{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import numpy.matlib\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "outputs": [
    {
     "data": {
      "text/plain": "      指数代码_IdxCd 指数名称_IdxNm  交易日期_TrdDt  收盘价_ClPr  指数月收益率_IdxMonRet\n0              1       上证指数  2010-01-29   2989.29           -0.0878\n1              1       上证指数  2010-02-26   3051.94            0.0210\n2              1       上证指数  2010-03-31   3109.11            0.0187\n3              1       上证指数  2010-04-30   2870.61           -0.0767\n4              1       上证指数  2010-05-31   2592.15           -0.0970\n...          ...        ...         ...       ...               ...\n1579          41       上证公用  2021-08-31   2067.54            0.1198\n1580          41       上证公用  2021-09-30   2609.22            0.2620\n1581          41       上证公用  2021-10-29   2354.39           -0.0977\n1582          41       上证公用  2021-11-30   2215.75           -0.0589\n1583          41       上证公用  2021-12-31   2573.07            0.1613\n\n[1584 rows x 5 columns]",
      "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>指数代码_IdxCd</th>\n      <th>指数名称_IdxNm</th>\n      <th>交易日期_TrdDt</th>\n      <th>收盘价_ClPr</th>\n      <th>指数月收益率_IdxMonRet</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>上证指数</td>\n      <td>2010-01-29</td>\n      <td>2989.29</td>\n      <td>-0.0878</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>上证指数</td>\n      <td>2010-02-26</td>\n      <td>3051.94</td>\n      <td>0.0210</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>上证指数</td>\n      <td>2010-03-31</td>\n      <td>3109.11</td>\n      <td>0.0187</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>上证指数</td>\n      <td>2010-04-30</td>\n      <td>2870.61</td>\n      <td>-0.0767</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>上证指数</td>\n      <td>2010-05-31</td>\n      <td>2592.15</td>\n      <td>-0.0970</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>1579</th>\n      <td>41</td>\n      <td>上证公用</td>\n      <td>2021-08-31</td>\n      <td>2067.54</td>\n      <td>0.1198</td>\n    </tr>\n    <tr>\n      <th>1580</th>\n      <td>41</td>\n      <td>上证公用</td>\n      <td>2021-09-30</td>\n      <td>2609.22</td>\n      <td>0.2620</td>\n    </tr>\n    <tr>\n      <th>1581</th>\n      <td>41</td>\n      <td>上证公用</td>\n      <td>2021-10-29</td>\n      <td>2354.39</td>\n      <td>-0.0977</td>\n    </tr>\n    <tr>\n      <th>1582</th>\n      <td>41</td>\n      <td>上证公用</td>\n      <td>2021-11-30</td>\n      <td>2215.75</td>\n      <td>-0.0589</td>\n    </tr>\n    <tr>\n      <th>1583</th>\n      <td>41</td>\n      <td>上证公用</td>\n      <td>2021-12-31</td>\n      <td>2573.07</td>\n      <td>0.1613</td>\n    </tr>\n  </tbody>\n</table>\n<p>1584 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index=pd.read_excel(r'E:\\大三下study\\蒋志强\\実験5\\data\\2010_2021.xlsx')\n",
    "index"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "outputs": [
    {
     "data": {
      "text/plain": "      code  name        date    close  monret\n0        1  上证指数  2010-01-29  2989.29 -0.0878\n1        1  上证指数  2010-02-26  3051.94  0.0210\n2        1  上证指数  2010-03-31  3109.11  0.0187\n3        1  上证指数  2010-04-30  2870.61 -0.0767\n4        1  上证指数  2010-05-31  2592.15 -0.0970\n...    ...   ...         ...      ...     ...\n1579    41  上证公用  2021-08-31  2067.54  0.1198\n1580    41  上证公用  2021-09-30  2609.22  0.2620\n1581    41  上证公用  2021-10-29  2354.39 -0.0977\n1582    41  上证公用  2021-11-30  2215.75 -0.0589\n1583    41  上证公用  2021-12-31  2573.07  0.1613\n\n[1584 rows x 5 columns]",
      "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>code</th>\n      <th>name</th>\n      <th>date</th>\n      <th>close</th>\n      <th>monret</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>上证指数</td>\n      <td>2010-01-29</td>\n      <td>2989.29</td>\n      <td>-0.0878</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>上证指数</td>\n      <td>2010-02-26</td>\n      <td>3051.94</td>\n      <td>0.0210</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>上证指数</td>\n      <td>2010-03-31</td>\n      <td>3109.11</td>\n      <td>0.0187</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>上证指数</td>\n      <td>2010-04-30</td>\n      <td>2870.61</td>\n      <td>-0.0767</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>上证指数</td>\n      <td>2010-05-31</td>\n      <td>2592.15</td>\n      <td>-0.0970</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>1579</th>\n      <td>41</td>\n      <td>上证公用</td>\n      <td>2021-08-31</td>\n      <td>2067.54</td>\n      <td>0.1198</td>\n    </tr>\n    <tr>\n      <th>1580</th>\n      <td>41</td>\n      <td>上证公用</td>\n      <td>2021-09-30</td>\n      <td>2609.22</td>\n      <td>0.2620</td>\n    </tr>\n    <tr>\n      <th>1581</th>\n      <td>41</td>\n      <td>上证公用</td>\n      <td>2021-10-29</td>\n      <td>2354.39</td>\n      <td>-0.0977</td>\n    </tr>\n    <tr>\n      <th>1582</th>\n      <td>41</td>\n      <td>上证公用</td>\n      <td>2021-11-30</td>\n      <td>2215.75</td>\n      <td>-0.0589</td>\n    </tr>\n    <tr>\n      <th>1583</th>\n      <td>41</td>\n      <td>上证公用</td>\n      <td>2021-12-31</td>\n      <td>2573.07</td>\n      <td>0.1613</td>\n    </tr>\n  </tbody>\n</table>\n<p>1584 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index.columns=['code','name','date','close','monret']\n",
    "index"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "outputs": [
    {
     "data": {
      "text/plain": "code            1        32       33       34       35        36       37  \\\nyearmonth                                                                   \n201001     2989.29  3013.19  2338.58  2328.31  2487.20   4325.84  3275.82   \n201002     3051.94  3012.64  2407.88  2378.33  2638.66   4414.94  3421.93   \n201003     3109.11  3064.59  2427.83  2392.62  2633.05   4348.36  3345.19   \n201004     2870.61  2878.92  2260.45  2207.50  2395.73   4161.42  3638.46   \n201005     2592.15  2513.56  2069.46  1981.80  2157.30   3981.44  3687.59   \n...            ...      ...      ...      ...      ...       ...      ...   \n202108     3543.94  1544.61  3424.67  3111.24  3749.15  15592.87  8970.98   \n202109     3568.17  1699.18  3021.62  2999.62  3632.99  17203.02  9500.47   \n202110     3547.34  1463.15  2937.69  3101.17  3911.24  18238.92  9194.52   \n202111     3563.89  1360.96  2949.79  2933.02  3764.14  18011.08  9502.19   \n202112     3639.78  1442.91  2922.49  3007.69  3799.83  18327.31  9287.54   \n\ncode            38       39       40       41  \nyearmonth                                      \n201001     3555.01  2509.45  2457.17  1966.32  \n201002     3627.34  2576.54  2405.64  2045.31  \n201003     3845.23  2776.39  2304.18  2061.80  \n201004     3449.79  2728.58  2125.24  1953.54  \n201005     3065.19  2470.64  1847.16  1742.98  \n...            ...      ...      ...      ...  \n202108     4730.65  4531.86  2713.74  2067.54  \n202109     4770.62  4428.19  2585.23  2609.22  \n202110     4820.51  4553.92  2736.45  2354.39  \n202111     4658.56  4783.52  3067.99  2215.75  \n202112     4780.38  4740.97  3220.39  2573.07  \n\n[144 rows x 11 columns]",
      "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>code</th>\n      <th>1</th>\n      <th>32</th>\n      <th>33</th>\n      <th>34</th>\n      <th>35</th>\n      <th>36</th>\n      <th>37</th>\n      <th>38</th>\n      <th>39</th>\n      <th>40</th>\n      <th>41</th>\n    </tr>\n    <tr>\n      <th>yearmonth</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>201001</th>\n      <td>2989.29</td>\n      <td>3013.19</td>\n      <td>2338.58</td>\n      <td>2328.31</td>\n      <td>2487.20</td>\n      <td>4325.84</td>\n      <td>3275.82</td>\n      <td>3555.01</td>\n      <td>2509.45</td>\n      <td>2457.17</td>\n      <td>1966.32</td>\n    </tr>\n    <tr>\n      <th>201002</th>\n      <td>3051.94</td>\n      <td>3012.64</td>\n      <td>2407.88</td>\n      <td>2378.33</td>\n      <td>2638.66</td>\n      <td>4414.94</td>\n      <td>3421.93</td>\n      <td>3627.34</td>\n      <td>2576.54</td>\n      <td>2405.64</td>\n      <td>2045.31</td>\n    </tr>\n    <tr>\n      <th>201003</th>\n      <td>3109.11</td>\n      <td>3064.59</td>\n      <td>2427.83</td>\n      <td>2392.62</td>\n      <td>2633.05</td>\n      <td>4348.36</td>\n      <td>3345.19</td>\n      <td>3845.23</td>\n      <td>2776.39</td>\n      <td>2304.18</td>\n      <td>2061.80</td>\n    </tr>\n    <tr>\n      <th>201004</th>\n      <td>2870.61</td>\n      <td>2878.92</td>\n      <td>2260.45</td>\n      <td>2207.50</td>\n      <td>2395.73</td>\n      <td>4161.42</td>\n      <td>3638.46</td>\n      <td>3449.79</td>\n      <td>2728.58</td>\n      <td>2125.24</td>\n      <td>1953.54</td>\n    </tr>\n    <tr>\n      <th>201005</th>\n      <td>2592.15</td>\n      <td>2513.56</td>\n      <td>2069.46</td>\n      <td>1981.80</td>\n      <td>2157.30</td>\n      <td>3981.44</td>\n      <td>3687.59</td>\n      <td>3065.19</td>\n      <td>2470.64</td>\n      <td>1847.16</td>\n      <td>1742.98</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>202108</th>\n      <td>3543.94</td>\n      <td>1544.61</td>\n      <td>3424.67</td>\n      <td>3111.24</td>\n      <td>3749.15</td>\n      <td>15592.87</td>\n      <td>8970.98</td>\n      <td>4730.65</td>\n      <td>4531.86</td>\n      <td>2713.74</td>\n      <td>2067.54</td>\n    </tr>\n    <tr>\n      <th>202109</th>\n      <td>3568.17</td>\n      <td>1699.18</td>\n      <td>3021.62</td>\n      <td>2999.62</td>\n      <td>3632.99</td>\n      <td>17203.02</td>\n      <td>9500.47</td>\n      <td>4770.62</td>\n      <td>4428.19</td>\n      <td>2585.23</td>\n      <td>2609.22</td>\n    </tr>\n    <tr>\n      <th>202110</th>\n      <td>3547.34</td>\n      <td>1463.15</td>\n      <td>2937.69</td>\n      <td>3101.17</td>\n      <td>3911.24</td>\n      <td>18238.92</td>\n      <td>9194.52</td>\n      <td>4820.51</td>\n      <td>4553.92</td>\n      <td>2736.45</td>\n      <td>2354.39</td>\n    </tr>\n    <tr>\n      <th>202111</th>\n      <td>3563.89</td>\n      <td>1360.96</td>\n      <td>2949.79</td>\n      <td>2933.02</td>\n      <td>3764.14</td>\n      <td>18011.08</td>\n      <td>9502.19</td>\n      <td>4658.56</td>\n      <td>4783.52</td>\n      <td>3067.99</td>\n      <td>2215.75</td>\n    </tr>\n    <tr>\n      <th>202112</th>\n      <td>3639.78</td>\n      <td>1442.91</td>\n      <td>2922.49</td>\n      <td>3007.69</td>\n      <td>3799.83</td>\n      <td>18327.31</td>\n      <td>9287.54</td>\n      <td>4780.38</td>\n      <td>4740.97</td>\n      <td>3220.39</td>\n      <td>2573.07</td>\n    </tr>\n  </tbody>\n</table>\n<p>144 rows × 11 columns</p>\n</div>"
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index['date']=pd.to_datetime(index['date'])\n",
    "index['yearmonth']=index['date'].dt.strftime('%Y%m')\n",
    "index.drop(columns=['monret'],inplace=True)\n",
    "index\n",
    "index_new=index.pivot(index='yearmonth',columns='code',values='close')\n",
    "index_new"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "outputs": [
    {
     "data": {
      "text/plain": "        日期_Date  月无风险收益率_Monrfret\n0    2010-01-29          0.001553\n1    2010-02-26          0.001604\n2    2010-03-31          0.001619\n3    2010-04-30          0.001616\n4    2010-05-31          0.001646\n..          ...               ...\n139  2021-08-31          0.001969\n140  2021-09-30          0.001980\n141  2021-10-29          0.002027\n142  2021-11-30          0.002055\n143  2021-12-31          0.002079\n\n[144 rows x 2 columns]",
      "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>日期_Date</th>\n      <th>月无风险收益率_Monrfret</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2010-01-29</td>\n      <td>0.001553</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2010-02-26</td>\n      <td>0.001604</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2010-03-31</td>\n      <td>0.001619</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2010-04-30</td>\n      <td>0.001616</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2010-05-31</td>\n      <td>0.001646</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>139</th>\n      <td>2021-08-31</td>\n      <td>0.001969</td>\n    </tr>\n    <tr>\n      <th>140</th>\n      <td>2021-09-30</td>\n      <td>0.001980</td>\n    </tr>\n    <tr>\n      <th>141</th>\n      <td>2021-10-29</td>\n      <td>0.002027</td>\n    </tr>\n    <tr>\n      <th>142</th>\n      <td>2021-11-30</td>\n      <td>0.002055</td>\n    </tr>\n    <tr>\n      <th>143</th>\n      <td>2021-12-31</td>\n      <td>0.002079</td>\n    </tr>\n  </tbody>\n</table>\n<p>144 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf=pd.read_excel(r'E:\\大三下study\\蒋志强\\実験5\\data\\rf.xlsx')\n",
    "rf"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "outputs": [
    {
     "data": {
      "text/plain": "          date  monrfret yearmonth\n0   2010-01-29  0.001553    201001\n1   2010-02-26  0.001604    201002\n2   2010-03-31  0.001619    201003\n3   2010-04-30  0.001616    201004\n4   2010-05-31  0.001646    201005\n..         ...       ...       ...\n139 2021-08-31  0.001969    202108\n140 2021-09-30  0.001980    202109\n141 2021-10-29  0.002027    202110\n142 2021-11-30  0.002055    202111\n143 2021-12-31  0.002079    202112\n\n[144 rows x 3 columns]",
      "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>date</th>\n      <th>monrfret</th>\n      <th>yearmonth</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2010-01-29</td>\n      <td>0.001553</td>\n      <td>201001</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2010-02-26</td>\n      <td>0.001604</td>\n      <td>201002</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2010-03-31</td>\n      <td>0.001619</td>\n      <td>201003</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2010-04-30</td>\n      <td>0.001616</td>\n      <td>201004</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2010-05-31</td>\n      <td>0.001646</td>\n      <td>201005</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>139</th>\n      <td>2021-08-31</td>\n      <td>0.001969</td>\n      <td>202108</td>\n    </tr>\n    <tr>\n      <th>140</th>\n      <td>2021-09-30</td>\n      <td>0.001980</td>\n      <td>202109</td>\n    </tr>\n    <tr>\n      <th>141</th>\n      <td>2021-10-29</td>\n      <td>0.002027</td>\n      <td>202110</td>\n    </tr>\n    <tr>\n      <th>142</th>\n      <td>2021-11-30</td>\n      <td>0.002055</td>\n      <td>202111</td>\n    </tr>\n    <tr>\n      <th>143</th>\n      <td>2021-12-31</td>\n      <td>0.002079</td>\n      <td>202112</td>\n    </tr>\n  </tbody>\n</table>\n<p>144 rows × 3 columns</p>\n</div>"
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.columns=['date','monrfret']\n",
    "rf['date']=pd.to_datetime(rf['date'])\n",
    "rf['yearmonth']=rf['date'].dt.strftime('%Y%m')\n",
    "rf"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "outputs": [
    {
     "data": {
      "text/plain": "      code  name     date_x    close yearmonth     date_y  monrfret\n0        1  上证指数 2010-01-29  2989.29    201001 2010-01-29  0.001553\n1       32  上证能源 2010-01-29  3013.19    201001 2010-01-29  0.001553\n2       33  上证材料 2010-01-29  2338.58    201001 2010-01-29  0.001553\n3       34  上证工业 2010-01-29  2328.31    201001 2010-01-29  0.001553\n4       35  上证可选 2010-01-29  2487.20    201001 2010-01-29  0.001553\n...    ...   ...        ...      ...       ...        ...       ...\n1579    37  上证医药 2021-12-31  9287.54    202112 2021-12-31  0.002079\n1580    38  上证金融 2021-12-31  4780.38    202112 2021-12-31  0.002079\n1581    39  上证信息 2021-12-31  4740.97    202112 2021-12-31  0.002079\n1582    40  上证通信 2021-12-31  3220.39    202112 2021-12-31  0.002079\n1583    41  上证公用 2021-12-31  2573.07    202112 2021-12-31  0.002079\n\n[1584 rows x 7 columns]",
      "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>code</th>\n      <th>name</th>\n      <th>date_x</th>\n      <th>close</th>\n      <th>yearmonth</th>\n      <th>date_y</th>\n      <th>monrfret</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>上证指数</td>\n      <td>2010-01-29</td>\n      <td>2989.29</td>\n      <td>201001</td>\n      <td>2010-01-29</td>\n      <td>0.001553</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>32</td>\n      <td>上证能源</td>\n      <td>2010-01-29</td>\n      <td>3013.19</td>\n      <td>201001</td>\n      <td>2010-01-29</td>\n      <td>0.001553</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>33</td>\n      <td>上证材料</td>\n      <td>2010-01-29</td>\n      <td>2338.58</td>\n      <td>201001</td>\n      <td>2010-01-29</td>\n      <td>0.001553</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>34</td>\n      <td>上证工业</td>\n      <td>2010-01-29</td>\n      <td>2328.31</td>\n      <td>201001</td>\n      <td>2010-01-29</td>\n      <td>0.001553</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>35</td>\n      <td>上证可选</td>\n      <td>2010-01-29</td>\n      <td>2487.20</td>\n      <td>201001</td>\n      <td>2010-01-29</td>\n      <td>0.001553</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1579</th>\n      <td>37</td>\n      <td>上证医药</td>\n      <td>2021-12-31</td>\n      <td>9287.54</td>\n      <td>202112</td>\n      <td>2021-12-31</td>\n      <td>0.002079</td>\n    </tr>\n    <tr>\n      <th>1580</th>\n      <td>38</td>\n      <td>上证金融</td>\n      <td>2021-12-31</td>\n      <td>4780.38</td>\n      <td>202112</td>\n      <td>2021-12-31</td>\n      <td>0.002079</td>\n    </tr>\n    <tr>\n      <th>1581</th>\n      <td>39</td>\n      <td>上证信息</td>\n      <td>2021-12-31</td>\n      <td>4740.97</td>\n      <td>202112</td>\n      <td>2021-12-31</td>\n      <td>0.002079</td>\n    </tr>\n    <tr>\n      <th>1582</th>\n      <td>40</td>\n      <td>上证通信</td>\n      <td>2021-12-31</td>\n      <td>3220.39</td>\n      <td>202112</td>\n      <td>2021-12-31</td>\n      <td>0.002079</td>\n    </tr>\n    <tr>\n      <th>1583</th>\n      <td>41</td>\n      <td>上证公用</td>\n      <td>2021-12-31</td>\n      <td>2573.07</td>\n      <td>202112</td>\n      <td>2021-12-31</td>\n      <td>0.002079</td>\n    </tr>\n  </tbody>\n</table>\n<p>1584 rows × 7 columns</p>\n</div>"
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_rf=pd.merge(left=index,right=rf,on='yearmonth',how='inner')\n",
    "index_rf"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "outputs": [
    {
     "data": {
      "text/plain": "     close41 yearmonth  monrfret  close40  close39  close38  close37  \\\n0    1966.32    201001  0.001553  2457.17  2509.45  3555.01  3275.82   \n1    2045.31    201002  0.001604  2405.64  2576.54  3627.34  3421.93   \n2    2061.80    201003  0.001619  2304.18  2776.39  3845.23  3345.19   \n3    1953.54    201004  0.001616  2125.24  2728.58  3449.79  3638.46   \n4    1742.98    201005  0.001646  1847.16  2470.64  3065.19  3687.59   \n..       ...       ...       ...      ...      ...      ...      ...   \n139  2067.54    202108  0.001969  2713.74  4531.86  4730.65  8970.98   \n140  2609.22    202109  0.001980  2585.23  4428.19  4770.62  9500.47   \n141  2354.39    202110  0.002027  2736.45  4553.92  4820.51  9194.52   \n142  2215.75    202111  0.002055  3067.99  4783.52  4658.56  9502.19   \n143  2573.07    202112  0.002079  3220.39  4740.97  4780.38  9287.54   \n\n      close36  close35  close34  close33  close32   close1  \n0     4325.84  2487.20  2328.31  2338.58  3013.19  2989.29  \n1     4414.94  2638.66  2378.33  2407.88  3012.64  3051.94  \n2     4348.36  2633.05  2392.62  2427.83  3064.59  3109.11  \n3     4161.42  2395.73  2207.50  2260.45  2878.92  2870.61  \n4     3981.44  2157.30  1981.80  2069.46  2513.56  2592.15  \n..        ...      ...      ...      ...      ...      ...  \n139  15592.87  3749.15  3111.24  3424.67  1544.61  3543.94  \n140  17203.02  3632.99  2999.62  3021.62  1699.18  3568.17  \n141  18238.92  3911.24  3101.17  2937.69  1463.15  3547.34  \n142  18011.08  3764.14  2933.02  2949.79  1360.96  3563.89  \n143  18327.31  3799.83  3007.69  2922.49  1442.91  3639.78  \n\n[144 rows x 13 columns]",
      "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>close41</th>\n      <th>yearmonth</th>\n      <th>monrfret</th>\n      <th>close40</th>\n      <th>close39</th>\n      <th>close38</th>\n      <th>close37</th>\n      <th>close36</th>\n      <th>close35</th>\n      <th>close34</th>\n      <th>close33</th>\n      <th>close32</th>\n      <th>close1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1966.32</td>\n      <td>201001</td>\n      <td>0.001553</td>\n      <td>2457.17</td>\n      <td>2509.45</td>\n      <td>3555.01</td>\n      <td>3275.82</td>\n      <td>4325.84</td>\n      <td>2487.20</td>\n      <td>2328.31</td>\n      <td>2338.58</td>\n      <td>3013.19</td>\n      <td>2989.29</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2045.31</td>\n      <td>201002</td>\n      <td>0.001604</td>\n      <td>2405.64</td>\n      <td>2576.54</td>\n      <td>3627.34</td>\n      <td>3421.93</td>\n      <td>4414.94</td>\n      <td>2638.66</td>\n      <td>2378.33</td>\n      <td>2407.88</td>\n      <td>3012.64</td>\n      <td>3051.94</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2061.80</td>\n      <td>201003</td>\n      <td>0.001619</td>\n      <td>2304.18</td>\n      <td>2776.39</td>\n      <td>3845.23</td>\n      <td>3345.19</td>\n      <td>4348.36</td>\n      <td>2633.05</td>\n      <td>2392.62</td>\n      <td>2427.83</td>\n      <td>3064.59</td>\n      <td>3109.11</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1953.54</td>\n      <td>201004</td>\n      <td>0.001616</td>\n      <td>2125.24</td>\n      <td>2728.58</td>\n      <td>3449.79</td>\n      <td>3638.46</td>\n      <td>4161.42</td>\n      <td>2395.73</td>\n      <td>2207.50</td>\n      <td>2260.45</td>\n      <td>2878.92</td>\n      <td>2870.61</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1742.98</td>\n      <td>201005</td>\n      <td>0.001646</td>\n      <td>1847.16</td>\n      <td>2470.64</td>\n      <td>3065.19</td>\n      <td>3687.59</td>\n      <td>3981.44</td>\n      <td>2157.30</td>\n      <td>1981.80</td>\n      <td>2069.46</td>\n      <td>2513.56</td>\n      <td>2592.15</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>139</th>\n      <td>2067.54</td>\n      <td>202108</td>\n      <td>0.001969</td>\n      <td>2713.74</td>\n      <td>4531.86</td>\n      <td>4730.65</td>\n      <td>8970.98</td>\n      <td>15592.87</td>\n      <td>3749.15</td>\n      <td>3111.24</td>\n      <td>3424.67</td>\n      <td>1544.61</td>\n      <td>3543.94</td>\n    </tr>\n    <tr>\n      <th>140</th>\n      <td>2609.22</td>\n      <td>202109</td>\n      <td>0.001980</td>\n      <td>2585.23</td>\n      <td>4428.19</td>\n      <td>4770.62</td>\n      <td>9500.47</td>\n      <td>17203.02</td>\n      <td>3632.99</td>\n      <td>2999.62</td>\n      <td>3021.62</td>\n      <td>1699.18</td>\n      <td>3568.17</td>\n    </tr>\n    <tr>\n      <th>141</th>\n      <td>2354.39</td>\n      <td>202110</td>\n      <td>0.002027</td>\n      <td>2736.45</td>\n      <td>4553.92</td>\n      <td>4820.51</td>\n      <td>9194.52</td>\n      <td>18238.92</td>\n      <td>3911.24</td>\n      <td>3101.17</td>\n      <td>2937.69</td>\n      <td>1463.15</td>\n      <td>3547.34</td>\n    </tr>\n    <tr>\n      <th>142</th>\n      <td>2215.75</td>\n      <td>202111</td>\n      <td>0.002055</td>\n      <td>3067.99</td>\n      <td>4783.52</td>\n      <td>4658.56</td>\n      <td>9502.19</td>\n      <td>18011.08</td>\n      <td>3764.14</td>\n      <td>2933.02</td>\n      <td>2949.79</td>\n      <td>1360.96</td>\n      <td>3563.89</td>\n    </tr>\n    <tr>\n      <th>143</th>\n      <td>2573.07</td>\n      <td>202112</td>\n      <td>0.002079</td>\n      <td>3220.39</td>\n      <td>4740.97</td>\n      <td>4780.38</td>\n      <td>9287.54</td>\n      <td>18327.31</td>\n      <td>3799.83</td>\n      <td>3007.69</td>\n      <td>2922.49</td>\n      <td>1442.91</td>\n      <td>3639.78</td>\n    </tr>\n  </tbody>\n</table>\n<p>144 rows × 13 columns</p>\n</div>"
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tempo1=pd.DataFrame\n",
    "count=0\n",
    "for i in index_rf['code'].unique():\n",
    "    count+=1\n",
    "    tempo=index_rf[index_rf['code']==i]\n",
    "    tempo=tempo[['close','yearmonth','monrfret']]\n",
    "    tempo.columns=['close'+str(i),'yearmonth','monrfret']\n",
    "    if count==1:\n",
    "        tempo1=tempo\n",
    "    else:\n",
    "        tempo1=pd.merge(left=tempo,right=tempo1,on=['yearmonth','monrfret'],how='inner')\n",
    "tempo1"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "outputs": [
    {
     "data": {
      "text/plain": "     close41 yearmonth  monrfret  close40  close39  close38  close37  \\\n0    1966.32    201001  0.001553  2457.17  2509.45  3555.01  3275.82   \n1    2045.31    201002  0.001604  2405.64  2576.54  3627.34  3421.93   \n2    2061.80    201003  0.001619  2304.18  2776.39  3845.23  3345.19   \n3    1953.54    201004  0.001616  2125.24  2728.58  3449.79  3638.46   \n4    1742.98    201005  0.001646  1847.16  2470.64  3065.19  3687.59   \n..       ...       ...       ...      ...      ...      ...      ...   \n139  2067.54    202108  0.001969  2713.74  4531.86  4730.65  8970.98   \n140  2609.22    202109  0.001980  2585.23  4428.19  4770.62  9500.47   \n141  2354.39    202110  0.002027  2736.45  4553.92  4820.51  9194.52   \n142  2215.75    202111  0.002055  3067.99  4783.52  4658.56  9502.19   \n143  2573.07    202112  0.002079  3220.39  4740.97  4780.38  9287.54   \n\n      close36  close35  close34  close33  close32   close1  \n0     4325.84  2487.20  2328.31  2338.58  3013.19  2989.29  \n1     4414.94  2638.66  2378.33  2407.88  3012.64  3051.94  \n2     4348.36  2633.05  2392.62  2427.83  3064.59  3109.11  \n3     4161.42  2395.73  2207.50  2260.45  2878.92  2870.61  \n4     3981.44  2157.30  1981.80  2069.46  2513.56  2592.15  \n..        ...      ...      ...      ...      ...      ...  \n139  15592.87  3749.15  3111.24  3424.67  1544.61  3543.94  \n140  17203.02  3632.99  2999.62  3021.62  1699.18  3568.17  \n141  18238.92  3911.24  3101.17  2937.69  1463.15  3547.34  \n142  18011.08  3764.14  2933.02  2949.79  1360.96  3563.89  \n143  18327.31  3799.83  3007.69  2922.49  1442.91  3639.78  \n\n[144 rows x 13 columns]",
      "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>close41</th>\n      <th>yearmonth</th>\n      <th>monrfret</th>\n      <th>close40</th>\n      <th>close39</th>\n      <th>close38</th>\n      <th>close37</th>\n      <th>close36</th>\n      <th>close35</th>\n      <th>close34</th>\n      <th>close33</th>\n      <th>close32</th>\n      <th>close1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1966.32</td>\n      <td>201001</td>\n      <td>0.001553</td>\n      <td>2457.17</td>\n      <td>2509.45</td>\n      <td>3555.01</td>\n      <td>3275.82</td>\n      <td>4325.84</td>\n      <td>2487.20</td>\n      <td>2328.31</td>\n      <td>2338.58</td>\n      <td>3013.19</td>\n      <td>2989.29</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2045.31</td>\n      <td>201002</td>\n      <td>0.001604</td>\n      <td>2405.64</td>\n      <td>2576.54</td>\n      <td>3627.34</td>\n      <td>3421.93</td>\n      <td>4414.94</td>\n      <td>2638.66</td>\n      <td>2378.33</td>\n      <td>2407.88</td>\n      <td>3012.64</td>\n      <td>3051.94</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2061.80</td>\n      <td>201003</td>\n      <td>0.001619</td>\n      <td>2304.18</td>\n      <td>2776.39</td>\n      <td>3845.23</td>\n      <td>3345.19</td>\n      <td>4348.36</td>\n      <td>2633.05</td>\n      <td>2392.62</td>\n      <td>2427.83</td>\n      <td>3064.59</td>\n      <td>3109.11</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1953.54</td>\n      <td>201004</td>\n      <td>0.001616</td>\n      <td>2125.24</td>\n      <td>2728.58</td>\n      <td>3449.79</td>\n      <td>3638.46</td>\n      <td>4161.42</td>\n      <td>2395.73</td>\n      <td>2207.50</td>\n      <td>2260.45</td>\n      <td>2878.92</td>\n      <td>2870.61</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1742.98</td>\n      <td>201005</td>\n      <td>0.001646</td>\n      <td>1847.16</td>\n      <td>2470.64</td>\n      <td>3065.19</td>\n      <td>3687.59</td>\n      <td>3981.44</td>\n      <td>2157.30</td>\n      <td>1981.80</td>\n      <td>2069.46</td>\n      <td>2513.56</td>\n      <td>2592.15</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>139</th>\n      <td>2067.54</td>\n      <td>202108</td>\n      <td>0.001969</td>\n      <td>2713.74</td>\n      <td>4531.86</td>\n      <td>4730.65</td>\n      <td>8970.98</td>\n      <td>15592.87</td>\n      <td>3749.15</td>\n      <td>3111.24</td>\n      <td>3424.67</td>\n      <td>1544.61</td>\n      <td>3543.94</td>\n    </tr>\n    <tr>\n      <th>140</th>\n      <td>2609.22</td>\n      <td>202109</td>\n      <td>0.001980</td>\n      <td>2585.23</td>\n      <td>4428.19</td>\n      <td>4770.62</td>\n      <td>9500.47</td>\n      <td>17203.02</td>\n      <td>3632.99</td>\n      <td>2999.62</td>\n      <td>3021.62</td>\n      <td>1699.18</td>\n      <td>3568.17</td>\n    </tr>\n    <tr>\n      <th>141</th>\n      <td>2354.39</td>\n      <td>202110</td>\n      <td>0.002027</td>\n      <td>2736.45</td>\n      <td>4553.92</td>\n      <td>4820.51</td>\n      <td>9194.52</td>\n      <td>18238.92</td>\n      <td>3911.24</td>\n      <td>3101.17</td>\n      <td>2937.69</td>\n      <td>1463.15</td>\n      <td>3547.34</td>\n    </tr>\n    <tr>\n      <th>142</th>\n      <td>2215.75</td>\n      <td>202111</td>\n      <td>0.002055</td>\n      <td>3067.99</td>\n      <td>4783.52</td>\n      <td>4658.56</td>\n      <td>9502.19</td>\n      <td>18011.08</td>\n      <td>3764.14</td>\n      <td>2933.02</td>\n      <td>2949.79</td>\n      <td>1360.96</td>\n      <td>3563.89</td>\n    </tr>\n    <tr>\n      <th>143</th>\n      <td>2573.07</td>\n      <td>202112</td>\n      <td>0.002079</td>\n      <td>3220.39</td>\n      <td>4740.97</td>\n      <td>4780.38</td>\n      <td>9287.54</td>\n      <td>18327.31</td>\n      <td>3799.83</td>\n      <td>3007.69</td>\n      <td>2922.49</td>\n      <td>1442.91</td>\n      <td>3639.78</td>\n    </tr>\n  </tbody>\n</table>\n<p>144 rows × 13 columns</p>\n</div>"
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_rf=tempo1\n",
    "index_rf"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Python37\\lib\\site-packages\\openpyxl\\styles\\stylesheet.py:226: UserWarning: Workbook contains no default style, apply openpyxl's default\n",
      "  warn(\"Workbook contains no default style, apply openpyxl's default\")\n"
     ]
    },
    {
     "data": {
      "text/plain": "          date     mkt     smb     hml yearmonth\n0   2010-01-29 -0.0900  0.0710 -0.0400    201001\n1   2010-02-26  0.0190  0.0488  0.0018    201002\n2   2010-03-31  0.0178  0.0348  0.0101    201003\n3   2010-04-30 -0.0778  0.0035 -0.0433    201004\n4   2010-05-31 -0.0952  0.0093 -0.0457    201005\n..         ...     ...     ...     ...       ...\n139 2021-08-31  0.0488  0.0070  0.0832    202108\n140 2021-09-30  0.0094 -0.0191  0.0488    202109\n141 2021-10-29 -0.0096 -0.0051 -0.0983    202110\n142 2021-11-30 -0.0025  0.1014 -0.0599    202111\n143 2021-12-31  0.0255  0.0129  0.0579    202112\n\n[144 rows x 5 columns]",
      "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>date</th>\n      <th>mkt</th>\n      <th>smb</th>\n      <th>hml</th>\n      <th>yearmonth</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2010-01-29</td>\n      <td>-0.0900</td>\n      <td>0.0710</td>\n      <td>-0.0400</td>\n      <td>201001</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2010-02-26</td>\n      <td>0.0190</td>\n      <td>0.0488</td>\n      <td>0.0018</td>\n      <td>201002</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2010-03-31</td>\n      <td>0.0178</td>\n      <td>0.0348</td>\n      <td>0.0101</td>\n      <td>201003</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2010-04-30</td>\n      <td>-0.0778</td>\n      <td>0.0035</td>\n      <td>-0.0433</td>\n      <td>201004</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2010-05-31</td>\n      <td>-0.0952</td>\n      <td>0.0093</td>\n      <td>-0.0457</td>\n      <td>201005</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>139</th>\n      <td>2021-08-31</td>\n      <td>0.0488</td>\n      <td>0.0070</td>\n      <td>0.0832</td>\n      <td>202108</td>\n    </tr>\n    <tr>\n      <th>140</th>\n      <td>2021-09-30</td>\n      <td>0.0094</td>\n      <td>-0.0191</td>\n      <td>0.0488</td>\n      <td>202109</td>\n    </tr>\n    <tr>\n      <th>141</th>\n      <td>2021-10-29</td>\n      <td>-0.0096</td>\n      <td>-0.0051</td>\n      <td>-0.0983</td>\n      <td>202110</td>\n    </tr>\n    <tr>\n      <th>142</th>\n      <td>2021-11-30</td>\n      <td>-0.0025</td>\n      <td>0.1014</td>\n      <td>-0.0599</td>\n      <td>202111</td>\n    </tr>\n    <tr>\n      <th>143</th>\n      <td>2021-12-31</td>\n      <td>0.0255</td>\n      <td>0.0129</td>\n      <td>0.0579</td>\n      <td>202112</td>\n    </tr>\n  </tbody>\n</table>\n<p>144 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "three_factors=pd.read_excel(r'E:\\大三下study\\蒋志强\\実験5\\data\\3factors.xlsx')\n",
    "three_factors.columns=['date','mkt','smb','hml']\n",
    "three_factors['date']=pd.to_datetime(three_factors['date'],format='%Y-%m-%d')\n",
    "three_factors['yearmonth']=three_factors['date'].dt.strftime('%Y%m')\n",
    "three_factors"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "outputs": [
    {
     "data": {
      "text/plain": "     close41 yearmonth  monrfret  close40  close39  close38  close37  \\\n0    1966.32    201001  0.001553  2457.17  2509.45  3555.01  3275.82   \n1    2045.31    201002  0.001604  2405.64  2576.54  3627.34  3421.93   \n2    2061.80    201003  0.001619  2304.18  2776.39  3845.23  3345.19   \n3    1953.54    201004  0.001616  2125.24  2728.58  3449.79  3638.46   \n4    1742.98    201005  0.001646  1847.16  2470.64  3065.19  3687.59   \n..       ...       ...       ...      ...      ...      ...      ...   \n139  2067.54    202108  0.001969  2713.74  4531.86  4730.65  8970.98   \n140  2609.22    202109  0.001980  2585.23  4428.19  4770.62  9500.47   \n141  2354.39    202110  0.002027  2736.45  4553.92  4820.51  9194.52   \n142  2215.75    202111  0.002055  3067.99  4783.52  4658.56  9502.19   \n143  2573.07    202112  0.002079  3220.39  4740.97  4780.38  9287.54   \n\n      close36  close35  close34  close33  close32   close1     mkt     smb  \\\n0     4325.84  2487.20  2328.31  2338.58  3013.19  2989.29 -0.0900  0.0710   \n1     4414.94  2638.66  2378.33  2407.88  3012.64  3051.94  0.0190  0.0488   \n2     4348.36  2633.05  2392.62  2427.83  3064.59  3109.11  0.0178  0.0348   \n3     4161.42  2395.73  2207.50  2260.45  2878.92  2870.61 -0.0778  0.0035   \n4     3981.44  2157.30  1981.80  2069.46  2513.56  2592.15 -0.0952  0.0093   \n..        ...      ...      ...      ...      ...      ...     ...     ...   \n139  15592.87  3749.15  3111.24  3424.67  1544.61  3543.94  0.0488  0.0070   \n140  17203.02  3632.99  2999.62  3021.62  1699.18  3568.17  0.0094 -0.0191   \n141  18238.92  3911.24  3101.17  2937.69  1463.15  3547.34 -0.0096 -0.0051   \n142  18011.08  3764.14  2933.02  2949.79  1360.96  3563.89 -0.0025  0.1014   \n143  18327.31  3799.83  3007.69  2922.49  1442.91  3639.78  0.0255  0.0129   \n\n        hml  \n0   -0.0400  \n1    0.0018  \n2    0.0101  \n3   -0.0433  \n4   -0.0457  \n..      ...  \n139  0.0832  \n140  0.0488  \n141 -0.0983  \n142 -0.0599  \n143  0.0579  \n\n[144 rows x 16 columns]",
      "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>close41</th>\n      <th>yearmonth</th>\n      <th>monrfret</th>\n      <th>close40</th>\n      <th>close39</th>\n      <th>close38</th>\n      <th>close37</th>\n      <th>close36</th>\n      <th>close35</th>\n      <th>close34</th>\n      <th>close33</th>\n      <th>close32</th>\n      <th>close1</th>\n      <th>mkt</th>\n      <th>smb</th>\n      <th>hml</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1966.32</td>\n      <td>201001</td>\n      <td>0.001553</td>\n      <td>2457.17</td>\n      <td>2509.45</td>\n      <td>3555.01</td>\n      <td>3275.82</td>\n      <td>4325.84</td>\n      <td>2487.20</td>\n      <td>2328.31</td>\n      <td>2338.58</td>\n      <td>3013.19</td>\n      <td>2989.29</td>\n      <td>-0.0900</td>\n      <td>0.0710</td>\n      <td>-0.0400</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2045.31</td>\n      <td>201002</td>\n      <td>0.001604</td>\n      <td>2405.64</td>\n      <td>2576.54</td>\n      <td>3627.34</td>\n      <td>3421.93</td>\n      <td>4414.94</td>\n      <td>2638.66</td>\n      <td>2378.33</td>\n      <td>2407.88</td>\n      <td>3012.64</td>\n      <td>3051.94</td>\n      <td>0.0190</td>\n      <td>0.0488</td>\n      <td>0.0018</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2061.80</td>\n      <td>201003</td>\n      <td>0.001619</td>\n      <td>2304.18</td>\n      <td>2776.39</td>\n      <td>3845.23</td>\n      <td>3345.19</td>\n      <td>4348.36</td>\n      <td>2633.05</td>\n      <td>2392.62</td>\n      <td>2427.83</td>\n      <td>3064.59</td>\n      <td>3109.11</td>\n      <td>0.0178</td>\n      <td>0.0348</td>\n      <td>0.0101</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1953.54</td>\n      <td>201004</td>\n      <td>0.001616</td>\n      <td>2125.24</td>\n      <td>2728.58</td>\n      <td>3449.79</td>\n      <td>3638.46</td>\n      <td>4161.42</td>\n      <td>2395.73</td>\n      <td>2207.50</td>\n      <td>2260.45</td>\n      <td>2878.92</td>\n      <td>2870.61</td>\n      <td>-0.0778</td>\n      <td>0.0035</td>\n      <td>-0.0433</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1742.98</td>\n      <td>201005</td>\n      <td>0.001646</td>\n      <td>1847.16</td>\n      <td>2470.64</td>\n      <td>3065.19</td>\n      <td>3687.59</td>\n      <td>3981.44</td>\n      <td>2157.30</td>\n      <td>1981.80</td>\n      <td>2069.46</td>\n      <td>2513.56</td>\n      <td>2592.15</td>\n      <td>-0.0952</td>\n      <td>0.0093</td>\n      <td>-0.0457</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>139</th>\n      <td>2067.54</td>\n      <td>202108</td>\n      <td>0.001969</td>\n      <td>2713.74</td>\n      <td>4531.86</td>\n      <td>4730.65</td>\n      <td>8970.98</td>\n      <td>15592.87</td>\n      <td>3749.15</td>\n      <td>3111.24</td>\n      <td>3424.67</td>\n      <td>1544.61</td>\n      <td>3543.94</td>\n      <td>0.0488</td>\n      <td>0.0070</td>\n      <td>0.0832</td>\n    </tr>\n    <tr>\n      <th>140</th>\n      <td>2609.22</td>\n      <td>202109</td>\n      <td>0.001980</td>\n      <td>2585.23</td>\n      <td>4428.19</td>\n      <td>4770.62</td>\n      <td>9500.47</td>\n      <td>17203.02</td>\n      <td>3632.99</td>\n      <td>2999.62</td>\n      <td>3021.62</td>\n      <td>1699.18</td>\n      <td>3568.17</td>\n      <td>0.0094</td>\n      <td>-0.0191</td>\n      <td>0.0488</td>\n    </tr>\n    <tr>\n      <th>141</th>\n      <td>2354.39</td>\n      <td>202110</td>\n      <td>0.002027</td>\n      <td>2736.45</td>\n      <td>4553.92</td>\n      <td>4820.51</td>\n      <td>9194.52</td>\n      <td>18238.92</td>\n      <td>3911.24</td>\n      <td>3101.17</td>\n      <td>2937.69</td>\n      <td>1463.15</td>\n      <td>3547.34</td>\n      <td>-0.0096</td>\n      <td>-0.0051</td>\n      <td>-0.0983</td>\n    </tr>\n    <tr>\n      <th>142</th>\n      <td>2215.75</td>\n      <td>202111</td>\n      <td>0.002055</td>\n      <td>3067.99</td>\n      <td>4783.52</td>\n      <td>4658.56</td>\n      <td>9502.19</td>\n      <td>18011.08</td>\n      <td>3764.14</td>\n      <td>2933.02</td>\n      <td>2949.79</td>\n      <td>1360.96</td>\n      <td>3563.89</td>\n      <td>-0.0025</td>\n      <td>0.1014</td>\n      <td>-0.0599</td>\n    </tr>\n    <tr>\n      <th>143</th>\n      <td>2573.07</td>\n      <td>202112</td>\n      <td>0.002079</td>\n      <td>3220.39</td>\n      <td>4740.97</td>\n      <td>4780.38</td>\n      <td>9287.54</td>\n      <td>18327.31</td>\n      <td>3799.83</td>\n      <td>3007.69</td>\n      <td>2922.49</td>\n      <td>1442.91</td>\n      <td>3639.78</td>\n      <td>0.0255</td>\n      <td>0.0129</td>\n      <td>0.0579</td>\n    </tr>\n  </tbody>\n</table>\n<p>144 rows × 16 columns</p>\n</div>"
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "three_factors.drop(columns=['date'],inplace=True)\n",
    "index_rf_factors=pd.merge(left=index_rf,right=three_factors,on='yearmonth',how='inner')\n",
    "index_rf_factors"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "outputs": [],
   "source": [
    "def get_rexc(index_rf_factors,rf):##这个函数用于在不停的循环中获取rexc\n",
    "    p41=index_rf_factors.iloc[:,0].values\n",
    "    p40=index_rf_factors.iloc[:,3].values\n",
    "    p39=index_rf_factors.iloc[:,4].values\n",
    "    p38=index_rf_factors.iloc[:,5].values\n",
    "    p37=index_rf_factors.iloc[:,6].values\n",
    "    p36=index_rf_factors.iloc[:,7].values\n",
    "    p35=index_rf_factors.iloc[:,8].values\n",
    "    p34=index_rf_factors.iloc[:,9].values\n",
    "    p33=index_rf_factors.iloc[:,10].values\n",
    "    p32=index_rf_factors.iloc[:,11].values\n",
    "    r32=np.log(p32[1:])-np.log(p32[:-1])\n",
    "    r33=np.log(p33[1:])-np.log(p33[:-1])\n",
    "    r34=np.log(p34[1:])-np.log(p34[:-1])\n",
    "    r35=np.log(p35[1:])-np.log(p35[:-1])\n",
    "    r36=np.log(p36[1:])-np.log(p36[:-1])\n",
    "    r38=np.log(p38[1:])-np.log(p38[:-1])\n",
    "    r37=np.log(p37[1:])-np.log(p37[:-1])\n",
    "    r39=np.log(p39[1:])-np.log(p39[:-1])\n",
    "    r40=np.log(p40[1:])-np.log(p40[:-1])\n",
    "    r41=np.log(p41[1:])-np.log(p41[:-1])\n",
    "    rexc32=r32-rf[1:]\n",
    "    rexc33=r33-rf[1:]\n",
    "    rexc34=r34-rf[1:]\n",
    "    rexc35=r35-rf[1:]\n",
    "    rexc36=r36-rf[1:]\n",
    "    rexc37=r37-rf[1:]\n",
    "    rexc38=r38-rf[1:]\n",
    "    rexc39=r39-rf[1:]\n",
    "    rexc40=r40-rf[1:]\n",
    "    rexc41=r41-rf[1:]\n",
    "    return rexc32,rexc33,rexc34,rexc35,rexc36,rexc37,rexc38,rexc39,rexc40,rexc41"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "outputs": [],
   "source": [
    "def get_Cov_Const(Cov_Sample):\n",
    "    diagnal_average=np.mean(Cov_Sample.diagonal())\n",
    "    sum_notdiag=0\n",
    "    for i in range(10):\n",
    "        for j in range(10):\n",
    "            if i!=j:\n",
    "                sum_notdiag=sum_notdiag+Cov_Sample[i,j]\n",
    "    sum_notdiag\n",
    "    average_notdiag=sum_notdiag/90\n",
    "    Cov_Const=np.linspace(1,100,100).reshape(10,10)\n",
    "    for i in range(10):\n",
    "        for j in range(10):\n",
    "            if i==j:\n",
    "                Cov_Const[i,j]=diagnal_average\n",
    "            else:\n",
    "                Cov_Const[i,j]=average_notdiag\n",
    "    return Cov_Const"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "outputs": [],
   "source": [
    "def get_Cov_Sample(R):\n",
    "    Cov_Sample=np.mat(np.cov(R,rowvar=False))\n",
    "    return Cov_Sample"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "outputs": [],
   "source": [
    "def get_Cov_Factor(mkt,R):\n",
    "    X = np.mat(np.concatenate([np.ones((len(mkt)-1, 1)), mkt[1:, None]], axis=1))\n",
    "    Y = np.mat(R)\n",
    "    AB_hat = (X.T*X).I*(X.T*Y)\n",
    "    ALPHA = AB_hat[0]\n",
    "    BETA = AB_hat[1]\n",
    "    RESD = Y - X*AB_hat\n",
    "    covfactor = np.cov(mkt[1:])\n",
    "    covresidual = np.diag(np.diag(np.cov(RESD, rowvar=False)))\n",
    "    Cov_Factor = BETA.T*covfactor*BETA + covresidual\n",
    "    return Cov_Factor"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "outputs": [],
   "source": [
    "def get_Cov_EWMA(Cov_Sample,yearmonth,Cov_EWMA):\n",
    "    if yearmonth==201801:\n",
    "        Cov_EWMA=Cov_Sample\n",
    "    else:\n",
    "        Cov_EWMA=(1-0.95)*(Rx_t-Rx_t_)*(Rx_t-Rx_t_).T+0.95*Cov_EWMA\n",
    "    return Cov_EWMA"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "outputs": [],
   "source": [
    "def get_Cov_Shrink(Cov_Sample,rexc32,Cov_Factor):\n",
    "    c=min(((1-2/10)*(Cov_Sample**2).trace()+Cov_Sample.trace()**2)/((len(rexc32)-2/10)*((Cov_Sample**2).trace()-Cov_Sample.trace()**2/10)),1)\n",
    "    c=c[0,0]\n",
    "    Cov_Shrink = (1-c)*Cov_Sample + c*Cov_Factor\n",
    "    return Cov_Shrink"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "outputs": [],
   "source": [
    "def get_omega_Shrink_df_min(Cov_Factor,A,b,yearmonth,columnsname,omega_Shrink_df_min=None):\n",
    "    if yearmonth==201801:\n",
    "        omega_Shrink_df_min=pd.DataFrame(columns=columnsname)\n",
    "        omega_Shrink_min = Cov_Factor.I*A.T*(A*Cov_Factor.I*A.T).I*b\n",
    "        omega_Shrink_min=omega_Shrink_min.A.reshape(-1)\n",
    "        omega_Shrink_min=np.append(omega_Shrink_min,yearmonth)\n",
    "        omega_Shrink_dfi_min=pd.DataFrame([omega_Shrink_min],columns=columnsname)\n",
    "        omega_Shrink_df_min=omega_Shrink_df_min.append(omega_Shrink_dfi_min,ignore_index=True)\n",
    "    else:\n",
    "        omega_Shrink_min = Cov_Factor.I*A.T*(A*Cov_Factor.I*A.T).I*b\n",
    "        omega_Shrink_min=omega_Shrink_min.A.reshape(-1)\n",
    "        omega_Shrink_min=np.append(omega_Shrink_min,yearmonth)\n",
    "        omega_Shrink_dfi_min=pd.DataFrame([omega_Shrink_min],columns=columnsname)\n",
    "        omega_Shrink_df_min=omega_Shrink_df_min.append(omega_Shrink_dfi_min,ignore_index=True)\n",
    "    return omega_Shrink_df_min"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "outputs": [],
   "source": [
    "def get_omega_Shrink_df_max(Cov_Shrink,A,b,uhat,yearmonth,columnsname,omega_Shrink_df_max=None):\n",
    "    if yearmonth==201801:\n",
    "        omega_Shrink_df_max=pd.DataFrame(columns=columnsname)\n",
    "        omega_Shrink_max=(gama*Cov_Shrink).I*A.T*(A*(gama*Cov_Shrink).I*A.T).I*b-(gama*Cov_Shrink).I*(np.matlib.eye(n=10,dtype=int)-A.T*(A*(gama*Cov_Shrink).I*A.T).I*A*(gama*Cov_Shrink).I)*(-uhat[:,None])\n",
    "        omega_Shrink_max=omega_Shrink_max.A.reshape(-1)\n",
    "        omega_Shrink_max=np.append(omega_Shrink_max,yearmonth)\n",
    "        omega_Shrink_df_maxi=pd.DataFrame([omega_Shrink_max],columns=columnsname)\n",
    "        omega_Shrink_df_max=omega_Shrink_df_max.append(omega_Shrink_df_maxi,ignore_index=True)\n",
    "    else:\n",
    "        omega_Shrink_max=(gama*Cov_Shrink).I*A.T*(A*(gama*Cov_Shrink).I*A.T).I*b-(gama*Cov_Shrink).I*(np.matlib.eye(n=10,dtype=int)-A.T*(A*(gama*Cov_Shrink).I*A.T).I*A*(gama*Cov_Shrink).I)*(-uhat[:,None])\n",
    "        omega_Shrink_max=omega_Shrink_max.A.reshape(-1)\n",
    "        omega_Shrink_max=np.append(omega_Shrink_max,yearmonth)\n",
    "        omega_Shrink_df_maxi=pd.DataFrame([omega_Shrink_max],columns=columnsname)\n",
    "        omega_Shrink_df_max=omega_Shrink_df_max.append(omega_Shrink_df_maxi,ignore_index=True)\n",
    "    return omega_Shrink_df_max"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "outputs": [
    {
     "data": {
      "text/plain": "     close41  yearmonth  monrfret  close40  close39  close38  close37  \\\n0    1966.32     201001  0.001553  2457.17  2509.45  3555.01  3275.82   \n1    2045.31     201002  0.001604  2405.64  2576.54  3627.34  3421.93   \n2    2061.80     201003  0.001619  2304.18  2776.39  3845.23  3345.19   \n3    1953.54     201004  0.001616  2125.24  2728.58  3449.79  3638.46   \n4    1742.98     201005  0.001646  1847.16  2470.64  3065.19  3687.59   \n..       ...        ...       ...      ...      ...      ...      ...   \n139  2067.54     202108  0.001969  2713.74  4531.86  4730.65  8970.98   \n140  2609.22     202109  0.001980  2585.23  4428.19  4770.62  9500.47   \n141  2354.39     202110  0.002027  2736.45  4553.92  4820.51  9194.52   \n142  2215.75     202111  0.002055  3067.99  4783.52  4658.56  9502.19   \n143  2573.07     202112  0.002079  3220.39  4740.97  4780.38  9287.54   \n\n      close36  close35  close34  close33  close32   close1     mkt     smb  \\\n0     4325.84  2487.20  2328.31  2338.58  3013.19  2989.29 -0.0900  0.0710   \n1     4414.94  2638.66  2378.33  2407.88  3012.64  3051.94  0.0190  0.0488   \n2     4348.36  2633.05  2392.62  2427.83  3064.59  3109.11  0.0178  0.0348   \n3     4161.42  2395.73  2207.50  2260.45  2878.92  2870.61 -0.0778  0.0035   \n4     3981.44  2157.30  1981.80  2069.46  2513.56  2592.15 -0.0952  0.0093   \n..        ...      ...      ...      ...      ...      ...     ...     ...   \n139  15592.87  3749.15  3111.24  3424.67  1544.61  3543.94  0.0488  0.0070   \n140  17203.02  3632.99  2999.62  3021.62  1699.18  3568.17  0.0094 -0.0191   \n141  18238.92  3911.24  3101.17  2937.69  1463.15  3547.34 -0.0096 -0.0051   \n142  18011.08  3764.14  2933.02  2949.79  1360.96  3563.89 -0.0025  0.1014   \n143  18327.31  3799.83  3007.69  2922.49  1442.91  3639.78  0.0255  0.0129   \n\n        hml  \n0   -0.0400  \n1    0.0018  \n2    0.0101  \n3   -0.0433  \n4   -0.0457  \n..      ...  \n139  0.0832  \n140  0.0488  \n141 -0.0983  \n142 -0.0599  \n143  0.0579  \n\n[144 rows x 16 columns]",
      "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>close41</th>\n      <th>yearmonth</th>\n      <th>monrfret</th>\n      <th>close40</th>\n      <th>close39</th>\n      <th>close38</th>\n      <th>close37</th>\n      <th>close36</th>\n      <th>close35</th>\n      <th>close34</th>\n      <th>close33</th>\n      <th>close32</th>\n      <th>close1</th>\n      <th>mkt</th>\n      <th>smb</th>\n      <th>hml</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1966.32</td>\n      <td>201001</td>\n      <td>0.001553</td>\n      <td>2457.17</td>\n      <td>2509.45</td>\n      <td>3555.01</td>\n      <td>3275.82</td>\n      <td>4325.84</td>\n      <td>2487.20</td>\n      <td>2328.31</td>\n      <td>2338.58</td>\n      <td>3013.19</td>\n      <td>2989.29</td>\n      <td>-0.0900</td>\n      <td>0.0710</td>\n      <td>-0.0400</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2045.31</td>\n      <td>201002</td>\n      <td>0.001604</td>\n      <td>2405.64</td>\n      <td>2576.54</td>\n      <td>3627.34</td>\n      <td>3421.93</td>\n      <td>4414.94</td>\n      <td>2638.66</td>\n      <td>2378.33</td>\n      <td>2407.88</td>\n      <td>3012.64</td>\n      <td>3051.94</td>\n      <td>0.0190</td>\n      <td>0.0488</td>\n      <td>0.0018</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2061.80</td>\n      <td>201003</td>\n      <td>0.001619</td>\n      <td>2304.18</td>\n      <td>2776.39</td>\n      <td>3845.23</td>\n      <td>3345.19</td>\n      <td>4348.36</td>\n      <td>2633.05</td>\n      <td>2392.62</td>\n      <td>2427.83</td>\n      <td>3064.59</td>\n      <td>3109.11</td>\n      <td>0.0178</td>\n      <td>0.0348</td>\n      <td>0.0101</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1953.54</td>\n      <td>201004</td>\n      <td>0.001616</td>\n      <td>2125.24</td>\n      <td>2728.58</td>\n      <td>3449.79</td>\n      <td>3638.46</td>\n      <td>4161.42</td>\n      <td>2395.73</td>\n      <td>2207.50</td>\n      <td>2260.45</td>\n      <td>2878.92</td>\n      <td>2870.61</td>\n      <td>-0.0778</td>\n      <td>0.0035</td>\n      <td>-0.0433</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1742.98</td>\n      <td>201005</td>\n      <td>0.001646</td>\n      <td>1847.16</td>\n      <td>2470.64</td>\n      <td>3065.19</td>\n      <td>3687.59</td>\n      <td>3981.44</td>\n      <td>2157.30</td>\n      <td>1981.80</td>\n      <td>2069.46</td>\n      <td>2513.56</td>\n      <td>2592.15</td>\n      <td>-0.0952</td>\n      <td>0.0093</td>\n      <td>-0.0457</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>139</th>\n      <td>2067.54</td>\n      <td>202108</td>\n      <td>0.001969</td>\n      <td>2713.74</td>\n      <td>4531.86</td>\n      <td>4730.65</td>\n      <td>8970.98</td>\n      <td>15592.87</td>\n      <td>3749.15</td>\n      <td>3111.24</td>\n      <td>3424.67</td>\n      <td>1544.61</td>\n      <td>3543.94</td>\n      <td>0.0488</td>\n      <td>0.0070</td>\n      <td>0.0832</td>\n    </tr>\n    <tr>\n      <th>140</th>\n      <td>2609.22</td>\n      <td>202109</td>\n      <td>0.001980</td>\n      <td>2585.23</td>\n      <td>4428.19</td>\n      <td>4770.62</td>\n      <td>9500.47</td>\n      <td>17203.02</td>\n      <td>3632.99</td>\n      <td>2999.62</td>\n      <td>3021.62</td>\n      <td>1699.18</td>\n      <td>3568.17</td>\n      <td>0.0094</td>\n      <td>-0.0191</td>\n      <td>0.0488</td>\n    </tr>\n    <tr>\n      <th>141</th>\n      <td>2354.39</td>\n      <td>202110</td>\n      <td>0.002027</td>\n      <td>2736.45</td>\n      <td>4553.92</td>\n      <td>4820.51</td>\n      <td>9194.52</td>\n      <td>18238.92</td>\n      <td>3911.24</td>\n      <td>3101.17</td>\n      <td>2937.69</td>\n      <td>1463.15</td>\n      <td>3547.34</td>\n      <td>-0.0096</td>\n      <td>-0.0051</td>\n      <td>-0.0983</td>\n    </tr>\n    <tr>\n      <th>142</th>\n      <td>2215.75</td>\n      <td>202111</td>\n      <td>0.002055</td>\n      <td>3067.99</td>\n      <td>4783.52</td>\n      <td>4658.56</td>\n      <td>9502.19</td>\n      <td>18011.08</td>\n      <td>3764.14</td>\n      <td>2933.02</td>\n      <td>2949.79</td>\n      <td>1360.96</td>\n      <td>3563.89</td>\n      <td>-0.0025</td>\n      <td>0.1014</td>\n      <td>-0.0599</td>\n    </tr>\n    <tr>\n      <th>143</th>\n      <td>2573.07</td>\n      <td>202112</td>\n      <td>0.002079</td>\n      <td>3220.39</td>\n      <td>4740.97</td>\n      <td>4780.38</td>\n      <td>9287.54</td>\n      <td>18327.31</td>\n      <td>3799.83</td>\n      <td>3007.69</td>\n      <td>2922.49</td>\n      <td>1442.91</td>\n      <td>3639.78</td>\n      <td>0.0255</td>\n      <td>0.0129</td>\n      <td>0.0579</td>\n    </tr>\n  </tbody>\n</table>\n<p>144 rows × 16 columns</p>\n</div>"
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=index_rf_factors\n",
    "index_rf_factors['yearmonth']=index_rf_factors['yearmonth'].astype(int)\n",
    "columnsname=['32','33','34','35','36','37','38','39','40','41','yearmonth']\n",
    "columnsname1=['32','33','34','35','36','37','38','39','40','41']\n",
    "columnsname2=[]\n",
    "for i in columnsname1:\n",
    "    tempo='close'+i\n",
    "    columnsname2.append(tempo)\n",
    "columnsname2.append('yearmonth')\n",
    "omega_Shrink_df_max=pd.DataFrame(columns=columnsname)\n",
    "data=index_rf_factors\n",
    "gama=3\n",
    "b=1\n",
    "data"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "outputs": [],
   "source": [
    "data=index_rf_factors\n",
    "index_rf_factors['yearmonth']=index_rf_factors['yearmonth'].astype(int)\n",
    "columnsname=['32','33','34','35','36','37','38','39','40','41','yearmonth']\n",
    "columnsname1=['32','33','34','35','36','37','38','39','40','41']\n",
    "columnsname2=[]\n",
    "for i in columnsname1:\n",
    "    tempo='close'+i\n",
    "    columnsname2.append(tempo)\n",
    "columnsname2.append('yearmonth')\n",
    "omega_Shrink_df_max=pd.DataFrame(columns=columnsname)\n",
    "data=index_rf_factors\n",
    "gama=3\n",
    "b=1\n",
    "rexc_min_list=[]\n",
    "rexc_max_list=[]\n",
    "yearmonth_list=[]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "outputs": [
    {
     "data": {
      "text/plain": "201806"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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     "metadata": {},
     "output_type": "display_data"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
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      "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>32</th>\n      <th>33</th>\n      <th>34</th>\n      <th>35</th>\n      <th>36</th>\n      <th>37</th>\n      <th>38</th>\n      <th>39</th>\n      <th>40</th>\n      <th>41</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.002895</td>\n      <td>0.003142</td>\n      <td>0.002445</td>\n      <td>0.002432</td>\n      <td>0.001979</td>\n      <td>0.001519</td>\n      <td>0.002411</td>\n      <td>0.001649</td>\n      <td>0.001944</td>\n      <td>0.001592</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.003142</td>\n      <td>0.005315</td>\n      <td>0.003617</td>\n      <td>0.004282</td>\n      <td>0.003652</td>\n      <td>0.003227</td>\n      <td>0.002832</td>\n      <td>0.003784</td>\n      <td>0.003603</td>\n      <td>0.002348</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.002445</td>\n      <td>0.003617</td>\n      <td>0.003400</td>\n      <td>0.003348</td>\n      <td>0.002714</td>\n      <td>0.002406</td>\n      <td>0.002486</td>\n      <td>0.002743</td>\n      <td>0.002789</td>\n      <td>0.002103</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.002432</td>\n      <td>0.004282</td>\n      <td>0.003348</td>\n      <td>0.005575</td>\n      <td>0.003963</td>\n      <td>0.003979</td>\n      <td>0.002326</td>\n      <td>0.004209</td>\n      <td>0.003591</td>\n      <td>0.002135</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.001979</td>\n      <td>0.003652</td>\n      <td>0.002714</td>\n      <td>0.003963</td>\n      <td>0.005375</td>\n      <td>0.004098</td>\n      <td>0.001762</td>\n      <td>0.002627</td>\n      <td>0.002753</td>\n      <td>0.002168</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>0.001519</td>\n      <td>0.003227</td>\n      <td>0.002406</td>\n      <td>0.003979</td>\n      <td>0.004098</td>\n      <td>0.005375</td>\n      <td>0.001486</td>\n      <td>0.004572</td>\n      <td>0.003828</td>\n      <td>0.001803</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0.002411</td>\n      <td>0.002832</td>\n      <td>0.002486</td>\n      <td>0.002326</td>\n      <td>0.001762</td>\n      <td>0.001486</td>\n      <td>0.002948</td>\n      <td>0.001883</td>\n      <td>0.002025</td>\n      <td>0.001540</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>0.001649</td>\n      <td>0.003784</td>\n      <td>0.002743</td>\n      <td>0.004209</td>\n      <td>0.002627</td>\n      <td>0.004572</td>\n      <td>0.001883</td>\n      <td>0.007612</td>\n      <td>0.005695</td>\n      <td>0.001589</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0.001944</td>\n      <td>0.003603</td>\n      <td>0.002789</td>\n      <td>0.003591</td>\n      <td>0.002753</td>\n      <td>0.003828</td>\n      <td>0.002025</td>\n      <td>0.005695</td>\n      <td>0.005703</td>\n      <td>0.001917</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>0.001592</td>\n      <td>0.002348</td>\n      <td>0.002103</td>\n      <td>0.002135</td>\n      <td>0.002168</td>\n      <td>0.001803</td>\n      <td>0.001540</td>\n      <td>0.001589</td>\n      <td>0.001917</td>\n      <td>0.001904</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
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    {
     "data": {
      "text/plain": "    rexc_min  rexc_max  yearmonth\n0   0.035418  0.058321     201801\n1  -0.052749 -0.072703     201802\n2   0.012194  0.014599     201803\n3  -0.042044 -0.040798     201804\n4   0.109819  0.125082     201805\n5  -0.076984 -0.072254     201806\n6  -0.006850 -0.017844     201807\n7  -0.071938 -0.062030     201808\n8   0.065199  0.079045     201809\n9  -0.146749 -0.111222     201810\n10 -0.014276 -0.033796     201811\n11 -0.013241 -0.047796     201812\n12  0.005479  0.007759     201901\n13  0.104988  0.092558     201902\n14  0.036945  0.033048     201903\n15  0.008778  0.006585     201904\n16 -0.042269 -0.049059     201905\n17  0.008102  0.008038     201906\n18 -0.011594 -0.004317     201907\n19  0.013416  0.011547     201908\n20 -0.030731 -0.030103     201909\n21  0.005717  0.014396     201910\n22 -0.024889 -0.034466     201911\n23  0.006650 -0.001162     201912\n24 -0.036259 -0.028789     202001\n25 -0.054076 -0.062344     202002\n26  0.014662  0.011234     202003\n27  0.001166 -0.000623     202004\n28  0.030164  0.017938     202005\n29  0.062553  0.054614     202006\n30  0.079884  0.050230     202007\n31  0.030287  0.021909     202008\n32 -0.058685 -0.059921     202009\n33 -0.003112 -0.004265     202010\n34  0.012693  0.012820     202011\n35 -0.001988 -0.022343     202012\n36 -0.041030 -0.055655     202101\n37 -0.030609 -0.022037     202102\n38  0.098415  0.087699     202103\n39 -0.014791 -0.004318     202104\n40  0.039589  0.050012     202105\n41 -0.016544 -0.018972     202106\n42 -0.144883 -0.144331     202107\n43  0.010782 -0.006397     202108\n44  0.212226  0.232152     202109\n45 -0.082449 -0.097364     202110\n46 -0.029791 -0.027414     202111\n47  0.076780  0.059633     202112",
      "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>rexc_min</th>\n      <th>rexc_max</th>\n      <th>yearmonth</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.035418</td>\n      <td>0.058321</td>\n      <td>201801</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-0.052749</td>\n      <td>-0.072703</td>\n      <td>201802</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.012194</td>\n      <td>0.014599</td>\n      <td>201803</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-0.042044</td>\n      <td>-0.040798</td>\n      <td>201804</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.109819</td>\n      <td>0.125082</td>\n      <td>201805</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>-0.076984</td>\n      <td>-0.072254</td>\n      <td>201806</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>-0.006850</td>\n      <td>-0.017844</td>\n      <td>201807</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>-0.071938</td>\n      <td>-0.062030</td>\n      <td>201808</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0.065199</td>\n      <td>0.079045</td>\n      <td>201809</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>-0.146749</td>\n      <td>-0.111222</td>\n      <td>201810</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>-0.014276</td>\n      <td>-0.033796</td>\n      <td>201811</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>-0.013241</td>\n      <td>-0.047796</td>\n      <td>201812</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>0.005479</td>\n      <td>0.007759</td>\n      <td>201901</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>0.104988</td>\n      <td>0.092558</td>\n      <td>201902</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>0.036945</td>\n      <td>0.033048</td>\n      <td>201903</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>0.008778</td>\n      <td>0.006585</td>\n      <td>201904</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>-0.042269</td>\n      <td>-0.049059</td>\n      <td>201905</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>0.008102</td>\n      <td>0.008038</td>\n      <td>201906</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>-0.011594</td>\n      <td>-0.004317</td>\n      <td>201907</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>0.013416</td>\n      <td>0.011547</td>\n      <td>201908</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>-0.030731</td>\n      <td>-0.030103</td>\n      <td>201909</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>0.005717</td>\n      <td>0.014396</td>\n      <td>201910</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>-0.024889</td>\n      <td>-0.034466</td>\n      <td>201911</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>0.006650</td>\n      <td>-0.001162</td>\n      <td>201912</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>-0.036259</td>\n      <td>-0.028789</td>\n      <td>202001</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>-0.054076</td>\n      <td>-0.062344</td>\n      <td>202002</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>0.014662</td>\n      <td>0.011234</td>\n      <td>202003</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>0.001166</td>\n      <td>-0.000623</td>\n      <td>202004</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>0.030164</td>\n      <td>0.017938</td>\n      <td>202005</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>0.062553</td>\n      <td>0.054614</td>\n      <td>202006</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>0.079884</td>\n      <td>0.050230</td>\n      <td>202007</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>0.030287</td>\n      <td>0.021909</td>\n      <td>202008</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>-0.058685</td>\n      <td>-0.059921</td>\n      <td>202009</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>-0.003112</td>\n      <td>-0.004265</td>\n      <td>202010</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>0.012693</td>\n      <td>0.012820</td>\n      <td>202011</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>-0.001988</td>\n      <td>-0.022343</td>\n      <td>202012</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>-0.041030</td>\n      <td>-0.055655</td>\n      <td>202101</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>-0.030609</td>\n      <td>-0.022037</td>\n      <td>202102</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>0.098415</td>\n      <td>0.087699</td>\n      <td>202103</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>-0.014791</td>\n      <td>-0.004318</td>\n      <td>202104</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>0.039589</td>\n      <td>0.050012</td>\n      <td>202105</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>-0.016544</td>\n      <td>-0.018972</td>\n      <td>202106</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>-0.144883</td>\n      <td>-0.144331</td>\n      <td>202107</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>0.010782</td>\n      <td>-0.006397</td>\n      <td>202108</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>0.212226</td>\n      <td>0.232152</td>\n      <td>202109</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>-0.082449</td>\n      <td>-0.097364</td>\n      <td>202110</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>-0.029791</td>\n      <td>-0.027414</td>\n      <td>202111</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>0.076780</td>\n      <td>0.059633</td>\n      <td>202112</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "                 32        33        34        35        36        37  \\\nyearmonth                                                               \n201801     0.357418 -0.100832 -0.529310  0.206624  0.313503  0.215825   \n201802     0.343466 -0.107641 -0.511672  0.218861  0.292094  0.218012   \n201803     0.338885 -0.107499 -0.508307  0.215658  0.296732  0.220088   \n201804     0.353566 -0.098111 -0.505363  0.218864  0.297915  0.200788   \n201805     0.345865 -0.100208 -0.507768  0.224891  0.297851  0.200657   \n201806     0.384866 -0.082042 -0.488653  0.183324  0.237131  0.185046   \n201807     0.391269 -0.080177 -0.489227  0.183422  0.231653  0.182378   \n201808     0.399716 -0.093724 -0.484552  0.214368  0.244513  0.194553   \n201809     0.383295 -0.090471 -0.483764  0.219049  0.248331  0.197293   \n201810     0.386205 -0.056534 -0.477041  0.210599  0.216955  0.193424   \n201811     0.369988 -0.062585 -0.483508  0.201299  0.217061  0.201981   \n201812     0.360260 -0.055717 -0.492730  0.191561  0.220942  0.209313   \n201901     0.380383 -0.046979 -0.484305  0.186089  0.213735  0.211826   \n201902     0.349648 -0.049117 -0.475523  0.194656  0.206550  0.226335   \n201903     0.323786 -0.095042 -0.445090  0.199750  0.244371  0.205611   \n201904     0.315797 -0.105272 -0.448098  0.198372  0.241313  0.205501   \n201905     0.322345 -0.099396 -0.446637  0.195837  0.227522  0.205731   \n201906     0.337673 -0.094487 -0.444229  0.195604  0.219514  0.199929   \n201907     0.324847 -0.100400 -0.447041  0.187987  0.223082  0.205538   \n201908     0.327922 -0.094073 -0.449770  0.188309  0.227018  0.205752   \n201909     0.332197 -0.094038 -0.442877  0.185591  0.219070  0.198557   \n201910     0.312971 -0.095887 -0.442665  0.188874  0.232936  0.202601   \n201911     0.315453 -0.094780 -0.434505  0.189074  0.230695  0.199387   \n201912     0.307343 -0.098166 -0.433008  0.187505  0.236339  0.202202   \n202001     0.291571 -0.130699 -0.429365  0.181216  0.254573  0.209464   \n202002     0.278928 -0.130947 -0.430323  0.179192  0.263169  0.210400   \n202003     0.260776 -0.134033 -0.431639  0.178240  0.272550  0.214493   \n202004     0.291532 -0.126641 -0.419731  0.172350  0.250162  0.208255   \n202005     0.274905 -0.130701 -0.425538  0.164165  0.263653  0.212560   \n202006     0.286116 -0.131372 -0.415995  0.156685  0.248387  0.208695   \n202007     0.283038 -0.125429 -0.401637  0.123813  0.251594  0.199674   \n202008     0.276133 -0.169838 -0.409517  0.078208  0.245856  0.204056   \n202009     0.278601 -0.170039 -0.415280  0.080961  0.234760  0.204968   \n202010     0.275254 -0.179491 -0.399075  0.085137  0.231075  0.203750   \n202011     0.272752 -0.180693 -0.397584  0.080989  0.232708  0.204703   \n202012     0.252133 -0.192857 -0.393436  0.088032  0.238755  0.210882   \n202101     0.230089 -0.192715 -0.395508  0.086009  0.232884  0.213297   \n202102     0.208332 -0.190851 -0.390246  0.086652  0.245894  0.218800   \n202103     0.190192 -0.187291 -0.387244  0.090706  0.249594  0.221333   \n202104     0.259741 -0.173947 -0.371678  0.090231  0.215190  0.209743   \n202105     0.247857 -0.173530 -0.369182  0.091230  0.220622  0.209740   \n202106     0.243391 -0.171958 -0.363927  0.094276  0.215337  0.208788   \n202107     0.230934 -0.169614 -0.358271  0.096893  0.220491  0.210802   \n202108     0.183667 -0.127263 -0.365629  0.098386  0.233636  0.224875   \n202109     0.168070 -0.131609 -0.363961  0.108840  0.229364  0.223263   \n202110     0.276713 -0.103869 -0.339442  0.122798  0.161306  0.201308   \n202111     0.241393 -0.099477 -0.334135  0.118430  0.184464  0.215605   \n202112     0.232124 -0.102502 -0.321871  0.121676  0.192262  0.216764   \n\n                 38        39        40        41  \nyearmonth                                          \n201801     0.027382 -0.051563 -0.018484  0.579437  \n201802    -0.028617 -0.031151  0.000443  0.606204  \n201803    -0.023731 -0.030823  0.000237  0.598761  \n201804    -0.017132 -0.037368  0.000196  0.586645  \n201805    -0.017752 -0.035562  0.002350  0.589675  \n201806    -0.005226 -0.025877  0.005488  0.605943  \n201807    -0.007683 -0.026653  0.005282  0.609736  \n201808    -0.015039 -0.025195  0.002995  0.562365  \n201809    -0.016866 -0.024788  0.001052  0.566871  \n201810    -0.046902 -0.014240  0.008957  0.578576  \n201811    -0.031472 -0.012032  0.011045  0.588222  \n201812    -0.003557 -0.015066  0.004874  0.580120  \n201901     0.004961 -0.011030  0.005233  0.540086  \n201902    -0.019112 -0.009467  0.012034  0.563997  \n201903     0.030489 -0.049400 -0.027520  0.613045  \n201904     0.053926 -0.058240 -0.022050  0.618752  \n201905     0.043953 -0.053710 -0.020713  0.625068  \n201906     0.037540 -0.054901 -0.021831  0.625188  \n201907     0.034879 -0.051826 -0.014669  0.637603  \n201908     0.035725 -0.053649 -0.014256  0.627022  \n201909     0.041969 -0.056970 -0.013954  0.630457  \n201910     0.046567 -0.056950 -0.019744  0.631297  \n201911     0.038408 -0.057549 -0.019018  0.632835  \n201912     0.043537 -0.056461 -0.019410  0.630120  \n202001     0.054305 -0.054710 -0.014983  0.638629  \n202002     0.061827 -0.051387 -0.014448  0.633589  \n202003     0.068385 -0.046338 -0.012292  0.629858  \n202004     0.058661 -0.052205 -0.015554  0.633170  \n202005     0.067203 -0.050913 -0.013698  0.638364  \n202006     0.068717 -0.052572 -0.012760  0.644100  \n202007     0.086061 -0.055484 -0.015840  0.654210  \n202008     0.132913 -0.039909  0.000577  0.681521  \n202009     0.132248 -0.034453  0.003952  0.684282  \n202010     0.133975 -0.036547  0.000311  0.685612  \n202011     0.134238 -0.035513  0.001749  0.686653  \n202012     0.130133 -0.032824  0.007488  0.691693  \n202101     0.150417 -0.025343  0.013418  0.687452  \n202102     0.153583 -0.022796  0.012958  0.677673  \n202103     0.153710 -0.020704  0.013269  0.676437  \n202104     0.151761 -0.024437  0.018065  0.625330  \n202105     0.159832 -0.023381  0.020669  0.616142  \n202106     0.154785 -0.024254  0.022205  0.621357  \n202107     0.156293 -0.024615  0.020626  0.616459  \n202108     0.141061 -0.010958  0.016320  0.605905  \n202109     0.152967  0.003024  0.023780  0.586262  \n202110     0.179989 -0.002541  0.046363  0.457378  \n202111     0.182043  0.001634  0.040941  0.449100  \n202112     0.182928  0.000367  0.033049  0.445204  ",
      "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>32</th>\n      <th>33</th>\n      <th>34</th>\n      <th>35</th>\n      <th>36</th>\n      <th>37</th>\n      <th>38</th>\n      <th>39</th>\n      <th>40</th>\n      <th>41</th>\n    </tr>\n    <tr>\n      <th>yearmonth</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>201801</th>\n      <td>0.357418</td>\n      <td>-0.100832</td>\n      <td>-0.529310</td>\n      <td>0.206624</td>\n      <td>0.313503</td>\n      <td>0.215825</td>\n      <td>0.027382</td>\n      <td>-0.051563</td>\n      <td>-0.018484</td>\n      <td>0.579437</td>\n    </tr>\n    <tr>\n      <th>201802</th>\n      <td>0.343466</td>\n      <td>-0.107641</td>\n      <td>-0.511672</td>\n      <td>0.218861</td>\n      <td>0.292094</td>\n      <td>0.218012</td>\n      <td>-0.028617</td>\n      <td>-0.031151</td>\n      <td>0.000443</td>\n      <td>0.606204</td>\n    </tr>\n    <tr>\n      <th>201803</th>\n      <td>0.338885</td>\n      <td>-0.107499</td>\n      <td>-0.508307</td>\n      <td>0.215658</td>\n      <td>0.296732</td>\n      <td>0.220088</td>\n      <td>-0.023731</td>\n      <td>-0.030823</td>\n      <td>0.000237</td>\n      <td>0.598761</td>\n    </tr>\n    <tr>\n      <th>201804</th>\n      <td>0.353566</td>\n      <td>-0.098111</td>\n      <td>-0.505363</td>\n      <td>0.218864</td>\n      <td>0.297915</td>\n      <td>0.200788</td>\n      <td>-0.017132</td>\n      <td>-0.037368</td>\n      <td>0.000196</td>\n      <td>0.586645</td>\n    </tr>\n    <tr>\n      <th>201805</th>\n      <td>0.345865</td>\n      <td>-0.100208</td>\n      <td>-0.507768</td>\n      <td>0.224891</td>\n      <td>0.297851</td>\n      <td>0.200657</td>\n      <td>-0.017752</td>\n      <td>-0.035562</td>\n      <td>0.002350</td>\n      <td>0.589675</td>\n    </tr>\n    <tr>\n      <th>201806</th>\n      <td>0.384866</td>\n      <td>-0.082042</td>\n      <td>-0.488653</td>\n      <td>0.183324</td>\n      <td>0.237131</td>\n      <td>0.185046</td>\n      <td>-0.005226</td>\n      <td>-0.025877</td>\n      <td>0.005488</td>\n      <td>0.605943</td>\n    </tr>\n    <tr>\n      <th>201807</th>\n      <td>0.391269</td>\n      <td>-0.080177</td>\n      <td>-0.489227</td>\n      <td>0.183422</td>\n      <td>0.231653</td>\n      <td>0.182378</td>\n      <td>-0.007683</td>\n      <td>-0.026653</td>\n      <td>0.005282</td>\n      <td>0.609736</td>\n    </tr>\n    <tr>\n      <th>201808</th>\n      <td>0.399716</td>\n      <td>-0.093724</td>\n      <td>-0.484552</td>\n      <td>0.214368</td>\n      <td>0.244513</td>\n      <td>0.194553</td>\n      <td>-0.015039</td>\n      <td>-0.025195</td>\n      <td>0.002995</td>\n      <td>0.562365</td>\n    </tr>\n    <tr>\n      <th>201809</th>\n      <td>0.383295</td>\n      <td>-0.090471</td>\n      <td>-0.483764</td>\n      <td>0.219049</td>\n      <td>0.248331</td>\n      <td>0.197293</td>\n      <td>-0.016866</td>\n      <td>-0.024788</td>\n      <td>0.001052</td>\n      <td>0.566871</td>\n    </tr>\n    <tr>\n      <th>201810</th>\n      <td>0.386205</td>\n      <td>-0.056534</td>\n      <td>-0.477041</td>\n      <td>0.210599</td>\n      <td>0.216955</td>\n      <td>0.193424</td>\n      <td>-0.046902</td>\n      <td>-0.014240</td>\n      <td>0.008957</td>\n      <td>0.578576</td>\n    </tr>\n    <tr>\n      <th>201811</th>\n      <td>0.369988</td>\n      <td>-0.062585</td>\n      <td>-0.483508</td>\n      <td>0.201299</td>\n      <td>0.217061</td>\n      <td>0.201981</td>\n      <td>-0.031472</td>\n      <td>-0.012032</td>\n      <td>0.011045</td>\n      <td>0.588222</td>\n    </tr>\n    <tr>\n      <th>201812</th>\n      <td>0.360260</td>\n      <td>-0.055717</td>\n      <td>-0.492730</td>\n      <td>0.191561</td>\n      <td>0.220942</td>\n      <td>0.209313</td>\n      <td>-0.003557</td>\n      <td>-0.015066</td>\n      <td>0.004874</td>\n      <td>0.580120</td>\n    </tr>\n    <tr>\n      <th>201901</th>\n      <td>0.380383</td>\n      <td>-0.046979</td>\n      <td>-0.484305</td>\n      <td>0.186089</td>\n      <td>0.213735</td>\n      <td>0.211826</td>\n      <td>0.004961</td>\n      <td>-0.011030</td>\n      <td>0.005233</td>\n      <td>0.540086</td>\n    </tr>\n    <tr>\n      <th>201902</th>\n      <td>0.349648</td>\n      <td>-0.049117</td>\n      <td>-0.475523</td>\n      <td>0.194656</td>\n      <td>0.206550</td>\n      <td>0.226335</td>\n      <td>-0.019112</td>\n      <td>-0.009467</td>\n      <td>0.012034</td>\n      <td>0.563997</td>\n    </tr>\n    <tr>\n      <th>201903</th>\n      <td>0.323786</td>\n      <td>-0.095042</td>\n      <td>-0.445090</td>\n      <td>0.199750</td>\n      <td>0.244371</td>\n      <td>0.205611</td>\n      <td>0.030489</td>\n      <td>-0.049400</td>\n      <td>-0.027520</td>\n      <td>0.613045</td>\n    </tr>\n    <tr>\n      <th>201904</th>\n      <td>0.315797</td>\n      <td>-0.105272</td>\n      <td>-0.448098</td>\n      <td>0.198372</td>\n      <td>0.241313</td>\n      <td>0.205501</td>\n      <td>0.053926</td>\n      <td>-0.058240</td>\n      <td>-0.022050</td>\n      <td>0.618752</td>\n    </tr>\n    <tr>\n      <th>201905</th>\n      <td>0.322345</td>\n      <td>-0.099396</td>\n      <td>-0.446637</td>\n      <td>0.195837</td>\n      <td>0.227522</td>\n      <td>0.205731</td>\n      <td>0.043953</td>\n      <td>-0.053710</td>\n      <td>-0.020713</td>\n      <td>0.625068</td>\n    </tr>\n    <tr>\n      <th>201906</th>\n      <td>0.337673</td>\n      <td>-0.094487</td>\n      <td>-0.444229</td>\n      <td>0.195604</td>\n      <td>0.219514</td>\n      <td>0.199929</td>\n      <td>0.037540</td>\n      <td>-0.054901</td>\n      <td>-0.021831</td>\n      <td>0.625188</td>\n    </tr>\n    <tr>\n      <th>201907</th>\n      <td>0.324847</td>\n      <td>-0.100400</td>\n      <td>-0.447041</td>\n      <td>0.187987</td>\n      <td>0.223082</td>\n      <td>0.205538</td>\n      <td>0.034879</td>\n      <td>-0.051826</td>\n      <td>-0.014669</td>\n      <td>0.637603</td>\n    </tr>\n    <tr>\n      <th>201908</th>\n      <td>0.327922</td>\n      <td>-0.094073</td>\n      <td>-0.449770</td>\n      <td>0.188309</td>\n      <td>0.227018</td>\n      <td>0.205752</td>\n      <td>0.035725</td>\n      <td>-0.053649</td>\n      <td>-0.014256</td>\n      <td>0.627022</td>\n    </tr>\n    <tr>\n      <th>201909</th>\n      <td>0.332197</td>\n      <td>-0.094038</td>\n      <td>-0.442877</td>\n      <td>0.185591</td>\n      <td>0.219070</td>\n      <td>0.198557</td>\n      <td>0.041969</td>\n      <td>-0.056970</td>\n      <td>-0.013954</td>\n      <td>0.630457</td>\n    </tr>\n    <tr>\n      <th>201910</th>\n      <td>0.312971</td>\n      <td>-0.095887</td>\n      <td>-0.442665</td>\n      <td>0.188874</td>\n      <td>0.232936</td>\n      <td>0.202601</td>\n      <td>0.046567</td>\n      <td>-0.056950</td>\n      <td>-0.019744</td>\n      <td>0.631297</td>\n    </tr>\n    <tr>\n      <th>201911</th>\n      <td>0.315453</td>\n      <td>-0.094780</td>\n      <td>-0.434505</td>\n      <td>0.189074</td>\n      <td>0.230695</td>\n      <td>0.199387</td>\n      <td>0.038408</td>\n      <td>-0.057549</td>\n      <td>-0.019018</td>\n      <td>0.632835</td>\n    </tr>\n    <tr>\n      <th>201912</th>\n      <td>0.307343</td>\n      <td>-0.098166</td>\n      <td>-0.433008</td>\n      <td>0.187505</td>\n      <td>0.236339</td>\n      <td>0.202202</td>\n      <td>0.043537</td>\n      <td>-0.056461</td>\n      <td>-0.019410</td>\n      <td>0.630120</td>\n    </tr>\n    <tr>\n      <th>202001</th>\n      <td>0.291571</td>\n      <td>-0.130699</td>\n      <td>-0.429365</td>\n      <td>0.181216</td>\n      <td>0.254573</td>\n      <td>0.209464</td>\n      <td>0.054305</td>\n      <td>-0.054710</td>\n      <td>-0.014983</td>\n      <td>0.638629</td>\n    </tr>\n    <tr>\n      <th>202002</th>\n      <td>0.278928</td>\n      <td>-0.130947</td>\n      <td>-0.430323</td>\n      <td>0.179192</td>\n      <td>0.263169</td>\n      <td>0.210400</td>\n      <td>0.061827</td>\n      <td>-0.051387</td>\n      <td>-0.014448</td>\n      <td>0.633589</td>\n    </tr>\n    <tr>\n      <th>202003</th>\n      <td>0.260776</td>\n      <td>-0.134033</td>\n      <td>-0.431639</td>\n      <td>0.178240</td>\n      <td>0.272550</td>\n      <td>0.214493</td>\n      <td>0.068385</td>\n      <td>-0.046338</td>\n      <td>-0.012292</td>\n      <td>0.629858</td>\n    </tr>\n    <tr>\n      <th>202004</th>\n      <td>0.291532</td>\n      <td>-0.126641</td>\n      <td>-0.419731</td>\n      <td>0.172350</td>\n      <td>0.250162</td>\n      <td>0.208255</td>\n      <td>0.058661</td>\n      <td>-0.052205</td>\n      <td>-0.015554</td>\n      <td>0.633170</td>\n    </tr>\n    <tr>\n      <th>202005</th>\n      <td>0.274905</td>\n      <td>-0.130701</td>\n      <td>-0.425538</td>\n      <td>0.164165</td>\n      <td>0.263653</td>\n      <td>0.212560</td>\n      <td>0.067203</td>\n      <td>-0.050913</td>\n      <td>-0.013698</td>\n      <td>0.638364</td>\n    </tr>\n    <tr>\n      <th>202006</th>\n      <td>0.286116</td>\n      <td>-0.131372</td>\n      <td>-0.415995</td>\n      <td>0.156685</td>\n      <td>0.248387</td>\n      <td>0.208695</td>\n      <td>0.068717</td>\n      <td>-0.052572</td>\n      <td>-0.012760</td>\n      <td>0.644100</td>\n    </tr>\n    <tr>\n      <th>202007</th>\n      <td>0.283038</td>\n      <td>-0.125429</td>\n      <td>-0.401637</td>\n      <td>0.123813</td>\n      <td>0.251594</td>\n      <td>0.199674</td>\n      <td>0.086061</td>\n      <td>-0.055484</td>\n      <td>-0.015840</td>\n      <td>0.654210</td>\n    </tr>\n    <tr>\n      <th>202008</th>\n      <td>0.276133</td>\n      <td>-0.169838</td>\n      <td>-0.409517</td>\n      <td>0.078208</td>\n      <td>0.245856</td>\n      <td>0.204056</td>\n      <td>0.132913</td>\n      <td>-0.039909</td>\n      <td>0.000577</td>\n      <td>0.681521</td>\n    </tr>\n    <tr>\n      <th>202009</th>\n      <td>0.278601</td>\n      <td>-0.170039</td>\n      <td>-0.415280</td>\n      <td>0.080961</td>\n      <td>0.234760</td>\n      <td>0.204968</td>\n      <td>0.132248</td>\n      <td>-0.034453</td>\n      <td>0.003952</td>\n      <td>0.684282</td>\n    </tr>\n    <tr>\n      <th>202010</th>\n      <td>0.275254</td>\n      <td>-0.179491</td>\n      <td>-0.399075</td>\n      <td>0.085137</td>\n      <td>0.231075</td>\n      <td>0.203750</td>\n      <td>0.133975</td>\n      <td>-0.036547</td>\n      <td>0.000311</td>\n      <td>0.685612</td>\n    </tr>\n    <tr>\n      <th>202011</th>\n      <td>0.272752</td>\n      <td>-0.180693</td>\n      <td>-0.397584</td>\n      <td>0.080989</td>\n      <td>0.232708</td>\n      <td>0.204703</td>\n      <td>0.134238</td>\n      <td>-0.035513</td>\n      <td>0.001749</td>\n      <td>0.686653</td>\n    </tr>\n    <tr>\n      <th>202012</th>\n      <td>0.252133</td>\n      <td>-0.192857</td>\n      <td>-0.393436</td>\n      <td>0.088032</td>\n      <td>0.238755</td>\n      <td>0.210882</td>\n      <td>0.130133</td>\n      <td>-0.032824</td>\n      <td>0.007488</td>\n      <td>0.691693</td>\n    </tr>\n    <tr>\n      <th>202101</th>\n      <td>0.230089</td>\n      <td>-0.192715</td>\n      <td>-0.395508</td>\n      <td>0.086009</td>\n      <td>0.232884</td>\n      <td>0.213297</td>\n      <td>0.150417</td>\n      <td>-0.025343</td>\n      <td>0.013418</td>\n      <td>0.687452</td>\n    </tr>\n    <tr>\n      <th>202102</th>\n      <td>0.208332</td>\n      <td>-0.190851</td>\n      <td>-0.390246</td>\n      <td>0.086652</td>\n      <td>0.245894</td>\n      <td>0.218800</td>\n      <td>0.153583</td>\n      <td>-0.022796</td>\n      <td>0.012958</td>\n      <td>0.677673</td>\n    </tr>\n    <tr>\n      <th>202103</th>\n      <td>0.190192</td>\n      <td>-0.187291</td>\n      <td>-0.387244</td>\n      <td>0.090706</td>\n      <td>0.249594</td>\n      <td>0.221333</td>\n      <td>0.153710</td>\n      <td>-0.020704</td>\n      <td>0.013269</td>\n      <td>0.676437</td>\n    </tr>\n    <tr>\n      <th>202104</th>\n      <td>0.259741</td>\n      <td>-0.173947</td>\n      <td>-0.371678</td>\n      <td>0.090231</td>\n      <td>0.215190</td>\n      <td>0.209743</td>\n      <td>0.151761</td>\n      <td>-0.024437</td>\n      <td>0.018065</td>\n      <td>0.625330</td>\n    </tr>\n    <tr>\n      <th>202105</th>\n      <td>0.247857</td>\n      <td>-0.173530</td>\n      <td>-0.369182</td>\n      <td>0.091230</td>\n      <td>0.220622</td>\n      <td>0.209740</td>\n      <td>0.159832</td>\n      <td>-0.023381</td>\n      <td>0.020669</td>\n      <td>0.616142</td>\n    </tr>\n    <tr>\n      <th>202106</th>\n      <td>0.243391</td>\n      <td>-0.171958</td>\n      <td>-0.363927</td>\n      <td>0.094276</td>\n      <td>0.215337</td>\n      <td>0.208788</td>\n      <td>0.154785</td>\n      <td>-0.024254</td>\n      <td>0.022205</td>\n      <td>0.621357</td>\n    </tr>\n    <tr>\n      <th>202107</th>\n      <td>0.230934</td>\n      <td>-0.169614</td>\n      <td>-0.358271</td>\n      <td>0.096893</td>\n      <td>0.220491</td>\n      <td>0.210802</td>\n      <td>0.156293</td>\n      <td>-0.024615</td>\n      <td>0.020626</td>\n      <td>0.616459</td>\n    </tr>\n    <tr>\n      <th>202108</th>\n      <td>0.183667</td>\n      <td>-0.127263</td>\n      <td>-0.365629</td>\n      <td>0.098386</td>\n      <td>0.233636</td>\n      <td>0.224875</td>\n      <td>0.141061</td>\n      <td>-0.010958</td>\n      <td>0.016320</td>\n      <td>0.605905</td>\n    </tr>\n    <tr>\n      <th>202109</th>\n      <td>0.168070</td>\n      <td>-0.131609</td>\n      <td>-0.363961</td>\n      <td>0.108840</td>\n      <td>0.229364</td>\n      <td>0.223263</td>\n      <td>0.152967</td>\n      <td>0.003024</td>\n      <td>0.023780</td>\n      <td>0.586262</td>\n    </tr>\n    <tr>\n      <th>202110</th>\n      <td>0.276713</td>\n      <td>-0.103869</td>\n      <td>-0.339442</td>\n      <td>0.122798</td>\n      <td>0.161306</td>\n      <td>0.201308</td>\n      <td>0.179989</td>\n      <td>-0.002541</td>\n      <td>0.046363</td>\n      <td>0.457378</td>\n    </tr>\n    <tr>\n      <th>202111</th>\n      <td>0.241393</td>\n      <td>-0.099477</td>\n      <td>-0.334135</td>\n      <td>0.118430</td>\n      <td>0.184464</td>\n      <td>0.215605</td>\n      <td>0.182043</td>\n      <td>0.001634</td>\n      <td>0.040941</td>\n      <td>0.449100</td>\n    </tr>\n    <tr>\n      <th>202112</th>\n      <td>0.232124</td>\n      <td>-0.102502</td>\n      <td>-0.321871</td>\n      <td>0.121676</td>\n      <td>0.192262</td>\n      <td>0.216764</td>\n      <td>0.182928</td>\n      <td>0.000367</td>\n      <td>0.033049</td>\n      <td>0.445204</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "                 32        33        34        35        36        37  \\\nyearmonth                                                               \n201801     0.450720 -0.295503 -0.516373  0.313675  0.284939  0.282092   \n201802     0.445562 -0.293574 -0.518813  0.332240  0.238658  0.280608   \n201803     0.408972 -0.271493 -0.528985  0.328140  0.238407  0.282831   \n201804     0.421562 -0.271162 -0.524328  0.352907  0.246609  0.259710   \n201805     0.421289 -0.270802 -0.521720  0.344797  0.250190  0.262168   \n201806     0.478637 -0.252143 -0.494145  0.187205  0.162918  0.257751   \n201807     0.490851 -0.246277 -0.500554  0.189097  0.153090  0.258869   \n201808     0.482625 -0.251445 -0.498390  0.193306  0.162281  0.268814   \n201809     0.492151 -0.262077 -0.496100  0.170952  0.162950  0.271815   \n201810     0.483734 -0.202911 -0.496655  0.166428  0.093568  0.283200   \n201811     0.494666 -0.202515 -0.485888  0.142432  0.051372  0.304934   \n201812     0.473196 -0.197380 -0.489581  0.118205  0.069920  0.312476   \n201901     0.462233 -0.195744 -0.490238  0.116638  0.080510  0.306695   \n201902     0.435848 -0.181546 -0.493077  0.123704  0.062671  0.326722   \n201903     0.400743 -0.201015 -0.491023  0.143957  0.134050  0.293204   \n201904     0.389751 -0.204587 -0.492819  0.147550  0.129786  0.302085   \n201905     0.395177 -0.200756 -0.494242  0.148053  0.114465  0.305748   \n201906     0.409925 -0.201746 -0.497280  0.143804  0.118793  0.293299   \n201907     0.401334 -0.201449 -0.500820  0.128627  0.126569  0.294401   \n201908     0.406111 -0.199002 -0.500738  0.130981  0.125809  0.294640   \n201909     0.408197 -0.200087 -0.495263  0.136639  0.117693  0.293084   \n201910     0.396187 -0.200778 -0.495234  0.120786  0.131887  0.292140   \n201911     0.402619 -0.201064 -0.491099  0.127604  0.127059  0.287479   \n201912     0.389060 -0.196895 -0.492133  0.116483  0.139395  0.284240   \n202001     0.371498 -0.203414 -0.492607  0.091863  0.166537  0.281698   \n202002     0.368106 -0.208413 -0.494626  0.084199  0.171185  0.281825   \n202003     0.349444 -0.206661 -0.491530  0.065080  0.187835  0.275505   \n202004     0.367264 -0.209107 -0.495617  0.104955  0.152002  0.276250   \n202005     0.352455 -0.207146 -0.498964  0.086906  0.169589  0.274306   \n202006     0.361628 -0.212030 -0.493098  0.081980  0.155998  0.276872   \n202007     0.371306 -0.207586 -0.486404  0.035296  0.173577  0.264982   \n202008     0.368445 -0.237103 -0.496230 -0.022374  0.198686  0.261200   \n202009     0.367706 -0.237196 -0.502838 -0.020048  0.190550  0.262952   \n202010     0.365596 -0.257009 -0.490053  0.011367  0.182843  0.263697   \n202011     0.362968 -0.256742 -0.489918  0.009399  0.185458  0.263465   \n202012     0.349447 -0.261101 -0.488516  0.003786  0.198536  0.264846   \n202101     0.332563 -0.257536 -0.478791  0.000260  0.217875  0.264776   \n202102     0.290690 -0.225113 -0.467248  0.001783  0.224192  0.271814   \n202103     0.279753 -0.208241 -0.471824 -0.007691  0.227143  0.274752   \n202104     0.346279 -0.195049 -0.428407 -0.033287  0.191784  0.288348   \n202105     0.337523 -0.193559 -0.429575 -0.036072  0.196484  0.286267   \n202106     0.329235 -0.184752 -0.429181 -0.021106  0.179930  0.288012   \n202107     0.319681 -0.185742 -0.425751 -0.028371  0.189591  0.288733   \n202108     0.244845 -0.058946 -0.444875 -0.083481  0.183279  0.279109   \n202109     0.223343 -0.047714 -0.439783 -0.091625  0.198536  0.276454   \n202110     0.238908  0.011179 -0.360270  0.043177  0.020783  0.340697   \n202111     0.179971  0.010254 -0.338674  0.055686  0.063241  0.310745   \n202112     0.169749  0.015764 -0.340385  0.047187  0.070362  0.310939   \n\n                 38        39        40        41  \nyearmonth                                          \n201801     0.171730 -0.081544 -0.139052  0.529317  \n201802     0.130623 -0.069543 -0.131125  0.585364  \n201803     0.141650 -0.064119 -0.130572  0.595169  \n201804     0.129519 -0.088537 -0.113688  0.587408  \n201805     0.132737 -0.087164 -0.114319  0.582825  \n201806     0.153236  0.016460 -0.104253  0.594334  \n201807     0.145145  0.021774 -0.112736  0.600741  \n201808     0.157238  0.019524 -0.116626  0.582673  \n201809     0.169229  0.023181 -0.104887  0.572786  \n201810     0.135305  0.029393 -0.099922  0.607860  \n201811     0.149140  0.032950 -0.100718  0.613626  \n201812     0.176104  0.037871 -0.102530  0.601719  \n201901     0.179715  0.037515 -0.102114  0.604790  \n201902     0.180656  0.022552 -0.097858  0.620329  \n201903     0.194016  0.011390 -0.131731  0.646408  \n201904     0.214618 -0.013483 -0.108227  0.635327  \n201905     0.205736 -0.008267 -0.110963  0.645050  \n201906     0.187926  0.001350 -0.118399  0.662330  \n201907     0.193701  0.003154 -0.114231  0.668716  \n201908     0.190226 -0.001519 -0.111854  0.665344  \n201909     0.191803 -0.014228 -0.099935  0.662099  \n201910     0.206229 -0.009538 -0.097269  0.655591  \n201911     0.194436 -0.011971 -0.095499  0.660438  \n201912     0.204549 -0.009001 -0.093664  0.657965  \n202001     0.225303 -0.000497 -0.092748  0.652366  \n202002     0.233601  0.010474 -0.095014  0.648664  \n202003     0.244760  0.030335 -0.098687  0.643920  \n202004     0.230068  0.024746 -0.098200  0.647640  \n202005     0.244030  0.031401 -0.098671  0.646094  \n202006     0.243099  0.029055 -0.090459  0.646956  \n202007     0.244819  0.041238 -0.089594  0.652367  \n202008     0.281719  0.070901 -0.080874  0.655631  \n202009     0.284094  0.076632 -0.081467  0.659616  \n202010     0.286146  0.072976 -0.090347  0.654785  \n202011     0.288262  0.073918 -0.090678  0.653868  \n202012     0.299807  0.076102 -0.090869  0.647963  \n202101     0.307778  0.077268 -0.097550  0.633358  \n202102     0.323344  0.063745 -0.097741  0.614533  \n202103     0.332479  0.061906 -0.096889  0.608614  \n202104     0.301924  0.061750 -0.086167  0.552824  \n202105     0.307921  0.061232 -0.083881  0.553661  \n202106     0.299941  0.051920 -0.079324  0.565327  \n202107     0.305640  0.057704 -0.083562  0.562077  \n202108     0.277101  0.127657 -0.151546  0.626858  \n202109     0.282924  0.127947 -0.155672  0.625590  \n202110     0.324761  0.006124 -0.035058  0.409699  \n202111     0.351675  0.007122 -0.028760  0.388740  \n202112     0.356605  0.007599 -0.026115  0.388294  ",
      "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>32</th>\n      <th>33</th>\n      <th>34</th>\n      <th>35</th>\n      <th>36</th>\n      <th>37</th>\n      <th>38</th>\n      <th>39</th>\n      <th>40</th>\n      <th>41</th>\n    </tr>\n    <tr>\n      <th>yearmonth</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>201801</th>\n      <td>0.450720</td>\n      <td>-0.295503</td>\n      <td>-0.516373</td>\n      <td>0.313675</td>\n      <td>0.284939</td>\n      <td>0.282092</td>\n      <td>0.171730</td>\n      <td>-0.081544</td>\n      <td>-0.139052</td>\n      <td>0.529317</td>\n    </tr>\n    <tr>\n      <th>201802</th>\n      <td>0.445562</td>\n      <td>-0.293574</td>\n      <td>-0.518813</td>\n      <td>0.332240</td>\n      <td>0.238658</td>\n      <td>0.280608</td>\n      <td>0.130623</td>\n      <td>-0.069543</td>\n      <td>-0.131125</td>\n      <td>0.585364</td>\n    </tr>\n    <tr>\n      <th>201803</th>\n      <td>0.408972</td>\n      <td>-0.271493</td>\n      <td>-0.528985</td>\n      <td>0.328140</td>\n      <td>0.238407</td>\n      <td>0.282831</td>\n      <td>0.141650</td>\n      <td>-0.064119</td>\n      <td>-0.130572</td>\n      <td>0.595169</td>\n    </tr>\n    <tr>\n      <th>201804</th>\n      <td>0.421562</td>\n      <td>-0.271162</td>\n      <td>-0.524328</td>\n      <td>0.352907</td>\n      <td>0.246609</td>\n      <td>0.259710</td>\n      <td>0.129519</td>\n      <td>-0.088537</td>\n      <td>-0.113688</td>\n      <td>0.587408</td>\n    </tr>\n    <tr>\n      <th>201805</th>\n      <td>0.421289</td>\n      <td>-0.270802</td>\n      <td>-0.521720</td>\n      <td>0.344797</td>\n      <td>0.250190</td>\n      <td>0.262168</td>\n      <td>0.132737</td>\n      <td>-0.087164</td>\n      <td>-0.114319</td>\n      <td>0.582825</td>\n    </tr>\n    <tr>\n      <th>201806</th>\n      <td>0.478637</td>\n      <td>-0.252143</td>\n      <td>-0.494145</td>\n      <td>0.187205</td>\n      <td>0.162918</td>\n      <td>0.257751</td>\n      <td>0.153236</td>\n      <td>0.016460</td>\n      <td>-0.104253</td>\n      <td>0.594334</td>\n    </tr>\n    <tr>\n      <th>201807</th>\n      <td>0.490851</td>\n      <td>-0.246277</td>\n      <td>-0.500554</td>\n      <td>0.189097</td>\n      <td>0.153090</td>\n      <td>0.258869</td>\n      <td>0.145145</td>\n      <td>0.021774</td>\n      <td>-0.112736</td>\n      <td>0.600741</td>\n    </tr>\n    <tr>\n      <th>201808</th>\n      <td>0.482625</td>\n      <td>-0.251445</td>\n      <td>-0.498390</td>\n      <td>0.193306</td>\n      <td>0.162281</td>\n      <td>0.268814</td>\n      <td>0.157238</td>\n      <td>0.019524</td>\n      <td>-0.116626</td>\n      <td>0.582673</td>\n    </tr>\n    <tr>\n      <th>201809</th>\n      <td>0.492151</td>\n      <td>-0.262077</td>\n      <td>-0.496100</td>\n      <td>0.170952</td>\n      <td>0.162950</td>\n      <td>0.271815</td>\n      <td>0.169229</td>\n      <td>0.023181</td>\n      <td>-0.104887</td>\n      <td>0.572786</td>\n    </tr>\n    <tr>\n      <th>201810</th>\n      <td>0.483734</td>\n      <td>-0.202911</td>\n      <td>-0.496655</td>\n      <td>0.166428</td>\n      <td>0.093568</td>\n      <td>0.283200</td>\n      <td>0.135305</td>\n      <td>0.029393</td>\n      <td>-0.099922</td>\n      <td>0.607860</td>\n    </tr>\n    <tr>\n      <th>201811</th>\n      <td>0.494666</td>\n      <td>-0.202515</td>\n      <td>-0.485888</td>\n      <td>0.142432</td>\n      <td>0.051372</td>\n      <td>0.304934</td>\n      <td>0.149140</td>\n      <td>0.032950</td>\n      <td>-0.100718</td>\n      <td>0.613626</td>\n    </tr>\n    <tr>\n      <th>201812</th>\n      <td>0.473196</td>\n      <td>-0.197380</td>\n      <td>-0.489581</td>\n      <td>0.118205</td>\n      <td>0.069920</td>\n      <td>0.312476</td>\n      <td>0.176104</td>\n      <td>0.037871</td>\n      <td>-0.102530</td>\n      <td>0.601719</td>\n    </tr>\n    <tr>\n      <th>201901</th>\n      <td>0.462233</td>\n      <td>-0.195744</td>\n      <td>-0.490238</td>\n      <td>0.116638</td>\n      <td>0.080510</td>\n      <td>0.306695</td>\n      <td>0.179715</td>\n      <td>0.037515</td>\n      <td>-0.102114</td>\n      <td>0.604790</td>\n    </tr>\n    <tr>\n      <th>201902</th>\n      <td>0.435848</td>\n      <td>-0.181546</td>\n      <td>-0.493077</td>\n      <td>0.123704</td>\n      <td>0.062671</td>\n      <td>0.326722</td>\n      <td>0.180656</td>\n      <td>0.022552</td>\n      <td>-0.097858</td>\n      <td>0.620329</td>\n    </tr>\n    <tr>\n      <th>201903</th>\n      <td>0.400743</td>\n      <td>-0.201015</td>\n      <td>-0.491023</td>\n      <td>0.143957</td>\n      <td>0.134050</td>\n      <td>0.293204</td>\n      <td>0.194016</td>\n      <td>0.011390</td>\n      <td>-0.131731</td>\n      <td>0.646408</td>\n    </tr>\n    <tr>\n      <th>201904</th>\n      <td>0.389751</td>\n      <td>-0.204587</td>\n      <td>-0.492819</td>\n      <td>0.147550</td>\n      <td>0.129786</td>\n      <td>0.302085</td>\n      <td>0.214618</td>\n      <td>-0.013483</td>\n      <td>-0.108227</td>\n      <td>0.635327</td>\n    </tr>\n    <tr>\n      <th>201905</th>\n      <td>0.395177</td>\n      <td>-0.200756</td>\n      <td>-0.494242</td>\n      <td>0.148053</td>\n      <td>0.114465</td>\n      <td>0.305748</td>\n      <td>0.205736</td>\n      <td>-0.008267</td>\n      <td>-0.110963</td>\n      <td>0.645050</td>\n    </tr>\n    <tr>\n      <th>201906</th>\n      <td>0.409925</td>\n      <td>-0.201746</td>\n      <td>-0.497280</td>\n      <td>0.143804</td>\n      <td>0.118793</td>\n      <td>0.293299</td>\n      <td>0.187926</td>\n      <td>0.001350</td>\n      <td>-0.118399</td>\n      <td>0.662330</td>\n    </tr>\n    <tr>\n      <th>201907</th>\n      <td>0.401334</td>\n      <td>-0.201449</td>\n      <td>-0.500820</td>\n      <td>0.128627</td>\n      <td>0.126569</td>\n      <td>0.294401</td>\n      <td>0.193701</td>\n      <td>0.003154</td>\n      <td>-0.114231</td>\n      <td>0.668716</td>\n    </tr>\n    <tr>\n      <th>201908</th>\n      <td>0.406111</td>\n      <td>-0.199002</td>\n      <td>-0.500738</td>\n      <td>0.130981</td>\n      <td>0.125809</td>\n      <td>0.294640</td>\n      <td>0.190226</td>\n      <td>-0.001519</td>\n      <td>-0.111854</td>\n      <td>0.665344</td>\n    </tr>\n    <tr>\n      <th>201909</th>\n      <td>0.408197</td>\n      <td>-0.200087</td>\n      <td>-0.495263</td>\n      <td>0.136639</td>\n      <td>0.117693</td>\n      <td>0.293084</td>\n      <td>0.191803</td>\n      <td>-0.014228</td>\n      <td>-0.099935</td>\n      <td>0.662099</td>\n    </tr>\n    <tr>\n      <th>201910</th>\n      <td>0.396187</td>\n      <td>-0.200778</td>\n      <td>-0.495234</td>\n      <td>0.120786</td>\n      <td>0.131887</td>\n      <td>0.292140</td>\n      <td>0.206229</td>\n      <td>-0.009538</td>\n      <td>-0.097269</td>\n      <td>0.655591</td>\n    </tr>\n    <tr>\n      <th>201911</th>\n      <td>0.402619</td>\n      <td>-0.201064</td>\n      <td>-0.491099</td>\n      <td>0.127604</td>\n      <td>0.127059</td>\n      <td>0.287479</td>\n      <td>0.194436</td>\n      <td>-0.011971</td>\n      <td>-0.095499</td>\n      <td>0.660438</td>\n    </tr>\n    <tr>\n      <th>201912</th>\n      <td>0.389060</td>\n      <td>-0.196895</td>\n      <td>-0.492133</td>\n      <td>0.116483</td>\n      <td>0.139395</td>\n      <td>0.284240</td>\n      <td>0.204549</td>\n      <td>-0.009001</td>\n      <td>-0.093664</td>\n      <td>0.657965</td>\n    </tr>\n    <tr>\n      <th>202001</th>\n      <td>0.371498</td>\n      <td>-0.203414</td>\n      <td>-0.492607</td>\n      <td>0.091863</td>\n      <td>0.166537</td>\n      <td>0.281698</td>\n      <td>0.225303</td>\n      <td>-0.000497</td>\n      <td>-0.092748</td>\n      <td>0.652366</td>\n    </tr>\n    <tr>\n      <th>202002</th>\n      <td>0.368106</td>\n      <td>-0.208413</td>\n      <td>-0.494626</td>\n      <td>0.084199</td>\n      <td>0.171185</td>\n      <td>0.281825</td>\n      <td>0.233601</td>\n      <td>0.010474</td>\n      <td>-0.095014</td>\n      <td>0.648664</td>\n    </tr>\n    <tr>\n      <th>202003</th>\n      <td>0.349444</td>\n      <td>-0.206661</td>\n      <td>-0.491530</td>\n      <td>0.065080</td>\n      <td>0.187835</td>\n      <td>0.275505</td>\n      <td>0.244760</td>\n      <td>0.030335</td>\n      <td>-0.098687</td>\n      <td>0.643920</td>\n    </tr>\n    <tr>\n      <th>202004</th>\n      <td>0.367264</td>\n      <td>-0.209107</td>\n      <td>-0.495617</td>\n      <td>0.104955</td>\n      <td>0.152002</td>\n      <td>0.276250</td>\n      <td>0.230068</td>\n      <td>0.024746</td>\n      <td>-0.098200</td>\n      <td>0.647640</td>\n    </tr>\n    <tr>\n      <th>202005</th>\n      <td>0.352455</td>\n      <td>-0.207146</td>\n      <td>-0.498964</td>\n      <td>0.086906</td>\n      <td>0.169589</td>\n      <td>0.274306</td>\n      <td>0.244030</td>\n      <td>0.031401</td>\n      <td>-0.098671</td>\n      <td>0.646094</td>\n    </tr>\n    <tr>\n      <th>202006</th>\n      <td>0.361628</td>\n      <td>-0.212030</td>\n      <td>-0.493098</td>\n      <td>0.081980</td>\n      <td>0.155998</td>\n      <td>0.276872</td>\n      <td>0.243099</td>\n      <td>0.029055</td>\n      <td>-0.090459</td>\n      <td>0.646956</td>\n    </tr>\n    <tr>\n      <th>202007</th>\n      <td>0.371306</td>\n      <td>-0.207586</td>\n      <td>-0.486404</td>\n      <td>0.035296</td>\n      <td>0.173577</td>\n      <td>0.264982</td>\n      <td>0.244819</td>\n      <td>0.041238</td>\n      <td>-0.089594</td>\n      <td>0.652367</td>\n    </tr>\n    <tr>\n      <th>202008</th>\n      <td>0.368445</td>\n      <td>-0.237103</td>\n      <td>-0.496230</td>\n      <td>-0.022374</td>\n      <td>0.198686</td>\n      <td>0.261200</td>\n      <td>0.281719</td>\n      <td>0.070901</td>\n      <td>-0.080874</td>\n      <td>0.655631</td>\n    </tr>\n    <tr>\n      <th>202009</th>\n      <td>0.367706</td>\n      <td>-0.237196</td>\n      <td>-0.502838</td>\n      <td>-0.020048</td>\n      <td>0.190550</td>\n      <td>0.262952</td>\n      <td>0.284094</td>\n      <td>0.076632</td>\n      <td>-0.081467</td>\n      <td>0.659616</td>\n    </tr>\n    <tr>\n      <th>202010</th>\n      <td>0.365596</td>\n      <td>-0.257009</td>\n      <td>-0.490053</td>\n      <td>0.011367</td>\n      <td>0.182843</td>\n      <td>0.263697</td>\n      <td>0.286146</td>\n      <td>0.072976</td>\n      <td>-0.090347</td>\n      <td>0.654785</td>\n    </tr>\n    <tr>\n      <th>202011</th>\n      <td>0.362968</td>\n      <td>-0.256742</td>\n      <td>-0.489918</td>\n      <td>0.009399</td>\n      <td>0.185458</td>\n      <td>0.263465</td>\n      <td>0.288262</td>\n      <td>0.073918</td>\n      <td>-0.090678</td>\n      <td>0.653868</td>\n    </tr>\n    <tr>\n      <th>202012</th>\n      <td>0.349447</td>\n      <td>-0.261101</td>\n      <td>-0.488516</td>\n      <td>0.003786</td>\n      <td>0.198536</td>\n      <td>0.264846</td>\n      <td>0.299807</td>\n      <td>0.076102</td>\n      <td>-0.090869</td>\n      <td>0.647963</td>\n    </tr>\n    <tr>\n      <th>202101</th>\n      <td>0.332563</td>\n      <td>-0.257536</td>\n      <td>-0.478791</td>\n      <td>0.000260</td>\n      <td>0.217875</td>\n      <td>0.264776</td>\n      <td>0.307778</td>\n      <td>0.077268</td>\n      <td>-0.097550</td>\n      <td>0.633358</td>\n    </tr>\n    <tr>\n      <th>202102</th>\n      <td>0.290690</td>\n      <td>-0.225113</td>\n      <td>-0.467248</td>\n      <td>0.001783</td>\n      <td>0.224192</td>\n      <td>0.271814</td>\n      <td>0.323344</td>\n      <td>0.063745</td>\n      <td>-0.097741</td>\n      <td>0.614533</td>\n    </tr>\n    <tr>\n      <th>202103</th>\n      <td>0.279753</td>\n      <td>-0.208241</td>\n      <td>-0.471824</td>\n      <td>-0.007691</td>\n      <td>0.227143</td>\n      <td>0.274752</td>\n      <td>0.332479</td>\n      <td>0.061906</td>\n      <td>-0.096889</td>\n      <td>0.608614</td>\n    </tr>\n    <tr>\n      <th>202104</th>\n      <td>0.346279</td>\n      <td>-0.195049</td>\n      <td>-0.428407</td>\n      <td>-0.033287</td>\n      <td>0.191784</td>\n      <td>0.288348</td>\n      <td>0.301924</td>\n      <td>0.061750</td>\n      <td>-0.086167</td>\n      <td>0.552824</td>\n    </tr>\n    <tr>\n      <th>202105</th>\n      <td>0.337523</td>\n      <td>-0.193559</td>\n      <td>-0.429575</td>\n      <td>-0.036072</td>\n      <td>0.196484</td>\n      <td>0.286267</td>\n      <td>0.307921</td>\n      <td>0.061232</td>\n      <td>-0.083881</td>\n      <td>0.553661</td>\n    </tr>\n    <tr>\n      <th>202106</th>\n      <td>0.329235</td>\n      <td>-0.184752</td>\n      <td>-0.429181</td>\n      <td>-0.021106</td>\n      <td>0.179930</td>\n      <td>0.288012</td>\n      <td>0.299941</td>\n      <td>0.051920</td>\n      <td>-0.079324</td>\n      <td>0.565327</td>\n    </tr>\n    <tr>\n      <th>202107</th>\n      <td>0.319681</td>\n      <td>-0.185742</td>\n      <td>-0.425751</td>\n      <td>-0.028371</td>\n      <td>0.189591</td>\n      <td>0.288733</td>\n      <td>0.305640</td>\n      <td>0.057704</td>\n      <td>-0.083562</td>\n      <td>0.562077</td>\n    </tr>\n    <tr>\n      <th>202108</th>\n      <td>0.244845</td>\n      <td>-0.058946</td>\n      <td>-0.444875</td>\n      <td>-0.083481</td>\n      <td>0.183279</td>\n      <td>0.279109</td>\n      <td>0.277101</td>\n      <td>0.127657</td>\n      <td>-0.151546</td>\n      <td>0.626858</td>\n    </tr>\n    <tr>\n      <th>202109</th>\n      <td>0.223343</td>\n      <td>-0.047714</td>\n      <td>-0.439783</td>\n      <td>-0.091625</td>\n      <td>0.198536</td>\n      <td>0.276454</td>\n      <td>0.282924</td>\n      <td>0.127947</td>\n      <td>-0.155672</td>\n      <td>0.625590</td>\n    </tr>\n    <tr>\n      <th>202110</th>\n      <td>0.238908</td>\n      <td>0.011179</td>\n      <td>-0.360270</td>\n      <td>0.043177</td>\n      <td>0.020783</td>\n      <td>0.340697</td>\n      <td>0.324761</td>\n      <td>0.006124</td>\n      <td>-0.035058</td>\n      <td>0.409699</td>\n    </tr>\n    <tr>\n      <th>202111</th>\n      <td>0.179971</td>\n      <td>0.010254</td>\n      <td>-0.338674</td>\n      <td>0.055686</td>\n      <td>0.063241</td>\n      <td>0.310745</td>\n      <td>0.351675</td>\n      <td>0.007122</td>\n      <td>-0.028760</td>\n      <td>0.388740</td>\n    </tr>\n    <tr>\n      <th>202112</th>\n      <td>0.169749</td>\n      <td>0.015764</td>\n      <td>-0.340385</td>\n      <td>0.047187</td>\n      <td>0.070362</td>\n      <td>0.310939</td>\n      <td>0.356605</td>\n      <td>0.007599</td>\n      <td>-0.026115</td>\n      <td>0.388294</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for yearmonth in data[data['yearmonth']>=201801]['yearmonth']:\n",
    "    index_rf_factors=data[data['yearmonth']<yearmonth]\n",
    "    rf=index_rf_factors.iloc[:,2].values\n",
    "    mkt=index_rf_factors.iloc[:,-3].values\n",
    "    rexc32,rexc33,rexc34,rexc35,rexc36,rexc37,rexc38,rexc39,rexc40,rexc41=get_rexc(index_rf_factors,rf)\n",
    "\n",
    "\n",
    "\n",
    "    index_rf_factors1=data[data['yearmonth']<=yearmonth]\n",
    "    rf1=index_rf_factors1.iloc[:,2].values\n",
    "    mkt1=index_rf_factors1.iloc[:,-3].values\n",
    "    rexc32_,rexc33_,rexc34_,rexc35_,rexc36_,rexc37_,rexc38_,rexc39_,rexc40_,rexc41_=get_rexc(index_rf_factors1,rf1)\n",
    "    rexc=pd.DataFrame({'rexc32': rexc32_, 'rexc33': rexc33_,'recx34':rexc34_,'rexc35':rexc35_,'rexc36':rexc36_,'rexc37':rexc37_,'rexc38':rexc38_,'rexc39':rexc39_,'rexc40':rexc40_,'rexc41':rexc41_,'yearmonth':data[data['yearmonth']<=yearmonth]['yearmonth'][1:].values})\n",
    "    averrexc32=np.mean(rexc32)\n",
    "    averrexc33=np.mean(rexc33)\n",
    "    averrexc34=np.mean(rexc34)\n",
    "    averrexc35=np.mean(rexc35)\n",
    "    averrexc36=np.mean(rexc36)\n",
    "    averrexc37=np.mean(rexc37)\n",
    "    averrexc38=np.mean(rexc38)\n",
    "    averrexc39=np.mean(rexc39)\n",
    "    averrexc40=np.mean(rexc40)\n",
    "    averrexc41=np.mean(rexc41)\n",
    "    R=np.concatenate([rexc32[:,None],rexc33[:,None],rexc34[:,None],rexc35[:,None],rexc36[:,None],rexc37[:,None],rexc38[:,None],rexc39[:,None],rexc40[:,None],rexc41[:,None]],axis=1)\n",
    "    Cov_Sample=get_Cov_Sample(R)\n",
    "    Cov_Factor = get_Cov_Factor(mkt,R)\n",
    "    Cov_Shrink = get_Cov_Shrink(Cov_Sample,rexc32,Cov_Factor)\n",
    "    uhat = np.mean(R, axis=0)\n",
    "    A = np.mat(np.concatenate([uhat[:, None], np.ones((len(uhat), 1))], axis=1)).T\n",
    "    up = np.mean(uhat)\n",
    "    b = np.mat(np.array([up, 1])[:, None])\n",
    "    Cov_Const=get_Cov_Const(Cov_Sample)\n",
    "    Rx_t_=np.array([averrexc32,averrexc33,averrexc34,averrexc35,averrexc36,averrexc37,averrexc38,averrexc39,averrexc40,averrexc41])\n",
    "    Rx_t=np.array([rexc32[-1],rexc33[-1],rexc34[-1],rexc35[-1],rexc36[-1],rexc37[-1],rexc38[-1],rexc39[-1],rexc40[-1],rexc41[-1]])\n",
    "    Rx_t=Rx_t[:,None]\n",
    "    Rx_t_=Rx_t_[:,None]\n",
    "    if yearmonth==201801:\n",
    "        Cov_EWMA=Cov_Sample\n",
    "    else:\n",
    "        Cov_EWMA=get_Cov_EWMA(Cov_Sample,yearmonth,Cov_EWMA)\n",
    "\n",
    "    ##\n",
    "    ##以上计算协方差矩阵部分完成\n",
    "    ##\n",
    "\n",
    "    uhat = np.mean(R, axis=0)\n",
    "    A = np.mat(np.concatenate([uhat[:, None], np.ones((len(uhat), 1))], axis=1)).T\n",
    "    up = np.mean(uhat)\n",
    "    b = np.mat(np.array([up, 1])[:, None])\n",
    "    if yearmonth==201801:\n",
    "        omega_Shrink_df_min=get_omega_Shrink_df_min(Cov_Factor,A,b,yearmonth,columnsname)\n",
    "        omega_Shrink_df_max=get_omega_Shrink_df_max(Cov_Shrink,A,b,uhat,yearmonth,columnsname)\n",
    "    else:\n",
    "        omega_Shrink_df_min=get_omega_Shrink_df_min(Cov_Factor,A,b,yearmonth,columnsname,omega_Shrink_df_min)\n",
    "        omega_Shrink_df_max=get_omega_Shrink_df_max(Cov_Shrink,A,b,uhat,yearmonth,columnsname,omega_Shrink_df_max)\n",
    "    w_min=omega_Shrink_df_min.loc[omega_Shrink_df_min['yearmonth']==yearmonth]\n",
    "    w_min=w_min.iloc[:,0:10].values\n",
    "    w_max=omega_Shrink_df_max.loc[omega_Shrink_df_max['yearmonth']==yearmonth]\n",
    "    w_max=w_max.iloc[:,0:10].values\n",
    "    rexc_yearmonth=rexc[rexc['yearmonth']==yearmonth].iloc[:,0:10].values\n",
    "    rexc_min=np.sum(w_min*rexc_yearmonth)\n",
    "    rexc_max=np.sum(w_max*rexc_yearmonth)\n",
    "    rexc_min_list.append(rexc_min)\n",
    "    rexc_max_list.append(rexc_max)\n",
    "    yearmonth_list.append(yearmonth)\n",
    "\n",
    "\n",
    "    if yearmonth==201806:\n",
    "        display(yearmonth)\n",
    "        display(pd.DataFrame(Cov_Const,columns=columnsname1))\n",
    "        display(pd.DataFrame(Cov_Sample,columns=columnsname1))\n",
    "        display(pd.DataFrame(Cov_Shrink,columns=columnsname1))\n",
    "        display(pd.DataFrame(Cov_Factor,columns=columnsname1))\n",
    "        display(pd.DataFrame(Cov_EWMA,columns=columnsname1))\n",
    "    elif yearmonth==201908:\n",
    "        display(yearmonth)\n",
    "        display(pd.DataFrame(Cov_Const,columns=columnsname1))\n",
    "        display(pd.DataFrame(Cov_Sample,columns=columnsname1))\n",
    "        display(pd.DataFrame(Cov_Shrink,columns=columnsname1))\n",
    "        display(pd.DataFrame(Cov_Factor,columns=columnsname1))\n",
    "        display(pd.DataFrame(Cov_EWMA,columns=columnsname1))\n",
    "    elif yearmonth==202011:\n",
    "        display(yearmonth)\n",
    "        display(pd.DataFrame(Cov_Const,columns=columnsname1))\n",
    "        display(pd.DataFrame(Cov_Sample,columns=columnsname1))\n",
    "        display(pd.DataFrame(Cov_Shrink,columns=columnsname1))\n",
    "        display(pd.DataFrame(Cov_Factor,columns=columnsname1))\n",
    "        display(pd.DataFrame(Cov_EWMA,columns=columnsname1))\n",
    "\n",
    "rexc_df=pd.DataFrame({'rexc_min': rexc_min_list,'rexc_max':rexc_max_list,'yearmonth':yearmonth_list})\n",
    "display(rexc_df)\n",
    "omega_Shrink_df_min['yearmonth']=omega_Shrink_df_min['yearmonth'].astype(int)\n",
    "omega_Shrink_df_min.set_index(['yearmonth'],inplace=True)\n",
    "display(omega_Shrink_df_min)\n",
    "omega_Shrink_df_max['yearmonth']=omega_Shrink_df_max['yearmonth'].astype(int)\n",
    "omega_Shrink_df_max.set_index(['yearmonth'],inplace=True)\n",
    "omega_Shrink_df_max"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.06223184687261751\n",
      "0.0006953712813297907\n",
      "0.01117388148150492\n",
      "0.06296779072031662\n",
      "-0.0017313728920606772\n",
      "-0.027496167044368733\n"
     ]
    }
   ],
   "source": [
    "rexc_min_std=rexc_df['rexc_min'].values.std()\n",
    "rexc_min_mean=rexc_df['rexc_min'].values.mean()\n",
    "rexc_max_std=rexc_df['rexc_max'].values.std()\n",
    "rexc_max_mean=rexc_df['rexc_max'].values.mean()\n",
    "sharpe_min=rexc_min_mean/rexc_min_std\n",
    "sharpe_max=rexc_max_mean/rexc_max_std\n",
    "print(rexc_min_std)\n",
    "print(rexc_min_mean)\n",
    "print(sharpe_min)\n",
    "print(rexc_max_std)\n",
    "print(rexc_max_mean)\n",
    "print(sharpe_max)\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 2000x800 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "###绘图###\n",
    "x = omega_Shrink_df_min.index.astype(str)\n",
    "x=x.values.tolist()\n",
    "print(type(x[1]))\n",
    "for i in range(len(x)):\n",
    "    x[i]=x[i][:3]+'\\n'+x[i][3:]\n",
    "plt.figure(figsize=(20,8))\n",
    "names = {}\n",
    "for j in range(10):\n",
    "    names[f'y{j + 1}'] = omega_Shrink_df_min[omega_Shrink_df_min.columns[j]].values.tolist()\n",
    "    plt.plot(x, names[f'y{j + 1}'],label = omega_Shrink_df_min.columns[j])\n",
    "    plt.legend()\n",
    "    plt.grid()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 2000x800 with 1 Axes>",
      "image/png": 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sWGy3odlh9Ple4rCyPQoRFYyq6xhXJoKU6SpYxmQFKU2fNtiYmNR+YsJ7T3rCb/5tI6LJ+pMp7BuPYd94FHvGozgYiUGZ9PbJJgpY53ZgQzpI2eBxzvj7OSIiolwMTRiaEBERXVKHI3H8pHcE9w8GEE23aLCJAl5T4cNdNWW4wuM45woRTTd6ZxtBShzHogkcjSbQFktM+cCdUW4xZatSVjiNqpSlTtsZ214p6R7dJ9PBSHs8mV0fSimnfZxJAJrsRjDSYrdhscOK5nRQUmY2sfUEERERUY64quHFcAx7x6PpipTotPM5tditEy29vE5edEJERDOKoQlDEyIiolkXVzX8biiIn/aN4PlQLLu9xW7FnbWleGOVHyXmmbtiMKVpxhwh6RDlWLpCpfMME5NmJpxf4bShxmZBVzyFtrhRMXIqnoJ8hrdB5RYTWuxWLHbYstUjLQ4rGmxWtlIhIiIiukC6rqMtnjQClHQ1yolJ894BgM8kYb3HiY1eoyLlco8DTonzwBER0YVhaMLQhIiIaNaciCbws75R3Dswlp2TxCQAN5X7cGdNKbb5XJe02iKqqGiNpVt7Zdt8xTF4hmqRDLsoYJHdihaHUTFiBCNGSOLh5OxEREREl0RAVvB8aKKl1wuh2JR2rZIArHLZsdHjxFafC9f43dm2r0RERGfD0IShCRER0YxKaRr+NDKOn/SO4tlgJLu9zmbGnTVluKPKjwqruYAjnGo0paA1OtHiqz8po95mMeYbSQcjNVYz2z4QERERFRlZ03EkPcF8piKlN5k/wbxZELDZ68QrSj14RZkHLXYr26QSEdFpMTRhaEJERDQjOuNJ/KJvFL/sH8OIbFRuiACuL/PgzpoyXOt3Q+KHUyIiIiKaZb2JVDZEeXIsjLZ4fkuvJrsF15d68IpSL7b4nLCKYoFGSkRExYihCUMTIiKiC6ZoOv46FsJPekfwxFgYmTcKlRYT3lxdirfUlKLOZinoGImIiIhoYeuIJfHYaAiPjYbwbDCSN1edQxJxTYkbryj14OWlHlQVWUU0UaHtDkbQHkvCJAowCwJMggCLaCwz983Z+8g7zpy3z1g3CwIr+KnoMTRhaEJERHTeBpIyftE3il/0j6Ivp/3BNSVu3FlbileWejkBOhEREREVnYiiYkcgjMdGQ/jraGjK3HaXuex4RakH15d6sM7j4MldWtC+dmoQn+/on/HnFYG8MMU0KVzJBDN3VPvxd3XlM/71ic6GoQlDEyIionOi6TqeDkTw074RPDIyDjX9rsBvlnBHVSneVlOKRQ5rYQdJRERERHSONF3HoUg8W4XyQiiG3BNfpWYTXlZqVKFcW+KG12wq2FiJLrVvdA7iP9uNwGSbzwWzIEDWdSi6DlnXIWv6xH1tYvvU+xc+Brso4shVq2GX2EKPLi2GJgxNiIiITkvXdbTHk3hkJISf9Y3gVDyV3bfZ68SdNaW4udwHG9/EEhEREdEcN5yS8fioUYXy5FgIYVXL7jMJwCavy5hMvtSDJQ5OJk/z17e7hvC5tj4AwMcWVeMfmyov+Ln0dHCSCVRSuYFKbvAy6f77j3ahPynjF2ua8fJSnrelS4uhCUMTIiKiLE3XcSyawK5gBLuDUewej2A4p2WBWxLxN1V+vK2mFCtc9gKOlIiIiIho9siajj3jkWwVyolY/mTyDTZLNkC50ufiRUQ0b3ynawifSQcmH15UhX9uqirIOD7c2o2f9o3i7bVl+MLSuoKMgRYuhiYMTYiIaAGTNR0HIzEjIAlG8Nx4FOOKmneMVRRwhceBv6n049ZKH5ySVKDREhEREREVRmc8ib+k50F5JhBBKucUmV0UcXWJC9eXefByvwc1NksBR0p04b7XPYRPnTQCk39pqsK/LCpMYAIAj46M486DHai1mrFv60pWdtElxdCEoQkR0ZwUUzUMp2REVA1NNgucJp7IPxcJVcML4Rh2pytJ9oaiiOW0HQAAhyRik8eJLT4ntvhcWOd28Mo5IiIiIqK0qKpiZ2CiCqU/KeftX+WyYb3HiQqLGRUWE8otJlRYzCi3mFBuMXN+BipKP+gZxr+d6AUAfLCxEh9pri7oeGKqhpU7DyKh6Xhi4zJ2OqBL6nxyA852RUREsyqqqBiWFQynFAyn5PTSWB/J2T6UUhDNOdEvAFhkt2KVy45VLlt6aUe11bzgr0aJKir2hqLZSpL9oVjeVXEA4DNJ2OxzYovXhS0+F1a77DCLC/v7RkRERER0Ok5Jwg1lXtxQ5oWu6zicM5n886EYDkcSOBxJnPbxbklEeTZQMadDFSNYKUsvKywmlFlMsIgMWGj2/TAnMPmnxkp8uIAVJhkOScRVJW48NhrCX0ZDDE2oaLHShIiIzouu6winK0JyA5DhlJIOQXK3K4hr2tmfNIdVFOAQRQQmtZPK8JslrHTascpthCirXXYsdljn9QePgKxgz3g0OyfJwUgM6qR/vcstpnRA4sRWnwvLnTaICzxcIiIiIiKaCaMpBU+OhXAylsSIrGAoJWMoqWBYNj77JLXzO7VWYpLygpSJkGUibCm3mFFqNsHEC5/oAvykdwQfOd4DAHh/QwU+3lxdNBcfZsa20ePE79cvKfRwaAFhpQkREV20U/EkfjMwhv7k1OqQ8/1QYBcFlGVL100oNxvrZZOuwiq3mOGWRAiCgOGUjCORBA5F4jgSieNQJI6TsQTGZBU7gxHsDEayz28WBCxz2rDSZcNqlx0r01UpJea5+c/cUFLG7vFout1WBEejCUz+jtfZzNjidWGrzwhKmu3WonkTTEREREQ0n5RaTLityj/tvsxFZZODlOHU1HBlOCVD0YGAoiKgqFMmop9MAFBqNuFavxtfXFrH9sV0Tn7WNxGYvKe+uAITAHhFqXGyel8oitGUglLL3PzcTvMbK02IiCjPaErBVzsH8OPeUchn+CfCKYnTBCATIUi5eWLdmQ5CLlZC1dAaS+BwJI7D4bixjMQRVqevZqm1mrNtvVa57FjttqPBZimaCgxd15HQdAylZOzJhiRRtMWnfnha7LBmK0k2+1yo50SURERERERziqbrCCoqhlIyRlIKhlIKhpIyhtPVKyOZoCWlYDSlIPdTzhqXHT9b04xKq7lg46fi94u+UXyotRsA8O76cny6paaoApOMl+89hsORBL6xogF/c5pAkmimFVWlybe+9S18+ctfxsDAANauXYtvfOMb2LRp02mPDwaD+MQnPoEHHngAY2NjaGxsxFe/+lXcdNNNsz1UIqIFLa5q+EHPML7eOZgNIbaXGPNhTFcd4ijARIc2ScRatwNr3Q4gPX+druvoTqTSAUoiG6R0JVLoTcroTcp4dDSUfQ6nJGJVuhrFqEqxYbnTftbXo+s6UrqOiKIhqqqIqhpiqoaIOnE/qmqIKBP7oqqGSO4+Vc1uzzxmcpstwLiibKXLlp2PZIvPiXILPxwREREREc1loiDAbzbBbzYBzjMfq+o6xmQFh8JxvO9oF16KxHHT88fxi7XNWO7kPBA01S/7R/Ev6cDknXXFG5gAwPWlXhyOJPCX0RBDEypKs1ppcs899+DOO+/Ed77zHWzevBlf/epX8Zvf/Aatra2oqKiYcnwqlcK2bdtQUVGBj3/846itrUVnZyd8Ph/Wrl17Tl+TlSZE85um6xiTVQQVBY02Kye2ngGqruM3A2P4UscA+pIyAGC1y45PttTgGr+7wKO7cCFFzbb1yixbo4lpW4uJAFocVixx2KAiE4zkhyFRVYUyS/9iSgKw1u3IVpJs8jrhm6OtxYiIiIiIaGZ1xpN4y0vtOBlLwmMS8cPVi3BVydz9rEYz757+MfzTsS7oAP6urgz/vri2aAMTAHh+PIqb95+AWxJx5KrLeG6HLonzyQ1mNTTZvHkzNm7ciG9+85sAAE3TUF9fj/e///346Ec/OuX473znO/jyl7+MY8eOwWw+tytqk8kkksmJNiahUAj19fUMTYjmmEwYMpiSMZCUMZiSMZiUMZhSMJg0tg2ljO2ZE9duScR2vxvX+T24zu9GLdsVnRdd1/HEWBj/3taHo9EEAKOd1ceaq/H6ypKiaWE1kxRNx8l4It3ay6hKORSJY1RWzut57KIAhyTBKYlwSiJcmXWTCEfu/exNyl83iXDlbHdIIqR5+P0mIiIiIqKZEZAV/O3BDuwej8IsCPjv5fV4I6/QJwC/GRjDB44agcnf1pbhv5YUd2ACGBdvrnnmMEZlBfeta2EISJdEUYQmqVQKDocD9913H1772tdmt991110IBoN46KGHpjzmpptugt/vh8PhwEMPPYTy8nK8+c1vxkc+8hFI0vSTXX3mM5/BZz/72SnbGZoQFQdN1zEqG71azzUMORd2UUB8UsXAUocNLyt142V+Dzb7nLCKl7591FzxUjiGf2/rw9MBYzJ1r0nCPzZW4u7aMtgK0HarkHRdx1BKweFIHO3xJGyiEWg4ckIOl2li3SGKMPEqGCIiIiIiusQSqoZ/OtaFB4eCAIB/aarCh5oqi/4EOc2e+wfG8P6jXdAA3FVTii8srZszvw8fONqJewcCeFd9OT67uLbQw6EFoCjmNBkZGYGqqqisrMzbXllZiWPHjk37mPb2djz++ON4y1vegj/+8Y84efIk3vOe90CWZXz605+e9jEf+9jH8M///M/Z+5lKE1p4dF1Hf1LGyVgSJ2MJdCVS8Jok1Nks2Vu1xcyTnTMoqWlojyXRlxOGGCGIcsFhSJnZhEqrCZUWMyqtZlRZzKiwmlFlmdhWbjFBEgS8FI7jibEQHh8NYX8ohuOxBI7HEvhO9zDsoohtJS5c5zdClEUO6+x9I+aQrngSX+wYwP2DAQCARRBwd10Z/rGxEiULtB2UIAiotBq/Wy8r9GCIiIiIiIhOwyaJ+PbKRtTbLPhG1xD+36kBdCdS+PKyOlh40eCC89vBQDYweVtNKT4/hwITwJjX5N6BAB4bCTE0oaJTVGfINE1DRUUFvve970GSJKxfvx69vb348pe/fNrQxGq1wmrlydCFJKqoaIsn0ZYOR9pixnpbPIlYevLq05EEoMpiRn1OkGLczKizWVBrtcC+wK6yPxe6rqMvKeNIJI6j0QSOROI4EkmgLZ6YdhLryQQApWYTqqxmVFiMZSYAqbSYsuFIucV8Xn0sL/c4cLnHgX9uqkJAVrAjEMYTo2E8MRbCYErBY6MhPDYaAtCLJrsF1/k9eJnfjStLXHCepnptvgrICr7WOYgf9owglS4wvK2yBB9ZVIUGO/+GEhERERERzQWiIOATLTVosFvw0eM9uGdgDH3JFP5v9SJ4TAvrc+5C9uBgAO890gkNwFuq/fji0ro512L7Wr8bZkFIn+NLoMVhK/SQiLJmLTQpKyuDJEkYHBzM2z44OIiqqqppH1NdXQ2z2ZzXimvFihUYGBhAKpWCxcL5ChYKTdfRk0hlw5CTMeMPaFu6quF0TALQZLeixWFFk82KcUVFTyKFnmQKvQkZsq6jNymjNykD49Fpn6PMbMoLUupslomQxWqGd55fjR9VVLRGEziSDUeMoGRcUac93peu5jFCkMkVIqYLCkMuRInZhFsrSnBrRQl0XcfRaAKPj4bwxFgYe8ajOBVP4Ue9I/hR7wgsgoAtPqcxF0qpG8sctjl1Ncb5SKgaftg7gq91DmZ/hlf5XPjk4hqsdTsKPDoiIiIiIiK6EG+rKUON1YJ3Hj6FpwMRvHr/CfxiTTPqONfnvPe7oSDee9QITN5U7ceXl9XPucAEANwmCVt9TuwIRPDYaIihCRWVWTv7a7FYsH79evz1r3/NzmmiaRr++te/4n3ve9+0j9m2bRt++ctfQtM0iOmywuPHj6O6upqByTwVVtS8QCTTWqsjnkRCO30JQ6nZhMUOIxxpcdiy640262lPzmvpeQt6Ein0JFLoTi97EjJ6ksZ6VNUwIisYkRUcCE//td2SOKlKxQhY6q3GernFNCdOwGu6js54CkeiRjByLB2SnIqnMN133iQASxw2rHDZscJpw0qXHStdNlRZzEX3egVBSI/Pjvc1ViKiqNgZiODxsRAeHwuhJyFjRyCCHYEIPtsG1FjNuC49ofx2v3teXJ2j6ToeGAzg8+39RkgIYIXThk+21OA6v7vofmZERERERER0fl5e6sFDly/GW1/qQGs0gZufP46frWnGGl4gN289PBzEPxw5BVUHbq/y47/naGCScX2pFzsCEfxlJIR31VcUejhEWbM2ETwA3HPPPbjrrrvw3e9+F5s2bcJXv/pV3HvvvTh27BgqKytx5513ora2Fp///OcBAN3d3Vi1ahXuuusuvP/978eJEydw99134wMf+AA+8YlPnNPXPJ8JXejSUHUd3YlUNhw5mdNWayilnPZxZkHAIrs1JxyxYrHDhhaHdVbmXtB1HcFMZUomTElXqWQCljF5+mqLXFZRQKnZBJ9Jgs9sQolZQonJBJ9Zgs8kwW/OrKf3pY+dzcm3g7KSbat1NJLAkagRkpyunVmlxYSVLjtWOI1gZKXLjsUO67zokarrOtriSTwxGsbjYyHsCkbyAjpJADZ6JqpQVrvsc+4NyI6xMP69rQ8HI3EAQLXVjI8sqsLfVPkhzbHXQkRERERERGfWm0jhLS+141g0AYck4nurmvCKUp4Tm2/+NBzE3x8+BUUH3lBZgq+taJjzn/FPxZPYsvsoTAJw5KrL5sVFrFS8zic3mNXQBAC++c1v4stf/jIGBgawbt06fP3rX8fmzZsBANdeey2amprw4x//OHv8rl278MEPfhAHDhxAbW0t3vGOd+AjH/lIXsuuM2FoUlx+NxTEv7Z2n7a1EwCUW4yqkcUOG1rsE+FIvc1SdJO2R1UVvZkwJXNLTtzvT8rTVmmcC7soGCHLacKWkvS2M4UtsqajLZ7AsUi6tVY0gaOReLbSYDKbKGCp04aVOeHIcqcdZZb53YIsV1zVsCsYwRNjRiuvk7Fk3v4yswnX+t14WakH15S4UVrE35vDkTj+o60PT4wZZVJuScQHGivxjrpyODhXDxERERER0bwVUlT83aEO7AhEIAL4/NI63FVbVuhh0Qz588g43nGoA4puzE/69XkQmGRc/dxRnIgl8b1VTXhNha/Qw6F5rKhCk0uNoUlx0HUd/9s9jM+19QEwqi8yVSOZapFMODKfUmRZ09GfNCpSgoqCoKxiTFYQVFQEZRUBRUFAVhFMbxuTjWPOPH39mWXCFqckoiueyk7yPVmdzZwOR+xY4TKCkkV2a9EFU4XWGU/iyTFjMvmnAxFEc6pxBABr3Q6sTlfetDisWOI0Ar5CvlnpTaTwxY5+/GYgAB1Gldbba0vxT41VRR3yEBERERER0cyRNR3/2tqNXw+MAQDe21CBTzRXz7nuCZTv0ZFxvOPQKci6jtdV+PCNFY3z6lzOZ0/24n+7h/E3VSX4xorGQg+H5jGGJgxNCkrVdXzyRC9+2DsCAHhHbRk+u7h2Xv1Bn0mariOiagjI6UBlmrBlYv3sYYtTErNzjqxw2bHSacNyp23eT2A/G1Kahj3jUTwxFsYToyEciSamPc4iCFjkyA8FF1+CUHBcVvCNriH8oGc422LsNRU+fLy5Gk1266x9XSIiIiIiIipOuq7jq52D+GLHAADjM+LXlzfMaktwmj2PjYZw98EOpHQdt1b48K15FpgAwLOBCF5/4CT8ZgkHt62eNxU0VHwYmjA0KZioquI9Rzrx55EQBACfWVyDd9aVc9LpWTA5bImoKuptFtTbLLyKZJYMJGU8G4zgRDSBtngSJ6MJtMeTeXOiTFZhMWWrqozqFBuWOKyou4jqlKSm4Se9I/jKqUEE0q3vtnid+FRLDa7wOi/oOYmIiIiIiGj+uG9gDB881g1Z17HJ68SPL1sEPy+mnFMeHw3h7enA5JZyL76zsmneBSaAUSG1+plDGFdU/OGKJdjA8xo0SxiaMDQpiOGUjLe91IED4RisooBvrmjEq9mLkOY5TdfRk0ihLZbEyVgSJ2MJnIwl0RZLYiA1/VwywETLuhaHFUuy1SlGsOI+TXWKpuv43VAQ/9Xej65ECgCwxGHFJ1tqcH2ph+EkERERERERZe0MhHH3oQ6EFA3Ndit+ubaZXQnmiCfHQrjrYAeSmo6b04GJeR4GJhnvPnwKDw4F8Y+NlfhYc3Whh0PzFEMThiaX3MlYAm9+sR1diRT8Zgk/uawZG5kM0wIXVtR0mJJAWyyJE+llezyJ5BmqUyotJrSkA5RMdQoAfLljAAfCsewx/7qoGndU+efllSZERERERER08VqjCbzlpTb0JGT4zRJ+dlkz1vN8TVHbMRbGnQfbkdB03FjmxfdWze/ABADuHxjDe492YaXThsc3LS/0cGieYmjC0OSS2h2M4O0HOxBUVDTZLfjlmhY0O3jlAtHpqOnqFKMiJZGuUDHClaGUcsbHOiUR72uowDvry+GUZm++FCIiIiIiIpofhpIy3nqwHS+F47CJAr61shE3l/sKPSyaxtNjYbwtHZjcUObB91c1wSLO//loxmQFq3ceggZg39aVqLNZCj0kmocYmjA0uWQeHAzgA0e7kNJ1XOFx4KeXNaPMwh6ZRBcqpKjZypTcdl/DKRmvqSjBh5oqUW4xF3qYRERERERENIdEVRX/cLgTj45yDtpitTMQxtteakdc03F9qQc/WN0E6wIITDJu3X8Cz41H8YWldXh7bVmhh0Pz0PnkBjy7TRdE13V8q2sI/9HeDwC4qcyLb65shENaOH/MiWaDxyThCo8TV3hYLk1EREREREQzwylJ+NFli/BvJ3rxo94RfPpkHzrjKfz7klpIDE4K7tlABG97qQNxTcfL/QsvMAGAV5R68Nx4FI+OjDM0oYJjaELnTdF0fOJED37SNwoA+Pu6MnxmMf+RJSIiIiIiIiIqVpIg4L+W1KLBZsFn2/rww94R9CZT+PbKxgXZ/jmmamiNJnAkEsfRaBxJTYdTEuGSJLgkES6TsXTmrLskCS6TCKckwS4KM1KpszsYwVsPtiOuabjO78b/LcDABACuL/PgP9v78UwwgqiqLsjfSSoeDE3ovERVFe8+3Im/pMs5P7u4Bu+sryj0sIiIiIiIiIiI6CwEQcA/NFSgzmbB+4524s8jIbz+hZP42WXNqLDOz1bQuq6jNynjSCSOI5E4DkcSOBqNoz2WhHYRzysJyIYszpxAJXs/G7QY69MFMn1JGX9/+BRiqoZrS9z40epFsC3QLi7LHDbU2yzoTqSwMxDBDWXeQg+JFjCGJnTOhpIy3nawHS9y4jAiIiIiIiIiojnr1RU+VFnNuCt9nufm/SfwizXNWOq0FXpoFyW3esQISOI4Gk1gXFGnPb7MbMIqlx0rXDa4JQkRVUVU1RBRNUQU1ViqKqKKsYyoGqKqEbWoOhBSNISUi4leDNtLXPjRZQs3MAGMQO/6Ug9+2DuCv4yEGJpQQTE0oXNyPJrAW15qR3ciBb9Zwk8va8YGL+dcICIiIiIiIiKaizZ6nXj4iqV480tt6Iin8Or9J/DD1U3YVuIu9NDO6nyrR0wCsMRhSwckdqxy2bDSab+g6hpN1xHLBCuqioiiIXoOYUvmuNzHxTQNL/d78LUVDbAv4MAkIxuajI5D1+tmpP0Z0YVgaEJntSsYwdsPdmBcUbHIbsEv17RgkcNa6GEREREREREREdFFWOSw4g9XLMXbD3ZgbyiKO15sx1eW1+MNVf5CDy3rfKtHSs0mIxRx2bHSZccqlx2LHdYZmydEFASjvZZJAjA/W5oVylafCw5JxGBKwcFIHGvcjkIPiRYohiZ0Rr8dDOAfj3YhpevY4HHgJ5c1o9TCXxsiIiIiIiIiovmg1GLCveta8IGjXfj9cBDvO9qF7kQK/9RYeVFX+uu6Dg2AqutQ9cxShzppm6Lr0HRAhQ5Z09GdSBkBSTooOVv1SCYcWek0Kknm69wsC4FNEnFNiRt/GhnHX0ZCDE2oYHj2m6al6zq+2TWE/2zvBwDcXO7FN1c0slSQiIiIiIiIiGiesUsivruqEfVtFny7ewhf7BjAQ0NBWAQBKnQoutGWStUBJR1+aMhZz24HNOjZ9ZmSqR5Zka4cmenqESoe15d6jNBkNIQPLaoq9HBogWJoQlMomo6Pn+jBT/tGAQDvqivHpxbXQGIfQSIiIiIiIiKieUkUBHxqcQ3q7RZ84ngPjkUTs/r1JAGQIEASBGM9vaywmI25R9KVI6tcdpRbTJzfYoF4eakHAHAgHMNQUmblEBUEQxPKE1VUvPNwJ/46FoIA4N+X1OLv6soLPSwiIiIiIiIiIroE/ra2DNtLXGiLJSEJAkzpMEOEAFM63BCF3HUYxyBnPTcIQSYQmdgmAgxBaFqVVjPWuu14MRzHY2MhvLm6tNBDogWIoQllDSZlvO2ldrwUicMmCvj2ykbcVO4r9LCIiIiIiIiIiOgSanHY0OKwFXoYtEBdX+o1QpMRhiZUGGz8RwCA1mgCNz1/HC9F4vCbJdy/bjEDEyIiIiIiIiIiIrqkri8zWnQ9GQgjqWkFHg0tRAxNCM8Ewnj1/uPoTcpotlvxx/VLsd7rLPSwiIiIiIiIiIiIaIG5zGVHpcWEmKphVzBS6OHQAsTQZIF7YDCAO15sR0jRsNHjxO+vWIImu7XQwyIiIiIiIiIiIqIFSBQEXF/qBQD8ZSRU4NHQQsTQZIHSdR1f7xzEe450QtZ13FLuxb3rWlBq4TQ3REREREREREREVDiZFl1/GQ1B1/UCj4YWGoYmC5Ci6fjX1h78V3s/AODd9eX43qom2CX+OhAREREREREREVFhXVXiglUU0JVI4XgsWejh0ALDs+QLTERRcefBdvy8fxQCgP9YUovPLK6FKAiFHhoRERERERERERERnJKEbT4XAOAvI+MFHg0tNAxNFpCBpIzXvXASj4+FYRcF/Gj1IvxdXXmhh0VERERERERERESU5/oyY16Tx0Y5rwldWgxNFohj0Thufv44DkbiKDWbcP/li/Gqcm+hh0VEREREREREREQ0xStKjXlN9oxHEZCVAo+GFhKGJgvEoyMh9CZltNit+OP6JbjC4yz0kIiIiIiIiIiIiIimVW+zYIXTBg3AE2PhQg+HFhBToQdAl8b7GyogAnhzTSn8Zv7YiYiIiIiIiIiIqLhdX+rB0WgCfxkZx+srSwo9HFogWGmyQAiCgPc1VjIwISIiIiIiIiIiojkhM6/J42NhKJpe4NHQQsHQhIiIiIiIiIiIiIiKzhUeB/xmCeOKir2haKGHQwsEQxMiIiIiIiIiIiIiKjqSIOBlfmNC+L+MhAo8GlooGJoQERERERERERERUVG6viwdmoyOF3gktFAwNCEiIiIiIiIiIiKionRtiRsmATgRS+JUPFno4dACwNCEiIiIiIiIiIiIiIqS12zCZq8LAPDYKFt00exjaEJERERERERERERERev6Us5rQpcOQxMiIiIiIiIiIiIiKlqZeU2eDUYQUdQCj4bmO4YmRERERERERERERFS0Whw2NNutkHUdTwXChR4OzXMMTYiIiIiIiIiIiIioqLFFF10qDE2IiIiIiIiIiIiIqKhlWnQ9NhqCpusFHg3NZwxNiIiIiIiIiIiIiKiobfI64ZZEjMgKDoRihR4OzWMMTYiIiIiIiIiIiIioqFlEEdf60y26Rtmii2YPQxMiIio4XdcxFE5g54kR/HBnB/7rj0fxlyODSCpqoYdGREREREREREUi06KLoQnNJlOhB0BERNNTNR19wTg6RqIYGE+g3G1Fvd+OuhIHbGap0MO7YIFoCscHw+lbBK3p9WBMzjvuezva4baacP3KSty8phpXLSmD1TR3XzcRERERERERXZyX+T0QAByKxNGXSKHGZin0kGgeYmhCRHNCOCHjaH8Yh/vGcbgvhI6RKEocFtSV2FHvd6A+s/Q74LLOnT9tuq5jLJpCx0gU7SNRdIxE0TGcXo5GkVK0aR9X4baiwe9Ag9+BuvSyvsSOhlIHKt02iKJwiV/JVOGEjBNDERwfMMKR44NhtA6GMRxOTnu8KACNpU4srXTB77TgiWPDGAgl8MALvXjghV64bUaAcsuaaly1uBwWE4sliYiIiIiIiBaSMosJ6z0O7AvF8NhoCHfWlhV6SDQPzZ0zi0S0YAyFEzjcF8KRvlA2JOkcPfcJvkocZtT7HUagUmKECplQpdZnL0iVRjSpGEFIzq19JIqO4QhCCeW0jzNLAhpLnajx2TESTqJ7LIZwUsFQOImhcBL7OgNTHmORxIkwyW+fCFdKHGgodcBjM8/oa0vIKk4ORdA6EMbxoXA2JOkNxk/7mLoSO5ZWutM3F5ZWurG4wpX3s9E0Hfu7Anj4YD/+eLAfg6EkHtjfiwf2GwHKK1dW4ZY11di2uIwBChEREREREdECcX2pF/tCMfyFoQnNEkHXdb3Qg5hJoVAIXq8X4+Pj8Hg8hR4OEZ2BpunoHIvlhSOH+0IYiUxfiVDjtWFljRerajxYUulCICajZyyG7kAMPYE4usdiCExq8TSdSo8VdSU51SklDtT5jYCl2muDSbqwE/ApRUN3IJatFDEqRyLoGIliMDT9awIAQQBqvHY0lzuxqGzi1lzmQm2JHVJO1Yiu6wjGZHQHYugai6F7LJ5eGt+H3kAcinbmP+teuzmnSsWerlIx7tf47KcNIFKKho6RKFoHwzgxGDZCksEwOsdiON2/JJUeazYcWVbpxpJKF5ZUus+7GkjTdDzfFcDDLxkBylBOtYrHZsIrV1Xh5jXV2NbCAIWIiIiIiIhoPjsaieO6va2wiQKOXHUZHBd4HocWlvPJDRiaENElkVI0HB8M40hfCEf6jZDkaH8YkeTUKgtRAJrLXVhV40nfvFhZ7UGJ8+x9KsMJORugdKeXPYE4egJGsBBNnXlicUkUUO21ob7EqNIwlo5s5UaZy4qhcAIdw1G0ZVtpGcFIdyAO9QyBRanTMhGKlDvRXObEojIXGktnbo4SRdUwEEpMBCnpUKVrLIaeQAwjkdQZHy8KQLXXnn3tZW4rusZiOD4QRsdI9LSBTInDjGVV7pzqEaOCxOeY+d6imqZjX2cAD7/Uhz8eGshr9+WxmXBDJkBZXAYz3zgRERERERERzSu6rmPDriPoTcr42WWLcH2Zt9BDojmAoQlDE6KCmjz/yJG+EE4MhSGrU//cWE0ille5sxUkq2o8WF7lgd0y8y20dF1HICZnqzK6x+LZKpWedLiSUqefQyRDEHDaqgoAcFiknEoRIxxZVObColInvI6ZbYt1IaJJBT2BiSClO33rSn9PEvKZX7/basLSqomWWkb1iBtlLgsE4dLPo6JqOvadGku38BrIq1LyOcx45cpK3LymBle2lDJAISIiIiIiIponPnq8Bz/uHcGdNaX40rL6Qg+H5gCGJgxNiC6ZkUgSB3vHz2n+Ea/djJXV6eqRWqOCpLnMecHtsGaapukYCifTgYoRqvQEJgKW/vE4NB0wiQIa/I68qpFMO61Kj7Ug4cFM0HUdw5GkESalg5ThcBL1fmP+kWVVblR5bEX7+lRNx56OMfzxYD/+dKg/r6rG5zDjhpVGBcpWBihEREREREREc9pfR0N4y0vtqLGa8fzWlUV7roKKB0MThiZEs07XdXzriZP4n78cx3Qdm4z5Rzx5FSS1Pvuc/kdMVjWMRVPwOy086V7kVE3Hcx2jRoBycACj0YkApcRhxqtWV+Gmy6qxtbm0aEI7IiIiIiIiIjo3CVXDip2HENc0/HXjMqxy2Qs9JCpyDE0YmhDNKlXT8enfHcLPd3cBAFrKnViVDUe8WFnjgf8c5h8huhQUVcOejjH84WA/Hjk0gLGcAMXvtBhzoFxWjS3NfgYoRERERERERHPEXQfb8eeRED66qAr/1FRV6OFQkWNowtCEaNYkZBUf+NULePTIIAQB+MyrV+GuK5sKPSyic6KoGp7rGMMfXurHI4f6EYjJ2X1GgFKJdfU+LElPZu+ymgo4WiIiIiIiIiI6nZ/3jeJfWrux3uPAw+uXFno4VOQYmjA0IZoVwVgKf/eTfdjXGYDFJOKrt6/DTZdVF3pYRBdEUTXsbh/Dwwf78MihgbwAJaPWZ8eyKiNAWVrpwtJKNxZXuGAzSwUYMRERERERERFl9CdTuPzZIxAAvLRtFcot5kIPiYoYQxOGJkQzrjcYx10/3IOTQxF4bCZ8/84N2NxcWuhhEc0IWdWwq20UT7YO4/hgGMcHwxgKJ6c9VhSAplInllS6sKzSjaVVbiyrdKOpzMm5boiIiIiIiIguoVfubcVLkTi+trwBt1f7Cz0cKmLnkxuw7wgRndXR/hDe/qM9GAwlUe214Sd3b8LSSnehh0U0Y8ySiO1Ly7F9aXl2WyCaygYorYNhHB+MoHUgjPG4jPaRKNpHovjz4cGc5xDQXOZKhyiudHWKG/V+ByRRKMTLIiIiIiIiIprXXlHmwUuROP4yOs7QhGYMK02I6Ix2tY3inT/dh3BSwdJKF378t5tQ47MXelhEBaHrOobDyWyIcnzACFRODIYRTanTPsZmFrGkIqfFV7oypdprgyAwTCEiIiIiIiK6UC+EYrjx+eNwSSKOXLUaFpEdIGh6rDQhohnxh5f68M/3vIiUqmFTkx/fv3MDvA72h6SFSxAEVHhsqPDYcPWSiaoUTdPRNx43qlIGItkKlRNDESRkDQd7x3GwdzzvudxWE5ZWGUHK8ioPblxdhQqP7VK/JCIiIiIiIqI5a63bjnKLCcMpBc8Fo7jaz84odPFYaUJE0/rhzg78+8NHoOvAjaur8JXb13Hya6LzpGo6Okej6RAlYlSoDITRMRKFouX/82sSBdywqgpv3dKILc1+VqEQERERERERnYMPHuvCr/rH8M66cnxuSW2hh0NFihPBMzQhumCapuOLjxzDd3e0AwDu2tqIT716FedkIJpBKUVDx0g0G6I82zaC/V3B7P7FFS68dXMDXr++Dh4bq7uIiIiIiIiITuePw0HcfegUmuwW7Nq8ghch0rQYmjA0IbogKUXDh+97EQ8e6AMAfPhVy/AP17TwHxuiS+BIXwg/f64TD77Qi1h6fhS7WcKt62rw1i2NWF3rLfAIiYiIiIiIiIpPRFGxcuchpHQdOzcvx2IHW1/TVAxNGJoQnbdIUsG7f/Y8dp4cgUkU8MXb1uC29XWFHhbRghNOyHjwhV78bHcnjg9GstvX1fvw1i2NuGVNNVvlEREREREREeW440AbngyE8emWGvxDQ0Whh0NFiKEJQ5MpErKKlKrBbTWxaoCmGAon8Lc/2ovDfSE4LBK+/ZYrcO0y/gNDVEi6rmPvqQB+trsTjxzqh6wa/1z7HGb8zfo6vGVzI5rKnAUeJREREREREVHh/aBnGP92ohdX+lx44PLFhR4OFSGGJgxNpnj4pX6895f7YZYElDgs8DstKHVZUOKwoNRpQYnTWPqdVpQ4zShNL/0OC0ySWOjh0yxqH47gzh/uQU8gjjKXBT98+0asqfMVelhElGM4nMS9+7rxy+e60BuMZ7dfvaQMb93SiJcvr+DfaiIiIiIiIlqwOuNJbN59FJIAHNm2Gl6zqdBDoiJzPrkBf3sWiGA8BQCQVR1D4SSGwslzfqzXbobfaQQtmZDF77LAnw5fJt8cFonVLHPEC10B3P3jvQjEZDSWOvDTuzehsZRXrhMVm3K3Fe+9bjHefU0Lnmwdws93d+LJ48N4+sQInj4xgmqvDW/a1IA7NtajwsPerURERERERLSwNNqtWOqw4XgsgSfGwnhtZUmhh0RzGCtNFpB4SsVYLIVANIXRaApj0STGovKkZSp7C8ZlXMhvh9Uk5oUoZS4rbr6sGi9fUcEwpYj89egg3vvL/UjIGtbUefHDt29Emcta6GER0TnqHovhF8914d593RiLGsG4SRRww6oqvGVLA7Y2l/JvLhERERERES0Y/97Wh291DeENlSX45srGQg+HigzbczE0mRGqpiMYS+UFKWOxFMYiRugSmLRvNJpCStFO+3xXLS7Dv92yAsur+HMptHv2duHjvz0EVdNxzdJyfPstV8BpZeEZ0VyUVFQ8cmgAP9vViX2dgez2lnIn3rqlEa+/og5eu7mAIyQiIiIiIiKafbuDEbz2hZMoMUk4dNVqSLyQkHIwNGFoUhC6riOWUvNDlmgKh/tC+PnuTqRUDaIAvGlTA/75+qUoZVXDJafrOr7+15P4ymPHAQBvWF+Hz7/+Mpg5FwLRvHC0P4RfPNeJ3+7vRTSlAgDsZgmvWVuDt21txOpab4FHSERERERERDQ7FE3H6mcOIaio+N3li7HJ5yr0kKiIMDRhaFJ0usdi+PyfjuKPBwcAAG6rCR94+RLcdWUTLCaesL8UFFXDJx86jF/t6QIAvO+6xfjQK5eyfQ/RPBRJKvjtC734+a5OtA6Gs9vX1vvwti2NuGVNNWxmqYAjJCIiIiIiIpp57znSiQcGA3h/QwU+0VJT6OFQEWFowtCkaD3XPorP/eEIDveFAABNpQ58/KYVuH5lJU/ez6J4SsX7f/UCHjs6CEEAPveaVXjb1qZCD4uIZpmu69jXGcDPd3fijwf7IavGP/leuxl/s74Ob9nSiEVlzgKPkoiIiIiIiGhm/HYwgH840onlThue3LS80MOhIsLQhKFJUVM1Hffv78GX/9yK4XASAHBlSyk+ectKrKjmz2ymBaIpvOMne7G/KwiLScTX71iHV62uLvSwiOgSG4kkce++bvzyuS70BOLZ7beuq8G/vHIZ6v2OAo6OiIiIiIiI6OIFZAWrnzkEVQf2bFmBBjunByADQxOGJnNCJKngf588ie8/3YGUYsx3cvvGBnzolUtRxvlOZkRPIIa7frgHbcNReGwm/N/bN2Jjk7/QwyKiAlI1HU8dH8LPd3fh8WNDAACLJOLOrY1438sWw+ewFHiERERERERERBfutftPYPd4FP+5pBbvqCsv9HCoSDA0YWgyp3SPxfCFR47h4Zf6ARjznbzvZYvx9m1NsJrYc/9CHekL4e0/2oOhcBI1Xht+cvcmLKl0F3pYRFREDvWO4/N/OopnTo4CADw24+/vnVubOOcJERERERERzUnf7hrC59r6cJ3fjV+tbSn0cKhIMDRhaDIn7ekYw7//4QgO9o4DABrT8528kvOdnLdnT47gXT97HuGkgmWVbvz47o2o9toLPSwiKkK6rmPHiRF8/o9HcWzAmDS+1mfHv9ywFLeurYUo8u8vERERERERzR0noglcvecYLIKAo1ethpMXZRMYmjA0mcO09HwnX8qZ72RrszHfycoa/jzPxe9e7MOH7j0AWdWxaZEf379zA7x2c6GHRURFTtV0PLC/B//96HEMhBIAgFU1HnzsxhW4aklZgUdHREREREREdG50XcfW547iVDyFH61uwo3lvkIPiYoAQxOGJnNeNKngf59sw/eebkdK0SAIwB0b6/HP1y9DuZvznZzOD55ux388fBQAcPNl1fjvN65lix0iOi8JWcUPn+nA/z7RhnBSAQBcs7QcH71xOVZU899VIiIiIiIiKn6fPNGD7/eM4M3VfvzP8oZCD4eKAEMThibzRk8ghi/86Rj+kJ7vxJWe7+Rvi3C+k4Ss4thAGMcHwkipRtAjQEgvATG9IgAQBCG9RP5xudtzHmt0JxMgTj4mfRwE4KnWYfz42VMAgLdf2YRP3bKSbXWI6IKNRVP4xuMn8PPdnZBVHYIA3HZFHT70yqVs90dERERERERFbcdYGG98sQ0VFhMOXLnKOC9HCxpDE4Ym887eU8Z8Jy/1GPOdNPgd+PhNy3HDqqqCzHcSSyk42h/CwZ5xHOoL4VDvOE4MRaBqhf/f6aM3Lse7tjdzHhgimhGdo1F86c+teDgdXltNIu6+ahH+4doWeGxs/UdERERERETFJ6VpWLHzEKKqhkfWL8U6j6PQQ6ICY2jC0GRe0jQdv32hF1985BiG0vOdbF7kxydvWYnVtd5Z+7qhhIwj6WDkcF8IB3vH0T4cwXT5iN9pwcpqDxwWCToA4/8uHbqO9H0dWs460sfomWNOt54+Xs8eb6xoOcdYTCLu3rYIr15bM2vfCyJauF7oCuDzfzyGPafGAAAlDjM+8PIleMvmRlhMYoFHR0RERERERJTvHYc68PDwOD7UVIl/XVRd6OFQgTE0YWgyr0WTCr7zVBu+t6MdyfR8J29cX48P3bAUFW7bRT13MJbCod4QDvWNZ0OSjpHotMdWuK1YXevF6hqPsaz1otprY4UHEc1buq7jr0eH8IVHjuHkUASAUfn34Vctw82XVfPvHxERERERERWNX/eP4p+OdWON245HNywr9HCowBiaMDRZEHoCMXzxkVb8/sU+AIDTIuG9L1uMu7ctOqfJz0ciSRzqHU/fjKCkJxCf9tgary0bjKyu9WB1jRcVnosLaIiI5ipF1XDvvh585bHjGE5X/q2t9+HjNy7H5ubSAo+OiIiIiIiICBhOyVjzzGHoAA5cuQpVVraYXsgYmjA0WVCe7xzD535/BC+m5zup99vx8RtX4FWrjflOdF3HYCgdkPSlA5LecQyEEtM+X4PfYQQjtV6srvFiVY0HpS7rpXxJRERzQjSp4AdPd+C7O9oQS6kAgFesqMRHb1yGxRXuAo+OiIiIiIiIFrqbnj+O/aEY/ntZPd5Sw4v8FjKGJgxNFhxN0/HgAWO+k8GQcdXz+sYSuG0mHOoNYSSSnPIYQQAWlTmxusabDUlWVXvhdTB1JiI6H8PhJL721+P41Z5uqJoOUQBu39iAD75iCavyLqH+8Tj2nQrg+c4A9nWOoWs0hhqfHfV+Bxr9DjSUOrLrtSV2WE1nr8qkfJqmI6VqSMgqkoqGpKxBFIEarx2iyPZ0RERERETF5iunBvDFjgEscVjxj42VeGWZFx5+FlqQGJowNFmwYikF33mqHd99qg1JRctuFwVgSYUbq9KttVbXerGyxgOX1VTA0RIRzS8nhyL40iPH8OiRQQCA3Szh77c3453bm/n3doapmo6j/aF0QBLA/s4AeoPTt5icjiAA1R6bEaKUOtDgTwcqpU40+B0ocZjnxBw1uq4jklQQjMkYj8tGkKGoSMrppZIfcGTXFRWJ0x2T83jjmInnTKnatONwWU1YUe3GymoPVtZ4sLLaiyWVrnNqF0pERERERLPneDSBa/ccQ+advFkQsL3EjVsqvHhVmRclZn5WXSiKLjT51re+hS9/+csYGBjA2rVr8Y1vfAObNm066+N+/etf401vehNuvfVWPPjgg+f0tRiaEAD0BuN44Pke+BxmrKr1YkWVB3YLT1wQEV0Ke0+N4b/+eBQvdAUBAGUuK/7pFUtw+8Z6mCWxsIOboyJJBS90BbKVJC90BRBNt0TLEAVgZY0H6xtKsL7JjyUVLgyGEugai6FrNGYs07fYpMdO5rKaplSoNKTv1/jssJhm/ueoqBqCcRnBWAqBmIxANIVgTEYgfd/YnrtuLGW1MNf/iAJgM0tQVH3aMEUSBSwud6VDFCNMWVHtgd9pKcBoiYiIiIgWruPRBH47GMDDw+M4Hpto128SgG2+iQCl3MLuM/NZUYUm99xzD+6880585zvfwebNm/HVr34Vv/nNb9Da2oqKiorTPu7UqVO46qqr0NzcDL/fz9CEiIhoDtF1HY8cGsAXHzmGU6MxAEBzuRMfedVyvHJl5ZyoYigUXdfRG4wbVSTpkOTYQAjapHdsLqsJlzf4sKHRjw1NJVhX74PzHCp6dF3HaDSFztEYutMhSu766eb8yhAFoNprz6tQacipWPHazYjL6pTgIxuGxKaGIWPRFMIJ5YK/Z1aTCI/dDLtZgtUkwmoWYTNJeUurSYItvTSOSS9NImzmiW22nH22MxxjSgeAiqqhfSSKI30hHOkP4UhfCIf7xhGIydOOtdprw8pqI0DJBCoNfkfRtffSdR2BmIzBUAKDoQSGwkkMhRKQVR2ra71YW+9FhZvt94iIiIhobmmNJvDwcBAPDwdxODLx2UcEsMXnwi3lXtxU7uOk8fNQUYUmmzdvxsaNG/HNb34TAKBpGurr6/H+978fH/3oR6d9jKqq2L59O+6++248/fTTCAaDDE2IiIjmIFnV8Ks9XfjaYycwGk0BAC5v8GH7knIsq3JjaaUbTaWO7AnohUhRNRztD2Nf5xj2dQbw/KnAtMFFXYkdGxpLsL6xBOsb/VhW5YY0CyfaE7KKnkAsW6HSOTYRqHSNxZCQp29RlWESBSiTE57z4LGZUOK0wOewoMRhRonDAl96WeIwp7entzkt8DssRVdNqus6BkNJHOkfzwtTMgHiZE6LlBeirKzxYGmle1bae2XCkKFwAoOhJAZDCQyHk5PCkSSGwomzVvHUeG1YW+8zbnU+XFbnZSs+IiIiIpoz2mNJPDwcxB+Gg3gxPNHuWACw0evELeVe3FzuQ62N1eLzQdGEJqlUCg6HA/fddx9e+9rXZrffddddCAaDeOihh6Z93Kc//Wm89NJL+O1vf4u3v/3tZwxNkskkksmJSb5DoRDq6+sZmhARERWRcELGd59qxw92tk856W6RRLRUuLCs0oUllW4sq3RjWZUbtb75Obl2KCFjf2cgW0lyoDuIuJzfLksSBayq8WB9Y0m2kqTSU/ir+nVdx3A4mQ1Q8qpVxmIYDk+8JzNLQjb4yA9AJtZLnPn7vXbzvA7QwgkZrQPhbIhypD+EYwNhpJTp23u1lDvz5klZUe1Gqcs67XPruo5gTMbQlAAkHY6EExgKJTEcTp52bpbp+J0WVLitqPTYUOE2vvbB3nEcHwxPqX4SBGBJhQtr64wgZV29D8uq3GzLR0RERERFryuexMPD43h4OIh9ofyLna7wOHBzuQ+3lHvRaJ/+/TgVv6IJTfr6+lBbW4tnn30WW7duzW7/8Ic/jKeeegrPPffclMfs3LkTd9xxBw4cOICysrKzhiaf+cxn8NnPfnbKdoYmRERExWdgPIE/vNSH44NhtA5GcGIwfNr5NRwWKR2iuLA0HaQsrXSjwm2dM+29VE1HbyCO57vGsq22WgfDmPzuy20zpQMSo4pkbb0XDsvcu2I/llIQiMnw2s1wWqQ583MqpPNt71XpsWJltQe1JXaMRlJ51SEXEoZUeGyodFtR4ckEI7bsernLetr5a6JJBQd7x/FidxAv9gTxYvc4eoPxKcdZTaLRzqvOh7X1Xqyr96HB7+DvBhEREREVrb5ECn8cGccfhoJ4bjyK3I9va1x2I0Cp8KLFUfgL2+jczdnQJBwOY82aNfj2t7+NG2+8EQBYaUJERDSPaZoxf0frQBitg2EjTBkIo304etoTwF67Gcsq3Vha5TKW6VvJJZhgW9N0hBIyRiLGPByjkSRGoimMRVIYjSYxmrc0Ji6f7p1Wg99hBCRNRiXJkgrXvKyqoQtzvu29cpU4zHnBR6ZKpNJjBCQVbivK3VZYTTPf+msonMBL3eN4sSeIA91BvNgdRGiauWp8DjPW1hmVKOvqfVhT5z1tBQ0RERERUSENJWX8ccSoQHkmEEHup9QVTls2QFnmsPHCoCJXNKHJ+bbnOnDgAC6//HJI0sSHOE0zfhVFUURraytaWlrO+DU5pwkREdHcp6gaTo3GsiGKUZkSxqmR6JSWQBkVbiuWVbmxpMKNZVVGdcqSSvcZ51jQdR2RpDIl7BiLpjASMe5n16MpBKKp856vwywJWFXjxYbGEmxoKsEVjSWcQJsuSCSp4Fi/EaIMhZIoc1mMYCQnDJmNeVAulKbpODUazVaiHOgO4khfaNpAtN5vzwYpa+t9WF3jLbq5aoiIiIhoYRtJKfjzyDj+MBzE04EwlJyPhkscVtxS7sPN5V6sctkZoBShoglNAGMi+E2bNuEb3/gGACMEaWhowPve974pE8EnEgmcPHkyb9u//du/IRwO42tf+xqWLl0Ki+XMV5EyNCEiIpq/ErKKtuFIOkwx2nu1DobRE5jaFiijrsSOZZVu1PsdCMVlozIkE5BEUufV0ijDbTOhzGWF32lBqdOCUpc1vZy07rSixDG/5+kgOh9JRcWx/nBeNUrbcHTKcZIoYGmlO12N4sXlDSVYUuHih08iIiIiKgpBWcGjoyH8YSiIJ8fCSOWcYm+yW9JzoPiw2GGFCEAQhPQSECFAFJDdTpdGUYUm99xzD+666y5897vfxaZNm/DVr34V9957L44dO4bKykrceeedqK2txec///lpH3+29lyTMTQhIiJaeCJJBSey7b0i2cqU3InJz8RhkYwAxGVFmdMyse6aWM8EIX6nZVZaGxEtVKGEjIM9RiVK5jbd/7sVbiuuXlKO7UvLsG1xGcrY0ouIiIiIikBYUfGX0RAeHg7i8dEQ4ufZnUAA0iGKkF0X0sFK7r5MR+eJ0EVIhzATYUzm+KVOG35yWfPMvtA57nxyg1mfYfT222/H8PAwPvWpT2FgYADr1q3DI488gsrKSgBAV1cXRJFXXxIREdGFc1lNuLyhBJc3lORtD0RTOJ4OU3qDCfgcZvidFpSlq0D86SBkLk66TjRfeGxmbFtsBCGA0TZvIJTAi91BHOgex4HuAA50BzEUTuL+/T24f38PAGBVjccIUZaUYX1TCcNMIiIiIioIt0nC6ytL8PrKEkRVFY+PhvGH4SD+MhpC7Bw6G+gAVB1QM1PO69n/XDCXxPfGF2PWK00uNVaaEBERERHNLwlZxfOdAew4MYynj4/gSH8ob7/dLGFzsx/b05UoLeVs5UVEREREhaXqOmRNhwZAhw5dBzQAmp7epgNaznYdOrScYwCk709sn/w8es4x2efRddglEZe5HYV54UWqqNpzXWoMTYiIiIiI5rfhcBLPnBwxQpQTI1PaeVV7bbh6SRmuXlKOqxaXocR55nkRiYiIiIhofmNowtCEiIiIiGhB0HUdxwbCeDodoDzXMYaUMtEGQRCAy2q9uHpJGbYvKcflDSWwmNgemIiIiIhoIWFowtCEiIiIiGhBSsgq9nSMZUOUYwPhvP1Oi4StLaW4ekk5rl5ShkVlTrbyIiIiIiKa5xiaMDQhIiIiIiIAg6EEnj4xgqdPDGPniRGMRlN5+2t9dmxfarTy2tZSBq/DXKCREhERERHRbGFowtCEiIiIiIgm0TQdR/pD2RBl36kAUupEKy9RANbW+3D1knJsX1KGtfU+mCW28iIiIiIimusYmjA0ISIiIiKis4ilFDzXMYanjxuTyp8ciuTtl0QBTosEt80Mp1WCy2qCy2aGK73utJrgTi9dNpOxP3M/c0tvt5pEtgGjS2IsmkLHSARtw1G0D0fRMRJB+3AUoYQMp8UEu0XKLh0WCQ6LyVhaJTjMOeu5+/LWjaXdLEEU+TtNREREc8P55AamSzQmIiIiIiKiouKwmHDdsgpct6wCANAXjGPnCSNA2XlyBMGYjFBCQSihXPTXMonCtGFKXtBiM8FllVDpseGqxWUodVkv+uvS/JSQVXSOxtA+HEH7SE44MhJFMCaf4ZHJGR2H3SzlBy7psMVuNsGZXreaJIiCAEkERFGAJAiQRCG9LXcd02wzjhfFSfvT26Y8LnusALMkwmoSYTNLeUurSYSJFWRERER0Bqw0ISIiIiIimkTVdAyHk4gkFUSTCiJJBeHExHrmFk0qiCTy74dztkdT6gV9fUEA1tT58LJlFXjZ8gqsqvHMm6v6QwkZz54cwVPHjTZpwZiMtfVerG/0Y0NjCS5v8MFt49wymqZjIJRA+3AU7elqESMgiaA3GMeZPsnX+uxYVOZEc7kzvXTB77AgLquIphTEUyqiSQVxWUUspSKWVBBLqYimVMRTSnqpIpYytscmrc91JlGYEqhYTCKsZgm2aZcirCYJtrMsrSYRtnQVTqYax55eMqghIiIqLLbnYmhCRERERERFQNN0RFMKokkVkaSMSFJNhyyZdRnRlJoXyBwfDONwXyjvecrdVly7tBzXLa/AVUvK4JlDoYKq6TjYO44dx4ex4/gwXugOQtVO/zFUFIBlVR5saCzBhqYSbGjyo9Znv4QjvrRCCTmvjVYmHOkYiSAha6d9nNtmQnO5C81lTuNW7sKiMiMksVukWRuvpulIKCqiyXSwIivZ9Wwgkw1mVKRUFaoGaLoOVTNumfWJbTn7dR36NNune7yqG+OZ/JyaDqQUDUlFQ1JWkVS0vPmLCsEiibCZxXRbNBNs0wQrDos0dbsl977JWObuS6+zBSAREdGZMTRhaEJERERERHPYYCiBJ1uH8MSxYTx9YjivYsUkCtjQVILr0lUoiytcRXeydDCUMEKSEyPYeWIYgUkto5rLnNi+tBzXLC1HpceG/V0BPN8ZwL7OMXSPxac8X7XXhvWNJekgxY/lVe45deV+QlbRPRZDx0g0e8tUkIxEUqd9nEkU0FDqQHOZC83l+eFImctSdD/3YqZpuhGiKCoS8vTLpKwhcZZlbhCTmGaZUFTEUxriKQUxWT1jRdBM8trNWFvvw7o6L9bW+7CmzodyN1v8ERERZTA0YWhCRERERETzRErRsO/UGB4/NoTHW4fQPhzN219XYjfmZllejq3NZbNaZXA6CVnFvlMB7DhhVJMcGwjn7XdbTbhycSm2Ly3H9iXlqPc7Tvtcg6EEnu8MYO+pMTzfGcDhvtCUyhSnRcK6Bl9RtfTKBCOnRmM4NRJFx2gUnaNRnBqJoW/8zO20KtzWbBut5nRbreZyF+pK7DDPoXCI8um6ng1TYikVcdmoyMm0RTPWFcRTGmIpJe+4RN4xmXZp6pTnOlMFTa3PjnX1Pqyt92JtnQ+ra71wWjm1LRERLUwMTRiaEBERERHRPNU5GsUTx4bwROswdrWPIqVMnDS1mkRsbSnFy5YbE9yfKZy4GLquo30kih3Hh/HU8WHsbh/NayUlCMCaWq8Rkiwtx7p63wWf/I+lFBzoDuL5UwHs6wxgf1cA4YSSd4woAMurPNjQVGJUpMxSS6+ErKInEEPHiBGMnBpN384hGHFZTWgqc6Cp1AhEWtLzjSwqcxY88KG5S1E1xGUVnaMxHOgO4sXuIF7sCeLEUGTK76MoAEsq3EaIUu/D2jofllW5GcwREc0zuqpBT2kQ7QzKczE0YWhCREREREQLQCylYFfbKB4/NoQnjg2hbzyRt39xhQvXLTPmQtnQ6IfFdOEnR3MncN9xfBi9wfw2WhVuazYkuWpxGfxOywV/rTNRNR0nhsLYeyqA50+NYV9nAD2BmWvplVQyrbRi6Bw1WmmdbzDSWOrEolInmsqcaCp1oKnMiVIn22nRpRNOyDjYO44Xu8ezQUr/pL8PgBG0rq41KlHW1nuxrt6HBr+Dv6tEREVCVzVoMQVaVIYalY31mAwtsx6VocVkqJn1qAw9qcLS4EbFe9YVevhFhaEJQxMqEC2VQnz/CxCdTpjraiH5fHyzSURERESXhK7rOD4YwROtQ3j82BCe7wzktbVyWU24ekkZrltWgWuXlaPCYzvj86majkO943jqNBO4WyQRGxeVYPsSIyhZXuUu2HvfwVAC+04Zc6KcqaXX5Q2ZSpQSVHls6ByN5VWLdIxEzzsYaSx1YFGZk8EIFb3BUCIboLzYPY4Xe4JTqrYAwOcwp0MUH9bVe7GmzocyF+dHISK6WBcagFwIU7kdVR/aMMOvYG5jaMLQhC6xRGsrgvfdj9Dvfgd1fDy7XXQ4YK6tNW51den1GljS65LXW8BRExEREdF8Nh6X8fSJYTx+bAhPtQ5jNJo/4fjqWg9etqwC1y6vwNo6HyRROPsE7uVObF9iTOC+udkPh6U42z7EUgoOdAWxr9No6fVCZwDh5NSTw6fjtEhGlUimUqTUaKPVWMoJ2Gn+0DQdHaNRI0jpDuJAzziO9oWmnSelrsSenmjeCFNW13qK9v9/IqJC0zUdyZNBRPcNQAkkLzoAgQCIDhNEh9m4Oc3GfacZkmNiPbvdYYZoN0EQ+X4lF0MThiZ0CajhMEIPP4zgffcjcehQdrtUVgZBEKAMD5/1OUS3OxuqWOpqpwQskss1my+BiIiIiBYITdPxUu94ei6UIbzUM5633++0oMxlwfHBSN7285nAvZipmo7jg2Hs65xo6TUek9GQDkQyc40YIQmDEVq4koqKY/1hvNgTzM6R0jYcnXKcKABLK924orEE21rKcGVLKUpmqSUfEdFcocUVRJ8fRHRXH5TRqS0RAZw9AHGa8rczAJkxDE0YmtAs0XUdsb17MX7//Qj9+VHoifQfQLMZ7uuug+8Nt8G5bRsESYKWTELu7YPc2wu5tye97EWqx1iqo6Nn/XqS1zu1UqWuFpb0NtExNz+0EhEREVFhDYeTeLJ1CE+2Gq23MlUYMzmBOxHND6GEjIM949kQ5UB3EEPhZN4xggCsrPbgqsVluHJxGTY1+WG3SAUaMc2E4XASsZSCGp+d/w4QnUWqL4Lo7n7EXhiCLhvVeoJVgnNDJawtvmwAIjnNEGwMQAqFoQlDE5ph8uAQxn/7WwQfeAByV1d2u2VxC3y3vQHeW18Dk99/Xs+pxWKQ+/rSQUqPEbD0TIQrajB41ueQ/P6JSpWGBpTccTvMNTXn+/KIiIiIaAGTVQ3PdwYQjKWwaVHprE3gTkTzx8B4Age6A9jdPoZn20amVKlZJBGXN/iwbXEZti0uw9o6L0w88T5n7G4fxd/+aC/isgpJFFDjs6HR70RDqQONfgcaSx1o8BtzOjmtbNNGC5OuaIgfHkVkVx9Sp0LZ7eYqB5xba+BYVwHRyvC4mDA0YWhCM0CXZYSffBLj992PyNNPA5qRFIsOBzw33wzfba+Hbe3aWSvbVyORiUqVdJiS6u3JbtNCoSmPcV6zHQ3f/e6sjIeIiIiIiIhoOkOhBJ5tG8XOkyN49uQI+sbz29K4rCZsafbjyhYjRFla6WILvCL1fGcAd/7fc4imVIgCoJ3lrGGZy4IGvwONpc70ciJUYatDmo/UUBKR5wYQ3dMPLZye+00UYF9dCteWGlgWefh7X6QYmjA0oYuQbG9H8L77Mf7QQ3kttOzr18N3223wvOqGomiLpYZC2SqV1KlTGP7v/wEEAS1/eRSWurpCD4+IiIiIiIgWIF3XcWo0lg1Qnm0bxXhczjum3G3FlS2l2UqUWp+9QKOlXId6x/Gm7+9GOKFg2+JS/ODOjRiPy+gcjaJzLIau0Vh6adwPxuQzPp/DIuUEKTmhit+JGp+N1Ud0SSiyjOcffhCuEj+Wb9sOyWQ+7+fQdR2pjhAiu/sQPzSaTRNFtxnOTdVwba6C5LHO9NBphjE0YWhC50mLRhF65BEE77sf8RdeyG6Xysrge+2t8L7+NlibFxVwhGfXdfc7EH32WZS+852o+OcPFno4RERERERERFA1HUf6QnimbQTPnBzBno4xJBUt75imUkc2QNnazEnlC6F1IIzbv7cLwZiMjU0l+Mndm+CwnLn11nhcTgcpUXRlQpXRGLrGYugbj+NMZxwlUUCtz56uSslv+dXgZ9svmhmKLOP3X/k82p/fAwBw+Uux/ubXYs3Lb4DFfvYLorWkitiBIUR39UEeiGW3W5o8cG2tgX1VKQQTw7+5gqEJQxM6B7quI/7CAQTvvw+hPz0CPZb+4ydJcG3fDt8bboNr+3YI5vNPoAsh9Oij6P3AP0IqLcWSJx6HYOGbTCIiIiIiIiouCVnF/q4Anj05imfaRvBidzCvBZQgAKtqPEaI0lKGjZxUfta1DUdw+3d3YSSSwtp6H37+jk1w2y7uXEhSUdETiKeDlEmVKmMxpCYFZ5P5nRbUl9hR73cYtxIH6v121Jc4UOOzw8IT1XQWuYGJyWyB1eVCNDAGALA5XVh3w824/MbXwOHxTnmsPBxDdHc/ovsGoSdVAIBgFuG4vALOLdWw1Lgu6WuhmcHQhKEJnYEyOorxBx9C8IEHkGpry263NDbC+4bb4L31VpgrKgo4wgujyzJOvuzlUIaHUfuV/4HnxhsLPSQiIiIiIiKiMwolZDzXPoZnThqVKCeGpk4qf0WjD9tayrBtSRnW1HJS+ZnUNRrDG7+7CwOhBFZWe/Crv98Cr2N2Lx7VNB2D4YRRlZKuVMlUqHSOxqa0c5tMFIAqjw11k8KUer9RpVLhtkIUOafEQqYqMn73PxOByWs//CnUrliFo08/gb2/ux+B/l4AgMlixerrrseGW14HT1kFEsfGENnVh+SJYPa5TKU2OLfWwLm+EqKdFVBzGUMThiZFQQkEEH3mWcT27AFEAZLbA8nrgZhduiF5PJA8HogeDySXa9aqOnRFQWTnTozffz/CTzwJKAoAQLDb4bnhBvjecBvs69fP+Ymahr/+dYx8+3/h2LwZjT/5caGHQ0RERERERHReBkMJPNs2gmdOjuKZkyPonzSpvNtqwg2rq/CxG5ej1DW/5hDQVR1qMAFlNAFlNA5lJA5lNAGpxAr3tlqYymZ27pfeYBxv/M4u9AbjWFLhwq/fuaUovqehhIzusVj6Fkd3IL0eiKMnEENCPnOVisUkos5nT4cq9imVKj6Hec6f/6HTmy4waVyzLrtf01S07X0Oex76DQbaTsAi2tHiWYvlZVtgUdO//wJgW+aH68oaWBf7IDCEmxcYmjA0KQhd15E8ehSRHTsQeWoH4i++CGhn/odsMtHhMAIUtxui12MELZlQxe2eCF08bmNbTugiOp1T/tFLdXUheP8DGP/tb6EMDWW329auMSZ1v+kmSK75U1In9/fj5MtfAWgamv/4MKzNzYUeEhEREREREdEF0XUdHSNRPNM2imdOjGBX+8Sk8iUOMz7zmlV4zdqaOXUCPC8YGYnnhSNKIAGopzlNJwD2NeXwXFcPc5XzoscxFErgjd/dhVOjMSwqc+Ked25Bhcd20c8723Rdx3Akie4xI0DpTrf7yoQr/eMJqNqZT3W6rCbU5YQpDX5jPTOvClt/zV2qIuP3X/kC2vY9B5PZgls//Ek0rbl82mOTXSEMPXIEensCIowWgEk1jlHHIGpuWoPaTWvm1N8WOjuGJgxNLhk1HEb02V2I7HgK0R1PQxkezttvXboUzquugmi3Qw2HoIXCUEMhaKEQ1PDEuhaNXvxgRNEIW9IBCwQBicOHs7ulkhJ4X/MaeG97PWxLl1781ytS3e95LyKPPw7/XXei8mMfK/RwiKiI6brON4FERERENGeomo59p8bw6d8dxrGBMADg5csr8J+vuwxV3uI54Z8NRjJhSCYcGU1AGUsAZzqpbxJgKrUbtzIbTCU2JI6NIdEayB5iW+GH+7p6WBsu7LzXaCSJO763GyeGIqgrsePed21FjW9mq1gKRVE19I8n0pUpUytVhsPJMz5eFIB6vwOLypxYVOZEc5kTi8pcaC53ospjY9uvInYugYkua4i9NIzI7n7I3eHsdqHMjM7UEew9+AcoWgoAUL10OTa95g1oWb8JgsggbT5gaMLQZNbouo5UWxsiT+1A5KmnENu/P9vqCgAEhwPOrVvh2r4dru1Xw1xdfW7PqyhQw2Fo4TDU8RC0cAhqyLhltk0JXdLBizY+Dl0+Tb9LQYDzqqvgu+02uF923YKYHD2yYwe63/kuiB4Plux4CqKteN44ElFh6bqOoY42nNy7Cyf37kagvxfLtl6NrX/zFvgqqwo9PCIiIiKic5JSNHznqTZ84/ETkFUdbqsJn7h5BW7fWH/JLgrSVR1qIL+N1rkHIyJMpbaJYCQbktgheSzTtgJK9UUQfrIb8YMjQPqprc1eIzxZ7Dvn1x2MpfCm7z+Ho/0hVHls+M27t6Le77iQb8GcFE+pRoVKJlDJCVc6R6OIptTTPtZmFtFU6kRzuTMdqriwqMyJlnInfI75f76pmE0JTP7139C09orsfiWYMCZ23zsALZo+jykJcKwph3NrNSz1bgiCgOBAP/b94QEcevIxqOlzjf7aemx8zW1YcdU1kEyzO98PzS6GJgxNZpQWiyH63HOI7NiB6FM7IPf15e23LFpkhCTXbId9wwaIBQgmtEQiL2DRwiFosRjs69adc3AzX+iqirZX3gC5txfVn/88fK97baGHREQFpCoKeo4ewsm9u9G27zmER4enHCNKEi572Q3Y8vrb4fKXFmCURERERETnr3UgjA/f/xJe7A4CALYtLsUXXr9mRkMAXdehDMeR7BiHMhibCEkCyfMIRuzGepkRjpwuGDkX8nAM4ad6ENs/lP365joXPNfVw7ai9IzPG07IeOsPnsOLPeMoc1lx77u2oLl8/rQsv1i6rmMonET7cBQdI1F0jETQMRJF+0gUXaMxKGf4eZc4zNkgZSJUMW42s3QJX8XCYwQmX0Tbvt1TAhNd0RB+shuhJ7qzre8krxXOLdVwbqyE5Jr+HGY0GMD+P/0OB/78MFLxGADAVVqGDTe/Dpe9/JWw2OZHZdZCw9CEoclFS3V1TVST7NkDPZXK7hMsFjg2b85Wk1gaGws4UprOyHe/h+GvfAX2tWvRdM+vCz0cIrrEUok4Tr24Hyf37kb7/j1I5rRANFmtWLRuPRZv3ApPaTmee/BenHpxv7HPbMG6V92Cja+5DQ6Pt1DDJyIiIiI6Z6qm44c7O/D/Hm1FUtFgN0v41xuW4a4rmyBdQDChazrkgSiS7eNIdYwjeSoELXqa7haZYKTMnl81UmaH5L7wYORcKMEEIjt6Ed07AD09MbqpwgH3dfVwrCmHIOV/7VhKwV0/3IO9pwIocZjx63duxbIq96yNb76RVQ09gTg6RiI5oYpx6x9PnPGxtT57XoiyqNxo+1VX4rig31GakBuYSGYzXvuvn8wGJqnuMAL3H4c8YIQe1mYvXNtqYFteOuX/j9NJxmJ46bE/4fmHH0Q0aLTIszldWPeqV+PyV93Cz81zDEMThibnTUulENu7F9H0JO6pU6fy9ptrauC69ho4t2+Hc/NmiHYmqsVMGRnBiWuvAxQFi377AGwrVhR6SEQ0y2LjQbQ9vwcn9+5C58ED2VJiALB7vGhZvxmLN25Bw2VrYbZY8x7bfeQgdv76Z+hrPQIAsNjtWH/z67D+5tfC6lg4pfpERERENHedGoniI/e/hOc6xgAA6xtL8MXb1mBxxZkrKXRVQ6o3YgQkHSEkT41DT0xq0WQSYW1ww1znvqTByLlQIylEnulD5Nk+6Elj3JLfBvf2OjjXV0Iwi0jIKu7+8V482zYKt82EX/39Fqyu5cnemRJLKRMhSjpQaR+Jon04glBCOe3jLJKIhlIH/mZ9Hd65vZnzTZ4nVZHxh69+ESf35gcmuqxi/C9diDzdA+iA6DTD95oW2NeUXfD3WEmlcOTpx7Hv9w8g0G904DFZrLjsZa/EhlteB095xUy+NJolDE0YmpwTua8PkR1PG223du+GHotN7DSZ4Fi/Hq5rroHrmu2wNPOP91zT88EPIvynR+C743ZUf+YzhR4OEc2CwEAf2vbuxsl9u9HbehTI+SfdW1mFxRu3YvHGLahZuhyieOaScF3X0XFgH3b++mcYPtUOALC5Pdh06xuw7oabpwQtRERERETFRtN0/HJPF77wp2OIJBVYTCL+8eVL8M7tzTBLxkTOuqwi1R02ApKOcaQ6Q9lKjQzBKsHS6IF1kRfWRR5Y6twQTMU9EbSWUBDZ1Y/Izt5sZYzoNsO+rQYfaevHoydG4LRI+NnfbcYVDSUFHu3CoOs6AjEZHSMRtA3nhyodo1GklInfu0/dshJ3X7WogKOdW04XmCQ7xhG4/wSUkTgAwL6uHL5Xt0ByzsxcJJqm4uSeXdjz0H0YbD8JABBEEcu3XYONr7kN5Q1NM/J1aHYwNGFoMi1dlhE/cACRHTsQefIpJE+cyNtvKi+H85rtcG3fDueVV0Jysa/lXBZ9bg+67roLosOBxTt2QHI5Cz0kIrpIkydyH+nuzNtf2bwYizdsweKNW1Ba33hBYbeuaTj+3LN45t6fI9DXAwBwlfix5bY7sPq66znxHREREREVvb5gHB//7UE82ToMO4Bb/B68q7kCnpEEUt3h7NwGGaLDBEuTNxuSmKtd59y+p9hoKRWxvQMI7+iFOp4EAISg40FRxvVvXY2NKysLPEICjICvbzyOe/f14Ot/PQFBAL7/tg14BX8+Z2UEJl/Cyb27jMDkX/4NDSvWYvxPpxDd3Q8AkDwW+F63GPYVszNnp67r6Dr0IvY8dB+6Dh7Ibm++YiM23voG1C1fNStfly4OQxOGJlME77sPg1/8ErRweGKjKMK+bl12Enfr8uWsJplHdF1H+003I9XRgarPfBold9xR6CERFaVkLIrQyDDCI8OIjQdhdbng9Prg8JbA6fXBbLMVdHyqoqDnyCGc3LcLJ/c9h8joSHafKEmoW3kZFm/YjJYNW+ApK5+xr6upKo7seBzP3vdLhEeMyeO9lVW48g1vxvKrrjlr5QoRERERUSFoMRnJU0YVycDhYdjHkpCQf65DdJvTAYlxM1U4Ct5ma6YpKRU//P7zWN0dQwOM9+6CRYRzczXcV9dC8rCSvBjouo6P//YgfrWnG3azhN+8eytbp52BqijpCpOJwKTK0YzAAyegBo2Q0LmpCt6bFkG0mS7JmAbbT2LPQ/fh+HPPZLs/1CxbiU233obmyzdCEIu7Sm0hYWjC0GSKyFNPoftd74ZUUgLn1VcZbbe2bYPk8xV6aDSLxn7yEwx+/guwLl+ORb99gKEYLTiaqiISGDVCkeEhYzk6nA1JQiPDSMVjZ3wOs9UGh88Hh9eXDlPSgYqvZOK+z5cOWOwz8v9ZKhHHqQPPGxO5v7A3byJ3s9WWnsh9CxZdvhG2Wa4KVGQZB//6CHY/cA9i40EAQGldA7bd/lYs3riVf1eIiIiIqKDUcArJjnGj1VbHeHbS51xBs4BdchIHoCLot+JDt1+GKxr9BRjtpaFpOj58/0u47/keWEUBP9u+FI2tIcj96c8VkgDnhkq4t9fBVMo5awtNVjXc/eO9ePrECCo9Vjz43m2o9vLnMtnkwOTW938C3k43YvuHABhz+ZS8fglsi30FGV+gvxf7fv9bHH7qMaiKMZdNaV0DapevBHRAh54NVYyFDl3XgeyZ+cz99DHpA3NP3Wf2Tzwkc3/ieYzjdXgrqnDtnX83q695rmFowtBkCi2RQLK1FbbVqyFIvDp4oVCDQZy45lroySSafv0r2NetK/SQiGZUIhrJhh/hkWGERofz7kfGRqHr2lmfx+72wF1WDofXh2Qkguh4ELHxIJRU8rzGY7JYJ8KVaYOWiaXF7sgLHKLBANqe34O2fbunnch98YbNWLxxKxpWr4XJYjmvcc0EOZHA/kd+j72/uy8b4lS1LMG2O+5E42XrGJ4QERER0azTZRVKIIlUTxip9JwkmbkLcpnK7dkqEssiD0w+G/50sB+ffOgwRiJJCAJw97ZF+JdXLoPdMr/Okei6jk8+dAg/390FSRTwzTddjhsvq4au60gcDyD8eDdSnSHjYBFwrCmH+9p6mKsK19Jb13XoKRVqWIYWTkEwiZBKrBCd5gXzOSOUkPGG/30WxwcjWFHtwW/evRUu66WplJgLplSY3P5RWF7UoUVkQABcV9bAc0MTxCL4/zkSGMP+P/0OLz76x7NepDmbypuacecXv16wr1+MGJowNCHK6vvoxzD+4IPwvva1qPnC5ws9HKJzpioKooExhEaGJoKQ86wSAQDJZIK7tBzusnJ4yoylu9RY95RXwF1aBrN1agsuXdchJ+JGgBI0QhQjTAkY68FANlyJBYOQk4nzen2S2ZwNVQBgoP1k3kTuvspqLN60FYs3bEH10mVF0w4rEY3g+T/8Fs8//FD2NdevvAzb7rgTtctWFHh0hafrOpRUEslYDKl4DMlYFJqqobSuHjYn5wojIiIiOhMtpUINJqEEElADSaiBRHZdCSSME6STCYC5ypkNSKxNXkju6S8yCsZS+NwfjuCB/b0AgAa/A1+47TJc2VI2my/rktF1Hf/x8FH8384OCALw1dvX4dZ1tVOOS3aMI/REN5LHA9lttpWl8FxXD0u9e+bGo2hQIyloYRlqOAU1nIIWTkGNyHnrWjgFXZ56sZtgFiGV2GAqsaaXNkgl1uxyLocquqZDi8rQojIknxWizYTusRhe9+1nMBJJ4bpl5fj+nRtgktjaSVUUPPy1L+HEnmfhsHpx8+b3QuxWAQCmCjtKblsKa2PxnQNOxqI4uvMpxMPjAAABAiAI+b+zk+4LQvqYKfsFZA+b5v7Ec2cfCLvHg6Wbt83Wy5uTGJowNCHKih84gFN3vAmC1YolTz3JlmxUtDRVRfeRg2jd9TROvbgfkdHzqxLJBCKe0nK4yyqy951e3yXpISonEtlQJS9oCQamBC6p+NSr4QCgsnkJFm9MT+Re11DUHwBi40E89+Bv8OKjD2dLj5uv2Ihtt78NFU3NBR7dhdE0FalYHMlYNBt4TIQfsWm2RZGKxZCMx5DK2a6p6rTP76usRkVTMyoWtaByUQsqFrXAkQ7NiIiIiBYCLaWmg5DkxDI4cX/aUGQSwSLBVOnITtpubfRAdJjPaxxPtA7h4w8cRP+4cRHQmzc34GM3Lofbdn7PU2z+359b8c0nTgIAvnTbGrxxY/0Zj0/1RhB+shvxQyPZ1j7WxT64r62HtcU77eeRzMl+NZyClgk/Iimo4dwgxLivx5XzGr9gkSC6zdBlDVoodfbjzWJOiDIRrkg+Y5vourShihESycb3I28pG+FR5vuVDksy33PBLMKxoRLubbU4FE/i9u/uQlLRcNfWRnz21tWXbPzFKDcwWeS5DBurb4KQAiAC7mvq4XlZAwQzgyU6NwxNGJoQZem6jo7X34bk0aOo+OhHUPr2txd6SERZmqai99gRtO7aiRPPPZOdMyNDlExwl5XBkxOCZKpEMgFJoSdqvxByKplXvSIn4qhdvmpGJ3K/VEIjQ9h9/69x6MnHoGtGyLVs69W48o1vgb+mrqBjU2QZoeFBBAf6ERzsR2hkGMlo9LSBx/lWC52RIMBqd8DicEDXdURGR6Y9zOUvnQhRmowgxV1aVtSBGREREdHpaMl0KBKcWiWiBhLQomc/iS5YpUkVBbmVBlYIdtOMvFcKJ2R84U/H8IvnugAA1V4b/ut1l+G65RUX/dyF8M3HT+D/PXocAPC5W1fhzq1N5/xYeSiG8FM9iL0wBGjGaUJLvRvWZu+kqhDjxD/O50yiJEByWSC6zZDcFkhuC0TX1HXRZYFonaiu12UNynj+71Fu4KaGU2cdh2AWIfmsU36HMlUr5xKqaCl1oiJmuhAkG4bI0BPnFxJBMIIiPalm79tWlOJwjQ13PXYUAPDpV6/E325bdH7PO0+oioKHv/4l9Ow7iI3lr0K13bg4z1zjRMkblsJSw0p+Oj8MTRiaEOUJ/PoeDHzmM7A0NaH5T3/kyTgqKF3T0HeiFa27duD47mcQDYxl99lcbizZfCWWbt6GsoamS1YlQhcv0N+LZ3/zSxx75ikAgCCIWHXty7H1tjfBUz57HzxTiXg2FAkO9GN8cADBwT4EBwcQGhnOa3l2rkxmCywOB6wOByx2J6wOe3rpSG93wmI39ueuG0vjOLPVlve7Gw+HMHSqHUMdbRjsaMPQqXYE+nunHZ/d7UFFuhIlU5Hiq6ji/wtERERUFLSkAnkgBrk/CmU0nnciW4udQyhik6YNQ6QSG0y+mQtFztWutlF89IGX0DlqtP59/eW1+OQtK1HivPTzCF6oHzzdjv942DjJ/vGbluOd21su6HmUQALhHT2I7h0ElDNU/QuA6DRPhCEuC0S3BZJ7IgDJrM/Wz1NXNKOVWzA3mJsI6NTQ2UMVmMS83z0A+eHIadqGnZEkQHKaIbrMxvfBZTa+N870Mne7wwyIQLJtHJGdvUgcm/hsHPSY8bXQOJ6Egv+9cwNesbLyPL9Dc1umwkQ7GsNa/7Uwi1ZAEuB5RSPc22shsG0ZXQCGJgxNiPKokShObt8OLRZDw49/DOeWzYUeEi0wuq5joO04Wp99Gsd3P4Pw6HB2n9XpxOKNW7Fs69VoWL0WkomT3c1lQ6fa8cy9P0f783sAGHPKrLn+Rmx+7Rvh9JWc9/Ppuo54OGSEIQNGGJIJSIKD/VOqkyYzW23wVVXDV1kNT3kFbC53XsCRF36kgxLJdGnaMqTiMQx1dmCoow1DHe0Y6jiJkZ6ubMVOLovdnq1EqWhqRuWiFvhr6yFKxTHXDREREc0/uqZDGUtA7o9CHohml+rYmatzBbvJOBHtm34uCtFefO/34ykV//OXVvzfzg5oOlDmsuBzt67GTZdVF3poZ/Wz3Z345IOHAAD/fP1SfODlSy76OdVwCtE9A9CicjYMMU74p6tDnGYIUnFfjKkrGtTxZE4ruJxQJZiEOp4854oZwSwaFTGunMoYp3kiEMkJQi4mJJKHYog804vo80PZ0GoQGn4nKXjj3euwqqX0gp53rlEVBY/9zzdQ3luBCnsDAMDS6EHJbUtgrnAUeHQ0lzE0YWhCNEX/Zz6D4K/vgfvGV6HuK18p9HBoAdB1HUMdbWjd9TRad+1EaHgwu89it6NlwxYs23o1mtZefslOUtOl03f8GHb++qfoPvwSAMBktWL9Tbdiwy2vh82VX0ataxoigbG8MCSYDknGBweQjEXP+LVsbg9KKqvhrayCr6oGvpylw+ubU9V1SiqFke7OdEXKSQx1tGG46xRUeWp/b5PZgrLGpmw1SuWixSitb4TJPDf/f9J1HdB16NABPX0fulGMk92uZz/cZu4b72R1AAIkkwmSycSqHCIiovOkxeScYCSG1EAUykD0tFfZix4LzFVOmMvtkPy2vMoR0VZ8oci5eqErgA/f9xJODEUAAK9aVYXPvXYVKtzF2RL43n3d+PB9xvvt91zbgn+9Ydmceu9bSLqqQR1PZStTlEASgoCp1SCT2oZdCmpURnR3PyK7+rLz/MShw7G+EpUva4Cp1H5Jx3MpKSkZz3/pV6gM1cEkmqFLgO+mZri21kAQ+btNF4ehCUMToikSx46h47WvA0wmLHnyCZjKygo9JJqHdF3HSHcnWp99Gq27diA40J/dZ7ba0Lx+E5ZdeTUWrV0Pk2XulLvThes8eAA7f/1TDJw0+itbnU6secWN0BQ5HYwYLbUU+cwTPbr8pdmKEV9ldXbdW1kFm3N+97JVFQVjfT3pipSJ9l5yIj7lWFGSUFrXgIpFLSitrQcEAZqiQNNUaOp0NwWaqqWXKjRNm+Z44xhdVaGqSnqpQk8fo6rqxLb0YzLBR27YkQk39GmCj5kmiCIkkxmS2QTJZIaYDlMkyQTJbIZkMkE0mbMhi2Q2Q5JM6ePM2W3Zx+Vsz3tc+r4oiRAlCYIoQhQliJIEUUzfl6SJfTnLzHH598W8+wx/iIhopumqDmUklg5IYtmgRB1PTv8AkwhzlcMISKqcMFcbS8k5Ny/SOBdJRcW3Hj+Jbz/ZBkXT4bWb8albVuL1V9QWVSDx0IFe/NM9B6DrwN3bFuGTt6woqvHRxdMVDaN7+3HiD22oV9M/WwGwrSyF++paWBo98+pnnugNofN/d8KpuAEAWoWImruumNchEV1aDE0YmhBN69TtdyD+4oso/+AHUfaudxZ6ODSPjPZ0o3XXDrTu2omx3u7sdpPFiubLNxhByeUbYLYW5xVaNLt0XUfbvufwzD0/w0h357THCKIIb3klfFXV8FZW51WLeCurYLZYL/Goi5uuaQgM9GPoVE6Q0tGGRCRc6KHRTBIEiOkgRpCMUCUTpkhmM2wuN+wut7F0u2FzeWBzuWB3G0uby5Pe7obN6WI7NyKiBUaNpCaqRzIttoZigDL9aSDJZzVCkeqJgMRUal+wV3cf6Qvhw/e/iEO9IQDANUvL8W83r0BLuQtigb8njxwawHt/uR+qpuPNmxvwn69dPa9OnlO+7tEoPv2N3bgxIWArJgJLc50L7qtrYV9dNqfn+NAVDeOPdyL0eBdEiJC1JIRNTjTetoW/1zSjGJowNCGaVvDBB9H/0Y/BXFODlr88CoEnT+giBPp7jYqS3Tsx0nUqu10ym7Fo3Xos3Xo1WtZvgsXGq0LIoGkqWp99Gu3798LpK8mrGHGXlXM+m4uk6zrCo8PpAKUd44P96aoFU7YSwliXjBPxJtNEVcSkmyBKRhVFznGCJEGS0ifvRQmiyVhm2mFln18UAUGA8flGMD7oTFoXIKSPMT4EGdvz141j0veR2Ye8fRPrAqDr0FQFqpK+yTJURYGmyOltcnaflrkvT96WOU7OuT/dc008n1GZY1TiZCp0dE1LV+6oRpWOpk3sy2zPPEbVoOvnOcHoBbI6nLC5J4IW26TAxe5ywZYOXOwuD2xuN6x2ByteiIiKlK7rgKJDl1UowaQRiuTMPaKFp7b3BADBIhnVI9X51SNzua3WbFFUDd9/ugNfeew4Uuk5JuxmCYsrXFha6cayKheWVLqxrNKNaq/tkpzgfeLYEN75s32QVR23XVGHL79hzayEOMlYDHIiDsligclshslsWVDvCXRdT7+H06Bras66lrPP2J63b5r7efv0yfd12N0elFTXTmljnOuFrgDu+N5uVCvAv1WWYvloKhuASl4rXNtq4NxYVZRzBp1JqieMsfuOQxmIAQD64m0o/ZsVaLmKc/HSzGNowtCEaFpaIoET11wLbXwc9d/9DlzXXFPoIdEcMz40gNZdO9H67NMYOtWW3S5KJjStvRzLtl6Nlg1bYHVwcjYionOV/eB9muBFV7Vs+7RMWzQllUIiEkYiEkY8swyHJ7aFw0hEQkhEImedF+hMBEFMV624YXMbFSsWmx0Wu3Ez2xzGes42Y90Bc+5x1ktzIomIqBB0TYeuaNBlDbqiAemlLk9sy90/ZTn5MWdbZr6OcpbQXQBMpfaJ9lqZ1loltgVbPXKh2oYj+Ozvj2B3+2g2PJnMbTVhSaULy6rcWFo5cStzWWbs38BnTo7gb3+8FylFwy1rqvG1Oy6HNAM/S0WWMdLZgf6TrRg4eRz9bScQ6OuZcpzRwtQCk8W4ZdfToUretsy62QyTxQqT2TwRwOTet1hgMuc+lwWADkXOXOAyaSnLUBQZqqxM2T/lMVP2T31M9iIaWTba0qbDjEKwe7woqa5FSXUN/DV12aW3shomsxl/PNiP9/xiPwDgP165DK/RzIjs7s/OeyJYJDg3VsK1rRYmf3F3edBlFeOPdSGyowfQgYQaw4HAE7j83a9DywYGJjQ7GJowNJlCTql49AeHsfHmJlQ08vuykA1+/gsY+8lP4LruOtT/f/buO06Ou74f/2va9n6936kXS7LVbDVbLhhj2caFFiA2NpBAIOQXSChpkARIqAlgyJdiDCYEjLFlg21cZFu2im01W72ddL3f9j7t8/tjdvd2r0gn6e727vR+Ph6rmZ2Z3fvc6cruvObzfv/Pj4o9HDIDRAYHcOr1HTj5+g70njmd287xPBqWXYmF6zZh3pp157wqhhBCSPHomoZUPIZkNIJUNIpUPBOqRCND22MxpGKRTNgSQzIWgZoeo779xeA4mCwWmCxWSFabEaxYLJAKAhdbbl0aFsJIFiN4kcxmiGYzJJMZgiRREEMIySkILhQdTNHAVAaMEVIMDyIwxvb8bcYxbMR+6MU/rcJZREhVQ+GIqcoBscIG3kTVBSaSpjO0+eM41RfDqb4oTvZFcbovirMDcahjfB/47CbMLx8epjjgsV1Yj8e9rQHc+9AeJBUN71hSgR99aCWkiyjJlC3z2nvmFHpOn0TvmVMYaD0LTVVHHMtx/JTNiJ1xMmVUucxtaN2Y1c1x3Nj78vdzPOKhAGIB/zk+FA9XWRm81bXo1OzY1s0Qljz40gc24Z0rFyB5eBDRHV1Q+xKZBwDWpSVwbKqFqd457V4vpVvCCD5+Guqg0aOxLXYMb4e245a//VvMXbW2yKMjsxmFJhSajPDabw/i4Laj4AURK25sxNJNdRBN2YamQw1Os7+4pwtNVaHKMlQ5DU1RoMjpzH1j2/B1TZGhZLbpmorqBYvRuGIllXzJkz7bgrO33grwPOZtexFSdXWxh0SmEVWW4e9sx0B7KwbbW9F96jh6Tp/M7ec4HnVLl2HheiMosbncRRwtIYSQyZSdzZKdyWIELjHIySTkVAJyMgkllczczyyz2/K2Y5LebnAcbwQoZjNE07BlZrtkMucdY4Qu+cFLwdJsGfF8gpSpG575FIbeOrHM/ezdzP2hAwsfN2z70Jcke7/weEEUIVmm9xWihIwHYywvxMgEGaOu592XM4GErGWWefdHO17VocvjmHUxVXgOnMQbN3HkEqNsyy2z68MfM9bxeUvw3LR6L3+5kVUdLYNxnOqLGmFKbxSn+2No9cfH/DNY4TLnQpSFFU7MrzBKfTnMI89fvN0Rwod/9iZiaRXXLSjDT+5dBbM4vkAsHgqi98wpYwZJ8yn0njmFdHzkLFSLw4mqeQtQmb3NXQCby23McFXk3PkXTZGhKopxnkZWhvYpsjELRE5DzWzX8vfJmX3K0GOM/UrBcaosAxwHURQhSBIEUTKWeeti/nZRgiANHSuO8Rhh2POJoxyTLWPLjRqG5K1zE3/uTE4lEezpRrCnC8HuLgR7uhDILOVkYszH8ZIJJVXV8FbVoso5FyXhckiDQ2Gaqc4Jx6YaWJeWghOm7ncE03ToMQVaVIYWU6DHjKXaG0fi7QEAgMLLeKPnj+hNt+KOz/0DBSZk0lFoQqHJCAe3vYhtP/3e+Q/kuEyAIoIXpdy6IIpGnfK8+wXH5PYND2KMdSP8OHfgoeb+uA5tn4gpkVanCwvXX4slm65H5bwF9EISQNt9H0HizTdR+lefRNlnPlPs4ZAiYLqO8EA/BttbMdDegsH2Ngy2tyLY0z3ySiKOQ+3ipVh4zSbMv3o97B5vcQZNCCFkxmGMQU2nMyFKojBgSSWhJDPbU9nAJTV0bCo/hElBTaehpNPQtZFXws5GJqsVdo8Pdq8Xdo8PDq8Xdm8JHB4v7F6fsc3ng8lqo9e3ZFpKnQoi/OxZKL1jn+ybNAJ3zpAB4hjhQ8E6NzKsGOMxBUEIlb0ieZKyhjMDQ7NSTvVGcaovhq5QcszH1HqtBTNSXBYJn/3d24ikVKybU4KH718DizR6YKKkUuhraTbCkUxAEhnoH3GcIEkob5qLqrkLUDl/IarmLoC7opL+nkxDjDEkwiEEu7sQ6DFCFH93J46fOAMpHoSAkefNXFIpFrhXo9GxFAJnhHCqSQNbKMJxdQ28DbUQJWnE4847ljGCED2av5ShxxToiXO/Xhu09uG1E7+BxmkUmJApQ6EJhSYjnHx9J7Y/8jPIyTTklAwwDYCeuc0MufqYeTUvRZM5VyuzYF0yQddUnNm/B4lwKPcc3qpqLN54PRZv3AxPZVXxPpkii/zpT+j6289CLCvDvJdfAncRfyzJzJGKxQrCkYH2Fgx2tENJjf5C3eJ0oay+EaX1DShraELTilVw+EqmeNSEEELI6PJnIiuplDETOROoqHLeMn/fKMco6VThvsxSTaehKnKxP81xE03mvGDFN2zdlwtZLI7pV56DzE7KQALhZ1qQOhEo3CHkzcCQhLz1ofv8WPtMmUDCxIMTM/fP8TwUXJDpLppScLo/htN9UZzsjeVmqPRHxy6NubrBi18+sBb2zEwUXdPg72zPBCRGL5LBjvZRL4IrqalD5VxjBknVvAUorW+kihwzXDip4L0/2om+7h4sd8j45EoX4v3dxmyV7k7EggGYeRvmua7CPNdVsAh2AICip9ESPYweqRWcS4TJZIXF5IRVdMDC22HmLTDBAomZIGomiJoAXhHApzlwygUOkgd4uwmCUwLvMEFwSODtIt46+gIO7H8GvCDijs99CXNXUQ8TMjUoNKHQ5JwC3XG88NBR+LtiYIxh2eYqrLmtHhzHoKuq0QhLVTPrI+/rWna7OubxQ+vGTRDF0cONzLqUCUAEkzR6EJIpHXahdE1D2+G3cXzHKzi99/WC2txVCxZhyaYbsHDdRlidl9f3CpNlnL7hRmiDg6j53vfgeufNxR4SmQCaqiDQ1ZkJSIzyWgMdbYj5B0c9XhBF+GrrMwFJY25p93jppAohhJDLmq5rmbIhCpD5m8gh87cxu8j9rczsz90d/fiR24c9Lvc8HFQ5jVgwaNQ4DwYQDwYQDwUzywBiAeN+OjGyvMpYBFGEzeM1whRPXqDi88GRuW+xOzINcLVcI1ym66Osa8Z9xsbcN57H55rtcpxRTiXTSNhoBpwts2Iy3iNIprz9knExVaacylS8btF1rTB4GxbKKam80C5t7FPSw8O6FJR0GqLJhLL6JpQ1NKG8sQmeiqqLeq8z3egpFZGX2hHb3Q1oDOA5ONZVwbm5DrxNmtKyMITMVMG4bAQo/bHMrBSjzNeCcju+s6UBsY6WXKmt3rOnR+0/5vD6UDlvYS4gqZgzD2abvQifDZlsHYEE7vrRLgzGZNy4qBw/uXc1hExonCv31d2JYFc3cCYNX7gMdhjnv3SmQ9HTMAvWC/qYOtOR0uJIa3GktARSWty4ryeg8DJUXoUmadAlDTDxkKzZsqcWSBYLwv196Dh6iAITUhQUmlBocl6qouH1rWdw6OVOAEBJjQM3f2wpfFWz9w+pnEygee8bOLbjFbQfPpi7+oIXRDRdtQpLNl2POSvXQjRdWDO2mar/v/4b/h//GPb161D/858XezjkAjDGEPUPYrCjFQNtRjgy2N6KQHcndE0b9TGusgpj5kheQOKprKariwghhJAZTEmnEA+FEAv6Ec+ELPFgJmjJhCyxUBCpaKTYQ51U+WFKdj0bwgjZ2erZ+vWm7D4TOI7LBRnDZyCpBaFHatQGyRNFMltQ2tCI8oYmlDXMQVlDE8rqG2dMXxumM8T39iLyQhv0uHEZsmWhF+4tcyCV24o8OkKKT9c0KGmj/KSSSuVucjoJJZXOlaFUUknj91DBfeNxga7OgioaWZLFisq583MBSeW8BXD6Sqf+kyRF81Z7EB/4yRtIqzo+sr4RX7lj6ZjHMsaM0onb26C2xIa2cwy6xKCJKlRBhcrLkJFGmiWR1pNIaXEklSgSchhJOZq7cEBJXVzZVF4Qcftnv4R5qykwIVOLQhMKTcat9fAgXn7kOJJRBaLEY+P75mPJxupZf5V5LODHid2v4fiO7ehvPZPbbrbZseCaDVi86XrULlo6K674Govc2YUz73gHwBjmPvcnmBobiz2ky5quabkX0UMvpodeLKdiMQx2thsBSUfrqI37AON7uLS+AaX1TSjLLEvrGmC20RtWQggh5HKlKgoSoWAmTAkgHsxfN4KVeDAAOZE4R+NbYeyGuNl17jyNc3lh1O1gDKqq5hoBa6oy1FxYMRoEa5nGwdntxSSaM1fMmrNXzxr3RbMZksmcW0oWM0RT3nEWY3sqHsNAWwv6W1vg72gb/fPhOHgrq1HWOMcIUxqNmSkOb8m0eq+WPhtC6I9nofQYr03FMivct82BdaGvyCMjZGKoioJ40I+ofxDRgB+pWOaEcTr//dro97Pv5TTlQmsajY4XBJTWNxrhyNwFqJq/EN7qGvD8+BrCk9nr2cM9+KtfHwAAfOX2JfjIhqbzPkYNpMAUDbzDBN4qXnRZw2wP46EgJVVw0UF2m5q9SEFRMGflalTNW3hRH4+QS0GhCYUmFyQeTuOlXxxDx/EgAGDOVWW4/sOLYLFfHn0uBjvacHzHKzi+81VE/QO57c7SMizeuBlLNl2Pktr6Io5w8rT/5V8i/upr8D3wACo+//fFHs6MwBiDqsjDQo38K4UKw47cFUKp1LD15CW9kOYFAd6qGpQ1GKFIWUMTSusb4Cwpm1ZvpAkhhBBCJhpjDLqmQpWNQEXNhSoKNFnOC1uy+5W8MGYoiGEMmcDDPCLwEC2Woft5oYiYmaEyUXRNQ7CnG/1tZzHQ1oKBVmMZDwVHPd7qdBkzURqaUN5ozErx1dRN+exhNZBC+NmzSB7xAwA4iwjXTfVwrKsCJ8zeC8/I7KLIacQCfsQygUjUP4hYYBBRvz+zHBx1dsfF4ngeJosVksUCyWKFZDaPfd9sgSlvu7O0HOVNcyCZzBM2HjK7/M/2M/jGcyfAc8DP7luNGxZVFHtIhEw7FJpQaHLBmM7w9ksdeOPJM9A1BofXjJvuX4KaBd5iD23KMF1H54mjOPbaKzj1xk7IyURuX3njXCzetBmLNlwHh3f2XDUVffkVdP7VX0HweDDv1e3gzfQCLCsVjyHQ1YlgTxcCXR0IdHci0NWJUF/vRU0/HS9eEDIvlI0Xy9kXyiarFb7q2lxpLW91LUTp8gg2CSGEEEIuN/FQMDMbJROmtLUg0N1p9IAZJtunrjxT2qu80SjzZXE4JnxcelpDdHsHojs6AZUBHGC/ugqudzRAuEwuuiMzg5JKIZoJPmIFgYgRkMT8g0iOs3ShIElw+krhKCmB1ekqCDpMmT4NufuZwMN4L2fNrUsWKwRRpAvcyKRhjOGLjx/Go/s6YDMJeOwT67C02l3sYREyrVBoQqHJRetvi+CFh44i3J8EOGD1uxqxeksjhMvsaiFVlnH2wB4c2/EKWt7al+sTwXE86petwJJN12Pe2nUwWS6sYdZ0wzQNzTe9A2pPD6q/+Q2477ij2EOaUrquITIwgGB3Zy4UCWTWx3NFkZh3JZDxYnmUq4KyL5bzlrkX06NsE0R6s0kIIYQQQkZSZRn+zvZckJJd5l/slc9ZWmaEKA1NKGucA29VDcw2G8w2O0wW6wWVImY6Q+KtfoSfa4UeNcqJmee64bl9LqTK2dsXkxgYY2C6Dk1ToasqNNVY6pqW6xU6pDAUGBEScGPeOc+xAJe3IZ2IZwKQQcT8/swyO2NkYMxyxsOJJjOcJaVwlpTA4SvNrJfm1h0+IyihsIPMBIqm4yMP78GuZj8qXGY89amNqHTPjP5YhEwFCk0oNLkkckrFjt+dxondPQCAiiYXbv7oUrhKZ3ZAcLESkTBOvbELx3a8jJ5TJ3LbRbMZ89esw+JN16Nh2ZXghZlZR3TgRz/C4Pd/AOvKlWj8v18XeziTQk4mEOzpLpgxEujpQrCn65xlsRy+Eviqa+GrqYW3KrOsrIbV6YRoNlPtWEIIIYQQUlSMMUQG+tDf1oKB1hYMZMp8hfv7zv1AjoPZaoMpE6LkwhTr0H2TzQ6L3Q5Lyg7rcQF8wDh1wLklON9ZD8eKiqK9B9J1LVN+TTXKrmXLs6nZsmwKdE2FrutgmdvIda1g++jHaiO3s3Mfkz3FYpxk5wCOg3G+nTO2cZmT/3nbjfWhk/LGcZmIILcvc3z+et5zMt0oW5cNNDRNg64q0DUtb1te6KFp0PL2G9vyApG859A0DZiBp44kizUvBCnJrRuzRoyl2W6nQITMKuGkgvf8z26c7o9habULv/vLdbCbp7aEIyHTFYUmFJpMiNP7+rD91ychJ1WYLAKu+9BCLFhTWexhFVWotwfHd27HsR0vI9Tbk9tuc3uwaMN1WLLpepQ3zR3zRRdjDJqiQJGzTbHSUGXjll3PNsxSZXnoODm7bdhxcua4dBqaIkOyWGFxOGCxO2BxOI2b3QGLwwFzZpvV4cysO2C22qAODqL5+hsATUPTU0/BsnDBVH05JxTTdUQD/lwoEuwZmjkSC/jHfJwgSfBW1RjhSHVmWVMHb1U1TFZqnk4IIYQQQmaedCKeazZvlPc6i8jgAOREHJo6vlKzVsGJFb7r0OBYCgBQ9DSOhXbjVHg/dGgFwYvFZocpP3zJWxdEcajHjDpsqShQVXXEtvzjVEUxTuArCtTM9tHKlJGpxfE8BEEsmLHEkHd6afiZprxTT4XHsdFWRzxJwakrBpisRiBihB+Fs0SyM0XMNno/Ry5PHYEE7vzhLvjjMm5cVI6f3LsawkU2eidkNqHQhEKTCRMZTOLFnx9D79kwAGDRNZXY9IEFMFku75SaMYbe5lM4tuMVnNz9WkEtVG9VDawutxFo5AUeSibgmE5X6HAcD7PdDjGZghCJwlZRAfeKK2G2O2B1GCGLORfA5IUxdgdEk+mCP162aaeuaWPcVOiaPsoxme26ltunptMI9fXmzRzphJpOj/mxbW4PfDW18GVmjGRnkDhLy2jGCCGEEEIIuWyosox0Io50IoF0IoZ0IgE5dz8OOZaEvdMCr98HnglgYOhBK04m9iKa8COdSExqj78LxnEQRQmClLmJEgRJBC+I4HkeXOY2tC6MsX30Y859rACO4wr3cXzmBD/LvPVjABtaz52CYZnogA1tY4xl3i+yobeNjBkhA0Ph8+bWh2a28KIIQZTACwIE0fgaFCxFYdz7eVGEIIiZ58wsBQG8KEEQhAsq70YImXoH2oP4s5+8gbSq4yPrG/GVO5YWe0iEFB2FJhSaTChd07H32Vbsf7YVjAHuMive8dGlqGikry8AaKqK1oMHcHzHKziz702oijyux/GCCMlshmg2QzKZIZpMQ+t5S9FkMo4zWTLLvG15xwmiBDmVRCoWRToeQyoWQyoeQyoWNW7ZbZn1cwUM4yGazMZsFZtRw3jU0EPXoKtDy5H1bicWL4jwVFYNzRqpqYOvuhbe6hpY7BPfCJMQQgghhJDZgjGG5KFBhP/UAi1kvFcwNbrguW0OTLXOguNURYacSCAVj0HOhC250CURRzqZXU9AU9VckJENNcRcuJEfdBi3XAAy7DGCNPrjeEGg8kqEEDKKZw/34K9+fQAA8K93LMV96xuLOyBCioxCEwpNJkX36RBe/PlRxIJp8DyHq989B1e9ox4cTfHLSScSaD96ENDZyBAkF3gYoYcgFne2jirLSMVjSMdjSEYiaPn7v0MqEIDltlvBz5s7ImQxwpcY0vH4hIcfvCAaVzONuIngBX7kfl6AIElwl1fkZoz4qmvhLq+csb1lCCGEEEIIKRa5M4rQ02chtxoz6AW3Ge5bm2BdXkqBBCGEzGA/2t6Mbz53EjwH/Oy+1bhhUUWxh0RI0VBoQqHJpEnFFWz/9QmcOTAAAKhd5MVN9y+B3W0u8sjIpfI/9HP0f+tbsCxZgsbHfz92XxZdN64cy5vNwnHcsKBjHMFHZjvH8fRGjBBCCCGEkCLQojLCz7cisb8PYAAn8XBeVwvHtbXgTXQxEiGEzHSMMXzx8cN4dF8HbCYBj31iHZZWu4s9LEKKgkITCk0mFWMMx3f3YMejp6DKOiwOCTfcuxhNy0uLPbTLBtMZFFmDnNQgp1TISRWaoqNyjhuCdHG1ZdVgEM3XbQaTZTQ+9jtYly2b4FETQgghhBBCpgOm6oju7EL0lQ6wtAYAsF1ZBte7miDSBXGEEDKrKJqOjzy8B7ua/ah0WfDkpzag0m0p9rAImXIUmlBoMiWCvXG88NBRDHbEAADLNtdi/T1zIUp0RdJYGGNQ0hqUlBF2pJMqlGzwkVILQhA5qUJOaSO3Z7ZhlJ/cxuWluPWTyy565kbX5z+PyB/+CPc9d6P6a1+7xM+WEEIIIYQQMp0wxpA65kfomRZogRQAQKp1wHP7XJgb6P0zIYTMVuGkgnv+Zzea+2NYWu3C7/5yHezm4paNJ2SqUWhCocmU0RQdrz95Bgdf6gAAlNTY8Y6PLkVJ9eXRdJsxhmRUQcSfRHQwZSwDaaQTCuSkBiUThqSTqhGUJFVM5E8cz3MwWUWYrAJiwTR0jWHj++ZjxQ11F/V8iQMH0PbBD4GzWDD/tVch0M8QIYQQQgghs4LSG0foj2eQPhMGAPBOE9y3NMJ2VTn1qSSEkMtARyCBO3+4C/64jJsWl+PHf74aAv3+J5cRCk0oNJlybUf9eOkXx5CMKhAkHhvfMw9Lr62ZFb0q0gkFkUwgEhlMIerPX09ClS+8KTrHczBZBCPwsBihh7EUje3Z9eHbc8cb9wVpqB/IoVc6sePRU+BFDu/5/GqU1TsveFyMMbTc8W6kT59GxT/+I3x//uELfg5CCCGEEDL9McaghdNQehNQeuNQe+Ngig6pzglzowumGie4iyz7Ot1pcQVyRxRaJG28luY5cIKxBMcZAYKQWXIo2F9wfHZf9vjMvqH9mX08B/AoeG/EGAM0Bqbqxk1jQHZdZWCaDmSW2W3QRtmv6pljMo/X8vcPrTNFh9wWMWarixycm2rh3FwH3kxVAggh5HJyoD2IP/vJG0irOm5aXIEPX1OPDfNKIQmz828+IfkoNKHQpCgSERkv/fIY2o8GAABNK0pxw58vhsUhFXlk56aktYKZIsODETmpnvsJOMDhMcNZYoGrxApniQUWhzR6GGI11kVp4pufM8bwp/93GC0HB+Eut+J9/7AGJsuFT7UM/PrX6Pv3r8I0by7m/PGPsyL4IoQQQgi5nGlxBWpf3AhIssveeK6XxagEDqYaB0wNLpgbXDA1uCA4TVM36AnCNB1KTxxyRxRyexRyRxTqYLI4g8mELAAArThvw63LSuF+VxNEH9WyJ4SQy9Uzh3rwqf87kLvvtkp459IKbFlejfVzSyhAIbMWhSYUmhQN0xkOvtyB17eega4x2D1m3HT/EtQu9BZtTJqiIxoonB1izBwx1pNR5bzPYXVKcJVac8GIq3QoIHH6LBfdfH2ipeIKHv3qHsSCaSy8uhI33b/kgp9Di0Zx+trrwJJJNPzqEdjWrJmEkRJCCCGEkImmyxrU/kQuFMkGJHpUHv0BPAexzAqp0g6p0gZO4CG3RZBui0CPjXyNLJRYcgGKucEFsdw27co6qeE05PZILiCRO2OAOnJmuFhqhVhqBWC8h0HmxoYth9Yxrn3QL/LttcCBE3hwIgeIPDiBAyfy4AQeEDPrme0F+0XeeOyI/cZzcSKfO14sscJUc3mUUSaEEHJuBztCePxAJ5493IvBWDq33WOTcMvSSmxZXoV1c0ogUoBCZhEKTSg0KbqB9iheeOgoQn2J3EwMAAAHcMY/yE1g4Dhw2X2ZjQX7OORtK3zsuY5nDIiH0oiH06M2Tc9ntolDM0VKh4KR7DZpBk1b72kOYet33wLTGW78yGIsuqbqwp/jn/8Zocd+D9eWLaj5zrcnYZSEEEIIIeRiMY1B9SeNYKQ3DqUvAbU3DjWQGvN1r+A1G+FIhRGQSJV2iKVW46T68OdnDFoghXRbxAhRWiNQ+xMjnpuzCDDVZ2aiNLpgqnOCN03d62Zd1qB0xYyApD2SKbk1MiDiLCJM9U6Y6pwwZ5a8bfJmwxeEK9kyXNlwJfP2uyAUyZYCI4QQQqaYpjPsaQngmcPdeO5ILwZjQ39HfXYT3rm0EluWVeGaOb4ZFaCkVQ2HO8N4syWAN1sCeKs9iGvmlOB7H7gSNtOFV2UhswOFJhSaTAtKWsPO353CsV09xR4KRBMPZ94MkfxAxFVqgXkS3zQVw75nW/HmH85CNAt435dWw1tpv6DHJ48cRet73gNIEuZvfwViSckkjZQQQgghhIzF6DsiGz1H8spqKf2JMcs78XbJCEUq7EYwUmmDVGEDb760EwR6UoXcHskFKXJHFGx4bz8ekKocudkopkYXRLf5kj5uFmMM6mByaAZJRxRKTwwYPomEB6RKO0x1TpjqXTDVOyGWWCmUIIQQQs5D1fRMgNKD5470wh8vDFBuuaISty2rwtqm6RegJGQVb7WH8GZLAHta/HirPYT0KDNNr6r34OGPrIHHNvNKjpJLR6EJhSbTSnggiXRCAWMAGMCMfwAgs40ZdzP/DB03bF/eY7Pbct+++cfnfUfb3Ca4SqywOqXLqjeHrjP84Xtvo+tkEKV1Dtzz+VUQpQu76q/lve9D6vBhlP/d51DysY9N0kgJIYQQQoieUqEGUtCCKaiBNNTBRK7/CEuN3neEM/EQK+yQKmy58lpSpR2CY2pOAjCNQemNQ24NZ4KUKLRwesRxgtsMU+NQXxSp0m6UkDoPPaFA7owZQU0mKGGj9BrknSZj9ki9E6Y6F6Rax5TOdiGEEEJmI1XT8WZLAE8f6sFzR3oQTAyV7Sx1GAHKlmXVWNvkg1CECxMiKQX7W4OZmSR+HO4MQx1WIrPUYcLaJh/WNvpQ7rLgS08cRjipYEGFA488cDUq3dTf63JDoQmFJoQgHkrj0a/tQTKqYNn1tbj2/Qsu6PGhxx9Hzz/+E6S6Osx9/jlw/PS6ioAQQgghZKZgqg41mIIWTEMNpPICEmOpJ0aGATn5fUeyAUmFDYLXMu1mT6ihNOS2MOS2KNJtkVFngnAmwQg46p0wN7phqneCkwQjgOkYKrOlDozSrF3kjeb0mRJbpnoXBLfpsro4ihBCCJlqqqbj9bN+PHOoB88d7UWoIEAx411XGD1Q1jROXoDij6WxtzWQmUkSwLGeCIaf0a5yW3B1kw9rm0qwtsmHuWX2gtcIp/qi+POH3kRfJI0ajxW/+uhazCmjXl+XEwpNKDQhBADQdsSPpx88CAB41yeWYc6VZeN+rJ5I4PR1m6FHo6j72c/g2LhhsoZJCCGEEDKjMZ1Bi8jQMoGIGkwZ65mlFpXP22OPt4sQvBaIPgtEn/W8fUdmAj2tGUFIprm83B4ZOXOGAziRB1NGadZeYsmV2DLVOY1ZKjP0a0EIIYTMBoqm4/UzQwFKODkUoJQ5zbj1ikpsWV6N1Q1e8JcQoPSGU3izxY89mZDkdH9sxDFNpXasbfQZs0mafKj1Ws97IUVHIIF7f74HLYNxlNhN+MX9a7Gs1n3R4yQzC4UmFJoQkrPr8Wa8/WI7zDYR7/+ntXD6xj/9sPerX0Pwf/8XjptuRN2DD07iKAkhhBBCpi/GGPSEOmYooobSY/YYyeJM/FAo4rVAyCzFEgsEr/mSe47MBExnUPsTRoDSagQpWiAFAODMQsEMElOdE4J9dvUdJISQfJqqQ0lrY9xUqGk9t+6rdqBpRSnNrCPTiqLp2NU8iGcO9eD5o72IpIZmzpY7zbh1WRW2LK/CqvpzByiMMbQHErlZJHtaAmgPJEYct7DCmQtI1jb5UOG6uPJag7E0PvLwHhzpisBhFvGTe1dh/dzSi3ouMrNQaEKhCSE5mqrjiW/tR39bFFVz3bjzs1eBH2fDrvTp0zh7+x2AIGDeS9sgVVZO8mgJIYQQQopPGUwieWQQclsk12eEyaP3FsnhOQhesxGE+CyZgMScC0p4++XVY2+8tKgMPaVSs3ZCyLTGdIZUXEE6oY4acIwVfqhjhiIa9POE7cOVNzix4T3zUD3fO0mfJSEXT1Z17DozFKBE8wKUSpcF71pWiduWV+GqOi84DmjujxWEJL2RVMHz8RywtNqdKbflw5pGH7z2ievbFk0p+ItH9uP1s36YBB7f/7MrccsVVRP2/GR6otCEQhNCCoQHkvjd1/ZATmlYfWsjrr5jzrgf2/bhP0di3z6UfvrTKPv0pyZxlIQQQgghxcEYg9qXQPLIIJJHBqH0jry6EQB4lykvFDHnhSMWCG4znfQnhJAZRtcZklEZibCMeCiNeDiNRERGPCwjEU4jHjLuJ8IydH1yTp8JIg/JLEA085DMIiSzUHDjeQ5n3x6AkjbC+6YVpVh311x4K+2TMh5CLlVa1bCreRBPH+rBi0f7EE0XBiiypiMQlwseIwkcVtR6crNIVjV44bRM7ozTlKLhb377Fp4/2geeA75+1zJ8YG39pH5MUlwUmlBoQsgIp/f14YWfHQU44N1/cyVqF/nG9bjw08+g++/+DmJFBea9tA2cOPtLRxBCCCFk9mOMQemKZYISP9TBvMbjPAfzXDcsC30Qy6xGUOI1g5OE4g2YEELIuGmqngk/0khkA5C8ZXZ7MiqPaCZ9LiaLAMkyMtjI3sQxtp/reGEclSASERl7nm7BsZ3dYDoDx3NYurEaa25rgs01cVffEzLR0qqGnaeNGSgvHOtDLBOgWCQeK+u9uDrTtP2qeg8sRXidpWo6/nHrETy6rwMA8IVbFuET182h2cGzFIUmFJoQMqpX/vcEju3shs1lwvv/ae24Xlzpsozm6zZDCwZR+8MH4bzxxikYKSGEEELIxGM6g9weQfKIH8kjg9BC6aGdIgfLfC+sV5TCutgH3kb9NAghZDpKxmSEehNGCBIpDEOyy1RMOf8TZXAcYHWZYHebYXObYHeZYPOYjft5220uEwRxfKWuJ0ugJ47Xt55B66FBAIBkFrDynfVYcVM9JBMF+2R6Syka9rYGYDMJWFbjganIP09ZjDF88/mT+J/tZwAAH9/UhC+9a/ElNbIn0xOFJhSaEDIqRdbw2H/sQ7AnjvqlJbjtU8vHVUai/9vfhv9nD8G+aRPqf/qTKRgpIYQQQsjEYBpDuiVszCg56oceHSoHwUk8LIt8sF5RAssi32XRjJ0QQmYaxhgCPXG0HhpE66FB9LZEgHGcyeJ5zgg73GbY85a5cCSztDpNM+7kaNepIHY/3oz+tigAwO424ep3z8HCa6pm3OdCyHTx09fO4mvPHgcA3LOyFt+4ZxnEcfYEJjMDhSYUmhAyJn9XDI/95z5oio7198zDVe84f71Gub0dZ25+J8BxmPvC8zDV1U3BSAkhZGJokTSSR/2Qu2IQ3EYPArHUajRmdlBjZkJmI6bqSDWHkDwyiNQxP/TEUC1tzizAutgH6xWlMC/wgqcrcwkhZNrRVB3dp0NGUHJ4EJHBwibRTp8Fdk82+MiGINlgxAhHLHZpVveaYjrD6f19eOPJs4j6ja9PSY0d6++eh/qlJUUeHSEz0+/3d+ILjx+CpjPctLgcD35wZVHKhpHJQaEJhSaEnNPRHV3Y/uuT4HkOd39+FSoaz/+z0v7RjyG+axdKPv4xlH/uc1MwSkIIuXiqP4nkUaP8jtweHfM4ziRALLFALLFAKLEa6z4rxFILBBc1dSZkJtFlDelTQWNGyfEAWKZhLgDwNhGWJSWwLiuFZa4H3DQpB0EIIWRIKqag7agfrYcG0X7UDzk19HtcEHnULPSiaXkJGpaVwumzFHGk04um6Di0vRP7/9SKdOYigbrFXqy/Zx5Ka51FHh0hM8+2Y3341P8dQFrVsbbRh599ZDVck9yUnkwNCk0oNCHknBhjeP6nR3HmQD9cpRa87x/Xwmw9dzmKyIsvouuvPwPB58O87a+AN1GzOULI9MEYg9qfyPUpUHriBftN9U6Y53qgxxWo/iRUfwpaOH3u0g4CZ8xKyYUpecGK10InXQmZBvSUitSJAJJH/UidCIApem4f7zTBurTEmFHS5AYnUAhKCCHTTbA3jtZDfrQcGkDvmXBBU3arU0LDslI0LS9F7SIvTBYqoXguqbiCfc+24vD2TugaAzhg0dWVuPrdc+DwUshEyIV486wfH/vlPkTTKhZXufDLB9ag3Ek/RzMdhSYUmhByXumEgke/thdRfwrzVpfj5o8uPWeJGqaqaL7hRqj9/aj+zrfh3rJlCkdLCCEjMcagdMWMoOToINSB5NBOHjA3uY2GzktKILjNIx+v6lADKeM2mIQWSOUCFTWYArRzvETiAMFjzgtUrAWzVajcDyGTR08oSB4LGKW3TgcLflYFj9n4ub+iBKZ6F80WI4SQaUbXdPScCaMl058k3J8s2F9SY0fjslI0Li9FRSP9Hr8Y4YEk3njqDJr39QMABInHlTfWYeU7G2A6z8WShJAhR7vDuO/nezEYS6OhxIZfPXA16ktsxR4WuQQUmlBoQsi49J4NY+u3D0DXGa7/8CIs2Vh9zuMHfvAgBn/4Q9jWrEHDrx6ZolESQsgQpjPIbZFcQ2ctlB7aKXCwzPcaDZ0Xl0CwX/wUaqYzaKE01EAmRPEbgYqWWeZfzT4a3ikZgYrPArHMBvNcN0y1TnrjTy4aYwxaMA25PYJ0WwRaRIZUboNUZYdUZYdYYp3V319aVEbymDGTLH0mDOhDb2HEUmsuKJFqHNSniBBCppl0QkH70QBaMmW30nl9pniBQ80CDxqXl6JxWSlcpdYijnR26WuJYNfjp9HTHAZgzNxZs6UJSzZVQ6Dm1oSMS5s/jg8/9CY6AkmUOc145IG1WFxF55tnqmkXmvzwhz/Et771LfT29mLFihX4wQ9+gLVr14567E9/+lM88sgjOHLkCABg1apV+PrXvz7m8cNRaELIhTnwfBte33oGosTjPV9ajZJqx5jHKr29aL7hRkDXMeeZp2GeO3cKR0oIuVwxVUf6bNgISo75oceU3D7OxMOy0GcEJQt94KegbANjDHpsqMxXbhlIQfMnCxpO5+OsIizzPLAs8MK8wAtxlNkvhGQxRYfcHYPcFoHcFkG6PQI9qox5PCfxkCrtuRAle+PNM+uKUj2tQu1PQhlIQB1IQulPQM2s55fTkyptmaCkFGKFjYISQgiZZsIDiUzZrUH0nA5Bzwu7LXYJDVeUoHF5KeqX+Gj2wyRijKHl4CBe33oGob4EAMBTYcO6O+ei6cpS+vtJyDj0R1K49+d7cKI3CqdFxM8/sgZrGn3FHha5CNMqNHn00Udx77334v/9v/+Hq6++Gv/93/+Nxx57DCdPnkR5efmI4z/0oQ9hw4YNWL9+PSwWC77xjW9g69atOHr0KGpqas778Sg0IeTCMJ3hjw8eRMexAHzVdrz3i6shnqOsTMenPo3YSy/B++d/jsp//IcpHCkh5HLCFA2pU0Gj9NbxAFhqKIjgLCKsS3ywLi2FZYEHnDS9SmHpCcUo+ZUJVJSeOFKnQwWfAwCIFTZY5nuNEKXJNe0+DzK1tEga6bYI5LYo5PYI5K7YyBJxAgep2gFzvROCxwK1PwG5JwalNwGoo89+EnwWSFV2mHJBigOC11zUkySMMehRGUp/EupAIhOMJKH2J6BF5DEfJ9U6ckGJRFciE0LItKLrDH1nw2g9PIiWQ34Eh/WX81bajNkky0tROccNfhbPjpyONE3HsR3d2PtMC5KZizCq5rmx/p55qGxyF3l0hEx/4YSCj/5yL/a1BWEWefzPh1fihkUVxR4WuUDTKjS5+uqrsWbNGjz44IMAAF3XUVdXh7/+67/GF7/4xfM+XtM0eL1ePPjgg7j33ntH7E+n00inh0pzRCIR1NXVUWhCyAVIRGT89qt7kIzIWLqpGps/tGjMY2M7dqDj438B3unE/NdeBW+lkxaEkIlxzobODmmoofMcN7gZVlKAaQxyZxSpU0GkTwchd0QLm9CLPMxz3JkQxQOxnK6cn82YpkPpiWdmkEQht0UKS81l8A4JpnoXzA1OmBpcMNU4Rg3XmM6gDhoBnXGLQe6JQx8jgOAsQm5WiqnKYYQplbYJD+6Yphvh4UACSiYUyS5ZWhvzcbxTglRmg1hmhVhug1Rmg1Rph+AyTej4CCGTLxVXkIopcPjMEOnigFknnVTReTyA1kODaD3iRyp/NjDPoXq+O9efxFNOfQCmAzmp4sALbTi4rQNq5rX2vFXluObOuXCX0Xt7MvnklArJJMzIsrJJWcOn/u8AXj7RD4Hn8K33LMfdK2uLPSxyAaZNaCLLMmw2G37/+9/jzjvvzG2/7777EAqF8NRTT533OaLRKMrLy/HYY4/htttuG7H/K1/5Cv71X/91xHYKTQi5MB3HA/jD998GGPDOj1+BeatGzgQDAKbrOHPzO6F0dqLqa1+D5567p3aghJBZRYsrSB3zG0HJaA2dl5bAuqx01jV01hMKUs2hXIiihQtPbgtuM8zzjVJelnke8LaL789Cik+LK0aZrUw/EqUzNrIvDgdIlXYjHGlwGbNJfJZLCs+0uAKlJ5YXpsSh9CdGzmDJfHyx1JqbjSJVG7NTeKfpvGPQU2peKa28klr+VEHvkREfr8SaF4xklqVW+n4nZIZjOkPnySCO7ezG2YMD0FXj94DNbYKrxAJniRWuEgtcpVY4SyxwlVrg8FogiDPrgojLjZLWMNgRRX9bFP3tEfS3RnPlnrLMNhH1S0vQtLwU9Ut9MNPv82krFkzhzT+24MTrPQAzesssu64Wq29thMVRnP83pjOkkypScQVWpwlmKts24zHGEPWn0N0cQs/pELqbwwj1JdCwrAS3fWpFsYd3URRNxxd+fwhPvNUFAPjn25bgoxubijwqMl7TJjTp7u5GTU0Ndu/ejXXr1uW2f/7zn8err76KN99887zP8Vd/9Vd4/vnncfToUVgslhH7aaYJIRPnjSfPYP9zbTBZRbz/H9eM2YRv8Kc/xcB3vgvL8uVo+t2jUzxKQshMp4XTSB7NNHRuCRfMuBDLMg2dl14+DZ0ZY1D7E0idChohSkuksNQSB5jqnLleKNRQfnpjuvH/mc70IpHbo1AHkyOO46wizPVOmOpdMDU4YapzTkn/EabqUAaSw8KUGPT46L14eLtohCiZmSmC02TMasnrOTLWjBbA6Dsklg2FImKZDVK51WhcTydICZlVYsE0TrzejWO7ehD1p3LbBYmHNjwoHobjALvHnAlRMmFKJlxxllrg8JjBz7BZpjOZqmjwd8bR3xbJ3KII9sQx2tkjd7kVjctL0bSsFJXz3NRgfIYZ7Ixi9xNn0HEsAAAwWUWselcDll9fe9GzwxhjUGU9N9MslTCW6biS2aYa27L74wrScRXphJL7HuM4oKzeidpFXtQs9KJqrgeSmWarTXdMZwj0xNF9OoSeZiMkiY8ymxoAPvSv18BTMTNnoOk6w1efOY6f72oBAHz6+nn43M0LLov3rjPdrAlN/vM//xPf/OY3sX37dixfvnxcH5N6mhBy8TRNx5PfeQu9Z8OoaHLhrr9bOeqLXtXvx+nN1wOKgsbHfw/r0qVFGO3E0mUZejgMLRKBFo5Aj0Zy61okDD1s3GfpNGxrVsN5yy0Qvd5iD5uQaY8pGuTuOOSOKJTOKOTO2IgTyFK1HdalpbBeUQKpwl6kkU4fTNGQbonkQhS1v/AqzmI1lGc6g55UocdkaFHFWMaUEfdZWgNn4sGZRfAWAZxZAG8WwFlE8MPWObMA3iJmlgI4s2g8dga94dCTKuSOqBGStBshyWilp8Rya6bUljGTRCy1TpvwK9tjRM6fkdITG9F8/VxyJbXKjbJaUiYgEdznn6lCCJm5NE1H22E/ju/qRtsRf+6Ep8kqYsHaCizZUI3SOgdScQVRfwqRwRQi/iSigylE/ClE/UlE/Knzhio8z8HhM+fClOHhit1tmja/U2caTdMR6IrnwpH+tggCXfGCxu1ZdrcJZQ0ulDc4UZ5ZWp1UOnE2aD/mx+7Hz8DfFQMAOH0WXHPnHMxdWY50Qs2FG/m3dC4UKdyfjqvQxui1Nh6iiYcqFz6eFzhUNLlQu9CL2kVeVDS5aXbaNKCpOvrbopmAJITeM2GkE4UX4vA8h7IGJ6rneVA134NDL3eg80QQq7c04urb5xRp5JeOMYYfbT+Dbz1/EgDwwavr8e/vvgIC/S2a1qZNaHIp5bm+/e1v46tf/Sq2bduG1atXj/tjUmhCyKWJ+JP43df2Ip1QsfKd9Vh317xRj+v67OcQefZZeN73PlT928gSeVONMQaWTBpBRyQCPTIUeuiRsBF+RKND65G8MCQaBUulzv9B8gkC7BvWw71lCxw33gTBQSd6CWEag9IXh9IZg9wZNYKSvjgwynsmU4Mr16NE9I2cSUqGqKE00qeNAGXUhvLlNqOM10U0lGc6g55QoMcUaFHZWGbDkGheKBIzjhmz1NJE4pAJWvLDlLxwZUTokglbJB7QGJimg6kM0HSwzH2ome0aA9RRtucfn9mffz/3vPmPzz5mlBN9nEmAqd5p3BpcMNc5Z2TJKaZoUPoSuSBF7olBjysQS4ZCEbHcCqnMBp5KaBByWQn1J3B8dw9O7O5BIm+2WfV8D5ZsqMKcleWQTOP7e8QYQzKqIDKYNIKVTJASHcwsA6lcia+x8CIHp8+SmZmSmaFSYoHTZ4XTZ4bNbabG4wB0TUewN5ELR/rbovB3xkY9wW1xSLlgJBuS2D1Tc6EGKQ5dZzj5Ri/e/MPZMWcHXAhe4GCxS7A4JFjsEsw2Mbee226TYHGIMNuHtgsij1gwja6TAXSeDKLzRBCxYOF4RIlH1XwPahcaM1HK6p30Mz4F5JSK3rNh9DSH0X06hL7WyIjQWzQLqJrjQtU8D6rmeVDR5Cr4e3BqTy9e/PkxuEot+PC/r5vxF9f835vt+McnD4Mx4NZllfiv918Js0izoqaraROaAEYj+LVr1+IHP/gBAKMRfH19PT796U+P2Qj+m9/8Jr72ta/h+eefxzXXXHNBH49CE0Iu3Zm3+vHcj48AAG7/6xWoX1oy4pj4nj1ov/c+cDYb5r/2KgSHo2A/03UwWTZu6TSYLENPy2ByOrdNl2WwdOYYOQ09nd03tG3ocdnnyTwulYYWjUCPRHNBCRRlxDgvCMeBdzohuFwQXC7wbhcElxuCywneZaxD1xDd9hJSR48OPcxshuP66+Hacisc114L3kxvJsjsxxiD5k/lwhG5Mwale5Q+Dcg0s651wlTrgKnOCanWCcE+804gTwcX0lDe1OAES2nQYkNhiB4dtowr455JkMVZRQgOCbzDBMEpQXCYwDuGlrxFhK5oYGkNLKVBT6vQU8Z9PaUay/Sw+ykNLK1e8FimC6HEAnO2zFa9C1Klna54JjNO9qQpXblLxqIqGs6+NYBju7rRdTKU2251Slh0TRUWb6iCt3LiLyRiOkM8LBszVPypgnAl6k8hGkiDnSfU5wUODq8ZTp8FTp8FDl82VMnen32N6pnOEOofCkgG2qIY6IiOuIIfMHqRlNUPzR4pb3TB4TXP+JOZ5OIosoaDL3XgwPNtUFIawAFmq5gLOsyZoMOSF3TkByFmu7FPMgsT8j3EGENkMInOE0F0nQyi82QQyWjhe3+TVUR1JkSpXeSFr2r6vRbTNB3RwRRCfQkE+xIIZW7h/gQ4noPNZYLNbYbdPbS0u82wZZZWpzTlZQoTERk9zSEjJGkOYbAjOqJMn8UhGbNI5rlRPd+D0lrHOceppDX8/PM7oaY13PP5Vaic457kz2LyPXu4B//fb9+GrOnYMK8EP/7z1XBMQdldcuGmVWjy6KOP4r777sOPf/xjrF27Fv/93/+N3/3udzhx4gQqKipw7733oqamBv/xH/8BAPjGN76Bf/mXf8H//d//YcOGDbnncTgccAw7KTsaCk0ImRiv/uYkjrzaBatTwvv/aS3sw8q/MMZwdsttkM+ehVheDvD8UDgiy5ceYFwsQcgLPdwQnE4Iblcu9MitO11D6263cbzDAY4f34uQdEsLIs88i8gzz0Buaclt5x0OON/xDri2bIH9mqvBifSHkswOWiQNuSMzgyRTZoslR/ZA4MwCTDVD4YipzgHBTW+6J8v5GsqPCwfwNtEIQbJhiEMC7xzlvl2atD4UjDEwRR81TMmFLulh9/NDGEUDJ/CAwIETeXACBwjGsuC+yBnHZZacwAHi0HEQMtuzx+Xdz3++7HbeIszIWSSE6DrDQHsUnScC6DoZRE9zGIwBy2+oxap3NVIDXpLj74rh2M5unHyzd6jsCgfUL/FhyYZqNC4vLWrYpms6YqF0rvxXtuRXNDNLJR5Mj1pqajiry5QLUYxAxZy3boHJKk7b1zPGSeVUbvbIQFsE/e1R44T3MJJZyAQkmZCk0QlXqXXafm6keFRFg5LWYLZJ02oWB2NG34yuzCyU7tOhESWhrE4JNQu8uZ4o7rKp+R5njCERkXOBSKgvgVB/EqG+BCIDyXH9LhoLxwFWpykXomSX2ZAlt91luqjfydnfI9lSWz2Zpu3DOUssBSGJp8J2wV/bbQ8fw8k3e3HFtTW47oMLL3is09Gu5kH8xSP7EJc1LK914+GPrEGJgy6onW6mVWgCAA8++CC+9a1vobe3F1deeSW+//3v4+qrrwYAbN68GY2NjfjFL34BAGhsbERbW9uI5/jyl7+Mr3zlK+f9WBSaEDIxVEXD7/9zP/xdMdQu8uKOz1w54kqN4G8fRe84fi7BceDMZnAmEzizCbxkyrtvBm8y5dazx3AmE3iTOW+7BD6732QGZzZDcDoKQw+nC7z9wv9gXwrGGNLHjyP89DOIPPss1N7e3D6hpASuW26Ba8sWWK+6kt6IkBlDTyiQu7IltozlqI2eBQ6magekWocxk6TOOa36NFxuhhrKh5A6HYTaGy8MQ5ymoVkhzrzZIXbJCA4IIbNa9iRT9krdrlMhyKOE34Bx1eja25qwZFM1NXW+TMkpFc37+nF0Zzf6WyO57Q6vGYs3VGPx+io4Z0h5TV1niIfSiAZSiAWMICUbqGSXo82+GE6yCAUhSmHAYoHNNXZfFV3Toco6FFmDKmvGetpYV2TdWKaN7cY2Derw+6M8Jn99tNmaosSjtM45VGKr0QVPuY1eq5FZRdcZBjuiub9v3c2hET/TDq/ZKOW1yIvahV44vJf2+0tJa5lAJFEYkPQlII8SVmaJJh6eCptxKx9aggMS4TTiYRnxcBqJsFxwPxmRR8zwOBeLQxqaseIqDFVyIYvLhPBAcigkOR1CfJQLsEpq7Kia60HVfDeq53ku+WsHAB3HAvjD99+G2S7i/m9snDWzXA92hHD/L/YiEJcxp8yOX330atR4rMUeFskz7UKTqUShCSETJ9ATx2P/sReqrOOaO+dg1S2NBfsZY0gdOQqmKEYYYioMQzjJBN5sAsTpe1XWRGK6juSBAwg/8wyif3oOWiiU2yfV1MB1661w3bYF5gULLouvB5kZxtOoHQDAGb0zsuGIqdZhlCCaJS9wCSFkNsqWM+nMljMZFoCbrCJqFnhyV+JG/SnsfrwZwV7jylJvpQ3r7p6HxmUl9NrlMsAYQ19rBMd2duP0vn6oaePEH89zaFpRisUbq1G32DetrjifCIwxpONqYZgyLFxJxc4/iz5bAsxkFUcEIOfryTIReJFDaY0D5Q0ulDU4UdHogrfSNuXlfAgpNk3V0dcayc1E6W0Jj/gZdJdbc/1Qahd6YXWaRjyPrjNE/UmE+pK5QCRbVutcPV84zpiN4amww1NhhbfCBneFDd4KG+xu80WFlrrOkIzKSOSFKgXLiIx4yFjq2sX/vuEFDuUNTlTN86B6ngeVc92wTEJZZV1n+OUXdyERkXHrJ5ehaUXZhH+MYmnuj+Heh95EdziFKrcFv/roWswrdxZ7WCSDQhMKTQiZMMd3d+PlR06A4znc9bmVqJo78+tNTgWmKIi//joizzyD6IvboCeGprWa5s2Fe8sWuLZsgam+voijJFON6QxMyfR5SKlGSaGUUVoIug6mw2i0rTOjNnfeMreuMeMqo7z7YHnHa5njGQq3sWHPoTOwlAalf/RG7YLPYvQgqXXCVOuEVOMAb55dtb4JIWS2iYfTuXrvnSeCiPpTBftFiUfVPDdqF/nGbJyraTqO7ejGnqdbcieKaxZ6sOGe+Sirpzf9s1EqpuDkm704tqsbge54brunwoYlG6qx8JpK2FwjTyheThRZM2ap5M9QCeaVAAvJ5+2rAgDgAMkkQDQLkEw8RJMA0ZRZNwsQJQGSmc9sEyBmjpHMeeuZx4smPnOMsZ5toE0IKaTIGnqbw7kLCAbaIiNmbZTU2FGz0AtB5Id6jQwmzxl4WhxSQSCSnTniLrNCkIrzs8h0hlRCyYUp8ZCMRMSYsZLIC1niYRmaokMyC6jMNG2vnudB+bCm7ZNp5+9P4+C2DsxdWYZb/mLZlHzMqdIdSuLen+9Bc38MHpuEf7x1MZZUuzC3zAHLLOufNdNQaEKhCSEThjGGF39+DKf39sHhM+P9/7h2Uq40mM30VAqx7dsReeYZxLa/CpbX78WyfDncW26F813vglReXsRRknMZ0Wsh09zaCD/y+yqoQ4FIfs+F7PHp0UsnFBs1aieEkJkpnVDQdSqUC0mCPfGC/TzPoWKOK3clbWWTe9wnctJJFQeea8XBlzqNJvEcsOjqSlz97rlweKlG90zHdIauU0Ec29mNM28P5E4MihKPuavKsWRDNarmuWmG0Tjpmo54WEbUn4Iqa5lQZGToIYg8fU0JKbJ0UkX36RC6MjMx/V2xMY8VJB6ecutQKa3KoXBkJp8XYYxBTmmQTHzRZqMNdETxu6/thSDyuP+bG2CeZT0Cg3EZH/nFXhzsCOW2cRxQ47FiXrkD88ocxrLcgbllDnjtl/fFCVOFQhMKTQiZUHJSxaNf34vIQBJzrirDLX9xBb3Yv0haNIroi9sQefppxN94A9Azl/hzHGxXXw3XllvhuvlmCG6a0TOVmMaQPOZH6rgfekLNa0I91Iwal9C0bwQe4C0iOIsI3iyAMwvGNG2BM5Y8Z/QCEox1jucADsZ6dhuXOS7vMRyHcz9H9jg+81wiD6nKTo3aCSFkhlBkDT3NoVzJkYH2aOHVshxQVuc0QpJFXlTNdcNkubSG7pHBJN546ixO7+0DYJxUv/Id9bjq5vpLfm4y9eKhNI6/3oPju7oRGRyaiVRa58DSjdWYv6Zi1p24IoSQc0lGZXSdCqH7VBDguEzPESs8FTY4vRbqATRJGGP47b/vQaA7juv/fBGWbKgu9pAmXDyt4nsvncaBtiCaB2IIJcYu9VhiN2FuJkSZV+bIrVe7LfRefQJRaEKhCSETrr8tgse/uR+6xnDdny3AFdfVFntIM546OIjIc88j8swzSL711tAOSYJj40a4btsC5/XXg7fZijfIWU6Lyojv7UX8zR5oozS9G4EDOLMA3iyCswjgLSJ4ixF6GCFI3j5zZp9l5PGgqwwJIYSMw4i67GfDI2qVeyttuZCkZr4XFsfknPDubQlj9++b0XMmDACwuUy4+t1zsGhd1azrcTHbpOIKOo4HcGpPH9qO+HNlpEwWAQvWVmLJxmoqvUYIIWTK7X+uFW88eRbV8z2463Mriz2cScUYQyAuo7k/huaBmLHsj+FMfwzd4dSYj7OZBMwtmJVix7xyBxpK7JCoZ9UFo9CEQpMR9JSKwV8chevGeljme4s9HDJDHXypAzsfOw1B5PGeL65Gaa2j2EOaNeTOLkSefRaRZ55B+uTJ3HbOaoXzhhvguvVdMDU0QHC7Ibjd4Ew0dfNiMcYgt0cRf70bicODRk8QALxdgm11BaRS67AQZGidkwS60ogQQsiE0nUGOaEiFVdyt2BPAp0nA+huDueacWc5vGbULvIafUkWeKe0VBZjDGffGsDurWcQGUgCAEpqHNhwzzzULfFN2TjIuWmajr6WCDqOBdBxPID+1sL6/VXz3FiysRpzV5ZPWe16QgghZLhoIIVH/mE3AODer6+H02cp8oiKI55WcXYgjuaBaC5Mae6Poc2fgDpGxQuR59BQYsuV98ov9WU300zgsVBoQqHJCKE/tSD2aicAwLaqAp4tTeBp2jW5QIwxPPujQ2g97Ie30ob3fmkNJGoMPeHSp08j/MwziDzzLJRePwR3HTirF1r/UbB0FADA22zgPW4Ibk8uSMndPMaSz23zDG2zXJ4vQgCAKRoSBwcQe70HSl7dWlO9E/Z11bAtKwVHzTMJIYRcpGx98FTMCD7SuRBEHXa/cFs6qZ6z35XVKeV6ktQs9MJdZi36bEVN0XH41U7se7YV6YQKAKhfWoL198xFSfX0vqiGMQZ/VwztxwJIx1WU1TtR0eSCwzuzS1WGBxJoP2qEJJ0ng1BShWGbr9qOhitKsHh9FbyV9iKNkhBCCCn05HcPoOtUCNfcOQerbmks9nCmFUXT0eZPGDNSMrNTssuErI35uGq3BXPLHbiixo0v3LJoCkc8/VFoQqHJCHpaRfi5VsTf6AGY0fTXc8dcWJeVzug3B2TqJWMyHv33PYiHZSxaX4Ub711c7CHNCowxaME0lO4Y5J44lO4YlJ44tFB66Bg1DfnMNsinXwDU5EV9HM5szgQrnlzAUhCuDAteBF8JpIqZ3aBeDaQQe6MHiX290DMndiBysK0oh2NdFUy1VI6CEELISKqiIdibMEKOmIJ0QjUCkYSCdGxY+JEw1tkl9L8yWQSY7RIsdgkOrxk1C4yQpKTaPm1nOabiCvY904rDr3ZC1xg4DliysRprb58Dm2v6zIqNh9PoPB5A+/EAOo4HkYyMLMlpdZlQ0ejK3cobndO6t0c6qaLrRND4nI75C/qTAIDFIaFusS93m8oZSYQQQsh4HdvVjVd+dQLeKjv+7F/W0jnKcWCMoSecGpqVMmCU+TozEMNgbOg1ztJqF575zKYijnT6odCEQpMxpdsiCD5+Cmq/ccLVstgHz53zILrpRTQZv65TQTz1X2+BMeCm+5dg4dWVxR7SjMJUHUpfIheMyJklS49+pYDgNYOTBKj9CQAAZxFgu8oJcz2DHotAC4ehhUPQwmHo4TC0UDizLXMLGfugjX0lwrmYFy2C56474brtNoglJRf9eU8lpjOkm0OI7e5G6mQgdwWv4DXDcU0VbKsrIdin74kQQgghxRPxJ3H0tS4c29mDVHzshp1jEU08LHYpF4BY7OKw+yO3me0ihBlclzrUn8DrW8/g7FsDAADJLGDlLQ248sY6iEUo/6QqGnqaw+g4ZgQl/s5YwX7RxKNmoRd2txn9bRH4u+KjBl6eClsmQHGhosmF0hoHBKk4/0+6pqO/LYqO4wG0Hw2grzVSMGZe4FA11426JUZIUlbnnLZhGyGEEJKVTqp4+O93QlN1vO8f1lCPrUsUSsi5MMUiCbjzqppiD2laodCEQpNzYqqO6PYORF7pADQGzizAfUsj7FdX0QtrMm57/ngWe59phWQWcN2fLYCv2gFPhW1Wl+tS0hqi/hQ0TQfTGZhuJPxMZ5kloOfuGyfuWVIFAilwwRQQTIEPpcFFZXCj/OZlHKA5TFCdJqgOCYpdgmKToAs8JBOPRo8Zydc6cqGn4DLBeWM97KsrwJ3nRAtjDHo8nglUQka4kgtVhoUs2f2hMNRAYChsEUU4rrsO7jvfDed1103Lvip6UkV8fx/ib/RAHRyajWOe74FjXTUsi3z0e44QQsgIjDF0ngji8PZOtB4azPV/MNtF2N3mXNhhzoQd57ovSrP3tdD5dJ8OYdfvT6O/zSgn6vCacc2dc7FgTcWk/v1ljCHQE8/18Og+FYKq6AXHlNU7jVkXS3yomuMuCD8UWcNgexR9rRH0t0bQ1xoZMXMDAHiRQ2mt05iN0mTMSHGXWSftc4sMJtFxPICOY0bJrWwptCxPhQ31S4zPqXq+ByYL1TAnhBAy8zz3kyM4c6AfK26qw8b3zC/2cMgsRqEJhSbjovTFEXz8NOR2402NqcEF7z3zIZXbijwyMhPomo6n/vttdJ8OFWx3eM3wVNjgrbDBU2mDp8K4Ob2WGXGyWpE1hPuTCPcnEB5IItSfyN2Ph0eWcshn4wG3wBXcrGN8zrLOENaGbhGNIaqfs6Q5fNV23Pm3V0E/GURkW1uudJdYaoXrHQ1Gub0J/hproRDCzz6L8NYnkTp8OLdd8Hjguv12eO66E+bFi4s+hVbpjSP2ejcSB/rBMidJOLMA++oK2K+pglRGv9cIIYSMJKdUnHyjF4e3dyLYm8htr13kxbLNtWhcXgp+Brx+mU6YznBqbx/eePIMYkHjtUp5gxMb3jMP1fO9E/ZxkjEZnceDaD/mR8exwIjXaTa3CfWZkKR2ke+Cy4Ulo3JeiBJFf2tk1JlHZptozETJzkhpdF10aTI5paLrVCgX/oT6EgX7zTYRtYt8qF/iQ+1iL1wl1ov6OIQQQsh00nJoEM/+6BBsLhPu+4/14Gfw7FsyvVFoQqHJuDGdIf5GD8LPtYLJGiBwcF1fB+fmOmqITM4rGZWx99lWDHZEjZrfsbFLWIgSD3c2TKmwwZsXqEz1VXGKrCEyLBAJ9ScRHkgintdDZDQmqwiTiYOD4+DiACcABxgcDBjrs0jxHBIij4TII2nikZQEqCIPjufB8QDHceB5LrfODVvnOaD1sB+JiIzKOS7c8TdXQRQ4xN7oQfSVDuiZN/BStR3udzbCvMA7KSFGurkZ4SefRPipP0AdGMhtNy9cCPedd8J9+20QS0sn/OOOhWk6kkf9iL3eDbklktsuVtjgWFcN21Xl4GfxzCdCCCEXL9gbx+FXu3Di9Z5cw2zJLGDRNZW4YnMtfFXUKPtSqbKGgy93YP9zbbmvcdOKUqy/ex48FRd+MYOm6ug9E8708AhgoCNacLWJIPGonu8xZl4s9sFXbZ/Q10OMMUQGk0aQ0hJFX2sYA+0xaKo+4linz5ILUCqaXCird446G1vXGQY7orkG7r1nwtDzSm5xPIfKOS7ULfahfkkJyhqcFOIRQgiZdTRVxy++sAupuILbP7MC9UtmRllwMvNQaEKhyQVTQymEtjYjdTIIwDjp6L17PswN9DUk45eKKwj1JRDsTSDUF88sjRkbujb2rxq72wRPpT0XqHgqjXDF6bv42SmqrCE8kES4P4nQQKJg9kj2qsexmG0i3OU2uMus8HpN8EgC7JoOKaZAG0hAHUgAI98fAyIHqcIOqcoOU7UDUrUdUqUd/ASEQv6uGLZ+5wDSCRX1S3249ZPLIYg89LSK2I4uRHd05XqimJrccN/SOGk/v0xVEd+9G6GtWxF76WUwOXNlpyDAce21cN95JxzXbwY/SeW7tKiM+Js9iO3phZ5t5MoD1qWlRmP3JnfRZ74QQgiZfnSdoe2IH4e3d6LjWCC33VNhw7LNtVh0TSVMVipvNNESERl7nm7BsR1dYAzgeQ5XXFeDNVuaYHGM3V+MMYZQX2KoPNWpENRh/d9KauyoW1KC+sU+VM1zT3n/FE3TEeiKo681gr6WMPpaowj2xkdMHeY4wFftQEWjExVNboADOo4H0Hk8OGL2irvMmutLUrvQS9+ThBBCLguv/uYkjrzahQVXV+Ad9y8t9nDILEWhCYUmF4UxhuShAYT+cNa4cp0DHOuq4XpnA3gzvVgnF0/XdEQGU0ag0pfIBCtxhPoSSEbHnp0iSDw85dbMzBR7bmaKt8IGk1WEqgwFI0PhiBGQjCsYKbMa4Ui5FZ4yK1xWATaFAYEklO44lJ4YtDFKcvE2EVImGDFVOSBV2SGWWc/bW+RS9J4N46n/fguqrGP+6nLc9MDS3NWGWlxBdHsHYq93A6rxa92y2Af3OxshVU7e1bJaOIzIn/6E0NatSB08lNsuuN1w3XYb3HfdBcvSJZccYjDGILdFEHu9B8kjg0AmhOMdEuxrK2G/ugqi23xJH4MQQsjslIorOL6rB0de6xzqU8EBjctKsXxzLWoXeWdECdGZLtAdx+4nmtF2xA/AeC22+tZGLLuuNtdfJBVX0HkimAtKooHCviJWp5TrS1K32Af7NPzbLydV9LdH0dcSRn+r0SflXDOZTRYBtYuGPid3GZXcIoQQcvnpPRvG49/cD9HE4/5vbqQ+XWRSUGhCockl0eIKws+cReJAPwBA8JjhuWserAt9RR4ZmS70tIrUySCSR/1Inw2Bt4gQvBaIXjNEnyWzboHgNYO3S+c8YZ5OKEaQ0jsUqIT6Egj1J6CrY/96MttFoxnmOX6DmawiPOWFwYi73AZ3iQV8TIbaE4fSHYfcE4PSHc/N1BhOKLEYM0eqhmaR8C5TUWYztB/145kfHYKuMVxxXQ2u/cCCgnGooTSiL7Ujvq/X+NpwgO3Kcrhuqoc4yXWv02fPIrz1SYSfegpqf39uu3n+fLjvusso31VWdkHPqcsakm8PIPZ6N5SeeG67qcEFx7oqWK8opVKChBBCRjXYGcXhVzpxak9frim42SZiyYZqXHFdDVyldHK6GDqOB7Dr983wd8UAAK5SC+ZeVY7u5hD6WyPIf3fKixyq5g6V3CqtdczIgCsWTOcazPe1RqBrOmoWelG/2IeKJhfVbieEEHLZY4zhf//lDUQGkrjp/iVYeHVlsYdEZiEKTSg0mRCp00EEnzgNLXPFvvXKMnhumwPBMTkld8j0psUVpI4HkDw6iNTpYG42w/lwEm+EKD4jRMmGKcbSAt4mjho+6DpD1J/MlfjKBiuhvgQSkaHZH7lgJBOI5IckFrsEltKg9MQgd8ehdMeg9MSh9CdyMxUKCBykyskprzWRTu/rwwsPHQUYsPrWRlx9x5wRxygDCUReaEPy8KCxQeBgX1sJ1w31EJyT+zPMNA3x3a8jvHUrotu2FZbv2rgR7rvuguOG689Zvkv1JxF7owfxfX1gSdXYKPKwXVkGx7pqmGock/o5EEIImZk0TcfZtwZweHsneprDue0ltQ4sv74W89dUQJriEk5kJF1nOPF6D978w1kkhs3q9VbaULfE6OFRPd8zai8QQgghhMw+e/54FnufaUX9Eh9u/8yVxR4OmYUoNKHQZMLosobIC22I7eoCmFGSyH3bHNiuKqeeAZcBLZJG8qg/N6Mkv4+HWGKB5YpSWBf6wHQdWjANNZCCFkxBDaahBlND/SbOgTMLEL3mvNkplrz7ZvC2kbWu00kVUX8KdrcJFocxk4UxBi2cNspqdccg9xhLbYwyXZxFhKnabpTYqrLDVOOY9PJaE+nIa1149f9OAgA2vnc+VtxYN+pxcmcU4edbkT4dAmCEWI6NNXBeWwt+Cmpka5EIIn96DuGtW5F8++3cdt7thnvLFrjvuhPmxUug9iUht0cgd0Qhd0ShDiRzxwo+CxzXVMG+umLU7wdCRsMUBXo8Di0Wg569JRJgmgYwBug6mK4DOgPYBa7reuZ+3rqmjziGscw6z0FwOCG4XeBdLgguNwS3C4LLBd7tBm+f2GbFhFyO4uE0ju3sxtHXuhDPnITneQ5zVpZh2eZaVM2lflfTkZxScXh7J0K9CVTN96BusQ9On6XYwyKEEEJIEYT6E/j1v7wBjgPu+88N07IMJ5nZKDSh0GTCyR1RBB8/DaXXKI9jnu+B9675EOlNzayjDiYzQckg5PZowT6pyg7r0hJYryiFWGE778kHpuhQw2logRTUYMoIVoLZYCUF/Rz9TLI4izBKmGIB75SgDg71HlF64tAT6qjPIXjMkKodRkhSZcwgETzmGX/yZN+zrXjzD2cBADd9ZDEWXlM15rGpMyFEnmuF3GH8n3JWEa7NtbCvqwY/RVfcps+2ILT1SUSffxVMdUDwNYH3zoHgbQDHjwxDzAu8cKyvhmUB1Zq/nDBNy4UcWiwOPRbNrMegZ+7n1qNR6PHMvmjmMXFjnaVS5/9g04UgQHA6wbszgYrLNSJgGRG2ZLfb7eD4mRH2EjLRGGPoa4ng0CudOHOgH3pmFqnVZcLSTdW4YlMN7B56s00IIYQQMlP8/hv70NcSOefFoYRcLApNKDSZFEzTEX2tC5GX2gCVgZN4uG5uhGNDNZ3QnMEYY1B6E0gdHUTyiD8XjGWZ6p2wXlEK69KSCe+JwRQNajBdMDslu64FUtDj5w9VCvAcpHKbUVarKtuk3T5rZycwxrDrsWYcfLkDHM/hXZ9Yhqblpec8PnXMj/DzbVD7EwAA3mmC66Z62FdXTMosGz2tQu6MQW6PZmaRREYNy5gchxZqheDUYFszH65b10PyUQmu2UYNBhF7ZTsSB/ZDj0QzAUjcCEmiUWjxOFgiMaEfk7NYwDsdEOwO8DYbIGZKAvI8wPOF6zwHcHzmPgfuQtd5PvP4/HUe0DRo0Si0SBh6OAItYtz0cBhMucDfc8PxvBG4uFx5YYsRvIilJbAsXw7bVVdBcLsn5gtKyDSgKhqa9/Xj0CudGMi7wKNyjgvLNtdi7spyCNTvihBCCCFkxjm8vROv/fYUyuqdeN8/rCn2cMgsQ6EJhSaTShlIIPjEacgtEQCAVOuA954FMFXZizwygKm6MfugNw51MAnBbcwwkCps1Cw6D9MZ5M4okkeMGSWaP++KbB4wz/HAekUJrEtKILiKd4WmLmtDIUowb7ZKIAUtKkP05TVor3ZAKreBky6v/2emM7z8yHGceKMXgsTjjs+sQPV873kfk3irH5EX26CFjPJlYokFrnc0wLq87KJDUKYzqP0JpNsjuZBE7U8YDenz8ZxREq3OCbFMgnxmH6LPPYHkgQNDh7hccG25FZ6774bliitm/Kygy5nS3Y3oSy8jum0bEvv2AZo2rsdxJhN4pxO8w24EHk4neIcDgsMO3mGs8w67ERjY89YdjsxxDmMWhjS9Q1M9lYIWjkCPhI0wJRwxwpXcemZfLmwZCl5YevTygyNwHMzz58O6aiVsq1bDtnoVpEpqrEhmnmgghSOvduHYzm6kMhdWCCKP+WvKsWxzLcob6LU/IYQQQshMlozJ+MXnd0HXGf7sX66Gr7r45xrJ7EGhCYUmk47pDPF9vQg/2wKW0gCeg/O6WrhuqJ+Sk9ZMZ1ADKai9cSh9CSh9cSi9CaiDSaN+/HD5MxCyPSyqHVPS02G6YJqOdEvYCEqO+Qv7jYg8LPM9xoySxb5ZOzNjttI1HX/68RG0HhqEySLgzs+uRFm987yPY6qO+Js9iLzckZvVI1XZ4bql0SiJdZ6gQovIkDuGAhK5MwYmjzwhLnjMMNU5jVu9E6YaBzhpZEmwdEsLwk89hfBTf4Da05Pbbp4/D+4774L7jtshlpWd9/MixcUYg3zmDKLbtiH64jakjh4t2G9etAiO666DWF42FHLYHRCcjlzgwTsc4E2mIn0GM4eeTkMLZwKWSHRY2BKG0tWN5IEDkFtbRzxWqq6GdfUqI0RZtRKmuXMpnCTTjq4zhHoTGOiI4uxbA2g5OIDsOxeHz4xl19Vi8YYqWB30+4IQQgghZLZ45keH0HpoEKtuacA1d84t9nDILEKhCYUmU0aLpBF86gxSR/0AALHUCu/d82GeMzFlQIzm3jKUvjjU3kw40peA0pcAVH3Ux3AWAVKFHWKpFVooBbk7DpYco9eFN9PrIjtTodoBwW2aNSeOmKIjdTqI5FE/Usf9BT0/OLMAyyIfrFeUwLLAB948NX0tyORQZQ1//MFBdJ8OweqUcPffrYKnwjaux+ppDbGdXYi+1gmWNkIPU6ML7lsaYW40fpZ1WYPSnVdmqz0KLTzyKnfOJMBU6zDCkTonTHUuCK4LO5nFdB2JN95A6ImtiL744tDV9IIAx7XXwn3XnXBu3gyOTqpPG0zXkTp0CNGXXkL0xW2FJ+k5DtaVK+G86SY4b7oRpjqqSzvV1MFBJA4cQHL/fiT27Ufq+HGjgX0eweOBdeVK2Fatgm31KliWLJn2s3SKTU+noQ0OgqkqpPr6WfPaoVg0RYe/O4aB9igGO2IY6IjC3xWDKhd+r9Yu8mLZ5lo0LisBPwllJQkhhBBCSHGd3teHF352FA6fGfd+dT21BCAThkITCk2mXPLIIIJPNed6FdivroT7XU3gLeOfyaHFZCh9ibzZIwkovfHcSdzhOImHWG6DVGGDVGmHVGGDWGmH4CoMPYzgJW00DO+OQc4ss2WJhuNtYsFsFKnaDrHUBk6YGb+k9ZSK1MmAEZScCBZc+c/bRVgWG43cLfM8VLJslkknVTz53QMY7IjB6bPg7r9fBYd3/OXVtLiC6KsdiO3uyYWS5jlu6CnV6HUzPKfkAKnCBlOdKzeLRCy3TegLGi0SQeRPzyH8xBNIHjyY2y54vXDdfptRvmvRogn7eGT8mKIgvmcPotu2IfbSy1D7+3P7OEmCbf06Iyi5/nqIpWP32iFTT4vFkTz4NpL7DyCxfz+SBw+CpVIFx3AWC6wrVsC2apVR1uvKK8HbZ/fUeMYY9GgU6qAfWsAPddAPNeCHNuiH6h+5TY8P9QATvF7Y1qwxbmvXwDx/vtHXhoxKTqoY7DSCkcH2KAY6Ygj2xKGPMltYNAsorXGgYo4LS9ZXU4kGQgghhJBZTpU1PPz5nZBTGu763FXnLUFOyHhRaEKhSVHoSRXhP7UgvqcXAMC7TPC+ey6sSwtPlukpNVdSK3/2iB4boxkuz0EssxrhSIUdUqWxFHyWSzo5qycUyD1xI0zpiUHpjkHpT4w8MQwAIg+p0pYLUaQqI1ThTVM7O4PpDEzWoKc1sMwtu65FZaROBJA6HQS0oR9rwW2CdWkprFeUwNTgnjHhD7k4iYiMJ769H+H+JLxVdtz9uZWwOC7sanE1nEb0pXbE9/UW/DzwTskISLKzSGod4M1TV+IufeYMwlu3GuW7BgZy282LF8Nz111w3X4bRC+9mJpMeiKB2M6dRlCy/VXokUhuH2+3w3HdtXDedBPs114LweEY9TkOvtSBfc+2guMBi12C2SbCbBu+HFq32Av3iyaeruifYEyWkTp+HIl9+3MzUrRQqPAgQYBl0SLYVq+CddUq2FatglhSUpTxXgimqtCCQaj+TPDh90P1B6D5B6H6A1D9g0YoEghA8/vBlDFei4yBkySA48BkuWC74HbDumY17JkgxbxwITjh8pzRmYjIGOyIGgFJhzGTJDyQHPVYi11CaZ0DZXVOlNU7UVrngLvcBp6uLiSEEEIIuay8/MhxHN/dgyUbq3H9h+lCSTIxKDSh0KSoUmdCCG1tNvqLALAuLYFQas3NIBlrhgc4QPBZjGCkwpYLR8RS65TNiGCKboQ4PXHI3bFMoBIftU8DOKMcWbYJeTZQEfLqamdDjly4MUbgoadVMFkfWs/fJw8dz5TRS5INJ5Zajf4kS0sg1TroBONlJuJP4olvHUA8lEZFkwt3/M2VMF3ArK8sZSCB1LEABK8ZpnonBLd5WnwvMVVFfNcuhLY+idhLLw2d5JQkODdvhvvuu+DYtAmcePn0LJpMajCI2CvbEd22DfFduwqajws+H5w33gDnTTfBtm7defuQHNvZjVf+98QljYcXuFGDFotNhHlYCEOBy8Vhug757Fkk9h9AYv8+JPfth9LdPeI4U2Oj0RdlpVHSS6qru+SvL2MMLJ2GnkwWLFkqBT1zY7llGnoqmVvq8UQmFBmaFaKFQsAFvtTlHQ4IJT6IJaUQS0py60PbfBBKSiCWloJ3OABFQfLIEST27EVi714kDhwASxaGArzLZZQ+y4QolsWLLuh3FNMZQv0J9J4No7clgr6zYUT9KZhsIqwOEyx2ERa7BEt23SEZN3vezSFBMguT9jPAGEM0kMoFI0ZQEkN8jNd9Dq8ZpXVOlNU5jGW9Ew7v9Pg7QwghhBBCiqvrZBBP/tdbMFlF3P/NDRBH6YtKyIWi0IRCk6JjiobISx2IvtYx6swNwWWCmCmplZ09Ipbbpnzmxnhkm84r3TEoPUMlvvSoPOrxvEMCOBghhzy+kOOC8Rw4swDeLIAzZZYWAeZGN6xXlBglkuikw2Ut0B3HE9/Zj3RcRd1iL7b81QoI0uwrFaMGg4g88yzCTzyB1LFjue1CWSncd9wBz113wTxvXhFHODMpPT2IbnsJ0W3bkNi3D9CGgmOpttYou/WOm2C98spxXz3fcmgQf/qfQ2AMuOrmeixYW4F0XEU6oSKVUJBOqEjnlqOsx9VRS/dcCNHEo7zBher5HlTP86BijuuiAsXLkdLTg8T+A0geMPqipE+fHhFGiGVlsK5aBcvixYCuQU+lwVLJYcvhwUfeMhOOTDiOg+D1ZgKQEoglJRBLSyD4sksfxNJMQOLzgbdYLunDMUVB6uhRxPfuRWLPXiT374eeSBQcwzscsK5aacxEWbvW6CGTF6Kkkyr6WyLobQmj92wYfS0RpBOj92e7ELzIwWKXYHUUhimjLjPrZqs4YmavrjOE+hK5YGSgPYrBzijS8VHGyAGectvQDJI6J0rrHdS8nRBCCCFkGmGM5W7Z+8OX4912vn0ulwv8eUrZMp3hkX/cjVgwjVv+4grMXVk+MZ8ouaxRaEKhybQhd8cQ29UNzsQPldYqt4G3zfzmslpUzpuRYsxKUf1JYLSfKB7gTKIRbmRuBYGHeWg56jazCN7EZ7aJgMhRKELOq68lgif/+y2oaQ1zV5bj5o8tndUlTlInTyL8xFaE//hHaIFAbrtl+XJ47r4LrltvhUB/F0bFGIN85gyi27Yh+uI2pI4eLdhvXrgwF5SYFy684N8/vWfDeOq/3oKq6Fi0rhI33Lv4gp+DMQYlrY0SquSFK3EFqQsIXDieQ1mdA1XzPKie70HVPDedyB0nLRxG4q23kNy/H9H9byN8ugtJ0YWU2QfZ5ATHGDjoANONdaaP6z7HdCB7X+DBSwJ4SQJvEsGbpKGbJIG3mCCYJHAWEwSTCZLNDHu5C1JpSUFAIni9RS2NxVTVKH+2Z48xG2X/fuix2NB+cEj6GpBcei2i5YsR0D0IhfQRrydEiUdZgxOVc9yobHLDU2mDnFKRiilIxZWRy7iCVExFKiYjFVehqRd7IQeDCTIkLQEpHQVjQMxWBY0b+VqOFzj4qu25GSRldU6U1DoonCSEEEIImUYGBwfx6KOPYiCv7PVUampqwr333nve94Svb23Ggefb0bSiFLd+cvkUjY7MZhSaUGhCikRPa1AHEgDPFQQfEKkkDCmOjuMBPP3gQegaw5JN1dj8wQs/4T3TMFlG7LXXjPJdr74KqMaVz5zJBOdNN8F9992wr7vmsu0vkKXH40ifPo3oSy8h+uI2yK2tQzs5DtaVK42g5KYbYaqru+iPE+yN4/FvGbOeGq4owbs+uQyCMLWznrKBSyyQRs+ZELqbQ+g5HUY0MHJWg7fShqrMTJSqeW64SqxTOtbpSE6qiAZSiPpThcvMeiIy+szLYuAFDk6fBc4SC1yl1szSAleJsW5zmYr+OzAVTaNjx1F07W9Df1cKQdUFVRz5fWZjUZT6GKoWl6Nu/SKUNnku+mdH13Wk+/2ItfUi3jWIRF8IycEIEsEEUlEZ6aSKtMxBhgmKZIci2qFIDmji2LNueC0NR6wLzlgHnNEOeExx+OrcsC2YB/P8+cZt3lzwVvoZIoQQQgiZLiKRCB566CGEw+FJ/1gcN3TBb3apZaoYfOADH8CiRefuVeLvjuG3/7YHvMDh/m9svOB+rYQMR6EJhSaEEJLTvL8fL/zsCBgDVt7SgHV3zi32kKaMOjiI8B+fRviJJ4xyQhliZSXc7343PHfdCVNjY/EGOEm0WAxqby+U3j6ofb1QenqNZW+fsb2vr6CJO2A0tLatu8YISm64AWJp6SWPIx5K4/ff3IdYII3yRhfu/NurIJmnT1gVDaTQ0xxC9+kQupvDCPbERxzj8JkzAYoRpHirZlf5Q8YYUjFlRCgSyazHAqlxlYUSTXwurLC5jNk6TDfKODHGjB5fI+4z6Dry1jPbGYbWR7uvM+gMI+5rig52nhJugmSM01VqgbPECldJJmApscJVaoHFIU3o/y/TGQI98VyJrd6zYQR7EyOOE0UOPlsK7lgrbM174ew5ApMSze3nLBZYr7wStjWrYV+7Fpbly8GbzcbHkGUo/QNQ+/ug9vVB6euD2tsHtb8PSl8/1D5j+3ib3HM2G6TycoiVleDLK6CXVEPzVEB1+qDZvGAcD0eiG+auU5BPn0L69OlRe94YT8ZBqq+DZcGCoSBlwQKYGhqo7xQhhBBCyBRLJBJ4+OGHMTAwAJ/Phw9+8IMwm82jhhsXu+18r6W3bduGnTt3ory8HJ/4xCfOW6br0a/twWBHDNd9cCGuuLbmIj9zQgwUmlBoQgghBY7u6ML2X58EAKy/Zx6uekd9kUc0tRhjSB09hvATTyD8zDPQ866qsa5aBc/dd8H5zlsgOOxFHOX5McagR6NQenuNk6O9vVB7eqH09ULt7TOWPb3Q4yNP/o+Gd7ng2LgBzptugv3aayE4HBM21nRCwdbvHIC/Kw5PhQ13//3KaV/6KhmT0dMcNoKU5jAG2qMjTsJb7BKq5rkz5bw8KKtzgJ/imTMXQtcZ4qH0iBkisbx1dRz9t8w2Ec4SixGMZMKR3LLEAot9YsOGi6FrOmKhtBH6DKYQ9SdzAVDEn0Q8mD5vT3jRLBQEKcZyaNaK2Sae8/NMxZVcONJ7Noz+1gjklDbiOFeZFZVzXKhscqNyjhslNfbc9xHTdaRPNxtN5TO3/JKDgDFzztTQADUQgOb3j/trJJSUQKwoh1RRmVlWQCyvgFhZYaxXVIB3OC74/1KLxZA+fdq4ncouT0ELBkc9npMkmObOzQtS5sMyfz7E6uqifx8RQgghhMxGsizjkUceQWdnJ5xOJx544AF4vd4pH0cymcT3vvc9pFIp3HXXXVixYsU5j3/rxXbsfrwZVXPduPvvV03RKMlsRaEJhSaEEDLC/uda8caTZwEAN9y7CIvXVxd5RMWhp9OIvfwyQlu3Ir5zF6AbJ4w5q9WYYVFRAU6Shm4m0/jvmyRwkimzHGX/Oa6iYYxBD4czV4kbs0OyYUh2lojS2wuWGHmV+mh4l8s4CVpZCamyAmJFJaSqSmNZaWyfyJAkn6po+OP3D6L7dAg2lwn3fH4VXKUzr0SPnFLR1xIxynk1h9B3NgJVKQwYRLOAyqa85vJNLoimiZ9NM6KnS9zo25LK9m+JD/V4SWXuJ2MK4sH0qP1chrO5TYVhyLBgZDb0pNBUHbFgGlF/0phJ408hMjgUrMTD6dH7kuUxWQQ4SwrLfgkih77WCHrPRhDqG2UWiVlARaMTFZmApLLJBatz/AFitudQYu9exPfsQWLvPmiDgwXHcJIEsSITfpQb4UcuFKmohFRRDrGsDJxpaoNLdXAwF6akTp3KrDeP+XuMt9sLZqSY58+HeeECiEV4Q08IIYQQMltomobf/OY3aG5uhsViwf3334+KioqijWfHjh146aWX4PV68alPfQriOWYgx0Np/PJLu8AY8OF/Xwd32cx7X0mmDwpNKDQhhJARGGPY/cQZvP1iOzgOuOUvl2HOlWXFHlZRKX19CD/1B4SfeKKwp8dkEYRRQxcAUAcGwJLJ8T2N2w2xstI4QVpZNRSKZMIQqaICvL04s2Z0neGFnx3BmQMDkCwC7vrcSpTVOYsylommqToG2qPoPm2EKD1nwiNKV/ECh/IG51Bz+blumG1DtXdVWRsj7Mhsiw81sk/FC5vd69rFvWTjeQ4OnzkXhjhKhoUiXgsEafrOlpkqmqIjGjBmpeTPVolkQpXkOHu3uMutRjgyx42KJhdKqu0TOhuJMQa5pRVKVyfEsjKIFRUQPJ4ZM0OD6TqU7m5jRko2SDl1CumWllwPquGE0lJYli6B+7bb4XzHTeAtY/daIYQQQgghQ3Rdx9atW3H48GGIooh7770X9fXFrTwhyzK+973vIR6PY8uWLVizZs05j//D995Cx/Eg1t7ehDVbmqZolGQ2otCEQhNCCBkVYwyv/OoEju/ugSDyuP2vV6BmIV3ByxhD8u23Ed+5C3oqCSYrYIoMpijGTc4ss9ty9xUwOe+4YffHOgF4LoLXC7Gq0iifU1kxtKyshFRZaZTPmaaNlRlj2PHoaRze3gle4HD7X69A7SJfsYc1abL9KoyeKCH0nA4hHh52Yp0DXCUWqIqOdEKFppy/FNa58AIHs02E2SbBYjeWZpsIs91YWmwSzPbsfglOnxk2txk8PzNOqE9niqwZJc6GzVBR0irK6p25kGS6l6GbrpgsQ25rK5iRkj51CkpHR8FxvMsF921b4L77HliWLpkxYREhhBBCyFRjjOG5557Dm2++CZ7n8YEPfAALFiwo9rAAAHv27MGzzz4Lh8OBz3zmMzCdY0b0iTd68NIvjsNdbsWH/vUaev1HLhqFJhSaEELImHRNx3M/OYKWg4OQLALu/NurUN5Avy8nA9P10QMVufA+mJ67Yjzb3Hkmyi8Bd/PHlmL+6uJN+S4Gxhgig5nm8pkG8+H+kbOHOA5DYYdNhMUu5YIQc14QYhl+3y5BNPH0JoFcVvR4HOkzZxB79TWEtj4Btbsnt8+8aBE8d98N1+23UQkvQgghhJBhXnvtNbz88ssAgLvvvhvLly8v8oiGqKqKBx98EKFQCDfddBM2btw45rFySsXDn98JVdbxni+sRkUTnb8gF4dCEwpNCCHknFRFw9MPHkTXyRAsDgl3/91KeCundxN0Mr0d392Dlx85DgDY+N75WHFjXZFHND3Ew2mE+hIwWcTcjBCTWQBHMz8IuWBM15F44w2Efv84otu2gcnGzC5OkuC48UZ47rkH9vXrwAkT31eIEEIIIWQm2bdvH55++mkAwC233IJrrrmmyCMa6eDBg9i6dSssFgv+5m/+BtZzVFR44aGjOL23D8uur8W1758es2XIzHMhuQEVsCaEkMuQKAm49RPLUVbvRCqm4A/fexvRQKrYwyIzVNsRP1753xMAgKturqfAJI/dbUbNAi/K6p1wlVphtooUmBBykTieh339etR89zuY/9qrqPjnf4JlyRIwRUH0uefQ8fGPo/nGm9D/ve9BHlbWixBCCCHkcnHs2DE888wzAIBNmzZNy8AEAJYtW4aysjKkUins3r37nMcuvLoSANC8rw+admkljwkZDwpNCCHkMmWyirj9r1fAU2FDLJjGH7//NpKx8TU6JiSrryWC535yGExnWHh1JdbdObfYQyKEXAYEjwe+D30ITU88jqatT8D74Q+Dd7uh9vbC/z//D2fecTPa7vsIwn/4A/QUXRQwncnt7Yjt2AktFiv2UAghhJAZ7+zZs3j88cfBGMPKlStxww03FHtIY+J5Pje+N954A7FzvBaoW+yF1SkhGVXQcSwwVUMklzEqz0UIIZe5aCCFJ761H7FgGuUNTrz7b6+CySIWe1hkBgj1JfD4t/YjFVNQv8SHWz+1HIJA12MQQopDT6cRe+klhB5/AvHdu4HM2xze6YRry63w3HMPLFdcQX2Bpgk9mcTgj34E/8O/AFQVEEXYrrwS9k2b4Ni0EeZFi8Dx9DeFEEIIGa/u7m784he/gCzLWLx4Md773veCn+Z/Sxlj+NnPfoauri6sXbsWt95665jH7vjdKRx6uRPzV5fj5o9dMYWjJLMF9TSh0IQQQi5IsDeOJ759AKmYgpqFXtz26eUQJaoJT8YWD6fx+Df3I+pPUdhGCJl2lO5uhLZuRfiJrVC6unLbzQsWwHPP3XDdfjtEn6+II7y8xV59Fb3/9u+5/xuxvBxqf3/BMUJpKRwbNsB+7SbY16+H6PUWY6iEEELIjDA4OIif//znSCQSaGxsxIc+9CFIklTsYY3L2bNn8cgjj4DneXzmM5+Bx+MZ9bj+tgge+499ECQeD3xzI0xWev9JLgyFJhSaEELIBetvi+DJ774FJa1hzpVleOfHl4KnWQNkFHJSxdbvHsBgRwyuMivu+ftVsLlMxR4WIYSMwHQdiT17jObxL7yQax4PSYLz+uvhec89sG/YQM3jp4jS14++r38d0eefBwCIVVWo/Od/gvOGGyB3dCC+cydiO3Yi/sYbYInE0AM5Dpbly+DYaMxCsSxbRv9nhBBCSEYkEsFDDz2EcDiMqqoq3HfffbBYLMUe1gX55S9/iZaWFlx55ZW48847Rz2GMYbf/OubCPYmcMO9i7F4fdXUDpLMeBSaUGhCCCEXpfNEAH988CB0lWHxhipc/+FFVMaEFNAUHU//8CA6TwRhdUq45/Or4C6zFXtYhBByXlo4jMizzyL0+BNIHTmS2y5WVMB9553w3H0XTA0NRRzh7MU0DcHf/BYD//Vf0ONxQBDgu/delH36U+Dt9hHH67KM5IG3EN+5A7EdO5E+ebJgP+92w7FhPewbN8G+cQOk8vKp+lQIIYSQaSWRSODhhx/GwMAAfD4fHnjgATgcjmIP64J1dnbiZz/7GTiOwyc/+UmUj/G3fd+zrXjzD2dRu8iLd/9/V03xKMlMR6EJhSaEEHLRzr49gOd+fBiMAVfdXI/1d88r9pDINMF0hhd/fhSn9/VDMgu487NXobyB/tYSQmae1IkTCD3xBCJ/+CO0UCi33bZmDdz33A3XzTeDt1EgPBFSx46h58tfQerwYQCAZflyVP3rV2BZvHjcz6H09Q3NQtm9G3okUrDfvGgRHJs2wr5xE2xXXQnORLMfCSGEzH6yLOORRx5BZ2cnnE4nHnjgAXhncDnL3/72tzhx4gQWL16M97///aMeExlM4lf/9DrAAfd9fT0c3pk1o4YUF4UmFJoQQsglOb67Gy8/cgIAsGRjNTbcM4/qhV7mGGPY9VgzDr7cAZ7ncNunV6BuCfUDIITMbLosI/byy0bz+J07h5rH2+1wbdkC1623wtTUCLG0lMpBXSAtFsfgD76PwK/+F9B18A4Hyj77t/C+//2X9LVkqorkocO5WSipI0dy/28AwNtssK1blwtRTLU1E/HpEEIIIdOKpmn4zW9+g+bmZlgsFtx///2oqKgo9rAuSX9/P370ox8BAD7+8Y+jpmb0v+FPfHs/eprDWHf3XKy8mWYJk/Gj0IRCE0IIuWRvb2vHrt83AwAcXjM2f3gRGpaWFHlUpFjeeqEdu58wvh9uun8JFl5dWeQREULIxFJ6ehB+8kmEntgKpaOjcKckQaqogFRdDamqClJNNcSqKuN+Zhs/w2qHT6botm3o/erXoPb2AgBct74L5V/84qSU0VIDAcR37c6FKFogULDfNGfO0CyUNavp/4kQQsiMp+s6tm7disOHD0MURdx7772or68v9rAmxNatW3Hw4EHMmTMH995776jHHN3Rhe2/PomSGgc+8M9rp3iEZCaj0IRCE0IImRBdp4J4+VcnEBlIAgAWravEhvfMh8UuFXlkZCqdfLMX2x4+BgBYf888XPWO2fGCnBBCRsN0HYm9+xB+4nHE9+6F2tcPaNp5HyeUlBiBSl6wIlUPhSuCxzPr+4Qp3d3o/erXEHv5ZQCAVFuLyi9/GY5NG6fk4zNdR+rY8VyAknz77YL/O85shm3t2qFZKE2Ns/7/hBBCyOzCGMNzzz2HN998EzzP4wMf+AAWLFhQ7GFNmGAwiB/84AfQdR333XcfmpqaRhyTiit4+As7oasM7/+ntSitnXk9XEhxUGhCoQkhhEwYRdbw5lNncfDlDoABNrcJmz+4EE0ryoo9NDIF2o/58cyDh6DrDCtuqsPG98wv9pAIIWRKMVWF2t8PpacHSnc3lO7Msqc7d58lEud9Hs5mKwxVqqshVQ/NVhHLy8GJM7MUJlNVBH71vxj4wQ+Mr4UoouSBB1D6yU+At1qLNi4tEkH89TdyIUp25kuW4PFAKC2B4PFA9HoheLzGNq/XuHncxnavsZ13OsHxfJE+G0IIIQR47bXX8HLm4oS77roLK1asKPKIJt6zzz6LPXv2oKamBh/72MdGvcDhTz8+jLNvDeCqd9Rj/T3Uh5WMD4UmFJoQQsiE6z0bxsuPHEew1zgxNH9NBTa9fz6sDmq2Olv1t0Ww9btvQU1rmL+mAu+4fwk4nq7IJYSQfIwx6OFwJkjpgdLVPbSeWWqDg+d/IkGAWFEOqaoa5jlNsG/cBPuGDRAc9sn/JC5B8tAh9Hz5K0gfPw4AsK5ahaqvfBnm+dMrZGeMQW5uNprJ79yBxN59YIpyYU8iCEao4vFA8FLQQgghZGrt27cPTz/9NADgne98J9atW1fkEU2OaDSK73//+1AUBR/4wAewaNGiEcecfWsAf/rxYdg9Ztz79fXg6X0qGQcKTSg0IYSQSaEqGvY+3Yq3XmgDY4DVKeHaDyzEvFUTX6OcFFeoP4EnvrUfyaiC2kVe3PbpFRBEOulDCCEXQ0+loPb2Zmam5M9WySx7e4HRTuBLEuxr1sCxeTMcN1wPU23t1A9+DFo0ioH/+i8Ef/NbgDHwbjcq/v7v4L777hkREuiJBOT2dmihELRgEFooBDUYhBYMDW3L3kIh6OOYTTSqUYIW88JFKPnoA0WdhUMIIWRmOXbsGB577DEwxrBp0ybceOONxR7SpNq2bRt27tyJ8vJyfOITnwA/7LWFpuh4+As7kU6ouOP/uxJ1i3xFGimZSSg0odCEEEImVX9bBC/98jgC3XEAwNyrynDtny2EzUWzTs5F1xkig0kEe+IIZG7BngTi4TQqGl2oX+JD3ZISuMuKexIlEZHx+Lf2IzKQRGmdA3d9diVM1plZMoYQQmYCputQBwahdHdB6e5G6vARxF55BXJbW8Fxpnlz4dy8GY7rr4d1xYqilPNijCH63HPo/frXoQ0YM2jc774D5Z//PMSSkikfz1TR02kjTMkPVMYKWjLr5wpaTHPnoua734Vl4eypQ08IIWRynD17Fr/+9a+haRpWrlyJ22+/fdb35Eomk/je976HVCo1Zhmy7b8+gaM7urFoXSVuvG9JEUZJZhoKTSg0IYSQSaepOvb9qRUH/tQGXWcw20Vset8CLFhbMetfwJ2PrumIDKYQ6M4LR3rjCPYmoCn6eR/vLrOifokP9UtLUL3AA5Nl6k6KySkVT373LQy0R+EqteDuv18Fu9s8ZR+fEELIkHRLC2KvbEds+3Yk9u8vaGouuN2wX3stnNdvhn3jRghT8N5H7uhA77/9O+I7dgAATI2NqPzKl2G/5ppJ/9gzkS7LmUAlL2Tp78fgT38KbWAQnMmE8i9+Ad4/+7PL/rXTRNJ1HYlEArquw2w2w2Qy0de3CBhj0HUdmqZB1/UJX7fb7aipqYHH46H/XzKrdXd34xe/+AVkWcbixYvx3ve+d8Ssi9lqx44deOmll+DxePDpT38a4rCLRbqbQ9j67QOQLALu/+ZGSCahSCMlMwWFJhSaEELIlBnsjOKlXx7HYEcMANC4rATXfXARHN7Zf6Jd03SE+4fPHIkj2JeAro7+51WQeHgrbfBV2eGrtsNbaYfVIaG7OYT2owH0nglD14ceywscqua5Ub+kBPVLfSipcUzaG0NN1fHMjw6h41gAFoeEe/5+FTwVtkn5WIQQQi6MFokgtmMHYttfRey116CHw0M7BQG2VavguP56ODZfB3NT04R+bKYo8D/8Cwz+8Idg6TQ4SULJX/4lSv7i4+BNNMv0QqmBALq/9CXEX30NAOC46UZUf/WrEDye4g5sGtM0DfF4HLFYDPF4vGB9+DKRSCD/NAfHcTCbzQU3i8Vy0yMxZwAA46JJREFU3vXh94efrJvuNE2DqqpQFAWqql7S+rn2Z8OM4aHGVJ1qstlsqKmpyd2qq6tht0/vXlCEjNfg4CB+/vOfI5FIoLGxER/60IcgSVKxhzVlZFnG9773PcTjcWzZsgVr1qwp2M8Yw6/+6XVE/Snc/NGlmL+mokgjJTMFhSYUmhBCyJTSNB1vvdCOvc+0QFcZTFYRG94zD4vXV82KK780VUeoP4FAdzwTkCQQ7I0j1JeAro3+Z1Q08fBWGsGIr8oOb5UdviobnCXWczapk5MqOk8G0XEsgPZjfkQGUwX7bS4T6pb4jFJei32wOifmZBXTGbb94hhO7emDaOJx52dXoqKR/o4SQsh0xFQVyYMHEXvlFUS3b4fcfKZgv6mhIROgbIZt1Upwl3CCJXHgAHq//GWkTzcDAGzXXIPKL//LhAczlxvGGIKPPIK+b38HUBSIlZWo+dY3YRt2Qmg2UxRl1NBjtG3JZPKCn5/juAk9cS+K4riCF1EUc+HBaLfsDIyJPkbTtIJAQ9fPP7u5GHiehyAI4Hn+otc5jkM4HEZvb++on6fX6y0IUiorK2GigJfMMJFIBA899BDC4TCqqqpw3333wWKxFHtYU27Pnj149tln4XA48JnPfGbEz/KbfziLfc+2omFZCW771MgSXoTko9CEQhNCCCkKf3cMLz9yAv2tEQBA3RIfNn9oIVwlM6PRqaboCPYlcjNHsstQfxJMHyMcMQvwVdqMWSNVRkDiq7LD6bOAO0c4Mh6MMYT7k2g/FkDHMT86TwahynlvDDmgvN6ZCVFKUDHHBUG4uKnaux9vxlsvtoPnOdz6qeVoWDp769ITQshsI3d05Mp4xffuLWgqzzudcGzaCMfmzbBv2gTR6x3Xc2qhEPq/8x2EHvs9AEDw+VDxhc/Ddccds+KCiOkiefQouj/7OaN/Dc+j9BOfQOlffbIo/WomGmMMZ86cQWdn56iBSDqdvqDn4zgOdrsddrsdDoejYDl8m81mA8/zUBQF6XQ6d0ulUuddH35fluVJ+gpNHUEQIEkSRFGEKIrnXb+QYwVBuKDgYyKpqore3l50dXXlbn6/f8RxHMehoqKiIEgpKyu7bEockZknkUjg4YcfxsDAAHw+Hx544AE4HI5iD6soVFXFgw8+iFAohJtuugkbN24s2B/sjeP/vvImOJ7D/d/YMGEXFc5UjDGkEyrioTR0jaGs3lnsIU0rFJpQaEIIIUWj6wwHt3XgzT+ehabokMwC1t89F0s31VxyiDDRGGPwd8XRdmQQbUf86D0bGTMckSxCLhDJhSPVdjg85in7vDRFR8+ZENqPBdB+LAB/Z6xgv8kioGahF/VLS1C/xAdX6fjCqoMvdWDnY6cBADd+ZDEWXVM14WMnhBAyNbRYDPFduxHbvh2xV1+FFggM7eR5WK+8Eo7rN8O5eTNM8+aNCEAYY4j88Y/o+89v5B7ree97UP65z1H5qEmix+Po/erXEN66FQBgXbUKNd/6JqTq6iKP7OJomoYjR45g165d6O/vP+exgiCMGYQMX1qt1qKc5NZ1fVzhSnZd07RcUDDajeO4S9o/1jGCIIwabExGWDGdJZNJdHd3FwQpsVhsxHGSJKG6urogSHG73RQKk6KTZRmPPPIIOjs74XQ68cADD8A7zgseZqu3334bTz75JCwWC/7mb/4GVmvh+9zH/mMv+tui2PT++Vh+fV2RRjn58gORWDCNWDA1bJlGLJSGmjZ64FU0ufCeL6wu8qinFwpNKDQhhJCiC/Ul8PKvjqOn2ai5XrPAg+v/fBHcZcXtkSGnVHSeCKLtiB9tR/yIhwqvcjRZxUw4YjPCkUx5LbvHPO3eRMXD6UwZrwA6jgWQiisF+z0Vtlwpr5oFXkjmkY3xTu/twwsPHQUArLtrLla+s2FKxk4IIWTyMV1H6tAhRLdvR+yV7UifPFmwX6qthWPzZqOM19o1ULq60Ptv/4bE628AAMzz56HyK1+BbdWqYgz/shP+49Po/cpXoMfj4N1uVP37v8F1883FHta4pdNpHDhwAK+//joiEWPWsclkwuLFi+F2u0cNQiwWy7R7fUVmF8YY/n/27js8jvLc//97ZntR79W9G1ewMYSSUIwJBAgJBJKQRg8k56TC+Z2QwvkmJKQAoUMKNYAJJgmhBmICNtWWsQ2WcZGtbvW6fWd+f8zualfNlixpVe7Xdc01fffZlS3tzmfu5+no6EgIUWpra/utIIoOLh8/PorTKeP7ibETDof5y1/+wt69e7Hb7Xzta18jL0/G6dA0jXvuuYfGxkZOOukkTjvttIT9H7xWxZtP7SF3eiqfv2FihgTxgUhni6+fYCQxEDkcu8tC7rQUzv3WstFt+AQjoYmEJkIIMS7oms6O16t5a8M+QgENs0Xl+PNnccwniwcd12NE26DrtB3yxEKS2j1tCeOQmC0qRfMzmLYoi9JFWaRmT8wv77qm01jVSeWHzVR+1NKnakY1KxTOTqdkYSbTFmWRWeiiZncr//j9B2hhnSWfLOYTF82ZkK9dCCHEkQnW1tL1+ut0btyI56230eMuGqpOJ3owiB4MothsZF97LVlf+yqKjAMwpgKVldR893v4duwAIP0LF5N3ww2o47gf+66uLt555x3ee+89fD5jLDaXy8Xxxx/Pscce2+eOYCGSTdM0mpqaEoKUQ4cO9Ts+SmZmZkKQkpGREeuWzGw2y2dnMWI0TWPDhg3s2LEDs9nMZZddRmlpabKbNW7s2rWLJ598EovFwre//e2E7so8HQH+fMMmdE3n0p+sJiPflcSW9jUagYgrw0ZKhg1Xhh13hi0y2XGnG8tma98bJoWEJhKaCCHEONPe6OXfj5ZTs7sVgPyZaXzqsvmj9mEmFAhT83FbJChp6jOYemq2nWnHZDNtcRZFc9In5QcKvzdETXkrBz9qpurDFjpbEt8DV5qVgC9M0B9m9spczvzGonHXfZoQQojRo3k8dL/9dmww+XBjEwCuk04i/6YfYS2ZvN1bjHd6IEDj739P8wMPAkbFT+FvfoN97twktyxRS0sLmzdvZtu2bYRCIcC4wHziiSeyZMkSLBZLklsoxJELBoOx8VGi3Xv1Nz5Kb9ExXaJdoo3Ecn/bol2sxYc0R7I8nHN6Lw91inYhN9gkEum6zosvvsg777yDqqp84QtfYO44+52fbLqu8+CDD1JTU8OqVas4++yzE/Y/d+cHHNzZzLFnT2f1Z2YmqZWJave0sfmZvTTXdksgMk5IaCKhiRBCjDu6rvPRm7Vs+utegr4wJrPKqnNnsOz0EtRhDl4er6PJG6smqd7dSjjYc6eYalYompPOtMVGUJKW65hSH9aj1TaVH7VQ+WELtR+3Eoq8P0Vz0zn3+mWYLFOnr2shhBCJdE3D99Eu0MLYjzlmSv2NHM+6Nm2i9oc3EG5qQrHZyLvxBtIvvjjpP5+amho2bdrErl27iF5OKCoq4sQTT2T+/PlTavwMMbkd6fgoYugOF6o4nU4yMzPJysqKzbOyskhLS5uUv2P+85//8NprrwFwwQUXsHTp0iS3aHzav38/Dz/8MKqqcv311yeM9fLxe/W88oePSM2286Wb1yT1b2XQH+btZ/exfWM1xF11l0Ak+SQ0kdBECCHGrc4WHxsfLafyI2Nw2dxpKXzqsgVkFbkPc2aicEijdq9RTVK5s5nWek/CfneGjdLFWUxfnEXRvAysdvOIvYaJLhQMU7e3nbZDHuYdny/vjRBCCDFOhZqbqb3hRrrfeAOAlDPOoODmn2FKTx/Tdui6zr59+9i0aRMVFRWx7XPmzOHEE09k2rRpSQ9zhBgLuq4TDocJhUKx+Vgs67pO/OW7I1k+0uMOtxx97sNNY8FkMpGRkdFvoJKSkjJhAhVd1/F6vXR1dbFnzx5eeeUVANauXcuaNWuS3Lrx7aGHHqKiooKlS5dywQUXxLYHA2H+9P03CfrDfPZ7KyiYnZ6U9tV83MprD++K9Xax4IQClp9ZSkqmXQKRcUBCEwlNhBBiXNN1nfK36tn09B78nhCqSeHYs6ez4qxpmAapOulq9fVUk5S3EowrcVVUhYJZaUxbnMW0xcaYHfLlXQghhBATna5ptDz0MA2//S0Eg5gLCii69Vc4jx39wW7D4TAffvghmzZt4tChQwCoqsrixYs58cQTZYBiIUSMpmlHHLAMNmmaRldXF83NzbS0tNDc3ExzczOtra2EwwN3cWQ2m8nMzEwIUqLLbrd71L8bapoWC0K6u7v7nUeXu7u7+4yh098A56Kv6upqHnzwQRRF4ZprriE3Nze279U/f0T52/UsOrmIUy+dN6btCvhCvL1hHzterwHAlWHj5E/nkavVotjsWEuKMefloZgkOEkmCU0kNBFCiAmhu83Pxsd3c2C70Y96VrGb0y5bQE5pCgBaWKO+oiMWlDRXJ5bDO1KtTFuUybTF2ZQsyMDmlL6zhRBCCDE5eXd+SM13v0PwYCWoKtnfvJbsq68elQswgUCAsrIy3nrrLdra2gCwWCysXLmS448/nvQxrnQRQghN02hvb08IUqLLbW1tfUKIeFartU91SnTudDoHDFQ0TcPj8RxxEDLUS6x2ux2Xy8UxxxzDKaecIjf9HaEnnniC8vJyFixYwMUXXxzbXrWrhb/fvg2b08zXfvUJTOaxqTyq2tXMa3/+kK52Y3yvUm0vs3Y9idJYm3igxYK1sBBLSQmWkmKsxSVYiouxlhRjKSnBlJIyJu2dyiQ0kdBECCEmDF3X2fP+Id54Yg++7iCKqrD4lCK8nQGqPmrB7wn1HKxA3vTUWDVJTkmKDF4uhBBCiCkj3NXNoZtvpv1vfwPAeeyxFN76KywFBSPy+N3d3bz77ru8++67eL1e4zmcTlavXs1xxx2H0+kckecRQoiRFA6HaWtrSwhSovP29vZBwwybzRarTFEUJSEI8Xg8Qw5CHA4Hbrcbl8t12LnZLN0kD0dDQwN33303AFdccQVFRUUAaJrOwzduors9wLqrj2HmspwRf249HCZw8CC+Dz+ic2c5ZftcVJqNqhabr4UFux8js7XcONhkwjZzBnogSKC2FoLBQR/blJaWGKiUFGMtKTG25eejyL+XoyahiYQmQggx4Xg6AvzniY/Zt7UhYbvNZaZ0oRGSlC7KxOG2JqmFQgghhBDjQ/vf/079T36K5vFgSkuj4P/9Hymnnz7sx2ttbeWtt95i69athELGDSsZGRmccMIJLFu2DItFqnmFEBNTKBSitbW130Clo6PjiB7D6XQecRBiku6XxsSGDRv44IMPmDlzJpdddlls+6a/7mXbK5XMWp7DWVcdc1TPoQeD+Pfvx/fhR/g+ikzl5egeD80Z8ymf90X89kwAiureZJFlF+4Fc7AvWoh94UJsc+ei2u3GY4XDhBoaCFRVEayqJlBtzINVVQSqqwk3Nw/eGJMJS2EhluKiSKBSYlSoFBtzNS1NKpWOgIQmEpoIIcSEtW9rAx+/d4iMfCfTFmeTNyMVVapJhBBCCCESBA4epOa738O3cycAGZdeQu4PfhC7QHMk6urq2LRpEx9++GHsbuqCggJOPPFEFi5cOGEGVRZCiOEIBAIJgYqiKH2CEKfTKUHIONTa2srvf/97NE3jsssuY+bMmQA0VXfy5P+9h2pW+PqvPnHEXXhrfj/+j/f0hCMffYR/9270QCDhuJDJzt65n6c273gA3I4wJ63LYcapC1Gsw7/BU+vuJlBdQ7C6qm+wUl3dpx29qSkpfSpUrNOn4zr++GG3aTKS0ERCEyGEEEIIIYQQk5weCNBw2+20/PGPANjmzqXot7/BNnv2wOfoOhUVFWzatIl9+/bFts+aNYsTTzyRGTNmyN2qQgghxr1//vOfvPfeexQVFXH55ZfH/nY9cfM7NNd0c+oX57HopKI+52keD77y3YkByd69EAr1OVZ1u7EvNCpHmrMX8+5uN92dYQCOObWY48+fidU+ut1m6ZpGqLHRqEqJVadUEayuIVhVRaixsd/zrDNnMuv5f45q2yaaoeQG0hmaEEIIIYQQQggxASlWK3k/+D6uNWuoveEG/B9/TMXnPk/ejTeSftHnE8IPTdP46KOP2LRpE3V1dcb5isKiRYs48cQTKRihcVGEEEKIsXDyySezbds2ampq2L17N/Pnzwdg7qp83tqwj4/fPRQLTTqef57Of2/E99FHBPbvh35qCEzp6dgXLTJCkkgXW5biYvzeMJue3kP5W/VAmNQcB6ddNp/CORlj8joVVcWSl4clLw/nscf22a95vQRravpUqJjz88akfZOVVJoIIYQQQgghhBATXKixkdobbqR70yYA3GvX4v7B92no6qKuro4PPviA1tZWAMxmMytWrGDNmjVkZIzNRR8hhBBipP3rX//izTffJCcnh2uuuQZVVelq9fHQ/2wGHb78/9bA1jepuf5bCeeZc3NjFSTRgMScn9+n0vLA9iY2PlZOd3sAFFj6yRJWnz8Ti1W6bJuIpNJECCGEGIbofQTSJYUQQgghJhJN02gF2r79LQ4sXEDN3r20Oez4H3gg4TiHw8GqVatYtWoVLpcrOY0VQgghRsiJJ57I+++/T2NjIzt27GDp0qW4M+wUzc2gZncrH79TT9Yf7gMgZd1ZpF9wAfYFCzDn5Az6uL7uIG8+tYfd79QDkJbr4LTLFlAwO320X5IYJyQ0EUIIMSmFw2E8Hg9erxePx5Mw9bfN4/Hg9/tj50eDE0VRjnh5uMeZTCYKCgooLS2ltLSU7OxsCW6EEEII0a9gMEhDQwN1dXXU19dTX1/PoUOHCAaDPQflGV1yKJpGamcn+Xl5zDntNJYsXYr1KAaqFUIIIcYTh8PBiSeeyKuvvsq///1vFi1ahNlsZt7qPGp2t1K+8QArPvwQ1eEg/6abMB9BdeX+bY28/vhuPB0BFAWWnl7K6nNnYJbqkilFQhMhhBDjXigUSgg6Bgo94rfHByDDEa06GateLBsaGvjggw8A44NfNEApLS2loKAAs1n+ZAshhBBTjcfjiQUj9fX11NXV0dTU1O/nE4vFQl5eHgUFBeTn55ObloZ23/14XngRAOeucky33wYSmgghhJhEVq9ezdtvv01bWxtbt25l1apVzFqey+t/+Zj2Do0udwnTLvzUYQMTb1eAN57cw573DgGQke/kU5ctIH9m2li8DDHOyJgmQgghRpWu6wSDQXw+H36//7BT9Lj4IORoAhCHw4HT6UyY+tvmdDqx2+0oioKu67Ep+hp6Lw9l3+GO9fv9VFdXU1lZSU1NDaFQKOE1mM1mioqKKCkpobS0lJKSEhwOx7DfEyGEEEKML7qu097enhCO1NfX097e3u/xTqczFo7k5+dTUFBAZmYmqqr2Obb9b3+j7qc/Q/d4sM6eRekDD2CRQd+FEEJMIu+++y7PP/88brebb33rW1itVp7/1RtU7A9SUruRdQ98E0vewAOj79vawOt/2Y23M4iiwPIzSznunBmYLVJdMpnImCZCCCFGRCAQOGzYcSRhyEjl8wOFHYOFIP1dPBiP5s2bBxhVNXV1dVRWVsYmr9fLwYMHOXjwYOz43NzchGqUtLQ06dJLCCHEsPj9fvbt20draysul6vPNNGqHTVNw+Px0NHRQWdnJ52dnQnL0fVAIIDFYsFsNmOxWI54eTjnmEw9F13C4TDNzc0J3WvV19fj9Xr7fT0ZGRkJ4Uh+fj4pKSlH/Hc/7bzzsC1YQNUVVxLYu48DX7iE0gcfwDZnzoi830IIIUSyrVixgs2bN9PW1sa7777LJz7xCXL2/5sKPkFD0RpM2f2PYeLtDPCfJz5m75YGADIKXJx22QLyZsiN+FOdVJoIIcQUo2kaXq+Xzs5Ourq66Orqii333pbQN/ZRUhQFm8122Mlut2Oz2foEIRMpABlJuq7T3NycEKK0tLT0OS41NTVWhVJaWkpeXt6UfL+EEEIcmc7OTj7++GPKy8vZv38/4XB4wGPtdnu/YYrL5cLtdiesR6s2R4vP5+sTfvRe7urqQtO0UWvDcCiKEgtQ/H5/n6pSAFVVycnJSQhH8vLyRqy6NFhbS+UVVxLYtw81NZWSu+/CeeyxI/LYQgghRLJt27aNZ599FrvdztVnn03NxZewac3PCVpTOPf6pZQuyoodq+s6e7c08J8nPsbXFURRFVacWcpxn56BySLfoyeroeQGEpoIIcQkEQ6HDxuCRJeHciFhoLAjGm4caRBisVikEmKEdHV1xQKUqqoq6urq+vxMrVZrLEApLS2lqKhIBn4VQogprrGxkd27d1NeXk51dXXCvoyMDIqKivB6vXR3d8emoYYPqqr2G6YMFLJEKzBCoVDs80p/VSHR5UAgcMRtcbvdpKSkkJKSQmpqap9lm81GKBQiGAwSDAYHXB5s3+GWB2K1WhPGH8nPzyc3N3fUq3rCbW1UXXMt3rIyFKuVot/+hpTTTx/V5xRCCCHGgqZp3HPPPTQ2NrLM72fehmepOP37VISmM3dVHmd8fREAno4A//nLbvaVNQKQVeTiU5ctIHeaXEee7CQ0kdBECDFJhEKhWBdYhwtDPB7PkB7b6XTidrtjFxTi5/HLVqtVwo5xLhAIUFNTkxCk9L6opKoqBQUFCUGK2+1OUouFEEKMBU3TqK6ujgUlzc3NCfsLCwuZP38+8+fPJycnp8/fe03T8Pl8CSFKdOrq6uqzbThjkEUrSYfyOcZms/UJQnqvu93uhC6xkkHXdUKhUJ8wxWw2k5GRkbSKUM3no+Y736XrtddAVcm/6UdkfOELSWmLEEIIMZJ27drFk08+iSkU4tPP/ZPs2x7mH080YraqfO1Xn+DAjibeeGIPvu4gqqqwYt00jl03HZNZqkumAglNJDQRYtLQdT32Zd3j8cTmXq8XVVVjfUUf6dxkMo1JAKBpGoFAoN9xPwZa7m/fYF1l9EdV1VjoMVgYMhH7JxdHTtM0Dh06FAtQDh48SGdnZ5/jon2k5+bmkpeXR25u7oCDyAohhJgYgsEg+/fvZ/fu3ezevZvu7u7YPlVVmTFjBvPnz2fevHkj/n0pGAz2G7AMFLT0/iqqquqAVSHxk81mG9F2T0V6KET9T39G2/r1AGRfey3Z118nN8oIIYSY0HRd556f/IQGRWFhdzef/9WveOzHb9Pe4CWjwEVrnfG5KKvYzWlfWUBOSUqSWyzGkoQmEpoIMW5F71iMBiC9w5D+to10n9RDCVn6C12OJAwZzp2Wg7FarbhcrkErQlJSUnA4HHLBW/Sh6zrt7e0J46I0NDT0e6zZbCYnJ4fc3NzYlJeXN6QBZ4UQQowtj8cTG59k3759Cd1C2Ww25syZw/z585k9ezZ2uz2JLe0RX8USDoflc0wS6LpO05130XTXXQCkf/7z5P/4JhS5sUYIIcQEFTx0iE2XXMrGk09CVRSu/9a32Lu5jXf/UQGAalI49uzprDhrGiaTfOaYaoaSG8inISHEUendbcPhApD+7io8EtHQwOVy4XQ6cTgc6Lqe0Gf0YPP454x2j+D1ekfyreiXqqp9xv6IXx9oufe6XEAQR0NRFNLT00lPT2fJkiUAeL1eamtrOXToEA0NDbEpFApRV1dHXV1dwmPY7faEECW6PFKD0wohhBialpaWWLdblZWVCZ91UlNTmTdvHvPnz2fatGnjsrpUVVWcTidOpzPZTZmyFEUh5/rrMOdkU/+zm2lbv55QczNFv/k1qvx9F0IIMQG1/PFP5NXWUuDxUOd0snHjRs449Ww+erMWV7qNU784n+xi6aZaHJ5Umgghhm3//v2sX79+WOGDzWaLBSDxYUh/25xOJxaLZdjt1HWdcDh8ROHK4eahUAir1XrYkCO6bDab5e58MWFomkZra2ssQIkGKs3NzQOGnSkpKQkhSl5eHtnZ2Uf1f1YIIURfuq5TV1dHeXk55eXlfSoG8/LyYt1uFRQUyOcPMSQdr7xC7Xe/hx4I4Fi+nJJ77saUnp7sZgkhhBBHLNTayt5PnYbu9WL+zW947L13URSFa665pt+x2yY0XYdAF3Q3QndTZN6YuJ5aCGf+X7JbOq5I91wSmggx6vx+P3fddRcdHR2AcRf6kQQg0fl4vONRCNG/YDBIc3Nzn6qU9vb2fo9XFIXMzMw+VSkyXooQQgxNKBTiwIEDsfFJop+7wPhdO23atFhQkpGRkcSWisnAs2ULVddci9bRgXXWLEofuB9LYWGymyWEEEIckcY7fk/T3XdjX7iQ6X99mieffJLy8nIWLFjAxRdfnOzmHV4oAJ6mvuHHQMsh3+CPl7sIrt08Nm2fICQ0kdBEiFH30ksv8dZbb5Gens4111wjA3IKMQX5fL4+VSkNDQ0DVp9Fx0vJz89n0aJFzJw5U0IUIYSIo+s6XV1dsaBkz549CeOkWSwWZs+ezfz585kzZ450bSVGnO/jj6m64kpChw5hzsuj5IH7sc+dm+xmTQjhri7anlpPoKICxWZDsVlRrFZUmw3FaqyrNpuxz2pDsVoS1xP2W1GsNlSbFaRyXQghDivc1c3eT30KraODottvJ3XtmTQ0NHD33XcDcMVJBRTRAKoZVFNkMoNi6rsttj1+X3S72rMcv18x9doe9/j+jgHCj17rvv5vShyUxQWubHDlRKa45fQSWHDuCL/TE5uEJhKaCDGq6uvrue+++9B1nUsvvZS58kVKCBERveDXuyolOl5KvLS0NJYtW8by5ctJly5AhBBTjMfjobGxsc/vyt7Bs8vlio1PMmPGDOn+UIy6YF0dlVdcQWDvPtSUFEruvgvnccclu1njVrijg5ZHHqHl4UfQBqjCPSqqimKzoVqtkTAmEqrYrKhWWyygMbndmDKzMGdlYsrKwhyZosuqhKxCiEms+Q9/oOHWX2OdOZOZz/0DJXJz3oa/Ps0HO3Yyk4NcxjNJbuURUEz9ByAJy9H1bLC6kt3iCUVCEwlNhBg1mqbxxz/+kerqahYuXMhFF12U7CYJISaA+PFSKioq2L59Oz5fTznxrFmzWL58OfPnz5fu+4QQk4rf7+83HOnq6hrwnJycHObOncv8+fMpKiqSqjwx5sJtbVRd+028W7eiWK0U/vpWUs88M9nNGldCra20PPwwrY88ihb5/2ydPp3Us9ehaxq6P4Du96MF/LFlPRBAD/jRouvR/YFg3HoAgsERb6/icGDOzMSUnYU5MwtzdtaAIYspLQ3FZBrxNgghxGjQ/H72nnY64aYmCn7xC9IvON/Y4Wun9eGv8PvaY9Aw8dkZHnIcOpoWNqZwdFmLTLox18M9y5qOputxc9B0zVjWiWynZz06ocSWddWKbrYbk8kWmVuNZZPVmFQrusmCrpjRdT02AQnrQ5lycnK45JJLkveDGYfGXWhy1113ceutt1JfX8/SpUv5/e9/z6pVqwY8fv369fzoRz/iwIEDzJkzh1/+8pecffbZR/RcEpoIMbref/99nnvuOaxWK9ddd538PxNCDEswGKS8vJytW7dSUVER2+5wOFi6dCnLly8nLy8viS0UQoihCQaDNDU19QlI2traBjwnLS0tNu5TdMrOzpZqEjEuaD4fNd/9Hl2vvgqKQv5NPyJDLr4Qam6m5c9/pvWxx9E8HgBsc2aTdfXVpJ511oiEDXo4bAQskRAluqz7/Wj+SAgTDPSs+/xoXZ2EmlsINTcRbm4h1NJCuKmJUHMzelw3f0dEVTFlZmLOzOwVrmQnhiyZmZjS01GcTulCTAiRNK1/+Qv1P/0Z5sICZr/0EorFAl0N8OhnoX4H/1TP5D1tUbKbOeby8/O5+uqrk92McWVchSZPPvkkl112Gffeey+rV6/mtttuY/369ezevZvc3Nw+x2/evJmTTz6ZX/ziF5xzzjk8/vjj/PKXv2Tr1q0sXrz4sM8noYkQo6erq4s777wTn8/HWWedxfHHH5/sJgkhJoGWlha2bdtGWVkZnZ2dse1FRUWsWLGCRYsWYbfbk9jCiU3XdcLhMOFwmFAoFJvHLx/Ntt77dV3H4XDEJqfTOeC61WpN9tsjxJCFw2FaWlr6VI60tLQw0Fcrt9tNbm4uOTk5sXAkJydHfreJcU8Phaj/2c20PfUUAFnXXE3Ot741JS+QBxsaaPnjn2h94gn0SLWsbf58sq+5hpQzTo91BTPe6LqO1u0h3NJMqLmZcHNzr3ClmXBTcyxkCQ+nizFVRXW7Ud0uTC53ZDmy7najutyoKW5jObrudmNyu+KOdaM6nePmfdQ1DT0UMgKrYNCoBgoGMaW4MaWlJbt5QogIPRRi39qzCNbUkPe//0vml74IrQfgkQugZT+4cuj67GM8+orxXVNV1VGZFEU57P7o387o8khNAz2m1WqloKAgiT+d8WdchSarV6/muOOO48477wSM7jlKSkq4/vrrueGGG/ocf/HFF9Pd3c1zzz0X23b88cezbNky7r333sM+n4QmQoyev/71r+zYsYOCggIuv/xyTFKyLYQYQZqmsXfvXsrKyti9ezeapgHGwMeLFi1ixYoVlJSUTMkLNYPxer00NzfT1NREc3NzbGpvb4+FGuOV2WzuN0w53LL8/RFjQdM02traEoKRxsZGmpqaCIfD/Z5jt9v7VI7k5OTgckl/02Li0nWdprvupinynT7tcxdS8JOfoEyR7jSD9fU0P/gH2p56Cj0QAMC+eDHZ116D+5OfnHSfS/RgkFBrayxcCbc0E2pqjs1DLc1G2BIJYPSR7EpMUVBdrsMHMG43itlkhBlxgUbCFA07BpsGOYZBPj+ZMjKwTp9uTNOmGfMZ07GWlqI6HCP3fgghDqv973+n9gc/xJSVxexX/4Xavg8e+Sx01UN6KXz5WcialexminFiKLnBqH7KCQQCbNmyhRtvvDG2TVVVTj/9dN56661+z3nrrbf4zne+k7Bt7dq1PPvss/0e7/f78ceVmnZ0dBx9w4UQfezfv58dO3YAcM4558gFKyHEiFNVlblz5zJ37ly6urrYvn07W7dupampiW3btrFt2zaysrJYsWIFS5cuxe12J7vJYyYUCtHS0hILROIDEk+ka5AjpaoqJpMJs9kcm8cvH+02RVHwer2xyePx9LusaRqhUIjOzs6ECqMjYbVa+4QpbrebvLw8CgsLyc7Olr9T4oj4fD5aW1v7ndra2mLhbW8WiyUhFIkup6SkTLoLqEIoikLOdd/EnJND/U9/SvvTfyXc1EzR7347qS8QB6praH7gAdqfeSYWDDiWLSP7m9fi+sQnJu3/dcViwZKbi6WfnkF603Ud3esl3NWF1tWN1t2F1tXVs97VlbitM7LcHbc/so9QCHQ9tm3cMZlQzGZ0v59wayve1la8ZWV9DjMXFGCdPq0nTJk+Hdv06ViKiowug4QQI0bXNJruvx+AzK98BfVQGfzlYvC1Q+5C+NIzkCqVFmJ4RjU0id6F1btP8ry8PMrLy/s9p76+vt/j6+vr+z3+F7/4BT/96U9HpsFCiH4Fg8FY9deqVasoKipKcouEEJOd2+3mhBNOYM2aNVRVVVFWVsbOnTtpbm7mlVde4dVXX2Xu3LmsWLGCWbNmTYoL5Jqm0dHRkVAtEg1H2tvbB+z2ByAlJYWsrCyysrLIzs4mKyuL9PR0rFZrn5BjPAwores6fr//sMFK72VfpDuUQCBAIBAYcKwIs9lMfn4+BQUFFBQUUFhYSE5OzqT4dyKGJhwO09HRMWAw4vV6Bz3fZDKRnZ3dp3okLS1tXPxfEmIsZVx8EeasTGOck40bqfza1ym+527MGRnJbtqIClRW0nTffbT/7e+xagPnsceS/c1rcR5//KQNS4ZDURQUpxPV6YTDZywD0nUdPRBICFGMUKWz3xAm3NkFmoZisSROVks/26wJ6/Teb7GgWKy9HsOa+Fhmc2ysGq27m0BlJYEDB2KT/8ABAgcOorW3E6qrI1RXh+ettxNfpNmMtaiop0JlxvTYsjk3d9x0SybERNL12msE9u5DTUkh47gceOR8CPmgZDVc+iQ4JtffJzG2Jnw97Y033phQmdLR0UFJSUkSWyTE5LNp0yZaWlpwu9186lOfSnZzhBBTiKIolJaWUlpayllnncXOnTspKyujurqa8vJyysvLSUlJYdmyZSxfvpzMzMxkN/mwBupOq7m5edCutKxWaywQiQ9IMjMzsdlsY/gKjp6iKNjtdux2OxlDuNimaRo+n6/fYKW9vZ26ujrq6uoIBAJUV1dTXV0dO9dkMsUqUaJhSm5uLuYp0r3MZOb1egcMRdrb2wesFolyOp1kZGT0O6Wmpko4IkSclNNPp/SPf6DqmmvxbtvGwS9+idIH7scyCW6q8u+voPm+e2l/7p8Q6YLPueZ4sq+5BteqVUlu3eSmKAqKzYZqs0FWVrKbMyjV5cK+YAH2BQv67Au1tkaClIM9ocpBY1n3+Yzlgwfh9dcTzlPs9oTKFGMy1k3p6RLUCdEPXddpuv8BADJOX4rp718HPQxzzoTPPwRWZ5JbKCa6Uf2WGO0a4dChQwnbDx06RH5+fr/n5OfnD+l4m8024S4UCDGRNDc388YbbwBw1llnyYClQoiksdlsrFy5kpUrV9LQ0MDWrVvZvn07nZ2dvPHGG7zxxhtMnz6dFStWsGDBAixJ6AIhEAjQ3d2Nx+OJzTs7OxOCkcG601JVlYyMjD7hSFZWFm63e8p/aVZVFafTidM58JcgTdNoaWmhrq6O2traWJDi9/upra2ltrY2dqzJZCI3NzchSMnLy5MgZRwJh8MEg0E8Hs+AwUi0AmkgJpOJ9PT0AYMR+S4hxNA4V65k+mOPUnnFlQT27+fAJZdS8sAD2OfNTXbThsW/Zw9N99xLxwsvQKSq03XySWRfcw3O5cuT3DoxkZgzMjBnZPT5d6NrGqGGhrjqlLhQpboa3efDv3s3/t27+zymmpaGdfo0nMuWk/75z2GbPXusXo4Q45rn7bfxbd+OYjWTqT8NugZLLobz7gKTdIUnjt6YDAS/atUqfv/73wPGF9nS0lKuu+66AQeC93g8/OMf/4htO+GEE1iyZIkMBC/EGNN1nYcffpiKigpmzZrFl770pSl/wU4IMb6EQiF2797N1q1b2bdvX2y73W7nmGOOYcWKFRQUDK8fW13X8fl8fUKQ+OXe24500PVod1q9w5H09HTpQmoUaJpGa2trLECJhin9XWxXVZXc3NxYt17RIGW0Q7j4Lsv6q6TpvS0YDGKxWLBYLJjN5thy/DTQ9sOdM5y/9bquEwwGY12n9Z4G2zfY8Uf6f8rlcg0YiqSkpEi1iBCjIFhXR+UVV8S6Rim+684JVZHh27WLpnvupfPll2Pb3J/6FNnXXI3jmGOS2DIxlejBIMGaGvwHDhA8eDDS1ZcRrITq6voc71ixgoyLLyJl7VpUuaFRTGEHv/pVPG+/Q8bcLvJXdMDqa2Dtz0E+84lBDCU3GPXQ5Mknn+QrX/kK9913H6tWreK2227jqaeeory8nLy8PC677DKKior4xS9+AcDmzZs55ZRTuOWWW/j0pz/NE088wc9//nO2bt3K4sWLD/t8EpoIMXK2b9/OM888g9ls5tprr50Q3d4IIaautrY2tm3bRllZGe3t7bHtBQUFLF++nMWLF6Np2qDhR/w+j8dz2G59+mMymXC5XDidTlwuFy6Xi8zMzFhAMhG705qMdF2nra0tFqBE5/2NcaEoSr9BitVq7fexg8HgEY3VEr/N6/UO69/baOgvUIluU1V1wABktNs0WLXIQD8LIcToCre3U3XtN/Fu2YJitVJ4662krj0z2c0alHfHTpruuYeu116LbUs580yyr7m63y6XhEgWzes1xk/Zt4/255+n698bY13HqamppJ13HhkXfR7bnDnJbagQY8xbtpUDl3wRFJ3Z5xzCcu7/wEnfA7nJVxzGuApNAO68805uvfVW6uvrWbZsGXfccQerV68G4NRTT2X69On8+c9/jh2/fv16/vd//5cDBw4wZ84cfvWrX3H22Wcf0XNJaCLEyPB6vdx55510d3fzqU99ipNPPjnZTRJCiCOiaRoVFRVs3bqV8vJywpEvl8NltVoTQpBo91ADbbNarVKVN0Hpuk57e3tCt161tbX9dqmmKAo5OTlkZmbi9/sTgpAjrY7oj9lsxuFw4HQ6E+a9l61WK6FQiGAwGJt6rw9l39H+P4lnsViwWq2DTkdyTPxkMpnk/5UQ45Tm81Hzve/R9a9XQVHI+9H/knnppcluVh+esjKa7rmH7v8YXQ+jKKSuW0fW1VdhnzsxuxYTU0vwUAPtG56h7an1BOO6G3WsWEH6RZ8n9ayzpPpETH4hP1UXnkrX7jbSZngovPnHcOzXk90qMUGMu9BkLEloIsTIeO6553j//ffJzs7m6quvlv7dhRATksfjYfv27WzdupWGhgYAHA7HEYUg0bn8/pvadF2no6OjT5DS3d096HmKogwaegwUiiRjLB4wxg45khBG07RBQ5BoNYoQYmrRw2Hqf3YzbU8+CUDW1VeR8+1vj4uw0/PeezTefTeet942NphMpJ3zabKuugrbzJnJbZwQw6CHw3Rv3kzbU0/R+dq/E6tPPvMZ0i/6vASBYnLyd+L7/YVUPFgF6My847vYzrwi2a0SE4iEJhKaCHFUqqqq+MMf/gDAV7/6VaZPn57cBgkhxFGKjhdhtVrlgq44arqu09nZSV1dHe3t7djt9j4BiM1mk39rQogpRdd1mu6+m6bf3wlA2oWfpeCnP0VJws0Huq7jefttmu66G8/77xsbzWbSzj+P7CuvxFpaOuZtEmI0BBsaaH9mA23r1xOsqYltdyxbRvrFF5N61lpUhyOJLRRihHQ3waMXUvPMAToOOkn5xAqKH3ws2a0SE4yEJhKaCDFs4XCY+++/n0OHDrF06VIuuOCCZDdJCCGEEEIIMUG0PvUU9T/5KWga7lNOoei23yVctNU1Dd3vR/P50P1+dJ/PWPb50Hx+dL8Pzesz5j4/us/bsz1+3edD8/vRvV5jHv84Xi/hlhbjCS0W0i/8LFmXX4G1uChJ74oQo0vXNLo3RapP/v1viHQVqqakRKpPLsI+T6pP+qPrOsGqKrzbtqFrGpb8AiwF+Zjz81FlHMLxoa0SHrmAwMEK9v0zF3SFGc/8FfvChclumZhgJDSR0ESIYdu8eTMvv/wyDoeD6667DpfLlewmCSGEEEIIISaQzldfpeY730X3+zFlZqJYLLFAQ/f7x6QNitVK+kUXkXX5N7Dk54/JcwoxHgQbGmjf8KxRfVJdHdvuWLrUqD5Zd9aUrj7RQyF8u8rxbt2CZ8tWPGVbCTc29XusKTMTS34+5oICLHl5mAvy40KVAix5uShJ6lp1ymgoh0cugM5a6j4opG0XuE46idIH7k92y8QEJKGJhCZCDEtbWxt33XUXwWCQz3zmM6xYsSLZTRJCCCGEEEJMQJ6tW6m65lq09vaBD7JYUG02FLvdmDvsqDa7sW6392yPX7fbUGx2VIfdmNttKHZHz3a7DcXhwJKfjyktbexesBDjjK5pdG9+KzL2yWuJ1Sfnnkv6xRdhnzcvya0cfeGubrwfbMO7ZSuerVvxbt+O7vEkHmSx4Fi0CNXpIFhXT7C+Ht3rPfyDKwrm7GwjVMnP7wlTCvJjYYs5OxvFZBqdFzfZVb0Hj38evK0E7XPY91gAPRhk2qOP4Dz22GS3blzSdR1N8xEOe9HRsFmzk92kcUVCEwlNhBiWJ554gvLyckpKSvja174mfbELIYQQQgghhi3c2Yl/zx4Umw01LviIhRtJGO9EiKko1NhIW7T6pKoqtt2+dAkZF0WqT5zOJLZw5AQPHcK7datRRbJ1C/7y3aBpCceoqak4ly/HsXIlzhXLsS9ejGq3x/bruo7W3k6wvp5gXR2h+vpImFJHKBKqhOrr0YPBwzfIbMacm2NUqOTn961WKcg3KvIUZaTfiolt77/gyS9D0ANFx3Ko4RRaHvkLjpUrmf7Yo8lu3bDpephw2EdY86KFvYTDHsJhb9y6N269Z184HLc/fl2Le4ywF03rCftSU5Zw3HEbkvhqxx8JTSQ0EWLIysvLeeKJJ1BVlauuuoq8vLxkN0kIIYQQQgghhBAjRNc0PG+/TeuTT9H56qs91SduN2mfOdcY+2T+/CS38sjpmoZ/7168W8vwbN2Cd8tWgjU1fY6zFBXhWLkC54qVOFYsxzZ7NspR3iSqaxrhlpZ+wpS6WLVKqKEBwuHDPpbj2JUU33Yb5mypCgBgx9Ow4WrQgjDrU4TX3smesz6D7vFQcv99uE8+OdktjAmFugkEGvD7G415oJGAvwF/oIGAvxF/oIFQqCsWaGja2HRRCZDiXsSqVX8fs+ebCCQ0kdBEiCEJBALcddddtLe3c+KJJ3LGGWcku0lCCCGEEEIIIYQYJaGmJto2bKDtqV7VJ0uWkHHxRaSuWzfuqk80vx/fjh14tpbh3bIFT1kZWkdH4kGqim3+PJwrVuJcuQLHihVYknRTqB4KEWpqGrBaJVhfFxtPxVJURMl992KbPTspbR033n0Anv8+oMOiz8IF99F47wM03XkntgULmPHMX0e9KkfXdUKhzkgYciguCImbBxrw+xsIh7uH/Tyq6sBkik5OTKoDNboe2dfvuuqMnafG7TPWnZhUOyaTA0WRbuF6k9BEQhMhhuTll19m8+bNpKenc+2112K1WpPdJCGEEEIIIYQQQowyXdPwvPMOrU89Ree/XoVIl1Oqy4Vj+XJUpxPV4UBxOiLLxrrqdKA4HMa60xHZ5jS2Rc5RHY6jGig91NqKt2xbbNB2386dfbrEUhwOHEuX4lyxAsfKFTiWLsXkdh/VezKWAgcOUHnVVQQPVqKmpFB8x+241qxJdrPGnq7Dxlvg9VuM9eMuh3W/QvP62POp09Da2yn63W9JXbfuKJ5CIxhs7Qk//A2R6pC4SpHIfCgVISaTE6s1B5s1F6stB5stD5s1B6s1F5stF7M5BZPJmRCSqKpdumRLgqHkBtKBqBBTXH19PW+99RYAZ599tgQmQgghhBBCCCHEFKGoKq41a3CtWUOouZn2DRtofWo9wcpKut988+ifwGKJBSgJAYwjLliJrjudqA47gQMH8WzdSmDfvj4PZ8rOxrliRaSKZCX2+fOOKphJNuv06Ux/4gmqr7se75YtVF5xJQU//QnpF16Y7KaNHU2DF34A7z1grJ9yA5x6AygKrU+tR2tvxzptGilnnjmshw8Emtn2wdfo6tqNroeO+DyzOTUSfMQFItZcIyCx5WGzGctm88QJ6cSRk9BEiClM0zSee+45dF1nwYIFzJ07N9lNEkIIIYQQQgghRBKYs7LIuvxyMr/+dbxbtxKorELzetC9XjSPF83rRfN4+m7zetE9nsg+Yz02lkcwiBYM9u1G6whZZ86MBSTOFcuxlJZOujv0zRkZlP7pj9T9z/9Hx3PPUff//S+Bg5Xk/Ne3j3rslXEvFIANV8GHzwAKrPsVrL4SAC0QoOWPfwQg68orUEzD626q4sBddHZ+GFu3WDKNShBbbiQMyY1bz4mEIbmYTPajfnli4pLQRIgpbOvWrVRXV2O1WjnrrLOS3RwhhBBCCCGEEEIkmaKqOI89Fuexxw7rfF3X0YNBI0iJhChad1zYEgtcouGLJy6A8WDOycG5ciWO5csxZ2SM8Ksbn1SrlcJbf4W1tJSmu++m+f77CVZXUfCLX6DabMlu3ugIdMOTX4J9r4Fqhgvug2M+F9vdvuFZQo2NmPPzSTv33GE9hddbRU3N4wAsOeY+srJORlWlhxVxeBKaCDFFdXV18a9//QuAT37yk6SlpSW5RUIIIYQQQgghhJjoFEVBsVrBasWUnp7s5kwYiqKQ863rsZSUUHfTTXQ8/wLBunqK77oTc2Zmsps3sjwt8NjnoeZ9sDjh4kdg9umx3XooRPODDwKQ9fWvGf+ehmF/xW3oepDMjE+Qk3P64U8QImKS13gJIQby8ssv4/P5yM/PZ9WqVclujhBCCCGEEEIIIcSUl37B+ZQ+8ABqairesjIOXPwF/Psrkt2skdNeA388ywhM7Olw2d8TAhOAjhdeJFhVhSkjg/TPfa7/xzmMrq7d1Nf/DYBZs757tK0WU4yEJkJMQfv372f79u0AnHPOOZiG2S+kEEIIIYQQQgghhBhZruNXM/2Jv2ApLiZYVcWBSy6h+513k92so9e0B/64Fpp2Q0ohfP1FKDku4RBd02i+/34AMr9yGarTOayn2rf/N4BObu7ZpKYuOdqWiylGuucSYooJhUL885//BOC4446juLg4yS0SQgghhBBCCCGEEPFsM2cy/aknqb72m3i3baPy8sspuPlnpJ9/frKbdmS6m6HhQ2jYBYc+hIaPoH4nhLyQNRu+vAHSS/uc1rXxdfx79qC6XGRceumwnrqt7X2aml5FUUzMnPHfR/tKxBQkoYkQU8ybb75Jc3Mzbreb0047LdnNEUIIIYQQQgghhBD9MGdmUvrnP1F74410vvAidTfcSLCyiuzrr0NRlGQ3zxDwQGO5EY40fNQTkHQd6v/4omPh0ifBld1nl67rNN13LwAZl16CKTV1yM3RdZ29+24FoKDgc7hcM4f8GEJIaCLEFNLc3Mwbb7wBwNq1a7Hb7UlukRBCCCGEEEIIIYQYiGq3U/Sb39BYUkrz/ffTdPfdBCorKfj5/0Md5gDpw6KFoaXCqB459FHPvGU/oPd/Tvo0yFsEuQshb6Exz54Hav8jRnjeeRffB9tRbDYyv/KVYTWzuXkj7e3vo6o2Zky/fliPIYSEJkJMEbqu889//pNwOMysWbNYvHhxspskhBBCCCGEEEIIIQ5DUVVyv/PfWKeVUvfjn9Dx3HME6+oovvP3mDMyRvbJdN2oEolWjEQDksbdEPL1f44zKxKMLILcBZC7CHLngy1lSE8dHcsk/cILMWf3rUQ5fNM19u3/NQDFxZdhtxcM+TGEAAlNhJgydu7cyf79+zGZTJx99tnjp4xTCCGEEEIIIYQQQhxW+oUXYikspPpb38a7ZQsHvvAFSu+7D+v06cN7QH9n4pgjhz4y5t6W/o83OyBnXq/qkUXgzoWjvM7k3bGD7s2bwWwm6xtfH9ZjHDr0HF1d5ZjNKUyfdvVRtUdMbRKaCDEFeL1eXnzxRQBOPvlksrKyktwiIYQQQgghhBBCCDFUrjVrmP6Xx6m68iqCBys5cPEXKL7rTpzHHntkDxDyw8s/go9fgLbK/o9RVMicGVc9EplnTAfVNGKvJV60yiTt05/GUlQ05PM1LcD+/b8DYFrplVgs6SPZPDHFSGgixBTw6quv0t3dTVZWFieeeGKymyOEEEIIIYQQQgghhsk2ezbTn3qSqmu/iW/7diq/9nUKfv5z0s495/Anb/kzvHtfz7o7v2e8kWhAkjMPLI5Ra39v/r176XzlX6AoZF15xbAeo7b2Kby+SqzWHEpKvjqyDRRTjoQmYtLQdR2/3093dzfd3d34fD4KCwtxu93JblpSVVdX8/777wNwzjnnYDbLf3shhBBCCCGEEEKIicycnc20h/5M7Q9+SOcrr1D7/e8TqKok+5prBu6SPeiFN35rLH/y/4PjLgdn5tg1egDNDzwAQMrpp2ObNWvI54dC3VQc+D0AM6Zfj8nkHNH2ialHrp6KcS0QCMRCkOjk8XgG3BYOhxPOV1WVuXPnsmzZMubMmYPJNDolhONVOBzmueeeA2Dp0qXMmDEjyS0SQgghhBBCCCGEECNBdTgouv02Gn7zG1r+8Eea7vg9wYOVFNz8MxSrte8J7/8JuuohrQRO/C8w93PMGAtU19D+3D8ByLryymE9RlX1nwkEmnA4SiksvGgkmyemKAlNxJgKBoN9Qo/+QpDo9mAwOOTnsFqtuFwuTCYTTU1NlJeXU15ejsvlYunSpSxbtozc3NxReHXjz7vvvkt9fT12u50zzjgj2c0RQgghhBBCCCGEECNIUVXyvv99rCWl1N98M+1/+xvBujqK77gdU3p6z4EBD7xpjPnByd8bF4EJQMsf/wDhMK4TT8RxzOIhnx8MtnLwoDEeyswZ/42qWka6iWIKktBEjLhAIEBDQwP19fXU19fT0NBAZ2cnHo8Hv98/5Mczm824XC5cLhdOpzO2HD/Fb7dYen45NjQ0UFZWxvbt2+nu7mbz5s1s3ryZoqIili9fzuLFi7Hb7SP58seN9vZ2XnvtNQDOOOOMKd9NmRBCCCGEEEIIIcRklfGFi7EUFVHzX/+F5913OXDJpZTcdy/W0lLjgPf/AN0NkF4Ky76Y3MZGhBobaXv6r8Dwq0wOHLyXcLgLt3sBeXlHMKaLEEdAQhNxVLq6umLhSHRqbm5G1/UBz1FVdcDAo79tVqt14L4YDyM3N5e1a9dy+umns2fPHsrKyvj444+pqamhpqaGF198kYULF7Js2TKmT5+OqqrDfSvGnRdeeIFgMEhJSQnLly9PdnOEEEIIIYQQQgghxChyn/QJpj3+OFVXX02gooIDF3+B4rvuwrl4Lrx5m3HQyd8H0/ioxmh56CH0QADHsmU4Vx035PN9vjqqqx8GYNas76Eok+e6nkguCU3EEdE0jZaWlj4BSVdXV7/Hu1wu8vPzyc/PJy8vj/T09FgQYrfbhx2CDJfJZGL+/PnMnz+fzs5Otm/fzrZt22hsbGT79u1s376d9PR0li1bxrJly0iPL1+cgHbv3k15eTmqqnLOOedMqjBICCGEEEIIIYQQQvTPPm8u0598guprrsX34YdUfvWrFH7tZFI9TZAxHZZekuwmAhBub6f18b8AkHXVlcO6VlhRcQeaFiA9fRVZmaeMdBPFFKbog5UETEAdHR2kpaXR3t5OampqspszIQUCAQ4dOkR9fX3CfKDxRbKysmIBSXRKSUkZ41YPna7r1NTUUFZWxs6dOxO6DpsxYwbLly9nwYIFCd19TQSBQIC77rqL9vZ2TjzxRBnLRAghhBBCCCGEEGKK0Twear7/A7pefRWAnCUdZN3wS5QVX0pyywxN99xD4+13YJs3jxnPbhhyaNLdvY+33zkL0Fi58inS01aOTkPFpDGU3EAqTaa4zs7OfrvX6o/ZbCYvLy8hHMnLy8NqHR8DRw2VoigUFxdTXFzM2rVrKS8vp6ysjIqKithks9lYvHgxy5cvp6ioaMwrZIbj9ddfp729nbS0NE45RVJ2IYQQQgghhBBCiN50XScc9hAKtRMMtqEoJszmVCyWNFTVMSGuAQ1GdTopvuN2Gr79BVr+tZPG7akEnv6IgmOCKEm+QVjzeGh5yOhWK+uKK4b1Xu/f/ztAIzv7dAlMxIiT0GSK0DSN5ubmPgFJd3d3v8e73e4+1SOZmZmTtpsnq9XKkiVLWLJkCa2trXzwwQeUlZXR3t7Oli1b2LJlCzk5OSxfvpwlS5aM20HVDx06xFtvvQXA2WefPWEDLSGEEEIIIcTEpGkBfL4avN5KPN5KQsF2FMUEioqCEresxuaK0mu5zzYTKErPMkpkv6nnMSPnRM83m1NwOIpRVVty3xAhxKgzwo9ugsF2QqE2gsHIFGonFLcc3R6KLbej64F+H1NRLLEAxWxOw2JJNebmNMzmFMyWyLIlNbItLXJsKiaTa9wELkqwm7yS97Cs9HNoawbtz2wgWFdH8e23Y0piDz1t69cTbmvDUlpK6llrh3x+R8d2GhpfABRmzfzOyDdQTHkSmkwRb7/9Ni+//HKf7Yqi9OleKy8vb0J0rzVaMjIyOPXUUzn55JM5cOAAZWVl7Nq1i8bGRl5++WX+9a9/MWfOHJYvX86cOXMwmUzJbjJgBGPPPfccmqYxf/585s2bl+wmCSGEEEIIISahUKgTj/cgXm8VXm8lXu/ByLwSn68O0JLdxAgFu60Ah6MUh3MaDsc0nI5pxrqjFLN5fN4MJ8RUpOs6muZH03yEQl0Eg61xoUc7wUgYEkoIQNoJBlsJhdrR9dCwnzsakIAeeawwuh4kGGwmGOy/N5bBH8+M2ZxqhC7mVMyRMCUWwES2GWFLKmlpyzCZnMNu/6DevQ+8rWQePwfr5b+i5rvfx/PW2xy45FIK/u9mbDNnoqamjmnIowUCNP/xTwBkXf4NFPPQL0/v2/drAPLzz8ftlutfYuRJaDJF5OXlYbFY+nSvlZubK9UIA1BVlZkzZzJz5ky8Xi8ffvghZWVl1NTUsHv3bnbv3o3L5WLJkiUsX76c3NzcpLa3rKyMqqoqrFYr69atS2pbhBBCCCGEEBOXrmv4Aw14PZU9oYivKhaMBIOtg56vqg4cjhIcjlKslkx0dNA1dMKg6+ho6HqvZXR0XQM93HN8/DJhjCFZBzo3bJyPhq5rBINthMNd+Py1+Py1tLa93aedFksWzgECFYslc9zcKS5EMsSHGGHNhxb2oWn+2HJY86KFe+/3Edb8aGFv33M0H+HIMcb5kX3RbZrvqNusKFYslnQsljQs5nQjmLBk9FpPx2JOjxyXjtmchsnkjP1/j1athEIdkcqV9kh40xGZtxMMdRAKdUQqVqLzdkKhDnQ9iK6HCAZbCAZb8B5Bu+32IlaueAK7vfCo34MEvnbY/Htj+ZQf4l7yKaY99ihVV19DYN8+Dl5yKQCq242luBhLURHW4iIsRcVYovOiIkxu14g2q+Pvfyd06BDm3FzSzj9/yOe3tGyipXUTimJl5oz/GtG2CRElA8FPEeFwGEVRJm33WmOpoaGBbdu28cEHHyR0b1ZUVMTy5ctZvHgxdrs94RxN0wiHw7F5dBrJ9ffffx+fz8fatWtZs2bNWL8tQgghhBBCiAlE0/x4vdWxICQ6ebyV+HxVaJp/0PMtlsxI0FAaCxqik9Wak/TAQdd1gsFm4zV5DsbCH09kHgy2DHq+yeQ2QhSnEaT0BCrTsNnyIt2ACTHx6bqGz1dHd/fHdHfviUx76fbsJRz2JKVNqmrFYs4wQo3+QhBzWmRfWiQAMbarqj2pv3uMoMkbC1CioUso1GEELcFeAUyoHY/nAMFgCw7HdFaufBKbNXvkGrTxl7Dx55A9D659C1Sjp5TgoUPU/+SneHfsINzUdNiHMaWnYykqigUrluIirNH1wkJUh+OIm6SHw+w7+2yCByvJ/eEPyfraV4f0knRd5733L6CzcwclxV9l7twfDel8MbUNJTeQ0ESIYQqHw+zZs4eysjL27NmDphkl6CaTCavVmhBsjNV/s/z8fK644opx02WYEEIIIYQQIpFRERFCj1ZSxE+E0bUwEO67r9cxeuQYIhUWPY8ZSjw2shwKtuH1VkW61arE768HBv6eoigm7LaiSFBQEgsMousTvWurni7GKiMVNQcj6wcj783AVNWK3V6K09kTpDhjgVE2oBAbdyW2rPTZnuxgSUwtuq7j99fRFQ1HuiIBiWcf4XD/491GKYoZVbWjqjZMJjuqasek2lFNNmPZ5DD2qXbU2H5jn2qyY1IdqKbI/tg24/F69sedoyZ3kPKx5PPVsWXrxfh8Nbjd81mx/HEslrSjf2BvG9y2BPzt8Lk/wuIL+z1M83oJ1tYSrK4mUFNDsLqGYHU1wRpjHm5vP+xTmbKzsRQVYi0q7hOsmAsLUeN6uOl4/nlqvvNdTGlpzH7tVVTX0KpYDjW8wM6d12EyuThhzWuR37lCHBkJTSQ0EWOsq6uL7du3U1ZWRmNj4xGdYzKZYpOqqke9brFYWLFiBZmZmaP8aoUQQgghhBADCYc9eH01+LzVeH1VkXl1bD0U6kh2E2NMJmevKpFpOOxGQGK3F06pC5fxwmFfT3dkkSoVj/dAZMyWmqMaO6Gv+FAlGqYQtxzdTq9jFPoNZFAgIYzpvS3uuOhqwuP0rEd29tpO3OMpmExObLa8nsmal7BuMh35HehiZETDke7uPXTFKkeM6pGBwhFFseB0TsflmoPLNRe3aw4u1xxsttwpF2Ikg8dzgC1bv0Ag0Ehq6jKWL3vo6IPpf/8cXv8l5CyAazbDMHueCXd1GQFKTU+YEqjuWde6ugZ/AEXBnJsbCVMK8W77gGBlJdnXX0fON785pLZoWoh33j0Lj6eCGdO/xcyZ3x7WaxJTl4QmEpqIJNF1nZaWFsLh8IABR3SbEEIIIcRkp+s6uh5A0wKRftHj53HLes+yrgUIa370+GP0YN876CN36zPAdmOsg1DCHfn93ok/yHYgckdtP3fP9nenbewO2ujxjl530kbOiTxmz7JduvqZQDQtgM9XGwlCquICkWq83qphDRocpShmFEVFUcyAmrBuzE1xkxklcgxKf8fGzVExmV2xShFnbNyOLKl2GCJNC+H31xrdfHkOxlWoGN2bjcS4DJOJ2Zzab5gSP1mt2SiK9JYwVPHhSHf33riAZC/hcP8XshXFjNM5IxKOGJPbNQeHY5oEI0nW1bWbLVsvJRRqIyNjDUuX/AGTyTa8B/O0wO1Lwd8Bn38IFp0/om2N0nUdraODQKwyJS5cqTXCFd3bd1QX1elk9muvYkpPH9Lz1dQ+SXn5/2CxZHLCmtcwm1NG6JWIqWIouYEMBC/ECFIUhaysrGQ3QwghhBBTlBFShIzwQe8dPsRNeqDv9khwYYQWAfSEQKNn0gcIO/oGIkYbJrrQSN5QPghFsWKK6+bEbE7BZHJjNqdEJjdmk7FsMsdtTzgmBZPJJQHMUdL1MH7/IbzeqkilSE1cxUgVfv8hBuvWCsBsTsFuL8FhL8LuKMFhL8bhKMFuL8ZqzUJRLP0GHGL8U1VzrDKHzE8k7DPGMwhg/PswJmNwej2yX+uzXQeILeuRZWKD3hvHRpd7thvH6nHnxT1n9N9nbFtkNeE4ep17pMfoCceFwp34/Yf6nTTNGxksu4Pu7j0DvqeKYsJqzcFmzY0EKfnYbD3LVlsudls+JpN7SoZ8RjhSn1AxEg1IBgtHHI7psYoRl9uYOx3TJRwZp9zueSxf9ie2ln2J1ta32Pnh9Ryz+K7h/bzeussITPIWw4LPjHxjIxRFwZSWhiMtDceiRX3267pOuLU1rkKlmlBdPe5TTh5yYBIO+6iouAOA6dOvlcBEjDoJTYQQQohJStMCkQEHOwiFeg06GGxHR6cg/zzjS78QYlTpuoam+QiHvcakedHCXsJhT2w9HPaghX29tkWO03qOja73Dieiy4e7mJtMimJFVeMnW+Ky0rOsJGy3oKg9d9X3udte6dlOZLsaf/c9qnG+YkKh//P6PqYJHR0t7EPT/IQ1X2TZF1n2xy0nHhPWjHVj2Rv52RvHG8vGfl0Pxt4bXQ8QCgWAzqN+n42wxZ0QqpiiwYs5JRK+uOOCFmNZNdl7LsLqGqD1XNhNWOcw++PXdXQ0oyIIrZ/Hj1ycVRQUVONnFuuKKG59oP2oceNDxK8PvN94LIVwuBuvtxpfpEIkWjHi89cetvslVbVjtxfjcBTjsJdgj82LcNhLRqY/ejHhKIoy/DvDJxld1wmHu/D56/H7DxGIBCm+uGW//xD+QGMkqKw3xpIZ5FegyeTEGglWohdM47sKI7ZlgC7KIHasEtf9WLSLMiXuMeIfM+FcXe8zXlBChWL8GEPEjzXUs1/TDzNukR4GEscpGoiimHA4ZuByze4JSFxzcDqno6rWAc8T41Nq6hKWLnmAbR98jaamV/lo1/dZtPA3Q6vE8rTAO/cay6feMOxuuUaCoiiYMzMxZ2biWLLkqB6ruuYR/P567LZCigovHaEWCjEwCU2EEGKKC4e9BIOtBIItBAOtBIOtBIMtBINtBEPGoG+xi0iYIhcwouuJ3UUQ7TaC+AtPkeOJdinR6/jIY/ZctIouW3pdTOu5uDaVyvc1zU9wgNAjGDK2x5aDkWNCHQSD7Wha31Lo3g4cuJPCwouZMf06bLbcMXhFQkx8gUALXV276Ooqx+OtROsVcoTDnsiFcU8sJDmS/4+jwfi9OkBQoVjjgglrbJuq9tqu9Pod3M/j9Ak/+pkrinVK3iE8GF0PRwKUSOgSDWXCHkKhTkLhLkKhTsKhTkKhLkLhTmN7KLo9us1Yj4Yw4XAX4XDXYQezFgNTFDN2e2FcIFIcCUlKsDtKsEq3VkIMSlEUzOYU3OYU3K45Ax6n62ECgaZeVSr1+P0NkVDF2BYKdRjjBXkP4PUeGLsXMk4Y4ch0XK7ZsS61jHBkhoQjk0xGxmqOWXwX23dczaFD/8BkcjF/3v8d+d+czXdAoAvyl8D8c0a3sWMkGOzgwIF7AJgx89sSTosxIaGJEEJMIuGwLxJ4tBIMthlBSLA1LgxpjQQkrbHjNM2f7GYPWeJFwH6ClYSLfgPv73M3s2KUPve+E1aPdIdg3CWr99wtG9tmHN/TpUJ8lwt6r/Mid9TGnafpoVjXBcFgeyQIMcKRkegX27iTOA2LOQ2zJTWynIrPV0NL6yZqah6jru6vlJR8lWmlV8rdsUJE6LqG13uQzq5ddHXuMuZdu476QrSq2jCZnJhUhzHmhckRWbejmpyRdUfcfmdsPXqs8butbyiR+PtuaoXME5GimDCbXYDrqB8r2i1QKBwNWeIClriwJbYvEsjEAphwF+GwL6Eqo98KD6V3RUf8uhK3XY3cvK3Gna/0+ziRV9Dn72ysK6OEahW9199eLeHvckLFSz9VLjo9f6dV1RbpMqsoMvh5sdGllqMYmy1P/v8IMQYUxRQb22Qw4bAnEqgYYUo43N3TDRkkdlVmbEjoZiyxyzIOcywJ6/FdlCXcQNbrxrKEMYnixyCidzWjURWpRqsjBxy/yBy7ycxiSUVV5ULxVJGd/UkWLfwtOz/8L2prn8BsdjN71g2HD066m+Cd+43lU2/sVXE1cVVWPkAo1I7LNYeC/AuS3RwxRUhoIoQYEl3XCQZb6O7eh8ezD6+vBqslE4ejBIejFLu9OPLlX4wUf6CJ7u49BAPNkSCkJ/AIBloJhox5INg67DuZFcWK1ZKBxZKOxZKBxZppzM3GwFhGcBBXaq5rkdLzcJ+yc/ocG9kWOza+fD16bPwUPS+Y0Dd+Qn/MehhdT96d22NP6Qk+YqGHEYBYzGmY45ctRiBiHJuG2Zwy6EWf1tZ32LfvVto7yjh48F5qah5nWumVlJR8BZPJOYavUYjkCoc9dHV9TGfXR0YVSecuurp3Ew57+j3e4SjF7V6AyzkLs9kdGfC7J/BQ44KQaOihRkIPGbdAjIZot0Amkw2s2clujhBCjCiTyYnTOQOnc0aymyLEmMjL+zThsIdd5TdQWfkgZlMKM2ZcN/hJm26HYDcULIN568aknaPN72+ksupPAMya+R25oUGMGQlNhBD90rQQPl813Z59eLr30e3Zj8ezj+7u/YRCbYOea7FkRUKUkshde6WxUEXu2huYpgXxePZH7mQuj0y7CASahvQ4imI2Ao/IZLVk9glDrHH7LZaMyKCx4/culJ6BjXsGHjYGKu5vgGN/bNDi/o/x9xrU2N8zsHGkW5PYnbORO2N7tkXvklV63X2rJBzf+5ieu28HOsaE2ZxihB2WSCBiTo2EHtHgY3QusmZkrGblyvU0Nb3Kvv2/obv7Y/bt/zVV1Q8xffo3KSq8WEr+xaSi6zqBQENc9chHRjdbngr6GwtEVW24XfNwu+fjTllIinsBbvc8GXxSCCGEEEKMqsLCzxMKd7Fnz/+xv+J3mMwuSku+1v/BXQ3w3oPG8if/Z9JUmRw4cBea5iU1dTnZ2WckuzliCpHQRIgpLhTqxuPZj8ezn+7uvbFwxOM5iK4HBjhLwW4vwuWahd1eQjDYYgye6a0iFGojGGwmGGymo2Nb3zMVi9E/dDRI6RWqTJWLUMFgK52du2LBSGdXOd3dewd4z5VY4NQThCSGHhZrZmybyeQe1wHIcCiKEhnjxJLspkxKiqKQk3M62dmfpP7QP9i//zZ8vio+/vgnVFb+gZkz/4v8vHMnbODp9zdy6NA/aGt/H1WxGF0b9VMFEKsGiHWFlLjdZHJO2PdgqoqG0V1d5UY4EuliKxhs6fd4qzUbt3tBJBhZQErKQhyO6aiqfGQWQgghhBBjr7Tka4RCXVRU3MaePf+H2ZRCYeHn+h646XYIeqBoJcw5c+wbOgq83kpqav8CwOxZ35901znE+CbfAIWYAqJ31Rpdau2n27MXT/d+uj37Bu2XXVVtOJ0zcTln4XTNwuWcidM5C6dzOiaTo99zgsEOfD4jQPFG595KvN4qfL4adD2I13sQr/dgv+ebzWmxAMUIVHpCFZutYMJdNNe0EF7vgbjqkV10dZbjDxzq93iTyW3czeyeT4p7Pm73AtzuudJNkhgTimKiIP988nLPpqb2SQ4cuBOfr4qPPvouBw/ex6xZ3yM761MT4sNqOOyjsekV6us30NLyptEd3AgwxolwJIYpaq+wpXfoYnZjtxVgsxdgtxVGKofG/3s40YRCnZEwOjr2yEd0d++JdO/Xm4rTOZOUlAUJIYnNljPm7RZCCCGEEGIwM6ZfRzjUSWXVH9hVfiMms4u83LjutzoPwXt/MJZPnTxVJvv2/w5dD5GVeTIZGauT3RwxxSi6rvfth2AC6+joIC0tjfb2dlJTU5PdHDHF6LqO31+PpgUiA7f1DJrZM0CmqdeyArGB446u+x1NC+D1Vka61IoPR/YTDncNeJ7FkoXLNatPOGK3F45ol0C6HsbvPxQJUarx+ipjFSpebyXBYPOg5xuDBBbEKlQslnTM5tTIWA/ReUrcemrkzvCx+cAQDLYnVI50dZXT3f3xgAOtG/3hz49csDPmdnuxXEwV40Y47KGq6iEOVt5HKNQJQFrqcmbN+v64/NCq6xptbe9TX7+BQw3PJ/zeS01dRm7OWhTVQjjsIRz2Eg570CLzsBaZh31o0f1az3H9dds0XCaTG7u9IBKkFGK3F2K3FRhzeyE2W96kGehT18PGe6r5euaaFy3sI6z5InMvWthvzOOPCxvrPcd6CWv+yDxuu+YlHPYPWB1phNHzSHEvxJ1iBCQu11xMJvsYvxtCCCGEEEIMj67rlJf/D7V1T6EoFpYuuY+srFOMnS/eCG/fDcXHwTdemRShSWfnR7z73rkArDru76SkLEpyi8RkMJTcQEITIY6S399AS+tmWls209K6Gb+/7igfcaAwRY10C6PEgpfY2AiKiq5r+P11A95NrSgm7PYSXK7ZsVDE5ZqJ0zkTiyX9KNs8MkKhbny+6rgqlZ5QxeerGjB8GIyimDCZUmLjRZjM7sgg2Sn9BC6pmM3uWOASDWB6V7foehivtzLWH35X9266Onfh89f22waTyYnLNa+nciRlPm7XPMxm97DeJyHGWjDYxsGD91NV/RCa5gMgM/MkZs36Hqkpi5PcOvB4Kqirf5b6+mfx+apj2+22QvLzzyc//wJcrpnDfnxd19E0f0/YokXDlt6Bi7cncAl7CGs+wmEPoVAnfl8dPn/dgN1C9Wa15kTClEKjQiWybLcbQYvVkjXqAauua4TD3QSD7YRCHQRD7YSCHXHL7YRCncZyZF8w1EE41BV5T3yDdPM4Ouy2QtwpCyPVesbc4SiRgdeFEEIIIcSEp+thdn743zQ0/BNVtbNs6Z/IMJXA7Ush7Icvb4BZn0p2M0fEtg++QXPzRvJyz2Hx4tuT3RwxSUhoIqGJGEWhUCetre/Q0rqJ1ta36O7ek7BfUUyoqg1d19B1DdAiQcbY/FczmVxGl1rRyhHnLJyumTgdpRP6zmVd1wgEmuK6+qomGOogFOoklDDvIBTqIhTqQNdDI/LcquqIBC4pqKoVj6cCTfP2e6zdXhTpUmt+bLBgh2OaXLATk4Lf30DFgTuprX0y9v8rN/dsZs38Dk7njDFtSzDYxqFD/6SufgMdHWWx7SaTm9zcdRTkX0B6+nHj7v9eOOzF56vD5681ghRfbc+yvxafr/aIAmJVtWKz5UeClEKjYiWhWqUAs9mFpgVjvxsHDD9CHZHAo/dyJ6CN2GuPdW2m2lFNdqNLM9Xes646UE22yDyyXY07zhS/bovbbqxHg24hhBBCCCEmK00Lsn3HNTQ3/xuTyc0KzypS33kKSo6Hr784KapMWlvfZWvZJSiKmeNXv4TTOT3ZTRKThIQmEpqIEaRpftrat0YqSd6is3N7r2oOhZSUhWRmnEhGxgmkpx/b73gfxn81rd8wRdfDxnY00Ptu7zkvbnvcsXrkopbdXoTNmifdOxG9M9zXK1DpjAtaOvsJXOLnnYTD3QM+vqracbvmGt1rpSzA7TLGIbFY5PeOmPw8noNUVNxO/aG/A3pkLJQLmTHjW9jtBaP2vJoWoLn5derqN9DU9O+4KgaVrMxPkJ9/ATk5Zww45tJEoOs6wWBrJEipjQUsvsiy31+H33+IIwniVdU2rAq9/h7HqL5Lw2IxKvEs5jRjm6XvssnsjoznYk8IO8ZbgCWEEEIIIcREFA772PbB12lrewdLUGPFB+24P7cBZp6S7KYdNV3X2bL1Itrbt1JUdCnz592c7CZNKOFwmGAwmOxmJJXFYsFkMvW7T0ITCU3EUdD1MJ2dH9HaupmWls20tb8f644myuGYTmbmCZGgZDUWS0aSWitGk6aFCIe7EgIXLezD4SjF6Zwe6S5NiKmrs6uc/ft+Q1Pza4BRSVBc9GWmTbsaqzVzRJ5D13U6Orcb45Qceo5gsDW2z+1eQEH+BeTlnYvNljsizzcRaFoQv/8QPn9dJFipxec3qlb8keVQqCPhnJ4uCNOM7gotaUYIEu2OsJ/wwxxZN5kmbpWiEEIIIYQQk1Eo1EXZxlPpUFuxhkwc+4lXcDinJbtZR62x6VW2b78SVbVzwprXsNnykt2kCaOrq4vq6mom2aX+IVMUheLiYtzuvl3iS2gioYkYAl3X8XoP0BIZk6S19W1CobaEY6zW7FglSWbmCdjthclprBBCjENtbe+zb/9vaGt7FzC6yCotvZzSkq8Ne+wen6+W+vpnqavfgMezP7bdas0hP+8z5OdfQErKghFp/2QUCnUSDLZHxmlKkZBXCCGEEEKIyaStiuDdK9hyjJNulxm7vZiVK5/EbstPdsuGTdfDvPPuOXR3f8y0aVcze9b3k92kCSMcDrNnzx6cTic5OTlTtgcaXddpbGzE4/EwZ86cPhUnQ8kNzKPZUCHGK7+/MVJJsqnfwdtNJjcZGavJzDiBjIwTcLnmTNlfOEIIcTjp6ceyYvnjtLT8h737fk1X10dUVNxGdfXDTJ9+LUWFlx5RtUIo1EVD44vU122gte3t2HZVtZOTcyYF+ReQkXECqiofXw5HxvcQQgghhBBiEnvjN1gCAZa3rmRLdgCv9yBlZZexcsVfsFqzkt26Yamv/zvd3R9jNqcyrfTKZDdnQgkGg+i6Tk5ODg7HxO2ueiTk5ORw4MABgsHggN10HQm56iCmhFCok9a2d2lp2URr6+Z+Bm+3kpa2nMxIJUlKyhK5KCeEEEOgKApZWaeQmXkSDQ3Ps2//7/B6D7Bnz/9RVflHZsz4Nvn55/f53arrYVpaNlFf/ywNjS8ldIeYnr6agvzPkpu7VgIAIYQQQgghhABoPQhljwBgO+UmludOY8vWi/B49rFt29dYseKxCff9SdP87K+4DYBp067GYklLboMmKLnhe+TeA7kqLMZM4gDoOhAd5FxPGBg9fsD0voOh6z3HoRsDoccGRI8bMB0dLeyhrX0LLS2bhz14uxBCiKFRFJW8vHPIyVlLXd1fqai4A5+/ll3lP+Rg5f3MmvkdcnLW0t39MXX1z1Bf/3cCgYbY+U7nDPLzLyA/73wcjqIkvhIhhBBCCCGEGIfe+DVoIZh5Kkw7AQewfNkjbNl6MZ1dH7Ltg2+wfNmfMZmcyW7pEaupeQKfrxqbNY+S4suS3RwhJDQRR0/Xw/j9h/D6avD5avB5q425rwavrxq/vw5NCyS7mTJ4uxBCjCFVtVBU9AXy88+nuuYRDhy4F49nHzt2fhOLJYtgsDl2rNmcTn7eueQXXEBqyhK5O0YIIYQQQggh+tNSAWWPGcun/k9ss8s1k+XLHmZr2aW0t29h+45rWbrkPlT18N0kJ1so1EXFgTsBmDHjermpWYwLEpqIw9K0EH5/fSQIqU4IR7y+Gvz+OnQ9NILPqEQGrFUjF85UFMWYosvGMSoKJlAUFFRQ1J65oqIoJlLcC2XwdiGESCKTyc600isoKvwCBysfpKrqjwSDzSiKhezsT1KQfwFZWaeiqtZkN1UIIYQQQgghxrf//Br0MMw6DUpXJ+xKSVnAsqV/oGzbV2hpeYOdH/43ixfdMe67n6+s+hPBYAsOx3QKCj6X7OYIAUhoIgBNC+Dz1SVUh/jighF/4FCvrq36UhQLdlsBdkcRdnsxdnsRDnt0uRCTyY4ReJgSQo+eECQ+DJE7jIUQYrIxm1OYNfO/KSn+Mh2dO0lLXSoVf0IIIYQQQghxpJr3wQd/MZY/+T/9HpKWtoIlx9zLtg8up7HxJXaV38DCBb+KXHMbfwKBZiorHwRg1szvoKqWJLdIjKV77rmHe+65hwMHDgCwaNEibrrpJtatW0dLSws//vGPefnll6msrCQnJ4fzzz+fm2++mbS00R/zRkKTKSIQaKKzq7xP11k+Xw1+/yFAH/R8RbFitxfiiIQgdkdxZLkIu70Imy03Uh0ihBBCDMxqzSY769RkN0MIIYQQQgghJpb/3GpUmcw5E4qPHfCwzMwTOWbxHezY+U3q6zdgNruZO+fH4/Im5QMH7yUc7iIlZRG5ueuS3RwxxoqLi7nllluYM2cOuq7z0EMPcd5551FWVoau69TW1vLrX/+ahQsXcvDgQa6++mpqa2t5+umnR71tim6MrD1pdHR0kJaWRnt7O6mpqcluzrhRXfM4u3f/aMD9qmqLVYUYYUhk2WEsW63Z4zaVFkIIIYQQQgghhBBi0mraC3cdB7oGV/wbilYc9pT6+r/x4UffBXSmT7uGWbO+N/rtHAKfr5bNb52GrgdYtvTPZGWdlOwmTVg+n4+KigpmzJiB3W5H13W8wcF7DRotDovpqAK6zMxMbr31Vr7xjW/02bd+/Xq+9KUv0d3djdncfy1I7/ci3lByA6k0mSKcjmm4XHMilSHFOOyFRjDiMKpFrJascZk4CyGEEEIIIYQQQggxpb3+SyMwmbvuiAITgPz88wiFu9m9+0ccOHgPJnMK06ddNcoNPXL7K25H1wNkpB9PZuYnkt2cScUbDLPwppeS8twf/WwtTuvQI4dwOMz69evp7u5mzZo1/R4TDTsGCkxGkoQmU0Rm5okcv/rFZDdDCCGEEEIIIYQQQghxpBp3w85Id0SfvHFIpxYXXUo41Mnefb9i375fYTa5KS7+4ig0cmi6uvdQV/cMALNmfV9u5J7CduzYwZo1a/D5fLjdbjZs2MDChQv7HNfU1MTNN9/MlVdeOSbtktBECCGEEEIIIYQQQgghxqNolcn8c6Bg6ZBPnzbtKkKhLg4cvJvdH/8Yk9lFQf75I9/OIdi//7eARk7OmaSlLUtqWyYjh8XERz9bm7TnHop58+axbds22tvbefrpp/nKV77C66+/nhCcdHR08OlPf5qFCxfyk5/8ZIRb3D8JTYQQQgghhBBCCCGEEGK8adgFO42KDE69YdgPM3PmdwiFu6iufphdu36A2eQkJ+fMEWrk0LS3b6Ox8WVAZebM7ySlDZOdoijD6iIrGaxWK7NnzwZg5cqVvPfee9x+++3cd999AHR2dnLWWWeRkpLChg0bsFgsY9KuifHuCSGEEEIIIYQQQgghxFSy8RZAhwWfgfxjhv0wiqIwd86PCIe6qKt/hh07v82sWd/BbivEbE7FYknDbE7BbDbmqjo6F6Z1XWfvvl8BUFDwWdyuOaPyPGLi0jQNv98PGBUma9euxWaz8fe//73PwO6jSUITIYQQQgghhBBCCCGEGE8OfQgfPWssH0WVSZSiqMyf/wtCYQ+NjS+yd+8tAx5rMrkiIUoqFnMaZksqZnNqwrrFHN2WuG4yuQYco6Sl5U3a2t5BVa3MnPHto35NYmK78cYbWbduHaWlpXR2dvL444+zceNGXnrpJTo6OjjzzDPxeDw8+uijdHR00NHRAUBOTg4m09C6ARsqCU2EEEIIIYQQQgghhBBiPNkYCTUWXQB5i0bkIVXVzOJFv+XAgbl0dn1EKNRJKNROKNhBMNRBONwFQDjcTTjcjd9fP+TnUBRTLGAxQpZUzJFKltbWdwAoLvoydnvhiLwmMXE1NDRw2WWXUVdXR1paGkuWLOGll17ijDPOYOPGjbzzjvHvJdp9V1RFRQXTp08f1bZJaCKEEEIIIYQQQgghhBDjRd122PV3QIFTjr7KJJ6q2pg5s/8qD00LEQ53Egx2EAoZUzDUQSjY3rPcz3owsq7rQXQ9TDDYSjDY2u9zmExupk27ekRfk5iY/vCHPwy479RTT0XX9TFsTSIJTYQQQgghhBBCCCGEEGK8eP2XxnzxhZA7f8yeVlXNqGoGFkvGkM/VdR1N8yWEKL1Dl1Cok6ysU7FaM0eh9UKMHAlNhBBCCCGEEEIIIYQQYjyo3Qblz4Giwik/THZrjpiiKJhMDkwmBzZbXrKbI8RRUZPdACGEEEIIIYQQQgghhBD0jGVyzOchZ25y2yLEFCWhiRBCCCGEEEIIIYQQQiRbzRb4+AWjyuTkHyS7NUJMWRKaCCGEEEIIIYQQQgghRLJFq0yWfAGyZye3LUJMYRKaCCGEEEIIIYQQQgghRDJVvQd7XgbFBKd8P9mtEWJKk9BECCGEEEIIIYQQQgghkmnjL4z5sksgc2Zy2yLEFCehiRBCCCGEEEIIIYQQQiRL5Tuw71VQzXCyVJkIkWwSmgghhBBCCCGEEEIIIUSybPy5MV/2RciYntSmCCEkNBFCCCGEEEIIIYQQQojkOLgZ9m8E1QInfy/ZrRFCIKGJEEIIMfUEPMYH8w+egJb9yW6NEEIIIYQQQkxd/45Umaz4MqSXJrctQoyhe+65hyVLlpCamkpqaipr1qzhhRdeiO2/6qqrmDVrFg6Hg5ycHM477zzKy8vHpG3mMXkWIYQQQiSHrkPrAah+z5iq3oVDO0EL9RyTMx/mrYN5Z0PRSlBNSWuuEEIIIYQQQkwZFW/AgTfAZIWTvpvs1ggxpoqLi7nllluYM2cOuq7z0EMPcd5551FWVsaiRYtYuXIlX/ziFyktLaWlpYWf/OQnnHnmmVRUVGAyje51C0XXdX1Un2GMdXR0kJaWRnt7O6mpqclujhBCCDG2At1QW2aEI9XvQ/W70N3Y9zh3PqSXGMfGByiuHJiz1ghRZn0SrK6xa7sQQgghhBBCjCZ/J7RUGBX3rRXGcmuFcaNZ0GcMxK6ajRvJYsvRddNh9g+2PsAxHz4L9dvhuMvh079J9rsjJiifz0dFRQUzZszAbrcbN08GPclpjMUJijLs0zMzM7n11lv5xje+0Wff9u3bWbp0KXv37mXWrFn9nt/nvYgzlNxAKk2EEEKIiUrXjQ/70XCk6l049CHo4cTjVAsULIHiVVBynDFPKzY+yHjbYO+/YPcLsOcVI2DZ9qgxmWww8xQjQJl7FqQWJuVlCiGEEEIIIcQR0XXjO000DOkdkHiakt3Cvkw2+MR3kt0KMZkEPfDzJH1//5/aYd18GQ6HWb9+Pd3d3axZs6bP/u7ubv70pz8xY8YMSkpKRqKlg5LQRAghhJgo/F1QuzWuiuS9/j/0pxRGwpFIQFKwFCz2vscBONLhmM8ZUzhojHXy8YtQ/k9oOwh7XjYm/hsKl8PcdUaIkn/MUd09IoQQQgghhBDDEg5BR3ViMBKbH4BA1+DnO7MgYwZkzkic21KMKnwtHJmHjBvSem/rd/1wxwzyOPPOgrSiMXnrhBhvduzYwZo1a/D5fLjdbjZs2MDChQtj+++++25+8IMf0N3dzbx583jllVewWq2j3i7pnksIIYQYj6JVJFXvGlUk1e9Fqki0xONMViMUKV4FxcdCSaSKZCSev7HcqEDZ/YLx/MR9ZEgtNj7cz1sH008Cs+3on1MIIYQQQgghAIJeaD0YCUP2JwYjbZWgBQc5WTG+E2VM7xuMZM4Ae9pYvQohxsRE7p4rEAhQWVlJe3s7Tz/9NA8++CCvv/56LDhpb2+noaGBuro6fv3rX1NTU8OmTZv6dL0VNVLdc0loIoQQQowH/k6o2RIZrD0yaLu3pe9xqUVGBUnJqkgVyZKxCSy6GuDjl4wqlH2vJX4As7ph9mlGFcqcM8GVNfrtEUIIIYQQQoyeQDd4WiAcMCrSE+aRZW2A7f0uh47gmMhyZz101g7ePpPVCEX6qxjJmCY3dYkpZbCgYKI5/fTTmTVrFvfdd1+ffYFAgIyMDB588EEuueSSfs8f92OatLS0cP311/OPf/wDVVW58MILuf3223G73QMe/+Mf/5iXX36ZyspKcnJyOP/887n55ptJS5MEWAghxBjQwhDy93xoj18OByAUXfYbH+j7HBuM7Is/tr/HiT82aHwZadrdTxWJDQqXRbrZikzJKtt258KKLxtT0AsV/4Hdz8PuF6GrHj76mzEpKpQcH6lCORuy5ySnvUKIiU3TwN8OvnZj7CUtDCl54M4DkyXZrRNCCCEml6APDu2Emq1Gd8C1ZdC4m4RK82SwpfZfKZIxwxhvUTUlt31CiBGnaRp+v7/ffbquo+v6gPtH0qiFJl/84hepq6vjlVdeIRgM8rWvfY0rr7ySxx9/vN/ja2trqa2t5de//jULFy7k4MGDXH311dTW1vL000+PVjOFEEJMVroO/g7wNEN3szH3NEXWm4ygove6vz25bU4riasiOc4YN2Q83iFlccDctcb0aQ3qthldeH38AtTvgMrNxvTKTZA1OzKQ/DooWQ0mGU5NHAVdh5DPCO6CHuMLftBjrIcDoJojk8m4sB5bj0yxbSZQLYnHyxg9Iy8UAF+bEXr42uOW2/puj4Yj0WVfBwNeqHHlQEo+pBQMPHflyIUUIYQQoj/hIDTs6glHarZCw0fG+Bq9mazGjVwmS2TZGrfc37b+lnsff5jzVAu4so1gxJkpn9GEmMRuvPFG1q1bR2lpKZ2dnTz++ONs3LiRl156if379/Pkk09y5plnkpOTQ3V1NbfccgsOh4Ozzz571Ns2Kt1z7dq1i4ULF/Lee+9x7LHHAvDiiy9y9tlnU11dTWFh4RE9zvr16/nSl75Ed3c3ZnP/F1n8fn9CutTR0UFJSYl0zyWEEJNNKGB0VxULOZp7pth6JPyIrg/az+0RMNmM0MJk6fmyYLbFfbC3gjm6HL8/evwR7re4jIAktWBk3qtkaqsyuvDa/TxUvJH4M3BkwJy1MPt0445xW6ox2GJ0PtBg9RNByG9c5PW1R+6Ojy53QMATuUhv6nUR/3DrvbYp/T3GAI9zuC+Xum5UNuk6oMfNe23TtV779X7O6XWcHk4MM4KeXkGHN26KWw/1sy3+uJBvdPvljb1/loFDF9VsBH+9j1XUyNzUsx6/rc+y2uvYAc4b9LFUQDF+1orSs5wwVwfZF9kPg+zr5zwtNHDQ0Xs55D36n4vZYfQ7rpqMbgKP9Pe6ohpVKYcLVxyZxs9DCCGEmIw0DZr39IQjtVuNm5xCvr7HOrOhaAUUroDC5caUkjf2bRZCDNlE7Z7rG9/4Bq+++ip1dXWkpaWxZMkSfvjDH3LGGWdQW1vL5ZdfzpYtW2htbSUvL4+TTz6Zm266iXnz5g34mOO6e6633nqL9PT0WGACRn9kqqryzjvvcMEFFxzR40RfwECBCcAvfvELfvrTnx51m4UQQvTi7zS6W/J39lxI1bW4i6NarwuoWuJxCcdEz2OAY3ptC4d6BSRHUQVicRljbDizjC8CzizjziVnZq/1LOPimcUeucPJLHc1DUd6Cay6wph8Hcb4J7tfgD0vgbcVtj9hTP0xWSMhSjRISe1Zt6cOvC+2P7JtqNU54ZARbvg7eu5w93ckBh++9rjljr7L4dEvDx6S6AX3/sKQyUK1GIMMWhw9/2+1sHFRXwsbF9e1kDGFI3Mt2LcbvKjosfRzEUEcHVsaONKM8MOeDo70Xsvpfbfb04z1+P/Pmmb8beisi/R1PsC865Dxc+6sMybKBm6baokEKPlxgUpBT7Dizov8/ciUbsGEEEKMb7oObQfjutjaZkyBzr7H2lKNboALV0SCkuVG1bt8/xFCjKE//OEPA+4rLCzk+eefH8PWJBqV0KS+vp7c3NzEJzKbyczMpL6+/ogeo6mpiZtvvpkrr7xy0ONuvPFGvvOd78TWo5UmQgghjkJnPTx6odGv7XiiqEawEQ05olNsPRKGxO+3OJLd6qnLngqLzjemcAiq3zUqUCrfiYQUnUboEP0iFw70VA8dDZOtV6CS1hOm+Lv6hiOBrqN9pT2skeeLhjj2NLA6jQu4sQv6obgL+6Fe2wc6pr/1Qe6418MQDo/c6xpU7woFUyTIiE7OnmVzP9vijzPb4/bFBSLx28x2Y3m4F7A1LfL+BHu9t9GAJRj3Pgd73vfBjo9W2GhhYx79ecfP++zvb9tg5+g9y9H9CZU+HL4KaKD5gPv6qUZSTD2BRny4MdCyLXXkuslSVeP3uyvbqM4b8Gcchu7Gw4cr3Y3Gz7i9ypgOx5YWCdzj/v70WY+bHOnSRZgQQojR01GX2MVWbZlxc0FvZgcULO0JRwpXQOZMqbQUQohBDCk0ueGGG/jlL3856DG7du06qgaBEXx8+tOfZuHChfzkJz8Z9FibzYbNNg77exdCiImqpQIeOR9aD4ArF2acFNdNi9rT3UtC9y9qr+5c4tZj3b/03qYMfJ5qMrpy6l0JYk+XD/cTlckM004wpt40zQgu/J09YUq0isPfGTd1JIYtvfdFw4+wHzx+o7u2oYh2AxQLPCKhR8JyWt/t0XDEljL2F0g1zbhwrw8QvAzWRdOAXTMNdnzvbRPwbkRVBVSpGpjMVFNP5chgQgHobhg8WOmsN6rk0I2KR387tFYcYUOUyN+y/kKW/sKWTON3zEB/53TdCJdD/ri533gdCXP/8I9Djws3o+Flf8Glc+BjzI6xHb9K1422h3xxrzVuPeSLTIFe2/zG7zVnpjE54n42FufE/P3Wm6YZfx+9rUbXeY5MyJiW7FYJIYbD09ITjESDks66vsepFshf3BOOFK2A7HkyrqAQQgzRkH5rfve73+WrX/3qoMfMnDmT/Px8GhoaEraHQiFaWlrIzx/8y0tnZydnnXUWKSkpbNiwAYtFvtAKIcSYqd8Jj37W6NokYzp8eYNxF5IQo0lVI0FEKlA0/MfRwj3hS0Ko0m7Mgz6wufsJPNIjlSjWkXpFY0dVQZ2A7RZiPDBbIa3YmAajhY3qtPixtBKmlr7bfO2Abtzx620x+pM/EorJuGhvdfUNOcKBo37JYyah+7z+wpdelWeq2jfUCPcTcsTmccHIaHSPaLbHhSgZPV14xgdejsyewMWZBVb36AUtIb8xTlA0/PC2Rqb45X72+dr6dkk49yw48dtQumZyBENCTFaeFqj4D+zfaMxb9vU9RlEhZ34kHImMQZK3eOhd1QohhOhjSKFJTk4OOTk5hz1uzZo1tLW1sWXLFlauXAnAa6+9hqZprF69esDzOjo6WLt2LTabjb///e8TauAaIYSY8CrfhscvMi705C6CLz9z+Lt0hRhP1Ei3QfY0SEt2Y4QQk4Zq6rk4zpwjOyccNC5cDxqytCRuC3QaVWPdjcZ02HZZjAtjJmuvuc0IhPqbm6z97Is7FwVCXiNkDnog6DWmUGQe2+aJHBO3LeTtaZsW7KnMGWtmu/Fa4ufR19l7nxaK/JwiPwtvS09Y01lrTEdKtfQKVTL6CVki66p54PDD19YrDGmDYPfRvScWp3GDQGcdfPyiMRUfByd8C+Z/WrqRE2I8CHRD5Vuw/3UjKKnfQZ+x6DJnGcFIdLD2giVGyC6EEGLEjUp93oIFCzjrrLO44ooruPfeewkGg1x33XV84QtfoLCwEICamhpOO+00Hn74YVatWkVHRwdnnnkmHo+HRx99lI6ODjo6OgAjrDGZ5IOcEEKMmo9fhqcuMy54lBwPlz5hfNkXQgghxNCZLODONaYjFfL3XLwPegYPQ0zW8dddpa4bYUNCuOLtG6wkbIuEM1ooEmTEhxrWoQUg0ffqaKondN2oWIwPUXqHXN7oelwoFvYbQVFXvTGNCqVnzCBHRq+p17aEY9J77jpv3gebfw/bHofq9+CpLxsXYU+4HpZeYlQDCSHGRjhkdLO1f6MRlFS903e8upwFMPMUmHEKTFsj38+EEGIMjVqnho899hjXXXcdp512GqqqcuGFF3LHHXfE9geDQXbv3o3H4wFg69atvPPOOwDMnj074bEqKiqYPn36aDVVCCGmtu3r4dmrjQsWs8+Aix42Bq4WQgghxNgx2yC1wJgmIkXp6XqLzGS3ZngUxeiu0ZZy5GN/6LoR/PQJWnqHLNH1VqMSqb+wo99QJLI+2Hg3RyprFpx7G3zyf+Cd++C9B4wuf577L/j3z2H1VXDcN+TCrBCjQdehYRdURCpJDmwyKgzjpRbDzFMjQcnJUvUvhBBJpOi6rh/+sImjo6ODtLQ02tvbSU1NTXZzhBB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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_max = omega_Shrink_df_max.index.astype(str)\n",
    "x_max=x_max.values.tolist()\n",
    "for i in range(len(x_max)):\n",
    "    x_max[i]=x_max[i][:3]+'\\n'+x_max[i][3:]\n",
    "plt.figure(figsize=(20,8))\n",
    "names = {}\n",
    "for j in range(10):\n",
    "    names[f'y{j + 1}'] = omega_Shrink_df_max[omega_Shrink_df_max.columns[j]].values.tolist()\n",
    "    plt.plot(x_max, names[f'y{j + 1}'],label = omega_Shrink_df_max.columns[j])\n",
    "    plt.legend()\n",
    "    plt.grid()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['201\\n801', '201\\n802', '201\\n803', '201\\n804', '201\\n805', '201\\n806', '201\\n807', '201\\n808', '201\\n809', '201\\n810', '201\\n811', '201\\n812', '201\\n901', '201\\n902', '201\\n903', '201\\n904', '201\\n905', '201\\n906', '201\\n907', '201\\n908', '201\\n909', '201\\n910', '201\\n911', '201\\n912', '202\\n001', '202\\n002', '202\\n003', '202\\n004', '202\\n005', '202\\n006', '202\\n007', '202\\n008', '202\\n009', '202\\n010', '202\\n011', '202\\n012', '202\\n101', '202\\n102', '202\\n103', '202\\n104', '202\\n105', '202\\n106', '202\\n107', '202\\n108', '202\\n109', '202\\n110', '202\\n111', '202\\n112']\n",
      "['201\\n801', '201\\n802', '201\\n803', '201\\n804', '201\\n805', '201\\n806', '201\\n807', '201\\n808', '201\\n809', '201\\n810', '201\\n811', '201\\n812', '201\\n901', '201\\n902', '201\\n903', '201\\n904', '201\\n905', '201\\n906', '201\\n907', '201\\n908', '201\\n909', '201\\n910', '201\\n911', '201\\n912', '202\\n001', '202\\n002', '202\\n003', '202\\n004', '202\\n005', '202\\n006', '202\\n007', '202\\n008', '202\\n009', '202\\n010', '202\\n011', '202\\n012', '202\\n101', '202\\n102', '202\\n103', '202\\n104', '202\\n105', '202\\n106', '202\\n107', '202\\n108', '202\\n109', '202\\n110', '202\\n111', '202\\n112']\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 2000x800 with 1 Axes>",
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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = rexc_df['yearmonth'].astype(str)\n",
    "x=x.values.tolist()\n",
    "for i in range(len(x)):\n",
    "    x[i]=x[i][:3]+'\\n'+x[i][3:]\n",
    "plt.figure(figsize=(20,8))\n",
    "names = {}\n",
    "for j in range(2):\n",
    "    names[f'y{j + 1}'] = rexc_df[rexc_df.columns[j]].values.tolist()\n",
    "    print(x)\n",
    "    plt.plot(x, names[f'y{j + 1}'],label = rexc_df.columns[j])\n",
    "    plt.legend()\n",
    "    plt.grid()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "rexc32=r32-rf[1:]\n",
    "rexc33=r33-rf[1:]\n",
    "rexc34=r34-rf[1:]\n",
    "rexc35=r35-rf[1:]\n",
    "rexc36=r36-rf[1:]\n",
    "rexc37=r37-rf[1:]\n",
    "rexc38=r38-rf[1:]\n",
    "rexc39=r39-rf[1:]\n",
    "rexc40=r40-rf[1:]\n",
    "rexc41=r41-rf[1:]\n",
    "rexc32"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "R=np.concatenate([rexc32[:,None],rexc33[:,None],rexc34[:,None],rexc35[:,None],rexc36[:,None],rexc37[:,None],rexc38[:,None],rexc39[:,None],rexc40[:,None],rexc41[:,None]],axis=1)\n",
    "R"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "Cov_Sample=np.mat(np.cov(R,rowvar=False))##将列作为一个变量\n",
    "Cov_Sample"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "X = np.mat(np.concatenate([np.ones((len(mkt)-1, 1)), mkt[1:, None]], axis=1))\n",
    "X"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "Y = np.mat(R)\n",
    "AB_hat = (X.T*X).I*(X.T*Y)\n",
    "ALPHA = AB_hat[0]\n",
    "BETA = AB_hat[1]\n",
    "RESD = Y - X*AB_hat\n",
    "covfactor = np.cov(mkt[1:])\n",
    "covresidual = np.diag(np.diag(np.cov(RESD, rowvar=False)))\n",
    "Cov_Factor = BETA.T*covfactor*BETA + covresidual\n",
    "Cov_Factor"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "c = 0.5\n",
    "Cov_Shrink = c*Cov_Sample + (1-c)*Cov_Factor\n",
    "Cov_Shrink"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "uhat = np.mean(R, axis=0)\n",
    "uhat"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "\n",
    "A = np.mat(np.concatenate([uhat[:, None], np.ones((len(uhat), 1))], axis=1)).T\n",
    "A"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "\n",
    "up = np.mean(uhat)\n",
    "up"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "b = np.mat(np.array([up, 1])[:, None])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "Cov_Sample"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "diagnal_average=np.mean(Cov_Sample.diagonal())\n",
    "diagnal_average\n",
    "# for i in range(10):\n",
    "#     sum"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "\n",
    "sum_notdiag=0\n",
    "for i in range(10):\n",
    "    for j in range(10):\n",
    "        if i!=j:\n",
    "            sum_notdiag=sum_notdiag+Cov_Sample[i,j]\n",
    "sum_notdiag\n",
    "average_notdiag=sum_notdiag/90\n",
    "average_notdiag"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "Cov_Const=Cov_Sample\n",
    "for i in range(10):\n",
    "    for j in range(10):\n",
    "        if i==j:\n",
    "            Cov_Const[i,j]=diagnal_average\n",
    "        else:\n",
    "            Cov_Const[i,j]=average_notdiag\n",
    "Cov_Const"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "c_new=min(((1-2/10)*(Cov_Sample**2).trace()+Cov_Sample.trace()**2)/((len(rexc32)-2/10)*((Cov_Sample**2).trace()-Cov_Sample.trace()**2/10)),1)\n",
    "c_new=c_new[0,0]\n",
    "c_new"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "Cov_Shrink = (1-c_new)*Cov_Sample + c_new*Cov_Factor\n",
    "Cov_Shrink"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
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