Monti_Sebastiano_Lab0.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hands-on exercise\n",
    "\n",
    "In this simple example you are required to perform a simple linear regression with scipy. Find all the information on the function in the documentation: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.linregress.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assignment\n",
    "\n",
    "1) Load the provided .csv file with the used car data\n",
    "\n",
    "2) Use a linear regression to estimate the car prices from the year, kilometers or engine power. You can make a simple 1D regression from each one of the parameters independently (as an optional task you can also try a 2D or 3D regression combining multiple cues)\n",
    "\n",
    "3) Firstly perform the estimation using the scipy linregress function (or alternatively you can use the sklearn.linear_model.LinearRegression class).\n",
    "NB: check the documentation of the two methods!! In particular be aware of the number of outputs (in case use \"_\" to avoid the return of a specific output).\n",
    "\n",
    "4) Have a look at the correlation coefficient to see which of the 3 features works better\n",
    "\n",
    "5) Then implement the least square algorithm: you should get exactly the same solution of linregress !\n",
    "\n",
    "6) Plot the data and the lines representing the output of the linregress and least square algorithms\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: nbAgg\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "%matplotlib inline\n",
    "%matplotlib notebook\n",
    "%pylab\n",
    "import csv\n",
    "from scipy import stats\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn as sl\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.250000e+05 2.001000e+03 4.000000e+01 1.371110e+03]\n",
      " [1.500000e+05 2.001000e+03 4.000000e+01 1.298700e+03]\n",
      " [5.000000e+03 2.001000e+03 6.000000e+01 1.232430e+03]\n",
      " ...\n",
      " [2.000000e+04 2.015000e+03 2.600000e+02 4.949238e+04]\n",
      " [1.000000e+04 2.015000e+03 3.000000e+02 3.854269e+04]\n",
      " [2.000000e+04 2.015000e+03 3.000000e+02 3.968332e+04]]\n"
     ]
    }
   ],
   "source": [
    "# Load the provided data file with the used car data (you can also have a look at it with any text editor)\n",
    "\n",
    "filename = \"data/km_year_power_price.csv\"\n",
    "lines = csv.reader(open(filename, newline=''), delimiter=',')\n",
    "\n",
    "# place your loading code here\n",
    "dataset = np.array(list(lines)[1:], dtype=np.dtype(float))\n",
    "print(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use linear regression to estimate the car prices from the year, kilometers or engine power. \n",
    "You can make a simple 1D regression from each one of the parameters independently \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinregressResult(slope=1509.0938253352238, intercept=-3018309.4849569635, rvalue=0.6767043169095446, pvalue=2.07223115991105e-174, stderr=45.625811824073196)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1ebffa85460>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# linear regression with linregress (estimate price from year)\n",
    "\n",
    "# your code.....\n",
    "#km = dataset[:, 0]\n",
    "#year = dataset[:, 1]\n",
    "#power = dataset[:, 2]\n",
    "#price = dataset[:, 3]\n",
    "\n",
    "#fig, ax = plt.subplots(figsize=(10, 6))\n",
    "#ax.plot(year, price, 'o', markersize=3, label='Data')\n",
    "#ax.set_ylim(0, 57000)\n",
    "#ax.yaxis.set_major_locator(ticker.LinearLocator(10))\n",
    "#ax.set_title('Car price in function of the year')\n",
    "#ax.set_ylabel('Car price')\n",
    "#ax.set_xlabel('Year')\n",
    "#ax.grid()\n",
    "#param = stats.linregress(year, price)\n",
    "#print(param)\n",
    "#ax.plot(year, param.intercept+(year*param.slope), label='Linear regression')\n",
    "#ax.legend(loc='best')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.plot(dataset[:, 1], dataset[:, 3], 'o', markersize=3, label='Data')\n",
    "ax.set_ylim(0, 57000)\n",
    "ax.yaxis.set_major_locator(ticker.LinearLocator(10))\n",
    "ax.set_title('Car price in function of the year')\n",
    "ax.set_ylabel('Car price')\n",
    "ax.set_xlabel('Year')\n",
    "ax.grid()\n",
    "param_1 = stats.linregress(dataset[:, 1], dataset[:, 3])\n",
    "print(param_1)\n",
    "ax.plot(dataset[:, 1], param_1.intercept+(dataset[:, 1]*param_1.slope), label='Linear regression')\n",
    "ax.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1509.09382533522\n",
      "-3018309.4849569555\n"
     ]
    }
   ],
   "source": [
    "# (Optional) linear regression with linear_model.LinearRegression() (estimate price from year)\n",
    "# Recall that in Python a mx1 matrix is different from a 1D array -> need to reshape\n",
    "\n",
    "# your code.....\n",
    "X = dataset[:, 1].reshape(-1, 1)\n",
    "#print(X)\n",
    "y = dataset[:, 3].reshape(-1, 1)\n",
    "#print(y)\n",
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X, y)\n",
    "m_1 = regr.coef_[0, 0]\n",
    "print(m_1)\n",
    "q_1 = regr.intercept_[0]\n",
    "print(q_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1509.0938253351871\n",
      "-3018309.48495689\n"
     ]
    }
   ],
   "source": [
    "# (Optional) perform linear regression with a manually implemented least squares (estimate price from year)\n",
    "# You should get exactly the same solution of linregress !\n",
    "\n",
    "# your code.....\n",
    "m_year = np.mean(dataset[:, 1])\n",
    "m_price = np.mean(dataset[:, 3])\n",
    "\n",
    "num = 0.\n",
    "den = 0.\n",
    "\n",
    "for i in range(0, len(dataset[:, 1])):\n",
    "    num += (dataset[i, 1]-m_year)*(dataset[i, 3]-m_price)\n",
    "    den += (dataset[i, 1]-m_year)**2\n",
    "    \n",
    "m_1_ls = num/den\n",
    "q_1_ls = m_price-(m_year*m_1_ls)\n",
    "print(m_1_ls)\n",
    "print(q_1_ls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinregressResult(slope=1509.0938253352238, intercept=-3018309.4849569635, rvalue=0.6767043169095446, pvalue=2.07223115991105e-174, stderr=45.625811824073196)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1eb806411c0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the data and the lines representing the output of the linregress and least square algorithms\n",
    "\n",
    "# your code....\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.plot(dataset[:, 1], dataset[:, 3], 'o', markersize=3, label='Data')\n",
    "ax.set_ylim(0, 57000)\n",
    "ax.yaxis.set_major_locator(ticker.LinearLocator(10))\n",
    "ax.set_title('Car price in function of the year')\n",
    "ax.set_ylabel('Car price')\n",
    "ax.set_xlabel('Year')\n",
    "ax.grid()\n",
    "param_1 = stats.linregress(dataset[:, 1], dataset[:, 3])\n",
    "print(param_1)\n",
    "ax.plot(dataset[:, 1], param_1.intercept+(dataset[:, 1]*param_1.slope), label='linregress')\n",
    "ax.plot(dataset[:, 1], q_1+(dataset[:, 1]*m_1), label='LinearRegression')\n",
    "ax.plot(dataset[:, 1], q_1_ls+(dataset[:, 1]*m_1_ls), label='Manual linear regression')\n",
    "ax.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinregressResult(slope=90.33080718820231, intercept=-1303.912698987995, rvalue=0.7085500315263968, pvalue=2.670876398432412e-198, stderr=2.4999213289302586)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1eb807d7a00>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# linear regression with linregress (estimate price from power)\n",
    "\n",
    "# your code.....\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.plot(dataset[:, 2], dataset[:, 3], 'o', markersize=3, label='Data')\n",
    "ax.set_ylim(0, 57000)\n",
    "ax.yaxis.set_major_locator(ticker.LinearLocator(10))\n",
    "ax.set_title('Car price in function of the power')\n",
    "ax.set_ylabel('Car price')\n",
    "ax.set_xlabel('Power')\n",
    "ax.grid()\n",
    "param_2 = stats.linregress(dataset[:, 2], dataset[:, 3])\n",
    "print(param_2)\n",
    "ax.plot(dataset[:, 2], param_2.intercept+(dataset[:, 2]*param_2.slope), label='Linear regression')\n",
    "ax.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinregressResult(slope=-0.054251513309725635, intercept=16692.345302519738, rvalue=-0.25728674299884974, pvalue=4.6897144107235236e-21, stderr=0.0056622275650525895)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1eb806c2eb0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# linear regression with linregress (estimate price from km)\n",
    "\n",
    "# your code...\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.plot(dataset[:, 0], dataset[:, 3], 'o', markersize=3, label='Data')\n",
    "ax.set_ylim(0, 57000)\n",
    "ax.yaxis.set_major_locator(ticker.LinearLocator(10))\n",
    "ax.set_title('Car price in function of the km')\n",
    "ax.set_ylabel('Car price')\n",
    "ax.set_xlabel('km')\n",
    "ax.grid()\n",
    "param_0 = stats.linregress(dataset[:, 0], dataset[:, 3])\n",
    "print(param_0)\n",
    "ax.plot(dataset[:, 0], param_0.intercept+(dataset[:, 0]*param_0.slope), label='Linear regression')\n",
    "ax.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.25728674299884974\n",
      "0.6767043169095446\n",
      "0.7085500315263968\n"
     ]
    }
   ],
   "source": [
    "# Have a look at the correlation coefficients to see which of the 3 features works better\n",
    "\n",
    "# your code......\n",
    "print(param_0.rvalue)\n",
    "print(param_1.rvalue)\n",
    "print(param_2.rvalue) #These features are the most correlated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1325.2284004    80.43004469]]\n",
      "[-2661196.78674901]\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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MHOeMTTPjcvE7RtSKfmH22vUOt7r7HG8NzewNJWrd2cy+38gr6U15gMp7Um8lwZm98IsD3XiFezxxwRm+mnd+PGoyXDBYWIGp+3FF1+sWF+ZCy/7wEWYJlGaq8ki5iJDEDmYwExtjZo72eReNGrMEwHmen0iS5fEtVqbIQJ7Jf+6Hcgrmel5b35Ogh5/6rHh7f51bhScWGa4sWDf+AxeNa+B2at833Z58B7vete7xO3SyE0Dc4OOngZm5wOntCkuvNVqHvNqXPxosnUTkkLNlvGlK1eudNyh8vrKil3N32RX88ceS+rUXO6lxkxNhFphIwMztU0n7gPHA2k7kD5savvcXJSb2mv53NV8LNfMy8lx3PyxcW7Yj50MM7x6DK+Bmax+uhTcvCtux4Im7EsvvVQ4BY3cNDA36OhpYK49cE5CncqvQO2XISlusisxvpQ/buoUU3vjIk4gVSY3asf0gVZaaJ1uGLwGZi9DXQ6Exuw1MCsLh9slRW0ca5HjqAkSxLlhsx9XboYv19Yrxa+rz8pJc14D84HIXsbsdH/5l38pGcYauWlgbtDR08BceeAUmYuZksh6VouPE3PheLRf3oe7dKfFHWiuZvYj/maWKCeLuVM/tpfAzP5SC/EqBpWkIJK/xutjrvcaczPkNTB7yfpWG7hKiWwI2OXm9noMeHU85cbNIMGazHvOyVpgbtfWnYRV9cazMAygMxaUDazXSVIYjsUCFkx728hNA3ODjp4G5n0HrjzU6Y9//KPUDiZpymkbj/ZL8xw12npMU5oiubhS86FWQ0Cul2JT9dupH5uLMc31k2neVH3wOmsTgZnaoFOZOB1Tddx4Sxo6vU89H7bT61Q7jvHyBEKy8iezqY0sx5ekRFp+yslx1f6uZIZXGnsib+D7r47grT4Cs4Fj58bwoSOi8BdLHA4+ixfzlG6if/mXf8Fjjz02mWI64NfSwHzART45N9TAXMryo7QFu++YEmZIEn1ZTmOFeQ5NbQRBp9ovzyHQctGsFJtpZ1fzWIIxk1BwV1+vjKN9ljjV5NUC6sWCx4Wbi94pp5wyORO47CqNDsxeAz9NtAS9amlZJzoonDt8ZxjH7KaVx66rOXjrUzvw8s4k2sI+eU8HUgWs6jRwyRKIRs7jlBnejfldaezVYtefeeYZfPOb38RDDz20z2OwzCo3xiol6/vf/358/vOfl3f+ox/9qMRX89q33norWJ2KjYRHmsWZEIibi5tuuglXXXWVfMd3+2/+5m/wyCOPyN88juUmVfvqV7+Kb3/72/Lnn/7pn+KWW25xtRHRwOxmFk6hYw9lYK4U6qSKrKshormYIUluciOTxEVWtJt80NSE6R+1J96g2VflrmaN3nJzNYG5ra3NcV5oEma4gDApSa3mJTBTGyebXANz5REgMFMTdLMRdLOceL1xmczxTWTy+Mh3X0ZbNICA6ZPHzOSLGIyn8LXzZmL5sr1Z8uxm+HqMePv3lWLXb7/9duGI0LV0+eWXS31sbmQIjATm66+/Hu9973v3ETsBlZnzqGUz6uAd73iHxFrT0sbP+K5+//vflw07w7Dou6Y74b//+78FeH/5y18KgNPF85//+Z8SB85ELX/xF38BWu1IBjz33HMFuD/84Q87HnINzI5FNbUOPNSA2Z4IRL2U9lCn8tEpZz47GT36v/hS1spuVX4dLgQsMkG2sjJXE+C5INDsWKlSD0sEMmzHqT/VqYnda2CmqdOrykBeA4/XwMn5xo2gV8DsNTluMl0V6VwBH/3uakRDJkL+EjAnswWJMf7X82Zj6RLneeVrvbflsevU+AmK1HA/8IEPiD+bwPi3f/u3VYGZ40VtWeVoP++88/CJT3xCmN006xN8lRWBnzNigz7siy66SICWGwC2L3/5ywLed9xxB6677jpxyfz93/+9fHfffffJz6OPPupkGZJjNDA7FtXUOvBQAWanoU7lo0OfH8HRTVEBO8g6HW2ew3vRHMe+KnN1LXMyYyy5I3dqliTBjOZs+swPlsbsdck+mmqZ0cwrH7PXwElrApPMuOE0OJ1jPM7rIh+TXWDizqe34Cev70FL2A8LQDxdwLlLQnjvyrZJ95Pb5cg6zMz5/a1vfWsf8VJj5jvE95TjRG2Y7iRujmnhUo3gy6gMmqkZWsd3VW2gCb68BjVzvot33nnnWG1pAu/999+Phx9+WDR0AvYVV1whl6V5/ZprrnEVwqWB2c3bMYWOnc7AbA91IiOZvh9V59YJu5rDxNAe+nOZ4tFpY8pAarNOmMfUMGiupumb/aWWTfO0k8YdOhnITok8TglmXmrM4/VBOpEHj/Hah9rowMw5Q6Dwql41zbEsxDFZBSay+SJ++OIu/HJtL3wGcP7KGTiuNY1IKOh4Q+p07tiPI0DS/HzbbbftczrfU5qg+a5+5zvfwRe+8AVxzZQDM03OTBakgJnWMKVNE8zpVlLAfNddd41p0zRtP/DAA2PATFBW2jS1+E996lMamMczoI12znQE5kpkLi7YqrKSmzGiv8gNwYrXZrgIk2jU8qOqtIIES2p3BH4uBG5IM9R+qB06reHs1PftJTDTfUD/2umnn+5mGBwf2+jATDM/F3SnFcMcC2b0QAIE+RKMdfeiOSlSMtH7em2OZ//uuece0Wq/8pWv1OwuLWk0fZPoWc2UTU7H3XffXdGUfeGFF+LKK6+saMq+9tprZfOhTdkTnTENeP50AuZaVZ24YLO5TUXolmDFe1BroC+yHGTZP+6UyeosN1eruGSSPpw2tzWcazG/7ffUwFx9BLzWmL0GZppUSWYiUcmLdiAqP3FDSp+uV1o/5UJGNs3y//RP/zQmJv7N51NWt5/+9Kf42Mc+JqlvaWJmfxT565xzzhGeCft58803iylbkb9oFePmlO6qe++9V8hedvIXQfyMM87A008/LX5qO/mLZDACudOmTdlOJTXFjpsuwEyTsJ3MVW6qHk/+ag6VWz8uzykHWWWuZpwyzdR8IcvN1WSK8mXlC+m0ud1sOCGlUePhdbkIURsvr6CkQlLsoSlO3QJ8Lm5O6LtzswFxKg8edyA0ZhJ3vPIBMwqAGhbJRl40ulio5blxzbjphxeVnwpFC7tHMmgK+cXXTK2f749Xmws+L8OSaLUg4Us1kicvuOACyYNP7gc3OP/2b/8mtaf5HcOlqM0zBOvrX/86qA2zMT0vAZXvN9+VG2+8EVdffbV8x/eBLG9F6OJx/F41Aj2Bm41EMZrW3YQxamB2M3un0LHTBZhVUoxqIOE0HWX50NA8RUa0U3Mxz2dfyOhkshBqx8pcTV8wfcKVGrVUkjve/va3O54dTotSqAvSfE5to9z/J/Gho3HUBGb2k8BgL8agQkzsoSZK5jyfi0U5aNvB2/5vZv4iK9tJ/WPHwhg9kLHg3Ew4JcS5vT41Zq+BmSzeavPEbX/Lj6cFiKCstL6JXq/8fBVR4CaGv1Yf1u6O4/YnN2PXcAYBn4F3r5yBU1rjmDmjy7PNBftDghb9xn/1V3812SI6oNfTwHxAxT15N5suwExgUxpzJekwJpEao5vqTbyOW3OxSgZCHzPNWNSOSfqot8vleUz/R9an0+bWClBOSlOmdYIZ+0czPxdtfk7Qrddn1U+7T78cwMuBnH9zE6CKNPAaBGh7+sZKWnk5yFcDda+B2WvWNP2VZOp6BcwkZtG/7CbKwOl85HF8x7gJdpPzvdr1GS517QOvIZ4poCMaQLZgoT+ZxcVLfLj4+EWePQP7QxM21wqSuBq5aWBu0NE7VIDZaXKN8mHkQu+ktrLdXE1yDbVkJhlwauYluD355JOgb8ppc2sFoH+MCzN9XDyXwM44aAKyPYGK0pSdArPT/qrj7EUOFKhXA/RyYFcVl+yJIexATv8+QZvAUwns+dlENPUDAczUNivFrbuVc6XjSWbkRpHlQL1oJBhyE1wtiU08k8crO4bl1qvmtYh5ulrjcV/86TrMaAqOvUcDySy6All88aLDxZTsVfu7v/s7cbe48ed61ZeJXFcD80SkdxDPPVSA2W2RCDUk9bRSxh9XMlc/8cQTrkCW93N7jtOiFOpZuGgyVpqbBRJVqM1X8mUeSGCeyNSvVE2JyRn4OTcc1cBegXo1/3kt7Z2kPsaneuVjJtGHLhCvgJmWHEYBeAVqtUoybuhJ4CuPrxcNmDHJTUETf//u5Vg+o3Ie+jXdcdz0yNp9gJnFLBZGc/jHC9/mKhuf23n26U9/Gu95z3skQUgjNw3MDTp60wWYudhSm6rWnIYKlZ9fSSulBk2yBwGZ91XJQOxVctyC7HiA2WlRCpqwSYqiDNioCVDLrNYaBZgr9d9pkYZyUK/mQ7fXROYxJPYp4K7kQ69lhndSRYkEIeZLJ/HOi8a4fHIInMbKu+1DtZKMlPc/PLIWm/uSmNEUksv2xLNY1BHBly8+vKJliaSvzz20But7EmgNByQdZ65QxGXLgfNPXOFZTWz27ZOf/KRk5FIELrdymCrHa2CeKiPhsh+HCjA7YSRXEp0d/GiuJlAzIQhNv4pdXclcfSCAuVZRCi6EXCQJVAQUlhLkYkyNr14McSMDMzdLNME7Tbri8nWRZBLM+ETgLAdt+9+VvlMFUmqBNy00zJVNH7Ndo58stwKJd05KhLqVizqeG1ZyCCgje0vlCvjze1ejMxqAfzTvdb5ooS+RxT1XHoNo0Kx4y/5EFvc9vxPPbxlERyyIS4+bg8jQZk/TlrIjLDLBlJh0RzVy08DcoKN3qACzm2xc9qEk+PGHiySBjmbAWuxqdS6BmUQupz5mpTG7OadS7msu/txMEJBppiYg05/IfjiNldbAXP1lnmicsSqcUs3MTmCmf1wR8BTAqwpK9cLXKmnxdlD3uqwkuRx07zDft70VLQvXPfAahlM5FGHAkPrNBppCJu740FEwmdbLYZvoGDi5zWWXXSZVoOptYp1c62Aeo4H5YEp/Avc+VIDZSTaufRaSUXM1w4sIaPQr0i/rxBzJ65DIxdAnN5qO23Psua/t5DMu7ATk8kIIPIY+zHqx0o0OzNyEuAlvc/P6eA0KDLNjIRMCcHmrVEHJiQneXhaR8emcF9y02X3plczz/MzN/GV/Sfziu0atv7x999lt+OZTW1Ckg5lsfJ+BG85dgkuPm1tzCAjqI+m8EMV4jtfMdXbmkksukVjkSmVY3cyXg32sBuaDPQLjvP+hAsxckBiDWi/lpTJX02RNAhHNvzSDuynhyKFg6BN325UW2GpD5fYcavDUsGj2pPZMbZ6AVI04pOKrzzzzzJqzRQOzdxpzvdeUyVeYY93NvKl3TX6vQJ2mbM4TatYqw1utELfyWsf1YtXJY+A7RI3Zbi2i2frT//O6+JWZ/1q1Uxa3CQGsWntp2xDu+f02dA9n0dUUwJ+fugDY85a8j1754dmXd7/73ZLYw214pZOxOJDHaGA+kNKexHtNF2BWpr9qoqmnLdLUTf8kwY6JBQhwBDynaSzL78uQIGYEcrN48Bzm165FzFL3Yb+YAYlmQ/rz2Od6izkBl/c466yzamsoxeJYdqNJnGpjl7KHS0329blRYWtUjZlJZjgHnFpm3MpvPOFe5Wb1Wr50Zrni9wqUqXFzXsYLJm59IY2ZURM+0yff54pApgDc8b5l+2nv/L57OI3P/N+bYvhmxi+GWpH8dfniDC4480RH74lb+ajj+Y6wwhT98Y3cNDA36OgdKsBcKbMWtQHFrub3XMwZ42lfFKtly6o33FxgGS/sJlEEq8fQjFlN42V/6cOj/5iNyUDYP6d1n50mMeFxKu1gveccz/eNDswrV670LGWm18DstSmexEhuAOlKYVPREvFUBn/z4DrAshANGKB5uj+Zx+yYD9ccF9uHSKeiK57vAX6xzUJnxBR/NMG6L2XhtK4cPvInS8dSxtq1+InEqNvnMjdH5Il4VexjPO/NeM7RwDweqU2Bcw4VYLYn8KDpTrGraa6uxa4eL2mMvkIminBTJYhmTJroymOLuVAxZpkaPUv2MSEITexuNw1Ok5hoYK7+YhI9YGBbAAAgAElEQVTYvARmbs7obnHr23W6lHidi1uVL62kaf7fS7vwn7/fhnzBQjhooiVs4u/OXYaj57Xs133O1Z+9vhv3PLMNM5uDYoqXjelIFqd2ZPDBE+fvZ4pXpnlezE02OQXsdlBnLDmJcnzfyhvLPTLXNTXq9773vWJlY55spu+ldeDWW2/F+eefL6cx4Q2zh3E94MaChDIyvtn4TCwL+cgjj8jfPO5zn/vc2O2++tWv4tvf/rb8zdrMzN/tdl5oYHb6Zkyx4w4VYKbYf/WrX8kOWJmrVU7oWkPiljSmrkWSFRfwcgJWrXuVgzlJZwRjsqyZsJ9aiB3oacamOZtattPmJIyr0YFZVe5yKhM3x3kNbARmchPcsPnd9N9r4lS1cLWdQ2n8f09vwe7hDAaSOUkwcvGqWfjIyfOrdn/PSAY3/N+boH+apmwSwEjn/tD8OC45tzpPQsWo1wtfK/etU7t/8MEHwapR5KQwpzwjGvjz2c9+VqIx+Hysj8x78DMCMwGV76eqLMUQK6byZRIafsb85KqyFK/JWHVa51h7mcBrryxFvzbzDHAesKCFvbIU78PYajdNA7MbaU2hY6cLMPNFofm1vNnN1fTLMlVgubm61nBwx0vSmNuwCS7gLHZAjdxpU2DOPtNcTSINizFwQajkq1YpNunLdtqmCjBz8fECfGppbE5lVOs4r4GZZn6vZMPn8jqzWLXqXve/sBMvbhvC/NZSERcmC+lNZPH35y1HW7R6spvXd43gu89ux9aBFOa2hnHVqfMxsumVfSILCNzFooWg3zfhIeamlOsEIxceeughsUrxb4Itcxecd955+Nd//Vd85jOfkapQBGZuvqvVYmbOcIKvIp2yjCPXhRtuuEGqRRFoCfRsLJzBTfgdd9whMdQk6U2kFjOvqYF5wlPi4FxgugKz3VxNcxRNa0yuwVzUbgChHmms2qiRZLN06VLHGZYIxjRl01TFnT7N1fWKX4xHm58KwOyluXY6AHO9cLaJrBReZxajpkiOBEHF3m779UYMp/Noi+wF4W2DaVx31mLJ/lWrSex30YJ/1M+sOAr87Kev7cGv3+pFrmDhhAUt+ODxc9Ecrp5/24nseD+CJ0O/7GvF1772NdC19YUvfEFyFBCYGeFA4iWtW6oRfBleSTM11x6WjuUxCnyZF+H222+XYiV33nnn2Kb/vvvuw/3334+HH35YTNcE7CuuuELOI/eANZ/JqnfTNDC7kdYUOna6ATO1SJqbGONLYKPJSPls3cYJc5jGU46R53ETwHvXKxagqlGpGsiM/+R5TjYPZMDSD1YvBMw+3aY7MJN8RJl6xaY9EBqz18BMsqAT5n+tZYrs6Oe2DGL1jmFEA36cvrQdh82MST1iulv47tnbz17fg8ff7EEk4EN/omTKbo8G8E8XrkAkUDnrV6X7EzS5saOMHnuzBz98YSdmNIcEtGkmP2Z+i4D9RBrnDyMd7MDM4h8f//jH8fTTT4vsagEzTc4Ms1LATHeTkgdN2yRwKmC+6667xt5fmrYfeOCBMWAmKCttms/8qU99SgPzRAa2kc6dLsBMDZnaADVcVWqxPOSELxVBrF5YkX38nDKZy8ecO1vukqvVvWV/CSL8YUEBashMZkI/stMCA/SDUTN3Y2afCsDsJfO4nBU82e8igdnLesleMtYpi1oJTNzI6v9e3oVnNw2iNRKQECbGJtPM7BveJVoik/HYG7Xlv/nR63hzd0KyfhkGsKwriq+9fyW6mpznBacfmO855/yND6/BzqEMBpM5YXmzChUTkHz1krdJ+s7xNmq/TCxC64vaIH/rW9/CF7/4xbGICWq9dFN9/vOfF8JWNVM2XWd33313RVM283CzelUlU/a1114rbixtyhZawaHXpgswK18y/UDVtM164UjVduhuyzHyOqziQ0IIf+yNWi41eu6auYvmJkKFVFH75cvotFau0xSb5RpzrbSf3IiQsU7/NjUDeyhK+b/HG5pCkz394l7E6noNzF6Tp7wG5snYFA2lcvi3X2zArOYQAqN5r0nUWtARwRmdKXHflM/7HYNp/O2P30DE74PhMxAN+iXZyJ8eNRNXnLTX7M0azP3JnJi8K+XPpgWLm6NTTz0VV39/NTb0JOU4Lt7pfAEt4QDuuXLVhICZPuVzzz1XiJXVmtKY6WOmJs2NiCJ/0V1Gkz59zzfffLOYshX5i9YKbiz43t97772SxMRO/iKI0xpAJYImcTv5i2Qwt2UotSm7QTF9ugAzxW/381QaDgICwyDchDDxOk60zPL7se4xzdhKcyCJhOZqMqlpqiYAl2vu9bTs8ns4zeRlP6+aOZ8LHkGNWgL7Ta1dZYvifewMVnuNZF6bAFsO4uUpHu3fc2ElMLuxXDh9vTQw15bUZPj3mfjjG09sxoK28NgmmGAdDvhwwbyczB/G2NsbiV+3/mojZrWUyF9s/ckslnZFcdP5pbzaz20ewA9f6kYyW0DY78Mlq2bhzOWlPO+q0SJGNxGzo11172ps6UuhOWTKMUxAEgua+NFfnoCwC/N4ucSoDb///e8HK3E5AWbmQmC4FM34fBeYylNVpeJGnIBKMGYfb7zxRgm1YuP7RT/1o48+Kn/zOH6vGoGewM1Gothtt92mw6WcLgSNftyhBMzjCWEaLzAzRIKmLgISAZkvIc3V1CSqxSJW07KrzbHx+L9/85vfyI5caatc6KjBM06amwia0skAd5pgpFb5xEpgzs+4WBGUFaDXAvFywK8Xx6mBufaKNBmsb5ZjvP3JTdjcm8JwJi/ErKBp4IoT52Ku1SugXO7Cof/3M//3BtqjQYT8Pgk32jGUlmpR/CHYf+ln65h/RJKJ8B/Mqf2Zc5cJeKtG9w0BkyGC/+9Hr2MwkRPNm8d2xAJS4/nWS4+cEAGM76sKVWr09V1rzA06gtMJmAkmfOGrtfGEMClgdlP1iX4w+n5JRKNpXSUEqUfoKtey602p8fi/VT5uAiRDsmhSp/ZOLV6FZHkdx0ztgSY9xUCvppFX+rxS7ma7iZ0yZ6N/3/4571VP/vXkLVrdc89J4phq2dmcXKPWMV6bsicCzDQzr9kdh88wsLEvgf94cgvyxSIMy0A46MNfnr4QR4QGZINXifT4o5d24ceru0ffUQPz28K48d2lcKkn3+rDLb/aiExhbx5tAvgnzliIi46aNSYybuq46WU88Pf/uB2/Wd+PuS2l+s7dI1kcObsJ/++cJRMahjVr1kg4FN+VRm8amBt0BA8lYGY8shtylRrSci2z2lDTlE5TMLVPghy140pVdqqdzwWH/rlyRmu1451m8rKfz2dRhTno56pkUj8QwExyjZs84vZnqGVip2+cgE4mvkowwb8r1UKu5T9XWny5H71RgJlkq9d2jmAwmcXizihWzGoS5vJ4gX9jbxL//ssNkhyErTeexby2sPiCSbiiFp3KFXHNkcCShfMrEhg5X9f3JLG+J4HWiB/HLWgVDZn+6afW9+PuZ7aiMxYU4CeZi7WaP376Alx1yoKx4WeIIH2/nD9MOPLdP2wHY52pMS/viuLPT1vgikxW6d3iOvGlL30Jjz/+eIOu6nu7rYG5QYfwUAJmt+QqNaQkYpBsUi3EhFoatU9mFFPaJ02qXNzdhO2QJELCSHkMaK2p5dT/TR83CSkMI2OMJjXkamZhr4GZLgWvqgORuEYgppWivNlrIVczs1cqo8jrqDrIBAZqg9SYa5HjePx4NPTxAqf9Wenv/a9ntwuI0sScyVs4YWELLl41e1zATLnd+PBabOlPYUZTKQ75D5sHxSx97PxSAh0C6a6hDD5xpIGjD1sslqJ67en1fXjghV2SbIQgzHSbrM9MTTlbKEq/P332Yrz3mL0Mb8YRcx6TK8LGvnGTULCAWc3Bccm8vJ/kojDJB+OJG71pYG7QETyUgJm+KZrZyokp9YauEpubCwLZm/RHMTsYNXF7RjF+zlYJIKrdjyEXZGgz05fTVguY2UdqkFzICDhMeEKCCs2AtYprHAhg5sLqhTm4FjA7lWklQFelMEk8ohzZqoG70tR5jD1nsxMNnWb+emU56z3Hb9b1iWl44WjiDmqzTIlJs/D6V56vW4+7/PoE+k/e96r4bQmY1LwJwn3JLM5e3imhT/TzzmoJ4UOLMli6ZEnVjHcE0afW98n5z2wawJyWkNRZJmv7je4RMUtnC5YAdNBv4tPnLMax8/eCPDeY5ETQneBV4zulYoq9useBuq4G5gMl6Um+z3QCZrvJspKY3Ppw1TXsxSVUQhBqyNSKCLwkupRrR1w8uEAvW7bM8YgRNLl4uylZWAmYVegYAZnAQCBhCBb7WK1Qhr2TqszfeDQ+Jw/rpTnYC2C2P5OblJYcByVLpxo6rS808atyifW0cvv3itBHX+7GnoQk3lBt22AKf3bCPOx56yXXwMw45Q/954uSGMRvGiWClkEtGeInJjAznvn6c5ZgePtbMucrFX+gCZu5r8m6TuUK8nvVvGYs6oiKKZxaeHPYFHN2Ll+UZCHXnrV4LCSLz8LNMF1FjCX3qv3sZz8TpvT3vvc9r25xwK6rgfmAiXpyb3QoATNJHWRKq/R4TiVJLYaZgLgo0ERNMx015FrmOh5HBikLxjttBFIuyKpknpPz7MBMEGC2ImrrDAmrlBLUSdUrlXfcS2BmOkI3JTGdyILHcNEmCdCNpcLptXmcl2Z4e1YrVS6xnlZu/57jzzHbOGLg2d3AnGY/AqaJrGUgngU+dkIXenZsFlAzTD+2DuUwmC5i+YwY5rZFqpqBe0YyuPaHr4kp2yoChq+U9OF9x8wWLZn/Pu+IGZjTGpa88nSVsIADgXdDT0J8xstmRPGFn67D81tLJvBEJi++Zb/pwztXdMrvjb0JAf3u4dLnzCT2t+cuxYL2vSk7+/r6hLDIAjFeNVaNopWM6TIbvWlgbtARPJSAeTymYpqpCcxc9Ajo9BmXl2WsNPQECGo/BHSnjVo476NMpU7OIzDT9ElNkcQzErt4frWqVnwWZiOqVfXKa2D2Mq0l5U4SnhsZOpGzOsZLYObY06LBIhbjbbxGIp3F/760C+v2JFAkkloWzlgYwRGdfpkjLe2duPPFEbzak0euCMT8wLkLgHMXBSvGo6/tz+O2Z3qQKwA5FowYDXyIhUw0h5iX2hDCFUF0cOsaSUfZkzZw9++2SmwxD6cW/PyWQRZoRjjoRwEGdg1nQKb3iQtbQS52fzyHXLGI2c0hIZTRPD6/PYxbPrBSwJ2NPA7+MBe1V+0HP/gBmILzG9/4hle3OGDX1cB8wEQ9uTc6lICZpmKnhCwmAqHmOTg4KFosF5tq6TUrjQg1V57L85w2LprUgJyav3ksWdZ8JjLAqSXW2zTQjMxFrZKpUfVTA3P1EZvqwKx6TtPwtoEUEpkCZreGBBg5rsz8tdFciFue2IT8aGQSIY+JOf77qlWY3WTuw2anO+Z3m4fwzT8OoD1ECCZjuoitwxaIuEF/SXumafuYmX5cviSHrkgEP3mpF5nBEbRkEvDF4wjER4DBIaSKQLOviHQ4JqFRA0YICxd0YebcLjzdncXuYhDpcARmSwua2psxkCrgtktXjmnNTObB94pauVftnnvuEcsT6yE3etPA3KAjOJ2AmYsITYDVWj1CljA8e3sFkHktmpRJFmNGLicFKez3ZfYgmt2onTptTs3fNJHTh83Fg/2khlUPkFUfnMRyew3ML7zwgmxY3GZgcyJHrzVmL6sz2fNAO3lWt8cojfxLL/uxfXBvNSR1nevPWYSPn75ov8uu3j4kyT/6U3nJie3PpGAMDaM5G0csmxbwbUsNYWYhiT/tTCAWjeGZrQkkM0UkqJKTPQ2gEAkjnEmLP5mtYPpx2JwIVkQKcuxT2zKijZNcRpa3zzRRjMbw3lMXo2VmB/ytrRjM55EwDCyiKbu5GUZTE4xI7epUbuXEvNiMl/7nf/5nt6dOueM1ME+5IXHWoUMJmKmR0v9YHlvMBZEgR1MyAY6AzJAY5WN1myqTkufOnj9uSCr1zN9cLLhp4OaBzG1uFmj6rBXKVT4LCIp8/lr+ca+BmclXqLVrYN53dLwGZnX9v3oqj2yF/evJi1pwzZmLMT9mYhYyZFrBGhpCYk8f7vrpK0j3DqApXTKPJ+GH3yoiWszCsCyMBKOIWHlcPDePmbNn4/E3+7AnnpVwLba0YQpb7KiZEWymr9oCDpsRxTHzmmAZBl4aKGDDzgEkRgjOe5MEzW8yccJsPwrFIvKmCXbb8vkQIEHOMOALBpFftQrGscfWzOvuJsHMLbfcIvyHz372s/sM0GWXXSbJTVTGuq985St45zvfKe8jU3LSVUbr1a233orzzz9fzqUrjNWmyO3genLTTTfhqquuku+4UWIFqkceeUT+5nEsiKEaNXbWcmZjGUj2q17mu/L3XQOzMxycckcdSsBMPyzBTZnBCNKqwhPN1ATkSr5Xt6kyOch8WVn03E1YRzXzN83qJIbxN/vIOGfFwGXcK/NOO03W4STJyoEAZvreSRCa7EaZ06LglY9ZZS2baNnESs9N6wddDW7KeLqRnyoA8VdPpOFPJtGcjpd+Mgn5PdtKY4mZQTCTwoqZTVgxKyZmajKoH3plDwZ9QeQLRUSyKaSKBvI+P6LFUsIRCxZCfhPvOCyC+QsW4Dcvb8Vb4uMGijCQDYTQXMzANAyJU2Zj7PHijoj8zTCqvpyF3VYIRiEv3xsWcOzcJrxjaQtj08RXzjjmnM+H1lmzUBgeRnH+fOQuvBC5fH4/E7w9c5yypNlD1spTwRIoGQ7Hd4quIYImyaJqg04TOjkcbHyPFCh/8pOflONVEYt3vOMd8r5yfvMzgrkqYsFQRc4hbqoZkkXgtRexYG5sWsBIPlNpQakssKgGgfvDH/6wmyFn3xu+OFPDP4CrERs9+FACZgV8fCmoHTPZBkGOf9eKqR1PmBXjh6mhM5GG00bzN89TjFOywPmCE2joP6ZZvXzHTJ8h01s6ZTg7qRPtNTBzUSNbvRYBzanMyo8jMFNLceqnd3sfr4FZVU5y26/y4614HIXBQRgjI7AGBoDBQeR7e7Ht9dfxqzVxpLMl8LM3ErhYOpGmZjKq3768Ey3NYfRbfjzy3FbkU2kxSXOhHApEEMsk9wllYoKPY9qymDd/Pn61JYHVu9OIZpNIhKJoyiSQ95kIhIIomnRMG1IuMp8vIGwVYBbysKgVx5rQXhg1d1uQeOZLjpktZDCarQmOSCTQxipyzEV/9dUwontzadeSmz3BjD0rnPo3XU///u//LkRKJpIhiHMzTIBkyUd7YzEYFrrgBpzgXa3sIy1mBF+12WLFKCoGN9xwgxSmINBWKvt43XXXydqkyz7qso8TXQsO+vm1fMx8KQlyfOkIbmRX2zXPWp0fT5gVFxDej6DptNH0zVAQAjDPZZ+p+TEhSrWNr5O4ZPv9nWQ/08BcfcS8BGZqeHQ10DVRr1mZDKzBQTE10+RcHBrCYM8grHQSLSggn0ihN56BPz6CjpntoEWZGiY3f6sHAxL6VN5mNgcFaIOmD3FfAMcv7cTSsIWRZBY/eH6naMVSxYlm63QCASnfaMplgn4fTl7aiWJ6AIuWLcP9L3ZL0hDLKsAy/YhkM/AXSto19eW9GbEh1ygWLQxZJoI0j5slPzNZ35migfefsQzBfI7l48AEI2yt7e0IXH45fIv294nXk12972nCJpB+5CMf2e/Qv/7rv8ZDDz0k/fjRj34kFjFGa9gr2xF86aqhxk2SJTP6qRDNL3/5yzIGt99+OxgyyJAsVU/9vvvuw/333y8Zx2i6JmBfccUV0gduwK+55hrhu7hpWmN2I60pdOx00phV7KddvDRPEfDom+X3NPmyZJwbCw9z89Kc5CYjF3faPI9VcJw09pO7bprWWXKRgMzf9RqBgrtyp2ZhJ/5yr4GZWnu1JBT1nrfe915rzPQV0hzphSmbwEz/+ymnnAKLJMZR4BUAtv+b4JRMAuEwEAohYwEvbBqQ8CNqnp3IYcgIAumUmH/JuD5pUSvCzVHs3L0bG3stvNk9sp8oQwE/MuEIfMUCmos5iS9mnDITgzy5rg95C0j6w+ITph7Dak5MMBJEEYe1BTEnZmLb1q1YsHAhfvLanlFgJpwDRZ9Pzg0WcvJjbwRmCwaGYCKcy0pqTSsSwVDOwKJwAecs2/seqCiJ9gsvhP/MM+tNh3F9/+lPfxoXXHABPvjBD1Y9/+c//zn+8R//URKR0OJmB2aanEluVMDMdUDlv6dpm5tvBcx33XXXmDatso0pYCYoK22apu1PfepTGpjHNaINeNJ0BWZqzyq2l6Ym+mYJfm7NyxxSN2FWagowhpkmcPp/azVuFkj6ommd5mhq807BnNd1W8rSSVpSr4GZWjs3HrVCtsb7KjUKMEsVNJqZbYCb6+3F1ldfxZKODlgjIwKqqokJORKBEQjAMk10D6Wxuz+BYDGP3ryBLbsGERLfrIWBnIFQwIc5kZIvlybjGU0hHD+/STapvxsMozttIZxOwipayPv9SPiCaLGy8Pt9yBl+GKaJS4+fg45YEGv2JPGz13tgZtIIFrJiyi76TCyd04LzV81lnUYxTZMEtmnDBixetAh/eGsPXtjUD59A7l4NmX8lgxGYxQLC+RIznLWTk6Eo/Ik4CtGYlG4MZ1NojwZw+tJ22VioRvcOteQZ117ranPtZj7RZ0wTs6qpXO1ckigfeOABnHXWWVVN2YzKuPvuuyuasnn9K6+8sqIp+9prr5W8+9qUrU3ZbubulDyWIEdAVKFEs2bNEpO1Yv+Ox7zMB60XZlVJGCSZUTutZpbkxoHaMTcKTJdJHzLJaARoVs5x2pzEJduvxcQJNI2TrFKpETAIbtwsKPapSvto/20nzrixPvCeBGY+LzdMk93II6DsvfIxu9GYrVSqBLw2kzNNz/L38DCQ39fPmy8UsLu7W1wsY0AcDMqGkqAnAJjNYuP2frw+VITP70MglcSOgZSYoGn+JWu5r2gilE0LqYpxx0G/Ifmo3314B3oGB/D7gRB2JwsIhoPwFYvIpLPIZ3OYGaKZ2UIwFEQ2FMapi9qwMAKs2zmIX63tkz7Z2zHzmnH60o59PuN8lmiBTQNY15PAcIFAb8IwfZKBjFsFmtWplWcMEyb9yvQfw0CskMWFK9pkM8DG3NzlxJ++VAq+q67C7OXLJ3vqjF2PrGkCI8ldqpG3QBO0yihHF9J73vMecTnRX0z3kyJ/nXPOOfI5ORQ333yzmLIV+YuuLVq5uC7de++9INnLTv4iiLNeOgvn0CTOjbcif9HXTSB307Qp2420ptCx00lj5qJMjZAmZ/6UmxvJ6KS/mKZsN208ea9J2KJZUvmP1P0IvrwegY/mLb6girhFv9X69evFVOq0OQl/sl+rGpFNpfPkgkJNln0j4Cpmq/13OXGGYG4HcTtoV8r1zIWKC5yTCkRO5aCO4xwgccdNuU039yAw06LBZ7RyuTEf7xjg2jRg+kSdNpqHC4EAdvb0YNH8+RgeSaE/ZwnYdplFhFCUMKFcOIpfvd6NlmJW0lZS9izvyBAjFn0YDkVRTGeEMW1KqBI12SK6Qj586Mg29Pb0YE90Fp5b14NQPit5rhmbTI3ZHwmJJtuUzyASMPGOwzulrOOLW4fwB2btKmvzWsO4eNXeWsn8WgHzs5sHsXMwLf5jIV0ZPmwYKaBg+GD4DOTgQ54wXSwiXMgCfhNthTRWzWnGSYtKzOf9mmFg+0knoXnVqqobS6fyrnXcn/3Zn+Ef/uEf9nl3Scqkhss1hGNP1xFBlwxsWiEYLkXLGt+Dr3/962PaNjeJBFSCMd+nG2+8EVdffbXcnu/c9ddfL+ZwNh7H71Uj0BO42UgUu+2223S41GQMcCNcYzoBs8obXC3Wjws2Q5/ow3PTnCb+sF+TAMzdLne/bNxxUxumf0nFIJdvHNz6pXldgn+lnNjVnq+85jMXB2rItAoQKHktLjqUpdPGhVdVX7KDtj1cxQ7stGpwjPhTXnGpXDsvB/l6cZyTCcxkCYMartJyh4aw9o9/xLLOTslmReZzvcZwoc19SbCeca5gYV5bCEfMaRZQlcQYgQCMQgHUrnOGge6+PpixDqzb3COAJlm1wmEcu3wGmgtZxFNZ/GZLHC1NVG/pmwVe2jaIlEXtGGhNjYxptgR1AQAm7YiG8ZHTF6JvyxZ0ds3AM5uGsCllIW/5EClkkU6m5TjR1A1Iqs0PHDdbiGB/2DyAl7YNS9ywCr4pWBbaIwEpycj7+AJ+GMEgNm3diiXLlwso/3rNHoQNC+FiHqlMHkNGAFnDJz7qYjoLM5OCFQ6LP5v3TdPH3BLDR4/ugFFmTeBzmKecgvXz5omFyW2FuHrjZP/+kksuwde+9jVXxE031z+Qx2qN+UBKexLvNZ2AWVXyqSYegiOJR27jROsl/qh0PwKUCmWiFkrfGLVj+o0INpWaU7+0/Vwn4U/24xXDnKY3PhcBWZHNFIFM+Zh5nlsztZOpSasGzZ00ZVcC8mqAzs8FrAxjLKdzOZBz88XjeH27tl7tOQiudm1XgHg0sYaYmwnOtkbeAhm29TYI6pS1uxN4becw0gUL6UAIWZhY0BLAeYubEDANCfUxfD5Y6TRyySS6SVQstLIANIqmKRUjctkcZsf8OHpGGIVMFr/bOCAs5mA0hJQZxNpNPcj6TGEuUzuWcQMkrjgTisi/o/k0PnTSXPSOjKCtYyZefKsbI6msaNpDyTxS+YKwrMVcbZXGvVSYIoSt/Sn8dE0vMj6/MKwZl8y7tIVNtAcMRFDA4Z1hLOqI4LWN2wX4GXa1M1nAy705JHMFzI2aOGZJJ9Zv7cOW3cOSQ3swEEFnPj0G9iz32BEN4P0nzIMRi4lMaLqX55k3D4Err8Sba9eCLiqCs1eNyUFoUnaTTtervkz0uhqYJyrBg3T+oQTM5VqsU5GPJ+81gdcWXnoAACAASURBVJj+X/q3STxzspjX80tX6q+T8KdyYKYc6G9ndjNqyOUZuLwGZlotuEFRyRqcjoM6zl55qdzUzuciQ5Z+ObGgJBLi0zUZl5pMIpROI5BKIZhMSpIN+jgJsjRB2n8zHSTBqdzHaQfmfNEqZZ+qkAGBZudiOIyH3ujF0EgGTcUMfJaFrOHHsBHEOUfMxIqucOmR+Hkmh56+EQz37saGuB+743mkDD+ihayUQpzXFhEiFFuvFcDz24ZhJBPIZAvYHs+JWTgwynZmvHDaH0aHvyAkraxhYn7UhwsWRyROfgBNosGzwhM1XxaSSNE5HQhKrDFoIgdw/tu6cHhXBLlUBo++tAMDqZzEOKuSj2G/D5QB3d8Ml6LZejBvwReNCVP7nPlRzJndhqLhgy+V3GeT8+owsHrtDmRyRd6ulIjEAs5Y2o63zW4qyUVU92b5Z/DDH4bR3i4WL/rgnUQsuJ1X6vizzz4b//u//+uqytt47+X1eRqYvZawR9c/lIBZabFvf/vbXUnTaXpNAhqTFFALJSgQaEkgcap1jkejd8Ky5sMSzAgqDMmiD5kxlNVSYvI54sk0Xt81gg29KUmreOTcZizrijp+lloCnszFtTgaVqRCi/o2bUJmzx7MCocFkJEqxesSaGiaZmpHnkPrCmWifg+m8hhKE94sNAcsUC9U2rkyuxO8OUbBSBO6E0UMZ4tCuprXGsKc9ijMpiZJESlELV4fPvxodTeGk1mk4BNTtmkVxYd73OwoTljYKn1jFaiXtg+Jr5cJN9IFlJJyZJOjuq+FBV0xXHjSElg08aZSyBaKGEzmMZjK4ZGNCURzKaT9IeTMgAA99wpkPfuLeRBAzz1iBma2htA3MoJne01s7C2FUvmsIvyFPPzFAgJGqaQjjQTExIuPniXhUmx9iSxe2DqEkXRetGX+Zn8LhiH35QYgUsxitj+HUCSGQTOICIp43+Gtku1rnxYMIpPJ4Y8b+rB9MI1E3kIxEMDRC9tw2rIu6buw1jm22Sz8F18Mc7SalLK2eMFPUH0kYfPXv/61aOaN3jQwN+gITidgtmt6lYaDizCrMZE16abVS6/J+9J3TECmmZpaKLVRZgdycy9qesz85KbsX710oQQf+shJOFOJSmiypqm3WuPzPPFmt6RU7BzNBEXgOvuwDiztmngaTTLDaUEYj9YjhKvt21HcubP0s2uXhPZI3C+TXyQSSGcyJR8kEYY/BLNRcOAmSeJw1Q8JS/1JbOxLwzdaPIFgS8YxmcwSgzsK4pJTfedO7MxFMZItIOw3kGeZxZyFZU0W2nx5MPEHAYX3IUi/kghiY3cSGX9QgFY2CIYPyxZ14u1zw4gnMnhsbb+AFzNVDiZz6AvEJE0mW8YMIu8PYEGwgMuO3R8oXhyy8Is1fUKqoseZQMyYYUIzNXSatgm6R3YGcebiZrGU/GRDVio7lYKp9oYy0bTOxv/7fT5ceNRMKeeoGjVaZgQbylv4weuD3Okgms/AtFgK0kLWDKC9JYg204CvkJfKVtTyqY3HYhHM7YrBIGmOrpxsFlYuj/6BBPK5nIRGqVSd9nlpHncc/BdcMPaRl6F26ibM1kfuhpfg72b9mcixGpgnIr2DeO6hBMwUM+sXuwFLnlMtvaYiTpHURbAjINtfZrf3opZN9qYijDmZFtVY1ioci4DM0Cj2jexvJzHZ8XQO9z+3TRZltVhTM4sF/HjPUZXDrJz0VR1DYCbrm5uXes3KZlHcvh3Wjh0CxKIFp9MoJpIYMYPIDA6LGbU5GoLZ0oz47t3IJpP7XJtAzPjfMVAYjbklaFNze2FDL2KmJWkf2YYzBXS2N2HFvLaS/5cgTg0un8dba9ZjTy6MllhYAMbMZpDIFtAS9uOIURMs/aNFsqyHhrBzMIlH1idg5HMSmsQ7EP9mNflw0twQ1qQDWLszhWAxJ6bcAX8Y4WwG6WBENO9ILg16fltDPpy0pA2xpig6WyMwQyHxQ//6rX68uHlA2NQ0y5tWAYFCXs7ZB+AMAx89caakmPzxWyR6lbRqNnUk44W5oeCGw/T5MKclhHS+KPPgsIVdCMfCkrM6OTSC7z67fRTEDSQDYeR8pjwfI50C/oDEUadTWdHG2adUMIK5zQG855i5ko0MtCyEwygmEkJ+q9SMmTMR+NjHZNxU8zKdq8jCssS3zE220/zz9ebwwfxeA/PBlP4E7q2Bub7wyuOfVfISgp4iTlXK++wWmMdjai9nWfMaDFnhD01xDEuy59HmgsPFl37vam04lcMDz23DrJbgGFjRdEn27cWrZtcXWJ0juJmoRuCRVJPbtqGwfTuwezeKvb0lAlAwWDJLU9MCsDnnx2B3n/h4CTIExsVdUTE1p00TXSyQUYHZq4CIDGJeM54r4vmtw2iL+lH0+cW0mx8aQhAWVs3bP8563eZt6C7E0NTRKjHEbATmpmgQRy2fLSFUynzO77aP5PDw6z3wM6WkaKJkNhtCjrro6Fl4ozsuZC4m1mAUcl/OFHM6tV02mr4Juq2+AjpDQCoQRmfEh65AHiOJLJLpAt5IhcQ3HMumBJAJ/3nTj4LPFO2Zf/N6nzi5C4nhYTy6Nol8Jie+XTYSyXi3he1hSfYRzxQwmCnAiEURYOnFTBrt4SDOO2YOAsGAyPs/ntyMZAEir+YM5VAe5QwxT4ebm2RzwYxk/f4wzp4VHNvAlATiB5jrmmNrjwQIBhH48z+Hr4zk5WXJUAXMLLBCYqRTgt+EXwgPL6CB2UPhennpQxGYSe5w6vel7FUY0zHHHCMmYfpqCSwEt1p1kAnMbu5FDfypp56Sc5w2xbKmVsy+0WxNxjUBuVJhDmr3vE+t6kv8/mev7sLOobT4GEn46R7O4LQl7Vg5p0TGmUizAzOZtwTi4tatYpYWJvQouDGciFmuwLAkGzt6JBTDhg27pMCBjKNlYTidx+LOKALFjCRqaZ8zp7Tg5/Oi9YrGy2vw2vwZvR7JTy9vG0Y+GhXfL48byAJz53ZgSagAo4yVvXXbNuxpno9s/6CQnTL+AOLw49g2E51hZRwuSYea9tM703hjfXfJJG74pAZx0e/HYbObcdGRM7G5N4GfvbJLzN8knBVGfeJ2+dJMPWdOB2aEfJJ9a9twFsFIEG1GQdjNTMVJbTtjBpAzg4hlkxU0ZuDCJX5hwa8bgvxkfYFSKUXDh/aoHzOiJtJ5C37TxFAyi5gfKObyiKEIMqZPXdKGBXPaJSzq1XXd+OW2lOTMppapilvQ6EDtmOk7I9k0mkM+5AsWisGQmLcXtQbxjsO79p8+Pp8UqRBXQCYD/0UXwVy1ar/j6OphClqnRVvczlM+C4GZIYRu1gi39zlQx2tgPlCSnuT7HGrATL8vyV9udsM0Zavk8WSEMuzJiZmL/myapVWJxnpDx0XBrV+aGjO1RG4eaB4mINfqm5NkKezHYDyFP2wexLaBlPgbyZQ9Zn6LaHwTaYzXXf/kk+hIJtHEnM/xeMlcPLogE4h9DJUp0zzH7tnUhIGeQWwaSKO5OSrATQY0Nfo5zQG0WGlkCgV0dpVIRKKJRaOwaphM+ws+vLVzSIhVxG9q3yx5GAyHAG4O2MdRgN7c349Zza3YlDHRN5IRbZD5ooUkRXChJu73y6KeMf34r99uRiaRElAyGa88qjVzk3PU3Gb0xLN4fssgdlpBGMMjyJGcxiRfTPxhBpH1+8W0vaIzLKkpSSB7Iw50FpVPnESs3FgxiYDPh+6MhaQAdEo0Wt50xYJ2nLa0FSOJBHxNHfjRizuRSufE/xw1Cjh3WQs6Z7eLq+C17iRe78mgUDpV/leIRHBMF7DITMt8Zt7r57YkMZIpyjEkuuUiEcmDHSnmxfeeH40046+sP4RIPoPjF7Tg5MX75oDnGNLVQLmJe2D5cgTOO6/iNGNuAPqAnbx/45mn3JSyAIUG5vFIz5tzJrbieNMnz686nYCZwrInk68kPKa6YxxztVhi+zmMiaXpl+UhudASZJ2cp67Be5Hh6abggVPzt8ogRqBl2BGr3DhZrGji5rm1MmMRmFXMcCpXFDCmH3c8zUomRRu2qBHz98AA9gwPoykWQ5TarvIvhsMCajyeWi7BdgzkCNzUnPmTTCIZT2HtziHRmGnKZn+HskUsXTwLgdQwssPD6Fy8uKR9q8aFnwA9MrKPFkwgIAt6aIi1g1nwwS+pIPfZf4yeW8zlsGXHDskFzftK6BBhiaBNcztN56MmaKu5GSN7+vE/r/QgFYogwDzTJGYJUQp4/7Gz0RkLSh7rBzenRAOnhKkBJ4IRMF1IOJeCX8hqwNJO1i02MRCIYNfWPWJyjgZ90oehVB5GKIAZbTGkLVNCmJjReiBTkIxhS8NFnL2kDcVcWiIFntlZkA0BU3Xyv0y+iHBrEy5b2S65s5/dNIDXdo0gb5jIBMMI5rMSivWut3Vh2YyYMLF/9EqPyIwaOq0GwUwaAfq3JW02SzrSpF5qQ+FmhPNZ0cyXdQRw6jy6SHxCGhO/OAlnsRjMYBBmOIzg1VcjwPjuCqWE3aREHc985btB4Od7ojXm8Uhw8s/RwDz5Mj3gV6wHzKzSwpSKteovU/tkUhD+pgZKkhITebhhS/PBndyrXED1gFnl0+aOXnIqW5Y8C7V4J81pFjPeR4ULObmuOoaaqQDwli0obtsGiz5iEpWoSY6G+jAtZCQWQ6y1FVYoBB8LMyh/MH+TeZ3LlTTepiZhUNPkLQvlqI9512Aae+JZFIJB5ANBzDByWNwaQiqZQNLvxwzes1IjCSwSQZHmccvC+riFjVt7kM4VBaA622M4fkkn2lsiJeY2QXeUfMQ+EJgXdnYKMYugSsJYeThQT9HExu0DopFu6qdZmem7TIz4wwhkU2jzG7jsxLnyeZ8Rwgtv7EDWMDBg+WGlEkgn88IQJ0mMQM70mM3NEeTDEaSzBNUM2sJ++A2IxptKpIUc1hoLItLWIrHHsYCJvoERBGDJs5hNMSR6ehAfGsZPNqQlmQjvwZaFD8m8hTa/JVpywudH3DIRyabEx63aCQtacfLiNgz6gvjOsztk8xLLpUuVo7ghIAGMSVPo988XJR92we9HUzohVG/uWWbEArhoZYeEqtGNkGdYG8c7kZA83/0XXIB0c7OY3Nm4EbZnf+MmmZnzOOcrpX6lJWwigEo+CdNsMqxwOjRtym7QUZxuGrMClGrDUat+MU3WBGSCO32w9CPzRaf2yJ36mS7LzLmtlcw+VwNmPhe1dyY7YXIOAjE1cfaXfaxF5rLLwmkWM6fATA2UAJzfvAX9a9Yj1d2DYNBEa0eLmIIJYBKTqpjNhQJ6du1ClPmGZ80qabBlg1VuzhbtmSUOqU2P+m4RiyGZziEzFEfAb4imS6AYYdjOwAA6KzC+xQ9K7XeU+NWXzOG3a3sks1YykYaRIzPaEjbyKUs60DqzvaS9c1Mwen9ubDrmLcRbIxaGevoRNg0s7IhKhiw+x0jRJ6bpkFWQ8J+tAyn0J7LiDqAI6Ec+9oi5OKrNL6bvbSM5MR13+gvIWcCWwTT6cz6ETANBw0LMZyHAcKf5bchk8wJ2b3bHMZxmso+SWM1QEIvndWBhGOgI0jAQwpahLPoH4miNBOR5GCu9ZTCFbDCE4aEUQgyjGg2P6kMQZjqJbDhWYnVn0vuMCDVd5t6e1xlDIRRC1Aes7x4WbTxQzIvWW7CKsodpChowWX854MPWBFniJfKaaks6o3j3sfPERYBEYh+Cnv+882DayqTaU72qRDIMD+S7qdLvlieYkYIfDD3z+ytmh6uWx125thj2+L73vU9y7qvGZEGszczyjYz9J4fjm9/8pvSDoZTMk00g57VvvfVWMHMYG11MLAHJtYN9uummm8ACGWzsJ8tCPvLII/I3j/vc5z43ds+vfvWr+Pa3vy1/szbzLbfc4sr9pi6kgVkD85SQQD1gLi+TyJefu3ACHF8evmxd9E/azGjc3f/2t7+V8m5umttayQqY7YQxbhIIyKxsQ02BgGw3pzshc9n7TE2blgD60Wq1asBM87BoxGRO79hRImaZJjb2p9AzkoHP75ckHq2ZFJZ3RcbCrez3YhKWXLQFQ/GcgA19unNawwg1RUvmbC7YoyZuAVNWoaJZetSkrNjZ+/U/FJLqYqlsFjNmzy75fEcTVUicM/3WBFiSwXw+bNnUjVd3jqAvnhF/KE32TDtpRiM4tjOAE+ZE5RbZSBO2bt0tfmwrNYhMoAV7RjIo+gPIBsJoziVx8sJWdDaHsDkJbNneK8+kEnUwiQY3G6FICMvmtGL5zFhpc0CiWX8cL63rRmvQJ4lPNvUlkLN8mNkUQmS03OFQpojTjpiNLl9eEos8tHq3/C5EoijQ/zsSl03B2JSNxRBMp8TMzcbPqXWbBjXiIobzBobNEGLFPNLBMNLZEriGAqYQ1lL5UjUrauOMiyYwZ/2lSlTRXFrY3qlACE25tGwe6ITmJqEr5seZ8/yYMXMmhtMF3PNyD8K5vYU8yCj/0InzMD9Q2KekJfvoO/xwBD7wgbqvF9/DeuGEBD0nOdvtqV+5DvzHf/yHFJFhMZlzzz1X0n6St8E6yCSd8TM21lJ+8MEHJQkJAZXES1VZito21xKGT/IzckBUZSkWp+GawBwCrL1M4LVXlmLBClrlaGljQQt7ZSneh6Uo3TYNzG4lNkWOP9Q0Zr5gK1askJJsBDuCHhmeBORqCS/GQ8ri8LotychzFDlNacjcwfNF5k8l/7YTMpd9qqn0ostXHC4JIGIhU7JXlbexDQ5NjmvWSByx1d29t1whfaqj4MmkE0xGEu5qQ2C0sEOfERQG7iz/vrmmeZ9tA4PYPmShOegXrW3YH0HEb2Bly/5mYbS2SkEDxc6WmGRFsFJs69EEF9Ruk319YGUvey7l/QhlDKVKJrG9P4Un1vUilS1KTmnGHkezKbSGTNE0T1/WDn97G556fpOko6RGTi2dUcLUgKlwkpmdNwNYMK8dpy3vwuZdQ3h5VxyZvCXgEDMsRIwCjp/bhJmdzSVNkYQ0bhCGhwUIVw9b2LNnCEYmg/5ERohU89rCJV9tvoihYAQXLYyIn78vXcBTuzIopDMoptLiTyb5i0UxogEf+swIzPiI9LW0ubQE6I2Av8QIN1ir2QQLKiYKQJRJT3J5KF61wKxYOYB0KCLELsqEZmqyyknuUo3Zvii3cC6NFj/wzmUt8BfSktzljYQPf3x1CzIFAyOBMELFPJqRw58s7cBh3JjYmtHaisDHP16yStRpToC53jWqfc/KUXxnCahf+cpXJIsftV6l5arzuIZceumlUpSG6wi1ZQI423nnnSflGvk92eMEX5Wbn59z7WGZSFaLItBefvnlct6Xv/xlIZzdcccduO6668RNNdFazKVNWQVH/XgFdJDO0z7mgyT4ybxtPY2ZGX1ojmKaTVVNiSkq67V6vt9K5/MFPuyww1xlECIwc2GjFk/tmFpyLcKZU5+x6h83Iy9v7MbWQqtogE1hP84+rAuHz95XBpSjZLx68EEUN2wo+WVpdh4lN9mfl+Sjt5IGOqy92lEyk0dbLIgFC2aUiFGjJRAJkm/s7Mdwuoj2WV3wpVPw5XIS7nTYjJjEAyvgZRxsfyKHDTuHkExlMCtsYGlbCGEyjGxNwJqhUYmElHwcA2ZFKLNp4OLv5oaC9bCHs/jp5iSyyRQio4CjWNPUVltntMMaiWMwlUVzNIiCP4htwxmkC4ZsKEhaomk5ZhQxa0473jM/LJr0r9/qGyPMMQ91OhDG8Ys70FbIYA5jw9vbkOsfFOYySVwU6UA6j96iD+n+fuyJW+hNlMzqVnMr3hbJY1uqiLjlx7xAHtlsAZ2xwFgY286RHBAMIMukHfkiMkX2qyhpRf35UoKPfYAQQD4QQHPAJ1oxCWejyrUQvlLBsJSEjFh5Ibkx9WYyFBGWt73Rv372YZ3C0p43vwvF+BAS/f2YsXQpnt/Qg+d2ptCOUmGNghnAcAE4cXEHTlzcNpawhWYF/2WXwZw3r94rKN97Ccy8PrkkX/ziF/GLX/yian9YE5kbv89//vOSwc7OayH40hpFMzXXFZY45TEKfPn+UeNmStw777xzrLTkfffdh/vvvx8PP/ywmK4J2FdccYWcx2I411xzzVhkiCNBjR6kgdmNtKbQsdNNY6Z5ioBS3lTiDWa+IpmL2X2q5YquNDzjAWZuAkgec5J6kjtzmsAYI03GNH3GTsKseDyZttyJO2lrt+zE936/BUvnz0FLJCDaFnNEf+SUBehiKcHRRjlmH38cxeee23tZajQ0J4+M7HMrMnff3NyLqGSMKrGkCfqMK2aqRdpSjeZmFOmrbWrC6vU7MJIuoqu9tBmgNXQonsbh7UG0BkqXJkt3qH8Yf9geRzEUQVM2gVS2IIzpExeV5V9Wpm4m+0gmkTAMzGRKzrKyjGNhOby+lCjswRvbhzCUziGes8Q0nSIF2jTR1hwpJdsYGJbkGLOjppiPaa7PjZq9mRWN5uJkMIxVbX6ctbwDm/qSWL19WFji8UAYURSRizMVZ6kqlr+5CSd0+tEbZ2WnUmjWkXOaJP0nNxTbeoexOh1DKp5AzjAlHpghSaak2mR1J0NKMs5pDqI1aGBHbwLbUuRWFRHOZVEwTeR8ftFyS6Uo9m/UmhmKNdNfEBM4N0UpfylJiSQDsUrpOqm1J0mKi0QR792/HjPDtz56yvwSgz4QQCKbxUg2Kz7YbbuH8dir3WItCEoIWCkW+p2Hd2Ju617N2DznHPhPO83J1JV5RTNvPVO2o4tVOYj+YJq0CZCV2he+8AU89thjYybocmCmyZlriwJm+qWVNk1NnBYwBcx33XXXmDZN0/YDDzwwBswEZaVN85lpTlchm26eTwOzG2lNoWOnOzDbWcx8QQiANBO5rec6HmB2UpKRoEpAptmMYEwN+KSTTqrJGrdPH6c+Y3XOU69twU9W78CJb9vL4t7Sn8S7jpiJY+a3ivmVoSJ7HnsMzc89N1Y32V59SUJbTBNmJgNfJCKs5P7hNLpHmJXLEIBrb41i8czmMeYvwY4aIDW5bes3Y8dQHq1NYTHBEnCpmTFWWuKkR7XadTuHJI66JRxAIcCfIJJ9gzh+YauUBxQAJ7Ob8dCj/ud4by8yDJcqyxhFMPK1tZU2CTSNkxEdz+KZdb2IGQUkEmkMWT7syRiIWDkxVbOMYtIMIGP5sDzCuFwLvSMZAUtuQIQkbgbAvCLHz4lJprDHNgxhzVBefLGMW86NqqJkQef8ARRyBUn/yThpXoPuBGr8XR1knwNb+lPYmSwlJzHzOWRTpUIUcj/GC4/GODe1RpEs+hHIJEXjJdAyTlhBMXNzk7BFZnU5QMdDMTRlEpIRLBmMCBATkMvbpcfNwYxZ7WJmv2/1nrFMYnKcBRw5I4L3HM4Qq6LEng/09OKt3iSC8GHO3E681T2Czdv6xnh/K2bGhNWtTJO+pUtFW3ZqbeWGm8B5+umne7aC0mJ177334oc//OF+9/jSl74kZK3HH398zApWy5R95JFHSvnISqbsCy+8ENS8K5myr732WiF4alP26Cbds9GewheersBMAKb/mCZrmoNVfd5quaXrDdF4EpNwh8sdNclk5Y2ATO2djE8CMl9EMkPdMrndlqR85s2tePDF7TjJBsyb+5J49xEz0FIYlOxhc5NJzCSTdDTRIslvUpmJ/mb+SLYoal2mfF6k2TuXQyaVQS6Tk1AcsnIJ2jSBBzIZ0ZRZbpHPGM9kMBKIIU4CVcFCU9CP+e1h8deOabXptLCPmXGMGqJqvQUfjlvSiVlGthRKRbM0E1MwgYjfj3iWaSrTmMnKQLScKNIXTd0E8FENkYBPUEzlCuj3hUqpMhMJuV8mFEbLKMjRFE1szYfCME0ffMmkxBUzvpi+Zm4WFkcNHH34HARNH558aSsSku7SJ1oifc2ifwYCyEdCyGdKm4JTFrfCVyiifzCOboZUSYlJkqgs+P0+mCRwZVJg8RDZgEixiVJeam4IlFmZ10qEYmjJJkum77KJxo0DQTqWSZTipIMxhHLMxR0SYhZZ1exfjhsn1oCWUhiWmJr/7IS5mB818Pz6HrywZXC/a7d3NOPy0xaL7BOpLP7rjSH4Rq0p3GixEMiKpbMwkMyhE1m0RUbNIXye5mYErr5aLCNOGzeNdA8xN4BX7ec//7mA7/e+9719bkHz9k9+8pN9QJkHfPzjHxcLgSJ/MQ8/N9oE7JtvvllM2Yr8dfzxxwv5iy4qgj/JXnbyF0Gc1gDmP6BJ3E7+IhmMQO62aY3ZrcSmyPHTDZgZ8kTAYzwiXwACnt0krFJYKr+P02Fgqkzu1N0kGKlU+Yk+UNU/mrmpvduzkPHFrVWSsby/9FnxmVeuXOnoUbbu2oPvPL0ebZ0z0RYNyKKZTozg2NgQFs6ZicXUVulXJhiTXMWsTFzwR/2yKp2lmC9VyIvtzgIMsRgK+bz4pAXIo1EUyeYeBXZumkTpCgQlr3UwmSjVNea9CODUxH0+DGeBNX1ZRAMmAn4fEvAD/gDOOnwmgs3Rkp9yNMcyNweM8WUSjcGRJCLNrWIelopFNlM3iWqrdwzDMv3wxaLIJtOSQGNRR1QSbvzP+gRCybhoqARJxjYz3rcrFkQim8cQAgiF/PCxiEYkJqUSV7QHcNayTmwayuGZLYNispUNSy6PJHGV2rdpIFzMYdjyI5ZN4Pj5rcKQfqM7Ib+jQb88TzxbQCoSwzyjFEc+kMqLiZmMZvp97eQrypDpSVmJivJjIziXNwIvze2MVyYrLGCVWNcMYyIw08+swF+dS/l/9JzDEM6mJQPci9tKmxpePhmKyvkLQwV88LgS6enH6+Po3tW3372vPGmeuB8kPSo3cuQpGAYCl18OX4187ZUmM61fLGJxyimnOJrr4zmIbGsCI83MqrHoColcy5YtE8Bl4zrATQI3/gyX4jvNdebrX/86qA2zcfNNQOU7/LM3wgAAIABJREFUzWe+8cYbcfXVV5fGqVjE9ddfj0cffVT+5nH8XjUCPYGbjUSx2267TYdLjWdAG/Wc6QbM3KEyvpdm60ppN8mgZH5rArabRtLJySef7Ci7lrquXTtnGA930twwkAHOjUGl/pWHc9Xro9Na0eo61NBXr92EeHQ23tzaAyPZj1OWduDEI1cgxAIQP/gBjGxWCjmwBGF/skRCoq+4PRoUtu8+IUyjF2YGLWo/hWRSyFxjTEouyOn0Prmu2QcubE3NzbLQ+yJhFKmxMTFFOo38KMua4L6lL4mtrB3MurxBEwu6wmgqpEu1hJkHOxbDrqSFzd1JyfPMRhwIBwNieu7qasORHX7R1ghem5MWNg3l0YnMGJFtKJUDzaxtszvxyG/XCRjzSkw6IuZ3nw+x5ggSRQND6SLmd0SQsIBoPoeQlcesiCnhTf3JLDb2JpEtFpGLNEkmrMRgHCPBGFpzpRhsuV4oiHBTVGoo0zROTZs1k/ldD0KIZJKIhfzIBEIYylrCepbUmmWNfuFQISd9ZdEKxZzmYXQZiK+4WCiFOhGIrYJ8ng6ExAdtTx4i54yVxgRWLJ+Ds2eVNNzfbujHS7uSY6QwtTmY2RTEBwjMwSC+9eTGvVncbP1k6tFj57eMZXKzwmHJgR0444x6U3u/7+mDZ3wxXT1etR/84AcgEH/jG9/w6hYH9LpaYz6g4p68m003YKa5i1pateak7GGlc8mMpCnKTfJ8audMk8m4YYZiEJCrbRjUPVU4Vwu1PAeN7G1qzdSynTSCImMrKSOy0qkFMOaSqTDT//VfsPpKWk+v5cemnhHE8lnZ7dOkO6s5VMoJ3do6ZhYWTcjvR3ZwGLsGU0Ikoimbx7W3xUoqFjM7jSb3IOmqd3AQ6aIPg/kAMumshNrMnd2KaKpUg5hNwKZQKsVIjZupQfPDI/J5rK1ZNDmJQR9JSz3ikUBECFJWquQrZSasAkOE8haazTyscAQxs+RLTRV8iAV9AkRMx7krUQSiMXQ2+QHDhx0jOfioGaazQDYngEbTNaPK2I9MOCqm7lLiLFa2CqAt4pfSh0w8wnzg1ETZmK0rls8IcYydJ2GMpDJmDUsZJhK+IDoLablW2gzCymaQC0cwI+KXNJe76dNm8QufXwpO0IcvBRsNS0zh/z977wFl11leDe/Tb793ijSjUZcsybJsq7hgW7hhYzAllAABh54QSiDt//ORL8m/frKyVn5YCXyEEAiEhBogpgVsMMYYV9zlLlmyepnebz/9X/s594zujGakGVkyQp53rVkjzT3tvufes9/nefaztxVEetdTR5zC5vvl61UzJa1R8YijcNag9Qbon9uRketatWIhVicbtpDJJH6+ewx7DgwcA+QdWRNv2twlmZPP37ELrmpIHVparhrWmgsSKrJqgKVZQ2rwqdUroN90U2QsMsfBCJSfXfYDn67x1a9+VZylPvWpT52uU7yox50H5hd1uk/dyV5qwMy6MwfTyHMZJJ1Qj3q2TG4CMclfXCjQrYaAPBuSC23tyMqerUk7SWNkZtP56niDIEZGKFmiBGVGHXFajnKY9je/Kb7H8djVVxLJRD2fk/5a+iLbboB1axZBrVYk5SwSm9TCDoF9QxWJKBOpBDymTv0Qa7oKKKgNYY8mR6f+oREcHPeRTiUkWiSoWnYV6xampdbL2u/+MRtjqiVErEUWMFb1ZHHgpLNI1MpY3pqUKP7Rg2PY3luKWM1egBHFkui6zVKQbsmJbKdXYV04qvPquoYFWRML0zp818Xu0Ybal+7B1U3AtaXFh6IeZSdExY0EOgicFCGhgxPFOEz48nfibaY1i8UtSdhjJRwYrgkwCdCq0baLszraUgZKtofDo/VjarV13RJhE8pXmoGPrSvz6MxaGC/VcP/OAehT2p0kg28mJeKeOpjypiBIxq1FJYimdHfMGWjex1c0SXPTCepjL18i0a9IsZKBzxp9pSKqYbc/PwpP16Wdim+c9/zi1W24dm2baI9/8afPTMqKxOdgJoDiJiTBZQpZvOWz/xt6IT+Xr97EtiwD8fO7efPmk9p/Njt98YtflBT0Jz7xidlsfsZvMw/MZ/wtmv4CX2rAPBsTh+lmarYpZkbHjMr5EGEkytas2epY87ysoZEMNpsWK27P+jLfE4X3pxuxshkVjRjtc4HAiIB64Rx83fn+90VEpHns6ClCUUKYjNJo2JDOiBXgBStaYDYkNmNVrXLVxu7uMeTMSHaSsVZJs9AS2ljW1RqZTzS1Lu3tGcJQNUB7IQPfSkB1HZSqDla1pZDKp/F4fw1auYK0EfXQDpQjAli+ox1GpSh/Izv5vEUZPLJ/FDsHqkimEqiGCkpeiKqvSATbaQHdQ2VhRxMoY+1pim0wQlZtG77jita2RRY1SWumjs7FbXCrdYyOlsVrmZE7R6wJnQldKJoqka1Td5AJKLlJxazIoEIAUFUlvay7DpKmikUtKRyqRpaSHExNE7Q9iRwVWGooJheKZeHSBQY2L85JGv9nOwaPua0xqzp+gUBMdjVTzKxDx4MZgbJmIeHZoqstBLJpCGLcnprjf3zjBii8VyxFEJSDQLgFrMN/66FDkuKPI+1M4OLdV61ExokyFD/eMYxd5TBiozdF8KzNi9EIQvxyw7X4sw9cj43T+FzP5vHJxS7LQSdahM7mWDNtQ0lNZrma5TFfyPF+0/vOA/Nv+g6c5PlfasA8177feFpPFMlSxo+ATGIT08PU2eZDhISQ2epY81xPPfWU1L+ntvvMdHtZs+Z5mGZvHgRcRtOxhi+FTuhCxYcbU+xxnY69yt7DD0e70qyBJg+6jsNjNXSP1VAgU9rQpS+5BR5WLWkVIlez0AgJVXsGKxK1Epi9TFbcktiju7QlGR2b7FthDlWxt2cYQ1Ufre1sneEOrjCj17QmoNSq2NVfQZ6F4sbYO1KFkc9jWT6yVuRCoVi1cWFHGgOjFdy9ow+qpsJLpETzuqpoWARbwJLkKQFlVUFdN1EPdYk0WxIabM/HsJpA2q426quR4lVbxsTS1hR2VVWUx0rQ/Uhoo2IlEDK1nUxgoUoZSw+DZVeiSEaTjD5ZJ+cQhS1xZfKR00O0pnT0jDti1BCRsZLSBkXQLFspZO0ozWyqnCoD53RmoNWqePDIUdEWvl6RlHRNEtriv6wbSDr2MSIi3DZKfavQPTdiXGs6fGq/h1HrlSwO/IgAtqJg4XVXnDPhyBWT5Mq2L4piO0uB8AC0ahUmb5mu46LF2chHWgEWZi389Jk+VPyIaMa6OHW+84moTr13xQY8uGIz/ubV52Dr6taTelrxO8auAWauTteg4hdZ1n/yJ39yuk7xoh53Hphf1Ok+dSc724CZadrYmWa6WWLfL7/gFAGYy5gpkiUwEpCp/kNApm5unLJm2pwAydrybAdbrBjVzrbPmu+F0XBz3Y1RdOyOQ0Bm1B4PRvJki5PZ6j7yCLwHHphwc5qwYBTHIQWH6wqK42WJnDJJA0tSZBerR92iSiWJonrG69g7WBXwa+tqh1UpSVp5VXtqUquToHY2i94jPTgwVEeqvRWWocMuR+1C65e2iNDH0z0lWKznsu3ItDBcdZB3a1iWN+Vt0FKQNdvzF2WlvvnrI2XsPzgIz3EFuMh6VhQaJzJ6DlG30kgoAfR6XdLzHIzk+hVL5CsJUrz22JJxQcaU6J09xf0VV1jIDLej7qsASToo0SzB91gIPwYUY6Z0POesuZNVPuar0rrEtHGcno6u92jtl/vwesjidg0dxdBEiguHBhAzGrV1SzZKOXV5j2yjmiB/sU0LCrIJTWrZ3L48WhYQZmweKVtHRC9JWXPNZOl47eWr0OJHiwCqrf3gyV44Ip6SktpyxrfBeYkXHYOuCq1Or27R/hRwvmxxQnq8Q80CK+wPD/nIhi6qmQJu3/IqyZx88z2bRO70ZAb5Ecz2kCF9ugZT2BTr+eAHP3i6TvGiHncemF/U6T51J3upATPrrHSEYfP/XMbUSJYPCQJy7G1MIJ1aQ56rjjWvZ7oWq+NdZ3MEzEUCAZkLEwIyo+6p18T6GcH/4kJBUtixf/DEOdh7bJrwx8cReB5cqc2aMFz7GBco10rgscNFuMWyPKB7QwNWrYqVbUlRdxLVr5gIRAEQRuS6Lr3bYyxPk2xtO8ioARbnoz5mMqnvPlDEgKuCKWOSkfjgX5rSGiAQXenSJe0CFAFroP0VWRyMQsfwcFFAMzBMMZhgBB4TsRoZ6UioI52GLvXxUPqyGU0SOJwAspjIWRrGKw5sakQjFDnNMJmGOz7Opq1Ig5ptUVNuTtVICsGMgClbieY0la6ow8368tE9GPHqwp6emazIYzi6AVs14Ws05YgiXYIsQZc/jOh5PpLPKP/JSD0wLWmj4t9rVgqB58s95H6RZ3IE0LwuejlfuzSBFkuVDM8teyo47GgTfc7xW2xJGrAMVVq0akOjk3y6eZ8ypopXn9+OXFubLA7u3jOC54bruP/C66C0tuAvr1+Fy1a2zOVrN2lbLjhJdJxta+DJnOjjH/+4CILQTepsGPPA/Ft6F19qwDxXFnN8W9mmwRQXH1wEZIIfSVpTnaiaPwZz1bHmvnMVQGEbFoGW/rSM2nlN0y0S4utiqv3ZX/wCG3bsmLDcEwYt9cLJZq9UIjBxPXl4m7kswinSlvGxBsuOiIAYLXkxgmAgRPnI8xdn0ZVLRMcn6Yu/m/prR6kUVSohl88jaBRlSfiiN69mGHhmVy88n+IfIZBMwHTqYl2Yy0a+wjl4MKhtHQLd43VsZ4SdtlCs+ThSoWCGgkLgyDkJGIZlwlE1lH1GxQoCTRWSlep7IrdpUFOaKXKEUT05mUQul4I3VkQx1NBqhGhPatLXvH/MgZJJI1mviitTDLMEewKtaFSHvhCwCNJS9/UdiVCbe4xjpjVfY9o7ZjTTKIICJtJnTGvGwIMV+JIJyDhV6cum8QhTzbweZibYNsXFBYGfoF0xk2gJbJkfZgIYfXPBM6qasOg05TvIWlRii8LzUqhjVSrAxhUFEY65+ZG+Yxds0o6twksloPohKqEqxiOcNS0gQ9wFXA+/e14OuXxOtuWcjF3/GgwvXY2V7Skhgb2QwQU1f07kjPZCzsHeYto2vvWtb30hhzlj9p0H5jPmVsztQl5qwHwistR0s8eUH3WvCWrsvyX4TReNTt13tt7HzfuxHYSM7NkIoBCUWS/me2LdjXXtEzG/az09OPjJT2IZtaRpepBMSquUACiVoWxPWLjU0PYyGZxj+uhqSUY+xvwhOajhrXxkuIyHD5cFHOjh61m0EnRwUUcKS+LacuPNEQPI6kYuC7uvR/7K91n3QzxbAirlGnTHFvDiA511XjeZxmj/CA7WFaimgUWqg3PaUsLOptY0Nb6FrQ0FTiaLGj2KmVpWFCQ1iI8x1cgyRpRqJZHLBaPjSG4zBlVuzy5hGjWwfrrCCmD7IRhfjtk+DM8VBy7Pj2Q5Je2t6ygpTEtXpUeYMazDjIDUdo1Iq7rRhsTXaMhBgQ/WeOM6ueZ5sDxbSGBxhD3d549krxVGdH+qto98Ukc2ZYh/8nNHxkSKMx6Oash5yUrnZ4HvmyIyHLElNoGbpQIueAj4Fv2sNSVSAdM09FWDiVo5j8UMBFPlGzrTWJUMkcwm8cvtg9Ds+oTVJBcJKR24arEm56WARvWcc1DdunVaX2RqDUz3c7zPL3v2mW1hl8PpGkxhU6eaoh5nw5gH5t/Su3i2ATMfCDRgmGnMRJaaCZBJoGKEzBQwVbqYIj4R+MXHmqtcJvejQArbmHiumUasHsb6MqVGuQCYjX6wSCd+5Ss4tHcvVq5aFUXCZOkyYjYMSS8/ergoIEUd66BaQ61m49wWS0CJ7T5UcVpYSMHMZ9E/MIY7d/TDTCaQ9p3IgtANsXXjUiwzWYONUrSsQ9PcYUwxo37pWh2qZaGzq128hMdKdSGKcRTrLoYrLhYv75Qa8/M94/AcR6Je1dAl4l2YNZDWVezrKwpjuML0rRJIfdMiWDBdq6sSLTMqZMsOU+0lJxAf4TRTzY3eIVfTkW/NiYIYxUJKdV/kRGmNyB7t6lhRonpGe1wI8D2KfCdr3UEoWtk1zRBLxSh6DKTfmb1VBLVmT+OYOa1ZptTMbT/AmJ4Sicw43T71nrP9iQQxLkg4+us+xmCgTfWQCAPpc45HbE7B7dkvzSg9ngNJ3+s6XIU/qgAwF1l01aKcKt97stFazF5tjubUONPlN2xegjWZaKNHD43j3l4HSugjyRS5ouBliy2sWZiRbgRlwQLo73mPMNlj32MxRnGcSf9vfo1ZKC6CmZUiaJMdzYVw/JsLY+7PLocY1Pn6dEI9J/s4fu973yuGEbH38ske50zZbx6Yz5Q7McfreKkB81RW8kyAzJQZAZlfekbITIHzgTMXxbCTqWezRsy2Jup7H/OQrtXkmhghx+phfFAxmj8eMNuuj+7hMoxbf4xceQRHDhzAqpUrpYYskbLr4choFYdHqugtUtc6i5agLi1HBGOmqQnIKoU+oKLg19GZNbF3qILnS0DCqUlEKf4TuorzFmUl7ewYlvTTdo/WMBZoSKkBdhUDjJZtOUY2aaLq+li8ICtSmxSnYA3zQNFFIaFjbHAMI8UaMozorCiVPFxxBMQJBEylx0QrAi1BODZloA60kJwA5JK6gOkBzxTzBg7WYtmqlHFq2Ex2MYBDIzVROuN+lMXM1KvQNZKZKB6ioxYoNDEUUJZ2IrKTG329+fpRx61I2ztEyfaPYU4zvd0KWmqGMl8ksTF9zSjWdCYDNIGR/dfJwENXV6u0YJWGxlCuR2UGDs6J/GaLmpWR1Dlr0QmDcxTCtqN0PRcIXChQa5vnM0IPCf6NGuhTZDzZV01Gd5wa5/EZtd+0NiOOUzzl9x7vARnbTP+zp5qp93NTHq5YWUCK/srvex/UaTTiT/R4IjCTwNkM2PG/+blnuYZaAs2vN4P5TJF4/PcY5GcC87e//e0ijbl169aJS6XmNfWu+d384Q9/iDe+8Y3yGp8RlOOMOx/YasU0OAcXEXSaov4BF/J/+7d/O+HrzOCB7lPU5Obgds3tWZ/85CfFx5mDFpCf+cxnTnrxMQ/MJ/rEnaGvv9SAuZmVPPWWxD2/sToYATnuJ+aXj3VcRqizHSdTz+a5+RBpPg+lCNkSxeNRGCU2vOB1EJjZY91shUcg5sOTnsL9xTp+9EQP2n5xCzKDvUhYBnLFHiQKCwSEyBjOpwxxWmKKdnclRC504bg+2tMGxm0PZT2Fc1ot6PVI0IJRmtg75nIY6R0SkCRbm8IdxZoX6VM3+mbJQi76CpZ05FCsuHi+dxyeFwFC3lTh1OvImpqknwVgkkm0az62LM7hgUEHhwcqWGBE9oSEoN5xGxlLk4UC25ni1qG4hsvtWPssGSmJBKmDTQeripWEXqsKy5jRX7MTE/uaE8kEhutRbZh2i5ZoSHtSi26xGHmGkgqXh65hwVeitquaYSLlkBh3lNTFuahZSUnxx8xpuVdMM9OeEZHUJ99za9oUIw0CLLMNBGi2GjHyJogTgc3QQwa+pJOrAeQe5lOmZDuYbaBbVl03kLFpLxkZbFDfm8ekDrcbTm7P4ntMpixkQpLGFGG9SyqfDOwgQNKLepPjOedihzX73zl/odxjfm7+56k+qcfHdXPOsZ9I4F0XLUTuxhuhzdBXP9vvznTbMTPEz/tUcaDjgfl0AM+/TQXzAwcO4LbbbpMOh+uuu07aD8kfYc80s1jstqDONWvQMTATUPn32MDiFa94hXxPuYDn31iWig0s2DVBzWx+r2nxSOBtNrCgLjYXA7R4pG52s4EFz/P7v//7JzV188B8UtP2m9/ppQbMXMlSkSu2YuMd4JeU9SuCIlfUbHuaKvDBLxxX2XPpSZ6tKlfzp6D5PIwO2HJFJirPyyh6qkczHzJclV955ZWStrxr1yAeOzgmD+X1nRmRyEzffxdWHtghpxmvuuipu1jSkkNO9aIotOxKnbG9LYf9AyURzWCK18wkpe+Y/ajUgo7HgZEqEq0t6FJtHBipY9xTqDyNbMoSZ6SkrqI9ZaAeAPtHqmIJybqozval8XIEYiGQTRqSHmY0W0iwJ1dHqebCDAMsa0kgm9TxXG8FtUQKJmuxnidRGoGZBK2hkagNiIPiIeRt8feaVgOVQBdiWNlIQVEChJoBz43UuiTiZZrZ9yNLxSYd6lh/ulmbmj3VdT/AsJIQFnRc02UaOyZ7yaJC1SQifePaPMqlqpg/xCOqQRuSlrY0VRY2TIszaj6/K4uRuo9DZU/mksBpK5pobVu+L8BMMOfnlPvQB5n7i7Y1SxHJJFr1UExCyGwfKEXkLiqZ7Sv70MiNaGJ+l80UWv06WlI6yoGCPk8/hoEdX7e8xzBAu6Xg9y5aLNdb8oH/eKxP6vWynpJ0RYCsFuBdb7kMLTfddFoebCRTMpqey3fweBfSHJkT9KmH/+1vf1s08RnlMiL+8z//8wnTjGuuuWYSMB/P8pEtXQTf6SwfWb8m0E5n+fjRj35UylinwvJRFlezrbudljt2ag46obt/ag7323GUsw2YRcnKOap+NPUuNEeYsUwlAZnp4+NJYXJFzRTUXHqSSVThfnOREOT28YqePddcYfNnJlcrPlz4QLn66qtx7+4h3PHcgLQq8cFNO0fricfw6v5nJ1KfVdvDnr4xbFq5AEE2JyIWY8UaxpwQqxfl4CiapLNJuqJJwYr2FA6VPGTSZEzr0k60d8zGspSCFlNB/1AJfeO2RIcEX9Zms215ZHUFh7pHMEYN63JJwJdM4iE1Ml5IM1pTVVHxYno0aRk4XPVRK0ascInCdUVal+i4RHqUEJXsKkxDh8LjlWuRJaNuCKHLgw5DV7C+TRc97N7hipC4mGYlKFqOLVKatGSUNqwpg/XXkCxmMowbg57GhfY8amNFUQmbDLQ6Ep4jvcmuRqGPmjg3vfeyJaKp/Z1tEcmNo5jICJM8UDRkEhRiUWRx4NoeLl+aFj/nh/YM4MneGgic7G2O5TK7dA8ZLUrVD5QaKfo4la2oWNKeRTKMtLmXtyRwwFHRX3KRtGsYLE8WKKHtY8apSP08nWS9vYaB4lEvZumJVjWJzgm8tIO0WEdWQ3zkiiXiET1csvH9J3oiVnfTCHI5vO+f/y+0dHaelocf2w85b3PJWs31Qgi+3//+96eV7G0GZi66SdDk4jketGokY5xp6mw2K5F2TOL8h3/4B1lgf+5znxNd+y996UsT5ScuBr773e/iJz/5iaSuCdgkoHFQo/9DH/qQdF6czJgH5pOZtTNgn5caMJNgwnQRmZ0EZDpNMUI+kTb1yUh5Tif+cbxbzmtjv3Ts0czI4EQ2k1ws0JKSD43P3rlHAE1s9gBYe3fB+eGPsKIlMVGjYhtU91ARG1cskEinFio44mpi5rAgbUjLVO94HZrvQnEdhK4v7Ta6pogwRJBMCWGobrtIWRq6R0mkUqAnTbS25tHXP4q0EkgP8/aKhhavIjaIBO3BkoPxmgdNV1G3UmLuQGtEejHnFrTg6Z182EdPe0ZmfPCbuobO9gyKHjBY9VBBxApP8fo8N1LlEhHQCKmShoL2pAJPMXB43JEab8qPerDZomTksjDrVVTrkwmChGnbSEgPMkdci826NaxdkMaRsdpEKpuvl8xUdN4wFMEQEfjQdLRmE3jv5UtlwfG5u/ZFjk+U3Gy0d6W8epPoCtudQly8LC8OTLt7RoRQ5Tu+SGGSRMZS9oJCEno2A1SqIBO+eRDE24M60ikTdSsJ2A46jKgWzwwCF2LxEqSmmbKIYbZiWd7AO162TDIWX7r3gGQNop5oFzUzFVllaJrU5JlFYNL77etysohiKeOHT/ZNug5G+fddcgO+/hevOOF36WQfe8wescwzF57HXM9Fr+c777xTWiOnjhMBM1POFC6KgZm63hQL4mBqm5yTGJhpKxlH00xt//d///cEMBOU42iazyqS0eaBea538rd8+5cSMDNCZhQai3gQkGfr4nQyUp6zIZrx40NAJvDzh+kx/symV7NvvI7HDo5i29M78IarL8Y9u4ck3UkATfb3oOv2H6FvtCI+xmzVIUOWJK1kUEVnS0FaYvaP2nA9H+0moOgGlHIJauAIC3u4TnISWcgAs8e5tBGRjVwPLolQ7Hc2E1KjXgAHBslJTsSEDjIZjA6OC6jzevjDB/pohXXtqDapmhZsiylrUyKxXUO25JkNJQTFJEOXNV4Pl6xsxc5yiLGxCnzdQMGroa5oKJKd7Ndg6qpE2WQ5k+xlaRSs1tAbmJGqVqNYSshvS5toK6TwxJAjESXBNQLaNNJObZJUJv/OtHlXIYm94y5K7INWmWbWJc1MVyaSqhK0U+S/G65aN25eIm1oP3loH3aPe5Imjs9D8EyotIwkcEZ15teev1AWU+PlCn6yt4qgZjdam9iapYpvNq9jxA5E+CO2bCTxjTV0iomckwolrT5E1rYbpcNJHAsaPdFcQNACkj3MHGsWpHD9uQuk1PHdbT2wQ3WSrSNr3XTQEpVr3s8wxKsvXg7LMmCPjuPnz0WftTjN+NDKi9C7ej1+8oGNs/5OzfXRGS+kZ9NKONdjx9szu0WHN8rXHg+Y5bORyQjxKwbfG264AYya3/KWt4iA0Ve+8pVpU9n0a37nO985bSr7j//4j2XhMZ/KPjr786nsk/00n0H7TZfKZlTJ1iXWb5liYhqKBI+5jJOR8mSfMb1dZzJ2ZxqadTOmr7lCZ5qcBC/ud6JezSOjNfz7/QeE1TvY14uWBZ3ozFkYq3lYHFSx6u5bUKfOM60QGzlHKTcpChy4WNa5EDv39qGayGCJ5ghhamCsgr7QwIaONPZ2jwuRK9KgCBGoGlTTxLkFHemEIYA6NFJBUKuiKxcRkTjPQ1UPhUIC+cDB4XEXFV+BbkSpURtRbTStRvXSNI0fDBN1DzskAAAgAElEQVTnJgL0lV0821eN6r8NIK3qJgLdwJZWDd3DVQwqFlL1ihCb2A7EvuhCWxahqsMtFoWMxMg+awBHkIJOuc2YwdSQoGSan8xvzl9JMYRtzWiQdo9U4GLkLQIfjdanTR1JXLsqj/t2D2F7bxkVMzGJ7MV6LxXIROoylUbZDfA7qzJI6Aoe7a7iiSPjgBuBIUVMOnMkT/koGxaSSog2zT/aZ5xJQ6uUkbIiL2kCPUsEPAcJYhRhKQWqaH6zBq0pIXT2VSPEqoIJ1XPRO1I5Ro2M52ZkTYGSeNDikc5QgWXhW08Moj5WRBhE759qZ7yfZqMuTWAWKdbGE5IRNNnpXCCQXX6obQnuveBarCkAX7ppo3zHTscgMYtgOF00eyrOx88kI15G5uSanAiY/+AP/kCuJSZ/XXvttfKM4TWSyc1Udkz+IpmM5C+2en3zm98EyV7N5C+COAmc9913n4B7M/mLZDAC+cmM+VT2yczaGbDP2RwxEygIqPyiMTJmhMwvzV133QV+ieYyWB9iu8Zc5ADZ+8zUdDPRjOfkdTEC53VRpYuAHPs8z7b3+ebHjuCp7nEsLiSxf99+LFm+HL1jdVy3PA3va1+DNzIqUc54zRXmb2shjZJqYmx4HKXRcVx2Tgd21lS0+zXY9ZrUtTXDQm/Zx9qODO7uc6DbdakvUhCjTiay62HzqnZJQTPlzZ7jwWqAfD4JVTfQXXLgqCpaUybyoYtzWkw8d3hU0tdMTS/KGmhNGegZq8oc+Mk0WhUH7QkFOnzcedCBHSiN9ptAnIoYATPqHteTMO3IvKF5LM5bKCQNPFcOJfJPODbsRBIJuy6LFoIb24NIZmPWYMuKFjFR+J/Hu3Gg6AnAsI+Yixaej9vLYkTcqBRRrGKamdmJ2/cxmxApe8UjmzLRtrBVIv7x0ZIIs3D/pKXDpZmDU5d6MzEtYahCznrF2jb521PdJTw5Hsi+rkZ2dwWKYWJRe0ZaoxiP9ozVRDSFkblCBTA9IZEvxVMYBfO+MCuwPG/JYufI+OSaMq+T2YAs68qNweNefF4XLl2WF6U32mbevzf6vPBKg0QCy61QXL0IyrxmXi/vobDf2b/tB6gnkpJh+NWF1wsZ733n6fi9KzcIK/l0DKaGGcmSCX06BuePOtl8ZjS3U1E/m8DJRTMXHfyuknBJ8Ga7FCN5kjI//elPg9EwB7/7BFSCMT9bbMEiqzv+/pPdfeutt8r/uR1fjweBnsDNQaLYZz/72fl2qdNxw8/kY55twMy5JvOaLEsCH7/IBL7Ye5ivE5hZL5oLYfFkWp/Y5tTcYxwvFLiqprEEFwqscTeP2S4A/vXufRirkX2rY+++A1h7zkp0D5bw0b4HEXT34JEDowIG/b4OJwTaFReu2xD8sG1kF7YDReo+eyhkEkjwIWv7qHs+LF3Djt6iABkfvNS3Jiiw4Hl+Z0YsBAfHCOh1WAhFYCSkf2+9jgWMSF0Ho6EmTOMlBlnUurTt9BUdqTW3J4BQVzFc1zBergmgUECE7GCvUhE/5dg+kf3LRjqB4VH6Cx9L2FqSYZ1cRY8TeQXX1SjNTDUuEfdgD28DSCkIefHKAs4/ZxF+9Ngh9BcpkMHtIuIU+3FXFiyodtQWRsUsXgfbv1jrH3BCGE5EviLjmc5OnZonfdYkk7GvOh5xfzWBnq1Eoq0dKtAtC9df0CntTl/59WE4km5WpJ/aaGheLykkZIEwUnVQrPsSIVNNjOQyLgzYisVoNR7MRmQKGZiVkmzfPIRl7jkSVYtgiJkALRvfuqFNTC5IsPve470TEp90uqL4yYZFWWxc2SoZhe0DNTx6qAhqrohUKI0xqLKqarh77eUYKnRgYdbE/9qi4vJNG2btWT7X5yJV7qi4N1uDl7kePwZmLo7n8myY63lezO3nI+YXc7ZP4bnONmAm+BF4SeYiIE+3er/77rtx1VVXzWkVejKtTzEDnP2J/LJzZc3rIiDPFFWwbYs/0zno0HqRkpntGRP3Pj+Erz98CDXHx+h4CW35LG58/j68IV3Bo4fGcMjRYLHFploTe0NuRxchpoADNZS+WCdQYaWSqFAqUtGQTVq4fGVBtr/18SNioSgPchHlUMT8IGXqUbTkBnCTKSxtSSDj2Tg8VkdHZ6uYPXBwG7ozLV/aLlrUh3tGpZ5JaDUMDRYjypqLkmoIQYpqXLQ87Mpb4m7E90pQJHDBMuF6oZC3RKVsws/Yx9YlGaxsMfH9J/oEeBgdN7OqWR+X+jPTv5qB9qCGlBpixFZEsYvtS82jLanBKuQwMFaFX6OilbScoEhzinpVQJEq0BRV4Wubl+SEvPbowTFhr5PFTZKV3qBcsUWMjGqKfPiNBUfK0oVcVrE9GBR5YV29sTjgtdAsgin/g64uGYJmEJ7qXMXt21IGXrl+gehXf3v7KNI272gkfsKaOOdENw0kyGhXIZ+f7qKNKu049CiVzyiawiSW50Y95lqI917aJVPzTE8JD+0fhRMojRq8K2z0nWu24Mj6LbKwYhvblYtC/M0bNh+z2DxVjyvqyFN2draWqHM97zwwz3XGXpzt52vML848n/azUETkeGxmspiplHUixnPzhZ5M6xPTw6wZMeXFiJ3tWM2R+3QTwd5JptKaPWeZOrxtez/uen5I6qitaQMbl+TwtQcOSStRrVrDFYeewiWVbrS15bBn/wCq1Siy01iv1HTUPCCTpKQlH8EhlqRN+HUbCS2yUeSHn5Hh+Ytz4q37q52DeLoYysM8Th8zkm7PJdHjaQhsGwnfQYJ/y5jY42jogo1swpAHNUG1Z6wu1o9F28OOiirCFWYYSqq54gSSvs4GVKoyUA0VkYpkBoCKY4ca3sUmacmqAsuuTfQbx2Vjzt8N57ZLXfnW7UOS1mU6Oh5MyyYLOTjUz67SClJBS5KLExW7aiqyTmQ3KUPSuMDSvAHXC9BXoRlE1D/N4/J1IXixXzleICjAxgU6tiw08bPtIyLnSfER6nDHvdEEb56P80EGPCU2GcFy8cDrm3rNBFOSrHTXwc+fPDLpI0JCVrNgSfwiU/nvuLhL7ve/3XdQZEJtLTLu0HndCPCaDQvQ3TeM9mwKv949HKXGm8oCjIzZA86/8/6RUPf+yyP1uYNlDzfvGJW0PAlvHN2FTjx5+Y2RdCl9oh0fixMe/uO9l4gQz+kY5GuQ+DVVY+BUnYsLaQqKkPcxHzGfqll94ceZB+YXPodnxBH4BePDZabBFoSLL754Tg+QubQ+xQpiZGxykcAWjNmyv6eLzJ88PI7/fOCAODbxgUkt6d0DJXmsVp0AHbu2YdPQQaiBh/a0hXLNwfBYVerDlI4MTVNckgik1Jik4YRvWNCSCWzIRH68NccTVS1GuqypHqqrUFkftV2pKfqWhe5qiLXpEJ7joXusDo9zTGZ2KiO16kibOWJck3FMQKIvsgiHuCFCtupomngj12qORIkpJSKNke0ttd1UGnYQwqjXULFSExKaBA6ppza8ieN7y17rxQUL9w0GE9syAo80ph1sWmhJjZv9vyRUMTx10lkY5WKD2BYdia5KgWbg969YBss08YX7DsJTyG7WJNVNQGSKnFkDnTaRQnYLcUFHAucusPDTXUUMu1E9uzliZ+SazlooBbpojzcLlzRHv5TBZB2c0fE7L+qUuvkX7zs48RGOzSmYlue0UzKTjlSMiBOmjj+5ejls18O//movDDpzGQkYgSvbMILmYoeOXbZPGe/J3w0uEGwuaqj41XiJTPC3X71Wiu0/vG/3JE1uHvtHm18DJZ3GgmxU22bEvLUjwCfecsm0xKlT8WCgwxt7mE/U2niy5yoWi6IHQJLZ2TLmU9m/pXfybEtl8zacCJgffPBBbNq0aU4pt9m0PvEBRWAlILPfkilrqozNhWg2XWT+9QcPSc23k1aKjfHz7X1wvBAvc/qw9tc/h2kakgJe0Z4W9az+koOSzgowkGmQmlgvznp1UU+iuAfH2pUdAt57Dg0JKMt6xjLRmtREJIQR55GRKpxiWVqoyGjmCpY60BxlMxlFWZR5TJry0CcJi9KOGTVETosIaPx7LZkR8OQpYo3nuL84AhJPLB0ZyQ1qKeQa28bvWSLlhIU6Fxn2UYZxxUoL+BDcqL7FXmTxLFaAxfkkqq6H/qovaWZbpyxmNFiHFjKXyG/6KCQ0vO3ixQhSafzLXfsm9KX5Wiz2ES9AqEPNRdEExDV0uinTyRFLXBZMBS1wRfK0uUJOljRBmPeE19oc6acNRTIdvQ4JYJFEKAGfvdBmcLR3O54XS6MYi4aUoaK/7EianMYScUTM6NlImLBqZTT8KSY9rZrr4TyPk0jhquUZbGyLmMlfffCwsMM5mFu5fcO16G5ZJBE227yYdmfW5ANrHFy39ZI5ZaLm8tgkkZJynLNd5M7l2NyWfcaU22Q75dky5oH5t/ROvhSBma0IZFefKK3cfEuPp7HN7cjYJiAL2K1dO5FumysDfLrI/PuPd+PBvSOSYiY5i05Hd+8eQm6gFzds/xX8WhWabgqwthdSGA50DI2UJqwNSSQqmil0KY7sW6vXkUomMFJxhNVNo4oDroGEy75ewDUsAdplaWC05KAcqNBMDfm0JbXqYpV1SF+iNaamfZKIKhVhHbO1h6IVMfDyoU2y2JiWmGBZS6qb59FIoNJFkYutSvGoGEnZNqp8TgRxEpHz+KzNlo2EEJEE5FU9InE1fJB5XQTlhWkdV6/IY9v+EewpS0MTUu7Rc5F1zZo7Xwk0HRXNRKvqY4EF7B22JyQ0mz8H6aQOM5fHcP/IRG+yLFAkuq8KWYqRPXuXudBY3ZbCFatb8N3HesSNiqOiGsLY5sKA1y3dWuK/7Eu0mzcgy5MhmHIPSTLjgkaOrVOtK7KanG6wzi58gKaaNbfje6WetkONb5YnGvnBOD1OG0slkUTat7G+PYFLlhdkDjlufqIXvdVQovRdHefg8RUb5e+XLMvjilUtYtF5/bp2PPP4I9IaOFU29lQ9Gp944glxd5vL93Yu56ayGN2l2Md8tox5YP4tvZNnIzCztsv06EyDXzyC51xW3lMZ1vGxaSNJQGYUyocG2dbN9am5AjPTaWwLYao9HoeGK/jrHz+HvmKkssUU6rlGHZvvvQV2uQrX9SStmE5baHHKwn6uRETjKLq10sjaFbE/1DQVrudJOpaP9iULstg1YqPmK2ILqGkKEqEHr+6iM6nAUiCRuMuUeMh6aZSuJgCwP9n0IqejcRhSb2yBI73UzYMRm0UpzDAQJataIin/bld82DRgoF9jY5CBTHZys351JBOpI5O2oJk6BsquoDXZzAbr3FT/atQ+43oUr+nC1QtxxboO/ODXe9A9bos1I+u78SBYdXW1oBooGB8cE9Y5FctIfusJLQHaqWNDCxnZaTwzyjSxLqn1qpUCwkAWM4yCGbUzLc3o87yuHF63YQH+9e79KLuhCH4w8mWaPR5xrzVngXOVzkXRdLlioxhLaDZdiGJZqKg6UjVe39G54/2ZytiOdyMJjptyquPMAsVJWBPnnHXoHl65Lg/TtGBRi1vTpJ7OjEL/SA0/eaYP/dkF+OmF18v7JGjf9pFLRHwlHiwRsTXwVNowNs//tm3bpM+Y7lKnY7DvmNrY5IWcLWMemH9L7+RLEZi58mZKbDp1n5lu41QXJwIoAZlauQRkOtFMRxiZKzBTXITsUwrpx2NXfxn/dMduYWQTFFsCG5f9+hb44+NwrBQC1j8dG5Ya4spVBdxzoIKxmiuqUdVECloYouZHKlKMYCmDyfanJHwsylno9Q3U6JFsmNLvWlNJxlJxTjIQw4X9I0dbc3hNBERbn0y0IjykDA1VKwmnZk/UWQlSJDSJQlYhGxGjnLqQhdozFvIJDTsH66LJTPB1CAiNSJJsbvbraoEHJQQ2Lc5iTUcG/72tJ3KB0iKt6niQOMbab81MCAmNafvNS/N46kgRz1WUSAVsIs2cFt3sDQVN1MgoYRlD3FQxjubPxCuXqXBynbht16gAr63pEp1G18mIN0rFx2NRIYEt53bh0f0jGOgfO0boI96OKW2ZQ6cuKWlZ7ISaHC+tR05R/BtTyu1pU+r3R6oBqkpUQ+ZrzTXreIEirV2qTiq81NHZskUA530BI/QwFOGR1a0WLl+WlgWm1Px9f9K/D9oG/r8VN2JQS6M1qeDDW7LYvDg94ZvM0g3dlOiiFFsrnmoCFRfU7FaIe/5P9SOXqXL2LFP442wZ88D8W3onX4rAzC8gZe/m0nZB2UwKylPBJyZ1EZApdnC8B9BcW7MoTEBdXBLG4vGTp3px9/NDYuNoV+u46JHbMd43jEzgwNBCDFc92Gw7goqVbSmJtPpHSsLGpngGgZmA1ZI2hdXNmq8wd3UTucBBUgkwxFRzrSFCQXauqmBRVxuKpTrGxqboM1sp5KSPNkolMxhjipyym0yPV3xE9oVOXaJJpmlJREonTYhoJqPbIEAbGdKBhz4qiDUYymzXqZoJAZuEFglZEmS4oLigK4uLluXxpfsPomhMVrIi4autPY9W1UO9VMZwnU5MEdO8mkxBKZYmUsFskeJ5xT7S0uWaaXbBUTKSwjym0hkXILETFhcWWS3EpS0u8q3t+NmOQWnfYhrdashcNj8CaIhRNy2kfUe0wzn/Q1pSMhfxiN2ouFiZegw5NvuTQw/JBlCzZYzlCs4z3cD6ija8MLK05L1nliFyz2JaP4x0xNnHLcxwBTWaUhgmtHo9Wrw0rsWxEnjdBZ1YnZyZMKm/5S0IV60SYqEaesf4JXPhypZAErNiq0W+T3Y+EKibfZJn+v+JUuAsQZEbMp0q16l4/FI0hFrWsU/yqTjmb/oY88D8m74DJ3n+lyIwk91JKb25CBWQ/EXSWGx6QX3c2UQE99xzj/iszrY1i+Iot9y7DU9UCzg0Wsf6zixWtSfx7Ue65aG45Zn7sWi8TzSSC6orqddDYyQWqWJawIc45RMH3CgNGbW/RKYQqxakMRAYog4V+xgT9NgWVAjsyEKQhChVEbUnthYNVjz0Bqw/21LDLSfSyNQrAniMtklAM5OUiNSxtiuLfSN17OgtSZSsUOgDqtRHDQVY3paUhQHZw4zo0xaVwHTsHapJtBf1S0eDZgtLOvJAKXKmImQsb0lKm9jnHh+BUSnJduKNzLSuUwN7kPlzcLQ+QXLie6MwCaNbbiNmE6om4hjrOtK4fGULvvXgIVEyixYBdKIykRYd7aPlEOmdpuOVGkLVNZTrfsQab5K5jK4nAV9VkWrS4ZY5bhCyuNTggogLIwqGNKfsm7/C4jBlV6GZBmqKBjY3WaYuhhd7BipwHRf1ap1C67Lg4DlioRQeRywhOcIQbiodiYlUSzB0TchdbrEc3WtFEVb7DesXQs2ko5o+RV6ahnbxxdBvuOGETxi6nDX7gotNJa/PdYWQGQN287+b/8ZInd+pmYCbgkHUkOd3MAb62XwHT3jhjQ34Xf3GN76Bm2++eba7nPHbzQPzGX+Lpr/AlyIwM1XMWvBsNHcZwVIYhGxpPlBohj6XGhrrVSTEzHaVv7tvDB/4+mPwVFOkKG0vkEiZdd7Ldj+CdT27QEsEalVTY7rmhRgsRbVnAqpJO0QqTFHrt16PwIbp4VwGrTowNlKU3ud40FFJ9xzkTeo4W8KeZi2a5+X/dw9V0VMNhDjkKfqEfjLZxmvbEtBc1nsDEQ+5eHU7xhQTt287iJpmTchAsm5M1vhyKxCFLPoFi1G9eAoDlUSkWhXHa5F8ZaRBbeQyGKu6KI6MR6SmfBZKuYwBmEKAit2guM+StrS4UPXUIiBm1MlIPOU7kY8yrREbITi3P39RFi9b2SIReLNfMueGYMd0fd6rCbix/YuDtVqWBJqjX0bWTEXT3znpRzKWzYPHkpQyvY2VyI2Kg/vBMBFoGpxQkYUVQZ3LAZV+1XZFgJvzwMF7wgXT687vEKWuZ3uKeLKnLK1O8TxwO4N935YhtXzL96TMcf257TjSO4B1a5ZJ7/nB/qIsjiifuqw1OUH0khPFkpqVCpTOThjvfjcUPXIsO96YCswn2n661wnOU4E8/j/7i/m9jcGcoM/PERe9s4nIuQ2j8pnA/Oc//7k4PFHL+lQMPjfe8573iGAQBYXoz9zMHTkV5zjRMeaB+UQzdIa+fjYCM7+wrJHNNCjtR+LX8VxqSPbiF4uCH6xHM/XNFfVcWp94fhJiWHebbV3s/9zxPL798CEsacvIA4QPnv1DFVx4ZAcuPfC4AA17mYNAwZoV7TgwUsPY0LhEPgRm8pirmoHFZoiFC/Pir0xt63LVRkfOAnuiWd/loMwjyUjcJ5W0cO7ihqNOGGC8VMfFXSlR+Prx8+MCqKJdTXtAqkelTUlfU9yjz9VRDYAlhoflBQt39djQ65TQjBCK4LukJYmVyxfi/ucH4FdqEyDMtHXSsZFLaML4JlalTV0WJbzeobIjKVvaLIprlaZJK5YwmRvkJF5TRg3w7os6sW+sjlv2VaGzlk25UN+VtDozB7SrZPKd9WqeiNkFvgemsosNVvXUz4xGZTQfSDRkOvl6lSpgbl0ibzKw2ZrEaFVY45YmgiY2lcpUDQ6VwAwrEvoIA7RQ0cX3YQa+iMAQqBVVw7hmyTXy/jLVLpE1WeuajlVmNC/8LNS8QBjQlDbd1V/BL3qcSelxRuJIJNCherIYofkFBWkypo5SpYIL1i/DiqNdd8d8RUSjWzfwWHcZDw44eOCCq/Gal5+L912+ROrapxuYj3d8fpcoDNQMrJwTftdnE5ET0KVVkKRBw5j44aKZUTK/63R4o241OSPMivH3yY5XvvKVeNvb3gbaQd5xxx346Ec/Cj57TmWUf6JrmwfmE83QGfr6SxGYWSNmOmw6X1eSuahlzVUunWCWLl060f4xVyIXb/mJeqYJeNt7SkI+WtqSxNcfPIDbn+lDOsU0cSCCFoWDu3H1rgfQnjYkWqcyFYlgFy7Oo+r5eHgwitRIQGIKlA+rhWkDa7P0JVbRWwswUg+wYXFeyGPbDgxLFEiAE9ERRDKNJGMRRAmE46qBpa0pdGo+nhu2UaE5A40u6A5laljTmRVv46cPDKPYVINmGpiSl2EjmiF8st91y7I8tizN498fOIxRPSlRI6NDXgNT5HxfRsKCr2moekA+beB3zl+Ih/cNY1d3caKtKBbvIETwWiZq0Lks3rJlEUZ6B3Drc2MiJdosZSmSk+kMTLeO0ImMJtjepSuquFYlm9Lo8VeVKXSCLeu6TFFznUHwZCqayxmLzl1U9VI0kQolmHamdbCS3jscRbtTJTQpiMIFFC+gux4Aiag1zHMc1LmUmCJQwmsptObk70a9KvyA+MHuplIIShVJv3OxwMEFA2U3mXGIRigqa6xxS7nAt7F13UJsXN4a9bI3yhdcLIDZD8/DXbuGsLO/jLvXXoG9C1fKUd64sQN//7p1Mz7F+JkjB4Nlm9M1TkVEzmuLo/I48ubv73znO6I5wMU40+UEaarv0ZjiZAa19SkJzDZKLgI42Any7W9/+0WNmueB+WTu3hmwz0sRmPnlY/qLwBsPrrhZwyKBJQbkqXXhkzG/oLsMmaTTaWPXXV+ELJ44OIaa5yNlRenbX+4YlKhYQYDW4X5c/+xdyJsRSFSdiJnNwXphNptEdzmqM1JXmupUVhhgbXsSquuib6wqKdDYGYiEJ/YZl3VGqnRrighSV61px5Gig53lAF7djowPSAIzDVQCVaJNGh6QBcw+3NesSGNZVsd/Pnh4IvplhBgrXzEalYot64xBiFTSwLolrbhn37goaglTGIEcVw8CpOFLLZnsYy5WxO3JVME5cgKgbkR90PHgdS9qTQtg1qs11EtRby5JUQNqalIUGe+zojWJshfikK3BcmpC8qI/smXzuJPzz2R9ZzIJmAkTPUUXzDGwzsvtKBPKa8mYqsydlBEUiA0lI1kumkZr3rQM7La0gSCZljS2M1aU98ysA7MFI6o1aTERXzdVwPjeBgNdUup534FDG003kDS20SCP8Zhkti8spHDJ6jZZuPz4qX7UKNDGzwaJYCS9+S7ev3Wp9DZPHTz+l+8/hOc7VuO+NUcJiNzysY+/XD6X0w1GovysM6I9XeNUAfNM1/e1r31NXN8+9alPveC3wNaud7zjHdL6GA/6NX/oQx/Cm9/85hd8/NkeYB6YZztTZ9h2L0VgJgBzMEXN1TL9kOlGxQh6xYoVMxK1mMomueVE7NHmW/zoo4/KCnw6j9q7dw3g/7llp0hGxkScjKXAtl0xmMjXxnHD9rtFgWtdVw57hqoYKtmi1MXo2Aw8SaXyoauokUORY5hiGXjN4qQQsx49MCKQIzoWjeewl0qjQAUw+uzqKtREApetXySg8KPHDkuvK0fElE6gJbQRpDNC3KKyFTF3RVtKyF+37RiUY1OXuW6lhAlMgY91C9Oo1h2MlOpi4JAgU9gy0eOqQlwjCSvt1CT6JJgsVh3Q3KG/ZEufNgeBhaBenkIMoxkF3Z0uLKgYLdUn/Iy5Tyz0Md3XjLrdBMDuohN5GquGeBpLSlzavRSELmVCozYiLi6YGj5ch0T4ZFmzt5l7yHl8G11pth1RGtUXXXCS9Rzfx7eeGZnUj80aNuvQLUYoLV68F6zls/2J5YIwl4U7yjr65MEWtAVZUxZXfVUPdeio6fR/VmRhQVESyToETF3zjoXCzH/V+R1C8vrCnbsbf4/IYdyPqf0PX9qJjHJsuYfmIf+yvYwfb3y1sMKbxy8/dik6mtTnml9j+YjtTM3dBKf6UXe6gfnf/u3fxAv97/7u717wpROYb7rpJvFkjgdT2x/+8IfngXmOszuvlT3HCTtTN+fqnQ+KmQbrSExZE2D5b9aaCdJxymmm/Ujk4oPnRNs1788vKM0rptP3/V8/fBa3PNUnxdKYMERI6koBl3Zlse4XP4BVKYkX7tqONLrHajg8WheikpCNAk2Y4ioAACAASURBVE/qkbaVRJauTg3wJaBdtnE5+vvHsf3Q8KS3w3Rmm1vBqvY0vExWouLaaBHrOzMCLvftObo9xS0kCvVdnNeRQjVQxXO56oVCgsobCoarvqSfWU9ubgUiSJXrHkaqrihqsTUoH9hiPTkdeCrpDFqTKoaHipOuNwZlzk/cesV0LQF1YcZCX+moO5RYHEodmfXzyAaSiwQBeSVER0pH4Djod1S5XtZ8kw13KX75Fxei4iv3H4aFV57bJi1f33u855iUNLdTNRV6NgfLrYmDFBdYjPY1Q8O4RwlN+lMbsA1TgJ0p+1etbxc5Uy4Q6IPMhQeVziy3LrrWHlvEmBpvtIl1pDW8e0unpJm/eE+0oJzOYSqeNBLMXra+Ey9bYKJUruMbj/YcQw7jtjddshj5ha1CEKTNZjzKPvCm2gaMpluO+Rr8+i8uRy4ZpWWnDi5waXFKouPpGFy4ssbczPo+1eeh7zG/23/1V3/1gg89n8p+wVM4cYB5YD51c/kbPdLxgJmvsV2KNSRGyKwDzZYxfTLmFxQzYRRORxw+XG59ug/7hirSnvL/3roTT3VPBiJO3ELdxUf6H0PpwGFJi7I+SDZuVdExHuhSR4w9hqnixMiZvciMhmKDAtaMGTnvq6sTNoCsP7Ov2U8ksL6gI62EKNZcDNZ9rOosYNj2sXs4ShezNs10NiNz1lWXtUR2jCSOicKUSRBT5FoItM2gzP27WhI44uiwbU9cpSYe/NMoWfG1lW1JjNd9HPTYmlWX87JuangObDNybGJ9OTJw0LG8I4tLVrXhvx7rkfcdkokecJvIf5lpWnHSYmRKRbJsEutXLcTT+wcxMFSaNs2cT5uoW2loroMsPMkmMPX/+LAn8xZ1VR8dTEvfuGEhBmsBfrS7iEQ9slvkPJMxHiqRYAgvmlkBSlr+0dUrkUma+NqDh9FXdiX1z4wCbSLjVH2zOEhb2sTbtiySk9LUYibxk5qRFDY3Fz9v37JINKxHKw7+fXtx2rT+uy5ZjExCB1QVSiaDsFYT8C9ddR2uvWeyFWb8jrd9fKtock83uNClPkCzMM6pfAiwLsw+49OZKv/kJz8pugR/+qd/ekou/brrrsPb3/72CfLXRz7yEUltz5O/5ja988A8t/k6Y7eeDpj5xWb9iGlsSvqRJX3BBRfM6T2ciMg13cFiMRMtmcWbv/Qwesaihx4BI6lCGL/Ngz23N+27F8vLQ5LWlUjRTEbbO5F+dDykDUdRIonHRotT/CrZxj5rqG4ogh12qEChgUPoI2sAHWkTxYqDWrUuuteRiAedhyJxkGZ7QYLNxiV5PNpdAgGAABkLYrBPOO6J5nXFWtEfuagdP3rsyKTWLJorsB96KsBxv9/d2IH+Yh337i+J0QTr2CQnGZKuDSI2OBcJlOtEiEtXFIRMRrAiILHtq7lliPNB1yym9mkreW2XhWUFCz99ph/ba9oksGJqnKS1169Mo1ipS1ZitOpKvZuLAGbWp2pP85rZU0xN6Vu2D2HvuCNpeVpXMgHMtDLnmr+5bywZ+odbl8LI5fDpO/dJNM7zxp7QBF1D5taduMesixP8+fn93EP9skiKdcWZOaG4CLfnIoqkMrZKcXu2ug2FBm6+f++kz0x84PdetkQWLAqJSWyFMk2oK1YAr74R13z2IWl/ax4dWRN3fOxlM4IKuxho/nC62oFejFQ5U9gUDfrgBz84p+fCTBuTZMp2KQYBfOZ8+ctfPm0Ll5muYb7GfEpu5Yt/kLOxxtwMzHyg0d+YTGv2QNLxiXKa/LJs2LBhThN+PCLXTAeiihfbLj55Ty9ufWbghOfbuudhvKK4B3U9gYM2taiPVYXiQRjRst3J9B0R2AgpHCGQxXYgRUCBAiNst5HI1otMGQhsy5OBANtPtg8f03PLCJzgMEmr2krg+gsW4Y5tByf9vXnbmp4QQhOjPqaN/+jlK/Dv9x+QOjYHU7rRoiKU65V+XSVKMzO6XFEwBcCeLwYThg78O2u8FPEg4IjKmEJnKuD8rgxetjyPLz9w5JjULiutXV2taEvpSNh1qbmyTstxd08dO/YOSBTLBQ9XJPEi5MNXLsdg2cEPnuiNjC1A0hnnwxeAjn2Yo2sO8K4tnehIqvjmAwfE9jBuo2pePMU3nPeLRLM/u7xLsgAS/U6pnXNbqV+bKQFrzsc5C9J45bntIqf5uQd6pD49NT3OqN5gyr5BQKMM6YUr2+AEIb5w3yFJ6YfiSBGR17hY+POrlkJhuaehKa8UCjDe/34oiQS+/0Qv/vGX+4T9z13YJvX3r1uLG9YvmPHzy37/WJLzhB/yk9iA5Exmn05XqpyXxBQ2j//ud7/7JK7wzNxlHpjPzPtywqs6G4GZYBxLBJKBzfou67yx+D3rP319fXOOmI9H5JppohlFUGHstf+xY8Iqceq2rPMyMr7wyLM4/8hOrGnT0TdSh+PSbpCa0IbUaAXWooZWSZMmXSeKoBQqdh3lFTc7HDE6JVs6Nm8gGC7uzGKZFeLhA5PT6JSR5AOcETHBrcZIPQyQ823pz2UNNR7clrVlAj+BnIAROUYZ4tS0ZXkLHjs8LiDqayoM10WSQh8NswkCLdPuIryh6EDKguU68G1X9LZJDIsHmdfsa055UQaBKeoNFAdZUcBnHhtu8mxmX3FC3uvmBSa2rorqpLTA3DNYhS3iEioeH3SiqN9zZH45P/SJ/tPrVmPb4XHctXtEFjXsQeb8cWEw1c2JEfk1a9qwsj2Frz50GCU/ssNsVt/iuYWophmyYKHMyPsb/cD/+MjgtCYZ8XvmfWLmYpHm4V0XdSLIZvF/7tg7kR6XOnojPZ5LsQUrcqlgDf+61S04Z0lBottfbduPXb2RSlo8Ni7O4opVrUf/oGnw334TelOtEmkz0/DowTHctn1Qsii/c0EHLlicO+6zhO5rJDpRsvZ0DKbKuci95JJLTsfh5Zg0sHjVq16Ft771raftHC/2geeB+cWe8VN0vrMRmOn4RNIV7eEIyFNt4thbSNIXdXfnMkhuYU16NuYXz/WW8F+PHMbh/hFcsSKLf/71oLTcTB0EKD5QlwwcxJaDT4uy1kIrQMkOEdiMXI/dq55MI1GrSPsU69ZsEaJDE6NWRqIENUZtEvGZSeR9W9KyPBVxfWFKg26q2FHUkJ7wD1ZhU3SB+sqpJMK6LSIdBABeI639ukteZDahaBLJSv8xz49A6tyhx2uNzqOlUhhzFSScqlxDbCDB9893zCisqJoS+TMVHqe3S2Z6QjGsea5aUibG9AR0pw63HpGVQmpg12uoaxGzm4zxOGXMNDdVvQ6Mu/jFnhGUPRW2roOq0r4bRHKlbNVqLBSomf2OizrxwL5RPNtXmURQ43tltiFrR/Ma09tvPG8BlrYk8MX7D0mGI66lR0Q1LmqAtMfyQ6QqznOQdMWo9PO/3D2jHCfr45E4iYZ0JoH3X3MOFN/HZ36+S+Z7qlkGyxbSGh0CuaSO129dC70cLboC3cCTww6e292LwHNx0Yo2Ifo11+0eWLYJf9OXF+Y327c+fNUKMNU9l0E28549e+b8nZrtOShVS8U+ivWcrsFWJtaEX//615+uU7zox50H5hd9yk/NCc9GYGa9iw+K6VqUOGsEbqa257q6pwDBsmXLJCV+vPHg3mF85DtPodpwpY+TiNMZUVIHu2NsAOfddys81mDVQGqlDE6b3BAnTldPZZCzq/AaNowMacxMWlSkSFpqHjHIUUmLkQ+ZwPQEZrR3TnsCz/ZVJSrTPE+iY4Ia+6AJmnXmoNmfG0Z62xTTiNOeU1nV7OVl9Mu3y/Qwo+K43juVQSwylOmMkNXs0mRN5pmITQQqaoJzETJQ8+UchmtLdExAFzvJprQ4r/ktF7ZjWdbEfzw5JJKezABwwUJ9axK5mmvGvD+MFN+4aRG++1h3o6Vrcjqf8+pIa1pEeOOi6Pp1bVjWksCnt41KzTo2pWAkHguh0J2LKEim/PqODJJJA+lsGj95sneCgR3RBiKWONPMJm03FU1q1levzOPSDgsuFHz2oX7xUp5ao9+0JAe2OdGve92qDgwNjIqaGeU2l7QkooWBaeJAby9WtLVNAuXBhUvwhsEVUnKIW+oIzl94+wVS7pjtYHmI/I2NGyOv5lM9GJGTOLV58+ZTfeiJ473vfe+TPuPrr7/+tJ3jxT7wPDC/2DN+is53NgJzrOwz0xTRkIJpt7kSVZhKY2vViWT6bvjsfTg4Oj2zdeo1feS8NBb/7Ac42D0sD0cCCNuCJOptRHPxPiRaXZiLpDcPVTyMB5oQiFJqgEUZA3tHjwJzDIgEWUbU485RZjD7nqPSbxjVTX22P4VSa7WUEIWUgZFylJqeOqZr1dnUaeLpsgG7OhnwYgAnMDAtTHZ1xrOxtsVCyfbQWzw6R1Nr2zH7mpFwIZvAJStbcNfOAdT9SOiD6XNKjU47NB1vuHw1FieBL9yx65gaNN+zmc/CqFVhsKqrAMtaU7hqTRu+ta0XQzV/Alibj88FDYlgfiotkfb1K9JYvrwD//KLXWKVScEWRuJkX/O6kwkTi1si4l7g+TgyUpX0P3uhPT+YqL83n8NPJMWaMe3WsaQtiVw2hdGRkshqPtNdkuwAywc0uIj70t/XIHLRT5vkNqqOcRHDVPaSgoUbN3TIve4+cgRL166NyF6lEpRcDl9bex0+/+jgFL458OZNHfi7186s9DV13sfHxyULNVdC5WwfY/zOcjF9uoCf10FBENaZT2dL1mzf76nabh6YT9VMvsjHeSkCM1ffrP3OlUjCfdhOwZ/pBpmjjBpe962D074epTSPDkY/f9ZzP7RyET3jk0GG6VPfMJCqRx7CVT2BdOhg6wVLxf7wG3fulFYqgVeChaYjNAxhX3uqKmpatPtb3cJ2Khf7B45aN8Y1aGo8U2gjdkjiOVPpBNakgOf6JtcleR5xPGqkyfl/9g0zSiTj+bmecZQp0dUYrAszcuTxswkNRo3CJNG737RAE1WzO/qjXmOm3xmJMlpk9JtVA4Sida3IomJRPoHrtyzHP/7qgIAf08QEt6mgWdVMETHJ+HW8el072rMW/umBPolwpxKyGOVTApRGFVmnKsSm1oyJB/ptPLmzZ9r7xwhU0TVUQhWqYeAdW1fA0jV84/79GByrTrDG42UP6+BXr22X6PWHT/SilsxIml7azVxfom7Wy8murllJpODidauzsGjKoCn40f4qgmIpIugpodTr48GFDOetxbfxvssWS3ZjZznEPU8ePubaSR5b1ZZEd08Plq5eDSWbBTo7oW3Zgr/ZVsJPnx08Zp8rVhXwpXdcOOunEU1eKNJDlbvTMQj8Bw8eFJnM0zXe9KY3ierX6UyXn65rn+m488D8Ys/4KTrf2QjMjBZI/pppsF7FtPTll18+p1mcyZXq5seO4NsP7UO97uC161vw+UdGpz0uJS2ZlqVgCOubr33ml1hYGZWH83SDaVA1n0O15qCi6KL4xbaVjqyFp7rHG4pPvvTBEgzEHjHFuqstaVEelRKfWUvDroEK2K7ElG8Mrvx/AS7ZVLJk4C9dU/HOa9fi87/aN9HGw2uLI9pYl5np7bjNh0pgjay9vI06o2MjIREdI8j2bEJWD/RNWlWwcOWKHEaLVXz3sZ5pW53SYl2pSCqf/dIXt4doD+u49UBwjDiJZBjMJBYkVYmgKVfKtPerNywURbPP3blHUvFTx9JCQnyN6dy1Zlk7WrIJhJqGHfsH8bNdIzJPscgH33NKCdFqQnybU4Usrl6eQ87ShOx128EqDhwanKhXx+ciwWrT0jx29pVw5+GqtIpxHrhAs30y5k1Y6QSMWqSrTYD+vYu6RAHt6WKIXz9x4JhIdur78JMp/NEVi6GZJr53/1701sh6jxjvvGelZAbtSxbiL960Ec/19WHT9ddDtSw5DCPqT/5iL76zrfeY+XnNhgX41BvXz/r7Qd4G5Wzn2ukw2xOcbuDnddx444340pe+dNrew2zf66ncbh6YT+VsvojHeikCM0GbputzTVlNdaXiAuBvv78N3392bFZ3bFHOxFDFhef5eOWOe7B0tEce0o4SiU8IGJDQ1ajtUk2qLamjt+zCVjRkHPoaH2UzTz0pI97OjIFqpS5SjxxM2xK4YhZyvA/ZwgQDAg4BlIDGBQJJRNRmHq56Un9mmxXBj6Qv8T2WdijARhTpSm23kQrg8bh4kL+TuGRXpKf3tRd0SnTPKHWgZAvJiMpgtzzTj+nIXlvP7cDhuoJcYGNdawJK4KFSHMVPeg3odhT9uuLsFEXlicDFoowuC56MATh+iIqvIJ8xcGT4aCaC+0irlqLizRe0oqslJRaOimli+0AN2ugoAqeOBw5VJgEi3146n8E7X74KznhRfLF/sLsEv1iSt86FwLinoMR0trxnBUaDtU0P7CNlFz96sh9aoz+5IuIsKnJuTRj1ZFQTJNd1ZHDF6lYoySRu23YQ+waP1uCFPc6siEriniGKZqJGHgb4yDWroBfy+Mu7etFj5jCaLmA0lZfFEUchoeLTV5rCjaDYDT+3HPz9Hw8ewRfuP3LM5/dDW5fggy9fNgHgMZDz93S2p8PDw2L8sn797MF8Vl+axkYEfnZSnHfeeXPZbU7b0jnue9/7nqgAni1jHph/S+/kSxGYmXKmE85VV101p7tGwQAKk1AxjC1Xz+3ahT/65exqyTzRx1+5Gp/91V5c+Pw2rBo+JAzoZvGJqRfTlrGwZFErntodRTQkA/EB3WzmEO/DlDKZvNcvz+C+vSNRm47Ouqd9DLM7Sh9rIhKSSxDQ/AljDIIwXyMQ0zWJzGBCfMJ3ReyjM6mgVLanJabxeU9yVZwa5/WaloE/vngh+sbr+J+neqPgvDFYM29ma4uDk6Lio5csEILaoaEybt8xCJda3JqBnAGMeWxvmszAppTmGzYvBjH4O0/2oUqjJEUTpjhBfEKYpKlm/5p1WSSySfQOlXH34chII2bAswQgZKiGrrSpBNhcUHHp8pyInnzuiXFZdDQPMteZbfBNC7aiYiFcvHFTpxDpuh0FP3zowDELJC4m2nNJ1PwQyzsKWNuVh0plLV3Hv/5qt0iaMs/A1rho7aNIyltpacF4poBBK4+LLjoHf/3WiKm85R/uFnb+1JHWgds/tPEY0iK5GJ/6xR5885HuY/b5g8sW46NXRyYvzUAebzhVvYq6AIxq6aAUvxb/not/+UxfSB6f37nTBfw8LzNotGek7sDZMuaB+bf0Tr4UgZkPpHvvvRfXXHPNnO4ae6IZbZOByodNoXMZXveVZ2Z1jKSu4JH/fQ3e96HPY+P+JyMvX92aALGpByHIXbZxGQ4d6J9EkuJ2ZTMNy4tkKznitqhWr4YlCxN4epg9zva0ik+EqopF8pIPTSPYtODBfSMiSMKUr+77IhRCkCTBij24BFq2YhHkLmtx0FdT0UdR5SljOmJYVz6BN718Db545/MI6kcXMbEYB7MDwrL2oz5nppfftqULo2NjuPnZyNSBjORyMgM1CEW6kosHMZ4gixwBXn9uG1a1WLjt2X7sH64eQ/aKa7lyLMNEIp3E+y9sg6GE+Ny9B6U+H/d5cxsuXLR0SljuoqqlK3j50gQSWojnqwqeOlSddm5XFQwUnQALMwbOW9mOXNKU6Pe/Hu3F8Hgt6kFvtDUxo8Cw/K9ftVosMmm7qGhRPzVTGH9/Tw9GjSTGGtHvSKqAUiIjdel8imItwCvObcfHX3mO9B1zXPlP92O4elQ1LL49y1sSuO1j05dtPnHLTtz8xLGp7A9sXYY/v271pDvM700zUMf/7+/vF2IWVbMYlZ8IyGNi2lQAPx6QMxon8K9bN3tC2qy+mE0bsUuDWgWzaYec67F/U9vPA/NvauZf4HnPRmDmlFCQ4Hhjrt7KZIU+/sSTuOuQg312Bota0vjdTR1419efmvY0bzh/Ie7aM4xiPZK7pIfx1W4fOu/7xaTtWedlRJcM3EnRJGvLf3hhK7750GGJaKcOggfrr5l6BUU+sBHibee3YWCshvsOlRoewVGkxXRnDLo8HwlIHOe1KFiZA356MIqybM2Ufl2ysxk9TgVaPjjfdtVq/OrJbgyOHxUA4b7TtToRh7YsyWNDVw5fe/iIRNMkbxGU2L9MQGwJHSGpMU2rGDq2LM2iTalg+3CInYOO1M/F/5htSg0jCOqGsy+b9fplrUlcs7ZdmOo0eaiINOfkRQmvm3rQrO+S7UxlrPO7shgZG8dXdtoTiyMulEiMYz/0y5Zm5ZgpU5e0u/gbmya++/BhjBSPEs8ku9BIj3946yJ4YQgqz9m2g8ODVYR2HQ8N0uaSaf+I4Cb1fMlEqPjAlV1QCwXo7e0wOjthLFoEc9EiXPnPj0wrSEO+wN1/EXkes3+5efzNj3fgR0/1H/NZoaLZx65dNfF3en//9Jk+yZI821PE7c8NTbPPCnzs2uOndLlIZXmHv1lbpsd585gJyKcC93RAzuPEQC1tcgMDIGmTyn3TAfoL1Z/mOZgm5+LbatTgX+Cj9YzYfR6Yz4jbMPeLmAfm488ZiWIUTmDv8z8/5eOZwZldq5qP9NV3b8IX7z2ARw5E9edFY3141fa7phWVYASYbS9gse6hWHWgpRO4fIGBjtYcvnzvfuk95iAYizQkVcAUBR2FFAbKDrKmguuWprCmxcLD+4ax7fCxxhjc/2UXLsOevX3SU9uVtyQ6JeP5P58aluM162MzKieAx0xy/s6bwNKsgueKKkbVBNL1itSnm1udCgkNFSeQRQbPw6ic7T0lBoX0IKYyWBgiwTR+6GNFVsOClAHfD9BquGg1A7QvWID/fqJfZC6bFwfxYuQ95+UxVo3S8Lz++KH82fuPiABHrL4Vb58KHFy6KC293ytaU1iQ1jA8NIRaMomfPT1+TO815+qKVS3YtLxVtKSpZHaEtfFAxa93D2BgvC4LnYgNH3EDuLj6vylzaZpiz3jztl7Rt6awiO2zDGGhlEiLDCej4JFUHmOpPH76h+sF2Piz7UgZP9xZhaqEGK6G6D/q/THx0VrXZuJf3rBcjFdI+fvDm5/HgZFoEbqy1ULvuC0tZfGg0cbtH7tcTFA46B72wW8/PelDL8pzzLyEkeSpqan46DUr8f4rohrzdINpZbYcsq9/6dKlp8SYYSYgZ22Z1qxMkzOajYGc19X8b34O4v/LYqzRTzb1N/ebml7nfjw+5XtPRep97k/i07PHPDCfnnk97Uc9W4GZD7rmL+3UiTxRxMz9mZ5jmo5kkKKaw+/++2Ozuh8EsV/9xVZc+5lfy/YtlTG87pk7xId4prF5aQFbNy3Hj7cPYEc3U7iKPCQTNJZw3Mi8QZi7Ueo3qYX4k+vWoDw8ilue7sdhzxCVqRV5EwdGJkezPCetBf/y/2/vTMCjqs7//2Yy2RMCCXtERNS6L4i48qOuFZdqtdVqa8Vdf1WrVq21tlqty9+61Fq3qhWtuPKzrtQFFRdcqoiAQEUFBNmUNRCyZ/7P50xOvNzcWXPvOJO853nyQDJ37jn3e8493/Pu+wyU9fXN8uzM5SauGSekskgL6oVNhkVoVnFrk/QpaJPhfUtk4doWk44zWq0puvmxkRNT25QXkhBqWSgoL09+sUOJTFvWKAtWtacLbW2VSH6erA2hrm4yBSWwK6Ma5zs7VubJyIEFRhqiApdJCpOXJw+/v1iWtRVuUiDDDvKXP9jOqHwjdXXmeVbWNUllaaGMn/6N8RR3Sr5ghv33jNFbSKStTT79aqXMWFonawrKZEi/cvlkSdSBi7FTCjLaInLCHkNkSFWJrK+tk4nvLJANLZGojT+UH00Egso/FDK/2yC4yw7ZWvKKiuSvH62WeY1hWVdSIWtKess3pZUmOYm74c0+44r9zZ//8NxcmTh9ecL1tf+W5XLR3lWGyMc9vVzWd2iuo2PvXZgnew4ukAW1Edl5QJEcs2OV8YqHyPkZ87fpm3jQ2w5xzIvmIY+YA9XTZ4+SIX02lYC5Fv8MCJkEPoRGlZWVJRxzuhdQShKJHO0XfeHf4WypSuRuInfeCy0HanKczLoqfaf7vEF8T4k5CFQzcM+eSsxTpkwxzl/u0zEbD/GSixcvNpLA0KFDJRwOy2X/N1P+NdO7CIUzPpn/j92+v1x91LYy8vo3TaamI2e+bEoZIjmivrSezBCBKXIfaZVTD9hGpi9aJzPmLfNMw0k+6YbCImMLxcN685pqOWLrSrlryvwOD2JUqpCHdb6yy6e+oEgqIi1y5r6by7/+u0bmrW02hOcsVGGvpUCCCb1qa5G9h1fJ5gN6y/hpy4x0Rkd8BjGhisYZievIYU3j2c8ZM0wmfrRUvlnfKA3tVZusatxdtIHvDxtYItuXNUtbc6u8s6xNapuj95GiQmlpih5G3O3MfTYztbRfXdIos79cbQgfVTrNZh1zahcG9S42BDX7q6/llUUR43QHgdt7m5Sb+RS5iH7G/X6895YyqF8vGf/Ol/LNuoaoREm8dbsOoTUvXzYUlxm7Lz84Y7141ZHSWFJm5t0rOYv7Ob6/dZXcecIu0tjSKrtd92bCt51wuEsPGS4/HzVElq7dKAf99X3P77x5/ghTMcxK4vbfuoYGOfn5qN3e3UrDIoSh9ynOl3P3GSBjturTQeYQOnhj46VQBc5ReHcHKVnSF+GJNTU15h30gyzdRG4OmJGICfMi4xcOZhTKSKXeesJJ+44vUGL+jicg3e57KjHj/EVtV0iX5iwLOWDAANli2DB5Z0GtzFxSKyOHVsrNr3wuc5Z/m6TDife1R20nj3+4RBqbW01CidP320KKQhEZefWrMvaTV6Wqbs0mBINaEbsxntOtBUUyZnCxjBreV/7y6hcmvtnZSOJBiEyxI590XlGx/HTEAFm0olamzu8cM41jVX5bi3FcgqyRQPfbYaCM3KxS7pj0SfR5qd2cHzZOaB1hWu2eSSQkKYq0yskjY5cQ3QAAIABJREFUB8mnK+rk/YXRPqy0bsnPnZqTGsAn7TlE7p2+0kjYzsQesdJt/nDHvrJZ/94m3WR443oTAgSpQoI2raUTD6TMscOLZGmDyBsr8gSCxIpPiQ/jWW3qR3+rXaA61PYDCmXnXm3y2ucbTRIUbOk4tZG0g+pdppBGfsG3UnOeyK/2Hy7FxQVy7UufycbCUqktrjC2/NrSXmIdsTpSb4lIaWFI3rlob5kzd66c+MzqpF7HbfqXGcl08twVcv6Tczy/g+NZ+1FFKkvC8tSZe0hVWaH854uVMm6Ct+Phm7/eV/qWRatpOVtza5vscu0bnv38/djh0rs4T8ry26S1pbmD1JFaIXakVt4RWzIVsobArCRu/7V/g8jTIVP6QFOFTRkpuVev+MUzkgI6xkWQMl7YFK+4/PLL5bTTTgv0sNGVsab7XSXmdJH7jr/XU4l56tSpJiUnGwo2LOzI2K8oehEuKJSf3j9N5i6vNf6/KG77lhTIirrOHq9M36d/PEjem79Kzn1spnHYYSvdqX+xDH5tktSs3dTjFW3pqB1qpH9liSxY+LUJI2pCchzUW2Z9EXXciXpFQ67RJB6oGIdWlQpOO+VlRbLH8P4m3eRjH3wlqz28cLnHrsOqZca6NgmHC2TPzctl50EVRlV700vzDHHhfe3MhoWdsb6oVIob601yD6oJ7bFFH3nn85XywbKNJiYWVbwly/WFpSZjlm2odPfZcTPZfUCxSYO5yeGioMgQIGksncE8EPkJI2vMc1Fq0RAmOcEoT9nW0nFg4F7GdhxplZGDymT0sEp5cvYqWVDbuknhCq4z3u4FReZZi6m+FWmVXWqKZWBlWCZ+HnWC4yDSQg7r9sEgBXMQwA68obhcaoYNkt+csKfk9+0rO981yzhqxWvM95HbVcrhA+ukuGqQnPx/nbNvmWdwVI7AnosD2hOnj5RX5qyQX030Jub9t66SL1bWyw6DK+SXY7aQLfuWmbChaTNny0VTvRPTzPlDVD3u1fb581uy1lEljGuY749/5x2hQMat2bNnmwppmHTQKFmytpK4+3f+jmoYidqLvL1IHSK3GfkgY+y9/C2oxhivvvpqmTx5sjzyyCOBZSwLavzJ3leJOVmksuy67krMbBZWdeUFOXHMqMhQW2O7YiOwVajue3uh3DL5c5MWkVN/ayQizS1Ru6q7bdW/VP511p4mhhQvV9tGf/aebLPii02IC49oCOrk3fqbRBtT5q00Djd4V6NabgwVSGNBoZE0ndmqUDMesFnIZIua11Qia9c2SmlpoUlq8U1jniFyBockC5mTkvOSH2wT7buxUVY3R6S1doNUlxXK3W8t9FyBFf2qZFRVvjQ0t0pJQcjE8Rb3KpcpC9ZK3ap1mxROwF5NLHXvkrCsbAmZVKBblUSkf1lYhvcrk8enfZvS0llKkm02VBSWtjCpLcPSr6JIKooLpKW1VWYvXmvGjyqeghC2WQ9swoTKi8Ny2F7DpbwgJHf9+5MOL3YItSk/bDArbG6SUFGh8YJmNvqUFMjP9h4qhQVhuXbSPFPWsilcaAi4tgQpuMJIwU29e0tzqEC2qwrJid/Ll5K8FjP3Z0/xqgkmUhyOJnoB+p2qw3Lu7qWyy47byxerG+S4+z9O+JbzvRuO2k6O3HmgfLxorZw4frrnd2b87n+koJ2gWNPYd6l93Np7iJz22H89vzPzijESNmUgO7dl6xrkR/d8ILUNUSdG5nrCKbvLtgPLN7nYKbnirZxOCJEtvwoJepG3++9cj2c3UrmbvN0E3xXS5p0fN26cSe952223dZSDTThpOXiBEnMOThpD7onEjBRAvCIbAAkLcDpytlPGT5P3Fq4xmapsa2ppk+0Hlsjc5fUdyTU271Miz/7vnvJ/Hy+Ta16ISokIRbsumiUjFkU9X40jUn64w6bLRnjqPlvIg+8tkrrGFqkrKTcq2FaJSHFjgwnpsZ7XpsBEXp4cvcsg2bpfidz73hJpXLveSJOQDqOztkz+pfiFCb1qbpRDty+XtvV1MmOVyJqGiPmsV16LbNzY2QEN2+9O5REZu+MA+WjxOpm6osmUGSTrF+SDQ5BtPA/qdrJu/ebAYbJ+9TqZ+NEy2dheO9qpHscLu1VCxpGMZyzLa5Fjd0E9vl4+/qqz57hbNU6fP929RpZsbJGiigoZVibR2O1QSG6autRIvuGWJongrd6eb5vvDCwPS0MkJIP6VcqYkVtKdU1/CVVVycET5sma0t6dHLEG9SqSp87awzi3UV7SNlSdO1wzxfPNPmH7QtmuV0RCrY1Szcmp3dN3eX1ErnzPm8yZe9YRSUd+PqrGxAlD/vO/2SBH3PWBZz+zf/99cw2qXaorkdyGA2VDS6uMvP6tTofFeNKvs4Mla+vNWIb17ey8RWggUjJkTGxyV0gw0bYIOWO3RsLmXeRZ3bZxJ6nb/3M943ITdiz1Otcyn88884xRW19//fWmxGM66vZEz5RNnysxZ9NspDCWnkTMtnQcEgcvJLGXlZWVndC6/OnZ8vTHy6SwnZh5oZGGjx8xQA6vaZKKmm1MqNHAyqiX6D1vLZBbJkel421WzJd9P3uvIwRni+KIcYRCtCK8Z+zOg6RvVYXc/PpC2ZAXkqL29JsQqrS0SGV+ROqbotIMJE/ZviN2GihzNobkzQ/nt5s1o+5m8GVLOCz1+UWGjG2Y0LZbD5IDBhfL1M9Xyexl6zuIFZUscb7O0oFI8eRwHjU4X0orSuWNzzdKXmvnkDCIH5t4fWGU/DF9XnrwcHlm+hL5fHmtFLS2GrUxTmGUkOxITtJYZ6Rg7MbblZOec4Dc+/aXhvCdDcJHTW7rKfMZ9vWLD/2eRDZsECkoMMk68Kym3fL6QlOa0lShorBEYVmHFPzYJQdLuF8/Uz3J2Xa85vVNYsXtZ9YJy+u12f7q1z3fpmO3ypcTdu1riMs6CyHxLV65Xsbe/ZHnd247qELqGpqlLK9ZCkMRQyxIhnVtYTn35XWdSBZv8rd/tbuxuUJWSK5OL+ibJ38h97+zaJO+fj92Gzlhj5oUdoBvL2X8SJMUo4Akq6ur07pPsl8ijSeknG7IFeQcS53uJHMOGkjIzBN7AMVrcF477LDD5Nhjj012uDl5nRJzTk5bz5CYqc+MDZlQCGzIeJVSxAKbmZeKbtGqjXLU3e8bZy5Tso+C94X58sS4nWT10oWdykWuq2+Svf7fm9J/zXLZ54sP2u23IkhiJ+4xSD6dv0gG9h9oiiCECB8SkQdmrZaNq9ZJY1GJCYVC8uOw8NPdB5vY5DUbm03BCsoR5lf2kjc+Wiizl9Z2nPCxozbnhaRfpNEkmlhb32yyQg3bvJ/sv3mpoDZ+4J1FRjVtm6XCxoIi48iF5y5OT9WlBXLKyAGmPvNL81ZHnaDwP6YkM/WZKZLR1iZ8z9ZZ3rpSZLvqsLy4sMV48+LV3FxACtAGc18vZ6+j991KykMRefitb1X8jM2p7jaSf2GZhNpazPP8euy2EmnPJW3+ragwCTlum75GXvm6TdaV9pL1xdEQKxqYvX5hNAGHu+1141sdKlznZ7/Ys0Yus6p/15diEfMtP9xSDt01mrLS2VbXNcp+N7/j2f8Hl40WamPb5iSWMx6bK3O/ru8oBYnNf+9BITlui2ZD4NZe65YI/7u6RZ6aU2uqXJ2+92ay42a9DQGlKglCWEjJaJEIGwrSM5nn5n3E8xoHL2tCCmoLRf1PrWWiMH75y18KGjPs9Dh5plr6NagxBnVfJeagkA34vt1ZYsaTFGmDcAhOyJzMbYjHjBkzjFowllQwZ2mt3PjKZ7Jg5UbZpn+5XPaDrWVAqZgQjlGjRnWalccmfSRz73hA8loIH2owZInkgmpxGeX2hgyJSkSEaJSWytJlq+WFmcujqmIqARWVyMg+IZPFapNWWirS0CDzlq2Tlz9dbaRRckATNoXqlYQZh+3YX+qaWiVcWipFQsHiKBn//a2F5v4m0xbZqUJRL+b9tqqWuSvrpaGxRbbtHZb9aspM2skZqLHnr46GK7UTHdqCknBIGsvKJW/9ekOWw/uWypitkaYi8sCsNbKmvkXKmqLFJXhGvMKJqXbGkfcrFtmnpkAkPywTvgxJaWO9qTZl4qELis3hhLCuaK7ukEkJut02g+XksbtIflWV5PXrJ6Hq6mjaShF5td2T2W33/9//2cIkx/Bqpz/8sbzj8mLnWW87bgc5aFvvUp67XjvFFMVwt2fOHiVb9++sBkY9vNef35IGZ7mtdq/t9y4dHdP2u6K2US791xz5+Kt1UXNI/7CcsXOxjNg5mlELLHG8cqt5vey3SIsQczyvaacXNUk1CA/EzyJWSVO/tiHinymfSupODslBhlyB2YQJE+S6666Tv/71r3LkkUemfGDx67m/q/soMX9XyHex3+5KzNRFxiZHHCSSsQ2LsnDNmjVLBg4caLxNk22owCF0d7nIttpaaXzgAWlYvVYWramX4qo+JosXatnWtrZogfrNN48m5igoIDbLEDgOOLOW1kp9U5vZ5IduVh1N4LEx6u1MFimuxabaVlQkE16fJ7V1EGC0UTTh2BE1Ut2nTCJhUloWRIszRyKyccMG+ee7i6WxMapippnwrMIiOXXXvlJVVWFUxDhhSWmpRGprZYUpNLHcOLvZPqCkms0HyBFDS2RNXZNJTVlOXeLycok0NsoXi1fJS3O/redLH6UhkZ/s0l/+u3yDrG9okSFVxbJ1vzKTGhRp6bXPVsun6yh7WCDrCoqlrrhcqgf0khXhClmY30vWl1fK5n0K5ZzdSjdJjmEdgpiHhV8ukrv/G5bP17QYMwMxvgMri+ThcSNMOJFXe2f+avnVk58YLQKwkkhjQK8iefacUZ3SW9rv/+rxmfLavFUdkiye1f3Ki+Tl8/cytmKvdseUBXL/u4ukub3CF5Wmzho9VM7ab4u4S431MWf+Yln85ULZcZstzdpNVfK1HRjzS3uokxeZ8zc0SUjKtFje017hUOnYnBkP5L9o0aKMqMlRX1900UXy1VdfycMPP2wOxj2xKTHn6Kx3V2ImWQCbSqy8t0i+SMuos5JtSODTpk0z8c8dG2BjozQ++KC0rXDlKC4sNMTaWltr7HZICKHCQgmTrIF42zhxnthGW+vrJURtZeRSJCDCedpE3l+wRhaurJPq4pDsvVm59Cmi7GKe5EGudXXS3NRkVPYQ4IrmYpPmsZbyf6jjmxtkYK9COWr090RqN3W+yisrM8UUnv3gS0F6M1m+KElZUibHb1tp6jrnkXmpqEgi69dHDwyRiLABfrJ4tcxblyf1kZD07VMqh27ZS8qLC8zz8yzOH6moML+P/2StPP7ZBmkLh+UnIwbLmfsNNSaDeV9vMJm6hlQWdBCLJRhIBBUkz4Yn/camFnl3aYvMrxXZrCJf9h9WKv0cWa68SGXS7K/lnre+lG82NMnONb3ksh9sZUKQ3A0iwdb64dz5cueckNQ2Rj2wUfLfdMz2pjxjrAZ2T01fZpwCwZDnO3qXgXFJFpJkTdKwJbuzXCW7RpO5zibVIC80UisHVP6WjDTONdiirX08VhyzUyJn/lCT8x1s13wWZOPwfOqpp8qPf/xjufLKKzsdyoPsO9vurcScbTOS5Hi6KzGj9mMDj9XctZWTgYsN5r333pPRo0ebyyM4nzz2mLTOn995Y2/P4xspL5fa5culhdhO8i5v2GDGxQ+xs5AXhJ1fUCDh/HyzeRnyJgaUmrv19VEbo2EFRyCso0fsrq1r15p83ja1ZVllpeSVlct/Plshn365UlpaI7JZ72LZZ7dhnUoWdtwqL0+aS8pkxqdLZfGqOimuKJURg8pkYP/eURV8u3TF9Y3NzbKivl7CffrIwK22ksK+fU05Qgie8eTRvyuFYjIYe11jnZKQfiyROCVJZ1hOLOnQ/h0CQjrMD4elpDiaqtItLTI3aFz4+7bbbiuhgkJzICIpzJ5b9DGOf341ewCgPwo04P+QrpSczJg4XOJwBWbpHgAYMxglkshtYhKuA2cbBuUmc+fvaLbSfX6e6d5775Xbb79d7rvvPqG+crr3SgbLXLhGiTkXZsljjD2VmFFzs1FgZ062sRm9/fbbMmbMGPOVpmeflZYZm1aXQvVrVNaQLqplVMWFhdHwJg4K/DQ3d5T6YzMxRF1QYEJ+UIs3I5WuWxfd/IqLpW3jRok0RZ3DIGnnT17v3tKyapWJbcVjt/eAARKmkhIk2n4wYTxIo+GK8qiavCMntPeTQ6htvXpJfju5GrJtl3wZz/xvvpF1zc3GSQhNQJAN6Z9DFCFtkHJXnZLi2WohLQ43FC6hH66NlyTDTTCp2kvpBynZSpJBVzUi7zvrHtNOV9Tkycw3axenKzQqRD/wbMlI5KnYx22qUN4LHMlw7ELz8OCDD6akCUvmeXL1GiXmHJ257krMbAxIzbEaajxO58SEJtvYqMmxzUm8+e23pfn996PEaxJ8RIx6t62lxdheIcUQBRloxFCu88hRjAqaIgAOaRQCxN67SUPtXVZmpOK2dmmb59tItqQVK4w0He7dO1rNaMMGcw1jdRJ4qKTEeGqHIfeKCsnv08d8p6CqSvJ7995U3VxeblTnzmZTlqKWBzMONKkSUbI4m0NPU5MhEZyFUH96hbWlcr9E10LISJI2+5szBCpeggwn2YARayqeRGgldBwSsbcSboU5JUjJzhaDgLQgSQ6kQTbqlePghSPZlltumfI6AUfGnIxEfv/99wsFaXgf6I9QKPD83e9+F2g6zyDx8/PeSsx+opnBe/VUYkZ1SEN6SKWxCYyuqjLSsm0dUjLk3B7SYyo2NTYawotFtHhbIwnbhgrY2G9jNEPiSNHr18tK7MkNDdJ/yBATCy3t90HaRY2MLbettFRaSkqkuaREGgsLjfTdWFAgTY74T7fNEMkGUuJfSzBs6KiRbarEICU7DhT0RZk/DgB+lRSMhSmHt88++8xIyhwA0slw1bEO2lW88dTpSMkcNmiWxC3W8Ryw0nG4og98LdA42MQkQR4AmDvmDS9v1OTuxD2pvGfJXAsZk7nroYcekhtuuMFEXpCIhZ/jjjsuUDt9MuPLhmuUmLNhFtIYQ08lZqQVTuTY9VJpUydMkN2+/NIQpJOQSX5BEgyjQm5PgmEcqhyqY3NNOBy11bZf09F3e1hUp787BhfJz5dVLS2yJi9P+g0fLtU1NUYq38TBKg3HGmsz7FSNqN3ZCpwI2bFaCDZ3p1ToReZWMkxFqkbSQmpFoiN0J8gDALDabFqDBw8OvFoSGKNt4NBhw5KsZBiPyMHeFpCI5XDl5YDFXKFxsKrkoGOFObwhJTNn2OW7anJI9E6ilj/zzDPNYRF7ctCHgETjydbPlZizdWYSjKunEjMbJHZZNslkW9vy5TLnqqtkWE1NR01iwobyUCG3hzh1SL/EniLFEhqFzRcid13TcS1kiqQNcVvHKce//A1Hq/lLlhgPWiQDd/hXss+QzHVOZysve2Q6zlbx1LsQDqE0SK3YrYPOOAWJYP/0yqaVDD6pXoOEjC25q8k7nAlJ4pE5RI4mAHLE78Cp/XBrQ6ydNtVncl5PERi0DjZ5T1fulei71px07rnnyiWXXGLKNaZy+Et0/+72uRJzjs5odyVmK43EmhbUbUhonO6TaThlNfzjH7Jw1izpN3iwsdOGm5slhB3b7S1N5qV2py+BjHHCgsBt6JCLfIW/Exrl4XUNWUEittAGkmuQDdUnkpafzlbxbIVIdPzY7FapSIWpqmWdavJMOEA5U1xy4EglZj6dObYZtXCYQ5XMWknkpe6sBJXINu7WgkD+qMlRz5PBK+i1yToiWcjzzz9vEofsuuuu6cDUo76jxJyj091TiRlVGCSEM0yi1lZfH41VXr9eVq1bJ81r1khrezgWMauRkhJDuqHKSvOTX10tBb16SbiqSgr5f58+UlRcnFKqRKQ6pBDCn5Dqg/Z+ZnPlAMDmx2GlwjquJQInzc95LjZ1yJX+rEOSUypE8otFLE7vXadEGItc0I6gJudaSDJoNbktBIGq1ZlPO024En7Nlmfs27evMc+kapOOJ427D1bWW525Yd6wy8eag3TrMrsfGA0Xsck82x133BFIGs/jjz/erBFbHINCFwceeKDZJ37xi1+Y9xFN1a233iqHHnqoGSLvzRlnnGHCKFnLV1xxhZx88skJ5ytTFygxZwppn/vpqcRMogpUcDvttFNcRNmEGl99VdrWrDHJNSz5GimXH5ysPCri2BhOJ7FYMnHbZ91xnIyLgwMerZkIa8FhBy9hNj1U5alKoqksSQjA9gdh4Umbbn+JbLSW2E2ZxNZWQx5IdW7ydpNKV8iEMeFYyBxy4AhaLe/sLxMOV7YcJOuT9RIrDMque6RqVM2xEpG4Hd/camnev0mTJhm1NfWTTzrppLTXS6J1inbKOv9Nnz69g5TPOusss06vvfZakyjlgAMOMKl+MRPwN8ic7GJo4XbffXd5//33TfrfbGhKzNkwC2mMoacSM+o+HMBiqcNM7C/hT9aRi9CmGAk+UoE9ln0WEkHqsWpd+oqV79hLOknVzsbzcThBCkDFyiEgSLs1GFFNCKkcsmJTD7o/YlvZNNE2WCe/ZFS7scjE60DlLBjB/GFLtmaAoJ/P5p0mlIxDTtD9ccDBwQtHMrQOyUrlyfok2BrqPAfE+O9//9tIpDjN4ehF/WTS4eKsF3QjLPKYY44x0jJaD94Tkr/QDjnkEDMeMouhwiepiU3Ty9/RcF188cVBDzGp+ysxJwVT9l3UU4mZ0zGn3hEjRmwyKUERcryZZ0OHsNj42WBtaT+b7ziWStf5d5vRykuV6yZy6/zEd9hgM+Gxi92afglJClpNzgbPRgquSJHpxEAn62hFXyahTHuoFBIXeMZSqSdLZvHWi9PDG6kc9XWQzZnCk/USZKELmwDmww8/lKuuuso8GxIqhyy86JGYiVUOql144YWmZjNrZ+LEieYwwEGAd802yBfcycXNWubdtYcFbOBoSyiakQ1NiTkbZiGNMXRXYra5f2NBgmTKC2XLvllCtpuslVjTgDTpr9hylIyFU3ZX1J7OjFZeRO5U6yIRQhyodb2kb+ffuqrWxTaIxJMJZyswYPOGlDEBEAedqiYh6clrv5ADHupNpGT6hNDB2oY5uSV0m2c6Gbu4V3pKpFb6Y+6CLs/II/IcaAF4rnRTeKaCKXP45JNPGlK+6aabTL1kPzRVqYyBa1988UX5/e9/bxzNUEs7iRmbMgdMS8wcOq00jWobNb8Sc6qIx77eOxGxf/fPyjv1VGLG+Qi1HCUcnaRsN4EgNwM2OVuQntAnNvQgCYTn4xRPtjMbbsVitKQRTyJnrIwtVqyyLRTCv04iQeJAjYz0yKEj6MIFHHJwJmO8SDNW6xDUS2c9oFHPQ1jJJiaJFTPuJHBL7JCiMyUov7NukVg5xDmlclTqfq8hzD2QMkleIKcg3wnmiWe79NJLzYEZr+tUk//4PdeEfz3++OMmBW8sVTbOo8RRqyrbb/S/vZ8Sc3DYZvzOiSRmbFcff/yxIWa/7cixHtZKdBSJR0WHXTfoRAxI4xAWpImElU46RrdaNxaRW+c2a5uHrFD1eTlbOW2zXVkcYEoMNAedTBSBYKwQFocOm3LSD/V0LAzA0s4hxMjBKpbNNp45w86BldTjjZn7s0Y5dGBDDdr0wLNzADjllFPksMMOkz/96U+BvxduvNkPOLzaw8C7775rxoK5C3sxuFvnL1Ly8ncOnddcc405SFjnL0xjOH+lkuq3K+s/0XdVlZ0IoSz9vCdKzMbTurFRpk6dagjLrVZ0/p7MRpbM1BIzzQvMhghBBi3RQZJIyGyuSKxBx9BaOyQbOvY2Dh6ozL28023uaXCzaSgTqXa9pEEIC4K0at2gpXJn+k4kJZyCgmxO224yhw5rzogXZmYlcw5bXjHjVrvCs0FSxM/7dYjywopDAEUnUFvffffdxrHKT8kc2zR2aVu0BoK98847zaF43Lhx8sorr5h3g7nFGZL/syewpnhHiSAAK0gYrQyY3XzzzXL44Yebx8G0cPrppxsyZtyXX365CevKlqbEnC0zkeI4uisxA4PTLsTvbscu/malD/dm5vzdmRIxVRLnuxAktkgIMhOOOoRtcKK3dtYgJTowxDvYKZUnm2jCajXcalwvz2mnNAhRsEki5WDbw+vaKZUH8bwccDgE0B+E5bfa2P3aggH9QRgcAoKoz+yOGbex/djLeT47D15hfvGSkSSLDeaO888/32ggkDitnTbFLSzu5bx3OJIddNBB5jpsv08//bS89tprhpiJyrjgggs63SPbw6CSxUiJOVmksuy6nkDMfjh2OW2D8UjcZlKCHCzxIH2w2bG5pqJSTHWpWO9uDg8cApIlyFT7sdezsXMAYEO3+Z/TvVei71kPdaQaq0YEU4jLPR+xqjx5SeWJQowgJWfO6UyodXFgQ7uC/wHFJ/yUIL1wBj9UyfSDvdxL85BszLgz5Mm51m0qUOLlIUsOc1deeaVJ3EElqCAOU17PCkkT5oQkHI+Ysz0MKtH7Yj9XYk4WqSy7rrsTsyVU/s2EY5fTM5gwHbdN0E0iVqWYqiTuXEYcBlAhZyrXNH1DkBBWJuys9OckSMgjnhrZ6aHulr5j4e8lAXIt2gfrMMc1QZIkzwghox6FGNLxB0h1e2Ee7SHAr2Q28fB/+eWX5dFHHzV+AZA1ByvW/vjx40051aDbz3/+c+M4R1UqiPmNN94wNnSe/Y9//KPxOaFlexhUsjgpMSeLVJZd112JGWcgSjRiM6I+Kz9IWEGGQWHzhKxoSJDJSFduL123Ct1JLF4kjkoXQuY52VysVB6UBOJM3UnISNAx0OCDRI5XrPUOTlZVmuyrZlW6zpAyJDuwZw7t5zbxiFchCKd3us0pnQqJW1V5JsozggvPZMtdkv0uaJ8H+iRZB0Un0FIbMthQAAATc0lEQVT84x//6DDrsKasvTvZOUvnOoj3pZdeksmTJ5tDD6F8qM/pm2QmpNJEc4C5iXnP5jCoZJ9fiTlZpLLsuu5KzLxg5NS19VnZ3CEwNlVL1kh79gfi5v+WxJHIkiVxNnDsyNjKSBDC/VPZlJNdEk4SZyNHHQcJ4P3s9pz2QxJ3jgtVJpnSkHSScURK9pniXceGjZ2VxiEgaNU8/bBO2JAJD/IKEYrlk+AV7mQzt8VybOPvHDI4ROIciC05mcNcV7HlAEmooM2r7fdBx2t8b7/9tiFlbMr8ZKJP5zjw9H7uuecEiT1WwhnSaf75z382CU2yPQwq2TWgxJwsUll2XXclZi+YITYkTDZfm4+a/yMd8S8/EDmfsXmxcTqJ2yl983cI8amnnjLqLyTkTCS0QKpD0sFGh3d3rDq0XZXEnRIgfULKbGiZSGoB+XEAoE9iSYPO381asc5W/Mum7Ica2W2XdavRidtlnUFStnBCMvHi6ZIaa8LWhMYcEHRhFHBFy4DH9RNPPGEcvGxCn0xug+TYfuGFFzqRMhIzGgratGnTjEc4Bxak6GwPg0oWPyXmZJHKsut6EjEnCz0bGA1bn5u4LaHPnDlTZsyYYTx0uY7vWInbSwq3n9kNP1WJ2klWfjsFxSJxJFZskJAVhMEY/JbE3XMCUaHtQLWaicQkNjwIG73fuMZab2CIhsVWN+PAY53bEiV94XO3c1s8idyaNDiQkjEMjRHah6Dj53l2DryEEnGwIhQqndSo8d7ZeKFQtiIURMshD7K1qUQ52HAwIeUmuKMl43NU3bZqVLaHQSW7lykxJ4tUll2nxJzehHCiPuKIIzqKYCDBQtr2B8mbjclK4PwLyUN22GUtUTvV6m51OhsuBE5JOTYQNjYkyKDjdelryZIlpkoS6lxsu04pzcYnu2OUu+LYxizgbQ1OmajKRH+QFapyiM7WL05vNST/LVTWECTSKnOZji9ALOcqr/hlDgE0HMtYd5hoYhG5VwrQ5J/s2ysZH/HB5J0mrve0004LRHUdLxSKtJm5WhEqHcxjfUeJ2U80M3gvJebMgc2GxQ9SIQRuVeiWzJ3qdAjKSuJspnisQshOqdwSOX/zy2PYJu3AiQyJtavxs8mQOIcVSB5iQFL2SvDiVK2nQ2bOWWYObKw3iSZIiJKqBiPVVeMsz4jEmik1Mh7XHBqZS54xnkQeq6qWF5HHSjrC/VEd42D1yCOPGO/yTDVnKBSHkFytCOUnXkrMfqKZwXspMWcQ7BS6YpMcPXq0nHjiifL973/fqJSdBM7/rRTOZ0hE2JtjObTxd1SKOPx4SUZsqEjIqACxIwedCAUo6BNHK2ytVmL1Io6uSOJuErcOZZAUBNnVg0cyUwoxIiXj2AVBJoqdTuaeia4hpt3aS1HRJ2uXjlVVyz0H7qQjqKr5LuRIdAAFHvC5oDpT0Icei4UNhfrDH/6Q0xWhEs1tKp8rMaeCVhZdq8ScRZPhGooz9jreKK0kjle4lcKdDm3WLg6RQ+JIb0hsSNwQMCpd7OVsaPxuJXH+35XqUrHGzHgZH3ZWNm9U5alu3slI4s5kL0jcEAfEzLMTy2olQb/Srrqfl+e0nuwcdoJOi0r/TskcJ7Zki2uk8xY4PdSJgCAUiagE7LWsNWzAVGfKRHOGQjHH7lKNuVQRyk+8lJj9RDOD91JiziDYWdCVzYKGZAw5nnfeeWYDxV6OFOT0VickiwZBu23h/I4EblXrkB1SWSKCRT2PXZdrsSVnIgTKOpTRJxt2rJAnPx3bbHlGJHKeMxPOVhDSrFmzjDmAg0AmJHP6/O1vf2sKwqC6xm6e6eYVChVPld1dQqGSwVmJORmUsvAaJeYsnJQMDgkbpLU/ekl8SKaWxN0e6jbsDOkIaR3ig7C9QsyQUAlZ2W233eTggw82Unmy6tV04eAQYitP2ZShiQ4OqUriXvZXVNdgYmtQd9Umnuj5nRoInhNsM9FYO1SEwv/hhhtuMBqITLdYoVA4nOVqRSg/MVRi9hPNDN5LiTmDYHfjriAHVMfW7m0lb6RypDgSOyCtQsYQPdKcJXGnE1tXEr044cVuTdgVEivSYxCe7G4Sp0+cysACqdV+7qzkFMuxzTq3pUriaDnQQPBvUMUuvA5s1Eu+7rrrTFGII488MqGmJIilj90e5zIS3tgMdKwr7NysQ/JwYy7JtYpQfmKlxOwnmhm8lxJzBsHuoV1NmjTJJOzAiY0GceFMZCVwG1LmDDWzHuqEFyVK9GId29icORyQipX/B11Yw06nMxY6lpd3LEncHXIWi8TdKT/BBAcvDh+ZSuPJ82IWwLGL5BwkDME/wO9GKCL3xquaBD5HH3206cJZppHf99prLxMfTUOljh2Z0EK0IldccYVJsdnTmxJzjq4AJeYcnbgeMGyb6MUWsXfHiFvythnbiGuF2JCQISscn2KlXE030YsbdpsxzEqsftjMnSQeK1Yc6ZxnhbDRCiQqgpKqJO61vHAQpNYw1ZmoDBWUDRtyZd7oi5KMTmLu7mUa/X6tlZj9RjRD91NizhDQ2k2gCEDeY8aMkcsuu0woypAo0QvOWUjVTjW6V4w4n0N8XrZp+iBjmE3Cksh+7QcAEDJhUMS2oxGwJoRY8ck2zCkVSdxN4jjL3XvvvXL77bfLfffdZ3JJZ6KhYUmWmLtLmUa/cVVi9hvRDN1PiTlDQGs3gSOQaniZV6IXp0OblcQJJyPrmiVuSBHHJyTzww8/fJOkL34lenGD5czGhpd3OqFX8SRxp2RuSfzpp582hwA0ERxkzjzzTBMOtc8++2TEwcyLmLt7mUa/XxIlZr8RzdD9lJgzBLR2k5MI2Bhx7Lk4suFYdOmll8oee+xhwqCIC7cEjq0clTYqdCTteOlW+SzZFJiQJrZkxkIilkx4P0PiL774otx4441GA0HxCZvkBo9nnj/o5ibmnlCm0W9MlZj9RjRD91NizhDQ2k23QACSxCkJD2gvqRbyJHTMmTfdeqg7s7XhmY5kSvy32w7uLD86ffp0kwBm7NixaSViSQd0xnXbbbfJgw8+aH6QkL+L5iZm9xi6Y5lGv3FWYvYb0QzdT4k5Q0BrN4qAAwGb6IUkLl7Z2gi7ooYxKmSkb9TJfiZ6iTUZSP+orFHXY0+OVVY0E5PpJTF39zKNfuOqxOw3ohm6X08j5mRKxSERoWa89dZbO8rAxQvHuP766014B04zxOn+5je/kRNOOCFDM6jddEcEcCqDGEk1id3aJnrxksTdiV5Yh5B4vFrikD2ki8MaPxwUpkyZIueee65ccsklcvbZZweeACbWvF111VXm2VGdk18c5zs8tY899lhzUOH5UOd3xzKNfq9lJWa/Ec3Q/XoaMQdRKo4cwfvuu6/x8qUQPSq2//znP0JMqzZFIJMIxEv04gwv4/+8C6QKxd6Nupwf4oYJSQqixYpPtrWTUz0QBzHG7nZPJeYcndGeRszuaQqiVBxVi3AAQk1JvCypAe+8805D1F3dhHAwwhln7733FrxmtSkC6SLgTPTywQcfGAcvKlEF1WLFJ2vt5KAQF7QhecHdPTN3zvkHSAemnk7MfpeKo0A8qQCJ+6QwBI20hZDoa6+9ZrITdaWA+49+9CPjMATpKzGns+L1O981Am7bsdZODm5GlJiDwzbQO/dkYva7VBwVdiDjxx57TPbbb7+OefNLKr///vtNXV9q3ELKSsyBvhp684AQcBIzB0x3iUaczwhFI/UnNmZixrmGRn5u7OwcdrUlRkCJOTFGWXlFTyVmv0vFQcok8ye8xJ0ZyQ+pfMGCBXLcccfJm2++KY8//rg88cQTxjln3rx5vqrLn3nmGVOXmZAZVJ30RVYlbYqAXwgkIuaeWjvZL3yd91FiDgLVDNyzJxKz36XiiDX94Q9/KOPHj5cDDzxwk1nzQyonphSyx/ubxP308+STT8qFF14oBx10kG/qcnIhQ/6TJ082MbN4wELQSC3aFAG/EEhFld2Taif7ha8ScxBIZviePY2YgygVRxYkysuRM9k2VG4fffSRPPfcc6bkISkdaekUcEeCwHHMEiT1fiHNUaNGmRAXmh/q8pNOOsl4lxMqQwsitIwQGDJHLVy40IQAoZbHHq/kn+EX/zvszk3MWjs5uMlQiTk4bAO9c08j5kDBdNzcb6ncOW4kZreN2Q91+YgRI4yNHHU5KShHjx5tMk7xQ/PDie28884zITq33HKLUByBSkUcbH77299mamq0n+8IgVjxycRpa+3kYCZFiTkYXAO/qxKz/xAHIZXHI2Y/1OWQLmFYNTU1JpYV+/Xxxx9vSJPatn5J5RAz8bLU0UViPuqoo8zPCy+8kLK9/NlnnzVJJii0cM4558hf/vKXDpggfZyH0FjQ0DpQeUpb8AggERPPb7VExxxzjPFbiBcqGPyoemYPSsw5Ou9KzDk6ce3D9tOJDec1fvCKpU2YMEEeffRRef75583vfkjleOH+5Cc/kVmzZgl5pw877DC56667jCo+VXs53rqQPPZ21PtOYmbsqMixl5O1jQMGXu2o6rUFi0CsHNfxQgWDHVHPvbsSc47OvRJzjk6ciPitLscDe+LEiSbci7wEJ598sgwdOlQgf7+kchKtQMrU9oWYkcqpo3zxxRd3TESy9nL7BVSkZLFyEjMq+Z/97GcdqVGx+ZN/+m9/+1vuTniOjDwWMcfzr8iRR8u5YSox59yURQesxJybExeEupzwKNS9qIjJR4xz2R133CE333yzL05skC+hV3//+987KhYh2T7yyCNGlW1bspJ5PGJGLX/PPfd09EMfDz30kMmBnmqIWTyVueZJ7/z+QMzEGuNLQM3qa6+91uTujhevnJtvYfaPWok5++fIc4RKzDk6cRkatt9SOWFlkCabNaFYOP3gzQ7B0VKRzBMRMwcAUpfSOABQaOTXv/61rypzzZPeeSEuWrTIzCkHvQceeMDMKREKbmJ2xitnaDn3uG6UmHN0ypWYc3TiMjDsIKRyEqUQjrVkyRKzceMJjo0ZNWeq9vJ4xHz44Ycbm7it8uWlyvZDZe6eBjQCHDKQ1v2UzG0/uZgrvbq6WsjFTWgchSoGDRpkHueQQw4x/gx45msLBgEl5mBwDfyuSsyBQ6wdJIFAOpJ5PGL+5z//aZy9nM5flBJ0pkr1Q2XufDTypI8bN85UFps7d66vkrntJ9tzpeOMhyMeqmvapEmTDCYcxDiQUdAFbQmHvv3331/mz59vDmXagkFAiTkYXAO/qxJz4BBrBwkQSFcyR41Mcora2lojfROeQw1tvL4Jl7rgggs6PMpPP/10ufzyyztG4pfK3N4wVp50PvdLMs+FXOlI9MS949hHyB01n2+88Ubjr8BnseKV9SUJBgEl5mBwDfyuSsyBQ6wdZBkCfqrMebR4edL53A/J3J0rXYuYZNmiytLhKDFn6cQkGpYScyKE9PPuhIDfKvN4edLBzQ/JHG2AO1e6EnN3WpXBPYsSc3DYBnpnJeZA4dWbZxECQajMY+VJJ3GKX5I56VET5UrPIph1KFmEgBJzFk1GKkNRYk4FLb1WEUgOAb8lc2evXrnSkxuVXtXTEFBiztEZV2LO0YnTYWctAkFI5krMWTvdWT0wJeasnp7Yg1NiztGJ02ErAoqAIpAAASXmHF0iSsw5OnE6bEVAEVAElJi75xpQYu6e86pPpQgoAoqASsw5ugaUmHN04nTYioAioAioxNw914ASc/ecV30qRUARUARUYs7RNaDEnKMTp8NWBBQBRUAl5u65BpSYu+e86lMpAoqAIqASc46uASXmHJ04HbYioAgoAiox6xpQBBQBRUARUAQUgUwikJfJzrQvRUARUAQUAUVAEYiPgBKzrhBFQBFQBBQBRSCLEFBizqLJ0KEoAoqAIqAIKAJKzLoGFAFFQBFQBBSBLEJAiTmLJkOHoggoAoqAIqAIKDHrGlAEFAFFQBFQBLIIASXmLJoMHYoioAgoAoqAIqDErGtAEVAEFAFFQBHIIgSUmLNoMnQoioAioAgoAoqAErOuAUVAEVAEFAFFIIsQUGLOosnQoSgCioAioAgoAkrMugYUAUVAEVAEFIEsQkCJOYsmQ4eiCCgCioAioAgoMesaUAQUAUVAEVAEsggBJeYsmgwdiiKgCCgCioAioMSsa0ARUAQUAUVAEcgiBJSYs2gydCiKgCKgCCgCioASs64BRUARUAQUAUUgixBQYs6iydChKAKKgCKgCCgCSsy6BhQBRUARUAQUgSxCQIk5iyZDh6IIKAKKgCKgCCgx6xpQBBQBRUARUASyCAEl5iyaDB2KIqAIKAKKgCKgxKxrQBFQBBQBRUARyCIElJizaDJ0KIqAIqAIKAKKgBKzrgFFQBFQBBQBRSCLEFBizqLJ0KEoAoqAIqAIKAJKzLoGFAFFQBFQBBSBLEJAiTmLJkOHoggoAoqAIqAIKDHrGlAEFAFFQBFQBLIIASXmLJoMHYoioAgoAoqAIqDErGtAEVAEFAFFQBHIIgSUmLNoMnQoioAioAgoAoqAErOuAUVAEVAEFAFFIIsQUGLOosnQoSgCioAioAgoAkrMugYUAUVAEVAEFIEsQkCJOYsmQ4eiCCgCioAioAgoMesaUAQUAUVAEVAEsggBJeYsmgwdiiKgCCgCioAi8P8BOzwkAJ7kHnQAAAAASUVORK5CYII=\" width=\"432\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# (Optional) 2D linear regression with linear model (estimate price from year and power)\n",
    "\n",
    "\n",
    "# your code......\n",
    "X1 = dataset[:, 2].reshape(-1, 1)\n",
    "X_2d = np.column_stack((X, X1))\n",
    "#print(X_2d)\n",
    "regr_2d = linear_model.LinearRegression()\n",
    "regr_2d.fit(X_2d, y)\n",
    "m_1_2d = regr_2d.coef_\n",
    "print(m_1_2d)\n",
    "q_1_2d = regr_2d.intercept_\n",
    "print(q_1_2d)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter(dataset[:, 1], dataset[:, 2], dataset[:, 3])\n",
    "ax.plot_trisurf(dataset[:, 1], dataset[:, 2], (m_1_2d[0, 0]*dataset[:, 1])+(m_1_2d[0, 1]*dataset[:, 2])+q_1_2d, color='r', alpha=0.5)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}