04_eofs/eof_with_iris.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Eigendecomposition of the Iris dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In EOF analysis, we seek a solution to the following equation.  For a matrix **A** we seek a set of vectors $\\mathbf{V}$ which makes the following equation true:\n",
    "\n",
    "$A\\mathbf{V} = \\Lambda \\mathbf{V}$\n",
    "\n",
    "$\\mathbf{V}$ are called the _**eigenvectors**_ and $\\Lambda$ contains values ($\\lambda$) along the diagonal called _**eigenvalues**_.  The equation tells us that we may be able to find a new set of (eigen)vectors for which the values in our data matrix **A** can be expressed simply as a magnitude along these new eigenvectors.  As we discussed in class, if **A** is a square symmetric matrix _m x m_ like the covariance matrix, the _m_ eigenvalues are all real and positive and the eigenvectors that are paired to these eigenvalues are orthogonal.  It is these pairs of _m_ eigenvalues and their respective eigenvectors that we are after in EOF analysis of our datasets.\n",
    "\n",
    "So what's so cool about eigenvectors and eigenvalues?  First, eigenvalues give us some indication of how the common patterns of variance are re-distributed into the new eigenvectors.  If we call each eigenvector and eigenvalue pair a **mode** then for each mode the eigenvalue can tell you how much of the total variance in the original data is captured by the new eigenvector associated with it. The eigenvectors are also *orthogonal* to one another, which means that they are independent from one another (something that is useful for regression modeling, for instance).  Finally, when the eigenvectors are projected (multiplied) onto the original centered data, they produce new a data series the size of the original data series but that are now uncorrelated with one another and related to the pattern present in the eigenvector.  \n",
    "\n",
    "However, before we get to more complex examples using spatiotemporal data and how to interpret eigenvalues and eigenvectors in those contexts, let's take a look at a simple example with the Iris dataset you used in Homework #1:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This short notebook will walk you through the basics of the simple Iris 2-dimensional empirical orthogonal function calculation. \n",
    "\n",
    "Let's first import our packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy as sp\n",
    "import xarray as xr\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# nicely render figures in the notebook \n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "%matplotlib inline\n",
    "\n",
    "# you can omit the line below if you'd like, but I really don't like the default fonts in Python, so I switch to Helvetica\n",
    "plt.rcParams['font.family'] = 'Helvetica'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll read in the Iris data (same data you used on Homework #1) and take a look at the resulting DataFrame::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>species(id)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)   sepal width (cm)   petal length (cm)  \\\n",
       "0                  5.1                3.5                 1.4   \n",
       "1                  4.9                3.0                 1.4   \n",
       "2                  4.7                3.2                 1.3   \n",
       "3                  4.6                3.1                 1.5   \n",
       "4                  5.0                3.6                 1.4   \n",
       "..                 ...                ...                 ...   \n",
       "145                6.7                3.0                 5.2   \n",
       "146                6.3                2.5                 5.0   \n",
       "147                6.5                3.0                 5.2   \n",
       "148                6.2                3.4                 5.4   \n",
       "149                5.9                3.0                 5.1   \n",
       "\n",
       "      petal width (cm)     species(id)  \n",
       "0                  0.2     Iris-setosa  \n",
       "1                  0.2     Iris-setosa  \n",
       "2                  0.2     Iris-setosa  \n",
       "3                  0.2     Iris-setosa  \n",
       "4                  0.2     Iris-setosa  \n",
       "..                 ...             ...  \n",
       "145                2.3  Iris-virginica  \n",
       "146                1.9  Iris-virginica  \n",
       "147                2.0  Iris-virginica  \n",
       "148                2.3  Iris-virginica  \n",
       "149                1.8  Iris-virginica  \n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('iris.data') # use Pandas to read in the csv data\n",
    "df #let's look at our Pandas dataframe\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "In the code block below, we'll create a new DataFrame with only the sepal length and sepal width from Iris setosa.  For this we'll use Pandas `.loc` with a long chain of specifics.  Note that the species selection (rows) and columns are in the square backets of the `.loc` method - so the row selection uses the `.str.contains` applied to the species id column, and then the column selection is the two sepal related columns, enclosed together in square brackets.  The `.copy()` at the end ensures that sepal going forward will be not affected by any changes to the original df. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
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       "  </thead>\n",
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       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
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       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
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       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
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       "      <th>15</th>\n",
       "      <td>5.7</td>\n",
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       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>5.2</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>5.5</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.2</td>\n",
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       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>5.5</td>\n",
       "      <td>3.5</td>\n",
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       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>4.4</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.4</td>\n",
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       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>4.5</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>4.4</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5.3</td>\n",
       "      <td>3.7</td>\n",
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       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.3</td>\n",
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      ],
      "text/plain": [
       "    sepal length (cm)   sepal width (cm)\n",
       "0                 5.1                3.5\n",
       "1                 4.9                3.0\n",
       "2                 4.7                3.2\n",
       "3                 4.6                3.1\n",
       "4                 5.0                3.6\n",
       "5                 5.4                3.9\n",
       "6                 4.6                3.4\n",
       "7                 5.0                3.4\n",
       "8                 4.4                2.9\n",
       "9                 4.9                3.1\n",
       "10                5.4                3.7\n",
       "11                4.8                3.4\n",
       "12                4.8                3.0\n",
       "13                4.3                3.0\n",
       "14                5.8                4.0\n",
       "15                5.7                4.4\n",
       "16                5.4                3.9\n",
       "17                5.1                3.5\n",
       "18                5.7                3.8\n",
       "19                5.1                3.8\n",
       "20                5.4                3.4\n",
       "21                5.1                3.7\n",
       "22                4.6                3.6\n",
       "23                5.1                3.3\n",
       "24                4.8                3.4\n",
       "25                5.0                3.0\n",
       "26                5.0                3.4\n",
       "27                5.2                3.5\n",
       "28                5.2                3.4\n",
       "29                4.7                3.2\n",
       "30                4.8                3.1\n",
       "31                5.4                3.4\n",
       "32                5.2                4.1\n",
       "33                5.5                4.2\n",
       "34                4.9                3.1\n",
       "35                5.0                3.2\n",
       "36                5.5                3.5\n",
       "37                4.9                3.1\n",
       "38                4.4                3.0\n",
       "39                5.1                3.4\n",
       "40                5.0                3.5\n",
       "41                4.5                2.3\n",
       "42                4.4                3.2\n",
       "43                5.0                3.5\n",
       "44                5.1                3.8\n",
       "45                4.8                3.0\n",
       "46                5.1                3.8\n",
       "47                4.6                3.2\n",
       "48                5.3                3.7\n",
       "49                5.0                3.3"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "sepal = df.loc[df[\" species(id)\"].str.contains('Iris-setosa'), [\"sepal length (cm)\",\" sepal width (cm)\"]].copy()\n",
    "sepal # take a look:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have a new Pandas DataFrame with 50 rows and 2 columns. Let's scatter plot the data using Pandas object plotting ability.  To do this we tell Pandas the column name of the x and y values, and we can also pass it some plotting options.  The second line tries to set the aspect of the plot to be equal in both x and y directions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 451,
       "width": 331
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sx1 = sepal.plot.scatter(x=\"sepal length (cm)\",y=\" sepal width (cm)\",color='Red',title='RAW DATA')\n",
    "sx1.set_aspect('equal', 'box')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll now calculate the covariance of these two measurements.  There are (at least) two ways to do this.  In the first way, we turn our Pandas DataFrame into a simple NumPy array using `.to_numpy()`.  We then remove the mean of the data columns (we'll see why in a moment) and calculate the covariance using Numpy's `np.cov`.  Note that we pass [two additional inputs to `np.cov`](https://numpy.org/doc/stable/reference/generated/numpy.cov.html).  First, `rowvar=False` tells NumPy that our variables are in columns and the observations of those variables are in rows (the default in NumPy is the reverse).  Second, we still need `ddof=1` to get the unbiased covariance. \n",
    "\n",
    "Alternatively, we could get the covariance directly from Pandas, using the [`.cov` method](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.cov.html). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.12424898 0.10029796]\n",
      " [0.10029796 0.14517959]]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <td>0.124249</td>\n",
       "      <td>0.100298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <td>0.100298</td>\n",
       "      <td>0.145180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   sepal length (cm)   sepal width (cm)\n",
       "sepal length (cm)           0.124249           0.100298\n",
       " sepal width (cm)           0.100298           0.145180"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# take Pandas dataframe, put into NumPy array, remove mean, calculate covariance\n",
    "sepal_array = sepal.to_numpy()\n",
    "sepal_array = sepal_array - sepal_array.mean(axis=0)\n",
    "\n",
    "# covariance from array \n",
    "Cn = np.cov(sepal_array,rowvar=False,ddof=1)\n",
    "\n",
    "# or keep in datafrom, calculate covariance, then move Covariance to NumPy array\n",
    "Cp = pd.DataFrame(sepal).cov(ddof=1)\n",
    "\n",
    "print(Cn)\n",
    "Cp\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare the results from the two methods and convince yourself you understand how the two approaches work to calculate the covariance matrix. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have our covariance matrix, we can calculate the eigenvalues and eigenvectors -- we'll do our first Singular Value Decomposition (svd)!  As usual, there are several ways to do this.  Numpy has `np.linalg.svd` which will calculate the complete SVD of the covariance matrix.  This will work OK for now, since our dataset and covariance matrix are quite small.  Alternatively, SciPy provide `sp.sparse.linalg.svds` where you can specify how many eigenvalue/eigenvector pairs to calculate.  This can be much, much faster for datasets with a large number of variables. \n",
    "\n",
    "Singular value decomposition of a square symmetric matrix allows us to factorize the covariance matrix to the product of matrices containing the eigenvalues and eigenvectors:\n",
    "\n",
    "$\n",
    "\\mathbf{A} = \\mathbf{Q} \\Lambda \\mathbf{Q}^{T}\n",
    "$\n",
    "\n",
    "Where **Q** contains the eigenvectors and $\\Lambda$ is a diagonal matrix containing the eigenvalues, $\\lambda$.  \n",
    "\n",
    "We'll use NumPy here, since our data are a manageable size:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# do the SVD\n",
    "U, S, V = np.linalg.svd(Cn) # could use Cp or Cn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output contains U and V with the eigenvectors and S with the eigenvalues.  Let's take a look at S first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.12424898 0.10029796]\n",
      " [0.10029796 0.14517959]]\n",
      "[0.23555675 0.03387182]\n"
     ]
    }
   ],
   "source": [
    "# The eigenvalues are in S\n",
    "print(Cn)\n",
    "print(S)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unlike other programming languages, `np.linalg.svd` returns S as an array instead of a diagonal matrix, so the total variance in the system is the sum of the elements of S:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.26942857142857146, 0.2694285714285714)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_variance_original = np.sum(np.var(sepal_array,axis=0,ddof=1))\n",
    "total_variance_svd = np.sum(S)\n",
    "total_variance_original, total_variance_svd # same total variance\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's look at U and V, the matrices we expect to hold the eigenvectors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.66941065 -0.74289258]\n",
      " [-0.74289258  0.66941065]]\n"
     ]
    }
   ],
   "source": [
    "print(U)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that in this simple case, U == V, but also because this is 2 variable case that U.T == V as well.  My warning to you is that, generally speaking but especially in Python, be sure you know whether the columns or rows of the method or function you are using contain the eigenvectors.  In more complex cases, the answer may different substantially. \n",
    "\n",
    "Let's check if the columns of U are orthongonal.  Remember that we can check for orthogonality from the dot products our two vectors: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.3183939343219683e-16\n"
     ]
    }
   ],
   "source": [
    "print(np.dot(U[:,0],U[:,1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Orthogonal? The number is 0 within floating point machine precision, so these vectors are orthongonal. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the original data and then the eigenvectors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(-0.75, 0.79, '13.0%')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rrzVrZHMsXgwXXuh83ukWApzu+KhRZrrvSy+ZJF5ERETKucxMU3zj9dfh8GHn44cPm8dff5mpWaNGmTce48eb/fkKM3u2qUTpSZs2eabfvXthxAizhqqgN0fJyae2eXj2WVPs4403ICys+K/111+muuXChSaJTE+H+vWpHRnJjcAXpfl/bNhgio0A3HsvNGtWmt5KTUmqVCht2pgiQAVp1qzgEcHCHDhw6nm9eq5ds3u3/eM6dVx/vfwOHza/T3JccYVzNeGCtGxpbuylpZmPv/vO/G7N0ayZScA3bjT/x8JizLtmN+8UYjDXTpli2ocPP31MIiIi4uP27oU+fWDlStevsSxzR33FCvj448Knnf37r30NVXnxyy/Qq5dZ2+QKy4KvvjLT8b7+2oy6uiI724wwvPqqvVImwN9/U+3vv/kceAII+vVXM3pbXI8+al7njDM8f8PABZruK1JCX35p1mLu22f2C3XF7Nn2jy+9tOSv//PP9ht2bdq4dl1goJmZk2PbNvvxnj3Nv8ePw8iRBfcxcSIkJJjnUVHOe66OHGl+z734IgQHuxaXiIiI+Kj0dLPRekEJakAANGpkik80aFDw9YcPm20Gli3zbJxl6a+/zNqnwhLUBg3M3frAQOdjhw6Z5Hb16tO/zokTZvT1hRecE9R8LgLqX389xMWdvt+8li07VZTkscdKN4riJkpSRUooNNRMY61b10wjPp0tW8wsjxwtW8Jll5X89X//3f5xcWZlnCwAB5jfsXkNH26WlYAZDe3b14zYZmaa7W5GjTKFk3LkL9S0eLF5tGljtsYRERGRcu7++0/dnc5x1llmi4SDB2HXLvjtN5Ow/fOPuXuffzTv+HHo188+Fa2s5J/yVVo5iWPOlg05mjSBd94xa3b37DFT9DIyzCb17dvbz83IMH0UNG06rxEjnEc5wNwYOPdc+x6HQMDRo2bE+48/XPu/WJYZRQXzNfORKXBKUkU8ICPD/MwfOABr15qbUm3bmhtnADVqFLy1S3EkJ9s/Ls7MjoYNTz1PSzPx5mja1CyVyPHVV2Ydb+XK5tgLL5wq1tS/P9x446lzs7NNxWSA//3PtXW6IiIi4sM2b3Z+0xIRAWvWwKBBznvp1atn7mYnJEDr1vZjBw/a79jnuPVW88bJHY/809vq1DEb27vT22+bqb55tWplPifDhpk3ejmCgkwhqRUrnO/e79oFTz1V+Ov8+KNJevO67TZTeXPXLjNi8e+/HHj7bZLynvPvv+bGgitmzDi1l+CYMU5Jr7coSRVxs4wM8/MdEGBGWS+91OxdmrMG9OKLzXpOV6fnFib/zbu8vw9Pp1o1+8epqfaPhw41iWpQEavWBw+GqVPtbZMnm1HX//s/15dZiIiIiA97913nbRGmTTt9VcR69cyd7vzrfqZPL16lyuJYsQL++99THwcFwcyZ7t0HLyPD3LHPKyTEjHbmHQXILzDQvHFq3tze/tFHhY8ujx1r/3jcOHPDIG8RqipVSL/uOtoCtslxCxYUXoglR2bmqfWnkZFwyy1Fn1+GlKSKlLHq1c3Nr9JKT7d/XJwbX/nPzd8XmBtwGzeaPU7PPddMb27SxMwgWbzY/J7NmRYMJgl/+mnz9+CVV1yPRURERHzY/Pn2jzt2NAUpXHH22eaudl7//us8ddgdkpPNGqW8CfDYsRAd7d7XmTED9u+3t40aZaabnU5QkPNIcno6fPCB87kZGfYR4C5d4MEHC+16D3B7/sa820AU5N13T637eukl1ypwlhHfiUTET+RsL1WpUsHHv/vOFLfLW1G3JPJXOS/s9QqS/4Zo3mQzrwsugPfeM7NJDh82a1JnzjTbfeX36qsm+b7rLjj/fPuxzEzz+zc62sy6CQ42f7fuuqtibaEmIiJSrmRmOu+X2aFD8fro1s25LSnJua00srLMVNq8yePVVxdeAbI0Pv7Y/nHlynDnna5ff/31ZuQ1r88+cz5v+3b7KMLNN5+26xXAicaNTzXk3S8xv5QUeP5587xTJ/P58iFKUkXcrEoVc/Pr+HE4dszUEZgyxflG3htvmASwpPL/fjtNwbciz80//be49uwxa1BDQ51vEO7cadbj3n23mYVz8KD5vGzfDh9+aApIvfZa6V5fREREPGD/fue74sWt/Boa6tyWlVXymAoyZgzEx5/6uGFDswbJ3Q4ftr8OmOSuOJ+T4OBTWynk2LTJbPGT17//2j92cV/VrEaNTn2Qf21YXq+8YraoyHnuY5SkinhQ5cpmGcQtt5hq4BMn2osJPfVU8ZLLvPInqfnXlRblyJFTzytVcu6ruJ56Co4eNQWi8u4Ze+AAdO16qrZAYKBZi9utmylKB+bv1EMP2Ys1iYiIiA8oaC1RcavzFrQJvasbzLti3Tqz511e77/v3tfIsXy583rakkwnzl/p17Kct42pVcv+cf6KmYUI3L371Ae1axd80u7dMH68eX7DDaXbE9FDlKSKlKHbb4eBA099vH+/GV0sibw3ysD5BlxRcm6cgVlCUZoqvJs3m5uVTZrAAw/Yjz36qClAB3D55Wba8Nq1ZolFUpIpjpdTnOmRR5y3wxEREREvqlnTuUDS0qXF66Og7VPyV/0tqePHTWXgEydOtV1/vang6Anr1jm3lWQ/wYLW9G7aZP+4WTN70an804wL0BEIylv4pEWLgk989llTTKRSJeciUD5CSapIGevf3/7x5s0l6yd/cbj8S0aK8vffp57nLRBXEiNHmtHQF16w/y49fNgU/wOTwC5YYLZUyxEQAPfeayofg/k7k79qvIiIiHhZr172j3/8EebOde3aZcucCy916uQ8SlhSL75oT+5CQ+HNN93Td0EKqpbbsmXx+4mIcG7Lv69p1ar29bxxcUWuj6oPfJS/8dprnU/cutVM7YNT1TF9kJJUkRKYPt3c4Mp5FCdBzJuowamtaYor/82xjRtduy472/x+ytGuXcleH+Dbb2HhQnNDMP96/h9/PDUj5vbbC16SAnDPPWYdL5iiUiIiIu6QkpLCli1bWLNmDVu2bCElJcXbIZVPI0c6V30dMMC8ASjK6tX2zdRzPPGEe+L6889Td7pzvPCCy2s3SyT/lK/69Z232HFF3brOVStzpp7l9eij9o8fegiGDLHHcfw4VefM4SfANn7RrVvBI7aPP25GF2rUMNsy+CglqSIlULmyWWKR8/jtN9evzTsjBU6/zVhhmjaFc8459XH+pQyF2bDBvia1uEX6cmRnnyqa97//OU8ZzrtkJW+hufyqVj11QzV/RXcREZHisCyLuLg4+vbtS+3atYmIiKBdu3ZERERQu3Zt+vXrR1xcHFb+YkBSuFatnKeEHj0KV14JMTEwZw788495g3PwoFnHdM89Zqua/H/Y770XevRwT1z3328qVeZo3RqGDXNP34VJTLR/3KRJyfpxOJzXbeVdi5XjiiucKwdPmmTeAIaHm8InNWtSZ9gwbJGEhsJbbzn3t2oVfP21ef7II55Zt+smSlJFSiAy0v7xqlWuX5t/OUNhywVckXcGztatrm07NnPmqed16phZNyUxdaopiHTNNdC5s/PxvIXuNmwovJ89e8zfNih5wi4iIpKQkEDLli3p0qULX331FVn5KshmZWUxc+ZMunTpQsuWLUnwxF6d/uqxx0whiZypTzm+/tqs/2zQwKxvrFPHFBJ67z17ZUiHw4wKFpQ4lcTixc7TiJ97zrP7fGZkmG1b8irNG5f8U8wKm1r3zjvmhkB+ycmm2Ee+ze6zq1aFL780+wjm98gj5t9GjZwLifgYJakiJdCihbmBlWP6dOcR0oJkZtqXE9SqVbL19jnuusv+8dixRZ//zz8wYcKpjwcOLHyP1KKkpZmKvoGBhVct/89/Tu3dOnly4WtvH3741L6tV1xR/FhERESWLFlCx44d2exioYfNmzfTsWNHlixZ4uHI/Mi995o74sVdgxkQAF98AS+9VLpKjXk9+aT940svNXfNPamgJLKg6seuyj9NuLBtGipVMqPVDz98qtpkIbYA+2bNKni0+uuvYeVK8/zZZ0u/tYOHKUkVKaF77jn1/M8/4b//dd5KLK/0dLNuM+/6/gceKPj3zZQp5vd4zuPWWwvu86KL7KOpM2eavUcLkpoKffqc2narShWztKEkxo0zN/DuvLPwkeAzzoBBg8zztDQzYjtlirkJaVlmDe3118Mnn5hzKleGoUNLFo+IiFRcCQkJxMTEkFqcvdiA1NRUYmJiNKLqqtWroV8/14tg5MjOhjvuMEmqO/ZHjY2Fn36yt40ZU/p+Tyfv1OIcJVmPWti1BfWfI2dU4NdfzWhoq1am8nKVKhAeTnrPngwEWgKZ+af7gfm8P/64eX7BBWZdq49TkipSQvfdZy+C9P77ZiTwyy/NiGV2tvmdsH27OdaqlbmRmCMyEh58sPRxjB9v/z13zz1mScbWrWZ099Ah87pt2sD3358675ln4Mwzi/96e/ea35M1asDo0UWf+8orp6oHHzgAt91mktegIJNgx8aeOvfll0tfaVhERCoWy7IYPHhwsRPUHKmpqdxyyy1ao3o6b7xh9pLLnxyCuZvesKFZH9mwYcFTbg8fNklSz55m3WpJZWebqVx5XX656dfTcqZ95VWS6WiF9edKwnvuueYN0/r1ZtQhIwMSEznw0Ud8ChQQoTFx4qmqmS+9ZJLegmzebEYwWrUy0/2qVjVFUG64wbzBLcOfEyWpIiVUowZ89pl9ScGqVebnOGdpRqVKJvEaOtQsG8hxzjkwb557ZlpccIEZocz5fZOVBe++a0Y4K1c2v2NuvNFe0bdfP+eCca56+mlTL+HRR01Ru6LUqWOqz7dqZW/P+3s5MNAUXhoxomTxiIhIxbV8+XKXp/gWZtOmTawo6ablFcEHH5g/0vmTqosvhk8/NcWRdu82VSR37zbJ0+zZ0LWrc19Ll5piS66skSrIp586rx9yV7Xg06lWzbktZxuDksh/bUH9u0Na2qlRhfbt4brrnM85dsyMclx0kVmXtmGDGeXIyDD7Fn75pXmDGxUFW7Z4Js58lKSKlEK7dhAfb36m88vOdr7hFBgIt9xiChyVZBSzMDfeaP4e5C/SVtDrP/yw+R1fktoCW7aYm3Hh4a6PAjdtCmvXmhoKHTua2SmVK5ute+64w/weLOm0YxERqdgm5C204AP9+J3t200V3fwefdT8cR8wAGrXth8LDTXFlL791lRZzClQkSM+vsj9Pov00kv2j1u0gKuuKllfxVXQyMKxYyXvL//ov6eS1PHjYdcu87ygQiJHjpwqdlXQaHFe69ebYirx8e6O0onD0vwGKaWkpCSanCzBnZiYSHjeikIVhGWZNe3z5pnR1L17zY3EnO1VLrzQTAW++WaTtHlKSopJIr/5xiwZOXTIxHDOOdCliym0VFCxN1ctXGj2P+3UqeCKviIiImUlJSWF2rVrO1XxLYnAwEAOHjxIaGGbeldUQ4aYLU/yGjrUTNly1bRp5g59XnXrmu1cirOmc/ly5zcf775btgUtKlWyjwJ36WJGh0vizDPtW9pceqlZ91sChb4X37/fvAlMSTEj2LNm2S+0LHNDYe5c507DwsybyKQk5/WytWvDmjX2vRDdTEmqlJqSVBERESlrW7ZsISIiwq39tSjNvnD+5vhxs27n6NFTbTVrwo4dpsBEcVxxxanKsjm+/bbgKcGF6dfPvo9ejRpmhLB69eLFUhpNm5rprzkuvLDw7QtOJzjYPhJ7443w+ecl6qrQ9+LDh8Obb5piIJs2wfnn2y/87DO46aZTHzscpujKww+f2sYiIwO++gpGjjT7Bua48krnbYDcSNN9RURERKTcOZo3eXKDI0eOuLW/cm/jRnuCCmZqbXETVID+/Z3bfvzR9et37TJbqOQ1YEDZJqhgikPllZRUsn727XOeKtysWcn6Ksxff5kpvGDWV+VPUMG+d6HDAR9/bIpk5R1wCg42exauW2ePccEC+Pln98ach5JUERERESl3qrs5QalRo4Zb+yv3du50brv44pL1dcklzm1797p+/fvvOxdbGjy4ZLGURv4kNSXFPmXXVdu2ObflrzJZWk8+aUbDQ0LMlg757dxp305o0CCTjBamcWOzpiyvOXPcE2sBlKSKiIiISLkTHh5OYGFbaRRTUFAQYWFhbunLbxS0b2dJRlHBTBvOrziVcadNs3/crJmpVFvWoqKc20oymrhunXPbFVcUv5+i+p8xwzx/6CGzNVB++av05l83XJAuXeyjrL/+WvIYT0NJaimlpaWxa9cuMktTglpEREREiiU0NJSYmBi39BUTE6OiSfnVrevc9u+/JeuroKnUDRq4du3PP5t1sHm56etebD16OLctXlz8fvJvedSsGZxcU+oWjz5qiiLVr2/WkhYk/9fS1ZoyeeMszZ63p6EktYQ+/fRTLrnkEqpXr05YWBhVqlQhIiKCcePGcaKYez9FR0fjcDiK9YiOjrb1sWzZMpeua1jQnRQRERGRcmjYsGE+1Y9fKeg945o1JeuroOmtZ5/t2rWxsc5t115bsjhKq0kTs+1NXl9/bTapd9WRI86Jbd++pQ4tR5W4uFMVh595xhSYKkj+rYOSk117gbzrcPP34UZKUktg6NChDBw4kISEBHKKI1uWxZYtWxg5ciTR0dEeX3yff3rLn3/+6dHXExEREfE10dHRpa7wGxkZSadOndwUkR+JiHCepjt/fvHWkubIX/TI4Sh4VLIg+ZPUkBDvTPXNMWiQ/ePkZHvV4dP58EPnglSuTLV1gQM4I2cv2ebN4c47Cz85f7Kdf0p1QZYvt6/BvfDC4oboMiWpxfT+++/z/vvvA9C5c2dWr15NWloaf//9N6NGjcLhcLBy5UqGFmPPps8//5zt27ef9vHGG28AUK1aNV599VVbHzlJ6k033VRkH2vXrnXTZ0JERETEuxwOB9OmTSMkJKRE14eEhDB16lQcDoebI/MDgYFw9dX2tvR0eOCB4vXzww/w5Zf2to4dCx6pze+PP8zWKXm1bw+VKxcvBncaOtQkynk9/njBU5rz27oVRo+2t3XoAJGRbgltIFA5Z63piy+afV0L07QpXHTRqY+nTj21jrUge/aYfXPz8uSItiUuS09Pt+rWrWsBVqtWraxjx445nfPYY49ZgAVYa9euddtr//nnn1bNmjUtwPrkk0+cjvft29cCrJdfftltr+mqxMTE3P9zYmJimb++iIiIVGyLFy+2QkJCct+PuPIICQmxFi9e7O3QfdvGjZYVFGRZZoXjqcfDD1tWdvbpr1+50rLq1HG+Pj7etdd/913na194oXT/p7zi4pz7b9r09Nc99JDzdVdfbVkZGYVf88cflnXuuc7Xffddqf8biYmJVmWwtuf0eemlrl04fbo9loAAy3rgActKSjp1TkaGOa9xY/u5vXqVOu6iaCS1GObMmcP+/fsBGD16NJULuIvz0EMP5U7FnZi/THMJHT9+nH79+nHo0CEGDRrEwALKQ+eMpJ5zzjlueU0RERGR8qJ79+7Ex8e7PPU3MjKS+Ph4unfv7uHIyrnISBgxwrn91VehbVv47DM4+d44V2amGT297TYzSnjggP14TrsrVq92bvPmVN8czzzjXOho3jxo1w4WLrRvl7Nvn/l8RUWZkeG87rrLbVV9/ws0y/kg34zLQg0YAFdeeerj7GwYP94UUWrSxGy5U7Om2Zpm165T59WqBW++6Za4CxPk0d79zLJlywAz3faqq64q8Jy6devSpk0bVq9ezZIlS9zyumPHjiUhIYEGDRrwZiHfEDlJ6rnnnuuW1xQREREpT6Kioti4cSMrVqzgnXfeITY2lqw8BW2CgoKIiYlh2LBhdOrUSVN8XfXSS2aq5yef2NvXrYObbjLrS+vWNYnLsWNmzWpB29cAXHYZvPuu66+dP0kNCjLJsbfVqAFffQWdOpkp0Dl++cUkfSEhZl/Rw4fhn38K7uPSS+G119wSjuPwYZ44+Ty9Wzeqduzo4oUO+Pxz6N7duShW3gJJeeX835s3L3G8rlCSWgzx8fGA+SVYqYg53hdffDGrV6/mr7/+Ij09napVq5b4Nbdt28bYsWMBGDduHDVr1nQ6Z//+/aSkpACnRlIzMjI4ePAgNWrUKPXm1EmFfZOetHv37lL1LyIiIuIOOTsgREdHk5KSQnJyMkeOHKFGjRqEhYVpm5mSCAw06xXPOsskrPm3XbQsM1q4b1/R/fTpY/qpUsW11z182KzhzOuii6BaNddj96S2bU0hqd69Tax5pabC778Xfm379vDNN85rW0so9J13qAGcAA4/9hjFyjxCQ82WOPfdB5MmmdHUwlx0EUyf7rY1tEXRdN9i2L59OwBnn6Zk9plnngmYir87d+4s1WsOHTqUY8eO0aFDhwKn+cKpUdSGDRsyf/58oqKiqFatWu4v43PPPZdnn302N5EtriZNmhT5uPTSS0v8/5Py5fBh+OADb0chIiJyeqGhobRo0YJLL72UFi1aKEEtjYAAGDMGEhLg+uvNiKarmjeHyZNN8aTiJGVr15oEOC9fe88ZHW1GTwuZYekkJASefRbi4ty3fUtSEtUnTQJgCnDi/POL30dwsKk6/MsvZnp3ZCSccYa5odCkibnB8PnnZs/aMkhQQSOpLsvIyCD95HB+vXr1ijw372hnabaimTdvHnFxcQA8//zzhZ6Xk6Tu2bOHG2+8scDjo0ePZtq0acydO5cLPVguWvzX7t1mBssvv5gCdg895O2IREREpExFRpqpnsnJZluZlSth/XqzLvXQIZPs1K4NYWFmtLBLFzOVNN/WiS7p1s05SXW36OjSv0bTpmY9akKCSeS++86Moh4+bKrr1qsHrVqZLXf69zdTo93pqadwHDtGGvAM0Ks0fUVGmjWpPkBJqosOHjyY+/x003er5JnGcOzYsRK/5tNPPw1A165d6VjE3PK8e6ReccUVjBo1inbt2lG1alU2btzI+PHj+fTTT9m+fTs9evRg48aN1KpVy+U4EvPuh1SA3bt3azTVz23bBj17wo4d5uORI6FBA7j5Zq+GJSIiIt4QFgb33mseYkRFmUdZmzyZpOeeo0n+Qk7lnJJUFwUEuD4zOm9iGhwcXKLXmz17NgkJCQA8/PDDRZ4bGhpKz549ueCCCxg3blxudWGANm3aMH36dM4//3yeeeYZkpOTeeWVV3LXuboiPDy8RP8H8R/r1p1KUHPcdpu5Odizp1dCEhERERE/pTWpLqpevXru89TU1CLPTUtLy31eUKEjV+RU8W3evDk9evQo8tzhw4ezcOFCXn/9dVuCmtcTTzxB48aNAYiNjS1RTFJxDRgAr7xibztxwixR+Okn78QkIiIiIv5JSaqLqlevnptw7sq7T1ABkpOTATP6mlNEqTi2bduWuxb1rrvuckuJ9KCgINq1awdQ6mJOUjGNHAkPPGBvS001tQKKKmAnIiIiIlIcSlKLIafg0KZNm4o877fffss9v3LlysV+nY8++gjLsnA4HNxwww3FD7QQOdvmlHQKslRsDgf8739mVDWvffvMlN89e7wTl4iIiIj4FyWpxdC5c2cAtmzZUmgxoePHj/P9998D0KVLlxK9zqxZswBo167daUdiv//+e9q0aUObNm1Yu3ZtkeeuW7cOgBYtWpQoLpGAAJgyxRTcy2v7djOiWsJdjkREREREcilJLYb+/fsDZv/T119/vcBzJk2alLsf6aBBg4r9Ghs3bsyt1tvThYo0TZo0Yd26daxbt46pU6cWet6sWbNy+42JiSl2XCI5KleGWbOcC9j9/LPZOq0UBa1FRERERJSkFkdkZCTXX389AG+88QYzZsywHV+4cCEPndw8sk+fPrRp0yb32M6dOwkODiY4OLjIQkiLFi3KfV7UtjM5mjZtmjvC++677/LOO+9g5dvvKS4ujjvuuAOARo0acffdd5+2X5Gi1KgB8+fD2Wfb25cuhVtugexs78QlIiIiIuWfktRieu+99wgPDycrK4v+/ftzySWX0L9/f9q2bcuVV15JWloaTZs2za3Om8OyLI4dO8axY8c4fvx4of2vXr0agMDAQJf3Hn3vvfeoX78+2dnZ/Pe//+XMM8/k+uuvp3///rRu3ZouXbrw77//UrVqVaZPn05oaGjJPwEiJzVoAIsWQf369vYZM+DBBz2//7aIiIiI+CclqcVUr149fvzxR7p27QpAQkICM2bM4KeT+3B0796dVatW5W73Ulxr1qwBTNGlvNveFOW8885jzZo1XHfddTgcDpKSkoiNjWXGjBn88ssvAPznP/9hxYoVuaOuIu5w7rlmRDX/t+obb8Crr3onJhEREREp3xxW/rmh4rJNmzbx448/sn//fho2bEjbtm2JiIjwakx///03K1asICkpiaCgIBo0aMBll11G8+bNPfaaSUlJNGnSBIDExETCw8M99lrim5YsMYWTTpywt0+dCoMHeycmERERkYrAH9+LK0mVUvPHHwwpvunT4eab7W2BgTBnDlx5pXdiEhEREfF3/vheXNN9RcQtBg6EcePsbVlZ0LcvnJzFLiIiIiJyWkpSRcRtHnwQRo60t6WlwdVXw7Zt3olJRERERMqXIG8HICL+5eWXYfduM/03x/790LMnrFoFjRp5LzYRkeJKSUkhKSmJo0ePUr16dcLDw1UlX0TEwzSSKiJuFRAAkyZB/u2Ad+wwa1MPH/ZKWCIiLrMsi7i4OPr27Uvt2rWJiIigXbt2REREULt2bfr160dcXJzTvuQiIuIeSlJFxO0qV4avvoI2beztv/wCvXvDsWNeCUtE5LQSEhJo2bIlXbp04auvviIrK8t2PCsri5kzZ9KlSxdatmxJQkKClyIVEfFfSlJFxCOqV4d588xeqnktXw6DBkF2tlfCEhEp1JIlS+jYsSObN2926fzNmzfTsWNHlixZ4uHIREQqFiWpIuIx9evDokXQoIG9/csvYcQI0Ew5EfEVCQkJxMTEkJqaWqzrUlNTiYmJ0YiqiIgbKUkVEY86+2xYsABq1LC3v/UWvPSSd2ISEcnLsiwGDx5c7AQ1R2pqKrfccovWqIqIuImSVBHxuIsvhlmzoFIle/sTT8CUKV4JSUQk1/Lly12e4luYTZs2sWLFCjdFJCJSsSlJFZEy0a0bTJvm3H7HHWbtqoiIt0yYMMGn+hERqeiUpIpImenfH15/3d6WlQX9+sGPP3olJBGp4FJSUoiNjXVLX7NmzSIlJcUtfYmIVGRKUkWkTA0fDo88Ym9LT4err4atW70Tk4hUXElJSU7bzJRUVlYWycnJbulLRKQiU5IqImXupZdg8GB728GD0KsX7NrlnZhEpGI6evSoW/s7cuSIW/sTEamIlKSKSJlzOOCjj0xSmtfOnabt0CGvhCUiFVD16tXd2l+N/KXMRSqSzEyzIbpIKSlJFRGvqFTJ7Jfatq29feNG6N0bMjK8EpaIVDDh4eEEBga6pa+goCDCwsLc0pdIuZOaav6Ad+0Ks2d7Oxop55SkiojXVK9uKvs2b25vX7ECbr7ZFFUSEfGk0NBQYmJi3NJXTEwMoaGhbulLpFzZv98kp/PnQ3a2qZS4cqW3o5JyTEmqiHhVvXqwaBE0bGhv/+oruP9+sCzvxCUiFcewYcN8qh+RcmXnTrjiCli9+lRbRgbccIOmRUmJKUkVEa876yxYsADyL+WaMAFefNE7MYlIxREdHU1ERESp+oiMjKRTp05uikiknNiwAS67DH77zd5eq5ZZ0xMc7J24pNxTkioiPqF1a7OEpXJle/uoUTBxoldCEpEKwuFwMG3aNEJCQkp0fUhICFOnTsXhcLg5MhEftmIFdOwIu3fb25s0MVN9L7/cO3GJX1CSKiI+o3Nn+PhjU/03r7vugjlzvBOTiFQMUVFRxMbGFjtRDQkJITY2lqioKA9FJuKDvvoKevaEw4ft7ZGRsGoVtGjhnbjEbyhJFRGfcsMN8MYb9rbsbNO+apV3YhKRiqF79+7Ex8e7PPU3MjKS+Ph4unfv7uHIRHzIhAnQrx8cO2Zv79AB4uMhPNw7cYlfUZIqIj7nvvvg8cftbRkZcM018Ouv3olJRCqGqKgoNm7cSFxcHH379nXaniYoKIh+/foRFxfHhg0bNIIqFYdlmTU4997rXNUwJsZUQaxVyzuxid8J8nYAIiIFeeEF2LMHJk8+1fbvv2Z20apVulErIp7jcDiIjo4mOjqalJQUkpOTOXLkCDVq1CAsLEzbzEjFc+IEDB1acJGIu++Gd94BN+03LAJKUkXERzkc8MEH8M8/Zi/VHImJ0KsXfPedbtiKiOeFhoYqKZWKLS3N7HtaUHGI0aPhqaeci0mIlJKm+4qIzwoKghkzoF07e/vmzXDddZCe7p24REREKoQDB6BbN+cENSAA3n8fnn5aCap4hJJUEfFpISEwdy6cf769/bvvYOBAyMryTlwiIiJ+7e+/TTGkH36wtwcHm+q+d93lnbikQlCSKiI+r25dU4+hUSN7e2xswfUbREREpBQ2bTL7nOavVlizJixZAr17eyMqqUCUpIpIudC0KSxcCGecYW9//3147jnvxCQiIuJ3vvvOjKAmJ9vbw8Ph++/hiiu8E5dUKEpSRaTcuOgimD0bqlSxtz/zjCmyJCIiIqXw9dfQvTscOmRvb9HClNZ3cQ9hkdJSkioi5UqnTjB9unOdhnvuMX9bRUREpATeew/69IFjx+zt7dubEdQmTbwTl1RISlJFpNzp0wfeftvelp0NAwaYv6MiIiLiIssyU5Luucf8Mc3r//7PrEGtXds7sUmFpSRVRMqlYcNg1Ch7W0YGXHut2aJGRERETuPECRg6FMaMcT52552mim/VqmUfl1R4SlJFpNwaMwaGDLG3HToEvXpBYqJXQhIRESkf0tOhb9+Cizo8/bSpTBgUVPZxiaAkVUTKMYfDLKG59lp7e1IS9OwJBw96Jy4RERGfdvCgKZA0e7a93eGAd9+F0aOdiz+IlCElqSJSrgUFweefw2WX2dt//dUspUlP905cIiIiPikx0Wwxs3Klvb1KFZg500z/FfEyJakiUu5VqwZz55oK+XmtXAn9+5slNyIiIhXeli1w+eXm37zOOAMWL4brr/dOXCL5KEkVEb9QuzYsXAhhYfb2b74xRZYsyztxiYiI+ISVK+GKK8yamLwaN4bvvoOOHb0Tl0gBlKSKiN8480yTqNasaW//8EN49llvRCQiIuIDvvkGunWDf/+1t19wAaxaBS1beicukUIoSRURvxIZaf4WV6libx8zxhRZEhERqVA++ghiYsw+bXn95z9mc/GmTb0Tl0gRlKSKiN/p0AE++wwC8v2GGzYMZs3yTkwiIiJlyrLguefMfqfZ2fZj11wDS5dCnTreie00UlJS2LJlC2vWrGHLli2kpKR4OyQpY0pSRcQvxcTAhAn2NsuCm26C+HjvxCQiIlImsrLMndmnn3Y+dvvtEBtrqg76EMuyiIuLo2/fvtSuXZuIiAjatWtHREQEtWvXpl+/fsTFxWGpyESFoCRVRPzW3XfDM8/Y244dM1vTbNzonZhEREQ8KiMD+vUreI3LqFFm+m9QUNnHVYSEhARatmxJly5d+Oqrr8jKyrIdz8rKYubMmXTp0oWWLVuSkJDgpUilrChJFRG/9swzcNdd9rbDh6FXL/j7b+/EJCIi4hGHDkGPHmakNC+HA95+20z/dTi8ElphlixZQseOHdm8ebNL52/evJmOHTuyZMkSD0cm3qQkVUT8msMB77wD111nb9+1C3r2hAMHvBOXiIiIWyUnm6IM331nb69cGb74Au691ztxFSEhIYGYmBhSU1OLdV1qaioxMTEaUfVjSlJFxO8FBZlCSu3b29u3bjW1I9LSvBOXiIiIW/z6K1x2GWzaZG8PDYVFi6BvX+/EVQTLshg8eHCxE9Qcqamp3HLLLVqj6qeUpIpIhVC1qtma5sIL7e0//gg33ggnTngnLhERkVL54Qe44gpITLS3N2pkKgVGR3slrNNZvny5y1N8C7Np0yZWrFjhpojElyhJFZEKo3ZtWLgQwsPt7XPnmiJLuhkrIiLlyty50LUrHDxobz/vPFi1Clq18k5cLpiQvwS/l/sR36IkVUQqlCZNzMynWrXs7ZMmwVNPeScmERGRYps0CXr3hvR0e/ull8LKldCsmTeicklKSgqx+Ys7ldCsWbO0j6ofUpIqIhXOhRfCnDkQHGxvf+EFU/xQRETEZ1mW+YM1ZIjZDzWvq66CZcugbl3vxOaipKQkp21mSiorK4vk5GS39CW+Q0mqiFRI7dvDjBkQkO+34P33w8yZ3olJRESkSFlZcN99Zr/T/G69Fb7+GkJCyjqqYjt69Khb+zty5Ihb+xPvU5IqIhXW//2f817nlgUDB8Ly5V4JSUREpGAZGdC/v9lXLb/HHzfTfytVKvu4SqB69epu7a9GjRpu7U+8T0mqiFRod94Jo0fb244fN/uq/vKLd2ISERGxOXwYevVynurjcMCbb8KLL5rn5UR4eDiBgYFu6SsoKIiwsDC39CW+Q0mqiFR4Tz0FQ4fa21JS4MorYccOr4QkIiJi7NoFHTtC/q1WKleGzz8303/LmdDQUGJiYtzSV0xMDKGhoW7pS3yHklQRqfAcDlMw6frr7e27d0PPnrB/v3fiEhGRCu633+Dyy2HDBnt7jRqwYAHccIN34nKDYcOG+VQ/4luUpIqIAIGBMH26uVmd17ZtcPXVkJrqnbhERKSCWr3aVPnbudPe3rAhxMdDly7eictNoqOjiYiIKFUfkZGRdOrUyU0RiS9RkioiclJwMMyeDZGR9vY1a8zN6sxM78QlIiIVzPz5Jgk9cMDe3rw5rFoFrVt7JSx3cjgcTJs2jZASViMOCQlh6tSpOMrRWlxxnZJUEZE8ataEhQvhzDPt7fPnmyJLluWVsEREpKKYMsWUn09Ls7e3bQsrV8JZZ3klLE+IiooiNja22IlqSEgIsbGxREVFeSgy8TYlqSIi+YSFmUS1dm17+9Sp8MQT3olJRET8nGXBSy/BbbeZ/VDz6tkTli2DevW8E5sHde/enfj4eJen/kZGRhIfH0/37t09HJl4k5JUEZECtGgBc+dC1ar29pdeMtX+RURE3CY7G0aMMPud5jdoEMyZA27eW9SXREVFsXHjRuLi4ujbt6/T9jRBQUH069ePuLg4NmzYoBHUCiDI2wGIiPiqyy6DL76A3r3tN7VHjDB1K8pxUUUREfEVx47B4MHmD05+jzxi7o5WgHWXDoeD6OhooqOjSUlJITk5mSNHjlCjRg3CwsK0zUwFoyRVRKQI11wDH3wAQ4acarMsc2O7bt1yX1xRRES86fBhiImBuDjnY+PHm7uiFVBoaKiS0gpO031FRE7j9tvh+eftbcePmxHW9eu9EZGIiJR7u3dDp07OCWqlSvDZZxU2QRUBJakiIi554gm4915725EjcOWVsH27d2ISEZFyats2uPxy+OUXe3v16qacfP/+3olLxEcoSRURcYHDAW+8AX372tv37DFFF/ft805cIiJSzqxdC+3bw44d9vb69WHFCujWzSthifgSJakiIi4KDISPPzazs/L6/Xe4+mo4etQ7cYmISDmxcCFER8P+/fb2c86BVatAVWtFACWpIiLFEhwMs2fDRRfZ29euNaOsmZneiUtERHzcxx/DtddCWpq9/ZJLTIJ6zjneiUvEBylJFREppjPOgAULoGlTe/uiRaYKcHa2d+ISEREfZFnw6qtmm5kTJ+zHunc3hZPq1/dObCI+SkmqiEgJNG5sktI6deztH39c8F7sIiJSAWVnw0MPmf1O87vpJpg7F2rUKPu4RHycklQRkRI6/3yYNw+qVbO3v/IKvP66V0ISERFfcfw43Hyz2e80v4ceMnc1K1cu+7hEygElqSIipdCuHXz5pSmqlNcDD5ht7kREpAJKSTEV9Qr6Q/C//5lHgN6GixRGPx2llJaWxq5du8j0sWophw4dYu/evWRrcZyIx111FXz0kXP7LbfAt9+WfTwiIuJFe/eaCr75/wAEBcEnn5hRVBEpkpLUEvr000+55JJLqF69OmFhYVSpUoWIiAjGjRvHifyL4l3UsWNHHA7HaR/vvfdegddnZGTw3HPP0axZM2rVqkXDhg0JDg6mc+fOxMbGlua/KyKnceutMHasvS0zE2JiICHBKyGJiEhZ++MPuPxy+Plne3tIiFkfMnCgd+ISKWeUpJbA0KFDGThwIAkJCViWBYBlWWzZsoWRI0cSHR3NkSNHit3vn3/+WeKYUlJS6NixI08//TQ7d+7Mbc/MzGT58uVcf/313H///SXuX0RO79FHIf+P2dGjcOWVUIofbxERKQ9++skkqH/9ZW+vVw+WL4cePbwSlkh5pCS1mN5//33ef/99ADp37szq1atJS0vj77//ZtSoUTgcDlauXMnQoUOL1W96ejq7d+8GYM2aNWzfvr3Qx8AC7sLdfffdrF27FoB77rmH33//nfT0dDZs2EBMTAwAb731Fh9++GFp/vsiUgSHw9THuOEGe/s//0DPnuZfERHxQ4sXmym++/bZ288+2+yB2qaNV8ISKa8cVs5QoJxWRkYGTZo0Yf/+/bRq1Yo1a9ZQOV9Vtscff5yXXnoJgLVr19LGxV9KmzdvJjIykmrVqpGamlqsuH766Sfatm0LmAR1woQJtuPZ2dl06NCBVatWUbduXRITEwkODi7WaxQlKSmJJk2aAJCYmEh4eLjb+hYpj44dM+tUly2zt19yidkOT7sNiIj4kenTzZqP/Mu9Lr4Y5s+Hhg29EpZUHP74XlwjqcUwZ84c9u/fD8Do0aOdElSAhx56iMCTZT4nTpzoct85U33PPvvsYsc1efJkACpVqsTo0aOdjgcEBDBy5EgA9u/fz+zZs4v9GiLiuipVIDYWWrWyt69bB336mF0JRETED7z2mtlmJn+C2rWrmeKrBFWkRJSkFsOyk8Mi1apV46qrrirwnLp16+aOni5ZssTlvnOS1HPPPbfEcUVHR1OvXr0Cz+nRowcBJ0udFycuESmZ0FBYsACaNbO3L1kCt99u9ncXEfEXSUlJfPPNN0yePJlvvvmGpKQkb4fkWdnZMHJkwZV6+/c3RZJCQ8s+LhE/EeTtAMqT+Ph4AKKioqhUqVKh51188cWsXr2av/76i/T0dKpWrXravvMnqZZlcfDgQU6cOEHdunVzR2fz++eff9i6dSsA7dq1K7T/kJAQzjvvPLZu3cqmTZtOG09ep/tDk7OWVkTsGjWCRYugfXs4OQkDMDPDGjY02+SJiJRX2dnZvP7667z22mskJyc7HQ8LC+PBBx9kxIgRuTfK/cLx4+Zu4/TpzsdGjIBx47QHqkgp6SeoGLZv3w6cfkrumWeeCZhEM2+l3aLkJKkhISHccccd1KlTh7p169KwYUNq1KjB1VdfzYoVK5yu27FjR+5zV+PKe40rmjRpUuTj0ksvLVZ/IhXJeeeZJUnVqtnbx40zDxGR8mj69OlUq1aNhx56qMAEFSA5OZmHHnqIatWqMb2ghK48OnIErr224AT1lVfM9F8lqCKlpp8iF2VkZJCeng5Q6JTaHDVr1sx97upWNDlJ6ujRo5k4cSL//vtv7rH09HTmz59PdHQ0jz32mO26AwcO5D53Na6SbI8jIiXXti189ZXZxz2vkSPNvu4iIuXJSy+9xM0338yxY8dcOv/YsWPcfPPNuYUly61//oHOnU0l37yCgmDaNHj4YVPmXURKTUmqiw4ePJj7/HTTd6tUqZL73JVf4FlZWbmjm9WqVeO5557jjz/+4NixY+zZs4eJEyfSuHFjAF5++WVeffXV3GvzJrOuxuXqH5UciYmJRT7WrFlTrP5EKqJevWDSJOf2225zfr8jIuKrpk+fzuOPP16iax9//PHyO6L6559mD9R16+zt1arBnDkwaJB34hLxU1qT6qLirKXImwS6stXLoUOH6NKlCw6Hg2eeeYb//Oc/uccaNGjA7bffTq9evbj00ktJTk7m2WefZciQIdSuXbtEcRV3+xl/KGMt4gsGDYI9e+CRR061nTgB119vikBqGz0R8WXZ2dkMGTKkVH0MGTKEAQMGlK81qgkJcOWVzptd161rCiRp2ZOI25Wj3xDeVb169dznp9vHNC0tLfd53qm/halTpw4LFy5kwYIFtgQ1r8aNG/Pkk0/m9r/45NBLSeJyJSYR8YyRI+GBB+xtqalmX9Xff/dOTCIirnj99deLPRsrv2PHjvHmm2+6KaIy8O230KmTc4LarBmsXKkEVcRDlKS6qHr16rnJ3a5du4o8N6eAQEBAQG6xIndo37597vOc6cE5G/cWJ66zzjrLbTGJSPE4HKaq74AB9vZ9+6BnTzPSKiLii1577TW39DOuvFSN+/xzcwfx6FF7e6tWsGqVqYwnIh6hJLUYLrzwQoDTbuHy22+/5Z5fuXJlt71+3m1vctafnnfeeQSdrMZSVFxZWVm5xZlat27ttphEpPgCAmDKFOjWzd6+fbt5P5SS4pWwREQKlZSUVGgV35L05fP7qL7+urmbmJlpb+/cGVasMHuMiYjHKEkths6dOwOwZcsWEhMTCzzn+PHjfP/99wB06dLFpX4HDx5MmzZtuPPOO4s876effsp93qJFC8Akqzn7oy5atKjIa1NOvvN1NS4R8ZzKlWHWLIiKsrf//LNZo1rKGXUiIm6VkJDg1v7Wr1/v1v7cxrLg0Ued12UA3HADLFgAZ5xR9nGJVDBKUouhf//+gNn/9PXXXy/wnEmTJuUmg4NcrPQWGBjIunXr+Pjjjwu9s5iVlcX48eMBs4a1Q4cOuccGnJw3+McffzBnzpwCr8+5tnbt2lx11VUuxSUinlWjhtlDNf8Wx0uXwq23Qna2V8ISEXGSd8s7d9i3b59b+3OLzEzzy/eVV5yP3XcffPYZ5NnBQUQ8R0lqMURGRnL99dcD8MYbbzBjxgzb8YULF/LQQw8B0KdPH9rkKdW5c+dOgoODCQ4OpkePHrbrbrvtNsAUE4iJiWHnzp2240ePHuW2227j559/BuCxxx6zbTdz22235VbgvfPOO23Tfi3L4vnnn8+NdfTo0bYtckTEuxo0gEWLoH59e/vnn8NDD5mb+iIi3lanTh239ne6vd3L3NGj8H//Z/Y7ze/FF+GNN8xaDREpEw7L0lug4ti3bx9RUVG5I55RUVE0b96cP//8M3c6btOmTVm1alXu3qZgCh3lFCzq1KkTy5cvt/X7wAMP5I7OBgUFcdlll9GkSRMOHTrEypUrOXz4MABXXXUVc+bMcSrdHh8fT7du3cjMzCQoKIhOnTpRp04d1q5dy/bt2wHo2bMn8+fPd3vZ96SkpNwCTomJidqyRqQE1q0zBSTzF+l++WX7ljUiIt6Q92+9O/jU+4V9++Dqq2HtWnt7YCB89JEZXRXxYf74XlxJagkkJydzyy23sHTpUqdj3bt3Z8qUKbYEFU6fpAJ8+OGHPP300+wpoLxnSEgII0aM4JlnnrEVUMpr5cqV3HLLLbkFknJUrlyZO+64g/Hjx7u1kFMOf/zBEPGGxYvN+6QTJ+ztU6fC4MHeiUlEJEd4eLhbiieFh4cXWtujzG3fbkqr598DrFo1+PJLU81OxMf543txJamlsGnTJn788Uf2799Pw4YNadu2LREREaXqMzMzk++++44NGzaQmppKzZo1Oe+882jfvj3VqlU77fWWZbFixQo2b95MamoqZ555JtHR0TRs2LBUcRXFH38wRLxl+nS4+WZ7W1AQzJkDvXp5JyYRETBb0OQsayqN8ePHM2LEiNIHVFrr18OVVzrv/VWnDsybBycLU4r4On98L64kVUrNH38wRLzptdfMetS8qlWDuDjtGy8i3pOdnU21atU4Vory41WqVCEtLc3tS4+Kbdky6N0bjhyxtzdtagoFnH++V8ISKQl/fC+uFeAiIj7mwQedk9S0NDMVeNs278QkIhIQEMDEiRNL1cfEiRO9n6B+8YUZQc2foLZsCatWKUEV8QFKUkVEfNArr8DAgfa2/fvN0qndu70Tk4jIwIEDGTt2bImuHTt2LAPz/2Ira2+9Bf37w/Hj9vZOnSA+HvLVFBER71CSKiLigwICYNIkyLdjFTt2mAGAkwW/RUTK3GOPPcYnn3zi8pZ2VapU4ZNPPuGxxx7zcGRFsCx44gm4/37nvb369IGFC6FmTa+EJiLOlKSKiPioypVh5ky45BJ7+y+/QEwMlGJZmIhIqQwcOJC0tDTGjx9PWFhYgeeEh4czfvx40tLSvDuCmpkJt98OBY0ADxsGM2ZAcHDZxyUihVLhJCk1f1ysLeJL/vkH2reHP/6wt99wA3z2mfaXFxHvS0pKYv369ezbt4969erRunVr33g/kJpqflnOn+987Pnnzeiqw1H2cYm4kT++Fw/ydgAiIlK0+vVNscnLLjMJa44vvoAGDeCNN/QeS0S8Kzw83PfeGO/fD9dcA6tX29sDA+H992HIEO/EJSKnpfvvIiLlwNlnw4IFUL26vf2tt+Dll70Tk4iIz9qxA664wjlBrVoVvv5aCaqIj1OSKiJSTkRFQWwsVKpkb3/8cZgyxSshiYj4ng0b4PLL4bff7O21a8PSpWZ0VUR8mpJUEZFypFs3mDbNuf2OO2DevLKPR0TEpyxfDh06OO/V1aQJfP+9WTchIj5PSaqISDnTvz+MH29vy8qCfv2cZ7aJiFQYM2eazaRTUuztkZHwww/QooV34hKRYlOSKiJSDo0YAY88Ym9LT4err3ae4SYi4vcmTDBVfI8ft7d36ADx8VDINjki4puUpIqIlFMvvQSDB9vbDhwwAwm7dnknJhGRMmVZMGoU3HuveZ5XTIwpjV6rlndiE5ESU5IqIlJOORzw0UfQq5e9fedO03bokFfCEhEpGydOwJ13wgsvOB+7+2748ktTzVdEyh0lqSIi5VilSuZ9WNu29vaNG6F3b8jI8EpYIiKelZYG118PEyc6Hxs9Gt591+yHKiLlkpJUEZFyrnp1U9m3eXN7+4oVcPPNpqiSiIjfOHDAlDqfM8feHhAA778PTz9tppqISLmlJFVExA/Uq2eWXjVsaG//6isYPtx5qZaISLn099+mGNIPP9jbg4Nh1iy46y7vxCUibqUkVUTET5x1FixYADVq2NvfeQdefNE7MYmIuM2mTXD55fDrr/b2WrXg22/huuu8E5eIuJ3XktR9+/axfft2/v77bw4cOOCtMERE/Err1jB7NlSubG8fNargpVsiIuVCfDxccQUkJ9vbw8Phu++gfXvvxCUiHhFUFi+SmprK3LlzWbJkCT/++CN//vknx/PtY1W5cmWaNm1K27ZtiY6Opnfv3tSpU6cswhMR8SudO8PHH0P//vZpvnfdBfXrw7XXei82EZFii42FAQPg2DF7+4UXwsKF0KSJd+ISEY9xWJbnVipt2LCB1157jS+//JKMkyUmT/dyjpML3QMDA+nduzePPfYYUVFRngpR3CApKYkmJ/9AJCYmEh4e7uWIRATgrbfg/vvtbcHBsHSpmTEnIuLz3nvP7IGanW1vb98evvkGatf2TlwiPsQf34t7ZCR13759PPLII3z88cdkn/ylUqVKFVq2bElkZCRNmjShbt26VKtWDcuyyMjI4MCBA+zatYstW7bwyy+/cPToUWbOnMlXX31Fnz59eOWVV2jWrJknwhUR8Uv33Qe7d8PYsafaMjLMSOr330OLFt6LTUSkSJYFzz4LY8Y4H/u//4PPP9ceqCJ+zO1J6rvvvsuTTz7JoUOHqFmzJjfddBO9e/emY8eOVM6/SKoQWVlZ/Pjjj8yYMYPp06czc+ZM5s6dyyOPPMLjjz9OlSpV3B22iIhfeuEFk6hOmXKq7eBB6NkTVq0yy7lEfMWvv/7K0qVL2bt3Lw0aNKBr16600N2UiufECTN6+sEHzsfuvBMmTICgMlmxViGkpKSQlJTE0aNHqV69OuHh4YSGhno7LKng3Drdt127dqxdu5azzjqLkSNHcsstt1CtWrVS9Zmens7UqVN59dVX2b59O+eeey7btm1zU8TiDv44xUDEn2RmQu/eMH++vT0y0tQiqVXLK2GJAObG9H333cfkyZNzlwblFRwczG233cZbb71FYGCgFyKUMpWebtafzp7tfOzpp83oqvZALTXLsli+fDnvvPMOX3/9NVl5NtQODAwkJiaGYcOGER0dnbsUT3yXP74Xd2t13/379zN58mS2bdvGPffcU+oEFaBq1aoMHTqU33//nWnTphEQoF1zRESKo1Il+OILaNfO3r5pk9mxIT3dO3GJPP/881SqVIl33323wAQVICMjg3fffZdKlSrx/PPPl3GEUqYOHoTu3Z0TVIcD3n0XRo9WguoGCQkJtGzZki5duvDVV1/ZElQwN45mzpxJly5daNmyJQkJCV6KVCoyt46knjhxgiAPT7/Izs5Woupj/PHujYg/2r/f7ODw22/29pgY+PJL0CCVlKUhQ4YwadKkYl93++23M1H7KfmfxETo1Qu2bLG3V6kCn34K11/vnbj8zJIlS4iJiSE1NdXla0JCQoiNjaV79+4ejExKwx/fi7s12/N0ggooQRURKaG6dWHRImjUyN4eG2uWf3mu1ruI3fPPP1+iBBVg0qRJGlH1N1u2mJLj+RPUmjVhyRIlqG6SkJBQ7AQVzFaSMTExGlGVMuXRLWgKk5mZedqtaACXCy2Jd/nj3RsRf7ZhA3TsCIcP29tHjzZLvkQ8KSsri0qVKrn0PqAwDoeDzMxMrVH1BytXmpLj//5rbw8LM3ugRkZ6Jy4/Y1kWLVu2ZPPmzSXuIzIykg0bNmiNqg/yx/fiZTIsuW/fPh588EHOP/98QkJCCA4OpmrVqkU+3LGeVUREnF10kVnylf8+4DPPFFxMU8Sd7rvvvlIlqGDecA8fPtxNEYnXzJ4N3bo5J6gXXGDKjytBdZvly5eXKkEF2LRpEytWrHBTRCJF83iSmpycTKtWrXjjjTf4/fffSU9Px7Islx4iIuIZnTrB9OnONUjuuafgopoi7jJ58mS39FPS6cLiIz780EzjzV8w67LLzEbOZ57pnbj81IQJE3yqH5HT8fgi0qeffpo9e/YA0LVrVy655BKqavNlERGv69sX3n7brEfNkZ0N/fvDt99C+/bei03806+//lpoFd/iSk9P59dff9U+quWNZcFzz5mpG/ldcw3MmAGaTedWKSkpxMbGuqWvWbNmkZKSon1UxeM8nqQuXrwYh8PBiBEjGDdunKdfTkREimHYMNi9G/LWocnIMO8Vv/8eIiK8F5v4n6VLl7q1v+XLlytJLU+ysuC//4X33nM+dvvt8P77UAZFOCuapKQkp21mSiorK4vk5GQlqeJxHp/uu2/fPgAGDRrk6ZcSEZESGDMGhgyxtx06ZHaDSEz0Skjip/bu3evW/nbt2uXW/sSDMjKgX7+CE9RRo+Cjj5SgesjRo0fd2t+RI0fc2p9IQTyepNavXx+AY8eOefqlRESkBBwO877x2mvt7UlJJlE9eNA7cYn/adCggVv7a9y4sVv7Ew85dAh69DD7XeXlcJg1B88957xAXtymevXqbu2vRo0abu1PpCAeT1K7desGwPz58z39UiIiUkJBQfD556ZmSV5btsD//R+kp3snLvEvXbt2dWt/0dHRbu1PPCA5GTp0gO++s7dXrgxffGFfFC8eER4e7rbtmoKCgggLC3NLXyJF8XiS+uSTTxIcHMwrr7zi9rUoIiLiPtWqwZw5kH+J38qVppjSiRPeiUv8R4sWLQgODnZLX1WrVtV6VF/366/mztemTfb20FBYtMhUbxOPCw0NJSYmxi19xcTEaD2qlAmPT/4/55xzmD17Ntdccw09evSgbdu2tGnTxqVv8BdffNHT4YmISB516sDChXD55WYAJMc335giS++/r1l5Ujq33XYb7777bqn7uf32290QjXjMDz+YCmz51ws0amR+yVx0kXfiqqCGDRvGzJkz3dKPSFlwWB7ekDQzM5PBgwczY8YMHMV8Z+OuSmTiWUlJSTRp0gSAxMREwsPDvRyRiJTWpk1wxRVw+LC9/emnYfRo78Qk/iErK4tKlSqVaj90h8NBZmam26YwipvNnQs33OC8TuD8880IatOm3omrArMsi5YtW7J58+YS9xEZGcmGDRuK/X5ePM8f34t7fLrvG2+8kZugWpZVrIeIiHhHZKQZPa1Sxd4+ZkzBxTlFXBUYGMiYMWNK1ceYMWOUoPqqiROhd2/nBLVdO7OvlRJUr3A4HEybNo2QkJASXR8SEsLUqVOVoEqZ8XiSOmXKFAAuuOACli1bxqFDh8jOznbpISIi3tOxI3z2GQTk+0tx773ORTpFimPUqFElnq57++23M2rUKDdHJKVmWfDCC3DHHWY/1LyuugqWLoW6db0TmwAQFRVFbGxssRPVkJAQYmNjiYqK8lBkIs48nqTu2LEDh8PBG2+8QXR0tBZbi4iUIzEx8M479rbsbBgwAOLjvROT+IeJEyfy3HPPuTwy43A4eO6555g4caKHI5Niy8qC++4z+53md+ut8PXXUMIRPHGv7t27Ex8fT0REhEvnR0ZGEh8fT/fu3T0cmYidx5PUWrVqAdBU0ztERMqloUPNWtS8jh0zW9Ns3OidmMQ/jBo1iszMTO69995Cq/5WrVqVe++9l8zMTI2g+qKMDFP+O//dLIDHH4dJk6BSpbKPSwoVFRXFxo0biYuLo2/fvk5T54OCgujXrx9xcXFs2LBBI6jiFR4vnDRo0CA+/fRTPv/8c/r16+fJlxIv8cfF2iJiZ1kmWf3gA3t7WBisWgVnnumduMS//Prrryxfvpxdu3bRuHFjoqOjtc2MLzt8GK67DlassLc7HPDGG2Z0VXxeSkoKycnJHDlyhBo1ahAWFqaZj+WMP74X93iS+uuvv3LJJZfQrFkz1qxZQ/Xq1T35cuIF/viDISLOTpww2xrOnm1vv+ACUw+lTh3vxCUiXrBrF1x5JWzYYG+vXBk+/thU9xWRMuGP78U9Pt23RYsWfPnll+zYsYO2bdsybdo0duzYwfHjxz390iIi4kZBQaaQUvv29vatW+HaayEtzTtxiUgZ++03s5ly/gS1Rg1YsEAJqoiUWpCnX6BatWqA2S9127Zt3HbbbS5d53A4OHHihCdDExGRYqpa1WxN06EDbNlyqv2HH+DGG03V3yCP/2UREa9ZvRquvhoOHLC3N2xoEtTWrb0Sloj4F4+PpGZkZJCRkUFWVpb2SRUR8QO1a8PChZB/NtHcuXD33Wb9qoj4oXnzoEsX5wS1eXOzOF0Jqoi4icfvdz/zzDOefgkRESljTZqYRPWKK+DQoVPtkyZBo0bw/PNeC01EPGHyZLjzTuc9UNu2NclrvXreiUtE/JKSVBERKZGICDN62q2b2YUixwsvmET13nu9F5uIuIllwUsvwRNPOB/r1Qu+/BJUFFNE3Mzj030Loqm8IiL+oX17+PxzCMj31+S++2DmTO/EJCJukp0Nw4cXnKAOHmwWqCtBFREPKLMkNS4ujuuvv57atWuzZMkS27HffvuN2rVr06dPHxYvXlxWIYmIiBtcdx289569zbJg4EBYvtwrIYlIaR07BgMGwFtvOR975BGYMgUqVSrzsESkYiiTJHXMmDF069aN2bNnc/jw4QLPOXToEF9//TVXXnklQ4YMITs7uyxCExERN7jzThg92t52/LhJYPPvUiEiPu7wYbMH6hdfOB8bPx5efhkcjrKPS0QqDI8nqStXruTZZ5/Fsizq1q3LE088Qet81d/Cw8MZNWoUzZo1w7IspkyZwpgxYzwdmoiIuNFTT8HQofa2lBSzbG3HDq+EJCLFtXs3dOoEcXH29kqVzEbJI0Z4JSwRqVg8nqS+dXKayPnnn8/WrVt57rnnqF+/vu2ckJAQxowZw+bNmxkwYACWZTFu3DhSUlI8HZ6IiLiJwwFvvw3XX29v370bevaE/fu9E5eIuGjbNrj8cvjlF3t79eowfz707++duESkwvF4krpq1SocDgdjxoyhVq1aRZ4bHBzMRx99RIMGDUhLS2PZsmWeDk9ERNwoMBCmT4cOHezt27bBNddAaqp34hKR01i71lRCyz/toX59WLHClPEWESkjHk9S//nnHwAiIiJcOr9q1apceumlAPz5558ei0tERDwjONgU/YyMtLevXg033ACZmd6JS0QKsXAhREc7T3c45xxYtQqiorwSlohUXB5PUqufLE1enKm7gYGBgLaqEREpr2rWNO97mzSxt8+fb4os6de7iI/4+GO49lpIS7O3X3KJSVDPOcc7cYlIhebxJLVVq1YAfPLJJy6dn5qaSnx8PADNmjXzVFgiIuJhYWGwaBHUrm1vnzoVnnzSOzGJyEmWBa+8YvY7PXHCfqxHD7N/VL4aIiIiZcXjSeqgQYOwLIt3332XsWPHkpWVVei5hw8f5qabbuLgwYNUrVqVHj16eDo8ERHxoBYtYO5cqFrV3j52bMHbL4pIGcjOhgcfhEcfdT42cCDMmWOKJYmIeInD8vCcWsuy6Ny5M/Hx8TgcDsLCwrj22mu59NJLqVevHg6Hg6SkJFavXs2MGTNIOznd5IUXXuCxxx7zZGjiJklJSTQ5OacvMTGR8PBwL0ckIr5m7lzo3Rvy3qd0OODzz806VREwS4OSkpI4evQo1atXJzw8nNDQUG+H5XNK9Xk6dgxuvdX88OX30ENmdDWg+GMY+tqJeI8/vhf3eJIKcOTIEXr37k3cyT23HIVsAJ0TyogRI3jttdc8HZa4iT/+YIiI+02cCHfcYW+rXBkWLIAuXbwTk3ifZVksX76cd955h6+//to24yowMJCYmBiGDRtGdHR0oe8fKgK3fJ5SUsweUUuXOh/73/9MklrWMYlIqfnje/EySVJzTJo0ibfffpv169cXeLxDhw48+eSTmuZbzvjjD4aIeMYLL8CoUfa2GjUgPh5at/ZKSOJFCQkJDB48mM2bN5/23IiICKZNm0ZUBaw065bP0969cOWV8PPP9vagIJgyxUzzLeuYRMQt/PG9eJkmqTn279/Phg0bOHDgAFlZWdStW5fWrVtTt27dsg5F3MAffzBExDMsC+67D955x97esKEpJHrWWd6JS8rekiVLiImJIbUYm+eGhIQQGxtL9+7dPRiZb3HL5+mPP6BnT/jrr/wnwqxZplBSWcckIm7jj+/FvZKkin/xxx8MEfGcrCzo3x9mzrS3N28OK1dCvXreiUvKTkJCAh07dixWkpMjJCSE+Pj4CjEq55bPU3Y2XHUV7NtnP6FePbMnVJs2ZR9TBfjaiZQlf3wv7tbqvkOGDGHHjh3u7NJm27ZtDBgwwGP9i4iI5wUGmq0ZO3Wyt//+O1x9NRw96p24pGxYlsXgwYNLlOSA2arulltu8fu91N3xeXrv+uuxoqOdE9SzzzZTF4qZoOprJyJlxa1J6vTp0zn//PO57bbb2LJli9v63bhxIwMHDiQiIoJFixa5rV8REfGO4GCYPRsuusjevnYt9O0LmZneiUs8b/ny5S6tYyzKpk2bWLFihZsi8k2l/TzdBLyzcyeO/AnlxRebKQvnnlvmMUHF+NqJSOm5NUndsGEDHTp0YOrUqbRs2ZJevXoxa9YsMjIyit1XSkoKn376KV27dqV169Z89tlnXH/99W5NfkVExHvOOMNU9m3a1N6+aBEMGWLWr4r/mTBhgk/146tK8/97EJgOVMp/oFs3WLHCLAIv45g80Y+I+C+PrEmdOXMmDz/8MDt37sThcBAUFESrVq247LLLaN26NbVr16ZWrVrUqlWLwMBAMjIy2LdvH4mJiWzevJmffvqJNWvWcOLECSzL4pxzzuGtt96iV69e7g5V3MAf58GLSNn57Tdo3x4OHLC3P/IIvPyyd2ISz0hJSaF27dq2rUpKKjAwkIMHD/rlXpwl/Tw5gFeBAjeSGTDAVPGtXLlMYyqIP3/tRLzBH9+LB3mi0759+3Ldddfx7rvv8uqrr5KcnMxPP/3EunXrXLo+J29u3rw5w4cP54477qByCX+pioiIbzv/fJg3z+yVmpZ2qv2VV6BRIxgxwmuhiZslJSW5JckByMrKIjk52S8TnZJ8nioBk4CbCzh2YPBg6kyeDAEln0Cnr52IlCW3TvfNq1KlStx///3s2LGDb775hltuuYWmTZtiWVahD4fDwQUXXMCDDz7Id999x2+//cawYcOUoIqI+Ll27eCLL0xRpbweeAA+/9w7MYn7HXVzVawjR464tT9fUdzPU3VgDgUnqA8Dfw4bVqoEtSQxnY6/fu1ExD08MpKaV2BgINdccw3XXHMNAAcPHuTPP/9k9+7dudXhzjjjDOrXr09ERARVq1b1dEgiIuKDrr4aPvoIbrvN3j54MNSta5bTSflWvXp1t/ZXo0YNt/bnK4rzeaoHzAfy1+nNBG4HPgFud8OIpb52IlKWPJ6k5le7dm1q165d1i8rIiLlwK23wp498Pjjp9oyMyEmxtR70faK5Vt4eDiBgYFumTYaFBREWFiYG6LyPa5+ns4GFgH56/SmAn1OHnPX50lfOxEpSx6b7ltRpKWlsWvXLjLdtF/C+vXrGTZsGBdccAGhoaFUq1aNZs2acdNNN/Hdd9+55TVERHzZo4/CfffZ244ehSuvhD//9E5M4h6hoaHExMS4pa+YmBi/XdPoyufpYmAVzgnqPqAzJkEF932e9LUTkbKkJLWEPv30Uy655BKqV69OWFgYVapUISIignHjxnHixIkS9Tlu3Djatm3Lu+++y2+//caRI0dIT09n586dfPbZZ3Ts2JFHHnmkwGuXLVuGw+E47aNhCcvOi4iUFYcDXn8dbrjB3v7PP9Crl/lXyq9hw4b5VD++qqj/X1dgBdAgX/t2oD2w1sV+3BmTN/oREf+lJLUEhg4dysCBA0lISMitRGxZFlu2bGHkyJFER0cXuyDA7NmzGTlyJCdOnOCss87iww8/ZP369WzevJkPPvggt6z0q6++ysSJE52u/1PDCyLiRwICYNo06NzZ3v7HH3DVVaCaK+VXdHQ0ERERpeojMjKSTp06uSki31TY5+lGzBrU/Cs61wOXA7/naXP350lfOxEpK0pSi+n999/n/fffB6Bz586sXr2atLQ0/v77b0aNGoXD4WDlypUMHTq0WP0+9dRTADRu3Jg1a9Zwxx130KpVKy688ELuvPNOfvzxR+rXrw/Ak08+SUZGhu36nCT1pptuYvv27YU+1q5di4hIeVClCsTGQqtW9vZ166BvXzh+3DtxSek4HA6mTZtGSEhIia4PCQlh6tSpOBwON0fmWwr6PA0HPgfy73mwFOgE7MnT5onPk752IlJWlKQWQ0ZGBqNGjQKgVatWLFy4kEsvvZSqVavSpEkTnnvuOR599FHATAf+6aefXOp348aNbNy4EYBHHnmEunXrOp3TuHFj/vvf/wKwd+9eli9fbjuek6S2atWKZs2aFfrIGZEVESkPzjgDFiyAZs3s7YsXw+23Q3a2V8KSUoqKiiI2NrbYyU5ISAixsbFEVZAKWjmfp+rVqvES8HoB58wArgJS8rR58vOkr52IlAUlqcUwZ84c9u/fD8Do0aML3L/1oYceIvDkRn8FTcstyKZNm3Kfdytij4U2bU4VmN+8ebPtWE6Ses4557j0miIi5UWjRrBokdmGJq/p06GQZfpSDnTv3p34+HiXp49GRkYSHx9P9+7dPRyZb+keHc2Ozp15tIBjbwADgLyTCsri86SvnYh4mpLUYli2bBkA1apV46qrrirwnLp16+Ymk0uWLHGp3927d+c+L6qw0bFjx3Kf56yFzZGTpJ57bv46f6WXlJRU5CNv/CIinnDeeTBvHlSrZm8fN848pHyKiopi48aNxMXF0bdv39ybvDmCgoLo168fcXFxbNiwoeKNwh09CtdeS51585wOPQaMACy883nS105EPKnM90ktz+Lj4wHzi7lSpUqFnnfxxRezevVq/vrrL9LT06latWqR/d59993cdNNNANSpU6fQ8+bMmZP7vHnz5rnP9+/fT0qKmeiTM5KakZHBwYMHqVGjRqk3zNYUYRHxBZdeCl99BddeC3mLqI8cCQ0bwsCB3otNSs7hcBAdHU10dDQpKSkkJydz5MgRatSoQVhYWMXdqmTfPrj6ashfSyIwkPS33uKW6Giu9/LnSV87EfEUjyepjz/+OAMHDiQyMtLTL+Vx27dvB+Dss88u8rwzzzwTMKOdO3fu5IILLijy/JCQkNOu7Zg/fz5Tp04FTCKbd1pwzihqw4YNmT9/Pi+99BLr16/PHW0955xzuPnmm3nwwQf1B0NEyrVevWDSJBg82N5+661Qrx706OGVsMRNQkND9XcKYPt26NkTfv/d3l6tGnz5JVWvuooW3omsUPraiYg7eXy678svv0yrVq1o2bIlY8eOZceOHZ5+SY/IyMggPT0dgHr16hV5bs2aNXOfF3crmvwsy2L8+PH07t2brKwsAF566SVbUpuTpO7Zs4cbb7yRn3/+2TYd+M8//2T06NG0bt2aLVu2FDuGxMTEIh9r1qwp1f9RRKQ4Bg2CV16xt504AddfDy7WqxPxXevXw+WXOyeoderAsmVmDyYRET9XJmtSLcti8+bNjBo1inPOOYfLL7+ct99+m71795bFy7vFwYMHc5+fbvpulSpVcp/nXUdaXOvXr6d9+/Y8+OCDZGZmAjBq1CjuuOMO23l590i94oorWLhwIf/++y8ZGRmsXbs2dyrx9u3b6dGjB//++2+x4ggPDy/y0ahRoxL/H0VESmLkSBgxwt6Wmmrev//xh1dCEim9ZcugY0fYs8fe3rQprFwJ7dp5Jy4RkTLm8SR127ZtjB07ljZt2mBZFpZl8eOPPzJ8+HDCw8Pp3r07U6ZMyV1T6asCAlz/VOVNTIODg4v9WocOHeK+++6jTZs2/PDDDwDUr1+f2NhYnnvuOafzQ0ND6dmzJ8OHD2f58uX07NmTmjVrUqVKFdq0acP06dMZPXo0AMnJybySfwhCRKSccThMwaQBA+zt+/aZWZL53+OL+LwvvoArr4T8M7BatoRVq+D8870Tl4iIF3g8ST333HN59NFHWbNmDTt37mT8+PF06NABh8NBVlYWS5cuZciQITRs2JAbb7yROXPm5E5r9SXVq1fPfZ6amlrkuWlpabnP8079dcXcuXNp0aIFb7/9NllZWQQGBnLPPfewdetWevfuXeA1w4cPZ+HChbz++utO1fVyPPHEEzRu3BiA2NjYYsUkIuKLAgJgyhTIv3PXX3+ZEVUfv/cpcspbb0H//nD8uL29UyeIj4eTf79FRCqKMt2CpkmTJgwfPpwVK1awa9cu3n33Xbp3705gYCAZGRl8+eWX9O7dm7CwMB5++GF+/fXXsgyvSNWrV89NOHft2lXkucnJyYAZfc0pouSK//3vf/zf//0fe04OAXTp0oX169czYcIEatWqVbLATwoKCqLdyWlCO3fuLFVfIiK+onJlU/H34ovt7T//bNao5n/PL+JTLAueeALuv988z6tvX1i4EIp5s1tExB94bZ/U+vXrc/fdd7No0SL++ecfpkyZwjXXXAPAP//8w2uvvUZkZCTR0dHMnDmT7Oxsb4Wa68ILLwRg06ZNRZ7322+/5Z5fuXJll/r+7LPPePjhh7Esi2rVqjF58mSWLl3q1qrIOdvmlGQKsoiIrwoNhQULIH/h9aVLTdVfH/jzIeIsMxNuvx3GjnU+du+98PnnoL/XIlJBeS1JzWvTpk2sX7+ejRs3AmbfrZz1q/Hx8dx4442cd955zJgxw6txdu7cGYAtW7aQmJhY4DnHjx/n+++/B8xIqCuOHTvG8OHDAZNALlmyhFtvvdWla7///nvatGlDmzZtWJt/L7V81q1bB0CLFr5WuF5EpHQaNIBFi8w2NHl99hk89JDzIJWIV6WmQu/eZr56fi+8YKb/FrJ8R0SkIvBKkpqVlcXixYsZOnQojRo1olOnTrzxxhvs3LkTy7I477zzePbZZ5k3bx4333wzVapU4a+//uKmm25i0qRJ3ggZgP79+wOmWvHrr79e4DmTJk3KLQI1aNAgl/pdvHgx+/btA+DNN9/k8ssvdzmmJk2asG7dOtatW5e7j2pBZs2alVsFOCYmxuX+RUTKi3PPhfnzIf+206+/Dv/7n1dCEnG2fz907Wq+WfMKDISJE830X4fDO7GJiPgIh2WVzf3ljIwMFi5cyKxZs5g7dy6HDx8GyN3Ps0mTJtx4440MGDCAi/MtLjpw4ACDBg1i4cKFtGjRgs2bN5dFyAXq06cPs2bNIjAwkOnTp3PjjTfmHlu4cCF9+vQhLS2NPn36MHPmzNxjO3fu5PyTlfk6duzI4sWLc4/dddddfPjhh9SsWZM1a9bkTsstSt26dXOLOXXp0oW4uDgCAgJ48803GTZsGI48f+Di4uLo06cP//77L40aNWLr1q1u3XA7KSmJJk2aAGZP1fDwcLf1LSJSXIsXw9VXm71T85o2zeyxKuI1O3ZAr15wcllQrqpVTXXfk8ueRESKwx/fi3s8Sf3000+ZNWsWixYtyq16m/OSderUoW/fvtx000106NChyH4SEhJo06YNlStXJiMjw5MhF2nfvn1ERUWRlJQEQFRUFM2bN+fPP//kp5O7yDdt2pRVq1blVtMF2LFjB2eddRYAnTp1Yvny5bnHunbtyrJly4oVx+TJk3OnBG/bto0OHTrwzz//AGZf07Zt21K5cmW2bt3KL7/8Apj9XefNm5c7bdld/PEHQ0TKt+nT4eab7W1BQTBnjskRRMrchg3mm2/3bnt77dowdy5cdpl34hKRcs8f34sHefoFbr755tw1pmCq5F533XUMGDCAnj17FrplSn7p6ekALhci8pR69erx448/csstt7B06VISEhJISEjIPZ6z72vjYpSL37t3b6liOu+881izZg3Dhw/nm2++ISkpKTeJzvGf//yHN998k7Zt25bqtUREyoOBA81eqSNHnmo7cQL69IG4OLj0Uu/FJhXQ8uVw3XXO+yI1aWIWU6tWhIiIjcdHUgMCAqhcuTK9evXipptu4tprr6Vq1arF7mfv3r1s3bqVkJAQ2rRp44FIi2/Tpk38+OOP7N+/n4YNG9K2bVsiIiK8GtPff//NihUrSEpKIigoiAYNGnDZZZfRvHlzj72mP969ERH/MHIkjBtnb6tbF1auhPPO805MUsHMnGnumuTfDyky0mwxExbmnbhExG/443txjyepEydOpG/fvpxxxhmefBnxIn/8wRAR/5CdDYMHm+m/eTVrBqtWQaNGXglLKooJE+C//3UuL92hA8yeDaXcA11EBPzzvbjHq/sOGTJECaqIiHhFQABMmgQ9etjbd+yAK6+EkzX8RNzLsmDUKLPfaf4ENSbGTPFVgioiUii3rkmNj493Z3d07NjRrf2JiEjFU7mymXHZuTOc3C4agF9+geuvNzuBVKnivfjEz5w4AXffbe6O5Dd0KLz9tvZAFRE5DbcmqdHR0batT0rD4XBwIv/+ASIiFVxKSgpJSUkcPXqU6tWrEx4e7tYtpfxVjRomGb38cji5ZTQAy5aZ6cCffWZGXSsSfS95QFoa3Hijqdab35gxZnRVe6CKiJyW2/8kW5bltoeIiJjfq3FxcfTt25fatWsTERFBu3btiIiIoHbt2vTr14+4uDj93jyN+vXNLMv69e3tX3wBI0Y4z8r0R/pe8qADB6BbN+cENSAAPvgAnnpKCaqIiIvcWjhpxYoV7uoKMPuJiu/zx8XaIr4iISGBwYMHs3nz5tOeGxERwbRp04iKiiqDyMqvhATo1AmOHrW3jx0Ljz3mnZjKgr6XPOjvv6FnT9i61d4eHAyff262nxER8RB/fC/u8eq+4v/88QdDxBcsWbKEmJgYUlNTXb4mJCSE2NhYunfv7sHIyr9vv4WrroLMTHv75Mlw661eCcmj9L3kQRs3Qq9esGuXvb1WLZgzB9q3905cIlJh+ON7cZ9cgbNx40bi4+M5rLKLIlJBJSQkFDupAEhNTSUmJoaEhAQPReYfunWDadOc2++4w6xd9Sf6XvKg+HiznUz+BDU8HL77TgmqiEgJ+WSS+sILL9C5c2em59/YTkSkArAsi8GDBxc7qciRmprKLbfconWFp9G/P4wfb2/LyoJ+/WD1au/E5G76XvKg2Fizt1H+G+oXXmg24Y2I8E5cIiJ+wK3VfYsyd+5cli1bxuHDh4v8Y3fs2DEWLFgAQFpaWlmFJyLiM5YvX+7SusGibNq0iRUrVhAdHe2eoPzUiBGweze88sqptrQ0uPpqWLkSzj/fa6G5hb6XPOS998weqNnZ9vb27eGbb6B2be/EJSLiJ8okSb3rrruYOHGiy+dblkVQUJD+IIpIhTRhwgS39aPfo6c3dqxJVD/++FTbgQOmDs6qVdC4sfdiKy19L7mZZcGzz5rtZPL7v/8zRZKqVi3zsERE/I3HCyf98MMPtD+5JiMoKIizzz6b5ORkUlNTqV69OnXq1CErK4s9e/Zw4sQJHA4H1157Lffee6+KNZQT/rhYW8RbUlJSqF27NllZWaXuKzAwkIMHD2rvSxdkZpocY+FCe/tFF5llh2ec4Z24SkPfS2524oQZPf3gA+djd94JEyZAUJlNUBMRyeWP78U9vib1o48+AuCCCy7g77//ZuvWrWzZsoUqVarQqFEjtm/fzt9//01KSgovv/wyAQEBpKSk0LlzZ0+HJiLic5KSktySVABkZWWRnJzslr78XaVK8OWX0LatvX3DBrN7SEaGd+IqDX0vuVF6OvTtW3CC+swz8P77SlBFRNzI40nqTz/9hMPh4NFHH6Vhw4YANGnShKioKP744w/++usvAIKDg3n44Yd59tlnWbFiBS+88IKnQxMR8TlH82/eWUpHjhxxa3/+rHp1mDcPmje3t69YAYMGmaJK5Ym+l9zk4EFTDnr2bHt7QAC8+66Z/utweCU0ERF/5fEkNefOa6tWrWztzZo1A2DDhg229gceeIBq1arx2muvkZ6e7unwRER8SvXq1d3aX40aNdzan7+rVw8WLYKT91RzzZwJw4ebJYnlhb6X3CAx0Wwxs2qVvb1KFfNNMXSod+ISEfFzHk9Sc+7k5v9jmZOk5oyk5qhWrRqXX345R48eZfHixZ4OT0TEp4SHhxMYGOiWvoKCgggLC3NLXxXJWWfBggWQPyd75x148UXvxFQS+l4qpc2b4fLLYcsWe3vNmrBkCcTEeCUsEZGKwONJauOTZRG3bdtma2/WrBmWZbFx40ana2rVqgU4J7AiIv4uNDSUGDe9+Y2JianYhW5KoXVr+PprqFzZ3j5qFBSjWL1X6XupFFauhCuugKQke3tYGHz3nRldFRERj/F4ktqxY0csy+Lll18mMzMzt/2SSy4BYPHixbbCDtnZ2axbtw5Am4eLSIU0bNgwn+qnourSxWxLk3+54V13wZw53ompuPS9VAKzZ5s1qIcO2dsvuMBM+42M9EpYIiIViceT1HvuuQeA77//nvPPP58xJ/cWu/jii6lTpw579uxhyJAhJCUlsWvXLu69917+/PPP3HNERCqa6OhoIiIiStVHZGQknTp1clNEFdcNN8Abb9jbsrPhxhvhhx+8E1Nx6HupmD78EK6/3rmc82WXwfffw5lneicuEZEKxuNJart27Xj00UexLIsdO3bwySefAOBwOHj22WexLIuPP/6Ypk2b0qRJEz44Wd49KipK29CISIXkcDiYNm0aISEhJbo+JCSEqVOn4lDFUbe47z54/HF7W3o6XHMN/Pqrd2Jylb6XXGRZMGaMGSbPzrYfu+Ya+PZbqFPHO7GJiFRAHk9SAcaOHUtcXBzDhg2zJZ7Dhg3j5ptvxrIs2+Oiiy4iNja2LEITEfFJUVFRxMbGFju5CAkJITY2lqioKA9FVjG98ALcequ97eBB6NkTfH37UH0vnUZWFgwbZvY7ze/22yE2FqpVK/u4REQqMIflAws/ExIS+O6778jIyKBly5b06tWLgIAyyZ/FDZKSkmjSpAkAiYmJhIeHezkiEf+RkJDA4MGD2bx582nPjYyMZOrUqf6fVHhJZib07g3z59vbIyMhPh5O1vzzWfpeKkBGBtx0k0lE8xs1yoyu+vsosoiUe/74XtwnklQp3/zxB0PEl1iWxYoVK3jnnXeIjY21FZsLCgoiJiaGYcOG0alTJ/+flullqanQtSusXm1v79DB7K9atap34nKVvpfy+PdfuO46U603L4cD3n7bjK6KiJQD/vheXEmqlJo//mCI+KqUlBSSk5M5cuQINWrUICwsrGJtDeID9u83u5P89pu9PSYGvvwS3LQ1qcdV6O+lpCS48krYtMneXrkyfPop9OnjnbhERErAH9+LB5XVCx08eJBFixaRmJjI4cOHXdpe5sXytGu6iEgZCA0NrTiJhI+qW9eMml52Gezefao9Nhb++1+YMKF8zBCtsN9Lv/5qFhMnJtrbQ0Phm2+golQyFhHxYWWSpD733HM8//zznDhxoljXKUkVERFf1LQpLFxopvmmpJxqf+89aNQInn7ae7FJEVatMtV6//3X3t6okfmCXnSRd+ISEREbj1cnmjp1Ks888wyZmZlOVXxP9xAREfFVF10Es2ebGaJ5PfOM2W5TfMycOdCtm3OCev75ZtNbJagiIj7D40nqu+++C0Dz5s357rvvSEtLIzs726WHiIiIL4uOhunTnaf3Dh1qEljxERMnmkXD6en29nbt4PvvzdC4iIj4DI8nqb/++isOh4OXX36Z9u3bExwc7OmXFBERKTN9+5pisHllZ0P//rBypXdikpMsy2xye8cdZj/UvK66CpYuNYuMRUTEp5TZZqTnn39+Wb2UiIhImRo2DJ580t6WkWGWP7qwLal4QlYW3Hef2e80v1tvha+/hpCQso5KRERc4PEktUWLFgBs27bN0y8lIiLiNc89B0OG2NsOHYJevZwLyYqHZWSYoex33nE+9vjjMGkSVKpU9nGJiIhLPJ6kDhkyBMuyeP7557XOVERE/JbDYar7XnONvT0pySSqBw96J64K5/Bh8wmfOdPe7nDAm2/Ciy+Wjz2CREQqMI8nqXfccQdXXnklCQkJdO3alYULF7J//36OHz9+2oeIiEh5EhQEM2aYPVTz2rIF/u//nOv2iJvt2gUdO8KKFfb2ypXh88/N9F8REfF5DqsM9npZs2YNHTt2JDMz0+VrHA5HsfdVFe9ISkqiSZMmACQmJhIeHu7liEREvOvAAbOH6q+/2tuvu84M8AWVyS7lFczWrdCzJ/z9t729Rg1TarlzZ+/EJSLiYf74XtzjI6nr16+nS5cu2idVREQqjDp1YOFCaNzY3j57timypD9xbvbjj3DFFc4JasOGEB+vBFVEpJzx+L3c0aNHk5aWBkDHjh2JiooiNDQUh9aDiIiIHzvzTJOoduhglknm+PBDk7w++6zXQvMv8+ZBv37Oc6mbN4dFi+Css7wTl4iIlJjHk9TVq1fjcDh48sknGTNmjKdfTkRExGe0bAnffAM9esCxY6faR482g3xDh3ovNr8weTLceafzHqht25rktV4978QlIiKl4vHpvkeOHAFgwIABnn4pERERn9OxI3z2GQTk+4t7770QG+udmMo9y4KxY+H2250T1F69YNkyJagiIuWYx5PU5s2bA+RO+RUREaloYmKct+zMzoYBA+C777wTU7mVnQ3Dh8MTTzgfGzzYDF1Xr172cYmIiNt4PEm96667sCyLjz/+2NMvJSIi4rOGDoWnn7a3HTtmtqbZtMk7MZU7x46ZzP6tt5yPPfIITJkClSqVeVgiIuJeHl+TeueddzJr1izefvttLrzwQu666y5Pv6SIiIhPevZZ2L3bFE/KceiQmaG6apUptlRWkpKSSEhI4MCBA9SpU4eoqCivb1tQZEyHD5sh6bg45wvHj4cRIzwSU0pKCklJSRw9epTq1asTHh5OaGioR15L3EtfO5Hyy+P7pL7yyiukp6fz6quvkp6eTrNmzejQoQONGzcmODi4yGufzn/LWXySP+7NJCLiKSdOQJ8+ZlZqXhdcAN9/b7av8ZTs7Gxef/11XnvtNZKTk52Oh4WF8eCDDzJixAgC8i+i9WJMT91xB3d9/TWOX36xH6xUCaZNg/793RqTZVksX76cd955h6+//pqsPOteAwMDiYmJYdiwYURHR2u3Ah+jr51URP74XtzjSWpAQECJfwlk5S+GID7JH38wREQ8KT0duneHlSvt7ZddBt9+C9Wquf81p0+fzpAhQziWt8xwIapUqcLEiRMZOHCg+wMpZkzNgUWA00YyNWqYylNdu7o1poSEBAYPHszmzZtPe25ERATTpk0jKirKrTFIyehrJxWVP74XL5PbpJZlleghIiLij6pWNSOpF15ob//hB7jxRjPa6k4vvfQSN998s0sJKsCxY8e4+eabeemll9wbSDFjaguspIAEtUEDWLHC7QnqkiVL6Nixo0tJDsDmzZvp2LEjS5YscWscUnz62on4F48nqdnZ2SV+iIiI+KvatWHhQsh/w3vuXLj7brPLijtMnz6dxx9/vETXPv7440yfPt09geThSky9gDgg/0YyvwOzH30ULr7YrTElJCQQExNDampqsa5LTU0lJiaGhIQEt8YjrtPXTsT/eHy6r/g/f5xiICJSVjZvhiuuMAWU8ho1Cp57rnR9Z2dnU61aNZdHUAtSpUoV0tLS3LZG1ZWYBgETgfx1en8CrgJS3ByTZVm0bNnS5VG4gkRGRrJhwwatcyxj+tqJ+Od78bKpipBPdnY2+/btY/v27Rw4cMAbIYiIiPiEiAgzepq/luDzz8OECaXr+/XXXy9Vggpm6u+bb75ZukDyOF1MDwPTcE5QFwHRwD4PxLR8+fJSJTkAmzZtYsWKFW6KSFylr52IfyrTJPWrr76iR48ehIaG0rBhQ84991zq169PzZo16dGjB9OmTSMzM7MsQxIREfG69u3h888h/8Dgf/8LM2eWvN/XXnutdIGdNG7cOLf0A4XH5ABeA14p4NgnwLVA3smc7oxpQmnvBri5H3GdvnYi/qlMpvumpqZy0003MXfuXIACiyLlTLE4//zz+eSTT1RtrRzxxykGIiLe8OGHkH878cqVYdEiiI4uXl95fze7gzt+vxcWU2VgCjCggGv+BzwCFPRmxR0xpaSkULt2bbfsKBAYGMjBgwe1F2cZ0ddOxPDH9+JBZfEigwcPZu7cuViWRaNGjbjuuus4++yzOeOMM9i7dy87d+5k3rx57Nmzh61bt9KlSxe+++47WrZsWRbhiYiI+IQ774Tdu+GZZ061HT8O110H330HF13kel/uLgazfv36Ur/xKSimGsAsoFsB5z+EGV31ZExJSUlu2/IuKyuL5ORkJTplRF87Ef/l8SR13rx5xMbG4nA4GDNmDE888USBhQ6ys7OZP38+9957L4mJidxzzz18//33ng5PRETEpzz1lElU33vvVFtKCvTqBatWQbNmrvXj7poP+/btK3Uf+WOqDywA8s+dygRuBT4tg5iOHj1a6j7yOnLkiFv7k8Lpayfivzy+JnXixIkADBw4kFGjRhVaiS8gIIBrrrmGuXPnEhAQwA8//MC2bds8HZ6IiIhPcTjg7bchJsbevnu3SVT373etnzp16rg1rnr18m8GU3x5YzoXWIVzgnoEuJrTJ6juiql69eql7iOvGjVquLU/KZy+diL+y+NJ6o8//ojD4eDWW2916fyWLVty2WWXAbBq1SoPRiYiIuKbAgPh00+hQwd7+2+/wTXXgCvbQbq7tkPr1q1L3UdOTJcAK4Fz8h3/B1PBd0kZxhQeHk5gYGCp+wEICgoiLCzMLX3J6elrJ+K/PJ6kHjx4EKBYxRvOPPNMAP755x+PxCQiIuLrgoPhm28gMtLevno13HADnK4Yfnh4uNvedIeHh7ulEEd4eDgD6tRhOWaqb15/ApcDrq6kdVdMoaGhxOQfti6hmJgYrWksQ/raifgvjyeptWrVAoqXcOYktpUrV/ZITCIiIuVBzZqwYAHkv887f76pAny6+vwPPvigW+J46KGH3NIP06fz8b//kn+S5jpMgvqnN2IChg0b5lP9iOv0tRPxTx5PUnOm9nz22Wcunb9//36+++47AJo3b+6xuERERMqD8HCzBU3t2vb2KVPgySeLvnbEiBFUqVKlVK9fpUoV7r///lL1AcC4cXDzzQRmZ9ual2Cm+BZn7pTbYjopOjqaiIiIUvURGRlJp06d3BSRuEpfOxH/5PEkddCgQViWxXvvvcfbb79d5LmHDx+mf//+pKWlERoaSpcuXTwdnoiIiM9r0QLmzoWqVe3tY8fCW28Vfl1AQEBuAcOSmjhxYqFFD12SnQ0PPQQjRzod+hRTJKm4NVpLHVM+DoeDadOmERISUqLrQ0JCmDp1au6e71J29LUT8U8eT1L79+9P165dyc7OZvjw4URGRjJ27FiWLl3Kpk2b2LhxI/Pnz+fhhx/m3HPPJS4uDofDwbPPPkvV/H+NRUREKqjLLoMZM0xRpbyGD4cvvij8uoEDBzJ27NgSvebYsWMZOHBgia4FzCavgwfDa867na5t356bMdvNlGlMhYiKiiI2NrbYyU5ISAixsbFuL1QlrtPXTsQPWWXgyJEjVrdu3SyHw2E5HA4rICCgwEfO8QceeKAswhI3SUxMtAALsBITE70djoiIX/voI8syq1FPPSpXtqxly4q+7pNPPrGqVKmS+/u6qEeVKlWsTz75pHSBpqRYVo8ezsGCZb3yimVlZ5d9TC5Yt26dFRER4VJMkZGR1rp16zwek7hGXzupqPzxvbjHR1LB7GO1ZMkSJk6cSOvWrbEsq8BH+/btmTdvHq8VcMdVREREYMgQeP55e9vx43DddbB+feHXDRw4kLS0NMaPH19o1d/w8HDGjx9PWlpa6UYr//kHOneGxYvt7UFBMG0aPPwwOBxlG5OLoqKi2LhxI3FxcfTt29dpi5OgoCD69etHXFwcGzZs0CicD9HXTsR/OCzrdLUB3W/fvn388ssvHDhwAIC6dety8cUXUzt/VQgpF5KSknK3GEpMTHTLlgAiIlI4y4L77oN33rG3N2wIq1bBWWedvo+kpCTWr1/Pvn37qFevHq1bt3bP7+8//4SePc2/eVWrBl99Bb16lX1MpZCSkkJycjJHjhyhRo0ahIWFaauSckJfO6ko/PG9uFeSVPEv/viDISLi67KyoH9/mDnT3t68OaxcCfXqeSGohAS48kozkppX3bowbx5ceqkXghIR8W/++F68TKb7ioiIiHsFBsLHH0P+nTN+/x2uvhqOFrdkbml9+60JJn+CetZZZnhXCaqIiLioTJLU1NRUnnrqKS655BJq1qxJUFAQgYGBRT6CgoLKIjQREZFyKzgYvv4aWra0t69dC/36QWZxS+eW1GefwVVXOWfGrVubBFX7nouISDF4PBM8dOgQ//nPf/j9998B0OxiERER96lZExYuhMsvh507T7UvXAh33AFTpoBHt4AcPx4efNC5vUsXiI0FrQEUEZFi8niSOmbMGLZt2wZA/fr16dGjB40bN6ZSpUqefmkREZEKoXFjWLQI2reHkzUJAVNIt2FDePllD7xodjY89hi8+qrzsRtuMC9epYoHXlhERPydx5PUOXPm4HA46Nq1K19//TXVqlXz9EuKiIhUOOefD3PnmgHM9PRT7a+8Ao0awYgRbnyxzEyzF87HHzsfu/9+M7oaoLIXIiJSMh7/C5KcnAzA008/rQRVRETEg/7zH/jyS1NUKa8HHoDPP3fTixw9CtdeW3CCOnYsvP66ElQRESkVj/8VCQkJAaBRo0aefikREZEK7+qr4aOPnNsHDzYFeEtl3z4zVLtokb09MBAmTzbTfz26AFZERCoCjyepF198MUDuulQRERHxrFtvhRdftLdlZkJMDPz8cwk73b7dLHpdu9beXq0afPONeVERERE38HiSevfdd2NZFq+99pqnX0pEREROeuwxuO8+e9vRo3DllfDXX8XsbP16Uz74ZKX+XHXqwLJlZvsZERERN/F4ktqnTx/69+/PsmXLGDRoEEeOHPH0S5aptLQ0du3aRaabN6PLzMxk9+7dpKamluj6Q4cOsXfvXrKzs90al4iIlA8Oh1keesMN9va9e6FnT/jnHxc7WrYMOnaEPXvs7U2bwsqV0K6dO8IVERHJVSZb0Jx77rmEhITw6aefMnv2bP7zn/9Qv359KleuXOh1DoeDiRMnejq8Evv0008ZN24cP//8M5Zl4XA4aNGiBbfffjvDhw8nKKhkn9rVq1czevRoli1bxrFjxwCznrd///489thj1K9fv9BrMzIyePXVV5k4cSI7T26WV6lSJdq3b8/9999PTExMiWISEZHyKSDA7ASzbx/ExZ1q/+MPM/i5fDlUr15EBzNmwKBBZq5wXhddBAsWmL1vRERE3MxhWZblyRcICAjAcbKIQs5LOU5TVCEn6cvKyvJkaCU2dOhQ3n///UKPt2/fngULFlCjRo1i9fvJJ59w6623Fvr/DgsLY8GCBbRs2dLpWEpKCt26dWNt/rVCedx33328+eabxYrJFUlJSTRp0gSAxMREwsPD3f4aIiJScocPQ6dO8Msv9vYePWDOHCjwnvGbb5p9a/K/TYiOhq+/hjPO8EywIiJSLP74Xtzj033PPPPM3EfTpk1p2rSpra2gR845vuj999/PTVA7d+7M6tWrSUtL4++//2bUqFE4HA5WrlzJ0KFDi9Xvxo0bGTJkCFlZWTRt2pTZs2eTkpLCgQMHmDp1KmeccQbJycn07du3wCnAd999d26Ces899/D777+Tnp7Ohg0bckdQ33rrLT788MNSfgZERKS8OeMMM/DZrJm9ffFiuP12sK0MsSx4/HEYPtw5Qe3b13SkBFVERDzI4yOp/iQjI4MmTZqwf/9+WrVqxZo1a5ymLD/++OO89NJLAKxdu5Y2bdq41Pc111zDvHnzCAkJYcOGDZx99tm244sWLaJXr14AvPrqq4wcOTL32E8//UTbtm0Bk6BOmDDBdm12djYdOnRg1apV1K1bl8TERIKDg4v3ny+CP969ERHxR9u2mQK9+/fb2x96CP73P8y03jvvhKlTnS++91544w3nTVhFRMSr/PG9uHbbLoY5c+aw/+Rf9tGjRxe4pvahhx4i8OQfcFfX1O7du5f58+cDcOeddzolqAA9e/bkoosuKrDfyZMnA2b96ejRo52uDQgIyE1q9+/fz+zZs12KS0R8T0pKClu2bGHNmjVs2bKFlJQUb4fkk5KSkvjmm2+YPHky33zzDUlJSd4OySecdx7Mm2d2jclr3Dh4+rF90Lt3wQnqCy/AW295PEH1xe9vX4zJF+lnTkTcyvJBTz75pNW5c2dr+fLl3g7FZujQoRZgVatWzTp+/Hih57Vr184CrHPOOcelfj///HMLsABr5cqVhZ736KOP5p6XmJiY237BBRdYgNW9e/dCrz169KgVEBBgAdaQIUNcistViYmJBcYlIu6RnZ1tLVu2zOrTp48VGBiY+/MGWIGBgVbfvn2tZcuWWdnZ2d4O1auysrKscePGWWFhYbbPUc4jLCzMGjdunJWVleXtUL0m53NUp85AC45bZj7vqccn3GRvCAy0rIkTPRqTL35/+2JMvkg/cyK+wR/fi5dpknr48GHr77//tnbu3FnoY9u2bdbll19uBQQEWC+++GJZhndaF154oQVYV1xxRZHn5SSzDofDSktLO22/w4YNswArKCjIysjIKPS8vMnsggULLMuyrL179+a2jRo1qsjXyUlm27Vrd9qY8kpMTCzysWbNGr/7wRDxFevWrbMiIiIKfAOY/xEREWGtW7fO2yF7xSeffGJVqVLFpc9TlSpVrE8++cTbIZc558/RzU5JahDHrUV0tyywMitXtqw5czwaky9+f/tiTL5IP3MivuP/2bvv8Ciq/Y/j700hQEIHgRCK2OgloAgoBBRDUSEKiqKAYuVeG1jgCjZQ8KfSFFG4WBBsFwUbVUoA6USkWykJJRBaIAkhJPP7Y8yazSZhk2yZbD6v58njembm7HcPZ3fnu3PmHCWpRfTzzz8bbdq0MQICAgr199FHH3kjPJeVK1fOAIyBAwcWuN9rr71m7yi7d+++aL09evQwAKNevXoF7rd27Vp7vdOmTTMMwzA2bNhgL/vggw8KPP6mm24yAKNmzZoXjSknV76A/O2NIWIFS5YsMUJDQwv1HgwNDTWWLFni69C9aty4cYVqo+y/cePG+Tp0r8mvjWrxjFOiGsoZYyltjGs93EZW7N9WjMmK9J4TsRZ/TFI9fk/q6dOniY6Otq8n6urfddddR9++fT0dnsvOnTtHWloaADVq1Chw38qVK9sfnzlz5qJ1Hz9+vMj1Zh9bmONdiUlEfCsuLo6YmJg8Z/MuSEpKCjExMcTFxXkoMmuZM2cOI0eOLNKxI0eOZM6cOW6OyHrya6POwG7e4EkmOpSnEEZ3FrCeyzzWRlbs31aMyYr0nhMRb/B4kvrJJ59w7NgxAIYOHcqnn37KY489BkCzZs348MMP+e9//8t//vMfIiIisNlsvP7668TGxhIaGurp8Fx24sQJ++Ny5coVuG9ISIj9cXp6+kXrPnnyZJHrzT62MMe7ElNO8fHxBf5t3LixUPWJSMEMw2DgwIGFPlnOlpKSwqBBg+xrU/urrKwshgwZUqw6hgwZQpbD+iv+Jb82uh1YDFQG3mI4/fnMYXsml/y9R023t5EV+7cVY7IivedExFs8nqTOnTsXm83Go48+yjvvvEP//v2ZPHkyDRo04Pfff6dfv37cf//9jB07ll9++YWWLVsyevRotm7d6unQCiUgwPWmypkEurLUi6t151VvUeIq7PIzERERBf7Vrl27UPWJSMFWrlzJzp07i1XHjh07iI2NdVNE1jRp0qRC/+iWW3p6OlOmTHFTRNaTVxsNBb4Esn/2DMDgIwYTyY+5jr4MWEB6erBb28iK/duKMVmR3nMi4i0eT1L3798PQL9+/RzKGzduzPnz5x2S0SpVqjB9+nTOnz/P0KFDPR1aoYSFhdkfX+yX1tTUVPvjnEN0L1Z3UeotSlyuxCQivpN7rWNf12NVEyZMcEs9b731llvqsaLcbTQGmIrzl//3nGcHtwG5h6xGAl/z5pvuSyqs2L+tGJMV6T0nIt7i8ST18OHDAISHhzuUN2jQAIA9e/Y4lLdt25YmTZqwYcMGtm/f7unwXBYWFmZP7g4dOlTgvgcPHgTMq5z16tW7aN3Zi++6Wi/ApZde6nBsYY7PPlZErCc5OZl58+a5pa6vv/7ab9d0TEhIcPhMLG5d/rimY842CgT+C4zKY79pwB3Aec4APYA/c+3RjYMHx3LgQPHbyIr924oxWZHecyLiTR5PUrPvk8x9la9BgwYYhsEff/zhdEyTJk0ALHevY3ZcO3bsKHC/X3/91b5/mTJlXK43MTGRpKSki9YL0LJlSwCuvPJKgoKCLhpXZmYmf/5pnni0atXqojGJiG8kJCSQmZnplroyMzPddlJpNe6epMZqt5i4Q3YblQPmAXndSTgac/jvP3cIHgWi//5vTnfz2GPnKe4tl1bs31aMyYr0nhMRb/J4ktq0aVMAFi1a5FB+xRVXALB+/XqnY7J/hcyecMkqunTpAsCuXbuIj4/Pc5/z58+zZs0aALp27VqoesG5nXJavnw5AC1atKB69eqA+SNAu3btAFi8eHG+x27evNnerq7GJSLed/bsWbfW56+zeeec2dwdrPZ94w7Hjx+nKvAjcEuubZnAg8DYPI/8E+gJOPbFb79tyJtvFi8mK/ZvK8ZkRXrPiYg3eTxJve222zAMgzFjxjBjxgz7bLSdOnXCZrOxevVqfv75Z/v+O3fuZMWKFQDUqlXL0+EVSv/+/QFzFsBJkybluc8HH3xgTwbvvfdel+rt3LmzfTj0pEmT8pwdcNeuXSxZsiTPeu+66y4A/vjjD7777rs8n2PiRHOJgapVq9KzZ0+X4hIR78t5n7k7VKhQwa31WUW1atXcWt/FlvAqiSKyslgDdMhVngbchjn8N39b/t4rw6H02Wfhk0+KHpMV+7cVY7IivedExKs8vRBrSkqKUb9+fcNmsxkBAQHGlVdead/WvXt3w2azGRUrVjSGDBliPPjgg0aVKlUMm81mlC1b1jh8+LCnwyu02267zQCMwMBA4/PPP3fYtnDhQqN8+fIGYNx+++0O2/bt22eEhIQYISEhRrdu3ZzqnTJlin0R3ieffNLIyMiwb/v999+NK6+80gCMevXqGWfPnnU4NiUlxYiIiDAAo2bNmsb27dvt27KysowxY8bY63777bfd0QwO/HEBYRFfOX36tBEYGGh/TxXnLygoyDh9+rSvX5JH5Pzcccef3312bdtmXKhZ0zDA4e84GB0K1TZ3567CCAoyjIULixaWFfu3FWOyIr3nRKzLH8/FPZ6kGoaZZHXu3NkICAgwrrjiCofyChUq2BPYgIAAw2azGTabzZgwYYI3Qiu0o0eP2hNCwIiMjDTuvPNOo23btvay+vXrGwcPHnQ4bu/evfbtnTt3dqo3KyvL6N69u32fBg0aGH379jVuvPFGIzg42ACMsmXLGj/++GOeccXGxtr3CwoKMm644QbjjjvuMC699FJ7ndHR0UZmZqbb28Qf3xgivtS3b1+3nAT269fP1y/Fo+rUqeOWdoqIiPD1S3Gv2FjDqFTJKUE9AEaTIrXRMKdENTTUMDZuLFp4VuzfVozJivSeE7EmfzwX90qSmu3o0aPGjh07HMq2bdtm3HzzzUblypWNsmXLGldffbXxv//9z5thFVpCQoJxww035PnB261bN6cE1TAunqQahmGcO3fOGDp0qBEUFORUb6NGjYxVq1YVGNeaNWuMyy67zOnYMmXKGEOHDjXS09Pd8fKd+OMbQ8SXli9f7pYTwRUrVvj6pXjUW2+95ZZ2mjhxoq9fivt8/bVhhIQ4Jag7wIgoRhtFRW12SlSrVzeMX38tfIhW7N9WjMmK9J4TsSZ/PBe3GUZx5+orvXbs2MH69etJSkqiVq1aXH311faJoorjyJEjLFu2jISEBCpVqkTjxo3t9/BejGEYxMbGsnPnTlJSUqhXrx5RUVEevb83ISHBvhROfHw8ERERHnsukdLAMAyaN2/Ozp07i1xHs2bN2LZtm0ufGyVVVlYW5cuXJz09vch1hISEkJqaSkCAx6do8Lz33oN//QuyshyKf7LZuNkwOFXEakNCQjh7NpXBgwOYM8dxW4MGsG4dFOYrxor924oxWZHecyLW5Jfn4r7MkMU/+OOvNyK+tmXLFiM0NLRIVylCQ0ONLVu2+PoleMXs2bOLdUVn9uzZvn4JxZeVZRijRztdPTXAMHr3Nj774AO3tFF6umHcdJPzU7RqZRiFvQ3Tiv3bijFZkd5zItbjj+fi+hlLRMSCIiMjmTdvHqGhoYU6LjQ0lHnz5hEZGemhyKxlwIABjBs3rkjHjhs3jgEDBrg5Ii+7cAEefhjGjHHe9tBDMHcu/e+7zy1tVKYMzJ0Lbdo47rN1K8TEQGEurlmxf1sxJisq9e85EfEOX2fJUvL54683IlaxZcsWo2nTpi5doWjWrFmpuZqT2+zZs42QkBCX2ikkJMQ/ruakphrGrbfmfQX1xRfNK6w5uKuNEhMN47LLnJ/yjjsMo7Bz81mxf1sxJisqle85EYvyx3NxJalSbP74xhCxkqysLGPFihVG3759nZbKCAoKMvr162esWLHCyMqVlJQ2mZmZxsSJE/OdgTQiIsKYOHGiR2Y597rjxw2jQwfnTDEgwDCmTcv3MHe10R9/GMYllzg//eOPO+XGF2XF/m3FmKyoVL3nRCzMH8/FNXGSFJtf3qwtYlHJyckcPHiQM2fOUKFCBerUqUPFihV9HZblJCQksHXrVo4dO0aNGjVo1aqV/3w2xcdD9+6wa5djeUgIfPaZOfbWBcVto7g46NwZzp51LB83DkaMcLkaB1bs31aMyYr8+j0nYnH+eC6uJFWKzR/fGCIilrRzp5mgJiQ4lleuDN9+C9df79VwfvwRevaEjAzH8g8/hMGDvRqKiEip5Y/n4po4SUREpCT46Se47jrnBLVOHVi92usJKsCNN8KsWc7lDzwACxZ4PRwREfETSlJFRESs7ptvzIzw1CnH8kaNYO1aaNbMJ2EB9O8PEyc6lmVmQr9+sGGDb2ISEZGSTUmqiIiIlU2fDrfdBufOOZa3bw9r1kC9er6JK4cnn4RnnnEsS02FXr3g1199EpKIiJRgQd58stTUVOLj4zl+/DgXLly46P6dOnXyQlQiIiIWZBjwyivw0kvO2265BT7/HMqX93pY+Rk/Ho4cgU8++afs+HGIjjYv9oaH+y42EREpWbySpP72228MGzaMH3/8kYzcsyvkw2azuZTIioiI+J3MTPjXv+D99523DRkC770HQV79nfmiAgJg5kw4ehQWL/6nfP9+6NEDVq2CSpV8F5+IiJQcHh/uu3fvXtq1a8fChQs5f/48hrk2q0t/IiIipU5aGvTtm3eCOmoUzJhhuQQ1W3AwzJ0LV1/tWL5tG/Tu7TxiWUREJC8e/5Z79dVXOX36NAB333033bt3p3r16gQHB3v6qUVEREqWkyfNbG71asdymw3eeQeGDvVNXIUQFgY//AAdO8Lvv/9THhsL995rjlIODPRdfCIiYn0eT1KXLl2KzWbj6aef5vXXX/f004mIiJRMCQnmuNgdOxzLy5SBTz+F22/3TVxFUKOGOeS3QwfzPtVsc+fCE0/A22+bebeIiEhePD7c9+jRowDcddddnn4qERGRkmn3bjOjy52gVqwIS5aUqAQ126WXmmulVqjgWD51Krz2mm9iEhGRksHjSWrVqlUBKG+hGQhFREQsY+1ac2xsfLxjee3a5rDfzp19E5cbtG4N8+ebF4NzGjUKPvjAJyGJiEgJ4PEktW3btgCsX7/e008lIiJSsnz3Hdx4o3kvak5XXQXr1kGLFr6Jy426djWXpck9vPehh+D7730Tk4iIWJvHk9Rhw4YBMG7cONLS0jz9dCIiIiXDzJkQE2PO5ptTu3awZg3Ur++buDzgjjtg8mTHssxMs3zdOt/EJCIi1uXxJLVz585MnjyZ3377jc6dO/Pjjz9y/vx5Tz+tiIiINRkGjB0LDzxgZmo59ewJy5ZB9eq+ic2DHnsMRoxwLEtLg5tvNm/JFRERyebW2X0bNmyY77bg4GC2bNlCdHQ0QUFBVK1alXLlyuW7v81m488//3RneCIiIr6VmQmPPw7vvuu87b77zLVR/XiJttdeM2f7/eijf8pOnIDoaPOKap06PgtNREQsxK1J6r59+1zaLyMjg8TExAL3sWluehER8SfnzsE998BXXzlv+89/zKurfv7dZ7PB9Olw9Kg582+2+Hjo3h1WrYIqVXwXn4iIWINbk9RBgwa5szoRERH/cOoU9OkDsbGO5TabebPmY495PaTk5GQSEhI4e/YsYWFhREREULFiRY8/b3AwfPkl3HADbNjwT/mOHXDzzRd4553fycg449WYRETEWtyapH744YfurE5ERKTkO3TIvEy4fbtjeZky5rS3d9zhtVAMw2DlypVMnTqV+fPnk5njntjAwEBiYmIYOnQoUVFRHh3RFBpqzux73XXw66//lK9dG0Rk5C7gDiDLqzGJiIh1eHziJBERkVJrzx5o3945Qa1QARYt8mqCGhcXR/PmzenatStfffWVQ4IKkJmZydy5c+natSvNmzcnLi7Oo/FUrw5vvrmDoKCjubbcDrzjk5hERMQaPJ6kBgQEEBQUxIEDB1zaf+/evQQGBnL55Zd7ODIREREPWr/evFSY+/uvVi3z5ssuXbwWytKlS+nUqRM7d+50af+dO3fSqVMnli5d6tGY+ve/lgsXbgRO59r6KDDK6zGJiIg1eOVKqmEYLu8bHx+PYRjEx8d7MCIREREP+uEH6NoVjh93LL/iCli7Flq18loocXFxxMTEkJKSUqjjUlJSiImJ8cjVS8eYtgO9gfRce40BHvBaTCIiYh1uvScVoEOHDnmWx8TEEBISUuCxWVlZ7P57sbTQ0FB3hyYiIuJ5H34IDz7ovAbq1VebyWuNGl4LxTAMBg4cWOgENVtKSgqDBg1i27ZtbrsfNO+YYoEBwJc4/n7+HnAU+NajMYmIiLW4PUldv349NpvN4eqpYRj8/PPPhaonJibG3aGJiIh4jmHA+PHmcjK5de8O//sfhIV5NaSVK1e6PMQ3Pzt27CA2NpaoqCgPx/QV8BgwNUdZIPA5cCOw1mMxiYiItbg9Se3UqZPDL5uxsbHYbDauueYaypYtW+CxNpuN6tWr06VLF4YMGeLu0ERERDwjMxOeegreftt528CB8N//mmuveNm7777rtnrclRAWHNO7QG0c70ctB3wHXA/s8khMIiJiLTajMDeMFkFAQAA2m429e/dSr149Tz6V+EhCQgJ169YFzHuKIyIifByRiIgXpafDvfeaV0pze+45GDfOXA/Vy5KTk6latarTLL5FERgYyIkTJ4q9ZqnrMf0XyP1jdTzQAUhwa0wiIiWdP56Lu/1Kam7ZV1YvdhVVRESkxDl9Gvr0gZUrnbdNmgRPPOHlgP6RkJDglgQVzKVgDh48WOyE0PWYHgYuAW7JUVYXWARcB5xyW0wiImI9Hk9SV+b1xS0iIlLSHT4MPXrAL784lgcHw6xZ0L+/b+L629mzZ91a35kzZ4pdh+sxZQJ3Aj9iXj3N1hRzEqWbgHNuiUlERKzHrUmqq2uhukrDg0VExJJ++w2io2HfPsfyChVg3jy44QafhJVTmJsnaapQoUKx6yhcTGmYV1LXAI1zlF8PfAb0dUtMIiJiPW5NUi+99FK31WWz2bhw4YLb6hMREXGLjRuhVy9ISnIsr1kTFi6E1q19E1cuERERBAYGumXIb1BQEHXq1PFBTCeAaGAdkPP5+2CzTSM8vPgxiYiI9QRcfBfXGYbhtr+srCx3hiYiIlJ8CxdCly7OCerll8PatZZJUAEqVqzotuXcYmJi3HLvZ9Fiige6A6ccSg3jQSZO1P2oIiL+yK1XUlesWJFn+bp163j++ecxDINevXpx22230bhxYypWrEhaWhr79u0jNjaWTz75hOTkZJ566imeeeYZd4YmIiJSPLNmwZAhkHuUT5s2sGABXHKJb+IqwNChQ5k7d65b6nGXosW0A7gVWAL8MxHjyy9DrVrwyCNuC09ERCzA40vQ/Pbbb1x99dWcP3+ezz77jD59+uS779GjR4mJiWH9+vW8+eabPPXUU54MTdzEH6e9FhGxMwx44w1zOZncbroJvvoK3Hz/p7sYhkHz5s3ZuXNnketo1qwZ27Ztc1gD3XcxxQD/AwLtJQEBMHcuuOmisYhIieOP5+JuHe6bl/Hjx3PmzBmGDx9eYIIKcMkllzB//nzCwsJ4/vnnOX36tKfDExERyV9WFgwblneCOmAAfPedZRNUMOd3mDVrFqGhoUU6PjQ0lI8//thtCWrxY5pHmTLDHEqysuCuu2D1avfEJyIivufxJHX58uXYbDb69u3r0v41atSgS5cupKens2jRIg9HJyIiko/0dDMRnTTJedvTT5vDf8uU8XpYhRUZGcm8efMKnRSGhoYyb948IiMjLRXT99/fzAsvOJanp8Ott8KOHW4MUkREfMbjSWpiYiIAISEhLh+Tva+7l7QRERFxSXKyOYPv5587b3vrLXP4b4DHv0Ldplu3bqxatYqmTZu6tH+zZs1YtWoV3bp1s2RML70EDz7ouP3UKejeHXTqICJS8nn8G7Z69eoArC7EOJzNmzcD7l/jTURE5KKOHIGoKFi2zLE8OBjmzDGH/5ZAkZGRbN++nRUrVtC3b18CAwMdtgcFBdGvXz9WrFjBtm3bPHIF1V0x2Wzw7rvm1dOcDh40E9UTJzweuoiIeJDHJ0665557+PTTT6lVqxYbN2686I28U6ZM4cknn8Rms7Ft2zaXf2EV3/HHm7VFpJT6/XeIjoa9ex3Lw8Lg66/Bg1cWvS05OZmDBw9y5swZKlSoQJ06ddyyzIw3Y0pLgxtvNFf/yal9e/jxRyhf3sMBi4hYgD+ei3s8Sf35559p164dmZmZ1KxZk7Fjx3LHHXc4XSVdu3Yt77zzDl988QUAt956K/PmzfNkaOIm/vjGEJFSaPNm6NkTjh1zLK9Rw1wftU0b38QlBTpxAq6/Hnbtciy/+WaYNw+C3LrYnoiI9fjjubjHk1SADz/8kIcffpgLFy5gs9kICAigfv36VK1aldOnT5OQkMC5c+cAc2r6Zs2asXLlSqpWrerp0MQN/PGNISKlzJIlcNttkJLiWN6wISxeDJdf7pu4xCXx8dChAyQkOJYPGQIzZpjDg0VE/JU/not7ZdaH++67j1WrVtGuXTsMwyAzM5O//vqLzZs38/vvv5OWloZhGAQEBHDfffexZs0aJagiIuIdc+aYkyTlTlAjI81xpEpQLa9uXVi0CCpXdiyfOROnmYBFRMT6vDYI5tprr2XdunX88ssvLFy4kO3bt3P8+HECAwOpUaMGbdq0oU+fPvZfAURERDzurbfM5WRyu/FG8x7UChW8H5MUSdOm5rK13brB34OzABg7FmrXhqFDfRebiIgUjtfv1GjZsiUtW7b09tOKiIj8IysLnnkGJkxw3nbXXfDRRyViDVRxdN115qpBt91m/hNn+/e/4ZJLwMUl20VExMdKziJvIiIi7nD+PAwcmHeC+tRTMHu2EtQSrHdveO89xzLDgAEDIDbWNzGJiEjhuPVK6t133w1AnTp1eOONNxzKCstmszFnzhy3xSYiIsKZM3D77bB0qfO2N97Ie+ivlDgPPgiHD8OLL/5Tdv68ua7q6tXQooXvYhMRkYtz6+y+AQEB2Gw2rrrqKnb9PRd8dllhGIaBzWYjMzPTXaGJB/njjGIi4ocSE80JkrZscSwPCoIPPoB77/VNXOIRhgGPPgrvv+9YXrs2rFsH9ev7Ji4REXfzx3Nxt15JrVevHjabjfDwcKcyERERn/nzT4iONv+bU2gozJ0L3bv7Ji7xGJsNpk6Fo0fN9VKzHT5sdoU1a6B6dd/FJyIi+XNrkrpv3z6XykRERLwmLg569DCzlZyqV4cFC+Dqq30Tl3hcYCB8+incdJM5zDfbr7/CzTfDsmXm7xQiImItmjhJRET8148/QufOzgnqpZeaa6AqQfV7ZcvCN99As2aO5Rs2wB13QEaGb+ISEZH8eTxJ7dixI2PHjmXTpk2efioREZF/fPYZ9OwJZ886lrdqZSaoV1zhk7DE+6pUgYULIfdS7AsWwMMPm/evioiIdXg8SV23bh0vvvgi1157LZdccgkDBgzgk08+4WjuX7VFRETcZeJEuPtu58tkXbua65DUquWbuMRnIiJg8WIzYc3pww/h+ed9E5OIiOTN40nqXXfdRXh4OIZhkJSUxGeffcbgwYMJDw+nTZs2PP/886xevVoz+YqISPFlZcGzz8KwYc7b7rjDvHRWsaL34xJLaNwYvv8eypVzLB83Dt5+2zcxiYiIM7cuQVOQ33//nRUrVrBy5UpWrlzJkSNHzAD+nvm3QoUK3HDDDXTv3p3u3bvbp1EW6/PHaa9FpATKyIAhQ+CTT5y3Pf64eXU1QFMxCHz3HcTEQM7fx202+Pxz87cMEZGSxB/Pxb2WpOa2Z88eVq5cyfLly4mNjeXYsWNmQH8nrY0bN2bHjh2+CE0KyR/fGCJSwpw9C337muM5cxs/3ry6quXQJIeZM+GBBxzLypSBRYugSxffxCQiUhT+eC7usyQ1p8zMTD755BNeffVV/vx7DTubzaYhwCWEP74xxPuSk5NJSEjg7NmzhIWFERERQUUNy3RixXZKSEggLi6O48ePU61aNSIjI737OXDsGPTqBbkn6AsMNDORQYO8F0sJYsW+5G1jx8Lo0Y5lFSrAqlXm/FoiIiWBP56Lu3WdVFdduHCBjRs3Ehsby6pVq1i7di1nc82+WNq+KEVKI8MwWLlyJVOnTmX+/PkOP0wFBgYSExPD0KFDiYqKso+yKI2s2E5ZWVlMmjSJCRMmcPDgQaftderUYdiwYTz55JMEeHKI7V9/QXQ0/PGHY3n58jB3rrk+qthZsS/50vPPw5EjMHXqP2VnzpjdZu1ac6UiERHxPq9cST137hzr1q1j1apVxMbGsmHDBs6dOweYX5gAoaGhdOzYkS5dutC1a1fatGnj2RMbcRt//PVGPC8uLo6BAweyc+fOi+7btGlTZs2aRWRkpBcisxYrttOcOXMYMmQI6enpF903JCSEmTNnMmDAAPcH8vPPZjaRmOhYXq0a/PADtGvn/ucswazYl6wgMxPuvBO++sqx/Ior4KefoEYN38QlIuIqfzwX93iSet1117F582Yy/l4GIPvpQkJCaN++vT0pbdeuHUFBPrmwK8Xkj28M8aylS5cSExNDSkqKy8eEhoYyb948unXr5sHIrMWK7TR+/HhGjhxZ6OPGjRvHiBEj3BfI8uXQp4952Sun+vXN+1Kvusp9z+UHrNiXrOTcOeje3VydKKerrza7WliYb+ISEXGFP56LezxJDQgIwGazYRgGtWrVYsiQIdxwww20b9+ekJAQTz61eIk/vjHEc+Li4ujUqVOhTpazhYaGsmrVqlJxdceK7TRnzhzuueeeIh8/e/Zs91xR/eILuPde5zVQW7SAhQshPLz4z+FHrNiXrOjUKejUCbZvdyzv0QO++QaCg30SlojIRfnjubhXklT7k9lsVKtWjU6dOhEVFUWnTp1o0aKFJ59evMAf3xjiGYZh0Lx5c5eGG+anWbNmbNu2za/vl7NiO2VlZVG+fHmXhvjmJyQkhNTU1OLdyjFlCjz5JOT+6oqKgvnzoVKlotfth6zYl6zs0CFo3x4OHHAsHzgQPvpIE0SLiDX547m4x2/63Lx5MxMnTuS2226jRo0aJCUl8fXXX/PEE0/QunVrqlWrRp8+fZg4cSJxcXFYYLJhEfGQlStXFutkGWDHjh3E5h6T52es2E6TJk0qVoIKkJ6ezpQpU4p2sGHAyJHwxBPOCWrfvuYVVCWoTqzYl6wsPNwcLV6tmmP5rFlm9xMREe/w+hI0v/76K6tXr7b/7du3zwzk758nK1SowHXXXUdUVBRPP/20N0OTIvLHX2/EM/r168fcuXPdUs+XX37phoisyYrtFBERkecsvkWpJz4+vnAHZWTAgw/Cxx87b/vXv2DyZHO5GXFixb5UEqxfD127QlqaY/mkSebvJCIiVuKP5+I+Xyf14MGDrFq1itWrV7N06VKtk1oC+eMbQ9wvOTmZqlWruuV9HRgYyIkTJ/xyqSortlPO97g7FOpzIiUF7rgDFixw3vbqq+blLY3BzJMV+1JJ8sMP0Lu3OftvTp99Bv37+yYmEZG8+OO5uE/XeDl58iQ///wzv/zyC7/88gvx8fGl4p4XkdIoISHBbT88ZWZmuuWqnhVZsZ3i4uLcEM0/tm7d6tqOSUnm5azcCWpgIMycCf/5jxLUAlixL5UkvXrBjBnO5QMHwrJl3o9HRKQ08eqaL/Hx8axevZo1a9awevVqdu/ebb8HNfu/devWJTo6mujoaG+GJiIedvbsWbfWdyb30iN+wortdPz4cTdE8o9jx45dfKd9+yA6Gn77zbG8XDn48ku4+Wa3xuSPrNiXSpr77oMjR8zfQ7JlZEBMjLlcTevWvotNRMSfeTxJff/99+33nyYkJNjLs5PSsmXL0qlTJ6Kjo+nevTuNGzf2dEgi4gNhbl5osEKFCm6tzyqs2E7Vcs8iU0w1atQoeIdffjEXrTxyxLG8alX4/ntz+lW5KCv2pZJoxAg4fBjefvufsjNnzKVp1q6Fhg19F5uIiL/yeJL66KOP2tdJzdaoUSN7Utq5c2fKli3r6TBExMciIiIIDAx0y/DDoKAg6tSp44aorMeK7eTuNTJbtWqV/8aVK80bAZOTHcvr1YNFi0A/ZLrMin2pJLLZYOJE8zeT//3vn/LERPNi/08/wSWX+C4+ERF/5JV7UitWrEhMTAzvv/8++/btY9euXUycOJHo6OgSm6BmZGRw+PDhIi2Onp8jR47w0ksv0aZNG6pXr05ISAi1atWiZ8+ezJo1i6ysLLc9l4i3ZX8OuENMTIzfTuBixXaKiIhwW4ISERGR/4QOc+eaZ/25E9RmzcxLVkpQC8WKfamkCgyETz6BLl0cy//4w7x31c0jq0VESj2PJ6mrVq0iKSmJr776igcffJB69ep5+ik9asOGDfTs2ZMKFSoQHh5OWFgY4eHhDBs2jKNHjxa53mXLltG8eXNefvll4uLiOH78OOfPnycxMZGFCxcyaNAgbrzxxjzvMcrIyCAoKAibzXbRvz179hTn5YsUy9ChQy1Vj1VZsZ2GDRvmlnqGDx+e94apU81ZfM+fdyzv1AlWr4ZSehWvuKzYl0qqkBCYNw9atnQs37wZbr/dueuKiEjR+XwJmpJk9uzZDB48ON+hU3Xq1GHhwoU0b968UPXu37+f1q1bc/LkScqXL8+IESPo3r07lSpVYvfu3bz++uusW7cOgHvuuYdPPvnE4fjff/+dK6+80qXn2r17N40aNSpUfBfjj9Nei2cYhkHz5s3ZuXNnketo1qwZ27Zt8+uZwK3YTllZWZQvX5709PQi1xESEkJqaioBATl+HzUMGD3aXE4mt9tugzlzoISOuLECK/alku7wYejQwZzbK6cBA2DWLAjw6boJIlIa+eO5uD5KXbR9+3aGDBlCZmYm9evX55tvviE5OZnjx4/z8ccfU6lSJQ4ePEjfvn0LPQT4rbfe4uTJkwQEBLB48WJGjx7N1VdfzZVXXknv3r1ZtWoVUVFRgJko//zzzw7HZ68t27BhQ/bu3Vvg32WXXeaW9hApCpvNxqxZswgNDS3S8aGhoXz88cd+f7JsxXYKCAhg5syZxapj5syZjgnqhQvwwAN5J6iPPGLO4qsEtVis2JdKutq1YfFiqF7dsXzOHHjuOd/EJCLidwxxSa9evQzACA0NNf7880+n7YsWLTIAAzDeeOMNl+vNzMw0atSoYQBGnz598t1v1apV9vqfe+45h23vvPOOARg9evRw/QW5UXx8vD22+Ph4n8QgJcuSJUuM0NBQe79x5S80NNRYsmSJr0P3Kiu207hx4woVT/bfuHHjHCtKSTGMm282DPNaquPfK68YRlaWx15DaWTFvlTSbdhgGOXLO3fft97ydWQiUtr447m4rqS6IDExkQV/Lyb/4IMP0jCP+eajo6Np0aIFQKGuNiQmJtrXDLzxxhvz3a9t27b2x7mHbWVfSdVVUikpunXrxqpVq2jatKlL+zdr1oxVq1bRrVs3D0dmLVZspxEjRjB79mxCQkJc2j8kJITZs2czYsSIfwqPH4cbbzSXk8kpIACmTzeH/+rKnVtZsS+VdNdcA199BUG51kkYPty8qioiIkWnJNUFK1eutC+h069fv3z369GjBwB79uxxWBO2IIcPH7Y/rlWrVr775bwPzMh1G3F2knr55Ze79JwiVhAZGcn27dtZsWIFffv2JTAw0GF7UFAQ/fr1Y8WKFWzbts3ty6CUFFZspwEDBpCamsrEiRPznfU3IiKCiRMnkpqayoABA/7ZsH8/XHcd/H2fvV3ZsuasNA8+6MHISzcr9qWSrnt3yOt36cGDYckSr4cjIuI3PL5Oqj9YtWoVYH6Bt2nTJt/9WrdubX+8Y8cOl25abt68uT1RrVq1ar77fffdd/bHV1xxhcO23EnqhQsXOH78OMHBwVSpUqXY9xJdLOHOmWiLFIbNZiMqKoqoqCiSk5M5ePAgZ86coUKFCtSpU6dUL3mRkxXbKSAggCeffJInn3yShIQEtm7dyrFjx6hRowatWrXK+/Nv+3bzrP7QIcfyKlXgu++gY0fvBF+KWbEvlXQDB5prqOa8H/XCBXPG35UroYDTBhERyYeSVBfs3bsXgPDw8AKHuOVcXmdf7mn/8hEcHFzgFVQwZ/999tln7f9/xx132B8bhsFff/0FwOnTp+nevTsrV660X3mtXr06vXv3ZuTIkUUeDpw9W5iIJ1WsWFEnyC6wYjsVuPZptlWr4NZb4fTp3Aebs9A0aeK5ACVPVuxLJdUzz5iz/k6a9E/Z2bPQo4e5xK8GOomIFI6G+7rg+PHjANSoUaPA/SpXrmx/fObMGbc899KlS2nXrh1HjhwBzGF27du3t28/fPgwaWlp9m2LFy92GBqclJTEzJkzadmyJV999ZVbYhIRKZSvv4abbnJOUJs2NYf9KkGVEs5mg7fegv79HcuPHYPoaEhM9E1cIiIllZJUF5w8eRKAcuXKFbhfzqusxVlLEODo0aMMHjyYm266icS/v92io6OZPn26w37ZQ33BvOL50UcfcfjwYdLT0/njjz948cUXCQkJISUlhbvuuou1a9cWOpb4+PgC/zZu3Fis1yoifmzaNOjbF3J/Jl53HaxebV5JFfEDAQHw0Udwww2O5X/9BT17gpt+uxYRKRXcOty3a9eubqvLZrOxbNkyt9V34MABUlNTC31c7dq1Hdf1K0DOxLRsEdf2y8zM5N1332X06NGc/vuqQ5kyZRg9ejQjR450mujiwoULREdHExYWxrRp0xyu9l522WW89NJLdO7cmW7dupGRkcHw4cNZl3vCkovwhwWBRcTLDANefBHGjHHe1rs3fPYZXOSHP5GSJiTEHDgQFQU5lzSPi4PbboMffoAyZXwWnohIieHWJHXlypXYbDan2WcLI/t4dy8cPnDgQGJjYwt93IwZMwgLCwMgJSWlwH1zJsE5h/66avfu3QwcOJDNmzfby7p168aUKVNo1KhRnsd06dKFLl26FFhvly5duP322/nyyy9Zv349hw8fpnbt2oWOT0TEJRcuwNChMGOG87aHHoKpU53X7RDxExUrwoIF0KED/D2lBQA//mjO+jt7tnnVVURE8ufWs4ROnTq5Pbm0grp167JlyxYO5Z6RMpeDBw/aH1966aWFeo4VK1bQp08fkpOTAfMq6IQJE7j11lsLH3AeOnbsyJdffgmYEzEpSRURj0hLM2/M+/Zb520vvmj++eH3hEhOtWqZS9B06GDel5rts8/MbW+9pbeBiEhB3H4l1aqKE9vevXuZP38+iYmJJCUlUb169Tz3+/XXX+2PW7Zs6XL9v/32G71797ZPtvTUU0/x2muvFXnIcF6Cg4Ptjy92b62ISJGcOAG33GJOZ5pTQIB59fSRR3wTl4gPXH65eUU1KgpyDsSaOBFq1zZnBBYRkbxpwIkLcg6nXbRoUb77LV++HIAWLVrkm8jm5ZlnnrEnqBMmTGDChAkuJahpaWlcffXVtG3blvfee6/AfbOHEAcHB9vXUxURcZv4eLj+eucENSQE5s5VgiqlUtu25j2quUe3P/ssfPKJb2ISESkJLJmkjho1iq5duxbpHlJP6Ny5M+Hh4QBMmjQpz3tud+3axZIlSwC49957Xa775MmTLFy4EIC7776bp556yuVjy5UrR1JSElu2bOHdd98lKysrz/3i4+P5/PPPAfMe19DQUJefQ0TkonbuhPbtYdcux/LKlc0b8WJifBKWiBXcdJM5629u998PBfzuLSJSqnk1SU1OTiY+Pp4DBw7k+/f777+zYsUKYmNji7RciicEBwczYsQIALZs2cKwYcO4cOGCffsff/xBTEwMWVlZ1KtXj0cffdSpjquuuoqyZcs6XSFduHAhGRkZAPTv3599+/Zd9C8pKcl+/H333QfA9u3bGTJkiNPkTvv27aN3796kpqYSEBDACy+84J5GEREBWLPGXE4mxz35ANSpYy4xc911volLxEIGDIA333Qsu3DBXJ1p0ybfxCQiYmVemV5x69atPPDAA/yccz52F2RfvbSCf//73yxYsIBFixYxadIk5s+fT9u2bTl16hSxsbFkZGRQtmxZPvjggzyvVKanp+e5dmrOdU5dnSRp0KBBfPT3z7LPPvssCxcuZP369Xz00Ud8/fXXdOzYkSpVqpCQkMC6devsSfCrr75Ku3btivDqRUTyMH8+3HUXnDvnWN64sXmJqF49n4QlYkXDh8OhQzBhwj9lKSnmGqpr18IVV/guNhERq/F4knr69Gmio6NJSkoq1NI0119/PX379vVgZIVjs9mYP38+w4YNY/r06farmtkaNWrE9OnTuf766wtVb2JiYrHiKlu2LMuWLWP06NFMmzaN5ORk+/DhbPXq1eO1115jwIABxXouERG76dPh0Uch920G7dvD999D1aq+iUvEwt54AxITYc6cf8qSkiA62kxUa9XyXWwiIlZiM4qzqKkL3nnnHR5//HFsNhuPPvoo1113HevWrePtt9+mWbNmDB8+nMzMTP766y9mzZrFwYMHGT9+PM9YeNq7I0eOsGzZMhISEqhUqRKNGze2xPI7p0+fZsWKFfz+++9kZmZStWpVWrVqRZs2bQgMDPTY8yYkJFC3bl3AvP81IiLCY88lIj5mGPDKK/DSS87bbrkFPv8cypf3elgiJcX583DzzbB0qWN5q1YQG2uusyoiUhj+eC7u8SQ1KiqK1atX8+ijj/LOO+/Yyxs2bMjhw4c5fvw45f8+oTl58iQ33HADu3btYv369bRq1cqToYmb+OMbQ0TykJkJ//oXvP++87YhQ+C995ynMRURJ2fOQJcusGWLY3nXruayNSEhvolLREomfzwX9/jESfv37wegX79+DuWNGzfm/PnzbN261V5WpUoVpk+fzvnz5xk6dKinQxMREVelpZmzvOSVoI4aBTNmKEEVcVGFCmYyetlljuXLl8PAgc6j6EVEShuPJ6mHDx8GnCdBatCgAQB79uxxKG/bti1NmjRhw4YNbN++3dPhiYjIxZw8ad40N3++Y7nNBlOnwpgx5mMRcdkll8DixeZ/c/ryS3jqKXNkvYhIaeXxn73LlStHRkaG09IoDRo0wDAM/vjjD6djmjRpwu7du9m4cSPNmzf3dIgiIiVGcnIyCQkJnD17lrCwMCIiIqjoyZvYEhKgRw/YscOxvEwZ+PRTuP12zz13MXi9nS5i9+7dLFu2jMTERGrWrMkNN9xA48aNfRaPWMNll8HChdC5M5w9+0/5lCkQHg7PPee72EREfMnjSWrTpk1Zt24dixYtcrjH9Iq/51pfv3690zHJyckAHDt2zNPhiYhYnmEYrFy5kqlTpzJ//nwyMzPt2wIDA4mJiWHo0KFERUW5dwK33bvNK6jx8Y7llSrBN9+YZ9YW4rN2ykdmZiaPPfYYH374IedyL9ODOTv7fffdx9tvv+3Rye3E2iIjYd48cymav1eMA2DECHO230GDfBebiIiveHy472233YZhGIwZM4YZM2Zw8uRJAPtsuKtXr3ZYP3Xnzp2sWLECgFqai11ESrm4uDiaN29O165d+eqrrxwSLzAToblz59K1a1eaN29OXFyce5547Vro2NE5QQ0Ph9WrLZeg+qyd8jF27FiCg4OZNm1angkqwLlz55g2bRrBwcGMHTvWo/GItd14I3z8sXP5kCHmvasiIqWNx2f3TU1NpUmTJhw4cACbzcbll1/Or7/+CkCPHj1YvHgxFSpUoF+/fgQEBDB37lxOnTpFSEgIe/fuVaJaAvjjjGIiVrB06VJiYmKcbpcoSGhoKPPmzaNbt25Ff+Jvv4U774TcydVVV5k30dWvX/S6PcBn7ZSPIUOG8MEHHxT6uPvvv5+ZM2e6PR4pOSZOhGHDHMvKlzcnVGrXzjcxiYj1+eO5uMevpJYvX54ff/yRTp06AeZwrGxvv/02YWFhnDlzhg8//JCZM2dy6tQpAF577TUlqCJSasXFxRU68QJISUkhJiam6FcK//tfiIlxTlCvvRZ++slyCarP2ikfY8eOLVKCCvDBBx/oimop99RTkHuZ+NRU6NUL/v59X0SkVPD4ldScjh07xtGjR2natKm9bPv27fznP/9hzZo1nDt3jubNm/Pss8/St29fb4UlxeSPv96I+JJhGDRv3pydO3cWuY5mzZqxbds21++9NAx49VUYPdp5W69e8MUXEBpa5Hg8wSftVIDMzEyCg4MpzteqzWYjIyND96iWYllZMHgwfPKJY3n9+uYo/FyLJYiI+OW5uMevpOZUo0YNhwQVoHnz5nz33XecPHmStLQ0Nm7cqARVREq1lStXFivxAtixYwexsbGu7ZyZCf/+d94J6n33mbO6WCxBBR+000U89thjxUpQwUy8n3jiCbfEIyVTQADMnGnOWZbT/v3mRNunT/smLhERb/JqkioiIhf37rvveq+ec+fM+0/z2vc//zHPloOD3RKPu3m1nVzw4YcfuqWeog4XFv8RHAxz58LVVzuWb9sGffo4j8YXEfE3Xh3ue/LkSebOncv69es5dOgQaWlpVKpUiUsuuYR27drRrVs36lvsfie5OH8cYiDiK8nJyVStWtVpdtqiCAwM5MSJE/mvD3rqlHnGm/tKos0GkyfDY48VOwZP8Wo7uWD37t00adKk2LFk27Vrl9ZRFY4dgw4dIPeS8n37wuefg0aFiwj457m4x9dJzTZu3DheffVV0tLS7GWGYdjvA/rggw8ICAigf//+TJgwgRo1angrNBERy0hISHBL4gXmPZIHDx7MO/k6dAi6d4ft2x3Ly5Qxb4a74w63xOApXmsnFy1btswtsWRbuXKlklShRg1zQu0OHSAx8Z/yuXPhiSfg7bfN35RERPyNV4b7Pvzww4waNYrU1FQMwyAkJIRGjRrRsWNHIiMjqVq1KoZhkJmZyaeffkrHjh2Jz702n4hIKXD27Fm31nfmzBnnwj17oH175wS1QgVYtMjyCSp4qZ0KITFnBuEGhw4dcmt9UnI1bAgLF5pvz5ymToVx43wTk4iIp3k8SZ03bx4zZsywz8I4f/58Tp48yc6dO1m9ejWbNm3i2LFj7Nixg0GDBmEYBn/++SeDBw/2dGgiIpYTFhbm1voq5D6zXb8errsODhxwLK9VC1atgi5d3Pr8nuLxdiqkmjVruikSU7imcJUcWreG+fPNgQ45Pf886BZmEfFHHk9Sp06dCkDr1q1Zu3Ytt956KyEhIU77NWnShA8//JDx48djGAYrV65k69atng5PRMRSIiIi3Lb8SFBQEHXq1Pmn4IcfoGtXOH7ccccrr4R166BVK7c8rzd4tJ2K4IYbbnBLLNmioqLcWp+UfF27miPxcw/vfegh+P5738QkIuIpHk9St2zZgs1m4/nnnyfUhSUMnn32Wa688koAfvrpJ0+HJyJiKRUrViQmJsYtdcXExPxzn+WHH0Lv3pBjXgAArrkG1qyBBg3c8pze4rF2KqLGjRtTtmxZt8RTrlw53Y8qebrjDpg0ybEsM9MsX7fOJyGJiHiEx5PU7MmDc6+PWpDWrVsDkJKS4pGYRESsbOjQoe6rxzDgtdfg/vvNs9mcevSA5cvN2VlKILe2kxvcd999bqnn/vvvd0s94p8efxxGjHAsS0uDm2+G3bt9E5OIiLt5PEm94oorADh69KjLxxz/eyha9lTKIiKlSVRUVKF+2MtLs2bN6HzddeYZ7fPPO+8waBB88w24MMLFqtzWTp07uyWet99+2z5jfVHZbDYmT57slnjEf732mvkWzunECYiOhoMHfROTiIg7eTxJHTJkCIZh8N///tel/Q8ePMiqVasoX748PXv29HB0IiLWY7PZmDVrlku3SOQlNDSUWTNmYLv7bnjnHecdnnvOHP4bHFzMSH3LHe308ccfFzuxzBYYGMgrr7xSrDpeeeUVt91rK/7LZoMZMyD3aVJ8vLmy1KlTPglLRMRtPJ6kPvjgg1x33XXMnj2bJ598kuTk5Hz3/f3337n55pvJyMjg//7v/6hUqZKnwxMRsaTIyEjmzZtX6AQsNDSU72bPpvXIkfC//znvMGkSjB/vN4srFqed5s2bR2RkpFvjGTVqVJGH695///2MGjXKrfGI/woOhi+/hHbtHMt37DBvPz93zjdxiYi4g83IvmnUQ6ZMmcLZs2cZN24cqamphIWFcf3119O6dWuqVq1KcHAwx44dY926dcTGxnLhwgUaN27MHQWs0/fCCy94MmQppISEBPvQ7Pj4eCIiInwckYj/iIuLY+DAgezcufOi+zZr1ow5b75Ji+eeg19+cdwYHAyzZkH//h6K1LcK204ff/yx2xPUnMaOHcsLL7yAK1+xNpuNV155RQmqFElSEnTsCL/95lh+221mEqsL8yL+zx/PxT2epAYEBLg8lMowDJf2zcw9+Yf4lD++MUSsxDAMYmNjmTp1KvPmzXP4DAwKCiImJoahQ4fSuXZtbN27w759jhVUqADz5oGbl0mxGpfbqXNntw3xLUhmZiZPPPEEM2fO5Fwel7XKlSvH/fffz+TJkzXEV4pl3z7o0AEOH3Ysf+QRePddvxk4ISL58Mdzca8kqe6WlZXl9jql6PzxjSFiVcnJyRw8eJAzZ85QoUIF6tSpYy6fsnEj9OplXlbJqWZNWLgQ/p41vbTIt518ZPfu3axcuZJDhw4RHh5OVFSUlpkRt9q2Da6/HnLfVfXKKzB6tG9iEhHv8Mdz8SBPP4ESShER96lYsaJzsrVwIfTtC6mpjuWXXw6LF0PDht4L0CLybCcfaty4sZJS8agWLcwJu6Oj4fz5f8pfeAFq1YIHH/RdbCIiheXxiZNERMSDPv4YbrnFOUFt2xZ++qlUJqgipVVUFMyZ4zy895FH4NtvfRKSiEiR+CxJzczMJCkpiYMHD7o0sYSIiORgGPD66zB4MOS+Tz86GlasgEsu8UloIuI7ffvC2287lmVlwZ13mr9biYiUBF5NUtesWcP999/P5ZdfTkhICDVr1qR+/frEx8ezfft2evbsyZo1a7wZkohIyZOVBU89BSNGOG+75x7zkklYmPfjEhFL+Ne/4PnnHcvOnTMHXbgwAbaIiM95JUlNT0/n3nvvpXPnznz88cf89ddfZGVlYRiG/Srq+fPnWbRoEVFRUYwZM8YbYYmIlDzp6XD33TB5svO2p582h/+WKeP9uETEUsaMgdxL9p48Cd27Q0KCb2ISEXGVV5LUIUOG8Omnn2IYBlWrVmXgwIGMHz/eYZ/atWvTokULsrKyeOmll/hfXovQi4iUZsnJ5gy+X3zhvO2tt+CNN8ADM6qLSMljs8H778PNNzuWJySYierJk76JS0TEFR4/m1myZAmffvopAM8//zyHDh3io48+4tlnn3XYLzw8nM2bN9O3b18Mw+Dpp5/2dGgiIiXHkSPmrCjLljmWBwebM6UMG+aTsETEuoKCzN+0rr3WsXznTrj1VkhL801cIiIX4/Ekddq0aQDcc889jBkzhuDg4Hz3DQoKYvr06YSGhpKQkMC6des8HZ6IiPX9/jt06AA//+xYHhYGP/xgDv8VEclD+fLw/ffQqJFj+Zo1cNddcOGCb+ISESmIx5PUjRs3YrPZuD/3jRH5qFy5Mh07dgRgz549ngxNRMT6Nm+Gjh1h717H8ho1YOVK6NbNJ2GJSMlRrZq5ZHJ4uGP5N9+YkyxpkQURsRqPJ6kn/77pITz3J2MBatSoAcDRo0c9EpOISImwZIk5xPfYMcfyhg1h7Vpo08YnYYlIyVOvHixaBJUqOZZPnw4vv+ybmERE8uPxJLVWrVoA/Pbbby4fk52cVqxY0SMxiYhY3uzZ5iRJKSmO5ZGRZoJ6+eW+iUtESqzmzc0VqkJCHMtfftmcZElExCo8nqRGRUVhGAbjx48nM/eC83lISkqyr5V6zTXXeDo8ERHrefNNuPde55vFbrzRHOJbs6ZPwhKRkq9TJ/j0U+eJwIcOhfnzfRKSiIgTjyepTzzxBIGBgaxdu5Zbb72VgwcP5rtvfHw8d9xxB2lpaURGRtJGQ9lEpDTJyoLhw+GZZ5y33XWXOUlShQrej0tE/Mptt8HUqY5lWVnQvz+sXu2bmEREcvJ4ktqyZUtefvllDMNg0aJFNGzYkBtvvJGhQ4fa9xkzZgx9+vThqquuIjY2ltDQUKZPn+7p0ERErOP8efPq6YQJztueesoc/lumjPfjEhG/9MgjMHq0Y1l6urk0zY4dvolJRCSbzTC8M6fb5MmTGTlyJOfOncNms+W5j2EYXHLJJXz++edERUV5Iyxxg4SEBOrWrQuYV8MjIiJ8HJFICXPmDNx+Oyxd6rztjTdA60aLiAcYBjz8MMyY4Vhep45563u9er6JS0QKxx/PxT1+JTXbE088wZ49e3jmmWdo3LgxYCalhmEQEBBAq1atGDNmDLt27VKCKiKlR2IidOninKAGBcGsWUpQRcRjbDZ4913z6mlOBw9C9+5w4oRv4hIR8dqV1NwyMjI4ceIEAQEBVKlShaCgIF+EIW7gj7/e+Lvk5GQSEhI4e/YsYWFhRERE+Hw2bSvG5HF//gnR0eZ/cwoNhblzzbNEKZKEhATi4uI4fvw41apVIzIy0qefTVbs31aMSXwjNdVccnntWsfy9u3hxx+hfHnfxCUirvHHc3GvXUnNLTg4mJo1a1KjRg0lqCJeYBgGK1asoG/fvlStWpWmTZvSrl07mjZtStWqVenXrx8rVqzAm79bWTEmr4mLgw4dnBPU6tVhxQolqEWQlZXFhAkTiIiIoG7duvTu3Zv777+f3r17U7duXSIiIpgwYQJZWVleiceK/duKMYnvlS8P330HTZo4lq9bZ06mlHuicRERjzO8aOPGjcamTZscyuLi4oy+ffsajRs3Nq699lpj7NixxtmzZ70ZlhRTfHy8ARiAER8f7+twJA9btmwxmjZtav93KuivadOmxpYtW0plTF6zdKlhhIUZhnlL2D9/l15qGL/95uvoSqTZs2cbISEhLvWnkJAQY/bs2R6Nx4r924oxibUcOGAYdeo4fzQNGWIYWVm+jk5E8uOP5+JeSVJ//fVX45prrjECAgKMRx991F6+c+dOIzQ01AgICDBsNpths9mMgIAAo2nTpkZiYqI3QhM38Mc3hj9ZsmSJERoa6tKJafZfaGiosWTJklIVk9d8+qlhBAc7nwW2amUYhw/7OroSady4cYXqS9l/48aN80g8VuzfVoxJrGnHDsOoXNn5I2rUKF9HJiL58cdzcY8P9z116hRdunRh06ZNGIZBuXLl7NvGjBlDamoqNpuNQYMG8cgjj1C+fHl2797NQw895OnQRPxeXFwcMTExpKSkFOq4lJQUYmJiiIuLKxUxec3EiXD33ZCR4VjetSvExkKtWr6JqwSbM2cOI0eOLNKxI0eOZM6cOW6Nx4r924oxiXU1bWoO/S1b1rF87FhzkiUREa/wdBY8evRow2azGdWrVzdWrVplL09NTTXKly9vBAQEGC+//LK9fPHixfYrqvv27fN0eOIG/vjrjT/IyspyeWhffn/NmjUzstw4xsuKMXlFZqZhPP2086UJMIw77zSMc+d8HWGJlJmZ6fIQ3/z+QkJCjMzMTLfEY8X+bcWYpGSYP98wAgIcP65sNsOYO9fXkYlIbv54Lu7xK6nff/89NpuNUaNGcf3119vLV61aRVpaGgAPPPCAvfymm27iqquuAuCnn37ydHgifmvlypXs3LmzWHXs2LGD2NhYN0VkzZg8LiMDBg2CN9903vbEE/DppxAS4v24/MCkSZNIT08vVh3p6elMmTLFLfFYsX9bMSYpGXr3hmnTHMsMwxwMou4gIp7m8SR17969AHTu3NmhfPny5QA0bdqU8PBwh23NmjUD4ODBg54OT8RvveumcVnuqseddbkzJo86exZuuQVmz3beNn68Ofw3wGeTrJd4EyZMcEs9b731llvqsWL/tmJMUnI89BC89JJj2fnz5rqq27b5JCQRKSU8fnaUmpoKQFhYmEP5qlWrsNlsTskrQGBgIACZmZmeDk/ELyUnJzNv3jy31PX111+TnJxc7HqsGJNHHT0KXbrA4sWO5YGB8NFH8NxzYLP5JDR/kJCQ4LYfMhMSEkhISChWHVbs31aMSUqeF16Ahx92LEtOhh49YP9+38QkIv7P40lqzZo1AThw4IC97MiRI2zatAmAG264wemYPXv2AFC1alVPhyfilxISEtz2I09mZqZbkgErxuQxf/0FHTvC5s2O5dmLEQ4a5Ju4/Ii7J/PZunVrsY63Yv+2YkxS8thsMHUq9OnjWH7oEERHQ1KST8ISET/n8SS1Q4cOAEycONG+gPrw4cPJysoiODiYG2+80WH/2NhYtv09hqRly5aeDk/EL509e9at9Z05c6bYdVgxJo/4+Wfo0AH++MOxvFo1WL7cvPwgxXb8+HG31nfs2LFiHW/F/m3FmKRkCgw0b5/PMbUIAL/+CjffDIWcOFpE5KI8nqQ+8cQTACxYsICIiAgiIiL4/PPPsdls3H777fZhwNu3b2fMmDH06dMHm81Gw4YNadeunafDE/FLuYfXF1eFChWKXYcVY3K75cuhc2dITHQsr18ffvoJ9JnmNtWqVXNrfTVq1CjW8Vbs31aMSUqucuXgm2/g72lD7DZsgDvvdF5ZS0SkODyepLZv355x48ZhGAZHjhzh0KFDGIZBlSpVePXVV+37vfbaa7z00kucPn2akJAQZs6c6enQRPxWRESE/d7u4goKCqJOnTrFrseKMbnVF19A9+6Q+2pTixawdi38PWu5uEdkZKRb62vVqlWxjrdi/7ZiTFKyVakCCxdC3bqO5T/8YN63ahi+iUtE/I9XppV89tln+emnn3jwwQe56aabeOihh9iwYQMNGjSw72MYBsHBwdx8882sX7+eTp06eSM0Eb9UsWJFYmJi3FJXTEwMFStWLHY9VozJbaZMgbvucr6UEBUFq1ZBrhnMpfgiIiLcljRlj/IpDiv2byvGJCVfRIQ5H1yVKo7lH34Io0b5JiYR8T9eW/ugffv2vP/++yxatIj33nuPyy67zGH7+++/z5kzZ/j2229p0aKFt8IS8VtDhw61VD3urMudMRWLYcDIkeZ6p7kvIfTrB4sWQaVKvomtFBg2bJhb6hk+fLhb6rFi/7ZiTFLyNW4M339vDgHO6bXX4O23fROTiPgXm2FocIYUT0JCAnX/HvsTHx9f7CsS4h6GYdC8eXN27txZ5DqaNWvGtm3bsLlpqRQrxlRkGRnwwAMwa5bztn//GyZNMmcbEY/JysqifPnypKenF7mOkJAQUlNTCXDDerVW7N9WjEn8x3ffmbP+/j0vJmDOBvz553DHHT4LS6TU8cdzca0iL+KnbDYbs2bNIjQ0tEjHh4aG8vHHH7v1xNSKMRVJSgr07p13gvrqq+bwXyWoHhcQEFDs+QtmzpzplgQVrNm/rRiT+I9bboH333csMwy4915YscI3MYmIf1CSKuLHIiMjmTdvXqFPUENDQ5k3b57bJ6exakyFkpQEXbuas4fkFBgIH3wA//mPeSlBvGLAgAGMGzeuSMeOGzeOAQMGuDUeK/ZvK8Yk/uOBB2DMGMey8+fNK6y//OKTkETEDyhJFfFz3bp1Y9WqVTRt2tSl/Zs1a8aqVavo1q1bqYrJJfv2QceOsHGjY3m5cjB/Ptx3ny+iKvVGjBjB7NmzCQkJcWn/kJAQZs+ezYgRIzwSjxX7txVjEv/x/POQ+7bl5GRzwvO9e30Tk4iUbEpSRUqByMhItm/fzooVK+jbt6/TshRBQUH069ePFStWsG3bNq9cObFiTAX65Rdo3x5++82xvGpVWLbMXNFefGbAgAGkpqYyceLEfGf9jYiIYOLEiaSmprr9CmpuVuzfVoxJ/IPNZt7lcPvtjuVHjkB0NBw75pu4RKTk0sRJUmz+eLO2v0tOTubgwYOcOXOGChUqUKdOHZ8vL2HFmOxWrjTvQU1OdiyvV8+cwbdxY5+EJflLSEhg69atHDt2jBo1atCqVSuffjZZsX9bMSYp2c6dM6+exsY6ll9zDSxfDkW8NVpELsIfz8WVpEqx+eMbQ8Ru7lwYMMC8ySqnZs3MBNVNa3WKiPiDU6egUyfYvt2xvEcP+OYbCA72SVgifs0fz8U13FdEJD9Tp5rrKOROUDt1gtWrlaCKiORSubL5+129eo7lCxeakyzp0oiIuEJJqohIboYBo0aZ653mPqO67TZYvNg8ExMRESfh4ebHZLVqjuWzZsHIkb6JSURKFiWpIiI5Xbhg/tz/6qvO2x59FL78EsqW9X5cIiIlSKNG8P335uTnOb3+Okye7JuYRKTkUJIqIpItNdVc3O+DD5y3jRljDv/NNSOqiIjk7dpr4X//c/7YfPJJ+Pxzn4QkIiWEklQREYDjx+GGG+CHHxzLAwJgxgxz+K/N5pvYRERKqF69zI/Q3AYONFfvEhHJi5JUEZH9+6FjR1i/3rG8bFmYN88c/isiIkVy333w2muOZRkZEBMDP//sm5hExNqUpIpI6bZ9O3ToAL/+6lhepQr8+CPceqtv4hIR8SMjRphz0eV05oy5NM1ff/kmJhGxLiWpIlJ6rVoF118Phw45lkdEwJo15tVVEREpNpsNJk2Cfv0cyxMTIToajh71SVgiYlFKUkWkdPr6a7jpJjh92rG8aVNYtw6aNPFNXCIifiowED75BKKiHMv/+MO8d/XsWZ+EJSIWpCRVREqfadOgb19IT3csv+46WL3avJIqIiJuFxIC8+dDy5aO5Zs3w+23w/nzPglLRCxGSWoRZWRkcPjwYVJSUnwdipPU1FQOHTpERkaGr0MRsRbDgBdegKFDzcc59e4NS5aY96KKiIjHVKoECxdCgwaO5UuWwP33Q1aWT8ISEQtRklpIGzZsoGfPnlSoUIHw8HDCwsIIDw9n2LBhHC3GDRUvvPACNpvton/9+/fPt45PP/2UNm3aEBYWRp06dQgJCaFp06a89dZbXLhwocixifiFCxfgoYfM9U5ze+ghmDvXedV5ERHxiNq1YfFiqF7dsXzOHHjuOd/EJCLWoSS1EGbPnk3Hjh1ZuHAh6TmGCR4+fJiJEycSGRnJ9u3bi1T3n3/+WazYHnnkEQYMGEBcXBzG31eIDMNg165dPP3000RFRXHmzJliPYdIiZWaao4j++9/nbe99BK89x4EBXk9LBGR0uzKK82lqcuXdyx/802YMME3MYmINShJddH27dsZMmQImZmZ1K9fn2+++Ybk5GSOHz/Oxx9/TKVKlTh48CB9+/Yt0hDg7CR1ypQp7N27N9+/t99+2+nY999/n/fffx+ALl26sGHDBlJTUzlw4ACjRo3CZrPx008/8cgjjxSvEURKohMnoFs3+PZbx/KAADM5ffFFc9pJERHxumuuMQey5P6dcPhw+PRT38QkIr5nM4zcN2ZJXm6++WZ++OEHQkND2bZtGw0bNnTYvnjxYrp37w7AG2+8wdNPP12o+mvUqEFSUhIbNmzgmmuucfm4c+fOUbduXZKSkmjZsiUbN26kTJkyDvuMHDmS8ePHA7Bp0ybatm1bqNguJiEhgbp16wIQHx9PhCadEauIjzfXNti927E8JAQ+/xz69PFJWCIi4mjWLBg0yLEsONi80tqtm29iEikp/PFcXFdSXZCYmMiCBQsAePDBB50SVIDo6GhatGgBwMyZMwtVf3JyMklJSQBcdtllhTr2u+++sx/78ssvOyWoAMOHDycwMLBIsYl4S3JyMrt27WLjxo3s2rWL5OTk4lW4cye0b++coFauDD/+qARVRMRCBg6E1193LMvIgNtugy1bfBOTiPiOklQXrFy50n6fZ7/cq1Dn0KNHDwD27NlDQkKCy/VnD/WtXLky1apVK1Rsy5cvB6B8+fL07Nkzz32qV69uv3q6dOnSQtUv4kmGYbBixQr69u1L1apVadq0Ke3ataNp06ZUrVqVfv36sWLFCgo94GPNGnM5mYMHHcvr1DGXmLnuOve9CBERcYtnnoEnnnAsO3sWevQw11IVkdJDSaoLVq1aBUBQUBBt2rTJd7/WrVvbH+/YscPl+rOT1Msvv9xelpyczKFDhxwmaCootsjISIKDgy8a219//UVaWprLsYE5hKCgv8OHDxeqPhGAuLg4mjdvTteuXfnqq6/IzMx02J6ZmcncuXPp2rUrzZs3Jy4uzrWK5883x4adOuVY3rgxrF0LzZq5JX4REXEvm82cMCn3QgbHjpl3biQm+iYuEfE+Jaku2Lt3LwDh4eGEhITku1+9evXsj/ft2+dy/dlJakREBK+++ioNGjSgUqVK1KlTh/Lly3PNNdfw4YcfkpXHwmHZseU1BDmv2AzDYP/+/S7HBlC3bt0C/wpzD60ImFf0O3XqxM6dO13af+fOnXTq1OniIwGmTzdn8T13zrG8fXvz6mqO96iIiFhPQAB89BHccINj+V9/Qc+eoIUKREoHJakuOH78OGBOblSQypUr2x8XZrmX7CR1/vz5jBo1yiGJzMrKYtOmTdx///1ER0dz9uxZ+7Zz587Zr4p6KjYRd4uLiyMmJqbQs2CnpKQQExOT9xVVw4CXX4aHH3ZeBf6WW8x7UKtWLUbUIiLiLSEh8PXXkGOAGgBxceY9qufP+yYuEfEeJakuOHnyJADlypUrcL+cV1kvNkw3p5xrpN53331s3ryZlJQUkpOTWbBgAe3atQPgxx9/5M4777Tve+LECftjT8UG5ixhBf1t3LixUPVJ6WUYBgMHDizSMk1gJqqDBg1yvEc1MxMefdRc7zS3IUPMM53ci/CJiIilVawICxbApZc6lv/4Iwwe7Px7pIj4l1Kzev2BAwdITU0t9HG1a9cmIMC1XD5n8le2bFmXn6NBgwZER0dz++238+CDDzps69GjB926daNXr14sWbKEBQsWsHjxYqKjo12OqzixAX4xjbVYw8qVK10e4pufHTt2EBsbS1RUFKSlwd13m/eh5jZ6tHl1VWugioiUSLVqweLF0LGjeV9qts8+M7e99ZY+4kX8ValJUgcOHEhsbGyhj5sxYwZhYWEAF736kzMJzjm89mIutixMUFAQkyZNokmTJgDMmzeP6Ohoe1yejE3End5991231RPVsiXceqt5r2lONhtMnWpeXRURkRLtiivMK6pRUZDzVGfiRAgPh0IuSy8iJYSG+7oge3HcQ4cOFbjfwRzLXVyae3xKMTVu3Jiqf99Tlz0pU1hYmD3hdDW2gIAAhwmeRLwlOTmZefPmuaWuDV99RWbHjs4JakgIzJ2rBFVExI+0bWveuRGU69LKM8/AJ5/4JiYR8axSk6Rmr3Va2L8HHnjAfgUzMTGRpKSkfJ/j119/tT9u2bKl219D0N+fzjnvP82O7WJL3mTH1qRJE8qUKeP22EQuJiEhwWmZmaJoDKzOyiJw927HDZUqmePCbrut2M8hIiLWctNN8OGHzuX33w+LFnk/HhHxrFKTpBZHly5d7I8XFfBJuHz5cgBatGhB9erVXap78uTJtG3blmuvvZbzBUxXFx8fz9GjRwHzqmru2Hbt2kV8fHyex54/f541f19x6tq1q0txibhbzpmpi6o9sAZwGgsQHg6rV0PnzsV+DhERsaZ77oE33nAsu3AB+vaFTZt8E5OIeIaSVBd07tyZ8PBwACZNmuQ4s+jfdu3axZIlSwC49957Xa67cuXKbNmyhQ0bNvDtt9/mu98bOT6VY2Ji7I/7/73itWEYTJo0Kc9jP/jgA5KTkwsdm4g75byHuihuAZYBTgvJXHUVrF0LzZsXq34REbG+p5+GYcMcy1JSzDVUf//dNzGJiPspSXVBcHAwI0aMAGDLli0MGzaMCxcu2Lf/8ccfxMTEkJWVRb169Xg0j/vhrrrqKsqWLes0s26fPn3s95U+8sgjrFq1ymF7VlYWkydP5p133gGgV69eXH311fbtzZo147a/hzdOnjyZL774wuH4RYsWMXz4cABuv/122rZtW5QmECm2iIgIAgMDi3TsEGAe4LTQ0rXXwk8/Qf36xYxORERKijfeMCd2zykpCaKj4cgR38QkIu5lM/K6LChODMOgZ8+e9uG+DRo0oG3btpw6dYrY2FgyMjIoW7Ys33//PTfccIPT8Q0aNGD//v32unL66quv6N+/vz3xbdGiBY0bNyYjI4NNmzbZh/Feeuml/PTTT9SuXdvh+GPHjhEZGUlCQgIAkZGRXHHFFfz5559s3rwZgPr167N27Vr7FWF3SkhIsE8uFR8fryVrJF/9+vVj7ty5hTrmeWBsXht69YIvvoDQUHeEJiIiJcj583DzzbB0qWN5q1YQG2uusypSWvjjubiS1EJIT09n2LBhTJ8+3eFKKkCjRo2YPn06119/fZ7HFpSkAqxevZrHH3+crVu3Om0LCAjgjjvuYOLEidSqVSvP+g8ePMigQYNYtmyZ07Zu3brx0UcfeSRBBf98Y4hnrFixwuX7ogOAKcC/8th2uHt3an/7LQQHuzM8EREpQc6cgS5dYMsWx/KuXc1la0JCfBOXiLf547m4ktQiOHLkCMuWLSMhIYFKlSrRuHFjOnXqhM0NK0pv3bqV9evXc/z4cUJDQ6lbty6dOnWiRo0aLh2/Y8cO1q9fT1JSErVq1eLqq6+madOmxY6rIP74xhDPMAyD5s2bs3PnzgL3CwFmA33z2Da9Rg0ePHIEW4DuVhARKe0SE6FjR/jzT8fyO+6Azz4DfVVIaeCP5+JKUqXY/PGNIZ4TFxdHp06dSMm5KnsOlYBvgNzz9GYBz5Qpw4B164iMjPRwlCIiUlL8+Sd06AB/L4Jg9/jjMGkSuOEagoil+eO5uH5fEhGvioyMZN68eYTmcS9pOLAK5wQ1HRgUEkL3779XgioiIg4uuwwWLoTck8hPmQL/93++iUlEikdJqoh4Xbdu3Vi1apXDUPSrgLVAi1z7ngYebdCAp9aupVu3bl6MUkRESorISPj6a+epCkaMgI8/9k1MIlJ0SlJFxCciIyPZvn07K1asYGSXLvwE5F5I5mTZsvw2YwYz//pLV1BFRKRA3brlnZAOGWJeaRWRkkNJqoj4jM1mIyolhdfWr6darm2Zl19Old27ufqBB9wyKZmIiPi/u+6CCRMcyzIzoW9f2LDBNzGJSOEpSRUR3/nwQ+jdG9LSHMuvuYbAtWuhQQOfhCUiIiXXU0/BM884lqWmmstr//qrb2ISkcJRkioi3mcY8NprcP/95k/cOfXoAcuXg4vLLomIiOQ2fjzcc49j2fHjEB0Nhw75JiYRcZ2SVBHxrsxMc12A55933jZoEHzzDeQx86+IiIirAgLggw/MpDSn/fuhZ084fdo3cYmIa5Skioj3pKebNwy9847ztueeM4f/5p6aUUREpAiCg2HuXGjb1rH8l1+gTx84d84nYYmIC5Skioh3nD4N3bvD//7nWG6zmautjx+vFddFRMStwsLghx/g8ssdy1euhHvvdb7jRESsQUmqiHje4cPQubN5VpBTcDB89hk88YRPwhIREf93ySWweDHUrOlYPneu+fVjGL6JS0TypyRVRDzr11+hfXtzfFVOFSrAokVw552+iUtEREqNhg3NtVIrVHAsnzoVxo3zTUwikj8lqSLiORs2QMeO5kwVOdWsCbGx0LWrb+ISEZFSp3VrmD/feeqD5583J1kSEetQkioinrFwoZmEHj/uWH755bB2rXm2ICIi4kVdu8InnzhPgfDQQ/D9976JSUScKUkVEff7+GO45RZz9fSc2raFn34yx12JiIj4wJ13mvP15ZSZCXfcAevW+SQkEclFSaqIuI9hwOuvw+DBzlMmRkfDihXmDBYiIiI+9PjjMGKEY1laGtx8M+ze7ZuYROQfSlJFxD2ysuCpp5y/9QHuuQe+/dZcC0BERMQCXnsNBg1yLDtxwlwt7eBB38QkIiYlqSJSfOnpcPfdMHmy87annzaH/5Yp4/24RERE8mGzwYwZ0LOnY/mBA2aieuqUT8ISEZSkikhxJSdDr17wxRfO2956C954AwL0USMiItYTHAxffgnXXONYvmMH9O4N5875Ji6R0k5njiJSdEeOQFQULFvmWB4cDHPmwLBhPglLRETEVaGh8MMPcOWVjuWrVsGAAc5TLIiI5ylJFZGi+f136NABfv7ZsTwszPy2v/tu38QlIiJSSNWrw+LFULu2Y/nXX8Njj5nzAoqI9yhJFZHC27TJTFD37nUsv+QSWLkSunXzSVgiIiJF1aCBucR3xYqO5dOmwdixPglJpNRSkioihbN4MXTpAklJjuWXXQZr10KbNr6JS0REpJhatoRvvnGe6++FF+C///VNTCKlkZJUEXHd7NnmInIpKY7lkZHw009moioiIlKCRUWZ0yrYbI7lDz9srqYmIp6nJFVEXPPmm3DvvXDhgmP5jTeaQ3xr1vRJWCIiIu7Wty9MmeJYlpUFd95p/iYrIp6lJFVECpaVBcOHwzPPOG+76y5zkqQKFbwfl4iIiAf9+9/wn/84lp07B7fcArt2+SYmkdJCSaqI5O/8efPq6YQJztueesoc/pv7xh0RERE/MXYs3H+/Y9nJkxAdDQkJvolJpDRQkioieTtzxrz/9NNPnbe98YaZuAboI0RERPyXzQbvv29+HeaUkADdu5sJq4i4n84wRcRZYqI5g+/SpY7lQUEwaxY8/bRv4hIREfGyoCD44gu49lrH8p074dZbIS3NN3GJ+DMlqSLi6M8/oWNH2LLFsTw0FL7/3hz+KyIiUoqUL29+BTZq5Fi+Zg3cfbfznIIiUjxKUkXkH1u2QIcOZqKaU40asGKFeROOiIhIKVStGixaBOHhjuXz58O//gWG4ZOwRPySklQRMS1dai4Od/SoY/mll5rz7V99tU/CEhERsYr69c1EtVIlx/Lp0+GVV3wTk4g/UpIqIubkSL16wdmzjuWtWsHatXDFFT4JS0RExGqaN4dvv4WQEMfyl14yJ1kSkeJTkipS2k2YAAMGQEaGY3nXrhAbC7Vq+SYuERERi+rUyfx912ZzLB861Bz+KyLFoyRVpLTKyoJnnoHhw5233XknLFgAFSt6Py4REZES4LbbYOpUx7KsLOjf35xQSUSKTkmqSGmUkQGDBsGbbzpve+IJ8+fh3OOYRERExMGjj8Lo0Y5l6elwyy2wY4dvYhLxB0pSRUqbs2fNb8/Zs523jR8PEydCgD4aREREXPHyy/DAA45lp05B9+5w4IBPQhIp8XQmKlKaHD0KXbrA4sWO5YGB8NFH8NxzzjfYiIiISL5sNpg2DW691bH84EEzUT1xwjdxiZRkSlJFSou//oKOHWHzZsfy8uXhu+/M4b8iIiJSaEFB8Nln5lLjOe3eDTffDKmpvolLpKRSkipSGvz8s/nN+ccfjuXVqsHy5dCjh2/iEhER8RPZv/k2buxYvm6dOZnShQu+iUukJFKSKuLvli2Dzp0hMdGxvEEDcw3Udu18EpaIiIi/qVrVvKOmTh3H8u++g0ceAcPwTVwiJY2SVBF/9vnn5lXSM2ccy1u2NBPUK6/0TVwiIiJ+qm5dM1GtXNmxfOZMePFFn4QkUuIoSRXxV5Mnw113mcvN5BQVBbGxULu2T8ISERHxd02bmldPy5Z1LB8zxpxkSUQKpiRVxN8YBowYAU8+6bytXz9YtAgqVfJ6WCIiIqXJddeZkynlXtXtX/+Cr77yTUwiJYWSVBF/kpEBgwfD6687b/v3v81vy5AQr4clIiJSGvXp43zl1DDg7rvNQU0ikjclqSL+IiUFeveGWbOct736KkyZYq6HKiIiIl7z0EPw0kuOZefPm1/Z27f7JCQRy1OSKuIPkpKga1dYuNCxPDAQPvgA/vMfc7VxERER8boXXoCHH3YsO30auneH/ft9E5OIlSlJFSnp9u2Djh1h40bH8nLlYP58uO8+X0QlIiIif7PZYOpUc/hvTocOQXQ0HD/uk7BELEtJqkhJ9ssv0L49/PabY3nVqub6qDff7Ju4RERExEFgIHz6KVx/vWP5r7+aX9cpKb6JS8SKlKSKlFQrVkCnTnDkiGN5vXrw009m8ioiIiKWUa4cfPONuURNTuvXw513Oq8aJ1JaKUkVKalWrYLkZMey5s1h3Tpo1Mg3MYmIiEiBqlQxV4OrW9ex/IcfzPtWDcM3cYlYiZJUkZLqhRfgwQf/+f9OnczENTzcdzGJiIjIRUVEwOLFZsKa04cfwqhRvolJxEqCfB2AiBSRzQbvvguJiRAUBHPmQNmyvo5KREREXNC4MXz/PdxwA5w790/5a69BrVrw2GO+i03E13QlVaQkCwqCL76AL79UgioiIlLCdOhgfo0H5Dojf+IJ+N//fBOTiBUoSRUp6cqWNacMFBERkRLn1lvh/fcdywwD7rnHnCNRpDRSkioiIiIi4kMPPABjxjiWnT9vrqv6yy8+CUnEp5SkioiIiIj42PPPw9ChjmXJydC9O+zb55OQRHxGSaqIiIiIiI/ZbDBlCtx+u2P5kSMQHQ3HjvkmLhFfUJIqIiIiImIBgYEwe7a5qlxOv/0GN98MKSm+iUvE25SkioiIiIhYRNmy8M030Ly5Y/nGjdCvH2Rk+CYuEW9SkioiIiIiYiGVK8OiRVCvnmP5woXw4IPm7L8i/kxJqoiIiIiIxYSHw+LFULWqY/nHH8PIkb6JScRblKSKiIiIiFhQo0bwww9Qrpxj+euvw+TJvolJxBuUpIqIiIiIWNS118L//mdOqpTTU0/BF1/4JiYRT1OSKiIiIiJiYb16wYwZjmWGAffeC8uW+SYmEU9SkioiIiIiYnH33QevvupYlpEBMTHw88++iUnEU5SkioiIiIiUACNHwr//7Vh25gz06AF//eWbmEQ8QUmqiIiIiEgJYLPBpEnmeqk5JSZCdDQcPeqTsETcTkmqiIiIiEgJERgIs2ZBVJRj+R9/mPeunj3rk7BE3EpJqoiIiIhICVK2LMyfDy1bOpZv3gy33w7nz/skLBG3UZIqIiIiIlLCVKoECxdCgwaO5UuWwJAhkJXlk7BE3EJJahFlZGRw+PBhUlJSfB2KiIiIiJRCtWvD4sVQvbpj+ezZ8NxzvolJxB2UpBbShg0b6NmzJxUqVCA8PJywsDDCw8MZNmwYR4twt/pLL72EzWYr9F9OGRkZBAUFuXTcnj173NUUIiIiIuJjV14J338P5cs7lr/5JkyY4JuYRIpLSWohzJ49m44dO7Jw4ULS09Pt5YcPH2bixIlERkayfft2j8cREOD4z7Zv3z4yMzM9/rwiIiIiYj3t2sHcuRAU5Fg+fDh8+qlvYhIpjqCL7yIA27dvZ8iQIWRmZlK/fn2mTJlCly5dyMjI4Pvvv+fxxx/n4MGD9O3bl7i4OEJDQ12q98knn2Tw4MEX3W///v306NGDtLQ03n33XYdtf/75JwANGzZk2bJlBdZTp04dl+ISERERkZKjRw+YORMGDXIsHzwYatSAbt18EpZIkShJddHIkSM5f/48oaGhLF++nIYNG9q3DRw4kJo1a9K9e3d+++03pk2bxtNPP+1SvZUrV6Zy5coF7nP+/HnuuOMO0tLSeOCBB3j44YcdtmcnqVdddRUNct89LyIiIiKlwsCBcPgwjBjxT1lGBtx2G6xcCW3a+Cw0kULRcF8XJCYmsmDBAgAefPBBhwQ1W3R0NC1atABg5syZbn3+YcOGsWnTJq666iqmTJnitD07Sb3sssvc+rwiIiIiUrI8+yw88YRj2dmz0LMn/H3KKGJ5SlJdsHLlSgzDAKBfv3757tejRw8A9uzZQ0JCglueOzY2lqlTp2Kz2Zg5cyblypVz2ic7Sb388svd8pwiIiIiUjLZbOaESXfe6Vh+9Kg59PfvU1oRS9NwXxesWrUKgKCgINoUME6idevW9sc7duwgIiKiWM97/vx5HnnkEQCGDBlCx44d89wvd5J64cIFjh8/TnBwMFWqVHGaDbiwLpZwHz58uFj1i4iIiIj7BATAxx9DUhJkT1dyxRUwa5aZxIpYnZJUF+zduxeA8PBwQkJC8t2vXr169sf79u0r9vOOHz+ePXv2UKlSJV5//fU89zEMg7/++guA06dP0717d1auXGmffbh69er07t2bkSNHFnk4cN26dYv2AkRERETEJ0JC4OuvISrKnPX3hx/MCZRESgIN93XB8ePHAahxkXd2zgmQzpw5U6znPHr0KP/3f/8HmPekVq1aNc/9Dh8+TFpaGgADBgxg8eLFDsvjJCUlMXPmTFq2bMlXX31VrJhEREREpOSoWBEWLYLly5WgSsmiJNUFJ0+eBMjzftCccl5lzZkoFsX48eNJSUmhSpUqPPnkk/nu92eOO+Dr1q3LRx99xOHDh0lPT+ePP/7gxRdfJCQkhJSUFO666y7Wrl1b6Fji4+ML/Nu4cWNRXqKIiIiIeNgll0BYmK+jECmcUjPc98CBA6Smphb6uNq1axMQ4FounzMxLVu2bKGfK1tiYiLTpk0D4OGHH6ZixYr57nvhwgWio6MJCwtj2rRpDld7L7vsMl566SU6d+5Mt27dyMjIYPjw4axbt65Q8RT33loRERERERFXlZokdeDAgcTGxhb6uBkzZhD2989PKSkpBe6bMwm+2NqnBZk+fTrnzp0jMDDQPnFSfrp06UKXLl0uus/tt9/Ol19+yfr16zl8+DC1a9cucnwiIiIiIiKeouG+LsieOOjQoUMF7nfw4EH740svvbRIz5WZmcmMGTMA6N69O/Xr1y9SPbnlnBl4//79bqlTRERERETE3UrNldSVK1cW+di9e/cyf/58EhMTSUpKonr16nnu9+uvv9oft2zZskjPtXjxYuLj4wHo379/kerIS3BwsP3xxe6tFRERERER8RVdSXVBzuG0ixYtyne/5cuXA9CiRYt8E9mLyZ6Bt0yZMtx6660F7puWlsbVV19N27Ztee+99wrcd/PmzYCZrGavpyoiIiIiImI1SlJd0LlzZ8LDwwGYNGkShmE47bNr1y6WLFkCwL333luk58nKyuL7778HoH379gVOmATmFdGkpCS2bNnCu+++S1ZWVp77xcfH8/nnnwPQrVs3QkNDixSfiIiIiIiIpylJdUFwcDAjRowAYMuWLQwbNowLFy7Yt//xxx/ExMSQlZVFvXr1ePTRR53quOqqqyhbtmyBs/7GxcVx9OhRADp16uRSbPfddx8A27dvZ8iQIU6TO+3bt4/evXuTmppKQEAAL7zwgkv1ioiIiIiI+EKpuSe1uP7973+zYMECFi1axKRJk5g/fz5t27bl1KlTxMbGkpGRQdmyZfnggw/yvFKZnp5+0bVTN2zYYH/cvn17l+J69tlnWbhwIevXr+ejjz7i66+/pmPHjlSpUoWEhATWrVtHRkYGAK+++irt2rUrxKsWERERERHxLiWpLrLZbMyfP59hw4Yxffp09u3bx759++zbGzVqxPTp07n++uuL/BwbN260P77mmmtcOqZs2bIsW7aM0aNHM23aNJKTk1m4cKHDPvXq1eO1115jwIABRY5NRERERETEG2xGXjdYSoGOHDnCsmXLSEhIoFKlSjRu3JhOnTphs9l8Gtfp06dZsWIFv//+O5mZmVStWpVWrVrRpk0bAgMDPfa8CQkJ9mV64uPjiYiI8NhziYiIiIjIP/zxXFxXUougVq1alrwqWalSJfr06ePrMERERERERIpMEyeJiIiIiIiIZShJFREREREREctQkioiIiIiIiKWoSRVRERERERELENJqoiIiIiIiFiGklQRERERERGxDCWpIiIiIiIiYhlKUkVERERERMQylKSKiIiIiIiIZShJFREREREREctQkioiIiIiIiKWoSRVRERERERELENJqoiIiIiIiFiGklQRERERERGxDCWpIiIiIiIiYhlKUkVERERERMQylKSKiIiIiIiIZShJFREREREREctQkioiIiIiIiKWoSRVRERERERELENJqoiIiIiIiFiGklQRERERERGxDCWpIiIiIiIiYhlKUkVERERERMQylKSKiIiIiIiIZShJFREREREREctQkioiIiIiIiKWoSRVRERERERELENJqoiIiIiIiFiGklQRERERERGxDCWpIiIiIiIiYhlKUkVERERERMQylKSKiIiIiIiIZShJFREREREREctQkioiIiIiIiKWoSRVRERERERELENJqoiIiIiIiFiGklQRERERERGxDCWpIiIiIiIiYhlKUkVERERERMQylKSKiIiIiIiIZShJFREREREREctQkioiIiIiIiKWoSRVRERERERELENJqoiIiIiIiFiGklQRERERERGxDCWpIiIiIiIiYhlKUkVERERERMQylKSKiIiIiIiIZShJFREREREREctQkioiIiIiIiKWoSRVRERERERELENJqoiIiIiIiFiGklQRERERERGxDCWpIiIiIiIiYhlKUkVERERERMQylKSKiIiIiIiIZShJFREREREREctQkioiIiIiIiKWoSRVRERERERELENJqoiIiIiIiFiGklQRERERERGxDCWpIiIiIiIiYhlKUkVERERERMQylKSKiIiIiIiIZShJFREREREREctQkioiIiIiIiKWoSRVRERERERELENJqoiIiIiIiFiGklQRERERERGxDCWpIiIiIiIiYhlKUi3IMAyOHj3KiRMninR8amoqhw4dIiMjw82RiYiIiIiIeJaS1GK4cOEClStXxmazsW/fvmLXd/DgQR544AGqVq1KzZo1qVatGmFhYdx1111scVHpUgAAO79JREFU3br1osd/+umntGnThrCwMOrUqUNISAhNmzblrbfe4sKFC8WOT0RERERExNOUpBbDvHnzOH36tFvq2rZtG61bt2bmzJmcOnXKXp6SksLnn3/Otddey2effZbv8Y888ggDBgwgLi4OwzAA84rsrl27ePrpp4mKiuLMmTNuiVVERERERMRTlKQW0bZt23jqqafcUldaWhoxMTEcO3aMsmXLMnnyZBITEzl79iw//vgjzZo1Iz09nfvvv5/t27c7Hf/+++/z/vvvA9ClSxc2bNhAamoqBw4cYNSoUdhsNn766SceeeQRt8QrIiIiIiLiKTYj+7KbFCgjI4P33nuP3377jQ0bNrB582ZyNt3evXtp0KBBkep+8803eeaZZwD44osvuOOOOxy2Hz16lCuuuILk5GR69erF999/b9927tw56tatS1JSEi1btmTjxo2UKVPG4fiRI0cyfvx4ADZt2kTbtm2LFGd+EhISqFu3LgDx8fFERES4tX4REREREcmbP56L60qqi1JSUnj88cd555132LRpE+7M7T/88EMAWrVq5ZSgAlxyySUMHjwYgAULFnD48GH7tu+++46kpCQAXn75ZacEFWD48OEEBgYCMHPmTLfFLSIiIiIi4m5Bvg6gpChXrhxjxoxxKFu+fDkrVqwoVr2JiYns2rULgH79+uW7X48ePZgyZQqGYbBs2TLuueceewwA5cuXp2fPnnkeW716ddq2bcuGDRtYunRpoWNMSEgocHt8fLz9cc4EWkREREREPCvn+be/TJaqJNVFISEhjBo1yqHswoULxU5SV61aZX/crl27fPdr3bq1/fGOHTucjo+MjCQ4OLjA4zds2MBff/1FWloa5cqVcznG7OEDrrjmmmtc3ldERERERNzn2LFjRb4F0Uo03NfH9u7da3/csGHDfPerWbMmISEhAA7L3WQfX9CxAPXq1QPMGX/3799f1HBFRERERMSiEhMTfR2CW+hKqo8dP37c/rhGjRoF7lupUiWOHj1qX0rm3LlzpKWluXRs5cqV7Y8LuxRNzuG8edm7dy+dOnUCYO3atYW68iqFd/jwYfsV640bN1K7dm0fR+T/1Obepfb2PrW5d6m9vUvt7X1qc++Kj4+nQ4cOADRq1MjH0biHklQfO3nypP3xxYbgZl9JTU9PB+DEiROFPjbn8a4qzAxhdevW9YsZxUqK2rVrq729TG3uXWpv71Obe5fa27vU3t6nNveusmXL+joEtyg1SeqBAwdITU0t9HG1a9emUqVKHojIFBDg+ojr7OQyu/MV5dicx4uIiIiIiFhNqUlSBw4cSGxsbKGPmzFjBg888IAHIjKFhYXZH6ekpFCxYsV8981OsrOH7uY+tiA5E/ScQ39FRERERESsRBMn+VjO+zcPHTqU737JycmcPXsWgEsvvRQwk9TshLOgYwEOHjwImFdfsydREhERERERsZpScyV15cqVvg4hT02aNLE/3rFjR743O//666/2x61atXI4fu3atQ7L0hR0fJMmTShTpkwxIhYREREREfEcXUn1sQ4dOtgnNVq0aFG++y1fvhyAwMBA+0y6AF26dAFg165d+c7Ce/78edasWQNA165d3RK3iIiIiIiIJyhJ9bHQ0FBuueUWAD799NM81zY6d+4c06ZNA6B79+4Oy830798fMNc/nTRpUp7P8cEHH5CcnAzAvffe687wRURERERE3EpJqpdcddVVlC1bNs+Zdf/zn/8QFBREWloa/fr1c1ha5syZM/Tv35/9+/cTGBjI2LFjHY5t1qwZt912GwCTJ0/miy++cNi+aNEihg8fDsDtt99O27Zt3f3SRERERERE3MZmGIbh6yBKqpdeeomXX34ZgL1799KgQYN8923QoAH79+8HzKueuY0fP56RI0cC5uy7119/PYGBgaxcuZJTp04BMHbsWJ5//nmnY48dO0ZkZCQJCQkAREZGcsUVV/Dnn3+yefNmAOrXr8/atWsJDw8v8usVERERERHxtFIzcZLVjRgxgipVqjBixAhOnTrFd999Z99WrVo1xo4dyyOPPJLnsTVq1GD9+vUMGjSIZcuWERcXR1xcnH17t27d+Oijj5SgioiIiIiI5elKqsWkpaWxePFi/vrrLwICAmjYsCHdunWjXLlyLh2/Y8cO1q9fT1JSErVq1eLqq6+madOmHo5aRERERETEPZSkioiIiIiIiGVo4iQRERERERGxDCWpIiIiIiIiYhlKUkVERERERMQylKSKiIiIiIiIZShJFREREREREctQkioiIiIiIiKWoSRVRERERERELENJqniVYRgcPXqUEydOFOn41NRUDh06REZGhpsj852MjAwOHz5MSkqKr0MpNazc5urjrjly5AgvvfQSbdq0oXr16oSEhFCrVi169uzJrFmzyMrKcttzWZWn+kpx/71OnTpFYmKiX/4buLvNt27dytChQ2nUqBEVK1akfPnyNGjQgLvvvpvVq1e75TlKMqt+HqqPi9X5xee4IZKHjIwMo1KlSgZg7N27t9j1JSQkGEOGDDEqV65sAAZghIaGGv379zd+/vnnix4/Z84cIzIy0rDZbAZg2Gw2o0mTJsabb75pZGRkFDs+X1i/fr3Ro0cPIyQkxN4mtWvXNp566ikjMTGx0PW9+OKL9noK85fT+fPnjcDAQJeO2717t7uawmvc3ebZRo8e7VKb3XnnnfnWoT7uuh9//NGoXr16gW3dpUsX48yZM07H+kMf91RfKc6/V1pamvHKK68Y9evXtx8bHBxsREVFGV9//XWRY7IKT7T5m2++aQQFBRXYB5955pk8j122bJlLfbhmzZrFedk+44n2vv76611qs2nTpuV5vPq46zp37lzoc5HOnTs71OHvfTynDRs2GIBRv379YtflT5/jSlIlT19++aW9gxY3Sf3ll1+MGjVq5PsBExISYnz66af5Hv/www8X+AHVsWNHIzk5uVgxetsnn3xS4IlynTp1jG3bthWqzqIkqQEBAQ51/Pbbby4fa9UT+Px4os2z3X333S61WX5Jqvq46/bt22dUqVLFAIzy5csbr7zyirFx40bj119/NebPn2+0b9/e/hz33HOP0/ElvY97qq8U59/r9OnTxtVXX11gXI899lhxX7rPeKLN58+fbz/+0ksvNWbMmGFs3brV2LlzpzF9+nSjbt269u3//e9/nY6fPn26S324JJ7Ae6qPh4eHu9RmeSWp6uOFa/OiJKldu3Z1qMOf+3huQ4cONaD4Saq/fY4rSRUnv/zyi1GnTh17pyxOkpqammo0bNjQAIyyZcsakydPNhITE42zZ88aP/74o9GsWTP7trzeOO+99549ji5duhgbNmwwUlNTjQMHDhijRo2y/+J39913F+MVe9e2bduMMmXK2D+QvvnmGyM5Odk4fvy48fHHH9uvYF955ZXG2bNnXa735MmTxt69ey/6t3LlSqNcuXIGYLz33nsOdSxcuNAAjIYNG160nvPnz7u7aTzGU22erV27dgZgTJkypcA2O3r0qNOx6uOFa+/HHnvMAPMHltWrVzttz8jIMKKiouxtGhcX57C9JPdxT/WV4v579e/f3x7Xo48+avz+++9GWlqasW3bNiMmJsa+bfr06e5qCq/xVJs3b97cAIzw8HDj2LFjTtsPHjxoXHLJJfaT8LS0NIftzz33nP15C+rDBw4cKNbr9zZPtXdqaqr92I0bNxbYZnklY+rjhWvzw4cPu3Q+MnnyZAPMHxy3bNniUIe/9vHcvvzyS3tiWZwk1R8/x5WkinH+/HljypQpxr///W/j6quvtn8guSNJfeONN+z1fPHFF07bExMTjYoVKxqA0atXL4dtaWlp9iF9LVu2NNLT052OHzFihL3+TZs2FTlOb+rVq5cB5nDnP//802n7okWL7K/pjTfecOtzp6en238pe+CBB5y2v/POOwZg9OjRw63P62uebvPsfrphw4ZCHac+Xrj2zszMtI/K6NOnT777rVq1yl7/c88957CtpPZxT/aV4vx7bdq0yeHEJrfMzEyjQ4cOBmBUr17dKdmyMk+1+bZt2+zHTJo0Kd/9XnnlFft+CxcudNjWt29fAzBef/31wr0oC/NkH9+xY4c9GSos9XHPfAf9+eef9lvAZs+e7bTdH/u4YRjG/v37jf/7v/8zBg8ebL+Ik/1XnCTVHz/HlaSKcfLkSadL+u5KUps0aWIARqtWrfLd5/HHHzfAvP/h0KFD9vKcQ47nz5+f57HHjh2z/wL1yCOPFDlObzly5Ij9R4Ann3wy3/1atGhhAEajRo3c+vz/+te/DMC46qqrjNTUVKftTz31lAEY//73v936vL7k6TY/ffq0vZ8mJSUV6lj18cK196FDh+zt9c477+S7X2pqqn2/m2++2WFbSe3jnuorxf33yh6mFhwcnOdIAcMwjK+//toe++eff+5SXFbgqTb/9NNP7fXu2LEj3/0WLFhg3+/NN9902Na6dWsDMObOnev6C7I4T34efvPNNwZgNGvWrNBxqY+7/zsoPT3diIyMNADj3nvvzXMff+zjhmEY8+bNy/d8u6hJqr9+jmt2X6FcuXKMGTPG4a9Lly7FrjcxMZFdu3YB0K9fv3z369GjBwCGYbBs2TJ7+fLlywEoX748PXv2zPPY6tWr07ZtWwCWLl1a7Jg9beXKlRiGAbjWJnv27CEhIcEtzx0bG8vUqVOx2WzMnDmTcuXKOe3z559/AnD55Ze75TmtwNNtnt1mlStXplq1aoWKTX28cO19+PBh++NatWrlu196err9cXYs2UpqH/dUXynuv1d2XFFRUdSoUSPPY2+66SYCAgIKFZcVeKrNS3M/LognPw+L017q4+7/Dho3bhxxcXHUrFmTKVOm5LmPP/ZxgCZNmjidczdo0KBYdfrr53iQV55FLC0kJIRRo0Y5lF24cIEVK1YUq95Vq1bZH7dr1y7f/Vq3bm1/vGPHDqfjIyMjCQ4OLvD4DRs28Ndff5GWlpZn8mUV2a8pKCiINm3a5Ltf7jaJiIgo1vOeP3+eRx55BIAhQ4bQsWPHPPfL/aVw4cIFjh8/TnBwMFWqVMFmsxUrDl/wdJvn9UWanJzM2bNnqVatGiEhIReNTX3ctfZu3ry5/QS/atWq+e733Xff2R9fccUVDttKah/3VF8pzr/X0aNH2bNnD1DwZ3xoaChXXnkle/bscfiMtzpPtfnDDz/M3XffDVDgD1v59eOkpCSSk5MBuOyyywA4d+4cJ06coEKFClSoUOEir8yaPPl5mPt9bxgGJ06c4MKFC1SvXp3AwMA8j1MfN7nzO+i3335j3LhxALz11ltUrlzZaR9/7eMAV155pdM5948//si+ffuKXKe/fo7rSqp4zN69e+2PGzZsmO9+NWvWtJ/I53yTZh9f0LEA9erVA8wvnf379xc1XK/Ifk3h4eEFJi/Zrwko1gdXtvHjx7Nnzx4qVarE66+/nuc+hmHw119/AXD69Gm6d+9OWFgYtWrVolq1alxyySU88MAD9i/7ksLTbZ7dHhEREbz66qs0aNCASpUqUadOHcqXL88111zDhx9+mOdaY+rjJlfbOzg4mFq1alGrVi3KlCmT5z779+/n2Weftf//HXfcYX9ckvu4p/pKcf69cv67uRqXOz7PvMVTbR4aGmrvx/lZsGABH3/8MWAmsjfeeKN9W3b/rFWrFgsWLCAyMpLy5ctTp04dKlasyOWXX85LL71kP8kvKTz5eZjdZqGhoTzwwANUq1aN6tWrU6tWLSpUqECvXr2IjY11Ok593OTO76BHHnmE9PR0rr/+egYMGJDnPv7axz3FXz/HlaSKxxw/ftz+OL/hA9kqVaoEwJkzZwDzF7O0tDSXjs35K1z28VaV3SbefE1Hjx7l//7v/wAYNmxYvlegDh8+bG/zAQMGsHjxYofhZklJScycOZOWLVvy1VdfFSsmb/J0m2d/mc6fP59Ro0Y5fIFnZWWxadMm7r//fqKjozl79qx9m/p4Zftjd72mpUuX0q5dO44cOQKY/bh9+/b27SW1j3uyrxTn36swn/HZx1u9/2bz1fvTMAwmTpxInz59yMzMBMwfGUNDQ+37ZH/mHDlyhDvvvJOff/7ZYTjwn3/+ycsvv0yrVq3st9xYnafbO7vNXn75ZWbOnMnJkyft29LS0liwYAFRUVGMGDHC4Tj1cZO7+vgPP/xgH6U3duzYfPfzxz7uSf76Oa4kVTwm55fAxYaGZP/yk33CeOLEiUIfm/N4q8puE2++pvHjx5OSkkKVKlV48skn890v59WjunXr8tFHH3H48GHS09P5448/ePHFFwkJCSElJYW77rqLtWvXFisub/F0m+dst/vuu4/NmzeTkpJCcnIyCxYssA+f+fHHH7nzzjvt+6qPu+81HT16lMGDB3PTTTeRmJgIQHR0NNOnT3fYr6T2cU/2leL8exXnM97qfPH+3Lp1Kx07dmTYsGFkZGQAMGrUKB544AGH/XL24+uuu45FixZx8uRJzp07x6ZNm+xDiffu3ctNN93k8O9kVZ5s78zMTPuVn/LlyzNmzBj++OMP0tPTOXLkCDNnziQ8PByA119/nTfeeMN+rPq4yV19/IUXXgDghhtuoFOnTvnu54993JP89XNc96T6gQMHDpCamlro42rXrm2/gukJ2TdYuyK7w5ctW7bIx+Y83pOK096uvi53vabExESmTZsGmPdBVaxYMd99L1y4QHR0NGFhYUybNs3hF7XLLruMl156ic6dO9OtWzcyMjIYPnw469atK3JshWHlNm/QoAHR0dHcfvvtPPjggw7bevToQbdu3ejVqxdLlixhwYIFLF68mOjoaPVxN7ymzMxM3n33XUaPHs3p06cBKFOmDKNHj2bkyJFO95lZuY8XxJN9pTj/XsX5jLc6b74/T506xejRo5k2bZr96ukll1zC+++/T58+fZz2r1ixItHR0TRq1Ii33nrLoZ+3bduWOXPmcNVVV/Hiiy9y8OBB/u///s9+D6BVebK9T506RdeuXbHZbLz44otce+219m01a9bk/vvvp3v37lxzzTUcPHiQl156iSFDhlC1alX18b+54/P6m2++IS4uDoBnnnmmwH39sY97kr9+jitJ9QMDBw7M816Ki5kxY4bTL7TuFBYWZn+ckpJSYIKUfUKcPZQg97EFyXkyndcN+O5WnPbOfl3eek3Tp0/n3LlzBAYG2idOyk+XLl0uOqtzly5duP322/nyyy9Zv349hw8fpnbt2kWOz1VWbvOZM2cWuD0oKIhJkybRpEkTAObNm2dPlLKpj1cu9HPt3r2bgQMHsnnzZntZt27dmDJlCo0aNcrzGCv38YJ4sq8U59+rKHF5o/+6g7fen99//z0PPvigfYh6YGAgDz30EK+++ipVqlTJ85gnnniCJ554osB6//Of//D+++9z6NAh5s2bZ/kTeE+2d7Vq1Vi0aFGB+4SHh/P8888zdOhQUlNTWbJkCf3791cf/5s7voOyZ/G94ooruOmmmwrc1x/7uCf56+e4hvuKx9StW9f++NChQ/nulz0TKsCll14KmG+a7DdBQccCHDx4EDB/Dcp5U7gVZbeJq68J/mmTwsrMzGTGjBkAdO/enfr16xepntxyzgxs9Ul8wLttnp/GjRvb7wXOHnamPl709l6xYgXXXnutPUG97LLL+Oabb1iyZEm+CWphWK2Pe7KvFOffy9XP+JzHu/u95SneeH+++eab3HrrrfYEtWvXrmzdupV333033wTVVUFBQfZbDazQhy/GCp+HOd/32Z/T6uOm4rb5b7/9Zr8X9aGHHnLLLOolrY97kr9+jitJ9QPZ6yMV9s+TV1EB+5UjoMDpqn/99Vf741atWjkdf7GprrOPb9KkSb4zfrpTcdo7+zUlJiaSlJR00dcE0LJlyyLFuXjxYuLj4wHo379/kerIS85p6r21FEpJafOCBAWZA1dytpn6uKkw7f3bb7/Ru3dv+6yOTz31FDt27ODWW28t4qt15os+fjGe6ivF+fe68sor7f26oLgyMzPt95jl/Iy3Ok++Pz/77DOeeeYZDMOgfPnyfPjhhyxbtoxmzZoVL+gcsvtxSRl+6uvPw7ze9+rjpuK2+X//+18Mw8BmsznMuF5cJa2Pe4q/fo4rSRWP6dChg/0m64KG2mQvIhwYGOhwI332sLxdu3bZk63czp8/z5o1awDzV2iryznU0JU2adGiBdWrVy/Sc2XPTlqmTJmLnsCnpaVx9dVX07ZtW957770C982+ehUcHFwiFtn2ZJtPnjyZtm3bcu2113L+/Pl894uPj+fo0aOAeVU1d2zq46738WeeecY+s+CECROYMGGCSycoJb2Pe6qvFOffq1y5cvYrGYsXL8732M2bN9t/VCgJfTibp9o8PT3dPpSxbNmyLF26lMGDB7t07Jo1a2jbti1t27Zl06ZNBe67ZcsWwPEzx8o81d4DBw6kbdu2TnMG5Jbz1oHsNlMfd8930Ndffw2Y63Be7EqsP/dxT/Hbz3FDJA8vvviiARiAsXfv3iLX07dvXwMwypUrZxw5csRpe1pamlG/fn0DMHr16uWwbfv27fYYhg0blmf906ZNs++zadOmIsfpLefPnzfCw8MNwGjTpo2RlZXltM/OnTuNgIAAAzDeeOONIj1PZmamcckllxiA0blzZ5eOadCggQEYzZs3NzIzM/Pc58CBA0b58uUNwOjZs2eRYvM2T7b5Rx99ZO9///vf//Ld77HHHrPvt3HjRnu5+njh2vvEiRNGcHCwARh33313oWMryX3cU32luP9e77zzjv05v/322zyf48477zQAo2rVqsa5c+dcissKPNXm3377rf2Y6dOnFyqmffv22Y/917/+le9+X331lX2///u//yvUc/iKp9p78ODBBmCEhIQY8fHxee5z4cIFo3Xr1gZgVKtWzUhNTbVvUx8v3nfQtm3b7Me/+OKLF93fn/t4fjp37mwARv369Yt0vL9+jitJlTwVJkm98sorjZCQECMkJMRpW1xcnBEUFGQAxvXXX28cP37cvi05Odno3bu3ARiBgYHGzz//7HT8bbfdZt/++eefO2xbuHCh/WTy9ttvL9Lr9IUpU6bY2/bJJ580MjIy7Nt+//1348orrzQAo169esbZs2edji+ovbNt2rTJ/hyjR492Ka6XX37ZfszgwYOdnnvv3r32L/GAgABj/fr1Lr5i3/NUm586dcqoXLmy/cQmNjbWYXtmZqYxadIkw2az5flDjGGojxemvefMmePwRbp3796L/h07dsx+fEnv40XtK/v27bO3Z7du3ZzqLc6/V0pKihEREWEARs2aNY3t27fbt2VlZRljxoyx1/3222//f3t3HlZVtf4B/HsO85CgIIMKKBQJDiiDZSg4zzml4FDqdbqpdbNuWj/MtDK1zKHhllmpTVdNK4csRxBsQBNzRtAr5jyQJIgIiO/vD56zOsezDwKCnPT7eR6eB/faa+111l4H97vX2mtXRTPcUdXR5mPGjBEA4u7uLpmZmeXqx3l5eSp/+/btVR997733zC5IExMTpXbt2gJAfH195fLly1XcKtWnOto7OTlZ9cHIyEg5fvy4SXpeXp488cQTap+bL+DZxyv3d8Vgzpw5qn22bt1arjrdzX1cS3mC1Hvx7ziDVNJUkSDVMBIKaHenWbNmqXR3d3d59NFHpW/fvuriHoDMmDFDM++FCxfUFweAhIeHS3x8vERGRqptAQEBcvr06dv9yHfMjRs3pFu3bqr+DRs2lAEDBkinTp3UKJGjo6Ns2bJFM/+t2lvE9K7Y999/X656FRQUyMMPP6zy1apVS7p37y5DhgyRmJgYVTcAMmvWrEp99ppSnW2+atUqdSMGgDRv3lzi4+Olf//+4ufnp7Y3atRIzpw5Y5affdycpfZ+9dVX1fby/gwfPlzl/7v38cr2laysLJWuNbPids9XcnKy2s/W1lY6duwocXFx0qhRI1Vm165dLY5eW7PqaPMOHTpUuB8vWbJE5c/IyFAzZQBIgwYNpF+/fhIfHy9hYWFqu5OTkyQmJt6BVqo61dXHJ06cqNJtbW2lbdu2MmTIEOnRo4e4ubmptB49emj2U/bxire5gWFGnY2NjcnNlrLczX1cS3mC1Hvx7ziDVNJUlUGqiMjChQtNglLDj4eHh3zwwQdlln/q1Cnp2LGj5n/cnTt3/ltdvBtcu3ZNxo8fbxLcGH4aN24sKSkpFvOWp72HDRum9snOzi53vfLz8+W5554TJycnzfb29/eXL774okKf1VpUZ5unpKRIixYtNNtMr9fLoEGD5OzZsxbLZx83Zam9J0yYUOGLe+MgVeTv38cr01fKczF5O+dLROTHH3+UoKAgs7z29vYyfvx4KSwsrIqPXyOqus2bNGlS4X5sHKSKlI6q9OnTR83SuPnn4YcfNnm04O+kuvr4okWLxMfHR7NcFxcXmTJlihQVFVmsF/u4qfIGqf7+/gKUPmZREXdzH79ZVQSpInff33GdiAiI7oCCggJs3LgRx44dg16vR2BgIDp37lzu1TMPHDiA1NRUZGdnw8fHB1FRUWjSpEk117p6nTt3Dlu3bsWpU6fg5uaGkJAQxMTEVMny7Lfj8uXLSEpKwpEjR1BSUoI6deqgRYsWiIiIMHmp9t9Rdbb5nj17kJqaij/++AMuLi7w8/NDTEwM6tatW6787ON3zt+9j1dXX7md8yUiSE5OxsGDB5Gfnw9/f3+0a9cOPj4+t10va2CN388TJ04gOTkZp06dgq2tLby9vdG6dWs88MADNVqvqlAd7V1cXIzt27dj3759yM/Ph7u7O4KDgxEdHQ1nZ+db5mcfv/Pu5j5eXe6Wv+MMUomIiIiIiMhq8BU0REREREREZDUYpBIREREREZHVYJBKREREREREVoNBKhEREREREVkNBqlERERERERkNRikEhERERERkdVgkEpERERERERWg0EqERERERERWQ0GqURERERERGQ1GKQSERERERGR1WCQSkRERERERFaDQSoRERERERFZDQapREREREREZDUYpBIREREREZHVYJBKREREREREVoNBKhER1Yh27dpBp9OhXbt2dyTf39X06dOh0+mg0+lquipUDsePH1fna+nSpXf8+H/88Qfq1q2L9u3b3/FjV0RMTAz8/f2Rn59f01UhIivEIJWIiKiGjRgxAjqdDj4+PjVdFfqbe+GFF5CdnY3p06fXdFXK9Oqrr+LkyZNWX08iqhkMUomIiIjuAr/++isWL16Mtm3bIjY2tqarU6Z27dqhbdu2WLBgAQ4cOFDT1SEiK8MglYiIiOguMHHiRIgIXnjhhZquSrm8+OKLuH79OiZMmFDTVSEiK8MglYiIiOhvbu3atfj5558RHByMHj161HR1yqV79+548MEHkZKSgrVr19Z0dYjIijBIJSKyIr/88gsGDRoEPz8/2Nvbw93dHS1btsTUqVORk5NTZt49e/Zg5MiRaNSoEZycnFCvXj307dsXGzdutJjHsMDLggULcP36dSxcuBBRUVFwc3ODu7s7OnbsiG+++abM4+bn52PevHmIjo6Gu7s7HBwc0LBhQ8TGxuLdd9/Fn3/+WZmmuC1r1qxB79694evrC0dHRwQFBeHJJ5/EwYMHNfc3Xuxm27ZtKCoqwty5cxEZGYn77rsPtWrVQnh4OObPn4+SkhKLx7148SKee+45BAUFwdHREQEBARgyZAh2794NACbHAP56FvXTTz8FAJw/f17tc/z4cc1j7NmzB0OHDoW/vz8cHBxQr149xMfH47fffqt0exUXF+Ojjz5Cx44d4eHhAXt7e/j5+aF169aYNWsWzp07p5nPsIjViBEjAACJiYno27cvvL294eDgAH9/f4wePRonT54s8/gbNmzAgAEDUL9+fTg4OMDLywvdunXDl19+iRs3bmjmadiwIXQ6HZ588kkAwPLlyxEdHQ03NzfUrVsXnTt3RlJSktp/1apVaN++PWrXrg0XFxeEhYVh3rx5FssHgEOHDmHcuHF48MEH4eTkBDc3N4SGhiI+Ph6bNm2CiJT5uQy2bNmizuuMGTMs7peXlwdHR0fodDrEx8eXq2yDmTNnAgBGjx5d5iJbq1evRs+ePeHt7Q17e3v4+Pigd+/e+O6778z2Xbp0qar3uXPncPLkSUyYMAGNGjWCs7MzgoODMWXKFOTm5gIo7b/PPPMMAgMD4ejoCB8fH8TFxVn83ul0OowaNQoA8NZbb1Xo8xLRXU6IiMgqvPHGG6LT6QSA5o+vr6+kp6dr5n399dfFxsbGYt4hQ4ZIQUGBWT5D+owZM6Rt27YW848ePVpKSkrM8mdmZkpAQIDFfACkbt26kpaWZpY3NjZWAEhsbGyF2qmsfAUFBdKvXz+LdbGzs5M5c+aY5cvKylL7LFu2TJo1a2axjPj4eM16/frrr+Lp6amZx9bWVpYuXar+nZSUJCIiw4cPt3icrKwsERGZNm2a2vb111+Lvb295v4ODg6q3Io4f/68hIWFlXkOXVxc5IcffrB4LoYPHy6TJk2ymN/T01NOnDhhlr+oqEiGDh1a5rHbtGkj58+fN8tr6Hdjxoyx2I56vV6+++47GTNmjMXyR4wYodku77//fpnfKQDSrVs3KSoqMsln3JeWLFkiIiIlJSXi4+MjAKRly5YWz8WyZctU3tWrV5d12kzs27dP5cvMzNTcp7CwUOLj48v8PCNHjpQbN26oPEuWLFFpa9askTp16mjmi46OlrS0NPHw8LDYfw4cOKBZr8zMzFvWnYjuPQxSiYiswE8//aQu1Lp37y6bN2+WjIwMSUtLk3nz5qmLw8jISLO8CxYsUHlbt24ta9eulcOHD0tiYqI8/vjjJoHqzYwvIgHI0KFDZdu2bXLo0CFZuXKltGjRQu0zY8YMs/yGIMXR0VFmzJghaWlp6tjjxo1TebUuzKsjSDUOUIcNGyYpKSmSnp4u33zzjbRu3Vqlffjhhyb5jAMLV1dX0el0MmbMGPnpp58kMzNTvv32W2nYsKHaZ9OmTSb5z5w5oy7QPTw8ZO7cuZKWlib79++Xt99+Wzw9PcXW1tYsSL148aJkZWXJY489poK5rKwsycrKkuLiYhExDVIdHBzE19dX3nnnHdm/f7/s3btXZs6cqQLXkJCQCrWlyF+Bsl6vl+eff15SU1MlIyNDtm/fLgkJCWJnZycAxMvLyywgM5wLV1dXdZ6/+uorycjIkNTUVJOgSKv/jRo1SqXHxcXJ5s2b5fDhw5KcnCxPPfWU6PV6ASARERFy9epVk7yGINXZ2VkAyODBg2X79u2Snp4u8+fPV+1taJugoCD5/PPPZf/+/bJhwwaJjIxUx/7tt99Mys7MzFT5mzRpIitWrJD09HTZs2ePLF++XKKiolTet99+22JfMgSpIiL/+te/1HbDDYibDRgwQACIu7u7FBYWlvscGvqIp6enxX2MA/kBAwZIYmKiHDx4UNasWSOtWrVSaR9//LHKYxykOjs7i6+vryxatEjS09MlJSVFHnnkEZVub28vOp1Oxo0bJ9u2bZO9e/fK66+/rs5hnz59LNbN8N2aOXNmuT8zEd3dGKQSEVkBw0iPt7e3WSAgIpKcnKwuBlNTU9X248ePi6OjowCQrl27auZNSEhQeZOTk03SjEc7nnrqKbO8eXl5alTRyclJsrOzVdr58+fVyO8HH3yg+bnGjx+vys/JyTFJq+ogdfny5epYr7zyilm+oqIiadeunQAQNzc3uXz5skozDiwsfZ6dO3eq9IkTJ5qkGQIABwcH2bdvn1nejIwMqVWrllmQenN+b29vs7zGQaq/v7+cPXvWbB/jc5yRkWGWbklJSYkKMF944QXNfd58802LwZzhXACQdu3amQWSJSUlqv+4u7ubpCUmJlpsT4NPP/1U7fPmm2+apBmP4E+ePNks77Bhw1R6cHCw5ObmmqSfPHnS4g0Yw2d2dXWVCxcumJWdl5cn3t7emsGXpSA1NTVVbZ87d65ZmQUFBepm0ahRozTbwxJDv+7Vq5dm+tatW9WxtcouLCyUkJAQASBhYWFqu3GQ6uHhYTYafuzYMZPvzc03f0T+uhHh6OiobrzczBCct2/fvgKfmojuZnwmlYjICvz+++8AAL1eD1tbW7P0mJgYTJw4Ef/85z9Nnjf7z3/+g2vXrsHW1hYfffQR7OzszPJOmzYNHh4eAIDFixdrHt/V1RWvvfZamdsLCgqwatUqlXbhwgUEBgYiKCgIAwcO1Cy3TZs26vfLly9r7lNV5s6dCwAIDQ3FSy+9ZJZuZ2en9rl8+bLFZ23btGmjnnM0FhUVhQYNGgAA/ve//6ntubm5WL58OYDS5wGbNWtmljc4OBjPPfdcBT+RuYSEBM13qXbr1k39bly3W8nNzYW3tzeCgoIwePBgzX3Kcw51Oh0WLVoEJycnk+16vR59+vQBAPz555+4dOmSSnvnnXcAAB4eHhaf0xw2bBhatWoFAPjwww8193F2dsbUqVPNtoeHh6vfn3nmGdx3330m6Q0aNICXlxcAmD0zW1BQgKCgIMTFxaFu3bpmZbu6uiIsLAxA+fv1Qw89hKCgIADQ7HsbNmxAfn4+AGDIkCHlKtMgLS0NANCiRQvN9HfffRcA4ODggDlz5pil29vbq/65d+9ezeffJ0yYAD8/P5NtjRo1gru7OwDAx8cHo0ePNssXEREBALh27Rqys7M169e0aVMAwM6dO8v9nC8R3d0YpBIRWYGGDRsCAM6ePYuhQ4fi7NmzZvvMnz8fCxcuVBftALB+/XoAQNu2bc0uIA3s7e3Rs2dPAEBKSormPm3btlUXmzfr2rUrHB0dAQCpqalqe9OmTXH06FEcPXpUBcE3M96/Oi8+L168iF9//RUAEB8fD71e+7+38PBw+Pv7A7DcFl27drV4nHr16gEArly5oralpqaisLAQANCrVy+LeR999NEyPkH5dOzYUXO7p6en+v2PP/4od3nu7u7qHBqCrpuV5xwGBgbigQce0EwztBnwV7uVlJRgy5YtAIB+/frBxcXFYh379+8PoDT4PnPmjFl6REQEXF1dzbbXqlVL/R4ZGalZtiGovjnQfPnll3H06FF88sknmvkuX76Mw4cPA6hYvx40aBCA0gXSbl6MyhC4+vr6ol27duUu88KFC8jLywMA1KlTxyz9xo0b2Lp1KwCohaO0DBw4ED/88AN++OEHzZtdlt67amjnli1ban7vjG9cWAroDX+78vPzcfr0ac19iOjeYn67noiI7rhJkyZh5cqVyMnJwbJly7BixQpERUUhJiYGHTp0QExMDJydnU3yFBUVIT09HUDpSF1ZQkNDAZSuYltcXGx2EdqyZUuLeR0dHdGoUSOkp6drBglAaWCUkpKCI0eO4Pjx4zh+/DjS09MtrlBb1fbs2aN+L09bnDhxApmZmZrphtE1LYZ2M14R9siRI+r3kJAQi3ktBXEVUb9+/TLrBQDXr1+vVNl5eXnYvn07Dh8+rM5hZmYmMjIybpm3PG0G/NVux44dUwGr8U0XLYGBger3kydPmgS9AMz+bWA848A4YNVSVqB56NAh7NixA1lZWapdfvvtN5MbFeU1ZMgQvP7667hx4wZWr16tRuyLi4uxbt06AGXfZNFifENLKwDNyspSQWxZbe3m5mYyIn+zW7XzrdoYsNzOxjdZTp8+rWYsENG9i0EqEZEVuP/++7Fr1y5MmTIFX3/9NYqLi7Fjxw7s2LEDc+bMgZOTE3r37o2pU6eiSZMmAIBLly6pi74PP/zQ4nRIYyKCnJwcs6BCawRGK72goMBk+9GjR/H0009j48aNZheg9vb2aNKkicXXT1Ql49HDwYMHW5y6asx46qkxe3v7Ch3b+BU7lkapAKjR6Ntx83TaqnD+/Hk8++yzWLlypVmAq9fr0bx5c+zbt6/MMiraZsbny9vbu8x93dzc1O9agWFVtKuWFStWYNKkSZqvz/H19YWLiwvOnz9foTJDQ0NVe37zzTcqSN26davqR+Xpu8YMU4QB7f5n3NZaU5fLq7raGYDJDbjKBP9EdPdhkEpEZCUCAwOxbNky5ObmYsuWLdi6dSuSk5Nx8OBBFBQUYMWKFVi3bh1SUlIQERFRodEWY4apqca0pvcZu3btGgDTgGHfvn2Ijo7GlStXoNfrERMTg4cffhj3338/GjdujBYtWmDVqlX4xz/+Ual6VkRl2kKrHSrDOEAra0TO0vN4Nen06dOIiopSo3FRUVFo06YNgoODERwcjJYtW2Lv3r1o3759tdXhVtNljQPB8ozWVYWXX35ZPYvt7u6Obt26ISwsDIGBgWjatCkaN26MDh06VDhIBUqD0H379mHbtm3IyclB7dq11VTfoKCgW44sl8XGxsZsm/F7fct6f2pNMu4DWp+BiO49DFKJiKxMrVq10L9/f/Us3qlTp/Dxxx/jtddew9WrVzFz5kx8/fXXqF27NmxsbFBSUoKRI0dqLh6jRWva3sWLF8vMY5i2azwCO2XKFFy5cgWurq5ISkrSfO7P+AK5OhlPF1y0aBE6d+58yzy3Cswrc+wTJ05oLpwElC5IY21mz56Ns2fPwsbGBmvWrFHPLhurjnNo3GZaI5XGDIuKAX89u12dzp07h1mzZgEofQZ49erVms+8VrZdBg8ejISEBDXF9/HHH8eaNWtUWkUZ3zgy3EwyZtzWp06dslhOYWEhsrKyAJQuiOTg4FDhulSWcb3Lej6ZiO4dXDiJiKiG/fnnnxg0aBAGDRqkGcg0aNAA06dPxxNPPAEAavqsnZ0dmjdvDqA0yGzYsKHFHzs7O2RnZyM3N1dzpOKXX36xWL+DBw+qKYMPPfSQ2r59+3YApQuuWFqY5tixY+VpgtsWHh6uRlPz8vLKbAvDKqNFRUVVcmzj53kNiwFpMawAbE0M5zAmJkYzQAWq5xwGBgaq4CopKanMfQ0BXFBQ0G1NVy2v1NRUNe355Zdf1gxQAVT6eeuAgAC0bt0aQOliSdu3b8eFCxcAAEOHDq1wecY3jrRuNgUFBakR6B07dlgs58svv0RISAhCQkJw9erVCtfjdhhPSfb19b2jxyYi68QglYiohjk7O+Pbb7/FihUrsHr1aov7GZ5HNB45MYy2btq0yWTEyVhxcTF69uyJqKgoTJ48WXOf5ORkiwvkvPXWWwBKp9Qaj1AaRj8sTbXNycnBZ599ZvHzVCV3d3c1JXXx4sUWR7lOnDiBli1bIioqCitWrKiSYzdv3hyNGjUCUNpWWtN6ExMT8fnnn1sswzAN03hBpjvhVuewqKgICxcurPLj6vV6tRLyunXrcPToUc39Dhw4gF27dgGoXABXGcajepbaZeXKlWWOSt6K4RUzmzZtUt+RiIgING7cuMJl1a1bVz3XqzUqbfwaoKSkJLUqsTERwccffwwACAsLK/PZ6upgWJDNwcHB4gJNRHRvYZBKRFTD7O3t0aVLFwDAvHnzsG3bNrN9Nm3apIIc4xGvcePGwcvLC4WFhRg+fLjZSEpOTg6GDh2KvXv3QqfT4f/+7/8063D9+nXEx8ebXHiLCN58800sXboUQOmqo4ZgDICa1rpq1SocOHDApLxdu3ahY8eOJuVV1TOglkydOhV6vR4HDx7E888/bzZSmpGRgZ49e+LatWvw9PTE2LFjq+S4er0e06dPB1B6sf3II49gxYoVyMjIQGpqKqZMmYIePXqUufCMYUrmxYsXsWbNGhw7dgzFxcVVUr+yGM5hSkoKEhMTTdIyMzPRo0cP7N69W22rynM4adIk2Nraori4GP379zcbmTxx4oQKTD08PPD0009X2bHLYjxde/bs2SZBqyFoN37OujJtEhcXB1tbWxQUFGDJkiUAoGZKVIbhnbCWFimbPHmymt5+89+J69evY/LkyWo2xbhx4ypdj8o6dOgQANzWs/ZEdJcRIiKqcXv27BFHR0cBIACkefPmEhcXJ4899pg8+OCDanvjxo0lNzfXJG9iYqI4OzsLAHFycpLOnTvL0KFDpUuXLuLi4qLyTps2zey4hrSQkBABIPb29tKpUyeJj4+X4OBglR4YGCjnzp0zyfvVV1+pdDs7O+nSpYsMGDBAQkND1bbZs2erfVq3bi0JCQkqf2xsrACQ2NjYCrVVWfmMj+fl5SV9+vSRIUOGyCOPPCJ6vV59xg0bNpjky8rKUvmWLFli8djR0dEWjz19+nTR6XSqHOMfBwcHWbx4sfp3cnKySd7169eb5cnKyhIRkWnTpqltlhw5cqRc9b/Zzz//LDY2NgJAdDqdxMbGSlxcnISHh6vy3njjDbVPs2bNZOzYsSp/ec7hRx99ZPaZDObPn2/Sh9q3by+DBw+WDh06iJ2dndq+du1as3IDAgIEgAwfPlzzuEuWLFFlp6ena+5jKCM+Pt5ke48ePVTeevXqyYABA6R79+5Sp04dASChoaEycuRIASC2trYycOBA2bhxo4iUvy917drV5LNfuHDB4r638s4776i6WjJv3jx1PBcXF+nZs6f069dPtQEA6dChg1y/fl3lMW7Dm8+dgaU21CrD0nkw/I0z/vtARPc2BqlERFYiKSlJAgMDNYMcANKrVy85e/asZt5du3aZBBbGP/Xr15dly5Zp5jMOYN977z0V7Br/xMbGyvHjxzXzv/XWW2Jvb2+Wp1WrVrJr1y4REWnbtq1JWQbVEaSKiHz55Zfi4+Oj2RaRkZGye/duszxVEaSKlAZ9AwYMEC8vL7GzsxNfX18ZOHCg7N69WzIyMtQxdu7caZLvxo0b8vzzz4unp6fY2NhIvXr11LmuziBVROS///2vuLq6mrVV48aNZdOmTSIi8sQTT6jtAQEBKu/tBqkiIl988YXF8xUSEiKJiYma5VZnkHrp0iXp1KmT5s2GcePGyeXLl2Xfvn0m3xdDu5e3Ly1dulTt169fP4v7lcfvv/+ubpDs37/f4n6ffPKJ1K5d2+xz2dnZyZNPPilXrlwx2f9OBKknT55U6VrfTSK6N+lEbrH2OxER3TE3btzAjh07sHfvXly6dAk2NjaoX78+oqOjTabaahER7Nq1Czt37kRubi68vLzQuHFjtG7d2uIUOsOzkNOmTcP06dORk5OD77//HidPnoSLiwuio6PVVEJLzpw5gw0bNuDcuXOoW7cuWrVqhbCwMJWel5eHVatWqamw1fk6E4PCwkIkJSXh0KFDKCkpga+vLyIjIyv1zF9VSUpKQocOHQCUTmX18/Orsbrc7NKlS/j+++9x4sQJ1KlTB2FhYWpxH6D0ueaVK1ciKysLTZo0Qd++fav0+EVFRUhOTsahQ4dw9epVeHp6IiIi4pZ9r7qlpqbil19+QXFxMfz9/dGlSxeTdwofPHgQGzZsgF6vR1xcHOrXr1/usg8dOqTeefzdd99ZXLiqvDp16oStW7fitddew0svvWRxv6tXr2Lz5s04cuQISkpKEBAQgPbt29/yfbXVZd68efj3v/+N8PBwpKWl1UgdiMj6MEglIrqH3RykUsWJCBYvXgwRQXh4uMXAaubMmZgyZQrq1KmD7Oxsq31nJd0Zhnex+vv7Iysr67afxVy/fj169eqFwMBAHDly5G/xbKeIIDQ0FIcPH8aqVavw2GOP1XSViMhKWP9fMCIiIium0+nwyiuvYMyYMXjqqaegde/33LlzWLBgAQCgd+/eDFDvcUVFRWo13fHjx1dJQNmzZ0+0adMGx44dw7p16267vDth48aNOHz4MCIiItRK5UREAINUIiKi2zZixAgApe+b7dKlC9avX4/09HTs3r0b7733HiIjI3Hx4kXUqlWLI9b3qCtXriAxMRF79+7F2LFjcfbsWdx3331Vtso0ALz//vuwtbXF7Nmzq6zM6jRr1iwAwIIFC3jjhohM2NZ0BYiIiP7uEhISsHPnTmzcuBFbtmzBli1bzPapXbs2li1bhoCAgBqoIdW07OxsdOzY0WTbiy++WKXvJG3WrBmeffZZzJkzB5s3bzZ5r7G1+fHHH5GSkoLHH38cbdq0qenqEJGV4TOpRET3MD6TWnVEBF999RU+++wz7Nq1C5cuXYKbmxv8/PzQu3dvjB07tkIL69Dd5cyZMwgNDUV+fj78/PwwatQoJCQkVPkI4tWrV9G0aVN4eXkhNTW1SsuuSp06dUJaWhoOHz5cY4s2EZH1YpBKREREREREVoPPpBIREREREZHVYJBKREREREREVoNBKhEREREREVkNBqlERERERERkNRikEhERERERkdVgkEpERERERERWg0EqERERERERWQ0GqURERERERGQ1GKQSERERERGR1WCQSkRERERERFaDQSoRERERERFZDQapREREREREZDUYpBIREREREZHVYJBKREREREREVoNBKhEREREREVkNBqlERERERERkNRikEhERERERkdVgkEpERERERERW4/8BeMho4Jk/Bl0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 435,
       "width": 468
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.scatter(sepal_array[:,0],sepal_array[:,1],color='black')  # there are the center (zero mean) values\n",
    "ax.set_xlim(-1,1)\n",
    "ax.set_ylim(-1,1)\n",
    "ax.set_xlabel('sepal length anomaly (cm)')\n",
    "ax.set_ylabel('sepal width anomaly (cm)')\n",
    "\n",
    "stretchLine = 1 # for display purposes\n",
    "\n",
    "# the vector described by the first eigenvalue (column 0 of U)\n",
    "lx1 = ax.plot([-U[1,0],U[1,0]],[-U[0,0],U[0,0]],color='red');\n",
    "\n",
    "# the vector described by the first eigenvalue (column 1 of U)\n",
    "lx2 = ax.plot([-U[1,1],U[1,1]], [-U[0,1],U[0,1]],color='blue');\n",
    "ax.set_aspect('equal', 'box')\n",
    "\n",
    "# the explained variance in percentage\n",
    "expvar = np.round((S/np.sum(S)) * 100)\n",
    "\n",
    "# place the explained variance for each new eigenvector on the plot\n",
    "ax.text(0.70,0.75,f'{expvar[0]}%',fontsize=18,color='red')\n",
    "ax.text(-0.75,0.79,f'{expvar[1]}%',fontsize=14,color='blue',horizontalalignment='center')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice too that each eigenvector is unit length:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "print(np.linalg.norm(U[:,0]))\n",
    "print(np.linalg.norm(U[:,1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can demonstrate how the eigenvectors allow us to express the original data on the new 'axes' or basis that the eigenvectors provide.  Let's (matrix) multiply the eigenvectors and the original centered (zero mean) data -- this will project the original data into the space of the eigenvectors.   This essentially makes the eigenvectors the new axes for the data space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'SECOND EIGENMODE')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 435,
       "width": 593
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rot = U @ sepal_array.T\n",
    "\n",
    "plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.scatter(rot[0,:],rot[1,:],color='black')\n",
    "ax.set_xlim(-1,1)\n",
    "ax.set_ylim(-1,1)\n",
    "ax.plot([-1,1],[0,0], color=\"red\")\n",
    "ax.scatter(rot[0,:],np.zeros(rot[0,:].shape),color='red',marker='o',facecolors='none')\n",
    "ax.plot([0,0],[-1,1], color=\"blue\")\n",
    "ax.scatter(np.zeros(rot[1,:].shape),rot[1,:],color='blue',marker='o',facecolors='none')\n",
    "\n",
    "ax.set_xlabel('FIRST EIGENMODE')\n",
    "ax.set_ylabel('SECOND EIGENMODE')\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.11.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}