00_Plotting_in_python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting in Python\n",
    "In python, like in any programming language, you can plot graphs. Making high-class graphs of data or simulation results is usually one key component of your published work, be it a thesis, report, or journal-publication.  \n",
    "\n",
    "There exist a bunch of different plot libraries in python which all serve more or less the same purpose (making your coding visible) but have different utilities.  \n",
    "\n",
    "One note: Do not use ```pylab``` as suggested by [IPython core developers](link) \n",
    "\n",
    "\n",
    "### Pandas\n",
    "To create a proper dataset for plotting, we use [Pandas](http://pandas.pydata.org/), a high-level library, perfectly suited to handle your data.  \n",
    "Structuring data or tools to analyse data are just some features of Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import some libraries\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the Dataset\n",
    "Before we start to analyse the data by inspecting it on a plot, we need to load it so that Python knows, under which _name_ we address the data. We use Pandas for this, as it is well suited for working with structured data. Being built on `numpy`, it is also quite fast.\n",
    "We'll load a Porosity - Permeability dataset of a (fictitious) Sandstone unit. There are a lot of simplifactions with this porosity and permeability field, mainly that there is no spatial correlation of porosity values. and hence permeability values, as we relate porosity and permeability by a fractal law (see Pape et al., 1999, _Geophysics_ __Vol. 64, No. 5__):  \n",
    "\n",
    "$$\\kappa = 31 \\phi + 7463 \\phi^2 + 191(10 \\phi)^{10}$$\n",
    "\n",
    "The dataset comprises points in space (x and y coordinates), which store information about porosity and permeability.  \n",
    "Let's load the dataset using Pandas:  \n",
    "\n",
    "* x\\_[m] and y\\_[m] are the coordinates in space in [m] (i.e. distance from a datum)  \n",
    "* por\\_[dec] is porosity \n",
    "* logperm\\_[mD] is permeability in m$^2$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>por</th>\n",
       "      <th>perm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000</td>\n",
       "      <td>14900</td>\n",
       "      <td>0.13</td>\n",
       "      <td>3.201680e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4000</td>\n",
       "      <td>7900</td>\n",
       "      <td>0.13</td>\n",
       "      <td>3.201680e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12900</td>\n",
       "      <td>4100</td>\n",
       "      <td>0.07</td>\n",
       "      <td>4.072250e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5800</td>\n",
       "      <td>13900</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2.135360e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7000</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.29</td>\n",
       "      <td>9.215890e-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       x      y   por          perm\n",
       "0  10000  14900  0.13  3.201680e-15\n",
       "1   4000   7900  0.13  3.201680e-15\n",
       "2  12900   4100  0.07  4.072250e-17\n",
       "3   5800  13900  0.25  2.135360e-12\n",
       "4   7000   2000  0.29  9.215890e-12"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = pd.read_csv('data/por_perm.csv') \n",
    "ds.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this presentation, we will use x, y, por and logPerm for looking at different kind of plots and plotting libraries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matplotlib \n",
    "Matplotlib is undeniably the most used plot library. It comprises a lot of different functions, and you can basically plot everything you like using Matplotlib. In notebooks, it is also possible to use matplotlib with magic functions, namely:\n",
    "```python\n",
    "%matplotlib inline\n",
    "%matplotlib notebook\n",
    "```  \n",
    "The key difference is that the `inline` option provides a static plot, while the `notebook` option enables you to pan, zoom, or read data directly from the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# a usual way to create adjustable plots inline of notebooks is to create a figure space and add \n",
    "# the plot as a subplot\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "fig.add_subplot(111)\n",
    "b = plt.scatter(ds['x'],ds['y'],s=ds['por']*100,c=ds['por'],label='porosity')\n",
    "\n",
    "# to know the color coding, it is always good to add a colorbar\n",
    "cbar = fig.colorbar(b)\n",
    "\n",
    "# axes schould always be labeled\n",
    "plt.xlabel('x [m]',fontsize=14)\n",
    "plt.ylabel('y [m]',fontsize=14)\n",
    "\n",
    "#getting the axes ticks in a bigger font is often desireable, for axes, labels..everything\n",
    "plt.tick_params(axis='both',labelsize=16)\n",
    "cbar.ax.tick_params(labelsize=14)\n",
    "cbar.set_label(label='Porosity [-]',fontsize=16)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bokeh\n",
    "Bokeh is a stylish interactive plot library, which allows you to chose data within your plot and *grey out* other stuff.  \n",
    "Its interactive nature is quite similar to using ```%matplotlib notebook``` (btw, try to replace ```inline``` with ```notebook``` in the plot above and see how the figure (-options) change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"1001\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"1001\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"1001\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.4.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1001\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"1001\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.4.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.io import output_notebook, show\n",
    "from bokeh.plotting import figure\n",
    "from bokeh.models import ColumnDataSource, LinearColorMapper\n",
    "output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"28d02e07-95c3-4b8a-8ab9-fa47bdc42d24\" data-root-id=\"1003\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = {\"54056d87-d275-4cff-9ba5-5ae53ce81b13\":{\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"1013\",\"type\":\"LinearAxis\"}],\"left\":[{\"id\":\"1018\",\"type\":\"LinearAxis\"}],\"plot_width\":800,\"renderers\":[{\"id\":\"1013\",\"type\":\"LinearAxis\"},{\"id\":\"1017\",\"type\":\"Grid\"},{\"id\":\"1018\",\"type\":\"LinearAxis\"},{\"id\":\"1022\",\"type\":\"Grid\"},{\"id\":\"1031\",\"type\":\"BoxAnnotation\"},{\"id\":\"1043\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"1002\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"1029\",\"type\":\"Toolbar\"},\"x_range\":{\"id\":\"1005\",\"type\":\"DataRange1d\"},\"x_scale\":{\"id\":\"1009\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"1007\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"1011\",\"type\":\"LinearScale\"}},\"id\":\"1003\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"callback\":null},\"id\":\"1005\",\"type\":\"DataRange1d\"},{\"attributes\":{\"callback\":null},\"id\":\"1007\",\"type\":\"DataRange1d\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"plot\":null,\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1031\",\"type\":\"BoxAnnotation\"},{\"attributes\":{},\"id\":\"1009\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"1011\",\"type\":\"LinearScale\"},{\"attributes\":{\"axis_label\":\"x [m]\",\"axis_line_width\":{\"value\":2},\"formatter\":{\"id\":\"1048\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"1003\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"1014\",\"type\":\"BasicTicker\"}},\"id\":\"1013\",\"type\":\"LinearAxis\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"size\":{\"units\":\"screen\",\"value\":10},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1042\",\"type\":\"Scatter\"},{\"attributes\":{},\"id\":\"1014\",\"type\":\"BasicTicker\"},{\"attributes\":{\"data_source\":{\"id\":\"1039\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1041\",\"type\":\"Scatter\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1042\",\"type\":\"Scatter\"},\"selection_glyph\":null,\"view\":{\"id\":\"1044\",\"type\":\"CDSView\"}},\"id\":\"1043\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"plot\":{\"id\":\"1003\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"1014\",\"type\":\"BasicTicker\"}},\"id\":\"1017\",\"type\":\"Grid\"},{\"attributes\":{\"axis_label\":\"y 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5200,14000,8400,11400,15000,10300,4100,1700,6300,5700,12300,10600,10300,13800,700,8100,4100,7300,8300,1200,1900,8200,7200,13400,2700,9100,2000,13200,600,12300,15000,300,9600,2700,14200,12900,13000,14800,13100,2400,11900,13000,4100,12200,10200,2700,8600,9100,14800,6000,3800]},\"selected\":{\"id\":\"1051\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1052\",\"type\":\"UnionRenderers\"}},\"id\":\"1039\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"plot\":null,\"text\":\"Porosity plot with Bokeh\"},\"id\":\"1002\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"1027\",\"type\":\"ResetTool\"},{\"attributes\":{\"fill_color\":{\"field\":\"por\",\"transform\":{\"id\":\"1038\",\"type\":\"LinearColorMapper\"}},\"line_color\":{\"field\":\"por\",\"transform\":{\"id\":\"1038\",\"type\":\"LinearColorMapper\"}},\"size\":{\"units\":\"screen\",\"value\":10},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1041\",\"type\":\"Scatter\"},{\"attributes\":{},\"id\":\"1028\",\"type\":\"HelpTool\"}],\"root_ids\":[\"1003\"]},\"title\":\"Bokeh Application\",\"version\":\"1.0.4\"}};\n",
       "  var render_items = [{\"docid\":\"54056d87-d275-4cff-9ba5-5ae53ce81b13\",\"roots\":{\"1003\":\"28d02e07-95c3-4b8a-8ab9-fa47bdc42d24\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1003"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = figure(plot_width=800, plot_height=600, title='Porosity plot with Bokeh')\n",
    "\n",
    "color_mapper = LinearColorMapper(palette='Magma256', low=min(ds['por']), high=max(ds['por']))\n",
    "\n",
    "p.scatter(x='x', y='y', source=ds, size=10,\n",
    "          color={'field': 'por', 'transform': color_mapper})\n",
    "p.xaxis.axis_line_width = 2\n",
    "p.yaxis.axis_line_width = 2\n",
    "\n",
    "#p.title.text = 'Porosity plot with Bokeh'\n",
    "p.xaxis.axis_label = 'x [m]'\n",
    "p.yaxis.axis_label = 'y [m]'\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seaborn\n",
    "Seaborn is another visualization library, and is built on top of `matplotlib`. It provides an interface tailored to generate  aesthetic statistical plots. A more detailed information can be found [here](https://seaborn.pydata.org/) and [here](https://seaborn.pydata.org/introduction.html#introduction).  \n",
    "A special characteristic of seaborn are pre-built [plot-styles](http://seaborn.pydata.org/generated/seaborn.set_style.html#seaborn.set_style), such as `darkgrid, whitegrid, dark, white, ticks` etc.  \n",
    "It also let's you set your plot [context](http://seaborn.pydata.org/generated/seaborn.set_context.html), such as `paper, notebook, talk, poster`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(39.974057250000016, 0.5, 'y [m]')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dtLQ0jh49yvTp0/nuu++wWCwMGjSIxx57DFmWGTt2LMOHD6dz585MnToVVVU588wzufLKK1m5ciWLFi2KxnfLLbfwq1/9irFjx1a7F6fTySOPPMKqVavo2LEjgwcPZsOGDSxdupQPPviAv//97yxdurTGOL766qtqYy9btoyePXty88038+GHHzJ69GiSkpLYtm0bc+fOZfbs2axbtw6Hw0F+fj7Z2dk88sgjXHDBBRQXFzN8+HC+/fZb7HY7QI33WDlO5d9h69atPP3002zbto127doxbty4aOIdO3Ysffv2ZeXKlRQUFHDOOefwxBNP0KNHj2Z4t5zeVO8Wq+JE6Q1ithji2i2WCIjKRRBXXC4XCxYs4KyzziItLY333nuPV155hRdffJE1a9Zw8cUXc9ttt+F2u4GKed60tDRWr17NiBEjePTRR9m5cydLlizhs88+IxgM8oc//AGAOXPmYLPZWL16NUuWLOGHH37gk08+qTb+5Zdfzu9+9zsuvvhili1bxjXXXMOmTZuiO48PHjzId999x1VXXXVS7I8++iihUIiVK1fyyiuv8NVXX8W8x5riOHHsSrxeL9988w233377Sdf69ttvufLKK1m9ejW//vWvmTRpEoFA7fPuNY0DFRXN+PHj+eUvf8maNWv461//yssvv8yHH34YPWfJkiX89a9/ZeXKlZhMJmbPnl3reIL4kN4llceeH01O7hkE/CEC/iABf4i+eWe0ucQCYp+LIA7cdNNN6PV6AAwGA7169Yp+YC1ZsoRx48Zx3nnnAXDbbbfx/vvv8+WXX9K3b18ArrnmGgwGA5qm8emnn7Jw4ULat28PwCOPPMKFF15IaWkpVquVVatW8dlnnzF48GCWLFmCJNX+/ahr16707duX5cuXc+utt7J8+XIGDx5MWlp1S9lgMMjnn3/O4sWLsVqtWK1Wxo4dy7///e+TrlnfOK688koMBkPM+erc3FyuvPJKAMaPH8+bb77J2rVrOeuss2q9r5pYsWIFHTp0YMKECQD84he/YNy4cfzrX//i6quvBiqm+LKzs4GKRFWX6T5BfEjvksp9j4zA6wngdgawp5jiNhWWaIjKRdBoFi1axPr161m/fj2rV69m3rx50Q+vI0eO0LVr12rnd+3atZqGUceOHYGKqiccDtOlS5fosQ4dOmAwGDhw4AB33XUXv/71r6MLj+PGjWP37t2njG/kyJF89NFHAHz44YfRRfqqOJ1OwuEw6enp0ecyMjJiXq++cVTeXyyq3qtOpyM9PZ2DBw+e8p5q4ujRo9WuCRW/7wMHDkR/bteuXfTfSUlJzbo2JqjAajPRJTOtzSYWEMlF0MR06dKFffv2VXuuuLi42gdcZXtj+/btMRgM1c4vLS0lFArRrl07fvrpJ0aNGsXHH3/M559/TlpaGo8//vgpY7jiiivYuXMn69evZ8+ePfzyl7886ZzU1FQMBkN0obNy7FjUN47a2jcPHz4c/bemaRw4cIAuXbpEK8Gq7aBOp7PG61TSuXPnavcAFS3LlZWgQNBciOQiaFKuvfZa3n77bbZt20Y4HGb+/PkcPXqUiy+++KRzJUlixIgRPPfccxw5cgSv18uf/vQncnJyyMzM5O9//zt/+tOf8Pl8tGvXDlmWq/XtV2IwGPB4PNGfbTYbF198MU888QSXXXYZsnzyRjaDwcDVV1/NCy+8gMvlori4mLfffjvmPdUWx4ljn4o1a9bwxRdfEA6HeeWVVzAYDOTm5tKuXTtsNhtLliwhEonw8ccfs2vXrhrvsZJhw4ZRVlbGW2+9RTgc5scff2TBggVcc801dY5JIIgHIrkImpSRI0cyceJE7rnnHgYOHMj//vc/5s+fX61yqcr06dPp0aMHI0eO5OKLL0av1/PSSy9Fj6mqyiWXXEJeXh5ut5uHHnropGtcfPHFFBQUREX7KuPYvn07I0aMqDHWhx9+mHbt2nHJJZcwbty4aq8/Mcaa4og1dm0MHTqUN998k4EDB/LNN9/w6quvRtdnnnzySd577z0GDBjA//73Py699NJa7xHA4XDw+uuv87///Y+8vDymTJnCxIkTufHGG+sUj0AQL0QrsuC04Mcff2Ty5Ml8/vnnp2wCEAgEjUd0iwnaNIqiUFhYyIsvvsj1118vEotA0EyI/2mCNo3b7ebGG2/E5/Nx6623tnQ4AkGz0VgnynfeeYdhw4aRm5vLX/7yl3p3FYrkImjTdOzYkY0bN7JgwQIsFktLhyMQAMecKPeVJawTZX5+Pn/729+YPXs2X375JXv37uWNN96oVwxizUUgEAiaiZIDTha9vYrNGwui8i99crIYPW4I6Z1T4jbOF198wdy5c3n//fcB2L17N2PGjGH16tXRc3766ScmTJjAqlWrgAq1jLy8PFavXs2cOXMIh8M8/PDDAHz//ff88Y9/PEkRojZE5UJFBg8Gg3EXOxQIBIJKSg44eWz6+2zasBeTyYDJlIzJZGDjhr08Nv19Sg444zZWbU6UVc+pyYlSVdVqtsaSJNXb4kEkFyAUCrF161ZCoVCdzv/hhx+aOKLGI2KMH60hThFjfGjKGBe9vYqAP4TVJldxotRhtckE/CHefXtV3MZqrBPl0KFDWbJkCTt27MDn8/Haa69V29BbF0RyaQBV/0CJiogxfrSGOEWM8aGpYvS4A2zeWIDZYox53GwxsmljAV5PfMZvrBPlhRdeyMSJE5k0aRIjR47koosuwmq11isGkVwEAoGgifF4lKjbZCwkqcKl0h2nBf7GOlEePXqUX/3qV3zxxRf873//o1OnTpx99tn1ikEkF4FAIGhibDY56jYZC1WtcKm02xPDiXLbtm1MnDgRp9PJ4cOHefHFF7nuuuvqFYNILgKBQNDE2Owmeudk4fcFYx73+4L0ycnCajtZ964hNNaJcsiQIVxxxRVcfvnljBw5kmHDhnHttdfWKwaxQ18gEAiagZvGDWHHtv14PQpmixFJ0qGqGn5fEJPZwOhxQ+I6Xt++ffnPf/5T7Tmr1crGjRujP/fo0YN//OMfMV9///33c//99zd4fFG5CAQCQTOQ3jmFmU/dQE6/7gQCoeijb/9sZj51Q1z3uSQConIRCASCZiK9cwr3T7sKr0fB7Q5gt5viNhWWaIjk0gZxKQolSgCXouCI4V1SGx53AI8rgM1hwnZscVFVnWiqE52UgiSlNEHEjcfjUXB7AthtJmxt9D+roO1gtcltNqlUIpJLG6LI7WLOhnzW7isiFAxi2LODQV0zmdwvl0z7yaZaVSnZX8bi11eyef1eJL0ONaIx+BI7I27cSJJ+AzqdHk2LYDAMwWy7G31SZjPdVe2UlLh45901bNpcRU6jTxZjRg0iPb32exYIBE2HSC5thCK3izuWL8UXDmEzGNGXlyMnG1hVXMim0hJevXJkjQmmZH8ZM+9/F78vhMVasXvY7jjKgMFvUnYoTFqHdJKTDYBKKPQN4aMbcaS90eIJpqTExcMzPsAfCGExH18g/W5jAdu3H2DWjOtOiwTj9ikccgVw+xTslrb9bVjQehAL+m2EORvy8YVDOIwy0jHPdkmnw2GU8YVDzN2QX+NrF7++Er8vhM1uim7yuujSVZhMYfw+I0cP+QHQ6SQkyYGmefF7Zjf9TZ2Cd95dgz8QwmatLqdhs8r4AyHeWbymhSNsWvYfdjHrrc+4ZdYiZn/0PbfMWsSf/v5f9h92xX0sb7mfnw8V8XPh/iZT8hW0LUTl0gZwKQpr9xVhM8SWlrAZjKzZV4QrqOAwVv9m63EH2Lx+Lxbr8edlU4DuPfaiKDKSXkfAHyQSUdHrK76L6HR2QqFVqKoLSWqZysDjUdi0uQCLOfY9W8xGNm0qwOtVsFrb3rf5/YddTH1pKX4lhFWW0WnlmIwG1m0r4Ic9JTw7ZQRd2jf+b3NQOcLbPy1jw8EfCQXKQQJ9sZmcQB8m3Hhpm+twEsQPUbm0AZxBBUmni1YsJ1J5zBlDN8njCiDpddVkKUzmAJomgaaj4lkdkYgaPa7TSYAeTS2L743UA7cnUAc5DQlXG/2W/caH+fiVEHazqVrVZjeb8ClB3vxoXaPHOKgc4Ynv57KmeAtBdwQpokcq1xPJ9LO++xoe/tM7cVXyFbQtRHJpA6QYZVRNQ63BMqDyWEqMzjGbw4Qa0arJUgT8JnQ6FXQaFc9q0aoFQNNUIIJOSo3vjdQDu81UBzkNFUec5DQSCbdPYcOOYqw1dAJaZZn124vw+BsngvjP4k845HKBIpGk16PT6dChQx9OQmfUcJ1/IK5KvoL4Uhcnyko++eQTJk6cGNfxRXJpAzhkmbyumXhCsaUlPKEgg7pmnjQlBsdkKfp3x+c9/kGkBEzs3dUdWVZQIyoms/GE5OLGYBjSLFNinrCfff7DeML+6nHbZPr0zsLnj33PPn+QPn2y2uSUmNunRIUOY1F5zOVteHLxlvvZUvYTIbcacxxdSI/Wzc/GH3bHTcn3dMLjUdi3vwxPE/3u6uJECRVeVgsXLuTBBx+Mu5+VWHNpI9zVL5fNpSW4gkp07UXVNDyhIJZkA5P75db42lETL2LHD/vwuANYrEYkSWLlf4eQ3rUYszVIWvuKCkXTVDTNjU5nxWy7u0nv50DgKO/sXcGmsl1IOh2qppGT2oObuw+nsykNgDGjB7F9xwE8XqVat5jPH8RsMjBm1KAmjbGlsFvkqNBhrA/+ymOORiRWT9gHKkg6CV2M6VYdOjRNB+YIbnegze/ZiBfN1Tq/du1aunbtyrBhwwCYMmUKY8aMYdq0adXOe+ONN/j000+59dZb+f777+M2PojKpc2QaXfw6pUjGZLRDV84RCASwRcOMSQjq9Y2ZID0Lqk89vxocnLPIOAPEfAHObDPzPo195La/lfo9Qqq6kPTvBiMFzV5G/KBwFEe3vIm35XtxKQ3HnsY2FC2k4e3vMWBwNGKuNMdzJpxHX1zsggEgsfkNIL0y8li1ozrsMjJ7N97CI/Tf4oRWxd2i0y/nhl4a/Ae8SoK/c/JxGZu+Ae+LdkCEqiaGvMbrYYGOg38+rgp+bZ1Klvnv9tUgMlkPOZGaeS7jQU8POMDSkri1+VXFydKgBEjRvDee+/RrVu3uI1diahc2hCZdgdPXXIZrqDCN+vXc2H//jGnwmKR3iWV+x4ZgdcTwO0MYE8xYbVV7tB3oall6KTUZpkKe2fvCvyRILYkc/Q5SSdhSzLjKfezcO8KHjj3hoq40x08cN8VeL0KLncAh92Er8zH4uc+Ysvqn6MbQnsNOYtRd19Gp8x2TR5/c3Dr1bn8uLcUtz8QXXtRVQ2vomCRjUy4amCjrm9NMtMr9WxWujYSdmvo9dWrF80QQVdgJucXZ4iqpY5UbZ2vpLJ13uNVeGfxGh6474q4jFUXJ0qADh06xGW8WIjKpQ3iMMqky6Y6J5aqWG0mumSmRRMLgCQ50Cd1b7Y1lk1lu7DoY38btuhlNpbtwhOu3gVmtcp07ZKKr8zHzFtfY9OqnzBZDZgsRkxWA5u+2cHjt75GadGRJr+H5qBLewfPThnBwHOz8AdDBMMR/MEQuedmxa0N+bcZV9DB4QBZpTwSQdM0NDQiyeVoQR2OrZ3jruTbVqlP63w8qIsTZVMjKhdBQuEO+0/RVi0h6XS4wz5sySf/R1n84qcEfAo2R5WqR5KwOcx4nH4Wz/6Me58Z3WTxNydd2jv44y2X4vErrFq7niF5/Rs1FXYiHeV2PHLBZBb8tIz1B38kqJSj04G+0EzfYA7j//grsc+ljtSndT4eTSjZ2dl89tln0Z9jOVE2NSK5CBIKe7I52jodK8GomoqqadiTLScd8zj9bFn9M5Yapmksdpktq3bidfmxVkk+rR2bWaaDwxTXxFJJR7kdU3tNwFfup8R5FM0vkX5RmpgKqydVW+drbsKIX+t8Xl4eDz30ECtWrGDIkCExnSibGjEtJkgobMlm+qT2wBeJvfnRF1HISe0Rs2rxOH3HNoRWf1uHJQgkQyRJQtLrcJf5miT2towlyUyP9hmc2a2LSCwNoLlb5+vqRNmUiMpFkHCM6T6c7e4iPOV+LHoZSSehaiq+iIJZL3Nz9+ExX2dLsRzbEKoiSRKBZChoL+G0SOjQUAFbmQGfTo35eoGgKWnu1vm6OFFWct1113HdddfFdXxRuQgSjs6mNGb1Gk+/1LMIREIEIkECkRD9U89mVq/x0X0uJ2JLMdNr8Jn4PAqBZPg+U0+ZRYde1dCroAurKOkmpv/rM/Ydjb+4o0BQG6dqnW9rCt6ichEkJJ1NaTxw7g14wgHcYR/2ZEvMqbATGXXP5fy0qZAf7SoRSU+yCpoGEVVFL0l07piCTwnx+op1PHbDpc1wJwLBcWK1zrdFFQkQlUuL41IUCpxOXDVsiDvdsSWb6GpuX6fEAtApsx33zx1HKN2MLqxFd6ubrTJduncgyaDHKhtZt6sYd0D8ztsCNUkEJTKVrfNtNbFAM1cu8+fPp6CggMcff7za87Nnz2bTpk3Mnz8fqOjJnjlzJh9//DEWi4Vp06Zx5ZVXAlBaWsqDDz7Ili1byMrK4plnnuHss88GKoTaHnvsMfbt28fgwYN55plnsFqtzXmLdabY5WJufj5ri4rR63RENI1BmZncmTuQjGZsF2yLGFPNpHWwI3fWEylX0SdJSFW00SSpotXZ5VOwm9ruf+62Tl0kggQtR7NULuFwmBdffJFnn332pGM7duzgtddeq/bcW2+9RVFREV999RXPP/88M2fOpKysQt79kUceoU+fPuTn53P99dczffp0oO5CbYlAscvF75YuZXVhIRaDAbPBgMVgYFVhAZOWLqPYJdYDasJb7udA4BDe8pq/pdrNFSrRSDqSjUnVEgsca/vUNBzCtbHVUleJIEHL0SzJ5amnnmLr1q3ceOON1Z6PRCI88sgj/Pa3v632/PLly5kwYQIWi4W+ffsyZMgQPv74Y7xeL6tXr+aOO+7AYDAwduxYSktL2bVrVzWhNlmWmTJlCkuXLm2O26s3c/Pz8YVCOGRTdddI2YQ3FOTl/MZ7cbQ1DipHmPvzQh7c/Ax/2vYyD25+hrk/L+KgcvKOe4dZZvDZqRj0B5CTT05CXiXIwB4ZomppxVSVCDr+f6hCIsgfUVi4d0ULRyholmmxO++8kw4dOjB79mwOHToUfX7+/Pn069ePs846i4KCgujzBQUFZGdnR3/u1q0bu3fvprCwkHbt2mGxWKod27VrF/v3769RqC0lJaVJ768+uBSFtUXF2GqQZrEZZdYUFeFWFOw1+HWcbhxUjvDUtlcIRIKYq7Qmb3HuYKdnL9PP/R0d5QrNsFB5EQfds7l24DcM7ulHQ6Xw0Jms/ukSyrypeJUgFtnAxOGN094StBz1kQiq61qdIP40S3KJJY62e/duli5dyj//+U8+/vjjasf8fj9ylQ9WWZY5dOjQSc9XHgsEAnUWaquNrVu31vncDRs21PncqpQEAoSDQZTy8hrPCUcifL1+PemN1AFqaIzNSV1i/DD8Dc6IC5POiMLxzZUS4NRczNv0DlclXwhSKUmOJ0EXAM2M3WjHFVDo1uEn0lMKmP+/33JmaneuOq8bJXt+pmRPfONsaU6XGA9HPISCwQpDuxoIaWFWb8ynvd5W7+vXNcZ+/frV+9qnEy3SiqyqKg8//DAPP/xwTCE1WZaria4pioLJZEKWZUKhULVzFUXBbDbHRajt/PPPx2iMLSxXlQ0bNjT4jeVSFJJ3/IRsMNQgb6IRCYUY2r9/oyqXxsTYXNQlRm+5n/mb/0OqMQVJd/IsrqyZKFHLOKf3uZS5/4FXUUmSOkWPO6xWIppKuNzJM+MOk91xepPE2dKcTjF6wn7e/nYtst5Yo0SQFpEYnJNb78qlNfwe60p9Gpw++eQT/vnPf/L6669Xe76mJqy60CKtyCUlJWzdupW7776b/v37M3PmTNauXcs111wDVIiuFRYWRs+vnCbLysri8OHDBALHv70WFhbSvXt3srOzT5paa26htrrgkGXyMjPwBGNXVJ6gwqDMTDEldgxP2HdMrDL2W1XSSejQ4QwewBdcjV5nP+kcvU7CmJRCKJJPRBXNEq2dxkgEJQoer8K+A2V44qSCfCKNdaKsrQmrrrRIcunSpQtbtmxh/fr10eyal5cXlSq44oorePXVV/F6vWzcuJFVq1YxfPhwbDYbubm5zJkzh1AoxIIFC0hJSeHMM88kLy+PgoICVqxYgaIoLSLUVlcm5+ZiNRhxKYGo772qabiUAFaDkTtzxXpAJbZkyzGxythTIKqmoqFh0YcBPboaklDF83rK1bKmC1bQbIzpPhyzXsZT7o++N1RNxVPur1UiqKU5UOrimTmfcscfFvDgrA+44w8LeGbupxwoje+Xnro2OL3xxhssXbqUW2+9tdrzNTVh1YeE3EQ5fvx4srOzGT58OFOnTmXWrFmkp6cD8OSTT7Jt2zby8vJYsmQJL7zwAjqdrkahtkQkw+Fg3sgRDOmWhS8Uwh8K4QuFGJKVxbyRI8Q+lypYk8yc7zgLfyT2Nzx/JMD5jrOxGdKBCFoNSaji+QhJUmrTBStoNhoqEdSSHCh18dBT/2bDlgJMsgGTnIxJNrBhcwEPPfXvuCaYxjpR3nnnnbz66qu0a9dwc71mXXO5++7YvusniqYZDAZmzJjBjBkzTjq3Y8eO0c2WJxJLqC1RyXA4+NNll+JWFMoUhVRZFlNhNfDbjCvY6SnAW+7DrDdFu8X8kQAmvcxvMy5HL6VgMQ7Gq3xDki7lpGtENDdW+UL0zWB4JmgeGioR1FIs+OfaWp0oF/xrLQ9OvjwuYzXWiTIeDpUJWbmcTthlmayUlIRMLIkiTdNRbsf0c39Hr5RzUNQggYiCogbpnXJOtTbkjva70UtWylVntILRNJVy1YlestLRHvvLjaB1U1+JoJbA41XYuLWwVifKjd8Xxm0NRjhRChKSRJSm6Si3Y/KZN+Er90e/pVqSqht+GZIyyWo/n4Pu2fiCq0HTAxGs8lA62qdgSMpskdgFp8bj9ONx+rClWLCltB0jt0rq5kSpw+0JVKtsGopwohQkHJXSNL5QCJtRjmo2rSosYHNJSYuvCVmSzCcllaoYkjLJSHuGiOqiXC0jSUoVU2EJTGnRERa/+ClbVv+MpNehRjR6DTmLUXdfRqfMhs/3Jxp1c6LUsNuEE6Wggbj9CkWHnLj9ianI21akafSSA2NS97gmFrdPofigE7cvMf92DcUVUCg84sTVhCrRHneAIwd9eNzH24dLi44w89bX2LTqJ0xWAyaLEZPVwKZvdvD4ra9RWnSytE9rxWaVyTm/W61OlDkXdItL1QLCifK0Yt8RF/M/Xce3O49PNQ04K4PbLh9I13aJ8c1aSNPE5rBbYdZbn7FhR3HUPbD/OZlMuGogXdonxt+uIewrc/HKl+tYt6cYvaQjomrknpHBHcMG0jU1PvdVsr+Mxa+vZPP6vYTCId59eSO9B2Qz6rah/OPFTwn4FGyO45WoJEnYHGY8Tj+LZ3/Gvc+MjkscicDY3+axbWfNTpRjr8+L63jxcKKsqQmrLojk0kDcfgWXT8FhkbGba/+g3XfExf2vLsMXDGEzHn9T5f9UyNaCEp6/Y0RCJBinoqDX6WLueoaKCkav01HWTMnF41FwewLYbSZsLeTbvv+wi5c/3oom6bHKcvRvt25bAT/sKeHZKSNaZYLZV+ZiysJl+IMhrHLFTndV1Vi7q5AtxSW8dPOIRieYkv1lzLz/Xfy+EBarjE6JIMsGNubvZtvmIpR9h7HZY/9dLXaZLat24nX5sTraxhpM504Onpz+Gxb8ay0bvy88/kWldxZjrs+jc6fW9z6qDZFc6sm+Iy7eWvUTez/cUucKZP6n6/AFQzhM1VsQHSYZl19h/qfrePSmlndFTJFlIlqFHH2N0jSaRmoTJ5aSEhfvvLuGTZsLkCQJVVXp0yeLMaMGNbsV7Bsf5qOEI3RIPS6bIUk67GYTbn+ANz9axx9vafm/XX155ct1+IOhasrQkqTDbpJxBRRe/WodM69t3H0tfn0lfl8Im/34OoIkSdjsJpxHvfjLNRxSDcoLkoSk1+Eu87WZ5AIVCebByZfj8Vb54tRGDcPEmks9qKxAtu93YjEkYzYmYzEkk/9TIfe/uox9R07eBOX2K3y7sxhbDZplNtnItzuLE2INJhGkaUpKXDw84wO+21SAyWTEZDJgMhn5bmMBD8/4gJKSppNvCUdc+MOFhCMVY7h9Cht2FCMb9DHPt8oy67cX4WnE384VUCg82rTrHbHGXLenGKtcw3vSaCR/d+OcOj3uAJvX78VSwwenxWZCiWiEyyMxj6uqihrRsKdaYh5v7disMl07p7bZxAKicqkXlRWI2ZgU7fg4VQXi8h2baqqlBVFf6Yp4ium15mBybi5bSkpxKYFq3WKeoNIs0jTvvLum1o1m7yxewwP3XRHXMQPhfex2zqNMWYdOp0fTIqSZcpHLRyNJOjSt9vZRl1fBVs+/XbHTxbxv1pFfcHwdJ7d7BpOGDCQjpWmrM5dfQS/VMv0p6dBLOpz+hjt1elwBJH3N7/vkZD2yKRmvRyE1RgLxuRX6DO3ZpqqW0w1RudSRhlYgDsuxqSZVi/k6Va2YakoUV8SWlKbxeBQ2bS6odaPZpk0FeOMo9hcI72NT6V2UBfLR6yzodWb0OgtH/Ws5EP4/LGZnVP/tRCrbRx31/PZZ7HQx+b1lrNlbiOVY9WsxJrNmTyF3vL+EtcWFuGqoHitpTOeawywTOebGGQtV1YioGimN+LJjc5hQI7W971WsDjMWi4zH6UdV1ejzHqcfs1Vm1N2XNXh8QcsjKpc60tAKxG6WGXBWBvk/FVZbc6nEowTJ7dktIaqWSlpKmqZuG80kXO4A1jhNJ+x2ziOi+knSH1dT1ukkkvR2yiMuLrv4e975Z39sMWZnvIrCwHOz6l21zPtmXUW7d5X3Q1gXwWkK4NQUbl/xb9LMJgZ1zuKu3nlk2o4n9P2HXbzxYf5JnWv9u9X9G77DJDMwO4P83YUxKxNPMMigHt0a5dRps5vo3b87G9ftqbbmUonPG6TvoDMZPX4Ii2d/xpZVO6P7XHIu6smNU9rWPpfTEZFc6khjKpDbLh/I1oISXH4Fm3y8W8xzzBXxtssTUwXZ3sx6Z3XbaKbiiPFh1RDCERdlyjr0UmyPC71kI7NrEXZLb9z+QLVuMa+iYJGNTLiqfn87V0Ahv6AYW5X1jiDl7OIIETSSdBLBcAQ5KZlV+/ey6fABXhv+GzJtDvYfdjH1paX4ldBJnWvf/hDhggvOr3Pn2u8uHsj3+0pwBZRqHYyeYBCL0cAdwxr/nhw18SJ2/LAPjzuAxVpxv6qq4vMGMVsMjLptKJ26pHLvM6Pxuvy4y3zYUy1iKqyNIKbF6khlBeIJxt4E5VGCDDgrI2YF0rWdg+fvGEFuz274QmH8wTC+UJi8c7ISpg05EbDZZPr0zqp1o1mfPllxq1rCqgudrnaZ/iR9Mrdd3o2B52bhD4aij9xzsxrUhuwKKBUVWJX1jhI8FYkFiQp3mgqfDYdRxhcKMmfzWqCic82vhLCbTdXW/OxmE0o4wpsf1X2Da9dUBy/dPIJBPY69J0MV78lBPbLi0oYMkN4llceeH01O7hkE/CGCSjkBf4i+eWfw2POjSe9yXKHa6jDTpXuHZkssbXVDbCIhKpd6UFmBlLm8mGStXhVI13YOHr3p0nrtjzkdGTN6ENt31LzRbMyoQXEbK1lyoGkRNNSYCUbTVDQipFna8cdbLsLjV3B5FRxWud5TYZU4THJFBXas3bscFQ9B9FQkC+3YQ3+sRddmMLLmQAHFZS427CjGWkMlKRv00c61usbWNdXBzGsvxR1QcPoVUsxyo6bCYpHeJZX7HhmB1xNg9TffMvjCAVjjJHHSEGqaVmztG2Jj0Vgnyvfff5958+bhcrno168fjz/+OJ06dYr5+liIyqUeVFYg53ZJaXAFYjfLZHZIEYmlBtLTHcyacR19c7IIBIIEAiECgSD9crKYNeO6uO5zSdY7SJUHElG9MY9HVA9pplwkrWLBxWaWyeiY0uDEAhXJJTcrA49SUZ1FUAHdsXqlYtrIYkhGX0V6R9JJFDtd0e60WEi6451r9cVukunWLiXuiaUqVpuJdh0tLZ5Ypr60lG+3F2I2GqKPddsKeOClZew/3HwupW6vQnGpE3eCOlF+//33vPDCC7z66qusWbOGzMxMHn300XrFICqXetK1nYNbhpzNWef+QlQgTUR6uoMH7rsCr1fB5Q7gsJviNhV2ImekTGJTcCvlERd6yYZOJ6FpKhHVg16yku34HT9SEtcxJ104kC0HSnAGFMxyMug0VEBTNSRJooP1ePeAqmmomkpGiiPanRZzPUrT0DWgc+10ouq0YiUnboi94vymNRk7cMjFW0vy+e7Homjl1O8XmdwyMpfOHeL3xamqEyXAlClTGDNmDNOmTat23htvvMGnn37Krbfeyvfffx99vrS0lAkTJtCjRw8A/t//+3+MGzeuXjGIyqWBiAqk6bFaZbp2SW2yxAJgSu5Kn04vkWYeRETzEVH9RDQfaebB9On0EqbkrnEfMyPFwdwbRjA4uxvBYAQ5kky5pmI1GumW6sCgP75p0xMKMqhzFhmpDvr1zMBbg7eOEorQ/5zMRlVVbZnKDbE1TStWboj1B8ubLIYDh1w8+NxS1v9QiFk2RB/fbi1k2nNLOXAocZwof/WrXzFx4sTozytXruSss86qVwyichGc9piSu3Je+8cJR9yEVSfJUgrJVVqTm4KMFAezrqpo99526BCPfPtfAuVhko6ttaiahicUxGIwclfvCkHDW6/O5ce9pTE71+Rkfb07104n3D6l9mnFY8d8SrjJYnhrybHKqUpHqSTpsFtk3F6Fvy/N5/8mxmdvT2OdKKuyevVq5s6dy5tvvlmvGETlIhAcI1lvx5zcrckTS1XsskxuZibzL72eIV264wuH8IXD+MIhLuzaPdqGDNClvYNnp4yI2bl256/r3oZ8OmK3yNFpxVhUHrPIyU0yvtur8N2PRVhNsTcIW81GNvxQhCdO3WvxcqL87LPPuOeee3j22Wfp3bt3vV4rKhdBq8Vf7iUQ8WLSWzEnxe6CqYrHHcDjUbDZ5Jgb+1qSTJuDP194Oa6ggjMYIMVowhHD+qBLewd/vOXSkzrXNmzY0AJRtx7sFpl+PTP4dnthtTWXSio3xJqNTfORWNfKyeVVsMVBrSMeTpT//Oc/+ctf/sKcOXPIzc2tdwwiuQhaHUdDB/mi9F/s9v2ApJNQNZUe1vO5uON1pBk6nnR+yQEni95exeaNVVSWc7IYPW4I6Z1Tmv8GasFhlGMmlROxmRveDn26Utu0YuWG2AMFPzfJ2FUrp9qcKOPVkNFYJ8qNGzfypz/9ibfffpsLLrigQTGIaTFBq+Jo6CAL9j7NLu9WjJKM4djjJ9cW3vjpSYoOF1Q7v+SAk8emv8+mDXuPKSwnYzIZ2LhhL49Nf5+SA86WuRFBs1PbtGJT+/LYrTJ9z8vEG4i9QdjrD9LvF5lxqVqg8U6UCxcuRFEUxo0bR05ODjk5OfWuXkTlImhVfFH6L4IRBVNSRbtuOFRO2UE3fm8QyVzOs1/9iTMKLol6sC96exUBfwirrfoiqtUm4/UovPv2Ku6fdlVL3Y6gmalpWrE5GH9tLtt2leD2KlirbBD2+oNYTAZuGVn/qafaaIwT5bPPPsuzzz7bqPFF5SJoNfjLvez2/YCsr5gzD4fKObD3MH5vxXw2oSSM5/jYvOFHHr/1NXZv28/mjQWYLbEXUc0WI5s2FuD1CAmQ0414bIitL507OHh66kgGXNANvxKKPgZckMXTU0fGdZ9LIiAqF0GrIRDxIumkqFRL2UE3qqoi6Y99R9J0oOmwpSfj3q2w+OX/1UFlWYfbHahW2QgETUXnDg7+b+JleHxVKqcEsduINyK5CFoNJr0VVVPRNBVVpWIqrKpNrk4DnYbmT8Jil9i5YS+Rru1Q1aRaF1HtCdY5Jmj72CxtN6lU0qzTYvPnz6+mT/Pyyy8zdOhQcnNzmTp1Kl5vhcZTeXk5jzzyCP3792fYsGEsX748+prS0lJuueUWcnJyuPbaa/npp5+ix9avX89VV11Fnz59mDx5cvR6gtaFxx1gf9FRPO5AtefNSVbOsPwCJRJALVfR6YAqOUMnq0R220FJQpIkkvQ6zj47Hb8v9iKq3xekT06WqFoEgiagWZJLOBzmxRdfrLZA9Omnn/Kf//yH999/ny+++IJQKMRf//pXAN566y2Kior46quveP7555k5cyZlZWUAPPLII/Tp04f8/Hyuv/56pk+fDtRdqE2QuJTsL+OFx5dyz5hXeeSed7hnzKu88MQySvaXRc+5pNP1GPUmQroAGlqFhLBOQ2eKQFBP+RddgOMe7DeNG4zJbMDrUaIb6FRVw+tRMJkNjB43pCVuVSBo8zRLcnnqqafYunUrN954Y/S5srIy7rjjDtLT0zGbzVx33XVs3rwZgOXLlzNhwgQsFgt9+/ZlyJAhfPzxx3i9XlavXs0dd9yBwWBg7NixlJaWsmvXrmpCbbIsM2XKFJYuXdoctyeIAyX7y5h5/7tsXLcHk9mIyWTEZDawMX83M+9/N5pg0gwdGdv9Qc529EZO0aMll6MzqER22gm+fRZaWcXivc+t0GvIWZxxdmdmPnUDOf26H1NYrnj07Z/NzKduSLh9LgJBW6FZ1lzuvPNOOnTowOzZszl06BAAo0aNqnbOypUrOfvss4GK3aTZ2dnRY926dWP37t0UFhbSrl07LBZLtWO7du1i//79NQq1paSkNN3NCeLC4tdX4veFqu2clyQJm92Exx1g8fyvue+REUBFgrkuYxL9Ilfxl2nz8R+MYE62RDdI+txKNQ/29M4p3D/tKrweBbc7gN1uElNhAkET0yzJ5VTiaEuWLGH58uX885//BMDv9yNXUS+VZZlDhw6d9HzlsUAgUGehttrYunVrnc9tDXIbrSXGgC/EulU7MBr1+P2+k87RSRrrvtnON193xGSurv107biL+er9Lez+viS6b+CMXp0Z9tsLKD64l+KDe0+63oEGKOg3xe/Sr4bwa0HMOiNmydDo67WWv3eiU9cY+/Xr18SRtG5avFtswYIFvPTSS7z22mtkZmYCFQmjquiaoiiYTCZkWSYUClV7vaIomM3muAi1nX/++RiNsfdEVGXDhg0J/8ZqTTHuLzqKyfwtphpE/QACUpAzup9Nl8yT/TZ+deXFTerBHu/f5YHAUd7Zu4JNZbuQdDpUTSMntQc3dx9OZ1PD/ERa0987kWkNMdaVxjhRaprG888/z/vvv095eTm//OUvmTFjRr0+T1t0E+Xs2bN57bXXWLBgAb169Yo+n52dTWFhYfTnymmyrKwsDh8+TCBwvIuosLCQ7t27k52dTUFBQbXX1FeoTdAy2Bwm1EhtirVqRctwSs1v7Ob2YG8oBwJHeXjLm3xXthOT3njsYWBD2U4e3vIWBwJHWzpEQTPg9ikUH3TijpMK8ok01onyP//5DytXruSjjz7iq6++4vDhw61Hcn/58uUsXLiQRYsWRddaKrniiit49dVX8Xq9bNy4kVWrVjF8+HBsNhu5ubnMmTOHUCjEggULSElJ4cwzzyQvL4+CggJWrFiBoij1FmoTtBw2u4ne/bvjq8Hy1ecN0ntAdota5MaLd/auwB8JYksyI0WtjCVsSWb8EYWFe1e0cISCpmT/YRez3vqMW2Yt4t6//ZtbZi3iT3//b9wtluva4PTGG2+wdOlSbr311mrPX3PNNbz77rukpaXh8/kIBAKkpqbWK4YWSy4LFizA4/FwzTXXRIXRKrVtxo8fT3Z2NsOHD2fq1KnMmjWL9PR0AJ588km2bdtGXl4eS5Ys4YUXXkCn09Uo1CZoHYyaeBFmiwGPO4CqqkBFxeJxBzBbDIy6bWgLR9h4PGE/m8p2YdHHTpIWvczGsl14woGTjnncAfbvKztp709L4AoFKPCU4Qq1fCytif2HXUx9aSnfbi/EbDREH+u2FfDAS8vimmAa60RZ+Zk6b948hg4ditfr5ZprrqlXDM265nL33XdH//3uu+/WeJ7BYGDGjBnMmDHjpGMdO3Zk/vz5MV8XS6hN0DpI75LKY8+PZvH8r9n87Z7o4nzfvDO48dahpHep37cml6LgVBRSZBlHDda2zY077EfS6aIVy4lIOglJp8Md9mFLrkhAiWQXUOx1MueHVawtLUSv0xHRNAZ1ymLyLwaTYW3eWFojb3x4zInSXLUjUofdbMLtD/DmR+v44y2XxmWseDlRTpgwgbFjxzJ9+nQeffTR6F7EutDiC/oCQSXpXVK575EReD0B3M4A9hRTvafCil0u5ubns7ao+PgHYGYmd+YOJKOF19/syWZUTUPVtJgJRtVUVE3DnlzRal9pFxDwhzBbjqvobtywl+3b9jfrPp1ir5Pfrfwn3nAIm8EYbURYVbKXzUf288pFvxUJphbcPoUNO4qx1vBFxyrLrN9ehMevxEVMM15OlEajEaPRyN13383/+3//r16vFarIgoTDajPRJTOtQYnld0uXsrqwEIvBgNlgwGIwsKqwgElLl1Hsiu+8dn2xJZvpk9oDXyT2dJIvopCT2iNatVS1C6jURqu0Cwj4Q7z79qpmi33OD6vwhkM4jHKVtSIdDqOMNxxi7g+rmy2W1kh9nCjjQWMbnF599dVqDQDhcBibzVavGERyEbQZ5ubn4wuFcMim6h+AsglvKMjL+etaOEIY0304Zr2Mp9yPqh1bW9JUPOV+zHqZm7sPByrWWBLFLsAVCrC2tBCbIXYsNoORNaUFuEPCuqAmqjpRxqIpnCgb0+B0wQUXsHDhQvbu3YvX6+Vvf/sbI0aMqFcMIrkI2gQuRWFtUTG2GiyCbUaZNUVFuOuxqbYp6GxKY1av8fRLPYtAJEQgEiQQCdE/9Wxm9Rof3efi8Sh1tgtoapxBBX2ta0U69DodZUGxwF8TdotMv54ZeGt4/3kVhf7nZMbNX6axTpSDBg3irrvu4pZbbuGyyy4jMzOTe+65p14xiDUXQZvAqdTxA1BRsLfwAn9nUxoPnHsDnnAAd9iHPdkSnQqrxGaTo/t7WtouIMUoE6l1rUgjommkGlt/q3hTcuvVufy4txS3P4BVlo87USoKFtnIhKsGxnW8xjhRAtx8883cfPPNDR5fVC6CuOFx+tm/9xAep7/Zx06Rj38AxiL6AZggnWMAtmQTXc3tT0oscGzvT05WQtgFOAwm8jp1wxOKHYsnFGRQpyzshsT53SYiXdo7eHbKCAaem4U/GIo+cs/N4tkpI+jSvm1t+BaVi6DRlBYdYfGLn7Jl9c9Ieh1qRKPXkLOiPvbNgUOWycvMYHVhIQ755A9rT1BhSLesFq9a6sNN44awY9t+vB6lWreY3xdsdruAu34xhC1HDuAKKtW6xTyhINZkA5N/MbjZYmnNdGnv4I+3XIrHX8WJshmtlpsTUbkIGkVp0RFm3voam1b9hMlqwGQxYrIa2PTNDh6/9TVKi440WyyTc3OxGoy4lEC0glE1DZcSwGowcmdufKcdmpr0zikJYxeQYU3hlYt+y5D07vjCIfzhEL5wiCGdu7eJNmRP2M8+/2H8aujUJ8cBm1kmo2NKm00sICqXNonbr3DQHcDtV7A38Zt38YufEvAp2KpoekmShM1hxuP0s3j2Z9z7zOgmjaGSDIeDeSNH8HL+OtYUFUX3uQzJyuLOgS2/z6UhJJJdQIY1hSdzr8QdUigLBkg1mlr9VNiJIqIBJUD+tuJGiYgKKhDJpQ2x74iL+Z+u49udxYSDQeau3MmAszK47fKBdG0X/w9Wj9PPltU/Y6nhw85il9myaidel7/ZBCUzHA7+dNmluBWFMkUhVZZb1VRYTVhtcsJ40NgNcqtPKnBcRNQfCWLRV7SvRyhnQ9lOtrmLqnXvCeqPmBZrI+w74uL+V5eR/1MhFkMyxmQ9FkMy+T8Vcv+ry9h3JP4bCD1OH5JehyTFfhtJkoSk1+EuO9mjpamxyzJZKSltIrEImobYIqI6ISIaJ0RyaSPM/3QdvmAIh6n6bm6HScanhJj/afw3ENpSLMek8tWYxyt97O2plpjHBYKWojEiooK6IZJLnHEpCgVOJ65m3Kzn9it8u7MYWw1GZzbZyLc7i3H74xuTLcVMr8Fn4qthl3ilj32ie6wITj/qIyIqaBhizSVOtKRgost3bANhLbu59TodLl/8F/hH3XM5P20qxOP0Y7HLNfrYCwSJRH1FRAX1R1QucSCWYKJJn8TK3Xu4/YMlTS6Y6LAc20BYi25RRNNwWOK//tApsx2PvnE7fYb2JOANEfAFCXhD5FzUk0ffuL3Z9rkIBPWhviKi8cbtVyg65Iz7bEJV1q9fz1VXXUWfPn2YPHkyXq+3xnM/+eQTJk6cGPPYBx98wBVXXFHv8UXlEgeqCiaGw+UcPuTF76/YzXxU72Xyy+/z6rjr6+1JUlfsZpkBZ2WQ/1MhDtPJCcSjBMnt2a3J2pI7Zbbj3mdGN6mPvUAQb8Z0H852dxGecj8WvYykkyo2hp4gIhpPqnZ0Vs5wNEVHZ6XN8VNPPUVubi4PPfQQc+bMYdq0adXO0zSNRYsW8fTTTzNgwICTrnPo0CGefvrpertQgqhcGk1VwcRwuJwDRUfx+4LHRAclkjXYFXbz8AOLKNlf1mRx3Hb5QCxGAy6/Eq1gVFXD5VewyAZuu7zpNxC2Fh/7E/F4FPbtL8PTDArDgsQhlohoiPBJIqLx4sSOTrMxuck6Ohtrc1zJjBkz6u1AWYmoXBpJVcHEw4e8qBENSX88Z+uQ0Esa7lCIxfO/5r5H6idbXVe6tnPw/B0jju9zCUdQpTB552Rx62UDmmSfS2unpMTFO++uYdPmKi6PfbIYM2oQ6eni9xUvIqqTctUJupqnZVqKE0VE9/ywkwvPHdQkY1Xt6KyksqPT5VeY/+k6Hr0pPk6Utdkcp6SkRJ8fMWIEt912Gx988AHff/99tWt89NFH6PV6Lr30Ur755pt6xyCSSyOpFEwsL4/g9werJRYADQ0NcBiNbP52D15PoN4mWHWlazsHj950KW6/wqr89QzJ7d/kO/RbKyUlLh6e8QH+QAiL+bhu13cbC9i+/QCzZlwnEkwjCZUXcdA9G19wNaAnKdVP8dFL6Gi/G0NSZkuHVw1bsglbsokSqeDUJzeA+nR0xuP/bGNtjo8ePcqLL77IO++8w+7duxsUg5gWaySVgok1+YSU6yHNo8eo6YkYYFtxaZO3KdvNMh3tpnq9Sd1eheJSJ+44OeElOu+8uwZ/IITNWn1fkM0q4w+EeGfxmhaOsHUTKi+i4PBteJVvkLCi11lAk/Eq31Bw+DZC5UUtHWKzUp+OznjQWJvjWbNmMXHixBqTT10QlUscmJyby8Z9Bziq95KsaejQoaFRrockFboe0bMlU+GINcL/rfocbTUJ4+t+4JCLt5bk892PRdFv7/1+kcktI3Pp3KFtfnP3eBQ2bS7AYo79LdJiNrJpUwFer4I1Ts6ApxsH3bOJqF6SpJQqz0okSTbKVScH3bPJSHumpcJrdqp2dNbkzxPPjs7s7Gw+++yz6M/1tTn+3//+x8qVK3n66aeJRCIoikL//v1Zv359nWMQlUscyHA4eO26a+mRbCOs04joNSJ6aO/Rc16hgR+7hThsjWAzGrDKxoTxdT9wyMWDzy1l/Q+FmGVD9PHt1kKmPbeUA4fqF5unzMf+3QfxtIDcS31wewJ1cHmUcDWDy2NbJKI68QVXo9fZYx7X6+z4gquJqC333m9uKjs6PcEaPHGUIAPOyojbNHZjbY63bNnC+vXrWb9+PfPmzSMrK6teiQVEcokbGQ4Hr477LYO2GTlnI+T9aOT8QiPFaeWE0DBqOtI62IDE8XV/a0k+fiWE3VJ9ashukfEFQvx9aX6drlNScJgX7n6Tey95nEdveJ57L3mcv93zFiUFh5sy/AZjt5miLo+xqHB5VHE0g8tjW6RcdQJ6dLrYHy8Vz+spV5uuezIRac6OzsbaHMcDMS0WR9K7pDLr2ZtYPP9rNn+7h6AhzBFrBLtsIK29jeTk6r/uqr7uzS2w6PYqfPdjEVZT7Kkhq9nIhh+K8PgUbLWU6iUFh3l89Iv4vQoWhynadbXxyx/ZsWE3jy66h/Ss9k11Gw3CZpPp0zuL7zYVYIsx7eXzB+mbkyWmxBpIxVRYBE1TYyYYTVOBCElS0+z7SlRO7Ois3OfSVB2djbU5riQ3N5dPPvmk3uOL5BJn0rukct8jI/B6AmwrLuX/Vn2OVY79Ad6Svu5un3Js+qe2qSEdLm/tyWXxs//B71WwVRGnlCQJW6oFT5mPfzz3Ife+OD7e4TeaMaMHsX3HATxepVq3mM8fxGwyMGZU07Sjng7opRQsxsF4lW9I0qWcdDyiubHKF6KX2uaaXm1U7eh0+RQcFrnNdnSKabEmwmozcXZ2OpqOhPR1t1vkY9M/tU0NaThq+fbuKfOx5evtWBw1KMs6TGxeuQ2v0x+XmONJerqDWTOuo29OFoFA8JjLY5B+OVmiDTkOdLTfjV6yUq46j1UqACrlqhO9ZKWj/e4Wja+lsZtlMjuktNnEAs2cXObPn8+jjz4a/XnRokVceOGFDBgwgOeeey76fHl5OY888gj9+/dn2LBhLF++PHqstLSUW265hZycHK699lp++umn6LH6aOk0B5Vtyp5g7PZCT1BhUGZmi3iO2K0yfc/LxBuIvcDo9Qfp94vMWqsWT5kPSS+dws9Fwn008TbPQUWCeeC+K5j30i08Neu3zHvpFqbed4VILHHAkJRJVvv5WOULUfES0XygU7DKQ8lqPz/h9rkI4k+zJJdwOMyLL77Is88+G31uy5YtvPzyyyxatIgPP/yQFStW8OWXXwLw1ltvUVRUxFdffcXzzz/PzJkzKSurWPx75JFH6NOnD/n5+Vx//fVMnz4dOK6l8+CDD7J27VpkWWbOnDnNcXu1ksi+7uOvzcUiG3B7qy8wur0KFpOBW0bm1vp6W6oFNaKews9FxZ5mjXvs8cRqlenaJVWsscQZQ1ImGWnPcGanj+je4e+Ulz1LRtrTIrGcJjRLcnnqqafYunUrN954Y/S5jz/+mBEjRtCtWzc6derEuHHjWLJkCQDLly9nwoQJWCwW+vbty5AhQ/j444/xer2sXr2aO+64A4PBwNixYyktLWXXrl111tJpbip93Yd0y8IXCuEPhfCFQgzJymLeyBEtus+lcwcHT08dyYALuuFXQtHHgAuyeHrqyFPuc7GlWug19Bx8rhqUZV0Bel90LtaU1qU1JogvesmBMak7aIn9JUMQX5plQf/OO++kQ4cOzJ49m0OHDgGwd+9efvnLX0bPycrKYuHChUDFhp/s7OzosW7durF7924KCwtp164dFoul2rFdu3axf//+OmnptASJ7OveuYOD/5t4GR6fgsur4LDKtU6FncioB67hp+/24CnzVesW87kCmG0yN069ugmjFwgEiUqzJJdYEgJ+v7+aFIHRaIzq3vj9fuQqH76yLHPo0KGTnq88FggE6qylUxtbt26t87kbNmyo87kn0ly7PxoSY2kDxrnuoUv48p317N60L9p11SMng2E392Pf4QL2Ha5Zr6kxv8fmpDXEKWKMD3WNsV+/fk0cSeumxVqRT9S+CQaD0eQgy3K1Y4qiYDKZkGWZUChU7TqKomA2mxutpQNw/vnnY6xBWK4qGzZsSPg3VnPH+KurLsHr9OM+6sWeZq3TVFhr+D1C64hTxBgfWkOMrYUWa0XOzs6msLAw+vPevXuj01onHqucJsvKyuLw4cMEAsfn+AsLC+nevTvZ2dkUFBRUe019tHQEjceaYqbLGR3FGstpghLx4AztR4l4CEbceELFBCPulg6rVeDyKxQdduJKYCfKe+65h169epGTk0NOTg733HNPvcZvscrliiuuYMqUKVx77bVYLBYWLFjA3XffHT326quv0rt3b3bu3MmqVauYNm0aNpuN3Nxc5syZwz333MM//vEPUlJSOPPMM8nIyOChhx5ixYoVDBkypN5aOgKBoG64QiWsPbKYIt9mdJQjaYcw6sox6R3odDrSTf04P+0WrMldWjrUhGPfURevrVjHt7uKkXQ6VE1jYI8MJg4fSNe0xHKi3LFjBx988AFnnnlmg2Joscqld+/eTJ48mQkTJnDNNddw2WWXRZPB+PHjyc7OZvjw4UydOpVZs2aRnp4OwJNPPsm2bdvIy8tjyZIlvPDCC+h0uhq1dFo7roBC4VEnrsDpIYUvSGxcoRKWFM+k0LcRg07CoBWjx09YC+EpP4qOZA741/Pl/ml4w02vX9Wa2HfUxb1vLiN/ZyFmY4UTpdmQzNqdhdz71jL2HU0cJ8pAIEBJSUm1Jqn60qyVS2VlUsno0aMZPXr0SecZDAZmzJjBjBkzTjrWsWNH5s+fH/P6sbR0WivFThfzvllHfkFxdJE8t3sGk4YMJCNFTPUJWoa1RxYTUv3IehtE9gIRdCShA1Qi+CNO7MmdCEU8bD36d/I6TW/hiBOH11ZUOFFW3ZUvSTrsZhm3X+H1Fet47IbEcKLcuXMnsiwzfvx4du3aRd++fZkxY0a9/F2E/EsCUux0Mfm9ZazZW4jlmM+2xZjMmj2FTH5/GcXO00eqXJA4KBEPRb7NGCULaOWgeQB99LgOPSE1gKapJEsWSgIbCEU8LRdwAuHyK3y7q7hmoVjZyLpdxbjjNEPRWCdKv99Pnz59eOKJJ/j8889p167dSVNqp0IklwRk3jfr8IUqvLYl3TEpfF2F17Y3GGLeqpaT6RecvigRDzqddEzpuBzQHXtUUPkvlUjFeUgEI+KLEFTYHEs6XfT/84lIUsWxRHGizMvL45VXXiE7OxuTycTvf/971qxZU6+tHSK5JBiugEJ+QTG2GpSU7bKR/L3FNdoqCwRNhay3oWnqMSHKJEA79qig8l8S+orzUDHqxRQuVAhVqppWs4itWnEsnk6Ujeme/eqrr6ppOoZCIfR6PUlJdV9JEcklwXAFlOi3mKqUoxKkHFVXYZPqjHMLo8ejsG9/GR6PSFqC2Mh6G5mW3gRVH+iSQGcDItHjGhEMkgmdTiKs+kg39cOgt7VcwAmEwywzoEdGzUKxSpCBPTKwmxLDiVJVVZ588kkKCgoIBAI8++yz/PrXv65XchF+LgmGwyRHv8VIOh1ByinBg4cgoENDQ9Yn41NDp7xWXSgpcfHOu2vYtLkgKt3Sp08WY0YNEurAgpPIazeKksAOlIgHo64TOs2HRjkaFWsuZn0KoYiHZMnM+Wm3tHS4CcXtwweytagEt1/BKh/3EPIqQSyygYnD4+9E+dhjj/GHP/yBwYMHR50or7rqKj766CO6dKm5VfySSy7hlltuYezYsXg8Hi666CJmzZpVrxh0mlZDnXYaEQwG2bp1a8Ls0H/4w/+yZm8hsimJXRwhgoYeHTp0lKsRDMlJZKak8MKgK7GpydhtppMcFesSY0mJi4dnfIA/EIppmNXUviatZTd0a4izOWN0hUrIP7KYwhP2uch6B5JOR7qpP+enjTtpn0uzxhhUcAYDpBhNOIx1rwaaOsZ9R128vmId66ruczkzk4m/HBDXfS6JgKhcEpBJFw5ky4ESdpcfJZKkkYSEBkRUFb2kp5PZyr7DZYx7cxFnFdlQVZWcC7ox9vo8Oneq+xv0nXfX4A+EqiUmSdJhs8p4vArvLF7DA/dd0QR3KGjNOAzpXNb5PpSIFyXiRtbbkdAIRlwY9Y4WnQor8rh4afMa1h4ojFbigzpncVfvPDJtLf/h3TXNwWM3XIo7UMWJMk5TYYmGWHNJQDJSHPx55OUga6ARXQi0Go10sdk4eNBNuT+C2xEhyazHJBvYsLmAh576NwdK69ad4/EobNpcgMUcu1KzmI1s2lSA1yvWYASxkfVWUgxdkPVWDHobNkNGiyeW2//3Aav3F2BJNmBJSsaSbGDV/r3cvuLfFHkSp3PNbpLJbJ/SZhMLiOSSsFjkZDpYrZzZvh1ZaSn0aJ9GF4cNtzuAqmro9RI6IKxXo9WGPxBiwb/W1un6bk+gwilSqqU1UpJwuWN7tQgEicZLm9fgC4dwGE9o4TfK+EJB5myu2/8NQXwQySVBSTGaUFUVHWDQ69HrdERUDX8ghCRVLOxrQHLk+J/QYjay8ftCPHWoNuy2iutXOlCeiKpqqKqKw153VWmBoKVwBRXWHijEZohdidsMRtYcKMBVg+W4IP6I5JKgOIwyeZ274Qkdb11UIxXJBh1EJEjzGUhWj/8JK6oNHW7PqasNm02mT+8sfP7YrZE+f5A+fbKE9a+gVeAMHqvEa9qkqNMh6SScQVGJNxciuSQwU3oPwmIw4goqFa3JegkVjXJJRa/qyD5sqXZ+RbWhYbfVrdoYM3oQZpMBj1eJVjCqquHxKphNBsaMGhT3exIImoLKSr/GTYqahqqppBhFJd5ciOSSwGTaHLw2/DcM6dIdXziEEikn2ZSEzZlEn6IUTOHqzX4+f5CcC7qd1JZcE+npDmbNuI6+OVkEAkECgRCBQJB+OVlN3oYsaLu4fQrFB5244yRlUhdiVfpV8YSCDOqcVa+2ZEHjEK3ICU6mzcGfL7w82rcfdId56tnl+AMhVLP+pL0pY6/Pq9f109MdPHDfFXi9Ci53AIfdJKbCBA1i/2EXb3yYz4Ydx5W8+5+TyYSrBtKlfdN/UZnSexCbD5fgCirYDMboPhJPKIjFYOSu3vX7vyFoHKJyaSU4jDJZ9lTOzujIk9N/Q7/eWQSUUPTRv3cWT07/Tb32uVTFapXp2iW13onFX+7lSLAEf3nNLneNwRP2s89/GE/Y3yTXF8SH/YddTH1pKd9uL8RsNEQf67YV8MBLy9h/uOnbgE+s9H3hML5wiAu7due14b9JiH0ulbgCCoVHmtanqbFOlD/++CM33ngj/fv356abbqKoqKhe44vKpRXSuZODBydfjser4PYEYu7Qb2qOhg7yRem/2O37AUknoWoqPaznc3HH60gzdGz09Q8EjvLO3hVsKtsV/Qaak9qDm7sPp7MpLQ53IIgnb3yYj18JYTcfX9Oo8Cox4fYHePOjdVxxftP/3U6s9Ou7Q7+p2Vfm4pUv17FuTzF6qaIDNPeMDO4YNpCuqYnjROl2u7n11luZMWMGl112GS+99BIzZsyo0UsrFqJyacXYrDJdO6e2SGJZsPdpdnm3YpRkDMceP3u+Z8HeZzgaOtio6x8IHOXhLW/yXdlOTHrjsYeBDWU7eXjLWxwIHI3TnQjigdunsGFHMVY59vvQKsus316EP1jebDFVVvqJllimLFxG/u4KnyazocKrae2uQqYsXMa+ssRxovz888/p06cPV1xxBZIkcccdd/Dggw/WKwaRXJoZl1+h6LATV5xVjZuTL0r/RTCiYEqyHPP2AEknYUqyEIwE+PLgB426/jt7V+CPBLElmatshpOwJZnxRxQW7l3R6HsQxA+3T4m2wcei8phPCTdzZInFK1+uwx8MYa/q0yTpsJtkfMEQr34VP5+m2pwoqzJixAjee+89unXrVu357du307FjRyZPnkxubi733HNPNQfLuiCSSzOx76iLGe//l5tefJfJ85dw04vvMvP9/8bVN7s58Jd72e37AVkfu6XTqDexy7uVQMTXoOt7wn42le3CUsP1LXqZjWW78ITFfoVEwW6Ro23wsag8ZpGTmzmyxMEVUFi3pxhrDT5NNqOR/N2J40Tp8XhYtmwZY8eO5euvv6Zbt25Mn14/y2qRXJqBfUdd3PvmMvJ3FmI2Jlc8DMms3VnIvW8ta1UJJhDxIkXdCE9GOuZA6C9vmL2tO+yPOvapqko4XI6qqtWuL+l0uMMNS16nM65QgELvUVyh+CZmu0WmX88MvDUY2HkVhf7nZGI2nr5LvC6/gj6GT1MlkqRDH0efpsY6USYnJzNo0CAGDRqEwWBgypQprFmzpto1T8Xp+9duRl5bsQ5fMITdXF192G6WcfsVXl+xjsduuLQFI6w7Jr0V9ZgbYawEox5zIDQnNUzA0J5sJlxejvNIACUQBp0ONA2T2UC7FCv6pIrFfXuy5dQXEwBQ7HMyb8fXrDu0N9p8kdshm9/1vJAMS0pcxrj16lx+3FuK2x/AKstVvEoULLKRCVcN5EDBz3EZqzXiMMtEqvg0nYiqakRUjRRzfNaIsrOz+eyzz6I/19eJsnv37uzatatKfGotZ8dGVC5NjMuv8O2uYqym2OWwVTayblf8yuGmxOMO4DwQItNwDkok9rffYCRAD+v5mPQN+/D3Hg3j3Qm+cgXdMWFNnSTh94fYV1KGO+gnJ7UHtmSx07ouFPuc3LXmH6w9tAdzkhFzkgFzkpE1h3Zz19p/UOxzxmWcLu0dPDtlBAPPzcIfDEUfuedm8eyUEc2yzyWRcZhkBmZn4FVq2OQZDJJ7RuI4UV566aV8//33/Pe//yUcDjNnzhwuuuiiOvldVSIqlybG7Vei0zyxqLQ0dvmUhJXfLtlfxuLXV7J5/V4kvQ5sATrfHqI8NYLFYI1+Gw5GAhj1Ji7ueF2Dx1rwz7XIP6cQuiiAmlyOVK5Hp9Mh6XWUS+UEPOXcPGB4HO+ubTNvx9f4IkHsVZKxpNNhTzbhCgV4Zcc3PNH36riM1aW9gz/ecikev4LLq+Cwytji9E28LfC7iwfy/b4SXAEFm/G4OZ8nGMRiNHDHsMRxouzatSvz5s3jz3/+M9OmTaNPnz78+c9/rlcMIrk0MXazHPVjqakcVjUNhyUx/xOW7C9j5v3v4veFsFiPTXeEktn/Sg8cv9qPrr+f5KQkNFTOtF3QqH0uHq/Cxq2F2GUrlg3dKetxkEA7L1qFhy6WQzbkH1OxDjPH9yYTAFcogCsUQKdJqCqkGGUcNbT21uea6w7txZoU+zq2ZJn8Q3twhxTshvi9/2zmiqTi9ioUlzqxW2TsCaD64HEH8LgC2BwmbC2g9t011cFLN4/g1a/Wkb/7+D6XQT2yuGPYgLjucwHo27cv//nPf6o9Z7Va2bhx40nnXnfddVx3XfUvhXl5eSxZsqTB44vk0sQ4zDIDemSQv7Ow2ppLJV4lSN5Z3RK2aln8+kr8vlC1/4ySJGFWUzn6T5nMAxnc8vsLMSfZGjwVVklVjxkpYKDj1gwiSRFUQzlSKAl9uZ6AEsLtCTT73p6monI9ZHXpblzBIIHyMEbViCVsY2iXbCb3yyXT3rAPHVcoEG2AiEVUKTjkj2tyOXDIxVtL8vnux6Lot/N+v8ik7xkt86XgxMpbjWj0HpDNqNuGkt4ltVlj6ZrqYOa1FU6UTr9CirntOlHWmFymTJlS54vodDpmz55d78HXrFnDE088QWlpKeeddx5PPPEE3bt3Z9GiRcydO5dgMMioUaOYOnUqUNHxMHPmTD7++GMsFgvTpk3jyiuvBKC0tJQHH3yQLVu2kJWVxTPPPMPZZ59d75iagtuHD2RrUQluv4JVNlZZ7AxikQ1MHB6/cjieeNwBNq/fi6WGD3KL1cj3a/ZhDDkwxUFttqrHTOWeCX25Hn25Hqi/6nOiU7ke4goHOOwLoGmg10mE9CHKpaOs3KexqbSEV68c2aAE4zCYUDW15qq5UinYEL8P/QOHXDz43FL8Sgir6fh7/duthaz/vpwLLriAzh2ab/0lZuWtqmzM382OrcU89vzoZk8wUOFE2VaTSiU1Luh/+eWXnHPOOad89OzZky+//LLeA0ciEe677z5mzpzJt99+S05ODo8//jhbtmzh5ZdfZtGiRXz44YesWLEiev233nqLoqIivvrqK55//nlmzpxJWVkZAI888gh9+vQhPz+f66+/vt492U1J1zQHfxs/gryzuuEPhfEHw/hDYfLOzuJv40fQNS0xFzs9rgCSPvbmuHI0QnpQ9eB2xqe11WaVyTm/W60eM/VRfU50KtdDAqFIRWKRdOh0OvRIqDqNkBzAFw4xd0N+g67vMJgY2KE73vLYzSKesEJuh+y4Vi1vLTkmA2ORo+8bSdJht8gooQh/X9qwe2koVSvv4/FI2Owm/L4Qi+d/3azxnE7UWLlkZGTUuXr56KOP6j2wy+XC6XSiqiqapiFJEkajkY8//pgRI0ZEd4yOGzeOJUuWcPHFF7N8+XLuvfdeLBYLffv2ZciQIdHzV69ezfPPP4/BYGDs2LG88sor7Nq1ix49etQ7tqaga5qDx26oKIddPgWHJfG/udgcJtSIVq2SUHQq+5LCuKUIOg1Uu8arn+Zzx7WD49IRNPa3eWzbeQCPV8FiNjZa9TlRqVwPMUkGSkI+9CdUFhI6AroA7QyprNlXhCuoNEjKZFLPoXxfth9XKIAtWT6uFBxWsCYb+V3PC+N1S7i9Ct/9WFRjZ6Rs1LPhhyI8PgVbM6wx1qXy3vztHryeANY2Ug0nEjVWLp988km1n30+H16vt9qjpnPrQlpaGtdddx3jxo3jggsu4L333uOhhx5i79691WQLsrKyov3WBQUFZGdnR49169aN3bt3U1hYSLt27bBYLNWOVe3TbilOVPW1m2Qy26ckfGIBsNlN9O7fHd8x22RFp/KTQcEtRdADqBoWk4GNP++Lm/Jt506OJlF9TjQq10M0AN2xRxV0x57QdBVTWs4aNiieigxLCnPybmRwxzPwlwfxl4fwlwcZ0vEM5uTdGLd9LlAHGRhdxTFXHWy440FtlTcQXd+LV+UtqM4pF/SXL1/OzJkzcbvd0ec0TUOn07Ft27YGDxyJREhJSWHBggX07t2b2bNn88ADDyDLcrVdpEajMSpZ4Pf7kat00MiyzKFDh056vvJYIFC/N83WrVvrfO6GDRtqPX404uV/wR/YVX4QHRWe92cmdWK48TzS9NZ6xdVQThVjXTg/N41N63/myCEnhzskEdZ06I9t+NJJOszWJPSoHHV5+MvfP2LMxT3jEuPw3PYM6u3A7w9jNidjlpPZX/wz+4sbfUsNIh6/y6p4I2H8SgAD+mOyKVq1BKOhoQGKL0AwAgXbt3M4qXb5lNpivJbOXJrWHm8khFVvwKIlU7p9F6XxuR0A/EqYQEAhEgnVuMYTCijs+Xk7pcVNLwUT8IUI+BXUSBhdjASjqhqhYITde3/iwMHj8dT1b92vX7+4xdoWOWVy+fOf/8zdd9/NsGHDkKT47bn89NNPKS4uZuDAisXs+++/n/79+9OvX79qEgPBYDCabGRZrnZMURRMJhOyLBMKhapdX1EUzOb6LVSef/75ddoktGHDhlrfWAcCR5m75U38+iApBsexqQiVgkgZi7UNzDpvfJPLxp8qxvpw/gXn8/dXv2Tpnj3otYpv1WabTFp7K8nJFW8hk6yx97DC2ef+os57G+IZY1PSVHFe9N0h1h7ag00z4g2F0Ff5AIygYtFMaEkGLunejYtya58OTJTf5Tc/uln/QyGWGO+BQ0ddXNjvLIYOab6pzfVfHmTjuj3YrCdPe3ncAQZeeBYXDj0eT6L8HtsCp8wWoVCI0aNHk5mZSdeuXas9GkNJSQnh8HGVVEmS0Ol0mEwmCgsLo89XnSbLzs6udqxymiwrK4vDhw9Xq1QKCwurTa81J21N1Te9Syo3T76ETl1SyMhsR+YZHejUOSWaWOC48m1zTXm0BSb1HIolyYjJoEeng4iqoWkaEVQkTYdBMWFJNjC5X25Lh1pnxl+bi0U24PYqUSFLVdVwexVkg55bRjbvvYyaeBFmiwGPOxCVMFFVFY87gNliYNRtQ5s1ntOJUyaXm266iTlz5tTqYtYQ8vLyWLNmDd988w3l5eXMmzePrKwsJk6cyAcffMCePXs4ePAgCxYsiMoWXHHFFbz66qt4vV42btzIqlWrGD58ODabjdzcXObMmUMoFGLBggWkpKRw5plnxjXmutBWVX3txxZg9Ul69DEq2Mo2YUcb6eRqDirXQ4Z2OpOOFjNysp4IKoaIAUcwjYu69mhwG3JL0bmDg6enjmTABd3wK6HoY8AFWdwx8vxmbUOGii9Gjz0/mpzcMwj4QwT8QQL+EH3zzmixNmQ45kR5NHGdKOfNm0dOTk700atXL3r27Elpad0nUk85LXb22WczdepUXn755ehz8VhzOe+88/jTn/7EE088wZEjR+jVqxezZ88mIyODyZMnM2HCBAKBAKNHj44ml/Hjx7N//36GDx+OxWJh1qxZpKenA/Dkk08yffp08vLyyM7O5oUXXkBXw+axpqSqqm8sqqr6tiZ9rErl22+3F1ZzG6zEqygMPDdLyH3UkwxLCk/0vRp3SMEZ8iMhEYlAiiwnlNFVfejcwcH/TbwMj6+KDIxFjvu6VV1J75LKfY+MwOsJ4HYGsKeYWqw7rNjpYt4368gvKI52QuZ2z2DSkIFkpCSOE+WkSZOYNGlS9Oc//OEPOBwOOnXqVOcYTplcnnzySX7/+9+Tl5eHXq+v84XrwtVXX83VV5+sazR69GhGjx590vMGg4EZM2YwY8aMk4517NixXhacTYU92Vy73MuxTW2tUdW3Lsq3goZhN8hx3W+SCNgscrO0HNcVq63lkgpUJJbJ7y3DFwphk43RtvA1ewrZsr+EuTeMiFuCqepECRWb4seMGXNScnnjjTf49NNPufXWW/n+++9jXuvrr79mw4YNLF++vF4xnHJaLBgMctttt/GLX/zipA2UgpOxJZvpk9oDXw2qwb6I0mpVfYXyrUDQcOZ9sw5fKISjqhOlTofDJOMNhpi3KnGcKKvy3HPPcf/995/UkXsqTlm5jBo1ir/97W/cddddJCUJKbK6MKb7cLa7i/CU+7Ho5ahqsC+iYNbL3Ny99ar6CuVbgaD+uAIK+QXF2GpworTLRvL3FuNWFOyNFCyFxjtRVpKfn4/H44nKbNWHU2aLL7/8kh07dvDaa69hMpmqrWOsWxe/TNuW6GxKY1av8Szcu4KNZbui5W//1LO5qfsvm7wNuTmoVL49XfG4A3g8Cjab3CIKu4LWhSugRO01YlG5wdTpj09yaawTZSXLli3jN7/5TYOWRE6ZXP74xz/W+6KCigTzwLk34AkHcId92JMtrXIqTFCdkgNOFr29is0bC5AkCVVV6ZOTxehxQ0jvnNLS4QkSFIdJjtpr1CgimkBOlJWsXLmS119/vUEx1LjmcvfddwMwcODAGh8nnis4jscdYH/RUQhAV3N7kVjaACUHnDw2/X02bdiLyWTAZErGZDKwccNeHpv+PiUHnC0d4kl43AH27yvD425dre9tDYdJJjcrA08NTpRuJUhu94y4VC3QeCdKgIMHD+LxeDjrrLMaFEONlcvKlSv5/PPP0TTtlBf5+muhLFpJInlHCOLLordXEfCHsNqOfwBIkg6rTcbrUXj37VXcP+2qFozwOKLCSjwmXTiQLQdKcAYU7FW6xdxKEKvRwKQhieNECRUb3dPS0hqszFJjcmnXrh2zZs2q00XS0lr/GkI8SFTvCEHj8bgDbN5YgNkSe0HWbDGyaWMBXo9SLfm0BJUVVsAfwmw5riy9ccNetm/bz8ynbhAJpgXISHEw94YRzFu1jvy9x/e5DMnO4ndDBsR1nws03omyV69efP755w0ev8bk0piLnq7U5Npos5vwuAMsnv819z0yogUjbLtU2gQ7DCYchvhPQXo8SlRFNxaV8jdud6DFk0trqrBONzJSHMy66lLcShUnyjhNhSUaorc4TgjviJah0iZ43aG90Zbv3A7Z/K7nhXGVk7fZ5JNcMqsSdcls4c6x1lRhnc7Y5babVCqJn8zxaY7wjmh+Km2C1x7agznJiDnJgDnJyJpDu7lr7T8o9jnjNpbNbqJ3ThZ+X+wFWb8vSJ+crBb/wK5PhSUQNCUiucSJqq6Nsaj81mtPEVVLvKi0CbYnm6rteLYnm/CGg7yy45u4jnfTuCGYzAa8nuqKv16PgslsYPS4IXEdryFUrbBikSgVlqDtc8rk8uWXXxKJRJojllZHVWXTE10bT8TnDdJ7QHaLTIm5QgEKPGW4Qm3n22qlTbA1KXalYEuWyT+0B3cofqqz6Z1TmPnUDeT0604gEIo++vbPjusieWPah1tLhSVo+9RJuNLr9XLllVcyYsQIevXq1RxxJTSH/AoPf/jfk5RNr78xhx0/7MPjDmCxGqMtoD5vsEW8I4q9Tub8sIq1pYXodToimsagTllM/sVgMqwpzRpLvKm0Ca51x7NOwhnyx1UQMr1zCvdPuwqvR8HtDmC3m+L2QR2v9uGbxg1hx7b9eD1KtW4xvy+YMBWWoO1zyuTy2WefsXnzZj766CPuuusuzGYzI0eO5JprriEzM7M5Ykwoip0unvvuB1S9Pqay6czHr+ar9zax+ds90f/UffPO4MZbm3efy8FwgEdX/hNvOITNcDzOVSV72XxkP69c9NtWnWAcBlNUYbrGHc+aSoqhfm6kdcVqk+P67T+e7cOVFda7b69i08aC4+/D/tmMGjtYtCELmoU6dYv17t2b3r17M336dFavXs1f/vIXZs+eTd++fbnhhhsYOXJki3intATzvllHoDxCR+txyfxKZVNnQOFfP+9gVgJ4R3xwZBfe8lA1TxBJp8NhlHEFFeb+sJonc+svRpcoOAwmBnboztpDe7DHUD/whBUGdzyj1cjYx7t9uCkrLIGgLtRpQT8UCvHf//6X+++/nylTppCUlMRDDz3EhAkTWLRoEb///e+bOs6EoFLZ1JQUW8StqrKp1WaiS2Zai62x/OAvw2aI3Y5qMxhZU1oQ1/WIlqDSJtgVCqAeU5JQNQ1XKIA12cjvel7YwhHWjfq0D9cXq02mS9dUkVhqoFKmqbnlcVyKQoHTiUtpuv+DjXGiBFi+fDm//OUvGTBgAJMmTeLIkSP1Gv+UlcuDDz7I559/TkpKCldffTX33nsv2dnZ0eMZGRkxjb3aIpXKpjSTsmlDcQYVJB21rkfodTrKgoFW880+FpU2wa/s+Ib8Q3ui+1yGdDyDO+K8z6UpaU0bNNsKNck0nT+waaeui10u5ubns7ao+Pg6aGYmd+YOJKOeopK10VgnysOHDzNz5kwWLVpEZmYmDz/8MC+88AJPPPFEnWM4ZXKRZZl58+bRv3//mMczMjJ4++236zxga6ZS2ZQa9NbirWzaUFKMMqpGresREU0j1dj621FPtAlOMZhbXcJsLRs02wq1yTRt+jbC+Rec3yTro8UuF79burTCidIoH18HLSxgc0kJ80aOiFuCaawT5YEDBwiFQqiqClTs0zMaY1fWNXHKabHHH3+8xsQCFVo1F1xwQb0Gba1UKpsGymO3Zsdb2bShOAwmfmFOxROK3Y7qCQUZ1Cmr1X0I14bdINPNmtYq70m0DzcvVWWaKpN5pUxTMFDO4vlNI8Q7Nz+/wolSrr4vyyGb8IaCvJyfOE6U5513Hr179+bqq6+mT58+bNy4sd7q92ITZT2ZdOFATEl6nAGl2jy/M6DEXdm0MVzXrgfWZAOuYPU4XUEFa7KByb8Y3MIRCqrSGjZotgVOJdNkNCVFZZriiUtRWFtUjM1Yw74so8yaoiLccVqDaawTZTAY5IwzzmDJkiWsX7+eCy64gJkzZ9YrBpFc6klGioOpfX/B4Oxu+IJhfKEwvmCYIdlZzL1hRNyVTRtKx2QTr1z0W4akd8cXDuEPh/CFQwzp3L3VtyG3RZprg2YlHqef/XsP4XH643rdROfUMk26JpFpcioKel3tTpR6nY6yOCWXxjpRLly4EIPBwLnnnovZbGbatGl8/PHH1a55KoRwZQPoYJaZNXRIwiubZlhTeDL3StwhhbJggFSjqVVOG50uNEf7cGnRERa/+ClbVv8cXcjuNeQsRt19GZ0y28V1rESkqkxTTetbOuIv05Qiy0S02p0oI5pGapw+RxrrRFlSUlLt56SkJCRJqteWE1G5NAK7LNMtLSUhE0tV7AaZLFuqSCythKZqHy4tOsLMW19j06qfMFkNmCxGTFYDm77ZweO3vkZpUf1aTVsjp5JpCgbKm0SmySHL5GVm4AnGHtcTVBiUmZkwTpSDBw9m2bJlbNu2jVAoxPPPP8/FF1+MwWCo8zVEchEIThMWv/gpAZ+CzWGOugtKkoTNYcbvVVg8+7NTXKFtMGriRZgtBjzuQLQbSlVVPO4ARlNSk8k0Tc7NxWow4lJO2JelBLAajNyZG38nyhdeeIHBgwcTDAajTpQ5OTns37+/1tcPHz6cu+66i7vuuouhQ4fidDrr1YYMYlpMIDgt8Dj9bFn9M5YaqiGLXWbLqp14XX6sjqaRzKnEFVBw+RUcZhmHqfmr6fQuqTz2/GgWz//6JJmmXwxIbTKZpgyHg3kjR/By/jrWFBVF97kMycrizoHx3ecCjXeiHD9+POPHj2/w+CK5nMYoEQ9KxIOstyHrbS0dTkLgUhScikKKLONI8OnO+uBx+o4tZMeerJAkCUmvw13ma7Lkcsin8Oi//8u6PcXoJR0RVSP3jAzuGDaQrqnN2wiT3iWV+2LING3YsKFJx81wOPjTZRVOlGWKQmobNg1r0eRSVFTEo48+ytatW+natSuzZs3i/PPPZ9GiRcydO5dgMMioUaOYOnUqUNHxMHPmTD7++GMsFgvTpk3jyisr9LFKS0t58MEH2bJlC1lZWTzzzDOcffbZLXl7CYsrVMLaI4sp8m1Gp5PQNJVult7kthuFw5De0uG1CLXtnG4L2FIsxxay1ZgJRlVV1IiGPdUS49WNZ1+ZixfW/oCm12OtFHxVNdbuKmRLcQkv3Tyi2RMMgNXWMtp/womyCYlEIkycOJFhw4aRn5/PzTffzNSpU9myZQsvv/wyixYt4sMPP2TFihV8+eWXALz11lsUFRXx1Vdf8fzzzzNz5kzKysoAeOSRR+jTpw/5+flcf/31TJ8+vaVuLaFxhUpYUjyTQt9GDJIp+ijwbWRJ8UxcoZJTX6SNUblzenVhIRaDAbPBgMVgYFVhAZOWLuNgoHVrsAHYUsz0Gnwmvhr0yXxuhV5DzmqyquWVL9ehRCLYTfLxDYSSDrtJxhcM8epX8dtAKEgMWiy5fPfddxgMBsaPH48kSVx//fU899xzLF++nBEjRtCtWzc6derEuHHjWLJkCVAhpDZhwgQsFgt9+/ZlyJAhfPzxx3i9XlavXs0dd9yBwWBg7NixlJaWsmvXrpa6vYRl7ZHFhFQ/st6GTlfx59fpJGS9jZDqJ//I4haOsPk51c7pfxcWtHCE8WHUPZdjtsh4nP7qC9lOP2arzKi7L2uScV0BhXV7ahZ8tRmN5O8uxt0GkrjgOC2WXLZv30737t2ZPn06ubm5jBkzBpPJdJJsQVZWVjRJFBQUVBPN7NatG7t376awsJB27dphsViqHRPJpTpKxEORbzNGKfbUh1GyUOjbjBKpWT21rVGXndM/OJ1x2zldH9zhAEW+I7jD8dnQ1ymzHY++cTt9hvYk4A0R8AUJeEPkXNSTR9+4vcn2ubj8Cnqplg2Ekg79McFXQduhxdZcPB4PK1as4M9//jMzZ87k7bffZsqUKbRv377aLlKj0RiVLPD7/chV5illWebQoUMnPV95LBCo33/KrVu31vncpl74iwcnxhjQHSFkDBHRat4IVa4LsX7zakxa82yoa+nfY0kgQDgYRCkvr/EcCR1fr19Peh13NzeWwxEfH3l38FP4MDp0aGj0NHTgSsvZtNfXvCZS19/lhTeeTb+rsvB7gphtRkxWI8UH91J8cG+c7qA6vlAYfyCAnKTH7z9ZEUDVNJTyCHt/2sEhQ8v3GNX199ivX78mjqR102J/yeTkZM4880xGjBgBwG233cbLL79M165dq0kMBIPBaLKRZbnaMUVRMJlMyLJMKBSqdn1FUTCb6zd/fP7559dJ+XPDhg0J/8aKFaMS8bB9z7sYJDk6JVYVTVMJqRr9zxmMrLe2SIzNjUtRSN7xE7LBUOPOab/HzdD+/ZtlAXa/v4y/bFiITxck1Ww/ppyrsrvcxZvBLfy13810MZ/cKpsIv8sT8bj8eFwBbA4TQ0s8rNz+Mx0c9pPOcwUULjo3m4sG5bZAlNVJxN9ja6XFkkv37t2rmddoWkUniyzLFBYWRp/fu3dvdJosOzubwsLCqL1yQUEB/fv3Jysri8OHDxMIBKKJqLCwsNr0mgBkvY1MS28KfRtjth4HVR9ZlpxmSSyJQuXO6dWFhTjkkysTV0Chh2xFFwaaobln/q4v8ZUHq7lrSjoJe7IJdzjA/F1f8cgF1zZ9II2gtPgoi+d9zpb83UiShKqqZOdmkWzR4Qoo2IzHbZw9wSAWo4E7hrWNrjzBcVpszWXw4MH4/X4WLFhAJBJh/vz5ZGRkcPvtt/PBBx+wZ88eDh48yIIFC6KyBVdccQWvvvoqXq+XjRs3smrVKoYPH47NZiM3N5c5c+YQCoVYsGABKSkpnHnmmS11ewlLXrtRGCQzSsSDplUs6mqaihLxYJDM5LYb1cIRNj+xdk4HQ+UUlh6l7KAH10ond979d5594RNKSlxNFoc7HGD9kT1Yk2JnMWuSkfVHduOJ0xpMU1BafJSZk99m05pdFfIyFgMmi4Fda/bS4RsnvdM7VUyThSpEXwf1yGqxNuSWxKUoFLgS24nynXfeYdiwYeTm5vKXv/yFSCS21UhNtFhysVgsvPXWW3z00UcMGDCA//73v7zwwgv07t2byZMnM2HCBK655houu+yyaHIZP3482dnZDB8+nKlTpzJr1izS0yv2ZTz55JNs27aNvLw8lixZwgsvvFAvkbXTBYchnWszHiPLkkNIDUQfWZa+XJvx2Gm5z6Vy5/SQbln4QiHcfoXi0jKMRzR6l9qx65IwmYx8t7GAh2d80GQJxhXyI+mkWpRzJSSdhDOUuErGi+d9TsAXxOY4wSvFYQJXiIxtQd67czQvj72W9+4czcxrf3VaJZYit4v/++IzRv5zIRM+/ICR/1zI9C8+o8gd3/dUpRPlgw8+yNq1a5FlmTlz5px0nqZpLFy4kAcffBCtiglifn4+f/vb35g9ezZffvkle/fu5Y033qhXDDpNq8FW8TQiGAyydevWNr/mciJKxIsScSPr7S0yFZaIv0e3ovCXuZ+x4/v9pJorpqb8fh9mc8VCuser0DcniwfuuyL+Y4cDjF31Mma9sYb1HxV/JMQ7Q+7Ellx9Ci8Rfpcel5/7fvsSJosxpuKwx+NDQs/f/nU31gR11WzK32OR28Udy5fiC4ewGYxRJ0pPKIgl2cCrV44k0x6fRPvFF18wd+5c3n//fQB2797NmDFjWL16dbXz5s+fz6effsrgwYP5/vvvmT9/PgBPP/004XCYhx9+GIDvv/+eP/7xjyxbtqzOMQjhytMYWW8lxdDltFpjORW6MOzZcqBG6ReL2cimTQV4a1DVbQz2ZBP922XjLY99bW95kP7tzjgpsSQKHlegQkamVq8UCXccPGQ87gD7i47icTdsijAcceEPFxKONN0054nM2ZCPLxzCYZSr76cyyvjCIeZuyI/bWI11olRVtdoXbUmSKCoqqlcMLd/3JxAkEG5P3T4gXe4A1hrcDBvDbT0u5gfnPtzhANYkI5JOQtVUvOVBLElGbusxLO5jxgubw1QhI1OLVwqo2FMargJQsr+Mxa+vZPP6vVE/mt4Dshl129A6CU4GwvvY7ZxHmbIOnU6PpkVIM+WS7fgdpuSuDY7rVLgUhbX7irAZYs+M2AxG1uwrwhVUcNSw56o+NNaJcujQoUybNo0RI0aQkZHBa6+9RjgcrlcMonKJI66AQuFRJ6567jRWIh6cof0oEU8TRSaoK3bb8Q/IWKhqRVejI87TOsGIG0+omHZGPX/tdzMD2/fAHwlFH7nte9TYhpwo2BxmeuWeUaPETDAQpldujwZPiZXsL2Pm/e+ycd0eTGYjJpMRk9nAxvzdzLz/XUr2l9X6+kB4H5tK76IskI9eZ0GvM6PXWTjqX8um0ikEwvsaFFddcAYVpFM4UUo6Hc4EcaK88MILmThxIpMmTWLkyJFcdNFFWK31m+EQlUscKHa6mPfNOvILiqMtlrndM5g0ZGCttsdCQDLxsNlk+vTO4rtNBdhiVCY+f5C+OVlxq1q84f18f/QtSgPfoUNCQyXd1I/7z7kFjctxhvykGMwJOxV2IqMm/ZKfthTjcQaw2I3RVmSfO4jRlMyoSZc0+NqLX1+J3xfCViU5SZKEzW7C4w6weP7X3PfIiBpfv9s5j4jqJ0l/fK+NTieRpLdTHnGxx/UKMLLB8dVGilFGPYUTpapppCSIE+XRo0f51a9+xYQJEwBYtWpVvYWAReXSSIqdLia/t4w1ewuxGJOxGJKxGJNZs6eQye8vo9gZe05XCEjGF7dfoeiQE3ccJETGjB6E2WTA41WiFYyqani8CmaTgTGjBjV6DKhILF/un0aJfwNJOjNJkpkknZkD/vV8uX8aOsrItLRL6MTiCfvZ5z+MJ1yxjtIpI41H546jz+AzCfhC0UfOkDO5eeoQOmWkNWwcd4CN6/aQLCcTOaaLVhWL1cjmb/fg9cRegwlHXJQp69BLsb996yUbRwP5qDpfg+I7FQ5ZJq9rJp5QbA96TyjIoK6ZcZkSg8Y7UW7bto2JEyfidDo5fPgwL7744kl+L6dCVC6NZN436ypED6uYHkk6HQ6TjDOgMG/VOmZddelJr6sqIFlJpYCkEvGQf2Qxl3W+rzluoVWz74iL+Z+u49udx6XyB5yVwW2XD6Rru4Z13qSnO5g14zreWbyGTZsKCIUiaFqQfjlZ3DxqEOnp8eno+f7oW4RVP4YT3gMGvY1QxMPWo38nr1NiqnsfCBzlnb0r2FS2K9r1lJPag5u7D6dzRhr3zroerzuA2+nHnmLGam+4V0rJASevz/2cg24/kldCQ8NsTKadw0zyMTHMynUytzMQU0I/rLrQ6fQxlSmg4veu0/RouqbT1burXy6bS0twBZWY3WKT+8VPoaDSifKxxx7jD3/4A4MHD446UV511VV89NFHdOnSpcbXDxkyhCuuuILLL7+cpKQkbr75Zq699tp6xSCSSyNwBRTyC4qxybEX6eyykfy9xbgVpZp0SH0EJEUnV83sO+Li/leX4QuGqu36zv+pkK0FJTx/x4hGJZgH7rsCr1dh1epvGTJ4QFwX8IMRN6WB70iu4T2QLFkoCWwgFPFUSz6JwIHAUR7e8ib+SBCL3hSVqNlQtpNt7iJm9RpPZ1MaVrup0S3HJQecPDb9/ajnfcWMkg6/EkYJuenawU5ykj66TmZPiT1esuRA0yJoqDVKH2lE0GlN9/8t0+7g1StHMndDPmv2FUWTy5CMLCb3Gxi3NuRKGutEef/993P//fc3eHyRXBqBK6BUdA/Vtkh3TO31xOSi00m1f4vSScf2oIjkUhPzP12HL3hC1ShVVI0uv8L8T9fx6E0nV431wWqVad/OHPfOsFDEjY5TvAc0iWDElXDJ5Z29K/BHgtiSjnd9SToJW5IZT7mfhXtX8MC5N8RlrEVvryLgD2F3mFH8Ify+EJJeQqfXoUZUjrj8pLez4fMGyck9o0bjr2S9g1R5IGWB/GprLpVEVA9p5kEEtKYxS6sk0+7gqUsuwxWs4ngap6mwREOsuTQCh0mu6B6qYR+qqmmoqkaK+QTFZr2t4puSdvLcMRA9Jsf4TyCowO1X+HZnMbYaNr3aZCPf7iyOyxpMU2DQ29E4xXsAFaM+sXave8J+NpXtwqKP/SFu0ctsLNsVF4kajzvA5o0FmC0Vf+O09raK6jSigqahkyoqGJfTj9liYNRtQ2u93hkpk9BLFsojrmrSR+URF3rJSrbjd42Oua44jDJZjpQ2m1hAJJdG4TDJ5GZl4FFiL9K5lSC53TNOUtOtFJAMqrEXD4Oqj26W3qJqqQWXT0F/rDKMhSTp0Ot0uHyJmVyMejudTH0Jx3gPqBEVJeQmTd874aoWd9h/ipbaCvkad7jxC+Mej1Jtz1FSsp4umWmYLcaKRgtNQ9M0zu2TwWPPjz7lPhdTclf6dHqJNPMgIpqPiOonovlIMw+mT6eXmnSfy+mImBZrJJMuHMiWAyU4Awp2+fginVsJYjUamDQkttprXrtRlAR2oEQ8GCVLtBU5qPpOWwHJ+uCwyESOVYY1bdiLaBoOS+J+M7wgbTxHlG2EIh6SJQuRsMrRQ25Cmhc1mMwX88rZeNa/GDXpkgZ3WcUbe7L5FC21KqqmYU9u/PSSzSaftCkzKVlPx84OVFUjHIoQDpdzz/RrsNrq9nc2JXflvPaPE464CatOkqUUksUMQZMgKpdGkpHiYO4NIxic3Q1fsELp1RcMMyQ7i7k3jKhxn0trF5D0eBT27S/DU8OGuabGbpYZcFYGnmANrZ1KkAFnZWA3n/pDp6XuxZrchYu7PE1nc3+UsJfS0lJCER+enzuz651h6IKpbFrzM49PfpvS4qPNGltN2JLN9EntgS8Se9rLF1HISe0Rl/Zpm91E75ws/L6T/8aSpCMcLienX/c6J5aqJOvtmJO7icTShIjKJQ5kpDiYddWluBUFp18hxSzXyVjKYUjnss73tbiAZH0oKXHxzrtr2LS5ILpBrk+fLMbEsUW3rtx2+UC2FpTg8ivY5CoeIUoQi2zgtstr9whJhHuxJnchr9N0Xpz3Dtt/3IFR7yCiGACQpApJFY8zwOJ5X3DvrOubJaZTMab7cLa7i/CU+7Ho5ahEjS+iYNbL3Nx9eNzGumncEHZs24/Xo2C2HP8b+31BTGYDo8cNidtYgvgiKpc4YpdluqWl1NuxsLUISJaUuHh4xgd8t6mgQnrDZGgWKfqa6NrOwfN3jCC3Z7cKj5BjlWPeOVmnbENOpHvxuPxs/mY/UrB9NLFUxWI3siV/F94qIo3+ci9HgiX4y5tuX0ZNdDalMavXePqlnkUgEiIQCRKIhOifena0DTlepHdOYeZTN5DTrzuBQCj66Ns/m5lP3UB655S4jSWIL6JyEdSZd95dgz8QqiaLIkk6bFYZj1fhncVrmkSKvja6tnPw6E2X4vYruHwKDotcp6mwRLqXU6sJS1E14ZDs4YvSf7Hb90O0YuhhPZ/2nNUssVbS2ZTGA+fegCccwB32YU+2NJmSQHrnFO6fdhVej4LbHcBuNzVoKkzQvIjKRVAnPB6FTZsLsJhjt/42pRR9XbCbZTI7pNR5jSWR7qWqmnAsKo6pqBYfC/Y+zS7vVoySjOHY42fP96zS/4ujoYPNEm9VbMkmuprbN4tEjdUm06VraptILK6gQoG7DFewZZ0ov/vuO6677jr69u3Lb37zG7777ru4jS+Sy2mAy69QdNiJqxF7PuojRZ/oJNq9nEpN2OcO0iu3B/n+jwhGFExJlujmS0knYUqyECbElwc/aJZ4BQ2nyONi2jefMGLZ24z/778Ysext/u+bTynyNL8TpaIoTJkyhd/97nesX7+e22+/nbvuuuskWf6GIpJLG2bfURcz3v8vN734LpPnL+GmF99l5vv/Zd/R+r+RW0qKvilIxHsZNemXmC1GPM4A6jFhRlVV8TgDmK1Grr1jILt9PyDXsHnRgJFd3q0EIk0jvChoPEUeF7f/7wNW7y/AkmzAkpSMJdnAqv17uX3Fv+OaYNauXUvXrl0ZNmwYsiwzZcoUli5dWu2cgwcPcuGFF3L55ZcjSRJXXnklqqrW2xSsJkRyaaMc8irc++Yy8ncWYjYmVzwMyazdWci9by2rd4KplKL3+WO3/vr8Qfr0iZ8UfVOSiPdSm5rwo3PHYe2UjFSbZBA6dEj4y4UnUKLy0uY1NTtRhoLM2bw2bmPVxYmyW7duPPPMM9Gft2zZQjAYJDMzMy4xiAX9NsqHPxTiC4aqrUFIkg67WcbtV3h9xToeu6F+ultjRg9i+44DeLwKFvPxtlCfPxhXKfrmIBHvpVMNasJQ0R2mHpMFiim8iIaGijkpsXb0CypwBRXWHiis3YnyQEGzO1FWcuDAAe69997/396dx0dR348ff83sfeUAuS85FA+uABJOL6pYUbzqt/hFRNQWBMT6pYXaXxFRq5VaxQNvW1sEabXW+2irIhUES7gtInIkgYQQSLL37OzOzO+PkCWBBLJkdhPg83w8eDzMXvPeyZr3zud4v/nZz36G06SeMuLK5RTkjyh8u78Kr6v+D7LX6eDrHXsIpNgxs6YU/cC8bkSjsUPLQqtL0T90//UZ3+fSWIF4lOLwQQK16l215PfizXLRsWvrOhWF3VYvPTznozSweVElRk9vH1yW9BZeFE5MVezQPN8xO1HKVMXMmedLpRPljh07uOmmmxg3bhy33nqrKccHceVySgpEFKRj1X86VMnZH1bIcqX2LaV2KXp/IEp2lqvFDoWVRCp5Zcdy1h7clVy2O7h1D27veREd3bkn1XsBuKTdDeyJ7iCaCOOwuJLvKaZFsWHn4rapNXMSMifHcWie75idKHVyHObM8zW2E+U333zD7bffzpQpU5JdJ80irlxOQVluJ4ZxjGrNhyo5N6XultfrpFPH3Bb7x7gkUsn/FSzh6wM7cVscuC123BY7q/d/z12r/8x35Yc7fbb091Kjlb0tE8+cTS9fX1RdIaYpqLpCL19fRmg30MretrlDFBqQ7XAytEPXY3ei7NAto50oA4EAU6ZM4a677jI9sYBILqekbLeTc9rmEIpWf5B1rbrIn65VJ5uQEmNIz84pX7WcTF7ZsZxwIkaWrbqZVTyhUV4eorI0zJ4DFUxd9kceW/hxRnfiK1qQKrUERTvxSfdW9rZc33kqM856lMndf8WMsx7l+s5T8ZDTqOcHQgp7yqoINNN+pPoE/RFKig4S9EdMeT0zznM6zOg/DI/dgT+mJL/46YaBP6bgsTuY3n+oaceq6US5cOFChg8fTiwWS3aizMvLo6SkhE8++YTy8nIee+wx8vLykv82bdpkSgxiWOwUddX5XXlu9XaKSirQlAQSEgYGFqeVtq193DH62HW3TmaBeJS1B3fhtVYnz3hCo6SkCl3XkWUZKSERbafyn3/s4tv7S9M+x+JX97H64DKKwxuT1a+7evqT33r8CRcodVk8Kc2vlJb7efXtNaz7b3Fy8cKg87sw6Zp8OrRpnvmlsj0VLHv+Mzat2Zms7dYvv+cJV4FOx3k2UxdfNi+Nvo5FG1fzVWlhclhzZKczmdZvKF18me1EeeONN3LjjeY0dauPSC4mCgaiBIMKPp8TX63J2FAiQjAexmfz4K3VvS+dLDGdrP9GqbRrJDwSSGAg4fDr5ByIYYnVP2R2KvCrkWRfEYCKinAysUD1sl0wcOZaiexXk6Ve/FEFf0Qh2+2s092yabHs4+0981H1SJ3WCoXh9ZRGt2WkAnZpuZ/Zv3+HiKLidR1eGfefLUX89/t9PDrrmownmLI9Fcyf9mei4Rgen/NQTDobvvqe7zYVc9+zt6SUYFrCeW6MLr5sfjtyTHUnyliUHIfrlG0Y1iKSy+rVq5kyZQobN24EYOnSpTz77LPEYjHGjx/PrFmzgOoVD/Pnz+ejjz7C4/EwZ84crrzySgDKysqYPXs2mzZtolu3bixYsICzzz47I/HvK61i6Z9XsnF9req6ed24/Kbz+EJdxRb/9uS3lD7ZZ/OjzmNo62ydvnhKKvnjov9QUaHgs0i4ZbD7HJyR48FptRIKKbz+55XcM2ds2mJoTtl2d7KvCAZEIrFkYoHqZbtIYFUt2N021mzexb1vfMz64lIssoSmG+T36MxPLxpCp9ym/dFdfXAZqh7BWavplyTJOC0+FC3ImoPLuLzDz5p0jON59e01RBSVLM8Ry9I9TgIhhT+9s4Zf3nF5WmM40rLnPyMajuHLPvwlTJblE64C3RLOcyqyHadue+MazT7nEo1GmTdvXnJX8qZNm3juuedYunQp77//Pp9++inLly8H4NVXX6W4uJgvvviCJ554gvnz51NZWQnA3LlzGTBgAGvWrOGGG27g3nvvzUj8+0qrmHfvG2wo2H2osq4Nl8tOwXffcf+GZ1h/cCtO2ZH8t6lqG49sfYH9ysH0xFNSyX13L+FgRRSLRUKSZayGRKIqxv6iShJxDbfHwYb1hYSaqRdLumXZXAxu3Z1QQkHTdKDu6hzdpuM74MKasKBadXaeofL1rmI8hzaaeuw2Vu8oYsaSd9lbefScjK5XoSV2o+tVx4xD0YIUhzfikOsfvnLIHorCG1G09FU2DoQU1v23uOFl6W4HBd8UE8xgx86gP8KmNTvxNFAjrL4q0MfSEs6zcLRmTy6PP/44o0Yd7n390UcfMW7cOLp27Uq7du245ZZbePvttwH48MMPmTx5Mh6Ph4EDBzJixAg++ugjQqEQq1at4qc//Sl2u52JEydSVlbGjh070h7/0j+vJBpR8R66tIfqb4X6iEo0i4ZSqSHXqgPltbqJagpv7vkkLfEse3kF4ZCKLFcnFgAkCdkio+sGFQdCh2pnSQROgjpgJ+r2nhfjsToI6zEMDvVLx0CzacgJmY47qodcihxRNMkg2+06vGtalshyOQnHVF784uvka2qJYoKVv6CyfCz+iluoLB9LsHI2WqL+chmKFkQ61q76Q/cpWsDMt15HIKwkf9/1qbnPn8EJ/lSqQDdGSzjPwtGaNbmsX7+eTZs2MWnSpORtu3fvrlO2oFu3bskkUVhYSPfu3ZP3de3alZ07d1JUVETr1q3xeDx17kt3cgkGomxcX4jbU/dboW5PoLYPIcctRCJq8qqshtviYov/O8IJc1bH1Iln7e7qqrEGGLWWIusOA72VQSihkEhU19XKOgnqgJ2oju5cHh80gaFte2HzWonLGrrNwHfAzVkFHXFEbcQlnUpLHK/DjqWeP3Q+h4M1O/cQVhNoiWL8FbehqiuRJC+S5EGSvKjql/grbqs3wTgtPoxDu+rrU3OfM43dELM8zkO10o5VR80gO4PLsBtbBTorp3Hzky3hPAtHa7Y5F1VVmTdvHr/73e+Qam0qikQidXaROhyOZMmCSCRSpzSB0+mkvLz8qNtr7otGU/tmvmXLlkY/tqCggIMHIsRVFUnS6tynWVUMzUAyJAzDIBQKY7XWzeOqEWfV+jW0ks37wB/cH0aNq0iyhsMmo8Q19ByD8NA4alcdDECCaGkpAwMd2fbdN6Yd+0QVFBSk9fWvpAv9Pdks/scm4kEDj2xDk1VCeoyAlkDyGHhcFiKR+hN9PKERUuMU7roPp+Mguu4Dan/LtyHLB6nadR+VgRlHPd9l60ilZSc24+hEHpei5Go9+WbDNlPea0PnsmtbF98VVeJyHv2/e0RJ0LtrLt99m5nPQk2MHXpksfOb/bg8RzdHi4ZVevZpy7bt/23065p5nhv7mRw0aFCj4zsdNVtyefrpp7n00kvp3bs3e/bsSd5+ZNmCWCyWTDZOp7POfYqi4HK5cDqdqKpa5/UVRcHtTm1lVp8+fXA46h+brq2goIBBgwYRDERZ8sfNOJ32Opf4umwnapFAA0mS8Ho9dSaUdUMHXWZ4/3w8Jq4eCwaivP7cepxOB1majiLrVF2rYNhBilWvktINg1hXjaI2YTqc24OO7tw6r+FXo1TFFHIcTrLt6b2yqTmP6TYIGNJ3IK8t+4oNGwoPDZPoDBt4Jp9rpXjdhwsJYokjW+PoCRt63IqmxslyxsjO+g5JalN/XS/DicPxHWf27IUs110A0EvtVO8qppgexifncFXnGaasYjrWuezYtRdzfv8O4aiKt1YdtVAkRqtsFz+/Y2xGVovVjrHz3O48MO3PREIxPFmO5EKYcCBGbqssZvz6f1JaLWbWec7UZ/J00GzJ5Z///Cfl5eW89tpr6LqOqqoMHjyYyy67jKKiouTjag+Tde/enaKiomTVzsLCQgYPHky3bt04cOAA0Wg0mYiKiorqDK+lgy/LRf+8bmwo2F2ngZGsWrHv86K0C+CxuOokFoCIFqVfzjmmJpZkPIPPZP3Xu7BaZfSLDHCApFT/4TQMsNusdGrbiigqr+z4grl9rwVgT6iKRd+sZHVZERZJQjMMhrXrxrTzh9PZm2NqnM2hoVIvyt//yZqdRWRnGzg67sSaVQmGBJJBpCKLQdJQfI4okmQ55pi+YVgw9Eo4Irlk29tzbed5rDm4jKJa+y+6eQaS3/rHOC1uguoe7JYsHA0M2/gjCoGIQpbbSXYjmqEdqUObbB6ddQ1/emcNBd8c3udyQd9uTLpmSLPsc6mpAr3s+c/ZtGZHMrnkjejFj6ekvs/leOe5JSxDPt00W3L5+OOPk/+9Z88efvjDH7J27Vo2btzIjBkzuPbaa/F4PCxevJi77roLgCuuuIIXX3yR/v37s337dlauXMmcOXPw+Xzk5+ezaNEiZs6cyV/+8hdycnLo1atX2t/H/94ygm1bSwgFFdyew98K5ZW5WMaFcZxhQTf05FLkiBbFZXHyo85j0hLP+DsuZNs3ezkYDhDsoGKPW8AqoWs6slWiY5dW2GwWLIaDtQd3EoxH8cdiTFnxJqG4is/uQJaqr3BW7tvNxoMlvHDhj06JBAPVpV5ql3mZcvEQvjmwC2evjVisOoZmAUNCM3TcrQKQ9R+q4sPpbGhAAxWJDR3QkOTco+6D6j98l3f4GYoWQtECOC1ZJPQAmyv+QFl0HRIyBjrtXYPo02oSXltHoLofz0uffs1/duxJ/k6G9OzMHaOH0KlVagmhQ5tsfnnH5QTDCv6QQrbXia8J5X/McKwq0CeivvPstHhNjNg8mRghWLt2LfPmzWPv3r0MHz6cBQsW4PXWPR/r1q3joYceYvfu3XTr1o25c+cycODAOo954IEHaN++PT/96U9TOn6zrxY7Uv/+/Zk2bRqTJ0/m6quv5vLLL0/WxLn11lvp3r07o0ePZtasWTz00EO0b1/9jeThhx9m69atDB06lLfffpuFCxfWmctJl/Ydcpj/yI3kDTrzUGXd6n+De/fm/rwZDGx9HooeI6opKHqM/jnncO+5U9K2z6V9x1zmPXETnc7PAQ0MHQzdwON10LFLa2w2C8ChTYYyVWqERd+sJNRAn4lQXOXZb1alJdaWoFNuNqMv1HE5JbS4BV2vLsnhdTjokn0GGnHWGlux24djGPWXEzGMAHb7iKOGxI7ktHjJsXckoQdYXjKHfZECrJIbq+zGKrkpjaxleckcQvES9lb4Te3HU8PncdK5XU6zJ5ba6qsC3RQ157klJpY9oSruXfMB1378Krcv/wvXfvwqv1rzIXtCVaYex4xOlOFwmHnz5rFkyZITiqFFbKLs3LkzmzdvTv580003cdNNNx31OLvdzv3338/9999/1H1t27bllVdeSWeYDWrfIYd75owlFFQIBKJkZbmSw2Tn051wIkIgHibL5jF9KKzeeDrmct2P+vN9MIwDGzarfNTQXPUmQx0ZmdVlx+kzUVZIQFXIsrecP0hmCSfC7FF30dHXCsMLmm5gkSUsh86XxXCxlz1I7mlI8Q3oehWSlJUcdjGMAJLkxe27q9HH3FzxKnE9gv2IDX92iw9VC7Kl4k98vGKw6f14hOa1J1SVsRGC2p0oAWbMmMHNN9/MnDlzko+p3YkS4Morr2T+/PkUFxdz1llnMWvWLLKzs5P3p6rFXbmczLw+Jx075daZfwHwWN10cLXJSGJJHlO2M6RND2JS/KjEAhBKxBjcugcJHSzHKs8vSVgkiUqT+ky0NOFEONlLwyLL2K2WZGKBQ1d4SESMXLJb/QG7fRSGEULXwxhGCLvjQrJb/QGLtXHd+2JagLLoOmwNbPizyR5KwmvZULTD9H48QvPK5AiBGZ0oH3zwQR599NGUF0bVaBFXLkJ63N7zYr6p2ksgHsVrdSTnfUKJGB6rg9t7XoTH6kQ7VJ6/oT4TmmGQa1KfiZbGY/Uke2nI9cyn6IaOjoHX6sFibYsvdwG67sfQK5Hk3OMOhR1J1QJIHHvDn25IOB0xjET9XSWb0o9HaB5+NZrREQIzOlG2adOmSTGIK5dTWM1GwiFn9CSiqcl/+Wf05PFBE+joziXb7mJou+P0mWjX7ZQcEoPq5NLbdw7RBjo8RrUoneiM23r4SkOWs7FYz0w5sQDYLVkYHHvDnywZKDFHWvvxpFMwEKWkuILgKVwBIlVVMSWjIwSiE6WQdh3ducztey3BeJQqNUKO3Y3PVvcDNv38EWw6WIo/ptQZCw6qMbw2O9POH95M0WfG2A5XsTu8k3AijKtWh8fooZV9Axls2rEclizauQayL1JQZ86lRlwP09EzmAFde7Jme1GdOZcaISXG0LO6trirln0llSx7eQUb1+5GtkjomkH/C7oz/vZRtO9Y/0q600WOI7MjBKITpZAxPpuLLp7WRyUWgM7eHF648EeMaH8m4bhKJK4SjquM6HDmKbUMuSFnONowvddMzs06j5geQ9EUYnqM87LOZ3qvmWRJ5pYN6dvqVmyyG1ULJq9gDENH1YLYZDd9Wk3iJ6OH4HHaCUSUZJkUXTcIRBQ8TnuL68ezr6SS+fe8zvqvd+FyO3C5HLjcdtav2cn8e15nX0llc4dYr0gixMHYPiKJ9Ba1zPQIQUvoRCmuXASgOsE8nH8lAVWhMhYl1+E6ZYfC6nOGow2TzpxMJBEmlAjjtXqSQ2GFFB3n2anx2jpyccdH2VLxJ/ZFC5CM6n0uHdwX0KfVLXhtHfG2gidvHcfLn37N17X2uQw9uxt3XHpByvtc0m3ZyyuIhNU6fYxkWcaX5SIYiLLslX8z6spOzRhhXRXqfj4v+xs7w98kr1R7evtwBmel7ZiZHCGo6UQ5b948fvGLXzB8+PBkJ8qxY8fywQcfsHLlymQnysceeyz53D/96U/069evyTFIhtHAwO5pJBaLsWXLlpTLv7RkIkbzpDNOVQsS0/w4LNn1DpMBBKIK/rBCtsfZ4FBYc57LYCDKzJtfxHWotMyRdF0nGlG5/Rf5jBxlXivfE1Wh7mfx7keJaQpOi+vQIgqdmBZFixlMOe9+WtnbpuXYe0JVPPvNKr4qKzxcCaN9N6add2pUwqhNXLkIQjOyW3wNJpUaWa6Gk0pLEPRHkS3HKutfXV4/ElLrvT/TPi/7GzFNwVV7kYYk47J6qIxVsHz/W1zfeWpajn06jRCI5CIIQpP4sl3oWnXp/oauXHTdwO09ugJypkUSIXaGv8FpqX/i3I6DHaEtRLUwLkv9e5HMkGV3nrJJpYaY0G8mwaoIJbvLCTayIVI6ZWpSUzg11RRMDTfQcCwcitH/gu643LYMR3a0qBZCPlZjMSQkZCKJ+kv9CI0nrlwyrKz4IMue+oRNq75PLtfsN+Isxt91Oe26pKfeWEMamtS8uO31aRtzFk5NNQVTg4EoHm+tEvqhGG6PnfG3j2Jv6c7mDhOXxYt+qHlYvUVIMTDQcVuPPVQpHJ+4csmgsuKDzL/tJTas/A6X147L48DltbPhy208cNtLlBUfzFgsNZOaO0JbcMhO7If+fR/czOLdC6hQ92csFuHkV1MwNS+/B9GISjQSIxpRGTi0B/OeuKnF7HNxW7308JyP0sCmWZUYPb190jokdroQVy4ZtOypT4iGFXzZh2v1yLKML9tNsCrCsqf/wd0Lji7YmQ7HmtSMJsJpndQUTk3tO+bys7njCAWjBKqiZOW48PpaXtmgS9rdwJ7oDqKJMI5am2ZjWhQbdi5ue31zh3hKEFcuGRKsirBp1fd4fPVP4nmynGxauZ2Q3/w5mEBIYU9ZFYFDY+LHm9R0WFzJSU0zBUMKe0srCTYwNi+cGrw+Fx27tEopsShakCq1BEVL/1xHK3tbJp45m16+vqi6QkxTUHWFXr6+jNBuEEPCJhFXLhkSrAofWq5Zfz6XZRnZIhGoDOPNNqd6cmm5n1ffXsO6/x7uPjjo/C5cM7b7MSc1ZUlOTmqaMTxQWubn5b+uZP22YmxYkDWDvL5dGdD75B16CIYUAsEoWT4XPu+pveonnfzqPlYfXEZxre6RXT39yW89Pq3dI1vZ23J956lEtTCRRBC31YfL4qGgrCBtxzzdiOSSIb4cz6Hlmnq9CUbXdXTNICvXnD+4B/1RFv7+HSKKitd1uEPmf7YUsbWwiHPGJ7DZ6p/U1A3dtEnNdd/v4e4/vkOlNY7lDAlDguy4leiWXRRsNOjbpy8d2rWs3ebHUlrmZ/Gbq1m/pehwa96+XZl4w9CT6n20BH51X7197wvD6ymNbuPazvPS3p7YZfE0y/yKX43iV6Nk210tthPlG2+8wfPPP4/f72fQoEE88MADtGvXrtHHF8NiGeLLcdNveC/CwQaWawYU+o04y7Srlk/WFBFRVLI8zuTeA1mWyPI4CQUgWtqqwUnNmBY1ZVJzT5WfaX99l0prHJskYUHCYoDfFqewrUpQi7P4b6ubdIxMKi3z86tH/k7BpkJcTjsupw2X007BxkJ+9cjfKS07se6QJ8ofUygMVOKPnZzDjKsPLkPVIzgtvuSXHEmScVp8qHqENQeXNfhcTa8iltiNpldlKFpz7AlX8et173Hj5y/z01Wvc+PnLzN33fvsCVeZepymdqLcvHkzCxcu5MUXX+Srr76iS5cu3HfffSnFIJJLBo2fOQa3x0mwKoKuVxcs1HWdYFUEt9fJ+LsuN+U4gZDC9uKqhptNuR1s/6IdVslBNBFGP1Q8UTf05CSnGZOaT33+FSFVxS7JSFQnOAkJqyGjSTqVZ8D6zUUnzRzM4jdXE4mq+Lx1E7bP6yQSVTOWKIuDfuZ8+THj3v0zt/7zb4x79888V7yV4mBmk1tTKFqQ4vBGHA00TXPIHorCG1G0unuv1EQxeypm833ZVewun8T3ZVexp2I2aqI4E2E3yZ5wFdO/+gury3fhtjpwW+24rQ6+Kt/J9NV/MTXB1O5E6XQ6mTFjBu+8806dx9TuRCnLMldeeSW6rlNcXExZWRmTJ0+mZ8+e2Gw2/ud//oeNGzemFINILhnUrktr7vvDTxgwqjfRkEo0HCMaUsm7sDf3/eEnpu1zCYQVJIljlOOQ0CMersiaVu+k5sQzZ9eZ1DyRTZb+qMJ/ivdgqb9tCRZDIuTQ0S0QCLb8vh/BkML6LUV43PUnbI/bkZFEWRz085N/vcWqkkI8Njseqw2Pzc6mYCU/+fTvJ02CUbQg0rE2Mx66T9ECydvURDGFB24npHyJjBeL5EHGS0j5ksIDt7f4BPP8tn8T1mJk2Vx1OlFm2VyE4jFe2PalacdqaifKH/zgB9xxxx3J+1asWMFZZ6VW1FPMuWRYuy6tuXvBTYT8EQKVYbJyPaYNhdXI8jgxDI5RjqO6VEeX7M6c5zl6UrNGUzZZ+qMKVoslecVypOrbdeLoZLXA5apHCgSjyRpZ9ZHl6tpagWA0rRP8z2z8ivChVrnJY0sSXquVsBpj0cbV/HbkifU8zySnxYdxrM2Mh+5zWg63O9gfeBpND2GVc5K3SZKMVcohoVexP/A0nVstOOq1WgK/GuXr8t14rfV/Nnw2J2vKd7WoTpQ1Vq1axbPPPssf//jHlGIQVy7NxJvtpuOZbUxPLABZXidndckhFK2/d0QoEmPQ+V3wHepk6LJ4aO1of1Riacomy+xDhRZdThuafnThbQMD3YC8c7qcFKutsnyuZI2s+tQk7HQmSn9MYXXpcVrllhaeFHMwTouPLp7+xPT6l7vH9DBdPf1xWqonoDW9inBsFZYGeutYpCzCsVVoesu8cvOrUWRJPmYnSlmSqVLN2YpgVifKf/zjH8ycOZPHHnuM/v37pxSDSC6nqDH5XaubTYWOaDYVUvC47Ey6Jv+Yz6+9ybLmm2XNJsuYFmX5/reO+fxsl5P8bp2xe2zIknRUgokbBlmqhTtuHNGEd5k5Pq+TvD5dCUfqT9jhSIy8vl3TmiirYoeuno73B8qkVrnpNrT1eOyyG+WIpmmKFsQuu8lvPT752IReBViOOYwGFhJ6y2xKlm13oRt6w62rDQPd0Mmxm/Nls3v37hQWFiZ/PlYnygkTJjBp0iTuueeeOve9+eabzJ07l0WLFnHppZemHINILqeo1tkuHp11DRf07UpEUZP/LujbjUdnXUOHNg0vmzVrk+XUkUPIdjvJauXC7bSh6waarqOi43XamTnq/JNq+e7EHw3F7bITPCJhB0MKbpediTekt1dJjuPQ1dPx/kCZ1Co33bLt7bm28zy6efJQ9WjyXzfPwKOWIVcPhWnJJHSk6ts1rHLLKDNzpGy7iyFtziSUqP+qMhhXyG/TvcV0oly/fj2/+c1vePnll8nPP/YX0YaIOZdTWIc22fzyjssJhhX8IYVsrzM5FHYsx6sc29hNlp1zsnn06jG8sOprNu0tw5vtIqFp5HftzIyLh1G24/sTfm/NoUO7bB6+9zoW/2016zcXJfcODe7fjZszsM8l2+FkaIeurCoprDPnUiOoxhjR8cx672upsu3tubzDz1C0EIoWwGnJSg6F1WaRc/A4hhNSvsQq5Rx1v2YE8DpHYpFb7peVqb1HsbmyBL8axWdzHu5EGVfw2hxM6T3StGM1tRPlkiVLUBSFW265JXm73W5nzZo1jY5BJJfTgM/TuKRS43iVYxuzyXJPlZ/nv/yaNYV7kGUJSYLzO7Rl6ogLOKdd9WKAstTfSrPr0C6b2dPGNNsO/Rn9h7HxwL6jWuWGEglauXxM79/8nR5PhNPirTep1NY26y6i6gYSehUWKSu56VIzAlhkL22z7spQtCemsyeHRUN/zAvbvmRN+a7kIpkRbXvw094j6ezJMfV4AwcO5L333qtzm9frZf369QDceOON3HjjjfU+98iEcyJEchGOUlM5dkdoS53CljViWpRevr5HXbUEA1GC/ihBKc4vPvwHYVXF56z5A2hlc8k+Zr/7Cc/eOI7OOc3/DTOmBVC1AHZLFg5L/RPFDfF5nc2yEKGLL5uXRl/Hoo2r+aq0MPkHqp8vl3mjr6aLr/nPa7rYrV1obXuaA8FnSFjXIVusgIbXOYq2WTOwW7s0d4jH1dmTw4MDryKgKlSpEXLs7lO2aVizJpfPPvuMxx57jLKyMs455xweeOABevbsydKlS3n22WeJxWKMHz+eWbNmAdUrHubPn89HH32Ex+Nhzpw5XHnllQCUlZUxe/ZsNm3aRLdu3ViwYAFnn312c769k9qxKsceuclyX0kly15ewca1u5EtEt910Yi2sdLhjOw66/mzXU6qogrPr/yah8Ze1lxvjVC8hM0Vr1IWXYeEjIFOe9cg+rSahNfWsdniaqwuvmx+O3IM/phCVSxKjsPF91u+OaUTy77CAyx77D02/ftbZIsXm3MIeaM7c9Xt19GxQ4/mDi9lohNlGu3bt49f/vKXPPjgg3z99ddccskl3H333WzatInnnnuOpUuX8v777/Ppp5+yfPlyAF599VWKi4v54osveOKJJ5g/fz6VldWrQ+bOncuAAQNYs2YNN9xwA/fee29a4/erUYpCFfjVk2NlTqosWhajs6fSxXXeMTdZ7iupZP49r7P+61243A6sHjuBbIlEOEFJcQVxVavzullOB1/tLGLbzn1EIvGMv69QvITlJXPYFynAKrmxym6skpvSyFqWl8whFC/JeEwnKtvhpFtWbp05lmAgSsneSoKBU+dzua/wAA/c9BQbvtiKy+fE5XUiy1mseruMh/73VfYVHmjuEIV6NNuVS2lpKTfccAODBg0C4KabbuJ3v/sd77//PuPGjaNr164A3HLLLbz99ttcfPHFfPjhh9x99914PB4GDhzIiBEj+Oijjxg3bhyrVq3iiSeewG63M3HiRF544QV27NhBz549TY17T7iKlw5uYufnq5Lf5vPbdGdKGsZMm8PeSj8vLP+ar3ftwSJLaLqbIb2uYPyw3nRv1fGoobBlL68gElbxZVWvUFIsOhJgtchomk7FgRDtOlZ/o47HNSoOhggqMebNfwv8MdauOshNt4ygfYecjLy/zRWvEtcj2C2H54skScZu8aFqQbZU/Imh7dL7xSQdKg5GePzRD9i4vjBZUHNAXreMntt0WfbYe0RCCr5aRV1lWcaX6yFYGeYvv3+fu5+6tfkCFOrVbFcueXl5zJkzJ/nzihUr6NSpE8XFxXXKFnTr1o0dO3YA1Wu1u3fvnryva9eu7Ny5k6KiIlq3bo3H46lzX83zzFJTG+ib6IG01wZqDnsr/cxY8i5rdhbhcdhw22147DbWbC/j//1lNRWBRJ3HBwNRNq7djafW3INNkzCo3iQpWyQikRiaZhCPa5TsqSQcjiFJEj67HbvDwvqC3cy79w32lVal/f3FtABl0XXYGqhnZZM97IsWoGagp4iZ9pVWsfjl9Wwo2I3LZcflsuFy2TN6btMlWBlm07+/xZNd//JqT7aLjSu2Eqoyvw+S0DQtYkL/22+/5f777+fBBx9kyZIldXaROhyOZMmCSCRSpzSB0+mkvLz8qNtr7otGUxsa2LJlyzHvf+ngJiqiATwWO0qt17YCB5UAv1n5d+5o3S+lY6ZTQUFqvSleWfcdlcEQHpv1qPdXEQjxm799yO15h+exDu4Po8ZVJOWIoa8AVPnApoGhG4RCYUKBGAlNQ7dK5IQgHooeKqWiU1UZ5OnH3+H68X2a8naPS5XKibnjxGl4B7uOSsHGldiNNnVuT/VcZtLfXt9CLJbA5ZZQlMO/N9lCxs5tYxUUFKBLYQwphGR4kY1jV94+uNePGlchWv/eHgA1HmfVitW07mTOnFNjf9c1oy5C/Zo9uaxdu5bp06dzzz33cPnll/PWW2/VKVsQi8WSycbpdNa5T1EUXC4XTqcTVVXrvK6iKLjdqe127dOnDw5H/aU1/GqUnZ+vorU3GyUaPeq1nYaLHYkQZ/U9v0VM1BUUFKT04fdHFXZ+sYnWWb56d4A7nQY7AwpnnXc+WYdKuwQDUV5/bj1Op6NOza2zq3Q2+GIkrAZSQsLtcnHwYATDJmMzJHqF3bjcMpFIBLfbjdNpsKc4TO+zz8fbQKdOM8S0AGXFL2OVnA3Ws0oYOoPOGlFn2CzVc5lJwUCUvXvW4HRa6/28Z+rcNsbX6z/G03kllcrXSJIFw9Bo5cqne/YUXLZO9T4n2CPMMtsnuFzOBvsgkZAYfuFQvDlN393ekn/XJ5tm3aG/YsUKpk6dyrx58xg/vrrUQ/fu3SkqKko+Zvfu3clhsiPvqxkm69atGwcOHKhzpVJUVFRneK2pMl0bKNP8EQWLLDX8/mQJiyxRFTn8rd+X5aL/4DMJH1EJ2BWXGVDswFcFNreVSDyOLku0Vqz03+/GlZCPem1ZlgikeRLaYcminWsg8QbqWcX1MO1dg+oklpYuGFSQZRnpeAU1m3mCPxrfS8j7BJXRNVgkDxbJjUXyUBFZzYayGUTje+t9ni/XQ79R5xD21x9/2B+l/4XnppRYglURSnaXExRDaWnVbMmluLiYn/3sZyxYsCC5nBjgiiuu4K233mLXrl3s37+fxYsXJ8sWXHHFFbz44ouEQiHWr1/PypUrGT16ND6fj/z8fBYtWoSqqixevJicnBx69eplWryZrg2UadluJ5puNPz+dANNN8hx1/32O/6OC3F77AQD0To9ahIHY/Td72DpzTfy9LVj6bvDRu9y51GJpea1dd0gKyv9ZUv6troVm+xGPaKelaoFsclu+rSalPYYzOTzOdF1HeN4BTUzcG6PZWfV8+iSgtWSVacxmNWShaaH2OV/ocHnjv/51bh9ToKV4bp9kCrDuH1OfjzrqkbFUFZ8kCd/sZSfjX2Mebc8z8/GPsaTs1+nrPhg099gigLxKMXhgwTi6Uv6a9euZezYsQwYMIBp06YRCh3dMmPdunVcf/31DBw4kOuuu45169YBYBgGjz/+OMOGDeOCCy5gzpw5KU8zNFtyefPNN4lEIsyaNYu8vLzkv86dOzNt2jQmT57M1VdfzeWXX55MLrfeeivdu3dn9OjRzJo1i4ceeoj27avrDz388MNs3bqVoUOH8vbbb7Nw4UKkBr6Fn4hM1wbKtGyXkyHdOxNS6i/MGIzFyO/ROTkkVqN9x1zmPXETefk9iEZUopEY0YjKwKE9mPfETZzVvT3ndW3PoP5nEgnX/9qRcIwBed0yMmzjtXXk4o6P0sE9mIQRIaFHSBgROrgv4OKOj54U+1xq82W56J/XDUVJ1Ht/Js9tQ+Kav3oozKg/wVlkHxXRNcS1ALpehZbYjV6rw2T7bmdw39KZ5F18HtGgQjSkEA0q5F1yHvctnUn7bmccN4ay4oPMv+0lNqz8DpfXjsvjwOW1s+HLbTxw20sZSzAlkUoe3Px3Jq58jplrFzNx5XM8uPltSiLmFtxsaifK9957jxUrVvDBBx/wxRdfcODAgZRL7kuG0cBX1dNILBZjy5Ytx5xzgUOrxVb/hYOhAGd4s4+qDbRo6I9bzHLkExk7rlktFo6p+ByOZO2sYCyGx2HnmQnj6JTb8KRpKBglUBUlK8eF94jS8/tKq5h37xtEIypuT/Vrh0JhMCy43HbmP3JjxpfMqlqQmObHYck+5lBYSx+H31daxey7/wxYkudW1w0i4VizndvaIvEi1pdNJRalwXlQSa/ibM9Z6PFNyfkYu30Ebt9dWGrtvA9VRQhUhMhq5U1pKOzJXyxlw8rv8NXT4iJYFWHAqN7cveCmtP6uSyKV/F/BEsKJGF5rTW0xnVAihsfq4PFBE+joNqfw5ueff86zzz7LG2+8AcDOnTu5+eabWbVqVfIxRUVFPPPMM3UahuXn5/Paa6/Rq1ev5Jx2eXk5d999N1dffTU33XRTo2MQVZFTUFMbqK/rDCKJGJGESiQRY0TbHi0qsZyoTrnZPDNhHMN6diWsxomoccJqnGE9ux03sQB4fS46dml1VGIBaN8hh/mP3EjeoDOJRlWiURU1pjFwcPdm++Nnt/jw2TtnZI7FrygUVlXhb6BZU1O075DDxDvy6pzbaFRt1nNbm03OxjA0DOqvaGwxonSUd6Gp65EkL5LkQZK8qOqX+CtuQ6vVYdKb46Zjj7Ypz7FsWvU9ngau3jxZTjat3E7In945mFd2LCecOLITpUyWzUU4EeOVHV+YdqymdqKUJAmXy8Xzzz/PqFGjCIVCXH311SnF0OyrxU42nT053NG6H2f1Pf+UrA3UKTeb+ddeRiCqUBVRyHE7jxoKO1HtO+Rwz5yxhIIKgUCUnTu3MXLUyVlosbH2+P08u2YNq4v3YJEkNMNgWJcu3Jk/hM7Z5pVradXazT1zRiXPbVaWq9lXh9WwWbLJdQ6hNLYCOLo4ZQ67sUpWLJbD39qr2xxno+tVRIJP48s98Q6TwaowskWqd7UZVG/IlC0Sgcpjt5BoikA8ytqDuxrsROm1Olh7cCfBeBSfrenzY2Z1opw8eTITJ07k3nvv5b777uPxxx9vdAziyuUEZdmddPW2OqUSCxwuHyLFDbq2zjEtsdTm9Tnp2CkXl9t23MeGE2H2K/sJJ9L3P3667PH7mfLOO6wqKsJjt+O22/HY7awsKmTqO++yx29+18Sac9tSEkuNHjlTkQ0nCc1fZyGFrlXglqJY5fZohopu1N0zJUlZqOpK9CZ0mPTleNA1I7kY4Ei6rqNrBlm5x95z0xR+NXKc1aZyi+xE6XA48Hg83HXXXXz++ecpxSCuXASgetx+6Z9XtpjyIQdi5XxQ+j7bgt8m57Z6+85hbIerOMPR5vgv0Iz8ikKVovD0V18RVlWynYf/h5YliWynC78S5bk1X/Oby5uvgGcmuWyd8IbuwZu7ioroGiTDgoFGjv0sDL2UUKI0+Vir7MZlPQOLZDtUVt+CoVfCCfZq8eW46Te8V4NzLuGAwoBRvdPScrxGtt2dXG1aX4Kpvs/cTpT/+Mc/kj8fqxPl7bffzpQpU+o0DHvxxReJx+NMnz4dgHg8js+X2vCxuHIRkpPtLaV8yIFYOc98/xRbA//FITtwyE4csoOtgf+y6PunOBArz2g8jbXH7+dX//gH1y1ZyuS/vcV7324jGFNRNe2ox/ocTr4qLiaQhjmYlsqit+G8Mx4gv+Mb5LV/nj5tn6Y45kfXY4B0aImyTFyPEFT3oBnxZIdJqYkdJsfPHIPb4yRYFam7nLkqgtvrZPxdlzf5/R1Lls3F4NbdG1xtGkrEGNy6hylDYtD0TpR9+/ZlyZIl7N69m1AoxJNPPsm4ceNSikEkF4Glf15JNKLi9TmTO+1lWcLrcxKNqLz+55UZjeeD0vdRNAWP1YN8aE+ELMl4rB6imsIHpe9nNJ7GOHIIzGG1IAFhVaWoquqoBCNLEhZJovI0Si41bJYs3LaubK16E0WPE6Q1Vqn6/EhS9e/aQCeaOIBhBLDbRyA3scNkuy6tue8PP2HAqN5EQyrRcIxoSCXvwt7c94ef0K5LazPe2jHd3vNiPFYHgXgU/dDQoG7oBOJRPFYHt/e8yLRj1XSiXLhwIcOHDycWiyU7Uebl5VFSUsInn3yS7ERZezvIpk2bGDZsGNOnT2fSpElcfvnldOnShZkzZ6YUgxgWO80FA1E2ri/E7al/Cbbb42DD+kJCQSUj4/jhRJhtwW9xWer/BueyuNgW/JZIIoy7nkZmzeXZNWvqDIFZJBlJqq54oOk65eEwnbIONyTTDQPNMMh1tqy5kUypXUS0RO+Bx7IBCwk0LICEhIShB8DaGrfPnA6T7bq05u4FNxHyRwhUhsnK9aR1KOxIHd25PD5oAq/s+IK1B3cerqp+Rk9u63mRacuQazSlEyXAhAkTmDBhwgkfXySXU0wwECUYVBrsleJXFKpiCjkOJ9lOZ7J8iNyI8iGZSi41pXTqjUeSkZAItaDk4lcUVhfvwVerr4pFlvHYbYRUFVmSCKsqmq5jObRiKRhTGNG1G1mnaXJRtQASMpIko+JiuzaAjvIusqQKDCQkyaBKzyHb90idfS5m8Ga7M5pUauvozmVu32sJxqPJ1aZmDYW1NCK5nCKOnJCPRqJ1eqUUB/wsKljD6r3FyQnyYZ26MKl3/+rVMrpRb4LJdPkQj9WTLKVTX4LRDR0DA28LSSwAVYqCRTq6Lltbj5dovArN0JGQSOg6kiQRjCl47Q7uzB/STBE3P7slCwMdw9CTCWa3fh4W4liJEzcsxIw4ve3nNneoaeGzuU7ZpFJDJJdTQH273zVNZX3Bbr7dWsKUX1/B/1v7OeG4is/uSCaXlXuK2FC2j/yBHdi5tqTeK5NIOEbeoDMztrTVY/XQ23cOWwP/xVNPAolqUc7NOq/FXLUA5DidaIZx1Eogm8VCt5wcykIhQqqKmkigahojunXjziHm7nM52dQUEd0XKaiziVXDhoYNVQ/SwT34pCoiKtQlksspoPaEfA1ZlnC7nYSCCnPf/4Rwa+q0w5UliWyHE39MofJ8G67/2gkFlXrLh9x0y4iMvp+xHa5id3gn4UQYl8WVHJuOalFcFidjOzSuUGGmZDudDO3SmVVFRXWWHUN1gvE57Fzaowczhg0l1+k8bYfCjtS31a0cVLYeKhrqObTsWCeuh0/KIqJCXWK1WDMJxiPsjRwgGG/apqnjTcjbsuzsSARwW+vfsOizO9hYWcbP549rMeVDznC0YXqvmZybdR4xPYaiKcT0GOdlnc/0XjNb5D6Xafn5eO0O/Eo0WVlaNwz8ShSv3cHdw4fRLSdHJJZaTrUiokJd4solw0qjFby2+1M2VO5IDk/l5fZkwpmj6eBqlfLrHTkh73RGcbmiVP99c5OwGcgGoBtgOfr58qG5AnuOo05pluYuH3KGow2TzpxMJBEmlAjjtXpa1FDYkTpnZ/P8NeN4bs3XfFVcnCz1IobAjs1r68jQdvc2uoiocPIQySWDSqMV/HrTH4loMTwWV7IqakHldrYGinmo360pJ5iafh5ZWRVcdMm/6d59F7ouAxqFu3vw6b9HoktWaGA1mH5oriDn0Ddqr8/ZokqHuFt4Uqmtc3Y2v7n8MgKKQqWiiCGwFNgtPpFUTjFiWCyDXtv9KREths/qrlMV1Wd1E9EUluz+NOXX9GW5GD7Sw/j/XUr37jtRFAeqaicWs9O9x05u/vHrDPYl8McUtHpqKwXVGMM6dakzHyM0TZbTKYbAhNOeSC4ZEoxH2FC5A08DmwM9FifrK3cQPIHOdNdeX4DTqRIJO4DqpGUYEuGIA4tb5cpz/k1FMMr28gPsrfSjalr1fEBMwWOzM21QflPemiAIKTJrzvVYmtKJsra33nqLK664IuXji+SSIYF4JDm/UZ+aiqmBeGrVf3W9CqulgFatO+D2OJItb3XDQHVARHfSr0MhAxxOsiUnQVVl98FK/IrCiM7dePHKa+iSJeYDBCETSqMV/G7rG0z9z1P8cuMrTP3PUzy29Q1KoxWmHqepnShrlJeX8+ijj55QDCK5ZEiWzZ2c36hPTcXULFtq8wuGXoUkWbDZbLRrn03XbmfQsXMu1iwrskXGIlswDJlWdpXucjZ95DPooLoZ5evCI5dcJhKLIGRIzZzrusrtuCyOQ//sFFRu59ebXjU1waxevZpOnTpx0UUX4XQ6mTFjBu+8806dx+zfv5+RI0cyZswYZFnmyiuvRNd1iosPN2e7//77U24SVkMklwzx2dwMyO1JWKt/2CusKeTl9kx5164k51R3+TtUCM9ikbFYZZSEjkWWkNCRJZ1wvLrchVWSaW13s353KYHo6Vc0URCaSzrmXBvS1E6UAB988AEWi4XLLjuxthAiuWTQzWeOxm1xEkxE6lRFDSYiuC1OJpw5OuXXlOUc7PbhGEYweZumH746ctljbCvvSTReq6eILGGRJaoiIrkIQiakc861Pk3tRFlRUcFTTz3F3LlzTzgGkVwyqIOrFQ/1u5VBuWcR1VSiWoyopjI49+wTWoasaEGq1BIsntswcKMmKkjoiUNXLAZuWxQl4eCT7ZfWeZ6uG2i6QY5brGYShExI15xrQ5raifKhhx7ijjvuoE2bE9+wLPa5pEkoESEYD+OzefBaD1dg7eBqxc/PvZFgPEogHibL5kl5KMyv7mP1wWUUhzeiGVClhgkr7ejvtHC2ey92ix2fK8Gmku58vusyKiJ1k1YwFmNYz65paWGcCX41il+Nkm13kW0/eYv/+dXo4QrVJ/H7EI6v9pxrw50oU59zbUhTO1H+61//YsWKFTz66KNomoaiKAwePJi1a9c2OgaRXEy2XznIm3s+Zot/e7ImVp/ss/lR5zG0dR5uSHSiVVH96j7e3jMfVY8g4aQkUoVuGNjtUbZo2bxXfDGqpuIP26C4PfGIgc9hJOuFBWMxPA47P73o5KvIuydcxfPb/s3X5bsP98Jo050pvUfS2ZPT3OE12p5QFYu+WcnqsqLkTv5h7box7fzhdPbmNHd4QhrUzLmuq9yOz1pPq2VNYVDuWaZ2ovzVr37Fp59+yogRI47bifLIvi2bNm1K/veaNWuYN28eH3/8cUoxiGExE+1XDvLI1hfYVPUdTtmR/LepahuPbH2B/crBJh9j9cFlKIkwFt1NuRJGNwwskgVNtyNLcc5wF6IYHfFrNnoPyGZYz66E1TgRNU5YjTOsZzeemTCOTrmZWSVm1nr+PeEqpn/1F1aX78JtdeC22nFbHXxVvpPpq//CnnCVOQGn2Z5QFVNWvMmqfYV4bHbcNjsem52V+3YzZcWb7AlVNXeIQpqkY861IU3tRGkGceViojf3fExUi9UZBpMlGa/VTSgR5s09nzCt1/+e8OvvLt3LppKviAQBSSXqiVf3EbFISJKEptvJduzHIqm4ZRtbAiW8eeVY0KAqopDjdmZsKMzsGmrPb/s3YS1GVq1vdrIkkWVz4VejvLDtSx4c2LKqJddn0TcrCcXVBitUP/vNKh7Ov7IZIxTSpWbOdcnuT1lf6/+Lwbln879nXnpC/18cS1M7UdbIz89P+aoFRHIxTSgRYYt/O25L/X+83RYXW/zfEU5E8NRzWXw8pWV+Hn7uLbqMSSDLdgzZQKJ6cl41NOxWC5IkYSBhldXqRluSTJUaoau3VUbnV8yuoeZXo3xdvhuvtf734LM5WVO+i4CqkGVvufNIfjXK6rIifPb6K1j77A6+Kits8e9DOHFmzLmeLE65YbHGlDxIh2A8fGjFx7Hb857oapDFb64mVCVhsQAYULPaWAIMg4SmAwYSBgndnuzmmGPPfDtXs9fz+9VocjVNfWraIlep6SulYYaqWP0dK2vIkoRFkqiMmbMcVWi5fDYXndxnnLKJBU6x5NKYkgfp4rN5Dq34OLo4JBxuz3siq0GCIYX1W4pwWnyES9thsceRDAmLJlfnGElC13Ussoo/1hbNsBPR4+S36Z7xb8DpWM+fbXclV9PUpzkTaSpyHIc7VtZHNww0wyDXcer+wRFOH6dUcmlMyYN08Vrd9Mk+i4hW/yaliBalT/bZJzQkFghGkz1bKjb0QU9YsdhV7HG5+sIFHbs1gaZbKQ71xq9GcclWpvQe2cR3lbp0rOfPtrsY0uZMQon6z20wrjRLIk1Vtt3F0HZdCaqxeu8PqjGGtevW4t+HIDTGKZVcGlPyIJ1+1PkKXBYHoUS4zmqQUCKMy+LkR53HnNDrZvlc6LqOrhvEQ172/vMiwiXtsVoTeBISTsmgNJjDuv0XUBmzMqJtD2a1Hdwsy3PTVUNtau9ReKwO/OoRnR7VKF6bo1kS6YmYfv4IvDY7/phS933EFLw2O9POH97MEQqCOSTDaOCvwElo0aJFlJWV8cADDyRv69OnD//6179o3759g8+LxWJs2bLFlBiq9CArtY3s1kuRDQldMjhT7sgISz9y5BNvhrTs/a1s31WJ23W4XbHFrmJxqQQrZbp2PoPLLjsTr8WOR66/pXGm/DWyhu3xMtzy0RPXET3G2bb23OhOfZ9NeSLCO/7v+UY5iIyEjsH5zjO4JrsnbU7girC57I9HeevgTr6JVCADOtDH3YrrWveg7Sk8Bn+qGTRoUHOH0KKdUqvFUil5UJ8+ffrgcNS/kqe2goKCY36wRnMx4UQkuRrkRIbCjtSxcy9+9cjfiURVPG7HoU2RLsKVMbJcdmb/9Bo6tDu8d+V4MaZTx2h3fr3pVSKagsfiTG54DGsKuZZs7u73Yzq4Wp1QjFcwioCqUKVGyLG7MzKElI5z+UNGElAVKmNRch2uJr+P5vx9N5aI8fRySg2Lde/encLCwuTPDZU8yASP1U0HVxtTEgtAh3bZPHzvdQzq342ooib/De7fjYfvva5OYmluZtdQO1KW3Vm9vPokn5vIsjvp5ss96d+HINTnlLpyaUzJg5NZh3bZzJ42hmBIIRCMkuVz4fO2zD9Mp9N6fkFIVUO1B820du1a5s2bx969exk+fDgLFizA6/XWecy6det46KGH2L17N926dWPu3LkMHDgQgJkzZ7J8+XIs1fsfGDVqFE899VSjj39KJZeakgfz5s3jF7/4BcOHD2fWrFnNHZbpfF5ni00qRzrRGmqCcCpqbO3BpqrZlvHII4+Qn5/Pr371KxYtWsScOXOSj6npRDlv3jwuu+wyPv74Y6ZPn87nn3+O0+lk27ZtvPXWW/Tq1euEYjilhsXgcMmDdevW8cwzzxyVqQVBEJpDJmoP1mhqJ8poNMq+ffvqrL5N1SmXXARBEFqi2rUHayp51NQejGoKb+75xLRjNbUT5fbt23E6ndx6660MGzaM6dOnU15enlIMIrkIgiCkWSq1B83Q1E6UkUiEAQMG8OCDD/LZZ5/RunXrOkNqjXFKzbkIgiC0RKnUHjRjhWmqnShvv/12rrnmmmQnyqFDhzJ06NDkY/7v//6PYcOGoSgKTmfj5nvFlYsgCEKapbP2YH0auy3jm2++YcKECUyaNIl77rknefsXX3zBhx9+mPxZVVUsFgtWa+OvR0RyEQRBSLN01h6sz9ChQyksLOTTTz9FUZTjdqKs3eIYQNd1Hn74YQoLC4lGozz22GP88Ic/TCm5iGExoKYCjqqqjX5O7UvOlkrEaJ6TIU4RozlSidFutyM1UKT1SD/qfAXbg4WEEmHcFldyKXJEizap9mB9GtqWUVJSwtixY/nggw9YuXJlshPlY489lnzun/70Jy655BImTZrExIkTCQaDXHjhhTz00EMpxXBK1RY7UcFgkO+++665wxAE4STT2JJRNar3uXzCFv93SEgYGPTNPpsbTN7n0hKI5EL1JWA4HMZmszX6W4ggCEIqVy61mV17sCUSyUUQBEEwnZjQFwRBEEwnkosgCIJgOpFcBEEQBNOJ5CIIgiCYTiQXQRAEwXQiuQiCIAimE8klBWvXrmXs2LEMGDCAadOmEQqFMnbszz77jCuvvJJBgwYxYcIEduzYAcD111/PgAEDyMvLIy8vj9/+9rdAdaG6uXPnMnjwYC666KI6dYLKysqYNGkSeXl5XHvttaZtIH300Ufp27dvMpYf/ehHACxdupSRI0dywQUX8Pvf/z75+OaI8d13303Gl5eXx4ABA+jduzfr1q1rEefylVde4b777kv+bPa5M+szfGSczz33HKNGjSI/P59Zs2YlXzccDnPuuefWOef/+te/MhLnkTGa/fttSozhRJj9yn7CiXDK76uxGhNfzed+4MCBXHfddaxbty5533//+19+/OMfM3jwYP73f/+X4uLi1AIwhEaJRCLGsGHDjOXLlxvRaNS45557jN/+9rcZOXZpaalxwQUXGGvXrjUSiYTx0ksvGWPHjjUSiYTRv39/IxQKHfWcl156yZg0aZIRCoWMgoICY8iQIUZFRYVhGIbxk5/8xHj88ceNWCxm/PnPfzauv/56U+KcPHmysXz58jq3bdy40Rg5cqRRWFho7Nu3z/jhD39ofP75580W45GeeOIJ484772z2c6mqqvHkk08a55xzjjF37lzDMMw/d2Z8huuL8+OPPzZ++MMfGqWlpUY4HDZmzJhhzJ8/3zAMwygoKDBuuOGGel8rXXHWF6PZv98TjbFc2W+8uusPxr2bZhv/b/Mc495Ns41Xd/3BKFf2N/r9NUZj4otGo8awYcOMjz/+2NA0zfjggw+MoUOHGtFo1PD7/UZ+fr7x0UcfGZqmGU8++aRx2223pRSDSC6N9Nlnnxk/+tGPkj/v2LHDGDZsWEaOvW7dujofjFAoZJx99tnGd999Z4wePbre51x33XV1/tDfc889xpIlS4xgMGicf/75df4nGzFihPH99983Oc7hw4cbJSUldW777W9/ayxYsCD58+uvv27cfffdzRZjbdu3bzeGDBli7N+/3/j++++b9VzOnz/f+MlPfmLMmzcv+QfR7HNnxme4vjhff/114+9//3vyMZ999lnyj/DSpUuNe++996jXSWec9cVo9u/3RGIsV/Yb87b82piz8efG/VvmGg98M8+4f8vcQz//2tQE05j4CgsLjV/84hd1bhsyZIjx3XffGX//+9+NKVOmJG+PRqPGt99+m1IMYliskRrT2S1d8vLy6jTqWbFiBZ06dWL79u0YhsH111/PiBEjuPfee5OXvoWFhXTv3r1OvDt37qSoqIjWrVvj8Xjq3FczzHaiKioqqKys5P7772fo0KFMmjSJXbt2sXv37jrnrVu3bsljZTrGIz3xxBNMnjyZNm3asG3btmY9l3feeScvvvgirVsfri9l9rkz4zNcX5zjx4/n2muvTf68YsUKzj77bAC2bdvGjh07+MEPfsAll1zCCy+8AJDWOOuL0ezf74nE+EHp+yiagsfqqdOJ0mP1ENUUPih9v1HvrzGa2ony22+/pW3btkybNo38/HxmzpxJTk5OSjGI5NJIqXZ2S5dvv/2W+++/n1/+8pfouk6/fv147rnn+PDDD6msrOTRRx8FIBKJ1Gnq43Q6iUajR91e+76mqKioYMiQIcyYMYMVK1YwZMiQ5Dhv7fPmcDiS5yzTMdZWVFTEmjVrmDBhAkCzn8s2bdocdVskEjH13JnxGa4vztrefvttPvzwQ6ZNmwaA2+1m6NChvP3227zyyiu88cYbvPfee2mNs74Yzf79phpjOBFmW/BbXJajm3UBuCwutgW/JWLSHExTO1EGg0HeffddJk6cyL///W+6du3Kvffem1IMouR+I6XS2S1d1q5dy/Tp07nnnnu4/PLLAbjqqquS9991111MmTIFqP6foHa8iqLgcrlwOp1HtRZQFAW3u2nF83r16sWrr76a/HnatGn84Q9/oFOnTnXiiMViyXOW6Rhre//997nsssvw+XxA9XlsKeeyxpGfuaaeu3R/hhcvXswzzzzDSy+9RJcuXQCYPXt28n6v18uECRNYvnw5kydPzmicZv9+U40xnAgjS9JxO1GGEmHc1qY3DGtqJ0qbzcawYcMYNmwYADNmzGDYsGHEYrFGV4EWVy6N1NjObumyYsUKpk6dyrx58xg/fjxQvfJp1apVyceoqordbk/GW1RUVCfe7t27061bNw4cOFDn23VRUVGdS+gTsWHDBpYtW5b8Wdd1NE2jTZs2deKoPdST6RhrW7FiBZdddlny55Z0Lmscedymnrt0foaffvppXnrpJRYvXky/fv3q3L53797kzzXnNdNxmv37TTVGj9WDbhjH7UTpNSGx1LynpnSiPPPMM+usLtP1+uM+FpFcGqkxnd3Spbi4mJ/97GcsWLCAK6+8Mnl7IBDgkUceoby8HL/fz1NPPcW4ceMAuOKKK3jxxRcJhUKsX7+elStXMnr0aHw+H/n5+SxatAhVVVm8eDE5OTn06tWrSTE6nU4WLFjApk2biMfjLFy4kHPPPZfx48fz1ltvsWvXLvbv38/ixYuT5y3TMdbQdZ2tW7fW+SPYks5ljSuuuMLUc5euz/CHH37IkiVLWLp0aXKupcb27dtZuHAhsViMHTt2sHTpUq6++uqMx2n27zfVGD1WD7195xDV6h8yjWpRevvOMeWqBZreifKyyy5j8+bN/POf/yQej7No0SIuvPDClHrXiNViKSgoKDCuuuoqIy8vz5g+fboRDAYzctzHH3/c6N27tzFgwIA6/w4cOGA88sgjxrBhw4zBgwcbc+fONWKxmGEYhhGLxYx58+YZQ4YMMS655BLjk08+Sb5eWVmZcdtttxl5eXnG9ddfb2zfvt2UOP/+978bl156qTFgwADjtttuS64cW7p0qXHRRRcZQ4YMMZ544onk45sjRsMwjAMHDhi9e/c2dF1P3qZpWos4l0899VRyhZNhmH/uzPoM145z/PjxxnnnnVfns3ndddcZhmEY5eXlxp133mkMGjTIGDlypPHaa69lLM7aMabj95tqjOXKfuP+DK0Wayi+vXv3GgMGDDD27t1r/PWvfzXOPvvso/6ubNy40TAMw/jqq6+Ma665xsjLyzMmT55slJWVpXR80c9FEAQhQw7Eyvmg9H22Bb9NdqI8x3cuV3YYyxmOYy+WONmI5CIIgpBhkUSYUCKM1+oxbSispRHJRRAEQTCdmNAXBEEQTCeSiyAIgmA6kVwEQRAE04nkIgiCIJhOJBdBOMKll15Kv379mD59ekrPGzNmDH369EnW1RKE05lILoJQj8cff5xFixal9JxPPvkkWa9KEE53IrkIp4W33nqLQYMGUVZWBsCSJUu4+OKL8fv9x3zemjVrGDduHE888QRDhgxh5MiRfPTRRzz55JPk5+czcuRIPvjgg0y8BUFISVM6UT7//PN1uof269eP3r17J///aQyRXITTwvXXX8/QoUOZP38+u3bt4ve//z0LFixoVDHEbdu2AbB69WpuvfVWZs2aha7rfPnll0yZMoUHHngg3eELp5hIIsTB2D4iifS0So9Go8ycOZPZs2ezevVqnE7nUVfiiqIwY8YMpkyZwtq1a/nJT37C9OnTURSFqVOnsn79+uS/MWPGMHHiRNq1a9foGERyEU4bDzzwAOvXr2fy5MlMnDiRIUOGNOp5FouF6dOnI8syQ4cORdM0br/9dmw2GxdffDFVVVUn3IteOL1UqPv5W/FzLPr+l7y6+2EWff9L3trzPBXqflOPs3r1ajp16sRFF12E0+lkxowZvPPOO3Ues3//fkaOHMmYMWOQZZkrr7wSXdcpLi6u87h///vfFBQU8POf/zylGERyEU4brVu35vLLL6e0tDRZEbcxXC5Xsjy7xWIBICsrCwBJkoATK0kunF4q1P0s3v0oO0JbcMhO7If+fR/czOLdC0xNME3tRFnb73//e+65556jGqcdj0guwmlj8+bNvPvuu4wdO5Zf//rXjU4INQlEEJri87K/EdMUXFYPUq02xy6rh5gWZfn+t0w7VlM7UdZYs2YNwWCwTquPxhLJRTgtKIrC7NmzmTp1Kg8//DAHDx7k5Zdfbu6whNNEJBFiZ/gbnA20OXZYXOwIbSGqmdPmONVOlDfddBPjxo1LdqKs8e6773Ldddclr9hTIZKLcFr4/e9/j9Pp5Pbbb8fpdPLggw/y9NNP8+233zZ3aMJpIKqFqlsZH7PNsUwkETTleE3tRFnjyI6tqbCe0LME4STz//7f/6vzc35+Pps3bz7u8/Lz81m7dm3y53PPPTe5egygc+fOdX4WhPq4LN7qVsaGXm+CqW5zrOO2+kw53tChQ/nVr37Fp59+yogRI47biXLChAlHvcb+/fsJBoOcddZZJxSDuHIRBEFIM7fVSw/P+SgNtDmOaVF6evvgspjT28XlcrFo0SIWLlzI8OHDicVizJo1i5KSEvLy8igpKeGTTz6hvLycxx57rM6elk2bNgGwb98+WrVqhSyfWJoQ/VwE4QiXXnopBw4cYNSoUSnt0h8zZgx79+7lwgsv5Nlnn01jhMLJqHq12AJiWhSHxYUsyeiGnvx54pmzaWVv29xhmkYkF0EQhAypUPezfP9b7AhtQULGQKentw8Xt73+lEosIJKLIAhCxkW1MJFEELfVZ9pQWEsjkosgCIJgOjGhLwiCIJhOJBdBEATBdCK5CIIgCKYTyUUQBEEwnUgugiAIgulEchEEQTgFNaUTJcCHH37IpZdeygUXXMDUqVM5ePBgSscXyUUQBCHDFC1IlVqCoplTqPJITe1EeeDAAebPn89LL73EypUrycrKYuHChSnFIJKLIAhChvjVfXxSupDXds3kb8VzeW3XTP5RuhC/us/U4zS1E2VpaSmqqiZ7HsmyjMPhSCkGURVZEAQhA/zqPt7eMx9Vj+CQqxuGGYZOYXg9pdFtXNt5Htn29qYc61idKHNycpK3NdSJ0maz0b9/f6666iosFgtdunThr3/9a0oxiCsXQRCEDFh9cBmqHsFp8SXL7kuSjNPiQ9UjrDm4zLRjNbUTZSwWo0ePHrz99tusXbuWvn37Mn/+/JRiEMlFEAQhzRQtSHF4Iw65/jpiDtlDUXgjinb0pPuJaGonyiVLlmC32zn33HNxu93MmTOHjz76qM5rHo9ILoIgCGmmaEGkY3SirLlP0QKmHK+pnSj37dtHPB5P/my1WpFlGUmSGh2DSC6CIAhp5rT4MA51oqxPzX1OS5Ypxxs6dCiFhYV8+umnKIpy3E6UkydPrnPf8OHDeffdd9m6dSuqqvLEE09w8cUXY7fbGx2DSC6CIAhp5rT46OLpT0wP13t/TA/T1dMfp8VryvGa2oly9OjRTJ8+nenTpzNq1Ciqqqp48MEHU4pBlNwXBEHIgIZWi8X0MHbZbepqsZZAJBdBEIQM8av7WHNwGUXhjcnk0tUzgPzWPz6lEguI5CIIgpBxihZC0QI4LVmmDYW1NCK5CIIgCKYTE/qCIAiC6URyEQRBEEwnkosgCIJgOpFcBEEQBNOJ5CIIgiCYTiQXQRCEU1BTO1G+9tprXHTRReTn5/O73/0OTdNSOr5ILoIgCBkW0wIE1T3ETCpUeaSmdqJcs2YNTz75JE8//TTLly9n9+7d/OEPf0gpBpFcBEEQMiQUL+Grsof5qPg2Piv5Pz4qvo3VZY8QipeYepymdqJcvnw511xzDf369cPlcjF16lTee++9lGIQyUUQBCEDQvESlpfMYV+kAKvkxiq7sUpuSiNrWV4yx9QEc6xOlLVva6gTpa7rddoay7JMcXFxSjGI5CIIgpABmyteJa5HsB/RidJu8RHXI2yp+JNpx2pqJ8pRo0bx9ttvs23bNsLhMC+99FKd/i6NIZKLIAhCmsW0AGXRddga6ERpkz3sixagakFTjtfUTpQjR47kjjvuYOrUqVxzzTVceOGFeL2p1UATyUUQBCHNVC2AxHE6USIT0/ymHK+pnSgrKir4wQ9+wOeff86//vUv2rVrx9lnn51SDCK5CIIgpJndkoXBcTpRouOwZNd7f6qa2oly69at3HHHHVRVVXHgwAGeeuoprr/++pRiEMlFEAQhzRyWLNq5BhJvoBNlXA/T3jUIu8VnyvGa2olyxIgRXHHFFYwZM4ZrrrmGiy66iGuvvTalGETJfUEQhAyoWS0W1yPYanWijOthbLKbizs+itfWsbnDNI1ILoIgCBkSipewpeJP7IsWICFjoNPeNZg+rW45pRILiOQiCIKQcaoWJKb5cViyTRsKa2lEchEEQRBMJyb0BUEQBNOJ5CIIgiCYTiQXQRAEwXQiuQiCIAimE8lFEARBMJ1ILoIgCKegxnSi/OCDD/jBD35AXl4eEydOZPfu3aYdXyQXQRCEDItrfiLxIuImFao8UmM6UZaUlDBv3jyefvppCgoKGDx4MPfff79pMVhNeyVBEAThmKLxveysep5K5WskyYJhaLRy5dM9ewouWyfTjlO7EyXAjBkzuPnmm5kzZ07yMR07duSLL77A4/EQjUYJhULk5uaaFoNILoIgCBkQje9lQ9l0ND2CRfZW1xZDpyKyGr+ymQHtnjEtwRyrE2VOTk7ydo/HQ0FBATfffDMej4fXXnvNlOODGBYTBEHIiJ1Vz6PpEayWrDqdKK2WLDQ9xC7/C6YdK5VOlH379mXjxo3ceeed3HnnnaiqakoMIrkIgiCkWVzzU6l8jUWuv5ujRfZREV1DXAuYcrxUOlHa7Xbsdju33347iqLw3XffmRKDSC6CIAhpFtf9SJLlOJ0oLcT1KlOO15hOlKtXr2bKlCnJn3VdR9M0fD5zCmmK5CIIgpBmNjkbw9CO04lSwybnmHK8xnSi7N27Nxs2bOBf//oX8XicZ599lh49etC1a1dTYhDJRRAEIc1slmxynUPQ9KP3mgBoepBWrnxslixTjteYTpS5ubk8/fTTPPnkkwwfPpzNmzfz5JNPIkmSKTGIkvuCIAgZUL1abAaaHsIi+5KdKDU9iEX2mrparCUQyUUQBCFDovG97PK/QEV0DRIWDDRauYbSPfunp1RiAZFcBEEQMi6uBYjrVdjkHNOGwloakVwEQRAE04kJfUEQBMF0IrkIgiAIphPJRRAEQTCdSC6CIAiC6URyEQRBEEwnkosgCMIpyKxOlA888AAvvvhiyscXyUUQBCHDNL2KWGI3mkmFKo9kRifKcDjMvHnzWLJkyQnFIJKLIAhChqiJYvZUzOb7sqvYXT6J78uuYk/FbNREsanHqd2J0ul0MmPGDN555506j6npRHnuuecSi8WO6kQ5a9YsFEVhzJgxJxSD6EQpCIKQAWqimMIDt1fXFpOykrXFQsqXRNUNdDvjFezWLqYcy4xOlA8++CBt2rThl7/85QnFIK5cBEEQMmB/4Gk0PYRVzqnbiVLOQdND7A88bdqxzOhE2aZNmybFIJKLIAhCmml6FeHYKixS/XXELFIW4dgqNN1vyvFEJ0pBEITTQEKvAo7diRIsJPRKU44nOlEKgiCcBqxyDnDsTpSgYZVz670/VaITpSAIwmnAIufgcQxHMwL13q8ZATyO4Vjk7HrvT5XoRCkIgnCaaGi1mGYEsMheU1eLtQQiuQiCIGSImihmf+BpwrFVgAXQ8DhG0DZrximVWEAkF0EQhIzTdD8JvRKrnGvaUFhLI5KLIAiCYDoxoS8IgiCYTiQXQRAEwXQiuQiCIAimE8lFEARBMJ1ILoIgCILpRHIRBEE4BTW1E+Ubb7zB6NGjGTx4MFOmTKGsrCyl44vkIgiCkGG6XoWW2I3eQjtRbt68mYULF/Liiy/y1Vdf0aVLF+67776UYhDJRRAEIUO0RDHByl9QWT4Wf8UtVJaPJVg5G62FdaIsKytj8uTJ9OzZE5vNxv/8z/+wcePGlGIQnSgFQRAyQEsU46+4DcMII0m+Q2X2dVT1S+IV68lu9QcsLaQT5Q9+8IM6r7dixQrOOuuslGIQVy6CIAgZEAk+hWGEkeXsOp0oZTkbwwgRCbasTpQ1Vq1axbPPPsvPf/7zlGIQyUUQBCHNdL0KVV2FJNXfiEuSslDVlegtrBPlP/7xD2bOnMljjz1G//79U4pBJBdBEIQ0M/QqJOn4nSiNFtSJ8s0332Tu3LksWrSISy+9NOUYRHIRBEFIM0nOwTCO34lSaiGdKNevX89vfvMbXn75ZfLz808oBpFcBEEQ0kyWc7Dbh2MYwXrvN4wAdvsI5BbSiXLJkiUoisItt9xCXl4eeXl5KScZUXJfEAQhAw6vFgsh1epEaRgBJMlr6mqxlkAkF0EQhAzREsVEgk+jqiup6URpd4zE7Z1xSiUWEMlFEAQh43Tdj6FXIsm5pg2FtTQiuQiCIAimExP6giAIgulEchEEQRBMJ5KLIAiCYDqRXARBEATTieQiCIIgmE4kF0EQBMF0IrkIgiAIphPJRRAEQTCdSC6CIAiC6URyEQRBEEwnkosgCIJguv8PupVZhbwpf7YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 427.185x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "# Set style of scatterplot\n",
    "sns.set_context(\"notebook\", font_scale=1.1)\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "# Create scatterplot of dataframe\n",
    "sns.lmplot('x', # column name of x axis\n",
    "           'y', # column name of y axis\n",
    "           data=ds, # Data source, so our Pandas Dataframe\n",
    "           fit_reg=False, # we do not want to fit a curve\n",
    "           hue=\"por\", # we color by porosity\n",
    "           palette=\"viridis\", # choose viridis colormap\n",
    "           scatter_kws={\"marker\": \"D\", # marker style\n",
    "                        \"s\": 70}) # marker size\n",
    "\n",
    "# Set title\n",
    "plt.title('Porosity distribution')\n",
    "\n",
    "# Set x-axis label\n",
    "plt.xlabel('x [m]')\n",
    "\n",
    "# Set y-axis label\n",
    "plt.ylabel('y [m]')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One benefit of seaborn for example is to have extensive options for histograms, which can be generated rather easy. Let's create histograms for porosity and permeability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,2)\n",
    "sns.distplot(ds['por'], ax = axs[0])\n",
    "sns.distplot(np.log10(ds['perm']), ax = axs[1])\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Altair\n",
    "Altair is somewhat different from the other plot-libraries, as it takes a descriptive approach towards plotting. What does that mean? It will best become clear by showing the plotting code, but let's say:  \n",
    "If you type \"mean x\" for your x-axis label, it will directly plot the mean of your x-data.  \n",
    "\n",
    "Altair is built around the pandas data frame. [Pandas](link) is a nice library to handle data in a csv format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "var spec = {\"config\": {\"view\": {\"width\": 400, \"height\": 300}}, \"data\": {\"name\": \"data-986ff343158f29b79ca0b2cacf1df118\"}, \"mark\": \"circle\", \"encoding\": {\"color\": {\"type\": \"quantitative\", \"field\": \"por\"}, \"size\": {\"type\": \"quantitative\", \"field\": \"por\"}, \"x\": {\"type\": \"quantitative\", \"field\": \"x\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"y\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v2.6.0.json\", \"datasets\": {\"data-986ff343158f29b79ca0b2cacf1df118\": [{\"x\": 10000, \"y\": 14900, \"por\": 0.13, \"perm\": 3.2016800000000004e-15}, {\"x\": 4000, \"y\": 7900, \"por\": 0.13, \"perm\": 3.2016800000000004e-15}, {\"x\": 12900, \"y\": 4100, \"por\": 0.07, \"perm\": 4.07225e-17}, {\"x\": 5800, \"y\": 13900, \"por\": 0.25, \"perm\": 2.13536e-12}, {\"x\": 7000, \"y\": 2000, \"por\": 0.29, \"perm\": 9.215889999999999e-12}, {\"x\": 200, \"y\": 6800, \"por\": 0.17, \"perm\": 4.35093e-14}, {\"x\": 7200, \"y\": 4000, 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\"y\": 6000, \"por\": 0.28, \"perm\": 5.65795e-12}, {\"x\": 2800, \"y\": 3800, \"por\": 0.23, \"perm\": 8.26735e-13}]}};\n",
       "var opt = {};\n",
       "var type = \"vega-lite\";\n",
       "var id = \"dd15371d-873e-4372-8380-126ccbe1b688\";\n",
       "\n",
       "var output_area = this;\n",
       "\n",
       "require([\"nbextensions/jupyter-vega/index\"], function(vega) {\n",
       "  var target = document.createElement(\"div\");\n",
       "  target.id = id;\n",
       "  target.className = \"vega-embed\";\n",
       "\n",
       "  var style = document.createElement(\"style\");\n",
       "  style.textContent = [\n",
       "    \".vega-embed .error p {\",\n",
       "    \"  color: firebrick;\",\n",
       "    \"  font-size: 14px;\",\n",
       "    \"}\",\n",
       "  ].join(\"\\\\n\");\n",
       "\n",
       "  // element is a jQuery wrapped DOM element inside the output area\n",
       "  // see http://ipython.readthedocs.io/en/stable/api/generated/\\\n",
       "  // IPython.display.html#IPython.display.Javascript.__init__\n",
       "  element[0].appendChild(target);\n",
       "  element[0].appendChild(style);\n",
       "\n",
       "  vega.render(\"#\" + id, spec, type, opt, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== \"scripterror\") {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ],
      "text/plain": [
       "<vega.vegalite.VegaLite at 0x1a1ce49400>"
      ]
     },
     "metadata": {
      "jupyter-vega": "#dd15371d-873e-4372-8380-126ccbe1b688"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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"
     },
     "metadata": {
      "jupyter-vega": "#dd15371d-873e-4372-8380-126ccbe1b688"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import altair as alt\n",
    "alt.renderers.enable('notebook')\n",
    "#from IPython.display import display\n",
    "dsa = ds.copy()\n",
    "alt.Chart(dsa).mark_circle().encode(\n",
    "    x='x',\n",
    "    y='y',\n",
    "    color='por:Q',\n",
    "    size='por:Q'\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Honorable Mention: Plotly\n",
    "[Plotly](https://plot.ly/python/) is another interactive plotting library, specialized on sharing your code and your plots online. It heavily uses ```matplotlib.pyplot``` for its features and you can create high class figures with plotly.  \n",
    "But the main purpose of plotly is to share your plots. It is also available for multiple programming languages, so translating your graphs from one language to another is really simple.\n",
    "\n",
    "*\"__[Plotly is a]__ python plotting library for collaborative, interactive, publication-quality graphs.\"*"
   ]
  }
 ],
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    "equation": "Ctrl-E",
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  "toc": {
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    "hover_highlight": "#DAA520",
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