notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Практическая работа №8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Задание 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Улица</th>\n",
       "      <th>Гараж</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Понедельник</th>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Вторник</th>\n",
       "      <td>98</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Среда</th>\n",
       "      <td>75</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Четверг</th>\n",
       "      <td>91</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Пятница</th>\n",
       "      <td>78</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Улица  Гараж\n",
       "Понедельник     80    100\n",
       "Вторник         98     82\n",
       "Среда           75    105\n",
       "Четверг         91     89\n",
       "Пятница         78    102"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.DataFrame(\n",
    "    data={\n",
    "        'Улица': [80, 98, 75, 91, 78],\n",
    "        'Гараж': [100, 82, 105, 89, 102],\n",
    "    },\n",
    "    index=[\n",
    "        'Понедельник', 'Вторник', 'Среда', 'Четверг', 'Пятница',\n",
    "    ],\n",
    ")\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Коэффициент корреляции: -0.9999999999999998\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    \"Коэффициент корреляции:\",\n",
    "    f\"{data['Улица'].corr(data['Гараж'])}\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Задание 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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_device_pixel_ratio', {\n",
       "                device_pixel_ratio: fig.ratio,\n",
       "            });\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",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\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",
       "    fig.rubberband_canvas.style.cursor = msg['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",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\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",
       "                img.type = 'image/png';\n",
       "            }\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",
       "                img\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 https://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",
       " * https://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.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\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\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\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.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\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",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(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",
       "    // 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": {
      "text/html": [
       "<img 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XZTMl5qFhA6u+sak/PkcFdCmABMQbUToT/AK4BvLaN8Hlq5Ef5wLNcxlAoI/jlg6gEEoZgmwfwHvLzWYLVd0LCXC4BWl2PIMV39k4wkIBn1ffPFFyY2x1113nRnMo2BAjwFjkm+6i2mB5VxYG46CxDJaMhWvk5uAIFHAbARmbJCwIIHJHbhic3M0QMYSJiwhKsUsNwFBgoS9M9gAi2/1sbE6d6kUliwhKUDBch08E218dpWAYMAWzYjhs4B9QrYldwkW+Ocb/MffE9+LlWRAmMRHzO7ghCeUyZMnrzYDBz3jszJYyoQTriopmJ3DXpRo3xWWAWKJW6UFTKKleVjuh5m5UiXeno022ijvpvVoRg22kHxFp5UVsq09ARkwYEBd0gy9oXsSn7Ev6PzzzzfNRrIYXS6bpN+3WZ5aiiF/TwW0KsAERCsZ+hWsAlgeguVPKPiWHt/WFysuE5D48i8sr8HANsnxlLn+YXlLdCQtlm7gm/P4UZT4ZjlaglRsn0saCQjWwOduoM6nLzbTYuAZFQy+4v+fWwcDPQzKcfQmBvf4BhSJXHwDOpbERPtfbrrpJrNOPrfkS0DwLTa+zUbJHZRitgYDFgyqsZ8BG+yTMMuXgOCb92jNeb4BazSAxuwAklOchlVuAgIdMcjEt/Fff/21We4T3/iNGYLo5CBs/sc3yrYlnoDg9DMc9Vqq4DhpfCOPgsQTCWjuEqe4DcyaYD8TEias5wd/7HuKF+znio4uRhIe2Y+egV/R3iAkgdG36KV8zfd7xDgOC4gKeBdb0lPsHfi8Yu9MFMtYVlVMi7gtLKuKTt6CLphlixdsdsfyNBQkhW+//bY5fKNQSTKYz+oYXny+sbwU8Qy98BlBEpjEZ3whgSVyUcFSPOyrK1Rc9Y2VxBbrUAENCjAB0UCBPlSNAtgIiuUZ0R/6QoPUeINdJSDxE30woMUabpz+k2QwG/+jiVmDUkcHx/3H5udo43EuSFd/ZOMzIFjeE23yLxY4GESg/dHJQIVmLDBgRrKGfRrRevckAYm7XqIlF/Hn8yUgiAsM3DAQ7Natm0l0ooKEBHsKUDCAxQA7CbN8CQhsYOkZ7jLJPQIaiVV0KlW0/h93wiRNQDAgxulKpQ5ViGuBJSxJju4tpXd8QBol1qXqYKYpPmDPN2MBG0gowBFalONrvoMIsBci+sIBWkezJaV8zfd73HeCI11RMJMVbQivxFY8MUWSkHs8dzGb8dOa8mmIOMQSpeieC5x0h43/GIyjH8o9gSzJYD7OGzM3uUlPPn8//vhjs+8CJenBDbn3gOCz+eyzzxob8b47ic9IxHHqV5Ss4e8AZuGwHDLfKViu+sZK4oF1qIAGBZiAaKBAH6pGAQz84pfV4Y9ZqT/2LhIQ/CHFhtFowBMfHCUZzAIANjFj4FBuKXbbu6s/svEEBMscoj0upXzFN/DRwA0DzGiTaFQPCQf27CSZUcl9V6G7FvIlIKiL5Rw4mQiDGXyzilkIlGjPEHzFN8wYtCdhVigBueGGG0yigILZlOiUKCwHxOlo2MCM92A/Q9IEBBufMZAqdqlcIRalLnsrxRC/jw9IsYEex52WKpi1i3/LDy5YZhYvmHXCz5LcA5P7vuhCxPjPseEciSwKTr7CLEylBTNY0aECOAoXM3qVltxv5yu1g2Qk36lOiGt8cVEu6ySnYFXiayUJCA7qiI7JxkwpltFGy/ySJCDwE4eP4HMSfemR1HcuwUqqFJ+rJgWYgFQTTbYlcwVyz47HAAdLsooVFwlI/AQWXAAX3ZGB9yYZzOYu90BSgQErTo7CyTn4VhN3O0R7KAoNsnPbmUYCgmVl+BY8ScF6/OiSOeyFyT2aOL6BNho0YgYCx6XiW1cMYLGUJipJbqIvpA1miTCQRMEJO5iFwDe2+PYYA7d4YpWEWaEEBN/AYqM5Znai5UpItPAzfEuLZXpIKFCSJCB4D/YVYRYHBYkS9pFgIzOSKPw/vuWOYqOQX0l4FXomnoBg9iva5F7MJr6Rj58WheVVuTd7x++vgP+YicI39zg0APzxLXb8hLRSS4OiJA9+JTmAopj/mGHDoQUoOO0Nsw+VFuwLyr1bpxJbxQbKuEMICVg5iZKWBAQJA2ZaokMrkDgigYxK0gQEz2MJHxJk3IKetDABSaoUn6smBZiAVBNNtiVzBeLHv2LQjjXypYptAoJverHcBgNt/KFE8oF9ElFJMpjFRvLoqEkkMDjFq9ga8fi3hb5nQIpdsparNW7Bxjp8FJzgE78DAWywsTb6Vh+bSKNvnAsxw8lRDz/8sPl1uTMgqIPZCCQomH347LPPzB4hnDSUOyuShFmxgT6WVSG5wOAZ74nudYAPGCgieUBJkoDg6GEcI4uCk7omTpxodCtUsI4+vgeg3G/F89mND/wLXdiXWw9Lq+IXX+JzEb+dHQN6rPePCr69jpbCFWobllVhKRtKvhkQJJHRJYXwGQPLSgtmerDBGwV9RL47KZLaxkls0abz6PLIpHXLfS6+pBB1cadQ/BQwjRcRxvexICnF/o14KScBieohAcHhE9HSNFyKGe9TeRFhuZHF56tNASYg1UaU7clUgfgaYvzhjY7jLeaUbQISX3ee77jZUoNZDBBxtCcSGZR8S1Vy/ccFgNEyGN8JCJZJxM/lL6Qt9lvET4GKD7xRBwNOzAagYJYHsxGl7mrAADb6ZrOSBCR+ERxmrbBs5T//+Y8ZpMVjpRQz+FwsAYGP0WAbMy1IvrCkBLMtmImJ2pkkAYn2lOCd+Zax5eqPCwnjm29dJyBYVx8lRMU+V/E9L3gOfJFARSV+alHSZCF+wES+BCR+8hpmUHJvtS+nc0Kii4QXBUuBMIuFWchKCg6MwBLNqGAmLMmdP+W+C6zRB+KzhoI9LLgYMV6SDOZLzTTl86vSzwz6iQ4dOpi9QNAZF3Vixq9cn3N9QtsRIyhYhovPd7ztrmaHy2XE56mAFgWYgGghQT+CVwAbWHFiDJa+oAwZMqTu3P1ijbNJQHCDM2Y/cIITlsFgcImNkPFS6g8zZgJw4ks5gxOssY5uhvadgGApEQZ2pZKF3IEwBhrgE5X4GvtiF8tFz4Mr6kcblStJQLDUA8vCcDIUZmdwMhNK7rfvpZihTqmlTvh2H9/yx9+TO8uTJAFBcooBKwpOvorvccoX1/GEGL93nYDkLjEs9NmKJ8k4yQj84zET36+Bma9o5qKQPcxAxBOYfAkIZofwTXdUcpOecjo5nLYW33ydm0CXYwuneUGDqG+ysVXsvdj/Al1R0A8hXnIvJdSWgMTjADNO0R6eeDuT+Bx/Pj7jhPZjVjD3AlImIOVEMJ+tRgWYgFQjVbYpEwVy93/gtCAce1qq2CQgOPkF67tRCh35W2owO2/evFUu1Cv17SiWNGFgFA1KfScgaGuhzbBxreP7NTBoxmAoXuIXhyVJQOKnEsFOJQkI6sUvisT/YzkTlknF9yuUYoZ6pRKQ+AAcz2M5FpbpxZPNJAkIvnWPkq5SCQiWs2GWJb5cyHUCgsEgblrH56ZYiX+LjpvJMSiMl/j+nyQJSG5ilS8BwUAfPKOTkMAaX0RUWuKzT4UuAkxqG0cGR5vicSQ0jmB2WdAXYa9KdG8JTnrDbEhuSTKY9zUDgv0q2O+DZVJI0NCG3Ltz4H8Sn6N2ItEFN/SrKDj4AV8U5RYmIC6jj7ZCVIAJSIjU6LM6BfBHF+vDo+NrceEcBpVJbkCuNAGJNtBigIf34SSffMsqSg1m8a086kUnt+TbrBsXPH7xGH6eRQKCzfFIQgrNguA0MgyGovXX+fZ3YACGJAUF39Zi0FyIFwaU2KQabWi3SUAweMaSj2hgnm+QWopZkgQkvvEcz+cbwCZJQLA8DckOSr6N/PHYiC9rin7uOgGB3UI3vUfvxHKX+IbzfPs7cNEjdEbBfRuYvShUEBsYqEbLFPFcvgQEP8cBA9jXg4LkDSfTlZo1KvTeW265pe6uGcQ6BvVYrldJiWuCOMcJfRjouyi4IwifyUhDJHfwPV9JMpj3lYAgKYv2e2D5XHSfSa7fSXyO6uBzFh3yUOwkNCYgLiKPNkJWgAlIyPTouwoFcNIV9hLgHo6o4Ejb6BbuUk5WmoDE7RYaDOGZJIPZ+B98DNxxkVzu0gkMJPFtbnQyT/T+LBIQvPu0004TbB7NTRqwPAubrKOjVXFKE5amxZdfoX7ukhrohG+5cwv2aOBEo/jJYnim0hkQ1MXSnCjhQ/KTmzgmYVZqBgTvwV0Y0TfS2L+Ab3njJUkCEp9JwvIxnJiW714GJHQ4nSr3qN40EhC0AUcxRzdPx9uEWRrwBzcUrOfH/pf4DBN+Hj+tDP9faH8L4mT//fdf7ajeQp85LK9DshJdwAjdcQxu/GCI3BjDZnXolptc4OQxnMgWnSyF5XDYz4MZnUIFFwEiNqL9I/Hn4rMgWJo3cuTIkje1o/24EwOfn9zPfmQbLJBIoyBhxT6KQodYJBnM+0pAIv8xa4cLNnNjJPp9Ep/xbHyPFz5riMVCd5gwASn1l5G/r3YFmIBUO2G2LzUF8EcWAyB865d7gV3uyS/FnIifhoJjTfHtOAqOzcw9OjM+GIxsYuYFm0wLzQYkGcyOHTt2lYEIkgoM8GEbA0gMgDDAjG4/x5KC6DJA3wkIkgEsJcFyFwzO4AuOt8X6dly4hqVH0RIYaJTvwrhIO9h69NFH6/BggIZv15EUYH8GkkosocCyCuw92X777eu+MbVJQEoFZRJmSRKQUu9JkoBgQIulhFEigSVcWDePWQMM2JBMYVAcbTyOxwbe7zoBOfLII82eGRQMVLEfCYNe8MK3+uAVHRmM5BSDe3w7n1vgF/bJIDlBwecHt5j37NnTLKPC/TGwh0QDSRxO9sKMRnQRY7GkH7MNiKVoBg62cXjCwQcfbJaOQbcvv/zSzI7gQAUc/1rodC/sH9hjjz3qljzCVyRY8BMDZ+z9QrKF419xOzv+XeikOMQxNqNHF/bBFtji6GG0D0kJ2orkCfuH0DdFdwtFR0fn6ohBdqdOncwyPbQTd88Um1lJMpj3nYCAQXQHSL7PTBKfseQKs6TRyYfoL7HUrVBhAlKqd+Lvq10BJiDVTpjtS02B3G9QXb8o3wA3XwJSaj9EksEsfMfAMffEmnxtwnp5fAMbXXDnOwGBLvhWEcs8sPSjWLnqqqvk4osvLvgIZq/wzTSWyxUrGJhhcIfBbbR2vlYSEOhy5ZVXmj1GpQpmArG3Jr5Z23UCgoE/BtDwqVjBvR5gVez+C3zrjU3j0WEAhewh3jCwxj0f+LyhFEtA8Hssl8LeotwvJwq9o9jxwjjRC5+5aF9BKQ7FjqpGcoHZkfiMbSl7+H2+BASzeDgUIDqaGIkpLsIsVpIM5n0mIEjmouS5kN9JfEYCg+PJUZKc1McEJEnU8ZlqVoAJSDXTZdtSVUBDAoKBBDa/FytJExAMFLGUAsusorsz4nYxqMSgD98SZ3kRYTTwxylXOJI13y3W+EYcR7VGdx8U0wdJyIABA+oGD/FnMYjFRlqsZ4dNm4sIywnGJMx8zYBEfmO5zkUXXVS3tCjeHiwzwhIcDHxd+JWrVb4BKU5hw5KgfEfd4tt8MMPMQamCfVu4WDHfgBwzFZhpwMlImPkpd2CMk6zAErNw8f0jcZ+wrAr38OCCzV/+8pcF3cUxsUjubrvttlVm+OIVMEODvWFIujErUahguRd8wl6V3MMZ4nVwCSfussC+BlxqCV/jJb4xHzO3mE3C5Y2hJCD4fCN5wrJTG5/jB1Tg7hkktuVcQMuLCEt9Svn7alSACUg1UmWbvCgQH4RjGQiWs9iW+JKYQt+w276jVH18Y4u2YWCPZU74Q4p19MXWsJeyafv7YgN/3HmBP/hY0oI//lh/j+VBpY7pzfUJm8Ox9wUJCZa14FtvDGALreG2bVOI9bG0CcvcoDeWvOFCRQw8MUhNcuBCpW0uNPDHDBiYYQkTlr5gvw+WDcYvGEz6TrQJyymx9AoDbXDfe++9V7nMMKmt3OewPAkJDmIM9qFS+feLAAAgAElEQVQVfN1qq63MMsJCew/yvQ/JA/bhIHGK9rkg7rEMEcupELvlFCRw2N+E2RXMBKE+fEMyhIF5ufbKeTefpQJUoHYVYAJSu+zZcksFqjUBsZQllepJZh5SeTGNqlCg3JkHFU7TCSpABagAFSioABMQBgcVqFABJiAVCldBNSYgFYhWRVWYgFQRTDaFClABKoCDP1a62CFIKalADSrABMQfdCYg/rTW+CYmIBqp0CcqQAWoQOUKMAGpXDvWrHEFmID4CwAmIP601vgmJiAaqdAnKkAFqEDlCjABqVw71qxxBZiA+AsAJiD+tNb4JiYgGqnQJypABahA5QowAalcO9ascQVwqVd0KRlOzMHpS7Zlzpw5govHUHC6UHQpoa3d0OszAQmdoJ3/TEDs9GNtKkAFqIA2BZiAaCNCf6gAFaACVIAKUAEqQAWoQBUrwASkiuGyaVSAClABKkAFqAAVoAJUQJsCTEAUEcEFVU899ZS0bduWlz8p4kJXqAAVoAJUgApQASoQKYCLWGfPni3777+/NGvWjMJUoAATkApES6vKPffcI3369EnLPO1SASpABagAFaACVIAKOFLg7rvvlmOOOcaRtdoywwREEe9XX31VunTpIgjoLbfcMnXPFi5cKG+//bbstNNO0rRp09TfxxdkpwBZZ6e9zzeTs0+1s30XWWerv6+3k7Mvpct7z/Tp080Xxq+88op07ty5vMp82ijABERRIPzrX/8yyQCSgh133DF1z/773//KSy+9JHvvvbest956qb+PL8hOAbLOTnufbyZnn2pn+y6yzlZ/X28nZ19Kl/ce3+O18rwL42kmIIo4+Q5odmyK4KfsClmnLLAS8+SsBIQHN8jag8gKXkHOCiDkccH3eE2nCnZeMQGx089pbd8BzY7NKT7VxshaNR5nzpGzMynVGyJr9YicOEjOTmR0bsT3eM15AxQYZAKiAELkgu+AZsemCH7KrpB1ygIrMU/OSkB4cIOsPYis4BXkrAACZ0BSgcAEJBVZKzPKBKQy3VirtAL8I1Zao2p4gpyrgWKyNpB1Mp1Cf4qcdRL0PV7TqYKdVyoSkGnTpsntt98ub775puAujEMPPVSGDh1qWjZx4kSZNGmSOW1ggw02sGut8tq+A5odm/KAcOgeWTsUU7EpclYMx7FrZO1YUKXmyFknGN/jNZ0q2HmVeQJy3XXXyYUXXig//fSTaUm9evWkb9++MnLkyLoEZM8995Sbb75ZBgwYYNda5bV9BzQ7NuUB4dA9snYopmJT5KwYjmPXyNqxoErNkbNOML7HazpVsPMq0wRk/Pjx0r17d9lss81k2LBh5g6MDTfcUPr161eXgKB5G2+8sTmW9oknnrBrrfLavgOaHZvygHDoHlk7FFOxKXJWDMexa2TtWFCl5shZJxjf4zWdKth5lWkCsu+++5plV++++660a9fOtKR+/fqrJSAHHHCAzJgxQ2bOnGnXWuW1fQc0OzblAeHQPbJ2KKZiU+SsGI5j18jasaBKzZGzTjC+x2s6VbDzKtMEBJff7bzzzvLMM8/UtSJfAoL9H2PHjpXFixfbtVZ5bd8BzY5NeUA4dI+sHYqp2BQ5K4bj2DWydiyoUnPkrBOM7/GaThXsvMo0AWncuLFgduPhhx8umoDst99+8sYbb8h3331n11rltX0HNDs25QHh0D2ydiimYlPkrBiOY9fI2rGgSs2Rs04wvsdrOlWw8yrTBGTrrbeWH374YZWlVbkzIMuWLZNNN91UWrduLW+99ZZda5XX9h3Q7NiUB4RD98jaoZiKTZGzYjiOXSNrx4IqNUfOOsH4Hq/pVMHOq0wTkIsuusgct3vNNdfI2WefbVqSm4BceeWVMmjQIMG/8Xw1F98BzY6tmqNp1baRdW2wJufa4IxWknVtsCZnnZx9j9d0qmDnVaYJyLfffivbbbedzJkzR4444gg57LDDpHfv3nLggQfKCSecYPZ93HPPPeaUrHfeeUeaNm1aUWsXLVok1157rbz99tvmTpF58+aZo37/9re/rWZv+fLlJiG644475LPPPjMzL/DlvPPOkwYNGtQ9P3v2bONXvnL88ceb+uUWnwE9be4CeWHqLGn1w2z5fO22sk/HzWTLFj8r12U+H4gC/CMWCChLN8nZUsCAqpN1QLAsXCVnC/FSrOpzvJZiMzI1nWkCgpZ/9NFHJvl47733zB0gK1euNP9GwX9vtdVWMm7cOOnQoUPFQkXJQosWLWSnnXaSxx9/vGACcuqpp8ott9wi/fv3lz322ENeffVVk6jg5zfddNNqCQguTYT/8QJfd9ttt7L99RHQs+cvlvMfnCJvzv5GWq2zUs7ruFyumdJAPl9cT3Zpu4EMPaKjtG22Ttm+s4JuBfhHTDcfV96Rsysl9dsha/2MXHhIzi5UdG/Dx3jNvde6LGaegECOFStWyGOPPSZPP/20IFnA/7dq1Uq6desmhx9++CozD5XIt3TpUnPDesuWLc2Fh2uuuWbeBGTq1KlmRub000+X66+/vu5VZ5xxhtx4443muOBtt93W/DxKai655BKzPMxFSTugZ81fLD1vflW+XfKjcTc3AcHP1m+8pow9tTOTEBdAFdngHzFFMFJ0hZxTFFeZabJWBiQld8g5JWEtzaY9XrN0L4jqKhIQn0oVS0CQTAwePFj+/e9/r7K8atasWeaekniyEU9A8HOURo0aWTUl7YDuNeI1M/MRlXwJCH6HmZD7T97dqi2srEsB/hHTxSMtb8g5LWX12SVrfUzS8Iic01DV3mba4zV7D/VbCCYBmTt3rmyyySbWihZLQPbff38zy4E9Irllo402kh122EEmTJhgfhUlIE2aNBHsMUHB0quzzjrLLNeqpKQZ0Njz8ZsbXl7FrUIJCB568ow9uSekEohK6/CPmFIwjt0iZ8eCKjZH1orhOHSNnB2K6dBUmuM1h26qNpVpAnLvvffK0UcfXVKgm2++WS6++GJz6odtKZaAYHnVWmutZTar55Ydd9xRfvzxR8EyLZRPP/1UjjvuOLNxHscEI0G67bbbBEF57rnnmo3sxcoXX3wh+Cdepk+fLrh08cUXXzRLwVyWe974RO5987NVTG7YaKX0/cUKGT2jvnz1/f/tu4lK711aS+9d27h0gbYyVGDhwoUmNhHHlR7mkKH7fHVCBcg5oVBV8BhZVwHEBE0g5wQiZfAIvqzu2rWrGS/i7ypL+QpkmoDgVCkcw3vOOefk9RwD8hNPPFEmTpxo9oRg0G9biiUg7du3F8x04H25BRvSv/rqK/n4448LuoATtPbee2957bXXzOZ62CtULrvsMrn88svz/nrYsGFF69pqwPpUgApQASpABagAFaAClSkwc+ZMM3ZlAlKZfqiVaQKyyy67GHjY9P3Xv/61rhWYacDG7j//+c9m0/gpp5wiQ4YMESx3si2uZkAK+YHN9IcccojceuutctJJJxV0lzMgtiRZvxwF+C1aOWqF+yw5h8uuXM/JulzFwnyenHVy4wyIPZdME5AlS5ZIr1695IknnjBH2d59993y5ptvmoH7Bx98INtss43cfvvtsuuuu9q39P9bcLUHpJBDU6ZMMcunrrrqKrNsrJyS5ppC7gEph0T1Pct1xNXHNF+LyLk2OKOVZF0brMlZJ+c0x2s6W+zeq0wTEDQHR+5ihgOJBi72++STT8w+jIEDB8r5558va6yxhtNWF0tAkDBgpqXQKVj4PRKLYuXhhx82RwfjIkJcSFhOSTugeQpWOTSq61n+EasunoVaQ861wZkJCDnXjgI6W5r2eE1nq916lXkCEjUHS67++Mc/mksIcR/Ivvvu67al/99asQQEU2o46arQPSCTJ0+Wjh07GkvYD7Lhhhuu4uP3338vnTt3NpcqYn0gblEvp6Qd0LiE8DDeA1IOkqp5lgPTqkFZtCHkXBucmYCQc+0ooLOlaY/XdLbarVdqEhA0a8yYMWbTefPmzWX8+PFOT4IaPny4mbLGjMugQYNMotGzZ0+jJvZsRInFySefbPZv4CZ0JBO4CX3UqFEyYMAAGTFiRJ36OP3q66+/ln322cdskMcpWKNHjzazJ5hFufDCC8sm5SOgeRN62ViqogIHplWBsWQjyLmkRFXzAFlXDUp+qRAgSh/jtQBlKcvlTBMQHGObW9544w3B6Vc4KhRLmaKCmZE777yzrMbFH27btq1Z3pWvIMHo16+f+RVmSHAyF5ZQzZkzx9yefsIJJ6y2HAy+IOH48MMP5ZtvvjEb5HEUG25NR0JTSfEZ0NO/WCAvTJklLX+YLXPWbiu/6rgZ7/2oBFogdThYCQSUpZvkbClgQNXJOiBYFq6Ss4V4KVb1OV5LsRmZms40Aalfv37exiPZWLly5Sq/w89wzG01F98BzY6tmqNp1baRdW2wJufa4IxWknVtsCZnnZx9j9d0qmDnVaYJyEsvvbSK95h9wLnKOEkK92lgqRQSj6jgZ9VcfAc0O7ZqjiYmILVD938t5We6dqiTdW2wJmednH2P13SqYOdVpglI3HUcyYujeJ966in57W9/a/Zd4DSsWiq+A5odW+1EF1nXBmtyrg3OnAEh59pRQGdLfY/XdKpg55WKBAQnSv3mN78RAMWMxxVXXGE2ceOm9FoqvgOag5XaiS6yrg3W5FwbnJmAkHPtKKCzpb7HazpVsPMq8wTko48+kgMOOEA+/fRTOe+88+SRRx4xG7ujSwhxW3qtFN8BzcFKrUQW14vXCml+pmuFND/TtUKan2mdpH2P13SqYOdVpgnIxIkTzYlRWH51zz33CI62XbZsmbns789//rPZdI5LCgcPHmxOmar24jug2bFVe0T9r31kXRusybk2OHMGhJxrRwGdLfU9XtOpgp1XmSYgjRo1ksaNG8ujjz5q7tyIl2nTpslJJ50kSFJwod+NN95Y8fG2dhL5q+07oDlY8cc26zeRddYE/LyfnP3orOEtZK2BQvo+kHP6GlfyBt/jtUp81F4n0wRks802kwkTJsjmm29eUKdbbrnF7AdZtGgRj+F1HE3s2BwLqtgcWSuG49A1cnYopnJTZK0ckCP3yNmRkI7NMAGxFzTTBGTevHmy8cYbl2wFbhk/7bTT5OGHHy75bMgP+A5odmwhR0t5vpN1eXqF+jQ5h0qufL/JunzNQqxBzjqp+R6v6VTBzqtMExA716uvtu+AZsdWfTFUqEVkXRusybk2OKOVZF0brMlZJ2ff4zWdKth5xQTETj+ntX0HNDs2p/hUGyNr1XicOUfOzqRUb4is1SNy4iA5O5HRuRHf4zXnDVBgUE0C8v7778uMGTNk4cKFsnLlyrzSHHvssQokS88F3wHNji09ltosk7U2Iun4Q87p6KrRKllrpOLeJ3J2r6kLi77Hay581mYj8wTk2WeflVNPPVVmzpxZUBskJLigEMfyVnPxHdDs2Ko5mlZtG1nXBmtyrg3OaCVZ1wZrctbJ2fd4TacKdl5lmoBMmjTJHL+L5OLII4+UqVOnmn9w6hUSEiQn3377rfTr10823XRTGTRokF1rldf2HdDs2JQHhEP3yNqhmIpNkbNiOI5dI2vHgio1R846wfger+lUwc6rTBOQww8/XMaNG2eO4u3WrZv0799fxowZUzfTgQ/egAED5MUXXxQkK7gPpJqL74Bmx1bN0cQZkNqh+7+W8jNdO9TJujZYk7NOzr7HazpVsPMq0wSkRYsW0rJlS5NcoOQmIPjZ0qVLpW3btrLffvvJ6NGj7VqrvLbvgGbHpjwgHLpH1g7FVGyKnBXDcewaWTsWVKk5ctYJxvd4TacKdl5lmoA0bNhQevbsKffee69pBWY77rjjDlmwYIGss846dS3r1auXvPLKK4L7QKq5+A5odmzVHE2cAakdupwBIev1alGCmmgz/07rxOx7vKZTBTuvMk1AWrVqJdttt52MHz/etGLgwIEyZMgQmTx5smy77bZ1Levevbs8//zzsnjxYrvWKq/tO6DZsSkPCIfukbVDMRWbImfFcBy7RtaOBVVqjpx1gvE9XtOpgp1XmSYgXbt2la+++kqmTZtmWvHoo49Kjx49zKlYw4cPNz/7+OOPTZLSrl07s0G9movvgGbHVs3RxBmQ2qHLGRCy5gxItcYA/07rJOt7vKZTBTuvMk1Ahg4dKhdddJG89957suWWW8qyZctk8803l08//VQ6deokmCHBzAeWZA0bNkzOPPNMu9Yqr+07oNmxKQ8Ih+6RtUMxFZsiZ8VwHLtG1o4FVWqOnHWC8T1e06mCnVeZJiDz5s2TRx55RLp06SJbb721aQlmObDn48MPPzT/X79+fTn++ONlxIgR5rjeai6+A5odWzVHE2dAaocuZ0DImjMg1RoD/Dutk6zv8ZpOFey8yjQBKeb6Bx98YO4A6dChgzRv3tyulYHU9h3Q7NgCCQwHbpK1AxEDMEHOAUBy5CJZOxJSuRly1gnI93hNpwp2XqlNQOyaFWZt3wHNji3MOKnEa7KuRLXw6pBzeMwq9ZisK1UurHrkrJOX7/GaThXsvFKTgGD/xzvvvFN31O4mm2wi22+/veCo3lopvgOaHVutRJYIWdcGa3KuDc5oJVnXBmty1snZ93hNpwp2XmWegCxatEj++Mc/yp133in473hp0qSJHHfccXLFFVdI06ZN7VoaQG3fAc2OLYCgcOQiWTsSUrkZclYOyKF7ZO1QTMWmyFknHN/jNZ0q2HmVaQLy3XffCY7inTJlimkFjtvFrecon3zyibkPBGWbbbaRf/7zn7LuuuvatVZ5bd8BzY5NeUA4dI+sHYqp2BQ5K4bj2DWydiyoUnPkrBOM7/GaThXsvMo0ATnjjDPkxhtvlH322cf8G0fxxgs2op9++unmKN7TTjtNrr/+ervWKq/tO6DZsSkPCIfukbVDMRWbImfFcBy7RtaOBVVqjpx1gvE9XtOpgp1XmSYguOdjxYoV5rLBxo0b523J999/L+3btzfH8X7++ed2rVVe23dAs2NTHhAO3SNrh2IqNkXOiuE4do2sHQuq1Bw56wTje7ymUwU7rzJNQBo1amRuPr/33nuLtuK3v/2tuSV9yZIldq1VXtt3QLNjUx4QDt0ja4diKjZFzorhOHaNrB0LqtQcOesE43u8plMFO68yTUBw+WDr1q1lwoQJRVtx4IEHmtvR33//fbvWKq/tO6DZsSkPCIfukbVDMRWbImfFcBy7RtaOBVVqjpx1gvE9XtOpgp1XmSYg2NNxwQUXyOuvv26O3M1XsBF91113lauvvlrOOussu9Yqr+07oNmxKQ8Ih+6RtUMxFZsiZ8VwHLtG1o4FVWqOnHWC8T1e06mCnVeZJiBwHZvM7777bvPvo446Stq0aWNahFOw7r//frM5vU+fPnLDDTfYtTSA2r4Dmh1bAEHhyEWydiSkcjPkrByQQ/fI2qGYik2Rs044vsdrOlWw8yrTBKRBgwbG+5UrV0q9evXytqTQ7/D8Tz/9ZNd6ZbV9BzQ7NmUBkKI7ZJ2iuIpMk7MiGCm7QtYpC6zEPDkrAZHjhu/xmk4V7LzKNAHBnR+FEo8kzZo1a1aSx4J5xndAs2MLJjSsHSVrawmDMEDOQWBy4iRZO5FRvRFy1onI93hNpwp2XmWagNi5Xn21fQc0O7bqi6FCLSLr2mBNzrXBGa0k69pgTc46Ofser+lUwc4rJiB2+jmt7Tug2bE5xafaGFmrxuPMOXJ2JqV6Q2StHpETB8nZiYzOjfgerzlvgAKDTEAUQIhc8B3Q7NgUwU/ZFbJOWWAl5slZCQgPbpC1B5EVvIKcFUDI44Lv8ZpOFey8UpOA4I6PGTNmyMKFC82m9Hzl2GOPtWut8tq+A5odm/KAcOgeWTsUU7EpclYMx7FrZO1YUKXmyFknGN/jNZ0q2HmVeQLy7LPPyqmnniozZ84s2JLoJKzly5fbtVZ5bd8BzY5NeUA4dI+sHYqp2BQ5K4bj2DWydiyoUnPkrBOM7/GaThXsvMo0AZk0aZJ07tzZnIR15JFHytSpU80/F154oUlIkJx8++230q9fP9l0001l0KBBdq1VXtt3QLNjUx4QDt0ja4diKjZFzorhOHaNrB0LqtQcOesE43u8plMFO68yTUAOP/xwGTdunEyYMEG6desm/fv3lzFjxkg004EP3oABA+TFF18UJCutW7e2a63y2r4Dmh2b8oBw6B5ZOxRTsSlyVgzHsWtk7VhQpebIWScY3+M1nSrYeZVpAtKiRQtp2bKlSS5QchMQ/Gzp0qWC+0L2228/GT16tF1rldf2HdDs2JQHhEP3yNqhmIpNkbNiOI5dI2vHgio1R846wfger+lUwc6rTBOQhg0bSs+ePeXee+81rcBsxx133CELFiyQddZZp65lvXr1kldeeUXmzp1r11rltX0HNDu20gExbe4Ceer9ebLghx/lZ2uvKQdss7Fs2eJnpSsqe4KslQFJyR1yTklYhWbJWiGUFFwi5xREdWDS93jNgcvqTGSagLRq1Uq22247GT9+vBFm4MCBMmTIEJk8ebJsu+22dWJ1795dnn/+eVm8eLE6AV065Dug2bEVpjd7/mI5/8Ep8ubsb1Z7aJe2G8jQIzpK22b/S5JdxkEatsg6DVX12SRnfUzS8ois01JWl11y1sUj8sb3eE2nCnZeZZqAdO3aVb766iuZNm2aacWjjz4qPXr0MKdiDR8+3Pzs448/NklKu3btzAb1ai6+A5odW/5omjV/sfS8+VX5dsmPBcNt/cZrythTOweThJB1Nfcc/2sbOdcGZ7SSrGuDNTnr5Ox7vKZTBTuvMk1Ahg4dKhdddJG89957suWWW8qyZctk8803l08//VQ6deokmCHBzAeWZA0bNkzOPPNMu9Yqr+07oNmx5Q+IXiNeyzvzkfs0ZkLuP3l35VH1f+6RdRCYrJ0kZ2sJgzFA1sGgsnKUnK3kS62y7/Faag3J0HCmCci8efPkkUcekS5dusjWW29tZMAsB/Z8fPjhh+b/69evL8cff7yMGDHCHNdbzcV3QLNjWz2asOfjNze8nDjMnjxjzyD2hJB1YqRBP0jOQeMry3myLkuuYB8mZ53ofI/XdKpg51WmCUgx1z/44ANzB0iHDh2kefPmdq0MpLbvgGbHtnpg/OWZj+T652Ykjpgzf/0LOfPXv0z8fFYPknVWyvt9Lzn71TvLt5F1lur7ezc5+9O6nDf5Hq+V41soz6pNQEIR0KWfvgOaHdvq9C5/7H0Z9ersxFj7d24rg7r/3+yd5kLWmum4842c3Wmp3RJZayfkxj9ydqOjayu+x2uu/ddgL9ME5IYbbpCtttpKfv3rX2vQInMffAc0O7bVkXMGJPOPAR2wUICfaQvxAqtK1oEBq9Bdcq5QuJSr+R6vpdycTMxnmoBgf0e/fv1k5MiRmTRe20t9BzQ7ttUjgHtAtH0q6E85CvAzXY5aYT9L1mHzS+o9OSdVyu9zvsdrflvn521MQPzonOgtvgOaHVt+LDwFK1G48iGFCvAzrRBKSi6RdUrCKjNLzsqA/H93fI/XdKpg5xUTEDv9nNb2HdDs2PLjwyWEh/EeEKexTWN+FOBn2o/OGt5C1hoopO8DOaevcSVv8D1eq8RH7XWYgCgi5Dug2bEVhs+b0BV9MOhKYgX4mU4sVfAPknXwCBM1gJwTyeT9Id/jNe8N9PBCJiAeRE76Ct8BzY6tNJnpXyyQp96fJ999/6Os22hN2X/rjYO49yO3ZWRdmnU1PEHO1UAxWRvIOplOoT9FzjoJ+h6v6VTBzqvME5ADDjhALrzwwkSt2GuvvRI9F+pDvgOaHVuokVK+32RdvmYh1iDnEKlV5jNZV6ZbaLXIWScx3+M1nSrYeZV5AlLO7ebLly+3a63y2r4Dmh2b8oBw6B5ZOxRTsSlyVgzHsWtk7VhQpebIWScY3+M1nSrYeZVpAtK/f/+yvB81alRZz4f2sO+AZscWWoRU7i9ZV65dSDXJOSRadr6StZ1+odQmZ52kfI/XdKpg51WmCYid69VX23dAs2Orvhgq1CKyrg3W5FwbnNFKsq4N1uSsk7Pv8ZpOFey8YgJip5/T2r4Dmh2bU3yqjZG1ajzOnCNnZ1KqN0TW6hE5cZCcncjo3Ijv8ZrzBigwyAREAYTIBd8BzY5NEfyUXSHrlAVWYp6clYDw4AZZexBZwSvIWQGEPC74Hq/pVMHOKyYgdvo5re07oNmxOcWn2hhZq8bjzDlydialekNkrR6REwfJ2YmMzo34Hq85b4ACg0xAFEDgDIgiCFXqCv+IVSnYnGaRc21wRivJujZYk7NOzkxA7LkwAbHX0JkF3wHNjs0ZOvWGyFo9IicOkrMTGYMwQtZBYLJ2kpytJUzFgO/xWiqNyNgoE5CMAcRf7zug2bEpgp+yK2SdssBKzJOzEhAe3CBrDyIreAU5K4CQxwXf4zWdKth5xQTETj+ntX0HNDs2p/hUGyNr1XicOUfOzqRUb4is1SNy4iA5O5HRuRHf4zXnDVBgkAmIAgiRC74Dmh2bIvgpu0LWKQusxDw5KwHhwQ2y9iCygleQswIInAFJBQITkFRkrcwoE5DKdGOt0grwj1hpjarhCXKuBorJ2kDWyXQK/Sly1knQ93hNpwp2XmWagDRo0CCx9/Xq1ZOffvop8fMhPug7oNmxhRgllflM1pXpFlotcg6NWOX+knXl2oVUk5x10vI9XtOpgp1XmSYg9evXFyQWrVu3Nv8uVWbNmlXqkaB/7zug2bEFHS5lOU/WZckV7MPkHCy6sh0n67IlC7ICOevE5nu8plMFO68yTUBatGghX375pbRr105OOukkOe6446RZs2Z2LQq4tu+AZscWcLCU6TpZlylYoI+Tc6DgKnCbrCsQLcAq5KwTmu/xmk4V7LzKNAHBkqqxY8fKrbfeKs8//7w0bNhQevbsKSeffLLsueeedi0LsLbvgGbHFmCQVOgyWVcoXF9oPxwAACAASURBVGDVyDkwYBbukrWFeAFVJWedsHyP13SqYOdVpglI3PWPPvpIRowYIWPGjJFvv/1WtthiCxkwYID07dtX1l13XbtWBlLbd0CzYwskMBy4SdYORAzABDkHAMmRi2TtSEjlZshZJyDf4zWdKth5pSYBiZqxdOlS+cc//mFmRd544w3p3r27jBs3zq6VgdT2HdDs2AIJDAdukrUDEQMwQc4BQHLkIlk7ElK5GXLWCcj3eE2nCnZeqUtAli1bJvfdd5/ccsst8vrrr8shhxzCBMSOccHa7NhSElahWbJWCCUFl8g5BVGVmiRrpWAcu0XOjgV1ZI4JiL2QahKQGTNmmCVYo0ePNkuwttxyS7ME69hjj+USLHvOeS2wY0tJWIVmyVohlBRcIucURFVqkqyVgnHsFjk7FtSROSYg9kJmmoAsX75cHn74YZN4vPjii7LmmmvWbULfa6+97FsXmAXfAc2OLbAAsXCXrC3EC6gqOQcEy9JVsrYUMJDq5KwTlO/xmk4V7LzKNAHBMbxfffWVbLbZZjyGV0R8BzQ7NrsPT0i1yTokWpX7Ss6VaxdaTbIOjVhl/pJzZbqlXcv3eC3t9mRhP9MEJH4RIf67WMFFhTNnzqxIo0WLFsm1114rb7/9tkyaNEnmzZtnTtf629/+tpo9zMpcc801cscdd8hnn31mLkk84YQT5LzzzpPcm9vLeTaJ474Dmh1bEirhPzNt7gJ5YeosafXDbPl87bayT8fNZMsWPwu/YWzBagrwM107QUHWtcGanHVy9j1e06mCnVeZJyDluL9ixYpyHq97dvbs2WaWBTMuO+20kzz++OMFE5BTTz3VbIDv37+/7LHHHvLqq6+aRAU/v+mmm1Z5fznPJnHcd0CzY0tCJdxnZs9fLOc/OEXenP2NtFpnpZzXcblcM6WBfL64nuzSdgMZekRHadtsnXAbSM+ZgNRwDLD/rg345KyTs+/xmk4V7LzKNAGxcz15bRztO3/+fGnZsqXg8kPsNck3AzJ16lTZbrvt5PTTT5frr7++7gVnnHGG3HjjjfLuu+/Ktttua35ezrNJPfUd0OzYkpIJ77lZ8xdLz5tflW+X/Gicz01A8LP1G68pY0/tzCQkPLwFPeZnuopglmgKWdcGa3LWydn3eE2nCnZe1UQCEpeoWAJyySWXyODBg+Xf//63mTGJyqxZs6Rdu3aC31955ZXmx+U8mxSR74Bmx5aUTHjP9Rrxmpn5iEq+BAS/w0zI/SfvHl4D6XFeBfiZrp3AIOvaYE3OOjn7Hq/pVMHOKyYgMf32339/M8uBPSK5ZaONNpIddthBJkyYYH5VzrNJEfkOaHZsScmE9Rz2fPzmhpdXcbpQAoKHnjxjT+4JCQtxQW/5ma4SkAmaQdYJRKqCR8hZJ0Tf4zWdKth5pSIBmTJlitlf8fLLL8ucOXNMi7BcCkfxYp9Fx44d7VoZq11sBgTLq9Zaay2zWT237LjjjvLjjz+apVco5Tybz/kvvvhC8E+8TJ8+Xfr06WOOJMZSsLTLwoULzclbaFvTpk3Tfh3te1Lgnjc+kXvf/GyVt23YaKX0/cUKGT2jvnz1fb1Vftd7l9bSe9c2nrzja9JUgJ/pNNXVZZusdfFIyxtyTktZO7v4srpr165mvIgxFEv5CmSegGCvBU6YwolSK1euXK0Fa6yxhjmVCvswXJRiCUj79u0FMx0TJ05c7VXYkI4jgz/++GPzu3Kezef3ZZddJpdffnneJg0bNszYZ6ECVIAKUAEqQAWoABXQpQBOZT3nnHOYgFhgyTQBeeaZZ8xSpsaNG8vJJ58sv/vd76Rt27aCI3dxctVdd91lLilcsmSJPP3007LvvvtaNPX/qnIG5H8S8psV63BSaYAzICqxeHGKn2kvMqt4CVmrwJC6E+ScusQVvYAzIBXJtkqlTBOQAw88UJ577jmz5AgzDPnKa6+9ZpZidevWTZ544gnrFhdLQMrZ11HOs0md9r2mkGtLk5IJ6znuAQmLl0tv+Zl2qaZuW2Stm48r78jZlZJu7fger7n1Xoe1TBOQn//852btHGZCihUkH4D99ddfW6tWLAG5+OKLZciQIQVPwcLvr7rqKuNDOc8mddp3QLNjS0omvOd4ClZ4zFx4zM+0CxXDsEHWYXCy9ZKcbRVMp77v8Vo6rcjWaqYJSKNGjeSwww6Tv//970VV6N27t4wdO1a+//57a7WKJSCYUsNJV4XuAZk8eXLdhvhynk3qtO+AZseWlEx4z+ESwsN4D0h44Cw95mfaUsCAqpN1QLAsXCVnC/FSrOp7vJZiUzIznWkC8stf/tKcLDVjxgzBZvN8BQnDL37xC3N54EcffVSxUMOHDxd8kHGb+qBBg0yi0bNnT2PvkEMOqUsssBfl1ltvNTehd+7c2dyEPmrUKBkwYIDZjxIv5TybxHHfAc2OLQmVcJ/hTejhsqvUc36mK1UuvHpkHR6zSjwm50pUS7+O7/Fa+i3y/4ZMExCcfoUTn3D07A033CDrrbfeKgosWLDAnH41ZswYc9rA0KFDK1YIm9s/+eSTvPWRYPTr18/8DgkP3nPHHXeYI4FxHPAJJ5wg559//mpJUjnPJnHcd0CzY0tCJfxnpn+xQF6YMkta/jBb5qzdVn7VcTPe+xE+1rwt4Ge6SsHmaRZZ1wZrctbJ2fd4TacKdl5lmoB88803svPOO5sTr5o0aSIHHHCAOQULBckCLv1DEoJbyN966y1Zf/317VqrvLbvgGbHpjwgHLpH1g7FVGyKnBXDcewaWTsWVKk5ctYJxvd4TacKdl5lmoDA9blz55rlTePHj8/bkoMOOsgsidpkk03sWhpAbd8BzY4tgKBw5CJZOxJSuRlyVg7IoXtk7VBMxabIWScc3+M1nSrYeZV5AhK5P2vWLHnllVdMQoKChKNLly6y2Wab2bUwoNq+A5odW0DBYekqWVsKGEh1cg4ElAM3ydqBiAGYIGedkHyP13SqYOeVmgTErhnVUdt3QLNjq464SdIKsk6iUvjPkHP4DJO2gKyTKhX2c+Ssk5/v8ZpOFey8YgJip5/T2r4Dmh2bU3yqjZG1ajzOnCNnZ1KqN0TW6hE5cZCcncjo3Ijv8ZrzBigwmGkCcsUVVySWoF69enLppZcmfj7EB30HNDu2EKOkMp/JujLdQqtFzqERq9xfsq5cu5BqkrNOWr7HazpVsPMq0wSkfv36gsRi5cqVda3A/6PEf4b/x8+XL19u11rltX0HNDs25QHh0D2ydiimYlPkrBiOY9fI2rGgSs2Rs04wvsdrOlWw8yrTBGT06NGreI+k47jjjjObz48//vjVWta3b1+71iqv7Tug2bEpDwiH7pG1QzEVmyJnxXAcu0bWjgVVao6cdYLxPV7TqYKdV5kmIPlcx6wILgUcOXKkXcsCrO07oNmxBRgkFbpM1hUKF1g1cg4MmIW7ZG0hXkBVyVknLN/jNZ0q2HnFBMROP6e1fQc0Ozan+FQbI2vVeJw5R87OpFRviKzVI3LiIDk7kdG5Ed/jNecNUGCQCYgCCJELvgOaHZsi+Cm7QtYpC6zEPDkrAeHBDbL2ILKCV5CzAgh5XPA9XtOpgp1XTEDs9HNa23dAs2Nzik+1MbJWjceZc+TsTEr1hshaPSInDpKzExmdG/E9XnPeAAUGmYAogMAZEEUQqtQV/hGrUrA5zSLn2uCMVpJ1bbAmZ52cmYDYc8k0Acl3D8hll10m22+/vfTo0WOV1vEeEHvYuRbYsbnXVKtFstZKxq1f5OxWT83WyFozHXe+kbM7LV1aYgJir2amCUi+e0AKNYn3gNjDZgLiXsNQLPKPWCik7PwkZzv9QqpN1iHRqtxXcq5cuzRrMgGxVzfTBCT3HpBSzeE9IKUUKu/37NjK0yvkp8k6ZHrJfSfn5FqF/iRZh04wmf/knEwn308xAbFXPNMExN796rLgO6DZsVVX/BRrDVnXBmtyrg3OaCVZ1wZrctbJ2fd4TacKdl4xAbHTz2lt3wHNjs0pPtXGyFo1HmfOkbMzKdUbImv1iJw4SM5OZHRuxPd4zXkDFBhkAqIAQuSC74Bmx6YIfsqukHXKAisxT85KQHhwg6w9iKzgFeSsAEIeF3yP13SqYOdVpglIu3btEnuPTegzZ85M/HyID/oOaHZsIUZJZT6TdWW6hVaLnEMjVrm/ZF25diHVJGedtHyP13SqYOdVpglIOadgoZkrVqywa63y2r4Dmh2b8oBw6B5ZOxRTsSlyVgzHsWtk7VhQpebIWScY3+M1nSrYeZVpArL22mvLsmXLpGvXrnLyySdLz549ZY011rBrUcC1fQc0O7aAg6VM18m6TMECfZycAwVXgdtkXYFoAVYhZ53QfI/XdKpg51WmCcjXX38tI0eOlNtvv90sr9pwww3luOOOkwEDBsimm25q17IAa/sOaHZsAQZJhS6TdYXCBVaNnAMDZuEuWVuIF1BVctYJy/d4TacKdl5lmoDEXX/66adlxIgR8vjjj5ulVvvtt59JRLp37y5YqlULxXdAs2Orhaj6vzaSdW2wJufa4MzPNDnXjgI6W+p7vKZTBTuv1CQgUTPmzp1rZkTuvPNOmTNnjvTq1Uvuvfdeu1YGUtt3QHOwEkhgOHCTrB2IGIAJcg4AkiMXydqRkMrNkLNOQL7HazpVsPNKXQKC5rz88ssyfPhweeCBB+Tggw+WRx991K6VgdT2HdDs2AIJDAdukrUDEQMwQc4BQHLkIlk7ElK5GXLWCcj3eE2nCnZeqUlAFixYIKNHj5Zbb71Vpk+fLuuvv7707dtXTjnlFOnQoYNdKwOp7Tug2bEFEhgO3CRrByIGYIKcA4DkyEWydiSkcjPkrBOQ7/GaThXsvMo8AXnrrbfM3o/77rtPlixZIrvttps5Eeuoo46Shg0b2rUusNq+A5odW2ABYuEuWVuIF1BVcg4IlqWrZG0pYCDVyVknKN/jNZ0q2HmVaQKy0047yeTJk6VJkyZyzDHHmMSjY8eOdi0KuLbvgGbHFnCwlOk6WZcpWKCPk3Og4Cpwm6wrEC3AKuSsE5rv8ZpOFey8yjQBiS4i3HHHHaVp06ZFW4Kb0J977jm71iqv7Tug2bEpDwiH7pG1QzEVmyJnxXAcu0bWjgVVao6cdYLxPV7TqYKdV5knIEndRwKyfPnypI8H+ZzvgGbHFmSYVOQ0WVckW3CVyDk4ZBU7TNYVSxdURXLWicv3eE2nCnZeZZqAfPLJJ2V536ZNm7KeD+1h3wHNji20CKncX7KuXLuQapJzSLTsfCVrO/1CqU3OOkn5Hq/pVMHOq0wTEDvXq6+274Bmx1Z9MVSoRWRdG6zJuTY4o5VkXRusyVknZ9/jNZ0q2HnFBMROP6e1fQc0Ozan+FQbI2vVeJw5R87OpFRviKzVI3LiIDk7kdG5Ed/jNecNUGBQRQIybdo0c/v5m2++KfPnz5dDDz1Uhg4dauSZOHGiTJo0Sfr06SMbbLCBAsnSc8F3QLNjS4+lNstkrY1IOv6Qczq6arRK1hqpuPeJnN1r6sKi7/GaC5+12cg8AbnuuuvkwgsvlJ9++slog83muIBw5MiRdQnInnvuKTfffLMMGDBAm35O/fEd0OzYnOJTbYysVeNx5hw5O5NSvSGyVo/IiYPk7ERG50Z8j9ecN0CBwUwTkPHjx0v37t1ls802k2HDhkmXLl1kww03lH79+tUlINBo4403FhzV+8QTTyiQLD0XfAc0O7b0WGqzTNbaiKTjDzmno6tGq2StkYp7n8jZvaYuLPoer7nwWZuNTBOQfffd1yy7evfdd6Vdu3ZGG9wNkpuAHHDAATJjxgyZOXOmNv2c+uM7oNmxOcWn2hhZq8bjzDlydialekNkrR6REwfJ2YmMzo34Hq85b4ACg5kmIOutt57svPPO8swzz9RJkS8Bwf6PsWPHyuLFixVIlp4LvgOaHVt6LLVZJmttRNLxh5zT0VWjVbLWSMW9T+TsXlMXFn2P11z4rM1GpglI48aNBbMbDz/8cNEEZL/99pM33nhDvvvuO236OfXHd0CzY3OKT7UxslaNx5lz5OxMSvWGyFo9IicOkrMTGZ0b8T1ec94ABQYzTUC23npr+eGHH1ZZWpU7A7Js2TLZdNNNpXXr1vLWW28pkCw9F3wHNDu29Fhqs0zW2oik4w85p6OrRqtkrZGKe5/I2b2mLiz6Hq+58FmbjUwTkIsuusgct3vNNdfI2WefbbTJTUCuvPJKGTRokODfeL6ai++AZsdWzdG0atvIujZYk3NtcEYrybo2WJOzTs6+x2s6VbDzKtME5Ntvv5XttttO5syZI0cccYQcdthh0rt3bznwwAPlhBNOMPs+7rnnHnNK1jvvvCNNmza1a63y2r4Dmh2b8oBw6B5ZOxRTsSlyVgzHsWtk7VhQpebIWScY3+M1nSrYeZVpAgLXP/roI5N8vPfee+YOkJUrV5p/o+C/t9pqKxk3bpx06NDBrqUB1PYd0OzYAggKRy6StSMhlZshZ+WAHLpH1g7FVGyKnHXC8T1e06mCnVeZJyBwf8WKFfLYY4/J008/LbNnzzb/36pVK+nWrZscfvjh0qBBA7tWBlLbd0CzYwskMBy4SdYORAzABDkHAMmRi2TtSEjlZshZJyDf4zWdKth5pSIBsWtC9dT2HdDs2Kondkq1hKxLKVQdvyfn6uCYpBVknUSl8J8hZ50MfY/XdKpg5xUTEDv9nNb2HdDs2JziU22MrFXjceYcOTuTUr0hslaPyImD5OxERudGfI/XnDdAgcHME5Dp06fLqFGjzOlXJ510krkRHT/DiVe4/XzzzTeXyy67TDp27KhArnRd8B3Q7NjS5anJOllropGeL+ScnrbaLJO1NiLp+EPO6ehqa9X3eM3WX431M01APvzwQ+nUqZMsWbLEbDhv3ry5vPLKK/KrX/1K5s6dW6fXuuuua07Batu2rUYNnfnkO6DZsTlDp94QWatH5MRBcnYiYxBGyDoITNZOkrO1hKkY8D1eS6URGRvNNAE5/vjjzezHwIEDzQzIFVdcYY7lxSb0e++91yQk559/vowePVpOPfVUGT58eMZypft63wHNji1dnpqsk7UmGun5Qs7paavNMllrI5KOP+Scjq62Vn2P12z91Vg/0wQER+s2bNhQ3n//faMNkg8cx/vCCy/IXnvtZX62dOlScxP6+uuvLx988IFGDZ355Dug2bE5Q6feEFmrR+TEQXJ2ImMQRsg6CEzWTpKztYSpGPA9XkulERkbzTQBady4sRx66KFmtgMFlxDed999snDhQsHvotK9e3d5/vnnZfHixRnLle7rfQc0O7Z0eWqyTtaaaKTnCzmnp602y2StjUg6/pBzOrraWvU9XrP1V2P9TBMQzGrgro/777/faNO/f38ZM2aMLF++fBWtjj76aHnkkUfMXpFqLr4Dmh1bNUfTqm0j69pgTc61wRmtJOvaYE3OOjn7Hq/pVMHOq0wTkK233lqaNWsmL730kmnFyy+/bG5Gx96QeMFyrE8++cT8U83Fd0CzY6vmaGICUjt0/9dSfqZrhzpZ1wZrctbJ2fd4TacKdl5lmoBgZuPJJ5803+QUKlh2tfHGG8uvf/1rGTt2rF1rldf2HdDs2JQHhEP3yNqhmIpNkbNiOI5dI2vHgio1R846wfger+lUwc6rTBMQzGh8/vnnsttuu0mDBg3ytgTH795www1yxBFHyEEHHWTXWuW1fQc0OzblAeHQPbJ2KKZiU+SsGI5j18jasaBKzZGzTjC+x2s6VbDzKtMExM716qvtO6DZsVVfDBVqEVnXBmtyrg3OaCVZ1wZrctbJ2fd4TacKdl4xAbHTz2lt3wHNjs0pPtXGyFo1HmfOkbMzKdUbImv1iJw4SM5OZHRuxPd4zXkDFBgMIgE577zz5KGHHqqT69prr5WePXsqkM+tC74Dmh2bW36arZG1ZjrufCNnd1pqt0TW2gm58Y+c3ejo2orv8Zpr/zXYyzQBwZG7Scrtt98uEydOlGOPPdY8fuKJJ8oee+yRpGpQz/gOaHZsQYWHlbNkbSVfMJXJORhU1o6StbWEQRggZ52YfI/XdKpg51WmCUj9+vWlXr16JVuwcuVK81zu/SAlKwb2gO+AZscWWIBYuEvWFuIFVJWcA4Jl6SpZWwoYSHVy1gnK93hNpwp2XmWegGy//fbmNvRiZdy4cTJlyhQmIHasV6vNjs2xoIrNkbViOA5dI2eHYio3RdbKATlyj5wdCenYDBMQe0EzT0D69esnI0eOLNqSQjek2zdflwXfAc2OTRf/NL0h6zTV1WObnPWwSNsTsk5bYR32yVkHh1wvfI/XdKpg5xUTEDv9nNb2HdDs2JziU22MrFXjceYcOTuTUr0hslaPyImD5OxERudGfI/XnDdAgUEmIAogRC74Dmh2bIrgp+wKWacssBLz5KwEhAc3yNqDyApeQc4KIORxwfd4TacKdl5lnoBgD0iPHj1kzTXXlEaNGskGG2wgbdq0ke22207WW2890zouwbKDXKg2O7Z0dNVolaw1UnHvEzm711SrRbLWSsatX+TsVk9X1piA2CuZeQISb0L8RCz894477ijHHXecvPDCC/Lggw9yE7o971UssGNzLKhic2StGI5D18jZoZjKTZG1ckCO3CNnR0I6NsMExF7QTBOQ0aNHmxasWLFCfvjhB1myZIl88cUXMnv2bJk0aZJ8+umnqxzTy2N47YHHLbBjc6unZmtkrZmOO9/I2Z2W2i2RtXZCbvwjZzc6urbCBMRe0UwTkFLu4+jdyy+/XMaOHWsSESQmuBOkWbNm0rhx41LVg/u974BmxxZciFTsMFlXLF1QFck5KFxWzpK1lXzBVCZnnah8j9d0qmDnleoEJGoabj1//fXX62ZDRo0aVXcrul3zddX2HdDs2HTxT9Mbsk5TXT22yVkPi7Q9Ieu0FdZhn5x1cMj1wvd4TacKdl4FkYC8+uqr8vHHH9e1tEuXLtK+fXu7lius7Tug2bEpDIKUXCLrlIRVZpaclQFJ0R2yTlFcRabJWRGMmCu+x2s6VbDzKogExK6J4dT2HdDs2MKJDVtPydpWwTDqk3MYnFx4SdYuVNRvg5x1MvI9XtOpgp1XahKQZcuWyeTJk2XOnDmmRS1bthQc0bvWWmvZtTCg2r4Dmh1bQMFh6SpZWwoYSHVyDgSUAzfJ2oGIAZggZ52QfI/XdKpg51XmCQhOv/rjH/8ot956qyxatGiV1jRp0kROPvlksxF97bXXtmtpALV9BzQ7tgCCwpGLZO1ISOVmyFk5IIfukbVDMRWbImedcHyP13SqYOdVpgnI0qVLZd9995XXXnvNtKJjx47Stm3buhOv3n33XfPz3XffXZ577jlp2LChXWuV1/Yd0OzYlAeEQ/fI2qGYik2Rs2I4jl0ja8eCKjVHzjrB+B6v6VTBzqtME5Crr75aLr74YsGm8ptuukm23XbbVVrz3nvvyWmnnSYvv/yyDB48WC644AK71iqv7Tug2bEpDwiH7pG1QzEVmyJnxXAcu0bWjgVVao6cdYLxPV7TqYKdV5kmINttt53MmzfPnHDVtGnTvC3BsiyceLXRRhsJ7gWp5uI7oNmxVXM0rdo2sq4N1uRcG5zRSrKuDdbkrJOz7/GaThXsvMo0AVlnnXXk4IMPlvvuu69oK4466ih5/PHHZfHixXatVV7bd0CzY1MeEA7dI2uHYio2Rc6K4Th2jawdC6rUHDnrBON7vKZTBTuvMk1A1l13Xdlrr73kscceK9qK7t27yz//+U/57rvv7FqrvLbvgGbHpjwgHLpH1g7FVGyKnBXDcewaWTsWVKk5ctYJxvd4TacKdl5lmoBg78c777wjU6dOlXbt2uVtyaxZs2SbbbaRHXfc0ewFSbt89tlnMnDgQJkwYYKZ4sbyL5zEdfrpp9fdxA4f6tWrl9cVbKp/9tlnK3LTd0CzY6sIU5CVyDpIbGU7Tc5lSxZsBbIOFl1ZjpNzWXJ5e9j3eM1bwzy+KNME5K677pK+fftKq1atZNCgQdK7d29p1KiRaf73338v//jHP+Syyy6Tzz//XMaMGSPHHHNMqtJ88cUXstNOO5njgLH5fdNNNzWnbz344INyzjnnyLXXXlv3fiQge+yxh5xyyimr+NSiRQtzslclxXdAs2OrhFKYdcg6TG7lek3O5SoW7vNkHS67cjwn53LU8ves7/Gav5b5e1OmCQiaOWDAALn99tvrZhSaNWtmWj9//nzz75UrV5pnbrnlltRV+cMf/iDDhw83My2dO3eue9+pp55q7il5//33ZYsttjA/RwKChOjuu+925pfvgGbH5gydekNkrR6REwfJ2YmMQRgh6yAwWTtJztYSpmLA93gtlUZkbDTzBATtf+ihh+SGG26QN954Q3AjOgpuQN9tt93M0qfDDz/ci0w4lQsb3XEqV7zgnhLMdlx66aVyxRVXrJKAjBw5Un788UfBhnrb4jug2bHZEgunPlmHw8rGU3K2US+sumQdFq9KvSXnSpVLt57v8Vq6rcnGuooEJGr6Tz/9JF9//bX535///OeyxhpreFUFsxtIfHKP+8X/Izk54IAD5Mknn6xLQBo3biy4yX3FihXSsmVLOemkk8y9JpX67Tug2bF5Da9MX0bWmcrv7eXk7E3qzF9E1pkj8OIAOXuRueyX+B6vle1gABVUJSBZ69WjRw+z+Rwb37GXIyrXX3+9nHnmmeaixCg5wexMr169pEOHDma52N///nezX+SII46QBx54oGRTsN8E/8TL9OnTpU+fPvLiiy+ahCftsnDhQsGHCBv8C93DkrYPtO9HAbL2o3PWbyHnrAn4ez9Z+9M6yzeRc5bqF373u+++K127dpW3337bjKFYyleACUhMMyQQ3bp1k5133tlsOG/durW88MILctZZZ5mlWW3atFlteVZc8qOPPtpsnEcdBGaxEcwecwAAIABJREFUgs31l19+ed5Hhg0bZk7fYqECVIAKUAEqQAWoABXQpcDMmTPN4URMQCrnkmkCUujo3XzNwaZvAE+7jB49Ws4++2z55ptvzKtwKheSESQMWGaFY4MLFRwn3LFjR7noootk8ODBRV3lDEjaJGk/rgC/RauNeCDn2uCMVpJ1bbAmZ52cOQNizyXTBKR+/fpltQB7LXwUbITHUiv8G3eQYE9HkyZNzJIrzHAUKgsWLBBcrnjiiSfKbbfdVrarvtcUcm1p2YiCrUDWwaIry3FyLkuuoB8m66DxJXaenBNL5fVB3+M1r43z9LLME5B+/foJTpLSXMaNGyeHHXaYjBo1SuBvoRIFJC4y/NOf/lR2k3wHNDu2shEFW4Gsg0VXluPkXJZcQT9M1kHjS+w8OSeWyuuDvsdrXhvn6WVMQEoI/d1335kjeJcsWSLTpk0zS7K++uor2XDDDVepuXz5cjn00ENl/PjxgmN7sUm93OI7oNmxlUso3OfJOlx25XhOzuWoFfazZB02v6Tek3NSpfw+53u85rd1ft7GBCSm87x588xRuzgNCxvQsU/jjjvuMPtBnnnmGdl1113N0zgR66WXXpKDDjrI3JaOo4Pvv/9+mTx5srk0ccSIERXR8x3Q7NgqwhRkJbIOElvZTpNz2ZIFW4Gsg0VXluPkXJZc3h72PV7z1jCPL2ICEhN70aJFZokVLkTELMf6669vTsUaNGiQOW43Ko888oi5MR03oyP5aNiwodkrgntAii3RKsXVd0CzYytFpHp+T9bVw7JYS8i5NjijlWRdG6zJWSdn3+M1nSrYecUExE4/p7V9BzQ7Nqf4VBsja9V4nDlHzs6kVG+IrNUjcuIgOTuR0bkR3+M15w1QYJAJiAIIkQu+A5odmyL4KbtC1ikLrMQ8OSsB4cENsvYgsoJXkLMCCHlc8D1e06mCnVeZJyA43rZZs2YlW+HrHpCSjqT4gO+AZseWIkxlpslaGZCU3CHnlIRVaJasFUJJwSVyTkFUByZ9j9ccuKzOROYJSDmK+LoHpByfXD7rO6DZsbmkp9sWWevm48o7cnalpH47ZK2fkQsPydmFiu5t+B6vuW9B9hYzTUCyb74uD3wHNDs2XfzT9Ias01RXj21y1sMibU/IOm2FddgnZx0ccr3wPV7TqYKdV0xA7PRzWtt3QLNjc4pPtTGyVo3HmXPk7ExK9YbIWj0iJw6SsxMZnRvxPV5z3gAFBpmAKIAQueA7oNmxKYKfsitknbLASsyTsxIQHtwgaw8iK3gFOSuAkMcF3+M1nSrYecUExE4/p7V9BzQ7Nqf4VBsja9V4nDlHzs6kVG+IrNUjcuIgOTuR0bkR3+M15w1QYJAJiAIInAFRBKFKXeEfsSoFm9Mscq4NzmglWdcGa3LWyZkJiD0XJiD2Gjqz4Dug2bE5Q6feEFmrR+TEQXJ2ImMQRsg6CEzWTpKztYSpGPA9XkulERkbZQKSMYD4630HNDs2RfBTdoWsUxZYiXlyVgLCgxtk7UFkBa8gZwUQ8rjge7ymUwU7r5iA2OnntLbvgGbH5hSfamNkrRqPM+fI2ZmU6g2RtXpEThwkZycyOjfie7zmvAEKDDIBUQAhcsF3QLNjUwQ/ZVfIOmWBlZgnZyUgPLhB1h5EVvAKclYAgTMgqUBQm4B8//33Mn36dNlggw2kbdu2qTRem1EmINqIVI8//CNWPSyLtYSca4MzWknWtcGanHVy9j1e06mCnVeZJyBPPPGEjBo1SpYvXy49e/aUPn36CH7Wt29f+eabb0zrjjzySLnnnnukQYMGdq1VXtt3QLNjUx4QDt0ja4diKjZFzorhOHaNrB0LqtQcOesE43u8plMFO68yTUBeeOEF6datm6xYscK0ol69ejJy5Ej5wx/+YP5/2223lalTp8qiRYtkxIgRcuKJJ9q1Vnlt3wHNjk15QDh0j6wdiqnYFDkrhuPYNbJ2LKhSc+SsE4zv8ZpOFey8yjQBOeigg2TChAly3XXXyd577y3nnXeeTJo0SZYuXWoSj/bt28uMGTOkY8eOssMOO8jEiRPtWqu8tu+AZsemPCAcukfWDsVUbIqcFcNx7BpZOxZUqTly1gnG93hNpwp2XmWagLRq1Upatmwpb7zxhmnFtGnTZJtttpH9999fnnzyybqWHXjggfLOO+/IvHnz7FqrvLbvgGbHpjwgHLpH1g7FVGyKnBXDcewaWTsWVKk5ctYJxvd4TacKdl5lmoCsscYa0rt3bxkzZoxpBZZi4WfY/4F9IVHp16+f3HXXXWafSDUX3wHNjq2ao2nVtpF1bbAm59rgjFaSdW2wJmednH2P13SqYOdVpglI/fr1BckF9n1EJd/P+vfvb5IUJiB2sHNrs2Nzq6dma2StmY4738jZnZbaLZG1dkJu/CNnNzq6tsIExF5RJiD2Gjqz4Dug2bE5Q6feEFmrR+TEQXJ2ImMQRsg6CEzWTpKztYSpGPA9XkulERkbzTwBwclXSQtnQJIqlew5dmzJdKqGp8i6GiiWbgM5l9aoWp4g62ohWbwd5KyTMxMQey6ZJiC4YLCcBGTWrFn2LVZswXdAs2NTHAyOXSNrx4IqNUfOSsGk4BZZpyCqQpPkrBCKiPger+lUwc6rTBMQO9err7bvgGbHVn0xVKhFZF0brMm5NjijlWRdG6zJWSdn3+M1nSrYecUExE4/p7V9BzQ7Nqf4VBsja9V4nDlHzs6kVG+IrNUjcuIgOTuR0bkR3+M15w1QYJAJiAIIkQu+A5odmyL4KbtC1ikLrMQ8OSsB4cENsvYgsoJXkLMCCHlc8D1e06mCnVdMQOz0c1rbd0CzY3OKT7UxslaNx5lz5OxMSvWGyFo9IicOkrMTGZ0b8T1ec94ABQaZgCiAwBkQRRCq1BX+EatSsDnNIufa4IxWknVtsCZnnZyZgNhzYQJir6EzC74Dmh2bM3TqDZG1ekROHCRnJzIGYYSsg8Bk7SQ5W0uYigHf47VUGpGxUSYgGQOIv953QLNjUwQ/ZVfIOmWBlZgnZyUgPLhB1h5EVvAKclYAIY8LvsdrOlWw84oJiJ1+Tmv7Dmh2bE7xqTZG1qrxOHOOnJ1Jqd4QWatH5MRBcnYio3MjvsdrzhugwCATEAUQIhd8BzQ7NkXwU3aFrFMWWIl5clYCwoMbZO1BZAWvIGcFEDgDkgoEJiCpyFqZUSYglenGWqUV4B+x0hpVwxPkXA0Uk7WBrJPpFPpT5KyToO/xmk4V7LzKNAHZZ599Entfr149ee655xI/H+KDvgOaHVuIUVKZz2RdmW6h1SLn0IhV7i9ZV65dSDXJWSct3+M1nSrYeZVpAlK/fn1BYrFy5cqSrcBzy5cvL/lcyA/4Dmh2bCFHS3m+k3V5eoX6NDmHSq58v8m6fM1CrEHOOqn5Hq/pVMHOK28JyODBg2XUqFHSq1cvufTSS2XttdeWFi1ayJdffimtW7eWIUOGSOfOnYu2pk2bNnatVV7bd0CzY1MeEA7dI2uHYio2Rc6K4Th2jawdC6rUHDnrBON7vKZTBTuvvCUgc+fOlVtuuUWuuuoqOeOMM+Qvf/mLLFq0SJCY/PWvf5WlS5ea5GTo0KEmIanF4jug2bHVTpSRdW2wJufa4IxWknVtsCZnnZx9j9d0qmDnlbcEJHIT+z4++ugj+fzzz+s8//TTT+W8886TBx54wMyMnH322XLRRRfJOuusY9e6wGr7Dmh2bIEFiIW7ZG0hXkBVyTkgWJaukrWlgIFUJ2edoHyP13SqYOeV9wSkZ8+e8tJLL8nXX3+9muevvvqqnHXWWTJp0iTZeOONzWxJ//797VoYUG3fAc2OLaDgsHSVrC0FDKQ6OQcCyoGbZO1AxABMkLNOSL7HazpVsPPKawKCxOPAAw+Ubt26ySOPPFLQ87vvvtvMgMyZM0d22GEHs1xrr732smtpALV9BzQ7tgCCwpGLZO1ISOVmyFk5IIfukbVDMRWbImedcHyP13SqYOeVtwQEez0GDhwou+22m9x3330l93l8//33cs0115g9IfjvHj16yEMPPWTXWuW1fQc0OzblAeHQPbJ2KKZiU+SsGI5j18jasaBKzZGzTjC+x2s6VbDzylsCgk3oOEY3vsG8Xbt2Jb3Hhw//8BjeklKV/QA7trIlC7YCWQeLrizHybksuYJ+mKyDxpfYeXJOLJXXB5mA2MvtLQHJ5yruASmnrFixopzHg3vWd0CzYwsuRCp2mKwrli6oiuQcFC4rZ8naSr5gKpOzTlS+x2s6VbDzKtMExM716qvtO6DZsVVfDBVqEVnXBmtyrg3OaCVZ1wZrctbJ2fd4TacKdl4xAbHTz2lt3wHNjs0pPtXGyFo1HmfOkbMzKdUbImv1iJw4SM5OZHRuxPd4zXkDFBhkAqIAQuSC74Bmx6YIfsqukHXKAisxT85KQHhwg6w9iKzgFeSsAEIeF3yP13SqYOeVigTk6aefNrekv/nmmzJ//nzp06eP3HnnnaZlTz31lPnn3HPPlU022cSutcpr+w5odmzKA8Khe2TtUEzFpshZMRzHrpG1Y0GVmiNnnWB8j9d0qmDnVeYJyBlnnCHDhw+XlStXSpMmTWTRokXSr18/GTlypGnZlClTZPvtt5dhw4aZSwqrufgOaHZs1RxNq7aNrGuDNTnXBme0kqxrgzU56+Tse7ymUwU7rzJNQMaMGWOSjU6dOsltt91mEg2cjBVPQNC8Nm3aSPv27eX555+3a63y2r4Dmh2b8oBw6B5ZOxRTsSlyVgzHsWtk7VhQpebIWScY3+M1nSrYeZVpArL77rvLhx9+aP5p3ry5aUm+BKR79+4ydepUmT17tl1rldf2HdDs2JQHhEP3yNqhmIpNkbNiOI5dI2vHgio1R846wfger+lUwc6rTBOQpk2byt577y2PP/54XSvyJSDYE4Jb0HEjejUX3wHNjq2ao2nVtpF1bbAm59rgjFaSdW2wJmednH2P13SqYOdV5glI165d5bHHHiuagOyzzz4yefJk+eabb+xaq7y274Bmx6Y8IBy6R9YOxVRsipwVw3HsGlk7FlSpOXLWCcb3eE2nCnZeZZqA7LjjjvLll1+apVVrrrmmaUnuDMjChQvNHpCtt95aXn75ZbvWKq/tO6DZsSkPCIfukbVDMRWbImfFcBy7RtaOBVVqjpx1gvE9XtOpgp1XmSYgQ4YMkUsuucScboVTrvIlIKeddpo5oveGG26Q3//+93atVV7bd0CzY1MeEA7dI2uHYio2Rc6K4Th2jawdC6rUHDnrBON7vKZTBTuvMk1AsKdjt912k/fee0922WUXOfTQQ+Xiiy+WPffcU3r06CFjx46VV155RTBTMnHiRFlrrbXsWqu8tu+AZsemPCAcukfWDsVUbIqcFcNx7BpZOxZUqTly1gnG93hNpwp2XmWagMD1//znP+bY3SeffFLq1atn7gOJl27dusndd99dd0qWXXN11/Yd0OzYdMeDS+/I2qWaem2Rs142rj0ja9eK6rRHzjq5+B6v6VTBzqvME5DI/XfffVdwIzr2g6xYsUJatWolSD4wM1IrxXdAs2OrlcjiiTm1Qpqf6Vohzc90rZDmZ1onad/jNZ0q2HmlJgGxa0Z11PYd0OzYqiNukrSCrJOoFP4z5Bw+w6QtIOukSoX9HDnr5Od7vKZTBTuvmIDY6ee0tu+AZsfmFJ9qY2StGo8z58jZmZTqDZG1ekROHCRnJzI6N+J7vOa8AQoMZpqAXHHFFYklwP6QSy+9NPHzIT7oO6DZsYUYJZX5TNaV6RZaLXIOjVjl/pJ15dqFVJOcddLyPV7TqYKdV5kmILjzI9/G83xNwnPLly+3a63y2r4Dmh2b8oBw6B5ZOxRTsSlyVgzHsWtk7VhQpebIWScY3+M1nSrYeZV5AtKlSxc5/vjjE7Wib9++iZ4L9SHfAc2OLdRIKd9vsi5fsxBrkHOI1Crzmawr0y20WuSsk5jv8ZpOFey8yjwBwRG8I0eOtGtFldT2HdDs2KokcBI0g6wTiFQFj5BzFUBM2ASyTihU4I+Rs06AvsdrOlWw84oJiJ1+Tmv7Dmh2bE7xqTZG1qrxOHOOnJ1Jqd4QWatH5MRBcnYio3MjvsdrzhugwCATEAUQIhd8BzQ7NkXwU3aFrFMWWIl5clYCwoMbZO1BZAWvIGcFEPK44Hu8plMFO68yT0A6dOgge+65p6y55prSsGFDWW+99WSjjTaSLbbYQjp16iQ/+9nP7FoYUG3fAc2OLaDgsHSVrC0FDKQ6OQcCyoGbZO1AxABMkLNOSL7HazpVsPMq8wQkn/s48QpljTXWkIMPPliuu+46adOmjV1LA6jtO6DZsQUQFI5cJGtHQio3Q87KATl0j6wdiqnYFDnrhON7vKZTBTuvMk1AXnrpJeM9jtf98ccf5YcffpBvvvlGvvjiC/nwww/lxRdflM8++0xat24tkyZNkubNm9u1Vnlt3wHNjk15QDh0j6wdiqnYFDkrhuPYNbJ2LKhSc+SsE4zv8ZpOFey8yjQBKeX6ypUr5ZJLLpGrr75aLrjgAhkyZEipKkH/3ndAs2MLOlzKcp6sy5Ir2IfJOVh0ZTtO1mVLFmQFctaJzfd4TacKdl6pTkDQNCQh7du3l0aNGsn7779v11rltX0HNDs25QHh0D2ydiimYlPkrBiOY9fI2rGgSs2Rs04wvsdrOlWw80p9AoLmYanW559/Lsccc4xda5XX9h3Q7NiUB4RD98jaoZiKTZGzYjiOXSNrx4IqNUfOOsH4Hq/pVMHOqyASELsmhlPbd0CzYwsnNmw9JWtbBcOoT85hcHLhJVm7UFG/DXLWycj3eE2nCnZeMQGx089pbd8BzY7NKT7VxshaNR5nzpGzMynVGyJr9YicOEjOTmR0bsT3eM15AxQYzDQBadCgQWIJcDTvTz/9lPj5EB/0HdDs2EKMksp8JuvKdAutFjmHRqxyf8m6cu1CqknOOmn5Hq/pVMHOq0wTkPr16wsSi3XXXdf8U6rMmjWr1CNB/953QLNjCzpcynKerMuSK9iHyTlYdGU7TtZlSxZkBXLWic33eE2nCnZeZZqAHHTQQfLkk09KkyZN5MILL5RzzjnH3IZeq8V3QLNjq51II+vaYE3OtcEZrSTr2mBNzjo5+x6v6VTBzqtMExC4/vTTT8u5555rjtjFbee486NXr152rQq0tu+AZscWaKBU4DZZVyBagFXIOUBoFbpM1hUKF1g1ctYJzPd4TacKdl5lnoDA/RUrVsitt94ql112mcyfP1/22GMP+ctf/iKdOnWya11gtX0HNDu2wALEwl2ythAvoKrkHBAsS1fJ2lLAQKqTs05QvsdrOlWw80pFAhI1YcGCBfKnP/1JbrzxRrPhHPd+DB48WFq2bGnXyjJqf/bZZzJw4ECZMGGCmeLGJYgnn3yynH766Wa/Srzcfvvtcv3118vHH38szZs3l969e5skCpcmVlJ8BzQ7tkoohVmHrMPkVq7X/6+9LwG7amrff0SahSIqCUkoUQiRjPkQDWSITzIkP0OGzD6zImNEkgiZKlOFIlPiR/WhJEopNGnwaVCi+l/3+v33+533dM777nP22ms/65x7XZcL71nDs+772Ws/95o2ec4VMX/zk2t/ucvFcvKcC1ru8rqO19z1zF1LqgRI0O3Zs2dL79695fXXX5eqVavKNddcY/4/38A+LJwLFy6Uli1byqpVq+SSSy6RBg0ayPjx42XEiBHmfMp9991XUtW9994r1157rZx88smCsyzffvutEU7HHXecjB49OmyTpfK5dmgObHnR5GUhcu0lbTkbTZ5zhszbAuTaW+pyMpw85wSXs8yu4zVnHXPYUKIC5Keffiqzqx9++KHccMMNAmGAVZDy8kfF7bLLLpNHH31UJkyYIK1bty6p7uKLLzZbxHBOpUmTJrJkyRJzXuWYY46RN954oyQfto1deeWVRoBAlOSaXDs0B7ZcGfI3P7n2l7tcLCfPuaDld15y7Td/Ya0nz2GRcpvPdbzmtnduWktUgATX8JbX1Y0bN5rtT+vXry8va6TfmzdvLqtXrzZbqlLTZ599Zs6l3HzzzXL77bcLtl5deOGF8v7778sRRxxRkvWPP/6QWrVqSefOneX555/P2RbXDs2BLWeKvC1Arr2lLifDyXNOcHmdmVx7TV9o48lzaKicZnQdrzntnKPGEhUgbdu23eRcRVn9/uCDD2KFBasbW265pUydOrVUO/h/iBNsr8K1wT169JBBgwYJBEf6trBWrVrJ77//Lt99913Otrp2aA5sOVPkbQFy7S11ORlOnnOCy+vM5Npr+kIbT55DQ+U0o+t4zWnnHDWWqABx1MfQzXTo0MEcPscHD3fccceScjho3qtXL2nWrJkRJ+3bt5dPP/1Uli1btkndnTp1knfffVdWrlxZZrvYVoZ/UtOMGTPkrLPOEmw9g+CJO8FGPEQtWrSQGjVqxN0c608QAXKdIPgOmybPDsFOuClynTABjponz46AzrGZr7/+WjCJPmXKFBNDMeWOAAVICmY4cI5zHQcccIA5cL7TTjsJVl2uuOIKszUL5z6wPeuoo44SiIUFCxZsgjhuwnrllVfMLV5lJdyWddttt2XMcv/995vbt5iIABEgAkSACBABIkAEdCGAy5JwOREFSP68qBIgCOjnz59veoND53Xr1s2/Z3mWHDp0qDlIvnz5clMDtlhBjEAwwKYvv/ySKyB5YstiySHAWbTksHfZMnl2iXaybZHrZPF31Tp5doV0bu1wBSQ3vDLldiZA/vzzT6MUcZg7PfXv3998T2Pu3Lmlftpll13k8ssvN1fipn+DI3rXs9ewbt06s9UK/27atKlsscUWUr16dfOF9pdeeolnQOIEn3XHggD3EccCq7pKybM6SmIziFzHBq2qismzKjpKjOEZkOi8OBMgOMD98ccfm4PbQcKtVrgxatSoUYKbrurUqSMNGzY0P0OMLF682AiPk046SV599VWnIiQVWnyPpGPHjvL0009Lt27dzAF0HETPdgsWzoEMGzYsZ3ZcOzQHtpwp8rYAufaWupwMJ885weV1ZnLtNX2hjSfPoaFymtF1vOa0c44acyZAzj77bHnhhRdKXaX72GOPmdWNPffcU/ANjWOPPbZUt3GYG+cvcN5iwIAB5ovkrhNutMKqDYQTPjaILVm//vqrOQ/Srl0787HEIAXfAXnzzTfNNq1ck2uH5sCWK0P+5ifX/nKXi+XkORe0/M5Lrv3mL6z15DksUm7zuY7X3PbOTWvOBEjPnj3NykHqtzwOPPBAmTlzpvln++23z9hjrILsscce5p/PP/88VlQWLVpkrtrFbVg4gI5bqgYPHmzOg0AM4YrdIPXt21euv/56kxcfHcRHCvERw6OPPtpc1ZtPcu3QHNjyYcnPMuTaT95ytZo854qYv/nJtb/c5WI5ec4FLXd5Xcdr7nrmriVnAuSmm26SPn36mGC+Zs2apoe4+hWrHiNHjiyzx2Gvto0K26pVq8wWKwgdrHJss8025lasW265RRo1arRJ9fg6Os6u4DaE7bbbTs444wxzs1XVqlXzMsW1Q3Ngy4smLwuRay9py9lo8pwzZN4WINfeUpeT4eQ5J7icZXYdrznrmMOGnAmQF1980XzjAuc9jj/+eNPFatWqmRWHMAJk3LhxAoFQyMm1Q3NgK2RvKt03cl0cXJPn4uAZvSTXxcE1edbJs+t4TScK0axyJkDw0T5cY4uP+eEjfhUrVpSWLVvKnDlzZNasWVK7du2MPVmyZInsvvvusuuuu5qP5hVycu3QHNgK2ZsoQIqH3f/2lM908bBOrouDa/Ksk2fX8ZpOFKJZ5UyAwEx8X+Ohhx4yN0o9++yz8swzz8ill15qRMkDDzxgPvCXmt577z3zoZdvvvlG+vXrZ8oXcnLt0BzYCtmbKECKh10KEHK9dTFCUBR95ntaJ82u4zWdKESzyqkA+euvvwTnOcaMGWOu3MX5j+HDhwu+EYKEcxS4XQpp3rx5gtUPXM97xBFHyDvvvGNWTQo5uXZoDmyF7E0UIMXDLgUIuaYAKVQf4HtaJ7Ou4zWdKESzyqkAgakQFI8//rg5vI2tV2Wl+vXrm6t3e/fuXfDiAzi4dmgObNEeHp9Kk2uf2MrfVvKcP3a+lSTXvjGWn73kOT/c4i7lOl6Luz9J1O9cgKR28pdffpEFCxbImjVrSv6MDw9WrlxZdtxxR3MVbjEl1w7Nga14vItcFwfX5Lk4eEYv4+T62wUrZOz0RbJi7V+yVeWKclzTHWTPHbcqHnAV9TROnhV10ztTXMdr3gEUwuBEBUgI+4oqi2uH5sBWPO5FrouDa/JcHDzHJUDmLl0t14yYKl/MXb4JkAc23FbuPWUfaVi7WvGArKCnfKYVkJDBBNfxmk4UolmlRoCsW7dOvvrqK5k/f77pEW7M2nfffWXLLbeM1kOPSrt2aA5sHjlHRFPJdUQAPSlOnj0hyoKZtrn+celq6fTYRPntj7+yWrdN1Yry2sWtKUIs8Be2Cts8h22X+cpGwHW8Voh8JC5A1q5dK//6178EH/VL/85H9erVzRkQfNwP27IKPbl2aA5she5R/+0fuS4OrslzcfAcxwpIl4GfZVz5SEcUKyGvXHRw8QCdcE/5TCdMQJbmXcdrOlGIZlWiAgS3X+Hq3c8++8z0Yp999pGGDRsKzoHMnTtXvv76a/P3gw8+WMaPHy+VKlWK1lvlpV07NAc25Q5h0TxybRFMxVWRZ8XkWDbNJtc483F8/wmhLXz78sN4JiQ0WtEy2uQ5miUsnYqA63itENFPVID07dtXbrjhBjn00ENlwIAB5nsgqQnf/7jkkktkwoQJcvfdd8u1115biByU9Mm1Q3NgK2h3KtU5cl0cXJPn4uAZvbTJ9YPvzpSHx5d9K2Uqsr2O3l16Hd24eMBOsKc2eU6wGwUYKxsjAAAgAElEQVTXtOt4reAAFJFEBUjz5s1l0aJF8sMPP0iNGjUy4ottWbvttpv5bsjUqVMLkQMKkIJmVUfn+BLTwUPcVpDnuBHWU79Nrm8bNV2enjg3dOfObd1Qbmm/d+j8zJg/AjZ5zt8KlkxHgAIkuk8kKkCqVasmJ554orz88stl9uS0006T0aNHy+rVq6P3WHENrh2aA5tiZ7BsGrm2DKjS6sizUmJiMMsm11wBiYEgS1Xa5NmSSawmge+2FSLoiQqQmjVrSps2bWTUqFFlYtu+fXv5+OOP5ffffy9EDrgCUtCs6ugcX2I6eIjbCvIcN8J66rfJNc+A6OE13RKbPOvtpX+WuZ4w9g+h8i1OVIDg7MeXX34p06ZNk1133TWjtT/++KM0bdpUWrRoYc6CFHJy7dAc2ArZm0r3jVwXB9fkuTh4Ri9tc81bsHT6jm2edfbSP6tcx2v+IVS+xYkKkOeee07OOeccqV+/vtxyyy1y5plnSpUqVYzV+Dr6Sy+9JLfeeqvgi+nPPvusdO3atfweeZzDtUNzYPPYWXI0nVznCJin2cmzp8TlYbZtrvERwo78DkgeTMRbxDbP8VpbPLW7jtcKEdlEBQgA7dGjhzz55JPm6l2k2rVrm38vXbrU/Hvjxo0mz+OPP16I+Jfqk2uH5sBW8C5V0kFyXRxck+fi4Bm9jINrfgldn//EwbO+Xvpnket4zT+Eyrc4cQECE0eOHCn9+/eXzz//XPBFdCR8Af2ggw6SSy+9VDp37lx+Twogh2uH5sBWAE4TsgvkOiRQnmcjz54TmIP5cXI9Y+EKGTt9kfy+5i+pWaWitNt7B373IwdubGaNk2ebdhZbXa7jtULEV4UACYD9+++/ZdmyZeZ/a9WqJVtssUUhYp61T64dmgNb8bgXuS4OrslzcfAc1wpI8aDnT0/5TOvkynW8phOFaFapEiDRuuJ/adcOzYHNf58J2wNyHRYpv/ORZ7/5y8V6cp0LWv7mJc86uXMdr+lEIZpVFCDR8LNa2rVDc2CzSp/qysi1anqsGUeerUGpviJyrZ4iKwaSZyswWq/EdbxmvQMKKkxUgGS7ejcTLjikPnv2bAWQxWeCa4fmwBYfl9pqJtfaGInHHvIcD64aayXXGlmxbxN5to+pjRpdx2s2bNZWR6ICpEKFCub2K9x0FSZt2LAhTDZv87h2aA5s3rpKzoaT65wh87IAefaStryMJtd5weZdIfKskzLX8ZpOFKJZlagAqVy5srn1qm3btnLRRRdJp06diu7geSp9rh2aA1u0h8en0uTaJ7byt5U854+dbyXJtW+M5Wcvec4Pt7hLuY7X4u5PEvUnKkBw49WQIUPMd0CwvWr77beX7t27m+9+NGjQIAk8Em3TtUNzYEuUbqeNk2uncCfWGHlODHrnDZNr55An0iB5TgT2cht1Ha+Va5CHGRIVIKl4jRs3TgYOHCijR48WbLU69thjjRBp3769YKtWMSTXDs2BrRi86v/6SK6Lg2vyXBw885kmz8WDgM6euo7XdKIQzSo1AiToxoIFC8yKyFNPPSXz58+XLl26yIsvvhitl56Udu3QDFY8cQwLZpJrCyB6UAV59oAkSyaSa0tAKq+GPOskyHW8phOFaFapEyDozoQJE+TRRx+V4cOHy4knnihvvvlmtF56Utq1Q3Ng88QxLJhJri2A6EEV5NkDkiyZSK4tAam8GvKskyDX8ZpOFKJZpUaArFixQoYOHSpPPPGEzJgxQ7bZZhs555xzpGfPntKoUaNovfSktGuH5sDmiWNYMJNcWwDRgyrIswckWTKRXFsCUnk15FknQa7jNZ0oRLMqcQEyadIkc/bj5Zdflj/++EMOOuggcyPWaaedJpUqVYrWO89Ku3ZoDmyeOUgEc8l1BPA8KkqePSIroqnkOiKAnhQnzzqJch2v6UQhmlWJCpCWLVvKV199JdWrV5euXbsa4bHPPvtE65HHpV07NAc2j50lR9PJdY6AeZqdPHtKXB5mk+s8QPOwCHnWSZrreE0nCtGsSlSABB8ibNGihdSoUaPMnuCDhePHj4/WW+WlXTs0BzblDmHRPHJtEUzFVZFnxeRYNo1cWwZUaXXkWScxruM1nShEsypxARLWfAiQ9evXh83uZT7XDs2BzUs3yctocp0XbN4VIs/eUZa3weQ6b+i8KkieddLlOl7TiUI0qxIVIPPmzcvJ+p133jmn/L5ldu3QHNh885D87SXX+WPnU0ny7BNb0Wwl19Hw86U0edbJlOt4TScK0axKVIBEM73wSrt2aA5shedD2XpErouDa/JcHDyjl+S6OLgmzzp5dh2v6UQhmlUUINHws1ratUNzYLNKn+rKyLVqeqwZR56tQam+InKtniIrBpJnKzBar8R1vGa9AwoqVCFAFi9eLEOGDDEfIMTXz5Hq1asnbdq0kXPPPVfq1KmjAKr4TXDt0BzY4udUSwvkWgsT8dpBnuPFV1Pt5FoTG/HZQp7jwzZKza7jtSi2ai2buAAZOXKkdO/eXVatWiUbN24shRMOnuN2rKeeeko6d+6sFUNrdrl2aA5s1qhTXxG5Vk+RFQPJsxUYvaiEXHtBU2QjyXNkCGOpwHW8FksnEq40UQEyefJkOeSQQ2TDhg3SoUMHOfvss6Vhw4YC4TF37lx57rnn5LXXXpPNN99cJk6cKPvvv3/CcMXbvGuH5sAWL5+aaifXmtiIzxbyHB+22mom19oYicce8hwPrlFrdR2vRbVXY/lEBQhWNV5//XUZMWKEdOzYMSM+ECDI16lTJ5OvkJNrh+bAVsjeVLpv5Lo4uCbPxcEzekmui4Nr8qyTZ9fxmk4UolmVqADB2Y7GjRubsx9lpcMOO0xmzpwpOCtSyMm1Q3NgK2RvogApHnb/21M+08XDOrkuDq7Js06eXcdrOlGIZlWiAqRy5cpmdWPYsGFl9qJr166CsyJr166N1lvlpV07NAc25Q5h0TxybRFMxVWRZ8XkWDaNXFsGVGl15FknMa7jNZ0oRLMqUQGC8x5Vq1aVb7/9tsxe7L333rJ69WpzLqSQk2uH5sBWyN7EFZDiYZcrIOR662KEoCj6zPe0Tppdx2s6UYhmVaICpEePHjJ48GC59tpr5Y477jCHzVMTbsW6+eabpU+fPnLBBRfIwIEDo/VWeWnXDs2BTblDWDSPXFsEU3FV5FkxOZZNI9eWAVVaHXnWSYzreE0nCtGsSlSA/PLLL7LffvvJ8uXLpUGDBtKlSxdzCxbSvHnzZPjw4WbVo1atWgKy69evH623yku7dmgObModwqJ55NoimIqrIs+KybFsGrm2DKjS6sizTmJcx2s6UYhmVaICBKZPmzZNcMbjm2++MT3BFbxIwTdBmjVrZs6ING3aNFpPPSjt2qE5sHngFJZMJNeWgFReDXlWTpBF88i1RTAVV0WedZLjOl7TiUI0qxIXIIH5H374obkNa8GCBeZPdevWFdx+1bZt22g99Ki0a4fmwOaRc0Q0lVxHBNCT4uTZE6IsmEmuLYDoQRXkWSdJruM1nShEs0qNAInWjcIo7dqhObAVht+E6QW5DoOS/3nIs/8chu0BuQ6LlN/5yLNO/lzHazpRiGZVogKke/fucuihhwr+zSTmnEvLli1lypQp0qJFi9gh4cAWO8RqGiDXaqiI1RDyHCu8qion16roiM0Y8hwbtJEqdh2vRTJWaeFEBUiFChWkW7duMmTIEKXwuDXLtUNzYHPLb5Ktkesk0XfXNnl2h3XSLZHrpBlw0z55doNzrq24jtdytc+H/BQgilhy7dAc2BSRH7Mp5DpmgJVUT56VEOHADHLtAGQFTZBnBSRkMMF1vKYThWhWUYBEw89qadcOzYHNKn2qKyPXqumxZhx5tgal+orItXqKrBhInq3AaL0S1/Ga9Q4oqJACRAEJgQmuHZoDmyLyYzaFXMcMsJLqybMSIhyYQa4dgKygCfKsgASugMRCQuICJPjuR3m9Q76///67vGxe/04B4jV9qo3nS0w1PdaMI8/WoFRfEblWT5EVA8mzFRitV+I6XrPeAQUVJi5AqlWrJrVr1w4FxY8//hgqn6+ZXDs0BzZfPSV3u8l17pj5WII8+8hafjaT6/xw860UedbJmOt4TScK0axKXIDwFqz/EujaoTmwRXt4fCpNrn1iK39byXP+2PlWklz7xlh+9pLn/HCLu5TreC3u/iRRPwVIEqhnadO1Q3NgU0R+zKaQ65gBVlI9eVZChAMzyLUDkBU0QZ4VkJDBBNfxmk4UollFARINP6ulXTs0Bzar9KmujFyrpseaceTZGpTqKyLX6imyYiB5tgKj9Upcx2vWO6CgQgoQBSQEJrh2aA5sisiP2RRyHTPASqonz0qIcGAGuXYAsoImyLMCErgCEgsJiQqQoUOHSqNGjaR169axdM63SilAfGPMH3v5EvOHqyiWkuco6PlVllz7xVe+1pLnfJGLt5zreC3e3iRTe6ICJJku623VtUNzYNPrC7YtI9e2EdVZH3nWyUscVpHrOFDVVyd51scJLHIdr+lEIZpVKgTI9OnTZf369dK0aVOpUKGC/Pnnn/LQQw/JxIkTZdttt5XLL79c9ttvv2g99aC0a4fmwOaBU1gykVxbAlJ5NeRZOUEWzSPXFsFUXBV51kmO63hNJwrRrEpUgPz+++/Srl07mTRpkulF48aN5cMPP5Tzzz9fxowZU9KzKlWqyOTJk2XPPfeM1lvlpV07NAc25Q5h0TxybRFMxVWRZ8XkWDaNXFsGVGl15FknMa7jNZ0oRLMqUQFyyy23yB133CG77LKLNGvWTN555x054ogjZNy4cXLRRRdJx44dZeTIkfLEE0/IueeeK0899VS03iov7dqhObApdwiL5pFri2Aqroo8KybHsmnk2jKgSqsjzzqJcR2v6UQhmlWJCpC9995bFi1aJHPmzJGaNWvK008/Leedd57sscceMmPGjJKeNWnSRDZs2CAzZ86M1lvlpV07NAc25Q5h0TxybRFMxVWRZ8XkWDaNXFsGVGl15FknMa7jNZ0oRLMqUQGy1VZbyTHHHGNWOZBWrFghW2+9tXTp0kVeeumlkp6dfvrpMmrUKFm9enW03iov7dqhObApdwiL5pFri2Aqroo8KybHsmnk2jKgSqsjzzqJcR2v6UQhmlWJChAcOO/WrZsMGTKkpBeZ/obtV88++6w5qF7IybVDc2ArZG8q3TdyXRxck+fi4Bm9JNfFwTV51smz63hNJwrRrKIAiYaf1dKuHZoDm1X6VFdGrlXTY8048mwNSvUVkWv1FFkxkDxbgdF6Ja7jNesdUFBh4gJk3333lQ4dOpRAceutt0r631577TWZOnUqV0AsOwwHNsuAKq6OXCsmx6Jp5NkimMqrItfKCbJkHnm2BKTlaihAogOauAAJ24XNNtuMAiQsWCHzcWALCVQBZCPXBUBiiC6Q5xAgFUgWcl0gRJbTDfKsk2cKkOi8JCpAhg4dmlMPzjnnnJzy+5bZtUNzYPPNQ/K3l1znj51PJcmzT2xFs5VcR8PPl9LkWSdTruM1nShEsypRARLN9MIr7dqhObAVng9l6xG5Lg6uyXNx8Ixekuvi4Jo86+TZdbymE4VoVlGARMPPamnXDs2BzSp9qisj16rpsWYcebYGpfqKyLV6iqwYSJ6twGi9EtfxmvUOKKiQAkQBCYEJrh2aA5si8mM2hVzHDLCS6smzEiIcmEGuHYCsoAnyrICEDCa4jtd0ohDNqsQFyMaNG2XYsGHyxhtvyKxZs2TlypWCv6UnHEKfPXt2tN4qL+3aoTmwKXcIi+aRa4tgKq6KPCsmx7Jp5NoyoEqrI886iXEdr+lEIZpViQqQdevWyQknnCDvv/9+RtGBrkF4BIJkw4YN0XqrvLRrh+bAptwhLJpHri2Cqbgq8qyYHMumkWvLgCqtjjzrJMZ1vKYThWhWJSpA+vTpIzfeeKO0b99eHnjgAbn99tvl+eefl7Vr18qcOXPk5Zdflvvuu0969uwp99xzT7SeelDatUNzYPPAKSyZSK4tAam8GvKsnCCL5pFri2Aqroo86yTHdbymE4VoViUqQPDBwV9++UXmzZsn1apVk3PPPVeeffbZUt/7mDBhghxxxBEyaNAg6d69e7TeKi/t2qE5sCl3CIvmkWuLYCquijwrJseyaeTaMqBKqyPPOolxHa/pRCGaVYkKkOrVq0ubNm3krbfeMr0477zz5JlnnhFszdp8881Lenb44YfL6tWrZfLkydF6G6L0/Pnz5bbbbpP33ntPFi5cKHXq1DE23nTTTdK4cWNTw9y5c2WXXXbJWBv6MHjw4BAtbZrFtUNzYMuLJi8LkWsvacvZaPKcM2TeFiDX3lKXk+HkOSe4nGV2Ha8565jDhhIVIDVr1pTjjz9eXnzxRdPlyy67TAYMGCAQATvssEMJDGeeeaaMGjXKHFCPMy1fvlyaNm0qf/75p9n2BZHxww8/yOOPP27OokybNk3q169fIkBOPvlkOeWUU0qZ1KhRIznooIPyMtO1Q3Ngy4smLwuRay9py9lo8pwzZN4WINfeUpeT4eQ5J7icZXYdrznrmMOGEhUge+21l9SqVUuwzQqpf//+csUVV8jIkSOlQ4cOJTBAFCxbtsysSMSZIDQuvvhiefPNN825lCDBHgiNBx98UHr16lUiQHB+5c4777RmkmuH5sBmjTr1FZFr9RRZMZA8W4HRi0rItRc0RTaSPEeGMJYKXMdrsXQi4UoTFSA40/Haa6/JokWLpFKlSma1YY899pCddtpJBg4cKPXq1TNnPx577DEjCF5//fVY4erbt69cf/31MmnSJNl///1L2vrss8/kkEMOkSeeeEIuvPDCUgIEIgSpSpUqkW1z7dAc2CJT5k0F5NobqiIZSp4jwedVYXLtFV15G0ue84Yu1oKu47VYO5NQ5YkKkDFjxsj5559vREaw4nDVVVeZlQZseULCFbw4oD5lypSSMxhxYfXFF19Iq1atzBYq3L7VsGFDI4quvPJKWbVqlREmW221VYkAwRkW/B0JW6+weoMVlHyTa4fmwJYvU/6VI9f+cZaPxeQ5H9T8LEOu/eQtV6vJc66IucnvOl5z0yu3rSQqQLJ19aWXXjKrHb/99psRHTgbsvvuuztBBtuwsKqBtoOEQ+ivvvqq2S6G9NNPP5kbuTp27CgNGjSQBQsWGBEFh7z66qulX79+5dqK7WTpW8pmzJghZ511lnz44YfSvHnzcuuImgFnamBzixYtpEaNGlGrY3nFCJBrxeRYNI08WwRTeVXkWjlBlswjz5aAtFzN119/LW3btjWT44ihmHJHQKUAyb0b9krgsDu2fB1zzDFmVQMHzyEocA5l7NixZjUmU1q/fr3gti5s15o5c6bstttuZRp16623mtu2MqX777+/3PL2esyaiAARIAJEgAgQASJABMIiMHv2bMGOHQqQsIhtmo8CJAWTN954wxw2x6pAs2bNSn4ZN26ctGvXznwM8ZprrsmKNsTLSSedVHJWpCxauAKSv9OyZO4IcBYtd8x8LEGefWQtP5vJdX64+VaKPOtkjCsg0XmhAEnBEB88XLx4sXz77bebIIuzH9iKNXr06KyoT5061Wyduuuuu+SGG27ImR3Xewq5tzRnirwtQK69pS4nw8lzTnB5nZlce01faOPJc2ionGZ0Ha857ZyjxihAUoDGDVxI33//fSn4cRAeB84PO+wweeedd7JSg3MinTt3Nh8ixAcJc02uHZoDW64M+ZufXPvLXS6Wk+dc0PI7L7n2m7+w1pPnsEi5zec6XnPbOzetUYCk4IwPC2Ib1cSJE+Xggw8u+WXEiBFy6qmnmsPp+O7Hr7/+Kttvv30phtasWSOtW7eWb775RrA3EFcJ55pcOzQHtlwZ8jc/ufaXu1wsJ8+5oOV3XnLtN39hrSfPYZFym891vOa2d25aowBJwfnTTz81txrgmyS4ThcHyXEIHTdcbbvttvLll1+aL7Tj9it8GPHII480X0bHLVhDhw6VOXPmSJ8+feS6667Liz3XDs2BLS+avCxErr2kLWejyXPOkHlbgFx7S11OhpPnnOByltl1vOasYw4bogBJAxvnOG6//XaZPHmyERYQHrgRCysfO++8s8n91FNPGcGBrVrLly8327NwDdvll19uDqHnm1w7NAe2fJnyrxy59o+zfCwmz/mg5mcZcu0nb7laTZ5zRcxNftfxmpteuW2FAsQt3mW25tqhObApIj9mU8h1zAArqZ48KyHCgRnk2gHICpogzwpIyGCC63hNJwrRrFIlQHC2Yv78+aZH9erV2+ScRbSu6i/t2qE5sOn3CVsWkmtbSOquhzzr5semdeTaJpp66yLPOrlxHa/pRCGaVSoECD789/DDD8sPP/xQqjf4+jm2NfXs2TNaLz0p7dqhObB54hgWzCTXFkD0oAry7AFJlkwk15aAVF4NedZJkOt4TScK0axKVIBs2LBBunTpIq+99prgqtutt97anLPYbLPNZN68efLbb7+Z/8ah7+HDh5v/LuTk2qE5sBWyN5XuG7kuDq7Jc3HwjF6S6+Lgmjzr5Nl1vKYThWhWJSpABg4caG6bwvc3+vXrJyeeeGKp3owZM0Z69+5tDntjlaRHjx7Requ8tGuH5sCm3CEsmkeuLYKpuCryrJgcy6aRa8uAKq2OPOskxnW8phOFaFYlKkBatWplxAX+qVOnTsaeLFq0yAiUJk2ayOeffx6tt8pLu3ZoDmzKHcKieeTaIpiKqyLPismxbBq5tgyo0urIs05iXMdrOlGIZlWiAqRGjRrmilt8Qbys1KlTJ3n33Xdl5cqV0XqrvLRrh+bAptwhLJpHri2Cqbgq8qyYHMumkWvLgCqtjjzrJMZ1vKYThWhWJS5Ajj32WBk5cmSZvejcubOMGzeOAiQa15uU5sBmGVDF1ZFrxeRYNI08WwRTeVXkWjlBlswjz5aAtFwNBUh0QBMVIPvvv7/Mnj1bZs2aJbVr187Ym6VLlwpuw8JXyfFxwEJOrh2aA1she1PpvpHr4uCaPBcHz+gluS4OrsmzTp5dx2s6UYhmVaIC5JFHHjHX7DZr1kweeOABOeqoo0r15oMPPpArr7xS8HVyXNN7ySWXROut8tKuHZoDm3KHsGgeubYIpuKqyLNiciybRq4tA6q0OvKskxjX8ZpOFKJZlagAWb9+vZx00kny9ttvmyt2t9tuO3MNLxKu4V2yZIm5nvf444+XN998UypUqBCtt8pLu3ZoDmzKHcKieeTaIpiKqyLPismxbBq5tgyo0urIs05iXMdrOlGIZlWiAgSm41sgDz74oPTv319+/vnnUr1p0KCBXHrppXLFFVcUvPhAx107NAe2aA+PT6XJtU9s5W8rec4fO99KkmvfGMvPXvKcH25xl3Idr8XdnyTqT1yApHYaAmTBggXmT3Xr1pWddtopCUwSa9O1Q3NgS4xq5w2Ta+eQJ9IgeU4E9kQaJdeJwO68UfLsHPJQDbqO10IZ5VkmVQLEM+ysm+vaoTmwWadQbYXkWi01Vg0jz1bhVF0ZuVZNjzXjyLM1KK1W5Dpes2q8ksooQJQQATNcOzQHNkXkx2wKuY4ZYCXVk2clRDgwg1w7AFlBE+RZAQkZTHAdr+lEIZpViQqQXXfdNbT1OKSOK3sLObl2aA5shexNpftGrouDa/JcHDyjl+S6OLgmzzp5dh2v6UQhmlWJCpBcb7XCgfVCTq4dmgNbIXsTBUjxsPvfnvKZLh7WyXVxcE2edfLsOl7TiUI0qxIXIN26dZMhQ4ZE60WBlHbt0BzYCsRxQnSDXIcAqQCykOcCIDFkF8h1SKA8z0aedRLoOl7TiUI0qyhAouFntbRrh+bAZpU+1ZWRa9X0WDOOPFuDUn1F5Fo9RVYMJM9WYLReiet4zXoHFFRIAaKAhMAE1w7NgU0R+TGbQq5jBlhJ9eRZCREOzCDXDkBW0AR5VkBCBhNcx2s6UYhmFQVINPyslnbt0BzYrNKnujJyrZoea8aRZ2tQqq+IXKunyIqB5NkKjNYrcR2vWe+AggoTFyC43QqpYsWKUqlSJdl6662lTp060qRJEzn44IOlU6dO5v+LIbl2aA5sxeBV/9dHcl0cXJPn4uCZzzR5Lh4EdPbUdbymE4VoViUqQBo2bCgQIOvXr5e//vpL1q5dKytWrJCNGzeaXuG3KlWqyEMPPSTnn39+tJ56UNq1QzNY8cApLJlIri0Bqbwa8qycIIvmkWuLYCquijzrJMd1vKYThWhWJSpAMpmOq3YXLVok33//vYwfP14effRRWbVqlXz88cdyyCGHROut8tKuHZoDm3KHsGgeubYIpuKqyLNiciybRq4tA6q0OvKskxjX8ZpOFKJZpU6ApHfn888/N8IDW7GGDx8erbfKS7t2aA5syh3Connk2iKYiqsiz4rJsWwaubYMqNLqyLNOYlzHazpRiGaVegGC7h1++OEya9YsWbBgQbTeKi/t2qE5sCl3CIvmkWuLYCquijwrJseyaeTaMqBKqyPPOolxHa/pRCGaVV4IkGhd9Ke0a4fmwOaPb0S1lFxHRdCP8uTZD55sWEmubaCovw7yrJMj1/GaThSiWUUBEg0/q6VdOzQHNqv0qa6MXKumx5px5NkalOorItfqKbJiIHm2AqP1SlzHa9Y7oKDCRAXI4MGD5ZprrpEXXnhBjjvuuIxwvP3229K1a1d54IEHpFu3bgogi88E1w7NgS0+LrXVTK61MRKPPeQ5Hlw11kquNbJi3ybybB9TGzW6jtds2KytjkQFyNFHHy3Tp0+X+fPnS4UKFTJigyt669WrJ/vss4+MGzdOG35W7XHt0BzYrNKnujJyrZoea8aRZ2tQqq+IXKunyIqB5NkKjNYrcR2vWe+AggoTFSB169aVZs2aydixY8uEol27dkao/PLLLwogi88E1w7NgS0+LrXVTK61MTd+tOkAACAASURBVBKPPeQ5Hlw11kquNbJi3ybybB9TGzW6jtds2KytjkQFSOXKlaVz584ybNiwMnHBFqyRI0eaDxUWcnLt0BzYCtmbSveNXBcH1+S5OHhGL8l1cXBNnnXy7Dpe04lCNKsSFSD169cXrIJ88cUXZfbiwAMPlJ9//lkWLlwYrbfKS7t2aA5syh3Connk2iKYiqsiz4rJsWwaubYMqNLqyLNOYlzHazpRiGZVogLk9NNPNx8XxBfP27Ztm7EnH374oRx55JHmQ4QjRoyI1lvlpV07NAc25Q5h0TxybRFMxVWRZ8XkWDaNXFsGVGl15FknMa7jNZ0oRLMqUQGClQ985bx69epy0003yQUXXCA1a9Y0PVqxYoUMGjRI7rrrLlm5cqV89NFH0rp162i9VV7atUNzYFPuEBbNI9cWwVRcFXlWTI5l08i1ZUCVVkeedRLjOl7TiUI0qxIVIDD94YcfliuvvLKkF9tuu6357+XLl5f8rV+/fqXyROuy3tKuHZoDm15fsG0ZubaNqM76yLNOXuKwilzHgaq+OsmzPk5gket4TScK0axKXIDA/I8//lj69u1rVjnWrFljelSlShWzLevaa6+VNm3aROulJ6VdOzQHNk8cw4KZ5NoCiB5UQZ49IMmSieTaEpDKqyHPOglyHa/pRCGaVSoESNAFfPNj2bJl5n9r166d9dsg0bqst7Rrh+bAptcXbFtGrm0jqrM+8qyTlzisItdxoKqvTvKsjxNY5Dpe04lCNKtUCZBoXfG/tGuH5sDmv8+E7QG5DouU3/nIs9/85WI9uc4FLX/zkmed3LmO13SiEM0qCpBo+Fkt7dqhObBZpU91ZeRaNT3WjCPP1qBUXxG5Vk+RFQPJsxUYrVfiOl6z3gEFFSYqQHbdddfQEGy22WYye/bs0Pl9zOjaoTmw+egl+dlMrvPDzbdS5Nk3xvK3l1znj51PJcmzTrZcx2s6UYhmVaICpEKFCgJhsXHjxlC92LBhQ6h8vmZy7dAc2Hz1lNztJte5Y+ZjCfLsI2v52Uyu88PNt1LkWSdjruM1nShEsypRAdK8eXOZNm2a7LTTTtKnTx8588wzo/XG89KuHZoDm+cOk4P55DoHsDzOSp49Ji9H08l1joB5mp08l0/ctwtWyNjpi2TF2r9kq8oV5bimO8ieO25VfsEIOVzHaxFMVVs0UQGCFY3BgwfLv/71L1myZIm0atVKHnroITnwwAPVAhanYa4dmgNbnGzqqptc6+IjLmvIc1zI6quXXOvjJA6LyHN2VOcuXS3XjJgqX8z973fjgtwHNtxW7j1lH2lYu1octPAWLAuoJipAAvvxpfM777xT+vfvL+vWrZMzzjjDfBekfv36FrroTxUUIP5w5ZulfIn5xlh+9pLn/HDzsRS59pG13G0mz5kx+3Hpaun02ET57Y+/soK6TdWK8trFrWMRIa7jtdw9R38JFQIkgGnu3LnSu3dvGTlypPkQ4dVXX20+RFi1alX9SFqw0LVDc2CzQJonVZBrT4iKaCZ5jgigR8XJtUdkRTCVPGcGr8vAzzKufKTnxkrIKxcdHIGBzEVdx2vWO6CgQlUCJMDjk08+kV69epklrrp168rdd98t//znPxXAFa8Jrh2aA1u8fGqqnVxrYiM+W8hzfNhqq5lca2MkHnvI86a44szH8f0nhAb87csPs34mxHW8FrqzHmVUKUAC/J599lm54YYbZOHChdKiRQuZNGmSR9Dmbqprh+bAljtHvpYg174yl5vd5Dk3vHzOTa59Zi+87eR5U6wefHemPDx+VmgQex29u/Q6unHo/GEyuo7XwtjkW55EBcjmm28eCi9c04vretevXx8qv6+ZXDs0BzZfPSV3u8l17pj5WII8+8hafjaT6/xw860Ued6UsdtGTZenJ84NTeW5rRvKLe33Dp0/TEbX8VoYm3zLk6gAadiwoREWYdOPP/4YNquX+Vw7NAc2L90kL6PJdV6weVeIPHtHWd4Gk+u8ofOqIHnelC6ugHjlwlmNTVSAFAaE9npBAWIPS9ZUGgG+xIrDI8hzcfCMXpLr4uCaPG/KM8+AFIbvU4Ao4pECRBEZBWYKX2IFRmiW7pDn4uCZAoQ8Fw8CmXvKW7D89wAKEEUcUoAoIqPATGFgWmCEUoAUB6Fl9JLPdHG4AHnOzDM+QtiR3wHx+iFwKkA+/vjjSGC1adMmUnnthSlAtDPkr318ifnLXS6Wk+dc0PI7L7n2m7+w1pPn7EjxS+hhvUhnPqcCpEKFCjkdOk+FDIfV//77b50oWrKKAsQSkKxmEwT4EisOpyDPxcEzekmui4Nr8lw+zzMWrpCx0xfJ72v+kppVKkq7vXew/t2PdCtcx2vlo+BfDqcCpLxbr+bNmyfVqlWT2rVrZ0SSt2DZdTAObHbx1FwbudbMjj3byLM9LLXXRK61M2THPvJsB0fbtVCAREfUqQApz1yskHTr1k2GDBlSXtaC/N21Q3NgK0g3ytgpcl0cXJPn4uCZKyDkuXgQ0NlT1/GaThSiWUUBEg0/q6VdOzSDFav0qa6MXKumx5px5NkalOorItfqKbJiIHm2AqP1SlzHa9Y7oKBCChAFJAQmuHZoDmyKyI/ZFHIdM8BKqifPSohwYAa5dgCygibIswISMpjgOl7TiUI0qyhAouFntbRrh+bAZpU+1ZWRa9X0WDOOPFuDUn1F5Fo9RVYMJM9WYLReiet4zXoHFFSoRoB89913stdee0nPnj1lwIABCqBxb4Jrh+bA5p7jpFok10kh77Zd8uwW7yRbI9dJou+ubfLsDutcWnIdr+Vimy95VQiQ+fPnS9euXWXChAkyaNAgOe+883zBz6qdrh2aA5tV+lRXRq5V02PNOPJsDUr1FZFr9RRZMZA8W4HReiWu4zXrHVBQoVMBcuyxx5bqMr7rsXjxYpk1a5b5xkeTJk3kyy+/lEqVKimAxr0Jrh2aA5t7jpNqkVwnhbzbdsmzW7yTbI1cJ4m+u7bJszusc2nJdbyWi22+5HUqQHDNbqZUsWJF6dy5szz44INSp04dX7Czbqdrh+bAZp1CtRWSa7XUWDWMPFuFU3Vl5Fo1PdaMI8/WoLRaket4zarxSipzKkA++uijUt2GIKlZs6Y0btxYKleurASS5Mxw7dAc2JLj2nXL5No14sm0R56TwT2JVsl1Eqi7b5M8u8c8TIuu47UwNvmWx6kA8Q0c1/a6dmgObK4ZTq49cp0c9i5bJs8u0U62LXKdLP6uWifPrpDOrR3X8Vpu1vmRmwJEEU+uHZoDmyLyYzaFXMcMsJLqybMSIhyYQa4dgKygCfKsgIQMJriO13SiEM0qCpBo+Fkt7dqhObBZpU91ZeRaNT3WjCPP1qBUXxG5Vk+RFQPJsxUYrVfiOl6z3gEFFVKAKCAhMGHixIly6KGHyvPPPy977rln7JatXLlSpkyZIi1btpQaNWrE3h4bSA4Bcp0c9i5bJs8u0U62LXKdLP6uWifPrpDOrZ0ZM2bIWWedJZ988om0bt06t8LMbRCgAFHkCMOGDTMOzUQEiAARIAJEgAgQASKgGwFMGOM7dky5I0ABkjtmsZVYunSpjB07Vho2bChVqlSJrZ2g4kDBu1pxib1DbCArAuS6OJyDPBcHz+gluS4OrsmzTp7XrFkjc+fOlXbt2knt2rV1GqncKgoQ5QTFaR73MMaJrq66ybUuPuKyhjzHhay+esm1Pk7isIg8x4Eq69SAAAWIBhYSsoEDW0LAJ9AsuU4A9ASaJM8JgJ5Qk+Q6IeAdN0ueHQPO5pwhQAHiDGp9DXFg08dJXBaR67iQ1VUvedbFR5zWkOs40dVTN3nWwwUtsYsABYhdPL2qjQObV3RFMpZcR4LPm8Lk2RuqIhtKriND6EUF5NkLmmhkHghQgOQBWqEUWbhwoTzxxBPSo0cP2XHHHQulW+xHBgTIdXG4BXkuDp7RS3JdHFyT5+LguRh7SQFSjKyzz0SACBABIkAEiAARIAJEICEEKEASAp7NEgEiQASIABEgAkSACBCBYkSAAqQYWWefiQARIAJEgAgQASJABIhAQghQgCQEPJslAkSACBABIkAEiAARIALFiAAFSDGyzj4TASJABIgAESACRIAIEIGEEKAASQh4NksEiAARIAJEgAgQASJABIoRAQqQAma9W7duMnTo0Kw9vPPOO+XGG2+UuXPnyi677JIx33nnnSeDBw8uYJQKo2vz58+X2267Td577z1zPWedOnWkTZs2ctNNN0njxo1LOrl+/Xrp16+f4fTnn3+WnXbaSc4//3zp3bu3bL755oUBRgH3IgzPfJ4LwwHwfOL5feedd+Q///mP7LbbbnLRRRfJpZdeKptttlmpTj755JPy8MMPyw8//CDbbbednHnmmXLrrbdKlSpVCgOMAu5FWJ7TOQ8gOeqoo8y4z0QEfEOAAsQ3xnKw97PPPpPZs2dvUgIvqsmTJ8uUKVOkRYsWJQLk5JNPllNOOaVU/kaNGslBBx2UQ6vM6hqB5cuXS9OmTeXPP/+Unj17GjGJQOTxxx83gcq0adOkfv36xqyLL77Y/P3cc8+VQw45RCZOnCjPPPOM+fuAAQNcm872ckAgLM+BAOHznAO4yrJiEqFly5ayatUqueSSS6RBgwYyfvx4GTFihFx11VVy3333lVh87733yrXXXivg+4QTTpBvv/1WHnnkETnuuONk9OjRynpGc1IRyIVnjOUYszHGpyZ8wwsihIkI+IYABYhvjEW0948//pAddthBGjZsKFOnTjW1BQELVkOwKsLkFwIQFBAQb775prRv377E+JEjRxpB+eCDD0qvXr2MEGnevLmZQYUIDdLll19uApavv/5amjVr5lfni8jasDzzefbfKS677DJ59NFHZcKECdK6deuSDuE5x8djp0+fLk2aNJElS5bIzjvvLMccc4y88cYbJfnwzF955ZVGgECUMOlEICzPsB4CpGvXrvL888/r7AytIgI5IkABkiNgvmcfNmyYnHXWWWYGDTNp6QIEIgSJS/f+MN23b1+5/vrrZdKkSbL//vuXGI4VMMyYIWC58MILzXa7u+++W+bMmVNqy92PP/4ou+66q/mdAlQv72F5ThUgfJ718lmWZZgoWL16tVnJTE3BM33zzTfL7bffLth6hWf7/ffflyOOOKIkKyaaatWqJZ07d2bAqtgFwvKcKkCGDBkif/31l1SrVk1xz2gaESgfAQqQ8jEqqBzHHnusfPDBB/LLL7+YcwKpAqR69epmyR8JW6+uuOIKM7POpBuBL774Qlq1amW2ykFYYnULgQtmQMEnhMlWW20l7dq1M6scixYt2qRD8IX99tvP7Ddn0olAWJ4DAcLnWSePYazC6saWW25ZskodlMGqNYJWbK96++23pUePHjJo0CCB4EifNMKY8Pvvv8t3330XpknmSQCBsDwHAqRq1aqydu1a2bBhg9SrV8+IzxtuuEG22GKLBKxnk0QgGgIUINHw86o0DrBiL/E//vGPUnuDf/rpJ+nevbt07NjR/L5gwQLzUvv3v/8tV199tTm0zKQbAWzPwWz3b7/9VmIoDqG/+uqrZiYUCdurENTg7E96wlkgzKphmxaTXgTC8MznWS9/YS3r0KGDmQzA6iT2+AcJWyexnRLPMsQItlx++umnsmzZsk2q7tSpk7z77ruycuXKsM0yn2MEwvIMszDB1KVLFzM5uHTpUnnhhRfMuSBssx0+fLhjy9kcEYiOAAVIdAy9qSHYwoHBKv2weXoncFvS4YcfLljynzlzprmBhUkvAqNGjZLHHnvM7AXHCwpCAsIRh9PHjh1rluvBIVY6ELCkJ2zV+vXXXzfZ8qG3x8VpWRieMyHD59kvf0FgiWf5gAMOMKuauK0OK9dYlcbWLJz7wConDh/PmDHDTBqlJ9yE9corr8jff//tV+eLyNqwPGeD5IwzzpCXXnrJ+Ebbtm2LCDl2tRAQoAApBBZD9mGvvfaSxYsXm5dVpUqVyi2FYOekk04qOUNQbgFmSAQBHD6FoMSKVeoh8nHjxpltV/fcc49cc801XAFJhB17jYblOVuLfJ7tceGiJlyhjm2UuP0MCVusIEZwvS6233z55ZdcAXFBRMxthOE5mwmYaNpnn33MGUCc72MiAj4hQAHiE1sRbMU5gAMPPDCn61aD/cZ33XWX2WfKpBMBHD6FsMT1m+kJZz+wFQu34fAMiE7+wloVluds9fF5Dou0nnzr1q0zW63wb6xmYq8/zvZgKw5mvnkGRA9XUSwpj+dsda9YsUJq1qwpF1xwgdk2zUQEfEKAAsQntiLYirvk8Z2Hzz//3AiRMAnnB3CLCj5ahw8SMulEYI899jCGff/996UM3LhxowlWDjvsMLOfHCKyT58+WW/Bwu8Qm0w6EQjLczbr+Tzr5DUXq15//XVzVu/pp58WfGgWQSdESLZbsHAOBDcfMvmFQDrP2azHqje+F4MPVt5xxx1+dZLWFj0CFCBF4AKYXcFBRuz/zzRLjr3/22+/fSkk1qxZY+6f/+abb8zHDLEHmUknAvgAGbbX4KOCBx98cImR+GjZqaeeWnK9Lm7Awk1X2b4D8tVXX5nlfCadCITlmc+zTv6iWoUbrXBWCzdeYRzHlixwjfMgWN1E0Bqk4Dsg6d8GimoDy8ePQDae09/RONeFMWHMmDHmrCY/GBw/N2zBLgIUIHbxVFlbMPOJQ+j4Ym56wowablE58sgjzRezcUYE+1LxvQjMmF933XUq+0Wj/g8BHCrHAUSc68G1yThsjr3BmB3ddtttzV5xfHwS6aKLLjJnevAldAhMiBbMpmIWdeDAgYRUMQJheebzrJjEkKbhqmxctYtbkjD5gy9mYyUa50FwsxWu2A1ScLkI8uKjg/hIIT5iePTRR5urepn0IhCWZ9x89tFHHxl+cVMl3te4YACTRhy79fJLy8pGgAKkCDwEsyQ4A/Dzzz9L3bp1N+nxU089ZQQHtvDgBYdtO7iWFV/IxiF0Jv0IYJ84Pkw2efJkIyAhPHCLDj4siBnSIOFGnHvvvdcEM7iWGYdZzz//fHNInXfJFwbPfJ7181iehfh+D7ZYYcssVjm22WYb8zzfcsst5pa79IRJBVzRi9Xq7bbbTnA70m233Sb4bgSTXgTC8owLKCAqIS4hPjDZhDNB+A4I/ISJCPiIAAWIj6zRZiJABIgAESACRIAIEAEi4CkCFCCeEkeziQARIAJEgAgQASJABIiAjwhQgPjIGm0mAkSACBABIkAEiAARIAKeIkAB4ilxNJsIEAEiQASIABEgAkSACPiIAAWIj6zRZiJABIgAESACRIAIEAEi4CkCFCCeEkeziQARIAJEgAgQASJABIiAjwhQgPjIGm0mAkSACBABIkAEiAARIAKeIkAB4ilxNJsIEAEiQASIABEgAkSACPiIAAWIj6zRZiJABIgAESACRIAIEAEi4CkCFCCeEkeziQARIAJEgAgQASJABIiAjwhQgPjIGm0mAkSACBABIkAEiAARIAKeIkAB4ilxNJsIEAH9CCxdulTGjRsn06dPl2XLlsmqVatkyZIl8vrrr0uVKlX0dyDFwvnz58szzzwjN954Y8lfM/3Nq07R2LwQoC/kBRsLEQEikIIABQjdoSgR+PDDD+WII46Qo446St57772MGDRs2FDmzZsnH3zwgbRt27YocWKn80Ng3bp1ct1118mAAQME/52attxySxk/frwceuih+VWeUKmffvpJdt55Z3n//ffNs4N08803ywsvvCCzZ89OyCo2mwQC9IUkUGebRKCwEKAAKSw+2ZuQCFCAhASK2XJGYMOGDXLyySfL6NGjZb/99pPbbrvNiI1tttkm57q0FTj44IPlq6++kuOOO05+//13I86vvfZa6du3rzZTaU/MCNAXYgaY1ROBAkeAAqTACWb3MiNAAULPiAuBxx57TP7nf/7HiJCXX35ZKlWqFFdTzuvFiuAll1win3zyidlCdsoppxjxUbVqVee2sMFkEaAvJIs/WycCviNAAeI7g7Q/LwQoQPKCjYVCILDHHnvIf/7zH/nhhx+kRo0aIUowCxEgAkSACBCB4kKAAqS4+GZv/z8CH3/8sRx++OHmH4iRTCnbGZDg7xs3bixVbP369dKiRQuZOnWq2Ss/d+7cTar9/PPP5cEHH5QJEyYIDihvu+22stdee8npp58uF1xwwSb5g7Yy2Zepjb///lsef/xxc1j4+++/N8VQ/7nnnisXXnihbL755qF9YMyYMTJy5Ej57LPPBIdO0b9GjRrJaaedJlddddUmM/toE+2kpurVq8tOO+1kZsqvvPJK2Xrrrc3PmfJmM+ycc84x+YP0888/y5133invvPOOLFq0SGrWrGm2OF1//fVywAEHlKoGHOyyyy7mb/vuu698+eWXmzQDHurXry9//vlnVt6ee+45eeKJJwy3wBg4nHHGGXLFFVdI5cqVS9nWoEEDOeuss6RVq1by9NNPy4wZM6RChQqy5557Srdu3aRHjx6yxRZblLIDZ4w++ugj+fHHHwWcp6YTTjhB3nrrLfOndJ/D3wIxnQ2/W265RW699dZSP//xxx/y8MMPyyuvvCKzZs0yvzVt2lR69uwpwDs9bbbZZhmxWblypTRu3NjwUNazlF5ftmcoyJfpd/T9pZdekjfeeEP+/e9/G59MxfWiiy4y/19eAs/16tUTYLBgwYISn0wt9+mnn0rr1q2lTZs2hpfUBCyxrS5bSj0zFvhfJmxwjqJJkyayZs0ag3mqj2fDG21m+i14njJxnfq8ZfodlyNgFQsXI+DZwmrWgQceaJ7XY489NnZfQAOrV6+WRx99VIYPHy4zZ840Yw3GDZzRu/zyy42P4dkZOnRoefSa3/HcIX+QMIahj+B1xYoVsuOOO8rxxx8vN910k9StW3eTOn/77TczxowaNUrAE3wmNaXjmI8/B89tOvdo5+qrr5b777/f4A8bcG4sSMFz8OSTT5rxDP6DMQ7j8jXXXMPVyFAewkwaEKAA0cACbXCOwNdff20CUgQACBAzpVwFyKBBg0xwiZRJHCDgw0sdZwRatmwpu+++uxEhCGrxgsOseXoKbEgPCvEiTm8DL+2TTjrJBKtbbbWVHHnkkSZgxaFhBIodO3aUESNGhArSYMcOO+xgXm4ITBGgY8//F198IXg5o27c7pQqaIIgqHnz5gZbJJTBixZ9gziAAEMAhS08gwcPLtVd2IZAJL2vEBfnn3++yTtt2jTTNnDDSgPOWCBAQGCBoB4Hok899dSSelMFCP74v//7v0YYpKZ+/fqZF3c23sApuIXQQNsI0NAn2IB98LjEINiChP4ddNBBJu/atWtLeEDdOHgOHtq3b2+CvdRgOZsAAcbt2rUrFXyk+0gQyOy2226lDrZjBWbixImSHiz9+uuvcswxxxi/A8cQzfATYAi+sMXqkUceKdVMtoD4hhtukD59+pi8cQsQ4IltX7Vq1TKiGoEjgmfYDTGRKZDLNrBAQD/wwAMm6MV2ufQEIQ1/fv7556Vr166lfg4ECAQKhGiQ4NM4jB9WgJx55pny4osvmuJJCRCIOIisOXPmCIQz/Bm3tEF0YTwBRhDZqcm2LyxcuND4I26KwzkpPO/YtgibcN4IgXivXr3MeAGMg4Qb5TBBUqdOHXMmKTVhvAgueQCHECPoDziDsIGAhdBBWTw/eA8ECfkwRkyZMsVMbgAfTBQhZXumbAqQO+64Q/71r38ZW/H8p25vxLsDkxvwG0zu7L///gazyZMnG/EI4Yj++HbDXrbnlH8vbAQoQAqbX/YuCwIIrPFywYCOWWe8lNJTLgIEs2oQFJip+uWXXzYRB1hxQZCJl8Zrr71mZvaChBl1vGgwI5eeYBfqS5/5zhQE4EWNmbO9997bBLt4uSLhBY9bi7AigsASAWaYhJlmzMClvswQQCNwwgFriKB//vOfJVVlm4WFYIHgAs4Ieps1a5ax+fJe4sAA4gYiBIIBM5rAAQmBSJcuXczLGoEFZjiRAgGCIBt/x0oMZkeDhDoxu4oZcQRd6aIO9aIMgl282MExEgL1E0880QRECGbvu+8+8/fU1Yh0HrBKAAEDwYtVMARVQcokQBAIQcgBt4oVKxoRl2kFBFwfffTRJshK7Vs2PoIVFcws33PPPSUrWYsXLzZ9QjDz9ttvlwrqMvkbzgAgcKtdu7bx0bgFCJ4T+B3sBx5BQsCMZwd2g0MEjOUl+AJs32effUyQm5qCGXIEwVghSV3hQj7c/IXZceCbKpaDGfowAgRC+JBDDjF+B+ySEiAQw8AUzzR8J5hph19D+GJiBLgGEwrov21fgO/Ch/H8PvXUU2aMDBKeX/ABntJTWatLQV4E5ZiowE10r776qpmgQcK4j+f2oYceMkH8pEmTSqqHD+F5xKQLVhjg30HK9kyVN3Zl+j3TCgjG58suu8xMrMCP8I5KTcFkCeyDCMEEAhL6d/HFFxv8eClEeU8/f9eCAAWIFiZoh3MEsIUGWzoQsAwcONAEJEFAC2NyESAY9O+9917BVp2zzz57k0AWARKCOgTNyBs24QWzfPnyTa5yzRQEIHjGasDYsWM32TqBZXy8fDFjG2y5CWtDej7MAiIQ79Spkwn8y3s543ccyH7zzTfNCz01mEmtu7yXOF7ICOAxUwsbUoNQ1NO5c2cTZCA4DL5VkRqkgN9nn3221LabYIUBL3P4Q7oAQVAN8YjtV9jClpogptCXatWqmVljBKqpAiQTD/gbZmvRVwiLIGUSINhKh6ACM6KY/UXAn0mAYCvaP/7xD7OFDys1ZfGBYBvBDVajEASnb1kCPxBr8BUI0CBl8jds+cAWrsDn4xYgZfkrVqEwi44VRgjxMAmTAFgdxKpe6tY9jAXYioZAEKuW6QnXK0O4DRs2zATuQQorQMAhxAdm4XFhAWbr0wUIfAn+DcGfnmxtwcIKA1bNEPDDotTLPwAADmNJREFUt4JZ/qC9YJUI9mG7Txy+AOyx2rD99tvnfGYqjADB6t/tt99unm2sjqYmiKtdd93VjAcQXFhxQMI7Afnh3/jv1BSnAMGEDlbeMCGCLbrbbbddqbYhwDGxgskzrLQFE0xBJvwd/UG/sDobZjtimOeEeYhAXAhQgMSFLOtVjwBmpBH4BWclshmc/h2Q9EAZgST292N2PgjqUgNZvDiwJQovCIiJXK5jxZkJzISnByLpQUhwLz9eWthik54Q9CDAwCw6VkSCmbPySIJYwZYuBPzYHoWZQ9SFQB4rGQjCg5Tp5YyVAogC7M8HJt9+++0m5x+C8uUJEAQSCCiwyoOZwPSEbU3YZoaZWwTlSKlBCmY7EXzj35j9R4KIwjYlzJRixjuVt7/++ssEZ5hdxHYPCI30BAGC7XzYY46tV4EAycYDygN7rDagTcyyIqULEOAGkYfVp++++874VzYBEvQ7fetUJj4gkiGA77rrLsH2qUwJB+fRb/hJkNL9DZhhiwu2u6FO7EF3JUAgoiAcgQe2XsEf8XzAzzCjD6EbJuG8AWbd04UbVusgDr755huzmpiesHIFYYItgxC9QQorQBAIY1tX7969zcoNVifTBQhm/LHSBxEYzNpn4wJ/z+cMCJ5htIsVPmCRngKxihUE+GAcvnD33XebyQI8j3guc0lhBAgmLDB+Bytn2bjENkIISySM4diKhtUp+AHEUZDiEiCYoMGzhC2tGHMxyZKeArEGoQ3/z5Swgomze+ALvDERAc0IUIBoZoe2xY4ARAH2CONFg/9OTcGZhPIECF4cyIu96HhxpQdrCDYRdEIAYM962AThgVlQvAgRrKam9DaCl2b6doLUMkGwjC0VCLLKSgjqEOhjq1CmWXeUTZ/FL+tgObZy4fdga1SmtssTIBAxWInItm8/CJhwZgXBG1J6kAJ+ENxDCGHmE4IDM73Bdq5UAQKBCnuxBQMrHJlShw4dTJAYBKPB9o2yeIBQwVmR1PMo6QIEwSm2dSFYxWxsWdgMGTJEzjvvPHOgFqslZQVLWFHBykp5CedpIMCClOpv8AfMWkN8BoFpvgKkPDtSfQ9CEEF+cG4iU1ngiOc1TEL/EOhBXEJsQXRhHMCzAT/B85wpYdshVn2w6pK6lTKMAMEYg8AQ51kg6tFeJgGCFQesuOH5R8CZOhue6fxX2EsdUs8DwedxcUM2QY/JCkyWAJfUCRCbvoCVJqw49e/fXy699NIwtJXkCSNAsOqJCaZsYhKiB2dcUsU7fA7jFfjFxBGEddgzIOV1INWfg8kKnGfCikZw0B3iFqtv6QmrjViVCZNSV3TC5GceIpAEAhQgSaDONr1AIMwWLAz0hx12mLnFKgiMbAkQHBDFDDkCIoiG1JSPAMHsP4L0MAIk2IaAMygQIQjIEAQhIEIgmL5aANsyHULHDDVWCLDnHnv3MdOa7YBkVAESXCxQlgBB8IZAES9//IPbjPDyRwCdjmkYAYIVF6xABAIkOIQeRYBAeCIoAe9BEFwWNljNgPhIP9+TabY2EHFYvcD2m7JStluZINixzRAzxpg5DhMIpreT7XKFIF8g/lMDtiBgxsobVl2wVQwBMnwS/oXAPpdVGLSF2XfMwiPgx1ajQKBB1KXf6BbYBkGA4BQiF74WpDACBNsDcYYEQTcuNyjrJiTYhJU++CdWHlNT+lbBTM9eav5Mh6fLEyAQ6liBLUuARPWFpAUIgn2saKWvHmKMw/OEQ/iYqEhP2W7BynSDHMpm8ufU7ZrAGWMjVsYgiCHu05/PYEzGNtpgu1i25xfPZurBei9euDSy6BCgACk6ytnhsAiUJ0AQFGDvOG5vwSxbsGyeHsgioMS2Fsx+4kB2cBVteXZgfzpmWPFSwou+LAFS3hYslMUsHtoPswUr2N+faetCsCUoWxCU/nJGEImbhjDzjmAPs66ZUnkCJNiChdUBBKDpCSsRWJHItgULL3zMPGNFCbhi2xS22ATbtdJ5C7MFKxB1wRasMDyUtwUL5xggalBncGNXWdgEM/LvvvuuOYwepEwCJAiAsbqClZ+wKcAGK0cI9IENtorAr6MIkLJW19K3nAELbEPJNJsdnIPJVYCgDeybh2DEygkuG4BNeEayfVwRzzl+x6pA6gH18gQIJiiwrQ7t4awNttuUJUCycWPrDEiwBQsruJhdT0+BoM+2BcuGL7jagoVtSZku+cDqB1ZBUrdgBThg1RkCEwIMYzzG7Ti2YGFrJ7ZU4VxQcOkFJrXgG6nnOILJLmyzwpk+JiLgOwIUIL4zSPtjQ6A8AYKXEYIOzKIisAtSpgAhOISOw6vBla/lGY5ZbcxuZ9pyVNYh9PStIWgHL2C8uMIeQscWBAS0WDHB2ZbUFAiBsAIEZVEX6sQqCERNPgIkOIQOXjCjm/5Nk2ArXLZD6MH3XhDgY1UHCecGsIqBlAnT4BA6DoFjm1NqQiCMvfqph9Dxe3BzWaZD6AEO2Q6hBz6VLjqzCRCIYPyGgBjbxFLFbaZgKVihwQxq6pWm5fligA1WBXANbbBigHKuBAgO50L0YGtQ+u1A3bt3N7c45SpAYH9wKxiCUfgFZuVxODxTCi5gyLTCVZ4AwSobOEkVikkKkOAQOkQkhHP6xEiwDTDbIXQbvpB6CB0rPak3YJXnk2H8LjiEjqtrsW0uNWGVA2IQK83pW5ZSt2Glipc4BAgmEFK/b4LV9JdffnmTm/KwRQsHz/HMo+/plwaUhxd/JwLaEKAA0cYI7VGDQHkCJJgtxfaP1BdnpkAWZwOw1xsve8xu47+DlOkaXgSKCNgx04wXc/rZiUxtYFYbQQOCYoiQYN946vWvYa/hxZaEAQMGmC0pEEDB7WC4nQViCtsEwgoQvMxRD7adYOsOZl7zESCp1/DiADWERmAXrjbGYdps1/CmBqYIYhFsIi/OWAQfBcyEKbZOQNhgKxw4RMCCFHzPA39LvYYXvwVbojB7iutFg0OsOAuElRfMpma7hhc+hVUqrKilXg2dSYAgEIFIxewtDrHCr1JTtmApEJfgBGWxzz01YeYbgib12wrABkE//BGz+Di7EMzOhgkE0/kub7Ur0+/BAdv0m+TAEYI2rDTmI0CCG+ICG9E3rGylJ3COSwvwbKXzh7xlCRCseuFyCDw7qbPXSQoQ2BxgiiAYIju4WQ6rb9hqhhXDTNfw2vSF4KA4zjpB2KZe9gDfAu6Zru4O43fBxx7ht3g+IDaR8OxgIgg3pmXa4goBilVbiCxsxwtSHAIk/QICnBPEyiyuH8azGFz9DRuCFSP4OewKxqPAPogprJxjnGUiAtoRoADRzhDtSwyB8gQIDMu0Vzzbh7ogEPDSQyCNGdTgQ4R4yaR+iBAvFwT6SNi2FHzcMBWITG0gAMMtQLjuFwFC8CHC4AN42J6EJf4w1zNCVGGPPW6+wnkEiJpgpjAIuMPsQ8e2M6yioD5sV8FMY7YD8OUFpeg/9t1DvOEljZuhcLAeQQZuZYKQwNWouNkoSGGClCBvNt6CDxHi7Erqhwix4oAD5cA3dbsOZlaxDQzBZfBBSLSBwABBBTiCYEpdwQkOoSMfPkKW/qXtdGxQN2b9cQMbtpTBX4IvvpcXLCEQhrjANiDMegNDCB/s+cfec2w9Sb+VKPV6avQjVUDngnFgW3lcZ/od1yEDf/g5fChYEUGAjIPUeL7yESCoD9ih39nO7mCbDlb+IA7BFbbMpF8DXZYAQb+RH6tmsDtISQsQPNPY7gM/wvMcfIgQdgEXBOhYMUwfe4L/t+ELsAHCHKIbs/rBhwgx8ZL6IcL0F0FYv8PKB7jBuJv6IUK0l+lDhGgXq744XwS+UlfbXAgQ9BPjA8QusMAkRzBmQzihL+gTvtkCoQzfxZiD/mBbXKZv2yT2EmXDRKAMBChA6B5EIAsC5QkQDP4IftID+myBLJpBoIjZUwTMCGZwwxICfMz+BVt8MFuK/cCYpUdwkCllawOrKZi9w4syuF4Y9WMmD4F0+ralssjHWQ8IJqzGYMUDe8GxPQXXlpa1Dz21TogOBMh4kWJ1JtO1pmGD0iAfBAdWP7DvH6s7CBBQP86W4EvAqSlskIIyZfGGFz5WcBCgA2McEMU3IMBRpkP1EJRYbcLZHYgv1A0eEDzgIHg6D4EAgRBA/vQrf9MDctSLlRaIGQjC9G8CoD9lXc2KmW3MNuNgK1ZkIBRRB2ZUMSuOFYXgiuAAG/w700pLLhiH5TqbQMHNYdjyCPEEHjAzjv5DLOd6E1eqn2DGGJhm+t4L8mG1CF/GxtY48Bd8sC+1jvIESKarZpMWILAfYh4rYVghgAiDmMZzBFyxWpaeAjFqyxdQP1Y5IPKwmoUVSjwf8D+caQJuqV+cD+zJxe9wmQNWzvDv4EOTWA2BL2F8ChICfIhYTJRk2kLpSoDAHowvODeUSQTiqml88wcfUMR7BGIJK6YQcji/h+eBiQhoR4ACRDtDtI8IEAEiQARiQwA3tSEIhaDBjUfYJslEBIgAESAC8SJAARIvvqydCBABIkAEFCOAq26x0oc9/zjvxEQEiAARIALxI0ABEj/GbIEIEAEiQAQUIYBtR/giPC4GeOutt8y2oxkzZpTadqbIXJpCBIgAESg4BChACo5SdogIEAEiQATKQiA4P4CzHDhHggPsOIfDRASIABEgAm4QoABxgzNbIQJEgAgQASJABIgAESACRAAXv2zM9ilawkMEiAARIAJEgAgQASJABIgAEbCMAAWIZUBZHREgAkSACBABIkAEiAARIALZEaAAoXcQASJABIgAESACRIAIEAEi4AwBChBnULMhIkAEiAARIAJEgAgQASJABChA6ANEgAgQASJABIgAESACRIAIOEOAAsQZ1GyICBABIkAEiAARIAJEgAgQAQoQ+gARIAJEgAgQASJABIgAESACzhD4f0ncWtIG1BHpAAAAAElFTkSuQmCC\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_fig = plt.figure()\n",
    "\n",
    "plt.grid(True)\n",
    "plt.title('Диаграмма рассеяния', fontsize=20)\n",
    "plt.xlabel('Число автомобилей на уличной стоянке', fontsize=12)\n",
    "plt.ylabel('Число автомобилей в подземном гараже', fontsize=12)\n",
    "\n",
    "plt.scatter(x=data['Улица'], y=data['Гараж'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Задание 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataframe dimensions: (2001, 7)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>volumefrom</th>\n",
       "      <th>volumeto</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-12-20</td>\n",
       "      <td>462.76</td>\n",
       "      <td>431.10</td>\n",
       "      <td>461.20</td>\n",
       "      <td>121531.64</td>\n",
       "      <td>54316484.95</td>\n",
       "      <td>441.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-12-21</td>\n",
       "      <td>445.54</td>\n",
       "      <td>423.99</td>\n",
       "      <td>441.78</td>\n",
       "      <td>121990.88</td>\n",
       "      <td>53270215.60</td>\n",
       "      <td>437.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-12-22</td>\n",
       "      <td>443.22</td>\n",
       "      <td>433.25</td>\n",
       "      <td>437.59</td>\n",
       "      <td>65785.54</td>\n",
       "      <td>28845536.39</td>\n",
       "      <td>437.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-12-23</td>\n",
       "      <td>444.78</td>\n",
       "      <td>434.28</td>\n",
       "      <td>437.03</td>\n",
       "      <td>63247.04</td>\n",
       "      <td>27877349.10</td>\n",
       "      <td>442.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>460.62</td>\n",
       "      <td>441.76</td>\n",
       "      <td>442.43</td>\n",
       "      <td>79604.01</td>\n",
       "      <td>36096662.15</td>\n",
       "      <td>452.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         time    high     low    open  volumefrom     volumeto   close\n",
       "0  2015-12-20  462.76  431.10  461.20   121531.64  54316484.95  441.78\n",
       "1  2015-12-21  445.54  423.99  441.78   121990.88  53270215.60  437.59\n",
       "2  2015-12-22  443.22  433.25  437.59    65785.54  28845536.39  437.03\n",
       "3  2015-12-23  444.78  434.28  437.03    63247.04  27877349.10  442.43\n",
       "4  2015-12-24  460.62  441.76  442.43    79604.01  36096662.15  452.98"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bitcoin = pd.read_csv('bitcoin.csv')\n",
    "bitcoin.drop(['Unnamed: 0'], axis=1, inplace=True)\n",
    "\n",
    "print(f'Dataframe dimensions: {bitcoin.shape}')\n",
    "bitcoin.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2001 entries, 0 to 2000\n",
      "Data columns (total 7 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   time        2001 non-null   object \n",
      " 1   high        2001 non-null   float64\n",
      " 2   low         2001 non-null   float64\n",
      " 3   open        2001 non-null   float64\n",
      " 4   volumefrom  2001 non-null   float64\n",
      " 5   volumeto    2001 non-null   float64\n",
      " 6   close       2001 non-null   float64\n",
      "dtypes: float64(6), object(1)\n",
      "memory usage: 109.6+ KB\n"
     ]
    }
   ],
   "source": [
    "bitcoin.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Задание 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>high</th>\n",
       "      <th>predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>38304.55</td>\n",
       "      <td>35798.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>39945.35</td>\n",
       "      <td>33582.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>39842.90</td>\n",
       "      <td>33409.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>40854.81</td>\n",
       "      <td>37391.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>40408.95</td>\n",
       "      <td>36680.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>38879.60</td>\n",
       "      <td>36843.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>37307.10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>36481.62</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>37512.80</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>37910.19</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>38225.18</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>39482.46</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>39273.49</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>37911.76</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>36453.81</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>36790.39</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>34061.09</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>37528.42</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>38387.20</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>37665.85</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          high   predict\n",
       "1981  38304.55  35798.57\n",
       "1982  39945.35  33582.86\n",
       "1983  39842.90  33409.38\n",
       "1984  40854.81  37391.58\n",
       "1985  40408.95  36680.78\n",
       "1986  38879.60  36843.33\n",
       "1987  37307.10       NaN\n",
       "1988  36481.62       NaN\n",
       "1989  37512.80       NaN\n",
       "1990  37910.19       NaN\n",
       "1991  38225.18       NaN\n",
       "1992  39482.46       NaN\n",
       "1993  39273.49       NaN\n",
       "1994  37911.76       NaN\n",
       "1995  36453.81       NaN\n",
       "1996  36790.39       NaN\n",
       "1997  34061.09       NaN\n",
       "1998  37528.42       NaN\n",
       "1999  38387.20       NaN\n",
       "2000  37665.85       NaN"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "date_to_cost = pd.DataFrame(\n",
    "    data={\n",
    "        'high': bitcoin['high'],\n",
    "        'predict': bitcoin['close'].shift(-14), # [:-14]\n",
    "    }\n",
    ")\n",
    "\n",
    "date_to_cost.tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "high       float64\n",
       "predict    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_to_cost.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_36a8d_row0_col0, #T_36a8d_row1_col1 {\n",
       "  background-color: #b40426;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_36a8d_row0_col1, #T_36a8d_row1_col0 {\n",
       "  background-color: #3b4cc0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_36a8d\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_36a8d_level0_col0\" class=\"col_heading level0 col0\" >high</th>\n",
       "      <th id=\"T_36a8d_level0_col1\" class=\"col_heading level0 col1\" >predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_36a8d_level0_row0\" class=\"row_heading level0 row0\" >high</th>\n",
       "      <td id=\"T_36a8d_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_36a8d_row0_col1\" class=\"data row0 col1\" >0.973826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_36a8d_level0_row1\" class=\"row_heading level0 row1\" >predict</th>\n",
       "      <td id=\"T_36a8d_row1_col0\" class=\"data row1 col0\" >0.973826</td>\n",
       "      <td id=\"T_36a8d_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1ecb3237970>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_to_cost.corr().style.background_gradient(cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Задание 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "X = np.array(date_to_cost['high']).reshape(-1, 1)\n",
    "y = np.array(date_to_cost['predict'])\n",
    "\n",
    "regression_btc = LinearRegression().fit(X[:-14], y[:-14])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  828.85493461,   812.36599182,   810.14448501, ...,\n",
       "       36320.93085599, 37143.25224349, 36452.52640815])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_btc = regression_btc.predict(X).flatten()\n",
    "prediction_btc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Предсказанная (от high) стоимость Bitcoin, $</th>\n",
       "      <th>Реально зафиксированная стоимость Bitcoin, $</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Дата</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37307.10</th>\n",
       "      <td>36109.006767</td>\n",
       "      <td>34611.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36481.62</th>\n",
       "      <td>35318.571662</td>\n",
       "      <td>35662.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37512.80</th>\n",
       "      <td>36305.973987</td>\n",
       "      <td>37290.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37910.19</th>\n",
       "      <td>36686.493207</td>\n",
       "      <td>36683.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38225.18</th>\n",
       "      <td>36988.110634</td>\n",
       "      <td>37576.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39482.46</th>\n",
       "      <td>38192.014118</td>\n",
       "      <td>39229.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39273.49</th>\n",
       "      <td>37991.915723</td>\n",
       "      <td>36861.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37911.76</th>\n",
       "      <td>36687.996555</td>\n",
       "      <td>35538.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36453.81</th>\n",
       "      <td>35291.942307</td>\n",
       "      <td>35798.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36790.39</th>\n",
       "      <td>35614.233153</td>\n",
       "      <td>33582.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34061.09</th>\n",
       "      <td>33000.802748</td>\n",
       "      <td>33409.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37528.42</th>\n",
       "      <td>36320.930856</td>\n",
       "      <td>37391.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38387.20</th>\n",
       "      <td>37143.252243</td>\n",
       "      <td>36680.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37665.85</th>\n",
       "      <td>36452.526408</td>\n",
       "      <td>36843.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Предсказанная (от high) стоимость Bitcoin, $  \\\n",
       "Дата                                                     \n",
       "37307.10                                  36109.006767   \n",
       "36481.62                                  35318.571662   \n",
       "37512.80                                  36305.973987   \n",
       "37910.19                                  36686.493207   \n",
       "38225.18                                  36988.110634   \n",
       "39482.46                                  38192.014118   \n",
       "39273.49                                  37991.915723   \n",
       "37911.76                                  36687.996555   \n",
       "36453.81                                  35291.942307   \n",
       "36790.39                                  35614.233153   \n",
       "34061.09                                  33000.802748   \n",
       "37528.42                                  36320.930856   \n",
       "38387.20                                  37143.252243   \n",
       "37665.85                                  36452.526408   \n",
       "\n",
       "          Реально зафиксированная стоимость Bitcoin, $  \n",
       "Дата                                                    \n",
       "37307.10                                      34611.58  \n",
       "36481.62                                      35662.59  \n",
       "37512.80                                      37290.83  \n",
       "37910.19                                      36683.51  \n",
       "38225.18                                      37576.98  \n",
       "39482.46                                      39229.66  \n",
       "39273.49                                      36861.50  \n",
       "37911.76                                      35538.40  \n",
       "36453.81                                      35798.57  \n",
       "36790.39                                      33582.86  \n",
       "34061.09                                      33409.38  \n",
       "37528.42                                      37391.58  \n",
       "38387.20                                      36680.78  \n",
       "37665.85                                      36843.33  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_btc = pd.DataFrame(\n",
    "    data={\n",
    "        'Предсказанная (от high) стоимость Bitcoin, $': prediction_btc[-14:],\n",
    "        'Реально зафиксированная стоимость Bitcoin, $': bitcoin['close'][-14:],\n",
    "    },\n",
    ")\n",
    "prediction_btc.index = bitcoin['high'][-14:]\n",
    "prediction_btc.index.names = ['Дата']\n",
    "\n",
    "prediction_btc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Задание 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Наклон линии регрессии: 0.9575460391470993\n",
      "Y-перехват: 385.7409295389625\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    f\"Наклон линии регрессии: {regression_btc.coef_[0]}\",\n",
    "    f\"Y-перехват: {regression_btc.intercept_}\",\n",
    "    sep='\\n',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Задание 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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_device_pixel_ratio', {\n",
       "                device_pixel_ratio: fig.ratio,\n",
       "            });\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",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\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",
       "    fig.rubberband_canvas.style.cursor = msg['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",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\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",
       "                img.type = 'image/png';\n",
       "            }\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",
       "                img\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 https://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",
       " * https://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.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\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\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\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.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\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",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(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",
       "    // 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=\"1280\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bitcoin_fig, ax = plt.subplots(figsize=(12.8, 12))\n",
    "bitcoin_fig.subplots_adjust(0.075, 0.075, 0.975, 0.95)\n",
    "\n",
    "plt.title('Зависимость между high и стоимостью Bitcoin', fontsize=20)\n",
    "\n",
    "plt.scatter(\n",
    "    x=date_to_cost['high'], y=date_to_cost['predict'],\n",
    "    alpha=0.5, label='Диаграмма рассеяния',\n",
    ")\n",
    "plt.plot(\n",
    "    X, regression_btc.predict(X),\n",
    "    color='crimson', linewidth=3,\n",
    "    label='Простая линейная регрессия',\n",
    ")\n",
    "\n",
    "plt.xlabel('Наивысшая стоимость Bitcoin, $', fontsize=14)\n",
    "plt.ylabel('Стоимость Bitcoin, $', fontsize=14)\n",
    "\n",
    "plt.grid(True)\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Задание 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataframe dimensions: (3479, 8)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Room</th>\n",
       "      <th>Parking</th>\n",
       "      <th>Warehouse</th>\n",
       "      <th>Elevator</th>\n",
       "      <th>Address</th>\n",
       "      <th>Price</th>\n",
       "      <th>Price(USD)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Shahran</td>\n",
       "      <td>1.850000e+09</td>\n",
       "      <td>61666.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Shahran</td>\n",
       "      <td>1.850000e+09</td>\n",
       "      <td>61666.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>79</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Pardis</td>\n",
       "      <td>5.500000e+08</td>\n",
       "      <td>18333.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>95</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Shahrake Qods</td>\n",
       "      <td>9.025000e+08</td>\n",
       "      <td>30083.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>123</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Shahrake Gharb</td>\n",
       "      <td>7.000000e+09</td>\n",
       "      <td>233333.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Area  Room  Parking  Warehouse  Elevator         Address         Price  \\\n",
       "0    63     1     True       True      True         Shahran  1.850000e+09   \n",
       "1    60     1     True       True      True         Shahran  1.850000e+09   \n",
       "2    79     2     True       True      True          Pardis  5.500000e+08   \n",
       "3    95     2     True       True      True   Shahrake Qods  9.025000e+08   \n",
       "4   123     2     True       True      True  Shahrake Gharb  7.000000e+09   \n",
       "\n",
       "   Price(USD)  \n",
       "0    61666.67  \n",
       "1    61666.67  \n",
       "2    18333.33  \n",
       "3    30083.33  \n",
       "4   233333.33  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price = pd.read_csv('housePrice.csv', thousands=',')\n",
    "\n",
    "print(f'Dataframe dimensions: {price.shape}')\n",
    "price.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3479 entries, 0 to 3478\n",
      "Data columns (total 8 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Area        3479 non-null   int64  \n",
      " 1   Room        3479 non-null   int64  \n",
      " 2   Parking     3479 non-null   bool   \n",
      " 3   Warehouse   3479 non-null   bool   \n",
      " 4   Elevator    3479 non-null   bool   \n",
      " 5   Address     3456 non-null   object \n",
      " 6   Price       3479 non-null   float64\n",
      " 7   Price(USD)  3479 non-null   float64\n",
      "dtypes: bool(3), float64(2), int64(2), object(1)\n",
      "memory usage: 146.2+ KB\n"
     ]
    }
   ],
   "source": [
    "price.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Room</th>\n",
       "      <th>Price</th>\n",
       "      <th>Price(USD)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.479000e+03</td>\n",
       "      <td>3479.000000</td>\n",
       "      <td>3.479000e+03</td>\n",
       "      <td>3.479000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.744000e+06</td>\n",
       "      <td>2.079908</td>\n",
       "      <td>5.359023e+09</td>\n",
       "      <td>1.786341e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.167266e+08</td>\n",
       "      <td>0.758275</td>\n",
       "      <td>8.099935e+09</td>\n",
       "      <td>2.699978e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.600000e+06</td>\n",
       "      <td>1.200000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.900000e+01</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.418250e+09</td>\n",
       "      <td>4.727500e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.000000e+01</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.900000e+09</td>\n",
       "      <td>9.666667e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.200000e+02</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000e+09</td>\n",
       "      <td>2.000000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.616000e+10</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.240000e+10</td>\n",
       "      <td>3.080000e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Area         Room         Price    Price(USD)\n",
       "count  3.479000e+03  3479.000000  3.479000e+03  3.479000e+03\n",
       "mean   8.744000e+06     2.079908  5.359023e+09  1.786341e+05\n",
       "std    3.167266e+08     0.758275  8.099935e+09  2.699978e+05\n",
       "min    3.000000e+01     0.000000  3.600000e+06  1.200000e+02\n",
       "25%    6.900000e+01     2.000000  1.418250e+09  4.727500e+04\n",
       "50%    9.000000e+01     2.000000  2.900000e+09  9.666667e+04\n",
       "75%    1.200000e+02     2.000000  6.000000e+09  2.000000e+05\n",
       "max    1.616000e+10     5.000000  9.240000e+10  3.080000e+06"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Задание 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "quantile = price['Area'].quantile(.99)\n",
    "\n",
    "price = price[price['Area'] < quantile]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Room</th>\n",
       "      <th>Price</th>\n",
       "      <th>Price(USD)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3444.000000</td>\n",
       "      <td>3444.000000</td>\n",
       "      <td>3.444000e+03</td>\n",
       "      <td>3.444000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>102.653020</td>\n",
       "      <td>2.068525</td>\n",
       "      <td>5.124715e+09</td>\n",
       "      <td>1.708238e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>51.376075</td>\n",
       "      <td>0.740871</td>\n",
       "      <td>7.100481e+09</td>\n",
       "      <td>2.366827e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.600000e+06</td>\n",
       "      <td>1.200000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>68.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.400000e+09</td>\n",
       "      <td>4.666667e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.850000e+09</td>\n",
       "      <td>9.500000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>120.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000e+09</td>\n",
       "      <td>2.000000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>405.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.500000e+10</td>\n",
       "      <td>2.833333e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Area         Room         Price    Price(USD)\n",
       "count  3444.000000  3444.000000  3.444000e+03  3.444000e+03\n",
       "mean    102.653020     2.068525  5.124715e+09  1.708238e+05\n",
       "std      51.376075     0.740871  7.100481e+09  2.366827e+05\n",
       "min      30.000000     0.000000  3.600000e+06  1.200000e+02\n",
       "25%      68.000000     2.000000  1.400000e+09  4.666667e+04\n",
       "50%      90.000000     2.000000  2.850000e+09  9.500000e+04\n",
       "75%     120.000000     2.000000  6.000000e+09  2.000000e+05\n",
       "max     405.000000     5.000000  8.500000e+10  2.833333e+06"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "      <th id=\"T_4967f_level0_row1\" class=\"row_heading level0 row1\" >Room</th>\n",
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       "      <th id=\"T_4967f_level0_row2\" class=\"row_heading level0 row2\" >Parking</th>\n",
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       "      <td id=\"T_4967f_row2_col4\" class=\"data row2 col4\" >0.437644</td>\n",
       "      <td id=\"T_4967f_row2_col5\" class=\"data row2 col5\" >0.213059</td>\n",
       "      <td id=\"T_4967f_row2_col6\" class=\"data row2 col6\" >0.213059</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_4967f_level0_row3\" class=\"row_heading level0 row3\" >Warehouse</th>\n",
       "      <td id=\"T_4967f_row3_col0\" class=\"data row3 col0\" >0.122530</td>\n",
       "      <td id=\"T_4967f_row3_col1\" class=\"data row3 col1\" >0.128165</td>\n",
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       "      <td id=\"T_4967f_row3_col4\" class=\"data row3 col4\" >0.199383</td>\n",
       "      <td id=\"T_4967f_row3_col5\" class=\"data row3 col5\" >0.126712</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_4967f_level0_row4\" class=\"row_heading level0 row4\" >Elevator</th>\n",
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       "    <tr>\n",
       "      <th id=\"T_4967f_level0_row6\" class=\"row_heading level0 row6\" >Price(USD)</th>\n",
       "      <td id=\"T_4967f_row6_col0\" class=\"data row6 col0\" >0.797784</td>\n",
       "      <td id=\"T_4967f_row6_col1\" class=\"data row6 col1\" >0.570775</td>\n",
       "      <td id=\"T_4967f_row6_col2\" class=\"data row6 col2\" >0.213059</td>\n",
       "      <td id=\"T_4967f_row6_col3\" class=\"data row6 col3\" >0.126712</td>\n",
       "      <td id=\"T_4967f_row6_col4\" class=\"data row6 col4\" >0.162221</td>\n",
       "      <td id=\"T_4967f_row6_col5\" class=\"data row6 col5\" >1.000000</td>\n",
       "      <td id=\"T_4967f_row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1ecb7d85ea0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price.corr().style.background_gradient(cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_06f77_row0_col0, #T_06f77_row1_col1, #T_06f77_row2_col2, #T_06f77_row3_col3, #T_06f77_row4_col4, #T_06f77_row5_col5 {\n",
       "  background-color: #b40426;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_06f77_row0_col1 {\n",
       "  background-color: #f39778;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_06f77_row0_col2 {\n",
       "  background-color: #4b64d5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_06f77_row0_col3, #T_06f77_row3_col0, #T_06f77_row3_col1, #T_06f77_row3_col5, #T_06f77_row5_col2, #T_06f77_row5_col4 {\n",
       "  background-color: #3b4cc0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_06f77_row0_col4 {\n",
       "  background-color: #4257c9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_06f77_row0_col5 {\n",
       "  background-color: #f29274;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_06f77_row1_col0 {\n",
       "  background-color: #f39577;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_06f77_row1_col2 {\n",
       "  background-color: #536edd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_06f77_row1_col3, #T_06f77_row5_col3 {\n",
       "  background-color: #3c4ec2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
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       "  background-color: #4a63d3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
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       "  background-color: #dfdbd9;\n",
       "  color: #000000;\n",
       "}\n",
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       "  background-color: #6b8df0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
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       "  background-color: #7093f3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_06f77_row2_col3 {\n",
       "  background-color: #afcafc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_06f77_row2_col4 {\n",
       "  background-color: #a9c6fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_06f77_row2_col5 {\n",
       "  background-color: #5977e3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_06f77_row3_col2 {\n",
       "  background-color: #96b7ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_06f77_row3_col4 {\n",
       "  background-color: #485fd1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_06f77_row4_col0 {\n",
       "  background-color: #4f69d9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_06f77_row4_col1 {\n",
       "  background-color: #5673e0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_06f77_row4_col2 {\n",
       "  background-color: #9abbff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_06f77_row4_col3 {\n",
       "  background-color: #5572df;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_06f77_row4_col5 {\n",
       "  background-color: #465ecf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_06f77_row5_col0 {\n",
       "  background-color: #f29072;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_06f77_row5_col1 {\n",
       "  background-color: #dedcdb;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_06f77\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_06f77_level0_col0\" class=\"col_heading level0 col0\" >Area</th>\n",
       "      <th id=\"T_06f77_level0_col1\" class=\"col_heading level0 col1\" >Room</th>\n",
       "      <th id=\"T_06f77_level0_col2\" class=\"col_heading level0 col2\" >Parking</th>\n",
       "      <th id=\"T_06f77_level0_col3\" class=\"col_heading level0 col3\" >Warehouse</th>\n",
       "      <th id=\"T_06f77_level0_col4\" class=\"col_heading level0 col4\" >Elevator</th>\n",
       "      <th id=\"T_06f77_level0_col5\" class=\"col_heading level0 col5\" >Price(USD)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_06f77_level0_row0\" class=\"row_heading level0 row0\" >Area</th>\n",
       "      <td id=\"T_06f77_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_06f77_row0_col1\" class=\"data row0 col1\" >0.788524</td>\n",
       "      <td id=\"T_06f77_row0_col2\" class=\"data row0 col2\" >0.256229</td>\n",
       "      <td id=\"T_06f77_row0_col3\" class=\"data row0 col3\" >0.122530</td>\n",
       "      <td id=\"T_06f77_row0_col4\" class=\"data row0 col4\" >0.182814</td>\n",
       "      <td id=\"T_06f77_row0_col5\" class=\"data row0 col5\" >0.797784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_06f77_level0_row1\" class=\"row_heading level0 row1\" >Room</th>\n",
       "      <td id=\"T_06f77_row1_col0\" class=\"data row1 col0\" >0.788524</td>\n",
       "      <td id=\"T_06f77_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_06f77_row1_col2\" class=\"data row1 col2\" >0.274778</td>\n",
       "      <td id=\"T_06f77_row1_col3\" class=\"data row1 col3\" >0.128165</td>\n",
       "      <td id=\"T_06f77_row1_col4\" class=\"data row1 col4\" >0.207735</td>\n",
       "      <td id=\"T_06f77_row1_col5\" class=\"data row1 col5\" >0.570775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_06f77_level0_row2\" class=\"row_heading level0 row2\" >Parking</th>\n",
       "      <td id=\"T_06f77_row2_col0\" class=\"data row2 col0\" >0.256229</td>\n",
       "      <td id=\"T_06f77_row2_col1\" class=\"data row2 col1\" >0.274778</td>\n",
       "      <td id=\"T_06f77_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_06f77_row2_col3\" class=\"data row2 col3\" >0.428808</td>\n",
       "      <td id=\"T_06f77_row2_col4\" class=\"data row2 col4\" >0.437644</td>\n",
       "      <td id=\"T_06f77_row2_col5\" class=\"data row2 col5\" >0.213059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_06f77_level0_row3\" class=\"row_heading level0 row3\" >Warehouse</th>\n",
       "      <td id=\"T_06f77_row3_col0\" class=\"data row3 col0\" >0.122530</td>\n",
       "      <td id=\"T_06f77_row3_col1\" class=\"data row3 col1\" >0.128165</td>\n",
       "      <td id=\"T_06f77_row3_col2\" class=\"data row3 col2\" >0.428808</td>\n",
       "      <td id=\"T_06f77_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_06f77_row3_col4\" class=\"data row3 col4\" >0.199383</td>\n",
       "      <td id=\"T_06f77_row3_col5\" class=\"data row3 col5\" >0.126712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_06f77_level0_row4\" class=\"row_heading level0 row4\" >Elevator</th>\n",
       "      <td id=\"T_06f77_row4_col0\" class=\"data row4 col0\" >0.182814</td>\n",
       "      <td id=\"T_06f77_row4_col1\" class=\"data row4 col1\" >0.207735</td>\n",
       "      <td id=\"T_06f77_row4_col2\" class=\"data row4 col2\" >0.437644</td>\n",
       "      <td id=\"T_06f77_row4_col3\" class=\"data row4 col3\" >0.199383</td>\n",
       "      <td id=\"T_06f77_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "      <td id=\"T_06f77_row4_col5\" class=\"data row4 col5\" >0.162221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_06f77_level0_row5\" class=\"row_heading level0 row5\" >Price(USD)</th>\n",
       "      <td id=\"T_06f77_row5_col0\" class=\"data row5 col0\" >0.797784</td>\n",
       "      <td id=\"T_06f77_row5_col1\" class=\"data row5 col1\" >0.570775</td>\n",
       "      <td id=\"T_06f77_row5_col2\" class=\"data row5 col2\" >0.213059</td>\n",
       "      <td id=\"T_06f77_row5_col3\" class=\"data row5 col3\" >0.126712</td>\n",
       "      <td id=\"T_06f77_row5_col4\" class=\"data row5 col4\" >0.162221</td>\n",
       "      <td id=\"T_06f77_row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1ecb7d2e8f0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price.drop(['Price'], axis=1, inplace=True)\n",
    "\n",
    "price.corr().style.background_gradient(cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Задание 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = price['Area'], price['Price(USD)']\n",
    "\n",
    "n = np.size(X)\n",
    "m_X, m_y = np.mean(X), np.mean(y)\n",
    "\n",
    "SS_Xy = np.sum(y * X) - n * (m_y * m_X)\n",
    "SS_XX = np.sum(X * X) - n * (m_X * m_X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Задание 11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Наклон линии регрессии: 3675.282602461848\n",
      "Y-перехват: -206455.00915985904\n"
     ]
    }
   ],
   "source": [
    "coef_price = SS_Xy / SS_XX\n",
    "intercept_price = m_y - (coef_price * m_X)\n",
    "\n",
    "print(\n",
    "    f\"Наклон линии регрессии: {coef_price}\",\n",
    "    f\"Y-перехват: {intercept_price}\",\n",
    "    sep='\\n',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Наклон линии регрессии: 3675.2826024618457\n",
      "Y-перехват: -206455.0091598588\n"
     ]
    }
   ],
   "source": [
    "np_X = np.array(X).reshape(-1, 1)\n",
    "\n",
    "regression_price = LinearRegression().fit(np_X, y)\n",
    "prediction_price = regression_price.predict(np_X).flatten()\n",
    "\n",
    "print(\n",
    "    f\"Наклон линии регрессии: {regression_price.coef_[0]}\",\n",
    "    f\"Y-перехват: {regression_price.intercept_}\",\n",
    "    sep='\\n',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Задание 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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_device_pixel_ratio', {\n",
       "                device_pixel_ratio: fig.ratio,\n",
       "            });\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",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\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",
       "    fig.rubberband_canvas.style.cursor = msg['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",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\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",
       "                img.type = 'image/png';\n",
       "            }\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",
       "                img\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 https://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",
       " * https://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.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\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\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\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.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\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",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(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",
       "    // 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": {
      "text/html": [
       "<div id='6e768a07-d6c7-4396-bd78-d2113faef0bc'></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "price_fig, bx = plt.subplots(figsize=(12.8, 12))\n",
    "price_fig.subplots_adjust(0.075, 0.075, 0.975, 0.95)\n",
    "\n",
    "plt.title('Зависимость между площадью и стоимостью дома', fontsize=20)\n",
    "\n",
    "plt.scatter(\n",
    "    x=X, y=y,\n",
    "    alpha=0.5, label='Диаграмма рассеяния',\n",
    ")\n",
    "plt.plot(\n",
    "    X, intercept_price + (coef_price * X),\n",
    "    color='crimson', linewidth=3,\n",
    "    label='Простая линейная регрессия',\n",
    ")\n",
    "\n",
    "plt.xlabel('Площадь дома', fontsize=14)\n",
    "plt.ylabel('Стоимость дома, $', fontsize=14)\n",
    "\n",
    "plt.grid(True)\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "pygments_lexer": "ipython3",
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