oBg4oL934FzoOqqL2HtYGy87JjasmQoI8Kv5vTMK4UoWjDiUdEyJe46xLsEbiK2HwEfpsNuP1APqsaA75NmFMbMKngo5qvtAnNPEulhypccHP568hzysmaoI81k2tk8ZuGfomPXINS4deAXamgUdlHs14FIWBayZWmcfWF9GQVDHVS2dnqAn7MxJuVFAv4LhB6AOP/ZSWzwAVZQMZGdAUIlO7N9ZZCMfClUlHWXM9tGh3LXNeI94k/C+pjnWzlrIZJwMMuKGhBE8smFNWM55Nt5Pr2PpyXy8m2rLn89Ok+Pv/qvxfdZoz5a9eundUpZO2OsAUyADjCgzPOXcsA4PGjm6b+W1lZwexsFhPmsn0v6P7LH1m6/GYZfi/b6I12fwn8nt7Zx6MP9/D4wz1s3qscmfSBiGtOUzAfq/tYG/3G8CNAmzJg3Z6VCTAI/BaGmwAj8gK4j+tw6cpLhd/9KoK9k1wIjx4nEb9vnQq/KeHWqw2hv47tC6An1HtU8W3Eqr7NRh/uuhE0Qj4B96jkk3DPnJLzmjGYorGXK5bx9fL5deRz6xLuCdjHZar61sSDQ1YmzwIC+FUrAvoxeUevCTsIhG99/mcuV9bdyRve7IwzC1yIBSRQdGJ1ogSE0uVZ1oEf12IdP7fj9TFMFOuS/eJ9fSZfOdtwFhdirHM46NaWj3//nfsZADwHW4/yITIAOMqjM8Z9myQASHeZfuPZ3Lt3D81m+wGQyrGbN29msf9G9DvhOI4AfEcVQsAsyceIDt4l6pZQ+H28j8cf7Qrot3WPbnjpce5MBRL2xdBvVlOgDgL8qJgj7CP0e2YG+iIz+PUfCzDNzFEYwd+2Jex7SOBXhfekDhxxTicNlWJqwoWXk0W33utTUAuDxe7x3UCo97ohnwR9ds+JNwj56K5LuLch4J6AfKUNGFNnC/kYhy+fvyHBXgz4CmL5OnR9dmhjeNKYZJ9fDgts3r2D6s5W3501rJyIAbhw7To0fbDvWt8HzXbILJBZYKIt0A0VE4DIuj1JmMhlG2FcS5BoIwjl+uRzub6JMJTTOJcMAI7z6PZ+bhkA7N1W2ZZDtMCkAEDGyLn35j8jQoTizBxK8wsoTM8c+6a8Vqvh0aNHXdbO1H9DvPiG2JTIpFoug26+WXzGIRo2a0pYgO6kdOl98tEeHn24i60HNRCapZWSKoHfvCZj+JUGzK6rzZitbLdMgqEvDQ/4CVdeqvoe1FrAL3J6jX13+KypPjRjdR9rJhxR+nBjDoMQ1XITexuJii922920Ud1tHus+3dkb1Wi0Ad/UBgzCvljNd1aZdRmTiG65EurdQL4gYR+Xc7nr0LTMzTK7jXRbgL9XR8F7uupu3f/0RJOZ+QLypSkB/TjpZqYWPdFonRsEHhD6gJHva7ds48wCmQXOxwLM7CwVi7aAgRIMEhYSHDYRxqAwAYZyHT9zJEAMmghCQkcuO3IfsT6Zl9vJ/R1EkXc+JxYfJQOA52rukT1YBgBHdmjGu2OTAgBru2Vs3PmoYzAV3HjlC9CNo9+U3717F1SUJSVT/43md8HzPJHht9GQiRX4YLW+vp5l9R3N4boUvars2ELh9+TjPQH+yo/p5pICvujOqystl1669pp9gK/OFqnoY6Zbwj5O2pw1FHVYUPfgPaoJVZ/L+mEVYc/KucPnrFiaUPRJhZ9U9/WSnZfQo1FxpZtu7KorYvPFyTfCoLdMwYrmxFCPSj4CPqniI+g7q8QbTLhBxV6hcFNAvgT2FQo3YFl01R1+spJL8UXJOtmzBbxmU8Twa1T24DQauPG5z6d+v7nd/XfePNSuYeWRn55GforTVKby69nyR2z45C2gtgEsvwTMZLG3TmvObP/MApfdAlEUiJiLAgoSIgpwKOGghIpc10QYuS2oKNZ1TdzfFbAxEm1x4jau3C+ZD108fVrB//RLrwmzZTEAL/vVM3j/MwA4uO2yPU9hgUkBgE8/+Qj1vXLLUvwTvfZ8erBsblStVvH48eMuy66urmJmZuYU1s52HbYFqNIk/AuCbvUSYe2NGzegaVkWzGHbfNzao5Kv/KSOxx9J2EfoV9tNj3WXqPvmNFWo/JitdyA3XAUi6YXMeBsDv+nTK3jCpi8gH5NzCFfeR7WBs/KKcVYAfTmO3Re79OpLhWPVfVRLJmCPbrqtJBybNrxeVYaqD7Mos+sS8hkx6BPzhb0zuQQl5FsHoV4hfxP5ws0Y9N1ELreaZdQ9E6uPb6MiEU2tivreLhr7e/Ccbne2tRc+IxR8aeXeW2+I1W3gN50p/IZ5qVQ3gMevt1ssLQMrLwN6ptYdpplP2xa/Q3wtxEAXR/3OeiG3kduxRPFMazlZn2zR8bmhMuFWeuKpqh8giBs71FbHu6rO11ZJL3Kqiik9/b/nrufDFX0+vqRtwb7OG+kvm9iuHRwdi++44/Gl5ZKZLobY83zUBmxXV4ArVvr/moofgG33Ug72nSFUrufS2635AXZ6bDft2Ou59Ky/9SDAtuufOG5Hnc81y4R+4OXwpDx/9zLGk7xNBgAnefQv8Nwn4QYUBgHuvvk6KCdPytKNW5heXD7S8r7vC5fSvb098E8IgdKtW7cGe9i/wPEd10Mzlh/dfXd3d1NPcWFhAZwGgjPjarTsvIQFnIaHjU8rImkHJ86nZYW1FIDx+hKFH+cHVfcpphq7yBL4TQv1nGqdTjUW2jHse1yT0O9RTcTxO02h27F5bQoGFX6crpVS+8kYiCLphki+IePxSVVfA069tz/1UEIYhR0Zly9OwCFqAfm2oagnPSL1f6aKYkglnwB8naCPkO9KBvn6N2m2R4cFAt+HXdmX0K+yf2zyjrkrVzG/lq48C3wvU/ilXFlhFMGPodCxoCdqRw3gXURMUYQZwhNm+L37PSBwW0dwFB2Vq18FjFwXQJL7yvsQj5e0JZbjNllfy5nQUuKxEsg8dtwuONUJkzrbTjrT+fntvIV8Smb0ZhDig0YzBXZ106/DEEwe5fmCJW1xoPhhhB9VmCm2/UGrjwe27d5GfvhiMYdlKx0k/UO50gopexioxTY+4m7yQjGHG/l0OMt2CdQGKbfyFp4r5lJ3/cfdGmoHXiz3eoyrOROfLaW7lv94v47ygICKkO4L04XUbrxVbeCpM5gL64yu4SuzpdR236/buG+3vyu92oDbFTQV35hLf8lxp+Hg48ZgMf50RcG/XphO7crDpot3a4P/D/o3C9OpMZo3HQ9vVKWX0SDlW3NTyMXf5eZ77yGs1fDg/n28+O/+nWguUwAOYtXx2CcDgOMxjpfuLCYBAFbL29j89JOOsVFw89Uv9PQHm+6lhEy5XA7T0+k/OJdu0C95h13XxZMnT7qSsySnpOs6rly5gkIh/U/SJT/1rPt9WoDqPkIpKvs2CPw+rQi138HXuHxXP9MB+wj9CgO68rKL2rQp1H3CpffGNIwrJSgDxgJke8KNtwP0nVrZR0VF4sp7jbCvJF15p9sPWb4XoLLVlAo+gr7EbXezgcZ+rw8EEfT8nsyu21Lxxck3SltQtR5hYV/jroosugLwFW4J2Cdddwn5rmbuun3ZMtv4JAtQ2UeFX31vT2Tu7TVgpVUo4tpLL5/U/LGfE4gRfZB/hERVopawS6znvQhA8Qg1EhU49SDs2idpL1FVsW25TkKWpH1TVfH8EfDkUdMFJ7FtC57JPnWCNK5J2k+AGlVZ/3I+/b/Wg6aL907xcP/zC9NQnrwJVJ902XVj4bN4E4N7d3Q+3Hc2TFXWj/bTw0f0MvBfmy2lKsmoRvrBbq2XJlK3+eJ0EYvmYQBINd1/LfMaHqx8bip/pOLrb7f3B2sUwHEA8L+Vq3DCwbLQXgQA/Ml+fWCFWgYA5SU0agCQ/zMjz0XkOAhdWWulErQOj7HOe0T9tX9CWK/j0dYWXvrOL4tzygDgwLeHS79jBgAv/RBezhOYBAD45OMPxB/0pBSmZ3HluRcu54BNeK8rlQo2NjZABeDBUiwWQTdtQsCsTKYF7KqLjbsVMW1+Kmun0Q2Z6PAzrSlC3Sem2JV3kMy8wspUBq6VZEw8Qr/1aeizg7mS8cE92HMk7HtcFzWz8XLdaYpiqDDYx6slqe67WhJZhHm8yg6Tb1DJ163oY1KO3vxdmGG32lLxSdDXVvWpeq+wsL8ztCyCfrrpEvDdil12CfquQ1VP707dX2+yrcfZAoRfCWRToICQit+dR++/A6fRBjz7IcCrPQFfrCUwU0St6DrMYglWaUrUiqaJ9WybKpzbhfT7BhUtm64X96EN+Hqx+ayu4ctHqHsI0wjVBimXUt2Tc6E++nH36ZaWsbn4ytDUPZ2NnxYAfnW2hOkUeDuqAPDlUh5rR7hm/t32fm8/JykX41kBwJt560iIfVYKwAwAygG+jArA18v7iJpNhI4LuA5Cx0HkugL6Hby49YV5mFevtq7mTgBov/02/K3tDAAO8sMzhvtkAHAMB/UynNK4A0C65Nx765+73H+Xbz6DqYXFyzA8WR9jCxD4EfwRAB4sdPNdWlrC3NxcZq8JsoDnBti6X8VmDPzoylvd6XYpYeygaU268spJFcsDwz4A6pQJK4F9BH5rJRCw9VuiIIK/3ZCgjy68hH5P6ojsU6riNEXGF7xaEu682loJjqGisi3VfAL0bcm6ssXser24TxHy1WTCjTirroB8YnkLmjm4y81xdjOMBQn5Wkq+WxL25dehaVn2zn6vuXHYnjCO+X12bAEAACAASURBVGIYnyuIFWRiPlbB8XLmcknXUuEJbfBBvSniZQmwR4VtR02FWtKuhHPd3w/Gnnopdu87+HLxp76CvYh3nXYxLAtWsYRcsQjdPDqxz2VT9+RVFd+cT3fv+7Th4KMB3fvoSvvts3DvC0N8u/omNL/jXqUawK1vYitU8c+Vwd37vjk3leqqS4XlP51CAXgUAGwEIb6/Wx3460w30rSYb3QB/vtTKADPCgBSaUpYl1ZOowA8DgD+014NlSAU8QdZDtZiXbySLwWSwrkVyxCqxbTyTs0GwfCh/7HHjGbS/pyhte49Bzf/qN7Edo+uxd13KAiV6VEuy/ds50TX4oPtJX2zNBWvTqV74zymSnhAl2U6U1DFmla2XA/3DrgsH9W/tP353Uj+H7oPHyFkKId6HeWGjTvdoca7dj94DG16Grnnnmtt8/JUHlRNszgffQT3wcMMAA58BxuvHTMAOF7jeWnOZtwBYGV7E1v3Pm3/OCsqbr76RahZcohLc43S5ZcJWTozMiedN01TuPzSRTsr42uBwAux87iGzXtVbN2rYPN+FTuP6qDrRVL414puvO1JKv3SYjP1ainG7jOuxvHw4rh4jJPXb2zJsOEJuEc1n5xq8DYbgN8LfDumtzHsM9aK8GZzsE0NNT/C/k6zFZuPkC/we3GRiqCZtS7A1xmf76wgn6aVJOBLpg6XXcPIwi70eq2OynZCxRrx0paALoF1XObXVaxPIB4iLBo6Zo8IbM+H8GYYb58C444752G499G91/e8rmQda5aBl+OH2sr2Frbu3Wl14x0CQKgw8wVYxaKYdD09JtrBvtMOX5xJf6j9abWBxwM+LPPhnq6kaYUglA/4gxQmOvjWEQDwru3gw/pg8b34IP3zi+nuuIPG9xIP55VH+FfuPTAxQasw8cfsdey4PghmZLKJ9sdiOYY7XC+XZelMTPHKVD41mQSVeh/VpX2T/Vv7Ju3EB0wDTARUaTEA6ap7rxm329HHdr/afewc2+QYhH9p7RJ2M45ctw264VZyjKTdzm2nNK0V5+zgNXUQenXZ8ggbiCMrgKmoQnWbVhL337b9uvvbhnTdNun3N3yQ70i2z+haIApDhA1CfwVaKf2+a7/xBvxyepzxk85MzedQ/NrXUjcL9vakC3C5jFuvvCK2yVyAT7Lo+H6eAcDxHduRPrNxB4CPP3wvjssjh6E4O4/VZ9pvZUZ6cLLOoV6vC/iX5vLLmIzLy8tZpt8xu04Iq3Ye1aS6j8BPwL4aQpKEuNDJW4A+XSr7OM8MvadR9vFBw1gtyuQX8cQMuEofsQCjIBSJOLynjTboowtvZTA3u86hjTQF4VIB9pQJ29JQC4Fq3cP+llTy+V4fkG+K6j05tSEflXyDq2COuwxVNdeKwyfddgn8ZGw+qvyyh7HR+BITAHhMtBBGcOOEC4QNXLdg6EJVl1aoRiLYI88+qJg76cyOU/d8t1xFc8D4Xut5Ey8W01Wir+3VsO+nyzk8xxH/GZq1GpiMQ1FVrNx8pnUfWLUMvBIDQHoYMMEYs83nZ2bxiVlAxchBPSKz6HG2YHbPLx0BAOkCTPjVbxEK6GMC/N+3HQEWuR3vnQmUkbBKAW994rM4C2uic+bnhDJHuSwzg+qeH3TsK9s72H4C3JL1SR+OyqAqwbH8HWiBudgoaaCndV9pVoB7/9gdnzE/B1z/Sjfx69fA2faZBTILjLQFRDxU20ZQqwnoFtYbsmbohgjQl5aQ/1x6LNbmhx/Ce/io5/NTDANqIQ81n4daKMC8efPYfcf9+btnw034hhkAnPAL4KJOf5xvQHxzT/ffzuAMK7efQ2luPtXcW1tb4o/77OxsBpUu6oI8cNxms4n79++LeEtJ4RitrKxkSVlGZIxO0w3PCVqwb/uhhH5U+oUdyriCKmEf1XwzqqyLp0ioET89ihh4IuOtcJUtiRh5qpkOOA6eo1A57TsS9D2tw39al/NbDfoTDmwSttuMgLqmwpky0TBU1IMIVTtAZbcJ3+0R8jEmXxyHTwC+0pYEfaXNM3PXZdy9fP6GmJLYfPnYfdcyVzLIN/BVMZwdCU4IkZgxk0AvqQn4CO8I/I6Dd3QRY3bLtPJfdiotMNNvb58pWHimkK7gJliku+MghZlZP9NjJk7f99CsVGDXqvAZz+lAYbZeK04stWwa+HxHJk6n0YCZz4vrmxkzqSYjyCIsY807Cj8jTEvWSaDG5WS9grymHJlAoeYHcMKoDeTi9lugThyrDe0SYDeI3cZuH/53uP/fgWY7DjQUFbjxdcBKd2EeOxtkJ5RZYAIsEPm+yK4rYF88cb6VfjrFBgR1xa9+JdU6dAF2Pvyw6zNF1wTgUwoFqPlCF/AjAOynjPPzdz92mPRtMwA46VfABZ3/ON+A9jefYvvBvQ5wpOHGq1+Aqh5+yGe2308//VSApgQCzs/PZyDwgq7LzsMy7h+z/rLQ5XdtbQ2WNViShRE4nYntQqPiYvthFdsPath6IGvGpEuCJ/NbOSXUfNJ1N4F+RqeP0YDW0xdyMJjtthP25XpLFhPUXHgbDfgbDXgbMejbqCNqHhMQ5ph+8h5jE/IFEephhIahoaHFoK/hIejJLTiEXtiFWdyCQTUf6w7QpxmDufWdZF5FMUSSDSbeEHCvK8PuKhSlN4B60nGyz7stwGuGgI7QTkxR2AXwCIck1AtFjCiqyQ4WAkCCukHLRQTi/+FeDVSTnVQS0EZ3fwnaFFCpd5RCjRlq664Hp7IPe28Xnl2TKrc4a66oO9RvMwtLWF6/KdpmBsrThBU46Vyyz4dsgd17wOa73Y3OPwMsPT/kA2XNZRbILHARFnAfPID38CFCe4CwAwpQ+hf/Qii9D/1mVqvwHj+BWiToK0AtFqEO8dljnJ+/L+I6uKzHzADgZR25S97vcb4BPXr/XTTr7UDJpbkFrNx+NnXENjc3sbvbjvXAN/a3b9/OMsqOyPXN8SGkZZZfultlZXQtQBdeZpWloq/8uIbth3XsPKyivi+VNXy4prsuYR9BH6cpVUFRlUqZUxU+oC/mpapvTar6zLUi1MLJb2ZboG+ToE/CPn+T7iL9J+WgkqoRQgA+MQVAnesUBXWPyQdOPktF9WAUd2AUt2JX3XZtFLehav336+Sjcgx0AfmEkk+APllT1WdZa1DV3sBpL8fKtjlsASry3qvbAvAxvlWi1OvVVp+byh+pJPvPO5W+XXST4z5byB0J1E5S6iXQjFCN8Ix3cK4juFs29SMzh1JNFyISQC/ZlxBOE8tyXT9u/2EYoLG3h1p5B43KfldysKPsy8Qd00vLmFtd63UIsu1GxQJeE7j7PSDsuFcaBeDmN4EB3LRH5bSyfmQWmCQLhM2myL6rzc6mnrZ79y6cO+1Y7/3YRs1ZyH/hC0LVd95lnJ+/z9uWl/l4GQC8zKN3ifs+rjcgz3Vw/+03ukZm9ZnnUZw9nCk2CALcuXOnK84c3YDpZpqV0bAAFTCnhkOjcSpj0wuOSWPfxfajGnYe1oTr7s7DOnaf1lvx+ui+S7jXCfsI/4aioGECjJWChHxXJexjDD/VOhoQS9ddV4A9JuHwt+J600ZY9/oaGyquBNijii8BfYR+gVT49VJUox6758Zwr7gtsupS0UeFn6L02FAvB+vYhpAvl7vW4aobw778DeRyVzPI16c9Ozcn/GXiCsI7KvOSmuo8uSwVfIwjd95KvX5i6vE7SvUtQZ2pKmDyi7UjXIAJ6nilMsFCJ+xLIN8pzDm0Xbfu30VtZxuEgCcV3bBQml8QU+L6e9I+2ecjaIFHrwO1je6OXfsyUFwYwc5mXcoskFkgdByElQqCag1hVdaR64LutaVvfiPVQP7ODuw33zrWeIppSiVfsQitVJJqvkJBtHtRZVyfvy/Knpf1uBkAvKwjd8n7Pa43oL2nT7Dz6H5rdFRNx81XvpAq897Z2cH29nbXSN66dUu4m2bl7C1AKMMxYL20tHT2B8yO0LcF6L5bflJH+TGnWmveaUhlBdV7Cegj7ON8SZMwYBhFLegwrhRhXCnFdREGE3Toh902eLzID+Hv2PC3bBGXz9+0W8Av6imOHsT1mLjqUs3XCfk47/bC5lQfRqEcq/i2QOUeJ+myu31mSTdoA0VhTL51FApxXL5WfL4bmZJvCBclE2XcbToS6gVhC/oRDPdSjlPqnSam3nHZb5lMgv0zO8AeIR8zbArQx/WqBH/9KOt6Od+L3ubJxx+gsd8RB+5Ah/gfgfGBSwuLyBVL2Qunix6w0x7fqQJ3f9Cd+GP6KnBFZt3MSmaBzAIXa4EoCBBWqwgI/PYrCKjKdo5OdlT8+teg5g7HiyU0rP+ASX6ki0kL8hH0TU1BKxZBADhqZVyfv0fNzqPenwwAjvoIjWn/xvUG1KzXUN3eQn23jCDwMcUYPjdvHxpFZpel+o8qwKRMTU2JOHNZOXsL0O6M78dsvyy0O+2flfO3gABeVU8o+HafNlB+FIO+J3WxnqiN6r1SC/BJZd/QFH3xnzfhwtsB+8wrRajT5qEHcvY3rLrwmAF3W8I+oejbthGUm525f1KNyf29iGCvDfcSt10CPs6fjHJC6LlKC+wR6rUh3zb0fBmKenIrg462phXixBvrKAjAtx5Dv1uwLCbeyNzle7UtgR4zztqxUo/zzSDEjbyFtKyk3P7vy4PH1Dsu++33ylXRj7SSADvCOkNVYcXAjio9TlOadmS23l5tMY7b1fd28fSTAwHdFRWFmVlMLSyiMD2T+oJwHG0xMedk7wEbPwUIAzUDuPktQB89EDAx45Gd6ERbgEo+v1wWsC+s7ItkHT38yWrZjNl6mbU3rXhPn0pVH2HfJXHvH9fn74m+yAc4+QwADmC0bJfTW2Dcb0ARH+iqFWiGmerKw7h/jC/XWW7coBtcelbC01s8ayGxAGP68fpzO7IuMgHL+vp6luTjDC+TMIxQ3Wm2QJ8Afk8aYpmKPotBkWMFH2sJ+RTQnXeYbtiEenTZNVYLMFZYU9WXh2J0Q6uw4Qmo5+80JejrmCLneHc+ZjdNAF8C9YTLbiDXnxxFL4Jm1WAUCPYYj49TG/KdZSy+5BIwjPk4Jh/hHl11Y9BXuAnTWBjqmJzhZXfhTTO2HqEaoR5rMQUS+nHdUcq9s1LqESwysUZauW874rmIsM+MIZ8AfIqSjXeKwZi5t7qzhcb+PtaefzH1AZD/Be69/QYC30N+alq49xZn56HpWUzLC/9ynmUHqMjd/RTQc8B09mL3LE2dtZ1Z4DgLBHt7aLz+zwMZSTF0WM88A2OMxBnj/vw90EBP4E4ZAJzAQR+FU57kGxDVf8z86/ttDFAsFnHt2rVRGJqx7oNt23j06FGX8jI5YSb6mJmZGevzP4+Ta9Y9kWV3f6OBvU1bJOagso/rFD+UkE8FijHgS+aHkXW38/yUvC7cdQXoI+QTsI9Z1dqxVwj5BOCj224n7NuxEcZuxmk2Owj47FjNl0C/k910CfiqMAoJ3IsBn1iW0E/Vj3ZJGc44qiLunky8QSWfBH3Jsq5nitjT2Jng77u7VTAL7iDluUIOtwrpWcePS35BF9pcDO0sTSr1LEUVKj1LVVHUVOS1dBf2Qfo5ifswkUdlaxNU9yVSkqNi/dI+3N7I5WCYWRb5SbxesnPOLJBZ4GwsEPm+dOXd24dxdS01Wy5dfmvf/e6Jqj9F16Tr7tQU1KlpaFMlEa9v3MokP3+P21ie5nwyAHga62X7DmyBSb4BVSoV4X7aWa5fv47CGP7QDHyBnMGO1WpV2J0umJ2F2X3pApzZv3eje26AypYtoB4BH0GfBH4N+DUPxTi7rqwVFDXG65NgYthFLerQlwn4CgL46YR+KwWoJUP84QuqroB7dM8VsK8cq/p2mOHtsBaP1wcBXgL1bCr3xLJMusH1JwI+JYCe32sDPgH2ymJZ53yhDFXvL/nHIHbT9RkJ9HKEfNeRi2FfPnctTrpxcYGoBzmfi9qHbrdU7tWDEA0xBfJ6CEK8VMxh2Uq342my317PmXiplJ4h8ONGEwSMBHoC8Kmq+G6xZly9rAzfAmEQoLqzjf3NDXiOfegAdOm98uwLwz9w1mJmgcwCmQUyCwgLRJ6HYH8fwe6urKvVFtjLfeYlGKurqZZq/OhHIrFHqyQx+2ZmoE1Pi0lhco4hxY8e5eGa5OfvUR6X8+5bBgDP2+LZ8YQFJvkGdJep4x2ndSXQ7Zfuv1k5OwuUy2VsbW0dOgATrly9ejVLvJJietf2sR9DPtYEfqz3N+sIKh4KCeTTYshHVV8cD+wsRlKbtaAv5WEsEfLlW7BP0VT45SaCXcI9ObXmd5uA3w18mS2VEI8Ah8k2EqjXCfiOd/CNoJoNCfcKZZE1tz1fhpHfhZ7fPdMYfIl9ZcKNqyKzrgR915ATSj4J/QwjU7T2ei1SqSfhXjwR+PkBGmEINzxaxXdcTL0f7FYFNOylUAGb0wjyJMxbMPQjwWIv7WXbDMcCbpP3vI0eMvkquPG5z0MfwaDvw7FE1kpmgcwCmQXO1wJU77WAH6FfB/A72BNj7QpyL76Y2kHnzqcI6zUB+lQCv6kpKBMahmGSn7/P9+od7aNlAHC0x2dsezepN6BarSZcUDtLloDi7C5zqrkYa3Fv73AWRir+aHsqACexMCZfbbeJyjYnO57kfGPbhtKQSr4E9LGWE6Cd1VtSXYWxmBeAT18qwFjKQ5uxeECENU+CvT1H1ruOUPR1qvg43s1Yrdc8AvI5J3hkqkZDADyjIEGegHzJMjPr5nehGm2Af5bXDpNpWNYacrm1DsAnQR8hn2kuQVEyd87TjAGvmR/s1QT4G6Qcp9T750oDW65UelKdl6cKli64hHxxnaw7s+/UICc14fvwmmDm3v3NpyKW73GFgd9LcwuYXlxGrlSacMtN4Ol7NlB9CszdZLDaCTRAdsqZBYZnAcZMlcBvD8HernDvxTEv4DqPTHfd4le/MrzOjGlLk/r8PabDOfBpZQBwYNNlO57GAuN2A6JrEF2ATgrsff/+fTAOXVKoQLt58+ZEyM5Pc70Msu/BTL+dbTDW3/LyMpj8Y1xLRBfFiotquYnKji0ScFR2mqhuNVDfbiLcc5BXIKAEoV5BUVrzjBd2ZkUF9LkcmHVXW8hDmzKhWhoiVUHk+gj3XPi7TQR7joB8CeCjco9wrxm74Yo6hn2Joo9w72i+F0Eza0KxJ9xzhVJvT8A9oeKLa81ontmpH2w4AXz53FXk8nTLvQYxLwDfNZgmr9EsWcAgA2IHIWpBIBR4NT/Asmkcqag7LqbeScemUu9nZoqpm9WDAAqk6ztj811UEZmngwguk474oahdP4Tjh/C4TkzcJoQf153rxPowElMQzwfJcquW+3I91ZSMtMB5fm/5/MYXDpxPPkvWs2/iOyu+uxGYhJg19z+O1R+0Js1LG7OmGxc/5zJvZWKZNeSyxvFQFfEig8vJvIYQamMPaq0MNfS79hf7xG2xXc3KwZhZgDUzD13XoWsKdFUVbeucOpfFvAJDU8XEz5hJ2dDlPma8jus5PwluaBf1XRj6cR/+GKhvAdYUsPIykJ8d+iGyBjMLTIoFDrnq9nLivKdT1TczA/OZZ7L75wk2G7fn714ukWybwxbIAGB2VVyIBcbpBtSkqu+Dd4QSJz8ts/xREXDwT3yj0cCDBw+67J0lnjiby4+Zfqm07HS1To60uLiIhYWFsznwObbKB/H6riNUfNWyg2oC+bZteHSD3XeQiyTgy6tJLWHfsBNuHDptqgRnc9BnLRGLT7E0KLoq3uSGToCw4sLfdwTkiwghCPY64R5hXxjDvnj+OOWeojnQ8/vQcwR6ybTfDfnye1C1k/PvDnMIVTUvE23kqOIj2OOUzK/BslZACJiVwS3ATLo1fhcY6DuGfawPJt9Yz5t4sZgeU+/1Sh3b7snXBt1zC1p7opKvpKko6r2NISEYoVvTC2AnkxvA8QPYrlzf9AM4XijqJmuPy1yfzEuAx324HdsT86zFctCCewLyxYBvwFwkgw/MJdyz5New6G4f2/OGVkBFn0JTS7+WhnHaBgGhpsKkIpo1k7kk86JWxGeWrsW13JbL3C6ZknU5Q0XO0FpTXswn69qfFUwNOQbCP8sXQMMw0Ki0sXcf2HinozcKcOVVYPrKqPQw60dmgZGzAF/6HPWSw/noI7gPHh7fZ768mZ6BNjcLbXZWxu+bUE+eQQZ3nJ6/Bzn/bB9pgQwAZlfChVhgnG5A2w/uCVehpBhWDusvv3rIrgRSdAFOClUDt27dGmsV2kVcXM1mU8C/zizL4manKCBwnZ6evohu9XVMod6ruqjFgK9GwLfbRG2nCbdsC3CGhi+URYmKL4F8uVil0tcBB9lYV6BNWWASDir46KYLqnfcAEGDgZpdeE7QAnsJ4BNgLwF8cX2U82UX2Mvtx5AvqdugTzMbg5zBKfdRBMCzrCvI5TitIRe761pcttZgGHPZ2+hTWrlz96ofoOIHqDIhgx+Cy8zI3EtZNHR88Qil3gf1Ju7ZjkgQpESAGkZQAkAJIgGoOQUxjLPdAHWXcC5Aw/UFuLM91lyOwV5SE9x1znuDuRn3cn7ZNkOwQBThevMhtKg7CmgIFVV9SoC/YAIUuYSDhIQFU0fe1MR8UhMScr5o6uicF+ssbivXc577i+0suT3bHRt1o9sA7n4f6LxWdAu4+U1Ay5IrDeHbmDUxJhbg72pYqyHY2YFfLlMOjsKXvpR6dvzcfuPN7s8Shd/cHDROGfA71ZUxTs/fpzLEhO+cAcAJvwAu6vTH5QbEH7Z7b7+BwHNbppy7chXza9cOmZbwj8koEhfgpaUlzM/PX9QQjO1x0+IsjlKmX4KE+r6D+p6DGqddB/XdJhplBx5dXysOlIYPC0BOBXLCNVcq+Kzzgnvx1aHkdai5WL2nKIiorGr6sOs+HJ8uuRGcDqDntNR6UsGXijuUEJpVhU6gl6tAsyox2KvE6/ahxbDvPN1xD34hDGMeOesKCPMsazWeZy1j8lkW3XPNsf0ejeKJvbZXw74v4QzdS1vqNwHnpGqurYzrUNAJgBdgthEJWFd3JLxLaoJEAe8cP+WijaApIVQliOv2vKJEUJUQCuKay53z8Wf8nD6prIXrqsI6nmfd4c/a5plyZYI3W1tHCiJOUBBGPBprrpPz/Ez0KFIRRKrYJoi0uFYRhnJZtndxbskXeX0pUYjoiNiZM94+5rxd0T1XNbGvT6OuFbP4bkMYMF7nBIGEgwkYlJCQwFBHySI8ZC2XC2Kd3JbLYp1JsNje/0JcpvklffAaYMvrpFWufgkoLQ3BUlkTmQUutwVE8o7dXfjb2/C3dxC57WcknlnxGz8HNSVhEverf//7YDw/Afs4zcxMbMKOs7gKxuX5+yxsM0ltZgBwkkZ7hM51XG5ADBD++MP3uix7/TOfg5kvHGltAsDd3V2srKxMbAKKs74UaV8m/2AxDAPXrjGe2tnCmsAPYVdd1PfcFuCr7zVhM1kFs9NWXUR1H6obgCo9qvcSwMdav4gYYaYqXXMBGRvMlS6FjVBmxyXEI9Sj+y1j7rGmu+6hovjQc9UY7MVQj8u5CnSrImsx7Ys4fIram2rrbK4TRSTPoHovZ60KuCeUfEK1x3kJ/DSNCDYrw7KATzddJ0BNgLdkClBuetixbZRdG3uejchtYsqtw/FsuL4Dx2vCD5pivqwZqGkawtAHIg+6GrTAnKYG0BBAU2NAp4YwFA+5yEZBsZFHHYvRNridTpjH7eO6azmBfeJztnWR1+qwrJ/eThDDQALBINRjUKjTiggjXUwRdITgZwaCKIcgshDCius8Qi4rOUTIIVJyCKM8QqWEICoiVIoIUUSkFKAqGjQ1jsuntmP0ydh8QqItkWQcqy9Zn3ZbTBN9Ms6giCHI4YrjDibLjDdI1UlY3weqOwhUE/78VRmTkC8q4riFIk5h4EPb34Cbm4Wn83wZl1DGLkxiGoo6hFgn4iKG7fiHMlZiiCCQn4kpiaEYx2E821GdrNYZW1EqDhPlYVy3oGGiRNQEUCRsFNAxUSjGsDFRNLIdulAfq1Qsfwpsvd9t6JnrwOrLk2X87GwzC3RYIHQcqfLb3hbwjy+Mjyq5z7wEY3U19ePI9zPgd4ZX1rg8f5+hiSai6QwATsQwj95JjssNaPPuHVR3tloGJvgjAMzKxVtgY2NDxAC8evXqwKCVrrjNhieSaYhpn5MDe8+By0y0+67ITgvbh+oFQqEnwJ7CrJ9SsXdR2T35HByqVEoBHuFdDPQI9uphhGogXXGp4DsU/YxgxKxLoGcR5MVwj/Px1AJ+BHzWRbjgHr7GNK0gYZ5J19wVmBZBn4R8Eu6tCPinqpmL1knf0CBwYbsNVO0a6s0aas0abKeBhltD023A8RpwPVn7vo0gsOGHTYRBE1HYRBQ5UCAnFS50xYGiRfA1A75qiNpVLUF81A4gnEcDL+Hd1O5tYQkPcOPQZypC5GCLKd+qmzDRrTo46Zyzz8/KAgp0vQRdn4auT8X1NAxjVk76HAxjRrjMc1nvWH9aEM+sktXyNnafPIbvJtm7Fax/9hUYudxZnfCR7dJrgABRJF0JZVIWQkLGa0xqJl1JErWI+ThZi0zaIrd1GeuRsSC9JCZkktgljg0Zf5YoYhlzMokxSbd1GVcyEO1lpdsCTOSSqAyFWlGAQwkN53QXn/V+irwOEVPRMlToVhH11a+ikM+11IxSsdjel21mJbPAuFkgoGuvUPltI6hUez49Y3UFuc98puftsw2HZ4Fxef4enkUms6UMAE7muLfOmn9G//Iv/xJ/9md/hnfeeQd7e3sCmHz729/Gb/7mb+Lll8/mjeY43IDKjx9i98mjriuIrr90Ac7KxVtAZJeMokMxFj26+lVdNGsS7NlVD/a+A2e3CW/PQVBzEdb9GOqFoG7QUiFg3kVDvcSqIqtnrMZL3G4J9moCA0VmcQAAIABJREFU7EWoEvq1nusiqIYtlHeaVY9rgj0uJ1M1Xh8vm3XQvXFUCsGeUO2ZyxLqmcvCBdcUsI/zhHvLAjJMShFxdcImgqAu4FsQEMTV0XTrMayrwRZKujpcMTXg+e1tuS8iOSloQoUDTWkKUGeorlDXnaYwbloDBdRRRB0lUXvi23RSifB5vC5caQ8WtnEXtwTkIyhMaoK+7PH6JLtezs/53ac7vmkuwDQWYLAW8/Ny3oiXzQUYxkIra7YAfzsx+PMS8Ne2wczSChbXb15Oowyx11QnMsEMY1jKuJYyjiVd5JNlruuct11fbCenzvmOZcdHwwtENudxKXSu/5LyIaaVetcp/SR8HnuYOvY0Gf+w05VZKBFjF2fp9txWMCYu0YnbM92gD25zokpxXIyencdIWoAuvY3XX0fYsHvun1rIQ5ufh76wIJJ3ZIk7ejbdUDcch+fvoRpkQhvLAOCEDjxPu16v4zvf+Q7+5m/+RliBSSmmpqaEeyqLZVn4kz/5E/z6r//60K10mW9AfLDYun+3S/knDaRg/eVXwCQgfDh3XVfYMCtnZ4Fm00HkKWjWPdg1T0C9Zs1Fc7cJd99FUHER1DxEtoeoGUBxA+ghYMZAz1QUAffOPCtuHybweO3EcfXsEEKtx7oZhmiqDly9jtAgyJOTKqBeMrUBnoB78XpFHcXkAypMkw/2SzDNxdZ0CPKZS5ca7EUR3QEbR06Eck2X6roamgLU1WJYx30ksItCG4jkpApYJ0HdKEHazkt8Eyt4CMZBHQzLvYB3UcRoqEr7+Ooe2FSJMzzTnVD+PiSTdC9sx+BrL0tawt+P7tKOBCg/C+NtWBPUjhFlGdzgwqaEgFFzCUF9FgqmYehSaajrVBjOwNCpMJxBvjiDqy99dnySUpzKbmezM12nqTI8GHOTSXQaDAPAhDqibocESJZrjNPp+Ki1Pk+g5MX9lt1SnuC28rjLWPejFXwUHY75fDYWbbeauD4LcBi7Nks36LZ7cwIOE3fnLsiYuEN37MuM01nJLNCrBer//TWEjWN+p+kBMzMLfXEB+uKiiOuXlYu3wGV+/r54641PDzIAOD5j2feZ/Oqv/ir+4i/+AsvLy/jDP/xD/NIv/ZKIl/bkyRP87u/+Lv74j/9YqKf+9m//VigCh1ku6w0oDAI8/eQj2NX9Q+aYv3odc6tr4sGM7qeVSkXEnitkP3onXjoCmNo+mkwu0fAE0HNqHpw9B17VhVf1ENRdhA1fTJHrA54DNQyQi0wQ5MmJME9m/B214hLk0eUWLpp6A02tDteow9Pr8PUGfKOOUK9DNRtgVlvVYJ2AvQZUwj7tkLPuSJ2momgdap0E6iUKnQT0LQkVnyky5Goj0/+TQB1hnE+Q59PtVUI7qayrx8q6BoKQsK4hgJ0S2UJZpymH1Ucjc9IDdMSFgSbyaCKHJWymIr5dzOJTPNtz6yoCFIReUGoGp1GBFoUIYQCKKSZFTJZIvKKqFnTNgq5bMFjHk6Ia8nNF1kpr3mjNK4ouXMAVhesMqIoOuY7t61BUucxrM6nlNu1lKJqIaQcQ7nHiPD/vhH7ndw+SUJCKZ8IRQkEJBuXkH1PLz8LQFduFoYco8hBGHqLQay+LeUdC7FCqTenq3bmcqFDF98SvwPeZ8f78wCRP3a0ZcCsmQv9o22tWAGvGRX56BnmR4EeGC0gyencu012Z45uV0bEA3acT1SFhIVWIrToGiYk6Me3zBsFinAioc7+TlIpTaOBn1fdF8p6kNJDDa+FLoNp5HAoTqiQZm0WyFQJFoyOjc6xElIlYJGhkhmiCx87M0ElMRq7j/noGFi/d5SFCFeztiXiq+hHJCp07d+Devdd1boquQZsn8FsQ+ylnHHv70hl2BDp8WZ+/R8B0Y9WFDACO1XD2fjI//OEP8fWvf10kRvjxj3+Mz33ucNy63/7t38bv//7v44UXXsD77x8IeNz7oVK3vIw3IN918eTjD+DaB994KVi8fgMzyyst+Le/LwEhHwjpUl0sFk9psdHfnUkwCPGchg+3KWun6sLdJ8Tz4Nc8BA1OPqKmj8gJAC+A6kVQQ/GoHwM8RUC8UQR5EUKEehOhbiPUG3C1BhytAVdvwNNseAR5egMBPzfqiAwb0BtQDAI8Qj175CHewSuNyhm63Ek3vPnYDY/1fLyuDfoYw+ssH5il26uLMKQ6ToKI9pQss267urZdYGvw/AZ8sV8dIZV1EWEdXWCprLtcoI4PrMzymiRv8ENNJHGQE9cnmV/lcigSPcSfxVlhZYZYtStjbLLOjwzYSg6uYsFVTDiw4Ch5eDAQhkwMoWG+XoYS0llXp4YcUOQUqiZ2ivNQCcVUCc74MkkTiSA05KDCggKLdaTCIFBrbatBETBNBuJXBVRj8gh5P5Xz7SQSXK+qioi1KWpVAdUx3IbzyWdcp2ucVBjxdlS8iHVqUivgOrrXsTZ1OXWuy2J59fZbJIF6HZ5XgR9U4XfWfgUeJ28XvrcPz9+F5+21JgLEXuFhAv6cfRNRcAz4ywXIzbjQ8727thPsSuUg73cyPqGMUzgHw5R1+z4o74eaVhzJF1C9jdpkbpUoFak2JCCUisSk9tFoepjaeA1BsyrcpRk/0fYivKt/BttBvp3UKNnP8UUyraxIC/AemsRUFFDQ7AaHhIRindnO8tze7uA6uczPCR8z1eJwr7Jgfx/exgb8zS2RuVebKqHwsz+behDG/2v804+g5ixoi4tC5Sdce9XxAOLDtezotHYZn79Hx3rj05MMAI7PWPZ1Jr/xG7+BP/3TP8Vv/dZv4Q/+4A9S963Varh16xa2t7fxve99D9/4xjf6OsZxG1+2GxCh35OPPoR/IJYQf+hWbj2L4uycOF0q/xhHsbPwwff27dsDJ6IYmtGPaIhQxWcgcduH1wwkvKt7Atq5NRd+3RNTYAcIbR+hQ4AXAC4BXgjFD6EFEbQoEtCO2WxlfXEJMA6eaqT4CDWCu3gS84R4TYSaLYGdmG/A0234uo1ArJfbRboNCJjXHFm3y16uE6qc+JDafqBNHmQZSyt+sBUPtzHoM2b7TpjBDK2hUAnFQE4khkiAXbKuW0VEZZ0EdYxTx1h2EuyxHU7SBVbGqmMcpvMoBGx+qMMLdVH7kQ4vMOBHWvf6+PPWdvG2Yp9QbpvsI9cdXJbQLtk+ycgq95HZWZP9E7AXRDJj6zCK0LPwy6qriLS45nIPQevVfRcK/dVTSrBgiTZ4f5D3CQatFKlTB3QMHsbZnr4NAkBDU2DpGhjXK6lFQgBdlYkBdJkcIKfzQZUPv7pYL5Uz8sE17QG3lKOqRiYcmGTQSFUiISChoOuV4bk7cDl5ZVGLZW8H9b0d7D2qwXMZfD5dbagNAP5Oc5XIeyxVz8l9Viqg5QsUvixJFNGLYrvTJjk5TV+zfXu0wOb7wO6n3RsvPAssPndkA0ze0lYn0pVZKhUFZOwAhS036E6X6NjtmTBSbi9h5EkqxR7PZqw244udBAby3prcZ+U6qUzMG6q45+aSe298/+W24l58YJn39aTN5L4+il4lwxpIuvF6Twn9NlJj+hW/9lWo+Xz673ytBq00ObGXh2Xzi2znsj1/X6StxvnYGQAc59E94tyq1SquXLkiYgCeBPZ+7dd+DX/+538O1kwUMqxymW5AdPu9//YbCIJu90tN07H67AvIxT9+afCP9lpbWxOxFYdVhDTfC+E5gZhcQjvbh19z4RHcNXyhsguaHkI7QOAEUnHnBoj4AO6GUAIJ7tSQ6rsIRAkEd3zuTyDesPrbbztCZac5iASQc1pTJKAdl+P14vP2vPw82UcCvUBrinYizeu3GyO+PbNqxjGtRBZNmUFTZs9MlCqzsTpFrmMGTrosMvmDTB7BOgZ1cfZWuvcJ1z5R87OmUMwRyrm+rJOMrwLwMdurmDoB3eBuyomqLQFlXmjEsM0QIC6BbO31Eqi1lgMdXgLqjtxettUJ9w62lwC/Eb8ITt09Ar9wfvA4pUrNg2qnK6oETAwjjFAumVPb6zwbSFzrGMernQxAJguYyumYzhuinsoZmOZyrr3M9TN5Q8DGcX5wDXwP9956A3zpEPg1qTb0qSSsQLUaMGdsRNouXHcTjrMlaroyj1LRtFIHGEzU1Ex0xPkkXIJcn8HCCxi5Rhl48Fr3ga1pYP1rlB6fW4cOqhRbYPAQLOyIrUh3ZzeOoxiDxARKEig2+X8wKydagKpzvswRYJAgUWR/lmCR83LqmI+3TdZ3vyTq3rb90ih5eSTrs34BFLoufCr9NjZOzN5rPfsMzPX1E+2UbXA5LHCZnr8vh0UvZy8zAHg5x+1Uvf67v/s7/MIv/IKAUuVyWST/OKr81V/9FX7lV35FuLHypjGsctluQJXtTWzda78BZqKPK8++ACOXEyY5CP/4Zy0MIizNryBn5gWo8x0fXt2HTzdYO4BvE9L5CGJFXeCGwi028qSyTsA6X8I6+BLUKYR1USTc3SS0O1+lXcQ4U4qPSHPFFKouQjFPMOciOrjM9WKdE28jgZ7Yt6Nuf05YNzhAGtb1eV7tMMOlrk1B00vCdUzT8mJS1Rw01YrjljHmGV0r6RpJd02KqCJ4PuFcU9QJqBNwj2AvcgSYQ+TISSSOOBqChpESK9AkZOtSs8WA7TA0OwDfBKzT2jAuPALaBR1quvhYsm25fTQm8ZTO6xpKO47QQgk1n4JIV6EwAU5wWCEl0kksyXtYXyWKhKJPtEs1cFZG0gJUx8wWDAEDOc0WzI55uW6uYGKuaGJe1Abmi6Z4yL0s4HDj009QK2+37J+fmsHc2lXkS4dfugm1u1+B4zyF42yIuhnX7XUb8LzyaI6nPt1KmmQJOLiMwzWzoU9fmvEbSUN3dsp3gI2fArVNuZYxIW98HbCG91L3omxwMJ4i3Z4JFm0vzuwcu0En2Z5byVziJC6tWIuuL7JIJzEV/fD8Yn9elO3O+ri8dyfqQ0JCAkdRi0kqzFvz4vN4fRy2IvmM7tcCMsKHFQTIeU1Yezsw6xURCoMhMEQojDgMBtXtnGctwmeUSjCvX4dx5cpZn3LW/jlZ4LI9f5+TWSbuMBkAnLghR0vR96UvfQk/+tGPjrXAW2+9hVdffVW4rzKrLd1Zh1E6b0D/53/8D5ib7f/PlEj4oKXDy7p/PESSEaSS0jEfAYbIECvd67idFiUTYEdApDBaFWNWEceoDLmOQISB5jL3kPVUVEAOhtiSkxVFKITpD8tVTYM/ULDxCEYYoMRA73SBEnKbdl1VVXgaw1aHiFRPwDVCukiVdch1nBfwzhewTm7nQVWaKGr7cp3mxbUrjmEjBxfmQJeChhAlMED84dJknDEMpkjiKDJ5QFphX9nnQcvMEe02wjwafknGVmPMNbp8wkAQxjHYRM04a4qoI7HMeUW4cBasWoeIQF6HEWFcoMJxC8KpjbHbkvhu3N8Hl6nk08F4bX4rvhvdQtm2hkZkwoPZgnqJ2i30IxToDi3201v9iHp0J90NSnDTxicKsKJ3u773Y+tKUICNdBeTFW2nn6a6tj2u3WWt3BXQvZ+D1EMLtSjd7WVe3YOhDAbFGqGF6hHtzrFdZnOOb1cRZQlxdtkoyTKrAqGuiUl8Ts0nN6sFsMOifHGgyXWMy87buV50oKlhHGNPNinj7VEvGkEPAxhhCJ1xOqMAZhBAI6ZVgEjTUS+uCGSbxOdLulJ0yjD9esuTuJ3z9mRLh6qBSnFVwG5xR2Mdz1vOHvJuleLCeL1smRBbbsuOyVruw8/kMr9Lm9Ya+IDqB5xCOR9GMLwaioxHF0QCsPdbHmsrqbsUwgZmI7qoDlaeqIuIUpLlWJGDhXDw79ymOg9fYeTV7qJHHq4pu5gRqkINM1Qc5jUUTfkgyUQBJl2g+eAolhWYOpdVsawtrEPJzxxqN/I9KJsf9GUEAjuvVoWi67CuvYQwBbr4dh3N974Po1BCbmYO+hGuap0Hptqd3wXxk1tchJKf7YodCXiw1CoW7XcQBoxRyJiFVXjBvohl6DGmoc/1vF6qwlWZiVM6S0B1LSF8StHpEt//JSZaClUg0NP/hyXtqoop47XqUg2uqTmoiiVq8WJJi+dFUp14smYQTS2l9lepbgOe3dfYtTbWTUTT6d8NpV4GnHpP7TImqKklCvdpqHoBWLidvm9tC6gP+LvBG9vSC4fb3X8EPHkDKC4DczcPfy7ux6qcVC2e582W86yH89+5J2Nd8EauT/dnqURkJucEFBIsEiISFopELh4/5zxjKnZsl3wu1svtxT5eAELLYRUj8FAg4D0ifICnGmgY6f8bC54NIxzsZbWv6Kib6f93Cl4TRti/xwrvJ76qoWamZ9p9sXwPM076/+6D9mzqJrZzMygXZhBaeRkLtyMmbup8/HkSO7cdN5e/DzKOrpzagJHxeAk6O2PxJuBxWGN8sJ3kjtz5gitZJ/rS6qsizrnzfMSyngBTxiM+v0Rfw7RHBgCHac3L21YGAC/v2A3c89/7vd/D7/zO7wgV4H/6T//p2HYePXokMtmyMDvw6upqT8c9SS3Itr785S+Ltn7nr/8HzC71nx7+Ou5hCVup/XkLn4cvAtP3X1bxGGt4nLrju/isyIA5SFnEJtZxP3XXD/ECaugfgrKxWeziNj5JbfcOnsEeZHzCfksJVTyP9Ae2+1jHNpb7bVJsn4ONz+Cd1H1p+adYG6hdHT5ewRup+25hCQ9wo/VZ6KsIAxWBryD0NTEv1nE+/qxz+WPvChp+Drafhx3XTd/C8/59rJvd1yDBS9O00DSYwfT4U3lr/1YqUFtQd3FramMgO3CnDyrXUkHSlFLDC9ODK3k/ra5gJzx8PTFFxCszB2Ik9dH7x7UFPA7SHz6/NDN4AqLtxgzueulvrn9m6gMo6mAPE3vNPD72VqEoPhBPCqGfEuDV/BZ0LQHxNEI8z1n+S08gvVgOE4YnsrfWwwY+Vvcg22LbQTwf4IVwHSbhawvyd7Tdus7agLDzocZXtvBR/q3UEXmm+UWY4Rz00IURNGEETjw1oYXesZdwxQzx2uLT1HZfLQNXaoM99Np6hP+2lj42L+0CN6qDtRuqCl5/bg5zuTnMW/OYzc1izprDfG4eK48fo/Tpp8ipBvKaCYvABEykQlgPUIjNZ0+GMhTL8TqGOOS62rf+V5EcgMH/m14g3OtYGxvvYPrBP1LMLUIgdtUMiygF36K9tPL/5b4JRzn8UmQ52MbX3PT7XS9fvX+wvox9lWEBustMWMG/dP6plyZSt/mh+XlsaouHPiOw/MXm9wZql/fU16zPY0NZSN3/3+BHiAYELB9E63gYpd97/rX2JtbyPlYKwLKYFKzkIZbncjIZjfgWMjlR5MGPmvAZViG0YS9dg710JY5XGMcx9GQsw8LGI2gHQor0ahg7p6FeTP9vM7PvwRgw+YRjqqhOHQbC7NdU1YN1RKzPk/rt6Sr2Z9LbLdZ95AdSEFPdXIC9dlsqIIW7dNt9urD5GPnN+zBUJmTp816hW8C3/vf009r6ENj5WL4dGaQ8+/NA2ktrexfY/lhCQgERY4DYCRITwNha17FdYV6CxoOFLzIIqcYEQPJ75gYhmm4oMjkLcNgBCROQaPNzVyZiYW0zXE6jgbBaRVirAXVOddANltsQWHqBvHdzPnn/s52fwSez8vnnYHl+9z7mmoO92Nm3Snh/vv1/tLPtW/uPsdzYHeTqwm5uCh/Opbvq3t57hCX76BdGnqZjJzeNndzMkRBxoE6N8U6ElW2wKeFmAjmT9YnCMvksUVcm0JNQMUlEJuBonKRMwFG211Jmyva1WJ3Zmbjs4D4i4VlHYrOD2z59/AiffV6+PHnw4EHrGX+Mhyo7tRQLZABwAi+L3/zN38Qf/dEf4Zd/+Zfx13/918dawLZtFAoSzv3kJz/BF7/4xZ4s1s+bkQwAAhkAlJdVGgBklkcCuojALq5DgrsgBndc51MWAez4eTi+CSew0CSoC3IgqLvm7mBKoatsvO1JZO7AVf53YXoWtBf8u4cAYLIrH1pty4KjHw0CMwAYj3tPAFAmj4h1XgKsKS0ZTdg9H2+347u4oxOK+RLWqXGt+PhZf6H1BpeJReJHeYRaBEePKH/rUNNKWCdLhKr6EA9z6XD8BfvrUKPBXhLY6gbu5n6aeo99zv4K9CPUgSfdlJtaFZ9a6VDn83tXsNCglrl/GFoxA7y2mA6qRxEA8jbxn6+lx726XQGe3+uGBVRuEwbmVRN5zUBBtVDQLORVQ9TJclEz8cJv/F+pw1B777vY+OH/e9IQSbd+QkLmV2Ko1hgMNn/uf0ZdKbQSCNBFr2J7CLfvYPneP4DJ1JPtk/kTDwbgMgFAns+b+ou4a6Q/jH9b/Ukvp5y6zXEA8FvqmzCQrvKhp8ByXoJBAsHVgoLVopwnR1PWvwo89/Opx4z+8Y8RNJ7CDxsCGnqsQxt+ZMs6bMh1EWsq79qK4gwASpP6moK92XQvhELDR0HEJlVhqiWY2hQMdQqmOg2TMRc5r03DUPKHAeFxAHD3HrD57sDXGp7/xXR4WN0AHr8+eLu3/xWQplQ7GL8wUSh2qhVb6xL4mNRUQr4EpCnKfBfYuxfDyljb3dVOhxpSANhYUp4c1yimg1BagF4yoq0BIesBK9pvvIGgUkHk96bKpycNVYYEgf78IvxnnhcqRMJFvtBx4hc7+OBdBNs78AgNwxCeF8CN92MCGAkVQ7hB3Ba34bZBiB29iHfn1mVbftCV3OU0ALBm5PHOYroq9lp1E1epjO0ooaKinJvGdn4a+2ZpaDYf/ELO9jwPC/iVbTz6k18Th8oA4HlYfDSPkQHA0RyXM+1VAgC/853vgDH+jisZAOy2TqYAlPboVACKl8yBgjDsnGLFDJUtBHgC4nFegRLZCAvvwKH6JVLEw6vDOgLyu1ehOFdFW0HA/RQQAB6U0tHJGpECVfg+qlAithvhjZkaotACQkvUUcA6hxfsOpbEdvG2dNQW872pAwYBgMmVE6gKHDMdBF4GAJgAN1r600YJ2+KhiX/SvZZSzYqaeCXntgCd2KdD7Sb/yidqN6mI61y3oTzC/eJ2rHjzBKhLoN2X934uBnH93xb31Dv4aPZu6o5f3vkWIuUIlbACNA0FBEZp5SIA4PP2l6FF/amEtcgXqr6GVsUHxfQH1y/uLmPBHkwtfRwA/FzZwlpDg8JYlH2Ws1IA9gsA++n2D54tYjm/jKXCEpbyS6JeLixjeXMbxffeRpHAUDNF2Ih+ys1f/j+gFWcP7WLfewuP/8ufH1ofUiXDe2oAUFzl+LJO5p0wQtMH3rnyb/E0msF+w0O54WK37gpX6FFUAPIkP1LW8W7u+VTTXQQAPG4MKdDzZ28id+0V3F4s4vZSCbeXiri5UBRxvfDDPwao+uqhiKRfEcemAS9qwJlbQmP1Bhx3C66zBcfdhOtuwXE2UdzZnhAFYK8A8CQDay1AKOHglEicpX7lf0E+f01kae56mX0qAKgAL/xieocqj4Enb57U2aM/f+bbgJ4CQ+kG/XBwNS9ufhOwUkJdOFXg7vcH7+/1rwBULR4sdMf95O871iYgMQaIAiQmcDBlfvF5oNitEm786EcIqlWgfKcbRMb/VsTBWrCxHVaD/zn1pQXkX3xOhAlArjusgf322/C3toHGtoxFIdpI9heNdvxv7WwX0OfnkP/cy4CRR2TkRdgJgkACwfq778F9/Bh+k/A/hE+QyJdDASdCRRm+grCRtVgvtotgqzru3fosbJ/3f0UoGqlKJ3Qs7GxiceshGlBR0XLYtKawpZfQFKr0/l/+DT742Z4XbYEMAF70CIzG8TMAOBrjcK696McFuDNWwFm5AP/H/+eXMLvYvwvwWrSBpShd0v5T9VkwUtUgZTnawmrUHQRc/jxG+EC9Bcap63SvS346CaGSIuLxHSizURlXojiQ9IGoI/eUa6grxY490gP2tzdof16KKljDo5ajoUjUIdqP8CS8JlyLRSw5ArpAurOJJCXC7YxvO6Vrm4B0YmIcOe5fR3XqE7iRAk/AuhjYRcBs+Tr05oJwj+M+/ZRQcXFn9b3UXVbLqyg56fGCTjpGpAT4ZDVdPbW0t4gZ+2pqEwkIFBEdYyhIsKhETLohQeFbJa0FFhHmBFhkve5UcC1qtPSETNRycOSS5VDV0MiZcDr+qL9rL4lYisn1Jf80RphXqriRk/EME3WahGpcI0GaVL+1oZoEdXL9Pc1Fw+R/ZelCKiY1QMn1se5Itz8lVrTJduUgMqKljCEplXbtY0rT7Uzdx/5M45Ad9aaK9e30h/OTxo2fV/KPsL2Q7k5z++GLvTSRuk0jt4Gni+kP2rcfPsdMGfK8j/gD7BoqHAYbPSBGcPRt7E23v8udB1/afRZqNFh8TFffw+50eviB5d11aGFBjgmTcHDsRR0D1XheDz0B/czAAQEgy34+wp1lN9VGtzdNzNjpaosE08odkytMzrM0rBAfraYDvms7U1ioBRBRK8MqtCj9+GmdauoK3lxPj7G4vm3jSqWtypLulyECcceT84RgjM16sPhKhO8e4Vp8oxbhmf3BfjN4nL+/mn4jvNIAXtqV0I+xYYt6DiUthyktjynO6/l4Po9pPY+Cluu63K79j/9bKgBsPnwXG989/gXecV+ctX/7H2AstBV1tGPV8bH98C6e/tf/G00PaPiA7Ueoe1KZyN8L4cIcuz8nrssibmK8/qeFz2PXPOxSy2vyK5X+XYAZczcwTHyYewnlFNdinuOXvB/3dY8Qv4HiugE+wVU8iZbEubV/E+W18xXlPehUDg9Q7kfLeBB1/5aREVyby+MXrHdxK2/jSknFWhG4UlQxQ+/+XlRPy58Bnv+F1B4Fb/wF/P078MI6vKAmamZqp3tyCMZ+9UTG5BAeoshDEMXrIg90AW7MpL9gKOxXYTj9xyVjJ33DQH0uvd18tQ7T7i0rcxQFQiGZ/PdbJpQGAAAgAElEQVRijMXKdLprcc4OBnQt/v/Ze9Petpb1XPBZE2dKpAZqli1LtjxtD9ve4xlzT246nXQ3cE8DF2kEyIB8bgTpv5A/ECQf8i1IEHSAbqBvTwkuOsm9yTlJ9t62t+1te3uUZM0DSZHiPK1FsvHWIjVYpERSpERSb0GFWiRr1ap6qrjEetb7vg89VyTLQrNdEumy2cZgt03AZh+HHW7Ys6QIOwC7OghV3h8frvQjqphH6anlwTkiIa8qFqGIrJpCI42maq7FZPG1Xt9340AXpn4MWPb/Li19mokCy1812lugGgGoZ4D3/1RXu2TZV8znIVstwOjHgPvgdy7z5g309XVg42nFdiVFhkSBcSskxeuG7cIIMHQT8EwcqEHt5sNhYP2J+XS7jiS7HbBPjwNEWPZPHzgzu7AglHmx+RwQ8QlrT5KmwHFj2oxR6bt24MQi3dwo7unKN0CmvG8yiVW6H5bj4uoFIhSJWDz4HpGNCesAIs5pYd0o8j5LR2dsHkomAoMsHYmkLEjic2rXJC/NWLtmyIwCyCCz/DpasGNFGhExeake/S8qJ19xG+6iGbtw/++R8vGHZfk8ej8nadiS9kIVlVul5nuKcbiQLO2WzBjBwvJTEKs0NtN4YX97Zh/IHltBEIcfytHndmTgwt5vor3+HW6r3F55bFQ3isq/e8gS3Q5zPRzE4eD6qIQR1UiJfav5u40JwNq/U91ckwnAbp7dKmP7q7/6K/ze7/0e2kUEhE2Qm78I8wX6sWzAKJhZL+jIGBlk81lk8hlkjb3yw/fK9dJG+kBdep/OpVJ8VnpNx5Rp431ek0tzwW1xY9Bw4MKOgv6sBXbazGsOODQ7HKoDdtVhlt4BuGZm4RmeRI+FrA4aI4vOK9atGLcRDCLz5i2K+uFNruJ2wXb9OmRnhY1QKzrTIW3Sj3T6MVyOg7a/28Gcjqexg0SxR1Vw2WmDV2vM2rBeWEzLqbzIel5HPBdHOBvGTmZnL2fN43Cm9H52B+F0GHG9sdhO9faxUn2LbMGIawQjzhGMukYPlGOuMWFVqBKZwKllCNDaCSdzWAolsbidwtJ2EouhpCgpk7hBs1OPTcW0z4XpQTPP+Mw82ecQAiWcaJOeR07fEZaPudx2KZePKb5iUKg6ZzJrKDRgeVwvxprmhc02Drt9Yo8ktE/Cbh2DzToMmSzMiRwqk4LVlIPJoi4ZNC3JRH16Alsqd1/Tg58P3yvVm/ppZZGRE7sW/1RYqR1KZL1KRFKjafILwF6BQMmlgMVf1NRqPpmGHgjDCEWheNywz0wAY/cA18HY1ET+EVlHBKCkKlCcNshOO2S7TRxLVsvxxPvwR0Bv5fADePf35rw0kioQgLvNvP/nxoV3vFOAr8qDUyJuicBtJPWMAiO3K5+59hhIVn4oeuylnIPA+P3K1YgIja0f20TFCtYe4CJ5kFRIwbclq9DqTRMhSJlITSIDqRTiYbIFsdEflSwyTdLStM4sQI0uw7bzTpCb5rn0YImIUCIU99rZa9usZz5ck/DC8ZkgYMtkKLVJ1+zV/ZjILpSMNkrnlNqjh1nl9sptUUnvl9M/F+7sGsUwAdjYcuq2s5gA7LYZrWE8//AP/yAEQNxuN8LhMFS1+mbib/7mb/Dbv/3bGBsbw3HCHjVcercKqxDVg1b716UNExGNKSN1iBhM6yZBSJ+VycL9ZUrfe5/qlF9TKV4bqY4jF92pIkZDgCtT3bUi2CthxScJopCIwF5rr5ktZtlj7dk99lg94j0qy1lTKls/tP9qac8eFrJZZF+/hhE+bDFIVgLWmRloY5WtSNtzRGfTK7oXfB1JIkE+/BXSgKbiksMKzykRgY2gQA9FQumQmTMhbKe3Dx3Te8F0UNzTTjMpkiJIwXH3OIgQpJLyhGtClHQvqcmS7DQ73UXXovUdTGSxVCIG3xM5uJ3A+2ASy6GUsHppZqIg8VMDTkEGEkEoiMGSS7FwJ+Z0CAGhHq2HkM6sI5NeQzqzJkhB83i9RBDWZ11VP8yyIAFtRA6WCEJR2smacBKWD92L67/A8WcIk9ZsiVAksrCciUzc/3rfsbBmK0msey5UjtVHarLb7/bqVWtXtENtlyXbSx4GRADaDgsQIZcEFn9ZdVxkxWaEY4L4yyf23XclwHn7CuRLX5ruuvtSIZNBIRqBHHgI2dLgb6bhW0Bvlf/97/6/yhafx8+OqTbdV0XJeuGfAKP+8Bnisi0jAMeAkVuVR7b2rUliN5KOJACfAeQi30git+0LX1Y+M/AG2GlQuE61AdO/UrldcjUncrGRRGI9Vay7EV0Dtl7U1ar5ENS0bM9O/Qzkj0FkJRnd3LtBHjAcA7AuQLusMhOAXTahtQwnHo9jZGQEyWQS//Iv/4If/vCHVU8jS0GyGPzd3/1d/OVf/mUtzddUhwnAmmDiSiWFRdqQExGY1JOCIBRlldf0WTlT3YSe2H1Nx6e5Ye9NmkSggwIefpDejEtI2hu37CBrwjIxSKqmdFwuSdV0/2t6nwhETW7wB/A5WYli47i2BnLDEf6BHyR1cAC22VlIFrbaPGpJ+LM65lIZpI4gQ/o1FVMOK/ramAg8btnTeqF7TSAdQDAVFIQglYFUYPfYn/KDMj0gOY3k1ty7pOCkexKTPZOg8kLPBQzYB5gcbOEkkNXF+k4aC9sJLAaTeE/ldlKQg5vRBjfzVfpLRoETfQ5cFqSgu1SaBKHTyhaiR01zsVgQ1oNECqYFKbi6RxSm15DJbqBYCp/QquUiy3aTGCSLwV2C0Dwmq0JF2e9e3KpetFm7xFQQ6SW8SfbIw0I2A31jE/r6RslKnz6jvpfrFWGdnIDl2p3KYijULqk3l4nNA2WJsBfWUiWiUsBSPi6ahFqlmIVUbeXBvv6W2hCnH9Fe+fP+GcBbWQ0YwgKQ7hkNxOcjUpHIxUpp6d+ArBlepu5EVpBkDVkprT4y4yE2kpw+YPxe5TMpNmZLCMDXwE7l+NDHDoGsYi/9tHK10EKJHD+2lcMVmkwAHrjA5V/bVQrn/XcDc9OFpzAB2IWTWsuQ/uAP/gB/8Rd/ARIE+dM//dOKpyQSCVy6dAnBYPBYorCWa+6vwzegehHj+s1CgNyjy+QhuQXSBn631ONI5BKCNNxfkksgvaZ6dExlzS7PxSK8CWA0VISt5GEa8EhYHWyc/GsUC3JTJnKQstfqRZ+9VJbfs3n3Prd5z62rYT6RQOblKxSSyYNQyxIc9+9DcVWO09LovHTjeUSObWR1LKSyyBwRKJRcgy85bBiwdC9pQfcKcjMWZGDSj63klnmcMo/Lr1tNEpK18YekIBGEU71T4n7AloOt+yYms4YgAxeCCSwEElgImsdkQUjxtJqZxjx2QQQSOXh5yIVrIz24MuQ2BUg4HYsAxUnM5QJIp1f3kYOrgiwk0jCb3Tq2jZNWsFh8+wjCSeFibHdQOQmL5XwQ+flEEvraKvStrYoP5HYxliCs821XGo9DfNL5avn5ZXfOXbfOSmQl9YJiOCvVVZbJzVq4ku9GxDtMVpbJS9HcPlKTLN8qWW5SveT2PpflfeTph23sc0vd7QMpTZN7caUUXS+5LJf6u7/fu22Xxv3hZ6Q2PVhlTURWAHKRF2k/lqXXH7a9/zXhUI2wpHbDZcvCfeTth32r1D7NWzXLQsLB//IwIbwfz6PIYiYAW/4V7bQLMAHYaTPWpP5+/fXX+PLLL2GxWPDo0SPcunXYrPuP/uiP8Cd/8ieYnZ3FG4qh0cTEBGATweSmTh0BIjeIRBSEYCkTaRjNRsXrWC5m5mxs73U2CiW4A8t2HC+GMih0QFwnsiLst/Wj396/V9r7BUlYfp+siui421ySKbB4dn7BDCBeStbLM7BMHAwGfuqLr8MuSHFoVjM5LKazIsB2tdSjKph2WDHYqJtWh+HyYXfpwQS5FW8mN7GR2MBGcgObiU1R0mt6v5XWy2QhfLHnoiADRe6ZwsXei8KakC2HW7e4yGpwI5LG/C4xmMB8wMw7qcaENyr1luIIkuvwjdEeXC/nkR54HGzNXO/s5imWsnAlNklBsiAksrD82jAajLFWY0cOWQ+SFSERhPYLsNvHIMsU8L9zkxEOQ19ZqRiOY/+oKH6fNjoqsmzt7DF37mxxz9sSgQPE4H6la4hwXhOl37Ecg78tZ+9UOsUE4KnA3J4X+Z3f+R389V//NQYHB/Fnf/Zn+PnPfw5N00Bqv3/8x3+MP//zP4csy/j7v/97/OxnP2vqIPgG1FQ4ubEOQ4BEWYgoJMKQMpGFosxGYSwsIuAuIqSkEMlGEM1EzZLIxTMUJzgOYiILBRlo7xflgG3gwGsSL6D3Oy1GmbG9jczrNxBiILdvs5XUcQuhyucUDHstm8PSMUTgTZcdozYmJT6EkR460H1gPbGOtfga1hJrB0qyIiTBk2YnVVIx0TMhCMFpz7TIM54ZQQ5aFd50Nxvv/e2FElmTDAyahKCwGgwksB5pXtxJshYkQpCIwdvjHtwa70W/i+f1JPOq69E9y8H0iohBmE6v7LockwJz65IEm3VExB50CEKw5GIsygvQtN7WXfqELet+P3JLy4ct7z9oV/F6hMWfOjAASTYV1jkxAoxAbQjw/rs2nLq9FhOA3T7DR4yPYgD+1m/9Fv72b/9W1CIxEBIG2dkxg+BbrVZBAv7+7/9+01HiG1DTIeUGuwCBHMWfezdH0qqwXrwIbXLywA9cchEsk4ZEBpSJwZ3sDiKZCA6U2YhQOCVysZ0SkQZEBJYJwQ/LIceQ+IyUldvFJbGQywkIZY79d+KlREGp1zNEBOYOuQZbZAk/8rqhSKfvHn/igZ1xA/RQgUhAIghX46tYja1iJb6C5diyeE1xVJuZZEkW7sRECF7qvSRIQTom60FWNm8m0ofbIndiiis4F4hjLpDAnJ8IwjhWwiSYdfJrj3vtuD3hwZ0SIXhzrJfjCp4cVtECKRlns37TvVi4GK+Y8QcFUbgqYhO2MqmqBw5BBpbzBdgdF8R75HZ8lv9zM2/fHbC4P4CDLEEbHoY2Ps4hOFq5QLjtrkeA999dP8U1DZAJwJpg6t5KZFVASr8UD/D7779HNBrF6OgofvVXfxV/+Id/iBs3brRk8HwDagms3GgHI1BIp5F6+BDFfcIJZHVmvXbtRD94iRgg0rBMEFIsMspEDn54TO8RqVhsJPB0k7GneGVEBO7PZXKQ3qNjIhJV+exjx+mbm5BsNqheb5NR6N7myDWYYgQuprJIl2IEXnHacNHO1kfNnnWKQUjCJCuxFSzHlwU5SMRgmSCke0SzEqkUEwl42XMZV/qu4IrXzPR9PUtyoVnja+d2MnpexBkkUnDeH8c7fwJv/XHx3kkSRaugGIJkIUjE4O2JXswOuaEqbH11ElwrnWsYyZKl4MouQWiShfR6DcWi+TCqFUmWbaW4g0QIEjF4sWRFeAE22wgkilHWwlRIpZD85sGBK0iaJqz9tPExfgDXQuy56fODAO+/z89cHzVSJgB5HZwJAnwDOhPY+aJtjEDmzRuhdHcoSYBlfByWqSlIauvJLrIyJBIwlA4JgjCUCYljKsPpD15nwqemblpp6ohsIBJwyDkkCIZh57Ao6fWwY1i8bjVJmI9EkHr6VHTPOsMxAuv9itFDqM2sjrVMDh/3OKFWiY35PJ4SloEXbBa41NZuROsdQyfXp9iDFGNwMbaIxaiZl2JLoqTvf7MSCRCVycByedl7GUT0c2otAomsgTebMbzciOEV5c0Y3m7FkTtCpfu4Htk1BR+N9eLOpAd3yFpwwoORXhuTvMcBd4LPSb3YtB7cTw7SsZl13fTeaUWSJA12+3gpzmCZIDRLUi2WZa2myxayWfE7RlIq38PTL17ACG5DdjhgmRiHOjxctW5NF+RKjAAjcACBx6/mcP+GKY7CMQDP7+JgAvD8zv2ZjpwJwDOFny/ehgiQ6ERucRG51VUhSvZhooDX1pnL0IZ8bdN7siwioRMSL9jObIuSyMJgKnjwdToorBDPIpGrIlkMjjhHBCG4vywfNxqXkDYzqUffolhyEabxqUM+2K5e5U1LEyc7mc/j33YSuy0OaCom7dauVg5uInwNN0Xf2TIp+D76HvOReSxEFoQgSTMSfTdJeORq31Vc67uGq/1mSYIknFqLgJ4vCBXil+smMfj9ehQv1qNI643HkfS5rSYZWCIFyWLQaW39Q6vWItU5rRtGXBCBqRIhWCYGzfiDGwCaqzZdRoYsA222MUEOOuwXSy7FF0tkIZGDFpCHQ25lFfrmhvmgbHy8IrD5eBzFbBZKfz+TyZ2z9LinbYwAWYY/WAzjl++CIr+eX8L6n/8eE4BtPGen0TUmAE8DZb7GIQSYAORFwQhURiAfiwnRiUKystuW2ueF9fJlyE5nR0FIMciIICRyMJgOHi5L75H14Wknh+oQxOCIawRjrjFxPOoaNbNzVAibEFnxYcq+fy+Cln+YZJcT9ps3hRUDp5Mj8DqRFkrCHyaXouCC3YIRqwaZ4waeHOgaW0jqSbyP7BGC81GTGKQYhM1I9P0jUrBMDN4YuCFIfE6tRcDIF4T78PO1CL5bjeLZakS4EJNScSOp7Dp8d9KLu5MefDzpwaUBF+QqVr6NXIPPqQ2BQiEnlIsPEoTLu9aDhUJzY4SWeyVlZTiiPtiTA7BafLBah2B1j6H3R/8N7I5JyG0QwqM2BLkWI9AZCJBXBd3Hiez7xbsgHi6GkTX2yH8jts0EYGdMZUt7yQRgS+HlxqshwAQgrw1GoDoCxUJBEEu5lWVUjOouS7BMTsJy4ULXWZoRUUjxyqplf8ovPmtm3LLj1qIma4IMJGKCCELK9JrKwZ0C3GuRQ9YKkqYKS0B1cPC45vnzIxCgWIH/HI7DKFYnITRJEsrB4zYNziquZQxy6xFI5BLCSvDdzjuR53bmRJnQ96w3G+3FoH0Q1/uv40b/DRAhSMfk3s+ptQikc3m83Ijiu9UInq2ZpCCJjTSa3DZVWAnepTzpFcdeJ6t+N4pnM84TrsW5ANIpciUmUnAZqXKZWkY+X//3V8oCql+CXOV5njFeRKFfFe7DDscUHCLeYKl0TMFqHYZU4aFbM8bLbTAC3YZAIJ7B1wshfDUfwi/ngtiMZqoOkQnAbpv9xsbDBGBjuPFZJ0SACcATAsinnwsEKCh2dm4ORqhyLC7ZZhXWgOeNZKInnKR4TBZH/qQfRAqK49S+46QfuULrAqbvX4BW2YJhxYtRpR8jah9GlH6Mqma+OPER+q/dPqDmfC4WbxMHmS0UsJLOCSvAo4hAumS/pmLCZsGgRWUXsibOQaNNiRiPyc1dUpAIwbfht0KE5KRiQ2QVKAjBEil4s/8mPDZPo13l82pEIJTI4hlZCa5E8JSIwdUIYhmjxrMPV5sacJYsBE1LQRYYaRjKpp9I31+KLbhLCqYOEoSH4g7mADUgQaFwhNWe2UiA4SsiP1S9u7JsLbkTEyk4JY4FSeiYgqb18b296TPNDXYSAtGUjm8WifDbxlcLIWHxV2tiArBWpLq7HhOA3T2/bTs6JgDbdmq4Y22IgB4ICCKwmK1MaNnv3mEF2g/mbT9JSAQEEYSbiU1BRmyltrCV2BKuyCclIWpZLr2KC+M9k5joncS4exzjrnFMuCfEMYmWKDKLWtSCY75YxHomh5VMDqljBAxssoxxmwVjNg1WmdVKa8H3NOuk9JQgBYkMfB1+jTfhN8Ji8KSk/aR7EjcHbuLW4C1RkhuxVWFl6VbObaFQxGIoKQhBshSk/HozBqNB12GHRcGt8V58LFyHvcJ1uN/Fc9jKOWy0bV2PCnIwGZ1HcuEFsivzyGUCwqKQYhIeSDKQ7wOMwSJQm2ZIxW6pag8cjkuCDHSK0jy22y9C4e96o1PJ57UxAqmcgUdLO/hqYVtY+X2/EcURThGHRkJRF8ja+sdXBjHrzuE3Pv9I1GERkDae9BZ3jQnAFgPMzVdGgAlAXhmMQH0IFA0DuWVyC1458GSdYgLa79yprzGuLRDQ87qwGiRSkDKpoYqc3NglC1vtaqzK6i4hSKTgJBGF7gmTIHSNQ1NOsFPq0nkmcjeYM4RFYEg/2vJoxmHDJQeTB52wFOi7RqIjRAa+DpmkIJGDFHOw0UTfr1nvLD4a+AgfDX4kShIekThmZKOQ1nQeBZ4nYZGnwkpwR5RHuaUd1+hknwM3RntwbaQHV4fdohz32nkejwOuxZ8XdR251TXoa6soGgcFZPL5lCACc0YQWW8G2Z4wUsYKUqklGEYrRMEk4VLsFC7Fl+BwTpsEoXMGFo1FRVq8FLj5JiJAFn7fLodF/L6HS2G8WIvW/UCFVNl/fHkQP5kdxA+mB9DrMH9L8v67iRPVwU0xAdjBk9fJXecbUCfPHvf9LBHIJ5LIvnuHfCQCyBKcn3zScYIgZ4lfPdcmlWMSLiFSkAjC9cS6yPS6XLaSICThkWHHMCZ6JkCWTRd6LuyWZD1oUTh2FikEExG4kdEPuQdLAH7c52YLwHoWfZvVpe/gSmwFL0Mvzbz9UpCCaSPdcE9J9fv24G0z+24LUtCpdZaoUsODP8MTN6PpXStBIgSfr0eQ0RtXpnVbVcyWyMCrI25cHTbJQVYePp1J1jc3kZ2fR7HKQxhJkaGNj4t4xZJ28EEWuQ4TEZhKLSKVNst0imIPLiGfb5zwrzZyVe0tkYFECk6XyMFp2O0TIBVjTozAWSLgj2UE2fdoyST9SHypHgs/6rvLquKzqT58Md2Pn1wZxIzPVfEBCe+/z3Km2+faTAC2z1ycq57wDehcTTcPtgUI6H4/itms+HFdLRk7O+wa3ALsy00SOUGCJGUycC2xhvW4SRKuxVbhTwda5mJM5CCJkhAxSFaDRA6WMwmUkHDJeUrkHryV1QUZGCtZogxbNdxyV1ZiThh5xPN5DFlYQbjT1km+kBcxBIkQfBV6he+3vxekIAkINZLouzTjmcGdwTuCECRikL5XbCXYCJq1n6PnC3izGRcWgk+Wd/Bk5WQCI+Urj3nsuDzkwsygyyx9dOzetYCpvYdc8ygE6DdI5uWrw1VkCdrIKCwXL0C21md9TdbduVzQJAYFOUglEYRLwtW4WNSbOimSZBGxBZ3OGTgdM3CS1aDzsniP4hByYgSajQCpqs8F4niyHMGTlR1B+i2H6hdWsqoy7l/04svpAUH63RrrhaocH+6E99/NntHObI8JwM6ct47vNd+AOn4KeQBtjoDuDyDz8iUUr0cIhSguV5v3uPu6l82lsfTqG6ysv8K6EcK6sY2NfAibahJr6Y0TWTEdhZYqqSK+ILk6ClKw94I4pkzKqd1ObER10z14zGaBV1MrQvU6kRZ1VEnCiFXDqFVDb5W63bcyu29EZIk7vzOPF9svRCZScCGy0DAB77V6BRl413dXZFId5liCrV8324ms6Ta8QoTgDp6tRpHWD7qWNtqLQbcVl4kMLOdBF6Z9Lvjc1q6/JzaK2VHnEVmXevQIhUTJYk8CtOFhWC5ehGy3N/2SxWIemcw6kqn3JYLwPVJJ8zib8zf5erKwDiQykCwGy8QgEYWKUvmhUpM7wM11CQLhZA7fiYccZjgEuqclsvWLJqmyhNsTHnw53S8IP4qRatPqt17l/XeXLKwTDoMJwBMCyKc3hgDfgBrDjc9iBGpBoFgoIPXgAQrpzG51bWQYlkuX6n4iX8v1uM7RCAgRl7dvhauUZWJcELK0eQpnwliNr+7mlfiKeRxbFSrHrUjk6li2FJzqncJUzxSoJCtCu9r8TVsrxnDSNgvFIv45HD/kMuxUTOEQsgq01fAk/aT94PNbi0AilxAWgs+3nwtC8EXwBQLpQEMXJYtaIgGJDLzjuyPKPltfQ23xSbUjYJCV4FYcrzZieLUZw5utGF5vxhFNN88SjFyJL/lcmB50YppIwUEiCZ240O+ExveBIyfLCAaRfvE91MFBWKamoLjOxpXeMBJ7VoOp9yWSkMjB9ygUGrMMrjxwM86gi4hByq4r4tjhmIai2Gpf2FyzKxGg2Kd0v3qxRg8xTAu/pQas+wgcsvAj4Y5Pp/pEJsKvGeENeP/dlUuv7kExAVg3ZHxCMxDgG1AzUOQ2GIHKCJBQSHZ+4dCHIibPxKQgoT6MycNYthaBQiYjRFyI/JNqUKWN5+KCDBSkYGxVuDzSMZVEHDY7SZCESzGRgRd7LwpiUJS9Uxi0D3aVhcxmNocX8aNjyPVpKsiFmMhAjST0OHU8AkS6kxr4s+Cz3Uyuw0ahfmsMAoOIdHIbvjd0Dx8Pfcxuw6e0QsQ8xjLCffh1iRB8sxnD++0kyL2uWYksbib7Hbg04MSlQddeOehEv9PSVffESpiRwEd2cRHI52G7dq0qrPlEom09DEyrwQ0kk/OCDKTStCBcAMUhbF7asxh0Oa/A5ZqF03lFqBPL5ywcR/Mwbe+WckYBb4nsW4/ixXoEz9ei4nWj6uf0IOLeRa9J+F3sw0fjvbCq9Vv4HYca77+PQ+h8fM4E4PmY57YbJd+A2m5KuENdhAC5/2bn51DM5iqOStJUWCYmRIBuSa3sItlFcHTEUETso6UlaGNjkC1Hi3uQZdNyfPkQMUjkYCQbafp43ZpbEIGUL3ku4VKvmcdcY1Dk5v9AbfoAPmgwlDOwmM4ifIyCMJ1G1F+/RRVuwj6LBoXVY1s9PafafsbICCvB/aQgCf80ksi9niwDiRCkfNlzuSO/H42MvR3OIeub98GkiK+1EEhgLpDAfCCBxe1kw5vyauPqsamYImtBQQ46MTXgwtSAExcHHHBYOvt/KnkQ6BsbyC0u7gp8OO59DKW3tx2muWl9yOXCSKYWkBKk4IJJDibnkc1uNu0akqQJ8RHTUnB2t7TZRiFJx8dra1pHuKETIUD3FiL3Xm/GSoRfVDyAyOUbFzEiFfO7k158POnBJxf7hLK5cgoPG3n/faKl0DUnMwHYNVPZWQPhG1BnzRf3tnjW9+AAACAASURBVPMQKObz0FdXQdaAxZIowoejICtACtStjY5CUjqPyOm8Wane49zaulB3pjmxzl6B5vM1NLxoNoql2JKwFFyKLu0e0+tGRRKqdcQiW0R8QbIWnPZMC3JwundaWEZ1gkJxKl8AWQOSgnC6cPwPeVmS4LOomHXaWFm4odXZ/icREU8iPt8Fv8N3ge/wNPAUcztzDcUSdGku4S5cJgRv9N/oiO9F+89SfT0ksREKsj8fiAtCkIjBOb9JDDYrvuD+HlFMwYsDTkz1EyFI5KBDlBf6nLBb2vv/rLG9LbwHCqmDogRKbw8c9+7VB3yH1jbdiUvWgrvk4BzS6RUAzbEwVRQnTEvBq3C5rgmLQcqq6u5Q1Lqj23T/D8SzItwAkX0UboDK98EETmJcbNNk3Br34O6kR7jy3p3wwNdzNi7jvP/ujrV60lEwAXhSBPn8hhDgG1BDsPFJjEDdCBRyOfEkn57oV/vtKlktsFy4CG10pCb31Lo7wScciQBttiiYenHf02R1yAcbuQsfYw1YK7SkWOxP+gUhKHJ0CYvRRSzGFoVbZDOTIimYcE+YpCBZC5aIQXIpbsc4gyIeo54XZKA/Z4AUhaslsgD8lT43iAzkdD4QIHf858HnggwkUpBiCqaNo13IKyFDIiLCZXj4Hu4P3cetwVssLHKGS6hQKGIzlhGkIFkMLgRNi8GFYBIkRtKKNNJrw2SfAxf6KTvFcfm1x3G05Xcr+lNuk/4H0QMoI1zFLVaW4Lh/v21dfVuJTbntfD4jXIcTyTkkKSfeieNMZrVpl7fZJuAWpOAeMWi3kyI5Wws2DeRSQ/GMbj4Q8Cfwzh8XsfuI7AslK3vO1Hp9+mlA1sCkyvvxBbLw82J22N028UR5/13rTHZ3PSYAu3t+23Z0fANq26nhjnUpAvlEUhCBFLS7WpLtNlhnZkRAb06nh0D6xQsYwcNuh0T+2a7OQh0YaGlnUnrKtBiMmaSgIAdjZpnJ7wnJnLQTFGeQ3IZnPDOCHKRMx+RabFPP5mn4h2Mi8m87Z2AzqyOY0w9x5qQWfNNdWQUynS/AIkvsJnzShdLm51PMwLc7bwUZ+Nj/GE/8TxDKhOruNVnQEgl4f/i+IARvD95um+9B3YPpshOiKR0L2yYxSLEFF4NJvN9OYGk7dSK3v6NgIrdiQQoSOdjnwARlL5V2jHrsLSEQyN2XYtNSrmbipPp8sE5faomybzcsm3w+VXIfnkMi+a5EDL5DNtucB2vCWtB1FW73dbhd1+FyXxfCI7Js7Qb4Wj4G+i7PB+N45zfJPgoRQMTfZrQ5v20u9jvw0bhHEH4Ut+/GaA/cNq3l42r0Arz/bhS57jqPCcDums+OGQ3fgDpmqrijXYZAPh43icDtyhtW243r0IaGumzU7T0cstIkleBKJCD1nBSciZg9beEWshrcTG7ifeQ93kffmxaD0UVx3MxYg7IkY9w1vksI7icGz9KVWC8UEcjp2MrqCJXiBX7c48RAlRhfT2JJUHzBAQuJh6gYZAGR9v7iNal3ZEFKAj1EBBIhSHktsVZ366Q0/NHAR/hk+BN8Ovwpbvtus4Vg3Si29gQSGdmIpIW1IMUbJDdiIgaJINxoEqFQaQQUGmy4x4bxfaTgOJGDXjvGvHbxmVqnYrERDgurv0KqsjWr0uMW/3cUj6e1oHZp67oeFZaCicTbEjFI5VsYRuzEI5YkFU7njHAfdrtvCGKQCMLz6kJMMfpWwinxfaS8JL6X5nEw3hxrXrLsu9jvxPWRHkH0EeF3Y6wXvfb2JfsqLTTef5/469cVDTAB2BXT2HmD4BtQ580Z97i7EMhHoyYRuM/lR3Y64fj0k65XN2zXmdT9frEhK1YQpyA3bduVK21jnUlKxPuJQSIFFyIL8Kf8TYOXXIkneyaFlSAJKsx4Z8QxuRer8ukG2s8WCgjkDIxbtYrfD1L++6dw7IDFIDkJk5rwYIkMtNe5QW8akNzQqSMQSAX2CMHAYxFHsN5EFoJEApYJwVsDt6ApnbXZrHfMnVyfSAiKNSgIiJBJQphlSqgWtzKVCUKyFKRMpKAoPTaMeRwY9dh2rZLEA6e5ORj+QMUu0f8a6/Q01KEh/i3Q5EmjhwXZnB9JIgUTbxBPvBElxRwsFhtTI9/fRbv9giAEe9w34Rb5BjStOwjcZNbA6k4Kq+E0VsMp8d0qE37rkTSOiNxR9yw6LAquDruFMAfl66M9mB1yw2k93d8ddXe8hhN4/10DSOegChOA52CS23GIfANqx1nhPp1HBIydHUEE5iNR2G7eqCo+UUinhQUaqwa3dpUUslnTGrCKhaY6OADr5cuQbe3hMvshGhQvjawEiQwsk4JUkrBCsxIRI+Q2XCYEr3ivCIJw2Dl8ZhtWih/4In50XDiXoggykCwIPapyZn1t1jxwO7UjQOI8ZBn4aOuRKN+E39QtLGJTbEJUhKwDPx35FCQqctpEeO0j5pr7EUjlDEEOLoeSZhlOYUWUSazvpE8kMFAr0i6riutyAtdyYfTbFPQ5NHicVlF6nRaRPZcuwHrpEv+frxXUJtUrFLJIJheQSLw2LQYFOfgauh4+8RUorqBJCN4QpGBPz01omvfE7Ta7ASLQt6KZPZJPkH0prO6YhF/4hLH5KvWXVHcpHudlnwtXhtzCuo8IP4rLKZ+CIm+zMaylPd5/14JS99dhArD757gtR8g3oLacFu7UOUaAiEBy9ZGqiBuknj5FIZGANjYOy/hY08QpzjHkRw5d39wUVhqVFJwlVYFlagra+HjHkEgUZ1AQg9EFzEfmBUFIuZnEoFtzC1KwbC1I5WXvZfRae1u+zBZSGSykanc10iRJEIHkJtyvqdC6dLPRcuA79AKxXAxP/U8FIfit/1u8Dr8GudzXk5yaUygMfzb8GT4b+UysdXKn59RZCJBKMZGAJiloEoTkzkjEx1o4hXj25JZhZUQuRjcwlDos9JGwOLDYM4KCwwGf2wZSMvb1WMXxIB27rRjqse2+53VUtoTuLOTbu7dkLZjLBRFPvEIi/gpxyolXSKeXT9xxu20S7p6P0NNzCz3uW4IcVFXnidut1oCRL8Afz2Izkhau8lRSDD6y3NuMprEZyZxYfOOozquyJIQ5Lg+5cNnn3i0vDjhgVdtbmbvZk8L772Yj2pntMQHYmfPW8b3mG1DHTyEP4BwhkI/FkPr28e6IJUWGNjoKbXISspUDUbdqKRQyGWTfvKmqzKi4XbDfvt3RZCwRg2QhWCYFyyXFHmxW8jl8ICtByrPeWVGSInGzradyhQKCOQP+nC5iAVbXEj48sh953WAX4WbNeOe1k8gl8CTwBN9ufYsHWw+EhWC9hGCfrU+4CxMZSKQgucpXe6DTeQid3x6TiAG5Pq6V3R/FsWkVRWVaz9cMjlLI43ZwHlrBJBUNWcGq24eA3QvUoWyuKRL6nVb0uywYcJnlYKk0X1vR77QI8tDj0M4dyVLzhDRQ0TDiwkowHn8prASJHCTxkWJRb6C18ikynM5pQQYKUrDnlhAekeWjlanJao9i7AXiGQRiVO4dBxPZ3fdCyWxTXXSrDZSI6osDTlwacIqSSL/yscYhOARsvP8+wdeki05lArCLJrOThsI3oE6aLe7reUegmkotZAna8DC08QkortY9PT7v+JuxAedQ1A/+wFe8Hjju3u1KeIgQEdaCO/OCHCzn7fRhteRGACA3YhIb2SUG+2YFOeixNSdeEsUE3NYNoSRMqsL6EQGK7LKMH/W5Kw6DrECYxGlkhjv7HLIQfLz1GA+3HgorQVIdrjeNOEeEu/Dno58LQnDQweru9WLY7vXp/hBJ6cKSijIJlJA14UbULNcjGWwnDlomD6QjmI6sY9vuwYp7CLrS+rhmTosCj4PcjDV4HRZxLFyQ6b2SC3KPXROCCj02DT12VRyfN+usRtdboZATJKAgBePfIx77HonkG9D7tSa9oCKl25Ey7EjqDiQND3TpCnLSJaSLo0gafYimFeykcsJaj1xyU7nayeda+3FcPSL5SCGblLKJ4CsTfVSSmzunoxHg/TevEEKACUBeB2eCAN+AzgR2vigjUDcCxUIBmZcvqyrUlhtU+7zQJiag9PUxYVE3ysefQORfdmEB+kbJMk6W4Pz0U8gOx/End1GNnczOLhlIwgpEDFKZ0BNNGaXP7sMskYElQvBK3xVccF+AIjfuJiQ26UZeWAcSIZjMH3T1nLBZcM1lr9j/+VQGgayBfosqXIW9mgKlDkudpoDCjZw5ApFMRLgKlwlBWvf1pune6V0y8P7wfbgtlUnnetvl+u2FQNEwUEiloPT0iI6RlRa5W1J8ta2Y6XoZ3gphJacIcRJ6TSRhM0UUmoWIVZWxRwyq4piEGFwW1SytiijNY7N0WhVxbNcU2DQFVk0WpU1VQJaL3fRAhVSpaX4pExmXzBkgsYxENo9EJotwbAM78XXsJAKIJEKIpeNI6lakdTuShh0pQfQ5xOtc4Whrv2bN6XHtuG2qiME3QSrXfXZB9onsdWDcaxdzyalxBHj/3Th23XQmE4DdNJsdNBa+AXXQZHFXGQEA+UQSueUlGIEAjvJtJEKKYtNpI8OQFP6h1uzFk49EkHnzFtrwECwXLza7+Y5sjwi2reQW5iJzggwsl+RabJRc3U4yMBJfoPhqwoW4bxZX+66KY4rB1khK5QvYLlkGhnQDd3ucIh5gpfRNJIGYsWdlQcrC3hIRSArDvaoCmQnBRqaho88hS9iHmw8FIfjN5jd1x9Ikhe0bAzfw+cjnIt8evA2L0h4EQEdPzBl3nmL5UtgIih3r+OxTyJba5jRnFIQbJ5GEwo0zViqFS6f5mlw9yfKrkxOJPtjUEiFYIgctigwiGslFVGRVhkWR9l4rMiyqJO6zdD6VdMulBzEkFEGvKYQrfSbIxWJR/EQiQpWOzLL0evezIshK3MhTWYBOZb4AXbxXEO+Xj7NGYZfky+gFZA0i/MySzuukRDiTOvVIrw0jvaRUbZYjHhtGSyVZgHJqHQK8/24dtp3UMhOAnTRbXdRXvgF10WTyUM4VAmRZkFtdBYlUHCVdKJGwAcUJHBtrW8XaTp04ssqkJMmVA/4bwSDyiQQsFy5UrdOpY6+n33pBx3J0WRCC73be4W34rSj9KX89zVStSzHWiAwk12FR9s1iyDFUl4UJbQJp81iJxKOYgv8cjh/ZVzqPFIWJDCTrQCYEmzK1HdfIWnxtlwwkYjCUCdU1BiK5Px76GF+MfCGsBIngZkGRuiA808pk9Zd9/x762p7aujo4CPtHN5vaLxIsIWtBivdGcd22EznxOvRBSe+Hk9lTUTdu6gC5sYYQsMg59Fpj6LVG4bXrGOohUm8AEwPjmBicgq/HgeEeG/qclrr+PzbUGT7pSAR4/80LROwfivTonBMjcMoI8A3olAHnyzECTUagkM1CX1+Hvr5xKDbd/ktZr1yGZXy8yVfn5qohUMznkXrwAIVMFrLdBuvMDGgjyGkPAXKnJFKwTAhSfDWKNZirI15SNTw9Vo9pJeg1CUHKU71T0OT6rRq2sjqex1N1TR0Rgl5VEVaCQ+QOx1a4deHXDZXpZz0pbJOYCFkHkrBIvS7yXqtXiIkIC8HRzzHmGusGaLpyDGWrv0I6c2h8ths3oA35zmTchUJRxIvbSemIlErxOrn/PfOY3otnDETTel2iJmcysHNwUbsmo8dWRI81A5cagV3ehFPbgVtLwG0xs6t03GOJw65mqmrIyLIdvT230eu5B4/nU/T23G2p4vA5mJ4TDZH33yeCr2tOZgKwa6ayswbCN6DOmi/uLSNwFOFk+P3Ira6hkEweqCapCpxffglJ5cDMp7WCyAokt7R84HJqfx+sly+fu3iB9WBOrsLLsWVhIUgKrEQKvgu/QzAdrKeZinVJcGTGO4NrfdcEIUglWVg5tKPjN5ZjB5KbMKkKR/e5AtfSqdtuB4as9ROPtbTNdToHAVrbL0Mv8WDzgchPA09B1rH1JLJ2LbsLk7BIs8Ry6ukD1z2IQCWrv/01FE8vbFevdtx9n9yR4xldkIGxjIFYunysI5Yux7gzS4p5R/HuUiLunfk6SfHvsgaonfOYSHBlf0xEh8WMiViOj2iKrJjZFF0xBVfEcUmExaIe9C6g/0Xp9BKi0e8QjX2HWOwpEok3KBbrFwGRJAVu9014PJ8IQtDTex+a1nsep+pMxsz77zOBve0uygRg203J+egQ34DOxzzzKM8XAkY4DH1tDca26X5mmRgXxFOlRGQhWauVA5WfL6RaM1oSCkl+9RWKH4hMiKvJEiwTE6ZbMBOyNU9AKB3aJQOJFKS8GFmEUTRqbqNSRQkSLvRc2I0pSC7ElAfsA1XbpZhQEcMAEYLhXB6J/NGbr5/0uWGt4CZOrsVzqaxwHfZoClsJnmgmO+/ktJEWJCBZBxIh+Dr0WsQqqzXR2r3ef10Qgl+MfoE7vjuwKtZaT+d6TUAgH40i8+oVKln9SRSz7tIlEYu3mwQv6oWNLBApfl66JJJRLil+Xlk4g47JrTlH8fcoG+XjoiAQxXsiF0GCG4UiZYDaLh8TOZbf9z4k0J/A3izN1yLMg/gzYwiqsgRVkaDKFHuQjmVo4j1ZvK/JZklKyBQ7TwiZlARN9r8Wn2synBZT+ITiEp5GyufTiMWeIxp9gkj0sSgNI9rApSW4XLN7hKDnU1gt1f8PNnABPmUfArz/5uVACDAByOvgTBDgG9CZwM4XZQROBQEzTuAaLJMTkO2V1U3TpCzsD0Bxu0SsQHVoiImpJswOxf7LvnuHfKTyD3HJahFuwdrQUBOudj6byOVzwsWybCkoyvDbut0sK6E3aB8URCDla/3XhCvxuLvyRp4IwR2yDtQN7OgHCUGHIuOH3soqr6RE/DS251qsluII9moKeogUVFWxEeV0PhAgl/hH/kf4ZuMbQQquxFfqGng5fmCZEOT4gXXBV1dlIpv05WVkFxcrinF1qtVfXSBw5bZEoFgsIJV6v48QfIxUarGhvjqdl+H1fA6v9wt4vZ9C07wNtcMnHUaA99+8KpgA5DVwZgjwDejMoOcLMwJnjkAxl0Piq68OiIiQuzCRgEQGKu7KxMWZd7yDOqD7/cjOz6OYrazaSBtFIgLZArM5k0ob8/XEuiAC3+y8EeQgZVInPmlyaa5d1+EyOXjJc+lQXEGy7CMiMKwbsMgSph22ipd+l8xgKZ09sltEIJKgiEkIKnCz2vBJp7Fjzqd1XCYDyUJwJ7tTV9/7bH34bPgzYR1Iedg5XNf5XLkyAoVMBplXr0FK8B8mtvrjVdOOCORy4RIh+C0ikW8Rj79AsW7rebIQvIY+QQZ+LiwFVZV/IzY637z/bhS57jqPLQC7az47ZjR8A+qYqeKOMgJNRyC3soLs/ELVdmWnE9rIsCAEZSu7ljU6ARQjKre0hNzaWlXFZtXng/XSVMfFiWoUk9M+j6yriBAUxGCJFFyMLiLfQOyk/X3fH1ewTArWEleQ2ngYSSBSZzxBsgd0lcjAa67KVr2njS1fr/UIFIoFEReTCMGvN7/GE/8TZPKHxSaO6snFnosmGTjyBT4Z/gQui6v1He+yK+iBALJv36KoHw49wFZ/XTbZXTycfD6FaPSpIAMjkYeIxp6iUDj6YdQhsrsUQ9Dr/RJ93i/h8dyDLPPvxFqXDe+/a0Wqu+sxAdjd89u2o+MbUNtODXeMEWg5AvrGhiCmSKn2yERxcvr6oA6PQB0cgFQhnlnLO9sFF6B4i9m5ORjhKpY8siQsL63T05BYNbblM54xMpiPzO8SgkQMEslCsdlOkmRJBpEtwn247xqu9ptlr/VggPWVdBZhPS/iCeYooFUdiSwCP/dUJnDIAlGRJJE5dScC2XwWzwLPBBlIpCCJi9QTP1CRFNwavCXIQCIFbw7chCqzSNRRqyWfSCL18OHhKhJguXhR5PMc6687v2nnY1RE/sXi3yOy8wiR6ENEIo+RzyfqGjypDHs9n6Cv74ciO51X+PtwBIK8/65reXVtZSYAu3Zq23tgfANq7/nh3jECrUZABM4m0ZCNDRjb2xXjGe3vg6SpIGs1bXgYSi8rxjUyP0YwKNyCKwWOV3rcsN+7xz+cGwG2CefkC3ksx5fxJvTGdCGmMvymbvfLSl0ZdY7uxhQUxGDfVfgcPjHX6XxBqAsTGRjT8+L4KEpw3GbB9SoWgC8TaaxncnAqsnAZdisKXKosSptyUFWyCZBxE22AQDQbxcOth/h642sRP3A1vlpXr8i9nawCyxaCJIzDZNZhCOm+nVvZw1a2WWG7fh2Kx1MX3lyZEWhnBAoFA/H499jZ+QY7O18jEv0WhUJ9FscWiw99fWQdSITgD2C1+tp5yKfeN95/nzrkbXlBJgDbclq6v1N8A+r+OeYRMgK1IlDIZqGvb8DY2jzWKlCyWOD8wZe8SawV3A/qFQsFodScW14+4E5mv3sHqpcDbTcIa0tOI5Lcn/IL9+HX4de7FoMUo+2kieK0lcnAsqXghHuCtOGQyBcQ0Q3EBDGYR2qfqjS5/07YLBUv/9VOoqoysSZJghQsE4J0TEQhWwuedCbb6/y1+NqudeCDrQcggrCeNOIc2SUDPxv5DF4b35MIP7pvpx8/Rj6egDrkg+3KFUiaVg+0XJcR6DgEyEIwGnsuyEAiBcl9uFisHNe42uBczln09f8Y/f0/gaeX3IUr///qOHAa7DDvvxsErstOYwKwyya0U4bDN6BOmSnuJyNweggIq8BIBMbWFoxAAMV9xEO5F6QsTOIVnE6GQFHXBQlI8QHJzdp+69bJGuSzTw0BIlXK8QQFMRh6g8XYIihe20mSU3Ni1jtrqg+X3IhJbARQBRkYz+cxaFHhrOAmXigW8Y+hWN2XJ7ERam/EqmHYyoRG3QC28Qlk1UrrlNyFyULwaeAp9IJec48lSGIdknXgl6Nf4q7vLizK+d28UyiHfCwGbWSkZgy5IiPQTQjk82khKhLe+Qrh8L8Ja8Fj3Uf2AaAoLmEd2N/3E0EI2mzn77vE++9u+kY0PhYmABvHjs88AQJ8AzoBeHwqI3AOECABC3IN1jc3kd/ZUz10fPoJFFflGGSZd+8gO5xQfYOQLed3o1jP8iik00CxWFUEhNyGSbSF4kyp/f31NM11TxEBih9IcQSJDCxbC9LregiXSt3VZA2XvZf3rAX7rqKS2EjcyOPrSH2xm/Zfb8ZhwyVH5UDuRC7KHFfwFFdTay6V0lN47H+8SwhSHMx6kk2x4d7wPRE/kAjBGc9MV1mC56NR0IMZdWCgHli4LiNwbhHQ9R2Ew0QG/ivCO/+GTKY+63iKF0hE4HmyDuT997n9uhwYOBOAvA7OBAG+AZ0J7HxRRqAjESCSSt/aQiGRhP2jmxXHUMhkkPzqa/MzEg/xekXMQHVwkF2lTjDrqUePhNsZJaW3B5apKWExyKn9ESDy733k/UFrwfAbJPXkiTpPllkUq60sNFJWIe6xeIULcDxfABGCCWE1WEC+eLzQyB23A74qFoDP4ylkC0WMWTUMWTV2Gz7R7LXPycFUUMQNJOtAshLcTm/X1blB+yA+H/ncdBke/QID9s4lzvT1dWTm5oTQlePePchOZ11YcGVG4LwjQB4k6fSSsAw0CcGv6xIUIevA/r4fYWDgV9Df/1NYLN35wJP33+f9m1LaJhXpG8OJEThlBPgGdMqA8+UYgS5HgKzUsvMLh0dZIgOVgUGhJCxbK1sZdTk8DQ2PrP/SL8jF5mBiIrAhONviJHITpjhtZStBKl+HXiOcCZ+4fyQsshtXsO8qZvtmMeYcQ7pYRMIoCBfiuFEQxGC6cNBd+QdeV0XXYlIW/kU4vitMokqScBces1lAisScugMB2oqQRWCZDCRLwXpVsckytawu/PHQx7Cr9rYHp5jPI/v2LfQt/25fifxz3PsYksrqyG0/gdzBtkWABEVise8QCv0CofAvEI+/rKOvEnp77mBg4GcYGPh3XaUszPvvOpZBF1dlC8Auntx2HhrfgNp5drhvjEDnIbDfUu2o3hN5RS5WZBkoOxydN9BT7HH6xQsYwepWOYqn13QNZovAU5yV5l+KyJdgOigsBYkMFGX4NZohNkIqr0QElq0EqZzunYYkqUjm80JwJJkv4LLDWtGdczmdxdtkZRVIEhIhq8ARqwWaLDUfGG7xzBDI5XN4FnwmCMGvNr7Cq9ArFI/Upz7YVYtswd2hu8JVmEhBWoOy1F5K1IVUCunvvxeW7R8m66UpcW/lxAgwAs1BIJsNIhz+JbZDvxAWgoZRu0CRzTYuLAMH+n8Gr/dTyHLnPkjm/Xdz1lOnt8IEYKfPYIf2n29AHTpx3G1GoE0RIGs1PRBAfnu7onhIpW6TpQVZBRIZqLjdbTqys+sWKU+SIAuJhRTSlUkY6h0TgWc3R628MomNfKhA/D76/sRiI6qsivhtZLFVJgbpuNfae2g4j6JJ7OjGkcMk6o8ERMZtFng1tppq5Zo4q7YjmQhIVVhYCG58jY3kRl1dIdVrUhUuE4JDzqG6zm92ZYpvm3n9+oASe/kaJHRlmSaSnEntZuPO7TEChICwDow/M60DQ2QdeNjToRpSiuIUMQMHB38NA/0/hap21m9H3n/zd4AQYAKQ18GZIMA3oDOBnS/KCHQ9AuRSZWyHYAT8MEIhoHB8lAuyCqS4S5wqI1ArESi7nLBMTEAdGhKxrDh1HwIZI4O5nbldF2IiCElsJJOvThDXisKoc1RYagmLQa/pQjziHEVQz2M9k0PoGCKQrkNWgZM2iyAEFSZQaoW+o+qRxepKfEVYBhIZ+HDrYd1xLckKleIGUgzB+8P3QQrYp5Go77nFJeSWlg5dTlIVWK9ehebznUZX+BqMACNQQoCsA0Ohf8b29n9BKPyvKBTSNWEjSRb09X2BwYFfw8Dgr8Jqaf84pLz/rmlqu74SE4BdP8XtOUC+AbXnvHCv5vM7iwAAIABJREFUGIFuQkAoCYfDIOvAfDhc0dqCxmudvgTLhQvdNPSWjGWXCFxaQiGTrXoNSdMEptroaEv6wY22FwJGwcBybFmQgmWLQSoj2T317kZ7TC7EZB1IZOBU7xV4XFPQrBdQkLQjm9QkCZN2C6YdtkYvzed1CAIkdvP99ve7hOCL7Rd1Wamqkopbg7d2CcGbAzdBVqrNTsVcDplXr2CEdw41TeEoSOCKxT+ajTq3xwjUh0A+n8VO5Gtsb/+TIASz2c0aG5DQ2/uxsAz0Df4a7PbJGs873Wq8/z5dvNv1akwAtuvMdHm/+AbU5RPMw2ME2gwBIq/ykYggAymuHW3Gysn5+WccD7CO+aqFCLRdvwZteLiOVrlqNyFAlk7+lP8AIUixBdcSayceJsVyG3dPYsQ9g37XNEZ7ZjDacxk91v4DbpPkEnzd1f5CECcGhBs4gEAsF8OjzUdCWZisBFfjq3Uh5Nbc+GT4E3w++rmIH0iK1yd1x80nEsg8f17xwQmp1duuzrLoR12zxJUZgdYjQP/HEok32A79V2xv/1fEYs+AGmORulzX4Bv8dQwN/SYcjqnWd7bGK/D+u0agurwaE4BdPsHtOjy+AbXrzHC/GIHuR4B+1BViMUEGUiB2+61bFQctiK5AwHRpZXfCQxgJfDY3kVtdRSG15zIjWSxwfvkFuwF3/1ep7hHGc3FBCr7deStKIgVJ/ZWsuE6aHFpviQycFuTgfzt2Czf7Z2BVOjdg+0kx4fMhVK+JDCR34W82vwGtwXrSsHNYuApTDEEqB+z1uflRKIrMy5coGvmDl5UA68yMCJvAiRFgBNofAXIV3t7+RwSDf4/wztcoFmv7v+VyXYXP9xsY8v3GmZOBvP9u/3V2Gj1kAvA0UOZrHEKAb0C8KBgBRqDdESDxi+zCeyhuF6yXL0PxeNq9y2fSPyJU86EQ9LU14d5mmboI61TlJ97Gzo6wxCT3YNnKxMyZTFibXZTIv8XookkMEim480aUzXAhViQFU71TuOy9jFnvrHAnvuiZQaTQg3G7lUVD2mwttLo7+UJeKAqXCcHvgt+BXNjrSSRgUyYE7w/dh8viqnp6bm0N2bm5Q0ZD9JDEduM6VK+3nktzXUaAEWgTBAwjLtyEg9v/IIRE8vnDat6VunrWZCDvv9tkAZ1xN5gAPOMJOK+X5xvQeZ15Hjcj0BkIFLJZpL755oCiMLlqWWemIds4rli1WcwnkpAtGmiDWymlnz8XIi2QALW/XxCBSv9B183OWCHcy1YiUHYhJoGRsqUgHVOswWKNLlhH9c+h9WDEfQkTvTO42X8V9wev4pr3Mhyao5XD4rbbDIGUnsJj/+NdQpCsUetJRDBTzMAyIXh78DYsyt69L/t+8ZDgB4lO2W7e5Acg9QDNdRmBNkZAxA3c+UpYBga3/xG6Hq6pt3tk4H8Hh+N04lDz/rumqen6SkwAdv0Ut+cA+QbUnvPCvWIEGAETAQrWrm/5D8EhKTK0yUkhGsJKt/WtlkImg+TXXx+2hrFaBBGojYwwuVofpOeuNhE2c5E5QQqWyUFyJ04btak2HgWYBAmjrnFc7bsiLAUpk+XguGsciqycO6zP44CDqaBwE6ZMLsPBdLAuGGyKDXd9d/HpyKeCFLzWdw25129g+AOiHXXIB9u1a/y/oy5UuTIj0DkIFIt5RCLfIhD4zwgE/zNyue2aOt/TcxfDQ/89fEO/2VI1Yd5/1zQdXV+JCcCun+L2HCDfgNpzXrhXjAAjYCJA6sHZuXkUkpXdOmS7TcRvUgcHGbIaEahkDXPgVLYKrBFJrrYfgUKxgPX4uogrWCYFqWyG4Ahdx67aMd07jSt9V3DZc1mQgkQOem3svtnNK5GsUN9H3+8Sgo+2HiGp1+bmV8ZFCIr47uFXoqO4MfMlZm79mOPJdvOi4bExAvsQaIwMlNHX9wMMD/0PGBz891BVd1Mx5f13U+Hs2MaYAOzYqevsjvMNqLPnj3vPCJwHBGgDqK+vI7e4iKJeOU6U2ucV8QFlp/M8QHKiMZISJuFp+P2HA+J/0LJEVoHDwyIztieC/dyenMglhMDIfkvBuZ05pIxUUzAhMYgyIUikIB1f8lwShCGn7kOAYgW+DL3ENxvf4MHWA3wX+K5m8RqpWERRktBv68enw5/ik5FPRDnpnmRCsPuWCo+IETiEQCNkoCxbMTDwM2EZ2N//E9Drkybef58Uwe44nwnA7pjHjhsF34A6bsq4w4zAuUWgmMshu7gEfWP9kPuqAEWCUHK0XLwISVXPLU61DrxoGEJdWd/YQD52vCKn0uOGSmTg6Ci7ztUKMteriABZC24kNgQp+Dz0Ft+H3mIxModgcg1FFE6MGrkRT7gnQEIRRArOeGdwxXMFkz2TUGW+N5wY4DZqgNzOn/qfCgvBl2/+Bd8n55GqMzysz+ETRKAgBYc/wbh7vI1GyF1hBBiBViBQJgP9gb8TrsK1xAxU1R4M+X4TIyM/B7kLS5LUUNd4/90QbF13EhOAXTelnTEgvgF1xjxxLxkBRmAPAbJgy757h3wkWhEWEr4gkRB1aKjhH2fnDe98PC6IwOOsAmWHHc7PPz9v8PB4TwEBvVDEciqOb7ffYX7nHTbjC9iMLYgykYs0pQearOFi70VBDFKe9kwLi8Ex1xjHF2wKwmfXSDm0QUrS8WI8j29Cj/Fw66FQtq43jTpHBRFIMQSJFBx2DtfbBNdnBBiBDkKgUNAR3vk3+Lf+H6EonM8fb6HucExhePg/YGT4P8BmG61rtLz/rguurq3MBGDXTm17D4xvQO09P9w7RoARqI6A7vcjOz+PYjZXsZL97h2oXo4PVs8aOs4q0DJ1EdapqXqa5LqMQN0IxI081jI5bGV16MUi4tkwNmIL2IovIJR4j3ByEQuRBeQKlb/79V7QqlhxqffSLilYJgdHXaOQJbne5rj+KSJA96wMCXwE94RCFLcL9o8/hqQoCKQCeLD5wMxbD7CV3Kq7d0QQEyF4f+g+7g/fF4QxJ0aAEehOBIj8C27/F/j9/y9CoV+gWKwcemZv9BK83s8xMvxz+Hy/DkU5XsWe99/duXbqHRUTgPUixvWbggDfgJoCIzfCCDACZ4QAbf5yy8vIra4CheJuLygmoP3OnTPqVXdcliwtja0tocJM7teUnF98DtleObZa+sULyA4HVJ8Piru5AbO7A1EeRb0IFIpFhHQDm1kdgZwBen3DZceYzQKKBbcSXxFuxPM786C4gq/Cc/ALN+K9e0G919xfn9Rkp3qnhKWgyL1myRaDJ0G1eecWUimkX3xfUSTKdnVWhCvYnyie7Gp8VVgGPtx8KMpQJlR3h8hCkIhAIgSJGKT10KgrYN0X5xMYAUbg1BDQ9R34A/9ZkIGRyMNjr0vkn2/w1zE88nN4PZ9BqvIAifffx0J5LiowAXguprn9Bsk3oPabE+4RI8AI1I8AbQSzc3MwQmERC9D56acsWlE/jBXPoE1zPhxGPharav1HKs3JB3s/jslVmIhA1TcExcXCLE2ainPdjFEoIpDTMWjRoMmH4y7ROv3lTgKxXBKBxLJwHQ4mFrGdWMQ6WQ6mt5uGn0W2CGKQxEbIcrCcKcagRbE07TrcUHUEjFAImVevDgtDSRCCUJbx4+P40ZohF2FBCG49xLdb32Inu1M37OQifG/o3m6e6pliQrBuFPkERqC9EUin17G19X9ic+s/IZ1ePrazdvskRkf+o4gXaLUOHajP++9j4TsXFZgAPBfT3H6D5BtQ+80J94gRYAQaR8DY3kYhnRZiIJUSbfiMzU0zPqCiNH4hPvMAAtnFReQWlyqiQurBqm8Qms/HpCyvm5YhsKMbeBRNVm1fN+LIZpYRSS1jI/ZeuBCTOnEk25z4gnRhRVKEgIQgB/cRg/TaZXG1bOznreHc0hIo5t+HSdJU2G7cgNrX1xAkJE5Da+LR1iNhIfit/1vEcrG62+qz9eFj38eCEPx46GPMemc5xmTdKPIJjEB7IkC/I6OxJ9jc/E8IBP4OhnG0iJokKejv/xWMjv5H9PeRirAK3n+359yedq+YADxtxPl6AgG+AfFCYAQYgfOEAMUNzLx8BdlmhWVqSqjasuvWyVdA8psHICvM45LsckIdHBRZcTEhchxe/HntCCykMlhIZWs6QZUkDFpUDGgq5HwMSzGTDCyTgnQczx2vjF3TxUqVhhxDwn2YiMGySzEd91p762nmXNetFO+vDAjdW+wffVQ1REEjwBEhSK7lRASSdSCVjRDGLs2F277bwmWYiMEbAzdAcSc5MQKMQGcjkM9nsL39j8IqMBT6F+AYFXuLxYfRkf8RhcIPceXKF2Lwq6urGK/BYrmzkeLeV0KACUBeF2eCABOAZwI7X5QRYATOAIFioYDUgwcopDO7V6dNo3V6Gmp//xn0qHsuSSrCRiAg8n58jxqhbLdBHRgwyUCPp3vA4JGcGQIJI4+tnA5/VkcyX6ipH+RM3KepuO6yw66Ygh9k4UGx4YgQ3M1R87gRAuiojgzYB3atBcvEIImQeG0sYLQft6Pi/VG4Adu1qy236iZCkNYAWQgSGfjY/xjhTLimdba/EqlR3+i/gbtDdwUheGfwDjw2vgfWDSSfwAi0EQLZbABb/v8bm5v/B5LJuSN7Fgwa+J9+a4UJwDaav7PoChOAZ4E6X5MtAHkNMAKMwLlBgIRCsnPzFcdLBJT10hQTUU1YDfloVBCBeiBQVaF5/2VYsKUJoHMThxCohwyUJQn/rs8NKo9LRPiUScH30fegvBhZRCAdOO7Uuj4nN9Ky8EhZlZjK80gUHRnvb3oalsnJurBtVmUiimn+iQgsE4KkOtxIIoGZO747wmX4ru8uxl3jbJ3eCJB8DiNwxgjQfSEW+w4bG/87/IG/BakKf5iYADzjSWqTyzMB2CYTcd66wRaA523GebyMwPlFgKzUsvPzyO9Uj/mleD1C6IIt0k6+TuhHcCEaFUSgEQjuKgl/2LL1yhVYxscqXrCo65A07eSd4RbONQJEBvpzppJw3MgfwoKERe72OCpiFMoZyBYK6LeosMqmhWClRC7DS9ElQQjtEoPRRaE6S5ZjzUpEDBIR+KErMb3fjeEM8okk0k8eo/jBvJ003l+z5mN/O3TPW0+sC0KQ8pPAEyzHjhcLqNSXflu/IATJOpDch6/3X2e34VZMGrfJCLQQAcNIwB/4O0EGEilYTkwAthD0DmqaCcAOmqxu6ioTgN00mzwWRoARqAUBsibJLiygkKguGMBEYC1I1l5HKAlHIshvb8MUatlzw3b+4EvI1sPxsOic5L/+KySr1XQV7uuD3NvblSRH7UhyzZMikM4XEMzpCOYMhHUDRQA3XHaM2Sqr9z6NpUR9Sm5VEXEDiQz0qEpNFoO5fE6QQAvRBbyPmOIjRBAuxZZgFIyTDmf3fI/Vs0sKCgESzyWQVZnP4Wub70whl0Mxk0Exm0UhmxXHosxmoQ0NQRur/CCAFMZJabycWhHvr2kT8UFDwVQQjwOP8XjrMZ4GnuLdzjsUxaqrL6myiut91wUZeHvwtiAGh5wHlUXra5FrMwKMwGkikEi8xfrG/4atrf8Lm5shdgE+TfDb9FpMALbpxHR7t5gA7PYZ5vExAoxAJQSEGvDWFnKLiyhkqgsHMBHYmvWTTyRgBIMoptOwXb9e8SJEGKaePD3wGVn9KH19ImYjEYKSpTJp05pec6vdhoBRKGJbN9CnKbBUsO4rFIv4p3Ac+eJhwkaRJBE7sF9T0K+pcKr1qYrrBV1YB5ZJQeFWHF3AYnQR9FmzEglQkArxfmVici0ec401XZmW4vQViOBLpw8RfPQ+CtWJLyL/bLNXKg47/ewZjJAZa++04v01C/8P2yFL0WfBZ4IMpPwi+AKZ/N4DkXquO+Icwa3BW7g1cEuU1/qvsZVgPQByXUbgDBDI57N49ux/xb17fyCuziIgZzAJbXJJJgDbZCLOWzeYADxvM87jZQQYgf0IkDCIvrGJ3PLSkfHqtLFR2GZnGbxTRCD7/j1yS0e7zyk9bkEIKl4vFLIOPMJF8xS7zpfqEgTIQvDbaHVL4f3DJPdgIhKJFKRcFhSpFwqyClyLr+0qEhMpSORgs4lBi2zBhd4LuwIkZDVIJOGFnguwqbZD3S7m88JiD7JcVWm3VjXwSpiogwNCxbdSyrx5A31zUwg2nVW8v3rnsdb6el7H6/DrXUKQSMFGhEUEOSqrmPXOmqRgiRiccE+0jQVorZhwPUag2xHg/Xe3z3Bt42MCsDacuFaTEeAbUJMB5eYYAUagIxEwicAN5JaXKxKBthvXhYsap9NDIPXkCfKRaM0XlBRZkIC7hKDbXfO5XJERqIRARDewmM4JV+FKVoBHoeZQZFx12jFgUZsCLhGDZDFYdiEul0QMZvPVrZjrubhqFGHTJUxahnDJOoYL1mFMqIMYUfsxovXDqblgnZiA9fLlis3ut9Sr57pUV3G74Pjkk4qnGTs7Ihao4nLV22zH1Sfr9JX4irAS/C7wHb4Lfof5nfmG3IZp8OQa/tHAR/ho8CPc7L+JmwM3WWG641YFd7jbEOD9d7fNaGPjYQKwMdz4rBMiwDegEwLIpzMCjEBXIVCJCJQdDjg++5StKE55pssiIhSzkfJRMRs/7Bq5BlNswW4URTjlaeDLgTxXi4gYeWznDJET+cNCIpWA+tzjQk+drsH1Ap4v5LGR2BDuw2VSULgVRxeQNtIVm3NkirDnAKsO2EqlVS9COUarxKE6YPeNQr5xBWRZRnncPS5KijWYezcHfX3j6CFIEDE/JZtNxPcUx1Q6HMK1n9NhBBK5BJ5vPxek4LPAMzwPPkdcjzcMFbl/ExFIhOCNgRu40X8DDq2yCE7DF+ETGQFGoCoCvP/mxUEIMAHI6+BMEOAb0JnAzhdlBBiBNkdgPxFonZmpav0nhC0SSWgjw5CU+mKAtTkEbdc9EgvIl8jAfDiMYr46W6EO+WC/caPiGEgNmuaKCAdOjEAjCJAqMKkDh3QSEskLleAPkyZJ+GmfuyIJTZaFz+NpeDUFXk2FV1XqjiF4VL/JXbdg6AjoOwdUiYkYJAES38IO3On6hSjomimrhNeT0uHxyhpuZQYxm/agr3dY5H7PCHzecfg8E+jrHRKuw0TOMzHfyKrbO4dUpWkuiRB8sf1ClET+NiIuIjahkEBxIYkIJEKQFIfJlbiSK/jJes5nMwKMACHA+29eB0wA8ho4MwT4BnRm0POFGQFGoAMQICIQklR1w5r+7jsYYdM9TRsfg2VsjIUpTmFeaV7y0SjyOzsgMjAfO2gNY7s6C210tGJPSFiEiFsiIhSPZy+7nKfQc75ENyKQNPK7ZCC5CxvFInwWDXd6KpPMS+ks3iUPCj8QYeghMlBT0KsqwnKQhEaOJPpyOQjhDcrJ5N5xOoOjSHD/swfYWHiGraQf/pQf/uQWtlJ+7GRMoY2jUkYDXl6UK1chsZQqfbYpNgw7hzHqGjWzcxQjrhGQkAXlQccgNFk77vL8eRUEyErwZejlLiFIVoKNxhKkSyiSIpSkiRQkQpBJQV56jEDzEOD9d/Ow7OSW2AKwk2evg/vON6AOnjzuOiPACJwpAqRkm3r46EAfKA4dEU/axARk2+FA+mfa4S6+eFHXQXHC8jsR5HfCsN+5UxF/Ig4Tv/xlRTVSEWPM02vGEezthex2s6hIF6+ZVg2NXNfj+QKIunNXcf99EksKV+KjUvl8IgN7UYA7m4WV1HWTCZPsS6ZA675aOiqmXm5lBdn5hUOnUizBYCaIDT2EdWMbK7ofi7lNLGY2kFByyGpAQT6alGwUV7JCIxKQSEIiBIcdw4IgpNeUhxxD6LP1QZaqkI+NXrhLz6N1uJHcECrD5D78cvslXoVeNaw4vJ8UvN53XSgOX+u7htm+WTg1fnjSpcuIh9UiBHj/3SJgO6xZJgA7bMK6pbt8A+qWmeRxMAKMwGkjkH75EoY/UPmyEqAODEAbH4fq9Z521/h6VRAgyz+yAKwpyRIUt9skA3t6zNJqrelUrsQIVEOAiJl/CseFleBxSV1ZgeLfhJTNiaqqLMGjKpiwHb8O6WGE88c/rmi9bGxvCwJQdjqEW65wzXU4BGkuYvN9YMVXjjO4FFvCWmJNiJGQUjGV64n1qrEGjxtfvZ+Tyi0RgZQp5qA4du69JqKw397PloRVgCUhGXIVLlsKEik4tzMHo3g0GX3cPE26J3G176ogBamkPGAfOO40/pwR6GoEcvmcuD+KHF8X9046Firvywt48j8/EeNfXV3F+Ph4V2PBg6uyVSjSLwJOjMApI8AE4CkDzpdjBBiBrkGALM705WXhAnxUolhzRARqw0OQ1OYognYNiKc8ECMYRObdu4pKz7V0xXbzBjSfr5aqXIcRqIpAglyGkylEI1FEMzmkq4hfqCvLIBJwfyI34Sl7ZQKQYhGqkrTrOkxCOK0mrWn7EsqEdglB2tzSRpeESTaTm/An/ScmmOpZSmRJSJaCRBCSReGgfXD32Gc336PPvFYvFJnjtmaMDN7uvMX329+LTFaCpCzdaDzB8lwRAVgmA694r4iYgpM9kyASlxMj0A0IpPTU7n2O7nW7ObEp7oHBVLDq90gP63j7v7xlArAbFsIJxsAWgCcAj09tHAEmABvHjs9kBBgBRoAQIFfgHBGBgQBwxKM8SVWgDg3DMj4G2ckuU2e5eihmmhBwKeVCJltTd5xffC6spT5MwgV5exuyu0dYVbHIQU1wnotKRJAVab0lEihQjseFcFAxZ1r1SVYL5M+/AAmDkNJwRM/vqgzL20FY3rw5gNOYzSLiC+4mWYJsdwhRm5d5IGq1wu5ywe1yosdmFbEE3YoirAfPIpH1YDAdFBtlckkVZYkcpJJiEFZTK25lf8mVmIhCIgjJapAIq/IxlfSaMn1G6sfn6TtNxMab8BtBBpbzYmwRJD5ykmRVrEJspEwIUknZY/OcpFk+lxFoOgIUDiGQDGArtSXuUYFUAFsUKzW5JYg+unfFcrGGr8sEYMPQddWJTAB21XR2zmCYAOycueKeMgKMQHsjUPj/2bsTMLmqOuH/v9q6qrqrl+wLARIIZGFJCBiEqOCOOs7IjPMODA6joOKD8r6+juMrogOC4jgz6qi4IYrvwzv6MDOK6+iA/lljQgTCFiEJhISQfeut9u3//E51daq7b3V1Vddy763v9amnqrvuPfecz7k5cn59lnhcUrt3S2b/fslnspNmNrBwgYSWL7d3gdood1p3Jhg4OGg2F8kNRyeUXjcNibxmnaVK5uhRiT/5VCGg4/eJN9Itvp5uExDUtdg8OsWywoYObcTt2qKOBvuGhgqBvpH3yXasVgx9rvT5Kh6ZXF4GNBg4OCRDj26UWCYnGY9H8uGwrJw7S3r7ek3AT/+QUHy29N6/OzokuTITijp9Xon4CpuLdPu9JigY8rV+PT3N90BywHS09w3vK7xH98n+Yd2YpPBZR9Jk85O3qY18qHQDEw0WajBwVmiWzAzPNO/6s/l9aJbMCM0wr75gnytHuWlQUEcKFgOCGiDUnYinO31Y601HZJ424zQ5ve90WTpjqSztWyqn9J7CLsSNfKjbNG39g8Sx5DHTpugfJg7HD5vPGuAzGyKZTZEOmHMaeRAAbKSuc9ImAOicunJVTgkAuqo6KQwCCNhAIJ/JSHr/fkm/ssfsyml1BJeeKh0nnWSD3JIFKwEd0afBm2z/gOQGB0xg0DdjhoTPOssSLLVzpyR3vFQWsxAUjJiXriuoG4xoAMfjbX0Ahidg+gK6HEDqpZ2SGx6qGPy3ulv4nNWWa4VqcCxz8JAJ9MWDHTKYzcv8YEC8FsFknVb8+/7hqgqjOw9rQDDi98nCYKDspiVVJdqAk7XTrtOMtWM+2kkf6agXR+fod6lcYVRlKw+dgtwT7DFTjDU4WAwMFn/W0W4aJCx96SYaTvwDgY6SeqH/BXn+yPNmxKC+NEhYjxGdOkJT1xbUYKAGB/Vdg4P6O6YRt/IJt9+9tZ3U0XjaRhyNHy28J47KkfgRE+DTlwb49F1/3+w/Jui/9UWRRXJC9wlyQuQEWdS9SAKDAbn0VZcaTNYAtN8z1awcEQBslvS4++RyOfnRj34kv/nNb+Spp56SHTt2yMknnyzLly+X97///fK2t72tYs4OHjwot912m9xzzz2ya9cu8fv9cuqpp8pll10m11xzjUQikYppPPLII/KNb3xDNmzYIPv375c5c+bImjVr5EMf+tCU8lDxBmVOIABYqxzXIYAAApUFzDqBr7xipocWpwebxfkvvFB011mrQ3eqJTBU2baZZ5hlmtPpMaO0Su8ff/ppyRw+Ul2WdJORri6zwUho2bLqruXspgtoYL/cGp767zy++cmq86RtgQaDOxYvFv/MmVVfX3rBwWRanhyy/oPDVBJe1d0p84LWbVI0m5VOr9fWQSr9N9qf7DejeoojevSzdvz1ZX4XP2iCAs0OAFTy14BWMSDYG+w1n/W9p6Nn9F2Diubnjl4TYNTP3R3dttsVWYO1ujlMaUBw27Ftpg7qcQS8ATm552QzlfjU3lPllL5TzLv+LuCzfn7rcV/SaJ6Ablajo4L13/OxxDHzWUfkFX/W9/HBPr2mVYcG+nXn8oWRhWbH8oVdC80O5iboFzlBIh0T4wD0v1tVW/a6LwHAFtTH0aNH5V3vepc8/PDD5u7617e+vj45duz4sN93vOMd8sMf/lB6enosc/joo4/KpZdeKvv27TPfa7Avk8lIIpEwP69YsUJ+8YtfmIBgueOzn/2s6Ku4D8zMmTNNHoo/axDxm9/8pngbMFKABqgFDx63RACBthPIJRKS3rtX0nv2in/O7LLTf3WkWfypp8Q/b54EFi4U3xT+gNR2mDYscGLrVskeOSJTXUuwtAg6uqvr/LWWpdIRpBoQZrRgcytd//srF42Njv7MDgxKLhqVsmsQfVaZAAAgAElEQVRAZjIy/FDhvyXLHbrWnxn9WRwJGqn/1PBsPi9DmayZPqzvg5nc6HqClQTX9UWkyz9xUwzdWOTBo0OiKwh2+XS0oFe69I8Y+tnnFZ1abDUisdL9WvV9cQpgceqfvmswQQOExdFC5rv4obqMZGtkOXW0oQYXNBgYCURMQLD4Mr/riEh3oPA7/awjDfU8fZnPHREJ+8NNCSJqIEd3HNYRghoQ3Hp0q9mRuF6jNn0en9lkpBgU1CnES3qXyOKexdIZ6GxkNZC2hYAG5KLpqAylhmQ4PWzeB5ODMpAaMO86Ys+8xv1Og3vTWVuv3pWh638Wdxov7jpeDPBp0E9fIX+o6tvS/66azJUXEABsQbX+yZ/8ifzqV7+SWbNmyXe+8x1585vfbAJ9Q0NDZjTf9ddfL3v37pW//uu/ln/7t3+bkEMN+q1evVp0BOBb3/pW+Zd/+Rc588wzTeBu/fr1ct1118mTTz4pK1eulE2bNkmXxaLv3//+9+Xqq68Wn88nN998s3z4wx+W3t5eicVicuedd8rHP/5xE0y86aab5MYbb6y7Eg1Q3UlJEAEEECgroMEc0ZFEJet9lZ6sgSQNEhYPXT/Ov2ChBObNLTtiEG77CORSKcnpOoKDQ4XgUclmD+VyqbtDh1autPx69HnQjR5G1nwzowb1pVOI9cXagtN+APTfpVn7cWBgdC1Iq3U8Q2eslMC8eZb3i258dHTKvwn29ej6j4Xp3vpe7t/8tDNfIQFdE3A4m5PBkaCgBgaHsjnRYGHx0ODem2b1WD5LR1IZeXxw4pqYpbfVIGBxjcFCcLAQIAy0aOORepnquncaHNRRg8X34tTC0mmG+lkDHE48NIiowcBicFDfw4Gw2fhEP+u7BtBG30c+a+DQ6qXBEP39VKbpapBo1+AuExDU4KBOJ9aX7iQ93V2IS+tCAzfFYKC+F1/6e9rPgpRu8KI7Quv07eJLA3ixTEzi6bhEM1HRfw/F3+ln/W44NWwCfPo+lB4a/bke08Ab+e9Jp5jr2p1m85+R3cI10De/c74UA336bjV6rx75ov9dD0Xnp0EAsMl1+LOf/cyM/guFQvL000/LaaedNiEHGrxbu3atpNNp+e///m95y1veMuYcHZl3++23y+te9zr57W9/K4Fx07l0FN95551nphXfeuutJqBYesTjcVm4cKH09/ebAOQHP/jBCXn4+c9/Ln/2Z39mAoQ7d+6URYsW1VWKBqiunCSGAAII1CyQz2Ylun699RpiXo/4Z80W/7y54p89mynCNSs3/8JcMnl8Q4jiTrDxwiwBPYKnLZWOE0+0zFjs8cdFR5+VPYo7wI4EBE1g0HwO84xMUtVmjceRDV+Km79IbpItvEfSCiw6QUKnn26ZcvrgQRNM0Cnd3mCw+Q9aFXfUP1THcjkZzhQCg5l8XlZEJu5urUnuiidla/T481rFbcTv8cj5fV0mGOj2I51LS3+i36wxptMVdcSb+ZwofNbf6c/FqY060qmV0xYbXR86VVeDgbqBiu7+q5/1ffTlD475Ts/v8HWYlwYldRSYempw9XDssFn7UQNM9Tw0UKkjBHXkoE4hLr70dzoFu5GHBtx0NKpuoqLPQfGzvusUdX1P59OSzqbN9/p86SuVTZX9rGsyJjNJMe8lL70mkU2Ya0uDfPq74s96vhsOHd1a3LBH34u7eevO3hroK+72rd/5vK1rl+h/u+Fpm34ZCABO37CqFDQw9/jjj5sRdv/8z/9c9tp3vvOd8stf/lI+85nPmBF6xSMajcqCBQvMaEFNR9frszruvvtusxbg0qVLZdu2bWP+0nTXXXfJlVdeKatWrZLNmzeX/SuUrkOoaxTecsst8ulPf7qqclY6mQaokhDfI4AAAs0RyBw5IvGnnq54M91Qwj9njpkmrBtTMIKhIpntTjABKA0GDg2Jb+Ys8UW6JuRRgzTRhx+uaVMJna8ZWHiChJZZB6tsB9KkDKlpXIOqQ0Oja3JWc2tfb490nntuNZc4/tzno3F5OV775hpvnNUjPotRqgPpjGyLJc3IwbC3MIIw7PVIpwtGDk6l0k0QNhMbXeNMA4Klr+I0SQ0YFqdLFj+7OXA4FbtmnKNBSJ1WrEGi4rvf4zfTpUv/P7e4XJPmqThqUQN4GuAzSwnou+RNQC8nY3/XjHI4/R46hV2DscUdtkvfx+/Erbtza4DZCQf9byfUUuPzSACw8cajd9CNP3StPh2Bp1N1L7zwwrJ3/7u/+zv58pe/bEbh/fSnPx09T6fnXnXVVWZtvxdeeKHs9XoPXdNPp/E+8MADctFFF42eq58feuihioG9H/zgB/K+971PlixZIi+++GJdO3s0QE188LgVAgggUEEgF49Let8+88onK3e6dVqhf+4cMy3R19vYEQtUXnMFdDpx/LHHalpXUHPasfhkCZ5yimWmE88/bwKLOlLQGy68dDqxt8zU9OaWvLF3iz32mJmiPZXDGwqKt6dXNPCnU3p1Om87btCT0tGCWR0xmJVoNicx/ZzNia4PONkR8nrldTO7LU95JZGSPw7HLb/TkYMmIKhTi72FKcVzOgKOn1I8lWeu0jkmiJ2Jm6CgBgSL66uZNdZSg4WpmKkhM1rOvI+8dOqmnqvvdp+eWcmA750loMFUXYdS16XUDWxKN7IZv9GNflfcOVs/u3VjF/rfznqGG5VbAoCNkrVIV6fkFjfl0HX85s+fX/buf/EXfyE/+clPzG6+3/72t0fPu+KKK8zmILpL77e+9a1Jc6/rA957771yww03yOc+9zlzbiqVMtOP9f/IN27cKOeff37ZNHQdwhNOOMF8v337djOasF4HDVC9JEkHAQQQqJ+A/n9D9uhRSe/dJ5kjh3WBnoqJh88604wM5HCXgI4W1A0odD1Bfc/F9D0m+dTkAeLQyhUSKPPfN8M61dwiwKy70npMQLBTvOHQ8cCgBgiDwbr+AbKetaRr+Onai7obb/ZYv/h6uiVY5r+Vktu3S2r3KxNv7xGz6Y6vr0+8vRr067X9VN56GtaSViaXF90hWIOC+tKgoP6sAUI9Zgb8cl7vxNGt+t22aEJ2xqc+7TDo9cr5vV0S8nlrySrXlAgUN2goDQoW13Irru1m1nsbWfdNA4Zm7bdxvyudTlrPNfuoLHsIFNd9LK4FqVOmzeeSNSH1Zx2lp2vlmQ1mRt414Ge+6+g260cyU2FsndL/tscz3upcEABsYg3otF1d90+PdevWlb2zjt5bvHix2eTju9/9rrz//e8fPff1r3+9GdF32223mY07Jjs+9rGPyVe+8hUzik83/dBj165dJm09hoeHLTcIKU2zuDPwgw8+aNYcrNdBA1QvSdJBAAEEGiOgAaDMoUOSPnDABDesDp0W3LVunXgs1tpK798vmf37xaNBndIRX6GQ5fmNKQWp1ltAA4C6S3AhKFh4z0ajo4G9zvPONaPWxh/5KexYa5lXXW8wFDYjC8sFFutdxsnS0zJnjh41gXJdx690047JdlbWf0vxZ54V8XoKG3X09Zlgn748fn8zi+Dae+nGI/GcTncUiVjsLKwF3zwYk0OpdFUGC4IBOaubHV2rQmvCyfoHK93NVzeLMBtIZEfe0/Exa9FpwLB0bbrSn3V9Ok3DrHGXTY9+1t+Zn0e+13XwNIBZXBev+Fmn3brx0NFzui5icY1E3VxFPxd/Ln7Wqa96nllj0V9Ya7HDO/ZnsxajL2TWY9RgXvG9uJnL6O9HztHpzhyNEaD/3RhXp6VKANCixjQwlkxO/a+DlSq9s7NTwmHrRY6trv3f//t/y7/+67+anYGfe+45s2FH8Vi2bJlZ009HAV5++eWT3vrzn/+8WbtPRwLqWn56bNiwwUw91o1DdDRgpUM3KdGpxj/60Y/MmoJTPbSBmezQEZC60Ykeu3fvrvsmI1PNJ+chgAACCFQW0A0lMgcPSkaDgSXTGAML5ktoxQrLBBJbt0l6zx7L73S3UjP9MxgsTAXVoGBxSqiNR3xVlmrfMzTAp8ExDYJZBYR1/bvYHx6rGSi0fJkESv57qDSh+JYt4vEHjo8eLD5TdQis6SY52WPHJHPkqGSPHZVczHr6aDE/Xa9ZZzmlWX3UwAT8vHRwa34Qpnmh7i48NDJaUEcMxvVVYUqx3vKCvoh0lwkqTjNLXO5gAV1rrzQwWFyHT39X3HDDbK5RfI1stmHW6RtZm09HMWqAUTcd2RvdKweiB2R/dL/sj+2XQ7FDZhOXRo901GmvCyILZGHXQvO+KLJIFnQtkPld881LN69gNJ2DH9SRrBMAdH4d1qMEBAAtFD/wgQ/IHXfcUQ9fk8ZNN90kN95445TS0/vqtF9dL1Cn/urn0qO7u9uM3Pv1r38tl1xyyaRp6hTha6+9Vs4666zRkYc//vGP5d3vfrfMmzdP9u/fXzFPOkV406ZN8qUvfUl0ROFUj2r+T4IA4FRVOQ8BBBBovYAGeXRUoAYDg6efLv6ZMy0zFX/ySckcPVZ9hj1i0gyvWlX9tVxhWwFdWzB76FBh1GA8bgJpuUR8StPMtVDhc1aLf8aMCeXTAN3wgw9ZltsTKAQFPaFwYRSqBppDITPFVt+tApWakE57zh49Ykb5Zfr7p5xHvTZ0xkqzNiaHcwSKIweLAUHdqXhPIi1ZHUo4cszu8MuaHutpxc4pKTl1ooCORNTA4O6h3fLy4Mvmvfh6ZegVM2qx0YeO6tNAoAYF53XNGxMcnNc5z/xOp+RW0/9rdJ5Jf6IAAUCeChUgAGjxHLQiAKij8f7+7/9evva1r5kc/dVf/ZUZdTe+IS0GAHVEn47sm+yYLACo6w/qKLxKBwHASkJ8jwACCCBgJRDduLHiaKlycv7ZsyR89tmWXye2bpXMocOiGyV4giExowk1mKOjCYuBnY4ORlk54LHUUTD5ZNIEBPMaFCwGBuMxyScSY6bXdq270HJtPA3WxTZtqqm0upmNjiz0z549er1uhJN47vmq0vNGukxwUnfH1qm9TOmtis+WJ78YS8iLseOzgTwi8poZ3WaDEA4E7CKgowwPxg6KBgJfGX5F9gzvMZ+L74fih5qWVZ3Sa4KBIwFBfZ/bOdf8PLdrrswNzzUjCXWHY47WCBAAbI273e5KANCiRpo9BVg32NCA3+bNm01uPvnJT8qtt95q+VeUaqYA68Yfn/nMZ6Y1BVg3/tAdgJkCbLd/uuQHAQQQsLdA+sBBs3FEaWBH1xWcyhE44QQJLTvd8tT4U0+Z6ZiVDg3uFIKEhZfuNKu7qfpnzap0Kd/bREBHDeZHRgwGFiywzNXo2no15nn8yEKd7h5d/3vJZzOS1EBgICCegL8wrVdHFPoD4unqEv+8eRKYN1f8c+e2xS7GNfI69jLdaOSR/iFJ5fKyMBiQUztDBP8cW5vtm3Fd73Dv8F4THNTA4L7oPvOzeUX3munFzTx0fb/Zodkyp3OOCQ7qa0648Fl/p59nh2ebHXlZC7D+NUMAsP6mTkyRAGCLa624o69uEDJ79mz53ve+J3/6p39aNldsAtLiCuP2CCCAAAI1C5h14nRkl470SiQKI7/Me0LyibjkR3bxDC49VTpOOsnyPtFHN5mNJ2o5dLdi3bXY6ki9skfy8Zho4NC8NHBY/KxBII+OAeKwm4COAMwcOjj6HOV09KDFTsOabw3u5YaGJDswYDYUMZt2XHiBGUFaesQee0zSBw9Jctu2wq89HvF2doqvu9sEkc304ZHnwexgbJ4VDTR3FEahBoNmZ+zx6drNjvxMLqCbhYS93rIbiuCHgNMFdPOUMUHBkeCgrj+o6xDq6MJWbHTi9/hlZnimCQiaoGDn7NHgYDFIOCs8S/Slm4xwTE2AAODUnNx+FgHAFtVwOp02u/jqLr96vP3tbzc79erafJMdV1xxhdkA5EMf+pDoFN/Jjre85S1y3333yQ033CA6GlAP3dxENyTRaTcbN24UneJb7tizZ8/o5hw6SlFHA9broAGqlyTpIIAAAu4RMCO+4vHCiL1xQZliKYcffHA0UFhtyQOLTpDQ6WVGFk62ZqFHTDBQR4GNBgVLgoMmOGSx8221+eP8+gjkdSfYkSBz5vARSe/bK7ordW5gUPLplJlarAG6wMIFErn44gnB3dTOnZJ4/nkTBDRBv0ik6p2rO89dYzb8GH/oLsqJ7duPP0slgebiKEOCzfV5DkgFAQSmJ6CbmRyOHy5sShLdb4KFpe8HYgeaPopwfIl07UENBOr0Yh09aAKDoVnms/5OA4n6rr/Tacrt3L7S/57evwe3XE0AsAU1mUgkzEYcv/rVr8xuvF/84hflox/96JQapDvvvFOuuuoqOfXUU83uvOWOeDwuM2fOFL3X/fffLxdffPHoqRdddJE89NBDcsstt5hdgssdP/jBD+R973ufLF68WHbs2DGl/E2VkwZoqlKchwACCCBQKqAjvvLJkVGDqWRh5FcyadaSM6+RUYRWasFTlkjH4sWWoNMaWThvroTPOMMy3dTLL5uRjrohxehLg4elP/tYE6meT3l2eLiwa/XBwqYjE4Jwuax4fH4JrzrbchMbDSBm9u+XxPNba86W1chCTUzzFtv0h/LparA5oIHmwPFpxyWBZ9+sWUw5rrlWuBABBOotkMqmzEhBDQaa9+gB89m8dEfj2H45Ej/SkpGE48sa8oXM9GITGCx56e/6gn3mu9L37o5uV01Fpv9d76ffmekRAGxyvenIu0svvVR+9rOfSVdXl/znf/5nxd18S7MYjUZlwYIFolOGH3/8cVmzZo1lCe6++2657LLLzKi9bdu2jQne3XXXXXLllVfKqlWrzLqD5f4S8ra3vU10s5FKgcJaCGmAalHjGgQQQACBSgK6zqAJCOpUY53yWRIcDCxaNGbDh9K0hh9+WPLpTKXkLb/vOHGRBE87zfK72BNPSLZ/YPJ0vZ7jAUFdY06Dgx0BM4JMp6paHfrfE+08kmG8yfGgn649GZ9SPXadv9ZMA7b0TaUKOxaPPEujz5EJOKdER/JNdkQuvshyI5rMkSMSf+rpKeXP6qTOV51nRiWOPzR/yeeeK6xTWBJgLqxhqC8NOvsLn71sZFFzBXAhAghULZDNZeVY8pgJEB6KHZKD8ZF3/Tl+yPxOA4bHEsckL8d33676RnW+QNch1IBg8dUb7DWf9b34Mj93jP055B+7rESds1VzcvS/a6Zz1YUEAJtcnTrl94Mf/KD5j3YNAr7zne+sOgfXXHON3H777fLa175Wfve735lRhKXHsWPH5LzzzjOj9nQzkeuvv37M9zo6cOHChdLf32+mEet04vGH5u1d73qX+Hw+2blz5+hU4KozW+YCGqB6SZIOAggggMB0BTSYltqxwwR1TNBQ31PpikGe4n0nHVm48VHLUWhTyXNgwXwJrVhheWr86acle+yYiN8vHhM0HAnumJ/9hd9rsEffdYRh6c/Fc6aSCRufo2tB6tRe3QhkqkE/ncKtu0zrzr+6a6+xqeEw04xHgsuFgGBhBKoG4iSXk/BZZ1mmWssuw6UJld0NeXBQYo89PqWSePy+wnMxEhzUIGFwyRKzzuH4w+zUnEoROCyB0Q1ChrJZmRHwT8mbkxBAYGoC6VxajsaPmmnH+jLBwfghORwrfC7+TkcU6rl2PXRdwp6OHvPSQKH5HCz8PPo+8p2OMtTf67u+dJRio/64R//brk9Mc/NFALC53rJkyRITUHvPe94jOhKvlmPfvn2yevVqOXjwoLz5zW+WL3/5y3LmmWeadf3Wr18vH/nIR+Spp56SlStXyqZNm8xIw/GHrjd49dVXmwDfZz/7Wbnuuuukp6dHYrGY2YjkE5/4hJk+fNNNN8mNN95YSzYnvYYGqO6kJIgAAgggUGeBYvDDBAZHgoP6bgKE6UKAUAOFHYtPlkCZNXyHH37EnFvLMe2RhWVuOvlmKK+YTVaOBw41iOg7HkT0FYJHJqCon2sMoNXiMf6azOHDEn/6mYpJ6W7QWmazY29PT8M6VxUzolOABwdNwNIED4vPkgYQp/iMNGxk4dq14otM/O9FHQUZ3fhooWg6UlWDzSZ4WBJoLgagi4Fl38j3Pp8ZYWmeFxccuXxedidSsiOWNGOUXjujWwJeNgdyQdVSBIcJ6P83D6WHzNRiDQoeSRwxn81r5LP+Xnc51lcym3RMCf1e/2jwsBgUjAQiJjio75GOcZ8D3YXfjbzrOQHf2MFBxcLT/3bMY9DQjBIAbCjv2MQPHDgg80em8syYMcMyMDc+O4sWLZINGzZMyOWjjz5qRujt37/ffBeJRCSTyZignR4rVqyQX/ziF2atwHKHBvduvvlmEzjUvzTomoFHjx41P+uhIw2/+c1vircBU0VogJr44HErBBBAAIGWCSRfeqkwmlADhroBhQkcFj5XmunUsJGFCxdIaPlyS5P4U09J5sjRqXvpmnUjwUBdny60bJnltRqsM2shasBQA0Q+r8hIALHwu5Fgor6P/HeHjqbLHj1qRuvpaLXxh+4qrVO3rRxN0G/uXBP4s9qMY+oFbM6ZZkThaFC5NMA8EnzWYHMuJ51lln7RkZCJPz5Xc2bLjiwcGJDY40/UnO5km6HEn91igsum7k1gsUKgWZ8zfTb0vcm7cuuovw39wxLP5UYtFoeDcnqXPaf61VxhXIiAywS0X6u7HWtg0AQE44WgoE5JLgYMB5IDZvpxf7LfvPR8Jx8d3g4TFOwKdJmgYfE925+Vb727sIno7t276z7Dz8lm7ZR3AoBNrO3//u//rmq9P82absDx0ksvWeZSRwB+/etfl3vuuUd27dplRvNpwO/yyy8303o1KFjpeOSRR+S2224zQUYNUM6aNUvOPfdcufbaa6vOa6V7lX5PALAaLc5FAAEEEHCjQCEoOPLKZI5/Thc+++fMttykQi2GH1k/5SnK4+0aNrJw7lwJn2m9GYoGe3RjjikdGlT0+cxuvXqEzjhDAvPmWl5ausaiNxwaHenXbrsy6+Y42SOHS56hkRGqo8/V5OtbRi56neVozmmvWVhuZGE0KrrxTq1H+JzV4p8xY8LlGhRObt9ePrhcDDZrILE0EG1+9k0aWHxqKCYHksdH8+rYPx0FGNJgNgcCCLhGIJFJjAYDi4FBfddA4UBqwHxnPiePfx5KDdlq/UKrykgfTcvWjxU2tyIA6JrHteqCEACsmowL6iFAALAeiqSBAAIIINCuAjo10wQPNcBTEjyU4s8j72N+HtnkpGPJYrPmm9Uxnd2QJ12zsNqRhSWZC0wyYjG9d6/kEonCmn49Pe36OFQst5ndURpwLg08ZzISPOUUyzTSBw5IYssfK6Zf7oRmjyzUUaPR9b+vOb/hs840QWSr4+iWLbIh4xUxwcLC+6JgQFZ2hY5Phy8dyVoMNjZ5tGLNhedCBBCoWUA3OtEgoAYHB1ODJjio7+aVHDSBQ30v/k6/1/P1FctM3K2+5oxMciEBwEaoOi9NAoDOqzNX5JgAoCuqkUIggAACCDhIwASBsoVRdeXWZUvt2iW5RFLymbQ5txhgLHzOSj6bEclZ79IYWHSChE4/3VJkSrshl7HUkX1dF1zgIGn3ZHU0cFgMMutzoIHkTEnw2Xw38ruR7/W5kWxGutats3zWbDuycPUqy1G3Zrr5Qw/L8+KTPXJ8xJ+OAjxf0mK9l3ThOdDp7sHly8uuE5rYqiNyPGOmxYtXp0NrkHHkvTg1vty7ex45SoJA2wlkchkZTg2bYGAxQFgMDurP+nk4PVw4Jz1k3vXn0t9n84X/b5/sIABYSag9vicA2B71bLtSEgC0XZWQIQQQQAABBKYkoGvRmZGFGuzRgOLIaENPKCS+MsuP6LTM7PBwIaiYzZngkL5PFlA0wZOAX3x9MyS0ckVLNxyZEgwnTVlA14PUIODoc6TB5dGgc3bk+RgJOuvvxwWduy68QLyhievv6SYrU90N2SqznWvOEV9f34SviiMLdSuBDeKXrBzf/GOu5OQsmbzzPdk09qH/7/4pu40/MXj6adKxaJHl9bqep/4bKwYSzSYuOirR4x35nbewpqLF73TzFqtdofVGZsMaHdXoHbm+5txzIQII1EOguM5hNB01gcHR91Th5+LvtP99yztuMbdkCnA95J2ZBgFAZ9ab43NNANDxVUgBEEAAAQQQqItAcWSiCSYWg4r62e8v7CLLFMq6ODs5EfOMjASd9RnxhMOjm8WUlksDi+lXXjEbpphRiGPesyK5QlDRvGsgetzR+arzxNfdPTEAWLIb8ovilZ3iG3POeZKR3kl29QmffZaZpj7+0Gd++MGHaq6a0PJlEli40PL64YceGl1Hs9obTL4B0UbJxUo2SSgNLOquyBpU1CCjxWc1KJff9L59hXVNzfWewr97/Vz8eeTdBB71u+L3eq+AX7zBYLXF5HwE2kqA/ndbVXfZwhIA5DloiQANUEvYuSkCCCCAAAIIIICAjmTToKIGCEcDzznR6eZmlNy4Q0cApnfvNuem0mn5fSIraQ0gaiAxl5O+XFbWSGE9Tqs4YNlNS1Ips6FPrYeOjA3Mn295+dADD5Sdrl/pfsGlp0rHSSdZnhb9/e/NMgG1HB0nnSjBpUstL4099phkB4dqSVYmXX/06aclc/RoIWBsAoclwUkdyakBzNJAY0lg0dfTLR0nn2yZp8yhQ5KLRo8HKXUatwY9i9cXfx697/Ggpj5j+scNq8M8j/pslgZCa1LhIgTGCtD/5olQAQKAPActEaABagk7N0UAAQQQQAABBBCYpsCueFK2RhNjUjmnp1PmdARMQLA4krU42lCn01qtu6nTaVM7d465xlw7mkYxyKjBypHRiyV3DZ15hgTmTtwhW4Obw/c/UHMpJ5taPK0dyBefXHbDmeimTZIbjtaU58k2Coo/+aRkjh6rKV3/7FkSPvtsy2vjW7ZI5sAUdzYfl4Kvt0c6zz3XMl1dLiG1+5Wx35m4ZTGAWQhiauxydFNpJ3wAACAASURBVJRk4QcTwC6XX93QJ7N//0gQVKewj0xjN0HRkbRMdGAkiFn8rIFMv0+Cp55qmV+ddp85fKTw3aTpjKQ9ct/iwO7ACSdYpqubO2UHBkfTLWSnJM+j+SumW/IuIr7eXstRwhqk1020TDlL0ygWoDSZ0tHnI589waB1uvm8mR4/ujiAxbVj7jd+ZHtx1GtNT+rULqL/PTUnt59FANDtNWzT8tEA2bRiyBYCCCCAAAIIIIDApAK5fF4eOTYsCQ3UjRw9fp+8ui/ScLnR4GAuZ4KKViMWNQCY0Sm1xUCivo+8zO/MyMecSH4kWDnuu44lS8puWjL88MOFTV9qOCbdgXzjo4XATA2HBpFCy8ptQLRZsv39NaQqZkdo3Rna6og/u0UyB2sMAPb1SueaNZbpJrZtk/Qre2rKrwaau159vuW1yZdektRLO2tK1xMISOS1r7G8NvXKHklu21ZTunpR9xteb3lt+uBBSTy7peZ0u16zTrwdHROuzxw7JvHNT9acbufateKLTBy9mR2OSmzTpprTnXSU8PqSUcLjAodjlsiwCCqGli8f3dmc/nfN1eOqCwkAuqo6nVMYGiDn1BU5RQABBBBAAAEEEBgrsCeRki3DJWvhiUhxFKCbrXK6Tl8xsDgyjdoEFEd2GTcBxvG/Hwkw6gYrVpusqFdi6zbJJxOF4KRu+qLByWK6Iz8Xgpe6C/nI9G1zn7x0nLhIgqedZskee/zx4yPJqqwY/7y5Ej7jDMur4s88I5lDh6tMsXC6b0afdJ5zjuW1uit0es/emtLVacVd56+1vDa54yUz2rSWoyUBwAMHJbGl9gBg5DXrxOOCAKD+e4tOZ5mAM86QwLzCKGH637U8/e67hgCg++rUESWiAXJENZFJBBBAAAEEEEAAAQsBDXit7x+WWMlmIs0aBUiFjBXQuii3WZCOKjTr6pnA4khwsrj+Y0mgUYOWxXUhi0FIb6TLcoq13l2DaTr9dTRIqWkW0x0JTI4PVBZ/9s2YUXaqbuL55yW9d19NVaz57VpbLgC4Q1I7d9WUrgbSNKBmdaReeUWS27bXlK7Ol+1+fZkRgAcOSGLLH2tLV8TktxEBQA2wWq3fqLvcxzb9oeb8lhsBOO0AYMkyAfS/a64eV11IANBV1emcwtAAOaeuyCkCCCCAAAIIIIDARIF2HQXIs9A4AV1HrhiwHA1KFoOLGqQsTjsvjrIsBi7zYtbq88+aZZk5nfqa04ClXlfcBMd8LPxsfl98Fb8f+XqyNQAzR45Iet/+kXRL0ipNc1x6Izc0b+WmQmu6yRdeHM3vmHwW0y5Nt1iUkfLpVGgduTj+MFOAn3rqeJlLCKZSqwQAp6LEOXYWIABo59pxcd4IALq4cikaAggggAACCCDQBgLjRwF2+32yrCskMwP+Nig9RUTAfQJmFGhJgHHCZ6/XcrSpXqfB29GjmE7xF+PTHfe92VzEYgdyDfiOro1Zek25z+PybjYgGpkKTf/bfc9rLSUiAFiLGtdMW4AGaNqEJIAAAggggAACCCDQYoG9iZTsSqRkaWfQ7ALMgQACCNhRgP63HWul+XkiANh8c+7IIqQ8AwgggAACCCCAAAIuEJhs/TkXFI8iIICASwQIALqkIqdZDAKA0wTk8toEaIBqc+MqBBBAAAEEEEAAAQQQQAABBKoRoP9djZZ7zyUA6N66tXXJaIBsXT1kDgEEEEAAAQQQQAABBBBAwCUC9L9dUpHTLAYBwGkCcnltAjRAtblxFQIIIIAAAggggAACCCCAAALVCND/rkbLvecSAHRv3dq6ZDRAtq4eMocAAggggAACCCAwDYGj6YzsT6ZlZSQ8jVS4FAEEEKiPAP3v+jg6PRUCgE6vQYfmnwbIoRVHthFAAAEEEEAAAQTKCmjg78VYUo6lM+ac1d2dMjfI7sA8Mggg0FoB+t+t9bfL3QkA2qUm2iwfNEBtVuEUFwEEEEAAAQQQcLmA7gi8vn9YYtncaEm7/T65oC/i8pJTPAQQsLsA/W+711Bz8kcAsDnO3GWcAA0QjwQCCCCAAAIIIICA2wT2JlLy7HB8TLEYBei2WqY8CDhPgP638+qsETkmANgIVdKsKEADVJGIExBAAAEEEEAAAQQcJsAoQIdVGNlFoE0E6H+3SUVXKCYBQJ6DlgjQALWEnZsigAACCCCAAAIINFhgXzIlzwwxCrDBzCSPAAJVCND/rgLLxacSAHRx5dq5aDRAdq4d8oYAAggggAACCCBQq4DVKMCIzycXzmAtwFpNuQ4BBKYnQP97en5uuZoAoFtq0mHloAFyWIWRXQQQQAABBBBAAIEpCzAKcMpUnIgAAk0QoP/dBGQH3IIAoAMqyY1ZpAFyY61SJgQQQAABBBBAAAEVYBQgzwECCNhJgP63nWqjdXkhANg6+7a+Mw1QW1c/hUcAAQQQQAABBFwvYDUKcFV3p8wLBlxfdgqIAAL2EqD/ba/6aFVuCAC2Sr7N70sD1OYPAMVHAAEEEEAAAQRcLmA1CrDT55UL+yLi9XhcXnqKhwACdhKg/22n2mhdXggAts6+re9MA9TW1U/hEUAAAQQQQACBthDYn0zL00OxMWVdEQnLiaGOtig/hUQAAXsI0P+2Rz20OhcEAFtdA216fxqgNq14io0AAggggAACCLSRgI4CfHQgKoOZ7GipO7weeU1ft/i9jAJso0eBoiLQUgH63y3lt83NCQDapiraKyM0QO1V35QWAQQQQAABBBBoV4EjqYw8PhgdU/zTOkOypDPYriSUGwEEmixA/7vJ4Da9HQFAm1aM27NFA+T2GqZ8CCCAAAIIIIAAAkWBJwaicjidEb/HI0vCQTkp3CE+1gHkAUEAgSYJ0P9uErTNb0MA0OYV5Nbs0QC5tWYpFwIIIIAAAggggMB4gaFMVvYkU3JqOCQBpv7ygCCAQJMF6H83GdymtyMAaNOKcXu2aIDcXsOUDwEEEEAAAQQQQAABBBBAwA4C9L/tUAutzwMBwNbXQVvmgAaoLaudQiOAAAIIIIAAAggggAACCDRZgP53k8FtejsCgDatGLdniwbI7TVM+RBAAAEEEEAAAQQQQAABBOwgQP/bDrXQ+jwQAGx9HbRlDmiA2rLaKTQCCCCAAAIIIIDAOIFYNiedPi8uCCCAQMME6H83jNZRCRMAdFR1uSezNEDuqUtKggACCCCAAAIIIFC9QDKXkx2xpOxOpGR1d6fMDQaqT4QrEEAAgSkI0P+eAlIbnEIAsA0q2Y5FpAGyY62QJwQQQAABBBBAAIFmCLwcT8r2WFKy+by5nY4AXNcXEY/H04zbcw8EEGgzAfrfbVbhZYpLAJDnoCUCNEAtYeemCCCAAAIIIIAAAjYQ2JdMyTND8TE5WRkJy6JQhw1yRxYQQMBtAvS/3VajtZWHAGBtblw1TQEaoGkCcjkCCCCAAAIIIICAYwXy+bxsHIjKUCY7Woag1yuvmRERH6MAHVuvZBwBuwrQ/7ZrzTQ3XwQAm+vN3UYEaIB4FBBAAAEEEEAAAQTaWeBwKiNPDEbHECztDMkpncF2ZqHsCCDQAAH63w1AdWCSBAAdWGluyDINkBtqkTIggAACCCCAAAIITEfg8YGoHElnRpPQ0X+vnRGRDi+7Ak/HlWsRQGCsAP1vnggVIADIc9ASARqglrBzUwQQQAABBBBAAAEbCegU4A39w2NydGKoQ1ZEwjbKJVlBAAGnC9D/dnoN1if/BADr40gqVQrQAFUJxukIIIAAAggggAACrhR4Zigm+5LpMWVbNyMiXT6fK8tLoRBAoPkC9L+bb27HOxIAtGOttEGeaIDaoJIpIgIIIIAAAggggEBFgXg2J48cG5J8yZmzO/yypqer4rWcgAACCExFgP73VJTcfw4BQPfXsS1LSANky2ohUwgggAACCCCAAAItENgaTciueHLMnc/p6ZQ5HYEW5IZbIoCA2wTof7utRmsrDwHA2ty4apoCNEDTBORyBBBAAAEEEEAAAdcIpHN5Wd8/JKnc8XGAnT6vXNgXEa/H45pyUhAEEGiNAP3v1rjb7a4EAO1WI22SHxqgNqloiokAAggggAACCCAwJYE9iZRsGY6POfe0zpAs6QxO6XpOQgABBMoJ0P/m2VABAoA8By0RoAFqCTs3RQABBBBAAAEEELCpQD6fl00DURnIZEdzqKP/XtMXkZDPa9Ncky0EEHCCAP1vJ9RS4/NIALDxxtzBQoAGiMcCAQQQQAABBBBAAIGxAgPpjDw6EB395YmhDlnaGZKAl2nAPCsIIFC7AP3v2u3cdCUBQDfVpoPKQgPkoMoiqwgggAACCCCAAAJNE9BpwLFsTpZ3haTb72vafbkRAgi4V4D+t3vrtpqSEQCsRotz6yZAA1Q3ShJCAAEEEEAAAQQQcJFALp9n4w8X1SdFQcAOAvS/7VALrc8DAcDW10Fb5oAGqC2rnUIjgAACCCCAAAIIIIAAAgg0WYD+d5PBbXo7AoA2rRi3Z4sGyO01TPkQQAABBBBAAAEEEEAAAQTsIED/2w610Po8EABsfR20ZQ5ogNqy2ik0AggggAACCCCAwDQEMrm8+NkQZBqCXIpAewrQ/27Peh9fagKAPActEaABagk7N0UAAQQQQAABBBBwoICuC7grnpId8aSc19MpvQG/A0tBlhFAoFUC9L9bJW+v+xIAtFd9tE1uaIDapqopKAIIIIAAAggggMA0BA6nMvJ8tLAzsB69fp+s7e0Sj8czjVS5FAEE2kmA/nc71Xb5shIA5DloiQANUEvYuSkCCCCAAAIIIICAwwR2xJLyQiwxJtdnRMJyQqjDYSUhuwgg0CoB+t+tkrfXfQkA2qs+2iY3NEBtU9UUFAEEEEAAAQQQQGAaAjr99/f9w6MjADWpDq9H1vV1S4D1AKchy6UItI8A/e/2qevJSkoAkOegJQI0QC1h56YIIIAAAggggAACDhQ4lErL5sHYmJyfFO6Q5V1hB5aGLCOAQLMF6H83W9ye9yMAaM96cX2uaIBcX8UUEAEEEEAAAQQQQKCOAk8MRkXXAyw9LuyLSMTvq+NdSAoBBNwoQP/bjbVafZkIAFZvxhV1EKABqgMiSSCAAAIIIIAAAgi0jUA0m5XfHxuWfEmJ+/w+eRUbgrTNM0BBEahVgP53rXLuuo4AoLvq0zGloQFyTFWRUQQQQAABBBBAAAGbCGyPJuSleHJMbpZ3heSkcNAmOSQbCCBgRwH633aslebniQBg8825o4jQAPEYIIAAAggggAACCCBQnUA2n5cN4zYE8Xl0Q5CIhHze6hLjbAQQaBsB+t9tU9WTFpQAIM9BSwRogFrCzk0RQAABBBBAAAEEHC6g6wDqeoClx5yOgJzT0+nwkpF9BBBolAD970bJOitdAoDOqi/X5JYGyDVVSUEQQAABBBBAAAEEmizw7FBM9ibTY+66qrtT5gUDTc4Jt0MAAScI0P92Qi01Po8EABtvzB0sBGiAeCwQQAABBBBAAAEEEKhNIJXLyfpjw5LOH98SJOj1iu4KHPB6akuUqxBAwLUC9L9dW7VVFYwAYFVcnFwvARqgekmSDgIIIIAAAggggEA7CuxLpuSZofiYop8Y6pAVkXA7clBmBBCYRID+N4+HChAA5DloiQANUEvYuSkCCCCAAAIIIICAiwSeGIjK4XTGlGh2h19WdIUlzGYgLqphioJAfQTof9fH0empEAB0eg06NP80QA6tOLKNAAIIIIAAAgggYBuBWDZnNgRZ2hmS+az/Z5t6ISMI2E2A/rfdaqQ1+SEA2Br3tr8rDVDbPwIAIIAAAggggAACCNRBIJ/Pi8fDun91oCQJBFwrQP/btVVbVcEIAFbFxcn1EqABqpck6SCAAAIIIIAAAggggAACCCBQXoD+N0+HChAA5DloiQANUEvYuSkCCCCAAAIIIIAAAggggECbCdD/brMKL1NcAoA2eg5yuZxccsklct9998nFF18s999//6S5O3jwoNx2221yzz33yK5du8Tv98upp54ql112mVxzzTUSiUQqlu6RRx6Rb3zjG7JhwwbZv3+/zJkzR9asWSMf+tCH5G1ve1vF62s9gQaoVjmuQwABBBBAAAEEEECgskA6l5ehbFZmBvyVT+YMBBBwtQD9b1dX75QLRwBwylSNP/FTn/qUfOELXzA3qhQAfPTRR+XSSy+Vffv2mfM12JfJZCSRSJifV6xYIb/4xS9MQLDc8dnPflb0peuG6DFz5kw5duzY6M8aRPzmN78pXq+37oWnAao7KQkigAACCCCAAAIIIGAEDibT8lw0IZl8Xtb1RSTEzsA8GQi0tQD977au/tHCEwC0yXOgo/j+4i/+wuRGA3KTBQA16Ld69WrREYBvfetb5V/+5V/kzDPPNNetX79errvuOnnyySdl5cqVsmnTJunq6ppQyu9///ty9dVXi8/nk5tvvlk+/OEPS29vr8RiMbnzzjvl4x//uAkm3nTTTXLjjTfWXYkGqO6kJIgAAggggAACCCDQ5gLaH3hqKC4HU+lRiTkdATmnp7PNZSg+Au0tQP+7veu/WHoCgDZ4DrZu3SqvetWrJBwOyzvf+U753ve+N2kAUEfm3X777fK6171Ofvvb30ogEBhTCh3Fd95558mOHTvk1ltvleuvv37M9/F4XBYuXCj9/f3yne98Rz74wQ9OUPj5z38uf/Znf2YChDt37pRFixbVVYoGqK6cJIYAAggggAACCCCAgBF4digme5PHA4D6u7O7O2V+cGyfAS4EEGgfAfrf7VPXk5WUAGCLn4Ph4WFZu3atbNu2zQTzHnzwQTPqrtwIwGg0KgsWLJChoSF5/PHHzXp9Vsfdd99t1gJcunSpSdvj8Yyedtddd8mVV14pq1atks2bN4/5rjQtXQPwN7/5jdxyyy3y6U9/uq5SNEB15SQxBBBAAAEEEEAAAQSMgK79t75/SFK5wjI/evg9HrmQqcA8IQi0rQD977at+jEFJwDY4ufg3e9+t/z4xz8203j/7u/+zqzJN1kAUKfnXnXVVWZtvxdeeKFs7nWUn67pp9N4H3jgAbnoootGz9XPDz30UMXA3g9+8AN53/veJ0uWLJEXX3yxbKCwFkIaoFrUuAYBBBBAAAEEEEAAgcoC+5NpeXooNubE2QG/rOmduDRQ5dQ4AwEEnC5A/9vpNVif/BMArI9jTal88YtflE9+8pPyl3/5l/Lv//7vJo1KAcArrrhCfvjDH5pder/1rW9Nel9dH/Dee++VG264QT73uc+Zc1OplIRCIbNe4MaNG+X8888vm8bevXvlhBNOMN9v377djCas10EDVC9J0kEAAQQQQAABBBBAYKLAU0MxOTBuKvCKSFhODHXAhQACbSZA/7vNKrxMcQkAtug5+N3vfmc28Fi2bJnojr66i+9UAoCvf/3rzYi+2267zWzcMdnxsY99TL7yla+YUXy66Yceu3btksWLF5vPOv3YaoOQ0jSLOwPr1GRdc7BeBw1QvSRJBwEEEEAAAQQQQACBiQKpXE429EclmcuNfun1eOSCvi7p8vkgQwCBNhKg/91GlT1JUQkAWuBoYCyZTNbtCens7DQbfBSPl19+Wc4991xzjz/84Q8mCFg8Ko0A1HN1TT8dBXj55ZdPmsfPf/7zZu0+DTTqWn56bNiwQS688EKzcYiOBqx0nHbaaWaq8Y9+9COzpuBUD21gJjt0J2Nd+1CP3bt3132Tkanmk/MQQAABBBBAAAEEEHCrwKFUWjYPjp0K3Ov3ydrerrou7+NWP8qFgFsECAC6pSanVw4CgBZ+H/jAB+SOO+6YnmzJ1bqm34033mh+o2vyveY1rzEbePzkJz+RSy+9dMx9KgUAu7u7zci9X//613LJJZdMmkedInzttdfKWWedJU8//bQ5V9cb1HUH582bJ/v3769YRp0ivGnTJvnSl74kOqJwqkfppiOVriEAWEmI7xFAAAEEEEAAAQQQqE3gj8NxeSUx9g//SztDckpnsLYEuQoBBBwnQADQcVXWkAwTAGxyAPDqq68203F17b8vfOELE+4+1QCgjujTkX2THZMFAOfPny86Cq/SQQCwkhDfI4AAAggggAACCCBgX4FsPi8b+ocllj0+FVhz++q+iPT4mQps35ojZwjUT4AAYP0snZwSAUCL2mvUFODvfOc7ZvOON73pTWZKrs9i7Y1KAcBqpgDrxh+f+cxnpjUFWDf+0B2AmQLs5H/m5B0BBBBAAAEEEECgnQX60xnZNBAdQ9Dl88oFfRHRdQE5EEDA3QIEAN1dv1MtHQHAqUpN87zHHntM1q1bJzryTqf/zp492zLFSgFANgGZZkVwOQIIIIAAAggggAACbSiwPZqQl+Jj1zk/ORyUZV2hNtSgyAi0lwABwPaq73KlJQDYpOfgq1/9qnz0ox8VXcOvt7e37F0HBwdFX8FgUObMmWPO6+npkS1btpjPV1xxhdkAREcS6hTfyY63vOUtct9998kNN9wgOhpQD914RDckyefzsnHjRtEpvuWOPXv2jG7OsX37dtHRgPU6aIDqJUk6CCCAAAIIIIAAAghUFsjpf//3R2U4mx09eUk4KKcRAKyMxxkIOFyA/rfDK7BO2ScAWCfISskUA4CVzrP6vq+vT44dO2a+uvPOO+Wqq66SU0891ezOW+6Ix+Myc+ZMs+nI/fffLxdffPHoqRdddJE89NBDcsstt5hdgssdP/jBD+R973ufLF68WHbs2FHXncJogGp5ErgGAQQQQAABBBBAAIHaBYYzWbMeYNjnlTMiYZkR8NeeGFcigIBjBOh/O6aqGppRAoAN5a0+8f/7f/+vvPe97zUBOw3cjT+i0agsWLBAhoaGzFTiNWvWWN7k7rvvlssuu8yM2tu2bduY4N1dd90lV155paxatUo2b95cNrD3tre9zaxVWClQWH0pRWiAalHjGgQQQAABBBBAAAEEpidwOJWRPr9P/F7W/pueJFcj4BwB+t/OqatG5pQAYCN1a0i7UgBQk7zmmmvk9ttvl9e+9rXyu9/9TgKBwJg76WjB8847z4zau/XWW+X6668f872ODly4cKH09/ebacQ6nXj88bOf/Uze9a53mY1Kdu7cOToVuIYiWV5CA1QvSdJBAAEEEEAAAQQQQAABBBBAoLwA/W+eDhUgAGiz52AqAcB9+/bJ6tWr5eDBg/LmN79ZvvzlL8uZZ55p1vVbv369fOQjH5GnnnpKVq5cKZs2bZKurq4Jpfz+978vV199tQnw6cYj1113nVlrMBaLyfe+9z35xCc+YaYP33TTTXLjjTfWXYkGqO6kJIgAAggggAACCCCAAAIIIIDABAH63zwUBABt+AxMJQCo2X700UfNCL39+/ebUkQiEclkMiZop8eKFSvkF7/4hVkrsNyhwb2bb77ZBA49Ho9ZM/Do0aPmZz10pOE3v/lN8Xq9dZeiAao7KQkigAACCCCAAAIIIDAtgaFMViI+b13X/p5WhrgYAQTqIkD/uy6Mjk+EEYA2q8KpBgA12zoC8Otf/7rcc889smvXLjOaTwN+l19+uZnWq0HBSscjjzwit912m2zYsEEOHDggs2bNknPPPVeuvfZaueSSSypdXvP3NEA103EhAggggAACCCCAAAJ1FdABAC/FU/JCLCFLO0NySmewrumTGAIItFaA/ndr/e1ydwKAdqmJNssHDVCbVTjFRQABBBBAAAEEELClQCqXk2eG4nIknRnN36t6u9gh2Ja1RaYQqE2A/ndtbm67igCg22rUIeWhAXJIRZFNBBBAAAEEEEAAAVcLHE1n5LGB6JgyBr1euaCvSzoasBSQqzEpHAI2FaD/bdOKaXK2CAA2GZzbFQRogHgSEEAAAQQQQAABBBCwh8CLsYS8GEuOyczsgF/O6elkPUB7VBG5QGBaAvS/p8XnmosJALqmKp1VEBogZ9UXuUUAAQQQQAABBBBwr4CuAfj4YEx0NGDpcVpnSJawHqB7K56StY0A/e+2qepJC0oAkOegJQI0QC1h56YIIIAAAggggAACCFgKJHM52dA/LKlcfsz3rAfIA4OA8wXofzu/DutRAgKA9VAkjaoFaICqJuMCBBBAAAEEEEAAAQQaKnAklZHHB1kPsKHIJI5ACwTof7cA3Ya3JABow0pphyzRALVDLVNGBBBAAAEEEEAAAacJvBBLyI7x6wF2+OWcbtYDdFpdkl8EigL0v3kWVIAAIM9BSwRogFrCzk0RQAABBBBAAAEEEJhUQNcDfGwwJsfGrQd4eldIFoeD6CGAgAMF6H87sNIakGUCgA1AJcnKAjRAlY04AwEEEEAAAQQQQACBVggksjnZODBxPcC1vV3SF/C3IkvcEwEEpiFA/3saeC66lACgiyrTSUWhAXJSbZFXBBBAAAEEEEAAgXYTOJzKyBPj1gMMeb1yQV9EAl5Pu3FQXgQcLUD/29HVV7fMEwCsGyUJVSNAA1SNFucigAACCCCAAAIIINB8ge3RhLwUT4658ZyOgJzT09n8zHBHBBCoWYD+d810rrqQAKCrqtM5haEBck5dkVMEEEAAAQQQQACB9hQYvx6g3+ORMyNhmRsMtCcIpUbAoQL0vx1acXXONgHAOoOS3NQEaICm5sRZCCCAAAIIIIAAAgi0UkDXA9zQPyydPq+c3d0pYZ+3ldnh3gggUIMA/e8a0Fx4CQFAF1aqE4pEA+SEWiKPCCCAAAIIIIAAAgiIDGeyJgDo9bD2H88DAk4UoP/txFqrf54JANbflBSnIEADNAUkTkEAAQQQQAABBBBAAAEEEEBgmgL0v6cJ6JLLCQC6pCKdVgwaIKfVGPlFAAEEEEAAAQQQQAABBBBwogD9byfWWv3zTACw/qakOAUBGqApIHEKAggggAACCCCAAAI2F9gVT0o6n5elnSGb55TsIdC+AvS/27fuS0tOAJDnoCUCNEAtYeemCCCAAAIIIIAAAgjURSCXz8sfh+OyN5k26ekG7DRIwAAAIABJREFUIfPZHbgutiSCQL0F6H/XW9SZ6REAdGa9OT7XNECOr0IKgAACCCCAAAIIINCmAvl8Xv4wEJX+THZUQDcIOb+3S7r9vjZVodgI2FeA/rd966aZOSMA2Ext7jUqQAPEw4AAAggggAACCCCAgHMFXo4n5floYkwBQl6vvLqvSzq8XucWjJwj4EIB+t8urNQaikQAsAY0Lpm+AA3Q9A1JAQEEEEAAAQQQQACBVgpsGY7LnkRqTBZmBPxybk+n6IhADgQQsIcA/W971EOrc0EAsNU10Kb3pwFq04qn2AgggAACCCCAAAKuEdB1AB8bNxVYC3diqENWRMKuKScFQcDpAvS/nV6D9ck/AcD6OJJKlQI0QFWCcToCCCCAAAIIIIAAAjYUSOZysrE/KvpeeqyMhGVRqMOGOSZLCLSfAP3v9qtzqxITAOQ5aIkADVBL2LkpAggggAACCCCAAAJ1FxhIZ2TTQFTyJSnrBODzertEpwRzIIBAawXof7fW3y53JwBol5pos3zQALVZhVNcBBBAAAEEEEAAAVcL7E2k5Nnh+Jgy+kd2Bu5iZ2BX1z2Fs78A/W/711EzckgAsBnK3GOCAA0QDwUCCCCAAAIIIIAAAu4S2BpNyK54ckyhwl6vrO3rkiA7A7ursimNowTofzuquhqWWQKADaMl4ckEaIB4PhBAAAEEEEAAAQQQcJdAPp+XzUMxOZzKjClYt98nr+rpEr+XnYHdVeOUxikC9L+dUlONzScBwMb6knoZARogHg0EEEAAAQQQQAABBNwnkM3n5Q8DURnMZMcUbk5HQM7p6XRfgSkRAg4QoP/tgEpqQhYJADYBmVtMFKAB4qlAAAEEEEAAAQQQQMCdAroj8Kb+qMRHdgbWtQBXdXfKrA42BHFnjVMquwvQ/7Z7DTUnfwQAm+PMXcYJ0ADxSCCAAAIIIIAAAggg4F6BaCYrjw5ERYN/a3o6JcJGIO6tbEpmewH637avoqZkkABgU5i5yXgBGiCeCQQQQAABBBBAAAEE3C0wkM6YzT9CPq+7C0rpELC5AP1vm1dQk7JHALBJ0NxmrAANEE8EAggggAACCCCAAAIIIIAAAo0XoP/deGMn3IEAoBNqyYV5pAFyYaVSJAQQQAABBBBAAAEEqhDQDUN8HnYGroKMUxGoSYD+d01srruIAKDrqtQZBaIBckY9kUsEEEAAAQQQQAABBBoh8HI8KTvjKVnb28UU4UYAkyYCJQL0v3kcVIAAIM9BSwRogFrCzk0RQAABBBBAAAEEEGipQD6fl22xpOyKJ00+Ij6fvKq3SwJeRgK2tGK4uasF6H+7unqnXDgCgFOm4sR6CtAA1VOTtBBAAAEEEEAAAQQQcIbAjlhSXoglxmS2z++Tc3u7mA7sjCoklw4UoP/twEprQJYJADYAlSQrC9AAVTbiDAQQQAABBBBAAAEE3CaQyuVk00BUYtncmKLNDPhlTU+neFkT0G1VTnlsIED/2waVYIMsEAC0QSW0YxZogNqx1ikzAggggAACCCCAAAJign+bBoYllcuP4Zjd4ZfV3QQBeUYQqLcA/e96izozPQKAzqw3x+eaBsjxVUgBEEAAAQQQQAABBBCoWWA4k5U/DEQlnR8bBJwXDMjZkbB4GAlYsy0XIjBegP43z4QKEADkOWiJAA1QS9i5KQIIIIAAAggggAACthEYHAkCZscFARcGA3IGQUDb1BMZcb4A/W/n12E9SkAAsB6KpFG1AA1Q1WRcgAACCCCAAAIIIICA6wSOpTPy+GBMcuOCgCeGOmRFJOy68lIgBFohQP+7Fer2uycBQPvVSVvkiAaoLaqZQiKAAAIIIIAAAgggUFHgSCojTwxGZexkYJHF4aCc3hWqeD0nIIDA5AL0v3lCVIAAIM9BSwRogFrCzk0RQAABBBBAAAEEELClwKFUWjYPxibkbWlnSE7pDNoyz2QKAacI0P92Sk01Np8EABvrS+plBGiAeDQQQAABBBBAAAEEEECgVGB/Mi1PD00MAi7rCsnJYYKAPC0I1CpA/7tWOXddRwDQXfXpmNLQADmmqsgoAggggAACCCCAAAJNE9iTSMmW4fiY+50ZCcvCUEfT8sCNEHCbAP1vt9VobeUhAFibG1dNU4AGaJqAXI4AAggggAACCCCAgEsFXo4n5floQjwisqq7U+YGAy4tKcVCoDkC9L+b42z3uxAAtHsNuTR/NEAurViKhQACCCCAAAIIIIBAHQR2xZPS6fPKnA6Cf3XgJIk2F6D/3eYPwEjxCQDyHLREgAaoJezcFAEEEEAAAQQQQAABBBBAoM0E6H+3WYWXKS4BQJ6DlgjQALWEnZsigAACCCCAAAIIIOAKgWgmK11+nyvKQiEQaLQA/e9GCzsjfQKAzqgn1+WSBsh1VUqBEEAAAQQQQAABBBBoisDBZFqeHIrJKZ1BWdoZaso9uQkCThag/+3k2qtf3gkA1s+SlKoQoAGqAotTEUAAAQQQQAABBBBAwAgcSqXlycGY5Ec8Tg4HZVkXQUAeDwQmE6D/zfOhAgQAeQ5aIkAD1BJ2booAAggggAACCCCAgGMFEtmcPHxsaDT4VyzIiaEOWd4VEo9H9w3mQACB8QL0v3kmCADyDLRMgAaoZfTcGAEEEEAAAQQQQAABxwrsTqTkueH4hPyfEOqQlQQBHVuvZLyxAvS/G+vrlNQZAeiUmnJZPmmAXFahFAcBBBBAAAEEEEAAgSYJ7EmkZItFEHBBMCBnRsKMBGxSPXAb5wjQ/3ZOXTUypwQAG6lL2mUFaIB4OBBAAAEEEEAAAQQQQKBWgf3JtDw9FJtw+bxgQM7oCovfy3TgWm25zn0C9L/dV6e1lIgAYC1qXDNtARqgaROSAAIIIIAAAggggAACbS2guwE/NXR8Q5AiRofXY3YHPiEYYDRgWz8hFL4oQP+bZ0EFCADyHLREgAaoJezcFAEEEEAAAQQQQAABVwmM3xW4tHARn0+WR0IyM+B3VZkpDALVCtD/rlbMnecTAHRnvdq+VDRAtq8iMogAAggggAACCCCAgCMEjqQysnkoJrl83jK/czoCclaEacGOqEwy2RAB+t8NYXVcogQAHVdl7sgwDZA76pFSIIAAAggggAACCCBgB4FoNitbowk5nMpMyE6f3ydr+yJ2yCZ5QKAlAvS/W8Juu5sSALRdlbRHhmiA2qOeKSUCCCCAAAIIIIAAAs0U0ADg1mhcotnc6G1f3ReRHr+vmdngXgjYSoD+t62qo2WZIQDYMvr2vjENUHvXP6VHAAEEEEAAAQQQQKBRAvl8Xl5JpuWFaELm6q7AkbDlrXT9wFg2JzMCfun2edkwpFEVQrotF6D/3fIqsEUGCADaohraLxM0QO1X55QYAQQQQAABBBBAAIFmCqRzedH/dXi9lrd9cjAmB1Np853X4xGdKtzr90lfwCd9fr8EvJ5mZpd7IdAwAfrfDaN1VMIEAB1VXe7JLA2Qe+qSkiCAAAIIIIAAAggg4ESBB48OSTJ3fKrw+DJ0+rwmKKjThzUw2O33mUAhBwJOE6D/7bQaa0x+CQA2xpVUKwjQAPGIIIAAAggggAACCCCAQKsE4tmcPHxsqKrba+hPg4AaDJwfDJipwxwIOEGA/rcTaqnxeSQA2Hhj7mAhQAPEY4EAAggggAACCCCAAAKtEtBdg3fEkjKQyZp1AKs9lnWF5ORwsNrLOB+BlgjQ/24Ju+1uSgDQdlXSHhmiAWqPeqaUCCCAAAIIIIAAAgjYXSCVy0l/Oiv9mawJCPanM5KvkOlX9XZZjgDM5fOiU4u7fF4zWlCnD+tLf2b6sN2fBPfmj/63e+u2mpIRAKxGi3PrJkADVDdKEkIAAQQQQAABBBBAAIE6CmgQb0gDgZmsDI4EBcePEnzjrB7xWawHqNdt6B+ekBudPhzRdQRHAoMRn08ifq8Ey2xQUsfikBQCQv+bh0AFCADyHLREgAaoJezcFAEEEEAAAQQQQAABBGoQ0B2FdXSgBgRT+Zws7wpbprInkZItw/Ep36HD65FiMFDf53UE2H14ynqcOFUB+t9TlXL3eQQA3V2/ti0dDZBtq4aMIYAAAggggAACCCCAQI0Czw3HZXciVePVIq+d0S1hn3fC9ToqUaclW406rPlmXNg2AvS/26aqJy0oAUCeg5YI0AC1hJ2bIoAAAggggAACCCCAQAMFkrmcGSU4lCm+ZyWem9omI36PR94wq8cydweTaXlyKCZhr1e6/F7p0inEPq/06fqCfl8DS0TSbhCg/+2GWpx+GQgATt+QFGoQoAGqAY1LEEAAAQQQQAABBBBAwHECOn1Yg4LDWQ0MZmUom5PhTHbCRiMazFvbF7Esn+5Y/EIsYfmdbjAyLxiQ+R0Bs84gBwLjBeh/80yoAAFAnoOWCNAAtYSdmyKAAAIIIIAAAggggIANBPL5vMRyGgjMyZAJDOakx++VUztDlrl7eigm+5Ppijnv9HllfjBg1hLUXYg5EFAB+t88BwQAW/wMPPTQQ3LbbbfJxo0b5eDBgzJ//nw56aST5F3vepe8//3vl54e6+HfxWy/9NJL8vWvf13+67/+S3bv3i1dXV1y+umny9/8zd/IVVddJYFAoGIJf/WrX8l3vvMdeeKJJ+Tw4cOyYMECueCCC+QjH/mIXHjhhRWvr/UEGqBa5bgOAQQQQAABBBBAAAEE2k1AdxbW0YPVHBoM1ECgBgQJBlYj575z6X+7r05rKREjAGtRq8M1/+f//B/553/+Z9G//Ph8PpkxY4YcPXpUciPrQ8ycOVPuv/9+Ofvssy3v9stf/lKuuOIKGRwcNN/39vZKLBaTdLrwVyEN3v30pz+VOXPmWF6v97nmmmvkjjvuMN97vV7p6+szeSj+/NnPflY+/elP16G0E5OgAWoIK4kigAACCCCAAAIIIICACwWy+bxEszmJZrNm1OBwtrDGoK45WOno9fvk/DJTiytdy/fuEKD/7Y56nG4pCABOV7CG67/whS/Ipz71KdEg31e+8hX5q7/6KwkGg5LJZOSBBx6QT3ziE7J582YzGu+ZZ56RWbNmjbnLs88+K+eff74J+GkQ8HOf+5wsXrxYstms/OY3vzGj93bu3ClveMMb5L777jPBvfHHP/zDP8gtt9winZ2d8uUvf1muvPJKCYfD0t/fL1/96lfl5ptvNsHIO++8U9773vfWUMrJL6EBqjspCSKAAAIIIIAAAggggEAbCehgkv5MVg6k0nIgmSkbDFzWFZKTw8E2kqGo4wXof/NMqAABwCY/B/oPb8mSJdLR0SEbNmywHOE3MDAgZ5xxhuzZs0e+8Y1vyLXXXjsml29961vl3nvvlcsvv1x++MMfTijByy+/LKtWrTLBPP1ezys9NA+lAUNNb/zxta99Tf7X//pfZlTg3r17TXCwngcNUD01SQsBBBBAAAEEEEAAAQTaWUCDgQOZrOxPpeVgMiOJkpGBr53RLWHfxEEh7ezVbmWn/91uNW5dXgKATX4OdErt5z//eTPK74tf/GLZu//kJz+Rhx9+2AQCdT3A4rFr1y455ZRTzPp+ugagjhK0OjTtT37yk/LGN75Rfvvb3445RUf+6QjAP/3TP5Wf/exnltfr6D+dfrxlyxa566675D3veU9dpWiA6spJYggggAACCCCAAAIIIIDAqEB/OiMHUoVA4KruTmTaXID+d5s/ACPFJwDYxOcglUrJiSeeKMeOHZMdO3bIokWLqr77jTfeaKbnWgX2ShPTKcA60tDj8ciLL75oPuuhfxnSAKJ+Xymwd9NNN4muA3jxxReb9QjredAA1VOTtBBAAAEEEEAAAQQQQACB6gQOJNMSy+ZkcbjD9Bs53CtA/9u9dVtNyQgAVqM1zXN1t953vOMdZoOO9evXmzX7/vM//9ME4jRIp2v9rV69WnRK7jvf+U7Lu61bt05+//vfyz/+4z+KbiQy2bFs2TLZtm2bfPe73x0dRbh9+3azU7Ae+/btMzsPlzt0irLmVf/PIJFImGnL9TpogOolSToIIIAAAggggAACCCCAQHUCupnIxv6o6OYiswN+ObM7LB0Wa8dXlypn21WA/rdda6a5+SIA2ERvXc9PN+jQNfm+/vWvy5//+Z/LQw89ZJmDv/3bvzXndHd3j/leR/Lp6D3dBViDiZMdmv4999xjRvHplF89HnzwQTOib/bs2XLo0KFJr9cdhnV3YT106vFJJ51UNy0aoLpRkhACCCCAAAIIIIAAAgggMGWBXD4vjw5EZSiTHb0m6PXK2d1hmRHwTzkdTnSOAP1v59RVI3NKANBCd3h4WJLJZN3cdadd3UTj+uuvNyP3Pv7xj8umTZvMS4Nzb3/7282mHM8//7z86Ec/MjsD61RdqzX6QqGQyZuOArzgggsmzeMHPvABueOOO+Saa66Rb3/72+ZcTf+v//qvzSjArVu3ViyjrjWouxPraMBXv/rVFc8vnqANzGSHjj5cu3atOWX37t01TYeecmY4EQEEEEAAAQQQQAABBBBAwAgcSqVl82DMUmNpZ0iWMCXYdU8KAUDXVWlNBSIAaMFWDJzVJGpxka6lp2v3/c3f/I38v//3/8w6gIcPHzZBNd2td/zx4x//WN797nebX//617+WSy65xHw+cuSIGbmnx3PPPSfLly+fNIs6Rfif/umfzHTin//85+bcL33pSyYAef7558vGjRsrFnHevHly8OBB0TzpiMKpHtWsIUEAcKqqnIcAAggggAACCCCAAAIITF/gSCojzwzHJJXLT0isz++TM7s7pZOdg6cPbZMUCADapCJanA0CgBYV0KgAoK7td++995o7lk7LtXoGitN33/SmN8l9991nTikNAOpoQV3jb7JjsgCgjubTAGSlgwBgJSG+RwABBBBAAAEEEEAAAQScJ5DM5eTZobgcSWcmZN7n8ciyrpAsCtVvHXjnCbknxwQA3VOX0ykJAUALvUZNAX7Pe94j//Zv/2buWGlNvbvvvlsuu+wyWbhwoezZs2c0l9VMAX7/+98v3/ve95gCPJ1/IVyLAAIIIIAAAggggAACCLhUQJeeeimekhdiCcsSzu7wyxmRsOgagRzOFSAA6Ny6q2fOCQDWU7NCWsURebomoAYZJ5sm++STT8o555xjUhwYGJCenh7zmU1Amlhh3AoBBBBAAAEEEEAAAQQQaAOBY+mMGQ0Yz+UmlDbg8cjKSFjmBQNtIOHOIhIAdGe9VlsqAoDVik3jfN3V93/+z/8pUwkA6gYhuk6f1+s1wULdRESPdevWmQ1AdDMRDShOduhGH9u3b5fvfve7oqMB9dCf9fd66EYc8+fPL5uE3kfvp4HKRCIhHR31G/5NAzSNB4lLEUAAAQQQQAABBBBAAIE6C2RyedkaS8ieRMoy5fnBgKzoCkvA66nznUmu0QL0vxst7Iz0CQA2sZ5++9vfypvf/GZzx5dfftlsBlLu0M1CdNOQ0047TbZt2zZ6mm4mcvPNN8sb3/hG0fTKHTt37jSjBTV498ILL8gpp5xiTtUh3vpZv7/rrrtEpyWXO3TzEl2r8KKLLpIHHnigrlI0QHXlJDEEEEAAAQQQQAABBBBAoC4CukvwluH4hA1CNOx3YV9Euvy+utyHRJonQP+7edZ2vhMBwCbWjgbfdOMOHYX3+c9/Xj71qU+VvXtxw5C//Mu/lH//938fPU/XDtQAXiAQkJdeekkWLFhgmcYXv/hF+eQnP2kZKLzlllvkH/7hH8bsDjw+kVwuJ2effbZs2bKlYqCwFkIaoFrUuAYBBBBAAAEEEEAAAQQQaLxAKpeTPw4n5GAqPXoz3RTk5HCw8TfnDnUXoP9dd1JHJkgAsMnVdtttt8l1110nkUhE/vCHP8jy5csn5OA//uM/5H/8j/8hfr9fnnjiCTnrrLPGnFMMDuomIT/84Q8nrCWoQcLVq1dLf3+/+f7yyy8fc73+41+8eLFks1n59a9/LZdccsmEPHz1q1+Vj370o9LX1yd79+4dnYJcLy4aoHpJkg4CCCCAAAIIIIAAAggg0BiBfcmUPDeckB6/T87t6Zx0HfvG5IBU6yFA/7seis5PgwBgk+swGo3KmjVrzLRe3djjG9/4hrz97W+XmTNniv6j1F17dYqvjsD7xCc+ITqSb/zx7LPPytq1ayUej5vg3q233joa0PvNb34jH/7wh80uw294wxvk3nvvFZ9v4hBtHQGoIwF1PcIvfelL8rd/+7cmyKdBw3/9138132ke7rzzTnnve99bdyUaoLqTkiACCCCAAAIIIIAAAgggUHeBRLawMUjIZ70TcDqXl5zk2Sm47vL1S5D+d/0snZwSAcAW1J6uv/eOd7xD/vjHP47eXYOBg4OD5mcd+ffpT39abrjhBvPZ6vjlL38pV1xxxeg1vb29EovFJJ0uDNG+8MIL5ac//anMmTPH8noN7l1zzTVyxx13mO91s5EZM2bIkSNHRn/W9f80H404aIAaoUqaCCCAAAIIIIAAAggggEBzBf44HJf9ybSc3hWSRaH6bRzZ3FK4+270v91dv1MtHQHAqUrV+bxUKiW33367Wd9v69atcuzYMZk9e7bZdffv//7vzQi/SoeuAfi1r31N/uu//kt2795tRvPpGoNXXnmlXHXVVWadwErHr371K/n2t79tphpr8G/evHkmeKi7FV9wwQWVLq/5exqgmum4EAEEEEAAAQQQQAABBBCwhcDRdEYeG4iO5mVGwC8rIyHpspiFZosMt2km6H+3acWPKzYBQJ6DlgjQALWEnZsigAACCCCAAAIIIIAAAnURyOXz8vv+YYmNTBEuJqq7BZ/aGZLF4Q7xevQnjlYL0P9udQ3Y4/4EAO1RD22XCxqgtqtyCowAAggggAACCCCAAAIuEtC1/56LFqb/Wh1dPq+cEQlLX8B6WSsXUdi+KPS/bV9FTckgAcCmMHOT8QI0QDwTCCCAAAIIIIAAAggggIDzBQ6l0man4ESusFnI+OOEUIcs7QyySUgLq5r+dwvxbXRrAoA2qox2ygoNUDvVNmVFAAEEEEAAAQQQQAABNwtk83l5IZaUXfGkZTF9Ho8JAp4YYlpwK54D+t+tULffPQkA2q9O2iJHNEBtUc0UEgEEEEAAAQQQQAABBNpIYDCTlS3DcRnKZC1L3enzyvKusMzuYFpwMx8L+t/N1LbvvQgA2rduXJ0zGiBXVy+FQwABBBBAAAEEEEAAgTYVyOfzsiuRkhdjSdGRgVbHvGBAVnV3tqlQ84tN/7v55na8IwFAO9ZKG+SJBqgNKpkiIoAAAggggAACCCCAQNsKJLI52R5LyD6LTUIWh4NyeleobW2aXXD6380Wt+f9CADas15cnysaINdXMQVEAAEEEEAAAQQQQAABBKQ/nZHnoonRacFBr1fW9UXE7/Wg0yQB+t9Ngrb5bQgA2ryC3Jo9GiC31izlQgABBBBAAAEEEEAAAQTGCui04D3JtGyPJmRZV0gWhjosifQ8j4fAYL2fH/rf9RZ1ZnoEAJ1Zb47PNQ2Q46uQAiCAAAIIIIAAAggggAACVQmkc3kJTDLy7/loXGKZnJzWFZJuv6+qtDm5vAD9b54OFSAAyHPQEgEaoJawc1MEEEAAAQQQQAABBBBAwJYCsWxO1h8bkuK2IQuCATm1MyS6czDH9ATof0/Pzy1XEwB0S006rBw0QA6rMLKLAAIIIIAAAggggAACCDRQ4Jmh2IQNQ3Qy8InhDjklHJQOL4HAWvnpf9cq567rCAC6qz4dUxoaIMdUFRlFAAEEEEAAAQQQQAABBBoqkMnlZX3/sCRzOcv7+DweWRIOysnhDtHPHNUJ0P+uzsutZxMAdGvN2rxcNEA2ryCyhwACCCCAAAIIIIAAAgg0USCbz8uueEpeiidFP1sdHd5CIHBRiEBgNVVD/7saLfeeSwDQvXVr65LRANm6esgcAggggAACCCCAAAIIINASgVQuJzviSdkdT42uBzg+IwQCq6sa+t/Vebn1bAKAbq1Zm5eLBsjmFUT2EEAAAQQQQAABBBBAAIEWCuimIC/GEhPWBSzNEoHAqVUQ/e+pObn9LAKAbq9hm5aPBsimFUO2EEAAAQQQQAABBBBAAAEbCQxlsrItmpAj6UzZXK2bEZEun89GubZXVuh/26s+WpUbAoCtkm/z+9IAtfkDQPERQAABBBBAAAEEEEAAgSoEjqUzsiOWnBAIXBgMyJndnVWk1H6n0v9uvzq3KjEBQJ6DlgjQALWEnZsigAACCCCAAAIIIPD/t3cnQHoU5cPAn2x2NyQQOSQCghIIBVrRBESjBBUlluABliglyCGHFioiKpZaaCnHn0MKlUtFThEBFVEQREAUMSAiCJ8Kn6CixAgIAUIg926yX/X8K/k2yV7Zfd+e2Xl/XUVBeGfm6f71pN+dZ3u6CRAY1QJrJwIHmv23sqcn2uwaHJ6/R/Ut37DKSwA2jNKF1kfAALQ+Wo4lQIAAAQIECBAgQIAAgd4Cz3V1x4LuFbHt+HF9wnSv7Ik7n1sYL+lsL46ZMLatZQE9f7ds16/RcAlA90EpAgagUtgFJUCAAAECBAgQIECAQEsIzFmyLB5etHR1W7cY1xGTN+iMjTvaW6L9vRvp+bvlurzPBksAug9KETAAlcIuKAECBAgQIECAAAECBGovkF79vWP+wli6cuU6bd20oz0mj++MSZ0dtXdY1UDP3y3T1QM2VALQfVCKgAGoFHZBCRAgQIAAAQIECBAgUHuBtE7gPQsWDdjODce2xeTx42KrcR21XyfQ83ftb/khNVACcEhMDmq0gAGo0aKuR4AAAQIECBAgQIAAAQKrBJasWBlzli6Lx5Z2xYqenn5hOsaMiW026Cz+GV/TdQI9f/t7kQQkAN0HpQgYgEphF5QAAQIECBAgQIAAAQItJdC1sif+s3R5kQxcvrL/RGBCeUlnR7x8fGdsVrN1Aj1/t9Qt329jJQDdB6UIGIBKYReUAAH5FVihAAAgAElEQVQCBAgQIECAAAECLSmQ1gV8fFlXPLpkWSxese7agL1RZm6yUWzUPrY2Tp6/a9OVI2qIBOCI+Jw8XAED0HDlnEeAAAECBAgQIECAAAECwxXo6emJp7u6Y86S5fFsV/c6l0mbhLxu4w2He/lKnuf5u5Ldkr1SEoDZyQVMAgYg9wEBAgQIECBAgAABAgQIlCmwsHtFzF26PB5b1hVphmAq0yZOiC3H9b1DcDp+wti2UbdpiOfvMu+y6sSWAKxOX7RUTQxALdXdGkuAAAECBAgQIECAAIHKCqR1Ah9ftjyeWt4du75oQp8JvjRzcPb8hdHd01PsHJw2DZk4Sl4T9vxd2Vsva8UkALNyC7ZKwADkXiBAgAABAgQIECBAgACB0SIwb3lX3P/84jWq+6L2sbH1Bp2xVWdHtLeNqWxTPH9XtmuyVkwCMCu3YBKA7gECBAgQIECAAAECBAgQGG0C/+f5xfHU8q4+q902Zkxs2dleJAPTGoJVKxKAVeuRcuojAViOe8tHNQC1/C0AgAABAgQIECBAgAABAqNCIL3+m2b/pc1DBivj29qKNQRfOq4jNqzIK8Kevwfrtdb4XAKwNfq5cq00AFWuS1SIAAECBAgQIECAAAECBAYQWLxiZTy2dHk8vqwrlq1cOahVWiMwJQJTQnBcW9ugxzfrAM/fzZIdXdeVABxd/VWb2hqAatOVGkKAAAECBAgQIECAAIGWEkgzAtNswP8s7Yq0NuBgZYtxHTF94oTBDmva556/m0Y7qi4sATiquqs+lTUA1acvtYQAAQIECBAgQIAAAQKtKpBmAj6xrCv+s3R5pBmCfZVdXjQhJnV2lEbk+bs0+koFlgCsVHe0TmUMQK3T11pKgAABAgQIECBAgACBVhB4vntFPL5sefx3WVcsX9lTNLljzJjYY7OJkTYKWbt0r+yJ/7toSWzR2RGbd7bH2D6OaYSb5+9GKI7+a0gAjv4+HJUtMACNym5TaQIECBAgQIAAAQIECBAYRCC9IvxM14p4YtnyYu2/HTfcoM8z0ud/eWFJ8VlKEE7qbI8tm5AM9Pztlk0CEoDug1IEDEClsAtKgAABAgQIECBAgAABAhURSDsL97WGYEoGbt7RXmwekv7d3rbu7MH1aYLn7/XRqu+xEoD17dtKt8wAVOnuUTkCBAgQIECAAAECBAgQaKJAev33tmefj/99Ubj/klJ/m3W0F7MD0zqC48eu/27Cnr+b2JGj6NISgKOos+pUVQNQnXpTWwgQIECAAAECBAgQIEBgfQRSAjCtF/jU8u54tqt7yKdObB+7+lXhjdrHDuk8z99DYqr9QRKAte/iajbQAFTNflErAgQIECBAgAABAgQIEMgrkHYSTonAJ5d1DTkZOHn8uH7XFly79p6/8/ZnVaNJAFa1Z2peLwNQzTtY8wgQIECAAAECBAgQIEBgvQWWr5UM7O8V4RkbbxibdLT3ef1FK1bEhmP//+xAz9/r3Q21PEECsJbdWv1GGYCq30dqSIAAAQIECBAgQIAAAQLlCaTXhJ/u6i42Cpm3vDu6e/43HdjZNib22HRijBmz7uYgKfl35/yFsUFbW2ze2V5sIrL4yf/Gdtu+vDh37ty5sc0225TXKJFLE5AALI2+tQNLALZ2/2s9AQIECBAgQIAAAQIECAxdoKenJ+Z3r4inlndF+5gxscOEDfo8ec6SZfHwoqVrfDbv8cfi4OlTJQCHzl3LIyUAa9mt1W+UBGD1+0gNCRAgQIAAAQIECBAgQGB0Cdy3YFExa7B3kQAcXX3YrNpKADZL1nUHFJAAdIMQIECAAAECBAgQIECAAIHGCaRZgnc+tzAWr1gpAdg41tpcSQKwNl05uhoiATi6+kttCRAgQIAAAQIECBAgQGB0CCzsXlHMAnx6eXfM7+qOp7wCPDo6rsm1lABsMrDL9y0gAejOIECAAAECBAgQIECAAAECzRVIG4k8+OijsfOU7YtANgFprneVry4BWOXeqXHdJABr3LmaRoAAAQIECBAgQIAAAQKVEfD8XZmuKLUiEoCl8rducANQ6/a9lhMgQIAAAQIECBAgQIBAPgHP3/msqxxJArDKvVPjuhmAaty5mkaAAAECBAgQIECAAAEClRHw/F2Zrii1IhKApfK3bnADUOv2vZYTIECAAAECBAgQIECAQD4Bz9/5rKscSQKwyr1T47oZgGrcuZpGgAABAgQIECBAgAABApUR8Pxdma4otSISgKXyt25wA1Dr9r2WEyBAgAABAgQIECBAgEA+Ac/f+ayrHEkCsMq9U+O6GYBq3LmaRoAAAQIECBAgQIAAAQKVEfD8XZmuKLUiEoCl8rducANQ6/a9lhMgQIAAAQIECBAgQIBAPgHP3/msqxxJArDKvVPjuhmAaty5mkaAAAECBAgQIECAAAEClRHw/F2Zrii1IhKApfK3bnADUOv2vZYTIECAAAECBAgQIECAQD4Bz9/5rKscSQKwyr1T47oZgGrcuZpGgAABAgQIECBAgAABApUR8Pxdma4otSISgKXyt25wA1Dr9r2WEyBAgAABAgQIECBAgEA+Ac/f+ayrHEkCsMq9U+O6GYBq3LmaRoAAAQIECBAgQIAAAQKVEfD8XZmuKLUiEoCl8rducANQ6/a9lhMgQIAAAQIECBAgQIBAPgHP3/msqxxJArDKvVPjuhmAaty5mkaAAAECBAgQIECAAAEClRHw/F2Zrii1IhKApfK3bnADUOv2vZYTIECAAAECBAgQIECAQD4Bz9/5rKscSQKwyr1T47oZgGrcuZpGgAABAgQIECBAgAABApUR8Pxdma4otSISgKXyt25wA1Dr9r2WEyBAgAABAgQIECBAgEA+Ac/f+ayrHEkCsMq9U+O6GYBq3LmaRoAAAQIECBAgQIAAAQKVEfD8XZmuKLUiEoCl8rducANQ6/a9lhMgQIAAAQIECBAgQIBAPgHP3/msqxxJArDKvVPjuhmAaty5mkaAAAECBAgQIECAAAEClRHw/F2Zrii1IhKApfK3bnADUOv2vZYTIECAAAECBAgQIECAQD4Bz9/5rKscSQKwyr1T47oZgGrcuZpGgAABAgQIECBAgAABApUR8Pxdma4otSISgKXyt25wA1Dr9r2WEyBAgAABAgQIECBAgEA+Ac/f+ayrHEkCsKTeefjhh+NrX/taPPjgg/HPf/4zFi5cGNttt11sv/32cfDBB8f73ve+GDNmzIC1+9e//hXnnntu3HjjjTF37tzYcMMNY8cdd4xDDjkkjjjiiOjo6Bi0dT//+c/jO9/5Ttx3333x9NNPx1ZbbRW77bZbfOITn4iZM2cOev5wDzAADVfOeQQIECBAgAABAgQIECBAYOgCnr+HblXnIyUAS+jdT3/600XibsWKFUX0cePGxfjx4+O5555bXZtdd901rrnmmth22237rOENN9wQBx10UDz//PPF5xtvvHEsXrw4urq6ij+n5N21114bkyZN6vP8lStXxlFHHRUXXXRR8XlbW1tssskm8eyzz67+84knnhhf+tKXmiJkAGoKq4sSIECAAAECBAgQIECAAIE1BDx/uyGSgARg5vvgtNNOi+OPP75I+qVE4Mc+9rF42cteVsz2S8m8NJvvK1/5Svztb3+LXXbZJe68884iOdi7PPDAA/H617++SPilJOD//M//xOTJk4uE4k033VTM3nv00Udjzz33jF/+8pdFcm/t8uUvfzlOPvnkmDBhQnz961+PQw89dHUS8uyzz46TTjopUpLw0ksvjcMOO6zhSgaghpO6IAECBAgQIECAAAECBAgQWEfA87ebQgIw8z2QEnRppt6iRYviiiuuiA9+8IN91uCpp56KnXfeOZ544on40Y9+FPvvv/8ax+21115xyy23xIEHHhhXXnnlOtf497//HdOnTy9mFKbP03G9S/rL3zthmK63djnnnHPi2GOPLWYFPv744+skIUdKZwAaqaDzCRAgQIAAAQIECBAgQIDA4AKevwc3aoUjzADM2Mt//vOfi8TcRhttFAsWLOhzZt6q6nz0ox8t1ub7zGc+U6wVuKrMmTOnWCcwre+X1gBMa/b1Vb761a/GF77whZg1a1bceuutaxySZv6lGYD77rtvXHfddX2en2b/TZs2rVij8PLLLy/WJWxkMQA1UtO1CBAgQIAAAQIECBAgQIBA3wKev90ZSUACMON9kF7r3WmnnWKDDTaIF154Idrb2/uNfuSRR8Yll1xSvC58yimnrD4uvR6cXs/tK7HX+2LpFeC0qUh6tfiRRx4p/juVnp6eIoGYPh8ssXfCCSdEWgfwLW95S9x2220NlTIANZTTxQgQIECAAAECBAgQIECAQJ8Cnr/dGBKAme+B9ArwK1/5yvj73/8eF1xwQXzkIx/pswbp1d80+y7tynv77bfHm9/85tXH7b777vG73/0uTj/99Pj85z8/YAtSsjElHS+88ML48Ic/XBybYqedglNJcbbccst+r3HXXXcVm4mkJOLSpUujs7OzYWIGoIZRuhABAgQIECBAgAABAgQIEOhXwPO3m0MCsIR74Le//W28853vjOXLlxdJucMPP7yYnZc240hr96WZdmnG32OPPVZs5pF2C+5d0rFp9l7aBfhd73rXgC3Yb7/94qc//Wkxiy+98ptKSiimGX2bb755zJs3b8Dz06Ykac3CVNKrxy9/+csbJmYAahilCxEgQIAAAQIECBAgQIAAAQlA98CAAl4B7oNn4cKFsWzZsobdOim513sn37/+9a/Fq73XXnttnzG23XbbYobfAQccsM7n6fXhVLc0C3C33XYbsI5phuFFF10URx11VJx//vnFsVdddVWx+UiaBfjwww8P2sa01mB3d3ek2YBveMMbBj1+1QEpwTdQmTt3bjG7MJU//OEP/a5lOOSADiRAgAABAgQIECBAgAABAgTWEUhv/82YMaP4/2kvgbQpqNJ6AhKAffT5qsRZo26HtJZeWrsvlfQacJqNd95550WaYZdKerU2JQjTxiCppFdu3/72t8e3vvWtYr2+VeWZZ54pZu6lkpKIr3jFKwasYnpF+Iwzzoh99tknfvaznxXHpg1FPvvZz8brX//6+P3vfz9oE7fYYotIuxJfc801kWYUDrWkNigECBAgQIAAAQIECBAgQIBAdQTSBJzXve511amQmmQTkADsg7qZCcA0q++HP/xhsRHIcccdF2mzj/Rq7dixYyMl+NIrwmn33rR2X9rhd/bs2TFlypSilr0TgA899FCxochAZaAEYJrNl2b1DVYkAAcT8jkBAgQIECBAgAABAgQIEBgdAtdff328+93vHh2VVcuGCkgA9sHZrFeA03p8aRZd2v03Jfr6e4W3q6sr0mYf99xzT+y7775x3XXXra7l+rwCnNYYvPjiiyv5CnDaVCQlMVOCcdKkSWvsiLznnnsW7f31r3/d0Js998Xq0o7cbgPF6z11fX1eHR9uXwz3vN5taMQ1Vl2vkddqVh2Hcr80qx1Dib32MVWqy3Dq3+x7YyR1Gs3n5h5rktVI78WRnp9jTGhkHYdyf+WON1CdqlSXodj1d0xd2jESg0afm3u8GWkfjvT8HGNNI8bU9ennRpqsT9y6/z2tmmsj+qbMa/RegittDLrDDjuUWR2xSxKQAMwInzbfSJtwHHPMMXHOOecMGPnee+9dPS03bfqR1gVMpS6bgAzU+KlTpxYfP/jggxl7p/Gh6tKOxssM/4rD3TxmuH0x3PN6t7AR11h1vUZeq1l1HErvNqsdQ4m99jFVqstw6t/se2MkdRrN5+Yea5LVSO/FkZ6fY0xoZB2Hcn/ljudnm6H0imPWFsg93oz078VIz88x1jRiTF2fO7WRJusTt79jq1af4bapLu0Ybvsbfd5wx5pG18P1yhWQAMzon2a6Pf3008VGHH1t8NG7KmmtwI022ijSTLmf//znxc7BqaSZgWkDkLRJSHrFd6CSNvpI2f0LL7yw2HE4lfTn9P9TSb9x3HLLLfu9RIqT4qX1/FI90lqFOUpdBvu6tCNHnw81xnC/uIbbF8M9r1k/3DaiPn1ZN+u6o+EH09xtH+q9vr7H1aUd69vuZh2fe6xpxMNqI++BRl6rWePhUPq+We0YSuy1j6lSXYZT/1Xn1KUdIzFo9Lm5x5uR9uFIz881JjSynoP1ec5Yg9WlEd8nQ4mR45iqueZoczNjDHesaWadXDu/gARgRvOUbHvyySfjRz/6Uey///4DRl65cmW86EUvikWLFsUvfvGL2HvvvYvj02YiJ510UsyaNStuvfXWfq+RZg2m2YIpefePf/xj9WYiPT09xX+nzy+//PI4+OCD+71G2rzkxBNPjD322CN+85vfZJOqy2Bfl3Zk6/ghBBruF9dw+2K45zXrh9tG1Kcv5mZdt78uzR1voFurSnUZwl+Bfg+pSztGYtDIc3OPNY14YGvkPdDIazVrPBxKfzerHUOJvfYxVarLcOq/6py6tGMkBo0+N/d4M9I+HOn5ucaERtZzsD7PGWuwujTi+2QoMXIcUzXXHG1uZozhjjXNrJNr5xeQAMxonpJ4N998c7H5x5lnnjlg5D/96U+x8847F8f897//LdbKS2XOnDlFAq+jo6PYvjttFNJX+epXv1psJtJXovDkk08udiLuvTvw2tdICchp06YVr+EOlihsNGFdBvu6tKPR/TuS6w33i2u4fTHc85r1w20j6tOXf7Ou219f54430D1XpbqM5O9GXdoxEoNGnpt7rGnEA1sj74FGXqtZ4+FQ+rtZ7RhK7LWPqVJdhlP/VefUpR0jMWj0ubnHm5H24UjPzzUmNLKeg/V5zliD1aUR3ydDiZHjmKq55mhzM2MMd6xpZp1cO7+ABGBG86997Wvx2c9+NsaNGxd33313TJ8+vc/o3d3dkdYLvPPOO4skXEoG9i577bVX3HLLLcVrxFdeeWUxy693SUnClDx87rnnis8PPPDANT5Pf/knT54c6TXj3rMLex909tlnx6c+9anYZJNN4vHHH4/x48dnlBKKQN8CvrjcGQQI5BAw1uRQFoMAgSRgvHEfECCQQ8BYk0O5+jEkADP2UUq4vfWtb43Zs2fHxIkTixl6hxxySGy99dbR1tYW8+fPjzvuuCOOP/74eOCBByLt+Jt2On31q1+9Ri3TZzNmzIglS5YUyb1TTz11dULvpptuiqOPPrqYKZh2TkqJwrFjx67TyjQDMM0EnDBhQqTE5Ic+9KEiyZeShmeddVbxWZoFeOmll8Zhhx2WUUkoAv0L+OJydxAgkEPAWJNDWQwCBCQA3QMECOQS8LNNLulqx5EAzNw/aTbdUUcdFTfccMPqyGlzjZR8W7Bgwer/l2bopZ2C02u6fZV0/kEHHRTPP/988fHGG28cixcvjq6uruLPM2fOjGuvvTbSxiN9lZTcS/W46KKLio9TAnLTTTeNZ555ZvWf0/p/X/rSlzILCUegfwFfXO4OAgRyCBhrciiLQYBAEjDeuA8IEMghYKzJoVz9GBKAJfXR7bffHpdcckmxQccjjzwSCxcuLNb2mzJlSjFzLyXnBtt1N60BmJKEN954Y8ydO7eYzbfTTjvFoYceGkcccUSxTuBgJe0wfP7558d9991XJP/SWoMpefjJT34ydtttt8FO9zmBrAK+uLJyC0agZQWMNS3b9RpOILuA8SY7uYAEWlLAWNOS3b5OoyUA3QcECBAgQIAAAQIECBAgQIAAAQIEaiwgAVjjztU0AgQIECBAgAABAgQIECBAgAABAhKA7gECBAgQIECAAAECBAgQIECAAAECNRaQAKxx52oaAQIECBAgQIAAAQIECBAgQIAAAQlA9wABAgQIECBAgAABAgQIECBAgACBGgtIANa4czWNAAECBAgQIECAAAECBAgQIECAgASge4AAAQIECBAgQIAAAQIECBAgQIBAjQUkAGvcuZpGgAABAgQIECBAgAABAgQIECBAQALQPUCAQK0Frrjiirj66qvjd7/7XWy22WYxc+bM+NSnPhXTpk2rdbs1jgCB6gr89re/jf333z9mzJgR119/fXUrqmYECIxKgTTGnHrqqfGXv/wlFi9eHDvuuGN87GMfi0MPPTTa2tpGZZtUmgCB6gm88MILcd5558UPfvCD+Ne//hXt7e0xefLkOOigg4oxZ8KECdWrdIvXSAKwxW8AzSdQZ4ETTzwxTjjhhHj/+98f++yzT8ybNy8uv/zyePjhh+O2226LN7zhDXVuvrYRIFAxgb///e9x/vnnFz8sr1y5Mvbee28JwIr1keoQGO0C5557bhx77LHxnve8p/j5Z9y4ccXPPBdeeGEccsghcfHFF4/2Jqo/AQIVEFi4cGG89a1vLRJ/xxxzTEyfPj1WrFgRd955Z3zrW9+KN73pTXHzzTf7pUMF+qp3FSQAK9YhqkOAQGME5syZE1OmTInDDz+8+KF3VZk/f34x62ajjTaK+++/vzHBXIUAAQKDCMydOze23XbbmDRpUlx66aVx9NFHx6te9SoJQHcOAQINE/jnP/9ZjCsHHnjgOom+s88+u3gDIj2cp7chFAIECIxE4LTTTosvfvGLcd9998XOO++8xqW+/vWvx3HHHVd8tssuu4wkjHMbLCAB2GBQlyNAYGgCv/zlL4tX4BYsWBCPPvpo8WDcV+np6Ykrr7yyeGB+8MEH47nnnoutt946Zs2aVfy2Kf2g21f5whe+EN/4xjfi6aefjokTJ65xyA9/+MM44IAD4o477ojdd999aBV2FAEClRdo9rgyEoBnn302brzxxmJGzgYbbBDbbbedBOBIQJ1LoASBKo8xiSPNukm/XPjHP/5R/BK0d0nJwfT/zjrrrGKGoEKAQLUFqj7e/OQnP4n0s82HP/zhdSDTK8HpFxG/+c1vYo899qg2dIvVTgKwxTpccwlUQeA73/lOfOITn4ju7u6iOv0lABctWlQk6m644YbiuLSuRErmpVl8qaTXWr797W8Xs/zWLun13vT57bffvs5n6fy0HuApp5wSxx9/fBVI1IEAgREK5BhXRljFNU6XAGykpmsRaL7AaBhjUh2vu+664uemtdf6+/Wvf1388jSti5x+EaEQIFBdgdEw3vTWSxM2li1bFmlNwLvvvrt4zks/59x6660xduzY6kK3YM0kAFuw0zWZQBkCab2rn/3sZ5FeQUm/Ders7Izly5cPmABMi1WnNfte8pKXRFrT5r3vfW90dHTEE088USTvvvnNbxY/4N5yyy3FD7W9yzbbbFP8xiltAtJXSa8Ap+un35YrBAiMToHc40pa0Ho45cwzz4wNN9xQAnA4eM4hUKLAaB5jerOldbne8pa3xEMPPVT88+IXv7hEVaEJEOhLYDSPN+ktrd5vZR122GFxwQUXFM9tSrUEJACr1R9qQ6C2AilBl377nMrUqVMjrQ2x11579ZsAvOuuu4o1alKi8N57741Xv/rV69h85jOfKV7z3WmnnYofaHuXNPsvzQxMC+73VdJrxLvttlv8+Mc/rq25hhGou0DucWXMmDHDIk1LEaz9wG0G4LAonUQgq8BoHmNWQaVX9NIvPG+66ab46U9/WmyKphAgUD2B0TzeLFmyJP70pz8VrwTPnj07zjnnnGIjojQRY7g/O1Wvh+pRIwnAevSjVhCovEBaADbtEnXkkUfGSSedVKzNl7aJT6WvV4DTcZdcckl88pOfLGYN9lXS7lPpITpdK33ZvPGNb1x9mBmAlb8lVJDAiAVyjysjrnCvC0gANlLTtQg0R2A0jzFJJL15cdRRRxUP4N///vdjzz33bA6UqxIgMGKB0T7e9Aa46qqr4oMf/GCkf6flnJTqCEgAVqcv1IRArQWuv/76Yqv49OptKmmX3v4SgGn9iK222irSGoBrJ/bWRkpTzC+77LJI/04bhawqA60BmH47lWbjWAOw1recxrWAQO5xpZGkEoCN1HQtAs0RGK1jTNpgLW30kX4+SrP/0sYfm266aXOQXJUAgYYIjKbxJr2unJY3mTZtWuy9997rtH/p0qXF0icf//jHi2WclOoISABWpy/UhEBLCQyUAEy7Xr397W8vNvxIybq0+Ud/ZdVvmNIrvf/5z39WHzbQLsCrdqayC3BL3XIa2wICzR5XGkkoAdhITdcikEdgNIwx//73v+Md73hHPPnkk/Hd73433v3ud+fBEYUAgYYKVH28ScszTZo0afUST70b/8wzz8Tmm28ep59+enz+859vqIuLjUxAAnBkfs4mQGCYAgN9qa2a0ffa17427rnnngEj/PnPf47p06cXO0ylTUVW7XqXrj9lypRiHcALL7xw9TXSDsAzZswoZiLef//9w6y90wgQqKJAs8eVRrZZArCRmq5FII9A1ceYrq6uYv3k9AvR9AbFDjvskAdGFAIEGi5Q9fHm+OOPjzPOOCN+9atfFRsv9i4p6ZdmCP7hD3+IXXfdteE2Ljh8AQnA4ds5kwCBEQgM9KV22mmnRfpSSbMAb7755gGjPPbYY5HW+0sl7Q685ZZbrj7+oosuKta+SbsH77vvvjFv3rz43ve+F0899VSkWYa9d6saQVOcSoBARQRyjCuNaqoEYKMkXYdAPoGqjzFp47O0W/lxxx1XzALsq2y//fbF+skKAQLVFqj6eLNs2bJ485vfHGkH4KOPPjrS8ktp+YGf/OQnkV5nPvHEE+PLX/5ytZFbsHYSgC3Y6ZpMoAoCA32pHXPMMXHeeefFBz7wgUiv6w5U0q5TEyZMKA754x//GK95zWvWOPzaa68t1gZMuwqn9W923333Irnot+JVuAvUgUBjBXKNK42otQRgIxRdg0BegaqPMQcddFBceeWVA6KccMIJ8ZWvfCUvnGgECKy3QNXHm9SgtCFj2qzx6quvjkceeSQ23njjmDp1anzuc5+LtKuxUj0BCcDq9YkaEWgJgaF8qaVdo9IafyNJALYEpkYSIFAIGFfcCPCvXMgAAAmSSURBVAQINFPAGNNMXdcmQKC3gPHG/dAMAQnAZqi6JgECgwo0alp7WufmZS97WRFv7VeAB62EAwgQqJWAcaVW3akxBConYIypXJeoEIHaChhvatu1pTZMArBUfsEJtK5A1Re2bd2e0XICo1fAuDJ6+07NCYwGAWPMaOgldSRQDwHjTT36sWqtkACsWo+oD4EWEWjU1vZprZu05s3WW29d7HqnECDQugLGldbtey0nkEPAGJNDWQwCBJKA8cZ90AwBCcBmqLomAQKDCgz0pfbCCy/EVlttFYsWLYrZs2fHG9/4xn6vd9hhh8Vll10WH/rQh+K73/3uoHEdQIBAfQWMK/XtWy0jUAUBY0wVekEdCLSGgPGmNfo5dyslAHOLi0eAQCEw0Jda+vzII4+MSy65JNKOwOecc06famnnqe233z7mzZs3aKIQOwEC9RcwrtS/j7WQQJkCxpgy9cUm0FoCxpvW6u9crZUAzCUtDgECawgM9qV21113xcyZM6OzszPuueeemDZt2jqCn/70p+Oss86KnXbaKR566CHCBAi0uIBxpcVvAM0n0GQBY0yTgV2eAIHVAsYbN0MzBCQAm6HqmgQIDCow2JdausChhx4al19+eUyaNCnOPffc2G+//aKjo6PY7ffkk0+Ob3/729HW1ha33HJLzJo1a9CYDiBAoN4CxpV696/WEShbwBhTdg+IT6B1BIw3rdPXOVsqAZhTWywCBIb8W610YFoD8IADDogbbrihOK+9vT0mTpwY8+fPL/48bty4Igl4+OGHkyVAgMCgSwsYV9wkBAiMRGAoD+R+dhmJsHMJEFglYLxxLzRDQAKwGaquSYDAoAJD+VJLF+np6Ym0029aD/CBBx6IBQsWxEtf+tJ429veFscee2xMnTp10FgOIECgNQSMK63Rz1pJoCwBY0xZ8uISaD0B403r9XmOFksA5lAWgwABAgQIECBAgAABAgQIECBAgEBJAhKAJcELS4AAAQIECBAgQIAAAQIECBAgQCCHgARgDmUxCBAgQIAAAQIECBAgQIAAAQIECJQkIAFYErywBAgQIECAAAECBAgQIECAAAECBHIISADmUBaDAAECBAgQIECAAAECBAgQIECAQEkCEoAlwQtLgAABAgQIECBAgAABAgQIECBAIIeABGAOZTEIECBAgAABAgQIECBAgAABAgQIlCQgAVgSvLAECBAgQIAAAQIECBAgQIAAAQIEcghIAOZQFoMAAQIECBAgQIAAAQIECBAgQIBASQISgCXBC0uAAAECBAgQIECAAAECBAgQIEAgh4AEYA5lMQgQIECAAAECBAgQIECAAAECBAiUJCABWBK8sAQIECBAgAABAgQIECBAgAABAgRyCEgA5lAWgwABAgQIECBAgAABAgQIECBAgEBJAhKAJcELS4AAAQIECBAgQIAAAQIECBAgQCCHgARgDmUxCBAgQIAAAQIECBAgQIAAAQIECJQkIAFYErywBAgQIECAAAECBAgQIECAAAECBHIISADmUBaDAAECBAgQIECAAAECBAgQIECAQEkCEoAlwQtLgAABAgQIECBAgAABAgQIECBAIIeABGAOZTEIECBAgAABAgQIECBAgAABAgQIlCQgAVgSvLAECBAgQIAAAQIECBAgQIAAAQIEcghIAOZQFoMAAQIECBAgQIAAAQIECBAgQIBASQISgCXBC0uAAAECBAgQIECAAAECBAgQIEAgh4AEYA5lMQgQIECAAAECBAgQIECAAAECBAiUJCABWBK8sAQIECBAgAABAgQIECBAgAABAgRyCEgA5lAWgwABAgQIECBAgAABAgQIECBAgEBJAhKAJcELS4AAAQIECBAgQIAAAQIECBAgQCCHgARgDmUxCBAgQIAAAQIECBAgQIAAAQIECJQkIAFYErywBAgQIECAAAECBAgQIECAAAECBHIISADmUBaDAAECBAgQIECAAAECBAgQIECAQEkCEoAlwQtLgAABAgQIECBAgAABAgQIECBAIIeABGAOZTEIECBAgAABAgQIECBAgAABAgQIlCQgAVgSvLAECBAgQIAAAQIECBAgQIAAAQIEcghIAOZQFoMAAQIECBAgQIAAAQIECBAgQIBASQISgCXBC0uAAAECBAgQIECAAAECBAgQIEAgh4AEYA5lMQgQIECAAAECBAgQIECAAAECBAiUJCABWBK8sAQIECBAgAABAgQIECBAgAABAgRyCEgA5lAWgwABAgQIECBAgAABAgQIECBAgEBJAhKAJcELS4AAAQIECBAgQIAAAQIECBAgQCCHgARgDmUxCBAgQIAAAQIECBAgQIAAAQIECJQkIAFYErywBAgQIECAAAECBAgQIECAAAECBHIISADmUBaDAAECBAgQIECAAAECBAgQIECAQEkCEoAlwQtLgAABAgQIECBAgAABAgQIECBAIIeABGAOZTEIECBAgAABAgQIECBAgAABAgQIlCQgAVgSvLAECBAgQIAAAQIECBAgQIAAAQIEcghIAOZQFoMAAQIECBAgQIAAAQIECBAgQIBASQISgCXBC0uAAAECBAgQIECAAAECBAgQIEAgh4AEYA5lMQgQIECAAAECBAgQIECAAAECBAiUJCABWBK8sAQIECBAgAABAgQIECBAgAABAgRyCEgA5lAWgwABAgQIECBAgAABAgQIECBAgEBJAhKAJcELS4AAAQIECBAgQIAAAQIECBAgQCCHgARgDmUxCBAgQIAAAQIECBAgQIAAAQIECJQkIAFYErywBAgQIECAAAECBAgQIECAAAECBHIISADmUBaDAAECBAgQIECAAAECBAgQIECAQEkCEoAlwQtLgAABAgQIECBAgAABAgQIECBAIIeABGAOZTEIECBAgAABAgQIECBAgAABAgQIlCQgAVgSvLAECBAgQIAAAQIECBAgQIAAAQIEcghIAOZQFoMAAQIECBAgQIAAAQIECBAgQIBASQISgCXBC0uAAAECBAgQIECAAAECBAgQIEAgh4AEYA5lMQgQIECAAAECBAgQIECAAAECBAiUJCABWBK8sAQIECBAgAABAgQIECBAgAABAgRyCEgA5lAWgwABAgQIECBAgAABAgQIECBAgEBJAhKAJcELS4AAAQIECBAgQIAAAQIECBAgQCCHgARgDmUxCBAgQIAAAQIECBAgQIAAAQIECJQkIAFYErywBAgQIECAAAECBAgQIECAAAECBHIISADmUBaDAAECBAgQIECAAAECBAgQIECAQEkC/w9zo14Yma11AQAAAABJRU5ErkJggg==\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as animation\n",
    "\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "%matplotlib notebook\n",
    "\n",
    "train_X, train_y = fetch_diabetes()\n",
    "\n",
    "n_alphas = 100\n",
    "alphas = np.logspace(-3, 0.5, n_alphas)\n",
    "\n",
    "coefs = []\n",
    "lasso_coefs = []\n",
    "for a in alphas:\n",
    "    reg = ElasticNet(alpha=a, l1_ratio=0.9, fit_intercept=False)\n",
    "    reg.fit(train_X, train_y)\n",
    "    coefs.append(reg.coef_)\n",
    "    reg = Lasso(alpha=a, fit_intercept=False)\n",
    "    reg.fit(train_X, train_y)\n",
    "    lasso_coefs.append(reg.coef_)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "xdatas, ydatas = [], []\n",
    "lasso_xdatas, lasso_ydatas = [], []\n",
    "lns = []\n",
    "lasso_lns = []\n",
    "for i in range(0, 10):\n",
    "    xdatas.append([])\n",
    "    ydatas.append([])\n",
    "    lasso_xdatas.append([])\n",
    "    lasso_ydatas.append([])\n",
    "    if i == 0:\n",
    "        ln, = ax.plot([], [], label='弹性网络回归')\n",
    "        lasso_ln, = ax.plot([], [], linestyle='--', label='Lasso回归', alpha=0.3)\n",
    "    else:\n",
    "        ln, = ax.plot([], [])\n",
    "        lasso_ln, = ax.plot([], [], linestyle='--', alpha=0.3)\n",
    "    lns.append(ln)\n",
    "    lasso_lns.append(lasso_ln)\n",
    "\n",
    "def init():\n",
    "    ax.set_xlim(alphas[0] , alphas[len(alphas) - 1])\n",
    "    ax.set_ylim(-800, 800)\n",
    "    ax.set_xscale('log')\n",
    "    ax.set_xlim(ax.get_xlim()[::-1])  # reverse axis\n",
    "    return lns\n",
    "\n",
    "def update(i):\n",
    "    for j in range(0, 10):\n",
    "        xdatas[j].append(alphas[i])\n",
    "        ydatas[j].append(coefs[i][j])\n",
    "        lns[j].set_data(xdatas[j], ydatas[j])\n",
    "        lasso_xdatas[j].append(alphas[i])\n",
    "        lasso_ydatas[j].append(lasso_coefs[i][j])\n",
    "        lasso_lns[j].set_data(lasso_xdatas[j], lasso_ydatas[j])\n",
    "    return lns, lasso_lns\n",
    "\n",
    "ani = animation.FuncAnimation(fig, update, frames = range(len(alphas) - 1, -1, -1), init_func=init, interval=50, blit=True, repeat=False)\n",
    "# ani.save('弹性网络.gif')\n",
    "plt.title('弹性网络回归', color='#264653')\n",
    "plt.axis('tight')\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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提交于 2021-07-23 17:48 . 06-弹性网络回归算法

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