{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hierarchical clustering basics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Learn in this tutorial how to use the `cnnclustering.cnn` module for step-wise hierarchical clusterings, where\n",
    "one cluster step does not deliver a satisfactory result. We will also show how to use a data set of reduced size for cluster exploration and how we can transfer the result to the original full size data set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Pre-requirements\" data-toc-modified-id=\"Pre-requirements-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Pre-requirements</a></span><ul class=\"toc-item\"><li><span><a href=\"#Import-dependencies\" data-toc-modified-id=\"Import-dependencies-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Import dependencies</a></span></li><li><span><a href=\"#Notebook-configuration\" data-toc-modified-id=\"Notebook-configuration-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Notebook configuration</a></span></li><li><span><a href=\"#Package-configuration\" data-toc-modified-id=\"Package-configuration-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Package configuration</a></span></li></ul></li><li><span><a href=\"#Dissimilar-blobs-showcase\" data-toc-modified-id=\"Dissimilar-blobs-showcase-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Dissimilar blobs showcase</a></span><ul class=\"toc-item\"><li><span><a href=\"#Getting-started\" data-toc-modified-id=\"Getting-started-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Getting started</a></span></li><li><span><a href=\"#Parameter-scan\" data-toc-modified-id=\"Parameter-scan-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Parameter scan</a></span></li><li><span><a href=\"#Hierarchical-clustering\" data-toc-modified-id=\"Hierarchical-clustering-2.3\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>Hierarchical clustering</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-requirements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:24.076407Z",
     "start_time": "2020-06-11T17:56:23.338439Z"
    }
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import time\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import pandas as pd                # Optional dependency\n",
    "from sklearn import datasets       # For sklearn test data set creation\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "import cnnclustering.cnn as cnn    # CNN clustering\n",
    "import cnnclustering.cmsm as cmsm  # Core-set MSM estimation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook was created using Python 3.8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:24.082769Z",
     "start_time": "2020-06-11T17:56:24.078683Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.8.3 (default, May 15 2020, 15:24:35) \n",
      "[GCC 8.3.0]\n"
     ]
    }
   ],
   "source": [
    "# Version information\n",
    "print(sys.version)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Notebook configuration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use `matplotlib` to create plots. A `\"matplotlibrc\"` file can be used to customise the appearance of the plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:24.099478Z",
     "start_time": "2020-06-11T17:56:24.085834Z"
    }
   },
   "outputs": [],
   "source": [
    "# Matplotlib configuration\n",
    "mpl.rc_file(\n",
    "    \"matplotlibrc\",\n",
    "    use_default_template=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:24.114674Z",
     "start_time": "2020-06-11T17:56:24.104169Z"
    }
   },
   "outputs": [],
   "source": [
    "# Axis property defaults for the plots\n",
    "ax_props = {\n",
    "    \"xlabel\": None,\n",
    "    \"ylabel\": None,\n",
    "    \"xlim\": (-2.5, 2.5),\n",
    "    \"ylim\": (-2.5, 2.5),\n",
    "    \"xticks\": (),\n",
    "    \"yticks\": (),\n",
    "    \"aspect\": \"equal\"\n",
    "}\n",
    "\n",
    "# Line plot property defaults\n",
    "line_props = {\n",
    "    \"linewidth\": 0,\n",
    "    \"marker\": '.',\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optionally, clustering results can be nicely collected in a `pandas` data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:24.133954Z",
     "start_time": "2020-06-11T17:56:24.116172Z"
    }
   },
   "outputs": [],
   "source": [
    "# Pandas DataFrame print options\n",
    "pd.set_option('display.max_rows', 1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Package configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:24.153314Z",
     "start_time": "2020-06-11T17:56:24.141047Z"
    }
   },
   "outputs": [],
   "source": [
    "# Configuration file found?\n",
    "cnn.settings.cfgfile  # If None, no file is provided"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:24.178373Z",
     "start_time": "2020-06-11T17:56:24.160759Z"
    },
    "run_control": {
     "marked": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'default_cnn_cutoff': '1',\n",
       " 'default_cnn_offset': '0',\n",
       " 'default_radius_cutoff': '1',\n",
       " 'default_member_cutoff': '2',\n",
       " 'default_fit_policy': 'conservative',\n",
       " 'default_predict_policy': 'conservative',\n",
       " 'float_precision': 'sp',\n",
       " 'int_precision': 'sp'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display default settings\n",
    "cnn.settings.defaults"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-11T14:38:11.051577Z",
     "start_time": "2019-09-11T14:38:11.034144Z"
    }
   },
   "source": [
    "## Dissimilar blobs showcase"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will generate a sample data set of three clusters that have very different point densities and are spatially not very well separated. As we will see,\n",
    "it can be non-trivial to extract all three clusters with a single set of cluster parameters. We will solve the problem by extracting the clusters in a two step procedure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-19T10:35:00.015683Z",
     "start_time": "2019-09-19T10:34:59.968481Z"
    }
   },
   "source": [
    "### Getting started"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:24.246003Z",
     "start_time": "2020-06-11T17:56:24.183929Z"
    }
   },
   "outputs": [],
   "source": [
    "# Generate blobs with quite different point densities\n",
    "dblobs, _ = datasets.make_blobs(\n",
    "    n_samples=int(1e5),\n",
    "    cluster_std=[3.5, 0.32, 1.8],\n",
    "    random_state=1\n",
    "    )\n",
    "\n",
    "dblobs = StandardScaler().fit_transform(dblobs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:24.304863Z",
     "start_time": "2020-06-11T17:56:24.250178Z"
    }
   },
   "outputs": [],
   "source": [
    "# Initialise clustering\n",
    "cobj = cnn.CNN(points=dblobs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:24.317903Z",
     "start_time": "2020-06-11T17:56:24.307037Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "CNN cluster object\n",
      "--------------------------------------------------------------------------------\n",
      "Alias :                         root\n",
      "Hierachy level :                0\n",
      "\n",
      "Data point shape :              Parts      - 1\n",
      "                                Points     - 100000\n",
      "                                Dimensions - 2\n",
      "\n",
      "Distance matrix calculated :    None\n",
      "Neighbourhoods calculated :     None\n",
      "Density graph calculated :      None\n",
      "\n",
      "Clustered :                     False\n",
      "Children :                      False\n",
      "================================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Get basic information about the cnn.CNN instance via cobj.__str__()\n",
    "print(cobj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:25.539681Z",
     "start_time": "2020-06-11T17:56:24.320552Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the original data\n",
    "_ = cobj.evaluate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the 2D plot of the generated points above we can already tell that this is probably not the easiest\n",
    "of all clustering problems. One of the three clusters is hardly visible. We also have generated a fairly large amount of data points. If we have a big amount of computer-memory or enough time, we can attempt to cluster the 100,000 points directly. In terms of speed and for data exploration it might be, however, a good idea to perform the clustering on a reduced data set. We can predict the clustering later for the full sized set on the basis of the reduced result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:25.549285Z",
     "start_time": "2020-06-11T17:56:25.541609Z"
    }
   },
   "outputs": [],
   "source": [
    "# To make the clustering easier, reduce the number of points\n",
    "# (start, stop, stride)\n",
    "robj = cobj.cut(points=(None, None, 100))\n",
    "# Equivalent to:\n",
    "#     >>> robj = cnn.CNN(points=cobj.data.points[slice(None, None, 100), :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:25.573629Z",
     "start_time": "2020-06-11T17:56:25.551165Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "CNN cluster object\n",
      "--------------------------------------------------------------------------------\n",
      "Alias :                         root\n",
      "Hierachy level :                0\n",
      "\n",
      "Data point shape :              Parts      - 1\n",
      "                                Points     - 1000\n",
      "                                Dimensions - 2\n",
      "\n",
      "Distance matrix calculated :    None\n",
      "Neighbourhoods calculated :     None\n",
      "Density graph calculated :      None\n",
      "\n",
      "Clustered :                     False\n",
      "Children :                      False\n",
      "================================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(robj)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We created a cluster object holding a smaller data set by using a point stride of 100, leaving us with only 1000 points that can be clustered very fast. Note, that using a slice on the original data like this does not require a copy. A plot of the reduced set also shows that we made the clustering easier just judging by visual inspection. We can now clearly spot three point clouds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:25.919311Z",
     "start_time": "2020-06-11T17:56:25.578031Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '1000 points')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the reduced data\n",
    "fig, Ax = plt.subplots(1, 2)\n",
    "_ = cobj.evaluate(ax=Ax[0], ax_props=ax_props)\n",
    "_ = robj.evaluate(ax=Ax[1], ax_props=ax_props)\n",
    "Ax[0].set_title(f\"{cobj.data.points.shape[0]} points\", fontsize=10, pad=4)\n",
    "Ax[1].set_title(f\"{robj.data.points.shape[0]} points\", fontsize=10, pad=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we pre-calculate pairwise point distances, we can plot the distribution of distances. This can give us a very basic estimate for a reasonable radius cutoff as one of the cluster parameters. For globular clusters, each cluster should be visible as a peak in the distance distribution around a value that is very roughly equivalent to the radius of the point cloud. For more complicated data sets this approximation is not valid, but we can still get a feeling for the value range of the radius cutoff."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:26.071139Z",
     "start_time": "2020-06-11T17:56:25.922176Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Looking at the distance distribution between points, can help to find\n",
    "# an initial value for the radius cutoff\n",
    "robj.calc_dist()  # Pre-calculate distances\n",
    "fig, ax, *_ = robj.dist_hist(maxima=True, maxima_props={\"order\": 5})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-19T10:35:37.964861Z",
     "start_time": "2019-09-19T10:35:37.962263Z"
    }
   },
   "source": [
    "### Parameter scan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Blindly starting to cluster a data set in a happy-go-lucky attempt may already lead to a satisfactory result in some cases, but let's tackle this problem in more systematic way, to see how different cluster parameters effect the outcome. This will take a few seconds to complete."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:36.896239Z",
     "start_time": "2020-06-11T17:56:26.073453Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# A scan of a few parameters shows that it is difficult to extract the\n",
    "# three clusters at once with one parameter set\n",
    "for r in [0.05, 0.08, 0.1, 0.125, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.75, 1., 2.]:\n",
    "    for c in [0, 1, 2, 3, 4, 5, 10, 15, 20]:\n",
    "        robj.fit(r, c, v=False)  # fit from pre-calculated distances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T10:55:56.099867Z",
     "start_time": "2020-06-11T10:55:56.085610Z"
    }
   },
   "source": [
    "Each cluster result will be added to the `summary` attribute of our cluster object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:36.918422Z",
     "start_time": "2020-06-11T17:56:36.898721Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CNNRecord(points=1000, r=0.05, c=0, min=2, max=None, clusters=Labels(111), largest=0.329, noise=0.332, time=0.010924577713012695)\n",
      "CNNRecord(points=1000, r=0.05, c=1, min=2, max=None, clusters=Labels(31), largest=0.326, noise=0.544, time=0.010438919067382812)\n",
      "CNNRecord(points=1000, r=0.05, c=2, min=2, max=None, clusters=Labels(14), largest=0.317, noise=0.627, time=0.010040998458862305)\n",
      "CNNRecord(points=1000, r=0.05, c=3, min=2, max=None, clusters=Labels(8), largest=0.315, noise=0.664, time=0.010024070739746094)\n",
      "CNNRecord(points=1000, r=0.05, c=4, min=2, max=None, clusters=Labels(3), largest=0.312, noise=0.684, time=0.01040029525756836)\n"
     ]
    }
   ],
   "source": [
    "print(*robj.summary[:5], sep=\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have `pandas` installed, you can convert the summary to a nice table as a `pandas.DataFrame`. This makes the analysis of the cluster results more convenient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:36.957793Z",
     "start_time": "2020-06-11T17:56:36.920815Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>points</th>\n",
       "      <th>r</th>\n",
       "      <th>c</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>clusters</th>\n",
       "      <th>largest</th>\n",
       "      <th>noise</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>1000</td>\n",
       "      <td>0.200</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>3</td>\n",
       "      <td>0.334</td>\n",
       "      <td>0.464</td>\n",
       "      <td>0.055377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>1000</td>\n",
       "      <td>0.400</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>3</td>\n",
       "      <td>0.956</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.070035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1000</td>\n",
       "      <td>0.450</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>3</td>\n",
       "      <td>0.668</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.068890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1000</td>\n",
       "      <td>0.125</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>3</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.659</td>\n",
       "      <td>0.035683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>1000</td>\n",
       "      <td>0.400</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>3</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.064144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1000</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>3</td>\n",
       "      <td>0.991</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.068568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>1000</td>\n",
       "      <td>0.300</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>3</td>\n",
       "      <td>0.629</td>\n",
       "      <td>0.272</td>\n",
       "      <td>0.058881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>0.050</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>3</td>\n",
       "      <td>0.312</td>\n",
       "      <td>0.684</td>\n",
       "      <td>0.010400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>1000</td>\n",
       "      <td>0.300</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>3</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.054430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    points      r   c  min   max  clusters  largest  noise      time\n",
       "52    1000  0.200  15    2  <NA>         3    0.334  0.464  0.055377\n",
       "86    1000  0.400   5    2  <NA>         3    0.956  0.039  0.070035\n",
       "96    1000  0.450  10    2  <NA>         3    0.668  0.058  0.068890\n",
       "35    1000  0.125  20    2  <NA>         3    0.330  0.659  0.035683\n",
       "83    1000  0.400   2    2  <NA>         3    0.981  0.015  0.064144\n",
       "81    1000  0.400   0    2  <NA>         3    0.991  0.004  0.068568\n",
       "70    1000  0.300  15    2  <NA>         3    0.629  0.272  0.058881\n",
       "4     1000  0.050   4    2  <NA>         3    0.312  0.684  0.010400\n",
       "65    1000  0.300   2    2  <NA>         3    0.950  0.042  0.054430"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary sorted by number of identified clusters\n",
    "df = robj.summary.to_DataFrame().sort_values('clusters')\n",
    "# Show cluster results where we have 3 clusters\n",
    "df[df.clusters == 3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The summary shows indeed that we got 3 clusters (as desired) for a few parameter combinations. Apart from the number of clusters, it is, however often also of interest, how many data points ended up in the clusters and how many are considered outliers (noise). In this case we expect 3 clusters of more or less equal size (member wise) and we may be interested to keep the outliers-level low. Of the results giving 3 clusters, 3 find the largest cluster to entail one third of the data points (which is good), but the noise level is around 50 % and higher (which is probably not what we want). Let's plot a few results to see what is going on here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:37.475445Z",
     "start_time": "2020-06-11T17:56:36.961701Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cluster attempts in comparison\n",
    "fig, ax = plt.subplots(2, 2)\n",
    "Ax = ax.flatten()\n",
    "for i, pair in enumerate([(0.050, 4), (0.125, 20), (0.2, 15), (0.450, 10)]):\n",
    "    robj.fit(*pair, rec=False)\n",
    "    _ = robj.evaluate(ax=Ax[i], ax_props=ax_props)\n",
    "    Ax[i].set_title(f'{pair[0]}, {pair[1]}', fontsize=10, pad=4)\n",
    "\n",
    "fig.subplots_adjust(\n",
    "    left=0.2, right=0.8, bottom=0, top=1, wspace=0, hspace=0.3 \n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "None of the above attempts was able to achieve a split into 3 clusters as we wanted it. One could now try to tinker around with the parameters a bit more, or resort to hierarchical clustering. As we see in the plots above (lower left and right), two different parameter pairs are leading to a clean split of different regions of the data, so why not apply both of them, one after the other.\n",
    "\n",
    "Before we do this let's have another close look at the cluster results we obtained. Using the `smmarize` method of a cluster object, we can visualize a summary table in a 2D contour plot, to evaluate the quality measures *identified cluster number*, *members in the largest cluster*, *points classified as outliers* and *computational time of the fit* versus the input parameters *radius cutoff* (*r*) and *similarity criterion* (*c*).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:37.782322Z",
     "start_time": "2020-06-11T17:56:37.478174Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Summarize the summary table as 2D contour plot\n",
    "# time\n",
    "fig, ax = plt.subplots()\n",
    "contour = robj.summary.summarize(ax=ax)[2][0]\n",
    "colorbar = fig.colorbar(mappable=contour, ax=ax)\n",
    "ax.set_yticks([0, 5, 10, 15, 20])\n",
    "colorbar.set_label(\"time / s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the *time vs. R/C* plot we can see how the total clustering time depends in particular on the neighbour search radius cutoff. Larger radii result in larger neighbourlists for each point increasing the processing time, so if one has the choice smaller values for *r* should be preferred."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:38.146452Z",
     "start_time": "2020-06-11T17:56:37.784923Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.05, 1.0)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Summarize the summary table as 2D contour plot\n",
    "# noise\n",
    "fig, ax = plt.subplots()\n",
    "contour = robj.summary.summarize(ax=ax, quant=\"noise\")[2][0]\n",
    "colorbar = fig.colorbar(mappable=contour, ax=ax, ticks=(0, 0.5, 1))\n",
    "ax.set_yticks([0, 5, 10, 15, 20])\n",
    "colorbar.set_label(\"noise / %\")\n",
    "ax.set_xlim(None, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ratio of outlier-points depends on the search radius *r* as well as on the similarity criterion *c*. We can somewhat denote from this that cluster results with a similar noise-ratio (that are presumably similar qualitatively) are achieved for a variety of *r*/*c* combinations. If we increase *r* and *c* at the same time, the noise-level stays constant. The desired amount of noise depends much on the nature of the underlying data set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:38.397383Z",
     "start_time": "2020-06-11T17:56:38.149537Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.05, 1.0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Summarize the summary table as 2D contour plot\n",
    "# largest cluster\n",
    "fig, ax = plt.subplots()\n",
    "contour = robj.summary.summarize(ax=ax, quant=\"largest\")[2][0]\n",
    "colorbar = fig.colorbar(mappable=contour, ax=ax, ticks=(0, 0.5, 1))\n",
    "ax.set_yticks([0, 5, 10, 15, 20])\n",
    "colorbar.set_label(\"largest / %\")\n",
    "ax.set_xlim(None, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ratio of points assigned to the largest cluster show a similar trend as the noise-ratio. This view could give a good hint towards reasonable parameter combinations if one already has an idea about the expected cluster size. It also shows for which parameters we do not observe any splitting (about 100 % of the points are in the largest cluster. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:38.728829Z",
     "start_time": "2020-06-11T17:56:38.401852Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.05, 1.0)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Summarize the summary table as 2D contour plot\n",
    "# cluster number\n",
    "fig, ax = plt.subplots()\n",
    "contour = robj.summary.summarize(ax=ax, quant=\"clusters\",\n",
    "                         contour_props={\"levels\": 100})[2][0]\n",
    "colorbar = fig.colorbar(mappable=contour, ax=ax)\n",
    "ax.set_yticks([0, 5, 10, 15, 20])\n",
    "colorbar.set_label(\"# clusters\")\n",
    "ax.set_xlim(None, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:38.983621Z",
     "start_time": "2020-06-11T17:56:38.731343Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.05, 1.0)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Summarize the summary table as 2D contour plot\n",
    "# cluster number with adjusted coloring\n",
    "fig, ax = plt.subplots()\n",
    "contour = robj.summary.summarize(\n",
    "    ax=ax, quant=\"clusters\",\n",
    "    contour_props={\"levels\": [-0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5],\n",
    "                   \"vmax\": 5}\n",
    "    )[2][0]\n",
    "colorbar = fig.colorbar(mappable=contour, ax=ax, ticks=(0, 1, 2, 3, 4, 5))\n",
    "ax.set_yticks([0, 5, 10, 15, 20])\n",
    "colorbar.set_label(\"# clusters\")\n",
    "ax.set_xlim(None, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probably most telling view is given by the *number of clusters* plots. The analysis demonstrates nicely that for this data set a splitting into 3 meaningful clusters is hard to achieve."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-19T10:35:00.015683Z",
     "start_time": "2019-09-19T10:34:59.968481Z"
    }
   },
   "source": [
    "### Hierarchical clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To use the hierarchical approach to clustering our data set, we will apply in a first step a pair of cluster parameters that will extract the lesser dense region of the data as an isolated cluster. That is we choose a comparably large value of the neighbour search radius *r* and a rather low value of the similarity criterion *c*. We can refer to these parameters as *soft* parameters, leaving the more dense regions of the data untouched and in one cluster. Remember that density in terms of the CNN clustering is estimated as the number of common neighbours within the neighbourhood intersection of two points with respect to a radius *r*. So more common neighbours (higher similarity cutoff *c*) and/or a smaller neighbourhood radius (smaller *r*) will result in a higher density requirement. To make this more clear let's have a look again at some clusterings. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:39.665756Z",
     "start_time": "2020-06-11T17:56:38.985758Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cluster attempts in comparison\n",
    "fig, ax = plt.subplots(2, 2)\n",
    "Ax = ax.flatten()\n",
    "for i, pair in enumerate([(0.18, 6), (0.2, 5), (0.6, 17), (1, 30)]):\n",
    "    robj.fit(*pair, rec=False)\n",
    "    _ = robj.evaluate(ax=Ax[i], ax_props=ax_props)\n",
    "    Ax[i].set_title(f'{pair[0]}, {pair[1]}', fontsize=10, pad=4)\n",
    "\n",
    "fig.subplots_adjust(\n",
    "    left=0.2, right=0.8, bottom=0, top=1, wspace=0, hspace=0.3 \n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Lower right__: Choosing a high radius cutoff ($r=1$, ), a soft density requirement, results in no cluster splitting. The data set as a whole forms cluster in which the point density is *at least* as high as our density requirement.\n",
    "\n",
    "__Lower left__: When we increase the density requirement ($r=0.6$, $c=17$), we observe a splitting between the broadly distributed lower density cluster and the more dense clusters. Within both the resulting clusters the point density is higher than required by the parameters but they are separated by a low density region and therefore split. This parameter set can be used for the first step of the hierarchical clustering.\n",
    "\n",
    "__Upper right__: Increasing the density requirement further ($r=0.2$, $c=5$), leads to a splitting within the broad cluster first, while the more dense clusters still remain united. Obviously, the point density in the region between the two dense clusters is higher than in parts within the low density cluster.\n",
    "\n",
    "__Upper left__: Choosing parameters hard enough ($r=0.18$, $c=6$) to achieve a splitting of the two dense clusters, does also completely split up the less dense cluster. We could use this parameter set in a second hierarchical clustering step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The central element of the hierarchical cluster functionality is the `isolate` method of a cluster object. After a clustering (with soft parameters) we can *freeze* the result before we start to re-cluster. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:39.765809Z",
     "start_time": "2020-06-11T17:56:39.668048Z"
    }
   },
   "outputs": [],
   "source": [
    "# After the first step, we need to isolate the cluster result\n",
    "robj.fit(0.6, 17, rec=False)\n",
    "robj.isolate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `isolate` method will create a new cluster object (a child cluster) for every cluster of a cluster result.\n",
    "In our case we get two new child clusters (plus one for outliers). The clusters are stored in a dictionary under the `children` attribute of the parent cluster object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:39.787963Z",
     "start_time": "2020-06-11T17:56:39.768093Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(<function cnnclustering.cnn.CNN.isolate.<locals>.<lambda>()>,\n",
       "            {1: <cnnclustering.cnn.CNNChild at 0x7f9086d3ba30>,\n",
       "             2: <cnnclustering.cnn.CNNChild at 0x7f9086d3bd00>,\n",
       "             0: <cnnclustering.cnn.CNNChild at 0x7f9086d3beb0>})"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The children dictionary of the data after isolation holds\n",
    "# a cluster object for each cluster found in the last step\n",
    "robj.children"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we will use different cluster parameters now for different clusters it would be nice to have something\n",
    "to keep the overview of which cluster has been identified under which parameters. This information is provided by `labels.info`. A label info tells us three things:\n",
    "\n",
    "  - `origin`: How have these labels been assigned? The entry `\"fitted\"` means, they were obtained by a regular clustering.\n",
    "  - `reference`: This is a related clustering object, i.e. the object that is associated to the data for which the labels are valid. For `\"fitted\"` labels this is a reference to the clustering object itself that carries the labels.\n",
    "  - `params`: This is a dictionary stating the cluster parameters (*r*, *c*) that led to each cluster. For `\"fitted\"` labels, each cluster has the same parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:39.821196Z",
     "start_time": "2020-06-11T17:56:39.790337Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelInfo(origin='fitted', reference=<cnnclustering.cnn.CNN object at 0x7f90884c64c0>, params={1: (0.6, 17), 2: (0.6, 17)})"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Information on used cluster parameters are attached for each cluster\n",
    "robj.labels.info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every single isolated child cluster is a full-fledged, completely functional cluster object itself. When we want to re-cluster a child cluster, this is no different to clustering a parent cluster. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:39.841267Z",
     "start_time": "2020-06-11T17:56:39.833641Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "CNN cluster object\n",
      "--------------------------------------------------------------------------------\n",
      "Alias :                         child No. 1\n",
      "Hierachy level :                1\n",
      "\n",
      "Data point shape :              Parts      - 1\n",
      "                                Points     - 678\n",
      "                                Dimensions - 2\n",
      "\n",
      "Distance matrix calculated :    None\n",
      "Neighbourhoods calculated :     None\n",
      "Density graph calculated :      None\n",
      "\n",
      "Clustered :                     False\n",
      "Children :                      False\n",
      "================================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "robj1 = robj.children[1]  # Child cluster 1\n",
    "print(robj1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note, that the `Hierarchy level` in the above overview has increased to 1. We can again plot the distance distribution within this child cluster, to see how this changed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:40.004702Z",
     "start_time": "2020-06-11T17:56:39.844410Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distance distribution for this cluster\n",
    "robj1.calc_dist()\n",
    "fig, ax, *_ = robj1.dist_hist(maxima=True, maxima_props={\"order\": 5})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then we can attempt the second clustering step. For somewhat inhomogeneous data sets, a typical artifact or side effect of the CNN clustering is, that very small clusters may be appear in the result. To filter those out because they are irrelevant, we can use the `member_cutoff` keyword argument of the `fit` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:40.244896Z",
     "start_time": "2020-06-11T17:56:40.007634Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time for call of fit: 0 hours, 0 minutes, 0.0472 seconds\n",
      "--------------------------------------------------------------------------------\n",
      "#points   R         C         min       max       #clusters %largest  %noise    \n",
      "678       0.180     6         2         None      4         0.494     0.117     \n",
      "--------------------------------------------------------------------------------\n",
      "Execution time for call of fit: 0 hours, 0 minutes, 0.0446 seconds\n",
      "--------------------------------------------------------------------------------\n",
      "#points   R         C         min       max       #clusters %largest  %noise    \n",
      "678       0.180     6         5         None      2         0.494     0.124     \n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'member cutoff 5')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now cluster the child cluster\n",
    "fig, Ax = plt.subplots(1, 2)\n",
    "robj1.fit(0.18, 6)\n",
    "_ = robj1.evaluate(ax=Ax[0], ax_props=ax_props)\n",
    "Ax[0].set_title(f\"member cutoff 1\", fontsize=10, pad=4)\n",
    "\n",
    "# When the result is satisfying, but also a few very small clusters are found,\n",
    "# this can be surpressed\n",
    "robj1.fit(0.18, 6, member_cutoff=5)\n",
    "_ = robj1.evaluate(ax=Ax[1], ax_props=ax_props)\n",
    "Ax[1].set_title(f\"member cutoff 5\", fontsize=10, pad=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a little extra feature a cluster object has a `pie` method that allows to visualize the current state and splitting on the different hierarchy levels as a pie-diagram (from level 0 in the middle to lower levels to the outside)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:40.349062Z",
     "start_time": "2020-06-11T17:56:40.248063Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.3, 0.3)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The cluster hierarchy can be visualised as pie diagram\n",
    "fig, ax, *plotted = robj.pie()\n",
    "ax.set_xlim(-0.3, 0.3)\n",
    "ax.set_ylim(-0.3, 0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have isolated one cluster in the first step and two others in a further splitting on a lower hierarchy level, the last step that remains, is to put everything back together. This can be done automatically by calling `reel` on the parent cluster object into which the child cluster results should be integrated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:40.358729Z",
     "start_time": "2020-06-11T17:56:40.352159Z"
    }
   },
   "outputs": [],
   "source": [
    "# Wrap up the hierarchical clustering and integrate the child clusters into\n",
    "# the parent cluster\n",
    "robj.reel()\n",
    "\n",
    "# Manually sort clusters by size (largest = 1)\n",
    "robj.labels.sort_by_size()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:40.566015Z",
     "start_time": "2020-06-11T17:56:40.360784Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.3, 0.3)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "robj._children = None  # Remove child clusters \n",
    "fig, ax, *plotted = robj.pie()\n",
    "ax.set_xlim(-0.3, 0.3)\n",
    "ax.set_ylim(-0.3, 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:40.656154Z",
     "start_time": "2020-06-11T17:56:40.567696Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "_ = robj.evaluate(ax=ax, ax_props=ax_props)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:40.665352Z",
     "start_time": "2020-06-11T17:56:40.658563Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelInfo(origin='reeled', reference=<cnnclustering.cnn.CNN object at 0x7f90884c64c0>, params={2: (0.6, 17), 3: (0.18, 6), 1: (0.18, 6)})"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now the paramater information becomes useful as they differ\n",
    "robj.labels.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:40.679191Z",
     "start_time": "2020-06-11T17:56:40.667648Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label\tr\tn\n",
      "--------------------\n",
      "1\t0.18\t6\n",
      "2\t0.6\t17\n",
      "3\t0.18\t6\n"
     ]
    }
   ],
   "source": [
    "print(\"Label\", \"r\", \"n\", sep=\"\\t\")\n",
    "print(\"-\" * 20)\n",
    "for k, v in sorted(robj.labels.info.params.items()):\n",
    "    print(k, *v, sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cluster result is satisfying. But we have clustered only a reduced set of 1000 points. We would like to predict the cluster label assignment for the full 100,000 points on the basis of the reduced set assignments. This we can do with the `predict` method of a cluster object. We call `predict` with another cluster object for which labels should be predicted as an argument. Similar to a regular `fit` we need to compute neighbourhoods for the points that need assignment, but this time we need relative neighbourhood between two data sets. We want to compute the neighbouring points in the small set `robj` for the points in the big set `cobj`. To allow the fast computation of theses neighbourhoods we could calculate a relative pairwise distance matrix for distances from points in `cobj` to points in `robj` (a so called map matrix). When we predict labels afterwards we should do the prediction separately for the clusters because the assignment parameters differ. This is where the label info comes in handy showing us the parameters used for the fit as an orientation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:53.938371Z",
     "start_time": "2020-06-11T17:56:40.681152Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distance matrix calculated in 0.29 seconds\n",
      "\n",
      "Prediction in 2.74 seconds\n",
      "\n",
      "Prediction in 4.73 seconds\n",
      "\n",
      "Prediction in 2.46 seconds\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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+s8H7+937za/FeYEar07X7tF0jYv8a1zrM7UxT+31c/UwvETtpmk8JBBCfBlGF9j9UsqFvZ5PI2odI9sAHJWbzw/ZMQgh3grgBwA+JqX89b2ej4bGdkLzQ921fhOGYvMBKeXf7uS1NDR2Cg/6mt4MhBB/hFqjOynl5B5P5x4IIRwwjDyjUsq37PV8HgbonImHD78F4++2mfKyO4paXPMxAL/7ICoSNfwGjDJvD1rDPQ2N7YDmB7Aw8EsAXtGKhMZDjgd2TW8B7wbw2QdRkajhZ2H0lvjNvZ7IwwLtmXgIIYT4MwAfATAgmyQOaTSHEOIkjDCMX5dSfmyv56OhsRPQ/AAIIX4FRvjFW6SU5/d6PhoaW4Fe07uHmiHiDoDLUsoP7PV8HhZoZUJDQ0NDQ0NDQ0NDY1PQYU4aGhoaGhoaGhoaGpuCViY0NDQ0NDQ0NDQ0NDYFrUxoaGhoaGhoaGhoaGwKO9K0TgixAMAJo8GQhobGg4v9AApSyq7tHljzgIbGQ4Ed4wBA84CGxkOCLfHAjiRgCyEyNpvNc/DgwW0fey+wvLyMYrHI/3c4HLBYLPxduVwGALS1tdV93uqcta5RrVaxsrICl8sFs9lcN0ZbWxtfi8YEgGKxiHw+D5PJcDS5XC4sLy9jZWUF1WoVVqsVJpOJx7ZarfxdPp/H8vIyTCYThBBoa2tDW1sblpaWUKlUUK1WjYYkQvC/aQy73Q7q6r20tIRqtYrl5WUIIWA2m/l3qWMAgMlkgtlsRrVahRACKysrkFLy/aFx6Bx60bkAYLUazTnpfCFE3bXoO3oXQqjNaO45jq6lXlP93mw2w2QyYXl5GRaLBRaLBVarFVJKlMtl/lxKyb+L7mm1WoXZbOZ7Q38Hug82mw0OhwNWqxWVSoWvValUeFz6u9psNiwtLSEej6NcLvN16Hy6vnovaFybzQYAKJfLkFJieXkZ+Xwe1Wp1WUpJ3U63DY8KD9BaI5jN5i3xQD6fx8rKCsxmM+x2e8s1r362srKCbDbLa9lms8Fut6NUKvHadDgc/MzRfFdWVrC8vIxCoYCVlRV+hi0WC0wmE1ZWVlAul2GxWHhOpVKJ1wldh9bG0tIS3w+6D9VqFZVKha9pMpl4bdD6NZvNvD7p3ysrK8wNdKzJZIKUkt/b2tqYE2h90fXV8wiNa13lgcZj6N/0TvOm82jt2my2ut9Ov5mOod9Da1K9B/SbTSYT3G432traYDabmQcAoFIxmoUTbxMPJBIJZDIZnhvxCP29q9UqX8dkMvG6pznTc5ZOp3eMA2r3TvPANvNAM1nAYrFgaWlp0zxQLpdRLBZ5/Ver1ToeoGeS9i4aT71OW1sbz71cLrP8QfMlmYLuD/EArSlaE0II5iLiDnU9k/xAa7+trY3vBR1L+yyApns6zUOVbej/Kic04xC6LmCsZVUeKJVKPP+lpSUA4PvYjJ9oDCklHA4HHA5HnQxA59G9o7HcbjcsFgsSiQTS6TSP5XQ6eZ8gTmrFAyaTiZ/LGg9UpJSrN3MD2BHPBIC7Bw8ePHLjxo0dGn53cfPmTYyPj/P/Dx48iCNHjiASieCFF17A2NgYAGB4eBjPPvsswuEwACASiWBkZAQAcPjwYf68FSKRCOLxOILBIMLhMF5++WW89tpr8Hq98Pv9OHjwIILBIB8DAOfPn8e5c+cwNjaG3t5euFwuhMNh9Pf3AwCSySSy2Sy8Xi9GRkbQ1dUFr9eLK1euYHFxEel0GqVSCT6fD21tbXA6nVhYWEA8HkdHRwcWFhZQLpdhtVqRyWSQyWRYWB0eHkZvby96enowPz+P0dFRJJNJuFwulEolWCwWRCIRFop9Ph8rIj09PahUKigUCiiXy1haWkJHRwc6OzuRyWQwPz+PRCJxj0JhsVjgcrng8/nQ2dmJQCCAarWKyclJzM/P15HbysoKbDYbk6DT6cTS0hIL48vLy3X3n65htVpZISCScLvdqFaryGQyTJjBYBAejwdmsxmRSARSSmSzWSYMk8nEih1tCC6XC/l8HolEAmazGQ6HAz09Peju7sZb3vIWHD9+HHfv3kU2m8Xrr7+OQCAAt9uN4eFh/pt+6UtfwrVr1xCLxWAymdDd3Y0nn3wShUIBnZ2dmJqagtPpRKFQ4PtB5OJwOFAsFnHr1i0AwMsvv4xisbi0pQXSGm94HggGgzh//jySySTGxsYwPDwMv9+P06dPbwsPxONxXLx4EZlMBl6vF0888QRzj8oDf/3Xf43XXnsNyWQSvb29OHjwIPbv3w8hBM+N1r4qTLfiAVojS0tLcLvdyOfzsFqtyOfzmJub4/VtMpnwtre9DU6nE+3t7ZiYmMDNmzdRqVTgcrnYiDE1NcWbaHt7OwDA7/fDYrGgUCiw4FwulxEKheD1ehGPxzE7O8trSl2bVqsVVqsVwWAQnZ2d6O/vRywWw9zcHHMWbcgul4uFPRKm8vk8KpUKcwyB1jwJTHRd2rjJwLO0tMRCUm9vL/x+PyqVCqanp1mgs9vtzHsWiwW5XI6VjX379iGVSiGXy0FKifb2doTDYRw7dgxHjx7FgQMHcOPGDUxPT6NYLCIQCODUqVMAAJ/PB6/Xi09+8pMYGRlBpVJBe3s7jh07hhMnTmB8fBxzc3MAgN7eXoRCIQBANBpFsVhEMBiE2+1GNBrFrVu38L3vfQ/5fH5VMt1+aB7A1nngwoULyGaz8Hg8ePLJJ+tkgXA4jEgksmkeuHz5MuLxeFMeWFxcRKVSgdvtRi6X4zU7NzeHQqEAwBDmn3rqKbz1rW8FALzwwgu4efMmnE4nXC4Xent7MTMzg8nJSd6PvV4vrFYrwuEwstksGy2KxSLcbjfcbjd6enoQjUYxOjpaZzAlRYwE4kAggM7OThw4cACxWAzT09OIxWJ1hgqn08kKCs05FosBAHK5HCsUzYyYNpsN1WoVTqeThXJSEE0mE3NROBxGsVhEIpFApVJBOp0GYPCK3++Hy+VCIpFgfvJ4PGTQg5SSuezw4cN48skn0dfXh+vXr+P69esoFAoIBoM4efIkAIM/l5eX8bnPfQ4zMzPI5XJwuVx48sknceLECeRyOUxNTSGZTKKnpwednZ0A6nkAAG7fvo10Oo1XXnkFhUJh07LATikTbygEg8E68qA/Qjwex9zcHGKxGJxOJzKZDOLxeB1J0MN0/vz5OmJphnA4XEc8k5OTWFhYwO3bt9HZ2YmBgQH+Ph6PY3FxEYAhIKbTadhsNrhcLvT09GB6ehperxft7e3o6+vDjRs3mHByuRw/mMFgkBdaPp/H/Pw8a9NkzchkMuju7kYqlWLrNgmlQgjMzc2hp6cHs7OzWF5ehtPphM1mw8zMDFvT6LylpSXWhsn7QSSxtLSEubk5tkaQFg2sCvqkyJCALqVEoVBgwXl5eZmFfdLkSfABDGsICSWql0C1RtAxNFZHRwdMJhMWFxfZegOAycrtdiMcDiORSEAIwRYYwLAqkseHBJClpaU6z8DU1BRKpRImJydZeCAQmV24cAEXL15EPp/H1atXkUwmkc/n2ZI5OjoKk8mEwcFBHD16FJFIBAMDA7hx4wacTicAsCXG7XYzSdU+0zywDjTjgXg8jlQqhdHRURQKBWQyGfj9/m3jgVgshtHRUQAG6Xs8Ht60gFUeqFarKBQKKJVKKBQK8Hq98Hq9uHHjBrxeL4aGhuDz+Xgeo6OjcLlcLXlgYmICuVyOPYBmsxmzs7NsKLDb7QCMZ2lhYQEDAwNIJpPwer1sJQ0EAjCbzRgbG4PNZmOLZ6VSgZQSS0tLMJvNyOVyLOB7vV7k83leA7R+aI2Skk/rm1AoFNg4oVo4af07HA5kMhn20tF3pDiolkl6J0MEYFgOOzo6kEwm2SBB87darcx74XAYd+8a0Tz0GxKJBPMgeR3IiEJzIS9DsVjExYsX8cwzzzAXe71eCCEwMzODiYkJAMDc3BxyuRwqlQoWFxdRLBZhs9mQTCaxb98+nDhxAnfv3kUoFEJvby9mZ2fhdrshhEA+n4eUknmgxgE6f3Kd2CseIKPl7du34fP5EAwG2bBw8+bNpjxAz3kmk0F7e3tLHigWi+jo6EBHRwcLzfl8Hnfu3GFDAu1bkUgEHo8HpVIJTqcTlUqFleWpqSn2VJJC4PV6YbPZUCgUeL3R2qpWq7xO8vk8fD4f77H0TtdVPXbECyQnkEBfKBQ4WoTWMckQJAeQoQBY9fCoUQRqJIWUkrmOrhMKhZBOp5HNZlEqlQCAjSHVahU9PT3weDwYGxuDyWRiT24ikWCvEQD2vBKPVCoVRCIRpFIpTExMYGFhAYcOHcLi4iKWlpbYIDg7O4uJiQkUCgXcuXMHQgjkcjlks1kUCgU2aJw+fRrHjh3D9evX0dnZid7eXszNzTEPFAoFOBwO9Pb28nOCLcgCWohYB8LhME6fPl1nBQCMBycSiSCZTCKZTGJwcJAVDcDY6FU0EkszkDVicXER7e3tcDgcGB0dhcPh4I2E3lOpFFKpFEqlEvr6+uByuXDy5ElYrVZ4PB5kMhmcPHkSJpMJ09PTyGaz6OzshM/ng9vtRiQSAWA81NPT05iYmMDy8jKsVitcLhdyuRyEEOju7kZHRwfC4TCTRSwWg9lsxvz8PDKZDKampmA2m7G4uAiv14tyuYzu7m72VNACokUei8XgcDjgdDpZeC+Xy7zBUvhFMxckLUDVhWi32+H1enmDJfeeam0gInI4HOzqJ5ckCdoAWOghyyuwGmqgLDq+Tnd3N4QQ8Hq9SCaTTI6kCDSS4PLyMrut6f/T09NwOBx45ZVX4PV60dnZiUqlArvdjmg0ipmZGdjtdkxPTyOfzyOTybDV1+FwoFwuw+PxIBKJYHh4GO9+97thMplw/PhxvPrqq7hz5w6WlpZQKBRw/PhxdHV1IRgM4sqVKwCw+uM1WqIZD5CwTxb7wcFBANg2HkgkEhgaGsLIyAji8Thu3boFs9mMgYEB5oGpqSncuXOHr/nWt74VJ0+exMTEBPPAmTNnkEgkMDMzA4/Hg6GhIQBoygMjIyO84ZLrfmVlBaFQCG63m61pQgi2ss3MzMBisSAWi/EmW61W4ff70d/fj5mZGfYM0rqmMA4KrSSQgUENSaR3CjGgtUTPtM/n443f7/cjlUqxEEKeAsAQ8ClUw+PxwO12s1GDfid5J8joQP+mMFRa1zQvEoJIsHK73UgkEsx5jWFUdE8p1KBQKDCfjY+Pw+Fw4Dvf+Q6CwSCPEQgEcOfOHVy9epWtzKQ4UYgKhVzSffd6vXjzm9+Mw4cPIxaL4Rvf+AYikcg9PPDlL38ZxWJRc8A6sVc8MDw8jFu3biEej+PmzZswmUxr8sBjjz0Gr9fL+6vP58Pp06cxMjKyYR6QUiKXy8FmsyEQCGBwcBB+v58NbOl0GrlcDpcuXYLVakW5XGZr/8rKCsLhMO9ptN6JI+LxOEwmEzweDywWC9rb21EqlVAul5HJZHhOFBqkhmUDYIVkaWkJ7e3tKBQKsNvtzH0kAxAHUAgXeQScTic8Hg+KxSIrJ3ROW1sbhBBwOBysaCSTyboQK5IJ1NBEr9cLj8fDXh0AdaGcNHa1WoXL5UKlUuEIjWKxyErH5OQkwuEwKpUK+vv74XQ6mQdKpRISiQQrI6VSCaFQCOVyGe3t7RyRQh5L4oHPf/7zWFxcZOVkcHAQTqeTwuE2zQNamVgnVCsBwWQy4eTJk+w+OnHiRN0xrTwarRCJRHD+vNGsNZVKIZ1OY3p6GoCxCaZSKdZEAcPNRfG2w8PDkFJibm6OPRLt7e28MMiqAQDHjx9HOp2GlBKzs7M4d+4cu+RWVlYQCASQTqc5LMflcqG7uxvHjx+Hw+HAuXPnEIvFMDU1hWw2i+npabhcLgghEAwG0d7ezmEPbW1t7KGYm5vjzZwUBvKSAOCNleZMVkRa/LSw6WWz2RAOhzExMYFYLIZMJsMLu1ksNF2bLJtWqxVOp5OtICQokDJB9zkajcJkMvHmTSAhhMJAiAAJan4J/Q5VWSsWi2zpJEJcWVlBKpWClBKBQKDu7x+NRpHP59mTY7PZEAwGWSCj3zA3N4dTp07hyJEjbJUii2WhUEAul8Nzzz0HAPjUpz6FYrG4Ao11oZEHTCYThoaGkBsEnJwAACAASURBVM1mMTg4iL6+vnssjlvlAfJ4Li0tIRqN3sMDQgh0dnbC6/Xi1KlT6O/vx507d5BKpZgHEokEpqamsLCwgIWFBQwNDeHUqVMYHx+v44F8Po94PM5heeSxCAQCbFjo7++Hy+XCK6+8wh63ZDKJaDTK1sFgMIju7m7s27cPLpcLhw4dwszMDBKJBFKpFLLZLOcR0TnkOSArPuX7ULw2cR0J+HSM3W6H1WpFLpfjsE7yXqprmkD8USwWObRTzRGjNU5KP3lnFhcXWXBXFZxisYhCoYC2tjbOQVNBCgp5TEl4UAWoTCaDUqmEYrHIQk0mk8HAwABbHEmwVPPhAMDj8cBms6FYLHJIE+VSBAIBFnZb8YDP58Pi4uJOhTq+IbHbPJBMJpHJZFh4XA8PqAJ0e3s7Ojo6AKCOBzo7O/HmN7+Zn6XR0VGMjIwgl8vV8QBZ5cPhMCwWC/r7+/Hss88ikUjgW9/6FiYnJ1EqlVh4p1wmj8eDxx9/nA0RFIa0sLDABj46XkqJZDKJUqnEvKLmSql5krT/q8I5ySvRaBSJRII9M2qeBrDqqaBohXw+z+eSQZO8FbR/0xwKhQLnlagyBikT9PnS0hLnUhIob5EMohQ61dbWhmAwyF4J4rX5+Xnmlt7eXiwsLCAQCDAPkFGAjCPt7e3weDwAwIYPUsYCgQBisRguXbqEXC7HeRWhUAhCCLz3ve/FZz/7WaRSKR3mtBE05iZsFiQ4Uwzw4cOH7xm/mUejFVTLBSkK/f39cDgccLvd7GkgSwQA9PX14dVXX8Xs7CznOdy5cwePP/442tvb2QU7PDyMTCaDdDqNr371q2wZvHLlCiqVCsdCkrUOMOIIKQkoGAzizJkz7Oa7fv06VlZWcPfuXXZj2mw2jp3M5/NwOBzo6upiqxq5IIlkhBDIZrNYWlri2EdaYEQUpVIJlUqF45PJMuFyuSClZPcyxSMTGhUJYDXpWs1roM2crJhEIKplFAArOGoSKGC4Tm/dusX5ICpp0TzIYkEuaAonKRaLnGhOv4uIin5zNpvlsI9ischEUKlUEAgE4HA42DVMipvD4cAXvvAFnDp1Ct/73vdw+/ZtRCIRtsYEg0FMTExgYGAAHo8HqVQqv64H/g2G7eCBRg4gAaJx7J3kAVVgnpmZwd27dyGEQCwWYx6gsYgHpJT4+te/jlgsVscDlMtjt9vh8/ng8Xg4npmU1+eeew4mk4mV2lKphLt37yIcDnMoj8vlYiEil8vxszo5OYm5uTnmgQMHDnCstip0q4nILpeLhSLiGdVySBbObDbLnkk1EbaRC2i9kUfA7/fD4/EgmUzW3U8ydqggLyZxAK3rTCaDmzdvsoel8Zr0e1XvJHkzVR6gYylsiX6/anQgRczlcnFMOoVZkbcomUyuiwcuX75M9yp934f9DYqHgQdo3I3wACmPTqcTjz/+OMsCxAMzMzNYWFhALpfD5OQkpJS4desW7zcWi4WT+kl5z+fz6Ovrw1NPPYWnn34a8Xic8wbUPEQ6d3BwEAMDAxyFABhrmAqIrKyswG63IxQK8TNOIcykfFNINVnyyQhI+Uuk9FMRilQqxZ837scq1HAjknHI26JGK5TLZY5gUItIAPUFGkgeAQy5gEKgCBSGTUUZ1BAnuo6qoJBnxuFwIJfLobOzE3fv3sXS0hL/LWguZLj1eDxsIKGQaYfDgT/6oz9i7006nUZ/fz970Z555hlMT0/TPOoJbwN45JQJVdsfHx+/b9ziWmhGDs3GP3LkSNN5NJJKo+Xi1KlTaG9vZ2vb0aNH2bVJwu3ly5cRiURw584djmEkqL/N7/cjnU7j6tWrKBQKbEknAmpvb4eUEvv374fT6cTMzAwnDR0/fhzve9/7ABix+/Sd1+tFT08PstksJxd96EMfQjAYxMzMDMdcxuNxTE5OcrIhCfEkkFAcNSkMtNiIENSqRiQIEHEkk0mqQsCbPFV7UAUKAp1P/6YqNqqFQf2eSIVclHQMYCxissBQ4hiFRqkkRlbSlZUVjjcnl3Ij4ZGnw+FwIJvN8kZBv4WsKSQAtbW1sXA1Pz8PwIgfP3r0KIfGkLAzMjICv9+P27dvw+124/r16xQqpnlgkzywXg5o5tlsJcRshAcSiQT8fj+EELh+/Trsdjvm5+fR09PD558+fRqAEadNPHD58mU2QDTyAIVNknBfqVRw8OBBDA4O4u1vfzsA4Dvf+Q4WFhbg9/vZ43f37l24XC54vV68//3vx/79+zkR+Pbt21heXsb8/Dwr8lT9xe12I5lM1lU9CgaDnNMQi8XqqruR0YK8CUIIjIyMsLCtrvvGtdgIEqwbcydIcFDDK1WoBgAq0kDVnbLZLM+PrgGAq6+RV8LhcNQVqFAVEJoLKX5zc3OwWq0olUosfABGhbf29nZOlC8Wi3zP78cD169fx+LiInmGHS1v0hsYb3Qe8Pl8HAZIY4+Pj8Pv9+P111/H3bt3USqVMDU1xc8xebTIIh6NRmG32yGlRF9fHz74wQ/i6aefxsjICO7cuYNMJoOurq66cB+bzYauri4899xzkFJyCBiFhOdyORbiSY6gqATiB1JkSLEgYyTxEsX+A8Z6KRaLWFhYqPMckBdD3fcbQfIEeShUYwTNhY5TZQM1zImOJc8DcUijV5RkAQBsZCRZjDwbBOIy8lTOzMxgcXGRw8jUJHSHw8FRCl6vF3Nzc5idnQVgeCnsdjtyuRznpBE/2Ww2vPLKK/D7/cRXm6rkBDyCQsRm4hbXQiM5rGf8tUhmYGAAly5dgs/nQygUQigU4o1gYmKCLXREXJlMBm63Gz6fj+PtQqEQDhw4wNelcb/97W+jUChgcnKSNzopJXp6euBwOHDy5El4vV5cunSJreN2ux1utxt/+7d/i/HxcRSLRdy4cQNutxt+v58t41arFUNDQ/iRH/kRJBIJXL58GXNzcxzaQFYScuvF43H09vaira2NYysLhQI8Hg88Hg+Wl5fZekGLmIR1ioMulUqcW0ECP1lViSAohAqoT+IGwNcki6G6oavxjaQsUEwygZQX2uQpSYpIoLFUIFknVTdro5BC50WjUQBGmTf6jEAhaplMBqFQCLFYDG63m8nWarViYmKCc1ro7yyl5CT7dDqNzs5Osp5smkAeVmwnD2wnB9B46+UBn88Hv9/Pz4KaG9DIAxQvTRvN66+/XleNZf/+/RwySRXf5ubmOIQum83i5ZdfxrVr1zA6OorFxUWEw2G86U1v4kRkKSWOHj2KZ555Bjdu3OCKQaVSCfF4HPPz80ilUpw0ePfuXbS3t/MaIstjqVSC3+9HLBar4wBKXm5ra2OhRQ1nAMA8QGERjWU5G1Eul/lvRp5JVRAhqOU01fVIcyZPYrlchsPhYAFItWiSAmI2m+/LA8ViEfF4nAUTp9PJyqDJZEIikYDFYuHwh43yQKFQ4HAJPKLKxMPCA16vd1M8QJEE5NkgpYdCme7evct5TuTt6u7uRjgcxr59+1AsFhGJRBCLxdDR0QGHw4Hr16/jxRdfRKFQwLlz55DJZNDZ2YnOzk4O1+nq6sLhw4fR39+Ps2fPYnx8nCtFpdNpxONx5HI5mM1mZDIZTExMsCKvGgRoHeVyubrwQuIK2rftdjtXUFK9gGSgpJwEtWhDI6gogurhobVH95K+Ix5QlQvyxiwvLyOZTMJms3GiuuoFIZBSpIZzNSofpOTEYjGeO8lcxAOpVApWqxWxWIzvfyMPULgUzSUUCnFoe6VS4VBPbIEHHjllYqNxizsxfiuSiUQiuHTpEuc3ZDIZPPvsszhy5Ahu3rx5zznBYBBerxcLCwvo6OiA3W5Hf38/hylFIhEmpkQiwSFLQH1tZIfDgf3798Pr9XJCFMUvp9NpvPjii0gkEpibm0Mmk6lzQ3Z0dLCF3GQy4a/+6q8Qi8Vw+fJlFhhUwZ8UgUwmA7vdzlWe1Oorat6EurhUK75a+lENP6LQrnQ6zaFBquVADVFQPQ2NCdh0vBqvqSoSBKrUoIYuqHWuG6EmaqnzJ9Ki0C4iIxKYpJScJEaVr2w2G1KpFBwOB5LJJNxudx1B2Ww2+Hw+ThBVa5WTxbf2W7e/2cwDjp3kga1wAIAN8QCBeODQoUMcO93IA+FwGCMjIyiVSnV5BR6PB1arFT6fjyu+UHlm8t5ZrVYkEgkuwUyhgolEAolEgjcnv9/PPEDHUy8DUu5J0a9WqywQk1JAlkDKHaI1pG7apLiTYN5oITSZTJwQXS6XkUql6u6VajkkHmjlvVDHJo5r9HiazWbmOPp95KFUOVcFhTi0tbVxH6DG0Czq/0FhXsSdVDiClBer1bopHiBLaE3QEfdM8hHAg84Dly9fruOB973vfdvGA/RsU4VFCh0mpaC3txe3b99GPp/n8ueXL1/mym7ZbJYrlUWjUXR1daG7u5sFd+IBqlJIpaXJIwGsCsszMzMcqlOpVPjZpPwFdX3SPk75AlJKFqzVvdflctWVn27kAcrFVPMumq1VUloAcCg2eRQaQRETZKylYg6NXgcCGTDJo6F6Uul3rqysIJPJcHiYqkARD1DlvUQiAbvdjmw2y6V8hRAcBmaz2eB2u9HZ2cnJ85T43SySYyN45JSJjcYtbgTkrqQwJCKPmzdv3hPOpNaMrlar+OpXv4obN25wHHIul8PIyAj6+/sRDocRDAbr6s3TeCdPnuTFQ1VM5ufnMTU1hXQ6XWfpcLlcCIVCbPVWq4pQLB7F4NL3yWSSS61lMhleFLRRkTAtpcS1a9eQTqeRTqc5iZiUhcbNm2IjqfEdVWug8UwmE9dpb3T9EUh4B1YrOgUCAbhcLqTTaf5ePZ9inFu5PIFVAYLmTaTTrC8FER3Ffzb+1magEDEqOUfkSUoO3W8iIfrbkiJH8ZjlchmdnZ3I5XIIhUIcAtHV1cXhUj09PZiamsL+/fuxsLCARCLB3g7qnYFHsJrTTvFAMw5oFjtNggaFLAwMDCASieDVV1/dEA9QCNPIyAgn31EPg/vxwNTUFIcMVSoVxGIxCCHwwx/+EIFAAB0dHVwxidZzPp9HNputa65I3lESpK9du8YVaKiogFpmlUDrhtYOKfW0eZLwTvlFZAhoVP5VLwKF/RGPkRFDXY/Ee2tZKYHVco/AalnIxqRKwFjPFC6pCkGNno1GULlZ8lJQ7XoakwwPxJXxeJzDsZaXlzkBs62tDYFAAJlMBj09PaxoBQIBtLW1wefzIRAI3MMD7e3tPM9HFbvJA0BzWUBdz9VqFS+//DJ78ylUJZfLYXR0lD2N1WqVy7/7/X6u1HPnzh2cPHkSFosFx44dQyqVwq1bt5rygNvt5sReAGzBptBaWjsejwdOp5MLJ1COJckGFJqUTCbR0dGBRCLBhVrIC0peCApjpmeODAu0B9L6UvdWu93OSeAkwKvhTUC9XEDXcTqdXOWNuEnFWsp+43FU1UptXttsf6dKdmrBCNUQQlD5iDxCpAiphlVSlijXgZ4tyrVSK9pRYZd8Ps+FGKhIjRBGZbr29nZEo1E4nU643W4OyVTC4zetUTxyygTQvDLTVqG6K4H6OGXgXhcmLYZMJoOvfe1rXOqrVCpx4zafz4dr165xYnfjQxmJRDAxMQEhBMbHx9HZ2cl9BQqFAsLhMJLJJA4cOFBX1aO3t5eTpYrFIpNSKBRCIBCAzWbD3Nwc4vE4nE4nIpEIC8AkpJNrnzasdDqNZDLJbnPawCmvgBajamlMJBLsFlTjlCkOmgiANuVWORDAasnZVCrFi1+tNQ3Ul5ZcyxqpJmACqAuVarw2CUSNAoZKFDSemmtBnqFgMMgCGrCaFFapVDgcxGw2s8VBFaaIkAKBAFtFAaMC0PDwMFwuF8eeCyEwODhY1w2VYjzz+fzabPoGxXbzQDMOWCt2emBggNfepUuXMDMzsykeAMB1z0dHR+t4IBqNorOzE9VqFUNDQ3U8QCFy9EwkEgmsrKzgwIEDCIVC3Hxpbm4ODoeD8x28Xi97OgEjFC8QCLDLXeUBUtxJ4FAVAgDsnVBjk1VBmno40OetYp/pcwp/II8kufXVc0gRoupqrUDrm85tJXSovKX+bdRrNsvdIA8jKW6pVKquNwBgCEvUP4A8pGSAIkWAEkR7enr439SzY9++fThy5AjfUyEEurq66viopjQ9stWcdoMHgNayAD0nqVQK3/3ud7npoM/n430wFouhr68PU1NTuH79el3ZZ2piNjExgXQ6jdHRUQwNDSGdTmNmZgaFQgGXLl1qyQOdnZ11FQutViumpqbwjne8A93d3VyqlMKo0+k0rFYr8wDJMtRXxul0IhaLYXZ2lkvGkpFAFcxVb9zy8jKPpZajBVaFeTK4AWi5dwOrXj+y1pNnolF+IINdq6gDujb9Ptp31XmoIANFYw5m43iN5wBGWCoJ+Gp5fDq/UqkgmUyy/FIul1mhoLlXq0bhmp6eHu7dQ4nb/f39OHDgACuBwGpSP8keNR6ozxrfAB5JZWIjWG+lh2buymbHhMPhuu+o0QsArqBCQqPdbsf169exf/9+DA0N1VWLUMeg8l+RSAQOh4PL/l29ehVerxdut5vDmMgNpzaFCofD7PrO5/Nwu91ssXS5XNyAzuVycRwwVUGhzZseaAr3IdDDT0IxfUehAWSVVBUG8gJQZRiyBtzPDUcNnMiV2iwGEVj1NqxFIGuRlTpOo5W02TGNggU16pJSwuVyweFw1HkkKElT7W2hlqpT620nk0kOfwLAidvRaJS7AlMN+0Qigfn5eS7/2dHRQe7YR9c0uQ5shQMa17v6uclkgtfrRSaTQTab3XYeoM7Wt27dwqVLl/DOd76TLXXZbJY3kVQqBb/fz2u+XC7D7XYjGAwikUiwwYDKk1J4o5RGNaOOjg5Eo1G2oBMHqJsqVWVRhQE1rK8xaRFAXRlW8hKsxxVPORA2m62lAYJKPlLZ5lZYy4NJIMV8rWObWUXNZjOHL1Gdd/rt5F0gq6T6bwoXIe4hLqP7VK1W4fP5uK8I5bSQdTOTySCXy7GxosaDWha4D3ZKFqC/eT6fRy6XYy43mYzOzsViEZ2dnRDCaFwopWQOoPLvdA3iAFrbNO/p6Wncvn0bt27dwunTp+H1evk4SvJPJpMIBALMBfF4HE8++ST6+/sxOjrKa54EW7PZjFAohOXlZXi9XoTDYczMzLARQPU6EpaXlzlxmvZ52suaVWAjZUptTgesrUzQ9/l8vi5RuZkMQTywVsGWVp67xjVNIcj3kwfod5EcQuu6Wq3W5YwCqOsloxor1IafNG+qFFculzmR3u12Y3Z2FjabDX19fTzHSCTC5agjkQhXtIROwN4ZbKTSQ6v4yPHxca4VPzAwAMB40KmrLZVNpHi+YDCIxx9/HFevXmUX582bN3Hw4EEAqBsrFAphfHyc4yTD4TCi0ShCoRCuXLmCUqmEmZkZeL1eTE5OYv/+/Th37hxyuRyXbqUScyqhkBuTQpRUqwC5PSmkAljtt0AWP9X673K56urJk4LQWPuZrJd0PTVkCLhXo28FGlcdj6Bu+I0WUhXrUSTWOrbxOo3fkfBCMdFut5uTRfP5fJ1wo+ZtkCWIxqRQBzqOLEvUDfPu3bs8NrmOqSEYKYa1eWplogW2gwPo893mgWq1ipmZGeRyOUSjUZRKJRw6dAi5XA6vvvoqWxo9Hg9cLhfcbjdisRisViu+9a1vIZfLMQ9QaBAJABQ+QzHB5I2ksCYK0SM3vdPprEuKJKiFIOidNn8A9/DAekGWPRKuG6Fa/1t5KDbCA83mp/JAs++Jqyh8xOFwwOFwMJc24wE1zpveVUGMcmDm5+frOiF3dnYin89z2UsSOqh/CADzPRPUYGyXLAAYa3dxcZHL9KocEAqFOPfF4XBwKOOdO3cwOzuLqakp3nupstPi4iICgQCA1ZwJKr1erVY5R4qs2OfPn0cwGEQul8PZs2fZm+V0OjkGPxqNYt++ffjCF76AaDTKHou2tjYOP6ZyxMFgkPMtaD1TWXkq3UwCOSnMFosFLpeLPXHNPPskL9BzrkY7rBdqLmNjTgJdh/Ivm4U/t4pgWCuqoRHNeIDkk8ZwTVLMyBBCBlf1OFKq1DxUNYSTwiYpt4WeATIykSJI/AiAczYArJLzBqGViTWwkUoPrWIvBwYG8NJLL8Hj8WBiYgKhUAgm02qDG4/HgwMHDnCJ01OnTiEUCmFubq6u3TmVgGsci65Jna6j0SiuX7+OgwcP4tVXX+VGSF1dXZienka5XObSsFR6FQA6Ozu5ZBslV1IXbOp2S+EDJDiQ1cTn83FMsxqWYLFY4HQ6uXQpJXWrsYDq4m3MNSACofrS64UaR0mejUZXJSk39Nl24n4WSlXRIMuix+Np6ooljw5VW6DKL43kFAwGuTEVxVFWq1UsLi5ySAeVnLVYLBx/qZWJtbEdHEAhTbvNA2fPnsXExASXHqRyxJSPQQYESoJOpVLo7OxEJBKp4wEyKlDhBbKcplIptLW1wWKxsBBCxwGrVZXa2tr4XlBFF6rY1MxaKKXkGvNAfYGEjQgS6nnvfVM7ntznxA+nc/jOWIa5hSyDzbBRBWaj56vJosRVFKLazEpqtVpht9tZYCOo5TSHhoa4VwCFZEQiEczNzfExVI8eACfGa6yN7eABqqJEa/z8+fPw+Xz3cAAA9ggGAgGu2kSlfvv7++Hz+QCsNrdNp9Ocn0EcEAwGcfbsWbz22mscM7+4uAifz4fZ2VnEYjGYTCZks1l+VsxmM27fvo2+vj7cunULiUSCrd9Uypms+cVikcMI1b1U9UoAhhxABQlCoRAsFguHSVKjx1aeATVvkKAKweuBuue+/1gIT/Ta8epkFi+OZerCr1pFP+wUD7QycqiNL8krSaDwaDKAqLKAmjfW19eHGzdusLeYOpxTRUzKqezo6MDKykqdh2Oz0MrEGtiOSg8mkwl9fX38fyKYxmZ3jcT03HPPscCgNp+jJlI01pEjRxAOh3H9+nVcuHCB3Zzj4+Po6Ojg0KF8Ps8ufYfDwZsN1Vin8BrqfK1WXSALptp1ltyPVOUBAAu76sZPpEFjkGWShAK1upTqwmyMcd7sgiZLJyVNqS7L+yVe7QbIatDoxiSQBZiOpbAIgslk4r9NJpOp+7uRIud2uznZm6zIDoeDiQaAe7d+78OGjXIArWMSPuj/28kDIyMjdce04gGPx8NhMX19fZyfQMq5x+NBOp1mRcJut7Nwmc1m4XQ6kUwmUSwW2bBAGzn1V6DwObPZzCWLVW8CccH8/DzH8JKLXlXyVVc9WQmbNZ3aqOL/8Q/3411DXrjaDOHjp58OYXyxhI99ew4vjmX2jAOa/S7qJN4suZNC38iQo3IFcRspDtFotM4iu7y8jO7ubr7XJPw1JLM/cuWhN4LtkAUorIkMOYT7cUAoFILP50Nvb2/dcSMjI2wQoHAn6ml1/fp1vPDCC5iZmeHmaRaLBcPDw5BSIhaLATDWqNvtRiqV4jKtFCJJPEBVw6iXCYXNCmF0kXe73UgkElzVkXiCnmNVYKfrqPsY9TxQLfVkhFCjFFRsVB74+If78e4hL5w1Hvipp4K4PJPHp74fYR54EAoRqKFfzUDFJcjzqt4bs9nMhVmmpqa4nC6FpVFVNypoYbFYuMmmkl9q2+zcH2plYrs6WbfCWpUe6NpquAxVQVDdoM2qtrRqcKP+/9ixY1xTmj6LxWJcJm5hYQGnTp0CYBDHZz7zGdy8eRPRaBS9vb0QwijLeuLECUxMTLAba3BwEIuLi+zyJFcalQmlbpOlUgkul4s9EhRLTVWcqKoQbf40lro5q90syVIOgJOeCFTtpVVJ1a1YBmizVEs8rjcnYrdA96zRlWm1WtHb2wvAcBtT8juBlCTyNmWzWSYYciVT6ARVraCScWQhrpGR5oEWWA8HkGCh1n8HNs4DwL3VXhp5AACmpqawsLCAhYUFDA0N8efNeIA29/3793MIVSqVYh5QY5KLxSLy+TwroWRIoDAnAHUhc/S80bMmhOAcKjUUTy1jSjxAyYIkSKg8Q2EPW12jH/9wPz54tP2ezw922PHpjwziX/y/E/j26L2Nn9895MWPvzmAp/a74Gwz49pcAf/4v9/e0lzWAxLA1OpTZL2lJoRUC78ZJ66srLD1UfXAOhwOCCHYu0keZrvdzr1/oDlgTWyEB0ZGRjA1NQW/339PSFQjD5w5cwaHDx9eUxagl3ocsH4eoHzIYDCIvr4+3kuSySQOHjzIioVaHYh4gCq9kQJqtVrZGEUeDcq/ymazbLSiZ7QxRImUClKGycNA1yHrOhVmaZWTuBFu+MuPHsJT/ffay07uc+HTHxnE//7FO3ix5q1U8ctnuvATjwfhajMhX67ir6/E8YmXFtZ93c2C7o2aQ0Yeh56eHiwvL9f131FRrRolc4nL1fLPBw4cYD6gv0s6nW4MO3/0wpy2s5P1WmjVsfL8+fNIJpMYGxvD8PAwWwLVxEg61+Px4Ny5c+jq6uKwBHXcxt+ilpMjSyRZI+laXq+XBeRLly7h8uXLmJiYQC6X4w64oVAIs7OznDths9lw6NAh9Pf349y5c0gmk+yhUC1e5XKZNVZ6yNxuNywWCxYXF+/RnIvFYl2yM72T6149nizxqveCEi2JcLYbasOYrbosdwqkUJCC4HQ6ORQkk8nUJWESqBoX9QygcAf1b6GGclCoGXXrzufzD+z9WC92gwfW4gDA6AovhNEtfnp6elM80Ox30PkqD0xOTgJY5YEDSlO6tXhgYWEBmUyGQ5ocDgeeeeYZfP/73+e+ERQ/DIAFThLuSSEgKyYdTwIvJWY2JhiqeRZqaCONabPZ2O0O1Mc4bwUf/3A/PnDEv+Yxn/yJfhQqEtmlZSxVJP7mZgrX5gr49EcG6457qt+Nv/zooV1RKFQeoLhysjhSPlujIEX3Xf1OjREnBcJms7GlMpPJNAoR648fe8DwIMgCQtW64wAAIABJREFUAHDx4kVIaRQ3WFhYwPDwMPx+f11IVCseII/CRnhACNGSB65cucLdqYUQ6O3tZWPSxMQEd7wWQuCJJ57AxYsXEYlE6kKeqBcUlXAnQdViscDv93PjQ/IwUggkJQ2r3ACs5imqnks1JJoMFSSPrFVpbT0wlIEAwp61nW5//A8PoFiRqKxITCaW8KnvR3Cix4mff0cXH+O2mfn/u6FQ0H5O3gOHw+glVygUuNFc495N3K0WnyFQ1MnRo0dZxqNCDJTIXeOBTRPvQ6tMbHcn681cm8KNSLjPZDIsRJCVIBKJ4MaNGxBCcH3gxrmqv2Vqagrnzp3D8PAwhBBIpVLcFyIcDsPr9XKJuAsXLvCipWQuYLVEGQDMzs7izp07AAw32ODgIDweD5ckVRvKqQI9hTHQOKVSid2XjbWdaQ4UBkXEopZfVCsnEYmQNbIxIWonsFVi2i2srKxwcjRZjVV3cbOkLQovc7lcdUnZVDGqUqnA6/WiXC5zYhzd+4c9Z2KveEC9LvGA3+/fNA80/o6zZ89idnaWEymJB6gJ0eOPP86hS1/72tcwODhYxwOkSFJS5NTUFIdA2u12HDlyBBaLBV6vl/tL0LNCfSXIoEAhcSTU0hxoTamhDMBqfwbaDNX45mbhPY0vGnOjePeQFz/79k68KeyAw3r/mGqTyQS3zRAUAODn39GF8cXmlRFP7Xc2/XynQAYcu92OSqXCPKAKaY28SflrVGWPhLpcLseWX5PJ6F1CZaQ3Env+oOJBkAWA1UpKHo+HlXe/318XErURHkgmk3jhhRfYUHHx4kWk02nOaQKAU6dO3cMD1WoVCwsLvCapoEG5XMYPfvADDl20Wq3o6Ojg3Emv18vPDBkXKTyaGksuLS3BbrcjHo9zYjUJvhTlQDIDcQrJAeTtVEueAvV783YY/N495MXv/P1edHvXF7FjrvEAALQ7Lfj0RwaRKjSXF37qdMeuKBPAKjfa7XZuwJnL5ZgHmhlcqtUqG4OpaR0ph+l0GnNzcxzWSrmzxNNbve8PrTKx052sWyESiWBxcZFLci4sLHDjGDXxSSUHqrAAGIJHI7nQeIDhonQ4HBgbG4PT6eTkZzqXyGd8fBxutxtjY2M4ePAgjh49ips3b7JL02KxYHZ2lrtZk3trfHwcgUCALQwUvtRYI31lZYXzJ+x2OyddUjwzCbYk6JIg0uidaNSQCaorX2MVZLGVUjKxUyWdZklqJFy0tbVheHgYlUoF0WgUU1NTWF5eRiqV4s6YpNhRyJvL5SLL5EObgblXPFCtVtkLQQI/gE3zgDoeYBgBCoUCFhYW7uEBWjMejwff/va3AQA//OEPmQeuXbvGz0ulUsHs7GydN8HtdmN0dJTLSDscDu5NQHMhIUEIweWiAXDXZrXJoqpYAKvNJOl5pfClZgq9mmS9Ffzyma46S+JmcbDD3vRzscv6NnlwzWYz7w1UIrvxfhOWl5fhcrlw/PhxLgkcjUaxsrKCaDQKj8fDSe/EAwALfQ9O3OcGsVccANSvW6/Xy/kLQ0NDOHDgwD05EOvlgWq1img0yk3IyAOxsLBQxwNSypY8cOnSJQ5xW1paQjQaRbFYRDKZ5D2DmtVSeB11W6fxqfIX7e9UtpRi7tXjqMITQQ1ZppActVJb433cjtDjVqGNG4Xf2XyeDsvuKt9qhbx0Os28qvKrKkPR54FAAENDQ9y4lIwLY2NjnP9G95xCodPpNKpb+CM8tMrETnaybgXVBSmEQH9/P06dOnWP4KAiGAzC7/czGZw5cwaxWAznzp2D1+vlhUuC9bFjxzihWQjBZR6pqlJvby+uXLnCDV5cLhdMJhM+9KEPobe3F9euXUMwGES5XOZOp7ShU2Oqs2fPsmWBNiiKo1NB1msiAtUyRkpCs0osdB4Jwa2qBGhF4l6QgEaJ1MBqHftW9xsAWxkGBwc53pVCGCqVSp0AqFbfqCmHD60ysVc8MDExwV6IM2fO3JPf1IhGHjh69CiHL1LFFhqPcp6oZCTxwJ07dzA3N8d5NK+99hpisRh3eyYesNvtGB0dRV9fH+LxOGKxGHMA8UAsFsN3vvMdLgPtcDjYEwGAY5spVJGeJTI+rNWYCaivoNKYZK0eo/ae2QzWG8qwVazssqxN9z6VStV5gEmQaJZXVq1WMT8/j1AohIGBAczPzyOVSnGYE5WLJE8mcXUND60ysRccAOwsD4yMjKCrqwterxdjY2Mc/SCl5KZ2PT09EEI05YEzZ84gm80imUzC6XRyDmUikajLR4pGo3VNEdva2uD3++vyF9XGbaR4kCdCDatr1beJQJyy3dXDKMfpRLcTXb6d5YHqDiwT1eDauK4pv0Tt4E1FMJrdx2q1ykahwcFBeL1epFKpuvAxtTqfWvK7NodHswP2TnSyXguqC9Lv96Ojo4NjHVuhkehisRi++MUvAgBisRiOHTuG/v5++P1++Hw+TE5OcnjKj/7ojyIUCtXFSlPoy927d7kx2Uc+8hGEQiGk02nuPNvd3Y1MJlPnwjKZTNxtmR5Oqv1ORASAPQy0AameCNUdtpaSQOdvpRLTGxUUy0w5L5RETaFhjQ181POafUaWovHxcbZcEeGTlVhtegeAk7lef/114CEWJIC94wG/3w+/3w+TyXTfOag8UK1WcenSJS6m4HK50Nvby+P5fD5kMhmu3U488MILLyAcDrMQMT8/j/n5eSwtLTEPAMDrr7+OdDoNp9PJFZTa2tpYEKW4bjUkoVwuc/4CJfvRxkbloNVyhDTWWooACQ/kbWt8prcqWGyXFXI9sJhM+OLzh/Cp70fx4lhmy+MRlyqhhhwmSve2sb68em4zUOjSjRs3aF3X5U8RHwOrfxuv10vNvR5agwKw+xwAbA8PXL58mY0GTqcT+/btg9/vx+HDhznfgT5/+umnAQAvvPACOjs7sW/fPgBoygPj4+Pc6C4UCkEIgenpaQ5ZUkuPkqGAin4EAgEujqCGLuZyOc5pIp6gZ7aVQVsVVElB2WpPFxXvHvLek+O0kzCbTPgvP3EAX7qa2DYeUEMN1bVN95iiQhrPA5obZCnq5PLly01lAUq0J0hp9ANLpVJY2QIhP9TKxG5jI+7UxmpPZKk4d+4cH+NwOLCwsID+/n4ARjt6akrj9XrrEjQPHz6M8+fPY3p6GgC4iyTN4ezZs3j55Zdx48YN5PN5hEIhHD58mC3WtKlQRSagvgRrqVSCx+OBz+dDqVTC4uIil5RrfGAp1KmVcKuGSzUmDe9EaBP9tu1wk272+uv9TUIILsvodDrh8XhgsVgQj8fZokAErULNg1GhCnXxeJxL79HfhoQ51UOk9p3YiYT3NzrWywPNOODIkSO4efNmXYdaKSXHVwOteeB973sfe0anp6frGg8Gg0EsLi7im9/8Ji5cuICxsTHY7XYMDg5ieHgYqVQKlUqF45zVTvUUqkjPpsPh4IZ01NG2Mf5Z5YD1rLtm62O96/XjH+7H6X43ystVBF3GlvXaXLFphZadghACJ/e58emPuPHDqVzTZOyN8ACtP4pdt1gsCIVCSCQSHI7WrOFeq5hyEkgoQZMajKkNBEk5ofOp/Gbt3Ic2AXuvsB08QHkWBOIBv9+P/fv348aNG9xfBsB9eaCjowOLi4v4xje+gQsXLqBYLGJqagrPPfccbDYbIpEIh8405ifR/pDL5binicfjQSqVQiwW40IK5KmgMu+tog/UIgyN3kl131nvmvn4h/vxzKAHK1LCZjbuR2Zpd3MhTULg2Tf58eyb/C15YL0gZcxut7Nx0WKxwOfzIR6P1xl818sDdF/z+Tw3p1ONFBSKpua9Uj+w2rmPXgL2XmC97tRm1Z78fj8GBgbYDUUdZ9/znvegs7OT+0ioaKwCMTAwgKmpKXZdAoZS8dJLL+HKlSs4f/48u8ULhQJbSpLJJIfK1OLi+AGjF31G7lCK1wdQpzhQVZb7NXhRNy3CTgmujQrLbmMj11YVLZfLhcOHDyObzWJxcREA2EOxkeuopK02DKM49UYiog1BuZbWKDaA9fBAKw44ffo0qrWqWsQD+/btq8uzaMUDdN2RkRG43W6cO3eOQxTdbje+8pWv4MKFCxgZGeFcCMAoL/vYY4/htddeg9frRbVa5Y2GPIdqbK7FYmHvpWpQUNf7ekOT1DAIQmPux1poVdZxNxWJZtf++If78S+/PFX3+UZ4QAjBuVEkOFKYEvFxKw9EMyWMhDtSDOnvozYSbPybqZ4jAA9HhYoHCBvhgVQqhdHRUQwNDaG9vR0DAwNIJBJ1z0xvb29d2HQ8Hsf09HRdX6n78YDL5cJXvvIVLuCwsrICj8eDF198EadOncKhQ4ewtLTExTpUHqAIBMqVtFqtbKBSIwwosZtyMdTciFYgGaKRB9ZrUGjFAy7b3hUQaOSBjRpK6b5R+Kjf78fgoOFlWVhYWLM8dqvPSYYjDiFDo9pMuDGvhUrz1+auPRPNsJXa063OvZ8bMxKJ4Ny5c0in03XVngDgpZdeQl9fHwYGBgAA+/fvx2OPPcbjxWIxXLp0CcViEQ6HA/v37wdgPDjj4+O4efMmAHDnaYfDgampKfT19XH1CHKJURlXqh8PGG7UQCDAD2kikair+04acLOmKaow0aqRTCOaKRNrLbZHJSGbYksXFha48VepVGLPw0ZiyMnqQzGURCbkPm2mwJHLMxwO48aNGwDwhr7pe8EDIyMjdYIAWRxHRkaQTqc576G7uxv9/f3sfQBa80A0GsX09DSHP5CyQDxQrVbZw0UVvbLZLGZnZ+s2bpvNhvb2dpRKJSwtLbHhQBUSSPhsXI903EbWqbrxreVFpNjnQx12BJxmWMwmrrT0oOE9w94tna92sY5GoxzPTJV11so1a8aTZrOZCyuoHX3J8EPGpMbzqGkp3uCeia32odgqD5AMQF3lSRbwer1cwOHUqVM4duwYnxuLxXD58mXmgb6+Pi7WQqGKlO8ErPJALpfjXBkKbcnlchgdHeV8JzqeQhDL5TKHPNH6p+pMwL17eaPS2gzNZAjar9SSsY345TNdeOdBL0qVKvoCbfDazeuqzrYXeGbQw//ejOyyvLzMzeUAYGJioi7PaaPRFopSwEZD8nKSAbERFDq/VR54wyoTW6k9fb9zWxGLaoUYGxvj7yhBy+PxIJlMclk3s9mMb37zm+jv78fhw4eRSCQAGK7qeDyOb37zm3jsscdw+fJlLC4uIp1Oo62tDYFAAPv27cPt27dhs9lw9epVXvSqNprJZJBMJmG1Wrn0q91ur2th//+z9+axcebpmdhT9/19VcUqVvG+RFIHW91ST6tHc7Xk6ajd0w7g8Ywxfxjeze4snFk48cAI1tkEAWIHCRJsEg8cZwF7YcQJHGfnWGN6tmfcI3naGkmtVovdOpuieN9VrPv46r7zR8376qti8ZBIUWSxHkBoUayryarn+73v+7zPQ5B3JjdLYNztQX+7D8ZRKCTkKBQKWF1d3ZUdHh0AqQNZLlf95MkFZLP7qFQqrK2t7Xrp6qDjRfHA0tISfD4f26gKQuPD5/r6Otra2rblgc7OTty4cQM6nQ7xeBz9/f0wmUzQ6/VYXFzkrAqg1l0llUphYWGhRtpIjj56vR6VStVSVC6FSyaTLI1pdCDYDTabIu639nm30GtUG6YTZE3bKWjhlQqcrrsdstks/H7/jtO4G/38yJ2JuJ4mzeT+R+8J+X3psPIrXm7aycRucyj2ggco08disdScBTweD4Dq9JDCLmkyQSYr6XQakUgEP/7xj6FUKuH1elGpVKDX63H8+HG0t7dDkiTmAa/XW3Ntr1QqCIfDiEajNQ6MNI0wGAw81aLrST6fRyQS4QlGPbYrJLbDZoXyXrmy7RdEgxrffcNdYxd7cVjAH321Aw6TBh8uJDZMMBuBft7RaHRXe2SUSA6AGwtarZZ3Iuo5gK4LsqZjazJRj914Tze6L/23XG6ccCu/HVnDWa1WXLhwAZFIBJFIBGtra/D7/ey8sLy8DL/fD0mS8PDhQ3i9XoRCIUiShPX1de5mkNzA5/Oxvpo8n0nXHAgEUKk8yXqgzgWAmg41daLkGRIE+dfyN93TyBJaeDrsNv9CvuAmz/mgrnMjUKeKOloAGt+wCfCieIA4IJFIoKurCyMjIzwNpM/9djxATitk6wdUD56rq6v8/0HWj3TbQqFQY9+czWZrbJ1pMlAoFHjJUu7WQqYLjQrc58UDF4cF/A9vd+/pY+4H3jllQ7tZg9/5m7kNxZBL0Nak626HnRYSm4E6zCRjy+VyUCqVfDihaVA9r5NvPQ65CcNW2G0ORf39p6ena5KoNys0NuOBQCCAW7duIRgMIp1Ow2g0IhqN1kws/vEf/xFerxfBYBDBYBCBQACBQAC9vb0cZEo24CMjI7wLtbKygmAwiGw2y+5sJK2WTx7lUmXS7DcqNul9tJfYjD8uDgv4z8459/S59gO//2U3zvWaG/IAGUTspKDYi51Pag6JosjZNEqlEpIkbdm0pPcHdsEDTVtM7MZ7uv6+5XK5ZuGpUcJt/f1sNhunVy4uLkKlUsHr9SKTyaC3txeZTAa3b9/G2toaS4+0Wi3m5qoLPQpFNahqbm4ONpsNlUo1nZa8xCkRG6geMKhA0Gg0TBTUbSBCoIh64EnOgxzk9lH/ptvsDdhoMaiF/UW9qwO9T+Q6VroNdSqo4xyPx+n9YHoRr30/8CJ5wGaz1fAA3VfOA8vLy7hy5QqAquwtEAhAq9Xi8ePHyOfzMJvNCAQCcLur3bpMJsOFoM/ng81mg0qlgiRJyGazPK2Qu3fQtLFQKHC2DHUWacGPIN+NamQ/2gi7kScetolEPV7rM2PyvzkNpbLx2tG5XvOeuL5sB5r4UMYQ/b7lAWLyXBCSs8oWcZ+vp+YLxG5zKOT3j0ajiMViiMfjmJ+fhyiKNbfdCQ/Mz88jkUhgbW0NQ0NDUKvVmJ6exszMDHQ6HTweD+bn51EulzE1NcU7DpIkIRaLsVSFFnQnJiYwNDQEALwoTZNQuZmHXLpIS/9yKU39eYAMW3ba8JK7RD0tmoMHXoKqQeF14djuJJHPAnqP0BmQrvnE1fLGULFYRCAQoN/dM/NA0xYTu/WeJpIYHR2t6UxslnC72XPSngOlTtKfeDyOzz77DKFQiEdTAwMD3KXo7e2FTqdDZ2cnLl68yOEylE5LOnun0wmns1rN0+I1FQ+xWIwPlZRASVIG6kTIC4JG7ksEjUbDnQ1Ca1Lx4kG/O/mCFVBbQJBLD00q5Ie/X91Hs68veh9xkHlgeXkZ09PTCAaDCIVCbM/c1dUFrVaLTCaD9vZ26HQ6DA4O4vz583j33XdZrri6usoZIuQGpNPpWLZEZgySJNV8rvV6fY1No/x7W32m5XbG9Ut8O8F333Djd15tg0alxKQvg2imiHO9h7+OVas279yOryT35TWQnIHsPemzTha/8mlEvaPOr77X4oAtQDwgiiLi8fimt9sJDxAHKBQKRKNR2Gw23LlzB4uLi7wfQdd1tVqNdDqN9vZ26PV6nDp1CiMjI/jggw+g1WpZujI7OwudTgej0QhRFJkjiAey2SybKRB0Oh0XJNSAkmO7Ben6ZuJOC4nm5YFNjFP2cSVRfj2QW3fr9foaEwa5gQ6dCX/19TPzQNMWE8CzeU/L9ZEEeWdCnnBLC4/0XI2ek+6bSCRgNpsxNDSEZDKJe/fuQZIk5HI55PN5zojo7OxkH3mbzQaXy4WhoSF8/vOfhyRJ+NnPfoZoNAqz2QxBEPigQXHrlUo14XZpaQnpdJqDyrRaLXQ6HcxmM1eqiUSi5sJCZFJPEgqFgi9UFHS3mQd6C/sPeR4IuXBQV0mun6e9CgDsN/2r32FTuzkdVB4YHx9HPB5HKBTiDiD9Lp1OJ0RRhCAIsNlsOHHiBE6cOAGHw4E///M/x8TEBJLJJBwOB2uxx8bGWOJGBeTc3Bwv99O0klxfyC6Umg4EmlbID55A9T1DgUfU8dypPKdeC/0i3Zj2C8lccV+mEgTKAlEqlXA6nbwXR8ucAGoWMFUqlbyQbOoF7GfNoajngYGBgZpiYnR0FMAT6eNOeIA4oLOzEy6XC+vr61hdXUUkEuFJATUH2tvbIYoi9Ho9RFHEpUuXcOHCBfT39+NP/uRPEIlEoNfr4XA4YDKZoNPpMDAwgHQ6jUwmA6VSiXQ6jc8++4ybDACYB6jwyOVySCQSNdd0WtyVH0rpvtRcJJ5olIXQCEeRB/yJ3UkYnxa5XI7dHJ1OJ5LJJHQ6HVuDE2fT1IKuYbvlgaYuJp4FjfSVJ0+e3NBh2MlSFy1mDQwM8MEAqCbXhsNhlploNBq0t7ezr7zVakVfXx9UKhW6urpw9epVKBQKJBIJ7joRGVQq1bTkSCSCl19+GQsLCwiHw/y41H0ymUyca6BQVAOvGhEHdR7lNoOUwih3DWoVEgcHxWKR3RjocECLvyR/ky9aKRQKDig8CgeJZ8F+8EA6nWbddKVSzYro7u6G2WyGKIowGAzo7++HIAhQqVT44Q9/uEGqRntRdrsdoVAI7e3tOHnyJB4/foxEIsETSfq9i6LIYXa0b1U/jaT3C/2bvKCQ//1pdP7vnLTu+LbNgtIeUyS53Fyfl2oWPgk0mS4UClxA0JSaDn/yw6ROp4Ner2db6hY2op4HlErlplOOnS5pv/HGG7h27RrzwMTEBCKRCB/+nU4nTp06hXw+j97eXubu06dPY2VlBT/5yU8QjUa58EilUtDr9XA6nTAajZiensZXvvIVpFIpLC8vQ5IkaLVaaLVa/lzTWcBkMtWE2MpB1w8KqZO/f+j6v1NnR8JR5IH50N6uJG7HA6VSiR39stksisUiZ9eQDJ7UKSqVCnq9nmX1u0GrmKjDZvpKeYeB7F9jsVhDzTTdRt7ROHfuHEZHR3H58mVEIhEYDAbYbDYUCgUIgoDR0VEEg0FOrszlcjh37hxWV1dx48YNaLVaPjhYLBbE43E+8APgXYfZ2VlEIhGuOCmEigJRaEGTQkxIOwvU5hUIggC9Xs8FDyUo13crWzgYoI42gJouEf2+abKkVqthMpnQ1ta25bj+qGM/eEChUMDhcCCXy0Gn08HtdqO/vx9erxeRSAQOhwPt7e3o7e3Fw4cPcePGDVQqFQSDQQiCgHQ6jUQiAYPBgEAgAKBqM6rRaLCysoJIJFKTWm8wGFgrTe8L4EmHupHc0Wq1cu4E7V89jT88IZopov8pfweHHR8uJLa/0TYgu9zTHUa4xaqc+aVOIy981oMOd/WSFmoGUeCgSqVCR0cHDAYDuz21sBGNeKB+2iC3g6fgSTkPNGo4fPOb30Q4HMbMzAxyuRxEUWRHrv7+fgBVG9lwOIyuri50d3ejUqngww8/RDweRywWQyKRgNFohCRJSCaTiMfjLHP5+OOPodVqsbCwwJkFer0eQLUgMpvNHGpIZ4X6JWxqIqpUKpZ50SG1kcXoTnAUeeDvHkR2/RhUQOjUCoy0V41TNuMBuWyp3oRFfh6gsDpRFJFKpWhC0Qqt2ytsp68kYojH45idncXw8DAAIBQKwe/31zg5RKNRJBIJWCwW7mwKgoB8Po9CocCdyJGREayurvJyJU0Vbt26hdnZWSQSCdhsNl6y1Gg0aGtrQ09PDxKJBPx+P08uyFualrYsFgvLkoLBIE8VaHwpr1Tlo8x4PM4dEXlaYguHD9TBUqlUsFgssFgsm+7GtFDFfvGAxWJBd3c3nE4njh8/zjkUZB8dCASQTCZx69YtJBIJ6PV6JJNJSJIEjUYDh8OBtrY25HI5+P1+aDQapNNpzo7IZDKckGsymZDP5xEOh5knKCGVRt1kwkCIxWJ8QKLO5LO8bz5aTOJMd/NLGuT4jxPRp7r9xWEBf/x2NxxGNULpAu6uptkNph6v9Zk3WFJuBfnOBE2otFot8vn8cwsTbQbslAfIDn5kZASVSqWGBzbjAJfLxdkPNpsNer0evb296OjowOTkJLLZLKsR9Ho9AoEAHj58yHtR9D21Wo2hoSEYjUbE43EEAgEYDAZuNlB32mw2w2Kx8B5NOBxmO3jqUssD7KggpUV+URS3zIfYCY4iDzwtqjslDug1CnjieURSpU3lYE/LAwBYnkYT6rpdl2f+5baKiTpsF25DY0+r1crEQcnS4+Pj7NgwOzuLmzdvQqFQwGg04uzZswCqcgRyZqlUKjh+/Dg6Ozvh8Xig0Wj4w+3z+ZBIJBCLxeDz+bC+vg6dTgelUgmLxYJSqQRRFNkLmsaYwWCQFytVKhW6u7sRCoWQSqVQKBT4jSO/uHR2diKfz9dIn8jGsoXDD/kFgHT5csvQFmoh54CTJ082vM12PDAwMAClUolAIID79++zBSTxAB3oaAdheHiYD3UUNBmNRhGNRrGyssI8AIBH05R83dPTg6mpKdjtduRyOcTjceh0Ol681mg06OnpQTwe584iJdfKu5AdHR1IpVI85QCqjQSSVOwGw079ru5/GPGdL7bveGeiXkveIejwzindlvf55sv2HR8iqIigiTQdEOl90MJGPA0PkAVsIx6Yn5/HvXv3AIBd3OSPZ7VaWYI0PDyMVCrFaoBMJsPNgeXlZUQiEaRSKeTzeRiNRqhUKpjNZqRSKbjdbjZloVwLypOg33F7e3sNB9CuFp0t1Go1HA4HIpFIjfSpVCrtyXngKPLAN16275gHvvf1vpoGwpDDgCHH1vd5Gh4Anpiz0Gdfp9Px+wW72J9sFRN4QhpbeccT6pcwyd0hFouxl69KpcL4+DhmZmbgcDhgMBgQiUTg9/sRiUQQDochSRLK5TJrFylMrqOjA1arFS6XC4FAgD/w5KTU0dEBnU4HtVoNj8cDk8kEq9WKrq4uTrkk3Ww6naaEYyYPAKyB1Gq1sFgs6OjogMlk4iTWFpoD8o4jFZjyUMN6KcRRxtNwALA1D6ytrWFiYgJjY2P4+OOPsbq6CoMCh5VfAAAgAElEQVTBwDwwMTGB27dvw+fzoVAo4NixYxgdHUU8Hoder4cgCFCr1fjc5z4Hk8mEx48fs0sbAG4cANWL/OrqKk6cOMEL9xaLBQ8fPuQLhCRJmJmZgUaj4YMK7V3U8wB1LOVSxr3ghJc6DdvfqMlwpnvzruH3vt6HLw1aEEoV8PPH8WcK6nIJ2h11JeXZQ+TcQxNw2f5bazyB3fGAzWZryAOCIHB+lNvtxqNHj3DixAkEg0F88sknWF9fr+EBSZIwNTXF0sTz588jnU5jZWUF5XIZyWSSJc60+5ROp+H3+zE0NMSH/mg0ivv37zPPx2Ix/sxTlozcNlQURYiiCLfbDVEUMTk5ueeF5vmBozeVuHTciovDQsOC4m9/9xhOdRjwaD2Dv/o4sOkkcivslAfkMjaaWlNYcaOA0qfFkS8m/H4/Ll++jEQiAUmS0NXVtan+GaiOPQuFAu7cuYNXX30Vo6OjuHLlCmZmZgCA0y1XV1d58QUAIpEIxsfH8cknn2BhYYEP9Pfv34cgCDCZTDh9+jTW19e5w+H3+3lXgbSu4XAYVquViaRYLEIURbaQDIVCfGgkHRwtTwGo6TSQZrpcLsPr9cJsNiMafbrRfAsHFxqNhvWTwJOCIpfL1XjQH3WQVCEajWJmZgYul4t1y5sFXG3FA8FgEAaDAcvLy1hdXUUikeBuUCQSwc2bN3H37l0sLS1Br9fzDpQoiqzHzmQyGBwcZOliJpNhgwTaZSLNs16vRy6XQ1tbG4xGI0qlEpswAGAeIEMGube8nAey2Sw7xe31Po1R09g2sdnxnS+2b0jH/d9+sxcWXfXSKxrU+P0vP3uh9Z0vtOMLA2b8xc1Aw8MKOfHR4ZH+jaQsLQ54gnoecLvd6OvrA7B10F0kEoHX68X58+cb8gDtOQFg57Tp6Wl89NFHuHPnDpaWlthpkfYuent7eZ9qeHgYs7OzAKpNQfocGwwGPm8Eg0Go1WrY7XY4HA7o9Xqk02m2h6Y/cgthuS00FZvk2JjL5WA2mzk4d69gUB9NHvif/9MefP5PH/HXF4cFfO+3+pgXX+sz49Ue4zM//j//vBN6tRLjK8lNeUBuF0vKFZpO7AUPHOpiYjtJ0k4wPT3NH9RUKsVJlRaLpWG4zS9/+Uv84Ac/AAAOmCN3hK6uLgDgiQQdENrb22G32/lwQUtSRqMRNpsNiUQCLpeLD/UzMzOw2Wx49dVX4fF4eAxZKBQ4KIvsvrRaLeLxOBYXF5FKpfh10qGBDjHkKyz3Fs/n89BoNJienuYKtYXmAemh6fAgTzluJp30bnmANM2zs7M10zylUomBgYGG99mKB06dOoVAIACfz8cXdqVSifb2dk6rjkQiHDJJkw0AGBwcRLlcxr1793D37l2WT6ytrXHwJFB1Z6OvdTodbDYbJ2pHo9GaLlO5XOaMGXqdjXhgdna2ZpF/L/HhQuKZum6HHSqlEn/7u8fwO38zh7/93WN7boWpUilxptuMv/yWuWHadqVSqZlA0oGSdubkoWaHHXvJA6lUCuPj40gkEuju7m54FvD7/fjRj36EGzduAADLA+t5IJ1Oo6+vD5lMBg6Hg/MlfD4fotEo8wBNNiqVChwOB0KhEHK5HH75y19iZGQELpcLoVAIKpWKreCdTic3jbRaLaxWK0udPR5PTSCdPLSMpK90LSBLaUmS4Pf7eWq111gIZ3mB+CjBbtTgu2+48dCbxp+83Q23sDEbbjdJ4waNCt8+345vn2/flAeoMU2/90KhUGMXv1sc2mJiJ5aMTwv6sEmSBK/XC0EQcP78+ZrHnZiY4L9ns1lcvnwZL730EpaXl2GxWNDX14c333wTjx49wuDgIBQKBU6dOoVIJIIrV65w0qAgCLwAA1SJ6N69e6ytzufzcDgc+OIXv4jbt28jHA7XhBLZ7Xbk83kkEglEIhGsr68DeCKBoIKFxqH0R24RClQnKQqFYsPWv/w2LU3t4YX84EhfN1PRuBc8QN7vQPWQ7nA44Pf74Xa78dOf/hRzc3Pb8sC7777L0oWuri6WDJCRgSRJvFhJZgvFYhEWiwUAWPfciAf6+/sxNDSE1dVVpFIpmEwm7izRMv38/Dwn6FKjgS4YlDFDS9nyhHs5D2zWoaLDxm7kTn/442X8+gmxYUJss+NUhwHf+3rfc/fU30ybTb83cu+j3YlmKSKAvecBADAYDPD7/QCAx48fbyhUwuEw7zHR1++++y5cLteOeGBtbY13Gdvb2wE84YG1tTVMT08DqHLS7OwsRFFEf38/fD4fstksHA4HstksjEYj9Ho9yuUyFhYW+H7pdJqbCGTqotFoeCGb3gNyoxWSau+F7KURfuPfTWPqvz29q4PzYcU7J23PJGd8Wmy1o0ERAMQJNJ3aCxy6YoK6D/Xe2FuNIbfC6OgoezFTmMzCwgI8Hg/C4TDW1tZw+vRpjI6OwuVyYWxsjDsRJC3w+/0wGAzw+Xy4cOECxsbGcOLEiRrt5dWrV/HgwQNYLBa4XC4MDw9ztkQqlYLBYGAdm8FgQC6XQzqdBlC1gqXXp1QquePpcrlY1ylJEtLpNLRaLZOMzWaDSqViWRWNNMkajNxZGnUi5RZxLRxukFMHXVSaAXvJAy6Xq8b7XZIkCILAPLC0tLQlD9Dt5QuLb731FudQEA+89957uHfvHhYWFiAIAiqVCrq7u3Hu3Dmsr69DEAQYDAbk83nk83lYrVaWOAHVgwaFTdKk0WKxoKenBxMTE5w3Qr7yDkd1c6+9vR2xWIx18vLDpZwHNht175XzV7kMbBEW3bTQqoCv7YO/vtO89eVcnhvQAIeO7OWTiEa5MLvhAblLYiAQwPe//3184QtfgEKhQF9fH0ZHR9HW1ga3283KBkq2344HfvrTn+Lu3buYn5+H1WpFuVzewANkoEDue/l8vsbaWaVSIZlM8mJ0T08Penp68OjRoxoHJ5p2kAOk1+tFKpWqkTqSFJp2KJ73db9UBo5gLYEe2/6EzG/FA9RcJB5o0CA6Gm5O8u4DaQ9pv6F+DLnTkafL5cKlS5f4g37t2jU+xFcqFUxMTLA7w7lz53DhwgUA1c5kZ2cn1tfXuTthMpl4+Ym0z5OTk4hGo1hcXESxWORR5+DgIMbGxhCNRnHv3j0Eg0FIkgSVSgVJkjA8PAyn04n79+/DZDJx+Jj8wk862Hg8zl1HvV7P3sEajYbzJ+QEQbq5rcLnWsVE80CeVNoM2CkPPI3sYWxsDE6n85l4oFQqbRr8RTxw/fp1BAIBrK+vI5vNQhRF9PT0oLe3F319fYhGo5iammIDBaAqUzt9+jSA6ufRZrMhGo1yR1GtVtcsZtPeF/BEytbd3Q2lUolUKlXzeacAStLO74fxwn1P+kgk3tZDrdofnfhLHUbc+INTSOVLmAtl8XcPIjUdynrJSx0OFTnUTyLq5Yi75QHaaUilUiwfpIO4JEnMA7/927+N7u5uNlSRZ1LIIecBv9+P9fV15HI53o/o6elhHvj0008xNzcHtVqNTCYDQRDQ09PDRUtbWxs8Hg8bs9DuBKVYE0eQ3MrpdLLDE71O+XuBJlVbNZr28jywGMkdSanTfk1lX+ow4v3vjELKlhFMFjbwwDYNxaPh5iTvPpDOmHzWNwuK2snIsz6ERpIkGAwGnhKQFIG6HRcuXMCFCxfg9/vx8ccfw+PxAADbt8l95ml02tHRAb/fzwf98+fP14xUlUol+7lXKhVEo1GoVCpks1nEYjF0dHSwL32pVGIfaRpvAuDDQSqV4twJYKMbS31x0QgtV6fmwQ5+l4eqatwJDzyL7OFZeODEiROYnp7GgwcPkEwmOXmcbKLlj2c2m9Hf349UKsWvl2yiCXIeMJvNyGQyiMViiEQi0Gg06O/vx/z8PHQ6HV8UJEmC0Wis0UTTjgzJNOoTbjebSD5P/M7fzOHuvxqDWXeoLjuHBmqVEi5BCUCDQYcel45b8Z0fLuIfZ6rL9NvwwKHlAGDzZOrd8IDdbse1a9d4skCfHzkPnDx5Er/5m78Jv9+P6elpJBKJHfNAOp2G0+mssYtXKBQ8iTQYDDAajbDb7TCbzchmswgEAhBFEX19fbxbSRLmRCLBmRH0u6Yl7GKxCEmS2PGNQIXEdueBvWws3phPHMliYr+gVikx5Hjy87103Ip/+cNF/OOstJMp89GYTNSnUZLkoB67GXnKOxORSASSJG3a9RwfH4dSqYQgCBw+ZbVaa56PRqd0/3A4jLfeegsXLlzAxMQElpeXMTQ0BIfDwWSUSqUQCoXQ1dWFdDoNl8sFvV6P0dFRxGIx1l+SzZsoisjlcpxYSWmW9Eev1yObzW5wdGqheVHfSWqmg8ROeGC3soed8ID8oEJFDXFA/XOSnNLtdqOnpwcWiwXnz5/nYiQajaKjowPBYBBLS0tIJBLw+XxYXFyEyWSCQqGA2WzGq6++ipMnT+Kzzz5DNpvl4EkqeEjeQIUFHSz0ej3bAb/IRsF/9e4K/vJbgy/s+Y8avvlyGxcTzYSdJFMDe3MWoGllJBLB0tLSrnlgaWkJbrcbvb29zAMOhwMLCwu8d0nNiUQigcePH2NqaoplzFqtFp/73OfQ19eHqakpDp0kCRRNoUnOQqYKxAPlcpldfF4EF4yvJPHt8+37/rxHGd94pQ0fPGceOFTFxHZplIS2tjbcuXOHtcz1EqiJiQksLCyw1KjR89Bj020FQWBiolRLQmdnJxKJBJNH/fM5nU50dXVBkiScOHECGo0GExMTWFxchCAI7KJSKpUQi8WgVCoRi8WYEIxGI8bGxtjRhTSOdJigAwWlHJNVZLlcZj96coPaL0lDCy8WOykYaRGrcsiqy53wAB02KP+lXgZBnURg86bEdjzQaEIit1St54G+vj5EIhEIggCr1YpEIoFgMMj3ocklBc+RjEkQBA64c7lcWFpagsVi4eyYtbU1biKQJaRCoeAmAu1F0I4EdTKfBy4OCzjXa97UovDqrIRMoQTDEbWK3W+UK83J9U9zFpifn+cE6qflATkH0JQPqIbPPisP9Pf3IxKJsDPjysoKVldXmRPI+S0cDqNQKGB1dZWXqXU6HXp7e+FyuTA/P4+2tjasr69Dr9cjEolAp9PBYDBwY5E60YlEAmq1mg0ZaH+OFrD3+hKwFQ9cnZXwb2/49mUZuYUqnuLMdzQmE8BGKcJmkNsgyjExMcGWjp988gkANCwogCp5LC4uIh6P45NPPsHw8DBsNhvOnTu3Iazm7NmzUCqVm0quPB4Pa6EB4O7du0gkEhAEAS6XCw8ePEA2m0U+n+f9iHQ6za4tt27dQmdnJ3Q6HXK5HCdUkg6aFqjov/KgO71ez5KGRq5NLRxNyNxcDpVeGtieB1wuFwYGBnDt2jVYLBYsLi7C6XSyZOHy5cusQV5eXsalS5c2fbzNeKD+YDI6OgoAW0otPB4PBEEAUN33+OUvfwlRFGGz2eByueDz+TgLhD7DuVwOgUAAer0ejx49gsFggCiKCIVCCAaDvFxPhwetVguNRoNSqcScQoUEsPHCQnaB2x0otjt0yK1Pv32+Hf/2hq9hkNL/9XGwdZDYJ/Tbt07RPszYyVmAeGBlZQWCIDwzD9TvadGEotG+xk55gORSHo8HqVQKIyMjsNlsfM2uVCq865TP57mg8Hg88Pl86OzshMfjQSaT4XwosnmmPTmVSoVEIoFyucx20pQ9Uu/stle4+l+cQJe1+r7bjAf+7JoP/XbdkbSLfhGwGXZ81D8aOxM7RTgchs1maxg+t7CwUHPbhYWFTYsJ6jjQUmMikYDNZmOSIG/40dFRBINBzM3NoVwu15DR9PQ0JiYm2GGBlrdmZ2cRjUbhcDjgdDoxODgIn8+HZDLJlq5ANXiMEjLJ9YkCcPR6fY0DC1m8yrsSCoUCJpOJLWLli1cajWazjf4WjhYOXTGxEyiVSvT29vLXxAPhcLhmX0mSpC3lD5vxAOVQ0JTT5XLxBEPOA+FwmBNxaUk6Ho9jYmICdrud07IVCkUND5DzGhUKXq8XANgOlowfSMqQy+Wg1WqRSCSgUCiQz+d5CkHyiHrTBY1GA61Wi0wms+2h4sIxC77xsh0ANiz1NbI+/f0vu/FbL9vwx+97cHVWwve+3oevjogAKpAyRQg7v8C18Izotm70sz9q2AsekE8fiAfofFHPAwB42iGfSsh5IJlMckjdvXv3UKlUkE6n8corr2BwcBCvvvoqgsEgh9vS5DGXyyEajeLOnTsQRRHBYBCxWAwmk4kTjamQoL9TA5FkUkajEbFYrCbEUKfTsSXsdrg4LGzKAz/9vVEuJAi//2U3/pNREb/x76ZxcVjAd77owrBTD4UCKJcrUCqb8vJzoNDf9vybCk3J5o30lITBwUGeSNDX9ZDbuAGAIAjw+XzcSSiXy9xhAKoe1B988AGA2mmH3+/Hw4cPcf/+fQCAKIowmUyYmZlhr2igOjINhUKwWq04fvw4dxaTySQWFhZY/lQqlRAIBGA2m/mDT0WHyWTiYiObzfKEolAo8Nfk8EJSKaB6ENnvRcwWDhyaks0344G2tjZYLBY+jDeSQgI744HFxUUoFAosLi4iFAo15AEKoSOjhsHBQV7onJ+fhyiKmJubw8DAAJLJJDo6OrgZQGnla2tryOfzWFlZgdvtRjab5WYAFRyCIHD4FPAkBIlSbSkBFXji5kSHDvnSZiN89w13zTTh0nFrTcfxwrDQ8H4dgg5/+a1B5Aol6GTSJv3+uCQeeTxa33GmTFNyALA3PBAKhRCLxWC1WtnWmSDngXv37iEWi3GAnXzaUS6XcffuXW4KDA0NYXFxkQ/7oVCIdzMkScLo6CjnxqTTaXZtzOVyCIVCyGazHH5JhYRGo2HjBsqOIRtQCqY1Go3MAzS9oPPBdtiOB4Yc+ob3G2k34LN//RJ0RzQB+0VjfDm505u2JhNyyPWU5XK5ZteBphCb7UzIR5EAMDAwAKVSiTNnzrCMqX6pSx5gRY89NjaGcDgMhUKBzs5OhMNhGI1GKJVKWCwWjqyPRqN48OABcrkcwuEwvvSlL+Gdd95BuVzGX/3VX8Hr9XJ3kbTQ5D+t0WjQ1dWFXC6HbDYLnU4HjUbDrk75fB6FQoH/yBeziESaKbiohRbk2IoH3nrrLfT39wNorJXeSx5QKpVwOp1s3xyJRDgLJpvNIpPJQKPRYHFxEeVyGaIo4u2338aZM2cQCoXw13/911hdXeX8CK/Xy4F0FGDX2dmJ1dVVVCoVGAwG7nqq1WpetsxmsxvC6ogTtgqRujgsNJQl0b/92TUftmsu6g7wjkSmUMaHCxKuzki4s5qCJ56HSqlAr02Lt45b8c8+74RJe3Bf/2ZI50r4nb+Ze9Ev44Vjr3igUqlAFEWcO3cOwBMZU/3UgvIp6GuadtAuRCQS4UkEBc6Ra+PNmzf5XCAIAr72ta+hr68P4+PjeP/995FIJBCPx5HP57mgoCLCaDTCZrOxk1uxWORGoUajQS6Xg0qlQjQa5RRs4AkX0ARkswnlTnigXClDhcaflYNeSDQrD0iZIv7wx8vP/XmaspgAwKTQyBZubGxsW2kTQalU4uTJkxtuJ+900BSCQNOOtrY21kdTCN3U1BSSySSPSqm7QJrHx48f4+2334ZSqYTD4UBHRwcikQji8TjK5TIvTmazWRgMBu426vV6ZDIZljrQ/gQVFUCVLHS6J+OuXC7XKiZaaGpsxQNb6a33mge6u7sRCASwsLBQY4oQiURgMBg4fLJUKsHlcuHUqVOw26tSAlEUoVarYTAYUCwWkUqlkMlkoFQqkclkIIoiwuEwTxfK5TLbRpMVJDm6kMOLWl2lfrKI3YoHSNLQCCRhyBYqMBzSacN7E1H8dz9bBQAMOXT46oiIZK6Eu2sp/B/Xffjpoyj+9p8cQ5vp8PwPlisVvPK/fvaiX8aBwV7wgM1m47BY+WPS4wHV6Ua5XObJhHzaQZMQeRPh2LFj0Gg08Hg8MBqNmJyc5OmDy+VCOp3GsWPHsLq6CqfTiXg8jkQiwdd6+nzrdDpotVqkUilYLBaWN8nlzEDtziSdBSjdXt5saITteOBcrxnJXAU246Y3O9BoRh4olir43P8+sf0N9wBNW0wA29vCycNsgsEgu7XI0Wjs2chJgqzdBEFAJBLB9evXMTo6ikuXLvFyo9frZWtXs9nMU4rJyUkAYC/od999F8PDwwiHwwgGg7woVa+HTCaro6tyuQyLxcKkUCwWYTAYEI1GaxIty+UyNBoNS6hahUQLRwEHhQfi8TjW1tagUCiwvr4OnU6H/v5+rK2tQaVScdGfz+exuLiI9957DwAwMzPDy9jkyAaAAyxjsRgfHnQ6He9MkXxB7upCEkeTycSHlu144LUe05bfH2k3IJM/vFyiVirwrTNt+KevO3FMJtMIJAr4vR8sYNKXwf90xYM//Xr/i3uRT4nFcPZFv4QDh53yAE0PAXBBT9gJDwBPdibsdjump6cxPT2N0dFRnD59Gl6vF3Nzc6hUKlhdrR5eOzo6IEkSCoUCcrkcNw38fj+uXbuG9fV1rK6uspyJ9qHkjYJYLMbL2NRkLJfL0Ol0vENFIB4wGAzI5/NIJpPbypzeGLJs+f3X+swtHjhg+Gw9tW/P1XTFhPxgsNXuhHx8+cEHH7CfOwB89atfbTj2r0/SlBMReVJfuXIFMzMzAIClpSW89dZbOHv2LH7xi19gfX2d9xq0Wi3a29uhVCpx4sQJPHz4kA8BCwsLSKVSWFhYQDgchiRJ3EmkRWparqZuQj6f50VKlUoFnU63waGlXC4jk8mwC1QLLTQrDiIPdHd3I5/PIxKJIJVKwel0Ip/PQxAE5PN5WK1WxONxaDQaRKNRLhJCoRDS6TRLGamIkNu7plIpFAoFzpsg21i6rRzFYhHxeLym0bAVBP32ya06zf6kuz4P/NbLdvxWg65ru0WD//7Xu/Gt/3sWV6biyJfK0KoOx//nv/lg/UW/hAOBp+WBaDSKe/fusXHJyMgIzpw5w8XFZo9dzwPkFlXPA2fPnoUgCOzIqFarubFIDk4mkwnJZBJ2ux0ejweVSgWRSIRtruVW8HLZIu1FyJsJQHUaQfkT8oKBgu7kDYrN8L2v9+1IqtjigYOFv7gZ2LfnaqpiolHa5WZe1PIuhd/vRzqd5kPEysoKOzVQgiX9Xf7Y9Dj02HTwJyQSCZYfkMzBaDQiGo3CYrFAp9Ohu7sbmUwGr7zyCmue/X4/FhYWEAgEWGdNJFMqlVgDLU+xValUbD1bLBY52VIOcoXZCbRabWsxu4VDiYPKA3a7nQMmyfhAFEXY7Xb2nu/p6eHpZTweh9/v5+kkgRYoSbJEnEAGC5SgWygUNri1EZfs1A5SqVQCiu138pp1e/e4q5okmy9VEEuX0G45+IeInz2KNsz4OGp4Fh4gCRFQNTWRJIllxsDmPEA7VfLHbcQDSqUSx48fx927d2G325HNZpFMJuFwOKBQKOB2u9n+XRRFZDIZTE9PY25ujncwKXuKZE30b/XNA3lwZX1YJZ0ddsoD5+qc2jZDiwcODvabB5qqmGg0xjx58uSWoVYAWJtIqJc41D8uUB1jEsEQUQUCAczNzSEYDMJsNqO9vZ27HydOnEA4HMb6+jpEUeTgmLW1NbS1tcFgMHB3QZIkRKNRZDIZPiCYzWZOr6Tbye1fiShUKhWHkdEi1rP4SLfsYls4rDioPNDW1objx48jFouhUqmwftput/NSdiqV4hC6cDjMy5rkymI0GlEul5kvyO2FeABAjaVso8/x0/BBuVxGplCBeRtnQcUOCo7DiNVoVWOuUSpgNRz85cufPYruy7LlYcCz8IDFYuHPFrCRAxo9bjQaxcrKCnp7e2t2MTbjgfPnz+PRo0dcjLS1tbG5AhUJtOuYSqWQTqcRCARqFqWNRiM7MFHzkPYe5I5tNI1o1Fh8GowvJ3eUCdHigYOBF8EDTVVMbDXGrIdc53ju3DnWSg8ODsLpdNY4udDjyB+7Hu+//z4+/vhjpFIpeDwevPzyy5yI7XK5cObMGaytrfHkwe12w+fzwWyuVvy0PxEKhRCPx1EoFKBWq/kQYbVa0dfXh3g8juXlZT70yO0fAfAC5k67j5sVG88rIbeFFp43DioPAEBPTw+CwSDW1tZgt9thMpmwtrZW41lvsVjg9XoRjUbZdU2pVEKn0/HtSqUSy6XoNtRooINFPQds1VjYytktkSvCrDv4F9Dngf9nvJrp8+UhC7Tqg92NTOdLrUJChmflgbNnz7KsiQLotuIBCp8lhMNhPH78GN///veZB06fPl3DA729vQgGg/B6vbDZbFAoFAiFQmwfT0Yq2WwWy8vLvERNEqf+/n52d4xEImzIQOcACq0kJYL8ei5vOtRjMx74wx8v49JxARpViwdaPNAYTVVMNFqI3O72cmcGucNTo8epX7KSJ2Lev38f0WgU8XgcgiDAbDbDarXW2MLR48fjcfaCJ12jXq/npU2gSgYKhQKCIMDpdOLEiRMYHR3Fj370IyQSCU6ulFs7arValjWQRIkOEGq1GiqVit0cqGOx1+mXLRws0HvjKO3IHFQeAACr1YqzZ8+ylAIA7zgYDAbo9XqWGFIzQRRFKBQKnDx5Et3d3VhcXMT8/DxSqVSNTppAexXywCqCRqOBWq3mXavNOpdy5ApHkyN+OSfhP9yPQKNU4LsXOl70y9kWRq0K333D3TB1/ChiNzxQj614YGBggN3YgGpD7xe/+EUND1gsloY8kM1mkU6neXeK9h0pfJLc3kqlEudLnTt3Dn19ffj000+Ry+WQyWT4ui+fRpCro5wDqJDQarUsjZKfAbbigUi6DJfl6BUTLR7YGZqqmAC2JgRg49LUVt+XW0E2uh+RCQXakENLLpeD2131Xm5ra6sJvenu7mYNtN1ux9DQEBQKBSwWCzweDzs60H6D3W5HV1cXyuUyrl+/jpWVlRoLt74J42oAACAASURBVEqlAr1eD6PRCKPRiEwmg0QiUbOYRZ1LoFq0lEql1uThiOBZZW6HHQeRB4LBIFZWViAIAsbGxjh912QyweVyMQ8A1e4mOa1oNBoMDw/z5HJ9fR1+v78mtVqlUkGj0XBTIpPJsPyJQFMLOkhQ/sx2+NlkrKG/fDNjPpTFv3p3GRUAf/RmJ078SjN90PHOSVurmJBhNzyw1YJ1/fecTmdN7oTL5cLs7Oy2PHDq1CnMz8/D4XAAAJxOJ7q6uuDxeHDv3j10dHTU2L/29vYil8vh0aNHWF9fRyQSqTkPUFNRr9fDYDA0XBynKaZWq2V7WHnBsdn14j/cD7d44JDzwPM8DzRdMbEVGi1kAWjYZZRrHxvdj8iFvk+6ab/fj7NnzzI5BINB7lpUKhX09/ezO8TS0hLLFgYGBhCPx2Gz2dixoaurC/F4HHNzcwiFQkgkEhsOCOVymQsFu92OWCyGVCrFqZdELjQmrZc/HNXD5lFBa/dlI14kD1gsFkiShAsXLsDpdGJ6enoDD6ytrbEbm9FoxPnz5/HZZ59hcnKSJQ2NOopGY9XgnXiAbF9pd0qlUsFoNHIGTX3HcjMe+LNrPvyTcw5YdEfjcuGT8vgX/34e8WwJ/+x1J/7pOeeLfkk7Rpvp6HWOnxVb8QClWsu/R8XEVjxAoCC87Xjg4sWLuHjxIq5duwZBEGC1WjEwMACg6v4UDAZRqVTQ29uL48ePY3JyEnfu3EEsFkM2m224RE27FAaDARpNNROBrOVp2klZN3JLavneVSP82TUffu8LziMjdTrMPKBXN/49Ps+z3tG4OvwK4XAY0WgUiUQCFotlw/KkKIoAwBZsoiiyO4sc09PTGzoW586dw9DQEMrlMubn53Hz5k2YzWbej+jr66vRRY+OjmJ0dLTmAGO1WtHV1YV0Oo3Ozk6k02l8+umn8Pv9KJVK7DEtB0lYtFototEoW0wmk0nuatJeBjlH0Dh0u72KVqHRQjNiP3ggEAhgZWUFHo8HoijiypUrNTwQiUQ25YGenh6Mjo5icXERY2NjCAaDuHv3LhKJBIrF4gbpEvCEB0gqqVAoYLPZOAwTqE5ASEJFU43teIBklHPBHM50N//lIpYp4p//fwvwxAv4xst2/Os3O1/0S3oqWPRqfO/rfa3diR1genoaKysrsFgssNlsNTwg//dYLIbbt2/j9ddf38AD0Wi05nsAGvKA1+uFIAi4cuUKXC4X+vv7mQccDgfeeOONDW5QHo8HqVQKiUQCVqsVt27dwv379zm0shEPAGAeiMViAMA7GADYAY5uR26RhO2u98lcGTZj8xcTh50H3KIWF4eFlpvT80K5XMbs7CwAwOfzsUZRjlgsxr7QFosFo6OjNYtc5P8ej8cbdiV+9KMf4caNG4jFYiw5ePjwIV566SW43W72kqf7koTi+vXrePz4MS9mer1eLC0tIRAIIJvNbvCEp2Ur2q2QJGmDlStpoikEh34GwJO8iq3QKiRaaEY8bx4IBoP4yU9+wiGVfX19APBUPBCNRtHR0YHZ2Vn+Wt6FJPkiSZeoAxmLxVjGKE+5pkwa+syXSiV+jK14gAqNYHJnltKHGal8Cf/i3y9gLpTFpeMi/sd3eg6lO81ObTyPMvx+P5aXl+Hz+eDz+TA8PMxNBKDq4kQKgZmZGQwPD7MtLPFANBrF7OwsRkZG+HvygoJ4wOPxIB6P87Ti4cOHGBsbQ0dHB/MAgJr7z83Nwe/38+f26tWrSKVSiEQivCcFVM8B8jBKMmsgvqB9KlrIllu+y6/vOzkPADg0+Qq7QdPwQK+5VUw8LyiVSoyMjECSJAiCALvdzh9k4IlzgyRJ3JUgOzm5Ljoej2/oWgLVTsfExASy2SxCoRD0ej0kSYJOp8PExASCwSC7OsRiMVy+fBn9/f2w2+24ceMGrl+/jkKhALfbDUEQoNVqa+zf6P+BDhDyiYO8kKgPpqEJhXzhmkimJYNpAcCRqhqfNw/cvXsXXq+XeaBSqfD+g5wH6DnK5TKGh4dRLpcb8gAFTsmtXungQJPHfD6PdDpd03Qg9xcCSR0qlQr/F9h6Akn//ncPIrh03NrwNs2AfLGMf/nDRTz0pvHlQQv+9Ot9UCkP3wECqNp4PgOOFAeEw2FYrVbmgf7+foyOjrJ8yWq14syZM1hYWMDw8DCrCuQ8cPv2bYyMjPD1vH5CQTyQy+Ua8kAoFGrIA4FAAO+//z4ePXqEQqHAO5HyxGv5tZwC7ywWCzKZTM1CNt2ezg/FYhEqlYp5QR6AuxP4EnkM6Q7H3sCzoKl4YGV/eeBAFRPbLUXu9jHb2tpgtVrZoo0ODfXPKT9YlMtlTE5O8iJmfaoldS3pvkajEXq9Hg6HA/l8HhaLhReio9Eorl+/jldeeYXJZXZ2FqVSCTdv3oTX6wUAtLe3w2AwoK2tDevr68hms3zwL5fLvAdBy9dEGI1C5uqTcun/SZ5N0UILBwmHnQeow0k8QJ9VoFr4+3w+RKNR9PT0AKh2IePxOKampnD16lWEQiEoFArmAZvNBkEQNhgn6HQ6mEwm3ocgFyd5oVC/VyG3hXyayePVWQnFUgnqJtRLl8oV/OGPl/HxUhKf6zHh//ztgUPbgZ0JZJpC4rQfHDA/P888QJ/detementoOQ+8/vrrGB8f5ymmfHrhcrnYLlbOA0D1s5fL5ZgHuru7AVR5QJIk/PznP8fDhw9516m9vR1KpRKSJMFkMiGfzyObzfLnWafTwWKxMA+YTCYkEomG13e69tNOZSOe2Ar/5oN1/OW3Bnf1ezioaCYeeBHBlQemmNhsqWmvH7ORxZv8eeSEIl/CunPnDvr6+jA6Ooq+vr4NXUuXy4XR0VEMDg4imUxCFEWcOHEC9+7dQyKRQCwW4+Li1q1bGBwchCAISCaT+OCDDzA/P88jSq/Xi0uXLqGtrY0PFsViEclkkp2eqCtZKpVgtVphNpsRCARQKpU2JRH5AaIlYWpBhgPTemkGHvj85z+PR48e4fHjx+jt7YXD4YDH48H8/DyKxSKsVisn4w4PDyOTyeDDDz/Ehx9+iNXVVajVaqjVaoRCIVy6dAl6vR7BYBAajQbJZJL1z1Sk0DK13W5HKpWCJEkbkq8JT5uATfjuG+6mLCQA4P/9NIR/mK4WjjajGn/8/mrD2/3Xb3bBbjwwl8yGGGk3PKtWusUB2Oj+tB0PDAwMNJxeuFwunD9/HpOTk5iamkJvby+cTic8Hg/m5uaYB8jRjXjg5z//OW7cuIFQKMTNAq1WizfffBMfffQRL1H7fD4+C5AjE1k+22w2ljo1snql/apnsQs/3Wl86vscFjQTD7xzyob/OPFMBcUz88CB+Yk0SqvcLYE8TQKmHEQok5OTAJ7opyVJQjwex8DAAHp7e/n2ZP9KC1xKpRIGgwHZbBavvfYauru7cefOHUQiET58kAyClsDow022jaIoQqlU4uLFi7h//z6Wl5dhtVqRSqUQj8eh0WhgMpk4JVulUsFgMKBQKCCXy9UcJNRqNfR6Pe9OtAqJFg4qmoUHKpUKTCYT0uk084BOp8PKygpyuRwikQgKhQJ0Oh1CoRAikQjW1tZ4wkjTR1EUMTo6CoVCgcePH8Pr9aJQKHDWjEajgcFg4IVqcmvKZrOcZUOfd41GA61WW7NDtRW++4Yb75y0QTAoIeoPzKVizxHPPPlZ0GGiEf7Lr7gP/CECAP747a5970ruJQ4SBwBb80Bvby8GBwdrcibqecBoNG7ggeXlZeTzeYRCIeTzeW4YrK2tceo9/btCoUBPTw++9rWv4f79+5idnYVCoeDHLJVKMJlMUKvV3HCk7Jp0Or1hv4Kso0npsB1aPFCLw8IDf/TVjqO5M/E0aZX79Zh0f+oikge8UqlsGFy1srKC1dVVGAyGmmTr8+fPI5VK4eOPP+augNVqrcmS8Pv9KBaLKBQKSCaTeO+993Ds2DF89tlnmJmZQSaTgUajQWdnJywWC2dV5HI5toItlUowGAw1IWWFQgEajYZ1kS2HpqOLrZJPDwqajQfoIKFUKvHlL38Zd+7cwZ07d5gj4vE4/z4EQUA0GmXZYjwex3vvvYeOjg48ePAAXq+XF6u7urqQTCYRjUaRyWSQSqWQy+VY9qjT6Wp+38VikUOs6LVvdZD47hvuI+Mp/wdvdOAP3jj4YVQ7RYeg2zK06qDzwEHkAPljPAsPGAwGTqz+0pe+BKPRiDt37sBoNMJsNiMWi6FcLkOtVnNzgCSLKysrzAMzMzNYX1/nfSi3241CocDucFRg6PV6qFQq6PV65PN5vu6rVCpWQMix2bnge1/vwzunbE/9szqMaDYeGHJsPaXc67PggSkmnjatcjePuVM9Jt1fFEWWMgBPiCgUCiEUCvHtBUFgIjCbzeju7saZM2f4wp3NZjEzM4N8Pg+fz4fOzk7WP5rNZqTTaZhMJqRSKdy7dw+BQABerxehUAiFQgFKpRJmsxknT56EIAhYWVnhQwQF1+RyOZjNZoiiyOQl37WggkKlUtVoq1tofhyG33Wz8YDT6cTg4CBeeukljI6OQhRFRKNR+P1+tm82m82IRqNQqVQQBAHFYpEPH/fu3cPk5CTi8ThSqRSHTVksFrjdbiiVSvj9fuaXdDoNoDqN1Ol0PK2koCr53gQdKhpJI//zLxweT/UWNuL3v+zGQ2+64UFiEx44MOSwnxwA7B8PDA0NMQ8IggCPx4NwOIx8Po9kMgmNRoNUKsWNAMqKSCQSmJiYYB4gO1eNRgNRFOF0OlEoFBAKhThXhqaSarUaGo2GpxIqlYqnmFRQ0LmgXrVwcVg4MoVEs+KP3+7G1dnJht/b6/PAgSkmgO3TKvfiMbfSYzYiFbp/vRe8fPmyvb2dF7nOnj0Li8UCu93OS11+vx8zMzMol8vQ6XTQarV8WCBv6Uwmg7a2NkiShFQqxf7SJFcolUpQqVSIRqPw+XzI5/McXFO/ZEnLVySdkjvAAGA3FzpgtBaxWzhIaGYeqJ8amM1mdnnR6XQQBAGFQgHr6+useSbpExX/lUoFoVAI6XSaMyPqDwKFQoFvX7+UTQ0FeYNBjp/+3mjT7kccJTylzOHA7EwA+8MBwIvjgXg8jlwux3tQbrcbyWSSecBgMCCZTCKRSCAUCkGlUiGXyyGfz/M+lE6nQzAYRDqdRiqV4nOC/HpOt6eppFydIJ9QkTucXP74v/xGz57+/FvYf3QI2i2nlHuJA1VM7Ac202M+zdLX9PQ0pqenkUqlYDaboVQq0dfXB4fDsaG7MTExgffeew9LS0uYm5tDPp+H3W6HVquFx+NBoVBgp4dIJIJYLMYfaIVCgUKhwA4MJFeicS2NOusrzHK5jFQqxX+XHxbq07NbaOEo4iDwAE0hC4UCS5eoq0hFPnnK53I59pK32+0oFAoIBAI8YdyMB9LpdM3BoV7ySBxC9784LGCkvXmtH48SukTti34JBx7PwgP1Rcb169efmgfu3bsHv98PtVoNk8mEcDjME8menh6srKxws4/OAcVikZ0dFQoF9Ho984DFYtlUaUByaPk0gnJ1qKlQf7+LwwJsJs3e/aBbeGH45iv2VjHxPLCZdnKrdFsATCyffvopJElCOp2G1+tFZ2cnH8rptpOTkyiXy4hEIrh58yaWl5d54SoajfJkoqOjo+ZD7Ha7EYvFauQMANi5iexltVotstksNBoN1Gp1Q0vYRqmW9daQ8gNIa5eihaOEnfBAvXe8/ICxVzxgNpuRy+Wg0+lQKBRgsVhYvuDz+ditSa/X81J2Pp9HPB7nYkCtVkOhUPCBQQ65e5tCoeCdic0cnVqBZ82DFptvj53wgDzlGkBNkTEwMIClpSXmAVEUYTQaYbfb2UK6EQ/QZJE+jyaTiSeWZN1Ki9XJZJLPApQPBYBVCTR5NJlM3IDYjAeoOKHnpeDK+sbiud4WDzQLTNr9mTIfuWJiM+2knFRisRii0Sgn1MqTMROJBBQKBV555RW0t7fDZDJxiuWVK1dQqVQgSRLGx8eh1+v5flQI0L91dHTA7Xajv78fwWAQqVQKyWQSarUaRqMRqVQKKpUKbrebCwCPxwONRsMLVSqVCjabjbXXhULjlNp6b3m6L/BE+rTZfVtooRmxHQ808o6XHzB2wgNra2t48OAB7zZQB1DOA8ePH4fZbOZJYiqVQiAQAFD1p6eDhNFohCiKPKnQarXIZDI8saTEXfkBpRHoIEH6aTpMAFV+GF9O4tufb38uP/MW9helcquc2A7b8UB9yrX8LAAACwsLsNlseOWVV2AwGJBKpdDd3c3uTouLi/jss8828IBGo2HDBADo7++H0+lEKpXi3cdkMolisciNBoPBwA5vkUiEpY9UIJjNZphMJgSDwYaNBYK8wUA7FfLwulKphPGVJL59vsUDzYBCaX8UKEeumAAaayflpELpto1gsVggSRI8Hg8UCgV6e3v5YixJEhKJBO7cuYP5+XkYjUaYTCY4nU5UKhU4nU4IgoBjx47BaDQiEAgwATgcDty9exfJZJJHmMViEaIo8tSgWCwiHA6ztKGrqwuiKEKSJCwuLtZ0JGjhqj45l75HB5RiscgE1kILRwlb8cDt27c3eMfLGw7b8YDX68VHH32EeDwOj8eD/v5+GI3GhjyQTqdhsViQTCbhdDqxurqKZDLJF3ug2rmkNGwAyOVyHDzV29sLm80Gg8GAxcVFXsImUOOgPvkaqBYser2ed7OuzkrwJ/JwWVoSmcMOs079rJkTRwrb8QClXDeCIAh49OgRH+iPHTvGt11YWMDExAT+/u//HoVCAcFgEJ2dnTAYDKhUKujo6IAgCOjs7EQmk2EeMBgMWFhYAABkMhl2YCqXyzAajRAEATqdDoFAgO1jjUYj+vv72c3N5/NtsH+maUd93pRCoeDJSDKZRCqVavFAEyFX2J+z3ZEsJjYDkYpczgDUJuSWy2VcvXoVy8vLbO8oiiKsVisEQcDMzAyWl5f5MJDP55HL5birqVQqYTQa8dprryGRSECSJEiSxGmZtJxFMqdEIgGHw4FkMolyuQyLxYJsNgu73c5FisVigVqt5q4HFSP1C9gE+ne9Xs9OUS200EIVLpeL020J1LWUB1htxQO3bt1CKBTiDqHH44HZbMb6+jqAnfFAMBiEUqlkNxaj0Qiv1wu9Xg9RFNn60WKx1PAANR9oqZKej0KuCJSonc/neVETALzx1iGiWfCNl+2tYuIZ0YgH6s8Ci4uLqFQqePToEXp7e+H3+2t44OOPP4YkSchmszAYDAgGg9BqtTU84HA4MDIygmQyCUEQIEkSTp8+zQqCeDzOjQPKk4rFYlyUWK1WdHR0wGKxIJfLobu7m3cwaPJI0we5bTwAzqShswMFYgJAMleCy7JPP+wWnhvM+v1J8W7qYmKnlm/12Gz0ScE1SqUSTmfVOlGhUPCyVblcxtTUFEwmE+c7UGeTOgLxeBwmkwnnz5/H+Pg4rFYrlpeXIUkSOjs7MTY2hoWFBeTzebaAjEaj7MjgcDjYQtZsNiOZTCKdTteMK+ULlZtpowuFQsPuRQtHEk09lnoWHtiKA3bCA59++invNGk0GnR1dXHQ3E55YGpqCslkkpc6yeJVp9PB7XYjn8+zO5ycB6gDqVKpuPNIvFCPbDa7QRLxFzcD+MtvtTTTzQCneceX+KbmAGDveYBC7BQKBZxOJ0RRRHd3N0RRxLlz5zA9PQ1RFKFWq2E2m6FQKHDixAksLCxs4IFf//Vfx/j4OGKxGCRJQkdHB0wmExQKBWKxGBKJBDcKaPJIGRPZbBYOhwMGgwE+nw+ZTIalURRoS9f5RhJIMmyhZWzCfCiHIUfLjOGwQ618KqO2Z+aBpi0mnsaVpdF9NyOdtrY2WCwW+HzV7Xi6CLe1tWF6eprzJSwWCwRBwMmTJ3H//n1MTU2xa0symQQAJhxBEFCpVDA3N4fu7m709vbi4cOHvGzZ3t6O5eVlFItF+Hw+qNVqhEIhTsMFwMmX1Imgg8dmU4eWJWwLMhwoW8i9xG54YCvIeSCZTEKSJF66vH79OtxuNwYHB5FMJtHT04OXX34ZV65c4cArsn4EtuaBTz75BDqdDuVymaUS2WwWc3NzrJ2mzztQywM0pZT/vRFaEsfmRTC542ZR03IA8Hx4gGSPgiCwWUIikcDZs2fhcrlw69YtKBQKdHV1QaFQ4M0334TVasXk5CRisRg0Gg0EQWAeGBgYwLVr19DV1YXV1VWEQiG8/vrrWFxchM/ng9vtRjQaxcrKCoDqYng6nYbBYMD4+DhPJhKJRM3ulPw6v1lgYaOm42wwi0vHd/UjauEAwBt/KuXJM/NA0xYTm1m+bQW/349bt27h8ePH6O7uhtVqZdKRFxhvvfUW+vv7eUk7Ho/j8uXLkCQJwWAQkiTxfS9cuACTyYTJyUnk83mo1WpUKhV2h3A4HHjw4AE++eQT+P1+6HQ6fmxycNLpdOjr68Pa2hoTj0qlQiwWg1arRS6X4z0K+h4Rg1qtbsmYWjiyeBYemJiYwN27dyFJEvr6+moOH/U8IAgCpqam0NnZyUuXS0tLCAaDKBQKaG9vx8svv4yxsTHMzc3x1FEQBJhMpm15IJ1OszyJ/m19fR2lUgmpVAqlUom7m5RbIw+mBJ44tmy1mC3HN162P8NPuoWDiL97EHnRL+FAYC95gO7f1tbGU4uenh48evQIFouFeWB1dRXxeByFQgF9fX34tV/7NYRCIYiiiLa2NmSzWfT09KCjowO3b9+G1WpFb28vlpaWcPv2bXZ5C4fD0Gq1iMfjsNlsiEQiMBgMCAQC/LnOZDIspSJjBgAsdaS9SblN9HYYduq3v1ELBx7vP47ty/M0bTGxmeWbHPKDAQBcvnwZH330EaLRKILBIM6cOcMkVN/V+MpXvoLJyUl+jkQiwWFxoijixIkT6Ovrg1KpxMDAAL74xS9ieXkZlUoFk5OTEEURKysrMJvN+Id/+AfMzs4ikUjAYDDg7t27yOVyMBqNAKpLXiRFyGQyyGQyyOfzbAlHUwbSVarVak7PVCqVSCaTW7o7tNBCs+JpeSAYDOIHP/gBgsEgotEoAKCvr29THhgZGeFDOwBenKznAUmSMDw8jEwmg+XlZVgsFkxPT0OlUjXkAY1Gg08//RTFYhFmsxmlUqmGB5LJJGKxGDcoqBNJTjGlUombEeQYQ65vLRwNXJmKtfYlfoW94oHp6Wk2ZyEOOHnyJIDqGYCwsLCAZDLJ7k/9/f1QKpWQJAnHjx+HzWbDysoK7HY7FhcXeccymUxiYmICU1NTXCBQgrbf72e1QrFYRKFQQDqdRjabRT6fh8FgqAmopIR7MoaQTzjr96daaF7o1a2diV1hM60joX7s+f+z9+axcd3ZueBXe93a9+Im7oskSrbFtGWpnVhOB2130Gl0DwJkxWAGmcFLMAO8oDGDNzN/zfsrfwQIHjqDzOsESQODIAM8oPPSnY7HkduOLLtttxZSlkSK4lbcqsjal1v7Pn/Q5+hXxeJOSpRUBzBkibVcVt373XO+853vWK1WLgZSqRR0Oh1kWYbT6dyR1RABqlar8cAk3fDX1tYwMDCAwcFBeDwexGIxzM3NweFwYHZ2FslkEmazGT6fjwErHo+jUqmgUqkgn89Do9HAZrNBp9MBAHp6erCxscHMoyRtaRrpfWlJlVarhVqt5qHMeDzeBpB2vHRxUByg65BwYGlpCX19fTviQHOSYrFYcOfOnW04MD4+DovFwjiwsbHRoIkWcaBarUKWZZ51KJVKUKlUMBqNPHRtMBjg8/m4E2E2m6FQKFAqlRqkTpIkQaPRQKvVNizC2y3a8oYXI9pdiSdxVBxYXFxEX1/fttdtlQsAWzgQDod5oJm21pPBgtfrRTwex+bmJv8slUpBo9FgcXGRCUBSIoTDYRgMBthsNphMJl52WywW4fP5mEAk+WUqleLll2QfrVQqodPpUKlUGpbj7hRtHHgx4vZa5qm8zwtbTACtLd8omhMDAA2FhEKhwPj4OD+fACadTmNgYIBfnwCKHBxkWeYh6Z6eHkxNTaG/vx8OhwOhUAi1Wg1zc3OsnVar1Q1dA51Ox8wiDVFSi9NqtSKZTMJgMECpVLI7A7EsdHzUyjQYDEilUpyM1Ov1lpKn9sK6drzIcRAcsFqtyGQyjAMGgwEDAwM74kBzkhKLxTA6OroNB9bW1mC1Wlm2kM1msbS0hEgkAqARB2q1GnQ6XYN0kSyeq9Uqz11JksTXtkKhwOjoKPx+PxMRNDOl0WhYbkGvudv13pY3PL+RKVaxECngh5+F2l2JpjgKDhiNRgwMDMDtduP27du75gKEAxMTE/B4PGz3nkqlkEgkMDQ0hLm5OaTTaZ6BoB0U9XodhUIBwFYBQo5NSqUSkiRBrVZDluUGlzbqONBzXnvtNczOzmJzcxPZbJZ/Tp3LSqXCZi27xdcH2iYMz2MUK1X4k2UsRQv4x/vxp4YDL3QxsVs0Mwlk+UZMQE9PDzyeraUtXq8XAwMDWFtbg8ViwfLyMtxuN4MTaalTqRRsNhvW1tbQ09ODVCqFmZkZBAIBzM7OMjOoVCoRj2+xRgaDAaVSCVarFVqtFhaLBfl8nvWSXV1dcDgcyOfzvHE3k8kwiJDfPICGgatSqcSLsAqFQsutmBTtQqIdL2s048CVK1cAAHfu3EFHRwdLFYG9cYDCZrMxDpjNZgQCAYTDYQwPD2N+fp7tonfCAZIsWiwWFAoF2O126HQ69pCnoWuSMJHUKZVK8aZc6lJQklEul/nv7ev9xYtNuYj/+H6gXUAcMvaLA/vJBShEHKjVapienmYcCAQCyGQyfC+njiQVCdSFKBQKsFqtKBaLuHTpEkqlEmRZZtt5wg/RdMXv98Pj8SAajQIAYw0tvKXFl61CJBa7rG176OcparU6Jtez+MO/X3wm7//SFhM7tT3FZXWirlKpVKK3txfJZJJZV0MWswAAIABJREFURhE4xNczm808yE1sYy6XY8Cg5KRaraJYLMJisaCzsxPnz5/nOYloNIrx8XEolUpsbm7is88+gyzLyOfzDAZqtRparRZKpRL5fB7VahU6nQ7VapWHrKjVuZNFbDva8TJHKxy4cuUKX6PA4XGgXC7jgw8+QC6XQ7FY5L0wu+GAw+GA1+tFZ2cnFAoFBgYG0NfXh1u3bmFmZgbr6+soFovsOU+dB7q2k8mtYTuSRQLgjmTz8sqdolxt48TzENVaDZFMBf/n+/52EXHEOAgOEAYAW2oAMlE4DA6QuoCuUYVCAavVyrsnXC4XYrEY3njjDQwPD+P999/H1NQUS51pRrJYLEKj0bDyYHNzc5u8sVqt8nvsh1istfOF5yIq1Tp8sQJ+62/mnulxvHTFRLPt604A0KyrdDqdmJycxPz8PICtDbhjY2Pbng9sSSFoQJI2WLtcLnzzm9/EgwcPeEkVuTCp1Wq8/vrr6Orqgtls5t0UVqsVDx48gN/vRz6fRzweZ4kCtUJVKhVyuRw0Gg26urp4EJwAo1Ao7MhC0KKadrycIXz/L91d42ngwNzcHHK5HPL5PBwOB3K5HEsed8KBs2fPwuPx8PbtoaEh1Go1fPLJJ2z7qFKpuLtBkqZKpcJsplarhdVqRSqV4oTjIGTC13rb8obTHu/NJPD9f1o98usIVqEvHQYAh8MB6mIkEgksLCxgdHQUt2/f3mY3u18cIDkVkX4ajQajo6P41V/9Vc4FCAfi8ThbwhJmlEol3jehVCphNBrR2dnJcxaJRALFYhEKheJAc5OSWrX3g9rxTOOvPg3iBzeDR36d48CBl6qY2I/X9E66Sq/Xy64slPDT8FUoFMLc3JOqMJlMsv+zQqGA0+lEZ2cn+vr60NnZiZmZGczNzSEWi0GtVuPMmTO4fPkyhoaGsLy8zItrenp6ePahs7MTuVyOF82R7pG2Vo6MjECn02FlZYUXWFGHQqVS8SAWFQ9kH9mOlzNUKhW3vWu12kuVSDwNHEgkEjyASVtuX3/9dbZ33AkHrl69ykPVZODg8/lgNBrR0dGBZDKJVCrF1zdJGwgTBgYGWP5AjyMHJ2JZxcKile+8Uft03D/acbgIyaVjKSQA8L2hXq+/dKzSYXGAioxbt25hdHQUNpsNAA6FAx0dHZiZmcH8/DzLnDweD771rW9Bo9E0zGb4fD4oFAruWsqy3GDYQhIpSZLg8Xig0+kQCoW4AyLOVTRf962IRbWqnR+c5vjgcfJYCgmg4T6wP//wFvHcFhOH2WZ5GK9pMcbGxrbJoEKhEK5fv46FhQUAW0CTTqdhMpnQ1dUFj8eDN998E2NjYyyBCofDWF5ehkqlQm9vL9555x2MjIzg/PnziEajuHXrFrxeL1KpFA9hAWDQWl9f5wTCYDBAo9FgcHAQ9+/f5w2YlGiQNRxtxaWEgm4iQOutmO14sUP0H8dz3Jk4rThA11RXVxfy+Txef/11fPe73+Xj3QkHLl++jFqthp/97GcAgKmpKZZUOBwO2O12xoPNzU02YtDr9fye9Xod4XCYLSBJSqFWqxukVXQOULFBiUWlLXM61fHjY3RpImYbz/nSuqeNA16vF2+88QYXI8ATHPjggw+4c3lQHOjv78d3vvMdnD17FuFwGLdu3UJHRweWl5dhsVhgMBig1+ths9nY+p2W2NE8Js1QFotFhEIhFItFNnRRKpXQarWscKAkks4DcaaqjQOnO47LrY0I56/O1Zdrad1ht1nux2u61XsRyzA2NsZtz1qthlgshmg02uAvrVAocO7cOQQCAWYuSQZBxzg4OAi/3w+NRoOOjg62hpuensbMzAwUCgXC4TCsViteeeUVvPLKK/j000+xurqKYDCIcDiMTCYDtVoNtVqNvr4+3LlzBwsLC8hms1xEEDAQC00ODuJmXIGZOtB30I7nIyiBFNlpuoEILe/nkoo+zThgNpthNpthsVhgNptx9epVANgXDsTjceRyOQDAwsIC+vv78bu/+7uYmppih6iHDx9yoqBWq6HRaLgzGYlEeB8N8KQTQVggXveUgABgd5dYrgyb4bm8NbzwMR/OH4qNJO286OBDWv2vMOC5LSaeFg40YwB1KERVQiwWY6dGAHz974YDQ0ND8Pv90Ol0bFEfDoc5FwiFQrBarRgaGsIf/dEf4eOPP8bGxgZMJhM+/vhjLC8vs1KBiIbFxUWsrKzwPgq6x9O9n8hFcaEd3RvoHMlXqrA9nyniCx/vzSQONSdFOaCY7zV1qQ6dCzyXZ8phGYW9vKbFIOB48OABQqEQgK3Ntu+++y6cTieDVzKZbGgPWiwWXL16FbOzs/jiiy8avrRQKISFhQUsLi6ip6cHPT090Ov1rJn8/PPPkU6nkcvlYDKZEAgEYLVaUa/XYbFY4PV6MTk5CVmW2VfabDbDZrNhcXER+Xy+ASyo2qR/o90T5PJEy6z24z3fjuczRGkbsAUYze5feE4TidOMAz09Pbh06RIWFxexvr6OL774AlevXmUZxG44cOfOHayvr8PpdMJkMiGRSCCRSCAQCLDDUzabRT6fZ9KAbGzX19e5qNHpdIw/VDgYjUa2mM3n80xIUFJZrVaxFC1iyCUd7Mtox4lHplg91JBlE/O47WfPezHxNHBgenoaP/vZzxCJRGA0GrG6uop33nkHwBPTltu3b2NgYAAWiwXB4FbB19XVhYmJCdy9e5ddHQkDYrEYwuEwgsEgzpw5w66NADAzM4NUKoVIJAKDwQBZlhEOh7kLce7cOUxNTbHMiXBep9NBq9ViYWGBzV/IZlq8xxOOVCoVlmBRcUnLb4vlNsF4GmMpWji0zLFZ2k7f+3HgwHNZTByGWaTYzWuagpiOtbU1TE9PQ5IkmEwmpNPpbcBls9l4mU0ikYDdbsfs7Cx++tOfYmNjA19++SXS6TTefvtt3Lt3D/Pz88xevPHGG7BarbBYLEgmkwiFQsjn8zAYDMwk3L17F6urq8jn89BqtYjH42z/SMyCqIUkWZTRaIRGo0E6nUahUGCXCFqGU61WUSgUoNFoALR3TbyIQd8pFZP0b7Qd+XmXt51GHIjH45BlGZcuXQIA3Lx5ExsbGwAAv9+/LxzI5XJQKBS8+XZ9fR3T09NYXl4GAHZ4kSSJu0vVahU2mw3BYJDlDHq9Hk6nE+VyGZFIhJMOKlDI/YWSB6VSiWq1in+8H8c7Z237/izb8XTi/7kdOfRzxVk5+vNFcfh7Gjhw8+ZNrK6uIpFIoLu7G7Ist9xVpVQq8c4778BsNkOWZUxMTCAajeL9998HAHz55ZeIx+PQaDRIJpO4d+8eqtUqkskkenp6oFQq2VZ+bW0NkiTx/OVHH32ESCSC5eVlmEwmlEolFAoFmM3mBmIBAJONZAnrdrs5F6jVarDZbCiXywiFQnzdU8FBsxfvPUrif/61jn1/lu14OvHnH20c6nlULNK1T1hAnemj5oDPZTFxEEbhMEEgQRpF6hSYzWYGKnF51bVr13iZTSqVwq1btxqAJhgMwufzQZZlZDIZ9pPP5XLo6OjA5OQkstksTCYT28GSP/3q6irW1tYQCoV46LJWq/Eym97eXmYizWYzMpkMTCYThoeHEQ6HUa1WUSqVoFQqkcvluAtBCQjJX9rx4gVJWkTGnIoJSja/2pj+XGYUpw0HxsfHoVKpYLfbsby8jEQiwUwjsDMOENv5+PFjZLNZKBQKfO1rX2vAgUAggIWFBS4AaX6KyABanqVWq2E0GlEul2EwGDAyMoJYLIZiscjuTpR4FItF1Go1doajuLEg44//iw9/8qYHl3razk6nIe75M4cethRJBfFSF4fwn1cMAJ4ODpjNZkiSxE5KFoulAQOAre5kNBpFrVZrwIGHDx82vN7k5CSuXLkCv9+PQCAAu90Oh8MBo9GIUCiEqakpSJLUkAsQZgQCATx+/Bi1Wg0ajYYlzMDW99nV1cXdRsIBmq/QarVIp9PIZDIN27HFfAB4UnjS+fbt83b0O3Vox7OPDx4nDyVvEs03msnnUqkEg8FA58HL5+a0H0bhsEFMh81mw6VLl2A2m+FwOBosIGlxTb1ex82bN5lVILkDdQMAoKOjAxaLpYHNcDqdqFar+MlPfoJ0Oo1sNove3l5mNGu1GlZWVhAOhxGPx1nCZLfbmVW22WwolUrY3NxEuVxmSZRareb3JztZWlpVr9cbnB3a8WIG7SBpLhbpe69UKtBqtcRGPbctitOEAx9++CEvuCLXJRGbxY4jFRm1Wg2BQAB+v5+11h6PZxsOJJNJHqbWarUwm828mMpqtQIAz1EZjUZ+fZrnSKVSbCFJuLTbIssbCzJuLMj49RELLveacHstg//xigev97WLi2cRP/wsfKjnkX1o89Zz+n/aSwDgucUA4ORxwG6349KlS/B4PDh//jyuXLnC70dzE4lEAisrK/jggw8acIAs2klWPDQ0hLW1NSYBqfswMzODaDSKUqkEs9kMk8mEixcvIplMolqtwufzIZlMsqKAuowKhQLpdBo6nY4lkFartWGRHbC1u6pYLLLbW6VS4ZxgJxz4wc0t69E2DpyOOOzQNUmbheu9Icg6GMCh9e7PbTFxkrEb00Fax3g8DovFwq4NZBUZCoWgUqnw6quvwmKx4MyZM7DZbJiZmYHJZMKZM2dQr9dhMpnwySefIBaLIZvNolarYW5uDnfv3sXrr7+OhYUFrKyssA7SbDYjmUyiWCyiWCzC6/Xy8yhBTKfT7OoSiUSQTqcbgIJOJKpS2wXFixukj91tORF5lQNojTAveRwUB7LZLKanp/nnIyMjuHDhAgAwmRAIBFCv13HlyhXcu3cPy8vLSKfTKJVKSKVS3DFYX1+H1WrF4uIiNjY2IEkSX9vlchnZbJa345bLZciyzJuz6TqnBIQWVgGNw9hkwrAbDlBRQf///p+cxZBLfyKfdztax53VzKGX0jWzzc0h/Ptz25k46RBx4N13322JAxRkgtKMA3Steb1e5PN51Ot1mM1mvPLKK8hms1haWkKxWEQymUQ2m0U0GkW9XmdJ1fLyMncUad6B/k7EUaVS4e5jtVrlHRO0n6JWqzGZUCqVthWYu0UzDtz89+fRaWlvyH6acRQcIFOenaRM4qLjw8aJFhOHsWs7LdGK6RBdI5LJJPx+P/+su7sbwFbLmFychoaG4HQ68eMf/5gHpMfHx3HmzBncvXsXZrMZPp+PtYx2ux2//OUvkcvlMDs7i0gkArVaDa/Xi9HRUUxPTyMcDkOj0bDOmVquOp2OrSBpM65CoeBdFEBjq4ue3y4oXtwQC82d4itgObHtRJVKBY8ePXouMQA4GA6YTCYMDg4CAC+dIxyIRqP4+c9/jkwmg3w+jytXrvDeCOpYRiIRZhfv3bsHSZLg8/kQiURgs9m4+xkKhXjTNXU6kskkOjo6WCudyWS4qKAtuaShFl1cKEjysFdy8ecfbeCvf3fweD7cduwZpUoNf/j3i0d6jX269Z2oo9vLgAOBQADAFg4MDQ0BaMQBq9WKhw8fIhgMIpPJwOv1oru7G4uLi+js7MTc3Bz/TKfTQaPR4IMPPoBSqWQcsNvt6OnpQaFQwMbGBsrlMtu+knOT0WhksrFQKPAyOwC8W0jUzdN/1MkAdiahKP7j+/42DjzFyJerR8IBUca222NwhFzgxIqJSqVyKLu20xwiA2Gz2WCxWOD3+7l4GBgY4CFJYKs9GovFUK/XeQhzZmYGvb296OjowMLCArq7u/Ho0SMYjUZYrVYYjUZMTU2xy4JWq0U2m+VldsQ+pNNpGI1GGAwG1mcGg0FotVoe4tZoNKyT1Gg0sNlsiMViLIEihw860dqFxYsVe32flGCeZOTzeSwtLb0wGAAcDgei0SgymQzjwJdffonh4WE4nU6WREmSBJ1OB6PRiGKxiGAwiHQ6za4sxWIRsiw36J61Wi10Oh08Hg9bQ0YiEUiSxNptGrQmpxdahkUJRbVahVarZQnkbjedGwsyPnicfOoD2vlyFTqVEkrlc2s6dKhI5I7usrdXYvg0ZiZeJhywWCyw2WzbcADYmmukbiNJETs6OpDL5WC1WmE2m7kg0Gg0kGWZCQcyTkmn0yxfJlm1JElQqVS83LJUKvH+GZEkUCqVjAO1Wo2lmOL+Kep+7nZK3FiQkcpXYJWerrilVKlCrVJC+ZIt3JXzJ6tCPNUbsJtvSAddDHUao9k14sqVKwCwrfvi8/kwODjIf1coFOju7kYul0Nvby8XAzabDfl8HmfPnkUkEmGXpnPnzuHhw4dQq9VcCFAyAoD3BjgcDqjVajidTlQqFfT396NcLsPv90Ov16NUKiGfz8Nms7GHPWnoiYmghEP88zmexWvHPoM6aF/ZGj6VPRMvAgYAh8cBkjgYDAa4XC4Eg0FIkgSbzQaTyYQHDx5wYt/R0QGtVss20DQPkUwmkc/nWcZGs1MKhQJ2ux2JRKIBB4CtRI6IBbVajUgk0pAs0AxWc0KxUzwLt6d/m5fx7XH7U33P0xDHuaCuOYhQ+qqgfGrZ2cuKA263G6lUCh6PB/l8niWQhANWqxWvvvoq7t+/zyRPR0cHdDodkskk27qTIoGuYZIqWiwWxpN8Ps9yqPX1dVSrVRSLRQBbs1uEJc2SR+piiu5uO8UvfOmnfk3+fK6NA8cdtDZgLyJprzixYoKGfij2sxDmtEuidtJQ05+hUAjLy8tQKBRYXl6G2+2G1+vFtWvXAGwxRORVT0Pa5LTw2muvoVAosCOM0WjEwsIC0uk0g0WxWGR7R51Oh2KxCJVKBY/HA51Ox8tzyCWGNNTVahWpVIqLEXEPBU3zkyRKp9MxYLWLihcvSDdJm5Of5tK6/S6He1Fx4Ld+67dw8+ZNxgGv14vNzU1mD7u6uuB2u9HX14fXX38ds7OzMBqNWFtb4/moarXKrm4qlQo6nQ7VahVqtRoOhwM2m60BB0ymrUHJQqHAg5fpdJplTeT4RTpqlUrF/0aL85qD3J7+w290YsCpP1GWsFav4z//IoQR98s3p3HYBXX7Cbo/UycbT3HPxMuMA5cvX4bVakVPTw8AYH5+Hl6vF8FgEHq9HkqlEt3d3XC73ejt7cXly5cxOzsLg8GAtbU15HI5FItFXmwp2sOTQYvD4YBOp8Pdu3eRz+d5tqpYLKJcLiOXy6FQKDSQk2QXTvkADe4DW9jRKhegXQe/OmiGSaeC6gS7htVqHT/8PISvD7x8g98niQOUD5BMHqdxz4RarT7QYqjnRRK1m2vE3Nwc1tbWWO5ADMyFCxfgdrtx69YtWCwW2O12pNNpnD17FgAQDocxPDwMi8WCvr4+ZiVcLhc++ugj5PN5lEolaDQaKJVKljUZjUbo9XrY7XasrKygWq1CluWGgUySSFDBQKFSqeB0Ovm1qWNRKBS2DWYplUpotVrWZ7bj+Y1arcZSF3LyOMmQJIlnh/a71wF4eXCArB+Brc+KcMDhcPC23Hq9jvn5eWQyGd50q1arYTKZYDAY4Ha7eUFdKpWCSqVCNBpFuVxGKpVCuVzm2SmxMwls4bTdbkc+n2f2cqdZKsKearXKA5l/eq3jRL3o33+UxA9uBvHwf794Yu9xGmMpWjjUgrqDhEqlemrFRBsHtnDg/PnzvLTu1q1bPFOhVCq58CccIPe4ZhyguQYiE0ji5HA42CmOlt+mUink83nodDqWPpHJg7j5WKPRoKuri13fKA9opVQgeWy9XueC4r/898MnaiP9w89D+MHNIP7k66+c2HuctqjX6/j/HiUPvaBuP0F7SGhxKU5jMQHs367tsBssn1W0Yk1CoRBWV1cRDAYRDAYxMjLSwMB4vV688cYbDJJms5nbi2azmRfVpFIpJJNJdnF47bXXMDc3x8NdRqMRKpUKw8PDGBwcRE9PD89B0F4JKiDo8bQEi2RNCoUCer0enZ2dMBgMWFlZYfBplWBqtVpYLBZks1naS/A0PuZ2HEO0Gqotl8sNXtM4wURCrVbj/Pnz+3rs84QDOzGnx4UDq6urkGUZnZ2dvLH+zp07KBaL0Ov1qNfrcDqduHTpEnp7e5HJZKBUKrG6uopYLIZ8Ps8Fgl6vh0qlgs1m42291I3Q6/Xo7u5mHKDdGM2kAREK5BZFkokf3Ayi36E7EenBndUMvv9Pq/jTax3QqU/MI+BUxmEXU7UKcdi2eSN6JpN5KnjexoGD4UB3dzfS6fSOOEBzFSIOOBwOFAoFuFwu3LhxA5FIBKVSiR0gnU4nkwG0oI7yApPJhEuXLiGVSsHn8yEUCvGGbDFUKhV3ULLZLOPEDz8L469/92SKifdmEvjBzSD+9FoHVKqn0kQ/FVGv40QLCQANM7NHxYFTYQ17lA2WwNNtie7EmsRiMdhsNoyOjkKWZfT39zMDIR7bwMAApqamGrSKxCgCW64Q8/Pz6OjogMlkQi6XY3Clm7/L5cL4+DgmJibQ19eHf/3Xf2W7OepIAE+YC4VCgXK5jEwmw7MRVqsVdrsdZ8+eRalUwvLy8o7tTLGNupeOkh4vWlC249kEDdvR8C7wxBpU3HR6WuIoOHAaMADAseCALMsshzQYDDAYDAiHw5BlmeUHdrsd586dwzvvvMO21I8fP+ZBTSr8dbqtZVOSJAEAz1JRh4pMH0jvTW5TrULsbIrbdv95OnHsxcSd1Qy7l7w1ZDnW134WMR/O49OlNM7YtXvOm6SLlUNbQLYKlUrF8zDUdSJcbk4WT0O8TDhgNpsxPT2Nrq4uAGA7d1mW98QB6kaIOGCxWBgH8vk8F/4Gg4ELikwmg3Q6zYUFWc/X63W89dZbyGQySKVS25ZZAuBlecVisSEXuLEg454/c+zdib/6NMgSnxcBB+6sZvBgI4eJM4Y9P6vF6PbP/yhB5wwtMSYcIDewo8apKCaOssHyabdEd2JNnE4nJicn+YIeGxvbdmwDAwO4d+8eFhYW2HFlcHAQ4XAYmUwGuVwOBoMBALg1urm5CZ/Ph2w2i3K5zAwC/QcAgUCAbeEoQdTpdKhUKjxkJbo3KRQKJJNJ+Hw+pFIp6PV6dnigxVhiVCoVLjT2U0wolUoeEm/Hsw2SNJHHuBinrdA7LA6cFgwAcCw4sLq6CkmSYDKZ2HLa4/FwMqLVanHmzBn8/u//PtRqNc6cOYP19XUsLi4yewlsYQDdNGKxGEwmE89X0LWZSqXg9/tx48YNdHd3Q5Ik3o4rSp2ad1WI8duvOo718xULCQD4ZEnGxS7Dsb7H04h0sYJYpoL3vpJqAcCvj1j2LCb+15+sHfuxmEwm1Ot1ZLPZU4/LLwsOmM1mfPTRRwCA6elpjI+Pw2q1YmFhYVcccDgciEQi3FX89re/jb6+PjgcDty7dw/z8/MolUrQarXcyQS27uNk9EL3crrG4/E4Jicnkclk4HQ6EYlEeFGuiAN0DlFxSsPaW3G8DW6xkABePBzYrZNTLNeOXeaoUCjY/fMkMOBUFBPAwTZYitX9026J7saa0E2W/mw+Np/Px1tuDQYDcrkcQqEQFAoFD2SJN+q1tTUMDw9jcnKSB6lNJhMkSUIqlcLNmzfR3d2Nr33tawgGg5BlmZlHGqbS6/U8qEVSBdJO01Zdh8PBiYZSqYQsyw2/Cy242W8rjIqa5sfux8e+HccXBPgAGhJIitNoBXwYHIhGow3//iwxADg6DpjNZpZGAODll/Pz89Dr9ezgNj8/D6PRCKVSydbSHo8HyWSSZQzVahXZbJYtaKkjQVEulxkHVCoVXC4XisUiO0gReUDdrJPcTXPPn8EPPwtvY+XpBvzfXXbBpDs1tywOOu7/8Bud6LBoEZRL+POPNlt2F2h4/U/e9GDErYdaqYBe86RD+FefBo+1KwGADTxowdlpLyaAlwMHaLEdsIUDoVCIMbsZB0j+5HA4UKvVYDQaAWzNVWazWdy/fx+vvvoqk5Aej4eJJJp1zOfzsFqtyOfzUKvVXEwQ0ZTJZODz+WCz2fh1dTodUqkU5waUO7S6l0cyx7P7dDccOClJ5XHEB4+T+Mf78WPBgX//X1eO/fjIdIdySSKHjisve2bIfNh2ZCuWT4yDSqQOGjuxJrFYjJfT0N9rtVrDANbg4CBSqRSCwSCMRiPPPJBNLAB2b/jwww/R0dGBUCgEt9sNYCv5q1Qq2NjYQDAYRFdXF2RZhslkgsfjwcbGBi+eqVar7FVNAKDX6/mmQkNXxWIRoVCIddM05ElzFmIicZBo1b1oFxJPN2q1GvL5PLt4iJ2ppu/imX0xx4ED1H2ja+9ZYQBwPDhgs9nw9ttvY3FxEbOzsywvcrvdbCMbi8UwOTkJpVKJrq4uSJIEhUIBo9GIVCoFg8HADlBmsxmVSgWSJHF3kwiHTCYDlUqFXC6HSCSC7u5uKBQKJBIJTkDo/XcqIo7DKraZhWyOH9wMQq9W4n+46jnS+5xERDKVhg3Be0XzY399xILLvSbcXjv8htvdgnBAXFi4Aw48szgOHKDdKXQvPa04QHsoLly4wDImo9GI3/iN3+AN161woLu7G/l8Hh6Ph+/lsVgM9+/fZ7dGMnMwmUxIpVKwWCy8n4pIBCIcLRYLL8ijbiQ5uI2NjUGhUGBzc5PnJEU5Zqvz5qg4kClW8L/8ZG3Xa+D7/7SK8Q4J/c7T5+72j/fjpxoHKK+jXI6+S1HydJR4JsXEUdqRzdW9Uqk8tETqsNGKNWlmKWq1GpaXl2GxWCDLMiYmJthXul6vw2Kx4OrVq9t0lMDWYjuFQoGlpSXkcjkeepJlGVqtFrFYDGazuYF56evrg8FgwPT0NIMKFQRqtZqLBUmSYDabkU6nEYvFEI/HodFoYLfbYbPZMD8/z8/ZiYFoDipOTsuNqR1b8iYqBGngmhaY1et13pL+VYL4TL6448IBm80Gq9UKl8v1TDEAOF4cuHnzJnK5HKamppDL5bi7EI/Ht+GA0+mEy+WC0WiExWLhpViSJCGbzbJjx9mzZ2FXy//0AAAgAElEQVQwGOB0OqFQKLCysoJIJMJMVXd3N3p7ezE1NYVoNMqs9m44cGNBxv/9ixD+p189+Ocez5Xxf/xsfV83z9trmVNZTPzjET3gD5KAHDSI6RYd/UQ7YHL2eZZxXDhAW6ZPMw6YzWbIsoxLly4xDkxPT+PChQt4++23AWBHHLh37x5yuRzLkqPRKMxmM9bW1qDT6ZDJZJDNZuF2u2E0GmE2m7GxsYF0Os0JIw1b9/f38/xlqVTCo0eP2AlOpVKho6MDer0ewWCQyYS9coEbCzLee5TAt88fvHMwH87vW9bz3qPkiTrIHSbem0kc+Rp+Gjggzr6SeoQsgL8y3zl9S+t2i6NIk1q1Fg/SEj3uEC98saih35EYBqVSyb7TxFZQiMf/6NEjbm8SKzg2NobHjx/DbDbD4/Egm81iZWUFOp2OOxfU+iS7V6vVCmDL6pUW33m9XtjtdrhcLiwuLiKZTEKv17MUwmQy8ZZN2kFAw9etgoBH3FnQto599kHe0ZQsiMPwzd/XV/FM1okeJw6MjY2dCgxoZiufFg5EIhEolUqcPXuWB6grlQoMBgPq9Tr/XKPR4JVXXkGpVILBYOBlmGQRLcsyvF4vS5+IwRaHr8Wg/TT/+Ys41Go1/t2VvdngeK4MlUKBX/jSB3IreSIN8KLLooHXot37SScU8VwZd9eyzEaexhCvfyIRyBqUZC7CHNUzY4JeJhygbgXhQDqdRl9fH9LpNO+eacYBi8XCOKBUKjE2NoZ4PI7+/n62gU4mk1hfX+fuQTMO1Ot15HI5/t4tFgsuXryIeDyOer0OrVYLo9HIDk7JZJK3bZO0kc6nVvd4tVoNtVqN/+1fNqFSqPCtc3sPS2eKVWRLNfz4y9iB9ijQY7993gadRonOZ4gDiVwZ/+/kwY6/OU66QyjiN7nxkWyNZE/N85SHiWdSTBzFreEow9qt4jDtVXoOsQ3AE0ZFtL9bWlpCMpmELMsYGBjYF2g6nU7Y7XaMjIxwIkAMYzweR7VahdPp5EHJaDSKWCwGh2NrCPLy5cvo6Oho0EN+9tlnKJVKCIVCGBwcRDQa5aIBAA97lUollMvlPU8sGvLWaDQAwFrq/Qad3M2a0pcpiBU4Cf05dSAsFgvq9TpL1yiZMJlMvO30q3gmxcRpwYGjSCzm5uawsrICu93ewKqKr3PSOLCxsYGNjQ10dXUhEong3LlzCIfDnLTY7XZMT09jbm4O8Xgcy8vLuHjxIvx+PxQKBTKZDIrFImq1GhYWFhAMBhsGLylBEQc2gScJBF3L/+nGBiZXU9yqB7aGsy92SbBLahTKNfzDZAz/16dhAIeb2RHZu18fseC3X3Xg9TNG2I2aA7/WYWInPfdhYrfk7DiCZuRoTo7es1wuQ6fTsRTuqMuqjhptHNiKnXCAXKGo02yxWJhg0Ov1OHPmDJu1kFQxHA4zDjgcDl5M9uWXX2JzcxOBQACSJKGzsxNzc3OQJImHrrVaLZaXl3lBnqitB9ASB4iUVKlU+NP/uoy3h8174sBffhI6dCL9g5vBhmHm337VgasDJpif0kwVzUccFw7QjNtJBJFI5OwounOq1WrO476apTo0DihOIpFTKBQz58+fPz8zM7PjY07DhkuxvQpsJeIAdj0u8TmiDhoAhoaGGoqJ6elp3Lx5kx8zMDDAxQe9X7M3NRUpSqUSTqcTc3Nz+Oyzz/Do0SP4/X6YzWZcvHgRnZ2d8Pl8ePToEdLpNHQ6HYaHh/Hqq69ifHych6hv3LiBL774ApVKBXq9HgaDAfl8HplMBrIsI5FIsJ7eaDSiWCwinU4zcFELjLR1wJOlNQedpzhNGt1nEeLvr9FouChrZcF30NcV29hqtRpWqxV6vZ4XGBUKBf6ZyWRCoVBgnSyAer1eP3YD7+cBB1phQCsLx52et7a2xkOSdrt9GwYAJ4sDRqMRn3zyCc9HdHV1YWJiAt3d3Wz/vLKygg8//BBzc3OoVCpwuVwwmUwIhUJIJpMIBAJs9Ug4kMvl2LgBeGIvStpaWngmFqn7wYGTKqL/4b8dxut9J7sd972ZxLH4votaZfrMxd0vRwnR+IIswKnTTO48xEZarVZkMhnahHwiGAC0cYDiqDgQjUaxuroKv9/PsiSbzYaenh6YTKY9ceD+/ft47733+J4/NDQEWZYRDocRiUR4zxRZiiuVSiYaRCabCmBKRqmjQVLJ/RTHGo2muTt+LPEv/24Mox7pWF9TjP3Mdew3KJ8iHCBlx1FxgIoHej1ajEo5G60RALa+B6PRiGq1ilQqhfpWHAoHntkA9rOUJlE0MwNzc3O872En7ab4HNJB7zT4qVQq0dvb2/D3nVgUAqVEIoF0Oo1r167xzz/77DNEIhFeMkNMoSRJPDSVz+exsLCAixcvwuPx4O2338b09DTee+89AFtuEWq1GtlslhfUkU0kncxk+SZqJMmHmrZzkuUcLaw5SCFxkGKi+bHNbMjzGCL7QJ/rfoB3t89Np9PxkD0N6JOMqVgs8tZTAJzEKZVKZpKOqpM8ajxrHGjFDgLYU8NNjyMJQjqdht1ub8mqHhQHrl+/jnQ6DbPZjHfffXdXHMjlcnC73QiFQiiXy9BqtQiHw+ju7sbIyAjOnz+PWq3GuGYwGGA2mxGNRnlBpih7IeaaMEZkz0X7aOp+lctlZLPZA7FqB7mWxXN/t+f94d8vNgwwAlts6F4DoXK+gmy5CpdRg3SxirtrWSxEChhx6xueu9eA+EGCcEDcB7TXZ0LfxU6PkSSpYSlpqVSCTqdj5x4AfN5Uq1VotVqWqJ0GkqeNA3vjgNPpRCqVQiAQQCQSgU6n43mXZhwg6aOIAz6fjx9PC+/S6TTvpqnX6zAajVzYEh6I2nqSTioUChSLRbYcJrvR/XbZ9prD2ilEi/tWz/2tv5nbhgN/8qZnz50OgWQRSiX2xIHjKiTodxGLMrqPHyW0Wi3L1RUKBfL5PPR6PQqFApOX4ndEe0MEGevzNTNxWqK5vdocO7Ud6Tk2mw2XLl1i9nCvoezd5jtisRgSiQQWFhYAbA1dud1ueL1enDt3DrOzswiFQpBlGdFoFF/72tcgyzKsVis2NzdRLBbZncHpdDIL0t/fj2Qy2eA5HQgEkEwmIUkSSqUS66YpaaDEQmyHUUu1v78f4XCYAWc/7IL4WruFWCjRyU0gRs5Tz2tB0ZwsEEsgguNOsdPnS90Nu93O2lYqDGkTugi6NOOi0WiaFxS+tO2iVtfofuUHS0tLLEHo6+vbUa99EByYm5tjDAgGg7zsaiccuHLlCtbW1mA0GhGPx5HNZpkwIByYmZnB+Pg4isUiXC4XDAYDHA4HFhe3djmYTCYmJcTlhlRwAmCbWWBrB47FYuHleOQW1irEhOGghAI9h/4kSdVOA8PNA4w3FmT86bWOlsOalWoN9/y5hn0WzXES7iqtcIC6PeKQZHPsZnBBy0nJWIMIH3pd6nxSUthswtCO5wMH3nrrLVy+fBmJRAJ9fX0IBoOIRCJs4rC+vg6j0YhYLMbD2Ol0GrVaDZ988gkymQxGRkawuroKt9uNM2fOIJPJMFFASS3NURSLRZbASJLE51KhUIAkSfB4PDCbzdy9IMvRvaKVZHKvx9OfJK2s1Wo7dvRb4cB/+m/6tlnKVqtVpIt1/MNkdFei4LhxoBUOiktkD1vcq1QqWCwWGAwGduojwkd0axJJIiIej6qOAF7yYqJZbwmgodXZil04iEaz1WN3aps6nU6k02n+u8ViYfC6evUqHj16xKxDLpfDp59+isHBQdhsNmaaZFnmi/nmzZsIBrcukOHhYX7PBw8e8Il1/vx5DAwM4NGjR/D5fLw0S6VSIZ/PswyGCgFyBiJGixxCdjvxRbZzrxCt58TnAzg2GcCzilbHXi6XmRVWq9UHclUhhy+NRoNSqcRArtFouHNE8hOxRU0uT6VSiRnSZ9mZeNax0/W8l4b7KDgAbA1WHlTS0QoHHj58yEOdsVgMuVwOCwsL+Na3vgXgCQ7k83m43W4YDAbo9XpsbGwwVvzar/0a3G431tbWsL6+jrW1NahUKkiSxJt0xe4AFaj1eh1ms3nHjdmt4qC6YMIEYtcPigE/uBnEg40cLveaUKjUoFcr950UnIS7SquioFKpcKFEw5D7TfJFSYparW74vKjDJBJCWq2WO5MkTaFDO9Zf9DmL5wUHvF4vJiYm8PjxY2xubnKC/+DBA9hstgYcmJ+fxxtvvIHl5WWsra0hFAqhv78fhUKB5yiou6nX6zExMQGPx4P19XUsLS1hbW1reSIVGHSuiDsq6FymfGWnRFjsrFEecZAQJValUunAOPD9f1rFP08neMHmQeYdjhsHdiIGyuVyg1LkIMoPmo8CwDuCqBCkPIP2y9BjicgkfP/qu2t3Jg4bzczAfoDhIC1Z8bGiLnNycrKBxfB6vbh27Rpu3ryJer0OWZYbLhiFQsEOTWT15vf7+XG0mGp+fh5ffPEFarUaIpEIMpkMotEo3G43gsEgD2IDwOLiIg93rq2tceJpMpkaWEC1Ws1e9RsbG9Dr9TCbzUgmk9z6bL64qQigVt5+o3koW3QietGCgIK0zVqttkGnLobYvaDEgZjFQqHQABTk7FUoFLj4E523tFot8vm8qM9UbXvDlyiar+f9JgiHwYFWe3LEzubY2BhWV1cbFkeSywvQGgcSiQT8fj97xLtcLszNzcHlcjEOBAIBJBIJDAwMIBgMwu/3IxwOw2q1IhQK4fz58wiHw1haWkKlUkG5XIYkSbBarVAqlSyXoV000WgUNpsNbrcbiUSi5SwEEQlHuYbpeVT8HuY1TtJy8aDR6vgpudJqtTxcK37/FMTIisPUhNPlchkbGxvc8aSuJf1clMaRrIGY0K/imQ1gn5Y4jTjg9/sBgA1WyAnOYrFAr9fDarWiVCohHo8jmUw24IDb7caDBw/wK7/yK+zoRtduvV7H8vIyG7iQZHt8fBx37txhHCDyyeFwQJZlqFQqGAwGKJVKFAoFFIvFBkKT7mliEkzzAIeVNpH2X7wv7uQut1ucJhxoFbQ8UJIkLvbpd6cQuxr0OYrFXalUwubmJududI0T2SguLZUkiXFBIIUPPTf10hcTzXFc2s1WHQhqmyaTSczPz0OWZaRSKdZiku80DWktLy/D7XYjFouhp6cH4XCY9Y2SJCGRSDArTTMRer0e6+vrCIfDqNVqWFlZQWdnJyKRCIrFInccKpUKFhYWsLCwwAO6CsXWls1yuQyLxQJJkpBMJll3n8/nuXCgwSulUol0Os1tc3FL7nHNObyIhYQY1WoVuVyu5eIYGqYSwZPmJOhzJr0rAQ0lGbSMiBgKcvQQ5yra0TpOCgdE6UQymcTNmzfR29vboMl+55132B0mlUrh9u3bnNTshAMUpGcGgHg8zjgQCoVgNBoRiUQQj8cRjUZRrVYRjUah0Wjw13/915xsAOB5LIPBAKvVytIJWnqkVqvZl56sqWmvDVmOisuRjiNedNtpsuZtVXiRG4uoS9fpdFxgEe6KElGSP9GuIiIkiPkkxvJFx9ejxLPEgUuXLkGW5W25AAB0dXUhHA6zI5ter2/4Hg0GA/9JBQYRCwAacICOYWFhgd2eKLk0mUzQ6/XQarXo6upCMpnke06lUmF5pVKp5CW5ZEFLyat4XK3UB4eJF/WcbVZ7NGMekYgkLycJOH3WhA+iTFyr1TYQiaIBhF6vRzabPTRR03BsR3p2O1pGqyU8ANgPWpa3qmPaVilqMWnyniRPBEA2mw0TExMMEvV6HcFgEBaLhatRh8OBUCiEr3/967DZbHzhko1cKpXizZkAWPMsOggUi0V4vV6o1WokEgkolUoYDAbW4IuSBmqviw4P1JEAngxLvei6XEr2qTtw0BCHIpvBQ6PRYGBgAKFQqGHGgmxfY7FYAyNMhZ7L5eIuB91IqANCiYckSQ3SunYcb7TCAXELLiUKFIQDlGysrKywY9xeOGCz2RCPx3lbrclkgsPhwOjoKOr1Oi5duoR4PM43KoPBgEgkwhIGcvWh86harUKSJHb5oMKiVCqhVCqxBJI26IotepE5p5tWkxXxCxmirOAwQZ9dU8cAwFZx193djbW1tQYTBVpOmkqlGrCDEg2n0wmHw8FuPQC4+0xyta/OmRczOzsFcVgcoIHtZDKJtbU1WK1WjI2NYWlpCXa7Ha+99hpeeeUVbG5u8p4KEQcAoLOzE2fOnEEgEIDBYIDRaITf72cCinBAoVAgEokgm82yjIhmIkl/bzQaeXs2zUmR/IkMYarVKnfFiVWn4phygxf5VBOv3cOSH1SANeMpAHbkJAKJOkXU3aR7frOtt8FggMViQTabbdg3QoUG5YhH+W7axcQJxG4uUfV6HT09PQ2WsqIWk7zega3Bq4mJCQCA1WqF1WrFO++8AwC4fv16w9AMXeg6nQ4KhQI2mw1jY2OsnTcYDBgeHuabCnU1SPJCoVKpOCEpl8vQ6/VQqVTs2CJKbQgciBWhpJbYLqqORdcJUdfbnFzsNoB4lNjLCeWor00FGrVi97oo6fMRH6PX63nruGi/aTabORmjZE6tVkOr1TKQi78XJQfxeBwdHR1IpVIsRaPFRMQ+C8f64qL7M4ydcIC24I6PjzcUc0fBAb/fj2g0CpfLBZfLBYVCAYfDgVQq1YADSqUSbrcbs7OzUCqVSKVSbBHa7B1PUjhirWiot1qtsnyOriuNRsPdMrKZJTygjcvEhovYADQmF63a+McRO73fcQXNMFESthcOkFSxmYAgYoIGJ6nLaDQamUggmQJhAnWZxKBOUSaTQUdHB+LxOOOARqNhmWqrwqUdxxuHxQGn04nJyUnMz88D2CIfx8bGMDAwAJ/Ph4mJCVy4cIGdn3bCgeHhYahUKiQSiYa9Ex6PB7Ozs0xKORwOtpYFnsjoaHM6LbokcoHuPXTPp3OLXInI9Y3uZ3T+0t9bSZ9EE5ajXqd7zW+IRc5x5QaiImA/0i7RaY2OTbSMputT7EKQVIn+jfBZluVtkvNarYZ8Pg+73Q6bzcZdCMJDwgGBBG7PTJxUHMb/ei+XKIVCgYmJiZYuUEqlEqOjo/D7/Uin07hx4wb7RFN4vV68++67ALaAidqOuVwOGo0Gc3Nz+IM/+APWUWYyGfT09HC7kxwd6ER8/Pgx28kplUoUi0UYDAa43W6WQtCwHl18VGyIA5kiSNAJTv9OFwIVPOVyGclksoFVp2TksLHbRUtJPzkZHVe3hCzxRDAkgBCdlGj4kQCAfk6ArNfr4XA4oNFoGix61Wo1NjY22OpRr9czA0SA3up3TSQScLlc8Hq9iMfj0Gq1UKvVkCSJmQhycajX6y82ZXwMcZw4QNeyLMsYHBzcFQdmZ2eRy+Vw9+7dbQOgIg5EIhG+0WxubuL+/fuwWq3ckSAcoK3aCoWCXaFINufz+VCv16HT6VimoFKp4HA4kMlkWPJIQYUEaXPFgUG6iRJ5ADy5IRLukEMbSSzp/Q5qRtAcrZIDSlA0Gg3frIU9K0cK+j1FskXEAZIk0A1cq9XyTAN9bvS763Q6OBwO7jaIycTGxgaTAgC4s7hT4UU40NnZuQ0HLBYLVCoVb0Z/0SVkxxVPEwe8Xi/6+vpYDj0/P496vc7FAEmfCAdkWcb6+jokSYJGo9mGA1arFbIss90s4cDi4iIsFgvff1ZXV3lWklycLBYLO0cS+Shq9iuVCmMDLUml84qSd/p/0SyA/iRTENEumQjHwxQVYkHeSjpMs0RHURQ0B3ULCbtEa126V9Ox0DFQh5mcKil3InlppVJhhQh9TjQwTbkAWX3vRsDk83n09PTA4/Egk8lAr9cDwLHiQLuY2CVatSf3AyA7uUSR9evo6CiWl5dbvh49PhwOw+fzYXp6Gp2dnXjzzTfZqYGkEL29vfjoo4+QTqcb7GoXFxfxb//2b8hms6xzNpvNCIfDCAQCCAaDcDgcDBI2mw3r6+tsMStWty6XC3q9nuVZxFxQMive3MRhH0qyCUSopUYFSjabZf2+yHYCh9sp0cpuTfyZ2C056o1TBECRhRS7MpQYiB0ZtVrNiRMlWwB4j0epVMLw8DBKpRJisRgqlQri8TgnKTqdjhM/WZZ3tePU6/WQZRlGo5FvLmazGd3d3ZidnYXBYIDBYCBG7KUfvtwtnhUOpFIpLC0toVAowOfz4cqVK7h48SIAbMMBkh0sLCwwO1gsFrG6uopCocAMdji8tX06mUzyMHehUEBXVxfrZzc3N1nWqNFokEqleDEWMdzUyRD/E7GAbqQiSycaDtjtdhgMBt62Tf9Or3PYoEKhFX4QDgBoaRpx0BCHm+v1Ol/rIg5QgkHLwEheQImFiE20A6RQKGBoaIiWSCGTyWBzc5PfhzTsKpUKqVRqx8+LCIxSqcQubyaTCVarFV1dXZidnYXJZEIsFkO1Wn1+rfKeUjwLHBgbG8ODBw9w//59dm1qhQPAk+Q+n89zN4QK891wgO7D9XodXV1dcDqdWFhYgCzLTLxRAezxePi6oW4nJcKUmNN5J6oXqHgXQ6fTwWq1ctFLx0/3u/12J1pd72RW0Or59Xqd5wz2u2RvpxBnlBQKRYOBijg32tyxIFKjuYtJS2WdTieKxSL6+/tht9tRKpWQSqUQj8cb9vyQzJFet9XvK34PkiQhn89Dp9PBYrGgu7sbjx8/PhYcaBcTu8R+fKZ3iubBrYGBAayursLr9cJm21qCMjc3t43lIDZicnISwNYJurq6yieV0+lEKBTC3NwcPv/884YKlRL9aDSK9957D3q9Hnq9HjabDbIs49KlS6jVatyVoJPv6tWrUCgUyGazyOfz7FVPw5zd3d188yUmUpQNkMyBkt1iscg31uYhrEwmw0wAJRskraLH7XSTF8GleTfDfmQRtVrtwGxkswZcXNgjHmuzVIN+Ji6Top9RC1h8TiqV4u9QqVRifHwcs7Oz/DxKOOj7pwFXUYtKoGGz2WA0GtHRseWvT8Wd0+mE3W6Hy+VCLBaD1WrF2toaqm1actc4SRxIJpO4desW3njjjW1OMhaLhW9ACoUC9+/fR09PT0sc0Ov1cLvdrHmORqMol8t4/PgxOjs7odfr0dHRAbPZjNHRUdRqNTx48IA7YR0dHbh27RoePnyISqWCdDrNXQNycuvu7mafeYPB0HAtiee3yWTiZJeSBJG1r9frnASLu0/oxk7ynr2uZ0rEm3Fgr9P5MMVKsxTDYDA0dBwoIRDfW5RtSpLUMBQpdnFEtjKbzUKr1SIQCMBut6O/vx8zMzMNQ5VKpRIdHR3w+/0Nn6lYSCmVSphMJrYNpo5EvV7H4OAgXC4XIpEIYrEYkUDt7uQecZpx4Pr161haWoIkSXC73UwMxmKxfeEAucKNjIzA4/FwLkH38Ww2i87OTj6vKB/QarWIRCIsgaSgfIA6BGQbS2QBJc503yPyjRJykk3ttYOJkvnmgkIkL5qDMKNZInzQIKKA5g3oums+FupQUteXdnkBT0gN+kwIC8jVKRAIwGazwePxcNFGuQC5v0Wj0YYcSzwGKjjcbjdMJhNMJhOTvoODg3C73ceGA+1iYpdotWTmIEEt0VqtxnZuCwsLbO24srLC+vXvfOc77OZEdrGbm5sAtlpR6XQa1WoVkUgE9+7dw/z8PFZWVuDz+XgokuQJ5MAgyzL0ej0UCgXGx8eRSqVYUxmNRmE0GpkFNxqNnDTQcC6wJZ8gTZ1Wq4XBYGiopsV9B11dXVAoFHzcdJGLIJJOpxk8ADArQqwF3ZzF5ITYNfq5OOS9V9CFJXYDDtL5EN9PoVDAYrFwUbJbUGJBg6kAeGaBJEYECiQhK5VKPKdisVjQ2dnJXuLVapX/PRQKNegqiSHVaDRsIUjLhKhdrFAoMDc3h2AwCLvdjm984xuIx+O4f/8+isViO5HYJY6CA6IsAkADDgDgBGBmZqYBAwBgYmICn3/+Od/Qh4aGEAgEYLFYGAempqbg9/sRiURQLpfZFpTOB9qGSjgwOjqKVCoFn8+HSCSCdDrNOmilcmtngcvl4g5lMw7Q+SkujiIcIAtpj8cDj8eDubm5bdJIOpdFdk6UHVDxIS7AosdSEU9JNckYxNhtRkHUSR8UB8TjV6vVsNvtiMfjDU5qraRV1G3J5/P8O4gspVgkyLLMzGEul2ONusPhwPLyMic/JpMJbrcbfr+/4fMTpRuSJPFzU6kUS0wLhQLu3r0Lu90OnU6Hb3zjG1hfX0c+n3+xXTKOIU4SB3w+H/R6/b5woKOjA3Nzc/jmN78JAPjggw/w2WefsQyKugOiUkDEAZJQNuOARqPB1NQUxsfHuRAlUkKn07EVPCW1lD/Q/ZGkzSTH1ev1cLlcCIfDfF6K8mdK6Kl7TsWJmA+I92/qzisUiobdCGJ+QLFTIUHXCfBEinmQoOMXZ5lEhzXxmqcgZYZIohKJUqvVeB6VihySfxPGUmeRLHkJPyVJgsPhwMrKCpMx5NZEHVBJkqDT6XhoW6vVMg5MTk424MDq6irQLiZOJg6ykKY5xJYoObLY7XaMjIzAarWiVqthamoKGxsb/BzSP3q9XnznO98BsMXkr62twe/3I5/PY3Z2lv+9VCqxg1N/fz+3IwcHB6FQbA1hK5VKXLhwAUNDQ/ibv/kbPHz4EMCTBSlU/TscDgwODmJycpKHdOkioVYkSXFIb0euQWQbp9FosLi4yAvmaPhSnJ2gat5ms2FjY6PBNYp0mqQnpguKEmMqmsQLc6eg9xG7HnTB7pZAiAykOERKoCNulabfhz4LcXiMQJEKCho+1el0Da9NiRY9lxiheDwOs9mMM2fOIBQKMTiazWZ0dHTw1lH6rGjQmkAkn8/zTgBin6llbTAY2CL4KxBs48AucVgcaJZFEIlAOBAIBJDNZltiAABcuHABv/d7v4cPP/wQBoOBfefD4TBmZ2eRyWSwsbEBpXLLAa5UKmFsbAyZTA1DSYYAACAASURBVAZarRbDw8OQZZl3RXzve98DAPzFX/wFFhcX2fGF3NjS6TTOnTuH1157DZ9++il0Ot02HBBtikluU6vVYLfbIUkSurq6YDQaMT8/3yCFoqJW7MiJ1zppvEW5FHUtqVDR6/Us+wOebI/eLSiRqlQq7Fy3n0ZccxeU3o/+pGtJNKSgmzj9GxEtZMUtMq0kB6PXa5Zi5vN5KJVKxONxOBwOlqORHafVam2JA4lEgjsnVKio1WpEIhEYjUb+zgOBALq7u8WFhJo9P5SXPE4SBzKZDAKBAICtc28nHKjVapiZmYHVasVPf/pT3i2RTCZhsVi4k0jdZ41Gg5GRkQYc+O53vwugEQfEeaVMJoNr164hnU7jk08+YUOPSqWCTCbDBAPd24xGI8tvCSOIdItGo0yYinNDwNZ5r9PpOCEnVQNhBN3/yXSgVqvBbDZDq9VyV45sUve6pqmAF7uCO8mgxKCEn467uUho3i5NxY/4XuL8B70GFRjNhCsRhDSHSfkGuWkZjUbu3JpMJlgsFjidTsiyDIPBwHkHkSyUO1HIsgy1Ws0W1IFAAD09PSRxAo6QC7STiD3isD7Tc3NzXESQ/Zvdbmdb2Hq9vm1zrNg2vXDhAtxuN65fv4719XUUi0X4fD5kMhl4PB5+Lk3p08VFTkzf+973ONF1OBz4+OOP8Ytf/AKRSIRZ7mw2y1pper233nqL5yooyaFhQEqEXS4XlEolBgYGYLVa0dvbC6PRiFu3brF9rDhkJVbTlUqFWTcaCiTWgRgDAhAa/lIqtzysgSfDjDuF6IIg+jHTxSuyHGJQYkTSDWIYRCYVwDYJBrUSCWwo8aAlWyJr43A4oNPpIEkSQqEQ2zkS0BAAkAykt7eXQaZYLLJmva+vDwqFggfoaJCLhvCBraQU2Or8RCIRBkHSZefzeXHI9dCLal6WOAwOxGIxJBIJHnikJAIAD0LS7IL4HPF93n77bZw7dw7Xr1/H48ePG3BAfJ7dbmcGO5VKIZfLwWAw4Dd/8zehVqu5pf2Xf/mXmJubQzgcRrVaZbtIjUbDPvaSJOHatWt4/PjxNhwoFArMmJHsoaenZxsOkGQSeJJoi/9Rl5U6GdQZERlVmv2gwXKz2YxarcY7NnaaixAlP3RTp2tLHAgFtjvHiYkSBW2TFh9LS/wIXyqVCrOm1Emh16djIUx0u93sgLe5ucmYTcWR+F65XA59fX1wuVzIZrNQKLZ2AUUikZY4QImIKOEgtpI6mnq9njvR5NiF9tzUvuIwOEC5AM0nitEKB+r1+o448KMf/QgAGAfofAXAS+zsdjtWVla4gDUajdtw4M/+7M/w6NEjJBIJLvZJCmcymbC6uoqrV6/CZDLhwYMHSCQS2NjY4Hk9OrdojqJareL8+fNwuVwwGo2o1+v46KOPIMsyXyt0PxaVBSTJJDKCSDHgiUEJGcNIkoRyuQyz2czS7VYD1hSiLFCj0TTILcU5h+bn08wh4VSzRJl+B8prxE5EuVzm96LcgK5rIgs0Gg2MRiOTvbXalk2wiFHlcpmVCrTjy+PxNFgId3Z2IhwOw263o7e3F+vr67Db7ZxLkEUwkTYkW83n83xsVIDQADfaS+tOV4RCIayuriIYDCIYDGJkZATXrl3D4uIilpaWYDKZkMlkWJJiMBjYbemf//mfcfv2bdy+fZuZiu9///tYWlpCPB6H1WpFZ2cnRkZGsLm5CbPZjOnpaZjNZqRSKbjdbrZmfOuttzA9PY2f/exnuH79OusoiSEcHR1lBiudTqNWq+GXv/wls4KvvvoqZmdnEY1GuZ1vMpmgVCphsVig0Wh4eJvkOtRGFatvGhiiC19c765SqfiYKLkhVoMeS772wO6zEZQ40PtTVS8yiuJ8gTj4SZILsl+l46VEX5RGUJIiemkT+0BdG+oSlctl7gLRgFuhUIDVakU2mwWAbYkNtXnD4TDvk7DZbNxNCIfD6OjowNmzZzmZALaG9iuVCuu59Xo9dyYosaNZCY/HA6vV2nZxOcGo1bbbu46NjeGLL75guUQqlYLFYsGZM2fQ09PDdpA///nP98SBs2fPIpvNwmQy8W6ajY0NlMtlxgGXy8U48KMf/QjXr19HMBjkBWcajQbj4+N8/ZKjSiAQ2BEHSMJAO2lEEwcRB+jaoQFEUb5EN8lischtfJHl02g0sNvtyOVyjA/hcLhhVqhV0BwWXev0XiIhADQyjqJsgZzS6Pqna0dM9MRiRZRakcSM8EHsrtK1TR1Mt9uNTCYDg8HAN3wqpMQ5rHQ6zXsIaFaFZtJyudw2HCiVSvz9UqJAzLNOp2MSoVgswmg0wuPx0O/WLiZOIEKhEFZWVjgXGB0d5b1TzThgtVrR3d29Jw78zu/8DgqFAuLxOACwvLgZByqVClwuFwqFAuPAxx9/jL/927/Fhx9+yDI9kuv09fXBaDRy5+WLL75ALBZDOp1GNpvFq6++iqmpKQBoMBSoVCq8kZuGskWpDeUA4nlN930ATDBSd1V8rCRJsNlsyOVyfA+j81tM8ptDLCBE8rDZeEFUBdDftVot4wCZJ5DEkt6PMIKudYXiyRZpynfEbgiRBGSfS/hJG8yNRiPS6TTnGCR3pAImmUzyYDZ1nmVZRi6XY7wfHx+HJEkIh8NIJBLI5XJ8bAaDgc0zRFykIuLChQt7dmn2inYxcQIRi8Vgs9kwOjoKWZbR39+PCxcuwOfzwWQyAQBMJhNGRkbQ398PYGtO4o//+I/x05/+dNvrvfbaa6jX65xAkIRpcHAQwBaY/OQnP+HV9sRWTE9P4+/+7u9w7949TE9Pc0Ku1WrR19eHkZERAFvL9JxOJ8sozGYz6vU6b9WmGyh1U/L5PFe+9+7d49YbAG57klUcAQ5V58Q20GuZzWYYjUZOUuiGThpNcqMSGT5xjoH+napv0owC4ItYDHKZEBMXUXJAEguSVxEbSyDj9XrZ5YIKEkoC6HMnfTTNhhBLmc/neZ6EBq0peaD/pwRPrVYjnU4zMMViMT4GjUbDiQNJz6gYpEIzl8thaWmJEyu9Xo9yuQyTyQSFQgGn04mJiQn8y7/8C0qlUntm4gSCtMmyLMNisUCpVPIyKsKB8+fPw+Px4M033+RZqYPgQGdnJ1555RWMjY1hdnYWs7OzcLvdO+LA4uIikw2SJGF4eBgTExMIh8PMEC4sLGB1dRUulwv1ep1vQoQBJHcgciCbzXLnUsQBumFSQi0OPgJPimeSZpD8jrqEdE0DW7JOKt5FaYBICtD1QZuixcRBxAFKdJqLHeqI0PAjJQp04ybcIvJnc3OT2VZ6DzpmYgIJW6irS7bMfr+f5VJUgABgDKPXUCqV/z97b/oc55lfi51e0Pu+bwAaIBZSAClx0TYejjQjlTSWPY5zy1N28uXGzlRS98P9kA9JVVL5B7J89/WNy77l3JolJduZGc2MNLE8lDTauIArSOxbdwO97xsa6O58aJ0f3wZBcOeQIz5VLBJgLy8a73Oe33LO+SGXy8Fms0Gr1cpsCJPJBKPReAsO0KbX6XRicnIS29vbyOfzIvykvo1BhtvtZnHr/r14n63brlwuJ3SmSqWC4eHhvgG194MDp0+fxtWrVxEIBBAKhVCpVPbFAY/H04cDZ86cwV//9V9jdXVVhh8CvWLmiRMncPToUSwtLYne5tKlS0gmk3IOrq2tQa/X99mQtlotpNNpVCoVxONxEVKTdaA8l9nBZzGAlD8WF+v1uhS4qPth4YKFvUqlInGMskixFwd4hnJvKjUNXMQB/j/pyeyENBoNWCyWPiE5q/rcg8FgEIlEoo/CRd0I6Yy1Wk323fb2tiQKGo0GxWJRYiVeCz8ndhEZPzGuoBUsf6Z2u41YLIbBwUFMTEwgFoshl8tBr9fD6/UiEonI67PgwWIwXzMcDsNisaDZbN63dupZMvEA63ae0xRqORwOGR4H9NTz586dk8dx6AzXq6++imPHjuHFF19ENBrFqVOn0Gq18PbbbyMajeLKlStIpVLIZDJYWlqSQVbFYhGRSEQsBUOhEABgZmZGxL70E+50OnC73TIJE+hVRVQqFTY3N8XbnF7EykMN6B38Op1OgglSBva28NiSZ0BvMBjEM5lJhZITya4AD29WBfdWNIB+DjMPfNqfKmdXKLsYygokNzsDIwYuyimQFCqxst/tdmG1WrGzsyPcVFYs+HNTTM7PLBgMolgsSvW11WqhWCyKtzSTMAZMX21mtNu96cP1el0oYQSibDYrbhc6nQ6pVEruOaDXho9EIojFYpL42Gw2AUNWVFZWVjA2NsahNaWHsyO+nusgHCAG8GugHwcsFsstgsu7wYHl5WVsbm4iFArBbrfD5XJhY2PjjjhA3rRK1Rtq5/F4cP36daHc7e7uYn19XapfPDgBCA4o+f+kMbBatxcHlAJsrVYLn8+HTCYjQmulCxK7ei6XSyp7DJBZ9VPuZ2IS/63T6WC326HRaMThZD9etJJORQoTAwa+XqPREFxgMkEccLvdaDabcLlcIi4FbuIT8cvhcAidAIB0WJmMKSkcxDFWkhuNhnRI2+12XzeJHcqDcMDj8Yhmw+/3S5JHnKKYNpFI8Pdbvv8d8GwB++MAYwG6ajEWAO4fB77//e/j2LFjt8WBWCzWhwPBYBBArxMC9O5v7k21uucMxs5iqVRCpVJBpVLB+vq67DvqjQBIl6z9lX20Xq9HPp+HRqORwiD3f71e77OL5p5zOp3IZDJC++a5pNRkRiIR6bApcUBZGFAmJQzGOXtBqVXaW1jcW4wwGAxot9tSSOVjuQdZhGN3kI8lpu5H6RwYGECr1RIKFxkhOzs7qFarfbQro9EosQNnzbBwW6/XBSOr1SoajQaMRqMkIt1uV2hTFy5cQKPRgN/vRyqVgt1uh8lkQiAQkD2vUqkQCASws7ODjY0N7O7uYmtri7+nzP3e/8+SiftcB3lO306oRaBYWVnB6OhoH3D89moSCH0bh10qjAwbsbq62ncIfutb3wIAXL16Vdpb5XIZ5XIZ6+vr8Hq9CIfDCAaDsNvtuHLlCq5cuYJ8Pi/TsYeGhqDT6eDz+WA0GrG8vCyD4rrdrmg7yuWyiDp5KIbDYVSrVeFjs1tAfq5Wq4Xb7RZQMJvN8Pv9yGaz0Gq1GBwcRKFQkI0EQA5Q5SJgsPLPAJhBAasJDHLU6t5EX1b5dDqdBCjKx+t0OnGo4esqux0Ug/HQLZVKkmTRJYltSiWXUimYpGPGzs4Oms0mksmk0D0YCJFiEAqF0Gg0EAwGoVKp8M1vfhONRgMffvhhX9dF2TY2GAyo1+sCYBRc7+7uwu/3w26346WXXsLg4CBGR0exsrIiXafTp0/j448/RiKRQKvVQiAQwNLS0jOa0wOuh4kDv72axMXFHKZe/S8w6ukFwrfDgWQyiVAoBIvFgnK5jJmZGSwuLkKj0RyIA2azWWa9nDp1Sq6b/HmaKrTbbRlo5/F4UC6XBQfy+bxQjlgYUM6IIQ7QR57zTLxer2h5mIwzwKCAGIAEAOwKMnFRCiH3apS0Wi2CwaAczkohtLJAoKRd8BpZrdtr1aoUQQK9IMVmswmGUPegLHZQ0MxuolarFa0MKRNKW0qLxQKz2SyC+bfeegtXrlzBb37zG+nk1Go1mZBss9kE48iHZ3GiVqsJDkSjUbEAXVlZQafTQSAQwMTEBH71q18J95xVXwDP3JweYN0OBw4SbT8uHHA4HLhy5QpmZ2eRTqfhcDhQKBQQCoWws7ODU6dOoV6vy/A6dgEDgYAMtmRFnIn3oUOHUCwWBQcYKDPpsFgs0j0DeoUzxheBQEA6MiyykRJIGp/JZEIqlRIzGHYQ+TjgpnGBkn5sMBgQDAZRqVSkQ8miAveNkolAjGA3gK+lZC60Wi3k83k5l1mQYcyhfB2lcxOLKrlcTjRzLB5y5tTu7q5gBKfcR6NRvP3227h06RLOnDkjWk6aYbBzyqIF9aEAxHGT+orh4WEcO3ZMrhkAjh49iunpafzwhz8UjOfnCeC+A4JnycR9rv08p/m3EjT4fWUgoUwiAOA//vwG/tP7i/L1a4MZPBe+ecN/+eWXQmmyWq2wWq24ePEiyuVyn7sCBxKVSiV88sknAHrB+eHDh3H69GkMDQ2JzezMzIwEqczi9Xo9rFarHFRs9YdCIRw9ehQmkwm//e1vZeBNOByWSd1cbMvx5t/d3e1rtSpbeGxlsrXYbDb7hGgejwcejwexWEw2p9I1CYAABrmDSqtU8g0tFosEBUqqBSt3rBxQoKhsZbKCy0pBLpdDPp/vm3oN3KxGMplot9soFAqIRqN9k69Z8fV4PGLLNjY2hkajgU8++QSFQgEAJGBhIEX9B4fNsONBIWW9XpcDidd1/PhxxONxPPfcc3jllVcwMTGBH//4xyL4JM8Tz3DgvtedcOB2ay8O7MWAPzsB6Jsx2Gw2+Z4SB/x+v9g6MoDkuhMO8EC22+24fPmydOGA3oHCe4r3G/enEgd++tOfylC1oaEhxONxqTIC/TjAA5ddgEAgIJaTLAgwADGbzdBoNKK/ACBWtWztUyzK5B6AzGhR6qS47xh0GAwGKYAoqZvsaFDYrOyWKMWWdrtdEoJMJiOfq5JvzefxOogx9Pint369XpeCi8lkQjgchsfjwdLSEj755BOhc1B/QptYfpYGg0Hc2tbW1hAKheDxeFAoFDA8PIzXXntNNHXHjx+H1WqFy+XC5OQkNBoNfvWrX6Hb7aJYLPJX1l/Vebbuad0JB9xu9y2xAHB/ODA/Py//3osDysftxQHSBI8cOYI///M/l4IkcYCWrh6PB51OR2IL4oPJZEK5XEYkEsGxY8dgNBrxs5/9TPj8TqcTiURCKuiMLVh1J7WXiQcNHxhQc58Fg0GxM6aLlNlsxtDQEFKpFAqFgmATAKFOAb14oFAoSKDOzh1wkxbFhIV0aHZPDQYDRkZGAPRs+6nVUmq9DAYDrFaraCgymcwtMyKUsYdyiJ1KpZL9DkDiEbPZjLGxMej1egSDQXg8HmxtbeHTTz8VrCA7gdhHrGVRtFAoQKvV4tSpU/D7/Uin0xgaGsJ3v/tdoVZ/85vf7MOBd955B++//77gwFfJhOkub/lb1rMg4j7XXs/pTqcjlYkLFy6IECrTsmMjv4Q3X57Al4sVXFzM4fi4G2+/NIj//UeXUahsY+/MwRt5B77cVGH3q+9/di2Nc+fOyewJk8kkFb5utwuv1wu73Q6bzYbjx4/jxz/+MYCb1b9ms4nh4WG89dZbyGQy+Lu/+zvkcjm0Wi3h4POQDYfDsNlschN7PB6Mj48jHA7DarViaGgIQC8woICLmTgAaUuazWYkEgmoVCqUSiWhCykrmJzITOoPp2yT96vVavHtb38bZ86cwcbGhlQx2WFgsKIULrMyykCcAjO6IdGNge4rQM8Jx+v1wmazIZFICA1AWeEklYAgxc+AgQdBhuAI9LjXlUoFNptN6B3BYFBAjYB28eLF3r2SyUgQAkCCOAITHWzYIuVnD/SCqfn5eTSbTelE8dAql8s4e/YsXnrpJfzFX/wFPvroIxHVfdUafoYD97luhwO0bGR16P3Pl7CjC+APXhjCB2djggODPgt+/K/LaLb6QeByHLBqPbg6pxMcWF9fFxxYXl5GJpNBNBqVg8zv90OlUt0RBwYHB/Hhhx8im81K6xuAiJ5jsZhwbYvFIgqFwi04MDw8jJWVFbTbbdnnpD8C/Tiwvr6O3d3eNPdKpQKLxSL3OLVGpCeRZpBIJIRXbDAYcOzYMVy6dEnoFKQ2MGAnjYsCcJ1OB6/XK9bT3O/UZrGbQm2H2WzG9PQ05ufnRdhKKierieQ7l0olEVQqtRfEAWIEkxh2Kil2bjQaYg+udFOhVXAul+tzWyMNgvcXtSpKh7xqtSqBWiwWwwcffIDh4WFsbW1hdHQUiUQClUoFpVIJhw4dwh/8wR9gfn5enLwAPJuA/QDrdjhQKBSQSCRuwYFqS4W5jRIcFh3+7XcncH2tsC8OfLoMtHcDyNdvxgOXL1/mTAAsLS0hn8/LuaxSqeDz+eR8PXHiBH784x+LYJu6JIrAf/KTn8gMGaW2wel0YmNjA16vF263G5VKBa1WC4ODg0KhpHU5k27OtapWq6hWq7BarVKMACCUHbqPsZoO9HDAbrfDbrfD4XBIIqE8r41GI8bHx7G0tCTxhNls7nOkUs5Wog16MBiUrgk7ckoNBfVVnNsQDofFFVFpjw3cpGrt7u5KAM6Cn7JwYbVaJenh2U03JZ/Ph3q9jmq1KoN+WVhNpVJYXFyETqdDPp+XIaSMGZTJEQsLxDvSyIlZ29vbeP/992+LA2NjY/jGN76BhYUF0cc+yHpqg4jb8ZQf11K2LzudDlZWVlAq9ajnHB6z0ghjrdmrGJ5dW5DnfjizhQ9ntm772ulav+X3xwkfrO0d6C//Sg4sh8MBk8kkXL6jR4+KcOvNN99ENpuVKnq328XCwgKsVivK5TLS6bS0LmnLGAwGUa/XUalUEAqF0O12USgU0Gq1UKvVxJaxUChIEJxOp6WtScoNk5OhoSGo1b2pmpxxwEomcNOD3ev1wufzidjYaDTKwTk5OdnHs2QlktQE/uFByqTCZrMJjYcARiGyw+FApVKRDbm7u4uNjQ2ZSeHz+aDX67GwsCD6EQDCI6feQqkhAXqBP19XWfWkY8vExAT0er1YdtJlhVQOJg4MFuiYRRBhq5b/z25QKpWC0+kUqgQPmYGBAXzxxRdwu924fv06jEYj1tfX8f3vfx9/9md/hs8//1xmluApdnJ5EnFgY2NDtE1l+PBZMoi1wgCAJH76RVKeexAGLKYBwNj3vf93MQzvdgqXL/9a2vgWi0W48zabDdFo9I44wEQ5FosBgBg65PN5WCwWeDweVCqVvm5FrVYTHCgUCsjlcuh2u8hkMlCr1X1JyV4c2NnZkerYzs6OVGiVhgqDg4Ow2+1y0DEQAXo44HQ6JYlgMM1Og9IZau/vxmw2C12RXu3dblc4x6Qx1mo1rKyswOl0SjcgHA4jkUhIF4JJDosFTIo4y4Y/N6+PFUoaVhQKBQwODkKlUh2IA6RcMTCh9oKdCSZFnONRKpWQyWTEDajVamF9fR3FYhE6nQ6fffaZFBjC4TBOnDiBEydOYHh4GFeuXGFg9VTbQz+JOLC+vo50On0TB7YCWCvqANzEgES2jv/pP5y97etulYC9I0B+thhAWDUPfTMmeK90/3E4HBgeHu7DgcXFRXEOA4D33ntPbOHX19flXOY0blo2p9NpMRKoVCryOJvNhvX1denmc05BpVLpczlicsJkR4kDZAAAN6mFhw4dwuTkJK5evYpsNguj0QitVotIJIJwOAy9Xo94PC7VeoPBIJaxpFwqtYzUOAE9OplK1RuMSUqysvvY6fTmYtAZq16vw+FwwGAwyM/HeCCTycjnTfMDtVotbAHGRDyziQOtVksoZsQRxnHJZFJiJXZBWSThAFJaeJfLZWg0GhlmzOdlMhmZjr69vS2GG3q9Hp9++qnQy4LBIP7oj/5IdHiXL1/m9dykxNzjeiqTiYN4yo9z8T1ZieSGBYCUagJrTcdBT7+nVdGEUQn+11DH3kfUuivDiSwWCyKRCPL5vNAgjhw5gj/8wz/Ee++9J+2warWKc+fOIZ1OY3Z2VuhGRqNRBtywMqZsE7LqZbFYcP78eWxubqJQKKBcLgs/mRWyra0tDA8PA+hV+9nt4EFP2hMPcHKpq9UqQqEQMpkMHA6HVFdPnjyJ69evixicFQe2Aakf4KZktYO0LVbvWTXweDzw+XwIhUK4ePGiJH8ajQa1Wg12u12Axmq1Chh0u13hhQI3J1mz/UpxmNIyTinG3N7eRiwWk2CLIrVSqSQaEovFIkLJdruNcDgswq+NjQ0RXimtHglUpIexWzMwMIBmswmNRiN2wGazWUSar7/+unBqv/rsDp4A+ISuJxUHrl271gv89BGs7R4DCg/nfRptLTa0LwHeUbRTfwOdTic4QHvWu8GBcrmM1dVVxGIxqUIajUYZXsQAenl5GTabTbpvpAxyXg07k0pfdHbwBgcHAdycf8GAmposBtwsKqRSKbRaLQwNDaHT6cBut4txxOTkJBYWFkQoSUGz2+2W4J5BAQDpknAQHgARhfL3pdPp+qgnvB4O0WK3lIUbpZCbWisA0kmhwQW7GsrKKznYjUZD6FvUYZXLZWSzWeGOsyPEPe7xeKDVahEKhbC2toZ8Pi8dm3q93ieMrdVq8j2gV+hgp7Rer0Oj0WBubk6CoOPHjytpMU9tMvEk4kCpVMLs7CyMRuNNHCje4QXuctXaeizgGNQtN8zdNRgMBpnTwt/n2tqaPP7IkSP4xje+gV/+8pdwOp0Aep9ZpVLBtWvXxHyB+huz2YxsNotYLCZORZwlwc7ezs6O/JwMtBngcz+wo0mM4f3ebreluAjc1DQZjUbEYjFMTEwgEAggl8vJLJpQKITjx4/jo48+ksfyb2oJlfNVSKW22+3Q6XTY2NgAANFjdLtdBINBmYvFPcSChNlshlqtRjabFazgHmKHQElrcjqdQtfkLA6lSQSxjnsxk8lIV3NwcBCZTEbYFzs7O/B4PMISYUHS5/PJvC3GNUye+LvRarUy5JbUSHZPSqUSFhcXxYQHuJm8ra+v3+J8ea/rqUwm9uMn/i7AA7g5kIbiIYvFgkRnFLObxjs/+V6X1oTOyL9Bo/4pTj7Xm0pNKgItWn/xi19IZX9paUmGzdCGNplMSsBM8FCpVMjn81hcXBRHJPKIWfWemZmRg7JWq/Xx+LmpaOPqcrkQCATwne98B++99x4uXLggbXmlJztdSgYGBnD16lXUajXZOBQOxmIx4Qyz4sJAPh6P9znC0CauUqnA4/EAgFQt6O0ciUTkOQQRejxvbm7KBmV1gcO3uHmBXtV/YmJC5jvQZo8CMNqwKQf01Ot1BbbePAAAIABJREFUGVbFZI1VH41Gg1AohKGhIRw/flz4pxaLBfPz8zCZTFhZWZFqJmkTrMDQdtTlcvWJwfm7qdfrkmBcvXoVNptN3EW+6tA8lSrsJxUHXC4X6vU6ivojwKMw3TV4oJn+72Er/wrj4+MislxbW8Pq6ioA4N1330U4HIZard4XB5rNJrLZLDwej1QriQPXr1+X/ZnP5+H3++F0OuHz+TAzM4NCoSDuJXsxALhJZyAOnDhxAu+++64EJspZESwG0Gq60+lga2sL2WwWer0eWq0W8/PzqFQq4thGXrfRaESz2ezzf2cnU61W9w0IJSZSiMi2Pi1aabvaaDQE58jbJiYwISKtieYPpCxyzguvgVxr/rw0UwiHw0JDyOVySCaTQrfyer04deoUQqEQ2u12Hw4osYQBi9IUwuFw9Am+abNbq9WkKk36yPz8PLrdLpxOJ5Osp9Ye+knGgUajgaL+uUfy6XZMYTQi/wbDmgvweDx97m0AcPHiRQm6SW9JJBJS9BscHJQENxAISDKqVqtRrVaxsLAg50ytVkMgEBBRPwfU8p5WiqRJ/yUmGAwGBAIBeL1eOfMYuHLvkiqt0+lw4cIFaLVaJJNJVCoV2cs3btyQjiW7si6XS2aukAIJ3CwotNtt6XBwH9CSlzotAOLKRjfIdrstduxkInDvp1Ip+Tfd2rxeL9RqtQyKo1CdDlWMB0i3AiBdxHK5LANO2+3erC273Y6jR49K8hAOh+FwOHD9+nXMzc1JrMWJ4MlkUhIdu70XG9K1s9FoSBeUnV1i55kzZ3Ds2DE4HA7GAvedUTyVycRefuJBQsdHuTiQZnl5GYlEoueOYBrGzKNIJBRrqzuCSmUdL774ohyCtVoNs7Oz2NzclBuXVUu1Wo2FhQVp9bNq5/P54PP50O12pVJBzjF1CWazWbh8GxsbcmMC6HM/YnWBrzMxMYFXX31VKhXkNJLb1263RSNA6zOlUNNiscggn0gkgkqlgmg0ivHxcSwvL0trn50AUgoAiNsV0OsM0MPebrdjbW1NOIgEHrfbLZ74tJkl95IZPrsryhYu7VtJk+B1EHw6nd60XlLR2L2g9SODA1rleTweqegS+AEgGo2K5/+hQ4dgtVrx93//9zIQzeVyIRKJIBgMiq4DAHw+n3QsACAWi8Hn80mQReoIgPoju1kf4XqSccDgm0Y6MXDnJ9/vMniwVR7DWKcjtESg136fn59Ho9HA1atXYTQakUgkJNklDvBAAXrUBuIADRUoziflLhKJYH19Xe5jYsDOzo44u7H61ul0sLm5iWKxiBdeeAHf+9738Itf/EKCXgbaPFxpOtBqtbC1tSUBCgDZi8PDwxgeHkaz2UQ4HJZ7vFwuo1gs9g18Ugb/9FHvdDpwuVziqEKs4/Xy4Kd/PHBTF0aKh7ILw8IF8Y+8Z/KnlYLvnZ0dlEolsQgeGBiQYXwshCgDgXA4jKNHj2Jtbe1AHOh0OviHf/gHbGxsQK/Xw+Fw4NSpU7BYLFhZWRGaGDnT7IxubGxIoqdYT2VBAXiycUDvnUI68ejCrF1DEHNZH77p6eHA5uamVPgp1Gbins/npYNAbROtzunayCToN7/5jXQOeIZTEzU7OysGKwzKDQbDLUYsjUYDm5ubUsQIhUJwOp1YWVkRm2kmHqTrkX68vb0tWggOYS2XyxgdHYXb7Uaj0RAcqNVqSCaTqFaroiPkH7IaqJXY2dmBy+XCwMCAaCGUGMFOBgN17uVarSZFRyYL7AQQT7VarXRJSLkCIN0BOuRxCB9xgJ0FxiPs1lqtVthsNtHfOhwO2O12DA8Pw+12Cw6USiX85//8n2W2RCQSwcTEBFKpFLa3t2G1WhEMBvsmYu/s7AhTYW1tTdlJvW+WwlOZTBxkt/Y4FwfSeL3e3g1kHcUXMcujf2NzCP/8r+/hww8/xPPPP49CoYBz587JZrJarbBYLKjVatDpdCII4uyBUCgkgLK6uopCodCXTACQTdTp9Kbhkl/darVEVFUsFoXewGCClfxf//rXYiGp1FxQiEThEysCtIdjBT2TycBisSAW6zlZeL1eHDp0SKqMFFMrNyC7LGxdkutJZxdqNhqNhugfSHFissEAx+12S0LGZIifDQMIh8Mh02k5ZE45a4ItZ4qhycEEet0dOmVoNBoUCgX4/X4UCgVks1kA6JuQm8/n4XA4MDQ0hEOHDmFxcRF2ux3dbhejo6NSgSCnmokX0EtMbty4ge3tbTz33HMYHh6G3W6X6i6Ap3Jg1ZOMA1+mfI/8fTu+V/FP//I3eP/993H69Gnk83l8+OGHwrO1Wq1wu919ia4SB+x2u1QGWYmMxWLSdQBuzpbY2NgQa1Wj0Qi32y0agWw2i1qt1ieWJC68++67Mr222WwKbYpfM9imBoNJD6lA5XIZ3W4Xi4uLCIfDGBoaEv96lUqFcDiM2dlZoSRSlEkhKN+H+5F0I9IAiQGkHQYCASksABANBakTSuEmCyisuBIXiUvKBIWCcFYmo9EoisUiSqUSjEajBE2ceL2ysiIB8kE4kE6ncf78eahUKpw4cQJWqxXpdPoWHCiXy6jVashms2g2m4hGo4IDXwVwDTyl60nEgWTdgowuinTG88jfd9fzEv7x//sb/OpXv8LJkyexsbGBS5cuSbDLc4Z6Bv7Z2NiQwYaVSkU0OR999BG2trb65i6xiEY7eSbtdEpkd67ZbEpSzeBZrVbjvffew8mTJ4XmpNVqhQrFfdhoNJDJZPpsWdklIBWw0WhgZGQEg4ODfRoQv98vxUXGKAMDA2KrWqlUZKYVq/OkXbK7R6c1t9stsy0Yk3BmFB9LPGBRhB2ZWq0mRVba2hMHSEH2er3SqRweHkapVEK5XBYc4GBAithZNGSsUavVkEgk4HA4cPToUajVasRiMdG+vPPOO1Cr1ZJM2Gw2vPLKK0ilUgiHwwCAfD6PwcFBoaUrcKB6v/fhU5lMABAP58e19hN4dTodbGxs9ITN5uexlgs/tuuxTf8FFv/p3wutga0zoMeV5YHPDJjTpuk3T2eHL774AtlsVg5tLpXq5mCblZUV4d95PB4RcisdDph4sMqQSqXwySefSIuSA3VyuZzwgZXzIoCbbkytVguxWAypVArdbleeQ4EzKQUA+ia70jGJr8MqDKuGShcUblpWJIxGIyYmJpBOp0WUnk6npRqhDHAYKNjtdhSLRWmzs1vBQXjj4+MCNKw00Pptbm4Oa2trUi0JBoN48cUXUavVRNTGa1pYWEA8HkexWMSZM2dw48YNBINBoZMEg0H88R//MX7zm98gl8tJJykYDCISiUh3Z3FxEZFIBABEnPegDg6/6/W1x4Gj/xWW/vnfIx6PY2BgQPjP5NTz8ykUCn04EIvFRPBbqVRw5coVXLhwoc/elfcg2//kEZtMJoyNjaHZbOLy5csSeHMv8TPpdDpIp9P42c9+1lfF9/v9iMfjffxmdhVYRWXlUKVSIZlMwuPxiPVrNptFNptFKBSSIJ5OLgMDA4hEIoIPyvfNZDKSVCnn43S7XXFJ4fRf0imSyaTgAwMGmkBoNBpEo1HBRtIsWKFkIMe5Hg6HA61WC+Pj4zh27BgqlQr8fj+uXLkieDQ4OIhwOIzNzc27xoGpqSnU63XYbLbb4sDQ0JDg+14c+KqD8dR2JoAnCwdK8GFN432AsOzel9Z/Aqu//RSbm5vioAhA+P3syLGzRr3k2tqaBPw7Ozu4evUq1tfXxT6ce4MTkwuFguCA1+tFKBQSfQgTbAbRLDK2220Ui0V8+umnkpA7HA6p5lMDRCYACwxKh6bd3V1kMhmhL9frdcTjcWSzWdhsNukgMvGhQ1yhUJCzm7HA5uYmvF6vFDZ3dnZgt9vRarUk8WKxxGq1wuv1IpFISHLS6XQk0aKWU6mdisViUsQljYpJhBIHwuEwpqamYLVacenSJVy4cEEYDWNjYwgEAtja2pK9PDExAZvNhqWlJWF1/PSnP8Xzzz8veop6vY5oNAqn04mtrS0kEgkYDAacPXsWU1NTsNlsmJiYkN8V18PAgac7knhMaz+BFwCsrq7CZrPheqKFtab3sV6T1mCD/fAfY2vm/+nzQld6yNPHPJlMIp/PI5VKSVbOacy0YlWKFxnk0xrR7/ejVCrJQW42m6XSpbQxZaBOVwNSqtjmZ6LAm5i8xr2JCKlArCjSqSKdTsNkMklAz+QA6HnRT05Owu12S4uUTges0Pj9fqlAKKdidrtdpNNpEZKxvVitVjE2NgaN5uakXwrUqM1gsEUwoUja7/djeHgYzz33HC5duiS0hlwuJ63ZUCgkHt1MWkgn8/l80i0JhUKIxWKoVCp9QQJ9uzmI7Pnnnxf3EC62Rj0eD06cOCHWub+r6t3TvJ5EHBgw3sQBZbUMgByMQ0NDsFqt4uSUzWbFWYQ0SVbv9uIAOb1M+Fn9X1hYQLlcFoE/9y+rf9znrHoSB2hduNc0gYc0sYEJBYsknOTOwgcTHOIUn8fEmcHC0tISarUadnd3pSM5OTkptB9SOkmF2NjYgEqlgt1uly7e5uam0Dbo4kLXOXYiAQhWkavNSbzj4+N9gkmj0YgrV66IScTQ0BCSyaT438fj8Wc48ASvg3DAarViZeO+DXHuexncI9IBVAppGRuYzWYZVktNRz6fl6Jgp9OR+Ul0CQMglXkm8sqBqt3uzTkldH6k6QkLC3x/FhyIKTybWYjkH51OJ4MfWWDg81Uqlbg+sppPZ7Rmsyk41el04Pf7EY1GUSqV0Gg0sLS0hN3dXemcVCoV6dqyOGAymeByudBsNjE3NydJA5MmADLTSYkDjEfYjXG5XPLZ8zlarRYTExNoNpvSHVWpVMhkMpiZmYHBYMDU1BSuX78u5iubm5syvycQCIjGyWKxwGazSTxRqVTQ6XSQy+XgcrnE/pmvk8lkZGbY+Pg47HY7Xn75ZQC3zkV7kPUsmbiLdbuBNMBXVmprv5sOsWv0DxA7+0OpApK/xwOdtB+z2Yy5uTkJvpn5k6/L7wE3tQ8MBACI41O1WoVWq0W5XO4b7kRgYcDAybDkWzebTbTbbaTTaWmXMqtXWrQpAyEmJqRO2e12oTDw2uj9TiGW0+nExMSECMY5S4FtWNIyAoGADH6rVquo1Wool8tIpVIiwHQ6nRgbG8Pu7i6OHTuGSCQiPvcej6dvGiaTiWQyKQAzOTmJF154QURuly5dQrlcRj6fF6G0Xq8XD+94PI54PA7g5mC/0dFRqFQqqVLW63WkUilxsCiVSjh27Bii0ajwYzkUx2g0SkuTXYhn68HWE4sDh3o4wEoiq+0Mbuluxnub1T/lbBgm2FwM8rkv6VvPPZrP55HNZvuCAnYIAUhFUym4ZoJBG0VW4Vi9ZzKjxBWliQB5z+Qks7swMDAgTkicuXPkyBHYbDb87Gc/w7lz5+Sa2Ak0mUxyQNNDn3oNFiMajYb4zpfLZbz44ovw+XxCAyEOUBDKggr3qsViwQsvvCBVQ1LJbty4gWKxKO5trIQ+w4GnYx2EAwB+J0bbWqO9r+vO/TX+b0/AfdgHfUkD1Re9pJM2onv3Eav6SvE0k3QWDQ0GA1wul+zpUqkkM6n4eBYFlG5mDPJpYsDXI/UH6FktkzalnEfFrgkAoVOy86csQFBv4XK5YLVaRcCcTqeh1Wpx/fp1iY/oCud0OpXiY7RaLZn6zdkczWYTTqcTkUgEpVIJp06dgtlsxmeffYZSqdRn0c+Yye12S7fGaDRiamoKPp9PaOPNZhPLy8vSQWWXkqwIjUaDixcvymBb5TwtJn4A+ujpzWYTXq8XKpUKKysrmJqaAtDDglQqBavVCqfT2ed29jDx4FkycYeVSqVkuBP57hR4LS8vY319HaVEGtCceuD3KqydR/zCT+Trbrt3uF/9x/9Rvhc5+edwRnvv1en2bi5uOG5KcuTNZjNcLpf4FyuTBiWlSbmUdma0K9zc3ITFYpFhbhz+wkMfQF8ywIonq+18LQb/XBqNBmNjY+KFzMqmsiJBlyxWCenyNDs7C4fDgUajIYDQaDTk0A6FQhgbGxPrSLfbjRdeeEGGtbA9u7CwgOXl5T5bOWouqPXgdVLXwErm9vY2RkZGhIqQTCaFDhGLxeByubC7u4tLly4hkUhgY2NDBgO53W7o9XpUKhVxZCDFhN7ZSs99v98vSQgHDwEQTYnD4YDD4YDNZsOxY8fgcrnupfqoudMDvu7rScaB7lfBg9ISkYLGS5cuIR6Po9lsSleAe1+pA2JiwUX6Gw+pZrMpWiwObKI2g85PTEiU3QUWDarVqgQppGHxb4vFgtHRUSwsLEilkLxsOrxotVp4PB688cYb8rvg4x0OB5rNpgy7MhgMsNlssFgsOHXqFHK5HLLZrNA0x8bGBAcsFgvW1taQSqX6uhXsyJDzHIlEEAgEEA6HEQgE4HA4bsEBJmupVEqMJhqNhthfr6ysYHV1tY8WRlx9QnDg2brDOggHzp8/j4sXL0LbsD52HFCpbmoH+fcL//PrMAZ6NJttI7D7khpL//GcCI+VugQAQgVWLtKWms2mFAxYhSf1iMUJ7ncmFMoiJ2MT5dBXUp6oadTpdFJxZ3zByj8fTye3d955BwsLC4IDygSENrSkANPqttVqIZvNwuv1iv27wWDA2NgYTp06hU8++UQo3fw5dnd3pchBHLBYLFCr1RgaGhLHNO7ZyclJmM1mLC4uCh5yjky1WkU2m5XOY6VSgdPplFiAv4doNCoailarJdrIra0tuN1usc0nBtDBEQDm5uaQyWRw/PhxqFQqvPbaa3Ie3CUO3Hcs8CyZOGAp25mkDimrOyMjI/jyyy/hN9ZQaJdRatsOerk7rp1mCdXUwi3fV35vp1mSf2sGDH1uAmybcxOwqs+qwd0sJhzK4CIej/fd7NQG0O2FnQ7lc6hHACAARIoE25k6nQ6bm5vQ6XQSNHS7XQQCAREj5vN5qFQqrK2t4ciRI2Id2Wg0oNVqYTKZ4HQ60Wq1MDc3h6tXr0o2r9Pp5HAdHR3FsWPHYLPZZMrm2tqacKUbjYZMtmUlNJPJiGD0448/hsPhkA5NsVjEK6+8ArvdjqmpKXz++ecIBAJwOp1CDysUCvj000+xuroqfG6CYTgcxssvv4xSqSRtZ04QZRUiGAxKgubxeGTqOSsRrBLx5wEgrlDPPffcXf/O8QwHDlz3ggPp3TrqHdMDvd+94oB6wCAdP+U+JM0gmUyKI9ndLhYd9sMB3pMM8okLyqCCOERaA9AvWqa2gM5nq6urMBgMkkioVCrhVQOQ/Tk7O4upqSnZg6QwsqLZarUwOzuL9fV1sYF1u91Qq9X74gATMI/Hg06ngytXrtz8jHd2hIZIHJiZmYFer98XB9rtNn77299iaGhI9jH54EtLS1hcXJRkS6vVwu12Y2RkBH6/H5VKRegdd4sDyWQS29vbYpn9EHDg2Tpg3QkHbDYbGo0GAiYV0juPFwfUajXG/vTPoPPFYIxYobPqodL0jw7RhoxAWIf6xf3jgf0KjOwmEDu2t7dx48YNKQZQ98AzHIAYLPD1WERgPKCcw8REg7RlxirEB41Gg2AwKBqBTqc3EHBxcRGnTp3C+fPncfbsWTGboZYpHo8jl8shFotJLMFk3ePxIBgMYmRkRDoYAMTRjgE/TR5YXGScUalUUCwWBQfonhWJRHD48GF0Oh0sLS3JUECljXs6ncbq6qpcq1arhc/ng91ux8jICE6ePInV1VUxUODvhSwOWtf6/X5EIhHE4/E+HOA8Cs4TUavVjy0WeBZEHLCU7UuCszKrU6vVmJiYwOLiIhoFwwO/n+/wG/AdfuOuH6812PrcBLh4mNM+8V4WqwnKRSGzcplMJqmC8BBXrr3UJbY1eZjye0qhEqsYyutXq9US2H/44Yeo1WrY3NwUgOK0x+XlZQkuCoUCRkZGJNFhQEXQ4BAqpZaBPOKtra1b7GuplUgmk0JZOHToEOx2u8x4YJuTAERdBelg5E0yMKDWgvcYK48Gg0EAb2FhAblcDj6fD9VqVehTpEK43e6+wXZsY96HNeJT6zH/ONa94MBu4cEh9Z5xQG/uowRxKauDe/f0nZZyCJzye0o6AwApZrAyuBeHaMPKYgIrfuxmKAMNCh7ptMQ9QhFluVzG7OwsFhYWsLm5KUMr9Xo9fD4fdnd3xcderVYjHA7D5XL1OU0pcaDT6SAejyOTyaBer2N0dBQGgwHXr1+XSl+lUhF3ubNnz4og3OfzIRgM4oUXXpDKIbskW1tbMBgMEtzXajVxkyH9IxgMwmazCbYBkM+WHQYOzkskEsjlctJ1NRqNCAaDWFtbQ6PRgNvthsViEcEpA4nflUXq7+u6Ew64XC5J9B43Dqh9M/BNbwM42EXOPOIALsbv+hr2ww1lLKDUOrDgyKImF+c1KeMFsg6oseD3XS6XUJmVw9iow+Ish/Pnz+PKlStIJBLiJmez2cS9iTNrkskkpqampMtJbZhKpcLRo0dFvB2LxZDJZLC5uYnR0VEEAgHMz8+LxoEGMLVaDaurqygWi2i1WvD5fHjllVfw4osvQq1Wo1QqoVqtCg4AvQ4jNRe7u7swmUxot9uiYaIFPouXpDARBwDA6/XKMFSLxQKDwYBEIgGr1SrJh9vtlvuPOPA4Y4FnycQB607+1W63G06nE+Pj4/j0gvqxh2Td9u4tbUnlYrvxYS9WKugUcbukRUmjIHgohc9GoxGFQgF6vb7v8KaTBCkEFBHRRYqJCrN1BjF6vV4mwJIjSBtZzlcg7Wpubk6qFo1GQ6wvnU6nXDN9tTUaDTwejwinI5EIrl+/jkQiAbfbDavVimg0ijfffFOG75nNZvF/93q98Pv9UtXlBHFWudhdWV9fF9DM5XK9gUfFolQwOWwukUjA5XJJJWVmZgaBQACVSgUnTpy4HzrDU+3k8qjXveDAx+c0wP4Mwke2drdrB+LA/azb0SCV/89kH4Dwl2/3WKWeg1RCAH2uLdRjETeUlc6BgQGpTlJnRWxQWkoDN93lcrmc2CvuhwMqlQqrq6vibNfpdIQuxS4PDR9oNU1NCOd0sLtZr9fx4osv4pvf/KY4zpBecePGDej1eoRCIdhsNtFKFQoFxGIxcYbjoq0j6QuXL19GMBiUjq3NZhPx6eDgICwWC5LJpGBsNBp9po94BOtOOMBJwuVyGaqS9rGgqsoag9p/FmpD5c4PBtBp3dtF3Q0OKAP+vQUN5VIKrgcGBoQGqFx0luI5zvlOxASaoSQSCTmfWTxgsk5KtdL0RLmfiQOlUkniAU6XB4BisSgmDHSVA3oYVygUZNhcrVaTAuL6+rpc90svvYTTp09LfHP48GFxoLNarRgYGIDdboderxcDCeIA0NOoptNp2O12cWOr1+vY2tqC3+/H8ePH5br5eQ0ODso8CovFgkQi8dhjgWfJxAHrTv7Vyv/f2MnilzPFx3p9rVr+wP9/FIkEF32ab7f2JjL7USxIk2IlkoGF0iqOVAez2SxaDAIHbdfI/+T1sI1KUKE92tzcHNxuN9LptEwEpYMSxd3c7AaDQcSNOzs7yGaz2NnZweTkpLwvAyJlNWJkZKRPlK5SqRCNRmUCNjsdrEIQeOnBHwgEhI9eKpWgUvWmErOSWSwWZTYHfw6DwYBMJoNoNCqA+Gw9vHUvOPDB2gZWU/c99+e+VjW9eOD/PyocUHY8DqJQsYjAx+29Hs5zYfeCHQzigF6vl0mwDodDuiIUPweDQeh0OuTzeTF94IwZJuf74UCtVsPS0pLY45LPzUGXpFvRyalaraJQKGB3d1d414VCASaTCd1uF8vLyzh58iReffVVtNttJBIJqFQqcZuKRqNiPc0iDJMiaiXsdjucTqfYTrPbWiwWYbVapaCSTqdRKBRkwnEul5NZHTabDZOTk4/iV/61XneDA2+99Rbm5+fxwUYJ29VHW1VQWWPQDn94T89xHPZi/b0bD/U62GG8m4IG9/7e7gXQw4FUKiUGLqQGsWhhMBhgNpslkWdywljB6XTKvqIjFQuVIyMjSKfT6Ha72NzchF6vx/b2ttjnLywsSDBPe23GHA6HAy6XC61WC2azWc57AFIE2N7elr24tLSEkydPSgEgEonAZrPBYDCI5oI2t3SRZIeG3ZSBgQHBtnw+LwkOY479cADo2V/z9WZmZqSQ+TjWs2TiARd/UcXi0mN/70Yx9tjf82EugoFyCAyDCaUHfCQSEecFihtbrRYOHTqEWq0Gj8cDnU6Hra2tPrEWAw+lqDGXy8nhPzAwIBVApU2t2+0WDcfg4KB4WpOTCEBoDPl8HvPz8zh8+DDi8bhQmzgB1ePxQKVSIRAIYHl5GdlsFmtra/I64+PjqNVquHz5MqxWK9RqNUKhEBYXF6HX6+F0OjE8PIyhoSE4nU6sra3h+PHjAICZmRl0u12USiVpdZLa8Ux0+XgXP2c9Hj8OZG7cW0Dxu1z7JTYUBzLhULqWAJAEIRAIYHx8HJlMRooH5BonEgkEAgFYrVYRPRsMBphMJhEp7sUBoCeCZvWRVVLSLIgDLAxYLBasrq726RYYjGQyGbhcLpTLZQwODiIWi/VNQuacDLVajUwmIxa3NpsNnU5HpuIuLS1hZGQE3/72t7G4uChaksnJSdF8zMzMwOfzYXR0FGq1GsViEcvLy0Lv8Hg8cDqdYg7xDAce7+pp7x59e1LtuXzPzzG4HkzH8bDWft0Ldi9pm8r9Tzo06VFerxcTExNotVpYWloSWrHb7RZRMgBxfGThMxqNSoJPa+VGo4FGoyEaS9qsktpktVplCCatZ41GI7a3t/tobUxauP8TiQTa7TaazSZmZmagUqnEACaZTAIA1tfXsb6+LvEHC5XtdlvsqMfGxvpw4PDhwwfiQKlUEhxg5+xxdSqfJRO3WalUCvPz81hbWxNOvtJSS7k+PL+Bz1Ye/zU+yUHEndqjXMrq5l5rWFb2C4W/fVZ+AAAgAElEQVSC8IHpR0/+MNuatVpNKvR0TzAYDJienka5XEa5XEYymcTw8DD0ej1cLpc4NhFUdnZ2MD4+jqNHj0obEehteq1WK4F9OByWCgQACUI48VqlUiGbzQr48fsUiapUKrhcLrmX4vG4DKADgNHRUbhcLmxsbMDpdMJqtcqcDn5GTqcTfr8farUa2WwW29vbYgH50UcfYWho6MB7lkOX7kWU+3Vc94oDc6nHe33dTgeF9XOP900f8jpI08E9RFeURCLRJ7bU6XSIx+NitcxBl5xvwcF9k5OTfTjA7kA0GhU753a7LY5XShxQUo6oe6KP+/Xr1/Hxxx/LdbJi6HA44PV6+3CAQQ9/LmWQv7KyIoNDV1dXcfz4cfh8PszPzwue2Gw2rK6uCrUiHA7D6XQiHA4jm80K/ZEdk42NjbvCga+qnc8c3Q5Yd4sDuVwOi+ku8nfveXJfS2WNQWXM3fmBe1a7+XDpkA9zKR2Z9i6yEdjBTyQSACCOT6RCKg0OGNxzGF2tVoPb7RaMWF1dxcmTJ1Gv1zE8PCyicYvFIsYvxAHa4QMQndLw8DBsNpvQGfm6wWBQ6J+c79PpdERzQY1oo9GA3++H2+2Gz+dDOBzG3NycDBxcXl7G888/D7/fLzhARsN+OBCJRPpwAOgNqKQb3O0wAHg4OPAsmdhn0bVhY2MDyWQS4+PjcDqdyOVyt/wyUqkULi8/fmpJe3fnqQ8i9i6ltRy/pr98o9EQ9xUCRigUErs3Po7dA5PJhEgkAo/HA5fLhfn5eUQiEZjNZoyNjcHhcCAej8NoNErLlS5VlUoFkUgEx48fx8zMjLQSTSYTwuEwTpw4IRuWyQ/dIhjsc8hOOp2G1WqF3W5HMpnE2NgYUqkUxsfHoVKpxLaTldS1tTUAwNjYmFjPbW5uSkKiHD41MjKCmZkZ0W4cOXJEqp1Aj/v55Zdf4uWXX+67b5WuJF8lQs8CiX3W04ADrfrvJ62NVEXiAak+5ATvxYFarSYD7jqdDtxut+DB5OTkLTjQarUwNjYmHUV2Jsxmcx/ve3p6Gq+++io+/LBXuCkUCrI3R0ZGcOPGDfj9fhneRd0SB5gpccDr9UrhYWxsTDRQxWIRq6uraLVaosv45JNPpCNJ2sbnn38ugcPExATsdrsMTCuXy/D5fEin04hEIveEA19pU4yP4/f6NK57wYFOp4PZtTKAB3N2PGipI2egcazd13Prycc4lvshLIq2AQhjgUUCOkpyUCypypx1BfT0iKREB4NBmVpPmlGz2cRLL70Eu92OGzd69C+32y3OdaQP/cmf/AlmZmawuNijlKpUKtmLnU4HJpNJcKBarSISifQV/VZXV5FMJlGtVgUHOPjyxIkTQpGemZkRobherxccGB4eRqFQQC6Xw9WrVxGNRuFwOA7EAc7kIQ7Mz8/vS9F7WDjwLJnYZ+VyOZkOXa1W+/yAlYu/BAuqAMyP9Rq3Lv7TY32/x7UYPHDORKPREG5xNpsVcLHZbCJ0ZluUz6fffDAYFHrB8PAwfL6e08Xg4KDoMRj8W61WLC8vy4Rqq9UKr9eLt99+GzabDfF4HBaLRXQJ4XBYOgkmkwlHjx4VnvKXX36J06dPQ6VSwWazYXh4GB6PB8ePH7/F8zmTySCVSgn1odvtDQS7ceOGiNANBgMuXbqEF154AU6ns69l6fV6kcvl5DU7nY64TSwsLGB8fBxnz57tq0rcMmTpGQ7su+4VB1S1NIDHOwF79aP/8Fjf73EtirI1Go10H+k/T7F2p9ORrh0A0UhwRobFYoHT6ZRD22w2Q6PRSFfTYrGIdTSdnTweDy5fvoxms4lr165henoar7/+OgDg2rVrOHr0qCTydIyq1+twu90wmUx47bXXMD09Da/Xi263ewsOcBEreLgPDg6KdouD80h1JA4APU40cUC5p99+++2HgQPP1j5rfn5e7LsBHIgDq6ursKCAR5VMqH0z951IAEBn98H1U66jAQy9PQFTyAaogPzVFBrpKpxTfhRmUw9Vk8EZFBQr7+7uIpfL9dnNs/vPydherxe1Wk3ojTs7O6K7IgvhyJEjsFgs8Pv9EjOEw2GEQiGoVCp4vV7Mzc3JcLnXX39dYoFyuQyLxQKtVouRkRHk83kZuOd2uxEOh/HWW28B6O0xu90Ou90uBcdoNHrzs9xDSf7TP/1TrKysQKvVol6vC5WLhdVAIICrV69CpVJheHgYDoejb0+/9dZbggP5fB7r6+viDFcoFFAqlW7pqj0sHPhaBhHXrl3DysoKRkdHMT09fcv/dzodLCz0vJyZgfLDJz3E7XZLsNHKLCBYbyHTCWLXMvrIr7+cmEXs3A8f+fv8Llan0xFf993dXRFS7ezsSKLBYTAUTGm1WjgcDhgMBnGKiUajGB4eluFxZrNZuhWvvvqqjLEPBALiEKG0WSSvkpNpK5UKlpeXpX3JFmilUpEAgstmsyGZTMpwq4P4in6/Hz/4wQ/wt3/7t1hZWYFKpYLf75ckii40RqMR5XL5FocGv99/y2t7vV58+eWXUkED0FdF2+tKgq+pNez94gAAXL9+XQ4A4kBx/RxsBS0qulF0bWOP/Prj537ye9ed5KLdMhMD0hD5h8Js0qM44T4cDouxQbPZFEtGGhNwmrTNZsPx48eRz+f7rBhp50gcKJfLMmAKAM6ePYupqSmoVCqMjIyIi9d+OEBqQyQSuS0O7J1E+6Mf/Qj5fB5qtVqonc1mE4FAQCboPkIc+Fqug3AglUphfX1deO5+v7+vcLQXBwqFArYzs7AV5h8JDqhdcw/0fPvY/lahrqMBjP6X09B7egF4PVVFI12FCkDy83XkryZx4n99A+aAFSp1/4hv36mI/Ns27MTQdyfQ2e0g/i9LD5xYaDQaGZZZq9XEtIV6SwCik+Tjadl65MgRweZAIACgp6PgLAbuS1ITg8Egtra2BAeIAUCPhuj1eqHRaFAqlfDZZ59henpacIAYYLVa8fbbb/ftxYWFBSQSCelkHBQPsHDxox/9CIVCQVyl1tfXAUBwgDTw0dHRA3GA8Wo2m5WiC/BocOBrl0xcu3YNP/lJb6rkuXO9g3gvgKjV6r6bY2JiQhKJs2fPolAooFKpYGpqCvF4HBsbG6ilUlA3v0R11wWT/zAMOg1arhcf+vXXC3HM/vR/eeiv+yStVquFarUqFTbSGWh9SLE225eFQkEsICm0SqVSfc5Hx44dw9TUFF5++WUAwMWLF6Vl6fP5YLVaRUcB9AILt9uN+fl5LCwsYH19Hel0GiaTSYZUeTyeW1qG165dk3kY165dQygUksz/dgAyPT2NH/zgB/j5z3+OdDotycPa2ppUEgKBACYnJzEzM4N8Pi8Vjf0mW/r9frz88stCZQL6bQyVriRftXG/dtaw94sDAORzPX/+PKLRKFwuF+LxeG/68eIiVKrPsGuKwjn0PBw2MzKaiYd+/eXk3O9tQQGAuMPQCIFzX1hQIOXBYDBIt3JgYACtVgvBYBCpVEqMDIAeplgsFgQCAbz55puyHy5evCgJo8/ng9frxcbGhlzH6OioBCTXrl3D1taWHPKHDh3a190nlUrh17/+NRYWFsT1ZXBw8I448Prrr8Pj8QgOsNsKQGhUtIZ9mDig0+kAoN+j82uy7oQDuVxO6CTlchlHjx7Ft771LYkF2Ll87bXXAECmshfWFtBqfYq2eQSu4RfQsU9iV3XvDBKVNQa1YxFdUwZqTQMq9Z2fc9DSmnU48t+9DHPAigGLDiqNGuoBDdTa/he2hGywhHrdFc8LIbm/7uqa1WpodGoMv3MYg2+PI3txC3N/d39FD862qdfrMnlbeS3EA41GI0MviRmkQQWDQanu0xmSA+LYwSuVSlhYWBDr6NHRUdk3FotFcKBQKGB2dhZbW1vI5/P41re+hUOHDklncC8OfPDBB1hcXBQK5tDQ0F3jwHvvvSd6La/XK8PygB5WqVSqPhxgUUSZrDC5UFKbgUeDA1+7ZGJlZeWWr/cGEfSNByDqfgByM/GAomCO/LaBgQGoqgUg/3kv6DWOQG30PNTr3/jsPz3U13sSV6vVQrfbhdlslimRtETb3d0VX/nt7W2ZJwH0qg7BYBB+vx+FQgHLy8tIp9NS1X/ttdfg9/tx/fp1cXUBIC4rR44cQTwex5EjR/Dqq6+K8KlWq6FUKqFWqyEWi+Hw4cO3iJlYAZiZmQEA6W7MzMxgYGDgjiJItVqN733ve33WrpFIBIlEAqlUSp63uLiIRCIhwlLqL/bSHu7GxtDv90vl9+u27hcHeBAQB5hokN9PJxGzqYqdrTNo5nRQDXrQ1bse6vVvXnj3ob7ek7jq9bpM16WHPAsLpD/QCpLc6VAoJH9bLBbMzc1hZWVF6E2ffPIJvvOd79wWB6xWK1566SUkk0m88cYbmJ6eRiqVEqznwX3t2jW8/vrr+1YCv/zyS8TjvcFgxIHZ2Vnpbu6HA8QPr9eLv/qrvxKxLxcFpzRZeJg48FWH92tXUADujAOs2nJ4GHDzd0UKGQD8/Oc/RyQSgd/vl6C0Wq1Cp6thO/GvULd3AHevuKj2zUBlXwW6KkC9A5WGQ9u0wLYN3a4aamMOXaig1jzcprFmQAPvC6F7ft7dJhK3PE+jge9UBK6jAcz93Tnkrybv6fl0d6JNs0ajEQo0CwqMESqViiQSFosFuVxOcGB+fh7Ly8uCA59++ineeOMN+bmIAzRRqFQqmJ6evgUHeA7cLQ5sbm4CgBgv3AsO/OVf/mUfDpRKJYkFotGonEGJRALpdBpAD2/W19fx1ltv3dKxeNQ48MRHEkpa0cOwthodHZUKBL/eu/x+P0ZGRrCxsSHKea/XC7fbjUQiIS2nwcFBhEIhnD59Gh9//DHS6TR2d3fRbDZ7FqStn8J+/L994GsGeoLrxQ/+t99bWsPeRWs28hnNZjMqlQp0Op3Mg9ja2kKr1RLtAofEbG1tiUiLLVG73Y4LFy5gYmIC+Xy+z22Kmgl6tNNpKZXqWfMYDAZJUkZGRjA8PNxHeWN1A4Dw6y0WC+r1OkZGRuR9bifcVXa7SJVIpVIolUri1qJSqfoCHwBIJpNQqVTC4d37+vtRH57G9bAxALh/HODvc25uDsvLyyLQs9vtOH36ND744APE43Hs7OyIlaCm9jNYnv9vHsp1t3e2sfjr/+NrgQOsJBIH2DlstVrQaDRwuVzQaDQoFoviSsaZDazqU2dVr9fFW/7MmTMAsC8OsOo5MTEBn88n997U1BS2trZw5MgRqNVqmaoL4BYcKJVKyGQy4gxDfOHau0+VVUN2uyYnJzE5OSnfp9Xr6upqX+cEeIYDD7LuhAMMwubn54VzfvbsWYyMjAgFrlQqIRAIwGazIRqNQqVS4YsvvpCOeaVSgcX+IXT2LNRW4201Dyq0AG1W8fXvz9LqtZj+d69i/Zdz90x94swJOjZxL9EFzul0Cg6QDs2ZDUwO8vm84ACnZJ85cwbT09N9ehgmEiwO3Q4H2BE5CAeKxaJ0FphMPAgOOJ1ORKNRwQGl4yQZFaRm7hdrPGoceKKTCeWHe1Bl914Wqw4HcaWBXoY6NDQkXxPEOJyIhxw9yePxOJLJpBxI9Xod22vnoPEegyVy/3SndnsXWzP/+HtNabjd2tnZ6ZtASXtFoBe07+zsyOTNbreLVCoFrVaLVqsFv98PnU6HQqEAoHfgptNp/OQnP8H4+DhsNhsGBwfhdDrhcrn6nBpsNhuuXbuGixcvitjKarX2cR6V9+bGxgasVqtYxw4ODgovmxu+UCiIfZzyHt7b7froo49k0IyykgD0hIB0ZlhYWEAgEOjrzOwVBP4+rEeBAcD94wAPp3g8jnQ6jV/+8pc4fPgwDh06JNzbXC4nvP5WqwVtoQC15yhM4ZP3fb1fpyRi79rd3YVWq0W1Wu2jOjFg4LA5tVqNdrvdN8eFhYi1tTXU63XEYjFsbm7uiwOdTkdcm5LJpNiqkspy4sQJbG1tyV6nzeR+OHD8+HGZRu1yuaTYsB8O7NftopXj3mqi1+uF3W5/hgOPEQeoh1ByztVqNYLBIN599120Wi2ZMcRFF75GowH7c16YvxsA8Jh9o5/ANfzOYQDYN6GgsFvvMmE7X8fGBwt9nQyKrmu1mugldToddnZ2UKlUBA84W6Ldbsuk+Wg02rPtXVxEo9FAPB7H5uam7EOVSoU33nhDnJD2wwEW/O4VBwA8chxgZwK4SdF+3OuJTib2qsz3y7buZ01PT982eOBSilKKxaKMQ6/VajKmfXl5WaajbmxsoNlsotlsQqvVwmQyQa1WY/PTv4b7aBIm/zR2Ww1ojQ50ux3sblehN3ugNdmhggqddguN/Aa2yxnYB5+HWjuAwup5LP7L//nAP+/TuliZbLVa4jNNwKYwqtvtihiLA6b0ej3y+bw4OpVKJbhcLiwtLaFYLMJkMmF6ehqHDh3Cc889B6BXpaxUKrDZbHA4HH3JRbVaxaFDh/qGv1y/fl2ukw4PpMS88sorcp9eu3YNMzMzKJfLKJVK+OCDDwRY2KpVVhhsNpvc53srCcpuyH7OUPeyN56WOROPCgOA+8cBVrKy2Sza7TYuXLgAs9mM8+fPSwBBP3G73Q61Wo302f8L1olNGP3PYbe1jQFTDwcAYMBoh1qjg0qlRqezg1Y1D6iAAYMN3c4O0tf/5WtZTOCiAQNxgA5ParUaRqMRGo1GggdaR9LLvdlsiojTZrPBZDLh2rVrCAQCCAQCGBoaEhy4fv26cOO5p8mlrlar2Nrawptvvgmfzyf77XY4oHRZSaVSIvRWqVS34AAn3BIHGCDkcjk899xz+3YZHiYOPA1zJp4UHGBiOTIyIkWuQqGAbreLCxcuwGq1Ip1O4+rVq2Lq4fCFH8p1/r6s/RIK19EApv/dq/K13mHct5PxlX1pXyfSaDRCrVZDr9ejXq/3TdluNpuiq2RXw263w2Aw3IIDPp9P4oG9OFAsFkX7cC84oKQd7sWB9fV1XL16Fa+99prMpGHCwtcB7h4H9tNM3O36vZ8zsVdl/jizrf3amxwElM/npfXu8/nkBt/e3hb7woGBAQl+c1f/Geuf/9/C+3+27n4xeeh0OvI5M3jg1waDAVqtVtqYdGxoNptQq9UwmUxStVBSh5T30+TkJEqlEorFIjY2NqQtWa1Wsbm5CZ/P11eZUt6bDodDDvW9Aiy2I0mZSqVSmJubQz6fx9TUFKLRKKampgDcBI/9OhhcD6NV+TTNmfhdYgCwPw7E43EsLCzIEDSHwyGCvN3dXezu7va1241GI3Z2dpC58o8ol/8e7fbXkp7+QEuJAwwWdDod2u22dCVIbyIOsKvIGTEqlUooQYVCQeY48J5yu91iFVssFsUulhgQCoUwOzuLI0eO7OuEsh8OKPdaIpGAzWaTyqMSB+x2uzhR8f0fFw48DXMmngQcGBkZwUcffQSr1YrV1VXBAyad1FQlk0nU63XBgdJSFr5vDd35Tb5Ga/idwzD6LJj7u3M49j+chm10f03Z8DuHUVkv3KK14HC7nZ0dqNVqmWTPeRPAzTlVxWIRPp8PmUxGOpbUKbJrAeBAHOCwTE60f1AcIJMCAH74wx/CarUiEomIayBjgY2NjT6q9N71JOHAE51M3Ek08qDrThxMtjfX19eFN83sl4dYLpcT33KgZ1FoNpvFgYTJxZNeAX6aFideq1QqtNvtPl95/l2r1VCtVqHRaFCtVqHX6+F2u3Ho0CH4fD5Eo9FbMn0eFt1uF1tbWzAajeh2uwiFQohEevZ3yq7Bne5NVtOsViuSySSSySRqtRpyuRy2t7cxOzsLu92OQ4cO4ciRIyK2Ii/3TtOrD9oTBz3maZoz8agxALg7HJifn5eKN528eHjkcjk5GEjFIQ6Qp99oNLC9vf0skXiIixNru92u4Cs96UmDKJfLIpik0YJWq8Xg4KAMe9orVmbiSEvoWq0mQk5l51D5nLvZa6TBAb37RIkD3/jGNzAxMSEOckp+/kFCzTvtiXvEgSd2PQk4oBTnAhCKC6cRZ7NZrKysSFGRSa5n3A/sdgDtA1ox/Z4t36kIPCdCdxR3j//F8/jyAOF2s9kUMTY7dGq1GmazGVqtFgMDA0KDarVaYqZgMpkwMjICm812RxyoVquoVquCA1ar9YFwYGFhASqVSnDAZDKhXq9jYuL/Z+/dYttKz2zBtTfJTXLzfteVutmSbckuW1VdjivpsjunTxWS6SB1Bg10v0zPIA+DOemHQWOAeZnz2DMYzDxkXgfobqBxMI3pp3lIkKTqdAVxJYhLdlkul+SL7iJFSbyJ5Ca5eSf3PDDfZ1I3S7bKsq29gAJsiSWL5N6L//d961trHOPj42i1Wl2FK0mfd+NlzwK7f7eXwWt7iCCc1NJI53JMZ6gPcLgGk7zmKSFVFEUMDAwgk8mgWCxCVVWkUinYbDaUSiV2dyqXy7BYLLw4RN00HS8P6k6SswMAlpa5XC5+HwDAbDajXq+zxWy9XkcoFMLExARLkJxOJ27cuMHhN+TQIcsy3nvvPRQKBXbz2G2pdti1Sd0K8qJ3Op14+vQpBEHA5uYmp213+pTTdCSfz8Plcu17iPjss894BLvbtYEec5i++E3LmTjJxbEX4YFEIoH19XXE43EsLy9DlmVYrVb09PTw8m8ymUSz2YTNZmP7wkqlglKpxI5DeiFxsqCGDsmeBEHgifDg4CCSySQsFgtKpRJkWeZ9C5ookbTliy++QDab5X0ov9+PSCSCxcVFGAwG+P1+7hy63e49XfHDrs/Oe83j8WB6epollS/KA0fhAHrcMXngtcZp80Cr1WLp6/LyMt555x243W4EAgHk83mIooiNjQ0OThRFEeH/6gI8f6rLnA7CUVyiJNfhzXKSOncG3ppMJlitVvh8Pp4YC4IASZJQrVZhMpmQTqcxPDzM2Q5ffPEFuzTt5gFRFBEIBGC323l38mV44M///M/x6NEjbi507oJShhnt6h2WYP/pp58emG9Bj3nerpGeM3EMdDrmLC0tsR6OlmSAZ9VZ58Lrzs4OlpeX0Ww2+esUnZ7P52G1WrusyYxGIzKZDC8BUR6CXkicPKg4INcFSZLg9Xpx7tw5WCwWxGIxrK6uIpPJoNlswu128/ThnXfeQSqVwj/+4z+ydVssFsOtW7e6HJMcDgdEUdxXwrQb+1X/+3Ur5ufncfv2bQ7PIrtaoH1T379/n4vXfD4Pr9fbpeel3AugvRxGzlKdeJ6+uPP3Oks5Ey/KAzMzM1AUhXkgGAyiv78fjx494gmkxWJBpVKB0+nkLiXd/8CzDwsdJwtN01CtVrl5YLVa0dvbi+9+97toNBq4d+8e0uk0MpkMLBYLTCYTPB4PpqamIIoiPv30Uzx48ABbW1vo7+/H1atXMT093cUDfX19nGD9PD3ybh44qGPp9XpfmAeOwgHA0XngrOVMvCgPrK6uwmq1IplM8l4USV5IsSDLMsucZVmG/329kHhZCKKAob+4eKALFBmzkKy51WohEAhgZGQEly9fRjKZ5Puycyrh8Xhw+fJlBAIBfPbZZ5idnWU54/T0NK5du8Y8oKoqAoEALl26hPPnzx97IrgfD5AaIRKJQNM0dnOkn0s7OouLiwiFQtjY2OgKxlxYWODiNh6P71FbAEfbNTopHjgTxQS9oDSe7FysIfJotVpdtlyCIEDTNHz99ddIp9O87yDLMoxGI7zetsZPkiTuSiqKwhc2TSNI16/j5CCKImsmgXaHkuzeVFXFj3/8Y1y8eBF37tzBzMwM7t+/zyPnsbExTExMYGZmhlOmq9UqlpeX8f3vfx83b97Ez3/+cxSLRaysrLAlaGfy8e5lx8Oq/93diqmpKQQCgX3HjqFQCENDQ6zTLpVKXe5OR8VR9MX0e52lnIkX5QFFUfD73/+eJUsOhwOJRAJ2ux0GgwGyLCMQCCAej6NaraJQKDAP0GKwLnM8eZBbFnG1pmmcSxGPx/HJJ5/gk08+wezsLKLRKGZmZqCqKiwWCyfiFgoF5oFYLIZAIID33ntvDw9cu3aNd6Z2d7WfxwP7dSxfJx44azkTL8MDDx8+ZB6gRpbNZuOptt/vRzQabeegjNhhcppP50m+ZRj483OQ+5xI/DGNezfofKZpGu9NOBwOqKqKH/zgB/D7/chms5ibm8OvfvUrmM1mWCwWXL58mYsM4oHNzU0Eg0G8++67mJycxPLyMlRVhc1mg6IoXff7wsICgO6l56PyAP39sEbkzMwMPwbAsXngqLtGJ8EDZ+IkQS8oaddpyW1wcJB3ITrDwjrddWw2G1qtFra2tlAsFvH1118/00L6/UilUujr68P6+jrsdjtfkCSp0XHy6LTgA9rFRKVSwfLyMtLpNHK5HH7605/ik08+gdfrRaVSYU/5wcFBhEIhjI6OspYaaOuvqaMxMDDAJEFYWFjgJXzqZpFjy3GdRvY7XBCheL1eOJ1Ozs7YrdGemJjA+vo6jzYnJib2/fnftr74TcR+PAC0g8CazWZXiijwjAdoKY72opaXl3Hv3j1UKhVIkgSLxQJBENDT08PL+8VikaVNOg98OyAeoEMdNW8ePXqEzc1NbG1t4ac//Sn+5m/+Bl988QV2dna6eIDupc4QqmQy+drzwFE4gH6+zgN78TI84Ha7USqV2HRhNw9omgaPx4NqtQrnBf31PikYJCMCV/sQuNqHaraEpf/3ITJzcZ5EEKiRK4oi7t27B5vNhs3NTfzt3/4tfvzjH8Pj8SAajUJRFHba8/l8cDqdXaGUyWQSyWQShUIBwWAQkUgEwWCQc1wAcMo9AKyvr7PM6CR5YHR0FBsbG/z1zl2NiYkJRCIRLoZP+yxwJoqJzhd0enqaL8AHDx5gaWkJxWIRsiyjp6cHw8PDcDgc3O1aXl7m/QfKkWg0GjzmpKqx0WigUCjwMlCr1dpz6NVxMuh8Xel9ajQabLumKAr+5V/+BZhnqrsAACAASURBVBcvXuTHWa1WWK1WnihNTU3hhz/8IWRZhizL6Ovrw+rqKlZWVro8wykIitDZzXK73XyTvozTyO6o+97eXmxvb6Onp2ePRjsUCuHjjz9+LjmcpL74bcFuHshkMnwoo9Ra6kDt5gFVVVGpVGC1WtmNgyYPPp8PkiSxFj6fz+u7Uq8Au19b2ldrtVrI5XLP5QG6lwCwHtrhcHCKfT6fh81mg6qqrxUPHJUD6LE6D3TjMB6YnZ1FMBhkK2GPx8M8kMvlEI/H4Xa7oWkaisUiJzM3Gg3YbDZYLBa2+a09Blzvv/mvfezzJSjLO1C38qgXqmg1WpCcZrjO+TH478/D1u96pb+P2SMfGIBH0lJFUdBoNJBOp7t4IJvNQhRFXqQG2tfDRx99BE3T4PP54HA44HA4cOfOHQDPzhuqqgIA7zR0SiELhQIf8r8tHgiFQl27GvR7vy5ngTNRTAB7X9DHjx9zWvHW1hY8Hg/sdjs7fADtilLTNNy5cwdWqxWJRAKqqqJcLqNYLPLiZaVSQSqV6nIT0PHtgg55ZAVZLpdZSqJpGhYXF/HrX/+aO0m0nEXFBADcuHGDkzMXFxehaRrm5uaQSCRgsVjgdrvR39/P18Pdu3f3dLPoJn6Z6r+zk0FLl+Pj4ygUChgZGdlX46gfEF4Mna/d48ePoSgKotEoisUiUqkUAoEANE3r4oGFhQU8ffoUm5ubKJfLkCQJxWKR7YkrlQp7iCeTSQ6upDBFHd8eaBeFZGXknEVL2evr67hz5w4EQdiXB+hg7vF4kMvluJBQVRWJRAJjY2M8mXqdeEDngJfDfjwwPz/POTE0mRobG+PD26effoqenh5WH1gsFpbHENbW1jgvAPezqJQrGPkPk5B7HPv+Hm8Cor9eRKvWgK3fBVtf+3ovbReQvLuB1P0YLv331+G73PvKf6/dtrG0fE0uSRRoC7QnB7/61a+wvb0NTdNQLpdZ8gzszwPEAQDQ09MDTdO67kOn04l4vP1vOxyOrkP+SfPAxMQE55u8rjxwZoqJTiQSCaTTab6oAHQtS3bq1tLpNP7whz9gaWkJ2WwW2WyWHUQymQw7tdAyMP0sHd8uOg9pVLxRcUGd5M8//xyDg4PIZDJwu90YHR3tKvQ6dYnnz59nrbvL5YIsyxgaGsL4+DiA9g0+MjKCsbExnm7t1jgeZyGrE52djM5FQI/Hoxem3xKIA7LZLJxOJ8rlMqxWK4rFImeM0Pu0s7PDi5aUGdIZjhSPx1Gr1ZgbCPqexLcPTdPYCtJgMKDZbHIxZzaboaoq7t27B7vdfiQeCAaDKJVK7eTiP/IA2TUCOg+8bSAeyOVy/PmvaRpSqRQEQeAQM6CdYvz73/8esViMdyay2SxLa4hPstksKxN25raxM7eNCz/5E3inQhBNBoiGN+u9nPwfvgNH2A3R1B1HtHV7Fcv/+hCL/88DfOd/DUE4hee12zZWEASYzWaUy2WWPhIPfP755+y66fP5MDAwsK8hQScP0E7l8PAwpqamkMlk8PjxY/h8Pnz00UcYGhoCsDco7jAeeJ5V6348QLkXrzMPnLlionOE5HK58J3vfAeLi4u8vBuJRLgapSW7TCaDWCyGnZ0dJg5yciJXIR2vHtSJIOKmsBqXywWv1wur1YrFxUXE43EuFAYGBvZ8+F+/fp2vCavVClmWUSwWkUwm8fTpU5hMJu4QdLopHBXPs2fr7GSMjIywRSHw/BHpUf3mdTxD5/shCAKGhoYwODiIO3fuIJVK7eGBdDqNra0tXtIlXiAPc5I76TgdkJNevV7nJWxyberr6wPQ7hgfhQco6ZYyZjRNw+bmJsLhcFcCss4Dbz463w9N03DhwgW0Wi1Eo1FYrVYoisKhgTR5pLwgKhgMBgMsFguazSbbx1NDqxPlZBFGy8BpPM2Xhmts/2uv7+YoYr9ZRiWlQt0uwD7wauVOAGByWvjPBoMBkiRx/gylYrvdbni9XlSrVeTzeU6g3tjYwBdffNFVCOzmAZ/Ph1KphGKxiDt37qCvrw9DQ0O4f//+kdzdduMoVq1vKg+cuWKic4TkdrsxNjaG0dFRzM3N8WI2LdkB7QRCt9vNgVNUPNB0wmQycSWsFxWvFiQjIY9pTdPYUSMcDvP+RKvVYpLZ3NzcEwS1W0P71Vdf4Te/+Q3sdjs+++wzhMNhnmC9iKvKwsICotEodxoPsmejrx3k8rIbRyEmHXuxmwP8fj8uXboEURQP5AHqWjYaDXZnqtVqMBgMsFqtfO/vd5DQ8e2h03KXuIAOEYFAAH19fVBVFfF4/Eg88PHHH2N4eJg7kNFoFOVyGXfu3GEXL+D4PJBIJDAzM4NcLtdlP6rzwOmhkwc8Hg/Gxsbg9XoxNzfXxdVAW9pGu1KkRCAjFk3TUKvV0Gq1ONNo92KwZ/LtfD8E8Y8yw1MK5BMNIr77f/0Im58vY+vTFdTrdRgMBjQaDQ6vDAaDcLlcvEtRrVZhNpuRTCYxNzcHRVGOzAN0DkgkEpx8fpz77ShnAfod3jQeODPFRKedXyd8Ph98Ph8fGjpBXSjS2ObzedbgORwOnk6QNk8vJl4taF9CEATUajX2nHe5XFhaWsL29jZ3IvP5PK5evQqHw4FIJIJIJIJbt25xd7Hz5l1dXeXxJQB2cgC63RSOmj4ZiUS4o0Ue1YfhqDrI47pG6OiWN9Ghjt6PiYmJA3nAbrcjGAxic3MTuVwOJpMJjUYDbrcbkiRxaJVeTLxadNrBAm1XNpI6CYKAhw8fcvL8cXjg8ePH2NzcRCAQANDmmng8zsUL8QCAI3HA3bt3oSgKlpaWcP78+X1Dr3ZD54FvB8c5C9CybTabRT6fh8Vigcvl4kaC0WhEq9VCT08PSqUS8vk8T8k6kX2UgHPIg7cJiZkoyskirEEbrEH7qf0eBsmI8A8uwGg0YfuzFeZfavw0m01sbGwgEolws+Hx48cQRRHj4+NH5gGaPBEPOBztPZjj8MBxzwKdv8vzcNo8cCaKid3b8SMjI3u0rp0LM0C37Rfp68rlMoee0CKfoigwm818qNUPEq8GnYm3ZOtKS5ZLS0uo1+vsuEJZCrlcDk+fPsXs7CzC4TAfLIPBYNe1MDo6inv37gFoWwN/73vfw/b2NncTfD7fkbsAtMRvs9mgadq+wTIvgk6t737p3Dr2Yre8yeVy7RlxH8YDAwMDnHhNPEBp1+VymbX7+s7UqwO93tVqlXXsrVYLpVIJKysrfAAAQAGNR+IBsoukBcu+vj6Ew2E8evSIeaAzi+B5HACAeQDAiXUNdR44Ho57FgiFQkilUhwO5vF4MDk5ibW1NQ6y1DQNiqLwVIKaXPQ9AIj84glCN8KweORX/6RPCBv/ZRGl7QKa1QZK8QJK2wVILgsu/ORPeEJxmgh9OITNXy+hWq2y9LlYLGJ1dZV3HxqNBmRZxvb2NrxeL548eYLZ2VkMDQ09lwfsdjtGR0cRDoehKMq+mSTP4wEKzyXzhpPigIOK41eJM1FM7K7YRFHsWqwC9lZ/FBpEdq+jo6OIxWJc6dZqNeRyOZjNZnZ20guJVwcaLQPdC++tVgupVAqiKLL8qVM7uby8DE3TEI1GUSqV8G//9m+4fv16FwlQd2J1dRWjo6MIBAJ7wmkeP37c9fsc1AVotVr4+uuvOR21003qRbFb67v7UKxjf+wnb3qeU9ZhPECuLtStpuAkHa8OrVarK/kWeOY1n0gkeO+B9lpsNtuReWD3giXwzOxhYmLiyJ1An8+Hr776inmALClfFjoPHB8vchagDjaFGpKF6BdffAEAzA3NZjvvi4qK3ShEcm90MZF9nERuIcV/N3tlTPy378IRfj0mLgazEVf+lw8x858+ZU5uNpt8HgDa90m9Xme3RuKBSCRyLB7oPA8chwd+85vfYH5+HrIss5nHy9yvRymOXxXORDHh8/lw//59Dvc4rGLrrPJKpRIymQw7vhiNRjSbTZjNZqRSKf6golwJvSv56tGZMN7p5tQpORNFEZIkwefzcXaIyWRCsVjEpUuXkM1mUSgU4HK5+AacmprC1NQUEokEPvvssz3BMEf1kqaQLPr9OsOQXhS7tb77HYp17MVJ8gBppGnZj8bn1JnUGwuvFrt5l96Dzq+bTCZemLVYLIhEImzzux8PdB4q9+OB46TLulwulEolthhfWFh46XtW54Hjg94zkjCPjIwc+vjOyU+pVMLOzg5kuV0QGI1GZDIZ1Go1nn4dZsSQuBNB4GrfyT2ZV4wr/+P3AACNUg3qVh6RXz7FNz/7HYZ/dAnhH+wfnPiqYfbKmP5P/w6zf/85gGdngkajwYvyRqPxQB64cOHCc3ng008/7QqMPE6uBKkl6PPhZaVIRymOXxXORDEBdKelHoTOKi8ajSIUCqHZbGJzcxObm5ucalssFvcUEGazmcdrOk4HnT7TtBhnt9tx6dIl9PX1YWRkBE+ePEEwGESxWMT09DQEQeARttPp3NMpWFhYYJlLPB7H0NAQk8tRvaTtdvuJdSOBoxcyOvbiJHiADiIE6kgCgNlsRr1e1/enThGdXUgyaXA6nbh8+TIcDgdkWUa1WoXH44EkSZicnHwhHvjwww+PzAEej4f3L04KOg8cH6FQCCMjI7h9+zYcDgfW1tYOXKTv5AFVVSHLMsLhML7++mtsbm5CURTmgU4jEEEQuOHQyTOZuTgiv3yKoR9eeDVP9luCUZbgOufH1N9+gK//z9tY/8VjeC4G4Rh+PSYUtp7uz1qSnZlMJvT29mJwcBB2u515wO12w2w2Y3JyEqIoPpcH6PvxeBzDw8NH5oGdnR309/dzqn0+n3/pe/Z14oAzUUzs7OywXzf9/TBtKx0WCoUCDAYDarUaVFXldEvqRtIHFekj9ULidEGkQS4boijCZrNhcnISoVAIoijC6/WiVCrh0qVLnHoLtPXwlGR7EBkUi0Wsr6+zlOAoi1ETExOcrkqdjOOCOpn0845TyOh4hpPggVwux8VDtVqFKIowGo3s7qbLHU8fndMh4meHw4Fr166hXC7Dbrez3/zAwAD6+/s56ZqcvI7LA8/DSfDA/Pw8Sy+npqZ0HnhBiKKIcDjMfz8KD5D+XhRFKIrCCcvAMx4AwDxw0HmAEpvf9IICaDspBd7tRzGaw87c9mtTTGjas8YicTUtyzscDly9ehXlchk2m+2V8oDP54PH4+FMq5s3b77QPbubB14XDjgTxcRRqzeSQVAHCmiPpWgEpqoqL/AA4IU/cgnRi4nTBdn2ksxJFEUOrfrwww8RCoVgMBjg8/lgtVrhcDjgcrk4tRjYe23QIYAmU4Ig7LGUPAxkM/eiNzvJK+iaXF9fx8cff3zkQ4yOZ3hZHqBFy1KpBIPBAJvNBoPBgEqlwo+jnSodp4fOYoI4WVVVPHjwAO+88w4fJCwWCwRB4BRrCocCXj8emJ+fx7/+678CAJtDUEGh88Dx8KI8oGka8wA1FalhBQClUoml0IfJnd6mgsJkNwMA6sXacx756iAaRHimQsjMxXl/ij7ft7a2unjAbrcfmwcikQhisRhUVT0WD5xE8X8YD5w2zkQxcdCb2KmLpqUVh8MBTdPQ09ODfD6PO3fuQFVV5HI5VKtVCIIAq9UKv9/PKbiZTEYvJF4j0EFCFEU0Gg1sbm7i3r17OH/+PILBIDRNQygU4qWq8fFxuFwujIyMcDdqt+f0zMwMTy+A42kdX+YDf2dnp0tSUygUdOvHF8RReYCcQGw2G/r6+hCLxXD79m2kUikUi0XUajVIkgSr1YpQKIRcLodischuITpOHyQ3ofe0UqlgbW0NDocDN27cgCAIkGUZdrud72mX6+DQrdPmgdXV1T1/P25ono42jssDsiyjv78fGxsbuH37Nra3t7l5aDabYTKZ4HQ6YTAYoKpq1+H1ILwtBYWylAYAWPy2U/5NujH48Tgyc3GeGNFCtqqqWF5eht/v50ml3+9HX1/fkXngo48+wszMDBRFOTYPvGzx/zrzwJkoJoBnb2IikcDjx4/RarWwtraGXC6HxcVFnD9/HoIgIJfLQRAErKyscEgNpd+azWYYjUbWRF64cAHLy8tdS8A6ThdE4lRQkO/80tIS/vmf/xnT09MIBoP8XlNMfWfS5G57t1CoOyUbeHXaxN0WlQ6HQ9dGvwSexwP0vWAwCFVVkc/nsbCwAFVVkU6n0Wg0IEkSHA4HTCYTjEYjvF4v6vV6V9Gn43RBPEANhWaziZ2dHTx8+BA7Ozu4fPkyZFlGKpViHvB6vcwB+3UbT5MHOu2q6e86XhzP44FgMIhkMolQKIRSqQRFUTjEMp1OQ9M0SJIEt9sNQRBgs9nQaDS6XJ2eh8gvnqAQyWLiv3sPJqvpW37GLwZlZQfNSgOei8Eu+9dWs4XtL9aQmIlCNBkQeLf/FH/LvaCJCS1gGwwG1Ot1nip9+eWX2NrawvXr1/lxBJ0HXgxnppgA9i5WOp1OPgAUCgVsbW0hnU5jaGiIrfYorIjCaCj9NplMIp1Os1OAjtcDkiR17bPQuFlVVUSjUXi9Xly+fBlDQ0NwuVzw+/0cTNSJ3Z2G09In72dNp08lXg6H8UA8HkcqlUIymeTXvL+/H9FoFMlkkpNVBUFAPp9HtVplWYMub3o9QHssZMZA+nVN07CzswNBEOD3+/HRRx/B6XTC5XLxvd2JgxKqT4MHdttVvy7dyDcZh/HAysoKMpkMCoUCzp07BwAYHBzE3NwcJEnipkK1WkWtVkOpVGKN/nFUCpm5OO78T7/Alb/7UzjHvNxJf11QThax+J9nYbJLsIfdMNokNIptN6eaUoFoEjHxN9OweF8vy9tiNAeg7bhF06Niscg8nc1mIUkSdnZ28P777+PKlStHPgsAOg/shzNVTHReJA6Hg23+4vE4FEXB/Pw8gPbC1djYGILBIMrlMmvriTTIs7xer8NoNEKWZRSLRX3x8jVAq9WCwWDgYqLVaqFWqzHRZzIZLC8vw+l0csdhv1TkVquFx48fdxHFaemTdV30yeIwHqD33ev1Ih6P4/vf/z76+vrQ39+PWCwGoL1kSYfSUqmERqPBPEDWfzpOD3TfGwwGiKIIg8GAarWKSqUCSZJQr9dRrVY5OOr69esIhdrhZNFotCuUbjcHAKd3P5JdtY6TwUE8sLy8jHQ6jUgkAkVRUC6XcePGDdhsNlitVkiSBFmWUavVkM1mWUJD15zJZDq2WuGbn/0OADD0FxfR9+EIDGYjWs0WBFP7+j0tuM77MfjxOJTlNNTNPOrFKgSjCItXhv9aH/pvjZ1q+vVBqObaVr3kugmAk8lJpqppGiqVCuLxOG7dusWWqjoPvBjOVDHRuXjl8XgwPT2NTCYDh8OBJ0+e8MgoFothaWkJ4+PjEAQBPT09qNfrWF9f37NcRQeJTucAHacHk8nEWulWq8X6VU3TYDKZUK/XkclkEIvFsLCwgFQqxePtzc1NOJ1OnDt3jsec9+/fx9DQkD4ReIvwPB64ePEiKpUKEokEvvrqK4TDYQwODkJRFKytrWFjY4PtX2mM3mq1+NrTmwqnC7rX6X0Buu17jUYjL9KSjI14QNM0LC4uYnp6ukv2eJphUDq+HRzEA+TeZLPZkM1msb29jaWlJSSTSd6rXF1dRT6fZx7olNLQvs6L8EDkF094nwIAvJd7MPUfb5zYcz4urH4bRn48eWr//ouCdjlMJhNMJhM7cBKMRiMkSUIqlcLVq1cxOzuLTCYDr9eLBw8eIJ/PI5/PIxwO6zxwRLzVxQQtVNEbTx7TncnGs7Oz2NzcRCKRQKlUgs1mw87ODjRNQzrdviAlSeKOAwA+oJLdGH1g7Q5J0vHq0Ww2YbVaOWyMFrGpK0EHwIcPH7KDg6IoiEajaLVa7NAgiiLrZ/P5PBRFObJzy0HYfT3qeDU4Lg8QcrkcarUaYrEYu37QyJxcmzRN412qZrP5Ql1JHScPct2r1Wrc/KH7WlVVtoheXV3lznInD3z66acsh8zlcrh9+zbC4fCefaoXgc4Dp4Pn8QAAzM3NoVAocNgoSRn/8Ic/8KG0t7eX9yYlSUKz2WR3NwqyJJvol0VmLs4NCx1HQ7PRZCcnaijQe9EpI6tUKvD5fEilUkin0ygUCsjn81hfX+8qBslwQeeBw/HWFhOdekh64wFwF/r27duw2Wx4+PAhtra2AAC9vb0IBAIYGxvDN998g2w2y3ZvnV7ybrcbrVaLiaRaraJarXJQjY7TQ71e7yJyQRBgMplgs9kgyzJkWcba2hrb+VFhUalUOJ368ePHcDgciMfjKJVKcDgcAF4urXK/6/FtIJDXHS/KA36/H9lsFslkEtlslqdbQLtglSQJJpMJrVYLdrsdlUoFJpMJqqqezhPV0QXabes80FFTqLe3F6VSCSsrK3C73bh27RoURenigaGhIZa9xuNx9PT08M/ReeDNw/N44Oc//zkKhQIqlQo2NzfhcrlQLpcxODiI5eVllEoldnCKx+Mol8uo1+vMAxaLBS6XC4lEAhaLBZVK5USmlB/87Ed6IXFM1AtVfu2Jl8kumprCFosFoVCIE8yJB7a2thCJRGA2m5HJZGCxWFAqlRAKhRCPx7vuVZ0HuvHWFhOkh+yMRvf7/YhEIpifn+c9h3g8jlqthmq1CpfLhUuXLkEQBDx8+BDZbBbl8jPtndVqZRenQCCAarWKlZUVlj/pi9ivBzRNg9Fo5BGn2WxGMBhEf38/arUa/+dwOCAIAt555x08fPgQXm97Ac7hcGBrawulUol3ZICXc2w4ylKXjpPHi/LArVu3AAC//vWvWTdNy5V2ux1GoxHj4+NwOp0oFArY2NhAPB4/1F9ex6uH1WrlQ4QoihgeHobX6+36PoA9PGC32+H1ejE3N4dwOIxEIgGXywW3263zwBuI3UGUu3mgXC6jXC6jUqmgVquhUqlgeHgYoihifHwc9+7dY3c3KlLpUPrOO+/gBz/4AX7zm9+gXC4jHo+fSHjl9f/9hzCa39oj2reGVrX7HNZqteBwOCCKIk+Xe3t7ObAWeMYDdrsd4XAYiqLA6/Wip6cHrVYLc3NzGBoaQjKZhMvlgsfj0XlgF97aK9Xn8+Grr77qikZPp9P43e9+xyFTFFISiUQAtDtXZBPX2YVsNpsQBAG1Wg1OpxMWiwUTExPI5/O4e/eunn79mqAz9dJgMMDr9cLv9+PKlSsYGRmBqqqoVCoolUp8oJicnEShUIDT6cT8/DympqZQKBR48Z46HC/bOXidYu/PEnbzgKZpcDgcuHv3LqrVKra3tw/kAVmWYTAY0Gq12HCBQuqGhobgdDoxOjqK5eVlKIrSxRs6Tg9UPNCBrre3lw99w8PDANof3uFwmO/vmzdvYm1tjXkgHA4jGo1ClmVUKhUEg0F2fdJ54M3D7gA6OmASD5AlvMlkQjQa5cn10NAQSqUSrFYryuVyV5aMyWRCIBCA0+mE0WhkyexJ8MCFn/wJzE7zyz3pMwqTw8wSJ3Jzq9frOHfuHD788EPYbDaWO+3mgR/96EeYnZ3F1tYWEokE7HY7Hj16BJvNhnK5jFAoBLfbrfPAPnhri4lQKITh4WE+KALA7OwsXC4XFEWByWRCtVrl8RXQ/hB68OABqtUqAMBsNqNcLne5AZCulvR11KXQi4nTBU2NKPmaxs7vvPMOrl69CqfTiSdPnqDVasHn8/E+xaNHjzA5OYnvf//7uHXrFkRRxNLSEkqlEoB2p4IOIPs5OhwVp2Uld9bRyQOapiGRSCAej8NisQBoHyqOwgO0SwO0dbeJRAJGoxFra2soFApsEakXE6cL2l8huN1uOBwODAwM4IMPPmC5WqvVQjQahdVqhd1uRyaTwcjICMbGxnDr1i2srq7C6XTyDo0oihgdHd0Tanlc6DxwOqCQUpIqJZNJliQB7cZitVqF0+mEx+OBIAjs9EOp953XFdkO7+zsYGVlBdvb24jFYsfKmTgMgenXK7fhzUI7kFLTNN5rk2UZwWAQvb29mJqawvr6OoB2c4n2pABgenqak+pbrRbvVBEPCIKg88ABeGuLCaDtya8oCoC2j3RPTw8fCOLxODweDyqVCmRZhiAIqFarfBhotVrweDzsPU2ymVKphGazic3NTQ5E0g8QpwuaSNA+i6qqsFgskGUZNpuNHVrS6TQSiQQcDgeq1SpUVeWb+OLFi/xnn8/Hbg5OpxNer3dffeNxF6h0i9fTAfFANBoFAOaBZrOJWCwGq9X6XB6w2WysodU0DRaLhYuIThtiHacH6izLssyNILLV9Hg8XEw2m02eQFssFqiqClEUEQ6H+d4OBAIQBAEulwv5fB6Tk5P7hlq+yBKlzgOng+fxgNfrRSaT4RA6QRD4/CAIAtxuN4xGIyqVCiwWC0wmE0RRRLFYZO39SexJXPm7P+0KiNNxPLRq7WLO5XLxTqvdbuf3J5vNYnV1lQOJy+UyisUiAOD27dv4y7/8S7aJ1Xng6Hiri4nO6o8Sjl0uFzRNw5UrV+B0OvH06VMeY1YqFb5ogLbOWpZljI2NQZIk5PN5rKysoFwuo1QqoV6v77Ec0/HqoWkaqtUqRFHkSVKlUsHOzg6GhoZQKBQQiUSYQLa3twEA6XQaNpsNTqezS7NIQXFEDrv1jQsLC1hYWMD6+jo8Hs9bs0D1toJ4wOVysXc48cDFixehadpzecDn82FoaIinD1tbW8jn86hUKuzgouN0QTxgt9vZwUVVVZhMJrjdbuaBbDaLTCbDdsDpdBpXrlwB8Ey7vLtzuLOz05UhsrOzg1Qqhdu3b/M1pXPA641OHnA6nXC73V08oCgK7t69i97eXsiyzOnW9HlBOUTk6Aa0F7jz+TzvVJGEGmhPLzqNG44K56jn5J70WYQA3ou02WwolUpQVRWqqqLZbGJhYQFPnjyByWRCNptFLpeDJElYXl7GlStX9pwFjsoDdE2dVR54q4sJoLv6CwQC2NnZwbVraBorqwAAIABJREFU17i67O/vR19fHwRB4HC6x48fI5FI4Ouvv0aj0eALkzT39Xqdcwz260a+KInoeHE0Gg0u7GhSUavVcP/+fYTDYWxsbEBRFDQaDTSbTbjdbn5PqavcKWPa3TUgfSORTz6fRzwex/nz5+HxeN6KBaq3GfR+TkxM8AcDAJ44HYUHzOa2hpk+UGq1GgwGQ5cUkkCaXH1a8WpRr9eRzWbhcDjQarUgiiJarRZPkhOJBFKpFMrlMmq1GuvfKchyPx4gdGqcW60Wbt++jXg8zjygc8DrD3pPOzvJQJsH8vk8hoaGEAqFIAgCLl++DK/Xi5///OfMA/V6HWazmZtVZB1rMBj2OLnR9QfgyGcB7+UeiKcYUvc2oJIpoVarIZ/Pd2VOVSoVqKrKNrC0dE87sZqmYXl5Gbdu3drXRphwGA+Mj4+fWR5464uJTuxXWNDEgvD+++9jbGwM//AP/8CjcvIqlySJF3HIa36/wwJJqfRi4tWi0Wiwzp08/1dXVyGKIvx+PxRFgdPp5EUqURQhyzLy+TxmZ2cPnDJ0difS6TSPvuPxOAqFwks7O+h4ddj9wbB7ctn59f14oNFowOl0IpfL8RTjoIYCoBcTp4FGo4FSqcTTCZPJhO3tbZ5CplIpeL1elMtl9Pf383t1GA/s16Gk1HQAKBQKOge8QdiPBzonFgC6gko7eaBaraLZbMLpdCKbze5bSABtDqBr66hOj67z/pd9amceG5+2l+ybzSaKxSKazSaMRiN2dnYQjUYRCAQgSRIqlQo8Hg8sFgvLoGw2G2ZnZ1nWdhQeIBt5oM0hZ5UHzlQx0Yn9CguqQhcWFiAIAi9nSZIEn8+HdDqNVqvFY05a0t0NPWTm9EBdhs5dlnK5DKvVikAgwJa+pIO/du0aCoUCFwXA/jZtnR2tu3fvwu12Y3x8XE/HfsPxIjxAeRQUXEWOTwSj0ag3Ek4R5N6ye0L8sjyw+wBKHJDP53Hz5k2dA95g7DexoPeT9iiIB8xmM7xeL+LxeJcpQ+dnPnHDcZ2dHEO6xOllUNzKIzPXPtg3m03UajWeTgLPGrx+f7toO3fuHKrVKhcUExMTLGOiovJ5PODxeHD+/HkUCoUzzQNntpjoxH6LMHa7HaOjo3C5XJiYmIDT6cTdu3e500We9PV6vWths5M4TmIZS8eLgQ54tVoNpVIJoijCbrfD7/fzoi0t4OXzeQ6mAw63aXsbXRh0tHEUHgDaXejh4WGoqopWq9XVAes8wJI1oc4BrxZ0qOs80Ok8oOMoOGgplniACkhBEDgNuVQqsWUsSR5NJhPLZ4/DA84RvZg4LlqtFrSWhsJaFt/87Hf89U57WDLQIVmaKIrw+XyQZZmXrCnZXOeBF4NeTOyDiYkJRCIR5PN5jI2N8ZKWIAhdQSd9fX0IBALY3NxkZ5d6va47u5wCOqVl5K7TaDSgKAoTh6Zp2NzcZBJxOBzI5/Po72/b8NGBsZMQqFNF+tf9NJQ63k7sxwOxWAwWi4Wdg2q1Gi5cuMC7NMVikQ8WOgecDqigA9ryEoPBoPOAjhdGJw9omtbFA0B7YqmqKs6dO4dsNot6vc4Lv8QBR1Yq6IqGY+Px/z3D04jdoGKCLONjsRhMJhMuXLiASqWCra0t+Hw+WK1WnQdeEnoxsQ/IzYccezRNg8fjwcTEBHK5HNxuN0qlEiKRCCqVCsxmM6dpUsidjlcHsuij1520qqIosptLpVJBLpfj7mQ2m8XIyAhnSADt0edu4rh79y5yuRwWFxd52fqsujWcNezHA/39/ZxJEQwGYTab8fDhQ1gsFlitVthsNmxvbx9ZI63j5CBJEh/aqKFAZgyUE5LL5aBpGtvB6jyg43kIhUK4du0auwHSHkRPTw/i8TiH1m1vb8NqtcLtdkOSpK4F/6NOJ1v1JkSD+K09lzcdrWYLrXoTtVwZpXgR8TuRPYWEJEkA2q+5JEl88KedSgopBNpTZuKB0dHRI/FANpvF0tISxsfHz7R7027oxcQhUBQFgiBgcXERwWAQ5XIZ3/nOd+ByubC4uIjt7W0Ui0UoisIyB13a8GohSRICgQA0TeOOEPBMw+p0OmG329nuk7Sv1FXKZrMA2qQyMjLCPzeRSGBmZga5XI41lKSnPqtuDWcVxAOzs7MIBoPw+/0YHR1FX18fnj59Ck3TUCwWORiPDrE6B7w6WCwWeDwenhZVKhUOraLcAMqcsFgscLvdnEEDAOvr6xBFEQ6Ho0vWoPOAjkQigbW1NS5GySXI7/dzIOajR48AtK8NVVXRaDRQr9f5ZxgMhiM1GTPzCQTfG/jWnsubiORXMVRzZShL6QMnEASLxYKenh6YTCakUil23wLaxQX9WRAEmM1mPh+QHO04PJDP5+F2u3Ue+CP0YmIXaIyVTqcBtJdwqBPZ09ODRCKBgYEBCIIASZKgKAoUReFDhH6AeHUwGo2wWq2wWq0AwH7gBoMBwWAQDocDg4ODGBkZQU9PD1qtFhYWFmA2myHLMmcJFAoFDAwMYG1tDYFAAEDbKlBRFCwtLTFRkI7yrLo1nCXs5gEqEFRVRV9fHyflplIpzjPIZDL8WJ0HXh0kSWLf/1KpxOYLdrsdbrebcyYGBgZw6dIltFotpFIp9qAXBAGrq6sIBAJwOp38c6kTqfPA2cRuDiAUCgU4nU709vbC6XRic3MTOzs7qFarKBaLKJfLe2SOR+UDwahPJQjNehOx/7KEyC+eHOnxkiRxk7BUKkHTNBiNRkiShHA4DEEQMDQ0BI/Hg9HRUeYBWZY5J4jOAIfxQDAYBAB+jM4DbejFxB+RSCSwsLCASCQCt9vN43BBELiQGBoagsvlQi6XgyzLyOVyrIvcTRYGg4FH7gflUeh4MXQm3VLarcViQS6XQ7PZhCRJcLvdmJ6exgcffID33nsPs7OzWFpaYs306OgoPB4PNjc3oaoqd5xcLhf8fj//PRQKIRwO49atW10aSR1vJw7iga2tLZRKJYyOjjIPUPFQqVRQLBYPtYmmzqReZJwcqKFjsVhgNpt5ubperzMPDA8Po6+vD1NTU+zOsrS0hHPnziEej2N0dBSFQoGd+TRNw8zMDK5fv46dnR2dB84o5ufnOYgMAEudNzc3AYAtZDOZDEtlSJt/0L6UwWBgOe5B5wF9Y+IZdh5uH7mQMJlMPGmg4sBqtXJTYWRkBH19fbh69SpPKBcXFzE6OopEInFsHvizP/sznQd2QS8m8KzyjEajiMfjHFpjs9mgqipbxrlcLiiKgtXVVcRiMRSLRVitVnZ26jwoUEV8kA+9jhcHTSQGBwc5lKZYLLJNpyAIqFQqGB4ehs/n406Dpmm8SJvP55HNZpFKpVAsFrG0tMQd51arhcXFRf73bt26hampqVN8xjpeBQ7jAU3TYLVamQccDgd++ctfYnl5GZVKBbIso1KpdFkSA902sXohcbIwGAywWq3o6emBqqrMt1arFaqqQpZlpFIpfPDBBxgZGcHIyAhmZ2c5b4IWaVOpFLLZLFqtFpLJJKanp3H37l04HA6dB84gEolEVxBZKBSC0+lEs9mEw+FAIpHA4uIirFYrUqkUYrEYCoUCrFYrP253EjbxwPOmlvE7Efiv9r2Kp/naw3v5aId0k8kEr9cLj8fDu5KlUomzpiwWCyRJwrvvvgu32808QOoEl8uFzc1NnQdeEnoxgbaPMNAeX6+srGB+fh6BQACyLMNut0MURQSDQZ5KxGIxrKysoNVqwWg0wuv1QlEU9jY3Go1wOByoVqv7htnoeHGQ1tFgMCCVSsFgMHSF1Xk8HpjNZgwMDHCXYWFhAYVCAYIgIJFIwOl04ubNm1hdXcW1a9cQi8UQiUQQCoXgdruRz+fZN9rhcPDCnY63G8/jAcLQ0BB+97vf4auvvgLQnjza7XYIgoBisYharcaHB5pIdB4udLw8iAdarRZyuRxLzcgS2uv1wmw2w+PxMA9QXoCqqlhaWsL58+dx6dIlxGIxjI6OIh6PszQKgM4DZxSdQWSqqmJ+fh4ffPABTyVkWebHptNpPH36FNVqlff3yF60VquxLakoiuzydhgyc3HU1SpMNvO3+hzfCByh90JNW+JYUorQWYz2J2RZ5vuaeKBUKuk8cILQiwm0NW8rKyvweDysme/v70c+n+cDBQBcvnwZALC8vIxEIgFRFOF2u9FoNOB2u1Gr1WC32+FyuWC32/HkyRMUCgV9MnGCoBFluVxGpVLhUBqj0Qiz2QybzYa+vj6eFJH0iUaUgiBgeHgYgUAAmUwGiqJw4dHX14dsNguXywUACIfDAHRN5FnBUXmg1WohEokgl8vxoq/H44Hb7eZpJdlFWiwWbGxs7Jlc6ng5UH4E5fxQyq3RaEQgEOAF6+fxgNfrRSwW4w6lpmnIZrMoFAqYnJyEIAgcYqfzwNmAz+fjILLFxUWMjIzA4/FAURQ8evSIeWBkZAS//OUvoaoqSqUS7HY7QqEQTCYTW42SIYCqqkgkEiyBOsygYev2GoZ+eOFVPuXXEupW/tDvi6LITo6qqqJYLKLRaHDxRpMi2qciySqZrlDgpM4DJwO9mEB38IjD4cCdO3e4CzE1NcWuQPl8++K2Wq1cINRqNe6G0cUNABsbG1wBA8/sSnXLyBcHOWXRnxuNBuvQjUYjLBYLvF4vwuEwB1I9edLWXFYqFdhsNpw/fx5erxd3794F0JaeDA8P49q1a8hkMohEIqx/3c9nWsfbi+PwAH2gUGidpmmo1WoA2oWuy+VCq9VCNpvlJV/6z2AwdDm96DgeqJCgrm+nBafBYIAsy+jt7YUkSTxV2tjYQLFY3MMDa2tr7C9/8+ZNAGCtPDm86droswXigYWFBbhcLkSjUUSjURSLxS4eoM+H9fV1dnqSJAmNRoMXgSkwkQ66QPssQGeG/RyeIr94At/VPtj7nHu+d5aw8enigd/rPAuYzWZOvSeZKVn0Ulq53+/HkydPeIKcSqVw9epVhMNhnQdOCHox8UfQBXL//n2kUimUSiXuTBOcTid3KAVBwPb2Nmq1Gmq1GiwWCwqFAgqFAi9Y7SYPk8mkFxMvAYPBAIvFAlmWOUCIfORlWcbAwAC+973voaenB8lkEslkEo8ePUJvby8GBgYgyzKGh4e7xpTUbaCv0WjT7Xbv8ZnW8fZjPx7wer0AgIGBAbjdbjgcDmQyGYTDYeTzeWQyGcTjcV6wNBgMiEQiKJVKXQcGOvySeYOO44Pud0q0paKMXneHw4GRkRGcP38e5XIZdrsd6+vr2N7ehtvt5qC6Th7weDzweDzIZDLI5XK8XEs/99KlS6f2fHWcHhRFQT6fZ4cfTdPY+Y94wGAwYGxsDIlEArlcjrMmaDqhKArLm0j2SPJoktrsh9m//xw3/o8fwmQ/m3Kn1Ndbz7WBJS4ltUInN9jtdly8eBG9vb2oVCool8tYXV2FJEm4cOECT4WoeQS0eYCc3dxud9f5T+eB50MvJjqws7ODfD4Pm80Gm80GURTZBo4+/MPhMJaWlmAymeByuVAsFrvGlYqiAHiWxEyTCrPZjGq1qvvPvwDoEEahVPV6nQ9pJpMJHo8Hg4OD+OSTTzA4OIhcLodkMolIJIJ6vY6dnR0MDAzA6XRiYmICqVQK0WgUTqcTiqLwBwQVJ3SQ0MeZZxOdPAC0NbbUtRocHEShUGAecDqdbAUJtO0JC4UCS2+owKCOJfHAUX3ndTwDyZhMJhN3JemQJkkSXC4XRkZG8PHHH6NYLEIQBOYB0q/7fL5DeYAWbCmQSueAswk6YHbyQLlc5kYW8cDQ0BAWFxdZBiVJEucUJBIJlMtl1Ot1dh4zmUzsNPa8fcqF/zyLqf9441t/rq8jEnciB36PFCAWi4UDaek1tlqtsNvtGB0dxccff4zt7W0sLi5idXWVlSXFYhF2ux3Dw8MIhULMA5qmYWVlBcFgkHcm9LPA0aEXEx2gD5p4vF0RU+Ktx+NBNBplEhkbG0M8Hsf4+DjK5TIWFxehaRp3Ger1OlfNsizv6U7oODokScLQ0BAURUGj0UC5XGarXYPBAIPBgP7+fvz0pz8F0O4oW61WbG9vQ9M0yLKMvr62O0Z/fz9SqRSPNMmRSxRFLC4uYnx8HENDQ/D7/Wi1WvyBok8nzhY6eaBUKsFqtTIPUGBVJw8MDg4imUyyJrdWq3XlHciyDJPJhHK5zJNMvZA4HmRZxoULF7C9vc3GFqIociKxJEkYHR3FX/3VX/GhoFqtolwuQ5IkOBwOnkIexgOlUgmhUAgulwvvv/8+AODx48e6vOGMgfanOs8DwLPpZCcPnD9/Htvb2/B4PDyZIBMG+qwymUw8yVBV9Uguj5m5OCK/fHrm9ieSX8UOnErY7XZ4vV6e+ubzeVaCiKIIp9OJy5cv44c//GH7ZyWTyOfzKBaLzOs2m42zIubn55kHZmdnUSqV+D/ieCokdB44HGe6mKBQGrpAQqEQrl27Bk3T4HQ6ce7cOaytrSGbzbKsJh6Pw+FwQBAElMtlVKtVXL58mS1K6QBiMpkQCATg9XpZN63j+AgGgxgZGUGz2UQ8Hsf29jYqlQpPeOx2O5PEZ599hrW1NWxtbcFut6PZbGJiYgLVahWtVguFQoF1kB6Ph5Ms6b3J5/OYmJgAAN6pWFlZwfvvv68TyFuK3RwAoIsHzp07x8t3tJwfiUSQTCZZ7qQoCoLBIC5evAhN03Dv3j0A6OIBs9mMp0+fcjiSjqPDaDSit7cXfr8fHo8H8Xgc0WiUu5GapnFiraqqePDgAVZWVlhuQuGVsVgMVqv1uTwgCAKuX78OQOeBs4L9zgK0N0EhhYVCgbNnqAhNJBKw2+0wm82o1WoYGRnhLCOanqmqys5iFJRGWSjPA+UsnJWCoq7W8PSf7u37PYfDgcnJSVitVhiNRkSjUcRiMQDgXTRZlvHuu+/CYDDgiy++wPLyMprNJsrlMnw+H4LBIAKBAIrFIiKRCObm5rjR0NPTg/X1df73vF4vLl26xJbhgM4Dh+HMFhP7XSBAOwGROljk2hCNRjEwMIBYLAZZljE+Po6NjQ18+eWXsFgsSKVSeP/995HNZtnvnJDNZmEymdgqstls8oIvLWIeNq04y7IoCp8pl8soFov8OpJrjsViQV9fH27evImtrS2oqorV1VWoqsquLdQNzmazePDgASwWC/L5PGteKX+iUCjg5s2bCIVCePz4cdfvsbOzo5PHW4iDPiQSiUQXD4yMjLCeXhRFFItFyLIMq9WKlZUVAEC1WkUgEMDg4CA2NzeRTCYBtCdrVqsVxWIRkiShVqt1TdY65To69oLkIdSJzGQyHFBFBziLxYJz587h8uXLWF9fZ6ctRVHgdDp5n61Sqeg8oGMPDjssKorCmnwy6chmszAYDCgUCnt4QBRFDA8Pw+l0YmlpCXa7HQaDAUZj+6ilqiqMRiMajQZP1oBn4Zb7TSvOUkGxdXt136+TWyM1benelmUZqqqy7PzGjRu4cOECB48qitK109psNvm+jkQivBfh8XjQ19fHC9okhQSeSd4IOg/sjzNbTOx3gez3GFEUuxZx6ANoYWEBY2NjKJfL2N7exsLCAley33zzDTs41Go1DlEiBxKLxYJGowFJkqBp2oHOLrRUeFhi5tsGGg2TXESWZTSbTZYu2Ww2lpCZzWYMDg4CAJrNJtbW1tBsNlGtVmGz2XjpirqRiqKgt7cXvb29XTKG3Z1pGnETdL3k24mDPiR2f10URfj9ft6HGhgYQD6fx/b2Nhe05XKZc08sFguCwSCi0SiazSaKxSKq1SosFgsH29GHI13rzysmyEv9LMijqNHSbDYhCAJqtRrzpNFoRC6Xg9vt5qLM5XIhGAxCVVVEIhG2jSZNtaZp+Oabb2A0GnUe0LEHJ8kDOzs7iEQiuHz5MgYHB1Eqlfj6VRSF96gsFgtLoQBwk/GgyWXkF09QiGQx9l9fhjVk3/cxbzqSX8W6Eq+pkUoFRKlUQiaTwfnz57nI83q9vJ/a29uLcDiMRqOB+fl5lEollMtl3rUym8348ssvOXy4p6cHAPDXf/3XCAaDfH/rPPBiOLPFxEEXyGFfc7vduHbtGi9Yff7553yBS5KEarWKgYEBRKNRJJNJLhpcLhdbSlJRQItdVCjQoaIT9DVRFLkL97ZNKTrtMok4jEYjms0mzGYzd4ILhQJ3dGw2Gx8gAGBmZgYjIyMwGo1wuVxMMi6XC6VSiYs2TdMwOTmJ4eHhLqem3V2GTotQXSP59uIgDngeNxAPLC8vQ1VV2O121tmur68jnU7D5/Mhn8+jUCjAbG47ssiyDJvNhkajAUEQ0Gw2kcvleEJJBQV9eHaCOpk03XgbQc0TcmUB2ntrZGJB8hCgbWhBO2nEA9988w2HCwaDQZY5UpiVzgM69sNJ8QDQnmRYrVbMz89DFEUMDQ2hVCqxbSxlH1DTrFaroVAo8A5Qp+Pjbh7IzMWRnU8gdG0Qff9+FPYhz7f7wrxCJL+K4ek/3eN7v/NMQIUW0F6epjBBg8EAq9WKwcFBWCwW+P1+FItFfPrpp9yICIVCaDQauHDhAjd2Sc5IPBAMBrucmnQeeDGc2WLioAvkqF+7dOkS/H4/fvvb32JoaAiCILBenzqI+Xy+S/oQCARQKBS449FoNDAwMICtrS2Uy2X2q++EpmnsXETdurepoKBiyWKxQBRFLpgkSYLdbofNZttziKCxptlsRiqVgtlsxpMnTyAIAgYGBlCr1eBwOOD1epFIJNDT04NyuYyBgQEMDw8DeH53gXSzOt5eHMQBR+WGqakpnDt3Dqurq7yTQ8u86+vrvGidz+fh8Xj4Oidnp42NDQDtQ7TH44GqqpzkvruYoMZCJw+8jbBYLDCbzbwXBQA2mw1msxnJZJKLKVmWIUkS6vV6Fw9Qwr3dbkc4HGaHl1qtpvOAjn1xUjzw29/+FmNjY3A6ndjc3EQ8HkcikUCtVuPPL+IBWZZhsVjYOY4+80wmE08z6PrvhKZpiM9Gkf5mC97LPQj/aAJmrwyD9Owo16g2YDQf/2jXajaRfrANz8XAK0vgrher2PpirWsiQbsPRqORQyfJubHZbGJlZYXlY6VSCT6fD2azGU6nE9lsFpVKBalUipetiQcITqcTqVQKU1NTGB4ePtKkQeeB5+PMFhPA0S+Qgx43NTWFQCDAeksKRerv7+dk3GaziYWFBbaLbDQaqFarnIxps9lw7do1PH78GPV6HcVicc9BobNaN5lMvFD8NoAsXwHAZDKxLEGSJDidzi7XFoPBAEmSeLFdEASEw2EkEgmWQxSLRfT398Nms/HBzefz4cKFC5ientaDZ3R04TgfEvs9dmpqClNTU6y7pn2earUKo9GIRCIBANjc3ITRaESpVIIsy+xIRPd2KBTipkO9Xkc2m933HqfuPRUUb0tRQc+JXg+Xy8WNBZvNxtMYOpRRaNi5c+dYippIJGCz2bhLTFNPnQd0PA8nwQOdZwGPx4Pe3l5kMhmUy2WW7RIPkE10qVRCoVCAKIpotVro6emBpmnIZDIoFov8/xI6D8XV1QK+/t9uo9VqwT0ZhOu8H8pSGpm5OK783Z/Cfd5/rNcg/WCbl5+H/uIiPJMhZB8lUIhkMfIfJmHx2WAwGY71Mw+CGs9j7f97tMe1SZIktoAXRZF3GEgSRmefarUKl8sFm82GQCCACxcu8P2uKArvUthsNv661WqFJEkYHBzE9evXceXKFT2U9gRxpouJ3ThoKfuw8Van60Nvby/S6TQePXoEs9mMcDiMaDSKTCbDYzcKrKHuBI3ix8fHMTMzg3Q6jZ2dHT4oUFaFyWSCwWBApVJh4nnTQcuVRqMRHo8HjUYDDocDmqbB6/VycWAwGOByuSCKIoLBIAYGBtDX14d33nkHqqpic3MTm5ub8Pv9fEgA2r7gg4ODEEUR09PTmJqaOuVnrONNwEnxwOrqKuv2S6USFEXpki7Q4dliscBms+G73/0uVFXFzMwMJElCOp3mqaTRaOTHU3eepFJvOkhDTqnzgiBAlmXU63We5uZyOX4tDQYD/H4/gsEgRkdHcePGDTx69Ih5gGQRnfkAOg/oOC6OywOdk4xWq4VcLsf7enQoTiaTKJVKEEWRP8cpaE2SJPT392NychKpVAp37txBPB5n56dOyZ/ZbIbVakW9XketVkNmLt51MP/mZ7/DhZ/8CYLvDRzpuRa38l0uSpFfPOmaFtDP9l7uwfh/Mw3pJcL0SNK0GwaDAWazmd2zqHloNpvh8/mwvr7epWSgqcPY2BjGxsYwOTmJR48ewWq1IhaL8WsryzLbPhMP3Lp1S+eBE4ZeTHRg98LVwsICL1s9zxKMPOZlWUZ/fz/i8TjrH0VR5OLA6/VyR5ImFUtLSxgcHMSVK1egKAqWl5dRKBT4ENJqtfiwnc/nUa/XoarqG3uQoE4BdWjImi0UCiGfz3OaaKVSYSeNgYEBfPe73wXQtojr7+/Hxx9/DAD48ssv8dvf/haCIHBRsbCwwN2IiYmJrtRrHToOw0nzgNvtRiwWY4mezWaD3+9HOp1GvV5HvV5HuVzG1tYWvF7vHh6gxUzq2FerVZZCUIDjmwg6UNEug8vl4oRqMr9QFIWXKC0WC8LhMILBIMLhMBwOB370ox9hamoKFy9e1HlAx4niRXiA/j4zMwNFUWC329Hf349EIgGXy4V4PM4TSrPZDJfLxQ0Dap7RTub09DSePn2KTCYDi8XCS9wmkwl2u50l0JVKhQPZOvH0n+6hnCxi8ONxiIaDr/tWs4nZv//8SK9JZi6OL//nX+LCT/4E/mt9h/7c3SjF81jdZxoBgAsqCo11u91QVZWX1pPJJCfee71e9Pf34+LFi2zdffPmTeaBO3fu8CL71tYWgsEgnj59CgCwWq0YHx/XeeBbgF5MdGD3wtVu7GcJRt2L/7+9M/tt67rC/XcocR4O50GiRFHUZFuWQ8WNpThxHGQboUVzAAAgAElEQVRw0Bs0LdC+5LX/0H27b30sGqB9CNIWsdsgddPWseopnjVRokZSpDiPGsj7IOwVUqYminLtaP0AI7WlQ5Hq2d/Za+21vpVOpykqvnDhAsbGxjA/P4833ngDs7OziMfj0Gg08Pl8iMfjNJ05m81Co9GgWCyiv78fQ0NDeP78OTUJieFLYlEVi0W0tbVRCcCrvpEQx7LVapVOZNra2khARZ+EmNmxvb0Nk8lETWhtbW00jXxgYAAXL16sO31YX1+HxWJBMBhENptFb28vTCYTJEnCwsIC/Xx2YGAOy0nogFqtxtLSEorFIvR6Pfx+P+bm5rCwsABgJwhZWlqCx+OBz+dDZ2cnnj9/jlgsRhvqcrkMSZKQy+UwMzNDG3FRV/wq0MjKWiQPhJtd7QnL1tYWnbyIvpNSqVTndCdOb6xWK1wuFz744AP09vZSiYKYEcA6wLSS4+hAKpXC48ePodPp8MYbb0Cr1VJmfW1tDdvb2xQ8P3v2DPF4HAqFgtyghoaG4PP58N577+HRo0eoVqsolUqYm5uj8uhMJkNDMMVGezfCBWq/Sdrx+6tH/t2Ik4W9yqm2N7dRKW9BoWpDZXMbKzfr+yJ2my1sbW2REY0YFCpsdNvb27G5uQmlUgmbzQatVovu7m5cvXoVVqv1BR2wWq0YHR1FJpOhHhYApLUA68BJwMFEDbWlCsDO0BKRiQAa34Dr6+tIpVLIZDLo7u5GoVDAwMAAAKCjowN//etf0dbWBovFQhH35uYmPB4PFhYWyPvY7/ejo6MDJpMJb731FsLhMEKhEMbHx2E0GnHz5k1qGlpaWoJCoUCpVKpr7hJHoSLqPomTi92bhb3mYIisY20GoL29HVarFaVSiRrOxTGkz+eDz+cj0c1kMigUCpBlmY52Z2ZmYLPZ6Lj5xo0b1LxmMpnIwleWZVgsFni9XmSzWfh8voaZ5EYDyxjG5XLB7/cjFAqht7e3rhYaaF4HDAYDVCoVzGYzubpYrVbE43FkMhnE43Ekk0n86le/QiqVgsfjocGMvb29AHYci1ZXVxEIBGiAYz6fp4ex+K8I3k9ifoVY040SGbW++cKdra2trW5atcisbm1tIZfLAdjpl7Lb7bDb7TAYDNBoNIjH42TvKOqdFQoFZSyBnTXMOsCcBM3qQDKZRDabRVdXF4rFIumAwWDA9PQ09QMFAgFoNBosLy9TQi0ajVKJ9EcffYRUKgVJksikRdjN5nI5LC4u1pVJlctl2guIdb+9vY300xie/L/v4RrvhtFnhtqio/ebmo7vOSTuIBQKBR7933+h+/8MwXLOBYWyDSq9il5TJBGEg5qwwQVATkx6vR7lcplOVpRKJcxmM9xuN7q6uuikV7ixiYoG0X8iKhd264Asyy/oQFdXFzKZDHp6elgHToBTHUzsdfOIB1U6nYbf79+3Wa9SqWBqaor+/uGHH0KhUGB+fh42mw2yLCMWi8FsNkOhUJBbiyhxEM1EKpUKsizj22+/xYMHDxCLxeByuaBSqWA0GhGJRKgsyul0QqFQIJlMQq/XI5vNkne9sJoVtmq1D/zj9Fo0cpao/ZrYRIiNvxALYXtZqVRgMpng8XgQi8WwtbVVZ3VrMpnw+eefkz+0UqmkqcOSJMFoNNLvf319HfF4vO733tXVhUAgQAI/MTEBi8UCi8VCw2dq4amWjGC3DoihdcKhzeFwHGgNuJ8OiL4nnU4Ht9uNjY0NlMtlKnsqlUoAQDayqVQKf/rTn/D48WPk83l0dHSQ9Ww6nUY0GsX29jbsdjs2NzfJ5SiRSNQNwRO1xeL1BcdxgxKfRehYLbU6oFKp6CRCWONWKhWaFmwwGJDJZOg0Upyu9Pf3Y2RkBJlMBvfv38f29ja2traQzWbJeEHUVLMOMK2kVTowPT1Nf6/VgUKhAKPRiFKpBI/HQ1l5k8mExcVFFAoFSJJEM6lqdSCXy8HhcMDhcMBkMmFpaYnWoM1mQ6VSwfr6Otrb25FIJCjbL4xL8tNJPH20cwJhPe+G3G9HZnod64+OfioB/HiysLm5Sf0VtcEDgLoAQpyiipJvg8EArVZLAz2LxSI2NzdRKBRQLpcxPj5Og/9WVlbQ19eHdDqNWCwGu91+oA54vd4XdEAkc1kHToZTG0zsdfM0GlQjPIgbBR8KhQL9/f3IZrMwGo3keZ5OpxEOh2kyrniod3Z2UtYNADVZ53I53LlzB//5z38QDodpuJWou25vb8fKygq5mgjbydo5FbUWsqJpWalUkpOUCGaaCShEhnF3plMIohAQtVqNtrY22Gw2CmjW1tbQ1taGYrGIQqFAmdmVlRUSlAsXLsBut2N2dpZOWy5duoRcLge9Xg+TyVRn5yh+fwKLxVLnFX2Q6PNUSwZorAON7o2zZ8/S/XEUHQiHw7h37x6tx76+PiwtLcFms0Gj0cBms9GmQGTebt++jdu3b1MjZ1tbGxwOB2XwlpeXKTEgSRK2trZQLBapZEhkAkWJoF6vp/UnflYzJZLipLFRYkH8u9ABrVZL83GUSiVKpRL9EY3WKpUKbW1tSKfT0Gg0kGUZn3zyCaxWK/7whz/AarUiEonA7/fDYDCgv7+fbHQB1gGmdbRSBwYGBpDJZGAymep0IBQKYWNjA2azGf39/fT9YuBlNBqlvoFisYjbt2/j3//+N1KpFL0HYVBSLpcRCoWg0+moLDCfz5OD5ObmZp0OWCwWKhsshrJYfBZ/YfN/FNrb21+4rvbfhLaI3g61Wk2BhdAhMWhOBBliFodwZ1peXka5XIbFYoEkSejv76c/B+mAGCAsYB04eU5tMLHXzbPXoJq9nB3ETSyO1MT33717F48fP0a5XIbZbIbf74dOp0MsFoPD4aBFv7W1BZvNhuHhYeRyOeRyORpglclk0NHRAb/fj1gsVrcpEMGGcHjYPeBGo9HAYDBAp9PBZDIhl8uRm0yt3ZxYvNFolDYnsixjc3OTTkIEIssojlKVSiVN8hRWd8DOKYMQLlHXKWqiFQoFzp07R0eUIuCRZRmhUAgGgwHFYhGpVArhcBiXL1/GxYsXYbPZXhCD+fl52rztzjbsZfUnHgC7N1JcQ3k6aaQD+008PaoOLC0tYWVlBcDOkKuhoSFcvHgR9+7dg8PhQDKZpASB1WolRxKRoQNA07OtVisSiQQ9oIvFIvL5PLm91J5GCj3Q6XRwuVxk2rC+vo6NjQ3SEYFwhxEaoVKpyFGpWCzSa4o/ot9BXNve3k522FqtFgDgcDjoc4jER+0k676+PszMzMBoNMJut6Orqwu5XA7t7e1wOByIxWIAgFKphDfffJN1gDkxWqkD1Wq1oQ6k02mo1WpoNBr09/fDbDbj2bNneOutt+qsoIUOTExMIJvNkj6IKc5utxuJRIISDEqlEplMhtazUqmkXkTh+CbLMnQ6HTKZDHQ6HVVflMtlbGxs0M8W5Zdi7YmAX/RsAD9WKQgbd1HOaDKZsLm5SY6TYnaUKGUSera1tUV7F1mW0dnZienpadK4wcFBrKysQKFQkA6IU5vBwUG8+eabLdGB2mCQp1wfn1MbTIibR9Q5+/1+AHsPqtktNt9//z2WlpZgNBqRyWSQTqdJQFwuF3w+H5aXl6HVamkqq9VqhcViwfLyMs6cOQONRoOZmRmMj49jbGyMrCT1ej20Wi1cLhfGx8dp81AqlaBWq6n5UmQnlUoltFotBRhCdLq6urCxsQGPxwOr1Yq7d+9ifX2dyiRUKhXVcwI7wpFMJrG9vY1SqVRnYSkyHCKA2NraovKBVCpFDWBikFQ+n0exWIRaraZabrGBkSQJIyMjePjwIZUwATsiE4vFkM/nkUgk4HK5MD09jdHR0RfEwOVy4dq1a0eqcax9AAA4sISN+eljs9lw9+5dyiSKe2GvTNZRdeDMmTMIh8OkAyaTCW1tbRgYGMDy8jLOnz8PYGdGzfj4OILBIPL5PPVWKZVK+Hw+Mh6Ix+PI5/NwOp2YmZmhjXqlUqFTwY2NDTop0Ol0pANiIvzdu3epZwnYGQo3NDSEXC5HgYDIHooTUHECKTRA2DhWq1WYzWaoVCpkMhkaNilqwLPZbF3dNLCjJ+FwmD5XJBKhU1PhgMM6wLxMjqsDt27dwvLyMqrVKlZXV+F2u/H+++/XDbkVOiBJElZXV2Gz2dDZ2QlZljE2NgZJkpDNZkkHQqEQlUcrlUr09fXhl7/8JaLRKPL5PKanp+HxeDAzM0P9SNVqlQY/inkrwlbaZrPRXIZYLIbV1VUUCgUKgAwGAwYGBrC9vQ21Wo1yuYxcLkcnnsCPp5MAKFionZEhyjbFtO/t7W3k83nkcjkaOif2KAAQiUTws5/9DGfPnsX8/DzsdjtSqRQ6OjqwsrJCOpDJZJDP5zE1NYVgMHhsHWgUDPKU6+NxaoMJ0Vx18+ZNGI1GqokUN+num6k2chXOK4VCATMzMzRYbW1tDZlMBh9//DEGBweRTqfR2dmJTCYDr9eLdDqNbDYLYCe6d7vdMBgMZAVnsVgwPDyMqakpmM1m+Hw+GI1G3L9/n6JyrVYLpVKJ2dlZFItFbGxswOl0IplMAgBlMZRKJfL5PPx+PzQaDfx+Px0jlkol2hAIp4NUKkWNnLVHpABIoNrb2+nnG41G+P1+mM1m2Gw2JJNJLCwsIBwOU722yWSC0WikI02TyYTOzk7YbDZ88MEH0Ov1WF9fJ7/nf/7znySCwWAQJpMJLpdrTxu3o06l3K+EjTm91N7ngr3uraPqwPj4OLLZLG1SrFYrZdCAnbKFvr4+FItFGI1GVCoVFItFBINBPHnyBHa7HefOnUM+n6ehbMPDw9S8OD09TdlLp9NJwYbI/re1tSESieDcuXO0kYhGo1hZWYEkSdDr9Whvb4fH48Hq6iq9V9HDILKOYg2KIVDt7e2kA3a7ndbq4uIiyuUywuEwnYCYTCbodDpEIhFoNBoAgNPpJB2Yn59HNBql+uk7d+6QhTbrAPOyOK4OrK2tIRQKAdi5x2RZpj3F2NgYMpkM/TEYDFhYWIDRaIQkSejq6kI8Hq/TAYVCgUAggGg0CofDgWvXrpEOiISETqeDwWDAo0ePkEgkoFKpqLRK9FWIYD6VSqG7uxtDQ0Po7+/H3bt3yVVSlER2dnZSX1btJG7xOxHBg06nQ1tbG1U/KJVKdHR0wOVyoVQqkY5Eo1EKdFwuF5U6C92x2+3o6emByWSCz+eDQqEgHRDlj5VKheZDtEoHDiphY47OqQ0mANDkVMF+dXK1WQpR0jA1NYVisUhBAwBkMhm6MYUTRDAYBAB88cUXAHaykGKQisgGZDIZWCwWfPTRRzh37hwymQwkSaLp2cCPTUXvv/8+JiYmyOqsWCziyZMnUCqVSKfTlBEQvRJqtRpra2vo7+/HzMwMNBoNZRq2trbg8Xiwvr5OHtfCBUZkN81mM9U4VyoVaDQa6HQ6ynqKDZHFYqHfh8FgwPb2Nrq6usiBwuVyQa/XU/AjGrJlWcbf//53Eg+lUolUKgVZlqn8ai+O4sDAR5nMboSlqKi/PahWthkdEFlG4cj0t7/9DcCPOmAwGGAwGOD1epHJZNDf3w+3202uUDqdDpOTk1Rm0NnZiUAgAK/X+4IOiMnbmUwGGo2GzBkkSUKhUMDc3Bw8Hg8NdRLlTJIkobOzk5q4a+0aJUkiQ4SOjg4qgRQ6IMsyTCYTent74ff7kc1mkc1mkU6nsbGxAZ/PB4fDgZ///Od4+PAhNYALHRAavLa2hnA4jHQ6jd7eXmi1WtYB5qVwXB2oVqvUIwmAhtXVvo7P5wOw00/wzTc7cx0ikQg++OADLC4uvqADo6OjcDqdlETIZrN4/vw5ne53dHSgr68PnZ2d+Mtf/oKFhQWYTCaoVCpMT0/DYrEgGo1Sj6ZWq4XVaiUXRaPRSKcXsizTaUGtxaywngZAwYMIOkqlEhKJBCULhDFMMBik05fvvvuOTjR9Ph+cTidZQAtXN9EHYjQaYTAYsLa2hsnJSczMzKCjowPATmDk8/lapgOsAa3nVAcTR72hROQbjUaRTqcpMyBqmYU1oc1me8EJQpblusYso9GI5eVl6i8QmQPRbKTX6/HkyROkUqk6B6hgMIixsTGMjY1hcnISDx8+xI0bN+oEQpRJ5XI5qNVqasKcmpqivgzRHOlyubCxsUFOM8lkktwkgJ3+hwsXLsDhcODu3bvU8KnX6+F2u1EqldDR0YFsNotqtQqXy0VDu/r6+vDuu+9idHQUExMT+Prrr6FSqTA/P487d+6QcMqyjK6uLqTTaaTTaahUKpw/fx5utxtGo3HPTMRRHRj2O7ZmTifNPFROWgeAnd6LarWK2dlZsp1VKpW0phvpwFdffYWNjQ2yURWTpcV8GhE0xONxMjkQ70NsPMRshrW1NTqVaG9vh9PpxOjoKNRqNSYmJqBWq0mnenp6qLHaYDAgl8uRw4xGo4HX68Vvf/tbXL16Fb/73e/w9ddfQ6lU4vnz53jy5AmVcfr9flitVqytrWF9fZ2SFawDzEnTCh04d+4cCoUC9TNVKhXSgNqyut0a4HQ6YbfbcfPmTdIB8fwdHh6G0WgkHYhEIrT2xCnG+Pg4+vr6cPPmTVQqFfz5z3+mIZdWq5Wma4uyR4VCgWw2i1AoRG6SxWKRAoRsNguLxUK6ITRJp9Ohp6cHo6OjyOVy+O9//wudTkf7AbGnUCgUGBsbw40bN8iN0ul04s0338Tnn38OAPjyyy8RDoeh0Whw/fp1VKtVBAIBSJKEvr4+rK6uUo/JSegAa0DrOdXBRLM3VO11wWAQ9+/fx9LSEgBQPd/Tp09fuE5YkwGghq3JyUmEw2E6BZBlGbIs0/cVCgWqP+zq6qLXF+8DAKanp7G4uEiNTaLsQLhHBQIBrKysIB6Po1KpUF203W6n40yR6ejp6UE8HkckEsH29jZ6e3tx8eJFVKtVxGIxbGxs0CIXNZqi+UpkQdxuN9RqNT799FN89tlnePz4Mf7xj39gZWUFDx48gEqlQjweh8fjwdzcHNxuNzweDx4/fgxgR2xFZhbYW9ibcWA4akkE89PmOA+Vk9aBkZERmEwmLC8v06Rb8WDdrQOJRIJOCER/ktfrhVqtRqlUgs1mQyAQwPT0NNbW1tDe3k7zHNrb26lMS5IkdHd3w2KxIJFI0EnHG2+8gWAwiGfPnsHv91M5pclkorIH0fskHNtE7favf/1rXL16tU4HhJ2t0AGVSgVgp4xKGDl0d3fD6/XWubY0gnWAOS6t0IFAIIDz58/j+++/p14i4MX7E6jXAPHzHA4H6YDoU6zVgefPn2N1dRWyLEOSJIyNjeHatWt0LzscDly/fh1OpxOpVIp0oKurC9vb2+jp6YFer8eHH36I3//+98hkMnTaIJKQKysrSKVScDgc6OvrQ3t7O5aXl+l9v/322+jv78ft27dJB2rLwvR6PfWPGY1GuN1ulMtlOJ1OfPLJJ3A4HLhx4wYmJycxOzuLRCJBp6fLy8sYHh6GXq+nCeHAyekAa0BrOdXBBND8DSWue/r0aZ0wiKh5d6ZDuAs0auwWzgpmsxl2ux02mw0TExPo6emhoU5er5easWoZHBzEwMAASqUSkskk3G431RaKWsPR0VHMzMxgenqaahGFZ3s+n4fVaqWyo2AwiFwuh1u3blEdthieMzs7i1KphFQqhUQigVAoRBsct9uNvr4+KBQKFAoFDA8PY2xsDABw7949pNNpJBIJqpkUDZcGgwH5fB6XL1+mcq7Ozk709PTQ72Kv/39acVTJg2qY4zxUTloH0uk0PUSBvXVAuKCI2SwjIyNoa2ujNXXp0iWMjY3h1q1bePbsGc2/sFqtsFqt2NzchMFgoLLGwcFBTE1NURP2O++8g2AwCKPRiH/9618olUqIx+PIZrN4+vQpNjc34XA44PV6m9aBarWKvr4+9Pb2QqFQ4Pz58xgcHDxwfR5XB1gDGKA1OgAA586do39v5Aq1lwa4XK46HbBYLHU60NnZiWKxCJfLhY6ODly8ePGFJuSenh709PRQiWBvby/Nn2pra8OHH36Iq1evIhwO48GDB0ilUmhvb6drRK+V6NkcGxvDF198QdOog8EglWt+9913KJVKtImfnJyETqeDw+HAe++9Rz+/WCxieHiY1rJwtkomk0gmk2TuIrRG7GFYB14vTn0wcVz2uoH3ynTs19gt/l57rd/vx9zcHH29Uqng6dOnsNlsiMViuHfvHra2ttDT04N33nmHbGgTiQTC4TDMZjPm5ubIJlI0fff19cHhcGB2dhYqlQq5XA5ms5msH7u6uiBJEoaGhui4dGRkBM+fP4fT6YTT6SSrWLEBCQaDGBkZAQAacQ/snDRoNBq4XC6Uy2W4XC7E43FYLBbodDpymxKN2Luv34vjHlXyoBqmVfyvdKBSqVBgX6lUSAc+/vhjxGIxKp0QwYcIeEQt8sDAAGUkVSoV0uk0vF4vCoUCWU/W1pLLsox33nkHExMTFHzE43HY7XYUCgWMjo5ShhU4mg4MDw/DZDJRQCauPUkdYA1gWslB63g/DTjoenHyWHuiUWtxXKsDfr8fnZ2duHbtGumA0WhENptFNBqlafCiZ0ps3IWltJgYHwqF4HA4qG9S6Igsy3j33Xdx+/ZtaiJfWloirREN42I2VK0OSJIEjUYDp9NJfVnCQtbj8eD8+fOUBKm9lnXg1YaDiQM4KFrd7wbevQAavdZ+QiP+t3g4JxIJ3L9/H2azGd988w0eP35MA21GRkbg8/ko6xEKhej4MZVKYXJyEhqNBpubmxgYGEBPTw/OnDkDvV6Phw8fUqNVNputm65ZrVYxPz9PDZxWq5X86NVqNbxeL9xuNzKZDBKJBK5cufLC72hsbAyLi4uIRCIYGxvDmTNnsLW1RU4PNpsNc3NzTVk0HiebxINqmMPyKugAsLOuTSYT5ubmkEwmcf/+fRQKBWpUNhgMZPgQCoVgNBpp8zE5OYlbt25R87TP58P4+Djy+TzsdjsePnwIWZaRSCSgVqsRi8UgyzIikQiWlpZgsViQyWQAAGfOnEEsFsOzZ8+gVqthsVig1+uRSCQwPDzc8Hd0kA709vYimUxCluVDJRN2//6bWbusAcxRaFYHGt2fzehANBqlQD0Wi5EOPHjwgKoOhA6Mjo4C+FEHREJA6IAwgPH5fNQH4XA48P3338NoNFLTdjweJxMEoQNi31GrAyaTCW63G9VqFZOTk7hy5coLn9nlcuHTTz+tc86qVqvUxzk0NIRsNtu0XTPrwP8ODib24bDR6mFu4P1e6zDXp9NpLC8vIxKJYGBgANFoFMlkkgbIFItF2O12ykKI5k2dTod8Pg+dTodUKkUnCV6vFwMDA7BarSgUCjRgb3Z2FhcuXEA+n0c+n6esw9TUFC24kZERZLNZykxGo1EAoGExjQTkN7/5DQkkAHLBEEe6wMlYNO4n/uzowByGV0EHahu5nzx5ApPJhGw2i2KxSDog7JhF0kHYVfb29iKfz0Ov1yMajZLDmyRJ6O3txeDgIG7fvg1JkvDDDz9QP8To6CiKxSJyuRwkSUIqlaKNvyRJCAQCtNnQ6XQoFAqYn5/fMxCo1QGRjRTDp8Q6FKUdrX6Q76UDrAHMYXkVdAAAPTMfPXpEOiASCvvpQCAQoOd2JBKp0wG/34/BwUFcv34dHo8HqVQKKysr8Hg8uHLlCtbW1qi3MplM1hk0BAIBGsh7GB0YHh6Gw+Go0wHeC7z+cDCxD62MVo/zWuJao9GImZkZTE5OQqvV1mUIXK6dHomvvvoK4XCY/OqFYMzNzcFkMtFQLTEfwmaz4dGjRygUCigWi1TeJJyhAFBTqU6nIwEZHh7GW2+9hcnJSTx69IgyH+Jz7V64tZkVIaKpVArVavXAxqpmOUj8j1smxZwOXgUdqL2uUqlgcnISLperTge0Wi2MRiOSySTu3btHE7JFIJHP5xEOhyFJEsrlMrq7u+mBf+nSJSwuLqKjo4N0QFg6Aju2rQK3241oNApJkjA+Po7t7W0sLy+/oAHAiw9w8e+161IMDBW8TB1gDWAOy6umA+IEwO12Q6fToVKpkDW8OL28desWtFotCoUCfvjhBxgMBszNzWFhYQHVavUFHejp6cH09DQ1QQubV2FHKyZjA/U6MDY2diwdEAmR2hKuVsJ7gZOHg4l9aGW0epzX2n2tXq+H1+vF5cuXyVu6r68P9+7dw9zcXJ3FaiAQICHo7u7GpUuX0N3dXbeY3nvvPQAgEdgtCrV/lySJyqnE9bUZhd1WeLsXbq0YijrMgxqtm+Uwgn2cMinmdPAq6IC4TmQF3W43JEnCZ599BoVCgUQiQc3U3377LUKhEDQaDSUPDAYDNW9arVa43W68/fbbdZvq/XSgFovFAlmW68oqa60vxWfaSwcaDY07yQf5QTrAGsAchldJB8QpocvlgiRJ+MUvflGnA5VKBV9++SVWV1fJVc1qtZIOiKGRTqcTly9fpvt/cHCQhmoajUZcu3aNZl2JAbcioSie363QAaEpvBd4feFgYh9aGa0e57XEtbdv38bo6ChF706nE1evXqXFurKyQkGESqWCy+WCwWAAsDNEZ3BwEGaz+YWovPbYUQhArSiMj48DeNF9Yq/PtdsOs3bhNnK2OKkFzEeXTCt4FXSgVgMGBgbqNECUAwgdEIOoxCAonU4HYEcDtFotaYjYAAgO0gFhY9vovTf6THs9wPdqMmUdYF5lXjUd6O/vp1P93Trwxz/+kU4gq9UqPB4PAoEA1tbWoNVqodPpEAwGX9ABl8uFa9euvfC+ahOGJ6UDvBd4veFg4gBa+ZA7zmuJUoQbN27QaURt/wEAdHR0UENWIBBAb28vecaLgVqHfW+HdZ9odO1+C/dlHify0SXTKl4FHRAaMDExgVQqhUwmU1ciVKsDYgCl1+t9KTrQ6DMd1eHqpGAdYFrFq6QD169fx8LCAoxGY90zdpaKWOwAAAWeSURBVH19nU4T1Wo11Go1AoEA3n//fVr7lUqF9OOgDP1e6+d10gHWgJOHg4mXRDMexo2uqXVBEIjFarFYyGe6s7MTFosFgUAAZ8+epQZOYCfbeJgpsft9vdaSbrfrwkEL92UeJ/LRJfMq0Qod8Pv9ZPU4NzcHh8NRl+l7WTpQ+76Axr75h3W4OmlYB5hXiePqAAAyXxH/FdhsNlgsFjgcDhQKBZw7dw49PT1QKBS4cuUKnWBKklSnH/txGB1otBcQ174KOsAacLJwMPESaMbDuNE16+vrdZ7vk5OTtED38qPffXohOE7zmHhvyWQS09PTVHZxVIcqhjlNtEoHFAoFTYcH/jc6UPu+7ty5QzXUzTjVMcxpohU6IMty3ZBMsa5rdUCWZUomACe7F0ilUpiamqLSq0YNzqwDP20UB38Lc1waLd5mrqk9ykylUpifn8fs7CyJzNmzZ8llKRAI1C3o3TWCx6kZFO9NDJYR3vOH+VwMc1r5KelA7fvKZrOkAY3eM8MwP9IKHdhNpVLBxMREnQ5cuXIF165deyl7AbH+xZ6ANeD0wcHES6CZxdvoGnFcGAgEaJplMpkEUL94XS4Xzp492/CocbewHOfziLpM4fLATU0Mszet1gFZluvKHYGXpwO178toNJIGNHrPDMP8SCt0YHBwkNax3+9HKBSivQDwow68rL2AWP9iT8AacPrgMqeXQDPNPwc1PWWzWUQiEUQiEfT39++7eGtrLW02W53QHPfzjI6ONjWpkmFOG63UAWDHYUUMlBTlBXvpQKMa7ePowO73JV6PdYBh9qfVOjAxMYF0Oo3p6ekj6YDooXr69GnT67b2fQWDQd4LnGI4mHhJNFMzuNc16+vrMJvNGBgYQCaTQU9Pz77NUeLY8+7duzQk7rC1mkd9b7U/lzcXDFNPq3RABAJCB2RZ3nM97663ru2nOI4ONHJ9afSzWQcYpp6fsg40gnXgpw+XOb2GiKyD2Wym6ZV7UVv2kMlkqKZx99daiRAtUb8ZjUYPfd3Tp08P/f0Mc1qpzTyazWZcunRpz4f07nUeCoX2/XqrYB1gmJOFdYB5VeBg4jXkKDWPtWJjMpnqptk2OgptxQJupsGsWcFhmNNIsxoAAL29vft+HWAdYJjXAdYB5lWBy5xeUw57THqU2uZmLOsa0cy0yVba1THMaaBZDXC5XHWTrlkHGOb1hXWAeRXgYOIUcJjaZqB1C7iZBjMed88wJ0cjDWAdYJjTBesAc1JwMMEQrVzAR20wa0ZwGIZpPawDDMOwDjBHgYMJhvhfL+BmHC4YhmktrAMMw7AOMEeBgwmmDl7ADMOwDjAMwzrAHBZ2c2IYhmEYhmEYpik4mGAYhmEYhmEYpik4mGAYhmEYhmEYpik4mGAYhmEYhmEYpik4mGAYhmEYhmEYpik4mGAYhmEYhmEYpik4mGAYhmEYhmEYpimkarXa+heVpIxarTYGAoGWvzbDMK1jdnYW5XI5W61WTa1+bdYBhnn1OUkNAFgHGOZ14Lg6cFLBRASADsBiy1+cYZhW0gWgUK1W3a1+YdYBhnktODENAFgHGOY14Vg6cCLBBMMwDMMwDMMwP324Z4JhGIZhGIZhmKbgYIJhGIZhGIZhmKbgYIJhGIZhGIZhmKbgYIJhGIZhGIZhmKbgYIJhGIZhGIZhmKbgYIJhGIZhGIZhmKbgYIJhGIZhGIZhmKbgYIJhGIZhGIZhmKbgYIJhGIZhGIZhmKbgYIJhGIZhGIZhmKbgYIJhGIZhGIZhmKbgYIJhGIZhGIZhmKbgYIJhGIZhGIZhmKbgYIJhGIZhGIZhmKbgYIJhGIZhGIZhmKbgYIJhGIZhGIZhmKbgYIJhGIZhGIZhmKbgYIJhGIZhGIZhmKbgYIJhGIZhGIZhmKbgYIJhGIZhGIZhmKb4/4rP6Qeq6XKCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 750x450 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# In the end the cluster assignment for the reduced (train) set can\n",
    "# be mapped onto another test set.\n",
    "# The assignment prediction can (and should) be done\n",
    "# for each cluster individually. \n",
    "go = time.time()\n",
    "cobj.calc_dist(robj)  # Distance map calculation\n",
    "print(f\"Distance matrix calculated in {time.time()-go:.2f} seconds\\n\")\n",
    "time.sleep(1)\n",
    "\n",
    "fig, Ax = plt.subplots(1, 3)\n",
    "\n",
    "go = time.time()\n",
    "r = 0.15; n = 10\n",
    "robj.predict(cobj, r, n, clusters=[1], purge=True, policy=\"progressive\")\n",
    "print(f\"Prediction in {time.time()-go:.2f} seconds\\n\")\n",
    "_ = cobj.evaluate(ax=Ax[0], ax_props=ax_props)\n",
    "Ax[2].set_title(f'1 {(r, n)}', fontsize=10, pad=4)\n",
    "\n",
    "go = time.time()\n",
    "r = 0.5; n = 17\n",
    "robj.predict(cobj, r, n, clusters=[2], policy=\"progressive\")\n",
    "print(f\"Prediction in {time.time()-go:.2f} seconds\\n\")\n",
    "time.sleep(1)\n",
    "_ = cobj.evaluate(ax=Ax[1], ax_props=ax_props)\n",
    "Ax[0].set_title(f'2 {(r, n)}', fontsize=10, pad=4)\n",
    "\n",
    "go = time.time()\n",
    "r = 0.3; n = 5\n",
    "robj.predict(cobj, r, n, clusters=[3], policy=\"progressive\")\n",
    "print(f\"Prediction in {time.time()-go:.2f} seconds\\n\")\n",
    "_ = cobj.evaluate(ax=Ax[2], ax_props=ax_props)\n",
    "Ax[1].set_title(f'3 {(r, n)}', fontsize=10, pad=4)\n",
    "\n",
    "\n",
    "fig.subplots_adjust(\n",
    "    left=0, right=1, bottom=0, top=1, wspace=0.1, hspace=0.3 \n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:53.951991Z",
     "start_time": "2020-06-11T17:56:53.940555Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label\tr\tn\n",
      "--------------------\n",
      "1\t0.15\t10\n",
      "2\t0.5\t17\n",
      "3\t0.3\t5\n"
     ]
    }
   ],
   "source": [
    "print(\"Label\", \"r\", \"n\", sep=\"\\t\")\n",
    "print(\"-\" * 20)\n",
    "for k, v in sorted(cobj.labels.info.params.items()):\n",
    "    print(k, *v, sep=\"\\t\")"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
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