{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d563033c",
   "metadata": {},
   "source": [
    "# Import tools/models be used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "fc9e6e26",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from math import pi\n",
    "from sklearn.linear_model import LinearRegression     # import linear model to be used\n",
    "import joblib                                         # to store model\n",
    "import os\n",
    "import errno\n",
    "from sklearn.model_selection import train_test_split  # this is very important"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "313e157e",
   "metadata": {},
   "source": [
    "# Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "bdc08ed0",
   "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>$M_{inv}$</th>\n",
       "      <th>$rapidity$</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>121.079631</td>\n",
       "      <td>-0.304619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>196.219429</td>\n",
       "      <td>-0.401002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>478.422633</td>\n",
       "      <td>-1.010334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>99.018601</td>\n",
       "      <td>0.097534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>423.950224</td>\n",
       "      <td>2.223391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>128.145991</td>\n",
       "      <td>0.246759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>576.782956</td>\n",
       "      <td>-0.554320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>35.437849</td>\n",
       "      <td>0.628855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>189.229229</td>\n",
       "      <td>0.462469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10000</th>\n",
       "      <td>443.127754</td>\n",
       "      <td>-1.858528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10001 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        $M_{inv}$  $rapidity$\n",
       "0      121.079631   -0.304619\n",
       "1      196.219429   -0.401002\n",
       "2      478.422633   -1.010334\n",
       "3       99.018601    0.097534\n",
       "4      423.950224    2.223391\n",
       "...           ...         ...\n",
       "9996   128.145991    0.246759\n",
       "9997   576.782956   -0.554320\n",
       "9998    35.437849    0.628855\n",
       "9999   189.229229    0.462469\n",
       "10000  443.127754   -1.858528\n",
       "\n",
       "[10001 rows x 2 columns]"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "directory = '../data/'\n",
    "filein    = 'checkLO.txt'\n",
    "file      = directory+filein\n",
    "data = np.genfromtxt(file)\n",
    "\n",
    "m        = data[:,0:1]\n",
    "rap      = data[:,1:2]\n",
    "x2       = data[:,2:3]\n",
    "\n",
    "m = np.concatenate((m),axis=0) \n",
    "rap = np.concatenate((rap),axis=0) \n",
    "x2 = np.concatenate((x2),axis=0) \n",
    "\n",
    "Data_ave = np.stack((m,rap),axis=-1)\n",
    "df = pd.DataFrame(Data_ave)\n",
    "df.columns = ['$M_{inv}$','$rapidity$']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0248b96b",
   "metadata": {},
   "source": [
    "# Split data into training and testing sub-sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "de4cf784",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for reproducibility\n",
    "#X_train, X_test, y_train, y_test = train_test_split(Data_ave, x2, test_size=0.4, train_size=0.6 , random_state=0)\n",
    "X_train, X_test, y_train, y_test = train_test_split(Data_ave, x2, test_size=0.4, train_size=0.6 )\n",
    "\n",
    "# rename data for convenience\n",
    "x2_train   = y_train\n",
    "m_train    = X_train[:,0:1]\n",
    "rap_train  = X_train[:,1:2]\n",
    "\n",
    "x2_test   = y_test\n",
    "m_test    = X_test[:,0:1]\n",
    "rap_test  = X_test[:,1:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2624e86c",
   "metadata": {},
   "source": [
    "# Now let's do some training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "b8ef444a",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Training and prediction with Linear Model'''\n",
    "#-----------------------------------------------------------------------------#\n",
    "# Input:                                                                      #\n",
    "# X_train  := training data set                                               #\n",
    "# y_train  := targets for training                                            #\n",
    "# X_trest  := testing data set                                                #\n",
    "# y_test   := targets for testing                                             #\n",
    "# filename := name for training storage                                       #\n",
    "# model    := name of the mode we want to use                                 #\n",
    "# ----------------------------------------------------------------------------#\n",
    "# Output:                                                                     #  \n",
    "# loaded_model := trained model                                               #\n",
    "# R2 := quality of the training on the testing set                            #\n",
    "# y_fit := predicted targets                                                  #\n",
    "# y_est := estimated targets                                                  #\n",
    "# ----------------------------------------------------------------------------#\n",
    "# Output labelling :                                                          #\n",
    "# Training(X,y,filename)[0] -> R2                                             #\n",
    "# Training(X,y,filename)[1] -> y_new                                          #\n",
    "# ----------------------------------------------------------------------------#\n",
    "\n",
    "def training (X_train, y_train, X_test, y_test,filename,model):\n",
    "\n",
    "    # train \n",
    "    model.fit(X_train, y_train)\n",
    "\n",
    "    # save trained model\n",
    "    joblib.dump(model, filename)\n",
    "\n",
    "    # load model \n",
    "    loaded_model = joblib.load(filename)\n",
    "\n",
    "    # R^2\n",
    "    R2   = loaded_model.score(X_test, y_test)\n",
    "    \n",
    "    # predicted targets \n",
    "    y_fit = loaded_model.predict(X_test)\n",
    "    \n",
    "    # estimated targets\n",
    "    y_est = loaded_model.predict(X_train)\n",
    "\n",
    "    \n",
    "    return R2, y_fit,y_est, model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "06b6bed9",
   "metadata": {},
   "outputs": [],
   "source": [
    "x2_train,x2_test;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "ca19e5ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "m_train,m_test;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "be16d388",
   "metadata": {},
   "outputs": [],
   "source": [
    "# name of the model for storage\n",
    "filename = 'storage/x2_LO_LinearRegression_model_not_thinking.sav'\n",
    "\n",
    "# train \n",
    "Training_machine_learning = training(X_train,y_train,X_test,y_test,filename,LinearRegression())\n",
    "\n",
    "# predict\n",
    "R2    = Training_machine_learning[0] \n",
    "y_fit = Training_machine_learning[1]\n",
    "y_est = Training_machine_learning[2]\n",
    "coefs = Training_machine_learning[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "3b225d13",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_est,y_train;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "1cf2c418",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_fit,y_test;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "49492be3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6180628385074418"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "93efe92e",
   "metadata": {},
   "outputs": [],
   "source": [
    "coefs;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56253913",
   "metadata": {},
   "source": [
    "# Let's plot and see what we have"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "c97b86ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    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",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            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 http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.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 icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.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",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "\n",
    "ax1.scatter(m, x2, label='data')\n",
    "ax1.scatter(m_test, y_fit,label='prediction')\n",
    "ax1.scatter(m_train, y_est,label='estimated')\n",
    "ax1.legend()\n",
    "\n",
    "ax1.set_xlabel(xlabel='m',fontsize=16)\n",
    "ax1.set_ylabel(ylabel=r'$x_2$',fontsize=16)\n",
    "\n",
    "ax2.scatter(rap, x2, label='data')\n",
    "ax2.scatter(rap_test, y_fit,label='prediction')\n",
    "ax2.scatter(rap_train, y_est,label='estimated')\n",
    "\n",
    "ax2.set_xlabel(xlabel='rapidity',fontsize=16)\n",
    "\n",
    "plt.savefig('Plots/x2_LO_Linear_not_thinking_1.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "3a651a0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    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",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            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 http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.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 icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.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",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABQAAAAPACAYAAABq3NR5AAAAAXNSR0IArs4c6QAAAERlWElmTU0AKgAAAAgAAYdpAAQAAAABAAAAGgAAAAAAA6ABAAMAAAABAAEAAKACAAQAAAABAAAFAKADAAQAAAABAAADwAAAAADIn4SfAABAAElEQVR4AezdB5xU1fn/8YciLF0FpMSCQlREA4mCYCcWFFDAKJZIBMWGmmBsMT8V7DFoYjQKJmJFRU0AFQsaK4ooQbEgEsSCoUtEii5S9j/f4/+Md2en7k69+zmv1zB37j333HPe97Iwz55SpyKSjIQAAggggAACCCCAAAIIIIAAAggggAACoRSoG8pW0SgEEEAAAQQQQAABBBBAAAEEEEAAAQQQcAIEAHkQEEAAAQQQQAABBBBAAAEEEEAAAQQQCLEAAcAQ31yahgACCCCAAAIIIIAAAggggAACCCCAAAFAngEEEEAAAQQQQAABBBBAAAEEEEAAAQRCLEAAMMQ3l6YhgAACCCCAAAIIIIAAAggggAACCCBAAJBnAAEEEEAAAQQQQAABBBBAAAEEEEAAgRALEAAM8c2laQgggAACCCCAAAIIIIAAAggggAACCBAA5BlAAAEEEEAAAQQQQAABBBBAAAEEEEAgxAIEAEN8c2kaAggggAACCCCAAAIIIIAAAggggAACBAB5BhBAAAEEEEAAAQQQQAABBBBAAAEEEAixAAHAEN9cmoYAAggggAACCCCAAAIIIIAAAggggAABQJ4BBBBAAAEEEEAAAQQQQAABBBBAAAEEQixAADDEN5emIYAAAggggAACCCCAAAIIIIAAAgggQACQZwABBBBAAAEEEEAAAQQQQAABBBBAAIEQCxAADPHNpWkIIIAAAggggAACCCCAAAIIIIAAAggQAOQZQAABBBBAAAEEEEAAAQQQQAABBBBAIMQCBABDfHNpGgIIIIAAAggggAACCCCAAAIIIIAAAgQAeQYQQAABBBBAAAEEEEAAAQQQQAABBBAIsQABwBDfXJqGAAIIIIAAAggggAACCCCAAAIIIIAAAUCeAQQQQAABBBBAAAEEEEAAAQQQQAABBEIsQAAwxDeXpiGAAAIIIIAAAggggAACCCCAAAIIIEAAkGcAAQQQQAABBBBAAAEEEEAAAQQQQACBEAsQAAzxzaVpCCCAAAIIIIAAAggggAACCCCAAAIIEADkGUAAAQQQQAABBBBAAAEEEEAAAQQQQCDEAgQAQ3xzaRoCCCCAAAIIIIAAAggggAACCCCAAAIEAHkGEEAAAQQQQAABBBBAAAEEEEAAAQQQCLEAAcAQ31yahgACCCCAAAIIIIAAAggggAACCCCAAAFAngEEEEAAAQQQQAABBBBAAAEEEEAAAQRCLEAAMMQ3l6YhgAACCCCAAAIIIIAAAggggAACCCBAAJBnAAEEEEAAAQQQQAABBBBAAAEEEEAAgRALEAAM8c2laQgggAACCCCAAAIIIIAAAggggAACCBAA5BlAAAEEEEAAAQQQQAABBBBAAAEEEEAgxAIEAEN8c2kaAggggAACCCCAAAIIIIAAAggggAACBAB5BhBAAAEEEEAAAQQQQAABBBBAAAEEEAixAAHAEN9cmoYAAggggAACCCCAAAIIIIAAAggggAABQJ4BBBBAAAEEEEAAAQQQQAABBBBAAAEEQixAADDEN5emIYAAAggggAACCCCAAAIIIIAAAgggQACQZwABBBBAAAEEEEAAAQQQQAABBBBAAIEQCxAADPHNpWkIIIAAAggggAACCCCAAAIIIIAAAggQAOQZQAABBBBAAAEEEEAAAQQQQAABBBBAIMQCBABDfHNpGgIIIIAAAggggAACCCCAAAIIIIAAAgQAeQYQQAABBBBAAAEEEEAAAQQQQAABBBAIsQABwBDfXJqGAAIIIIAAAggggAACCCCAAAIIIIAAAUCeAQQQQAABBBBAAAEEEEAAAQQQQAABBEIsQAAwxDeXpiGAAAIIIIAAAggggAACCCCAAAIIIEAAkGcAAQQQQAABBBBAAAEEEEAAAQQQQACBEAsQAAzxzaVpCCCAAAIIIIAAAggggAACCCCAAAIIEADkGUAAAQQQQAABBBBAAAEEEEAAAQQQQCDEAgQAQ3xzaRoCCCCAAAIIIIAAAggggAACCCCAAAIEAHkGEEAAAQQQQAABBBBAAAEEEEAAAQQQCLEAAcAQ31yahgACCCCAAAIIIIAAAggggAACCCCAAAFAngEEEEAAAQQQQAABBBBAAAEEEEAAAQRCLEAAMMQ3l6YhgAACCCCAAAIIIIAAAggggAACCCBAAJBnAAEEEEAAAQQQQAABBBBAAAEEEEAAgRALEAAM8c2laQgggAACCCCAAAIIIIAAAggggAACCBAA5BlAAAEEEEAAAQQQQAABBBBAAAEEEEAgxAIEAEN8c2kaAggggAACCCCAAAIIIIAAAggggAACBAB5BhBAAAEEEEAAAQQQQAABBBBAAAEEEAixAAHAEN9cmoYAAggggAACCCCAAAIIIIAAAggggAABQJ4BBBBAAAEEEEAAAQQQQAABBBBAAAEEQixAADDEN5emIYAAAggggAACCCCAAAIIIIAAAgggQACQZwABBBBAAAEEEEAAAQQQQAABBBBAAIEQCxAADPHNpWkIIIAAAggggAACCCCAAAIIIIAAAggQAOQZQAABBBBAAAEEEEAAAQQQQAABBBBAIMQCBABDfHNpGgIIIIAAAggggAACCCCAAAIIIIAAAgQAeQYQQAABBBBAAAEEEEAAAQQQQAABBBAIsQABwBDfXJqGAAIIIIAAAggggAACCCCAAAIIIIAAAUCeAQQQQAABBBBAAAEEEEAAAQQQQAABBEIsQAAwxDeXpiGAAAIIIIAAAggggAACCCCAAAIIIEAAkGcAAQQQQAABBBBAAAEEEEAAAQQQQACBEAsQAAzxzaVpCCCAAAIIIIAAAggggAACCCCAAAIIEADkGUAAAQQQQAABBBBAAAEEEEAAAQQQQCDEAgQAQ3xzaRoCCCCAAAIIIIAAAggggAACCCCAAAIEAHkGEEAAAQQQQAABBBBAAAEEEEAAAQQQCLEAAcAQ31yahgACCCCAAAIIIIAAAggggAACCCCAAAFAngEEEEAAAQQQQAABBBBAAAEEEEAAAQRCLEAAMMQ3l6YhgAACCCCAAAIIIIAAAggggAACCCBAAJBnAAEEEEAAAQQQQAABBBBAAAEEEEAAgRALEAAM8c2laQgggAACCCCAAAIIIIAAAggggAACCBAA5BlAAAEEEEAAAQQQQAABBBBAAAEEEEAgxAIEAEN8c2kaAggggAACCCCAAAIIIIAAAggggAACBAB5BhBAAAEEEEAAAQQQQAABBBBAAAEEEAixAAHAEN9cmoYAAggggAACCCCAAAIIIIAAAggggAABQJ4BBBBAAAEEEEAAAQQQQAABBBBAAAEEQixAADDEN5emIYAAAggggAACCCCAAAIIIIAAAgggQACQZwABBBBAAAEEEEAAAQQQQAABBBBAAIEQCxAADPHNpWkIIIAAAggggAACCCCAAAIIIIAAAggQAOQZQAABBBBAAAEEEEAAAQQQQAABBBBAIMQCBABDfHNpGgIIIIAAAggggAACCCCAAAIIIIAAAgQAeQYQQAABBBBAAAEEEEAAAQQQQAABBBAIsQABwBDfXJqGAAIIIIAAAggggAACCCCAAAIIIIAAAUCeAQQQQAABBBBAAAEEEEAAAQQQQAABBEIsQAAwxDeXpiGAAAIIIIAAAggggAACCCCAAAIIIEAAkGcAAQQQQAABBBBAAAEEEEAAAQQQQACBEAsQAAzxzaVpCCCAAAIIIIAAAggggAACCCCAAAIIEADkGUAAAQQQQAABBBBAAAEEEEAAAQQQQCDEAgQAQ3xzaRoCCCCAAAIIIIAAAggggAACCCCAAAIEAHkGEEAAAQQQQAABBBBAAAEEEEAAAQQQCLEAAcAQ31yahgACCCCAAAIIIIAAAggggAACCCCAAAFAngEEEEAAAQQQQAABBBBAAAEEEEAAAQRCLEAAMMQ3l6YhgAACCCCAAAIIIIAAAggggAACCCBAAJBnAAEEEEAAAQQQQAABBBBAAAEEEEAAgRALEAAM8c2laQgggAACCCCAAAIIIIAAAggggAACCBAA5BlAAAEEEEAAAQQQQAABBBBAAAEEEEAgxAIEAEN8c2kaAggggAACCCCAAAIIIIAAAggggAACBAB5BhBAAAEEEEAAAQQQQAABBBBAAAEEEAixAAHAEN9cmoYAAggggAACCCCAAAIIIIAAAggggAABQJ4BBBBAAAEEEEAAAQQQQAABBBBAAAEEQixAADDEN5emIYAAAggggAACCCCAAAIIIIAAAgggQACQZwABBBBAAAEEEEAAAQQQQAABBBBAAIEQCxAADPHNpWkIIIAAAggggAACCCCAAAIIIIAAAggQAOQZQAABBBBAAAEEEEAAAQQQQAABBBBAIMQC9UPcNppWxALl5eX2/vvvuxq2bt3a6tfnUSzi20XVEEAAAQQQQAABBBBAAAEESlRg06ZNtnLlSlf7vfbay8rKykq0JVS7JgJEXWqix7nVFlDwr0ePHtU+nxMRQAABBBBAAAEEEEAAAQQQQCAzgbfeesu6d++e2UnkDoUAQ4BDcRtpBAIIIIAAAggggAACCCCAAAIIIIAAAvEF6AEY34W9ORbQsF+f9BuIdu3a+Y+8I4AAAggggAACCCCAAAIIIIBAlgSWLl0aHYEX/C6epeIppkQECACWyI0KWzWDc/4p+Lf99tuHrYm0BwEEEEAAAQQQQAABBBBAAIGiEgh+Fy+qilGZnAswBDjnxFwAAQQQQAABBBBAAAEEEEAAAQQQQACBwgkQACycPVdGAAEEEEAAAQQQQAABBBBAAAEEEEAg5wIEAHNOzAUQQAABBBBAAAEEEEAAAQQQQAABBBAonAABwMLZc2UEEEAAAQQQQAABBBBAAAEEEEAAAQRyLkAAMOfEXAABBBBAAAEEEEAAAQQQQAABBBBAAIHCCRAALJw9V0YAAQQQQAABBBBAAAEEEEAAAQQQQCDnAgQAc07MBRBAAAEEEEAAAQQQQAABBBBAAAEEECicAAHAwtlzZQQQQAABBBBAAAEEEEAAAQQQQAABBHIuQAAw58RcAAEEEEAAAQQQQAABBBBAAAEEEEAAgcIJEAAsnD1XRgABBBBAAAEEEEAAAQQQQAABBBBAIOcC9XN+BS6AAAIIIIAAAggggAACRS1QXl5uq1evtm+++cY2b95c1HWlcggggEBtE6hXr541btzYtt56aysrK6ttzae9WRIgAJglSIpBAAEEEEAAAQQQQKDUBCoqKmzp0qX29ddfl1rVqS8CCCBQawQ2bdpkGzZssK+++spatGhh7dq1szp16tSa9tPQ7AgQAMyOI6UggAACCCCAAAIIIFByAqtWraoS/Ktfn68IJXcjqTACCIRaQAFAn/QLmwYNGlirVq38Lt4RSEuAf93TYiITAggggAACCCCAAALhEvjuu+9s5cqV0UZtt912bniZhpqREEAAAQSKR0BTM2iahhUrVrhK6Wd38+bNXSCweGpJTYpdgEVAiv0OUT8EEEAAAQQQQAABBHIgsG7dumipLVu2NL0I/kVJ2EAAAQSKRkA/m/3PaV+p4M9wv493BJIJFG0AUJHtqVOn2pVXXmlHHXWU696qMe56DR06NFmbUh7T5Ma77LKLK0vldejQIeU5yqDzxowZYz169LBtt93WmjZtap07d7aLLrrIFi1alFYZyjR37lw7++yzrVOnTtaoUSNr3bq1HXTQQXbnnXdasGtvqgInTpxoffr0ceP/NRGo2jFkyBCbOXNmqlOjxzXsY9SoUda1a1c3l4B+i6Bt7dMxEgIIIIAAAggggEA4BdavXx9tmP4PSEIAAQQQKG6B4M/q4M/w4q41tSsWgaIdAtymTZucGSmo+Omnn2ZU/sKFC61fv342f/78Sud99NFHptddd91lDz30kPXt27fS8dgP48ePt3PPPddN4OmPadW16dOnu9e9997rAp+K7idKyn/88ce7fME8n3/+uemleowePdquuOKK4OEq27NmzbIBAwa4iZ+DB9977z3TS216/PHHbZ999gkeZhsBBBBAAAEEEEAgBAIaAqykX4g3bNgwBC2iCQgggEC4BfSzWj+ztYCT/xke7hbTumwKFG0PwGAjd9hhBzviiCOCu6q9/c4779gtt9zils5u1qxZWuWoa23//v2jwb8zzjjDXnjhBZsxY4Zdd911riegJuJUUE6Bs0Rp2rRpduaZZ7rgnwKct956q7355pv2zDPP2LHHHutOU+89bW/ZsiVRMXb66adHg3+9e/e2KVOm2FtvvWUKLnbs2NGdqyCnAniJ0uLFi+3oo492wT9N9HzJJZfYq6++6l7a1r4lS5a4disvCQEEEEAAAQQQQCBcAv7/mxpapi+UJAQQQACB4hbQz2o/VYP/GV7cNaZ2xSRQtD0AFcDq3r27eylY9tlnn9nOO+9cIztNnKngnd41xFUBs7Vr16Ys86abbnK9/JTxj3/8o1188cXRc3r16mUKwmkIr4YIjxw50l588cXocb+hob3nnXeeC86p2+7rr7/ugnX++JFHHul6Bt5xxx0uCDdhwgT71a9+5Q9H31955RXXw087FMCbPHly9AeAvI455hjbe++93ZBkBfKOO+44N5lztID/v/F///d/tnz5cvdJPQYVvPTpwAMPdL3+Bg8e7PKoJ+Hdd9/tD/OOAAIIIIAAAggggAACCCCAAAIIIFBCAkXbA/Cqq65yvc+yORT4L3/5i82ePdt22203u/TSS9O6TRs3bjSdp6T5/i688MIq5ykIqF55Si+99JK7RmwmBeo+/vhjt/uyyy6rFPzzeTW/4DbbbOM+ajteUgBSSVF/BQt99N/n1VLgN954o/v41VdfuSCnP+bfFfhTgFFJcwgGg38+j/bpmNL9998fDRb647wjgAACCCCAAAIIIIAAAggggAACCJSGQNEGALPNp7nx1KtQaezYsWkvl/3yyy+75bZ13qmnnmp168YnCy5MMmnSJGWvlDRM16dgXr9P740bNzb1ulP64IMPbMGCBW7b/6GhyBp6rHT44Yfb9ttv7w9VetcQYj85aLy6PPHEE64XpE4aNmxYpXODH3w91WNS55AQQAABBBBAAAEEEEAAAQQQQAABBEpPIH40q/TakbLGI0aMMK2So1VyNWQ33aTFOXw6+OCD/WaVdy2U0aRJE7f/tddeq3Lcl6Peh23btq1y3O8IXiO2HM3zt2HDBpc1mM+f698bNGhgPXv2dB91jnoxBpOvi/YlKyd4LLYuwfJq2/ba8o22YPlam/PFaveuzyQEEEAAAQQQQAABBBBAAAEEEECgWAVqRQBw4sSJ9vTTT7vhtZrPL5M0b968aPbdd989uh27oUUztACHUvAcfVbPvf/+97/atGRlxB6PLSf4Od1yNPdgbE9CX06LFi2SBiPbtWsX7Unoz3GNqIV/aJWlGQu/tHMmzLZuVz9vh//5VRt4++vuXZ9HPDjbHVc+EgIIIIAAAggggAAChRa499573eIuWjRA86kXOh1yyCGuPnon5Vcg1bOQzr3RqDBNjdWjRw/3HVHPlV4DBw6s1JhVq1bZRRdd5KbPatSoUfQZ1EKcJAQQKKxA0S4Cki0WzYOnhTmU/vCHP9h2222XUdFffPGFy6/efVtvvXXSc7VasVYBXrlypeuppyW6lRT884GhRMN2fcEqwyd/7XifMy1njz328MWYLzdVGTpB9Zk7d270nGghKTZ8wDNRtqVLlyY6VHT7P1j8tf320Tn2n+Xr4tZt85YKe/r9Ze61a5um9qfB3WzPH7WIm5edCCCAAAIIIIAAAgggUNoCmibqmmuusVmzZrlFHn/yk5+4hSIHDRqUtGHLli1z89F/++237ntjqk4dSQvL88GTTjrJHnvssaRX/frrr03z48d2QEl6EgcRQCBvAqEPAGrFXi16oR9EWgE40+RXCW7atGnKU/0QYGVUrz8fAPRlaH+qcmLL0Dk+ZbucVHXRdX191J5MUjCQmcl5xZZ3+oKVdtYDs+2b7zanVTUFCQff+YbdOWRvO/DHrdM6h0wIIIAAAggggAACCJSigOYMv++++2ynnXYqil6G+TD85z//aSeccEJ0TnVd84033jDNw65ebr/5zW8SVkM949asWWOXXHJJypFhCQspwIEZM2ZEg3/9+vVzHWy0WKd6APq551Wt22+/PRr8UxuPPvroaCcajS5LN5XKc6WetTvvvLNr1j333GN+Dv1020k+BPItEOoA4Kuvvmp33323aXjuuHHj3A+oTIHLy8vdKZpXL1XyAT/l0291fPJl6HOqchKVoXOzXU6quuiavj7B9mh/bUjq+ZdJ8M+bKFio8x49qxc9AT0K7wgggAACCCCAAAK1WkC95ko9qVOEOpVoOKzmXL/iiivc9zsNjZ06darrBThgwADr0KFDlabqu+mDDz5oP/rRj6KLU1bJVKAdqe7Nv/71L1ezevXq2UMPPVQp6Besss+n+fFvvPHG4CG2EUCgCARCGwDUYhlnnnmmG3qr38KoW3Z1UllZmTvtu+++S3m6X6BDGTXfgU++DH1OVU6iMnRuNsv55ptvUtZF1/T1CbZH+1MlP8w4UT4NAdb8EcWaNGRbw37T7fkX2w6dd+Gj79qzIw+sVuA5tjw+I4AAAggggAACCCCAQGEFpkyZYppiqmXLlvb8889HR3f9/Oc/ty5duthHH33kgnz/93//V6mimpf93HPPdfv+9Kc/RUdZVcpUxB8WL17saqdef8Eef7FV9vl23XXX2EN8RgCBIhAIbQDwuuuus/nz57s57EaPHl1t6mbNmrlz0xkCq1WGfQoOr/Vl6FiqchKVoXOzWY4CgKnqomv6+gTbo/2pUjrzC6Yqo5DH3/hkVcI5/9Kt1/zISsEzP/mf9erYMt1TyIcAAggggAACCNR6gbXlG23Z1+W2PvIL1SYN6lnbFmXWrGyrWu8CQOEF3nnnHVeJ/v37R4N/2lG3bl03LPiqq64ynydY21tvvdU++OADO/TQQ23w4MHBQyWx7TuFbLVV8r+H6eYriUZTSQRCKBDaVYB9l+PDDjvMdcfWSsCxLx/c0rs/9uKLL1a6zT6QpTyrV6+udCz2g+/11rp16+jQWeXxZWg71eIYvgzljZ1HL9vlpKqL6uDrE1sXHQtzmjDz86w0L1vlZKUyFIIAAggggAACCBSpgEZfzFj4pZ0zYbZ1u/p5O/zPr9rA21937/o84sHZ7rhfWK9Im5G0WuqU4FdOVUYtmKCFJH7605+6edJ0TKu1xib1NDvllFPcXGMalaMeWF27dnXzyNVkYb0tW7aYvvtoXrr999/fWrVqZQrwaOHDbt26uf2LFi2KrY777Nui+f+UPv/882jbfBv1HkyJVpodNmyYO7dx48YWnPM8eG5wWz3tVPbee+8d3F1pO1dmumdK8eaza9u2rTvm87gPkT90j+Ql29tuu83vzuq7eiX+7ne/c/MK6hnRwpf6Hpxq0Q5fiUT3xt/LZPdZw4d9Pj0HSsrv9+ld5aeTqvNc+XLVy3L8+PHWt29fa9++vfs+rmf6oIMOcnMzBqfT8ucE32fPnm2nn366qfei5sHX6Dt9B9Zzpt6bTzzxRHRhT52ndvn5//TZP8fBdqs9JASKSaB+MVUmm3XxQ201GadeydKXX35pWtVI6eCDDzZ14fZJq+dqolcldenWXA/xkn7gLFy40B3q3LlzpSzqPacfHgqmqYxkKXg8tpzgSr7BfPHK88c1/2GnTp0qZVE5+gGnf5y0EpX/x6pSpsgH/WOlSWqVYuvidob0D/3Wedrc5Vlp3bNzl5nK47fWWeGkEAQQQAABBBAIoYDmXdbUK1pMLV7avKXCnn5/mXvt2qap/Wlwt5KfZ1mrpB5xxBFJF85QB4QhQ4bY5MmTK7EokPHee++519ixY+3hhx829UjLNF199dWmHmuxSd8R3n33XfdS+RMmTLBUq9vGlpHJZwU3FfjUnOOTJk2yU089NeHpc+bMsQ8//NAd/+Uvf1klX67NWrRo4a6p71Cxye/zefzxCy+80AU2tShGLr5TyUPBvmAwWM/ICy+84F6nnXaaHXjggb46oXzX9/Bjjjkm+mz4Rq5atcqmT5/uXnfccYc99dRT9uMf/9gfjr7/+c9/dgFvBcWDSR1m9Hr77bdN5ytAnenIuGB5bCNQaIHQ9gDMFuwBBxwQLeqVV16Jbsdu/Pvf/44Ol9Vv0GKTL0fDkv0/DrF59Dl4jdhyunfvHl1EJJgvthwFP2fOnOl2B8/x+Xxd9DlZOcFjsXXxZYXxXUNO9B/NbCSVs3zN9wvJZKM8ykAAAQQQQAABBMIkMH3BSht85xsJg3+xbVWQUPl1Ximn4447zjRf2vnnn+/mktN3CQXydtttN9csLTKhFVQV/FOPInVWUG8u5dOKs1p0Yscdd3RT+vziF79wv9zP1EMdGNSTbcSIEfbAAw/Y66+/7srRPHcKVinQoWmDTj75ZJs3b16l4nXO+++/b1rwQkk9rvQ59lXppAQfevfu7c7XYS2SkSz54xpye+KJJ1bKmg8z9bxUUiBJNj4pcOR72/k8OqbecbqvWvhDC4ZkOylY26dPn2jwT6sTP/300+450WIdWoxDi2IqeFWd5O9nsvus75s+n54DJeX3+/SeqkOOr1t1nisFPvVdVYFQTZmlgOszzzzjgnYvvfSSXXbZZabepQq6H3nkka4TjL+e3hVMVy9Y3UP16Lv55ptd4FRDuRU8lJ8C8bGBP7Vr2rRp0aKuvfbaSm3WcbWHhEAxCYS2B2A6wwM6dOjguqsnW7Ze3ZX1Wxz9cFVXZv1jqH+EY5N+a+VTvN+QDRw40P3wVx7lVRft2KR/RB599FG3W730YidP1Q80zRuhH2haYUm/jQgOC/bl6TdnvudevLrotyPnnHOO+yGnH8b6hyJe8m3SP7A6p7YkzTeTzbRuQ3bLy2bdKAsBBBBAAAEEECiUgHr+nfXA7IwXXdNiazrv0bN6lWxPQM0H9+yzz9rhhx8e5Q8Oab3llltMwQsNG3388cftqKOOiubThkYlKSihnl1z5861kSNHumBFpUwpPgwfPtxGjRrlrhHM+rOf/cwFcBSc1HUUqLz++utdkNDn0xBTvTRcWEn13HPPPf3hjN59ME+LY2hIcqIRSvp+p2mblIJBQ3+xfJjpO52GYK9cudIFkxRcatCggRvaqwCURl/5nokKsJ533nmuempbbADJ17sm7+rF6ad10j1SfXzS86RAs3qHPvfcc353Ru/+nqa6zz6fngMl5ff7MrlgdZ4rLfy5fPlyN+JOAddddtml0iX1ff744493f1c++eQTu+mmm9zQe5/pH//4h/terGG/Cq5roZNgUucZDe9VPECBRJ/UvuA9VZC3Om325fGOQD4E6AGYQlk/0H/961+7XPrNl35gxCb9oNB8A0oaQqzfgsQmBeI6duzodt9www3R4cLBfBdffLFbVUr7tB0v6bcTSvoHRXMR6DddwaThzJdeeqnbpR+8+oc9NmnIr/+HSb+10A+92KTfYPnfaOg/F4mGCceeF4bPmmw6m6lpw+yWl826URYCCCCAAAIIIFAIAQVzNOxXwbzqJJ134aPvVpqTqzrlFOqcoUOHVgr+BeuxceNG1wtJ+xRAig3++bzbbLONjRkzxn187bXX7OOPP/aH0npXZwgfsIl3gjoa+O8ksfOfxctfk33+u4m+2zzyyCNxi9LoJB/s8vl9xnyZKfg3btw4t+iHeodpvjkNv1WQVkkLUfoAlAKSCs5qeqlcLPyhBTd8z7qf/OQn0e+A3kTvur/6nprsPgfzl9q2AulTp0511f7rX/8atY9th+bZ9Kswq0dfMPnReep8Exv8C+ZTpyAFq0kIlLJA0T7B+kfs3khPOf8KBqn0j5vf799zeRP0D5/vjacegGeddZb7jZyG2SqYp/k7FJDThKv6QR8v6YeuVn/SDw31zlM3Zf2Qeuutt1ygTb+d8V2z9VsGBd3iJf0D4ru76x9i/dZQ7xoOoH8A9Fs6P1nvH/7wB9N/DOIl/eOkxUqUNKRAPRJlrpe21dVfSXnUnbk2Ja00V69u1V6e1TGoHymnTfOy6pzKOQgggAACCCCAQGgF3vhkVdrDfhMhzF++1mZ+8r9Eh4t6f2wAK1hZfT/w87mlChxpgQOf1CmhJknfUT799FMXtFJgRS/f48kfq0n5yc5Vr0M/P56GrsZLfr8WZzj22GMrZcmnmb47qaOEepapB5iM9B1MI7n0XVFJvSY1v6K+A+o7n08axaXeixrZpXNVhubqq07SnO5a/ENJ8yYmCk4pkKvvq2FMPvCqe9CvX7+kTfR/V5YsWRJd6FIn+AVd1INTzxEJgTALFO0Q4LvuussNuY2Hr/kp9Aom/RYtV0k/oDXPg37Do7kD/va3v7lX8Hr6bZDmpNCKWYmSztdvjPSbPHVTVrf62NSjRw8310e9eol7jem3FvpHWPM7aGiAXsGkH/6aY0KBykRJi5I8+eSTpm7s+q2HVk32Kyf7c9TrT3OAxBtm7POE8V0LdvTp0sZNNF3T9vXp0pYFQGqKyPkIIIAAAgggEDqBCTM/z0qbVE6vji2zUlY+C1GPrURJv9j3qVevXn4z5bvvyZQyYyCDVm3VCCd9L/AruAYOV9rUSCPfu63SgSx9UFD08ssvd0EYfecKLtagOc59hxANaY1daCOfZmquev3plSj99re/dfMzqiOJD2xqLkAteKK55jRXnqaVUq9G9STUsVTB3thraY45n+KNQPPH9K7vmPo+G7bk77um0tLw63ST/q7o+7CSArrq1KMeleqko3kCFUzU8HpNyxVv+q90r0M+BIpNoGh7ABYblFbS1USgCpJpMlUNr9VvGjRR7wUXXOAmD01n9a0zzjjDTa6rd/0Dqt9gtWzZ0tTrT6tsKbCp5cqTJfU01A9wBRzVA1BzJWiosn6IqeeeevGNHj06WRHu2L777usmKtU/tH4OA/0maq+99nL/+Oq3fspTG9MpPXfKSrOzVU5WKkMhCCCAAAIIIIBAEQisLd9o0+Yuz0pNnp27zFReqaVEo3TUjhUrVlSrOcFFKdIpQPOKK8ChHmqpgn8qT6v05jL5EUi6hu/t56+njg++t1u83pP5MvP1SfaueQzVG1Bzwl155ZUuqzpv+DnYtciEeghqOLPmBlRAUB03tMJsJsl76Bx9H0yWkg1tTXZesR/Lxn3ffffdXQBWfyc1qk9DinWv9P1YrhqZpyAtCYEwCKQfJs9za/MxtPezzz7LqFWaGFTdun3X7oxODmTWDxP1Iqxp0j+SwX8oq1Oego3XXHNNpYlQq1NO2M7ptUtL27VN0xoNTdmtTTPrucu2YaOhPQgggAACCCCAQI0Eln1dbpu3VNSoDH+yylm+przkRlwkG+0TnONbixqos0A6KVUQKFjGqlWr3PcIBQ3VAUDzjGs1Wc1Zrt516lygpGCWFiFUSmeRRZexmn9oBdb99tvPZsyY4QKAWqDEJx8QVCcMjaqKTfkwi71mvM+ai9Av/KFAn2yVNFRVi0io56d6B/qkjiSaxkm9+ZRHPQTTTcH7kaqXWjBvuuWXQj5/3/XsaFqsdJPyB5NW0laPTs0/qeHdCvhpoRf1ep0wYYJ7aZi1RuIlGmodLI9tBIpVoGgDgMUKRr1qh4D+ER1xSCe7IDI5dWSO6oxT48hCIjcP7kqX8YzlOAEBBBBAAAEEwi6wPrKARzbTug3ZLS+bdatOWcGAnwJxuVhZVAv+rV692lVv0qRJCRckCfYyq05bMj1HATAFAP/zn/+4Oc418ko94zREWUmrufrgZLDsfJgFr5doWz36tHCk5m0/4YQTotnmzJnjthXgjE3apwCg8mQSANx22x86Gmh6KT9nfWz5+lzdnnLxyiqmff6+q/3qyZfJMODYdijwrRWF9VLSnIAKKt52222meQPvu+8+02Iiv/nNb2JP5TMCJSPAEOCSuVVUNJ8C0xestN9Pfr/awb87h+xte/6oRT6rzLUQQAABBBBAAIGSEGgS+UVpNlPThtktL5t1q05ZCjL49Nxzz/nNrL5rdVolBZE0pVCi5OdYS3Q8Vc+zROcl2q958PyKtb7XnwKU5eXl7pR4w391IB9miers92tIr0ZWxS78oePq/acUO3eh9qlXo5LP4z6k8YembfJp1qxZfjPue6rjcU8q4M50nyt/39WTNXaNgJpWX8PjtTimFv7USEAlDe0OpnTrGTyHbQQKKUAAsJD6XLsoBT5Y/LWd9cBs+6Yav52OdBy06wftZQf++PsVlouygVQKAQQQQAABBBAooEDbFmVWr27kP01ZSPUj5bRpXpaFkoqnCM0N7nt3aQFBzR+X7aS5zpS08IHmoIuXFFS5//774x2K7tN85koqJxtJPbo0FFlp4sSJrm6a91xJCxP6lVzdjsAf+TALXC7upobzrl+/3kaOHBld+MNn9IE/BQlj0xdffOF2aVHJTNLee+9tfi7JBx54IOEQbc03mKtAcib1zSRvus/VgAEDosX+8Y9/jG5nc0Pz7PvelRoSHEy+ntqXrb8DwfLZRiDbAgQAsy1KeSUtoPkxfhsZ9lud4J8aruHCY19emPAf4JLGofIIIIAAAggggEAWBJqVbWV9urTJQkkWKadtyc3/l6rhCipoTj4lrVZ64oknusBSovM0RFYLeWSS/Aq7Clj51XWD52tuteHDh7uhj8H9sdvt2rVzuzTENNNFLGLL8p99L7+lS5e6xRk0D6GS5j5P1OMqH2a+fvHen3/+eecYXPgjmK9r167uoxaYCAZ0te1X5+3WrVvwlJTbDRs2tGHDhrl8Gj48ZsyYKuco0KvFJ7WKcimldJ8rrX58xBFHuKZpoZjgvJHx2qs1ALTicjBNmTIlOhw+uN9vK0D70UcfuY+xcwcqYO2HpC9cuNCfwjsCRSvAHIBFe2uoWCEE3vhkVY0W/lCd5y9fazM/+Z/16pjehM2FaCfXRAABBBBAAAEECilwSs+d7On3l9W4CionjEmLDr7wwgvu5VfrPfvss61Xr15uyKiCbfPnzzctEqIAhgJgfvGJdDw01Pb3v/+967U0dOhQN/+cFkFQLzQND9a8Z7Nnz7b9998/6dBKP6edehGqfueff75btMQH6jp16pROdSrlUa+uZs2auYCi2uQXevCBwUqZAx9ybRa4VKVNBdfUbqXgwh/BTAMHDnS2GuarHo5aHVhGV111lRv6K/dgb7bgucm2VY6Gpapn4aWXXuru469+9Su3eq3mUdSchBr+q0BZKQ0DzuS50iIqmitSAeOrr77aLeJx2mmnmYZI6++FFrx577337Nlnn3WL2uhenHTSSVHWW265xfRs9evXz83d2LlzZzdUW/Nfagi8/i74FbC1OnAwac5B2Wr4sRYI0ZBkBXL9MHb15PW9eYPnsY1AoQQIABZKnusWpcCEmZ9npV4qhwBgVigpBAEEEEAAAQRCKNBrl5a2a5umNfrF625tmlnPXX5YCCFMTFolWAtfKKimYbiLFi1yAbtEbcxkBWCVoeG0Y8eOdb38FNy44YYb3CtYvhaxUO8xBQYTJS120bNnTzdPmubs8/P2+fzVWX22UaNGNmjQINduv1BJly5d3Aq6vtx477k2i3dN7VPQT8HY2IU/gvkV4JO3FvnQnHLBlYy1qqyGemc6BFjla2ixAlu6R+otqt5tsT3c1EtQQ6d9b8FgvYp1O5Pnqn379vbGG2+4BWIU5HzzzTfdK1Hb4jlruLsWxtErXtKzpfkd4wVpL7vsMjv66KNdoFG9VINJPRJHjx4d3MU2AgUVYAhwQfm5eDEJrC3faNPmLs9KlZ6du8xUHgkBBBBAAAEEEECgqoB6P/1pcDdrXM0FQXTezYO7JhwSWvWKpbdHgTCtPKpeSOp5pCCYAj7qdaSFI9TT6PTTT3dDT7XybKZJAaHp06ebekS1bt3a9VrS0MsjjzzSHnnkETcHnwIfyZKCV5pf7vLLLzcNc23atGlW7klsb7/Yz4nqlGuz2OtqeOi1114bd+GP2LwKDk2bNs0F47SoROPGjd22engGe6TFnpfqs54L9dpUD0gN7dbQ4FatWlnv3r1dQFY900otZfpc7bTTTi7oN3nyZDdkXkN15aueeHq21aPwwgsvtFdeecXGjx9fiUM9KDXP5NBIT1j9nWrbtq37O6ZnWStwjxgxwt555x1ToC9eUs9B9dZVcFDBSN/7L15e9iFQaIE6kd/KRGYtIyGQXwF1U9eEqkr6h1O/hSx0WhAZunv4n1/NWjX+9duDrNN2zbJWHgUhgAACCCCAAALZFFiwYIFpjjAFlPyccNksP52ypi9YmfHiawr+3TlkbxZdSweYPAggEDqB6vzsLsbv36G7MSXQIHoAlsBNoor5EVhfjVV/k9Vs3YbNyQ5zDAEEEEAAAQQQqPUCB/64tT16Vi83HDgdDA37VX6dR0IAAQQQQACB9AWYAzB9K3KGXKBJNYegJGJp2jD5kIlE57EfAQQQQAABBBCoTQJ7/qiFTRt5kFtE7YGZn7kpWTZv+WGQUv26ddxqv1rwQ3P++QUmapMRbUUAAQQQQKCmAgQAayrI+aERaNuizOpF/oMZ/A9ndRun/6i2aV5W3dM5DwEEEEAAAQQQqFUCCuppATW9NI/y8jXlptEU+oWq/k/VrGyrWuVBYxFAAAEEEMi2AAHAbItSXskK6D+Wfbq0saffX1bjNvTp0pb/qNZYkQIQQAABBBBAoDYK6P9kBPxq452nzQgggAACuRRgDsBc6lJ2yQloaEk2UrbKyUZdKAMBBBBAAAEEEEAAAQQQQAABBGq3AAHA2n3/aX2MQK9dWqY9CXXMqdGPmpxa89OQEEAAAQQQQAABBBBAAAEEEEAAgWIQIABYDHeBOhSNgOaf+dPgbta4mguC6LybB3dlcuqiuaNUBAEEEEAAAQQQQAABBBBAAAEECADyDCAQI6CV6O4csnfGQUAF/3SezichgAACCCCAAAIIIIAAAggggAACxSJAALBY7gT1KCqBA3/c2h49q1faw4E17Ff5dR4JAQQQQAABBBBAAAEEEEAAAQQQKCYBVgEuprtBXYpKQD35po08yGZ+8j97YOZnNm3uctu8pSJax/p160RWDW5rWvBDc/5p+DAJAQQQQAABBBBAAAEEEEAAAQQQKDYBAoDFdkeoT1EJKKjXq2NL91pbvtGWrym3dRs2W9OG9axN8zJrVrZVUdWXyiCAAAIIIIAAAggggAACCCCAAAKxAgQAY0X4jEACAQX7CPglwGE3AggggAACCCCAAAIIIIAAAggUrQBzABbtraFiCCCAAAIIIIAAAggggAACCCCAAAII1FyAAGDNDSkBAQQQQAABBBBAAAEEEEAAAQQQQACBohUgAFi0t4aKIYAAAggggAACCCCAAAIIIIAAAgggUHMBAoA1N6QEBBBAAAEEEEAAAQQQQAABBBBAAAEEilaAAGDR3hoqhgACCCCAAAIIIIAAAggggAACCCCAQM0FCADW3JASEEAAAQQQQAABBBBAAAEEEEAAAQQQKFoBAoBFe2uoGAIIIIAAAggggAACCCCAAAIIIIAAAjUXIABYc0NKQAABBBBAAAEEEEAAAQSKSuDee++1OnXquNdnn31W8Lodcsghri56J+VXINWzkM692bx5s/3lL3+xHj16WPPmzaPP1sCBAys1ZtWqVXbRRRdZ586drVGjRtF8t9xyS6V8pfrh5ZdfjrZJ2yQESkmAAGAp3S3qigACCCCAAAIIIIAAAgggUFABBX4OPfRQFwhr2rSp7bfffjZ58uSUdVq2bJm1aNHCGjRoYB999FHK/MWU4aSTTrKRI0farFmzbO3atXGr9vXXX1uvXr3s5ptvdu0rLy+Pm4+dCCBQGAECgIVx56oIIIAAAggggAACCCCAQEkLDB061PWG6tChQ0m3I5PK//Of/7TDDjvMXnzxRRcIW79+vb3xxht27LHHuh5yycpSz7g1a9bYBRdcYLvvvnuyrEV1bMaMGfbYY4+5OvXr18+ef/55e++99+z999+3W2+9NVrX22+/3RYsWOA+X3LJJTZ9+nSXR/mGDBkSzZdqozY+V6lMOI5ANgTqZ6MQykAAAQQQQAABBBBAAAEEEEAgkUAYhkuuW7fOzjjjDNNw2J49e9oVV1zhevNpaOzUqVPt4osvtgEDBli8gOirr75qDz74oP3oRz+yK6+8MhFTQfanujf/+te/XL3q1atnDz30kOv5GK+iPt8+++xjN954Y7wsJb9Pw6UrKipKvh00oHYK0AOwdt53Wo0AAggggAACCCCAAAIIIJCBwJQpU+yrr76yli1bul5wffv2db0BH3/8cdejb+PGjS7IF1vkpk2b7Nxzz3W7//SnP1mTJk1isxT158WLF7v6tWnTJmHwTxl8vl133bWo20PlEKitAgQAa+udp90IIIAAAggggAACCBSjQPkasxWR+dH+O/v7d30mIVAEAu+8846rRf/+/U1z//lUt25dO+GEE9xHn8cf07uGyX7wwQdu3sDBgwcHD5XE9oYNG1w9t9pqq6T1TTdf0kI4iAACORMgAJgzWgpGAAEEEEAAAQQQQACBtAQ0pO7TV80eicwTdmMHszv2Nbvr59+/6/Ojv/r+eAkPvRs9enR09VCZaMGEa665xn7605/a1ltv7Y5ptdbYpPnWTjnlFNt5553dqqpagbVr166mOdaWLl0amz3tz1u2bHHz2Gleuv33399atWplCvCoLt26dXMruS5atChueb4t9913nzv++eefR9vmVx7WezAlWml22LBh7tzGjRsnXFwiWE6XLl1c/r333ju4u9J2rsx0z5TatWtX6Xr60LZtW7fP5/EZdI/kJdvbbrvN787qu3ol/u53v3O9ELXy7nbbbed6Jvp5+1JdLNG98fcy2X3W8GGfT8+BkvL7fXpX+emk6jxXGm6ta2jeQKXZs2e7bf19adiwoTvmDvz/Pz755BO3SMnRRx9tOldeeu20004uiPvss88Gs1fZDrY33tBp1UP1UdlKq1evdkO+9dyq56f+fh100EFxe4q6E/gDgRwKMAdgDnEpGgEEEEAAAQQQQAABBFIILJljNvlss5Xz4mes2Gz24ePfv1p3Nhs0zqx9t/h5S2SvFko44ogj7LPPPktYYy0uoYUTYleX1cqqWoBBr7Fjx9rDDz9s6pGWabr66qvtqquuqnKaAljvvvuue6n8CRMm2KBBg6rky9YOBTcV+Pz2229t0qRJduqppyYses6cOfbhhx+647/85S+r5Mu1mVbwVdJqvrHJ7/N5/PELL7zQBTYVsO3cOfL8ZjnJQ4uSBIPBekZeeOEF9zrttNPswAMPzPJVi7O4cePG2fnnn28ach0vffrpp9axY8d4h0zBbr0effRRF3C/5557rH79moVLtNLzUUcdVeXvuRZH0UuLx/z1r3+NWx92IpALgZo90bmoEWUigAACCCCAAAIIIIBA7RBY+KLZxFPMNq5Pr70KEt7T1+zECWYdIz0ESzQdd9xxbr40BSuOOeYY22abbdzqqeqFpKRFJtRD6aWXXnK9iU488US3yqx6NWmeubfeesv1YlLA4he/+IVpldZkPeLiMSlIop5sCu716tXLdtllFysrK7MvvvjClXfHHXeYFr04+eST7e23364UvBoxYoSpDZdffrlp/rv27dvbtGnT4l0m5b7evXu785csWeJ6RSULAGoRDSUNuZVJMOXDTD0vlZ566in75ptvTL0WldSb0ve283m0Xz3EFKDVwh9aMCTbScHaPn36RIN/GoYsP/UA/M9//mOab/Duu+92K/FW59pavVcp2X3WM+nzqS66j1oI5dprr41eMt05D2vyXM2aNcsFq3fYYQfXe1V/H/RMKNDmkz43aNDAmR1++OG2xx572Lbbbmv/+9//nJdWMZ47d64rR38f4gXIfVmp3vV86O/2qlWrnJ+CtBo2riHiKve///2v6Xr6ey43EgJ5EYisYENCIO8Ckf9YaOkk99I2CQEEEEAAAQQQQCC/ApEAQUWk91CF3guSFr9TUXFtu4qKUc0zf+k8nV9CadSoUdH//0YCWBXPPfdcwtrfdNNNLm9k2GjF008/HTdfJGhRERlW6PIdcMABVfJEejBFrxfp+VTluPZ99913Vfb7Hfo/eiRw5cqI9NLzuyu9R4JN7ngkcFlpf7wPBx98sMur99j029/+1h2LrDJbEenJFnvYfY4E2Sq23357l+/QQw+tkicbZlUKjdkRCbhVRIZguzpEetW5exNZ+bYiEvBy+yI9xioWLlzozooEaqP355FHHokpKTsfvZu+V11//fVVCtX9jfQ0dXXz373iPQvJ7o0KTfc+6znQdZS/Jind6+ka/pq67l577VURGQ6d8NKRgHZFJECZ8LiescgQXteGSNCyIjJ8t0reSFA+6qnt2OTrrvpEhvtWROZ+jM1SEekBXBEJtrtyIkHCKsdT7ajOz26+f6dSrR3H60YeTBICCCCAAAIIIIAAAgggkD+Bish3Xw37TbfnX2zNdN6UcyJfsfUduvSS5glTD6R4ST38br75ZnfovPPOc0MI4+VTr8ExY8a4Q6+99pp9/PHH8bIl3Kc5ypIt6hAJttnFF1/szn/iiSci1Lmz9sN51UMrEiyLW+dXXnnF9ZrSQZ/fZ8yXmeZf1DBT9UBUz7LgKsCqy3XXXed6Umr7lltucb3Jfv7zn1suFv7Qghsapqr0k5/8xC699FK3HfxD93f8+PFJ73Mwf6lvq0ed5thLlNQTMd78jT6/5u7T371IINo0nDwS3PWHqvWuYfaa+y82derUyQYOHOh2B3soxubjMwLZFiAAmG1Ryqs9AqxQV3vuNS1FAAEEEEAAgewKfBYZlpdozr90r7QiMhfcZ6+lm7uo8sUGsIKV0/BeP59bqsCRFhPwSfOJ1SStWbPGIr3DXNBKK9bq5Ye4+mM1KT/ZuT/72c+iQ4wfeuihuFn9fg1TPvbYYyvlyafZSSed5IY7a2ELDemUUc+ePd3ccZrnT2nx4sVumKcCcME53hRQ0pDnZs2auXNVhubqq07SYhda/EMp0uvMBSXjlaNAruabDHvS0N9M5zpU4FhDcefNm+eedz3zGsLcsmVLx6W5MKubFEzU8PlEyQ/Z1z3UQiEkBPIhwByA+VDmGuER0G8+9R/Wt/5u9tFTkd86Ryal9qlOPbPOkQmYuw836xCZaDfyQ5+EAAIIIIAAAgggEEdg1l1xdlZjl8rZOfL/rhJL6rGVKP373/+OHtLcfOkmvwhFuvmVT6u2RobO2pNPPum2k5375ZdfRnu3JctX3WMKimquOQXztEjKj3/842hRkaGs9o9//MN91oInsQtt5NNMldB8bnolSpGhuW7+RPWg9At/aC5ALXii+QI1Z6ICROrVqB5gOpYq2Bt7LT/vnvZ379499nClzz169HDzFlbaGbIPyf5OBZuqoN/f/vY3e+CBB9x8fHq2EiU989VNWlXbBxLjlaG5B31au3Zt0p6LPh/vCNRUgB6ANRXk/NojoBXq7oj8J+y+o83mPVE5+CcFv0Kdjiuf8pMQQAABBBBAAAEEKgtoFMW8qZX3VffTvCfNVF6JJQ3fTZRWrFiR6FDS/Vp0IJP0zDPPuEUQ1ENNgcBUSav05jIFe0v53n7+epF5EKO93eL1nsyXma9PsvcXX3zR9QbUwh9XXnmly6oelOecc44L/mmIqXoIqueZFulQQPCss85yKwUnKzf2mO/9p/1a9CNZatOmTbLDoTiW7O+Ub6AW+1BQXUPr33zzTUsW/NM5NXnmfe9Zf+3Ydw0j90lD30kI5EOAHoD5UOYapS9QS1eoK/0bRwsQQAABBBBAoOgE1iyp+ovU6lZSv4Bdu9SsrHl1SyjIeZpjLFEKBgO0imyyXkTBMlIFgYJ56Hyv5wAAQABJREFUtTKpAm4KGmoY60UXXeRWIu3YsaPrXaeVUpUUzIosuOG2czkHoC6g1WT3228/twKxAoCRRVPcdfWHDwhqfjfNuxeb8mEWe814n9W7TMElJQX6ZKuklZK1Yq96qal3oE8XXHCBm8dPvfmURz0E003B+6HehMlSMG+yfKV8LNnfKd+u3/zmN6ah00qag++0005z90R/dzS03DvuuOOObjXs2uDmbXivHQIEAGvHfaaVNRFQT76JkX+MM52kWvl13rCnzdp3q0kNOBcBBBBAAAEEEAiPwHeR/yNlM21Yl83SCl5WMOCnQNyee+6Z9To99thj0XnHJk2alHBBkmAvs6xXIk6BCoDNmDHDIqucmob17rPPPq5nnIYoKx1//PHmg5PB0/NhFrxeom316NN8clr444QTTohmmzPn+5FBCnDGJu1TAFB5MgkABoeQLl++3HbdddfYoqOfq9tDMlpACDbUC9MvMKPg94MPPpiwVfl+7hNWhAMIZFngh36nWS6Y4hAIhUAtX6EuFPeQRiCAAAIIIIBAcQk0aJLd+jT8vpdVdgstXGk//elPoxd/7rnnotvZ3Jg7d64rTkGkRKsRK0Nwbr141/c9puIdq84+zYPnVyb2vf4UoCwvL3fFxRv+qwP5MEvVHg3pveaaa1z9gwt/6Dz1/lOKnbtQ+/yqtT6P9qWT9tprr2i2WbNmRbfjbaQ6Hu+cQu7L9nOltmheSfXQVDrxxBPde7w/5s+f7+ZvjHeMfQiUugABwFK/g9Q/twK1fIW63OJSOgIIIIAAAgjUSoHm7SOLpSUeApuRSd3IgKZm7TI6pdgzH3DAAeZ7d40bN87UcynbadOmTa7IDRs2uDno4pWv4cH3339/vEPRfRo2qaRyspHUk69Pnz6uqIkTJ7q6+Z5aWs02uOpx8Hr5MAteL962hvOuX7/eRo4cGV34w+fzgT8FCWPTF1984XY1b57ZMHatIuvnvdOCFomGq2q+wVwFkmPbkq3P2X6uVC//zGs72XyZ+jtHQiCsAgQAw3pnaVd2BLK5Ql12akQpCCCAAAIIIIBAaQtovr7O/bPTht0j5ZTY/H+pGq7gh+bkU9LKvuqtpMBSoqQVRGN7nCXK6/f7FXZVrl9d1x/Tu+bUGz58uC1ZEpmvMUlq1+774KuGmKoe2Ui+l9/SpUvd6riah1BJwzYT9QzLh1mytj3//PPOMbjwRzB/165d3cepU6dWCugquPvUU0+5Y926ZTZlUMOGDW3YsGHuXA0fHjNmTPCSbltBrzPOOCPlYhdVTizwjlw8V506dYo+P4kC27o/t912W4Fbz+URyJ0AAcDc2VJyqQuwQl2p30HqjwACCCCAAALFKtB9eHZqlq1yslObrJVyySWXRBff8Kv13nDDDaZFQRTsmT59ut11111uzjgFS0aPHp3RtTXUVgEkpaFDh9rvf/97t+CHhvzed999tu+++7rg2/7775+0XD+nnVayPfvss23mzJluqOXHH39selUnDRgwwJo1a+ZO1YIafoEPHxhMVGauzRJdVyvJnn/++e5wcOGPYH4tOKEefhrmqx6OuqfPPvus29Y+HVO7M01aZVg9I5UuvfRSFyRVuW+//bapB6Xuj67VvXv3TIsuaP5cPFfqXeoXkNGq0kceeaRNnjzZLQoiIwW8dZ922WUXa926dUHbz8URyJVApM88CQEE4gqwQl1cFnYigAACCCCAAAI1FuhwoFnrzmYr51W/qO32MOtwQPXPL+IztaKpFr5QUE29lRYtWuSCdImqnMkKwCpDQaOxY8e6oMe3335rCi7qFUxaxEK9xw477LDg7krbWuyiZ8+eLvCnOfv8vH0+U6Jhqf54vPdGjRrZoEGDXLtXr17tsnTp0sWt1hovv9+XazN/ndh3Bf00b1zswh/BfArwyVuLfChI6gNRylO3bl3TsNNMhwDrXA0tVsBP90i9RR9++GH30jGf1EtQQ6d9b0G/v5jfc/Fcqb26Bxourr9P06ZNc6+gg1b/nTJlSqX7EzzONgKlLkAPwFK/g9Q/dwKsUJc7W0pGAAEEEEAAgdotUKeO2aDIXFtbVXNBEJ03cGxkLsFIOSFNCoSpN5565Z1zzjmmIJgCPvXr13cLR2jI6Omnn+6Gnmrl2UyTAkLqSaheT+rxpMU31JtQPaO0Wqp6kCmoliwpeKX55S6//HLTMNemTZtGh1kmOy/VsdjefrGfE52fa7PY62r+vmuvvTbuwh+xeTWEWUEnBeOaNGlijRs3dtvqfXbSSSfFZk/7s54LLeqiHpAa2q2ena1atbLevXu7gOzdd9+ddlnFkjFXz9UOO+zgekdefPHFbtVkWenvlJ7dUaNGud61e+wR+cUCCYGQCtSJ/FamIqRto1lFLKAJcPUDWEn/cPqu60VV5RUfmd2xb/aqdO5bkd9075a98igJAQQQQAABBBCogYBWxdQcYQoo+TnhalBc9U5dGJnfbeIpZhsTz3FXpWAF/06cYNbx51UOsQMBBBAIu0B1fnaXxPfvsN+4ImgfPQCL4CZQhSIVYIW6Ir0xVAsBBBBAAAEEQiOgIN6wp78fDpxOozTsV/kJ/qWjRR4EEEAAAQSiAgQAoxRsIBAjwAp1MSB8RAABBBBAAAEEciDQPrL66Yg3zE6darZHZCGEOjHDTutGpi3fY+D3x8+ZYab8JAQQQAABBBDISIBFQDLiInOtE9DKch8+XvNmh3SFuprDUAICCCCAAAIIIBAR0Fx+O0cWBtGrfI3Z2qVmG9aZNWxq1qydmX4xS0IAAQQQQACBagsQAKw2HSfWCgFWqKsVt5lGIoAAAggggEARCSjYR8CviG4IVUEAAQQQCIMAQ4DDcBdpQ+4EWKEud7aUjAACCCCAAAIIIIAAAggggAACeREgAJgXZi5S0gKaZ0YrzWnFuUySX6GOeWoyUSMvAggggAACCCCAAAIIIIAAAghkWYAAYJZBKS6kAqxQF9IbS7MQQAABBBBAAAEEEEAAAQQQCL8AcwCG/x7TwmwJ+BXqPnvNbNbfzeZFVqqr2PxD6Vqhbvf+Zlrwo8MB309m/cNRthBAAAEEEEAAAQQQQAABBBBAAIGCCBAALAg7Fy1ZAVaoK9lbR8URQAABBBBAAAEEEEAAAQQQqK0CBABr652n3TUXYIW6mhtSAgIIIIAAAggggAACCCCAAAII5FyAOQBzTswFEEAAAQQQQAABBBBAAAEEEEAAAQQQKJwAAcDC2XNlBBBAAAEEEEAAAQQQQAABBBBAAAEEci5AADDnxFwAAQQQQAABBBBAAIHiE6hXr56r1ObNm23Lli3FV0FqhAACCCBQSUA/q/UzW8n/DK+UgQ8IJBEgAJgEh0MIIIAAAggggAACCIRVoKyszDWtoqLC1q1bF9Zm0i4EEEAgNAL6Wa2f2UqNGjUKTbtoSH4ECADmx5mrIIAAAggggAACCCBQVALNmzeP1mfZsmW2Zs0aegJGRdhAAAEEikdAPf/0M1o/q31q1qyZ3+QdgbQEWAU4LSYyIYAAAggggAACCCAQLoEmTZq4HiTffvutG1K2ePFiq1OnDsPKwnWbaQ0CCIRAQMN+fc8/NUe9//QznIRAJgIEADPRIi8CCCCAAAIIIIAAAiERULBvxx13tEWLFpmCgEr6grlp06aQtJBmIIAAAuETUPBPP7v1M5yEQCYCBAAz0SIvAggggAACCCCAAAIhEqhbt67ttNNOtn79elu7dq0LBPoJ5kPUTJqCAAIIlLSAFvxQ4E/DftXzj+BfSd/OglWeAGDB6LkwAggggAACCCCAAAKFF9AXyaZNm7pX4WtDDRBAAAEEEEAgFwIsApILVcpEAAEEEEAAAQQQQAABBBBAAAEEEECgSAQIABbJjaAaCCCAAAIIIIAAAggggAACCCCAAAII5EKAAGAuVCkTAQQQQAABBBBAAAEEEEAAAQQQQACBIhEgAFgkN4JqIIAAAggggAACCCCAAAIIIIAAAgggkAsBAoC5UKVMBBBAAAEEEEAAAQQQQAABBBBAAAEEikSgaAOAK1assKlTp9qVV15pRx11lLVq1cotda1VyoYOHZoWX3l5uT3++ON2/vnn27777mvbbrutbbXVVu69V69eNnr0aFu6dGlaZSnTN998Y2PGjLEePXq4MrRaWufOne2iiy6yRYsWpV3O3Llz7eyzz7ZOnTq5pbxbt25tBx10kN155522adOmtMuZOHGi9enTx9q1a2dlZWXWoUMHGzJkiM2cOTPtMlatWmWjRo2yrl27WosWLax58+ZuW/t0jIQAAggggAACCCCAAAIIIIAAAgggUNoCdSoiqRiboEBfonTqqafavffem+iw2//ee+/ZAQccYGvXrk2ar1mzZnbXXXfZ4MGDk+ZbuHCh9evXz+bPnx83n4JnDz30kPXt2zfucb9z/Pjxdu6559qGDRv8rkrvPXv2dIHPli1bVtof/KDA5vHHH+/yBff77bp167rg5hVXXOF3xX2fNWuWDRgwIGEQtH379i6Aus8++8Q9vyY7//vf/9oOO+zgivjiiy9s++23r0lxnIsAAggggAACCCCAAAIIIIAAAnEE+P4dB6UW7iraHoDBe6FA0RFHHBHclXJ7zZo10eDf/vvvbzfccIM9//zz9vbbb9u0adPsrLPOsnr16rk8J598sj3zzDMJy1y3bp31798/Gvw744wz7IUXXrAZM2bYddddZ+oJ+PXXX7ugnAKPiZKue+aZZ7rgX5s2bezWW2+1N99801372GOPdaep9562t2zZkqgYO/3006PBv969e9uUKVPsrbfeMgUXO3bs6M5Vz0kFNhOlxYsX29FHH+2Cf/Xr17dLLrnEXn31VffStvYtWbLEtVt5SQgggAACCCCAAAIIIIAAAggggAACJSqgHoDFmCIBrIonn3yyYtmyZa56n376qXoqulekB2DKKr/++usVkV59FZHhtgnzRgJnFZGehq7MSOCsIhJ0i5s3Mhw2eu0//vGPVfJEAoEVkYCZyxMJyFU5rh0bN26siAz5dXkiw2wrPv744yr5RowYEb3OfffdV+W4drz88svRPJEAXkVkyHClfCtXrqzYcccdXZ5tttmm4quvvqp03H+Qofd89NFH/e7ou/b548OGDYvuz9ZGpNdftHxtkxBAAAEEEEAAAQQQQAABBBBAIPsCfP/Ovmkplli0PQCvuuoq1/tMPeWqk/bbbz975JFHbI899kh4uoa/+p53GuI7Z86cKnkjgTv7y1/+4vZrvr8LL7ywSh7NJ6heeUovvfSSzZ49u0qeyZMnWyTo5/ZfdtllrqdebCbNLxgJ2rnd2o6XIgFIt1u9F++44w7XizGYT3Ml3njjjW5XJPjnegUGj2t7+fLlNmHCBLdbcwhqOHFs0j4dU7r//vvdObF5+IwAAggggAACCCCAAAIIIIAAAgggUPwCRRsAzBedhtD6pCBgbIr0uLPVq1e73Zp7UPPrxUvBhUkmTZpUJYuG6foUzOv36b1x48bRuQg/+OADW7BgQfCwaSiyhh4rHX744QnnzVNQU4t5KMWryxNPPGGbN292xyO9+9x7vD98PZVX55AQQAABBBBAAAEEEEAAAQQQQAABBEpPIH40q/TaUe0aBxfjiBfcmz59erTsgw8+OLodu6GFMpo0aeJ2v/baa7GHzZez2267Wdu2basc9zuC14gtR/P8+foG8/lz/XuDBg1Mi4ko6Rz1YgwmXxftS1ZO8FhsXYLlsY0AAggggAACCCCAAAIIIIAAAgggULwCtT4A+Morr0Tvzu677x7d9hvz5s3zmxbvuD+oRTO0AIdS8Bx9Vs89rbqjlKyM2OOx5QQ/p1tOZI7AKj0JfTlauThZMLJdu3bRnoT+HNcI/kAAAQQQQAABBBBAAAEEEEAAAQQQKBmB+iVT0xxU9N1337WnnnrKldylS5e48wVGJst0x9W7b+utt05aC61WrFWAIwtxuJ56DRs2dPkV/ItMEOm2t99++5Rl+Az+2vE+Z1pOcC5EX26qMnRdtSmykIr5c3xdUr37gGeifEuXLk10iP0IIIAAAggggAACCCCAAAIIIIAAAlkUqLUBQA2lHT58eHQuvOuvvz4u69q1a93+pk2bxj0e3OmHAGufev35AKAvQ/tTlRNbhs7xKdvlpKqLruvro/ZkkhQ4JCGAAAIIIIAAAggggAACCCCAAAIIFF6g1g4BPu+88+zf//63uwNa3OOYY46JezfKy8vdfs2rlyr5gJ/yffvtt9HsvgztSFVOojJ0brbLSVUXXdPXJ9ge7SchgAACCCCAAAIIIIAAAggggAACCJSGQK3sAXjDDTfYXXfd5e7Q3nvvbbfffnvCu1VWVuaOfffddwnz+AN+gQ59btSokd9tvgztSFVOojJ0bjbL+eabb1LWRdf09Qm2R/tTpVRDhjUEuEePHqmK4TgCCCCAAAIIIIAAAggggAACCCCAQA0Fal0A8M4777Tf//73jk0r8j7zzDPRYa7xLJs1a+Z2pzMEdv369dEigsNrfRk6mKqcRGXo3GyWowBgqrromr4+wfZof6qUzvyCqcrgOAIIIIAAAggggAACCCCAAAIIIIBAzQVq1RDghx9+2EaMGOHUdtppJ/vXv/5lrVu3TqroA1kKhK1evTppXt/rTWX6obM6wZeh7VSLY/gylDd2Hr1sl5OqLqqDr09sXXSMhAACCCCAAAIIIIAAAggggAACCCBQ/AK1JgD4xBNP2K9+9SvbsmWLtWvXzl544YVKgblEtyq4eu5HH32UKJtt2rTJFi5c6I537ty5Uj71nvMBtGRl6KTg8dhy0q1LsJz69etbp06dKtXHl/P111/bsmXLKh0LftAw3TVr1rhdsXUJ5mMbAQQQQAABBBBAAAEEEEAAAQQQQKB4BWpFAFDBvsGDB7sgXcuWLe3555+3jh07pnVXDjjggGi+V155Jbodu6EFRfxw2f333z/2sPly5s+fnzToFrxGbDndu3ePLiISzBd7Mc0zOHPmTLc7eI7P5+uiz8nKCR6LrYsvi3cEEEAAAQQQQAABBBBAAAEEEEAAgeIWCH0AcMaMGTZgwAC3mEXz5s1t2rRp1qVLl7TvyiGHHGItWrRw+e+77z6rqKiIe+69994b3T9o0KDott8YOHCg37Rg3ujOyIbm5Xv00UfdLvXS23XXXYOH3RyAhx56qNun4cuJhvBOmjQp2nMvXl204nHdut/f+nvuuafSNYIffD2VN9EqycH8bCOAAAIIIIAAAggggAACCCCAAAIIFJ9AqAOAc+bMsX79+rmeeU2aNLGnn37atOpvJqlBgwb261//2p0yb948u+mmm6qc/sYbb9j48ePd/oMPPtjU6y42KRDnex1qFWI/XDiY7+KLL7avvvrK7dJ2vHTRRRe53RpyfO6559rmzZsrZfvyyy/t0ksvdfu23nprGz58eKXj+tC2bVv75S9/6fYrIPqPf/yjSp7HHnvMBUt1YMiQIe6cKpnYgQACCCCAAAIIIIAAAggggAACCCBQ9AJ1Ij3a4ndpK3DVX3vtNfv444+jtVBgywfFNBw1NrA1dOjQaF5tKMC233772YoVK9z+P//5z3bYYYdVyhP7YbvttjO9YtPatWttn332sf/85z/u0JlnnmknnniiNWrUyF566SW7/vrr3Yq6+qweh926dYstwn1WAPLoo4928xC2adPGLr/8cuvRo4cL+v3973+3f/7zny6fhui+/PLLVq9evbjlnHTSSTZx4kR3rHfv3jZy5Ehr3769vf/++3bddddFg4vjxo2zs846K24ZWtxDwdCVK1ea5gm88MILrX///i7v1KlT7eabb3ZDprWgydtvv53WfIlxL5Rgp3ov+nkRVZfgAicJTmE3AggggAACCCCAAAIIIIAAAghkKMD37wzBQpq9aAOACuhpyG26KTaOqeGrw4YNS/d0l2/UqFE2evTouOcoGNm3b19bsGBB3OMaXvzggw9Gg2hxM0V2KtB33nnnmebpi5cUEHzqqaesVatW8Q67fd9++60dd9xxrkdjvEwasnvFFVckbIs/58033zQNTU60EIh6Ck6ZMsX23Xdff0rW3vkBlDVKCkIAAQQQQAABBBBAAAEEEEAgoQDfvxPS1KoDoR4CnM07qZV033nnHbvxxhtdb0ANr23cuLHttttudsEFF9h7772XMvin+pxxxhk2e/Zs977LLrtYWVmZaWES9fobO3asvf7660mDfypDPQ0VJFTA8fDDD3e9FjVUWT3qTj75ZFPvyUSBTJ3vkwJ76jWonoh77rmnabVivfbaay+374MPPshJ8M9fn3cEEEAAAQQQQAABBBBAAAEEEEAAgdwLFG0PwNw3nSsUUoDfQBRSn2sjgAACCCCAAAIIIIAAAgjUFgG+f9eWO528nfQATO7DUQQQQAABBBBAAAEEEEAAAQQQQAABBEpagABgSd8+Ko8AAggggAACCCCAAAIIIIAAAggggEBygfrJD3MUAQSqCJSvMVuzxOy79WYNmpg1b29W1rxKNnYggAACCCCAAAIIIIAAAggggAACxSBAALAY7gJ1KH6Bigqzz6abvfV3s4+eMqvY/EOd69Qz69zfrPtwsw4HmtWp88MxthBAAAEEEEAAAQQQQAABBBBAAIECCxAALPAN4PIlILBkjtnks81WzotfWQUDP3z8+1frzmaDxpm17xY/L3sRQAABBBBAAAEEEEAAAQQQQACBPAswB2CewblciQksfNHsnr6Jg3+xzVGQUPl1HgkBBBBAAAEEEEAAAQQQQAABBBAoAgECgEVwE6hCkQqo59/EU8w2Rub6yyQpv87T+SQEEEAAAQQQQAABBBBAAAEEEECgwAIEAAt8A7h8kQpozj8N+800+Oebo/OmnBOZKzBSDgkBBBBAAAEEEEAAAQQQQAABBBAooAABwALic+kiFtCCH4nm/Eu32is+jCwc8lq6ucmHAAIIIIAAAggggAACCCCAAAII5ESAAGBOWCm05AVm3ZWdJmSrnOzUhlIQQAABBBBAAAEEEEAAAQQQQKAWChAArIU3nSanEChfYzZvaopMaR6e96SZyiMhgAACCCCAAAIIIIAAAggggAACBRIgAFggeC5bxAJrlkTm7tucnQqqnLVLs1MWpSCAAAIIIIAAAggggAACCCCAAALVECAAWA00Tgm5wHcZrvqbimPDulQ5OI4AAggggAACCCCAAAIIIIAAAgjkTIAAYM5oKbhkBRo0yW7VGzbNbnmUhgACCCCAAAIIIIAAAggggAACCGQgQAAwAyyy1hKB5u3N6tTLTmPr1jdr1i47ZVEKAggggAACCCCAAAIIIIAAAgggUA0BAoDVQOOUkAuUNTfr3D87jdw9Uo7KIyGAAAIIIIAAAggggAACCCCAAAIFEiAAWCB4LlvkAt2HZ6eC2SonO7WhFAQQQAABBBBAAAEEEEAAAQQQqIUCBABr4U2nyWkIdDjQrHXnNDImybLdHmYdDkiSgUMIIIAAAggggAACCCCAAAIIIIBA7gUIAObemCuUokCdOmaDxpltVc0FQXTewLGRuQQj5ZAQQAABBBBAAAEEEEAAAQQQQACBAgoQACwgPpcucoH23cxOnJB5EFDBP52n80kIIIAAAggggAACCCCAAAIIIIBAgQUIABb4BnD5Ihfo+HOzYU+nPxxYw36VX+eREEAAAQQQQAABBBBAAAEEEEAAgSIQqF8EdaAKCBS3gHryjXjD7LPXzGb93WzeVLOKzT/UuW7kr5FW+9WCH5rzj2G/P9iwhQACCCCAAAIIIIAAAggggAACBRcgAFjwW0AFSkJAQb2dIwuD6FW+xmztUrMN68waNjVr1s6srHlJNINKIoAAAggggAACCCCAAAIIIIBA7RMgAFj77jktrqmAgn0E/GqqyPkIIIAAAggggAACCCCAAAIIIJAnAeYAzBM0l0EAAQQQQAABBBBAAAEEEEAAAQQQQKAQAgQAC6HONRFAAAEEEEAAAQQQQAABBBBAAAEEEMiTAAHAPEFzGQQQQAABBBBAAAEEEEAAAQQQQAABBAohQACwEOpcEwEEEEAAAQQQQAABBBBAAAEEEEAAgTwJEADMEzSXQQABBBBAAAEEEEAAAQQQQAABBBBAoBACBAALoc41EUAAAQQQQAABBBBAAAEEEEAAAQQQyJMAAcA8QXMZBBBAAAEEEEAAAQQQQAABBBBAAAEECiFAALAQ6lwTAQQQQAABBBBAAAEEEEAAAQQQQACBPAkQAMwTNJdBAAEEEEAAAQQQQAABBBBAAAEEEECgEAIEAAuhzjURQAABBBBAAAEEEEAAAQQQQAABBBDIkwABwDxBcxkEEEAAAQQQQAABBBBAAAEEEEAAAQQKIUAAsBDqXBMBBBBAAAEEEEAAAQQQQAABBBBAAIE8CRAAzBM0l0EAAQQQQAABBBBAAAEEEEAAAQQQQKAQAgQAC6HONRFAAAEEEEAAAQQQQAABBBBAAAEEEMiTAAHAPEFzGQQQQAABBBBAAAEEEEAAAQQQQAABBAohQACwEOpcEwEEEEAAAQQQQAABBBBAAAEEEEAAgTwJEADMEzSXQQABBBBAAAEEEEAAAQQQQAABBBBAoBACBAALoc41EUAAAQQQQAABBBBAAAEEEEAAAQQQyJMAAcA8QXMZBBBAAAEEEEAAAQQQQAABBBBAAAEECiFAALAQ6lwTAQQQQAABBBBAAAEEEEAAAQQQQACBPAkQAMwTNJdBAAEEEEAAAQQQQAABBBBAAAEEEECgEAIEAAuhzjURQAABBBBAAAEEEEAAAQQQQAABBBDIkwABwDxBcxkEEEAAAQQQQAABBBBAAAEEEEAAAQQKIUAAsBDqXBMBBBBAAAEEEEAAAQQQQAABBBBAAIE8CRAAzBM0l0EAAQQQQAABBBBAAAEEEEAAAQQQQKAQAgQAC6HONRFAAAEEEEAAAQQQQAABBBBAAAEEEMiTAAHAPEFzGQQQQAABBBBAAAEEEEAAAQQQQAABBAohQACwEOpcEwEEEEAAAQQQQAABBBBAAAEEEEAAgTwJEADMEzSXQQABBBBAAAEEEEAAAQQQQAABBBBAoBACBAALoc41EUAAAQQQQAABBBBAAAEEEEAAAQQQyJMAAcA8QXMZBBBAAAEEEEAAAQQQQAABBBBAAAEECiFAALAQ6lwTAQQQQAABBBBAAAEEEEAAAQQQQACBPAkQAMwTNJdBAAEEEEAAAQQQQAABBBBAAAEEEECgEAIEAAuhzjURQAABBBBAAAEEEEAAAQQQQAABBBDIkwABwDxBcxkEEEAAAQQQQAABBBBAAAEEEEAAAQQKIVC/EBflmgiUtED5GrM1S8y+W2/WoIlZ8/ZmZc1LuklUHgEEEEAAAQQQQAABBBBAAAEEwitAADC895aWZVOgosLss+lmb/3d7KOnzCo2/1B6nXpmnfubdR9u1uFAszp1fjjGFgIIIIAAAggggAACCCCAAAIIIFBgAQKABb4BXL4EBJbMMZt8ttnKefErq2Dgh49//2rd2WzQOLP23eLnZS8CCCCAAAIIIIAAAggggAACCCCQZwHmAMwzOJcrMYGFL5rd0zdx8C+2OQoSKr/OIyGAAAIIIIAAAggggAACCCCAAAJFIEAAsAhuAlUoUgH1/Jt4itnGyFx/mSTl13k6n4QAAggggAACCCCAAAIIIIAAAggUWIAAYIFvAJcvUgHN+adhv5kG/3xzdN6UcyJzBUbKISGAAAIIIIAAAggggAACCCCAAAIFFCAAWEB8Ll3EAlrwI9Gcf+lWe8WHkYVDXks3N/kQQAABBBBAAAEEEEAAAQQQQACBnAgQAMwJK4WWvMCsu7LThGyVk53aUAoCCCCAAAIIIIAAAggggAACCNRCAQKAtfCm0+QUAuVrzOZNTZEpzcPznjRTeSQEEEAAAQQQQAABBBBAAAEEEECgQAIEAAsEz2WLWGDNksjcfZuzU0GVs3ZpdsqiFAQQQAABBBBAAAEEEEAAAQQQQKAaAgQAq4HGKSEX+C7DVX9TcWxYlyoHxxFAAAEEEEAAAQQQQAABBBBAAIGcCRAAzBktBZesQIMm2a16w6bZLY/SEEAAAQQQQAABBBBAAAEEEEAAgQwECABmgEXWWiLQvL1ZnXrZaWzd+mbN2mWnLEpBAAEEEEAAAQQQQAABBBBAAAEEqiFAALAaaJwScoGy5mad+2enkbtHylF5JAQQQAABBBBAAAEEEEAAAQQQQKBAAkUbAFyxYoVNnTrVrrzySjvqqKOsVatWVqdOHfcaOnRoxlzPPvusHXvssbb99ttbw4YN3bs+a3+66ZtvvrExY8ZYjx49bNttt7WmTZta586d7aKLLrJFixalW4zNnTvXzj77bOvUqZM1atTIWrdubQcddJDdeeedtmnTprTLmThxovXp08fatWtnZWVl1qFDBxsyZIjNnDkz7TJWrVplo0aNsq5du1qLFi2sefPmblv7dKzWpu7Ds9P0bJWTndpQCgIIIIAAAggggAACCCCAAAII1EKBOhWRVIztVrAvUTr11FPt3nvvTXS40n41T8G2v/3tb5X2Bz+ceeaZNm7cOBdcDO4Pbi9cuND69etn8+fPD+6Obit49tBDD1nfvn2j++JtjB8/3s4991zbsGFDvMPWs2dPF/hs2bJl3OPaWV5ebscff7zLFy9T3bp1bfTo/8fenUBHUeWLH/91ZyFsYV+FEQTZ3GBYlCMo6iAKOMo8hz8qbiOKPnVGcRvniaMz44ILOs4bWV1QVIRR8IkobqwaZBNlCQgIsiMIQlhCQtL/+l2sorrT3ekkndDp+t5zkq6+de+v6n4qzjn85t66j8jw4cPDnXbqFi1aJJdffrls3x5+l9qmTZvKe++9J126dHH6xOtgy5Yt0rx5cxNu8+bNJiEbr9hxiaP/WbzYXWRXdunDNewgctuX1nLiyH/LpQ9OTwQQQAABBBBAAAEEEEAAAQSKF0j4f38XPwRaxEEgYWcAusemiaKLL77YXRXz8UMPPeQk/zp16iRvvfWWLFy40Hzqdy2aHIyWLDtw4ID079/fSf7dfPPN8tlnn8mXX34pjz32mJkJuG/fPpOU+/bbbyPe28yZM0WTjZr8a9Sokbzwwgvy1VdfyYcffmhmJ2pHnb2nMxMLCwsjxrnpppuc5N8FF1wg06ZNM2PS5GKrVq1MX505OX78+Igxtm7dKpdddplJ/qWmpsr9998vc+fONT96rHXbtm0z49a2niuatBswWiStlBuCaL8rRpH889wfDgNGAAEEEEAAAQQQQAABBBBAIPEEEnYGoC5B7dq1q/nRZNnGjRulZcuWRjDWGYDr1q0zS3R1Wa3OYtMEly65tYsu6T3//PNl8eLFJuG1evVqk0Czz9ufOpvu0UcfNV+feuopue++++xT5jMrK8ss4dXraELu888/DzqvX/ScLhfWe9JltkuXLi1yLZ0Z+OKLL5q+EyZMkOuuu65InDlz5kivXr1MvSbwpk6dKikpxzes2L17t3Tu3NksSa5Tp458//33Urt27SJxdBm1XkPL5MmTTfLS3WjKlCkycOBAU3XjjTfKyy+/7D5d5uNK8/9ArLee5aTBIvkHYx+zJv8GTRRpdWHsfWiJAAIIIIAAAggggAACCCCAQDkIVJp/f5fD2Al5XCBhZwBqwk1n3Wnyr7Tlueeec96p969//Sso+acxq1WrJlqvRRN0zz//vDl2/8rPz5d//vOfpkoTePfcc4/7tDnu3r276Kw8LbNmzZIlS5aYY/cvTdRp8k/Lgw8+WCT5p/X6fkFN2tnH5iDklyYgtWjST5OF7uSf1uu7EkeMGKGHsnfvXtFZgaFl586dMnGilaCyir5DUJcThxat03NaXnvtNdE+niyaxLtxhkiD9rENX5f9anuSf7F50QoBBBBAAAEEEEAAAQQQQAABBMpdIGETgGUdub77T99fp6Vdu3bm3XrhYuo799q2bWtO6VLa0Fcizp49W37++WdzXmce6vv1whX3xiTvvvtukSYa2y7utnadfmpC0p51t2LFClm7dq37tOhSZF16rKV3794R35unS4h1lqGWcPfyf//3f1JQUGDO6+y+SMW+T22rfTxbmnYU+e8skeuni3S43FrWe3zGpTHxp1r1Vxw7r+/80/YUBBBAAAEEEEAAAQQQQAABBBBAIEEEwmezEuTmynIbGzZsEPvddbrMN1qxz+u0WF1q7C7z5s1zvtrtnArXgS4xrl792Pvi5s+f7zpz7NCOo8nGxo0bFzlvV7ivERpH311obx7ibmf3tT/T09OdhKf20VmM7mLfi9ZFi+M+F3ov7nieONZ3ArbsKTLwNZEHNorcvlBkiLU8WD/v32DVW8up9Twbfnjiz4FBIoAAAggggAACCCCAAAIIIFCZBJI2AZidfXz3Vp0BGK24z7v7aR/3d3e70Hi6aYZuwBHaR7/rzD1NLmqJFiP0vPvaes79PdY4urQ5dCahHUd3Lo6WjGzSpIkzk9Duo/fh+ZJhza5sYM0abdb52Kd+pyCAAAIIIIAAAggggAACCCCAAAIJKmCtXUzOsnnzZmdgzZo1c47DHeguw3Zx99M6+7vO7gu3mYbdTz81ju4CvGvXLjNTr0qVKua0Jv/spcXxuBcNWtI4HTpY76b7pdhjKi6GNtcxrVy50nGwYxT3aSc8I7Xbvn17pFPUI4AAAggggAACCCCAAAIIIIAAAgjEUSBpE4A5OTkOU40aNZzjcAf20l09p7P13MWOU1wM7RMax04A2jG0TXFxQmNoH7vEO05x96LXte8n1MW+p0if7qRqpDbUI4AAAggggAACCCCAAAIIIIAAAgiUv0DSLgHOzc119PSdeNGKnajTNocPHw5qascpLoZ2ihTHjqFtiosTKYb2jXec4u5Fr2nfT6iLnqMggAACCCCAAAIIIIAAAggggAACCCS+QNLOAMzIyHD08/LynONwB/bGGnquatWqQU3sOMXF0E6R4tgxtE1xcSLF0L7xjHPo0KFi70Wvad9PqIuei1bsZcaR2ugS4G7dukU6TT0CCCCAAAIIIIAAAggggAACCCCAQJwEkjYBWLNmTYeouOWrBw8edNqGLou14xQXQwNEimPH0DbFxYkUQ/vGM44mAIu7F72mfT+hLnouWonl/YLR+nMOAQQQQAABBBBAAAEEEEAAAQQQQCA+Akm7BNidgCpuQwr3bLXQd9fZcTQR9vPPP0dVt+M0aNDAWTqrHewYehyPe4lXnOLuRa9jjynURc9REEAAAQQQQAABBBBAAAEEEEAAAQQSXyBpE4DuXW9Xr14d9Um4z7dv3z6obaxxjh49KuvXrzd9Q2Po7Dk7gea+VtCFfvniPh8aJ9Z70VB2nNTUVGndunXQpew4+/btkx07dgSdc3/RZbr79+83VaH34m7HMQIIIIAAAggggAACCCCAAAIIIIBA4gokbQKwZcuW0rRpUyM/Z86cqE9g7ty55vxJJ50kLVq0CGrbo0cP53u0OIsXL3aWy5577rlOH/vAjrNmzZqoSTf3NULjdO3a1dlExN3Ovob9qe8ZXLBggfnq7mOft+9Fv0eL4z4Xei92LD4RQAABBBBAAAEEEEAAAQQQQAABBBJbIGkTgD6fTy6//HKjr7Ph7IRY6OPQenu2nLbXfu7Sq1cvqVWrlqmaMGGCBAIB92nn+NVXX3WOBwwY4BzbB1dccYV9KO62TqV1oO/lmzx5sqnSWXpt2rRxnzbvALzoootM3aeffhpxOfG7777rzNwLdy+//e1vxe8/9uhfeeWVoGu4v9j3qW21DwUBBBBAAAEEEEAAAQQQQAABBBBAoPIJJG0CUB/FXXfdJboEVsudd94phw8fNsf2L/2u9Vq0nbYPLenp6fLHP/7RVGdnZ8szzzwT2kSysrLkpZdeMvXnn3++6Ky70KKJuFatWpnqJ554wlku7G533333yd69e02VHocr9957r6nWJce33367FBQUBDXbvXu3PPDAA6audu3aMmTIkKDz+qVx48ZyzTXXmPqZM2fKf/7znyJtpkyZInpOy7XXXmv6FGlEBQIIIIAAAggggAACCCCAAAIIIIBAwgv4rBlt4ae0neBbnz9/vqxbt865C01s2UkxXY4amti64YYbnLbugwcffFCefPJJU9WpUyeTHNNEnL6vb8SIEfL111+bc9ru8ccfd3d1jnNycqRLly7y3XffmbpbbrlFBg0aJFWrVpVZs2aZfrqjrn7/8ssvpWPHjk5f98GMGTPksssuk8LCQmnUqJE89NBD0q1bN5P0GzdunLzzzjumuS7RnT17tqSkpLi7O8dXXXWVTJo0yXy/4IILTOJSlzsvX75cHnvsMSe5OHr0aBk6dKjTz32gm3t07txZdu3aZZKf99xzj/Tv3980mT59ujz77LOiSUbd0GTp0qVBG5m445T2WDcgsd+LqPfi3iiltDHphwACCCCAAAIIIIAAAggggAACwQL8+zvYw6vfEjYBqAk9XXIba4mUx9Rk28033ywvv/xyxFA33XSTjB071lkWG66hJiP79u0ra9euDXdaMjMz5Y033nCSaGEbWZWa6LvjjjtE39MXrmhC8IMPPpD69euHO23qdObilVdeKZpQDFd0ye7w4cPlkUceCXfaqfvqq69ElyZH2ghEZwpOmzZNzj77bKdPvA74H6B4SRIHAQQQQAABBBBAAAEEEEAAgcgC/Ps7so2XziT1EmB9kJoM0+W5mlTTd/zpTDld1quf+l2TaOPHj4+a/NM4upOuzhbUWYM6G1CX11arVk3atm0rd999t3z77bfFJv80jiYjlyxZYj5POeUUycjIkHr16onO+hs1apR88cUXUZN/GkNnGup4NOHYu3dvadiwoRmTzqi7+uqrRWdPFpf80zia2NNZgzoT8fTTTxfdrVh/zjjjDFO3YsWKckn+6bUpCCCAAAIIIIAAAggggAACCCCAAAIVI5CwMwArZvhc5UQJ8P9AnCh5rosAAggggAACCCCAAAIIIOAlAf797aWnHXmsST8DMPLQOYMAAggggAACCCCAAAIIIIAAAggggEDyC5AATP5nzAgRQAABBBBAAAEEEEAAAQQQQAABBDwsQALQww+foSOAAAIIIIAAAggggAACCCCAAAIIJL8ACcDkf8aMEAEEEEAAAQQQQAABBBBAAAEEEEDAwwIkAD388Bk6AggggAACCCCAAAIIIIAAAggggEDyC5AATP5nzAgRQAABBBBAAAEEEEAAAQQQQAABBDwsQALQww+foSOAAAIIIIAAAggggAACCCCAAAIIJL8ACcDkf8aMEAEEEEAAAQQQQAABBBBAAAEEEEDAwwIkAD388Bk6AggggAACCCCAAAIIIIAAAggggEDyC5AATP5nzAgRQAABBBBAAAEEEEAAAQQQQAABBDwsQALQww+foSOAAAIIIIAAAggggAACCCCAAAIIJL8ACcDkf8aMEAEEEEAAAQQQQAABBBBAAAEEEEDAwwIkAD388Bk6AggggAACCCCAAAIIIIAAAggggEDyC5AATP5nzAgRQAABBBBAAAEEEEAAAQQQQAABBDwsQALQww+foSOAAAIIIIAAAggggAACCCCAAAIIJL8ACcDkf8aMEAEEEEAAAQQQQAABBBBAAAEEEEDAwwIkAD388Bk6AggggAACCCCAAAIIIIAAAggggEDyC5AATP5nzAgRQAABBBBAAAEEEEAAAQQQQAABBDwsQALQww+foSOAAAIIIIAAAggggAACCCCAAAIIJL8ACcDkf8aMEAEEEEAAAQQQQAABBBBAAAEEEEDAwwIkAD388Bk6AggggAACCCCAAAIIIIAAAggggEDyC5AATP5nzAgRQAABBBBAAAEEEEAAAQQQQAABBDwsQALQww+foSOAAAIIIIAAAggggAACCCCAAAIIJL8ACcDkf8aMEAEEEEAAAQQQQAABBBBAAAEEEEDAwwIkAD388Bk6AggggAACCCCAAAIIIIAAAggggEDyC6Qm/xAZIQIVJJC7X2T/NpG8gyLp1UUym4pkZFbQxbkMAggggAACCCCAAAIIIIAAAgggEF6ABGB4F2oRiE0gEBDZOE9k4TiR1R+IBAqO9/OliLTvL9J1iEiLniI+3/FzHCGAAAIIIIAAAggggAACCCCAAAIVJEACsIKguUwSCmxbJjL1VpFd2eEHp8nAVe8d+2nQXmTAaJGmHcO3pRYBBBBAAAEEEEAAAQQQQAABBBAoJwHeAVhOsIRNcoH1n4u80jdy8i90+Jok1Pbaj4IAAggggAACCCCAAAIIIIAAAghUoAAJwArE5lJJIqAz/yYNFsm33vVXkqLttZ/2pyCAAAIIIIAAAggggAACCCCAAAIVJEACsIKguUySCOg7/3TZb0mTf/bwtd+026x3BVpxKAgggAACCCCAAAIIIIAAAggggEAFCJAArABkLpFEArrhR6R3/sU6zB9XWRuHzI+1Ne0QQAABBBBAAAEEEEAAAQQQQACBMgmQACwTH509J7BofHyGHK848bkboiCAAAIIIIAAAggggAACCCCAQBILkABM4ofL0OIskLtfJHt6fIJmvy+i8SgIIIAAAggggAACCCCAAAIIIIBAOQuQACxnYMInkcD+bda7+wriMyCNk7M9PrGIggACCCCAAAIIIIAAAggggAACCEQRIAEYBYdTCAQJ5JVw19+gzmG+HDkQppIqBBBAAAEEEEAAAQQQQAABBBBAIL4CJADj60m0ZBZIrx7f0VWpEd94REMAAQQQQAABBBBAAAEEEEAAAQTCCJAADINCFQJhBTKbivhSwp4qcaU/VaRmkxJ3owMCCCCAAAIIIIAAAggggAACCCBQUgESgCUVo713BTIyRdr3j8/421lxNB4FAQQQQAABBBBAAAEEEEAAAQQQKGcBEoDlDEz4JBPoOiQ+A4pXnPjcDVEQQAABBBBAAAEEEEAAAQQQQCCJBUgAJvHDZWjlINCip0iD9mUL3LCDSIseZYtBbwQQQAABBBBAAAEEEEAAAQQQQCBGARKAMULRDAEj4POJDBgtklbKDUG03xWjrHcJWnEoCCCAAAIIIIAAAggggAACCCCAQAUIkACsAGQukWQCTTuKDJpY8iSgJv+0n/anIIAAAggggAACCCCAAAIIIIAAAhUkQAKwgqC5TJIJtLpQ5MYZsS8H1mW/2l77URBAAAEEEEAAAQQQQAABBBBAAIEKFEitwGtxKQSSS0Bn8v13lsjG+SKLxolkTxcJFBwfo9/6z0t3+9UNP/Sdfyz7PW7DEQIIIIAAAggggAACCCCAAAIIVJgACcAKo+ZCSSmgSb2W1sYg+pO7XyRnu8iRAyJVaojUbCKSkZmUw2ZQCCCAAAIIIIAAAggggAACCCBQeQRIAFaeZ8WdJrqAJvtI+CX6U+L+EEAAAQQQQAABBBBAAAEEEPCcAO8A9NwjZ8AIIIAAAggggAACCCCAAAIIIIAAAl4SIAHopafNWBFAAAEEEEAAAQQQQAABBBBAAAEEPCdAAtBzj5wBI4AAAggggAACCCCAAAIIIIAAAgh4SYAEoJeeNmNFAAEEEEAAAQQQQAABBBBAAAEEEPCcAAlAzz1yBowAAggggAACCCCAAAIIIIAAAggg4CUBEoBeetqMFQEEEEAAAQQQQAABBBBAAAEEEEDAcwIkAD33yBkwAggggAACCCCAAAIIIIAAAggggICXBEgAeulpM1YEEEAAAQQQQAABBBBAAAEEEEAAAc8JkAD03CNnwAgggAACCCCAAAIIIIAAAggggAACXhIgAeilp81YEUAAAQQQQAABBBBAAAEEEEAAAQQ8J0AC0HOPnAEjgAACCCCAAAIIIIAAAggggAACCHhJgASgl542Y0UAAQQQQAABBBBAAAEEEEAAAQQQ8JwACUDPPXIGjAACCCCAAAIIIIAAAggggAACCCDgJQESgF562owVAQQQQAABBBBAAAEEEEAAAQQQQMBzAiQAPffIGTACCCCAAAIIIIAAAggggAACCCCAgJcESAB66WkzVgQQQAABBBBAAAEEEEAAAQQQQAABzwmQAPTcI2fACCCAAAIIIIAAAggggAACCCCAAAJeEiAB6KWnzVgRQAABBBBAAAEEEEAAAQQQQAABBDwnQALQc4+cASOAAAIIIIAAAggggAACCCCAAAIIeEmABKCXnjZjRQABBBBAAAEEEEAAAQQQQAABBBDwnAAJQM89cgaMAAIIIIAAAggggAACCCCAAAIIIOAlARKAXnrajBUBBBBAAAEEEEAAAQQQQAABBBBAwHMCJAA998gZMAIIIIAAAggggAACCCCAAAIIIICAlwRIAHrpaTNWBBBAAAEEEEAAAQQQQAABBBBAAAHPCZAA9NwjZ8AIIIAAAggggAACCCCAAAIIIIAAAl4SIAHopafNWBFAAAEEEEAAAQQQQAABBBBAAAEEPCeQ6rkRM2AEKkIgd7/I/m0ieQdF0quLZDYVycisiCtzDQQQQAABBBBAAAEEEEAAAQQQQCBIwBMzAPPy8uSll16SSy65RJo0aSJVqlSRGjVqSNu2beUPf/iDLFiwIAgl0pePPvpIfve730mzZs1MDP3U71ofazl06JA8/fTT0q1bN6lbt665j/bt28u9994rmzZtijWMrFy5Um699VZp3bq1VK1aVRo0aCDnnXeejBkzRo4ePRpznEmTJkmfPn2MS0ZGhrRo0UKuvfbamE1ivpAXGgYCIhvmirx9rciIFiIvni0y/sJjn/p98nXHzms7CgIIIIAAAggggAACCCCAAAIIIFBBAr6AVSroWifkMps3b5Z+/frJ8uXLo17/7rvvlmeffVZ8Pl+RdkqkybaxY8cWOWdX3HLLLTJ69Oiw/e0269evN/eyZs0auyros1atWvLmm29K3759g+pDv2gy8/bbb5cjR46EnjLfzznnHJk+fbrUq1cv7HmtzM3Nld///vemXbhGfr9fHnnkERk+fHi402Wu27JlizRv3tzE0WekydRKXbYtE5l6q8iu7OKH0aC9yIDRIk07Ft+WFggggAACCCCAAAIIIIAAAgiUQSDp/v1dBgsvd03qGYA6E86d/DvzzDPl1VdflaysLPn444/l4YcflurVreWZVnnuuefkmWeeCfu38NBDDznJv06dOslbb70lCxcuNJ/6XYsmB6Mlyw4cOCD9+/cXO/l38803y2effSZffvmlPPbYY2Ym4L59+0xS7ttvvw17H1o5c+ZM0WSjJv8aNWokL7zwgnz11Vfy4YcfmtmI2kZnNOrMxMLCQv0attx0001O8u+CCy6QadOmmTFpcrFVq1amr/qMHz8+bH8qXQLrPxd5xUraxpL8027aTttrPwoCCCCAAAIIIIAAAggggAACCCBQzgJJPQPwnXfekSuvvNIQdu/eXebNmycpKSlBpEuWLBE9l5+fL3Xq1JEff/xRUlOPvxpx3bp1okt0NZnYpUsXmTt3rllyawfRJb3nn3++LF682PRbvXq1SaDZ5+1PnU336KOPmq9PPfWU3HffffYp86lJSV3Cq9fRhNznnxdNDuk5vRe9p8zMTFm6dGmRa+nMwBdffNHEnDBhglx33XVB19Evc+bMkV69epn6yy67TKZOnRrksnv3buncubNZkqwm33//vdSuXdu0j9evpPl/IHTmnybz8q13/ZW0pFnJ5xtnMBOwpG60RwABBBBAAAEEEEAAAQQQiFkgaf79HfOIaRhOIKlnAH7xxRfOmB988MGgJJd9QhNdOjNPy969e0UTeO6iMwM18ablX//6V1DyT+uqVatm6vVY2z3//PN6GFQ0ufjPf/7T1GkC75577gk6r180Camz8rTMmjVLNDEZWjRRp8k/LToenakXWvT9gpq006LH4YomILVoMlSThaFJ0fr168uIEatj4AUAAEAASURBVCNMGzXRWYGUMAK6el6X/ZYm+afhtN+020SSexV+GDiqEEAAAQQQQAABBBBAAAEEEECgIgWSOgGom3/Y5ZRTTrEPi3y6E2nu9+rpu//ee+89075du3ai79YLV7ReNxTRoktpQ1+rOHv2bPn555/N+euvv170/Xrhyg033OBUv/vuu86xfaCx7eJua9fppyYkBw4caKpWrFgha9eudZ8WXYqsS4+19O7dO+K793QJsc4y1BLuXswJL//SXX6XvRH7st9IVj+uEtk4P9JZ6hFAAAEEEEAAAQQQQAABBBBAAIEyC4TPRJU5bGIEaNOmjXMjuow1UtHNObToBiCnnnqq02zDhg2ydetW812X+UYr9nmdWrtx48agprr02C52O/u7+1OXGNvvJJw/v2hSyI6jycbGjRu7uwYdu68RGkffXWgnOd3tggJYX9LT052Ep/bRWYyeLzpTz73L73u3x4dkEe9ZjA8kURBAAAEEEEAAAQQQQAABBBBAIJxAUicAr7rqKmcWmy5pLSgoKGLw9ddfywcffGDqBw0a5LTXiuzs4zu66gzAaMV93t2vJHH03YP2bMTQGDpzT5OLWtzXMhUhv9znQ+O4v7vbhYQwX+3zurQ5dCZhuPZJXafv+nuxu8iEy6w/jP+zlu0W/Vsq9fiz37e2ZbZmFFIQQAABBBBAAAEEEEAAAQQQQACBchBILYeYCROyQYMGZtffa665RvR9gF27dpW77rpLdGagJtS07tlnnxVdKtyxY0cZOXJk0L1v3rzZ+d6sWTPnONxB8+bNnWp3P620v+vsvuI209A4ugvwrl27zEy9KlWqmLia/LOXFsfjXjRoSeN06NDB3Essv+xkZaS227dvj3Qq8ep1t95Jg0v/rr/iRqTJxBzLI+PYkuvimnMeAQQQQAABBBBAAAEEEEAAAQQQKIlAUicAFWLAgAFmh15N7r388sui7+Bzl0aNGpndeW+55RZn+a19Picnxz6UGjVqOMfhDuylu3pOk4vuYscpLob2CY1jJwDtGNqmuDihMbSPXeIVx44X6dOdEI3UplLU68y/8kz+2QhHgv9m7Go+EUAAAQQQQAABBBBAAAEEEEAAgbIKJPUSYMXRd9e9+eab8v777zsz6NxoO3fulLfeekt0o47Qkpub61TpO/GiFTtRp20OHz4c1NSOU1wM7RQpjh1D2xQXJ1IM7RuvOBor6UtZd/ktCVCV6AnmkoSiLQIIIIAAAggggAACCCCAAAIIIOAWSOoZgAcPHpS+ffvK3LlzJSUlRe6//3658cYbRXcE1kTYV199JX/7299EN8q47LLL5LnnnpM//elPjk9GRoZz7N5R2Kl0Hdgba2hV1apVXWeslZ2/xCkuhnaKFMeOoW2KixMphvaNVxyNFa3Yy54jtdElwN26dYt0OjHqN1qbt+w6/h7Icrspv/WfYc0m5RaewAgggAACCCCAAAIIIIAAAggg4G2BpE4A/vWvfzXJP33EL730UtDyX51F17t3b7ngggvk4osvllmzZsmwYcPM9zPPPNP8VdSsWdP56whd1uuc+OVAk412CV2ia8cpLob2jxTHjqFtiosTKYb2jVccjRWtFPd+wWh9E+ZcRe3O264/7/9LmIfOjSCAAAIIIIAAAggggAACCCCQfAJJuwRYN8x45ZVXzBPTTT9C3/1nP0rdeffvf/+7+VpYWOj00Qp3Equ4TS3cM95C339nx9HE3M8//2xfOuynHUc3MHEv5bVjaKd43EtZ44S9+WSq1F15s6dXzIi6DqmY63AVBBBAAAEEEEAAAQQQQAABBBDwpEDSJgD13X579uwxD7VTp05RH27nzp2d86tXr3aO3bveuuudBq4D9/n27du7zojEGufo0aOyfv160zc0hs4qtBOL7msFXeiXL+7zoXFivRcNZcfRJGnr1q3DXSp56/ZvE9Hdecu7NLR2Vm7Ro7yvQnwEEEAAAQQQQAABBBBAAAEEEPCwQNImADVpZRdNrEUrulGIXdz9WrZsKU2bNjWn5syZYzcJ+6nvGdRy0kknSYsWLcyx/atHj+MJnmhxFi9e7CwBPvfcc+3uzqcdZ82aNbJjxw6nPvTAfY3QOF27dnU2EXG3C42h7xlcsGCBqXb3CW2XtN/zji/pLrcxplUXuWKUiM9XbpcgMAIIIIAAAggggAACCCCAAAIIIJC0CcC6detKZmamecJZWVkSLQnoToRp0s8uPisxc/nll5uvOhvOTojZ5+1Prbdny2l77ecuvXr1klq1apmqCRMmhN2NWE+++uqrpo3+GjBggHNsH1xxxRX2YVBbp9I6OHTokEyePNlU6Ww/Xf7sLvoOwIsuushUffrppxGXE7/77ruyf7+1DNYq4e7FnEjmX+lWcq48iyb/Bk0UadqxPK9CbAQQQAABBBBAAAEEEEAAAQQQQECSNgHo9/ulX79+5hFv27ZNHnvssbCPe+/evfLAAw845/r3tzZkcJW77rpL7FmBd955pxw+fNh1Vsx3rdei7bR9aNENR/74xz+a6uzsbHnmmWdCm4gmKXWjEi3nn3++6Ky70KKJuFatWpnqJ554wlku7G533333iY5Jix6HK/fee6+p1qTo7bffLgUFwUtdd+/e7ZjUrl1bhgzx4DvqMq2Zn76UcHxlr9NlvzfOEGl1YdljEQEBBBBAAAEEEEAAAQQQQAABBBAoRiDFSjw9okkp3ZyiWrVqogmfZCmnnXaa2dRDl/jOnj1bli5dapa/HjlyRDZu3Cj/+c9/5IYbbpB169aZIevMuEcffTRo+PXq1TNJvvnz54smEmfMmGGMcnNzZd68efKHP/zBxNVOmkgcOHBgUH/7S5cuXeSdd96Rn376ST755BPZvn276Hv99F2Fr7/+utxyyy2iMatWrSpTpkyRxo0b212dz5SUFPMuvkmTJpm2b7/9tmRkZJjzK1askD//+c/y2muvme+6XPi5554TTYSGFp3lqMuItY9+6jjq1KkjOTk5MnPmTLNhivpoef7558Veemwq4vRLZxfq/WnR3Zft2ZpxCl/2MKlVRH5cKbJrTdljaQS/tSS9/W9F+lrJ397W31hmk/jEJQoCCCCAAAIIIIAAAggggAACUQQS/t/fUe6dU/ET8FkJooA7nCaCdFMM/dGklX6efPLJ7iaV6liXuV511VWis9qilQsvvNAkBHX8oUV3B7755pvl5ZdfDj3lfL/ppptk7NixYRNudiNNNPbt21fWrl1rVwV9ahLsjTfekNBZiEGNrC/jxo2TO+64Q/Q9feFKt27d5IMPPpD69euHO23qdCbjlVdeaRKa4Rpp4nD48OHyyCOPhDtd5jrdydje1ER3Pnbvclzm4PEKsGGuyITLyh5N3/PXzppZmnFsSXrZAxIBAQQQQAABBBBAAAEEEEAAgdgEKsW/v2MbCq3KIOAkAAOB43nA0HfY2UlBOyFY2ZKCOutOl9d++OGHsnLlSjPbUZfr6iw7XWp79dVXy29/+9si7+4LddXZf5rkW7RokUkoaoJN+w8dOlQuvfTS0OZhvx88eFD+/e9/m1l+mhDUJJ4mwjQx+Kc//SnmZKvO3nvhhRfks88+MzMTq1evLrrj7zXXXGOW7NrLlsPehKvyzTffNO8T/Oabb4xLo0aNpGfPnibB2L17d1fL+B5Wiv8B0v8mXrQMdmWXfvC63Pe2L9noo/SC9EQAAQQQQAABBBBAAAEEECiDQKX493cZxkfX2ASsXJ/PyfyFJv4iJQW1XbRNNWK7NK28LFBp/gdo2zKRV/qK5JdiV2Dd6EPf9cdGH17+U2fsCCCAAAIIIIAAAggggMAJFag0//4+oUrJf3H/xx9/LI8//rj813/9l/zqV78yO9Rq4s9O/mmyz50YdJ9Lfh5G6HkBTd7pbr2azCtJYZffkmjRFgEEEEAAAQQQQAABBBBAAAEEylEg9Te/+Y3oj1327NkjixcvliVLljifmzZtsk/ziYD3BHS3Xp3JN/XW2JYD67Jffe8fM/+897fCiBFAAAEEEEAAAQQQQAABBBBIQAFra9LgUrduXbn44ovNj31GN9BwJwT1mIKApwQ0mfffWSIb54ssGieSPV0kUHCcQHf51Y0+ug4RadGDd/4dl+EIAQQQQAABBBBAAAEEEEAAAQROsECRBGC4+9HNLvr06WN+wp2nDgFPCFjL4aVlz2M/uftFcraLHDkgUqWGSM0m7PLriT8CBokAAggggAACCCCAAAIIIIBA5ROIKQFY+YbFHSNQzgIZmST8ypmY8AgggAACCCCAAAIIIIAAAgggEB8Bf3zCEAUBBBBAAAEEEEAAAQQQQAABBBBAAAEEElGABGAiPhXuCQEEEEAAAQQQQAABBBBAAAEEEEAAgTgJkACMEyRhEEAAAQQQQAABBBBAAAEEEEAAAQQQSEQBEoCJ+FS4JwQQQAABBBBAAAEEEEAAAQQQQAABBOIkQAIwTpCEQQABBBBAAAEEEEAAAQQQQAABBBBAIBEFSAAm4lPhnhBAAAEEEEAAAQQQQAABBBBAAAEEEIiTAAnAOEESBgEEEEAAAQQQQAABBBBAAAEEEEAAgUQUSO3Tp4907tzZ+WnRokUi3if3hAACCCCAAAIIIIAAAggggAACCCCAAAKlEEj99NNPRX/sUqdOHScZ2KVLF3N88skn26f5RAABBBBAAAEEEEAAAQQQQAABBBBAAIFKJJBq32sgEDCHe/bsMQnBcElBOyGoMwZJCtpyfCKAAAIIIIAAAggggAACCCCAAAIIIJC4Aql24k9v0efzBd2pfS40Kajtjh49GtSWLwgggAACCCCAAAIIIIAAAggggAACCCCQeAKpH3/8sSxevFiWLFliPn/44Yegu4yUFAxqxBcEEEAAAQQQQAABBBBAAAEEEEAAAQQQSEiB1N/85jeiP3bR2X7uhKAmBjdt2mSf5hMBBBBAAAEEEEAAAQQQQAABBBBAAAEEKpGA8w5A+57r1q0rF198sfmx63bv3u3MENSEoP5QEEAAAQQQQAABBBBAAAEEEEAAAQQQQCDxBYokAMPdcv369aVPnz7mJ9x56hBAAAEEEEAAAQQQQAABBBBAAAEEEEAgMQX8iXlb3BUCCCCAAAIIIIAAAggggAACCCCAAAIIxEOABGA8FImBAAIIIIAAAggggAACCCCAAAIIIIBAggqQAEzQB8NtIYAAAggggAACCCCAAAIIIIAAAgggEA8BEoDxUCQGAggggAACCCCAAAIIIIAAAggggAACCSpAAjBBHwy3hQACCCCAAAIIIIAAAggggAACCCCAQDwE/FlZWXL48OF4xCIGAggggAACCCCAAAIIIIAAAggggAACCCSYQGqPHj3E7/dL27ZtpXPnzubn17/+tXTq1EmqV6+eYLfL7SCAAAIIIIAAAggggAACCCCAAAIIIIBASQRSA4GAFBQUyKpVqyQ7O1smTpxo+vt8PmnTpo1oMtBODGpSsGbNmiWJT1sEEEAAAQQQQAABBBBAAAEEEEAAAQQQOIECqXptTfbZRROCWvRz9erVsmbNGnnrrbdMnbZr1aqVSQi++eabpo5fCCCAAAIIIIAAAggggAACCCCAAAIIIJC4Av5zzz1XqlWrZhJ+dvJPb1eTfXZiUOv1p7CwUNauXStvv/124o6IO0MAAQQQQAABBBBAAAEEEEAAAQQQQAABRyB13rx5zmy/pUuXypIlS8zPsmXLJCcnx2moB5oQdCcJg07yBQEEEEAAAQQQQAABBBBAAAEEEEAAAQQSTsBZAty+fXvRn2uuuca5ye+++84kA+3E4Ndffy379u1zznOAAAIIIIAAAggggAACCCCAAAIIIIAAAoktYBKAkW5RNwHRn6uuusppsn79epMUdCo4QAABBBBAAAEEEEAAAQQQQAABBBBAAIGEFYiaAAx317oJiP5QEEAAAQQQQAABBBBAAAEEEEAAAQQQQCDxBfyJf4vcIQIIIIAAAggggAACCCCAAAIIIIAAAgiUVoAEYGnl6IcAAggggAACCCCAAAIIIIAAAggggEAlECABWAkeEreIAAIIIIAAAggggAACCCCAAAIIIIBAaQX8L7zwgsyfP18OHDhQ2hj0QwABBBBAAAEEEEAAAQQQQAABBBBAAIEEFUi9++67za35fD6zuUenTp3k17/+tdif9erVS9Bb57YQSCCB3P0i+7eJ5B0USa8uktlUJCMzgW6QW0EAAQQQQAABBBBAAAEEEEAAAa8KpAYCATN2/Vy7dq2sW7dOpkyZ4nicdNJJQQlBTQw2a9bMOc8BAp4V0P92Ns4TWThOZPUHIoGC4xS+FJH2/UW6DhFp0VPESrBTEEAAAQQQQAABBBBAAAEEEEAAgRMhkGpfVGcA2sVOCur3LVu2yNatW+X999+3T0v9+vVl586dzncOEPCcwLZlIlNvFdmVHX7omgxc9d6xnwbtRQaMFmnaMXxbahFAAAEEEEAAAQQQQAABBBBAAIFyFPDfe++9MnDgQGnSpIlo4k9/NBno/rHr7c/du3eX4y0RGoEEF1j/ucgrfSMn/0JvX5OEL18i8pWVBPxxtYguF6YggAACCCCAAAIIIIAAAggggAACFSSQ+tRTTzmXWrNmjbzyyivy6quvyo8//miSgHpSk4Ga/KMg4HkBnfk3abBIvvWuv5KUo4dFPnzgWA+WB5dEjrYIIIAAAggggAACCCCAAAIIIFBGAb+7f9u2beXJJ5+UTZs2yaOPPioZGRnO6datW8vvf/97+dWvfuXUcYCApwQ0Ca7Lfkua/AtFspcHT7hM5MXuIppUpCCAAAIIIIAAAggggAACCCCAAALlJBCUALSvkZ6eLsOHD5dly5aJJv509t/69eulZcuWsnHjRt7/Z0Px6S0B3fAj0jv/Siuh8XQ5sS4rpiCAAAIIIIAAAggggAACCCCAAALlIBA2AWhf59RTT5WsrCw544wzTBLw6aefljFjxphNQOw2fCLgGYFF48tnqDqjUJcVMxOwfHyJigACCCCAAAIIIIAAAggggIDHBaImANWmbt26MmnSJKlatapJAg4bNszsDOxxN4bvNQHduCN7evmNWpOA024T6z+y8rsGkRFAAAEEEEAAAQQQQAABBBBAwJMCxSYAVaV9+/YyZMgQA5Sbm2veE+hJLQbtXYH926zkXEH5jv/HVSIb55fvNYiOAAIIIIAAAggggAACCCCAAAKeE4gpAagqN9xwg8HR9wG+8cYbUlBQzskQczV+IZAgAnkl3PW3tLddXsuMS3s/9EMAAQQQQAABBBBAAAEEEEAAgUovEHMC8PTTTxefz2cGvH//flm8eHGlHzwDQCBmgfTqMTctU8Ps90V0uTEFAQQQQAABBBBAAAEEEEAAAQQQiJNAzAnAtLQ0qV27tnPZlStXOsccIJD0AplNRXwp5T9MXWacs738r8MVEEAAAQQQQAABBBBAAAEEEEDAMwIxJwDz8/NFZ/7ZswB37drlGSQGioBkZFovw+xfMRBHDlTMdbgKAggggAACCCCAAAIIIIAAAgh4QiDmBOCsWbOC3vuXklIBs6E88QgYZKUR6HpsI5xyv98qNcr9ElwAAQQQQAABBBBAAAEEEEAAAQS8IxBTAlBn/w0fPtyo6CYgWho2bGg++YWAZwRa9BRp0L58h+tPFanZpHyvQXQEEEAAAQQQQAABBBBAAAEEEPCUQLEJwJ9++kkGDBggixYtcpb/qtC5557rKSgGi4D1H4DIgNEiaeW4IUg7a5mxLjemIIAAAggggAACCCCAAAIIIIAAAnESSG3ZsqVccMEFJqHXrFkzqV+/vhw5ckR++OEHmT17trz99tuSk5NjLqez//QdgF26dJFWrVrF6RYIg0AlEmjaUWTQRJFJg0XyD8b/xs+6Ov4xiYgAAggggAACCCCAAAIIIIAAAp4WsPJ5voC9sUc4CXvJr7bRY333n74PsEePHuGaU4dATAJbtmyR5s2bm7abN28WTT5XqrJtmcjUW0V2Zcf5tq1JuR0uE9H3DeqSY511SEEAAQQQQAABBBBAAAEEEECglAKV/t/fpRw33YIF/HbyT5N74X70vJ38089Ro0aR/As25JsXBayZgCsu/0iuL3xYPijoJkcDxa6mj1GpUGTVeyITrCTgi91FNNFIQQABBBBAAAEEEEAAAQQQQAABBMogkGrP8NMYdjLQjmcnBPV769atZezYsdKrVy/7NJ8IeFZA/9sYNuUb+S6vncyRdlJDDkkj314517dcHkybJFV9eWW30dmFr/Q9tuS41YVlj0cEBBBAAAEEEEAAAQQQQAABBBDwpEDqRx99JHPmzJFly5bJ999/L3v27JG8vDzJzMyUxo0bS9euXaVfv35yySWXFEkQelKMQXtbIHe/yP5tsnzDVin8cZ2V+KsrB6TasZ9ANVkfOEmW5LWVkWmjpK1/S9mt9D2D+r7BG2eI6PsHKQgggAACCCCAAAIIIIAAAggggEAJBXzWTKZACfvQHIEyC1SqdxDofyIb54ksHCey+gORQIEzfl36O7Owi0ws6C1ZhR2sevudfQE5x58t16Z8LH38iyXVZy3tLUtpaMW+7UveCVgWQ/oigAACCCCAAAIIIIAAAh4UqFT//vbg86moIadW1IW4DgKVUqCYzT40sdcvZaH5WVPYTIbl3yYrAy2tofpkgZUQ1J8L/Uvl5fRnyjb8H1dZScj5Ii2tjUEoCCCAAAIIIIAAAggggAACCCCAQAkE4rVzQQkuSVMEKonA+s+PvYMvxp1+dcnv5PS/SQ//8qAB/lfK3KDvpf6yaHypu9IRAQQQQAABBBBAAAEEEEAAAQS8K0AC0LvPnpFHE9CZf/ruPX0HXwlKdd8RGZM2Uk7zbTC9dHMQXQIcl5L9voi+g5CCAAIIIIAAAggggAACCCCAAAIIlEDALAHevn27TJ48WbKysuSHH36QvXv3SkZGhtSrV086duwoF110kVx66aVsAlICWJpWYgF959/UW0uc/LNHrEnAZ9NGyyV5T0pj356yv//PDqzvHszZLpKRadfwiQACCCCAAAIIIIAAAggggAACCBQrkHrrrbfKK6+8IkePHnUa674gPt+xzQxmz54tzz//vLRo0UL+93//1yQCnYYcIJCMArrhR4zLfiMNv51/s9kEpInsjtSkdPVHDpSuH70QQAABBBBAAAEEEEAAAQQQQMCzAv5x48ZJfn6+AdDEn70pcOjxhg0bpH///vLUU095FouBe0QgTu/aG2ztAPzH1KnxRatSI77xiIYAAggggAACCCCAAAIIIIAAAkkv4OwCrAm/U089Vdq2bSu1atWSI0eOyNatW+Wbb76RQ4cOmRmB2ubBBx+UDh06mGRg0uswQO8J6Dv2sqfHZdyX+hdJis9aThyv4rf+c63ZJF7RiIMAAggggAACCCCAAAIIIIAAAh4RSNWk3tVXXy1//etfTQIwdNyHDx+WiRMnyl/+8hfZs2ePmSF4zz33kAAMheJ7cgjs3yai79qLQ4lr8k/vp11/3v8Xh+dCCAQQQAABBBBAAAEEEEAAAQS8JuD/85//bBJ8OvsvXKlatarcfPPN8sUXX0jNmjVNk3Xr1smSJUvCNacOgcotkFeyXX8rdLBdh1To5bgYAggggAACCCCAAAIIIIAAAggkh4BfZ/7FUtq0aSNDhw51mi5dutQ55gCBpBFIr56YQ6nbSqRFj8S8N+4KAQQQQAABBBBAAAEEEEAAAQQSWsBfpUqVmG/wtNNOc9r+9NNPzjEHCCSNQGZTEV9K4g3nvPus+zq2M3fi3Rx3hAACCCCAAAIIIIAAAggggAACiSzgL8nNbdtmvR/tl5KZmWkf8olA8ghkZEp+m36JN56Tfp1498QdIYAAAggggAACCCCAAAIIIIBApRDwT5kyJaYb1c1AXnrpJaet7hZcmcru3bvlqaeeknPPPVcaN24sOvOxadOmcvbZZ8t9990nWVlZxQ7no48+kt/97nfSrFkz018/9bvWx1p0R+Wnn35aunXrJnXr1pUaNWpI+/bt5d5775VNmzbFGkZWrlwpt956q7Ru3Vr0PY0NGjSQ8847T8aMGSNHjx6NOc6kSZOkT58+0qRJE8nIyJAWLVrItddeKwsWLIg5RrI13Nn2mrgMqSAQpxl77P4bl+dBEAQQQAABBBBAAAEEEEAAAQS8KuCrXr164MUXX5TrrrsuosH69evlxhtvlPnz55s2tWvXlp07d0paWlrEPol0QpOct912m0Rbtnz55ZfLtGnTwt627pSsybaxY8eGPa+Vt9xyi4wePdpapRk56aOO/fr1kzVr1oSNU6tWLXnzzTelb9++Yc/blZqIvf322+XIkSN2VdDnOeecI9OnT5d69eoF1bu/5Obmyu9//3vTzl1vH/v9fnnkkUdk+PDhdlVcP7ds2SLNmzc3MTdv3mySqnG9QBmCLdu0V6qO7yFt/VtKHWV1YXNZH2gi/VIWljqG07HDFSIDJzhfOUAAAQQQQAABBBBAAAEEEEAgVoFE/vd3rGOgXdkFUnVGmib3NNmjM8HatWsnurw3Ly9P9I9EZ8bNnTtXCgoKzNU0waU7B1eW5N9rr71mxldYWCgNGzY0icAePXqY2Xc7duwQTcq9//77Ucfz0EMPOcm/Tp06yf333y+tWrUyfXVW4ddff23O6yy8f/zjH2GfyoEDB6R///5O8k93Vh40aJCZvTdr1ix54oknZN++fSYpp+Znnnlm2DgzZ840yUYdT6NGjeR//ud/zCzGPXv2yLhx4+Tdd981s/d0ZqLG1UReuHLTTTc5yb8LLrhA/vSnP5kZkcuXL5fHH3/cjO3hhx82MwOHDBkSLkTS1lWvkip35d8mk9P/JtV94ZOs0QZ/MFBF7sm/VTJ9h+KTAGT332jcnEMAAQQQQAABBBBAAAEEEEAAgWIEfFaCyJrgFjDNIs1e0/N6Tj81iaUz5SIlloq5XoWezs7OFk3Y6Uy5nj17mkSfzrILVzThmZ6eXuTUunXrzBJdXVbbpUsXkwzVJbd20QTq+eefL4sXL5bU1FRZvXq1SQ7a5+1PTbA++uij5qsmDXXZsbto0k+X8Op1NCH3+eefu0+bYz2ny4X1njRJqzsxayLSXXRmoM7o1DJhwoSwMzvnzJkjvXr1Mm0uu+wymTp1qqSkHN/4QpdLd+7c2SxJrlOnjnz//feisz7jWRL5/4HIyc2Xjn/7RLrLtzImbWSJkoCa/BuaP0zmF55hcQVkZvoDZZpJKA07iNz2JRuAxPOPj1gIIIAAAggggAACCCCAgIcEEvnf3x56DCd8qP5TTz016k3YyUFtpLPEdDltZUj+6f3eeeedJvlXv359MzMuUvJP24ZL/mn9c88957xT71//+peZsaf1dqlWrZpovRZN0D3//PP2KeczPz9f/vnPf5rvmsC75557nHP2Qffu3UVn5WnRmXtLliyxTzmfmqjT5J+WBx98sEjyT+v1/YKatLOPzUHIL01AatGknyYL3ck/rVevESNG6KHs3bs36N2PpjLJf9XMSJM+pzUySbyBeQ/LmsJmMY1Yl/1q+2PJP+3ik2HWTEJNCpaqpFUXuWIUyb9S4dEJAQQQQAABBBBAAAEEEEAAAQRsAf+KFSvkjTfeEJ0Jpu+M04Sf/aOz/nQp6t133y06m06TYbp5RmUoOhPvs88+M7d6xx13mKRWSe9bHd577z3TTZdG67v1whWttzdF0dmR7qSptp89e7b8/PPPpuv1118fMYF6ww03mDb6S5fyhhb3Owrdbd3tNCE5cOBAU6XPdu3ate7TokuRbZfevXtHfPeeLiG2d3oOdy9BQZPwy+BzTjajWhloKX3yRsigvIfkg4JucjQQvKQ6P5Ai0wvONucvyXtStL276HedEVjiJKAm/wZNFGna0R2OYwQQQAABBBBAAAEEEEAAAQQQQKDEAtaq1VS56qqrzI/21iWtmqzSRJ8u+wydHVbiK5ygDu7djXWzC7vojDZd4qo78EbbJEPbb9iwQbZu3Wq66jLfaEXP6+YeOrV248aN0rLl8UTQvHnznK7R4ugSY2tTFjl48KCz4YrT0Tqw42iyUXcyjlT0GrobsBbduMU9y3PhwoXO5iHR7kVnRGpi8+OPPxbto7MYK8t7HyO5lKS++yn15Fd1q8mmPYesbj5ZUNjB/NSQQ9LIt1dqyGE5IFVlZ6CO9VktamidEagzA0emjYptObAu+9WZfyT/orpyEgEEEEAAAQQQQAABBBBAAAEEYhMIns5k9dEZZE2bNjXJscqa/NOhL1iwwAjosl9ddquzHM866yyT+GvTpo2ZEXjKKaeY9/LprLhwRWc92kVnAEYr7vPuftrH/d3dLjSeJmPtd/q5+2g7vUdNLmqJFiP0fGgc9/dY4+jS5tCZhOZGkviX2ezmkrZFRqjJvvWBk+SbQGvzWVzyzw7wQ6CR3J5/p/xP3o0yp+CMiDMJV/Z+89g7/0j+2XR8IoAAAggggAACCCCAAAIIIIBAGQVSy9g/YbuvWrXK3FuLFi3MuwD//e9/F7lXneGnm3P85z//Ed1dVxOf7rJ582bna7Nm0d8D17x5c6etu59W2t91dl9xm2lonG+//VZ27dplZurZS641+WcvLY7Hveh9lTROhw7WzLQYi52sjNR8+/btkU4lTH3PNg3E7xMpPLZHTinuKyDd/avk2pRP5GK/tUmMr9CJcTTgM4nATwo7S5Y1u3BnoK6ZSdhvYxP597nWRSkIIIAAAggggAACCCCAAAIIIIBAnARS169f78w6i1PMhAizZ88ecx/6LsBvvvnGJN6efPJJsd9tt3z5cnn44Yflww8/FH1Xni4T1iW27g1OcnJynLHUqFHDOQ53oMk9u4TOKLTjFBdD+4fGsROAdgxtU1yc0Bjaxy7ximPHi/TpTohGapPo9boZyCWnN5YZy3eU+FZP822IuuQ31ReQ81OWmx/dZEQ3C9H3BX60cofoLsR6bQoCCCCAAAIIIIAAAggggAACCCAQDwH/6aefLsOHD5fDhw/HI17CxND36Gk5cuSIeY+hJvqGDh0qDRo0MO831PftTZ8+XS699FLT7ssvvyyy8UZubq45p78i7RJsN7ATdfo91NKOU1wM7Rspjh1D2xQXJ1IM7RuvOBrLC8XeDKQkY+3hXy6T0/8W2/v+rMBt/VtMe+1XYE033Ln/+N9dSa5LWwQQQAABBBBAAAEEEEAAAQQQQCCcgF8TZI8//rh5T97UqVPDtamUdRkZGc596+y+cDv46my/p59+2mn31ltvOcd64I6Rl5cXdC70izrapWrVqvah+bTjFBdDG0eKY8fQNsXFiRRD+8YrjsaKVnTZc7Qf3VikMhTdDKRNo+izP93j0Jl/Y9JGSnXf8b8H9/lIx9pe+2n/A0cKIjWjHgEEEEAAAQQQQAABBBBAAAEEECixgLMJyKZNm+TKK6+USy65JCk2fKhZs6aDYc/ycypcB6eddpqcdNJJpmbRokWuMyLuGKHLeoMaWl/sGYdaH7pE145TXAztGymOHUPbFBcnUgztG684Gita0fcLRvtp0qRJtO4Jc043Axk5sKNUS0+J4Z4CZtlvSZN/dmDt92zaaKmR7vxnaZ/iEwEEEEAAAQQQQAABBBBAAAEEECi1gF83wPjVr37lBPjkk0/kjDPOkAcffFAOHTrk1Fe2A/c76GLd7OLHH38MGqa7X3GbWtgbfWgA97X1ux1HE3M///yzVkUsdhx7qbLd0I6h3+NxL2WNY9+XFz5PP6mWjLm2c7FJQN3wQ5fzlqW082+Wpj8vKUsI+iKAAAIIIIAAAggggAACCCCAAAJBAn7dFEN3zP3LX/5i3i2nO83qEtOnnnpK2rZtK1OmTAnqUFm+6Mw+uxQURF9SaZ9PTQ3eFNm9661uJhKtuM+3b98+qGmscY4ePSq6KYuW0Bg6q9BOLLqvFXShX764z4fGifVeNJQdR11at24d7lKeqet5agOZPLR71OXAg63dfuNRqn3zajzCEAMBBBBAAAEEEEAAAQQQQAABBBAwAmatob6z7h//+IfZDde9XHbr1q0yaNAg+c1vfuMkgyqL23nnnefcqp1UcypCDr7//ntTYy8Ftk+3bNlSmjZtar7OmTPHrg77OXfuXFOvMVq0aBHUpkePHs73aHEWL17sLAE+99xznT72gR1nzZo1smNH5J1p3dcIjdO1a1dnExF3O/sa9qcmgRcsWGC+uvvY5734qTMBZ951nrx18znS94zGkuL3OQw15JD08S92vpfpIPt9a7eW/WUKQWcEEEAAAQQQQAABBBBAAAEEEEDAFgh62VirVq3kgw8+kGnTpgUlsWbNmiVnnXWW3HfffcW+f84OfKI/f/vb30paWpq5jXfffTfi7WgS7KeffjLne/bsGdRO3/92+eWXmzqdDWcnxIIaWV+03p4tp+21n7v06tVLatWqZaomTJggOssyXHn11Ved6gEDBjjH9sEVV1xhH4q7rVNpHeiy7cmTJ5sqne3Xpk0b92nzDsCLLrrI1H366acRlxOr2f79x5JQ4e4lKKiHvuiz7d6qnrx4TWdZ9nBv+XTYeTLt9nPlg+tbSqqvMD4SAWvGas72+MQiCgIIIIAAAggggAACCCCAAAIIeF4gKAFoa2jyTJcF//WvfzW7xmrCKj8/X0aOHGmWBYfulmv3S6TPevXqyZAhQ8wt6XsNJ02aVOT2cnJy5K677nLqhw4d6hzbB3reXhp85513yuHDh+1T5lO/a70WbeeOZyqtX+np6fLHP/7RfM3OzpZnnnnGPuV8ZmVlyUsvvWS+n3/++aKz7kKLJuI0SavliSeecJYLu9tpknbv3r2mSo/DlXvvvddU65Lj22+/Xewl0Hbb3bt3ywMPPGC+1q5d23G0z/N5TKBmRpq0blhTOjavLSfXDE76ltnoyIEyhyAAAggggAACCCCAAAIIIIAAAgggoAIpj1glHIUms3Tm2uDBg0WXyH733XdmZpsmzaZOnWpmknXv3j1c14Sp69atm7z99tuyb98+ee+992TXrl2iy5337NkjH3/8sdxwww1m2bPe8G233Sa33HJLkXvXRKIm+ebPny/btm2TGTNmiCbFcnNzZd68efKHP/xBli5davpp0mzgwIFFYmhFly5d5J133jGzDTUhuX37drNb8M6dO+X1118319aYen/63sXGjRsXiZOSkmLexafJTG2rY8vIyDDtVqxYIX/+85/ltddeM991ufBzzz0nfn/RHK8ubdZlxNpHP3UcderUEX22M2fOlOuvv142btxo4jz//PNiLz02FXH6pbML9f60DBs2TDIzM+MU+QSF0SW7i8bH7+LdbxepXj9+8YiEAAIIIIAAAggggAACCCDgSYGk+/e3J59i2Qfts2b3hV+PGhL7ww8/NLPb1q5daxKBOjvw4YcfDmmVeF91xp3OaFy3bl3Em9Mk3ujRo50lw6ENCwsL5eabb5aXX3459JTz/aabbpKxY8eGTbjZjfQe+vbtK2oYrmgS7I033pD+/fuHO+3UjRs3Tu644w6zWYtT6TrQxKcu5a5fP3ICSZOaV155pUlouro6h5o4HD58uETIDzvtSnugOxnbm5rozsfuXY5LG/OE9tME4IgWIrp8t6zFb21Gc/8GkYxKnhQtqwP9EUAAAQQQQAABBBBAAAEEyiyQdP/+LrOINwMUnR4WwUE3B1m+fLn8/e9/N7PUIjRLuGrdBXfZsmXy9NNPy9lnny1169Y1S3I14fT//t//k88//9wsvbXfFxhuAJoM0+W5mlTTd/zpxiC6rFc/9bvOChw/fnzU5J/G1Z10v/76axkxYoSZEagzCatVq2aWVd99993y7bffFpv80ziajFyyZIn5POWUU8wsQJ2pqDP1Ro0aJV988UXU5J/G0JmGOh5NOPbu3VsaNmxoxqRJuauvvtrMeCyv5J9eP+mKJuvaR0/cxjzmdlYckn8xc9EQAQQQQAABBBBAAAEEEEAAAQSiC8Q8A9AdRmds6Sy2Cy+80F3NMQIxCyTl/wOxYa7IhMtiNojY8PrpIi2DN6SJ2JYTCCCAAAIIIIAAAggggAACCEQRSMp/f0cZL6fCC1hrDUtedJaYvXyz5L3pgUCSCrSwknb1rF2Xf/qu9ANs2EGkRY/S96cnAggggAACCCCAAAIIIIAAAgggECJQqgRgSAy+IuBtAX2N5sZ5IgvHiexZX3qLtOoiV4wS6yWbpY9BTwQQQAABBBBAAAEEEEAAAQQQQCBEgARgCAhfESiRwLZlIlNvFdmVXaJuRRqnVhX53ViRph2LnKICAQQQQAABBBBAAAEEEEAAAQQQKItAzJuAlOUi9EUgKQXWfy7ySt+yJ/8U5+hhkcnXHfvRdwnGtjl3UrIyKAQQQAABBBBAAAEEEEAAAQQQiK8ACcD4ehLNKwI682/SYJH8g/EbcaBAZNV7xzYSebG7iF6DggACCCCAAAIIIIAAAggggAACCJRRgARgGQHp7kEBnZ2ny37jmfwLZdQlxTq7UGcZUhBAAAEEEEAAAQQQQAABBBBAAIEyCJAALAMeXT0qoBt+lPWdf7HQaYJRZxkyEzAWLdoggAACCCCAAAIIIIAAAggggEAEARKAEWCoRiCiwKLxEU/F/YQmAafdxjsB4w5LQAQQQAABBBBAAAEEEEAAAQS8I0AC0DvPmpHGQyB3v0j29HhEij3Gj6tENs6PvT0tEUAAAQQQQAABBBBAAAEEEEAAAZcACUAXBocIFCuwf5s1G8/arKOiS0XOOqzosXE9BBBAAAEEEEAAAQQQQAABBBAoVwESgOXKS/CkE8iL466/JcHJfl9EZx9SEEAAAQQQQAABBBBAAAEEEEAAgRIKkAAsIRjNPS6QXv3EAOisw5ztJ+baXBUBBBBAAAEEEEAAAQQQQAABBCq1AAnASv34uPkKF8hsKuJLqfDLmgseOXBirstVEUAAAQQQQAABBBBAAAEEEECgUguQAKzUj4+br3CBjEyR9v0r/LLmglVqnJjrclUEEEAAAQQQQAABBBBAAAEEEKjUAiQAK/Xj4+ZPiEDXIRV/WX+qSM0mFX9drogAAggggAACCCCAAAIIIIAAApVegARgpX+EDKDCBVr0FGnQvmIv286adaizDykIIIAAAggggAACCCCAAAIIIIBACQVIAJYQjOYIiM8nMmC0SFoFbghyImYd8qgRQAABBBBAAAEEEEAAAQQQQCApBEgAJsVjZBAVKZBzOE+27NghB+p2kEBFXLhhB5EWPSriSlwDAQQQQAABBBBAAAEEEEAAAQSSUMB6sRgFAQSKEwgEApL1/U8ye9YncuWmf0gb/5biusTnvM4yvGKUtfOwNeuQggACCCCAAAIIIIAAAggggAACCJRCgARgKdDo4i2BFVv3ybDJy6ThriwZkzZSqvuPVAyAJv8GTRRp2rFirsdVEEAAAQQQQAABBBBAAAEEEEAgKQVYApyUj5VBxUtg3tpdMnBMlqT9uPxY8s9XMcm/Q7XbiNw4Q6TVhfEaCnEQQAABBBBAAAEEEEAAAQQQQMCjAswA9OiDZ9jFC+jMv6GvL5FDeUdlZPooqV7Oyb/8QIrMLOwiEwt6S736F8q/mflX/EOiBQIIIIAAAggggAACCCCAAAIIFCtAArBYIhp4UUDf+afLfg/lFUh3/yppG8d3/tmJvncKesqmQEOpIblyQKrKzkAd67Oa4U5ZtVNycvOlZkaaF/kZMwIIIIAAAggggAACCCCAAAIIxFGABGAcMQmVPAK64cd3Ow+YAQ1O+SQuA5tfcJr89egNQYm+SIELCgOyc38uCcBIQNQjgAACCCCAAAIIIIAAAggggEDMArwDMGYqGnpJYOKCH8xwa8gh6eNfHJehn+PPjin5Z1/swJEC+5BPBBBAAAEEEEAAAQQQQAABBBBAoNQCJABLTUfHZBXQpbczV+40w2vs2yOpvsK4DFXjNPLtjTlWjSopMbelIQIIIIAAAggggAACCCCAAAIIIBBJgARgJBnqPSuwY1+u6BJcLdWt9/PFs9SQwzGFS/H7pFFmRkxtaYQAAggggAACCCCAAAIIIIAAAghEEyABGE2Hc54UOGht/GGXgxLfJJxu9hFL0QTkA+98K1+u3y26IQkFAQQQQAABBBBAAAEEEEAAAQQQKK0Am4CUVo5+SStQPf340tsdgbpyNOCPyzJg3f1Xd/qNtcxYvkP0p02jGjJyYEc5/aRasXalHQIIIIAAAggggAACCCCAAAIIIOAIMAPQoeAAgWMCjWtliC7B1XJAqsnMwi7HTpTxt8bReCUtuhvxwDFZMm/trpJ2pT0CCCCAAAIIIIAAAggggAACCCAgJAD5I0AgRKBmRpr0Oa2RUzuxoLdzXJaDssQ5ZC1LHvr6ElmxdV9ZboG+CCCAAAIIIIAAAggggAACCCDgQQESgB586Ay5eIHB55zsNMoq7CBrCps530tzsLqwuSwobF+ark4fTQLeM/kb3gnoiHCAAAIIIIAAAggggAACCCCAAAKxCJAAjEWJNp4T6H5KPfPuvWMD98mw/NvkYKBKqRy03z35t1p9jy0rLlWQXzqt2ZkjC77fU5YQ9EUAAQQQQAABBBBAAAEEEEAAAY8JkAD02ANnuLEJ+Hw+s/FGtV82BFkZaClD84eVOAmoyT/tp/3jVSYu+CFeoYiDAAIIIIAAAggggAACCCCAAAIeECAB6IGHzBBLJ6C77o65trPYScD5hWfIwLyHY14OHGjYQdb3myKZp/WWFCuhWJJSQw5Ja98WOcu3znzqd7t8tHKH5OTm21/5RAABBBBAAAEEEEAAAQQQQAABBKIKpEY9y0kEPC7Q89QGMnlodxk2eZnobrw6k69P3gg5x58t16Z8LH38iyXVV+goHZUUOdjyEql13m3ia9FDzrQSfy92E1m2aa9c8eKXTrvwBwHp7l9lxf1ELg6NG/Cb3Yh1IxF9J+HO/bmim5VQEEAAAQQQQAABBBBAAAEEEEAAgeIESAAWJ8R5zwvoTMCZd51n3r33+oKNMnPlTmtDjw7mR2fmneT/WS5qVU0u7tRazurQTmpl1CpqVswMwNN8G2Rk2ihp699StK9Vo0nGfikLzY9uSFKwdbRIw55h21KJAAIIIIAAAggggAACCCCAAAIIuAVIALo1OEYggoC+E7B7q3rmR5ff6gy8A0cKpEaVFGmUmVHsbLzqv7xLMFz4Hv7lMiZtpFT3HQl3ukidJgkLPxgokvmGSKsLi5ynAgEEEEAAAQQQQAABBBBAAAEEEHALkAB0a3CMQAwCuvS2pMtvG9fKkBS/TwoKA0FX0Jl/JUn+2Z39R613Ak4aLHLjDJGmHe1qPhFAAAEEEEAAAQQQQAABBBBAAIEiAmwCUoSECgTiL6AJwz6nNQoJHDDLfmOd+RfSWST/oMi020QCwUnFIu2oQAABBBBAAAEEEEAAAQQQQAABTwuQAPT042fwFSkw+JyTgy6nG35EeudfUMNoX35cJbJxfrQWnEMAAQQQQAABBBBAAAEEEEAAAY8LkAD0+B8Aw684ge6n1JM2jWo4Fxxs7fYbl7JofFzCEAQBBBBAAAEEEEAAAQQQQAABBJJTgARgcj5XRpWAArqRyMiBHaWatSGI7h7cx784PneZ/b5I7v74xCIKAggggAACCCCAAAIIIIAAAggknQAJwKR7pAwokQVOP6mWjLm2s7RM3yepvsL43GqgQCRne3xiEQUBBBBAAAEEEEAAAQQQQAABBJJOgF2Ak+6RMqATKZCTmy879uXKwbwCqW7N9NPdf3UDEHd948wMebL/KSLWBr5xK0cOxC0UgRBAAAEEEEAAAQQQQAABBBBAILkESAAm1/NkNCdAIGDtwpv1/U/yetYP8vGqnVJQeHxXXr9PpGHNKvJjzhFxVVubf2yVmelxvNkqx98tGMeohEIAAQQQQAABBBBAAAEEEEAAgSQQIAGYBA+RIZw4gRVb98mwycvku53hZ+Bp0m/H/iNFbnBrYR05GvDHZxmw3/rPuGaTItegAgEEEEAAAQQQQAABBBBAAAEEEFAB3gHI3wECpRSYt3aXDByTFTH5Fy3sAakmMwu7RGsS+7l2/UUyMmNvT0sEEEAAAQQQQAABBBBAAAEEEPCUAAlATz1uBhsvAZ35N/T1JXLIetdfacvEgt6l7Rrcr+uQ4O98QwABBBBAAAEEEEAAAQQQQAABBFwCJABdGBwiEIuAvvNPl/2WJfmn18kq7CBrCpvFcsnIbRp2EGnRI/J5ziCAAAIIIIAAAggggAACCCCAgOcFSAB6/k8AgJIK6IYfkd75V7JYPhmWf5scDFQpWTe7dVp1kStGifisnUYoCCCAAAIIIIAAAggggAACCCCAQAQBEoARYKhGIJLAxAU/RDpV4vqVgZYyNH9YyZOAmvwbNFGkaccSX5MOCCCAAAIIIIAAAggggAACCCDgLQESgN563oy2jAI5ufkyc+XOIlFqyCFp7dsiZ/nWmU/9HmuZX3iGDMx7OPblwLrs98YZIq0ujPUStEMAAQQQQAABBBBAAAEEEEAAAQ8LpHp47AwdgRIL7NiXKwWFgV/6BaS7f5Vcm/KJXOxfLKm+Qife0YDf7PKrG33ou/6sdbrOuXAHOhOwT94IOcefbcX7WPqmLhFfwLXBiN/6T1V3+9UNP/Sdfyz7DcdIHQIIIIAAAggggAACCCCAAAIIhBEgARgGhSoEIgkc/GXX39N8G2Rk2ihp698StqkmA/ulLDQ/utGHvutPk3zRi08WWMlC/fm/G86UMzOtWYRHDohUqSFSs4lIRmb07pxFAAEEEEAAAQQQQAABBBBAAAEEwgiQAAyDQhUCkQSqp6dID/+3Mj7tWcnw5UdqFlSvScLJ6X8z7/rT5b6xlGo1a4s0aB5LU9oggAACCCCAAAIIIIAAAggggAACUQV4B2BUHk4iECzQbMv78lraiJiTf3bv6r4jMiZtpOjMwVhK9fTwuXl9B+HanTmybPPP5lO/UxBAAAEEEEAAAQQQQAABBBBAAIFoAuGzDNF6cA4Brwqs+0yqvv/f1vv37HcAlgxCk4DPpo2WS/KetDpGfyfgwbyjTvBAICBZ3/8kr2f9IB+v2ul6B6FIit8nfU5rJIPPOVm6n1LPejVg9LhOUA4QQAABBBBAAAEEEEAAAQQQQMAzAiQAPfOoGWiZBLYtE3nraitE6ZJ/9rXb+TebjT70PX/RyoEjxzYAWbF1nwybvEy+22m9CzBM0Q1JZizfYX7aNKohIwd2lNNPqhWmJVUIIIAAAggggAACCCCAAAIIIOBVAZYAe/XJM+7YBawZeDL1VpGC3Nj7RGk52No1uLhSo0qKzFu7SwaOyYqY/AuNoUlCba/9KAgggAACCCCAAAIIIIAAAggggIAtQALQluATgUgCG+eJ7MqOdLbE9Zf4F0kNsXb4jVBSrWW9ew7mydDXl8ihX3YdjtC0SLW21346c5CCAAIIIIAAAggggAACCCCAAAIIqAAJQP4OEChOYNH44lqU6Hyqr1Aa+fYW6aNJwda+LXJzqz0y9p0Z4s/LKdImlgpNAt4z+RvRdwdSEEAAAQQQQAABBBBAAAEEEEAAAd4ByN8AAtEEcveLZE+P1qJU52rI4V/6BaS7f5Vcay0Lvti/WDQ5KJuPnTpaxS8zC7vIxILekmXeGRj7Bh9rrJ2CF3y/R7q3qleq+6MTAggggAACCCCAAAIIIIAAAggkjwAJwOR5loykPAT2b7P2/Ti2IUc8wx+QqnKab4OMTBslbf1bwobWZGC/lIXmZ01hMxmWf5usDLQM2zZc5cQFP5AADAdDHQIIIIAAAggggAACCCCAAAIeE2AJsMceOMMtoUDewRJ2KL55fiBFTvFtk8npf4uY/AuNoklCbd/Dvzz0VMTvH63cITm5+RHPcwJMeujrAABAAElEQVQBBBBAAAEEEEAAAQQQQAABBLwhQALQG8+ZUZZWIL16aXtG7JdV2F6eT3tRqvuORGwT7oS2H5M20swcDHc+tK6gMCA798dn5+LQ2HxHAAEEEEAAAQQQQAABBBBAAIHKI0ACsPI8K+70RAhkNhXxpcT1yi19O0uc/LNvQJOAz6aNtr7GtsHHgSPxX75s3wufCCCAAAIIIIAAAggggAACCCBQOQRIAFaO58RdniiBjEyR9v3jdvVNhQ2kuX9XmeK182+Wc/zZMcWoUSW+ycuYLkojBBBAAAEEEEAAAQQQQAABBBBIKAESgAn1OLiZhBToOiQut1WYUkXqNGsTl1iDrV2DtdSQQ9Lat0XO8q0zn/rdLql+nzTKzLC/8okAAggggAACCCCAAAIIIIAAAh4VYBdgjz54hl0CgRY9RRq0F9kV26y7sJF9PvH/18tSc8p1YU+XtPJS/0IZm/asXOj/WnS3YLscDfhlZmEXmVjQW+p2uEhqZqTZp/hEAAEEEEAAAQQQQAABBBBAAAGPCjAD0KMPnmGXQMBK3skA6717aaXdEET7jxWp39p6dV983smX4gvIxSlLgpJ/OiJNBvZLWShvpT8mT++6VWTbshIMlKYIIIAAAggggAACCCCAAAIIIJCMAiQAk/GpMqb4CzTtKDJoYsmTgCnWEtzB74icOVAk72D87ytKxGr7vhN5pa/I+s+jtOIUAggggAACCCDw/9m7E/ioqvv//++ZrJAAAmKQpayCgFatSEkFtxaxLt+qbflaC7X9uaDVVsWl7f+rfq2tVtuK27cKiq1W60Jbl1ap2KqtoiCLGzvIoiASQbYkkIXM/O/nhjuZSWaSSTJJZnmdxyPO3HPPPffc50XafDznfBBAAAEEEEAAgXQXIACY7m+Y50ucwJBTpB/MqV0OHEev5QcNU/DCl6ShX61tndvSGYRx3CxWk2on6PjUZGYCxvKhHgEEEEAAAQQQQAABBBBAAIEMECAAmAEvmUdMoIAzE3DZN17StQW36sWaMbI998JLdTBLL9R8WedV3aBRW/9XE5/eo2Wf7K5t0rWP5OuArLwWBHzuMmf5cTB8qHxHAAEEEEAAAQQQQAABBBBAAIEMEYiMXmTIQ9tjXn/99fI5e7t5P//+97+bfPqXXnpJ5557rvr166e8vDz3046tPt6yd+9e/eY3v9GYMWPUo0cPFRYWasSIEbr22mv18ccfx9uNli9frksvvVRDhw5Vp06d1KtXL51wwgmaOXOm9u/fH3c/Tz31lCZOnKhDDz1U+fn5GjhwoKZMmaIFCxbE3UcmNXxj7TZNenCB/vL5IF1efZWOrnxQX638jb5ReYv7eUzlTF1RfaUWBEY6LD6tKSnTpJnzZdcpv6s04syO4fpshbRxXsfcm7sigAACCCCAAAIIIIAAAggggECHCviCTunQEXTAzd9//32NHj06IlD22muv6aSTToo6GiOyYNuDDzqJHGKUSy65RDNmzHADijGaaN26dTrjjDO0evXqqE26deumJ554Qqef7uzb1kh5+OGHdfnll6uysjJqq7Fjx+qFF15Qz549o563yoqKCn37299220Vr5Pf7dfPNN+vGG2+MdrrVdZs3b1b//v3dfjZt2uQGU1vdaRt3YDP5LJi3t6r5iTw652Zp9tRiHVH1vvToWW080hjdjzxbmvRojJNUI4AAAggggAACCCCAAAIIpKNAKv7+nY7voaOfKeNmAAYCAV188cVu8O+QQw6Jy/+GG24IBf+OOeYYPfnkk1q40Mm06nzasRULDjYWLCsrK9OZZ54ZCv7ZGF555RW99dZbuvXWW92ZgLt373aDch988EHMcc2dO1cWbLTgX1FRke699169/fbb+sc//uHOTrQLbfaezUy0Z41VLrzwwlDw7+STT9Zzzz3nPpMFF4cMGeJee9NNN2nWrFmxusioegsCT5v9XouCfwZlQcNrZr+v4IBxce8hmHDglX93Ir97Et4tHSKAAAIIIIAAAggggAACCCCAQHILZNwMwLvvvltXX321Dj/8cJ1zzjn61a9+5b6hWDMAP/zwQ3eJri2rtVmDr7/+urvk1nuttqT3xBNP1OLFi5Wdna1Vq1a5ATTvvPdps+l+/vOfu4e//vWvdd1113mn3M/58+e7S3jtPhaQe/XVhplb7ZwtF7Yxde3aVe+8806De9nMwPvvv9/t89FHH9X3vve9iPvYwX/+85/QbMezzjpLzz77rLKy6vam2759u4499lh3SXL37t21fv16HXTQQQ36aU1Fqv0XiLfWbdf5D73dmkd2r33y4rEq7rSpNjuv7c3X3uXyhU4Acnh735X7IYAAAggggAACCCCAAAIIdJBAqv3+3UFMaX/bjJoBaEtNvVl6DzzwgHJzc5t8wXfddVdoqfB9990XEfyzizt37iyrt2IBOgsw1i/V1dW655573GoL4F1zzTX1m6i4uFg2K8+KBSOXLFnSoI0F6iz4Z+VnP/tZg+Cf1dv+gha08767X+r9wwKQVizoZ8HC8OCf1R988MG644477Kt27twpmxWY6eXxBR8lhMDtx0kkovMel3I6ICtwZVlCnoNOEEAAAQQQQAABBBBAAAEEEEAgdQQyKgD4wx/+ULYU94ILLgjNgGvsVdmyz+eff95tYjMGbW+9aMXqhw+vnVVlS2nrb6toCUZ27drlXmr3tv31opXvf//7oepnnnkm9N37Yn17JbytV2efFpCcNGmSW7Vs2TKtXbs2/LT7/Lb02MqECRNi7r1nS4htlqGVaGNxT2TIP0orqjV3eUlCnval5Vtl/WnIKdIP5rT/cuC8woQ8B50ggAACCCCAAAIIIIAAAggggEDqCESPRKXO+OMe6ezZs9097yzzrs2Si6ds2LBBn3zyidvUlvk2VrzzNrV248aNEU3feOON0LHXLlQR9sWWGBcU1M4KmzevYcZWrx8LNvbu3Tvsysiv4feo34/tXeglDwlvF9mD3NmRXsDTrrFZjJlatu6uUE0gMblyrJ+SPRW1lDYT8IfzpQtekEZ+w0kanBVJ7M+WDreswQn619T663Jo5D04QgABBBBAAAEEEEAAAQQQQACBtBdwIgLpX2z23ZVXXuk+qC1t7dWrV1wPvXLlylA7mwHYWAk/b9cNGjQo1DzefmwPQUvAYUlAwq+xjmzmogUXrYTfy62o94/w8/X7CT8Ob1evC/fQzr/88svu0mabSThy5MhozdK+rrwFWX8bQymrDMsi7PNJg8bX/liCjtJPJVumazP1LFiX78zCnO3s47iidiZqY/02ec6CidYfBQEEEEAAAQQQQAABBBBAAAEEMkogIwKA119/vbZu3aqvfOUroX324nnLtmegV/r16+d9jfrZv3//UH34dVbpHdvsvqaSaVg/FgDctm2bO1MvLy/P7deCf97S4kSMxTptbj/NCQB6wUp38FH+8emnTqArRUpBbr2Zea0cd2FejP4sOFc/QOcEBTcfPF79lIAA4HEXtXLkXI4AAggggAACCCCAAAIIIIAAAqkokPYBQFsCO2vWLDdD74wZM+SzGVdxltLS0lDLwsLG907zlu7aBTZbL7x4/TTVh11Tvx8vAOj1YW2a6qd+H3aNVxLVj9dfrM/wgGisNqlS37tbvrL8voQsA852+inqmt/4ozt7T2rjG9LChxRc9aL6BcNmDDZ+ZcyzwUNGyjdwXMzznEAAAQQQQAABBBBAAAEEEEAAgfQVSOsAYFVVlS655BJ35tzVV1+tI488sllvsqLiwF5tzlVNZQz2AnV2g3379kXcx+unqT7solj9eH1Ym6b6idWHXZuofqyvTCld8nM0cVSR5izd2upHnjiqt6y/mGXLe9Kzl0rbapefxx+ujtmjyoN5Wjf6dn2xGcHv2L1xBgEEEEAAAQQQQAABBBBAAAEEUk0grQOAt912m7uX3he+8AX97//+b7PfTX5+3UwtCyY2VrzEGtamU6dOEU29fprqwy6K1Y/Xh7Vpqp9Yfdi1ierH+mqseMueY7WxJcBjxoyJdTrp6iePHZCQAKD1E7Ose1V6arJUXR6zSXNPWPBvavU0dVvbRb9LHe7mPibtEUAAAQQQQAABBBBAAAEEEECgEYG0DQCuWrVKv/rVr9xHv++++yKW1jbiEXGqS5cuoeP6y3pDJw58KS+vC9rUX6Lr9dNUH9ZVrH68PqxNU/3E6sOuTVQ/1ldjpan9BRu7NhnPFQ/uqWFFhVpTErm8uzljHV7URWMH94h+ic38S3Dwb1Wgv66pvlTLg4OUtXyrSiuqG599GH1k1CKAAAIIIIAAAggggAACCCCAQIoLpG0A8K677nJnyg0ePFh79+7VU0891eBVLVu2LFT36quvuolCrOKss85yA4bhQaymklqEz3irv/+d9fP222+7wT3LSNxYIhCvH8tUHL6UN5Fj8R7anmn06NHeYYNPbyx2ov4zNWic5hW2d+T0SUdr0sz52tuCrMCdnUQid046KvoelLbnny37TcDMv+pgluYGRuvxmglaEBjhvJXaRcQ1gaBK9lQQAEzzP6c8HgIIIIAAAggggAACCCCAAALRBNI2AOgtg12/fr2+853vRHv2iLpf/OIXoeMNGza4AcDwrLc2o7CxEn5+xAgLvNQV6+evf/2rW2Htxo4dW3cy7Nv+/fu1bt06t6Z+Hzar0IJwFpQLv1fY5aGv4efr99OSZ8rOztbQoUND/WfqlyP6dtPMKcfqkj8u0b7q+BNzWPDPrrProxZL+HFgz7+o5+OsnFZ1qV52gn9l6hz1irLK+McctQMqEUAAAQQQQAABBBBAAAEEEEAgJQX8KTnqdhr0oEGD1KdPH/du//nPfxq96+uvv+6e79u3rwYOHBjRdty4uuyrjfWzePHi0BLg448/PqIPO/D6Wb16dWi2YoNGTkX4Per3c9xxx4WSiIS3q9+P7TO4YMECtzr8mvrtMuU46MzSe2vddj3x9seq3B9/IM2W/c6eWqzxh/WKTbVoVuxzzThzSta7MYN/1k1hXlYzeqMpAggggAACCCCAAAIIIIAAAgiki0DaBgAfeeQRN/uvBW5i/YQnBnnttddC7bwAni37/MY3vuG+a5tV5wXE6r98q/dm3Vl7uy68nHTSSerWrXb216OPPureJ/y8993G7JVzzjnH+xr6PPvss0Pfw9uGKp0vttx59uzZbpXN9hs2bFj4aXcPwK9+9atu3b/+9S/FWtr8zDPPaM+ePW67aGOJ6DTND5Z9slsT735d5z/0tv6xbKuc1bSNliy/T2cceaievHisXrpqfOyZf9ZLhWO88oVG+4v35Gn+RSrU3qjNs50xFXWtS2oTtRGVCCCAAAIIIIAAAggggAACCCCQlgJpGwBM1Nu66qqrZEtgrfzoRz/Svn37Irq2Y6u3Yu2sff2Sm5urH//4x271ypUr9dvf/rZ+E82fP18PP/ywW3/iiSfKZt3VLxaIGzJkiFttCU685cLh7a677jrt3LnTrbLv0cq1117rVtuS48svv1w1NZEz2rZv366f/OQnbhvbr/Ciiy6K1k1G1L2xdpu7719zkn/kZvl03pj+Kh7Ss0EwuAHani1SMNK/QZs4K7J9ARX5at99/UsmjurN/n/1UThGAAEEEEAAAQQQQAABBBBAIEMECAA28aJtBp0XMLMlurak9umnn5Z9t087tu9WLOB22GGHRe3Rznmz8a6//npNnTpVNuvQZg9aMO/UU0+VBeQ6deqku+++O2ofOTk5uvfee+X3+93ZeXbv//u//9PChQs1d+5cfetb39L999/vXmvLhadMmRK1n1NOOUXnnXeee+5vf/ubJkyYIPu05/jDH/7g7lH48ccfu+dvv/12de/ePWo/6V5pM/+mPrak2Uk/9lUH3Ovs+iZLVV326CbbxtGgUJEBau+SyWMHeF/5RAABBBBAAAEEEEAAAQQQQACBDBNI2yQgiXyPt956qz777DP9/ve/17vvvhsKnoXf48ILL9Qvf/nL8KqI7126dNGLL76o008/XWvXrtWDDz7o/oQ36tq1q/70pz/p6KOPDq+O+G7Xz5gxQ1dccYVKSkpCsw/DG40ZM0bPPvussrJi7/lmz2JLfOfMmeMGIi0YGV4syHjjjTe6gcrw+kz5bsvGp81+r9nBP8/HMgVfM/t9dwlw/SXhXhv3M7cg4rC1B2Xq1KAL24dw7OAeDeqpQAABBBBAAAEEEEAAAQQQQACBzBBgBmAc79mCYbY81wJ4tsefJQaxZb32accWRJs1a5Y7M6+x7iyTrgUQ77jjDo0ePVq2vLZz584aPny4rr76an3wwQc688wzG+vCPXfxxRdryZIlss/BgwcrPz9fPXv2dJOEPPDAA3rzzTd18MEHN9qPzTS057GAo80APOSQQ9xnskzD559/vubNm6ebb7650T7S+eT89Z+rOct+o1msLinVgvU7op2qq+vqJJnxxQ7U1jVs+lt1MEslwcjZmpaB+M5JRzW9FLnp7mmBAAIIIIAAAggggAACCCCAAAIpKuBzZjo1kdIgRZ+MYSe1gCUfsWCjlU2bNqlfv35JNd4f/mmJ5izd2uoxWTKQ3333S433M/t70ornG28Tx9kXar6sK6qvDLW04N/MKcc2noE41JovCCCAAAIIIIAAAggggAAC6SiQ7L9/p6N5Mj4TMwCT8a0wpg4VKK2o1tzlJQkZw0vLt8r6i1ks/t5vTMzTzTnxeM2EUHNb9jt7ajHBv5AIXxBAAAEEEEAAAQQQQAABBBDIXAH2AMzcd8+TxxDYurtCNYHmTYwt1F719u1QgSpUrnxtDfZQmTq7/ZTsqYiegXfLe9Kzl0rbVsYYSfzVqwL9tVgjZTMOLeGH7fkXa+9BC0jaM5Y7+xQWOLMEe3fLjz6++G9PSwQQQAABBBBAAAEEEEAAAQQQSGIBAoBJ/HIYWscIWGAsvhJUsX+FpmT9U6f6FyvbFwhdtj/o19zAaNmsvLKKr4TqQ1/WvSo9NVmqbn0W4Jrszio450G9O+S4mIE8W+lv+xo+Nv8jvbyiJCLAmeX3aeKoIjdwWDy4Z8zAYWjsfEEAAQQQQAABBBBAAAEEEEAAgZQSIACYUq+LwbaHgM2Ka6qM8m3Q9JwHNNy/OWpTCwaekbXQ/dn956cV/M4s+focU9vWZv4lKPinnAJlnfe4+g+JEmQ8MLJln+x2MxrHSmpisx1tv0P7GVZUqOmTjtYRfbtFfS4qEUAAAQQQQAABBBBAAAEEEEAg9QTYAzD13hkjbmMBWxLrTIqLWcb5l2p27i0xg3/1L+xW+qEqHjxNG97+u2R7/tmy3wTM/NMhI6UfzJGGnFL/lqHjN9Zu06SZ8+POaGxBQmtv11EQQAABBBBAAAEEEEAAAQQQQCA9BAgApsd75CkSKNAlP0fHDewetUeb+TczZ7oKfJVRz8eq7OTsDVg050KtnXNfQvb808TbpMvekvocHeuWspl/Ux9bor1xL2mu7cra23V2PQUBBBBAAAEEEEAAAQQQQAABBFJfgABg6r9DnqANBIKKNgUw6C77bW7wzxteZydo2HuhE7hLRPnICf75oo2xtnPb82/a7PeaHfzzhmZBwGtmv+9MWGxeMhTvej4RQAABBBBAAAEEEEAAAQQQQCB5BAgAJs+7YCRJImBZcpd8tLPBaCzhR6w9/xo0jlHRxbcvxplmVq96QXryO9KG12uXFde73BJ+RNvzz7IVD/Vt1lG+D91PO45VVpeUasH6HbFOU48AAggggAACCCCAAAIIIIAAAikiQBKQFHlRDLP9BLburojIkuvdebKT7Tepympn/z/76TVCOmdGxHLgxxd8FDbU+LIVzw84ewrWm/lo/RQP6RnWF18RQAABBBBAAAEEEEAAAQQQQCDVBAgAptobY7xtLlAeZc88myk30b+4ze/dohtsWyn94XTJyQZsCUFsBuPc5SVuV83JVrw60E/Tqi/T8uCg0DBeWr7V7c/2RaQggAACCCCAAAIIIIAAAggggEBqCrAEODXfG6NuQ4GC3KwGvff27VC2L9CgPmkqLKvwU5OlLe/Jm8HY3GzFtrzZshvbdV6pCQRVsqfCO+QTAQQQQAABBBBAAAEEEEAAAQRSUIAAYAq+NIbctgK9u+Uryx+ZYKPAyeKb9MWCgM9dpvLK/WpptmJLcGJZju16r5RV1nhf+UQAAQQQQAABBBBAAAEEEEAAgRQUIACYgi+NIbetgC13nTiqKOIm5cqPOE7ag89W6OBtb7cqW7EFAe/McfYUVG0G4MK8hjMik/b5GRgCCCCAAAIIIIAAAggggAACCDQQIADYgIQKBKTJYwdEMGwN9tD+YGr869L7vXtana34cP8mjfWvVLYzE7Koa4oEPyPeGAcIIIAAAggggAACCCCAAAIIIOAJpEZEwxstnwi0k0Dx4J4aVlQYuluZOmtuYHTouDVf9gQ7tebyJq/N3vRWk23iaWBZjyeO6i0SgMSjRRsEEEAAAQQQQAABBBBAAAEEkleAAGDyvhtG1oECPp9P0ycdrc5hCUEer5mQkBHdVn2+yoN5CemrLTs5zb9IF3ypR1vegr4RQAABBBBAAAEEEEAAAQQQQKAdBAgAtgMyt0hNgSP6dtPMKceGgoDzAyO1OtCvVQ8TCPq0NDhIU6unJX0Q0LIeH9czBZKftOqNcDECCCCAAAIIIIAAAggggAAC6S9AADD93zFP2AqB8Yf10uypxQeWA/s0rfpSWRCvpcXvCzoJNmZqXuAITaq6SR9lRe412NJ+2+o6X5WTWZiCAAIIIIAAAggggAACCCCAAAIpLUAAMKVfH4NvDwGbCTj3qhP05MVjdXy/XFkQrzXFS7Cx3JkJeGL5bTqv6gbNrTlWwdZ125ohxb42r24fxNiNOIMAAggggAACCCCAAAIIIIAAAsksQAAwmd8OY0saAdsTsHhIT13XY15CxmQJNmqLTwucpcVTq6/Ry4FjE9J3IFGBRF+W1OXQhIyJThBAAAEEEEAAAQQQQAABBBBAoOMECAB2nD13TjWBij3KWfNiQkZtCTYKtTeir0dqTos4bunBquAXWnpp5HVfGCvld42s4wgBBBBAAAEEEEAAAQQQQAABBFJOgABgyr0yBtxhAnu2SMGahNzeEmwU+XZG9JWIJCOrAv21R50j+m35Qcv3Omz5PbkSAQQQQAABBBBAAAEEEEAAAQQSLUAAMNGi9Je+AglOiFGoffWsLMnIZS3ODlwezNMN1d/Xcb419fpt4eHH8yVn1iMFAQQQQAABBBBAAAEEEEAAAQRSW4AAYGq/P0bfngK5BQm9W5k6NejPEoNMrZ7W7CCgBf/sul3OwuIsZ3ZhQorNdiz9NCFd0QkCCCCAAAIIIIAAAggggAACCHScAAHAjrPnzqkm0LWPZIkxElCqg1kqCXaP2tO8wJGaVHWTVgf6RT1fv3Jj4BBdU32p3gsM0Vj/yvqnW3dcWda667kaAQQQQAABBBBAAAEEEEAAAQQ6XIAAYIe/AgaQMgKWEGPEmQkZ7tzAaJU1slefzQScWHWHzqu6QS/WjNH+YOS/qpbp18v2O9D/mWbk3qP38i7RldnPJGR8oU5e+6W04XVn78NEpRYO9cwXBBBAAAEEEEAAAQQQQAABBBBoJ4HsdroPt0EgPQSOu0ha8Xyrn+Xxmglx9OHTgsBI98cyBo/zL9XPsp/UACfg54+Sn8MSi/RyUoAktKx7VbKfXiOkc2ZIfY5OaPd0hgACCCCAAAIIIIAAAggggAACbS8QOa2o7e/HHRBIbYGB42uDYa14CsvUuyDgBNSaUY72r9OdOTPc4F8zLktc020rFfzD6bXBwMT1Sk8IIIAAAggggAACCCCAAAIIINAOAgQA2wGZW6SRgM+Zemcz4XJalhDEknXYfn3OZoJxo4zybdDMnOkq8FXGfU1bNPRVl6vi8e/o/YX/dlYEsyS4LYzpEwEEEEAAAQQQQAABBBBAAIG2ECAA2Baq9JneArYM9rzHmx0E9DL12v5+8Zegpuc80OHBP2+8+cEK5b1whSbe9R8t+2S3V80nAggggAACCCCAAAIIIIAAAggksQABwCR+OQwtSQVs9pvf2T6z3+i4B2jLfi2zr2X4bU4p9q/QcP/m5lzS5m0P929Sj+2LNGnmfL2xdlub348bIIAAAggggAACCCCAAAIIIIBA6wScKAYFAQTiFtjynvSss4TX2ROvqWJxwjcCR+j+mrMP7PkX/7Jfr+/JWf/0vibVp43riqqRmvrYEs2eWqwj+nZLqvExGAQQQAABBBBAAAEEEEAAAQQQqBNgBmCdBd8QaFzAsuFaIow4gn/WkW0XeKx/rbJVY0eN9x3lrGX+nehfHOVMx1ed5l8oG9/eqhpdM/t99gTs+FfCCBBAAAEEEEAAAQQQQAABBBCIKUAAMCYNJxAIE7CZf09NlpxEGM0plrjDEnhYIo94iwXWhvo260T/e8r2BeK9rF3bZfuCKvLtdO+5uqRUC9bvaNf7czMEEEAAAQQQQAABBBBAAAEEEIhfgCXA8VvRMlMFbC2vLfttZvDP47Ig4J05M3Ra1e1OVayZgEHZfn9TnKW1pzqz/pI18Oc9k30Wal/o8PEFH6l4SM/QMV8QQAABBBBAAAEEEEAAAQQQQCB5BAgAJs+7YCTJKrDxjbiX/cZ6BEucMda/0tkLcGSDJjY70DL9JluyjwYDrVcRCAtmvrR8q0orqtUlP6deKw4RQAABBBBAAAEEEEAAAQQQQKCjBVgC3NFvgPsnv8CiWQkZY7SEHuP8SzU795aUC/4ZiE/OzMgDpSYQVMmeCu+QTwQQQAABBBBAAAEEEEAAAQQQSCIBAoBJ9DIYShIKVOyRVr6QkIGd5l/kJs7wOrOZf7Y/oC0RTsVSfzFzWaUlO6EggAACCCCAAAIIIIAAAggggECyCRAATLY3wniSS2DPFimYmMCW7evnJc5wOnWX/aZq8M9eUpk6RbyrwrysiGMOEEAAAQQQQAABBBBAAAEEEEAgOQQIACbHe2AUySpQ1bysv009xsgetfPmLOFHqu35F/5s1cEslQS7h6qy/T4Vdc0PHfMFAQQQQAABBBBAAAEEEEAAAQSSR4AkIMnzLhhJMgrkFiR0VPdeMF7nl/ZS1l8eUFgS3YTeoz06mxsY7cwA7By61cRRvUkAEtLgCwIIIIAAAggggAACCCCAAALJJUAAMLneB6NJNoGufZxsF87S1gQsA7ZZc4u25+org3MUrHgr2Z60WeN5vGZCRPvJYwdEHHOAAAIIIIAAAggggAACCCCAAALJI8AS4OR5F4wkGQXyu0ojzkzIyGzW3EVPrdHaD1fLl4CAYkIG1YJOVgX6a0FgROjK4UVdNHZwj9AxXxBAAAEEEEAAAQQQQAABBBBAILkECAAm1/tgNMkocNxFCRmVzZrbW1Wj+/7xfkL664hOyoN5uqb6UufWtXsZds7N0p2TjpLPVz8ncEeMjnsigAACCCCAAAIIIIAAAggggEA0AQKA0VSoQyBcYOB4qVfdjLfwU/F+D581t2JHYrIKe/d+ev8J3tc2/bTg39TqaVoeHOTex4J/s84bpiNyPpU2L5E+WyVV7GnTMdA5AggggAACCCCAAAIIIIAAAgg0X4A9AJtvxhWZJmCz286ZIf3hdKm6+VmB68+a2xrsoRr5laVAqyVtX8E/1XxN/539eqv7aqwDC2DazL/a4F9Qk3pu1P8cMk/d/vxy5P6Itl+iLZm2WZMWOGVmYGOsnEMAAQQQQAABBBBAAAEEEECgXQQIALYLMzdJeYE+R2vvuY8q+NR3VeCrjPtx6s+aswste+7cwHE63f923P3Eamj7Cq4P9tH+oF/ZvtYHFOvfZ0ugh6bt/6G751+WE8y79LBS/XjPneq8e420oX5r59j2NlzxfO2PzZq0wKljR0EAAQQQQAABBBBAAAEEEEAAgY4TYAlwx9lz5xQT+KTHWE2qukmrA/3iGrnNmrP28wJHhtoXaq+G+jbrrf2tW1LsdWj7CtYGFEd7VQn9rA3+jdQJw3pp6fdy9dOt02qDf/HcZdvK2lmT616NpzVtEEAAAQQQQAABBBBAAAEEEECgjQSYAdhGsHSbfgLlTgIPWwI7seoOjfWv1JSslzXRvzhi5p0tybVZeRaYq82Ua8kxgir2r3Da/1On1mvfGqXKYLZKg/luF3/2TdQZWtia7hpcG75v4bVHVKjzMxc0fwm0LZl+arL0gznMBGwgTAUCCCCAAAIIIIAAAggggAAC7SNAALB9nLlLGggUOEkvaovPCe6NdH9sRl+Rb6cKtc+ZiddJJcHu7ow873FH+TZoes4DGu7f7FUl7DPPt1+zc3+hXxb8j757/kXS83+RbNZdAkr4voXDDynUkYt+0vzgnzcOCwI+d5l02VvsCeiZ8IkAAggggAACCCCAAAIIIIBAOwqwBLgdsblVagv07pavLL/N6Ksrtvx2XbCv3g8OdT/t2Cvj/EudAN0tbRL88+5h+xHeVn2HjvBvrN1vL6fAO9Xiz/B9Cy3T74zxe+VrbWDxsxXSxnktHhMXIoAAAggggAACCCCAAAIIIIBAywUIALbcjiszTKBLfo4mjiqK66lt5t/MnOnNShgSV8dRGvm8GXaHHqWl43+nvcG8KK3iqwrft9CCfzOnHKtBG56M7+KmWi2a1VQLziOAAAIIIIAAAggggAACCCCAQBsIsAS4DVDpMn0FJo8doDlLtzbxgEF32W9zsgU30WHTp50Zdp++dKe2LHhZI1TVdPuwFtVOBmHLShy+b+Hwoi66c9JROqKnM+PxiRfCWrfi68q/SxV7pPyureiESxFAAAEEEEAAAQQQQAABBBBAoLkCBACbK0b7jBYoHtxTw4oKtaakLKaDJfxoiz3/Yt7wwIlD3/6FDo1coRz1kv1OwG9e4Aj9s+ZYLQiOcPYt7BHat/D0I3trytiBGju4h3w+p7PPVjk5TGqi9tPsSuun9FMCgM2G4wIEEEAAAQQQQAABBBBAAAEEWifAEuDW+XF1hglYUGz6pKNly2NjlclOtt9kLpXK1qyaM/SnwARn38J+oeCfjdmCf8VDetYG/6yiykngkchSGTtwmsjb0BcCCCCAAAIIIIAAAggggAACCNQJEACss+AbAnEJHNG3m7s3XrQgoGUFnuhfHFc/HdWowFfl7k9o+xTWL48v+CiyKrf1SUXCO1y7Kxh+yHcEEEAAAQQQQAABBBBAAAEEEGgHAQKA7YDMLdJPYPxhvTR7arG7HDj86Xr7dijbFwivSsrvtj/hnTkznLFFBuReWr5VpRXVdWPu2kfyxZ7tWNew6W/VwSydP/tjvbF2W9ONaYEAAggggAACCCCAAAIIIIAAAgkTIACYMEo6yiQBC5LlZft1xze/qN9+64v60hcOch+/QBUpw3C4f5PG+ldGjLcmENT6bWHLdC1hx4gzI9q09GBuYLS2VeVp6mNLtOyT3S3thusQQAABBBBAAAEEEEAAAQQQQKCZAiQBaSYYzTNXIBgMav76z/XY/I/08ooSWbCsfilXfv2qpD62/QoXBEZGjPGc+9/SaUf01uQvD3DmBwa1paSPvh3RomUH7wQOcy/cW1Wja2a/r5euGl+312DLuuQqBBBAAAEEEEAAAQQQQAABBBCIQ4AAYBxINEHAZqxNm/1eo9l/TWmrk1HXsuymwjJgG+9p/kWyfQvL1NkO3WJxzTlLt7o/VvG7HGdPwwSsAv6Sf41+X3O6e4/VJaVasH6Hm3DEreAfCCCAAAIIIIAAAggggAACCCDQZgIsAW4zWjpOFwHbs27SzPlNBv/seS2QZktdU6VYoLLItzPmcGuTmiyJeb45J05zkqNYf15pkHDEO8EnAggggAACCCCAAAIIIIAAAggkVIAAYEI56SzdBGzmn+1ZZ8tW4y2P10yIt2lStCvUvpjjSGRSk/rBxgYJR2KOghMIIIAAAggggAACCCCAAAIIINAaAQKArdHj2rQWsD3/bNlvc4J/BjLf2VNvU6BXytiUqVPMsR6sXTHPteREeLDR9lCct3Z7S7rhGgQQQAABBBBAAAEEEEAAAQQQaIYAewA2A4ummSVgCT/WlIRlxI378X3aGDxE/bUt7is6qmF1MEslwe71bh9UsX+FpjgJQiY6y3YTWeoHG696+j0V5mdr/GGpEzBNpAd9IYAAAggggAACCCCAAAIIINAeAgQA20OZe6SkQEv3qLN97or9K1PimW2/wvAEIKN8GzQ95wEN929O+PijBRsr9wfcJdazpxbriL7dEn5POkQAAQQQQAABBBBAAAEEEEAAAYklwPwpQCCKQGlFteYuL4lypumqRO6bF/Nuoy+Keao5J8L3KxznX6rZube0SfDPxlQ/2OiN05ZYXzP7fdmSawoCCCCAAAIIIIAAAggggAACCCRegABg4k3pMQ0Etu6ukO1RF2+xWX9DfZt1lO9DHe77ON7LWtbukJHS6b9RZffhLbv+wFWrAv21IDDCPbKZfzNzpqvAV9mqPhu7ODzYWL/d6pJSLVi/o341xwgggAACCCCAAAIIIIAAAgggkAABlgAnAJEu0k+gPK6sv3V75Z3q7JVnWW7bvOQUSGc/4Mzd9WvjCXeq33PfbFHQrjyYp2uqL3WG63N+gu6y37YM/oUHG2MZ2ZLr4iE9Y52mHgEEEEAAAQQQQAABBBBAAAEEWihAALCFcFyW3gIFuVmNPmBb7pUX88YW/DvvcanP0W4Tv/M5tXpas2fuWfDPrlseHOT2Ywk/2mLPP+85IoONXm3Dz5eWb5Utve6Sn9PwJDUIIIAAAggggAACCCCAAAIIINBiAZYAt5iOC9NZoHe3fGX5bXZcw9LWe+U1vKNTY8t+fzBHGnJK6HRR1zy9FTxSk6pu0upAv1B9Y19sJp61nxc4MtRsspPtt61K/WBjY/exJdcleyoaa8I5BBBAAAEEEEAAAQQQQAABBBBogQAzAFuAxiXpL2Cz0CaOKtKcpVsjHrY99sqLuOGAcdJJP5UGOp++uoDksk92a9rs92TbFC7XIE2sukNjnczDU7Je1sR6y5Et+64l4LA9+Gr3/Kvrx/YutPZtUSynx7uBw1Tj5hqy/RTr7hvrfmWVNbFOUY8AAggggAACCCCAAAIIIIAAAi0UIADYQjguS3+ByWMH1AsAtv1eeRGqBztJPr7/QkTgz86/sXabpj62RJY9t674nODeSPfHgnpFvp0q1D6VqZNKgt2dz851TcO+tWXGYotXjsta5v7YDMVp1ZeFlh2HDSHia2Fe40uvIxpzgAACCCCAAAIIIIAAAggggAACcQkQAIyLiUaZKFA8uKeGFRVqTUmZ+/htvVdehLHt93fug7XBv4o90p4tUlW51u4KODP/NjnBv7yI5uEHFuwrCzYM+Flg0AJ+BapQufK1NdjD/R5+bVt9tz0GZ+fe4u49GL78OPx+2c6S66Ku+eFVfEcAAQQQQAABBBBAAAEEEEAAgQQIpPUegO+8845uu+02ff3rX1f//v2Vl5enwsJCDRs2TN///vf1xhtvNIvwpZde0rnnnqt+/fq5fdmnHVt9vGXv3r36zW9+ozFjxqhHjx7ueEaMGKFrr71WH3/8cbzdaPny5br00ks1dOhQderUSb169dIJJ5ygmTNnav/+/XH389RTT2nixIk69NBDlZ+fr4EDB2rKlClasGBB3H2ka0OfM4Vt+qSj1flAQpC23CsvwtCCf//9mFTpBP6eniLdMVC6/8vSrFN02F++pvm+C/W7nLtV7F/uXGZLaxsrlql4ue532r+Xd4n+lXe9ns+7yf2045uy/9jYxQk9Z1mGZ+ZMly2jjlZOPrxXRAIQSwiytqRU723a5X7aMQUBBBBAAAEEEEAAAQQQQAABBJov4As6pfmXJf8VJ554ol5//fUmB2rBrlmzZik3NzdmWyOyYNuDDzozsmKUSy65RDNmzHC2aYu9z9m6det0xhlnaPXq1VF76datm5544gmdfvrpUc97lQ8//LAuv/xyVVZWelURn2PHjtULL7ygnj17RtSHH1RUVOjb3/622y683vvu9/t1880368Ybb/SqEvq5efNmNyhrnW7atMkNqib0BgnszJbcTnvsDTfwlu0LJLDnKF1Zso9xVzvrfKdL21ZGaRBZ1djS2g7JVBw5vKhHlojktKrbnXOR/67YvzqnOfsufmlAdy35aJf+uaJElhjEK5aUxfZltKXZNjuzsX/XvGv4RAABBBBAAAEEEEAAAQQyXSCVfv/O9HfVls+ftjMAP/nkE9etT58+uvLKK/WXv/xFCxcu1Pz58zV9+nT17dvXPf/YY4+5swEbQ77hhhtCwb9jjjlGTz75pNuXfdqxFQsONhYsKysr05lnnhkK/l188cV65ZVX9NZbb+nWW291ZwLu3r3bDcp98MEHMYczd+5cWbDRgn9FRUW699579fbbb+sf//iHOxvRLrTZezYzMRCIHay68MILQ8G/k08+Wc8995z7TBZcHDJkiHvtTTfd5AZHYw4mQ06MP6yXnpjUX20V/LMkHS/UfFnLJzwhnXqr9Per4gr+Gb+3tNYyE4eXDslUHD6ARr4f7t/kJiyp38T+U8Q/lpXo1hdX6aVlWyOCf9bWgoGWlOX8h97Wyb/9t95e/3n9LjhGAAEEEEAAAQQQQAABBBBAAIEoAmk7A9CCbd/73vf0zW9+U1lZDRMLbN++Xccff7zWrFnjsthswfHjxzcg+vDDD2VLdG1Z7ejRo91Zhbbk1iu2pNdmGy5evFjZ2dlatWqVG0DzznufNpvu5z//uXv461//Wtddd513yv20wKQt4bX7WEDu1VdfjThvB3bOxmJj6tq1q2yJswXrwovNDLz//vvdqkcffdQ1CD9v3//zn//opJNOcqvPOussPfvssxFGZnPssce6S5K7d++u9evX66CDDnLbJ+ofKfdfIDYvcZfgJur5f1j1I30S7BWRpOPSw0r1063TpOryZt9mXzBXV1dfqnmBL2qAr8Tdb8+W3CZrsYDnFdVXtnp4xw3srqsnDGNGYKsl6QABBBBAAAEEEEAAAQTSVSDlfv9O1xfRwc+VtjMAbQnspEmTIgJb4dYHH3yw7rzzzlCVzRCMVu66667Qnnr33Xefu99eeLvOnTvL6q1YgO7uu+8OP+1+r66u1j333ON+twDeNddc06BNcXGxbFaelddee01LljgBp3rFAnUW/LPys5/9rEHwz+ptf0EL2nnf3S/1/mEBSCsWGLVgYf0AqdnccccdbpudO3fKZgVmfMl19uVLYFkd/ILeDw7VumDfAxl6gzr3o1+0KPhnw+rkq9KM3Hudff4u0lO5v1QyB/9svKf5FzlZivfa11aVRRt3ujMCJ979upZ9srtVfXExAggggAACCCCAAAIIIIAAAukqkLYBwHhemDcLztra/nz1i+399/zzz7vVhx9+uGxvvWjF6ocPH+6esqW09bdV/Pe//61du3a55y+44ALZ/nrRyve///1Q9TPPPBP67n2xvr0S3tars08LSFrg08qyZcu0du1a97v3D1uKbEuPrUyYMCHm3nu2hNhmGVqJNhb3RCb9o2sfZ8u6rIQ8sS35LQnWBmm9Di3D8DAnU25rS7azj14X377WdtPm19ty6iLfzoTdxzI1T5o5X7ZfIwUBBBBAAAEEEEAAAQQQQAABBCIFokeiItuk7VFVVVXo2aIF5TZs2CBvL0Fb5ttY8c7b1NqNGzdGNA3PNuy1i2hw4MCWGBcU1M40mzdvXoMmXj8WbOzdu3eD815F+D3q92P7IHrJQ8Lbedd6n5YUxQt42jU2izGjS74TDB1xZkII5gZGH5j1V9ddu2UYrrtlh38rVGSg0mYEDvVt1lG+D93P5s4Q3FtVo6mPLWEmYIe/WQaAAAIIIIAAAggggAACCCCQbAIZHQC0vfC8YjP86peVK+uysEY7H94+/Hz4ddYm/Di8Xfj19t32EPT29Au/xs7ZzD0LLlpprI/65+v3E34cbz+2tLn+TEJ3IJn2j77HJuSJH6+ZENGPBbom+hdH1GXCQZlsL82giv3LdX/O3c7y5Uv0r7zr9XzeTe6nHf/Oqbfz1i6eYkHAa2a/32AWbjzX0gYBBBBAAAEEEEAAAQQQQACBdBXITtcHa+q5LEPu7bffHmrmLZsNVThfNm3aFDrs169f6Hu0L/379w9Vh19nld6xze5rKpmG9WNZgLdt2+bO1MvLy3P7teCft7Q4EWOxTpvbz8iRI92xxPMPL1gZq+2nn34a61Ry1m95z9mcse7PS0sHGQj6VBrMj7i8t29Hm2UYjrhREh3YMuge2qP7c+9xMxlHG5otEz4ja6H7szrQT9OqL9Py4KBoTSPqVpeUasH6HSoe0jOingMEEEAAAQQQQAABBBBAAAEEMlUgYwOAltzDlrZaOeecc9wMv/X/EJSWloaqCgsLQ9+jffGW7to5m60XXrx+murDrqnfjxcA9PqwNk31U78Pu8YrierH6y/WZ3hANFablKl39oLUs5c6WV5an7TC7wvqzpyZOq3KgonOhn1OKVCF+5lJ/1gSOEyP5P467mQlw539EWfn3qKp1dOcTMdHNkn1+IKPCAA2qUQDBBBAAAEEEEAAAQQQQACBTBHIyCXAtvT3pz/9qfuODznkED3wwANR33dFRV1gxvbEa6x4gTprs29f5N5mXj9N9WHXxurH68PaNNVPrD7s2kT1Y31lTNn4hrStbjl4a5/7cP8mjfXX9VeuyBmBre0/Fa4/2r8u7uCf9zyW2XhmznSN8m3wqmJ+vrR8q0orMnzfypg6nEAAAQQQQAABBBBAAAEEEMg0gYybAbh8+XJ3xp/ta2eBstmzZ6uoqCjqe8/PrwvMhCcMidbYS6xh5zp1sr3N6orXT1N92BWx+vH6sDZN9ROrD7s2Uf1YX40Vb9lzrDa2BHjMmDGxTidX/aJZCR+PJf1YEKhdUr012EM18itLgYTfJxk7rAxmK9/XsuCcBQHvzJkRMYMy2jPWBIIq2VOhLvk50U5ThwACCCCAAAIIIIAAAggggEBGCWRUANCy+p566qnauXOnsrKy9OSTT6qxTLhdunQJ/WGov6w3dOLAl/Ly8lBV/SW6Xj9N9WEdxOrH68PaNNVPrD7s2kT1Y301VpraX7Cxa5PqXMUeJ4vLCwkf0mn+RbLkH2XqrEBuF5UNOk3dNsxJ+H2SrcN9wRx1amHwz3sWbwalF0D16ut/llXW1K/iGAEEEEAAAQQQQAABBBBAAIGMFMiYJcBbtmzR1772Ndmnz+fT73//e3cmYGNvPTyI1VRSi/AZb/X3v/P6scDcrl27GrtlKGFIr169IpYDe33YxYkYS2v7afQh0unkni1OAtrEB5IswUWRb6c652Zp5pRj1e2Ey9JJLeqzlAfz9EFwSNRzza20GZRNlcK8rKaacB4BBBBAAAEEEEAAAQQQQACBjBDIiADg9u3bNWHCBK1fv959qffdd5++973vNfmCw7Perlq1qtH24edHjBgR0TbefmxZ8rp169xr6/dhswq9wGL4vSJudOAg/Hz9fuIdi3Xl9ZOdna2hQ4dGu1X611XVzexM9MOO6uHX7KnFGn9YL2ngeKlX5J+bRN+vI/tbFeivC6qu17G+NQkZhjeDMlZn2X6firrWLeGP1Y56BBBAAAEEEEAAAQQQQAABBDJBIO0DgLt379bEiRO1YsUK933efvvtuvzyy+N6t4MGDVKfPn3ctpY4pLHy+uuvu6f79u2rgQMHRjQdN25c6LixfhYvXhxaAnz88ceHrvG+eP2sXr1aW7du9aobfIbfo34/xx13XCiJSHi7+p3YPoMLFixwq8Ovqd8u7Y9zC9rsEe+5YJyO6Nuttn9nVqrOuLPN7tWWHVc4y3rfqBml/cHIv07seJ5T/7PAZfpW1U3apS6ymY+JKN4Mylh9TRzVm/3/YuFQjwACCCCAAAIIIIAAAgggkHECkb+xp9nj7927V2eccYbeeecd98n+53/+Rz/5yU/ifkpbKvyNb3zDbW+z4byAWP0OrN6bLWft7brwctJJJ6lbt9pAz6OPPqpgMBh+OvT9kUceCX0/55xzQt+9L2effbb3VeFtQ5XOF3tmS2xixWb7DRs2zP3u/cP2APzqV7/qHv7rX/+KuZz4mWee0Z49zv53Tok2FvdEJvyjqxMA9rXBUlJ/lnzWd3jp3DP8KCW+27Lei6qv1ZTq/9HRlTN1TdVUvVkz0gkG+txg37is5fqV/wG9lzdVN2c/ktBnKtS+mP1NHjsg5jlOIIAAAggggAACCCCAAAIIIJBpAmkbALQZbBa4evPNN913euWVV+qXv/xls9/vVVddJVsCa+VHP/qR9u2LDDrYsdVbsXbWvn7Jzc3Vj3/8Y7d65cqV+u1vf1u/iebPn6+HH37YrbfEJDbrrn6x5xkypHYPtV/96leh5cLh7a677jo3yYnV2fdo5dprr3WrbcmxzYasqYnc486WTHuB0oMOOkgXXXRRtG4yoy6/qzTizMQ/6wBnVqj1HV7acLlx+G0S9d2W9U5yZvbNCxypUb4N+mvuz3Vn7kwdn7XCCf5FBrltxt44pz6RpUyR2ba9vocXddHYwT28Qz4RQAABBBBAAAEEEEAAAQQQyHiB2shWGjJ85zvf0csvv+w+2SmnnKILL7xQy5Yti/mkFqSrP1vOGludBcxs6bAt0bUltRYcs0Cc7dd3xx136N1333X7tYDbYYcdFvUedu7pp5/WmjVrdP311+vDDz/Ueeedp06dOum1117TbbfdJgvI2fHdd98dtY+cnBzde++9Ouuss9zZeTaWG264QWPGjHGDfg899JD++te/utfacuEpU6ZE7cc87N5PPfWU/va3v7n7I1rg0pY7L126VLfeeqs+/vhj91p77u7du0ftJ2MqB50krXg+sY971HkN+2vD5cYNb9bymndqhuq+mrP1WuAYpxOfxvmXambOdBX4KlveaTOvrA5mqSTY8M+lJVW5c9JRDWbhNrN7miOAAAIIIIAAAggggAACCCCQVgI+Zzlq5FSdNHm8+stwm3qsAQMGaOPGjVGbBQIBXXzxxW7m4KgNnEoLMD744IPy+2NPqrSg3+mnn661a9dG7aZr167605/+pDPPbHzGmQX6rrjiCtksx2jFAoIvvviiDj744Gin3Tqbufitb31Lc+bMidrGnuPGG2/UzTffHPV8aystk7GX1MQyKIdnOW5t3wm9ft2r0p+cYF0gwcGtnzgB1k4H9v/zBlzhLLm+Y2CbZB32bpGoT9vf783AKC0LDNT/y35JnXzVieo6rn5eqPmyflp9sXr7dqhAFSpXvnbn9NL0KeNrk6rE1QuNEEAAAQQQQAABBBBAAIH0F0iZ37/T/1V06BMSADzA31gA0HtDFiyzIN+iRYtky2QtwGZLdadOnaqvf/3rXrNGP8vLy/W73/1Of/7zn91ZgBbEs0CYBQZtmbKNI55isxltNuArr7yiLVu2qKCgQJbx97vf/a67ZNdbttxUX0888YS7n+D777+vXbt2qaioSOPHj3cDjMXFxU1d3uLzKfEX0Jb3pD+cLlUnOBOwzSi8IMaMwtlOdupEzzZs8VtK3gsX1Byu0f41EUlFgs5ejT5brn2cs2TdsirX24szeZ+GkSGAAAIIIIAAAggggAACbSeQEr9/t93j0/MBgbQNAPKGk1sg6f8Csomx9zsB0G0rEw/5jfulXodLtuTXEoGE7wW44XXp0bMSf89M6/GggdKEn0tDTon0zTQHnhcBBBBAAAEEEEAAAQQyXiDpf//O+DfUPgBpuwdg+/Bxl7QV2PhG2wT/DOz5H9axWYbh8FlrNnMtt4tUVVrXhm/NF9i1UfrzBc4sQGdJ/ggnoBrvrEBbhr1ni+PvzPqMFqBt/ki4AgEEEEAAAQQQ+dSd+AAAQABJREFUQAABBBBAAIEOFyAA2OGvgAEkpcCiWe0zrKCTgdmW/NpPrxHSGXc6S473ts+9M+EuwUCdb89h0jcfkvocHfnkNtvTAr4LnXOrXozcg7F+gJZlxZF2HCGAAAIIIIAAAggggAACCKSEgDM9hoIAAhECNgts5QsRVe1yYMuNHzs7MgDVLjfOkJt8vkZ6yFkS/OY9jrET9LNi+zzaUm9bdr3ybw3tvQCtnbd21p6CAAIIIIAAAggggAACCCCAQIoJEABMsRfGcNtBwJaAWuCnI0pN9MzOHTGUtLynvdd/3iTde0ztjD9L8hLvPo/WztpbZmgKAggggAACCCCAAAIIIIAAAikkQAAwhV4WQ20nAdv/jZLeAjs3SHOubX6GZ8sI/dRkZgKm958Ong4BBBBAAAEEEEAAAQQQSDsBAoBp90p5oFYLWPIHCgKxBCwI+NxldcuIY7WjHgEEEEAAAQQQQAABBBBAAIEkESAAmCQvgmEkkUDXPk72WCc7LwWBWAKfrXASh8yLdZZ6BBBAAAEEEEAAAQQQQAABBJJKgABgUr0OBpMUAvldpRFnJsVQGEQSC7RXpugkJmBoCCCAAAIIIIAAAggggAACqSFAADA13hOjbG+B4y5q7ztyv1QTWPl3yTJGUxBAAAEEEEAAAQQQQAABBBBIcgECgEn+ghheBwkMHC/1GtFBN+e2KSHgZBQu374pJYbKIBFAAAEEEEAAAQQQQAABBDJbgABgZr9/nj6WgM8nnTNDyiEhSCwi6qXJD7yiH/5pid5at13BYBASBBBAAAEEEEAAAQQQQAABBJJSgABgUr4WBpUUAn2Ols57nCBgUryM5BzEnkC+5izdqvMfelsT735dyz7ZnZwDZVQIIIAAAggggAACCCCAAAIZLUAAMKNfPw/fpMCQU6QfzJG69W+yKQ0yS6A6mKWSYPfQQ68pKdOkmfP1xtptoTq+IIAAAggggAACCCCAAAIIIJAMAgQAk+EtMIbkFyj/PPnHyAjbVWBu4FiVqXPEPfdW1WjqY0uYCRihwgECCCCAAAIIIIAAAggggEBHCxAA7Og3wP2TW8D2dXv2Umn/3uQeJ6Nrd4F3AsOi3tOCgNfMfp89AaPqUIkAAggggAACCCCAAAIIINARAgQAO0Kde6aOwMY3pG0rU2e8jLTdBE72vxfzXqtLSrVg/Y6Y5zmBAAIIIIAAAggggAACCCCAQHsKEABsT23ulXoCi2al3pgZcbsIjPMvU6HKY97r8QUfxTzHCQQQQAABBBBAAAEEEEAAAQTaU4AAYHtqc6/UEqjYI618IbXGzGjbTcDnk071L4l5v5eWb1VpRXXM85xAAAEEEEAAAQQQQAABBBBAoL0ECAC2lzT3ST2BPVukYE3qjZsRt5vAuVnOEvEYpSYQVMmeihhnqUYAAQQQQAABBBBAAAEEEECg/QSy2+9W3AmBFBOoir28M8WehOG2kcDx/uUa7VupxcERUe9QVtlEANlmmVqg2f6s5RZIXftI+V0b9GUzCbfurlC5k2CkIDdLvbvlq0t+ToN2VCCAAAIIIIAAAggggAACCCAQTYAAYDQV6hAwAQvIUBBoRMCWAf8x93ZdUn2t5gWObNCyMC+rQZ2THliy5DILH5JWvRg5y9TntB9xpnTcRQoOGKf5G3bosfkf6eUVJbIZhV7J8vs0cVSRJo8doOLBPeWzgVAQQAABBBBAAAEEEEAAAQQQiCFAADAGDNUIuLOxLCDDMmD+MDQi0NlXrZk50zWp6iYtDw4KtXRidCqr2O/uAxiarbfxTelvV0g71ofaRXyxP2srnnd/Nvi+oFsrpkb06bW1YOCcpVvdn2FFhZo+6Wgd0bebd5pPBBBAAAEEEEAAAQQQQAABBCIE2AMwgoMDBMIEbCmmzcaiINCEQIGvUnfmzHBa1c3Sswl7Z9//lo6+5WXd/dAs7fndVxV85PTYwb969xgc/Fizc2/ROP/SemciD9eUlGnSzPl6Y+22yBMcIYAAAggggAACCCCAAAIIIHBAgAAgfxQQaEzAWYpJQSAegcP9mzTWvzKi6SjfBs3Jvl5XfXKNum5brOYu1LXAos0utH4aK3udvQGnPrZEyz7Z3VgzziGAAAIIIIAAAggggAACCGSoAAHADH3xPHacAgPHS72iJ3iIsweaZZDA5Kx/hp7WZu7ZDL7h/s2hupZ8iTa70Pop1F4N9W3WUb4P3U9/VammPf2es8Vg3SzEltyPaxBAAAEEEEAAAQQQQAABBNJPgD0A0++d8kSJFLDkCuc4Szv/4CzdrCYrcCJp07Gv0/yL3MDcAF+JO3PPgneJKN7swgWBESr2r9AUJ9B4qn+xsn2BUPf7g37N3Tlar/xjqr729W9KlaVxZRgOdcAXBBBAAAEEEEAAAQQQQACBtBUgAJi2r5YHS5hAn6Ol8x5X4Mnvyr9/b8K6paP0E7CAXJFvh6bnPKBEBf88pR9mPaefZz8Sc0ah3fuMrIVOduGF2v/Olcp2/6zWBQgVlmFYNrOVzMEeLZ8IIIAAAggggAACCCCAQNoLsAQ47V8xD5gQgSGnaO0Zs7U60C8h3dFJ+goU+1bEDNK15qnH+5fF3W/2/jLnVmHBP7uxl2H40bOk+4ulLe+1ZjhciwACCCCAAAIIIIAAAgggkEICBABT6GUx1I4V8DszASdW3aHzqm7QizVjZEsuw0u1c2yZXymZLTAh6502AUjohL1tTrISW9a+7tU2GSudIoAAAggggAACCCCAAAIIJJcAS4CT630wmiQW6N0tX1l+vxYERro/loShyLfT2fNtn8rUSQXO59/ybkriJ2BobS1g+TfG+T9o69skpn/b0/KpydIP5ki2zJ2CAAIIIIAAAggggAACCCCQtgKRU5jS9jF5MARaL9AlP0cTRxVFdORT7ZQ/++ysiohzHGSegM3Sy3J+UqY4QcDy2RcrGKi3XDhlHoCBIoAAAggggAACCCCAAAIIxCPADMB4lGiDwAGByV/+gnYufyVGFtZUivzwShGoFSjYtUbX3nm/vn/+FB3RtxssCCCAAAIIIIAAAggggAACaShAADANXyqP1EYCTtKE4rmX6iu5zv5pUUq2jw0Ao7BQlQICJ+35mybNHKqZU47V+MN6NX/EFXukPVukKmdZcW6B1LWPlN+1+f1wBQIIIIAAAggggAACCCCAQJsIEABsE1Y6TTsBS5bg7Jfms33TKAikmcBp/kX6aWWppj62RLOnFtfNBGwssGcbHm58Q1r4kLTqxdosw56LL0sacaZ03EXSwPFSQjOYeDfhEwEEEEAAAQQQQAABBBBAIF4BAoDxStEucwWcmX9usgSCf5n7ZyDNnzzbF3AT2qyr6qxrnn5PL53txOwWzYod2Bt8krRgprR9VXSZYI204vnan14jpHNmkGgkuhS1CCCAAAIIIIAAAggggEC7CBAAbBdmbpKyAjbL6dlLJYJ/KfsKGXh8ApbNepRvg6bvekC+P26OflF4YC96i4a125wl8384XTrvcWnIKQ3PU4MAAggggAACCCCAAAIIINDmAmQBbnNibpDSArbE0QIYFATSXGC4b5Nm596i4f4Ywb/WPL8F0J0l9LLZtBQEEEAAAQQQQAABBBBAAIF2FyAA2O7k3DClBGwZJAWBNBeoDvp1c84fVeCrbLsntSDgc5c5ewWSLKftkOkZAQQQQAABBBBAAAEEEIguQAAwugu1CEiWAGHlC0ggkPYC+5Snzm0Z/PMEP1vhJA6Z5x3xiQACCCCAAAIIIIAAAggg0E4CBADbCZrbpKDAni2RmU1T8BEYMgLxCHT17YunWWLaMKs2MY70ggACCCCAAAIIIIAAAgg0QyC7GW1pikBmCVQ5SxYpCCCQWIGVf6+dXZvftdn9llZUa+vuCpVX1aggN0u9u+WrS35ObT82Y9eC9vbvbW6B1LWP1IJ7NHtQXIAAAggggAACCCCAAAIIpIAAAcAUeEkMsYMELIhAQQCBxApYJuG1/5KKRkYG6WIE8ILOnoHz13+ux+Z/pLnLtyoQtoVgV1+5ftp3mc4K/luFO5bKFwzUjdWXJY04UzruImngeMnnqzsX4151DfiGAAIIIIAAAggggAACCKSXAAHA9HqfPE0iBZwZREEniOCzgAUFAQQSJ/DXH9T25XN2oTj0KCkrV9q0yKmLDODtHjhRvygp1l92DHbOeQG8oIr9y/WzrCd0pH+jfNtjDMv+vV3xfO1PrxHSOQ9IlaXSwoekVS9GLu9vLFgYo3uqEUAAAQQQQAABBBBAAIFUEvA5syvC5lOk0tAZayoLbN68Wf3793cfYdOmTerXr1/SPc6yT3brkwe/rYm+t5NubAwIgUwSWB3op2nVTgZhp/wu5x4N9H/Wgse3AGIc/3PnBgtnSH2ObsE9uAQBBBBAAAEEEEAAgeQTSIXfv5NPLf1GRBKQ9HunPFECBCwuPm32e3qk+msJ6I0uwgX2BDuFH/IdgSYFhvs36y+5N+vZ3JtaGPyzW8QR/LNm21ZKfzhdWveqHVEQQAABBBBAAAEEEEAAgbQQIACYFq+Rh0i0gO05tqakTPMDI7UhUJTo7jO6v0Lt0+3Vk7QkMDSjHXj45gl08lUr19dOy/GryxV44jx9vOwtle7eIW1eLC39a+2Pfbc9BCkIIIAAAggggAACCCCAQAoJsAdgCr0shtp+Ao8v+OjAzXy6d/85uivXWRJISYiA31mJ+dOc2foocEhC+qMTBNpCwF9Tqb5/Pl1+mznobT8YfqMBX5FO/Kk06ITIBCPhbfiOAAIIIIAAAggggAACCCSJAAHAJHkRDCN5BEorqp1soyWhAS0NWgICSqIFBrRoH7dEj4L+EIgtkOVrZNnwR29Jf/wvVRR+QZ+dNlM5/Y5RWeV+lVfVqCA3S7275atLfk7szjmDAAIIIIAAAggggAACCLSjAAHAdsTmVqkhsHV3hWoCdb/4bw12V03Qp0aDAanxaIwSAQQSLJBf9rF6//lM/b/q6zQv8MVQ71nOVNeJo4o0eewAFQ/uKZ/PJ/uPC59t26aqnZtVoAr17NFDgS59tLUih8BhSI4vCCCAAAIIIIAAAggg0BYCBADbQpU+U1rAZvB4ZZRvg6bnPEDwzwPhEwEEGgjY3oR/zLldV1X/UH8LjHPP239EmLN0q/vT76A8nVb4oY797K+a4FusbF8g1EdN0K/1gWP0SM1p7p6jWX5/g8BhqDFfEEAAAQQQQAABBBBAAIEWChAAbCEcl6WvgC3fszLOv1Qzc6arwFeZvg/LkyGAQEIEbG/Lu3Pu147qrhEzAd3/iLD3AQ2v2CxnQ8EGJcsJBk7MWuL+bA701C/3f1efLDtYNy1brMJeX9Bt5xypkQVlUlW5lFsgde0j5Xd1ZxPabGWWHDcgpQIBBBBAAAEEEEAAAQSiCBAAjIJCVWYL2N5dX8zaqJnZBP8y+08CT49A8wQsCPhQzm/1raqfa3lwULP/I0I//+eakXtv6KbB3VLwEecwLAlJ0Jeldzofr+m7TtCbNSNCJ+svObb9CAkQhij5ggACCCCAAAIIIIBAxgv4gk7JeAUA2l1g8+bN6t+/v3vfTZs2qV+/fu0+hpg3dP6V2PKro9WnamPMJpxAAAEEYgnsCXbSr/Z/Rzdk/6lNZxCvDvTTtOrL3GBj+FgK87K1t2q/wrYyVf0Aoe1JGCoVe6Q9W1RetlvbK7O1O/cQdSo8iEQmISC+IIAAAggggAACqS2Q1L9/pzZtSo2eAGBKva70GWxS/wW04XXp0bPSB5snQQCBtBXYG8zRldU/0j8Do+N+xgE9O+s33zxSR1R/oJq3H1LBhrnyB+v2Pt3v7Es41+nvicCp6jbiZF1wbE+N6VkhX9XeiGXIcd+QhggggAACCCCAAAIdKpDUv393qExm3ZwlwJn1vnnaeAQWzYqnFW0QQACBDhfo7KvWg85epW8GjtDvar7hJhKJWDMcZYSFO5ar2yNXqLPf2ZcwSrEkJWdkLXR/ytf+Rvlrq5wsxmGLBZxlyNXDzlDJ8O9q+8FfVoEz49C2TuiSnxPZ24GZhe7+hfKud2Yehu1lGHkBRwgggAACCCCAAAIIINBWAgQA20qWflNTwH5hXflCao6dUSOAQEYK2GrecVnL3J8NgSJdX32JFgVtf8C6Uqi9Guzbogn+Jbo4+wXlO5mL4ylRkyA5swVzVv9N/ZyfcmcZ8lXOMuSVGqTjhx6sCSMO0cl5q9V7zePKWTPH2cQw1n2cjCgjnZnWx10kDRzvxCzDliTHMzDaIIAAAggggAACCCCAQLMECAA2i4vGaS/g7IMV+xfWtH96HhABBFJcYJC/RLNzf6GFgeG6u+abztMEdWXWsxrjXyVLUpLoMtyZRTg79xZNrZ6mHR8W6ssfPaD+MWYWRt47IK143v2pOfhwfXLSXdrVqb+6VX2mg/P2q6CwWyjjceR1jR+VVlST/KRxIs4igAACCCCAAAIIZKgAAcAMffE8dgyBqvIYJ6hGAAEEUkPAJtN9OWu1nsy6rV0GbLMEZznZj22RbydnSXJzS9b2Verz5zPU1+khK2ypsWU81ogz5XNmCZb2HquteypVXlWjgtysiCXHlsts/vrP9dj8j/TyihLVhGU/aTT5SfhAw5crs0Q5XIbvCCCAAAIIIIAAAmkiQAAwTV4kj5EgAfvFj4IAAggg0CyB/BYE/sJvYPsO1i8+Wz58YJbgp85S46sPZDy25cx9/Tt1ypDOGjmgjx7+oFLvfdbweuvPgoFzlm51f4YVFWr6pKNlSVC27q5QeeV+Hbz9bRWtjrJc+UDw0V2ifOhRTpbkT0NZkrcGe6hUndTZ2fuwV2FeRDCy/jNwjAACCCCAAAIIIIBAsgiQBThZ3kSGjSNpsxDZLJA7BrIMOMP+PPK4CCCQ/AIVTsbjDwKD9SX/WoUHDL2sxY/XTAglQbEgYW/fDhXICfQpXxa0K1Nn2YxAmzE4Qhs0PecB2RLmporNbAxfPV3/fn5nyuXEUUWaUjxQxYN7OtsZHmjt/O9J+faP9fmOHdpXXaNOOVnq6QQMW7q8ualxch4BBBBAAAEEEIglkLS/f8caMPVtIkAAsE1Y6bQpgaT+C+jJ86XVLzb1CJxHAAEEEEgygU2BXvooeIjG+lfGDBJmqUYzc+5S1AQnzXye1c7MxGkHZibapf275+s3o/do0IYndfDml5UVyn4c2XFAfgUGnqDs4sukYRNV6sxGdGclRlniHHklRwgggAACCCCAQPMFkvr37+Y/Dle0UIAAYAvhuKx1Akn9F9CG16VHneyUbVRse6r79p/jzFQ5XMf41unErA90nH91xN5XbXRrukUAAQQyXiAQ9DkJUWxeX2JKeTDPTYKyM+gsMY5zVmH4nfc6y4kfrD5NrwWOCc1W3OcvcGcVTh47IHJWYfiF0b7bLHZLZuXuZ+s9ozMjsTn7Gob30Zzroo2HOgQQQAABBBBICoGk/v07KYQyYxAEADPjPSfdUyb1X0DO8jDd80Vp18dt5mZLyGyRWFaUfa/a7KZ0jAACCCDQJgL7nOXJtlC4k6+q1f3XOP/7MC8wSo/UTHSDgsOKurh7Fx7R18mMHK3Y/2ZtfENa+JC0ypm9bnsnRi1+aaTzH7ecpCoaON4Zbt1SZe35xOnjTef6F6T1/3GuDttTMXw/xPDrvHsQMPQk+EQAAQQQQCBpBZL69++kVUu/gREATL93mhJPlPR/AW15V3rwZMfSm0GREqwMEgEEEEAgjQRKg/matf90Lcj6kn76dWfWeP/uztM5gTtnZl5p3iHauWmVev3rSnXataZ5T33w4dp11P9T5epX1GvzP21BcnzXHzRQmvBzqd9oJ1D4b+n9p2oDh2HXB53x7e09Rv4vfVedvni2lH8gcEmgMD5jWiGAAAIIINAGAkn/+3cbPDNdNhQgANjQhJp2EEiJv4BsNsWca9tBg1sggAACCCDQPIEaZymzBdvCE6I0r4eWt7b/NOaEIZss1q68+0gVdDlIvo8XOEfhgUZnRuLAcc6sxP+SDj1GyiuUuvZxAoZdm+y3WQ2iBB4ti3ODPRe1r275NEufm0VMYwQQQACB5BdIid+/k58x5UeYnfJPwAMg0FYCYy6u7XnOdc4nMwHbipl+EUAAAQSaL5Dl7mPYMf/bFE/wz57I2hXuXCHtjPZ8TjBwo7Pnrv2ESoxlyt75sGBeufLc7M6lTnbngtws9e6Wry75thTbKY0si65x5ju+EThOj+3/mrMX7wgVOwljvpf9T53qX+wkbakLUAadpc/7h56qnJFnSs6MSTdAmddFqiyt3WORIGGtNf9EAAEEEEAAgZQRYAZgyryq9BpoSv0XiE/eVdXj/63cfSXp9RJ4GgQQQAABBJJVoNcIlZ/xf9qSP0y+j+apaPVjKtwwV76wPQ5tP925gdF6vGaCFmqUTjn8EH277+cav/TGuJZFVwazlefbH7dAg5mP4fsj9j5K+nytE+z8qLa/7gOcwOGw2u9OYpbyst3aXpmt3bmHqFPhQZEBywMjKK2objgz0Qtqxj1KGiKAAAIIINBQIKV+/244fGoSJEAAMEGQdNM8gVT6CygYCKjklmHqrW3Ne0haI4AAAggggECLBSy5yvZgN/X3b2+yj9WBfnrMCQT+LPsJFfgqm2yf6AYNgoPODbz5meEzJr2g5ROBU9VtxMk690v99Hl5pZ57d4veXr8jbA6iJQrzaeIRRYqVDbp09w7t2LJBlftKldepi3r0GaQu3XpEfbSowcXwZc/ho23h7Mao9yCAGfV9UIkAAgi0t0Aq/f7d3jaZdD8CgJn0tpPoWVPpL6AP/3Gvhr59YxLpMRQEEEAAAQQQqC9gK3+95Mb1zyXjsQUtp1VfpuXBQaHhFWqvevt2qEAVKle+u8y5zFnmPKyo0M0GPerQLlo+/0VVvfWgvlg2L2IPSAsuftBlnHKLL9Go4jNcjPnrP9dj8z/SyytKVBMIqtDp9TT/Ip2bPU9f9q9wlj3HKk7Ysv8Y6cuXSqPOiQkbdNDtHn9+c4VWrFql/OC+0Lj3+Qs0cVSRLvhSD43pWSFf1V43gU2svR6jBRBtdA32aySoGOulUY8AAgjEFEil379jPgQnWi1AALDVhHTQEoGU+QvI+T+2+37RR50Czv9ppSCAAAIIIIAAAgkU2BvM083Vk5WrGp2atURf8S9vENTzljnv83fW9JwZGhzc1OQI1vv667a8q/SvXYc6bYPOXocrdHnWczre6b+5QVLbD7F65LeUe8avpc4Hhe69bPMuPfXELJ1aWttvlq9uD0ULRi4OOkugnaDsaP9q55m8+ZDO5WFLp4MDxmn+hh2hIGWnQHlEALQk2EO2z6MXGO3ir9SRg/romFGjNLBv77pkNM5DNdgLMjTSJr6E7S1pGbZjBSgjemnJNREdcIAAAgi0r0DK/P7dviwZdzcCgBn3ypPjgVPlL6C9y15U57+cnxxojAIBBBBAAAEEMlYg4GR+9ocH0pqQqHCWUP+i+rtOkpN/abh/cxOtmz5tIbzPDzpCnSbeoi3rl6v3wtvUxedkT25F2aIi/brqbBU6y7ZP9VsAdFlEsNCyXe90wn/dVeYsia4LIu536hc4SVyeqxmnl5ykLmXOnEkrXX179a3D/Dp3WK5GHZwlnwX0CosaBvW8RDHz75fWzHWurAtehgcoZXs7ln5am/glp7O0baW06GHpozeda+rGI5+TwGbwSdKYqdKwiTFnTDoXRRYvkFj2mXOPstoZkvXGG21mZCjhTWRvHCGAAAIxBVLl9++YD8CJhAgQAEwII500VyBV/gIqn3WWCjaHZyhs7pPSHgEEEEAAAQQQ6BiBVFsW3RIle8ZVwX5OfueAhvq2OIHChr0EnQBd2aCv66PB31F5MF9Hzf+R8vc6gb22KJYt+qT/TxpyshPUq132XJp3iLZW5Ki8qkYFOX713b1Ynd/9vTPwF50RhAUfD4zHxru56Kt6xMlW/YdP+qmzs7TaWxq+z5evnocO1ujh/XXK8EM0tKiLuoTv52hBTxuDBS7rJ6XJ71p7By/wWFVetyzbzjSWsMa7psxJiudeV+gEVw9xg6ul6hTfUm2vD+f6RjN5H3DgAwEEEieQKr9/J+6J6SmaAAHAaCrUtblASvwF5PyflMDtA9z/Q9nmINwAAQQQQAABBBBAoM0FOiIoakui3wyM0rLAQJ2ZNV8D4khs40GUO8vE81QVMTOyNpnMsXonMEzH+tdoorN8PCsskGhzExvGQX2q6TVSldmF6vTpIud8XeDRm8sYfo3d45XAMdoYPFSnFGzU0Epn+Xj4rMcDA6xxaufWjNafa07UpmAvZy5mpSp8nTTq8MP1reNHqnhQD/nWviy9PUPB9f+JuK/N5HwzcIT+GThWi4IjnWtG1F4zuKd8laVuQFJhsyPLc3u6+2LasnBb8l2Yl62yyv21gVXnuHe3fHdU3r6Rspdt5cAScQuUVu7Y1DBxjgUmt69xA6Z7q2u0Pbu3dhUMipmx27pkZqYpUFJJICV+/04l0BQdKwHAFH1xqT7sVPgLKPjZSvnuH5vq1IwfAQQQQAABBBBAAIF2FdjvxN62BHqoKGu3E8CsievetuR7tTObs8C/X/31qfMf4RsWWwq/MDBcf68Zq6VOAh1fWFjSApjhyXNs78jBvk/0dd9CnZT1gQ7zb3ICqXV91jhjLPd3VZfgHqeXyGKxw+WBgXoqcKL8fb6k/xp1kEZ0d5aeb/j/27sPKDnKK9Hjt6d7erJmRjMagTQSAklIgEVYkMjRhrWPjAkmGNukZcELJpgnLwbzjM3jmSUYHPat8fHixfZ7mLiYXeIBs0QLDIggFgRCCAECZY0mhw717i2p2tU93dMzGvVMh39xhq741ff96qq6+lZqkyUrPpX1m7foq3rC8rHTIiudKdITqJZDZzXLOYfMkGPmtvw1geldZWlXZVpSM9PwhCkidoWmJSM/e11kzZsiUX2j+cRdRaYfLFI/NamC/gRkWX+HlHevlWCsJ+sbwd3y9UrPYdXDP59Xv6RaMFBIAoXw+7uQPAu1riQAC3XL7cB6f/zxx/KLX/xCHnnkEbH+iooKmTVrlpx22mly0UUXSXW1PvNkB3eFsANa+penZO/HTt7BLac4BBBAAAEEEEAAAQQQyJWAJe/s+kZLII70pTfbUydbX0T/YrrSiCYFg2UxqQ7otZG+rKJOTkoyphuO6DzlKfNZfWzeqP51SoO0BeqlTW9jjwwMyOzgWmnQ53Dabe9J69IFOvTK0XZ9E3e5XpFZGx3Q54dqElbnqyiLS8gK3dalq4ety94Q7k/AWrLU0rjm2h8IaZ0Cbl2t0JjO6eh/Ie2LRAO6Rk35BsukStcZ1qGg1j6o89h8PTqmW/9CUZsWdR+5GQmEZbM0S2d5o9TE2qUx3uYut7XdZbrU1qXLXd2Irieq9XD0L6RjKtzkqzg2FNV1BDQxq9eh6q3yDc5GvT2+Q8dbbTXn6dYhqENWa0frH3fHx7bVMOCWb+UGtI5VWld7H3ul1q1OPpMmqYj3yVS9erdS54gGKqSvvE5ilc1S27qX7HLIyVKnn/ncFcLv73z2K5a6kQAsli25ne2wpN83vvENaW9vT1vCnDlz5NFHH5Xddtst7fTtHVkIO6Abb/9/8r3V397eJrIcAggggAACCCCAAAIIIIBAkQu4SWBNjG6YeapMOeP/SCDkT7PmR+ML4fd3fkgVdy38if3ibimtGyTw5ptvulf5WfKvtrZWfvzjH8vixYvlqaeekvPPP9+d/7333pOFCxdKV5e+mayEOrusfv2Hb5VQi2kqAggggAACCCCAAAIIIIDASAXsCky7onHqB3eJ/G99VuXD1420COZHYEwE8i81PSbNZiUm8J3vfEd6enokpGconnjiCTn4YH2+xLbumGOOkdmzZ8sVV1wh7777rtx6661yzTXXeJOL/nPtll65NPhA0beTBiKAAAIIIIAAAggggAACCOwYAbvdePIrP5E1batk5zN/s2MKpRQEdpAAVwDuIMhCK+aVV16RZ555xq32eeedl5T889qyaNEi2WOPPdzBn/3sZxKJ2JMpSqMLfPSCvqFtfWk0llYigAACCCCAAAIIIIAAAgjsEAG7InCnFfdzJeAO0aSQHSlAAnBHahZQWQ8++GCitueee26i399TVlYmZ511ljuqra0tkTD0z1Os/ZPf/b/F2jTahQACCCCAAAIIIIAAAgggkEMBSwK2vPoTcaL2WhU6BPJDgARgfmyHMa/F888/766zpqZG9t9//4zrP/LIIxPTXnjhhUR/Uff0dUjth48XdRNpHAIIIIAAAggggAACCCCAQO4ELNmy9p5Lc7cCSkZghAIkAEcIViyzL1u2zG3KrFmz3GcAZmrX3LlzE5O8ZRIjirWn4zN9+bu95J4OAQQQQAABBBBAAAEEEEAAge0TaH7/3u1bkKUQyIEALwHJAWq+F9nX1ycbN250q9na2jpkdRsbG8WuEuzu7pZPPvlkyHn9E+0140N1a9asGWry+E7r4tl/47sBWDsCCCCAAAIIIIAAAgggUPgCIScinavflrrWvQq/MbSg4AVIABb8Jhx5Azo7OxML1dbWJvoz9XgJwK6urkyzDBo/bdq0QeMKZsTA8NtZMG2ioggggAACCCCAAAIIIIAAAmMqYM8C7H37cRKAY6rOyjIJcAtwJpkiHm9XAHpdOBz2ejN+VlRUuNN6e3szzlNUE8I1RdUcGoMAAggggAACCCCAAAIIIDA+AoG2D8ZnxawVgRQBrgBMASmFwcrKykQzBwYGEv2Zevr7+91JVVVVmWYZND7b7cJ2C/CCBQsGLZcXI2pb8qIaVAIBBBBAAAEEEEAAAQQQQKCwBcoqJxR2A6h90QiQACyaTTn8htTV1SVmHs5tvfb8P+uGc7uwV3C2Zwt68+XjZ/e6D4RrAPNxy1AnBBBAAAEEEEAAAQQQQKCwBCpnHl5YFaa2RSvALcBFu2kzN8yuAGxubnZnyPayjra2NvcFIDZzQT/XLzPHoCmbF/920DhGIIAAAggggAACCCCAAAIIIDASAUdnrplNAnAkZsybOwESgLmzzeuS99hjD7d+K1askGg0mrGu7777bmKat0xiRDH29HXIlDVPFWPLaBMCCCCAAAIIIIAAAggggMAYCvRVTxHhFuAxFGdVQwmQABxKp4inHXbYYW7r7PbeJUuWZGzps88+m5h26KGHJvqLtad748cSlHixNo92IYAAAggggAACCCCAAAIIjJFA5cm3jdGaWA0C2QVIAGY3Kso5TjzxxES77rjjjkS/vycej8vvf/97d1RDQ4McffTR/slF2b9p8+aibBeNQgABBBBAAAEEEEAAAQQQGDuBWKhaAjOPHLsVsiYEsgiQAMwCVKyT7Q28hx++9VkEv/nNb+TFF18c1NRbbrlFli1b5o6/7LLLpLy8fNA8xTbigy3F1iLagwACCCCAAAIIIIAAAgggMJYC9uy/4LmPiAQCY7la1oXAkAK8BXhInuKe+POf/1zstt7e3l457rjj5Pvf/757lZ8N33333fLrX//aBdh9991l0aJFxY2xrXVvdVbL4bq3DrGfLontTSMRQAABBBBAAAEEEEAAgR0pYMm/wKHfEZn6NzuyWMpCYNQCJABHTVi4Bey3335yzz33yDe/+U3p6OhwE4CprbHk3yOPPCJ1dXWpk4pyuCtQLc/E95UvBN8oyvbRKAQQQAABBBBAAAEEEEAAgdwIuMm/+ReIHHttblZAqQiMQoAE4CjwimHR448/XpYuXSp2NaAl+lavXi3hcFhmzZolp556qlx88cVSXV1dDE0dVhtaG6rkN7GFJACHpcVMxSzg2NGLdjviroUdWdbWWm39v5W7I+rnL3M4/bbemP7Ftz1EIy5l0ith6ZNyqZCIhOMRCcRj+kIhvfVD/xz9po3pp80X0P+01uLof/apU3VsXOez4a2dvYbIivYuRP7rnCIDEpJuqZNOXVNVtFeqdKjCXlwUcCRWVi69gXLpj4WlzIlJrfRLOBRxy4ppaVH9swc5lGltbP22nsDWGuo0XVZLC+q0Ku0r1zHWWT281yLFbZRWqkwbla5uNs7mtXVZuVtbqS3X5fztiduM28oI6OS4NjCqcwR0YwaCtvTW8mNai26plE6nQqKxkFRre2pCndounVvLtHIGtJw+maDrDKpDv/4/qn9x/c9x12/jI1pOl9To1Er9f6+W0yXl0agrYG0pC1ity1zbNi09Fg9Kg9Mp1cE+Xdq2kPdnW2prF3D7rIWqGSsTR4OizK3/1u0Y13UOuIpB/b9t34j+2RawEgISiZbpmJDbbisxpC0I61C5O5/VfZuxwemfZx/S/jKdaKNTO7ee+j93Wfvfti7dvz8bZ/OnzmuLpJt/W1HuNHe6LphuWW8+79PmTf03mq781PksJroCFW4dLY5tO+SyS11/LtdViGXjU4hbjTojgMB4CMQCIQme+EuRfU4fj9WzTgSyCgT0oNWOAekQGFMBSzROmzbNXecnn3wira2tY7r+TCtbsa5TvvDTZ+Uv4QtlcllHptlKbvxwDv5tnn79Rdgp9dKjP8gdTUjYj9ryoP50C4akP1Qj3fEK6eqPSVRfMNOjP8OjZVUyM7hGJsXW64/JuAyE6iQarpGgJlAGBqLSF7WUSb+byLDEhP3Yth2WpRfi+tfr/uQPuz/tqzUNYD/H7Ud3f6BSkzPl+kPefig7UuVYkqRf+4KysaxJljvTde6w1Jf1yCRnszTFN+g6ut15Izpnb3iiSFW9VGrdLYEwEJ4gFbWNMiGkWYe+Dunp7pTOvgFNSsQkpj9Se4OamtB1OlGt68BGqYh0S19ZhWysaJWB2lbZSXPotSFHopqc6Ylo8qJrvVT2rpEap0sqNPkQcwLSFQ9pnSolXFEt4UpNUQRrpUMTPdooCcUibrsr9Nf/hOpyTUxogqOvS3q7OmSgv1ud41JZEZbyqgYZqGqSLXFNC/VrgkOxJoX6pTnYoQmEfh1fo/WqklB/m5T3btDERVQimkVxogNSEdP26y/1/uqdpaP1aAnM/aJ0OdWy7sOlUr3uVdmt9y2Z6mxQk7hEKxqls3GuBJpmS72aVPVvkr5ITDq1Db09Wk7vZk2A6TasapGOiZ+Tjua/kXD1BJnSu1wmdb6rGaI+aStrlq6qnd1tVqaJrLg+JDlcXSdN5VGpibRpRqhHMxHaeJ3W0bZB1nX2SbtTo9tlolQ1tEhZ/c66TI1Eejqkd/OnUhbrl0m15dIY6JL4hvfE6VxrOTGta4NEaqdItLJBunqjsqFti7T3RqS7crJMnlAlc8vXSXOgXWO0UjbV7qZ1mipV5UFpqtG4CmuybSAmbVs2S3+vbquqWmlsmSY1zbrvqpxQcvsBGpwnAroPkk3vi6x+XaT9Y43dsEjLniLTDxSpn+ruo6Rzje6Qu9wE6lpp1P2BJhh71kpZpEti5bUSbpwqTnmdfLhyuXS+v1iqOj+UQHmVRFvmSVBvV7KkXbBthTT3rJSaeKd0l9XJZxUzpLNmhtTUNUp1RUhqnR6pj210y7SUZb/u27o72iXS3yM1tROkeadpbrqz46M3JbZhhSaowxKYsq80zpzv7tcG2j7V/W6vTGxskni4VjZv3iT9+u+5QvcVE3eeIXX1uh+2ztqr7ene9KlsaW939zVVgQFpjG5w9z3uzrG8wv2+6Onp0tk3SrBns5Q7fRIKVWh51fpvV/cduq+2/anovrA/EpGuiIglfDfV7i7dVVNkYtsbMqlrhVTrvrqqvkkqwzp/1wb9W6uZ5i7pj0akN2L7a01W2/dU/RQZqGySdZEa+TRWL3Ft904Dq6Quukn39WFpr5sl/S37SUPLzppUH5C47vfb1q+T3vbP9DuiR4K6v2uvnCoRPZswrX+FTIqs0V2efheUT5JY9UT9JopKqHudxLt1XzbQo0nvAf1CK9OxQem3/bbu3yrLYlJRpunuijpdpkUqa2qlPhjRJnZLe1eP9Pf1SCA2IGF9hnN5XYs41ZOkNxrTOmyUgb5uNwkd0+9dqayVYERPAfStk8pYh37n9Omq9KRBuEE2NO0vyyYcIqEtn8jsrr/ot3u37ocbtQ6a5O9Zr98nOr/WoVaz8hVan7jWMxTXb2K1LqvUxHvdVNkcnCxt7d1SPrBJKuP6za372OoJTVLXPEU6yxpkhT5/eV3f1hMku4U3Sl2sUyKRfv0eiUpdsF+q9UxBZ8SRtm7bdnqyI6inWiomSFBjv1q/R6viPXpCICQ9FZPcfXtIY31y93KpivVKRF1W1e0nH8tOUt2n46OfSX1kg1tHe9md/Vi345ON0iAdA2XSrMcjk2Wza2snD+zoIRAs1+8Dy76HpSPUKKGqOpla3iuBaLd82hGTzzSY6gY2yySNqKC6dQXrZUNwklRWVsnONUH9Ju+SSNtqCfVs0K0X0X8XdmSi7dXtGdKyQ+UhXYd66rZy9Pt+wNH4CU+S/opmqY23a7zocUcsKJsDE2VAjy3q9AiqQo1C+v1oxzkD1oZgndRWVcpO5T1SrsbtgVr5uHw3LT8grT0rtL3d+h1dLT16GBOObNG/Tve0VciOjIJan8pGGWicLR/GtfUdnTKx/zNp0GMkCVXqsVu5+51uJ7jMor98gtajSsfpdtZ9SkCPJTqCE2RDaLL7T3ZSdJ00aTxvPZESkzL9d7g+XittkbBuqy6ZKO3uCZ1AmR736H6oLdTiHmtVx/TkjB4TRTS+1oSmyfL+RtkSCckE3cafc96VqX0r9d/1gPpOkDWVM2VjeJqEdRvsFP/MLbPcsRM7WmV1LVPPfkePufr61K9NqnW9dvJL/1Xpn50Ms6M7+9dkR5RbT8nFyio1Fup0nB139eoxWUjWBiZLX7hOdglskPqoPShcj5v0pI+dZLJtaA79egxYpb51Ud3v6DaM6/q7nSrdSpW6z9G4CZVLp8bNZt0Xxwe6pVl97AjPThSt0fJfCe0rHU6t/I0sk1mBT90TVXYMFY3psa7aiZrrWiSo+y47ybfJqZeoE9TjnXZdX5/uXfQ4VVthV/XokbUO6XbS9lkX1WNT+/cd1n+Ptm/u1Thao/+Ke7REi9cmrYe1pk+Hu7RWA1q+o8fs9u+3UWOvJq7T9YSinXy0Eu3/cd2X9ugxqqPbPaT72X49y7klUCN6VOeWZ6cb7Vhc9zy6T9PYUQ/7dyEauz0ap9GIyupxsh3Z2slT3VoqY0e8to10+2vbrJ32vaKtd8vYEmiQlfEWHe6UOc7H+ktD404P8qy1MV13me7jwnE10j/7N2HlmkmdlatxYatP19l+1LrUE1Vbx2492WuLun8ZyrB5rZyOsP57PepyCR58kbuv9srIp898/f2dT0alUBcSgKWwlfOwjfm8A5rzPx+VWbEV8nD4Bxm/EPKF1L5w/F9aUR22qyZiYT3Qr22RuB4cR7q3iNPXrkk3/XLSL1CprpeagH75aoIlpl/G3frF2aYHA1tCzW6ybqr+gKkdWOf+gOlo2FMq5x0vu0yZLKEPn5be1Uv1mZF9EinTgx39zRnSy2f661oltsvh0qA/6hI/2PIFiHoggAACCCCAAAIIIIBASQp0tm+WzWtWuSeYKt0TvBWa0A8mnSBr6FmlJ2HWuolWTa9rslbvtChvkmj1ZD1BVSflbR9I1Ud/Etm4UjZGK2RlYKomw6tkaq0jM6bvIlPnLth6Ai7PhfP593ee0xVV9bgFuKg2J43ZEQKn7N8qd/7FkV9GvywXhR5OSrDtiPKzlWFJPbuVcElgTwk37CK7BD+Tmj69okHPetqZ+p76WbJp0gF6ZdU8PePaJLHeDqkN9Epzc7NMnT5TGryrJrKtaKTTd1ugX4x0CCCAAAIIIIAAAggggED+C9jFCekuUNDrwmVmovrTEn16045eSZnSTTtAZG/9025X/dPr2OkQKFgBEoAFu+moeK4E/tdX9pI//OUTuTn2dWnVm0K+EnpphycB7flhH+tl/59WzHFvg6nS2yojegtBb9NeUrvH52X6rrPlsEp7WtfgrlZHtQwezRgEEEAAAQQQQAABBBBAAAEEEEAgrQAJwLQsjCxlgaA+N+R/HLu73PLkcrksdqms1qdkXBR6aLuSgJboW+9MkNfCC6R395Nkp0lNiSv1dtUzUnYWiQ4BBBBAAAEEEEAAAQQQQAABBBDIpQAJwFzqUnbBClzy+dnySVuP3Pvqar0S8Ax5NL5A7gjfLC0BfRh5ls5u31216xnSNud02XXX3WXnyS2yMMsyTEYAAQQQQAABBBBAAAEEEEAAAQRyJUACMFeylFvwAjedso9Ma6yWW/VKwLedmbKg/zY5qGyZnB98WI4qe0P0xWqJLqpvK/1zfJ60zztXvnLquTLX/2aOxFz0IIAAAggggAACCCCAAAIIIIAAAmMvQAJw7M1ZYwEJ2JWAFx21m/zooXfcqwFfiu4pL8X3lFrpkcmBNv3slUioRg7Z93Ny1YnzxW4fpkMAAQQQQAABBBBAAAEEEEAAAQTySYAEYD5tDeqSlwKW1LvuxHnu34p1nfLiyk2yrrNfJtdVyMG7NcmsyXV5WW8qhQACCCCAAAIIIIAAAggggAACCJgACUDiAIERCFiyj4TfCMCYFQEEEEAAAQQQQAABBBBAAAEExl2gbNxrQAUQQAABBBBAAAEEEEAAAQQQQAABBBBAIGcCJABzRkvBCCCAAAIIIIAAAggggAACCCCAAAIIjL8ACcDx3wbUAAEEEEAAAQQQQAABBBBAAAEEEEAAgZwJkADMGS0FI4AAAggggAACCCCAAAIIIIAAAgggMP4CJADHfxtQAwQQQAABBBBAAAEEEEAAAQQQQAABBHImQAIwZ7QUjAACCCCAAAIIIIAAAggggAACCCCAwPgLkAAc/21ADRBAAAEEEEAAAQQQQAABBBBAAAEEEMiZAAnAnNFSMAIIIIAAAggggAACCCCAAAIIIIAAAuMvQAJw/LcBNUAAAQQQQAABBBBAAAEEEEAAAQQQQCBnAiQAc0ZLwQgggAACCCCAAAIIIIAAAggggAACCIy/AAnA8d8G1AABBBBAAAEEEEAAAQQQQAABBBBAAIGcCZAAzBktBSOAAAIIIIAAAggggAACCCCAAAIIIDD+AiQAx38bUAMEEEAAAQQQQAABBBBAAAEEEEAAAQRyJkACMGe0FIwAAggggAACCCCAAAIIIIAAAggggMD4C5AAHP9tQA0QQAABBBBAAAEEEEAAAQQQQAABBBDImQAJwJzRUjACCCCAAAIIIIAAAggggAACCCCAAALjL0ACcPy3ATVAAAEEEEAAAQQQQAABBBBAAAEEEEAgZwIkAHNGS8EIIIAAAggggAACCCCAAAIIIIAAAgiMvwAJwPHfBtQAAQQQQAABBBBAAAEEEEAAAQQQQACBnAmEclYyBSMwhEA0Gk1MXbNmTaKfHgQQQAABBBBAAAEEEEAAAQQQ2HEC/t/c/t/iO24NlFQIAiQAC2ErFWEdN2zYkGjVggULEv30IIAAAggggAACCCCAAAIIIIBAbgTst/iMGTNyUzil5rUAtwDn9eahcggggAACCCCAAAIIIIAAAggggAACCIxOIOBoN7oiWBqBkQv09fXJW2+95S44adIkCYXG5mJUu/TZu+Lw5Zdflp133nnklWcJBIYQIMaGwGHSqAWIr1ETUkAWAWIsCxCTRy1AjI2akAKyCBBjWYCYPGqBQowxu+3Xuwtv3rx5UllZOWoHCig8gbHJuhSeCzXOsYDtcObPn5/jtQxdvCX/Wltbh56JqQiMQoAYGwUei2YVIL6yEjHDKAWIsVECsnhWAWIsKxEzjFKAGBslIItnFSikGOO236ybs+hn4Bbgot/ENBABBBBAAAEEEEAAAQQQQAABBBBAoJQFSACW8tan7QgggAACCCCAAAIIIIAAAggggAACRS9AArDoNzENRAABBBBAAAEEEEAAAQQQQAABBBAoZQESgKW89Wk7AggggAACCCCAAAIIIIAAAggggEDRC5AALPpNTAMRQAABBBBAAAEEEEAAAQQQQAABBEpZgARgKW992o4AAggggAACCCCAAAIIIIAAAgggUPQCJACLfhPTQAQQQAABBBBAAAEEEEAAAQQQQACBUhYIONqVMgBtRwABBBBAAAEEEEAAAQQQQAABBBBAoJgFuAKwmLcubUMAAQQQQAABBBBAAAEEEEAAAQQQKHkBEoAlHwIAIIAAAggggAACCCCAAAIIIIAAAggUswAJwGLeurQNAQQQQAABBBBAAAEEEEAAAQQQQKDkBUgAlnwIAIAAAggggAACCCCAAAIIIIAAAgggUMwCJACLeevSNgQQQAABBBBAAAEEEEAAAQQQQACBkhcgAVjyIQAAAggggAACCCCAAAIIIIAAAggggEAxC5AALOatS9sQQAABBBBAAAEEEEAAAQQQQAABBEpegARgyYcAAAgggAACCCCAAAIIIIAAAggggAACxSxAArCYty5tQwABBBBAAAEEEEAAAQQQQAABBBAoeQESgCUfAgAggAACCCCAAAIIIIAAAggggAACCBSzAAnAYt66tC1J4OOPP5bvfve7sscee0hNTY1MnDhRFixYID/5yU+kp6cnaV4Gil9g/fr18vDDD8s111wjX/rSl6S5uVkCgYD7d84554wY4PHHH5eTTz5ZWltbpaKiwv20YRs/3M7i8Oabb3bj0uKztrbWjVeLW4tfusISeO211+T6669342vatGluXNg23X333cVi7Pnnnx9Rg4ixEXEV/cwdHR1y9913y6JFi+TII4+UWbNmSX19vYTDYWlpaZGjjjpKbrrpJtm0adOwLIivYTEx0zaBK664IvGdad+dzzzzTFYbYiwrUUnO4B17Zfu0fVq2jhjLJsT0jRs3ut+Nhx56qOy0007usdmUKVPkwAMPlH/8x3+UF198MSsScZaViBnyWcChQ6AEBDTR4+gPI0f/Lab9mzNnjvPBBx+UgARN9AQyxYKNP/vss73Zsn7G43HnggsuSBtX3jpsus03VLdixQrH4tBbJvXT4veRRx4Zqgim5ZHAEUcckXFb+rftmWee6fT39w9Zc2JsSJ6Snfjkk08OK8b05IajP1YyOhFfGWmYkEHgjTfecEKhUFL8Pf300xnmdtzvP74nM/KU/AT/d+JQ/XqiI6MV+7GMNEzwCdx7771OU1NT0r4rNeZOOOEE3xLJvcRZsgdDhSkghVltao3A8AXsQLW6utrd2evVN86Pf/xjZ/Hixc5TTz3lnH/++Ykvgblz5zqdnZ3DL5g5C1rA/4WvV2c5xx13XCIWRpIA/P73v59Ybr/99nPuuusu5+WXX3Y/bdhbz9VXX53Ry+LO4s+b1+LS4tPi1OLV4tamWRy/+eabGcthQv4IzJw5091melbZueyyy5z777/fjQs9s+zceuutztSpUxPb+4wzzhiy4sTYkDwlO9ESgLbvOuuss5yf//znzgMPPOBYfP35z3927rnnHufUU091gsGgG2d6VWDGfQfxVbIhtF0Nj8Vizvz589240itNE/uxoRKAxNh2UZfMQt6xz4UXXui89dZbGf9WrlyZ0YQYy0jDhG0Cv/vd75yysrLEvuuHP/yhY9+jS5YscU+w/+IXv3COPfZY55RTTsloRpxlpGFCAQmQACygjUVVt09Abxlwd/Z2ttoSKqmd3iKVOIC99tprUyczXKQCeuuv89BDDzlr1651W/jhhx8m4mC4CcD3338/cRXEAQcc4OgtvEla3d3djo23g1uLP7vKL11nByHeAbDFY2pncetdbXH00UenTmY4DwUWLlzoJmGi0Wja2m3YsMHRW4ET2/25555LOx8xlpaFkSqQKbb8OH/84x8TMaaPJPBPcvuJr0EkjMgi8NOf/tSNKSSZ70sAABkySURBVDtpddVVVyXiK1MCkBjLAsrkRAzZsdD2dMTY9qiV1jLvvPOOo4/ncWPt8MMPd7Zs2ZIRINNdGcRZRjImFJgACcAC22BUd2QCdiWWl1j51re+lXZhO5utzwV052tsbHQGBgbSzsfI4hbYngTgRRddlIgvu/ImXWfjvRi8+OKLB81i8dbQ0ODOY3Fo8Zius/j1ynn11VfTzcK4AhOwBLS3TS+99NK0tSfG0rIwcgQC3tXFditwakd8pYowPJSAPos2cUW6Jfz8J68yJQCJsaFEmWYC3vfg9iYAiTHiKJvA5z//eTfO7HvQTsBuT0ecbY8ay+SjAC8B0W8duuIVePDBBxONO/fccxP9/h69HFz0Fip3VFtb27AeZO1fnv7SFNAduvzHf/yH23j9gS0HHXRQWggbr8/2c6dZPNpy/s4enK5nIt1ReuWhWDym6/wvJtFb/dLNwrgCE/A/0FyfQTqo9sTYIBJGbIeAvfTKur6+vqSlia8kDgaGIaA/gKWrq0vsu8q//8q0KDGWSYbxO0qAGNtRksVbzrvvviv6WB23gXoi3n3p30hbS5yNVIz581kg/S/NfK4xdUNgBALeWzbtB9D++++fcUl7g6LXvfDCC14vnwhkFNArBuXTTz91p/vjJ90C3vTVq1fLqlWrkmbxYtRGevMlzbBtQG8ldt9ebYPEaDqhwhunV38mKp0u8UuMJXjo2U6BZcuWiT4H113aTlT4O+LLr0F/NgF9eL7oC9XE3lBvb6sfTkeMDUeJeUYjQIyNRq80lr3vvvsSDdVn4yb67aIPva1XNm3alBiXqYc4yyTD+EIUIAFYiFuNOg9bwH78WDdr1izRZ6hlXM7/w8hbJuPMTEBABfxx4o+fdDj+6f7lbF7/sH++1HIsfvXFEu5o/zKp8zFcOALPPvtsorLptr1/O6ebnlhYe/zT/cvZPP5h/3z+5a2fGEsVKcxhfRap+6NGXzYj+sxQ0ccKuA3Rl9EkNWi4cWEL+ePGv5xN8w/757Np/o748msUVr9dpe7Fz4033iiTJk0aVgOGGxtWmD92/MvZNP+wfz6b5u+IMb9G4fVbosbumKiqqpK6ujqZPXu2e7Wp3l6esTHDjQ0rwB87/uVsmn/YP59N83fEmF+jMPpfeuklt6L19fWij9qRO++8U/bZZx/3ZIY+i9m9InC33XYTfQ68e4VzulYNNz5sWX/8+Jezaf5h/3w2zd8RZ34N+ne0AAnAHS1KeXkjYLc7bdy40a1Pa2vrkPXSZ/8lrq765JNPhpyXiQiYgD9OssWXvqkzgeZfzl+OXaWqzwJMzJeuxytHn18i+pDidLMwrkAE4vG43HDDDYnannbaaYl+r8cfK8SYp8JnOoHf/va3EggE3D/bl9iPmkWLFsm6devc2b/73e/KN77xjaRFia8kDgaGELjiiitEX5glhxxyiJx33nlDzJk8iRhL9mBoaAF9UYMsX77cfVyB3WquL06T3//+93LMMcfISSedJO3t7YMKIMYGkTAiRcDiyroZM2bIJZdcIt/85jdl6dKlSXPZFX4/+tGP5OCDD5bPPvssaZoNEGeDSBhRwAIkAAt441H1oQU6OzsTM9TW1ib6M/V4z0mygw46BLIJjCS+vNiyMlPjyytnJDGarpxs9WV6fgnomzRFX1LkVsp+2Ngt3qmdFxs2Plt8EGOpegybwL777it29YPdsmkJQn9HfPk16M8kYI+cuP32290rhH/1q18NiqNMy9l4YmwoHaZ5AtXV1fK1r31N/vVf/1XssSivv/66PPHEE3L11VdLU1OTO5s9Q/mEE06QSCTiLeZ+EmNJHAykEdi8ebM71p4F+C//8i/uyXbbl61fv95NNr/yyivypS99yZ3nv//7v8VuE7aTtP6OOPNr0F/oApnviSz0llH/khfwP/A8HA5n9dDXw7vz9Pb2Zp2XGRAYSXx5sWVqqfHllTOSGE1XDlukcATs1t8rr7zSrXBLS4vcdtttaSvvxYZNzBYfxFhawpIZeeKJJyaSyLaPsZfK2DPb/vjHP7pX/v3sZz+TL3/5y0kexFcSBwNpBOw5pRdccIH78qrLL79c5s2bl2auzKOIscw2TPmrgD1POd0dEMcee6x7xZYlZywpaN+d9n156aWXJhYmxhIU9GQQ6O7udqfYnTPBYFAee+yxpBf32QlYe76pfUfatMWLF4u9bO+UU05JlEicJSjoKQIBrgAsgo1IE9ILVFZWJib4H7afGJnS491Sac8eoUMgm8BI4suLLSszNb68ckYSo+nKyVZfpueHwNtvv+3eyhSNRsWSdpakmTx5ctrKebFhE7PFBzGWlrBkRtqP58997nPu3/z5892raewHjN0+t3LlSvfKGbtN2N8RX34N+tMJXH/99e4zq6ZPny4//OEP080y5DhibEgeJm4TSJf883Ds+/H+++9PnAT753/+Z2+S+0mMJXEwkEbAHyN2dd9BBx00aC57EZv/5UZ33XVX0jz+MjgeS6JhoAAFSAAW4EajysMTsAcIe13qbZfeeP+nd4Yo2612/mXoL12BkcSXF1umlRpfXjkjidF05ZTuliicltszZo477jixN8/ZWWg7wBzqzc9ebFgLs8UHMVY4cTCWNT3zzDMTtzNdfPHFbux56ye+PAk+0wnY7XL/9E//5E6ypIv/MQPp5k83jhhLp8K4kQrYCxrsakDr7LmA/me0EWMj1Sy9+f0x4t3qm05hr732kqlTp7qT7LZgf+cvg+Mxvwz9hShAArAQtxp1HpaAna1pbm525129evWQy9gPcu8HtPeihSEXYGLJC/hfypAtvvwPD06NL68ciz970+JQnVeOvYHRf8vnUMswLT8E7AfLF77wBfeHiz2L7d/+7d/cKwGHqp0XGzYPMTaUFNOGErDnZlln+xi7vcnriC9Pgs90AvacUrvSxZIv9mbpu+++e9CfPS/L6/7rv/4rMd07niLGPB0+Ryuw5557JoqwW4a9jhjzJPjMJOA/7vbHS7r5vXnt+YD+zr8cx2N+GfoLUYAEYCFuNeo8bAF73bt1dsbQbrnL1NmZbq/zlvGG+UQgnYD/YNQfP+nm9U9Pja/hlmPxa8/1si61jHTrZFz+CNjbyO3qBbsV0zq7muass87KWsHhxoYVRIxl5SzZGeyEgdd99NFHXq8QXwkKetIIeI8VsP3WGWeckfbv3//93xNLXnfddYl57E311hFjCR56RingOE7aEoixtCyM9AnYlX1eF4vFvN60n970UCj5NQnEWVouRhaoAAnAAt1wVHt4Aocddpg7o52NXrJkScaF7MHCXnfooYd6vXwikFFg1113lSlTprjT/fGTboHnnnvOHW23FsyYMSNpFi9GbeRQ5bz66quJq1SJ0STCvB5ob2+Xv/3bv5V33nnHrecNN9wg3/72t4dVZ2JsWEzMlEXAf7WM/xEExFcWOCaPWoAYGzUhBWwT8L5DbdA79rJ+YswU6IYSOOKIIxKTvRPpiREpPd6JWu9WYG8yceZJ8FkMAiQAi2Er0oaMAvZmRK+74447vN6kT3vVuz0o3Tp7EPHRRx+dNJ0BBNIJ2G2c3q11dvXVSy+9lG42d7x3dZbNb8v5u6OOOkrq6+vdUb/73e/cty36p3v9/gf4n3TSSd5oPvNYwG6bW7hwobz22mtuLa+++mr53ve+N+waE2PDpmLGIQTuu+++xFT/W1yJrwQLPWkE7DvHrroa6s//YpCnn346Ma93oosYSwPLqBELWFLmySefdJezW9L9yRlibMScJbfAV77yFSkvL3fbbS/HytTZSfhNmza5kw8//PCk2YizJA4GCl1Av9jpEChqAd2J230Djl7O7eir3Qe19aabbnKn2zx6MDtoOiNKQ0Bf0JCIg7PPPntYjX7vvffcuLLYOeCAAxxN+CQtZ8M23ou/5cuXJ033Bn7wgx8k1m3xmNpZ3Fr8Wjn60ojUyQznoYDePufoCz8S2/Wyyy7brloSY9vFVhIL6Uktp7e3d8i23nrrrYkY1KSME4lEkuYnvpI4GBihgB0z2feS/WkCMO3SxFhaFkZuE/jP//zPQfslP87atWud/fbbLxFnt9xyi3+y20+MDSJhRIrAhRdemIghfQFbylTH6ejocPbdd9/EPC+//PKgeYizQSSMKFABO1tHh0BRC+jVN05VVZW7U9fbn5zrr7/eefHFFx19YLVzwQUXJHb2u+++u/sFUNQYNC4h8Pzzzzv2A9r7u/nmmxOxoLfYJsZ70xMLpvRceeWVieXsIFUflO7o28PcT/9B61VXXZWy5F8H7cDD4s/7IWVxafFpcWrxanFr0yyOX3/99b8uSF/eCpx88smJ7XnMMcc4S5cudd56662Mf3ZgmakjxjLJlPb4XXbZxZk4caJz/vnnO3r1sPPCCy84b7zxhmP7tl/+8peO7ce8fUo4HHb0Cpq0YMRXWhZGDkNgOAlAK4YYGwZmic5i+zG9pde55JJLnD/84Q/uiXo7zrH9lV417zQ1NSX2Y/rIFKevry+tFDGWloWR2wT0pR7O9OnT3ViyE+oXX3yxe5ytj9dxj/fnzp2biDNLFmbqiLNMMowvJAESgIW0tajrdgvYGcYJEyYkdu7ejyLv05Iv77///naXz4KFJ2BX+XnbfzifmVqoDwx2/u7v/m7Iss477zzH5huqs/ibPXt2xnIsfh966KGhimBaHgkMJ6b889iPoEwdMZZJprTHW8z4YyhTv7690HniiScyYhFfGWmYkEVguAlAYiwLZAlPHu5+7Ktf/arT1taWUYoYy0jDhG0C+hxJZ9asWUN+b9rxvL79PKMZcZaRhgkFJEACsIA2FlUdncCqVaucyy+/3L3Sqrq62tHn/bm3Z954442OviRkdIWzdMEJ7KgEoNfwRx55xNFn/Llnsu1qGzujbcOPPvqoN0vWz66uLsfi0W4btvi0OJ0zZ44btxa/dIUjkCkZk2n8UAlAr9XEmCfBpwno2+2dX/3qV87pp5/u7L333s7kyZPdRwXYFcMzZ8507AezXcE83O834ou4GqnAcBOAXrnEmCfBpyfwzDPPONdee63zxS9+0T0+t6ua7QotOwbSZ5Y63/rWt9I+vsdbPvWTGEsVYdgvYMfZdsfPgQce6F5Bb8frdpLMvkftzpvhdsTZcKWYLx8FAlYp/UFChwACCCCAAAIIIIAAAggggAACCCCAAAJFKMBbgItwo9IkBBBAAAEEEEAAAQQQQAABBBBAAAEEPAESgJ4EnwgggAACCCCAAAIIIIAAAggggAACCBShAAnAItyoNAkBBBBAAAEEEEAAAQQQQAABBBBAAAFPgASgJ8EnAggggAACCCCAAAIIIIAAAggggAACRShAArAINypNQgABBBBAAAEEEEAAAQQQQAABBBBAwBMgAehJ8IkAAggggAACCCCAAAIIIIAAAggggEARCpAALMKNSpMQQAABBBBAAAEEEEAAAQQQQAABBBDwBEgAehJ8IoAAAggggAACCCCAAAIIIIAAAgggUIQCJACLcKPSJAQQQAABBBBAAAEEEEAAAQQQQAABBDwBEoCeBJ8IIIAAAggggAACCCCAAAIIIIAAAggUoQAJwCLcqDQJAQQQQAABBBBAAAEEEEAAAQQQQAABT4AEoCfBJwIIIIAAAggggAACCCCAAAIIIIAAAkUoQAKwCDcqTUIAAQQQQAABBBBAAAEEEEAAAQQQQMATIAHoSfCJAAIIIIAAAggggAACCCCAAAIIIIBAEQqQACzCjUqTEEAAAQQQQAABBBBAAAEEEEAAAQQQ8ARIAHoSfCKAAAIIIIAAAggggAACCCCAAAIIIFCEAiQAi3Cj0iQEEEAAAQQQQAABBBBAAAEEEEAAAQQ8ARKAngSfCCCAAAIIIIAAAggggAACCCCAAAIIFKEACcAi3Kg0CQEEEEAAAQQQQAABBBBAAAEEEEAAAU+ABKAnwScCCCCAAAIIIIAAAggggAACCCCAAAJFKEACsAg3Kk1CAAEEEEAAAQQQQAABBBBAAAEEEEDAEyAB6EnwiQACCCCAAAIIIIAAAggggAACCCCAQBEKkAAswo1KkxBAAAEEEEAAAQQQQAABBBBAAAEEEPAESAB6EnwigAACCCCAAAIIIIAAAggggAACCCBQhAIkAItwo9IkBBBAAAEEEEAAAQQQQAABBBBAAAEEPIGQ18MnAggggAACCCCAQGkLDAwMyIsvvijvvPOOtLW1STgcll133VWOOOIImTRpUlacjo4OWbx4sSxfvlw6OzulsbFRZs6cKUcddZRUVFRkXZ4ZEEAAAQQQQAABBHIjQAIwN66UigACCCCAAAII5J1AIBBIqpPjOO6wJe6uu+46+fWvfy3Wn9qVl5fL17/+dbnpppukpaUldbKsWrVKrrnmGrn33nulv79/0PTa2lpZtGiRXHnllVJZWTloOiMQQAABBBBAAAEEcisQ0AO/rUd+uV0PpSOAAAIIIIAAAgiMs0C6BODbb78tCxculI8++ihr7VpbW+VPf/qTzJkzJzHvAw88IOecc457xV9iZIaeww47TB577DGxhCAdAggggAACCCCAwNgJkAAcO2vWhAACCCCAAAIIjKtAagJw5cqVctBBB8n69esT9bJ5GhoapLu7W+yW4NTObul98803paamRiz5d9ppp0ksFkvMFgwGZcKECdLe3i7xeDwx3us59dRT3SsFvWE+EUAAAQQQQAABBHIvwEtAcm/MGhBAAAEEEEAAgbwU+NrXvpZI/p1++uny9NNPu7fwbt68Wfr6+mTJkiVy8sknJ9X9gw8+kBtuuMF9zt/ZZ5/tJv+qq6vd23uXLl0qkUhEvOXtar999tknafn77rtPnnzyyaRxDCCAAAIIIIAAAgjkVoArAHPrS+kIIIAAAggggEDeCKReAWgVs5dz3HnnnfLVr341Yz3PP/98uf322xPTm5qaZK+99pLnnntOdtllF3n88cdl7ty5ien+nq6uLjn88MPljTfeSIw+4YQT5MEHH0wM04MAAggggAACCCCQWwESgLn1pXQEEEAAAQQQQCBvBNIlAG+77Tb5h3/4hyHraC8GsUTfli1bkuaz5OErr7wi8+bNSxqfOvDss8+6bwL2xttLRewW4aqqKm8UnwgggAACCCCAAAI5FOAW4BziUjQCCCCAAAIIIJDPAnvvvXfW5J/V357pd/zxxw9qyoUXXpg1+WcLHXnkkTJ9+vTE8nabsN0uTIcAAggggAACCCAwNgIkAMfGmbUggAACCCCAAAJ5J/D3f//3w67T/PnzB807kuUPOOCApOWXLVuWNMwAAggggAACCCCAQO4ESADmzpaSEUAAAQQQQACBvBY44ogjhl0//xV8ttDEiRNlzz333O7lU28nHnZBzIgAAggggAACCCAwYgESgCMmYwEEEEAAAQQQQKA4BOy5fsPtamtrk2a1hGC6ZwomzeQbSF2+s7PTN5VeBBBAAAEEEEAAgVwKkADMpS5lI4AAAggggAACeSxQX18/7NoFg8Gkee25gCPpUpePxWIjWZx5EUAAAQQQQAABBEYhQAJwFHgsigACCCCAAAIIFLLASK7gS23naJZNLYthBBBAAAEEEEAAgdwKkADMrS+lI4AAAggggAACCCCAAAIIIIAAAgggMK4CJADHlZ+VI4AAAggggAACCCCAAAIIIIAAAgggkFsBEoC59aV0BBBAAAEEEEAAAQQQQAABBBBAAAEExlWABOC48rNyBBBAAAEEEEAAAQQQQAABBBBAAAEEcitAAjC3vpSOAAIIIIAAAggggAACCCCAAAIIIIDAuAqQABxXflaOAAIIIIAAAggggAACCCCAAAIIIIBAbgVIAObWl9IRQAABBBBAAAEEEEAAAQQQQAABBBAYVwESgOPKz8oRQAABBBBAAAEEEEAAAQQQQAABBBDIrQAJwNz6UjoCCCCAAAIIIIAAAggggAACCCCAAALjKkACcFz5WTkCCCCAAAIIIIAAAggggAACCCCAAAK5FSABmFtfSkcAAQQQQAABBBBAAAEEEEAAAQQQQGBcBUgAjis/K0cAAQQQQAABBBBAAAEEEEAAAQQQQCC3AgFHu9yugtIRQAABBBBAAAEEEEAAAQQQQAABBBBAYLwEuAJwvORZLwIIIIAAAggggAACCCCAAAIIIIAAAmMgQAJwDJBZBQIIIIAAAggggAACCCCAAAIIIIAAAuMlQAJwvORZLwIIIIAAAggggAACCCCAAAIIIIAAAmMgQAJwDJBZBQIIIIAAAggggAACCCCAAAIIIIAAAuMlQAJwvORZLwIIIIAAAggggAACCCCAAAIIIIAAAmMgQAJwDJBZBQIIIIAAAggggAACCCCAAAIIIIAAAuMlQAJwvORZLwIIIIAAAggggAACCCCAAAIIIIAAAmMgQAJwDJBZBQIIIIAAAggggAACCCCAAAIIIIAAAuMlQAJwvORZLwIIIIAAAggggAACCCCAAAIIIIAAAmMgQAJwDJBZBQIIIIAAAggggAACCCCAAAIIIIAAAuMlQAJwvORZLwIIIIAAAggggAACCCCAAAIIIIAAAmMgQAJwDJBZBQIIIIAAAggggAACCCCAAAIIIIAAAuMl8P8Bn1Mt3cTXsdAAAAAASUVORK5CYII=\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1) = plt.subplots(1, 1)\n",
    "\n",
    "ax1.scatter(m_test, (y_test-y_fit)/y_test*100,label='relative % diff test')\n",
    "ax1.scatter(m_train, (y_train-y_est)/y_train*100,label='relative % diff train')\n",
    "ax1.legend()\n",
    "\n",
    "ax1.set_xlabel(xlabel='m',fontsize=16)\n",
    "ax1.set_ylabel(ylabel=r'$\\% diff$',fontsize=16)\n",
    "\n",
    "plt.savefig('Plots/x2_LO_Linear_not_thinking_2.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97025949",
   "metadata": {},
   "source": [
    "# Let's give it another go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "98a7e102",
   "metadata": {},
   "outputs": [],
   "source": [
    "# name of the model\n",
    "filename = 'storage/x2_LO_LinearRegression_model_smarter.sav'\n",
    "\n",
    "# training data \n",
    "\n",
    "kinematic = m*np.exp(-rap)\n",
    "\n",
    "kinematic_train = m_train*np.exp(-rap_train)\n",
    "kinematic_test = m_test*np.exp(-rap_test)\n",
    "\n",
    "X_train = kinematic_train\n",
    "X_test = kinematic_test\n",
    "\n",
    "# train \n",
    "Training_machine_learning = training(X_train,y_train,X_test,y_test,filename,LinearRegression())\n",
    "\n",
    "# predict\n",
    "R2    = Training_machine_learning[0] \n",
    "y_fit = Training_machine_learning[1]\n",
    "y_est = Training_machine_learning[2]\n",
    "coefs = Training_machine_learning[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "c51df2a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "6f0e9a15",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_fit,y_test;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "2b8f9e74",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_est,y_train;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "88d519bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00012255])"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coefs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "1b331f9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coefs*8160"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "416020bc",
   "metadata": {},
   "source": [
    "# Let's plot again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "9e3aa712",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    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",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            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 http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.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 icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.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",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1) = plt.subplots(1)\n",
    "\n",
    "\n",
    "ax1.scatter(kinematic, x2, label='data')\n",
    "ax1.scatter(kinematic_test, y_fit,label='prediction')\n",
    "ax1.scatter(kinematic_train, y_est,label='estimated')\n",
    "\n",
    "ax1.legend()\n",
    "\n",
    "ax1.set_xlabel(xlabel='kin',fontsize=16)\n",
    "ax1.set_ylabel(ylabel=r'$x_2$',fontsize=16)\n",
    "\n",
    "plt.savefig('Plots/x2_LO_Linear_smarter_1.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "ecdf81e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    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",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            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 http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.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 icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.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",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1) = plt.subplots(1)\n",
    "\n",
    "ax1.scatter(m_test, (y_test-y_fit)/y_test*100,label='relative % diff test')\n",
    "ax1.scatter(m_train, (y_train-y_est)/y_train*100,label='relative % diff train')\n",
    "ax1.legend()\n",
    "\n",
    "ax1.set_xlabel(xlabel='m',fontsize=16)\n",
    "ax1.set_ylabel(ylabel=r'$\\% diff$',fontsize=16)\n",
    "\n",
    "\n",
    "plt.savefig('Plots/x2_LO_Linear_smarter_2.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "223a95b9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
