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antvconst/reg.ipynb

Created June 19, 2018 13:14
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reg.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [],
"source": [
"from pandas_profiling import ProfileReport as profile\n",
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import scipy.stats as stats\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.linear_model import LassoCV\n",
"from sklearn.model_selection import GridSearchCV\n",
"from sklearn.linear_model import Ridge\n",
"from sklearn.feature_extraction import DictVectorizer\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.preprocessing import PolynomialFeatures\n",
"from sklearn.metrics import mean_squared_error, r2_score\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_excel('AirQualityUCI.xlsx')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Можно сразу дропнуть дату и время, так как для нашей задачи они не несут никакого смысла."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"df.drop(columns=['Date', 'Time'], inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Известно, что $-200$ кодирует пропуск значения. Заменим их на, собственно, пропуски."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"df = df.replace(-200, np.NaN)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Запустим профилировщик и посмотрим, с чем предстоит иметь дело."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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" border: 0;\n",
" margin-bottom: 2em;\n",
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" width:100%;\n",
" }\n",
"\n",
"\n",
" /* There is no good solution available to make the divs equal height and then center ... */\n",
" .histogram {\n",
" margin-top: 3em;\n",
" }\n",
" /* Freq table */\n",
"\n",
" table.freq {\n",
" margin-bottom: 2em;\n",
" border: 0;\n",
" }\n",
" table.freq th, table.freq tr, table.freq td {\n",
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"\n",
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" font-weight: 600;\n",
" white-space: nowrap;\n",
" overflow: hidden;\n",
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" td.fillremaining{\n",
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" max-width: none;\n",
" }\n",
"\n",
" td.number, th.number {\n",
" text-align:right ;\n",
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"\n",
" /* Freq mini */\n",
" .freq.mini td{\n",
" width: 50%;\n",
" padding: 1px;\n",
" font-size: 12px;\n",
"\n",
" }\n",
" table.freq.mini {\n",
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" .freq.mini th {\n",
" overflow: hidden;\n",
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" max-width: 5em;\n",
" font-weight: 400;\n",
" text-align:right;\n",
" padding-right: 0.5em;\n",
" }\n",
"\n",
" .missing {\n",
" color: #a94442;\n",
" }\n",
" .alert, .alert > th, .alert > td {\n",
" color: #a94442;\n",
" }\n",
"\n",
"\n",
" /* Bars in tables */\n",
" .freq .bar{\n",
" float: left;\n",
" width: 0;\n",
" height: 100%;\n",
" line-height: 20px;\n",
" color: #fff;\n",
" text-align: center;\n",
" background-color: #337ab7;\n",
" border-radius: 3px;\n",
" margin-right: 4px;\n",
" }\n",
" .other .bar {\n",
" background-color: #999;\n",
" }\n",
" .missing .bar{\n",
" background-color: #a94442;\n",
" }\n",
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" width: 100%;\n",
" white-space: nowrap;\n",
" text-align:left;\n",
" }\n",
"\n",
" .extrapadding{\n",
" padding: 2em;\n",
" }\n",
"\n",
" .pp-anchor{\n",
"\n",
" }\n",
"\n",
"</style>\n",
"\n",
"<div class=\"container pandas-profiling\">\n",
" <div class=\"row headerrow highlight\">\n",
" <h1>Overview</h1>\n",
" </div>\n",
" <div class=\"row variablerow\">\n",
" <div class=\"col-md-6 namecol\">\n",
" <p class=\"h4\">Dataset info</p>\n",
" <table class=\"stats\" style=\"margin-left: 1em;\">\n",
" <tbody>\n",
" <tr>\n",
" <th>Number of variables</th>\n",
" <td>13 </td>\n",
" </tr>\n",
" <tr>\n",
" <th>Number of observations</th>\n",
" <td>9357 </td>\n",
" </tr>\n",
" <tr>\n",
" <th>Total Missing (%)</th>\n",
" <td>13.1% </td>\n",
" </tr>\n",
" <tr>\n",
" <th>Total size in memory</th>\n",
" <td>950.4 KiB </td>\n",
" </tr>\n",
" <tr>\n",
" <th>Average record size in memory</th>\n",
" <td>104.0 B </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </div>\n",
" <div class=\"col-md-6 namecol\">\n",
" <p class=\"h4\">Variables types</p>\n",
" <table class=\"stats\" style=\"margin-left: 1em;\">\n",
" <tbody>\n",
" <tr>\n",
" <th>Numeric</th>\n",
" <td>11 </td>\n",
" </tr>\n",
" <tr>\n",
" <th>Categorical</th>\n",
" <td>0 </td>\n",
" </tr>\n",
" <tr>\n",
" <th>Boolean</th>\n",
" <td>0 </td>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <td>0 </td>\n",
" </tr>\n",
" <tr>\n",
" <th>Text (Unique)</th>\n",
" <td>0 </td>\n",
" </tr>\n",
" <tr>\n",
" <th>Rejected</th>\n",
" <td>2 </td>\n",
" </tr>\n",
" <tr>\n",
" <th>Unsupported</th>\n",
" <td>0 </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </div>\n",
" <div class=\"col-md-12\" style=\"padding-left: 1em;\">\n",
" \n",
" <p class=\"h4\">Warnings</p>\n",
" <ul class=\"list-unstyled\"><li><a href=\"#pp_var_AH\"><code>AH</code></a> has 366 / 3.9% missing values <span class=\"label label-default\">Missing</span></li><li><a href=\"#pp_var_C6H6(GT)\"><code>C6H6(GT)</code></a> is highly correlated with <a href=\"#pp_var_NMHC(GT)\"><code>NMHC(GT)</code></a> (ρ = 0.9026) <span class=\"label label-primary\">Rejected</span></li><li><a href=\"#pp_var_CO(GT)\"><code>CO(GT)</code></a> has 1683 / 18.0% missing values <span class=\"label label-default\">Missing</span></li><li><a href=\"#pp_var_NMHC(GT)\"><code>NMHC(GT)</code></a> has 8443 / 90.2% missing values <span class=\"label label-default\">Missing</span></li><li><a href=\"#pp_var_NO2(GT)\"><code>NO2(GT)</code></a> has 1642 / 17.5% missing values <span class=\"label label-default\">Missing</span></li><li><a href=\"#pp_var_NOx(GT)\"><code>NOx(GT)</code></a> has 1639 / 17.5% missing values <span class=\"label label-default\">Missing</span></li><li><a href=\"#pp_var_PT08.S1(CO)\"><code>PT08.S1(CO)</code></a> has 366 / 3.9% missing values <span class=\"label label-default\">Missing</span></li><li><a href=\"#pp_var_PT08.S2(NMHC)\"><code>PT08.S2(NMHC)</code></a> is highly correlated with <a href=\"#pp_var_C6H6(GT)\"><code>C6H6(GT)</code></a> (ρ = 0.98196) <span class=\"label label-primary\">Rejected</span></li><li><a href=\"#pp_var_PT08.S3(NOx)\"><code>PT08.S3(NOx)</code></a> has 366 / 3.9% missing values <span class=\"label label-default\">Missing</span></li><li><a href=\"#pp_var_PT08.S4(NO2)\"><code>PT08.S4(NO2)</code></a> has 366 / 3.9% missing values <span class=\"label label-default\">Missing</span></li><li><a href=\"#pp_var_PT08.S5(O3)\"><code>PT08.S5(O3)</code></a> has 366 / 3.9% missing values <span class=\"label label-default\">Missing</span></li><li><a href=\"#pp_var_RH\"><code>RH</code></a> has 366 / 3.9% missing values <span class=\"label label-default\">Missing</span></li><li><a href=\"#pp_var_T\"><code>T</code></a> has 366 / 3.9% missing values <span class=\"label label-default\">Missing</span></li><li>Dataset has 31 duplicate rows <span class=\"label label-warning\">Warning</span></li> </ul>\n",
" </div>\n",
"</div>\n",
" <div class=\"row headerrow highlight\">\n",
" <h1>Variables</h1>\n",
" </div>\n",
" <div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4 pp-anchor\" id=\"pp_var_AH\">AH<br/>\n",
" <small>Numeric</small>\n",
" </p>\n",
" </div><div class=\"col-md-6\">\n",
" <div class=\"row\">\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
" <tr>\n",
" <th>Distinct count</th>\n",
" <td>8988</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Unique (%)</th>\n",
" <td>96.1%</td>\n",
" </tr>\n",
" <tr class=\"alert\">\n",
" <th>Missing (%)</th>\n",
" <td>3.9%</td>\n",
" </tr>\n",
" <tr class=\"alert\">\n",
" <th>Missing (n)</th>\n",
" <td>366</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Infinite (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Infinite (n)</th>\n",
" <td>0</td>\n",
" </tr>\n",
" </table>\n",
"\n",
" </div>\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
"\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>1.0255</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Minimum</th>\n",
" <td>0.18468</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maximum</th>\n",
" <td>2.231</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Zeros (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"<div class=\"col-md-3 collapse in\" id=\"minihistogram-7076163972146195089\">\n",
" <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAAehJREFUeJzt3LHO0lAcxuF/iStlJ/QiTLwFEy/IzcTN0Utxd3X3LiBcQBsHB6mD4vLFN2JCW74%2Bz0ghpyT95fS0hWYcx7Emdjqdquu6qYflwR2PxzocDpOO%2BWLS0X7bbrdV9esLt207xy7wQPq%2Br67r/hw3U5olkKZpqqqqbduHCuTVu883vf/rhzd32pN1uh43U9pMPiI8EIFAIBAIBAKBQCCY5SrWWtx61avKla%2BlMYNAIBAIBAKBQCAQCAQCgUAgEKz2Psj/3KNgfcwgEAgEAoFAIBAIVrtIXyoPOC7LswjEFSnuxSkWBM9iBlk7/7ZyP2YQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAg8CzWCnmk/t%2BZQSAQCASLO8Xy4yeWxAwCgUAgEAgEAoFgcYt0lmmt907MIBAIBAKBQDDLGmQcx6qq6vv%2BybYf379NvTvcycu3n27%2BzJf3r5%2B8dj1OrsfNlGYJZBiGqqrqum6O4Vmw3ce/bxuGoXa73XQ7U1XNOEOWl8ulzudzbbfbappm6uF5MOM41jAMtd/va7OZdlUwSyDwKCzSIRAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAh%2BAmfaU7Y2t921AAAAAElFTkSuQmCC\">\n",
"\n",
"</div>\n",
"<div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-7076163972146195089,#minihistogram-7076163972146195089\"\n",
" aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
"</div>\n",
"<div class=\"row collapse col-md-12\" id=\"descriptives-7076163972146195089\">\n",
" <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
" <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-7076163972146195089\"\n",
" aria-controls=\"quantiles-7076163972146195089\" role=\"tab\"\n",
" data-toggle=\"tab\">Statistics</a></li>\n",
" <li role=\"presentation\"><a href=\"#histogram-7076163972146195089\" aria-controls=\"histogram-7076163972146195089\"\n",
" role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
" <li role=\"presentation\"><a href=\"#common-7076163972146195089\" aria-controls=\"common-7076163972146195089\"\n",
" role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
" <li role=\"presentation\"><a href=\"#extreme-7076163972146195089\" aria-controls=\"extreme-7076163972146195089\"\n",
" role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
"\n",
" </ul>\n",
"\n",
" <div class=\"tab-content\">\n",
" <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-7076163972146195089\">\n",
" <div class=\"col-md-4 col-md-offset-1\">\n",
" <p class=\"h4\">Quantile statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr>\n",
" <th>Minimum</th>\n",
" <td>0.18468</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5-th percentile</th>\n",
" <td>0.40087</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Q1</th>\n",
" <td>0.73677</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Median</th>\n",
" <td>0.9954</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Q3</th>\n",
" <td>1.3137</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95-th percentile</th>\n",
" <td>1.7256</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maximum</th>\n",
" <td>2.231</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Range</th>\n",
" <td>2.0464</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Interquartile range</th>\n",
" <td>0.57693</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" <div class=\"col-md-4 col-md-offset-2\">\n",
" <p class=\"h4\">Descriptive statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr>\n",
" <th>Standard deviation</th>\n",
" <td>0.40381</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Coef of variation</th>\n",
" <td>0.39376</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kurtosis</th>\n",
" <td>-0.5601</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>1.0255</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MAD</th>\n",
" <td>0.32846</td>\n",
" </tr>\n",
" <tr class=\"\">\n",
" <th>Skewness</th>\n",
" <td>0.25139</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sum</th>\n",
" <td>9220.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Variance</th>\n",
" <td>0.16306</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Memory size</th>\n",
" <td>73.2 KiB</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-7076163972146195089\">\n",
" <img 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86lHjx7y%2B/166qmn9MMPP2j8%2BPHKy8vjE4QAAKBBER2wNmzYoEGDBtnrx557ys3N1aRJk7R48WJJ0kUXXRSy3/LlyzVw4EC53W4tWLBAhYWFqqmpUefOnZWXl6cHH3zQ7tuiRQstWbJE99xzj/r37682bdooJydH06ZNC8MIAQBAJIrogDVw4EBZlnXC7b%2B2TZLS09NVWlp60vN06tRJ77///mnXBwAAmqcm/QwWAACAEwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYJgjAWvevHk6fPiwE6cGAABodI4ErPz8fHm9Xt19991at26dEyUAAAA0GkcC1q5du/Tyyy%2BrsrJSl156qXr27Knp06dr7969TpQDAABglCMBq2XLlhoxYoQWL16snTt3Kjc3Vy%2B//LI6duyoESNGaMmSJSd9CzsAAMCZyvGH3L1er6688koNHDhQLpdLGzZsUE5Ojrp27apVq1Y5XR4AAMBpcyxg7du3TzNmzFDv3r3Vv39/7dmzR4sWLdLf//53fffddxo6dKhuu%2B02p8oDAAD4pznyZc%2B///3v9cEHHyg1NVV/%2BtOflJubqw4dOtjbzznnHD344IN65plnnCgPAADgN3EkYMXExGjZsmW67LLLTtgnKSlJ27dvD2NVAAAAZjgSsF599dWT9nG5XDr//PPDUA0AAIBZjjyDNW7cOM2cObNe%2B3PPPacHHnjAgYoAAADMcSRgvf3228rMzKzXnpWVpQULFjhQEQAAgDmOBKx9%2B/YpNja2XntMTIz27dvnQEUAAADmOBKwzj//fH344Yf12j/88EOlpqY6UBEAAIA5jjzkPnbsWI0dO1bff/%2B9rrjiCknSRx99pCeffFLTpk1zoiQAAABjHAlYeXl5Onz4sKZMmaL/%2BI//kCR17NhRzzzzjO644w4nSgIAADDGZTn8pX%2BVlZVq06aN2rVr52QZjSoYDMrj8SgQCCgmJsbpcnCKMiYWOV0C8E/ZMHmI0yUAZwQn//46MoP1S0lJSU6XAAAAYJQjD7nv3btXt99%2Buzp16qTWrVsrKioqZAEAAIhkjsxgjRw5Ul999ZX%2B/d//XUlJSXK5XE6UgTDjlhsAoLlwJGCtXLlSK1eu1MUXX%2BzE6QEAABqVI7cIO3bsyKwVAABoshwJWH/5y19UUFCgb7/91onTAwAANCpHbhH6/X4dOHBAnTt3VkxMjFq1ahWyfc%2BePU6UBQAAYIQjAevxxx934rQAAABh4cgtwjvvvPNXl1O1cuVKDRs2TMnJyXK5XFq0aFHIdsuyNGnSJCUnJ6tNmzYaOHCgvvjii5A%2B1dXV8vv98ng88ng88vv92r9/f0ifzz//XAMGDFCbNm107rnn6rHHHpPD72cFAABnMEcCliR98803mjRpkvx%2Bv31LcOnSpdq6despH%2BPQoUPq3bu3Zs6c2eD2J598Uk8//bRmzpyp9evXy%2Bv16uqrr9aBAwfsPjk5OSorK1NRUZGKiopUVlYmv99vbw8Gg7r66quVnJys9evX69lnn9W0adP09NNP/5MjBwAATZ0jAWvVqlXq2bOnVqxYobfeeksHDx6UJG3atEmPPvroKR8nOztbf/7znzVixIh62yzL0owZMzRx4kSNGDFCaWlpevXVV/WPf/xDb7zxhiRp69atKioq0osvvqisrCxlZWVpzpw5ev/997Vt2zZJ0vz583X48GHNnTtXaWlpGjFihB5%2B%2BGE9/fTTzGIBAIAGORKwHnroIU2aNEnLly8PeXP7FVdcodLSUiPnKC8vV1VVlXw%2Bn93mdrs1YMAArVmzRpJUUlIij8ejvn372n0yMzPl8XhC%2BgwYMEBut9vuM3jwYO3atUvffPONkVoBAEDT4kjA%2Buyzz3TDDTfUa09ISNDevXuNnKOqqkqSlJiYGNKemJhob6uqqlJCQkKDdfyyT0PH%2BOU5jldTU6NgMBiyAACA5sORgNWuXbsGw0lZWZnOPfdco%2Bc6/oWmlmWFtDX0wtOT9Tl2a/BEL0udOnWq/dC8x%2BNRSkrKP10/AACIPI4ErJtvvlkTJkzQ3r177ZCydu1ajR8/XrfeequRc3i9Xkn1Z5n27Nljz0B5vV7t3r273r579%2B4N6dPQMaT6s2PHFBQUKBAI2EtFRcVvGwwAAIgojgSsKVOmyOv1KikpSQcPHlSPHj3Ur18/XXLJJXrkkUeMnCM1NVVer1fFxcV2W21trVasWKF%2B/fpJkrKyshQIBLRu3Tq7z9q1axUIBEL6rFy5UrW1tXafpUuXKjk5WV26dGnw3G63WzExMSELAABoPhx50WhUVJQWLFig//u//9OmTZt09OhRpaen64ILLjit4xw8eFA7duyw18vLy1VWVqa4uDh16tRJY8eO1ZQpU9S1a1d17dpVU6ZM0dlnn62cnBxJUvfu3TVkyBDl5eVp9uzZkqS77rpLQ4cOVbdu3ST9/BqHwsJCjRw5Ug8//LC2b9%2BuKVOm6NFHH%2BX7FAEAQINcVgS/a%2BCTTz7RoEGD6rXn5uZq7ty5sixLhYWFmj17tqqrq9W3b18999xzSktLs/v%2B8MMPGjNmjBYvXixJGj58uGbOnKl27drZfT7//HONHj1a69atU2xsrEaNGnVaASsYDMrj8SgQCDTr2ayMiUVOlwDgDLNh8hCnS0AT5uTfX0cC1l133fWr21944YUwVRIeBKyfEbAAHI%2BAhcbk5N9fR24RVlZWhqzX1dXpiy%2B%2B0IEDB3T55Zc7URIAAIAxjgSs//mf/6nX9tNPP%2Bnf/u3f1L17dwcqAgAAMMex7yI8XsuWLTV%2B/Hg99dRTTpcCAADwm5wxAUuSvv76a9XV1TldBgAAwG/iyC3CBx98MGTdsixVVlZq8eLFuuWWW5woCQAAwBhHAlZJSUnI%2BllnnaUOHTro8ccfV15enhMlAQAAGONIwFq1apUTpwUAAAiLM%2BoZLAAAgKbAkRmsSy655JTfgv7L7wkEAACIBI4ErEGDBmn27Nn63e9%2Bp6ysLElSaWmptm3bprvvvltut9uJsgAAAIxwJGDt379fo0eP1pQpU0LaJ06cqN27d%2BvFF190oiwAAAAjHHkG66233tLtt99er33kyJF6%2B%2B23HagIAADAHEcCltvt1po1a%2Bq1r1mzhtuDAAAg4jlyi3DMmDEaNWqUNm/erMzMTEk/P4M1Z84cPfzww06UBAAAYIwjAWvixIlKTU3Vf/3Xf%2Bnll1%2BWJHXv3l1z5sxRTk6OEyUBAAAY40jAkqScnBzCFAAAaJIce9FoMBjU3Llz9eijj6q6ulqS9Omnn6qystKpkgAAAIxwZAZry5Ytuuqqq3T22WeroqJCI0eOVGxsrN566y19%2B%2B23evXVV50oCwAAwAhHZrDGjRunnJwcffXVV2rdurXdfu2112rlypVOlAQAAGCMIzNY69ev16xZs%2Bp9Xc65557LLUIAABDxHJnBioqK0sGDB%2Bu1b9%2B%2BXfHx8Q5UBAAAYI4jAWv48OH6z//8T/3000%2BSJJfLpe%2B%2B%2B04TJkzQiBEjnCgJAADAGEcC1vTp07Vr1y55vV79%2BOOPuuKKK3TeeeepdevW9b6fEAAAINI48gyWx%2BPRmjVrVFxcrE2bNuno0aNKT0/X4MGD6z2XBQAAEGnCHrDq6up0zTXX6Pnnn5fP55PP5wt3CQAAAI0q7LcIW7Vqpc2bNzNTBQAAmixHnsG69dZb9corrzhxagAAgEbn2HcRzpw5U8uWLVNGRobatm0bsu3JJ590qCoAAIDfzpGAtXHjRl144YWSpM8%2B%2ByxkG7cOAQBApAtrwPr666%2BVmpqqVatWhfO0AAAAYRXWZ7C6du2qvXv32us33XSTdu/e3Wjn69Kli1wuV71l9OjRkqSBAwfW23bzzTeHHKO6ulp%2Bv18ej0cej0d%2Bv1/79%2B9vtJoBAEDkC2vAsiwrZP2DDz7QoUOHGu1869evV2Vlpb0UFxdLkv7whz/YffLy8kL6zJ49O%2BQYOTk5KisrU1FRkYqKilRWVia/399oNQMAgMjn2EPu4dChQ4eQ9ccff1znn3%2B%2BBgwYYLedffbZ8nq9De6/detWFRUVqbS0VH379pUkzZkzR1lZWdq2bZu6devWeMUDAICIFdYZrGO34Y5vC4fa2lrNmzdPd9xxR8g558%2Bfr/j4ePXs2VPjx4/XgQMH7G0lJSXyeDx2uJKkzMxM%2B030AAAADQnrDJZlWRo5cqTcbrck6fDhwxo1alS91zS88847xs%2B9aNEi7d%2B/XyNHjrTbbrnlFqWmpsrr9WrLli0qKCjQp59%2Bat9KrKqqUkJCQr1jJSQkqKqq6oTnqqmpUU1Njb0eDAbNDQQAAJzxwhqwcnNzQ9ZvvfXWsJ37pZdeUnZ2tpKTk%2B22vLw8%2B99paWnq2rWrMjIytGnTJqWnp0tqeIbNsqxfnXmbOnWqCgsLDVYPAAAiSVgDllNvb//73/%2BuZcuWnXRmLD09Xa1atdL27duVnp4ur9fb4Kcc9%2B7dq8TExBMep6CgQPn5%2BfZ6MBhUSkrKPz8AAAAQURz5qpxwe%2BWVV5SQkKBrr732V/t98cUXqqurU1JSkiQpKytLgUBA69ats/usXbtWgUBA/fr1O%2BFx3G63YmJiQhYAANB8NOlPEUrS0aNH9corryg3N1ctW/7/4X711VeaP3%2B%2BrrnmGsXHx%2BvLL7/UAw88oIsvvlj9%2B/eXJHXv3l1DhgxRXl6e/fqGu%2B66S0OHDuUThAAA4ISa/AzWsmXLtHPnTt1xxx0h7VFRUfroo480ePBgdevWTWPGjJHP59OyZcvUokULu9/8%2BfPVq1cv%2BXw%2B%2BXw%2BXXjhhXr99dfDPQwAABBBmvwMls/nq/eCU0lKSUnRihUrTrp/XFyc5s2b1xilAQCAJqrJz2ABAACEGwELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGNakA9akSZPkcrlCFq/Xa2%2B3LEuTJk1ScnKy2rRpo4EDB%2BqLL74IOUZ1dbX8fr88Ho88Ho/8fr/2798f7qEAAIAI0qQDliT17NlTlZWV9vL555/b25588kk9/fTTmjlzptavXy%2Bv16urr75aBw4csPvk5OSorKxMRUVFKioqUllZmfx%2BvxNDAQAAEaKl0wU0tpYtW4bMWh1jWZZmzJihiRMnasSIEZKkV199VYmJiXrjjTd09913a%2BvWrSoqKlJpaan69u0rSZozZ46ysrK0bds2devWLaxjAYCmJmNikdMlnJYNk4c4XQIiRJOfwdq%2BfbuSk5OVmpqqm2%2B%2BWV9//bUkqby8XFVVVfL5fHZft9utAQMGaM2aNZKkkpISeTweO1xJUmZmpjwej92nITU1NQoGgyELAABoPpp0wOrbt69ee%2B01ffjhh5ozZ46qqqrUr18/ff/996qqqpIkJSYmhuyTmJhob6uqqlJCQkK94yYkJNh9GjJ16lT7mS2Px6OUlBSDowIAAGe6Jh2wsrOzdf3116tXr1666qqrtGTJEkk/3wo8xuVyhexjWVZI2/HbG%2BpzvIKCAgUCAXupqKj4rUMBAAARpEkHrOO1bdtWvXr10vbt2%2B3nso6fidqzZ489q%2BX1erV79%2B56x9m7d2%2B9ma9fcrvdiomJCVkAAEDz0awCVk1NjbZu3aqkpCSlpqbK6/WquLjY3l5bW6sVK1aoX79%2BkqSsrCwFAgGtW7fO7rN27VoFAgG7DwAAwPGa9KcIx48fr2HDhqlTp07as2eP/vznPysYDCo3N1cul0tjx47VlClT1LVrV3Xt2lVTpkzR2WefrZycHElS9%2B7dNWTIEOXl5Wn27NmSpLvuuktDhw7lE4QAAOCEmnTA%2Bvbbb/XHP/5R%2B/btU4cOHZSZmanS0lJ17txZkvTggw/qxx9/1D333KPq6mr17dtXS5cuVXR0tH2M%2BfPna8yYMfanDYcPH66ZM2c6Mh4AABAZXJZlWU4X0dQFg0F5PB4FAoFm/TxWpL3vBgCOx3uwIouTf3%2Bb1TNYAAAA4UDAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMCwlk4XgN%2BGL1AGAODMwwwWAACAYQQsAAAAwwhYAACrek30AAAMHklEQVQAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhTTpgTZ06VZdccomio6OVkJCg6667Ttu2bQvpM3DgQLlcrpDl5ptvDulTXV0tv98vj8cjj8cjv9%2Bv/fv3h3MoAAAggjTpgLVixQqNHj1apaWlKi4u1k8//SSfz6dDhw6F9MvLy1NlZaW9zJ49O2R7Tk6OysrKVFRUpKKiIpWVlcnv94dzKAAAIIK0dLqAxlRUVBSy/sorryghIUEbN27U5ZdfbrefffbZ8nq9DR5j69atKioqUmlpqfr27StJmjNnjrKysrRt2zZ169at8QYAAAAiUpOewTpeIBCQJMXFxYW0z58/X/Hx8erZs6fGjx%2BvAwcO2NtKSkrk8XjscCVJmZmZ8ng8WrNmTXgKBwAAEaVJz2D9kmVZys/P16WXXqq0tDS7/ZZbblFqaqq8Xq%2B2bNmigoICffrppyouLpYkVVVVKSEhod7xEhISVFVV1eC5ampqVFNTY68Hg0HDowEAAGeyZhOw7r33Xn322WdavXp1SHteXp7977S0NHXt2lUZGRnatGmT0tPTJUkul6ve8SzLarBd%2Bvnh%2BsLCQoPVAwCASNIsbhHed999Wrx4sZYvX66OHTv%2Bat/09HS1atVK27dvlyR5vV7t3r27Xr%2B9e/cqMTGxwWMUFBQoEAjYS0VFxW8fBAAAiBhNOmBZlqV7771X77zzjj7%2B%2BGOlpqaedJ8vvvhCdXV1SkpKkiRlZWUpEAho3bp1dp%2B1a9cqEAioX79%2BDR7D7XYrJiYmZAEAAM1Hk75FOHr0aL3xxht67733FB0dbT8z5fF41KZNG3311VeaP3%2B%2BrrnmGsXHx%2BvLL7/UAw88oIsvvlj9%2B/eXJHXv3l1DhgxRXl6e/fqGu%2B66S0OHDuUThAAAoEFNegZr1qxZCgQCGjhwoJKSkuxlwYIFkqSoqCh99NFHGjx4sLp166YxY8bI5/Np2bJlatGihX2c%2BfPnq1evXvL5fPL5fLrwwgv1%2BuuvOzUsAABwhmvSM1iWZf3q9pSUFK1YseKkx4mLi9O8efNMlQUAAJq4Jj2DBQAA4IQmPYMFAIBJGROLTt7pDLJh8hCnS2i2mMECAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgWEunCwAAAI0jY2KR0yWcsg2ThzhdglHMYAEAABhGwAIAADCMgHWKnn/%2BeaWmpqp169bq06ePVq1a5XRJAADgDEXAOgULFizQ2LFjNXHiRG3evFmXXXaZsrOztXPnTqdLAwAAZyAC1il4%2Bumndeedd%2BpPf/qTunfvrhkzZiglJUWzZs1yujQAAHAG4lOEJ1FbW6uNGzdqwoQJIe0%2Bn09r1qxpcJ%2BamhrV1NTY64FAQJIUDAaN13ek5pDxYwIAEG6N8Tfy2DEtyzJ%2B7JMhYJ3Evn37dOTIESUmJoa0JyYmqqqqqsF9pk6dqsLCwnrtKSkpjVIjAACRzjO98Y594MABeTyexjtBAwhYp8jlcoWsW5ZVr%2B2YgoIC5efn2%2BtHjx7VDz/8oPbt259wH/w2wWBQKSkpqqioUExMjNPl4FdwrSID1ylycK1OzLIsHThwQMnJyWE/NwHrJOLj49WiRYt6s1V79uypN6t1jNvtltvtDmlr165do9WI/y8mJoZfMBGCaxUZuE6Rg2vVsHDPXB3DQ%2B4nERUVpT59%2Bqi4uDikvbi4WP369XOoKgAAcCZjBusU5Ofny%2B/3KyMjQ1lZWXrhhRe0c%2BdOjRo1yunSAADAGajFpEmTJjldxJkuLS1N7du315QpUzRt2jT9%2BOOPev3119W7d2%2BnS8MvtGjRQgMHDlTLlvy/4UzHtYoMXKfIwbU687gsJz67CAAA0ITxDBYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2AhYjz//PNKTU1V69at1adPH61ateqEfefOnSuXy1VvOXz4cBgrbn5WrlypYcOGKTk5WS6XS4sWLTrpPitWrFCfPn3UunVrnXfeefrv//7vMFSK071Wn3zySYM/U//7v/8bpoqbp6lTp%2BqSSy5RdHS0EhISdN1112nbtm0n3W/hwoXq0aOH3G63evTooXfffTcM1eKXCFiICAsWLNDYsWM1ceJEbd68WZdddpmys7O1c%2BfOE%2B4TExOjysrKkKV169ZhrLr5OXTokHr37q2ZM2eeUv/y8nJdc801uuyyy7R582Y9/PDDGjNmjBYuXNjIleJ0r9Ux27ZtC/mZ6tq1ayNVCOnn/4CMHj1apaWlKi4u1k8//SSfz6dDhw6dcJ%2BSkhLddNNN8vv9%2BvTTT%2BX3%2B3XjjTdq7dq1YawcvKYBEaFv375KT0/XrFmz7Lbu3bvruuuu09SpU%2Bv1nzt3rsaOHav9%2B/eHs0z8gsvl0rvvvqvrrrvuhH0eeughLV68WFu3brXbRo0apU8//VQlJSXhKBM6tWv1ySefaNCgQaquruarvxy0d%2B9eJSQkaMWKFbr88ssb7HPTTTcpGAzqb3/7m902ZMgQxcbG6q9//Wu4Sm32mMHCGa%2B2tlYbN26Uz%2BcLaff5fFqzZs0J9zt48KA6d%2B6sjh07aujQodq8eXNjl4rTVFJSUu%2B6Dh48WBs2bFBdXZ1DVeHXXHzxxUpKStKVV16p5cuXO11OsxMIBCRJcXFxJ%2Bxzop%2BrX/t9CfMIWDjj7du3T0eOHKn35dqJiYn1voT7mAsuuEBz587V4sWL9de//lWtW7dW//79tX379nCUjFNUVVXV4HX96aeftG/fPoeqQkOSkpL0wgsvaOHChXrnnXfUrVs3XXnllVq5cqXTpTUblmUpPz9fl156qdLS0k7Y70Q/Vyf6fYnGwTv1ETFcLlfIumVZ9dqOyczMVGZmpr3ev39/paen69lnn9UzzzzTqHXi9DR0XRtqh7O6deumbt262etZWVmqqKjQtGnTTnirCmbde%2B%2B9%2Buyzz7R69eqT9j2d35doHMxg4YwXHx%2BvFi1a1Pvf1549e%2Br9L%2B1EzjrrLF1yySXMYJ1hvF5vg9e1ZcuWat%2B%2BvUNV4VRlZmbyMxUm9913nxYvXqzly5erY8eOv9r3RD9Xp/r7EmYQsHDGi4qKUp8%2BfVRcXBzSXlxcrH79%2Bp3SMSzLUllZmZKSkhqjRPyTsrKy6l3XpUuXKiMjQ61atXKoKpyqzZs38zPVyCzL0r333qt33nlHH3/8sVJTU0%2B6z4l%2Brk719yXM4BYhIkJ%2Bfr78fr8yMjKUlZWlF154QTt37tSoUaMkSbfddpvOPfdc%2BxOFhYWFyszMVNeuXRUMBvXMM8%2BorKxMzz33nJPDaPIOHjyoHTt22Ovl5eUqKytTXFycOnXqpIKCAn333Xd67bXXJP38icGZM2cqPz9feXl5Kikp0UsvvcQnncLgdK/VjBkz1KVLF/Xs2VO1tbWaN2%2BeFi5cyCs1Gtno0aP1xhtv6L333lN0dLQ9M%2BXxeNSmTRtJ9X//3X///br88sv1xBNP6F//9V/13nvvadmyZad0axEGWUCEeO6556zOnTtbUVFRVnp6urVixQp724ABA6zc3Fx7fezYsVanTp2sqKgoq0OHDpbP57PWrFnjQNXNy/Llyy1J9ZZj1yY3N9caMGBAyD6ffPKJdfHFF1tRUVFWly5drFmzZoW/8GbodK/VE088YZ1//vlW69atrdjYWOvSSy%2B1lixZ4kzxzUhD10iS9corr9h9jv/9Z1mW9fbbb1vdunWzWrVqZV1wwQXWwoULw1s4LN6DBQAAYBjPYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMOz/AefbLkvANEKhAAAAAElFTkSuQmCC\"/>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-7076163972146195089\">\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">1.320841244259702</td>\n",
" <td class=\"number\">2</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">0.7797699428120062</td>\n",
" <td class=\"number\">2</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">0.7058010096166148</td>\n",
" <td class=\"number\">2</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1.4713255656117266</td>\n",
" <td class=\"number\">2</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1.6160477890039626</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">0.2239503487732882</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1.8350685576471888</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">0.9133141768209518</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">0.4526358842192853</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">0.6650527426787667</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"other\">\n",
" <td class=\"fillremaining\">Other values (8977)</td>\n",
" <td class=\"number\">8977</td>\n",
" <td class=\"number\">95.9%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"missing\">\n",
" <td class=\"fillremaining\">(Missing)</td>\n",
" <td class=\"number\">366</td>\n",
" <td class=\"number\">3.9%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"extreme-7076163972146195089\">\n",
" <p class=\"h4\">Minimum 5 values</p>\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">0.1846790209991702</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">0.18617943987720276</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">0.19096120854780632</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">0.1974686974243114</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">0.19875668744393402</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" <p class=\"h4\">Maximum 5 values</p>\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">2.139495892139178</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">2.1719321168014907</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">2.176616311974383</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">2.1806393191997313</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">2.2310357155831864</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"</div><div class=\"row variablerow ignore\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4 pp-anchor\" id=\"pp_var_C6H6(GT)\"><s>C6H6(GT)</s><br/>\n",
" <small>Highly correlated</small>\n",
" </p>\n",
" </div><div class=\"col-md-3\">\n",
" <p><em>This variable is highly correlated with <a href=\"#pp_var_NMHC(GT)\"><code>NMHC(GT)</code></a> and should be ignored for analysis</em></p>\n",
"</div>\n",
"<div class=\"col-md-6\">\n",
" <table class=\"stats \">\n",
" <tr>\n",
" <th>Correlation</th>\n",
" <td>0.9026</td>\n",
" </tr>\n",
" </table>\n",
"</div>\n",
"</div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4 pp-anchor\" id=\"pp_var_CO(GT)\">CO(GT)<br/>\n",
" <small>Numeric</small>\n",
" </p>\n",
" </div><div class=\"col-md-6\">\n",
" <div class=\"row\">\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
" <tr>\n",
" <th>Distinct count</th>\n",
" <td>97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Unique (%)</th>\n",
" <td>1.0%</td>\n",
" </tr>\n",
" <tr class=\"alert\">\n",
" <th>Missing (%)</th>\n",
" <td>18.0%</td>\n",
" </tr>\n",
" <tr class=\"alert\">\n",
" <th>Missing (n)</th>\n",
" <td>1683</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Infinite (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Infinite (n)</th>\n",
" <td>0</td>\n",
" </tr>\n",
" </table>\n",
"\n",
" </div>\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
"\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>2.1527</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Minimum</th>\n",
" <td>0.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maximum</th>\n",
" <td>11.9</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Zeros (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"<div class=\"col-md-3 collapse in\" id=\"minihistogram-8760257607184116197\">\n",
" <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAAdJJREFUeJzt3LHK01AYx%2BE3H65p99JchOAtCF6Qm%2BDm6KW4u7p7Fy29gAQHBxuHz7qIf/yqPWnwedZS3hTOj0PSQ7p5nudq7Hg81jAMrceycofDofb7fdOZz5pO%2B6Hv%2B6p6/MGbzWaJS2BFxnGsYRh%2BrpuWFgmk67qqqtpsNv8kkBdvPj75O5/fvfrrubR1WTctPTSfCCsiEAgEAoFAIBAIBAKBYJHHvMk1j2zhVuwgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCC4u%2BPurXgTCn/CDgKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgeC/Pax4jacecHS4cf3sIBAIBAKBQCAQCAQCgUAgEAgE/ge5IS%2BGWD87CAR2kDtj17kviwQyz3NVVY3j%2BMtn375%2BaX05q/f89Yebz/j09uXNZ/zOZZ1c1k1LiwQyTVNVVQ3DsMR4rrB9v/QVPK6b7XbbdGY3L5Dl%2BXyu0%2BlUfd9X13Wtx7My8zzXNE212%2B3q4aHtbfMigcBaeIoFgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQPAdG4lN4k2cDesAAAAASUVORK5CYII%3D\">\n",
"\n",
"</div>\n",
"<div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-8760257607184116197,#minihistogram-8760257607184116197\"\n",
" aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
"</div>\n",
"<div class=\"row collapse col-md-12\" id=\"descriptives-8760257607184116197\">\n",
" <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
" <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-8760257607184116197\"\n",
" aria-controls=\"quantiles-8760257607184116197\" role=\"tab\"\n",
" data-toggle=\"tab\">Statistics</a></li>\n",
" <li role=\"presentation\"><a href=\"#histogram-8760257607184116197\" aria-controls=\"histogram-8760257607184116197\"\n",
" role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
" <li role=\"presentation\"><a href=\"#common-8760257607184116197\" aria-controls=\"common-8760257607184116197\"\n",
" role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
" <li role=\"presentation\"><a href=\"#extreme-8760257607184116197\" aria-controls=\"extreme-8760257607184116197\"\n",
" role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
"\n",
" </ul>\n",
"\n",
" <div class=\"tab-content\">\n",
" <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-8760257607184116197\">\n",
" <div class=\"col-md-4 col-md-offset-1\">\n",
" <p class=\"h4\">Quantile statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr>\n",
" <th>Minimum</th>\n",
" <td>0.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5-th percentile</th>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Q1</th>\n",
" <td>1.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Median</th>\n",
" <td>1.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Q3</th>\n",
" <td>2.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95-th percentile</th>\n",
" <td>4.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maximum</th>\n",
" <td>11.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Range</th>\n",
" <td>11.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Interquartile range</th>\n",
" <td>1.8</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" <div class=\"col-md-4 col-md-offset-2\">\n",
" <p class=\"h4\">Descriptive statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr>\n",
" <th>Standard deviation</th>\n",
" <td>1.4533</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Coef of variation</th>\n",
" <td>0.67507</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kurtosis</th>\n",
" <td>2.6678</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>2.1527</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MAD</th>\n",
" <td>1.1175</td>\n",
" </tr>\n",
" <tr class=\"\">\n",
" <th>Skewness</th>\n",
" <td>1.3698</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sum</th>\n",
" <td>16520</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Variance</th>\n",
" <td>2.1119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Memory size</th>\n",
" <td>73.2 KiB</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-8760257607184116197\">\n",
" <img 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apUNFQEAAJhjS8A6e/as2rRpU6M9NDRUZ8%2BetaEiAAAAc2wJWHfeeaf%2B8pe/1Gj/y1/%2BosjISBsqAgAAMMeWSe4pKSlKSUnR3//%2Bdz344IOSpI0bN2rOnDmaN2%2BeHSUBAAAYY0vASkpK0qVLl5Senq7f/va3kqQOHTro9ddf15NPPmlHSQAAAMbYErAkafLkyZo8ebKKiorUsmVL3XrrrXaVAgAAYJRtAeuqiIgIu0sAAAAwypZJ7mfOnNGvfvUrderUSS1atFBwcLDfAgAAEMhsGcFKTEzUF198oeeff14RERFyOBx2lAEAAFAvbAlYOTk5ysnJ0X333WfH4QEAAOqVLbcIO3TowKgVAABosmwJWL///e81c%2BZMnTx50o7DAwAA1CtbbhEmJCSovLxcnTt3VmhoqJo3b%2B63vaSkxI6yAAAAjLAlYL366qt2HBYAAKBB2BKwnnrqKSP7ycnJ0dy5c5Wfn6%2BioiKtWbNGY8aM8W1PTEzU0qVL/V7Tt29f5eXl%2BdYrKio0bdo0/elPf9LFixf10EMPaeHCherQoYOvz4kTJ5ScnKxNmzapZcuWio%2BP17x583ikBAAAqJUtc7Ak6dixY5o1a5YSEhJ8twTXr1%2BvgwcP1nkfFy5cUM%2BePZWZmfmtfYYNG6aioiLfsm7dOr/tKSkpWrNmjVauXKlt27bp/PnzGjlypKqrqyVJ1dXVGjFihC5cuKBt27Zp5cqV%2BuCDDzR16tTvcdYAAOBmYMsI1tatWzVs2DD95Cc/UW5url566SWFhYVp9%2B7dWrJkid5///067Wf48OEaPnz4dfs4nU653e5at3m9Xv3xj3/Ue%2B%2B9p4cffliStGzZMnXs2FEbNmzQ0KFDtX79ev31r39VYWGhPB6PJGn%2B/PlKTEzU7NmzFRoa%2Bh3OHAAA3AxsGcGaPn26Zs2apc2bN/vdZnvwwQf9bt%2BZ8OmnnyosLEx33XWXkpKS/CbQ5%2Bfnq6qqSnFxcb42j8ejqKgo5ebmSpK2b9%2BuqKgoX7iSpKFDh6qiokL5%2Bfm1HrOiokJlZWV%2BCwAAuHnYErD27t2rn//85zXaw8LCdObMGWPHGT58uJYvX65NmzZp/vz52rlzpx588EFVVFRIkoqLixUcHKw2bdr4vS48PFzFxcW%2BPuHh4X7b27Rpo%2BDgYF%2Bfa2VkZMjlcvmWjh07GjsnAADQ%2BNkSsG699dZaw0lBQYFuv/12Y8d57LHHNGLECEVFRWnUqFH65JNPdPjwYX388cfXfZ1lWX4PQq3toajX9vmmmTNnyuv1%2BpbCwsIfdiIAACCg2BKwxo0bpxkzZujMmTO%2BkPLZZ59p2rRpGj9%2BfL0dNyIiQp07d9aRI0ckSW63W5WVlSotLfXrV1JS4hu1crvdNcJgaWmpqqqqaoxsXeV0OhUaGuq3AACAm4ctASs9PV1ut1sRERE6f/687rnnHsXGxqpPnz76zW9%2BU2/H/fvf/67CwkJFRERIknr37q3mzZsrOzvb16eoqEj79%2B9XbGysJCkmJkb79%2B9XUVGRr8/69evldDrVu3fveqsVAAAELlu%2BRRgcHKxVq1bp8OHD2r17t65cuaJevXrp7rvv/k77OX/%2BvP72t7/51o8ePaqCggK1bdtWbdu21axZs/Szn/1MEREROnbsmF588UW1b99eP/3pTyVJLpdLTz31lKZOnap27dqpbdu2mjZtmnr06OH7VmFcXJzuueceJSQkaO7cufrHP/6hadOmKSkpiZEpAABQK4dlWZbdRXxfn376qR544IEa7RMmTNCiRYs0ZswY7dmzR%2BfOnVNERIQeeOABvfLKK36Tzi9duqTnn39eK1as8HvQ6Df7nDhxQs8880yNB406nc461VlWViaXyyWv12s8lEWnZRndHwLXrtnD7C4BABqV%2Bvz8vRFbAtavf/3r625fvHhxA1XSMAhYaAgELADwZ2fAsuUW4TfnM0lSVVWVDhw4oPLyct1///12lAQAAGCMLQHro48%2BqtF2%2BfJl/du//Zu6detmQ0UAAADm2PZbhNdq1qyZpk2bprlz59pdCgAAwA/SaAKWJH355ZeqqqqyuwwAAIAfxJZbhC%2B88ILfumVZKioq0tq1a/X444/bURIAAIAxtgSs7du3%2B63fcsstuu222/Tqq68qKSnJjpIAAACMsSVgbd261Y7DAgAANIhGNQcLAACgKbBlBKtPnz6%2BH3m%2BkR07dtRzNQAAAGbZErAeeOABvfXWW7rrrrsUExMjScrLy9OhQ4f09NNP1/knaAAAABojWwLWuXPnlJycrPT0dL/2tLQ0nT59Wv/93/9tR1kAAABG2DIH689//rN%2B9atf1WhPTEzU%2B%2B%2B/b0NFAAAA5tgSsJxOp3Jzc2u05%2BbmcnsQAAAEPFtuEU6ZMkUTJ07Unj171K9fP0lfz8FasmSJXnzxRTtKAgAAMMaWgJWWlqbIyEj913/9l95%2B%2B21JUrdu3bRkyRLFx8fbURIAAIAxtgQsSYqPjydMAQCAJsm2B42WlZXp3Xff1X/8x3%2BotLRUkvT555%2BrqKjIrpIAAACMsGUEa//%2B/Xr44YfVqlUrFRYWKjExUW3atNGf//xnnTx5UkuXLrWjLAAAACNsGcF67rnnFB8fry%2B%2B%2BEItWrTwtY8YMUI5OTl2lAQAAGCMLSNYO3fu1KJFi2r8XM7tt9/OLUIAABDwbBnBCg4O1vnz52u0HzlyRO3bt7ehIgAAAHNsCVijR4/WK6%2B8osuXL0uSHA6HvvrqK82YMUNjx461oyQAAABjbAlY8%2BfP16lTp%2BR2u3Xx4kU9%2BOCDuuOOO9SiRYsav08IAAAQaGyZg%2BVyuZSbm6vs7Gzt3r1bV65cUa9evTR06NAa87IAAAACTYMHrKqqKj3yyCNauHCh4uLiFBcX19AlAAAA1KsGv0XYvHlz7dmzh5EqAADQZNkyB2v8%2BPF655137Dg0AABAvbPttwgzMzO1YcMGRUdHq3Xr1n7b5syZY1NVAAAAP5wtASs/P1/33nuvJGnv3r1%2B27h1CAAAAl2DBqwvv/xSkZGR2rp1a0MeFgAAoEE16BysLl266MyZM771xx57TKdPn27IEgAAAOpdgwYsy7L81tetW6cLFy40ZAkAAAD1zpZvEQIAADRlDRqwHA5HjUnsTGoHAABNTYNOcrcsS4mJiXI6nZKkS5cuaeLEiTUe07B69eqGLAsAAMCoBg1YEyZM8FsfP358Qx4eAACgQTRowOLp7QAA4GbAJHcAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAsIAOWDk5ORo1apQ8Ho8cDoc%2B/PBDv%2B2WZWnWrFnyeDxq2bKlBg8erAMHDvj1KS0tVUJCglwul1wulxISEnTu3Dm/Pvv27dOgQYPUsmVL3X777Xr55Zdr/HA1AADAVQEdsC5cuKCePXsqMzOz1u1z5szRggULlJmZqZ07d8rtdmvIkCEqLy/39YmPj1dBQYGysrKUlZWlgoICJSQk%2BLaXlZVpyJAh8ng82rlzp9544w3NmzdPCxYsqPfzAwAAgalBn%2BRu2vDhwzV8%2BPBat1mWpddee01paWkaO3asJGnp0qUKDw/XihUr9PTTT%2BvgwYPKyspSXl6e%2BvbtK0lasmSJYmJidOjQIXXt2lXLly/XpUuX9O6778rpdCoqKkqHDx/WggULlJqayo9VAwCAGgJ6BOt6jh49quLiYsXFxfnanE6nBg0apNzcXEnS9u3b5XK5fOFKkvr16yeXy%2BXXZ9CgQb4fqJakoUOH6tSpUzp27FjDnAwAAAgoTTZgFRcXS5LCw8P92sPDw33biouLFRYWVuO1YWFhfn1q28c3j3GtiooKlZWV%2BS0AAODm0WQD1lXX3sKzLMuvrbZbfDfqc3WC%2B7fdHszIyPBNmne5XOrYseP3rh8AAASeJhuw3G63pJqjTCUlJb4RKLfbrdOnT9d47ZkzZ/z61LYPqebo2FUzZ86U1%2Bv1LYWFhT/sZAAAQEBpsgErMjJSbrdb2dnZvrbKykpt2bJFsbGxkqSYmBh5vV7t2LHD1%2Bezzz6T1%2Bv165OTk6PKykpfn/Xr18vj8ehHP/pRrcd2Op0KDQ31WwAAwM0joAPW%2BfPnVVBQoIKCAklfT2wvKCjQiRMn5HA4lJKSovT0dK1Zs0b79%2B9XYmKiWrVqpfj4eElSt27dNGzYMCUlJSkvL095eXlKSkrSyJEj1bVrV0lfP8bB6XQqMTFR%2B/fv15o1a5Sens43CAEAwLcK6Mc07Nq1Sw888IBvPTU1VZI0YcIEvfvuu3rhhRd08eJFPfPMMyotLVXfvn21fv16hYSE%2BF6zfPlyTZkyxfdtw9GjR/s9V8vlcik7O1vJycmKjo5WmzZtlJqa6jsWAADAtRwWjySvd2VlZXK5XPJ6vcZvF0anZRndHwLXrtnD7C4BABqV%2Bvz8vZGAvkUIAADQGBGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwLBmdhcAwIzotCy7S6izXbOH2V0CANQrRrAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwLAmHbBmzZolh8Pht7jdbt92y7I0a9YseTwetWzZUoMHD9aBAwf89lFaWqqEhAS5XC65XC4lJCTo3LlzDX0qAAAggDTpgCVJ3bt3V1FRkW/Zt2%2Bfb9ucOXO0YMECZWZmaufOnXK73RoyZIjKy8t9feLj41VQUKCsrCxlZWWpoKBACQkJdpwKAAAIEM3sLqC%2BNWvWzG/U6irLsvTaa68pLS1NY8eOlSQtXbpU4eHhWrFihZ5%2B%2BmkdPHhQWVlZysvLU9%2B%2BfSVJS5YsUUxMjA4dOqSuXbs26LkAAIDA0ORHsI4cOSKPx6PIyEiNGzdOX375pSTp6NGjKi4uVlxcnK%2Bv0%2BnUoEGDlJubK0navn27XC6XL1xJUr9%2B/eRyuXx9AAAArtWkR7D69u2r//mf/9Fdd92l06dP63e/%2B51iY2N14MABFRcXS5LCw8P9XhMeHq7jx49LkoqLixUWFlZjv2FhYb7X16aiokIVFRW%2B9bKyMhOnAwAAAkSTDljDhw/3/blHjx6KiYnRnXfeqaVLl6pfv36SJIfD4fcay7L82q7dXlufa2VkZOill176oeUDAIAA1eRvEX5T69at1aNHDx05csQ3L%2BvakaiSkhLfqJbb7dbp06dr7OfMmTM1Rr6%2BaebMmfJ6vb6lsLDQ4FkAAIDG7qYKWBUVFTp48KAiIiIUGRkpt9ut7Oxs3/bKykpt2bJFsbGxkqSYmBh5vV7t2LHD1%2Bezzz6T1%2Bv19amN0%2BlUaGio3wIAAG4eTfoW4bRp0zRq1Ch16tRJJSUl%2Bt3vfqeysjJNmDBBDodDKSkpSk9PV5cuXdSlSxelp6erVatWio%2BPlyR169ZNw4YNU1JSkt566y1J0q9//WuNHDmSbxACAIBv1aQD1smTJ/XLX/5SZ8%2Be1W233aZ%2B/fopLy9PnTt3liS98MILunjxop555hmVlpaqb9%2B%2BWr9%2BvUJCQnz7WL58uaZMmeL7tuHo0aOVmZlpy/kAAIDA4LAsy7K7iKaurKxMLpdLXq/X%2BO3C6LQso/sDGsKu2cPsLgHATaA%2BP39v5KaagwUAANAQCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCsmd0FALj5RKdl2V3Cd7Jr9jC7SwAQYBjBAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAY1szuAgCgsYtOy7K7hO9k1%2BxhdpcA3PQYwQIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAAAGXCQZAAAJP0lEQVSG8ZiGOlq4cKHmzp2roqIide/eXa%2B99poGDhxod1kAUEMgPVaCR0qgqWIEqw5WrVqllJQUpaWlac%2BePRo4cKCGDx%2BuEydO2F0aAABohByWZVl2F9HY9e3bV7169dKiRYt8bd26ddOYMWOUkZFxw9eXlZXJ5XLJ6/UqNDTUaG2B9D9VAAh0jLgFlvr8/L0RbhHeQGVlpfLz8zVjxgy/9ri4OOXm5tb6moqKClVUVPjWvV6vpK//ok2rrrhgfJ8AgNrVx/s46s/Vvy87xpIIWDdw9uxZVVdXKzw83K89PDxcxcXFtb4mIyNDL730Uo32jh071kuNAICG4ZpvdwX4PsrLy%2BVyuRr0mASsOnI4HH7rlmXVaLtq5syZSk1N9a1fuXJF//jHP9SuXbtvfU1dlJWVqWPHjiosLGzwoc5AwnWqG65T3XCd6obrVDdcp7oxdZ0sy1J5ebk8Ho/B6uqGgHUD7du3V1BQUI3RqpKSkhqjWlc5nU45nU6/tltvvdVYTaGhofzDrAOuU91wneqG61Q3XKe64TrVjYnr1NAjV1fxLcIbCA4OVu/evZWdne3Xnp2drdjYWJuqAgAAjRkjWHWQmpqqhIQERUdHKyYmRosXL9aJEyc0ceJEu0sDAACNUNCsWbNm2V1EYxcVFaV27dopPT1d8%2BbN08WLF/Xee%2B%2BpZ8%2BeDV5LUFCQBg8erGbNyMbXw3WqG65T3XCd6obrVDdcp7oJ9OvEc7AAAAAMYw4WAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgBZCFCxcqMjJSLVq0UO/evbV161a7S2pUMjIy1KdPH4WEhCgsLExjxozRoUOH7C6rUcvIyJDD4VBKSordpTRKX331lcaPH6927dqpVatW%2BvGPf6z8/Hy7y2pULl%2B%2BrH//939XZGSkWrZsqTvuuEMvv/yyrly5YndptsrJydGoUaPk8XjkcDj04Ycf%2Bm23LEuzZs2Sx%2BNRy5YtNXjwYB04cMCmau1zvetUVVWl6dOnq0ePHmrdurU8Ho%2BeeOIJnTp1ysaK646AFSBWrVqllJQUpaWlac%2BePRo4cKCGDx%2BuEydO2F1ao7FlyxYlJycrLy9P2dnZunz5suLi4nThAj%2BIXZudO3dq8eLFuvfee%2B0upVEqLS1V//791bx5c33yySf661//qvnz5xv9VYam4D//8z/15ptvKjMzUwcPHtScOXM0d%2B5cvfHGG3aXZqsLFy6oZ8%2BeyszMrHX7nDlztGDBAmVmZmrnzp1yu90aMmSIysvLG7hSe13vOv3zn//U7t279Zvf/Ea7d%2B/W6tWrdfjwYY0ePdqGSr8HCwHhJz/5iTVx4kS/trvvvtuaMWOGTRU1fiUlJZYka8uWLXaX0uiUl5dbXbp0sbKzs61BgwZZzz77rN0lNTrTp0%2B3BgwYYHcZjd6IESOsJ5980q9t7Nix1vjx422qqPGRZK1Zs8a3fuXKFcvtdluvvvqqr%2B3SpUuWy%2BWy3nzzTTtKbBSuvU612bFjhyXJOn78eANV9f0xghUAKisrlZ%2Bfr7i4OL/2uLg45ebm2lRV4%2Bf1eiVJbdu2tbmSxic5OVkjRozQww8/bHcpjdbatWsVHR2tX/ziFwoLC9N9992nJUuW2F1WozNgwABt3LhRhw8fliR9/vnn2rZtmx555BGbK2u8jh49quLiYr/3dKfTqUGDBvGefgNer1cOhyMgRpID8/GoN5mzZ8%2Bqurq6xo9Lh4eH1/gRanzNsiylpqZqwIABioqKsrucRmXlypXKz8/Xrl277C6lUfvyyy%2B1aNEipaam6sUXX9SOHTs0ZcoUOZ1OPfHEE3aX12hMnz5dXq9Xd999t4KCglRdXa3Zs2frl7/8pd2lNVpX37dre08/fvy4HSUFhEuXLmnGjBmKj48PiB/KJmAFEIfD4bduWVaNNnxt0qRJ2rt3r7Zt22Z3KY1KYWGhnn32Wa1fv14tWrSwu5xG7cqVK4qOjlZ6erok6b777tOBAwe0aNEiAtY3rFq1SsuWLdOKFSvUvXt3FRQUKCUlRR6PRxMmTLC7vEaN9/S6q6qq0rhx43TlyhUtXLjQ7nLqhIAVANq3b6%2BgoKAao1UlJSU1/gcEafLkyVq7dq1ycnLUoUMHu8tpVPLz81VSUqLevXv72qqrq5WTk6PMzExVVFQoKCjIxgobj4iICN1zzz1%2Bbd26ddMHH3xgU0WN0/PPP68ZM2Zo3LhxkqQePXro%2BPHjysjIIGB9C7fbLenrkayIiAhfO%2B/ptauqqtKjjz6qo0ePatOmTQExeiXxLcKAEBwcrN69eys7O9uvPTs7W7GxsTZV1fhYlqVJkyZp9erV2rRpkyIjI%2B0uqdF56KGHtG/fPhUUFPiW6OhoPf744yooKCBcfUP//v1rPObj8OHD6ty5s00VNU7//Oc/dcst/h8lQUFBN/1jGq4nMjJSbrfb7z29srJSW7Zs4T39GlfD1ZEjR7Rhwwa1a9fO7pLqjBGsAJGamqqEhARFR0crJiZGixcv1okTJzRx4kS7S2s0kpOTtWLFCv3v//6vQkJCfCN%2BLpdLLVu2tLm6xiEkJKTGnLTWrVurXbt2zFW7xnPPPafY2Filp6fr0Ucf1Y4dO7R48WItXrzY7tIalVGjRmn27Nnq1KmTunfvrj179mjBggV68skn7S7NVufPn9ff/vY33/rRo0dVUFCgtm3bqlOnTkpJSVF6erq6dOmiLl26KD09Xa1atVJ8fLyNVTe8610nj8ejn//859q9e7f%2B7//%2BT9XV1b739bZt2yo4ONiusuvG3i8x4rv4wx/%2BYHXu3NkKDg62evXqxeMHriGp1uWdd96xu7RGjcc0fLuPPvrIioqKspxOp3X33XdbixcvtrukRqesrMx69tlnrU6dOlktWrSw7rjjDistLc2qqKiwuzRbbd68udb3owkTJliW9fWjGn77299abrfbcjqd1v3332/t27fP3qJtcL3rdPTo0W99X9%2B8ebPdpd%2BQw7IsqyEDHQAAQFPHHCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIb9P1GckIZ76zRzAAAAAElFTkSuQmCC\"/>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-8760257607184116197\">\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">1.0</td>\n",
" <td class=\"number\">305</td>\n",
" <td class=\"number\">3.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1.4</td>\n",
" <td class=\"number\">279</td>\n",
" <td class=\"number\">3.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1.6</td>\n",
" <td class=\"number\">275</td>\n",
" <td class=\"number\">2.9%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1.5</td>\n",
" <td class=\"number\">273</td>\n",
" <td class=\"number\">2.9%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1.1</td>\n",
" <td class=\"number\">262</td>\n",
" <td class=\"number\">2.8%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">0.7</td>\n",
" <td class=\"number\">260</td>\n",
" <td class=\"number\">2.8%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1.7</td>\n",
" <td class=\"number\">258</td>\n",
" <td class=\"number\">2.8%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1.3</td>\n",
" <td class=\"number\">253</td>\n",
" <td class=\"number\">2.7%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">0.8</td>\n",
" <td class=\"number\">251</td>\n",
" <td class=\"number\">2.7%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">0.9</td>\n",
" <td class=\"number\">248</td>\n",
" <td class=\"number\">2.7%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"other\">\n",
" <td class=\"fillremaining\">Other values (86)</td>\n",
" <td class=\"number\">5010</td>\n",
" <td class=\"number\">53.5%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"missing\">\n",
" <td class=\"fillremaining\">(Missing)</td>\n",
" <td class=\"number\">1683</td>\n",
" <td class=\"number\">18.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:34%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"extreme-8760257607184116197\">\n",
" <p class=\"h4\">Minimum 5 values</p>\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">0.1</td>\n",
" <td class=\"number\">33</td>\n",
" <td class=\"number\">0.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:16%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">0.2</td>\n",
" <td class=\"number\">45</td>\n",
" <td class=\"number\">0.5%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:21%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">0.3</td>\n",
" <td class=\"number\">98</td>\n",
" <td class=\"number\">1.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:45%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">0.4</td>\n",
" <td class=\"number\">160</td>\n",
" <td class=\"number\">1.7%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:73%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">0.5</td>\n",
" <td class=\"number\">217</td>\n",
" <td class=\"number\">2.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" <p class=\"h4\">Maximum 5 values</p>\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">9.9</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">10.1</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">10.2</td>\n",
" <td class=\"number\">2</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">11.5</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">11.9</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"</div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4 pp-anchor\" id=\"pp_var_NMHC(GT)\">NMHC(GT)<br/>\n",
" <small>Numeric</small>\n",
" </p>\n",
" </div><div class=\"col-md-6\">\n",
" <div class=\"row\">\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
" <tr>\n",
" <th>Distinct count</th>\n",
" <td>430</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Unique (%)</th>\n",
" <td>4.6%</td>\n",
" </tr>\n",
" <tr class=\"alert\">\n",
" <th>Missing (%)</th>\n",
" <td>90.2%</td>\n",
" </tr>\n",
" <tr class=\"alert\">\n",
" <th>Missing (n)</th>\n",
" <td>8443</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Infinite (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Infinite (n)</th>\n",
" <td>0</td>\n",
" </tr>\n",
" </table>\n",
"\n",
" </div>\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
"\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>218.81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Minimum</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maximum</th>\n",
" <td>1189</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Zeros (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"<div class=\"col-md-3 collapse in\" id=\"minihistogram-7108607739127898742\">\n",
" <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAAddJREFUeJzt3LGq02AYx%2BE3xbW5gNJchODuJHhBboKD4OW4C07u3kVKL6DFwcHGQety4I89tF%2Bac55nLembQn58/UJIN03TVI3tdrsahqH1WBZuHMfabrdNZ75oOu2v9XpdVX9%2BcN/3c5wCC3I4HGoYhn/XTUuzBNJ1XVVV9X3/IJBX779c/H3fP729ynlx387XTUur5hNhQQQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEs7y04dq86IFbsYJAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCB4Eg8rPoYHHPkfVhAIBAKBQCAQCATPdpP%2BGJdu7G3ql88KAoEV5IbcSl4%2BKwgEVpA7Y9W5LwJ5Atw8uB1/sSCYZQWZpqmqqg6Hw4PPfv380fp0np2X7z5ffMy3D28uPub1x69XmXO%2BTs7XTUvdNMPU3W5XwzC0HsvCjeNY2%2B226cxZAjmdTrXf72u9XlfXda3HszDTNNXxeKzNZlOrVdtdwSyBwFLYpEMgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQ/AZNTVikrWQg7gAAAABJRU5ErkJggg%3D%3D\">\n",
"\n",
"</div>\n",
"<div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-7108607739127898742,#minihistogram-7108607739127898742\"\n",
" aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
"</div>\n",
"<div class=\"row collapse col-md-12\" id=\"descriptives-7108607739127898742\">\n",
" <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
" <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-7108607739127898742\"\n",
" aria-controls=\"quantiles-7108607739127898742\" role=\"tab\"\n",
" data-toggle=\"tab\">Statistics</a></li>\n",
" <li role=\"presentation\"><a href=\"#histogram-7108607739127898742\" aria-controls=\"histogram-7108607739127898742\"\n",
" role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
" <li role=\"presentation\"><a href=\"#common-7108607739127898742\" aria-controls=\"common-7108607739127898742\"\n",
" role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
" <li role=\"presentation\"><a href=\"#extreme-7108607739127898742\" aria-controls=\"extreme-7108607739127898742\"\n",
" role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
"\n",
" </ul>\n",
"\n",
" <div class=\"tab-content\">\n",
" <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-7108607739127898742\">\n",
" <div class=\"col-md-4 col-md-offset-1\">\n",
" <p class=\"h4\">Quantile statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr>\n",
" <th>Minimum</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5-th percentile</th>\n",
" <td>28.65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Q1</th>\n",
" <td>67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Median</th>\n",
" <td>150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Q3</th>\n",
" <td>297</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95-th percentile</th>\n",
" <td>661.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maximum</th>\n",
" <td>1189</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Range</th>\n",
" <td>1182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Interquartile range</th>\n",
" <td>230</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" <div class=\"col-md-4 col-md-offset-2\">\n",
" <p class=\"h4\">Descriptive statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr>\n",
" <th>Standard deviation</th>\n",
" <td>204.46</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Coef of variation</th>\n",
" <td>0.93441</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kurtosis</th>\n",
" <td>2.2703</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>218.81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MAD</th>\n",
" <td>156.24</td>\n",
" </tr>\n",
" <tr class=\"\">\n",
" <th>Skewness</th>\n",
" <td>1.557</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sum</th>\n",
" <td>199990</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Variance</th>\n",
" <td>41804</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Memory size</th>\n",
" <td>73.2 KiB</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-7108607739127898742\">\n",
" <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%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%2B/X0KFDFR0drfj4eE2ZMkXV1dWNXT4AAAgRzSZgbd68Wc8//7y6dOni156bm6vly5crPz9f69at09GjRzVkyBCdOnVKknTq1CkNHjxYx44d07p165Sfn6/XX39d06ZNC8QwAABACGgWD3s%2BevSoxo4dq0WLFumRRx6x2r1er1544QUtWbJEAwYMkCS98sorSklJ0apVq5Sdna2VK1dq165dKi0tVXJysiTp8ccf1/jx4zVnzpyAP7w5Y1ZBQN//YmyZMzDQJQAA0CiaxQrWPffco8GDB1sh6qzi4mLV1NQoKyvLaktOTlZaWpqKiookSevXr1daWpoVriQpOztbPp9PxcXFjTMAAAAQUpr8ClZ%2Bfr6Ki4u1ZcuWWsc8Ho8iIyPVqlUrv/bExER5PB6rT2Jiot/xVq1aKTIy0urzQz6fTz6fz9qvrKz8d4cBAABCSJNewSotLdW9996rpUuXqkWLFvV%2BnTFGDofD2v/%2Bv8/V5/vy8vLkcrmsLSUl5eKLBwAAIatJB6zi4mKVl5crPT1d4eHhCg8P15o1a/TMM88oPDxciYmJqq6uVkVFhd/rysvLrVUrt9tda6WqoqJCNTU1tVa2zpo5c6a8Xq%2B1lZaWNswAAQBAUGrSAat///7avn27SkpKrC0jI0Njx461/h0REaHCwkLrNWVlZdqxY4d69uwpScrMzNSOHTtUVlZm9Vm5cqWcTqfS09PrfF%2Bn06nY2Fi/DQAANB9N%2BhqsmJgYpaWl%2BbVFR0erdevWVvsdd9yhadOmqXXr1oqLi9P06dPVuXNn64L4rKwsdezYUTk5OXrsscf07bffavr06ZowYQLBCQAA1KlJB6z6ePLJJxUeHq6RI0fq%2BPHj6t%2B/vxYvXqywsDBJUlhYmN5%2B%2B23dfffd6tWrl6KiojRmzBjNnz8/wJUDAIBg5TDGmEAX0dRVVlbK5XLJ6/XavurFfbAAAKhbQ37%2BXkiTvgYLAAAgEAhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwIWAACAzQhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwIWAACAzQhYAAAANiNgAQAA2KxJB6yFCxeqS5cuio2NVWxsrDIzM/Xuu%2B9ax/v06SOHw%2BG3jR492u8cFRUVysnJkcvlksvlUk5Ojo4cOdLYQwEAACGkSQestm3bat68edqyZYu2bNmifv366ec//7l27txp9ZkwYYLKysqs7bnnnvM7x5gxY1RSUqKCggIVFBSopKREOTk5jT0UAAAQQsIDXUBDGjp0qN/%2BnDlztHDhQm3YsEGdOnWSJLVs2VJut7vO13/yyScqKCjQhg0b1L17d0nSokWLlJmZqd27d6tDhw4NOwAAABCSmvQK1vedOnVK%2Bfn5OnbsmDIzM632pUuXKj4%2BXp06ddL06dNVVVVlHVu/fr1cLpcVriSpR48ecrlcKioqOud7%2BXw%2BVVZW%2Bm0AAKD5aNIrWJK0fft2ZWZm6sSJE7r00ku1fPlydezYUZI0duxYpaamyu12a8eOHZo5c6Y%2B%2BugjFRYWSpI8Ho8SEhJqnTMhIUEej%2Bec75mXl6eHH364YQYEAACCXpMPWB06dFBJSYmOHDmi119/XePGjdOaNWvUsWNHTZgwweqXlpam9u3bKyMjQ1u3blXXrl0lSQ6Ho9Y5jTF1tp81c%2BZMTZ061dqvrKxUSkqKjaMCAADBrMkHrMjISP30pz%2BVJGVkZGjz5s16%2Bumna13MLkldu3ZVRESE9uzZo65du8rtduvQoUO1%2Bh0%2BfFiJiYnnfE%2Bn0ymn02nfIAAAQEhpNtdgnWWMkc/nq/PYzp07VVNTo6SkJElSZmamvF6vNm3aZPXZuHGjvF6vevbs2Sj1AgCA0NOkV7AefPBBDRo0SCkpKaqqqlJ%2Bfr5Wr16tgoIC7d27V0uXLtXNN9%2Bs%2BPh47dq1S9OmTdN1112nXr16SZKuueYaDRw4UBMmTLBWvO68804NGTKEXxACAIBzatIB69ChQ8rJyVFZWZlcLpe6dOmigoIC3XTTTSotLdX777%2Bvp59%2BWkePHlVKSooGDx6shx56SGFhYdY5li5dqilTpigrK0uSNGzYMC1YsCBQQwIAACHAYYwxgS6iqausrJTL5ZLX61VsbKyt586YVWDr%2BRrSljkDA10CAKAZacjP3wtpdtdgAQAANDQCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwIWAACAzQhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwIWAACAzQhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADZr0gFr4cKF6tKli2JjYxUbG6vMzEy9%2B%2B671nGfz6fJkycrPj5e0dHRGjZsmA4cOOB3jv3792vo0KGKjo5WfHy8pkyZourq6sYeCgAACCFNOmC1bdtW8%2BbN05YtW7Rlyxb169dPP//5z7Vz505JUm5urpYvX678/HytW7dOR48e1ZAhQ3Tq1ClJ0qlTpzR48GAdO3ZM69atU35%2Bvl5//XVNmzYtkMMCAABBzmGMMYEuojHFxcXpscce069%2B9Su1adNGS5Ys0ahRoyRJBw8eVEpKit555x1lZ2fr3Xff1ZAhQ1RaWqrk5GRJUn5%2BvsaPH6/y8nLFxsbW6z0rKyvlcrnk9Xrr/Zr6yphVYOv5GtKWOQMDXQIAoBlpyM/fC2nSK1jfd%2BrUKeXn5%2BvYsWPKzMxUcXGxampqlJWVZfVJTk5WWlqaioqKJEnr169XWlqaFa4kKTs7Wz6fT8XFxed8L5/Pp8rKSr8NAAA0H00%2BYG3fvl2XXnqpnE6nJk6cqOXLl6tjx47yeDyKjIxUq1at/PonJibK4/FIkjwejxITE/2Ot2rVSpGRkVafuuTl5cnlcllbSkqK/QMDAABBq8kHrA4dOqikpEQbNmzQXXfdpXHjxmnXrl3n7G%2BMkcPhsPa//%2B9z9fmhmTNnyuv1Wltpaem/NwgAABBSwgNdQEOLjIzUT3/6U0lSRkaGNm/erKefflqjRo1SdXW1Kioq/FaxysvL1bNnT0mS2%2B3Wxo0b/c5XUVGhmpqaWitb3%2Bd0OuV0OhtgNAAAIBQ0%2BRWsHzLGyOfzKT09XRERESosLLSOlZWVaceOHVbAyszM1I4dO1RWVmb1WblypZxOp9LT0xu9dgAAEBqa9ArWgw8%2BqEGDBiklJUVVVVXKz8/X6tWrVVBQIJfLpTvuuEPTpk1T69atFRcXp%2BnTp6tz584aMGCAJCkrK0sdO3ZUTk6OHnvsMX377beaPn26JkyY0Oi/RgAAAKGjSQesQ4cOKScnR2VlZXK5XOrSpYsKCgp00003SZKefPJJhYeHa%2BTIkTp%2B/Lj69%2B%2BvxYsXKywsTJIUFhamt99%2BW3fffbd69eqlqKgojRkzRvPnzw/ksAAAQJBrdvfBCgTug3UG98ECADQm7oMFAADQhBCwAAAAbEbAAgAAsBkBCwAAwGYELAAAAJsRsAAAAGxGwAIAALAZAQsAAMBmBCwAAACbEbAAAABsRsACAACwGQELAADAZgQsAAAAmxGwAAAAbEbAAgAAsBkBCwAAwGYELAAAAJsRsAAAAGxGwAIAALAZAQsAAMBmBCwAAACbEbAAAABsRsACAACwGQELAADAZgQsAAAAmxGwAAAAbBa0AeuVV17RiRMn/q1z5OXlqVu3boqJiVFCQoKGDx%2Bu3bt3%2B/Xp06ePHA6H3zZ69Gi/PhUVFcrJyZHL5ZLL5VJOTo6OHDnyb9UGAACarqANWFOnTpXb7dZvfvMbbdq06UedY82aNbrnnnu0YcMGFRYW6uTJk8rKytKxY8f8%2Bk2YMEFlZWXW9txzz/kdHzNmjEpKSlRQUKCCggKVlJQoJyfnR48NAAA0beGBLuBcDh48qBUrVmjx4sXq3bu32rdvr9tvv1233Xab2rRpU69zFBQU%2BO2/%2BOKLSkhIUHFxsW644QarvWXLlnK73XWe45NPPlFBQYE2bNig7t27S5IWLVqkzMxM7d69Wx06dPiRIwQAAE1V0K5ghYeHa8SIEVqxYoX279%2BvcePG6S9/%2BYvatm2rESNG6O2335Yx5qLO6fV6JUlxcXF%2B7UuXLlV8fLw6deqk6dOnq6qqyjq2fv16uVwuK1xJUo8ePeRyuVRUVPRvjBAAADRVQbuC9X1ut1v9%2B/fXl19%2Bqb1792rLli16//331aZNG7344ou6/vrrL3gOY4ymTp2q3r17Ky0tzWofO3asUlNT5Xa7tWPHDs2cOVMfffSRCgsLJUkej0cJCQm1zpeQkCCPx1Pne/l8Pvl8Pmu/srLyYocMAABCWNCuYEnS119/raeeekrXXnutevXqpfLycr355pv68ssv9dVXX2nIkCG67bbb6nWuSZMm6eOPP9Zf//pXv/YJEyZowIABSktL0%2BjRo/W///u/WrVqlbZu3Wr1cTgctc5njKmzXTpzcf3ZC%2BJdLpdSUlIuYtQAACDUBW3A%2BsUvfqHLL79cf/7zn5WTk6PS0lL97W9/08CBA%2BVwOHTppZfq/vvv15dffnnBc02ePFkrVqzQBx98oLZt2563b9euXRUREaE9e/ZIOrN6dujQoVr9Dh8%2BrMTExDrPMXPmTHm9XmsrLS2tx4gBAEBTEbRfEcbGxmrVqlXn/fovKSnJCkJ1McZo8uTJWr58uVavXq3U1NQLvu/OnTtVU1OjpKQkSVJmZqa8Xq82bdqkn/3sZ5KkjRs3yuv1qmfPnnWew%2Bl0yul0XvC9AABA0%2BQwF3uleAi5%2B%2B679eqrr%2Brvf/%2B736/9XC6XoqKitHfvXi1dulQ333yz4uPjtWvXLk2bNk1RUVHavHmzwsLCJEmDBg3SwYMHrds33HnnnWrXrp3eeuutetVRWVkpl8slr9er2NhYW8eYMavgwp2CxJY5AwNdAgCgGWnIz98LCdqvCO%2B77z4tWLCgVvuf/vQnTZs2rV7nWLhwobxer/r06aOkpCRrW7ZsmSQpMjJS77//vrKzs9WhQwdNmTJFWVlZWrVqlRWupDO/MuzcubOysrKUlZWlLl26aMmSJfYMFAAANDlBu4LVtm1bvfnmm8rIyPBr37p1q4YNG6YDBw4EqLKLxwrWGaxgAQAaEytYdfj666/VqlWrWu2xsbH6%2BuuvA1ARAABA/QRtwPqP//gPvffee7Xa33vvvXpdrA4AABAoQfsrwtzcXOXm5uqbb75Rv379JEnvv/%2B%2BHn30Uc2fPz/A1QEAAJxb0AasCRMm6MSJE5o7d64eeughSWeuy3rmmWd0%2B%2B23B7g6AACAcwvagCWduUHo5MmTVVZWpqioKF122WWBLgkAAOCCgjpgnXX2pp8AAAChIGgvcj98%2BLD%2B67/%2BS1dccYVatGihyMhIvw0AACBYBe0K1vjx47V371799re/VVJS0jkfrAwAABBsgjZgrV27VmvXrtV1110X6FIAAAAuStB%2BRdi2bVtWrQAAQEgK2oD15JNPaubMmSH1SBwAAAApiL8izMnJUVVVldq1a6fY2FhFRET4HS8vLw9QZQAAAOcXtAFr3rx5gS4BAADgRwnagHXHHXcEugQAAIAfJWgDliR98cUXWrx4sfbu3avHH39cCQkJWrlypVJSUnTNNdcEujxcpIxZBYEu4aJsmTMw0CUAAEJU0F7k/uGHH6pTp05as2aNXnvtNR09elSStHXrVv3%2B978PcHUAAADnFrQB64EHHtDs2bP1wQcf%2BN25vV%2B/ftqwYUMAKwMAADi/oA1YH3/8sX71q1/Vak9ISNDhw4cDUBEAAED9BG3Auuyyy%2BTxeGq1l5SU6PLLLw9ARQAAAPUTtAFr9OjRmjFjhg4fPmzd0X3jxo2aPn26br311gBXBwAAcG5BG7Dmzp0rt9utpKQkHT16VB07dlTPnj3VrVs3/e53vwt0eQAAAOcUtLdpiIyM1LJly/Tpp59q69atOn36tLp27aqrr7460KUBAACcV9AGrLOuuuoqXXXVVYEuAwAAoN6CNmDdeeed5z3%2B/PPPN1IlAAAAFydoA1ZZWZnffk1NjXbu3KmqqirdcMMNAaoKAADgwoI2YL311lu12k6ePKm77rqLx%2BQAAICgFrS/IqxLeHi4pk%2BfrsceeyzQpQAAAJxTSAUsSfr8889VU1NTr755eXnq1q2bYmJilJCQoOHDh2v37t1%2BfXw%2BnyZPnqz4%2BHhFR0dr2LBhOnDggF%2Bf/fv3a%2BjQoYqOjlZ8fLymTJmi6upq28YEAACalqD9ivD%2B%2B%2B/32zfGqKysTCtWrNDYsWPrdY41a9bonnvuUbdu3XTy5EnNmjVLWVlZ2rVrl6KjoyVJubm5euutt5Sfn6/WrVtr2rRpGjJkiIqLixUWFqZTp05p8ODBatOmjdatW6dvvvlG48aNkzFGf/zjH20fNwAACH0OY4wJdBF1uf766/32L7nkErVp00b9%2BvXThAkTFBERcdHnPHz4sBISErRmzRrdcMMN8nq9atOmjZYsWaJRo0ZJkg4ePKiUlBS98847ys7O1rvvvqshQ4aotLRUycnJkqT8/HyNHz9e5eXlio2NveD7VlZWyuVyyev11qv/xciYVWDr%2BfAvW%2BYMDHQJAIB/Q0N%2B/l5I0K5gffjhh7af0%2Bv1SpLi4uIkScXFxaqpqVFWVpbVJzk5WWlpaSoqKlJ2drbWr1%2BvtLQ0K1xJUnZ2tnw%2Bn4qLi9W3b1/b6wQAAKEtaAOW3Ywxmjp1qnr37q20tDRJksfjUWRkpFq1auXXNzEx0XrQtMfjUWJiot/xVq1aKTIyss6HUUtnruvy%2BXzWfmVlpZ1DAQAAQS5oA1a3bt2shzxfyKZNmy7YZ9KkSfr444%2B1bt26C/Y1xvi9d111/LDP9%2BXl5enhhx%2B%2B4PsAAICmKWh/Rdi3b1/t3r1bxhj16NFDPXr0kCTt3r1bffr0UXZ2trVdyOTJk7VixQp98MEHatu2rdXudrtVXV2tiooKv/7l5eXWqpXb7a61UlVRUaGamppaK1tnzZw5U16v19pKS0svauwAACC0Be0K1pEjR3TPPfdo7ty5fu2zZs3SoUOH9D//8z8XPIcxRpMnT9by5cu1evVqpaam%2Bh1PT09XRESECgsLNXLkSEln7iC/Y8cOPfroo5KkzMxMzZkzR2VlZUpKSpIkrVy5Uk6nU%2Bnp6XW%2Br9PplNPpvOgxAwCApiFoA9Zrr72mzZs312ofP368MjIy6hWw7rnnHr366qv6%2B9//rpiYGGslyuVyKSoqSi6XS3fccYemTZum1q1bKy4uTtOnT1fnzp01YMAASVJWVpY6duyonJwcPfbYY/r22281ffp0TZgwodF/kQAAAEJD0H5F6HQ6VVRUVKu9qKio3qtDCxculNfrVZ8%2BfZSUlGRty5Yts/o8%2BeSTGj58uEaOHKlevXqpZcuWeuuttxQWFiZJCgsL09tvv60WLVqoV69eGjlypIYPH6758%2BfbM1AAANDkBO0K1pQpUzRx4kRt27bNuv5qw4YNWrRokR588MF6naM%2Bt/hq0aKF/vjHP573pqFXXHGF/vGPf9SvcAAA0OwFbcCaNWuWUlNT9fTTT%2Bsvf/mLJOmaa67RokWLNGbMmABXBwAAcG5BG7AkacyYMYQpAAAQcoL2GizpzA06Fy9erN///vfWrRQ%2B%2BugjlZWVBbgyAACAcwvaFawdO3ZowIABatmypUpLSzV%2B/Hi1atVKr732mg4cOKCXXnop0CUCAADUKWhXsO677z6NGTNGe/fuVYsWLaz2wYMHa%2B3atQGsDAAA4PyCdgVr8%2BbNWrhwYa3H0Vx%2B%2BeV8RQgAAIJa0K5gRUZG6ujRo7Xa9%2BzZo/j4%2BABUBAAAUD9BG7CGDRum//7v/9bJkyclnXng8ldffaUZM2ZoxIgRAa4OAADg3II2YD3%2B%2BOM6ePCg3G63jh8/rn79%2BunKK69UixYtaj2fEAAAIJgE7TVYLpdLRUVFKiws1NatW3X69Gl17dpV2dnZta7LAgAACCZBGbBqamp0880369lnn1VWVpaysrICXRIAAEC9BeVXhBEREdq2bRsrVQAAICQFZcCSpFtvvVUvvvhioMsAAAC4aEH5FeFZCxYs0KpVq5SRkaHo6Gi/Y48%2B%2BmiAqgIAADi/oA1YxcXF6tKliyTp448/9jvGV4cAACCYBV3A%2Bvzzz5WamqoPP/ww0KUAAAD8KEF3DVb79u11%2BPBha3/UqFE6dOhQACsCAAC4OEEXsIwxfvvvvPOOjh07FqBqAAAALl7QBSwAAIBQF3QBy%2BFw1LqInYvaAQBAKAm6i9yNMRo/frycTqck6cSJE5o4cWKt2zS88cYbgSgPAADggoIuYI0bN85v/9Zbbw1QJQAAAD9O0AUs7t4OAABCXdBdgwUAABDqCFgAAAA2I2ABAADYjIAFAABgsyYdsNauXauhQ4cqOTlZDodDb775pt/x8ePHW/fdOrv16NHDr4/P59PkyZMVHx%2Bv6OhoDRs2TAcOHGjMYQAAgBDTpAPWsWPHdO2112rBggXn7DNw4ECVlZVZ2zvvvON3PDc3V8uXL1d%2Bfr7WrVuno0ePasiQITp16lRDlw8AAEJU0N2mwU6DBg3SoEGDztvH6XTK7XbXeczr9eqFF17QkiVLNGDAAEnSK6%2B8opSUFK1atUrZ2dm21wwAAEJfk17Bqo/Vq1crISFBV111lSZMmKDy8nLrWHFxsWpqapSVlWW1JScnKy0tTUVFRYEoFwAAhIAmvYJ1IYMGDdKvf/1rtWvXTvv27dPvfvc79evXT8XFxXI6nfJ4PIqMjFSrVq38XpeYmCiPx3PO8/p8Pvl8Pmu/srKywcYAAACCT7MOWKNGjbL%2BnZaWpoyMDLVr105vv/22RowYcc7XGWPO%2BwDqvLw8Pfzww7bWCgAAQkez/4rw%2B5KSktSuXTvt2bNHkuR2u1VdXa2Kigq/fuXl5UpMTDzneWbOnCmv12ttpaWlDVo3AAAILgSs7/nmm29UWlqqpKQkSVJ6eroiIiJUWFho9SkrK9OOHTvUs2fPc57H6XQqNjbWbwMAAM1Hk/6K8OjRo/rss8%2Bs/X379qmkpERxcXGKi4vT7Nmz9ctf/lJJSUn64osv9OCDDyo%2BPl6/%2BMUvJEkul0t33HGHpk2bptatWysuLk7Tp09X586drV8VAgAA/FCTDlhbtmxR3759rf2pU6dKksaNG6eFCxdq%2B/btevnll3XkyBElJSWpb9%2B%2BWrZsmWJiYqzXPPnkkwoPD9fIkSN1/Phx9e/fX4sXL1ZYWFijjwcAAIQGhzHGBLqIpq6yslIul0ter9f2rwszZhXYej78y5Y5AwNdAgDg39CQn78XwjVYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwIWAACAzQhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2Cw90AUCwyphVEOgSLsqWOQMDXQIA4P/HChYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwIWAACAzZp0wFq7dq2GDh2q5ORkORwOvfnmm37HjTGaPXu2kpOTFRUVpT59%2Bmjnzp1%2BfSoqKpSTkyOXyyWXy6WcnBwdOXKkMYcBAABCTJMOWMeOHdO1116rBQsW1Hn80Ucf1RNPPKEFCxZo8%2BbNcrvduummm1RVVWX1GTNmjEpKSlRQUKCCggKVlJQoJyensYYAAABCUJO%2BD9agQYM0aNCgOo8ZY/TUU09p1qxZGjFihCTppZdeUmJiol599VX95je/0SeffKKCggJt2LBB3bt3lyQtWrRImZmZ2r17tzp06NBoYwEAAKGjSa9gnc%2B%2Bffvk8XiUlZVltTmdTt14440qKiqSJK1fv14ul8sKV5LUo0cPuVwuq09dfD6fKisr/TYAANB8NNuA5fF4JEmJiYl%2B7YmJidYxj8ejhISEWq%2BtL/fcAAARlElEQVRNSEiw%2BtQlLy/PumbL5XIpJSXFxsoBAECwa7YB6yyHw%2BG3b4zxa/vh8br6/NDMmTPl9XqtrbS01L6CAQBA0GvS12Cdj9vtlnRmlSopKclqLy8vt1a13G63Dh06VOu1hw8frrXy9X1Op1NOp9PmigEAQKhotitYqampcrvdKiwstNqqq6u1Zs0a9ezZU5KUmZkpr9erTZs2WX02btwor9dr9QEAAPihJr2CdfToUX322WfW/r59%2B1RSUqK4uDhdccUVys3N1dy5c9W%2BfXu1b99ec%2BfOVcuWLTVmzBhJ0jXXXKOBAwdqwoQJeu655yRJd955p4YMGcIvCAEAwDk16YC1ZcsW9e3b19qfOnWqJGncuHFavHix7r//fh0/flx33323Kioq1L17d61cuVIxMTHWa5YuXaopU6ZYvzYcNmzYOe%2BrBQAAIEkOY4wJdBFNXWVlpVwul7xer2JjY209d8asAlvPh9C1Zc7AQJcAAEGlIT9/L6TZXoMFAADQUAhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwIWAACAzQhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNwgNdAAB7ZMwqCHQJ9bZlzsBAlwAADYoVLAAAAJsRsAAAAGxGwAIAALAZAQsAAMBmBCwAAACbNfuANXv2bDkcDr/N7XZbx40xmj17tpKTkxUVFaU%2Bffpo586dAawYAAAEu2YfsCSpU6dOKisrs7bt27dbxx599FE98cQTWrBggTZv3iy3262bbrpJVVVVAawYAAAEMwKWpPDwcLndbmtr06aNpDOrV0899ZRmzZqlESNGKC0tTS%2B99JK%2B%2B%2B47vfrqqwGuGgAABCsClqQ9e/YoOTlZqampGj16tD7//HNJ0r59%2B%2BTxeJSVlWX1dTqduvHGG1VUVHTO8/l8PlVWVvptAACg%2BWj2Aat79%2B56%2BeWX9d5772nRokXyeDzq2bOnvvnmG3k8HklSYmKi32sSExOtY3XJy8uTy%2BWytpSUlAYdAwAACC7NPmANGjRIv/zlL9W5c2cNGDBAb7/9tiTppZdesvo4HA6/1xhjarV938yZM%2BX1eq2ttLS0YYoHAABBiWcR/kB0dLQ6d%2B6sPXv2aPjw4ZIkj8ejpKQkq095eXmtVa3vczqdcjqdDV4rEKpC6bmJEs9OBHDxmv0K1g/5fD598sknSkpKUmpqqtxutwoLC63j1dXVWrNmjXr27BnAKgEAQDBr9itY06dP19ChQ3XFFVeovLxcjzzyiCorKzVu3Dg5HA7l5uZq7ty5at%2B%2Bvdq3b6%2B5c%2BeqZcuWGjNmTKBLBwAAQarZB6wDBw7olltu0ddff602bdqoR48e2rBhg9q1aydJuv/%2B%2B3X8%2BHHdfffdqqioUPfu3bVy5UrFxMQEuHIAABCsHMYYE%2BgimrrKykq5XC55vV7Fxsbaeu5Qu5YFCEVcgwWEpob8/L0QrsECAACwGQELAADAZgQsAAAAmxGwAAAAbEbAAgAAsBkBCwAAwGYELAAAAJsRsAAAAGzW7O/kDgAXEmo39OXGqEDgsYIFAABgMwIWAACAzQhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM24TQMANDGhdFsJbimBpooVLAAAAJsRsAAAAGxGwAIAALAZAQsAAMBmBCwAAACbEbAAAABsRsACAACwGQELAADAZgQsAAAAmxGwAAAAbMajcgAAARNKj/WReLQP6o8VrHp69tlnlZqaqhYtWig9PV0ffvhhoEsCAABBioBVD8uWLVNubq5mzZqlbdu26frrr9egQYO0f//%2BQJcGAACCkMMYYwJdRLDr3r27unbtqoULF1pt11xzjYYPH668vLwLvr6yslIul0ter1exsbG21hZqy%2BsAANSlIb5%2BbcjP3wvhGqwLqK6uVnFxsWbMmOHXnpWVpaKiojpf4/P55PP5rH2v1yvpzP/QdjvlO2b7OQEAaGwN8Rl59pyBWEsiYF3A119/rVOnTikxMdGvPTExUR6Pp87X5OXl6eGHH67VnpKS0iA1AgAQ6lyPN9y5q6qq5HK5Gu4N6kDAqieHw%2BG3b4yp1XbWzJkzNXXqVGv/9OnT%2Bvbbb9W6detzvqa%2BKisrlZKSotLS0kZf7gwlzFP9ME/1wzzVD/N0YcxR/dg1T8YYVVVVKTk52cbq6oeAdQHx8fEKCwurtVpVXl5ea1XrLKfTKafT6dd22WWX2VpXbGwsf5z1wDzVD/NUP8xT/TBPF8Yc1Y8d89TYK1dn8SvCC4iMjFR6eroKCwv92gsLC9WzZ88AVQUAAIIZK1j1MHXqVOXk5CgjI0OZmZl6/vnntX//fk2cODHQpQEAgCAUNnv27NmBLiLYpaWlqXXr1po7d67mz5%2Bv48ePa8mSJbr22msDUk9YWJj69Omj8HDy8fkwT/XDPNUP81Q/zNOFMUf1E%2BrzxH2wAAAAbMY1WAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgh5tlnn1VqaqpatGih9PR0ffjhh4EuqdHk5eWpW7duiomJUUJCgoYPH67du3f79fH5fJo8ebLi4%2BMVHR2tYcOG6cCBA3599u/fr6FDhyo6Olrx8fGaMmWKqqurG3MojSYvL08Oh0O5ublWG3P0L1999ZVuvfVWtW7dWi1bttR//ud/qri42DpujNHs2bOVnJysqKgo9enTRzt37vQ7R0VFhXJycuRyueRyuZSTk6MjR4409lAaxMmTJ/X//t//U2pqqqKionTllVfqD3/4g06fPm31aY5ztHbtWg0dOlTJyclyOBx68803/Y7bNSfbt2/XjTfeqKioKF1%2B%2BeX6wx/%2BEJBn6v1Y55unmpoaPfDAA%2BrcubOio6OVnJys2267TQcPHvQ7R0jPk0HIyM/PNxEREWbRokVm165d5t577zXR0dHmyy%2B/DHRpjSI7O9u8%2BOKLZseOHaakpMQMHjzYXHHFFebo0aNWn4kTJ5rLL7/cFBYWmq1bt5q%2Bffuaa6%2B91pw8edIYY8zJkydNWlqa6du3r9m6daspLCw0ycnJZtKkSYEaVoPZtGmT%2BclPfmK6dOli7r33XqudOTrj22%2B/Ne3atTPjx483GzduNPv27TOrVq0yn332mdVn3rx5JiYmxrz%2B%2Butm%2B/btZtSoUSYpKclUVlZafQYOHGjS0tJMUVGRKSoqMmlpaWbIkCGBGJLtHnnkEdO6dWvzj3/8w%2Bzbt8/87W9/M5deeql56qmnrD7NcY7eeecdM2vWLPP6668bSWb58uV%2Bx%2B2YE6/XaxITE83o0aPN9u3bzeuvv25iYmLM/PnzG22c/67zzdORI0fMgAEDzLJly8w///lPs379etO9e3eTnp7ud45QnicCVgj52c9%2BZiZOnOjXdvXVV5sZM2YEqKLAKi8vN5LMmjVrjDFn/mAjIiJMfn6%2B1eerr74yl1xyiSkoKDDGnPmDv%2BSSS8xXX31l9fnrX/9qnE6n8Xq9jTuABlRVVWXat29vCgsLzY033mgFLOboXx544AHTu3fvcx4/ffq0cbvdZt68eVbbiRMnjMvlMn/%2B85%2BNMcbs2rXLSDIbNmyw%2Bqxfv95IMv/85z8brvhGMnjwYHP77bf7tY0YMcLceuutxhjmyBhTKzjYNSfPPvuscblc5sSJE1afvLw8k5ycbE6fPt3Qw7JdXUH0hzZt2mQkWYsGoT5PfEUYIqqrq1VcXKysrCy/9qysLBUVFQWoqsDyer2SpLi4OElScXGxampq/OYoOTlZaWlp1hytX79eaWlpfg/%2BzM7Ols/n8/tqKNTdc889Gjx4sAYMGODXzhz9y4oVK5SRkaFf//rXSkhI0HXXXadFixZZx/ft2yePx%2BM3V06nUzfeeKPfXLlcLnXv3t3q06NHD7lcribxd9m7d2%2B9//77%2BvTTTyVJH330kdatW6ebb75ZEnNUF7vmZP369brxxhv9nmubnZ2tgwcP6osvvmicwTQyr9crh8NhPbs31OeJgBUivv76a506darWA6YTExNrPYi6OTDGaOrUqerdu7fS0tIkSR6PR5GRkWrVqpVf3%2B/PkcfjqTWHrVq1UmRkZJOZx/z8fBUXFysvL6/WMeboXz7//HMtXLhQ7du313vvvaeJEydqypQpevnllyXJGuv5/uY8Ho8SEhJqnTshIaFJzNUDDzygW265RVdffbUiIiJ03XXXKTc3V7fccosk5qguds1JXX%2BHZ/eb4rydOHFCM2bM0JgxY6yHO4f6PIXm/eebMYfD4bdvjKnV1hxMmjRJH3/8sdatW3fBvj%2Bco7rmq6nMY2lpqe69916tXLlSLVq0qPfrmtMcnXX69GllZGRo7ty5kqTrrrtOO3fu1MKFC3XbbbdZ/S70N9eU52rZsmV65ZVX9Oqrr6pTp04qKSlRbm6ukpOTNW7cOKtfc56jc7FjTuo6x7leG8pqamo0evRonT59Ws8%2B%2B6zfsVCeJ1awQkR8fLzCwsJqJfLy8vJa6b2pmzx5slasWKEPPvhAbdu2tdrdbreqq6tVUVHh1//7c%2BR2u2vNYUVFhWpqaprEPBYXF6u8vFzp6ekKDw9XeHi41qxZo2eeeUbh4eFKTExs9nN0VlJSkjp27OjXds0112j//v2SzsyDVPv/Bf9wrg4dOlTr3IcPH24Sc/Xb3/5WM2bM0OjRo9W5c2fl5OTovvvus1ZHmaPa7JqTuv4Oy8vLJdVeHQtlNTU1GjlypPbt26fCwkJr9UoK/XkiYIWIyMhIpaenq7Cw0K%2B9sLBQPXv2DFBVjcsYo0mTJumNN97Q//3f/yk1NdXveHp6uiIiIvzmqKysTDt27LDmKDMzUzt27FBZWZnVZ%2BXKlXI6nUpPT2%2BcgTSg/v37a/v27SopKbG2jIwMjR071vp3c5%2Bjs3r16lXrNh%2Bffvqp2rVrJ0lKTU2V2%2B32m6vq6mqtWbPGb668Xq82bdpk9dm4caO8Xm%2BT%2BLv87rvvdMkl/h8TYWFh1m0amKPa7JqTzMxMrV271u/2KCtXrlRycrJ%2B8pOfNM5gGtjZcLVnzx6tWrVKrVu39jse8vPU%2BNfV48c6e5uGF154wezatcvk5uaa6Oho88UXXwS6tEZx1113GZfLZVavXm3Kysqs7bvvvrP6TJw40bRt29asWrXKbN261fTr16/OWxD079/fbN261axatcq0bdu2yd2C4Pu%2B/ytCY5ijszZt2mTCw8PNnDlzzJ49e8zSpUtNy5YtzSuvvGL1mTdvnnG5XOaNN94w27dvN7fcckudP7fv0qWLWb9%2BvVm/fr3p3LlzSN%2BC4PvGjRtnLr/8cus2DW%2B88YaJj483999/v9WnOc5RVVWV2bZtm9m2bZuRZJ544gmzbds269dvdszJkSNHTGJiornlllvM9u3bzRtvvGFiY2OD4vYD9XW%2BeaqpqTHDhg0zbdu2NSUlJX7/Tff5fNY5QnmeCFgh5k9/%2BpNp166diYyMNF27drVuUdAcSKpze/HFF60%2Bx48fN5MmTTJxcXEmKirKDBkyxOzfv9/vPF9%2B%2BaUZPHiwiYqKMnFxcWbSpEl%2BP/Ftan4YsJijf3nrrbdMWlqacTqd5uqrrzbPP/%2B83/HTp0%2Bbhx56yLjdbuN0Os0NN9xgtm/f7tfnm2%2B%2BMWPHjjUxMTEmJibGjB071lRUVDTmMBpMZWWluffee80VV1xhWrRoYa688koza9Ysvw/A5jhHH3zwQZ3/LRo3bpwxxr45%2Bfjjj831119vnE6ncbvdZvbs2QG/9cDFON887du375z/Tf/ggw%2Bsc4TyPDmMCYbbnQIAADQdXIMFAABgMwIWAACAzQhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwIWAACAzQhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM3%2BPzWTpuOB5ZvwAAAAAElFTkSuQmCC\"/>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-7108607739127898742\">\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">66.0</td>\n",
" <td class=\"number\">14</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">40.0</td>\n",
" <td class=\"number\">9</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">29.0</td>\n",
" <td class=\"number\">9</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">88.0</td>\n",
" <td class=\"number\">8</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">93.0</td>\n",
" <td class=\"number\">8</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">55.0</td>\n",
" <td class=\"number\">7</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">95.0</td>\n",
" <td class=\"number\">7</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">84.0</td>\n",
" <td class=\"number\">7</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">60.0</td>\n",
" <td class=\"number\">7</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">57.0</td>\n",
" <td class=\"number\">7</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"other\">\n",
" <td class=\"fillremaining\">Other values (419)</td>\n",
" <td class=\"number\">831</td>\n",
" <td class=\"number\">8.9%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:10%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"missing\">\n",
" <td class=\"fillremaining\">(Missing)</td>\n",
" <td class=\"number\">8443</td>\n",
" <td class=\"number\">90.2%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"extreme-7108607739127898742\">\n",
" <p class=\"h4\">Minimum 5 values</p>\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">7.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">8.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">9.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">10.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">11.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" <p class=\"h4\">Maximum 5 values</p>\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">974.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1042.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1084.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1129.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1189.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"</div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4 pp-anchor\" id=\"pp_var_NO2(GT)\">NO2(GT)<br/>\n",
" <small>Numeric</small>\n",
" </p>\n",
" </div><div class=\"col-md-6\">\n",
" <div class=\"row\">\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
" <tr>\n",
" <th>Distinct count</th>\n",
" <td>1420</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Unique (%)</th>\n",
" <td>15.2%</td>\n",
" </tr>\n",
" <tr class=\"alert\">\n",
" <th>Missing (%)</th>\n",
" <td>17.5%</td>\n",
" </tr>\n",
" <tr class=\"alert\">\n",
" <th>Missing (n)</th>\n",
" <td>1642</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Infinite (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Infinite (n)</th>\n",
" <td>0</td>\n",
" </tr>\n",
" </table>\n",
"\n",
" </div>\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
"\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>113.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Minimum</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maximum</th>\n",
" <td>339.7</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Zeros (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"<div class=\"col-md-3 collapse in\" id=\"minihistogram-889919381854366283\">\n",
" <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAAeJJREFUeJzt3L/O0lAcx%2BFfiStlJ/QiTLwFEy/IzcTN0Utxd3X3LiBcAI2Dg9RBcTH5Cgb6532fZwV6CjmfnEJbmmEYhhrZ4XCoruvGHpaF2%2B/3tdvtRh3zxaij/bZer6vq1xtu23aKXWBBTqdTdV33Z96MaZJAmqapqqq2bRcVyKt3n296/tcPbx60J8/TZd6MaTX6iLAgk6wgc3DrasDzZAWBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIHgS94O4t4NHsYJAIBAInsQh1lz9z6GfP3qYFysIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQuBZrZly/NS9WEAgEAoFAIBAIBAKBQCAQCAQCgUAwuxOF/uPqdrd%2BZk4sXs8KAoFAIJjdIRaP53qv600SyDAMVVV1Op3%2BeuzH929j7w5XePn2082v%2BfL%2B9V3GvsyTy7wZ0ySB9H1fVVVd100xPCPZfLzv9vq%2Br81mc9%2BN/kMzTJDl%2BXyu4/FY6/W6mqYZe3gWZhiG6vu%2BttttrVbjfm2eJBBYCr9iQSAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBD8BEX9ULJHqYcOAAAAAElFTkSuQmCC\">\n",
"\n",
"</div>\n",
"<div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-889919381854366283,#minihistogram-889919381854366283\"\n",
" aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
"</div>\n",
"<div class=\"row collapse col-md-12\" id=\"descriptives-889919381854366283\">\n",
" <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
" <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-889919381854366283\"\n",
" aria-controls=\"quantiles-889919381854366283\" role=\"tab\"\n",
" data-toggle=\"tab\">Statistics</a></li>\n",
" <li role=\"presentation\"><a href=\"#histogram-889919381854366283\" aria-controls=\"histogram-889919381854366283\"\n",
" role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
" <li role=\"presentation\"><a href=\"#common-889919381854366283\" aria-controls=\"common-889919381854366283\"\n",
" role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
" <li role=\"presentation\"><a href=\"#extreme-889919381854366283\" aria-controls=\"extreme-889919381854366283\"\n",
" role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
"\n",
" </ul>\n",
"\n",
" <div class=\"tab-content\">\n",
" <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-889919381854366283\">\n",
" <div class=\"col-md-4 col-md-offset-1\">\n",
" <p class=\"h4\">Quantile statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr>\n",
" <th>Minimum</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5-th percentile</th>\n",
" <td>43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Q1</th>\n",
" <td>78</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Median</th>\n",
" <td>109</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Q3</th>\n",
" <td>142</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95-th percentile</th>\n",
" <td>200.22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maximum</th>\n",
" <td>339.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Range</th>\n",
" <td>337.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Interquartile range</th>\n",
" <td>64</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" <div class=\"col-md-4 col-md-offset-2\">\n",
" <p class=\"h4\">Descriptive statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr>\n",
" <th>Standard deviation</th>\n",
" <td>48.359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Coef of variation</th>\n",
" <td>0.42767</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kurtosis</th>\n",
" <td>0.46408</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>113.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MAD</th>\n",
" <td>38.159</td>\n",
" </tr>\n",
" <tr class=\"\">\n",
" <th>Skewness</th>\n",
" <td>0.62141</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sum</th>\n",
" <td>872380</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Variance</th>\n",
" <td>2338.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Memory size</th>\n",
" <td>73.2 KiB</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-889919381854366283\">\n",
" <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzt3X9cVvX9//HnlcKlEVyJCBdMMmrqVNQpNEX75I8Wav5Ys62cxnAVzflrhHxM8rPSNsVVth%2B5WuuHtXTT7VY6Nx0DS1Fvgj9QStScFQkml6jhhbK8QDzfP7p5vl2BSfPA8YLH/XY7txvnfd7XOe/XuSCfvc%2B5zuUwDMMQAAAALHON3QMAAABobQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFmtv9wDaggsXLujYsWMKDQ2Vw%2BGwezgAALQJhmHozJkziomJ0TXXtOycEgGrBRw7dkyxsbF2DwMAgDapvLxcXbt2bdFjErBaQGhoqKTP3uCwsDCbRwMAQNtQXV2t2NhY89/hlkTAagEXLwuGhYURsAAAaGF23J7DTe4AAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDF%2BLJnoJVInJ9j9xCabPei0XYPAQCaFTNYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFAjZgZWdn65ZbblFoaKgiIyN111136dChQ359fD6fZs2apYiICIWEhGjChAk6evSoX5%2BysjKNHz9eISEhioiI0OzZs1VbW%2BvXJz8/XwkJCerQoYNuuukm/f73v2/2%2BgAAQOAK2ICVn5%2BvGTNmqLCwUHl5eTp//rySk5NVU1Nj9klPT9eaNWu0atUqbdu2TWfPntW4ceNUX18vSaqvr9fYsWNVU1Ojbdu2adWqVXrjjTc0Z84ccx%2BlpaW688479T//8z/au3evHn30Uc2ePVtvvPFGi9cMAAACg8MwDMPuQVjhxIkTioyMVH5%2Bvm677TZ5vV516dJFr7/%2Buu69915J0rFjxxQbG6sNGzZo1KhR%2Buc//6lx48apvLxcMTExkqRVq1Zp6tSpqqysVFhYmB555BGtW7dOBw8eNI81bdo0vfPOOyooKGjS2Kqrq%2BVyueT1ehUWFmZ98YD4qhwA%2BCI7//0N2BmsL/J6vZKk8PBwSVJRUZHq6uqUnJxs9omJiVF8fLy2b98uSSooKFB8fLwZriRp1KhR8vl8KioqMvt8fh8X%2B%2BzevVt1dXWNjsXn86m6utpvAQAAbUer%2BLJnwzCUkZGhW2%2B9VfHx8ZIkj8ej4OBgderUya9vVFSUPB6P2ScqKspve6dOnRQcHPylfaKionT%2B/HmdPHlS0dHRDcaTnZ2thQsXWlYf7BFIM0IAgKtLq5jBmjlzpt599139%2Bc9/vmxfwzDkcDjM9c//3NQ%2BF6%2BqNvZaScrKypLX6zWX8vLyJtUBAABah4APWLNmzdK6deu0adMmde3a1Wx3u92qra1VVVWVX//KykpzRsrtdpszVRdVVVWprq7uS/tUVlaqffv26ty5c6NjcjqdCgsL81sAAEDbEbAByzAMzZw5U2%2B%2B%2BabefvttxcXF%2BW1PSEhQUFCQ8vLyzLaKigqVlJRoyJAhkqSkpCSVlJSooqLC7JObmyun06mEhASzz%2Bf3cbFPYmKigoKCmqs8AAAQwAI2YM2YMUMrVqzQn/70J4WGhsrj8cjj8ejTTz%2BVJLlcLj3wwAOaM2eO3nrrLe3du1f33Xef%2Bvbtq29/%2B9uSpOTkZPXu3VspKSnau3ev3nrrLWVmZiotLc2cdZo2bZqOHDmijIwMHTx4UK%2B88opefvllZWZm2lY7AAC4ugVswHr%2B%2Befl9Xo1fPhwRUdHm8vq1avNPr/61a9011136Z577tHQoUN17bXX6u9//7vatWsnSWrXrp3Wr1%2BvDh06aOjQobrnnnt011136emnnzb3ERcXpw0bNmjz5s365je/qZ///Of67W9/q7vvvrvFawYAAIGh1TwH62rGc7ACE58ibD48BwtAS%2BA5WAAAAK0IAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALBYQAesLVu2aPz48YqJiZHD4dDatWv9tjscjkaXp556yuxz4403Ntg%2Bb948v/2UlZVp/PjxCgkJUUREhGbPnq3a2toWqREAAASe9nYP4ErU1NSof//%2B%2BtGPfqS77767wfaKigq/9X/%2B85964IEHGvR94oknlJaWZq5fd9115s/19fUaO3asunTpom3btunUqVNKTU2VYRh69tlnLa4IAAC0BgEdsMaMGaMxY8Zccrvb7fZb/9vf/qYRI0bopptu8msPDQ1t0Pei3NxcHThwQOXl5YqJiZEkLV26VFOnTtWiRYsUFhZ2hVUAAIDWJqAvEX4Vx48f1/r16/XAAw802PbLX/5SnTt31je/%2BU0tWrTI7/JfQUGB4uPjzXAlSaNGjZLP51NRUVGLjB0AAASWgJ7B%2Bipee%2B01hYaGauLEiX7tP/3pTzVw4EB16tRJO3fuVFZWlkpLS/XSSy9Jkjwej6Kiovxe06lTJwUHB8vj8TR6LJ/PJ5/PZ65XV1dbXA0AALiatZmA9corr2jKlCnq0KGDX/vDDz9s/tyvXz916tRJ3/ve98xZLemzm%2BW/yDCMRtslKTs7WwsXLrRw9AAAIJC0iUuEW7du1aFDh/Tggw9etu/gwYMlSe%2B//76kz%2B7j%2BuJMVVVVlerq6hrMbF2UlZUlr9drLuXl5VdYAQAACCRtImC9/PLLSkhIUP/%2B/S/bd%2B/evZKk6OhoSVJSUpJKSkr8PpGYm5srp9OphISERvfhdDoVFhbmtwAAgLYjoC8Rnj171pxpkqTS0lIVFxcrPDxcN9xwg6TP7n/661//qqVLlzZ4fUFBgQoLCzVixAi5XC7t2rVLDz/8sCZMmGC%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%2BpSVlWn8%2BPEKCQlRRESEZs%2Berdra2mavDwAABKaADlg1NTXq37%2B/li1bdsk%2Bo0ePVkVFhbls2LDBb3t6errWrFmjVatWadu2bTp79qzGjRun%2Bvp6SVJ9fb3Gjh2rmpoabdu2TatWrdIbb7yhOXPmNGttAAAgcLW3ewBXYsyYMRozZsyX9nE6nXK73Y1u83q9evnll/X666/r29/%2BtiRpxYoVio2N1caNGzVq1Cjl5ubqwIEDKi8vV0xMjCRp6dKlmjp1qhYtWqSwsDBriwIAAAEvoGewmmLz5s2KjIxUjx49lJaWpsrKSnNbUVGR6urqlJycbLbFxMQoPj5e27dvlyQVFBQoPj7eDFeSNGrUKPl8PhUVFTV6TJ/Pp%2Brqar8FAAC0Ha06YI0ZM0YrV67U22%2B/raVLl2rXrl0aOXKkfD6fJMnj8Sg4OFidOnXye11UVJQ8Ho/ZJyoqym97p06dFBwcbPb5ouzsbLlcLnOJjY1thuoAAMDVKqAvEV7Ovffea/4cHx%2BvxMREdevWTevXr9fEiRMv%2BTrDMORwOMz1z/98qT6fl5WVpYyMDHO9urqakAUAQBvSqmewvig6OlrdunXT4cOHJUlut1u1tbWqqqry61dZWWnOWrnd7gYzVVVVVaqrq2sws3WR0%2BlUWFiY3wIAANqONhWwTp06pfLyckVHR0uSEhISFBQUpLy8PLNPRUWFSkpKNGTIEElSUlKSSkpKVFFRYfbJzc2V0%2BlUQkJCyxYAAAACQkBfIjx79qzef/99c720tFTFxcUKDw9XeHi4FixYoLvvvlvR0dH66KOP9OijjyoiIkLf/e53JUkul0sPPPCA5syZo86dOys8PFyZmZnq27ev%2BanC5ORk9e7dWykpKXrqqaf0ySefKDMzU2lpacxMAQCARgV0wNq9e7dGjBhhrl%2B87yk1NVXPP/%2B89u3bpz/%2B8Y86ffq0oqOjNWLECK1evVqhoaHma371q1%2Bpffv2uueee/Tpp5/q9ttv16uvvqp27dpJktq1a6f169dr%2BvTpGjp0qDp27KjJkyfr6aefbtliAQBAwHAYhmHYPYjWrrq6Wi6XS16vl1mvAJI4P8fuIbRauxeNtnsIANoAO//9bVP3YAEAALQEAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxWwJWCtWrNC5c%2BfsODQAAECzsyVgZWRkyO1268c//rF27txpxxAAAACajS0B69ixY3rllVdUUVGhW2%2B9VX369NHSpUt14sQJO4YDAABgKVsCVvv27TVx4kStW7dOZWVlSk1N1SuvvKKuXbtq4sSJWr9%2BvQzDsGNoAAAAV8z2m9zdbrduv/12DR8%2BXA6HQ7t379bkyZPVvXt3bd261e7hAQAAfGXt7TrwyZMntWLFCi1fvlyHDh3S%2BPHjtXbtWo0aNUo1NTX6v//7P/3whz9UaWmpXUOExRLn59g9BAAAWoQtAeu73/2uNmzYoLi4OD344INKTU1Vly5dzO3XXXed5s6dq9/%2B9rd2DA8AAOCK2HKJMCwsTBs3btR7772nzMxMv3B1UXR0tA4fPvyl%2B9myZYvGjx%2BvmJgYORwOrV271txWV1enRx55RH379lVISIhiYmL0wx/%2BUMeOHfPbx4033iiHw%2BG3zJs3z69PWVmZxo8fr5CQEEVERGj27Nmqra29gjMAAABaM1tmsF577bXL9nE4HLr55pu/tE9NTY369%2B%2BvH/3oR7r77rv9tv3nP//Rnj179LOf/Uz9%2B/dXVVWV0tPTNWHCBO3evduv7xNPPKG0tDRz/brrrjN/rq%2Bv19ixY9WlSxdt27ZNp06dUmpqqgzD0LPPPtuUcgEAQBtjS8B6%2BOGHdfPNN2vmzJl%2B7b/73e/04YcfaunSpU3az5gxYzRmzJhGt7lcLuXl5fm1Pfvss/rWt76lsrIy3XDDDWZ7aGio3G53o/vJzc3VgQMHVF5erpiYGEnS0qVLNXXqVC1atEhhYWFNGisAAGg7bLlE%2BNe//lWDBw9u0J6UlKTVq1c323G9Xq8cDoeuv/56v/Zf/vKX6ty5s775zW9q0aJFfpf/CgoKFB8fb4YrSRo1apR8Pp%2BKioqabawAACBw2TKDdfLkSXXq1KlBe1hYmE6ePNksxzx37pzmzZunyZMn%2B806/fSnP9XAgQPVqVMn7dy5U1lZWSotLdVLL70kSfJ4PIqKivLbV6dOnRQcHCyPx9PosXw%2Bn3w%2Bn7leXV3dDBUBAICrlS0zWDfffLP%2B9a9/NWj/17/%2Bpbi4OMuPV1dXp0mTJunChQt67rnn/LY9/PDDGjZsmPr166cHH3xQv//97/Xyyy/r1KlTZh%2BHw9Fgn4ZhNNouSdnZ2XK5XOYSGxtrbUEAAOCqZssMVnp6utLT03Xq1CmNHDlSkvTWW2/pySef1NNPP23pserq6nTPPfeotLRUb7/99mXvmbp46fL9999X586d5Xa7tWPHDr8%2BVVVVqqurazCzdVFWVpYyMjLM9erqakIWAABtiC0BKy0tTefOndPixYv1%2BOOPS5K6du2q3/72t7r//vstO87FcHX48GFt2rRJnTt3vuxr9u7dK%2Bmzx0RIn90XtmjRIlVUVJhtubm5cjqdSkhIaHQfTqdTTqfToioAAECgse1J7rNmzdKsWbNUUVGhjh07NrjxvCnOnj2r999/31wvLS1VcXGxwsPDFRMTo%2B9973vas2eP/vGPf6i%2Bvt68Zyo8PFzBwcEqKChQYWGhRowYIZfLpV27dunhhx/WhAkTzE8ZJicnq3fv3kpJSdFTTz2lTz75RJmZmUpLS%2BMThAAAoFEOI4C/VXnz5s0aMWJEg/bU1FQtWLDgkvdzbdq0ScOHD9eePXs0ffp0vffee/L5fOrWrZsmTZqkuXPn6tprrzX7l5WVafr06Xr77bfVsWNHTZ48WU8//XSTZ6mqq6vlcrnk9XrbdCjjq3Jw0e5Fo%2B0eAoA2wM5/f22ZwTpx4oTmzp2rt956S5WVlbpw4YLf9qY%2BJX348OH6snx4uew4cOBAFRYWXvY4N9xwg/7xj380aUwAAAC2BKypU6fqgw8%2B0P/%2B7/8qOjr6kp/GAwAACES2BKwtW7Zoy5YtGjBggB2HBwAAaFa2PAera9euzFoBAIBWy5aA9atf/UpZWVk6evSoHYcHAABoVrZcIkxJSdGZM2fUrVs3hYWFKSgoyG97ZWWlHcMCAACwhC0Ba8mSJXYcFgAAoEXYErAeeOABOw4L4CoRaM9E47ldAL4qW%2B7BkqSPPvpICxYsUEpKinlJMDc3VwcPHrRrSAAAAJawJWBt3bpVffr0UX5%2Bvv7yl7/o7NmzkqQ9e/boscces2NIAAAAlrElYD3yyCNasGCBNm3apODgYLN95MiRTXqyOgAAwNXMloD17rvv6nvf%2B16D9sjISJ04ccKGEQEAAFjHloB1/fXXy%2BPxNGgvLi7W1772NRtGBAAAYB1bAtakSZM0b948nThxwnyi%2B44dO5SZman77rvPjiEBAABYxpaAtXjxYrndbkVHR%2Bvs2bPq3bu3hgwZoltuuUU/%2B9nP7BgSAACAZWx5DlZwcLBWr16tf//739qzZ48uXLiggQMH6hvf%2BIYdwwEAALCULQHroh49eqhHjx52DgEAAMBytgSshx566Eu3/%2BEPf2ihkQAAAFjPloBVUVHht15XV6f9%2B/frzJkzuu222%2BwYEgAAgGVsCVh///vfG7SdP39eP/nJT9SrVy8bRgQAAGAd276L8Ivat2%2BvzMxMPfXUU3YPBQAA4IpcNQFLkj788EPV1dXZPQwAAIArYsslwrlz5/qtG4ahiooKrVu3TlOmTLFjSAAAAJaxJWAVFBT4rV9zzTXq0qWLlixZorS0NDuGBAAAYBlbAtbWrVvtOCwAAECLuKruwQIAAGgNbJnBuuWWW8wveb6cnTt3NvNoAAAArGXLDNaIESN06NAhGYahwYMHa/DgwZKkQ4cOafjw4Ro1apS5fJktW7Zo/PjxiomJkcPh0Nq1a/22G4ahBQsWKCYmRh07dtTw4cO1f/9%2Bvz5VVVVKSUmRy%2BWSy%2BVSSkqKTp8%2B7ddn3759GjZsmDp27Kivfe1reuKJJ2QYhgVnAgAAtEa2zGCdPn1aM2bM0OLFi/3a58%2Bfr%2BPHj%2Bull15q0n5qamrUv39//ehHP9Ldd9/dYPuTTz6pZ555Rq%2B%2B%2Bqp69OihX/ziF7rjjjt06NAhhYaGSpImT56so0ePKicnR9JnX%2BOTkpJiPgy1urpad9xxh0aMGKFdu3bp3//%2Bt6ZOnaqQkBDNmTPnSk4DAABopRyGDVMx119/vXbt2qXu3bv7tR8%2BfFiJiYnyer1feZ8Oh0Nr1qzRXXfdJemz2auYmBilp6frkUcekST5fD5FRUXpl7/8pX784x/r4MGD6t27twoLCzVo0CBJUmFhoZKSkvTee%2B%2BpZ8%2Beev7555WVlaXjx4/L6XRKkpYsWaJnn31WR48ebdKlzurqarlcLnm9XoWFhX3l2lqLxPk5dg8B%2BK/sXjTa7iEA%2BC/Y%2Be%2BvLZcInU6ntm/f3qB9%2B/btZoi5UqWlpfJ4PEpOTvY77rBhw8xjFxQUyOVymeFKkgYPHiyXy%2BXXZ9iwYX7jGjVqlI4dO6aPPvqo0WP7fD5VV1f7LQAAoO2w5RLh7NmzNW3aNO3du9e8/6qwsFAvvviiHn30UUuO4fF4JElRUVF%2B7VFRUTpy5IjZJzIyssFrIyMjzdd7PB7deOONDfZxcVtcXFyD12dnZ2vhwoVXXAMAAAhMtgSs%2BfPnKy4uTr/5zW/0yiuvSJJ69eqlF198UZMnT7b0WF%2B8hGcYhl9bY5f4Ltfn4lXVS10ezMrKUkZGhrleXV2t2NjYrz54AAAQkGwJWNJnN5dbHaY%2Bz%2B12S/pslik6Otpsr6ysNGeg3G63jh8/3uC1J06c8OtzcTbr8/uQGs6OXeR0Oi271AkAAAKPbQ8ara6u1quvvqrHHntMVVVVkqR33nlHFRUVluw/Li5ObrdbeXl5Zlttba3y8/M1ZMgQSVJSUpK8Xq/fs7Z27Nghr9fr12fLli2qra01%2B%2BTm5iomJqbBpUMAAADJpoBVUlKiHj166IknnlB2drYZsP7yl79o3rx5Td7P2bNnVVxcrOLiYkmf3dheXFyssrIyORwOpaena/HixVqzZo1KSko0depUXXvttebMWa9evTR69GilpaWpsLBQhYWFSktL07hx49SzZ09Jn820OZ1OTZ06VSUlJVqzZo0WL16sjIyMJj8sFQAAtC22BKyHH35YkydP1gcffKAOHTqY7WPHjtWWLVuavJ/du3drwIABGjBggCQpIyNDAwYM0GOPPSZJmjt3rtLT0zV9%2BnQlJibq448/Vm5urvkMLElauXKl%2Bvbtq%2BTkZCUnJ6tfv356/fXXze0ul0t5eXk6evSoEhMTNX36dGVkZPjdYwUAAPB5tj0Ha/fu3fr617%2Bu0NBQvfPOO7rpppt05MgR9ezZU%2BfOnWvpITUrnoP1GZ6DhUDFc7CAwNTmnoMVHByss2fPNmg/fPiwIiIibBgRAACAdWwJWBMmTNDPf/5znT9/XtJnjzv4%2BOOPNW/ePE2cONGOIQEAAFjGloC1dOlSHTt2TG63W59%2B%2BqlGjhypm266SR06dGjw/YQAAACBxpbnYF38Kpq8vDzt2bNHFy5c0MCBAzVq1Cg%2BmQcAAAJeiwesuro63XnnnXruuefMT%2B4BAAC0Ji1%2BiTAoKEh79%2B5lpgoAALRattyDdd9992n58uV2HBoAAKDZ2fZdhMuWLdPGjRuVmJiokJAQv21PPvmkTaMCAAC4crYErKKiIvXr10%2BS9O677/pt49IhAAAIdC0asD788EPFxcVp69atLXlYAACAFtWi92B1795dJ06cMNfvvfdeHT9%2BvCWHAAAA0OxaNGB98WsPN2zYoJqampYcAgAAQLOz5VOEAAAArVmLBiyHw9HgJnZuagcAAK1Ni97kbhiGpk6dKqfTKUk6d%2B6cpk2b1uAxDW%2B%2B%2BWZLDgsAAMBSLRqwUlNT/dbvu%2B%2B%2Bljw8AABAi2jRgMXT2wEAQFvATe4AAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMVadcC68cYbze8//PwyY8YMSdLw4cMbbJs0aZLfPqqqqpSSkiKXyyWXy6WUlBSdPn3ajnIAAECAaNEnube0Xbt2qb6%2B3lwvKSnRHXfcoe9///tmW1pamp544glzvWPHjn77mDx5so4ePaqcnBxJ0kMPPaSUlBT9/e9/b%2BbRAwCAQNWqA1aXLl381pcsWaKbb75Zw4YNM9uuvfZaud3uRl9/8OBB5eTkqLCwUIMGDZIkvfjii0pKStKhQ4fUs2fP5hs8AAAIWK36EuHn1dbWasWKFbr//vvlcDjM9pUrVyoiIkJ9%2BvRRZmamzpw5Y24rKCiQy%2BUyw5UkDR48WC6XS9u3b2/R8QMAgMDRqmewPm/t2rU6ffq0pk6darZNmTJFcXFxcrvdKikpUVZWlt555x3l5eVJkjwejyIjIxvsKzIyUh6P55LH8vl88vl85np1dbV1hQAAgKtemwlYL7/8ssaMGaOYmBizLS0tzfw5Pj5e3bt3V2Jiovbs2aOBAwdKkt9s10WGYTTaflF2drYWLlxo4egBAEAgaROXCI8cOaKNGzfqwQcf/NJ%2BAwcOVFBQkA4fPixJcrvdOn78eIN%2BJ06cUFRU1CX3k5WVJa/Xay7l5eVXVgAAAAgobSJgLV%2B%2BXJGRkRo7duyX9tu/f7/q6uoUHR0tSUpKSpLX69XOnTvNPjt27JDX69WQIUMuuR%2Bn06mwsDC/BQAAtB2t/hLhhQsXtHz5cqWmpqp9%2B/9f7gcffKCVK1fqzjvvVEREhA4cOKA5c%2BZowIABGjp0qCSpV69eGj16tNLS0vTCCy9I%2BuwxDePGjeMThAAA4JJa/QzWxo0bVVZWpvvvv9%2BvPTg4WG%2B99ZZGjRqlnj17avbs2UpOTtbGjRvVrl07s9/KlSvVt29fJScnKzk5Wf369dPrr7/e0mUAAIAA0upnsJKTk2UYRoMOvYbEAAAUOElEQVT22NhY5efnX/b14eHhWrFiRXMMDQAAtFKtfgYLAACgpRGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsFh7uwcAAFe7xPk5dg/hK9m9aLTdQwDaPGawAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwWKsOWAsWLJDD4fBb3G63ud0wDC1YsEAxMTHq2LGjhg8frv379/vto6qqSikpKXK5XHK5XEpJSdHp06dbuhQAABBAWnXAkqQ%2BffqooqLCXPbt22due/LJJ/XMM89o2bJl2rVrl9xut%2B644w6dOXPG7DN58mQVFxcrJydHOTk5Ki4uVkpKih2lAACAANHqv%2By5ffv2frNWFxmGoV//%2BteaP3%2B%2BJk6cKEl67bXXFBUVpT/96U/68Y9/rIMHDyonJ0eFhYUaNGiQJOnFF19UUlKSDh06pJ49e7ZoLQAAIDC0%2Bhmsw4cPKyYmRnFxcZo0aZI%2B/PBDSVJpaak8Ho%2BSk5PNvk6nU8OGDdP27dslSQUFBXK5XGa4kqTBgwfL5XKZfRrj8/lUXV3ttwAAgLajVQesQYMG6Y9//KP%2B9a9/6cUXX5TH49GQIUN06tQpeTweSVJUVJTfa6KiosxtHo9HkZGRDfYbGRlp9mlMdna2ec%2BWy%2BVSbGyshVUBAICrXasOWGPGjNHdd9%2Btvn376tvf/rbWr18v6bNLgRc5HA6/1xiG4df2xe2N9fmirKwseb1ecykvL7/SUgAAQABp1QHri0JCQtS3b18dPnzYvC/rizNRlZWV5qyW2%2B3W8ePHG%2BznxIkTDWa%2BPs/pdCosLMxvAQAAbUebClg%2Bn08HDx5UdHS04uLi5Ha7lZeXZ26vra1Vfn6%2BhgwZIklKSkqS1%2BvVzp07zT47duyQ1%2Bs1%2BwAAAHxRq/4UYWZmpsaPH68bbrhBlZWV%2BsUvfqHq6mqlpqbK4XAoPT1dixcvVvfu3dW9e3ctXrxY1157rSZPnixJ6tWrl0aPHq20tDS98MILkqSHHnpI48aN4xOEAADgklp1wDp69Kh%2B8IMf6OTJk%2BrSpYsGDx6swsJCdevWTZI0d%2B5cffrpp5o%2Bfbqqqqo0aNAg5ebmKjQ01NzHypUrNXv2bPPThhMmTNCyZctsqQcAAAQGh2EYht2DaO2qq6vlcrnk9Xrb9P1YifNz7B4C0CbsXjTa7iEAVwU7//1tU/dgAQAAtAQCFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYLFWHbCys7N1yy23KDQ0VJGRkbrrrrt06NAhvz7Dhw%2BXw%2BHwWyZNmuTXp6qqSikpKXK5XHK5XEpJSdHp06dbshQAABBAWnXAys/P14wZM1RYWKi8vDydP39eycnJqqmp8euXlpamiooKc3nhhRf8tk%2BePFnFxcXKyclRTk6OiouLlZKS0pKlAACAANLe7gE0p5ycHL/15cuXKzIyUkVFRbrtttvM9muvvVZut7vRfRw8eFA5OTkqLCzUoEGDJEkvvviikpKSdOjQIfXs2bP5CgAAAAGpVc9gfZHX65UkhYeH%2B7WvXLlSERER6tOnjzIzM3XmzBlzW0FBgVwulxmuJGnw4MFyuVzavn17ywwcAAAElFY9g/V5hmEoIyNDt956q%2BLj4832KVOmKC4uTm63WyUlJcrKytI777yjvLw8SZLH41FkZGSD/UVGRsrj8TR6LJ/PJ5/PZ65XV1dbXA0AALiatZmANXPmTL377rvatm2bX3taWpr5c3x8vLp3767ExETt2bNHAwcOlCQ5HI4G%2BzMMo9F26bOb6xcuXGjh6AEAQCBpE5cIZ82apXXr1mnTpk3q2rXrl/YdOHCggoKCdPjwYUmS2%2B3W8ePHG/Q7ceKEoqKiGt1HVlaWvF6vuZSXl195EQAAIGC06hkswzA0a9YsrVmzRps3b1ZcXNxlX7N//37V1dUpOjpakpSUlCSv16udO3fqW9/6liRpx44d8nq9GjJkSKP7cDqdcjqd1hUCAF9B4vycy3e6SuxeNNruIQDNolUHrBkzZuhPf/qT/va3vyk0NNS8Z8rlcqljx4764IMPtHLlSt15552KiIjQgQMHNGfOHA0YMEBDhw6VJPXq1UujR49WWlqa%2BfiGhx56SOPGjeMThAAAoFEOwzAMuwfRXC51j9Ty5cs1depUlZeX67777lNJSYnOnj2r2NhYjR07Vo8//rjfJw0/%2BeQTzZ49W%2BvWrZMkTZgwQcuWLdP111/fpHFUV1fL5XLJ6/UqLCzsygv7nED6P1UA%2BCJmsNCcmvPf38tp1TNYl8uOsbGxys/Pv%2Bx%2BwsPDtWLFCquGBQAAWrk2cZM7AABASyJgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGCx9nYPAADQdiXOz7F7CF/J7kWj7R4CAgQzWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIDVRM8995zi4uLUoUMHJSQkaOvWrXYPCQAAXKV4DlYTrF69Wunp6Xruuec0dOhQvfDCCxozZowOHDigG264we7hAQBaCM/tQlMxg9UEzzzzjB544AE9%2BOCD6tWrl379618rNjZWzz//vN1DAwAAVyFmsC6jtrZWRUVFmjdvnl97cnKytm/f3uhrfD6ffD6fue71eiVJ1dXVlo%2Bv3ldj%2BT4BAK3DgMw37B5Ck%2BU/dofl%2B7z4765hGJbv%2B3IIWJdx8uRJ1dfXKyoqyq89KipKHo%2Bn0ddkZ2dr4cKFDdpjY2ObZYwAAAQ619Lm2/eZM2fkcrma7wCNIGA1kcPh8Fs3DKNB20VZWVnKyMgw1y9cuKBPPvlEnTt3vuRrmqq6ulqxsbEqLy9XWFjYFe0rULX1c9DW65c4BxLnoK3XL3EOmlK/YRg6c%2BaMYmJiWnh0BKzLioiIULt27RrMVlVWVjaY1brI6XTK6XT6tV1//fWWjissLKxN/kF9Xls/B229folzIHEO2nr9EufgcvW39MzVRdzkfhnBwcFKSEhQXl6eX3teXp6GDBli06gAAMDVjBmsJsjIyFBKSooSExOVlJSkP/zhDyorK9O0adPsHhoAALgKtVuwYMECuwdxtYuPj1fnzp21ePFiPf300/r000/1%2Buuvq3///raMp127dho%2BfLjat2%2B7%2Bbitn4O2Xr/EOZA4B229folzcDXX7zDs%2BOwiAABAK8Y9WAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgB5rnnnlNcXJw6dOighIQEbd261e4hNYsFCxbI4XD4LW6329xuGIYWLFigmJgYdezYUcOHD9f%2B/fttHPGV27Jli8aPH6%2BYmBg5HA6tXbvWb3tTaq6qqlJKSopcLpdcLpdSUlJ0%2BvTplizjv3a5%2BqdOndrgd2Lw4MF%2BfXw%2Bn2bNmqWIiAiFhIRowoQJOnr0aEuWcUWys7N1yy23KDQ0VJGRkbrrrrt06NAhvz5NqbGsrEzjx49XSEiIIiIiNHv2bNXW1rZkKf%2BVptQ/fPjwBr8HkyZN8usTyH8Hzz//vPr162c%2BPDMpKUn//Oc/ze2t%2Bf2XLl9/IL3/BKwAsnr1aqWnp2v%2B/Pnau3ev/ud//kdjxoxRWVmZ3UNrFn369FFFRYW57Nu3z9z25JNP6plnntGyZcu0a9cuud1u3XHHHTpz5oyNI74yNTU16t%2B/v5YtW9bo9qbUPHnyZBUXFysnJ0c5OTkqLi5WSkpKS5VwRS5XvySNHj3a73diw4YNftvT09O1Zs0arVq1Stu2bdPZs2c1btw41dfXN/fwLZGfn68ZM2aosLBQeXl5On/%2BvJKTk1VT8/%2B/1P1yNdbX12vs2LGqqanRtm3btGrVKr3xxhuaM2eOXWU1WVPql6S0tDS/34MXXnjBb3sg/x107dpVS5Ys0e7du7V7926NHDlS3/nOd8z/mWrN7790%2BfqlAHr/DQSMb33rW8a0adP82r7xjW8Y8%2BbNs2lEzefxxx83%2Bvfv3%2Bi2CxcuGG6321iyZInZdu7cOcPlchm///3vW2qIzUqSsWbNGnO9KTUfOHDAkGQUFhaafQoKCgxJxnvvvddyg7fAF%2Bs3DMNITU01vvOd71zyNadPnzaCgoKMVatWmW0ff/yxcc011xg5OTnNNtbmVFlZaUgy8vPzDcNoWo0bNmwwrrnmGuPjjz82%2B/z5z382nE6n4fV6W7aAK/TF%2Bg3DMIYNG2b89Kc/veRrWtPfwUWdOnUyXnrppTb3/l90sX7DCKz3nxmsAFFbW6uioiIlJyf7tScnJ2v79u02jap5HT58WDExMYqLi9OkSZP04YcfSpJKS0vl8Xj8zoXT6dSwYcNa7bloSs0FBQVyuVwaNGiQ2Wfw4MFyuVyt5rxs3rxZkZGR6tGjh9LS0lRZWWluKyoqUl1dnd85iomJUXx8fMDW7/V6JUnh4eGSmlZjQUGB4uPj/b7cdtSoUfL5fCoqKmrB0V%2B5L9Z/0cqVKxUREaE%2BffooMzPTbxa3Nf0d1NfXa9WqVaqpqVFSUlKbe/%2B/WP9FgfL%2BX32PPkWjTp48qfr6%2BgZfMB0VFdXgi6hbg0GDBumPf/yjevTooePHj%2BsXv/iFhgwZov3795v1NnYujhw5Ysdwm11TavZ4PIqMjGzw2sjIyFbxOzJmzBh9//vfV7du3VRaWqqf/exnGjlypIqKiuR0OuXxeBQcHKxOnTr5vS5Q/0YMw1BGRoZuvfVWxcfHS1KTavR4PA1%2BTzp16qTg4OCAOg%2BN1S9JU6ZMUVxcnNxut0pKSpSVlaV33nnH/L7Y1vB3sG/fPiUlJencuXO67rrrtGbNGvXu3VvFxcVt4v2/VP1SYL3/BKwA43A4/NYNw2jQ1hqMGTPG/Llv375KSkrSzTffrNdee828sbmtnIvPu1zNjdXfWs7Lvffea/4cHx%2BvxMREdevWTevXr9fEiRMv%2BbpArX/mzJl69913tW3btsv2bY2/B5eqPy0tzfw5Pj5e3bt3V2Jiovbs2aOBAwdKCvz6e/bsqeLiYp0%2BfVpvvPGGUlNTlZ%2Bff8n%2Bre39v1T9vXv3Dqj3n0uEASIiIkLt2rVrkMArKysb/N9KaxQSEqK%2Bffvq8OHD5qcJ29K5aErNbrdbx48fb/DaEydOtMrzEh0drW7duunw4cOSPqu/trZWVVVVfv0C8fdi1qxZWrdunTZt2qSuXbua7U2p0e12N/g9qaqqUl1dXcCch0vV35iBAwcqKCjI7/cg0P8OgoOD9fWvf12JiYnKzs5W//799Zvf/KbNvP%2BXqr8xV/P7T8AKEMHBwUpISDCnQS/Ky8vTkCFDbBpVy/H5fDp48KCio6PN6eHPn4va2lrl5%2Be32nPRlJqTkpLk9Xq1c%2BdOs8%2BOHTvk9Xpb5Xk5deqUysvLFR0dLUlKSEhQUFCQ3zmqqKhQSUlJwNRvGIZmzpypN998U2%2B//bbi4uL8tjelxqSkJJWUlKiiosLsk5ubK6fTqYSEhJYp5L90ufobs3//ftXV1Zm/B63x78AwDPl8vlb//l/Kxfobc1W//y16Sz2uyKpVq4ygoCDj5ZdfNg4cOGCkp6cbISEhxkcffWT30Cw3Z84cY/PmzcaHH35oFBYWGuPGjTNCQ0PNWpcsWWK4XC7jzTffNPbt22f84Ac/MKKjo43q6mqbR/7fO3PmjLF3715j7969hiTjmWeeMfbu3WscOXLEMIym1Tx69GijX79%2BRkFBgVFQUGD07dvXGDdunF0lfSVfVv%2BZM2eMOXPmGNu3bzdKS0uNTZs2GUlJScbXvvY1v/qnTZtmdO3a1di4caOxZ88eY%2BTIkUb//v2N8%2BfP21hZ0/3kJz8xXC6XsXnzZqOiosJc/vOf/5h9Llfj%2BfPnjfj4eOP222839uzZY2zcuNHo2rWrMXPmTLvKarLL1f/%2B%2B%2B8bCxcuNHbt2mWUlpYa69evN77xjW8YAwYM8HuPA/nvICsry9iyZYtRWlpqvPvuu8ajjz5qXHPNNUZubq5hGK37/TeML68/0N5/AlaA%2Bd3vfmd069bNCA4ONgYOHOj38eXW5N577zWio6ONoKAgIyYmxpg4caKxf/9%2Bc/uFCxeMxx9/3HC73YbT6TRuu%2B02Y9%2B%2BfTaO%2BMpt2rTJkNRgSU1NNQyjaTWfOnXKmDJlihEaGmqEhoYaU6ZMMaqqqmyo5qv7svr/85//GMnJyUaXLl2MoKAg44YbbjBSU1ONsrIyv318%2BumnxsyZM43w8HCjY8eOxrhx4xr0uZo1Vr8kY/ny5WafptR45MgRY%2BzYsUbHjh2N8PBwY%2BbMmca5c%2BdauJqv7nL1l5WVGbfddpsRHh5uBAcHGzfffLMxe/Zs49SpU377CeS/g/vvv9/8b3yXLl2M22%2B/3QxXhtG633/D%2BPL6A%2B39dxiGYbTcfBkAAEDrxz1YAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFjs/wGgbg4xl46IyQAAAABJRU5ErkJggg%3D%3D\"/>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-889919381854366283\">\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">97.0</td>\n",
" <td class=\"number\">68</td>\n",
" <td class=\"number\">0.7%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">95.0</td>\n",
" <td class=\"number\">66</td>\n",
" <td class=\"number\">0.7%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">101.0</td>\n",
" <td class=\"number\">65</td>\n",
" <td class=\"number\">0.7%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">68.0</td>\n",
" <td class=\"number\">63</td>\n",
" <td class=\"number\">0.7%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">114.0</td>\n",
" <td class=\"number\">63</td>\n",
" <td class=\"number\">0.7%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">96.0</td>\n",
" <td class=\"number\">62</td>\n",
" <td class=\"number\">0.7%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">99.0</td>\n",
" <td class=\"number\">62</td>\n",
" <td class=\"number\">0.7%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">107.0</td>\n",
" <td class=\"number\">61</td>\n",
" <td class=\"number\">0.7%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">119.0</td>\n",
" <td class=\"number\">61</td>\n",
" <td class=\"number\">0.7%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">116.0</td>\n",
" <td class=\"number\">60</td>\n",
" <td class=\"number\">0.6%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"other\">\n",
" <td class=\"fillremaining\">Other values (1409)</td>\n",
" <td class=\"number\">7084</td>\n",
" <td class=\"number\">75.7%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"missing\">\n",
" <td class=\"fillremaining\">(Missing)</td>\n",
" <td class=\"number\">1642</td>\n",
" <td class=\"number\">17.5%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:23%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"extreme-889919381854366283\">\n",
" <p class=\"h4\">Minimum 5 values</p>\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">2.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">3.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">5.0</td>\n",
" <td class=\"number\">2</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">7.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">8.0</td>\n",
" <td class=\"number\">2</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" <p class=\"h4\">Maximum 5 values</p>\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">312.4</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">321.6</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">325.9</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">332.6</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">339.7</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"</div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4 pp-anchor\" id=\"pp_var_NOx(GT)\">NOx(GT)<br/>\n",
" <small>Numeric</small>\n",
" </p>\n",
" </div><div class=\"col-md-6\">\n",
" <div class=\"row\">\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
" <tr>\n",
" <th>Distinct count</th>\n",
" <td>2467</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Unique (%)</th>\n",
" <td>26.4%</td>\n",
" </tr>\n",
" <tr class=\"alert\">\n",
" <th>Missing (%)</th>\n",
" <td>17.5%</td>\n",
" </tr>\n",
" <tr class=\"alert\">\n",
" <th>Missing (n)</th>\n",
" <td>1639</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Infinite (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Infinite (n)</th>\n",
" <td>0</td>\n",
" </tr>\n",
" </table>\n",
"\n",
" </div>\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
"\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>246.88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Minimum</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maximum</th>\n",
" <td>1479</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Zeros (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"<div class=\"col-md-3 collapse in\" id=\"minihistogram-7424980232871411859\">\n",
" <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAAdVJREFUeJzt2zGO01AUhtHrEa2dPooXgcQWkFgQHRIdJUuhp6VnF4mygFgUFMQUEJpBP8wo8xzDOW2k914kf7qy43TzPM/V2OFwqHEcW2/Lyu33%2B9rtdk33fNZ0t5/6vq%2BqH194GIYljsCKnE6nGsfx13XT0iKBdF1XVVXDMNwL5MWbjw9e7/O7V1c5F7ftct20dNd8R1gRgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIFjkbd5r8wYwT8UEgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgeCf%2BEfhY/gXIn/DBIFAIBAIBAKBQCAQCP7bp1iP8dAnX556rZ8JAoFAIBAIBO5BnpBf69fPBIHABLkxps5tMUEgWGSCzPNcVVWn0%2BneZ9%2B%2Bfml9nNV7/vrD0kf4rU9vX15lnct1crluWlokkGmaqqpqHMcltqeRzfvrrjdNU202m%2Bsu%2BgfdvECW5/O5jsdj9X1fXde13p6Vmee5pmmq7XZbd3dt7woWCQTWwk06BAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAsF3AZZPe8TcRisAAAAASUVORK5CYII%3D\">\n",
"\n",
"</div>\n",
"<div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-7424980232871411859,#minihistogram-7424980232871411859\"\n",
" aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
"</div>\n",
"<div class=\"row collapse col-md-12\" id=\"descriptives-7424980232871411859\">\n",
" <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
" <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-7424980232871411859\"\n",
" aria-controls=\"quantiles-7424980232871411859\" role=\"tab\"\n",
" data-toggle=\"tab\">Statistics</a></li>\n",
" <li role=\"presentation\"><a href=\"#histogram-7424980232871411859\" aria-controls=\"histogram-7424980232871411859\"\n",
" role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
" <li role=\"presentation\"><a href=\"#common-7424980232871411859\" aria-controls=\"common-7424980232871411859\"\n",
" role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
" <li role=\"presentation\"><a href=\"#extreme-7424980232871411859\" aria-controls=\"extreme-7424980232871411859\"\n",
" role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
"\n",
" </ul>\n",
"\n",
" <div class=\"tab-content\">\n",
" <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-7424980232871411859\">\n",
" <div class=\"col-md-4 col-md-offset-1\">\n",
" <p class=\"h4\">Quantile statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr>\n",
" <th>Minimum</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5-th percentile</th>\n",
" <td>38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Q1</th>\n",
" <td>98</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Median</th>\n",
" <td>179.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Q3</th>\n",
" <td>326</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95-th percentile</th>\n",
" <td>693</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maximum</th>\n",
" <td>1479</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Range</th>\n",
" <td>1477</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Interquartile range</th>\n",
" <td>228</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" <div class=\"col-md-4 col-md-offset-2\">\n",
" <p class=\"h4\">Descriptive statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr>\n",
" <th>Standard deviation</th>\n",
" <td>212.97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Coef of variation</th>\n",
" <td>0.86265</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kurtosis</th>\n",
" <td>3.4025</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>246.88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MAD</th>\n",
" <td>159.06</td>\n",
" </tr>\n",
" <tr class=\"\">\n",
" <th>Skewness</th>\n",
" <td>1.7158</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sum</th>\n",
" <td>1905400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Variance</th>\n",
" <td>45357</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Memory size</th>\n",
" <td>73.2 KiB</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-7424980232871411859\">\n",
" <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzt3XtcVXW%2B//H3DmRrDOxUgs1WNJpjdsGahI6ilXYDzctpaqaLSdr0oCwvGVppnjPjNJN0yqwpy8rTPRudHmoPm4xEK80H4AUlxRzzTJqabNEGN%2BoooH5/f/RzHbfgpekLy42v5%2BOxHg/2Wp%2B91vdDBG%2B/a%2B21PMYYIwAAAFhzltsDAAAAaG4IWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgWbTbAzgTHD58WNu3b1dcXJw8Ho/bwwEA4IxgjNGePXsUCAR01llNO6dEwGoC27dvV0pKitvDAADgjLR161a1b9%2B%2BSY9JwGoCcXFxkn74DxwfH%2B/yaAAAODNUV1crJSXF%2BTvclAhYTeDIacH4%2BHgCFgAATcyNy3O4yB0AAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlvGw5wiXMaHA7SGcspVP9HF7CAAANAlmsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWBaxAWvatGm69NJLFR8fr/j4eGVmZurjjz92ttfU1GjkyJFKSEhQbGysBg4cqG3btoXtY8uWLRowYIBiY2OVkJCgUaNGqba2Nqxm8eLFSk9PV8uWLXX%2B%2Befr5ZdfbpL%2BAABA5IrYgNW%2BfXs9%2BeSTWrlypVauXKlrr71W//Ef/6F169ZJkkaPHq25c%2Bdq5syZWrp0qfbu3av%2B/fvr0KFDkqRDhw6pX79%2B2rdvn5YuXaqZM2dq9uzZGjNmjHOMTZs26cYbb9RVV12l1atX67HHHtOoUaM0e/ZsV3oGAACRwWOMMW4PwpY2bdro6aef1q9%2B9Sude%2B65euedd3TbbbdJkrZv366UlBTNnz9f2dnZ%2Bvjjj9W/f39t3bpVgUBAkjRz5kwNHTpUlZWVio%2BP16OPPqp58%2BZp/fr1zjGGDRumL7/8UsXFxac8rurqavl8PoVCIcXHx1vtOWNCgdX9NaaVT/RxewgAgDNIY/79PZmIncE62qFDhzRz5kzt27dPmZmZKi0tVV1dnbKyspyaQCCgtLQ0FRUVSZKKi4uVlpbmhCtJys7OVk1NjUpLS52ao/dxpGblypWqq6s77nhqampUXV0dtgAAgDNHRAestWvX6mc/%2B5m8Xq%2BGDRumuXPn6uKLL1YwGFRMTIxat24dVp%2BUlKRgMChJCgaDSkpKCtveunVrxcTEnLAmKSlJBw8e1K5du447rvz8fPl8PmdJSUmx0S4AAIgQER2wOnfurLKyMpWUlOj%2B%2B%2B/XkCFD9NVXXx233hgjj8fjvD7661OtOXJGtaH3HjF%2B/HiFQiFn2bp16yn3BAAAIl%2B02wP4KWJiYvRv//ZvkqSMjAytWLFCf/rTn3TbbbeptrZWVVVVYbNYlZWV6tGjhyTJ7/dr2bJlYfurqqpSXV2dM2vl9/ud2ayj9xEdHa22bdsed1xer1der9dKjwAAIPJE9AzWsYwxqqmpUXp6ulq0aKHCwkJnW0VFhcrLy52AlZmZqfLyclVUVDg1CxYskNfrVXp6ulNz9D6O1GRkZKhFixZN0BEAAIhEETuD9dhjj6lv375KSUnRnj17NHPmTH3%2B%2BecqKCiQz%2BfTPffcozFjxqht27Zq06aNxo4dqy5duuj666%2BXJGVlZeniiy9WTk6Onn76af3jH//Q2LFjlZub63zSYNiwYZo6dary8vKUm5ur4uJivfbaa/rzn//sZusAAOA0F7EBa8eOHcrJyVFFRYV8Pp8uvfRSFRQU6IYbbpAkPfvss4qOjtatt96q/fv367rrrtObb76pqKgoSVJUVJQ%2B%2BugjPfDAA%2BrZs6datWqlQYMGafLkyc4xUlNTNX/%2BfD300EN68cUXFQgE9Pzzz%2BuWW25xpWcAABAZmtV9sE5X3AfrB9wHCwDQlLgPFgAAQDNCwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAsogNWPn5%2BbriiisUFxenxMRE3XTTTdqwYUNYTe/eveXxeMKW22%2B/PaymqqpKOTk58vl88vl8ysnJ0e7du8Nq1q5dq169eqlVq1Zq166dHn/8cRljGr1HAAAQmSI2YC1evFjDhw9XSUmJCgsLdfDgQWVlZWnfvn1hdbm5uaqoqHCWV155JWz7oEGDVFZWpoKCAhUUFKisrEw5OTnO9urqat1www0KBAJasWKFXnjhBU2ePFlTpkxpkj4BAEDkiXZ7AP%2BqgoKCsNdvvPGGEhMTVVpaqquvvtpZf/bZZ8vv9ze4j/Xr16ugoEAlJSXq1q2bJGn69OnKzMzUhg0b1LlzZ82YMUMHDhzQm2%2B%2BKa/Xq7S0NH399deaMmWK8vLy5PF4Gq9JAAAQkSJ2ButYoVBIktSmTZuw9TNmzFBCQoIuueQSjR07Vnv27HG2FRcXy%2BfzOeFKkrp37y6fz6eioiKnplevXvJ6vU5Ndna2tm/frs2bNzc4lpqaGlVXV4ctAADgzBGxM1hHM8YoLy9PV155pdLS0pz1d955p1JTU%2BX3%2B1VeXq7x48fryy%2B/VGFhoSQpGAwqMTGx3v4SExMVDAadmvPOOy9se1JSkrMtNTW13vvz8/P1%2B9//3lZ7AAAgwjSLgDVixAitWbNGS5cuDVufm5vrfJ2WlqZOnTopIyNDq1atUteuXSWpwVN8xpiw9cfWHLnA/XinB8ePH6%2B8vDzndXV1tVJSUn5kVwAAIFJFfMAaOXKk5s2bpyVLlqh9%2B/YnrO3atatatGihjRs3qmvXrvL7/dqxY0e9up07dzqzVH6/35nNOqKyslLS/81kHcvr9YadUgQAAGeWiL0GyxijESNGaM6cOfr0008bPFV3rHXr1qmurk7JycmSpMzMTIVCIS1fvtypWbZsmUKhkHr06OHULFmyRLW1tU7NggULFAgE6p06BAAAkCI4YA0fPlzvvvuu3nvvPcXFxSkYDCoYDGr//v2SpL///e96/PHHtXLlSm3evFnz58/Xr3/9a11%2B%2BeXq2bOnJOmiiy5Snz59lJubq5KSEpWUlCg3N1f9%2B/dX586dJf1wGwev16uhQ4eqvLxcc%2BfO1aRJk/gEIQAAOK6IDVjTpk1TKBRS7969lZyc7CyzZs2SJMXExGjRokXKzs5W586dNWrUKGVlZWnhwoWKiopy9jNjxgx16dJFWVlZysrK0qWXXqp33nnH2e7z%2BVRYWKht27YpIyNDDzzwgPLy8sKusQIAADiax3BL8kZXXV0tn8%2BnUCik%2BPh4q/vOmFBw8qLTxMon%2Brg9BADAGaQx//6eTMTOYAEAAJyuCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwLJotweAM0fGhAK3h/CjrHyij9tDAABEKGawAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABY5krAevfdd3XgwAE3Dg0AANDoXAlYeXl58vv9uu%2B%2B%2B7R8%2BXI3hgAAANBoXAlY27dv1%2Buvv66KigpdeeWVuuSSS/TMM89o586dp7yP/Px8XXHFFYqLi1NiYqJuuukmbdiwIaympqZGI0eOVEJCgmJjYzVw4EBt27YtrGbLli0aMGCAYmNjlZCQoFGjRqm2tjasZvHixUpPT1fLli11/vnn6%2BWXX/7XmwcAAM2eKwErOjpaN998s%2BbNm6ctW7ZoyJAhev3119W%2BfXvdfPPN%2Buijj2SMOeE%2BFi9erOHDh6ukpESFhYU6ePCgsrKytG/fPqdm9OjRmjt3rmbOnKmlS5dq79696t%2B/vw4dOiRJOnTokPr166d9%2B/Zp6dKlmjlzpmbPnq0xY8Y4%2B9i0aZNuvPFGXXXVVVq9erUee%2BwxjRo1SrNnz26cbw4AAIh4HnOyJNMESktL9frrr%2Bu1115TYmKiQqGQzj33XL3xxhu66qqrTmkfO3fuVGJiohYvXqyrr77a2cc777yj2267TdIPM2cpKSmaP3%2B%2BsrOz9fHHH6t///7aunWrAoGAJGnmzJkaOnSoKisrFR8fr0cffVTz5s3T%2BvXrnWMNGzZMX375pYqLi09pbNXV1fL5fAqFQoqPj/%2BR350Ty5hQYHV/%2BD8rn%2Bjj9hAAAD9BY/79PRnXPkW4a9cuPffcc7rsssvUs2dPVVZW6oMPPtC3336r7777Tv3799ddd911yvsLhUKSpDZt2kj6IbTV1dUpKyvLqQkEAkpLS1NRUZEkqbi4WGlpaU64kqTs7GzV1NSotLTUqTl6H0dqVq5cqbq6ugbHUlNTo%2Brq6rAFAACcOVwJWL/85S/Vrl07vfzyy8rJydHWrVv1/vvvq0%2BfPvJ4PPrZz36mRx55RN9%2B%2B%2B0p7c8Yo7y8PF155ZVKS0uTJAWDQcXExKh169ZhtUlJSQoGg05NUlJS2PbWrVsrJibmhDVJSUk6ePCgdu3a1eB48vPz5fP5nCUlJeWU%2BgAAAM1DtBsHjY%2BP18KFC094%2Bi85OVkbN248pf2NGDFCa9as0dKlS09aa4yRx%2BNxXh/99anWHDmr2tB7JWn8%2BPHKy8tzXldXVxOyAAA4g7gSsN56662T1ng8Hv385z8/ad3IkSM1b948LVmyRO3bt3fW%2B/1%2B1dbWqqqqKmwWq7KyUj169HBqli1bFra/qqoq1dXVObNWfr/fmc06eh/R0dFq27Ztg2Pyer3yer0nHTsAAGieXDlF%2BNBDD2nq1Kn11r/44othn%2BA7EWOMRowYoTlz5ujTTz9Vampq2Pb09HS1aNFChYWFzrqKigqVl5c7ASszM1Pl5eWqqKhwahYsWCCv16v09HSn5uh9HKnJyMhQixYtTq1hAABwRnElYL3//vvq3r17vfWZmZmaNWvWKe1j%2BPDhevfdd/Xee%2B8pLi5OwWBQwWBQ%2B/fvlyT5fD7dc889GjNmjBYtWqTVq1dr8ODB6tKli66//npJUlZWli6%2B%2BGLl5ORo9erVWrRokcaOHavc3Fzn0wbDhg3Tt99%2Bq7y8PK1fv975tOPYsWMtfTcAAEBz40rA2rVrV72Lz6Ufrs063oXjx5o2bZpCoZB69%2B6t5ORkZzk6oD377LO66aabdOutt6pnz546%2B%2Byz9eGHHyoqKkqSFBUVpY8%2B%2BkgtW7ZUz549deutt%2Bqmm27S5MmTnX2kpqZq/vz5%2Bvzzz/WLX/xCf/jDH/T888/rlltu%2BYnfBQAA0Fy5cg3Wz3/%2Bc33yySd64IEHwtZ/8skn9U71Hc%2Bp3L6rZcuWeuGFF/TCCy8ct6ZDhw7661//esL99OrVS6tWrTqlcQEAALgSsEaPHq3Ro0fr%2B%2B%2B/17XXXitJWrRokZ566qmw2SMAAIBI5ErAys3N1YEDBzRp0iT97ne/kyS1b99ezz//vH7zm9%2B4MSQAAABrXAlY0g%2B3Vxg5cqQqKirUqlUrnXPOOW4NBQAAwCrXAtYRycnJbg8BAADAKlc%2BRbhz507dfffd6tChg1q2bKmYmJiwBQAAIJK5MoM1dOhQ/f3vf9fDDz%2Bs5OTk4z5yBgAAIBK5ErCWLFmiJUuW6PLLL3fj8AAAAI3KlVOE7du3Z9YKAAA0W64ErGeffVbjx4/Xtm3b3Dg8AABAo3LlFGFOTo727Nmjjh07Kj4%2Bvt5DkysrK90YFgAAgBWuBKwnn3zSjcMCAAA0CVcC1j333OPGYQEAAJqEK9dgSdLmzZs1ceJE5eTkOKcEFyxYoPXr17s1JAAAACtcCVhffPGFLrnkEi1evFh/%2BctftHfvXknSqlWr9Nvf/taNIQEAAFjjSsB69NFHNXHiRH322Wdhd26/9tprVVJS4saQAAAArHElYK1Zs0a/%2BtWv6q1PTEzUzp07XRgRAACAPa4ErHPOOUfBYLDe%2BrKyMrVr186FEQEAANjjSsC6/fbbNW7cOO3cudO5o/uyZcs0duxYDR482I0hAQAAWONKwJo0aZL8fr%2BSk5O1d%2B9eXXzxxerRo4euuOIK/dd//ZcbQwIAALDGlftgxcTEaNasWfr666%2B1atUqHT58WF27dtWFF17oxnAAAACsciVgHXHBBRfoggsucHMIAAAA1rkSsO69994Tbn/11VebaCQAAAD2uRKwKioqwl7X1dVp3bp12rNnj66%2B%2Bmo3hgQAAGCNKwHrww8/rLfu4MGDuv/%2B%2B3XRRRe5MCIAAAB7XHsW4bGio6M1duxYPf30024PBQAA4Cc5bQKWJH3zzTeqq6tzexgAAAA/iSunCB955JGw18YYVVRUaN68ebrzzjvdGBIAAIA1rgSs4uLisNdnnXWWzj33XD355JPKzc11Y0gAAADWuBKwvvjiCzcOCwAA0CROq2uwAAAAmgNXZrCuuOIK5yHPJ7N8%2BfJGHg0AAIBdrgSsa665Rq%2B88oouuOACZWZmSpJKSkq0YcMG3XffffJ6vW4MCwAAwApXAtbu3bs1fPhwTZo0KWz9hAkTtGPHDv3P//yPG8MCAACwwpVrsP7yl7/o7rvvrrd%2B6NChev/9910YEQAAgD2uBCyv16uioqJ664uKijg9CAAAIp4rpwhHjRqlYcOGafXq1erevbukH67Bmj59uh577DE3hgQAAGCNKzNYEyZM0Guvvabi4mLde%2B%2B9uvfee1VcXKzp06drwoQJp7yfJUuWaMCAAQoEAvJ4PPrggw/Ctg8dOlQejydsORLojqipqdHIkSOVkJCg2NhYDRw4UNu2bQur2bJliwYMGKDY2FglJCRo1KhRqq2t/de/AQAAoFlzZQZLkgYNGqRBgwb9pH3s27dPl112me6%2B%2B27dcsstDdb06dNHb7zxhvM6JiYmbPvo0aP14YcfaubMmWrbtq3GjBmj/v37q7S0VFFRUTp06JD69eunc889V0uXLtX333%2BvIUOGyBijF1544SeNHwAANE%2BuBazq6mrNmTNH33zzjR566CG1bt1aX375pRITE5WcnHxK%2B%2Bjbt6/69u17whqv1yu/39/gtlAopNdee03vvPOOrr/%2BeknSu%2B%2B%2Bq5SUFC1cuFDZ2dlasGCBvvrqK23dulWBQECS9Mwzz2jo0KF64oknFB8f/yO6BgAAZwJXThGWl5frggsu0OOPP678/HxVVVVJ%2BuHThePGjbN6rM8//1yJiYm64IILlJubq8rKSmdbaWmp6urqlJWV5awLBAJKS0tzLsIvLi5WWlqaE64kKTs7WzU1NSotLW3wmDU1Naqurg5bAADAmcOVgPXQQw9p0KBB%2Bvvf/66WLVs66/v166clS5ZYO07fvn01Y8YMffrpp3rmmWe0YsUKXXvttaqpqZEkBYNBxcTEqHXr1mHvS0pKUjAYdGqSkpLCtrdu3VoxMTFOzbHy8/Pl8/mcJSUlxVpPAADg9OfKKcIVK1Zo2rRp9R6X065dO1VUVFg7zm233eZ8nZaWpoyMDHXs2FEfffSRbr755uO%2BzxgTNraGHutzbM3Rxo8fr7y8POd1dXU1IQsAgDOIKzNYMTEx2rt3b731GzduVEJCQqMdNzk5WR07dtTGjRslSX6/X7W1tc4pyiMqKyudWSu/319vpqqqqkp1dXX1ZraO8Hq9io%2BPD1sAAMCZw5WANXDgQP3hD3/QwYMHJf0wQ/Tdd99p3LhxJ5xZ%2Bqm%2B//57bd261bmIPj09XS1atFBhYaFTU1FRofLycvXo0UOSlJmZqfLy8rCZtQULFsjr9So9Pb3RxgoAACKXKwHrmWee0fbt2%2BX3%2B7V//35de%2B21Ov/889WyZct6zyc8kb1796qsrExlZWWSpE2bNqmsrExbtmzR3r17NXbsWBUXF2vz5s36/PPPNWDAACUkJOiXv/ylJMnn8%2Bmee%2B7RmDFjtGjRIq1evVqDBw9Wly5dnE8VZmVl6eKLL1ZOTo5Wr16tRYsWaezYscrNzWVmCgAANMiVa7B8Pp%2BKiopUWFioVatW6fDhw%2Bratauys7OPe11TQ1auXKlrrrnGeX3kuqchQ4Zo2rRpWrt2rd5%2B%2B23t3r1bycnJuuaaazRr1izFxcU573n22WcVHR2tW2%2B9Vfv379d1112nN998U1FRUZKkqKgoffTRR3rggQfUs2dPtWrVSoMGDdLkyZMtfTcAAEBz4zHGmKY8YF1dnW688Ua99NJL6tSpU1Me2jXV1dXy%2BXwKhULWZ70yJhRY3R/%2Bz8on%2Brg9BADAT9CYf39PpslPEbZo0UKrV6/%2BUTNVAAAAkcSVa7AGDx4c9vgaAACA5sS1R%2BVMnTpVCxcuVEZGhmJjY8O2PfXUUy6NCgAA4KdzJWCVlpbq0ksvlSStWbMmbBunDgEAQKRr0oD1zTffKDU1VV988UVTHhYAAKBJNek1WJ06ddLOnTud17fddpt27NjRlEMAAABodE0asI69I8T8%2BfO1b9%2B%2BphwCAABAo3PlU4QAAADNWZMGLI/HU%2B8idi5qBwAAzU2TXuRujNHQoUPl9XolSQcOHNCwYcPq3aZhzpw5TTksAAAAq5o0YA0ZMiTs9eDBg5vy8AAAAE2iSQMWd28HAABnAi5yBwAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWBbRAWvJkiUaMGCAAoGAPB6PPvjgg7DtxhhNnDhRgUBArVq1Uu/evbVu3bqwmqqqKuXk5Mjn88nn8yknJ0e7d%2B8Oq1m7dq169eqlVq1aqV27dnr88cdljGn0/gAAQGSK6IC1b98%2BXXbZZZo6dWqD25966ilNmTJFU6dO1YoVK%2BT3%2B3XDDTdoz549Ts2gQYNUVlamgoICFRQUqKysTDk5Oc726upq3XDDDQoEAlqxYoVeeOEFTZ48WVOmTGn0/gAAQGSKdnsAP0Xfvn3Vt2/fBrcZY/Tcc89pwoQJuvnmmyVJb731lpKSkvTee%2B/pvvvu0/r161VQUKCSkhJ169ZNkjR9%2BnRlZmZqw4YN6ty5s2bMmKEDBw7ozTfflNfrVVpamr7%2B%2BmtNmTJFeXl58ng8TdYvAACIDBEdsE5k06ZNCgaDysrKctZ5vV716tVLRUVFuu%2B%2B%2B1RcXCyfz%2BeEK0nq3r27fD6fioqK1LlzZxUXF6tXr17yer1OTXZ2tsaPH6/NmzcrNTW13rFrampUU1PjvK6urm6kLtGYMiYUuD2EH2XlE33cHgIA4P%2BL6FOEJxIMBiVJSUlJYeuTkpKcbcFgUImJifXem5iYGFbT0D6OPsax8vPznWu6fD6fUlJSflozAAAgojTbgHXEsafwjDFh6xo6xXeymiMXuB/v9OD48eMVCoWcZevWrf/y%2BAEAQORptqcI/X6/pB9mmZKTk531lZWVzgyU3%2B/Xjh076r13586dYTXHzlRVVlZKqj87doTX6w07pQgAAM4szXYGKzU1VX6/X4WFhc662tpaLV68WD169JAkZWZmKhQKafny5U7NsmXLFAqFwmqWLFmi2tpap2bBggUKBAI677zzmqYZAAAQUSI6YO3du1dlZWUqKyuT9MOF7WVlZdqyZYs8Ho9Gjx6tSZMmae7cuSovL9fQoUN19tlna9CgQZKkiy66SH369FFubq5KSkpUUlKi3Nxc9e/fX507d5b0w20cvF6vhg4dqvLycs2dO1eTJk3iE4QAAOC4IvoU4cqVK3XNNdc4r/Py8iRJQ4YM0ZtvvqlHHnlE%2B/fv1wMPPKCqqip169ZNCxYsUFxcnPOeGTNmaNSoUc6nDQcOHBh2Xy2fz6fCwkINHz5cGRkZat26tfLy8pxjAQAAHMtjuCV5o6uurpbP51MoFFJ8fLzVfUfarQTQeLhNAwCEa8y/vycT0acIAQAATkcELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBl0W4PAIAdGRMK3B7CKVv5RB%2B3hwAyoXMWAAARE0lEQVQAjYoZLAAAAMsIWAAAAJY164A1ceJEeTyesMXv9zvbjTGaOHGiAoGAWrVqpd69e2vdunVh%2B6iqqlJOTo58Pp98Pp9ycnK0e/fupm4FAABEkGYdsCTpkksuUUVFhbOsXbvW2fbUU09pypQpmjp1qlasWCG/368bbrhBe/bscWoGDRqksrIyFRQUqKCgQGVlZcrJyXGjFQAAECGa/UXu0dHRYbNWRxhj9Nxzz2nChAm6%2BeabJUlvvfWWkpKS9N577%2Bm%2B%2B%2B7T%2BvXrVVBQoJKSEnXr1k2SNH36dGVmZmrDhg3q3Llzk/YCAAAiQ7Ofwdq4caMCgYBSU1N1%2B%2B2365tvvpEkbdq0ScFgUFlZWU6t1%2BtVr169VFRUJEkqLi6Wz%2BdzwpUkde/eXT6fz6lpSE1Njaqrq8MWAABw5mjWAatbt256%2B%2B239cknn2j69OkKBoPq0aOHvv/%2BewWDQUlSUlJS2HuSkpKcbcFgUImJifX2m5iY6NQ0JD8/37lmy%2BfzKSUlxWJXAADgdNesA1bfvn11yy23qEuXLrr%2B%2Buv10UcfSfrhVOARHo8n7D3GmLB1x25vqOZY48ePVygUcpatW7f%2B1FYAAEAEadYB61ixsbHq0qWLNm7c6FyXdexMVGVlpTOr5ff7tWPHjnr72blzZ72Zr6N5vV7Fx8eHLQAA4MxxRgWsmpoarV%2B/XsnJyUpNTZXf71dhYaGzvba2VosXL1aPHj0kSZmZmQqFQlq%2BfLlTs2zZMoVCIacGAADgWM36U4Rjx47VgAED1KFDB1VWVuqPf/yjqqurNWTIEHk8Ho0ePVqTJk1Sp06d1KlTJ02aNElnn322Bg0aJEm66KKL1KdPH%2BXm5uqVV16RJN17773q378/nyAEAADH1awD1rZt23THHXdo165dOvfcc9W9e3eVlJSoY8eOkqRHHnlE%2B/fv1wMPPKCqqip169ZNCxYsUFxcnLOPGTNmaNSoUc6nDQcOHKipU6e60g8AAIgMHmOMcXsQzV11dbV8Pp9CoZD167Ei6QG/wBE87BlAU2jMv78nc0ZdgwUAANAUCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALGvWD3sGcHqKtGdo8uxEAD8WM1gAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyHPQPASfBwagA/FjNYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMu40SgANDORdGNUboqK5ooZLAAAAMsIWAAAAJYRsE7RSy%2B9pNTUVLVs2VLp6en64osv3B4SAAA4TXEN1imYNWuWRo8erZdeekk9e/bUK6%2B8or59%2B%2Bqrr75Shw4d3B4eAESsSLpeTOKaMZw6jzHGuD2I0123bt3UtWtXTZs2zVl30UUX6aabblJ%2Bfv5J319dXS2fz6dQKKT4%2BHirY4u0X04AgKZzpgfCxvz7ezLMYJ1EbW2tSktLNW7cuLD1WVlZKioqavA9NTU1qqmpcV6HQiFJP/yHtu1QzT7r%2BwQANA%2BXj53t9hBO2eLf3mB9n0f%2B7roxl0TAOoldu3bp0KFDSkpKCluflJSkYDDY4Hvy8/P1%2B9//vt76lJSURhkjAACRzvdM4%2B17z5498vl8jXeABhCwTpHH4wl7bYypt%2B6I8ePHKy8vz3l9%2BPBh/eMf/1Dbtm2P%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%2BIVKS0ud7cYYTZw4UYFAQK1atVLv3r21bt26sH1UVVUpJydHPp9PPp9POTk52r17d1O3ckIHDx7Uf/7nfyo1NVWtWrXS%2Beefr8cff1yHDx92aiK51yVLlmjAgAEKBALyeDz64IMPwrbb6m3t2rXq1auXWrVqpXbt2unxxx9v8ufGnajXuro6Pfroo%2BrSpYtiY2MVCAR01113afv27WH7aA69Huu%2B%2B%2B6Tx%2BPRc889F7Y%2BUnp1nUHEmTlzpmnRooWZPn26%2Beqrr8yDDz5oYmNjzbfffuv20E5Zdna2eeONN0x5ebkpKysz/fr1Mx06dDB79%2B51aoYNG2batWtnCgsLzapVq8w111xjLrvsMnPw4EFjjDEHDx40aWlp5pprrjGrVq0yhYWFJhAImBEjRrjV1kktX77cnHfeeebSSy81Dz74oLO%2BufT6j3/8w3Ts2NEMHTrULFu2zGzatMksXLjQ/O///q9T8%2BSTT5q4uDgze/Zss3btWnPbbbeZ5ORkU11d7dT06dPHpKWlmaKiIlNUVGTS0tJM//793WjpuP74xz%2Batm3bmr/%2B9a9m06ZN5v333zc/%2B9nPzHPPPefURHKv8%2BfPNxMmTDCzZ882kszcuXPDttvoLRQKmaSkJHP77bebtWvXmtmzZ5u4uDgzefLkJuvTmBP3unv3bnP99debWbNmmb/97W%2BmuLjYdOvWzaSnp4ftozn0erS5c%2Beayy67zAQCAfPss8%2BGbYuUXt1GwIpA//7v/26GDRsWtu7CCy8048aNc2lEP11lZaWRZBYvXmyM%2BeGXWosWLczMmTOdmu%2B%2B%2B86cddZZpqCgwBjzwy%2BKs846y3z33XdOzZ///Gfj9XpNKBRq2gZOwZ49e0ynTp1MYWGh6dWrlxOwmlOvjz76qLnyyiuPu/3w4cPG7/ebJ5980ll34MAB4/P5zMsvv2yMMearr74ykkxJSYlTU1xcbCSZv/3tb403%2BB%2BpX79%2B5je/%2BU3YuptvvtkMHjzYGNO8ej32D7Gt3l566SXj8/nMgQMHnJr8/HwTCATM4cOHG7utBp0odByxfPlyI8n5R21z63Xbtm2mXbt2pry83HTs2DEsYEVqr27gFGGEqa2tVWlpqbKyssLWZ2VlqaioyKVR/XShUEiS1KZNG0lSaWmp6urqwvoMBAJKS0tz%2BiwuLlZaWlrYQzyzs7NVU1MTdkrqdDF8%2BHD169dP119/fdj65tTrvHnzlJGRoV//%2BtdKTEzU5ZdfrunTpzvbN23apGAwGNar1%2BtVr169wnr1%2BXzq1q2bU9O9e3f5fL7T6mf8yiuv1KJFi/T1119Lkr788kstXbpUN954o6Tm1euxbPVWXFysXr16hT27NTs7W9u3b9fmzZubppl/QSgUksfjcZ4x25x6PXz4sHJycvTwww/rkksuqbe9OfXa2AhYEWbXrl06dOhQvQdNJyUl1XsgdaQwxigvL09XXnml0tLSJEnBYFAxMTFq3bp1WO3RfQaDwXrfh9atWysmJua0%2B17MnDlTpaWlys/Pr7etOfX6zTffaNq0aerUqZM%2B%2BeQTDRs2TKNGjdLbb78tSc5YT/TzGwwGlZiYWG/fiYmJp1Wvjz76qO644w5deOGFatGihS6//HKNHj1ad9xxh6Tm1euxbPXW0M/1kdena/8HDhzQuHHjNGjQIOeBx82p1//%2B7/9WdHS0Ro0a1eD25tRrY%2BNe9xHK4/GEvTbG1FsXKUaMGKE1a9Zo6dKlJ609ts%2BGej7dvhdbt27Vgw8%2BqAULFqhly5an/L5I7PXw4cPKyMjQpEmTJEmXX3651q1bp2nTpumuu%2B5y6k728xsJvc6aNUvvvvuu3nvvPV1yySUqKyvT6NGjFQgENGTIEKeuOfR6PDZ6a2gfx3uv2%2Brq6nT77bfr8OHDeumll8K2NYdeS0tL9ac//UmrVq064ZiaQ69NgRmsCJOQkKCoqKh6/wqorKys9y%2BGSDBy5EjNmzdPn332mdq3b%2B%2Bs9/v9qq2tVVVVVVj90X36/f5634eqqirV1dWdVt%2BL0tJSVVZWKj09XdHR0YqOjtbixYv1/PPPKzo6WklJSc2m1%2BTkZF188cVh6y666CJt2bJF0g99SPX/FXtsrzt27Ki37507d55WvT788MMaN26cbr/9dnXp0kU5OTl66KGHnFnK5tTrsWz11tDPdWVlpaT6s2Nuq6ur06233qpNmzapsLDQmb2Smk%2BvX3zxhSorK9WhQwfnd9W3336rMWPG6LzzzpPUfHptCgSsCBMTE6P09HQVFhaGrS8sLFSPHj1cGtWPZ4zRiBEjNGfOHH366adKTU0N256enq4WLVqE9VlRUaHy8nKnz8zMTJWXl6uiosKpWbBggbxer9LT05umkVNw3XXXae3atSorK3OWjIwM3Xnnnc7XzaXXnj171rvdxtdff62OHTtKklJTU%2BX3%2B8N6ra2t1eLFi8N6DYVCWr58uVOzbNkyhUKh0%2Bpn/J///KfOOiv8V2hUVJRzm4bm1OuxbPWWmZmpJUuWhN1uZMGCBQoEAs4f9NPBkXC1ceNGLVy4UG3btg3b3lx6zcnJ0Zo1a8J%2BVwUCAT388MP65JNPJDWfXptEU19Vj5/uyG0aXnvtNfPVV1%2BZ0aNHm9jYWLN582a3h3bK7r//fuPz%2Bcznn39uKioqnOWf//ynUzNs2DDTvn17s3DhQrNq1Spz7bXXNnjrguuuu86sWrXKLFy40LRv3/60u3VBQ47%2BFKExzafX5cuXm%2BjoaPPEE0%2BYjRs3mhkzZpizzz7bvPvuu07Nk08%2BaXw%2Bn5kzZ45Zu3atueOOOxr8eP%2Bll15qiouLTXFxsenSpctpceuCow0ZMsS0a9fOuU3DnDlzTEJCgnnkkUecmkjudc%2BePWb16tVm9erVRpKZMmWKWb16tfPJORu97d692yQlJZk77rjDrF271syZM8fEx8c3%2Bcf5T9RrXV2dGThwoGnfvr0pKysL%2B31VU1PTrHptyLGfIjQmcnp1GwErQr344oumY8eOJiYmxnTt2tW5vUGkkNTg8sYbbzg1%2B/fvNyNGjDBt2rQxrVq1Mv379zdbtmwJ28%2B3335r%2BvXrZ1q1amXatGljRowYEfbR4NPVsQGrOfX64YcfmrS0NOP1es2FF15oXn311bDthw8fNr/73e%2BM3%2B83Xq/XXH311Wbt2rVhNd9//7258847TVxcnImLizN33nmnqaqqaso2Tqq6uto8%2BOCDpkOHDqZly5bm/PPPNxMmTAj7oxvJvX722WcN/j86ZMgQY4y93tasWWOuuuoq4/V6jd/vNxMnTmzyj/KfqNdNmzYd9/fVZ5991qx6bUhDAStSenWbx5gz7daqAAAAjYtrsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWPb/AOOLe8A49JlDAAAAAElFTkSuQmCC\"/>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-7424980232871411859\">\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">65.0</td>\n",
" <td class=\"number\">37</td>\n",
" <td class=\"number\">0.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">89.0</td>\n",
" <td class=\"number\">36</td>\n",
" <td class=\"number\">0.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">41.0</td>\n",
" <td class=\"number\">36</td>\n",
" <td class=\"number\">0.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">57.0</td>\n",
" <td class=\"number\">32</td>\n",
" <td class=\"number\">0.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">61.0</td>\n",
" <td class=\"number\">31</td>\n",
" <td class=\"number\">0.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">51.0</td>\n",
" <td class=\"number\">31</td>\n",
" <td class=\"number\">0.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">46.0</td>\n",
" <td class=\"number\">31</td>\n",
" <td class=\"number\">0.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">72.0</td>\n",
" <td class=\"number\">30</td>\n",
" <td class=\"number\">0.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">111.0</td>\n",
" <td class=\"number\">30</td>\n",
" <td class=\"number\">0.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">122.0</td>\n",
" <td class=\"number\">29</td>\n",
" <td class=\"number\">0.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"other\">\n",
" <td class=\"fillremaining\">Other values (2456)</td>\n",
" <td class=\"number\">7395</td>\n",
" <td class=\"number\">79.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"missing\">\n",
" <td class=\"fillremaining\">(Missing)</td>\n",
" <td class=\"number\">1639</td>\n",
" <td class=\"number\">17.5%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:22%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"extreme-7424980232871411859\">\n",
" <p class=\"h4\">Minimum 5 values</p>\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">2.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">4.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">6.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">7.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">8.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" <p class=\"h4\">Maximum 5 values</p>\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">1345.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1358.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1369.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1389.0</td>\n",
" <td class=\"number\">2</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1479.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"</div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4 pp-anchor\" id=\"pp_var_PT08.S1(CO)\">PT08.S1(CO)<br/>\n",
" <small>Numeric</small>\n",
" </p>\n",
" </div><div class=\"col-md-6\">\n",
" <div class=\"row\">\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
" <tr>\n",
" <th>Distinct count</th>\n",
" <td>3246</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Unique (%)</th>\n",
" <td>34.7%</td>\n",
" </tr>\n",
" <tr class=\"alert\">\n",
" <th>Missing (%)</th>\n",
" <td>3.9%</td>\n",
" </tr>\n",
" <tr class=\"alert\">\n",
" <th>Missing (n)</th>\n",
" <td>366</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Infinite (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Infinite (n)</th>\n",
" <td>0</td>\n",
" </tr>\n",
" </table>\n",
"\n",
" </div>\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
"\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>1099.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Minimum</th>\n",
" <td>647.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maximum</th>\n",
" <td>2039.8</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Zeros (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"<div class=\"col-md-3 collapse in\" id=\"minihistogram-8823104925418689675\">\n",
" <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAAd9JREFUeJzt3DEO0lAcx/F/iStlJ/QQJl7BxAO5mbg5ehR3V3dvAeEAbRwcpA6Ki8lPMNBS8vmshL6SvG9e6WvajOM41sQOh0N1XTf1sCzcfr%2Bv3W436ZgvJh3tt/V6XVW/fnDbtnOcAgvS9311Xfdn3kxplkCapqmqqrZtZwvk1bvPV3/n64c3dzgTLnWeN1NaTT4iLIhAIBAIBAKBQCAQCAQCgUAgEAgEAsEsO%2BlLde3uu5335bOCQCAQCJ7iEut/HjyES1hBIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBE/xqMmj8u6t5bOCQCAQCAQCgUAgEAgEAoFAIBAIBAIbhQ/G5uJjsYJAIBAIBAKBQCAQCAQCgcBt3ifgrfP3YwWB4OFWEC%2Bi5pFYQSAQCAQPd4nF/Xne63KzBDKOY1VV9X3/12c/vn%2Bb%2BnS4wMu3n67%2Bzpf3r28y9nmenOfNlGYJZBiGqqrqum6O4ZnI5uNtjzcMQ202m9se9B%2BacYYsT6dTHY/HWq/X1TTN1MOzMOM41jAMtd1ua7Wa9m/zLIHAUriLBYFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUDwE3%2B4Uk0qcr4aAAAAAElFTkSuQmCC\">\n",
"\n",
"</div>\n",
"<div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-8823104925418689675,#minihistogram-8823104925418689675\"\n",
" aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
"</div>\n",
"<div class=\"row collapse col-md-12\" id=\"descriptives-8823104925418689675\">\n",
" <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
" <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-8823104925418689675\"\n",
" aria-controls=\"quantiles-8823104925418689675\" role=\"tab\"\n",
" data-toggle=\"tab\">Statistics</a></li>\n",
" <li role=\"presentation\"><a href=\"#histogram-8823104925418689675\" aria-controls=\"histogram-8823104925418689675\"\n",
" role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
" <li role=\"presentation\"><a href=\"#common-8823104925418689675\" aria-controls=\"common-8823104925418689675\"\n",
" role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
" <li role=\"presentation\"><a href=\"#extreme-8823104925418689675\" aria-controls=\"extreme-8823104925418689675\"\n",
" role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
"\n",
" </ul>\n",
"\n",
" <div class=\"tab-content\">\n",
" <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-8823104925418689675\">\n",
" <div class=\"col-md-4 col-md-offset-1\">\n",
" <p class=\"h4\">Quantile statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr>\n",
" <th>Minimum</th>\n",
" <td>647.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5-th percentile</th>\n",
" <td>810.38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Q1</th>\n",
" <td>936.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Median</th>\n",
" <td>1063</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Q3</th>\n",
" <td>1231.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95-th percentile</th>\n",
" <td>1507.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maximum</th>\n",
" <td>2039.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Range</th>\n",
" <td>1392.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Interquartile range</th>\n",
" <td>294.5</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" <div class=\"col-md-4 col-md-offset-2\">\n",
" <p class=\"h4\">Descriptive statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr>\n",
" <th>Standard deviation</th>\n",
" <td>217.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Coef of variation</th>\n",
" <td>0.1974</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kurtosis</th>\n",
" <td>0.33489</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>1099.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MAD</th>\n",
" <td>173.74</td>\n",
" </tr>\n",
" <tr class=\"\">\n",
" <th>Skewness</th>\n",
" <td>0.75593</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sum</th>\n",
" <td>9887500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Variance</th>\n",
" <td>47126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Memory size</th>\n",
" <td>73.2 KiB</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-8823104925418689675\">\n",
" <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzt3X10FFWexvEnkqQJTNICMek0IEYXEQmiEg4EUV7EBORlFXcFkUhmHIRBYDAgisw66IzAOoiMsqCyvosDuoCLgxMJirychHciBBlAQQiQENDQAQZCkLt/eKi1SQQcb1J08v2cU%2BfYdW9X/X5KyOOt6uowY4wRAAAArLnM7QIAAABqGgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFgW7nYBtcGZM2d04MABRUdHKywszO1yAACoFYwxOnr0qPx%2Bvy67rHrXlAhY1eDAgQNq2rSp22UAAFArFRQUqEmTJtV6TgJWNYiOjpb0/X/gmJgYl6sBAKB2KC0tVdOmTZ3fw9WJgFUNzl4WjImJIWABAFDN3Lg9h5vcAQAALCNgAQAAWEbAAgAAsIyABQAAYFnIBqzJkyerXbt2io6OVlxcnO666y5t3749aE6XLl0UFhYWtA0YMCBoTklJidLT0%2BX1euX1epWenq4jR44EzdmyZYs6d%2B6sqKgoNW7cWE8//bSMMVXeIwAACE0hG7CWL1%2Buhx9%2BWKtXr1Z2drZOnz6t1NRUHT9%2BPGjekCFDVFhY6Gwvv/xy0PjAgQOVl5enrKwsZWVlKS8vT%2Bnp6c54aWmp7rjjDvn9fq1bt04vvviipk6dqmnTplVLnwAAIPSE7GMasrKygl6//vrriouL04YNG3Tbbbc5%2B%2BvVqyefz1fpMbZt26asrCytXr1a7du3lyTNnj1bKSkp2r59u1q0aKE5c%2Bbo5MmTeuONN%2BTxeJSUlKQdO3Zo2rRpyszM5MnsAACggpBdwTpXIBCQJDVs2DBo/5w5cxQbG6tWrVpp7NixOnr0qDOWm5srr9frhCtJ6tChg7xer3Jycpw5nTt3lsfjceakpaXpwIED%2Bvrrr6uwIwAAEKpCdgXrh4wxyszMVKdOnZSUlOTsv//%2B%2B5WYmCifz6f8/HyNHz9en3/%2BubKzsyVJRUVFiouLq3C8uLg4FRUVOXOuuuqqoPH4%2BHhnLDExscL7y8rKVFZW5rwuLS392T0CAIDQUSMC1ogRI7R582atWrUqaP%2BQIUOcf05KSlLz5s2VnJysjRs36uabb5ZU%2BdNdjTFB%2B8%2Bdc/YG9x%2B7PDh58mQ99dRT/1wzAAAg5IX8JcKRI0dq0aJFWrZs2QW/yPHmm29WRESEdu7cKUny%2BXw6ePBghXmHDh1yVql8Pp%2BzmnVWcXGxpP9fyTrX%2BPHjFQgEnK2goOAn9wUAAEJXyAYsY4xGjBihBQsW6NNPP630Ut25tm7dqvLyciUkJEiSUlJSFAgEtHbtWmfOmjVrFAgE1LFjR2fOihUrdOrUKWfOkiVL5Pf7K1w6PMvj8TjfO8j3DwIAUPuEmRB9oNPw4cP17rvv6n//93/VokULZ7/X61VUVJS%2B%2BuorzZkzR3feeadiY2P1xRdfaMyYMYqKitK6detUp04dSVLPnj114MAB5/ENDz30kJo1a6YPP/xQ0vc3z7do0ULdunXTE088oZ07dyojI0NPPvmkxowZc1G1lpaWyuv1KhAIELZCSPKErAtPuoSsf6aH2yUAwCXFzd%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%2BffXt21f79u0LmrN371716dNH9evXV2xsrEaNGqVTp04FzVm%2BfLnatm2runXr6uqrr9ZLL71U5f0BAIDQFbIBa/ny5Xr44Ye1evVqZWdn6/Tp00pNTdXx48edOaNHj9bChQs1d%2B5crVq1SseOHVPv3r313XffSZK%2B%2B%2B479erVS8ePH9eqVas0d%2B5czZ8/X2PGjHGOsXv3bt1555269dZbtWnTJj3xxBMaNWqU5s%2BfX%2B09AwCA0BBmjDFuF2HDoUOHFBcXp%2BXLl%2Bu2225TIBDQFVdcobffflv9%2B/eXJB04cEBNmzbVRx99pLS0NP3tb39T7969VVBQIL/fL0maO3euMjIyVFxcrJiYGD322GNatGiRtm3b5pxr2LBh%2Bvzzz5Wbm3tRtZWWlsrr9SoQCCgmJsZ%2B86gSyROy3C7hJ1n/TA%2B3SwCAS4qbv39DdgXrXIFAQJLUsGFDSdKGDRtUXl6u1NRUZ47f71dSUpJycnIkSbm5uUpKSnLClSSlpaWprKxMGzZscOb88Bhn56xfv17l5eVV2hMAAAhN4W4XYIMxRpmZmerUqZOSkpIkSUVFRYqMjFSDBg2C5sbHx6uoqMiZEx8fHzTeoEEDRUZGnndOfHy8Tp8%2BrcOHDyshIaFCPWVlZSorK3Nel5aW/vwmAQBAyKgRK1gjRozQ5s2b9Ze//OWCc40xCgsLc17/8J8vds7Zq6qVvVf6/gZ8r9frbE2bNr2oPgAAQM0Q8gFr5MiRWrRokZYtW6YmTZo4%2B30%2Bn06dOqWSkpKg%2BcXFxc6KlM/nc1aqziopKVF5efl55xQXFys8PFyNGjWqtKbx48crEAg4W0FBwc/uEwAAhI6QDVjGGI0YMUILFizQp59%2BqsTExKDxtm3bKiIiQtnZ2c6%2BwsJC5efnq2PHjpKklJQU5efnq7Cw0JmzZMkSeTwetW3b1pnzw2OcnZOcnKyIiIhKa/N4PIqJiQnaAABA7RGyAevhhx/WO%2B%2B8o3fffVfR0dEqKipSUVGRTpw4IUnyer168MEHNWbMGH3yySfatGmTBg0apNatW6t79%2B6SpNTUVF1//fVKT0/Xpk2b9Mknn2js2LEaMmSIE4qGDRumPXv2KDMzU9u2bdNrr72mV199VWPHjnWtdwAAcGkL2YA1a9YsBQIBdenSRQkJCc42b948Z87zzz%2Bvu%2B66S/fee69uueUW1atXTx9%2B%2BKHq1KkjSapTp44WL16sunXr6pZbbtG9996ru%2B66S1OnTnWOkZiYqI8%2B%2BkifffaZbrzxRv3hD3/QCy%2B8oHvuuafaewYAAKGhxjwH61LGc7BCE8/BAoDQxnOwAAAAapAa8RwsAKG14sZqG4CajhUsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWhbtdAGqP5AlZbpcAAEC1YAULAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMlcC1jvvvKOTJ0%2B6cWoAAIAq50rAyszMlM/n09ChQ7V27Vo3SgAAAKgyrgSsAwcO6LXXXlNhYaE6deqkVq1a6bnnntOhQ4fcKAcAAMAqVwJWeHi4%2BvXrp0WLFmnv3r0aPHiwXnvtNTVp0kT9%2BvXT4sWLZYxxozQAAICfzfWb3H0%2Bn26//XZ16dJFYWFhWr9%2BvQYOHKjmzZtr5cqVbpcHAADwk7kWsA4fPqzp06erTZs2uuWWW1RcXKwPPvhAe/bs0f79%2B9W7d2898MAD5z3GihUr1KdPH/n9foWFhemDDz4IGs/IyFBYWFjQ1qFDh6A5ZWVlGjlypGJjY1W/fn317dtX%2B/btC5qzd%2B9e9enTR/Xr11dsbKxGjRqlU6dO2fkXAQAAahxXAtbdd9%2Btxo0b66WXXlJ6eroKCgr0/vvvq0ePHgoLC9MvfvELjRs3Tnv27DnvcY4fP642bdpoxowZPzqnR48eKiwsdLaPPvooaHz06NFauHCh5s6dq1WrVunYsWPq3bu3vvvuO0nSd999p169eun48eNatWqV5s6dq/nz52vMmDE//18EAACokcLdOGlMTIyWLl2qW2%2B99UfnJCQkaOfOnec9Ts%2BePdWzZ8/zzvF4PPL5fJWOBQIBvfrqq3r77bfVvXt3Sd8/QqJp06ZaunSp0tLStGTJEn3xxRcqKCiQ3%2B%2BXJD333HPKyMjQM888o5iYmPOeHwAA1D6urGC9%2Beab5w1XkhQWFqZrrrnmZ5/rs88%2BU1xcnK699loNGTJExcXFztiGDRtUXl6u1NRUZ5/f71dSUpJycnIkSbm5uUpKSnLClSSlpaWprKxMGzZsqPScZWVlKi0tDdoAAEDt4UrAeuSRRyq9rPdf//VfVi%2B99ezZU3PmzNGnn36q5557TuvWrVO3bt1UVlYmSSoqKlJkZKQaNGgQ9L74%2BHgVFRU5c%2BLj44PGGzRooMjISGfOuSZPniyv1%2BtsTZs2tdYTAAC49LkSsN5///0KN5tLUkpKiubNm2ftPP3791evXr2UlJSkPn366G9/%2B5t27NihxYsXn/d9xhiFhYU5r3/4zz8254fGjx%2BvQCDgbAUFBT%2BvEQAAEFJcCViHDx%2BusGokfX9v1uHDh6vsvAkJCWrWrJlzb5fP59OpU6dUUlISNK%2B4uNhZtfL5fBVWqkpKSlReXl5hZessj8ejmJiYoA0AANQergSsa665Rh9//HGF/R9//LESExOr7LzffPONCgoKlJCQIElq27atIiIilJ2d7cwpLCxUfn6%2BOnbsKOn7VbX8/HwVFhY6c5YsWSKPx6O2bdtWWa0AACB0ufIpwtGjR2v06NH65ptv1K1bN0nSJ598omeffVZTp0696OMcO3ZMX375pfN69%2B7dysvLU8OGDdWwYUNNnDhR99xzjxISEvT111/riSeeUGxsrO6%2B%2B25Jktfr1YMPPqgxY8aoUaNGatiwocaOHavWrVs7nypMTU3V9ddfr/T0dP3pT3/St99%2Bq7Fjx2rIkCGsTAEAgEq5ErCGDBmikydPatKkSfr9738vSWrSpIleeOEF/epXv7ro46xfv15du3Z1XmdmZkqSBg8erFmzZmnLli166623dOTIESUkJKhr166aN2%2BeoqOjnfc8//zzCg8P17333qsTJ07o9ttv1xtvvKE6depIkurUqaPFixdr%2BPDhuuWWWxQVFaWBAwf%2BpCAIAABqlzDj8pf%2BFRYWKioqSpdffrmbZVSp0tJSeb1eBQKBWr3qlTwhy%2B0ScIlY/0wPt0sAUAu4%2BfvXlRWsHzp7PxQAAEBN4cpN7ocOHdIvf/lLXXnllapbt64iIyODNgAAgFDmygpWRkaGvvrqKz366KNKSEj40edJAQAAhCJXAtaKFSu0YsUK3XTTTW6cHgAAoEq5comwSZMmrFoBAIAay5WA9fzzz2v8%2BPHat2%2BfG6cHAACoUq5cIkxPT9fRo0fVrFkzxcTEKCIiImi8uLjYjbIAAACscCVgTZkyxY3TAgAAVAtXAtaDDz7oxmkBAACqhSv3YEnS119/rYkTJyo9Pd25JLhkyRJt27bNrZIAAACscCVgrVy5Uq1atdLy5cv13nvv6dixY5KkjRs36sknn3SjJAAAAGtcCViPPfaYJk6cqGXLlgU9ub1bt25avXq1GyUBAABY40rA2rx5s/7t3/6twv64uDgdOnTIhYoAAADscSVgXX755SoqKqqwPy8vT40bN3ahIgAAAHtcCVgDBgzQ448/rkOHDjlPdF%2BzZo3Gjh2rQYMGuVESAACANa4ErEmTJsnn8ykhIUHHjh3T9ddfr44dO6pdu3b6j//4DzdKAgAAsMaV52BFRkZq3rx52rFjhzZu3KgzZ87o5ptv1nXXXedGOQAAAFa5ErDOuvbaa3Xttde6WQIAAIB1rgSshx566Lzjr7zySjVVAgAAYJ8rAauwsDDodXl5ubZu3aqjR4/qtttuc6MkAAAAa1wJWB9%2B%2BGGFfadPn9ZvfvMbtWzZ0oWKAAAA7HHtuwjPFR4errFjx%2BpPf/qT26UAAAD8LJdMwJKkXbt2qby83O0yAAAAfhZXLhGOGzcu6LUxRoWFhVq0aJHuv/9%2BN0oCAACwxpWAlZubG/T6sssu0xVXXKEpU6ZoyJAhbpQEAABgjSsBa%2BXKlW6cFgAAoFpcUvdgAQAA1ASurGC1a9fO%2BZLnC1m7dm0VVwOguiVPyHK7hJ9k/TM93C4BQIhxJWB17dpVL7/8sq699lqlpKRIklavXq3t27dr6NCh8ng8bpQFAABghSsB68iRI3r44Yc1adKkoP0TJkzQwYMH9d///d9ulAUAAGCFK/dgvffee/rlL39ZYX9GRobef/99FyoCAACwx5WA5fF4lJOTU2F/Tk4OlwcBAEDIc%2BUS4ahRozRs2DBt2rRJHTp0kPT9PVizZ8/WE0884UZJAAAA1rgSsCZMmKDExET9%2Bc9/1muvvSZJatmypWbPnq2BAwe6URIAAIA1rgQsSRo4cCBhCgAA1EiuPWi0tLRUb7zxhp588kmVlJRIkj7//HMVFha6VRIAAIAVrqxg5efnq3v37qpXr54KCgqUkZGhBg0a6L333tO%2Bffv05ptvulEWAACAFa6sYD3yyCMaOHCgvvrqK9WtW9fZ36tXL61YscKNkgAAAKxxZQVr3bp1mjVrVoWvy2ncuDGXCAEAQMhzZQUrMjJSx44dq7B/586dio2NdaEiAAAAe1wJWH379tUf/vAHnT59WpIUFham/fv36/HHH1e/fv3cKAkAAMAaVwLWc889pwMHDsjn8%2BnEiRPq1q2brr76atWtW7fC9xMCAACEGlfuwfJ6vcrJyVF2drY2btyoM2fO6Oabb1ZaWlqF%2B7IAAABCTbUHrPLyct15552aOXOmUlNTlZqaWt0lAAAAVKlqv0QYERGhTZs2sVIFAABqLFfuwRo0aJBef/11N04NAABQ5Vz7LsIZM2Zo6dKlSk5OVv369YPGnn32WZeqAgAA%2BPlcCVgbNmzQDTfcIEnavHlz0BiXDgEAQKir1oC1a9cuJSYmauXKldV5WgAAgGpVrfdgNW/eXIcOHXJe9%2B/fXwcPHqzOEgAAAKpctQYsY0zQ648%2B%2BkjHjx//p4%2B3YsUK9enTR36/X2FhYfrggw8qnG/ixIny%2B/2KiopSly5dtHXr1qA5JSUlSk9Pl9frldfrVXp6uo4cORI0Z8uWLercubOioqLUuHFjPf300xV6AQAAOMuVTxHacvz4cbVp00YzZsyodPzZZ5/VtGnTNGPGDK1bt04%2Bn0933HGHjh496swZOHCg8vLylJWVpaysLOXl5Sk9Pd0ZLy0t1R133CG/369169bpxRdf1NSpUzVt2rQq7w8AAISmar0HKywsrMJN7D/npvaePXuqZ8%2BelY4ZYzR9%2BnRNmDDB%2BX7DN998U/Hx8Xr33Xc1dOhQbdu2TVlZWVq9erXat28vSZo9e7ZSUlK0fft2tWjRQnPmzNHJkyf1xhtvyOPxKCkpSTt27NC0adOUmZnJTfkAAKCCag1YxhhlZGTI4/FIkk6ePKlhw4ZVeEzDggULfva5du/eraKioqAnxXs8HnXu3Fk5OTkaOnSocnNz5fV6nXAlSR06dHC%2ByqdFixbKzc1V586dnZolKS0tTePHj9fXX3%2BtxMTEn10rAACoWao1YA0ePDjo9aBBg6rsXEVFRZKk%2BPj4oP3x8fHas2ePMycuLq7Ce%2BPi4pz3FxUV6aqrrqpwjLNjlQWssrIylZWVOa9LS0v/%2BUYAAEDIqdaA5cbT28%2B9hGeMCdpX2SW%2BC805e4P7j10enDx5sp566ql/umYAABDaQvom9/Px%2BXyS/n8l66zi4mJnBcrn81X6mIhDhw4FzansGFLF1bGzxo8fr0Ag4GwFBQU/rxkAABBSamzASkxMlM/nU3Z2trPv1KlTWr58uTp27ChJSklJUSAQ0Nq1a505a9asUSAQCJqzYsUKnTp1ypmzZMkS%2Bf3%2BCpcOz/J4PIqJiQnaAABA7RHSAevYsWPKy8tTXl6epO9vbM/Ly9PevXsVFham0aNHa9KkSVq4cKHy8/OVkZGhevXqaeDAgZKkli1bqkePHhoyZIhWr16t1atXa8iQIerdu7datGgh6fvHOHg8HmVkZCg/P18LFy7UpEmT%2BAQhAAD4Ua592bMN69evV9euXZ3XmZmZkr6/mf6NN97QuHHjdOLECQ0fPlwlJSVq3769lixZoujoaOc9c%2BbM0ahRo5xPG/bt2zfouVper1fZ2dl6%2BOGHlZycrAYNGigzM9M5FwAAwLnCDI8kr3KlpaXyer0KBAK1%2BnJh8oQst0sA/inrn%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%2Bq2nTpmnGjBlat26dfD6f7rjjDh09etSZM3DgQOXl5SkrK0tZWVnKy8tTenq6G60AAIAQEe52AVUtPDw8aNXqLGOMpk%2BfrgkTJqhfv36SpDfffFPx8fF69913NXToUG3btk1ZWVlavXq12rdvL0maPXu2UlJStH37drVo0aJaewEAAKGhxq9g7dy5U36/X4mJiRowYIB27dolSdq9e7eKioqUmprqzPV4POrcubNycnIkSbm5ufJ6vU64kqQOHTrI6/U6cypTVlam0tLSoA0AANQeNTpgtW/fXm%2B99ZY%2B/vhjzZ49W0VFRerYsaO%2B%2BeYbFRUVSZLi4%2BOD3hMfH%2B%2BMFRUVKS4ursJx4%2BLinDmVmTx5snPPltfrVdOmTS12BQAALnU1OmD17NlT99xzj1q3bq3u3btr8eLFkr6/FHhWWFhY0HuMMUH7zh2vbM65xo8fr0Ag4GwFBQU/txUAABBCanTAOlf9%2BvXVunVr7dy507kv69yVqOLiYmdVy%2Bfz6eDBgxWOc%2BjQoQorXz/k8XgUExMTtAEAgNqjVgWssrIybdu2TQkJCUpMTJTP51N2drYzfurUKS1fvlwdO3aUJKWkpCgQCGjt2rXOnDVr1igQCDhzAAAAzlWjP0U4duxY9enTR1deeaWKi4v1xz/%2BUaWlpRo8eLDCwsI0evRoTZo0Sc2bN1fz5s01adIk1atXTwMHDpQktWzZUj169NCQIUP08ssvS5Ieeugh9e7dm08QAgCAH1WjA9a%2Bfft033336fDhw7riiivUoUMHrV69Ws2aNZMkjRs3TidOnNDw4cNVUlKi9u3ba8mSJYqOjnaOMWfOHI0aNcr5tGHfvn01Y8YMV/oBAAChIcwYY9wuoqYrLS2V1%2BtVIBCo1fdjJU/IcrsEoFZY/0wPt0sALglu/v6tVfdgAQAAVAcCFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGBZjX6SOwDURqH0UF8eioqaihUsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALAs3O0C8PMkT8hyuwQAAHAOVrAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFgW7nYBAIDaK3lCltsl/CTrn%2BnhdgkIEaxgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJZHlfNFAAAOeUlEQVQRsAAAACzjQaMAAFwkHoyKi8UK1kWaOXOmEhMTVbduXbVt21YrV650uyQAAHCJImBdhHnz5mn06NGaMGGCNm3apFtvvVU9e/bU3r173S4NAABcgghYF2HatGl68MEH9etf/1otW7bU9OnT1bRpU82aNcvt0gAAwCWIe7Au4NSpU9qwYYMef/zxoP2pqanKycmp9D1lZWUqKytzXgcCAUlSaWmp9fq%2BKztu/ZgAgJrhprHz3S7hoi1/8g7rxzz7e9cYY/3YF0LAuoDDhw/ru%2B%2B%2BU3x8fND%2B%2BPh4FRUVVfqeyZMn66mnnqqwv2nTplVSIwAAoc77XNUd%2B%2BjRo/J6vVV3gkoQsC5SWFhY0GtjTIV9Z40fP16ZmZnO6zNnzujbb79Vo0aNKryntLRUTZs2VUFBgWJiYuwXfgmrrb3X1r4leq%2BNvdfWviV6vxR6N8bo6NGj8vv91X5uAtYFxMbGqk6dOhVWq4qLiyusap3l8Xjk8XiC9l1%2B%2BeXnPU9MTEyt%2BwE8q7b2Xlv7lui9NvZeW/uW6N3t3qt75eosbnK/gMjISLVt21bZ2dlB%2B7Ozs9WxY0eXqgIAAJcyVrAuQmZmptLT05WcnKyUlBS98sor2rt3r4YNG%2BZ2aQAA4BJUZ%2BLEiRPdLuJSl5SUpEaNGmnSpEmaOnWqTpw4obfffltt2rSxcvw6deqoS5cuCg%2BvfXm3tvZeW/uW6L029l5b%2B5bovbb2Lklhxo3PLgIAANRg3IMFAABgGQELAADAMgIWAACAZQQsAAAAywhYVWT//v0aNGiQGjVqpHr16unGG2/Uhg0bnHFjjCZOnCi/36%2BoqCh16dJFW7duDTpGSUmJ0tPT5fV65fV6lZ6eriNHjlR3Kxft9OnT%2Bt3vfqfExERFRUXp6quv1tNPP60zZ844c2pK3ytWrFCfPn3k9/sVFhamDz74IGjcVp9btmxR586dFRUVpcaNG%2Bvpp5925Tu1fuh8vZeXl%2Buxxx5T69atVb9%2Bffn9fj3wwAM6cOBA0DFqYu/nGjp0qMLCwjR9%2BvSg/aHY%2B8X0vW3bNvXt21der1fR0dHq0KGD9u7d64yXlZVp5MiRio2NVf369dW3b1/t27cv6Bh79%2B5Vnz59VL9%2BfcXGxmrUqFE6depUlfd3Phfq/dixYxoxYoSaNGmiqKgotWzZUrNmzQqaE4q9T548We3atVN0dLTi4uJ01113afv27UFzbPW1fPlytW3bVnXr1tXVV1%2Btl156qcr7qxYG1n377bemWbNmJiMjw6xZs8bs3r3bLF261Hz55ZfOnClTppjo6Ggzf/58s2XLFtO/f3%2BTkJBgSktLnTk9evQwSUlJJicnx%2BTk5JikpCTTu3dvN1q6KH/84x9No0aNzF//%2Bleze/du8/7775tf/OIXZvr06c6cmtL3Rx99ZCZMmGDmz59vJJmFCxcGjdvoMxAImPj4eDNgwACzZcsWM3/%2BfBMdHW2mTp1abX1W5ny9HzlyxHTv3t3MmzfP/P3vfze5ubmmffv2pm3btkHHqIm9/9DChQtNmzZtjN/vN88//3zQWCj2fqG%2Bv/zyS9OwYUPz6KOPmo0bN5qvvvrK/PWvfzUHDx505gwbNsw0btzYZGdnm40bN5quXbuaNm3amNOnTxtjjDl9%2BrRJSkoyXbt2NRs3bjTZ2dnG7/ebESNGVGuv57pQ77/%2B9a/NNddcY5YtW2Z2795tXn75ZVOnTh3zwQcfOHNCsfe0tDTz%2Buuvm/z8fJOXl2d69eplrrzySnPs2DFnjo2%2Bdu3aZerVq2d%2B%2B9vfmi%2B%2B%2BMLMnj3bREREmP/5n/%2Bp9p5tI2BVgccee8x06tTpR8fPnDljfD6fmTJlirPv5MmTxuv1mpdeeskYY8wXX3xhJJnVq1c7c3Jzc40k8/e//73qiv8ZevXqZX71q18F7evXr58ZNGiQMabm9n3uX7q2%2Bpw5c6bxer3m5MmTzpzJkycbv99vzpw5U9VtXZTzhYyz1q5daySZPXv2GGNqfu/79u0zjRs3Nvn5%2BaZZs2ZBAasm9F5Z3/3793d%2Bzitz5MgRExERYebOnevs279/v7nssstMVlaWMeb7IHPZZZeZ/fv3O3P%2B8pe/GI/HYwKBgOUu/jmV9d6qVSvz9NNPB%2B27%2Beabze9%2B9ztjTM3pvbi42Egyy5cvN8bY62vcuHHmuuuuCzrX0KFDTYcOHaq6pSrHJcIqsGjRIiUnJ%2Bvf//3fFRcXp5tuukmzZ892xnfv3q2ioiKlpqY6%2Bzwejzp37qycnBxJUm5urrxer9q3b%2B/M6dChg7xerzPnUtOpUyd98skn2rFjhyTp888/16pVq3TnnXdKqrl9n8tWn7m5uercuXPQ91qmpaXpwIED%2Bvrrr6unGQsCgYDCwsKc7%2BOsyb2fOXNG6enpevTRR9WqVasK4zWx9zNnzmjx4sW69tprlZaWpri4OLVv3z7oUtqGDRtUXl4e9DPh9/uVlJQU1HdSUlLQl/KmpaWprKws6PaKS02nTp20aNEi7d%2B/X8YYLVu2TDt27FBaWpqkmtN7IBCQJDVs2FCSvb5yc3ODjnF2zvr161VeXl6lPVU1AlYV2LVrl2bNmqXmzZvr448/1rBhwzRq1Ci99dZbkuR8cfS5XxYdHx/vjBUVFSkuLq7CsePi4ip88fSl4rHHHtN9992n6667ThEREbrppps0evRo3XfffZJqbt/nstVnUVFRpcf44TkudSdPntTjjz%2BugQMHOl/4WpN7/8///E%2BFh4dr1KhRlY7XxN6Li4t17NgxTZkyRT169NCSJUt09913q1%2B/flq%2BfLmk7%2BuOjIxUgwYNgt577s/EuX03aNBAkZGRl2TfZ73wwgu6/vrr1aRJE0VGRqpHjx6aOXOmOnXqJKlm9G6MUWZmpjp16qSkpCRJ9vr6sT/vp0%2Bf1uHDh6uqpWpRO59fX8XOnDmj5ORkTZo0SZJ00003aevWrZo1a5YeeOABZ15YWFjQ%2B4wxQfvOHa9szqVk3rx5euedd/Tuu%2B%2BqVatWysvL0%2BjRo%2BX3%2BzV48GBnXk3r%2B8fY6LOyY/zYey815eXlGjBggM6cOaOZM2cGjdXE3jds2KA///nP2rhx43lrrGm9n/0Qy7/%2B67/qkUcekSTdeOONysnJ0UsvvaTOnTv/6Htrws/%2BCy%2B8oNWrV2vRokVq1qyZVqxYoeHDhyshIUHdu3f/0feFUu8jRozQ5s2btWrVqgvOrW1/z50PK1hVICEhQddff33QvpYtWzqfqPH5fJIq/t9ocXGxk%2BR9Pp8OHjxY4diHDh2qkPYvFY8%2B%2Bqgef/xxDRgwQK1bt1Z6eroeeeQRTZ48WVLN7ftctvr0%2BXyVHkOquDp2qSkvL9e9996r3bt3Kzs721m9kmpu7ytXrlRxcbGuvPJKhYeHKzw8XHv27NGYMWN01VVXSaqZvcfGxio8PPyCf%2BedOnVKJSUlQXPO/Zk4t%2B%2BSkhKVl5dfkn1L0okTJ/TEE09o2rRp6tOnj2644QaNGDFC/fv319SpUyWFfu8jR47UokWLtGzZMjVp0sTZb6uvH/vzHh4erkaNGlVFS9WGgFUFbrnllgofZ92xY4eaNWsmSUpMTJTP51N2drYzfurUKS1fvlwdO3aUJKWkpCgQCGjt2rXOnDVr1igQCDhzLjX/%2BMc/dNllwX%2Bk6tSp4/wfbk3t%2B1y2%2BkxJSdGKFSuCPtK8ZMkS%2Bf1%2B5xf2pehsuNq5c6eWLl1a4S/Jmtp7enq6Nm/erLy8PGfz%2B/169NFH9fHHH0uqmb1HRkaqXbt25/07r23btoqIiAj6mSgsLFR%2Bfn5Q3/n5%2BSosLHTmLFmyRB6PR23btq2GTn668vJylZeXn/fvvVDt3RijESNGaMGCBfr000%2BVmJgYNG6rr5SUlKBjnJ2TnJysiIiIqmqvelTrLfW1xNq1a014eLh55plnzM6dO82cOXNMvXr1zDvvvOPMmTJlivF6vWbBggVmy5Yt5r777qv0Y/w33HCDyc3NNbm5uaZ169aX3OMKfmjw4MGmcePGzmMaFixYYGJjY824ceOcOTWl76NHj5pNmzaZTZs2GUlm2rRpZtOmTc4n5Wz0eeTIERMfH2/uu%2B8%2Bs2XLFrNgwQITExPj%2BqMKztd7eXm56du3r2nSpInJy8szhYWFzlZWVuYcoyb2XplzP0VoTGj2fqG%2BFyxYYCIiIswrr7xidu7caV588UVTp04ds3LlSucYw4YNM02aNDFLly41GzduNN26dav0I/2333672bhxo1m6dKlp0qSJ649puFDvnTt3Nq1atTLLli0zu3btMq%2B//rqpW7eumTlzpnOMUOz9N7/5jfF6veazzz4L%2Bjn%2Bxz/%2B4cyx0dfZxzQ88sgj5osvvjCvvvoqj2nA%2BX344YcmKSnJeDwec91115lXXnklaPzMmTPm97//vfH5fMbj8ZjbbrvNbNmyJWjON998Y%2B6//34THR1toqOjzf33329KSkqqs42fpLS01Pz2t781V155palbt665%2BuqrzYQJE4J%2BsdaUvpctW2YkVdgGDx5sjLHX5%2BbNm82tt95qPB6P8fl8ZuLEia5/VP98ve/evbvSMUlm2bJlzjFqYu%2BVqSxghWLvF9P3q6%2B%2Bav7lX/7F1K1b17Rp0yboOVDGGHPixAkzYsQI07BhQxMVFWV69%2B5t9u7dGzRnz549plevXiYqKso0bNjQjBgxIuhxFW64UO%2BFhYUmIyPD%2BP1%2BU7duXdOiRQvz3HPPBf33CsXef%2Bzn%2BPXXX3fm2Orrs88%2BMzfddJOJjIw0V111lZk1a1Z1tFjlwoxx%2BdHIAAAANQz3YAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsOz/AFy70w9wyW4qAAAAAElFTkSuQmCC\"/>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-8823104925418689675\">\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">1099.5</td>\n",
" <td class=\"number\">12</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">954.25</td>\n",
" <td class=\"number\">12</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">986.75</td>\n",
" <td class=\"number\">12</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">925.75</td>\n",
" <td class=\"number\">11</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">888.0</td>\n",
" <td class=\"number\">11</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">988.25</td>\n",
" <td class=\"number\">11</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">890.75</td>\n",
" <td class=\"number\">11</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1009.25</td>\n",
" <td class=\"number\">11</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">969.0</td>\n",
" <td class=\"number\">11</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1072.0</td>\n",
" <td class=\"number\">10</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"other\">\n",
" <td class=\"fillremaining\">Other values (3235)</td>\n",
" <td class=\"number\">8879</td>\n",
" <td class=\"number\">94.9%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"missing\">\n",
" <td class=\"fillremaining\">(Missing)</td>\n",
" <td class=\"number\">366</td>\n",
" <td class=\"number\">3.9%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"extreme-8823104925418689675\">\n",
" <p class=\"h4\">Minimum 5 values</p>\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">647.25</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">648.75</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">654.75</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">666.75</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">667.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" <p class=\"h4\">Maximum 5 values</p>\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">1972.5</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1974.75</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1982.25</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">2007.75</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">2039.75</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"</div><div class=\"row variablerow ignore\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4 pp-anchor\" id=\"pp_var_PT08.S2(NMHC)\"><s>PT08.S2(NMHC)</s><br/>\n",
" <small>Highly correlated</small>\n",
" </p>\n",
" </div><div class=\"col-md-3\">\n",
" <p><em>This variable is highly correlated with <a href=\"#pp_var_C6H6(GT)\"><code>C6H6(GT)</code></a> and should be ignored for analysis</em></p>\n",
"</div>\n",
"<div class=\"col-md-6\">\n",
" <table class=\"stats \">\n",
" <tr>\n",
" <th>Correlation</th>\n",
" <td>0.98196</td>\n",
" </tr>\n",
" </table>\n",
"</div>\n",
"</div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4 pp-anchor\" id=\"pp_var_PT08.S3(NOx)\">PT08.S3(NOx)<br/>\n",
" <small>Numeric</small>\n",
" </p>\n",
" </div><div class=\"col-md-6\">\n",
" <div class=\"row\">\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
" <tr>\n",
" <th>Distinct count</th>\n",
" <td>3519</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Unique (%)</th>\n",
" <td>37.6%</td>\n",
" </tr>\n",
" <tr class=\"alert\">\n",
" <th>Missing (%)</th>\n",
" <td>3.9%</td>\n",
" </tr>\n",
" <tr class=\"alert\">\n",
" <th>Missing (n)</th>\n",
" <td>366</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Infinite (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Infinite (n)</th>\n",
" <td>0</td>\n",
" </tr>\n",
" </table>\n",
"\n",
" </div>\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
"\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>835.37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Minimum</th>\n",
" <td>322</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maximum</th>\n",
" <td>2682.8</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Zeros (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"<div class=\"col-md-3 collapse in\" id=\"minihistogram-4632079211006675337\">\n",
" <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAAdZJREFUeJzt3THOElEYhtFviO1AT5hFmLgFExdkZ2Jn6VLsbe3dBYQFMLGwkLFQbEzeCModxv%2BcFsh3SebJZQi5dNM0TdXY4XCoYRhaj2Xh9vt97Xa7pjOfNZ32U9/3VfXjDa/X6zmWwIKcTqcahuHXddPSLIF0XVdVVev1%2Bp8E8uLNx6tf8/ndq7%2BeS1uX66alVfOJsCACgUAgEMxyD/II3LfwJ%2BwgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgeDJHhx3i2sPm3PQ3PLZQSB4uB3kliNB4V7sIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBIKH%2B6nJ/8Q/6S6fHQQCgUDgI9aD8bHsscwSyDRNVVV1Op1%2Be%2Bzb1y%2Btl7N4z19/uOr5n96%2BvNNK7uNynVyum5ZmCWQcx6qqGoZhjvFP3ub93Cu4zTiOtdlsms7sphmyPJ/PdTweq%2B/76rqu9XgWZpqmGsexttttrVZtb5tnCQSWwrdYEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCATfAfQyTeImaHRaAAAAAElFTkSuQmCC\">\n",
"\n",
"</div>\n",
"<div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-4632079211006675337,#minihistogram-4632079211006675337\"\n",
" aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
"</div>\n",
"<div class=\"row collapse col-md-12\" id=\"descriptives-4632079211006675337\">\n",
" <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
" <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-4632079211006675337\"\n",
" aria-controls=\"quantiles-4632079211006675337\" role=\"tab\"\n",
" data-toggle=\"tab\">Statistics</a></li>\n",
" <li role=\"presentation\"><a href=\"#histogram-4632079211006675337\" aria-controls=\"histogram-4632079211006675337\"\n",
" role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
" <li role=\"presentation\"><a href=\"#common-4632079211006675337\" aria-controls=\"common-4632079211006675337\"\n",
" role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
" <li role=\"presentation\"><a href=\"#extreme-4632079211006675337\" aria-controls=\"extreme-4632079211006675337\"\n",
" role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
"\n",
" </ul>\n",
"\n",
" <div class=\"tab-content\">\n",
" <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-4632079211006675337\">\n",
" <div class=\"col-md-4 col-md-offset-1\">\n",
" <p class=\"h4\">Quantile statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr>\n",
" <th>Minimum</th>\n",
" <td>322</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5-th percentile</th>\n",
" <td>482.88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Q1</th>\n",
" <td>657.88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Median</th>\n",
" <td>805.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Q3</th>\n",
" <td>969.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95-th percentile</th>\n",
" <td>1290.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maximum</th>\n",
" <td>2682.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Range</th>\n",
" <td>2360.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Interquartile range</th>\n",
" <td>311.38</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" <div class=\"col-md-4 col-md-offset-2\">\n",
" <p class=\"h4\">Descriptive statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr>\n",
" <th>Standard deviation</th>\n",
" <td>256.82</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Coef of variation</th>\n",
" <td>0.30743</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kurtosis</th>\n",
" <td>2.6775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>835.37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MAD</th>\n",
" <td>195.12</td>\n",
" </tr>\n",
" <tr class=\"\">\n",
" <th>Skewness</th>\n",
" <td>1.1017</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sum</th>\n",
" <td>7510800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Variance</th>\n",
" <td>65954</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Memory size</th>\n",
" <td>73.2 KiB</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-4632079211006675337\">\n",
" <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzt3X9UVXW%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%2BHzg8PNzh3gAAcHmorq5WbGys/Xu4KRGwmsC5y4Lh4eEELAAAmpgTt%2BdwkzsAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADOOPPaPJJM7MdboLP0nR08Od7gIAIECxggUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADAvYgPXyyy/rhhtuUHh4uMLDw5WUlKRPPvnEbq%2BpqdGUKVMUGRmpdu3aafTo0dq/f7/fMfbt26dRo0apXbt2ioyMVFZWlmpra/1q8vPzlZCQoDZt2uiaa67RK6%2B80iTjAwAAgStgA1bnzp31zDPPqKioSEVFRRo6dKhuv/12bd%2B%2BXZI0depULV26VDk5OVq/fr2OHz%2Bu1NRUnTlzRpJ05swZjRw5UidOnND69euVk5OjxYsXa9q0afY59uzZo9tuu02DBg3Sli1b9NhjjykrK0uLFy92ZMwAACAwBFmWZTndCVMiIiI0d%2B5c/fa3v1XHjh319ttva%2BzYsZKkAwcOKDY2VitWrFBKSoo%2B%2BeQTpaamqqysTF6vV5KUk5OjjIwMVVZWKjw8XI888oiWLVumHTt22OeYMGGCvvjiCxUUFFxyv6qrq%2BV2u%2BXz%2BRQeHm520AEkcWau0134SYqeHu50FwAAv4CTv38DdgXrh86cOaOcnBydOHFCSUlJKi4uVl1dnZKTk%2B0ar9er%2BPh4bdiwQZJUUFCg%2BPh4O1xJUkpKimpqalRcXGzX/PAY52qKiopUV1d3wf7U1NSourrabwMAAJePgA5Y27Zt05VXXimXy6UJEyZo6dKluv7661VRUaGQkBC1b9/erz46OloVFRWSpIqKCkVHR/u1t2/fXiEhIT9aEx0drdOnT%2Bvw4cMX7NecOXPkdrvtLTY21sRwAQBAgAjogNW9e3eVlJSosLBQDz74oMaNG6cvv/zygvWWZSkoKMh%2B/cOvL7Xm3BXVht57zowZM%2BTz%2BeytrKzskscEAAACX2unO/BLhISE6J/%2B6Z8kSYmJidq0aZP%2B%2BMc/auzYsaqtrVVVVZXfKlZlZaUGDhwoSfJ4PNq4caPf8aqqqlRXV2evWnk8Hns164fHaN26tTp06HDBfrlcLrlcLiNjBAAAgSegV7DOZ1mWampqlJCQoODgYOXl5dlt5eXlKi0ttQNWUlKSSktLVV5ebtesXLlSLpdLCQkJds0Pj3GuJjExUcHBwU0wIgAAEIgCdgXrscce04gRIxQbG6tjx44pJydHa9euVW5urtxut8aPH69p06apQ4cOioiI0PTp09WrVy/deuutkqTk5GRdf/31Sk9P19y5c/Xdd99p%2BvTpyszMtD9pMGHCBC1YsEDZ2dnKzMxUQUGBXnvtNb333ntODh1NhE89AgB%2BroANWAcPHlR6errKy8vldrt1ww03KDc3V8OGDZMkPf/882rdurXuuusunTx5UrfccovefPNNtWrVSpLUqlUrLV%2B%2BXBMnTtSNN96o0NBQpaWlad68efY54uLitGLFCj300EP685//LK/Xqz/96U%2B68847HRkzAAAIDC3qOVjNFc/B%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%2B3V1dUaNmyYvF6vNm3apBdffFHz5s3T/Pnzm2ScAAAg8LR2ugM/V25urt/rN954Q1FRUSouLtbNN99s72/btq08Hk%2BDx9ixY4dyc3NVWFio/v37S5IWLlyopKQk7dy5U927d9eiRYt06tQpvfnmm3K5XIqPj9dXX32l%2BfPnKzs7W0FBQY03SAAAEJACdgXrfD6fT5IUERHht3/RokWKjIxUz549NX36dB07dsxuKygokNvttsOVJA0YMEBut1sbNmywawYPHiyXy2XXpKSk6MCBA9q7d28jjggAAASqgF3B%2BiHLspSdna2bbrpJ8fHx9v57771XcXFx8ng8Ki0t1YwZM/TFF18oLy9PklRRUaGoqKh6x4uKilJFRYVdc/XVV/u1R0dH221xcXH13l9TU6Oamhr7dXV19S8eIwAACBwtImBNnjxZW7du1fr16/32Z2Zm2l/Hx8era9euSkxM1ObNm9W3b19JavASn2VZfvvPrzl3g/uFLg/OmTNHTz755M8bDAAACHgBf4lwypQpWrZsmdasWaPOnTv/aG3fvn0VHBysXbt2SZI8Ho8OHjxYr%2B7QoUP2KpXH47FXs86prKyU9P9Xss43Y8YM%2BXw%2BeysrK/vJ4wIAAIErYAOWZVmaPHmylixZotWrVzd4qe5827dvV11dnWJiYiRJSUlJ8vl8%2Bvzzz%2B2ajRs3yufzaeDAgXbNunXrVFtba9esXLlSXq%2B33qXDc1wul8LDw/02AABw%2BQjYgDVp0iS98847evfddxUWFqaKigpVVFTo5MmTkqSvv/5aTz31lIqKirR3716tWLFC//Iv/6I%2BffroxhtvlCT16NFDw4cPV2ZmpgoLC1VYWKjMzEylpqaqe/fukr5/jIPL5VJGRoZKS0u1dOlSzZ49m08QAgCACwrYgPXyyy/L5/NpyJAhiomJsbf3339fkhQSEqK//e1vSklJUffu3ZWVlaXk5GStWrVKrVq1so%2BzaNEi9erVS8nJyUpOTtYNN9ygt99%2B2253u93Ky8vT/v37lZiYqIkTJyo7O1vZ2dlNPmYAABAYgiweSd7oqqur5Xa75fP5LuvLhYkzcy9ehJ%2Bt6OnhTncBAJoVJ3//BuwKFgAAQHNFwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQEbsObMmaN%2B/fopLCxMUVFRuuOOO7Rz506/mpqaGk2ZMkWRkZFq166dRo8erf379/vV7Nu3T6NGjVK7du0UGRmprKws1dbW%2BtXk5%2BcrISFBbdq00TXXXKNXXnml0ccHAAAClyMB65133tGpU6d%2B0THy8/M1adIkFRYWKi8vT6dPn1ZycrJOnDhh10ydOlVLly5VTk6O1q9fr%2BPHjys1NVVnzpyRJJ05c0YjR47UiRMntH79euXk5Gjx4sWaNm2afYw9e/botttu06BBg7RlyxY99thjysrK0uLFi39R/wEAQMsVZFmW1dQnjYqKUm1trcaOHavx48frn//5n3/xMQ8dOqSoqCjl5%2Bfr5ptvls/nU8eOHfX2229r7NixkqQDBw4oNjZWK1asUEpKij755BOlpqaqrKxMXq9XkpSTk6OMjAxVVlYqPDxcjzzyiJYtW6YdO3bY55owYYK%2B%2BOILFRQUXFLfqqur5Xa75fP5FB4e/ovHGqgSZ%2BY63YUWrejp4U53AQCaFSd//zqygnXgwAG9/vrrKi8v10033aSePXvqueee06FDh372MX0%2BnyQpIiJCklRcXKy6ujolJyfbNV6vV/Hx8dqwYYMkqaCgQPHx8Xa4kqSUlBTV1NSouLjYrvnhMc7VFBUVqa6u7mf3FwAAtFyOBKzWrVtrzJgxWrZsmfbt26dx48bp9ddfV%2BfOnTVmzBgtX75cP2VhzbIsZWdn66abblJ8fLwkqaKiQiEhIWrfvr1fbXR0tCoqKuya6Ohov/b27dsrJCTkR2uio6N1%2BvRpHT58uMH%2B1NTUqLq62m8DAACXD8dvcvd4PLrllls0ZMgQBQUFqaioSGlpaeratas%2B%2B%2ByzSzrG5MmTtXXrVr333nsXrbUsS0FBQfbrH359qTXnwl9D75W%2BvwHf7XbbW2xs7CWNAwAAtAyOBazDhw/rhRdeUO/evXXjjTeqsrJSH330kb755ht9%2B%2B23Sk1N1b/%2B679e9DhTpkzRsmXLtGbNGnXu3Nne7/F4VFtbq6qqKr/6yspKe0XK4/HYK1XnVFVVqa6u7kdrKisr1bp1a3Xo0KHBPs2YMUM%2Bn8/eysrKLj4hAACgxXAkYP3mN79Rp06d9Morryg9PV1lZWX68MMPNXz4cAUFBenKK6/Uww8/rG%2B%2B%2BeaCx7AsS5MnT9aSJUu0evVqxcXF%2BbUnJCQoODhYeXl59r7y8nKVlpZq4MCBkqSkpCSVlpaqvLzcrlm5cqVcLpcSEhLsmh8e41xNYmKigoODG%2Byby%2BVSeHi43wYAAC4frZ04aXh4uFatWqVBgwZdsCYmJka7du26YPukSZP07rvv6n//938VFhZmrzK53W6FhobK7XZr/PjxmjZtmjp06KCIiAhNnz5dvXr10q233ipJSk5O1vXXX6/09HTNnTtX3333naZPn67MzEw7FE2YMEELFixQdna2MjMzVVBQoNdee%2B2SLkcCAIDLkyOPaTDhQvc/vfHGG8rIyJAknTp1Sr///e/17rvv6uTJk7rlllv00ksv%2Bd0TtW/fPk2cOFGrV69WaGio0tLSNG/ePLlcLrsmPz9fDz30kLZv3y6v16tHHnlEEyZMuOS%2B8piG7/GYhsbFYxoAwJ%2BTv38dCVgPPfSQrr32Wk2ePNlv/5///Gft3r1bzz33XFN3qVERsL5HwGpcBCwA8HfZPQfrww8/1IABA%2BrtT0pK0vvvv%2B9AjwAAAMxxJGAdPny43vOppO/vzbrQs6UAAAAChSMB69prr9Wnn35ab/%2Bnn35a79OAAAAAgcaRTxFOnTpVU6dO1ZEjRzR06FBJ0t/%2B9jc9%2B%2ByzmjdvnhNdAgAAMMaRgJWZmalTp05p9uzZeuKJJyRJnTt31p/%2B9Cf927/9mxNdAgAAMMaRgCV9/wT2KVOmqLy8XKGhobrqqquc6goAAIBRjgWsc2JiYpzuAgAAgFGO3OR%2B6NAh3X///frVr36lNm3aKCQkxG8DAAAIZI6sYGVkZOjrr7/W73//e8XExFzwqewAAACByJGAtW7dOq1bt059%2BvRx4vQAAACNypFLhJ07d2bVCgAAtFiOBKznn39eM2bM0P79%2B504PQAAQKNy5BJhenq6jh07pi5duig8PFzBwcF%2B7ZWVlU50CwAAwAhHAtYzzzzjxGkBAACahCMBa/z48U6cFgAAoEk4cg%2BWJO3du1ezZs1Senq6fUlw5cqV2rFjh1NdAgAAMMKRgPXZZ5%2BpZ8%2Beys/P1wcffKDjx49LkjZv3qzHH3/ciS4BAAAY40jAeuSRRzRr1iytWbPG78ntQ4cOVWFhoRNdAgAAMMaRgLV161b99re/rbc/KipKhw4dcqBHAAAA5jgSsK666ipVVFTU219SUqJOnTo50CMAAABzHAlYd999tx599FEdOnTIfqL7xo0bNX36dN13331OdAkAAMAYRwLW7Nmz5fF4FBMTo%2BPHj%2Bv666/XwIED1a9fP/3hD39woksAAADGOPIcrJCQEL3//vv66quvtHnzZp09e1Z9%2B/bVdddd50R3AAAAjHIkYJ3TrVs3devWzckuAAAAGOdIwPrd7373o%2B1/%2BctfmqgnAAAA5jkSsMrLy/1e19XVafv27Tp27JhuvvlmJ7oEAABgjCMB6%2BOPP6637/Tp03rwwQfVo0cPB3oEAABgjmN/i/B8rVu31vTp0zV37lynuwIAAPCLNJuAJUm7d%2B9WXV2d090AAAD4RRy5RPjwww/7vbYsS%2BXl5Vq2bJnuvfdeJ7oEAABgjCMBq6CgwO/1FVdcoY4dO%2BqZZ55RZmamE10CAAAwxpGA9dlnnzlxWgAAgCbRrO7BAgAAaAkcWcHq16%2Bf/UeeL%2Bbzzz9v5N4AAACY5UjA%2BvWvf61XX31V3bp1U1JSkiSpsLBQO3fu1AMPPCCXy%2BVEtwAAAIxwJGAdPXpUkyZN0uzZs/32z5w5UwcPHtR///d/O9EtAAAAIxy5B%2BuDDz7Q/fffX29/RkaGPvzwQwd6BAAAYI4jAcvlcmnDhg319m/YsOEnXR5ct26dRo0aJa/Xq6CgIH300Ud%2B7RkZGQoKCvLbBgwY4FdTU1OjKVOmKDIyUu3atdPo0aO1f/9%2Bv5p9%2B/Zp1KhRateunSIjI5WVlaXa2tqfMGIAAHA5ceQSYVZWliZMmKAtW7bYgaewsFALFy7UY489dsnHOXHihHr37q37779fd955Z4M1w4cP1xtvvGG/DgkJ8WufOnWqPv74Y%2BXk5KhDhw6aNm2aUlNTVVxcrFatWunMmTMaOXKkOnbsqPXr1%2BvIkSMaN26cLMvSiy%2B%2B%2BDNGDwAAWjpHAtbMmTMVFxenP/7xj3r99dclST169NDChQuVlpZ2yccZMWKERowY8aM1LpdLHo%2BnwTafz6fXXntNb7/9tm699VZJ0jvvvKPY2FitWrVKKSkpWrlypb788kuVlZXJ6/VKkp577jllZGTo6aefVnh4%2BCX3FwAAXB4cew5WWlqaNm7cqOrqalVXV2vjxo0/KVxdqrVr1yoqKkrdunVTZmamKisr7bbi4mLV1dUpOTnZ3uf1ehUfH29fwiwoKFB8fLwdriQpJSVFNTU1Ki4ubvCcNTU19rjObQAA4PLhWMCqrq7Wm2%2B%2Bqccff1xVVVWSpC%2B%2B%2BELl5eXGzjFixAgtWrRIq1ev1nPPPadNmzZp6NChqqmpkSRVVFQoJCRE7du393tfdHS0Kioq7Jro6Gi/9vbt2yskJMSuOd%2BcOXPkdrvtLTY21tiYAABA8%2BfIJcLS0lLdeuutatu2rcrKypSRkaH27dvrgw8%2B0P79%2B/XWW28ZOc/YsWPtr%2BPj45WYmKguXbpo%2BfLlGjNmzAXfZ1mW34NQG3oo6vk1PzRjxgxlZ2fbr6urqwlZAABcRhxZwXrooYeUlpamr7/%2BWm3atLH3jxw5UuvWrWu088bExKhLly7atWuXJMnj8ai2ttZeQTunsrLSXrXyeDz1VqqqqqpUV1dXb2XrHJfLpfDwcL8NAABcPhwJWJs2bdLEiRPrrQB16tTJ6CXC8x05ckRlZWWKiYmRJCUkJCg4OFh5eXl2TXl5uUpLSzVw4EBJUlJSkkpLS/36tXLlSrlcLiUkJDRaXwEAQOBy5BJhSEiIjh8/Xm//rl27FBkZecnHOX78uP7%2B97/br/fs2aOSkhJFREQoIiJCs2bN0p133qmYmBjt3btXjz32mCIjI/Wb3/xGkuR2uzV%2B/HhNmzZNHTp0UEREhKZPn65evXrZnypMTk7W9ddfr/T0dM2dO1ffffedpk%2BfrszMTFamAABAgxxZwRo9erT%2B8z//U6dPn5b0/T1O3377rR599NEfvTfqfEVFRerTp4/69OkjScrOzlafPn30%2BOOPq1WrVtq2bZtuv/12devWTePGjVO3bt1UUFCgsLAw%2BxjPP/%2B87rjjDt1111268cYb1bZtW3388cdq1aqVJKlVq1Zavny52rRpoxtvvFF33XWX7rjjDs2bN8/gjAAAgJYkyLIsq6lP6vP5NHz4cO3atUtHjx5VbGysDhw4oH79%2Bik3N1dXXnllU3epUVVXV8vtdsvn813Wq16JM3Od7kKLVvT0cKe7AADNipO/fx25ROh2u7Vhwwbl5eVp8%2BbNOnv2rPr27auUlJQLfjIPAAAgUDR5wKqrq9Ntt92ml156ScnJyX4P%2BQQAAGgJmvwerODgYG3ZsoWVKgAA0GI5cpP7fffd5/cHmAEAAFoSR%2B7BkqQFCxZo1apVSkxMVLt27fzann32WYd6BQAA8Ms5ErCKi4t1ww03SJK2bt3q18alQwAAEOiaNGDt3r1bcXFx%2Buyzz5rytAAAAE2qSe/B6tq1qw4dOmS/Hjt2rA4ePNiUXQAAAGh0TRqwzn%2Bm6YoVK3TixImm7AIAAECjc%2BRThAAAAC1ZkwasoKCgejexc1M7AABoaZr0JnfLspSRkSGXyyVJOnXqlCZMmFDvMQ1Llixpym4BAAAY1aQBa9y4cX6v77vvvqY8PQAAQJNo0oDF09sBAMDlgJvcAQAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIY16YNGATSexJm5TnfhkhU9PdzpLgBAo2IFCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCM52AFuEB69hEAAJcLVrAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAsIAOWOvWrdOoUaPk9XoVFBSkjz76yK/dsizNmjVLXq9XoaGhGjJkiLZv3%2B5XU1VVpfT0dLndbrndbqWnp%2Bvo0aN%2BNdu2bdPgwYMVGhqqTp066amnnpJlWY0%2BPgAAEJgCOmCdOHFCvXv31oIFCxpsf/bZZzV//nwtWLBAmzZtksfj0bBhw3Ts2DG7Ji0tTSUlJcrNzVVubq5KSkqUnp5ut1dXV2vYsGHyer3atGmTXnzxRc2bN0/z589v9PEBAIDAFNDPwRoxYoRGjBjRYJtlWXrhhRc0c%2BZMjRkzRpL01ltvKTo6Wu%2B%2B%2B64eeOAB7dixQ7m5uSosLFT//v0lSQsXLlRSUpJ27typ7t27a9GiRTp16pTefPNNuVwuxcfH66uvvtL8%2BfOVnZ2toKCgJhsvAAAIDAG9gvVj9uzZo4qKCiUnJ9v7XC6XBg8erA0bNkiSCgoK5Ha77XAlSQMGDJDb7farGTx4sFwul12TkpKiAwcOaO/evU0zGAAAEFBabMCqqKiQJEVHR/vtj46OttsqKioUFRVV771RUVF%2BNQ0d44fnOF9NTY2qq6v9NgAAcPlosQHrnPMv4VmW5bevoUt8F6s5d4P7hS4Pzpkzx75p3u12KzY29mf3HwAABJ4WG7A8Ho%2Bk%2BqtMlZWV9gqUx%2BPRwYMH67330KFDfjUNHUOqvzp2zowZM%2BTz%2BeytrKzslw0GAAAElBYbsOLi4uTxeJSXl2fvq62tVX5%2BvgYOHChJSkpKks/n0%2Beff27XbNy4UT6fz69m3bp1qq2ttWtWrlwpr9erq6%2B%2BusFzu1wuhYeH%2B20AAODyEdAB6/jx4yopKVFJSYmk729sLykp0b59%2BxQUFKSpU6dq9uzZWrp0qUpLS5WRkaG2bdsqLS1NktSjRw8NHz5cmZmZKiwsVGFhoTIzM5Wamqru3btL%2Bv4xDi6XSxkZGSotLdXSpUs1e/ZsPkEIAAAuKKAf01BUVKRf//rX9uvs7GxJ0rhx4/Tmm2/q4Ycf1smTJzVx4kRVVVWpf//%2BWrlypcLCwuz3LFq0SFlZWfanDUePHu33XC232628vDxNmjRJiYmJat%2B%2BvbKzs%2B1zAQAAnC/I4pHkja66ulput1s%2Bn8/45cLEmblGjwc0haKnhzvdBQCXgcb8/XsxAX2JEAAAoDkiYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAsBYdsGbNmqWgoCC/zePx2O2WZWnWrFnyer0KDQ3VkCFDtH37dr9jVFVVKT09XW63W263W%2Bnp6Tp69GhTDwUAAARVh3DNAAAPQUlEQVSQFh2wJKlnz54qLy%2B3t23bttltzz77rObPn68FCxZo06ZN8ng8GjZsmI4dO2bXpKWlqaSkRLm5ucrNzVVJSYnS09OdGAoAAAgQrZ3uQGNr3bq136rVOZZl6YUXXtDMmTM1ZswYSdJbb72l6Ohovfvuu3rggQe0Y8cO5ebmqrCwUP3795ckLVy4UElJSdq5c6e6d%2B/epGMBAACBocWvYO3atUter1dxcXG6%2B%2B67tXv3bknSnj17VFFRoeTkZLvW5XJp8ODB2rBhgySpoKBAbrfbDleSNGDAALndbrumITU1NaqurvbbAADA5aNFB6z%2B/fvrr3/9qz799FMtXLhQFRUVGjhwoI4cOaKKigpJUnR0tN97oqOj7baKigpFRUXVO25UVJRd05A5c%2BbY92y53W7FxsYaHBUAAGjuWnTAGjFihO6880716tVLt956q5YvXy7p%2B0uB5wQFBfm9x7Isv33ntzdUc74ZM2bI5/PZW1lZ2S8dCgAACCAtOmCdr127durVq5d27dpl35d1/kpUZWWlvarl8Xh08ODBesc5dOhQvZWvH3K5XAoPD/fbAADA5eOyClg1NTXasWOHYmJiFBcXJ4/Ho7y8PLu9trZW%2Bfn5GjhwoCQpKSlJPp9Pn3/%2BuV2zceNG%2BXw%2BuwYAAOB8LfpThNOnT9eoUaP0q1/9SpWVlfqv//ovVVdXa9y4cQoKCtLUqVM1e/Zsde3aVV27dtXs2bPVtm1bpaWlSZJ69Oih4cOHKzMzU6%2B%2B%2Bqok6Xe/%2B51SU1P5BCEAALigFh2w9u/fr3vuuUeHDx9Wx44dNWDAABUWFqpLly6SpIcfflgnT57UxIkTVVVVpf79%2B2vlypUKCwuzj7Fo0SJlZWXZnzYcPXq0FixY4Mh4AABAYAiyLMtyuhMtXXV1tdxut3w%2Bn/H7sRJn5ho9HtAUip4e7nQXAFwGGvP378VcVvdgAQAANAUCFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGBYi36SO4DmKdAekMuDUQH8VKxgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAw1o73QEAaO4SZ%2BY63YWfpOjp4U53AbjssYIFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhvEpwkv00ksvae7cuSovL1fPnj31wgsvaNCgQU53CwDqCaRPPfKJR7RUrGBdgvfff19Tp07VzJkztWXLFg0aNEgjRozQvn37nO4aAABohghYl2D%2B/PkaP368/v3f/109evTQCy%2B8oNjYWL388stOdw0AADRDXCK8iNraWhUXF%2BvRRx/125%2BcnKwNGzY0%2BJ6amhrV1NTYr30%2BnySpurraeP/O1JwwfkwAaCp9pi92ugs/Sf7jw5zuAn6Cc793Lctq8nMTsC7i8OHDOnPmjKKjo/32R0dHq6KiosH3zJkzR08%2B%2BWS9/bGxsY3SRwBA03A/53QP8HMcO3ZMbre7Sc9JwLpEQUFBfq8ty6q375wZM2YoOzvbfn327Fl999136tChwwXfE6iqq6sVGxursrIyhYeHO92dFo/5bnrMedNivptWS59vy7J07Ngxeb3eJj83AesiIiMj1apVq3qrVZWVlfVWtc5xuVxyuVx%2B%2B6666qpG62NzEB4e3iL/42yumO%2Bmx5w3Lea7abXk%2BW7qlatzuMn9IkJCQpSQkKC8vDy//Xl5eRo4cKBDvQIAAM0ZK1iXIDs7W%2Bnp6UpMTFRSUpL%2B8pe/aN%2B%2BfZowYYLTXQMAAM1Qq1mzZs1yuhPNXXx8vDp06KDZs2dr3rx5OnnypN5%2B%2B2317t3b6a41C61atdKQIUPUujV5vSkw302POW9azHfTYr4bR5DlxGcXAQAAWjDuwQIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCzUM2vWLAUFBfltHo/HbrcsS7NmzZLX61VoaKiGDBmi7du3%2Bx2jqqpK6enpcrvdcrvdSk9P19GjR5t6KM3SunXrNGrUKHm9XgUFBemjjz7yazc1v9u2bdPgwYMVGhqqTp066amnnnLk73E57WLznZGRUe/7fcCAAX41NTU1mjJliiIjI9WuXTuNHj1a%2B/fv96vZt2%2BfRo0apXbt2ikyMlJZWVmqra1t9PE1N3PmzFG/fv0UFhamqKgo3XHHHdq5c6dfjan5zM/PV0JCgtq0aaNrrrlGr7zySqOPrzm6lDkfMmRIve/zu%2B%2B%2B26%2BGnytmEbDQoJ49e6q8vNzetm3bZrc9%2B%2Byzmj9/vhYsWKBNmzbJ4/Fo2LBhOnbsmF2TlpamkpIS5ebmKjc3VyUlJUpPT3diKM3OiRMn1Lt3by1YsKDBdhPzW11drWHDhsnr9WrTpk168cUXNW/ePM2fP7/Rx9fcXGy%2BJWn48OF%2B3%2B8rVqzwa586daqWLl2qnJwcrV%2B/XsePH1dqaqrOnDkjSTpz5oxGjhypEydOaP369crJydHixYs1bdq0Rh1bc5Sfn69JkyapsLBQeXl5On36tJKTk3XixP//w/Qm5nPPnj267bbbNGjQIG3ZskWPPfaYsrKytHhxYP3xaBMuZc4lKTMz0%2B/7/NVXX/Vr5%2BeKYRZwnieeeMLq3bt3g21nz561PB6P9cwzz9j7Tp06ZbndbuuVV16xLMuyvvzyS0uSVVhYaNcUFBRYkqz/%2B7//a9zOBxhJ1tKlS%2B3Xpub3pZdestxut3Xq1Cm7Zs6cOZbX67XOnj3b2MNqts6fb8uyrHHjxlm33377Bd9z9OhRKzg42MrJybH3ffvtt9YVV1xh5ebmWpZlWStWrLCuuOIK69tvv7Vr3nvvPcvlclk%2Bn8/wKAJLZWWlJcnKz8%2B3LMvcfD788MPWdddd53euBx54wBowYEBjD6nZO3/OLcuyBg8ebP3Hf/zHBd/DzxXzWMFCg3bt2iWv16u4uDjdfffd2r17t6Tv/6%2BxoqJCycnJdq3L5dLgwYO1YcMGSVJBQYHcbrf69%2B9v1wwYMEBut9uuQcNMzW9BQYEGDx7s9zcxU1JSdODAAe3du7dpBhNA1q5dq6ioKHXr1k2ZmZmqrKy024qLi1VXV%2Bf3b%2BL1ehUfH%2B833/Hx8X5/UDYlJUU1NTUqLi5uuoE0Qz6fT5IUEREhydx8FhQU%2BB3jXE1RUZHq6uoadUzN3flzfs6iRYsUGRmpnj17avr06X6r4vxcMY%2BAhXr69%2B%2Bvv/71r/r000%2B1cOFCVVRUaODAgTpy5Ij9R6/P/0PX0dHRdltFRYWioqLqHTcqKqreH82GP1PzW1FR0eAxfngOfG/EiBFatGiRVq9ereeee06bNm3S0KFDVVNTI%2Bn7%2BQoJCVH79u393nf%2Bv8n5892%2BfXuFhIRc1vNtWZays7N10003KT4%2BXpK5%2BbzQ9/jp06d1%2BPDhxhpSs9fQnEvSvffeq/fee09r167VH/7wBy1evFhjxoyx2/m5Yh7PxUc9I0aMsL/u1auXkpKSdO211%2Bqtt96yb/4NCgrye49lWX77zm9vqAYXZmJ%2BGzrGhd57ORs7dqz9dXx8vBITE9WlSxctX77c7xfQ%2Bfiev7jJkydr69atWr9%2B/UVr%2BR4340JznpmZaX8dHx%2Bvrl27KjExUZs3b1bfvn0lMeemsYKFi2rXrp169eqlXbt22Z8mPP//ViorK%2B3/k/F4PDp48GC94xw6dKje//3An6n59Xg8DR5Dqr86Bn8xMTHq0qWLdu3aJen7uaytrVVVVZVf3fn/JufPd1VVlerq6i7b%2BZ4yZYqWLVumNWvWqHPnzvZ%2BU/N5oe/x1q1bq0OHDo0xpGbvQnPekL59%2Byo4ONjv%2B5yfK2YRsHBRNTU12rFjh2JiYhQXFyePx6O8vDy7vba2Vvn5%2BRo4cKAkKSkpST6fT59//rlds3HjRvl8PrsGDTM1v0lJSVq3bp3fx9pXrlwpr9erq6%2B%2BumkGE6COHDmisrIyxcTESJISEhIUHBzs929SXl6u0tJSv/kuLS1VeXm5XbNy5Uq5XC4lJCQ07QAcZlmWJk%2BerCVLlmj16tWKi4vzazc1n0lJSX7HOFeTmJio4ODgxhpes3SxOW/I9u3bVVdXZ3%2Bf83OlETT9ffVo7qZNm2atXbvW2r17t1VYWGilpqZaYWFh1t69ey3LsqxnnnnGcrvd1pIlS6xt27ZZ99xzjxUTE2NVV1fbxxg%2BfLh1ww03WAUFBVZBQYHVq1cvKzU11akhNSvHjh2ztmzZYm3ZssWSZM2fP9/asmWL9c0331iWZWZ%2Bjx49akVHR1v33HOPtW3bNmvJkiVWeHi4NW/evCYfr9N%2BbL6PHTtmTZs2zdqwYYO1Z88ea82aNVZSUpLVqVMnv/meMGGC1blzZ2vVqlXW5s2braFDh1q9e/e2Tp8%2BbVmWZZ0%2BfdqKj4%2B3brnlFmvz5s3WqlWrrM6dO1uTJ092atiOefDBBy23222tXbvWKi8vt7d//OMfdo2J%2Bdy9e7fVtm1b66GHHrK%2B/PJL67XXXrOCg4Ot//mf/2nyMTvtYnP%2B97//3XryySetTZs2WXv27LGWL19uXXfddVafPn3sObcsfq6YRsBCPWPHjrViYmKs4OBgy%2Bv1WmPGjLG2b99ut589e9Z64oknLI/HY7lcLuvmm2%2B2tm3b5neMI0eOWPfee68VFhZmhYWFWffee69VVVXV1ENpltasWWNJqreNGzfOsixz87t161Zr0KBBlsvlsjwejzVr1qzL8qPUPzbf//jHP6zk5GSrY8eOVnBwsPWrX/3KGjdunLVv3z6/Y5w8edKaPHmyFRERYYWGhlqpqan1ar755htr5MiRVmhoqBUREWFNnjzZ7%2BPsl4uG5lqS9cYbb9g1puZz7dq1Vp8%2BfayQkBDr6quvtl5%2B%2BeWmGGKzc7E537dvn3XzzTdbERERVkhIiHXttddaWVlZ1pEjR/yOw88Vs4Isi0ewAgAAmMQ9WAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADPt/V/p2og2KmFMAAAAASUVORK5CYII%3D\"/>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-4632079211006675337\">\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">829.5</td>\n",
" <td class=\"number\">11</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">683.25</td>\n",
" <td class=\"number\">10</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">866.25</td>\n",
" <td class=\"number\">10</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">844.5</td>\n",
" <td class=\"number\">9</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">815.5</td>\n",
" <td class=\"number\">9</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">793.0</td>\n",
" <td class=\"number\">9</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">696.75</td>\n",
" <td class=\"number\">9</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">767.0</td>\n",
" <td class=\"number\">9</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">826.5</td>\n",
" <td class=\"number\">9</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">911.5</td>\n",
" <td class=\"number\">9</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"other\">\n",
" <td class=\"fillremaining\">Other values (3508)</td>\n",
" <td class=\"number\">8897</td>\n",
" <td class=\"number\">95.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"missing\">\n",
" <td class=\"fillremaining\">(Missing)</td>\n",
" <td class=\"number\">366</td>\n",
" <td class=\"number\">3.9%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"extreme-4632079211006675337\">\n",
" <p class=\"h4\">Minimum 5 values</p>\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">322.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">324.5</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">325.25</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">328.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">329.75</td>\n",
" <td class=\"number\">2</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" <p class=\"h4\">Maximum 5 values</p>\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">2327.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">2330.75</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">2541.5</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">2559.25</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">2682.75</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"</div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4 pp-anchor\" id=\"pp_var_PT08.S4(NO2)\">PT08.S4(NO2)<br/>\n",
" <small>Numeric</small>\n",
" </p>\n",
" </div><div class=\"col-md-6\">\n",
" <div class=\"row\">\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
" <tr>\n",
" <th>Distinct count</th>\n",
" <td>4408</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Unique (%)</th>\n",
" <td>47.1%</td>\n",
" </tr>\n",
" <tr class=\"alert\">\n",
" <th>Missing (%)</th>\n",
" <td>3.9%</td>\n",
" </tr>\n",
" <tr class=\"alert\">\n",
" <th>Missing (n)</th>\n",
" <td>366</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Infinite (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Infinite (n)</th>\n",
" <td>0</td>\n",
" </tr>\n",
" </table>\n",
"\n",
" </div>\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
"\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>1456.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Minimum</th>\n",
" <td>551</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maximum</th>\n",
" <td>2775</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Zeros (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"<div class=\"col-md-3 collapse in\" id=\"minihistogram6324860309995435255\">\n",
" <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAAeNJREFUeJzt3D2qE1EcxuH/XGwn6UNmEYJbEFyQ3QU7S5dib2vvLhKygAwWFmYsNDYXXxIh85E8T5kwnBM4P84wH2mGYRhqZPv9vrquG3tYFm6329V2ux11zFejjvZH27ZV9fsHr1arKabAghyPx%2Bq67u%2B6GdMkgTRNU1VVq9XqrgN58/zl6mO%2BfXx3g5nch/O6GdPT6CPCgggEAoFAIBAIBAKBQCCY5DLvUv3PZVuWzQ4CgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoHAw4oz4z32ebGDQCAQCB72FMu7HVzCDgKBQCAQCAQCgUAgEAgEAoFAcBf3QdzT4FbsIBAIBIK7OMV6dNeeYnr693J2EAgEAoFAIBAIBAKBQCAQCAQCgUDgRuED8tdCl5tdIB48ZE5mFwjz9Ki7ziSBDMNQVVXH4/HFdz9/fB97OtzI6/efrz7m64e3Lz47r5PzuhnTJIH0fV9VVV3XTTE8M7b%2B9O/v%2Br6v9Xo93mSqqhkmyPJ0OtXhcKi2batpmrGHZ2GGYai%2B72uz2dTT07gXXicJBJbCfRAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEgl%2BuH1IdEz/5xwAAAABJRU5ErkJggg%3D%3D\">\n",
"\n",
"</div>\n",
"<div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives6324860309995435255,#minihistogram6324860309995435255\"\n",
" aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
"</div>\n",
"<div class=\"row collapse col-md-12\" id=\"descriptives6324860309995435255\">\n",
" <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
" <li role=\"presentation\" class=\"active\"><a href=\"#quantiles6324860309995435255\"\n",
" aria-controls=\"quantiles6324860309995435255\" role=\"tab\"\n",
" data-toggle=\"tab\">Statistics</a></li>\n",
" <li role=\"presentation\"><a href=\"#histogram6324860309995435255\" aria-controls=\"histogram6324860309995435255\"\n",
" role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
" <li role=\"presentation\"><a href=\"#common6324860309995435255\" aria-controls=\"common6324860309995435255\"\n",
" role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
" <li role=\"presentation\"><a href=\"#extreme6324860309995435255\" aria-controls=\"extreme6324860309995435255\"\n",
" role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
"\n",
" </ul>\n",
"\n",
" <div class=\"tab-content\">\n",
" <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles6324860309995435255\">\n",
" <div class=\"col-md-4 col-md-offset-1\">\n",
" <p class=\"h4\">Quantile statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr>\n",
" <th>Minimum</th>\n",
" <td>551</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5-th percentile</th>\n",
" <td>883.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Q1</th>\n",
" <td>1226.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Median</th>\n",
" <td>1462.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Q3</th>\n",
" <td>1673.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95-th percentile</th>\n",
" <td>2029.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maximum</th>\n",
" <td>2775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Range</th>\n",
" <td>2224</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Interquartile range</th>\n",
" <td>446.88</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" <div class=\"col-md-4 col-md-offset-2\">\n",
" <p class=\"h4\">Descriptive statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr>\n",
" <th>Standard deviation</th>\n",
" <td>346.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Coef of variation</th>\n",
" <td>0.23775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kurtosis</th>\n",
" <td>0.077991</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>1456.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MAD</th>\n",
" <td>273.63</td>\n",
" </tr>\n",
" <tr class=\"\">\n",
" <th>Skewness</th>\n",
" <td>0.20536</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sum</th>\n",
" <td>13092000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Variance</th>\n",
" <td>119860</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Memory size</th>\n",
" <td>73.2 KiB</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram6324860309995435255\">\n",
" <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzt3XtwVGWe//FPC0kTs0kLxqTTEDFagEoYVi4DQTGAEohc1sEtUTRLXDfKSGAw8GMEahSdEVwFdGdZL8N6F0Vd0MWCicQLQSpB7nIdRAEJkjaAoQMMJAGe3x/8OD%2BbBEV5kpMO71fVqUqf8/Q53%2Bec6uST51zaY4wxAgAAgDUXuV0AAABAU0PAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlERuwpk%2Bfru7duysuLk6JiYm69dZbtW3btrA2ffr0kcfjCZvuuOOOsDYVFRXKzs6Wz%2BeTz%2BdTdna2Dh48GNZm48aNysjIUExMjFq3bq3HHntMxph67yMAAIhMzd0u4JcqKirS6NGj1b17dx0/flxTpkxRZmamtmzZotjYWKddbm6uHnvsMed1TExM2HpGjBihPXv2qKCgQJJ03333KTs7Wx988IEkqbKyUv3791ffvn21atUqffnll8rJyVFsbKzGjx9/TrWePHlSe/fuVVxcnDwez/l2HQAAnANjjA4dOqRAIKCLLmrgMSXTRJSXlxtJpqioyJmXkZFhfve73531PVu2bDGSzIoVK5x5JSUlRpL529/%2BZowx5tlnnzU%2Bn88cO3bMaTN9%2BnQTCATMyZMnz6m20tJSI4mJiYmJiYnJham0tPTnxorzFrEjWGcKhUKSpFatWoXNnzt3rt544w0lJSUpKytLjzzyiOLi4iRJJSUl8vl86tGjh9O%2BZ8%2Be8vl8Ki4uVocOHVRSUqKMjAx5vV6nzYABAzRp0iTt2rVLqamptWqpqqpSVVWV89r8v9OJpaWlio%2BPt9dpAABwVpWVlUpJSXH%2B7jekJhGwjDHKz8/XDTfcoLS0NGf%2BXXfdpdTUVPn9fm3atEmTJk3SF198ocLCQklSMBhUYmJirfUlJiYqGAw6ba644oqw5UlJSc6yugLW9OnT9eijj9aaHx8fT8ACAKCBuXF5TpMIWHl5edqwYYOWL18eNj83N9f5OS0tTe3atVO3bt20du1adenSRVLdO90YEzb/zDanR6TOdsAmTZqk/Px85/XpBA0AAC4MER%2BwxowZo4ULF2rZsmVq06bNj7bt0qWLoqKitH37dnXp0kV%2Bv1/fffddrXb79u1zRqn8fr8zmnVaeXm5pP8/knUmr9cbdkoRAABcWCL2MQ3GGOXl5WnBggX65JNP6jxVd6bNmzerpqZGycnJkqT09HSFQiGtXLnSafP5558rFAqpV69eTptly5apurraabNkyRIFAoFapw4BAAAkyWNMZD7Q6YEHHtCbb76p//3f/1WHDh2c%2BT6fTzExMfr66681d%2B5c3XLLLUpISNCWLVs0fvx4xcTEaNWqVWrWrJkkKSsrS3v37tULL7wg6dRjGtq2bes8piEUCqlDhw7q16%2BfJk%2BerO3btysnJ0cPP/zwOT%2BmobKyUj6fT6FQiGuwAABoIG7%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%2B52AQDs6DalwO0SAAD/DyNYAAAAlhGwAAAALCNgAQAAWBaxAWv69Onq3r274uLilJiYqFtvvVXbtm0La1NVVaUxY8YoISFBsbGxGjp0qPbs2RPWZvfu3RoyZIhiY2OVkJCgsWPHqrq6OqxNUVGRunbtqhYtWujKK6/U888/X%2B/9AwAAkStiA1ZRUZFGjx6tFStWqLCwUMePH1dmZqaOHDnitBk3bpzee%2B89zZs3T8uXL9fhw4c1ePBgnThxQpJ04sQJDRo0SEeOHNHy5cs1b948zZ8/X%2BPHj3fWsXPnTt1yyy3q3bu31q1bp8mTJ2vs2LGaP39%2Bg/cZAABEBo8xxrhdhA379u1TYmKiioqKdOONNyoUCumyyy7T66%2B/ruHDh0uS9u7dq5SUFC1evFgDBgzQX//6Vw0ePFilpaUKBAKSpHnz5iknJ0fl5eWKj4/X73//ey1cuFBbt251tjVq1Ch98cUXKikpOafaKisr5fP5FAqFFB8fb7/zgLiLsD6tfnyg2yUA%2BAXc/PsbsSNYZwqFQpKkVq1aSZLWrFmjmpoaZWZmOm0CgYDS0tJUXFwsSSopKVFaWpoTriRpwIABqqqq0po1a5w2P1zH6TarV69WTU1NvfYJAABEpibxHCxjjPLz83XDDTcoLS1NkhQMBhUdHa2WLVuGtU1KSlIwGHTaJCUlhS1v2bKloqOjf7RNUlKSjh8/rv379ys5OblWPVVVVaqqqnJeV1ZWnn8nAQBAxGgSI1h5eXnasGGD3nrrrZ9sa4yRx%2BNxXv/w53Ntc/qsal3vlU5dgO/z%2BZwpJSXlnPoBAACahogPWGPGjNHChQv16aefqk2bNs58v9%2Bv6upqVVRUhLUvLy93RqT8fr8zUnVaRUWFampqfrRNeXm5mjdvrksvvbTOmiZNmqRQKORMpaWl591PAAAQOSI2YBljlJeXpwULFuiTTz5Rampq2PKuXbsqKipKhYWFzryysjJt2rRJvXr1kiSlp6dr06ZNKisrc9osWbJEXq9XXbt2ddr8cB2n23Tr1k1RUVF11ub1ehUfHx82AQCAC0fEBqzRo0frjTfe0Jtvvqm4uDgFg0EFg0EdPXpUkuTz%2BXTvvfdq/Pjx%2Bvjjj7Vu3Trdfffd6tSpk26%2B%2BWZJUmZmpq699lplZ2dr3bp1%2BvjjjzVhwgTl5uY6oWjUqFH65ptvlJ%2Bfr61bt%2Bqll17Siy%2B%2BqAkTJrjWdwAA0LhFbMB67rnnFAqF1KdPHyUnJzvT22%2B/7bR5%2Bumndeutt%2Br222/X9ddfr4svvlgffPCBmjVrJklq1qyZFi1apBYtWuj666/X7bffrltvvVUzZsxw1pGamqrFixdr6dKl%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%2B%2B2HLc3Jy5PF4wqaePXuGtamqqtKYMWOUkJCg2NhYDR06VHv27Alrs3v3bg0ZMkSxsbFKSEjQ2LFjVV1dXe/9AwAAkcmVgPXGG2/o2LFj572eI0eOqHPnzpo9e/ZZ2wwcOFBlZWXOtHjx4rDl48aN03vvvad58%2BZp%2BfLlOnz4sAYPHqwTJ05Ikk6cOKFBgwbpyJEjWr58uebNm6f58%2Bdr/Pjx510/AABompq7sdH8/Hzl5eVp%2BPDhuvfee/XrX//6F60nKytLWVlZP9rG6/XK7/fXuSwUCunFF1/U66%2B/rptvvlnSqfCXkpKijz76SAMGDNCSJUu0ZcsWlZaWKhAISJJmzpypnJwcPf7444qPj/9FtQMAgKbLlRGsvXv36qWXXlJZWZluuOEGdezYUTNnztS%2Bffusb2vp0qVKTExU%2B/btlZubq/LycmfZmjVrVFNTo8zMTGdeIBBQWlqaiouLJUklJSVKS0tzwpUkDRgwQFVVVVqzZk2d26yqqlJlZWXYBAAALhyuBKzmzZtr2LBhWrhwoXbv3q2RI0fqpZdeUps2bTRs2DAtWrRIxpjz3k5WVpbmzp2rTz75RDNnztSqVavUr18/VVVVSZKCwaCio6PVsmXLsPclJSUpGAw6bZKSksKWt2zZUtHR0U6bM02fPl0%2Bn8%2BZUlJSzrsvAAAgcrh%2Bkbvf79dNN92kPn36yOPxaPXq1RoxYoTatWunzz777LzWPXz4cA0aNEhpaWkaMmSI/vrXv%2BrLL7/UokWLfvR9xhh5PB7n9Q9/PlubH5o0aZJCoZAzlZaWnlc/AABAZHEtYO3fv1/PPPOMOnfurOuvv17l5eV6//339c033%2Bjbb7/V4MGD9S//8i9Wt5mcnKy2bdtq%2B/btkk6Fu%2BrqalVUVIS1Ky8vd0at/H5/rZGqiooK1dTU1BrZOs3r9So%2BPj5sAgAAFw5XAtZvfvMbtW7dWs8//7yys7NVWlqqd999VwMHDpTH49E//MM/aOLEifrmm2%2BsbvfAgQMqLS1VcnKyJKlr166KiopSYWGh06asrEybNm1Sr169JEnp6enatGmTysrKnDZLliyR1%2BtV165drdYHAACaBlfuIoyPj9dHH32k3r17n7VNcnKyM9J0NocPH9ZXX33lvN65c6fWr1%2BvVq1aqVWrVpo6dapuu%2B02JScna9euXZo8ebISEhL0m9/8RpLk8/l07733avz48br00kvVqlUrTZgwQZ06dXLuKszMzNS1116r7OxsPfXUU/r%2B%2B%2B81YcIE5ebmMjIFAADq5ErAevXVV3%2Byjcfj0VVXXfWjbVavXq2%2Bffs6r/Pz8yVJI0eO1HPPPaeNGzfqtdde08GDB5WcnKy%2Bffvq7bffVlxcnPOep59%2BWs2bN9ftt9%2Buo0eP6qabbtIrr7yiZs2aSZKaNWumRYsW6YEHHtD111%2BvmJgYjRgxQjNmzPglXQcAABcAj7Fxu97P9OCDD%2Bqqq65SXl5e2Pz/%2Bq//0o4dOzRz5syGLqleVVZWyufzKRQKMeqFetNtSoHbJTRZqx8f6HYJAH4BN//%2BunIN1rvvvlvrK2ukU9c7vf322y5UBAAAYI8rAWv//v21nj0lnbo2a//%2B/S5UBAAAYI8rAeuqq67Shx9%2BWGv%2Bhx9%2BqNTUVBcqAgAAsMeVi9zHjRuncePG6cCBA%2BrXr58k6eOPP9aTTz7JxeMAACDiuRKwcnNzdezYMU2bNk2PPPKIJKlNmzb685//rH/91391oyQAAABrXAlYkjRmzBiNGTNGZWVliomJ0SWXXOJWKQAAAFa5FrBOO/1UdQAAgKbClYvc9%2B3bp3vuuUeXX365WrRooejo6LAJAAAgkrkygpWTk6Ovv/5a/%2Bf//B8lJyfL4/G4UQYAnJNIe4grD0YF3OdKwFq2bJmWLVum6667zo3NAwAA1CtXThG2adOGUSsAANBkuRKwnn76aU2aNEl79uxxY/MAAAD1ypVThNnZ2Tp06JDatm2r%2BPh4RUVFhS0vLy93oywAAAArXAlYTzzxhBubBQAAaBCuBKx7773Xjc0CAAA0CFeuwZKkXbt2aerUqcrOznZOCS5ZskRbt251qyQAAAArXAlYn332mTp27KiioiK98847Onz4sCRp7dq1evjhh90oCQAAwBpXAtbvf/97TZ06VZ9%2B%2BmnYk9v79eunFStWuFESAACANa4ErA0bNuif//mfa81PTEzUvn37XKgIAADAHlcC1iWXXKJgMFhr/vr169W6dWsXKgIAALDHlYB1xx136KGHHtK%2BffucJ7p//vnnmjBhgu6%2B%2B243SgIAALDGlYA1bdo0%2Bf1%2BJScn6/Dhw7r22mvVq1cvde/eXX/4wx/cKAkAAMAaV56DFR0drbfffltffvml1q5dq5MnT6pLly66%2Buqr3SgHAADAKlcC1mnt27dX%2B/bt3SwBAADAOlcC1n333fejy//yl780UCUAAAD2uRKwysrKwl7X1NRo8%2BbNOnTokG688UY3SgIAALDGlYD1wQcf1Jp3/Phx/fa3v9U111zjQkVAbd2mFLhdAgAgQrn2XYRnat68uSZMmKCnnnrK7VIAAADOS6MJWJK0Y8cO1dTUuF0GAADAeXHlFOHEiRPDXhtjVFZWpoULF%2Bquu%2B5yoyQAAABrXAlYJSUlYa8vuugiXXbZZXriiSeUm5vrRkkAAADWuBKwPvvsMzc2CwAA0CAa1TVYAAAATYErI1jdu3d3vuT5p6xcubKeqwEAALDLlYDVt29fvfDCC2rfvr3S09MlSStWrNC2bdt0//33y%2Bv1ulEWAACAFa4ErIMHD2r06NGaNm1a2PwpU6bou%2B%2B%2B03//93%2B7URYAAIAVrlyD9c477%2Biee%2B6pNT8nJ0fvvvuuCxUBAADY40rA8nq9Ki4urjW/uLiY04MAACDiuXKKcOzYsRo1apTWrVunnj17Sjp1DdacOXM0efJkN0oCAACwxpWANWXKFKWmpuo//uM/9NJLL0mSrrnmGs2ZM0cjRoxwoyQAAABrXAlYkjRixAjCFAAAaJJce9BoZWWlXnnlFT388MOqqKiQJH3xxRcqKytzqyQAAAArXBnB2rRpk26%2B%2BWZdfPHFKi0tVU5Ojlq2bKl33nlHe/bs0auvvupGWQAAAFa4MoL14IMPasSIEfr666/VokULZ/6gQYO0bNkyN0oCAACwxpURrFWrVum5556r9XU5rVu35hQhAACIeK6MYEVHR%2Bvw4cO15m/fvl0JCQkuVAQAAGCPKwFr6NCh%2BuMf/6jjx49Lkjwej7799ls99NBDGjZsmBslAQAAWONKwJo5c6b27t0rv9%2Bvo0ePql%2B/frryyivVokWLWt9PCAAAEGlcuQbL5/OpuLhYhYWFWrt2rU6ePKkuXbpowIABta7LAgAAiDQNHrBqamp0yy236Nlnn1VmZqYyMzMbugQAAIB61eCnCKOiorRu3TpGqgAAQJPlyjVYd999t15%2B%2BWU3Ng0AAFDvXPuqnNmzZ6tHjx4aPXq0Jk6cGDadq2XLlmnIkCEKBALyeDx6//33w5YbYzR16lQFAgHFxMSoT58%2B2rx5c1ibiooKZWdny%2BfzyefzKTs7WwcPHgxrs3HjRmVkZCgmJkatW7fWY489JmPML%2B88AABo0ly5yH3NmjX61a9%2BJUnasGFD2LKfc%2BrwyJEj6ty5s%2B655x7ddttttZY/%2BeSTmjVrll555RW1b99ef/rTn9S/f39t27ZNcXFxkk596fSePXtUUFAgSbrvvvuUnZ2tDz74QNKp70zs37%2B/%2Bvbtq1WrVunLL79UTk6OYmNjNX78%2BF/UfwAA0LQ1aMDasWOHUlNT9dlnn1lZX1ZWlrKysupcZozRM888oylTpjjP1nr11VeVlJSkN998U/fff7%2B2bt2qgoICrVixQj169JAkzZkzR%2Bnp6dq2bZs6dOiguXPn6tixY3rllVfk9XqVlpamL7/8UrNmzVJ%2Bfj7XkgEAgFoa9BRhu3bttG/fPuf18OHD9d1339XLtnbu3KlgMBh2l6LX61VGRoaKi4slSSUlJfL5fE64kqSePXs6j5E43SYjI0Ner9dpM2DAAO3du1e7du2ql9oBAEBka9CAdeZ1S4sXL9aRI0fqZVvBYFCSlJSUFDY/KSnJWRYMBpWYmFjrvYmJiWFt6lrHD7dxpqqqKlVWVoZNAADgwuHaRe4N5cxTeMaYsHl1neL7qTang%2BLZTg9Onz7duWje5/MpJSXlF9cPAAAiT4MGLI/HUyuU1Nc1TH6/X1LtUaby8nJnBMrv99d5inLfvn1hbepah1R7dOy0SZMmKRQKOVNpaen5dQYAAESUBr3I3RijnJwc53qmY8eOadSoUYqNjQ1rt2DBgvPeVmpqqvx%2BvwoLC3XddddJkqqrq1VUVKR///d/lySlp6crFApp5cqV%2BvWvfy1J%2BvzzzxUKhdSrVy%2BnzeTJk1VdXa3o6GhJ0pIlSxQIBHTFFVfUuW2v1xt2zRYAALiwNGjAGjlyZNjru%2B%2B%2B%2B7zWd/jwYX311VfO6507d2r9%2BvVq1aqVLr/8co0bN07Tpk1Tu3bt1K5dO02bNk0XX3yxRowYIUm65pprNHDgQOXm5uqFF16QdOoxDYMHD1aHDh0knXqMw6OPPqqcnBxNnjxZ27dv17Rp0/Twww9zByEAAKhTgwYs209vX716tfr27eu8zs/Pl3QqyL3yyiuaOHGijh49qgceeEAVFRXq0aOHlixZ4jwDS5Lmzp2rsWPHOncbDh06VLNnz3aW%2B3w%2BFRYWavTo0erWrZtatmyp/Px8Z1sAAABn8hgeSV7vKisr5fP5FAqFFB8f73Y5OEfdphS4XQLwi6x%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%2Bffpo8%2BbNYeuoqKhQdna2fD6ffD6fsrOzdfDgwYbuCgAAiCBNOmBJUseOHVVWVuZMGzdudJY9%2BeSTmjVrlmbPnq1Vq1bJ7/erf//%2BOnTokNNmxIgRWr9%2BvQoKClRQUKD169crOzvbja4AAIAI0dztAupb8%2BbNw0atTjPG6JlnntGUKVM0bNgwSdKrr76qpKQkvfnmm7r//vu1detWFRQUaMWKFerRo4ckac6cOUpPT9e2bdvUoUOHBu0LAACIDE1%2BBGv79u0KBAJKTU3VHXfcoR07dkiSdu7cqWAwqMzMTKet1%2BtVRkaGiouLJUklJSXy%2BXxOuJKknj17yufzOW3qUlVVpcrKyrAJAABcOJp0wOrRo4dee%2B01ffjhh5ozZ46CwaB69eqlAwcOKBgMSpKSkpLC3pOUlOQsCwaDSkxMrLXexMREp01dpk%2Bf7lyz5fP5lJKSYrFXAACgsWvSASsrK0u33XabOnXqpJtvvlmLFi2SdOpU4GkejyfsPcaYsHlnLq%2BrzZkmTZqkUCjkTKWlpefbFQAAEEGadMA6U2xsrDp16qTt27c712WdORJVXl7ujGr5/X599913tdazb9%2B%2BWiNfP%2BT1ehUfHx82AQCAC8cFFbCqqqq0detWJScnKzU1VX6/X4WFhc7y6upqFRUVqVevXpKk9PR0hUIhrVy50mnz%2BeefKxQKOW0AAADO1KTvIpwwYYKGDBmiyy%2B/XOXl5frTn/6kyspKjRw5Uh6PR%2BPGjdO0adPUrl07tWvXTtOmTdPFF1%2BsESNGSJKuueYaDRw4ULm5uXrhhRckSffdd58GDx7MHYQAAOCsmnTA2rNnj%2B68807t379fl112mXr27KkVK1aobdu2kqSJEyfq6NGjeuCBB1RRUaEePXpoyZIliouLc9Yxd%2B5cjR071rnbcOjQoZo9e7Yr/QEAAJHBY4wxbhfR1FVWVsrn8ykUCl3Q12N1m1LgdgkAGpnVjw90uwQ0YW7%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%2B%2B22NGzdOU6ZM0bp169S7d29lZWVp9%2B7dbpcGAAAaIQLWOZg1a5buvfde/du//ZuuueYaPfPMM0pJSdFzzz3ndmkAAKAR4hThT6iurtaaNWv00EMPhc3PzMxUcXFxne%2BpqqpSVVWV8zoUCkmSKisrrdd3ouqI9XUCANDQ6uNv5Ol1GmOsr/unELB%2Bwv79%2B3XixAklJSWFzU9KSlIwGKzzPdOnT9ejjz5aa35KSkq91AgAQKTzzay/dR86dEg%2Bn6/%2BNlAHAtY58ng8Ya%2BNMbXmnTZp0iTl5%2Bc7r0%2BePKnvv/9el1566Vnf80OVlZVKSUlRaWmp4uPjz69w/GIch8aB49A4cBwaB47Dz2OM0aFDhxQIBBp82wSsn5CQkKBmzZrVGq0qLy%2BvNap1mtfrldfrDZt3ySWX/Oxtx8fH8wFqBDgOjQPHoXHgODQOHIdz19AjV6dxkftPiI6OVteuXVVYWBg2v7CwUL169XKpKgAA0JgxgnUO8vPzlZ2drW7duik9PV1/%2BctftHv3bo0aNcrt0gAAQCPUbOrUqVPdLqKxS0tL06WXXqpp06ZpxowZOnr0qF5//XV17ty53rbZrFkz9enTR82bk4HdxHFoHDgOjQPHoXHgOEQGj3Hj3kUAAIAmjGuwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBq4FMnTpVHo8nbPL7/c5yY4ymTp2qQCCgmJgY9enTR5s3bw5bR0VFhbKzs%2BXz%2BeTz%2BZSdna2DBw82dFciyrJlyzRkyBAFAgF5PB69//77Yctt7feNGzcqIyNDMTExat26tR577DFXvvuqsfqp45CTk1Pr89GzZ8%2BwNlVVVRozZowSEhIUGxuroUOHas%2BePWFtdu/erSFDhig2NlYJCQkaO3asqqur671/kWL69Onq3r274uLilJiYqFtvvVXbtm0La2NrPxcVFalr165q0aKFrrzySj3//PP13r9IcS7HoU%2BfPrU%2BE3fccUdYG343NW4ErAbUsWNHlZWVOdPGjRudZU8%2B%2BaRmzZql2bNna9WqVfL7/erfv78OHTrktBkxYoTWr1%2BvgoICFRQUaP369crOznajKxHjyJEj6ty5s2bPnl3nchv7vbKyUv3791cgENCqVav0n//5n5oxY4ZmzZpV7/2LFD91HCRp4MCBYZ%2BPxYsXhy0fN26c3nvvPc2bN0/Lly/X4cOHNXjwYJ04cUKSdOLECQ0aNEhHjhzR8uXLNW/ePM2fP1/jx4%2Bv175FkqKiIo0ePVorVqxQYWGhjh8/rszMTB058v%2B/NN7Gft65c6duueUW9e7dW%2BvWrdPkyZM1duxYzZ8/v8H73Bidy3GQpNzc3LDPxAsvvBC2nN9NjZxBg3jkkUdM586d61x28uRJ4/f7zRNPPOHMO3bsmPH5fOb55583xhizZcsWI8msWLHCaVNSUmIkmb/97W/1W3wTIcm89957zmtb%2B/3ZZ581Pp/PHDt2zGkzffp0EwgEzMmTJ%2Bu7WxHnzONgjDEjR440//RP/3TW9xw8eNBERUWZefPmOfO%2B/fZbc9FFF5mCggJjjDGLFy82F110kfn222%2BdNm%2B99Zbxer0mFApZ7kXTUF5ebiSZoqIiY4y9/Txx4kRz9dVXh23r/vvvNz179qzvLkWkM4%2BDMcZkZGSY3/3ud2d9D7%2BbGj9GsBrQ9u3bFQgElJqaqjvuuEM7duyQdOq/vWAwqMzMTKet1%2BtVRkaGiouLJUklJSXy%2BXzq0aOH06Znz57y%2BXxOG/w8tvZ7SUmJMjIywr5/csCAAdq7d6927drVMJ1pApYuXarExES1b99eubm5Ki8vd5atWbNGNTU1YccqEAgoLS0t7DikpaWFfanrgAEDVFVVpTVr1jRcRyJIKBSSJLVq1UqSvf1cUlISto7TbVavXq2ampp67VMkOvM4nDZ37lwlJCSoY8eOmjBhQtjIOr%2BbGj8CVgPp0aOHXnvtNX344YeaM2eOgsGgevXqpQMHDjhfJH3ml0cnJSU5y4LBoBITE2utNzExsdYXUePc2NrvwWCwznX8cBv4cVlZWZo7d64%2B%2BeQTzZw5U6tWrVK/fv1UVVUl6dR%2BjI6OVsuWLcPed%2BaxOvM4tGzZUtHR0RyHOhhjlJ%2BfrxtuuEFpaWmS7O3ns30mjh8/rv3799dXlyJSXcdBku666y699dZbWrp0qf7whz9o/vz5GjZsmLOc302NH8/ZbyBZWVnOz506dVJ6erquuuoqvfrqq87FvB6PJ%2Bw9xpiweWcur6sNfj4b%2B72udZztvaht%2BPDhzs9paWnq1q2b2rZtq0WLFoX9UTkTn5FfLi8vTxs2bNDy5ct/si2fifpztuOQm5vr/JyWlqZ27dqpW7duWrt2rbp06SKJ49DYMYLlktjYWHXq1Enbt2937iY88z%2BK8vJy578Nv9%2Bv7777rtZ69u3bV%2Bs/FJwbW/vd7/fXuQ6p9ugYzk1ycrLatm2r7du3Szq1j6urq1VRURHW7sxjdeZxqKioUE1NDcfhDGPGjNHChQv16aefqk2bNs58W/v5bJ%2BJ5s2b69JLL62PLkWksx2HunTp0kVRUVFhnwl%2BNzVuBCyXVFVVaevWrUpOTlZqaqr8fr8KCwud5dXV1SoqKlKvXr0kSenp6QqFQlq5cqXT5vPPP1coFHLa4Oextd/T09O1bNmysNvUlyxZokAgoCuuuKJhOtPEHDhwQKWlpUpOTpYkde3aVVFRUWHHqqysTJs2bQo7Dps2bVJZWZnTZsmSJfJ6veratWvDdqCRMsYoLy9PCxYs0CeffKLU1NSw5bb2c3p6etg6Trfp1q2boqKi6qt7EeOnjkNdNm/erJqaGuczwe%2BmCNDw19VfmMaPH2%2BWLl1qduzYYVasWGEGDx5s4uLizK5du4wxxjzxxBPG5/OZBQsWmI0bN5o777zTJCcnm8rKSmcdAwcONL/61a9MSUmJKSkpMZ06dTKDBw92q0sR4dChQ2bdunVm3bp1RpKZNWuWWbdunfnmm2%2BMMXb2%2B8GDB01SUpK58847zcaNG82CBQtMfHy8mTFjRoP3t7H6seNw6NAhM378eFNcXGx27txpPv30U5Oenm5at24ddhxGjRpl2rRpYz766COzdu1a069fP9O5c2dz/PhxY4wxx48fN2lpaeamm24ya9euNR999JFp06aNycvLc6vbjc5vf/tb4/P5zNKlS01ZWZkz/f3vf3fa2NjPO3bsMBdffLF58MEHzZYtW8yLL75ooqKizP/8z/80eJ8bo586Dl999ZV59NFHzapVq8zOnTvNokWLzNVXX22uu%2B465zgYw%2B%2Bmxo6A1UCGDx9ukpOTTVRUlAkEAmbYsGFm8%2BbNzvKTJ0%2BaRx55xPj9fuP1es2NN95oNm7cGLaOAwcOmLvuusvExcWZuLg4c9ddd5mKioqG7kpE%2BfTTT42kWtPIkSONMfb2%2B4YNG0zv3r2N1%2Bs1fr/fTJ06ldugf%2BDHjsPf//53k5mZaS677DITFRVlLr/8cjNy5Eize/fusHUcPXrU5OXlmVatWpmYmBgzePDgWm2%2B%2BeYbM2jQIBMTE2NatWpl8vLywm5Rv9DVdQwkmZdfftlpY2s/L1261Fx33XUmOjraXHHFFea5555riC5GhJ86Drt37zY33nijadWqlYmOjjZXXXWVGTt2rDlw4EDYevjd1Lh5jOGRrgAAADZxDRYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMv%2BL%2B5XYtnjgb89AAAAAElFTkSuQmCC\"/>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common6324860309995435255\">\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">1490.0</td>\n",
" <td class=\"number\">10</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1363.0</td>\n",
" <td class=\"number\">9</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1488.25</td>\n",
" <td class=\"number\">8</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1400.0</td>\n",
" <td class=\"number\">8</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1533.5</td>\n",
" <td class=\"number\">8</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1538.5</td>\n",
" <td class=\"number\">8</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1479.25</td>\n",
" <td class=\"number\">8</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1430.0</td>\n",
" <td class=\"number\">8</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1623.0</td>\n",
" <td class=\"number\">8</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1601.5</td>\n",
" <td class=\"number\">7</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"other\">\n",
" <td class=\"fillremaining\">Other values (4397)</td>\n",
" <td class=\"number\">8909</td>\n",
" <td class=\"number\">95.2%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"missing\">\n",
" <td class=\"fillremaining\">(Missing)</td>\n",
" <td class=\"number\">366</td>\n",
" <td class=\"number\">3.9%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"extreme6324860309995435255\">\n",
" <p class=\"h4\">Minimum 5 values</p>\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">551.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">559.25</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">560.5</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">579.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">601.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" <p class=\"h4\">Maximum 5 values</p>\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">2679.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">2684.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">2690.5</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">2746.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">2775.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"</div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4 pp-anchor\" id=\"pp_var_PT08.S5(O3)\">PT08.S5(O3)<br/>\n",
" <small>Numeric</small>\n",
" </p>\n",
" </div><div class=\"col-md-6\">\n",
" <div class=\"row\">\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
" <tr>\n",
" <th>Distinct count</th>\n",
" <td>4679</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Unique (%)</th>\n",
" <td>50.0%</td>\n",
" </tr>\n",
" <tr class=\"alert\">\n",
" <th>Missing (%)</th>\n",
" <td>3.9%</td>\n",
" </tr>\n",
" <tr class=\"alert\">\n",
" <th>Missing (n)</th>\n",
" <td>366</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Infinite (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Infinite (n)</th>\n",
" <td>0</td>\n",
" </tr>\n",
" </table>\n",
"\n",
" </div>\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
"\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>1022.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Minimum</th>\n",
" <td>221</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maximum</th>\n",
" <td>2522.8</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Zeros (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"<div class=\"col-md-3 collapse in\" id=\"minihistogram-4858006153263363372\">\n",
" <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAAeVJREFUeJzt3L3K03AYxuEnL65J99IchOApCB6Qm%2BDm6KG4u7p7Fi09gAYHBxsHrcuLN63QfNTrGlvKP4X8eJqPphnHcayJHQ6H6vt%2B6mVZuf1%2BX7vdbtI1X0y62m9t21bVry/cdd0cm8CKnE6n6vv%2Bz34zpVkCaZqmqqq6rpstkFfvPt/8ma8f3txhS7jWZb%2BZ0tPkK8KKCAQCgUAgEAgEAsEsZ7HW6tYzX856rZ8JAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIHiIW03%2B5c9PcA0TBIKHmCBL5W%2B962eCQCAQCAQCgUAgEAgEAoFAIBAIBAIXChfGxcVlMUEgEAgEAoFAIBAIBAKBQCAQCFwHeQCeOn8/JggEi5sgHsDAkpggEAgEAoFAsLhjEO7PHcPXM0EgEAgEfmJxlf/1Z9ksgYzjWFVVp9Pp2Xs/vn%2BbenO4k5dvP938mS/vXz977bKfXPabKc0SyDAMVVXV9/0cy7Ngm49/f28YhtpsNtNtTFU14wxZns/nOh6P1bZtNU0z9fKszDiONQxDbbfbenqa9rB5lkBgLZzFgkAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCD4CTu4VU%2BI9Bj5AAAAAElFTkSuQmCC\">\n",
"\n",
"</div>\n",
"<div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-4858006153263363372,#minihistogram-4858006153263363372\"\n",
" aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
"</div>\n",
"<div class=\"row collapse col-md-12\" id=\"descriptives-4858006153263363372\">\n",
" <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
" <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-4858006153263363372\"\n",
" aria-controls=\"quantiles-4858006153263363372\" role=\"tab\"\n",
" data-toggle=\"tab\">Statistics</a></li>\n",
" <li role=\"presentation\"><a href=\"#histogram-4858006153263363372\" aria-controls=\"histogram-4858006153263363372\"\n",
" role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
" <li role=\"presentation\"><a href=\"#common-4858006153263363372\" aria-controls=\"common-4858006153263363372\"\n",
" role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
" <li role=\"presentation\"><a href=\"#extreme-4858006153263363372\" aria-controls=\"extreme-4858006153263363372\"\n",
" role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
"\n",
" </ul>\n",
"\n",
" <div class=\"tab-content\">\n",
" <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-4858006153263363372\">\n",
" <div class=\"col-md-4 col-md-offset-1\">\n",
" <p class=\"h4\">Quantile statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr>\n",
" <th>Minimum</th>\n",
" <td>221</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5-th percentile</th>\n",
" <td>460.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Q1</th>\n",
" <td>731.38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Median</th>\n",
" <td>963.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Q3</th>\n",
" <td>1273.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95-th percentile</th>\n",
" <td>1761.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maximum</th>\n",
" <td>2522.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Range</th>\n",
" <td>2301.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Interquartile range</th>\n",
" <td>542</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" <div class=\"col-md-4 col-md-offset-2\">\n",
" <p class=\"h4\">Descriptive statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr>\n",
" <th>Standard deviation</th>\n",
" <td>398.48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Coef of variation</th>\n",
" <td>0.38961</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kurtosis</th>\n",
" <td>0.078542</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>1022.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MAD</th>\n",
" <td>320.08</td>\n",
" </tr>\n",
" <tr class=\"\">\n",
" <th>Skewness</th>\n",
" <td>0.62786</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sum</th>\n",
" <td>9195800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Variance</th>\n",
" <td>158790</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Memory size</th>\n",
" <td>73.2 KiB</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-4858006153263363372\">\n",
" <img 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qS5UBAAAYI8rJ7lPnDhREydO1IkTJ9SvXz9J0qeffqpZs2Zp9uzZbpQEAABgjSsBKycnR2fPntWMGTP03HPPSZLat2%2BvV155Rf/yL//iRkkAIih9er7bJXwvW14Y6HYJAKKMKwFLksaPH6/x48ertLRUzZs31/XXX%2B9WKQAAAFa5FrAuSk5OdrsEAAAAq1w5yf3YsWP6yU9%2BohtuuEHNmjVTbGxs2AIAABDNXJnBGjVqlP70pz/p3/7t35ScnCyPx%2BNGGQAAAPXClYC1bt06rVu3TrfffrsbhwcAAKhXrrxF2L59e2atAABAo%2BVKwHr55Zc1depUHTlyxI3DAwAA1CtX3iLMzs5WZWWlOnTooPj4eMXExIRtLysrc6MsAAAAK1wJWC%2B%2B%2BKIbhwUAAIgIVwLWE0884cZhAQAAIsKVc7Ak6dChQ8rLy1N2drbzluCqVau0d%2B9et0oCAACwwpWA9fnnn6tLly4qLCzUhx9%2BqFOnTkmStm3bpmeffdaNkgAAAKxxJWD9/Oc/V15entasWRN25/Z%2B/fqpuLjYjZIAAACscSVg7dy5U//0T/9Ua31iYqKOHTvmQkUAAAD2uBKwrr/%2BegWDwVrrd%2BzYoXbt2rlQEQAAgD2uBKxHHnlEzzzzjI4dO%2Bbc0X3jxo2aPHmyHn/8cTdKAgAAsMaVgDVjxgz5/X4lJyfr1KlTuu2229SrVy/deeed%2BsUvfuFGSQAAANa4ch%2Bs2NhYLVmyRH/84x%2B1bds2XbhwQXfccYduvfVWN8oBAACwypWAdVGnTp3UqVMnN0sAAACwzpWA9eSTT/7V7b/5zW8iVAkAAIB9rgSs0tLSsMc1NTXas2ePKisrdc8997hREgAAgDWuBKzf//73tdadO3dOP/vZz9S5c2cXKgIAALDHtc8ivFTTpk01efJkvfTSS26XAgAA8IM0mIAlSQcOHFBNTY3bZQAAAPwgrrxFOGXKlLDHxhiVlpZq%2BfLleuyxx9woCQAAwBpXAlZRUVHY4%2Buuu05t27bViy%2B%2BqJycHDdKAgAAsMaVgPX555%2B7cVgAAICIaFDnYAEAADQGrsxg3Xnnnc6HPF/Jpk2b6rkaAAAAu1wJWPfee6/eeOMNderUSRkZGZKk4uJi7du3T6NHj5bX63WjLAAAACtcCVgnT57U2LFjNWPGjLD106dP1zfffKP/%2Bq//cqMsAAAAK1w5B%2BvDDz/UT37yk1rrR40apd/97ncuVAQAAGCPKwHL6/Vqw4YNtdZv2LCBtwcBAEDUc%2BUtwgkTJmjMmDHavn27evbsKenbc7AWLFigadOmuVESAACANa4ErOnTpys1NVX/8R//obfeekuS1LlzZy1YsEAjRoxwoyQAAABrXLsP1ogRI7Rx40ZVVFSooqJCGzdu/N7hat26dRoyZIgCgYA8Ho8%2B/vjjsO3GGOXl5SkQCKh58%2Bbq27ev9uzZE9amvLxc2dnZ8vl88vl8ys7O1smTJ8Pa7Nq1S3369FHz5s3Vrl07/fKXv5Qx5m/rOAAAaPRcC1gVFRV6%2B%2B239eyzz6q8vFyS9Ic//EGlpaVXvY/Tp0%2BrW7dumj9/fp3bZ82apblz52r%2B/PnavHmz/H6/%2Bvfvr8rKSqfNiBEjtGPHDuXn5ys/P187duxQdnZ2WJ39%2B/dXIBDQ5s2b9etf/1qzZ8/W3Llz/8aeAwCAxs6Vtwh3796t%2B%2B%2B/Xy1atFBJSYlGjRqlVq1a6cMPP9SRI0e0cOHCq9rPoEGDNGjQoDq3GWM0b948TZ8%2BXcOGDZMkLVy4UElJSXr//fc1evRo7d27V/n5%2BSouLlaPHj0kSQsWLFBGRob27dunW265RYsWLdLZs2f19ttvy%2Bv1Ki0tTX/84x81d%2B5c5ebmXvUNUwEAwLXDlRmsp59%2BWiNGjNCf/vQnNWvWzFk/ePBgrVu3zsoxDh48qGAwqMzMTGed1%2BtVnz59nCsYi4qK5PP5nHAlST179pTP5wtr06dPn7CrGwcMGKCjR4/q0KFDVmoFAACNiysBa/PmzXrqqadqzf60a9fue71F%2BNcEg0FJUlJSUtj6pKQkZ1swGFRiYmKt5yYmJoa1qWsf3z3Gpaqqqpxzyy4uAADg2uFKwIqNjdWpU6dqrd%2B/f78SEhKsHuvSEGeMCVtX11t8V2pz8QT3y709OHPmTOekeZ/Pp5SUlL%2B5fgAAEH1cCVhDhw7Vr371K507d07St0Hl66%2B/1jPPPOOcL/VD%2Bf1%2BSbVnmcrKypwZKL/fr2%2B%2B%2BabWc48dOxbWpq59SLVnxy6aOnWqQqGQs5SUlPywzgAAgKjiSsCaM2eOjh49Kr/frzNnzqhfv3666aab1KxZs1qfT/i3Sk1Nld/vV0FBgbOuurpahYWF6tWrlyQpIyNDoVBImzZtctps3LhRoVAorM26detUXV3ttFm1apUCgYBuvPHGOo/t9XoVHx8ftgAAgGuHK1cRXjyJvKCgQNu2bdOFCxd0xx13aMCAAd/rqrxTp07pyy%2B/dB4fPHhQO3bsUOvWrXXDDTdo4sSJmjFjhjp27KiOHTtqxowZatGihXO/rc6dO2vgwIHKycnRG2%2B8IUl68sknlZWVpVtuuUXSt7dxeP755zVq1ChNmzZN%2B/fv14wZM/Tss89yBSEAAKhTxANWTU2NHnjgAb366qvKzMwMu8rv%2B9qyZYvuvfde53Fubq4kaeTIkXr77bc1ZcoUnTlzRk899ZTKy8vVo0cPrVq1SnFxcc5zFi1apAkTJjh1DB06NOy%2BWj6fTwUFBRo7dqzS09PVqlUr5ebmOscCAAC4lMe4cEvyhIQEFRcX6%2B///u8jfWhXVFRUyOfzKRQKXdNvF6ZPz3e7BOBvsuWFgW6XAOBv4ObfX1fOwXr88cf129/%2B1o1DAwAA1DtXzsGSpPnz52v16tVKT09Xy5Ytw7bNmjXLpaoAAAB%2BOFcC1tatW/WjH/1IkrRz586wbZw4DgAAol1EA9aBAweUmpqqzz//PJKHBQAAiKiInoPVsWNHHTt2zHk8fPjwOm/0CQAAEM0iGrAuvWBx5cqVOn36dCRLAAAAqHeuXEUIAADQmEX0HCyPx1PrJHZOagfQ0EXbPdy4bxfgvogGLGOMRo0aJa/XK0k6e/asxowZU%2Bs2DcuWLYtkWQAAAFZFNGCNHDky7PHjjz8eycMDAABEREQDFndvBwAA1wJOcgcAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFjWqANWXl6ePB5P2OL3%2B53txhjl5eUpEAioefPm6tu3r/bs2RO2j/LycmVnZ8vn88nn8yk7O1snT56MdFcAAEAUadQBS5K6dOmi0tJSZ9m1a5ezbdasWZo7d67mz5%2BvzZs3y%2B/3q3///qqsrHTajBgxQjt27FB%2Bfr7y8/O1Y8cOZWdnu9EVAAAQJZq6XUB9a9q0adis1UXGGM2bN0/Tp0/XsGHDJEkLFy5UUlKS3n//fY0ePVp79%2B5Vfn6%2BiouL1aNHD0nSggULlJGRoX379umWW26JaF8A4GqkT893u4SrtuWFgW6XANSLRj%2BDtX//fgUCAaWmpuqRRx7RgQMHJEkHDx5UMBhUZmam09br9apPnz7asGGDJKmoqEg%2Bn88JV5LUs2dP%2BXw%2Bp01dqqqqVFFREbYAAIBrR6MOWD169NA777yjTz75RAsWLFAwGFSvXr104sQJBYNBSVJSUlLYc5KSkpxtwWBQiYmJtfabmJjotKnLzJkznXO2fD6fUlJSLPYKAAA0dI06YA0aNEgPPfSQunbtqvvvv18rVqyQ9O1bgRd5PJ6w5xhjwtZdur2uNpeaOnWqQqGQs5SUlPzQrgAAgCjSqAPWpVq2bKmuXbtq//79znlZl85ElZWVObNafr9f33zzTa39HDt2rNbM13d5vV7Fx8eHLQAA4NpxTQWsqqoq7d27V8nJyUpNTZXf71dBQYGzvbq6WoWFherVq5ckKSMjQ6FQSJs2bXLabNy4UaFQyGkDAABwqUZ9FeHkyZM1ZMgQ3XDDDSorK9O///u/q6KiQiNHjpTH49HEiRM1Y8YMdezYUR07dtSMGTPUokULjRgxQpLUuXNnDRw4UDk5OXrjjTckSU8%2B%2BaSysrK4ghAAAFxWow5YR44c0aOPPqrjx4%2Brbdu26tmzp4qLi9WhQwdJ0pQpU3TmzBk99dRTKi8vV48ePbRq1SrFxcU5%2B1i0aJEmTJjgXG04dOhQzZ8/35X%2BAACA6OAxxhi3i2jsKioq5PP5FAqFrunzsaLp3jwAIoP7YKE%2Bufn395o6BwsAACASCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgWaO%2BD9a1gFsfAADQ8DCDBQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsKyp2wUAAK5d6dPz3S7he9nywkC3S0CUYAYLAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsa%2Bp2AQAARIv06flul/C9bHlhoNslXLOYwQIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLuIrwKr366qt66aWXVFpaqi5dumjevHnq3bu322UBAHBZ0XTVY2O74pEZrKuwZMkSTZw4UdOnT9f27dvVu3dvDRo0SIcPH3a7NAAA0AARsK7C3Llz9cQTT%2BinP/2pOnfurHnz5iklJUWvvfaa26UBAIAGiLcIr6C6ulpbt27VM888E7Y%2BMzNTGzZsqPM5VVVVqqqqch6HQiFJUkVFhfX6zledtr5PAAAirT7%2BRl7cpzHG%2Br6vhIB1BcePH9f58%2BeVlJQUtj4pKUnBYLDO58ycOVPPP/98rfUpKSn1UiMAANHON6f%2B9l1ZWSmfz1d/B6gDAesqeTyesMfGmFrrLpo6dapyc3OdxxcuXNCf//xntWnT5rLPwbcqKiqUkpKikpISxcfHu13ONYWxdwfj7h7G3j2RGntjjCorKxUIBOrtGJdDwLqChIQENWnSpNZsVVlZWa1ZrYu8Xq%2B8Xm/Yuuuvv77eamyM4uPjecFzCWPvDsbdPYy9eyIx9pGeubqIk9yvIDY2Vt27d1dBQUHY%2BoKCAvXq1culqgAAQEPGDNZVyM3NVXZ2ttLT05WRkaHf/OY3Onz4sMaMGeN2aQAAoAFqkpeXl%2Bd2EQ1dWlqa2rRpoxkzZmj27Nk6c%2BaM3n33XXXr1s3t0hqlJk2aqG/fvmralPwfaYy9Oxh39zD27mnsY%2B8xbly7CAAA0IhxDhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2Ch3uXl5cnj8YQtfr/f2W6MUV5engKBgJo3b66%2Bfftqz549YfsoLy9Xdna2fD6ffD6fsrOzdfLkyUh3pcFbt26dhgwZokAgII/Ho48//jhsu62x3rVrl/r06aPmzZurXbt2%2BuUvf%2BnKZ301FFca91GjRtX6HejZs2dYm6qqKo0fP14JCQlq2bKlhg4dqiNHjoS1OXz4sIYMGaKWLVsqISFBEyZMUHV1db33ryGbOXOm7rzzTsXFxSkxMVEPPvig9u3bF9bG1tgWFhaqe/fuatasmW666Sa9/vrr9d6/hupqxr1v3761fu4feeSRsDaN%2BfWGgIWI6NKli0pLS51l165dzrZZs2Zp7ty5mj9/vjZv3iy/36/%2B/fursrLSaTNixAjt2LFD%2Bfn5ys/P144dO5Sdne1GVxq006faH301AAAIAklEQVRPq1u3bpo/f36d222MdUVFhfr3769AIKDNmzfr17/%2BtWbPnq25c%2BfWe/8aqiuNuyQNHDgw7Hdg5cqVYdsnTpyojz76SIsXL9b69et16tQpZWVl6fz585Kk8%2BfPa/DgwTp9%2BrTWr1%2BvxYsXa%2BnSpZo0aVK99q2hKyws1NixY1VcXKyCggKdO3dOmZmZOn36tNPGxtgePHhQDzzwgHr37q3t27dr2rRpmjBhgpYuXRrxPjcEVzPukpSTkxP2c//GG2%2BEbW/UrzcGqGfPPfec6datW53bLly4YPx%2Bv3nxxReddWfPnjU%2Bn8%2B8/vrrxhhjvvjiCyPJFBcXO22KioqMJPO///u/9Vt8FJNkPvroI%2BexrbF%2B9dVXjc/nM2fPnnXazJw50wQCAXPhwoX67laDd%2Bm4G2PMyJEjzT/%2B4z9e9jknT540MTExZvHixc66r7/%2B2lx33XUmPz/fGGPMypUrzXXXXWe%2B/vprp80HH3xgvF6vCYVClnsRvcrKyowkU1hYaIyxN7ZTpkwxt956a9ixRo8ebXr27FnfXYoKl467Mcb06dPH/Ou//utln9PYX2%2BYwUJE7N%2B/X4FAQKmpqXrkkUd04MABSd/%2BVxgMBpWZmem09Xq96tOnjzZs2CBJKioqks/nU48ePZw2PXv2lM/nc9rgymyNdVFRkfr06RP2eZsDBgzQ0aNHdejQoch0JgqtXbtWiYmJ6tSpk3JyclRWVuZs27p1q2pqasK%2BN4FAQGlpaWHjnpaWFvahtQMGDFBVVZW2bt0auY40cKFQSJLUunVrSfbGtqioKGwfF9ts2bJFNTU19dqnaHDpuF%2B0aNEiJSQkqEuXLpo8eXLYbHljf70hYKHe9ejRQ%2B%2B8844%2B%2BeQTLViwQMFgUL169dKJEyecD9G%2B9IOzk5KSnG3BYFCJiYm19puYmFjrQ7hxebbGOhgM1rmP7x4D4QYNGqRFixbps88%2B05w5c7R582b169dPVVVVkr4dt9jYWLVq1SrseZd%2Bby4d91atWik2NpZx/3/GGOXm5uruu%2B9WWlqaJHtje7mf%2B3Pnzun48eP11aWoUNe4S9Jjjz2mDz74QGvXrtUvfvELLV26VMOGDXO2N/bXm8Z5f3o0KIMGDXK%2B7tq1qzIyMnTzzTdr4cKFzom%2BHo8n7DnGmLB1l26vqw2ujo2xrmsfl3supOHDhztfp6WlKT09XR06dNCKFSvC/uBcit%2BD72fcuHHauXOn1q9ff8W2/Nzbc7lxz8nJcb5OS0tTx44dlZ6erm3btumOO%2B6Q1LjHnRksRFzLli3VtWtX7d%2B/37ma8NL/RMrKypz/Uvx%2Bv7755pta%2Bzl27Fit/2xwebbG2u/317kPqfbsGOqWnJysDh06aP/%2B/ZK%2BHdPq6mqVl5eHtbv0e3PpuJeXl6umpoZxlzR%2B/HgtX75ca9asUfv27Z31tsb2cj/3TZs2VZs2beqjS1HhcuNelzvuuEMxMTFhP/eN%2BfWGgIWIq6qq0t69e5WcnKzU1FT5/X4VFBQ426urq1VYWKhevXpJkjIyMhQKhbRp0yanzcaNGxUKhZw2uDJbY52RkaF169aFXcK%2BatUqBQIB3XjjjZHpTJQ7ceKESkpKlJycLEnq3r27YmJiwr43paWl2r17d9i47969W6WlpU6bVatWyev1qnv37pHtQANijNG4ceO0bNkyffbZZ0pNTQ3bbmtsMzIywvZxsU16erpiYmLqq3sN1pXGvS579uxRTU2N83Pf6F9vIn9ePa41kyZNMmvXrjUHDhwwxcXFJisry8TFxZlDhw4ZY4x58cUXjc/nM8uWLTO7du0yjz76qElOTjYVFRXOPgYOHGh%2B9KMfmaKiIlNUVGS6du1qsrKy3OpSg1VZWWm2b99utm/fbiSZuXPnmu3bt5uvvvrKGGNnrE%2BePGmSkpLMo48%2Banbt2mWWLVtm4uPjzezZsyPe34bir417ZWWlmTRpktmwYYM5ePCgWbNmjcnIyDDt2rULG/cxY8aY9u3bm9WrV5tt27aZfv36mW7duplz584ZY4w5d%2B6cSUtLM/fdd5/Ztm2bWb16tWnfvr0ZN26cW91uEH72s58Zn89n1q5da0pLS53lL3/5i9PGxtgeOHDAtGjRwjz99NPmiy%2B%2BMG%2B%2B%2BaaJiYkx//3f/x3xPjcEVxr3L7/80jz//PNm8%2BbN5uDBg2bFihXm1ltvNbfffrsz7sY07tcbAhbq3fDhw01ycrKJiYkxgUDADBs2zOzZs8fZfuHCBfPcc88Zv99vvF6vueeee8yuXbvC9nHixAnz2GOPmbi4OBMXF2cee%2BwxU15eHumuNHhr1qwxkmotI0eONMbYG%2BudO3ea3r17G6/Xa/x%2Bv8nLy2vwl0zXp7827n/5y19MZmamadu2rYmJiTE33HCDGTlypDl8%2BHDYPs6cOWPGjRtnWrdubZo3b26ysrJqtfnqq6/M4MGDTfPmzU3r1q3NuHHjwi5fvxbVNe6SzG9/%2B1unja2xXbt2rbn99ttNbGysufHGG81rr70WiS42SFca98OHD5t77rnHtG7d2sTGxpqbb77ZTJgwwZw4cSJsP4359cZjTBTcDhUAACCKcA4WAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADL/g%2BSInlvF0jpxAAAAABJRU5ErkJggg%3D%3D\"/>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-4858006153263363372\">\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">825.75</td>\n",
" <td class=\"number\">10</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">904.5</td>\n",
" <td class=\"number\">8</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">835.5</td>\n",
" <td class=\"number\">8</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">779.0</td>\n",
" <td class=\"number\">8</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1023.5</td>\n",
" <td class=\"number\">8</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1049.75</td>\n",
" <td class=\"number\">8</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">909.0</td>\n",
" <td class=\"number\">7</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">902.75</td>\n",
" <td class=\"number\">7</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">967.5</td>\n",
" <td class=\"number\">7</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">1001.25</td>\n",
" <td class=\"number\">7</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"other\">\n",
" <td class=\"fillremaining\">Other values (4668)</td>\n",
" <td class=\"number\">8913</td>\n",
" <td class=\"number\">95.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"missing\">\n",
" <td class=\"fillremaining\">(Missing)</td>\n",
" <td class=\"number\">366</td>\n",
" <td class=\"number\">3.9%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"extreme-4858006153263363372\">\n",
" <p class=\"h4\">Minimum 5 values</p>\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">221.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">224.75</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">226.5</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">232.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">252.0</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" <p class=\"h4\">Maximum 5 values</p>\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">2493.5</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">2515.25</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">2519.25</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">2522.25</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">2522.75</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"</div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4 pp-anchor\" id=\"pp_var_RH\">RH<br/>\n",
" <small>Numeric</small>\n",
" </p>\n",
" </div><div class=\"col-md-6\">\n",
" <div class=\"row\">\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
" <tr>\n",
" <th>Distinct count</th>\n",
" <td>4903</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Unique (%)</th>\n",
" <td>52.4%</td>\n",
" </tr>\n",
" <tr class=\"alert\">\n",
" <th>Missing (%)</th>\n",
" <td>3.9%</td>\n",
" </tr>\n",
" <tr class=\"alert\">\n",
" <th>Missing (n)</th>\n",
" <td>366</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Infinite (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Infinite (n)</th>\n",
" <td>0</td>\n",
" </tr>\n",
" </table>\n",
"\n",
" </div>\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
"\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>49.232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Minimum</th>\n",
" <td>9.175</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maximum</th>\n",
" <td>88.725</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Zeros (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"<div class=\"col-md-3 collapse in\" id=\"minihistogram-3015449722971000298\">\n",
" <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAAdVJREFUeJzt2j2u00AYhtHPV7R2%2BijeBYtgT9SU7IdFsItEWYAtCgpiCsgt%2BHl1g7i2E59TxopmIs2jie1ppmmaaman06n6vp97WO7c8Xisw%2BEw65hvZh3tp7Ztq%2BrHD%2B66bokpcEeGYai%2B75/XzZwWCaRpmqqq6rpOIL94%2B/7Tzd/5/OHdK8xkfa7rZk6LBLIV/7LYWZenpScAa2YHeQC37lRb%2BUv2P9hBIBAIBAKBQCAQCAQCT7Fu4L3G9thBIBAIBAKBQCAQuEnfICeGX84OAsFmdxCPbHkJOwgEAoFAIBAIBAKBQCAQCAQCgUAgeIgXhV76vb6tHk%2Bxg0AgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBYHXH3R1dfxyPcETeDgKBQCAQCAQCgUAgECzyFGuapqqqGobht2vfvn6ZezqsyJ/WxPWz67qZ0yKBjONYVVV93y8xPCu2%2B/j3a%2BM41m63m28yVdVMC2R5uVzqfD5X27bVNM3cw3NnpmmqcRxrv9/X09O8dwWLBAL3wk06BAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAsF3Q3hTgmNn4C0AAAAASUVORK5CYII%3D\">\n",
"\n",
"</div>\n",
"<div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-3015449722971000298,#minihistogram-3015449722971000298\"\n",
" aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
"</div>\n",
"<div class=\"row collapse col-md-12\" id=\"descriptives-3015449722971000298\">\n",
" <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
" <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-3015449722971000298\"\n",
" aria-controls=\"quantiles-3015449722971000298\" role=\"tab\"\n",
" data-toggle=\"tab\">Statistics</a></li>\n",
" <li role=\"presentation\"><a href=\"#histogram-3015449722971000298\" aria-controls=\"histogram-3015449722971000298\"\n",
" role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
" <li role=\"presentation\"><a href=\"#common-3015449722971000298\" aria-controls=\"common-3015449722971000298\"\n",
" role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
" <li role=\"presentation\"><a href=\"#extreme-3015449722971000298\" aria-controls=\"extreme-3015449722971000298\"\n",
" role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
"\n",
" </ul>\n",
"\n",
" <div class=\"tab-content\">\n",
" <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-3015449722971000298\">\n",
" <div class=\"col-md-4 col-md-offset-1\">\n",
" <p class=\"h4\">Quantile statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr>\n",
" <th>Minimum</th>\n",
" <td>9.175</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5-th percentile</th>\n",
" <td>20.338</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Q1</th>\n",
" <td>35.812</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Median</th>\n",
" <td>49.55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Q3</th>\n",
" <td>62.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95-th percentile</th>\n",
" <td>77.875</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maximum</th>\n",
" <td>88.725</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Range</th>\n",
" <td>79.55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Interquartile range</th>\n",
" <td>26.688</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" <div class=\"col-md-4 col-md-offset-2\">\n",
" <p class=\"h4\">Descriptive statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr>\n",
" <th>Standard deviation</th>\n",
" <td>17.316</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Coef of variation</th>\n",
" <td>0.35173</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kurtosis</th>\n",
" <td>-0.8185</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>49.232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MAD</th>\n",
" <td>14.424</td>\n",
" </tr>\n",
" <tr class=\"\">\n",
" <th>Skewness</th>\n",
" <td>-0.037974</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sum</th>\n",
" <td>442650</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Variance</th>\n",
" <td>299.86</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Memory size</th>\n",
" <td>73.2 KiB</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-3015449722971000298\">\n",
" <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzt3Xt0VNX9///XmJAh0GTkYjIMBIh%2BEIEgpUQh0RZUDCCXtrS1NhpBLWJBMAa8QFpFlMQbiMpHqrSKH0HRLpUvVo0JrQ2yuAQCUaCuQCtCuIRgDRNASLjs3x%2BunJ9DsNq6k8MJz8dasxazz5457/eajPNynzNnfMYYIwAAAFhzjtsFAAAANDcELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwLNrtAs4GJ0%2Be1J49exQXFyefz%2Bd2OQAAnBWMMTp48KBCoZDOOadp15QIWE1gz549SkpKcrsMAADOShUVFerUqVOT7pOA1QTi4uIkffkCx8fHu1wNAABnh5qaGiUlJTmfw02JgNUE6g8LxsfHE7AAAGhibpyew0nuAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFjm6YC1YsUKjRw5UqFQSD6fT0uXLv3auePHj5fP59PcuXMjxqurq5WVlaVAIKBAIKCsrCwdOHAgYs6mTZs0cOBAxcbGqmPHjpo5c6aMMY3SEwAA8D5P/9jz4cOH1adPH91000362c9%2B9rXzli5dqrVr1yoUCjXYlpmZqV27dqmgoECSdOuttyorK0tvvfWWpC9/ifvqq6/WFVdcoXXr1mnr1q0aO3asWrdurSlTpjROYwDOKKm5BW6X8B9ZP2uo2yUAZz1PB6xhw4Zp2LBh/3bO7t27dfvtt%2Bu9997T8OHDI7Z9/PHHKigo0Jo1a9S/f39J0oIFC5SWlqby8nJ1795dixcv1tGjR7Vw4UL5/X6lpKRo69atmjNnjnJyclz5hW4AAHBm8/Qhwm9y8uRJZWVl6a677lKvXr0abF%2B9erUCgYATriRpwIABCgQCWrVqlTNn4MCB8vv9zpwhQ4Zoz549%2BvTTTxu9BwAA4D3NOmA98sgjio6O1uTJk0%2B7vbKyUgkJCQ3GExISVFlZ6cxJTEyM2F5/v37OqWpra1VTUxNxAwAAZ49mG7BKS0v15JNPauHChf/2MN7pthljIsZPnVN/gvvXPW9%2Bfr5z0nwgEFBSUtJ/0wIAAPCoZhuwPvjgA1VVValz586Kjo5WdHS0duzYoSlTpqhr166SpGAwqH379jV47P79%2B51VqmAw2GClqqqqSpIarGzVmzZtmsLhsHOrqKiw2BkAADjTefok938nKytLgwcPjhgbMmSIsrKydNNNN0mS0tLSFA6HVVJSoksvvVSStHbtWoXDYaWnpztzpk%2Bfrrq6OsXExEiSCgsLFQqFnKB2Kr/fH3HOFoBIXvtWHgD8pzwdsA4dOqR//OMfzv3t27errKxMbdu2VefOndWuXbuI%2BS1atFAwGFT37t0lST169NDQoUM1btw4Pfvss5K%2BvEzDiBEjnDmZmZl64IEHNHbsWE2fPl3btm1TXl6e7rvvPr5BCAAATsvThwjXr1%2Bvvn37qm/fvpKknJwc9e3bV/fdd9%2B3fo7Fixerd%2B/eysjIUEZGhi6%2B%2BGK99NJLzvZAIKCioiLt2rVLqampmjBhgnJycpSTk2O9HwAA0Dz4DJckb3Q1NTUKBAIKh8OKj493uxzAdRwibFxcaBT4kpufv55ewQIAADgTEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlnn6twgBAA156Ur5XHUezRUrWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDJPB6wVK1Zo5MiRCoVC8vl8Wrp0qbPt2LFjuueee9S7d2%2B1bt1aoVBIN954o/bs2RPxHNXV1crKylIgEFAgEFBWVpYOHDgQMWfTpk0aOHCgYmNj1bFjR82cOVPGmCbpEQAAeI%2BnA9bhw4fVp08fzZs3r8G2L774Qhs2bNDvfvc7bdiwQW%2B88Ya2bt2qUaNGRczLzMxUWVmZCgoKVFBQoLKyMmVlZTnba2pqdPXVVysUCmndunV6%2Bumn9fjjj2vOnDmN3h8AAPCmaLcL%2BC6GDRumYcOGnXZbIBBQUVFRxNjTTz%2BtSy%2B9VDt37lTnzp318ccfq6CgQGvWrFH//v0lSQsWLFBaWprKy8vVvXt3LV68WEePHtXChQvl9/uVkpKirVu3as6cOcrJyZHP52v0PgEAgLd4egXrPxUOh%2BXz%2BXTuuedKklavXq1AIOCEK0kaMGCAAoGAVq1a5cwZOHCg/H6/M2fIkCHas2ePPv300yatHwAAeMNZE7COHj2qe%2B%2B9V5mZmYqPj5ckVVZWKiEhocHchIQEVVZWOnMSExMjttffr59zqtraWtXU1ETcAADA2eOsCFjHjh3Tddddp5MnT%2BqZZ56J2Ha6Q3zGmIjxU%2BfUn%2BD%2BdYcH8/PznZPmA4GAkpKSvmsLAADAQ5p9wDp27JiuvfZabd%2B%2BXUVFRc7qlSQFg0Ht27evwWP279/vrFIFg8EGK1VVVVWS1GBlq960adMUDoedW0VFha12AACABzTrgFUfrrZt26bly5erXbt2EdvT0tIUDodVUlLijK1du1bhcFjp6enOnBUrVqiurs6ZU1hYqFAopK5du552v36/X/Hx8RE3AABw9vB0wDp06JDKyspUVlYmSdq%2BfbvKysq0c%2BdOHT9%2BXD//%2Bc%2B1fv16LV68WCdOnFBlZaUqKyudsNSjRw8NHTpU48aN05o1a7RmzRqNGzdOI0aMUPfu3SV9eRkHv9%2BvsWPHavPmzXrzzTeVl5fHNwgBAMDX8vRlGtavX68rrrjCuZ%2BTkyNJGjNmjGbMmKFly5ZJkr7//e9HPO7999/XoEGDJEmLFy/W5MmTlZGRIUkaNWpUxHW16i/3MHHiRKWmpqpNmzbKyclx9gUAAHAqn%2BGS5I2upqZGgUBA4XCYw4WApNTcArdLwBli/ayhbpeAZszNz19PHyIEAAA4E3n6ECGA/x%2BrQgBw5mAFCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFjm6YC1YsUKjRw5UqFQSD6fT0uXLo3YbozRjBkzFAqFFBsbq0GDBmnLli0Rc6qrq5WVlaVAIKBAIKCsrCwdOHAgYs6mTZs0cOBAxcbGqmPHjpo5c6aMMY3eHwAA8CZPB6zDhw%2BrT58%2Bmjdv3mm3P/roo5ozZ47mzZundevWKRgM6uqrr9bBgwedOZmZmSorK1NBQYEKCgpUVlamrKwsZ3tNTY2uvvpqhUIhrVu3Tk8//bQef/xxzZkzp9H7AwAA3hTtdgHfxbBhwzRs2LDTbjPGaO7cucrNzdXo0aMlSS%2B%2B%2BKISExP18ssva/z48fr4449VUFCgNWvWqH///pKkBQsWKC0tTeXl5erevbsWL16so0ePauHChfL7/UpJSdHWrVs1Z84c5eTkyOfzNVm/AADAGzy9gvXvbN%2B%2BXZWVlcrIyHDG/H6/Bg4cqFWrVkmSVq9erUAg4IQrSRowYIACgUDEnIEDB8rv9ztzhgwZoj179ujTTz9tmmYAAICnNNuAVVlZKUlKTEyMGE9MTHS2VVZWKiEhocFjExISIuac7jm%2Buo9T1dbWqqamJuIGAADOHs02YNU79RCeMSZi7HSH%2BL5pTv0J7l93eDA/P985aT4QCCgpKem/rh8AAHhPsw1YwWBQUsNVpqqqKmcFKhgMat%2B%2BfQ0eu3///og5p3sOqeHqWL1p06YpHA47t4qKiu/WDAAA8JRmG7CSk5MVDAZVVFTkjNXV1am4uFjp6emSpLS0NIXDYZWUlDhz1q5dq3A4HDFnxYoVqqurc%2BYUFhYqFAqpa9eup9233%2B9XfHx8xA0AAJw9PP0twkOHDukf//iHc3/79u0qKytT27Zt1blzZ2VnZysvL0/dunVTt27dlJeXp1atWikzM1OS1KNHDw0dOlTjxo3Ts88%2BK0m69dZbNWLECHXv3l3Sl5dxeOCBBzR27FhNnz5d27ZtU15enu677z6%2BQQgA31FqboHbJfxH1s8a6nYJ8AhPB6z169friiuucO7n5ORIksaMGaOFCxfq7rvv1pEjRzRhwgRVV1erf//%2BKiwsVFxcnPOYxYsXa/Lkyc63DUeNGhVxXa1AIKCioiJNnDhRqampatOmjXJycpx9AQAAnMpnuCR5o6upqVEgEFA4HOZwIRqN11YCAC9iBctb3Pz8bbbnYAEAALiFgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWefo6WEBj4rIHAID/FitYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMtcCViLFi3S0aNH3dg1AABAo3MlYOXk5CgYDGr8%2BPEqKSlxowQAAIBG40rA2rNnj55//nnt3btXl19%2BuXr16qXZs2dr//79bpQDAABglSsBKzo6WqNHj9ayZcu0c%2BdOjRkzRs8//7w6deqk0aNH6%2B2335Yxxo3SAAAAvjPXT3IPBoO66qqrNGjQIPl8Pq1fv16ZmZnq1q2bPvjgA7fLAwAA%2BI%2B5FrA%2B%2B%2BwzzZ07V3369NFll12mqqoqLV26VDt27NDu3bs1YsQI3XjjjW6VBwAA8F%2BLdmOnP/3pT/XOO%2B8oOTlZv/71rzVmzBidd955zvbvfe97uvvuu/XUU0%2B5UR4AAMB34krAio%2BP1/Lly/XDH/7wa%2Bd06NBB27Zta8KqAAAA7HAlYL344ovfOMfn8%2BmCCy5ogmoAAADscuUcrDvvvFPz5s1rMP6///u/mjJligsVAQAA2ONKwPrTn/6kAQMGNBhPS0vTq6%2B%2B6kJFAAAA9rgSsD777DO1adOmwXh8fLw%2B%2B%2BwzFyoCAACwx5WAdcEFF%2Bi9995rMP7ee%2B8pOTnZhYoAAADsceUk9%2BzsbGVnZ%2Btf//qXrrzySknSX/7yFz366KN6/PHH3SgJAADAGlcC1rhx43T06FHl5eXp/vvvlyR16tRJTz31lG6%2B%2BWY3SgIAALDGlYAlSZMmTdKkSZO0d%2B9excbG6txzz3WrFAAAAKtcC1j1OnTo4HYJAAAAVrlykvv%2B/ft10003qXPnzmrZsqViYmIibrYcP35cv/3tb5WcnKzY2Fidf/75mjlzpk6ePOnMMcZoxowZCoVCio2N1aBBg7Rly5aI56murlZWVpYCgYACgYCysrJ04MABa3UCAIDmxZUVrLFjx%2Bqf//yn7rrrLnXo0EE%2Bn69R9vPII4/o97//vV588UX16tVL69ev10033aRAIKA77rhDkvToo49qzpw5WrhwoS688EI99NBDuvrqq1VeXq64uDhJUmZmpnbt2qWCggJJ0q233qqsrCy99dZbjVI3AADwNlcC1ooVK7RixQr17du3UfezevVq/fjHP9bw4cMlSV27dtUrr7yi9evXS/py9Wru3LnKzc3V6NGjJX35Mz6JiYl6%2BeWXNX78eH388ccqKCjQmjVr1L9/f0nSggULlJaWpvLycnXv3r1RewAAAN7jyiHCTp06Ndqq1Vddfvnl%2Bstf/qKtW7dKkj788EOtXLlS11xzjSRp%2B/btqqysVEZGhvMYv9%2BvgQMHatWqVZK%2BDGmBQMAJV5I0YMAABQIBZw4AAMBXubKC9cQTT2jatGlasGCBOnXq1Gj7ueeeexQOh3XRRRcpKipKJ06c0KxZs/SrX/1KklRZWSlJSkxMjHhcYmKiduzY4cxJSEho8NwJCQnO409VW1ur2tpa535NTY2VfgAAgDe4ErCysrJ08OBBdenSRfHx8WrRokXE9qqqKiv7efXVV7Vo0SK9/PLL6tWrl8rKypSdna1QKKQxY8Y4805dTTPGRIydbrXt1DlflZ%2BfrwceeMBKDwAAwHtcCVgPP/xwk%2Bznrrvu0r333qvrrrtOktS7d2/t2LFD%2Bfn5GjNmjILBoKQvV6m%2BermIqqoqZ1UrGAxq3759DZ57//79DVa%2B6k2bNk05OTnO/ZqaGiUlJVnrCwAAnNlcCVi33HJLk%2Bzniy%2B%2B0DnnRJ5mFhUV5VymITk5WcFgUEVFRc4J93V1dSouLtYjjzwiSUpLS1M4HFZJSYkuvfRSSdLatWsVDoeVnp5%2B2v36/X75/f7GagsAAJzhXLvQ6KeffqqFCxfqn//8p2bPnq2EhAQVFhYqKSlJPXr0sLKPkSNHatasWercubN69eqljRs3as6cOc7P8fh8PmVnZysvL0/dunVTt27dlJeXp1atWikzM1OS1KNHDw0dOlTjxo3Ts88%2BK%2BnLyzSMGDGCbxACAIDTcuVbhB988IF69eql4uJivfbaazp06JAkacOGDbrvvvus7efpp5/Wz3/%2Bc02YMEE9evTQ1KlTNX78eD344IPOnLvvvlvZ2dmaMGGCUlNTtXv3bhUWFjrXwJKkxYsXq3fv3srIyFBGRoYuvvhivfTSS9bqBAAAzYvPGGOaeqfp6en66U9/qrvuuktxcXH68MMPdf7556ukpEQ/%2B9nPVFFR0dQlNaqamhoFAgGFw2HFx8e7XQ6%2BpdTcArdLAHCGWT9rqNsl4D/g5uevKytYH330kX7%2B8583GE9ISND%2B/ftdqAgAAMAeVwLWueeee9prSJWVlaljx44uVAQAAGCPKwHruuuu07333qv9%2B/c715Jau3atpk6dqhtuuMGNkgAAAKxxJWDl5eUpGAyqQ4cOOnTokHr27Kn09HRdcskl%2Bt3vfudGSQAAANa4cpmGmJgYvfrqq9q6das2bNigkydP6gc/%2BIEuuugiN8oBAACwyrXrYEnShRdeqAsvvNDNEgAAAKxzJWDdeuut/3b7c88910SVAAAA2OdKwNq7d2/E/WPHjmnLli06ePCgfvSjH7lREgAAgDWuBKy33nqrwdjx48f1m9/8xtrP5AAAALjFlW8Rnk50dLSmTp2qxx57zO1SAAAAvpMzJmBJ0ieffKJjx465XQYAAMB34sohwrvvvjvivjFGe/fu1bJly3T99de7URIAAIA1rgSs1atXR9w/55xzdN555%2Bnhhx/WuHHj3CgJAADAGlcC1gcffODGbgEAAJrEGXUOFgAAQHPgygrWJZdc4vzI8zcpKSlp5GoAAADsciVgXXHFFXr22Wd14YUXKi0tTZK0Zs0alZeXa/z48fL7/W6UBQAAYIUrAevAgQOaOHGi8vLyIsZzc3O1b98%2B/eEPf3CjLAAAACtcOQfrtdde00033dRgfOzYsfrTn/7kQkUAAAD2uBKw/H6/Vq1a1WB81apVHB4EAACe58ohwsmTJ%2Bu2227Txo0bNWDAAElfnoO1YMECTZ8%2B3Y2SAAAArHElYOXm5io5OVlPPvmknn/%2BeUlSjx49tGDBAmVmZrpREgAAgDWuBCxJyszMJEydZVJzC9wuAQCAJuHahUZramq0cOFC3XfffaqurpYkffjhh9q7d69bJQEAAFjhygrW5s2bNXjwYLVq1UoVFRUaO3as2rRpo9dee027du3Siy%2B%2B6EZZAAD8W15biV8/a6jbJZy1XFnBuvPOO5WZmal//vOfatmypTM%2BfPhwrVixwo2SAAAArHFlBWvdunWaP39%2Bg5/L6dixI4cIAQCA57myghUTE6NDhw41GN%2B2bZvat2/vQkUAAAD2uBKwRo0apQcffFDHjx%2BXJPl8Pu3evVv33nuvRo8e7UZJAAAA1rgSsGbPnq09e/YoGAzqyJEjuvLKK3X%2B%2BeerZcuWDX6fEAAAwGtcOQcrEAho1apVKioq0oYNG3Ty5En94Ac/0JAhQxqclwUAAOA1TR6wjh07pmuuuUbPPPOMMjIylJGR0dQlAAAANKomP0TYokULbdy4sclWqnbv3q0bbrhB7dq1U6tWrfT9739fpaWlznZjjGbMmKFQKKTY2FgNGjRIW7ZsiXiO6upqZWVlKRAIKBAIKCsrSwcOHGiS%2BgEAgPe4cg7WDTfcoBdeeKHR91NdXa3LLrtMLVq00Lvvvqu///3vmj17ts4991xnzqOPPqo5c%2BZo3rx5WrdunYLBoK6%2B%2BmodPHjQmZOZmamysjIVFBSooKBAZWVlysrKavT6AQCAN7n2W4Tz5s3T8uXLlZqaqtatW0dse/TRR63s45FHHlFSUlJEmOvatavzb2OM5s6dq9zcXOfbiy%2B%2B%2BKISExP18ssva/z48fr4449VUFCgNWvWqH///pKkBQsWKC0tTeXl5erevbuVWgEAQPPhygpWaWmpLr74YsXExOijjz7S6tWrnduaNWus7WfZsmVKTU3VL37xCyUkJKhv375asGCBs3379u2qrKyMOA/M7/dr4MCBWrVqlSRp9erVCgQCTriSpAEDBjgn6gMAAJyqSVewPvnkEyUnJ%2BuDDz5osv3Nnz9fOTk5mj59ukpKSjR58mT5/X7deOONqqyslCQlJiZGPC4xMVE7duyQJFVWViohIaHBcyckJDiPP1Vtba1qa2ud%2BzU1NbZaAgAAHtCkK1jdunXT/v37nfu//OUvtW/fvkbbX/3lH/Ly8tS3b1%2BNHz9e48aN0/z58yPmnXrCvTEmYux0J%2BSfOuer8vPznRPiA4GAkpKSLHQDAAC8okkDljEm4v4777yjw4cPN9r%2BOnTooJ49e0aM9ejRQzt37pQkBYNBSWqwElVVVeWsagWDwdOGwP379zdY%2Bao3bdo0hcNh51ZRUfGdewEAAN7hyjlYTeWyyy5TeXl5xNjWrVvVpUsXSVJycrKCwaCKioqc7XV1dSouLlZ6erokKS0tTeFwWCUlJc6ctWvXKhwOO3NO5ff7FR8fH3EDAABnjyY9B8vn8zU4rNaY18O68847lZ6erry8PF177bUqKSnRc889p%2Beee87Zd3Z2tvLy8tStWzd169ZNeXl5atWqlTIzMyV9ueI1dOhQjRs3Ts8%2B%2B6wk6dZbb9WIESP4BiEAADitJg1YxhiNHTtWfr9fknT06FHddtttDS7T8MYbb1jZ3yWXXKI333xT06ZN08yZM5WcnKy5c%2Bfq%2Buuvd%2BbcfffdOnLkiCZMmKDq6mr1799fhYWFiouLc%2BYsXrxYkydPdr5tOGrUKM2bN89KjQAAoPnxmVNPjGpEN91007ea1xQXIW1KNTU1CgQCCofDZ/XhwtTcArdLAICzyvpZQ90uwVVufv426QpWcwtOAAAAp9OsT3IHAABwAwELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWHZWBaz8/Hz5fD5lZ2c7Y7W1tZo0aZLat2%2Bv1q1ba9SoUdq1a1fE43bu3KmRI0eqdevWat%2B%2BvSZPnqy6urqmLh8AAHjEWROw1q1bp%2Beee04XX3xxxHh2drbefPNNLVmyRCtXrtShQ4c0YsQInThxQpJ04sQJDR8%2BXIcPH9bKlSu1ZMkSvf7665oyZYobbQAAAA84KwLWoUOHdP3112vBggVq06aNMx4Oh/XHP/5Rs2fP1uDBg9W3b18tWrRImzZt0vLlyyVJhYWF%2Bvvf/65Fixapb9%2B%2BGjx4sGbPnq0FCxaopqbGrZYAAMAZ7KwIWBMnTtTw4cM1ePDgiPHS0lIdO3ZMGRkZzlgoFFJKSopWrVolSVq9erVSUlIUCoWcOUOGDFFtba1KS0ubpgEAAOAp0W4X0NiWLFmi0tJSrV%2B/vsG2yspKxcTERKxqSVJiYqIqKyudOYmJiRHb27Rpo5iYGGfOqWpra1VbW%2BvcZ6ULAICzS7NewaqoqNAdd9yhxYsXq2XLlt/6ccYY%2BXw%2B5/5X//11c74qPz9fgUDAuSUlJf3nxQMAAM9q1gGrtLRUVVVV6tevn6KjoxUdHa3i4mI99dRTio6OVmJiourq6lRdXR3xuKqqKmfVKhgMNlipqq6u1rFjxxqsbNWbNm2awuGwc6uoqGicBgEAwBmpWQesq666Sps2bVJZWZlzS01N1fXXX%2B/8u0WLFioqKnIes3fvXm3evFnp6emSpLS0NG3evFl79%2B515hQWFsrv96tfv36n3a/f71d8fHzEDQAAnD2a9TlYcXFxSklJiRhr3bq12rVr54zfcsstmjJlitq1a6e2bdtq6tSp6t27t3NCfEZGhnr27KmsrCw99thj%2BvzzzzV16lSNGzeO4AQAAE6rWQesb%2BOJJ55QdHS0rr32Wh05ckRXXXWVFi5cqKioKElSVFSU3n77bU2YMEGXXXaZYmNjlZmZqccff9zlygEAwJnKZ4wxbhfR3NXU1CgQCCgcDp/Vq16puQX9nMpMAAAQ9UlEQVRulwAAZ5X1s4a6XYKr3Pz8bdbnYAEAALiBgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABY1qwDVn5%2Bvi655BLFxcUpISFBP/nJT1ReXh4xp7a2VpMmTVL79u3VunVrjRo1Srt27YqYs3PnTo0cOVKtW7dW%2B/btNXnyZNXV1TVlKwAAwEOi3S6gMRUXF2vixIm65JJLdPz4ceXm5iojI0N///vf1bp1a0lSdna23nrrLS1ZskTt2rXTlClTNGLECJWWlioqKkonTpzQ8OHDdd5552nlypX617/%2BpTFjxsgYo6efftrlDgEA%2BHqpuQVul/CtrZ811O0SrPIZY4zbRTSV/fv3KyEhQcXFxfrRj36kcDis8847Ty%2B99JJ%2B%2BctfSpL27NmjpKQkvfPOOxoyZIjeffddjRgxQhUVFQqFQpKkJUuWaOzYsaqqqlJ8fPw37rempkaBQEDhcPhbzW%2BuvPRGBwA0rcYIWG5%2B/jbrFaxThcNhSVLbtm0lSaWlpTp27JgyMjKcOaFQSCkpKVq1apWGDBmi1atXKyUlxQlXkjRkyBDV1taqtLRUV1xxRdM2cQpCCwAAZ56zJmAZY5STk6PLL79cKSkpkqTKykrFxMSoTZs2EXMTExNVWVnpzElMTIzY3qZNG8XExDhzTlVbW6va2lrnfk1Njc1WAADAGa5Zn%2BT%2BVbfffrs%2B%2BugjvfLKK9841xgjn8/n3P/qv79uzlfl5%2BcrEAg4t6SkpP%2B%2BcAAA4DlnRcCaNGmSli1bpvfff1%2BdOnVyxoPBoOrq6lRdXR0xv6qqylm1CgaDDVaqqqurdezYsQYrW/WmTZumcDjs3CoqKix3BAAAzmTNOmAZY3T77bfrjTfe0F//%2BlclJydHbO/Xr59atGihoqIiZ2zv3r3avHmz0tPTJUlpaWnavHmz9u7d68wpLCyU3%2B9Xv379Trtfv9%2Bv%2BPj4iBsAADh7NOtzsCZOnKiXX35Z/%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%2B3SAADAGYiA9S3MmTNHt9xyi37961%2BrR48emjt3rpKSkjR//ny3SwMAAGegaLcLONPV1dWptLRU9957b8R4RkaGVq1addrH1NbWqra21rkfDoclSTU1NdbrO1F72PpzAgDQ1BrjM7L%2BOY0x1p/7mxCwvsFnn32mEydOKDExMWI8MTFRlZWVp31Mfn6%2BHnjggQbjSUlJjVIjAABeF5jdeM998OBBBQKBxtvBaRCwviWfzxdx3xjTYKzetGnTlJOT49w/efKkPv/8c7Vr1%2B5rH2NDTU2NkpKSVFFRofj4%2BEbbjxvozZvozZuaa2/NtS%2BJ3r6OMUYHDx5UKBRqpOq%2BHgHrG7Rv315RUVENVquqqqoarGrV8/v98vv9EWPnnntuo9V4qvj4%2BGb3BqtHb95Eb97UXHtrrn1J9HY6Tb1yVY%2BT3L9BTEyM%2BvXrp6KioojxoqIipaenu1QVAAA4k7GC9S3k5OQoKytLqampSktL03PPPaedO3fqtttuc7s0AABwBoqaMWPGDLeLONOlpKSoXbt2ysvL0%2BOPP64jR47opZdeUp8%2BfdwurYGoqCgNGjRI0dHNLzvTmzfRmzc1196aa18SvZ1pfMaN7y4CAAA0Y5yDBQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWB60YsUKjRw5UqFQSD6fT0uXLo3YbozRjBkzFAqFFBsbq0GDBmnLli0uVfvt5efn65JLLlFcXJwSEhL0k5/8ROXl5RFzamtrNWnSJLVv316tW7fWqFGjtGvXLpcq/vbmz5%2Bviy%2B%2B2LlQXlpamt59911nu1f7Op38/Hz5fD5lZ2c7Y17tb8aMGfL5fBG3YDDobPfqe63e7t27dcMNN6hdu3Zq1aqVvv/976u0tNTZ7tX%2Bunbt2uB18/l8mjhxoiTv/j1K0vHjx/Xb3/5WycnJio2N1fnnn6%2BZM2fq5MmTzhyvvm4HDx5Udna2unTpotjYWKWnp2vdunXOds/1ZeA577zzjsnNzTWvv/66kWTefPPNiO0PP/ywiYuLM6%2B//rrZtGmT%2BeUvf2k6dOhgampqXKr42xkyZIh54YUXzObNm01ZWZkZPny46dy5szl06JAz57bbbjMdO3Y0RUVFZsOGDeaKK64wffr0McePH3ex8m%2B2bNky8/bbb5vy8nJTXl5upk%2Bfblq0aGE2b95sjPFuX6cqKSkxXbt2NRdffLG54447nHGv9nf//febXr16mb179zq3qqoqZ7tX32vGGPP555%2BbLl26mLFjx5q1a9ea7du3m%2BXLl5t//OMfzhyv9ldVVRXxmhUVFRlJ5v333zfGePfv0RhjHnroIdOuXTvz5z//2Wzfvt386U9/Mt/73vfM3LlznTlefd2uvfZa07NnT1NcXGy2bdtm7r//fhMfH2927dpljPFeXwQsjzs1YJ08edIEg0Hz8MMPO2NHjx41gUDA/P73v3ejxP9aVVWVkWSKi4uNMcYcOHDAtGjRwixZssSZs3v3bnPOOeeYgoICt8r8r7Vp08b84Q9/aDZ9HTx40HTr1s0UFRWZgQMHOgHLy/3df//9pk%2BfPqfd5vX32j333GMuv/zyr93u9f6%2B6o477jAXXHCBOXnypKf/Ho0xZvjw4ebmm2%2BOGBs9erS54YYbjDHefd2%2B%2BOILExUVZf785z9HjPfp08fk5uZ6si8OETYz27dvV2VlpTIyMpwxv9%2BvgQMHatWqVS5W9p8Lh8OSpLZt20qSSktLdezYsYjeQqGQUlJSPNXbiRMntGTJEh0%2BfFhpaWnNpq%2BJEydq%2BPDhGjx4cMS41/vbtm2bQqGQkpOTdd111%2BmTTz6R5P332rJly5Samqpf/OIXSkhIUN%2B%2BfbVgwQJnu9f7q1dXV6dFixbp5ptvls/n8/zf4%2BWXX66//OUv2rp1qyTpww8/1MqVK3XNNddI8u7rdvz4cZ04cUItW7aMGI%2BNjdXKlSs92Zd3LomKb6X%2BR6lP/SHqxMRE7dixw42S/ivGGOXk5Ojyyy9XSkqKpC97i4mJUZs2bSLmJiYmNvgx7jPRpk2blJaWpqNHj%2Bp73/ue3nzzTfXs2VNlZWWe7kuSlixZotLSUq1fv77BNi%2B/bv3799f//d//6cILL9S%2Bffv00EMPKT09XVu2bPH8e%2B2TTz7R/PnzlZOTo%2BnTp6ukpESTJ0%2BW3%2B/XjTfe6Pn%2B6i1dulQHDhzQ2LFjJXn771GS7rnnHoXDYV100UWKiorSiRMnNGvWLP3qV7%2BS5N3PgLi4OKWlpenBBx9Ujx49lJiYqFdeeUVr165Vt27dPNkXAauZ8vl8EfeNMQ3GzmS33367PvroI61cufIb53qlt%2B7du6usrEwHDhzQ66%2B/rjFjxqi4uPhr53ulr4qKCt1xxx0qLCxs8H%2Bf/44X%2Bhs2bJjz7969eystLU0XXHCBXnzxRQ0YMECSd99rJ0%2BeVGpqqvLy8iRJffv21ZYtWzR//nzdeOONzjyv9lfvj3/8o4YNG6ZQKPRv53mlr1dffVWLFi3Syy%2B/rF69eqmsrEzZ2dkKhUIaM2aMM8%2BLr9tLL72km2%2B%2BWR07dlRUVJR%2B8IMfKDMzUxs2bHDmeKkvDhE2M/XfcDr1/8SqqqoaJP8z1aRJk7Rs2TK9//776tSpkzMeDAZVV1en6urqiPle6S0mJkb/8z//o9TUVOXn56tPnz568sknPd9XaWmpqqqq1K9fP0VHRys6OlrFxcV66qmnFB0drcTERE/391WtW7dW7969tW3bNs%2B/1zp06KCePXtGjPXo0UM7d%2B6U1Dz%2BW7Jjxw4tX75cv/71r50xr7/f7rrrLt1777267rrr1Lt3b2VlZenOO%2B9Ufn6%2BJG%2B/bhdccIGKi4t16NAhVVRUqKSkRMeOHVNycrIn%2ByJgNTP1f4hFRUXOWF1dnYqLi5Wenu5iZd/MGKPbb79db7zxhv76178qOTk5Ynu/fv3UokWLiN727t2rzZs3n/G9nY4xRrW1tZ7v66qrrtKmTZtUVlbm3FJTU3X99dc7//Zyf19VW1urjz/%2BWB06dPD0e02SLrvssgaXQdm6dau6dOkiydv/Lan3wgsvKCEhQcOHD3fGvP5%2B%2B%2BKLL3TOOZEf3VFRUc5lGprD69a6dWt16NBB1dXVeu%2B99/TjH//Ym325c249vouDBw%2BajRs3mo0bNxpJZs6cOWbjxo1mx44dxpgvv8oaCATMG2%2B8YTZt2mR%2B9atfndFfZa33m9/8xgQCAfO3v/0t4ivWX3zxhTPntttuM506dTLLly83GzZsMFdeeaUnvl49bdo0s2LFCrN9%2B3bz0UcfmenTp5tzzjnHFBYWGmO829fX%2Beq3CI3xbn9Tpkwxf/vb38wnn3xi1qxZY0aMGGHi4uLMp59%2Baozx7nvNmC8vqREdHW1mzZpltm3bZhYvXmxatWplFi1a5Mzxcn8nTpwwnTt3Nvfcc0%2BDbV79ezTGmDFjxpiOHTs6l2l44403TPv27c3dd9/tzPHq61ZQUGDeffdd88knn5jCwkLTp08fc%2Bmll5q6ujpjjPf6ImB50Pvvv28kNbiNGTPGGPPl13Tvv/9%2BEwwGjd/vNz/60Y/Mpk2b3C36WzhdT5LMCy%2B84Mw5cuSIuf32203btm1NbGysGTFihNm5c6d7RX9LN998s%2BnSpYuJiYkx5513nrnqqquccGWMd/v6OqcGLK/2V3%2BdnRYtWphQKGRGjx5ttmzZ4mz36nut3ltvvWVSUlKM3%2B83F110kXnuuecitnu5v/fee89IMuXl5Q22efXv0RhjampqzB133GE6d%2B5sWrZsac4//3yTm5tramtrnTlefd1effVVc/7555uYmBgTDAbNxIkTzYEDB5ztXuvLZ4wxriydAQAANFOcgwUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwLL/D11X2GEU3OX8AAAAAElFTkSuQmCC\"/>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-3015449722971000298\">\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">47.75</td>\n",
" <td class=\"number\">11</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">51.449999809265</td>\n",
" <td class=\"number\">9</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">54.5</td>\n",
" <td class=\"number\">8</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">55.949999809265</td>\n",
" <td class=\"number\">8</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">49.675000190735</td>\n",
" <td class=\"number\">8</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">57.925000190735</td>\n",
" <td class=\"number\">8</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">50.10000038147</td>\n",
" <td class=\"number\">8</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">49.050000190735</td>\n",
" <td class=\"number\">7</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">53.699999809265</td>\n",
" <td class=\"number\">7</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">47.97500038147</td>\n",
" <td class=\"number\">7</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"other\">\n",
" <td class=\"fillremaining\">Other values (4892)</td>\n",
" <td class=\"number\">8910</td>\n",
" <td class=\"number\">95.2%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"missing\">\n",
" <td class=\"fillremaining\">(Missing)</td>\n",
" <td class=\"number\">366</td>\n",
" <td class=\"number\">3.9%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"extreme-3015449722971000298\">\n",
" <p class=\"h4\">Minimum 5 values</p>\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">9.1750001907349</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">9.2249999046326</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">9.3000001907349</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">9.5999999046326</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">9.7999999523163</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" <p class=\"h4\">Maximum 5 values</p>\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">86.625001907349</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">86.950000762939</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">87.074998855591</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">87.174999237061</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">88.72500038147</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"</div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4 pp-anchor\" id=\"pp_var_T\">T<br/>\n",
" <small>Numeric</small>\n",
" </p>\n",
" </div><div class=\"col-md-6\">\n",
" <div class=\"row\">\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
" <tr>\n",
" <th>Distinct count</th>\n",
" <td>3368</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Unique (%)</th>\n",
" <td>36.0%</td>\n",
" </tr>\n",
" <tr class=\"alert\">\n",
" <th>Missing (%)</th>\n",
" <td>3.9%</td>\n",
" </tr>\n",
" <tr class=\"alert\">\n",
" <th>Missing (n)</th>\n",
" <td>366</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Infinite (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Infinite (n)</th>\n",
" <td>0</td>\n",
" </tr>\n",
" </table>\n",
"\n",
" </div>\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
"\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>18.316</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Minimum</th>\n",
" <td>-1.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maximum</th>\n",
" <td>44.6</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Zeros (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"<div class=\"col-md-3 collapse in\" id=\"minihistogram-3677506896709781474\">\n",
" <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAAdlJREFUeJzt3E2u0mAYhuG3xGlhTuguXIR7cuzQ/bgIdwFhAW0cOJA6UM5EfXKOCf2h1zUEkq8kvfP1LaTNOI5jTexyuVTXdVMvy8qdz%2Bc6nU6Trvlu0tV%2Ba9u2qn594f1%2BP8chsCJ931fXdS/nzZRmCaRpmqqq2u/3qwrk/ccvb/r8108fHnQk23Q/b6a0m3xFWBGBQCAQCAQCwSxD%2Bla8daivMtgvjR0EAoFAIBAIzCALY25ZFjsIBAKBQCAQbHYG%2BZ9rfbbHDgKBQCAQCAQCgUAgEAgEAoFAIBAIBALBU/yS7ldxHuUpAtk6z%2Bt6HJdYEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAn812SBPb3w9OwgEAoFAIBCYQXiVrc4tdhAIBAKBQCAQCASLG9I9gIElWVwgPI9nuPM1SyDjOFZVVd/3f7z34/u3qQ%2BHBfnbOXF/7X7eTGmWQIZhqKqqruvmWJ4FO3z%2B93vDMNThcJjuYKqqGWfI8na71fV6rbZtq2maqZdnZcZxrGEY6ng81m437X2lWQKBtXCbFwKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoHgJxwbVrgTdKwLAAAAAElFTkSuQmCC\">\n",
"\n",
"</div>\n",
"<div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-3677506896709781474,#minihistogram-3677506896709781474\"\n",
" aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
"</div>\n",
"<div class=\"row collapse col-md-12\" id=\"descriptives-3677506896709781474\">\n",
" <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
" <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-3677506896709781474\"\n",
" aria-controls=\"quantiles-3677506896709781474\" role=\"tab\"\n",
" data-toggle=\"tab\">Statistics</a></li>\n",
" <li role=\"presentation\"><a href=\"#histogram-3677506896709781474\" aria-controls=\"histogram-3677506896709781474\"\n",
" role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
" <li role=\"presentation\"><a href=\"#common-3677506896709781474\" aria-controls=\"common-3677506896709781474\"\n",
" role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
" <li role=\"presentation\"><a href=\"#extreme-3677506896709781474\" aria-controls=\"extreme-3677506896709781474\"\n",
" role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
"\n",
" </ul>\n",
"\n",
" <div class=\"tab-content\">\n",
" <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-3677506896709781474\">\n",
" <div class=\"col-md-4 col-md-offset-1\">\n",
" <p class=\"h4\">Quantile statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr>\n",
" <th>Minimum</th>\n",
" <td>-1.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5-th percentile</th>\n",
" <td>4.575</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Q1</th>\n",
" <td>11.787</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Median</th>\n",
" <td>17.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Q3</th>\n",
" <td>24.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95-th percentile</th>\n",
" <td>34.475</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maximum</th>\n",
" <td>44.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Range</th>\n",
" <td>46.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Interquartile range</th>\n",
" <td>12.613</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" <div class=\"col-md-4 col-md-offset-2\">\n",
" <p class=\"h4\">Descriptive statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr>\n",
" <th>Standard deviation</th>\n",
" <td>8.8329</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Coef of variation</th>\n",
" <td>0.48225</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kurtosis</th>\n",
" <td>-0.45675</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>18.316</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MAD</th>\n",
" <td>7.2456</td>\n",
" </tr>\n",
" <tr class=\"\">\n",
" <th>Skewness</th>\n",
" <td>0.3093</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sum</th>\n",
" <td>164680</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Variance</th>\n",
" <td>78.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Memory size</th>\n",
" <td>73.2 KiB</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-3677506896709781474\">\n",
" <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzt3X9UVWW%2Bx/HPSeSIBicQ4UCiMV1zLNQCStBK7QdK/rgz1m0aioFyqG6mIXodyXVntDtKU%2Bo05dQt%2B%2BWULa1bWjM2DFgO6hJUUErM5VhZYIFoIaBXD6T7/tFy305Q1vjA7sD7tdZei/3sZ%2B/93Q8Vn569zz4uy7IsAQAAwJhznC4AAACgqyFgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhQU4X0B2cOnVKn376qUJDQ%2BVyuZwuBwCAbsGyLDU3Nys2NlbnnNO5c0oErE7w6aefKi4uzukyAADolmpqatS/f/9OPScBqxOEhoZK%2BvIXHBYW5nA1AAB0D01NTYqLi7P/DncmAlYnOH1bMCwsjIAFAEAnc%2BLxHB5yBwAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhfNkz0EUkzyt0uoTvrHzheKdLAIAOxQwWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGBbQAWvjxo2aNGmSYmNj5XK5tHbtWr/tLper3eXhhx%2B2%2B1xwwQVtts%2BdO9fvONXV1Zo0aZL69OmjyMhIzZgxQy0tLZ1yjQAAIPAE9Gsajh07puHDh%2Bv222/XjTfe2GZ7bW2t3/pf//pXTZ06tU3fBx54QDk5Ofb6ueeea/988uRJTZgwQf369dPmzZv12WefKSsrS5Zl6bHHHjN8RQAAoCsI6ICVnp6u9PT0b9zu9Xr91l9//XWNHTtWP/rRj/zaQ0ND2/Q9raioSO%2B9955qamoUGxsrSVqyZImys7O1cOFChYWFneVVAACAriagbxF%2BHwcPHtS6des0derUNtt%2B97vfqW/fvrr00ku1cOFCv9t/paWlSkhIsMOVJI0bN04%2Bn08VFRXtnsvn86mpqclvAQAA3UdAz2B9HytWrFBoaKimTJni137fffcpMTFR4eHh2rZtm/Lz87V//349/fTTkqS6ujpFR0f77RMeHq7g4GDV1dW1e66CggItWLCgYy4EAAD84HWbgPXss8/q1ltvVa9evfzaZ86caf88bNgwhYeH66abbrJntaQvH5b/Osuy2m2XpPz8fOXl5dnrTU1NiouLM3EZAAAgAHSLgLVp0ybt3btXq1evPmPflJQUSdL777%2Bvvn37yuv1auvWrX59Ghoa1Nra2mZm6zS32y232332hQMAgIDULZ7BeuaZZ5SUlKThw4efse/OnTslSTExMZKk1NRUVVVV%2BX0isaioSG63W0lJSR1TMAAACGgBPYN19OhRvf/%2B%2B/b6/v37VVlZqYiICA0YMEDSl7fnXnnlFS1ZsqTN/qWlpSorK9PYsWPl8Xi0fft2zZw5U5MnT7b3T0tL08UXX6zMzEw9/PDD%2BvzzzzV79mzl5OTwCUIAANCugA5Y5eXlGjt2rL1%2B%2BrmnrKwsPf/885KkVatWybIs/fznP2%2Bzv9vt1urVq7VgwQL5fD4NHDhQOTk5mjNnjt2nR48eWrdune655x6NGjVKISEhysjI0OLFizv24gAAQMByWZZlOV1EV9fU1CSPx6PGxkZmvdBhkucVOl3Cd1a%2BcLzTJQDoBpz8%2B9stnsECAADoTAQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMCwIKcLAND9JM8rdLqE76V84XinSwAQYJjBAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGBXTA2rhxoyZNmqTY2Fi5XC6tXbvWb3t2drZcLpffkpKS4tfH5/Np%2BvTpioyMVJ8%2BfTR58mQdOHDAr091dbUmTZqkPn36KDIyUjNmzFBLS0uHXx8AAAhMAR2wjh07puHDh2vZsmXf2Gf8%2BPGqra21lzfffNNve25urtasWaNVq1Zp8%2BbNOnr0qCZOnKiTJ09Kkk6ePKkJEybo2LFj2rx5s1atWqVXX31Vs2bN6tBrAwAAgSvI6QLORnp6utLT07%2B1j9vtltfrbXdbY2OjnnnmGb3wwgu67rrrJEkvvvii4uLitH79eo0bN05FRUV67733VFNTo9jYWEnSkiVLlJ2drYULFyosLMzsRQEAgIAX0DNY38Xf//53RUVF6aKLLlJOTo7q6%2BvtbRUVFWptbVVaWprdFhsbq4SEBG3ZskWSVFpaqoSEBDtcSdK4cePk8/lUUVHR7jl9Pp%2Bampr8FgAA0H106YCVnp6ulStX6u2339aSJUu0fft2XXPNNfL5fJKkuro6BQcHKzw83G%2B/6Oho1dXV2X2io6P9toeHhys4ONju83UFBQXyeDz2EhcX1wFXBwAAfqgC%2BhbhmfzsZz%2Bzf05ISFBycrIGDhyodevWacqUKd%2B4n2VZcrlc9vpXf/6mPl%2BVn5%2BvvLw8e72pqYmQBQBAN9KlZ7C%2BLiYmRgMHDtS%2BffskSV6vVy0tLWpoaPDrV19fb89aeb3eNjNVDQ0Nam1tbTOzdZrb7VZYWJjfAgAAuo9uFbA%2B%2B%2Bwz1dTUKCYmRpKUlJSknj17qri42O5TW1urqqoqjRw5UpKUmpqqqqoq1dbW2n2KiorkdruVlJTUuRcAAAACQkDfIjx69Kjef/99e33//v2qrKxURESEIiIiNH/%2BfN14442KiYnRRx99pPvvv1%2BRkZH66U9/KknyeDyaOnWqZs2apb59%2ByoiIkKzZ8/W0KFD7U8VpqWl6eKLL1ZmZqYefvhhff7555o9e7ZycnKYmQIAAO0K6IBVXl6usWPH2uunn3vKysrSE088oV27dulPf/qTjhw5opiYGI0dO1arV69WaGiovc/vf/97BQUF6eabb9bx48d17bXX6vnnn1ePHj0kST169NC6det0zz33aNSoUQoJCVFGRoYWL17cuRcLAAAChsuyLMvpIrq6pqYmeTweNTY2MuuFDpM8r9DpErqs8oXjnS4BwD/Byb%2B/3eoZLAAAgM4Q0LcIAaAzBNrsIDNugPOYwQIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMOCnC4A%2BKFKnlfodAkAgADFDBYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgWEAHrI0bN2rSpEmKjY2Vy%2BXS2rVr7W2tra361a9%2BpaFDh6pPnz6KjY3VL37xC3366ad%2Bx7jgggvkcrn8lrlz5/r1qa6u1qRJk9SnTx9FRkZqxowZamlp6ZRrBAAAgSegXzR67NgxDR8%2BXLfffrtuvPFGv23/%2B7//qx07dug///M/NXz4cDU0NCg3N1eTJ09WeXm5X98HHnhAOTk59vq5555r/3zy5ElNmDBB/fr10%2BbNm/XZZ58pKytLlmXpscce69gLBAAAASmgA1Z6errS09Pb3ebxeFRcXOzX9thjj%2BmKK65QdXW1BgwYYLeHhobK6/W2e5yioiK99957qqmpUWxsrCRpyZIlys7O1sKFCxUWFmboagAAQFcR0LcIv6/Gxka5XC6dd955fu2/%2B93v1LdvX1166aVauHCh3%2B2/0tJSJSQk2OFKksaNGyefz6eKiopOqx0AAASOgJ7B%2Bj5OnDihuXPnKiMjw2/W6b777lNiYqLCw8O1bds25efna//%2B/Xr66aclSXV1dYqOjvY7Vnh4uIKDg1VXV9fuuXw%2Bn3w%2Bn73e1NTUAVcEAAB%2BqLpFwGptbdUtt9yiU6dO6fHHH/fbNnPmTPvnYcOGKTw8XDfddJM9qyVJLperzTEty2q3XZIKCgq0YMECg1cAAAACSZe/Rdja2qqbb75Z%2B/fvV3Fx8RmfmUpJSZEkvf/%2B%2B5Ikr9fbZqaqoaFBra2tbWa2TsvPz1djY6O91NTUGLgSAAAQKLp0wDodrvbt26f169fbM1LfZufOnZKkmJgYSVJqaqqqqqpUW1tr9ykqKpLb7VZSUlK7x3C73QoLC/NbAABA9xHQtwiPHj1qzzRJ0v79%2B1VZWamIiAjFxsbqpptu0o4dO/SXv/xFJ0%2BetGeiIiIiFBwcrNLSUpWVlWns2LHyeDzavn27Zs6cqcmTJ9ufMkxLS9PFF1%2BszMxMPfzww/r88881e/Zs5eTkEJwAAEC7AjpglZeXa%2BzYsfZ6Xl6eJCkrK0vz58/XG2%2B8IUm69NJL/fbbsGGDxowZI7fbrdWrV2vBggXy%2BXwaOHCgcnJyNGfOHLtvjx49tG7dOt1zzz0aNWqUQkJClJGRocWLF3fCFQIAgEAU0AFrzJgxsizrG7d/2zZJSkxMVFlZ2RnPM2DAAP3lL3/53vUBAIDuqUs/gwUAAOAEAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYY4ErBdffFEnTpxw4tQAAAAdzpGAlZeXJ6/Xq7vuukvbtm1zogQAAIAO40jA%2BvTTT/Xss8%2BqtrZWV155pS655BItWbJEhw4dcqIcAAAAoxwJWEFBQZoyZYreeOMNVVdXKysrS88%2B%2B6z69%2B%2BvKVOmaN26dbIsy4nSAAAAzprjD7l7vV5de%2B21GjNmjFwul8rLy5WRkaFBgwZp06ZNTpcHAADwvTkWsA4fPqxHHnlEw4cP16hRo1RfX6%2B1a9fq448/1ieffKKJEyfqF7/4hVPlAQAA/NOCnDjpT3/6U7355puKj4/XL3/5S2VlZalfv3729nPPPVdz5szRo48%2B6kR5AAAAZ8WRgBUWFqb169frqquu%2BsY%2BMTEx2rdvXydWBQAAYIYjAWvFihVn7ONyuXThhRd2QjUAAABmOfIM1syZM7Vs2bI27X/84x81a9YsByoCAAAwx5GA9corryglJaVNe2pqqlavXu1ARQAAAOY4ErAOHz6s8PDwNu1hYWE6fPiwAxUBAACY40jAuvDCC/W3v/2tTfvf/vY3xcfHO1ARAACAOY485J6bm6vc3Fx99tlnuuaaayRJb731lh566CEtXrzYiZIAAACMcSRg5eTk6MSJE1q0aJF%2B85vfSJL69%2B%2BvRx99VHfccYcTJQEAABjjSMCSpOnTp2v69Omqra1VSEiIzjvvPKdKAQAAMMqxgHVaTEyM0yUAAAAY5chD7ocOHdLtt9%2BuAQMGqFevXgoODvZbAAAAApkjM1jZ2dn64IMP9B//8R%2BKiYmRy%2BVyogwAAIAO4UjA2rhxozZu3KjLLrvMidMDAAB0KEduEfbv359ZKwAA0GU5ErB%2B//vfKz8/XwcOHDir42zcuFGTJk1SbGysXC6X1q5d67fdsizNnz9fsbGxCgkJ0ZgxY7R7926/Pg0NDcrMzJTH45HH41FmZqaOHDni12fXrl0aPXq0QkJCdP755%2BuBBx6QZVlnVTsAAOi6HAlYmZmZ2rBhgwYOHKjw8HBFRUX5Ld/VsWPHNHz48Ha/OFqSHnroIS1dulTLli3T9u3b5fV6df3116u5udnuk5GRocrKShUWFqqwsFCVlZXKzMy0tzc1Nen6669XbGystm/frscee0yLFy/W0qVL//kBAAAAXZojz2A9%2BOCDRo6Tnp6u9PT0drdZlqVHHnlE8%2BbN05QpUyRJK1asUHR0tF566SXddddd2rNnjwoLC1VWVqYRI0ZIkpYvX67U1FTt3btXgwcP1sqVK3XixAk9//zzcrvdSkhI0D/%2B8Q8tXbpUeXl53OoEAABtOBKwpk6d2uHn2L9/v%2Brq6pSWlma3ud1ujR49Wlu2bNFdd92l0tJSeTweO1xJUkpKijwej7Zs2aLBgwertLRUo0ePltvttvuMGzdO%2Bfn5%2Buijj/juRAAA0IYjtwgl6aOPPtL8%2BfOVmZmp%2Bvp6SVJRUZH27Nlj5Ph1dXWSpOjoaL/26Ohoe1tdXV27tySjoqL8%2BrR3jK%2Be4%2Bt8Pp%2Bampr8FgAA0H04ErA2bdqkSy65RCUlJXr55Zd19OhRSdKOHTv061//2ui5vn4Lz7Isv7b2bvGdqc/pB9y/6fZgQUGB/dC8x%2BNRXFzcP10/AAAIPI4ErF/96leaP3%2B%2BNmzY4Pfm9muuuUZlZWVGzuH1eiW1nWWqr6%2B3Z6C8Xq8OHjzYZt9Dhw759WnvGFLb2bHT8vPz1djYaC81NTVndzEAACCgOBKw3n33Xd10001t2qOionTo0CEj54iPj5fX61VxcbHd1tLSopKSEo0cOVKSlJqaqsbGRm3bts3us3XrVjU2Nvr12bhxo1paWuw%2BRUVFio2N1QUXXNDuud1ut8LCwvwWAADQfTgSsM4777x2n1%2BqrKzU%2Beef/52Pc/ToUVVWVqqyslLSlw%2B2V1ZWqrq6Wi6XS7m5uVq0aJHWrFmjqqoqZWdnq3fv3srIyJAkDRkyROPHj1dOTo7KyspUVlamnJwcTZw4UYMHD5b05Wsc3G63srOzVVVVpTVr1mjRokV8ghAAAHwjRz5FeMstt2ju3Ln6n//5HzukbN26VbNnz9Ztt932nY9TXl6usWPH2ut5eXmSpKysLD3//POaM2eOjh8/rnvuuUcNDQ0aMWKEioqKFBoaau%2BzcuVKzZgxw/604eTJk/3eq%2BXxeFRcXKxp06YpOTlZ4eHhysvLs88FAADwdS7LgVeSt7S0KDMzU6%2B%2B%2BqpOnTql4OBgtba26uabb9YLL7ygoCBHcl%2BHaWpqksfjUWNjI7cLA0jyvEKnSwD%2BKeULxztdAvCD4OTfX0eSTHBwsFavXq1//OMf2rFjh06dOqXExET9%2BMc/dqIcAAAAoxydKrrooot00UUXOVkCAACAcY4ErDvvvPNbtz/11FOdVAkAAIB5jgSs2tpav/XW1lbt3r1bzc3Nuvrqq50oCQAAwBhHAtaf//znNm1ffPGF/v3f/11DhgxxoCIAAABzHPsuwq8LCgrS7Nmz9fDDDztdCgAAwFn5wQQsSfrwww/V2trqdBkAAABnxZFbhHPmzPFbtyxLtbW1euONN3Trrbc6URIAAIAxjgSs0tJSv/VzzjlH/fr104MPPqicnBwnSkIn4MWdAIDuwpGAtWnTJidOCwAA0Cm61nfSAAACaraYr/VBV%2BVIwLr88svtL3k%2Bk23btnVwNQAAAGY5ErDGjh2rJ598UhdddJFSU1MlSWVlZdq7d6/uuusuud1uJ8oCAAAwwpGAdeTIEU2bNk2LFi3ya583b54OHjyop59%2B2omyAAAAjHDkPVgvv/yybr/99jbt2dnZeuWVVxyoCAAAwBxHApbb7daWLVvatG/ZsoXbgwAAIOA5cotwxowZuvvuu7Vz506lpKRI%2BvIZrOXLl%2Bv%2B%2B%2B93oiQAAABjHAlY8%2BbNU3x8vP7whz/o2WeflSQNGTJEy5cvV0ZGhhMlAQAAGOPYe7AyMjIIUwAAoEty7Muem5qa9Pzzz%2BvXv/61GhoaJEnvvPOOamtrnSoJAADACEdmsKqqqnTdddepd%2B/eqqmpUXZ2tsLDw/Xyyy/rwIEDWrFihRNlAQAAGOHIDNbMmTOVkZGhDz74QL169bLbJ0yYoI0bNzpREgAAgDGOzGBt375dTzzxRJuvyzn//PO5RQgAAAKeIzNYwcHBOnr0aJv2ffv2KTIy0oGKAAAAzHEkYE2ePFn/9V//pS%2B%2B%2BEKS5HK59Mknn2ju3LmaMmWKEyUBAAAY40jAWrJkiT799FN5vV4dP35c11xzjX70ox%2BpV69ebb6fEAAAINA48gyWx%2BPRli1bVFxcrB07dujUqVNKTEzUuHHj2jyXBQAAEGg6PWC1trbqhhtu0OOPP660tDSlpaV1dgkAAAAdqtNvEfbs2VM7d%2B5kpgoAAHRZjjyDddttt%2Bm5555z4tQAAAAdzrHvIly2bJnWr1%2Bv5ORk9enTx2/bQw895FBVAAAAZ8%2BRGayKigoNGzZMwcHBevfdd1VaWmovZWVlxs5zwQUXyOVytVmmTZsmSRozZkybbbfccovfMRoaGpSZmSmPxyOPx6PMzEwdOXLEWI0AAKDr6dQZrA8//FDx8fHatGlTp5xv%2B/btOnnypL1eVVWl66%2B/Xv/2b/9mt%2BXk5OiBBx6w10NCQvyOkZGRoQMHDqiwsFCSdOeddyozM1N//vOfO7h6AAAQqDo1YA0aNEi1tbWKioqSJP3sZz/To48%2Bqujo6A45X79%2B/fzWH3zwQV144YUaPXq03da7d295vd5299%2BzZ48KCwtVVlamESNGSJKWL1%2Bu1NRU7d27V4MHD%2B6QugEAQGDr1FuElmX5rb/55ps6duxYp5y7paVFL774ou644w6/TzCuXLlSkZGRuuSSSzR79mw1Nzfb20pLS%2BXxeOxwJUkpKSn2e7wAAADa49hD7p1t7dq1OnLkiLKzs%2B22W2%2B9VfHx8fJ6vaqqqlJ%2Bfr7eeecdFRcXS5Lq6urs2bavioqKUl1d3Teey%2Bfzyefz2etNTU3mLgQAAPzgdWrAOv0g%2BdfbOsMzzzyj9PR0xcbG2m05OTn2zwkJCRo0aJCSk5O1Y8cOJSYmfmN9lmV9a90FBQVasGCBweoBAEAg6dSAZVmWsrOz5Xa7JUknTpzQ3Xff3eY1Da%2B99prR83788cdav379GY%2BbmJionj17at%2B%2BfUpMTJTX69XBgwfb9Dt06NC3PjeWn5%2BvvLw8e72pqUlxcXH//AUAAICA0qkBKysry2/9tttu65TzPvfcc4qKitKECRO%2Btd/u3bvV2tqqmJgYSVJqaqoaGxu1bds2XXHFFZKkrVu3qrGxUSNHjvzG47jdbjtEAgCA7qdTA5YTb28/deqUnnvuOWVlZSko6P8v94MPPtDKlSt1ww03KDIyUu%2B9955mzZqlyy67TKNGjZIkDRkyROPHj1dOTo6efPJJSV%2B%2BpmHixIl8ghAAAHwjR1402pnWr1%2Bv6upq3XHHHX7twcHBeuuttzRu3DgNHjxYM2bMUFpamtavX68ePXrY/VauXKmhQ4faX0w9bNgwvfDCC519GQAAIIB0%2BU8RpqWltXk9hCTFxcWppKTkjPtHREToxRdf7IjSAABAF9XlZ7AAAAA6GwELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGNalA9b8%2BfPlcrn8Fq/Xa2%2B3LEvz589XbGysQkJCNGbMGO3evdvvGA0NDcrMzJTH45HH41FmZqaOHDnS2ZcCAAACSJcOWJJ0ySWXqLa21l527dplb3vooYe0dOlSLVu2TNu3b5fX69X111%2Bv5uZmu09GRoYqKytVWFiowsJCVVZWKjMz04lLAQAAASLI6QI6WlBQkN%2Bs1WmWZemRRx7RvHnzNGXKFEnSihUrFB0drZdeekl33XWX9uzZo8LCQpWVlWnEiBGSpOXLlys1NVV79%2B7V4MGDO/VaAABAYOjyM1j79u1TbGys4uPjdcstt%2BjDDz%2BUJO3fv191dXVKS0uz%2B7rdbo0ePVpbtmyRJJWWlsrj8djhSpJSUlLk8XjsPgAAAF/XpWewRowYoT/96U%2B66KKLdPDgQf32t7/VyJEjtXv3btXV1UmSoqOj/faJjo7Wxx9/LEmqq6tTVFRUm%2BNGRUXZ%2B7fH5/PJ5/PZ601NTSYuBwAABIguHbDS09Ptn4cOHarU1FRdeOGFWrFihVJSUiRJLpfLbx/Lsvzavr69vT5fV1BQoAULFpxt%2BQAAIEB1%2BVuEX9WnTx8NHTpU%2B/bts5/L%2BvpMVH19vT2r5fV6dfDgwTbHOXToUJuZr6/Kz89XY2OjvdTU1Bi8CgAA8EPXrQKWz%2BfTnj17FBMTo/j4eHm9XhUXF9vbW1paVFJSopEjR0qSUlNT1djYqG3bttl9tm7dqsbGRrtPe9xut8LCwvwWAADQfXTpW4SzZ8/WpEmTNGDAANXX1%2Bu3v/2tmpqalJWVJZfLpdzcXC1atEiDBg3SoEGDtGjRIvXu3VsZGRmSpCFDhmj8%2BPHKycnRk08%2BKUm68847NXHiRD5BCAAAvlGXDlgHDhzQz3/%2Bcx0%2BfFj9%2BvVTSkqKysrKNHDgQEnSnDlzdPz4cd1zzz1qaGjQiBEjVFRUpNDQUPsYK1eu1IwZM%2BxPG06ePFnLli1z5HoAoKtJnlfodAnfS/nC8U6XgADhsizLcrqIrq6pqUkej0eNjY3d%2BnZhoP2HFAC%2BjoAVWJz8%2B9utnsECAADoDAQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACJfm2BAAAMC0lEQVSGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAsCCnCwAAIFAkzyt0uoTvpXzheKdL6LaYwQIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGdemAVVBQoMsvv1yhoaGKiorST37yE%2B3du9evz5gxY%2BRyufyWW265xa9PQ0ODMjMz5fF45PF4lJmZqSNHjnTmpQAAgADSpQNWSUmJpk2bprKyMhUXF%2BuLL75QWlqajh075tcvJydHtbW19vLkk0/6bc/IyFBlZaUKCwtVWFioyspKZWZmdualAACAANKlXzRaWOj/QrjnnntOUVFRqqio0NVXX2239%2B7dW16vt91j7NmzR4WFhSorK9OIESMkScuXL1dqaqr27t2rwYMHd9wFAACAgNSlZ7C%2BrrGxUZIUERHh175y5UpFRkbqkksu0ezZs9Xc3GxvKy0tlcfjscOVJKWkpMjj8WjLli2dUzgAAAgoXXoG66ssy1JeXp6uvPJKJSQk2O233nqr4uPj5fV6VVVVpfz8fL3zzjsqLi6WJNXV1SkqKqrN8aKiolRXV9fuuXw%2Bn3w%2Bn73e1NRk%2BGoAAMAPWbcJWPfee6/effddbd682a89JyfH/jkhIUGDBg1ScnKyduzYocTEREmSy%2BVqczzLstptl758uH7BggUGqwcAAIGkW9winD59ut544w1t2LBB/fv3/9a%2BiYmJ6tmzp/bt2ydJ8nq9OnjwYJt%2Bhw4dUnR0dLvHyM/PV2Njo73U1NSc/UUAAICA0aUDlmVZuvfee/Xaa6/p7bffVnx8/Bn32b17t1pbWxUTEyNJSk1NVWNjo7Zt22b32bp1qxobGzVy5Mh2j%2BF2uxUWFua3AACA7qNL3yKcNm2aXnrpJb3%2B%2BusKDQ21n5nyeDwKCQnRBx98oJUrV%2BqGG25QZGSk3nvvPc2aNUuXXXaZRo0aJUkaMmSIxo8fr5ycHPv1DXfeeacmTpzIJwgBAEC7uvQM1hNPPKHGxkaNGTNGMTEx9rJ69WpJUnBwsN566y2NGzdOgwcP1owZM5SWlqb169erR48e9nFWrlypoUOHKi0tTWlpaRo2bJheeOEFpy4LAAD8wHXpGSzLsr51e1xcnEpKSs54nIiICL344oumygIAAF1cl57BAgAAcAIBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwLMjpAnB2kucVOl0CAAD4GmawAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAzjTe4AAHRRgfRtH%2BULxztdglHMYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBKzv6PHHH1d8fLx69eqlpKQkbdq0yemSAADADxQB6ztYvXq1cnNzNW/ePO3cuVNXXXWV0tPTVV1d7XRpAADgB4iA9R0sXbpUU6dO1S9/%2BUsNGTJEjzzyiOLi4vTEE084XRoAAPgB4kWjZ9DS0qKKigrNnTvXrz0tLU1btmxpdx%2Bfzyefz2evNzY2SpKampqM13fSd8z4MQEA6Gwd8Tfy9DEtyzJ%2B7DMhYJ3B4cOHdfLkSUVHR/u1R0dHq66urt19CgoKtGDBgjbtcXFxHVIjAACBzrOk447d3Nwsj8fTcSdoBwHrO3K5XH7rlmW1aTstPz9feXl59vqpU6f0%2Beefq2/fvt%2B4j5OampoUFxenmpoahYWFOV1Ot8CYO4Nxdwbj7gzG/cu/1c3NzYqNje30cxOwziAyMlI9evRoM1tVX1/fZlbrNLfbLbfb7dd23nnndViNpoSFhXXbfwmdwpg7g3F3BuPujO4%2B7p09c3UaD7mfQXBwsJKSklRcXOzXXlxcrJEjRzpUFQAA%2BCFjBus7yMvLU2ZmppKTk5WamqqnnnpK1dXVuvvuu50uDQAA/AD1mD9//nyni/ihS0hIUN%2B%2BfbVo0SItXrxYx48f1wsvvKDhw4c7XZoxPXr00JgxYxQURObuLIy5Mxh3ZzDuzmDcneOynPjsIgAAQBfGM1gAAACGEbAAAAAMI2ABAAAYRsACAAAwjIDVzT3%2B%2BOOKj49Xr169lJSUpE2bNjldUpeyceNGTZo0SbGxsXK5XFq7dq3fdsuyNH/%2BfMXGxiokJERjxozR7t27Haq26ygoKNDll1%2Bu0NBQRUVF6Sc/%2BYn27t3r18fn82n69OmKjIxUnz59NHnyZB04cMChigPfE088oWHDhtkvtUxNTdVf//pXezvj3TkKCgrkcrmUm5trtzH2ziBgdWOrV69Wbm6u5s2bp507d%2Bqqq65Senq6qqurnS6tyzh27JiGDx%2BuZcuWtbv9oYce0tKlS7Vs2TJt375dXq9X119/vZqbmzu50q6lpKRE06ZNU1lZmYqLi/XFF18oLS1Nx479/5ej5%2Bbmas2aNVq1apU2b96so0ePauLEiTp58qSDlQeu/v3768EHH1R5ebnKy8t1zTXX6F//9V/t/2FgvDve9u3b9dRTT2nYsGF%2B7Yy9Qyx0W1dccYV19913%2B7X9%2BMc/tubOnetQRV2bJGvNmjX2%2BqlTpyyv12s9%2BOCDdtuJEycsj8dj/fd//7cTJXZZ9fX1liSrpKTEsizLOnLkiNWzZ09r1apVdp9PPvnEOuecc6zCwkKnyuxywsPDraeffprx7gTNzc3WoEGDrOLiYmv06NHWfffdZ1kW/6w7iRmsbqqlpUUVFRVKS0vza09LS9OWLVscqqp72b9/v%2Brq6vx%2BB263W6NHj%2BZ3YFhjY6MkKSIiQpJUUVGh1tZWv7GPjY1VQkICY2/AyZMntWrVKh07dkypqamMdyeYNm2aJkyYoOuuu86vnbF3Dq927aYOHz6skydPtvnC6ujo6DZfbI2OcXqc2/sdfPzxx06U1CVZlqW8vDxdeeWVSkhIkPTl2AcHBys8PNyvL//8n51du3YpNTVVJ06c0Lnnnqs1a9bo4osvVmVlJePdgVatWqWKigqVl5e32cY/684hYHVzLpfLb92yrDZt6Fj8DjrWvffeq3fffVebN28%2BY1/G/uwMHjxYlZWVOnLkiF599VVlZWWppKTkG/sz3mevpqZG9913n4qKitSrV6/vvB9j3/G4RdhNRUZGqkePHm3%2BD6a%2Bvr7NjAo6htfrlSR%2BBx1o%2BvTpeuONN7Rhwwb179/fbvd6vWppaVFDQ4Nff8b%2B7AQHB%2Btf/uVflJycrIKCAg0fPlx/%2BMMfGO8OVFFRofr6eiUlJSkoKEhBQUEqKSnRo48%2BqqCgIEVHRzP2DiFgdVPBwcFKSkpScXGxX3txcbFGjhzpUFXdS3x8vLxer9/voKWlRSUlJfwOzpJlWbr33nv12muv6e2331Z8fLzf9qSkJPXs2dNv7Gtra1VVVcXYG2RZlnw%2BH%2BPdga699lrt2rVLlZWV9pKcnKxbb73V/pmxdwa3CLuxvLw8ZWZmKjk5WampqXrqqadUXV2tu%2B%2B%2B2%2BnSuoyjR4/q/ffft9f379%2BvyspKRUREaMCAAcrNzdWiRYs0aNAgDRo0SIsWLVLv3r2VkZHhYNWBb9q0aXrppZf0%2BuuvKzQ01J4l9Hg8CgkJkcfj0dSpUzVr1iz17dtXERERmj17toYOHdrmIWF8N/fff7/S09MVFxen5uZmrVq1Sn//%2B99VWFjIeHeg0NBQ%2B9nC0/r06aO%2Bffva7Yy9Q5z7ACN%2BCP74xz9aAwcOtIKDg63ExET7Y%2BwwY8OGDZakNktWVpZlWV%2B%2BquE3v/mN5fV6LbfbbV199dXWrl27nC26C2hvzCVZzz33nN3n%2BPHj1r333mtFRERYISEh1sSJE63q6mrnig5wd9xxh/3fkn79%2BlnXXnutVVRUZG9nvDvPV1/TYFmMvVNclmVZDmU7AACALolnsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGPZ/7vppi7iJPYgAAAAASUVORK5CYII%3D\"/>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-3677506896709781474\">\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">15.050000190735</td>\n",
" <td class=\"number\">15</td>\n",
" <td class=\"number\">0.2%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">23.049999713898</td>\n",
" <td class=\"number\">15</td>\n",
" <td class=\"number\">0.2%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">20.800000190735</td>\n",
" <td class=\"number\">13</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">24.0</td>\n",
" <td class=\"number\">13</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">14.575000047684</td>\n",
" <td class=\"number\">12</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">25.675000190735</td>\n",
" <td class=\"number\">11</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">15.350000143051</td>\n",
" <td class=\"number\">11</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">17.574999809265</td>\n",
" <td class=\"number\">11</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">13.75</td>\n",
" <td class=\"number\">11</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">21.349999904633</td>\n",
" <td class=\"number\">11</td>\n",
" <td class=\"number\">0.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"other\">\n",
" <td class=\"fillremaining\">Other values (3357)</td>\n",
" <td class=\"number\">8868</td>\n",
" <td class=\"number\">94.8%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"missing\">\n",
" <td class=\"fillremaining\">(Missing)</td>\n",
" <td class=\"number\">366</td>\n",
" <td class=\"number\">3.9%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" </div>\n",
" <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"extreme-3677506896709781474\">\n",
" <p class=\"h4\">Minimum 5 values</p>\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">-1.8999999761581</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">-1.3749999888241</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">-1.2749999910593</td>\n",
" <td class=\"number\">2</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">-1.1999999880791</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">-1.1249999850988</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" <p class=\"h4\">Maximum 5 values</p>\n",
" \n",
"<table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">42.824999809265</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">43.125000953674</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">43.424999237061</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">44.349999427795</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr><tr class=\"\">\n",
" <td class=\"fillremaining\">44.60000038147</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">0.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"</table>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"</div>\n",
" <div class=\"row headerrow highlight\">\n",
" <h1>Correlations</h1>\n",
" </div>\n",
" <div class=\"row variablerow\">\n",
" <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAApkAAAJNCAYAAAB6AG5rAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzs3Xl4TGf7wPFvdhEhIYlI7JFFQyu2UPRH6GWpNVTeFlUaGpHYSrWlQahK1dsiFEG1iqoliqJa%2BuLtaymJpfYQEYKQSCUh%2B/z%2BGJmaSUJyzmgs9%2Be65jqZM%2Be%2Bn2fOLLnnOZuJRqPRIIQQQgghhBGZlncHhBBCCCHEs0eKTCGEEEIIYXRSZAohhBBCCKOTIlMIIYQQQhidFJlCCCGEEMLopMgUQgghhBBGJ0WmEEIIIYQwOikyhRBCCCGE0UmRKYQQQgghjM68vDsghBAPc%2BXKFTp27Fji4xYWFlSqVIm6devSvn17Bg4cSKVKlf7BHgohhCiOiVxWUgjxJHuwyPTw8ChSQObm5pKamsrVq1cBcHFxYcWKFdSpU%2Bcf76sQQoi/SZEphHiiPVhkfvvtt/j6%2Bha73MGDBwkODiYjIwMfHx%2B%2B//77f7KbQgghDMg%2BmUKIZ4Kvry/jxo0DIDY2lj///LOceySEEM83KTKFEM%2BMV199Vff3sWPHyrEnQggh5MAfIcQzw9bWVvd3Zmam3mN//PEHK1euJCYmhrS0NCpXrkyTJk0YNGgQrVu3LjbfnTt3%2BP7779mzZw9xcXFkZGRgbW1N7dq16dChA2%2B99RZVqlTRi/H09ATg999/Z9asWezatQtTU1O8vb1Zvnw55ubmHDt2jBUrVnDq1CmuXbuGlZUV9erVo1OnTrz55pvFHriUlZXF999/z7Zt24iLiyM3N5fq1avz8ssvM3ToUOrWrau3/MGDB3nrrbd46aWXWLVqFStXrmTTpk0kJCRgYWGBt7c3gwYNolOnTkpWtRBCPJIUmUKIZ0ZCQoLub2dnZ93fn3/%2BOVFRUQBUqVIFDw8PkpOT2bVrF7t27SIwMJAJEybo5bp06RJvv/02165dw9zcnNq1a%2BPq6srVq1c5efIkJ0%2Be5KeffmLDhg3Y2NgU6UtoaCixsbF4eHiQmpqKo6Mj5ubm7Ny5k7Fjx5KXl4e9vT0NGjQgMzOT48ePc%2BzYMTZv3sz333%2BvV2hev36dIUOGcPHiRQDq1q2LjY0NFy5cYO3atWzatIlZs2bRrVu3Iv3Izc1l2LBh7N%2B/H3t7e9zc3IiPj%2BfAgQMcOHCAqVOn8sYbb6hb8UIIURyNEEI8wRITEzUeHh4aDw8PzYEDBx667Pvvv6/x8PDQeHt7a27evKnRaDSaNWvWaDw8PDTNmzfX/Pjjj7plCwoKND/99JOmSZMmGg8PD80PP/ygl2vgwIEaDw8PTf/%2B/TU3btzQi4uOjtZ4eXlpPDw8NN99951eXGFfGzVqpDl06JBGo9Fo8vPzNbdv39bk5%2Bdr2rRpo/Hw8NBERUVp8vLydHF//vmnplWrVhoPDw/N4sWLdfPz8vI0vXr10nh4eGg6d%2B6sOX36tO6x9PR0zaRJk3TP%2BejRo7rHDhw4oOtLkyZNNJs3b9Y9dufOHc3gwYM1Hh4empYtW2pyc3Mful6FEEIJ2SdTCPFUy8rK4tSpU0yZMoVNmzYB8Pbbb%2BPg4EBOTg7z588HYObMmfTs2VMXZ2JiQrdu3XQjmPPnzycvLw%2BAlJQUzp8/D8D06dNxcnLSi%2BvduzctW7YE4OzZs8X2q2vXrrRo0QIAU1NT7OzsSE1N5ebNmwD0798fMzMz3fLe3t6MHTuWTp06YWdnp5u/Y8cOTp8%2BjZWVFVFRUXh5eekeq1SpEjNmzKBdu3bk5ubyxRdfFNuXUaNG0aNHD919W1tb3fNOS0sjPj6%2BhLUrhBDKyeZyIcRT46233nrkMq%2B//jqjR48GtEeZ37p1CxsbmxJP6N6zZ0%2BmT5/OjRs3OHXqFC%2B%2B%2BCLVqlXjwIEDZGVlUaFChSIx%2Bfn5us3ZWVlZxeZt1qxZkXn29vZUqVKFv/76i/HjxzNixAheeuklTE21v/f79%2B9P//799WJ2794NgJ%2BfH7Vq1Sq2rSFDhrBv3z4OHTpEenq63r6pAB06dCgS4%2Bbmpvv7zp07xeYVQgg1pMgUQjw1DE/GbmJigpWVFXZ2dnh6etKpUycaNGige7xwNDI3N5cBAwaUmNfMzIyCggIuXrzIiy%2B%2BqJtfoUIFrl27xrFjx7h8%2BTKJiYlcuHCB06dPc/fuXQAKCgqKzeno6FhsO%2BPHj%2Bfjjz9mz5497NmzhypVquDr60ubNm1o37693r6kgG6U0dvbu8T%2BFz6Wn59PQkICjRo10nu8evXqRWIeLJ7z8/NLzC2EEEpJkSmEeGpMnjy5xJOxFyc9PR2AnJwcYmJiHrn8gyN6Fy9e5LPPPmPPnj16hWSlSpVo3rw5ycnJnDlzpsRcxY2Agna0sk6dOnz99df873//46%2B//mLnzp3s3LkTExMT2rdvz9SpU3XFZkZGBkCR0ckHPVh4Gx5VD9pLbz6MRq7JIYR4DKTIFEI8s6ytrQHtSN/GjRtLHZeSksLAgQNJSUnBxcWF/v3788ILL1C/fn1q1qyJiYkJ77333kOLzIfx9fXF19eXrKwsDh8%2BzB9//MG%2Bffs4efIkv/32G9euXWPTpk2YmJjojlwvLJiL82BxXNyR7kIIUR6kyBRCPLPq1asHaE9HlJeXh7l50a88jUbDwYMHcXZ2xsXFBUtLSzZs2EBKSgp2dnZs2LCBqlWrFom7ceNGmfuTk5NDYmIiGRkZvPTSS1SoUIG2bdvStm1bxo4dy08//cS4ceM4c%2BYMZ8%2BexcvLi/r163Pq1ClOnjxZYt4TJ04A2t0HateuXeZ%2BCSHE4yBHlwshnlktWrTA1taWzMzMEkcyt2zZwuDBg%2BnatSvXr18HtNdLB3BxcSm2wIyLi%2BPo0aNA2fZn3Lt3L926dWP48OHk5OQUefzll1/W/V2Yt/Cgnd27d5OYmFhs3m%2B//RaAJk2aULly5VL3RwghHicpMoUQz6yKFSsyfPhwAD755BM2bNigt3/lr7/%2BypQpUwDtKYcKRwHr168PwJkzZ/j55591y2s0Gvbu3UtgYCC5ubkA3Lt3r9T9eeWVV7C3tyctLY2JEyeSlpameywzM5OIiAgAatSogbu7OwBdunTB09OT7Oxshg0bpreJPiMjg48//pj//ve/mJubM378%2BNKvHCGEeMxkc7kQ4pk2bNgwEhMT%2BeGHH/joo4%2BYPXs2NWvW5MaNGyQnJwPQtGlTZsyYoYvp168fq1evJiEhgVGjRuHq6oq9vT3Xrl0jJSUFCwsLWrZsyaFDh8q02dzS0pK5c%2BfyzjvvsG3bNnbt2kXt2rUxNTUlMTGRu3fvYm1tzaxZs7C0tATA3NychQsXMmzYMC5evEivXr30rvhTeJqladOm0bx5c%2BOuPCGEUEGKTCHEM83ExITp06fTuXNnvv/%2Be44ePao7uXmTJk3o3r07AQEBuqIOtEdrr1%2B/nqioKH777TeuXLnCrVu3cHZ2pn379gwePJiKFSvSqVMnzpw5Q1JSEi4uLqXqj6%2BvL%2BvWrePrr7/myJEjXLp0CXNzc5ydnWnbti1Dhw4tkqtmzZps2LCBNWvWsGPHDi5cuMD169epUaMG7dq1Y8CAAUWuXS6EEOXNRCPnrhBCCCGEEEYm%2B2QKIYQQQgijkyJTCCGEEEIYnRSZQgghhBDC6KTIFEIIIYR4iqSmpvLqq69y8ODBEpfZs2cPPXr0oEmTJnTt2pXffvtN7/GoqCheeeUVmjRpwqBBg7h48aLR%2BylFphBCCCHEU%2BLIkSMEBARw%2BfLlEpe5dOkSoaGhjB49msOHDxMaGsqYMWN0p1yLjo5m5cqVLFu2jIMHD%2BLt7c2oUaMw9rHgUmQKIYQQQjwFoqOjGT9%2BPGPHjn3kcs2bN6dTp06Ym5vTrVs3WrRowdq1awH44YcfePPNN3F3d8fKyor33nuPpKSkh46MKiFFphBCCCHEPyA5OZmTJ0/q3QovClEabdu25ZdffqFbt24PXS4uLg4PDw%2B9eQ0aNNBdMczwcQsLC%2BrWrat3RTFjkJOxCy0TE%2BWx9erB%2BfPg7g7x8cpy3L%2BcnyK1asGePfB//wclXNu5VB643GCZ1a4Ne/fCK6/AQzZhPFJGhvLYOnXgyBFo1gwSEpTlMFfxlVCnDhw8CL6%2BytsHUHNScVdX2LAB%2BvaFq1cVp%2Bnm%2BIeiOGdniIqCYcPg/mXQFdlm1Ud5cPXqsGABjBwJZbgaURFKr4Hu6AiffQbvvw83bypv/3//Ux5bqxb88gu8%2Bqq674RTp5THgvbzlJenLkdWlrI4ExOwsYHMTCiv02Ebqw%2B2tsbrU1mo%2Bb9YgrXz5hEZGak3LyQkhNDQ0FLFOzo6lmq5zMxMrK2t9eZVqFCBu3fvlupxY5EiU6hnZwdmZtppeahSRdt%2BlSrq/qGoUbmytg9K/zEbw4Pr4XlsH6BSJW0fKlUql%2BZtbLTN29iUS/NPRicqVgRTU%2B20vNjaatdBeRUnhR5DkVKmtgtv5VlklncfnjABAQH4%2BfnpzStt4VgW1tbWZBn8QMnKysLm/vfCox43FikyhRBCCCEMmRp/j0InJyecnJyMnteQh4cHJ0%2Be1JsXFxdHo0aNAHB3d%2Bf8%2BfN06NABgNzcXC5dulRkE7task%2BmEEIIIcQzpGfPnhw6dIht27aRl5fHtm3bOHToEL169QKgb9%2B%2BfPfdd5w5c4bs7GzmzJmDg4MDzZs3N2o/ZCRTCCGEEMLQYxjJfJx8fHyYNm0aPXv2xM3NjQULFvD5558zadIkXF1dmT9/PvXq1QOgX79%2BpKenM3LkSFJTU2ncuDGLFy/GwsLCqH2SIlMIIYQQwtATXmSePXtW735sbKze/Xbt2tGuXbtiY01MTBg6dChDhw59bP0D2VwuhBBCCCEeAxnJFEIIIYQw9ISPZD4NZA0KIYQQQgijk5FMIYQQQghDMpKpmhSZQgghhBCGpMhUTdagEEIIIYQwOhnJNBAfH8%2BiRYvYv38/6enpVKtWjS5dujBixAjd5ZYSEhJYuHAhv//%2BOxkZGdjZ2fHKK68QFBSEi4uLXr5jx46xYMEClixZopu3adMm1q9fz7lz58jJyaF69ep06tSJESNGUKlSJTZv3syUKVMA0Gg03Lt3D2tra0zuX6Ls3XffpVu3bnzwwQd88803Rj%2BvlRBCCPHck5FM1WQNPiAmJoY%2Bffrg6urKpk2biI2NJSoqimPHjjF06FDy8/M5ceIEffr0wcrKijVr1hAbG8uqVasA6NWrl955q3Jycpg4cSITJ07UzZs0aRJffPEFAwYMYOfOnRw%2BfJjIyEjOnTtHYGAgGo2Gnj17EhsbS2xsLFu3bgVg69atunlBQUHUrl2bFi1asHDhwn92JQkhhBBClIIUmQ8ICwujd%2B/ejBo1iqpVqwJQr149vvjiC6pVq0ZiYiIff/wxXbt2JTw8nFq1amFiYoKrqyvh4eG0a9eOyZMn6/KtW7eOmjVr4ubmBsDvv/9OdHQ0S5YsoWvXrtjZ2WFubo67uzufffYZPj4%2BpKenl7q/b775Jt988w2pqanGXRFCCCHE887U1Pi354xsLr/v8uXLnD9/nqlTpxZ5zMHBgYULF3LlyhVOnz7NpEmTis3x%2Buuv8/bbb5OUlISLiwurV69m2LBhuse3bduGj48Pnp6eRWLt7e31RjxLo3r16jRq1Ijo6GjeeeedUsclJydz8%2BZNvXmO9erhZGdXpvZ1vLz0p0rUqKE89n4Rr5sqVVBQ/n24e1d5rLu7/lQJcxVfCcZoH9S9F%2BrW1Z8q5GavLK5mTf2pYpb1lce6uupPlbq/e1CZFb5%2Bal5HgLQ05bH16%2BtPn2ZKC5PCuPIsbIzRBzXfy2o9h0WhsUmReV/haKCDg0OJyyQnJz90GScnJ91ylpaWxMXF0bRpU93j169fx9nZWS9myJAhnDhxAtBuXg8PD6d3796l7rePjw/79%2B8vU5G5du1aIiMj9eaFjB5N6OjRpc5RrNWr1cWrNW9e%2BbYPYLBey8XSpeXb/qJF5ds%2BwIwZqsLnq2y%2BjL8XizFHbQIYN059DjVGjCjf9gG%2B%2BKK8ewBq95lXG29trS7eGNT0oQxb98STR4rM%2BxwdHQG4efMmdYsZBbl165ZumaSkJN1F5h905coVXa6kpCRAO9pYyMnJicTERL2Yr7/%2BWve3n58fBWX81ebs7MyuXbvKFBMQEICfn5/ePMcePeCbb8qUR8fLS1tgvvkmnDmjLIfakcx582DUKLhwQXketSOZkZEQEqKuD2pHMpcuhcBAOH9eWQ61I5mLFkFQkPL2Qf1I5owZMHkyXLqkOE2o/XeK4mrW1BaYERFw/%2BtAkfmW7ykPdnXVFpj//jdcvao8j5qRzBEj4Kuv4No15e0fO6Y8tn59bYE5dixcvKg8z/r1ymNBWyDm5qrLkZOjLM7UVFvc3btXfqOBT0If1JCRTNWkyLzP1dUVDw8Ptm3bRosWLfQeS0lJoUOHDnz66ad4e3uzfv162rRpUyTH%2BvXr8fb2xtXVldu3bwPoFY2dO3cmODiYCxcu6PbTVCs/Px/TMn4QnJycdKOuOvHx6jtz5gzExiqLTUlR3/6FC/Dnn8rjjfElqLYPGRnq%2B3D%2BPBw/rixWTZFpjPYB7txR34dLl%2BCBg/DK6oKjuuavXFH3WwMrFYVRoatX1RVYlSura//aNUhIUB5/6pS69kH7/I2Rpzyp/V4qKCj/Au9J6IMoF1KmP%2BDjjz9mw4YNREZGcvv2bTQaDadPnyYoKAhvb286d%2B7MzJkz2bdvH2FhYVy5coWCggISExOZPHkyv//%2BO5988gmA7lRGN27c0OVv3749/v7%2BvPPOO%2BzYsYN79%2B6h0Wg4d%2B4cH374IdevX6datWpl6nNycnKR0yYJIYQQQiU58Ec1Gcl8QMuWLfnuu%2B9YtGgRr732Gvfu3cPBwYEuXbrw7rvvYmFhgZeXF9HR0Xz11VcMGDCAtLQ07OzsaNeuHZs3b6bm/T3%2Bq1atygsvvMCRI0f0Nr/PmDGD7du388MPPzB16lSysrKoWrUqrVu3ZuPGjXiV8eCZI0eO0K1bN2OuBiGEEEI8h0WhsUmRaeDFF1985Lkna9WqxcyZMx%2BZq2/fvmzfvp2%2Bffvqze/atStdu3YtVX9q1qypd%2B7NB12/fp1z584xf77awxSEEEIIIYxLyvTHqH///iQkJBAXF/dY8n/77bcMGjRId05PIYQQQhiJbC5X7fl7xv8gS0tLIiIiiIiIMHruy5cvExMTQ1BQkNFzCyGEEEKoJZvLH7OmTZsSFRVl9Ly1a9fm%2B%2B%2B/N3peIYQQQvBcjjwamxSZQgghhBCGpMhUTdagEEIIIYQwOhnJFEIIIYQwJCOZqskaFEIIIYQQRicjmUIIIYQQhmQkUzUpMoUQQgghDEmRqZqsQSGEEEIIYXQykim0atdWHlujxt/TlBRlOS5fVt5%2BtWra6bVr6vI0b66%2BD9WqgbOz8jzXryuPrVjx72mlSspy5Ocrb9/c/O%2BphYXyPA4OymPt7P6eqsjz11/K4jIy/p4qzQFAHWvlsVZWf0%2BtVeS5c0dZXGbm31OlOeDvlanEvXt/T9XkycpSHmtqqv0c5ORAQYHyPGlpyuIsLMDGBtLTITdXefs5OcpjLS21fUhNVZfH1lZ5rBoykqmarEEhhBBCCGF0MpIphBBCCGFIRjJVkyJTCCGEEMKQFJmqyRoUQgghhBBGJyOZQgghhBCGZCRTNSkyhRBCCCEMSZGpmqxBIYQQQghhdDKSKYQQQghhSEYyVZM1KIQQQgghjE5GMoUQQgghDMlIpmpSZAohhBBCGJIiUzVZg0IIIYQQwujKfSTTz8%2BPmzdvYm6u7YpGo6FSpUr06NGDDh068O677%2BqWvXv3LlZWVpiZmQHQo0cPwsPDiY%2BPZ%2BrUqRw/fhwbGxsGDhxIUFCQLu6bb77hm2%2B%2BIS0tDVdXV0JCQujcuXOx/cnJyWH%2B/Pls376dlJQUrKysaNGiBWPGjMHNza3I8jNmzCAjI4NZs2bpzb9z5w5Dhgzh66%2B/pnLlygD897//5ZtvvuHEiRPk5uZSs2ZN3njjDf71r3/pxe7fv59ly5Zx7Ngx8vLycHFxoWvXrgQGBlKhQgUAjhw5wrJly1i4cGFZV7kQQgghHkVGMlV7ItbgtGnTiI2NJTY2lqNHj7Js2TI2bdrEgQMHdPNjY2MBiIqK0t0PDw8nNzeXoKAgGjduzMGDB1myZAmrVq1i%2B/btAOzZs4fFixezdOlSYmJiCAkJYcyYMVy5cqXYvkyfPp3Y2FhWrFhBbGwsO3fuxNnZmQEDBnDnzh3dcrdv32b8%2BPGsXLmyxDz9%2B/fXFZgrVqxg7Nix9OzZk//85z/88ccfTJo0iQULFvDZZ5/p4tasWUNwcDBt2rTh559/5siRI0RERLB//34CAgLIzMwEoFmzZlSsWJH169erfwGEEEIIIYzsiSgyDXl6etKiRQtOnTr1yGX/%2BOMPkpOTGTVqFJaWlrzwwgsMGjSIVatWAXDx4kU0Go3uZmZmhoWFhW7k1NCRI0do164dNWvWBKBy5cq8//77dOjQgZs3bwKQmZlJly5dqFy5crEjoufOnWPPnj306dMHgBs3bjB79mymTZtGjx49qFChAqamprRs2ZJPP/2UlJQUcnNzuXnzJp9%2B%2BilTp05lyJAhVK1aFVNTUxo1asTSpUvJzMzUG7kcNGgQ8%2BfPJycnp2wrWAghhBAPZ2pq/Ntzptw3lxvKzc0lJiaGAwcOEBoa%2Bsjlz58/T7169bC0tNTNa9CgAUuWLAHgtddeY%2BPGjXTr1g0zMzNMTEyYPXs2zs7OxeZ77bXXiIyMJD4%2BnlatWvHSSy9Rr149Pv30U90yVlZW/PTTTzg4OPDBBx8UybFmzRo6deqk69PevXsxMzPj1VdfLbJs27Ztadu2LQD79u1Do9HQtWvXIstZW1vTo0cPNm/ezIQJEwB46aWXsLCwYPfu3XTp0uWR66pQcnKyrmAu5OjiglPVqqXOoadwN4JidicotWrVlMd6eelPlfL0VB5bt67%2BVCkHB%2BWxxngdCgqUxzZooD9VSk3/7/841E0V8shUFlenjv5UsRp1lce6uOhPlcrOVhbn6qo/VSpT4YsAxnsvqikKCmPVFhYWFsriCgdSShhQ%2BUcU9l3pcwAoz0GU57AoNLYnosicNm0aM2fO1N13dnZmyJAhDBw48JGxmZmZWFtb682ztrbm7t27gLZo9fLy4pNPPsHLy4stW7YwadIk3Nzc8CymqBg5ciQNGzZk06ZNREREkJqaipOTE%2B%2B88w5vv/02AObm5jg8pBg4cOAAQ4cO1d2/ffs2VapUweIRH7Tk5GSqVKmiVzA/yMnJieTkZL15TZo0Yf/%2B/WUqMteuXUtkZKTevJDgYEJHjy51jmLNm6cuXq3Vq8u3fYAZM8q7B2Dw2v7jFiwo3/YBivnxVxZfq2x%2B6lSVCZj56EUeJSREfQ41xo0r3/YBnoR91g3%2BP5WZjY26eDU/XI2lhEGdUrlwwXj9eAakpKTw8ccfc%2BjQIczMzOjZsycTJ04ssnU2MDCQI0eO6M27e/cuAQEBhIeHc%2BvWLdq0aUPFihV1j9vb27N7926j9veJKDKnTJmCv7%2B/otiKFSty7949vXn37t3D5v4Hc/r06TRt2pQXX3wRgL59%2B7J161aio6OLHYUE7cFIfn5%2BAFy%2BfJmdO3fy%2BeefY2Njw%2Buvv/7IPl27do3q1avr7js6OpKWlkZOTk6RArKgoIC0tDSqVq2Ko6MjKSkpZGdnY2VlVSTvlStXcHR01Jvn7OzM%2BfPnH9mnBwUEBOien66PgYGwc2eZ8ui4uWkLzFGjlH8hXLumLA60I5irV8Obb8KZM8rzvPCC8ti6dbUF5uTJcOmS8jwpKcpj3dy0BWZIiPLXQe1I5oIFMHIkxMUpz%2BPurjy2Zk1tgTlrFpSw33VpDMlUVqjXqaMtMKdOhYQExc3zdY2PlAe7uGjfA5GRkJSkPI%2Bakcxx4%2BDf/4arV5W3f%2BKE8tgGDbQFZnCwuvdidLTyWFNTbYF57566z1V6urI4c3NtgXnrFuTlKW8/N1d5rIWFtsC8fl1dnvLyBI5kjhkzhurVq7Nv3z5u3brFiBEjWLFiBYGBgXrLLV26VO/%2B%2BvXriYyMJOT%2Bj88TJ07g6upq9KLS0BNRZKrh7u7OpUuXyMvL01XycXFxuN//R5WUlESjRo30YszNzYsdVbxw4QK9e/dmw4YNeHh4AFC7dm0CAwM5duwYp0%2BfLlWfTExM0Gg0uvvt2rVDo9Gwa9euIpvCf/vtN0JDQ9m1axcdOnTAwsKCjRs38sYbb%2Bgtl5mZybZt24qMWObn52Naxg%2BCk5MTTk5O%2BjOTktT9QwJtYfPnn8piL19W1zZoC8z7B4gpcv%2BsBapcugRnzyqPv35dfR/UvA75%2Berbj4tTVyAY44v9yhVVIyDn7jx6mYdJSIBz51QkyL6krgOg/Tyr%2BcFj8OO9zK5ehYsXlcereQ8VUvteVFMcPphDTR61xVlenrocxthcnZtbvpu9nxEJCQkcOnSIvXv3Ym1tTa1atQgODmb27NlFiswHXbx4kenTp7Ns2TLd//4TJ04UqY0ehyevTC8jX19f7O3tmTNnDtnZ2Zw5c4aVK1fSr18/QDsq%2Bd1333Hy5EkKCgrYsWMHBw8epFu3bkVy1a9fH29vb8LCwjh%2B/DjZ2dncu3ePPXv2cPDgwWL3qSyOq6srN27c0N13cHBg1KhRTJ06la1bt5KdnU1ubi7/%2Bc9/mDx5MoMHD6ZGjRpUrVqVsLAwPvvsM1asWEFqaiq5ubkcP36cwMBAbGxsGDlypF5bycnJuKjd90oIIYQQ%2Bh7DgT/JycmcPHlS72a4G1xJzp8/j52dnd6WUjc3N5KSkvTOfmNo2rRp9O7dm%2BbNm%2BvmnThxguvXr9O9e3datWrFsGHDiFMz6l%2BCp34k09zcnOXLlxMeHq7bv2DQoEG6ze8hISGYmZkRGhrKX3/9RZ06dViwYAENGzYEYNGiRWzZsoWffvoJExMToqKiWLhwIRO7WEeBAAAgAElEQVQmTODGjRuYmprSsGFDZs%2BeTevWrUvVpzZt2nDkyBH69%2B%2Bvmzd8%2BHBcXFxYtWoV06dPJzc3lzp16jBmzBgCAgJ0y/Xr14/atWuzfPlyFi1aRHZ2NjVq1KBLly4EBgbq7T8BEBMTw5QpU9SuRiGEEEI86DFsLi/2mIiQkFId6FzSMSig3d%2By8JSJDzp8%2BDDHjh3j888/15tfuXJlGjRowLBhw7C0tGTu3LkMGTKEbdu2YWtrW9anVaJyLzLLsj/A2RI2Q9apU4dly5YV%2B5i5uTmhoaElvoBBQUF6J263tbVl4sSJTJw4sVR9MjwJO0CfPn0YPHgwWVlZupOnA3Tv3p3u3bs/MmfLli1p2bLlI5eLjY1Fo9Hw8ssvl6qvQgghhCg/xR4TYXCsRUlKOgYF0B2HYmjt2rV07dq1SBtz5szRu//hhx%2ByYcMGDh8%2BTIcOHUrVn9J46jeXP4m8vLxo164dGzdufKztrFixgtDQ0BKPRhdCCCGEQo9hc7mTkxPe3t56tyLHSJTA3d2dtLQ0bt26pZt34cIFnJ2dix19zMvLY9euXfTs2VNvfkZGBhEREVx94MC8/Px88vLy9AbGjEGKzMdk0qRJrFu3jr/%2B%2Buux5D98%2BDDZ2dn07dv3seQXQgghxJOjbt26NGvWjJkzZ5KRkUFiYiILFy7UHYNi6OzZs2RnZ9O0aVO9%2BZUqVeJ///sfERERpKenk5mZyfTp06lZs6befpvGIEXmY2Jvb090dDRVqlR5LPmbN2/OokWLHktuIYQQ4rn3BF7xZ968eeTl5dGxY0f69%2B9Pu3btCA4OBsDHx4fNmzfrlk1MTKRKlSrFnhJx4cKFFBQU0KlTJ9q1a8fNmzeJiop65Pm8y6rc98kUQgghhHjiPIHnyXRwcGBeCRc%2BiTU4hV%2BXLl1KvFCLq6trkQOQHocnbw0KIYQQQoinnoxkCiGEEEIYegJHMp82sgaFEEIIIYTRyUimEEIIIYQhGclUTYpMIYQQQghDUmSqJkWm0CooUB9bUKA8j5pzc3l6aqcvvABmZsrzHD6sPDY/Xzs9dQoMjvArE2dn9X3Iz4e8PGU51JzY39z876ma02AkJSmPLbys2s2b6vJUUh5qFDdvKo8tPG3a7dvq8ly6pCyu8DsgLk77eVCqXj3lsS4uf08zMpTn%2Bf575bHVqoG/P/z8M6SkKE4T23yYojhra/ByhjNpzhhcJKZMfG79ojzY1hZq1YKrVyE9XXkeNzflsaJcSZEphBBCCGFIRjJVkzUohBBCCCGMTkYyhRBCCCEMyUimalJkCiGEEEIYkiJTNVmDQgghhBDC6GQkUwghhBDCkIxkqiZrUAghhBBCGJ2MZAohhBBCGJKRTNWkyBRCCCGEMCRFpmqyBoUQQgghhNHJSKYQQgghhCEZyVRN1qAQQgghhDC6p7bI9PT0ZPjw4Wg0Gr35GzduxM/PT285T09PLl68WCTH119/jaenJ/Pnzy829kF%2Bfn5s3LhRd7%2BgoIDVq1fTr18/mjdvjq%2BvL4MHD2b//v1FYpcvX87ixYt19%2B/du8eCBQvo0aMHTZs2xcfHh379%2BrF69Wrd8wkLC8PHxwcfHx8aN26Ml5eX7r6Pjw%2BHDx9my5YtzJgxowxrTQghhBClYmpq/Ntz5qneXL5nzx6WLl3KsGHDHrqcvb090dHRvPfee3rzN27cSKVKlcrcrkajITQ0lMuXLzNlyhSaNGlCQUEBP/74I0FBQfz73/%2BmY8eOAFy4cIEffviBzZs3A3D37l3%2B9a9/UbFiRaZOnYq3tzcajYYTJ04wadIkkpKSGD9%2BPOHh4YSHh%2Bv6GRkZye7du4v0Zc2aNezfv5/WrVuX%2BXkIIYQQogTPYVFobE/1Ghw0aBBz584lJibmocv16NGDH3/8kYKCAt2848ePk5OTwwsvvFDmdnfs2MHevXtZvHgxzZs3x9zcHEtLS15//XVCQ0O5cOGCbtm5c%2Bfi7%2B%2BPpaUlAIsXLyYzM5Ply5fTrFkzKlSogLW1NS1btiQiIgI7O7sy9WXgwIHMmTOnzM9BCCGEEOJxeqpHMl999VU0Gg3jxo1j06ZNJRZo7du3Z%2BvWrfzvf/%2Bjbdu2AKxfv55%2B/fqxd%2B9evWWTkpJo3rx5kRwZGRm6v3fv3k3Tpk1xcXEpslxgYKDu71u3bvHLL7/w4Ycf6uZt27aNnj17UrFixSKxTZs2pWnTpo941vr8/PyYNGkSJ06coHHjxqWKSU5O5ubNm3rzHGvWxKmMBa6Om5v%2BVIlq1ZTH1q2rP1UqP195rJeX/lQpBwflsQ0a6E%2BVsLBQHmuM9wGAgq0LOvXq6U8V8ij68SyVOnX0p4pVUPEa1qqlP1WqmO%2BoUqlfX3%2BqlL298lhjvRBqvpcKv0%2BVfq/eZ22tLM7KSn%2BqmK2t8tjC95DS9xJAerryWLVkJFO1p7rIBJg4cSKxsbF88MEHfPXVV8UuY25uTo8ePYiOjqZt27ZkZWXx888/s3Xr1iJFpouLS7GbpR/cVzM1NRWHUhQDhw4dwsnJiRo1aujmXb9%2BHWdnZ939nJwcXn75ZUC7GT4nJ4cdO3bg6ur6yPwAFSpUwMvLi/3795e6yFy7di2RkZF680KCgwkdPbpU8SUyyPmPexL2T129urx7AAsXlm/78%2BaVb/sAs2apCv9aZfNTp6pMQPHfZWXy0Ufqc6jxxRfl2z7AtGnl3QMoYT//0lL5s1Xt7y2gldoE8OKLymN/%2BUV9%2B0pJkanaU19kWlpa8uWXX9KnTx%2BWL1%2BOfQm/fv39/QkICCAjI4Nff/2Vpk2b4ujoqKhNJycnrl69WuxjGRkZmJmZYW1tTVJSEtWrV9d73NHRkRs3buj1//DhwwBcuXKFjh07FjmY6VGcnZ25fv16qZcPCAgocoCTY2Ag7NhRpnZ13Ny0BWZICDywq0CZqB3JnDEDJk%2BGS5eU5zl1Snmsl5e2wHzzTThzRnketSOZCxdCcDDExSnLoXYkc948GDVK%2BfsA1I9kzpoFH3wA8fGK0wypuFZRXJ062gJz6lRISFDcPF9XGKE8uFYtbYE5cyYkJirPk5SkLK5%2BfW2BOXYsFHPAZampHcmcNg2mTFH3QvTsqTzWzk5bYO7eDWlpitOcecFfUZyVlfbjEB8P2dmKm8cr7YDy4IoVtQXm8eNw967yPOKp9dQXmQC1a9dm%2BvTpvP/%2B%2B/j7F/%2BB9PLyon79%2Bmzfvp0tW7YwePBgxe116NCB9957r8ioJMD8%2BfPZs2cP27dvx9TUVG8/UIDOnTuzdetWhg0bhrXS7SAG8vPzMS3DLy4nJyecnJz0Z165or2pceEC/PmnsliD9ajIpUtw9qzy%2BNhY9X04c0ZdHmOsh7g4OHFCWez9fYdVUfM%2BANWbFwHtf1YVxf45FXUuaOuac%2BdUJKio8EfCgxITlf/YAHU/2EBbYKr54aZwEECP2hciJUV9H9LSVOW5d09d89nZKnMYY3P13bvlu9lbKRnJVO2ZWYPdunWjb9%2B%2BrF1b8giEv78/K1asID4%2Bnv/7v/9T3Narr76Kr68vw4cPJyYmhoKCAjIyMlixYgWrVq1i/PjxmJiY4OLiojdqCRASEoKNjQ3vvPMOMTEx5Ofnk5eXx/79%2B5kwYQK2trZlLj6Tk5OL3T9UCCGEEKK8PDNFJsBHH31Ew4YNS3y8e/fuJCQk0LNnT8zNlQ/impiYsHDhQrp06UJYWBgtWrSgY8eO7Nmzh6ioKDp16gRAq1atSE1NJfGBTVY2NjasXbsWPz8/ZsyYQatWrWjRogUzZ86kZcuW7Nixg2pl2HScnZ3NyZMnadeuneLnI4QQQggDcp5M1Z7azeVni9ksamVlxaZNm0pczt7enj8NNuOtXLlS97e/v3%2BJm9sNDwaytLQkODiY4ODgEvtoZ2dHx44d2b59O8OHD9eLDQwM1DsS/WEe1q9ffvmFhg0b4u7uXqpcQgghhCiF57AoNDZZg4/Z6NGjWbduHTk5OY8l/7fffsu4ceMeS24hhBBCCKWkyHzM3Nzc6N%2B/P8uWLTN67h9//BFvb29atTLCKSaEEEII8TfZXK7aU7u5/GnyqMteKtWrVy969er1WHILIYQQQqghRaYQQgghhKHncOTR2KTIFEIIIYQwJEWmarIGhRBCCCGE0clIphBCCCGEIRnJVE3WoBBCCCGEMDoZyRRCCCGEMCQjmapJkSmEEEIIYUiKTNWkyBRaGRnKY%2B/e/XuqNM/168rbd3DQTlNS1OVxdlbfBwcHdXnU9L9GDe301i3lecxVfCVUr66d3rwJSUnK83h5KY810utglassztLy76mVleLmoVYt5bGFr0P16pCdrTxPZqayOHv7v6eOjsrbV/NZqFZNO1X7ndC0qfJYa2vt1MsL7t1TnKZ%2BfWVxhfWRqysUFChuHixqKI%2BtUEE7dXCASpVUdEI8raTIFEIIIYQwJCOZqskaFEIIIYQQRicjmUIIIYQQhmQkUzUpMoUQQgghDEmRqZqsQSGEEEKIp0BKSgrBwcE0b94cX19fPvnkE/Ly8opdNjAwkMaNG%2BPj46O77d27F4D8/HwiIiJ4%2BeWX8fHxYcSIESQnJxu9v1JkCiGEEEIYMjU1/k2lMWPGULFiRfbt28f69evZv38/K1asKHbZP//8k2XLlhEbG6u7vfLKKwB89dVX/P7772zYsIF9%2B/ZRoUIFJk%2BerLp/hqTIFEIIIYR4wiUkJHDo0CEmTJiAtbU1tWrVIjg4mFWrVhVZNjExkb/%2B%2BosXXnih2Fzr1q1j2LBh1KhRg0qVKjFp0iT27t1LYmKiUfss%2B2QKIYQQQhh6DPtkJicnc/PmTb15jo6OODk5PTL2/Pnz2NnZUb3wXLiAm5sbSUlJ3Llzh8qVK%2BvmnzhxAhsbG8aOHcuJEydwcHDg7bffpl%2B/fqSnp3P9%2BnU8PDx0yzs4OFClShXOnj1LLTXn6TUgRaYQQgghhKHHUGSuXbuWyMhIvXkhISGEhoY%2BMjYzMxPrwpP831d4/%2B7du3pFZk5ODk2aNGHs2LG4u7tz8OBBQkNDsbGxwcfHB4CKFSvq5apQoQKZSi/CUAIpMoUQQggh/gEBAQH4%2BfnpzXMs5ZWxKlasyD2Dq0cV3rexsdGb37t3b3r37q2737ZtW3r37s327dt5%2BeWX9WILZWVlFcmjlhSZQgghhBCGHsNIppOTU6k2jRfH3d2dtLQ0bt26hcP9S%2BheuHABZ2dnbG1t9ZZdv349NjY2dO3aVTcvJycHKysrqlSpQvXq1YmLi9NtMr958yZpaWl6m9CNQQ78EUIIIYR4wtWtW5dmzZoxc%2BZMMjIySExMZOHChfTr16/IshkZGUyfPp1Tp05RUFDAf/7zH7Zu3UpAQAAA/v7%2BfPXVVyQmJpKRkcHMmTNp2bIltWvXNmqfn9mRzPj4eBYtWsT%2B/ftJT0%2BnWrVqdOnShREjRmBjY0N2djazZ89m%2B/btZGVl0ahRI8LCwnBzcwPA09OTb7/9Fl9fX7288%2BfP59ChQ6xcuRKAq1ev8sknn3Do0CHMzMxo3749YWFhekPOy5cvJzc3l3fffRfQDlEvX76cHTt2cPXqVTQaDW5ubvj7%2B/PGG29gYmJCWFgYW7ZsASAvL4/c3Fy9fTGioqK4du0ax44deyynHRBCCCGea0/gydjnzZtHeHg4HTt2xNTUlN69exMcHAyAj48P06ZNo2fPngwePJi7d%2B8SEhJCSkoKtWrVIiIigubNmwMwcuRI8vLyGDBgAJmZmfj6%2BvLll18avb/PZJEZExPD0KFDGTp0KJs2baJq1arEx8cTFhbG0KFDWb16NVOnTuXSpUtER0djZ2fHrFmzGD16NFu3bi11Ozk5OQwdOpQ2bdqwb98%2B7t27R3BwMHPmzCEsLAzQDmX/8MMPbN68GdDunPuvf/2LihUrMnXqVLy9vdFoNJw4cYJJkyaRlJTE%2BPHjCQ8PJzw8HICNGzcSGRnJ7t27i/RhzZo17N%2B/n9atWxthzQkhhBACeCKLTAcHB%2BbNm1fsY7Gxsbq/TUxMCA4O1hWghiwsLBg/fjzjx49/LP0s9EwWmWFhYfTu3ZtRo0bp5tWrV48vvviCsLAw/vzzT3788Ue2bdum2zdi/PjxxMfHo9FoMDExKVU7v/32Gzk5OUyaNAkzMzOsra2ZN28ed%2B/e1S0zd%2B5c/P39sbS0BGDx4sVkZmby/fff6x3Z1bJlSyIiIoiJiSnTcx04cCBz5sxh/fr1ZYoTQgghhHicnrki8/Lly5w/f56pU6cWeczBwYGFCxeyZ88ebG1tOXr0KCNHjiQ1NZVmzZrx0Ucf6RWYQUFBmJmZ6eXIzs6mSZMmABw/fhwvLy/mzp2rG6ns3LkzY8eOBeDWrVv88ssvfPjhh7r4bdu20bNnzyKnDgBo2rQpTZs2LdPz9fPzY9KkSZw4cYLGjRuXKqbY83TVro2TnV2Z2tZxd9efKlHM%2Bii1%2B7s46KZK5ecrj23QQH%2BqVI0aymO9vPSnSpir%2BErw9NSfKqVmHRae303led7ci79K2z/VPNRQsV9U4XtIzXsJoKBAWVydOvpTpapVUx5rrO8Eg9PFlEmFCvpThZQOphXGqR6MU9N/Kyv9qRJZWcpj1XoCRzKfNs9ckZmamgqgO/KqOH/99Rfp6ens3LmTlStXYmFhQXh4OEFBQURHR%2BsKy0WLFpW4T2Zhnr1799KoUSN%2B/vlnkpOTCQ0N5bPPPiMsLIxDhw7h5OREjQe%2B7K9fv46zs7Pufk5Oju50AhqNhpycHHbs2IGrq2upnm%2BFChXw8vJi//79pS4yiz1PV3AwoaNHlyq%2BREuXqotXy%2BA5lYuFC8u7B7B6dfm2f39/5XL1wQeqwherbF79btJT1SaA%2B/uAl5tp08q3fYDHsI9ZmdWvryrc9tGLPJTqM9LYqvzhDOp%2Bdf35p/r2Rbl55orMwvNN3bx5k7p16xZ5/NatW1haWpKfn8/EiROpWrUqAB9%2B%2BCGtW7cmPj6eBqUcSbG0tMTBwYGRI0cCUKtWLd59913Cw8MJCwsjKSlJ78z8hf27ceOGXo7Dhw8DcOXKFTp27IhGoynTc3Z2dub69eulXr7Y83S9%2BSZs3FimdnXc3bUFZmAgnD%2BvLIfakczISAgJgQsXlOdRO5K5cCEEB0NcnPI8t24pj/Xy0haYb74JZ84oy6F2JHPlShg0CM6eVZ6nRQvlsbVqaQvMWbNAxeXR3s1boLj5yZNhxgxVzbO4xlTlwTVqaAvMxYvh2jXleS5dUhZXp462wJwyBRISlLefkqI81s1NW2COGaPuOyEiQnlshQraAvPiRVWjcem1ir8s4KOYmmoLzMxM5YPSALY3VHyfWVlpPxSJiZCdrTxPeZGRTNWeuSLT1dUVDw8Ptm3bRguDf1YpKSl06NBBtyNsTk6O7rH8%2BwVGWQo8Nzc3duzYQUFBAab334wFBQW6HKamphQYfLo7d%2B7M1q1bGTZsWJEz9yuVn5%2Bva780ij1P1%2BXL2psa58/D8ePKYitVUtc2aP%2BZqPnVm6dwG%2BmD4uLgxAnl8WX4sVCiM2fggR3Ay0RNkVno7Fnl7QPY26vvQ2KiqmL/fK765pX%2B3gIgV%2BVnEbQFpprP9Llz6tpPSFCXwxifhQsX4ORJ5fEGJ6tWJCtLVR41BWJhvKocxthcnZ1dvpu9lZIiU7Vncg1%2B/PHHbNiwgcjISG7fvo1Go%2BH06dMEBQXh7e1NYGAgLVq0ICwsjNTUVDIzM5k1axbe3t64l2G/wq5du5Kfn8/MmTPJycnhypUrLFq0iF69egHg4uKiN2oJ2stH2djY8M477xATE0N%2Bfj55eXns37%2BfCRMmYGtrW%2BbiMzk5GRcXlzLFCCGEEEI8Ts9kkdmyZUu%2B%2B%2B47Tp06xWuvvUbTpk0ZNWoUrVq1YunSpVhYWPDVV1/h7u5O7969adeuHXfv3mVhGfenq1q1KmvWrCEhIYFXXnmFfv360bp1a9577z0AWrVqRWpqKokPbDezsbFh7dq1%2BPn5MWPGDFq1akWLFi10J0LdsWMH1cqww3t2djYnT56kXbt2Zeq7EEIIIR7C1NT4t%2BfMM7e5vNCLL7740KLR1tZWdx7K4pwtYZ8yw4vY169fn6ioqGKXtbOzo2PHjmzfvp3hw4fr5ltaWhIYGEhgYODDnoKOv78//v7%2BxT72yy%2B/0LBhwzKNwAohhBBCPG7PX1n9Dxs9ejTr1q3T2//TmL799lvGjRv3WHILIYQQzy0ZyVTt%2BXvG/zA3Nzf69%2B/PsmXLjJ77xx9/xNvbm1atWhk9txBCCPFckyJTtWd2c/mTZNiwYY8lb69evXQHGQkhhBBCPEmkyBRCCCGEMPQcjjwamxSZQgghhBCGpMhUTdagEEIIIYQwOhnJFEIIIYQwJCOZqskaFEIIIYQQRicjmUIIIYQQhmQkUzUpMoWWuYq3QmGsubnyPPn5ytsvKPh7qiaPpaXyWAuLv6dq8pT365CXp7z9wti8PHV5zMyUxxb%2BUzA1VZUnPVVZXGbm39P0dMXNQ1aW8tjCCz/k5KjLo9Goi9NolOeAJ%2BM74e5d5bEmJtppVpaqPEpfwsKvgJwcdR/HKk/C61BepMhUTdagEEIIIYQwOhnJFEIIIYQwJCOZqskaFEIIIYQQRicjmUIIIYQQhmQkUzUpMoUQQgghDEmRqZqsQSGEEEIIYXQykimEEEIIYUhGMlWTNSiEEEIIIYxORjKFEEIIIQzJSKZqUmQKIYQQQhiSIlM1WYNCCCGEEMLoZCRTCCGEEMKQjGSqVqY16OfnR%2BPGjfHx8cHHx4cmTZrQtm1bIiIiOHTokG6%2Bj48Pnp6evPjii7r7YWFhAMTHxzN48GB8fHxo27YtixYt0mvjm2%2B%2Bwc/Pj6ZNm9KjRw9%2B/vnnEvuTk5PDnDlz6NSpEz4%2BPrRq1YrQ0FAuXLigWyY7O5tPPvmEV155hWbNmvH6669z4MABvTx37tyhb9%2B%2B3Llzh4MHD%2BLp6cmSJUuKtPfBBx/wwQcfAOiWe%2BWVVygoKCiybFBQEJ6enhw8eLBI7IOuXLmCp6cnV65c0c1LT09nzpw5dO7cWbeexo8fz%2BXLl3XLLFq0iK%2B//rrEdSOEEEIIUZ7KXKZPmzaN2NhYYmNjOXr0KMuWLWPTpk0cOHBANz82NhaAqKgo3f3w8HByc3MJCgqicePGHDx4kCVLlrBq1Sq2b98OwJ49e1i8eDFLly4lJiaGkJAQxowZo1eAPWj69OnExsayYsUKYmNj2blzJ87OzgwYMIA7d%2B4A8PnnnxMTE8PatWs5dOgQr7/%2BOkFBQSQlJenl6d%2B/P5UrV9bNmzt3LjExMY9cHzk5Ofz%2B%2B%2B96827duqVbB2WVmpqKv78/CQkJLFq0iJiYGLZs2UKVKlUICAjg6tWrAAwdOpQffvhBr6AWQgghhJGYmhr/9pxR/Yw9PT1p0aIFp06deuSyf/zxB8nJyYwaNQpLS0teeOEFBg0axKpVqwC4ePEiGo1GdzMzM8PCwgJz8%2BK36h85coR27dpRs2ZNACpXrsz7779Phw4duHnzJqAdyRw1ahQ1atTAzMyM/v37Y2lpycmTJwE4d%2B4ce/bsoU%2BfPnq533jjDcaNG8ft27cf%2Bpx69OjBpk2b9OZFR0fTuXPnR66P4syfP58KFSrwxRdfUK9ePUxMTLC3t%2Bfjjz%2Bmffv2nD17FgBLS0v69OnDvHnzFLUjhBBCiIeQIlM1Vftk5ubmEhMTw4EDBwgNDX3k8ufPn6devXpYWlrq5jVo0EC3afq1115j48aNdOvWDTMzM0xMTJg9ezbOzs7F5nvttdeIjIwkPj6eVq1a8dJLL1GvXj0%2B/fRT3TLh4eF6Mfv37yc9PR0vLy8A1qxZQ6dOnfT6BPD%2B%2B%2B8TExPDBx98wKJFizAxMSm2D3379iUgIID09HRsbW0B2LhxIxEREaxdu1Zv2a1bt/Lrr7/qzTPc1L5792769%2B%2BPmZlZkbYefF4A3bt354svviAlJYVq1aoV2z8hhBBCiPJQ5iJz2rRpzJw5U3ff2dmZIUOGMHDgwEfGZmZmYm1trTfP2tqau3fvAtqi1cvLi08%2B%2BQQvLy%2B2bNnCpEmTcHNzw9PTs0i%2BkSNH0rBhQzZt2kRERASpqak4OTnxzjvv8PbbbxdZ/ujRo4wZM4aQkBBq1aoFwIEDBxg6dGiRZS0tLfnyyy/p06cPy5YtIzAwsNjn5OXlRb169di2bRsBAQEcOXIEMzMzXnzxxSLLdu/enVmzZunNu3LlCh07dtTdT01NxdHRsdi2DLm4uODo6MjBgwfp1q1bqWIAkpOTdSO9hRxr1cLJ3r7UOfS4u%2BtPlShhtLpUGjTQn5ZHH9zc9KdKVa%2BuPLbwM1LMZ6XU8vKUx97/4aabKqVmHd7fqqGbKuRZVVlc3br6U8Vqq0jg4qI/VcrCQllcnTr6U6WqKnwRwHifRxsb5bGF/%2BsM/ueVldKvpcJximLGK8pGTf%2BtrPSnSty7pzxWredw5NHYyvz2nTJlCv7%2B/ooaq1ixIvcM3jD37t3D5v4Hefr06TRt2lRXoPXt25etW7cSHR1d7EEzoD0Yyc/PD4DLly%2Bzc%2BdOPv/8c2xsbHj99dd1y61bt46ZM2cyatQohgwZopt/7do1qpfwj7127drMmDGDCRMm0KxZsxKfl7%2B/P9HR0QQEBLBhwwb69etXirVRPEdHR5KTk4t9LDU1lSpVquiNcjo7O3Pt2rUytbF27VoiIyP15oUEBxM6enTZO/wgg4O4/nELFpRv%2BwBPwu4LK1eWb/urV5dv%2BwATJ6oK/05l8zNmqEzAzEcv8ighIepzqGGwFalcPAmfx4YNVYWr3UZlZ6cyQTUPlQlQ94Pj2DH17Yty84%2Bewsjd3Z1Lly6Rl5en288yLi4O9/sjYElJSTRq1Ei/g%2BbmWBTzi/rChQv07t2bDRs24OGh/RDUrl2bwMBAjh07xunTpwHIz89n2rRp7Ny5kwULFvDyyy/r5ZZKGscAACAASURBVDExMUGj0ZTY565du3Lw4EHGjRuHp6cndsV8Ynv06MFnn33G6dOn2bVrF%2BPHjy/DWtHn5%2BfHzp07GTFihF4xqdFoCAwMpFGjRnq7AOTl5RW7af1hAgICdIV5IcdBg2DzZmWddnfXFphBQXD%2BvLIcakcyFyyAkSMhLk55HrUjmfPmwahRoOZgLIMR5jLx9NQWmIMGwf19d8tM7Ujm6tXw5ptw5ozyPG3aKI%2BtWVNbYEZEQAkHDJbGwNvzFcXVrastMCdPhkuXFDfPd7U/Uh7s4qItMCMj4YEDHMvs/kGGZVanjrbADAuDhATl7d%2B6pTzWWJ/H6dOVx1pbawvM06dVjcal1GmqKM7MTFtgpqVBfr7i5qmWck55sJWV9v2QkADZ2crzlBcZyVTtHy0yfX19sbe3Z86cOYwZM4b4%2BHhWrlzJ2LFjAW2B9d1339GhQwcaNmzIzp07dQWeofr16%2BPt7U1YWBgfffQRnp6eFBQUcOjQIQ4ePMjcuXMB7X6Me/fuZcOGDbi6uhbJ4%2Brqyo0bNx7a748%2B%2BoijR4/y22%2B/FTlACMDe3p4OHTrw/vvv4%2BvrS1UVm3mCg4P55ZdfGDduHOPGjaNOnTrcuHGDL7/8kuvXr/Pll1/qLZ%2BcnEyNGjXK1IaTkxNOTk76MxMTtTc1zp%2BH48eVxSrdNPeguDg4cUJ5vDH6cOEC/Pmn8ng1RUGhs2dB4dkNVBWZhc6cUd4%2BQAn7YJfJlSuqiouzKmp90BaYSut8AAouqesAaN9Laird%2BHh17SckwDkVBYoxPgtqP4%2BZmer7cO%2BeqjxqP5L5%2BSpzGGNzdXZ2%2BW72VkqKTNX%2B0TVobm7O8uXLOXfuHG3atGH48OEMGjRIt/k9JCSEAQMGEBoaSosWLViyZAkLFiyg4f3NDYsWLeK1114DtCOQUVFR%2BPj4MGHCBHx9fWnTpg1Llixh9uzZtG7dmtTUVFatWsWtW7fo3r273nk8N98ftWvTpg1Hjhx5aL8L98%2B0ecj%2BOf7%2B/pw7d46%2BffuqWkdVq1Zl/fr1VKlShbfffhsfHx/69etHXl4ea9asoXbt2rplExMTSUtLo3Xr1qraFEIIIYQwtjKNZO7evbvUy54t4Wd8nTp1WLZsWfGdMTcnNDS0xCPVg4KCCAoK0t23tbVl4sSJTCxh/6uqVavqNpuXpE%2BfPgwePJisrCwqVKiAr69vsX2vW7eu3nkzDZfr0KFDkbgH7xse8FOoZs2aReIcHR2LHBVfnG3btvHqq6/qnd9TCCGEEEYgI5mqPfdr0MvLi3bt2rFx48by7kqZ5OTksH79ekaNGlXeXRFCCCGEKOK5LzIBJk2axLp16/jrr7/KuyultnTpUgICAqhXr155d0UIIYR49sjJ2FX7Rw/8eVLZ29sTHR1d3t0ok%2BDg4PLughBCCPHseg6LQmOTIlMIIYQQ4imQkpLCxx9/zKFDhzAzM6Nnz55MnDix2Mtvr1mzhhUrVpCcnIyTkxNvvfUWAwYMALRXG2zWrBkajUbvioa///47FStWNFp/pcgUQgghhDD0BI5kjhkzhurVq7Nv3z5u3brFiBEjWLFiRZGrEv7666/8%2B9//Jioqipdeeon/Z%2B/O46qq9/2Pv5gFJ0hBHAFRgUoTh6Nl1pX0Z06l4kmv6cnU0hSQnIdODpTkdUzNHDtZ2skcMi1TM8vOPTfRlNSjoaJiKAYOmRMy//7Yso97gwprbcOO7%2BfjwWPJ2uvzWWuvvWV/9ue7hh9//JGXX36ZqlWr0r59e5KTk623Bre/rbYj3Xt7UERERERsnDx5kl27djFq1Cg8PT2pXbs2Q4YMYeXKlUWWTU9P56WXXqJx48Y4OTkRHh5OixYt2L17NwAHDhwgJCTkrhaYoE6miIiISFF3oZOZkZHBWbs7u/n6%2Bha9QUoxjh49ire3t82tsIODg0lLS%2BPSpUs2lzMsHBYvdP78eXbv3s24ceMAS5GZlZVFZGQkp0%2BfJjg4mBEjRtCkibE7TN2KikwRERERe3ehyFy1ahXz58%2B3mRcVFXXL64Pf7OrVq3h6etrMK/z92rVrt7xm9tmzZxk0aBAPP/wwnTt3BqBcuXI0atSIYcOGUblyZVauXMmAAQPYsGEDtWvXNvLUiqUiU0REROR30LNnTyIiImzm%2Bfr6lijWy8uLTLvbcxb%2Bfqs7Ev74448MGzaMZs2aER8fbz1BaOzYsTbLDRgwgHXr1rFjxw769OlTou0pCRWZIiIiIvbuQifTz8%2BvREPjxalfvz4XL17k3LlzVK1aFYBjx47h7%2B9PxYoViyy/Zs0a3njjDWJiYujfv7/NY7Nnz6Z9%2B/Y8%2BOCD1nnZ2dl4eHgY2rZbUZEpFoGBxmOrV//39NIlYzlu/IcxJDjYMq1f39wfhbQ047EVKvx76u1tPE9oqPHYevUs0%2BbNwcfHWA4XF%2BPrL3wdWrUCf3/jeb780nhseLhl%2Bs9/QmKi4TT1ehmLKxxlql0b8vIMrx5SU43HFg6npaebyzN9urG4wo5KbCxcvWp8/SNGGI%2BtUuXfUxPvxa%2ButzYcW9ENWgI7s5tw%2BbrhNLQ7/r2xwPLloUojqpzeb%2Bp1OPzAo4ZjPdwhEEhxb0BWgeE0hBgP/Y8SGBhI06ZNmTp1KlOmTOHXX39lwYIF9OjRo8iyW7ZsYdKkSbz77ru0bl30fXzkyBF%2B%2BOEH5syZQ%2BXKlVm8eDFXrlyhXbt2Dt1mnV0uIiIiYu8evOPP3Llzyc3N5amnnuK5556jdevW1puzhIeHs2HDBgDmz59PXl4eMTExhIeHW39ef/11AOLj46lTpw7PPvssLVq0YNeuXfztb3/D20yTpBjqZIqIiIjYuwevk1m1alXmzp1b7GOJN43ebNy48bZ5vL29iY%2BPd%2Bi2Fefe24MiIiIi8oenTqaIiIiIvXuwk/lHoyJTRERExJ6KTNO0B0VERETE4dTJFBEREbGnTqZp2oMiIiIi4nDqZIqIiIjYUyfTNBWZIiIiIvZUZJqmPSgiIiIiDqdOpoiIiIg9dTJNu%2B/2YEhICC%2B//DIFBQU289etW0dERITNvIMHDxITE0PLli0JDw%2BnXbt2TJs2jYsXL5ZqnVu3brXeLxQgLy%2BPDz74gB49etCsWTPCw8Pp0qULCxcuJDs7G4CFCxda7zXaqFEjQkJCbO4/umHDBvbs2WO9Z6mIiIjIveS%2BKzIBduzYwdKlS2%2B7zDfffEPv3r0JCgris88%2BY%2B/evSxcuJDU1FS6du1Kenp6idZ14cIFpk2bRmxsLGApMF9%2B%2BWU%2B/vhjhg0bxrfffktCQgJTp05l27ZtjBkzBoDBgweTmJhIYmIiS5YsAbD%2BnpiYyDPPPEPTpk3x8vJizZo1JvaGiIiIFOHs7Pif%2B8z994yBvn378vbbb7N3795iH8/Ozua1115j0KBBvPrqq1SrVg0nJyeCg4OZO3cu/v7%2B1hvLv/7667Rt25arV68CsHLlSlq2bGktQpcsWcLjjz/OAw88AMDatWvZt28ff/vb32jdujUVKlTA3d2dhg0bMn36dOrUqUNeXl6pnsu8efOsHVARERFxABWZpt2Xx2S2a9eOgoIChg8fzvr16/H29rZ5PDExkXPnztG1a9cisc7OzvTo0YNJkyaRm5vL%2BPHj6dGjB9OnT6dXr178z//8D/PmzaNatWrk5uayevVqFi5caI3ftGkTERERVKtWrUjuoKAgXn311VI9l0ceeQQ3Nze2b9/O008/XaKYjIwMzp49azPP188PPx%2BfUq3bKjDQdmqE3WtQKrVq2U6NqlTJeGxQkO3UqKpVjcfWrm07NcLMH0FHvQ7h4cZjQ0NtpwYZfSvXqGE7NcyrgfHYOnVsp0aVL28sztPTdmpUSIjx2IAA26lBFSsaj/Xysp0aT1S2r4OHh/FYd3fbqRFZWcZjpezdl0UmwJgxY0hMTGTs2LG8%2B%2B67No9lZGQAUPUWH/h%2Bfn7k5OTw66%2B/4uvry6xZs3juuef49ttv6devH0888QRgOaYzMzOTRo0aWWN/%2BeUXm98B2rdvz/nz5wHIysrivffeo3nz5iV%2BLo0bN%2Bb7778vcZG5atUq5s%2BfbzMvasgQoocNK/E6i/XGG%2BbizRo7tmzXD/DWW2W9BWW/H24c8lGmPvrIVHi8ydVHR5tMwDKzCWDiRPM5zDBZ6PPBB%2Ba3IS7OVHhL81uA3Z97IxnMhdevbyo80NzaAXNfug4fdsAGGHUfdh4d7b4tMt3d3ZkzZw7dunXjvffew%2BemLp6vry8AaWlpBBbT0jh16hRubm7WmAYNGtC8eXP%2B93//l8jISOtyaWlpeHt7437T1zhfX98ix3Nu2bLF%2Bu%2BQkBDy8/NL9Vz8/f05evRoiZfv2bNnkZOcfEePht27S7Veq8BAS4H52muQkmIsh9lO5tixlgLv1Cnjeey6u6USFGRZ/9ixcOKE8TxmO5mF%2ByE11VgOs53MMWNg2jRzr8M//2k8NjTUUmD27g1JSYbTjGtf/KE0d1KjhqXAnDcP0tIMr574jAHGg%2BvUsRSYkyfDzz8bzxMVZSzO09PyOiQlQWam8fXPnm08NiDAUmD%2B9a9w8qThNDuHGC90vbwsBeb%2B/XDtmuE0tPTabyzQ09NSYB49aup1SKlkvMh1d7f8n0hLAx3RdX%2B6b4tMgDp16hAXF8fo0aPp3r27dX7Tpk3x9fVlzZo1jBw50iYmLy/Peia6q6tl923atIl9%2B/bRrl07Ro8ezcqVK3FxccHZ2blIwfj0008zZ84czp8/T5UqVRzyPPLy8nAuRXHg5%2BeHn5%2Bf7cyMDMuPGSkpxr92mimuCp06BceOGY83UxUUOnHCVHGDv7/5bUhNheRkY7EuLubXb/Z1SEw0vw1JSabypJgYqQXLW8no9y0ATh0xtwFgKTCPmMhz4zhzwzIzzeVwRAvr5ElTeS5fNr8J166ZzFNQtq9Dlonh8kLZ2X/QYW91Mk277/dgx44diYyMZNWqVdZ5bm5uxMfHs2LFCmbPnk16ejr5%2BfkkJycTFRXFL7/8wrhx4wA4ffo0EydO5K9//StTp04lIyPDOhRdo0YNLl68SNZN/7t69uzJI488wgsvvMA//vEPsrOzyc/PZ9%2B%2BfQwePBh3d3cqV65cqueQkZFBDdMHgYmIiIiVTvwx7f57xsUYP348YWFhNvNat27Nxx9/zM8//0xkZCRNmjRh8ODB1KlThw0bNlC9enXy8vIYOXIkjz76KF26dKFChQpMnTqVxYsXs3v3bh588EG8vb1JvKmj4urqyuLFi%2Bnbty8LFiygdevWNGnShNGjR1OrVi02bdpEaCmPZdq7dy%2BtW7d2yL4QERERcYT7brj8cDFDJx4eHqxfv77I/NDQUGbf5rggFxcX/v73v9vMa9myJQcPHrT%2B3rVrV7788ktatvz3IeTOzs707NmTnj17lmibW7RoUex2g%2BVM%2BIKCAh577LES5RIREZESuA87j46mPXiXvfTSS3zzzTdcuHDhruR///33iY6Otjm5SERERKSsqci8yx544AHGjh3LrFmzHJ77hx9%2BICsry%2BaMdhEREXEAHZNp2n03XF4WOnbsSMeOHR2et1mzZjRr1szheUVERO5792FR6GjagyIiIiLicOpkioiIiNhTJ9M07UERERERcTh1MkVERETsqZNpmopMEREREXsqMk3THhQRERERh1MnU0RERMSeOpmmqcgUADr67jYcG%2BwD84BonxUc8zWW47ffDK%2BeBlfhb8CLV%2Bdz5JLxPFQwsQ1eN7bBaxVHTOTxyDEeWz8XFgGDct/hqME8l03cmCrkAVgB9Pl1HofPGs9Tr5fx2MBAiAfGtd9LSojxPH//2MlYYHg4xO8lfksTSEw0vP7/7lVgODbQ78Y%2B8FtGyjXDaaiz0VhcjRowLBze/i6ctDTj69%2Ben2A4NiT/xnsx/wMO5xvfho8Cjcd6eFimNWpAVpbxPL/5PWooztkZKgKXgxqRb2IfnD1gPLZ8ecv/yV9/hatXjecJMfF/WcqWikwRERERe%2BpkmqYiU0RERMSeikzTtAdFRERExOHUyRQRERGxp06madqDIiIiIuJw6mSKiIiI2FMn0zQVmSIiIiL2VGSapj0oIiIiIg6nTqaIiIiIPXUyTdMeFBERERGHUydTRERExJ46maapyBQRERGxpyLTtN9lD0ZERNCwYUPCw8MJDw%2BncePGPP7440ybNo1du3ZZ54eHhxMSEkKjRo2sv7/%2B%2BusAnDhxghdeeIHw8HAef/xxFi5caLOO5cuXExERQZMmTejSpQtbtmy55fZkZ2czc%2BZM2rZtS3h4OC1btiQ6Oppjx45Zl/ntt98YOXIkLVq0oEmTJrzwwgv89NNPNnkuXbpEZGQkly5dIiEhgZCQEBYvXlxkfWPHjmXs2LE28zZv3szzzz9PkyZNaNq0Kd26dWP58uXk5eWVaJ9u3LiRN954o0TLioiIyB/f%2BfPnGTJkCM2aNaNFixa8%2Beab5ObmFrvsjh076NKlC40bN6ZDhw588803No8vWbKEJ554gsaNG9O3b1%2BOHz/u8O393cr0yZMnk5iYSGJiIj/%2B%2BCPLli1j/fr17Ny50zo/MTERsDzxwt%2BnTJlCTk4OgwcPpmHDhiQkJLB48WJWrlzJl19%2BCVh25KJFi1i6dCl79%2B4lKiqK2NhYTp06Vey2xMXFkZiYyPvvv09iYiJbt27F39%2Bf559/nkuXLgHw2muvceXKFb766isSEhJo1KgRQ4YMKZLnueeeo1KlStZ5b7/9Nnv37r3tvpg5cyaTJk0iMjKSHTt2sGvXLsaNG8fq1at5%2BeWXS1RodunShUOHDvH999/fcVkREREpJWdnx/%2BYFBsbi5eXF//4xz9Ys2YN33//Pe%2B//36R5VJSUoiOjmbYsGH88MMPREdHExsbS3p6OgCffvopH374IcuWLSMhIYGHHnqImJgYCgoKTG/jzcqsFxwSEkLz5s05dOjQHZfdvXs3GRkZxMTE4O7uzoMPPkjfvn1ZuXIlAMePH6egoMD64%2BLigpubG66uxR8NsGfPHlq3bk2tWrUAqFSpEqNHj6ZNmzacPXsWgFmzZvH2229TqVIlrl27xqVLl/Dx8bHmOHLkCDt27KBbt242uf/7v/%2Bb4cOH8%2Buvvxa77p9%2B%2BoklS5Ywf/58unfvTsWKFXFxceFPf/oT77//Pvv27WPVqlVkZ2fz7LPPEhsba40dNmwYvXv3tn5r6dOnDzNnzrzj/hMREZE/tpMnT7Jr1y5GjRqFp6cntWvXZsiQIdZa6GaffvopzZo1o23btri6utKxY0eaN2/OqlWrAPjkk0/o3bs39evXx8PDgxEjRpCWlkZCQoJDt7lMjsnMyclh79697Ny5k%2Bjo6Dsuf/ToUYKCgnB3d7fOq1evnnVoulOnTqxbt46OHTvi4uKCk5MT06dPx9/fv9h8nTp1Yv78%2BZw4cYKWLVvyyCOPEBQURHx8vHUZNzc3AGbPns2iRYsoX748ixYtsj7%2B97//nbZt29psE8Do0aPZu3cvY8eOZeHChTg5Odk8vm3bNmrWrEmzZs2KbFfVqlWJiIhg8%2BbN9O7dm1mzZhEZGcmmTZu4fPkyCQkJfPbZZ9biOSIiggkTJnDgwAEaNmx4x/1YKCMjw1pMF3rgAV98fPxKnONmN2p169SIK1eMxwYE2E7LgqO2we7tVCq1a9tOjbh61XhsYKDt1Cgz21%2Bjhu3UsPBwY3GhobZTg8zsQ0ftg1v8%2BbwjX1/bqVEhIcZjHfVe9PAwHnvjI8Q6Ncpo86swzmzzrHx547GenrZTI8z8TTLtLhyTWdznr6%2BvL35%2Bd/78PXr0KN7e3lSrVs06Lzg4mLS0NC5dumQzqpqcnEyDBg1s4uvVq0dSUpL18Zdeesn6mJubG4GBgSQlJdGyZUtDz604v1uROXnyZKZOnWr93d/fnxdffJE%2BffrcMfbq1at42r1LPT09uXbtGmApWkNDQ3nzzTcJDQ1l48aNTJgwgeDgYEKK%2BUs1dOhQwsLCWL9%2BPdOmTePChQv4%2BfkxYMAA%2BvXrZ7PsK6%2B8wtChQ1m5ciUvvfQSGzZsoHbt2uzcuZP%2B/fsXye3u7s6cOXPo1q0by5YtY%2BDAgTaPZ2Rk4Hubv75%2Bfn7s378fsLx5JkyYwJQpU8jKymLu3Lk2b65y5coRGhrK999/X6oic9WqVcyfP99m3pAhUQwbdueC/3bGjDEVbtqkSWW7frg3tuG118p2/ffCocIl%2BO56e/G3P%2BTljj76yNzqza0dcMA%2BMKl377JdP9wb78Xq1ct2/WaKRDD%2BfetmZr5z/e//ml%2B/UQU43XmhUiru8zcqKqpEDbdb1UIA165dsykyi1u2XLly1rrpTo87yu9WZE6cOJHu3bsbivXy8iIzM9NmXmZmJuVv/O%2BJi4ujSZMmNGrUCIDIyEg%2B//xzPv300yIn3BSKiIggIiICgJ9//pmtW7cyY8YMypcvz5///GfrcuXKlQPgxRdfZPXq1Xz99df069ePM2fO2BR8N6tTpw5vvPEGo0aNomnTpjaP%2Bfr68u23397yuZ46dcqmCO3SpQszZsygatWqxX678Pf355dffrllvuL07NnT%2BtwLxcf7Gv5QqlXLUmBOmwa3OAz2jsx2MidNsvycPGk8jxmO2gaznczXXrN8sKamGsthtpP5xhuWbUhJMZ7HbCczOhrmzYO0NON54rc0MRYYGmopMHv3hhsdAyPGtTde5DpqH5jpZPbubdkNdg2bUtm923iso96LN/VFSs3NzVJgnjkDOTnG89x0lFapODtbCsyrVyE/3/j6k5ONx3p6Wv5LJCWB3Uf4fau4z9/bNZ5udqtaCLDWQ4U8PT25fv26zbzr169bl7vT447yh7iEUf369UlJSSE3N9c6VJycnEz9%2BvUBSEtL4%2BGHH7aJcXV1tQ553%2BzYsWN07dqVtWvXWlvJderUYeDAgezbt896BnmvXr3o168fTz/9tDU2OzubypUrA%2BDk5HTbA2Q7dOhAQkICw4cPJyQkBG9vbwD%2B3//7fyxYsIDvvvuOJ554wiYmPT2df/zjH7z66qvWefHx8QQFBXHlyhXmzJnDqFGjbGLy8vJwLmVL38/Pr0hr/sIFy48Zp07BTSfol8pvv5lbN1iKuyNHzOcpy20wMzxXKDUVjh41Fnv5svn1p6TA4cPG40t4gYXbSkszV1xw4yREw5KSTOVIMTFUXMjsPjBTmIClwDRT5Jp5DxUy%2B17MyjK/DTk55vKYfR3y883lcMRwdWZmGQ97G2R23xenuM/fkqpfvz4XL17k3LlzVK1aFbDUNP7%2B/lSsWNFm2QYNGnDw4EGbecnJydZaqX79%2Bhw9epQ2bdoAlhHhlJSUIkPsZv0hLgLVokULfHx8mDlzJllZWSQlJfHhhx/So0cPwNKVXLFiBQcPHiQ/P5/NmzeTkJBAx44di%2BSqW7cuDz30EK%2B//jr79%2B8nKyuLzMxMduzYQUJCAu3atQOgUaNGzJs3j9OnT5Odnc3cuXPJzs62fgOpWbOm9SytWxk/fjyVK1e2uWxAaGgoQ4cOZdSoUaxfv57Lly%2BTnZ3Nzp07GTBgAA899BA9e/YELMdvbtiwgbfeeou33nqL5cuX83//938268jIyKCG6QPQRERE5GaFBbojf8wIDAykadOmTJ06lStXrpCamsqCBQustdDNnnnmGXbt2sWmTZvIzc1l06ZN7Nq1i2effRawjPiuWLGCpKQksrKymDlzJlWrVi32fBEz/hCdTFdXV9577z2mTJlCq1at8PLyom/fvtbh96ioKFxcXIiOjua3334jICCAd955h7CwMAAWLlzIxo0b%2BeKLL3BycmLJkiUsWLCAUaNGkZ6ejrOzM2FhYUyfPp1HH30UgJEjR%2BLi4kLPnj3JycmhcePGLF%2B%2B3NrJbNWqFXv27OG555675XYXHp9pf5hAdHQ0YWFhfPDBB0ydOpXc3FwCAgLo0aMHffr0wdXVlfT0dCZMmMDIkSMJvHH0%2BuDBgxk9ejQbNmzggQceICsri4MHDxIXF%2BfoXS4iIiL3mLlz5zJlyhSeeuopnJ2d6dq1q/XyiuHh4UyePJlnnnmG4OBg3nnnHWbMmMGECROoWbMm8%2BbNIygoCIAePXpw%2BfJlhg4dyoULF2jYsCGLFi0qdgTYjN%2BlyNy%2BfXuJlz18i7GNgIAAli1bVuxjrq6uREdH3/LA2cGDBzN48GDr7xUrVmTMmDGMuc2ZKu7u7rddplu3brzwwgtcv36dcuXK0aJFi2K3PTAwsNjrZrZt25a2bdvecv3VqlUrcimBqKgooqKirL9/9dVXhIWFWQ8bEBEREce4G8PlZlWtWpW5c%2BcW%2B1ii3SE6rVu3pnXr1sUu6%2BTkRP/%2B/Ys9gdmR/hDD5fei0NBQWrduzbp168psGz744AOGDx9eZusXERERuRUVmSZMmDCB1atX85sjzloppc8%2B%2B4yHHnrIodezEhEREYt77ZjMP6I/xDGZ9yofHx8%2B/fTTMln3s88%2Baz2AV0RERBzrfiwKHU2dTBERERFxOHUyRUREROyok2meOpkiIiIi4nDqZIqIiIjYUSfTPBWZIiIiInZUZJqn4XIRERERcTh1MgWATR7djAe71wVmMs99BHgcN5YjwNP4%2BqsHAlP5W/XxkJViPM/Zs8Zjy9UD3uVv5V4Br2TjeWrXNh5bvQ4wiUXVJ0HOz8ZyXL9ufP11AoGprKgzHvJTjOdJTTUe69UAWEZ8xgA4dcRwmv/uVWAoLjAQ4oFx7feSEmJ49fz9YyfjweHhEL%2BX%2BC1NwO4OIKVy6JCxuHLlgCCGPXPC3PvpwAjjsT7BwDxW%2BESD7zHDafZd22Q4tuDGW%2Bj6dcjMNJzG8C50vfHpnp0NubnG13/mjPFYb2/L9Nw5uHjReJ6yok6meepkioiIiIjDqZMpIiIiYkedTPNUZIqIiIjYUZFpnobLRURERMTh1MkUERERsaNOpnnqZIqIiIiIw6mThtWhgAAAIABJREFUKSIiImJHnUzzVGSKiIiI2FGRaZ6Gy0VERETE4dTJFBEREbGjTqZ56mSKiIiIiMOpkykiIiJiR51M8%2B6rIjMkJIQnn3ySRYsW4eTkZJ2/bt065s%2Bfz/bt263zDh48yKJFi9i1axdZWVlUrVqVtm3bMmjQILy9va3LbdmyhQULFpCamoq3tzfdu3dnyJAhODv/u0kcFxdHeHg4nTt3BuDixYssWrSI7du3k5GRgbOzM2FhYfTu3ZuOHTsCMHDgQPbs2QNATk4OeXl5lCtXzprziy%2B%2BYMOGDXh4ePDiiy/enR0mIiJyn1KRad59N1y%2BY8cOli5dettlvvnmG3r37k1QUBCfffYZe/fuZeHChaSmptK1a1fS09MB%2BNe//sXo0aOJjY3lhx9%2BYMmSJaxbt47333/fmuv777/n0KFD1gIzIyODZ599lhMnTjBnzhwSEhLYsWMH/fv3Z8qUKfz9738HYOnSpSQmJpKYmMigQYNo1qyZ9ffExERq1KhB//79%2BeSTTzh27Njd2VkiIiIiBt13RWbfvn15%2B%2B232bt3b7GPZ2dn89prrzFo0CBeffVVqlWrhpOTE8HBwcydOxd/f3/i4%2BMBOH36NL169aJNmzY4OzsTHBxMu3bt2L17tzXfzJkz6du3r/X3adOm4e/vzzvvvENYWBju7u5UqFCBiIgIpk6dipubW4mfi7u7O926dWPu3LkG94aIiIgUJz/f8T/3m/tquBygXbt2FBQUMHz4cNavX28z9A2QmJjIuXPn6Nq1a5FYZ2dnevTowaRJk8jNzaV9%2B/a0b9/e%2Bvj169f59ttv6dKlCwD79%2B/n2LFjREREAJCXl8fWrVuZOHEiLi4uRfIXLlcanTt3Zvbs2Zw/f54qVaqUKCYjI4OzZ8/azPP19sbvgQdKvX4Aata0nRrh4WE8tkYN26lRlSsbj61d23ZqVLVqxmOrV7edGpGdbTzWUa%2BDp6fx2Dp1bKcGBfoZi3PULiA83HhsaKjt1KibDs8pFXd326lRwcHGY2vVsp0aZOatWPgnzcyfNgBXg5/ShR8xxXzUlIrdR2SpVKxoOzXi4kXjsVL27rsiE2DMmDEkJiYyduxY3n33XZvHMjIyAKhatWqxsX5%2BfuTk5PDrr7/i6%2BtrnX/lyhWGDRtGuXLl6NevHwA7d%2B4kLCzMeizlhQsXyM7Oxt/f3xqXkpJCjx49AMjPzycnJ4cDBw6U%2BLnUqFEDX19fEhISrMdz3smqVauYP3%2B%2BzbyoIUOIHjasxOst1vDh5uLNiooq2/UDjB9f1lsAgwaV7frvhddh4kRT4fEmVx8dbTJBfPEjLaXy0Ufmc5hh5ksnwLx55rdhzBhT4Q3MbwEBAQ5IYoKZIhGgXTvz29CypfHY1avNr9%2Bo%2B7Hz6Gj3ZZHp7u7OnDlz6NatG%2B%2B99x4%2BPj7WxwoLx7S0NAIDA4vEnjp1Cjc3N5uY48ePExMTQ5UqVfjggw%2BoUKECAGfOnKHaTZ0pHx8f3NzcrMd0AgQGBvLDDz8AkJCQwF/%2B8pdSPx9/f3/OnDlT4uV79uxZpGvqO2MGjBhR6nUDlg%2BT4cNh1iw4fdpYDrOdzKgomD8f0tKM5/n1V%2BOxtWtbCsypUyE11Xges53MQYNg0SIoxfvBhtlOpiNeh5v%2Bf5RanTqWAnPyZPj5Z8NpxvktMxRXo4alwJw3z9wuiN/SxHhwaKilwOzdG5KSjOdZu9ZYnLu75W/C6dPm3k%2BzZhmPrVXLUmBOmwanThlOcyTaeKHr4WEpME%2BehKwsw2ko4QBVES4ulgLz4kXIyzO%2B/lscWVYiFStaCsydO%2BHyZeN5yoqKTPPuyyIToE6dOsTFxTF69Gi6d%2B9und%2B0aVN8fX1Zs2YNI0eOtInJy8tj3bp1RERE4HpjDGPHjh0MHz6c5557jhEjRljng2V4Pf%2Bmd6mrqysRERGsXbuWbt262ZyBbkZubm6xw%2B%2B34ufnh5%2Bf3XjgxYvmxyVOn4bjx43FmhmXKpSWBikpxuPtDiEwJDUVkpONx5v5NCp05ozxAuv6dfPrN/s6mCnSC/38Mxw5Yjg85Zq51ZvdBSQmmtsAsBSYZvKYfS9kZ5vL4YgTGk%2BdMpUnM9P8JmRlmcuTm2tu/Xl55nI4Yrj68mUNe9%2Bv7rsTf27WsWNHIiMjWbVqlXWem5sb8fHxrFixgtmzZ5Oenk5%2Bfj7JyclERUXxyy%2B/MG7cOAB%2B/PFHhg4dyrhx4xgzZoxNgQmWoex0u67MhAkTOHPmDFFRUSQlJZGfn09WVhbbtm0jLi7OZgi%2BpDIyMqhu5jg8ERERsaETf8y7r4tMgPHjxxMWFmYzr3Xr1nz88cf8/PPPREZG0qRJEwYPHkydOnXYsGGDtaBbuHAhubm5vPnmm4SHh1t/Bg4cCECrVq04ePAgWTd1p6pVq8aGDRto0KABI0eOpHnz5rRs2ZIFCxbwzDPPsHnz5lJtf2pqKhcvXuTRRx81uSdEREREHOe%2BGi4/fPhwkXkeHh6sX7%2B%2ByPzQ0FBmz55923wLFy687eOhoaHUr1%2Bfr7/%2B2uaknIoVKxIbG0tsbGyJtjv6NmcRbNq0iXbt2lGpUqUS5RIREZE7ux87j45233cy77YRI0awfPnyu5I7OzubNWvWEBMTc1fyi4iI3K80XG6eisy7rFWrVoSFhbFhwwaH5166dCk9e/YkKCjI4blFREREzLivhsvLyqRJk%2B5K3iFDhtyVvCIiIve7%2B7Hz6GjqZIqIiIiIw6mTKSIiImJHnUzzVGSKiIiI2FGRaZ6Gy0VERETE4dTJFBEREbGjTqZ5KjJFRERE7KjINE/D5SIiIiLicOpkioWZ21KWL//vqdE8ly4ZX3/hveGzsiAz03ielBTjsV5elmlamrk8V68ajy382p2SAkeOGMtRUGB8/W5ulunp03DihPE806cbjy18L0ZFmdqXdTYai/P3//fUVBfk0CHjseXKWaZr18L168bzPPigsbjwcNi7FyIjITHR%2BPoHDzYeW736v6e5uYbTPPL%2Bq8a3oVYtGDGCBhtnwqlThtNkTr397Y1vxcnJMi1f3tx/6z9//oLx4IAAaDeFdv94HU6eNLERd%2BeueXeiTqZ5KjJFRERE/gNcu3aNuLg4tm/fTm5uLk899RQTJ06kfOEXcDtbtmxhwYIFpKam4u3tTffu3RkyZAjOzpaB7g4dOpCWlmb9HWDNmjUEBweXaHtUZIqIiIjY%2BSN2MuPi4jhz5gxbtmwhLy%2BP2NhYZsyYwcSJE4ss%2B69//YvRo0czZ84cnnzySU6cOMFLL72El5cX/fv358qVK5w4cYKvv/6amjVrGtoeHZMpIiIiYic/3/E/d1NmZiYbN24kJiYGb29vqlSpwsiRI1m3bh2ZxRxKdvr0aXr16kWbNm1wdnYmODiYdu3asXv3bsBShHp7exsuMEGdTBEREZHfRUZGBmfPnrWZ5%2Bvri5%2BfX4nir1%2B/Tnp6erGPZWZmkpOTQ4MGDazzgoODuX79OikpKYSFhdks3759e9q3b2%2BT%2B9tvv6VLly4AHDhwAE9PT/r06cPRo0epWbMm0dHRtGnTpkTbCioyRURERIq4G53HVatWMX/%2BfJt5UVFRREdHlyh%2B3759/OUvfyn2sWHDhgHgVXgiKuDp6QnA1TucCHnlyhWGDRtGuXLl6NevHwBOTk40bNiQ4cOHU6NGDTZv3kx0dDQrVqygcePGJdpeFZkiIiIiv4OePXsSERFhM8/X17fE8S1atODw4cPFPnbo0CHefvttMjMzrSf6FA6TV6hQ4ZY5jx8/TkxMDFWqVOGDDz6wLjtw4ECb5Z555hk%2B//xztmzZoiJTRERExKi70cn08/Mr8dB4aQUFBeHm5kZycjKPPPIIAMeOHcPNzY3AwMBiY3bs2MHw4cN57rnnGDFiBK6u/y4Lly1bxoMPPsijjz5qnZednY2Hh0eJt0kn/oiIiIjY%2BaOd%2BOPp6UmHDh2YMWMGFy5c4MKFC8yYMYPOnTtTrvD6uTf58ccfGTp0KOPGjWPMmDE2BSbAmTNnmDx5MqmpqeTm5rJmzRoSExPp1q1bibdJRaaIiIjIf4CJEycSGBhIly5dePrpp6lVqxavv/669fFOnTqxcOFCABYuXEhubi5vvvkm4eHh1p/CYfLRo0fzxBNP0Lt3b5o1a8bHH3/M4sWLCQgIKPH2aLhcRERExM4f8TqZFSpUIC4ujri4uGIf/%2BKLL6z/Liw2b8Xd3Z3x48czfvx4w9ujTqaIiIiIOJw6mSIiIiJ2/oidzHvNXe9kRkRE0LBhQ%2BtYf%2BPGjXn88ceZNm0au3btsjkOICQkhEaNGll/LzyO4MSJE7zwwguEh4fz%2BOOPF2nxLl%2B%2BnIiICJo0aUKXLl3YsmXLLbcnOzubmTNn0rZtW8LDw2nZsiXR0dEcO3as2OVXr15NSEhIkfmpqan06tWLnJwc1q1bR0hICJs2bSqyXN%2B%2BfZk3b57194KCAlatWkVkZCTh4eE0b96cXr16sX79epu4rKws3nzzTZ544gmaNm3Kn//8Z3bu3Gl9/OWXX2b//v23fJ4iIiJi3B/txJ970e/SyZw8eTLdu3e3/n748GH69euHp6cniYmJ1vkhISEsWbKEFi1aWOfl5OQwePBg2rVrx5IlS0hOTmbQoEEEBATQoUMHduzYwaJFi1ixYgV169Zly5YtxMbG8tVXX1GrVq0i2xIXF8eJEyd4//33qVWrFpcuXWLevHk8//zzbN26lUqVKlmXPXr0KFOnTi32OY0dO5aoqCjc3Nys81577TUeeuih2x4UO3LkSBITExk/fjyPPfYYbm5ufPfdd0yZMoVdu3ZZ1zdjxgz27t3LqlWr8PPzY%2B3atQwePJhNmzZRo0YNxowZQ1RUFJ999hnu7u4leBVEREREfj9lckxmSEgIzZs359ChQ3dcdvfu3WRkZBATE4O7uzsPPvggffv2ZeXKlYDlIqIFBQXWHxcXF9zc3Iqcil9oz549tG7d2lqAVqpUidGjR9OmTRubWz1lZmYyfPjwYq%2Bs/%2B2333LhwgUef/xx67yaNWvSokULYmNjyc7OLnbd27ZtY8uWLSxfvpy2bdvi5eWFm5sbTz31FMuWLWP9%2BvXs2LEDsHQyY2JiqF69Oi4uLjz33HO4u7tz8OBBwHKrqJo1a7J69eo77kMREREpHXUyzfvdj8nMyclh79697Ny5s0S3UTp69ChBQUE23bp69eqxePFiwHI6/rp16%2BjYsSMuLi44OTkxffp0/P39i83XqVMn5s%2Bfz4kTJ2jZsiWPPPIIQUFBxMfH2yw3ZcoU/uu//ovHHnusyPD8Rx99ROfOnYvkfuutt%2BjatSvx8fFMnDixyOPbtm2jSZMm1K5du8hj9erVIzw8nM2bN/Pkk08yZcoUm8e///57Ll%2B%2BTGhoqHVe586dWbp0Kc8//3yxz/VWir13asWK%2BD3wQKnyWFWvbjs14g63vLqtmjVtp0aZ%2BQtQt67t1CgfH%2BOxhR30UlxeooiCgrJdP8CNO1UYcuMWatapQTVqGIsrvHFHKW7gUbxirmlXYoV/K82OcISHG4sr/Bt1098qQ4r5O1li1arZTo0qxUWniyi84LbJC287OZmLMxpvZeb/syM%2BG06eNB4rZe53Gy6/edjZ39%2BfF198kT59%2Btwx9urVq9Z7bxby9PTk2rVrgKVoDQ0N5c033yQ0NJSNGzcyYcIEgoODiz2WcujQoYSFhbF%2B/XqmTZvGhQsX8PPzY8CAAdb7dX722WccO3aMuLg49uzZYxOfn5/Prl27ePHFF4vkrly5MrNmzaJv37786U9/okOHDjaPZ2Rk3Pb2UX5%2BfmRkZBSZ/%2BOPPxIbG0tUVJRNgRoeHs7Ro0c5d%2B4cVatWvWVee8XeO3XoUKJjYkqco1ivvGIu3qzhw8t2/QCzZ5f1FsDkyWW7frsvSGXCZIEzzGB9Vah3b3PxEGQ2gfkvXXv3mov/6CNz8Y4wYEBZbwH07Wsq3MTXDcBcnQw45v%2Bzmc%2BGF14wv36D7sfOo6P9LkXmxIkTbY7JLA0vLy/rvTcL3Xxfzri4OJo0aUKjRo0AiIyM5PPPP%2BfTTz9l7NixxeaMiIiw3jv0559/ZuvWrcyYMYPy5cvTtGlTZs6cycqVK4sdcr948SKZmZm3vC1UeHg4sbGx1uMzb%2Bbr68vJ23wrO3XqFPXq1bOZt3r1aqZOnUpMTEyRwrawW3vmzJlSFZnF3jv13Xfhpgu2lkr16pY/Iu%2B%2BC2fOGMthtpM5fDjMmgWnTxvPk5xsPLZuXUuB%2BeqrcPy48TxmO5mTJ8PEica//ZvtZE6ZYnkfmek%2BxMYaj/X0tBSYSUlg93ejNN7%2BzliV6etrKTA/%2BgjsBgtKZdgzJ4wHu7tb/k%2BcPg23OHSnRCIjjcWFhlp2QO/eltfBqB49jMdWq2YpMJctg/R043nMdjL79oUPP4RimgcldX3oCENxTk6Wzc/KMvffutxUg58L4JjPhjKkItO8e/4SRvXr1yclJYXc3Fxr0ZecnEz9%2BvUBSEtL4%2BGHH7aJcXV1tTkhp9CxY8fo2rUra9eupUGDBgDUqVOHgQMHsm/fPn766SfOnTvHpUuXrLdNysvLA6BZs2ZMnDjRehxmwW3%2B1w4YMIBdu3YRGxtrsx1PP/000dHRHD58uEiX9dChQxw6dIghQ4ZY1zt58mS2bt3KO%2B%2B8w2OPPVZkPYXb5uLicsttKU6x9069fNnyY8aZM8aLi0uXzK0bLB%2BqZgq8EhwjfEfHj5vLY3qcFctrcOSIsVgzn0aOWD%2BY%2B8JRKDPTVJ60NHOrP3vWZI7r181tAFgKTDN5bjop05CkJHM5bjoB1LD0dEhNNR5v5rCFQhkZcOqU4XCz/yULCkzmcMRwtZnPBvlDu%2Bcvxt6iRQt8fHyYOXMmWVlZJCUl8eGHH9LjxrfciIgIVqxYwcGDB8nPz2fz5s0kJCTQsWPHIrnq1q3LQw89xOuvv87%2B/fvJysoiMzOTHTt2kJCQQLt27XjllVf48ccf%2BeGHH/jhhx%2Bsx2P%2B8MMPdOnSBR8fH7y8vEi/zbdjJycnpk2bxvnz5/nxxx%2Bt89u0aUOXLl145ZVX%2BPrrr7l27RrXrl1j27ZtDBkyhE6dOtGmTRsA4uPj%2Be6771i7dm2xBSZg3YbqZo53ERERkSJ04o9593wn09XVlffee48pU6bQqlUrvLy86Nu3r3X4PSoqChcXF6Kjo/ntt98ICAjgnXfeISwsDLDcNmnjxo188cUXODk5sWTJEhYsWMCoUaNIT0/H2dmZsLAwpk%2BfzqOPPlqibWrVqhV79uyhVatWt1ymsDB%2Bwe54kvj4eNasWcPixYsZM2YMYDnpJzo62vqcLly4wMqVK3FxcSlygtHkyZN55plnAMuZ8g8//DA%2BZoZYRURERO6Cu15kbt%2B%2BvcTLHj58uNj5AQEBLFu2rNjHXF1diY6OvuWZ6oMHD2bw4MHW3ytWrMiYMWOsBd6dtGjRosh2RUZGMn36dGJunCjTvXv3Yo85bdasmfWSQzfr0aOHtRNbnAceeICffvrpjtv25Zdf3jaPiIiIGHM/dh4d7Z4fLr8XtWnThsqVK1uvaVkWjh49yqlTp1RkioiI3AUaLjdPRaZBb731Fu%2B88w45OTllsv5p06bx1ltvFXuCk4iIiEhZu%2BePybxXBQQE8Mknn5TZ%2BpcuXVpm6xYREflPdz92Hh1NnUwRERERcTh1MkVERETsqJNpnopMERERETsqMs3TcLmIiIiIOJw6mSIiIiJ21Mk0T51MEREREXE4dTLF4v/%2Bz3jsxYuW6b59cOiQsRxXrhhf/9WrlumBA5Yfo4KCjMcW3trTxwd8fY3n%2BeUX47FVqlim588bz5OXZ3z9DzxgmZ47B2lpxvOMGGE8NiQEPvgAZs%2BGW9xBrCS25ycYXj3A7t2mVg8HTOyD4GCYNw9mzYJjx4znuelOaaVSu7Zl2qMHtGhhfP0LFxqPDQ%2BH8eNhzRpITDScJut6geFYJydwB7KjR1BgPA2ev5w0FujuDtWrU%2B7XM5CdbXwDZs0yHuviYpkOH27ub0sZUSfTPBWZIiIiInZUZJqn4XIRERERcTh1MkVERETsqJNpnjqZIiIiIuJw6mSKiIiI2FEn0zwVmSIiIiJ2VGSap%2BFyEREREXE4dTJFRERE7KiTaZ6KTBERERE7KjLN03C5iIiIiDicOpkiIiIidtTJNE%2BdTBERERFxuHumkxkREcHZs2dxdbVsUkFBARUqVKBLly60adOGQYMGWZe9du0aHh4euLi4ANClSxemTJnCiRMnmDRpEvv376d8%2BfL06dOHwYMHW%2BOWL1/O8uXLuXjxIjVr1iQqKor27dsXuz3Z2dnMmzePL7/8kvPnz%2BPh4UHz5s2JjY0lODgYgPz8fJo2bUpBQQFOTk7W2H/%2B8594eXkBkJqayqhRo/jwww9xc3OjoKCATz75hE8%2B%2BYTjx4/j6upKcHAwvXr1omvXrtYcv/32G3FxcfzjH/8gJyeHhg0bMnbsWMLCwgB4%2BeWXiYqKolGjRo7Y/SIiInITdTLNu2eKTIDJkyfTvXt36%2B%2BHDx%2BmX79%2BeHp6kpiYaJ0fEhLCkiVLaNGihXVeTk4OgwcPpl27dixZsoTk5GQGDRpEQEAAHTp0YMeOHSxatIgVK1ZQt25dtmzZQmxsLF999RW1atUqsi1xcXGcOHGC999/n1q1anHp0iXmzZvH888/z9atW6lUqRLJycnk5OSwd%2B9e3N3di31OY8eOJSoqCjc3NwBGjhxJYmIi48eP57HHHsPNzY3vvvuOKVOmsGvXLqZOnQrAa6%2B9Rk5ODl999RWenp7MnTuXIUOG8M033wAwZswYoqKi%2BOyzz265bhERETFGRaZ591SRaS8kJITmzZtz6NChOy67e/duMjIyiImJwd3dnQcffJC%2BffuycuVKOnTowPHjxykoKLD%2BuLi44ObmZu2c2tuzZw/PPvustQCtVKkSo0eP5sqVK5w9e5ZKlSpx4MABQkJCblnkffvtt1y4cIHHH38cgG3btrFlyxa%2B/PJLateubV3uqaeeIiAggGeeeYb27dvz5JNPMmvWLPLz8/Hw8OC3337j0qVL%2BPj4WGOCg4OpWbMmq1ev5vnnny/xPhURERH5PdyzRWZhh3Dnzp1ER0ffcfmjR48SFBRkU/DVq1ePxYsXA9CpUyfWrVtHx44dcXFxwcnJienTp%2BPv719svk6dOjF//nxOnDhBy5YteeSRRwgKCiI%2BPt66zIEDB8jKyiIyMpLTp08THBzMiBEjaNKkCQAfffQRnTt3ti6/bds2mjRpYlNg3ryt4eHhbN68mSeffNLa%2BZw9ezaLFi2ifPnyLFq0yCamc%2BfOLF26tNRFZkZGBmfPnrWZ51ujBn7e3qXKY1W3ru3UiMxM47H16tlOjapRw3hsQIDt1KgqVYzH3jiMwzo1wsxXd0esH8ztAwe9DiEGd0NgoO3UMB8T%2B7BwZKaYEZpSqV7dWFy1arZTo8LDjceGhtpODbrpKCjDsWZyAGB0pKqwgXKLRkqJ3TgszVSsmRx5ecZjTVIn07x7qsicPHmydbgYwN/fnxdffJE%2BffrcMfbq1at4enrazPP09OTatWuApWgNDQ3lzTffJDQ0lI0bNzJhwgSCg4MJCQkpkm/o0KGEhYWxfv16pk2bxoULF/Dz82PAgAH069cPgHLlytGoUSOGDRtG5cqVWblyJQMGDGDDhg3UrFmTXbt28eKLL1pzZmRk4Ovre8vn4OfnR0ZGhs28V155haFDh7Jy5UpeeuklNmzYYC1Sw8PDOXr0KOfOnaNq1ap33EeFVq1axfz5823mRQ0ZQvSwYSXOUazZs83Fm7VgQdmuH2Dy5LLeApgzp2zXP3du2a4fIC7OVPgKk6t/4w2TCZhnNgGMGWM%2BhxkDBpiLHz/e/DZ89JGpcEcciHSjX2Cc0WK/0G0%2Bc343FSsajz1/3nHbIb%2B7e6rInDhxos0xmaXh5eVFpl03LDMzk/LlywOWYyybNGliPVEmMjKSzz//nE8//ZSxY8cWmzMiIoKIiAgAfv75Z7Zu3cqMGTMoX748f/7zn4vEDRgwgHXr1rFjxw46duxIZmYmfn5%2B1sd9fX05efLkLZ/DqVOnqGfXjStXrhwAL774IqtXr%2Bbrr7%2B2FrmFXdgzZ86Uqsjs2bOn9XlZt23IENi%2BvcQ5bNStaykwX30Vjh83lsNsJ3PBAhgyBJKTjecx28mcPBkmToTbvMZ3ZOYPanCwpcCMjYVjx4zlMNvJnDsXYmKMrx/MdzLj4uCvfzX1OvTJ/8BQXGCgpcB87TVISTG8elb43Hn05pZq1bIUmNOmwalTxvOY6WQOGADLlkF6uvH1r1ljPDY01FJg9u4NSUmG02Tv3Gs41snJUmDm5EBBgeE0uJ8/YyzQ1dVSYJ49C7m5xjfArnlTKi4ulgLz8uUy7UgapU6mefdUkWlG/fr1SUlJITc313qcZXJyMvXr1wcgLS2Nhx9%2B2CbG1dXVOix9s2PHjtG1a1fWrl1LgwYNAKhTpw4DBw5k3759/PTTT4BlKLt9%2B/Y8%2BOCD1tjs7Gw8PDysZ5sX3PTX5emnnyY6OprDhw8X6Z4eOnSIQ4cOMWTIEAB69epFv379ePrpp21yV65c2fp73o3/tC6lHIrw8/OzKX4BSEuz/Jhx/DiU4PjZYl25Ym7dYCkwDxwwHu%2BIbTh5Eo4cMR7/yy/mt%2BHYMTh40FisIz4Ijh2Df/3LePwtDmEplZMn4fBhw%2BGHTX64pKSYWj34mijSC506Za7YN1OYgKXATE01Hn/TyZ6GJSWZymOmOLw5h6k82dnmNiA311wOR5xYmpenIvM%2B9R9zncwWLVrg4%2BPDzJkzycrKIikpiQ8//JAePXoAlq7kihUrOHjwIPn5%2BWzevJmEhAQ6duxYJFfdunV56KGHeP3119m/fz9ZWVlkZmayY8cOEhISaNeuHQBHjhzhzTff5OzZs2Sy6jksAAAgAElEQVRnZzN//nyuXLlCu3bt8PHxwcvLi/Sbvsm3adOGLl268Morr/D1119z7do1rl27xrZt2xgyZAidOnWiTZs2ADRq1Ih58%2BZx%2BvRpsrOzmTt3LtnZ2TYdyMLc1c0Op4iIiIg42H9MJ9PV1ZX33nuPKVOm0KpVK7y8vOjbt691%2BD0qKgoXFxeio6P57bffCAgI4J133rFed3LhwoVs3LiRL774AicnJ5YsWcKCBQsYNWoU6enpODs7ExYWxvTp03n00UcBiI%2BPZ9q0aTz77LNkZmbSsGFD/va3v%2BF94wSaVq1asWfPHlq1amXdzvj4eNasWcPixYsZc%2BOYqXr16hEdHW1zqMDIkSNxcXGhZ8%2Be5OTk0LhxY5YvX27TydyzZw8PP/ywzVnnIiIiYt4fsZN57do14uLi2L59O7m5uTz11FNMnDjReuigvYkTJ7J27VqbUd2xY8fSs2dPAJYsWcKHH37IpUuXaNiwIZMnT6ZuKU7yvWeKzO2lOB7w8C3GoQICAli2bFmxj7m6uhIdHX3LM9UHDx5sc%2BH2ihUrMmbMGGshWBxvb2%2Bbs83tRUZGMn36dGJiYmzm9%2BjRw9phvRV3d/c7rv/LL7%2B8Yx4RERG5P8TFxXHmzBm2bNlCXl4esbGxzJgxg4kTJxa7/IEDB4iLi6Nbt25FHvv000/58MMPWbZsGXXq1GH27NnExMSwceNGmxvQ3M5/zHD5vahNmzZUrlyZHTt2ODz30aNHOXXqlIpMERGRuyA/3/E/d1NmZiYbN24kJiYGb29vqlSpwsiRI1m3bl2RE6PBcp7HkSNHipyvUuiTTz6hd%2B/e1K9fHw8PD0aMGEFaWhoJCQkl3iYVmXfZW2%2B9xTvvvENOTo5D806bNo233nqr2BOXRERExJx7sci8fv06J0%2BevOVPTk6O9YRlsNy45fr166QUc7mLpKQkcnNzmTt3Lo899hjt27dn8eLF5N/Y0OTkZJtcbm5uBAYGklSKKzbcM8Pl/6kCAgL45JNPHJ536dKlDs8pIiIid0%2BxN0Px9S16xZdb2LdvH3/5y1%2BKfWzYjWtde3l5WecVXj/86tWrRZa/fPkyf/rTn%2Bjbty%2BzZs3ip59%2BYujQoTg7OzNw4MBirz9erlw56/XHS0JFpoiIiIiduzG8XezNUKKiSnRnQ7BcSedW56UcOnSIt99%2B2%2BYa4YXD5BUqVCiyfKtWrWxOTG7UqBEvvPACmzZtYuDAgXh6enL9%2BnWbmOvXr9/yJKLiqMgUERER%2BR0UezMUB92VKSgoCDc3N5KTk3nkkUcAy3W/C4e57W3bto1z587Rq1cv67zs7GzrTWDq16/P0aNHrZdWzMnJISUlxWYI/U50TKaIiIiInbtxTKafnx8PPfSQzU9Jh8rvxNPTkw4dOjBjxgwuXLjAhQsXmDFjBp07d7YWjjcrKCggPj6e77//noKCAhITE/nggw%2Bsly%2BKjIxkxYoVJCUlkZWVxcyZM6latSrNmjUr8TapkykiIiJi5494ncyJEycybdo0unTpQk5ODk899RR//etfrY936tSJLl26MHjwYNq1a8e4ceOYNGkS6enpVK1alejoaJ599lnAcrnFy5cvM3ToUC5cuEDDhg1ZtGhRqU44VpEpIiIi8h%2BgQoUKxMXFERcXV%2BzjX3zxhc3vvXr1shkuv5mTkxP9%2B/enf//%2BhrdHRaaIiIiInT9iJ/Neo2MyRURERMTh1MkUi0OHzOdYs8Z4rN1lEkrF%2BcZ3pU8/NffV8%2BOPjcdWqWKZPvMMnD9vPE%2BTJsZjC69nNm0aFHN3hxIpxfXPiii8rEVcHBRzTbaS%2Bup6a8OxFStCS2DnkA%2B4fNlwGj4KNBbn4WGZTp0KWVnG17/v2ibDsZ6e0AA4Ej3P8NsA4JH3XzUWWLgTPDygmJMNSirreoHhWCcncAeyd%2B6lwHgaPMqV7NZ5xQoPh717cW/ZBBITDacZ8oqxJ1C7NowbB/HvVyc11fDqWXC%2Bp/HgoCB46y3Lz4kTxvOsWmU81gR1Ms1TkSkiIiJiR0WmeRouFxERERGHUydTRERExI46meapkykiIiIiDqdOpoiIiIgddTLNU5EpIiIiYkdFpnkaLhcRERERh1MnU0RERMSOOpnmqZMpIiIiIg6nTqaIiIiIHXUyzVORKSIiImJHRaZ5Gi4XEREREYdTJ/MP5PXXX2fjxo0A5ObmkpOTg6enp/XxJUuW0KxZs7LaPBERkf8Y6mSapyLzD2TKlClMmTIFgHXr1jF//ny2b99exlslIiIiUpSKTBERERE76mSapyLzPpSRkcHZs2dt5vn6%2BODn61tGWwQ4mzg8uDDWTA6AKlWMx3p7206Nuunwh1IrV852aoSTk/HYwm038xyAim7GY728bKdGeXgYi3Nzs50aVVBgPLZw240%2BB6tatYzF%2BfnZTg0y81YsjDWTA4DwcOOxoaG2U4Nq1zYWV62a7dSwSkHGY2vUsJ0aceKE8ViTVGSa51RQYObPmZQVM8Pl8%2BbNY/78%2BTbzooYOJTomxlGbJyIiYl7PnrBqVZmsesAAx%2BdctszxOe9l6mTeh3r27ElERITNPF8fH8jJMZ7Uzc1cfHa28VhnZ0v3LDPT3FfPLVuMx3p7Q0QEbN8OFy8az2Om61GuHNStC8ePw/XrxnIYjQPLaxAWBj/9ZHktDNqZ3cRwrJcXNGoE%2B/fDtWuG0xhuvLi5QfXqcOaMuf8OZl4GDw8ICICTJyEry3ieBhtnGgv084O%2BfeHDDyEjw/D6s6NHGI51cvr3nyQzbRT3lsbfi4SGwkcfQe/ekJRkOE38n/caiqtWDfr3h/feg/R0w6tn3G9jjQfXqAExMTB3LqSlGc9TRtTJNE9F5n3Iz88PP/uhLDOfiI7giP/N%2Bfnm8pw/b34bLl40l8dEcWZ1/brxPGYqs0KZmXD1quHwyyYKrELXrsHly8bjzRRnYPnvZCaHI94GWVkm85w6ZW4DMjJM5XDEGFtBgck8iYnmNyIpyVSe1JbmVp%2BeDqmpJhKcd8BwdVpamQ57S9lRkSkiIiJiR51M81RkioiIiNhRkWme7vjzB9W9e3ddI1NERETuWepkioiIiNhRJ9M8FZkiIiIidlRkmqfhchERERFxOHUyRUREROyok2meOpkiIiIi4nDqZIqIiIjYUSfTPBWZIiIiInZUZJqn4XIRERERcTh1MkVERETsqJNpnjqZIiIiIuJwTgUFBQVlvRFyD7h82XisszOULw9Xrxr/6nfxovH1u7mBvz/88gvk5BhOk3iutuFYT08IDYWkJMjMNJyGunWNxzo7Q8WKlpfS6Mtw/brx9bu6QpUqcP485OYaz1Pt%2BPfGg8uXh0aNYP9%2By/vRoN8efNRQnCNeA7g3XodKlYzFOTlBuXKW52Dm08Uz46TxYHd3/n979x0WxdW2AfxemiAaRQVL9I3GgCUEgyj2hmIsoIggFizRV0FjT4xYosaGJZagMWqUiDUWwKixYE%2BiiA3UWAgQjY0miAoCS5nvDz72ZSkKuzs7i9y/6/KCndmZ5/Gw7D6cM3MO6tYFYmMBuVzl00xY/oHKxzZoAMyaBfj6Ao8eqXwabPhRptqBtrbA9etAy5ZAeLjqCdy6pfqxxsbARx8B0dHqvaitrVU/Vg39%2B2v%2BnL/%2Bqvlz6jIOlxMREREVwuFy9XG4nIiIiIg0jj2ZRERERIWwJ1N97MkkIiIiIo1jTyYRERFRIezJVB%2BLTCIiIqJCWGSqj8PlRERERKRx7MkkIiIiKqQ89mS%2Bfv0aixYtwpkzZ5CdnY3u3btj/vz5MDU1LfLcefPm4fDhw0rbMjIy0L59e2zduhW5ubmws7ODIAiQyf43X%2BuFCxdQuXLlUuXDnkwiIiKid8CiRYsQGxuLEydOICQkBLGxsfjuu%2B%2BKfe7ChQsRHh6u%2BLdu3Tq899578PHxAQBER0cjKysLly9fVnpeaQtMgEUmERERURG5uZr/J6b09HQcPnwYkydPRvXq1VGzZk189dVXCAoKQvpblqJLTk7GV199hTlz5sDS0hIAcOvWLTRp0gRGRkYq58ThciIiIqJCxCgKExISkJiYqLTN3NwcFhYWpTo%2BIyMD8fHxxe5LT09HVlYWrKysFNsaN26MjIwMPHjwAM2aNSvxvN999x2sra3Rr18/xbZbt24hMzMTAwcOxJMnT9C4cWN8%2BeWXaNmyZalyBVhkSs7BwQGJiYkwMMj7UQiCAD09PTRr1gxz5sxB8%2BbNMXz4cNjb22PSpElKx4aFhWHEiBGIjIyUInUiIiIqg71792L9%2BvVK2yZOnFjk870kN27cwIgRI4rdN2XKFABQGs42MTEBAKSlpZV4zkePHuHQoUPYv3%2B/0nZjY2PY2NhgypQpqFatGnbt2oUxY8bg0KFDaNCgQanyZZGpA7799lu4uroqHj979gxz587FxIkTcerUKQkzIyIiqpjE6Mn08PCAg4OD0jZzc/NSH9%2BmTZsSO5bu3LmD77//Hunp6YobffKHyatUqVLiOQMDA2Fra1ukpzP/2sx8Y8aMQVBQEM6fPw9PT89S5ctrMnVQrVq14OHhgSdPniAlJUXqdIiIiEgDLCws8PHHHyv9K%2B1Q%2Bds0atQIhoaGiI6OVmyLiYmBoaEhGjZsWOJxISEh6N%2B/f5Hta9aswZ07d5S2yeVyVKpUqdQ5sSdTB8XGxmLnzp345JNPUKNGDQDA5s2bERAQoPS8nJwcKdIjIiJ655W3KYxMTEzQu3dvfPfdd/j%2B%2B%2B8B5F1r6eTkBGNj42KPef78OWJiYtC6desi%2B/7%2B%2B29cvXoVa9euRbVq1bB582akpqbC0dGx1DmxyNQB3377LZYuXYrs7GxkZWWhTp06cHR0hJeXl%2BI548aNK/GazLIq9sLjypVV/2tKT0/5qyoMDVU/9v%2BvZ1V8VdH/X7qikvw/7MrwB16x1GlCTfwY1GlCfX3lryorZj63Usv/Iarzw4TqbaiJnwGgGz%2BHAtPiqXScqscrqHFHq6beE0p52VmxatdW/qoyW1vVjmvaVPmrqkooTkpFE2%2BMGRmqH6um8lZkAsD8%2BfOxfPlyODs7IysrC927d8c333yj2N%2B3b184OzvD29sbAPD48WMAQO1iXqi%2Bvr5Yvnw5%2Bvfvj/T0dHzyySf4%2BeefUb169VLnIxMEQVDz/0RqcHBwwMSJE%2BHq6gq5XI7t27dj48aNWLduHdq1awcAGr/xZ926dUUvPP7iC0yaPFm9/wwREZEm/fUXYG0tSegOHTR/zgsXNH9OXcaeTB1iZGSE//73v3jx4gUmTJiAPXv2oKm6f4UWo9gLjytXBt5w99kb6enl9Rylp6v%2Bp9%2BrV6odB%2BT1VtSqBTx7BmRnq3yaeyl1VD62UiWgUSPg/n0gM1Pl0%2BD991U/Vk8vrxMwLU31H4Ncrnp8fX2genUgJQVQ50qOmk9uqn6wiQlgaQlEReW9HlX0qpGNSsdp4mcA6MbPQdUOZZks7/chMxNQpwvD%2BHms6gcbGADm5kBiolrvCb7b6qp8bO3awOjRgL8/UMKMM6Uya3/pp4tR0rQpsHs3MHQocO%2Be6gns26f6sZUq5XUHP3qk3hujRMpjT6auYZGpg6ZOnYorV65g%2BvTpCAoK0vj5LSwsig6Nv3ql/m%2BUOrPNZmWpFxvI%2BzBR4zxq1CQKmZnqnUcTb2rq/BjU%2BDxWyMlR8zyq/rFTUHq6WueR8lcB0I2fg7pjXIKg5jnUqbTzZWerdZ5Hj9RPIT5ezfOEh6uXwL176p1DE8PVmZmSDnuTdHh3uQ7S19fHypUrER8fj%2BXLl0udDhERUYVT3lb80UXsyZTYmTNnit3eoEEDXLt27Y3Hvmm%2BLCIiIlJdRSwKNY09mURERESkcezJJCIiIiqEPZnqY08mEREREWkcezKJiIiICmFPpvpYZBIREREVwiJTfRwuJyIiIiKNY08mERERUSHsyVQfezKJiIiISOPYk0lERERUCHsy1ccik4iIiKgQFpnqY5FJukEuV/8cWVlqncf22UnVY1etCqAtmqZcAl69Uv08hnVVP9bYGKj6EarGRwMZGSqdolpOjurxTUyAmlaomfQ3kJ6u8mkia7RT%2BdhKlYCGAB68Z4PMSiqfBom3VDvO1BSwtQWio4G0NNXjx8aqfmz16oCjI3D9OpCSovp53I%2BMVO3ADz4AFi6E8dJ5wL//qp7A6tWqH6uvn/fVxAQwMlL5NBuSPFTP4b1GAJZh1gsfIOm%2B6ue5peKL0dg47%2Bu%2BfSq/HwAAPvlE9WNtbfNeiIMGAeHhqp9HEFQ/liTFIpOIiIioEPZkqo9FJhEREVEhLDLVx7vLiYiIiEjj2JNJREREVAh7MtXHnkwiIiIi0jj2ZBIREREVwp5M9bHIJCIiIiqERab6OFxORERERBrHnkwiIiKiQtiTqT72ZBIRERGRxrEnk4iIiKgQ9mSqj0UmERERUSEsMtXH4XIds2vXLjRp0gTbtm1T2u7j4wMfH58iz3/8%2BDGaNGmCx48faylDIiIiordjkaljdu3ahSFDhmD79u3Izs6WOh0iIqIKKTdX8/8qGhaZOiQ0NBRJSUnw8fFBbm4uTpw4IXVKRERERCrhNZk6ZMeOHRg0aBCMjY0xdOhQ%2BPv7o2/fvor9R44cwalTp5SOyVXhT6OEhAQkJiYqbTOvXBkWFhaqJa6np/xVFUZGqh9raKj8VVVVq6p%2BbOXKyl9VZWys%2BrGVKil/VYU6f2prIj6ASmq8FPJfRuq8nADA1FS140xMlL%2Bqqnp11Y/Nfxmr83IGAHzwgWrH1a2r/FVV%2BvrqH6vOOQCgUSPVj61XT/mrqlR9T9DQ7yNsbVU/tmlT5a%2BqCA9X/Vg1VcSeR02TCYIgSJ0EAU%2BePEGvXr1w8uRJ1KlTBykpKejSpQt%2B%2Bukn2NvbK67HXLZsmdJxjx8/Rvfu3XH69GnUr1%2B/VLHWrVuH9evXK22b%2BMUXmDR5smb%2BM0RERJogkwESlSk1a2r%2BnElJmj%2BnLmNPpo7YvXs3srOz0b9/f8W27Oxs%2BPv7w97eXqOxPDw84ODgoLTNvHJlIC1NtRPq6eV13aSnq/6nX3KyascBeT2YdeoAcXFAVpbq53nyRPVjK1cGbGyAmzeB169VP0%2BtWqofW6kS0KAB8OgRkJmp2jnU7cn84APg339Vjw/ggZGVyscaGeV1HD19CsjlKp8Gz5%2BrdpyJSV6nzb17eb8Oqnr2TPVjq1YF2rYFLl0CXr1S/TyOf8xT7cC6dYHx44EffwRiY1VPYPp01Y/V189riFevgJwc1c9T6I/6MqlXD5g8GfDzy3tBquq//1XtOE28HwDAoEGqH9u0KbB7NzB0aN4vBVU4LDJ1QGZmJg4cOIAlS5agffv2iu1///03xo0bh5iYGI3Gs7CwKDo0/uqV%2BmMD6lzZrE5FkC8rS73zqPOJnO/1a/XOU6WK%2BjlkZgIZGaodq84HcsH4alRYmRrotJDL1ftcVfXvrXzp6eqdIyVFvfhA3stQrfP8%2B696CcTGqncOTbwWc3LUO8/9%2B%2Brn8PSpeudR9Xc5nzrvB4Bmhqvv3ZN02FtVHC5XH4tMHXD48GHIZDI4OzvDsMB1hXXq1IGVlVWR6YyIiIiIdB3vLtcBu3fvLlJg5vPw8MCvv/6KpIp2IQcREZGEOIWR%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%2BqlVVPjQhIQF7/f3h4eFR6l8Ujcdfty4vfoMGKp8HjRtrJgdV20BNSjl89JG08Qu9cZVFEzVzWLduLzw8PNCwoeo/hyYqJlEwvpSvA43k4B6gcvy969bBY%2BJE3fhdUCeHvXvVz2H6dEnaQWPvB4Kgfg7Hj0v2WlCHGv/1Eq1btxfr169X2jZx4kRMmjSpVMffuHEDI0aMKHbfDz/8gB49epQ6l7S0NJiYmChtMzY2xuvXr0u1vzQ4XE5qS0xMxPr164v8dVZR4jMH3YivCzlIHV8XcpA6PnPQjfi6koOuyb8Zp%2BA/Dw%2BPUh/fpk0bREZGFvuvLAUmAJiYmCAjI0NpW0ZGBkxNTUu1vzTYk0lERESkBRYWFjrTq2tlZYULFy4obYuOjoalpSUAwNLSElFRUUX2d%2B7cudQx2JNJREREVME4Ojri2bNn2LZtG7KysnDp0iUcPnxYcR2mm5sbDh8%2BjEuXLiErKwvbtm1DUlISHB0dSx2DRSYRERFRBdC3b19s3LgRAGBmZgZ/f38cP34cbdq0wdy5czF37ly0bdsWANCuXTvMnz8fCxYsgL29PX777Tf89NNPqF69eqnj6S9YsGCBGP8RqlhMTU1hb29fpms13qX4zEE34utCDlLH14UcpI7PHHQjvq7kUFGNHDkSzZo1U9o2bNgwtGrVSvG4du3acHNzg5eXF0aMGFHk%2BU2bNoWnpye8vb0xaNAg1KlTp0w5yARBjPuniIiIiKgi43A5EREREWkci0wiIiIi0jgWmURERESkcSwyiYiIiEjjWGQSERERkcaxyCQiIiIijWORSUREREQaxyKTiIiIiDSORSYRERERaRyLTCIiIhVdu3ZN6hSIdBaXlSQiegckJycjLCwMcXFx0NPTQ7169dCuXTtUqVJF6tTeaS1btsT169elTkNST58%2Bfetz6tWrp4VMSNcYSJ0AlU/Pnz/HwYMHERoaitjYWOjr66Nu3bro1KkT%2BvTpg%2BrVq0udolbcvn0boaGhSh/snTp1QuPGjStEfED64iY7Oxtnzpwp9rXYoUMHGBi8229z//zzD/z8/BASEgJzc3PUqVMH2dnZSEhIQEpKCnr27InJkyfjgw8%2B0Eo%2BycnJSj%2BHatWqaSWuVPHZTwM4ODhAJpMVu08QBMhkMty9e1fLWZEuYE8mlUlOTg5%2B%2BOEHBAQEwNraGra2tqhTpw5ycnKQkJCAa9euISoqCsOHD4e3t7eoH/BSFheXLl3CqlWrEB0djWbNmil9sN%2B5cwfNmzfHl19%2BidatW7%2BT8QHdKG4CAwOxbt06GBoa4tNPP1XKITw8HAAwefJkuLi4iJYDIF2xv23bNuzbtw8DBgyAk5MT6tatq7T/0aNHOHr0KAIDAzF48GCMHj1alDyysrJw4MAB7N69G9HR0YrCSyaTwdraGm5ubnBzc4O%2Bvv47F589mcCTJ08A5BWU/fv3x6FDh4o85/3339d2WqQDWGRSmQwbNgz29vYYMmQILCwsin1OXFwcduzYgevXr2PPnj2i5CFlcbF48WLcvXsXw4YNQ/fu3VGpUiWl/XK5HCEhIdixYwesra3xzTffvFPxAd0obr744guYmprC09MTNjY2xT4nPDwcAQEByMzMxI8//qjxHKQu9jdt2oTRo0fD0NDwjc%2BTy%2BXYunUrxo8fr/Ecbt68CR8fH9SvXx99%2BvRBy5YtFX94xsfH49q1azh69CiePHmCFStWlPizKq/xmzVr9tah4NOnT2s0ZmFNmzYtsScxn7Z6Eu3t7XH58mWtxCLdxyKTyuTRo0do0KCBxp9bFlIXF0eOHIGTk1Opnnvo0CH069fvnYoP6EZxc/36dbRs2bJUz7169SpatWql0fi6UOzHxcWhTp06Gj9vWXh5eWHmzJn48MMP3/i8qKgorFixAj/99NM7Fd/GxgbffvvtG58zYMAAjcYsLL%2BoEwQB3t7e2LRpU5Hn2Nvbi5pDwTgsMikfi0wqk82bN2PcuHGS5iB1cXHt2jXY2dlp9JzlKb6uKEuxLXV8sYp9DtVKT9d%2BBlIXeVLHJ93CKYyoTDZu3Ch1CqUuMAFovMAEgLFjx2r8nOUpfr7Y2FicO3dO8Tg3Nxfz58/H48ePtRJ/3rx5WolTkvwCMzc3t9j9KSkpiu/FKDAB3bnpJC4uDqdOnVJcm1fQkSNHtJrL48ePERAQgIMHD%2BLVq1eix9OVnwGRLnq3b7skjdOVN9T09HRs2LABx48fR0JCAszNzfHZZ59hwoQJMDU1FTW21G0gdXwg71IIDw8PdO3aFV27dgUAvHjxArdu3cLgwYOxZ88eUS6VKEgX2gEARowYgTVr1sDc3FyxLTQ0FDNnzsTvv/8uauy3XYenDWFhYfD29oaRkRFSU1MxadIkeHt7K/bPmzdP1B7ne/fuYcKECahZsyZmzZqFMWPGoF69epDL5Vi7di0CAgJEvflMrD8gypNZs2Ypvn/9%2BrXS43y%2Bvr7aTIl0BItMKrPY2Ng3fsCLPR9aZmYmhgwZgrS0NDg7O8PCwgKPHz/G0aNH8ccff2Dfvn0wNjYWLb7UH%2BxSxweAdevWoU%2BfPpg7d65im5mZGYKCgjBr1iysX78ey5cvFzUHXWgHAKhZsyb69%2B%2BP7777Dm3atMGaNWsQEBAALy8v0WNnZGRgxIgRb3zO9u3bRc1h9erVmD17Ntzd3REaGoqpU6fCxMQEI0eOBCD%2BHwPLly%2BHk5MTXr58CS8vL0yZMgWjRo0CAPj5%2BcHX11fUEZi3XY9Z0Tg7OxfZlpycLBS%2BvdwAACAASURBVEEmpAt4TSaVyZvuYtTWfGg//PADQkNDsWXLFqViMi0tDWPHjkX79u0xceJE0eJLfTep1PEBoHPnzjh8%2BHCxcxDGx8dj0KBBOH/%2BvKg56NIdtXv37sWKFStQq1YtGBkZYdmyZfj4449Fj2ttba3Ua1gcMX8XAKB169a4fPmy4mcRERGBzz//HD/%2B%2BCPatm0r%2BjWLrVq1QlhYGFJTU9G2bVvcvHlTcUOaXC5Hly5dEBoaKlp8XbB%2B/XrF9yVdNy/266A49%2B/fx88//4xDhw4hIiJC6/FJeuzJpDIxMTHR%2BjVWhR0/fhzLly8v0ltpamoKHx8fzJ49W9Q3VENDQ0nesHUlPpBX0Jc0yXXt2rW1ci1cpUqVNH6nsKree%2B89GBkZ4cWLF2jUqBHee%2B89rcQ1MjKS/LVQuXJlJCQkoHbt2gCATz/9FLNnz8b06dMRGBgoenwDAwOkp6ejWrVqGDdunFLP6bNnz975yfiBvEsW8rVo0ULpMaD9Xv%2BrV69i69atOH/%2BPCwtLTFjxgytxifd8e7/9pFGyWQyySfVffr0KZo3b17svubNm5dqiTN1GBgYiD4liS7HBwBzc3M8fPgQ//nPf4rse/jwoVZWfNLX19fatCxvMm3aNJw7dw5z5syBs7Mzli5div79%2B2PGjBkYMmSIqLF1YSCqZ8%2BemDhxIiZPnoxOnToBANzd3XHr1i0MGzYMWVlZosbv2LEjvv76a6xduxbTpk1TbA8JCcG6deuKHb591%2BzYsQOCIODFixdFfvcyMzOxYsUK0XPIzc3F8ePH8fPPPyMqKgrZ2dnYtGmT4jVBFRPvLqcy0YUPNX19faU7dwt69epVkfkKNU3qNpA6PpBXWKxatapILoIgYM2aNejcubPoOehCOwB5Q4KBgYFwc3NDpUqV8O2332LFihXw8/MTPbYYsyeU1YwZM/Dpp5/i1KlTStsXLFgABwcH5OTkiBp/3rx5MDAwKLKaj7%2B/P9q3b69UeL6r7t27hx49eqBdu3bw8PDAixcvAACRkZEYOHBgsSvwaFJAQAAcHR2xcuVKODo64ty5c6hSpQqsrKxEjUu6j9dkUpkcPnxY8p6BsWPHomPHjoobCwravn07Ll68KOqF/vPnz5f0Yn%2Bp4wNAamoqXF1dYWxsjN69e6NWrVpITExESEgIXrx4gQMHDqBmzZqi5rBp0yat3FzzNnK5HEZGRkW2JyQklLgqVkXy/PlzmJmZSZ3GO83T0xNVq1aFh4cHduzYASsrK3Tp0gUTJkxAkyZNsHLlStSvX1%2B0%2BE2bNsXQoUPh4%2BOj%2BF1o27Ytfv31V8VlFFQxscgklaSmpqJKlSo4dOiQYp7AGjVqaKUHKzQ0FF988QUWLlyIXr16wcDAAHK5HMHBwVixYgU2bdqk1R6egnfbGxsbo0aNGlqLLWX858%2Bfw8/PD2fPnkVycjLMzc3RrVs3TJgwQattkH/D2dWrVxXtUK1aNa30ogiCgL179xaZSsvDw0O0dboL27t3LwwNDeHq6gobGxvF8LS5uTmOHTsm%2BpRe%2Bf74448i7dClSxetxNaF%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%2BLHw6NEjYciQIcKaNWve6fiCIAhPnjx56z%2Bxbdy4UXBzcxMePHggCILya9HLy0tYsmSJqPEDAgIEJycn4dmzZ0rb4%2BLiBCcnJyEgIEDU%2BIIgCJ6ensKZM2cUj1u3bq34/uTJk8LAgQNFzyE4OFjo0qWLEB0drbT99u3bQteuXYXg4OB3Or4usLW1VXr86aefCnK5XKJsBOHx48fCihUrhDZt2gjt2rUTfH19JcuFpMUik8qkV69eSm/mBT/UIiMjhe7du2stl%2BzsbKXHcXFxWonr7u4uXL58WfG4YBtcvHhRcHZ2fqfjC4IgNGnSRGjatKniX5MmTRTb8r%2BKrV%2B/fsJff/2leFywHSIiIoRevXqJGn/AgAHClStXit0XGhoqDBgwQNT4gpBXWKelpSk9zpeZmSm0bNlS9ByGDBkinD59uth9J0%2BeFAYPHvxOx9cFhYvMgr8LUsrMzBT279%2Bvld8F0k2cwojKJC4uDo0aNVI8tra2VnxvZWWFpKQkreTx3XffISkpSTEUlpycDAcHB4waNUr0OdliYmJgY2OjeCwUuOLE3t5e9LW7pY4P/G%2Byd0EQ0L9/f9HvXi3Oo0eP0KRJE8XjgpcI2NjYIC4uTvT4dnZ2xe5r2bIlHj58KGp8AMjJyVG66ejAgQOK7w0NDbUyP2JMTEyJ1z526NABs2fPfqfj64Ls7GwcPHhQ8TgrK0vpMQC4uLhoOy0YGRnBzc0Nbm5uWo9NuoFFJpVJ/vrE%2BZNN%2B/v7K/ZlZGSIupxjvr179%2BLQoUOKazKBvCUN/fz88M0336Bhw4Zwd3cXNYeCH94XLlxQ2q6NyZ%2Bljl9wrlR9fX1J5k7V19eHXC5X/H%2BPHz%2Bu2JednV3sHd%2BalJubi7S0NFSpUqXIPrlcrpXr0CwsLBATE6Motguu0R0ZGYm6deuKnoNcLkdWVlaJ/19B5Mv%2BpY6vC2rVqqU0ZVb%2B%2B2E%2BmUwmSZFJxCKTysTKygqXLl1Cz549i%2Bw7f/58iZOka9Ivv/yCVatWoXXr1optMpkM3bt3h76%2BPvz8/EQtMt9//33cuXMHn376KQAoFTM3btxAgwYNRIutC/F1RcOGDREREYH27dsX2RcWFobGjRuLGr9Zs2Y4depUsR/ep06dQtOmTUWNDwDdunXD2rVrsWHDhiK9luvXr4ejo6PoOTRu3BgXL16Eg4NDkX2hoaGi/xykjq8Lzpw5I3UKRMXiZOxUJgMHDoSvry%2Bio6OVtv/zzz9Yvnw5Bg0aJHoOjx8/ViowC%2BrYsaPow5SfffYZli9fjszMTKXtmZmZWLVqFfr27ftOx9cV%2BavrPHv2TGl7cnIyli9fLvqqSJ6enlixYoXSXb0A8Oeff2L58uUYMWKEqPEBwNvbG3fv3sWQIUMQHByMixcvIjg4GJ6enrhz545W7i52d3fH0qVL8eTJE6XtUVFRWLJkiejvCVLHJ6KScQojKrM5c%2Bbg119/ha2tLWrXro34%2BHiEh4fD1dUVCxcuFD1%2Bu3btcP78%2BWKHQ%2BVyOTp16lRk7V5NysjIwKBBg5Ceno5%2B/fqhTp06iI%2BPx%2BHDh2FqaqqYt/BdjV%2BYvb09Ll%2B%2BrLV4%2BfKnELp16xYcHBxQu3ZtJCQk4MyZM2jRogU2b94seg6rVq3Cli1bUL9%2BfdSqVQtPnz5FYmIiJkyYoLU1xRMTE7Fs2TKcPHlScflA9%2B7dMWfOHK1NBv/ll1/i5MmTaNmypaIdbt26pfhD4F2PT0TFY5FJKrlw4QKOHTuGuLg4mJubo1evXlqb%2BHjcuHFwcXFBnz59iuz77bffFBMDiyk9PR0bN25UaoPPPvsMEydOROXKlUWNrQvxZ82apfi%2BpFWgtDE/YW5uLg4cOFCkHQYPHqy1ufnu3r2LU6dOITExEebm5ujZs6fSDUnakpubi%2BTkZJiZmUkyL%2BGpU6dw%2BvRpxWTovXr1QteuXStMfCIqikUmlTt//vknvvzySyxcuBA9evSAvr4%2BsrOzcfr0acyfPx/z5s0rtgAlzSlYZJakIkyCLbWjR4%2BW%2BrV%2B5MgRODk5iZyR9uXk5JS6qC7Lc4lIfSwyqUy8vb0xc%2BZMpWmMihMTE6NY4lEM27Ztw3fffQcDAwNUq1YNKSkpyM3NxaRJkzBu3DhRYubbvHkzRo8e/da7uLOysuDv76/x9bWljq8r5s%2Bfj%2BnTp6NatWpvfF5KSgpWr16t8Us53lZoy2Qy0YdqFy9ejLt372L48OFwcHAocgmJXC7H6dOnERAQgObNm2PevHkaz2H9%2BvVv3C%2BTyfDFF19oPG6%2BwYMHY8qUKWjXrt0bn/f7779jw4YN%2BOWXX0TLhYiU8e5yKpPx48djwoQJaNCgAZycnBTXZQqCgPj4eFy7dg1Hjx7Fw4cPlaYY0rRRo0ahT58%2B%2BP3335GcnAwLCwt06tRJscSlmAwNDdGvXz%2B4urrC2dm5yNq8T548wbFjx7B//35RbjqQOj4AXLly5Y37ZTKZ6OvHd%2BjQAQMHDkSnTp3g5OQEGxsbxbWocrkcEREROHr0KM6dOwcfHx9RcykoJSUFZ8%2BeRZUqVUQvMufOnYvQ0FCsXr0as2fPRvPmzVG7dm3k5uYiPj4et2/fhqWlJaZPn17sXfiaUNL1z1lZWYiIiICBgYGoRebKlSsxa9YsLF68WOk9KTc3FwkJCbh27RqOHz%2BOatWqYcWKFaLlQURFsSeTykwul2P//v3Ys2cPoqOjlaZOadasGQYOHIhBgwaJdvNJampqsXMTFufVq1eoWrWqxnOIiYmBn58fTp48idq1ayt9sCclJaF79%2B6YPHmyaNOnSB2/adOmip97cW8hMpkMd%2B/eFSV2QcnJydiyZQv279%2BP9PR0VK9eHYIgICUlBdWrV0f//v0xduxYmJmZiZ4LAERERCh6V9euXas0b6XYbt68ibCwMMTGxkJPTw/16tVDhw4dJLk%2B9NGjR5g6dSqSkpKwatWqEiet16Rz585hz549uHz5MtLT0wEAJiYm6NixIwYOHMjrM4kkwCKT1JKUlISnT59CT08PdevWVVp1RSxubm7w8PCAi4tLiYWsXC5HYGAg9u/fj6CgINFySUpKKvLB3rZt27cO4Zb3%2BN7e3ggPD0evXr3g5uaGTz75RNR4b5OTk4Pbt28rXov16tVD8%2BbNoaenvVna/P39sWbNGri7u8PHx0f0yeB11YkTJzB37lzY2dlh2bJlqF69ulbjC4KA58%2BfQ09PT%2BuxiUgZi0xSWVxcnOJOTm2sLJLv1atXWLRoEc6fPw9HR0el4bH4%2BHhcv34d586dQ6dOnTB79mx%2B0IgkKSkJwcHBCAoKgr6%2BPtzd3dG/f3%2BtFdj5EhISip2q5%2BrVq6IP2QPAixcvMHPmTFy7dg2LFi1Cr169RI9Z0LZt23DlyhVYW1tj5MiRSrMLjBs3TitTOQF5w%2BO%2Bvr7Yt28fpk2bhjFjxmglbuGVxp48eYJz587BxMQEjo6OooxkEFHpsMikMouKisLcuXNx8%2BZNCIIAmUwGa2trLF68WKtDc3///Td%2B%2BeUXXLp0CbGxsZDJZHj//ffRoUMHDBgwQNRczp49i%2BjoaIwdOxZA3gedq6srvvrqq2JXHtGk169fY/Xq1RgxYgTq1KmDFStW4NixY4p1xKdPn67VeTIB4Nq1awgKCsKpU6fQrl07uLm5oWPHjlqJ3b59e6xcuRIdOnQAkNeTtW7dOmzevBl//fWXqLEjIiIwbdo01KhRA2vXrtX6akvr169HcHAwHB0dce7cOVSuXBkBAQGKwqply5a4fv266Hk8evQIU6ZMUdxklb8alTYU/D%2BGh4djzJgxqFevHuRyOdLS0vDzzz/DyspKa/kQUQECURk8ffpUaN26tTB9%2BnQhNDRUiImJEc6dOydMnDhRsLOzE548eSJ1iqILDQ0VWrRoIWzdulWxLTU1VVi6dKnwySefCGFhYaLGnzlzpjBkyBAhMTFR8PX1FVxcXIRjx44JR44cEZydnYVly5aJGv9N7t69Kzg5OQlNmzbVWsydO3cKLVq0ENauXSs8fPhQGDx4sNCtWzfh0qVLosb96aefhI8//lhYvHixkJWVJWqskjg4OAjR0dGCIAhCenq6MHr0aOHzzz8XcnJyBEEQBFtbW9FzOHr0qGBnZydMmDBBePnypejxCvv0008V33t6eir9Xq5bt04YOXKk1nMiojzsyaQymTdvHmQyGb799tsi%2B%2BbOnQs9PT2trPojpc8//xx9%2BvQpdn30bdu24Y8//sDWrVtFi9%2B2bVscO3YMZmZmcHBwwPbt21G/fn0AeUtuDho0CBcvXhQtfmGpqak4duwYgoOD8ddff6Fr165wdXXV6o0WkZGR8Pb2RkJCAnr06IElS5aU%2BuYwVRVcm7zwuuH5xL75yc7ODteuXVM8TktLw%2BDBg9G5c2fMmDEDtra2RZa91LT8dqhXr16J7XD69GnR4hfsySy8GphcLkfbtm210ptLREVxCiMqkz///BO7du0qdp%2BXl5dW1muW2r1790q8zs3d3R0bN24UNX5OTg5MTU0B5A0NF7we0cLCoti7vcVw4cIFBAUF4fTp02jUqBFcXV2xYcMGrV8Dm56ejr179yIlJQUdOnTApUuX8Pvvv4s%2BIf/27dtFPX9pNGjQAOfPn1estmVqaorvv/8egwYNQuPGjUss%2BjRp6dKlWolTEkEQFJOsv//%2B%2B3jx4gXMzc0BAC9fvoSJiYlkuRFVdCwyqUyeP39e4k0%2B9evXR0pKiug5SL3aTHZ2domrhlSuXBk5OTmixQby1gpfsWIF5syZg379%2BsHf3x/e3t4A8uYM1Mad3l27doVcLoezszP27t0ryTQ5%2Bfr16wdjY2Ps27cPlpaWOHr0KBYsWICTJ09izZo1osVt3ry55FNpjR8/HlOmTMGQIUMwc%2BZMAMCHH36IVatWYeLEiaK/FgGgf//%2Bkq%2B4Y2triyZNmiAzMxPff/%2B9YpL6pUuXam25WyIqSnvze9A7oUqVKnjy5Emx%2B54%2BfaqVO4tNTU0RHByM169fix6rOA0bNsSNGzeK3RcREYE6deqIGn/OnDn4888/0b17d0RHR2P9%2BvXo1q0bOnbsiCNHjmD27NmixgfyZhZITk5GQEAAXFxc0KxZsyL/tKV9%2B/Y4cOAALC0tAQB9%2BvRBcHAw4uPjRY07atQo7N%2B/H1lZWSU%2BRy6XY8%2BePRg5cqQoOXz22WfYtWsXbG1tlbZ36dIFO3bs0EqBNWzYMISGhr71eb///juGDRum8fjXr19HYGAghgwZglatWimK%2BePHj6Nq1apanYifiJTxmkwqEx8fHxgbG2PBggVF9i1YsADZ2dlYvHix6Hl89dVXqF69OubOnSt6rMJ2796NX375BT/99JPSajvx8fHw8vJCr169FD2LYsnKysKZM2dw69YtvHjxAkZGRmjcuDF69%2B6tlYnHL1%2B%2B/Nbn2Nvbi55HvtzcXDx//hxmZmaKuTFzc3NFnSdT16bSKq4NtOHRo0eYNWsWnj9//tYVd3x9ffGf//xHa7kRkbRYZFKZ/Pvvv3B1dUXfvn3Rr18/mJub4%2BnTpzhw4AD%2B%2BOMPBAcH4/333xc9j%2BfPn6N37944evSoViaAL0gQBEycOBF//vknWrZsiVq1aiExMRHh4eFo06YNNmzY8NZ1xUkzEhMT4evri5MnTyI7OxsGBgbo3r07Zs2aVWS5TbFIOZUWoBttAEi74k5qaioSExPRqFEjAEBgYCDu3r0LR0dHtGnTRrS4RPRmLDKpzG7duoV58%2Bbh7t27kMlkEARBMU9mwTtuxfb69WtUqlRJlGu8SuPYsWM4e/YskpOTYW5uDgcHBzg6Oooed/jw4W%2B90ULsm1Ledl2sTCYTfd3uFy9ewMXFBXXq1IGbmxssLCzw6NEjxVD5oUOH3vmJ%2BHWxDQQtr7gTExOD4cOHo1u3bliyZAm2bduG1atXo2vXrggLC8OqVau0NmcrESljkUllJggCHj58CAMDA8WKPzdv3sRnn30mWcFXkaxfv17x/ebNmzFu3Lgiz5k4caKoOZRUZKakpODs2bOoUqUKrl69KmoOy5cvx%2BPHj%2BHn56dUdOfm5mLixIn44IMPFDfDaMPjx49x%2BvRpVK9eHQ4ODlpZaUbX2qCg3377DX379hU9zuTJk1GnTh3MnDkT%2Bvr66Ny5M0aNGoXRo0fj/Pnz2LJlC3bs2CF6HkRUFItMKpPXr19j9OjRqFWrlqLYSUpKQrdu3WBtbY0tW7YoLWsnFimHxw4ePPjW57i4uIiaQ77WrVvjypUrWon1NhEREZg%2BfTqqVauGtWvX4oMPPhA13meffYaNGzcqXgMFRUdHY8KECQgJCREt/r179zBhwgTUrFkTs2bNUlppJisrCwEBAe98G7yJvb19qa7dVVf79u0REhKCKlWq4MGDB%2BjduzdCQkLQoEEDZGRkoGPHjqL/wUNExeOFY1QmP/74IwwNDZUmY69ZsybOnj2L8ePHY9OmTZg2bZqoObxpeGzy5MmiD4/5%2Bfkpvo%2BLiytyN7lMJtNakSnl/IQF%2Bfv7Y82aNXB3d4ePj49iMmwxFfwjo7DGjRsjMTFR1PjLly%2BHk5MTXr58CS8vL0yZMgWjRo0CkPca8fX1FX3OVKnbAMibjL2416EgCIpZBsSclD4jI0MxldSNGzdQo0YNxfKeenp6WpnGiYhKoN0Fhqi8c3R0FB48eFDsvjt37gg9e/YUPYdJkyYJS5YsEbKzswVBEIROnToplpI7d%2B6c4OnpKXoO%2BVq1aqW1WMVp3bq1pPFTUlIELy8voVWrVsKxY8e0Grtt27ZCQkJCsfvi4%2BOFDh06iBrfzs5OyM7OFlJSUoSmTZsKcrlcsS8zM1No27atqPEFQfo2EARB2Ldvn2BrayusXbtWCAsLE8LCwoRLly4Jtra2isdi6tGjh/D06VNBEATBx8dHmDJlimLfjRs3tPKeRETF4zyZVCZJSUklDgE2a9ZMKz0nV69exeTJk6Gvr48HDx4gMTFRccNNmzZtRF/KryBd6UmUQkREBFxcXJCYmIigoCD06tVLq/Fbt26N3bt3F7tvz549ok%2BhZGBggPT0dFSrVg3jxo1TWmnp2bNnWplhQOo2APJWudq7dy9OnTqFCxcuoHXr1mjTpg0MDAxgb28veg69evXC119/jc2bN%2BO3335TjCJER0dj2bJl6NGjh6jxiahkLDKpTKpUqYLnz58Xuy8lJUUrS7hV9OGxK1euKP5lZ2fj6tWrStu0cY3mli1b4OnpiR49emDv3r2K9tcmLy8v/Pzzz9i0aRPi4uKQnZ2Nhw8fYvXq1di2bRu8vLxEjd%2BxY0d8/fXXkMvlmDZtmuISgZCQEHh5ecHZ2VnU%2BID0bZDP0tIS%2B/fvR0JCAoYMGVLigg1imDRpEho2bIhff/0V3t7eiqmSXF1dAeStikRE0uCNP1QmPj4%2BqF%2B/frF3L2/YsAG3b9/GDz/8IGoOjo6O2L59O%2BrWrYtZs2YhPT0da9euBQDcvHkTM2bMwIkTJ0TNIZ%2B2bm4o6G3TRMlkMtF7cwvmUFJvrjZ6lM%2BePYtvvvkGSUlJim21atWCr6%2Bv6NPWvHz5EnPnzsWaNWuUZlUYPHgwWrRoga%2B%2B%2BgqGhoai5gBI2wbF%2BfXXX7F69Wq8fPkS4eHhWo%2BfLyYmBo0bN5YsPhGxyKQyun//PlxdXeHq6oo%2BffrA3NwcCQkJOHbsGAIDA7Fz505YW1uLmsOqVasQERGBTp06Yf369fDz80PXrl0RHR2NefPmwdbWFjNmzBA1h3xSFJm6QJdW/JHL5QgPD0diYiLMzc1hZ2dX4SbD17U2uH//Pk6cOCH6yldEpNtYZFKZXb9%2BHfPnz0dUVJRiMnYrKyt88803aN26tejx5XI5Fi1ahOvXr6Nv376YMGECAMDGxgbW1tbYvHmzYjhdDA4ODoreu6dPn6JevXpFnnP69GnR4hcmCAL%2B%2BecfANBaz01qamqp2/jVq1damTNSClxpJo8ut4OzszMOHz4saQ5EFRWLTFLZo0ePFKvdFFdoaZu2hseCg4Pf%2BpwBAwaIFv%2Bff/7BlClTsGLFClSvXh3e3t6IjIwEAFhbW%2BOHH34QfTlBNzc3eHh4wMXFpcQhYblcjsDAQOzfvx9BQUEaz6FgsV8cmUyGU6dOaTxuvpiYGHh6esLBwUGylWakbgNA91fc2bRpk9auTSUiZSwyicqZzz//HB9%2B%2BCG%2B%2BuorfP311zAxMYGPjw9ycnKwbNkyZGRkiH5d7KtXr7Bo0SKcP38ejo6OsLW1Re3atZGbm4v4%2BHhcv34d586dQ6dOnTB79mxRlhcsqdiPiIjA3r170bx5c1GK23y6sNKM1G0A6EY7EJFuYpFJ7xyxh8cKLutYEjGXdWzdujUuXrwIQ0NDdOjQASdOnFAMXaelpaFLly5aW%2BHk77//xi%2B//IJLly4hNjYWMpkM77//Pjp06IABAwagSZMmWskjn7%2B/P1avXg13d3fMmjVL1EnhdXWlGW22AaAb7fD06VPo6%2Bujdu3aiIqKQlBQEIyMjNC7d%2B%2B33ihHROKpWFfHU4Xg5OQk6vnXr1%2BPqlWrolmzZijubzSx586sVKkSXr58iZo1a8LMzAzZ2dmKfXK5HKampqLGL8jKygrz5s3TWrySvHz5EjNnzsTVq1excuVK9O7dW/SYujaVlhRtAEjfDiEhIZg6dSqMjIzg6%2BuL2bNnw9bWFgYGBggICMCGDRvQvn17UXMgouKxyKR3jtjXX82cORNBQUFISEiAu7s7XFxcULNmTVFjFtSrVy9MnjwZy5Ytw9ixYzF37lzMnDkTcrkc8%2BbNQ7du3bSSR1xcHP766y80b968yDW5R44cEb3YzxcREYFpom1ewAAADblJREFU06bBzMwMQUFBWpuzs2bNmoiNjUXdunVx6dIlpZve7t27BwsLC63kAUjXBoD07fDDDz9g7dq10NPTw9SpU7Fw4ULFHJnHjx/H6tWrWWQSSYTD5VQu6cLw2M2bNxEYGIiQkBC0bNkS7u7u6Ny5M/T0xF3jQC6XY/78%2BTh06BDee%2B89vHz5Erm5uQCAVq1a4ccffxT17noACAsLg7e3N4yMjJCamopJkyYpTVfTsmVLXL9%2BXdQcgLxJ4b///nt4eHjg66%2B/1sqa6fl0ZSotKdsAkL4dWrVqpRiO//jjj3Hjxg3F9E2CIMDe3l4rCxQQUVEsMqncedPw2OXLl7U%2BPJaRkYHjx48jODgYDx48QP/%2B/TF9%2BnRRY%2Bbm5uKvv/5CSkoKXrx4ASMjIzx48AD//e9/lSYGF4uHhwfc3Nzg7u6O0NBQTJ06FRMmTMDIkSMBALa2tqJPxO3t7Y3z58/D09MTPXv2LPY5Yk6pJfVUWoD0bQBI3w7du3eHv78/srOz0bdvXwQGBuLjjz8GANy5cweTJk3S6pRiRPQ/LDKp3Onfvz%2B%2B%2BOKLEofHtmzZggMHDmg1p7S0NBw9ehQBAQF4%2BPAhbt68KVqs169fY/To0ahVq5biJqSkpCR069YN1tbW2LJlCypXrixafCCvcLl8%2BbLi%2BtOIiAh8/vnn%2BPHHH9G2bVut9GTqwspHxdHmSjO62gaA9tohICAAmzdvBgDUqFEDzZs3x0cffQS5XI5du3Zh6NChot6IR0QlY5FJ5Y4uDY9dvHgRgYGBOHPmDBo1agRXV1c4OTmJMmVPvvzhybVr1ypdC5qUlITx48ejXbt2mDZtmmjxAaBLly7Yt2%2Bf0nyc%2B/fvx5o1axAYGIi%2BfftqZbicCADOnz%2BP%2B/fvo2/fvsjKysLChQsRGxsLBwcHTJo0SfRLWIioeLzxh8qdatWq4d9//0V2djZycnIQGRmpGB67e/cu3nvvPVHjP3jwAMHBwfj111%2BRlZUFJycn/PLLL1qbrufEiRP46aefitxsVLNmTXz77beYOnWq6EVmz549MXHiREyePBmdOnUCALi7u%2BPWrVsYNmwYsrKyRI1fHnClmTzaaIcuXbqgS5cuiscbN24UNR4RlQ6LTCp3RowYgaFDhwIALC0tsX379iLDY2Lq3bs3zMzM4OzsjK5du8LAwAAvX75U6j0V8zq4pKQkfPDBB8Xua9asGRITE0WLnW/GjBlYuXIlTp06pSgyAWDBggVYunQpdu/eLXoOuk5bd9frOm20w4kTJ7Bz505ERkbi9evXMDU1haWlJdzc3ODi4iJ6fCIqHofLqVyScnhM6uvgunTpgoMHD8LMzKzIvpSUFPTt2xcXLlwQLX5pPH/%2BvNj8iDRt06ZN2LVrF4YPH46PPvoIxsbGyMjIQFRUFHbu3ImRI0dizJgxUqdJVCGxyCQqZ3x8fFC/fv1ib2bYsGEDbt%2B%2BLfqykgCQmpqKxMRENGrUCAAQGBiIu3fvwtHREW3atBE9vi7Qham0dIGU7dC5c2ds2rQJzZo1K7Lv3r178Pb2xrlz50TNgYiKx6uhqVw6ceIEhg8fDnt7e1hbW6NNmzbw9PTEwYMHpU5NdF5eXti6dSsWLVqEa9eu4eHDh7h69SoWLVqEzZs3Y/z48aLnEBMTA0dHR2zZsgUAsG3bNnz77bdISEjA5MmT8eeff4qeg9RCQkLQo0cPfPbZZzh27BgGDRqEyMhI3L17F4MHD8bFixelTlErpG6H1NRUWFpaFrvvww8/RFpamqjxiahk7Mmkckfq4TEHB4c3Lh0pk8lw6tQp0eIDwPXr1zF//nxERUVBJpNBEARYWVnhm2%2B%2BEX1eRACYPHky6tSpg5kzZ0JfXx%2BdO3fGqFGjMHr0aJw/fx5btmzBjh07RM9DSro4lZYUpG6HUaNGoUWLFpg0aZJilgkgby7Z7777DpGRkdi6dato8YmoZCwyqdyRengsODi42O0RERHYu3cvmjdvjqCgINHiF/To0SMkJyfD3Ny8yNKOYmrfvj1CQkJQpUoVPHjwAL1790ZISAgaNGiAjIwMdOzYUTHN1LtKl6bSkpLU7XD//n14e3vj2bNnaNiwISpXroz09HQ8ePAAZmZm8Pf31%2Boym0T0P7y7nModqYfHBgwYUGSbv78/AgMDMWTIEMyaNUvU%2BAU1aNBAkg/QjIwMxSouN27cQI0aNRR56OnpIScnR%2Bs5aZvUU2npCqnboVGjRvjtt99w%2BfJlREdHIy0tDSYmJrCyskKbNm20sgIWERWPRSaVOzY2Nli3bl2xw2Nr166FjY2N1nJ5%2BfIlZs6ciatXr2LlypXo3bu31mJLqWbNmoiNjUXdunVx6dIlpSH6e/fuwcLCQsLstEPqqbR0hdTtsGDBAixYsADt27fX6nKyRPR2HC6nckdXhsciIiIwbdo0mJmZ4fvvv69QQ3L5qw516tQJ69evh5%2BfH7p27Yro6GjMmzcPtra2mDFjhtRpio4rzeSRsh20sYQpEamGRSaVS9nZ2ZIOj23ZsgXff/89PDw88PXXX8PIyEj0mLpELpdj0aJFuH79Ovr27YsJEyYAyOtltra2xubNmxXD6URisrW1RXh4uNRpEFExWGRSuZM/PCYVb29vnD9/Hp6enujZs2exz9HGHd66KCYmBo0bN5Y6Da3hSjN5pGyHFi1aYOvWrXjTR1lF/X0kkhqLTCp3pB4ek3rFH9INUk%2BlpSukbgf%2BPhLpLhaZVO5weEz3OTs74/Dhw1KnISqpp9LSFVK3A98PiHQX7y6ncic3NxdXr17l8JgOc3JykjoF0Uk9lZaukLod3rQwAhFJi0UmlTuZmZnw9PQscT%2BHx6Tn5eUldQqi06WptKQkdTtwMI5Id3G4nModDo/phqdPn0JfXx%2B1a9dGVFQUgoKCYGRkhN69e7/1Orl3ga5MpSU1qdvhypUrCAsLw%2B3bt9GxY0cMGzZMtFhEVDYsMqnckfrGHwJCQkIwdepUGBkZwdfXF7Nnz4atrS0MDAxw%2BfJlbNiwoUJMjC31VFq6Qsp2WLFiBQ4ePIhWrVohLCwMY8aMwbhx40SNSUSlwyKTyh32ZEqvf//%2B%2BOKLL6Cnp4epU6di4cKFcHV1BQAcP34cW7ZswYEDByTOUlxST6WlK6Ruh86dO2Pr1q2wtLREWFgYFi9e/M7fdEZUXrDIpHKHw2PSa9WqFa5evQoA%2BPjjj3Hjxg3F9XiCIMDe3h5XrlyRMkXRsUc9j9TtUPCPzuzsbLRv3x6XL1%2BWLB8i%2Bp%2BKseYZvVPOnj2L3bt3w9DQEH5%2Bfti8ebPUKVU41apVw7///ouYmBjk5OQgMjJSse/u3bt47733JMxOO/j3eR6p26HgkpUFbzwiIunxN5LKnSNHjiAgIEBpeIzXYGnXiBEjMHToUACApaUltm/fjo8%2B%2BghyuRy7du1S7HuXcSqtPFK3g9RFLhGVjEUmlTuvXr1SzMtnZ2eH%2BPh4iTOqeEaOHImGDRvi/v376Nu3L7KysrBw4ULExsbCw8NDsZb5u4xTaeWRuh2ys7Nx8OBBxeOsrCylxwAq1BKfRLqE12RSuWNnZ4dr164pHtvb2/MaLNI63oCWR%2Bp2cHBweON%2BmUyG06dPaykbIiqIPZlU7vDvIt1w4sQJ7Ny5E5GRkXj9%2BjVMTU1haWkJNze3CtFzxJVm8kjdDmfOnJE0PhGVjEUmlTscHpPepk2bsGvXLgwfPhyjR4%2BGsbExMjIyEBUVhdWrVyMpKQljxoyROk1R8Y%2BdPGwHIioJh8up3OHwmPQ6d%2B6MTZs2oVmzZkX23bt3D97e3jh37pz2E9MiTqWVh%2B1ARCVhTyaVOxwek15qaqri5qvCPvzwQ6SlpWk5I%2B07e/asYqUZPz8/pKWlVchZDtgORFQSzpNJRGVmY2ODdevWITs7W2l7bm4u1q5dCxsbG4ky0578qbT8/Pzg5%2BdXYVeZYTsQUUnYk0lEZTZ//nx4e3tj586daNiwISpXroz09HQ8ePAAZmZm8Pf3lzpF0XEqrTxsByIqCYtMIiqzRo0a4bfffsPly5cRHR2NtLQ0mJiYwMrKCm3atIG%2Bvr7UKYqOK83kYTsQUUn4jkBEZbZgwQIsWLAA7du3R/v27aVORxK8ZzIP24GISsIik4jK7NChQ1iwYIHUaUiKU2nlYTsQUUk4hRERlZnUq7zoAk6llYftQEQlYZFJRGXWokULbN269Y1Dpa1bt9ZiRkREpGtYZBJRmTVt2vSN%2B2UyGe7evaulbIiISBfxmkwiKjMTE5MKP1xORERvxsnYiajMZDKZ1CkQEZGOY5FJRGXGq2yIiOhteE0mEZXZlStXEBYWhtu3b6Njx44YNmyY1CkREZGOYU8mEZXZ2bNnsXv3bhgaGsLPzw%2BbN2%2BWOiUiItIx7MkkojLr3Lkztm7dCktLS4SFhWHx4sU4fPiw1GkREZEOYU8mEZXZq1evYGlpCQCws7NDfHy8xBkREZGuYZFJRGWmp/e/tw4DA86ERkRERbHIJKIy41U2RET0NuyCIKIyy87OxsGDBxWPs7KylB4DgIuLi7bTIiIiHcIbf4iozBwcHN64XyaT4fTp01rKhoiIdBGLTCIiIiLSOF6TSUREREQaxyKTiIiIiDSORSYRERERaRyLTCIiIiLSOBaZRERERKRxLDKJiIiISONYZBIRERGRxrHIJCIiIiKN%2Bz9mkdfKbShd9wAAAABJRU5ErkJggg%3D%3D\" class=\"center-img\">\n",
" <img 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MWFhY0atRI97ixyMnYhZaaK3%2B4usKFC%2BDuDrGxytq4dxk/RRo0gH374F//gvh45e3cvq08tmFDOHoUWrWCK1eUt6PmdWjYEI4cgdat1Y2hsvt3d1ce6%2BQEW7ZAQAAkJipuxidX2a95Z2fYsQP694erVxV3T7Stn/LgevXgyy/h3/%2BGYpfcrDClr4OdHcyaBR98AKmpyvv/9VflsQ0awE8/wQsvqPtMOHxYeaypKdSsqf1cKXYJ04rS2NgqH4MRmGRmqAg2AWtryMoCNZth710e9pF7CFfE2rxoEREREXrzgoODCQkJKVe8vb19uZbLysrCyspKb17VqlXJzs4u1%2BPGIkmmUM/GBszMtNPKUKuWtv9atdR9oRhrDJWlssdQ2f2D9svIzKzSvpQquXut6tW1g6hevXL6t7LSJlgGX2CP1OPwQpiY/H2rxCFU6uVWiq8Due4LAIGBgfj56f%2BILG/iWBFWVlbcvXtXb97du3extrYu1%2BPGIkmmEEIIIYQhU%2BPvUejg4ICDg4PR2zXk4eHBqVOn9OZdvHiR5s2bA%2BDu7s6FCxfo2rUrAHl5eVy%2BfLnEJna1ZJ9MIYQQQoh/kH79%2BnH48GF27dpFfn4%2Bu3bt4vDhw/Tv3x%2BAQYMG8dVXX3H27FlycnKYP38%2BdnZ2tG7d2qjjkEqmEEIIIYShh1DJfJh8fHyYOXMm/fr1w83NjSVLljBv3jymT5%2BOs7MzixcvxtXVFYCAgAAyMjIYO3Ys6enptGjRguXLl2NhYWHUMUmSKYQQQghh6DFPMs%2BdO6d3Pzo6Wu9%2Bly5d6NKlS6mxJiYmjBw5kpEjRz608YFsLhdCCCGEEA%2BBVDKFEEIIIQw95pXMJ4GsQSGEEEIIYXRSyRRCCCGEMCSVTNUkyRRCCCGEMCRJpmqyBoUQQgghhNFJJdNAbGwsy5Yt48CBA2RkZFCnTh169uzJW2%2B9pbvc0pUrV1i6dCl//PEHmZmZ2NjY8NxzzzF69GicnJz02jt%2B/DhLlixhxYoVunnbt29ny5YtnD9/ntzcXOrWrUv37t156623qF69Ot999x0ffvghABqNhjt37mBlZYXJvcuTvfnmm/Tu3ZspU6awbt06o5/XSgghhHjqSSVTNVmDxURFRTFw4ECcnZ3Zvn070dHRrFy5kuPHjzNy5EgKCgo4efIkAwcOpEqVKmzatIno6Gg2bNgAQP/%2B/fXOW5Wbm8vkyZOZPHmybt706dNZsGABQ4cOZe/evRw5coSIiAjOnz9PUFAQGo2Gfv36ER0dTXR0NDt37gRg586dunmjR4/GxcWFNm3asHTp0ke7koQQQgghykGSzGJCQ0MZMGAA48aNo3bt2gC4urqyYMEC6tSpQ3x8PB988AG9evUiLCyMBg0aYGJigrOzM2FhYXTp0oX3339f1963335L/fr1cXNzA%2BCPP/4gMjKSFStW0KtXL2xsbDA3N8fd3Z1PPvkEHx8fMjIyyj3eV155hXXr1pGenm7cFSGEEEI87UxNjX97ysjm8nvi4uK4cOECM2bMKPGYnZ0dS5cuJSEhgTNnzjB9%2BvRS23jppZd47bXXSExMxMnJiY0bNzJq1Cjd47t27cLHxwdPT88Ssba2tnoVz/KoW7cuzZs3JzIyktdff73cccnJyaSkpOjNs3d1xcHGpkL963h56U%2BVqFdPeey9JF43VSozU3msu7v%2BVKl7u0RU6hgqu38XF%2BWxDRvqTxXyylMW16iR/lSxmirWYYMG%2BlO17VSUo6P%2BVKlmzZTHNm6sP1XKzEx5bFFC8aQnFmrGb4x1UFioPFatJ/21ewxIknlPUTXQzs6uzGWSk5Pvu4yDg4NuOUtLSy5evIivr6/u8evXr%2BNo8ME7YsQITp48CWg3r4eFhTFgwIByj9vHx4cDBw5UKMncvHkzERERevOCx48nZPz4crdRqo0b1cWrtWhR5fYPsGpVZY8AVq58uvsHmDVLVfgmld1//LHKBliutgEotlWlUgQFVW7/AAsWVPYIoHp1VeEqfnZq49U2cO9YBFWsrJTHVmDrnnj8SJJ5j729PQApKSk0KqUMkZqaqlsmMTFRd5H54hISEnRtJSYmAtpqYxEHBwfi4%2BP1Yr744gvd335%2BfhRW8Febo6Mjv/zyS4ViAgMD8fPz05tn37cvrFtXoXZ0vLy0CeYrr8DZs8raUFvJXLQIxo2DmBjl7aitZK5apf1ivXBBeTtqK5krV8KoUerGUNn9q61kzpoFH3wAV64obmZI3peK4ho10iaYU6fC5cuKu2dTzTeVBzdooE0wZ88Gg8%2BbCrejhKOj9v9g1Sq4fl15/3/%2BqTy2cWNtgvn223DpkvJ21q9XHmtqqk0wMzNVVeM0NWoqjjUxAY1Gcbi2jews5cGmptoE886dyq1IKiWVTNUkybzH2dkZDw8Pdu3aRZs2bfQeS0tLo2vXrnz88cd4e3uzZcsWOnXqVKKNLVu24O3tjbOzMzdu3ADQSxp79OjBmDFjiImJ0e2nqVZBQQGmFfxHcHBw0FVddWJj1Q/m7FmIjlYWm5amvv%2BYGPjrL%2BXxt2%2BrH8OFC3DihPJ41WUHI4yhsvu/c0f9GK5cgWIH4VXU2Vx13V%2B%2BrPz3FgC2RviREB9fOT82ily/ri7JPX1a/RguXVLXTkGB%2BjEUFhqnncpijOSwsPDJTDKFapKmF/PBBx%2BwdetWIiIiuHHjBhqNhjNnzjB69Gi8vb3p0aMHc%2BbM4ffffyc0NJSEhAQKCwuJj4/n/fff548//uCjjz4C0J3KKCkpSdf%2B888/j7%2B/P6%2B//jp79uzhzp07aDQazp8/z9SpU7l%2B/Tp16tSp0JiTk5NLnDZJCCGEECrJgT%2BqSSWzmLZt2/LVV1%2BxbNkyXnzxRe7cuYOdnR09e/bkzTffxMLCAi8vLyIjI/n8888ZOnQoN2/exMbGhi5duvDdd99Rv359AGrXrk2zZs04evSo3ub32bNns3v3br755htmzJjB3bt3qV27Nh06dGDbtm14VfDgmaNHj9K7d29jrgYhhBBCPIVJobFJkmngmWeeeeC5Jxs0aMCcOXMe2NagQYPYvXs3gwYN0pvfq1cvevXqVa7x1K9fX%2B/cm8Vdv36d8%2BfPs3jx4nK1JYQQQgjxqEia/hANHjyYK1eucPHixYfS/pdffsmwYcN05/QUQgghhJHI5nLVnr5n/AhZWloSHh5OeHi40duOi4sjKiqK0aNHG71tIYQQQgi1ZHP5Q%2Bbr68vKh3DeQBcXF77%2B%2BmujtyuEEEIInsrKo7FJkimEEEIIYUiSTNVkDQohhBBCCKOTSqYQQgghhCGpZKoma1AIIYQQQhidVDKFEEIIIQxJJVM1STKFEEIIIQxJkqmarEEhhBBCCGF0UskUWi4uymPr1ft7mpamrI24OOX916mjnV67pq4dDw/lsUVXXapdGxwclLeTna081srq76m1tfJ2Krt/GxvlsTVq/D1V0U7mJWVxRS9fdjZkZiruHlwrfx2QkaEsrvhKUNoGQFaW8ti7d/%2BeqmknP195bJGCAlXtmCQnKQs0N4c6dTBJT1P3PNS8kS0ttZ8F6emQm6u8naL39KMmlUzVZA0KIYQQQgijk0qmEEIIIYQhqWSqJkmmEEIIIYQhSTJVkzUohBBCCCGMTiqZQgghhBCGpJKpmiSZQgghhBCGJMlUTdagEEIIIYQwOqlkCiGEEEIYkkqmarIGhRBCCCGE0UklUwghhBDCkFQyVZMkUwghhBDCkCSZqskaFEIIIYQQRlfplUw/Pz9SUlIwN9cORaPRUL16dfr27UvXrl158803dctmZ2dTpUoVzMzMAOjbty9hYWHExsYyY8YMTpw4gbW1Na%2B%2B%2BiqjR4/Wxa1bt45169Zx8%2BZNnJ2dCQ4OpkePHqWOJzc3l8WLF7N7927S0tKoUqUKbdq0YcKECbi5uZVYfvbs2WRmZjJ37ly9%2Bbdv32bEiBF88cUX1KxZE4D//ve/rFu3jpMnT5KXl0f9%2BvUZMmQIL7/8sl7sgQMHWL16NcePHyc/Px8nJyd69epFUFAQVatWBeDo0aOsXr2apUuXVnSVCyGEEOJBpJKp2mOxBmfOnEl0dDTR0dEcO3aM1atXs337dg4ePKibHx0dDcDKlSt198PCwsjLy2P06NG0aNGCQ4cOsWLFCjZs2MDu3bsB2LdvH8uXL2fVqlVERUURHBzMhAkTSEhIKHUss2bNIjo6mrVr1xIdHc3evXtxdHRk6NCh3L59W7fcjRs3mDRpEuvXry%2BzncGDB%2BsSzLVr1/L222/Tr18//vOf//Dnn38yffp0lixZwieffKKL27RpE2PGjKFTp078%2BOOPHD16lPDwcA4cOEBgYCBZWVkAtGrVimrVqrFlyxb1L4AQQgghhJE9FkmmIU9PT9q0acPp06cfuOyff/5JcnIy48aNw9LSkmbNmjFs2DA2bNgAwKVLl9BoNLqbmZkZFhYWusqpoaNHj9KlSxfq168PQM2aNXnvvffo2rUrKSkpAGRlZdGzZ09q1qxZakX0/Pnz7Nu3j4EDBwKQlJTEp59%2BysyZM%2Bnbty9Vq1bF1NSUtm3b8vHHH5OWlkZeXh4pKSl8/PHHzJgxgxEjRlC7dm1MTU1p3rw5q1atIisrS69yOWzYMBYvXkxubm7FVrAQQggh7s/U1Pi3p0ylby43lJeXR1RUFAcPHiQkJOSBy1%2B4cAFXV1csLS1185o0acKKFSsAePHFF9m2bRu9e/fGzMwMExMTPv30UxwdHUtt78UXXyQiIoLY2Fjat2/Ps88%2Bi6urKx9//LFumSpVqvDDDz9gZ2fHlClTSrSxadMmunfvrhvT/v37MTMz44UXXiixbOfOnencuTMAv//%2BOxqNhl69epVYzsrKir59%2B/Ldd9/x7rvvAvDss89iYWHBr7/%2BSs%2BePR%2B4rookJyfrEuYi9k5OONSuXe429BTtRlDK7gTlVqeO8lgvL/2pUg0bKo9t3Fh/qtTdu8pjjfE6qGGs/tWswwYN9KcKNauqLM5YbwNVDTg760%2BVKvaZWiFOTvpTpVq0UB7bpIn%2BVKkyihHlcm%2B3Lt1UqcLCyu1f6fsAwMJCf6pEZRZRnsKk0NgeiyRz5syZzJkzR3ff0dGRESNG8Oqrrz4wNisrCysrK715VlZWZGdnA9qk1cvLi48%2B%2BggvLy%2B%2B//57pk%2BfjpubG56eniXaGzt2LE2bNmX79u2Eh4eTnp6Og4MDr7/%2BOq%2B99hoA5ubm2NnZlTmmgwcPMnLkSN39GzduUKtWLSwe8I%2BWnJxMrVq19BLm4hwcHEhOTtab17JlSw4cOFChJHPz5s1ERETozQseM4aQ8ePL3UapFi1SF6/Wxo2V2z/A/PmVPQIweG2fuv4BSvnxVxE7VHa/YIHKBpintgF4%2B231bagRHFy5/QMsWVLZI4B7u0xVGhsbdfFqCgBFyijqlEtMjPr%2B/0HS0tL44IMPOHz4MGZmZvTr14/JkyeX2DobFBTE0aNH9eZlZ2cTGBhIWFgYqampdOrUiWrVquket7W15ddffzXqeB%2BLJPPDDz/E399fUWy1atW4c%2BeO3rw7d%2B5gbW0NaPeN9PX15ZlnngFg0KBB7Ny5k8jIyFKrkKA9GMnPzw%2BAuLg49u7dy7x587C2tuall1564JiuXbtG3bp1dfft7e25efMmubm5JRLIwsJCbt68Se3atbG3tyctLY2cnByqVKlSot2EhATs7e315jk6OnLhwoUHjqm4wMBA3fPTjTEoCPburVA7Om5u2gRz3DjlHwjXrimLA20Fc%2BNGeOUVOHtWeTtqK5nz58PEiXDpkvJ21FYyIyK0X%2B6V8cFsrP7VVjKnTIG5cyE%2BXnEz/ROUJSeNG2sTzLffVvc22OE%2BSXmws7N2AAsWwNWryttRU8kMDta%2BFxITlfdv8AVZIU2aaBPMsWPh4kXl7Xz9tfJYMzNtgnn7NhQUKG9HTSXTxgZu3lTX/72CjSIWFtoE8/p1yMtT3k5leQwrmRMmTKBu3br8/vvvpKam8tZbb7F27VqCgoL0llu1apXe/S1bthAREUHwvR9/J0%2BexNnZ2ehJpaHHIslUw93dncuXL5Ofn6/L5C9evIi7uzsAiYmJNG/eXC/G3Ny81KpiTEwMAwYMYOvWrXh4eADg4uJCUFAQx48f58yZM%2BUak4mJCRqNRne/S5cuaDQafvnllxKbwn/77TdCQkL45Zdf6Nq1KxYWFmzbto0hQ4boLZeVlcWuXbtKVCwLCgowreA/goODAw4ODvozExPVfSGANrH46y9lsXFx6voGbYJ57wAxRe4dVKXKpUtQjn2Jy6TmA72ImtfBGNT2r/RLtbj4eFXJxWnqfH/2AAAgAElEQVQVCSKofxtgpnIAoE0w1WS6VRXuM1AkMREuX1Yef/Kkuv5B%2Bx5Q005%2BvvoxFBSoa0ft/4Pa/o2xuTovr3I3e/9DXLlyhcOHD7N//36srKxo0KABY8aM4dNPPy2RZBZ36dIlZs2axerVq3Xf/SdPniyRGz0Mj1%2BaXkHt2rXD1taW%2BfPnk5OTw9mzZ1m/fj0BAQGAtir51VdfcerUKQoLC9mzZw%2BHDh2id%2B/eJdpq3Lgx3t7ehIaGcuLECXJycrhz5w779u3j0KFDpe5TWRpnZ2eSkpJ09%2B3s7Bg3bhwzZsxg586d5OTkkJeXx3/%2B8x/ef/99hg8fTr169ahduzahoaF88sknrF27lvT0dPLy8jhx4gRBQUFYW1szduxYvb6Sk5NxUrvvkxBCCCH0PYQDf5KTkzl16pTezXA3uLJcuHABGxsbvS2lbm5uJCYm6p39xtDMmTMZMGAArVu31s07efIk169fp0%2BfPrRv355Ro0ZxUU3VvwxPfCXT3NycNWvWEBYWptu/YNiwYbrN78HBwZiZmRESEsKtW7do2LAhS5YsoWnTpgAsW7aM77//nh9%2B%2BAETExNWrlzJ0qVLeffdd0lKSsLU1JSmTZvy6aef0qFDh3KNqVOnThw9epTBgwfr5r3xxhs4OTmxYcMGZs2aRV5eHg0bNmTChAkEBgbqlgsICMDFxYU1a9awbNkycnJyqFevHj179iQoKEhv/wmAqKgoPvzwQ7WrUQghhBDFPYTN5aUeExEcXK4Dncs6BgW0%2B1vWLGX/3yNHjnD8%2BHHmzdPfz7tmzZo0adKEUaNGYWlpycKFCxkxYgS7du2iRo0aFX1aZar0JLMi%2BwOcO3eu1PkNGzZk9erVpT5mbm5OSEhImS/g6NGj9U7cXqNGDSZPnszkyZPLNSbDk7ADDBw4kOHDh3P37l3dydMB%2BvTpQ58%2BfR7YZtu2bWnbtu0Dl4uOjkaj0dCxY8dyjVUIIYQQlafUYyIMjrUoS1nHoAC641AMbd68mV69epXoY77BQapTp05l69atHDlyhK5du5ZrPOXxxG8ufxx5eXnRpUsXtm3b9lD7Wbt2LSEhIWUejS6EEEIIhR7C5nIHBwe8vb31biWOkSiDu7s7N2/eJDU1VTcvJiYGR0fHUquP%2Bfn5/PLLL/Tr109vfmZmJuHh4VwtdmBgQUEB%2Bfn5eoUxY5Ak8yGZPn063377Lbdu3Xoo7R85coScnBwGDRr0UNoXQgghxOOjUaNGtGrVijlz5pCZmUl8fDxLly7VHYNi6Ny5c%2BTk5ODr66s3v3r16vzvf/8jPDycjIwMsrKymDVrFvXr19fbb9MYJMl8SGxtbYmMjKRWrVoPpf3WrVuzbNmyh9K2EEII8dR7DK/4s2jRIvLz8%2BnWrRuDBw%2BmS5cujBkzBgAfHx%2B%2B%2B%2B473bLx8fHUqlWr1FMiLl26lMLCQrp3706XLl1ISUlh5cqVDzyfd0VV%2Bj6ZQgghhBCPncfwPJl2dnYsKuPCJ9EGp/Dr2bNnmRdqcXZ2LnEA0sPw%2BK1BIYQQQgjxxJNKphBCCCGEocewkvmkkTUohBBCCCGMTiqZQgghhBCGpJKpmiSZQgghhBCGJMlUTZJMoXWf654%2BUGbm31Ol7Xh4KO%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%2BfLluvt37txhyZIl9O3bF19fX3x8fAgICGDjxo265xMaGoqPjw8%2BPj60aNECLy8v3X0fHx%2BOHDnC999/z%2BzZsyuw1oQQQghRLqamxr89ZZ7ozeX79u1j1apVjBo16r7L2draEhkZycSJE/Xmb9u2jerVq1e4X41GQ0hICHFxcXz44Ye0bNmSwsJCduzYwejRo/m///s/unXrBkBMTAzffPMN3333HQDZ2dm8/PLLVKtWjRkzZuDt7Y1Go%2BHkyZNMnz6dxMREJk2aRFhYGGFhYbpxRkRE8Ouvv5YYy6ZNmzhw4AAdOnSo8PMQQgghRBmewqTQ2J7oNThs2DAWLlxIVFTUfZfr27cvO3bsoLCwUDfvxIkT5Obm0qxZswr3u2fPHvbv38/y5ctp3bo15ubmWFpa8tJLLxESEkJMTIxu2YULF%2BLv74%2BlpSUAy5cvJysrizVr1tCqVSuqVq2KlZUVbdu2JTw8HBsbmwqN5dVXX2X%2B/PkVfg5CCCGEEA/TE13JfOGFF9BoNLzzzjts3769zATt%2BeefZ%2BfOnfzvf/%2Bjc%2BfOAGzZsoWAgAD279%2Bvt2xiYiKtW7cu0UZmZqbu719//RVfX1%2BcnJxKLBcUFKT7OzU1lZ9%2B%2BompU6fq5u3atYt%2B/fpRrVq1ErG%2Bvr74%2Bvo%2B4Fnr8/PzY/r06Zw8eZIWLVqUKyY5OZmUlBS9efYuLjhUMMHVcXfXnypRu7by2MaN9adKWVsrj/Xy0p8qVcr7otw8PPSnj5qx%2Bq9RQ3msm5v%2BVKFnM5TFGe0lqFvxH786rq76U6WKfeZVSJMm%2BlOl6tRRHtuokf5UqXr1lMfa2elPFVL671D0cabmYw2A/JrKY4u2FCrYYqhz%2B7byWLWkkqnaE51kAkyePJno6GimTJnC559/Xuoy5ubm9O3bl8jISDp37szdu3f58ccf2blzZ4kk08nJqdTN0sX31UxPT8euHB8chw8fxsHBgXrFPqiuX7%2BOo6Oj7n5ubi4dO3YEtJvhc3Nz2bNnD87Ozg9sH6Bq1ap4eXlx4MCBcieZmzdvJiIiQm9e8JgxhIwfX674Mq1apS5ercehortxY2WPANasebr7BzB4f1fUf1V2r34VbFXbAMybp74NNZYsqdz%2BAebMqewRQECAqvCOKrt/9lmVDfCc2gaggsUTPTt3qu9fKUkyVXvik0xLS0s%2B%2B%2BwzBg4cyJo1a7C1tS11OX9/fwIDA8nMzOTnn3/G19cXe3t7RX06ODhw9erVUh/LzMzEzMwMKysrEhMTqVu3rt7j9vb2JCUl6Y3/yJEjACQkJNCtW7cSBzM9iKOjI9evXy/38oGBgSUOcLJ/5RUodmBThbi7axPMoCC4cEFZG2ormfPnw8SJUMoBXuV25YryWC8vbYL5yitw9qzydtRWMtesgZEj4fx55e1Udv9qK5kRERAcDMV2W6mozhm7FcUZaxX8t%2B4g5cGurtoEc9IkiI1V3o6aSuaSJTB2LFy8qLx/tZXMOXNg2jS4fFl5O8%2BpSLDs7LQJ5pYtkJqquJn/PTNaUZy1tTbBPH4csrIUd0/H/P0PXqgs1atrE8yoKOXvJ/FEe%2BKTTAAXFxdmzZrFe%2B%2B9h7%2B/f6nLeHl50bhxY3bv3s3333/P8OHDFffXtWtXJk6cWKIqCbB48WL27dvH7t27MTU11dsPFKBHjx7s3LmTUaNGYWVlpXgMxRUUFGBagV9cDg4OODg46M%2BMi9Pe1LhwAU6cUBZrOB4lLl2C06eVxxsjMTt7FqKjlcer2axU5Px57TdLZVHbv9LdNoqLiYG//lIcfvymuu5VvwT1VbyPi8TGqvt/uHVLXf8XL8LJk8rjDT5bFbl8Wd2PPjW7ABVJTYVr1xSHZ6jc6yErCzIU7v4BQJ4RNldnZlbuZm%2BlpJKp2j9mDfbu3ZtBgwaxefPmMpfx9/dn7dq1xMbG8q9//UtxXy%2B88ALt2rXjjTfeICoqisLCQjIzM1m7di0bNmxg0qRJmJiY4OTkpFe1BAgODsba2prXX3%2BdqKgoCgoKyM/P58CBA7z77rvUqFGjwslncnJyqfuHCiGEEEJUln9Mkgkwbdo0mjZtWubjffr04cqVK/Tr1w9zc%2BVFXBMTE5YuXUrPnj0JDQ2lTZs2dOvWjX379rFy5Uq6d%2B8OQPv27UlPTyc%2BPl4Xa21tzebNm/Hz82P27Nm0b9%2BeNm3aMGfOHNq2bcuePXuoU4HNRDk5OZw6dYouXboofj5CCCGEMCDnyVTtid1cfu7cuRLzqlSpwvbt28tcztbWlr8MNqGtX79e97e/v3%2BZm9sNDwaytLRkzJgxjBkzpswx2tjY0K1bN3bv3s0bb7yhFxsUFKR3JPr93G9cP/30E02bNsXdGJt1hBBCCKH1FCaFxiZr8CEbP3483377Lbm5uQ%2Bl/S%2B//JJ33nnnobQthBBCCKGUJJkPmZubG4MHD2b16tVGb3vHjh14e3vTvn17o7cthBBCPNVkc7lqT%2Bzm8ifJgy57qVT//v3p37//Q2lbCCGEEEINSTKFEEIIIQw9hZVHY5MkUwghhBDCkCSZqskaFEIIIYQQRieVTCGEEEIIQ1LJVE3WoBBCCCGEMDqpZAohhBBCGJJKpmqSZAohhBBCGJIkUzVJMoWWiYn6WBMT5e1kZyvv/%2B7dv6dq2qleXXlstWp/T9W0k5mpPLbouWdnq2unsvvv0kV5rJubdtqkiar3tFOMsjh7%2B7%2BnTk6Ku4fmzZXHurpqp25u6r4kU1OVxTVs%2BPdUzZXOLl9WHnv79t/TGzeUt9Oxo/LYGjW002ee%2Bfs1UcDLS1mcpaV26uqq7mUg3Vl5rJWVdurg8Pf6EE8VSTKFEEIIIQxJJVM1WYNCCCGEEMLopJIphBBCCGFIKpmqSZIphBBCCGFIkkzVZA0KIYQQQjwB0tLSGDNmDK1bt6Zdu3Z89NFH5Ofnl7psUFAQLVq0wMfHR3fbv38/AAUFBYSHh9OxY0d8fHx46623SE5ONvp4JckUQgghhDBkamr8m0oTJkygWrVq/P7772zZsoUDBw6wdu3aUpf966%2B/WL16NdHR0brbc889B8Dnn3/OH3/8wdatW/n999%2BpWrUq77//vurxGZIkUwghhBDiMXflyhUOHz7Mu%2B%2B%2Bi5WVFQ0aNGDMmDFs2LChxLLx8fHcunWLZs2aldrWt99%2By6hRo6hXrx7Vq1dn%2BvTp7N%2B/n/j4eKOOWfbJFEIIIYQw9BD2yUxOTiYlJUVvnr29PQ4ODg%2BMvXDhAjY2NtStW1c3z83NjcTERG7fvk3NmjV180%2BePIm1tTVvv/02J0%2BexM7Ojtdee42AgAAyMjK4fv06Hh4euuXt7OyoVasW586do0GDBkZ4plqSZAohhBBCGHoISebmzZuJiIjQmxccHExISMgDY7OysrAqOsH9PUX3s7Oz9ZLM3NxcWrZsydtvv427uzuHDh0iJCQEa2trfHx8AKhWdBGRe6pWrUpWVpai51UWSTKFEEIIIR6BwMBA/Pz89ObZF10q7AGqVavGnTt39OYV3be2ttabP2DAAAYMGKC737lzZwYMGMDu3bvpeO9KVoZt3b17t0Q7akmSKYQQQghh6CFUMh0cHMq1abw07u7u3Lx5k9TUVOzs7ACIiYnB0dGRGgaX7dyyZQvW1tb06tVLNy83N5cqVapQq1Yt6taty8WLF3WbzFNSUrh586beJnRjkAN/hBBCCCEec40aNaJVq1bMmTOHzMxM4uPjWbp0KQEBASWWzczMZNasWZw%2BfZrCwkL%2B85//sHPnTgIDAwHw9/fn888/Jz4%2BnszMTObMmUPbtm1xcXEx6pj/sZXM2NhYli1bxoEDB8jIyKBOnTr07NmTt956C2tra3Jycvj000/ZvXs3d%2B/epXnz5oSGhuLm5gaAp6cnX375Je3atdNrd/HixRw%2BfJj169cDcPXqVT766CMOHz6MmZkZzz//PKGhoXol5zVr1pCXl8ebb74JaEvUa9asYc%2BePVy9ehWNRoObmxv%2B/v4MGTIEExMTQkND%2Bf777wHIz88nLy9Pb1%2BMlStXcu3aNY4fP/5QTjsghBBCPNUew5OxL1q0iLCwMLp164apqSkDBgxgzJgxAPj4%2BDBz5kz69evH8OHDyc7OJjg4mLS0NBo0aEB4eDitW7cGYOzYseTn5zN06FCysrJo164dn332mdHH%2B49MMqOiohg5ciQjR45k%2B/bt1K5dm9jYWEJDQxk5ciQbN25kxowZXL58mcjISGxsbJg7dy7jx49n586d5e4nNzeXkSNH0qlTJ37//Xfu3LnDmDFjmD9/PqGhoYC2lP3NN9/w3XffAdqdc19%2B%2BWWqVavGjBkz8Pb2RqPRcPLkSaZPn05iYiKTJk0iLCyMsLAwALZt20ZERAS//vpriTFs2rSJAwcO0KFDByOsOSGEEEIAj2WSaWdnx6JFi0p9LDo6Wve3iYkJY8aM0SWghiwsLJg0aRKTJk16KOMs8o9MMkNDQxkwYADjxo3TzXN1dWXBggWEhoby119/sWPHDnbt2qXbN2LSpEnExsai0WgwMTEpVz%2B//fYbubm5TJ8%2BHTMzM6ysrFi0aBHZ2dm6ZRYuXIi/vz%2BWlpYALF%2B%2BnKysLL7%2B%2Bmu9I7vatm1LeHg4UVFRFXqur776KvPnz2fLli0VihNCCCGEeJj%2BcUlmXFwcFy5cYMaMGSUes7OzY%2BnSpezbt48aNWpw7Ngxxo4dS3p6Oq1atWLatGl6Cebo0aMxMzPTayMnJ4eWLVsCcOLECby8vFi4cKGuUtmjRw/efvttAFJTU/npp5%2BYOnWqLn7Xrl3069evxKkDAHx9ffH19a3Q8/Xz82P69OmcPHmSFi1alCum1PN0ubjgYGNTob513N31p0oYnJahQu7t4qCbKpWRoTy2aGdptTtNF/uBUmFeXvrTR81Y/at5HevX158q1KyKsrjGjfWnirm6Ko91dtafKmVrqyyuaJ8utft2qTnK1VifCQYHU1RI0fhVHq17rz5RYebm%2BlPF1Hw2V6miP1XC4AjoR%2BoxrGQ%2Baf5xSWZ6ejqA7sir0ty6dYuMjAz27t3L%2BvXrsbCwICwsjNGjRxMZGalLLJctW1bmPplF7ezfv5/mzZvz448/kpycTEhICJ988gmhoaEcPnwYBwcH6tWrp4u/fv06jo6Ouvu5ubm60wloNBpyc3PZs2cPzuX8gqhatSpeXl4cOHCg3ElmqefpGjOGkPHjyxVfppUr1cWrZfCcKsWaNZU9Ati48enuH2DyZFXhkSq7nz9fZQN8orYBUPv/rNbjsK/4woWVPQJ49llV4fUevMh9lfPsOPcZgBF%2BtKr50VRsE7B48vzjksyi802lpKTQqFGjEo%2BnpqZiaWlJQUEBkydPpnbt2gBMnTqVDh06EBsbS5MmTcrVl6WlJXZ2dowdOxaABg0a8OabbxIWFkZoaCiJiYl6Z%2BYvGl9SUpJeG0eOHAEgISGBbt26odFoKvScHR0duX79ermXL/U8XUOHQqTCr1Z3d22COWoUXLigrA21lcyICAgOhpgY5e2orWSuWQMjR8L588rbUVvJ3LgRXnkFzp5V3k5l99%2Bpk/LY%2BvW1CWZ4OCQkKG5mYMJiRXGNG2sTzIkT4dIlxd0T6f6e8mBnZ22CuXAhXL2qvJ2bN5XFubhoE8zZsyEuTnn/iYnKY93ctM9//Hh1nwnTpimPtbbWJpjHj4OKE1xfc%2B2oKM7cXJtgpqRAfr7i7ql3S8X/cpUq2gQzNhZycpS3U1mkkqnaPy7JdHZ2xsPDg127dtGmTRu9x9LS0ujatatuR9jc3FzdYwUFBQAVSvDc3NzYs2cPhYWFmN57MxYWFuraMDU1pbCwUC%2BmR48e7Ny5k1GjRpU4c79SBQUFuv7Lo9TzdMXFqftCAG2CeeKEslhjnAA2Jgb%2B%2Bkt5vNIv1eLOn9d%2BqSiVmal%2BDGfPVu6vf7X9F6v0K5aQoCq5OK0iLwFtgnn6tIoGzGPVDQC0CWasinZSU9X1Hxen/EcnwOXL6voH7Xvg1Cnl8Wp%2BeBbJylLVTrGvKUXy81W2YYzN1Tk5lbvZWylJMlX7R67BDz74gK1btxIREcGNGzfQaDScOXOG0aNH4%2B3tTVBQEG3atCE0NJT09HSysrKYO3cu3t7euFdgv8JevXpRUFDAnDlzyM3NJSEhgWXLltG/f38AnJyc9KqWoL18lLW1Na%2B//jpRUVEUFBSQn5/PgQMHePfdd6lRo0aFk8/k5GScnJwqFCOEEEII8TD9I5PMtm3b8tVXX3H69GlefPFFfH19GTduHO3bt2fVqlVYWFjw%2Beef4%2B7uzoABA%2BjSpQvZ2dksXbq0Qv3Url2bTZs2ceXKFZ577jkCAgLo0KEDEydOBKB9%2B/akp6cTHx%2Bvi7G2tmbz5s34%2Bfkxe/Zs2rdvT5s2bXQnQt2zZw916tQp9xhycnI4deoUXbp0qdDYhRBCCHEfpqbGvz1l/nGby4s888wz900aa9SooTsPZWnOnTtX6nzDi9g3btyYlWUc8GJjY0O3bt3YvXs3b7zxhm6%2BpaUlQUFBBAUF3e8p6Pj7%2B%2BPv71/qYz/99BNNmzatUAVWCCGEEOJhe/rS6kds/PjxfPvtt3r7fxrTl19%2ByTvvvPNQ2hZCCCGeWlLJVO3pe8aPmJubG4MHD2b16tVGb3vHjh14e3vTvn17o7cthBBCPNUkyVTtH7u5/HEyatSoh9Ju//79dQcZCSGEEEI8TiTJFEIIIYQw9BRWHo1NkkwhhBBCCEOSZKoma1AIIYQQQhidVDKFEEIIIQxJJVM1WYNCCCGEEMLopJIphBBCCGFIKpmqSZIphBBCCGFIkkzVZA0KIYQQQgijk0qmEEIIIYQhqWSqJmtQCCGEEEIYnVQyhRBCCCEMSSVTNUkyhRBCCCEMSZKpmqxBIYQQQghhdFLJFEIIIYQwJJVM1WQNCiGEEEIIo5NKphBCCCGEIalkqiZJphBCCCGEIUkyVZM1KIQQQgghjE4qmUIIIYQQhqSSqVqF1qCfnx8tWrTAx8cHHx8fWrZsSefOnQkPD%2Bfw4cO6%2BT4%2BPnh6evLMM8/o7oeGhgIQGxvL8OHD8fHxoXPnzixbtkyvj3Xr1uHn54evry99%2B/blxx9/LHM8ubm5zJ8/n%2B7du%2BPj40P79u0JCQkhJiZGt0xOTg4fffQRzz33HK1ateKll17i4MGDeu3cvn2bQYMGcfv2bQ4dOoSnpycrVqwo0d%2BUKVOYMmUKgG655557jsLCwhLLjh49Gk9PTw4dOlQitriEhAQ8PT1JSEjQzcvIyGD%2B/Pn06NFDt54mTZpEXFycbplly5bxxRdflLluhBBCCCEqU4XT9JkzZxIdHU10dDTHjh1j9erVbN%2B%2BnYMHD%2BrmR0dHA7By5Urd/bCwMPLy8hg9ejQtWrTg0KFDrFixgg0bNrB7924A9u3bx/Lly1m1ahVRUVEEBwczYcIEvQSsuFmzZhEdHc3atWuJjo5m7969ODo6MnToUG7fvg3AvHnziIqKYvPmzRw%2BfJiXXnqJ0aNHk5iYqNfO4MGDqVmzpm7ewoULiYqKeuD6yM3N5Y8//tCbl5qaqlsHFZWeno6/vz9Xrlxh2bJlREVF8f3331OrVi0CAwO5evUqACNHjuSbb77RS6iFEEIIYSSmpsa/PWVUP2NPT0/atGnD6dOnH7jsn3/%2BSXJyMuPGjcPS0pJmzZoxbNgwNmzYAMClS5fQaDS6m5mZGRYWFpibl75V/%2BjRo3Tp0oX69esDULNmTd577z26du1KSkoKoK1kjhs3jnr16mFmZsbgwYOxtLTk1KlTAJw/f559%2B/YxcOBAvbaHDBnCO%2B%2B8w40bN%2B77nPr27cv27dv15kVGRtKjR48Hro/SLF68mKpVq7JgwQJcXV0xMTHB1taWDz74gOeff55z584BYGlpycCBA1m0aJGifoQQQghxH5JkqqZqn8y8vDyioqI4ePAgISEhD1z%2BwoULuLq6YmlpqZvXpEkT3abpF198kW3bttG7d2/MzMwwMTHh008/xdHRsdT2XnzxRSIiIoiNjaV9%2B/Y8%2B%2ByzuLq68vHHH%2BuWCQsL04s5cOAAGRkZeHl5AbBp0ya6d%2B%2BuNyaA9957j6ioKKZMmcKyZcswMTEpdQyDBg0iMDCQjIwMatSoAcC2bdsIDw9n8%2BbNesvu3LmTn3/%2BWW%2Be4ab2X3/9lcGDB2NmZlair%2BLPC6BPnz4sWLCAtLQ06tSpU%2Br4hBBCCCEqQ4WTzJkzZzJnzhzdfUdHR0aMGMGrr776wNisrCysrKz05llZWZGdnQ1ok1YvLy8%2B%2BugjvLy8%2BP7775k%2BfTpubm54enqWaG/s2LE0bdqU7du3Ex4eTnp6Og4ODrz%2B%2Buu89tprJZY/duwYEyZMIDg4mAYNGgBw8OBBRo4cWWJZS0tLPvvsMwYOHMjq1asJCgoq9Tl5eXnh6urKrl27CAwM5OjRo5iZmfHMM8%2BUWLZPnz7MnTtXb15CQgLdunXT3U9PT8fe3r7Uvgw5OTlhb2/PoUOH6N27d7liAJKTk3WV3iL2Li442NiUuw097u76UyUM3hcV4uamP1UqI0N5rIeH/lSpe/8Litz74aSbPmrG6l/N63hvq4ZuqlCzKsriGjfWnyrm6qo81tlZf6qUra2yOBcX/alS1tbKY431mXCvcKBI0fjVPA/AoP5RbkUbAMvYEFh%2Baj6bq1TRnypx547yWLWewsqjsVX47ffhhx/i7%2B%2BvqLNq1apxx%2BANc%2BfOHazv/RPOmjULX19fXYI2aNAgdu7cSWRkZKkHzYD2YCQ/Pz8A4uLi2Lt3L/PmzcPa2pqXXnpJt9y3337LnDlzGDduHBM1r1oAACAASURBVCNGjNDNv3btGnXr1i21bRcXF2bPns27775Lq1atynxe/v7%2BREZGEhgYyNatWwkICCjH2iidvb09ycnJpT6Wnp5OrVq19Kqcjo6OXLt2rUJ9bN68mYiICL15wWPGEDJ%2BfMUHXNzKleri1TJ4TpVizZrKHgFs3Ph09w8webKq8EiV3c%2Bfr7IBPlHbAKj9f1br/fcrt3%2BAhQsrewTw7LOqwuup7L6cNYv7DMAIP1rV/GhSeHyDeDw80lMYubu7c/nyZfLz83X7WV68eBH3exWwxMREmjdvrj9Ac3MsLCxKtBUTE8OAAQPYunUrHveqRy4uLgQFBXH8%2BHHOnDkDQEFBATNnzmTv3r0sWbKEjh076rVjYmKCRqMpc8y9evXi0KFDvPPOO3h6emJTSrWvb9%2B%2BfPLJJ5w5c4ZffvmFSZMmVWCt6PPz82Pv3r289dZbesmkRqMhKCiI5s2b6%2B0CkJ%2BfX%2Bqm9fsJDAzUJeZF7IcOhUiFX63u7toEc9QouHBBWRtqK5kRERAcDGoOhFJbyVyzBkaOhPPnlbejtpK5cSO88gqcPau8ncruv1Mn5bH162sTzPBwKOOAwfIYmLBYUVzjxtoEc%2BJEuHRJcfdEur%2BnPNjZWZtgLlwI9w4UVOTmTWVxLi7aBHP2bCh2RowKK3ZwZoW5uWmf//jx6j4Tpk1THmttrU0wjx%2BHrCzFzVxz7fjghUphbq5NMFNSID9fcffUu6Xif7lKFW2CGRsLOTnK26ksUslU7ZEmme3atcPW1pb58%2BczYcIEYmNjWb9%2BPW%2B//TagTbC%2B%2BuorunbtStOmTdm7d68uwTPUuHFjvL29CQ0NZdq0aXh6elJYWMjhw4c5dOgQC%2B/9gv3444/Zv38/W7duxbmUzUfOzs4kJSXdd9zTpk3j2LFj/PbbbyUOEAKwtbWla9euvPfee7Rr147atWsrWT0AjBkzhp9%2B%2Bol33nmHd955h4YNG5KUlMRnn33G9evX%2Beyzz/SWT05Opl69iv3WdXBwwMHBQX9mXJy6LwTQJpgnTiiLVblJCdB%2Bmfz1l/J4pV%2BqxZ0/r/1SUSozU/0Yzp6t3F//avsvYx/sCklIUJVcnFZ50oZLl6Acx0KWzTxW3QBAm2DGqmgnNVVd/3Fxyn90Aly%2BrK5/0L4H7h3kqYiaH55FsrJUtZObq677/HyVbRhjc3VOTuVu9lZKkkzVHukaNDc3Z82aNZw/f55OnTrxxhtvMGzYMN3m9%2BDgYIYOHUpISAht2rRhxYoVLFmyhKZNmwLac0O%2B%2BOKLgLYCuXLlSnx8fHj33Xdp164dnTp1YsWKFXz66ad06NCB9PR0NmzYQGpqKn369NE7j%2Bd3330HQKdOnTh69Oh9x120f6b1fRIhf39/zp8/z6BBg1Sto9q1a7NlyxZq1arFa6%2B9ho%2BPDwEBAeTn57Np0yZciu3nFB8fz82bN%2BnQoYOqPoUQQgghjK1Clcxff/213MsWnWrHUMOGDVm9enXpgzE3JyQkpMwj1UeP/n/27jy%2BpnPv%2B/gnI4kpWomYRZCEGmIorWofKY8aYopTbqVVtFKSiHlsDWmpGzU7xh6KtmqocqqoanN696lQQh0aNbamiqHEEJmfP7bs2052KllrE47v%2B/XKa8na63dd117Z9v7t37WGcMLDw62/lyhRgpEjRzIyj%2BOvnnjiCeu0eV46d%2B7Ma6%2B9xu3btylatChNmjSxO/aqVavaXDcz53YtWrTIFXf37zlP%2BMlWsWLFXHHe3t65zoq3Z8uWLbRq1crm%2Bp4iIiLiAKpkmvbY78HAwECaN2/Ohg0bCnsoBZKamsq6deuIiooq7KGIiIiI5PLYJ5kAY8eOZe3atVy7dq2wh5JvS5cupVu3bviZOWtPRERE7NPF2E17oCf%2BPKxKly7N50bPrC4kAwYMKOwhiIiI/Od6DJNCR1OSKSIiIvIIuHz5Mm%2B//Ta7d%2B/GxcWFDh06MHLkSLu33/7kk09Yvnw5iYmJ%2BPj48Oqrr/LKK68AlrsNNmzYkKysLJs7Gv7www94eno6bLxKMkVERERyeggrmdHR0ZQtW5bvv/%2BeS5cu8dZbb7F8%2BfJcdyXcsWMHH3zwAUuWLKFevXrs37%2BfN998kzJlytC6dWuOHTtmvTV4zttqO9LDtwdFRERExMZvv/3G7t27GT58OB4eHlSqVIkBAwawevXqXNteuHCBN954g/r16%2BPk5ERwcDBNmjRhz549ABw8eJCAgID7mmCCKpkiIiIiud2HSmZiYiIXL160Weft7Z37Bil2HD16FC8vL5tbYfv7%2B3Pu3DmSkpJsLmeYPS2e7fLly%2BzZs4fRo0cDliQzJSWFsLAwzp49i7%2B/P0OHDqVBgwZmnl4uSjJFREREcroPSeaaNWuYN2%2BezbqIiIg8rw9%2Bt5s3b%2BKR4xbM2b/funUrz2tmX7x4kf79%2B/PUU0/Rvn17AIoWLUrdunUZNGgQpUqVYvXq1fTt25dNmzZRqVIlI0/NLiWZIiIiIg9At27dCAkJsVnn7e2dr1hPT0%2BSc9yeM/v3vO5IuH//fgYNGkSjRo2YMmWK9QShUaNG2WzXt29fNmzYQGxsLD179szXePJDSaaIiIhITvehkunj45OvqXF7atSowdWrV7l06RJlypQB4Pjx4/j6%2BlKiRIlc269bt453332XqKgo%2BvTpY/PYzJkzad26NbVq1bKuS01NpUiRIobGlhclmWJRo4bx2Oz7qVeuDDm%2BZeWbl5fx/qtV%2B99lZqbxdg4fNh6b/R%2B8RAlzz6V5c%2BOx/v6WZbNm4OtrvJ3C7v%2Brr4zHBgdblj/8APHxhpsp2chYXHYxoVgxMHW310OHjMdm/x84fhzucVvdv7R%2BvbG4okUty9Gj4fZt4/2/%2Bqrx2Oz/g15ecOfD2IivMlsbji2ZBc2AH7KeJcnE21KbqweMBXp4QLmalLv%2Bq/H3ZSCeYMOxHkAgkEAgxkeAiRH8Z6latSoNGzZk8uTJTJo0iT///JMFCxbQtWvXXNtu27aNCRMm8Pe//53mdj5Xfv31V3766SdmzZpFqVKlWLx4MTdu3KBVq1YOHbPOLhcRERHJ6SG848%2BcOXNIT0/nxRdf5OWXX6Z58%2BbWm7MEBwezadMmAObNm0dGRgZRUVEEBwdbf9555x0ApkyZQuXKlenYsSNNmjRh9%2B7d/OMf/8DLTJHEDlUyRURERHJ6CK%2BTWaZMGebMmWP3sfi7Zm82b978l%2B14eXkxZcoUh47NnodvD4qIiIjII0%2BVTBEREZGcHsJK5qNGSaaIiIhITkoyTdMeFBERERGHUyVTREREJCdVMk3THhQRERERh1MlU0RERCQnVTJNU5IpIiIikpOSTNO0B0VERETE4VTJFBEREclJlUzTHrs9GBAQwJtvvklWVpbN%2Bg0bNhASEmKz7tChQ0RFRdG0aVOCg4Np1aoVU6dO5erVqwXqc/v27db7hQJkZGTw0Ucf0bVrVxo1akRwcDChoaEsXLiQ1NRUABYuXGi912jdunUJCAiwuf/opk2b2Lt3r/WepSIiIiIPk8cuyQSIjY1l6dKlf7nNt99%2BS48ePfDz8%2BOLL75g3759LFy4kNOnT9OpUycuXLiQr76uXLnC1KlTiY6OBiwJ5ptvvsmnn37KoEGD%2BO6774iLi2Py5Mns2LGDkSNHAhAeHk58fDzx8fEsWbIEwPp7fHw8HTp0oGHDhnh6erJu3ToTe0NERERycXZ2/M9j5vF7xkCvXr2YPXs2%2B/bts/t4amoq48aNo3///gwePJiyZcvi5OSEv78/c%2BbMwdfX13pj%2BXfeeYeWLVty8%2BZNAFavXk3Tpk2tSeiSJUt47rnneOKJJwBYv349Bw4c4B//%2BAfNmzenePHiuLu7U6dOHaZNm0blypXJyMgo0HOZO3eutQIqIiIiDqAk07TH8pjMVq1akZWVxZAhQ9i4cSNeXl42j8fHx3Pp0iU6deqUK9bZ2ZmuXbsyYcIE0tPTGTNmDF27dmXatGl0796d//7v/2bu3LmULVuW9PR01q5dy8KFC63xW7ZsISQkhLJly%2BZq28/Pj8GDBxfoudSrVw83Nzd27tzJSy%2B9lK%2BYxMRELl68aLPO29sbnzuJcIFVqWK7NKJECeOxlSrZLo0y8wbg72%2B7NNuOERUr2i4fNEf1HxxsPDYw0HZpUECAsbiqVW2XhiUHGY/187NdGlW0qLE4d3fbpVFG/wjgsD9EyZLGY4sVs10a5uFhLK5IEdul0e4zjcc6YgjJycZjpfA9lkkmwMiRI4mPj2fUqFH8/e9/t3ksMTERgDJlytiN9fHxIS0tjT///BNvb28%2B%2BOADXn75Zb777jt69%2B7N888/D1iO6UxOTqZu3brW2D/%2B%2BMPmd4DWrVtz%2BfJlAFJSUvjwww9p3Lhxvp9L/fr1%2BfHHH/OdZK5Zs4Z58%2BbZrIsYMIDIQYPy3addMTHm4s0aNapw%2BwfIsV8LxZ1DLh7b/gE%2B/thU%2BCqT3b/7rskGWG%2B2AZg%2B3XwbZlSoYC5%2Bldm/Aqb/EM3Mj4D69c22UNNcuJkv/4C5r2sWZr7vxMc7YABGPYaVR0d7bJNMd3d3Zs2aRefOnfnwww8pXbq09TFvb28Azp07R1U734TPnDmDm5ubNaZmzZo0btyY//mf/yEsLMy63blz5/Dy8sL9rm/03t7euY7n3LZtm/XfAQEBZGYW7Kujr68vR48ezff23bp1y3WSk/fIkbB3b4H6tapSxZJgvv02/PabsTbMVjJHjYL334fTp423c%2BKE8Vh/f0uCGREBx48bb6d6deOxFStaErypU%2BHMGePtFHb/P/xgPDYw0JJg9ugBCQmGm%2BlZy/6hNPdStaolrxk3Dk6dMtw9q5LD7r1RXvz8LAnmsGFw8qTxdowmqe7ulgTz7FkwcxjP228bj3XQH%2BKHt4wnusWKWRLM/fvhztFUhjTz/tVYYJEilvfm336DlBTD/SdkGk9yixSxvBxPnjQ1BHmEPbZJJkDlypWJiYlhxIgRdOnSxbq%2BYcOGeHt7s27dOoYNG2YTk5GRYT0T3dXVsvu2bNnCgQMHaNWqFSNGjGD16tW4uLjg7OycK2F86aWXmDVrFpcvX%2BbJJ590yPPIyMjAuQDfuHx8fPDx8bFdefGi5ceM336DI0eMxeY4ZMGQ06fh2DHj8YcPmx/D8ePw738bj3dyMj%2BGM2fMJbqF3b8jShcJCabaOeJirvtTp4z/VwDg5i/mBgCWT/ZfTLRz%2B7a5/lNTzbVhagfeYfIPkZRkfgg3b5psp7jJ%2BeKUFFNzzskmpssdNITCo0qmaY/9Hmzbti1hYWGsWbPGus7NzY0pU6awatUqZs6cyYULF8jMzOTYsWNERETwxx9/MHr0aADOnj3L%2BPHjefvtt5k8eTKJiYnWqejy5ctz9epVUu76CtetWzfq1avHa6%2B9xvfff09qaiqZmZkcOHCA8PBw3N3dKVWqVIGeQ2JiIuXLl3fA3hARERFAJ/44wOP3jO0YM2YMQUG2B9o3b96cTz/9lN9//52wsDAaNGhAeHg4lStXZtOmTZQrV46MjAyGDRvGM888Q2hoKMWLF2fy5MksXryYPXv2UKtWLby8vIi/q6Li6urK4sWL6dWrFwsWLKB58%2BY0aNCAESNGULFiRbZs2UJgAU9a2LdvH82bN3fIvhARERFxhMduuvyInamTIkWKsHHjxlzrAwMDmTlzZp5tubi48Mknn9isa9q0KYcOHbL%2B3qlTJ7766iuaNm1qXefs7Ey3bt3o1q1bvsbcpEkTu%2BMGy5nwWVlZPPvss/lqS0RERPLhMaw8Opr24H32xhtv8O2333LlypX70v7y5cuJjIy0OblIREREpLApybzPnnjiCUaNGsUHH3zg8LZ/%2BuknUlJSbM5oFxEREQfQMZmmPXbT5YWhbdu2tG3b1uHtNmrUiEaNGjm8XRERkcfeY5gUOpr2oIiIiIg4nCqZIiIiIjmpkmma9qCIiIiIOJwqmSIiIiI5qZJpmpJMERERkZyUZJqmPSgiIiIiDqdKpoiIiEhOqmSapiRTAAhOjTMcG5gGnwD/lfYRCanG2rhxwnD31CoKXwAdz8znsIl2/rhqPLbedfgf4LnrX3HARDvljxuPrVUEPgc6n5nLYRPtFHb/JU1c%2BjUgAFYBPWvt44iL8Xb2/ORkLDAjGNjHqsMNID7ecP8dO2QZjq1WGWYCgyuv50S64WYIXG4srnx5GDQIZm/y49w54/2vu7LHcGztJNgEdEhaxSETN1vbWct4bPZN2KpVg1SD74sAFz3rGYpzdYXSwJ/eNUk38TpINfGe6nonw0hLM7cP5NGlJFNEREQkJ1UyTVOSKSIiIpKTkkzTtAdFRERExOFUyRQRERHJSZVM07QHRURERMThVMkUERERyUmVTNOUZIqIiIjkpCTTNO1BEREREXE4VTJFREREclIl0zTtQRERERFxOFUyRURERHJSJdM0JZkiIiIiOSnJNO2B7MGQkBDq1KlDcHAwwcHB1K9fn%2Beee46pU6eye/du6/rg4GACAgKoW7eu9fd33nkHgJMnT/Laa68RHBzMc889x8KFC236WLFiBSEhITRo0IDQ0FC2bduW53hSU1OZMWMGLVu2JDg4mKZNmxIZGcnx48et21y7do1hw4bRpEkTGjRowGuvvcYvv/xi005SUhJhYWEkJSURFxdHQEAAixcvztXfqFGjGDVqlM26rVu38sorr9CgQQMaNmxI586dWbFiBRkZGfnap5s3b%2Bbdd9/N17YiIiLy6Lt8%2BTIDBgygUaNGNGnShPfee4/09HS728bGxhIaGkr9%2BvVp06YN3377rc3jS5Ys4fnnn6d%2B/fr06tWLEydOOHy8DyxNnzhxIvHx8cTHx7N//36WLVvGxo0b2bVrl3V9fHw8YHni2b9PmjSJtLQ0wsPDqVOnDnFxcSxevJjVq1fz1VdfAZYduWjRIpYuXcq%2BffuIiIggOjqaM2fO2B1LTEwM8fHxLF%2B%2BnPj4eLZv346vry%2BvvPIKSUlJAIwbN44bN27w9ddfExcXR926dRkwYECudl5%2B%2BWVKlixpXTd79mz27dv3l/tixowZTJgwgbCwMGJjY9m9ezejR49m7dq1vPnmm/lKNENDQzl8%2BDA//vjjPbcVERGRAnJ2dvyPSdHR0Xh6evL999%2Bzbt06fvzxR5YvX55ru1OnThEZGcmgQYP46aefiIyMJDo6mgsXLgDw%2Beefs3LlSpYtW0ZcXBy1a9cmKiqKrKws02O8W6HVggMCAmjcuDGHDx%2B%2B57Z79uwhMTGRqKgo3N3dqVWrFr169WL16tUAnDhxgqysLOuPi4sLbm5uuLraPxpg7969NG/enIoVKwJQsmRJRowYQYsWLbh48SIAH3zwAbNnz6ZkyZLcunWLpKQkSpcubW3j119/JTY2ls6dO9u0/V//9V8MGTKEP//8027fv/zyC0uWLGHevHl06dKFEiVK4OLiwtNPP83y5cs5cOAAa9asITU1lY4dOxIdHW2NHTRoED169LB%2Ba%2BnZsyczZsy45/4TERGRR9tvv/3G7t27GT58OB4eHlSqVIkBAwZYc6G7ff755zRq1IiWLVvi6upK27Ztady4MWvWrAHgs88%2Bo0ePHtSoUYMiRYowdOhQzp07R1xcnEPHXCjHZKalpbFv3z527dpFZGTkPbc/evQofn5%2BuLu7W9dVr17dOjXdrl07NmzYQNu2bXFxccHJyYlp06bh6%2Btrt7127doxb948Tp48SdOmTalXrx5%2Bfn5MmTLFuo2bmxsAM2fOZNGiRRQrVoxFixZZH//kk09o2bKlzZgARowYwb59%2Bxg1ahQLFy7EycnJ5vEdO3ZQoUIFGjVqlGtcZcqUISQkhK1bt9KjRw8%2B%2BOADwsLC2LJlC9evXycuLo4vvvjCmjyHhIQwduxYDh48SJ06de65H7MlJiZak%2BlsZct6U7q0T77buFvVqrZLI27dMh5brZrt0qiyZY3H1qxpuzTK29t4rKP2Q2H3X6yY8VhHvBYByAg2FhcYaLs0yMw%2BvPPd2bo0qnx5Y3HZr2Ezr2WA2rWNxzrqtZjj7b1AsmscedQ6CtxOQbm42C6N8vQ0Hlu0qO3SCDOfDabdh2My7X3%2Bent74%2BNz78/fo0eP4uXlRdm7Pqz8/f05d%2B4cSUlJNrOqx44do2aOD6Tq1auTkJBgffyNN96wPubm5kbVqlVJSEigadOmhp6bPQ8syZw4cSKTJ0%2B2/u7r68vrr79Oz5497xl78%2BZNPDw8bNZ5eHhw686rLy0tjcDAQN577z0CAwPZvHkzY8eOxd/fn4CAgFztDRw4kKCgIDZu3MjUqVO5cuUKPj4%2B9O3bl969e9ts%2B9ZbbzFw4EBWr17NG2%2B8waZNm6hUqRK7du2iT58%2Budp2d3dn1qxZdO7cmWXLltGvXz%2BbxxMTE/H%2Bi3dfHx8ffv75Z8Dy4hk7diyTJk0iJSWFOXPm2Ly4ihYtSmBgID/%2B%2BGOBksw1a9Ywb948m3UDBkQwaNC9E/6/cleOXihmzizc/gE%2B/LCwRwCFXdwu7P4BzB%2Bu/NeHvNzTxx%2BbCnfES3noUAc0YkKPHubiBw0yP4ZZs8y3YZbZZNusu/IOQ%2B6awDOsRg3jsQ4urBVIFk733qiA7H3%2BRkRE5KvgllcuBHDr1i2bJNPetkWLFrXmTfd63FEeWJI5fvx4unTpYijW09OT5ORkm3XJyckUu1PyiImJoUGDBtStWxeAsLAw/vnPf/L555/nOuEmW0hICCEhIQD8/vvvbN%2B%2BnenTp1OsWDH%2B9re/Wbcreucr2Ouvv87atWv55ptv6N27N%2BfPn7dJ%2BO5WuXJl3n33XYYPH07Dhg1tHvP29ua7777L87meOXPGJgkNDQ1l%2BvTplClTxu63C19fX/74448827OnW7du1ueebehQb3btKlAzVlWrWhLM0aPh1CljbZitZM6cCYMHg5njli9fNh5bs6YlwezTB3791Xg7ZiuZM2ZYkov7cPz2A%2BvfbCXz3Xdh3Djjr0WAVYcbGAsMDLQkmD16wJ2KgRGDXzCe5FasaPkbzJgBeRyWni9Gq8He3pan//HHkKNgUyDffGM8tlo1S4IZHW3utXjX5FWBubpa9sXFi5DHeRn5YrQK6OJiSTCTkiCf55PaZeY1VLSoJcE8ehRu3zbezn8Se5%2B/f1V4ulteuRBgzYeyeXh4cDvHTr99%2B7Z1u3s97iiPxCWMatSowalTp0hPT7dOFR87dowad74enTt3jqeeesomxtXV1Trlfbfjx4/TqVMn1q9fby0lV65cmX79%2BnHgwAHrGeTdu3end%2B/evPTSS9bY1NRUSpUqBYCTk9NfHiDbpk0b4uLiGDJkCAEBAXh5eQHwf//v/2XBggX861//4vnnn7eJuXDhAt9//z2DBw%2B2rpsyZQp%2Bfn7cuHGDWbNmMXz4cJuYjIwMnAtY0vfx8clVmr9wwfJjxqlTxj9Xb9ww1zdYPkzycYhvngqYq9v1669w4IDxeKNTlHczux8Ku3%2BzlRewvBaPHDHRwJ2TEA1LSDDVxolK5roHS3JgJsEyM1UMluTq3Dnj8YcOmesfLM/fTDupqebHkJ5urh2z0%2B0ZGeaSXEcUtm7fLuRpb4MyMx3fpr3P3/yqUaMGV69e5dKlS5QpUwaw5DS%2Bvr6UKFHCZtuaNWtyKMeL/9ixY9ZcqUaNGhw9epQWLVoAlhnhU6dO5ZpiN%2BuRuAhUkyZNKF26NDNmzCAlJYWEhARWrlxJ165dAUtVctWqVRw6dIjMzEy2bt1KXFwcbdu2zdVWtWrVqF27Nu%2B88w4///wzKSkpJCcnExsbS1xcHK1atQKgbt26zJ07l7Nnz5KamsqcOXNITU21fgOpUKGC9SytvIwZM4ZSpUrZXDYgMDCQgQMHMnz4cDZu3Mj169dJTU1l165d9O3bl9q1a9OtWzfAcvzmpk2beP/993n//fdZsWIF/%2B///T%2BbPhITEynviMxERERErDIzHf9jRtWqVWnYsCGTJ0/mxo0bnD59mgULFlhzobt16NCB3bt3s2XLFtLT09myZQu7d%2B%2BmY8eOgGXGd9WqVSQkJJCSksKMGTMoU6aM3fNFzHgkKpmurq58%2BOGHTJo0iWbNmuHp6UmvXr2s0%2B8RERG4uLgQGRnJtWvXqFKlCvPnzycoKAiAhQsXsnnzZr788kucnJxYsmQJCxYsYPjw4Vy4cAFnZ2eCgoKYNm0azzzzDADDhg3DxcWFbt26kZaWRv369VmxYoW1ktmsWTP27t3Lyy%2B/nOe4s4/PzHmYQGRkJEFBQXz00UdMnjyZ9PR0qlSpQteuXenZsyeurq5cuHCBsWPHMmzYMKrembcKDw9nxIgRbNq0iSeeeIKUlBQOHTpETEyMo3e5iIiIPGTmzJnDpEmTePHFF3F2dqZTp07WyysGBwczceJEOnTogL%2B/P/Pnz2f69OmMHTuWChUqMHfuXPz8/ADo2rUr169fZ%2BDAgVy5coU6deqwaNEiuzPAZjyQJHPnzp353vZIHnNcVapUYdmyZXYfc3V1JTIyMs8DZ8PDwwkPD7f%2BXqJECUaOHMnIkSPzHIe7u/tfbtO5c2dee%2B01bt%2B%2BTdGiRWnSpIndsVetWtXudTNbtmxJy5Yt8%2By/bNmyuS4lEBERQUREhPX3r7/%2BmqCgIOthAyIiIuIY92O63KwyZcowZ84cu4/F5zhEp3nz5jRv3tzutk5OTvTp08fuCcyO9EhMlz%2BMAgMDad68ORs2bCi0MXz00UcMGTKk0PoXERERyYuSTBPGjh3L2rVruXbt2gPv%2B4svvqB27doOvZ6ViIiIWDxsx2Q%2Bih6JYzIfVqVLl%2Bbzzz8vlL47duxoPYBXREREHOtxTAodTZVMEREREXE4VTJFREREclAl0zxVMkVERETE4VTJFBEREclBlUzzlGSKiIiI5KAk0zxNl4uIiIiIw6mSKQDElw4xHlyyBrCIT0r2h9JHjbXh52W8/2rVgOl8UWMYaggDmQAAIABJREFUuJww3o7rL8Zjy9YC1vM/ZcOg4mHj7Tz1lPFYPz/gv/m8xghwPWm8ncLu/9Ah47HJQcB6ViWHwU3jf8%2BOHbIMxVWrBjOBwS/s40Qlw93zxSYn48HBwcA%2BZsY2gBx3ACmQ/04wFlekCFCVQR1PQUqK4e6nHhxsOJYq/sBcNlWJhPTjhpuJv7LFcKyHB5QrB9euQXKy4WYMc3e3LG/fhtRU4%2B2cOWM81uvO23piIly9arydJk2Mx5qhSqZ5qmSKiIiIiMOpkikiIiKSgyqZ5inJFBEREclBSaZ5mi4XEREREYdTJVNEREQkB1UyzVMlU0REREQcTpVMERERkRxUyTRPSaaIiIhIDkoyzdN0uYiIiIg4nCqZIiIiIjmokmmeKpkiIiIi4nCqZIqIiIjkoEqmeY9VkhkQEMALL7zAokWLcHJysq7fsGED8%2BbNY%2BfOndZ1hw4dYtGiRezevZuUlBTKlClDy5Yt6d%2B/P15eXtbttm3bxoIFCzh9%2BjReXl506dKFAQMG4Oz8v0XimJgYgoODad%2B%2BPQBXr15l0aJF7Ny5k8TERJydnQkKCqJHjx60bdsWgH79%2BrF3714A0tLSyMjIoGjRotY2v/zySzZt2kSRIkV4/fXX788OExEReUwpyTTvsZsuj42NZenSpX%2B5zbfffkuPHj3w8/Pjiy%2B%2BYN%2B%2BfSxcuJDTp0/TqVMnLly4AMC///1vRowYQXR0ND/99BNLlixhw4YNLF%2B%2B3NrWjz/%2ByOHDh60JZmJiIh07duTkyZPMmjWLuLg4YmNj6dOnD5MmTeKTTz4BYOnSpcTHxxMfH0///v1p1KiR9ff4%2BHjKly9Pnz59%2BOyzzzh%2B/Pj92VkiIiIiBj12SWavXr2YPXs2%2B/bts/t4amoq48aNo3///gwePJiyZcvi5OSEv78/c%2BbMwdfXlylTpgBw9uxZunfvTosWLXB2dsbf359WrVqxZ88ea3szZsygV69e1t%2BnTp2Kr68v8%2BfPJygoCHd3d4oXL05ISAiTJ0/Gzc0t38/F3d2dzp07M2fOHIN7Q0REROzJzHT8z%2BPmsZouB2jVqhVZWVkMGTKEjRs32kx9A8THx3Pp0iU6deqUK9bZ2ZmuXbsyYcIE0tPTad26Na1bt7Y%2Bfvv2bb777jtCQ0MB%2BPnnnzl%2B/DghISEAZGRksH37dsaPH4%2BLi0uu9rO3K4j27dszc%2BZMLl%2B%2BzJNPPpmvmMTERC5evGizzvvJJ/F54okC9w9ApUq2SyNKlDAeW6GC7dKouw6hKDA/P9ul2XaMcNR%2BKOz%2BzbwTO%2BjvUK2ysbiKFW2XhgUHG48NDLRdGlWkiLE4d3fbpVH%2B/sZjHfSH8PAwHpu9%2B4zuxmxGd6Orq%2B3SqBwfkQWS/bZu5u396lXjsVL4HrskE2DkyJHEx8czatQo/v73v9s8lpiYCECZMmXsxvr4%2BJCWlsaff/6Jt7e3df2NGzcYNGgQRYsWpXfv3gDs2rWLoKAg67GUV65cITU1FV9fX2vcqVOn6Nq1KwCZmZmkpaVx8ODBfD%2BX8uXL4%2B3tTVxcnPV4zntZs2YN8%2BbNs1kXMWAAkYMG5btfu8aNMxdv1uDBhds/wPTphT0CMPt3fNT7B9N/h5kmux861GQD2J9pKZCPPzbfhhnly5uLnzvX/BhGjjQVbjJNB8x/7zTrro8pQ8qVMz%2BGp582Hrt%2Bvfn%2BjXocK4%2BO9lgmme7u7syaNYvOnTvz4YcfUrp0aetj2YnjuXPnqFq1aq7YM2fO4ObmZhNz4sQJoqKiePLJJ/noo48oXrw4AOfPn6ds2bLW7UqXLo2bm5v1mE6AqlWr8tNPPwEQFxfHq6%2B%2BWuDn4%2Bvry/nz5/O9fbdu3XJVTb0nTIDDhwvcN2CpYI4bB%2B%2B%2BC6dPG2vDbCVz8GCYORPOnjXezsmTxmP9/CyJzbBh5toxU72pUMGS4M2ebW4/FHb/Zo4xdtDfYXBlY59sFStaEswZM%2BDMGcPdMzO2gfHgwEBLgtmjByQkGG9nwwZjce7ulgTz3DlITTXe/4wZxmMrVrQkmFOnmvpDJAw0nugWKWJ5OZ48CSkphpuhVCljca6ulgTz4kVITzfev9GPBbC8rT/9NOzeDdevG2%2BnsCjJNO%2BxTDIBKleuTExMDCNGjKBLly7W9Q0bNsTb25t169YxbNgwm5iMjAw2bNhASEgIrnfmIGJjYxkyZAgvv/wyQ4cOta4Hy/R65l2vUldXV0JCQli/fj2dO3e2OQPdjPT0dLvT73nx8fHBx8fHduXly5YfM06fhqNHjcWamZPJdvYsnDhhPP6XX8yP4eRJc%2B/KjnhNnD1rLtEt7P4d9Xcw0c4JEx/KYMlrzLwUiY83NwCwJJhm2jGTGYElwTTThiNOaDxzxlQ7ycnmh5CSYq4dM1P2YEkwzeT6jpiuvn5d096Pq8fuxJ%2B7tW3blrCwMNasWWNd5%2BbmxpQpU1i1ahUzZ87kwoULZGZmcuzYMSIiIvjjjz8YPXo0APv372fgwIGMHj2akSNH2iSYYJnKvrtqCTB27FjOnz9PREQECQkJZGZmkpKSwo4dO4iJibGZgs%2BvxMREyjliTkNEREQAnfjjCI91kgkwZswYgoKCbNY1b96cTz/9lN9//52wsDAaNGhAeHg4lStXZtOmTdaEbuHChaSnp/Pee%2B8RHBxs/enXrx8AzZo149ChQ6Tc9W2%2BbNmybNq0iZo1azJs2DAaN25M06ZNWbBgAR06dGDr1q0FGv/p06e5evUqzzzzjMk9ISIiIuI4j9V0%2BZEjR3KtK1KkCBs3bsy1PjAwkJkz//rw/4ULF/7l44GBgdSoUYNvvvnG5qScEiVKEB0dTXR0dL7GHRkZmedjW7ZsoVWrVpQsWTJfbYmIiMi9PY6VR0d77CuZ99vQoUNZsWLFfWk7NTWVdevWERUVdV/aFxEReVxputw8JZn3WbNmzQgKCmLTpk0Ob3vp0qV069YNv8K%2BRoaIiIhIDo/VdHlhmTBhwn1pd8CAAfelXRERkcfd41h5dDRVMkVERETE4VTJFBEREclBlUzzlGSKiIiI5KAk0zxNl4uIiIiIw6mSKSIiIpKDKpnmKckUERERyUFJpnmaLhcRERERh1MlUyxq1DAeW6mS7dKI69eNx7q7/%2B%2ByaFHj7Vy7Zjz2xo3/XZpp59Il47GlS1uWV6%2Baa6ew%2B1%2B/3nhs9t9/%2BnS4fdtwM4HLjcWVL29ZVq36vy9LQ/47wXhskSKW5YYNkJJivJ3AQGNxwcGwbx906QLx8cb7z%2Bdtd%2B2qWNGyrFoVXI1/zAXP7WN8DJUrw4QJBH46AX7/3XAzf8740FCci4tlWayYubfFsPU9jAdXrQovTubFb8bAqVMmBvGx8VgTVMk0T0mmiIiIyH%2BAW7duERMTw86dO0lPT%2BfFF19k/PjxFCtWzO7227ZtY8GCBZw%2BfRovLy%2B6dOnCgAEDcHa2THS3adOGc%2BfOWX8HWLduHf7%2B/vkaj5JMERERkRwexUpmTEwM58%2BfZ9u2bWRkZBAdHc306dMZP358rm3//e9/M2LECGbNmsULL7zAyZMneeONN/D09KRPnz7cuHGDkydP8s0331ChQgVD49ExmSIiIiI5ZGY6/ud%2BSk5OZvPmzURFReHl5cWTTz7JsGHD2LBhA8nJybm2P3v2LN27d6dFixY4Ozvj7%2B9Pq1at2LNnD2BJQr28vAwnmKBKpoiIiMgDkZiYyMWLF23WeXt74%2BPjk6/427dvc%2BHCBbuPJScnk5aWRs2aNa3r/P39uX37NqdOnSIoKMhm%2B9atW9O6dWubtr/77jtCQ0MBOHjwIB4eHvTs2ZOjR49SoUIFIiMjadGiRb7GCkoyRURERHK5H5XHNWvWMG/ePJt1ERERREZG5iv%2BwIEDvPrqq3YfGzRoEACenp7WdR4eHgDcvHnzL9u9ceMGgwYNomjRovTu3RsAJycn6tSpw5AhQyhfvjxbt24lMjKSVatWUb9%2B/XyNV0mmiIiIyAPQrVs3QkJCbNZ5e3vnO75JkyYcOXLE7mOHDx9m9uzZJCcnW0/0yZ4mL168eJ5tnjhxgqioKJ588kk%2B%2Bugj67b9%2BvWz2a5Dhw7885//ZNu2bUoyRURERIy6H5VMHx%2BffE%2BNF5Sfnx9ubm4cO3aMevXqAXD8%2BHHc3NyoWrWq3ZjY2FiGDBnCyy%2B/zNChQ3G965Jfy5Yto1atWjzzzDPWdampqRTJvkxaPujEHxEREZEcHrUTfzw8PGjTpg3Tp0/nypUrXLlyhenTp9O%2BfXuK2rlY6v79%2Bxk4cCCjR49m5MiRNgkmwPnz55k4cSKnT58mPT2ddevWER8fT%2BfOnfM9JiWZIiIiIv8Bxo8fT9WqVQkNDeWll16iYsWKvPPOO9bH27Vrx8KFCwFYuHAh6enpvPfeewQHB1t/sqfJR4wYwfPPP0%2BPHj1o1KgRn376KYsXL6ZKlSr5Ho%2Bmy0VERERyeBSvk1m8eHFiYmKIiYmx%2B/iXX35p/Xd2spkXd3d3xowZw5gxYwyPR5VMEREREXE4VTJFREREcngUK5kPm/teyQwJCaFOnTrWuf769evz3HPPMXXqVHbv3m1zHEBAQAB169a1/p59HMHJkyd57bXXCA4O5rnnnstV4l2xYgUhISE0aNCA0NBQtm3blud4UlNTmTFjBi1btiQ4OJimTZsSGRnJ8ePH7W6/du1aAgICcq0/ffo03bt3Jy0tjQ0bNhAQEMCWLVtybderVy/mzp1r/T0rK4s1a9YQFhZGcHAwjRs3pnv37mzcuNEmLiUlhffee4/nn3%2Behg0b8re//Y1du3ZZH3/zzTf5%2Beef83yeIiIiYtyjduLPw%2BiBVDInTpxIly5drL8fOXKE3r174%2BHhQXx8vHV9QEAAS5YsoUmTJtZ1aWlphIeH06pVK5YsWcKxY8fo378/VapUoU2bNsTGxrJo0SJWrVpFtWrV2LZtG9HR0Xz99ddUrFgx11hiYmI4efIky5cvp2LFiiQlJTF37lxeeeUVtm/fTsmSJa3bHj16lMmTJ9t9TqNGjSIiIgI3NzfrunHjxlG7du2/PCh22LBhxMfHM2bMGJ599lnc3Nz417/%2BxaRJk9i9e7e1v%2BnTp7Nv3z7WrFmDj48P69evJzw8nC1btlC%2BfHlGjhxJREQEX3zxBe7u7vn4K4iIiIg8OIVyTGZAQACNGzfm8OHD99x2z549JCYmEhUVhbu7O7Vq1aJXr16sXr0asFxENCsry/rj4uKCm5tbrlPxs%2B3du5fmzZtbE9CSJUsyYsQIWrRoYXOrp%2BTkZIYMGWL3yvrfffcdV65c4bnnnrOuq1ChAk2aNCE6OprU1FS7fe/YsYNt27axYsUKWrZsiaenJ25ubrz44ossW7aMjRs3EhsbC1gqmVFRUZQrVw4XFxdefvll3N3dOXToEGC5VVSFChVYu3btPfehiIiIFIwqmeY98GMy09LS2LdvH7t27crXbZSOHj2Kn5%2BfTbWuevXqLF68GLCcjr9hwwbatm2Li4sLTk5OTJs2DV9fX7vttWvXjnnz5nHy5EmaNm1KvXr18PPzY8qUKTbbTZo0if/zf/4Pzz77bK7p%2BY8//pj27dvnavv999%2BnU6dOTJkyhfHjx%2Bd6fMeOHTRo0IBKlSrleqx69eoEBwezdetWXnjhBSZNmmTz%2BI8//sj169cJDAy0rmvfvj1Lly7llVdesftc82L33qnFiuHzxBMFascqe1/nsc/z5dYt47Hly9sujapTx3hs9eq2S6MKcGmIXCpXtl0%2BaI7q38713PIt%2B33CZHXf6Esp%2B8YdBbiBh30FuNhxLg7aBwQHG4vLfo%2B6673KEDszUfmWfbFrsxe9LlXKeGy5crZLg1xcjMU5O9suDcvjIt754oj35lOnjMdKoXtg0%2BV3Tzv7%2Bvry%2Buuv07Nnz3vG3rx503rvzWweHh7cupOUpKWlERgYyHvvvUdgYCCbN29m7Nix%2BPv72z2WcuDAgQQFBbFx40amTp3KlStX8PHxoW/fvtb7dX7xxRccP36cmJgY9u7daxOfmZnJ7t27ef3113O1XapUKT744AN69erF008/TZs2bWweT0xM/MvbR/n4%2BJCYmJhr/f79%2B4mOjiYiIsImQQ0ODubo0aNcunSJMmXK5NluTnbvnTpwIJFRUfluw64ct6B64CIiCrd/gPnzC3sEMG7c490/QIUKpsLv3ALYsB49zMVDVbMNmP/StW%2BfufiPPzYX7wi9ehX2CKB/f1PhJe%2B9yV/6i7sJ5k8eh4wViJn3ZvP/mQx7HCuPjvZAkszx48fbHJNZEJ6entZ7b2a7%2B76cMTExNGjQgLp16wIQFhbGP//5Tz7//HNGjRplt82QkBDrvUN///13tm/fzvTp0ylWrBgNGzZkxowZrF692u6U%2B9WrV0lOTs7ztlDBwcFER0dbj8%2B8m7e3N7/99luez/XMmTNUz1EJW7t2LZMnTyYqKipXYptdrT1//nyBkky7905dtgzefTffbeQYiCXBXLoU/vjDWBtmK5kRETBvHpw7Z7ydHF8oCqR6dUuCOXAgHDtmvB2zlcxx4yx/x99/N95OYfc/erTxWHd3S4J59izkcdhKfsze5Gcoztvb8pn48ceQY7KgQAZ1PGU82N3d8n/i3DlT%2BwCD79kEBlp2QI8ekJBgvP8CztDY8PGxJJgrV4KdL%2B75dv268dhy5SwJ5qJFcP684WaShkwwFOfsbEkwb9wwlyyVfN/4NRId9t5cSJRkmvfQX8KoRo0anDp1ivT0dGvSd%2BzYMWrUqAHAuXPneOqpp2xiXF1dbU7IyXb8%2BHE6derE%2BvXrqVmzJgCVK1emX79%2BHDhwgF9%2B%2BYVLly6RlJRkvW1SRkYGAI0aNWL8%2BPHW4zCzsrLyHHPfvn3ZvXs30dHRNuN46aWXiIyM5MiRI7mqrIcPH%2Bbw4cMMGDDA2u/EiRPZvn078%2BfP59lnn83VT/bYXAo4n2L33qk3b1p%2BzPjjDzh92lismTfzbOfOmZtaOXjQ/BiOHTPXjpmkINvvv8PRo%2BbbKaz%2Bb982P4bUVFPtmP08vHjRZBspKeYGAJZ9YKadu07KNCQhwVwbL7xgrn%2BwJJhnzhiPv3bN/BjOnzf1pevO27xhmZkm23DEdLXZ92Z5ZD30F2Nv0qQJpUuXZsaMGaSkpJCQkMDKlSvp2rUrYKlKrlq1ikOHDpGZmcnWrVuJi4ujbdu2udqqVq0atWvX5p133uHnn38mJSWF5ORkYmNjiYuLo1WrVrz11lvs37%2Bfn376iZ9%2B%2Bsl6POZPP/1EaGgopUuXxtPTkwsXLuQ5ZicnJ6ZOncrly5fZv3%2B/dX2LFi0IDQ3lrbfe4ptvvuHWrVvcunWLHTt2MGDAANq1a0eLFi0AmDJlCv/6179Yv3693QQTsI6hnMljfkRERMSWTvwx76GvZLq6uvLhhx8yadIkmjVrhqenJ7169bJOv0dERODi4kJkZCTXrl2jSpUqzJ8/n6CgIMBy26TNmzfz5Zdf4uTkxJIlS1iwYAHDhw/nwoULODs7ExQUxLRp03jmmWfyNaZmzZqxd%2B9emjVrluc22Ynxa6%2B9ZrN%2BypQprFu3jsWLFzNy5EjActJPZGSk9TlduXKF1atX4%2BLikusEo4kTJ9KhQwfAcqb8U089RenSpfM1bhEREZEH5b4nmTt37sz3tkeOHLG7vkqVKixbtszuY66urkRGRuZ5pnp4eDjh4eHW30uUKMHIkSOtCd69NGnSJNe4wsLCmDZtGlF3TpTp0qWL3WNOGzVqZL3k0N26du1qrcTa88QTT/DLL7/cc2xfffXVX7YjIiIixjyOlUdHe%2Binyx9GLVq0oFSpUtZrWhaGo0ePcubMGSWZIiIi94Gmy81TkmnQ%2B%2B%2B/z/z580lLSyuU/qdOncr7779v9wQnERERkcL20B%2BT%2BbCqUqUKn332WaH1v3Tp0kLrW0RE5D/d41h5dDRVMkVERETE4VTJFBEREclBlUzzlGSKiIiI5KAk0zxNl4uIiIiIw6mSKSIiIpKDKpnmqZIpIiIiIg6nSqZYFODOTLnUqmVZ7tkDhw8ba%2BPmTeP916ljWe7dCwcPGm/H19d47JNP/u/STDunThmPLVbMsjx3zlw7hd3/q68ajw0IgFWr4O23IY87iOXHuit7DMXVrg2DBsE334Cdm33l29SDg40H%2B/vD3LkwYwYcP268nehoY3EVK1qWr7wCL7xgvP9Zs4zHBgfD0KGwejXExxtuJiszy/gYACcga/wEU22UPnXSWKC7O5SsQMnrZyE11fgAZs82Hut6J8UYPhzS0423U0hUyTRPSaaIiIhIDkoyzdN0uYiIiIg4nCqZIiIiIjmokmmeKpkiIiIi4nCqZIqIiIjkoEqmeUoyRURERHJQkmmepstFRERExOFUyRQRERHJQZVM85RkioiIiOSgJNM8TZeLiIiIiMOpkikiIiKSgyqZ5qmSKSIiIiIO99BUMkNCQrh48SKurpYhZWVlUbx4cUJDQ2nRogX9%2B/e3bnvr1i2KFCmCi4sLAKGhoUyaNImTJ08yYcIEfv75Z4oVK0bPnj0JDw%2B3xq1YsYIVK1Zw9epVKlSoQEREBK1bt7Y7ntTUVObOnctXX33F5cuXKVKkCI0bNyY6Ohp/f38AMjMzadiwIVlZWTg5OVljf/jhBzw9PQE4ffo0w4cPZ%2BXKlbi5uZGVlcVnn33GZ599xokTJ3B1dcXf35/u3bvTqVMnaxvXrl0jJiaG77//nrS0NOrUqcOoUaMICgoC4M033yQiIoK6des6YveLiIjIXVTJNO%2BhSTIBJk6cSJcuXay/HzlyhN69e%2BPh4UF8fLx1fUBAAEuWLKFJkybWdWlpaYSHh9OqVSuWLFnCsWPH6N%2B/P1WqVKFNmzbExsayaNEiVq1aRbVq1di2bRvR0dF8/fXXVKxYMddYYmJiOHnyJMuXL6dixYokJSUxd%2B5cXnnlFbZv307JkiU5duwYaWlp7Nu3D3d3d7vPadSoUURERODm5gbAsGHDiI%2BPZ8yYMTz77LO4ubnxr3/9i0mTJrF7924mT54MwLhx40hLS%2BPrr7/Gw8ODOXPmMGDAAL799lsARo4cSUREBF988UWefYuIiIgxSjLNe6iSzJwCAgJo3Lgxhw8fvue2e/bsITExkaioKNzd3alVqxa9evVi9erVtGnThhMnTpCVlWX9cXFxwc3NzVo5zWnv3r107NjRmoCWLFmSESNGcOPGDS5evEjJkiU5ePAgAQEBeSZ53333HVeuXOG5554DYMeOHWzbto2vvvqKSpUqWbd78cUXqVKlCh06dKB169a88MILfPDBB2RmZlKkSBGuXbtGUlISpUuXtsb4%2B/tToUIF1q5dyyuvvJLvfSoiIiLyIDy0SWZ2hXDXrl1ERkbec/ujR4/i5%2Bdnk/BVr16dxYsXA9CuXTs2bNhA27ZtcXFxwcnJiWnTpuHr62u3vXbt2jFv3jxOnjxJ06ZNqVevHn5%2BfkyZMsW6zcGDB0lJSSEsLIyzZ8/i7%2B/P0KFDadCgAQAff/wx7du3t26/Y8cOGjRoYJNg3j3W4OBgtm7dygsvvGCtfM6cOZNFixZRrFgxFi1aZBPTvn17li5dWuAkMzExkYsXL9qs8y5fHh8vrwK1Y1Wtmu3SiNu3jcdWr267NOrJJ43HVq1quzQqKcl47J3DOKzLB81R/Rt9HYLD/g61Df4ZHPFfAYAqJvZh9syMnRkaQ%2B0UlI%2BP7dKo4GDjsYGBtstHmdGZqjufIdalUXkUYvLlziFt1qUR6enGY01SJdO8hyrJnDhxonW6GMDX15fXX3%2Bdnj173jP25s2beHh42Kzz8PDg1q1bgCVpDQwM5L333iMwMJDNmzczduxY/P39CQgIyNXewIEDCQoKYuPGjUydOpUrV67g4%2BND37596d27NwBFixalbt26DBo0iFKlSrF69Wr69u3Lpk2bqFChArt37%2Bb111%2B3tpmYmIi3t3eez8HHx4fExESbdW%2B99RYDBw5k9erVvPHGG2zatMmapAYHB3P06FEuXbpEmTJl7rmPsq1Zs4Z58%2BbZrIsYMIDIQYPy3YZdM2eaizdr/vzC7R/grtdvoZk9%2B/HuH%2BDdd02FbzLZ/axZJhtgrtkGYORI822Y0auXufihQ82P4eOPTYU73XuTe7dhtpEKFczFm032HaFkSeOxOQoi8mh5qJLM8ePH2xyTWRCenp4kJyfbrEtOTqZYsWKA5RjLBg0aWE%2BUCQsL45///Ceff/45o0aNsttmSEgIISEhAPz%2B%2B%2B9s376d6dOnU6xYMf72t7/liuvbty8bNmwgNjaWtm3bkpycjM9d/8G9vb357bff8nwOZ86coXqOalzRokUBeP3111m7di3ffPONNcnNrsKeP3%2B%2BQElmt27drM/LOrYBA2Dnzny3YaNaNUuCOXgwnDhhrA2zlcz582HgQDh2zHg7ZiuZkyfDmDFw6pTxdsxWMmfPhkGD4Phx4%2B0Udv9mK5nvvgvjxpn6O3RIWmUorlo1S4IZHW38vwLApir3nr3JU8WKlgRz6lQ4c8Z4O0arwT4%2BlgRz5UrI8aW5QFavNh4bGGhJMHv0gIQEw81k7d1nfAxYEsysLFNN4HTurLFANzfL3yIxEdLSjA/gzkmshri4WBLMpCTIyDDeTiFRJdO8hyrJNKNGjRqcOnWK9PR063GWx44do0aNGgCcO3eOp556yibG1dXVOi19t%2BPHj9OpUyfWr19PzZo1AahcuTL9%2BvXjwIED/PLLL4BlKrt169bUqlXLGpuamkqRIkWsZ5tn3fUO89JLLxEZGcmRI0dyVU8PHz7M4cOHGTBgAADdu3end%2B/evPTSSzZtlypVyvp7xp3/tC4FnIrw8fGxSX4BOHfO8mPGiROQj%2BNn7bp501zfYEkwDx40Hp/HoRMFcuqUqQ81/vzT/BiOH4dDh8y3U1j9F%2BALU55OnYIjRwyHH7pirvsTJ0z%2BCdId8CXhzBlzyb6ZaVIcru6ZAAAgAElEQVSwJDdmkty7TvY0LCHBMe0UptRUc/FpaebacMSJpRkZhTrtbZSSTPP%2BY66T2aRJE0qXLs2MGTNISUkhISGBlStX0rVrV8BSlVy1ahWHDh0iMzOTrVu3EhcXR9u2bXO1Va1aNWrXrs0777zDzz//TEpKCsnJycTGxhIXF0erVq0A%2BPXXX3nvvfe4ePEiqampzJs3jxs3btCqVStKly6Np6cnFy5csLbbokULQkNDeeutt/jmm2%2B4desWt27dYseOHQwYMIB27drRokULAOrWrcvcuXM5e/YsqampzJkzh9TUVJsKZHbb5cqVu2/7VURERMSI/5hKpqurKx9%2B%2BCGTJk2iWbNmeHp60qtXL%2Bv0e0REBC4uLkRGRnLt2jWqVKnC/PnzrdedXLhwIZs3b%2BbLL7/EycmJJUuWsGDBAoYPH86FCxdwdnYmKCiIadOm8cwzzwAwZcoUpk6dSseOHUlOTqZOnTr84x//wOvOdF%2BzZs3Yu3cvzZo1s45zypQprFu3jsWLFzPyzjFT1atXJzIy0uZQgWHDhuHi4kK3bt1IS0ujfv36rFixwqaSuXfvXp566imbs85FRETEvEexknnr1i1iYmLYuXMn6enpvPjii4wfP9566GBO48ePZ/369TazuqNGjaJbt24ALFmyhJUrV5KUlESdOnWYOHEi1QpwZuNDk2TuLMDxgEfymAarUqUKy5Yts/uYq6srkZGReZ6pHh4ebnPh9hIlSjBy5EhrImiPl5eXzdnmOYWFhTFt2jSioqJs1nft2tVaYc2Lu7v7Pfv/6quv7tmOiIiIPB5iYmI4f/4827ZtIyMjg%2BjoaKZPn8748ePtbn/w4EFiYmLo3Llzrsc%2B//xzVq5cybJly6hcuTIzZ84kKiqKzZs329yA5q/8x0yXP4xatGhBqVKliI2NdXjbR48e5cyZM0oyRURE7oPMTMf/3E/Jycls3ryZqKgovLy8ePLJJxk2bBgbNmzIdWI0WM7z%2BPXXX3Odr5Lts88%2Bo0ePHtSoUYMiRYowdOhQzp07R1xcXL7HpCTzPnv//feZP38%2BaWbO7rNj6tSpvP/%2B%2B3ZPXBIRERFzHsYk8/bt2/z22295/qSlpVlPWAbLjVtu377NKTtX20hISCA9PZ05c%2Bbw7LPP0rp1axYvXkzmnYEeO3bMpi03NzeqVq1KQgFObn1opsv/U1WpUoXPPvvM4e0uXbrU4W2KiIjI/WP3Zije3rmv%2BJKHAwcO8Oqrr9p9bNCda1173nXZqezrh9%2B0cwWX69ev8/TTT9OrVy8%2B%2BOADfvnlFwYOHIizszP9%2BvWze/3xokWLWq8/nh9KMkVERERyuB/T23ZvhhIRka87G4LlSjp5nZdy%2BPBhZs%2BebXON8Oxp8uLFi%2BfavlmzZjYnJtetW5fXXnuNLVu20K9fPzw8PLid4xrWt2/fzvMkInuUZIqIiIg8AHZvhvIXdwIsCD8/P9zc3Dh27Bj16tUDLNf9zp7mzmnHjh1cunSJ7t27W9elpqZabwJTo0YNjh49ar20YlpaGqdOnbKZQr8XHZMpIiIiksP9OCbTx8eH2rVr2/zkd6r8Xjw8PGjTpg3Tp0/nypUrXLlyhenTp9O%2BfXtr4ni3rKwspkyZwo8//khWVhbx8fF89NFH1ssXhYWFsWrVKhISEkhJSWHGjBmUKVOGRo0a5XtMqmSKiIiI5PAoXidz/PjxTJ06ldDQUNLS0njxxRd5%2B%2B23rY%2B3a9eO0NBQwsPDadWqFaNHj2bChAlcuHCBMmXKEBkZSceOHQHL5RavX7/OwIEDuXLlCnXq1GHRokUFOuFYSaaIiIjIf4DixYsTExNDTEyM3ce//PJLm9%2B7d%2B9uM11%2BNycnJ/r06UOfPn0Mj0dJpoiIiEgOj2Il82GjYzJFRERExOFUyRSL3buNx7q4WJYrV0JGhrE20tON9%2B9652X86afm2slxWYkCKVfOsnz%2BeahRw3g7zz5rPLZECctyzBi4ft14O4Xc/1eZrQ3HliwJzYAf3lpFUpLhZthZy1icu7tluWgRpKYa7z/%2ByhbDsR4eEAgkDJyLnZt85FvwXINTZKVKWZbXr8O1a4b7z8rMMhwL4ARk7d1nrg3n/N06z67gYNi3D6eGDSA%2B3nAzw4cZ2w8VKkB0NMxaW4GzZw13z7SEUOPB/v4waxZMnAjHjxtvZ/Nm47EmqJJpnpJMERERkRyUZJqn6XIRERERcThVMkVERERyUCXTPFUyRURERMThVMkUERERyUGVTPOUZIqIiIjkoCTTPE2Xi4iIiIjDqZIpIiIikoMqmeapkikiIiIiDqdKpoiIiEgOqmSapyRTREREJAclmeZpulxEREREHE6VzEfIO%2B%2B8w%2BbNmwFIT08nLS0NDw8P6%2BNLliyhUaNGhTU8ERGR/xiqZJqnJPMRMmnSJCZNmgTAhg0bmDdvHjt37izkUYmIiIjkpiRTREREJAdVMs1TkvkYSkxM5OLFizbrvIsWxcfb21iDzs62ywfNxcV2aVS5csZjy5SxXRpVooTx2GLFbJcPmoP6L5lV6EPA3d1YnKur7dKou46CKbAiRWyXhlWubCwu%2B/%2BRmf9PD4vgYOOxgYG2S4MqVDAWl/12bvRt3SrN33hsxYq2SyOOHzcea5KSTPOcsrKyTLylS2ExM10%2Bd%2B5c5s2bZ7MuYuBAIqOiHDU8ERER80JD4c65CA9a376Ob3PZMse3%2BTBTJfMx1K1bN0JCQmzWeRctCklJxhp0dobixeHGDeNf/TIyjMWBpYJZsqRl/GbaWbPGeGyZMtC1K6xbB5cuGW%2Bnbl3jscWKQb16cOAA3LxpvJ1C7v%2BHrGdNDaF%2Bfdi/39wuqFbNWJyrq6VydPEipKcb7//aNeOxRYqAnx%2BcPAkpKcbbCfx0grHAcuWgf39YtAjOnzfcf9Z4g/3f4eQEZksoTg0bGA8ODISPP4YePSAhwXAzs17dZyjO2xteeQVWr7a8Ho2KPhVtPLhiRRg2DKZPhzNnjLdTSFTJNE9J5mPIx8cHHx8f25V//mkuQQPL/0ijbZj5RM6WkWGuHRMfiFaXLplrx8/P/Bhu3oTr1823U0j9Jzngjf3mTePfmQBSU831n55uro3kZHP9gyXBNNXO77%2BbG8D58%2BbbKGzx8ebbSEgw1c7ZF811f/EinD1rogFHTFefOVOo095SeJRkioiIiOSgSqZ5SjJFREREclCSaZ7u%2BPOI6tKli66RKSIiIg8tVTJFREREclAl0zwlmSIiIiI5KMk0T9PlIiIiIuJwqmSKiIiI5KBKpnmqZIqIiIiIw/3/9u47LIqr/Rv4d2mKGhUV7L9oDIiGaBBFxY5iBUUEsWCJPgox9sSIJXbFLiIxapSINUYBo8aCPYkiNlBjIUDiYwcCIoLAsjDvH7zswwoo7O7sLPr9XJcX7MzO3LeHZffmnJlz2JNJRERE9Br2ZGqORSYRERHRa1hkao7D5URERESkdezJJCIiInoNezI1x55MIiIiItI6mSAIgtRJkPQ0fRXIZJqdQ5aYoP7BRkZAzZpAcjKgUKh9mhPRtdU%2B9oMPAAcH4OJF4OVLtU8Da2v1jzUxAerWBZ4%2BBeRy9c8jdfwPU2%2Bof7CpKWBlBfz1F5CZqfZpkuq1VOs4IyPAzAx4/lyjlyJevVL/WG39HKpWVe84Q8P8Y9PSgNxc9eObpf6j/sEmJkD9%2BsDjxxo1woyNjdU%2Btn59YOpUwN8/Pw11rVotU%2B9AW1vg%2BnWgVSsgKkr9BO7dU//YChWARo2A%2B/eB7Gz1z9O0qfrHamDAAO2f85dftH9OfcbhciIiIqLXcLhccxwuJyIiIiKtY08mERER0WvYk6k59mQSERERkdaxJ5OIiIjoNezJ1ByLTCIiIqLXsMjUHIfLiYiIiEjr2JNJRERE9Jry2JP56tUrLF68GGfOnIFCoUD37t0xf/58VK5cuchz582bh8OHD6tsy8rKgoODA7Zt24a8vDzY2dlBEATIZP%2Bbr/XChQuoVKlSqfJhTyYRERHRO2Dx4sV4%2BvQpTpw4gfDwcDx9%2BhSrV68u9rmLFi1CVFSU8t%2BGDRtQtWpV%2BPr6AgDi4uKQk5ODy5cvqzyvtAUmwCKTiIiIqIi8PO3/E1NmZiYOHz6MyZMno3r16qhZsya%2B/vprhIaGIvMtK6ClpKTg66%2B/xpw5c2BpaQkAuHXrFpo2bQoTExO1c%2BJwOREREdFrxCgKExMTkZSUpLLN3NwcFhYWpTo%2BKysLCQnFL8OcmZmJnJwcWFlZKbc1adIEWVlZuH//Ppo1a1bieVevXg0bGxv0799fue3WrVvIzs7GoEGD8PjxYzRp0gRfffUVWrVqVapcARaZknN0dERSUhKMjPJ/FIIgwMDAAM2aNcOcOXPQvHlzjBgxAvb29pg0aZLKsZGRkRg5ciRiYmKkSJ2IiIjKYN%2B%2BfQgMDFTZNnHixCKf7yW5ceMGRo4cWey%2BKVOmAIDKcLapqSkAICMjo8RzPnz4EIcOHcL%2B/ftVtlesWBEtWrTAlClTUK1aNezevRtjx47FoUOH0LBhw1LlyyJTDyxcuBBubm7Kx//%2B%2By/mzp2LiRMn4tSpUxJmRkRE9H4SoyfT09MTjo6OKtvMzc1LfXzbtm1L7Fi6c%2BcO1q9fj8zMTOWNPgXD5FWqVCnxnCEhIbC1tS3S01lwbWaBsWPHIjQ0FOfPn4eXl1ep8uU1mXqoVq1a8PT0xOPHj5Gamip1OkRERKQFFhYW%2BOSTT1T%2BlXao/G0aN24MY2NjxMXFKbfFx8fD2NgYjRo1KvG48PBwDBgwoMj2devW4c6dOyrb5HI5KlSoUOqc2JOph54%2BfYpdu3bh008/RY0aNQAAW7ZsQXBwsMrzcnNzpUiPiIjonVfepjAyNTVFnz59sHr1aqxfvx5A/rWWzs7OqFixYrHHPH/%2BHPHx8WjTpk2RfX/99ReuXr0Kf39/VKtWDVu2bEF6ejqcnJxKnROLTD2wcOFCLFu2DAqFAjk5OahTpw6cnJzg7e2tfM748eNLvCazrIq78LhWrdJfeCwKIw1eioaGql/V9MEH6h9bMAVZMVORlYkGN/Epm1CTptSE1uL//2uI1FLwF3YZ/tIujrr/By29FPXidaDu/8HAQPWr2jRpBGNj1a9qql9f/WMLRkDLMBJaPFtb9Y6ztlb9qi5NfpcKfoaa/Cyzs9U/VkPlrcgEgPnz52PFihVwcXFBTk4Ounfvjm%2B//Va5v1%2B/fnBxcYGPjw8A4NGjRwCA2rVrFzmXn58fVqxYgQEDBiAzMxOffvopfvzxR1SvXr3U%2BcgEQRA0/D%2BRBhwdHTFx4kS4ublBLpdjx44d2LRpEzZs2ID27dsDgNZv/NmwYUORC4%2B//HIiJk8u3YXHREREOhETAzRtKknoDh20f84LF7R/Tn3Gnkw9YmJigv/85z948eIFJkyYgL1798Ja079Ci1Hchce1aplDkz83ZDJodnxKsvoHGxoC1asDqamABpcQXIypqfaxlSsDLVsCN24Ab7iJ760aN1b/WCOj/F6TpCRAoVD/PFLHr/vyL/UPrlAB%2BPBD4L//1agH5Lm51dufVAxDQ6BqVSAtTaOXIrKy1D9WWz8HdXvlDQyAKlWA9HTNeoKqvnys/sHGxoCFBZCYCOTkqH0a//3qd2WamwPDhwO7d%2Bf/LNQ1dUfpp4tRYW0N7NkDDBsG3LunfgKhoeofa2IC1KsHPHkCyOXqn0ci5bEnU9%2BwyNRDU6dOxZUrVzB9%2BnSEavILXgILC4siQ%2BOS92droyrKzdXoPC9fap5CRoZm59HG%2B7BCIe37ucbx3zJpcKlkZ2t0Hk1fjhq%2BFPXidVDCJVyllpenWaGtlUbIydHoPI81qHMLJCVpeJ6oKM0SuHdPs3NoY7haLpd02Jukw7vL9ZChoSFWrVqFhIQErFixQup0iIiI3jvlbcUffcSeTImdOXOm2O0NGzbEtWvX3njsm%2BbLIiIiIvW9j0WhtrEnk4iIiIi0jj2ZRERERK9hT6bm2JNJRERERFrHnkwiIiKi17AnU3MsMomIiIhewyJTcxwuJyIiIiKtY08mERER0WvYk6k59mQSERERkdaxJ5OIiIjoNezJ1ByLTCIiIqLXsMjUHItMAgDI0l%2Bqf7CBAVC5MmSvMtT/rUxPVz%2B%2BiQlQsybw6hUgl6t9ml45V9TPQVEVQGc4KH4DctLUP09KffWPNTUF6lqj7ot7QGam%2BueROH4UbNVPIQ%2BwBnAvzwqZGnxAyP9W77hKlQAzM%2BDRo/yXo7oePVL/2OrVgbp1gTt3gNRU9c8zKGSYegc2agQsW4aqy2cD9%2B%2Brn8D69eofa/T/P9oqVcp/f1DTqnsu6ueQ0wSAP6benwrEx6t/nnv31DuuQoX8r6GhQHa2%2BvGtrdU/1tYWuH4dcHMDoqLUP48gqH8sSYpFJhEREdFr2JOpORaZRERERK9hkak53l1ORERERFrHnkwiIiKi17AnU3PsySQiIiIirWNPJhEREdFr2JOpORaZRERERK9hkak5DpcTERERkdaxJ5OIiIjoNezJ1Bx7MomIiIhI69iTSURERPQa9mRqjkUmERER0WtYZGqOw%2BV6Zvfu3WjatCm2b9%2Bust3X1xe%2Bvr5Fnv/o0SM0bdoUjx490lGGRERERG/HIlPP7N69G0OHDsWOHTugUCikToeIiOi9lJen/X/vGxaZeiQiIgLJycnw9fVFXl4eTpw4IXVKRERERGrhNZl6ZOfOnRg8eDAqVqyIYcOGISgoCP369VPuP3LkCE6dOqVyTJ4afxolJiYiKSlJZZt5pUqwsLBQL3EDA9Wv6jAxUf9YY2PVr%2BqqWlX9Y6tUUf2qLlNT9Y%2BtUEH1q65pKb4GLaC1JjBS852xYkXVr%2BqqXl39Yz/4QPWr2ho1Uu%2B4evVUv6pL3R8CABgaqn5VV5Mm6h/boIHqV3Wp%2B2IueE/V5L0VAGxt1T/W2lr1qzqiotQ/VkPvY8%2BjtskEQRCkToKAx48fo3fv3jh58iTq1KmD1NRUdOnSBT/88APs7e2V12MuX75c5bhHjx6he/fuOH36NBqU8s1sw4YNCAwMVNk28csvMWnyZO38Z4iIiLRBJgMkKlNq1tT%2BOZOTtX9OfcaeTD2xZ88eKBQKDBgwQLlNoVAgKCgI9vb2Wo3l6ekJR0dHlW3mlSoBGRnqndDAIL8HLjNT/T/9UlLUOw7I78GsUwd49gzIyVH/PP/8o/6xVaoArVoB168D6enqn0fd3mQgv8ejceP8/0d2tvrnkTj%2BPajf66GtJlD3ZVSxImBpCcTGAllZ6sdPTFT/2A8%2BAOztgcuXgZcv1T9P99Oz1TuwXj1g4kQgMBB48kT9BGbMUP9YQ8P8kYm0NCA3V/3zLFyo/rENGgBffw2sXg1ocmPm1KnqHWdikv%2BzePIEkMvVj%2B/mpv6x1tbAnj3AsGHAvXvqn4fKLRaZeiA7OxsHDhzA0qVL4eDgoNz%2B119/Yfz48YiPj9dqPAsLi6JD4y9faj42oMmVzZq8CRbIydHsPGlpmueQnq7ZeTQe40R%2BdZWZqfl5JIqvjcw1bQJNX45ZWcCrV%2Bofn5qqWXwg/1dao/Pcv69ZAk%2BeaHYObdz4mJur2Xm08d776JFm59H0D0a5XLNzaGO4%2Bt49SYe91cXhcs2xyNQDhw8fhkwmg4uLC4wLXVdYp04dWFlZFZnOiIiIiEjf8e5yPbBnz54iBWYBT09P/PLLL0h%2B3y7kICIikhCnMNIcezL1QGhoaIn7hg8fjuHDh5e4v0GDBoiJiREjLSIiovfW%2B1gUaht7MomIiIhI69iTSURERPQa9mRqjj2ZRERERKR17MkkIiIieg17MjXHIpOIiIjoNSwyNcfhciIiIiLSOhaZRERERK8pz/NkZmZmwtPT841TJALAjRs34OHhAVtbWzg6OmL//v0q%2B8PCwuDk5ITPPvsMbm5uiCrjyk0sMomIiIjeEbGxsRg%2BfDiio6Pf%2BLwXL15g/PjxcHV1xZUrV7B06VL4%2Bfnh5s2bAIDIyEgsXrwYy5cvx5UrV9C/f3988cUXyCzDmr0sMomIiIheUx57MiMiIjBq1CgMHDgQ9erVe%2BNzw8PDUb16dQwfPhxGRkZo3749XFxcsHv3bgDA/v370a9fP9jZ2cHY2BijR4%2BGmZkZjh49Wup8eOMPERER0WvEKAoTExORlJSkss3c3BwWFhalOj4rKwsJCQnF7jM3N4e1tTXOnj2LChUq4Mcff3zjuWJjY2FlZaWy7eOPP8aBAwcAAHFxcRg0aFCR/ffu3StVrgCLTCrwwQdqH5qYmIh9QUHw9PQs9S%2BK1uNv2JAfv2FDtc%2BDJk20k4O6baAhqXPQVnxbDXPYsGGfpG2gjfht20qfAwbtUTv%2Bvg0b4Dl1avn/XTh8WPMcZs%2BW/vexUSP1TyQImudw/LhkrwVNaPBfL9GGDfsQGBiosm3ixImYNGlSqY6/ceMGRo4cWey%2B7777Dj169Ch1LhkZGTA1NVXZVrFiRbx69apU%2B0uDw%2BWksaSkJAQGBhb56%2Bx9ic8c9CO%2BPuQgdXx9yEHq%2BMxBP%2BLrSw76puBmnML/PD09S31827ZtERMTU%2By/shSYAGBqaoqsrCyVbVlZWahcuXKp9pcGezKJiIiIdMDCwkJvenWtrKxw4cIFlW1xcXGwtLQEAFhaWiI2NrbI/s6dO5c6BnsyiYiIiN4zTk5O%2BPfff7F9%2B3bk5OTg0qVLOHz4sPI6THd3dxw%2BfBiXLl1CTk4Otm/fjuTkZDg5OZU6BotMIiIiovdAv379sGnTJgCAmZkZgoKCcPz4cbRt2xZz587F3Llz0a5dOwBA%2B/btMX/%2BfCxYsAD29vb49ddf8cMPP6B69eqljme4YMGCBWL8R%2Bj9UrlyZdjb25fpWo13KT5z0I/4%2BpCD1PH1IQep4zMH/YivLzm8r0aNGoVmzZqpbBs%2BfDhat26tfFy7dm24u7vD29sbI0eOLPJ8a2treHl5wcfHB4MHD0adOnXKlINMEMS4f4qIiIiI3mccLiciIiIirWORSURERERaxyKTiIiIiLSORSYRERERaR2LTCIiIiLSOhaZRERERKR1LDKJiIiISOtYZBIRERGR1rHIJCIiIiKtY5FJRESkpmvXrkmdApHe4rKSRETvgJSUFERGRuLZs2cwMDBAvXr10L59e1SpUkXq1N5prVq1wvXr16VOQ1JPnjx563Pq1aung0xI3xhJnQCVT8%2BfP8fBgwcRERGBp0%2BfwtDQEHXr1kWnTp3Qt29fVK9eXeoUdeL27duIiIhQ%2BWDv1KkTmjRp8l7EB6QvbhQKBc6cOVPsa7FDhw4wMnq33%2Bb%2B/vtvBAQEIDw8HObm5qhTpw4UCgUSExORmpqKnj17YvLkyfjwww91kk9KSorKz6FatWo6iStVfPbTAI6OjpDJZMXuEwQBMpkMd%2B/e1XFWpA/Yk0llkpubi%2B%2B%2B%2Bw7BwcGwsbGBra0t6tSpg9zcXCQmJuLatWuIjY3FiBEj4OPjI%2BoHvJTFxaVLl7BmzRrExcWhWbNmKh/sd%2B7cQfPmzfHVV1%2BhTZs272R8QD%2BKm5CQEGzYsAHGxsb47LPPVHKIiooCAEyePBmurq6i5QBIV%2Bxv374dP//8MwYOHAhnZ2fUrVtXZf/Dhw9x9OhRhISEYMiQIRgzZowoeeTk5ODAgQPYs2cP4uLilIWXTCaDjY0N3N3d4e7uDkNDw3cuPnsygcePHwPILygHDBiAQ4cOFXlO/fr1dZ0W6QEWmVQmw4cPh729PYYOHQoLC4tin/Ps2TPs3LkT169fx969e0XJQ8riYsmSJbh79y6GDx%2BO7t27o0KFCir75XI5wsPDsXPnTtjY2ODbb799p%2BID%2BlHcfPnll6hcuTK8vLzQokWLYp8TFRWF4OBgZGdn4/vvv9d6DlIX%2B5s3b8aYMWNgbGz8xufJ5XJs27YNX3zxhdZzuHnzJnx9fdGgQQP07dsXrVq1Uv7hmZCQgGvXruHo0aN4/PgxVq5cWeLPqrzGb9as2VuHgk%2BfPq3VmK%2BztrYusSexgK56Eu3t7XH58mWdxCL9xyKTyuThw4do2LCh1p9bFlIXF0eOHIGzs3Opnnvo0CH079//nYoP6Edxc/36dbRq1apUz7169Spat26t1fj6UOw/e/YMderU0fp5y8Lb2xszZ87ERx999MbnxcbGYuXKlfjhhx/eqfgtWrTAwoUL3/icgQMHajXm6wqKOkEQ4OPjg82bNxd5jr29vag5FI7DIpMKsMikMtmyZQvGjx8vaQ5SFxfXrl2DnZ2dVs9ZnuLri7IU21LHF6vY51Ct9PTtZyB1kSd1fNIvnMKIymTTpk1Sp1DqAhOA1gtMABg3bpzWz1me4hd4%2BvQpzp07p3ycl5eH%2BfPn49GjRzqJP2/ePJ3EKUlBgZmXl1fs/tTUVOX3YhSYgP7cdPLs2TOcOnVKeW1eYUeOHNFpLo8ePUJwcDAOHjyIly9fih5PX34GRPro3b7tkrROX95QMzMzsXHjRhw/fhyJiYkwNzdHr169MGHCBFSuXFnU2FK3gdTxgfxLITw9PdG1a1d07doVAPDixQvcunULQ4YMwd69e0W5VKIwfWgHABg5ciTWrVsHc3Nz5baIiAjMnDkTv/32m6ix33Ydni5ERkbCx8cHJiYmSE9Px6RJk%2BDj46PcP2/ePFF7nO/du4cJEyagZs2amDVrFsaOHYt69epBLpfD398fwcHBot58JtYfEOXJrFmzlN%2B/evVK5XEBPz8/XaZEeoJFJpXZ06dP3/gBL/Z8aNnZ2Rg6dCgyMjLg4uICCwsLPHr0CEePHsXvv/%2BOn3/%2BGRUrVhQtvtQf7FLHB4ANGzagb9%2B%2BmDt3rnKbmZkZQkNDMWvWLAQGBmLFihWi5qAP7QAANWvWxIABA7B69Wq0bdsW69atQ3BwMLy9vUWPnZWVhZEjR77xOTt27BA1h7Vr12L27Nnw8PBAREQEpk6dClNTU4waNXJIf8MAACAASURBVAqA%2BH8MrFixAs7OzkhLS4O3tzemTJmC0aNHAwACAgLg5%2Bcn6gjM267HfN%2B4uLgU2ZaSkiJBJqQPeE0mlcmb7mLU1Xxo3333HSIiIrB161aVYjIjIwPjxo2Dg4MDJk6cKFp8qe8mlTo%2BAHTu3BmHDx8udg7ChIQEDB48GOfPnxc1B326o3bfvn1YuXIlatWqBRMTEyxfvhyffPKJ6HFtbGxUeg2LI%2BbvAgC0adMGly9fVv4soqOj8fnnn%2BP7779Hu3btRL9msXXr1oiMjER6ejratWuHmzdvKm9Ik8vl6NKlCyIiIkSLrw8CAwOV35d03bzYr4Pi/PPPP/jxxx9x6NAhREdH6zw%2BSY89mVQmpqamOr/G6nXHjx/HihUrivRWVq5cGb6%2Bvpg9e7aob6jGxsaSvGHrS3wgv6AvaZLr2rVr6%2BRauAoVKmj9TmF1Va1aFSYmJnjx4gUaN26MqlWr6iSuiYmJ5K%2BFSpUqITExEbVr1wYAfPbZZ5g9ezamT5%2BOkJAQ0eMbGRkhMzMT1apVw/jx41V6Tv/99993fjJ%2BIP%2BShQItW7ZUeQzovtf/6tWr2LZtG86fPw9LS0vMmDFDp/FJf7z7v32kVTKZTPJJdZ88eYLmzZsXu6958%2BalWuJME0ZGRqJPSaLP8QHA3NwcDx48wP/93/8V2ffgwQOdrPhkaGios2lZ3mTatGk4d%2B4c5syZAxcXFyxbtgwDBgzAjBkzMHToUFFj68NAVM%2BePTFx4kRMnjwZnTp1AgB4eHjg1q1bGD58OHJyckSN37FjR3zzzTfw9/fHtGnTlNvDw8OxYcOGYodv3zU7d%2B6EIAh48eJFkd%2B97OxsrFy5UvQc8vLycPz4cfz444%2BIjY2FQqHA5s2bla8Jej/x7nIqE334UDM0NFS5c7ewly9fFpmvUNukbgOp4wP5hcWaNWuK5CIIAtatW4fOnTuLnoM%2BtAOQPyQYEhICd3d3VKhQAQsXLsTKlSsREBAgemwxZk8oqxkzZuCzzz7DqVOnVLYvWLAAjo6OyM3NFTX%2BvHnzYGRkVGQ1n6CgIDg4OKgUnu%2Bqe/fuoUePHmjfvj08PT3x4sULAEBMTAwGDRpU7Ao82hQcHAwnJyesWrUKTk5OOHfuHKpUqQIrKytR45L%2B4zWZVCaHDx%2BWvGdg3Lhx6Nixo/LGgsJ27NiBixcvinqh//z58yW92F/q%2BACQnp4ONzc3VKxYEX369EGtWrWQlJSE8PBwvHjxAgcOHEDNmjVFzWHz5s06ubnmbeRyOUxMTIpsT0xMLHFVrPfJ8%2BfPYWZmJnUa7zQvLy988MEH8PT0xM6dO2FlZYUuXbpgwoQJaNq0KVatWoUGDRqIFt/a2hrDhg2Dr6%2Bv8nehXbt2%2BOWXX5SXUdD7iUUmqSU9PR1VqlTBoUOHlPME1qhRQyc9WBEREfjyyy%2BxaNEi9O7dG0ZGRpDL5QgLC8PKlSuxefNmnfbwFL7bvmLFiqhRo4bOYksZ//nz5wgICMDZs2eRkpICc3NzdOvWDRMmTNBpGxTccHb16lVlO1SrVk0nvSiCIGDfvn1FptLy9PQUbZ3u1%2B3btw/GxsZwc3NDixYtlMPT5ubmOHbsmOhTehX4/fffi7RDly5ddBJbH%2BJLyc7ODidPnkSNGjXw7NkzeHl5IS0tDUOGDMHUqVNhYCDuoOXu3buxZ88epKSkYPDgwRg2bBhcXV1x8OBBFpnvORaZVCbp6ekYN24cWrVqhRkzZqBly5bKHqvExESEhobq5MN97969WL58OYD8giI5ORnGxsb49ttvMWjQINHjr127FtWrV8eYMWOUH%2ByCIKBatWo4duyY6EWW1PH1QU5ODnx9fdGwYUNMnToVLVq0gFwuB5B/U9Cvv/4qau9NXl4exo0bh5s3b8LR0VE5ldbvv/%2BOTz/9FNu2bRP9w/3o0aNYvHgx/Pz80LVrV9jZ2eH777%2BHIAhYsmQJXF1dMXbsWFFzAIA5c%2Bbg4MGDsLW1VbbDnTt34OLiopP5EaWOLzVbW1tERUUpH9vY2GD69OkYM2aMTvOIiIjArl278PvvvyM3NxdLly6Fi4uLzv7gIj0kEJXBqlWrhHHjxgmpqamCIAhC69atlft8fX2FuXPn6iyXpKQkISQkRNi0aZMQEhIiJCcn6yTunj17BCcnJyEqKkoQBEGws7MTHj16JDx8%2BFAYOnSosG7dunc6viAIwuPHj9/6T2ybNm0S3N3dhfv37wuCoPpa9Pb2FpYuXSpq/ODgYMHZ2Vn4999/VbY/e/ZMcHZ2FoKDg0WNLwiC4OXlJZw5c0b5uE2bNsrvT548KQwaNEj0HMLCwoQuXboIcXFxKttv374tdO3aVQgLC3un4%2BsDW1tblcefffaZIJfLJcpGEB49eiSsXLlSaNu2rdC%2BfXvBz89PslxIWiwyqUx69%2B6t8mZe%2BEMtJiZG6N69u85yUSgUKo%2BfPXumk7geHh7C5cuXlY8Lt8HFixcFFxeXdzq%2BIAhC06ZNBWtra%2BW/pk2bKrcVfBVb//79hT///FP5uHA7REdHC7179xY1/sCBA4UrV64Uuy8iIkIYOHCgqPEFIb%2BwzsjIUHlcIDs7W2jVqpXoOQwdOlQ4ffp0sftOnjwpDBky5J2Orw9eLzIL/y5IKTs7W9i/f79OfhdIP3EKIyqTZ8%2BeoXHjxsrHNjY2yu%2BtrKyQnJyskzxWr16N5ORk5VBYSkoKHB0dMXr0aNHnZIuPj0eLFi2Uj4VCV5zY29uLvna31PGB/032LggCBgwYIPrdq8V5%2BPAhmjZtqnxc%2BBKBFi1a4NmzZ6LHt7OzK3Zfq1at8ODBA1HjA0Bubq7KTUcHDhxQfm9sbKyT%2BRHj4%2BNLvPaxQ4cOmD179jsdXx8oFAocPHhQ%2BTgnJ0flMQC4urrqOi2YmJjA3d0d7u7uOo9N%2BoFFJpVJwfrEBZNNBwUFKfdlZWWJupxjgX379uHQoUPKazKB/CUNAwIC8O2336JRo0bw8PAQNYfCH94XLlxQ2a6LyZ%2Bljl94rlRDQ0NJ5k41NDSEXC5X/n%2BPHz%2Bu3KdQKIq941ub8vLykJGRgSpVqhTZJ5fLdXIdmoWFBeLj45XFduE1umNiYlC3bl3Rc5DL5cjJySnx/yuIfNm/1PH1Qa1atVSmzCp4Pywgk8kkKTKJWGRSmVhZWeHSpUvo2bNnkX3nz58vcZJ0bfrpp5%2BwZs0atGnTRrlNJpOhe/fuMDQ0REBAgKhFZv369XHnzh189tlnAKBSzNy4cQMNGzYULbY%2BxNcXjRo1QnR0NBwcHIrsi4yMRJMmTUSN36xZM5w6darYD%2B9Tp07B2tpa1PgA0K1bN/j7%2B2Pjxo1Fei0DAwPh5OQkeg5NmjTBxYsX4ejoWGRfRESE6D8HqePrgzNnzkidAlGxOBk7lcmgQYPg5%2BeHuLg4le1///03VqxYgcGDB4uew6NHj1QKzMI6duwo%2BjBlr169sGLFCmRnZ6tsz87Oxpo1a9CvX793Or6%2BKFhd599//1XZnpKSghUrVoi%2BKpKXlxdWrlypclcvAPzxxx9YsWIFRo4cKWp8APDx8cHdu3cxdOhQhIWF4eLFiwgLC4OXlxfu3Lmjk7uLPTw8sGzZMjx%2B/Fhle2xsLJYuXSr6e4LU8YmoZJzCiMpszpw5%2BOWXX2Bra4vatWsjISEBUVFRcHNzw6JFi0SP3759e5w/f77Y4VC5XI5OnToVWbtXm7KysjB48GBkZmaif//%2BqFOnDhISEnD48GFUrlxZOW/huxr/dfb29rh8%2BbLO4hUomELo1q1bcHR0RO3atZGYmIgzZ86gZcuW2LJli%2Bg5rFmzBlu3bkWDBg1Qq1YtPHnyBElJSZgwYYLO1hRPSkrC8uXLcfLkSeXlA927d8ecOXN0Nhn8V199hZMnT6JVq1bKdrh165byD4F3PT4RFY9FJqnlwoULOHbsGJ49ewZzc3P07t1bZxMfjx8/Hq6urujbt2%2BRfb/%2B%2BqtyYmAxZWZmYtOmTSpt0KtXL0ycOBGVKlUSNbY%2BxJ81a5by%2B5JWgdLF/IR5eXk4cOBAkXYYMmSIzubmu3v3Lk6dOoWkpCSYm5ujZ8%2BeKjck6UpeXh5SUlJgZmYmybyEp06dwunTp5WToffu3Rtdu3Z9b%2BITUVEsMqnc%2BeOPP/DVV19h0aJF6NGjBwwNDaFQKHD69GnMnz8f8%2BbNK7YAJe0pXGSW5H2YBFtqR48eLfVr/ciRI3B2dhY5I93Lzc0tdVFdlucSkeZYZFKZ%2BPj4YObMmSrTGBUnPj5eucSjGLZv347Vq1fDyMgI1apVQ2pqKvLy8jBp0iSMHz9elJgFtmzZgjFjxrz1Lu6cnBwEBQVpfX1tqePri/nz52P69OmoVq3aG5%2BXmpqKtWvXav1SjrcV2jKZTPSh2iVLluDu3bsYMWIEHB0di1xCIpfLcfr0aQQHB6N58%2BaYN2%2Be1nMIDAx8436ZTIYvv/xS63ELDBkyBFOmTEH79u3f%2BLzffvsNGzduxE8//SRaLkSkineXU5l88cUXmDBhAho2bAhnZ2fldZmCICAhIQHXrl3D0aNH8eDBA5UphrRt9OjR6Nu3L3777TekpKTAwsICnTp1Ui5xKSZjY2P0798fbm5ucHFxKbI27%2BPHj3Hs2DHs379flJsOpI4PAFeuXHnjfplMJvr68R06dMCgQYPQqVMnODs7o0WLFsprUeVyOaKjo3H06FGcO3cOvr6%2BouZSWGpqKs6ePYsqVaqIXmTOnTsXERERWLt2LWbPno3mzZujdu3ayMvLQ0JCAm7fvg1LS0tMnz692LvwtaGk659zcnIQHR0NIyMjUYvMVatWYdasWViyZInKe1JeXh4SExNx7do1HD9%2BHNWqVcPKlStFy4OIimJPJpWZXC7H/v37sXfvXsTFxalMndKsWTMMGjQIgwcPFu3mk/T09GLnJizOy5cv8cEHH2g9h/j4eAQEBODkyZOoXbu2ygd7cnIyunfvjsmTJ4s2fYrU8a2trZU/9%2BLeQmQyGe7evStK7MJSUlKwdetW7N%2B/H5mZmahevToEQUBqaiqqV6%2BOAQMGYNy4cTAzMxM9FwCIjo5W9q76%2B/urzFsptps3byIyMhJPnz6FgYEB6tWrhw4dOkhyfejDhw8xdepUJCcnY82aNSVOWq9N586dw969e3H58mVkZmYCAExNTdGxY0cMGjSI12cSSYBFJmkkOTkZT548gYGBAerWrauy6opY3N3d4enpCVdX1xILWblcjpCQEOzfvx%2BhoaGi5ZKcnFzkg71du3ZvHcIt7/F9fHwQFRWF3r17w93dHZ9%2B%2Bqmo8d4mNzcXt2/fVr4W69Wrh%2BbNm8PAQHeztAUFBWHdunXw8PCAr6%2Bv6JPB66sTJ05g7ty5sLOzw/Lly1G9enWdxhcEAc%2BfP4eBgYHOYxORKhaZpLZnz54p7%2BTUxcoiBV6%2BfInFixfj/PnzcHJyUhkeS0hIwPXr13Hu3Dl06tQJs2fP5geNSJKTkxEWFobQ0FAYGhrCw8MDAwYM0FmBXSAxMbHYqXquXr0q%2BpA9ALx48QIzZ87EtWvXsHjxYvTu3Vv0mIVt374dV65cgY2NDUaNGqUyu8D48eN1MpUTkD887ufnh59//hnTpk3D2LFjdRL39ZXGHj9%2BjHPnzsHU1BROTk6ijGQQUemwyKQyi42Nxdy5c3Hz5k0IggCZTAYbGxssWbJEp0Nzf/31F3766SdcunQJT58%2BhUwmQ/369dGhQwcMHDhQ1FzOnj2LuLg4jBs3DkD%2BB52bmxu%2B/vrrYlce0aZXr15h7dq1GDlyJOrUqYOVK1fi2LFjynXEp0%2BfrtN5MgHg2rVrCA0NxalTp9C%2BfXu4u7ujY8eOOont4OCAVatWoUOHDgDye7I2bNiALVu24M8//xQ1dnR0NKZNm4YaNWrA399f56stBQYGIiwsDE5OTjh37hwqVaqE4OBgZWHVqlUrXL9%2BXfQ8Hj58iClTpihvsipYjUoXCv8fo6KiMHbsWNSrVw9yuRwZGRn48ccfYWVlpbN8iKgQgagMnjx5IrRp00aYPn26EBERIcTHxwvnzp0TJk6cKNjZ2QmPHz%2BWOkXRRURECC1bthS2bdum3Jaeni4sW7ZM%2BPTTT4XIyEhR48%2BcOVMYOnSokJSUJPj5%2BQmurq7CsWPHhCNHjgguLi7C8uXLRY3/Jnfv3hWcnZ0Fa2trncXctWuX0LJlS8Hf31948OCBMGTIEKFbt27CpUuXRI37ww8/CJ988omwZMkSIScnR9RYJXF0dBTi4uIEQRCEzMxMYcyYMcLnn38u5ObmCoIgCLa2tqLncPToUcHOzk6YMGGCkJaWJnq813322WfK7728vFR%2BLzds2CCMGjVK5zkRUT72ZFKZzJs3DzKZDAsXLiyyb%2B7cuTAwMNDJqj9S%2Bvzzz9G3b99i10ffvn07fv/9d2zbtk20%2BO3atcOxY8dgZmYGR0dH7NixAw0aNACQv%2BTm4MGDcfHiRdHivy49PR3Hjh1DWFgY/vzzT3Tt2hVubm46vdEiJiYGPj4%2BSExMRI8ePbB06dJS3xymrsJrk7%2B%2BbngBsW9%2BsrOzw7Vr15SPMzIyMGTIEHTu3BkzZsyAra1tkWUvta2gHerVq1diO5w%2BfVq0%2BIV7Ml9fDUwul6Ndu3Y66c0loqI4hRGVyR9//IHdu3cXu8/b21sn6zVL7d69eyVe5%2Bbh4YFNmzaJGj83NxeVK1cGkD80XPh6RAsLi2Lv9hbDhQsXEBoaitOnT6Nx48Zwc3PDxo0bdX4NbGZmJvbt24fU1FR06NABly5dwm%2B//Sb6hPw7duwQ9fyl0bBhQ5w/f1652lblypWxfv16DB48GE2aNCmx6NOmZcuW6SROSQRBUE6yXr9%2Bfbx48QLm5uYAgLS0NJiamkqWG9H7jkUmlcnz589LvMmnQYMGSE1NFT0HqVebUSgUJa4aUqlSJeTm5ooWG8hfK3zlypWYM2cO%2Bvfvj6CgIPj4%2BADInzNQF3d6d%2B3aFXK5HC4uLti3b58k0%2BQU6N%2B/PypWrIiff/4ZlpaWOHr0KBYsWICTJ09i3bp1osVt3ry55FNpffHFF5gyZQqGDh2KmTNnAgA%2B%2BugjrFmzBhMnThT9tQgAAwYMkHzFHVtbWzRt2hTZ2dlYv369cpL6ZcuW6Wy5WyIqSnfze9A7oUqVKnj8%2BHGx%2B548eaKTO4srV66MsLAwvHr1SvRYxWnUqBFu3LhR7L7o6GjUqVNH1Phz5szBH3/8ge7duyMuLg6BgYHo1q0bOnbsiCNHjmD27NmixgfyZxZISUlBcHAwXF1d0axZsyL/dMXBwQEHDhyApaUlAKBv374ICwtDQkKCqHFHjx6N/fv3Iycnp8TnyOVy7N27F6NGjRIlh169emH37t2wtbVV2d6lSxfs3LlTJwXW8OHDERER8dbn/fbbbxg%2BfLjW41%2B/fh0hISEYOnQoWrdurSzmjx8/jg8%2B%2BECnE/ETkSpek0ll4uvri4oVK2LBggVF9i1YsAAKhQJLliwRPY%2Bvv/4a1atXx9y5c0WP9bo9e/bgp59%2Bwg8//KCy2k5CQgK8vb3Ru3dvZc%2BiWHJycnDmzBncunULL168gImJCZo0aYI%2BffroZOLxy5cvv/U59vb2oudRIC8vD8%2BfP4eZmZlybsy8vDxR58nUt6m0imsDXXj48CFmzZqF58%2Bfv3XFHT8/P/zf//2fznIjImmxyKQy%2Be9//ws3Nzf069cP/fv3h7m5OZ48eYIDBw7g999/R1hYGOrXry96Hs%2BfP0efPn1w9OhRnUwAX5ggCJg4cSL%2B%2BOMPtGrVCrVq1UJSUhKioqLQtm1bbNy48a3ripN2JCUlwc/PDydPnoRCoYCRkRG6d%2B%2BOWbNmFVluUyxSTqUF6EcbANKuuJOeno6kpCQ0btwYABASEoK7d%2B/CyckJbdu2FS0uEb0Zi0wqs1u3bmHevHm4e/cuZDIZBEFQzpNZ%2BI5bsb169QoVKlQQ5Rqv0jh27BjOnj2LlJQUmJubw9HREU5OTqLHHTFixFtvtBD7ppS3XRcrk8lEX7f7xYsXcHV1RZ06deDu7g4LCws8fPhQOVR%2B6NChd34ifn1sA0HHK%2B7Ex8djxIgR6NatG5YuXYrt27dj7dq16Nq1KyIjI7FmzRqdzdlKRKpYZFKZCYKABw8ewMjISLniz82bN9GrVy/JCr73SWBgoPL7LVu2YPz48UWeM3HiRFFzKKnITE1NxdmzZ1GlShVcvXpV1BxWrFiBR48eISAgQKXozsvLw8SJE/Hhhx8qb4bRhUePHuH06dOoXr06HB0ddbLSjL61QWG//vor%2BvXrJ3qcyZMno06dOpg5cyYMDQ3RuXNnjB49GmPGjMH58%2BexdetW7Ny5U/Q8iKgoFplUJq9evcKYMWNQq1YtZbGTnJyMbt26wcbGBlu3blVZ1k4sUg6PHTx48K3PcXV1FTWHAm3atMGVK1d0EuttoqOjMX36dFSrVg3%2B/v748MMPRY3Xq1cvbNq0SfkaKCwuLg4TJkxAeHi4aPHv3buHCRMmoGbNmpg1a5bKSjM5OTkIDg5%2B59vgTezt7Ut17a6mHBwcEB4ejipVquD%2B/fvo06cPwsPD0bBhQ2RlZaFjx46i/8FDRMXjhWNUJt9//z2MjY1VJmOvWbMmzp49iy%2B%2B%2BAKbN2/GtGnTRM3hTcNjkydPFn14LCAgQPn9s2fPitxNLpPJdFZkSjk/YWFBQUFYt24dPDw84Ovrq5wMW0yF/8h4XZMmTZCUlCRq/BUrVsDZ2RlpaWnw9vbGlClTMHr0aAD5rxE/Pz/R50yVug2A/MnYi3sdCoKgnGVAzEnps7KylFNJ3bhxAzVq1FAu72lgYKCTaZyIqAS6XWCIyjsnJyfh/v37xe67c%2BeO0LNnT9FzmDRpkrB06VJBoVAIgiAInTp1Ui4ld%2B7cOcHLy0v0HAq0bt1aZ7GK06ZNG0njp6amCt7e3kLr1q2FY8eO6TR2u3bthMTExGL3JSQkCB06dBA1vp2dnaBQKITU1FTB2tpakMvlyn3Z2dlCu3btRI0vCNK3gSAIws8//yzY2toK/v7%2BQmRkpBAZGSlcunRJsLW1VT4WU48ePYQnT54IgiAIvr6%2BwpQpU5T7bty4oZP3JCIqHufJpDJJTk4ucQiwWbNmOuk5uXr1KiZPngxDQ0Pcv38fSUlJyhtu2rZtK/pSfoXpS0%2BiFKKjo%2BHq6oqkpCSEhoaid%2B/eOo3fpk0b7Nmzp9h9e/fuFX0KJSMjI2RmZqJatWoYP368ykpL//77r05mGJC6DYD8Va727duHU6dO4cKFC2jTpg3atm0LIyMj2Nvbi55D79698c0332DLli349ddflaMIcXFxWL58OXr06CFqfCIqGYtMKpMqVarg%2BfPnxe5LTU3VyRJu7/vw2JUrV5T/FAoFrl69qrJNF9dobt26FV5eXujRowf27dunbH9d8vb2xo8//ojNmzfj2bNnUCgUePDgAdauXYvt27fD29tb1PgdO3bEN998A7lcjmnTpikvEQgPD4e3tzdcXFxEjQ9I3wYFLC0tsX//fiQmJmLo0KElLtgghkmTJqFRo0b45Zdf4OPjo5wqyc3NDUD%2BqkhEJA3e%2BENl4uvriwYNGhR79/LGjRtx%2B/ZtfPfdd6Lm4OTkhB07dqBu3bqYNWsWMjMz4e/vDwC4efMmZsyYgRMnToiaQwFd3dxQ2NumiZLJZKL35hbOoaTeXF30KJ89exbffvstkpOTldtq1aoFPz8/0aetSUtLw9y5c7Fu3TqVWRWGDBmCli1b4uuvv4axsbGoOQDStkFxfvnlF6xduxZpaWmIiorSefwC8fHxaNKkiWTxiYhFJpXRP//8Azc3N7i5uaFv374wNzdHYmIijh07hpCQEOzatQs2Njai5rBmzRpER0ejU6dOCAwMREBAALp27Yq4uDjMmzcPtra2mDFjhqg5FJCiyNQH%2BrTij1wuR1RUFJKSkmBubg47O7v3bjJ8fWuDf/75BydOnBB95Ssi0m8sMqnMrl%2B/jvnz5yM2NlY5GbuVlRW%2B/fZbtGnTRvT4crkcixcvxvXr19GvXz9MmDABANCiRQvY2Nhgy5YtyuF0MTg6Oip77548eYJ69eoVec7p06dFi/86QRDw999/A4DOem7S09NL3cYvX77UyZyRUuBKM/n0uR1cXFxw%2BPBhSXMgel%2BxyCS1PXz4ULnaTXGFlq7pangsLCzsrc8ZOHCgaPH//vtvTJkyBStXrkT16tXh4%2BODmJgYAICNjQ2%2B%2B%2B470ZcTdHd3h6enJ1xdXUscEpbL5QgJCcH%2B/fsRGhqq9RwKF/vFkclkOHXqlNbjFoiPj4eXlxccHR0lW2lG6jYA9H/Fnc2bN%2Bvs2lQiUsUik6ic%2Bfzzz/HRRx/h66%2B/xjfffANTU1P4%2BvoiNzcXy5cvR1ZWlujXxb58%2BRKLFy/G%2BfPn4eTkBFtbW9SuXRt5eXlISEjA9evXce7cOXTq1AmzZ88WZXnBkor96Oho7Nu3D82bNxeluC2gDyvNSN0GgH60AxHpJxaZ9M4Re3is8LKOJRFzWcc2bdrg4sWLMDY2RocOHXDixAnl0HVGRga6dOmisxVO/vrrL/z000%2B4dOkSnj59CplMhvr166NDhw4YOHAgmjZtqpM8CgQFBWHt2rXw8PDArFmzRJ0UXl9XmtFlGwD60Q5PnjyBoaEhateujdjYWISGhsLExAR9%2BvR5641yRCSe9%2BvqeHovODs7i3r%2BwMBAfPDBB2jWrBmK%2BxtN7LkzK1SogLS0NNSsWRNmZmZQKBTKfXK5HJUrVxY1fmFWVlaYN2%2BezuKVJC0tDTNnzsTVq1exatUq9OnTR/SY%2BjaVlhRtAEjfDuHh4Zg6dSpMTEzg5%2BeH2bNnw9bWFkZGRggODsbGjRvh4OAgag5EVDwWmfTOEfv6q5kzZyI0NBSJiYnw8PCAq6sratasKWrMwnr37o3Jkydj%2BfLlGDduAJ0DCQAADedJREFUHObOnYuZM2dCLpdj3rx56Natm07yePbsGf788080b968yDW5R44cEb3YLxAdHY1p06bBzMwMoaGhOpuzs2bNmnj69Cnq1q2LS5cuqdz0du/ePVhYWOgkD0C6NgCkb4fvvvsO/v7%2BMDAwwNSpU7Fo0SLlHJnHjx/H2rVrWWQSSYTD5VQu6cPw2M2bNxESEoLw8HC0atUKHh4e6Ny5MwwMxF3jQC6XY/78%2BTh06BCqVq2KtLQ05OXlAQBat26N77//XtS76wEgMjISPj4%2BMDExQXp6OiZNmqQyXU2rVq1w/fp1UXMA8ieFX79%2BPTw9PfHNN9/oZM30AvoylZaUbQBI3w6tW7dWDsd/8sknuHHjhnL6JkEQYG9vr5MFCoioKBaZVO68aXjs8uXLOh8ey8rKwvHjxxEWFob79%2B9jwIABmD59uqgx8/Ly8OeffyI1NRUvXryAiYkJ7t%2B/j//85z8qE4OLxdPTE%2B7u7vDw8EBERASmTp2KCRMmYNSoUQAAW1tb0Sfi9vHxwfnz5%2BHl5YWePXsW%2Bxwxp9SSeiotQPo2AKRvh%2B7duyMoKAgKhQL9%2BvVDSEgIPvnkEwDAnTt3MGnSJJ1OKUZE/8Mik8qdAQMG4MsvvyxxeGzr1q04cOCATnPKyMjA0aNHERwcjAcPHuDmzZuixXr16hXGjBmDWrVqKW9CSk5ORrdu3WBjY4OtW7eiUqVKosUH8guXy5cvK68/jY6Oxueff47vv/8e7dq100lPpj6sfFQcXa40o69tAOiuHYKDg7FlyxYAQI0aNdC8eXN8/PHHkMvl2L17N4YNGybqjXhEVDIWmVTu6NPw2MWLFxESEoIzZ86gcePGcHNzg7OzsyhT9hQoGJ709/dXuRY0OTkZX3zxBdq3b49p06aJFh8AunTpgp9//lllPs79%2B/dj3bp1CAkJQb9%2B/XQyXE4EAOfPn8c///yDfv36IScnB4sWLcLTp0/h6OiISZMmiX4JCxEVjzf%2BULlTrVo1/Pe//4VCoUBubi5iYmKUw2N3795F1apVRY1///59hIWF4ZdffkFOTg6cnZ3x008/6Wy6nhMnTuCHH34ocrNRzZo1sXDhQkydOlX0IrNnz56YOHEiJk%2BejE6dOgEAPDw8cOvWLQwfPhw5OTmixi8PuNJMPl20Q5cuXdClSxfl402bNokaj4hKh0UmlTsjR47EsGHDAACWlpbYsWNHkeExMfXp0wdmZmZwcXFB165dYWRkhLS0NJXeUzGvg0tOTsaHH35Y7L5mzZohKSlJtNgFZsyYgVWrVuHUqVPKIhMAFixYgGXLlmHPnj2i56DvdHV3vb7TRTucOHECu3btQkxMDF69eoXKlSvD0tIS7u7ucHV1FT0%2BERWPw%2BVULkk5PCb1dXBdunTBwYMHYWZmVmRfamoq%2BvXrhwsXLogWvzSeP39ebH5E2rZ582bs3r0bI0aMwMcff4yKFSsiKysLsbGx2LVrF0aNGoWxY8dKnSbRe4lFJlE54%2BvriwYNGhR7M8PGjRtx%2B/Zt0ZeVBID09HQkJSWhcePGAICQkBDcvXsXTk5OaNu2rejx9YE%2BTKWlD6Rsh86dO2Pz5s1o1qxZkX337t2Dj48Pzp07J2oORFQ8Xg1N5dKJEycwYsQI2Nvbw8bGBm3btoWXlxcOHjwodWqi8/b2xrZt27B48WJcu3YNDx48wNWrV7F48WJs2bIFX3zxheg5xMfHw8nJCVu3bgUAbN%2B%2BHQsXLkRiYiImT56MP/74Q/QcpBYeHo4ePXqgV69eOHbsGAYPHoyYmBjcvXsXQ4YMwcWLF6VOUSekbof09HRYWloWu%2B%2Bjjz5CRkaGqPGJqGTsyaRyR%2BrhMUdHxzcuHSmTyXDq1CnR4gPA9evXMX/%2BfMTGxkImk0EQBFhZWeHbb78VfV5EAJg8eTLq1KmDmTNnwtDQEJ07d8bo0aMxZswYnD9/Hlu3bsXOnTtFz0NK%2BjiVlhSkbofRo0ejZcuWmDRpknKWCSB/LtnVq1cjJiYG27ZtEy0%2BEZWMRSaVO1IPj4WFhRW7PTo6Gvv27UPz5s0RGhoqWvzCHj58iJSUFJibmxdZ2lFMDg4OCA8PR5UqVXD//n306dMH4eHhaNiwIbKystCxY0flNFPvKn2aSktKUrfDP//8Ax8fH/z7779o1KgRKlWqhMzMTNy/fx9mZmYICgrS6TKbRPQ/vLucyh2ph8cGDhxYZFtQUBBCQkIwdOhQzJo1S9T4hTVs2FCSD9CsrCzlKi43btxAjRo1lHkYGBggNzdX5znpmtRTaekLqduhcePG%2BPXXX3H58mXExcUhIyMDpqamsLKyQtu2bXWyAhYRFY9FJpU7LVq0wIYNG4odHvP390eLFi10lktaWhpmzpyJq1evYtWqVejTp4/OYkupZs2aePr0KerWrYtLly6pDNHfu3cPFhYWEmanG1JPpaUvpG6HBQsWYMGCBXBwcNDpcrJE9HYcLqdyR1%2BGx6KjozFt2jSYmZlh/fr179WQXMGqQ506dUJgYCACAgLQtWtXxMXFYd68ebC1tcWMGTOkTlN0XGkmn5TtoIslTIlIPSwyqVxSKBSSDo9t3boV69evh6enJ7755huYmJiIHlOfyOVyLF68GNevX0e/fv0wYcIEAPm9zDY2NtiyZYtyOJ1ITLa2toiKipI6DSIqBotMKncKhsek4uPjg/Pnz8PLyws9e/Ys9jm6uMNbH8XHx6NJkyZSp6EzXGkmn5Tt0LJlS2zbtg1v%2Bih7X38fiaTGIpPKHamHx6Re8Yf0g9RTaekLqduBv49E%2BotFJpU7HB7Tfy4uLjh8%2BLDUaYhK6qm09IXU7cD3AyL9xbvLqdzJy8vD1atXOTymx5ydnaVOQXRST6WlL6RuhzctjEBE0mKRSeVOdnY2vLy8StzP4THpeXt7S52C6PRpKi0pSd0OHIwj0l8cLqdyh8Nj%2BuHJkycwNDRE7dq1ERsbi9DQUJiYmKBPnz5vvU7uXaAvU2lJTep2uHLlCiIjI3H79m107NgRw4cPFy0WEZUNi0wqd6S%2B8YeA8PBwTJ06FSYmJvDz88Ps2bNha2sLIyMjXL58GRs3bnwvJsaWeiotfSFlO6xcuRIHDx5E69atERkZibFjx2L8%2BPGixiSi0mGRSeUOezKlN2DAAHz55ZcwMDDA1KlTsWjRIri5uQEAjh8/jq1bt%2BLAgQMSZykuqafS0hdSt0Pnzp2xbds2WFpaIjIyEkuWLHnnbzojKi9YZFK5w%2BEx6bVu3RpXr14FAHzyySe4ceOG8no8QRBgb2%2BPK1euSJmi6Nijnk/qdij8R6dCoYCDgwMuX74sWT5E9D/vx5pn9E45e/Ys9uzZA2NjYwQEBGDLli1Sp/TeqVatGv773/8iPj4eubm5iImJUe67e/cuqlatKmF2usG/z/NJ3Q6Fl6wsfOMREUmPv5FU7hw5cgTBwcEqw2O8Bku3Ro4ciWHDhgEALC0tsWPHDnz88ceQy%2BXYvXu3ct%2B7jFNp5ZO6HaQucomoZCwyqdx5%2BfKlcl4%2BOzs7JCQkSJzR%2B2fUqFFo1KgR/vnnH/Tr1w85OTlYtGgRnj59Ck9PT%2BVa5u8yTqWVT%2Bp2UCgUOHjwoPJxTk6OymMA79USn0T6hNdkUrljZ2eHa9euKR/b29vzGizSOd6Alk/qdnB0dHzjfplMhtOnT%2BsoGyIqjD2ZVO7w7yL9cOLECezatQsxMTF49eoVKleuDEtLS7i7u78XPUdcaSaf1O1w5swZSeMTUclYZFK5w%2BEx6W3evBm7d%2B/GiBEjMGbMGFSsWBFZWVmIjY3F2rVrkZycjLFjx0qdpqj4x04%2BtgMRlYTD5VTucHhMep07d8bmzZvRrFmzIvvu3bsHHx8fnDt3TveJ6RCn0srHdiCikrAnk8odDo9JLz09XXnz1es%2B%2BugjZGRk6Dgj3Tt79qxypZmAgABkZGS8l7McsB2IqCScJ5OIyqxFixbYsGEDFAqFyva8vDz4%2B/ujRYsWEmWmOwVTaQUEBCAgIOC9XWWG7UBEJWFPJhGV2fz58%2BHj44Ndu3ahUaNGqFSpEjIzM3H//n2YmZkhKChI6hRFx6m08rEdiKgkLDKJqMwaN26MX3/9FZcvX0ZcXBwyMjJgamoKKysrtG3bFoaGhlKnKDquNJOP7UBEJeE7AhGV2YIFC7BgwQI4ODjAwcFB6nQkwXsm87EdiKgkLDKJqMwOHTqEBQsWSJ2GpDiVVj62AxGVhFMYEVGZSb3Kiz7gVFr52A5EVBIWmURUZi1btsS2bdveOFTapk0bHWZERET6hkUmEZWZtbX1G/fLZDLcvXtXR9kQEZE%2B4jWZRFRmpqam7/1wORERvRknYyeiMpPJZFKnQEREeo5FJhGVGa%2ByISKit%2BE1mURUZleuXEFkZCRu376Njh07Yvjw4VKnREREeoY9mURUZmfPnsWePXtgbGyMgIAAbNmyReqUiIhIz7Ank4jKrHPnzti2bRssLS0RGRmJJUuW4PDhw1KnRUREeoQ9mURUZi9fvoSlpSUAwM7ODgkJCRJnRERE%2BoZFJhGVmYHB/946jIw4ExoRERXFIpOIyoxX2RAR0duwC4KIykyhUODgwYPKxzk5OSqPAcDV1VXXaRERkR7hjT9EVGaOjo5v3C%2BTyXD69GkdZUNERPqIRSYRERERaR2vySQiIiIirWORSURERERaxyKTiIiIiLSORSYRERERaR2LTCIiIiLSOhaZRERERKR1LDKJiIiISOtYZBIRERGR1v0/no0Pv21Uwp8AAAAASUVORK5CYII%3D\" class=\"center-img\">\n",
"</div>\n",
" <div class=\"row headerrow highlight\">\n",
" <h1>Sample</h1>\n",
" </div>\n",
" <div class=\"row variablerow\">\n",
" <div class=\"col-md-12\" style=\"overflow:scroll; width: 100%%; overflow-y: hidden;\">\n",
" <table border=\"1\" class=\"dataframe sample\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>CO(GT)</th>\n",
" <th>PT08.S1(CO)</th>\n",
" <th>NMHC(GT)</th>\n",
" <th>C6H6(GT)</th>\n",
" <th>PT08.S2(NMHC)</th>\n",
" <th>NOx(GT)</th>\n",
" <th>PT08.S3(NOx)</th>\n",
" <th>NO2(GT)</th>\n",
" <th>PT08.S4(NO2)</th>\n",
" <th>PT08.S5(O3)</th>\n",
" <th>T</th>\n",
" <th>RH</th>\n",
" <th>AH</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2.6</td>\n",
" <td>1360.00</td>\n",
" <td>150.0</td>\n",
" <td>11.881723</td>\n",
" <td>1045.50</td>\n",
" <td>166.0</td>\n",
" <td>1056.25</td>\n",
" <td>113.0</td>\n",
" <td>1692.00</td>\n",
" <td>1267.50</td>\n",
" <td>13.60</td>\n",
" <td>48.875001</td>\n",
" <td>0.757754</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2.0</td>\n",
" <td>1292.25</td>\n",
" <td>112.0</td>\n",
" <td>9.397165</td>\n",
" <td>954.75</td>\n",
" <td>103.0</td>\n",
" <td>1173.75</td>\n",
" <td>92.0</td>\n",
" <td>1558.75</td>\n",
" <td>972.25</td>\n",
" <td>13.30</td>\n",
" <td>47.700000</td>\n",
" <td>0.725487</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2.2</td>\n",
" <td>1402.00</td>\n",
" <td>88.0</td>\n",
" <td>8.997817</td>\n",
" <td>939.25</td>\n",
" <td>131.0</td>\n",
" <td>1140.00</td>\n",
" <td>114.0</td>\n",
" <td>1554.50</td>\n",
" <td>1074.00</td>\n",
" <td>11.90</td>\n",
" <td>53.975000</td>\n",
" <td>0.750239</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2.2</td>\n",
" <td>1375.50</td>\n",
" <td>80.0</td>\n",
" <td>9.228796</td>\n",
" <td>948.25</td>\n",
" <td>172.0</td>\n",
" <td>1092.00</td>\n",
" <td>122.0</td>\n",
" <td>1583.75</td>\n",
" <td>1203.25</td>\n",
" <td>11.00</td>\n",
" <td>60.000000</td>\n",
" <td>0.786713</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.6</td>\n",
" <td>1272.25</td>\n",
" <td>51.0</td>\n",
" <td>6.518224</td>\n",
" <td>835.50</td>\n",
" <td>131.0</td>\n",
" <td>1205.00</td>\n",
" <td>116.0</td>\n",
" <td>1490.00</td>\n",
" <td>1110.00</td>\n",
" <td>11.15</td>\n",
" <td>59.575001</td>\n",
" <td>0.788794</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
" </div>\n",
"</div>\n",
"</div>"
],
"text/plain": [
"<pandas_profiling.ProfileReport at 0x1926b1b9d68>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"profile(df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Видно, что почти все признаки содержат некоторое количество пропусков. Нужно как-то решить эту проблему."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Признак **NMHC(GT)** на 90.2% состоит из пропусков. Информации о том, что пропуск в нём несёт некое значение нет, поэтому имеет смысл этот признак просто дропнуть."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"df.drop(columns=['NMHC(GT)'], inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Остальные признаки содержат не слишком большое число пропусков, поэтому попробуем эти пропуски заполнить. Сделаем разумное предположение о том, что за короткий промежуток времени каждый показатель меняется не слишком сильно. Тогда заполнить пропуски можно просто линейной интерполяцией."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"df.interpolate('linear', inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Выделим целевую переменную и матрицу признаков. Разобьём их на трейн и тест. Подготовив данные, можем переходить к построению моделей."
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"def prepare_data(df):\n",
" ##\n",
" ## Разобьём датасет на трейн и тест\n",
" ##\n",
" X = df.drop(columns=['C6H6(GT)'])\n",
" y = df['C6H6(GT)']\n",
"\n",
" X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
" test_size=0.3,\n",
" shuffle=False)\n",
" ##\n",
" ## Стандартизируем признаки\n",
" ##\n",
" scaler = StandardScaler()\n",
" X_train = scaler.fit_transform(X_train)\n",
" X_test = scaler.transform(X_test)\n",
" \n",
" return X_train, X_test, y_train, y_test"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"def simple_linear_model(X_train, y_train):\n",
" lr = LinearRegression()\n",
" lr.fit(X_train, y_train)\n",
" \n",
" return lr"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Для оценки качества получающихся моделей будем использовать метрики _RMSE_ и $r^2$. _RMSE_ выбрана как более наглядная версия естественной метрики для МНК, $r^2$ - как метрика, показывающая, насколько наша модель объясняет вариативность целевой переменной."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"# функция для быстрого тестирования модели\n",
"def test_model(model, X, y):\n",
" y_pred = model.predict(X)\n",
" print('RMSE:', np.sqrt(mean_squared_error(y, y_pred)))\n",
" print('r2:', r2_score(y, y_pred))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Построим бейзлайн-модель - линейную регрессию без регуляризации."
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RMSE: 2.239114751181447\n",
"r2: 0.8920881526426381\n"
]
}
],
"source": [
"X_train, X_test, y_train, y_test = prepare_data(df)\n",
"\n",
"lr_baseline = LinearRegression()\n",
"lr_baseline.fit(X_train, y_train)\n",
"\n",
"test_model(lr_baseline, X_test, y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Получилось довольно неплохо для начала! Попробуем улучшить качество предсказаний - посмотрим на зависимость таргета от предикторов в поисках нелинейностей и попробуем произвести какие-нибудь преобразования над ними."
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1926b703e10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1926d689c88>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"target = 'C6H6(GT)'\n",
"predictors = df.columns.drop(labels=[target])\n",
"\n",
"sns.pairplot(data=df, x_vars=predictors[:5], y_vars=['C6H6(GT)'])\n",
"plt.show()\n",
"\n",
"sns.pairplot(data=df, x_vars=predictors[5:], y_vars=['C6H6(GT)'])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Руководствуясь картинками, возьмём вместо признака **PT08.S2(NMHC)** его квадрат, а вместо **PT08.S3(NOx)** - обратный к нему (нулей там нет, так что всё хорошо)."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"df_transformed = df.copy()\n",
"df_transformed['PT08.S2(NMHC)'] = df['PT08.S2(NMHC)']**2\n",
"df_transformed['PT08.S1(CO)'] = df['PT08.S1(CO)']**2\n",
"df_transformed['PT08.S3(NOx)'] = 1. / df['PT08.S3(NOx)']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Снова построим обычную линейную регрессию по МНК и посмотрим на качество."
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RMSE: 0.2205251182827446\n",
"r2: 0.99895327510702\n"
]
}
],
"source": [
"Xt_train, Xt_test, yt_train, yt_test = prepare_data(df_transformed)\n",
"\n",
"lr_t = LinearRegression()\n",
"lr_t.fit(Xt_train, yt_train)\n",
"\n",
"test_model(lr_t, Xt_test, yt_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Скорее всего, дальше пытаться улучшить качество имеет мало смысла - текущая модель объясняет почти всю дисперсию целевой переменной."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Попробуем заняться отбором признаков. Для этого используем LASSO-регрессию с подбором параметра регуляризации перебором по сетке с кросс-валидацией."
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CO(GT) : 0.0\n",
"PT08.S1(CO) : 0.0\n",
"PT08.S2(NMHC) : 7.517110977683549\n",
"NOx(GT) : 0.03265693829766694\n",
"PT08.S3(NOx) : 0.0\n",
"NO2(GT) : 0.0\n",
"PT08.S4(NO2) : 0.0\n",
"PT08.S5(O3) : 0.0\n",
"T : 0.0\n",
"RH : 0.0\n",
"AH : 0.0\n"
]
}
],
"source": [
"lasso = Lasso()\n",
"\n",
"alphas = np.arange(0.1, 10, 0.1)\n",
"gscv = GridSearchCV(estimator=lasso, scoring='neg_mean_absolute_error', param_grid=dict(alpha=alphas))\n",
"\n",
"X_val, X_test, y_val, y_test = train_test_split(Xt_test, yt_test, test_size=0.5, shuffle=False)\n",
"\n",
"gscv.fit(X_val, y_val)\n",
"\n",
"for col, coef in zip(df.drop(columns='C6H6(GT)').columns, gscv.best_estimator_.coef_):\n",
" print(\"{:14}: {}\".format(col, coef))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Как видно, лучшая LASSO-модель исключила все признаки кроме **PT08.S2(NMHC)** и **NOx(GT)**. Попробуем применить МНК-регрессию только к ним."
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RMSE: 1.4894991616484148\n",
"r2: 0.9522474078394703\n"
]
}
],
"source": [
"X_train, X_test, y_train, y_test = prepare_data(df[['PT08.S2(NMHC)', 'NOx(GT)', 'C6H6(GT)']])\n",
"\n",
"lr_min = LinearRegression()\n",
"lr_min.fit(X_train, y_train)\n",
"\n",
"test_model(lr_min, X_test, y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Качество ощутимо ухудшилось, но стало понятно, что эти переменные действительно вносят наибольший вклад."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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