code/reproduce_Fig6+S15.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Import-libraries\" data-toc-modified-id=\"Import-libraries-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Import libraries</a></span></li><li><span><a href=\"#Load-data\" data-toc-modified-id=\"Load-data-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Load data</a></span></li><li><span><a href=\"#Process-data\" data-toc-modified-id=\"Process-data-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Process data</a></span><ul class=\"toc-item\"><li><span><a href=\"#Spatial-average\" data-toc-modified-id=\"Spatial-average-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>Spatial average</a></span></li></ul></li><li><span><a href=\"#Visualize\" data-toc-modified-id=\"Visualize-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Visualize</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-24T12:44:07.600244Z",
     "start_time": "2020-08-24T12:44:05.120939Z"
    }
   },
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import xarray.ufuncs as xrf\n",
    "from averaging import xarray_average\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import xclim as xc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-24T12:44:07.636485Z",
     "start_time": "2020-08-24T12:44:07.603603Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xclim   0.15.2\n",
      "numpy   1.17.3\n",
      "xarray  0.15.1\n",
      "seaborn 0.9.0\n",
      "pandas  1.0.5\n",
      "CPython 3.6.9\n",
      "IPython 7.1.1\n",
      "\n",
      "compiler   : GCC 8.4.0\n",
      "system     : Linux\n",
      "release    : 4.15.0-112-generic\n",
      "machine    : x86_64\n",
      "processor  : x86_64\n",
      "CPU cores  : 8\n",
      "interpreter: 64bit\n"
     ]
    }
   ],
   "source": [
    "%reload_ext watermark\n",
    "%watermark --iversions -v -m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-24T12:44:12.034608Z",
     "start_time": "2020-08-24T12:44:11.302261Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<title>Show/Hide data repr</title>\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<title>Show/Hide attributes</title>\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-41d3f8be-8009-40e5-aaaf-8cc80861d992' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-41d3f8be-8009-40e5-aaaf-8cc80861d992' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 48</li><li><span class='xr-has-index'>lon</span>: 96</li><li><span class='xr-has-index'>time</span>: 11688</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c01de014-dfa1-4940-8307-0d5c6f08bd2e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c01de014-dfa1-4940-8307-0d5c6f08bd2e' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1979-01-01 ... 2010-12-31</div><input id='attrs-e99a59e2-3797-4585-986f-517e898681e1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e99a59e2-3797-4585-986f-517e898681e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc001a24-8ae5-418a-80b8-ac46da67103c' class='xr-var-data-in' type='checkbox'><label for='data-cc001a24-8ae5-418a-80b8-ac46da67103c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([&#x27;1979-01-01T00:00:00.000000000&#x27;, &#x27;1979-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-01-03T00:00:00.000000000&#x27;, ..., &#x27;2010-12-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-12-30T00:00:00.000000000&#x27;, &#x27;2010-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>plev</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5c8a202f-3fd5-459d-8b89-9e8ccb13ff59' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c8a202f-3fd5-459d-8b89-9e8ccb13ff59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5d9b78e-ad74-4672-bff1-9266f86bf576' class='xr-var-data-in' type='checkbox'><label for='data-f5d9b78e-ad74-4672-bff1-9266f86bf576' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>plev_bnds</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd></dl></div><pre class='xr-var-data'>array(7000.)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-87.16 -83.48 ... 83.48 87.16</div><input id='attrs-a408608c-390c-47c5-9cbc-1be8f79cb4f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a408608c-390c-47c5-9cbc-1be8f79cb4f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d35a31c-cdd0-4fd2-8ee7-b40d09f21469' class='xr-var-data-in' type='checkbox'><label for='data-9d35a31c-cdd0-4fd2-8ee7-b40d09f21469' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><pre class='xr-var-data'>array([-87.159094, -83.478937, -79.777046, -76.070244, -72.361581, -68.652017,\n",
       "       -64.941949, -61.231573, -57.520994, -53.810274, -50.099453, -46.388558,\n",
       "       -42.677606, -38.96661 , -35.25558 , -31.544523, -27.833444, -24.122348,\n",
       "       -20.411238, -16.700118, -12.988988,  -9.277853,  -5.566714,  -1.855571,\n",
       "         1.855571,   5.566714,   9.277853,  12.988988,  16.700118,  20.411238,\n",
       "        24.122348,  27.833444,  31.544523,  35.25558 ,  38.96661 ,  42.677606,\n",
       "        46.388558,  50.099453,  53.810274,  57.520994,  61.231573,  64.941949,\n",
       "        68.652017,  72.361581,  76.070244,  79.777046,  83.478937,  87.159094])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 3.75 7.5 ... 348.8 352.5 356.2</div><input id='attrs-7efa6ed9-571f-45ba-9f43-9a692ab7fd33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7efa6ed9-571f-45ba-9f43-9a692ab7fd33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34b8c9b3-8d34-4fcc-aed6-6a6d9a3f7f33' class='xr-var-data-in' type='checkbox'><label for='data-34b8c9b3-8d34-4fcc-aed6-6a6d9a3f7f33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><pre class='xr-var-data'>array([  0.  ,   3.75,   7.5 ,  11.25,  15.  ,  18.75,  22.5 ,  26.25,  30.  ,\n",
       "        33.75,  37.5 ,  41.25,  45.  ,  48.75,  52.5 ,  56.25,  60.  ,  63.75,\n",
       "        67.5 ,  71.25,  75.  ,  78.75,  82.5 ,  86.25,  90.  ,  93.75,  97.5 ,\n",
       "       101.25, 105.  , 108.75, 112.5 , 116.25, 120.  , 123.75, 127.5 , 131.25,\n",
       "       135.  , 138.75, 142.5 , 146.25, 150.  , 153.75, 157.5 , 161.25, 165.  ,\n",
       "       168.75, 172.5 , 176.25, 180.  , 183.75, 187.5 , 191.25, 195.  , 198.75,\n",
       "       202.5 , 206.25, 210.  , 213.75, 217.5 , 221.25, 225.  , 228.75, 232.5 ,\n",
       "       236.25, 240.  , 243.75, 247.5 , 251.25, 255.  , 258.75, 262.5 , 266.25,\n",
       "       270.  , 273.75, 277.5 , 281.25, 285.  , 288.75, 292.5 , 296.25, 300.  ,\n",
       "       303.75, 307.5 , 311.25, 315.  , 318.75, 322.5 , 326.25, 330.  , 333.75,\n",
       "       337.5 , 341.25, 345.  , 348.75, 352.5 , 356.25])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-c782eff3-a1d1-4f6d-a8b1-7c882e62fa82' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c782eff3-a1d1-4f6d-a8b1-7c882e62fa82' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>accelogw</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bcbfcde6-906c-49d9-89b1-2dd73db901bb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bcbfcde6-906c-49d9-89b1-2dd73db901bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5eb92ed-ffef-4ea9-9372-5bc8fee1d9a7' class='xr-var-data-in' type='checkbox'><label for='data-e5eb92ed-ffef-4ea9-9372-5bc8fee1d9a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>[53858304 values with dtype=float64]</pre></li></ul></div></li><li class='xr-section-item'><input id='section-c3ad76f0-a31a-4f78-b578-addaa5caa2cb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c3ad76f0-a31a-4f78-b578-addaa5caa2cb' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:   (lat: 48, lon: 96, time: 11688)\n",
       "Coordinates:\n",
       "  * time      (time) datetime64[ns] 1979-01-01 1979-01-02 ... 2010-12-31\n",
       "    plev      float64 ...\n",
       "  * lat       (lat) float64 -87.16 -83.48 -79.78 -76.07 ... 79.78 83.48 87.16\n",
       "  * lon       (lon) float64 0.0 3.75 7.5 11.25 15.0 ... 345.0 348.8 352.5 356.2\n",
       "Data variables:\n",
       "    accelogw  (time, lat, lon) float64 ..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inpath = '/mnt/4data/CMAM/0A.daily/'\n",
    "ds = xr.open_dataset(f'{inpath}accelogw/accelogw_daily_CMAM_CMAM30-SD_r1i1p1_19790101-20101231_70hPa.nc')\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Process data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-24T12:44:14.136117Z",
     "start_time": "2020-08-24T12:44:14.112825Z"
    }
   },
   "outputs": [],
   "source": [
    "time_mask = (ds.time.dt.month == 12) | (ds.time.dt.month == 1) | (ds.time.dt.month == 2)\n",
    "ds_sel = ds['accelogw'].sel(time = time_mask)\n",
    "\n",
    "sel_dict_ea = dict(lon =slice(110,145), lat=slice(30,48)) \n",
    "sel_dict_hi = dict(lon =slice(70,102.5), lat=slice(20,40)) \n",
    "sel_dict_wa = dict(lon =slice(-125+360,-102.5+360), lat=slice(27.5,52)) \n",
    "\n",
    "sel_dict_ls = {'Himalayas': sel_dict_hi, \\\n",
    "               'West America': sel_dict_wa, \\\n",
    "               'East Asia': sel_dict_ea}\n",
    "weights = xrf.cos(xrf.deg2rad(ds_sel.lat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-24T12:44:16.636267Z",
     "start_time": "2020-08-24T12:44:14.819220Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Himalayas\n",
      "West America\n",
      "East Asia\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>accelogw</th>\n",
       "      <th>g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1979-01-01</td>\n",
       "      <td>20.411238</td>\n",
       "      <td>71.25</td>\n",
       "      <td>5.009432e-09</td>\n",
       "      <td>Himalayas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1979-01-01</td>\n",
       "      <td>20.411238</td>\n",
       "      <td>75.00</td>\n",
       "      <td>5.009432e-09</td>\n",
       "      <td>Himalayas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1979-01-01</td>\n",
       "      <td>20.411238</td>\n",
       "      <td>78.75</td>\n",
       "      <td>5.009432e-09</td>\n",
       "      <td>Himalayas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1979-01-01</td>\n",
       "      <td>20.411238</td>\n",
       "      <td>82.50</td>\n",
       "      <td>-2.866767e-01</td>\n",
       "      <td>Himalayas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1979-01-01</td>\n",
       "      <td>20.411238</td>\n",
       "      <td>86.25</td>\n",
       "      <td>-5.888247e-03</td>\n",
       "      <td>Himalayas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time        lat    lon      accelogw          g\n",
       "0 1979-01-01  20.411238  71.25  5.009432e-09  Himalayas\n",
       "1 1979-01-01  20.411238  75.00  5.009432e-09  Himalayas\n",
       "2 1979-01-01  20.411238  78.75  5.009432e-09  Himalayas\n",
       "3 1979-01-01  20.411238  82.50 -2.866767e-01  Himalayas\n",
       "4 1979-01-01  20.411238  86.25 -5.888247e-03  Himalayas"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_ls = []\n",
    "g_ls = []\n",
    "for hs_name in sel_dict_ls.keys():\n",
    "    print(hs_name)\n",
    "    sel_dict = sel_dict_ls[hs_name]\n",
    "    ds_sel_hs = ds_sel.sel(**sel_dict)\n",
    "    #ds_sel_hs = xarray_average(ds_sel_hs, dim = ['lat', 'lon'], weights = weights)#.load()\n",
    "    #ds_sel_hs.name = 'accelogw'\n",
    "    ts = ds_sel_hs.dropna('time').to_series().reset_index()\n",
    "    #sys.exit()\n",
    "    g_ls += [hs_name]*ts.shape[0]    \n",
    "    ts_ls.append(ts)\n",
    "\n",
    "df = pd.concat(ts_ls)\n",
    "df['g'] = g_ls\n",
    "df['accelogw'] = df['accelogw']*24*3600\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-24T12:44:17.478227Z",
     "start_time": "2020-08-24T12:44:16.639667Z"
    }
   },
   "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>time</th>\n",
       "      <th>accelogw</th>\n",
       "      <th>g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1979-01-01</td>\n",
       "      <td>-1.062378</td>\n",
       "      <td>Himalayas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1979-01-02</td>\n",
       "      <td>-1.030545</td>\n",
       "      <td>Himalayas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1979-01-03</td>\n",
       "      <td>-1.483128</td>\n",
       "      <td>Himalayas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1979-01-04</td>\n",
       "      <td>-1.021822</td>\n",
       "      <td>Himalayas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1979-01-05</td>\n",
       "      <td>-1.749732</td>\n",
       "      <td>Himalayas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time  accelogw          g\n",
       "0 1979-01-01 -1.062378  Himalayas\n",
       "1 1979-01-02 -1.030545  Himalayas\n",
       "2 1979-01-03 -1.483128  Himalayas\n",
       "3 1979-01-04 -1.021822  Himalayas\n",
       "4 1979-01-05 -1.749732  Himalayas"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_ls = []\n",
    "g_ls = []\n",
    "for hs_name in sel_dict_ls.keys():\n",
    "    sel_dict = sel_dict_ls[hs_name]\n",
    "    ds_sel_hs = ds_sel.sel(**sel_dict)\n",
    "    ds_sel_hs = xarray_average(ds_sel_hs, dim = ['lat', 'lon'], weights = weights)#.load()\n",
    "    ds_sel_hs.name = 'accelogw'\n",
    "    ts = ds_sel_hs.dropna('time').to_series().reset_index()\n",
    "    g_ls += [hs_name]*ts.shape[0]    \n",
    "    ts_ls.append(ts)\n",
    "\n",
    "df2 = pd.concat(ts_ls)\n",
    "df2['g'] = g_ls\n",
    "df2['accelogw'] = df2['accelogw']*24*3600\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-24T12:47:51.567414Z",
     "start_time": "2020-08-24T12:47:26.155444Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(1.3039843749999989, 0.5, 'Occurrence density')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABDYAAAJcCAYAAAACbCRKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzdd3hUVfrA8e+dySQzKZPee0hCAqGE3pvYO4pdwYJlBdvqiu7a11VXUXdl/dlgFWzoiq4uYAEVUHoJhBJaSA/pvU0mc39/3DBJgDQIJIH38zw8ZO4995wzQxJm3vue9yiqqqoIIYQQQgghhBBC9EK67p6AEEIIIYQQQgghxMmSwIYQQgghhBBCCCF6LQlsCCGEEEIIIYQQoteSwIYQQgghhBBCCCF6LQlsCCGEEEIIIYQQoteSwIYQQgghhBBCiC43b948goKCmDdvXndPRZzlFNnu9dTceeedLFiwoLunIYQQQghx1nn22WdP+LUQ56Le+PPg5uZGZWUlrq6uVFRUdPd0xFlMAhunaNq0aSxdurS7pyGEEEIIcdZRFMX+tbxlFee63vjz0BvnLHonWYoihBBCCCGEEEKIXksCG0IIIYQQQgghhOi1JLAhhBBCCCGEEEJ0MbW+DLWhrruncU6QwIYQQgghhBBCCNGF1Noj8OsgOPC37p7KOUECG0IIIYQQQgghRBdRVRvsmAX1ZRB8Q3dP55zg0N0TEEIIIYQQQgghzhqH50PBShjwTxS3+O6ezTlBAhtCCCGEEKJHevfdd7t7CkL0GPLz0DuoZUmw9y/gfxmE3dXd0zlnKKpsKHxKpk2bxtKlS7t7GkIIIYQQQgjRoyiKYv/6bPvYqVpKwFre8mBDNWy+BqxVMOK/4OgJDmYUR8/umeQ5RDI2hBBCCCGEEEKIzrCWa8tNmsv6FKrTIHI2lG3VjvlO1QIc4rSS4qFCCCGEEEIIIcSpKNsOJeu0QIZr3+6ezTlHAhtCCCGEEEIIIcTJspRA9mdgCtdqa4gzTpaiCCGEEEKIHmno0KH2r7du3dqNMxGi+8nPQw+l2iDrI1AbIHQmKPruntE5SQIbQgghhBCiR9q2bVt3T0GIHkN+Hnqogh+h6iCE3ApOvt09m3OWBDaEEEIIIYQQQpy1sktr+HVfPmU19YyM9GZgiDsGfRdUZag+DHnLwX0oeIw49f7ESZPAhhBCCCGEEEKIs0Z9g41t6SX8vC+fX1MK2JdX0eK8i6OekVHejOnjzZg+PsQFuKHTKa301gprBWR8CAZPCL4BlE5eL7qUBDaEEEIIIYQQQvRqqfkVrEzJZ/2hIjanlVBZZ0WvUxgU4s79k/swOsqbEE8Te3IrWHeokHUHi/g5JR8AT2cDoxuDHGP6eBPl69r+gPueh/oSiHoI9KbT/OxEeySwIYQQQgghhBCiV9qYWsRLK1JIyiwFwM3oQFyAG7H+bkT7uWI0aMU8M4prCPE0MTDEnYEh7tw7sQ/55bVszShlW3oJW9JKWJ58BIAZo8N57sqEVsdUU/8Jed+B36XgEnX6n6RolwQ2hBBCCCGEEEL0KpV1Vl5ZkcLiDemEepmYNT4SJwc9ge5GlFaWhdTU29h+qLjFMRdHB8bH+DIu2oeiKgtr9hfw0fp0Qr2cuWt8y6CFqqqQ8jQceg18zwe/C0/b8xOdI4ENIYQQQgghhBC9xur9BTy5NJmcshruHBfJHy+IpbjKwpr9hSfdp6Io+Lg6cVViMG5GB/66bC9+ZiNXDAoCQLXVw877IOsTCJ+lLUEp/KWrnpI4RRLYEEIIIYQQQgjRI5VVW6ioswJQXlvP/J8Psjz5COFezvzfzUNICHanuMpCXX1Dl4ynUxT+clk8VZYGHv1iBz6ujowON8HWm6HgB+j7DEQ/DjUZXTKe6BoS2BBCCCGEEEII0SNV1FlZs7+QPTnl/HdHNlV1VibF+jI5zo/iqnp7lkZimEeXjalXFJ69oh/3f7KdP32yimWj38CtLpnSPm9S7XMrlNbgqnPCvSOd2ayo1eltt3Ewozh6dsXUz1kS2BBCCCGEEEII0SOVVFv4fHMGO7PKCDAbuW10BMEep3cXkpp6G0kZZdyRqDK+5Ekcawr53vQmpeUXQrkWSJkQaehYYKOhCoo2t93GdypIYOOUSGBDCCGEEEL0SN9++213T0GIHuNc/Hn4ftcR5n61k4paK1Pj/ZgQ64uDTndGxvas38PE2llgrOf2Xc+S2hDJPRMaMDnqz8j4onMksCGEEEIIIXqkyy+/vLunIESPca79PHywNpW/LttLXIAbt40JIMBsPGNjO5ev4byymVgUM796LKJ/gj+b16WxeEM6t4+NwKA/M8EV0XHyLyKEEEIIIYQQokdQVZWXVuzlr8v2cnFCAP+6OfGMBjXCapcRevAmqnTB/OSxhHKHPvTxdWX60BDSiqr4cksmNlU9Y/MRHSMZG0IIIYQQQgghul19g43Hv9rJ0m3Z3DIqjOeuSCC3rOaMjO1gq2RI1Uv0qf0P1a6jWen0T+p1Zvv5gSEelNdaWZ6cy7LkXCZGhp2ReYmOkcCGEEIIIYQQQohuVW2x8odPtvHrvgIeOT+WOVOiURTljIztb1nHyIonMdny2GOahT7mKeqzjg+ojIv2obymnt8OFvLZViOP9T8j0xMdIIENIYQQQgjRIwUFBdm/zsnJ6caZCNH9zrafh7JqCxV1VgBKqy386T/JpBwp508X9uWKwUFkl2qBhbr6htM2Bwe1isGVrxJT+xnl+khWenxGkWEwiTon4MSZIhclBFBWU8//rcnm+lA9YebTNz/RcRLYEEIIIYQQPVJubm53T0GIHuNs+3moqLOyZn8hJdUW/v17GqXVFm4aEYaHsyNr9hfa2yWGeZyW8X0tmxhV8QQutmxSTLez0+UhGpT2a3noFIVLBgSyO6eML/aaeHRk5WmZn+gcCWwIIUQ3U5NmQdbHEDoDZdA7Lc/t/TMceh38LkYZsbSbZtg+Ne0dSF8ANenaAdd4iJmL4n9x69c01ELyHChLgsoU8ByNMubH49tlfw6H3oDKA2Awg89kiH8JxRhwup6OaMcfv9jBV9uyuG5YCH+/dlCLcy+t2Mu7q1OZEufHwpnDu2mG7Vu0Po1PN2aQVaLdkYvxd2XOlGimxPl36PrDhVVc9s+1qMCe5y+yH9+QWsTfv08htbCKGksDwZ4mbhgeyt0T+pyGZyGEEL3bkbJaPlx3GEuDjdvHRhLp43Lax9SrNQyqmkffmsVU6MJY5f4xBY7DOtWHu8nAqAgz/9nXwEPDK3GQLTm6nfwTCCFET2AMgZyvUK1V9kOqzQpZn4IptBsn1kHGYIj/K4xfD+N+B59JsOU61PLk1q9RG0BnhIh7we+iEzcpXgfb74SQm2HSNhi2BCpSYPvtp+d5iA4LcjeybGcu1Rar/Zi1wcbSbdkEe5i6cWYdE2A28vjFcfxvzji+nT2WMX28uXvRVvbmlrd7rcVqY85n2xgR6XXcORdHB2aOjeSLe0az8pGJzJkSzRs/HWDx+rSufxJCCNGLJWWW8t7aQwDcPaHPGQlqmCo3cXHJlfStWcx+4y2s8Ppvp4MaR102wIe8Kj2rM5y6eJbiZEjGhhBC9ATmAVCbC7lfQeht2rH8FaBzAu9xYCm2N1UzF2kZDNWHtaBH+CyInI2iaLFqNfUfkPkxVKeCgzv4XQj9XkIxeDRevxh2PQzDv4Tdj0J1GngMg0HvojhHnNT0lYDLWx6Iew41/X0o2ag9txNd4+ACA9/S5lSeDPVlxzcq2QimYJSoB7THzhGokffBrkdOap6i68QFmskrr+V/O3O5bpgWfPs5JR8nBx0jIr0ora63t/1iSybvrUklo7iaYA8TN48M446xkeh0WlG4D9am8p+tWaQXVWM2OTAp1o8nL43H3WQA4MstmTzz7W7ev20Yz323m8ziGgaFuvPqtYMI9XI+qflf0L9lxs9jF8bx8YYMtmWUEB9obuUqzcsrUogLMDMy0ouNh4tbnBsQ4s6AEHf741AvZ77fdYRNaSXcOjripOYqhBBnm1/35fPIkh2YTQZuHxuBp7PjaR3PyVZEQtW/CCv4lCpdEKvcPyLfcdQp9Tkm0h0fUwNL9po4L6Kui2YqTpYENoQQoqcImwGZHzUFNjIXaV9XH7Y3UdMXwv4XoP888BgCFbth5/2gGCDyvsZWOuj/KjhHQk2GFgTY9QgkLmway1YHB1+Fge+A3ghJs7RlISO/08Yp+g02XdX2fKP/hBLzp+MOq2oD5HwF1krwPLU3DXiNhpRnUPOWgd8lUF8EOV9qwRrR7a4fHsqXWzLtgY0vtmQxfWgoGcXV9jafbcrg9Z/289wV/RkQ7M6+IxXMXZqMQa9jxpgIABRF4enL+hHq5Ux2aQ3PfrubZ7/dzRvXD7b3Y7HaePvXg/z92kE4Oej44xc7ePLrZBbfORKATYeLmfnvTW3O9/7J0dw/Ofq44w02lWXJWvbJ0HDPNvv4OSWPn1PyWPbAeJYnt7/efVd2GVvTS3loaky7bYUQ4lywJa2Yez/eSoSPM9OHhuLidPo+kurVauKqPyS+5gP0ai2lvrez0nY/Vp1r2xfa6lEtRW02cdD5ck3fGj7Y4UJBtQ5fZ1sXzlx0lgQ2hBCipwi6HvY8gVp5EBxcoeBHSJgH+15oanPgJYh/ESVomvbYOQK1Tyqkv2cPbChRc5raO4ejxr8IW6ajDv7AntWBaoWEN1FcY7WHUQ/CzntRVVXbWs1jKEzY2PZ8DS0/AKrlu+D3SWCrBb0rDFuCYk44lVcExXMU6pBF2tKThhpt3j7nweAPTqlf0TWuHBTMi8v2criwChcnPWv2F/Dclf15/cf99jZvrTrAExfHccmAQEDLYLivuJrFG9LtgY07x0Xa24d6OTP34jjuXrSVedMH2bM6rDaV569MoI+v9mb07glR/Ok/O+3fswND3Fn+wPg25+vhbGjxOOVIOdPeXked1Yazo553bx1KXEDr2Rp55bXM/SqZd28d2u4b8VF/W0VxlQWrzcaD58Vyy6jwNtsLIcS5YE9OObd/uJkgdxPzpg8iObv95X8nQ1GtRNUuJaH6nzjbCsh0PJ8dLo8QHTYEa0Zp+x2oDVC5t+02Nk+ui6/h3SRXvtpn4t7Eqrbbi9NKAhtCCNFDKI6eqAFXaFkbBnfwnoBiCkNtPK/WFUBtFuycjZr8QNOFqhXsrUAt/FXLxqhMgfpy7T9nmwXqjoCxcas4nZM9qAFox20WqC8BRy8UvQlcOlns0DVWC4bUl0Hu15A0C3X0Dyjmk9/kXa3Yq2WbxMwF3/Oh9gjsfRJ2zobEBSfdr+ga7s4GLuwfwBdbMjEbDYyM8mpRX6Ooso6cslqe/DqZv3yzy37calObf8uy7mAhb/96iIP5lVTU1tOgqlgabBRU1uFv1irUOzro7EENAD+zE5YGG2U19Xg4O2I06Ino5PrsKB9Xlj8wnopaK8t35fLHL3bw+d2j6RvgdsL2Dy9J4pZR4SSGtZ3VAfDlvaOpsljZnlHKyytSCPUyMW1ISKfmJ4QQZ5O0wipuW7gJVycHFt054vQMoqoEWX5hcNVruDccosAhkd/N/6DQMPS0DBfl0cCIQAtL9pq4Z3AVinJahhEdIIENIYToSUJnQNJdWsZG36eOOdmY4jjwrVaXeKjV6bDpagi7Xbve4A1l22H7DLA11TxAOfbX/9H/ibUxTmYpiqJzbAqGeAxBLdsKh9+CY3Z66ZSDr4LHMJQ+jTU1zANQHZxh3VTUuOdQTPJBsbtdNyyUP36ZhIujAw+fH9vinK0xePHiVQNaXeKRVVLN7R9u5sYRYTx8fiyezgZ25ZTzwGfbsVib0noddC3fLSqN37NHxziZpSiODjp7MGRAiDs7s0pZ8FvqcTu9HLXuUBEbDxfzj1UHAFBVFZsKfZ5czgtXJnDTyDB726O1P+ICzBRW1PHmygMS2BBCnLP0rt7csmAjDTYbn989mhBPZ7JKqtu/sBOMVVs5r+xp/Oq3UK6PYK15PlmOUzld0QarYiRbjeeC+Dr++nM1y3KiSQwyHNfO1WrG/QTXi64lgQ0hhOhJfCaDzhEsReB/RYtTipM/qlMgVKWihNx84uvLtmmZF/1fRVH0AKj5yzs/j5NYinIc1abV8jgVDdXQ+Dzs7I9lLWtPMDbaG4NeR3G1hQv6t9wq1dfNCX+zE+nF1Vwz9MQf6pOzyqhvsPHUZf3QNwYvfk7J7/Q8TmYpyrFsNloEU471w0MTWjz+ac8R5v9ykP/eP46AxsySE/artt2vEEKczXRGN/yuf56SKguf3T2KaL8TZ8WdLA9rCv2r3ias4AdqFW82uz7LIeO1qErbv/NPVU29yra9aegbwElvZsGGYqbH1xzXbuLgIAlsnAES2BBCiB5EURTUiZtBVVH0J9g+rO9TsOsRVIO7tkWqrR7Kk6A2ByX6MXCJBmyQ+hZq4FXariKp8zs/j04uRVH3/kWbjykUrBWQvQSK1sCIr5u1eQpKt6CMXtF0rGJv4xKYImioQi3boY3v3njH3P9S2PkH1LT3tKUodbmw+zFwT0QxhSG6n6IofP/QBFRVxclBf9z5h6fG8sy3uzEbHZgc54e1QWVXdhlHymu5f3I0ET4u2FRY+NthLkoIYFtGCQt/O3yCkdrW2aUoL69IYUqcH4HuRqosVv6blMOGw0UsnDnc3uaV71PYkVnKp7O0DKljl6jszCpFpygtjn/4+2FCvZyJalw2s+lwEe+vTZUaG0KIc0pZtYWKOiuKwYjf9GcweATy0rQBeLk42jM16uobTn4AVcWvfiP9qt8nsP436hVnCgMf5RfLjW0XBu1AUVBNUIen4qiHQf4Wth1x5DJrDSb5hN0t5GUXQogeRnFo/U6GEnY7qt4ZDr0JKU+D3gSu8RBxr3bePAC1/2twcB7se05bstLvJdh26+mddF0eJN2h/e3gDuYEGPFfFL/zm7U5om1B29ymq7SdW45a27jE5jLtjocSeiuqtQLS3oE9cxtrj0yE+L+e3ucjOsW1jUKaN4wIw+So5701qfz9h30YHXTE+rtxW2Ph0PhAM89c3o93Vh/itR/3MTTckycvjWf2p9tP65wLKup4eEkSBRV1uBkdiAt048PbRzAx1tfeJr+8jvSizqVKN6ha0CSrpAYHnUKYtzOPX9SXm0dKYONkbNmypbunIESP0Zt+HirqrPy8Nx/faX/GMSCGgm9eonL6EtbsL7S3SQzz6HS/itpAiOVH4qs/wNu6i1rFmx3OD3PAdCMJQeHtFwbtSFFQADWg/TbNDAuwsCnHiR15jowKtnTqWtE1FFVV1fabidZMmzaNpUuXdvc0hBBCCCGEEKJHSC+q4g+fbGN3TjmFy96gatcqPtmQ3qJNYpgH2zuwQ0limAc7048QWfs1cdULcbNlUKEPZ6/pTg4br8KmaBmuiSEubEvNaLOvIZFBbEte1e6YQxIms23XL51qM3+rlikye2hli3YTB48h2NLOmL5TUZwlAH4qJGNDCCGEEEIIIUSXUFWVV3/Yx+6ccopXvU/VrvYDCa1xtJXinbuQK4rex6gWU+QwgLWu/yTbcSrqsTW4OpKN0clMjM4YFmDhu4Mmsit0BLtJXaUzTQIbQgghhBBCCCG6xMvfp/C/nblM7uvLh6/8t/MdqCo+1u1E1ywhtG4FDtSR4ziBPaZZFBiGn7ZdTk7VIH8LK1KNbD3iSLBbbXdP55wjgQ0hhBBCCCGEEO06WhS0Nf9NyuHd1alcMSiQkZHefNiJvg22ciLqviW6ZgkeDfupV1w4bLwah4g7WZ/bWBy6vriNHjpe8PN0MDlAgk89O/IcuSiqFsfj62mL00gCG0IIIYQQokdSmt2ZlbJw4lzXE34eKuqsLQqANneooJJ//36YWH9X7p7Qh51ZZe13qKp4W3cSXfs5YbXLcaCWIof+bHJ9gXTjpVgVFxKNRqhc2YG+Tt8yk44aFmghKd+R3YUGEv3ru3s65xQJbAghhBBCCCGEOGlFlXV8ujEDb1cnbhgehl7X9nIRg60cj7yvuKj433jaDlCPiTTDBRxwuooSfZzWqL4WqKW7MzE6I8K9AW9TA1tyHSWwcYZJYEMIIc5i6vbbwVqJMvzL7p6KEB3y0OfbqbI08P5tw7p7KkIIITqgxtLAovXajie3jQrHaNCDrR7VUtSinVKXS5B1PRGWHwi2/o6eeooJZZNyK2nKSKw2E9SowDEFQHtAJkZHKQoMDajnx8NGCqt1+DhLEdEzRQIbQgjRjdTN14K1CmX0iuPPVaTA6kQY+R2K79STGyDhTaD3p2+rBatg3/NQsQccXCHkFoh7FqWxIrpadQh+STj+wpHLUHyntN5v5UHY+wQUrwfVAn4XQv95KE5+TW0sRbDrj5C/HNBBwGVaG4N7Vz/NXuGujzZTbWng01mjjjt3ML+Cqa+vYdEdI5gQ63tS/T9/VQK9fcXBruwy3ll9iK3pJRRXWQj2NHHj8DDuGh/ZIpX8WJV1Vl5ZkcJPe/Ioqdauu3VUOLePjTyuraqq3LpgE78dLOTdW4dyYf/e88ZfCHH2aLCpfL45g6KqOu4YG4m3q7b16tEdShQFxvVTuGWSnmnll+BINTWYOaBMxBg9m3WHCntsMdCTlehvYeVhJ7YeMXBhVF13T+ecIYENIYToTqEzYMv1qNXpx+9fnvkhmMLAp/UP5q1RbfUoOsNZ8eFbLdsOm66GmCcgcSHUZEPybECF+L+2bDxqObjGNz129Gq9X2slbLwU3AfD6B8AG6Q8C1uuQx3zS9MH0G23QV0+jPxOe6O24z5ImgXDv+jqp9orXDcslHs+3kpmcTWhXs4tzi3ZnEmwh4lx0T6d7re+wYZBr8NsNHTVVLtNcnYZPq5OvH7dYII9TGzPLOGJpck0qCr3TuzT6nXPfbubTWnFvHH9YEI8TaxPLeLPXyfj5eLIlYODW7T9v9WHMOjPrg8DQoheoKG2RSbGit1lHMiv4qoB7kS616FatA/yjjWVDLJ9ReoHToT7KVTWqGQrg0hTRnGEeFRFzxDngaD80l3P5LQxO6n09bay7YgjUyMksHGmSGBDCCG6k9/F4OQPmYug71P2w6qtHrI+g4h7UBQd6p65kLcMarK09kHXQuxTKHrtzoia8izkr4CI++DgK1CdgXpxEey8r8VSFDXvezj4dy3zQdGBx3Do/yqKa6x2/mjmw9DPIe0dKNkIzhGQ8DqKz6Sm+VXshb1/geK1oNrAnAAD/w/FTQsqqBn/htR/QHWaFpyJuAci/tDm3epWZX8J5gEosU9oj136oMa9AEl3oMY8geLg0tTW4IVi7OCd66LftNdzwmYUg1mb9+D34cdgKFoLPhNQy3dB4c8wdjWK5witzYC3YMOFqFWpKC5RnX8+vdyUOD98XJ34cmsWj5wfaz9e32Dj6+3Z3DoqAp1O4cVle1i5N5+c0hp83Zy4bGAQD58fg5ODlmXz2g/7WJWSz8wx4cz/5SDZJTXsef4i5n61s8VSlF9S8vnXLwfZl1eBTlEYHOrB05f3o4+vKwBphVVMeu1X3rllKIvWp7Eto4RQT2eeu6I/Y5oFWA7kVfDyihQ2Hi7GpqrEBbjxyjUDifF3A+DzTRm8vzaVzJIaQjxM3Do6nJljIk7qe/bGEWEtHod5O7Mzq4wVu460GdjYmlHCNUNCGN3HG4BQL2eWbM4kKbO0RWBjW0YJH69P55vZYxnx4qpOz08IIU5EtZSAtbztRg16qNSWimzKMbAuzZkxwXUM90zHvSKHUHUboeoWPMuysKFjRbqNJz5q4NuNNt77+q4z8Cx6hmGBFvYWubCv2IHO354SJ+OcC2zk5uYyd+5c8vPz0el0TJw4kccee+zk3mwLIcQpUnQOqCE3Q9Zi1Ng/oyg67UTeMrAUQuit2mMHMwx6D4xBWlAieTboTRD756bOqg5B7lIY+hkoDqBzPH7AhiqIehDc+oOtGvb/DTZfizpxK4qu2Z3yfc9A/EvgEgMHXoRtt6Gel4Kid0atyYJ154H3eC1DwsEdSjeDqm3/pqa9BwdegoTXwT0RypNh5/3afMJnaW02XAYlG9p4YRxQLjqifW2rA72x5Xm9CRqqoTwJvMY2Hd98LaqtDlyiIeoBlMCrWh/DVqelv+qcmvVrBHRQsg58JmiBHQd3e1ADAO9xoDNq8z8HAxsOeh3XDAnhq61ZPHReDLrGAnGr9uZRXGVh+rAQAFydDLw2fSB+bkYO5Ffw5NJdGA06HpraFAxJL6piWfIR/u/moTjoFRz1uuPGq7Y0cNf4KPoGuFFjaeCfqw4w66Mt/PDwBAzN2r/6Qwp/vjSeSJ8B/GPlfuZ8tp3fHp+CyVFPblkN176znlFRXnxy10jcjA4kZZZitWlrXhZvSOetVQd47or+JAS7sze3nCeWJmPQ67hllJZJdeuCjWxNL2n1ddHrFJKfvbDV85W1VtxNbWejDA/3YuXePK4bFkqAu5FNh4vZd6SCOVOi7W3Ka+t56PMkXr5mIF7OJ/gZF0KIk2Uth4J2dh9xPA+A1BI93x00ckngPu6N/J0w2zbcOYKKQgHR5AU9w29HPJj+/H1nYOI9T4yXFTdHG1ty5ff0mXLOBTb0ej2PPvooAwYMwGKxcMcdd/Djjz9y4YWtvxkRQojTKnQmHJqnZQYcraWR+RH4TkUxhQKgxD7Z1N45HLX6US2jonlgw2aBwQtQnJpqGxxbqkAJuqbFY3XQe/BDAJRtA8+RTSeiHkLxv1hr0/dZyP5cC1B4joS0/wODOwz5uCkY4hrTdO2Bl6HfyyiBVzfONwK16iCkvWcPbDDoXbDVtvGiNAs2+54Pae+gZi/RMlVqc7XACUBtY/DDwQ36vaLNT3GAI9/B1ptRE/+NEnzdiYfwGgV6Z0j5M2rc86CqsPdJwNbUb10eOLWsFaEoOlRHH6g70sb8z27XDw/lndWH+O1gob2WxpLNmYyP8SXIwwTAg1ObvidCvZy5b1INH61PaxHYqG+w8cZ1g5rWZJ/ApQMDWzx+dfpABj73I8nZZQwJ87Qfv3tCFFPi/AF49MK+fJOUw94j5QwJ8+TDdWmYTQ7Mv2mIPRgS1ZjxATD/5wP8+YpdlRwAACAASURBVNJ4Lh4QaJ9vWlEVH29Itwc2Xr12ELX1Da3Os637IzsyS/l6ezbzb0psvRHw/FX9eeKrZEa9tAoHnYKiwPNXJjCpb1PNlyeWJjM13p8Jsb5YG6QonRCifR3KxABo6MCyCdWGY+1BQqp2snbYJoKcCrChI4849innk6UMplbxYIjvZOryzr5lJh2lV2BIgIU1GU4UVNYTLPGN065HBDbS09NZsGAB27dv5+DBgwwbNozFixcf1+7gwYO88MILJCUl4ebmxvTp05k9ezZ6vb7DY/n5+eHnp71BcHR0pG/fvuTm5nbZcxFCiM5SXKNRvcbbgxlqbQ4U/ASJi+xt1Jwv4fDbUH0YrJWN2RHH3N02hbUIapyIWnkQ9j+vZVhYirRlJKhQk9kysGFuVojT2LjNWl2B9nfZDvAa2zLD42j/tblQlws77kPdeX+zE1ZQmn5XK6bg465tjeJ/EWr8C5A8B5Lu1DIsYuZqGRONGS6Kkx9EPdB0kcdQVEsBHHoDWglsKE7+qEM+hl0Paq+toofgG8A88KwrZNbVIn1cGBnpxRdbMpkQ60teeS1rDhTy1o1NH9y/25HDh+vSSC+qptpixWpTOXb3v2APU5tBDYDDhVW8/tN+kjJLKKmqx6aqqCrklNa0CGzEBZjtX/ubtQyfokoLAHtyyhke4dUiw+Oo/PJa8srrePyrnTyxNNl+3GpT0Tf7PghwNx53bUcczK/gzo82c/eEKC5op8Dnwt/S2JFVysKZwwh0N7EhtYgX/reHUE9nxsX48MWWTA7lV/L6dYNOai5CiHNURzIxQFueeiK2OqjcB+W78K94lmsNhVj8HcimP+uVy8hWBmNRXE987TlsaEA9qzOMfL+3lMHya/u06xGBjQMHDrB69WoGDRqE1Wo9YZuysjJmzpxJdHQ0b7/9NhkZGbzyyivYbDYefvjhkxq3pKSElStXsnDhwlOZvhBCnLqwmbDzD6iWYsj8GAxeEHA5AGrx77D9doh9CnzPAwcPOPJfbblIc81rTbRm89VazYyBb4NToBYY+DURbMfsta40D1oc/XDXgbvDamObgf+C5ss3WvTTyaUogNLnEdSoh7WgicELqg5CytPgfPxuEXYewyG77QKfit8FMGUvqqVQy/RwcIcfQ5v6dfJvCugcnbtq05YJOZ3bu1BcPzyUuUuTKa228J+tWXiYDJzfT8uY2JxWzENLknh4agzjY3wxmwz8sPsIr/6wr0UfJsf234bc8eFmQjxNvDxtIP5mJxRF4fzXV1N/TLaCQ7NCmvbv2A5sr9K4GoWXpw1kcKhHi3PN41snsxTlQF4FN76/kWlDQnj0wr5tzqOqzsq8H/fx3m1D7Zkn8YFmdmWX897aVMbF+LDuYCH78yro9/QPLa697+OtDI/wYsk9o9t7ukKIs0iXZmIcy1II5buhYjdU7QfViqozsqVyGJ9lDiMkqC/BHpKG0BZvk42JobWEerQdwBddo0cENqZMmcLUqVr69QMPPEBJyfFvHD7//HPq6uqYP38+rq6ujB07lsrKSubPn8+sWbNwddWihFdffTU5OTnHXT9u3DjmzZtnf2yxWHjggQeYMWMGffq0XshLCCHOiMCrYdcjkP2ZlrkRclNTRkTxei0bI+Zxe3O1JqPTQ6h1eVpAYOD/oXiP046VbKZDAYvm3AdB7lL7zistGIO0YEB1GkrIja330ZmlKEePKIo9e0TN+QJM4Vp2RWvKd0AHC4kqjlqRSbVgJdSXgP+l2gnPkWAtQy3ZjOLZeCereJ02d8/jtzs9l1wyIJBnvt3N19uz+WJLJtOGBNszIraklRDsYWL2lKblKFkl1Z0eo6CijsOFVbxyzUBGRGo73CRlltqDER3VL8jM8uRc+84rzfmbnfBxdSKjuJqrElvPJOrsUpR9Ryq4+YMNXDk4mCcviT/xRc1YG9TGrJaWHel12tauAI9fHMd9k5rqbTTYVC7551qeuqwfU+P92x1DCNF7tBe0UKvTtYBF8dr2O2stE6NFhw1Qth1yv4GKXU3LLR39wGs8mBN4aedg3t/hzlWx1QR71LfdnwDggqg6JsaYwdLdMzn79YjAhk53fGrosdasWcO4cePsAQyASy+9lNdee41NmzYxZYpWb/brr79ut6+GhgYeffRR+vXrxx133HHyExdCiC6i6E2owdfD/he1D9ahM5tOusRATaZWY8JjOOT/ADlfdX4QRx8t2yFjIaoxUNs2de8THLekpT0R90LGQth2C2r0n8DgoS1tceuPYh6AGvtn2P04qoMb+F2gZYOUbYe6IyjRj2rPtxNLUVTVpu2w4nu+lmGS+zUcehOGfWYvtqpmLtIKeroPAhStxkb6B1rdjaP9lGyApHtgyIco7tqSCTXjQ3CL116bkg2w+zHo8xCKixbwVswJqD5TYOf9qAPna2/8kueA/+Xn5I4ozRkNeq4cHMSbKw9QVlPP9cND7ecifVzIKa3hv0nZDA714Nd9BSzb2flln14ujng4G/hsUwZ+bk7kltXy0oq9xy1pac+M0RF8tjGD2Z9u4/7J0ZiNBnZklRLr70Z8oJmHpsbw12V7cHVyYGJfX6wNKsnZZRRU1HHfJO17oTNLUVKOlHPT+xuZEOPDPROjyK/QgngKCr5u2p277NIabl2wkScvjmdqP3/cnQ0MC/fk5RUpGA16gj1MrD9UxDfbc3jq8n4ABLqbCGy2g/PRGhtBHqbjtt49W2RnZ3f3FIToHidYPpK18bWmBwUrOxawaIulECpStF1OKveDrUZblukcrRXmdusPTtoS/v8dNPL+DneuGeTFEI+yUxtXiNOgRwQ2OiI1NZVRo1reHQsKCsJkMpGammoPbHTE008/jYuLC3Pnzu3qaQohxMkLmwnp74HnKBS3OPthJfBK1Mg5sPtRaKjVCoz2/Yv2uBMURY86ZLF23eqh2s4h/f4Om6d1rh9TKOron7RCm+svBJTG7V7f1s6Hz0LVu2rBiJS/aAU63eK1rWhPVt4KrSipzaIFL0YsRTlaaPWoAy9BTYa2pMQlGga/jxJ8Q9N5a7WWTtvQLHOgch+kPKMFk5wjIOZJiLy/Zb9DFmnZNBsu1QIrAZdD/3kIuGF4GB9vyGBouCfRfm724xclBHDnuEie+24PdfUNTIj15eHzY3nuuz2d6l+vU5h/4xCe+243F7y5hkhvF566rB93fLS5U/0EeZj44t7R/G15Cje8twEFiAs08/K0AQDcMiocFyc97685zMvfp2Ay6In1d2XGmIhOjXPU/3bkUlxl4ZukHL5Jasoi1esUDv3tEgDqrTZSC6qoqGu66/mvm4fwyvcpPPj5dkqr6wn2NPGni/pyy8iw48Y4VwQFBXX3FIToMYL8Pdpv1JaGaqg80BjISNECGwAGT3AfDAFXaP+H6k0tLjtYoufxX8wM8bcwe7w/65IPn9o8hDgNFFXtwALUM+joUpRji4f279+fxx57jJkzZ7Y4PmHCBK666ioeeeSRDvW/detWbrrpJmJjY+2ZItdccw233XZbh+e4ZMkSlixZAmh1On755dyt+CuEEEIIIYTomE7VxWhvmYnHcC1jsjW2eqhJB2uFtvNadRqgakW4XWLANa4xa9FPW093gv4qLQpXfeVNaa3Cd9OLsHlNZnXSunanPyRhMtt2/cItl9xsP/bx8k9O2KajfXVFu+7oa+LgMQRbVrXdyHcqinN4u32J1vWajI2uMnToUPbt29d+wzZcf/31XH/99QBMm9a5O51CCCGEEEKIs0+HghZdWRfjWDarFsioOqBlZlSngloP6MAUBr4XglscmCJA1/7HQFWFx381c7hMz+LLSgh0tSGLw0RP1WsCG2azmcrKyuOOl5eXYzabT3CFEEIIIYTozZoXhJdlKaK7dHmWxUnKySu1fx3k76EFMqpStUBG1X7ta7VxeZsxWKuT4RIDQddBVedv7C7c6czyQyYeH1XBmBCpfil6tl4T2IiKiiI1NbXFsdzcXGpqaoiKOrcLuAkhhBBCnI2Cg5sKDfew1dPiXHKCQp4ndKrFPNsRMrKptpbt50FadsbRHcaMQU2BDJc+4NC04QKGzt8E3pRj4KX1blwQWcs9g6tOdepCnHa9JrAxYcIEFixYQGVlpX1nlOXLl2M0GhkxYkQ3z04IIYQQQgjRm3QqE6M71Jdry0mqDkH1oWPmVAX+l2tBC5eYloGMU5RfpWP2Tx6Emht4dXLZcdtZC9ET9YjARk1NDatXrwYgLy+PyspKvv/+ewAmTpyIyWTihhtuYPHixcyZM4dZs2aRmZnJ/PnzmTlzZostYIUQ4myjruoLEfei9Hm4u6ciRIeNfflnZowJ5+4Jfbp7KkIIcWI9JBMDANUGtblQcxiqD8PBV6AmUzunGLSdu5qLmdt+8dCTUN8As3/yoMKiY9FlRZidJFNK9A49IrBRVFTEgw8+2OLY0cerVq0iJCQEd3d3PvzwQ55//nnuvfdezGYzM2bMYM6cOd0xZSGE6DJqXQHs/yvkfw91R8DBA9z6QfSjKL7ndff0ejw15ys49Lp2R0ut17Z6jZyDEnpLy3Zp78KhN7TX2K0f9Ps7ive41vvN/QbSP4DyHdo2u25xEP04SsBlTW3WXXDi9dSu8SiTtnXZc+yJiirreGPlfn5JKaCgog6zyYFYfzfum9SH8TG+3T29XmFFci7zftpPRlE1Yd7OPHpBXy5KCGjzGlVVWfh7Gp9sTCeruAZ3ZwPXDAlh7sVNW0T/Nymbd1ancriwElcnA+OivXny0nj83Iyn+ykJ0WN0uJBnd7FWagGM6jTt75p0sDXOR+8KHkPAfai2rMQY2ljs867TPq1XNrqxOdeRN84rJc7betrHE6Kr9IjARkhISId2KomOjmbRokVnYEZCCHEGbb0RGmpg0DvgHAWWAihaC5ai7p5Z7+DoDTGPg2tf7a5W3nLYeS+qow+K/0UAqDlfwu5HIeEf4DUG0t+FTVehTtqGYgo7cb9Fa8FnIvR9Bhy9IPtz2HI96ugfmgIiwz4HW7OCarY6WDMcgq45zU+6+9338TZq6hv4+7UDCfd2pqjSwsbDRZRU13f31HqFreklzP5sOw9PjeGihAC+33WE+z/dxn/uHU1imGer1/112V5+TsnniYvjiAswU15bT0FF04ezLWnFPLwkiScviefC/gEUVNbx1De7eOjzJD6dNepMPDUhTqueUsizU2z1UJsN1emQtwxKt2j/1wOg0wp9eozUsjKcI8HRBzxHdHk2RnuW7a1mwQ4Xbk2o4qrY2jM6thCnqkcENoQQ4lyl1pdC8e8wchmKz2TtoHM4eAxr/Zqsz2DXgzB4IUrAZVpBvUOvQ8YCLY3VpQ/0+SNKyI1a+223goMHysC3tMcpz2oprmN/RfEcqR1bGQ1xL6CE3IiaNEsLqvhM0fq11WjreAe8iaJ31tq3MyaAuv9vkPkh1OWBwRN8zkNJXKCdK/oN9j4JFXtA0Wvrgwe9i2Lu3+nXUPGZ1PJA1GzUrE+017UxsEHqPyHkVpTwO7THCW+g5v8Eae9D/Asn7jdhXssDsX9GzVsBed9BY2BDcfRq0UTN+gwaqiH0tk4/j96krKaeTWnFfHznSMZG+wAQ4unMoFCPVq/5ensWT32zmzeuH8z5/fxRVZV316Ty6cYM8sprifB24d5JUVydGALA7E+3YTYZ+NvVAwB47Yd9zP/lIEv/MIYhjR/8R7+0ij9d1JerE0P44xc7KKm2MC7ah3fXHKLG0sAF/QN44coETI56gHbHBPjHygN8sSWzMQvFwIQYH16/fjAAG1OLeGlFCvvzKtArClG+Lvz92kH0DXDr9Gu48PfDjI7yZvaUGO35TnFjfWoRC39P461WAhuHCir5aF0a3z80nmi/E4+5LaOEQHcTd43XCquHejkzY0wEz367u9NzFKJH6knLR05EtUFdLlRnaFkYNelQmwNqg3be4A2mYC3I7hypbcOqc+yeuTZzsETP48uLSfS38JcxFd09HSE6TQIbQgjRnfSu2p+8/6F6jUHRt50qrqbOhwMvwvClTVkD+56F3K8h4U1wjYWSDbDzflSDB4r/xeA9AVLnN3VStEa7G1S0BjxHolYd0u4keY9valP8OzgFwKjlUJsFW28B1xiIfqxDY6q5X0Pqm5D4EZj7Q10BlGzSnoPNClumQ+gMSPxQWz5Stl0LcABqTQb8OqTt1y34RnugpsXro6pQ9Ku27V3cs43jWbT+ox5q2dh3qjbvzmio1II0rcn4N/hegGIK7Vy/vYyLox4XRz0r9+YxLMITo0HfZvuFvx3mzZX7WTBjGCOjvAF47cd9rEg+wvNX9qePryvbMkqY+1Uy7iYDU+L8GRXlzcLfD9v72JBahJeLIxtSixgS5klaYRW5ZbWMauwPYPPhYvzcnPjkrpHklNZy/6fbiPRx4f7J0R0ac0VyLu+vTeWfNw6mb4CZoso6tmdo2ytaG2zMWrSF64eH8o8bBlPfoLI7pwy9Ths7u7SG819f3ebrcFVisD1Qsz29hBljIlqcnxDjy6L16a1e/9OePMK8nPl1XwG3f7gZmw1GRnnx5CXx+Lg6ATA03ItXf9jHyj15nBfvR0l1Pd/tyGFyX7825yaEOAmqDeryoTZTq4eR/gFU7mnK5NMZwRSu3SgwhWt/fM+Hsi3dO+9jVNUr3PeDJ0YHhX9dUIpj27/SheiRJLAhhBDdSNE5oA5+D3beDxkLUc2DwGs0BE5D8Wy545O67zlIXwijVqC4a3eQVWuVlo0w8rumQIdzBGrpFkh7F44GNpIfQK3NBYM7lG2F2KegaLUWqChaA85RKKamu9Y4uMHAt1AUPbjFoQZOg8JfIPqxjo1Zk6EFRnynougM2h0pj6FaW2s51JeC/6UoLo3bdbv2bRrbKQgmbGz7hXNoebdarS+DlX20pSCKHhLeRPG7UDtpKdTulDn5t+zDyQ8Kf257nOZjpL0DNdkQfOOJz1ce0NKeh33R4T57Kwe9jtemD2Lu0mQ+3ZRB/yAzw8I9uWRA4HHLKOb9uI/PNmXw6axRJAS7A1BtsfLB2sMsvnMkIyK1rJdQL2eSMktZtD7dHtj4yze7yC+vxc1oYGdWGQ+fH8u6Q4X8YVI0G1KLCPd2JtDdZB/L1ejAi1cPQK9TiPZz49IBgaw7VMj9k6M7NGZ2aQ1+bk6Mj/HFoNcR7GFiYIiWhVJZZ6W81sp58f6Ee7sAEO3XVLzc382J5Q80Cw6egKux6W1XQWWdPRhxlI+rU4tlJcfKKK4mq7SG73bm8tq1g1AUhReX7+XOj7bw9X1j0OkUhoZ78taNiTy0JIna+gasNpXxMT7Mu25Qm3MToifo0XUx1AYtA7Ems+lPbVZTXQzFoP1f5jlG+z/PORwcfUHRteynB24x8vQaM4dK9Cy+0ZtA15zuno4QJ0UCG0II0c2UwKtR/S7WsiRKNkLBj5D6D9S+z6HE/ElrdPhfWqGxcb+huEY3XVy5V9vDftOVqDR7s6TWa3eGAMW1L6pTQGOmhq9WxyNoOhx4GdVWrx33ntByUm7xWlDjKGNg01rfDoxJ4DRtzj/Hofqer92h8r8MRe+E4uiFGnIrbLwc1Wcy+EyCwKvttS4UnYO2tKUzHNy0YIi1UgvA7Hkc1Tm8aXnPKVJzv4Y9T8LQxSjO4SdulLFQC+b4XdwlY/Z0Fw8IZHKcH5vTitmWXsrq/fm8v/Ywj13Y154h8e/f06iss/Lt7HFE+rjYrz2QV0md1caMhZtavMe3NqiEeGqBimg/V3zdnFifWoS3ixNh3s5cNjCQt34+QH2DjQ2pRYyK9G4xp2g/V/S6pg79zEaSMks7POYlAwL59+9pjH/lFybE+jAx1o+p/fxwctDj4ezItUNDuG3hJsb28WZstA8XDwgk2EO71kGvI6LZczwdVFXFYrXxxnWDiPLVgipvXDeIKfNWsyOrlMQwTw7kVfDMt7uZMyWaCbG+5FfU8dLyvTy5NNm+pEaIM63X1cWwWbTlI7VZWkA77R2o3Kf9PwegOIIpBDxHgSlU++MUoD0+w3UxTtVXKUaW7jfx4LBKEkL8yK6Ib7N9nerU5nkhuosENoQQogdQ9EbwPU/7E/sk6o77YP9fUfs0Lp/wGgMFP0HOFxD7ZNOFqk37e/hX2hurFp02+xXvPa4psOE9EcU5HNXRWytgVrQW4o6pM6EYjp0hYOvwmIopFHXSTi3IUPgz7JkLB/6GOnYNioMLyuD3UKNmQ/6PcGQZpDyLOuwLFL/zT2opiqLomoIh7oNQK/fBgb+Dz2Rt2Y2i1+60NVeXf3wWxwmoOUsh6S4Y/AGK/6UnbmOzQNYnEHa7Fpg5RxgNesbH+DI+xpcHp8bw+H928ubK/cxqrO8wLMKL1fvy+TYphwenxtivs6na9oELZgwjyMPUok8HfVPUYWSkFxsaAxujo7wJ9XLG09mRnVmlbDxczJ8u6tviWoO+5Z1RpdlYHRkzyMPEqj9OZN2hQn47UMSLy/bwj1X7+eb+sTg7OvDa9EHcMTaS1fsL+GlPHq/+sI/3bhvGxFjfTi9F8XV1orCy5Z3nwso6fN1a/9Dg62bEQafYgxoAkT4u6HUKOaW1JIbB278eYlCIB/dM1H4e4gPB2VHP9HfW89hFfVtkuAjRFTqcZdFewAK6py6GtQJqshqDGFna0sy6PKBxm1OdEVzjwWtcsyCG//GZGL3QwRI9T601MyqojjlDKzligdV709q8ZkhC5JmZnBCddO68+xJCiN7ELQ5Uq7bNKIB7IkQ9ABsuQ0VBiX2isV086JygJuP4IprNeU+AQ//Qll9E3t90LOPfx9fXaHduHRtT0Ru1ZSn+F6NGPwo/RUDJeq22BaCYB4J5IEQ/irrxSsj6GPzOP6mlKMez2dODFZ0jqnuiFmBpvltJ4SoIuKrNXtSc/0DSLBj8PkrQtNYbHvlWW/ISOrOdeZ3dYvxdsdpU6qxakbwBwWbuGhfJLQs2oijwwHkxje3ccHTQkVVaw5jG4qMnMirKmw/WpuLj6sTtYyPtxz7blHlcfY3259axMY0GPVPi/JkS5899k/ow/MWVbEkrYUKstoVtvyAz/YLM3DepDzMWbuKrrVlMjPXt9FKUxHBPfjtYaA9AAPx2sJAh4a3XcBkW7onVppJeVGVfDpNRXE2DTSW4MeukxtLQImsFQNeYomJT25yeEC30uiyL9tjqtYBFbY72/15tjhbgrm+2A5nBS9uhxD0RjCFakU+Dd7fsUHK61Vph9o8emBzgzalN9YKE6K0ksCGEEN1ItRTB1pu1QprmBO0De+lWOPQG+ExGMZiP3jNC8RiGOvI7bQmHoqDEzEVxcEONegj2PKEVzvQepy3HKN0E6FDC79QubqyzQU1G07IT7wmw8w/H19doR0fGVDMXa4EZj+Hg4Ao5X2pZIC7RqNVpWoE1/0vBGATVaVCeDBF3a/13cimKeuAVbRznSC2Ykf89ZH0KCa83NYp6ALbfieoxDDxHQ/r72m4u4Xc19bNde63sO7dkfwFJd0L8S+A1DrX2iNZQ53jcbihkLNT+vVzOjTtZJVUW/vDJNq4bHkJcgBlXJwd2ZpXxzupUxvbxwc3YlPEzKNSDxXeO5NYFG1GAOefF4OrkwN3jo/jb8r2gwohIL6osVrZnlKJTFG4aqS1LOlpnI7u0hlFRXo3HvHhiafJx9TXa05Exv9ySSYNNZXCYBy6ODny3MweDXiHSx4XM4mo+2ZjB+f388DcbySiuJuVIObeM1JYmdXYpyh1jI7ju3Q28/etBLugXwA+7j7D+UBFf3jva3uaV71PYkVlq36Z1XLQPCcFmHvvPTp6+rB8Az/9vD4NDPRjYWL/kvHg/nliazOIN6UyM8SW/opbn/7eHhGCzfdlMb6KqEo3pal0asICeEbRoTlWhvgSKftOKSdfmNAYx8rBnHioO4BSoLR3RG7VghikEGnf+6qlsaR90WV/P/25mX7GBDy8txt/F1mX9CtFdJLAhhBDdSe+q3Qk6PB+qU7U3ksYgCLoOYuYe11zxHN4U3ACUmLnQ9xktEyP1Tdj1ADiYtUyIPo80XXe0zoajF4qTducZ7wla8OHY+hod0d6YBnc4OA/2PKGtSXaNg2GfozhHoNblQdUBLaBTXwSOfhB8A/T540m8gGhBleQHtDtwepO2S8vgD1CCr296/kHTUS3FcOBlqDsCbv1hxDct62XUZLbsN/0D7fXZ85j25yiv8TDmR/tDteowFP4KQxad3Px7IWcnPYlhHvz79zTSCquwNNgIMBu5cnAQc6ZEH9d+cLPgBmjBjT9eEIuPqyPvrU3lL9/swtXoQL9AM/dMjLJfd7TOhqezAe/GQpujoryx2tTj6mt0RHtjmk0G3ll9iBeX78XaoBLj78o7twwl1MuZgoo6DhdW8odPsiipqsfH1ZGrBgdz76RO1oNpNDTci7duTOS1H/fxxk/7CfNyZv5NiS2Kr+aX15FeVG1/rNMpLJwxnGe/2831767HaNAzLsaHv1zaD11jlsb0YaFU1VlZtC6NF5ftwc1oYEwfb+ZeHHdS8xRnoZ6+XWpHqSpYy7QgdV2u9ndtrvY73lbb1M7grdWJMg/UAhjGIHDy1ZYoegw/6zIxOuK7A0Y+2+PMvYmVTAyzdPd0hOgSiiqh8FMybdo0li5d2t3TEEIIIYQQol1qdXrHAxsd+dDfkXan0peqasGYuiNQe0TbnaR8hxbMaGgK/KF31YIWxgBtSaPPFC3woW8jU6krn2NH23VzX2llei7/0ptYLyufX1lM8926sx3PY3XSuja7GpIwmW27fml3yKPtbrnkZvuxj5d/ckp9dUW77uhr4uAxBFtWtd3Id2rrxclFh0jGhhBCCCGEEGeBHr1dantUm5ZxUbGnKfPiaDDDVtPUzsHt/9m77/CoyryN498zJX3SSCChd4KAKF1FEMG1gHVVrOhr+9Pp/AAAIABJREFU37XXdW2LbV0Lq67Y1rIqiiC2taxdBFEREKVK75303mbO+8eZTEkdQmAScn+ua645bc555iSBzJ3n+T1WIeyEI63hJFHtrR4Z1WsvJQxslb0x6lPmhuu/TMRug6dPyA0KNURaOgUbIiIiItIs/fLLL77lwYMHh7ElLUQow0zCPcTEUwHle7yFPHdbz2W7oXy3Nc1qFXuc1fsicYg1lWpUmvWcMhbyFoWv/WH0y7JNvuXBA7ru8+v/8ZOL5ZlOXjwph44u1dWQQ4uCDRERERFploYMGeJbPlRHT4dWzNMBVDZ8subSG8M3fGS3FWJk/+gdPrIbyrPwTaUK1kwkke2sotFJw60i0JFpVuHp2hhG7dtbgaGnPuRb3tdCol9ujOS1ZbH834Ai/tCtmXyfiDQhBRsiIiIiIk2syadLDbWWwsHkLoXCVZC7yJo6tSrIKNsbXMDTFgkRKRDd2WpjZDsrvIhsC7aI4PZr+EiT25ZXye3fJjAgtYK/HFUQ7uaIHBAKNkREZJ+YW6fB8psxTs4Md1NEQjJr0Vb+9tEKVj5wUribIq3JoTL7iKfcCiqqAovyvVaIUb63WnBjeHtfpEJSd2vGq8i2VoiRegLk/VLnJeTAqXDDDZ9kYQLPnJBLpOpqyCFKwYaISJiZv10J5VkYw/wzLJm7/we/XATdb8DImNz4c3/TB7peg9Hj5iZoqVf7s6HtiU13vgDm6odg7cO17zxhM0ZkW+s407SmyN38MpRssn6Z7nghRl9/N13TU25N77ptulU9P6It9LgJo9u1/mN2fgCrH7Cm2o3pDn0mY6SfXn8bd7wL6x6HwrUQmeK9v7cEH5P1Paz8i1UELyodetyC0eXKxt2UZujWd5aQU1zOq5f6P5B98/turp2+mCtGdue2E/s0+tzH/ONbLjm6C1eNatw0qrU5dWB7xmS0bbLzVbdqVz73/XcFS7bmkhjj5IJhXbhhbE+MerrMb9hbyCOfrWLRpmzKKz30aufipnG9OK5PzXZmF5Vz8tNz2Z1fxuJ7TyA5NqKWM4rsB08ZlGVaYUX5XivA2PIqFG2AytzgYx0u699T12FWEc/ko6GywOqRYavje9OwHfj3ILWasiCOX3eU88wJ+XRJcIe7OSIHjIINEZFmxtw2HZb+CTIexuh+XePO4SnHqOsXzP1k2KPrnz5vf/S4CbpcEbxt8SQwDF+oAVihwZ7PoO/fwdXfmtKvdFe1110MJdvh8GchtqfVRdrt7xpt5sy3jul9L6SdDrv+C4svxDz6W4ykYbU2z9zzBfx6KfSbAql/sLpgL/0zpi0ao9ufrGOKN8GCM6DTJDjiVWts+fIbMSNSMNLPbIKb1Py8v3gbd763jDtPzuCykd0adY7ySg8RjgPz4SfKaSfqAJX/Lyit4KKXFzC8WzIfXTeS9XsLuX3WEmIi7Fw5qnudr7v89UV0So7hrStGEBNh562fN3PVG7/w1S2j6NImNujY22ct4bD0eHbn7z0g70H2zT4NMWkuTNOaGrU8E8pzIPdnb4CRWUvPC6zCnTHdIK631esiItX7nFLz338NH2m25m2L4MXf4jj/iFgm9NzV8AtEWjAFGyIizYi54RlYdQ8c/gJGx/OtbbX16Fj9EOz6AGP0L0HHkHw0bHoePBWYcRlQsgV+vwvz97sAMCZYU+aZOz+ENQ9B0VrrF9YuV0LPO3x/Ybb2PwxF66xfYl39YPCbGJHtagxFMUu2wvJbIPsHKziI7gS978bocO4+v3/DERdUMM4s2Wqd98hX/NsK11jvcdRCDFeG/8UJAfdx79eQ+R0cvwIjIsXaWH1++A1Toc1ojF5/sdZdGZhZc6yeIElv1N7AbdOh7SkYXa+21mO7Yfa8HdZPwex6jXX/Nr8EkekY/Z/0nzd3Iax/Cg7BYOOVeRt59LNVPHr2AM48siNQe4+OJ79aw2fLd/LlzaODjhnaNZnXf9xEhdtDz7ZxbM8t4e//W8Xf/7cKgE3/GA/A58t38uRXa9mYWUSbuAguHN6Za8f4e0V8vnwnT31t7Y9y2umT5uLZCwaR6oqsMRRlR24J9/13BQs3ZVNW6aZ9YjQ3jevNaQPb7/P7//C3HZRWuJly7kDfddfvKeTleRu44thutfbayC4qZ2NmEQ+f2Z/D2scD8JeTMnhl3kZW7MgPCjZenbeRkgo3143pyezVCjYOtJCnS22oJgYc/CEmphtKd0Dham/PiyxvcOF9BE6ZCuBIsIaNuPpZgUVkqvX/QVV4ocCiRcsqMbj1mwR6JlVy77hEKAp3i0QOLAUbIiLNhLlqMmz4FwyeidGuEbUAsr4HRzwM+wgwIao9zB0GnS6xgouq6+Quhl8uhF53QofzrKJvy663uhd3+zNm6S6rl0TGA9YH8cpCyF1Q93WX3WQViTvqc+v6hWuC39d3g6yApS7RnTGOW1z7vi2vgzMJ0gICgV0fW39J3Psl5sIzwfRAm2Oh79/9vTp2fQyJg2HDvzC3vQW2aGj7B8h4wApPAHJ+Bm8vC5/UcbDphbrb6ikDe1TwNns0lG633mNMF+u8qeNqnnfbm5ieCgybs+7ztzBPfLGal+dt4MWLBzdqqMfPG7JwRTl4/bJhmJikxUdx8tPfc86QTlw0orPvuGXb8vjzW4u57vhenHFEe5Zuy+OuD5YRF+ng0mO6saeglOvf/pU7TszgpP5pFJe7+XVLTp3XvffD5ZRVenj7yhG4ohys31sYtP+Ef85he25JHa+GDonRfHWLFdD8ujmHoV2TgnqEjOqdypSv1rAtp4ROyTE1Xp8U46Rn2zg+WLydgR0TiXLaeXvBFmIjHQzpkuQ7bvn2PF6Ys56PrhvJxkx9KtkfTV7IMxxME9yFVmBRnuUNLLzLFVlQng0ETOFp2MHZxgoqYrpZQ+ciUqDNcVC2q+5hI9LimSb8ZXYCuaU2/jM+i2hn43rSibQkCjZERJqDzG+soRVD329cqAHWB+6BL2LYI32bTMMOjjiMqDT/cRv+BW2Oxehzr7Ue1wuzaD2snwLd/mzVozArIP1MjKpeDvH96r5uyRZIPwMj/nBrPaZr8P5hH4BZzzSFRu3/FZmmG7a+Dh3PD3pPFG+yrrljFgx8ydr2+19h4R8xj5mDYdigeKM1BMQWCYPfhoo8WHELlO6EIW9brynbbRW1CxTZztpel9QTYMXtVo+QlOOhaD1seNp7vp1WsFG229pX/bxmpfVBJCq97vO3IPPWZvLtqj28eumQRteviHTaeezsw4l0+EMBm2EQF2mnrcsfIL08bwPDu7XhlhN6A9A9NY6NmUW8MGeDFWzkl1HhNjl5QBodk6wgoU+aq87rbs8t4aT+ab7eEtXDh//831Aq3XVPLeqw+3th7C0sIy0+OOxKcVnfr3sKymoNNgzD4M3Lh3P1tEX0n/wFNsMgMdrJa/83jLbecxWXV3LD279y/2n9SEuIUrCxv1pKIU93SUBYke1fdj8FpVutcDWQw2WFF9FdIGGQ1f6qehfOxNprW8T2sM4th6xpK2L4ZnMU9x2Tz2EpIUwTLHIIULAhItIcuPpZH77XPIyZfBSGM7ER5zgsOACoS+FqqB6eJB8Nax/GrMiH+MOtD+ZzhmCmjrWW08/EiEyt/XzdroVl12Pu+RJSxkDaaRiJg3y7jepDQEK150so3QadL6u2w2P9cn/EqxhxvQAwj3gVvjvc6n2SNMzqxYEBR76G4bTGqJjGk/DzqZhluzGqBxqh6nyZVWh04TlW+OOIt97/moeA1lUcr3daHAWllTz99VoGd0kmIXrfe6L0bhcXFGrUZd2eQo6vFp4M7ZrM09+spaC0gr7p8YzsmcKJT87l2F6pHNMrhVP6p9Emrvafh/87pit3f7CcOWv2ckyPFE7sl8aAjv6xTFXhyIFimib3fLicxJgIZl19FFFOOzMWbuFPb/7CR9eNJC0hiskfrWBI1yROHnBoBGEHUsjDR8LNNK3hIOXZAaFFNux41xr2V55Vc7iILcIKLmJ7QHRHiGjjfaRY26v/m6/hI63e6iwHD//o4rjOZVw6oDjczRE5aBRsiIg0B5FpMGQWzD8Z5o/HHP4JRoS3S7phA6r99disqHkOe2zNbfvKMDAMO+bwT6zicnu/sXpNrLoP8+gv/b0yAl/S+VLM1HGw5wvI/BZ+HIPZ43aMPvdYTW3sUJQtr0LSCAxX3+DtkWlgOHyhBmAVBzXsULLVCjai0iCqvS/UACDOO1NHyVarB0VtvTNq68URdHsM6PswZsYDVrHSyFTInG3tjPF29Y1sZ02FWP28hsP6MHKIaOuK4uVJA7jgpflc9PLPvHn5cBJirHDDZnhnrglQ6fHUOEdMxP7/GmIYBnabwbTLh7F4Sy7fr93LOwu38tjnq5h51VG+XhmBJg7tzKjeqcxetZcf1mXyx+d/5E/H9eBmb4+QfRmKkhoXSWZh8IfmzAJrva2r9mDlx/VZfLNqN7/d9wdfIPRQhwHMW5vJrEVbuX5sL35Yl8XOvBLeW7wd8N/PoQ9/zTWju3P7iRm1nrtVCqU3xsHoiWF6rKF7Fd7gonA15C3xr5dnW8P2AhlOK7BwJlj/hlQFF85k69keC4ahwEJCUloJN36dQHyEh8fH5FHPxEwihxwFGyIizYQR3QHzqC+84cYpmCM+waj6y1ze0uCD85fWfpLqbBFWQblAcX0g+6fgbdk/QlQHDIfVfd8wDEgaAUkjMHvdBXMGWX9VrCXYsNreEbpcDl0ux1z3BGx8FrzBRmOGopilO6yhOYc/X/P45KPArMQs2oAR6511onij9T6jvXUZko6CHe9jVhb6a2oUrbOefccMh73fQuBUrXu/td53AwzDDtEdrLZufweShvt7tCQNh10fBb8g81tIGHRI1dcASEuIYsZVIzj/pflc8PJ83rx8OEmxESTHRbByZ/Bf0FfuCKG+ARDhsOGuloH0bBvHok3BNTMWbsomPSGKuEjr+8cwDAZ3SWJwlyRuHNuLE56cyydLd9QabACkJ0RzwfDOXDC8M89/t57//LDRF2zsy1CUI7sk8ehnqyitcPvqbMxbl0m7+Eg6JtU+e1BJufUzaav2ocNmGHi8l512+TAqAtqwZFsud7y7lBlXjaBrmyYIMVuI9PRm1GPFUw4VuZCzEHJ+CggscqDC+6j+b50tyh9UxPbyBhbJ/md7nBXGKrSQEKS3Tah3/99/crE628lr47NJiakZJoscyhRsiIg0I0ZUujfcOAV+OhnzqP9Zhd7W/xNzy+vQZiTs/NAKJrwfrOsV3QWyf8Qs2Q72SGuGkB43wvcjrZlVOky0hm9seBoy7gfAzPnZ6oWQOs6a3i9vCZRsg7i+tV7CXH4rtD3R+qW9Mh/2fgUBvSwaNRRl6xvWXyrb/7HmvpTjIeFIWHI1Zr/HrW0rbrf+opk42FrvMBHWPgJLrsLsfY/1YWTFbd4hNd4hDd2uhZ9OwFz3OKSdZoURWXPg6G/87+33eyF3EcZRn1nr5Zmw431oM8oaDrP1Ddj5Phz1pb99Xa6ETS9grrgNOl9hfQDaOg0Gvb7v96EFaBsfxYyrjuLCl+dz/kvzmX7lCI7ukcKLczbwzsKtDOuWzOcrdrFocw7pCVENnq9jUjQLN2VzZl4HIhw2kmMjuPLY7pw2dR5PfrWG073FQ1/+fgO3n2j1wlm8JYcf1mYyqncqKa5IVmzPY2duCb3axdV6jckfreC4Pql0T4mjoKyCOWv2BB27L0NRTj+iPU9/vZbbZi3h+uN7sTGzkOe/W8+NY3sFzNiyi8e+WMX0K0aQlhDFoC5JJEY7uX3WUm4Y24sop40ZC7eyJbuYsX2t78/uqcFtzy4qB6BHahzJsS276GPIhTwd8ezYsePANwi8vS3yIX8Z5C72hha53uccK7xwF1Z7kWENR4tItmaDih8IEUlWaOFMsv4NLVx1cNovrcL2BVPq3Pf1pkimLY/l8sOLGN25/CC2SqR5ULAh4mWufgjWPmytdLwI44iXwtsgvFN4bnvTWul1t69rvxzajMh2mCM+h/nj4aeTYMT/oNfdsHoyuIutmUy6XgW7P234ZH3uhaXXw+x+1gfxCSUYCUdiDn7Lqgux7jErvOh5G3T1zhDiSLCCk43PQ2UuRHWEXnf6pp+tyWNN91q6zSpkl3Ic9P1Ho9+/aZqw5TXocB6GvbbCizbMoe/BilvhxxOsWUlSjod+j1qFQ7GmjTVH/M9q1/cjrSJ6aadB3wf950k+CvPIN2D1/bD6QYjtDoOmYSQN81+sbJdVUyPQtrfg97sA0+qdcdQXGEn+bu5GTFfMYR/Cijt8U7/SfwrGITjVa5VUVyRvXzmCC1/+mfP/PZ+3rhzOjWN78fiXqyktd3P6ke25eEQXvv69nsKsXjef0Ju73l/GqMdnU17pYdM/xtO/QwLPXTiIJ79ay3PfrSMlLpI/HdeDS47uCkB8lINFm3N4/adN5JdUkp4YxfVje/mmn63ONE0mf7SCHXmlxEU6OLpHG+4Zf1ij3nt8lJM3rxjGfR+u4NSp80iIdnLFsd244lj/LAQFpRVs2FtEhbcrSnJsBK9fNozHv1jNBS/Pp9Jt0qNtHP+eNJj+Her/i+whIdRCnqnjrKBgf5keq3Bv8SZvWFHVwyI3YD2PoBlFwOpt4UyyHgmdvcvJVq+u8j3gSARbPb9KO+ouYCvSlHYX2bhjdgKHpVRw+4iCcDdHJCwUbMghx9w6DZZc5Vs3JtQcJ21+EtA9eOC/MTpdfDCaJlKr2kI0IzIVRgdMsdrnHv/QjioZD9R7DgAjaXjweaq2p58B6WfU/hpXBgz/b93t7XQxBPzMGP2frPPYxjAMA8bW/1dOIyodBk+v/5i43jDik/qPaX8WtD+r7v3V7qsRkQIj59R7TgCjzbEw6qcGj2upppw7sMa2NnGRfH7TKN/6zSf09g3tqHLHSf66ELWdA2BQ56Sg81Q5qX86J/WvfVhCz7YuXr9sWK37AM4Z0olzhnTyrd9/ev86j22MjLR43rnmqJCvD3B4x0SmXT485Gsc1aMNm/4xvtFt3F8h97LAATQwC0OohTw9lZjFmxs8htId1nC0oKAiN/hRPbQwnFbg6UzyDhFJstaThlshiDPJCk1rkzhYQ0ek2fCYcOu3CZRUGvxrXC6RDddjFjkkKdgQqdJ5EqSOsZYjGjljgoiIyKFoX6ZLbehDf6iFPMszramwK3KtHhUVuVYvsqD1fGoUVw4KLXpay/GHW8dW9cCoKsrZmPaLNCMv/RbDD9si+fvoPHokuRt+gcghSsGGiJcR3dlfVFBEREQODLMyoDdFnjesyKu57Cnh4wX+D2qnDrODLdqaQcSZCFHp1nCQhIHW8c5E61FbaKHAQg4BH3/9m2/51HFHsHRnOU8scHFSt1LO61v3TE55lfEUmrXXyQpUZoYwZbxIM6VgQ8SrrhobQUNbko+Fvg9Z4+tzF1tjfzv9H/T+K5Tttbbv+dyqnN7mWOg3xT9rA2Du/Rq2/Afyl1t/iarMB3sMxPWG9mdD1z+HNGuCmfsLbJxqFTkr2239cmiLsmoEtJsAPW72zQRh/nYVbJtmvbDXXRh97vWfx1MOX3aGyjxrw+hfMFyHYW6fac2AUbASyrPAXWQVSHMdBh0vgk6TfEXxAMzC1bDmEcieZ01zaXNaM3m4+kHqWIxu1zb2yyIiIo100IePmB7r/4ySrd6QIq+O50JqDA3BZgUWjgRryuS43uDqz+mn/tN3hGfDU2Cr5YOXQgtpJU6/YqpvuWDtK9z43yxSoz08clz9U7sWlsOc3zc1eP5B/bs1eIxIc6VgQ2RfFG+wpuJ0F1vrpSVWGFK22+ouW7zRf+yez6F4M+boRb6ChmR+Z82gEKgy35qVIncRZM2DobMabkfeYtg+I3ibu9CaAjR/Kez5HPOYORg2B3S92h9sbJuG2ftuf3v2fuMPNRIGY7i8xfN2f2I9AlVkW8FF9jwoWAb9ngDALM+CH4639vvaUgElW6xH0Xpr9gkRETm4mmr4iOmB2B7ewMI7/KMi39u7It8bWOR7Q5Rapph0uKzAwhFvFSOO62v9P1rV88KRAI44qPq/KbBd+IONWkMNkVbqgXkuNudUMv30XBKj6p6eWqS1ULAhh7ygQqH7q3Q7pIyBbtfBni9g87+t7VtetiqlD3zRGtu79FrwlEDh71Z40PYE67jkkRDV3poWzuECDCjbCasfguL1sPsTzNxFGIlD6m+Hqz8c9g+I6eY9j93qAbL+n5D3ixV87PovtP8jRuJgzMQhVnBSshUyv7UqzQPs/MB/zsACqu3GQ5vRVjdfuwvwQPFmWHWPdZ1NL2D2uA0jKg0y5/hDjTajofuNVo+N0h2QPT847BERkebDU+6d4nQp5P0WHFBU5FnPlflQWUCNOhYA9jhvL4t477CQBKuWRUWev/eFw1Vz5hD1sBDZb++siuFPR7kY0X5XuJsi0iwo2BDZF7YoGPQmRkQyZuJQf7AB0OdvGJ0mAWDumAV7PrO2F60FvMFGyijYsATW/gOK1tX+y2LOAmgo2Egcav0Suv4pKFxV+zR1uQug/R+t5a7XwG9XWMtbXoPUcZieSv90obZIaH+u/7WpJ8D6J2HTi1Yw4S4ObqfptgKUqPHWL69VItOs7sMxXTEMO3jvh4iIHBym6bYC6LLdkLcUcuZb/9dU9ayoCioq8q0AvgbD38PCmWDVnnLGg2uA1VujvsACFFqIHCT9Uyq46dgE0OyuIoCCDWkNjq6lG+6P4xp3rrg+GBHJ1nJEm+B9SQHT9gXuq8gBwDRN+PkMyP6+/mtU5DbcjiVXw/b6p7oMOk/62bDyTuuX3d2fYJZnQ+4v/p4W7SZgRCRZ7XSXWENLitaEdv7kY6zaGwUrYcdM62GLwIztafVQ6XoNhqvhglUiIlI7s6p2RdkeKN9jhRbFm6FkG5TvtfaVZ3of2dQ6HMQW5Q0s4q2eg3F9/euJQ6xzOuJrHxICCixEmpmnxuUSYa+nsIZIK6NgQw55RvIxNbY1eiSiI95/XsMWfB5nYu2vMb1H5cz3hxqGHXrfA0kjrGEbax6xanSANZa5HmbJ9uBQo9t10PYksEdZhUm3vVXjPIY9ErPTJbB+CnjKYPvbkL/Cf47AnhW7/usPNeyxkPGANfTFZodlN0HB8qDzG/YozKO/ta6dNRcKV0PJZivoKFgJ22dijl5gzTojItKCNGnxzerHeCqt4Ls8CyqyAsKJHCu8KA/YVpFt9ZSrznD4wwlHPMT2hkTvssMFicOsYYHO+PrrUyi0EGlxNLWrSDAFGyIHS8lW/3L8QIxedwJYQ0IC9zWkdJt/2dkGo9/jvlVzzcN1v67LldbwEjxWCFG229oemQ6pY2tvZ+oJGN3+bJ27It+qMVKNaZoYzgTocZP1AEx3Mfx6Oez60Or6vOcL6/oiIi3JvhbfrKpZUVloDfcIfNicVi+LqnV3EbXG7EYEOGL94URsz+DwwuGyHinHQcHvNac1DZRwBJgVjX33ItKMrMnWxzaR+ugnRORgiQmYQit/GeamFyGmK2x+peFhH3WdpyILc+2jkDDIKgSaObvOlxkxXTDbnWzV1SgI6K3R8UKrHkZt58/6DnPbdOuX6Q1P+YbVBMldiLn0Wkg7DeJ6WXU2yrOtwqlV3KWhvz8RkebCU+EtolkVRhTWEloUgllu9azwlNd+HluUNQW2LQIi21pTcweGFL5HvDWteN6ihtvmcNUfaojIIaPMDTd+ndDwgSKtmIINkYMlcbA19CRnvvUXtOVW7wbssZAw2CrGGQIjsi1m+3Ngh3da2NWTvTvsVr2L7B/qfnGXq/0FQ6sEzoYC0O4UK9wo3mjV0fjtcmt7ZBrE9bGGmgQxreEpVUNUqnO4rNBDRCTMTE+5vxZFWaa3PkUmlHmfq6/XFuYCYAsII+IgOsMKNxwua6aQoLAizgo0Qh3uobBC5JCWVxlPodlw7bG4yniqoowpP7tYleU8sA0TaeEUbIgcJIZhwxw6C1beBXu/tP7KlzgE+j4Mm14IOdgA4PDnIbKd1UujPBvi+0Of+6zpVesLNlLHQUwPa2pZgKThGHG9g9tpj8Ec8RmsvAOy5lnjutscC4c9CkuuAaoFG7E9oOdfIPtHa6aXimyr/kZUmlU8tOcdGDFdQn9vIiIhMt3FASGFtx5FyRarrkR5jvXvUUW29e9kRX31MmzgTIKIZGvq7tiekDQM7PHW6wNDCocLbNHBAYRqVIhIiArLYc7vmxo8bvQR7UkAftwWwctLYrioXzH1DDgWafUUbMghx+h0cc1eCNWPmVBzijujzz3Q5559Ol9t5wEwjngJjnip5vaIFDji3zVfUNfxdW13xEK/x61HoNRxtb4H3+sMAzPtVGtYCUDHOt5XTBcYMrPmjqO/rHlsRApkTK7zmiIiobCmKc2uNsNHVQHNwJk/AtbdxXWczeYNIeKsHhQRbSCmC9hd/u2JQ61ZRRxxYI/RTCDN1KD+KjwtrVdemcGt3ybQLdHNXUfl85l+HkTqpGBDpBUwTTe4S6B4E+x8z9rocEGHc8PaLhE5NJmmxzvjR3ZwQOGb/SOrWlCR5R32UcecVVXhREQKRKSCq693uY21HrjsLoacBQ0P6Ug4EsyGZjKRcFv0yX3hboJIWJimyd1z4skssfH+yVlEO/XzIFIfBRsirUHWDzD/xOBtPe/AcLjC0x4RaTFMT4V/OEd5VsBypneIR1bwui+kqGPqaltkQEjRBuIP9y5XhRNtIDLVenZa64Y9KvSpV80o1akQkRbv81V5fLo+mjuGFzAgVSGsSEMUbIi0JoYdojtB58uhx63hbo2IHESmaVqzeFTkBAQUWQE9K7JqDzAq8+o+aWBI4UyG+AH+cMKZHBxgVC3bYzAaEzzsy9SrIiItWHaJwfM/7WRYejlXHVEU7uaItAgKNkRaASNlFNRRD0REWhatN0I8AAAgAElEQVTTNMFT4u0tkW0VyawKKHyFMrOD91etmxV1n9geB85Eb4+JtlYBzYjkWoIKb4HN/QkpRESkVh4T3l0VgwFMGZuLvZbyPyJSk4INERGRMDBNE9yF/hk7qnpOVF8O2uYNMDxldZ/YFg0RSd5hHEnWNM0RyVaPrbK91hTTjljr2bccY+0HaHMc2EL49cBTbhUyru89hjp8xBGPEZHU8HHS6vx7+hzf8lUXjA5jS0QOju+3RrI538F9J6bT0bUjaJ9+HkTqpmBDRERkP1g1KHIDwocc/7IvmMj196ioyPX3oqiveKUt2t+DwplkTa2cONTbYyLJ22ui6jnZ92zYo2tvZ/Hm0IZyuIsgK4SZQFLHWdevT6jDR0I5l7RK19w1zbesD3JyqNtRYOPrTZEMSC3nhD4JUK2TnX4eROqmYENERFo903R7w4eqACLwObdaOFEtxHAX1n9yR7wVRFQFElEdgtcDAwpnknc5qc6A4pDkqbSCl/q46+mlIiLSwlW44Z1VMcQ6TU7rVaphfiL7qNUGG/fffz/Tp09n9erV4W6KiIg0AavnRA5U5FULIPLqDiyqlhsaLmGL8vaeSLaeoztZs3k4E72BRGLw/qqwwpGIEcqwjtYulF4iKgoqIoewLzdGsbfYzv8NKCLGWcfU1yJSp1b529aiRYsoLi4OdzNERCSAb9aOytyAMCLw4Q0sKqvv84YX7gYqx/uGdiSBI8HqOeHq5w0ikvz7fOv+ZcMedXBugoiItDrrcuz8uD2SozqU0TNZU7uKNEbYg43Nmzfzyiuv8Ouvv7Ju3TqGDBnCtGnTahy3bt06HnzwQX777TdcLhfnnHMO1113HXa7fZ+uV15ezhNPPMFzzz3Hhx9+2FRvQ0Sk1bOKYRb5QwhfQOENImoEEnkB27zPeOq/iCMBnAn+HhKxPfzLTm9g4QsmEqqFE5EH5T6EQ0hFOjWUQ0Sk2SmuMHhvVQypMW5O7FYa7uaItFhhDzbWrl3LnDlzGDhwIJWVtSeUeXl5XHrppfTs2ZPnnnuOLVu28Oijj+LxeLj55pv36XrPPvssZ599NsnJyU3RfBGRQ4ZVZyLfGzbkBQQPef7hGkEBRX5wMFGZB6a7/ovYYwLCh0SITLNm7XAk+HtS+IKKRH844UgAZzyGsW9hdksX8qwi7jLI/r7+Y5p6KIfqYoiI7LeP10ZRWGFwUf8inK3rvziRJhX2YOP4449n3LhxANxwww3k5OTUOGbGjBmUlZUxdepU4uLiOOaYYygsLGTq1KlceeWVxMXFAXDmmWeyY8eOGq8fOXIkU6ZMYdWqVSxZsoSbbrrpwL4pEZGDzN9bIs/6IBwUTuQHBBMBy9UDjMqChi9kj/X3mHAkQGQ7iO0d0IsiARyJdawnYNgiDvzNOJSEOqtIOOpPqC6GiMh+WbLbydK9EYzrWkoHVwM9FkWkXmEPNmw2W4PHzJ07l5EjR/oCDIDx48fzxBNPsGDBAo4//ngAPvjgg3rPs3jxYtatW8fYsWN9244//njeffdd9eAQkbDxhRJVwUNlXs3nyvyAkCLwOeCYhnpLGHb/UA5vDwhiuvtCByuAiPeGFtWenQngiMewOQ/OTQlBSL0ZHPEYTTiNaFNeU8NHRERar9xSg4/WRtM5vpJRnfVvvcj+CnuwEYoNGzYwYsSIoG3t27cnOjqaDRs2+IKNhlxwwQVccMEFvvU+ffrw7bff7nN7Zs6cycyZMwFq7WEiIq2H6an09n7IDwgmAsKHwG01gouAR0OhBDZwuIJDiagO4OzrXw8KIwICjKrt9pig6ePCEQw0qVB6M6SOs4a4NKBJh3yEeM2Q2q8eDyIihxyPCe+tjsFjwtkZJdg1s6vIfmsRwUZ+fj4ul6vG9vj4ePLzQ/hFtIlNnDiRiRMnAnDWWWcd9OuLyP4zTTdUFnpDhgJvwFBQLYgo8A/RqMirZX8+uEOYYclwBIQPCVZAEdPFGzgEbHMm+EOJ6s/2uKaf074Jg4EWrzkP+RARkUPKT9sj2JDr4IzexbSJ1hAUkabQIoKNA2X16tXhboKI7CPTU+ENGAoCekFUCyYCnyuqBxTedXdhCFczrMDBF0C4wJkM0YGhRHzDy7bopg8lRERagQljDw93E0Sa1IbMUr7cEEVGmwqGpFXs02v18yBStxYRbMTHx1NYWPNDSH5+PvHx8WFokYjsC9P0eAtb5geEEgXVAop61isCwgpPKFOhGd5gweV/OBMhqqO/F4QjHpwuf2+JoEAizrs9DsNouA7QIS2UmS+geQ9ZOdhCvWeqnyHSoI9euSHcTRBpMmVueOCL7UQ6TM7sXcK+/s1DPw8idWsRwUb37t3ZsGFD0LadO3dSUlJC9+7dw9QqkUObf6hGYOBQ6A0dqm8PeLgLrSAi8DmU2TYAbBE1A4nIdhDby7+9queEo/qzyx9M2GMUSDSVUGa+gOY7ZCUcIUOo90zDWkREWpWnF8axLrOUi/qVEBdhhrs5IoeUFhFsjBo1ildeeYXCwkLfzCj/+9//iIqKYtiwYWFunUjzYJqm1ZvBF0AEhBHuwG3Vnt3Vt1cFFEWhXdhw1AwjItqAo2vwtqCHt1eEMx7sLqvnhN2FYY88oPdIWiGFDCIi0gws3OnkhV9jmdAvkb4peeFujsghJ+zBRklJCXPmzAFg9+7dFBYW8vnnnwMwevRooqOjOe+885g2bRrXX389V155JVu3bmXq1KlceumlQVPAirQkVhBR5g0WAsOF6oFDUXBviMDjqocSZmVoF7dFewOGOP9zZFuI7eFfDwgc/MfV8rBFqn6EiIiISB0Kyw1u/SaBji43N4xKY+HKEHoSisg+CXuwkZWVxY033hi0rWr9m2++oWPHjiQkJPDaa6/xwAMPcM011xAfH88ll1zC9ddfH44mSytl1Yko9ocN7oDQwRdOBAYOAaGDu459oQYRhrNmEOGMh6j0attdYI+reWxgWOFwYdjC/qMvIiLSoMlP/te/fPPpYWyJSOM99KOLbQV2Zp6RTUyEvdHn0c+DSN3C/ummY8eOIc1O0rNnT954442D0CI5FJimaYUQ7kJvCFEVJhTVEjJUhRNF3m1FAeFF4LFFQIjjIW2RAWFCnDeISICoDt7tsdWCiDh/AGEP2OfdZtgiDuj9kpbDLM+x6pzUJxyFPEOtZYEDaCDQUyFSEfF64OmPfcv6ICct0debIpn5ewzXHFnI0PQKtu/HufTzIFK3sAcbIlaRyqJqIURRcPBQPaCoCiFq27evIYRhDw4XqoKF6A7e5bjggKLO5VjfsA3D5jyg96y1abYf5sOhMh/2fl3/MeEo5LkvtSxyGziuzXGYDX29QbOKiIhIs5ZVYnDnd/FktKngpqGhTDMvIo2lYENC5huK4S7yBglFwSGDu7haGFFUSxhRy+tCmr7Ty7AH9GqI8z9Htff3grDHekOGusKHaiGGLUo1Ipq75vphXg4MFfwUEZEWzjTh7jkJFJTZePPUHCIbPwJFREKgYKOVMYs2QPaPwWGEuxAqi4MDiaAQwhtOuIv37WL2WG/IEOPtyRDrrwtRPZhwxAZsqwodYoKPsceBLUIhhIiIiIg0a++vjuKLjVHcOSKfjDYh1lQTkUZTsNHaLL8F9n4RvM0XHMR4Q4UYa1tkuzr2BQQPQcFEQEBhj8EwbOF5jyIiIiIiYbKtwMbkefEMTS/nioH7+IdBEWkUBRutzZC3oXSXP6CwRyuAkENDqMUrD3ItjpDqg0DrqREiIiJyCPOYcPu3CZjAlOPzsOvXbJGDQsFGK2PYoyG2W7ibIdL0Qq3LcLBrcYRSHwRUI0REROQQ8OrSGObviOTR4/LoFO8Od3NEWg0FGyLS5NRLIcxC7b2iWUVERESazJq9FTz+s4txXUs5J6Mk3M0RaVUUbIhI01MvhfDSrCIiIiIHVbkbbv44C5fTwyOj81Gte5GDS8GGiIiIiIjIfnh6URwrd1fw4kn5pMR4wt0ckVZHwYaIhE84hkyEcs0Qh8iENOQm1LZr+IiISA1XnD8q3E0QadAvu5y88Gss5xweyx+67Tpg19HPg0jdFGyISPiEY8hEKNcMdYhMKENuQm27ho+IiNTw70cmhbsJIvUqqjC49ZsE2se5uXdcIhzA0hr6eRCpm4INERERERGRRnjkRxdb8u1MPz0bV6TtgAYbIlI3zawsIiIiIiKyj77bHMFbK2O4fGAxI9pXhLs5Iq2aemyItAIh1YLQ1Kt+qnchIiIi9cgpNfjLdwn0TqrgtmEF4W6OSKunYEOkNQilFoSmXvVTvQsRkWbhqr++4VtWfQFpTu77Pp6cUhuvnpJD5EH6RKWfB5G6KdgQERERkWbp5bfn+pb1QU6ai4/WRvHJumhuG1ZAv9TKg3Zd/TyI1E01NkREREREREKwq9DGvXPjObJdOVcfWRTu5oiIl4INERERERGRBpgm3PFdAhUemHJ8Hg59khJpNjQURQ6KkIpX4gBC6M7XTItchvYeIaT3GeJ7DPmaKnIpIiIisl/eXBHN91sjefDYPLolusPdHBEJoGBDDo5QilcmDoXcEAo2Ntcil6G8RwjtfYb6HvflmiIiIiLSKBtz7fz9p3hGdSrjwn4l4W6OiFSjDlQiIiIiIiJ1qPSY3PJNApF2k8fG5GEY4W6RiFSnHhsiIiIiIiJ1eOGnAn7bE8EzJ+TSLtYT7uaISC3UY0NERERERKQWy/c6eHpeHqf2LGFCz9JwN0dE6qBgQ0REREREpJqySrjlmwTaxNh54NhQCsSLSLgo2BAREREREanm8QUu1uY4eXR8EolRZribIyL1MEzT1E/pfhg+fDgdOnQIdzP2SU5ODklJzXBWkRAVFBTgcrnC3YxG0/0PL93/8NL9Dx/d+/DS/W+cnTt3+pbT09MbfR7d//DS/W8ajf15COf9//XXX/F4PNhsNo488shGnaO53P/G0vd/00lKSuKVV16pfacprc6ZZ54Z7ibsl3vuuSfcTdgvuv/hpfsfXrr/4aN7H166/+Gl+x9euv/hFc77/8QTT5jp6enmE0880ehz6P6HV0u5/5oVRVqcMWPGhLsJrZruf3jp/oeX7n/46N6Hl+5/eOn+h5fuf+Pdeuut3Hrrrft1Dt3/8Gop9181NqTFOf7448PdhFZN9z+8dP/DS/c/fHTvw0v3P7x0/8NL9z+8dP/Dq6Xcf/vkyZMnh7sRcvD1798/3E1o1XT/w0v3P7x0/8NH9z68dP/DS/c/vHT/w0v3P7x0/w88FQ8VERERERERkRZLQ1FEREREREREpMVSsCEiIiIiIiIiLZaCDRERERERERFpsRRsiIiIiIiIiEiLpWBDRERERERERFosBRsiIiIiIiIi0mIp2BARERERERGRFkvBhoiIiIiIiIi0WAo2RERERERERKTFUrAhIiIiIiIiIi2Wgg0RERERERERabEUbIiIiIiIiIhIi6VgQ0RERERERERaLAUbIiIiIiIiItJiKdgQERERERERkRZLwYaIiIiIiIiItFghBxunnXYa06dPp7Cw8EC2R0REREREREQkZIZpmmYoB/br1w+Px0NUVBTjx49n4sSJDBgw4EC3T0RERERERESkTiEHG5mZmbz77rvMmjWL7du3YxgGffv25bzzzmPChAnExMQc6LaKiIiIiIiIiAQJOdgI9P333/POO+8we/Zs3G43MTExnHrqqUycOJG+ffseiHaKiIiIiIiIiNTQqGCjSlUvjnfffZft27cDMGDAAM477zzGjx9PZGRkkzVURERERERERKS6/Qo2AEzT5JtvvuHBBx9k9+7d1kkNg4SEBK655houvfTSpminiIiIiIiIiEgNjQ42du/ezaxZs3j33XfZvXs3NpuN0aNH88c//pGVK1cyY8YMsrOzufrqq7npppuaut0iIiIiIiIiIvsWbJimydy5c5kxYwbff/89lZWVpKSkcPbZZzNx4kTS09N9xxYWFnLppZeya9cu5s2bd0AaLyIiIiIiIiKtmyPUA5999lnee+89du7ciWmaDB06lPPPP58//OEPOBw1TxMXF8eYMWOYOnVqkzZYRERERERERKRKyMHGM888Q1xcHBdccAHnn38+PXv2bPA1/fv354wzztivBoqIiIiIiIiI1CXkoSgzZ87k1FNPJSYm5kC3SUREREREREQkJLZQD+zevTu5ubn1HrNz504WLly4340SEREREREREQlFyMHGpEmTeP/99+s95sMPP2TSpEn73SgRERERERERkVCEHGyEMmLFNE0Mw9ivBomIiIiIiIiIhCrkYCMUO3bsIDY2tilPKSIiIiIiIiJSp3pnRak+VeuCBQtqnb7V4/Gwc+dOPv30UwYPHty0LRQRERERERERqUO9s6JkZGT4DzSMBoejtGvXjmeffZb+/fs3XQtFREREREREROpQb7CxYMECwKqdcckll3DmmWdy5pln1jjOZrORlJREt27dsNmadHSLiIiIiIiIiEid6h2KMmzYMN/ymWeeybhx44K2iYiIiIiIiIiEU709NkREREREREREmjONGxEREREREZFmZcqUKbRv354pU6aEuynSAtTZYyMjIwObzcann35Kt27dyMjIwDCMhk9oGKxcubLJG9pcXX755bzyyivhboaIiIiISIswefLkWpeldQj16+9yuSgsLCQuLo6CgoID3zBp0eoMNi6++GIAHn/8cdLS0nzroZg2bVrTtK4FOOuss3j//ffD3QwRERERkRYh8I+lGhXf+oT69df3iewL1djYTwo2RERERERCpw+srZuCDTkQVGNDRERERERERFqseqd7DUVOTg6LFi0iKiqKo48+Grvd3hTtEhERERERERFpUMjBxvTp0/nggw946aWXSExMBGD58uVcccUV5OXlAdC/f39ef/11YmJiDkxrRUREREREREQChDwU5bPPPsMwDF+oAfDYY4+Rn5/PWWedxejRo1m2bBkzZsw4IA0VEREREREREaku5B4bmzZt4rjjjvOtZ2dns3DhQs455xweeOABAM455xw++eQTLrvssiZvqIiIiIiItHwvvvhiuJsgYaSvvxwIIQcbubm5JCcn+9YXL14MwLhx43zbhgwZohlCRERERESkTldddVW4myBhpK+/HAghBxsJCQnk5OT41hcuXIjNZmPQoEFBx5WXlzdd60RERERERESamb0l2ZSaxQ0e53LEkRiR2OBxsn9CDjZ69OjB7NmzycnJwW638+mnnzJgwADi4uJ8x2zfvp2UlJQD0lARERERERGRcMouKufO95by04ZMrjo7i4Q4s97jx7Q9TsHGQRBy8dBJkyaxd+9eRo8ezejRo8nKyuL8888POmbJkiVkZGQ0eSNFREREREREwumHdZmc9NRcZq/eQ0m5h9mLoms9rsJTwfd75/HAiod5deNrB7eRrVTIPTbGjh3L/fffz8yZMwE47bTTOP300337f/75Z4qLixk5cmTTt1JERERERA4JgwcP9i3/8ssvYWyJhENL/PqXV3qY8tVq/j13A91TYnn10qG88fNaZi3czfjD02ifbH2srjQr+T77Wz7a/Q7ZFVn0jevPmOQTw9z61sEwTbP+vjNSr7POOksFU0VEREREQmQYhm9ZH0Van1C//s3l+2TD3kJunPEby7bncf6wztw7oS8xEQ5+3rydC//9K20S3AztV8IWczHL3V9QSCZtjC4cbhtPO1sv/jRkNH3btgtb+1uLkHtsiIiIiIiIiLQGpmkya9E2/vbRCiKdNl64aDAn9U/z7Y+PdtCzUznL81ewp/xDimy7SKQ9x9qvoL1xWFAwIwfePgcbbrebjRs3kpeXh8fjqfWYoUOH7nfDRERERERERA62rVlF3PfRCmav3suRnRO5d3xf2sZHsS3HmgXFNE3mZ81nY/qjlKdvw1PWjqOiJ9HZNhDDCLmMpTShfQo2nn32WV5//XUKCgrqPe7333/fr0aJiIiIiIiIHGwLNmZz3fTFZBaWceJh7Ti2dyqrdhWyalchAMWeAmbmPcKSstnEkESPggvZsXwkEYdVYKS4w9z61ivkYOOll17imWeeweVycfrpp5OWlobDoZEsIiIiIiIi0vLNWLCFuz9cTlp8FFeP6kGn5Jig/ZvKl/N67r3kefZyeYcbyN/dCSPJQUG0we8bI2jXpgSbRqCERcjJxKxZs2jXrh0ffPABycnJB7JNIiIiIiIiIgeFaZo8+fVa/vXNWkb1TuXuUzL4ZXOub7/H9DC7aDqfFr5Aor0tNyS/wPi0obyyZzYAfbuVs3BlFFt3OeiSXhmut9GqhRxs7Ny5k3PPPVehhoiIiIiIiBwSKtwe/vr+Mt79ZRvnDO7I388awO78Ut/+Anc2b+U9wKrynxkYOYaJCX8lxuYKOkdaGzdJ8W5WbXbSoW0lDvvBfhcScrCRkpJCZaXSJxEREREREWn5Ckor+PNbi/l+bSY3jevFjWN7Bc1msqZsIW/m3U+Jp4Bz4u/g6Ogzap3txDDgsG7l/LAkmg3bnfTuXOHb5zY9bC3eVm87XI44EiMSm+6NtUIhBxsnnXQSX3/9NeXl5URERBzINomIiIiIiIg0qbzicgrKrD/WZxaUcdu7S9m4t4g7T85gwuHpbM8tAaCovIxPC17k66LXaWvvwjVtnqK9s2e9526T4CGtTSXrtjrpkl5BpNPaXuGpYPaeufW+dkzb4xRs7KeQ56K54YYbSE1N5YYbbmDr1q0Hsk0iIiIiIiIiTaqgrJK5azKZtWgbl/xnIVuyi7n4qC7ERzmZuyaTuWsy+e/vy/nz8kl8VfQaw6MncEubVxsMNapkdC2n0g1rt6gjwMEWco+NCRMmUFlZya+//sqcOXNwuVy4XK4axxmGwddff92kjRQRERERkUPDRx99FO4mSBiF++u/YW8hb/68GafdxlXHdqd9YrRv35LS2czMewQMDxcn3M/g6D/s07njY006t6tk0w4H3TtUEBNlNnXzpQ4hBxumaWK320lPTw/aVttxIiIiIiIitTn11FPD3QQJo3B+/b9euZv//LiJ5NgILj26K0kxVs+KMk8xHxQ8xfySj+ns7MsjfZ9k756ERl2jT9cKtu11sGpTBIMyypqy+VKPkIONb7/99kC2Q0RERERERKTJmabJi3M38I/PVtG1TSwXj+hCdIQ1dcmWit+Zlvs3Mt3bGBc7iZPjrqRjVAp7yW3grLWLjjTp3qGCdVud9OgYcuUH2U8hBxsiIiIiIiIiLUmF28O9Hy5nxsKtjO3bltG9UnHYbXhMD7OLpvNp4QvE29rw56Sp9Ioc1CTX7Nmxgs07nfy+0QnHNskppQGNDjby8vIoLi4OGpoiIiIiIiIi0hzklVRw7VuLmbcuk+uP78k5Qzoyb20Wue49vJX3AGvLf2Fg5BjOTfgLsbbGDT2pTYQTenUqZ+XGSJZuLcRo22SnljrsU7BRVFTEM888w8cff0x2djaGYbBy5UoAlixZwtSpU7npppvo16/fAWmsiIiIiIi0bO3bt/ct79ixI4wtkXA4WF//rdnFXPbaQjZlFfH42YdzzpBObMspZmnpd8zIe4RKyjkv/q8Mjz4VwzCCXuv2VJJTnlPv+d1E1ru/W4dKNu90snxbIQMUbBxwIQcbBQUFXHDBBaxdu5a+ffuSlJTE+vXrfft79+7NokWL+OSTTxRsiIiIiIhIrXbu3BnuJkgYHaivf15xOQVllQCs2JHHne8to8JtMuXcgQzqnMTazEweW/sQ/82dRSdHBhcn3k9bR+daz+U23awuWF3v9UaY7erdb7fB6MElTBzUjpUVaxr3piRkIVczef7551m7di3/+Mc/+OCDDzjppJOC9kdHRzNs2DDmz5/f5I0UERERERERqUtBWSVz12Ty7Oz1XPvWrxiGwZXHdqew1M1/ln/N6fNP4aM97zI29mJubPPvOkONpuSwg91mNHyg7LeQe2x89dVXjBw5kjPOOKPOY9q3b8+yZcuapGEiIq3F9YtvYubWWVzQ+TyeOnJK0L4HVjzM1HXPcUK7sbw14o0wtbBhP2XO57n1L7Akdxm7SnfxryP/yXmdJ9b7msdWTeGJ1f+sdd+Kk5aQGpnC6vw13LnsLlYXrKWgooB2Ue04s8Np3J5xKxG2iAPxViQEt76zhPcWb+PcIR157OyBQfse+ex3XpyzgeMz2vLqpUPD1MKGPTt7HV+s2MWGvUVEOGwc2SmRO07KoE+aq97XrdqVz33/XcGSrbkkxji5YFgXbhjb09eN+dOlO3lhzno2ZRVR6TbpmhLL5SO7cfbgjgfjbYmItFqmaTJn9R6+WLmbzskxXDSiC05nBe/nP8n3xbNItqfzZMZrkNs73E2VAyDkYGPXrl384Q9/qPeYmJgYCgoK9rtRIiKtTYfo9vx3+8c8POBBYh0xAFR6Knln67t0jO4Q5tY1rMhdRIarD+d2OpvrFt8Y0mv+3PMaLul6cdC2qxf9GcOA1MgUAJw2JxM7nUP/hP4kOBNYkbeSW5bcTqXp5m/97mny9yGha58QxadLdzL5tH7ERFi/TlS6Pby/eDsdEqPD3LqGzd+QxUUjujCwYyImJv/8cg0XvvwzX98yisSY2kOzgtIKLnp5AcO7JfPRdSNZv7eQ22ctISbCzpWjugOQFOPkuuN70iM1Dqfd4Jvf9/CX95bSJjaCMRkaZC0iciBUuD08+vlqvli5m8M7JvDHQR3Z4l7C21kPk+nezrExZzMh7k8cGd+eX3MbN42rNG8hBxuxsbFkZ2fXe8y2bdtISkra70aJiLQ2h8X3ZVfpbj7a/jHnd7F6Ony1+xui7JGMaDM8qIDV25tn8uy659lcvIUO0e25tNskrup+BTbDGl34/LoXmbF1FpuLNhHvTGBs2zFM7n8vCU6r2veMLTO5c+k9vDH8Ve5Z9je2FG/hyKQjeOqIf9IltnHdMse1G8u4dmMBuH7xzSG9JscwMu8AACAASURBVM4RS5wj1re+vWQ787N+5tnB//Jt6x7Xje5x3XzrnWI68kPWj8zP+rlR7ZSmk5Eez+78Uj5ZupNzh3QC4NtVe4h02BjWLZnc4grfse8s2sq/525gS3YxHRKjuXB4Zy47phs2b/fcl7/fwLu/bGNzVjHx0Q6O692Wu8b3JSHaCcCsRVv520creGnSEO7/eAVbs0sY2CmBx88eSKfkmEa1f9rlw4PWn5x4BAMmf8GiTTmMO6z2cdMf/raD0go3U84dSJTTTp80F+v3FPLyvA1ccWw3DMPg6J4pQa+5bGQ33lu8jQWbshVsiIgcALnF5Vw7fTE/rMtiTJ9URmbE81HhU3xfPIs29g5cl/wsPSOaZhpXab5CDjYGDBjA7NmzKSwsJC4ursb+PXv2MHfuXI477rimbJ+ISKtxQefzmb5lhi/YeHvLDM7rPJHNRZt9x0zb9BaPrnqCRw5/kMMTDmdVwSpu+e0OnIaTy7v/HwA2w8ZD/SfTJbYL24q38ddl9/LXpffw3OBnfOcp95Tz9JqpPHXkFCJtkVz/603cvuRO3jl6OgDzs37mvJ8uqre9N/W+npt639Bk7/+tzTNIjEhgQvopdR6zoXAjs3d/x4lp9fcglINj4tBOzFq01RdsvLNoG+cM7sSW7GLfMW8v2MI/v1rD/af1Y0CHBFbvKuDO95fhtNu45OiuABiGwX0TDqNTcgzbc0uY/NEKJn+0gicnHuE7T3mlh+e+W8djZw8k0mHj1neWcNcHy3wBxYKN2Vz6nwX1tvfaMT25dkzPWvcVlVXiMSEhxlnn63/dnMPQrklEOe2+baN6pzLlqzVsyympEbKYpsmP67PYsLeI207sU2/bRERk363alc9Vb/zCzrwS7j4lgx0s44ms68hyb2dUzLmMj7uGSJu/F2Eos51AwzOeSPMTcrAxadIkrrzySq666ioefPDBoH3r16/nnnvuoaysjIsvvriOM4iISH3+2PEM7l/xIBsKNxDriOPbPd/x9wEP8eiqx33H/HPNU9zX725ObT8BgC6xnbmh12b+s/F1X7BxdY8rfcd3junE3w67m0kLLmPqoKd9vToqzUoePfxherqsD3l/7nENN/12K6ZpYhgGAxMP59vjvqy3vUkRiU323t2mm7e3zOCcjn8k0l7zl4lT5p7GsrzllHnKuLjLhdx92J1Ndm1pvNMHduDhT39nY2YRsZF25q7Zy/2n9+OfX/qrvz/zzVr+enIGpwxIB6BTcgx/yi5m2vzNvmDj8pEBvXKSY7jz5AyueuMXppwz0Nero9Jj8sDp/emRav1x5apR3bnj3aW+79nDOybwvxuOrbe9ifWEFvd/vJLD0uMZ1Lnunqd7C8tIi48K2pbisr5f9xSU+YKN/NIKRvz9G8orPdhsBg+e3o8xfdRbQ0SksbblFNfYNnvVHv7+v1XERNh57Nw+fFnyAu/tnk6KvQPXJz9Hj4gja7wmlNlOoOEZT6T5CTnYOPbYY7nuuuuYOnUqEyZMwOGwXjp8+HDy8/MxTZPbbruNQYPUzUdEpDESIxI5Jf0kpm+ZSbwznqPbHEXHGH99jcyyLLaX7OD2JX/hjiV/9W13m25MTN/693vn8fTaqawtWEt+RQEe3JR7ytlTuoe0/2fvPgObqtoAjv+TNE3aJuledEF3KaPQAmVvFy5UxA0iKCqICIjixI2iOHAiDkAEFPRVEUGGbCijC1pK6YAO6N4j+/1QCNSuFEGG5/eF5t5zzzkJKSTPfc5z7LwAUEgVlqAGgJfSE51JR7m+HGdbZ+xkdo2WgFxsmwu2kFeXz30B9zZ7flGvT6nW13C4MoW5h1/lo/SPmRY69V+bn9A8R3s510Z6sWp/DhqlnD6BLo3qa5RUa8mvqGfOT8k8//Mhy3GDycw5b1l2HSvmk78yOFZYTVW9HqPZjM5ooqhai+fpQIKtjdQS1ADw0CjQGU1U1OlxsrdFKZfR0e3s0qb2ePW3FPZll/Lj5H4XpHq9ytaG358YSI3OwK5jJbz2Wyq+zvb0/9syFUEQBME6244WW342mc1sTCngr6NF+Dnb0bvnKebkzKHUeLLZLA3hv8HqwAbAlClTiImJYenSpSQmJlJeXo5EImHw4MGMGzeOvn37Xqx5CoIg/Cfc7T+WqfHTcZDZMztiZqNzJrMJgLe7vUUvl5hmr8+pzeXePeO4L+AeZofPxMXWmaTyQzxy4DF05rM1D2QSWaPrzuzoYDI3fNv8t5eiLD3+Hb1cYgjTNF+p3MfOB+wgTBOK0WzkqYRZPB78KDbSdv03JlwEd8b4MeOHBBxsbZg+svHfn+l08OL1W7sSHdB8JkRuWS0PfrOPu3v7M31kKM72cg7lV/LE9/HoDCZLO5u/BRwknHnPNjw+36Uor/yawq9J+Xw/KRZ/19brdbirFBRXaxsdK65qeOyhPptpJJVKLEGWyA6OHCus5uMtx0RgQxAE4R+q0xlZuf8ERwuq6drJRJ3/Vyyt3YK7zJ8PIpZgKmt+uaFw9Wv3J8LY2FhiY2MvxlwEQRD+8wa5D8RWKqdUV8r1Xtc1OuehdMdL6UV27XHG+o9p9vqE8kR0Jh2vdn3ZErzYcGpju+fxby5FOVV3ij8LNrEg6p22G9MQ4DGYDRjNRmza/9+YcIH1D3ZFLpNSWqvjmsjGqbvuagWeGgXHS2u5vYXtTpNzK9AbTbxwY2dLtsTmI4Xtnsf5LEV5+ZfD/JZ0khUP9yHYo2n9sL/rEeDMvHVHqNcbLXU2dhwrxlOjwNe55buDJrO5UZBGEARBaL+CynqW7TlOaW0dYdEHSVB8h0ln4AbVwwxzuJfuag/iy8SOJ/9V4hOhIAjCZUQikfDXkI2YMTdba+Lp8BnMSXoBR7mGER7D0JsNJJUnc6r+FNNCpxLo0AkTJj7PWMQo7xs4UHaQLzK/bPc82rsUpdpQQ1ZNFgBmTOTW5ZNccQhnubNlOc1rKW8SXxbP6v6rGl27/MQK7GX23Nzh5ib9rsr5EaVUQYQmArlUTmJ5Iq+nvsVNHUY1+/oI/z6JRMIfTw7CbDajsJE1OT99RCgv/XIYjdKGoeEeGIxmDuVVcKqynseHBtPRzQGTGb7akcV1Xbw4eKKMr3ZktXse7V2K8sLPh/gpPo8v7o9GYyensKoeAAdbGxwUDR+P5v1xhMSccpZParihc0tUBz7YmM7MHxKZOiyErOJqPv0rg2nDQyxZTws3pxPl54y/iz06o5EtR4r4KT6Pl2+ObPdzEoSr1f79+y/1FIRL6Hz+/lPyK1h1IBfUWdh3W0E8GYTLY7lDMwM3m4bAuTWFQUVR0KuXCGwIgiBcZlTylu8c3xdwD/Yyez4+9imvp7yFUqYkTB3KQ50aCodGOnbm9S6v8NGxT3gr9R16uUTzcuQLTNr/6EWdc2J5IqN3ns0iefvIfN4+Mp+xfmP4qOf7ABTUF5B9zg4v0LBrxPITK7jddzT2Nk3veNtIbPggfSGZ1VmYMeNn78uETuMaFUgVLj2VouWPE3f19sfOVsYX2zJ5e30aShspoZ5qHjhdODTCW8NLN3Xms60ZzN+QRnSAM3NGRTBlefxFnfPSPQ3vxXu+bLx18LThIZYlNYWVWo6XnC1Yp1HKWTaxNy/+fJibFu7A0U7OxIGdmDjwbBCwRmfk+Z+TOVlRj1IuI8jdgXfv7M4tUT4IgtAgOjr6Uk9BuITa9/cvwXHA3Szdn4ptyK9UOm9BI3VlvOZ1uiuGWoLKYF1hUFEU9OolMZvN5uZOhIeHN3qjWN2hREJKSso/ntiV4rbbbmPNmjWXehqCIAiCIAiCIAhXDZmdGtdR07GNNWMOXI1BVsUg+zFcr5qEUto0Qy/Sx47FSVta7XNc10F8m7ytzbGtaWdtXw/2iCVZ23q7oR5D8LNvfsmmYJ0Wb7H06tWrybHKykrS0tKQSqV4eXnh7u5OUVERp06dwmQyERYWhkajuagTFgRBEARBEARBEK5eB46X4fnEs5giN6BzTCdAHskYzdP4ypsvMi4ILQY2li5d2uhxYWEhd911F9dccw2zZs3Cz8/Pci4nJ4e3336blJQUvvyy/Wu5BUEQBEEQBEEQhP82k8nMe1v3seDYuxj67sRUbabmk3LGPTcPiVnaag0NUT/jv83qGhvz58/H0dGRDz/8sMk5Pz8/PvzwQ0aPHs38+fOZN2/eBZ2kIAiCIAiCIAhXh3OXu7ewKl64irX093+ysoo7175GmuIHJO5Gan+rp3ZFKeYaM0enpbfZr6if8d8mtbbhjh07GDBgQIvnJRIJAwYMYPv27RdkYoIgCIIgCIIgCMLVK7eslpzSGl7evYTo9QNIs19GqG0Ma3r9Ts3iEsw1IvAlWMfqjI2amhqqqqpabVNVVUVNTc0/npQgCIJwYTx6YCo1hhqW9PnqUk9FEKzy5Ip4anRGFj0Qc6mnIgiCIFxkSw9tZ0nhfEpsUpAbfblTPp9+rv05nnupZyZcaawObAQFBbFu3ToeeeQRvL29m5zPy8tj3bp1BAUFXdAJCoIgXM3u3zueWkMtq/uvanLuaFU6AzYPYWXf5Qz1GHxe/c/r9jpmruy7HcuOL+ephFnNnts0eD1dnbo0e+7N1HksONp4+aS30ovEaw8AoDVqeevIO2wu2MLx2uOobNQMdB/A852fwcdObM3Zkonf7qNWZ2T5pNgm544VVjHivW0smdCbQaHu59X/K7d24UrPTC+sqmfGqkTSTlVRXqvHTWXLyM6ezLw2DLVS3uJ11VoD89Yd4c+UAspqdfg423F/bAAP9j+7nezSPcf5LTGflPxKqrQGdj87DG/HplslC4IgXAkWlDwEZhV+xQ9yd8dbsbOVt1pHQxBaYnVg46GHHmLGjBnceuut3H///fTq1Qs3NzeKi4vZt28fS5cupaqqiokTJ17M+QqCIFxV7vG/m/FxD3GiNgd/e79G5747/j1+dr4Mdh/Y7n71Jj1yqRyN/Mrfqep239GM9BzR6NiLh14msTypxaDGGeHqMH7ot8LyWCaRWX6uMdZwuCKFp8KmEanpTIW+khcPz+Xu3ffz19CNSCVWr9b8T7kzxo9Hlh0gp7QWPxf7RudW7svBx8mOAcFu7e5XbzQhl0nRtPLF/0ohk0i4rosXs68Lx8leTnZxLc//nExZrZ4P7+7R4nVzfzlMXHYpC8ZG4etsx+7MEp77KRkXB1tuiWoItmn1RgaHuTOysyevrU39t56SIAjCBVGqK230WH5yJN1sRxDsJeeENhO0l2hiwhXP6sDGqFGjKCoqYv78+Xz88ceNzpnNZmxsbJg9ezY33HDDBZ+kIAjC1Wqk53DcFe58f2Ils8NnWo7rTXp+yF3NhE7jkUqkvHToFdaf2kB+3Uk8lO7c0uFmng6fgULWUAH8zdR5bDi1kYmBE1hw9ENya3PJvjGd6QmzGi1F2ViwifePfsSRyjSkEik9nXvwWpeXCVYHA5BZnUXspgF83etLvsr6mv1lB/C39+eNrq8y0P1snaW0yqO8mvI6u0r2YDKb6KyJYEHUfMI0DduwLTu+nE+Pfc6J2hx87XyY0Gk8EwMnNCoYZi07mR12srN3pKsNNfxZsInpodPavFYmkeGp9Gj2nIutC6v6LW907O1ubzL0r5Ecq84gVB3S7rn+FwwL98BNpeCHA7k8NfLstnt6o4mf4vO4P7YjUqmE19emsDG1kPzyOtzVCm7s1oHpI0NQ2DQEl+avT2PTkULG9wtg4ZZj5JXVkfLKdTyzOqnRUpQtRwr5eMsx0gqqkEokRPk58eJNnQlyVwGQXVzDkPl/8dl90SzZnc3BE2X4Odsz9+ZI+p0TYEkvqOKtdUfYm1WKyWwm3EvNvNu7EeKpBmBF3AkWbc8kp6wOXyc77u8bwPh+Hc/rPeuqUnBvnwDLY19ne+6LDWDxjqxWrztwoozbe/rSN8gVAD8Xe1buyyEhp9wS2Jg4MBCAgyfEHU1BEK4cNYZavshYxMJjnzY6PsJ1FI4q0yWalXA1sTqwATB+/HhGjhzJL7/8QmpqKlVVVajVajp37sxNN92Ej49I3RUEQWgPG6kNY/3GsPLEKmaFPWXJElh/6k9KtaXc7X8nAGq5mg97LsBL6UVaZRozE2ejlCmZFf6Upa+smmx+yf+Nr3stQiaVYSu1bTJeraGWR4MeIUITRq2xjnfT3uf+vQ+ybdhm5NKzd8rfSH2LlyNfIEgVyDtpC3hk/2PsH7kXexs78uvyuWnHaPq59WV1v5Vo5GoOlMVjMBsA+DrrW95L+4A3ur1KN8eupFSmMiNhFnKpnPGdHgBgzK672Ve6v+XXRWLDsVHN343+Oe9/aI1a7jr92rQmqyaLrn/0RCGzJdq5J3MiniHAwb/F9lWGagAc5Y5t9v1fZSOTcntPX1YfyOXJ4SFIpQ1f/DelFlBao2NMjC8AKoWc+WO64aFWkl5YxZw1h1DKpTw54mww5HhJDWuTT/HpvdHYyCTYyppmydTqjEwcGEiYl5o6nZEPN6Uz6dv9rJ8+CPk57d9Zf4TnRkXQya0rH2w8ytTv49kxexh2tjJOVtRxx2e7iQ104buJfVArbUjIKcdgaljzsnTPcT7alM7cmyPp4uNI6slKnl2TjFwm5b7YhgDF/Yv3cuB4y8EEmVRC8svXNnvuVEU96w+fok8nl1Zf214BLmxMLeDOGD+8HJXEZZWSdqqKqcOCW71OEAThcnOyqoLyunr0Jj2/Fa5hSe6XlOpLUFX3BNIs7URQQ7hQ2hXYAPDx8eHRRx+9GHP5V5w8eZJnnnmGwsJCpFIpgwcPZtasWed1R0YQBOFCuDfgLj469jFbi7ZbamksP/E9QzwGW2o9zAybbmnvb+/HE7VTWJz1daPAht6k5+OeH+KmcG1xrJt9bmr0+MMe7xH8ewSJ5UnEuERbjj8WPJmRXg3LP+ZEzGZ17hpSKlOIcYnmy8yv0cg1LIr51BIMCVKdra+04OiHzO3yIjd1GAVAgIM/mTVZfJ29xBLY+LDHe9QZ61ucZ2v/Ji/N/o7rvK/BXdH6cocY52gie3QmSBVEobaIBUc/4Mbtt7J92GacbJ2atNcatcw9/Ao3eF/fYpaH0GBsLz8+25rBjmPFlloaK/flMDDEnQ5ODdk100aczXjxc7Hn0SF1fLs7u1FgQ280seDO7riqFC2ONapb47pe74zpRre5G0jOq6Cnv7Pl+MODAhkW3rDV38xrw/g5IZ/UU5X09Hfmm13ZaOxsWHhPT0swJPB0xgfAws3pPDcqguu7elvmm11Sw7I9xy2BjXfu6E693tjiPJt7yz723QE2HymkXm9iZGdP5t3RrcXrAV65NZJnVycT++YmbKQSJBJ45ZYuDAkT70dBEK4spbW1PLvvfZKNv1NDKSpdMHZHH0JeHwj8dqmnJ1yF2h3YuNLJZDJmzpxJ165d0el0TJgwgQ0bNnDttc3fZREEQbjYAlWB9HPty/cnVjDUYzCn6k6xpXArX8R8Ymnzc97/WJT5Fdk1x6kx1GA0G5H+7ZuUr71Pq0ENgMzqTOYdmc+BsnhKdaWYzCbMmMmryyOGs4GNzpoIy89eyoYvi8XaEgCSKw4R69q7UYbHGQX1BZyqP8X0hBmNCn4azUZk59Ss8LZrWoTaGocqDhNfnsAzEU+32fZMYAYgEujlHE3Mxlh+yFnNpKCHGrU1mAw8cuBxag11vB81/7zm9l/Syc2BPp1cWLU/h0Gh7hRU1rMtvZiPzqkf8WtiPt/syuZ4SS21OgMGkxnp3778+zjZtRrUAMgqruG9P4+SkFNGWY0ek9mM2Qz55XWNAhvhXmfryXhqlACUVOsASMmvpFdHl0YZHmcUVtZTUKll9uoknl2TbDluMJmRnfM75uWotOKVaezlmyJ5amQYmUXVzPvjCK/9lsqrt7ZcF+arHdkk5pbz1fgYvB3t2JNZwqu/peDnbM+AkPbXLREEQfi3mc1m/izYyEvJr5NhTEdj9sHtxBRq8rvi7Wqke2QdRy/1JIWr0mUR2Dh+/DiLFy8mPj6eY8eOERMTw9KlS5u0O3bsGK+++ioJCQmo1WrGjBnDlClTkMlkzfTaPA8PDzw8Gu582NraEhYWxsmTJy/YcxEEQTgf9wTcxYyEpynTlbEiZxXOcieu82oIuO4piePRA1N5OmwGQzwG4yjXsPbkH7yR+lajPuxl9s113XicPQ/gb+/Pe1Fv46X0QoqEAZuHojPpG7WzkZ7970FCw5c7E22ni5rMDW3e7f4OPZ0bF0k8NwvjfJeiLM3+Dn97P4a4D2pzLn+nkqsIUYWQWdO4zoHepGfS/kfJqM5gTf8fms3mEJoa28uPZ9YkU16r48cDuTjZyRnZuSEIti+7lCdXJjB9RAgDQ9zR2MlZf/gU76xPa9SHnW3bH0MmfLMPX2c73rqtG54aBRKJhJHvbUVvbPx+tJGdfX+d+clkxfYqp1ej8NZt3Yjya/x3f27s8HyWonholHhoINhDhVop5+5Fe5g6LBgPTdMgSY3WwLsb0vjigWhL5kmEt4ZDeZV8sT1TBDYEQbgslOvKLcs2z2U2m9levINFmYtJqUylg8KX0OpxnEyJRYuUHqE6/DwNzWa3CcKFcFkENtLT09m6dSvdu3fHYDA026aiooLx48cTHBzMJ598wokTJ5g3bx4mk4np06c3e01bysrK2LhxI1999dU/mb4gCMI/dlOHUcxJeoEfc9aw/MRKxvjdYcmIiCvdh6+dL9PDzhbLzKnNafcYhfVFZNZk8X6Pd4l17QPAwbJ4qwIW5+rq2IVf8n+z7LxyLi+lF+4Kd47XHucOv9ta7ON8lqLUGupYnfsTjwU/cl7LB+uMdWTUZDLcc6jlmM6kY+K+yWRWZ7Km/w9tLm8Rzrqhqzcv/XKYn+LzWLU/h9t6+lgyIvZnl+HjZMeUYWeXo+SW1bZ7jKIqLVnFNcy7vRu9T9enSMgptwQjrNW5g4bfk09adl45l6dGgZtKwYnSWm7t0XKtsPNZinIu8+kgi9bQ/O+bwWg+ndXSuCOZ9Oy1giAIl1qVoZothX9ZHpvNZg5XpvBnwSby6vJwtXXhVq8xVB66lT3pNbhojPQIq8PBTvw7Jlxcl0VgY9iwYYwY0ZAy/MQTT1BW1vSOyIoVK9BqtSxcuBCVSkX//v2prq5m4cKFTJo0CZWqYa3s6NGjyc/Pb3L9gAEDePfddy2PdTodTzzxBOPGjSMoKKhJe0EQhH+TncyO23xv5Z209yjXl3NvwN2Wc0EOgeTV5bEm92d6OEexuWALv+T/2u4xXBUuOMudWJK9DA+FByfrTzL38GtIad+2pg8Fjmfp8e+YtP9RngyZikauIb48gXB1GJGOnZkV9hQvHZ6L2kbNMI8h6M0GEsuTKNQW8UTI48D5LUX5Jf8Xaow13O0/tsk5o9nIwM1DeThwoqWOxwuHXuYGr+vwsfehsL6Qd9PeR2fUMcbvdqAhU2NC3CQOVRxmaew3mDFTUF8IgKNcg1LW/qUH/yVKuYxbojrw/sZ0Kur0jO11drviTm4O5JfX8b+EPKL8nPgrrYi1Se3PjnRxsMXJXs73cSfwUCs4WVHPm+tSmyxpacu4vh35fu8Jpiw/yONDg9Eo5STmlhPqqSbCW8OTI0J4bW0KKoUNg8PcMRjNJOdVUFSl5dEhDZ8R2rMU5c+UAirr9HT1dcROLiPtVBVvrEslJsDZskVuXnkd9y/ey5zrIxjR2RNHezkxAc68te4ISrkMHyc7dmeU8HN8Pi/c1NnSd2FVPUVVWrKLawBIL6imtEaHr5M9jvZX/la5wn9DXl7epZ6C8A+ZzKaGgMapTeTX5+Nq68pYvzE4VPRm7Xo1tfU1RHTUEeynbxL4fXBJw84od4bH8nuRWJgiXBiXRWBDKm37Q/W2bdsYMGCAJYABDVvQzp8/n7i4OIYNGwbATz/91GZfRqORmTNn0rlzZyZMmHD+ExcEQbiA7gu4h2+yl9DLJabRVqOjOlzPI0GTeC75BeqNWoZ6DGZW2EyeP/Riu/qXSWR8EfMpzx96icFbhhPo0IlXurzEfXvHt6sfHzsf/jdgNXMPv8atO+9AgoTOjhG81/0dAMZ3egAHGwc+zficV1PewE5mR5g6lImBD7ZrnL9benw5IzyHNRsUMZvNHKvOoFRXajmWV5vHw/sfo1RXiqvClRjnnqwb9KulIGtObS4bCjYCMPyvxksIPu75oSUAIrTsrl7+LNtzgugAZ4I91Jbj13Xx4qEBnZj7awpavZFBoe5MHxnK3F9T2tW/TCph4d09mfvrYa55fxudXB144cbOTPh2X7v66eBkx6rJfXnj9yPc9cUeJEC4t4a3busKwH2xATgoZCzalsVbfxzBTi4j1FPFuH4d2zXOGbY2UpbuOU5GUTV6owlvRzuu6+LF5MFnb6ToDSYyi2qo0p5dBvbxvT2Z98cRpq2Ip7xWj4+zHU9fF8Z9fc7u5LNk13EWbjlmefzAV3EALBjbndE9fM9rvoLwb+vQocOlnoLQjJaWmZyrth6yy0v4X8Eqcuqz8bT1ZpLfVLooB7J6Vx37j2nxcZEx9+ZQ/spvfsmpyrUhA8/dywtEYEO4QCTmyyy/8UzGxt9rbPTt25d77rmHqVOnNjoeFRXFlClTmDhxotVjPPfcc5hMJt54441/vBvKbbfdxpo1a/5RH4IgCIIgCIIgCJdSTm1uo2Um5zKajSSXH2Jn0V6y6jJQ405n2Uj86cmpYluSMxToDRDqryfET8+D3QfxbfK2Vscb17X1Nh+NOpuhOXXtyjbn31Z/1ra50H092COWZG3r7YZ6DMHPXgSn/4nLImPDGpWVlajV6ibHNRoNlZWVVvdz4MABfvzxR0JDQ7n11lsBuP3223nggQes7mPlypWsXNnwy9XcshlBEARBEARBEIQrnd6kZ1/pAbYWbaVEV4qv0p9Y2X34S3qg08k4cMyWUyU2OKmNRIVqyAuOowAAIABJREFU0ThcVvfMhf+Qdgc2SktLWb9+PRkZGdTV1fH6669bjufm5hIaGopSefmuS46OjiYtLa3thq0YO3YsY8c2RBBvu63l4niCIAiCIAiCIDR2bj08sSzl4rNmiQlA/TlFveuMdewq3sOO4h1UGarxs/fjxg6juMt9Mt/Ex5FTYMOhDFtMZujcSUugr8Hq+kfVJQ3LRotOnTqv5yMIzWlXYOOHH37g9ddfR6vVYjabkUgklsBGcXExY8eO5ZVXXmHMmDEXfKIajYbq6qa/kJWVlWg0mmauEARBEARBEAThcuPjc3YHostsVfxV6e87mbQk2rknFfpKthftYHfJHrQmLWHqUIZ6DCHIIRCJREJxlYE9yUqKymW4OhrpHqpF1c4dT75+4NGGP7FuiYkgWMPqwMbOnTt58cUXCQsLY+rUqezYsYMVK1ZYzoeGhhIcHMymTZsuSmAjMDCQzMzMRsdOnjxJXV0dgYGBF3w8QRAEQRAEQRCEy9X5ZGK0pEhbxPy09/jj1AZMZhPdnbox1H0wPvYNQSijCeKSFWzZl47eJKVrsJaO3oY2t7oWhH+L1YGNRYsW4e7uzrJly1CpVKSmpjZpExYWRkJCwgWd4BmDBg1i8eLFVFdXW3ZG+f3331EqlfTu3fuijCkIgiAIgiAIgnA5ak8mRnPMZjMZNZlsL9pBSmUqNhIberv0YrD7INwUrpZ2OQUyft1mz6kSG3oE2OPqWYi9UmTaCJcXqwMbhw4d4oYbbmi03erfeXl5UVxc3O5J1NXVsXXrVgAKCgqorq7mjz/+AGDw4MHY2dlx1113sXTpUqZOncqkSZPIyclh4cKFjB8/vtU5CYIgXOmiN/RhQuCDPB48+VJPRRCs1v+tzYzrF8DDg4LabiwIgiD8awwmAwnliWwr2kF+fT4OMgeGewzl0eDJZNVkWdrVaSX8udeOAym2qOzNjB1ZzdiILnyTUHAJZy8IzbM6sKHX67G3t2+1TWVlJVKptN2TKCkpYdq0aY2OnXm8adMmfH19cXR05JtvvuGVV15h8uTJaDQaxo0b12T7V0EQhCtNsbaEt4/MZ1PBZgq0hWjkGiLUYUwNmcIQj0GXenpXlDW5PzP5wOOM9BzOd7FLWm2bW5vHM0lz2FG8E6VMyW0+o3m5ywvYSm2btN1TEsfonXcQogpm27DNF2v6V5SSai0LNh5ly5Eiiqq0aOxsCPVU8+iQIAaGuF/q6V321iad5LOtGWSX1GAwmuno5sBDAzpxR3Tr2/3lldfx4s+H2JVRglIu5ZYoH+bcEIGtzdnPX/9LyOOzrZlkFVejUsgZEOzKnFEReKgv3+LugiA0Zs0yE2uWmJyr2lDN7pI97CreQ5WhCk+FB2N8b6encw/kUjkuts5k1WRhNkPiUVvW77ajVishtpuWYTF1KGxBItaeCJcpqwMbPj4+HD58uNU2SUlJdOrUqd2T8PX1tWqnkuDgYJYsaf2DqiAIwpVmwr5J1BnqWNBjPp0cOlGsLWZXyR7KdGI76fbIrjnO3MOvEuvap822RrORe/c8gLOtM78M+IlSXRlTDz6JGTNvdnutUdtyXTlTDk5joNsATtWLCu5nPLrsIHV6I2/f0Y0AV3tKqnXszSqhrFZ/qad2RXC2lzNlWDBB7irkMgmbUguZvToJVwdbhoZ7NHuN0WRmwtf7cLKX88PkvpTV6pixKhGz2czcW7oAsD+7lOkrE5hzQwTXRnpRVK3lhZ8P8eSKBJZPiv03n6IgCP+ANctMWlpi8ndZNdn8kPMjB8riMZgNhKlDGeR+J6GqkCaBiqIyKb9utyc7X46vp4EHBtbi7WY836chCP8aqwMbw4cP58svv2TdunVcf/31Tc6vXr2atLQ0pk+ffkEnKAiCcDWr0Fewp2QvP/T9nkHuAwHws/elh3NUi9f8kLOa2Ulz+KTnR1znfQ1ms5mFxz5lSfYyCuoL6OTQkSkhjzHG73YAHt7/KBq5hvnd5wHwZuo8Fhz9kN8H/kKMSzQAUetjeK7zs4zxu52pB5+kVFfKYPdBLDz2KXXGOq73upa3ur2BvY0dQJtjAsxPW8Dy499TqC3CUe7IEPdBfBz9IQC7i/fwSsprpFamIZPICFYF8X6Pd4nQhJ/X66g36Zm8/3GejZjNzuJdlOpKW23/V+FWjlSlcfCavfjYNRRGezHyOZ5KmMWciNmo5WpL2ycTZjLWbwxmzPyWv/a85ne1qajTE5ddyrKH+tA/2A0AX2d7uvs5tXjNT/G5vPDzYRaMjWJkZ0/MZjOfb8tk+d4TFFTW09HVgclDAhndoyFjYcryg2js5LwxuisA89ensXDLMdY81o+e/s4A9H1zE09fF8boHr7MWJVIWa2OAcFufL4tgzqdkWsivXj1li7Y2coA2hwT4ION6azan3M6C0XOoBA33hvb8Pu4N7OEN9cd4WhBFTKJhEB3B96+ozthXmraq9/p1+2MCQM6sfpgLnHZpS0GNralF3G0sIqds4fRwanhd/HZG8KZvTqZmdeGoVbKOXiiDG9HOyYObCis7udiz7h+HXn5l9ZvTgmC8O+4kAU/W2M0G0mtPMKu4t0crU7HRmJDjHNPBrgPwEvp2aS9Tg/f/lXK6r0abOVmbh5UQ88IndVbuArCpWZ1YGPixImsXbuWGTNmsH79eqqqqgBYtmwZ+/fv588//yQgIID77rvvok1WEAThauMgc8BB5sD6U3/Sx7U3SlnrqeJfZHzJO2nv8V2fb+nr1nD39c3Uefyav5Z53V4nSBXE/rIDzEiYhZPckZFeI+jn2pcvMr+09LGzeDeuti7sKt5NjEs0mdVZ5NefpL9bX0ubPSVxeCo9+bHfCvLq8pm0bzJBqkCmhU61asxf89fyybHP+Dz6YyI0ERRrizlQdhBoWNv7QNwE7vG/i0+iF2IwGUiqSEYmafjymVubx4DNQ1p9He7wu80SqAF4I3Uefva+3OV/JzuLd7X5uu8rPUCoOsQS1AAY6jEErUlLYnkSA9z7A/BV1jcUaYt4Kmwa76a932a//xUOtjIcbGVsTC0gpqMzSrms1fZf7cji/Y1HWTwuhj6BDQXp5m9IY13yKV65JZIgdxUHT5TxzOpkHO3kDAv3JDbQla92nl3rvSezBBcHW/ZkltDT35ns4hpOVtQTG3i2wN2+rFI81Aq+m9iH/PJ6Hl9+kE5uDjw+NNiqMdcln2TR9kw+vDuKMC8NJdVa4k+UA2Awmpi0ZD9je/nxwV1R6I1mDudXIDu9AiSvvI6R721t9XW4tYePJVBzLrPZzK6MEjKLaph5bViL18cfLyPYXWUJagAMCnFHZzCRnFdBvyA3ogNceGd9GhtTChge4UFZrZ5fE/MZGtZ8sEQQhAvH2uUju0v2tNmXtdkYf1elryauNI7dJXsp15fjKHfkoU7j8bf3x8HGoUl7sxlSs+T8sduO8qoKokJ1XNO3rt1buArCpWZ1YMPR0ZFly5Yxe/ZsS2FPgNdea0jZjYmJ4d13322zDocgCIJwlo3Uhg97LmBGwtMsPf4dXRwj6e3Si5s73Ei0S+MPNW+lvs2S49+xpt8qujo1pJ3XGGr5LGMRq/ottyzBCHDwJ74snq+yvmWk1wj6u/Xj6aRnKagvQG2jIaE8kafDZ7KjaCdPhE5hV8luOjp0pINdB8tYahsV73R/C5lERqg6hJt9bmRb0Q6mhU61aszc2lw8FR4M8RiMXCrH196HKOfuAFQZqqjQV3Ct10g6OXQEIEQdbBnbS+nJ5iEbWn3dzs2o2FK4lV/yfmXz0NavOVeRtgh3ReM75q62LsgkMgq1RQCkVKYyP20B6wb+agm6CA1sZFLmj+nOM2uSWR53gsgOGmICnLmhqzc9TmdTnPHuhjS+jzvB8kmxdPFxBKBWZ+DL7VksfagPvTu5AA2ZBQk55SzZfdwS2Hj+50MUVtajVspJyq1g+shQdmUU89iQYPZklhDgao+349kv+SqlDa+P7opMKiHYQ82ort7syijm8aHBVo2ZV16Hh1rBwBB35DIpPk52dPNtyEKp1hqorDcwPMKTANeGLwfBHmeLl3uqFfz+xMBWXzeVsvHHrsp6PbFvbEJnMCGVSnj1lshWAxBF1VrcVIpGx1wcbJFJJRRVaQGIDnDmo7t78OTKBOr1RgwmMwND3Hj3zu6tzk0QhH/uQi4faQ+z2czx2hPsKt5NYkUSRrOREFUwt3S4ic6OEfR26WW5uXCughIpv++yJytPjoeLkbfvCkTtVNXqWFJz0zpUgnA5sDqwAdChQweWLl3KkSNHSEhIoLy8HLVaTffu3enSpcvFmqMgCMJV7aYOoxjpOZw9JXHsLzvA5oItfJrxOXMiZvNk6BMALMr4kmpDDRsGryVQFWi59mjVUepN9dy1+17gbL6owWzAz64hvT5EHYyHwsOSqdHRPoBbfW7mvbT30Zv07CzeTX/Xvo3mFKoObfRl3lPpyYGyeKvHvNnnRhZlLibmz1iGegxhmMcQrvW6BoVMgbOtM3f53cnY3fcy0L0/A90GcFOHG/G1b8iesJHaEKiyrl5TsbaEJw5O5/OYj3GUO1r5irdNa9Ty8L5HeTnyBQIc/C9Yv1eT67t6MzTcg33ZpRw8Xs7Wo4Us2p7FrGvDLBkSX+/Mplpr4JcpA+jkdvZOYXpBNVqDiXFfxXHu8m6D0Yyvc0OgIthDhbtawe7MElwdFPi72nNjN28+2pyO3mhiT2YJsZ1cG80p2EOF7Jy8aQ+NkoSccqvHvKGrN1/vzGbgvC0MCnVjcKgHIzp7oLCR4WRvyx3RvjzwVRz9g1zpH+zG9V298TmdPWEjk9LRrend0NaobG34/YmB1OgM7DpWwmu/peLrbG9Z3nM+0guqeOmXw0wdFsygUHcKq7S8+Xsqc9YkW5bUCIJwdag31rO3JI5dJbvJq8tHKVXQ17UP/Vz74qFsOUhaWy9h8z4l+1IUKG3N3DiglujOWkLsHPg6PrnVMcd1Ff8nCpendgU2zggPDyc8/PzWQQuCIAhNKWVKhngMYojHIGaGTWd6/EzeOfIej53e4rWPa282F/7Fmrz/MTPsbC0jk9kEwNI+3zRaVgEgl8otP/dzi2Vn8S7cFK70d+uHv70frrYuxJcnsLt4N891fvZv1zb+70GCBPPpsawZ08fOh13Dt7G9aAfbirbz0uFXGrIfBv2Gg409H/ZcwMNBE9lc+BfrT/3Jm6lv802fxQzzGNKupShpVWkUaAu4fdfYJq+J9y/+bB+6meBzskHOcFe4E1e6r9GxEl0pRrMRD4U7BdpCjlanMy3+KabFP2Xp14wZ71/8WR67lKEeg1ud43+BUi5jYIg7A0PcmTYihNk/JvH+xqNMOl3fIaajC1vTCvklIZ9pI0Is15nMDSnOi8fFNFpWAWAjOxt16NPJhT2nAxt9A13xc7HH2d6WpNxy9maV8vR1jZdtyGWNd2aTnDOWNWN2cLJj04zB7MooZkd6Ca+vTeGDTUf5+fH+2NvaMH9Mdyb078TWo0X8mVLAO+vT+OKBGAaHup/XUhSpVGIJhkR2cORYYTUfbznWYmDDXaVgf3bjosKlNTqMJjPu6oZMjk/+yqC7rxOPDG7YZjfCG+xtZYz5bDezrgtrlOEiCIJ1/q26GNY6VX+KvSVxzD38GlWGKryUXtzmM5po5x4oZIoWrzOaYH+Kgs37lGh1EnpHahkaU4+9Uiw7Ea58Vgc26uvrKS0txc3NDVvbpilIOp2O4uJiXF1dUSha/oUSBEEQ2haqDsFgNqA1NqSXd3PqxuSghxmz624kwIzTwY0wdSgKqYLc2jwGug9osb9+rn35NONz3BXuPBz4UMMxt74sy17epL5GW6wdUylTMtJrBCO9RjA1ZApd1kcRV7rPEhDo4hhJF8dIngh5nLt238eqEz8wzGNIu5aiRDlFsXXopkbn3kx9mwp9BW91ex3/FrIterlEs+DoB+TX5VuW4Gwt2oZCqqC7UzeUMmWTfr/O+patRdv4pvdi/Oz9Wn+R/qNCPFUYTGa0hoYK+l19NEwc0In7Fu9FIoEnhoecbqfG1kZKbnldkyKa54oNdOXL7Zm4qRQ82L+T5dj3cTlN6mu0PTfrxlTKZQwL92RYuCePDgmi1+sb2Z9dxqDQhi1sO3fQ0LmDhkeHBDHuqzhWH8hlcKj7eS1F+TuT2YzOYGrxfI8AZz7acoyTFXWWAMWOY8XY2kjpenqZT53O2ChrBUB6OkXFJL67CJcJs/nKejNas8QELs4ykzN0Jh2J5UnsLYkju/Y4MomMQW4DCdeEEejQqc1tWDNybVi3057CMhmBPnqu71+Lp0vL/95cTFPXrgRgXNdBfJu87ZLMQbj6WB3Y+Pjjj/n222/Ztm1bs4GN2tparr/+eiZMmMC0adMu6CQFQRCuVqW6Uibue4S7/e+isyYClY2KxPJEFh77lIHuAxrVkujhHMWqfsu5c9c9SJDwVNiTqOQqHgt+hJcPv4IZM7Gufagx1nCg9CBSiZQHOjYUdD5TZyO3No9+bv1OH+vLUwlPN6mv0RZrxlxxYiUGs5Gezj1wkDnwv7xfkEvkBDp04njNCZZkL+Na75F4K705XnOclMpUxne8H2jfUhQHG/smO6k4yjUYzYZGxxdnfs3irK/ZNbzhA9QQj8GEq8OYcnAacyNfolRXytzDr3FfwD2W1/zv/bop3FBIFee9c8vVpKxGx2PfHeTOXr6Ee2lQKWxIyq3gs62Z9A9yQ608my3U3c+JpQ/14f7Fe5EAU4eHoFLY8PDAQN74PRXM0LuTCzU6A/EnypFKJNzTpyEgdabORl55HbGBLqePufDsmuQm9TXaYs2YP+zPwWgyE+XvhIOtDb8m5SOXSejk5kBOaS3f7T3ByM4eeGqUnCit5cipSu7rEwC0fynKws3pRPk54+9ij85oZMuRIn6Kz+PlmyMtbb7dlc23u7PZPGMI0FAoNNRDzVMrE3luVATltXre+D2Vu3v5WV7z4REePLsmmaV7jjM4xJ3Cqnpe+S2FLj4ay7IZQRAaXG6ZGM3Jr8tnT0kcB8viqTfV465w56YOo4hxjmaQ+8Bma2ecq7RCyqubCtiTrsZZbeTua6sJ76injTiIIFxxrA5sbNu2jX79+uHk1PxWbk5OTvTt25e//vpLBDYEQRCs5CBzINq5J4syF5NVk43OpG1IKfW9ladCm/5b2tO5hyW4AfBU2JM8E/407gp3Pjn2GU8nPYvaRkWkYyRTgh+zXHemzoaLrTNuioa73P3c+mEwG5rU17BGW2Nq5I58lP4xLx96FYNZT6g6lK97LyLAwZ/C+iIyajKZuG8ypbpS3BVu3O47mqkhj5/PS2iVEl0px6ozLI9lEhnfxS5hdtKz3LjjFpRSJbf73sZLkc9ftDlcTewVMnr4O/H1zmyyi2vQGU14aZTcEtWBqcOaLv2JOie4AQ3BjRnXhOKmsuWL7Zk8//MhVEobOntreGTw2RoyZ+psONvLcT1dNDM20BWDydykvoY12hpTYyfns60ZvP57KgajmRBPFZ/dF42fiz1FVVqyiqt57Ltcymr0uKlsuTXKh8lDgs7nJaRGZ+T5n5M5WVGPUi4jyN2Bd+/szi1RZ5d3ldboyCyqsTyWSSV89WAvXvj5EHd8tgulXMatUT48e8PZYNuYGD9qtAaW7Mrm9bUpqJVy+gW58sz1IiAnCH93OWRiNEdr1LL25DpW5vxATm0ONhIbujl1pY9Lb6uyM6ChjsZfB5TsO6xALqtjRO86+narR35ehQgE4fInMVuZCxYTE8Mdd9zBM88802KbefPmsXr1auLi4i7YBC93t912G2vWrLnU0xAEQRAEQRAE4bQLvfVqW5kR1rZrqY3ZbCa7Jpt9ZftJLE9Ca9LhqfAg1rUP0c49sbdpuvNkc33pDbAnWcH2eCVavYSeYTqmjQgm25DU5vy7KgbxdXzrr4e1y0esaddWm49Gna2fdWb5ysUe82L09WCPWJK1rbcb6jEEP3vfNvsSWmZ1zM5gMFgVHdRqtf9oQoIgCIIgCIIgXL0OHDhg+Tk6OvqijHGptl5tr3JdOfvLDrK/dD/FuhIUUlu6O3XjvoB70Rq1Vn3/goYaOklHbdm0z46Kaimh/jquia3Dw8WEq9qGv9UdvqQK0zMBSDU7nru5miD8I1YHNvz8/Ni3b1+rbeLi4ujQwfp12oIgCIIgCIIg/LfExMRYfr7SColeCHqTnsMVKcSV7iO9+hhmzAQ6BDLcczjdHLugkCno6tjFqiwRgGM5NmzYY8epEhs6uBsYPbSGQB/DRX4W52/lkw07sa3EukwMQbCG1YGNYcOG8cUXX7Bo0SImTZrU5PwXX3xBSkoKEydOvKATFARBEARBEARBgCuj4GdzzGYzOXW5bC/awYaCjdQZ63CSOzHccxgxztGW+lftkVmgZcmfKo7lyHFSG7ljeDVdgvVIRRaE8B9kdWBjwoQJ/Prrr7z33nusW7eOAQMG4OnpSUFBATt27CA1NRVvb28R2BAEQRAEQRAE4aK4XAt+tqREW8rB8oMcLIunSFuMrdSWSE1nernEEKwKQiqRtrvP0gopW/YrSUrPR6mQcW3fWvp00WIjuwhPQBCuEFYHNhwdHVmyZAkzZ84kISGBlJQUJBKJJX2sR48evPPOOzg6Ol60yQqCIAiX3ooTK3km6Xmyb0y/1FMRBKv8sD+Hl345TMor113qqQiC0AprC35e7moNtSRWJHGgNJ7s2mwAghwCGeI+mAc63k9aVdp59VtRLWHrQTsOHrFFJoXbYh0JjTiOneK/t5xHEP6uXRv++Pr6smLFCg4fPkxCQgJVVVWo1WqioqKIjIxsuwNBEAShiakHn6RUV8p3sUssxzac+pNJ+yczOehhno2Yfd59R2/ow4TAB3k8ePKFmCoAt/jczHDP4Resv3OlVR7lmeQ5pFWlU6WvwlPpyWifm5kVPgNbqa2l3ercn1iY/gmZNZmobNQMch/Iy5Ev4Kn0aLHvjOoM5h5+nbjSOLRGHeGaUGaFzWCY51BLm+eSXySuZB9HqtLwULhz4Jq9F+V5XulmrEqkrFbHV+N7WY5tSi3g8eUHmTggkJnXhp133/3f2sy4fgE8POj8tlFtzk3dOzA0vOX3xj+xO6OExTuySMwtp6peT0dXByb078SdvfwatduTWcJra1M4WlCNp0bBI4OCuC82oM3+f4rP5YttWWQUVeNgK2NomAfvjY0CIKe0loFvb2lyzTcP9mJI2MV5voJwMV0pBT+bYzAZ2F60g1W5P5JaeQSj2YiHwoPrva6jp3MUzrbOAKhsHNrdd02dhG3xDVu3ms0QE6FlcM96hvgGcqAsu83rpQYV7oS23c5s22Ybof3kEkWbr79RL177f+q8djKOjIwUgQxBEISLZFXOjzyVMIsXOz/Hw0Hnt7xPZ9I1CgRcSHYyO+xkdhelb7lUzli/MXRx7IKj3JHDFSk8lTgLg9nIS5HPA7C3ZB+PH3iCl7u8wPVe11GkLWJ20hweOzCF1f1Xtdj3vXvGEeAQwI/9VmIvs+fb7KU8EDeB7cO20MmhIwAms4mx/mNIrTzCX4VbL8pzvBqtOZjLM6uTeeb6cCYM6HRefegMJmxt2p+SbQ2lXIZSfnFytA+eKCPcS83kwYF4qJVsTS/i2Z+SUcil3BLlAzQEIB78eh93xvjy/tgo9mWX8cLPh3B1sOX6rt4t9v31ziw++SuDOTeE08PPmXqDkayimibtvp3QmwhvteWxk534gCxcXlrLxMipzbX8fCVkY5zLZDaRUZ1JQnkCSRWHqDPWobJR0c+1L9HOPfCx87F6V5Pm1Gkl7EpUsDtZid4AUaE6hkTX46wxtaufeh2sS8lus924rv7nOVOhNVq9uc3Xv2NM24FuoXXnFdgQBEEQLo7PMxbxasobLIiazxi/24HmMzrePvIuv+WvZduwzY3a9HHtw+LMr9Cb9ISqQ8mpy2Xu4VeZe/hVAApvyQPgt/zfeefIu2TUZOJm68q4jvfzZOgTlg9gv+X/zjtp75FVnYVSpiRCE86imM/wULo3WYqSV5fHs0nPs6ckDq2pHh87H2aFzWC07y3tfv6Bqk4Eqs5+Mfaz92VnyS72lJzNnNhfdoAOdt5MDnoYgAAHfyZ2msCzyc+32G+JtpTMmizmd59HF8eGwPwLnefwecYiDlUcsgQ23uz2GgAfH/tMBDastHhHFvPWHWHeHV0Z3cMXaD6jY8GfR1l36CQbpg9u1KZXRxe+3ZWN3mgi2ENFXnkdb/x+hDd+PwJA9lujAPjj0EkW/JlOVnENripb7u3jz+NDgy3v2T8OneT9jQ3nlXIZYV5qPr6nJ+5qRZOlKPnldbz4v8Psyy5FazDSwcmOJ0eEcnP39u/s9vjQ4EaP73cNYE9GCeuST1kCG8v2HsdTo2DuLV0ACPZQk3CinC+2Z7YY2Kio0/P2H2l88UA0A0PcLcfDvTRN2jrby/FQK9s9d0H4t7SWiXHu8cs1G+NcZrOZE7U5JJQnkFieRKWhClupLV00kdzpdwcSiQSZ5J8FUnV6WLW7nFW7NdRppUQG6hjWqw535/YFNAThv6RdgQ29Xs+mTZtISkqisrISo9HYpI1EIuGNN964YBMUBEH4r3gzdR6fZXzBN72/ZMR5LPXYVbwHtVzDir7fYcaMt9KLoVtGcnfAXYzv+IClXWJ5EhP3PcL0sGnc4Tua+LJEZibORi1XMzFwAgX1hTyy/zGe6/wMN3YYRY2hptUt52YnzqHepGVN/1WobdRkVGc0Oj9w89BGd+T+zs/el+3DmqbTA2RWZ7Gl4C+u9brGcqy3SwxvpLzF+lMbuMZzJKW6Mn7K+x8jPIe1OIaLrTOhqhB+yF1NlHMUdjIlS44vQ2WjordLrxavE1o3f30aX+7I5PP7o89rqcfezBLUShu+ndAbM2a8NEqu/2A7Y2L8uC/dwNMaAAAgAElEQVT27J3D5NwKHvvuIFOGhXBrVAeSciuY81MyKoUN4/t3orCqnqnfx/P0teFc18WLWp2R+BNlLY77ws+H0BpMfD8pFrXShoyixneSR763lbzyuhav93Gy48+nBrd4vkprwFtzNtAQf7y8UXACYFCoO6sP5qI3mpDLmmaqbE8vwmg2U1ytZcR7W6mq19Pd14nnR3XG39W+UdvJSw+gNZjo6ObAQwM6cUMrWSCCcKFdLXUx2nKy7iTx5YkklCdSqitFJpERoQknyqk7nTUR2EptiXbuafUWrc3R6WFfioKdCUqq68oI8Tcyonc13m5Nv3MJgtCY1YGNgoICJkyYQGZmZqv7TYvAhiAIQvttLdrOnwWb+K7Pt+cV1ABQyhR8EPUuCpnCckwmkaGyUTWqPfFpxhf0c4tldvhMAIJUQWTWZPFR+senAxsF6M16bupwI372DXfgIzThLY6bU5fHjd43WDIhAhwap7Iuj12K3qRv8Xq5VN7k2A3bbia54hBak5b7A+7luc7PWM71conh85hPePTAVOqN9RjMBga7D+Kjnu+3OIZEIuGHft8zPm4iQWvDkEqkOMud+L7vUjyVni1eJ7RsR3oxm48U8tX4mPOuX6GQy3j7jm4ozinlL5VIUClkjTIQvtyRSZ9Orjw1smGNcqC7iqziGj7bmtkQ2KjUojeaub6rF77ODV/6w7zUtCSvvI7runjRuUND9oOfS+NAwdcP9sJgbPmzjo2s5dTyTakF7DpWzI+P9rMcK6rW0l/l1qidm8oWg8lMWY0OD03TbIsTpbWYzWY+2nyMF2/sjJO9LR9uSufuRXvY+NRg7GxlOChseO6GCKI7OmMjlbAxpYApyw/y7p3dLdkzgnC+2rOt6u6SPa22uRIyMZpTWF9EUkUSC9M/Jbs2GylSglVBjPQcThfHyAu2LFOnh7jDCnYmKqmpkxLoo+fFoT7Uq1IuSP+C8F9gdWBj3rx5ZGRkMGrUKO688068vb2RycSeQoIgCBdCuDqMSkMV89MW0Nu1F47y9u8wFa4JaxTUaEl6VToj/xY86ePSi/lp71GlryLSsTOD3AcyaPMwhngMZpD7AG7qcCNuCtdm+3s48CFmJT7D5sItDHIfwA3e19PdqZvl/JngSHss6vUp1foaDlemMPfwq3yU/jHTQqcCDQVG5yS/wFOhTzLUYzAF2kLmHn6NmQmz+Tj6w2b7M5vNzE6ag7OtM78M+Ak7mZJlx5czIe5hNgxei7eduMPdXqFeKqrqDXywMZ3oABcc7ZoGqNrsw1PVKKjRkmOF1Qz7W/CkV0cXPtiUTlW9nghvDQOC3bh2wTYGhrjTP8SNG7p44apq/vfhwf4dee6nQ2w9WkT/IDeujfSiq+/Z37kzwZH22p9dyrQVCbx0cyRRfk7n1ccZZjPojWZevimSQaEN2R7v3xVFr9c2sjG1gJu6d8DFwZZJgwIt13TzdaK0VsfnWzNFYEP4x660bVUvlDPBjMTyZE7WnwSgiyaS0T630M2xG2q56oKNpdVD3CEFuxKV1NRLCfLVMyS6mgBvI5HO/2fvzsOjLO/9j79nkkkyWcm+LwQS9k12QRRQa2u1SovgUrVarf1RrEtPrW1Pj7andW97qrW11Vq1WrEu1K1aRZGKsu8QIPuekH2bTDLL8/tjYCASwgAJQ8jndV1eTGbueZ7vPIw688l9f+8QNh974pmIfIHPwcbatWuZPn06jz322EDWIyIyJCWGJPDCpGdZtPYqvrF2Kf849+8MC/J8MTKbzBj0/O2xs5cZEKEBJ/dl7EgmPGuD/zH772xq2szqA2t4qfRlfrnnQVbOfdU7K+NI12ZezfyE8/mw9iPW1P2HS//zNW7P+R4/HH03cHJLUVKtqWCFUZG5uAwXd237L5aN/C6B5kD+L/9xpgybzPdyvgvAOMYSGhDK5Z9eyU/G/ogU69F9Ev5T/ynv13zA/q/s9oZGDw97gE/q1vD3shXcNeqOk75mQ1VCRAhPXz+Ba/68juueXs/fbp5JVKgn3DCbOGp2p9N99Nrw0KBTb/VlMpkIMJt44eYZbClr5j/5dbyysZyH39vLiltne2dlHGnJ9Azm5cbz8d461hbU8/U/fMZ3LxjBnQdnhJzMUpSNJY1869mN3HlRLt/8wm4n8eHB1Ld39bivvr2bQLOJ6LDeG33GR3hCmZzEw1+iIkMsJEaGUNVHbZPTo/nHpmP/+yZyJhiWcGrBX3/rLczICs3iaymXMSFqPAsTF5zSEpMvOhRorN0egu1goDF/WjsZSUNjyUlYjGeHGGugGh1L//H5E0VXVxcTJ048/kARETkpydZkVs59lUVrr+Lrny3hH+f+nZigGGKDYtnVsrvH2F2tu49xlJ4sZgsuo+cHpZyIHDY0buxx3/rGjaSEJBN+8DdRJpOJ6THTmB4zjR+MupPzPprPPyvf7DXYAEixpnB91nVcn3Udv8v/PX8ufMYbbJzMUpQjuQ03TsOJy3ARSCCdLjsBpp49CQ797D7GUslOl+eLoJmezzNjxs2xlxxI35KiQnj51llc/ed1XPP0Ov5280yiw4KICQ9iT3Vrj7F7qlqPcZSeggLNuL6QgYxMCGdTSc9fXW4saSQ5KoTwYM9HGZPJxNTMaKZmRvP9hTlc9Js1vL2jqtdgAyA5yso1MzO4ZmYGf1hdyLNri73BxokuRVlf1MBNf/WEGjf3sivMlMxh/Ht3bY/7Pi2oY0JaVK/9NQCmZXo++BfVdZAc5Znu3tHl5ECbndToY09/31PVSkLk8WduydB1IktMBsr/rX98wI7tq1p7LS+UvMi/at7vNcw49MuF/tTV7WkK+o91UdjsZkame2ZonGygMVi3cb3phT8CcMOEeTy3c42fq5Gzhc/BRk5ODlVVVQNZi4jIkJcYksgbc17lG58tYdHaq3jt3FeYGz+HJwqe5KXSl5kVO5N3qv/FhoZNpPiwfCI9NJ31DeupTltEkDmY2OAY/t+I73DxJ1/h4b2P8fW0K9jatJ0/FDzl7WOxqXEza+r+w/yEC4gPjmdnyy4qO6vIjej9w9NPdv6MhQnzyQ7Pps3ZxscHPiY3IueIGnyfEv9K+auEmIMZEzkGi9nC9ubt/DLvQS5LudS7zOZLSRdy17Yf8mzxc8xPuIBa+wH+e9f/MDFqAmmhnl0o3qn6F7/Me4DXzl1BsjWZadHTiA4axu1b7+LuUXdgDQjhhdKXKLWVcXHihd7zF7UX0+HqoMZeQ7fbwc6WXQCMisgdsO1zB7uEyBBevnU21z69jqv/vI6XbpnFuSPieOqTIl7ZWM6M4TG8t7uGTaVNJEcdf+eOtGgrG0saubIllaBAs2e5xXnZXP7Ep/zmg/187WDz0Kf/U8R/fWkU4NlydW1+PfNy44mLCGZ3ZQvVzZ09Zjsc6b43d3PBqHiy48Jp63Lwyf4DPcaeyFKUzws9ocY3Z2dy+eQUDrR5vgwGmEzepTDXzczk+c9Kuf+t3Vw7M4NNJU28urmC3y2d4j3Oc5+V8NznJXx09wWAp4/IRWMTuf+t3fzqyglEWS385sP9xIYFs3C0py/Mq5srsASYGJcSiclkYlVeLS+sK+GeS47dE0dkqC4xMQyDys5KdrbsYmfLLg501QEDH2YAdHSaWLcrmPU7g7F3e5qCXjC1nfTEU5uhoW1cRQ7zOdi4+eabueeeeygoKGDkyJHHf4KIyAkos5Uz7YNZ3p8PbUs6FCWExPP6nH8cDDcW8+qcFfxg1F38Ku8hOl2dfD3tSr41/Hrer/nguMe6Z/QP+MH2e5jx4Ry63F0c+FolE4dN4OnpT/HI3sf4v/2PEx8cx+05y7h5+LcAiLREsqFxE08XP0uro5UUawp3jbrDu/3sF7kNN/fu/ClVndWEB4ZxXvxc7h/3s5N67YGmQP4v/wmK2osxMEgPTeOm4TfwnRG3eMcszVhCu7ODvxT/lft2/5yIwEjmxs/hZ2N/7B3T5myloL0Qh+EEIDY4hpdnvciv8h7i62uvwmE4yQ3P4bkZzzBx2ATv8+7a9l981vC59+eFq78EwKaL1pERmn5Sr2koiI8I5u+3zOLap9dz9Z/W8eItM/n+whwe+fc+7N0uvjYlhW/OyuTDvNrjHuvOi3L58es7mffIx3Q73ZQ8eCnjU6N48tpz+M0H+Ty5uoC48GC+e8EIbjg3C4DIkEA2lTbx3OcltHY6SR4WwvKFOcfsM2EYBve9uZuqFjvhwYGcOyKWn1469qRe+6ubK+h0uPjTmiL+tKbIe3/qMCtrf+TZqSc9JpRnvzWdX7y9hxfXlZEQGcz/XDaux1avjR3dFNV19Dj2b5ZM5n/f3sPNz23CMAymZ8Xw0i0zsQYd7kvy+EcFVDZ1EmA2MTwujIe/MVH9NUQOchtuSjpKvWFGs6MZM2ayw4czJ+5crs5YSpmtbMDO39Ju4rPtIWzKC8bhNDFmeDe3zsuizerbrEsR8Z3J6GuLkyNs3LiRF198kU8//ZTrr7+ecePGERnZ+/TO6dOHztZ5ixYt4vXXX/d3GQK8XrGS2zYvAyAxOJGdl/RcC3n+xxeS15oHwFeSv8xfZzztfazN0Ubuv8Z5p+yvnv8hYyPHDGi9O1t28a/q9wHICE1jacaSkzrOnVt/wItlf/f+PHnYJP59/rv9UuPppGBDRERk4JXbKnyeseFLXwlfxp3OYzndTgrbC6m217K67hPane0EmALIjchlQtQ4xkWOJSwwrN/rOnJcQ7OZ/2wLYfv+IAwDJuZ0M3eynYQYd7+ec0LwPJ7d2veONOD7kg9fxp3OYz1+6eHPxsvfWXFazumvY3132vmMSdAubafC5xkb3/zmNzGZTBiGwZNPPonJdOytzvLy8vqlOJETMSt2hvd2bVctxR0lDA/LAjzrSfe27vU+/sX+AhsbN3lDjWGWYYyJGPhpvLtbdvPovl8DcG7s7JMKNmzOTt6servHfduat7O3dR+jI0f1S52nS2JwAm/OfcPfZYiIiJxxfO2LEUAALvpe3jCQvTN8tfXDw1/Yp1x46kteOl2d5LXuZXfLHva27aPL3UWIOYTciBwmRk1gdOQoQgKOvxTuVBXWdvHKJ2HsLrIQYIapY7qYM6mL6MijmycPZcXrNwOwpsYOSQP/9yJDg8/BxrJly/oMM0T8LcWaQkZohndK4br69d5gY0Pjxh67StR31VPQVsDICM+yqiP3X58RM23QvNffqX6XNmfbUfe/XP4K9437bz9UdOLchpsudxfWAGuPcEpEREQ8TqQvhi8zHvztt7f8xnv7ueIXTuoYzd3N7Grdw+6W3RS2F+HGTXhgOJOHTWRc1DiuSvuGz422T4VhQElVIJ9uCyG/vIrgIAtzJ9uZPaGL8FA1p+7N2z9/2PMnvs3EEPGFz8HG8uXLB7IOkX4xK3bG4WCjcT1XZ3pmQaxr2ABAdthwqjqrsbvtrGvc4A02Dj0OMDN2Zo9jNnU38VThn3mv5t+UdJTiNtxkhmVwWcpX+X8jbyP84HRGgC5XF48XPMnbVe9Q0lGCw+1kWNAwMkLTmTJsMt/PXU5iSAIJ/0ztcY7PGj7vcZ+vyzBWlP/De3tp+lW8XP4KAK+Vv8F/j/0xAaaAHuOPPMfHF3zAX0ue5+2qd7C7upgTN5tfTfxf0q1p/LnoGZ4pfpbKzioyQzO4M/f7fCN90VHnX1n5T14sfZmdLTtpc7QTHRTNnLjZ3J7zPcZF9Vyv/sVzv1T2Mm9VvcMB+wH+MuPPjI8a1+dSlJ0tu/hT4dN83rCeWnstFrOFdGsa8xMv8IY4xR0l/Hb/79jVsptqew0t3S1YzIGkWdOYn3A+t+cuJz44zqdrKyIiImcOwzCoslezq2U3H9SuorLT8zkhPjieefHnMT5qHBmh6ZgP7pJ1qOH0QHG5YU+RhbXbQ6iqCyQsxM3150eTml2MNViBhsjpduobyIucQWbHzuKV8lcBWNew3nv/odvnxc8lv62Azxo+Z13Deq7LvIYuVxfbmrd7xx45a6CovZhFaxdTdXAbsEP2te1n375f807Vu6yc+yrRQZ5t+e7e/kPv+Q+p66qjrquOzU1bWJR2BYkhCf3yWis7K/m0bi0AQeYg7h//M9Y1bqCko4Tarlo+PrCaCxMXHvP53974HQo7Dje6+3fth+xdu5/5CefzXMnh357ktxfw/7YsJzMsg+kx0wDPLIv/t+V2Xq/ouXTkQNcB3qj8J+9Wv8fT0//Il5Iu7vXcN2+8laKOYp9f6wslL3LPjh/jPNgIEqDL3UVe214qO6sOBxvtxfy9rGfy73A52N+ez/72fP5V8z6rLnifKEuUz+cWERER/3C4HeS3F7CnNY+81jxaHK2YMJEZmsGlyV9mXOQ4EkLiT2tNXQ7YkhfM5zuCaW4PIDbKxeXzOpiU282s+BFsblKoIeIPJxxsOBwOPv/8c4qKiujo6GDZMk+zxq6uLtrb24mOjsZs7n1PdpGBdmQoUdxRQq29lkhLJDtadh58fCaxQbEHgw3PLI0tTVvpcncBYA0IYdKwid5jLNuy3BtqzI07l29n30ygKYAnC57is4bPyWvby093/g+/n/o7AN6u8jTtjAyM5H8n3EeaNY367nry2wr5sHaV97cIb859g49qP+K3+Z593MdHjeNXE/73hF7rK2Wv4sazZnNBwnyig6L5etqVPLbPM73z5bJX+gw2Grob+e3kxwgOCObubT/E5rJRZivjuZIXuHn4t7gwcQG/3v9/bGzcBMDTRX/xBhvPlbzgDTVig2L4r9F3MyIsm88b1vGb/b+jy93Fss3fZ9NFn/e6dVpJRynfHfEdzo8/j4buRrLCMo9Z577W/fxwx73eHijjo8bxnexbiA+Jp6CtgJWVb3rHpoWm8dOxP2Z4WBYRgREEmS00O1p4vuRvfHTgY8ps5bxQ8hLfy/nuiVxqERGRAeNL/4wzoS/G6dLiaPH0y2jNo6CtAIfhINgcRG5ELmMjx3BV+mKKT+CXI/2lrcOzZevG3cHYu81kJDn48pxORmU5MA+OFcwiZ7UTCjbWrFnDT37yE+rr6zEMA5PJ5A028vLyuPrqq3nkkUf46le/OiDFihzPiPARxAfHU3dwb/J1DeuJC46j290NeIKPuKBYAMpsZVR3VveY2XFO9BSCzEEA5LXuZXPTVgAsJgvLc76HNcAKwM3Z3/JuCbmy8k0emvQA4YFhRFoisblshAaGkh2ezbjIcYQFhgLwX6Pv8p5nVuwMSo74n3JkYOQJ95c4cmbI4oPLRBanLfIGG+/XfEBzd/Mx92S/d8wPuSZzKQCvVbzOh7Ufea7BsCk8MNETsthcNm+wUdh+eHbHS6Uve28vzVjC2EjPspMLEi7gg9pV7GzZRauzlX9WvcUNWd886ty3jvg294/vuR1oma281zr/Xr7CG2qkhCTz1tyV3mu6IOECbh3xbe/Y3IgctjVv56/Fz7GnNY9mR4v3uYf40olcRETkdPGlf8aZ0BdjoLgNN+W2Cva27eWpwqfJb88HIDoompmx0xkTOYYRYdkEmj1fW2KCok9rsHGg0cxvP6vjo11RuN0wJtvBnEl20hP7btIqciJcB/89OJ6IwPBjfrYf6nwONnbu3MmyZcuIjo7m3nvvZceOHbzzzjvexydPnkxaWhoffPCBgg3xq1mxM3iryvPeXNewgbhgT5CREZpOqjWVYZZoAk2BOA0n6xo2sK7xiP4aMYf7a+xv2++97TAcLPn8ml7P5zAcFLYXMmnYRG7Iuo6H9j5Kjb2Gr/7nCgCSQ5KYNGwii9Ku4IrUr/XLa9zQuNG7jCQiMIKLEi8EIDs8mynDJrO1eRtd7i7eqPwn3xp+Q6/HODT7AiDaEu29PS3m8IenmKAY7+1mR7P39v72w9fm9wV/4PcFf+j1HHtb9/V6/1dTvnLM1/ZFR/49zE+4wBtq9ObBvEf49f7f9nm8liNeh4iIiPjX/bv/lw5XByZMjI0cw1eSLmFs1BgSgxP91szdbUB+WSDrdoZQWGEhOLCDqWO6mD2xi9go7XAi/c/hdvDxgeNvHTs/4QIFG8fgc7Dx5JNPYrVaee2114iPj+eJJ544asyECRPYvXvguw+L9GV27CxvsPF5wzrigz1rL2fGeGZEhAWGMjFqAluat/Jp/VrvjATwLFU5GR3ODgDuHnUn46PG8XbVu+xpzaOovZhqew3VNTW8V/NvDtjreswwOFkryg43DW1ztpHx9ohjjjtWsBFpifTePrRExnN/7/0nDOPE14weui5flBjc//t0O9wO/lj4J+/PX09bxOK0RYQGhvHvmg94ouBJANwn8TpERETk5BmGQaWtkry2fext3dvjsVGRuYyOGMWoiFzmxZ/n15mVXQ7Yti+YdTuDaWgJICLUzcIZndw8exQFXduPfwAR8Rufg40tW7awcOFC4uOP3aAnKSmJ1atX90ddIidt5hFLOva27qMkoAToGVrMjJ3BluatvF6xkg6X58t3gCmAaTFTvWNyInK8t60BIez80tYeYcAhHU6bdxaBYRh8Kelib9NMwzB4q+ptvr3pNgDeqFzpDTZMHA4TDvXK8IXdZeeflW/5NHZL81by2wrIObj7S3/JCc/x9i15dNJDXJ913VFjut3dmOj9Ny0n8huY3Ihc7zKZ1XWf9LjegHdZXGN3EzaXzXv/I5Me9O5Ys6LsFZ/PJyIiIqfO5rSxvy2ffW37eSDvIRq6GwFIs6b1GHdNxlJ/lNdDU6uZ9buD2ZIXhL3bTGqCk28sbGdctoOAAIgKDYAuf1cpIn3xOdiw2WxER0f3OcZut5/Ub3VF+tO4yLFEBkbS6mzFjRubqxPoGWzMjp3FHwqf8oYaABOixvfYunVs5Bjvso5Ol51Fa6/i29k3kWpNoaG7gbKOcv5Tvxa34ea1OZ6dOL766RXkhI9kWsxUkkISCTRZ+OiIdbN21+H/K8YEHf73aU9LHm9XvUtccBxRlkjGRI4+5ut7t/o9Wp2tAMQFxfLD0T84aswLpS+ys2UX4Gki+t/jfuzTtfPVNZlL2bHDE2z8z66f09DVwOToyTjc3VR2VrG9eQfv1/yb989/l4zQ9FM619L0q3iq8M+4DBeVnVVc8enXuWXEzcQHx1PUXsQblf/k7fNWkhAcT2hAqDfc+NWeB7k46SI+rV971E4pIiIiJ8uXZp8AAQTg4vh9GM6WxqBuw02Zrdyzc1zbPsptFRgYWAOszIyZQWJIIqMjcomwRHADR/ffOt0MA3aV23n5szDyii2YgLHZDmZPVP8MkcHI52AjMTGRgoKCPsfk5eWRlpbW5xiRgWY2mZkRO837W37wBABHzlqYGTsdEyYMjCPuO7p55x+mPs6itVdRZa9mR8tObt9651Fjzo2d7b3d1N3ES2Uv81LZy0eNA1iSsdh7e3rMNEIDrNhcnbQ6W7lp4y0AnBc31xuU9OblI2YffCXly9w4/PqjxrgMN/fu/AkA/6h4jR+PvYcAU8Axj3mibsy6ng0NG3m90jPj5YG9D/fbsb9odOQoHpjwv9y786e4DBfbW3bwvS3f9z4eGeiZRWMymfh29k38Lt+zTO7p4r/wdPFfAJgTN5u19Z8PWI0iIjJ0+NLsEzwNP31ZVjGYG4O2OFp4t/o93q/5N/ntBXS6OjFhIiM0nQsTFzI6Ipf00HSmx0w7Y5p3dztgZ0EQ63cFU9NQjTU4kLmT7cwY10VU+JnzC1qzM5x4cvseYwSdpmpEznw+Bxvz5s3j5ZdfZtOmTUybNu2oxz/55BO2bt3Krbfe2q8FipyMWbGzegQbM74QWkQHRTM6YhR5bYfXeR7qwXGk7PBsVs//kKeKnubfNR9Q1F6M03ASHxxHemg68xPO59Lkw40wb89Zxoe1H7GzZRf1XQ3YXDYiAyMYGzWGazKuZnH6171jhwUN4y8znubBvIfJa93r3XK2L9Wd1ayp+4/3568mX9rruEuTL+HHO3+KgUGNvYZPDqxhQeL84x7fV2aTmT9O+z1fSb6El8pWsKN5B82OFqIskSSGJDI9ZhqXJF1MqjWlX8534/DrmRI9mT8VPs36xg3U2GuxmCxkhKYzP/EC77gfjf4vQgOs/L3sFWrttWSHD2d5zjIc7m4FGyIiQ5ivsyy040DfulzdFHUUsb9tP/vb8qntOgBAZGAE4yPHMSoil5yIkYQdMQO2N5njs05DtT01tJjZuDuYLXs9y00SY51875JYhqUWEGQ57eUcl70b/rWnpM8xN0zIOD3F9LP4EcMBiLWG+7kSOZv4HGx85zvf4Z133uHmm2/muuuuo7KyEoDVq1ezceNGXnrpJeLj47nxxhsHqlYRn92es4zbc5b1OeaTBat8OtawoGHcM/oH3NPLko8vWpqxhKUZS3w6Lni2K12QcIHP45OtydR8rfdtUY+UZE2i9mtHbxl14GuVvY5//Jzf8vg5R+8mMifu3GM+B+Dy1Mu4PPWy49bT17kPyQhN73PMpGET+f3U3/V5jEBzIHeNuoO7Rt1x1GMn8vciIiJnF19nWZwXN/e4AcjZsnTEF27DTUVnJflt+exry6fUVorLcBFoCiQ7bDjTYqZxZerXaOpuOqH+WT9/6xcDWPVhh3Y32bArhPxyC2azwdjhDmaM7yIzycm0mBw2N52WUuQIS3/3IAA3TJjHczuPvxOIiC9OaCnKX/7yF+644w6eeeYZ7/3f/e53MQyDjIwMHn/8cWJiYvo4ioiIiIjI8fljloXNZWNzfd9LJgbz0hFfNHQ1kN9ewFtV77CxcZO3d1VKSArnxc0lN2Ikw8OGYzF7pjmMCM8+Y5aZHGKzm3h9fQuvb4qkqdWzu8n8aZ1MG9NFRNiZs9xERPqPz8EGwLhx43jvvfdYvXo127Zto7m5mfDwcCZPnszChQsJDDyhw4mIiIiI9EqzLE6Pdmc7BW2F5Lfnk99eSOPB3UvigmIZGzmG3IgccsJHEmGJ8HCKBy0AACAASURBVHOlfTMMKK8NYNOeYHYVBuF0NZKZ7OaiGZ2MGe7Z3UREzl4+JxFVVVVYLBbi4+NZuHAhCxcuHMi6RERERESOS7MsTkyns5O81r3ktxeQ31ZAtb0agBBzCCPDRzAv/jxywkfw5aRL2NK81c/VHp+9G3bsD2ZjXhC1DYEEWwymjOrihtkjaLTs8nd5InKa+BxsLFy4kCuuuIIHHnhgIOsRERERkbOYr0tMNMuifzjcDkptZRS0FVDQXkR5Z7m3T0ZWWCZfTrqEnIiRpFpTeuygdiI9M07Uxy8dbvA+/5oFJ3WMqroANu4OZmdBEN1OE8lxTi6f18GEnG6CLTA8OohG9c84I+3614cAvL6jFCZm+rkaOVv4HGxERkYSHR09kLWIiIiIyFnuRLZLlRPndDspt5VT0FFIQVshpbYynIYTM2bSQlNZkr6YiMAIssIyvX0yTre//uRZ7+0TCTa6HPD+9jZe3RhBVV0glkCDCSO7mT62i9QE10CUKgPg4yf+7PkTWP7OCv8WI2cNn4ONSZMmkZeXN5C1iIiIiIjICXAZLipsFRS2F/Fy2Stsb9mBw+3AhIkUazJz4mYzInwE2WHDCQkIYWr0OWdcs8++GAZUHghg896DszMc9STEmLh0ro1JOV2EBPu7QhE5E/gcbHzve9/j2muv5R//+AeLFy8eyJpERERERKQXLsNF+cEgo7C9kBJbKd3ubgCyQrOYGTOdEeEjGBGWTWhgqJ+rPXk2u4kd+UFszguittEzO2P8iG6umZGFLXQPA7hSRkQGIZ+DjTVr1jBjxgx+9rOf8fe//50JEyYQHx9/1DiTycSyZcv6tciBcP/99/PSSy+xb98+f5ciIiIiItIrp9vJrpbdrKr9iML2IoptJTjcDgCSQpKYHj2N7PDhZIdlc0HCvEE1G+OL3IZBUWUgm/OCySu24HSZSI339M4YP7KbkCAYGx3CZvXOEJEv8DnYeOKJJ7y39+zZw549e3odNxiCjU2bNmGz2fxdhoiIiIhID93ubko7yijqKKaoo5iyjjIchifISA5JYmbMdLLDsskOH054YLifq+0fLe0mtu0P5sn9FdQ0RxAS5GbqmC6mju4mKU69M0Tk+HwONp5//vkBKaC0tJRnnnmGrVu3UlBQwLRp03jhhReOGldQUMAvfvELtm3bRkREBIsXL+Z73/seASe4KXV3dzePPvooTz75JCtXruyvlyEiIiJy1vJ1J5OIwHCGBQ07DRWdPdqdHeS17qWovZiijiIqOitxGa6DPTJSmBU7kwsTPQ02wwLD/Fxt/3v+nXAKKwIxDBMTMgKZO7WZMcMdWHz+liIicgLBxowZMwakgPz8fD755BMmTZqE0+nsdUxLSws33ngjI0eO5Mknn6SsrIyHHnoIt9vNnXfeeULn+/3vf883vvENYmJi+qN8ERERkbOerzuZzE+4QMHGcbQ6WinuKKG4o5jijhKqO2tw4ybAFEC6NY3z489jeNhwssIysQZYAQZdw89jOdQI9Eh1TWbmnWNnSm43F2UNZ3NTtZ+qE5HBzOdg4/rrr+ecc87hjjvu6NcCFixYwIUXXgjA7bffTlPT0YvmXn75Zbq6unjiiScIDw9nzpw5tLe388QTT3DLLbcQHu6ZhnfllVdSVVV11PPnzp3LY489xt69e9m+fXu/vwYRERERkS8yDIO6rjqKO0oo6iimpKOEhu5GACxmC5mhGVyfdR0hASFkhKYTZA7yc8UDo7Hdyafbgtm6L5i6pp7Bxp3XtmJWI1AROUU+Bxvbt29n8uTJ/V6A2Ww+7pg1a9Ywd+5cb4ABcOmll/Loo4+yYcMGFizwTM974403+jzOli1bKCgoYOHChd77FixYwKuvvqoZHCIiIuJ3/bnkw9djBRCAi777GNhd9uMeBzyNLsttFf1yrMHI6XZS0VlJSUcJKyvfZGvTNjpcHYBnGcnwsCzmxJ1LVlgWqdYUAkwBZ81sjC9yOGFviYVt+4MpLC/HbYSSnujka+d38LuHDo9TqCHiO1/+GwtDc1mgz8FGZmYm1dX+mRpWVFTErFmzetyXkpKC1WqlqKjIG2wczzXXXMM111zj/XnUqFF89NFHJ1zPihUrWLFiBUCvM0xERERETkZ/Lvnw9Vi+fLGeGn3OcY8DYHPZ2FzfP8caDDqcHZR0lHr+sZVQbqvAaXiWVqeEpDAmcjTDw7IYHjac+OA4TGf5HqVuA0qrA9m+P4jdRUF0dZuIDHPz9VlRJGeWETfM7e8SRQY1X/4bC0NzWaDPwcbixYt5/PHHqaqqIiUlZSBrOkpraysRERFH3R8ZGUlra+tprQVgyZIlLFmyBIBFixad9vOLiIiIyOnlNtzUd9XzbvV7fFL3CcUdpdR11QEQYAog1ZrCuXGzGR6aRWZYJvMTzj8rZ2L0pr7ZzLb9QezYH0RzewBBFoNx2d1Myu0mK8XJ9JgRbG4q8Y6fvKD/Z4HL4JE1wxNupkfE+rkSOZv4HGzMnz+ftWvXcvXVV3PLLbcwYcIE4uJ6T55Pd/Bxsvbt2+fvEkREROQMpZ1AhrYuVxdltnJKbaWUdJRRaiul09UJgDXASlZoJtOip5IVlklGaDoWs8XPFZ9eHZ0mdhUG8WJRFfuqojCZDEakOblwZiejsxwE9XE57nzm7tNXqJxxLvufewC4YcI8ntu5xs/VyNnC52DjwgsvxGQyYRgGv/zlL485zmQysWfPnn4p7pDIyEja24/+YNHa2kpkZGS/nktEREQGr/4MI7QTyNBhGAYN3Y18UPMhH9d9QmlHGdX2agwMABKDE5gQNZ7M0Ay+kvxl6rrqMJuO3yfubNPt8PTN2J4fTGFFIG63ieEJBl+abWPiyG4iwgx/lygiQ5TPwcYVV1zht3WB2dnZFBUV9bivurqazs5OsrOz/VKTiIiInHl8DSPOi5t73ADkbG5yOdTZXV2U28ops5VRevCfDqenyWewOYiM0AwWJi4gK9QzGyM0MNT73KywTBq6G/xV+mnnckFhRSA78oPIKwnC4TQRFe7m3IldTMzp5tKRk9jcVOvvMkVkiPM52HjwwQcHso4+zZs3j2eeeYb29nbvzijvvvsuISEhzJgxw291iYiIyOA01JpcDmVuw01pRykbGjZSaiujzFZGjb3WOxsjITieMRGjyQjN4CvJl9DY3TgkZ2McyTBgT4Wdt7dY2VUYhM1uxhrsZlJuNxNHdpOR7NRuJiJyRvE52BgonZ2dfPLJJwDU1tbS3t7Oe++9B8D555+P1Wpl6dKlvPDCCyxfvpxbbrmF8vJynnjiCW688cYeW8CKiIiIyNDW6mjzzsYos5VTbivH7u4CPL0xMkLTvctK0r8wG2NEeDbNTc3+Kt2vDANqGgLYVWhhZ0EQzW3VBAYEMzrLwcScbkamOwgM6J9zvfHb1723r7xDjfiHmvUv/gMAe8IGQi7SL6mlf/g92GhoaOD73/9+j/sO/bxq1SrS0tKIiorir3/9Kz//+c+57bbbiIyM5IYbbmD58uX+KFlERER8oOabh/l6LXxd/uJ0Oym3VfTLsQazbnc3O5t3sfrAGm+Q0ezwBBNmzCRbk5gSPYV5cXMxgLjg2CE/G+OL6prM7CwIYldhEPXNAZjNBiNSndx0fiLWhHyCg/r/nCv/7w3vbQUbQ8+Gl171/Aksv2iFf4uRs4bPwca9997r0ziTycSvfvUrnwtIS0vzaXeSkSNH8vzzz/t8XBEREfEvNd88zNdr4evyl6G4lMZluKi11x6chVFBua2cGnstbtwARFuiyQzN4LywOWSEZpBqTSHI7PlWPjX6nCGz9aovmlrN7Cy0sKsgiJqGQEwYZKU4OXeinTHDHYRZDaZGR7C5yd+Vioj4xudg44033ujz8UM7ppxosCEiIiIymGn2RP8zDIPKziq2Nm07uKykgsrOShyGA/AsKUm3pjE/YQzzE87H4XYQYYnwc9VntpZ2E7sLg3ix1LM9K0B6opOvzLExLls7mojI4OZzsLFq1ape729ra2Pnzp08+eSTTJkyhbvv1r7UIiIiMnQMxdkT/ckwDFocLVR0VlJ+cDZGRWclNpcNgEBTIGnWVGbFziA9NJ300HTigmK9u/VpNsaxHQozdhUFUVHr+dg/ItHgopk2xo9wEB3p9nOFIiL9w+dgIzU19ZiPjR49mrlz53L55Zcze/ZsFi9e3C/FiYiIiMjZpam7ibzWvZ7lJJ0VVNgqaHO2AZ6+GEkhSUyIGsecuDkYGCSFJBJg6qeulUNAb2FGcpyTC2d0Mm5ENxdnTdb2rCJy1um35qHJycnMnz+f559/XsGGiIiIiNDubKfCVklFZyUVB0OMZkcLACZMxAfHkxuRQ7o1jfTQNFKsKVjMFkAzMU5Ec5uZPUUWXiqtYm+Vp1/NkWFGbJRmZojI2a1fd0WJjY2ltLS0Pw8pIiIiIoNAh7ODjY2b+PjA6oPLSiq8O5QAxAXFkRWWxayYmZhMJlKtKYQEhPix4sGtodnM7mILe4qCqKrzfKTPTjQUZojIkNRvwYbL5WL9+vVERKhxk4iIiMjZrNXRRmVnJZWdld4ZGT1DjFiywjJIs55LWmgaqdYUrAFWQDMxTpZheLZm3V0UxJ5iC7UNno/xqQlOLpppY2y2Q8tMRGTI8jnY2LhxY6/3O51OampqeP3118nLy9MyFBERETkhvuwqEhEYftZvCXsmOtTY89P6z1hTt4aKzioqbRW0HuyJARAfHMfwsCxSrSnMT7gAm8vmDTHk1BgGVNcHsGtHIx/tiaS+OQATBulJTi4518bY4d0Mi9BuJiIiPgcb3/zmN73dp3tjGAbTp0/nhz/8Yb8UJiIiIkODL7uKzE+4QMHGAHMbbhq6G6iwVVLZWUVVZ1WP3UlMmEgIjicnIodUawpp1jRSrMk9lpOcEz1FszFOkcsNZdWB5BVbyCux0NIegNnUQlaKm1kT7IwZ7iAiVGGGiMiRfA42li1b1muwYTKZiIqKYuLEiUycOLFfixMRERGR/ud0O6m111Jpr2Jt/edsbdpGtb2KLnc3AAGmAJJCkhgfNY5UawoLEubT6mgjOCDIz5WfnRxOKKywkFdsYV+pBZvdTGCAwch0Bwum2blq0hjyu7b7u8x+c/7SC/xdgvjRuC8tBCA3JsnPlcjZxOdgY/ny5QNZh4iIiAwizd3NtDnb+xxjd9n77Xy+LFfp73OeLTpdnVR1VlPVWeWdiVHbdQCX4QIgxBxCUkgS06KnkRqaQqo1lcTgBALNhz8mjo8ap5kY/ayzy8T+UgvvV9aysXAY3U4TIUFucjMdjBnuICfdQZBngxgiQwOgy7/19qebHrjZ3yWIHy24/VYAbpgwj+d2rvFzNWenobjEs193RREREZGhoc3ZzscHVvc5Zmr0Of12Pl+Wq/T3OQcbwzBocjQdDDGq+Wflm+xuzaOxu9E7JiIwnBRrCqMjR5FiTSElJIUvJV3E1uZtfqx86GhqNbOv1LPEpLQ6ELfbREx4F5Nyuxgz3EFWipPAAH9XKSKD3VBc4ulzsLFr1y5Wr17N0qVLiYuLO+rxuro6VqxYwcKFCxkzZky/FikiIiIih3W7uqmwVVLVWUWV3TMbo9peQ6erE/D0w0i1ppBuTWNmzAxSrSmkWFOItBy9e53ZZD7d5Q8Zh5p/5pVY2FtyeCeT+GgXcybZGZ3l4PJRE9navNXPlYqIDG4+BxvPPvssmzdvZtmyZb0+HhcXx2uvvUZZWRkPP/xwvxUoIiIiMlQZhkGbs43qzuqDAUY11fYaDtgP4MYNgMVsISUkmUnDJpISkkKKNZnkkCTOjZut5SN+4HRBcVUg69bX8+n+KFo7zJhMBhlJTr4028boLAexUW7veHMfzflFRMQ3PgcbW7duZebMmcfcGcVkMjFr1qxjbgsrIiIiIsfmdDspaCtgY+Nmqu3V3jCjw9nhHRNliSIlJJmFCfMxm8ykWJOJDYrVrAs/a+/09MvYV2qhsNxCt9NEsKWd7DQnC7Mc5GY4CLNqJ5ND/nLvM97b6rcx9Hz0uz8BUBHzJmnXXe7nauRs4XOwUV9fT1JS351rExISqKurO+WiRERERM5WhmHQ4mihyl5NTWcN1fYaquzV1NnrvLMwAk2BJIUkMS5yLMkhyd5ZGKGBoYCnl4hmY/iPYUBtYwD7SjxhRuWBAAxMRIa5mZTbzajMbhaNG8+udvUu6c0nL6/23lawMfTsfn+V509guYIN6Sc+BxtWq5XGxsY+xzQ2NhIUpG3ARETkzODLzh0w+DuD+/I6B/trHKzsri5q7DVUdVazvmGDZybGEb0wAKIt0SRbkxgfOZZz486ly2UnLjhOszDOMA6nZ4nJhg31/Gd/JC3tni6fqfFO5k+3MyrTQVKsi0OTm4Mt+vsTETldfA42Ro8ezapVq/jRj35EWFjYUY+3t7ezatUqRo8e3a8FioiInCxfdu6Awd8Z3JfXOdhf45nOZbg4YK+jxu6ZgXHoz6buJu+YYHMwySFJTB42ieSQJJJDkkiyJmENsHrHaCbGmaW5zUR+mYV9ZRaKKy04nCaCA9sZnubigql2cjMcRIRpiYmIiL/5HGwsWbKEu+66i5tuuon777+/R4Cxd+9efvazn9HU1MSSJUsGpFARERERf3Mbbpq6m6mx11Bjr6XGXsOTBX+k1FaGy3ABYMZMfEg8maEZzIyZQXJIEhclLqSis1KzMM5wLrdBaXUA+8ss7C+1UNvo+agcHeHinNFd5GY4WDRuPDvbtMRERORM4nOw8ZWvfIU1a9awcuVKrrzySmJjY0lMTKS2tpaGhgYMw+CKK67gq1/96kDWKyIiIjLgDMOgsauR/W35PUKMWnstXe5u77hhlmGMjhhFVlgmSSFJJIUkkRAcT6C550esZGsyVfbq0/0yxAftnSYKyi3kl1l4uKKMdnskZrNnF5OLZ9kYlekgbpjbu8QkKFDhlIjImcbnYAPgwQcfZMqUKfztb38jPz+f+vp6AHJycrj++utZvHjxgBQpIiIiMlA6nB0Hg4vD4UWNvRaby+YdEx4YTlJIItNjppMUkngwxEgkJCBEy0cGGbcbKg4EkH8wzKiq83wcDrO6mZUTSmxSDSPTHIQE+7lQERHx2QkFG+BZkrJkyRI6OztpbW0lMjISq9V6/CeKiIiI+JHNaaO26wC19lo+q/+cHS07qbXX9mi8GmIOISkkkYlR45kWMw2n4SQxOJEIS7gfK5dT1dzhYtv+IPLLLBSUB9LZZcZkMkhLcLFgeie5GQ6S4lxMjxnB5qZyf5crIiIn6ISDjUOsVqsCDRERETnjHBlgHJp9UWuvpdXZ5h1jDbASFxzHmMjRJIYkkhScSJI1icjASEwH1xxoJsbg5XJDRa1nVkZBuYXqujIMwgizuhmV6SAnw8GINCehIWr8KSJyNvA52Ni1axerV69m6dKlxMXFHfV4XV0dK1asYOHChYwZM6ZfixQRERH5og5nB7X2A9R21Xr+tNfS2N1IQ/fh7emDzEEkBieQG5HrCTBCEkkMSWBhwgK2NqsB5Nmkuc1MQXkgBeUWCistdHWbMJsM0hJdXDdvGGHxZSTFuTCb/F2piIj0N5+DjWeffZbNmzezbNmyXh+Pi4vjtddeo6ysjIcffrjfChQREZGhyzAM2pxtbGnayqf1nx2cheEJMzqcHd5xweYgEkISmRY9lUCz5WCAkcgwS1SvO5Fod5LBr9sBpdWBbN7cwNr8SOqbAwCICnczfkQ3I9MdZKc6sQYbTI3OYXNTsZ8rFhGRgWIyDMOnOXgLFixg+vTpPPTQQ8ccc++997Jx40Y+/PDDfivwTDdz5kxSU1P9XcYJaWpqIjo62t9lnLS2tjYiIiL8XcZJ0/X3L11//9L19x9de//S9fcvXX//+uL1r64+vENQcnKyP0o6IWfb9fc3X//+t27ditvtxmw2M2XKlNNR2oA4067/iTqT3v/R0dE888wzvT9o+GjChAnGr3/96z7H/PrXvzYmTpzo6yHFT6688kp/l3BKfvrTn/q7hFOi6+9fuv7+pevvP7r2/qXr71+6/v6l6+9fg/X6P/roo0ZycrJx4YUX+ruUUzJYr/8hg+X97/M8TKvVSmNjY59jGhsbCQoKOqHUReREzZ8/398lDGm6/v6l6+9fuv7+o2vvX7r+/qXr71+6/v5x9913U1VVxb333uvvUoa0wfL+9znYGD16NKtWraKjo6PXx9vb21m1ahWjR4/ut+JEerNgwQJ/lzCk6fr7l66/f+n6+4+uvX/p+vuXrr9/6fr7l66/fw2W6x9w33333efLwODgYFauXMn69euZMGFCj51R9u7dyx133EFpaSl33nknubm5A1Wv9JPx48f7u4QhTdffv3T9/UvX33907f1L19+/dP39S9ffv3T9/UvXf+D53DwU4Ec/+hErV67EZDIRGxtLYmIitbW1NDQ0YBgGV1xxBQ8++OBA1isiIiIiIiIi4nVCwQbAihUr+Nvf/kZ+fr73vpycHK6//noWL17c7wWKiIiIiIiIiBzLCQcbh3R2dtLa2kpkZCRWq7W/6xIREREREREROa6TDjZERERERERERPwt8EQGb9iwgS1btnDgwAEAEhISOOecc5gxY8aAFCciIiIiIiIi0hefgo0NGzZw3333UVxcDMChSR4mkwmA7Oxs7rvvPqZPnz5AZYqIiIiIiIiIHO24S1Hef/997r77bpxOJ/Hx8cycOZPk5GQAqqur2bBhAwcOHCAwMJBf//rXXHzxxaelcBERERERERGRPoON2tpaLrnkEtxuN/feey+LFy8mICCgxxi3282rr77Kr371K0wmE++99x6JiYkDXriIiIiIiIiIiLmvB5977jk6Ozt59NFHWbp06VGhBoDZbOaqq67i0UcfpbOzk+eff37AihUREREREREROVKfMzYuu+wyQkNDWbFihU8HW7JkCTabjbfeeqvfChQREREREREROZY+Z2xUVVUxZcoUnw82ZcoUKisrT7koERERERERERFf9BlsOJ1OLBaLzwcLDAzE7XafclEiIiIiIiIiIr7oM9iIj49n//79Ph+soKCAuLi4Uy5KRERERERERMQXfQYb06dPZ+3atRQWFh73QIWFhXz66adMnz6934oTEREREREREelLn8HGtddei9Pp5LbbbqOgoOCY4woLC7nttttwuVxcc801/V6kiIiIiIiIiEhv+twVBeCRRx7hmWeewWKxcPHFFzNr1iySk5MBqK6u5vPPP+eDDz7A4XDwrW99i3vuuee0FC4iIiIiIiIictxgA+CJJ57gj3/8I06nE5PJ1OMxwzAICAjgO9/5DsuXLz/qcRERERERERGRgeJTsAFQWVnJa6+9xpYtW6irqwMgLi6OqVOncuWVV5Kenj6ghYqIiIiIiIiIfJHPwYaIiIiIiIiIyJmmz+ahIiIiIiIiIiJnMgUbIiIiIiIiIjJoKdgQERERERERkUFLwYaIiIiIiIiIDFoKNkRERERERERk0FKwISIiIiIiIiKDloINERERERERERm0FGyIiIiIiIiIyKClYENEREREREREBi0FGyIiIiIiIiIyaCnYEBEREREREZFBS8GGiIiIiIiIiAxaCjZEREREREREZNBSsCEiIiIiIiIig5aCDREREREREREZtBRsiIiIiIiIiMigpWBDRERERERERAYtBRsiIiIiIiIiMmgp2BAREREREZFT9thjj5GSksJjjz3m71JkiDEZhmH4u4jB7Oabb+aZZ57xdxkiIiIiIiflvvvu6/W2yImKiIigvb2d8PBw2traeh2j95sMBAUbp2jRokW8/vrr/i5DREREROSkmEwm7219NZBT4ct7Se83GQhaiiIiIiIiIiIig5aCDREREREREREZtBRsiIiIiIiIiPSzvPdLqN5V7+8yhgQFGyIiIiIiIiL9aP+qMl6+dRX5n1T6u5QhQcGGiIiIiIiISD9pKm/j9Ts/IXlcLLNvHufvcoYEBRsiIiIiIiIi/cDZ5eKV//cRhgFX/WEBlpBAf5c0JOgqi4iIiIgMYU899ZS/S5Ah5Gx5v9la7HS3OY66f9Wjm6naUc/lD87BHGDC1mInNCrEDxUOLSZDmwefkkWLFvH666/7uwwRERERERG/MplM3ttn+9fM5oo2Ctb07J9RsfUAW1/JZ8S8VMZ+OQuAkfNSGZYW4YcKhxYtRRERERERERHxUWe3i9Lmzh73tdXa2PFGITHDIxl9caafKhu6tBRFRERERERE5DgMw+CtHdU8+G4eta12HslJIyMkGGeXi00v7iUwOICpS0dhDjAd/2DSrzRjQ0RERERERKQPuypbuOqpz7n971sZFhqE1RLAipomDMNg++sFtNd3cs7SXEIig/xd6pCkGRsiIiIiIkPY1KlTvbc3b97sx0pkKBhs77e6ti4e+/c+VmwqJzo0iAcWTeCqaek8snI3f9xQyqY1FdTsqGf0lzKJGzHM3+UOWQo2RERERESGsC1btvi7BBlCBsv7rdvp5rnPSvjdqnw6HS5unjOc5QtziLJaAPj6lFTe+aCIqn+XkTQljhHX5mKYj16C0mkGxR0DT8GGiIiIiIiICJ4+Gm9vr+Kh9/dR0dTJrOwYbl+QQ0ZsKG12B212zxavto5uzlvTRIc1gPxp0bTuqOn1eFdlaEeU00HBhoiIiIiIiAx5LZ0O7lqxjVV7DxAXHsQNszMZlRRJSYONkgabd5zhNmj9zU4snS5WXxQDDjuXEeLHykXBhoiIiIiIiAxpNS12bvjLBorq21k2fwSJkSEEmo/ea8MwDOpeyqdpfS2BF6WQlWXls5YOKru6SQ1W41B/0a4oIiIiIiIiMmQVHGhj0ZNrqWzu5K/fmsHVMzJ6DTUAGt8opunNUkZcmU3AxBjGhFkJM5vZ2NqBYRinuXI5RMGGiIiIiIiIDEmbSxv5+h8+p9tl4MWi2QAAIABJREFU8PKts5gzMu6YY5veLaP+5UIi5yUz5fZJmEwmAk0mzokMpbbbSXlX92msXI6kpSgiIiIiIiJy1muxddPW5fT+/GlBPf/zz93ERwTz2FWTGBZqoaLJRpfDdfRzP67kwF/3ET4jgaTvjsV0xA4oo0JD2NZmY2OrjfTgIEymo3dHkYGlYENERERERETOem1dTtbsrwdgY0kjK7dWkhpt5frZWRQe6KDwQAcAUzJ6btDa9nktNX/cQ+ikWJK/PwFTQM+FDwEmE1Mjwljd3EaJvZvh1mDvY27DoLmirc+6giIshEap+eipULAhIiIiIiIiQ4JhGHy87wAf5h0gJyGca2ZmEBwYcMzx7VvqqPrdTqyjhpF69yTMlt67OeSEBrOt3cbG1g4yQ4IwH5y14Xa4Kfisss+aRs5LVbBxitRjQ0RERERERM56LrfBP7dX8WHeAaakD+P62Vl9hhq2PY1UPbaD4IxwUn80GXPIsceaTSamRoTS5HRR1Nk1EOVLHzRjQ0RERERkCHvzzTf9XYIMIf56v9kdLv77n7vYUNzIvJw4vjQuqc9eGJ0FLVQ8uA1LopW0n5xDQKjluOcYYQ1ma5uNTa02sq3B3lkbMvAUbIiIiIiIDGGXXXaZv0uQIcQf77c2u4NvP7eJ9cWNXDohuc+dTwCaC1qo+OUWAqOCSP/pOQRGBvl0HpPJxPTIMN5vbGW/rYvRYVpecroo2BAREREREZGzUlNHNzc8u4HdVa387KtjCLH0/RW4u7qD1fdvxhwUQNp/TyUw5sTCicyQIOItgWxu6yAnNPj4T5B+oWBDREREREREBrUvbuUKUN/exZ0rtlPZ1MkvrxzP9Mxo1hc3HfMYXWXtVDywlQC3QdrPphKUYD3hOg7N2ni3oYW9HfYTfr6cHAUbIiIiIiIiMqgduZUrQGNHN39ZW0y73ck3Z2fidkO3yzjm89u31lP92x2YQgK44HfnU3IKq0jSgi0kBVnY0majy+k++QOJz7QrioiIiIjIEJaSkuL9R2SgnY7324FWO39aU0hnt4ub5w5nRHx4n+Ob/lVG5YNbsSSGkvmrmUTnDjul83tmbYRic7t5b9+BUzqW+EYzNkREREREhrDq6mp/lyBDyEC/3yqbO3l2bTFmk4lvnzec5KhjLycxXG4O/HU/ze+XEz4tnuTbx2MO6Z+vyCnBQUwMtxITagGbZm0MNAUbIiIiIiIiMuiV1Hfw3OclWC0B3DR3OHHhx27e6bI5qPrNTmzbG4i+PJP4a3Iwmft3e9bZUeGcNzyWtvq6fj2uHE3BhoiIn71x9xq2vZrPlKtyueKR83o89u8HNrD2jzvJXZDOtc9e7KcKj69kfTWf/WkXVTvraau1ccWj5zFlcW6fz2kqb+O3c1856v7rnvsSORekHT72umre+8V66vKbiUgIZc5tE5h+3Zh+fw3iu7tf2c5rWyq4aloaD39jUo/HHvhXHk99UsSC0Qn85cbpfqrw+NYXNfDn/xSxs7KF2tYuHvnGRBZPSz/u897eUcXvPy6kuL6d2LBgrp+dyXfOH+F9/NC1+SKrJYC8X1zSr69BREQOW1/UwLOfFRNlDeKmOVkMCz32Fq3dtTYqH9pGd7Xt/7N3n4FRVGsDx//bN2U3vZDeCYEQOqGFLs2KKBYE7A3rtXD1WtBXrwUFvVyvimBBRakKAtJ7CyEJhJYE0nvvbdv7YWHDmgQSBAlyfl/Mzpw5c3YzxJ1nnuccPB6LwHGUt9V+o95AXfmFJ/40utldlnELl4cIbAiCIHQCDl52HF+XzsQ50ShtFQAY9EaOrDyNg3fn/x9nU60e965ORE0OYfXzOzt07H3fjcOjm7PltY1j89OV8qxqvp+5id53hnH7/BFkxhWw7l/7sHNWEzEx8LKNX+g4Lwc1647m8+bN3bFVmr9O6A1GVsXn4u3Y8Vnk/2p1TQbCPDRM7uPD88sS23XM9uQinvkpkTdvimB4mDuni6uZvTIJtULGjMEBALxxcwQvT+hqddyU/+1nQKBzKz0KgiAIl8OGpHxeXpmEm0bF/UMCsVe1fZtbd6qCvA8TMRlN+L7aB9seLf8+mwxQnFpx4ZOGuvzZYQuXkQhsCIIgdAIe4c5UF9Zx/Ld0et9pznRI3ZaNXCXDf6An9eWNlrYJy1LY+0US5dnVOHjZ0W9aN6If6I70bPrkvoVJJK5IpSyzGrVWSegIH254dQA2DuaAQcLyFNa/vp+7F45lw5z9lGfX4B3lxq0fDsPJT3NJ4w8b5UvYKPPT7l9e2NWhY22cVGjcbVvdd+iHk2g8bJn01iAA3EIdyU0oZu+XSSKwcZWFd9FSWNXAb0fzufNspsO2U0Wo5FIGBDpTUaeztF0Wl82Xu9LIKqvD29GGewf68cCQQMs1+9XuNFYcziGztA6tjZwRYe68MqkbDjbmIN/yuGzeWHOchdP7MWftcbLL6onydeDDKVH4Ord+7VzMyHB3Roa7A/DC8iPtOmZ1fC6jw925b1AAAH4utjwxoo7Pd55h+iB/JBIJWrUCrVphOSYuo4yssjrmTY1qo1dBEAThQlpbxvV8vx3N54PfT9Gti5bJvX2wUcrabJuxIZOcd+KQu9ng83IvlF6d/+GR0D4isCEIgtBJ9J4aRvyyFEtg49zP5VnVljZxS0+x/aN4Js4ZhFekK0Up5ayZvQeZXMrAmREASKQSxr8ejZOfhsrcGta/sZ/1b+zn9vkjLP3omwzs/uwIt3wYg1wlY/XzO1n76l6mLzGnymfGFvD9jI0XHO+wJ6OImdXrT7/vnx/dir7RgHOAlkEP9qD7pOaARU58EcHDrNNDQ4b7kLgyFYPOiEwhFve6mqb292V5XLYlsLEsLoc7+vqSVVZnabM0NouPN6cw5+buRHo7kFxQzexVSShkUkuWg0Qi4fUbI/B1tiW3op431xznzTXHmTe1+fpq0hv5bMdpPpgShUou5R/LjvDK6iSWPDgQgNj0MmZ+HXvB8T45MoQnR4Zc8vtt0htRKay/MKsVMvIrG8gpr281yLI0NpswD3v6+ouMDUEQhEvxx2Vcz7crpZjfjxcQ6m7P/93ag5P51a22M+mNFC89TfLaTGy6O+H9jyhk9opW2wrXJhHYEARB6CR63hrMpndiKU2vRGmn4PTOHCbOGcT2j+MtbXZ9msgNr/S33Pw7+WkY+nhPDi05aQlsDHqwh6W9k6+Gsf8cwNKHN3Pbx8MtT8iNehOT3h6Ea7B5ObMhj0Tyy0u7MZlMSCQSvHq68tiG2y443vNLRi6F0k7BDa8OwK+fB1K5hOTNWSyftR19o4Goyeabz5rieoKGWpc12LmqMepN1JU1oPG4tKf1wuVxS5Q376w7SXpJLXYqGbtSiplzS3c+3pRiafOfran8c0I4EyO7AODrbMvjZXUsOZBpCWw8OLQ5mOXrbMvsCeE88t1hProjynLN6o0m3rqlh2XJvkdignhpxVHLNdvTx4H1T1vPUfNHjrZ/7ktsTJgbb/12nF0pxQwNcSWjtJaFu9MAKKpubBHYqGrQsS4pj5fGhf+p8wqCIAjWTCYTvx8rYPfpEnr6ODClrw9qReuZGo05NeT/5xiN6dWE3BaE9I5AJHLxYOTvRgQ2BEEQOgkbBxXdxvkTvywFtVZJQHQXHL2b112vLa2nMq+Wtf/cy2+v7rNsNxpMmEwmy+u0vXns/uwIJacraKjWYTIYMTQZqSmuQ+thTrmUq2SWoAaAxsMWQ5OR+sombB1VKNRyXAK0V/T92jmrGfJIpOW1d0836soa2PvFUUtgQ+jcHGwVjOvuybK4bLRqBQODnK3m1yitaSSvsoFXVifxr1+OWbbrjSZovmTZd7qEz3ac4XRRDdUNOgwmE00GI8U1jXho1QAo5VJLUAPAXauiyWCksl6Ho60StUJGgOuVTSm+e4AvmWW1PPxdHHqjCXuVnPuHBDB/SyqtTaT/S0IuRhNM7uPdcqcgCIJwSQxGE6sTcojPqiA6yIUbe3ZBKmn5R9hkMlGxKYfi71KQqmV4vRhFv9vDSMi6yNwZwjVJBDYEQRA6kd5Tw1j9/C6UdgpGPd/Hap/p7BLoN747BN++Hq0eX5FTzQ/3b6Lv3V0Z9XwfbJzU5B8rYcVTOzA0Na+hLpX94QvA2S8EJqP5bvOvLEU5n09vdxKWp1pe27vZUFNcb9WmtqQBqVyCrbP6sp5buDR39vPlH8sTsVPKeW6s9Uo4Zy8n3rk1kr7+Tq0en1Nex/3fHOLuAX48NzYMJ1sFx/KqeHppAk365mtW/ofIgQSJ1Tn+ilIUiUTCPyd046Vx4RRXN+Jsp2TvGXN6tF8bZSgTenhecGZ+QRAEof10BiNLY7M4VVDN6G7ujOrqjqSVoIa+opGCz09QG1+CXS8XPJ/ojvxPZpoKnZsIbAiCIHQiQUO8kCmk1JU1EH6Dv9U+ezcbNB62lGVW0+v20FaPzztagkFnZPzrA5HKzGmWKVuzOjyOv6IUpTX5J0qtJhL16ePOqY2ZVm3O7M7FK9JVzK/RSQwJcUEhk1JW18QN3a0Dbm4aFR5aFZllddze16fV45NyKtEZjLx2YwSys8GLbaeKOjyOv6IU5RyZVIKngzmwtjYxjz5+jrjYW/97SMyu4GR+Fa/fGHFZzikIgnC9q28ysORABpmlddwc5UV0UOurktQcLqbgf8cx1htwv78rjuN9LcGP9izjCmIp12uRCGwIgiB0IhKJhCc23obJZC4X+aORz/Vh/Rv7UWuVhI30waA3kn+slKqCOmKejMI50AGT0cT+RceJGB9ATkIRBxYf7/A4OlqK0liroyyjCjBnfVTm1ZJ/vBQbR5WlnGbz+4fITSxm5tKJACSuSEUql9KluwsSKSRvyebQdycZM7u/pd/+93Yj9tuTbJhzgH73hJMVV0jiilSmfDqiw+9JuDIkEgm/PxuDyWRCJW95zT43Jow31hxHq5YzMtwdvcHEsdxKCqoaeHJkCAGudhhNsHhPOuN7eBKfVc7iPekdHkdHS1FqG/VklNYCYDSZyKto4HheJY62Sks5zfu/n+JIdgU/PhwNQFltE+uS8hkU5Eyj3sjyuBzWJeXz86ODWvS/9GAWga52DAoWywEKnV9cXNzVHoJwHbmU6626Qcc3+zIoqmpkan9fevo4tmijb9BT+NVJKjbloPK3p8sbkah87a3atGsZVxBLuV6DRGBDEAShk1HZt5223vfuriht5ez9IomtH8QhV8lwD3NiwIxuAHh2c2bCm9Hs+d9Rts09jG9fd254dQDLn9x+Rcecd7SEb+5ab3m9/eN4tn8cT68podz2UQwANUX1Viu8AOz6TyIVuTVIZRJcAh245YNhVvNrOPlpmPbNDfz+1kEOfX8SjbstE96MFku9djL2qra/Ttw1wA8bpYwvd6XxwcZk1HIpYR4app+dOLRbFy1v3BTB5zvPMHdTMn39nXhlUjdm/ZhwRcd8NKeSuxcesLyetyWFeVtSuL2PDx/daV6ataiqkczSOqvjVsXn8O/1JzGZoI+/Iz89Ek0vX+sv2DWNetYezePp0a1nVglCZ9O3b9+rPQThOtLR6y23vJ4vdqVR06Bn+mB/Qt1bLk3fkFbFphcPUJVZjdON/rjeHYJUZHZeVySm82ecEzps8uTJrFq16moPQxAEQRAEQRAE4ao6f76Ly3GbeSKvimmLDtKgMzBjUECL1aeMjQZKV6ZRtjYTG2c1Lo91wy6y7WyLnh4a1q5NbXP/OZMmBrNu/Zk/3QZg8vhgqg8VX7BNSIw3jj4tAzZC+4mMDUEQBEEQBEEQBKFT2ZNawuPfH8ZGKeORYUG4a60nDa+JK6Lw62T0xQ1oh3dh5Mt9ic+uvOAcGmLujL8vEdgQBEEQBEEQBEEQOo0Vh3OYvfIoIe72vHNbD5ILaiz7dEX1FH2TTE1cMUpfO3zn9MO2mxNKO+XF588Qc2f8bYnAhiAIgiAIgiBcxy53+YAgXMiFrjeTycSnW08zb0sKQ0Jc+N+0vlTV60guqMGkN1L2WyalK9JAAm7TQnGa6IdELubSEERgQxAEQRAEQRAEQbjKdAYjr65OYllcDpP7ePPe5J4o5VKq6nXUHSujcNEpmnJrsR/gjvvMMBSuNld7yEInIgIbgiAIf2Mrn9lBU52OuxeOvdpDEYR2efanBGqbDCyc3u9qD0UQBEG4wnLKzStP1Tbqee2XY8RmlHP/4AAeGBpAUXUDdaX17P0wnuzfs5C5qnGd1R2bSGd0gO4Pc2mI+TOubyKwIQiCcBX9+OBmmup0zFw6scW+4tQKFoxZyX1LxhES43NJ/U96e/DfIq34zO5ctn10mKKUCpS2cnrdEcroF/oilbWdftpY08Tm9+JI3pxJXXkjjj729L+vG9H3d7e00TXo2fh/sRxbm4a+0UDwMC8mvT0Yraf4ctSWh749RF2TgR8fjm6x73RRNWM+3sV3DwwgJsztkvp/69YeXOuX7LHcSj7feYbDmeWU1Tbh7WTD3f39eGhYoFUK9vn0BiMhr25odd/MwQG8eXP3FttfWnGEZXE5vHZjBA8OFUsgC4Jw7dmVUkJVvY5v92dQWNXA5N7ehHpo2JlUSNlvmZStyQS9EVm0G/KB7tQopNS0NY+GmD/juiYCG4IgCFdRn6lh/PTIFsqzq3HytV7mK/7nZBx97Aka6t3hfg06IzKFFLVWebmGetXkJZXww/2biHmqF5Pnj6Aqv5a1r+wFE4yd3b/N4za8eYDM2AImzx+Bo489GfvzWfvKXuyc1UTeEgzA+jf2c3pHDnf+dxRqByUb5hzgxwc388jaW5BKW78Bvd7d2c+XR78/THZZXYtl934+lI23ow1DQ1w73K/OYEQhk6JVKy7XUK+apNxKXO1VfHxnL7wdbUjILuefq5IwmEw8Njy41WPkMimxr4622paQVcGjSw5zY88uLdqvOZLHyfxqXO1VV+Q9CIIg/BUKqhr4dl8G9ToD0wcFEOpiR/mmbEqXp2GobMJ+gDtDn45iR2LB1R6q0MmJwIYgCMJVFDrKFztXGxKWpzLq+T6W7QadkSOrTzNgegRSqYSN/3eQ5C1ZVObVYu9mQ4+bghj5XB/kKhkAWz+MI2VbNgNmRLB7wREqcmt49eR01ry8x6oUJWVbNrv/e4SilHIkEvDu5c6ENwbiGuwIQGlGFZ8OX85dX4zm4LcnyIkvwtFXw8Q5gwga4mUZX1FKOZvfO0TmwQJMRhMe4c7c/P5Q3MOcADi8NJl9C5OoyKnBwdueAdO7MXBmRJtPqy/k2No0PLo5M+KZ3gC4BGgZ+3I/Vj67k+FP90Jp2/qNcHZ8EVG3hxI4yHxT6OSr4fBPyeQkFhN5SzB1FY0krkjl9vkjCBpqfm+TPx7O/GHLyNiXb9kmWBsV7o6rvYrlh3N4fmyYZbvOYGR1Qi73RQcglUp4Z90JtpwsIq+iHjeNiht7evHc2FBUcvM1O3djMltPFTFzsD8Ltp8mt7yeE2+NZ/bKo1alKNtPFfHf7adJLqxGKpHQy9eR12+KINjNHoCMklpGzN3B59P68t3+DOKzyvF1smXOzd0ZfF6AJbWwmvc2nOJgehlGk4lwTw3v396TUA9zQPGn2CwW7k4ju7weH0cb7hvkz8zBAZd0zd49wM/qtZ+LLUdzKtlwrKDNwAaAu8Z6KcPNJwoJdbenX4Cz1fbssjr+77cT/PRINPcsPNjh8QmCIHQWX+w4g0Im4b6e3jgkVZD2yxH0RfWoQrS4PNYNVZAWO287SLzaIxU6OxHYEARBuIpkcim9poSSuCKFEc/2tmQJJG/Joq6skd53hAKg0ii5dW4MGg9bilPKWfvKXuRqGSOfbQ6GlGVUcWJ9OlM/H4VULkWmlLU4n65ez+CHe+De1QldvZ4dnyTy40NbeHLTZGSK5rKOLR/EMe7VAbgEObBjfgIrntrOs3unorSRU5lfy+IpvxEQ3YUZP05ApVGSm1iE0WCuH4hdcpJdnyYycU40XXq4UniqjDWz9yBTSOk/rRsA303bQPbhojY/F6lcyj+T7gNA32SwBHDOkavl6Or15B8vxb+/Z6t9+PXzIHlLFn2mhqH1tCMztoCi5HKGP20OkOQdLcaoNxEc05wR4+SnwSXIgazDhSKw0Qa5TMrtfXxYeTiHZ0eHWq7ZrScLKatt4o5+5rIpe5WCuXf0xF2jJrWomldWHUOtkPLsmOZgSGZpLeuSCvjfvX2RyyQoWyktqmsy8NCwILp6aqhvMvDp1lQe/jaOjc/FoDiv/YcbT/HqpG4EukbyyZYUnlqawJ6XR2GjlJFfWc+Uz/cTHeTMDw8NRKOWk5hdgd5ovmaXHMjkP1tTmXNzd3p4O3Ayv4p/rkpCIZMyLdofgPsWHeRwZnmbn4tMKiHpzXFt7q9p0ONg0/5slOoGHeuO5vPCuK5W23UGI7OWJvDc2DCCzgZ3BEEQrlW2EgkTa+UYPjpGaUE9EhcVitv8IUhDlcEIqRWixERol+susJGfn8/s2bMpKipCKpUyfPhwXnzxxUt6IiMIgnA59Jkaxp7/HSVtT65lLo34n5MJHuaNg5f5xuVctgKYMw+GPl5D7HcnrAIbBp2RyfOGY+fS9izh3SdZ1+HfOncY70UuIS+pBN8+7pbtgx+JJGy0+anz6Bf7cvSXMxSeLMO3jzsHvzmOWqvijv+OsgRDXIMcLMfu+k8i4/41gIiJ5nM5+WkoTa/i0JKTlsDGrXNj0DUY2hzn+X+SQ2J8iP32JEm/nqH7jYFUF9Wz89MEAGqK6tvsY9Lbg1kzew8fDfwJqVyCRCJh4tuDCB1h/oxriuuRKaXYOFin8tu7qqkpbrtfAab29+XznWfYc7rEMpfGz4eyGRbqhpej+fp7Zkyopb2vsy2Pj6jn2/0ZVoENncHIvDujcLlAOcWkP5RhfHhHT3rO2URSbiV9/Jws2x+JCWJUuAcAL4zryi+JeZwsqKKPnxPf7MtAayNnwT19LMGQ84MCC7al8uqkbkyI7GIZb0ZpLd8fyLQENj6cEkWDrn3X7B8dya5gdUIuC+7p3XajP/glIReDycTk3talaHM3JeOhUbXIChEEQbgWGIzWkyjdvKcS0mswaRTIx3kj6+6ERJSCCpfgugtsyGQyXnjhBSIjI2lqauKBBx5g06ZNjBvX9lMWQRCEK8kl0IGAaE8SlqUQEuNDVWEtZ3blMuU/Iy1tjq1N48DXxynPqqapVofRYGpxI+XgbX/BoAZAaXol2z6OJzehmLryBkxGEyYTVObVWAU2PLs1p75rPMwTadaWmm/2C46X4jfAwyrD45zqwjqqC+v49aXdrJm9x7LdaDBZfVHpyOScYaN8GfNyP9a+spdVz+1EppQR81Qvsg8XXfBmcv/iY+QeKebexWPRetmTcSCfjW8dxMlXQ/AlzFsiNAt0tWNgoDPL4rKJCXOjsKqBXakl/Ofu5hv3tUfy+GZfBpmlddQ16dEbTfzxu6q3o80FgxoA6SW1fLw5hcTscsprdRhN5ms2r6LeKrAR7qm1/OyhNZd0lNY0AXAir4r+Ac5WGR7nFFU1UFjVyMsrj/LPVUmW7XqjCdl5F5ing7rFse1xuqiaB789xCMxQdzQvfXsotYsjc1mfHdPnOya58nZe7qEXxPy2PDMsEsaiyAIwl+pIqfa6nVNk55XNp2ybpRXhzzGE1lvFyStfK8QhPbqFIGNzMxMFi1aREJCAqdPn6Zfv34sWbKkRbvTp0/z9ttvk5iYiEaj4Y477mDWrFnIZC3Trdvi7u6Ou7v5y7tSqaRr167k5+dftvciCIJwKfpMDWPN7L3meR+Wp2LjqCL8BvOT4sxDBax8ZgcjnutDSIw3aq2Skxsz2fpBnFUfStuL/0n/4f5NOPpquPm9oWg8bJFIYcGYVRiajFbtpPLzvlycvbczGS++VMW5FVhufm8o3r3drfadH4ToSCkKwNDHejLk0Uiqi+qwcVBRllHF1g/icPLTtHp8Y62ObXMPc/eXYyyZJ57dnMk/Vsq+L5MIHuqNvZsNhiYj9ZWNVlkbNSUNBA29cIBIMGdtzF6VREVdEysO5+Boo2BshDlj4lBGGc/+nMhzY0IZFuqG1kbBxuMFfLgx2aoPG+XFr9kHvjmEj5MN703uiYdWhUQiYezHO9EZrK9Zuaz5Ajv3k7Edy6ucu6zfm9yTXr6OVvvOv2YvpRQltbCauxceZHIfnxYlJRdyJLuCE/lV/OvGblbb958ppbC6gX7vbLFsMxhNvLPuBIv3pLN39qh2n0MQBOFKO70r1/JzYaOO/8Zl4pFovaKJ6qGuSGw6xS2pcI3rFFdRamoqO3fuJCoqCr1e32qbyspKZs6cSUhICJ999hlZWVm8//77GI1GnnvuuUs6b3l5OVu2bGHx4sV/ZviCIAh/WsTEQNa/cYCjq0+TsCyFqMkhloyIrLhCHLztGf5UL0v7iuzqtrpqU01xPaXpVdzywTD8B5ifHOckFrcrYHE+z+4unFiXYVl55XwaD1vs3Wwoy6qm520hbfbRkVKU5m0StGezR5LWpOHoY49HROt1t0a9EaPehOQPT+glUonl/Xr1dEMql3Bmdy49bgwCzE+XStMq8evr0ebYBLOJkV14Y81xVifksiwum8l9vC0ZEXEZ5Xg72jBrVHM5Sk55XYfPUVzdSHpJLe/f3pMBgeYsosTsCjp4yRLhpWV9Ur5l5ZXzeWhVuNqryCqr49bebWfydLQUJbmgmnu/OsAtvbx5ZWK31g9qw0+HsghwsWVQkPX1PXNIADdFWc/9cu9XB5nS18cyt4kgCEJnE3+qmMObM+mf14hUbf1AWgQ1hMulU1xJo0aNYsyYMQA8/fTTlJe3fCLy008/0djYyIIFC7C3t2fIkCHU1NSwYMECHn74YeztzbWyt912G3l5eS2OHzp0KB999JHldVNTE08//TQzZswgOLjtGcoFQRD+Cgq1nMibg9g/by/kAAAgAElEQVQxL576yib6TG1+uusa6EBlXg1Jv57Bu5cbqdtzOL4uvcPnsHVWYeOoIu7HU9i721KVX8umd2M7XMs6cEYEh39MZvmT2xg2Kwq1VkVuYjHuXZ3w7ObMiGd7s/Htg6g0CkKH+2DQG8lPKqW6uI5hj0cBHStFMRpN7F94jJDh3kikEo6vT2ffl0lM/Xy0ZeLKitwaltz3Oze8MoCuY/ywcVDh18+Dzf+ORaGW4eBlT/r+fI7+cprxr0ebPw9HFb2mhLLpnVhsndSotUp+f/sgXXq4EDC45fKagjW1QsYtvbyYvyWVynodU/v7WvYFutqRV1HPr4m59PJ1ZEdyMeuOdjw70tlOiaOtgqWxWbhrVORXNvDvDSdblLRczIxBASw9mMWsH+N5cmQIWrWCIzkVhHlo6NZFy7NjQvm/dSewV8kZ3tUNvcFEUm4lxdWNPD7C/B2hI6UopwqquGfhQWJCXXl0eBBF1Q0ASJDgpjFnB+VW1HPfooO8MqEbYyKaA2m1jXrWJOYxa1Roi/m/XO1VLZZ3lUvNfQaLiUSFPyE3N/fijQQBqKtsoKla1662JWcqOLgpA2NWLc4qKd6jvYkc6sOA+p0A2EU5szeh40FvQWhNpwhsSKUXr6fatWsXQ4cOtQQwACZNmsTcuXOJjY1l1Chz+uXq1asv2pfBYOCFF14gIiKCBx544NIHLgiCcBn1uasrh74/hW9fd9xCm1Piu40PYNCDPVj/5gH0jQZCYrwZ8Vwffp9zoEP9S2VS7vjvSDa8eYDPbliFc4CW8a8P5McHNneoHwcvex5YPolN78byzdT1IJHgEe7Eze8NBaD/tG4obRXsW5jElvfiUKhluIU5MXBmRIfOc76UbVns/E8ChiYjXbq7cM/isZaJVsE8cWrJmUoaqpos2+74bCRb3o9jxdM7qK9oxNHHnjEv9aP/tHBLm4lzBrHx7YMse3wr+iYjQUO9mPLpCEvARLiwu/r78f2BLPr6OxHi3lwWNL6HJw8ODWTO2hM06gzEhLnx3Ngw5qw90aH+ZVIJC+7uw5y1x7lh/i4CXex47cYIHvj2UIf68XK0Ydljg3h3/Snu+vIAEiC8i5b3JkcCMC3aHzuVjIW70nnv91PYKGSEedgzY3BAh85zzm9H8imrbeKXxDx+SWx+2CKTSjjz7kQAdHojacW1VDda3yCsOZJHk8HIlL4iA0P463h5iVWghPZpqtZZlZi0JkQdyTCHSez/6jh1NlLKo52554ZgtDbmOYPc1OZSVY2HG3DmSg9ZuE5ITKZ2FKD+hc5lbPxxjo1BgwZxzz338NRTT1lt79WrF7NmzeKhhx5q9zleffVVjEYj77777p9eDWXy5MmsWrXqT/UhCIIgCIIgCILQ2VXkVLca2DDoDOQkFLN12W7cFF5UK/Uc7+lMcD8PZvi5WU3GfI6mvxurfr94YGPSxGDWrb887a5GX5PHB1N9qPiCbUJivHH0aX3eMKF9OkXGRntUVVWh0bT8ZWu1Wqqqqtrdz+HDh1mxYgVhYWHceuutANx+++1Mnz693X38/PPP/PzzzwCtls0IgiAIgiAIgiBcS9pTZqJrtJ4PsaGqiYwD+WQcLEBXp8cghe3d9WSHe/BIoBdjXbRt9CQIl9c1E9i4XPr27UtycvLFG17A1KlTmTp1KmDO2BAEQRAEQRCEa9X589OJspS/n/bOi6Fr1JN5sPCCbXx6uwHmZeLT9uSRe7QEk9GERzdnmno583W9BpNRT/GK1xn73rJW+yguN6+KVlfY+qIRgnAprpnAhlarpaampsX2qqoqtFoRCRQEQRAEQRCES+Ht3bwiUCerUhcu4HIGLKA5aNEWk9FExoF8Yr87SWlaFTKlFP8BnvgP9mSdvp6VRRXoynIp/uVdDNUlbfYzcdZwy8/vfnrwouMShPa4ZgIbQUFBpKWlWW3Lz8+nvr6eoKCgqzQqQRAEQRAEQRCEv157JvKEiwcsLqaxpomsuCKyYguoK29E7aCk2wR//Pp70qiQ8ElWIYk19Yx20rB47stgaN+qKYJwOV0zgY2YmBgWLVpETU2NZWWU9evXo1arGTBgwFUenSAIwpUzb8jPDJgewZBHI6/2UASh3Ya8t40Zg/15JEYsqS4IgnCtMZlMlKZXkXmwgPzjpZgMJlyCtAx5LBKlnQKpTEp6fSMfni6gTKfnUW83xrpoWXxeUMPk2/6l3QXhz+oUgY36+np27jSvZ1xYWEhNTQ2///47AMOHD8fGxoa77rqLJUuW8NRTT/Hwww+TnZ3NggULmDlzptUSsIIgCNea2tJ6tn8cT+qOHKqL6lBrlbiHOTHsiSiCh3lfvIPrnEFnZPdnR0hckUp1YR0uQQ6Mnd2f0BEXXi5z/Zv7yY4roiilHHs3G57bO7VFm9M7c9g+L56ilArkSim+/Ty44ZUBuAY5XKm3c80orWlk3pYUtp8qpri6Ea2NnDAPDY+PCGZY6J97Ong9WBqbxar4HJILqjEB3b20/OOGrvQPcG7zmNKaRp79OZHkgmoq6nS42CsZ082DF8d3RatWWNqdKqji9V+PcyS7AkdbBfcM8Ofp0SF/eiU4QRD+Opcykefl0FSvJye+iMyDBdQU16NQywiI9sR/gCcad1t8eruRk1DMrvJqPs8pxl4u5a1gb8Js1S362pKYf9nHJwht6RSBjdLSUp555hmrbedeb926FR8fHxwcHPjmm2946623eOyxx9BqtcyYMaPF8q+CIAjXmp8f24qu3sAt7w/DOUBDbWkDGQcKqCtvuNpDuyZsnRvHkZWnueX9obiGOnJ6Zy4/PbKFh1bdSJcerm0eZzJC1O0hFCWXc2Z3y1Te8qxqlj68hYEzI5g8fwRNtTo2/zuWH2Zu5Jldd17Jt3RNePz7eOp1Bj6Y0hN/F1tKa5o4mF5KeZ1IQW6PA2ml3NjTizducsJGKWPRnnSmL4pl/TPDCHRt/SmnVCJhXHdPXhoXjpOdgszSOl779RhlK5v47719AKhu0DHtq1gGBjqzZtZQzhTX8OLyI9gqZTwcI0p3BeFa0Z4ykz9bYnKOyWSiIruGlG3ZpG7Pwag34uSrodeUULx6uiBTyCxtdQYji3JL2FBaSYSdmuf9PHBUdIpbSuE61ymuQh8fn3atVBISEsJ33333F4xIEAThr1Ff2UhmbCHTfxhP0FDzTPSOPhq8o9r+snJk1WnWvbaPyfOHEz7WH5PJxN4vkoj74RTVhXU4B2gZ+lhPoiaHALB81jbUWhU3vTsEgK0fxrFrwREeWn0Tvn3cAfgo+ifGvNSPqMkhrP7HLurKGgga5sXez5PQ1esJH+fPpLcHo7Qx/2/jYucE2PFJAvE/J1NTXI+Ng4rgYd5MnmeeMCzjYD6b/32IouRyJDIJrkEO3PLhMDy6tv20ui1HV51h6BM9CRvtB8CA+7Sk7clj38Jj3P7JiDaPm/TWIAD2fpHUamAjL6kEg87ImJf7IZVJARj2RBTf3L2B2rIG7JxbPp26XlTW64jNKOP7BwcyJMQcPPJxsiXK17HNY1Yn5PDaL8eZN7UXYyM8MJlMfLErjR8PZlFY1UCAix2PjQjitt7mTJtZP8ajtVHw7m3mEqy5G5NZsP00q54YTB8/JwAG/XsrL43vym29ffjHsiOU1zUxNMSVL3adob7JwA3dPXn7lh7YKM1fyi92ToBPtqSyLC77bBaKgphQVz6e2guAg2ml/HvDKVIKq5FJJAS52fHBlCi6erZcjv5iPrmrt9Xrd27twabjhexMLiLQNbDVY5zslEyL9re89nGy5b5ofz7bccay7ZfEPBp0Bj66Mwq1QkZXTw1nimr4ak8aDw0LFFkbgiBYNFQ1kZNYRPbhImqK6lHYyPHt647/AA8cvFpmxJfr9Ly96SRHSqu5ydWBe7u4IBd/U4ROolMENgRBEK5XSjsFSjsFyZuz8OvngUJ94T/L+xcfY8e8BO5ZPJaAgV0A2PrhYU5syGDS24NxDXIgO76INbP3YOOgJGy0HwHRXTiw+Lilj4wDBdg6q8k4kI9vH3dKM6qoyq8lYFAXS5vMQwXYu9sw44cJVObXsPzJ7bgEOhDzZFS7znlifTr7vkxiyqcjcA93pra0npz4YgAMeiNLH9pCn6lh3P7JCAw6I/nHSpFKzcGDitwa/jtm5QU/h563hVgCNfomA3KV9eemUMvIirv4DPAX4h3likwh5fBPKfS9KwxdvZ7ElafxjnK9roMaAHZKGXZKGVtOFtIvwAn1eU/zWrN4Tzrzt6SwaEY/Bga5ADB3UzIbkgp465buBLvZE59VzuyVSTjYKBgV7kF0kAuL96Zb+jiQVoqznZIDaaX08XMio6SW/MoGos/2B3AovQx3jYofHhpIXkUDT/4YT6CrHU+ODGnXOTck5bNwdxqf3t2Lrp5aSmsaSciqAEBvMPLwd3FM7e/LJ3f1QmcwcTyvkrMxL3Ir6hn78c4Lfg639va2BGr+qMlgpFFvwMFW0er+1hRWNfD7sQIGBjYHBBMyy+n/h99JTJgbH21OIae8Hl9n23b3LwjC5deRlUyuBIPeSOHJMrIPF1GcWo7JCE7+GnreFkz/+8IpSq5o9bij1XV8kl1EIyae9XNnqGPHA7qCcCWJwIYgCMJVJJNLuW3uMNbM3sPhpcl4Rjjj18+D7pMC8entbtV269zDHF56iplLJ1hKLJrqdOz/6hjTvx+P/wBPAJz8NOQeKSb2u5OWwMZvr+6jurAOlVZJ7tFiRj7fh/R9+Qx7IoqM/fk4+2tw6NKc/q6yV3LTu0OQyqS4hToSMTGA9L15xDwZ1a5zVuTWYO9uS3CMDzKFFEdve7x7mrNQGmt0NFQ10XWMH87+5uW63UKan/RrPGx5bMNtF/zcVPbNN38hMd4cWHSMwGhPnAMdSN+bx8nfMzAa/9yShY4+GqZ/P55lT2xj/Wv7MBlNeHZ3Ydq34/5Uv38HcpmUuXdEMXtVEj/GZtHdS0s/fycmRnah99lsinM+2pTM0tgsfnw4mh7e5rlJ6pr0fLU7nSUPDmTA2ZtyX2dbErMr+G5/piWw8a9fjlFU1YBGreBoTiXPjQ1j35kSnhgRwoG0UvxdbOniYGM5l71azju3RSKTSghx1zApsgv7zpTw5MiQdp0zt6Ied42KYaFuKGRSvB1t6OljvjZrGvVUNegZ3c0Dfxfzv5UQ9+Ynmh4aFeufHnbBz83+AoHLjzalYKeUM6abx0U//6eWJrD5RAENOiOjw92Ze0eUZV9xTSOeWuvAm6tGBUBRdaMIbAjCFdTeeTEux9KrHWEymajIrSE7rpDcIyXo6vWotUqCY3zw7eOOvZv576iylcCqwWRiRWE5K4rK8VYp+N+4bigzai/b2AThchGBDUEQhKssYmIgoaN8yTpUSHZ8Ead35LBv4TFGv9iXmFnmFPgDXx+nqVbHI2tuxiWweeLK4tQK9I0GlkzfyPnZoAa9EUcf89MUtxBH7N1syDiQj62LGmc/LT1uDGLXp4kYdEYyDuQTEN3FakxuoY6W8gsAjYcduYnF7T5n90mBHPj6OPOH/kxwjA+hI3zoOsYPuUqGraOKXneEsmT6RgIHdyFoiBcREwNx9DbfJMrkUlwCtO3+/Ca8Gc2a2XtYMGYVEgk4+WvpdUcYCctS2t1Ha6qL6vj1pd1ETQ4h8uYgGmt1bP84nuVPbmPG0olIpdd3+u2EyC6MDHfnUEYZ8ZkV7EwpYuHudF4c19WSIfH13gxqGvWsmTXUat6I1MIaGvVGZiyOtbqG9AYTPk7mL9gh7va4aVTsTyvFxU6Fn4stN/bswn+2paIzGDmQVkp0oIvVmELc7ZGd93tx16pJzK5o9zknRnbh670ZDHt/OzFhrgwPc2dMhDsquQxHWyVT+vowfXEsQ4JdGBLiyoTILng7mo+Vy6QEtDE3xsUs3pPOjwez+P6hgWjUF8/YeO3GbjwzOpT0klo++P0Uc9ae4N+TxapJgnC1/ZXzYrRHfWUjeUdK2PtFEmUZVUjlEjwjXPDt645biCOSi/x/rFynZ35WIcdrGxjppOFBb1eCnGzJEYENoRMSgQ1BEIROQKGWEzzMm+Bh3ox4pje/vrSbHfMTGPyI+WbFr78Hp3fkkLQmjRHPNNfmm85mJdyzaCwO3tb1sDJ58xeWgGhP0vfnY+eiJmBQF5x8Ndg6q8k9UkzGwQLGvNTvD8dKrV5LJM3nas85HbzseWrbFNL35nFmbx4b/+8gO+bH8/CvN6O0VXDb3BgGPdCd1J05JG/JYuuHh7l74RhChvt0uBTFzsWGuxeORdegp76iEY2HLZvfO4ST359Lk4397iQKGzk3vNK8pPjk+SP4OPonsg8X4t/f80/1/3egVsgYFurGsFA3nhkTyssrjjJ/SwoPDzNPUtkvwJmdyUWsSczjmTGhluOMJvM1tGhGP7wcbaz6lMuar9uBgc4cOBvYGBTkgq+zLU62So7mVHAwvYyXxne1OlYh+8N1e9652nNOL0cbtv5jOPvOlLAntZR31p3gk60p/PLkEGyVcubeEcUDQwLZmVLM5hOFfLgxmS+n92N4mNsll6Is2pPOx5uS+eaBAfS6wBwl53PXqHHXmAM5jrYK7vh8P0+NCsHL0QY3exUlNY1W7UuqG88ep2pX/4IgWLva5SMd0VSnI/9YKbmJxZRmVIEJ3MOdiLw1CO+ebihs2nf7d670pMFgZJaPGyOc2//AQRCuBhHYEARB6ITcQh0x6o3oGw0AePVwZfBDPfhumnkp7HPBDbdQR+QqGZW5NQQN8Wqzv4DoLuxbeAx7VxuiH+hu2Xb4p+QW82u0Z2ztOadCLSdstB9ho/0Y9ngUH/b7kay4QkJizBM1eka44BnhwrDHo1gyfSOJK1IJGe7T4VKU88+n8JRj0Bk5uSGD7jf+uRUgdPV6q6wVwJKlYfqTZS5/V6Ee9uiNJhr15us20lvLQ0MDmbboIBIJPD069Gw7DUq5lJyKegaHtL1yTXSQC1/tTsPVXsX9QwIt25bGZreYX+PiY2vfOdUKGaPCPRgV7sHjI4Lp/84W4jLKiQkzP2WN8NIS4aXl8RHBzFgcy8rDOQwPc7ukUpSvdqcxb3MKi2f2v+AyrxdyruSqSW8EoLe/E+9vOEWDzmCZZ2PP6RI8tCpLZoogCB3TnkwM+GuzMc6nbzJQeLKM3CPFFKVUYDKYsHOzoetoP7yjXOk61o+chOJ29WUwmVheWM7Ks6UnbwZ54atWXuF3IAh/nghsCIIgXEV15Q0se3wbve8Mw6ObMyo7BXlJ5rTRwCFeqDXNXya8o9y4b8l4ltz3OxIJDH+6Nyp7JYMf7sHGd2IxmcB/oCdNtTpyEoqQSCX0uyccwDLPRmVuDQHRnme3ebJm9p4W82tcTHvOmbA8BaPehE9vN5S2Co79loZMIcUlwIHyrGrifjxF1zF+aD1tKc+qpvBUGf2nmcfa0VKUnIQiqgrq8OzuTHVBHdvnxWMywpBHm5+MH/zmBLHfneCpbVMs20ozqmiq1VFdWIuhyUj+8VLgbOBGKSNslC8HFh1jxycJ5lKUGh1bP4zDwcsOr8i2b4yvB+W1TTzxQzx39vch3FOLvUrO0ZxKPt+ZxpBgV6tyiihfR5Y8OJD7Fh1EAjw1OhR7lZxHhgXx7vqTYIIBgc7UNulJyKpAKpFwz0DzCjfn5tnIragnOsj57DZn/rkqqcX8GhfTnnMuj8vGYDTRy88RO6WctUfzUMgkBLrakV1Wxw8Hsxgb4Y6HVk1WWR2nCqqYNtC8SklHS1G+2HmGuZuSmTe1F4FudhRVm5d3VitkaM9+ft/uy+Db/Rls+8cIALaeLKS8TkektwO2ShmpRdW8u/4Uvf0cLee+pZcXn2xJ5YXlR3hqVCjpJTX8b8cZnhkdKlZEEdpkMl2fwdprKRPjj4x6I5kHCziy+gwFJ0oxNBlRa5UEDe6Cdy83tF3sOvxvvriuiTlpeZw4r/RELZVe/MAOevfTgwBMmhjMuvVnLtJaENpHBDYEQRCuIqWtAp8+7hz4+jhlGVUYmgxoPO2IvDmImKd7t2jv06s5uAHm4MaoF/pi52rDvi+T+O1fe1HZK/GMcGboYz0tx52bZ8PWSYWdi/lmMCC6C0a9qcX8Gu1xsXOqtUr2/O8om96NxaAz4hbqyNQvRuPkp6GmuJ7StEqWPbGNuvIG7F1t6HlrMEMfj7rIWVunbzSwbe5hyrOrUdrKCR3py+T5w7FxaE67rytvoORMpdVxa17eTcaBAsvrzyf+AsCze+7EyVdD0BAvbv90BHu/SGLv50eRq+X49HZj2rfjWp1g7Xpiq5LR28+Rr/dmkFFSS5PBiKdWzS29vHhqVEiL9r3OC26AObjxjxvCcLVX8uXuNP71yzHs1XIiumh5dHhzps25eTacbBW42Jt/n9FBLuiNphbza7THxc6ptVHw+c4zvLP+JHqDiVAPez6f1hdfZ1uKqxtJL6nhiR9yKK/V4Wqv5NZe3jw2IvhSPkK+25+JzmBi1o8JVttv7+PDR3ea/y2U1TaRVtxcy66Sy/jh4GlOF9XQpDfi5WjDDd09eGJ482euVSv4/qEBvP7LcW5asAcHGwUPDQvkoWGtLyErCNezzp6J8UcGvZGS1AryjpVSeKIUXYMBhY0c715ueEe54RKgvei8GW05Ul3HgrWZ1DUamOXrzggnseqJcG2RmK7XEO1lMnnyZFatWnW1hyEIgiAIgiAIQgdU5FS3O7DRnlKO9rTraF8GnZHi1HLykkopPFmGvtGAQi3DI8KFyFuCkEolSOUXzqq40Dl1RhM/FZaxpriCQEcbnnJ3vWDpiXu0B4W5NW3uHxDjZ/n5XGZGW9qbsXE5212NviaPD6b60IV/5yEx3pYJ2IVLIzI2BEEQBEEQBEG4JrS3fEQil2DSX/j5bWcsMQEw6Ayk7cnj2No0Ck6WYWgyorCR0yXShS49XHELdkAql7Y7SNKW3IYmPskuJK2+ibHOWl6d0JWSpLILHlNvMLIlMf+SzykIV4oIbAiCIAiCIAjCdezw4cOWn/v27XsVR3JxHSkfaU/2RGfRVKuj8FQ5BSdLKU6pwKAzorST4x3lhlekKy5B2hYTWl8qk8nElrJqvs4rQSWV8JK/JwMc7FDLZZel/4vJzToJwIlj1YDIUhAuDxHYEARBEARBEITrWL9+zUt+iyr1v05dWQMFJ8ooOFFqWZpV7aDEt687kbcEYzSYkMou76S/VXoDn+cUE1tVS097G2b5uuOs+GtvCf87d+bZ/168XEUQ2ksENgRBEARBEARBuOraU2bSWctH2sNkMlGcWsGpzZkUniijqqAOAI2nLaEjfPDs7oKDl3k1kz9bZtKao9V1/Ce7iGqDgRldXJjk6oBUrJYk/E2IwIYgCIIgCIIgCFdde8pMOlP5SHvoGw2UnKmg8FQ5RcnlNFQ1gQRcArRETArAM8IFO2f1FR1Dk8HIt3klrC2pxEel4JXALgTaqC5+oCBcQ0RgQxAEQeiQhOUprH99P6+enHG1hyII7bI8Lps31hznxFvjr/ZQBOFvpz1ZFu2ZyBOu7WyM89WW1lsCGaVplRgNJuQqGW6hjoTf4IfCVoHK7q9ZNjynoYlX1h8jpayOcS5apndxQSW9PHN1CEJnIgIbgiAIV9nqf+yirqyBe7++wbIteWsWy5/YxqCHejD6xX4XOPrC5g35mQHTIxjyaOTlGCoAPW4KInSk72Xr73xFKeWse20/xafLaazWoXG3pcfNQYx4tjdyZfOkZhkH8vn97YMUp1agcbdlyGOR9J/WzbJ/3pCfqchpuRxd6Egfpn0zrs3zr39zP9lxRRSllGPvZsNze6de0vj+7v6x7AjldU0sntnfsm3ryUKe/DGeh4YG8cK4rpfc95D3tjFjsD+PxARfjqECcFOUFyPD3S9bf+dr0Bl4dfUxjudVcrqohr7+Tvz86KAW7Q6klfJ/606QUliDh1bFozHBTIv2v2j/qxNy+HJXOmeKa7BTyhjZ1Z2Pp/ay7N+ZUsz8LSmkFFSjlEvp6+/MKxPDCXKzv6zvUxDa0t4si/YucXotMuqNlGVWkRVXyJldudQU1wNg52ZDwKAueIQ74eyvvSwrmbSXwWRifUklPxaUYaeUMTvAk35auyt+XkG4WkRgQxAEoZM5siqVX1/ew9h/9mfQAz0uqQ99k+GK3Wgr1HIU6ivzvw+ZUkavKSF06e6CWquk4GQZa2bvwag3csMrAwAoz6rm+5mb6H1nGLfPH0FmXAHr/rUPO2c1ERMDAXhkzc0YDc1PB2uK6vjixl/pcWPQBc9vMkLU7SEUJZdzZnfLL+rtGd/1aFV8DrNXJjF7QjgPDA28pD6a9EaU8ivzFFGtkKFWXJl/D0aTCZVCyvRBAWxPLqKqvuWT6+yyOu7/+hB39vNh/tReHMoo57VfjuFip2RCZJc2+/56bzqf7TjDKxPD6e3rRIPeQHpxrVW/D38Xx/2DA5h3Zy9qm/S8t+EU939ziJ0vjrwi71cQBLPa0nqKUyooSi2n5EwlhiYjUoUUlwAt/gM98Qh3ws7F5qqMrbBRx4KcIk7WNtBPa8uc8eHUn6q8KmMRhL+KCGwIgiB0IvsXHWPze4e45f1hRE0OAVrP6Ng+L54T69N5cvPtVm38BngQ+80JDDojbqGOVOTUsOndWDa9GwvAnMwHATixIYPt8+IpTa/EzsWGfveGEzMrCsnZScRObMhgx/x4StOrUKhluIc7c+d/R2HvZtOiFKUyr4Z1r+8nK7YAfaMBB297Rjzbm8ibO/7E3SVAi0uA1vLa0UdDxv58MmMLLNsO/XASjYctk94yPxV3C3UkN6GYvV8mWQIbf/wyGf9zCiqNku43Xvim+1yfe79IajWw0Z7xXW8W7Unn/Q2neH9KJLf19gFaz+iYtzmFDcfy2fTccKs2/QOc+XZfBjqDkRB3e3Ir6nl3/a4An0UAACAASURBVCneXX8KgIz3JgHw+7F85m1OJb2kFhd7JfcO9OPJkSGWa/b3Y/nM32Ler1bI6Oqp4b/39MFNo2pRipJXUc/rvx7nUEYZjXoDXo42PDsmjJujvDr8/m2Vct69zZwRdaqgqtXAxvcHM/HQqphzizlQGeKuITGrgi93p7UZ2Kis1/HB78l8Ob0vw0Kbn2KHezZff0m5legNRl4aH45Mav4cHh8RzD0LD1JW24SznbLD70cQzmlPiQn8fcpHLkbfaKAkrZLilHKKUiqoK2sAwNZZjU9vd9zDHImaHELhqfKrNkaTycSmsiqW5JciRcIsHzeGO2lwsVGSc9VGJQh/DRHYEARB6CS2fhjH/q+OcdcXYwgb1fFSj4yDBag0SqZ9Nw5MoPG043/jV9P7zjD6Twu3tMtLKmHZE9uIeaoXPW8NJu9IMWtf2YvKXkH0/d2pLqpjxVPbGf1SPyImBNBUpyMnvu202d/+tQ99o4GZP01EZa+kJM36qdCCMSupzG1ZFnKOg7c9s7bc3uq+0owqTu/MpetYP8u2nPgigod5W7ULGe5D4spUDDojMoX1U3+TyUTCzyn0vDX4smeatDa+68ncjcl8tSeNL+7re0mlHgfTStGo5Xz7wABMmPDUqpnwyW7u6OfLtOjmzzQpp5Infohn1qhQbu3lxdGcSl5ZnYS9Ss7MIYEUVTfw1NIEXhoXzvgentQ1GUjIavvm4rVfjtGoN7L04Wg0ajlniq2vz7Ef7yS3or7N470dbdj8/PB2v8+EzAqr4ARATJgbK+Nz0BmMKGQtM1V2pxZjMJkoqWlkzMc7qW7QEeXjyL8mReDnYgtATx8H5DIpPx3K4q7+ftTrDKw8nEuUj4MIaghtuljAoiKnGjAHLDIPFl60v2u1fORiTEYTlXk1FJ+u5PBPyeQfK8VkMCFTSHENdiBoiBfuYY7YuTYH0hU2V+/WqqRJz/9yijhSU09Pexue8HHHVSlu9YTrh7jaBUEQOoEze3JJ2ZbNvYvHXlJQA0CuknHrh8OQq5pT7qUyCSo7BRp3W8u2fQuPETDQk1HP9wHANciB0owq9n5+1BzYKKzDoDPSfWIAjj4aADy6Ord53srcGrpNMM/sDuDkp7HaP+2bGzBcYNI4mbzlUnNf3baW/OOl6BsN9L27K6Nfap5npKa4nqCh1hkZdq5qjHoTdWUNaDxsrfad2Z1LeXY1fe++9HkfOjK+68We1BK2nSpi8cx+lzx/hUoh44MpPVHJz7tmJRLsVTLcNc2rBHy1J42BgS48PzYMgCA3e9JLavl8Z5o5sFHViM5gYkKkJz5O5t9/V0/r6/B8uRX1jO/hSYSXOfvB19n6mvn6/v7oDW1fs3JZx5ZHLK5pZIi9q9U2V3sleqOJ8tom3LUtV0TIKqvDZDLxn22nef3GCBxtlXy6NZW7Fx5gy/PDsVHK8HGy5fsHB/LED/G8/utxjCYT3b20fHP/9VsWJVzcxebEOLfv7xqwaIvJZKKutIHiMxWUnK6k5EwlunpzNopLoNYSyHDy1yL7f/buPDzK6mz8+HcmyWT2yb6zL2FfhEAEDIg7tNYFK4Iiltrqq7igb2urv9a664vVIq5Vq6JWLGBdQFRwQUXCLjsSwpKE7NvMJLPP8/tjkoExARJIGJLcn+vKlcmZM8+cuXlI5rnnnPu007K5U6EoCt/U2Hm9qAKfonBzegIXx5mDs9mE6CoksSGEEGeBpP6xuGxuvv7HFrqNTkZnaf02bEn9Y0OSGsdTkVdDv58lT7pnJfP1s1tw2tykDIqj94Q0nr/4A/qcl07vCWkMntLzuGuFx940mE/u/568rwvpPT6NgZf2JG3o0Yu4xuRIa1zz/Pm47B5Kd1fx+WPr+e7FbeTcNrzVxwHY9O+9pA9PCCZe2kJbjq+j6p9ixOb08o9V+xjVIw6LrvUV/vsnG0OSGseTV2Zn8s+SJ1k94/jH6n3YnB4GppqZ0DeBS55Zw3n9EhnfL4EpQ1KINzb//+im8T25/4MdfPNTOeP7JHDJ4BSGZliC9zcmR8JJUcDjU3jwl4PJ6R+4wHx2+giyHlnFqt2l/HJ4GmU2J39cuo2rz0nnl8PTqHN5+fsXP3HbO5v5983ZqNVyYdPVtGT5SFdZOtIS9TUuin4spyKvhvK8Whw1LgC0Fg0pg+NI7BNDQh8LfXLSz0jBz9aqdLh56lAJG6z1DNBrub1bEinRZ2a3FSHONpLYEEKIs4ApSc+M1y7ijes+5a2ZK5n1zqXB5IZKFfhE5lg+j7/JMTT60/+VrlKBOkLNrLcvpXBzGXnfFrFl8U+sfmojNy2e0mxyYNT0TPpOzGDfVwXkf3eEV6/6mPP+Zzjn3x2YEXIqS1EsaYEdHZL6x+L3KXx033eM//1QIiLVGBN1wYrzjeoqnKgjVejjQj/5tlc42PvFYaY83HSXitNxovF1FUkmLa/OGsqMf67j+ldzeXvOWCz6wBtqdTPnrNff9JzVt8E0aZVKRYRaxaI5Y9h8uIZv95Xz/oYCnlq5h8W/Ozc4K+NY12Z1J6d/Il/tKef7vAqufnEtt07qw90NM0LaeilKojGaCrsrpK3C7iZSrSL2OEtGEk2B///9ko/ubmLWRpFs1nKkYWyLfjiELiqCP005uiPQs9NHcO7jX7LpcDVZPY8/00p0Ti3doeTnEmK6xuwMd72HygNWKvfXUpFfi620HoAobQTxfWLoOzGdhL4xGOK1Z/WMB0VR+L7WzhsfHqLO7WNWajxTEyxEnMVjPpbJHPjwQ6vtOruJifYniQ0hhDhLmFMM3PTeFN64bgVvXvcps965FH2sFn28lpJdVSF9S3ZVtuiYEVFq/D+7oEzoG0PBxtB104c3lGJONRBtDFxkqVQquo1KptuoZCbdOZLnL1zGjk8OHHfWgyXVwOgZAxg9YwDfvvgjua/vCiY2TmUpyrEURcHv9aP4FIiEjHOS2PPZoZA++78tIm1oQpP6GluX7CNCE3FKhUxb6ufj60pSLFre+1021/1zHTNeXcfbc8YSa9AQZ9Swq9ga0nfXEetxjhJKE6nG97McSN8kIxsPhtbM2HCwilSLFmN0IOgqlYpRPWIZ1SOWOy/ox0XPrOGTbUeaTWwApFp0zBjbnRlju/Pi1/v51/cHgomNtl6KMrJHDJ/vDP0/911eOUMzLM3W1wAY3SMWgPzyOlItgdlSdS4vZTYn6bGBnx1uX7BoaKPGCxu///jjF+LnPn1+TbiH0C48Tm9IIsNaUgcKqKPUxPUwM/CyHmj0UcSkG1F1kBlOlR4v/ywsZ6OtnkHxBm6Oj6ObtmPV1PnTI8sBmDqlD8tX7A/zaERn0cXeggkhxNnNlKznpsVTeXPGCt647lNufPcyeo9L4/uXtrN58U/0GJvC7pUHKdhUhjnl5NPlYzKMHF5fivXKOiI0ERjitIy7eQivXP4RXz2zmaG/ChQPXfvPHVzwv6MAKNhcRv53RfSdmIEhQUfJzkpqi+tI7BvT7HOsePAH+k3qRnxvMy6bh7yvi0jsF3PMGFq+FOXHZfuIjI4kKTOWCI2aI9sqWPXkRgZN6RlcZpM1cyDr39zNp39bx+gZAzi8sZStS/YxbcGkkGMpisLm9/Yy5Je9iDY0nZqb+8Yu1r+1i7lfTgu2VR604q7zYCutw+f2U7wzkEBK7BdDpCaiRePrapLMWt773bnMfHUd1/1zHe/enM24Pgm8/E0+728oYEyvOFbuLGHjoWpSLU1rSfxcRqyODQeruLI2HU2kmjiDhpvP683lC7/jmS9+4lcNxUNf/Taf/70kUDdl8+Fqvt9XQU7/RBJM0ewsqqW4xhEy2+FYD360k0mZifROMGJzefjmp7KQvq1dirKv1Ibb56eqzk2928fOI4ECuoPTAstbrh/bg7fWHuJvH+9k5tjubDxYzZJNhSyYPjJ4jDfXHuTNHw7y5T2TgEAdkYsGJfO3j3fy2JVDseiieGbVT8QborlgQDIAkwck8dr3B/jHqn1cPiKwFOWpz/aSZtGGLK0RZ7eWLB9RRapQTpAgbtTVl5m4HV6qDlqpOlDLutd3Up5XE0hkRKqI7W4m84LuxPexEJthRB2pJmNk4lm5xKQ5iqKwqsrGouJKvIrCrNR4fn9hX4p/rAj30IQ4K0hiQ4gu6MAPxbwxfQUQuPC9+/trwzwicSxjoo7Z703hzZkreWP6Cm589zIm3TWS1f+3EY/Ty7Bf9SHrhoHs/eLQSY91/rxRfPzn7/lHzn/wunz87dAc0oYm8OsXJvPVM5v59vkfMSTomHDrMMbOHgSA1qTh8MYyct/chdPqxpJqYOIdI4Lbz/6c4ocVf/0Ba3EdGkMUvcencskDY0/ptasj1Hz7/I9UHrSComBJNzJm1kDO/e2QYJ/Y7iauf+NiVj6Uy4a3d2NK0nPZg9nBrV4bHfyhmMoDVq56dlKzz1Vf7aRif+gOLh/98VsOrju6detLU/4LwF3f/ZrYbqYWja8rSjRF8++bs5n5ai7XvbKOd24ey50X9OP/Pt+L0+3jVyPTuCG7B6t2n3yHhbsv6s+fl20n5/++wu31c/CJqQxJt/DCzHN45ot9vPB1HgnGaG6d1Icbx/UEwKyNZOOhat784SBWh5fUGC1zL+gX3H725xRF4cGPdnKk1okxOpJxfeJ5YOqgU379s/+1IWTpytQF3wFHt6rtFqfnXzdl8fAnu3hn3WGSzNH89ZeDQ7Z6rapzk19eF3LcZ64dwSOf7GLOmxtRFIWsnnG8e/NYdJpAEm1c3wT+MX0kr6zZz8tr9qONimBktxje/M2YNlnmI86Mli4fackFeFcr+Omyuak8aKXqgJXKg9ajMzIiVCQPjKP/+d0CiYxupiYz+jqSEpeHlwrL2FHnZLBByy0ZSaRGRzWZsSVEV6ZSfr4IVrTKVVddxbJly8I9DNFgy39+4r/3fnvCPj2zU7hp8dR2H0t1gY2tS/YBoDVrOHfOqV34fLNgC18+vTn4szFRxz2501EfZ/pyS0hiQwghhDg71BTa2jSxcbJ+bXmsM/2c9TUuUBT2fV1I5QErdQ31liKi1MT2MBHf00JcLzOx3Yz0GJPS4WNxaHMZKypq+XdJFZEqFTekxnNBnAl1w5KzcIzflJXIspXHXz7y5zuOfrDx2ILcEx6rpUtR2rJfOI511aV9sG04cVz75qSfUrF1cZSk84VoJzWFdr5+dgsQSB6camJj69K8kJ/t5Q7yvi6k/wXdT3lsqYPj+c2SQHKnq06fF0IIIUTAms1fBW/nnHN+GEdylOJXsJbUUXXQRtUhK1WHrDhr3QBEaiOI62Gm++gk4ntasKQbTusDn7PR/up6/ppXRJ7DxSiTnt+lJxLfSWZi7d4e+BDSEL0P6BfewYhOo3P87xDiOBov3o+lNXWcAkuHNpRQdbBpwb0tS/adVmJDa9bQIyvldIYmhBBCdEktqYkBHasuxj1P/0/w9oZ3dodlDF63j8Kt5fy0uoCqQ1aqD9vwunxA4H1LXE9zoODnpd1x1Lg7TLHP1vL4FZaVVfPBjnz0KhV3dUtifIzxjOzS4o5Wo3QznLCPr5XFk5uz6J/3Nnw/+awOIVpKEhuiUzvZxXt9tZPV/7eJ4p2V1BbZcVoDnwSYU/T0PDeV824bTlz3oxX1/X6F9W/uYtsHeVTsr8Xj9KI1R2NJM5A2LIFz5wwhsW8Mz4xfTE3h0e0tawrt/LXHa8GfG9frn0zjUhaA4Vf3ZdsH+1H8Cj+tLsBR6wpuB9qoYHMZ377wI0Vby6mvdhKljcQQryV5YDyDLu3BsCsDNRKOtxTF7/Oz8qFcindWUn3YhrPWhd+vYEzU0X10CuN/P5TUwc3viiGEEEJ0dC1JWnhcXg7lnrxejNTFOD5FUXBUu6g6bKO6IYlhLalD8QOqQCHt9BGJDckME7qY6OCFfUKfmA5T8LO1dtgdvFJYzhG3h4t7xXOtzoQl8szNrHX4/KzaWnzCPlPT2m+XMSFOhyQ2RJdWV+lk4zt7mrRXHbJRdcjG7k8P8rtPfhVMbnz97Ba++ceWkL71VU7qq5wU76ik17mpx905orU8Ti87lx8I/jzhlmHYSurJ//4IXpeP7R/uZ8yso8XuyvNqeGP6iuCnGwAuuweX3UPVIRsehyeY2Dgen8dP7hu7mrTXFtWxvWg/uz49wG/en0rGyKQ2eIVCCCHE2aWlhTxF63jdvsBuJYcDSYzqwzZcDQmkCI2amAwTfXIy6H9BN/weP1G6rnWJUuv18VZxBd9U20nWRPJAr1R+kdOj0yZwhGgPXeu3huhyjp0l0ejSv4wN1rvQWaI5f945JPSxoDVpiIyOwFXnYcfH+Wz7YD+OWjdrX9nBLx4ZB8DuTwOJBnWkikv/XzZJ/WOor3ZRdchK3jeFREQG1nf++sXJFGwq49MH1wGBgp+/fnFycAympJNvJbj704PBP/opg+JI6h/LsCv6kP/9ESAwm+PYxMZPqwuCSY3BU3txzrX9UfwKtcV1HMotafoEzVBHqpl4xwgS+sSgi4kmShuBx+Fl/7dF/PDaTnxuP1//YwvXv3FJi44nhBBCiK5FURTqK51UF9ioKbBTXRCYjeFvWJajj9OS0CeGuO4mYnuYMCUbUEe0rhhmZ+FXFL6qDmzh6vT7uSophquTYolWd656IUKcCZLYEF2aMVFH2pB4Nry9hyPbK6ivdgb/8DYq3FIWvB3dUJ8jIlJNfG8zqUMS0JoDbef9z/Bgv/Rhibjrjq6ZjYyOaHVNi61Ljy5DGXZFYKbFwMt68skDa/G6fBT9WEH5vhoS+wVmiESbo4L9LWkGEvrGYEkzoFKpGD1jQIueMyJSTd+JGfzw2g6KtpZjr3Dgc/tD+nSlNxxCCCGEODGXzU3ZT9XUFNioPmynutCGpz7wHihCoyYm3ciwK/sSpY0gtruJaGPHqXXWngqcbl4pKmd3nZMBei2/z0ikm1ZiI8SpksSG6NSaKx4a2/1obYvNi3/iwz+ceHvYxgrcAFnXD6BgUxkep49FN3wGgCFBS8qgeAZd1pORv+4fnLVxOmqL68j/PrDGUaVWMfRXvYFA4dPMC7sHl6hsWfITF/9pDAADLurBV09vxl7uYO0/d7D2nzuI0kaQ0CeGXuNSyf7NYCxpxhM+7/5vi3j7xs/w+45f7MxZ6zrt1yeEEEKIjsfn8VF7pI6aQnvDl426CmfgTlVgRmrqoDhiupmI7WbCmKRHHaHqcjMxTsTp9fNuSSUfldegVau5NSOR82OPbuEqhDg1ktgQndrJZkl89+KPwdt9J2YwZtZAtBYNR7ZVsPKhQJVmRTl6kT/8qn5Y0o1s/zCf4p2VVObXUlfhZP+aIvavKaI8r4bL/pJ92uP+cek+FH/geRW/wtNj32u237YP9nPhH0ajjlBjTNBxy/Ir2PTeXg5vKKEivxbrkTqKd1ZSvLOSXZ8e5NaVV55wV5jvX94WTGqkj0hkwu+HYkjUYS2uY8ncrxvicdovTwghhDijWrqTydmwQ8nZwu/zU1tcR02hLZDEKLBjK60Pvj/RmjVYMowM+UVv1JEqLOlGorRyaXEiW2z1vPFhIUV2FxNjjcxKTTijxUGF6Mzkt4/o0mqP1AVvX3x/FsmZcQAc/KH5itCKotBzbCo9x6YG2wq3lvPPX30EwI6P8oOJDdUxEzca3wS01NaleS3qZyutZ/+3R+g3KQNFUTAl65l058jg/Y5aF+/M/pyCzWXUFNop2FRGv0kZxz3esfGYeMcIMhu2lP1xWcvGI4QQQrSVttxWtTU7mXR1Oz7Op6bIjq2kPli7K0obgSXDSJ+cdGIyjMRkGIM7s8lsjJMrc3v415EKNljr6W7W8tfeqQw1nrzemhCi5SSxITq1QxuaFs1UR6jpdk5gV4/YbibK82oAWPPcVs65NpMj2ytYs/DHJo8DeP/WL1FHquiZnYo5xUCULpL93x6tnn7sjiT6WG3wtq20nq1L9xHb3USUNpK0oQnHHXPBplIq82sBiIhSc+lfxjbZq33XpwfJ/66xiOhP9JuUwc7lB/jh1R1kXtSDuO4mDPFabKX1VBfYjhnfiT+Jiu1momJ/4Ll/eHUHEZFqqg5Z+XL+phM+TgghhGhrLdmhBFp2YS0Ji6MUv4KtrJ7aosBykmM/1AA4vKEUc5qBgZf1IFITQUw3E4Y4bZP3IuLk3H4/H5bX8EFZDSpgZkocv7+wL2XbKsM9NCE6HUlsiE7t9WnLm7RpzRr+tP0GAMb+ZjCf/Pl7AHZ8fIAdHwdqV/TMTuXguqazNtz1HvK+KQr2+7nhVx/dTjWhjwVzqgFrcR1+n8IH89YAENfDxJ1rfn3cMW9ZcrRoaK9xqSE7nzSypBqCiY09nx/GUetC8SsUbik/7ps7c6qB3uPTjvu8AGNvGsy+rwsBOLC2mANrAzE4XjyEEEIIcfby+xTs5fXUFtVReySQxLCV1uNxBD7oUEepsaQZQh5z2YPZqNRSF+N0bbTW8a8jFZS6vYyzGJiVmkCCJhJNxJnf8cQdrUbpZjhpP1+EJK9ExyWJDdGlZc0cAIrCutd3UlNox5JmIGvWQFIGxPFGMxfyWdcPxBCvo+jHcuoqHLjsHjSGKBL6WBj6qz6MufFoEkIdoWb6Kxfw2SPrObKtIvgm4kQ8Ti87PzmaNBl4Wc9m+/WekE60MQqX3YPX5WPHx/n0zclg3O+GUrCxlOoCG45aFyqVKpDQmJBGzu0jTlqJvN+kDK596QK+WbCFyvxa9PFaRkzrz/Ar+7Bg0pKTjl8IIYQQ4eHz+Cn7qZpD60uoLQokMawl9fi9gd3NIjRqzKkGMi/qRqQmEku6AWNioLjngIKj719kZsbpKXF5eP1IBZtt9WRER/GXXqkMM4V32YnD52fV1pN/QDU1rc8ZGA2kZWQCYGlYziREW5DEhuhURl7Tn5HX9G/VY7KuH0jW9QObtP/t0JwmbQMu7sGAi3u0+NjpwxL5zftNd2Y5nihtZHA2yYlERkfw552zmrRfcv+YFj1Pr3NTm319AIMu68mgZhIqx+svhBBCNGpJXQyNKQq9RXvCPuLE3PUerMV1geRFcR21xXXYyxzBml5R2ggs6UZ6npuCJc2IJc2AMUF33JkYix5dGo6X0am4/H5e2VLAop+OEKGCG1LjmRJvIUoSRU3c/oe3AJg6pQ/LV+wP82hEZyGJDSGEEEII0SZaUhejb066JDZaSPEr1Ne4yP/+CAe+L6a2OLCk5Nit6LVmDeZUA8kD4ug9IRWfy48uNhqVbB96RiiKwg+1dSwqrqTc42VCjJFZqfHERclllhBnkvyPE0IIIYQQIsy8bh+2knpqiwOzMKwldSE7k6ACY6KO+J5mzGkGLKlGzKn6kGWmUhfjzMp3uPjXkQp21znpodXwwuRBJBW7wj0sIbokSWwIIYQQQogzxuf1U1NoO2k/z0l28uqoGmdh2ErqsBbXs3P5AUp3V1FX5YSGXWsjoyMwpxrIGJmIOdVA34npOGrdRGoiwjt4AUC1x8u/S6r4qtqGKULN79ITuCDOTI8UM4XFklgSIhwksSGEEEIIIU6oJbUzoGXJCI/Dy4EWzCroDFu0uh3eYALDWlqHreG7z+0P9jGnGjClHE1imFMMTZaSJA+Ia9eZGMu+fD94+6rJx9+5ravz+BU+qahhWVk1br/CLxIsTEuOxRAhCafWWP/9BwA4rInozBPCPBrRWUhiQwghhBBCnFBLamdA50hGnAqfx4etzIGttD7wVVLPl087qatwBPtE6SIxp+jpPioZU4oec4oBU7KentkpYV8+8vhrfw3elsRGU4qikFtr563iSkrdXkaZ9NyYFk9a9Il3mxPN++/iJxq+w2MLcsM8GtFZSGJDCCGEEKITauksC1WkCsWrnLBPZ10W0lp+n5/qwzaObK/AVlKPtbQeW0ldyDISdaQKY6Ke9GHxRERHYE42BGphmDRS0LMDOuhw8cTnu9lYYiUjOooHeqUyIszbtwohmpLEhhBCCCFEB9OSpIXH5eVQbulJj9WSgpNdbSaG36dQV+nAVlaPvXEWRpkDe4UDxdeQwVCBIUGHOdVA+ojEwCyMZD36OB3qiOa3VRUdR6XHy3slVXxdbcMUHcmctAQujjcTIckpIc5KXTax8be//Y13332XvXv3hnsoQgghhBCt0pKlIV0tGXEq/D4/dZVO7GWBJMbulQcp3VtNXbkD/zEJDH2sFlOyjuQBsXTPSsbr8mFM1BMRpQ7vCxBtzuHz89/yGj4ur8FPoI7G7Rf2w7arOtxDE0KcQJdMbGzcuJH6+vpwD0MIIYQQXURLl4VoTFHoLdozMKKuxev2YS2uC8zAaEhi2MrqqatwoviPLsMxJevRxUST1D8WU7IeU7IeY6IuZDcSmYnROfkUhVVVVt4vrabW62OcxcjMlDiSo6OwREdy8n18hBDhFPbExqFDh3jttdfYsmULeXl5jB49mkWLFjXpl5eXx8MPP8zWrVsxmUxcc8013H777US0sgqx2+1m/vz5vPDCC/z3v/9tq5chhBBCCHFcLS2+2TcnXRIbp8Hj8GIvb1hCUu7AXh5IZNRXO1EaNyJRgSFOizFZT8qgeIyJumACo+fY8BfyFGeWoihsstXzdnElhS4PAw1a7uuZQj+9/D8UoiMJe2Jj3759fPPNNwwfPhyvt/nCVLW1tcyePZu+ffvywgsvcPjwYZ588kn8fj933313q57v+eefZ9q0acTFxbXF8IUQQgghxBmkKArOWncwcZH//RFKdlVhL6/HdcysGHWEKlgDY8AlPVD8CqYkPYYEnSwhEQDsqbTzf/lH2FnnJE0TxR96pJBl1kuRVyE6oLAnNiZPnsyFF14IwB133EF1ddP1a++99x4ul4uFCxdiNBoZP348drudhQsXcvPNN2M0GgG48sorOXLkSJPHT5gwgaeffpo9e/bw448/ctddd7XvixJCCCGEzikBHwAAIABJREFUOAU+r5+awpNPeu8Ku5R4XT5qi+uoa5x5Ue7AVu6grsKBz+0P9tPoI9HHa0nqH4sxURf4StKjj9WijghcoMryEXGsYpeH90qq+H6bHXOEmjlpCVwUbyZSEhpCdFhhT2yo1SfPmK9Zs4YJEyYEExgAU6dOZf78+axfv57JkycD8MEHH5zwOJs3byYvL48LLrgg2DZ58mSWLFkiMziEEEIIEXYeh5cDLbgA7yyFQRW/gsPqDiQvKhzB7/ZyB45aV3ALVVSgs0RjTNQR39OMMUmHMSGQxOiTk07R1oqwvg7RMVR7vCwpq2ZVpZVIlYobh6Yx2a/B0Mql7UKIs0/YExstkZ+fT3Z2dkhbWloaOp2O/Pz8YGLjZGbMmMGMGTOCP2dmZvLll1+2ejyLFy9m8eLFAM3OMBFCCCGEEEe5HV7qKgKzLY5sK6d4RyX2Cif2Cgd+z9HZFxEaNcZEHXE9TKQO7YXf58eYqMcQrw0p4HksWTYgTqbO5+Oj8ho+Ka/FqyhcGG9mWlIsQ89JlZk8QnQSHSKxYbVaMZlMTdrNZjNWq/WMj+faa6/l2muvBeCqq646488vhBBChFtLdvmQHT66Fp/HR+WBWo7sqKCuwkldRcMsjAoH7rqjS2dUahX62GgMCToSelswNC4fSdASbdIEExWyfOTMmTByUriH0C7cfj8rK60sK6vG7vMz3mJkekocqdFR4R5alzZg8AQAkpL0YR6J6Ew6RGKjvezduzfcQxBCCCE6pJbs8tHRd/ho6RatqkgVilc5YZ/OUhPD5/FTX+WkrtJBXYUTe8P3ukoHzlp3SF+tWYMhQUvK4HiMCToMCYHkRb/zu1G8ozJMr0A055l7Xwz3ENqUT1H4eF8ZL+09TKXHxwijjhmp8fTWRYd7aAKY9funAZg6pQ/LV+wP82hEZ9EhEhtmsxm73d6k3Wq1YjabwzAiIYQQQnR2Ld2itSUzCzpSTYyQ5EWlk7pKJ5sX/0TVQWto3QtAY4jEENcw8yJeR/esJFx2T2DpSHTzbzNlRxLRXvyKwrraOhaXVlHk8tBXF83cbskMMerCPTQhRDvrEImN3r17k5+fH9JWXFyMw+Ggd+/eYRqVEEIIcepkKYcIJ6/bR32lk7oqJ+V5NRzZXnF05oXVHZK8iNJFEtvdRFxPM4Z4bWDmRbwWfYIOjS70raQsHxHhoCgKG631vFdaxSGnm4zoKB6f1J++lV6pwSJEF9EhEhs5OTm89tpr2O324M4oK1asQKvVMmbMmDCPTgghhGi9rrCUoy215bKQlvbryMtHFEXBaXVTfdhGXZUzkMSodARuVzlx/SyWGkMUhnhtw8wLLYZ4HYZ4Lfp4LRp9lCQsxFlJURR+tDt4r6SKPIeLFE0Ud3ZLYlyMkR494iisknNWiK4i7IkNh8PBN998A0BpaSl2u52VK1cCMHHiRHQ6HdOnT2fRokXMnTuXm2++mYKCAhYuXMjs2bNDtoAVQgghROfUlstCWtrvbF8+ovgVHLUu6ioDyYrA8hFnMHnhdfpC+mvNGgzxWpIyYwOJi7hA4qLf+RmU/1QTplchzgavLF0YvP27q28P40habpfdwb9Lq9hd5yQhKpJbMxKZFGsiQmZonPVWrfgnAIf3xdK937Qwj0Z0FmFPbFRWVnLnnXeGtDX+vHr1ajIyMrBYLLzxxhs89NBD3HLLLZjNZm688Ubmzp0bjiELIYQQQpwRHqe3IWnhor76aPLCXe/BWlKP4js660QVoUIfE40+XktcdxNpQxPwOL0Y4nXo46KJiGp+u9Rog+wQ0dX9c9nzwdtne2JjR7mNBflH2GZ3EBsZwW/TErggzkyUWhIaHcWXK19t+A6PLZDEhmgbYU9sZGRktGh3kr59+/LWW2+dgREJIYToLFq6fEFqWbQPn9dPTaHtpP06+7KQE/H7/Dhq3cEZF4VbyyndXRVIYFQ58dSHvu4oXST6uGgS+sSQ2DcGfbw2MPMiTovOEo3qmIs7WT4iOpO8eifvl1azeVs95gg1N6bGc3G8mWi1FKMVQpwFiQ0hhBCivbR0+YLUsmgfHoeXA11kWcjxKIqCy+6hZFcVhVvLAwmM6oYZGFVOnFYXiv9of3WECq0lGn2clrQh8egbkhaNX43FOiVpIbqKffVO/lNazWZbPcYINbeM7MZ4dyS6CEloCCGOksSGEEIIIcQpUpRAnYuaAhv1Na6GxEVg2Yij2kV9tQu/1x/ymGhTFPpYLXE9TOjjEgNJi9hAMqPvxHSObKsM06sR4uzx84TGjJQ4Lou30G9YsiT1hBBNSGJDCCGEEOI4FEXBU+8NJC2OSVY4qo8mMHzu0MRFlC4SfWw0pmQ9SQNi0cdq6T4qifpqF7rYaCI1zde6AFDLp9Cii/upIaGxxVaPKULNzJQ4Lo23yAwNIcQJSWJDCCGEaKGW1Ow4W+t1SL2R5imKgtvuob7GFUha1BxNXnz/so/aI/YmiYvI6IjALIt4LQl9LKQOjcdT70Ufq0UXG02UtunbK1k6IsSJ/VTv5OlVe/ihqEYSGkKIVpPEhhBCCNFCLanZcbbW6+iq9Ub8PgWnzY2j2omjxoWjxkV9jYut/9lHVYENRzNLRaK0EegalopY0gzBhIU+Nhp9rJYoXejbJ0laCHHqdtkdLCmrZpvdgSU6UhIaQohTIokNIYQQQnRYXrcvmLBw1Lgo+rGc0j3VwZ9/XpwTQGOIwpJmwJysJ3lALPqYhsRFTHTIjAtJWAjRPhRF4Ue7gyWl1eypdxITGcENqfHcdH5vqnZUhXt4QogOSBIbQgghhDgrKX4Fl92No8aNy+amcEt5YMZFbUMio9qFxxG6HapKrUJr1qCLiSaupxl9bDS6mNCvSE2EJC2ECAO/orDRWs+SsmryHS4SoiKZk5bA5DgT0Wo1+qgIJK0hhDgVktgQQgghRFh4nF4cNS4OrfdRsLn86MyLWhfOGhcOqxvFp4Q8JjI6IpigiO1mCty2aNDFatHFRNM3J012FRHiLOPzK3xXbWNZeQ2HnW5SNJHcmpFIToyJKLUq3MMTQnQCktgQQgghRJvzeXzUFNmp2F+Do8aNo/aYpEWtG0eNC6/LF/IYlRq05oakRXcTaTHR6CyBn3tkp2ArqSdSG4FKdfwLIdlVRIjWu+L8a9rluB6/wpoaG598WESB1UlGdBR3dktiXIyRiBP8PxadW9a4XwHQvZs5zCMRnYkkNoQQQgjRKl5XQ9IivxZnrQtHrbvh+9Hb7jpvk8dpjFHoLBoM8VoSelvQxmjQWaLpMSYZW5kDrUmD6jif3ib0tuCsdbf3SxOiS7r/tw+16fEcPj+rqqx8XFFDlcdHZpyB/+2RTJbZgFoSGl3eldP/DMDUKX1YvmJ/mEcjOgtJbAghhDiryLakrdeSmHlcTRMNzXHVeTi4rhh7eT22Mge2snrsZY7gz/ayeuqrXU0eF6WLRGfRoLVEE5thRBsTTfqwBBw1LnSWaLSWaCKimp9NkTIoHq9L6l0I0dHVen18WlHLyspa7D4/Q4w6bsuIYcp53SnaWhHu4QkhOjFJbAghhDirdNVtSU9HS2KWMTIRr8sbmFFhdYfMtHBa3Q3tzc+0iNJForVo0JmjSegbg86iIW1YAo4ad6DdEk1kdESzzykFOoXo/MrdHj4qr2V1lRWPojDGbOCKpBj66QO/o0+0fEwIIdqCJDaEEEKIDk7xK7hsbhzWo0kLZ8NtR8Ntt92Du76Z5SGGqMAuIhYNsd1NJGfG4q73NiQsAjMwIjWStBBCNHXY6eK1b/P4LL8CtQrOizFxRWIM6VpNuIcmhOhiJLEhhBBCnMU8Ti+20nqsJXXYSuqxltRjLW28XddwXz1+rz/0gSrQmjRoLRqMiXoSsy143T60luhgwkJr0jRZHiIJCyG6nkdf/Uvw9snqbSiKws46Jx+V17DZVo8uUs2UBAu/SIghQSOXFuLkPnjvMQA2fW9m1Pjbwzwa0VnIbx8hhBAiDPw+f2BWha1hhoXNjbPWHfL9i8c34LQ2LZip0UdiSjFgTtbTPSsZjSEKZ21gWUjj7ItoY2ghTklYCCGO579f/Sd4+3iJDZ+i8EOtnY/Ka8l3uDBHqLk2OZbfTOqNbXfNmRqq6AQ2rP0w8B0ksSHajCQ2hBCAFGwMt5bGXxWpQvEqp92nrY8VjvPC5/VTU2g7YZ+WFsxsS16Pj+KdFdRVOLBXOBu+O4I/28sDt+sqnSj+n8W2cZaFWYM+TktSdizmJB2GRD3GRF3wS6OPDFmz7nF5OZRbeoZfqRCiK3D4/HxZbeWT8lrKPV7SNFH8Pj2RnFgj0Wo1Fm0UJ/5NLIQQ7U8SG0IIQAo2hltL49+ST91b+sl8Wx4rHOeFx+HlQAvG31YURcFpdWMrrT8608LqxtV4u+Fnt92Dz+Nv8vgoXSRacyBpEdvdRP8LuuFpqGXR2H6iWRYeh5fqwzaqDze9hGjL1ymEEAAV9W7eKa7k8yordT4/A/RabkpLYLRZL1u2CiHOOpLYEEII0aUpioKj1oXL7glubWotrcdeVo+trB5baaDNVlaP1+Vr8vhIbURwlkVcDzOJfS14nL5AsqKhPVpqWQghOohDDhfLK2r5dkc+Xr/CWIuByxNi6G+QDzWEEGcvSWwIIYTolPw+hbpKBzVF9uCsCpfNjdPmafjuxtVw2+9rutxGa9ZgTNRhStLTbVQSxiQ9UdFqbBWOQMLCpCHarGmyY4gkLIQQHdk9+wrRqFT8sn8S5xNNanRUuIckhBAnJYkNIYQQHYrX7cNlc1O8s5Ij2yuOm6xw1XmgmfIgUfrIQFLCpMGYoENr1pA+IpGk/rGYkvWYkvQYk/RodE3/RNYU2lq0ZEgIIToCl9/PN9Why9tmpMRxUZyZgVkpkqQVQnQYktgQQggRdn6fn7rKQGHN0t2VFGwqDSYrXDY3TrsHl9WNy+5pdjmISg3RRg3Rpih0Fg0xGUaiTVGkDIzDUetuSGREBZaERKqbPL5vTjoxGaYz8VKFECLsqj1eVlbW8nmlFZsvtCbQVUmxYRqVEEKcOklsCCGEaBeB2hVu6sod2MvrsZcHdgexlzd+1QdvN7tDCBAZHUG0KQqtSYMl3Ui0MZCc0JqiSB+RiL3MQbQpCo0+KqToZiNZFiKEEEfl17t4/bs8Ps+vwKdAltnALxIs3BjugQkhxGmSxIYQQogWUxQFr8sXWO5hD8ygcNk8lO6pwuvyHU1UNGxx6nM33R0kIkod3LbUkmYkfXjiMVuZ6lEUhYr8WqKNUU3qVxxLkhZCCHFyXkVhfW0dKypq2VPvRBep5qI4C1MSLFI/QwjRaUhiQwghujhFUfA6fUcTFQ3JCpfdTd6aQqoO2gJLQhru83ubJitUahWGeG0wQZHUPxZjog5Dgg5Tw3djog5jkh6dRYPqBFsF1hTacNS42vMlCyFEp2f1+lhVZeWzyloqPT6SNZHMTo1n5qRe1O6sDvfwhBCiTakURWmmtJpoqbFjx5Kenh7uYbRKdXU1sbEdd/2kzWbDZOq4a+El/uEl8Q8viX/4SOzDS+IfXhL/EysuLg7eTk1NbfPjS/zD60zGf8uWLfj9ftRqNSNHjmy2T2vPN4l/eJ1N8Y+NjeW1115r/k5FdDlXXnlluIdwWh544IFwD+G0SPzDS+IfXhL/8JHYh5fEP7wk/uEl8Q+vMxn/+fPnK6mpqcr8+fPb7JgS//DqKPGXpSiiwzn//PPDPYQuTeIfXhL/8JL4h4/EPrwk/uEl8Q8viX/L3XPPPdxzzz1tekyJf3h1lPg33fNOiLPc5MmTwz2ELk3iH14S//CS+IePxD68JP7hJfEPL4l/eEn8w6ujxD/iwQcffDDcgxBn3pAhQ8I9hC5N4h9eEv/wkviHj8Q+vCT+4SXxDy+Jf3hJ/MNL4t/+pHioEEIIIYQQQgghOixZiiKEEEIIIYQQQogOSxIbQgghhBBCCCGE6LAksSGEEEIIIYQQQogOSxIbQgghhBBCCCGE6LAksSGEEEIIIYQQQogOSxIbQgghhBBCCCGE6LAksSGEEEIIIYQQQogOSxIbQgghhBBCCCGE6LAksdGFuN1uHn/8ccaPH8+wYcOYMWMG27dvb9KvtLSU2267jZEjRzJ27FgeeughHA5HGEbcudjtdh599FEmT57M8OHDueyyy3jjjTdQFCWkn8S/bRUWFpKZmdns1yWXXBLSV2LffoqKipg3bx5jxoxh+PDhXH755axZsyakj8S/fdxwww3Nnv8ulyukn8S//b355ptkZmZyxx13NLlP4t8+/vKXv3DppZcycuRIsrKymDlzJmvXrm3ST+Lf9ux2OwsWLGDatGmMGjWK8ePHc9ttt3HgwIEmfW02G3/605/Iyspi1KhR3HPPPVRXV4dh1J3LihUruP3225kwYQKZmZksW7as2X5y/re9vLw8brzxRoYPH86ECRP4xz/+gc/nC/ewOrXIcA9AnDmPPPIIy5cv59577yUtLY1FixZx00038eGHH5Keng6Ax+Nhzpw5REVF8cwzz2C1WnniiSewWq3Mnz8/zK+gY7vvvvvYsGED8+bNo3v37uTm5vLEE08AMHv2bEDi3x6SkpJYvHhxSJvT6WTOnDnk5OQE2yT27ae4uJhrr72WAQMG8Nhjj6HX69m9e3fIhbXEv32NHTuWefPmhbRpNJrgbYl/+6usrGThwoXExcU1uU/i335cLhfXX389vXr1wuPxsGTJEm6++WbeeecdRowYAUj828uRI0d4//33mTZtGnfffTcOh4NXXnmFX//613z00UekpqYG+951110cOHCARx55BLVazfz587ntttt49913w/gKOr6VK1dSVFTEpEmT+M9//tNsHzn/215tbS2zZ8+mb9++vPDCCxw+fJgnn3wSv9/P3XffHe7hdV6K6BKKi4uVAQMGKO+//36wzeVyKRMmTFD+9re/Bds+/vhjZcCAAcrhw4eDbcuXL1cyMzOVAwcOnMkhdyr19fXKgAEDlLfeeiuk/bbbblOmTZsW/Fnif2asWLFC6d+/v7J169Zgm8S+/dx1113Kddddp/h8vuP2kfi3n+uvv16ZO3fuCftI/Nvfn/70J+Xee+9t9t9D4n/meL1eZeLEicrDDz8cbJP4t4+6ujrF4XCEtFVXVysjRoxQnnvuuWDb5s2blf79+yvr168Ptv34449K//79le+///6Mjbczavy7a7fblf79+ytLly5t0kfO/7b30ksvKaNHj1ZsNluw7ZVXXlGGDRsW0ibalixF6SL27t2L3+9n/PjxwTaNRkNWVhZff/11sG3NmjUMHTqUbt26BdsuvPBCoqKi+Pbbb8/kkDsVn8+H3+/HZDKFtJtMppClKBL/M2P58uV069aN4cOHB9sk9u3DZrPxxRdfMGPGDNTq4//JkfiHl8S/fW3bto1PP/2Ue++9t9n7Jf5nTkREBCaTCbfbHWyT+LcPvV6PVqsNaYuJiSEtLY2ysrJg25o1a0hISCArKyvYNmzYMDIyMposWRStc6K/u43k/G97a9asYcKECRiNxmDb1KlTcTqdrF+/Powj69wksdFFNP4Bj4qKCmmPioriyJEjOJ1OAPLz8+ndu3dIH41GQ/fu3cnPzz8zg+2EjEYjl112Ga+++iq7d+/Gbrfz1VdfsXLlSmbOnBnsJ/Fvf3a7nTVr1jB16tSQdol9+9i5cycejweVSsX06dMZPHgwOTk5vPzyyyFJPYl/+/ruu+8YPnw4w4cPZ86cOezZsyfkfol/+1EUhYcffpjf/va3JCcnN9tH4t++FEXB6/VSXV3NG2+8waFDh5g2bVrwfon/mVNVVcWhQ4fo2bNnsK25+AP06dNH4n8GyPnf9pqLaVpaGjqdTmLajqTGRhfRvXt3ALZv387kyZOBwB/67du3oygKtbW1aLVarFZrk1kFAGazGavVekbH3Nk89dRT3HvvvVxxxRUAqFQq5s2bx5VXXhnsI/Fvf6tWrcLlcjFlypSQdol9+6ioqAACBfyuu+467rrrLnJzc3n22WcxGo3BxJ7Ev/1kZWVxxRVX0KNHD4qKinjppZeYOXMmH374IRkZGYDEvz0tXbqUyspK5syZc9w+Ev/2tWLFimCNGb1ezzPPPMOwYcOC90v8z5wnnngCg8HQ4vc+hYWFZ3J4XZKc/21PYhoektjowGw2W8hUvuPp06cPmZmZnHPOOTz55JMkJSWRlpbGv/71Lw4ePAi0bKqaCNWa+AM89thj/Pjjjzz++ON069aNTZs2sXDhQmJjY7nmmmvae7idSmtjf6xPPvmEfv36kZmZ2R5D6xJaE//GWRk5OTnBafjZ2dmUlJTwyiuvhMxYEi3T2vP/2B04Ro8ezbhx47jssst48803uf/++9ttnJ1Va+Jvs9n4+9//zgMPPNBkSr44Nafy+3/ChAksWbKE6upqPv74Y+bNm8crr7zC2LFj23OondLp/P199913+eijj3juueeIjY1tj+F1eqcTfyE6O0lsdGArV67kgQceOGm/vXv3AoEs+Z133snVV18NQL9+/Zg1axZvv/02MTExQCCTaLfbmxzDarUyYMCANhx9x9ea+O/Zs4d///vfvP7668E6J1lZWdTV1fHUU09x9dVXo1arJf4t1Npzv1F1dTU//PADt99+e5O+EvuWa038zWYzQJMLiOzsbJYtW4bdbsdoNEr8W+FUz/9GiYmJnHPOOezatSvYJvFvudbE/6WXXiI1NZUJEyYEP6Xzer14PB6sVisGg4GIiAiJfyucyvlvsVgYOnQoEEiylpWVsWDBAt555x1Azv/WONXfP6tXr+aRRx7h3nvv5aKLLgq5z2w2U1VV1eQYVqs1+DdEBJzu7//myPnf9k4UUzmn248kNjqwa665plWf9Pfo0YP//ve/FBQU4PF46NWrFw8//DCDBg0K1t7o3bt3k7VfbrebgoICpk+f3qbj7+haE//GmA4cODCkfeDAgVitVmpqaoiLi5P4t1Brz/1Gn332GV6vt0l9DZBzvzVaE//GT42Oradx7M8qlQqQ+LfGqZ7/x1KpVMHYg8S/NVoT/wMHDrBjx46QooiNsrKyeOeddxg9erTEvxXa4vwfNGgQy5cvD/4s8W+5U4n/pk2bmDdvHtOnT+e3v/1tk/t79+7Npk2bmrTn5+dz4YUXnvJYO6O2OP9/Ts7/ttdcTIuLi3E4HM3WkxFtQ9YfdEHdunWjd+/eVFdX8+mnn4YU0MrJyWH79u0UFRUF27788kvcbjfnnXdeOIbbKaSnpwOBQorH2rlzJ3q9PjglU+LfvpYvX86wYcOCNWeOJbFvHxkZGfTr149169aFtK9bt47u3btjMBgAif+ZVF5ezqZNmxg8eHCwTeLfPu666y7eeuutkK8BAwaQlZXFW2+9FVwSJ/E/cxRFYcuWLcH6MiDxb0/79u3j1ltv5bzzzjvuTIOcnBzKy8vZuHFjsG379u0UFBSQk5NzpobaZcn53/ZycnL47rvvQmZtrFixAq1Wy5gxY8I4ss4t4sEHH3ww3IMQZ8Zbb73FoUOHqKmpYe3atfzxj3+kV69e/PnPfw5+cterVy8+//xzPv/8c1JSUtixYwePPvooF1xwAb/+9a/D/Ao6rsTERL755hs++ugjTCYTVquVDz/8kNdee43rr7+eCRMmABL/9lRaWspjjz3GTTfdxIgRI5rcL7FvP4mJiTz33HPU1dWhUqlYunQp7777Lvfff3/wwk7i3z727NnDn//8ZzweD7W1tWzYsIH7778fp9PJE088ESxuJvFvH/Hx8WRkZIR8rVixgri4OG6++Waio6MBiX972bhxI48++mjw/N+5cyfz588nNzeXBx98MLgzh8S/fVRWVjJz5kw0Gg333HMPlZWVlJSUUFJSgt1uJy4uDoDU1FS2bt3KkiVLSE1N5cCBAzz44IP06dOHu+66K8yvomPLy8tj/fr17Nu3jy+++IKUlBQcDgdVVVXBD93k/G97/fr1Y/HixeTm5pKUlMTatWv5+9//zo033sjEiRPDPbxOS6X8fH6w6LReeeUV3nvvPcrKyoiPj+cXv/gFt99+OzqdLqRfSUkJDz30ED/88AMajYYpU6bwhz/8oUk/0Trl5eU8++yzrF27lqqqKtLS0rjiiiu46aab0Gg0wX4S//bxxhtv8OSTT/L1118fd8tFiX37+fDDD3nppZcoKCggNTWV3/zmN1x33XUhfST+ba+0tJQHHniA3bt3U1NTg8FgYMyYMdx1111NistJ/M+MG264gdjYWBYsWBDSLvFve4WFhTz11FNs3bqVqqoq4uLiGDhwILfccgsjR44M6Svxb3u5ubnMmjWr2fvGjBnDokWLgj9brVYef/xxvvjiC/x+P+effz73339/MPkhTs1zzz3HwoULm7T/PP5y/re9vLw8HnroIbZu3YrZbGbatGnMnTuXiIiIcA+t05LEhhBCCCGEEEIIITosqbEhhBBCCCGEEEKIDksSG0IIIYQQQgghhOiwJLEhhBBCCCGEEEKIDksSG0IIIYQQQgghhOiwJLEhhBBCCCGEEEKIDksSG0IIIYQQQgghhOiwJLEhhBBCiE7hueeeIzMzM/j1l7/8JdxDajeTJ09m8uTJZ/Q5q6qqQuKbmZl5Rp9fCCGEOJ7IcA9ACCGEEK2zfft23n33XdavX095eTmRkZGkp6czYcIEZs+eTXJy8gkfv27dOj744AO2bt1KWVkZHo8Hi8VC3759GT9+PJdffjkpKSkA+Hw+xo4di8PhIDc3F6PRGHKssrIyzjvvPAAee+wxrr766ibPd/3117NhwwbefvttsrKyAJpcFEdFRWE0GklNTWXQoEFcfPHFTJgwgYiIiFbH58orryQ9PZ0hQ4a0+rGnwuPxMG7cOEaNGsVLL710yseZOnUqarWajz/+uA1H13Z0Oh233347AB988AFFRUVhHpHF1DG7AAAOV0lEQVQQQggRIIkNIYQQooNQFIX58+fz6quvEhkZybhx47j00kvxeDxs2bKF119/nX//+9888cQTXHrppU0eb7fb+eMf/8iqVauIiopi9OjR5OTkoNfrqaqqYtu2bTz99NMsWLCA999/n0GDBhEREcGYMWNYvXo1GzduZNKkSSHH/OGHHwBQqVSsW7euSWLD4XCwdetW9Ho9I0aMaDKmxgtln8+HzWZj3759fPjhhyxZsoQhQ4Ywf/58evXq1ao4XXnllYwdO7ZVjzkdubm5WK1WLrroolM+xoEDB8jLy+O2225rw5G1LZ1Ox9y5cwFYv369JDaEEEKcNSSxIYQQQnQQzz//PK+++irp6em8/PLL9OvXL+T+zz77jP/93/9l3rx5xMTEkJ2dHbzP5/Mxd+5c1q5dy5gxY3jqqadITU1t8hx5eXksWLAAu90ebMvOzmb16tWsW7euSWJj3bp1aLVasrOzyc3NbXK8TZs24fF4yM7OJioqqsn9jRfKx6qoqODhhx9m5cqV3HTTTSxdupT4+PiTxidcvvjiCyIiIjj//PNP6xjAaSVHhBBCiK5KamwIIYQQHUBhYSEvvvgiUVFRvPjii02SGgCXXHIJf/rTn/D5fDz44IP4/f7gfR9//DFr166lZ8+evPzyy80mNQD69u3LggULOOecc4Jt5557LhBIYvxcbm4u55xzDhMmTKC0tJT8/PyQ+xsf03iMlkhISOCZZ55hzJgxFBcXn9byjmPdd999ZGZmUlBQwNtvv82UKVMYOnQokydP5qWXXkJRFAA+/fRTpk2bxogRIzj33HN56KGHcDqdzR5TURRWr17NqFGjiIuLC7Z98MEHTJ8+nezsbIYOHcrEiROZM2cOK1asaPY4q1atIj09nYEDB4Yc++2332bq1KkMHTqU8847j4ceegibzdbsMWw2G6+++iqzZs0iJyeHIUOGkJ2dzS233MKWLVtC+tbW1jJ8+HAuvPDC4Ov+uVtuuYXMzEy2b99+4sAKIYQQYSaJDSGEEKIDWLZsGV6vlwsvvPCERRuvueYaEhMTOXDgAOvXrw+2v//++wDMmTMHvV5/0ueLjDw6qbNfv34kJCSwZ88eqqurg+2HDx+mqKiI7Ozs4NKPxqUpjRp/bk1iA0CtVvM///M/ACxfvvy4F9+n4qmnnuK5555j2LBhTJ8+HZVKxTPPPMPChQt56623uO++++jRowfTp08nISGBd955h8cff7zZY23ZsoXy8vKQmRbPPPMM9913H+Xl5Vx22WXcdNNNjBs3jtLSUlauXNnkGKWlpWzbtq3JbI1HH32Uhx9+mNraWq699lqmTp3Kt99+y+zZs3G73U2Os3//fp599lnUajWTJk1i9uzZjB8/ntzcXK6//nrWrFkT7GuxWJgyZQoFBQWsXbu2ybGKi4tZs2YNgwcPZujQoS2OrRBCCBEOshRFCCGE6AA2bdoEwLhx407YLzIykrFjx/LJJ5+wefNmsrOz8Xq9bNu2DSBkeUprZGdn88knn5Cbmxus39E4G2Ps2LH069ePuLg4cnNzmTlzJhCYQbB7925iYmJCZiK01KhRo4iMjKSyspLCwkK6det2SmP/uZ07d/LRRx8Fi6zOnTuXiy66iNdeew2tVsuyZcvo06cPAG63myuuuIKlS5dyxx13NFkS09wSksWLF5OcnMwnn3yCTqcL6V9VVdVkPKtWrUJRlJBjbN68mUWLFtG9e3f+85//EBMTA8Ddd9/NrFmzKC8vJz09PeQ4ffr0Yc2aNcGZI41KSkqYNm0ajz/+ODk5OcH2GTNmsGzZMhYvXsz48eNDHrNkyRJ8Ph/Tp08/QSSFEEKIs4PM2BBCCCE6gPLycoDgbiUn0rjMpKysDAgsO/B4PADN7piSm5vLc889F/K1atWqkD6NCZFjl6OsW7cOg8HAkCFDUKlUjBkzhtzc3ODsitzc3OCuKiqVqrUvGY1GE7ygP3amyOm69dZbQ+JgNpuZPHkyDoeD6667LpjUaBzDlClT8Hg87N+/v8mxVq1axZAhQ5os7YmMjGx2R5efJx0gkByJj48PWf6zbNkyILAcpDEGANHR0cybN6/Z12UymZo9fkpKCpdeein5+fkcOXIk2D506FCGDBnC6tWrg+cXBOqxLFmyBIPBwNSpU5t9LiGEEOJsIokNIYQQootbv349CxcuDPlqSWIjNzeX0aNHB5etjB07lpqaGvbs2RPSt7XLUI7VlktQGjW3tCIpKQmAwYMHN7mvMQlSUlIS0r53714OHz7cZAnJL3/5S4qKipgyZQpPP/00a9asOW5djNraWjZs2MAFF1yAWn30bdmuXbsAGDNmTJPHjBo16rjb4G7atIk777yTiRMnMmTIEDIzM8nMzGTRokVAYNnLsWbMmIHX62Xp0qXBtm+++YaSkhIuv/xyDAZDs88jhBBCnE0ksSGEEEJ0AAkJCUDTi+vmFBcXA0cv1i0WS3BHksZZHMeaO3cue/fuZe/evfzrX/9q9pjdunUjIyODAwcOUFpayk8//URFRUXI0paf19loTGycbPnM8bj+f3t3F9JkG4cB/KpN3XJNJaVwmbMmRYZBttDwIEVHlKQrwrLNpA/MaIQRoQQlQXVSgWxYRBYWhgZmREduWqJNp63VWbUkTQoKZ1/ENKe+B7HR2lZvS8u97/U7fHz27N7YiRf/+7pHR/HhwwcA/icdgiWRSHyuucOZuXPn+vzNHSK4XC6v64FOMqmsrERlZSXmzJmDixcvYu/evUhPT0dZWRkGBga87m1ra/N0p3zLHYT4Ow1GKBQiJibG57rRaIRGo0F7eztSUlKg0Wiwf/9+HDhwwBOQfN/NsXHjRkRFReHGjRuesll3Hwu3oRARUahgsEFERBQC0tLSAMBv0eO3xsfHPaWh7q0NQqEQqampAHzLPX+FO8To6ury6tdwW7JkCeLi4tDd3Q2HwwG73Y74+HgkJiYG9X5WqxUulwuxsbFYuHBh0OueLkajEYsXL/baugJ8DUJKSkpw+/ZtmM1m6PV65OTkoK2tDXv27PEKF4xGIyQSic9UiztgcTgcPu/rcrn8bs2prq5GWFgYmpqaUFNTg4qKChw8eBA6nQ5JSUl+P4NIJIJarcarV6/Q2dnpKQ1duXIlli1b9svfCRER0d/AYIOIiCgEbN68GQKBACaTCXa7PeB9TU1NePv2LZKSkry2MWzduhUAcPnyZTidzqDW4P7n22KxoLu7G1FRUT6loGvWrMGDBw/Q0dEBIPiy0omJCZw/fx4AkJeXF9QzptPg4CCePHniM63xvXnz5kGlUqG6uhrp6el4+fIlnj17BgBwOp24f/8+1q1bh/DwcK/XLV++HAC8TrZxs1qtGB8f97k+MDAAhULhE7RMTEx4ymf92b59O2bNmoXGxkZPaWhhYeEPPxcREdFMwmCDiIgoBCQkJKC0tBRjY2MoKyvD8+fPfe4xmUw4efIkBAIBqqqqvDobNm3ahIyMDLx48QL79u0LuKXl48ePAdfgDinMZjN6e3uhVCq93gP4OsHx+fNn1NbWAgiuX8PhcKC8vBw9PT2Ij49HaWnpLz9jurk7SL7fQvLlyxe/IcLY2JhnW437pJSOjg6MjIz4PAMA1Go1AODChQt4//695/ro6CjOnTvnd00ymQz9/f1ePRqTk5PQ6/V+fy9ucrkcGRkZuHfvHhoaGiCVSlkaSkREIYXHvRIREYUInU4Hp9OJK1euID8/H5mZmVAoFHC5XLDZbHj8+DFEIhHOnj3rMykhEAhgMBhw5MgRtLa2IicnB0qlEsnJyRCLxRgeHobdbofNZkNYWJhn68q3YmNjkZyc7JkY8TeN4d6a4p5K+NnEhl6vB/B1quDTp0+w2+2wWq0YGxtDamoqzpw5M6X9GlOlpaUFCxYs8CkiHRkZQVFRERITE5GSkoL4+HiMjo7CbDajr68P2dnZnomKlpYWREREeB3B6paWlgatVotr164hLy8P69evh1AoRGtrK6RSKeLi4nxeU1JSguPHj0OtVkOlUkEoFOLhw4fo6+tDVlYW7t69G/DzFBUVwWw2Y2hoCFqtFiKR6De/ISIioj+HwQYREVGImD17NioqKrBhwwbU19ejt7cXXV1dEAgEkMlk2LVrF3bu3BnwSFiJRIKamhp0dXWhubkZNpsNNpsNLpcLUqkUCoUC5eXlKCgoCPiM9PR0T7Dxbb+Gm1wux/z58/HmzRsoFApPgWkgBoMBABAWFobIyEjIZDIUFBRApVIhMzPTZyJkJhgaGsKjR4+wY8cOn2NsxWIxDh8+DIvFApvNBpPJhMjISCxatAhVVVXYsmULgK8THO3t7Vi7dm3Ak0eOHj0KuVyO+vp6NDQ0IDo6Grm5uTh06BDy8/N97t+2bRvCw8NRV1eHW7duISIiAqtXr8bp06fR0tLyw2AjOzsbMTExePfuHbehEBFRyJk1OR3nqBERERH9YXq9HgaDAVevXvUbukyVxsZGHDt2DHV1dUF3iHR2dmL37t04deqUJ+z4mwYHB5Gbm4tVq1bh+vXrP71fq9Wip6cHT58+/QOrIyIi+jFObBAREdF/SnFxMQCgsLAQJ06cmPLnG41GREdHQ6lU/tYzBAIBsrKypnBlwautrcXk5CQ0Gk3Ae4aHh4PqTCEiIppunNggIiKi/wSLxeJ1isiKFStmTHAwE71+/Rp37txBf38/bt68iaVLl6K5uTng9h+n04lLly55XdPpdH9iqURERD/EYIOIiIjof8hisaC4uBhisRhpaWmoqqpCQkLC314WERHRL2OwQUREREREREQha+ZVjRMRERERERER/UsMNoiIiIiIiIgoZDHYICIiIiIiIqKQxWCDiIiIiIiIiEIWgw0iIiIiIiIiClkMNoiIiIiIiIgoZP0D9iy41+JlPU4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x648 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'font.size': 18})\n",
    "# Initialize the FacetGrid object\n",
    "bins = np.arange(-90,5)\n",
    "bins_fit = np.arange(-90,1)\n",
    "#bins = np.arange(-90,10,5)\n",
    "xticks = np.arange(-90,10,10)\n",
    "min_xticks = np.arange(-85,5,10)\n",
    "nh = len(sel_dict_ls)\n",
    "pal = sns.cubehelix_palette(nh, light=.7) # rot=-.25,\n",
    "colors = [\"amber\", \"green\", \"purple\"] #  , \"greyish\",\n",
    "pal = sns.xkcd_palette(colors)\n",
    "sns.set_style(\"ticks\", {\"xtick.major.size\": 10, \"ytick.major.size\": 10})\n",
    "g = sns.FacetGrid(df, row=\"g\", hue=\"g\", aspect=5, palette=pal)\n",
    "g.map(sns.distplot, \"accelogw\", norm_hist = True, kde=False, rug=False, bins = bins)\n",
    "\n",
    "g.axes[0][0].axvline(x=-6.66, lw=3, clip_on=False, color = 'k')#pal[0])\n",
    "g.axes[1][0].axvline(x=-5.07, lw=3, clip_on=False, color = 'k')#pal[1])\n",
    "g.axes[2][0].axvline(x=-7.13, lw=3, clip_on=False, color = 'k')#pal[2])\n",
    "\n",
    "quantiles = df.groupby('g').quantile([0.01,0.1])['accelogw']\n",
    "dist_name = 'exponweib'\n",
    "#dist_name = 'weibull_min' #\n",
    "dist_name = 'lognorm' #  # 'gamma'\n",
    "dist = xc.indices.generic.get_dist(dist_name)\n",
    "\n",
    "m_const = -1\n",
    "for i, hs_name in enumerate(sel_dict_ls.keys()):\n",
    "    ax = g.axes[i][0]\n",
    "    \n",
    "    arr = df2[\"accelogw\"][df2['g'] == hs_name]\n",
    "    arr.index.name = 'time'\n",
    "    \n",
    "    \n",
    "    arr = arr.to_xarray()\n",
    "    arr = arr.where(arr < 1).dropna('time')\n",
    "    sns.distplot(arr, norm_hist = True, kde=False, rug=False, \\\n",
    "             bins = bins, ax = ax)\n",
    "    ind = xc.indices.generic.fit(arr*m_const, dist = dist_name).compute()\n",
    "    \n",
    "    \n",
    "    mean, var, skew, kurt = dist.stats(*ind.values, moments='mvsk')\n",
    "    \n",
    "    textstr = '\\n'.join((\n",
    "        r'$\\mu=%.2f$' % (np.log(ind.loc['loc']), ),\n",
    "        r'$\\sigma=%.2f$' % (ind.loc['s'], )))\n",
    "    \n",
    "    textstr = '\\n'.join((\n",
    "        'Mean=%.2f' % (mean, ),\n",
    "        'Variance=%.2f' % (var, ),\n",
    "        'Skewness=%.2f' % (skew, ),\n",
    "        'Kurtosis=%.2f' % (kurt, )))\n",
    "    ax.text(0.45, 0.8, textstr, fontsize=14, verticalalignment='top', \\\n",
    "            color = 'C0', transform=ax.transAxes)  \n",
    "    \n",
    "\n",
    "    \n",
    "    ax.plot(bins_fit,dist.pdf(bins_fit*m_const, *ind.values), color = 'C0')\n",
    "    \n",
    "    arr = df[\"accelogw\"][df['g'] == hs_name]\n",
    "    arr.index.name = 'time'\n",
    "    arr = arr.to_xarray()\n",
    "    arr = arr.where(arr < 1).dropna('time')\n",
    "\n",
    "    ind = xc.indices.generic.fit(arr*-1, dist = dist_name).compute()\n",
    "    dist = xc.indices.generic.get_dist(dist_name)\n",
    "    ax.plot(bins_fit, dist.pdf(bins_fit*-1, *ind.values), color = pal[i])\n",
    "    \n",
    "        \n",
    "    mean, var, skew, kurt = dist.stats(*ind.values, moments='mvsk')\n",
    "    \n",
    "    textstr = '\\n'.join((\n",
    "        r'$\\mu=%.2f$' % (np.log(ind.loc['loc']), ),\n",
    "        r'$\\sigma=%.2f$' % (ind.loc['s'], )))\n",
    "    \n",
    "    textstr = '\\n'.join((\n",
    "        'Mean=%.2f' % (mean, ),\n",
    "        'Variance=%.2f' % (var, ),\n",
    "        'Skewness=%.2f' % (skew, ),\n",
    "        'Kurtosis=%.2f' % (kurt, )))\n",
    "    ax.text(0.275, 0.8, textstr, fontsize=14, verticalalignment='top', \\\n",
    "            color = pal[i], transform=ax.transAxes)  \n",
    "    \n",
    "\n",
    "    ax.axvline(x=quantiles[hs_name][0.01], lw=3, clip_on=False, \\\n",
    "                         color = 'k', linestyle = 'dashed')\n",
    "    ax.axvline(x=quantiles[hs_name][0.1], lw=3, clip_on=False, \\\n",
    "                         color = 'k', linestyle = 'dashed')\n",
    "    \n",
    "    ax.tick_params(axis=\"y\",direction=\"out\", pad=10, labelsize = 15)\n",
    "    ax.tick_params(axis=\"x\",direction=\"out\", pad=10, labelsize = 15)\n",
    "    ax.xaxis.set_minor_locator(plt.MultipleLocator(5))\n",
    "    ax.set_ylim(0,0.9)\n",
    "    ax.set_yscale('log')\n",
    "    \n",
    "def label(x, color, label):\n",
    "    ax = plt.gca()\n",
    "    ax.text(0.1, .35, label, fontweight=\"bold\", color=color,\n",
    "            ha=\"left\", va=\"center\", transform=ax.transAxes)\n",
    "\n",
    "\n",
    "g.map(label, \"accelogw\")\n",
    "g.set(xticks=xticks)\n",
    "# Set the subplots to overlap\n",
    "#g.fig.subplots_adjust(hspace=-.25)\n",
    "\n",
    "# Remove axes details that don't play well with overlap\n",
    "g.set_titles(\"\")\n",
    "#g.set(yscale=\"log\")\n",
    "g.axes[nh-1][0].set_xlabel('OGWD [m/s/day]', fontsize = 20)\n",
    "g.axes[nh-2][0].set_ylabel('Occurrence density', fontsize = 20)\n",
    "\n",
    "#plt.savefig(f'OGWD_distribution_hotspots_DJFonly_wfits_{dist_name}.pdf', bbox_inches='tight') "
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}