Recession Prediction XGBoost.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import warnings\n",
    "import itertools\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>RPI</th>\n",
       "      <th>W875RX1</th>\n",
       "      <th>DPCERA3M086SBEA</th>\n",
       "      <th>CMRMTSPLx</th>\n",
       "      <th>RETAILx</th>\n",
       "      <th>INDPRO</th>\n",
       "      <th>IPFPNSS</th>\n",
       "      <th>IPFINAL</th>\n",
       "      <th>IPCONGD</th>\n",
       "      <th>...</th>\n",
       "      <th>DNDGRG3M086SBEA</th>\n",
       "      <th>DSERRG3M086SBEA</th>\n",
       "      <th>CES0600000008</th>\n",
       "      <th>CES2000000008</th>\n",
       "      <th>CES3000000008</th>\n",
       "      <th>UMCSENTx</th>\n",
       "      <th>DTCOLNVHFNM</th>\n",
       "      <th>DTCTHFNM</th>\n",
       "      <th>INVEST</th>\n",
       "      <th>VIXCLSx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Transform:</td>\n",
       "      <td>5.000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.000</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>6.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>6.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/1/1959</td>\n",
       "      <td>2442.158</td>\n",
       "      <td>2293.2</td>\n",
       "      <td>17.272</td>\n",
       "      <td>2.922664e+05</td>\n",
       "      <td>18235.77392</td>\n",
       "      <td>22.0151</td>\n",
       "      <td>23.3984</td>\n",
       "      <td>22.2848</td>\n",
       "      <td>31.5847</td>\n",
       "      <td>...</td>\n",
       "      <td>17.791</td>\n",
       "      <td>11.326</td>\n",
       "      <td>2.13</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.04</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6476.00</td>\n",
       "      <td>12298.00</td>\n",
       "      <td>84.2043</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2/1/1959</td>\n",
       "      <td>2451.778</td>\n",
       "      <td>2301.5</td>\n",
       "      <td>17.452</td>\n",
       "      <td>2.944247e+05</td>\n",
       "      <td>18369.56308</td>\n",
       "      <td>22.4463</td>\n",
       "      <td>23.7142</td>\n",
       "      <td>22.4778</td>\n",
       "      <td>31.8164</td>\n",
       "      <td>...</td>\n",
       "      <td>17.798</td>\n",
       "      <td>11.343</td>\n",
       "      <td>2.14</td>\n",
       "      <td>2.46</td>\n",
       "      <td>2.05</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6476.00</td>\n",
       "      <td>12298.00</td>\n",
       "      <td>83.5280</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3/1/1959</td>\n",
       "      <td>2467.594</td>\n",
       "      <td>2318.5</td>\n",
       "      <td>17.617</td>\n",
       "      <td>2.934187e+05</td>\n",
       "      <td>18523.05762</td>\n",
       "      <td>22.7696</td>\n",
       "      <td>23.8577</td>\n",
       "      <td>22.5882</td>\n",
       "      <td>31.8164</td>\n",
       "      <td>...</td>\n",
       "      <td>17.785</td>\n",
       "      <td>11.363</td>\n",
       "      <td>2.15</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.07</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6508.00</td>\n",
       "      <td>12349.00</td>\n",
       "      <td>81.6405</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4/1/1959</td>\n",
       "      <td>2483.671</td>\n",
       "      <td>2334.9</td>\n",
       "      <td>17.553</td>\n",
       "      <td>2.993228e+05</td>\n",
       "      <td>18534.46600</td>\n",
       "      <td>23.2547</td>\n",
       "      <td>24.2022</td>\n",
       "      <td>22.9191</td>\n",
       "      <td>32.3184</td>\n",
       "      <td>...</td>\n",
       "      <td>17.796</td>\n",
       "      <td>11.403</td>\n",
       "      <td>2.16</td>\n",
       "      <td>2.47</td>\n",
       "      <td>2.08</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6620.00</td>\n",
       "      <td>12484.00</td>\n",
       "      <td>81.8099</td>\n",
       "      <td>1.0000</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>3/1/2022</td>\n",
       "      <td>17665.439</td>\n",
       "      <td>14442.9</td>\n",
       "      <td>125.880</td>\n",
       "      <td>1.549969e+06</td>\n",
       "      <td>669958.00000</td>\n",
       "      <td>103.6218</td>\n",
       "      <td>103.7185</td>\n",
       "      <td>104.0768</td>\n",
       "      <td>104.6939</td>\n",
       "      <td>...</td>\n",
       "      <td>112.165</td>\n",
       "      <td>128.206</td>\n",
       "      <td>27.53</td>\n",
       "      <td>31.72</td>\n",
       "      <td>24.74</td>\n",
       "      <td>59.4</td>\n",
       "      <td>368451.94</td>\n",
       "      <td>741659.17</td>\n",
       "      <td>5818.0406</td>\n",
       "      <td>26.9368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>4/1/2022</td>\n",
       "      <td>17703.693</td>\n",
       "      <td>14485.5</td>\n",
       "      <td>126.157</td>\n",
       "      <td>1.552467e+06</td>\n",
       "      <td>674719.00000</td>\n",
       "      <td>104.3022</td>\n",
       "      <td>104.7794</td>\n",
       "      <td>105.4442</td>\n",
       "      <td>106.0574</td>\n",
       "      <td>...</td>\n",
       "      <td>111.806</td>\n",
       "      <td>128.750</td>\n",
       "      <td>27.60</td>\n",
       "      <td>31.75</td>\n",
       "      <td>24.82</td>\n",
       "      <td>65.2</td>\n",
       "      <td>368249.89</td>\n",
       "      <td>738175.38</td>\n",
       "      <td>5817.0946</td>\n",
       "      <td>24.4850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>5/1/2022</td>\n",
       "      <td>17703.937</td>\n",
       "      <td>14503.3</td>\n",
       "      <td>126.061</td>\n",
       "      <td>1.541047e+06</td>\n",
       "      <td>677115.00000</td>\n",
       "      <td>104.2103</td>\n",
       "      <td>104.3621</td>\n",
       "      <td>104.8488</td>\n",
       "      <td>105.2218</td>\n",
       "      <td>...</td>\n",
       "      <td>113.167</td>\n",
       "      <td>129.299</td>\n",
       "      <td>27.84</td>\n",
       "      <td>32.14</td>\n",
       "      <td>24.94</td>\n",
       "      <td>58.4</td>\n",
       "      <td>368065.14</td>\n",
       "      <td>734877.03</td>\n",
       "      <td>5801.8214</td>\n",
       "      <td>29.1728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>6/1/2022</td>\n",
       "      <td>17653.307</td>\n",
       "      <td>14466.8</td>\n",
       "      <td>126.102</td>\n",
       "      <td>1.535482e+06</td>\n",
       "      <td>682585.00000</td>\n",
       "      <td>104.2229</td>\n",
       "      <td>103.8840</td>\n",
       "      <td>104.3465</td>\n",
       "      <td>104.5448</td>\n",
       "      <td>...</td>\n",
       "      <td>115.486</td>\n",
       "      <td>130.153</td>\n",
       "      <td>27.93</td>\n",
       "      <td>32.24</td>\n",
       "      <td>25.01</td>\n",
       "      <td>50.0</td>\n",
       "      <td>369195.45</td>\n",
       "      <td>733362.86</td>\n",
       "      <td>5786.0874</td>\n",
       "      <td>28.2445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>7/1/2022</td>\n",
       "      <td>17703.249</td>\n",
       "      <td>14527.1</td>\n",
       "      <td>126.360</td>\n",
       "      <td>1.535482e+06</td>\n",
       "      <td>682815.00000</td>\n",
       "      <td>104.8075</td>\n",
       "      <td>104.5397</td>\n",
       "      <td>105.0569</td>\n",
       "      <td>105.1478</td>\n",
       "      <td>...</td>\n",
       "      <td>114.953</td>\n",
       "      <td>130.275</td>\n",
       "      <td>28.02</td>\n",
       "      <td>32.35</td>\n",
       "      <td>25.09</td>\n",
       "      <td>51.5</td>\n",
       "      <td>369195.45</td>\n",
       "      <td>733362.86</td>\n",
       "      <td>5780.5942</td>\n",
       "      <td>24.5288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>764 rows × 128 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date        RPI  W875RX1  DPCERA3M086SBEA     CMRMTSPLx  \\\n",
       "0    Transform:      5.000      5.0            5.000  5.000000e+00   \n",
       "1      1/1/1959   2442.158   2293.2           17.272  2.922664e+05   \n",
       "2      2/1/1959   2451.778   2301.5           17.452  2.944247e+05   \n",
       "3      3/1/1959   2467.594   2318.5           17.617  2.934187e+05   \n",
       "4      4/1/1959   2483.671   2334.9           17.553  2.993228e+05   \n",
       "..          ...        ...      ...              ...           ...   \n",
       "759    3/1/2022  17665.439  14442.9          125.880  1.549969e+06   \n",
       "760    4/1/2022  17703.693  14485.5          126.157  1.552467e+06   \n",
       "761    5/1/2022  17703.937  14503.3          126.061  1.541047e+06   \n",
       "762    6/1/2022  17653.307  14466.8          126.102  1.535482e+06   \n",
       "763    7/1/2022  17703.249  14527.1          126.360  1.535482e+06   \n",
       "\n",
       "          RETAILx    INDPRO   IPFPNSS   IPFINAL   IPCONGD  ...  \\\n",
       "0         5.00000    5.0000    5.0000    5.0000    5.0000  ...   \n",
       "1     18235.77392   22.0151   23.3984   22.2848   31.5847  ...   \n",
       "2     18369.56308   22.4463   23.7142   22.4778   31.8164  ...   \n",
       "3     18523.05762   22.7696   23.8577   22.5882   31.8164  ...   \n",
       "4     18534.46600   23.2547   24.2022   22.9191   32.3184  ...   \n",
       "..            ...       ...       ...       ...       ...  ...   \n",
       "759  669958.00000  103.6218  103.7185  104.0768  104.6939  ...   \n",
       "760  674719.00000  104.3022  104.7794  105.4442  106.0574  ...   \n",
       "761  677115.00000  104.2103  104.3621  104.8488  105.2218  ...   \n",
       "762  682585.00000  104.2229  103.8840  104.3465  104.5448  ...   \n",
       "763  682815.00000  104.8075  104.5397  105.0569  105.1478  ...   \n",
       "\n",
       "     DNDGRG3M086SBEA  DSERRG3M086SBEA  CES0600000008  CES2000000008  \\\n",
       "0              6.000            6.000           6.00           6.00   \n",
       "1             17.791           11.326           2.13           2.45   \n",
       "2             17.798           11.343           2.14           2.46   \n",
       "3             17.785           11.363           2.15           2.45   \n",
       "4             17.796           11.403           2.16           2.47   \n",
       "..               ...              ...            ...            ...   \n",
       "759          112.165          128.206          27.53          31.72   \n",
       "760          111.806          128.750          27.60          31.75   \n",
       "761          113.167          129.299          27.84          32.14   \n",
       "762          115.486          130.153          27.93          32.24   \n",
       "763          114.953          130.275          28.02          32.35   \n",
       "\n",
       "     CES3000000008  UMCSENTx  DTCOLNVHFNM   DTCTHFNM     INVEST  VIXCLSx  \n",
       "0             6.00       2.0         6.00       6.00     6.0000   1.0000  \n",
       "1             2.04       2.0      6476.00   12298.00    84.2043   1.0000  \n",
       "2             2.05       2.0      6476.00   12298.00    83.5280   1.0000  \n",
       "3             2.07       2.0      6508.00   12349.00    81.6405   1.0000  \n",
       "4             2.08       2.0      6620.00   12484.00    81.8099   1.0000  \n",
       "..             ...       ...          ...        ...        ...      ...  \n",
       "759          24.74      59.4    368451.94  741659.17  5818.0406  26.9368  \n",
       "760          24.82      65.2    368249.89  738175.38  5817.0946  24.4850  \n",
       "761          24.94      58.4    368065.14  734877.03  5801.8214  29.1728  \n",
       "762          25.01      50.0    369195.45  733362.86  5786.0874  28.2445  \n",
       "763          25.09      51.5    369195.45  733362.86  5780.5942  24.5288  \n",
       "\n",
       "[764 rows x 128 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n=10\n",
    "df_cleaned=pd.read_csv('data\\current.csv')[:-1].dropna(axis=1, thresh=n).fillna(method='ffill')\n",
    "df_cleaned.rename(columns={'sasdate': 'Date'}, inplace=True)\n",
    "df_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MacroDataProcess:\n",
    "    # Stationarity transofrmation\n",
    "    # Add lag of the features\n",
    "\n",
    "    def __init__(self,macro_data):\n",
    "        self.data =macro_data\n",
    "        self.transformation_codes = None\n",
    "\n",
    "    def transform(self, df_col, code):\n",
    "        \"\"\"\n",
    "        Transforms each column of dataframe (df_col) according to code\n",
    "\n",
    "        \"\"\"\n",
    "        if code == 1:\n",
    "            df_col.apply(lambda x: x)\n",
    "            return df_col\n",
    "        elif code == 2:\n",
    "            df_col = df_col.diff()\n",
    "            return df_col\n",
    "        elif code == 3:\n",
    "            df_col = df_col.diff(periods=2)\n",
    "            return df_col\n",
    "        elif code == 4:\n",
    "            df_col = df_col.apply(np.log)\n",
    "            return df_col\n",
    "        elif code == 5:\n",
    "            df_col = df_col.apply(np.log)\n",
    "            df_col = df_col.diff(periods=2)\n",
    "            return df_col\n",
    "        elif code == 6:\n",
    "            df_col = df_col.apply(np.log)\n",
    "            df_col = df_col.diff(periods=2)\n",
    "            return df_col\n",
    "        elif code == 7:\n",
    "            df_col = df_col.pct_change()\n",
    "            df_col = df_col.diff()\n",
    "            return df_col\n",
    "\n",
    "    def stationarity(self):\n",
    "        \"\"\"\n",
    "        Clean macro dataset and perform necessary changes\n",
    "        \"\"\"\n",
    "        # Keep transformation codes for each variable in a dictionary \n",
    "        transformation_codes = {}\n",
    "        df_tmp=pd.DataFrame(columns = self.data.columns)\n",
    "        for col in self.data.columns:\n",
    "            df_tmp[col]= self.data[col].iloc[1:] \n",
    "            transformation_codes[col] = self.data[col].iloc[0]\n",
    "        df_tmp['Date'] = pd.to_datetime(df_tmp['Date'])\n",
    "\n",
    "        self.data=df_tmp\n",
    "        self.tansformation_codes = transformation_codes\n",
    "        # Make each feature stationary \n",
    "        data_transformed: DataFrame = pd.DataFrame(columns=self.data.columns)\n",
    "        for col in self.data.columns:\n",
    "            if col == 'Date':\n",
    "                data_transformed[col] = self.data[col]\n",
    "            else:\n",
    "                data_transformed[col] = self.transform(self.data[col], transformation_codes[col])\n",
    "        self.data = data_transformed\n",
    "\n",
    "    def add_lag(self,lag_values):\n",
    "        for col in self.data.drop(['Date'], axis=1):\n",
    "            for n in lag_values:\n",
    "                self.data['{} {}M lag'.format(col, n)] = self.data[col].shift(n).ffill().values\n",
    "        self.data.dropna(axis=0, inplace=True)\n",
    "        return self.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = MacroDataProcess(macro_data = df_cleaned)\n",
    "df.stationarity()\n",
    "lag_values = [1,3,6,9,12]\n",
    "df_macro= df.add_lag(lag_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPI</th>\n",
       "      <th>W875RX1</th>\n",
       "      <th>DPCERA3M086SBEA</th>\n",
       "      <th>CMRMTSPLx</th>\n",
       "      <th>RETAILx</th>\n",
       "      <th>INDPRO</th>\n",
       "      <th>IPFPNSS</th>\n",
       "      <th>IPFINAL</th>\n",
       "      <th>IPCONGD</th>\n",
       "      <th>IPDCONGD</th>\n",
       "      <th>...</th>\n",
       "      <th>INVEST 1M lag</th>\n",
       "      <th>INVEST 3M lag</th>\n",
       "      <th>INVEST 6M lag</th>\n",
       "      <th>INVEST 9M lag</th>\n",
       "      <th>INVEST 12M lag</th>\n",
       "      <th>VIXCLSx 1M lag</th>\n",
       "      <th>VIXCLSx 3M lag</th>\n",
       "      <th>VIXCLSx 6M lag</th>\n",
       "      <th>VIXCLSx 9M lag</th>\n",
       "      <th>VIXCLSx 12M lag</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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",
       "      <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>1960-03-01</th>\n",
       "      <td>0.003051</td>\n",
       "      <td>0.001833</td>\n",
       "      <td>0.018375</td>\n",
       "      <td>-0.013655</td>\n",
       "      <td>0.002594</td>\n",
       "      <td>-0.017956</td>\n",
       "      <td>-0.009113</td>\n",
       "      <td>-0.004586</td>\n",
       "      <td>-0.010303</td>\n",
       "      <td>-0.033820</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.037926</td>\n",
       "      <td>-0.000688</td>\n",
       "      <td>-0.027018</td>\n",
       "      <td>-0.038791</td>\n",
       "      <td>-0.030921</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-04-01</th>\n",
       "      <td>0.005328</td>\n",
       "      <td>0.004530</td>\n",
       "      <td>0.029396</td>\n",
       "      <td>-0.018200</td>\n",
       "      <td>0.024801</td>\n",
       "      <td>-0.016979</td>\n",
       "      <td>-0.001141</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008030</td>\n",
       "      <td>-0.021152</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.053379</td>\n",
       "      <td>-0.005654</td>\n",
       "      <td>-0.014082</td>\n",
       "      <td>-0.021701</td>\n",
       "      <td>-0.020784</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-05-01</th>\n",
       "      <td>0.005819</td>\n",
       "      <td>0.006057</td>\n",
       "      <td>-0.004988</td>\n",
       "      <td>-0.021704</td>\n",
       "      <td>0.010857</td>\n",
       "      <td>-0.009103</td>\n",
       "      <td>0.007979</td>\n",
       "      <td>0.008011</td>\n",
       "      <td>0.012578</td>\n",
       "      <td>0.008267</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.025753</td>\n",
       "      <td>-0.037926</td>\n",
       "      <td>-0.013266</td>\n",
       "      <td>-0.017855</td>\n",
       "      <td>-0.011197</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-06-01</th>\n",
       "      <td>0.003189</td>\n",
       "      <td>0.002195</td>\n",
       "      <td>-0.020563</td>\n",
       "      <td>-0.022945</td>\n",
       "      <td>-0.016411</td>\n",
       "      <td>-0.013795</td>\n",
       "      <td>-0.005726</td>\n",
       "      <td>-0.004602</td>\n",
       "      <td>-0.001143</td>\n",
       "      <td>-0.001192</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002181</td>\n",
       "      <td>-0.053379</td>\n",
       "      <td>-0.000688</td>\n",
       "      <td>-0.027018</td>\n",
       "      <td>-0.038791</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-07-01</th>\n",
       "      <td>0.002618</td>\n",
       "      <td>0.001653</td>\n",
       "      <td>0.001812</td>\n",
       "      <td>0.001729</td>\n",
       "      <td>-0.011463</td>\n",
       "      <td>-0.016127</td>\n",
       "      <td>-0.017175</td>\n",
       "      <td>-0.017252</td>\n",
       "      <td>-0.016038</td>\n",
       "      <td>-0.049437</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004001</td>\n",
       "      <td>-0.025753</td>\n",
       "      <td>-0.005654</td>\n",
       "      <td>-0.014082</td>\n",
       "      <td>-0.021701</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-01</th>\n",
       "      <td>-0.002206</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.002856</td>\n",
       "      <td>-0.020301</td>\n",
       "      <td>0.027850</td>\n",
       "      <td>0.014345</td>\n",
       "      <td>0.013049</td>\n",
       "      <td>0.012140</td>\n",
       "      <td>0.004331</td>\n",
       "      <td>0.010984</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024066</td>\n",
       "      <td>0.025145</td>\n",
       "      <td>0.021377</td>\n",
       "      <td>0.037173</td>\n",
       "      <td>0.041563</td>\n",
       "      <td>26.1429</td>\n",
       "      <td>21.2985</td>\n",
       "      <td>19.7806</td>\n",
       "      <td>16.9236</td>\n",
       "      <td>21.4680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-01</th>\n",
       "      <td>-0.001152</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>0.004807</td>\n",
       "      <td>-0.011067</td>\n",
       "      <td>0.018546</td>\n",
       "      <td>0.013547</td>\n",
       "      <td>0.013471</td>\n",
       "      <td>0.017674</td>\n",
       "      <td>0.014986</td>\n",
       "      <td>0.051463</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008885</td>\n",
       "      <td>0.028576</td>\n",
       "      <td>0.031610</td>\n",
       "      <td>0.022025</td>\n",
       "      <td>0.043892</td>\n",
       "      <td>26.9368</td>\n",
       "      <td>22.9143</td>\n",
       "      <td>17.7838</td>\n",
       "      <td>17.5754</td>\n",
       "      <td>17.1684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-01</th>\n",
       "      <td>0.002177</td>\n",
       "      <td>0.004173</td>\n",
       "      <td>0.001437</td>\n",
       "      <td>-0.005773</td>\n",
       "      <td>0.010626</td>\n",
       "      <td>0.005663</td>\n",
       "      <td>0.006186</td>\n",
       "      <td>0.007390</td>\n",
       "      <td>0.005030</td>\n",
       "      <td>-0.006981</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001072</td>\n",
       "      <td>0.024066</td>\n",
       "      <td>0.033118</td>\n",
       "      <td>0.017426</td>\n",
       "      <td>0.043917</td>\n",
       "      <td>24.4850</td>\n",
       "      <td>26.1429</td>\n",
       "      <td>19.1586</td>\n",
       "      <td>17.0618</td>\n",
       "      <td>19.1336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-01</th>\n",
       "      <td>-0.002850</td>\n",
       "      <td>-0.001292</td>\n",
       "      <td>-0.000436</td>\n",
       "      <td>-0.011001</td>\n",
       "      <td>0.011591</td>\n",
       "      <td>-0.000761</td>\n",
       "      <td>-0.008582</td>\n",
       "      <td>-0.010465</td>\n",
       "      <td>-0.014365</td>\n",
       "      <td>-0.046391</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002792</td>\n",
       "      <td>0.008885</td>\n",
       "      <td>0.025145</td>\n",
       "      <td>0.021377</td>\n",
       "      <td>0.037173</td>\n",
       "      <td>29.1728</td>\n",
       "      <td>26.9368</td>\n",
       "      <td>21.2985</td>\n",
       "      <td>19.7806</td>\n",
       "      <td>16.9236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>-0.000039</td>\n",
       "      <td>0.001640</td>\n",
       "      <td>0.002369</td>\n",
       "      <td>-0.003618</td>\n",
       "      <td>0.008383</td>\n",
       "      <td>0.005714</td>\n",
       "      <td>0.001700</td>\n",
       "      <td>0.001983</td>\n",
       "      <td>-0.000704</td>\n",
       "      <td>0.016928</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005345</td>\n",
       "      <td>0.001072</td>\n",
       "      <td>0.028576</td>\n",
       "      <td>0.031610</td>\n",
       "      <td>0.022025</td>\n",
       "      <td>28.2445</td>\n",
       "      <td>24.4850</td>\n",
       "      <td>22.9143</td>\n",
       "      <td>17.7838</td>\n",
       "      <td>17.5754</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>749 rows × 762 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 RPI   W875RX1  DPCERA3M086SBEA  CMRMTSPLx   RETAILx  \\\n",
       "Date                                                                   \n",
       "1960-03-01  0.003051  0.001833         0.018375  -0.013655  0.002594   \n",
       "1960-04-01  0.005328  0.004530         0.029396  -0.018200  0.024801   \n",
       "1960-05-01  0.005819  0.006057        -0.004988  -0.021704  0.010857   \n",
       "1960-06-01  0.003189  0.002195        -0.020563  -0.022945 -0.016411   \n",
       "1960-07-01  0.002618  0.001653         0.001812   0.001729 -0.011463   \n",
       "...              ...       ...              ...        ...       ...   \n",
       "2022-03-01 -0.002206  0.000132         0.002856  -0.020301  0.027850   \n",
       "2022-04-01 -0.001152  0.000083         0.004807  -0.011067  0.018546   \n",
       "2022-05-01  0.002177  0.004173         0.001437  -0.005773  0.010626   \n",
       "2022-06-01 -0.002850 -0.001292        -0.000436  -0.011001  0.011591   \n",
       "2022-07-01 -0.000039  0.001640         0.002369  -0.003618  0.008383   \n",
       "\n",
       "              INDPRO   IPFPNSS   IPFINAL   IPCONGD  IPDCONGD  ...  \\\n",
       "Date                                                          ...   \n",
       "1960-03-01 -0.017956 -0.009113 -0.004586 -0.010303 -0.033820  ...   \n",
       "1960-04-01 -0.016979 -0.001141  0.000000  0.008030 -0.021152  ...   \n",
       "1960-05-01 -0.009103  0.007979  0.008011  0.012578  0.008267  ...   \n",
       "1960-06-01 -0.013795 -0.005726 -0.004602 -0.001143 -0.001192  ...   \n",
       "1960-07-01 -0.016127 -0.017175 -0.017252 -0.016038 -0.049437  ...   \n",
       "...              ...       ...       ...       ...       ...  ...   \n",
       "2022-03-01  0.014345  0.013049  0.012140  0.004331  0.010984  ...   \n",
       "2022-04-01  0.013547  0.013471  0.017674  0.014986  0.051463  ...   \n",
       "2022-05-01  0.005663  0.006186  0.007390  0.005030 -0.006981  ...   \n",
       "2022-06-01 -0.000761 -0.008582 -0.010465 -0.014365 -0.046391  ...   \n",
       "2022-07-01  0.005714  0.001700  0.001983 -0.000704  0.016928  ...   \n",
       "\n",
       "            INVEST 1M lag  INVEST 3M lag  INVEST 6M lag  INVEST 9M lag  \\\n",
       "Date                                                                     \n",
       "1960-03-01      -0.037926      -0.000688      -0.027018      -0.038791   \n",
       "1960-04-01      -0.053379      -0.005654      -0.014082      -0.021701   \n",
       "1960-05-01      -0.025753      -0.037926      -0.013266      -0.017855   \n",
       "1960-06-01       0.002181      -0.053379      -0.000688      -0.027018   \n",
       "1960-07-01      -0.004001      -0.025753      -0.005654      -0.014082   \n",
       "...                   ...            ...            ...            ...   \n",
       "2022-03-01       0.024066       0.025145       0.021377       0.037173   \n",
       "2022-04-01       0.008885       0.028576       0.031610       0.022025   \n",
       "2022-05-01       0.001072       0.024066       0.033118       0.017426   \n",
       "2022-06-01      -0.002792       0.008885       0.025145       0.021377   \n",
       "2022-07-01      -0.005345       0.001072       0.028576       0.031610   \n",
       "\n",
       "            INVEST 12M lag  VIXCLSx 1M lag  VIXCLSx 3M lag  VIXCLSx 6M lag  \\\n",
       "Date                                                                         \n",
       "1960-03-01       -0.030921          1.0000          1.0000          1.0000   \n",
       "1960-04-01       -0.020784          1.0000          1.0000          1.0000   \n",
       "1960-05-01       -0.011197          1.0000          1.0000          1.0000   \n",
       "1960-06-01       -0.038791          1.0000          1.0000          1.0000   \n",
       "1960-07-01       -0.021701          1.0000          1.0000          1.0000   \n",
       "...                    ...             ...             ...             ...   \n",
       "2022-03-01        0.041563         26.1429         21.2985         19.7806   \n",
       "2022-04-01        0.043892         26.9368         22.9143         17.7838   \n",
       "2022-05-01        0.043917         24.4850         26.1429         19.1586   \n",
       "2022-06-01        0.037173         29.1728         26.9368         21.2985   \n",
       "2022-07-01        0.022025         28.2445         24.4850         22.9143   \n",
       "\n",
       "            VIXCLSx 9M lag  VIXCLSx 12M lag  \n",
       "Date                                         \n",
       "1960-03-01          1.0000           1.0000  \n",
       "1960-04-01          1.0000           1.0000  \n",
       "1960-05-01          1.0000           1.0000  \n",
       "1960-06-01          1.0000           1.0000  \n",
       "1960-07-01          1.0000           1.0000  \n",
       "...                    ...              ...  \n",
       "2022-03-01         16.9236          21.4680  \n",
       "2022-04-01         17.5754          17.1684  \n",
       "2022-05-01         17.0618          19.1336  \n",
       "2022-06-01         19.7806          16.9236  \n",
       "2022-07-01         17.7838          17.5754  \n",
       "\n",
       "[749 rows x 762 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_macro=df_macro.set_index('Date')\n",
    "df_macro\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Data manipulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* https://fred.stlouisfed.org/series/USRECD\n",
    "* https://research.stlouisfed.org/econ/mccracken/fred-databases/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Regime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1854-12-01</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1854-12-02</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1854-12-03</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1854-12-04</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1854-12-05</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Regime\n",
       "1854-12-01     1.0\n",
       "1854-12-02     1.0\n",
       "1854-12-03     1.0\n",
       "1854-12-04     1.0\n",
       "1854-12-05     1.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from fredapi import Fred\n",
    "key='####'\n",
    "\n",
    "fred = Fred(api_key=key)\n",
    "\n",
    "df_regime=pd.DataFrame(fred.get_series('USRECD'),columns=['Regime'])\n",
    "\n",
    "df_regime.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPI</th>\n",
       "      <th>W875RX1</th>\n",
       "      <th>DPCERA3M086SBEA</th>\n",
       "      <th>CMRMTSPLx</th>\n",
       "      <th>RETAILx</th>\n",
       "      <th>INDPRO</th>\n",
       "      <th>IPFPNSS</th>\n",
       "      <th>IPFINAL</th>\n",
       "      <th>IPCONGD</th>\n",
       "      <th>IPDCONGD</th>\n",
       "      <th>...</th>\n",
       "      <th>INVEST 12M lag</th>\n",
       "      <th>VIXCLSx 1M lag</th>\n",
       "      <th>VIXCLSx 3M lag</th>\n",
       "      <th>VIXCLSx 6M lag</th>\n",
       "      <th>VIXCLSx 9M lag</th>\n",
       "      <th>VIXCLSx 12M lag</th>\n",
       "      <th>Regime</th>\n",
       "      <th>Regime1</th>\n",
       "      <th>Regime2</th>\n",
       "      <th>Regime4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960-03-01</th>\n",
       "      <td>0.003051</td>\n",
       "      <td>0.001833</td>\n",
       "      <td>0.018375</td>\n",
       "      <td>-0.013655</td>\n",
       "      <td>0.002594</td>\n",
       "      <td>-0.017956</td>\n",
       "      <td>-0.009113</td>\n",
       "      <td>-0.004586</td>\n",
       "      <td>-0.010303</td>\n",
       "      <td>-0.033820</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.030921</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-04-01</th>\n",
       "      <td>0.005328</td>\n",
       "      <td>0.004530</td>\n",
       "      <td>0.029396</td>\n",
       "      <td>-0.018200</td>\n",
       "      <td>0.024801</td>\n",
       "      <td>-0.016979</td>\n",
       "      <td>-0.001141</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008030</td>\n",
       "      <td>-0.021152</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.020784</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-05-01</th>\n",
       "      <td>0.005819</td>\n",
       "      <td>0.006057</td>\n",
       "      <td>-0.004988</td>\n",
       "      <td>-0.021704</td>\n",
       "      <td>0.010857</td>\n",
       "      <td>-0.009103</td>\n",
       "      <td>0.007979</td>\n",
       "      <td>0.008011</td>\n",
       "      <td>0.012578</td>\n",
       "      <td>0.008267</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.011197</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-06-01</th>\n",
       "      <td>0.003189</td>\n",
       "      <td>0.002195</td>\n",
       "      <td>-0.020563</td>\n",
       "      <td>-0.022945</td>\n",
       "      <td>-0.016411</td>\n",
       "      <td>-0.013795</td>\n",
       "      <td>-0.005726</td>\n",
       "      <td>-0.004602</td>\n",
       "      <td>-0.001143</td>\n",
       "      <td>-0.001192</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.038791</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-07-01</th>\n",
       "      <td>0.002618</td>\n",
       "      <td>0.001653</td>\n",
       "      <td>0.001812</td>\n",
       "      <td>0.001729</td>\n",
       "      <td>-0.011463</td>\n",
       "      <td>-0.016127</td>\n",
       "      <td>-0.017175</td>\n",
       "      <td>-0.017252</td>\n",
       "      <td>-0.016038</td>\n",
       "      <td>-0.049437</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.021701</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 766 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 RPI   W875RX1  DPCERA3M086SBEA  CMRMTSPLx   RETAILx  \\\n",
       "1960-03-01  0.003051  0.001833         0.018375  -0.013655  0.002594   \n",
       "1960-04-01  0.005328  0.004530         0.029396  -0.018200  0.024801   \n",
       "1960-05-01  0.005819  0.006057        -0.004988  -0.021704  0.010857   \n",
       "1960-06-01  0.003189  0.002195        -0.020563  -0.022945 -0.016411   \n",
       "1960-07-01  0.002618  0.001653         0.001812   0.001729 -0.011463   \n",
       "\n",
       "              INDPRO   IPFPNSS   IPFINAL   IPCONGD  IPDCONGD  ...  \\\n",
       "1960-03-01 -0.017956 -0.009113 -0.004586 -0.010303 -0.033820  ...   \n",
       "1960-04-01 -0.016979 -0.001141  0.000000  0.008030 -0.021152  ...   \n",
       "1960-05-01 -0.009103  0.007979  0.008011  0.012578  0.008267  ...   \n",
       "1960-06-01 -0.013795 -0.005726 -0.004602 -0.001143 -0.001192  ...   \n",
       "1960-07-01 -0.016127 -0.017175 -0.017252 -0.016038 -0.049437  ...   \n",
       "\n",
       "            INVEST 12M lag  VIXCLSx 1M lag  VIXCLSx 3M lag  VIXCLSx 6M lag  \\\n",
       "1960-03-01       -0.030921             1.0             1.0             1.0   \n",
       "1960-04-01       -0.020784             1.0             1.0             1.0   \n",
       "1960-05-01       -0.011197             1.0             1.0             1.0   \n",
       "1960-06-01       -0.038791             1.0             1.0             1.0   \n",
       "1960-07-01       -0.021701             1.0             1.0             1.0   \n",
       "\n",
       "            VIXCLSx 9M lag  VIXCLSx 12M lag  Regime  Regime1  Regime2  Regime4  \n",
       "1960-03-01             1.0              1.0     0.0      0.0      1.0      1.0  \n",
       "1960-04-01             1.0              1.0     0.0      1.0      1.0      1.0  \n",
       "1960-05-01             1.0              1.0     1.0      1.0      1.0      1.0  \n",
       "1960-06-01             1.0              1.0     1.0      1.0      1.0      1.0  \n",
       "1960-07-01             1.0              1.0     1.0      1.0      1.0      1.0  \n",
       "\n",
       "[5 rows x 766 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast_windows=[1,2,4]\n",
    "\n",
    "df=pd.concat([df_macro,df_regime],axis=1).dropna()\n",
    "for i in forecast_windows:\n",
    "    df['Regime'+str(i)]=df['Regime'].shift(-i)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "split_date = '1975-09-01' # train and test set split date\n",
    "df_train, df_test = df[df.index < split_date], df[df.index>= split_date]\n",
    "target_col = 'Regime'\n",
    "feature_col = df.columns.drop(['Regime'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "import itertools\n",
    "parameters={'random_state': [100],'max_depth': [3,5,6],'n_estimators': [100,300],'objective': ['binary:logistic'],'booster': ['gbtree']}\n",
    "parameters_=list((dict(zip(parameters, x)) for x in itertools.product(*parameters.values())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPI</th>\n",
       "      <th>W875RX1</th>\n",
       "      <th>DPCERA3M086SBEA</th>\n",
       "      <th>CMRMTSPLx</th>\n",
       "      <th>RETAILx</th>\n",
       "      <th>INDPRO</th>\n",
       "      <th>IPFPNSS</th>\n",
       "      <th>IPFINAL</th>\n",
       "      <th>IPCONGD</th>\n",
       "      <th>IPDCONGD</th>\n",
       "      <th>...</th>\n",
       "      <th>INVEST 1M lag</th>\n",
       "      <th>INVEST 3M lag</th>\n",
       "      <th>INVEST 6M lag</th>\n",
       "      <th>INVEST 9M lag</th>\n",
       "      <th>INVEST 12M lag</th>\n",
       "      <th>VIXCLSx 1M lag</th>\n",
       "      <th>VIXCLSx 3M lag</th>\n",
       "      <th>VIXCLSx 6M lag</th>\n",
       "      <th>VIXCLSx 9M lag</th>\n",
       "      <th>VIXCLSx 12M lag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960-03-01</th>\n",
       "      <td>0.003051</td>\n",
       "      <td>0.001833</td>\n",
       "      <td>0.018375</td>\n",
       "      <td>-0.013655</td>\n",
       "      <td>0.002594</td>\n",
       "      <td>-0.017956</td>\n",
       "      <td>-0.009113</td>\n",
       "      <td>-0.004586</td>\n",
       "      <td>-0.010303</td>\n",
       "      <td>-0.033820</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.037926</td>\n",
       "      <td>-0.000688</td>\n",
       "      <td>-0.027018</td>\n",
       "      <td>-0.038791</td>\n",
       "      <td>-0.030921</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-04-01</th>\n",
       "      <td>0.005328</td>\n",
       "      <td>0.004530</td>\n",
       "      <td>0.029396</td>\n",
       "      <td>-0.018200</td>\n",
       "      <td>0.024801</td>\n",
       "      <td>-0.016979</td>\n",
       "      <td>-0.001141</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008030</td>\n",
       "      <td>-0.021152</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.053379</td>\n",
       "      <td>-0.005654</td>\n",
       "      <td>-0.014082</td>\n",
       "      <td>-0.021701</td>\n",
       "      <td>-0.020784</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-05-01</th>\n",
       "      <td>0.005819</td>\n",
       "      <td>0.006057</td>\n",
       "      <td>-0.004988</td>\n",
       "      <td>-0.021704</td>\n",
       "      <td>0.010857</td>\n",
       "      <td>-0.009103</td>\n",
       "      <td>0.007979</td>\n",
       "      <td>0.008011</td>\n",
       "      <td>0.012578</td>\n",
       "      <td>0.008267</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.025753</td>\n",
       "      <td>-0.037926</td>\n",
       "      <td>-0.013266</td>\n",
       "      <td>-0.017855</td>\n",
       "      <td>-0.011197</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-06-01</th>\n",
       "      <td>0.003189</td>\n",
       "      <td>0.002195</td>\n",
       "      <td>-0.020563</td>\n",
       "      <td>-0.022945</td>\n",
       "      <td>-0.016411</td>\n",
       "      <td>-0.013795</td>\n",
       "      <td>-0.005726</td>\n",
       "      <td>-0.004602</td>\n",
       "      <td>-0.001143</td>\n",
       "      <td>-0.001192</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002181</td>\n",
       "      <td>-0.053379</td>\n",
       "      <td>-0.000688</td>\n",
       "      <td>-0.027018</td>\n",
       "      <td>-0.038791</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-07-01</th>\n",
       "      <td>0.002618</td>\n",
       "      <td>0.001653</td>\n",
       "      <td>0.001812</td>\n",
       "      <td>0.001729</td>\n",
       "      <td>-0.011463</td>\n",
       "      <td>-0.016127</td>\n",
       "      <td>-0.017175</td>\n",
       "      <td>-0.017252</td>\n",
       "      <td>-0.016038</td>\n",
       "      <td>-0.049437</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004001</td>\n",
       "      <td>-0.025753</td>\n",
       "      <td>-0.005654</td>\n",
       "      <td>-0.014082</td>\n",
       "      <td>-0.021701</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-04-01</th>\n",
       "      <td>0.001930</td>\n",
       "      <td>-0.001197</td>\n",
       "      <td>-0.000993</td>\n",
       "      <td>-0.020529</td>\n",
       "      <td>-0.009139</td>\n",
       "      <td>-0.009328</td>\n",
       "      <td>0.000329</td>\n",
       "      <td>0.008725</td>\n",
       "      <td>0.023516</td>\n",
       "      <td>0.050581</td>\n",
       "      <td>...</td>\n",
       "      <td>0.028593</td>\n",
       "      <td>0.019431</td>\n",
       "      <td>-0.005318</td>\n",
       "      <td>-0.010488</td>\n",
       "      <td>0.013654</td>\n",
       "      <td>24.0618</td>\n",
       "      <td>26.7976</td>\n",
       "      <td>38.3682</td>\n",
       "      <td>29.7672</td>\n",
       "      <td>20.7668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-05-01</th>\n",
       "      <td>0.007316</td>\n",
       "      <td>0.004107</td>\n",
       "      <td>0.020924</td>\n",
       "      <td>0.009738</td>\n",
       "      <td>0.058955</td>\n",
       "      <td>-0.000991</td>\n",
       "      <td>0.009185</td>\n",
       "      <td>0.013555</td>\n",
       "      <td>0.025621</td>\n",
       "      <td>0.062751</td>\n",
       "      <td>...</td>\n",
       "      <td>0.056546</td>\n",
       "      <td>-0.000165</td>\n",
       "      <td>0.009669</td>\n",
       "      <td>-0.007443</td>\n",
       "      <td>-0.008827</td>\n",
       "      <td>22.6474</td>\n",
       "      <td>22.9871</td>\n",
       "      <td>27.2538</td>\n",
       "      <td>29.1721</td>\n",
       "      <td>22.2272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-06-01</th>\n",
       "      <td>0.023406</td>\n",
       "      <td>0.007327</td>\n",
       "      <td>0.021235</td>\n",
       "      <td>0.008720</td>\n",
       "      <td>0.044103</td>\n",
       "      <td>0.003630</td>\n",
       "      <td>0.006453</td>\n",
       "      <td>0.009647</td>\n",
       "      <td>0.018414</td>\n",
       "      <td>0.034640</td>\n",
       "      <td>...</td>\n",
       "      <td>0.036943</td>\n",
       "      <td>0.028593</td>\n",
       "      <td>0.030845</td>\n",
       "      <td>0.000785</td>\n",
       "      <td>-0.009733</td>\n",
       "      <td>21.0543</td>\n",
       "      <td>24.0618</td>\n",
       "      <td>28.2313</td>\n",
       "      <td>36.7628</td>\n",
       "      <td>23.7680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-07-01</th>\n",
       "      <td>0.006165</td>\n",
       "      <td>0.001643</td>\n",
       "      <td>0.005497</td>\n",
       "      <td>0.013904</td>\n",
       "      <td>0.014398</td>\n",
       "      <td>0.016583</td>\n",
       "      <td>0.021567</td>\n",
       "      <td>0.024914</td>\n",
       "      <td>0.038894</td>\n",
       "      <td>0.059588</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030292</td>\n",
       "      <td>0.056546</td>\n",
       "      <td>0.019431</td>\n",
       "      <td>-0.005318</td>\n",
       "      <td>-0.010488</td>\n",
       "      <td>18.9576</td>\n",
       "      <td>22.6474</td>\n",
       "      <td>26.7976</td>\n",
       "      <td>38.3682</td>\n",
       "      <td>29.7672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-08-01</th>\n",
       "      <td>-0.003654</td>\n",
       "      <td>0.008005</td>\n",
       "      <td>0.006577</td>\n",
       "      <td>0.016617</td>\n",
       "      <td>0.026232</td>\n",
       "      <td>0.019927</td>\n",
       "      <td>0.019467</td>\n",
       "      <td>0.019162</td>\n",
       "      <td>0.030667</td>\n",
       "      <td>0.062685</td>\n",
       "      <td>...</td>\n",
       "      <td>0.028442</td>\n",
       "      <td>0.036943</td>\n",
       "      <td>-0.000165</td>\n",
       "      <td>0.009669</td>\n",
       "      <td>-0.007443</td>\n",
       "      <td>17.4815</td>\n",
       "      <td>21.0543</td>\n",
       "      <td>22.9871</td>\n",
       "      <td>27.2538</td>\n",
       "      <td>29.1721</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>186 rows × 762 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 RPI   W875RX1  DPCERA3M086SBEA  CMRMTSPLx   RETAILx  \\\n",
       "1960-03-01  0.003051  0.001833         0.018375  -0.013655  0.002594   \n",
       "1960-04-01  0.005328  0.004530         0.029396  -0.018200  0.024801   \n",
       "1960-05-01  0.005819  0.006057        -0.004988  -0.021704  0.010857   \n",
       "1960-06-01  0.003189  0.002195        -0.020563  -0.022945 -0.016411   \n",
       "1960-07-01  0.002618  0.001653         0.001812   0.001729 -0.011463   \n",
       "...              ...       ...              ...        ...       ...   \n",
       "1975-04-01  0.001930 -0.001197        -0.000993  -0.020529 -0.009139   \n",
       "1975-05-01  0.007316  0.004107         0.020924   0.009738  0.058955   \n",
       "1975-06-01  0.023406  0.007327         0.021235   0.008720  0.044103   \n",
       "1975-07-01  0.006165  0.001643         0.005497   0.013904  0.014398   \n",
       "1975-08-01 -0.003654  0.008005         0.006577   0.016617  0.026232   \n",
       "\n",
       "              INDPRO   IPFPNSS   IPFINAL   IPCONGD  IPDCONGD  ...  \\\n",
       "1960-03-01 -0.017956 -0.009113 -0.004586 -0.010303 -0.033820  ...   \n",
       "1960-04-01 -0.016979 -0.001141  0.000000  0.008030 -0.021152  ...   \n",
       "1960-05-01 -0.009103  0.007979  0.008011  0.012578  0.008267  ...   \n",
       "1960-06-01 -0.013795 -0.005726 -0.004602 -0.001143 -0.001192  ...   \n",
       "1960-07-01 -0.016127 -0.017175 -0.017252 -0.016038 -0.049437  ...   \n",
       "...              ...       ...       ...       ...       ...  ...   \n",
       "1975-04-01 -0.009328  0.000329  0.008725  0.023516  0.050581  ...   \n",
       "1975-05-01 -0.000991  0.009185  0.013555  0.025621  0.062751  ...   \n",
       "1975-06-01  0.003630  0.006453  0.009647  0.018414  0.034640  ...   \n",
       "1975-07-01  0.016583  0.021567  0.024914  0.038894  0.059588  ...   \n",
       "1975-08-01  0.019927  0.019467  0.019162  0.030667  0.062685  ...   \n",
       "\n",
       "            INVEST 1M lag  INVEST 3M lag  INVEST 6M lag  INVEST 9M lag  \\\n",
       "1960-03-01      -0.037926      -0.000688      -0.027018      -0.038791   \n",
       "1960-04-01      -0.053379      -0.005654      -0.014082      -0.021701   \n",
       "1960-05-01      -0.025753      -0.037926      -0.013266      -0.017855   \n",
       "1960-06-01       0.002181      -0.053379      -0.000688      -0.027018   \n",
       "1960-07-01      -0.004001      -0.025753      -0.005654      -0.014082   \n",
       "...                   ...            ...            ...            ...   \n",
       "1975-04-01       0.028593       0.019431      -0.005318      -0.010488   \n",
       "1975-05-01       0.056546      -0.000165       0.009669      -0.007443   \n",
       "1975-06-01       0.036943       0.028593       0.030845       0.000785   \n",
       "1975-07-01       0.030292       0.056546       0.019431      -0.005318   \n",
       "1975-08-01       0.028442       0.036943      -0.000165       0.009669   \n",
       "\n",
       "            INVEST 12M lag  VIXCLSx 1M lag  VIXCLSx 3M lag  VIXCLSx 6M lag  \\\n",
       "1960-03-01       -0.030921          1.0000          1.0000          1.0000   \n",
       "1960-04-01       -0.020784          1.0000          1.0000          1.0000   \n",
       "1960-05-01       -0.011197          1.0000          1.0000          1.0000   \n",
       "1960-06-01       -0.038791          1.0000          1.0000          1.0000   \n",
       "1960-07-01       -0.021701          1.0000          1.0000          1.0000   \n",
       "...                    ...             ...             ...             ...   \n",
       "1975-04-01        0.013654         24.0618         26.7976         38.3682   \n",
       "1975-05-01       -0.008827         22.6474         22.9871         27.2538   \n",
       "1975-06-01       -0.009733         21.0543         24.0618         28.2313   \n",
       "1975-07-01       -0.010488         18.9576         22.6474         26.7976   \n",
       "1975-08-01       -0.007443         17.4815         21.0543         22.9871   \n",
       "\n",
       "            VIXCLSx 9M lag  VIXCLSx 12M lag  \n",
       "1960-03-01          1.0000           1.0000  \n",
       "1960-04-01          1.0000           1.0000  \n",
       "1960-05-01          1.0000           1.0000  \n",
       "1960-06-01          1.0000           1.0000  \n",
       "1960-07-01          1.0000           1.0000  \n",
       "...                    ...              ...  \n",
       "1975-04-01         29.7672          20.7668  \n",
       "1975-05-01         29.1721          22.2272  \n",
       "1975-06-01         36.7628          23.7680  \n",
       "1975-07-01         38.3682          29.7672  \n",
       "1975-08-01         27.2538          29.1721  \n",
       "\n",
       "[186 rows x 762 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.iloc[:,:-4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 6/6 [00:09<00:00,  1.57s/it]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'random_state': 100,\n",
       " 'max_depth': 3,\n",
       " 'n_estimators': 100,\n",
       " 'objective': 'binary:logistic',\n",
       " 'booster': 'gbtree'}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "from sklearn.metrics import accuracy_score\n",
    "from tqdm import tqdm\n",
    "\n",
    "acc=[]\n",
    "accuracy=[]\n",
    "for param in tqdm(parameters_):\n",
    "    tscv = TimeSeriesSplit(n_splits=3)\n",
    "    model = XGBClassifier(**param)\n",
    "    for train_index, test_index in tscv.split(df_train):\n",
    "        X_train, X_test = df_train.iloc[train_index,:-4], df_train.iloc[test_index,:-4]\n",
    "        y_train, y_test = df_train.iloc[train_index,-4:], df_train.iloc[test_index,-4:]\n",
    "        model.fit(X_train, y_train)\n",
    "        \n",
    "        y_pr = model.predict(X_test)\n",
    "\n",
    "        # scoring\n",
    "        a=np.zeros(len(forecast_windows))\n",
    "        c=0\n",
    "        for i in forecast_windows:\n",
    "            a[c]=(accuracy_score(y_test.iloc[:,c],y_pr[:,c]))\n",
    "            c+=1\n",
    "        acc.append(np.mean(a))\n",
    "    accuracy.append(np.mean(acc))\n",
    "\n",
    "best_param = parameters_[np.argmax(accuracy)]\n",
    "best_param"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 559/559 [06:27<00:00,  1.44it/s]\n"
     ]
    }
   ],
   "source": [
    "predicted=[]\n",
    "binary=[]\n",
    "\n",
    "roll=0\n",
    "\n",
    "X=df.iloc[:,:-4]\n",
    "y=df.iloc[:,-4:]\n",
    "\n",
    "for i in tqdm(range(len(df_train)+forecast_windows[-1],len(X))):\n",
    "    model = XGBClassifier(**best_param)\n",
    "    if roll>0:\n",
    "        X_fit = X.iloc[i-roll:i-forecast_windows[-1]]\n",
    "        y_fit = y.iloc[i-roll:i-forecast_windows[-1]]\n",
    "    if roll==0:\n",
    "        X_fit = X.iloc[:i-forecast_windows[-1]]\n",
    "        y_fit = y.iloc[:i-forecast_windows[-1]]\n",
    "\n",
    "    model = model.fit(X_fit, y_fit)\n",
    "\n",
    "    X_predict = X.iloc[i:i+ 1]\n",
    "    y_pred = model.predict(X_predict)\n",
    "\n",
    "\n",
    "    binary.append(y_pred)\n",
    "\n",
    "    y_pro=model.predict_proba(X_predict)\n",
    "    predicted.append(y_pro)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use(\"dark_background\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>Regime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1976-01-01</th>\n",
       "      <td>0.004690</td>\n",
       "      <td>0.009284</td>\n",
       "      <td>0.006291</td>\n",
       "      <td>0.001999</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-02-01</th>\n",
       "      <td>0.006110</td>\n",
       "      <td>0.005652</td>\n",
       "      <td>0.013377</td>\n",
       "      <td>0.001888</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-03-01</th>\n",
       "      <td>0.003431</td>\n",
       "      <td>0.011341</td>\n",
       "      <td>0.012460</td>\n",
       "      <td>0.003685</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-04-01</th>\n",
       "      <td>0.003587</td>\n",
       "      <td>0.002261</td>\n",
       "      <td>0.006149</td>\n",
       "      <td>0.034024</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-01</th>\n",
       "      <td>0.001885</td>\n",
       "      <td>0.001937</td>\n",
       "      <td>0.001436</td>\n",
       "      <td>0.045229</td>\n",
       "      <td>0.0</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>2022-03-01</th>\n",
       "      <td>0.000725</td>\n",
       "      <td>0.000266</td>\n",
       "      <td>0.000210</td>\n",
       "      <td>0.000838</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-01</th>\n",
       "      <td>0.001043</td>\n",
       "      <td>0.000366</td>\n",
       "      <td>0.000437</td>\n",
       "      <td>0.000425</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-01</th>\n",
       "      <td>0.001364</td>\n",
       "      <td>0.000440</td>\n",
       "      <td>0.000499</td>\n",
       "      <td>0.001327</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-01</th>\n",
       "      <td>0.004920</td>\n",
       "      <td>0.000757</td>\n",
       "      <td>0.004664</td>\n",
       "      <td>0.001314</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>0.000602</td>\n",
       "      <td>0.000824</td>\n",
       "      <td>0.001032</td>\n",
       "      <td>0.001182</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>559 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0         1         2         3  Regime\n",
       "1976-01-01  0.004690  0.009284  0.006291  0.001999     0.0\n",
       "1976-02-01  0.006110  0.005652  0.013377  0.001888     0.0\n",
       "1976-03-01  0.003431  0.011341  0.012460  0.003685     0.0\n",
       "1976-04-01  0.003587  0.002261  0.006149  0.034024     0.0\n",
       "1976-05-01  0.001885  0.001937  0.001436  0.045229     0.0\n",
       "...              ...       ...       ...       ...     ...\n",
       "2022-03-01  0.000725  0.000266  0.000210  0.000838     0.0\n",
       "2022-04-01  0.001043  0.000366  0.000437  0.000425     0.0\n",
       "2022-05-01  0.001364  0.000440  0.000499  0.001327     0.0\n",
       "2022-06-01  0.004920  0.000757  0.004664  0.001314     0.0\n",
       "2022-07-01  0.000602  0.000824  0.001032  0.001182     0.0\n",
       "\n",
       "[559 rows x 5 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df=pd.DataFrame(np.concatenate(predicted),index=df_test.index[forecast_windows[-1]:])\n",
    "pred_df=pd.concat([pred_df,df_test.iloc[forecast_windows[-1]:,-4]],axis=1)\n",
    "pred_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>Regime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1976-01-01</th>\n",
       "      <td>0.004690</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-02-01</th>\n",
       "      <td>0.006110</td>\n",
       "      <td>0.009284</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-03-01</th>\n",
       "      <td>0.003431</td>\n",
       "      <td>0.005652</td>\n",
       "      <td>0.006291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-04-01</th>\n",
       "      <td>0.003587</td>\n",
       "      <td>0.011341</td>\n",
       "      <td>0.013377</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-01</th>\n",
       "      <td>0.001885</td>\n",
       "      <td>0.002261</td>\n",
       "      <td>0.012460</td>\n",
       "      <td>0.001999</td>\n",
       "      <td>0.0</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>2022-03-01</th>\n",
       "      <td>0.000725</td>\n",
       "      <td>0.000640</td>\n",
       "      <td>0.000155</td>\n",
       "      <td>0.000254</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-01</th>\n",
       "      <td>0.001043</td>\n",
       "      <td>0.000266</td>\n",
       "      <td>0.000208</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-01</th>\n",
       "      <td>0.001364</td>\n",
       "      <td>0.000366</td>\n",
       "      <td>0.000210</td>\n",
       "      <td>0.000219</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-01</th>\n",
       "      <td>0.004920</td>\n",
       "      <td>0.000440</td>\n",
       "      <td>0.000437</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>0.000602</td>\n",
       "      <td>0.000757</td>\n",
       "      <td>0.000499</td>\n",
       "      <td>0.000838</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>559 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0         1         2         3  Regime\n",
       "1976-01-01  0.004690       NaN       NaN       NaN     0.0\n",
       "1976-02-01  0.006110  0.009284       NaN       NaN     0.0\n",
       "1976-03-01  0.003431  0.005652  0.006291       NaN     0.0\n",
       "1976-04-01  0.003587  0.011341  0.013377       NaN     0.0\n",
       "1976-05-01  0.001885  0.002261  0.012460  0.001999     0.0\n",
       "...              ...       ...       ...       ...     ...\n",
       "2022-03-01  0.000725  0.000640  0.000155  0.000254     0.0\n",
       "2022-04-01  0.001043  0.000266  0.000208  0.000121     0.0\n",
       "2022-05-01  0.001364  0.000366  0.000210  0.000219     0.0\n",
       "2022-06-01  0.004920  0.000440  0.000437  0.000122     0.0\n",
       "2022-07-01  0.000602  0.000757  0.000499  0.000838     0.0\n",
       "\n",
       "[559 rows x 5 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_=[0]+forecast_windows\n",
    "for i in range(0,len(f_)):\n",
    "    pred_df[i]=pred_df[i].shift(f_[i])\n",
    "pred_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_df.columns=['Nowcasting','1 month ahead','2 months ahead','4 months ahead','Actual']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABIEAAAE/CAYAAADRztNjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOz9d5wkV3nvj3/OOVXV3ZNng8KuFgmEJCSCBEIiCCFAGCFsEOALXyEyXAQ22NjAta65119h/AVffibaJl1dMMkgbLBNuAicEGCiwAQLISOJVVittHHydKg65/n9cUJV9XTP9ISdnZl+3q/XSDsdqqt3q+qces7n83kEAALDMAzDMAzDMAzDMAyzpZHHewcYhmEYhmEYhmEYhmGYYw8XgRiGYRiGYRiGYRiGYfoALgIxDMMwDMMwDMMwDMP0AVwEYhiGYRiGYRiGYRiG6QO4CMQwDMMwDMMwDMMwDNMHcBGIYRiGYRiGYRiGYRimD+AiEMMwDMNsAfbs2YOZmRlIyUP7WnPVVVfha1/7WtfnL7nkEtxzzz3ruEf9y7XXXotPfvKTANb3mN+7dy8uvfTSY/45DMMwDHOs4ZkiwzAMwyzB3r17MT8/j5mZGdx33334q7/6KwwODh7v3Spxzz33YHh4GMaYNd82EWF2dhYzMzOYmZnBxMTEmn/GsWCtbtw//elP47LLLgu/ExFOP/30FW1LSonvf//7+MM//MPSYzfddBPe+MY3hsce/OAH4zOf+QwOHjyIqakp/PKXv8Sf//mfY/fu3QBs4UlrHf5N9u3bh7e85S0r+4I9cuqpp4KIoJQ6pp/TK70e81ykYxiGYZgcLgIxDMMwTA8885nPxPDwMM477zw88pGPLN3E9wPnnnsuhoeHMTw8jPHx8WW/f6MUDo43xhi84hWvwH//7/8dZ511FgDgTW96E4gI73nPewAAp59+Or7//e9j//79eOQjH4nR0VFcdNFFuOOOO/CEJzwhbGv//v3h3+QJT3gCXvnKV+KKK644Lt9rJfAxwTAMwzDrDxeBGIZhGGYZHDhwAF/72tdw3nnnhcce85jH4Nvf/jYmJibwk5/8BJdcckl4bnx8HB/96Edx77334ujRo/j7v//78Nyv//qv48c//jEmJibw7W9/Gw9/+MPDc3/wB3+Affv2YXp6Grfeeiue8pSnAAAuuOAC3HTTTZiamsL999+Pd73rXQAWqjROPvlkfOELX8CRI0dw22234b/+1/8atn3ttdfis5/9LD7+8Y9jenoaN998M84///xl/12MjIzg4x//OA4ePIg777wT/+N//A8IIQAAL33pS/Fv//ZvePe7340jR47gLW95C5IkwZ/92Z/hrrvuwv33348PfvCDqFarYXvPetaz8OMf/xhTU1O4/fbbg/rmZS97GW655RZMT0/jjjvuwNVXXx3es337dnzpS1/CxMQEjhw5gm9+85sQQuATn/gEHvCAB+BLX/oSZmZm8N/+239bsP833ngjnvvc5wIALrroIhARLr/8cgDApZdeih//+Mfhu3zrW98CAHzjG98AAPz0pz/FzMwMnv/854ftveENb8CBAwewf/9+vOxlL+v69/bzn/8c7373u/GRj3wED3nIQ/DmN78Zr3jFK4Ki5S1veQu+/e1v441vfCPuvfdeAMChQ4fwvve9D5/97Gc7bvPOO+/Ed77zHZxzzjnhscc97nH4wQ9+gMnJSfzgBz/A4x73uPDcYsdHt2Psm9/8JgBgcnISMzMzeOxjH7tgP6699lr87d/+La6//npMT0/jRz/6ER7xiEeE5/fu3Ys/+IM/wE9/+lPMzc1BKbXo+XPaaafhxhtvxPT0NP7xH/8RO3bsCM+1H/OdzrWBgQHccMMN2LVrV1BNnXzyyRBC4JprrsHtt9+Ow4cP47Of/WypuPmiF70Id955Jw4fPow3v/nNXf8tGYZhGGYzQvzDP/zDP/zDP/zT/Wfv3r106aWXEgDavXs3/exnP6P3vve9BIB27dpFhw8fpssvv5yEEPTUpz6VDh8+TDt27CAA9OUvf5muv/56GhsboyiK6IlPfCIBoEc+8pF04MABuvDCC0lKSS95yUto7969lCQJnXnmmXT33XfTySefTADo1FNPpQc96EEEgL7zne/Qi170IgJAg4OD9JjHPCa8hohIKUUA6MYbb6T3v//9VKlU6Nxzz6WDBw/SU57yFAJA1157LdXrdbr88stJSklvf/vb6bvf/W7X709EdPrppy94/OMf/zj9wz/8Aw0NDdGpp55K//mf/0mveMUrCAC99KUvpTRN6XWvex0ppahardJ73vMe+sIXvkDj4+M0NDREX/ziF+ntb387AaALLriAJicn6alPfSoJIWjXrl101llnEQB6xjOeEb7/E5/4RJqbm6NHPvKRBIDe/va30wc/+EGKooiiKKInPOEJHf/dOv388R//Mf35n/85AaA//MM/pNtvv53+1//6X+E5/2/80pe+lL71rW91/fu45JJLKE1T+uM//mOKooguv/xympubo7Gxsa6fHccx/exnP6NDhw7RW97yltJz9913H730pS9d9Ji85JJL6J577gm/P/jBD6Z9+/bRk5/8ZAJA4+PjdPToUXrRi15ESim68sor6ejRo7Rt27Ylj49ej7FOP9deey21Wi36zd/8TYqiiN74xjfSr371K4qiKPyb/PjHP6ZTTjmFqtXqkufPd77zHXrXu95FSZLQxRdfTNPT0/TJT36y4/50O9fa/64A0Otf/3r67ne/S7t376YkSehDH/oQffrTnyYAdPbZZ9PMzAxdfPHFlCQJvetd76I0TRc9lviHf/iHf/iHfzbRz3HfAf7hH/7hH/7hnw39s3fvXpqZmaHp6WkiIvrnf/5nGh0dJQD0B3/wB/SJT3yi9PqvfvWr9JKXvIROOukk0lp3LAZ84AMfoLe+9a2lx2699VZ64hOfSKeffjodOHCALr300nDz7H++8Y1v0Fve8hbavn176fHiDfEpp5xCWZbR0NBQeP7tb387/dVf/RUB9kb9n/7pn8JzZ599Ns3Pz3f9/kREU1NTNDExQRMTE/S+972PpJTUaDTo7LPPDq+7+uqr6etf/zoBtnBy1113lbYzOzsbijkA6LGPfSz96le/IgD0oQ99iN797nf39O/x93//9/S7v/u7BNhizT/8wz90LFItVQR6ylOeQj/96U8JAN1www30yle+MhTDbrzxRnrOc54TvstSRaD5+flSceTAgQOheNLt57rrriMiojPOOKP0eJqmdNlll4XfX/va19LExATNzMzQ//7f/zt8ptaaJiYmaGpqioiIPv/5z1McxwSAXvSiF9H3v//90na/853v0Etf+tIlj49ejrFu3+naa68tFRSFELR///5QnNu7dy+9/OUvD88vdv7s2bOH0jSlgYGB8Nxf//VfdywCLXaudSoC3XLLLaHoBYBOOukkarVapJSiP/qjP6LPfOYz4bmBgQFqNptcBOIf/uEf/uGfLfHDdjCGYRiG6YFnP/vZGBkZwSWXXIKHPOQhwZZy6qmn4nnPex4mJibCzxOe8AScfPLJ2LNnD44ePYrJyckF2zv11FPxxje+sfS+PXv2YNeuXbjjjjvwe7/3e3jLW96CgwcP4jOf+QxOPvlkAMArX/lKnHnmmbj11lvxgx/8AL/+67++YNu7du3C0aNHMTs7Gx676667QqgwANx///3hz/Pz86jVaotmtDzqUY/C+Pg4xsfH8frXvx47duxApVLBXXfd1fUzimG8O3fuxODgIH70ox+F7/vVr34VO3fuBGA7Pd1xxx0dP/vpT386vvvd7+LIkSOYmJjAM57xjPD3/2d/9me4/fbb8Y//+I+44447cM0113T9Du1897vfxZlnnokTTjgB5513Hj7xiU9gz5492L59Oy688MJgf+qFI0eOQGsdfp+fn8fQ0FDX1z/hCU/As5/9bHz84x/H+973vgXb8v/eAPD+978f4+PjeO9734s4jsPj+/fvx/j4OEZHRzE6Oop6vY6Pf/zjAOwxUPy3AfJ/n6WOj16OscUo/rsTEfbt24ddu3Z1fH6x82fXrl2YmJjA/Px8aT87sdi51olTTz0Vf//3fx8+8xe/+AW01jjxxBOxa9eu0j7Oz8/jyJEjvX59hmEYhtnQcBGIYRiGYZbBN7/5TXzsYx/DO9/5TgD2hvaTn/xkKJCMj49jaGgI73jHO3DPPfdg27ZtGB0dXbCde+65B29729tK7xscHMT1118PAPjMZz6Diy++OOSevOMd7wAA3H777bjqqqtwwgkn4B3veAc+97nPYWBgoLTt/fv3Y9u2baUixAMe8ICQL7MWHD58GK1WC6eeemrXzyCi0uvn5+fx0Ic+NHzfsbExDA8Ph7+PTh23kiTB5z//ebzzne/EiSeeiPHxcXzlK18J2UOzs7N405vehNNPPx3PfOYz8YY3vCHkJxU/vxP1eh0/+tGP8PrXvx4333wz0jTFd77zHbzhDW/AHXfcccxu/CuVCj7ykY/gTW96E37rt34LZ511Fl74wheG5//lX/4lZBX1yvT0ND796U/jmc98JgB7DBT/bYD832ep46PbMbbU36dnz5494c9CCJxyyinYv39/eKy4ncXOn/vuuw/j4+Ol4/sBD3hAx89c7FzrtN/33HMPLr/88tLn1mo17N+/H/fdd1/pO9RqNWzfvr2n784wDMMwGx0uAjEMwzDMMnnve9+LX/u1X8O5556LT33qU3jmM5+Jpz3taZBSolKp4JJLLsHu3btx//3344YbbsAHPvABjI2NIYoiXHzxxQCA6667Dq95zWtw4YUXAgAGBgbwjGc8A0NDQzjzzDPx5Cc/GUmSoNFooF6vB5XJC1/4QuzYsQNEFFQPRQUKAOzbtw/f+c538Kd/+qeoVCp4+MMfjle+8pX467/+6zX7OzDG4G/+5m/wtre9DUNDQ3jAAx6AN7zhDfjUpz7V8fVEhOuuuw7vec97gvpn165deNrTngYA+MhHPoKXv/zleMpTngIhBHbt2oWzzjoLSZKgUqng0KFDyLIMT3/608N7ABuu7YtH09PT0FqHv48DBw7gQQ960KLf4xvf+AZe97rXhcDnG2+8sfR7J+6///4lt7sYb33rW3HXXXfh4x//OOr1Oq6++mq85z3vCeqmt7zlLbj44ovxrne9Kyhotm/fjrPPPrvrNgcHB3HllVfi5z//OQDgK1/5Cs4880y84AUvgFIKz3/+83HOOefgy1/+8pLHR7dj7NChQ9BaL/ndzz//fDznOc+BUgq/93u/h2azie9973sdX7vY+XP33Xfjhz/8If74j/8YcRzjoosuCkWudhY71w4cOIDt27djZGQkvP5DH/oQ3va2t4Wi0o4dO/CsZz0LAPC5z30Ov/Ebv4GLLroIcRzjrW99K6TkKTPDMAyzdTjunjT+4R/+4R/+4Z+N/NMpW+YDH/gAfe5znyMAdOGFF9KNN95IR44coYMHD9KXv/xl2rNnDwE2oPdjH/sY3X///XT06FH6/Oc/H7Zx2WWX0Q9+8AOamJig/fv309/8zd/Q0NAQPfzhD6fvf//7ND09TUeOHKEvfelLIST6k5/8JB04cIBmZmbo5ptvpiuuuIKAhXktu3fvpi996Ut05MgRuv322+nVr351+Nxrr7025Kp0em/7T7dg6LGxMfrkJz9JBw8epLvvvpv+6I/+iIQQBCzM0QFAlUqF3va2t9Edd9xBU1NTdMstt9Dv/M7vhOef/exn009/+lOanp6m2267jZ72tKcRAPrt3/5tuv/++2liYoI+8YlP0Gc+8xn6kz/5EwJAv/d7v0d79+6l2dlZuueee+h//s//Gbb3rGc9i+666y6amJigN77xjR2/29Oe9jQiohAi/NCHPpSIiJ7//OeH17R/l1e/+tW0f/9+mpiYoOc973kdM2e65RGdf/75NDk5Saeddlrp8Y9+9KP0qU99Kvx+1lln0Wc/+1k6dOgQTU9P06233kp//ud/TqeccgoBeSbQzMwMzczM0OHDh+nLX/5y6d/poosuoh/+8Ic0OTlJP/zhD+miiy4Kzy12fHQ7xgCbwXTw4EGamJjomHl07bXX0t/+7d/S9ddfT9PT0/Tv//7vIcS729/LYufPAx/4QPrmN79JMzMz9I//+I/0F3/xF12DoRc71z7ykY/Q4cOHaWJigk4++WQSQtDv//7v06233krT09N0++2309ve9rbw+pe85CV011130eHDh+nNb37zkvlS/MM//MM//MM/m+VHuD8wDMMwDMMwzKq49tpr8eAHPxgvfvGLj/euMAzDMAzTAda2MgzDMAzDMAzDMAzD9AFcBGIYhmEYhmEYhmEYhukD2A7GMAzDMAzDMAzDMAzTB7ASiGEYhmEYhmEYhmEYpg/gIhDDMAzDMAzDMAzDMEwfEB2vDz548CDuuuuu4/XxDMMwDMMwDMMwDMMwW45TTz0VJ5xwQsfnjlsR6K677sIFF1xwvD6eYRiGYRiGYRiGYRhmy3HTTTd1fY7tYAzDMAzDMAzDMAzDMH0AF4EYhmEYhmEYhmEYhmH6AC4CMQzDMAzDMAzDMAzD9AFcBGIYhmEYhmEYhmEYhukDuAjEMAzDMAzDMAzDMAzTB3ARiGEYhmEYhmEYhmEYpg/gIhDDMAzDMAzDMAzDMEwfsGQR6CMf+QgOHDiA//iP/+j6mve973247bbb8NOf/hSPfOQj13QHGYZhGIZhGIZhGIZhmNWzZBHoYx/7GJ7+9Kd3ff7yyy/HGWecgTPOOANXX301PvjBD67pDjIMwzAMwzAMwzAMwzCrJ1rqBd/61rdw6qmndn3+iiuuwCc+8QkAwPe//32MjY3hpJNOwv333792e8kcEwZHt+GsC58EAbHkaw/duxd33/LvG2Z/1pr1+H4MczxZzfnF5wfDbG74/Gc2AsdzntcOH9cM098Ur0f9eD1Ysgi0FLt378Y999wTft+3bx92797dsQj0qle9CldffTUAYMeOHav9aGaVPO6KF+Pi33xlT6+dn57EO178xA2zP2vNenw/hjmerOb84vODYTY3fP4zG4HjOc9rh49rhulvitejfrwerLoIJMTCaj4RdXztddddh+uuuw4AcNNNN632o5lVEsUVNOtz+IvfvmLR113y/Ktx7pOfuWH2Z61Zr+/HMMeTbudXdXgQozt34sCv7lzwHhlJvPwdH8TY+Gnrs5MMwxwTVjq+8vjIrCXdjkMhBOJKBa1GA3vOeQhe/K4/gYxiSCmx7+e/wEd/95o13Q8+rhmG8dejn934f/vyerDqItC+ffuwZ8+e8Pspp5yC/fv3r3azzDogpIDRGjNHDy76uub8bMdi37Hen8c+79l4/POeg/dc+XKQMcfsc9fr+zHM8aTb+f6EF70GT375C3Hda34ft33/h+HxKEnw4nf+CU544B6k09xIkmE2M72O9+3w+MisJd2Owye97IV4xutfg1u/9V084BEPxfThg3j/y38bl/3WK/HY/3IF5qePQmfZmu0HH9cMw/jrUb9eD1Y9s//iF7+Il7zkJQCAxzzmMZiamuI8oE2CELKn4gqRgZDH/iawfX9OfOBp2H32mXjgo849pp+7Xt+PYY4n3c736tAgVBThJe/6/7DnIQ/GyWeejqe++uX4w6/8LR725Cdi5uhRgAAZqeOw1wzDrAW9jvft8PjIrCXdjsPRE3YCAPY87GxIpfB/XvtGTB88hLv/4+cAgOEd29d0P/i4ZhjGX4/69XqwpBLo05/+NJ70pCdhx44duOeee3DttdcijmMAwIc//GF85StfwTOe8QzcfvvtmJ+fx8tf/vJjvtPM2iCEhOmpCETrowRq2x8h7Wee+2tPxq9++OPw+GN+81l41DOehg++8nVr8rnr9f0Y5njS7XxXcYTG3BwuPFnjdT99Hz76y3EAAv/57e/hs3/0Njz+ipfjgWefiKRaRWN2bv13nGGYVdPreN8Oj4/MWtLtOJRKojk3jz/5tWcjrlTQnJ8HAEwfOgIAGNm5HZP3H1iz/eDjmmEYfz3a/oBToKJVm6M2HUt+46uuumrJjbzudWtzM86sL0IKEC09KTRGQ4h1UAK17Y9UVnnw8Kc+Cf/wv94TsqbOfNyFOPXch63Z567X92OY40m38z2KY8xPTuPu//wufu0lT8SN73k3/u2r/4aJ/VbRedGzrNIz5iIQw2xatu/ZjerQwLLfx+Mjs5Z0G4eElDBaO2vGfHh8+tBhAMDIzp1ruh98XDMM469H23fv6suiMF8B+xghZE9FIDIUCjLruT/+hBw9YSdOe+QjwuM79pyCuFJZs89dr+/HMMeTbud7FMfQaYrmzAwAYGDuQCgAAcA5p28HIJDUauu1qwzDrDGjO3ciSpJlv4/HR2Yt6TYOCSlBRLjkkofhbW97cXg8LwKtsR2Mj2uG6Xv89SiqxAAEzn/0Gfid3+mfgGguAvUxQkqQ6dzJrUgvhaK1oH1/hJKoz8wibTRx7tOeEh7fvmc3AKxYuvewpzwRV/3pteH3Ux/x0BXuMcNsHrqd7yqOkaUpIpf5c8EFZ5SeTyJbjE1qa1d4ZRhmfRFSQggsO/dgvcZ/pj/oNg5JpwR67nMfj2v++39BHNv53ezEJIzWGNm5Y033g49rhmH89cgvkLzoJZfi7X/6kuO8V+sHF4H6GCF6s4P5EL9jHZrVvj9SKrTm6/jFv30XD3/qkwAAg+NjqA0PAcCKVjUB4KyLHovzf+Pp2HbKLgDASQ9+oP38PgwFY/qHbue7imPoNEMU2eP//EeXi0AC9j21ocFjv5MMwxwT/Pi2XBXteo3/TH/QbRyyN2MGlYptC797t1X+kDGYOXIUI2sdDM3HNcP0Pf565O8nk0qCOO4fhSBf/foYn4oexzFGR8cXeZ39vzzmRaBy1wghBYzRuOOmH2HsxBMwcsJO7HjAKeH5KIlX9Dm+iHTGYx6NysAARlxXCrXC7THMZqBbV5YojqDTFMopgR71qAeVz3WXxTU0Mrwu+8kwzNojpQQEkNSqy3qfz+I71uM/0x90G4eksgGtScXOwx7wgDwDaPrwEQyvtR2Mj2uG6Xv89cjfT1YqFSR9dC/IV78+RjoP9vOf90p86P2f7/q6ZzzjfAA45iF6fn/y3xWMNth3yy8BAKecfRZ27MmLQGqFSqABdzN7xmMejQeefy6kst+rUlt+aCbDbBbazy+PimNkWW4HGx4ewJln7spf4FZth0aG1mU/GYZZe4ISqLpMJZA7/zlEl1kLuo1D/masWrU3YHv25Pav6YOHMbrGwdB8XDMM469HXiHrxQD9Uhzuj2/JdMQG8RlsG9+BnTtO6via7dtHcOGF1h4i5bGVyPn9yX+3Mr39/3kbjDHYffaZIQ8IWIUSaGQEAPDgC8/HGRc+OjzOwbfMVqb9/PLkdjCFVisF0JYLxEUghtn0+Jvd5dvBqPR+hlkN3cYhqSSMtnYwoF0JdHjtlUB8XDNM3+OvR1Elt4MB6BtLGF/9+pggg4tiKKUQxwuVNa95zdORuIC+oeFjWyRplwlLKUHaoFWv4/Bd92D3Q84s28HildvBslYLw9u34YIrngHATgYqXARitjDdZPgqjpC1WogihTvuuB+zs3U8upALJNyq7eAxPv8Zhjl2iBXbwXx2Sv+1z2XWnm7jUDETCADOPOPhuOrKqwEAM4eOYGjb+Jp28+LjmmEYISRAhCix97n+vvLMMx6KZ/3GC47nrq0LXATqY6zShhBH9qCvVss3eUkS4bWv+w0YowEAI6PHNhjW70/+ey4bvvcX/4ndZ59ZsoOtNBi6NjKMX37vJgA2aNq3IE0G+CaX2bq0n1+eqBAM3Wym+Pd/vwPnP/rBhVc4JdAwK4EYZrMiXLhfXF1mEciwbYZZO7qNQ0LaTCBfBNq16zy86KrfAgBMHzoCKSWGtnXPrlwufFwzDNNeBI5dEejXnvosvObq/3Y8dmld4atfHyOElcH5VuvtRaArr3wiTjppHD+8yWbyjI+PLhogvVb7E353LUMBYN8vfoltu07GSWecjsn7DwBYTTD0MPb/5+04dNc9AIADd9wJYGk72OP27MIfXHzhij6TYY437eeXx9rBUiilkGUa//GzO3HOOXvy97kJe22IM7MYZrMiV9odzJ3/3EWJWQu6jUNSljOBhgYHEbm56fRhu1A3soaWMD6uGYYRQtoFElcL8oqgOI6PeQTKRoCvfn2MleUSIuWKQJVyEeSCC87AxMQsfvrTvQCAyy57Pv7hc9/DB/78b/BrT73imO2PRypVUgIBQGWghvtvt/uzEiVQUqtBxRHq0zP45Xd/gMbcHA7ddZd9rrp4Eej1j3s0/sclj1v2ZzLMRqD9/PKoOEKWpogiiSzTqNdbbd0R7IR9YGB5CgKGYTYQQkJgFXYwwbYZZvV0G4fa7WADAwMhomD60BEAwMgahkPzcc0wjBAyFIAAIHLOmNjFpGx1uAjUx/jg5aiLEmh0bBBHjkyjPt8AAOzYfiIajTpGx7bh937n2mO2P8Xfi0ogz/13/ArAypRANRduW5+ZwVfe90G87wWvRLNeBwDES0yOH7tnFyquYMYwm43288sTFYKhs8zAGANZkMj6OXJ1kItADLNZ8S3i2Q7GHE+6jUNSloOhlUzc4wrThw4BWGMlEB/XDNP3CClKlrAoZiUQ0yf4gD7lKp+VNiXQ2NgQJifnUK83AQADA4M4OnEY//r1LyNZYR5PL/vjkVLBuN/r09M4eu99AIADTgmkOgRZA8Bp5z0Cv/aaV3R8zncGq0/PoDE7h4N770LWagFYPBj6AaMj2DUyBCkFYsWnDbP56B4Mbe1gtgikobWBKhzj0lWBastUEDAMs4EQAgCxHYw5rnQNhlbWJlapxEjTDNrYcSeKIswcOQpjDEZ2sB2MYZi1w9vBfBnIiyKiOOqLNvFb/xsyXfGDcR4MXb7JGx8fLBWBKpUaGo15pGmKKIrXXEbbPjkQUpR+v/dWqwY6sPdOAN2VQI/69afh6a99FUZOsNLhhz75YvyX//caAMDAyDAAoD4zG17vi0BJtfvk+HF7doU/VyNWAzGbj26T7yiOnR3MFoGMoY5KoNoyFQQMw2wc/Hid1JZbBGLbDLN2LNYdzGiDajXBr351P4y2tydRFMFkGnMTkxjeuWPN9oOPa4Zh2jOBfEaut4VtdTUQF4H6mKXsYEEJ5OxglWoN9fo80tQWTfxJstb74/Et4j0//OIN+ME/fBnzU9P287uokSoDNsD2rMddAAB48steiMf85rMgI4Wa63BUn54Or8+a9vvEi2QCPaZQBKr0gU+U2Xp0k+GrQnewXAmUH+N+MaS6zJtHhmE2Dj77YNlKIO1bafN0kVk9i9nBrBIowm233Qfti0DKzjNnDh9ZWzsYH9cM0/e028FUZOe+XhyhtrjzY2t/O2ZRbJcGguoSDD02NoipyTmIyBZbkriCRqOOLEsB5K301np/wu+y/PvN//oNfPaP3haUO92UQJVBWwQ68/GPwdD2cZx63sMhpcTw9m3BDjY/PRNen7as0ilZROnwWFYCMZuc9vPLo+KoYAcz0Lq8QurHx0p17S2gDMOsDytuEU+cncKsHd3GIeEW/SqVGHfdeQBa2+PVL0hMHzqCkWOiBOLjmmH6FSFkqRDsrze+GMRKIGbL4u1W3ZVA1g72iMfbtuiRKwJ5JdBrL3wkfvral6/5/uS/5y3ii2St1O1PFyWQLwI99gI87MlPDL7OkZ07C0qgoh3MFbW6TI5rcYRzT9qJuyateqgabe2LArM1aT+/7GMSKopKmUA+h8uvgHhrWKW6tkVfhmHWDz/RXWyxoxOGbTPMGtJpHAJcMLSxdrC5uQbSli0UeXvG9KHDa1oE4uOaYRghBYSSaDWs40Uqr0C01x0uAjFbFrsik2cCFYOh4ziCEMNI4lPx6JottqgoCZlAAHDG9jGcfcJ2KLk2g6jfn/C77LxiFIpA3ZRAAwMwxmBo2zie8soXQ6cZAGD0hB2ouUygxmxeBPKFpm7dwc7fdRJipfCNvXfb7bMSiNmEtJ9fQD7BzkJ3MB2UQL7440/vZAXd+BiG2RjkSqBl2sEMB+gya0encQhwwdCuCNRsZkEJ5GMHZo4cxfC2bWu2H3xcMwwjhISUEmnTFoG8EigKdjAuAjFbFBvQR6E7WLXQHWt0dAD77xnDOWc9FwOxr4jGqBfsYANOiTO2RoGxfn88sosSSKdL28Hu/PHPAADbT9mNn/3z1wEAIzt3oDY8jPrMbGklyk9Ikkrn7+GtYN+48x67fVYCMZuQ9vMLsFYwAMjSVikYGsgHPz9H5iIQw2xmxMpaxBNnpzBrR6dxCACkkBCwx1qzmYLI3Yy5FfnZiQmoOApK79XCxzXDMEJISCWR1p0SSPoiECuBmC2OcEF8wQ5WqaI6NIikVsP4+BCyTCKKEiT+ZIBCvT6PlivCVF0m0PgyVxaX2h+PVKqzEsgpkVSXYOjq4AAO37MvdBP7/ue/CJ1lGHFKoHohDwjIJwPdAjMfMDqCw3Pz2O8sZJwJxGxG2s8vwHYGA7AgGBpYaAfjIhDDbF589sliXTA7wdkpzFrSaRwCrBJICTvfazYzCFG+GZt3dvzB8bE12Q8+rhmGEVJCKoVWs2wH81m5aosXifluto8RUoKMDistp593Ht72hn8GAKhDd4buDL4bliRRCoauORXBWBcb1Ur3J/wuBKiDEsi43JLF7GDNuXn87J++jtrwMO744Y8xe2QCozt3YmBkGPWZtiKQW5XqJpMfrsSYbrbQyKytjDOBmM1I+/kF2M5gAErB0D4TKNjB3P9jPu4ZZtMihIAA28GY40uncQhwK/LOeuymWgByy/LcxCQAYHBsDEf37V/1fvBxzTCMLQJJtOYbwCBChmxQwrMdjNmqCCFAhjAyasOSh0fH0Ko3cPO/fgO7T9sFndnDIw5qHLKZQK47ly8Cja+ZHUyUZMJCyXBD2o5upV1b1FcGbRHoX677OP70N54HozWmDh3C8M7tqI4MLVsJNFKpYKbZQiOzE5eK4tops/loP7+A3A7mi0BGd1ACuYl5HG/twZBhtjSr7g7GAbrM6uk0DgF2BT6CfdwvQAJ5q+a5yUkAwOD46JrsBx/XDMMIIawSqF4H0KEItMWLxFv72zEdec2F5+FHv/XSIMutuUlhJUnQmJvDj/7vP6KiKAzEyreMNsZ2B3NKIG+LGq8dGzuYXTFaOFkAgKzVguqgBIqrFUil0JyfBxHBuMLN9KHDGN25AwMjI6X28ABCPlC3ItBwJcF0s4mWZiUQs3npJMP3SqA8GNoUgqHd+e9GCVYCMczmRQibCbTc7mB+fGTbDLMWdLODSamCHczovDBTdcfr3MQUAKsEAoCHPukJeN61/33F+8HHNcMwQkrISKHVqLvf7bVHcXcwZqvyqNN24eEn7USsFIgMhDvIB6sV6DTF0X37bRHIKYGUXzEhXWoR74sh47Vah09ZPu1dI2zL0IWyYcDmAkUdMoGqg4MAgObcfOnx6YOHXTD0EBozs6XnfO5Q1CUYeqSSODuY3RfOBGI2I526skQL7GDFYGh7/ueDotzy0liG2doQomVnArFthlk7unYHkyIsOBSVQA/ZMYoXn/vQBUqghz3lEpz/zKeveD/4uGYYRgqXCeSDoZ0yULn7Yu4Oxmw5zn3EAwEAlSgGGYJ0KyG1JIFOMxy9dz8qyiDzSiBXiBHGoF6fC0ognxW0ZkqgdjvYEkqgTplAvnNEY3au9PjUocMY2jaOwfExzE9Pl55byg42HOxgVgmUsCKC2YR0kuH7c8gWgWTHFvHKZwOBup4jDMNsbPJgaLaDMcePbnYwISUiaR/3ncEA4BVnnYr3P/PX0Jidg06zoAQa2bljVeMRH9cMw8hIQQjkdjA3TvoFT8VKIGarESlf9ImtEsgd9JU4hs4yzE9NQ+lWrgQKuTwGBB26c/lW6WvWIr69O1iXFvEAkLU6K4F8Eag5X1YCzRw6DMAWeurtSqBgB+vcbWzE2cFYCcRsZnqzg+mQw9WeCSQFEK9RwZdhmOPDcm+c2TbDrCVdu4PJPBgavghEhPPGh1GNI1QihbnJyaAEGjlhB4B8DFsufFwzDONtX+GeMSiBvB1sa18f+G62D5GwB3k1id0NnwuAVjG0K/CIxlxQAkUmzwRSygQ7WMWdHONr1R1MlIOg7WRhESVQh8G/0sUONuWKQABCMPTY2CB2796ONNjBugVDJ5hpttDk7mDMJqb9/AI6dQfrEAytnBJoBXkiDMNsDGwmEC2/O1iwzbBiglk9ncYhwN5sRS4TiGDnWLX6JMYSe5syWqlgbnIqVwLt2A7ALt75eWsv7Dh1D055yJmYP2rnc3xcM0z/IqMIEEDLFYG8HcwXfzgTiNly+IM8iWK3GuKUQEZjfM6qZETaBJE7GXwuDxlEMRXsYPZ929asCCTC6gxgT8JOLeKB7plAlQFnB+uQCeSpOzvYm970XPzzv/x/iwZDx0qiFsecCcRsetrPLwCICt3BlJKuRbydiIdB0K+MAEjWKP+LYZj1o5h7kixzvGbFBLOWdBqHAGu/iJQvyNhjbXQqbwU/Wq1gbsIqgVQcY2jbOAAg6qLg7sbj/5/n4nl//Id8XDMMA+Xu55rODiacSEL6YGi1ta8PW/vbMR3Ji0ARimsgD9z7E/zW3XcBACKdD9KRzwQigzhCaBGfuInlsbKDLdkivkMmUNXbwebKmUDTJSWQLXSNjQ1iZGQgTAY6bW/YFZpsdzDXIp6VQMwmpKMdLMrtYHEcdVQC+TFQCFp2e2mGYY4/Nt9AgGj5uV7hZnmLy+KZ9aG7HUwgchNSIey8a3T6vvD8SEEJ5FVAABAnyzueK7UaVBTxcc0wTMj+afoc2RB/4Oe/W/t+j69+fYhXv0ZKIWTikcH2w3dh2GgIAcSFsDyfCSTIIKmIoARK3IbWLhi6bP8SYvkt4rtlAs1NTEKnVv7r7WCVSowoUmFCEsUJhJQYr1Xx8BOt33zETZinm7bw1UgzVgIxm5L28wvI7WDGndPFTKCFwdBAskwrCcMwxx8VRQABRmeQSi0rR4UDdJm1pNM4BNhijG/EI0UEEGF06j4cSu3i25hXAo2NYnhnXgRarhIoqVUhleLjmmEYq/gRQKNevmcUwQ62tcskW/vbMR3xmUBKSERO1TI6ex/irAkAGEoSVFQ+SQx2MGNQrUqbCUQGkbs5XDslUJsdTC3SIr6VIoq728HaM4GICDNHjgAA6jO2CBQnEZQqr0rFlQreeNEF+OeXXwnA5gEBwIwvAmUZK4GYTUn7+QXkdjDhzoGOmUCFIlC8RtZPhmHWDxkpEACdZVCCcNKuHT2/N79Z5ukis3o6jUOAzwTyr4kwMD+BOGvg5y1bMBqtWiXQwOgIxk48IbyvUyzAYsTVqlUC8XHNMH2PVAoCQMvdMwZDqvAt4rf2oj9f/fqQsPIhBIaqdgDdfmRveH4oiVHxBRaivEU8GdRqElmW5oUh5MHQD3rgWfj1y5+3iv1qs4Mt1iI+zVvE7zrrDDzuec8BUFQC1Re8Z8rlAs0XlEBSSsB/BNlVop2DAxivVVGLIwxXcjsYADQyzUogZlPSfn4BuRJImIVFoKGhUZz+oLOgOBiaYTY1PvfAZBketb2Of/vG23p+L4WMMF78YFZPp3EIcC3i3SEWqQQjzgr2n8aOPyPVBHMTk1BRhBMedFp4X7eurt3I1aw+AJaPa4bpV7wdzOfIepGivy6wEojZcng7GEHYIgcRxg/fBeNWRIaTBNWKvdkTZCB9lYQMBgdjpGleBDowO4fhSoJISjz9sufit1/zhyveLxsYmBd9pFxCCeRWgB7z3GfiOW9+A4SUqAwOoDlf77jS5HOBfIv4SiV2n+P/PoC4Wgk5QDsHBgpFoIISSPGkgdl8tJ9fQKG9blAC5cHQT33K8/Bn/+uvwvVCAJwJxDCbED/R1ZnGYGxwwomjPb83KCa4ixKzBnQahwAXDO0tyCpGrTENLSMccap02x1sEgCw68wHh/d16+raDT+G+cVQPq4Zpn/x95nadb32VwOvENzqRWIuAvUhUghAACQEhpIYg3NHUG3NY2J8DwBgbLCGOLIDpaEUwpVGJTRGRgagdQZk9oS5zxVUxmtVJHECtYoCiZSqdyVQK1cCJQM26G9wfBTVwcEFodCeqYOH0Ko3kDlVT+JajxYt4Um1iiFX+Nk+UFtgB2tpVgIxm5P28wsAlLeDYaESaHBwCLXaQK4EArESiGE2IUopgACdpZCweYC94sdgts0wa0GncQiwRRnfHSxSCYgMSEiQW6h42iWPwPV/YW36J59xenhfvFw7mLc0B0E8H9cM06/Y7FlTuNcU4XEgj0XYqmztb8d0REAAIxUQgME4ws5Dt4MAHNphV1dO2jYCre2hoUWaV0ZhMDJi7VaU2kLKfTO24DJerSCOk1VJ52zXiLISqJOiB3DdwZxlzecAje7caZVAbXlAnm98/NP4xJv+Z/g9KIFEkEYhqeWFnx0DNQy7VaZiJlCVM4GYTUj7+QUAkbeDFTKBfDB07Iq6IRNILL+9NMMwxx/pir06TSEFIY6XUQRiJRCzhnQahwCrBIp9ESiuQGcpSAjESQVTjSZOGhvGnt22LfyOB5yCzHWpXW4wdKVWc/vBSiCG6XuE7ZrpxzmCtYSxEojZskgAqEQwQmBbFOHk+3+OX0YjaFaHAQAnjg0jy+yhYZAFJZBwSiAA0K0GgFwJNFarIorjVZ0wVibcrgTqXATK0rxFvC8CDe/cjsrgABrznYtAR++9D7/45rfD7+12MEAgrlWDHWxHQQlUzARKWAnEHEdOHh7EJaftWfb72s8vAOEcEs7yWVQCxVECKVVQAglwMDTDbEZyO1galK+9rnBygC6zlnQah/zj3g6WxBXotAWCQBzHmGo0UZMKlSQCQJBK4ei9NjNo2UoglwkU7GB8XDNM32KvA4UiEOW5QAAXgZgtiLeDGSEwKiVISPzfGQUt7Q3hztHBoASq16fDDSKMxshoZyXQtloVcRSvyg5mV4jKRSCjuxSBCi3ifRj06M4diyqB2vF2MFnwgyXVSm4HG6xhpFKBMYS51LbQZiUQc7z5wyc+Dn9z5RXLfl/7+QV0ygTSIRMockqgPEgeqA3UVr7jDMMcF3yLeJ1mYdIXx70tZrAdjFlLOo1DACCUbRHfaLRQqVShsxYgbBFoutlE1dsz3HB05N79AJafCZS0ZwLxcc0w/YtXArlxziqB8nvC1dzTbgb46teHCMAe+FJCgLDvlPNw98Q0tAvg2z4yBJ1JZDrFT//pn/MiEC20g+33mUDVKqLIK2tWdlgJdzJ6fIv4h57zSFx15dWl1xaDoSvuxnTkhJ2oDgz2XARaqASyoYHDrrhk7WAJZlqtUBlucncw5jjz0BO2Y6DHG7gi7ecXkBeB/CmQZaagBLLP+dVZImBgiItADLPZCEqgNA1B71GPixlsB2PWkk7jEGBX3GMl0WymqFZr0FkGEgJJkmCq0UTFjVKmZeeeR/f5ItDyW8TbHXH/4+OaYfoWgU5KoPyawN3BmC2HhAuGVgINAew/+eGYmpkKRaBtgzVoLdFo1JHV5yF8DYj0giLQ/QU7mJflrnRlxQd0lX8nPPXSZ+JFV/1W6bVZq4UojiGECEqgkR3bXXewZSqB/O6SXSUarpTtYN4KBgBNVgIxx5lzTtiBeAUS1fbzCwCiOLLnkjumrR3MduTLi7q+CCQwyEUghtl0KH9+u0wgAIgitoMx60+ncQiwxZg4Emg0WqhWazBZCkAgimJMNVtI/ELczDSAvAi0HDuYECLk2rESiGEYqwRCoQgkUBQqKraDMVsNIdx/4giHto/AqAhzs9PQyhZFxgdr0JlEfX4OulUPSiAyBiMj7iYws/ao+2ZdMHStEpQDK1YCtcmEpZIgozFQG4RSZeVD1rKfr+IYSc0rgZZnB1sQDA1gYKCKilP67BgYwEilEkKhAZsJVFGsBGKODzsHB7B9oAYpRamrXS90s4NlaRqKQFrnLeJjrxJy2SEEcBGIYTYhMooACGStVlACLdsOtsVXRJn1oZsdTEqFSAk0mxkqlRqMtkqgOIow1WggcavzrZkpAMCRffcCWJ4SqPjaPBiaj2uG6VcEhO1E2MUOxplAzJZD+P8IgZHxUQDA7NwsICQoUnjS489GpiXm5mZhWo1yJpBTAiGz6piZZgszzRbGCnawlXooFwRDCwljCLXa4IJtZqnrDJHEIRh6ZMcOVAZWUAQqnAWjtYHw5+3ODjZdKAI1dYYKK4GY48TZO7eHPy9XDdQpkFPFse2051QBxWDoSNkJsy+SEgEDg1wEYpjNhlIKRFZBK+GVQMu0gy236swwHegaDC0FYiWsHaxShdYZIJwSqNFCpO1xm85Z9flKgqF9HpD/PL8/DMP0KUIAtIgdjFvEA5dddhluvfVW3HbbbbjmmmsWPD8yMoIvfvGL+MlPfoKbb74ZL3vZy9Z6P5k1RELYfs8E+HlgvVkHADQhMJgkyFJgdm4GlLVCdzAyGqOjg/YNWR6UPFFv2GDoeLWZQG12MGV/HxgYLAfUwraIB6y/28t7x04+EUmtiubcXE+f5+1gvvsRAIzU8pDBTnawBmcCMceRUhFomYNTJxm+iqOSEqgUDO2VfU4uTwTUBrg7GMNsNqQbs7K0FRSEPReBDNvBmLWjmx1MKoU4ksEORr4IFEeYbjYRZQQiQjpri0CTBw5aK/MygqHjYhGI7WAM0/e4RKBwTeLuYO0vkBLvf//7cfnll+Occ87BC17wApx99tml17z2ta/FLbfcgvPOOw9PetKT8K53vSsUBJiNRwiGBhC7CWG9YQsncxr4ztdvxr37plCvz4HSZiEYmkLhBNoWYepphslGA+O1KuLYKQdWeNJYmXAhGNq1iB+oDS7Ybtay6pzBMatkas7XMbLD3iA3VmgHM5nGiLOWHZmvY/tArYMdjJVAzPHjITu3hT/Hyyy2tp9fABDFMXSadcwEUu7GUflMIAjUBpbXiYVhmOOPzwSy3cG83bO3ccz4IhDbZpg1oNM45I+tyAVDV6o1GG1AQkCpCFONps2mzAzS+VlkaYr5ySmkzday7GB+wdB+KNvBGKbvcUogP84RRLk72Ba/Piz57S688ELcfvvt2Lt3L9I0xfXXX48rrii3JyYiDA8PAwCGhoZw9OhRZFl2bPaYWTU+EohIIHIhkU3XcSGFxNH7J3H4SB3z83NAloZgaJAJN4uipARqYqxazARahR2MyhNOYwxqA7YIVLSE+UygAVcEOnz3PeG55SqB/DmetlKMuAnF3okpqwSqlu1grARijidFJVC03CJQ4fzyqDiGLimBzEIlkMrtYLUaF4EYZrMhlQIIMFrDp16yHYw5HnQah7w1K45yOxiZDAQgUhGmvBq7keEX//oNfObNbwURIWu1FtjBVBxjYHSk42fH1Xz8kmwHYxgGrkU8dVYCtefRbjWWvIvYvXs37rknv8Het28fdu/eXXrNX/7lX+Lss8/G/v378R//8R94/etf37EFJLMxEBAhEd23yTQgGBBaQmEoSTBQG8R8fQ7QaUkJ5LNDhLFFvnqWYaJex3itisipv1ZaORUylwl7S1lRCVQ8GbPUFoG8EujQXcUiUG9KoGo1cftr/xJ0mmLIFYHunJhCpCROGhos2cFa3B2MOY6cvXM7tDtHlm0Hkx3sYJG1gym1MBPIn2++OwIBqNaW146XYZjjj1f1GaMh3GS3VyWQn8uxYoJZCzqNQ37hMFYSzUaGKIrta4SAiiJM1d0crKkxf/QIfvLVfwbgusS2KYEufeWL8fuf/VjHz+6YCcTHNcP0LbYoTWGcIwCGW8TndKqStxd4LrvsMvzkJz/Brl27cN555+Ev//IvgzKoyKte9SrcdNNNuOmmm7Bjx45V7DazGiSAzBgQAOEOdhIEIwipkBhKYgwMDKI+PwehszwTCBSyeaRbpUm1wUSjaYtAQTmwUiWQXDDhNNpmAgFlJZBeUAS6OzzX6KFFfLEzii8CZWmK4cR+hzsnp9xzckF3MCXlslUYDLNaxmtVnDw8hF8engCwkmDo3uxgXgmkVOzeZ19LJELhlGGYzYMfk43Oi0DLzgTiMY9ZAzqNQ0EJFEs0mnkGJQkBJctKIG/jB4CsuVAJdPKZD8bQtvGOn13OBHILmnxcM0x/Q8VMIAEy3B0ssG/fPuzZsyf8fsopp2D//v2l17z85S/H3/3d3wEA7rjjDuzduxcPechDFmzruuuuwwUXXIALLrgAhw8fXu2+MytEAGilWUnyRkJAC0ImIwxXEtRqA5ivz0GaLCiBfDFIKQlhNLQbRKebTYxUKkhCJtBKg6Ft14hzzj4PV135KrtfRqNW62QHs4WZgVFXBLpzeUqgkG2E3A6mWxmG3IrpryYmw/PTbZlAADgXiFl3HrLD5gH97MBBACsJhl7YlSVK2u1geSZQpMrKPiKgWuWsN4bZbAQlkNZhPF++HYxvlpnV02kcCkqgSKLV8t1oc0XqVMMWgaitCJR2CIYe330yZJdju5gJJNz4ycc1w/QzrkU8dwfrzE033YQzzjgDp512GuI4xpVXXokvfvGLpdfcfffduPTSSwEAJ5xwAs466yz86le/OjZ7zKwaAaCV6ZLkDQIggVAEUirC/PwchNEIr3JjcxQpKJ1BC3fjqA0iKXIl0IqDoRWIDJ50yeV4yVW/DRCghArFn5IdrFVWAh25e194rpciUHEi4TNPdCvFoJss3zkxFZ4v2sGa7gaZc4GY9ebsE2we0M/uPwRgBZlA7vwqouIYWda5Rbw/3/z5bDOBqpyhwDCbDBm5TCCjw2JOz3Ywpwzkm2VmLeg0DvmCTBwppH7NjWwwtFQqLMTVJ8QCJVC7HWzbySd1XYjk7mAMwxQJdjA3zi3IBNriSqAl72S11njd616Hr33ta1BK4aMf/ShuueUWvPrVrwYAfPjDH8af/Mmf4GMf+xh+9rOfQQiBa665BkeOHDnmO8+sjKAEKjxGEtCCoGWMISevrdfnUFEyPyEoX0GUZGBcEUgTQQmZt4hf4U2ikPZkVEpBqQiSgFjlA37nYGgbADg/PY3ZoxMY2ja+bCWQVzpkaYoBNxkpFoFmOyiBOBeIWW/O2DaOZpbhjqOTAFbSHUwskOGrOEbabC4IhibKi0A+GJ5g5frV4WHUp6dX9V0Yhlk/pFoDJZDk4i+zejqNQ74QE0cSzukPcpEFUirMOOX3vXcMtymBmiU7WFKrYXB8rOtnl5RAIROIj2uG6WdKwdAAUp0/t9UzgXqSM9xwww244YYbSo99+MMfDn++7777cNlll63tnjHHDAEBQ/ZGD14NJAWMIGgVYzCJASLMz8+hEkWgtqJOFEko0jDSynC1MVBFJdBqMoGMCZVXRRJJqQhUVAL5FvFjAGxb+OlDh20RqIdMoOJEwqv9dKYxICUaaYb9M3mHsaIdrOmuDpUVfkeGWSmVKMJ8mqEV7FrLtYN1CIaOIzRmZ9vsYAZEhRXSQiZQJAjD28e5CMQwmwjfIt5kutAivjc1q58cS1ZMMGtAp3HIWy6SWME1ns2VQFKh4Y/BVrpACVQZHAi/j+86qbBNZbvhFSgGQ/ubOz6uGaafsS3iczuYQLG5ed93B2O2IGS7gbk+YRYpYQSBogTSBT/XG/OoRhFMuAu0/4siBUUGxp0cmghKylW3iJfSBgb6wTkyorsSqC0YujU/j+lDVn3WWKYdTHk7WJqiJgVmWi3Mpynq7jM6ZQKxHYxZbyIpkRmDzHcHW+YKhT+/Sttc0CJewxhTCsbzEAAlCEPbt63sCzAMc1ywSiABY3TIwHvYky/CS9/99iXfy3YwZi3pNA75OV8cSWjtFDrklEBCIpMAQUBlKSoF+5cNhs4zgbbtOjn8uVPgc9ypOxgf1wzTt1g7mMntYAB0KRh6a18ftva3YzoiAJhCOzwATgkEmMi1Tdcp5ufnUI0UTHhfLiNXZECu2KNN8IvZ967gpPH+bDImKIkUCSRRNzuYVwLZIlBzvo7pQ4eh0wyZy/B54hMfhre+9YUdPy9JIpi9E9D//KuwvzrLUBMI3cAOz9cBADMdMoEqXARi1plICqTaINW+RXzvxdbi+VVExTGyVnswtIEpFoG8HYyASKJr5xWGYTYmKvbdwbKgBLr4yt/EQ57wuCXfy3YwZq3oNg75gk0SK+jMvYYo2MEq1QQmjqCyJmoFS1faakEl+Rxx2+68CKQ62B3LdjDfHYyPa4bpR8L1qKQEAjLN3cGYLYwAoAvqHmMMpLJKIER2kFQ6xXx9DtUogvZt2937o0hBwYCcSkf7SaJ73UpOGr8aQ2TyIGgjEKt81UfJvPCiQybQKFr1BsgY3Pyv38APv/iV8JpnP/ux+P03PLvj51UqMeiXR0A/uLegBMpQpbwIdMQVgTorgbb2hYHZeHglUOom0MsJhi6eX0VUHCFLy8HQxlA5NN6d+cYQlCAMsxKIYTYVyi2mGK1DZl+cRKFr2GKEFvGsmGBWSbdxyK62ky0CtSmBhFCoVmOYKEGkW6jVauF9WbOFuKAMGj85t4N1VgLlqiHJSiCG6WuK16O8RTyQUf8ogVjO0IcIQrB4CScRV3Fsi0CJHWCVTlGft0WgzI/XBTtYBIKREaSUQQkkwsrNCpRA0t9omlBEUmZpJVBtZBhzrp37z2/8N/z8xn8rvF5CdclNSZIIMAQYCpMBozWqwuBgq6wEKheBvBKIi0DM+hKKQE6Nthw7WPH8Km0zjm3xsxAMTURtSiB7frdSjQTESiCG2WT4YGidZcEOpu9P8cBs6XPZW3dYMcGslm7jkJASErYwY4xT6IBcEUiiUomh4xgqS1GrFpVATUSFYOjxghKoUzZlMRMotIjn45ph+hJ/7lslUO6OMZTPrdUWz3/d2iUupiMCZAs+Du2KQJkkJAO225a3g1UihdRo9z57wkSRRAzAyAhRFAelkK8SrSQYuqQE8plAJFCJCkqgYjC0y+uRUnYNgl6sCFSpxP5sR6S8RBlItM7tYHPeDsaZQMzxJ1YSupgJtIxg6O5KoIWZQFqXM4G8ZDZNNUyryUoghtlkKNe502QapO0Y1tg3h/FGxbaPX4RgB2PFBLNKuo1DQsnQoINI+T+EIlC1mkBHMSLdQrVQyLFKoFzdU1QCdZqHljOBVGmfGIbpL8L1yJhyMDRnAjFbGiuAAQAYI6CNRhRHODzQQN3dYBbtYJnLICnawSJBIKkQx0m4KRXuJFpZJpA/GSkUe5SRSEpFoIVKIABoOcVOO0sWgdxfQqz8NyMkOsOs2/b9s3OYbjTD9wOAllMCcRGIWW+sEoiCHWxZSqDC+VUkt4OVg6E7ZQKlqQHSFiuBGGaT4W1fRufdwSSsKngpSxjbwZi1ots4JKWEcguTvggk4BuYSFSrMXSUQOkWKoWiT9ZslZRA23afHDqCdbp5S2pVNOZs51cOhmaY/iac+0S5HQwozX85E4jZcggAvgPekaaCJgMVx0gV4Uv/+mUAgDJ5MPTM/Fx4JxEhihRiAWipEMcxtFlZJtDrHvso/OS1L7PvDbI8EwZvRQJJnA/45SJQGv7c7NINTCnZtYqbJFGwuXhFBRGQZFlQ/rz3Oz/Eb37mH0rv40wg5ngRuoPpFWQCFc6vIiqydjBfBNLauGDo4radEijToIyVQAyz2QhKIKNDa3ghrLZ3ySIQ28GYNaLbOCRKRSDXut3bwWCVQJlKEGXNkhIobbUQuUyguFrB8PZtmLz/oH1/hzlaXK2gOWvni5LtYAzT1xSvR8EORuXuYGwHY7Ycgmy7TQDQRsIYEyaJX/7XL9rH63MwRqMSRdh/8D7/TmufihRiIYISKARH+//3uLJy2tgoTnPdvYqyPF9EiowoKYGiQj4QGQPtCjLNRZRAxf8XKSqBKoXnE61DBtD+mVl84857Su/zmUCJYiUQs75EUiI1OlcCrcQO1p4JlHg7WFswdIcW8WlqIHTGSiCG2WT4iSwZE851WwQSPSiBnB2cFRPMKuk2DkkpEYUhJ0K9Pg8phJuieTtYAqVTVCplO5iUEiqKghXs8D373DY7ZwLVZ2fd86wEYph+xp/7hkwY54jKmUCsBGK2HgRop8b1ktvIrQ5OzNgB8hv/8vcArOKl5fJ3SAhA2yJQIm0mUBzFC1vE93hzqqQIN7K+kwMRlZRAlS5KICBXA3XLBJKLFIGsEsj92a8YOSXQbMFq1k7T5SlU4619YWA2HsrbwUIw9DJaxBfOr+JjKoqWzATy50kr1RAmw9B2LgIxzGbCB0N7JS+QZ/z5BaBueOuO2OKTYebY02kc8o8r6ZTkIkKz2YASEoasFqhajaFFBGkyxAX7V9pqAgCiSoLxXTYU+sg999ptdpj3xdUqmrPeDubnnnxcM0w/EjoIGgrjHJGALnQHW0m8yWZia387piuEvO07kQktZKecqubgvr0AgEoUoeGLQBCAtiuJsRQwUtnuQlTOBOq1cqqERKx8OF8uy8szgRYvAul0iSKQLyZ1kPPZYOiyHcyvh86m2YLXexqcCcQcJ2JvB3ODVbQsJdBCGb5XAGQFO1jIBCoMgv5PWWYgjUZ1cLDUapdhmI2Ncos8KCyySPhW8Um3t7m3uPGdbTPMKuk0DgHeDuZfE6HRrENJAQMCkUClEsNAQRqDJClnAgFAXKlgfJdVAh3xSqBO3cEKmUAyFIH4uGaYfsRfjwwVgqGRZwIZY1bU6GgzwUWgPkQQhUwgQYAhCquBrVaK+VaKIeezrkYKTWe7IgDIDJJIIZHSKoHipNQiHug9TV25wVdJUbaDeQWPkagkxSJQufDiw6EXywQq/r9IJzuYXw+dLeQNteMzgSpb/MLAbDxCi3gnW12tHczfGJaVQD4TqDgx9plABlLY9w+NsxqIYTYLMvI5QPljfgJcvKnuBHcHY9aKrnYwlWcCSRmj0ag7OxiBIFCtJjBCQYCQxIUusc0WIi1wztnnYfzkk6DTLM8E6jAPjasVNLwSyKvQ+bhmmL6kGFSfdwfLM4FaaZPtYMwWhKzkDVhoB9NZhtlWiuHElkSqRSWQs4PV3HNGKsRRDNNmB+tdCeRazktZkgkrnwnk7GDaWMlvdzvY8jOBinYw3x0scje7iymBmqwEYo4TkRS2CLSiYOiFMvzIFX51loZzxBiz0A7myDIdchuGOByaYTYNapEikA/W7UawgwlWTDCro6sdTORKIKUSNJuNvAhEAoODFWjY+V+lkA2Ztlo4Yb6K//mm/x+Gt2/D7NGJkBUpO8zRkmo1LBrmRSA+rhmmHwl2MDK5HQwiKOHTVotbxDNbD0FAhnwQJhBkHEFnGcgYzLRaGEq8EihCM/VKIAFkBgPuOROCoVfWIl52LAIVuoMZ4ZRAqdvusVECJUEJZPdnLuteBAKAZpZxdzBm3YmkRKrNylrEF84vj1f/ZS2rBErded4eDC38gJga+MOew6EZZvPgVbRS5OO+t4NFca92MJ4uMquj0zgEOCWQ9JmSVgmkpIQmAhmBkZEBGHe7ksi8CJQ1m5Bk54mDYyOYm5paokV8LdjBVLCD8XHNMP2IP/dLdjACvFAxy1LuDsZsQYhCpVNAgITtvKXdTeBss4Xhkh2sqAQyGEjshNLawWJk7S3iezxp/CAcS1mSCYcJKwSGBoYhpf38KG4rAjmFUqu+eBEo6lCwKbeId8ojdzbMpt3tYIDNBaqwEohZZ0KL+NAdbBnB0EvawSQyp3Jrt4P5RdtMGyTuJOE28QyzefBjsizkn3gFRLxUMLS7ANRqFZx99p5jtIdMP9DNDiakRCR8ESjJ7WDGABAYHR0GOYV40qYEEmSP74HRUcxNTMI4pazsGAxdQWu+DqN1wSLJt0EM048Ur0fFFvG+O1iWpcGZslXhq18/UugOJmGVQCqOQ9CyVwLZ7l3KWqDIRUlrwoC3gwm1ukygkhLIBwZS6f1jw+OIIteWvS3AMiiBVtsiPpIwxhSUQHrR/W5kGSqsBGLWmUhK6IIdbHlKoPz88gQlkAuG9kUgYxZmAhEZZJlB7LrisRKIYTYP/lzvaAdbIhMIsNeExz/+HPzLv77tmOwf0x90GocAO2f0drA4TtBs1qGECE0QxkZHQcIvGuZzr6zZggAQqQiD42OYn5qGcR1cfeFzZGQcZzz4HKgogooitBoNWwRSCsYYDoZmmD6l/XpEZFwwNABBMEZzJhCzFSEYmWcCkbCqAK+smW2lGEpiVJwix4YhU7CD1eI8EyiKYmhaYSaQ24dYlZVAxfdLKVFxc9QkKa9Yap8J1MUOlncH61wE0u5mOlEKRAax6NUOpjkTiFl3YuVaxLtg6GVlAnVYgQ2ZQC4YOsvy50yhDkpk/5NlBpUkQmN2jpVADLOJCEqgDkWgpZRAgJ0cDwxUMTIycEz2j+kPuiqBlArB0HFcQaPZsN3BnEVjeHQERthjWIlyEcg3LhgYGSkrgdw88vn/5WX4s3d8FHGtCgBIG03ozBaBiAwrgRimT+l0PSISIBIQgkrxJFuVrf3tmM6YQjA02SJQFMchUM/bwXzuje+IRUKAtEEt9nYwhSROoNvsYL1mAil3AkZFOxgZKCmRZrklq1qz/0/aJqurDYY2WR6wS4aCMmlOL64E4kwg5njg7WC2e4FZWXcw6t4dLCso4HRbEYhcEahWSzB7dAJD21kJxDCbBR8MXbKDYRlFIGObR0QRTxmZldNpHLKPC/jhLI4rwQ7mrc/DQ8O5EqhQBEpbTfiYq6GxUcxNTYUbOunmaAMDQxgdGUelZieSuRLIzvu4CMQw/cmCIpBzvBgjIIXNCuIW8czWgwj+Hk8AgBAd7WBe7dLINIpKIP+4kRGiOAlKIF8E6jVoT3awgxmXCTQ7NxNeNzDoAgHbuphk6eqCoUnn2SpEBpEvAqVL2cE0t4hn1h3likAAkBmzIjuYMR2CodvsYEC5CAQIgAjNZobR0UHMHT2K4W2sBGKYzYKfyIpiMLS/qV6iOxhgb9qjOOo4ljJMr3QahwB7fPpMoCSuoNmwdjC/wDg8PAzyyu5C0cbbwfw25ienod3g5eehkVO0Dw6PAgBa9XqwgxGxHYxh+pXcDuavR+QygawSCG3xJFuRrf3tmI5Y9U8hGFpaVYAPhp5ptjBarQS1SzPLbFt5ADCEmnvct4jXoUV8WYa7FMEO1qYEklJidj4vAsU+E6irEmj5RaAkiYJsOC8CSRgAjbZVqnZsJhDbwZj1JZIydAZLtUG0kmBo6mYHk6UiUHswNMFgbr5pt1WfYSUQw2wicjvYwmBo1aMdLI6jLd8phTm2LKoEsmsNqFRqzg4mkbk52sDAEIzwRaD8GE5beRFIEDA3MQny3cF8ww8XJD08OmLfU2+Ui0CsBGKYvsSf+/5e0BeBiOyCiSHDwdDM1sQgzwTySiCfCXTX5BSGKwl2jQwDsMoXAtnCUWZCAcR2Byu2iF+uHaxDi3hDkEqVikBR7Io1C4pAPhh6BUWgSgxy6dixFFbuLgSyKFqyiNXUnAnErD+RzOXx6bKVQPn5FbaX5EUg1ZYJVLaD2dn5fN116cvmORiaYTYRMtjB8sdyO1gPSiBnB7Pb4GkjszI6jUOAaxEvCEQCSik0m84O5gaiWm0g2MEktSmBCp1u56amghLIF4F84XJwxCmBGk2YTENGiu1gDNPHhOtR4R5WGwAkIISLJ9niCx989eszvPomFIHISuKKmUC3HZkAADz8xJ0A2oKhNRXsYC4YekF3sN5OGj+ZtMHQuSxPSon5Rl7Y6dYdzCuXWivsDkamrAQSENBSQiwheW9wJhBzHIilyotA2iwzGLpd9gqoqDc7mM8Emp+3RdcBZZC4fAWGYTY+nVvEO7tM1JsSKIr8TTVPG5mV0WkcAgAhFSJJ0No+33B2MK8EqlZqoUW8ooISqNnKtwFgfnIq2PxDYxA3Xx0asouaaaMBrTMoFbEdjGH6mIXXI4IhCkoggLg7GLO1iN1k0IiCEkhJZwezK/2+CPSIk/IiEFFBCaQKdrAkCTemAuXBdym8EkjJcncwpSJkOoMWLrg5KIHK6huvBGqsIBMoSaKwGmWDoQ2kdEWgJVaGmpnmFvHMulNWAumVBUN3yATqFAxtjIAx9npAJABj0GjY30cHI0i+EWSYTYNyuSgdu4MlvSiBTLiZ5iIQs1K6dQeT0trB6g37eKNRh5ICqfYLgLVgB5PID+KsEAwNAuYmpmBca0sfDO0zgQaGrB2sVbSDtXWjZRimfwh2MFMoAnklkOTuYMwWJPZ5Pu53QQCUgIryTKA7J6fQynRBCWQHVasEMkEFY2RkM4F8i3ivBOpRPic72cFcEBcRIXOju1cCLbCDpSmM1siazc7bdzPeTnK+UjC0KwIJCBgpl7zBtUogtoMx64vvDgYAmaGV2cGoYAdbpDuYLQLZ6wHIrpTUnR1sZDDmiTPDbCKCNaakBHLjYy9KIGMQcRGIWSWdxiH7uG0R32ra8a3ZbJTsYAIRyHcF0xQWBLNWGmyNAsDc1BSMt4PJstJtsKAEyu1gJpwHDMP0F7k9tVgEomAHYyUQs+XwRSAqDJxCylJ3MG0Iv5qYxENP2AGgoAQCSkqglGyewIpbxJeCoXNZnpIKBIKWvghkANCCItB9v7wdd998S/ftq+7y9bISSLiAQKsEWqrya5VAXARi1peoEJSZ6uUqgTrYwXx3sFbnYGgipwSCABEhzTQajRbGhpIlLZMMw2wcFrWDxUuPZUQGiu1gzCrpbgezLZmbTTsnqzfmoYRE6iIKMi1DJhA0oVKxYxcZA7h5nMk0GjOzIeRV+Pmfm6sNDg4CKLaI993B+HhmmH4kXI+CmwXQhWBogLb8eLe1vx2zgMgd0KbQHUxE1g7mg6EBawkbdMGxrWIwtCYkrjKaGiopgUJXhl67g/lJaJsdTEoJQyYUgVRkIATCSqTne5/7Av7iRVd3377yK0GdM4H85EE55ZEUVgm01KSAM4GY40FZCbTcTKCl7WBa588ZLWDITsCJ7H+EkDh4cAqjg8mWl8gyzFZCqu7B0L1lAlEYfyMe+5gV0tUOpqwSyBeBms0GIiXRcgsTOpMwftVem1AEAvKCUmNmDgCCEsgvAno7WG3AFoHSRrNQBOJgaIbpVxZejwhGwymBvB1sa493fPXrM4IdzB38AgJCKURJEuxgAPBLlwsEuGBoomAHq7jiSjPLbKC0K6ZkmbVl9ZwJ5JVASoZwPiJygX0E7e1gykAI6qmLSWn7IROoix3M7XcsZJAFa9FLEUgHNRTDrBe2CGSP2VQvtztYfn55lJfUZx3sYCRAoQiUK+UOHJjE2LA9D3u1fTIMc3zxFmfVSQmU9GoHYyUQszo6jUOAnTMqAaQt+3ij0QAAZNqOQbqgBCJNqFQKC4Lu/q0xbTvK5kogvwjoikA1pwSqN6C1hlJsB2OYfsZfj0xQAtlgaADBDsbdwZgtReRDnd0qoBQAAagMDAQ7GADcduRo+HMj0yAiGCFAmUEiFeppijTLEMcJjFuJ8UHNvSuB8kwg/x6vBCIiZJKsQkeRLQIt04K1VDC0zzBSIr/J7SUTiFvEM+uNFAKyEAydGRNC3nt6f+H88kRBCeS7g+XPkREgFDOBCEJKHDw4idHhCgCwjJ5hNgm5Eqh4w+szgXoZy8gPl1wEYlZMp3EI8HYwgptCImvZIlArXVgEEgU7GJDHEDRmZt2225RATulWqw0AsJlApI3NBGI7GMP0LeF6RMUFUADcHYzZqiQ+E6jQHcwIQnVosGQH++XhshKIYItA0IRhqXB4vo40bVk7WFAC+SJQb4dVKRjar/IQWZkuCI1Iw9AMhEBHO9hSLNUi3q8gRa7o5FvEL3XS62VacRhmtfjjLe8OtkI7WEkJtHh3MLiBsagEOnhgEuOuCMQdwhhmc+DHNH/K1uvNoIDwN8kvfMQ5+MILn9tlCxRyBLkIxKyUTuMQ4IOhAS9GT5u2CNT0RaBMwvh5WZsdzNv6m94O5sYx4Y95VwCtVgeQNpt2QbNkB2MlEMP0I+F65NWDAnkwtCQI0fv97GZla387ZgE+tNmENpsCJIDK4EDJDlZWAlk7mBESppVhDBHunJhCmqU2GNoN6Lq1XDuYfV0si3YwYzN6ABwcaEBUvo75+SaEoB5XLAvbX6oIFJRAtumoVQKJsC/dWG4oL8OslgVFoOUGQxfOL0+wg6ULg6GJBAADYwyIADJeCTSFsZEK+mGFhGG2Cr5g65VAjUbeVclnppy/+yQ88bQ9XbfBSiBmtXQahwDfIp6Qpfb5VlACuWYlC+xghSKQOy6bs/MAcmuHbLeDDQyi7tRCWmehRTwrgRimPwl2MG8B8y3iUbCDbfF5Ll/9+oxkQSaQXeOLCt3BAODA7DymG7ao024HG4PCnZPTyLIUcRyHG1OdrdwOVgzoUiqyFjMBjI3VcODAJITAsu1geYv4znYwFzmERJ2Ibdt2QgobPrjU/qfGFqp4AYlZLyJ3LKerVQJ1sIOZTEOptiKQkQA0iIzrDmYghMSBA5OII4WKJFQHKjjppPHVfjWGYY4x3g6mlL2O1OtNwF0T/OJKrGS4zrQj2A7GrAHdgqGFVFAScM3AkLkFxWYrt4OFhct2JVAoAtkCT2gR3xYMPTAwiLmJSfsa3yKeg6EZpm/Jr0ftdjBRsINt7evD1v52zAL8BI6QT/a8MLdoBwNshzAAaGlfBJIwzQxDkFYJlLZKLeJ1Zt+/omDotu5griE9RoZrOHhwcoVKoO5BliUlECIoqUKL+KXaX/uiF1vCmPWiXQmUmWUGQ3cqAiVxyPFakAlEEhAGxmirBPJ2sIOTAICByOB/vvm/4Hvff9eqvhfDMMcePyZ3VAI5O1gsZejY2Y5dLLJwEYhZKd27g0lIQdBZWQnUdOOTziS08+8L05YJ5P7fnKsDKBaBfJEzzwQKRSCtbQMSDoZmmL7Fj4v+miEEOTuYzcsVYus3QOHRvM/w3cFI5plA5BdYCnYwAPjlkaOo+8IQGRghICYaEBCuCJQiKrSIN24Zp9c0dT/hVG12MClV8IyPjQ7g4MEpCEnB290rSwdDu9cBLnfIFYGWuLlONReBmPVloR1secHQHe1gSRIKv+2ZQN4ORmRsMLSzgx04MAkAGIgIT7rkodixY2Q1X4thmHUgV0V4JVDL690LSiCFqFuBR5C7JvQ+vjNMO93sYELY7mA6EzDGhAXFlptTZloi9e8xpi0Y2v4/9UWgYAezx7W3g1UqNcwWikA2E4jtYAzTr4ioHFQv4CPGvBLIbHklELc46jOiBZlACKobnZWLQJ/+6S9wZN6uyBARSErIo/b3Oyen8OigBHJFIJ1Zr/UqgqFB5JRAds9GRwdw6OAkTtyedzbrlaUygepu8iABCIi8O9gS++9vxGMpUV/WHjHMyvD5P6tXAuWBnEm1itRZPhcWgSSEMPa8p7IdDABGEo1HPPzUBQGfDMNsPHxIrlcC1etNtHcH89cTIfL8n/B+5EqgKNrak2Lm2NFpHAKsakcJINMCWbMe8iIzt0KvMwHtLRvt3cHc/1vzNhOoVokwEuswj/N2sEpSKdvBlELGdjCG6VuktNcGUwiGtgueVhVkg6G3dpmEr359RlACoZAJFJRAZTvY127fizfc8K/29S4TSDjLyJ2TUyETSLsVGlsEMr1nAhXtYCGLwK0UgZBIQhQpqwQSa6cEEkIgSeLQWtSvJAkhocXSLeJTtoMx60xQAml7sKZ6mZlAHVZg42oFrbotYy4oAkHCK4GCHUwKHDw4BQA4bShFHEeIIlYFMMxGJ1hjSnawcnBu4tVCHa4rslAYYjsYs1K6KoFci3hjBOqNelgg9EUgIonMZHaVvl0J5OaMTbdg+YbffxauPH0y2PqjoHSLQxFIa5cJZMySjUAYhtmaqKjcIl6A8hbxEtwdjNl6+AkeiaISyNJuBytCZEKYtAbh3unZ3A7mVnVIZzBG91wE6qQE8rtFIFSU3a4tAi1fht6tCBTHdjv+b0ASCt3BZFg17UZQAvFkmFkncjuYHaxSs8zuYKE1bz75Tmo1tOp24tzeHQwkIQS51wu7OiIkDh+ehjGE04Z9CLzkTAWG2eDIkI/XQQmk8mBoAB1zgUSpCMSFX2ZldBqHgLxFvDESzUYjNA3xRSAAyLIUGoBYoARyr23Yseykk8YwGBFqtQRArnRTEJibnATQZgdjJRDD9CX+Xs+UWsR7ZwhZ8QF3B2O2EkEJJIqZQM4O1qYEKkLGZgIBwHxkW+qlaQtJoUU8GQ3jOmf1QqlFfCgC+ZWiYhFockVKIC99b1cr+AmEnzwIH5MtpMsEWqpFvLeDbe2LA7Nx6BQMvbLuYLkMv1KrojVfVAIVJ+YSQno7mD23hZQwxmByuoGqyrfDaiCG2bgIIcL578fcRiOFLwJ5pYS/nqgO458QBBMygXjayKyMrnYwKaEEgYxCs1kPx2BL5wuTWZYiA5wSKJ8L+jlj6saykaEaAGB81P4/Ui4YWgL/3+9eDMAHQysYbhHPMH2LX9AwXgkkkCuBgh1sa18ftva3YxbgB1dTWP3o1h2sCBGFwtFc7FZeshRRoUW8LQLpngfVUov4QlC136eqstv1drDlprR3UwIliS8CWWSwg4meWsRzdzBmvcmLQLkdbFlKoA4y/KRWQ6vhlUAK2imB4tifH94OJmBMnp1wdHK+vG9cBGKYDYssdNX0Q2ij0cqfdw/mSqCFRSApBHcHY1ZNdzuYhBQASKLRbASVeFrIqUzTFjQBwhhUq5XwuHQzudQd00PDbUUgbwcD8MTHPAgnnDCWt4g3rARimH5FuLGPSkoggoAAkc0G6tXZslnhq1+fES2qBOrNDlZP3CpNW4t40hra6FUFQxetJRVXnTl8eHpFsrxuRSC/iuQfFeSVQAJGiiUzgTLNdjBmfYnblECpMctSonWS4cfVasEOlmcCxbGV0QtJbZlA5SLQXXcfdu/l84BhNioqUmGlx5+r9XoLC+xgkjOBmGNLNzuYDYYmEBQajflgSSzawdK0icwQJBnUatX8zb7BhztuR0Zs8Wds2L7Gr/aTscf7Ix5xWsEORqwEYpg+xV8zyMUsSJcJJODmvAJL3g9udrb2t2MWkGcCuX96UcwEWswORqGjWKPilEBpirjQIp6MhtGm52JNORg6D6q2+0fBcjIxMQsiWnZFVoUchMXtYNIHRPfaIp6VQMw6o9qLQNogWkagpQiDXaE7WK3aMRg6FIEEgWCVQHbF1H7ekYk5AMD3f3hHeC/DMBuTooLWX0eajWIRyD6fhPDoDkWgwkNcBGJWSqdxCMhbxAtEaDQbYW5YVAK1Wi2kxkC0FYH8YqI/joedEmhsxL4milx+kLGffe65D7RdbEMmEGfaMUw/4sdG41vECwEi3yzIQK4ghmSzwaP5JmbnzlFccMEZy3qPn8B16g6WZYvZwUxQDzVq9sRJ21rEw2gQmZ6VQH61JyqEy4ZMICBkAk1MzALofbth+0vYwXIlkP976K0IVGwRzzDrgS/4lFrEL8MemWdtGZxxxi4873lPQDJQK7SIlwuKQFKSbQHvlUDufL33/ik0tcDPb93v3stFIIbZqKgOdrB6vQWiNiWQXzTpZAeTovB6HveYlVEch0qPOyUQoNAsdAdLdbEI1ERqCIIMqtVaeNwXefxxPDIyAMAWgYo3cKSdEujcB4YW8WwHY5j+RXYKhvb3gyAIKXrOuN2s9PTtLrvsMtx666247bbbcM0113R8zSWXXIIf//jHuPnmm3HjjTeu5T4yXfj9378C//crb1nWe7yFiQoqAkIPdrBCMHTLF4Gy1BaBKM8E0sb0nN0TlECFjlx5EYhQUQbGGMzM1F1xqbzdnTtHceGFZ3bf/iJ2MCIKEw3hksAIPhNoCSWQkyhv9YsDs3HwqjMfSm7tYMvJBPKSeIOrr346Pv2ZN2FgoL1FvN12EpRABiB3XhQCND/y6e/hr+8YQ7OZhfcyDLMxkSq3gyklobVGq5WP9b5Nrr+edLSDSRHsYHy+MyulOA4VkS4TSIoIjUY9FCLtCr098JrNhlUCGY1qtRre5/HzMa8EGh2qhDygZma74RkDnHuutYOpKGI7GMP0MT4vL9jBhFUpChLunpMzgSClxPvf/35cfvnlOOecc/CCF7wAZ599duk1o6Oj+MAHPoBnPetZeNjDHobnPe95x2yHmZzBwSoGBytLv7CAt2p5O1hRCbSoHawQDN0asCfOzMwUBgeHAJEP7GYZmUB+oFcFJZAP+SMAiSRXALK2lPaT0RbBru26fT9Z7agEKqiRfSYQwSqBlswEcsonzgRi1osF3cH0clvE5133kiSCUgonjycd7WCRC4aW0tvBULKDzcw0MNVSQQHIygCG2bgUlUCRksgygyzTQdnjx9UQDN3BZqokB0Mzq6c4DhVRyhWBZOzsYPYY08aOQQDQaDaQagNJBtVKxb2vYHVsVwINJ4jcY77LmNYSZ5+9BxKG7WAM0+f4e1Wt8+5g/tJEMBBScHewCy+8ELfffjv27t2LNE1x/fXX44orrii95qqrrsLf/d3f4Z577gEAHDp06NjsLVMiitSyV+WCEkgUlUCWRbuDGQMtJNJYgpwSaHLyKABgx/YT7DaWmQlUCob2mUBhtwhKAM2m3SebCVQ+XIeHaxgcrGIpFiqBYtcH0CLIQBDZYGghl5QHp65qzJlAzHrRXgRKl2sH81kMZMI146RhgVawgyloH3ju7WCKXIv4shLIuOPfuNGSlQEMs3GRkSoUcASyTCNNNdozgbwSqLMdjIOhmdVTHIeKJIlr1iFjNJt1+DqktisQAIBGoxEygSpVXwQqWh0VKpUYcWwfGx2qQDmrWAY3ZmmJOI6we3stt4PxPI5h+hKpyspEKe2toYRTAglWAmH37t2huAMA+/btw+7du0uvOfPMMzE+Po6vf/3r+OEPf4gXv/jFa7+nzAKUkmHA6/k9vkVnIYg5bdkbQZMt3h1sZmAQNz92OyL3mZOTRwAAO3eeZItKQQnUqx3M7kOsCoUXkReppDBBtk5GQ4jyduM4Qhx3/qziRLVjdzAqFoEIwpnilqUE4skDs0744m0xEwjovGrfidCVxZhwPuysZmjNL1QCeTuY3TQVlEBuG65Y5BX93B2MYTYupWBoZbO/vPUTyNXBIROow7impAjrJlwEYlZKcRwqEscRiAClYjQajZAXaaigBGpYJZAwGpXEFYFk8dhWQQUEACODcbCDuUxoTE7abpinnjRoi6NEnAnEMH2KCJlAHZRARJBqYWOhrcaSFYROUsl2KWcURTj//PNx6aWXolar4bvf/S6+973v4bbbbiu97lWvehWuvvpqAMCOHTtWs98M8psv6/M3S7zav8dVPgvB0GmaQiJC1kMmUD1RiNwxMTlllUA7d5xo7WVkbCbQMu1gVgnkpOnucCMiSFBeBCIDKeLS++NYQSnlEt0Xyos7/RlwdrCiEsiYEAxtCvlE3fCZQGwHY9aLXAlkj1ufDRRLBW26n7cef34ZY8J1Y2c1Q6veCOfHgmBo5VZhvRKolNOAYAdjJRDDbFxUFAW5bxxZJZDW+fjXbgfrpHBVzkbm/8wwK6E4DhVJYgUyAkJINBrzkO512hirGhK2CNSChiSDpLJQCSSlCnlAhgijg7kdzMR2e7fffggjo9tw6snDuFPKks2ZYZj+wi/4m0ImkKE8l9YPhVLKBdesrcKSo/m+ffuwZ8+e8Pspp5yC/fv3L3jNV7/6VczPz+PIkSP45je/iXPPPXfBtq677jpccMEFuOCCC3D48OE12P3+xgc6LucmzK/yhRbxEEjTFoDFM4HyG7/cTjLh7WA7TwJQUAKtJBg6ZBQJv1tQIi8CGVrYxcErkjp9f9W2+lmkUumUCUTLyATiFvHM+pIXgexglfoOdT3ekIUVWMqD23dUNVr1ejh/OnYH80ogKtjBXBE0VwJxEYhhNioyUsitXxJpqsO57h8DbEEZ6GwHszYyLgIxq6M4DhVJKhG0697VbDZCVIAmApE9Vuv1OlpaQ5BBknTKBMqVQNMNYLigBEJiX9dqEW6++W48aPewC0znYGiG6VdCd7DQIj5XAhljwj3qVlYDLXn1u+mmm3DGGWfgtNNOQxzHuPLKK/HFL36x9JovfOELuPjii6GUQq1Ww2Me8xj84he/OGY7zVj8ZGw5N2HhprEw8KWptYMtHgxtAAK0pvB53g52ws6TrR2MXBGox5WVcot4+2f/XhICEhQygYzRC4pA3grWyRJWnKi2//0kSdQhEwihO9jSmUDcIp5ZXxYqgVzBpsdjMJfhUzg3qoqwYzTpqgRSigAYEGwLzRDq6Y5/fwpxEYhhNi4Lg6E1dF4D6i0YWglkmotAzOoojkNF4jiCdp6tRrPQHYwoFIzm5wtFoDBGlYOhvRLoyLzBQEWhVrOZkeTUr1kK/Oyne3HarhH7OIiVQAzTp4SMMheFIgsNEAwMpCo3T9iKLGkH01rjda97Hb72ta9BKYWPfvSjuOWWW/DqV78aAPDhD38Yt956K7761a/iZz/7GYwx+D//5//g5z//+THf+X4nWpESyA2uhe5gadMqgZaygwHWAuLtJLOz08iy1NnBDroi0MIuXt3wkt+iHSwfkKmkBKJOSqBFvv/imUBxORPIt4gXgJZiaSWQZiUQs75E7vwoBkPbx3ssAvksMDKlDJ8HnTKG7wYlULlFvN20DYYuK4HctYCDoRlmw1PKBHJ2sGImkLdvh2DoDteUSEnMN303QD7fmZVRHIeKJLGCcUqgRqNe6g7mrRqNRh2tmoY0urBQUbSDyaAEOjprcPp2hR07x+x2hD12tZY4cmQaA1XXGpqVQAzTt/ix0bjrkRQIXTN9i3igz4tAAHDDDTfghhtuKD324Q9/uPT7O9/5Trzzne9cuz1jlmQlRaDIH8yF1Y9Wa2klkJfLGZMrgYgIk1MT2LnzJGDmkC0C6d5axBfVQrGSkAuUQFam1mrlSiB0VQItPIxlYTWzl+5g0jYEdJlAvdnBOBOIWS+8VaM9GLrXY1AWZPhKKey/fwonnziKB5+2rasdzKrlCSCUirt5dzC7bQ6GZpiNi1R5JlDkuoOZYiaQmwgnPhi6qx3MdcXk851ZIbKLHSyOFLS2zzUbRTuYCQuQrVYTzczO1+IllECH5+x7du4Ys9uR9njPtECrlRWOYdrSN3gMw3TH36v6pkiyaAcjA6UEYLCl28Rv3W/WB+R2sN7/GdszgWx3sKUzgcjd+OlCEQiwlrAd20/M7WBEPWUCFSXnRTtYUe2jRG4H01rneUEOX/xZyg62MBg6nxQDeYt4EjYTaKki0HJVGAyzWpRqUwIVgqF7oSjDjyKJufkWJlsSZ52+c2ERKCkEQzslkNE6t4NptoMxzGZBFc7PKLIBz8U+En5OkETdu4NFSoYgeLaDMSulux1MwRinBCrZwYCjE1MAfBEogyCDqIMSqJgJdGTG3tRt3z4GANDCjVlaotXKoKS0OZDEdjCG6VekD44PLeIFCF4JRGHerbZwoZhH803MipRA7ZlAAmi2bNvMxexgxrhMoLYi0MTkEWzffgJICAgn3e00iWxHFYo9He1gUpSDoY1G++Hqiz8rsoMVJyHGBkPDKYGWqvrmwdBb98LAbCzyTCCnBNK5EqgaRThpaHDR9xdl+EpJGAIONSKcc+bJoYjcrgSyp5VvEZ+30l2oBOLzgGE2KrKYCRQpmwlUtIMJVVqU6aQEiiIROoqxHYxZKd3sYHGSK4GsHSzvDjYzMw8gLwJJYxBHtlNsezB0UAK5ItC2bTb7xyuByMgwp7QfwS3iGaZfCd0KXUielLA2FBSUQMCSESGbma37zfqA1WQCUaHTT9bsRQnkckB0mxJo4qgbiAUEqOdMoG5KoGAHA0EKhAFbaw2sWAnUIRjaaf40EaRXAkFAi6W7g6Wa7WDM+uKLQP7YK6rR3njRBfjuq1+06PvzFVhrBzMETLYUTtm9LZxHvggUQjcjAYBgjO8O5gdM+9leNsvKAIbZuJSVQAJpmgVVD2Cl7kVFYbvCNUkiSClgWAnErJLiOFQkUQrGF4Ga9VJ3sCyz89JWq4VmmkGQRtShCCSlVQIZY3Bkxr4nFIFcJpApFIGU7QYSxjWGYfoLKZ0SSOct4kMwNJkQK1JUHG41eDTfxITWrh2KIF3fExRAhSKQy91ZPBPIr/5TyX7mO4SRkBBE0Fr3VgQSbUUg6VPY80wgJVHqDrawCLSYEmiJFvG+yxLI2sFcMLTNBFp8/7lFPLPeLFACFTrU7R4Zwu6R4Y5dfTz5Cqw9fwkCjUwgihTGxqyKyIfFlruDFVrEt03gNSuBGGbDI1UUCra2O5gpZwJJWVrQaL+ODA5WIYCCEojHPWZlFMehIlYJ5FrENxpBKW6IkLm8jlbaRCNNIchAKV8EKtvBhodrmJ1tYHbeFjrHx20RKNUZhDAgUkhTVgIxDNNJCSTg7zNNwQ7GmUDMhsQXY5anBHI3cv6mMkuRtXrsDkYC2mCBHQywLd1zJdDyg6HbM4GEyO1gj3/Abpx67z3orgRaWKVdyg6mU3vSZ74I5JVAhYJUN1IOhmbWmfYikG8RHymJIZfhM1KpdH2/KAVDSxCAhlt5PeGEMbvtdjtYBHS0g2m2gzHMZqGY0RfsYMUikJAhFBooW7UBYGCgAiEIhrgIxKwOsUgwtCm2iA92MILWrgjUaqIZikB2ztceDD0yUsP09Dy0MZhrGoyNDtn3NhuQ0gCkykoggItADNOnhO5gBTuYHxmLnXS3cng8X/02MSvrDuYGYfdPn6atUATyCemdMCEMtj0Y+qjdnhAQZGwmUE/B0O2ZQPY9otAdTDk72HPPORMPOrAf3TKBVhIMnbqJQEZeCQSQzwRaYv9ZCcSsN3EoAjkFW0EJNFyxRZvRxYpA7vwiYxBFCgSBemq3dcIJo3bbhSIQeT+0KLaIdyskpmwH4yIQw2xcVGGRJI6k7Q5W7I4pZbi+AAvHtYGBCgQA7+DhIhCzUorjUJE4lkEJ1GjkdrCSEqjVRCNLIY2Bkr4IVG4RPzQ8gJmZOozWmG1ojIzZIpDWGlIZAFGhCGRjB9gOxjD9iS/u+MVRKw7MOxPmwdBbd8zbut+sD8i7gy0/E8gHQ2dpC1mzBWNMqIZ2gowGAV2VQHC9u4zprUV80Q4WSxmKP0EhJASUBFrN1CqF3B1nsSLrFUCdvv9iLeLjWCF1FrgMxe5ggO4hGDrvzMSnD7M+LFQCeTWawmBipfFj1cWUQMWOBxIkBGbm7TnQuQik3XnTwQ4WCsJ2m9wymmE2LlKpvEW8KwIVh3opxKJ2MKsEAmcCMaumOA4VKbWIL3UHM3kmUNpCK9MQhbbu3ZRAtghkMDpii0BZlkG1FYFkmGry8cww/YjvBK2zDFGkIIQVNADl6BNWAjEbkhUFQ6tyi/i01ULaai2aBwTkq/+Gyp83NeWVQLblpjamp0F1QTC09CdbcUXSKoFiKe2BSlQa9FeqBIoiBZ26m2kfDA0DgrDKoCVbxOuw3wyzHiwoAgU1msjtYIsVgbwCMNjBBKbnrQKw3Q4WRRGIbDFXFO1gPkze54P5fWMlEMNsWFS0hBIIAhV3DQEWdgcLRSBW/jGrpDgOFYkiCaMFjNFI0zSsvFs7mB1vWq0WWt4G7W5dOnUHm5lpoSYSzLUIw8M+7y5FFBGEiNByC4DBDsbzOIbpS3xxxziFvB36igum7nVbeOFj636zPiAvAvX+zxgmeO7oTtMWDt+9D0fuuXfR9xmtAbKS8OLnTRTtYO51K2oR77uD+dwfmdvB/A2wICrJfxdTApWKQG3Px3EE7W54M68wIgIJgQxiyUmB76zCmUDMehFJEdrCA8VgaIUhpwQarSQd3wugpOKJIgUIiemZBoCiEsgVlqIoKIasEsjZwfwqrmY7GMNsFqTr3glY202aapTcOARU3TUEWCh993Yw7gbIrJZ2NanHZgIJtFpNACjZwYqZQH7xQ0IijqMFwdAjIwNIGw/CU3c9FrNNwvDwAABAZymUskWgNM07Adl9YjsYw/QjYWEzS3M7GHI7WD8ogbZu37M+YGV2sEJ3MLJFoG996rP41qc+u+j7vIfboD0TqGAHIwIRLZmpA3QIhvbdwXxxyNvBWmkotgjSXZRAywuGjiKZB0O7ia0gA4JCBuqp6ptqzUogZt2IpAyFH6BoB5MFO1i16/vzrixOCSQkpqYbAKrY2a4EUl4JJKw8luz5H4KhfXcweDvY1h0gGWazI0st4hcGQwugpASKOnUHExSUQFwEYlZKcRwqEikJIhGsX6rUIj4vAnklEGWE4eEaVOHmTEmrBMqyCEOVBKkB4qo9rslkUIogZdwWDM3dwRimX/HOE784KkQ+LhoyiCLfIn7rznH56reJWZEdLLRgt//3Ky9LESwgxp44fvWk0ahDmxZICEgB6F4zgYqZPUUlUChS2dc0myliHxpd6AoBFLuDLa9FvIoUMlcESoMSyIAApNRb1TczhjOBmHUjUqpUBMrc+RjLQnewag9KIMqVQPW5Bubnm0EJpF1hSUVRKBYBBJAA0UI7GLgIxDAbHhVFIRMojkQHOxhQiQtKoE7dwcBKIGb1dO0OFrsikCvy5N3BDDLtMoE6FYE6KYFSAyls0HTkrJBGp5DKuCKQLzS5feJ5HMP0JSETSKe2CGQftY85ixiAUrF5q8FXv03MyopA7p9c5nawXlisLbTWdZCwuT22RXwP3cFcESnTphQMHbKB3ESz1coKSiDTUQm0pB2sUyaQs774fmjCGJAQ0NRbt4hUG0Q8GWbWiUiKLkoghWFXBBrtJRjaOJuXkGjVGzh6dGZBMLRSEYw714QoBkO32cH8vnEwNMNsWMot4iWyzJSKQCBRLgJ1CYYmCBhjuAjErJjiOFTE28F05kObO3cH8+MeaV8EKgdDDw/X4Nxj0EaBjB+zUihloGRSCIb2LeLZDsYw/YjvVmi06w7mxjnAzm/9rWwvwobNytb9Zn2An4x1UsJ0fU/RDoZlFoEIMB1W/4kaVgnkXtdTEcjtR8vZqkLxxw3IcZQXgbztShqDpCBbz5VAy7ODxXEEk3klkH3MKoHKiofFyIxhOxizbrTbwfyfB5M4FCNHFm0RXw6GhpRo1euuCDRmt9nBDgZB9rwwpqAEcnYw8lJZPg8YZqNSDoZWTgmUPy8AJIVMINlJCeTuk7XmIhCzcroGQysBIiDTvkDjVuiJQmEoTVtIfVu7rFwE0jpDHMeI4wiZ8/hryLBo6e1gSsXlTCDBSiCG6Vd8F2mt8+5gKHYHU1s/E4ivfpuY1djBfIv4XotAPhMoD4MtHjpNAAJSupXCZbSIb2RZKRjav9dvv9lMC8HQBpVKsQjUvTvYYi3io0iGYOhiEcgIu/+9ZBqlbAdj1pFIyhCKCeTdwcZreQ7Q4i3i/eTbZXpJ5YpAsxgYsO8rdQczOj9vCGU7mJfkczA0w2x4pFLhXLU3yRpE+fjYbgeLVAclkF0X5SIQsyqK41CROJIwBkH1k9vBCJnOQmxBq6AEGhkZCHawVqsZCpm+TkRGheOcdAYpDZQqZwIJcBGIYfoVrwSymUDSjXOuCASC4mBoZiPjCyXLKgL5JT0hASKkPWYC+dUYQwuVQELaTKBIShjTmxLIK36aWiNRCkKSk5o7D2bkM4syxKpmP4cMKgW1w2JFsJ7tYKVMIAGjdU/yYFYCMetJt2Do8ULhZ1E7mCvQAu58kAqtRhNHjzbCa3x3MGsHKwRDw7buDRN4XxAWnAnEMBsdHwxNxiCO7QJI2Q4GVJNCtkqbEsgGQwMQwhWB+HxnVkZxHCoSKQlDIrSDL9rBbvnFT3DCzpMBIFcCaWPbwU/ZY7HVaoUFQt94zJDM7WCUQUiClFEpE4g4GJph+hZbAKagkC8qgYgoBEVv5YWPrfvN+oCluoP91m89A9//wbvL7ylmAonlKIE0QAJ++C5+powOI0psUUQb3VN3Lb/S08h0UAIR5XlCfjWy1coKwdAa1WonO9hSRaDy81GkFtjBhNHBDtaTEkgbxDwZZtYJWwTKb9xSF848VlACLWoHc+cX4M5dZZVAE0dnw2tCJlAUhYKsEC4Y2uSTZeNm2Z0KwgzDbCy8Hcy4UPg01SgKMQQEkmhhJtDQ9nGceu7DMDBQgda2CKy14QwwZsUUx6EikbJKIKPblEBk8K9f/7/4f//4dQDKSqBiMHQrbYWoACoWgbzizWSQkiClKtjBCAKCi0AM06fk6naT28EKSiCvB9jKCx989dvELGUHu+hxvw7TurSkzJEFJZCAHTx7wRgDsusmCz6zWrsfw6MtN5CbBZkCnfCvaRXsYGQoBHCV7WCufTxRaGVb/PyVKIFMUAL5bWuQECBtegoBS41Z0EqXYY4V8YJMIHsX5y1g2pgl7WA+jFMpCaFUCIYO2yxmArkik5AiD4Z2x7vvDtbpWsAwzMZCKgWjDchQaBFftIMBQFLI1fMK1ye95Cq86oPvQZJEViErBdvBmFVRHIeKRJG0wdC+O5jPBGp7bSsogcqZQK1WE7GzNBJ5y5mCIav2gdEhCNpvQoVMIJ7HMUw/ElrEu05gAvn1xphcCbSV7WA8mm9i8iJQ53/GWm0HGvUqzn/U48JjXglEoftWb3Ywv3rTqTtYrVYJLd1tMHTvSqCW7w4mRUkJpIpKIDfQC5NnAhXDoDsHQ3dvER/HKrTDLiuB7A2u6GGSq40JCiWGOdZYlV3RDuaUQFWrBLpvZm5JO1hJCSQUWo0GjnZSAjk7GOCjw4TrDuauHV5vH+xgPIwwzEZFRRHIWMl7FIKh7cBnjIYgIImKdjB7Xg+MjqA2PISkGsMYe/5nmd7Sq6LMsaU4DhWJpLCZQCEY2j5uqHsRyGYCuS63hUygkAMEmwlEIEjSCGuTfuHPF4FYCcQwfYkQEhAIXS9LwdAFJRAXgZgNyVJ2MKXsoHjpk38jPJYvekgIiGXYwcgGxGLhjV+tlgBSIJZq2S3ii8HQRMVMIF+kSkMAsyAdMoGKFrDF7GBpmnXJBLKTCd8iXhqrBDLa9DQpSDUrgZj1I5Ii5AABeTD0NmcH2z8z27MdTEU2KNZ3B/MUg6FDfpCbKVs7WO6VBgBIBa01K4EYZgNji0DGFYGkUwLZ54i0DYYuFoHceJu4a0u1VrXdAQUrgZjV0dUOFgkYsl16gPwY1G1FID/uUabLdrCCEijc1hibCWSVQCYogbyNOQRDc4t4hulLciUQdbSDAVR63VZk636zPmApO5hyxZiLn/A0VCp2Quel3n5o7bkI5Abujnawqi0CKSlgyPSUCVQMhpZSQEpnB1P+O+VKoGJ3sKSDEqjT9/fdwVqtzkUgynyba5f+HlrE97b/meFMIGb96BoM7YtA0zM928EiJWGADnYwpxRSUbB8lZRAhYFQZxmk9BkhfB4wzEYlSmIYrUGG8u5gxgfvagCiZAfzCzTJwAAAoDZQtUogyd3BmNXRzQ4WK1uwye1gvjtYuWDklUA+E8jPF1tpC1EoZLrFCuG6gwlAgiCk/Vwybs7HLeIZpq8RoQiknR0MBSUQ8kwgVgIxG5GlikBSRQAIAwODePxjn2wfC6v5dqhM07SnzwpKoA4dgWq1BBACSkrXIr4HJZA7+Rq+JahSVgnkHo9dEajZTBGrQot4J/ntVQnUamUL/n6iSIVwW//tFZkVZALx6cOsD2pBJpD9s7eA3Tszi2ocIelSmJSqHAxNBKT1LnawqFAE8u2iqSybJ2OLpVnGRSCG2chESdJmBzNhEYjIQKBsB4valECVaiXYwbTubZGEYTpRHIeKKGWVQL5FfN4drPy60B0sMxgqKYFaYRySwo1HJK3iTRAEaUi5UAnEdjCG6V9EQQlk7WD5BUcDeSbQFh7ztu436wOWsoNJGWFgsIXDRw7g0qc8074nSF+9Uqb3TCACYDoogbwdLAqZQL3bwZr+xlMqe2PZoTuYn5RKY0IHiKWUQHkRKO2YCeSLQJk7BRQZACKseC4Ft4hn1pNYlYtAAJBpg3GXCXTvtC3mdMsFsiuweYt4Q6KrHUwVlEB5TYlKAZpGGwxUBzE1mXARiGE2MFYJZEL4ZZpmZTsYoVQ89nl9Sa0GAKgOuCIQB0Mzq6Q4DhWxmUCiYAfLu4MVSfP+76VMoFbadApW4xY/XTC0WagEglN/K2f94GBohulP8kygghIo2MEMZwIxGxt/89VJCQPYIpCUhJt++E2cd+5j3GN5EcgqgZZjBxMgLCw8VauJC4a24bXLCYZ+2HkPtL8rOzlQwQ6WK3lCMDQVi0BFJVCnYOj8/R27g2nXKaJw2hOswqGXIpBtEc+nD7M+tNvBANsmPokUjCHc54tAzi7ZjhAiLwJFCgRvB1uoBIqiCNoXgQrnQnHF1BiNCx50Pm7/z9P5ppBhNjAqjmGMDgqMYncwcgHwSWE8952ZKgO2CFSpJLY7mJDQWvP5zqyY4jhUJFICZNBzdzBhjM0Ekj4YugUVRZienkfkikA2f8gWgQTyTCDli0XCBr+yEohh+hNRygQqB0MT5UqgrdwMga9+m5RioaW7EkhBSkKjPhdC86QQoeKxnBbx3sfdHgwtpUSlEttMICFgTK9KIPv+Ux90ov1dqtAdzIbNLmwRb4tA3g5W7A62uB2sc4t4O5lI3SkgQDYY2pjgM18MVgIx64ktArWFZLpV0bk0xWSjAQAYdcqgdoQsBENLCbNYMLSKYPyKbOFUKNnBtEE1qcLoqJDFwDDMRiNKEpAmePlPlpk85N1pe+OSHaysBKpUk5IdjJV/zEopjkNFIiVAlCuBcjtYexHIvlcQMDRk7WBaa2idIVIRjh6dRVRoFe9v5ERBCRTHsZ0XCluU4iIQw/QneRHIChCKwdDk75XBSiBmA1LsztW1CCQUhCQYMsE7LYUECeF0QMtvEd+eCVStuo4MIROot5VCP8gL9z2UlCAiSLeNuKgEkkUl0MJMoM52MLdClC7cnyiSoc219kUgIqsEWkYmUMxFIGadiKRE6tQ5Ht8pZbbVwlTTFnNHq92UQDJ09YoiaZVAjQbm5hpotWwylg+GVkoFJZD3QgugbAczBklkPytJugdSMwxzfIkS2+K9qATyk1prBxNBbatNnsvnlUBJxQZLsx2MWS3FcaiIkgJEeYt4rxRvLwL5MVCSwfDwoB2rXBFIKYWjR2dyJRCUDUAXgCoogaLIFoH872wHY5j+RAqr/jHGds70NSBDBkT+LrmsiN9qbN1vtsUpFj6KBaEiQigXhqddldOqdXyf+OXbwRZ2B6vV3A1gUALlBafF8IM8CooicqHSWmtEBSWPt11JMkjiTkqgxexgCzOBImehAQpFIBAIVsnUUyaQ1qwEYtaNTkqgLBSBUkx5JVCXNvFehu8LnDYTyL7HW8LyYGh70wfk2VztwdBG61AEiuPaqr8fwzDHBhsMbQpKIB0mtX5cT5Rv1KDDAk1QArkikhC2vTwXgZiV0tUOJgEiEcYdKcSCzmBAQQlkDIaGBp0SKIPWGlIpHDkyE5RAMNYOxkoghmE6YQvAFPLyrP1LwIBghMiDoVkJxGw0ihOxbkogIRSkoLDyolSUt8W0x3rvRSBDEBAwotyRLFcC2cKO93Qvuf+ivQhktze0bdxWZVWhO1ihRXwcd1ICLTyMF2sRH8cR0BYMHVoDGoLouUU8nz7M+hDJhZPiYAdrtTDVcEqgWpcikJPh+3PBtoivA0CwhBXtYCGgM/IFYyop5MgYxK7YG8edLWgMwxx/ojh23cHyIlA+qbWpeLFSSLVGZgyUFBBSInYh83ES2a5g0nYD5CIQs1IWt4MhH3eEWKACAgqZQGQwODAQVKvaxRAcPTqD2C1OgFwnMGlDoH13MK8EUoIAiZ6U3wzDbD18AdiKF6wSSDgxgy0g29dt5WvE1v1mW5yyEqh7EUhICuGPURSFLB7fIzZtLVMJJNqVQG7AlQLSnTzA0pXTIK9zE0qlJJKBKs677NIFdjCvuBHGIO7QHWz5wdAydAczIreD2QeoZzsYK4GY9SKWqkMwtP19ptnCVNPaOhdVAhGF85YIaNXte44enXWDnp8k58HQUVSWynuVnDEGsUrca7gIxDAblXYlUJrqwhhnALLXl1QbaDeu+fbwAJAkebdAtoMxq8GPQ+0opwQKwdBSLgiFBvIikCSN2kAtLFhobY/piaOzeUadESETSILCqn4cJ0jTDFIgxCL0ov5mGGZr4YtAuRLIPm7IwAivlkBPObGbFb7ybVJ6KQIB0q1+5Eog4exgPhWolfaYCRRuQMvdu9rtYCF8domJYlACOcWPlBJCCMRxAm10oUV8GhQ3gnQo+JS7gy0/GJp8dzCnbBLu70gYwS3imQ1HJEWHFvF2QjzbSjHdbMIY6t4i3tstlbeBlJVAWufbVkUlUNu5E1ZOtM6VQBFnAjHMRkX5TCAUlEBhUmsgIJAoidQYZIaghERlYCC8Py40Usg0cRGIWTF+HGpHSQGQQJbZcUcIAd2hWJQW7GBGC1SrCbTWNrMK0mYCRU6dTgJk7Gq+FBSUQN4O5lXx9vP4mGaYfkNI2yJ+QREIACDD79wdjNlw9GIHA6Szg9lDes85Z2F425h9yo2vPhR2KfzATbLcIj4ogYQo+biXUgItCIZ2xRgpfRtaVwRqpkE1JEt2sEI3k47B0MUiUPn5KFKh25kJodN+wtGjEkibEKbJMMcaJWWYAHt8RtBcKwURMNNqYaSrEsgGcvrzIjMGOrXn/tGjs8EKBlglUMgE8kogd36ErkLGIFaxew0XgRhmoxIl1g6GQjC0KiiBBIBISaRaQ5O1gxWVQHGcB8WzEohZDd2Dob0SaHE7GGDHPUEGhgSqtaqdL0YCBIGjR+fC/E2QgDFWCaQE4Os8IRgaJs/HFKLjZzEMs3XxofDG2LmxVwsaIhgUWsRv4Uwg7u27SeklGBpQLgzPTv4eedmvYWznDtDUfQBWFgyNBZlAvghkCzsh2G9JO5gvsebB0H6fqJi3U7jvFWSQxN6CInHfvaO4f/8IomXaweI4Ct3BjPCdJLwSCD1nAkXcVYJZJ2wwdLsdzCuB7Dk81WhirJsSyAVy+vM2bebF37vvOogjR/JW8UpFeZeWtmtLsIPpPBNIKS4CMcxGJUoSNAyVgqHz8dnawRJplUDaEJQQJSWQUtIqgRJWAjGro2swtGvL44uNqkMGnic1BpKsEqhSSaB1hiRRIANMTs6H10kgFIGkUwMBQBzFaLVSJCBfA2I7GMP0IcEOprVVArnHDRkUlUCcCcRsOIpFoE6ZOAAAsnYwcpWUKI5DILRFLKNFvFuVEV2UQFJAue5awNInTcgmispFIEDAkA2GzrTJbWOwEmDv947jCFOTVUxP1VCtjC7cvlPptFrpgiJZFMlcCeTtYEEIJHpKgrct4rdudZjZWHQsAum8OxgATDebi9vBCsHQc1PT4bl3vOPzeNxj32RfJ4Rru2uLQL5Ln18pLXmopS8CdW5LzzDM8SeKYzd+F4tA/rx2SiAp0dIGmqiUCdScryOOZMj605oWUR4zzOJ0C4a2djCETCDZxQ4GABkRBGkQ+SKQRhIrEASmphrhdZIEtBEQksrB0HFBCeQzIdkOxjB9R1C2u7zMPBPIdQdD3h3szO3jeP7Dzjpeu3rM4CvfJqU3O5gdAIuBeILIZgKRWJ4SyNvBuhWBRDkYein5nGxvEa9k6FpmiKCkQKap1IFLkAmqnzhWaDXtnyuVExZsf8lMIFcEImdpCUogoiARXAxWAjHrSWclkC8C2XN4stFc0g7mC6Kzk3kRqF5v4t57jwCwKiAgn4wXu4MBuXyWtEYkvYWTi0AMs1GxwdBFJZApKB9sESiWstQdLHFKoOlDh+1YnLEdjFk9i9nBAIEsswsaSsiudrBUG5cJJPMiUEWBCJieruefRQJkbNNXKQFDdmEjiROkqYYC5YsbPJdjmL5DiDwTyHYHc1mx/sJRUAL910efiw8967LjuLfHBh7NNynFwo/qUgQi8sHQTgkUxfYgL6hrlm0Hk92DoW0RyK3kLBUMHexgLhjar8gAtjuYUwLFslwEiqM8E6jVckWgeGERqNwiPv/7EUJASpnbwWSbHQyyNyUQZwIx60isZMgA8qR6GXYwKdxAZ4/ZYhGoiFfa+cl4rqIrK4GEya8hiotADLNhiZK4pL5I0yws0khJAIkQPG+MgRK5Emj60GFEKrd5p9winlkFfhxqR3k7mC7awboUgbwdjASSJIHRGarVGEQCMzP5fFYSYIyAEIRIFsc0aweTIl/wYyUQw/QfZSVQbv8iMiCRZwRJpVCJ7M9Wg698m5SixalTJpAtZAjng85bP7uemQDBdQfrVQnklDNtSqBq1Slp/MmzzGDoXAmUv56IoCSQZVTqwCWpaAdTaDV9QWrHgu37iWrapgQKxTO/yiQVCHkwNGcCMRuRTkogXxQq2sFGuhaBFIgMaoM1AMDMxGTH1/mbw3wy7p8pB0Orwq6wEohhNi5RkrjTt5MdjApKoNwO5jOBpg8eghKADkog2tKdUphjix+HigQbBsk2O1iXTCBtIEjbTKDEKoEqFQWQwMx0bgcTZFvES2mV5dp1HovipEN3MJ7LMUy/YRWx/v643CKeit3BpEKiFGKlsNUuFVwE2qQsZQdLEntjVrKDRVGwgwGuCNRzJpBrzRlaxC/MBAIALDcTyAdDu6qrINsVIpLCqW3KSiDlikBJkiBNldvWtgWDeDc7mG8nT4agjUEUxbbiW3iv7GFVKONMIGYdiVxwa5EQDO1CnicbTYxWOhdkhLNqjp9sVXMzR6c6vk45pZ3WLjjaT5K9Eih0XiluO17mt2EYZj1QrptmuTuYCYs0wrfNFgKp0TYYutAdbOrgYUiJ0F5eczA0swpEITLAkySRW5vsrTtYS2trByOBOI6hnRIIAOr1LLxOAjBklUA2XsCOaXHoDkZ5l9otHPzKMExnigpAawezaDIgiFKL+MQtfmy1+z6+8m1Syt3BFh6UkbuZs3Yw1+Yu8sHQAiB7dC/XDkayvQiU28HcC92vPdrBisHQwt5q2gBbgTTrZAezRaChoVEAAnEyDSESnLL7tPL2QxEo7VgwI012wqsikMy9oJJET0qgVJsQmsswx5qoQ7cUHww95+xg860Ug0nngowQEmQMxk7cCQCYOXy08+d4O5hbkRWhCJRvB7DnSb5tLgIxzEYk8kWgLsHQSlIpGDozthlDUrOKwalDVgnkrzzacBGIWTl+HCrii0AA8q6UUna1g7W0ti3itS8CadRqdtyKoiRsR5BwjT5sEShNM2itQ4t42zaeg6EZpl+xdtCiEsi3iM9dL8bY8TJx416yxca/rfVt+oilikCxa6UuJUEInwnklECFG7veg6G9fap7MDQACPKZQEu0iA8lVpcJVCj2WCUQbCZQMc/H6BBcOzw0bt8eHQAAnHXWw8vb93awVHe1g2kyiOPYFp+8HQxLq5gABGtOxCtIzDrQ2Q5mf59xRaBGplGLuxSBpA3k3OaKQNNHJzt/jju/stSumgpRnogHOxgVirPo0p2QYZjjSuSLwgVVRZbpYOmyuZgCStiMMU0GSkpUBmwRqD456d7lzvukwkUgZsX4cahIktg8H6DYHQxdu4M1swySDLLMZkNqo1F189Bq1R63jWYDkgSIBKQAokggTTWyLHXB0JktgIZMoC3m8WAYpgdkGBqLLeK10eGelsgqZ70SKNlidmgezTcpS9nBfICyEBREOlYJRCCvuAF17NTQiW7B0NVqmx0s1IqWUgK5FRi3HSElRGG/bEcS0yETyH7+4OAYAEDT/QAyPGRBEci+Lk27ZAKZzkogAblkAQvIOzPFPCFmjjFC2POlWxHIZwI1XOZBp0FKCAEig9FQBJro+Fn+vPGT8VwBVJbNK3eeG2oAYCUQw2xElLOF27WfshKIYIIi19rBjLODSSS1GprzdWT1eft+dyHY84iHcxGIWTF+HCpilUC+COTtYN27g1klkEa9niGKImidYWAgdtuyyvRGsw5JeXMUJQVarRRpluYt4gXyxUtezGOYvqOoBFJqYTC0/TNxEYjZeJSVQAv/GatV6+mXkuCVru3B0L0WgICCEkgszAQyxuR3i8YHyvaqBMozgVwFyBWB0NEO5pVAgwOjAIBGYwpSTeOsMx9W2r6UttNEe0tbnwlklUDkMoFEyDgR1NuqECuBmPXCH2MLWsS32cF8EajaoSgc7GAnbLfbamULXgPkNlLfScWfGAvsYPBS2TpArARimI2IVwIttIMpgMiv6SASNoMv2MEGamjV69ANF7Trz3ulOi46MUwvLGUHM4XuYO3ZQZ5mlkEag0YjQxRZO9jAgC12Vip23tto1W2OHdnmKJGSVgmUpoij2DYMEeDuYAzTx3jZAVAOhtaGguCBnDrWz8O5CMRsCIoTsSQ6Ex/8y8+Vnq85T78sBEMrqew9nf9XX04RyAdDL8gESjAzUy8ogXx3sMUPrfbuYMqdgALCtutzRaBi7o6kXMZeq9kiUL0xgyiewWmnnVHavlISWhtobUr7kiuBAG1sy3kbDO0ygSB7zASyk5WYi0DMMcYPPr7o40kXKIHsMVmNFhZlvAx/dIctAmndeYKtQot4WyQKmUDu/yFLxF1EtJ4HcRGIYTYkPhMIhaE+TV0RCATpxu1I2BbxmvJg6Fa9Du0bR7jXGUhWAjErprMdLFcC+XHHdgfrogTKMggyaNTtfFDrDIODVgFUdUWgesMqgQABJQmRsplAadoKSiAlEY7/XiIAGIbZWohCh2el8gK1aVcCqaISaGtdK3r6NpdddhluvfVW3Hbbbbjmmmu6vu7Rj340sizDb/7mb67ZDjKdyTNvMig5ijMefHbp+aoLbC52B4uiCDA29RwQC1ZkFsN0CYau1iq2CBQygVwxZSklUFswdBRFeTA0bFRQtogSqFYbQRRp1OsNRJFBzXnBw/ZdEShzN8b+76ucCURQUQSSIreDCbHkvgN5e25WAjHHmu5KINcdrE0JVOmoBLIy/BFXBPLnRTvBRpmVM4Hau4N593Sm57gIxDAblCjYwahkB1OuNa5ymXwRrM1Gm7xFfHO+DnJFIOUyBjsVgXbv3o4nP/kR6/OFmE1NJztYpRKH9cjeuoMZQGdotux8UGuNwUF7fCaFIpBw81wprRKo1cqQZk4JlLYFQ/M8jmH6DiFkEENEkQrqQ0Pk7pN9oyJVCIbuMyWQlBLvf//7cfnll+Occ87BC17wApx99tkdX/eOd7wDX/va147JjjJlfDGjXm9BCAWlohAGDSAURaQgn+WMSEUgrXPbFfVeBPJ2MCntiRKKQNUYc3ONhUqgJaqlvkX8nJObWymeKNnBWpkuFVkE6VCgqVaGESeZ9XYrm+3jC0RAWQlU/PsqFYEMOSWQyLuaQZSqw93w7bk5E4g51nQrAoVgaNcivhnsYJ2VQDCE0R1jALorgSLl7WBlJVAo8vpgaDd0pNkciBSvpDLMBiQqdQss2MGUsplAyp/P1g6myXcH80ogOz7XRkfsFigvFHt+93efiS9+6Y+O2XcY3rEdD3zUucds+8z6IeRCO1i1VgGZcjD0Yt3BUq0Bo9FqGacEyotAlcTZwZrz4fVSEqKoYAeLE6cEEnnWHQdDM0wfIoJINopUuCc2pIMFmtDnwdAXXnghbr/9duzduxdpmuL666/HFVdcseB1v/M7v4PPf/7zOHjw4DHZUaaMzwFqNtNQGKnVBsPztZodDEUhE0iqCNDaFj1QyPnpAXJFDxEpZJkO2Tq1WoJ6vQXt7WLIrWeL4ZVARybtYJ3bwfzzVrbuiyzaGKcEcidiMoQ4TpGmGfw9b6VSybffVgTKlUDuL4NgJ7xRBBISsmAH60UJ5K05rARijjV5Eah8vqbGwBhCPWu3g3XOBBJKIk58kaezEii0iG9XAvnuee5iEkGBiJCl9vytVQdW+O0YhjlWeCUQ8qageYt4KiwQCbuwkRnfHWwArfk64G7KB8bGANjWuaotg3BwsIrBwSqGh8tq3LXiiS/+f/DKv3znMdk2s74IsdAOZpVA5WBoawfrvFDR0hrCaKQtG9gqZT6/q7gszHo9LwKpkAlklUChRbzkFvEM08+UW8TnBWqtDcgPjkSuRbydV2+1hf8lv83u3btxzz33hN/37duH3bt3l16za9cuPOc5z8GHPvShtd9DpiO+GNJotMIAVqvlN2I+IM92RnAHuVJOaWNZlhLIS+bixHbtCplAFVsE8jeoprdMIK8EOjwxk38fIWwmkCjaweznzKcZhDGhQJMkw1BRCzrToQjkO0PY7XUuAsWxfbEgG/4VRbG1g4V5iegpE0j77mBcBGKOMXEXJdChuXnsn5kNUvqmK+xUOimBhICKo3DB75oJFLrqpQveDxTsYEIig0aa1QEAtYFBMAyzsSgqgbzYIcvsOEpEYVFEwS5saEMlJVDklELVkWEAgCFRyukD7E08AJxwwtgx+Q5JrYba8FBPXTuZjY0QC2MIKtUkjGFZoQjUbY3SdwfLUntzpiIRFiu8Emi+PhderyQhihRarQxZliIOmUACUnF3MIbpV2x4vL12JEkM7ebQhkxogmS7aPaxEqiTTLK9kv/e974X11xzTdc0f8+rXvUq3HTTTbjpppuwY8eOZe4qU8RP3hqNFMIdrMVcnGrVFkSkzMMfpYpsi3hXCVrq36uILxjFSYIs0yU7WL3ezEP8vGJgyWBo23b28NG8COTtYBACShDSTCNy+z6fphCUF4HiaBBStpCmeRHITwD89oyhDkqgQot4yu1geYt40ZO1hZVAzHrRzQ727m//EI//358KvzeWsINFcZx3P+hmB3Pv9SuyQQkUAqKdfUQoaDLIUpsZMsBFIIbZcPgsH2pTAinXIt7PDRQRBk/YgZ2nn4ZISiQDtkV8ErqL2feaDnawOLHXjJ07R47Rd7DbTwaOjdKIWT86BkOXlEB5dzDdZX6aagNhDLLMxhMkcd7a2avB642yHSyOFdJU22BopwTynwOwHYxh+hKR28HiWEE7x4sxeTC0baAgw+LHVisCLZnouW/fPuzZsyf8fsopp2D//v2l1zz60Y/G9ddfDwDYsWMHnvGMZyDLMnzhC18ove66667DddddBwC46aabVr3z/YwvZjSbhSJQSQmUB0OHNrCuLawP3FteJlD+Wq1NKELVahUcOjQFPWD8CwH0YgeTgBA4dHQGgM0UGUxbeMQvfoD9Qw+EEoRWqhGrXAk0SFbGXqsNQsoYUjVdQcpu04cCAuUW8UA+cfX7LWDVPNYOlheBpBAwPRR2/A35VpMGMhsPXwjN2go3jSzD/bNZ6Xegmx1MQCVx6UawE8plArVSbwdz70c5QDMSChoGaWozQwYHhpb7tRiGOcaUlUB2jEvTrKAE8plAwPYHPRDbto9j9vBRJLUaWvVGsH3n6mEsCIZOQhFo9Jh8B9+xsDowgMbM7DH5DGZ96BQMXa0mXexg3YKhNQQZHD4yi5NPknjgg07soAQq2MEkIY4kWq0UaWqVQGlqP8fPo1kJxDD9RykYOo5gXBFak8kdIURbOhNoySLQTTfdhDPOOAOnnXYa7r33Xlx55ZW46qqrSq950IMeFP78V3/1V/jyl7+8oADErC1+ItZotELnnmIRqOqKQMVgaOlaxGutobNsWd3B/OoNwduo2jKB3Lby7mBL2cEEIIFDh6cBjCOKYiRpihMO3wUx8kBIQTYTyG2nnqaQzg62bZtXkTWtEsjNcys92MGKwdCGyIZlC0CYPPOkJyWQ8UqgrXVBYDYe3ZRA7SzVIj6KY5Ar7iylBMrStkygoAQqF4FarghUzCNjGGZjEDKBUFQCGUglXdcTXwQiDOzYDgOgksSoDNTQnJ8PBR5/O94pE+hY28F8m/vKIOeObXY6BUMnpe5gTgkk5KLdwSQZHDk8jzQ1OOOMkyCEtSVXnBp+fn4mvF5JQqUS22DorIXBweFcCeTtYJwJxDD9hxDWGQOnBNK5EkhI+zuBIJUsdAfbWteKJb+N1hqve93r8LWvfQ2/+MUv8Dd/8ze45ZZb8OpXvxqvfvWr12MfmQ74YkajkQbvYq2a34gVM4F8TcOrYbIsg04zGNNZDdCJMHAToA3aikDNEFobFDVLFEekUwLVGy0QgDgZQkR2cBeC8kwgd8LNp1mwgz1k126MTu0H0ECaZkhiO5AvLxNI2O5gcQwjRJggyx4zgbJQBGIZMXNs6bUItFh3MCkVhBTIXMvnpVvEO4VRmw3M/z+SChqEVstOvtkOxjAbD68EEhDwwXc2GFqVi0AExEODICEQJwniatUpgey1xDilRvP+0UWUQMfGDiYjbwfjItBmR0q1MBOoYAfzXSmtHaxLdzCjIckgjmPMzTXtMRyUQHYOODefZwJJSRgcrNpg6DQt2cGCMpztYAzTd4hCZ+g4jmC07w5mIKVElhkAtvt03yqBAOCGG27ADTfcUHrswx/+cMfXvvzlL1/9XjFLUrSDxVU7kFULmUBJxa4ACknwx6ySymUCuS4NK8gEAgQMEVTIBErQbKQFJVCvwdC2PWeWaZAUIAgoX1gBQQlCs5VZ2xisEkiQgRQSr3j4mXjIrf+E2898iO1UltgBvFK0gy2hBBJw3cG8EijYwWTBC9odnwkUb7ELArPx8IXQpZVAdmJb6XBMCne+kU4BqCVbxKetln2fe9yfEcEOJiNnB7NFJbaDMczGI4o7KYFcJhAZxLE9nyUZGCFhhMBAEkNKiVYHJVA2VQtjsudY28EiV4iqshJo09PJDlZWAuV2sFaXRcpWpqFASJIKmk2NLNO5HczNAeuFIpAQwPBwLQ+GjmK0WlbpqpSEydgOxjD9iF3+LyiBslwJFCnlFkvL3cG2WhGIr3yblKIdDJ26gyW5Hcwfs74tLIEgpFhmMHS+KmNooRLI+7dDi/clTpRISkC6IpDbf18EimGVQMUW8b47mJAK2wYHoHQKQ3WkqUaSyNJ39n8/tgikS39fYeWHKHSfMEKUWmDLZSmB+BRiji1qmXawStylRbwUIPeapexgXgkkpD8vyrL5WCoYQWg2XYt4VgIxzIYjzwQS8KJVrwQyxmX7kYEEkIGQGYPYvaeYCeSvFoSFCzzeDrbzhGNTBPJKILaDbX7s4mNZ4VOtJDCmLRhaiK52sKa2RaA4ssXKm//jThw4MAEgLwLNzefZUUIQhodr/3/2vjtQsqqw+3fOLdNeb9sLS1lYRJooiCxFQbGASDRoooagURPrF0v8zGeJGjUmUWMkMQgYG0aDhqIGRLq0lV12l6LAAsv219vM3HbO+f445d6ZN6/ssuW9t+f3x76ddufOe/fOPed3fgWJDob2GimB7DjOwuJwgySlUyVQ1g4GwJBAsh1MjX0tCWQxG5C1g+nQ1iwJpK1RlIpU8q0ygTgAELJvdjAAAiQTDC0zgZK9VAK5LgUU0SN045DaH59wEALECctUxEslECEUzfkCCASSqCIHtA6v+cyAWuGZoh2MIA0ezP5LCZnWygakmUC2It7iQEMTjfEMlUCNM4GIJIHUSuu0drA6JZA2TBKq7WAeGBEIQ2kHayo1z/TjWFhYHCQ0roiXBQuCc3ieY67ZI0PDqFYq8NT3R7YdbGxwWL5YTGzPPFjB0DlrB5vzILSBEijfqB2MTmoHi9Rz8r4Hx3Fx9z2b8a4rvgEgJYEq5QwJlKmIj5USKI5rFweJtfVbWBx+mCQTiBkSiINAgBJi5qK+O7/mfPPr0xxGSEmgCDDB0OlqvKfCFOUFUEm+CX3BdrCoGioSSCuBcjXB0JhhJpDnOBklkDRjuYoE8pS0N4ySjBIoBhEChFC0qKpYHiWIYwaq1ArZdjDHcaa1g+k95qmlXP6OZqAEitWXhVUCWRxo7JdgaEJlKKcigSZTAjn1SiAdDE3rlECOVAJFcQhCBEolawezsJhtcHQwNCGGBGJMZutxwQEiQNW1fWB3H6rlilnpjKpVowT61bdkq6sAIHgtEaSJop4DpARybDD0vAEh1Iwle45YgVNedwFyXtYOJm1aU7WDhVrx6rpwHAdJkqBSkYsRvp8DYwzVatYOlrbiJXEMz/MbVMTbcZyFxeGG2kwgxyyOcpFRAhGpBKLqu8LawSxmBTSpEQYRhGiQCaRUMYRI1U2SxMoOBnAhQPZaCaSImfFxCFC4rmNk4EEQmYycmbaDuY4Mhk4SBq58mVSfjERuK46ZmQBX4kSFRlM05SXZk0QJ4jiBQ2tDAeX7EzDGzUmtyR89qKUw5z44hMkEAmbWDqaDsG1FvMWBRkoCNR4Ua4Rsqop4aQfjRgk0s0wgEwytt0O1HcwDJ0Lmi7jckkAWFrMQru+BMwYCSQLpamzqSDsYIQBR5HL/rt2olivmGh5Vq0blE4WR2SYXpCYc2tjBDrQSyJJAcx5ZO9jpb34j3vLZ/ws/505UAmVsGvUItRLI89RiX2Jel88XkCQxgiAwz9cinzhmiJNY2cEk2eS6WuFqx3EWFocfau1gPKlTAjEZDO05qVLQkkAWswJa3RMEMVIlUMYO5vkglCsSSK6WOJTCiN8IAWd7QQIpZjQYr0AQSQIVCnKVsVFFvDMTJRCBUQJBpESMr37GUWLsVsYOBoqSWt1kKhSQUvneuRolEAVjbHIlkKhTAkHvt5YHT31qJFq1ZAcPFgcYuoFuOiUQ4wIJ45PawSilgFECTWMHq6uI18QoIZIkdR0XnEoyyXG4tYNZWMxCuJ6PJIrl+a9IW0Be5yQJJIwdrG/nLlTGy2YxJqxUTTtYrJQTAgDntSTQAbeDeZYEmi/I2sFkC10OuXwuQwJl28EaX++0EijvuHBdD4wxs6Dp+3nESYxA2ZTleypSM4qlEsjNKIGc9LpmYWFxmIGkwdC+7yrSJ80EYkoJ5GXms4ddRbzF7ES2HUxfQAv5dJDkeh6omsC5roOEJYAgUgkkDWF7GQytSKByGaCaBJLKm2o1owSCto9M0w5GCUAJ4piBQZFAauHHV/RMlLGDVeMEVAiw2Ier9kXETNrBHH0ST18R39AOhtQOpgNwp1MDaVWGtYNZHGjoC9B0JBAgc4FyU9jBoMifSZVA6rWRIYH069MVU604lCQQg+tyWxFvYTEL4foekjiS5K1S3gLy+4BzBoI0x29wTy/K4+MNlUBJEpttCp7awYGUBMrlPLS07H+ixmYCzR9k7WDa5lcsFaDdyXpxYio7WKSO4YKyIcrFPmURy+XAkgRhtZJ5T20HY4jiqLYi3rFKIAuLwxUExCgTPc8FS+pIIC5AAPiZrwerBLKYFahpB2tkB/N8k5Xjuo6ShEuyo156OxPoEyUYLwPUgevSjBIoNGSLqZSe1g7mpHYwoV+l7WBqEBqnwdA69DZJPDhcqRkiVQ+6dRArtj60VyQQJakBjAGGBSJQJNE0+68zgawdzOJAY6ZKIECeJw3tYFS23gk+dTuY4yo7WKyDofV5IffhJaecibwimzUJ5DgcRVsRb2Ex6+D6WglEQSlBGGo7mFTKIqMECqMElbHxGiWQsYNNoQTK5TxUKiGAA6MG0mRBvmSJ5rkOQlM7mA4tL5Typh0sUeM8h9Ap2sHkcwpaEc4SoyDKKSVQHEUm8JUYO5isiPd939gidWmKXuSwsLA4jECAmor4BkogQkTNYr8lgSxmBRoqgTLB0K7rGRms60klEBFQFfFqMLcXJJCW2wbjZcBx4LoO8nl5EQ6C2ExQ9ekxnR3MddKKeJ0JpFcgPSHfK44SuJQgShgidXJSzkEVCcRjmQkknhzAol2PNrCDTSSBdCYQAUxFfMVzwHIqPFtNuKeriddKIMeuIFkcYJhMoEmImyyChCHX4CJFiG4H0yRQ43PfdfSkT5FARgkkf77tbe9BR3sXAECo89exSiALi1kJ1/eQRBEIIXAcgjCUih6HpplAOhg65gzlsawSKMjYwVIlUCM72I4dAwCA7u6W/f4ZbCbQ/IGsZJbHm6tInEIxD9bQDjZ1MLQ+bmuVQHkkSSwtYubape1gsh1MvkY+5rkzW/SzsLCYfyATMoFqSaCEqUiVDElsK+ItZgVcV9bZxXGqpMlmArlKCRTHiXkuAVFZOJJ02atg6MzqIKFuXSZQaJQxBDOsiKdpRbws4SNGdaDtXlGcwHUcJJyn2xccVA0URCQ/P2IGwvleVcRT9Z4A8GxnE3afJie2lDigHFi8ePmU+x9zqwSyODiYaTsYoJVAje1gMhg+rb5sBJ0JpIMztUKOEk2SOjjxxS8FAHBHKopch9dYUS0sLGYHXM8Di2J1/hPVJiqvz4zJVU4dDB0zjjiOze2oUjFKIE0CTZYJtH17PwCgp6dtv38GawebP8i20uqsp3wxD8Zr1emEkCmUQOo5RtWamPwgTQIJxo0SSCvi45ghiWtJIH1820wgC4vDECRLAqVKIJZVzBNh1PiAzQSymCVwHIokke1Xal5XQwJ5rswEqlRCaQfjTM3nUiWQXnWZCfSFOypXIAiBn/NrgqETVnvBptOwpdl2MF0Rr08zTxFJYRjDoxQx50YJRAQHUTWiPJZ2MCQcFAJ5XYcLPcjNKoHk/qQkUJoJlHBmgrYpIVg8XsQ/fenaKfdfT8htJpDFgcbekEBhwiYJhqYgDgWmsYO5rgvGEnBeK6WnmZWQU04+Xd0JowTKqhAtLCxmBxzfRxJLO1hWCUSpWhgiaSZQzDmYEHVKIB0Un44VRIN2sO3btRLoQNjBNAlUmOaZFrMdhFIz6XI9OV7LF3LgTJFAPG0HY6LxNcoogXiqatVjWUopkjgG542UQLHJttL2M2qeY+1gFhaHGwiyFfEumJrHGiVQIpVAXubrwdrBLGYFZOOXJEG0HSybCeS4LggVqFYjFQw9MRNon9rBylVwAfg5ry4YWl2EZ1gR79DUDiaIIxvCCCBA4CiSJ1IV8TFjRnlDBYOj7GIi4VIJpFQNxRnYwWozgVIJsqNtYNRBa+ihtaV9yv3XQdi2HcziQEOrzWYeDN3YDkapY0ggHRBbD9fxkCRJmvFVN0gWgFECCUVEOw43OUEWFhazB5PZwShVwdCZTKCYcTAu4ABIogicMfi+Jy3b6nujXglECIHnudip7GA9PQeABLJ2sHmDWjuYynrK+0bNrkkah9JJ7WCRUYVnSaD0epawBDxh4KR2ISOOmWm9TEmgmRWZWFhYzE9klUDcZALJ+xiTDZqOzQSymG2QFi+OJOGpHSzbDuZ4oBSKBKLgPAFR7WC6jJ3vjRJIrw5WKuCCwPc95PPyIh4Ekckr0YTptJlAyg6WJAygVNrBKAVzXLhqv6IwhudQJFwY0oWyJD1oM0ogACjk6oOhJ6+Ip4RI4gly4KGVQM2FFuSYA8dx4KlAykaIrRLI4iDBMUqgxoPiLCa1g1EK6lJAB95NFgztOEiSpKY5kHOWIXUFSiUZAs2IAOMclAp4nt9gaxYWFocS2WBohxIEQZoJlNQpgSLGDNF8y7/+BwA5MI7jBOlCqKghgbRSaGSkjLGxygENhrYk0NyHDIaubQfL5f0JdjBKMK0djJprWVITbRDHMTjnEIbg0XawxBQe6KmPvqxZO5iFxWEIQiCUJySrBNKKRJ0JVKsEml/fFfPr0xxGkHYwJmXaDYKhHdcDoRzVapjJBIIJhgbkislMoS/cYUUqgbycV2MHixQRQ40Pe2oSKFsRDyqVQMQh4NQ17V9RlCg7GDOZQC6LzDakEihJSaDM5He6YGgKGMaKM2aqQl0v3UYuN7n8XA+W51tImMXsgyYa4xlkeIWMNWwHM+ejqA2+q4ej7GBZkkgIUaME0uBEgDEBSoUMoreSeguLWQXXU0ogSuE6NFUCOQ5YkgBEZIKhubHg3P2f1wGQmSlRlMBVrYECsiJeX09zOXl/GMbo7R1B14EMhraZQHMehFJz7dFKIN/3TEmHJoEcQqetiDdKIF6rBGIsgeDc2P1pg2BorYZ3jFrIXrssLA431FbEZ5RAIl0sJQTGKQJYJZDFLIG2g+m5HGNJrR2MuqDKDuZ5LpjgqR0MBELsZSaQrpGtVCAgWdOsHSyps5bNLBiaKCWQI/eJUjDHM0qgUCmBYsaNEshJUhIIyg4mJlUC8XRQUacEcjJ2sCSJTVVodpabz9jL6mHtYBYHC+5eKYEmzwQCAAg+qRUMkK2CSSYTCJDnvg6G7uvbY+7nRIBxAUq5ea2FhcXsgaszgUh9JhA1C0OpHYwZC47+zvE8F3HM4OrvFCGtNPo66quJfBQl6OsbOTDB0J61g80XEEIntIP5OdfYs2rbwSbJBGKTZwIBSC1f0JbmicHQwtrBLCwsiCxJAuoygVhKAlEKuDYTyGK2wXUlyaEvnuXKGCilpibdcVxQkrGDMabsYEK1g+1lJhDXdrAAXMgcAG0Hq1ZDQwLp02M6JZAMhkaGBAJAHakEUtuKogQupbIdTK8e1SiBRK0dLGNJ0SSQbkHSg1bXlfJ2SmnGDpaYxwWAhKimifz0SiBrB7M40NDNBDMLhm5sB6PKfkmEmNQKBkg7GEvqlUDcBEM/t/VpI6nXJJCW2/vWEmZhMavg+h5YFIEQCtfJtoNpO1imHUwFQwPpyqdUAsXwnFQJlLWD6XYlSQKNHrCKeBYn8HI50AYqR4u5A9kOpo4xrfDy3UwmkMqWJASTrXlMrIhPapRAq8J+XPeWN4BDEzzyfqkEkse/UFMfvfZnlUAWFocntMvF992JSiAupBIoY4ieb+4PO4Odo9B2ME0CVSrjANKGMIe6AOFpRTzn5jDWmUAs2XslUByEYFyoinipvAmCGHFSu+pC6dQXVUoIQGrtYIJSMOrCySqBqKyI12GAOVJNN8Jqg6GzgbiTV8TL35tTEwwdS1JKYTgvV4umUgIB0i9qK+ItDjT2tiLebxQMTalZ9Z9SCeTodrBsJhA3K6WVahlbnvkDuODgRAbo6Qpez7ckkIXFbEKaCUTguhRhqJUWFEmS1GQCJYyb7xiHpPboOGY1+XjZdrCsHayvdxiLF3cgn99/3wOaKKiMjsr3K9oWwrkMQslEJZCXKoH4TNrB1PiQZJRA2UyghayM1x1zJNgEJVBigqd1hIJVAllYHM5IK+JlbEptO5hUuqeZQOUotplAFrMDjuvIUGh1naxWywDScGjquIBgSsrtgAsOIgDCRU0r1kyhTxQQgjiK4XoOXnT26eq9Q0PS6ANq+kwg2Q7GuTDB0Jw64E5KAkVRNMEOdsTS3WYbhNUqgXINK+Jr7WCe50oSiFIItUQUJ4kJhmacYzQnV4umygQC5KTcKoEsDjT0ysPMSKDGmUA6+JJgaiWQ67omK0xfCIXg8F1t+wixcdNDKFfLANEXSS2ntSSQhcVsguN5kgQidZlAVCr+ZDuYUt4yZiw4+rpmMoGUJSurBKKug+b2ZvnaKMFddz2KBQva8fgTV+I1rzl1v+0/AFRGJAmUt5awOY1aO5j823qeAy5IjZpnqnawVAmkmy7jmtdSwZD33HQipx6T7WBybOc4rnofnQlkx3EWFocbCOTc1lPXNx2DkH53cNmsqZ4/HkXIWSWQxWyADnsWhgTSSiC5UkapAwG56i9JIAYCHe66D0ogdVJQShGFsnb+6FNeDCGUzDZWMl71/Gkr4gkBIYAQkowRhIATCka9lAQKpR0s5tyE4pIo3WfCaoOh8zMIhnYVeSZXOuXvIwgC5HLytf/7wC+RqIFBPj+1Eii2JJDFQcDe2MGmagcDMG0mkON6RpKvzx0hOIpqBT6KI3z3e9/Et2/6dwC1SiDfzzXYooWFxaFCTUW8SxFqO5hSEoMAVC3w1NrB0iKFOE7gu+m5LUkgB+e8821417/8AwA5Bvj+9+/AOWd/EtVqhB//18f3y/7rPCBNAtlcoLkNQtIgVk3wyfEpqVHzSDvYdO1gjSviHXW/EPI6pq+bURSbvCDX9RCGsbGDTTdetbCwmI+QykRdGKTJnywJRKkwmUBSCWRJIItZAGkH46blIAgkCaTDoSlxATBDAjHO1TKeMH7ovWoHU2wTUSRQLudjSZuDWM1LtVJHm8DoNCeKVgIBigQCwIkrlUBqkhqGsSSBGEOkJ6TV2GyDcNTYwepJICdiOKq5xdwG0kwghxIIqq10Vfi+3N++oV5wJRG2SiCL2YC9s4NNEgxNyMwygZRCAMisigiOoiKXwyhCEFQxODqgHoNVAllYzFK4vi8VP5TCc2uVQIlRAulgaG7UF9oOppVAXkYJpNvB2hYuQOfCLgAw27377kdx1X/cgpaWItraXrh1y5BAwyMALAk010FoAyWQSyE4aspFpB1sknYw9bzJ7GD6eAZLwCGQqOtdHDNjB/M8D1GUpO1gmXHc4sXLsfYVF7zgz2phYTHLIaVAqRJIfY3U2sEIqCWBLGYbdDtYSgIpO5jKBCLUgVCr/p7nyJpn9VpB9iUTKJXNhkGEgiuwqiXGuv4iHNdFpJVA6k2cmVTEEwIhiNofAkZkMLTLa9vBEs5NRXw8HpptEK7sYIqJ8usygY7s5/j2ueeY2/L3pjOBKPThXylXYXIBBQwJNF0mUGwzgSwOAlxKwTjHJGPiGshg6MaZQIDMR5jODqZtokYJxFMlkJbTa2UgE7DB0BYWsxSpEkgGQ2fbweK6TKCYszQTSF3IdTuY56bnNucUjkPhep653kcZhe7OnZIgXry48wXv/8RMoKkXZixmN2qCoZUSyKForASaZNFDLzhmg6Gzz3WEvm4lEASIYk0CJTVKoCiKQUxuUDqOe9Mb346/+fiXX/iHtbCwmOUgEDyrBJLfTcwogWQmkFYMjkeRJYEsZgekHSxVAoVRBUBKAlHiQIgEScIzmUBEKYHSQOSZQk/6CCEYH6tCCODmx2M81FeE6/uIk/pMoKkPLaqUCYBj7GAJXDCnPhha28FUeGU5bQejXNTYwbInp+NQuEygWeUETbCDKRIKAMrlapr9LgT0cGKqdjBADpqtEsjiQEM35M0EQZLAcxwzidOQg1xpBJ26In5iJhAXHCVNAqmVVNOiYDOBLCxmLVwvEwztOQgCef46WgmETDtYRglUmwkUm3NbiYml0tb3TKZKFKVjiZ07BwEAixd3vOD910RBWSuBilYJNJehg6EJIXA1CURk2HjbyE78/flr5X10cjsYIBWvWSUQAKPycdQIrp8NYVdT1YxNoygNhvZcH1GUmG1k28FKpSYTq2BhYTG/IZAqgTRBbRY5uaqIV02D4zYY2mK2wLSDaSVQKJVAeRUMTYjMAUozgVQ7mOxil1Xoe5UJpC6WlOIH//MIrtnk4V++caPcF881El2HUjDGpg+GJgSgBMTYwQgEdcGpCxLrID+VCZQJhuZ1djCSmRv7DjXkk+NQUC7gUQohREoC6WBoQkww9Hi5YlY0pS1tZkqgRG3fwuJAwqEUyWR9uXXQoZk5p9YSpge5FJjaDua4EzOBOEex0ARAhrXL++T7cC5Mw4olgSwsZg8IIXA8F0kUgRIKh9YqgZJYK4HSYGhdjavtYFoJ5GfbwTiB6zpwfd+skOrtAvuZBHLrM4Hs5HwuQwdDO5njySECnBP0DG/D+192irqPTmoHA+SxmiqBaskgrQSqsDJ6S4GMDIC0g0VKyer5Pgb7OxBsl3bGrB1MW59txp2FxfxHjRJIt4MJrQiSdjA9my1Hka2It5gd0HYwXXUZ1SmBCHHAOTMV8ZoEIkJAqAHevtnBCG745jX46/P/FONDw3JffN/YwXxXvu+0wdCUKiWOYzKBBHXBqAsiBJKYgyu7VdYOhkDtc86BA6kG0iCcm4moJIHUAwmHo05c16UqE4gCah/HxyumRhSCzDgTKGbM2sEsDjh8h6bH/zQI1DldbwkzmUAEUwdDO64ZTKfSWIZSqdYOppVAWTuYJYEsLGYPHKWClUogCkJQkwkUq0wgauxgmYr4unawGiWQsoN5vm8yVbJ2sF27hgAcKBLIKoHmMrQdTGc9ATJCQAjAZSHyngtKCCglk7aDAXKxwyiBuFauKhJIrdpTdS2MYq0EipHEMdqGtuGDxyxEZbwJvFJS+5UqgYpFueCRn2b8Z2FhMddBatrBdJwYzyxyOo4w5Szj8zATaGKC6CFEe3s7PvzhD2PlypU1X8oWE+E6bRgbJbj0j1Yi4ttx9jlnI+Lb8Wd/9ha85Y9fh4hvRzwC9HSfja3P+VhzFEDinbj/pFcgIg5O8npx7BvPwTvOn1mVK3UctOR6sfq970D0Z38EAPCLBZT2jOHKr38DzaUc7qcuTj/uNHC6B6993VosW9GKr3/96xgaGpq4PaUEAknbwUAccEeuEI1VhGRoqSNJIG1H00nUeReOAByRHidUMOT8PMIwAKU0JYiYaNwORig457V2MAhwdSM3jRKI2WBoi4OAvOuiOkPCNtBKoPpcIPV9OpOK+EpVqgqzSiDX8YA4QqTtYMYqlm0HkxPFyy9/Ff707efiled9akb7bGFhsf+hg3eTWLaDEQBBph2McyZzUaYIhjbtYEq5wSFMRbzrN84EqlZDDA2NY9Gi/WEHk0PUqiaBrB1sTkPbwVx1reCcwyECQhC46tpS1CGtUyiBQpbAE7UKIP1TWjcInCQBAxBFmYr4hKNj6Hkcv6AZT+/sxWDzIrlfmUygkiKBcvk8MLafPriFhcWsRI0SSAg4yLaECTgO0naweP7ZwWYVCfThD38Yv/vd7/B3f/d3NZWPFhNxzDGLAULQ31tGV9cSDA7tQkf7IgwM9GJwaABHH3UcKA1QqY6ira2EgUGBYrEJzeP9qDouRvxWjA31YWywb0bvRx0XC1ceg+HenaiMDQMACi3N6Fy6BLuffgYdbQUsdfPYPV5GsWcZxsdG8NKXvQQf/vCH8ZnPfGbC9hylTKDEwWD7MggQEBAwKg/Jnz3VLE9Op9YO5sQcghIQ3wEVtQcw4Rx+LgeMNVICZUkgpkgoiiRJUKmENcHQIFLxkJ82E4hbO5jFAUfedQ25Mx1CowSqt4NRAAyUTG0Hc13PKASNIkhbSSEnlAAgjFoobQfTwdAvecnROPvsF8k64JmkWVtYWOx3aBKIRTEIoSBEGCWQQx1wzhHFcU0wtLbguHVKIH1uczDTDuZklEBZOxggLWGL9qMSKKhUwJLEKoHmOLQdTB+bwdi4ygQCXC6vLU2KIGJi8utUmCTImUyg2uuVJoEoY5IEUir1OE7k2E9lYS5+9g94/sUnyP3KjOMKKv/OKoEsLOY/hODw/fp2sNQO5jiyPCHhHEGSzDsl0Kyawa5cuRK//OUvLQE0ExACwgQKGVaSC65k33LKJiAvrkS1gcHMx9Tj+zI/ywq0MhYxvS1CCCCkSuYXv/gFVq5c2XAzlKjPQCiGW5eo/wNcZZmQRNZ+OpQi5sxkDrlMgHgUcClcQeBkPgMVzPi4HYeC6sdUODYgMw6ShMGh8r0Zi2tJIIUwjqZVAtmKeIuDgbzrGHJnOmiyaCIJJM8vOq0dzJlgB8u2ttQrgViDivhCMQdKKVpa7ITNwuJQwfW0HSxqaAfjjCFWJBAXAkKkrSgT2sE8eV3lEOAiVQI5DZRAALBr1+B+DYZmcYKwUkHekkBzGpRSCJZmAlVGRk07mKfImSZNXk5jB0Md+aPJIL1q72QCofXPOI7hsARcCBQro+gc3AqgLhhaK4GmGf9ZWFjMbZAJdrDaMS/jAo4rBQYRY4gZtyTQgQQhxBJAMwQB4MUcPTWBjQI0QwJJZkYYgoOg/qK6NyyQfm7GOJUyP2lmkHougbw4T2bro1DB0KR2sqqVQF7CIFTwcsKFsYM5IIDnAA6Fo2/rPeMcOV9euHUwtNwob1gRTxwXYRhKEkh9Pv0pozicdiUoZhzuPPtCsJh9kEqgmZJA8nlZO1j2HKSETK0EclzToMJMAxhH+pWiLpLGKjaRBCoW5YSxvb1pRvtsYWGx/2HsYGoSLO1gaTA041w1JHEk6sKnM4EoqW0Hy2n7juCpHczzJyWBdu7cTySQIrNZHCMsV6wSaA7DLE4KbprBgpFhdV+WBNLH2nQkkL5O6QmbPHb1iJKylPwBlB0sjuDwGFsDjvFiOxbvfFTuWwMlkCWBLCzmL/T3EefM2MH0V066AAq4LgEV0o0SMWZJoPkOIQT+8R//0dz+67/+64Z2poONiy++GMcdd5y5/cEPfhSnv/ysjIJFgHNeQwIJCAghJhAxAo2Jmb1FNixaw6iCpsl00hXxRIVCA3KQqjOBPJaAq+DlmLHaYFyPgjgEDgi87ARXMORyjZVAWTtYHCWglIC6LoKwOtEOBmkHy+WnHgTEnJvAMAuLA4Wc68zYDhY0sIMRqi9wsulgSiVQTUW8vhCmpJHOTjDtYBBpMLRfSwK1tdkmHwuLQwWdu6IVElk7GKVpcQQR0jYDIGMHq28Hk+e0EMLYwdzc5HawXfuLBNJKoCSRJJDNBJqzyF6H9LEZjsmsJ3DAE/I4bcppO9jU7WCpEqj2p6dmNVSNGcOMEoixBJTFiImDofYVKATy/bOZQLodzJJAFhbzF/r7CFklkHpMt2QyFQxNOEPMhWwlpMQoZecDLAlUhyAI8KY3vQmdnZ2Heldq8MY3vhFr1qwxt//1X/8JD957DwCp8CFETtYoyZBASuINMlEDJJ+wFzvQ4Lmixg4mTPuQCdaZAjoY2qGNlUBuwqRMjzqIOUeUVS94jrSDEQIvc/EmgpsLt+PQVCU0IRhatUg4HsIwUEqgWkRxNK0SKFHB1RYWBxJ51zXV79MhtYNNVAJxlsBx6LQV8ayuIp5zZs6PdFs6GJo0UALJn1YJZGFx6GAygWJNAmWCoVVAbxQloDxBbESzKhg6kwkURwk8vzYYWlbEe6AN2sEAqQTyfQ+dnS0v6DNYJdD8gRmX8tQOFpXH5YNMwFMTr9QONkUmEGMgE+xg8qdeGHTU7ShMM4EAgCYxhJcDczzTjKf3LZ8vmmbb6TIhLSws5i5SJVAaDK1bgYSOO+AcrieVQIkQZh46n9RAlgSqQ5Ik+I//+A985CMfmfDY8uXLcdttt2Hjxo247bbbsGzZMlBKsWXLFgBAa2srGGM466yzAAB33303jjzySJRKJVxzzTXYtGkTNm7ciDe96U0AgCuvvBLr1q3Do48+is9+9rPmfb70pS/hsccew8aNG/HVr34VZ5xxBi666CJ89atfxYYNG7Bq1Sp88e//Gedf+DoAwG9uvxUf//jf4Kabf47f3H4rjj12NQDZtvajH/03fvTDG/CVL/09br/j12hr78AE2csMIDBR9WMygai2g8lw5+kpIGUHIwSEOrVKIG0HYwk4k0ob2Q6WmQS7KhMIpGayS3maCUQpTfOCMkogz3PAVGWotIMFdUog+Z9oBplAMbNKIIsDj32xg9UqgeSxzxIG152aBHJrKuJ5zc/stsxjyARDq3PPKoEsLA49dCaQIYFQmwnEWEoCJeq6p8N469vBcjoTiGfsYH7WDlYfDD0A4IXXxOt2MJbITCBLAs1d6GtHNhg6HpdNlFQVDgBASTfRTWcHqwuG5pwBQsCnmgSSx7I+5s3iH4tA/AKY45sYAKoW80qldOHCKoEsLOYv0u+jjBKogR3McwmIYEhULhAwv0igWdUOlsXFH/8wFh979H7d5s7fP4Ub/uHr0z7vW9/6FjZt2oR/+Id/qLn/X//1X/G9730P3/ve93D55ZfjX/7lX3DJJZfgySefxJo1a3DEEUfg4YcfxllnnYUHH3wQS5cuxZYtW/DlL38ZIyMjePGLXwwAaGtrAwB86lOfwtDQECil+M1vfoMTTjgB27dvxyWXXIJjjz0WgCSWRkZGcOONN+Lmm2/G9ddfD2AiyTIwMIDXXvh6vOOd78BHPvJ/8OUv/SPe//4P4d5778HPfvY9HHPcGXj7n75NPTsNjn4hqM0EMv/VadRTvpYSSSzp1hEN5mSVQBye4yBm3IRWOpSmwdCEoJCd7ApeEwytSSDBau1gmgRy3IwSiOgPIH/EyfTtYAnn8Bx/yudYWLxQ7E0wdNQgGFpL3XmSwMnRKe1grtsoEyhVAulVUm0HExzm3NHncqkkB89WCWRhcejgGCWQPJ9lMLRSQ9C0Ip5yF5rCSRoogaIogZ/LgwMqQFpnAnlw6ORKIECSQJs3P7fvn8EogRIE5Qqau2aXQttierzqlRfhXX/+EfQN9GIQtXawJJAkkBunJOJM7WCU1yqAGGOm6Q4AKJfHZBDGNSQl5QncQisSxzMkkJvTCxiWBLKwOByQRhtklEAizS0DpDKWUAEiBGKBeUkCWSVQA4yNjeF73/sePvjBD9bcf8YZZ+BHP/oRAOD73/8+XvGKVwAA7rnnHqxduxZr167Fl770JbziFa/AaaedhnXr1gEAXvWqV+Fb3/qW2c7w8DAA4C1veQsefvhhbNiwAccffzzWrFmD0dFRBEGA73znO7jkkktQqVRmtM+//OXN4JzjsUcfx4oVKwAAp576EvzP/0jS6M4778Lw8Ih8skl53YtfSl2zGJAmqdfawWamBCIg4EJWUk+mBBKMq2BoeULqE1AHQ/uU1k52OauzgynUV8RnSKAgrCJJGBK1Wqp3Jkpm1g5mK+ItDjRyzt5nAuUyFymiVkYZSyQJOo0dLGET7WBmW5pQUo8xyNYxxuIJwdBWCWRhcehgMoG08hUCQRAZ1QPnWgnEUiWQDtfNZAJFUdI4GFopgZIG3ydZEuiFIEsC2UyguYnj15yE9rYutLbIY0HaweTfNalIEshJUiXQTNvBiJhIAtHMtcoogYKohqR0eAK32AzmeNBrf54innQeEADkLQlkYTFvocfFkgTS80ilABLpLUplJlCiMoEAwHfmz7xv1iqBZqLYOZD4+te/jvXr1+Paa6+d9DlaCXPPPffgve99LxYvXoxPf/rT+NjHPoZzzjkHd999N4AMSZLBypUr8dGPfhSnnXYahoeHce211yKfz4Mxhpe+9KV45Stficsuuwzvf//78cpXvnLCe6tcZYMoCsEFl1JbNXCqsW41VObsAwtU4warI4HUw2ImSiDIfAEdJqvBVDC0y2RFvA6GBqT9quBBJv+5FC6hyGVIIJpRArmuYzKBRMxqlUBJSgJF4yEAoBqENZ8vTmK0NbVO+RlsRbzFwUBur+xgzLxGo0YJ5NAJIa5ZOK47oSKeMZ5mAtXZwfTXmhDMtoNZWMwiuBkrFaCVQLG5FnLOEIYxKCdIoO1gSgmUaQeL4wTFQh5V9TjPBENTEiFpwCnv2rWfSKBsMLS1g81J5PMFDA714Y67f4WVF/+ZageT14pYLXK6cXp9K/kzsIOxxOT5ZIOhtfoHABx1jfqvn6/DPb9+yNzv8kTZwSYqgUqllATKTZMJaWFhMXdhlEAiVQIJnm3WBphqv6WCgwkgTmwm0GGDoaEh/OQnP8EVV1xh7rvvvvtw2WWXAQD+5E/+BPfeey8A4MEHH8TLX/5ycM4RhiEeeeQRvOc978E998jg5ltvvRXvf//7zXba2trQ0tKCcrmMkZER9PT04MILLwQgL0Ktra341a9+hQ9/+MM46aSTAEh1UnNzc2YPiVnFMOwlFzVVl7/73UO46KI3AgDOPusstLW1mtfuK2peWUMCIf1/3fNWrjwar77gkrrtEHAh4E2mBEoYwAUcVREPwNTE62Bon1Lk1cnLIdnanJ835JerVjd5HQnE1coo9aQSCACCiiKB1GviJJ6+HYxxePOIEbaYndinTCBvYjA0Y2zaYGhZEV8buFmrBFLEqnrMtCnwiSSQVQJZWBw6OFoJVEcCaSWQroinghkSSKtuHUpAKQWltEYJJDiH0MHQngeHCDT6OomiBP39o1i0aH8pgWLE1QB+waoz5hryuQKCoIrY2BJpSlCq8ZeTyQRq1setmPw6FSYsQwKlGXaNlECP/34Hrrnm1wCAgueCQBaQJNQBEbrZUl6zCoUsCWSPNQuL+YpsUL1WAmVbAgGpCKJUyEwgSPIZsCTQYYN/+qd/QldXl7n9wQ9+EJdffjk2btyIt7/97fjQhz4EAIiiCNu2bcMDDzwAQCqDmpubsXnzZgDAF77wBbS3t2Pz5s145JFHcO6552LTpk3YsGEDHnvsMVxzzTX47W9/CwBobm7GzTffjI0bN+Kuu+4yAdU//vGP8bGPfQzr16/HqlWrJuwrIbL9R1bEy/v+5Rv/jLPPPhc/+MH/4Lxzz0Hvnl6Uy+MTKuNngnRNJmMHq8kEUnYw+UDNay+5+E/xfz70uZr7KJGhsl5NJhCBoA4YIfCSBLo8XodCp3YwCjgEHqUmEygmSgmUy8FxqPSdk4kkkOc5YIrNdRwfYRAAAKrVsGb/oiSeUTuYY5VAFgcY+RdcEa+DoRPVjjcFCeS6YEwO1o0SiGeUQKRWCUSobNsTIjGZQIYEskogC4tDBh2+yzPB0EGQkkCcsdQOppVAXFfEU/i+urbGaeECU3YwvW2HAGwSwcbOnQNYtB+DoeMwhKcUGxZzB/l8AWEYIFJEj0MdQ1AmgSSB3IwdrKRJoKnsYIzBaaQEEul10tWPZ/KGdOg0czxEhBslkJ9XSqBMJpBtB7OwmL/IBkPra1197AnnQiqBOAcHMu1g82feN2vtYIcKWbVNb29vjTx069atDa1ZALB27Vrz/+uuuw7XXXeduV0ul/Fnf/ZnE15z+eWXN9zWy172sgn33XfffTj++OPN7U9/+mNw++UF9IK152KwMoggTPDYo4/j0je9Ge3tXRgZHcVb3nIJlizpxOJlJ+AVZ56JOIoAvao3hdx2Agzhk70r2w6mHtR19BmiqbOzB76fg+t6JnSWCCn3rc8EAoCEOnAZg6vuidWJp3/CpSYTqKAGiTGFUQI5Dq0ZmfKYw1HMrbSDcQAUru8hjBqTQEkST98OZjOBLA4CZEX83tnBaiviNQkUKyXQVMHQXkYJpDKBMs83djD1GKVUkUBSCeR5rllVsUogC4tDB9MOpr4TCBFKCZTawepJIJ5pB9MS+SiKTT4K5xxcEPiGBBLgSj3r+j4KzU0YG5BWsJ07B/drJlAcymu0m8shCcOpXmYxi5DLFRAEFcRq7Oc4DlyqCEr9N80SNerYmmp8GrGJSiDGGEhWCaSDozMLKE0628rxEPLYjDmLTXLcXyxmlEC+JRwtLOYr0gVNhrzJBKoVSHABFQwt7WBaiODNIyWQJYHmAYgQIAQI1KqKlrQuWrQY119/I3I5D5VqhE9/6tPqBfun1jybCcQzmUAQtadSV2cPABm6Nzo2bJ7HhYDv+4gzUmAAiCmFxxJQUWsDy9rBiEvhUGou6okjSSDfz8lBbkbtIGJZjQ1oO1gCwIfr5lIlUCVQz9bEU4x8vtAwz8nsJ2M2E8jigCPvuTNWAumLVG07mFbETR0MbWwidZlA2XawVEKrLJWOJoE4fN83KiDAZgJZWBxKaLWOVkroinhHT8CVHczhCZgmijPtYJroiWMGX5FATFXE+3l53aXg0N8O513xdrzsTW/A589/IwBg164hHH/88hf0GbKZQHEoxwlezrck0BxCPl/A6Ogw4kj+zRxKDQlEoYLIkwhcyHyO5hkogaKEwUV6fdI/a+xgWsmabR7T5wR1MVQdN3awYrEJrz1mFd63Sir/R0aGkLNKIAuLeQuqiBxZES//T+pIIMYFKFEkEMi8bAezJNAcBan5vySBkiRBwhKTZfPss1tw7rmvwMqVC7C7P0azV8QLj1WskQLJe2QvvPo/5P/rlEAAUCjWkkBMKYGk8iA9FGPqwE0YHFqbU6ADooknlUAA0JLLgVMCQQHBYuSUHSxLAtVmAqWZKI7nm0ygSqV2UBkrS4zv5xCGARpBVsRbEsjiwCGnFD3aizwTVON4UjuY49BJ7WA6UD6tiNcrrOnz69vBjB0MUgmUJYGsEsjC4tDBtIMZJRAQBBEKBXmOphXxPphKBjCZQDVKoAQ5P1UCCU5MoYNIYgj12p4jVtRUuPf3jaCrq+UFfYZ6OxgAeLkcqhh7Qdu1OHjI5/LorVMCaXLPUcNEJ4kQgiCJE6MEmjITiMkjliUpwTOhHUwdyyyZGDrNHA9PP7PF2MEKhRJee8wqnNXm4bdJjLHxEdsOZmExj2FIIMbSTCCTkKMWO0FMO5gkgWwwtMUsAakjY/QKfRBUzWM1CpYMUWOInL2xg2U3k4HMAqJyU0TuV30wdHubHBhm/dYmGNrzzeBAbzumBB5L4Krdq7eDBZyZ0UNbPgfhEAhCAJZk7GC1SiBtB/M8F1xNggUhCNXqlLaD6f2O1aR7qoaI2LaDWRxgaDJnpkog/dzcJHYwqQRqvC3HUSSQOvbTdrDEfL/Ut4NRHTQtakmgIIgmJYFOOfkMfOCv/nafssksLCxmhjQTKCWBoiipCYaOtR1Mf0eIRplACfK5NBNICAJPZygkMRgIXN9HS08XHNcFVd891WpKOO0rHNcFZwyCcyRKCeTm/GleZTGbkM8XVSaQJoFcY1V01TjOTSJUBUE1QwJNIQSSwdBENl5qMMbqMoEmKoHMth0X27bvhFAK8Xy+gI5CAS4hqI6PIAwC2w5mYTGPoa3GnKftYKRuPiczgTio4OCEzsuK+PnzSQ4zkAyBQzKxzdoSBkiCRjTK8qn7OVOIRq9QBFTN+2Qq4il1DAGT9VtTwLSDmZwg9VhIqMwEUkenDobWP4OEQT/Yms+BU0A4BIQl8P2JSiCR8Np2MP0YIQiDrBIopa/0RHiq1aCYceTmESNsMfugj6+ZtoMBcoCcPS6JUtSxeGZKIF6nAGLqvBOcT2gHM5lAdUqgHTsGJrWDnfnyV+JNb3w7Xnnu62f8mSwsLPYOrlJbcE3qsgRCiDQTSAVDE84ydrC0HUyvjsYxl9l9nJvvBG0PA0/ABEGuVERrd7d8TCmRtbo2n9930sbxXKPkyCqBLOYOcvk8qkE1EwydtoPpMZ6bRKhyoBLHGTvY1EogABBJquBmLKnJBEpJoPTaqeMDGPUwPDKIgDMQIVDIF9Gumuei8VEEYRU5e5xZWMxbGCUQZ7XtYEIYIQXXSiDBIcj8tIPNiAR69atfjd///vd46qmn8IlPfGLC429729uwceNGbNy4Eb/97W/x4he/eL/v6OGMlpyPpS3NNffVV7VnlUDp3bw2z7k+gXmvaaAJ7wxRRwJJJZAwz3IyjKnOKqK6sloIeL5vqkM1IkC2g6ndYyrkWUvxqnFSRwIRVTfGTDtYbSYQr7WDqUGlADFWr0ollGSaViMpO9hUvvD+SgVNOR9FNdi2sNjfMEqgeG+UQEldJlB9MPQkJJBWAtUFQ2vlkBBighKIOKptjHD4GRJo585BFAo55HITzw3duvcX7/qobWCxsDhAcHxfTYDVwob6DnF0OxjniEKpBGJ1SiBKUiWQEPoaLkxwtG4LI5yBCyBfKqGlW+apaJKmWpWT/kLhBZBArmsm8YklgeYkCvkCwrBqFvscxzHHpp58eUmEgMtrl8ntmUKprosSrr3mn8199RXxph0syZJAqR1seGgAQ+UqAIG2lhI6FAkUV8cQhlYJZGExn0GoJoHSTCBKau1gOhiacgZOqIkkOaxIIEopvvWtb+HCCy/EmjVr8Na3vhXHHXdczXOeffZZnH322TjxxBPx+c9/Hv/xH/9xwHb4cERrLofuUt0FKev0QmMSiPOsEmg/WC8aCoE0CSTfIyWbUiWQhraDGRKIc3iuZ6xXRgkEwGXM+MWNEkidgOU4NiRQWz4HppVAPGMHq1MC0QZKIEFSEqhaCWscc4l6z6mUQDtGxgEAS1psAK7FgYFu+ZppO5h+bpYE0iv/SSztYHxSO5h8Lz1Y10QPNyRQqgSqbwcDODzfR6mUKoGAxrlAuXweQVBFd/dC/PGbr5jx57KwsJg5XN9DEkfp+a+us9l2sDiUDUmMqHO/RgmkB8lpla4mhPXiCOEMTBC0LeyBrybRXl6TQJK0yeaE7S0cz2ugBLJ2sLkE2Q5WRaz+jg51zLGpiUYniVDhUHYw1eA1FQmkjsO77rzZ3MdYYkig8TCC08AOVszYwYZHBtE7PAYCoLlUMEqgpCJJIJsJZGExf2GU70oJlCQMjqsWLdXck6vSJWLsYPOvIn7aT/LSl74UTz/9NJ599lnEcYwf//jHuPjii2uec//992N4eBgA8MADD2Dp0qUHZGcPBq6++mrs2bMHmzdvPujvffHFF9cQbHfccQdOPfVUUEpASG1uOcmSQOpABdTALo6wZOli3HHHbxs2W4kJ/5kppFLm2WefRWenzPkRyvqVtYOJDCnlZEggbQfTgc86EyhJapVAoZBKoAkV8WqAWg4jEwzdms+BEwAOBeG8YTA06u1guj6e0JpgaEJgyLJEK4GmWA3aPirDKRc3WxLI4sAgzQSaOQkUJKxxRXwcT2kH0xdAbf+aoATi3Gyrvh0M4PC9nJnw7dopa6Lb2iaeG/lcAdt3PId77v01Lnnjn874c1lYWMwcfj6PqBpkzn99zqaZQDxSzUomJyibCaSqupG2qGiLTk5NkB0iwDjQfcQK8777Xwkkr8VxoEigvFUCzRXotlZJAsm/o+u6cD0PLE4MCeSyCBUuUInjVK0zRShQqDLyck662JHNBBoJw2ntYCMjQxgJQhAhkPN8owTiQVkqgaxK1cJi3oIYO5jMBIrjRM1X0+8dIYgkgTiDIM7haQdbsmQJtm3bZm5v374dS5YsmfT5V1xxBX71q1/tn707BPjud7+L17zmNYfkvd/4xjdizZo1E+7XyhmaVfOoC5y8ToqakNUwDEy2eWMhkL6xdyyQTMyZkAydKoH0ljObpU4DEigjPU/bwdLJZyikEshTO63JH83CjgWRCYZuzeXACEAcAirSinjBMicyS9vBPM9NlQ11djBK0s+XqMFEPj+FEkiRQPVWPQuL/QUd8Lx3wdAJchklkG5i0UqgSe1gbq0dzARD84wSyOSJ1LaDEcInZAIBQHt7AyVQTiqBnn3uKTQ3tc74c1lYWMwcXj6POAjNOauVGNTYwRiEmiBzpQTSFpxGSiAIYc57T5FAFAJMEPSsTKvgNUmjM4FeSDh0bSaQCob2LQk0V6Ctv0FQRaIygVzHheN7SKLIEI1uEqHMBKpJkhI1U7SDaVV4tgCBc2YygcbCyBSLNLaDuRgaHsBYGEklvaBoUcctD8oqE8gqgSws5it0EYoQHJ7nIlYFQkSk80A96iaCQ9CUBPIOJxKoUYNLI3UJAJxzzjm44oorGuYGAcC73/1urFu3DuvWrUNXV9de7urBwT333IPBwcEpn3PttdfiyiuvxO23344tW7Zg7dq1uPrqq/H444/j2muvNc+77LLLsGnTJmzevBlf/vKXzf1jY2P4whe+gEceeQT3338/enp6cMYZZ+Ciiy7CV7/6VWzYsAGrVq0CALz5zW/Gz2/7DX52z2+xdu1ZANTfRP0JcsUSvv2j6/CLX9yGTZs24aKLLsLwyAD8HAOlDr7xjX/Ff/3XL/GD7/4nckpGvWz5Mvzgmm/j3rvvwt13343Vq1cDAF7/+tfjgQcewPr16/HrX/8aPT2y2r2jowO33HILbr3xenz9n/6h5pioyQQiAFQmkOaKKKUYHpa/z6Kyg2klEOPSQhLX2U+qnMNLEjhaxq7uT9QJOBaEYEoK1ZTzkciNggoOv4EdDImA41BDBOmBbNYOJjOBAEcNKvR7TTUQ2DGm7GCtVglkcWCw70qglATSk7JUCdSYUNKZQMxUxPOan4Lzhu1gScIlCeRPJIEaKoHyBYRhgDCsglJq8kUsLCz2H/xCHlG1aq7XaSZQeh0UsTq3nTo7GCFGpUGIGixzbjKBdGiuQ+RCVHeWBKpTAr0gO5jbIBjaKoHmDHTmWxgGZnHBdRy4no8kiuH7LgQXcFmMCuMI4gQ0oxSfDKkSKJ2MZSviR4IQHkSNCggASr6PSAAgclw6FkYgQkBk4wOiqmwH8y0JZGExX6EFCpxz+L6LKErguh6k40U3bKvnGhJo/lXEu9M9Yfv27Vi2bJm5vXTpUuzcuXPC80444QR85zvfwYUXXjgpiXLVVVfhqquuAgCsW7duyvf92tfehRNPWjXd7u0VNj7yDD7yke/sl221t7fjvPPOw0UXXYSbbroJZ555Jt71rndh3bp1OPHEE9Hb24uvfOUrOPXUUzE0NIRbb70VF198MW644QY0NTXhgQcewN/+7d/iK1/5Ct797nfji1/8Im688UbcfPPNuP766837uK6LN19wPl5z4YX42//3adxx16tUdo08OoMwxF+/+wpsrw5jfJzhgQcewJo1x8J1GVatOhKXX/5O/Ou//j0++3dfx6tfcwFu+/H38Xdf/Dw+9v/+Dg8/eB9efPyxuPLKK/HKV74S9957L04//XQAUtH18Y9/HB/96Efxmc98Bvfeey+u+sFP8IrTX4LL3/kOs39SHZANhob6mdrBdvbuRD5fMMHQjskEUu1gur1ES3mFQC5J0KJIq3o72FglQCIAHTnLiIDjUlAhUjtYVu3ApB3MVQQPT7g88utIoDYCeJoE4rodbHJJcDVOMFipYolVAlkcIOwbCZSgLTNR0pMyTQJNpgSarCI+VQZl7GCKJCUqE4gQAc/zGiiBJpJAuVweI6PDJr8snysgisIJz7OwsNh3+IU8oiAwxG3SUAnEgDzAHW3Bkd8NSxZ14g/j/WpLE+1gniJuXQeoCIKerB1MqWd1JtD+C4bWSiCbCTRXoC1V1aCSZgI5LlyPgMWKBArk/eWEg2RImyntYNqW4daTQPL1I2GILiGQ1JWOlDwPEQeiKEK5PIaxSCqBePZpUYAwIlOqwC0sLOY2TDsYy9jBHEc6XggF5xwCcm5LBQccZ15WxE9LAq1btw5HH300Vq5ciR07duCyyy7D2972tprnLFu2DD/72c/w9re/HU899dQB29nZhJtuugkAsHnzZuzZswePPvooAOCxxx7DypUrsWLFCtx5553o75cDqR/+8IdYu3YtbrjhBoRhiJtvloF2Dz/8MM4///xJ3+dnP/sZKCF4YtNGfGiFHmilSiBBKT7wib/Biae/DEEQYcmSJViwYCEA4Pnnt2LTpo1YvXopHn3sUSxesgSFYhEnn3Iyvv3NryFJIgiVowNIgu+//uu/sGjRIvi+j2effRYAsHbtWrzpTW8C4OPWX/+mhuQTXNR4zQxBZSriKfoHetHVvRClYl07GJcWEh16q21aA2qSurytBUAaDK1PwF/dtgE8ZngLJPmSQMB1KSj4hIr4sTACkkItCaSVDSB1mUDCVJfqYOjcNAOBHaPj1g42R+E4LnK5HCqV8qHelUlhgqEZw7nnvBbnnv1afPpz75/yNTIYOrVhaftEEkk72KRKIEU46Qwg/bNaHcfI6JBqB1OrtEYJlNrBfM9HqSTPl50mE2iiHSyfLyAIKikJlC9gdGx4mt+EhYXF3sDYwRRxq1s4dTA04yxVzDq1drAPfuD1uO9rPwQAEKJJIG7Oe8/TJBAFE0DH4kWZ992PmUCNgqGtEmjOoJBVArE0E4j7DpI4RlPOA6ry7zseM7iZxY6p28EmZnMwloAKBsY5KlGMZGwcN/7jv9S8rsn3MFQu44tf/qh8T6UEIpn4AMQhgmDqPEgLC4u5DeKkiyGusoO5ricjTiiB4BxMEJ25AtD5mQk0LQnEGMP73/9+3HLLLXAcB9dccw0ef/xxvOc97wEAfPvb38anP/1pdHZ24sorrwQgV5xOO+20F7Rj+0uxc6AQqgEJ59z8X992XdesujVCthKdMWYmX5O9j0MIOOPmeYQoS54AXvemS9He0YGLLroATz21A88++ywKKuAuDEOj0GGcw825oJRidHQUF1x0KQZ3b0NQHjPv9c1vfhP//M//jJtuuglnn302PvvZz5rHUrtXHSbYwXR2kAR1HAwM9KJSGc8EQ2ubl1ICqd8fU6s4vXECOMDKNpkXYpRA6uetd2zEww88ibe8/3K5HSFAXAoHYkI72EgQok0pgUzGgbrgC0LN307bwcxEGLodbOqBwPbRMdsONkfxx2/+c7zhdX+Mt779lYd6VyaFzvYJE4aTTnwZXn7Geen5NglCxmqyElylqEuSaGolUF0mkH7eDTf9EL/63xvwR5+7ykwos+1gjHFQKuDUVcQDtUqg17/+NFx88enI5woIg8AQsDaA08Ji/8PP51EeGcnYwZQSKCOD19fJVAkkv1fyvocXvWg1Robz0MNEwTm4WhxxPfmd4roUTBBQxwFLEjiua5SH+yUTKBsMrZRAnlUCzRloO30QVDJ2MBfcg8oEchGOhfAAjCcJchkl0EzawWrsYJyDco4gYQgZA2UMD/z0f2pe1+R7GKlWcPc9twAAxqIIrQAIT9+35AGLF7eAUgrP8xGrLCMLC4v5A6dRMLRTUG4wCsG5HGera6Kg7rwkgWakafrVr36F1atX46ijjsLf//3fA5Dkz7e//W0AMuuno6MDJ598Mk4++eQXTADNBzz44IM4++yz0dnZCUop3vrWt+Kuu+6a8jVjY2Nobp6oKtGDuJqfQoBDoKm5BYP9/WCM4ZxzzsHKlSvrMnuyWxIoj49j+/YdeP2FF5h7X/ziFwMAWltbsWPHDgDAO9/5TvP43XffjT/5kz+BEALnv/I8dHR0ZLafIYFApA1MpDQQpQ76B3pRqZQb2MGkEihtI5I/d6sWkBVKCaRzCrQdrBInZhAAADEEqOfAAeDXtYONhiHABVzXmaAEknaw2naw1A42fSYQIMOhFzf4m1nMfixatAwLFy6d1Zk0eRMMnaBUaoLjOCgUilO+JkhYTTC0sYNFUwdDO1STQDqjS355jI4OY8szf1AV8bXtYERlDBEqQ96LxTyq1RBBEKFaDWuUQBdd9DJc8a4LpBIorBolUMGSQBYW+x1ePoeoUp1gB3MyFfGpEkgrYNVtIXDGy96AjQ8vQ3NTu3p+RgnkSiLG91xEauI+tGu3eV9gP2UCeTYTaC4jn5fXqiAIjDrcdV24ftoOlozL42Q8ZqhkF0h54+sUAKMez7m1SiAkEYIkQZiwGoJIo+T7GI/S9xhVwdCUpfcdvbITf3b5OWr/7bXJwmI+gjrpddAEQ6t2MEKozMwDBZRogDguGBfgXBx+JNDhhB/96Ee4//77sXr1amzbtg1//ud/vk/b2b17Nz75yU/ijjvuwMaNG7F+/XrceOONU77mxz/+MT72sY9h/fr1JhgaSEkTDWm5koqbm37+c6w58UT8z//8L/7kT/4ETzzxBLKSHZPVo14DAB/7yEdx2ZsvxYP3/xaPPfYYLr74YgDAZz/7Wfz0pz/F3XffbWxsAPC5z30Oa9euxa03/QznnXM2tm7dWrP9bDsY1NsQkqpqtBKoVJoYDO16nrnY6yySX/z05wCAle1KCWTawZQ9JU6MHBgAYqUEcgmQ9/MyWFCduKNhBMLE5HawoDYY2lVqIQ4Bzvm0g4CdY+NY2FyCN488oocLioqUbG/rPMR7MjlymUygppIkRfXPyRAkSU1FvF45T+JoL+1gdcHQWTuYUQLJ7VEqz7dSqYhyWU7WhobGa5RAnV1yv/P5PMIwqLGDWVhY7F/4hUKtHSyqzwTiQKIu3K5SAulGJi7Q1dUFIQhOPulsANoOptrD1HeF7zuI1HYHnt8OIBsMvb8zgcKa7VvMfuhcnSComuPQc71MO1gtCRTM0A4WNVICMQawRJJAdWpYjZLvoZwhgXQwtMtTtU9TzkGhIM+H3CxeILKwsNh3ELXwwbNKINc1djDOmZwy6yZcdY2MGDu8MoEON9TnHTXC5Zdfbv6/detWnHDCCQ0fu+6663DddddNeH1W7XP99debIOj77rsPxx9/vHns3HPPBQCctLAHw0ODOHXNceoR6VPkEBgaGsblF70eowWKp7fsAiDl183NAmeccZohZ75z7dXIRw4KALbv2IE//fP3YGDX8wgr4+b9brzxxoZE1eDgIF796ldjwcpjEJTH8Ffve495TGTyf0DUf7lUArnqpBlQSqCurgUAgBXHrwGg7GCej4QzOEjtYNueex7D1cDYwZKMHSxmDAnnNYOFRHBQT56Ued+H53k1drB6Ekhk2sGiKEMCEWEmwpRShGEwrRJou6qJX9zchK3Do1M+12J2QdsT29o6sad3Ytj9bEA+UxGvSdSmphb09u2a9DVBkqCQUQK5Jhi61uZVj5QESoOga3+y1A5W0w7G4FIBwYBSsWhsIMPDZbRmlEDd3a0QQuaJBEGqBLLZCxYHEgtWrcTS44/Dwzf96lDvykFFGgwtr8+xtoPpTCDGQPW1UNnBdGW34ALNbc0YCIDVx5wEQGb26fPecWSLiu+7iAI5qe7ftgOrIW1oQNYO9sIygeKM3T4OQ2NvtZj9MEqgsAqi2mFdx4XrUlSr4/B9D6wij5/RKII7UzuYbgfLED23/eZGXJo7F0vAJlUCNfke+ssVc3s8jEAh4IgYAkDsFVByCaijWvDsAoWFxbyEQ9Osu7QiXpFAhEIwDg6SUQKlJNBhVRFvceihKzMdYweDVAJpyQ1q43o0JyOvoaLmvvT+fUCjFyolUD0ISSun+wd6Ua2WUVKDgBedK6vuGePwXA9CrT4mSSIT2YXAjtFxLG6Wz9cS9Sf6BrBhVy8A1NjBIs5BlYKHCoZCvmgqP8ejCITXkUBqNTNO0kGttoO5ngvOOajjIAyDaTOBdoyomngbDj3noO2J7e2zVwmUr1ECyWOsqWnqY204CNGSz8FVkz2tBOLK5jWZEkh7pJNJK+KFsZbodjAZDM1BlBKoUCyYyV+9Eqi7uwWcye+KrBLI2sEsDiROf/Mb8eZPf+JQ78ZBh5fPIQ6CjBJIB0OnbV9E88Hqe2bp0i6AyuIJwWsHulk7mOO46BoZATbuQaCyega2SSu5uz/tYJmKeECSQFYJNHeQzzVQAnkeXN8Hi2N4vguugqHHorjODjaTTKB0sePJpx7Dnp1bESYMYZJMogSqtYONRVIJRFmCxM2BOR4KLgF1RM3+W1hYzC8Q0w7G6trB5DhXt4OZpmknqwSyJJDFQUKWX9FkUDYTSOgWrprXyPuEEBneJkvU6GqxvWODRP0OodYiYjaLiUqgcqWMglJe5FVwNadEZQKpyWYSm+DJHWNj5vNqO9hVv9uIV1wlG0uySqBYcFDV6kU4l5kpCUesFENSCeRkgqHl9sJM4J8hgVwXgnEQIpvDpm8Hk0qgpTYces5BK4Fmsx0snwmGLhkSaGo72KKjTgAlBIuaVQaXIoFEIo/3SZVAOhxWDbC19UP/lJlAde1gqiJeXxOLpVolUDYTqKurBYzLb6owrNpgaIuDgnxTCV4+ZwKRDxf4+TyiakoCJXXtYJwzOKb5RJ77y5d3AwTo2zMMxihcl8GNA5TG+yF4RglEXRz/9FPgv3wKYSC/V4Z27wGLE0PShGEMzvl+C4YGZE28VQLNHWirb5ghgVzHlXYwXRGv28GiBNUkqwSaPBPIBLTWET1510ntYM5Eo0PJ81CO6+xgAAhjigTykXcIHKqUQFalamExL6EXPTlLM4FcbQcjsh1MZJVAxg7G55UdbP58knmKbB4QrZHz1CmBamrasyRQAyVQ3c+9Qb3mR/BUCWTioNV/XE/W7Q2PDKJSKZsMFk0CgTrwPM/kEMRJbAaZO0ZTm1rcYNKavS9IGNycsnEJJkkgxhFxjjBhkyqBoiQdDOhMIM9zIAQHdeiMlEDaDmaVQHMPxTmhBHIQJQxcCKMA0oqgyeC09gBIj0lP5RqwZGo72GTtYKkSKBsMrXzSqiJez68L+XxDJZDjUHR2pkqgwGYCWRwk5IrSkuIXD5/jzPV9UMeRJNCkdjAOqhduVNDzsmVSCbT12T1gjKKltYrOJ+/Bix67uVYJRF0UqwHABEKVATba2y+VOpng5iCIX6AdLM0EAqwSaK5BkyhBmB6HruvC9WQmUC7ngZYjxG4OURKjGu27HUzedk0wNKXEqGE1mnyvVgmkgqHBORLHB3Nc0CQxSqDp4gAsLCzmJoiTRhsYJRB1Vc6tVAJxQRpmAuWmaPSea7Ak0CxHlvhxSJ0SSAjoP+FkdjBTJU0gL3bI3Le3aPC6mkwgxf4IyZ+iVGxCnEQQQqBaLSOfL4BSx5BAwpEVnHrFJ4kjo0LQChsAiHlj+0qgBoflMIKvBpqEc+SVEijiqipUUPDwvAmZQGFUrwQScFwXnHEQ6iAMqtMOAkbDCGNhhMVWCTTnUMhkAs1W6EEtpY6xr02nBIrzkvw5olN+Ljencj5U1s90wdA6oF2HP+uJn1T9pZcMzlhGCaTk84WUBBoZLhsSqLNT7jNjSgkUVBFaEsjiIECTP7nDiATylII1aweL6uxgnDM4+pKuzv1ly7oAQrBtax8Yo3BcDr77GbgsBlFlCXobxVDm6bGyPI9H+/omkDSVSvjCg6Fr7GCRJYHmEPL5AuI4AmNJTTB0th0sP1DBWFMPGGM1SqCZ2cEmKoFCxtLHMyQRIUBTzkc5M+7TdjABgsTLgVEPJGagRglkSSALi/kIY4tmTGbbRdIOBjXOFaxWCeR48joWz7Ng6PnzSeYpaI0SSBE+KnyZIyVgakmgiXawxplA+0ELVJcJpJVAjuMil8ub9q1yWSp7isVSarFyXLiuB32t37LtKfz8hh8AqFUCmdraOmhL2HgQAY76PQiGQr6UsYMxUA4I0QxPWca0EmgCCQQ5EeacgToUQRjMSA68c3QMS60SaM5BK4E6ZrUSyEXI0lBoAMYWNhmSggxUX9HZAQBwPTlp4nVZP/VwlHyemYr4dOUfqLWD6cdlMDTXDdPoXrQY5bI853fvHkJ7exNyOQ9dqhmM81QJVNUkkJXcWxxA5AqaBCoe4j05ePCVBUsGQ9e2g2Ur4lMSSCuBusEJ0N83CpZQOJSjQ0hSlyIlhAlxUFTXTz6uSaABxEEtCVSthi+wIt6zSqA5jHy+YBSf+jj0PB+OUgKViIPCeITRloWSBMr8radqB9NKoPpsjrzrIlCZQEAtSVT05GJIvRIIetky54E7HkicpIsa9tpkYTEvYca7vNYOphuvOWfgBEYJRNU1UtrB5o+1fP5omuYpapRANZlAsoSLTksCKTtYtjY+De/Ze0zggERqETHbT/chVLkf1WoZgFQHGRJIyeu0Emjrzudw5/evBlCnBJpk0qpXe8aqIaCYWcq5rCVlAhHjiJIERHAABKWSyifRdrBMJlC1GqlMIEdlAlGEYXXaCTcAbB8dt3awOQZKHaNAmd1KIKcmFBqYPhjaKTaDjTtY3i7JIFdlAkEp6iZVAumLYqYinmdWY7N2MEAq6oiqiNcLrsWmklECbdvWD0CGzXZ3SxLouecGAKxAGFbBOUMURVYJZHFAkSspO1jh8DnOtBIoqgZGARvrTCCdhcA5XBAIQkxd7rLl3eBDFSRRgiQh8JIqupSCigKmxIEQioIigcZ27MaWYAOSKEIUBPALqXqiWo2Qf8EV8ZlMoMCSQHMJ+XwBgVKMGRLI9eB6LpIoRqd8CGMtC8B4UhMMPZUdLGqg9NG3Q5UJVP94SZFA5QnB0LIplpRcJGMECFimHcwqgSws5iO0HUxwkQmGdgEmW3ClEp4aJRD1NAlkg6HnLZYuXYrbb78djz/+OB599FF88IMfPKjv/5ZL34RXnHqKuX3HHXfg1FNPNbdpnZyHCwFodZDIKIaoeYqBbBQTuOmBdWhra9un/dM2r5r71Jtce+21eNWFrzPPBIBqUDETykpFkkCFYgk5lRmgmVVNSvGMFLjWDjYJCaQms6Plao0SKJ+rt4PJACVN6GglUJBRAgn1HEcxwdShCIKZKYF2jI5hibWDzSkUCqkqYLZnAgWZUGgAaCpNbQdrampB5JcMMemq2mc9mZquIt7Ywbioea4+LzS0Yo4xDsfVAfUEYSTPy+3bJQm0bFkXurslIfX0U73qtfI9wrBqG1gsDii0AuhwsoNp9VMcBPAVCRypFi+TCcQZXEEgHMfct2xZFxLO4VACxin8kSGzTUkCAYCAw4Gc+m7YtfEJXPlnfynfr6Ed7IUogersYJENhp5LyOfyCAJZyd6oHayjzMEJMNbUjSRJ6uxgkwdDA5ANYA2VQIkZG+Yz2R1NviaB0nFfzDggpO2jqZsjEAMQEQNjkdr/w+c7w8LicAKl8ruBsyStiM/M/wTnEISY+BCqFPWyIn7+UCfz55PsByRJgr/+67/GmjVrcPrpp+Ov/uqvcNxxxx209/+jN70JZ5x8cg3RoomfhHGTCaTv45lmLgLZgtK2cEFDJVB2o6liZy+lQKJuQ6jNF0qDoeV9Y2Oj5rFyRdnBCiXk9CBRK4HUc7IrftMFQwMwkt+RSpAqgQSXbUMJR8i4GQxQnk6kBdMkUFD3WTgc1wFnDITqYOjpJ6h7xstYUCpN+zyL2QPdDBbH0axWAuVdF2GS1JJA0yiBSqVmRH4Ji5rkZ6xXAk1uB5tYET/RDpbNBOIgKhNIj7UpgCiRr9FKoCwJJJVAQEuL3KcgqCKfP3xsOhYHHzoTyD+M7GBZJZCvMsGiSAdD61YUDpcoEoikJBBjHL7rgYDAGR4021TrLCBUwFFqPwBwM18n9UqdajXavxXxVgk0p5DPFxFqJZAal0o7mIskjtEymmC05ENQF4wxk/MIGMH2pAgTNqEdLKfsYFEyMTOoSV0Hs3YwQF7XBAH8VoK2BQKIGEZH5CKkb481C4t5icbB0I4Z58p2MJrJBEpJIKsEmqfYvXs3NmzYAAAYHx/HE088gSVLlkx43rXXXosrr7wSt99+O7Zs2YK1a9fi6quvxuOPP45rr73WPO+yyy7Dpk2bsHnzZnz5y18294+NjeELX/gCHnnkEdx///3o6enBGWecgfMvfC0+9P8+jQ0bNmDVqlUAgEsuvRT/efOvcMNv78dLTj8DALBmzRr8582/wi/uuBs33HwDlh1xhCSBmpvQ1NEOQin+5m8+h9/+9n5s2rQZf/EXH6yxg73znW/H/97wU6xf9xBWr14NACgWi7j66qvx0EMPYf369bjooosAACtWrMDdd9+Nhx9+GLf94ga89LRUmfTNb34T6357H7575b9jwYIF5n7NC42Pj5j7jB2s1IRcXl6MqWot0mRUkmnrGqwGqCpSaLJMIC35HR6rpEogPlEJBEiFkJ746x3MZgLJuwVcxwHncnIrK+KnXwkaCkL4roOCZ92VcwU6D2jXru1obWk3K+GzDXpQq4mfarUybTB0qdSM0C9hgVqBd5UMXgsJJ7ODORMq4uvtYKLWDsY5qOOAJQzqLUAEQZzI12zfLgmfpUu7TCbQju3DAIDOTjkhD8KqtYNZHFBoBVC+dPiQQDoTKA4C+Eo5E6vJbzYTyCNUKYEcNDcX0NpakoNcdUJveeAxMzF3CCBjCAW8IF1A8TIq5Pp2sGr1BQZD12UCJVYJNKcgcyHrM4E8UMcBjSIUR2IMKpKQsVo7GJuiIh5Awxr4/BR2sKI/0Q4GSMWrAAEKLjoWtoEIYGxILmBaJZCFxfxENhjaKIEcXRGv2sFADBtNXA+UOjYT6GDhr973f3HUkcfu120+veX3+Na//f2MnrtixQqcfPLJePDBBxs+3t7ejvPOOw8XXXQRbrrpJpx55pl417vehXXr1uHEE09Eb28vvvKVr+DUU0/F0NAQbr31Vlx88cW44YYb0NTUhAceeAB/+7d/i6985St497vfjS9+8Yu445b/xf23/wb//r3vq8A6wHU9vPP1F+KUs9birz72Mfzg5l/giivehR9ffRW++6Mfom3REWgJhkFIelC7nocrr/xn3HvvRjDG8dBD9+GYo1djy/rHAQCDg4N4zcVvxpteez4++tGP4t3vfjc+9alP4fbbb8cVV1yB1tZWPPTQQ7jtttvQ29uL888/H2EY4qVnnYd//+bXcMvNN+CSSy7B6tWr8dIzX45jXnQ8bvufG3HbzT8HAIyMDiGMAlM1DaTB0KWmFnhKNmDsYGKiHQyQaqCjOtsnJ4HUZHZovGpYXSo4crkCBJOS4BolUEFatlI7WFizPT2pFZyDKiWQJgumwrAaELflczXBhhazF5oQ3LnreSxfvgotLW0Yzqx6zxbk6zKB9vTuNP9vbS1h8eIOPPHENvN8SimKxRIiv4gFimx1lZpNK1intYOp83D9+i24557HzONCcBBKcPkpJ+AdJx2PuzPtYK6r7ZgEsQp/rlZD9PePSotJwjA0NI6REXnOdXfLczEIZka0WljsCwghh2VFfI0SSE1+w7C+HYzDIwTCoSCUYtmybgBAnDDkFAnUHIZ4eHgAL1u6GG5GCeQG6bXTyyg24iBEKWM3r1YjtLTsO/lmlUBzGzXB0GoBwfd8ADGOpARUAP35dPFhpu1ggK5qrlMCOY4Khp4YHK3tYOMTFv9UC1DeBZSVeXxwDEIw2w5mYTFPQeuUQEmcpMHQlIAzptrBVEsuofD9HCLG0JqfP9eg2bn8fYhRKpVw/fXX48Mf/jDGxsYaPuemm24CAGzevBl79uzBo48+CiEEHnvsMaxcuRKnnXYa7rzzTvT394Mxhh/+8IdYu3YtACAMQ9x8880AgIcffhgrV64EIPM0gFoJ669uvAEAsHHDBixetly+Zt1DuPwDH8L7PvghLF68WDZwZfI6XM/F+ee/Fg8++KBSFR2No488Mt3mr/4XALB+w3rz3hdccAH+5m/+Bhs2bMCdd96JfD6P5cuXw/M8XHXVVdi0aROu/rdvYvUxRwMA1q5di+uuuw4sSbCnrw933HkHAGkHE0mM0dHhmt9XRSmBWlrb4SjlD9GtRVqhENeu0OwYHUfMGqsWgLQdbGi0DLgq5yAK4ft5qQRizOQJUc5QLKrcHkU6VcM6EkjZwTQZ9NxzT6FYLGHpkpWT7gMAjFTldtpsiOCcga5b37HzeQBAe1vXodydSaEzDrQdbM+enSgpJdDnPvc23H3Pl2uerz9XlCvBowRdxYJRQmg5/nQV8YzJ8/D7378Dr73ws+ZxHQz90qWLcOaKpfCiCMSptYNxThBlOKbt2/uxdFk3urpb0dc3gkpFnrM9C1ISqGBJIIsDhGwY9OHVDiY/dxSkJFCkJr86GJpxBp9SCNeB5+awYoVU80axUgIJgWVFD4/19mOgWjV2dEJQQwLlMsPIeiXQfskEyowL4iiEZ5VAcwa1JJCyg6kYgBf78qLRp+yKSZLULKJNFQwNyEXARplAUgmk2sFqMoHkcTNRCSSzP0jBA3y5vfGhCgjhyOcL6OpqwamnHrUXn9rCwmK2I7VFp0ogSmmNHQwkrYgXhML3/HlXET9rlUAzVezsb7iui+uvvx4//OEP8fOf/3zS54WKQOCcm//r267r1qhg6hFnJa+MmcmXtmtkL2yxGrgFUWye9/Pr/xu7H38cLzrrbFx9zbfxhb/+CB56+H4zuFt5xBH40z+9AieffAoGBgZx443/Lb3N6poaqP2tfW+CSy+9FE8++WTNvn7mM5/Bnj17cOKJJ6Jn+VHY/vTj5rFsBT1AwJQlqzWfQ3+lWrOdqgqGbmlrgyMyQVsM0GryiUqgsUlVQEBqBxscTe1gSViRJBAbkY8riwsRzEyQdQhRvRKIcw6POibrZP0jDwAATj75dGzf8dyk+zEcaBJo/rDD8x26cn2nJoHaO/Dsc4dwhyZB3nUwVA2MBay3dyeOPfYEAMDpZxyLzs4WFIs508ilVUKhL4/1pS3NyKkQbBUfNm1F/GTfXboJsEO1/7SVy6DUAetTZgAAdn1JREFUQZJw+DmtLiBgIr1AbtvWj+XLu9HXN4L+/lFQdT4uWiQzgoKwOiO1nYXFviCr/jmclEC+uhbFNZlAWgmUroD6lIK4FIRQHHO0tKAHcQzfceHFVbR4Lh7vG8ArViyFSwgAAkJqlUB+xmoeBUGdHWzfM4EcTUpbJdCcRT5fMA2x2g7m+z4Wox/ntzYhXFxChQNtgGoHm1lFPNBYCZT33BolUG4GSiDOOQQFQo/CVyRQdbiCUjNHzs/hk598M9717gvQ2vLHe/8LsLCwmJVIlUBMKoFUlqVe7OSMQxDHKIEEofB8f97ZweYPnbWfcPXVV+OJJ57A1772tRe0nQcffBBnn302Ojs7QSnFW9/6Vtx1112TPp8Sgkq5jGKpqSbsTq+e+EoySyBJnu1bt+Kqb/8bbv/NnTjquDUgIl3ha21tQ7VawfDwMHp6evDyl5+ttqYYTVFzEwBwyy234AMf+IC5fdJJJ6lttWLXrl0QQuAtl77RkEZ33303LrvsMlAC9HR145yzz4YgUtXTiAzRSqCm5lbZ1oVsJpAES2pXaO59fjse3rln0t9ZoC70/SPjxusSh1XklBIoZMzU+snqeLUSrIglXV2qIRVAFJwzEEqwc+fz2NO7E6ecdPqk+wAAQ8YOZpVAcwVFowTaCmAuKIGaUK2WMTwyhKZSC1zXwdFHHYPe3U3o6Wkzz9eKoUiRQItbmkxlM3WmVgI5jisHxJMMvrUdTB/nrdUKqEOxY8cAWpqVqo8TMJJ+f23f1o+lSzvRrZRAMmidY+myDgBAULXB0BYHDtlGsMOpHczYwYKJdjAnswKaow6IJ78XVq5cCcYYwiiB73koVqQ99vHefvSVq4oEAkAF3DBASAngUeQzOWH1JE1QjWacCdS5dAledN5ac9tRGXs1mUB1SiOL2Y1croBqnRKIAHjjts1ociiqL18MplbaGWNG3Q3MpB2sVgmkCaGQJWZs2DATKK5XAknbR4W6RgkUjAZwHIFcroAjVi1Ec3PRlBtYWFjMfej5ss4E0l89xg6m2m95rDIyqQPP81U7mCWB5iXOPPNMvOMd78B5552HDRs2YMOGDbjwwgv3aVu7d+/GJz/5Sdxxxx3YuHEj1q9fjxtvvHHS53uU4tYb/gfveN9f4ua77jbB0JQSCACtbXJS51CCSy65FD+5/S785u7fYtWqI3DT9T8FMiTQU1uewh/+8AQ2b34U11xzDR7Z+DBIlvFpwAJ9/vOfh+d5Jsj685//PADgyiuvxDvf+U7cf//9OPKIIzBelmTOz3/+czz11FNY98CD+PtPfxr33HsvhAASl6I559fW2UOGPkdRiKaWFmMHgw7m0jaVujyd7/xuE8675seT/s4iddaOV0PTFMbCAL6v2sESBuKrCbBgyOfl75Cow75aRwJxkwkkzO9ywyMP4KQTX2YGMI0wYpVAcw4FnQm0U+bpzNaGMN9xEKqK+PHyOMbHR+E4Dk45ZTX69vTgiccWYfHibvN8bXnUJNCSlmZjDdHn5FSZQFMpGPUKiVECVaoglGLTpudAqSKYOYGgqcB027Y+dHa2YPnybvT3jcq8LpFg6VL5+5btYIfP5Nzi4CJrATu87GBpJpCnyJQorFcCyUk09eX3wqJFS7Fr1xAY5/AcB8WKrId/vHcAA5UqPAoYJVAYoOI6QM5FnmaUyy8gGPoVf/Jm/Ok//J257ahcoholUBjB9f0pr8cWhwYLFrTh5S+vbdMt5AtpO5g67nLBOOjDO/DLwWE4i1qQJJoEkn9nXQgyrR2sbjKmCaEgYYi0HWwG7WCMSTtY2fFAFAkUjlfhekAun8eyZXKBSP+0sLCY+yAkXQzxfWkHA9JxruAClFLwOKMEUiSQtYPNU/z2t7+d0eDi8ssvN//funUrTjjhhIaPXXfddbjuuusmvL65Oa14vv7663H99dejyfew8Xfr8IZXvByuQ/HM7j6ce+65WNbajK5SESMjw3jD6aeBEoorv/E1/Pe//xueGxtH18KVyFeG4boUlBJQxkBzDj73uU/g4YefBgCceNIqRMIBgcAbTj8NA/BBC814eP16nHvuuQCAIAjw3ve+d8K+Pv300zjxxBMBAF1LjsDf/X2aQfKBD3wAuVIR3SuWo/fZrVixtB2eR0Bigpacb2xSGpVKGaVSsyGBuLaNaJtKXCvTnQ7aDhYkCcYqIdoBJFEAv71LkUAJXEUCEc6M4sBVk9QwnGgHcxwXIuRmoLxhwwN4zQVvwhErj8Ezz/6h4X7oz9n6ArIPLA4utBKot28X4jhCe/vsJIGymUDl8hjGy7K15GUvfRGqVQ8QBMcduwb33fcoAKCppEmgArgAlrQ0gRcLQDh1MHRzcyvWHHdiTUNfPQRXSiA1wWwPqqCOg02bngVRJBDnBNxJJ326Iay9vQn9/aPI57rAWGxCaMOwqtRBFhb7H7nS4UoCFcA5RxJFph0sUmUTOguBcY6C50KoUWBnRw927twOwgU830UhGMVIGKG3XEF/pQKvTgk05hK05xwUnDoSKFebCTRTO1hTexu8XA5+oYCoWjV2MF5DAslrrZvzEdeNLywOLT760TfhfX/5WrQ0vwVcqXhkO1gFQBoMvWz7esCluG5wAOf7bqoEUn/nSpyg4HnT2sHCJKlR+uTV8RIkSWoHcyfawSp1SqCYJYjhIKBpJlA8HsF1BYqFIpYtk+fP8uXdWL9+y97+WiwsLGYhSI0dzDVktFa8C85BHAc8YaAABKXwfd9WxFscGHiKdCjHMVxKTQgjJUSSJOovRQlJV/TVNVKXKLRWqugYG61V/QAZ5Y++KRrePz0E6jkyvS1CCIQQ4C5FwnnD9PRyZRylYhOotqUp9kfvRX0w9HTQkt9qkmC8KlebWBzC86QdrJowuDmVhyJUdTwAVw2CKxPsYAyUOuCMmVWrDRtlO9zJU1jCsu1gFnMDxWIJjCUIwwDDw4Non6VKINkOxtBUasb4+CjGx2VQ/UknHYNqRQ5qj1yVtihqO1ilWkWZuEoJpNRwk1TEH3P08bj2qptxwotOxXe/9y+T7osOzOswJFAASin27BnGwNA4AAEuCISbkkDbtvWZ//f1jSCXLyCOQ3R1tcBxKKqBtYNZHDgcrplAXj6HWF2XtBIoVKRJNgsh57oQjgBjIZqaOrBjxwASzuE5Lvyogp1jUvkrlUAEhEglkBcGCFwK+HUkUBCCOo4hcKrVCJ7nwpnBymmhVeaeldqk7UbbwbLjgkS3pvr2WjvbsGBhG4rFHFau7AEg7cWe58viEqR2sNaxXrCzj0aSI/B9F7r7g6n/6HDo6drBZEV8AxIoZmaB0Fc5d69ffSTe+uI16C9XJgx7d4yM4g8xQ4iUBEqqMSgVaG5O7dZ64cLCwmLuQy+GQGXUajI6zQRiIISkdjDiwPdsJpDFAYKrDiotVdW5QJIEIiY12iHEXExNlg7nQMKRiyNQADRhNbkeQiixjeF+9pb8Qeb9JrBA8l5lWyMEGA1CtDQIb6xWyygWm0wmEE+ZLfk59pIECpMEnAvEjGO0LAcaPArheTmAcQRxAk+rfxBLmxjSTIRqtTa8mjOpAOKcGxKor283tm1/FiefPDkJFDOOchTbTKA5hEKhhIoKKx8aHpgjSiBpBwOA49YcjTCQJNCSxWnznyaBevt2oUJ9LGlpNhMmQhvbwd7wuj9GPl/Ee//qj/DT67876b5oeWyzUhZ0hKE5T7Y81w9KBTivJ4H6zf91JlAUy8loZ2eLVAJZO5jFAYJW/1THxg+rTCA/n0ekFkY81cIURnWZQJyj4LpgBIiTMfh+C3buGAATAp7jSBJoVJLOfZUqKADPdQFlB6s6BCTnoqTanoBUqaMtYdWqJG1m0hBWbJEkULFN/pxKCWRzgWYfOjvl32316iUAYBSeWgm0RJF8Iy0LwV60FC1FH56XUQLtrR0sYXVKIPn/iNUqgd5z2kn42dsuAeMcb//vX0zYDmMcAePYU/XwyGOyKIJVYziOQKs6JgFrB7OwmE/Qbg+qFfKJdqjIAhQdD6JJIG0Hq8SxURXOB1gSaJbAoxRCCCNV1SsclBBJkmhlEE2VQHqhJGEcEEJm6wjAiZIaLZBALQukSaC9poIaXJQNoUQIIAQIIQgSBs+h9XQRKpUyCvkCHFXPxXUt2CSZQNMhSNIgQU0CiSSC5/gAEwhZAk81I3lOaEggV60OVaNaJVAcJ3BcFw64WS0FgA2PPIiTT3zZlJPV4SCwSqA5hGKxZMLKh4YG0N4+Owd4uvI2tYPJSdnCBSugCdmOjqXm+UVlB+vr343AK2BpWzqI1Yd0vRKou3sRdux4Dk9veWLKfRFCKgQAoK9cQWscGQXjlm1DIFRAcALipefB9u0pCdTfLzOBdGVwV1cLgqAKx3HgefPnomoxe5BTeVhj/QOHlR3My5BAvjq3wkDbwZQSiDHkXQeMAAJVcJbHjh0DYJzDdSi8qIwdI5J0HihXQSDgOy6ISOAmEQKPAL6DUqaGO66G5v0BmNbCmeQCFeuUQNTVSqDaTCAA8HxbEz/b0NEhrz2rV8vrUV6NvXQm0KuPkTmX25a/BFxQdLTJc5PXK4F0vqOYOhg6Ygw5Jz32NCEUJKkSKOc6OH3ZImwfGcNJ3/oufvPM1gnbEYJDMI5NQwW87rJ/1hsHdThKTWlz5bLlVglkYTFfQNRiiKaRa5RAlEBoUUDC5YyVEHiej/5KFZ7jNHS7zEVYEmiWwKMUMecTqi0dQuQKvuJLHEKMrUNTMnrFhJd8BJzBjRPESTYIGjXGK/PIPiqCshA8awcDQIipdXdp7eFVqYwjnyuAahubPv20vS3au0yga9dvxgd+cRsAYGRcTix5HMFXSoQgTpArSGVEzk3QolZ1JlMCVauhDOEukFQqCOA3t9+EYrGEs9e+ZtJ9GQlCqwSaQygWSqgqJdDw8ADa2joO8R5NhEMJXIdKO1hTM8bLYygrEqhakYNT1xtHsdBj1IFNpSYkSYyhoQFEuSYsbi6ZE55OogTq6V6I3r7d0+6PEByeGmg/vHMPKIButfHnd5eNEojkUrI0ihLs2SMDZvv6RpHP5815193dYgghawmzOBDQmUBjA4OHlR3ML+RTO5hpB2uUCeSBEaBYZAhDV5JAGSXQrrFxAEB/pQoipELZjeV2Q5cCOQclbyolkLw9k1wgTQJpRZDbMBjaKoFmKzo75VhLK4FySgmk28GO6GgHACReAZwTdLXLc1PbvhKjBJqZHSxirKZJN9cgEyjvuljU3ITnR0bNuLQeQnCTYURKBSSMg8QcDhUoFAoQMcNYf2iVQBYW8wh6od8ogRQZbexgnIE4FCLh4GrO7Xk++stS2dg1T8YTlgSaJXAdioRxcCGQcJ7awSitsYNRQuCApAEfAALGwQjgNHmocg4SMwQZUY2o0/zsqx0MAOpDgeozgQgBYk0COfUkUBn5XB5ELf2wRF2UqVYC7Z0d7LHefnz/kccAAGPjVQgCCBbDV4PcIE7gqwBgFwxNTXJwqRsl6tvBqtUQQgAdBWKsMwCwafPv8Pzzz+D1r33LpPsyHITzhhk+HFCoUwJ1zEIlUDboskkrgZQdbHxMVbJjGyj1sWihXH3VtrFyeQys0IIW34ejrm6O05gE6u5ZhN6+XdPuj+DCKIEe3iFJox713bKtP2hIAgFpOHRf3whyuQLKZTmx7O5uNSRQzoZDWxwAaOJntK//8LKDFfKIFAnkTqYE4lIJlECgo8NBHLnYvXtEZvo5BBTCZAL1V6QSyHNdeKEcBIcuAXJujTTekDS5ejvY1ModQggKLZJEKOpMIFdXxMtxwaJFHTjjpKUABFyrBJp10Hawo49ZDADIq+tAGFbhORRLW1NVKmcU3Z1ybFZvB6soEmhGdrBGmUBJgjDTDrakucmQmY0guIBQY9ZCczPGowiUcVCHw3Nz4Pc+D+8/12P58oVoaWmfya/CwsJiloMat43+/qnLBOJcLpiECRI17/X9HPrKSsk+T8YTlgSaJdBKIACIMhc3R6uAaEoCeYQqb4e8byiMsRvSipVvloMvkSWBJuRE7ysJJCZYvFBPAoFIexrSsGuNSmUcOS8HoRQ/kZZ5a3vbFM1E02FstAo4BGCx/P1ArijlVUYKj0O4jvzduNSFAEFYZwerVgJJAhWJGShr/OJXP8WLjj8FK1cc1fD9h6oB2m072JxBsVBCuSIHhgODvfD9HJqbWw/Ke6869SRc8L4rpn2eHtTGQl58yuUxQ6BUyj7CMMDQ8DMAgKOOlNW8xWITypVxVCrjECU5YM3HOgtEbjdrBysWS2gqNaNvxkogRQLtVCSQ+s7qHROgRJJANF+qeZ3OBZLtYHmMqQG5toMBQMEqgSwOAHLFIpI4RmVk9LCzg+n2LF9nAumKeCeVwTuUIoFAV7dqTqo4YFyglcrzOlUCVQwJ5KrFEze/DL1DrWgt5I0SUb/nRBJo6mtjvrnZXHOLdcHQWgn0iU9cin/7ypvx6iXjaGotNd6QxSGB41C0t9fZwZR9PgiqOL6nC4467ggAxgm6O+XzBdeLE6kdjE+jAgJUMHSDTKAwYWBcgHGOnOtgUXMTdoxOQQIJbhTt+aYmjEcxPA44DgchHpJd43DLEeKxFbj2qpsmjA0tLCzmHnRboaPbqdXaKBdcKoC4/EkqsVzwAOArOxgAdJfmx3jCfps1AKUU69evx0033XTQ3tNzHJz7mgtx3HHHIVQy1zvuuAMvOukkgKR9Xw4lsqrVy/zphEAYyQtosU0FISfMDPbABGiYAELgpgfWob1dr2bsHRkk+Z7GSqB//9aVeM1rXg9CCBzVsFBvBxsZHULeywOK/EnUoBSZjIJ9xdhYVc5yWQKqlEZBnCBfkqtPY8MjSBJ10jsOBCEIg1oSKAgiMA60lxyQuvT3W379c8RxhNde+OaG7z8ShGi1drA5g6wdbGBQNlh1dhwcz/8pr381Xv2X75rW0mBWOZX6plweUxXuCQCCXbu2Yc+e5wAIHHmkbAjT2UHl8jiSgjz2cyoQ1rQKZpRAPd2LAGBmSiDBTYD97/sGEBKCLiHPNbdYAofMBHKLdSTQzmGMjQcolwPkcgWMjklLW3d3K4JQ28Hmx6qKxexCrlhAVKkirFQOKxJIBkPLc8vVCglF0Gg7tK8WlmIhkMvL7wiW5KUdTF3md9bZwTzHgRsp4rbpxagmBfiU4ob/uhc/+dFd+KvLPgyHE/PdpjOBprODFTMqkVJrvRJIjheWLutCEMRY0x7iO994xz79XiwODNraJKGza9cglizpRFNTAfm8Doau4tTFC2uWEAUn6O5SC3TqcqSVQEGcTJsHBExUAun/69bYMGHoKOTRks9NrQTKkECF5iaMRxE8EBSKMTin4IPyGK72E3R0dGPliqOn/4VYWFjMahg7mFIC8TolkGAclFCQKjMkkMwEkkrYTqsEmr/40Ic+hCeemDokdX/DpRTnv/a1WLNmTY0SiEIqgQLl5885DighIG76pxMQ4IQgUZk8MQBfhRwDgFtN4I1UTHW8ltnukyBo0op4uSeOQ7HyiAXmM2Xx6KPr4VAHbqQGn4q4omrF74XY1EZHKyAuBeEMRE1MK3GMfFMbAGB4cEhe0LkcyApCzATUfBbOEYYxOkrUrGxqjIwM4bf3345XnfeGhu8/HIQ2GHoOIWsHGxjoBQB0HCQSqKVTNpF1LV825fP0yiZUxpWuh3c9OUDesWsb9uwZQLEYGSVQU6kJ5fIYKpUyopwkY3zdCqSWPLJKoG5NAvVOVAKdtLAH737Jiea24MKQQIPVAAOuh25F3Oabm5AIjoQR5Jqaarbzu4ESrt/aDkIIcioTaGhovCYTKJebHxdUi9mFXKmIsFJBWKnCy+fShZF5Dr+QBkO76voaaSWQui7n1CA4Fhy5nJyANzd3yLZRBT15rsYJOGdwHReeIoFY0zDy7bJNad0Dt+HhDffh6BWrcdRgM1pa5ULTTO1gWRLItIPVZQItWtSBdeufxe/6CjjjJavguofH33IuQOcB3Xff7wEAxxyz2OS8BUEVpyxegLJSpBIAUULQ0yX/zvpwSxJtB4untYIBUn3dksuZumatnA3VdkLGsLJdEorT2sEU6VRokUqgguPC88cBIUBH5XkUD8nnHL/mpGn3zcLCYnaDEG0Hk7e5UiRyrqrhlRLICRNEGRJI28GsEmieYsmSJXjd616H73znO5M+59prr8WVV16J22+/HVu2bMHatWtx9dVX4/HHH8e1115rnnfZZZdh06ZN2Lx5M7785S+b+8fHxvAf3/gannh0M9avewiLFy7Eiae9BOdf+Fp89atfxf/e+1ssW7kSBMAFb7gI/3n9Tbj55jtw0stOR9HzsOqY1fjPn9yI++77LW688WdYtWoVKHUQJZJIGQ0jfPoLX8QD992HRx99FH/5wf8DAHCVQuYv3nUF/veGn+KRDRuwevVqAECxWMTVV1+Nhx56COvXr8dFF10EAFixYgXuvvtuPPzww7j9lptx2qmnmM/xzW9+E49u3ozvXvnv6O7uhhBSFuzl5OAtVzdI2/To78AFRy6Wj2sS6MjT5DbFDFZ/JoNWArlE1CiBisriM9Qnw2njGPAdFwBFVJcJJARHECbobHIaThY2bVqH9vbOhmTBiG0Hm1MoZirijRKos+egvHdztySBeo5YMeXz9KCWaBKoPIYFC9rg+3KAvGvXNvT2jqCpJcTRR68BoJRAlTLKlXFEnrxI5WI9+WukBFoIAOhroAT6zHln4l9e90pD5maVQMNBiH7XRZdavS20NGMsiNHbXwF1nBrVRa57IYZECc1dncjnCgjDKvr6RtCZtYMVLAlksf/hFwoIyxVESsLtFw4PtWbWDuaZRRZ53lPqgDGWsZty5PLyPO7pXlhDAu0eL5v/x4zBdRwUq33gnouXnlnG+s3Sjvr9a/4ZX/nqJ/GNa76MYuLgvW/9EIA0GHqmJBDn3LSDmYp4pQRavLgDu3YPYWRXC9bdvxKLFs2+MP/DFZoEuv8+uXi6evVSk/MWhAFOWbQAW0fGzPPHIwcLFqi/ueJ7snaw6UKhAeCZwWFQSrBSkYbZTCBAKoGOaG8DgBnYweT/pR0sQpPvIY4H4SYBHLVoQtWxvMaSQBYWcx5EjWtdV4+Ls+1gFJwxUErhBAyxl5JAlThGNY7nTSaQO/1TDg3+8TXn4sSF+3dStnF3Lz76v3dM+Zyvf/3r+PjHP47m5uYpn9fe3o7zzjsPF110EW666SaceeaZeNe73oV169bhxBNPRG9vL77yla/g1FNPxdDQEG699VZcfPHFuOGGG1BqasJj6x/GP33+8/jk5/4OH3jfe/HTb/8bfvWLX+BnN9yAW2++CUd3toMSAtd18c43X4RjX3I6PviR/4MNDzyAS9/+Dlx33XfxjW9ejRXLV2H7ju1wmooYrXJs370LSAS+9ZUv4fe9fRgdHcddd9yBo447Dk8rddPAwABec/Gb8cbXnIuPfvSjePe7341PfepTuP3223HFFVegtbUVDz30EG677Tb09vbi/PPPRxiGeMkZZ+Gqf/8WfnXD9bjkkkuwevVqnHDCCTj5rDNx2w034fqfXgdAqnKaKYHvuTWKmkqljMFoFB1CDgirlSpEp8BT6x7GiiPPNOF8+wJJAhG4FIYEqkYxSk1yQDk6OIJmAGEAdOZ9RH4BQT0JxAWiMEFnk9vQ9731+S0AgOXLVmFQEQcaQ0EIh1I0+R7Go33PNrI4OCgWS6gaJdDBtYO1dM2MBNJtJ8ST50u5PIbjj18O15Xnyc6dz8PLDaNUCtHdtRDFYklmApXHUKmMgzsuRN5DLooAVxK0QC0J1N29EJxz9Cs1lEbBc3HequVwKMWSliZsHR6F4AIOpRiqBuBCYIfj4cXVCj5x1stQzPk4cdNvkAureHTRq5FvLiFUstmupbItpmPxIuTzBQRBgL6+kRo7mA2GtjgQyBWLCJUdDAD8YhFBhtiYr8gGQxdLeYwB5vpKKYEQHAVFDoWcw3EEBEL09CwG2y0J4bKgiDPfFXGSoFAooOPZXtDTlsDxHNz94O/xmpe8FC05+R217uF7sacU4Ngj1sgB8wwr4jUJNLx7j2kHW9C9GG2BZwojFi1qx65d69G22Ee14uO4447Btm19k27T4uCho0OOlx966ElwzrF69RKwRLXQRQFetKALt41IIoYQoBI5aG5SkyiRrsADwHA1QGUGJSHPDA0DAI5ob8OTA0PIeSoTSJFJQZJghTquplMCgRCElaoMht45jKWtzRgdG0OLGDTP8+Iqwmgca447eUa/EwsLi9kLPcdLlUDqp+AgxIVgHD4hcBKB2FckkCok6CtX5w0JZJVAGbzuda9Db28v1q9fP+1zdV7Q5s2bsWfPHjz66KMQQuCxxx7DypUrcdppp+HOO+9Ef38/GGP44Q9/iLVr1wIAojDEPXfejj1xiEc3PoJjjjwSQGrTivTKAyG4/Ze/gADw8MO/w+Jly0AIsOnh3+HyK/4SH/nIR7B48WJUq1VQhyJOGMbGqhgLArzqDRfjttvvwLqHH8aRq1fjiNXHmn2/8eabAQDr16/HypUrAQAXXHAB/uZv/gYbNmzAnXfeiXw+j+XLl8PzPFx11VXYtGkTrvmPf8MxR8tg5LVr1+K6664D5xy7e3txz733YHy8ih07BrBjxwDgEORyPlq6a1uXBvk4cooE6t+2C2EYYvOv7wTwwu1gcCk8ClCRDgKamlsRcwGRSFl6GALdOReRX0JYbwcTHGHE0JR3kM9NVAI9v02ueq5YfuSEx4bVgLv9MFlpnsvwPB+u6xklUBBUUKmUD4oSiBCCpk65gt2zcvmUz+1okQQm9eXFZnx8DMcfvwKuK49vrQQqluSxvXzZKpSUHUwHSPNSHrlIZ4HI7WbtYD3dizAw2GfyGDReuWoFCsqOsUKtzAvB4TgOhpTN5NZSE9Y3NePzrzoLH3/2afSM96M1LqMYhsgrSxihFO1LpOWse/kyUEoRhlX094/WtIPZiniLAwGdCaSVQIdLQ5iXz5lMoK7uNgDp9ZVSB5xzYzeN1KQ5jseUEkg+b6Quoi9iCfKeB0EI6MuXYVfvKB55XNrBmhQJFAchAvX9tKBnsbGDzTQTqP/57SYY+tWveB1WDjeBM47Ozhb4vocdO/qBWJJXLzr+xfvwm7E4ENBKoJ07B/Hcc7048xVr8IlP/DEA4MjmInKui22jqRIoYunUg3MCxpg5Pr923+/whh/8bNr31CTQqo42AFklEFPvkVbI75wmE4gQgmBsXGUCxSj5PoaHx9Eqhszz/LiK8epTWL7sCLQ0t027fxYWFrMXWgnk1NvBGDd2sJIippNcGgwNyKKErnliB5u1SqDpFDsHAmeeeSYuuugivPa1r0U+n0dLSwu+//3v4+1vf/uE54aqCpVzbv6vb7uua/zNjZAkCeASFEs5DFeq8NRkS18EI8YAIVfsoigC4xxBHBt59M0//xke2/4kVh9zKq6+5tu44op3YePTfzDByitXrMSfvvd9ePuFr8au3j7807//G/ymdPAbBiFAPLCEmdBIQgguvfRSPPnkkzX7+pnPfAZ79uzBiSeeiM7Fy7HruafNY4a0EQIAQRjG2L17CJ7nAJTAJRTUddDS043RXrliN+KGICpY6IQ1r8Bjv99o1EIvXAlE4TtIK+iJVPTEQgBqknv33X/AWb6DwC8hrlttEoKbxrKuZg/16O/fg3J5HMuXHTHhsRElFW7L57AtI3u2mH0olSRBoTOBANkQdjCUQIWWZlPZ3D0NCXTRhZcCiLFylcz70UogxkMATdixaxuamxNDAq1YfiSaSs2oVMaNyomX8sip76JGdrDu7oUNrWCvW30kGOdwKMUKJbcXgsOhKQkUCoHrFi7BD795Jd79jsswSDrx8t4n0D42ioIigVp7utPPu1S2xoRhgP6+UbzkJUdn2sEOj8m5xcGFXyygPDyM8DAjgfxCwdjBurtb8eyO9PpaKjXX2MFCdf/4+CC6uxchUbeHk9rrcZww+CDoPaILS1tyuO2WjRhRix9aCRSHISJHvm7hwiV4fttjAKZvB8uSQEuPkxb1Rd2LQUHQXGxC52JJnO/cMQCqCh5WHbF6n343FvsfmgQaGBjDk0/uwGtecyqef64No0PAOUdJJej20XEsgcwEipNssQmpWYTor1RNA89U2DNeQTmKcYTK/UmDoVM7GACMBuGU6mwhpP2jOjaGfHNqB3tuqIyj2QgAICYUOVZGawcFGHDccS/Ggw/dPbNfjoWFxayDIYHUV5FuJBSCgVDZDtas55I5iiSJ4fuaBKqie56MJawSKIP/+3//L5YtW4YjjjgCl112GW6//faGBNBM8OCDD+Lss89GZ2cnKKV461vfirvuustMxOBQlEp5jIWRIVNGxsbQ3NwMAUkEmTYfLhDH6UVy4Yrl2L59K7797f/A7b+5Eyec8CJQxwFXk7uWlhaUqxVUxkaxculinHnueUAmn0eYn6ny5pZbbsEHPvABc/ukk04CALS2tmLXrl0QQuCP/+hSQxrdfffduOyyy0ApRXdXF84680zz2jhmACVw1PYXrFppHqvmBaCUOl4uj3/+xmfNyfhCMoFGRysAJfAoMXYwRykoIi4MMfS1f7wZrQ5B2Zk4KOWcI47lPnS1NOZHn9/2DJY3VALJAfdsbgi74FUX40tf+Pah3o1DjmJBBibrdjAAGBzsOyjB0FoZN9o/MC0JtHKJtIudedZrAEgSaM3xyzEyMiIVeLu3o7d3GIVCDM4THHP08XAcF+PlcbS2yYsVKxbgqUGxq652SWZy19O9CL119fCEAK89ZhV+8Yct4FxgpVqZ54oUGlITP8E4iOvgmw+sxy0vfzme8+REoGN0DPlm+TvuXLrYbLd7iZwMBEFV2cGywdCz97yxmLuQwdCpHexwaAhzfR+UUkTVALmcZ5qbhOC49JJ34KLXX4b1jzyQkkBq8WhwqBcLFiwGU+ORgbB2IasaReAAnj9GKib/99ebzOS62U+VQKEigaQSKLWDldrbsOz44xruc7G1BdXRMYwPDCLf3ARCKRZ0yryyjtYuLFokg6Z3PN8HV6lIFi6YuBhjcWjQ2dmCJGEYGSlj08ZnEYYxHr3zUZyw+Ub8vzNOw1MDQxgI9GIpR5xRAgkQkwe0t3h2aBhHTlACpcHQwNRWMEBe1wihqCol0FA1QGehgNHBceTDMURuHlGuGT4vY8lyF4wl1hJmYTHHYexgamostC2VMRBCwRlHkyKSWd5FHEfwtBKoXLXtYBZTY/fu3fjkJz+JO+64Axs3bsT69etx4403opSXBxEnQKmUQ8I5IsaRcI7rrrsOH/vYx7B+/Xr0LFsGx5BAXBIrCq++6GL85Ce/wj333oNVq47A97//A1CHGk/1pk2bsOnRR/HTO+7CZ/7561i//ncQTnblZaLt6vOf/zw8zzNB1p///OcBAFdeeSXe+c534v7778eRq45AuSwnzj//+c/x1FNPYfPmzfjSZz6L3z7wQM32OADCJ5JAXqmAMJFKmVt/8xNs2/YMCFEV8S9UCeRSeE5KArmqcShKmLlvWXs7HALsLk9caRKZ33NnU2MSaNu2Z7B82aoJ9+uJ8WwOhz7n7Atx+svOQak0dd7VfEdBVZhnlUD9A73o7DzwJFCzygN65ncbkC+VJtglNSilWLJAToK4sm9UqmUcf/xybH3+YfzgR/+GOI4wODgOLhiqwSCOO+4kAJIsWrJErqwnhQIcpm0gctvZAXd390L09dYqgU5dvBCLmpvws8efxI6xsVQJxDmoygQCZIYDpRS5YhHUcTAUc8TURfv4mLGDdao8oPHBIXQtkrawMAzQ3z8K3/fgK6+1tYNZHAjoTCATDH0YkECeWoiIgwBHHrkIutLzlee+Hu//y0/h7ntuwec+/6E0E0gNdLdtfxZNpWbkC/J31B/Uqiee7B/Ac8Oj2J4QDIcU9z3wFEaVCro5nyqBYsrBOMPChUtq7GCv+os/w/uu+VbD0oViawsqo6MoD4+AUorFy1agmJff0x2tnVislEBjo1wpiQVaWhbtr1+ZxQtER0cThoYk2fJ3f/djHHfs+7Do+V0olQfww91bcdZVPzTjPEI4ksyh1dbag94GatSZ4JnBYaMEyrsOGOfGzqhbwnZMQwLJIFiCYHwc+eYm3Lt1B3zXQaG/Cq9cRphvQeIX4UYVLFnUhC3P/ME2hFlYzHEQxf44SpghMtlkhBIIztGkFIos7yDKkEB9lcq8aQebtXawQ4277roLd911V8PHLr/8cvP/rVu34oQTTmj42HXXXYfrrruu5rXFnI+zjjkS445Aa1MON998I37+s5/BdxwEu+/D8ccfDwBY0dqC97z5UgAAcwT6+vrw+rVngAD4t299Az/86bV45pkhLOhZjMHBQSzsajdKIAB434c+iMWei3ycoNziI1fMAdTBG05/KQbKEVq7FuLh3z2Mc889FwAQBAHe+973TvisTz/9NE48UdZEty9Ygi/909fNY1o5tPDoIxGWKxjamV7IOSTDSERKAhFKkSsWMB7IENpbb/tveb+2g71AJRBxCHxKwdR2PGUxCRkDVfctU7LzLX39E7YhBAcXQDVk6GxqXD+79fktuOD8N6JQSIOFAWBErXK1z2Il0DFHy2Nr6ZKV+MOTmw/x3hw6aCVQJasEGug7KHaw5i45mXl63Xqc9JpXoXvlcow2OBYXL1qOnCttVP/zy59g5UlnYeHCdrS3N+F3Dz+Aa/9T5npxztHfPwrGXRx1pMz9KpfH8OIT5fsEfgGAPDZdUxEvz4Xm5lYUCkX09dcqgS48ehUY5/jfp57Fu15yYk0mEKUUg5oEYhzUcVBokaRiGIYY95vRrmT1ANCxbDFYkuDZDZtMjX0QVtHXJyee7e1FJEmM/Cw+bw43HNvVgZMXLcB1m5841LvyguEXZCZQqgSaH6t3U8FX1quoWsVRRy0yet/TX3YOtm1/Fp/7wodVJpBWAsmB7h/+8BjOXQu0tMrvjt5qbXFCEMcIGMdtGwexs/kYhEGISihJHq0E4oyBJQnGgnEs6FmCJGGI4wSFgo9FK45ErlhA57Il6Hvu+ZptF1tbUBkZRWVE2m+OPvZF5rHO9i4sXqyIayH/fl5+DAhaUSyWar7HLQ4NOjpbMDAgF/cqlRA7dw5jWTGPsFTC46NPYrAapOM8cERRuhCxeNEqbNi4bp/e95mhEbzyyJUAgJLvmzwgYOZKIJkJJJVAXcuW4u6t2xAlDNEf+hEe0YJqaRmIAIqVYTQXPTz//Ga8/IzXgFL6ghYuLSwsDh3095Fj4kjk/TITSLaDlRSRLAou4uG4RgnUnPORcx2ziDJXMSeVQL7joDU3ddvEbEXBlxO7wVE5cCmVcuBCGAmrRphZrdcefcbln6vYWkS1GplsHUKIDLLKvIbFCcrFIp4bHoEg0uLBfQeJ48gbAICJiqCpIJN/SIMHRE0LGAAwdZvHMRYcKWXbup63GsjGhWokB5DGDjaDWtDJkGYCERAuf5deXk5EgziBA/k7PKpHTvQf27Zt4sfg8nP0jUboKDUmgXQ49LK6XCBjB5sm++BQYPnyblx66StN8PGypSsP7Q4dYhQbKIEGBvtQKJTMYwcKLV1S+fPM7zYAqG0IK7S0YNExMnj9qCOPM+q17//4O3jPX74Jxx8v7WOPPVY7gertHYHrVcwFqlIex9FHLwQgMEpLEOqcpU5tJpCuh6+3g71kyUI83juAwWqArcMjGSWQAKUUw2pyqKs0Cy3qPAuqqORb0DE6hkKTtoMtQcuTT+Ks3bvQpj57GAbo6xsFABMObZVAswcfP+tluPqSC5FzG38HzhXoRYewXD7MSCClgA1CHH30YrPCefRRx+GJJzaaiWtBkUBVZTVfv0F+J7W0yfN093ilZrv6+sjjBAIESRQZO1hLLr3uRUGAsWAcCxdKFWC1GqFQyJnvugWrJtq4NAlUHpbfC0esOka+JwS6OnqwaFE7BgfH0NYmr9/cGwAArDri2Anbsjj46OxsxsDAqLn9ijNfhaZwFLzDxcqVCwCk4zxCgeqYvIbEMUVHxwI8veX3+/S+zwwNo+R7WNhUwsuXL8Eju/aYx/TkbOcU9fBAelxXR8dQaGlGOYrxwPadOELk4FdjjPp5xH4BrrZBkwEUiyWsXHH0Pu2zhYXFoYdWpLr1djDOTDB0U5KAEYDmXERRCN+X1zmdWTYfGsLmHAlECcHRnW1Y1dHWiI6Y9dAD66HRskwfLzVeAY8yKwyxuphxOBAgcPwc9vSPgSgZm/4psiRQkoBRByMJg4D8Q/ttPvpZaoPaa8plkheIBiQQ1+xqkpjBn85jECoTKFSDTy0TfiFKIF0R7zkUhMmBqadk7dU4gksIkiTGccqesmHLUw0+h5zU9o1E6Cg1PjW2Pt+4IUwrgWajHexv//aP8a/f/IS5vfRwJ4EaZAINDOqa+APbENbc1YGwUkXvs1sRVqroWZmSQBd+4C/wge9/G9R1cNRRx0Ek8pjShPBUJFCpKdXXj5fHsfrYJQBilL0mCHUuuqYiXm6vu1vaKeql+Cct6sEju6Vab+vwKJa2NMOhBC4BBCEZJZC0gxWapRJo1+5tiFt60BRU0a6UQCsW9ODNT/4Bby94KKlv7GpVtoPJfWhBNagib4OhZw1OXbwQrkOxWrXYzVXoRQeZCXT42MH8jB3sqKMWYbwsz9fOjm48+dRj5nm6UjtIEjDG8Mwzz6Ovbzfa2iUJtHO0tuBAXx+ZWrBiUQwuBMbDyLSDAdISNh6VsXCBvNaOjAj0LFiD1gWSwFl4VAMSqEUrgeT3wvKlqxBEAQKXSRJocQd27hxET4/MGBtn8vv6qKMsCTQb0NHRhMHBlGz5owveAJfFGPYqWL5C/t3zyjIIBwjUMTk+JsdLW7bsm+rwmcFhAMDLli7CS5YsxK1PP2ce0613UzWDAZlx39ZtaOpoR9eKZfjNlq04cVEPHAC91EGFeMgJgSTiWH6kPL+sJczCYu5Cz1ldPX9G1g4mg6FLSYzIJXA9mQlk2sHKcoGkex6MJ+YcCbSspRk51wUhxMiZDzW6ulpwzDFLZvRcj1IIQiAEUC6HaGsr4cijFmHVqoVw1CSts7MHnd1poGrCOHzfR+wXUS20IQiBUs8i5EtyMqtXWOqVQABko5gAKJEH/dhYNSXP9hMLpCs2a+5z1N+GMTR3dqDU1oqc8lBSpUSIIkUC6ZPwBUhr4zgBA+BTCgq53VxRTk6rUYyc66BcHsfSlhYw6mHzU483+BwChFD0DgXoKFG4DVbCd+58HkkST2gI40JgJAjROgtJoFNOPQrVagmccwwM9B72JJDJBKoJhpakx4EOh27p6sTYwACEEOjfug3dR6Th0CtPOgG5YhE9K1fgqCOPw8iA3CetEnzJaUdj9+4h9PYO12yzt3cYnZ3puVkuj+HYY5ciTgKUc83m4qa9z9oO1qNIoGwm0IKmIhY1N5kV1eeGR+A6FEtbmuFRCiAlgQRXdjBF+Nxxz68wTOUAeZW6723jI2hW53XXiKzbDUMZDA3I784wrCJ/CIKhT3jl2bjsC//voL/vbEaT72G1siwe39M4r2quQC86hNWqqUs/HJRAOhMoqgY46ujF6O+XZA4B8IcnHzXPKyi76fU3PIjPfuZHYIzjmWefRLOqv94xOlqzXX191CRQopS8o2Fk2sEAGQ5dYQE6O3vguh5271iMJT2vQ2sg32/hkZMrgSrD8nthyaLlGBgbQORwdHctwKJFHdi1axA93QsRiwQj4Tg8L8Gxq180YVsWBx+dGTtYT/civGyVXCR7vjxulEAl3VBLgGBcLnCUFQn09NP7RgI9OySPl3efJiMLbnn6WfPYjJVA6rjeeMtvwDnHqa97NX695Tnz+PqRnfj+z34AAOgfYDjtZcsxPDyI4447cZ/22cLC4tDDtINpGsEEQ0s7mOAcpThG4AKuS2uCofvUotJ8CIeeUyRQaz6HzlLBBJMWvdlBAnV0NKO5uYC2tumtJC4IGJETtrGxCnzfQz7nobWthNXHLoXnOWhpaQP1MoOqJEGh0AQBAu44iJgDITjyTfUkUEqi6IGa47lpG5gQKJeDfbaDyTdrZAeDZJkUqOOAK6kdVe/Rs2qlGZQTpfjRSiCqArpEg8DqvUHMOTzqwCfy4p8vtYKxBNU4Qs5xUK6Mo5hUUfEK6FcT7JqPoYJvn9w+Dt8hOOWUiS1gjCXYsfP5SRvCZlMmUHtbJ0588Uvwohctx/hoHqOje/D0lt9j2ZKVh3rXDim0EqhcSQeHAwNKCXSAw6GbOzsx1i/tkL3PbUWPagjzC3ksOloeU4uPPRpHHXkshgekTUuTQOed92LcfvumCdvcvWsIy1cUjM2D0ASLF3ciCKsIRB5ckctOvR2sZxEYSzA4lGYSnbRQDtg37FJKoCE5EVzR1gqfEnBCjB2MM20Hk3axgT178Mt1MiB+dVMT/vylJ+OYgX5cNyJ/z13DkgQKwsCQQIfSDvbSN70Bp1382knDuQ9HnLxogWmwPH5B7e/lnCOW4YuvOutQ7NY+wVcDtKhSAU8Y4jA0CxHzGWkmkFQC9Q/I848xhqcziou8WuS45/7H8cUv/gQA8MyzfwAohQDBTkXIaOjrox5bxIoEGo8ikwkESCVQhYeglKKnZxGCiswUWzFSQvn5PcYerkEISYOhVSbQgs6F6B8bREQ5ursXYXFGCVTlEUYCjlJziGOOXrN/fmkWLwidnc2IqY/T/+hivPqCS1AK5N/x8d4BrFBKoKYmee5xAsRBhCRhGB/PYXCoD0PDA/v0vs8Nj4BzgQuOOgJ95Qo2ZO1gM1UCqeN6tK8fTz/0ME55/auxfuceDKqJ3lNDg9i45UkAQH9fhDWrF+CJ3z+C421DmIXFnIWed2qFfNYORqlsBytGMUJHwHUdxFFKAg2o74bukiWBDiq6igWECcNzQ/KLv+B5h3qXQAhBU5Oc+Pf0tE77fCqARAj4vo9du4awYcMWbHm6D7t2lOF7Lo45Zjk814Mg1JR4RTFDsVBEkiSIohAUFCN7+hCr+lU9sNXtYADAYmkPcTJEWbkSgr+A3B2ZCdTgfl6rBPLyOTBl8dIH2IIjjzCrsLo1LDJ2sBceDA0AMWco+B5cRQIdd/wpeG7rFoQJQ951US6PwQ/HMZRMoWiiFL9/Xk581649vuHztj6/ZYIdDABGgmBW2cHe+xcfxz/9w/cQxyWMjeXAMYTt/7+9846zorwe9zPt1u0dFnaXJk2kg4KIYhd7RU1M1BhTjNEUJaYXf0ajicb4jb0k9mg0aKKCHTtIXzossJTdZfvdW+ZOeX9/zN0L69LrAu+j5wPMnfvemTlzZt45c8qGNfTocWS31g2FvCiVeHxLzYt2p+D+Lg6dWZifLgS9Ycky8nuUkl1cSI9BA9I5yv2OGUZ+fhGtTfW4rsByXAYNKqNbtzzefWd+pzGff/5DMjN9JBLNAHTv7jllorEorqshAt41YEuLeM8+jh48gpWrlnYobjmsm5cONz+dDuZN5stzsvBpCmKrSCDXdVA1NR0JFGuN8OjLT+MqKmODWfzxlAlszM/nz4uWs6a5lYIWz/llJuLEYibxuJl2Ah2MFvHlx3hRBGVDtm3nRyIjS706URtaI50igX543Ch+OmEspakaUF2d9vuNmQrdTsbiR0SL+PaaQKpr07NnQTpCo7p6NYnElnTw9u5g8a3qEa6uWk59fi/W9RxBJNrx4bn9/uimrh92qh5QxEyS6fdRnBFiXFkplmmSwPts5PDjcN0AetirwdcnlktRRXmHDmH+jDCqphFrbiURaYOkQ1Yoi6ZoE6bqkJ2VQ1FRPps2epFAMSdBJKmQkWnSs0efdJ0GycHB7zcIhwOM7j+Uuy85l3POvozouqVsjsZYVl1LRkaQvLxMQqnSBy4gbJfGxghtkQBVVcv2+Lctx6U6lbY4Y+WaDo1v27uDbYrsuHB4+3kNMOe/b1HQswc9hgzi/TXrcRWFtQ1N1KbqY61fVoPf0GhsWkdZWW8yM3c+55dIJF2P9jIkhtb+7NoxHaylbrOXDmZo6PpXuoOl5hQFh8FLpUPKCZTh89FqmgggZttdIhIoHA6k0qxiZGaGCAa3X7Ba11RwBULxUV7WF5/PhxDQrXtPsrLyqa5uRlG87zuug5s6SS3bSXXBaCNpW6gomNFYuthle8ehrSOBhOsihIumb3GURSLtE8A9rKa0nUidr9YE8gUCOHjVqDUFEtEoxb0r8LenrwkXFNLt2NM1gfay04JpO+gCArEWHBSOPnokH858i6Tj4Nc1YrE2/Mkom6KxbX6/fT+aIyabo4IJJ2w71Hzp0gWUl/VhQP8hHZY3J0yyU5FAmrqHx3gfoWk6xx17EpqmsXxxMUnTICfXYv36KkKhMHl5hZx55kjuu+/b+31bevQo4NRTu85bs+ysHOLxWAenaTQawTQT5O3nmkBeOpjnDFn8wccADDrh+LQjomH9BgYO8sLMzUhTunPPySd7y97ZhhNo1qwVzJgxl8Iiz44qKnIBaG1rw3UUhM+7Tm4dCRQMhhk8aBizv/y4w1jDuhWxsqGJSKrrT3VrBNcVVORk40tFCDS3F8h0XFR1SzqYGY2yflM1EX+YAS2byAoEWN53PI0bNvLlhk3kpt72ZhTnc/SkE9i8uZX8gqxUJNCBfaNSUN6TcKrrWflQ6QRqZ2T3YtY2tzJzzXoGbeUE0lSFCeU9ADg51e2xq9N+v2mvB2TG4mkHyeFMezpY9+JMr5tfo/cQvGKrekAAAV3Hdra01AaoqlpGJKuE9T1HdHCSw5b745aaQB3Twf5x0dnM+OalBNuimKp3bT3j9AsByCpqZbm5ge45JWRoAfJ6bEl3D6U6drbXA6LFu740xZpJ4P2G6wbZtKmJoqJuRJMxopZKTm4cXTcYPKjr3FuORMr69kS4gl6fLGXM0iWUB33QsJ7FdQ2sXeu9TKioKCKcemtuWjaGqlFf30Ys6tvjotDttNcFmr5VKhhs1R2sbVfSwbx748K338cyTUZOPp175izknREjaKpvoC7q2VD1Qu83MnO8F7C33rL/508SiWTf0+741fXU82e7E8jxCkPPn/4OIdNEZIdTTiATIxV40pRI4LiuLAx9IAn7DDRVSXejiFvWHkcCqYqSDoXeFueffz5CCPr377/DcX74wx9SVJSHQLBmTR2u6+4wGiicCtNWdD+KovD979/Iww8/gqEbOK5DOJyLmVDQdJekGUekwtVQNDRNJxaPIjTFC8lxBaqqpfOZoWNNIPDqAmm6nvbdtEW8Sd22Mrp2mW18+atOoEBmBmYiCaqCpqjUVa31nEDtkUBCgKKkIxLajXFvMR0HEjY5NWuJZhSiqioffjQd03bwaxrxtgg+K05V6iG80364qVxQx2VtM0yYMAh1G9v2n9eepbFxM9/7zs86LG+OJygKh3ju0nNYeMM1GNrBM69hQ8eQmZlNW3QdrS3ece/TN0D1+jUAlPXszZ//8i1+cOM5DBzYc49+Q1UUQrtgg3+59zreePM3DB/eOXrqQKMoCuOOm8SChbM7fdbQuJmC/P3nBNIMg1B2FpF6zxlSu3oN9evWM/ik4ykbMoiG9Ruo+mIuA7r1R3VsJnXL5dPqjQBMOvkYVq3alJ5Uf5X/d/uLFBTZmMkWvvPdM7Fth9a2KLqjEXO8txV+v3ceuK7L0CGj0HWDOXM/6TDOsG7F6Sgg8N60bohEvEggVUUobBUJ5KJoKj2PHkSkoTF9/VnteA9uc8MlGFoxvYt78+XGWrISUTTb5J6f38zrF56JvayBwpQTKHiA08EqhnoO3LbGpnREkARGdC9hzsYaKuvq6ZWbTUaqm+XwbsVkpaIcT+5TtqMhugz+lMNnixModkTUBPKl9NSjW3tEoGeXW9cDAq8AeFOiYxv4ddWrcVKO53jiq93BvPujY1k4lp1O325LJhnZvYSTepdhaBojmxuxdIHjOAwaOAwhYmTmqFRt9FLRshM+Svr0To8byuroBNJT2+uEdaJWSncJnbq6CNlZubQlY0QdjeycOK7rMHzYsXtzuLocJ3x9Cj/611MdoqW6Mud+/2pEZR3h1EvG3JpKegR0lmxuYM0a715SXl5EKOQ5J6OxKD6fn4Z6BSEUli+v3O7Yu8LqpmYAZqxa22H5e6vX8c95lVjOjl8utp/XAIm2KIs/+JjhZ57K5lCYpeUVtDU0Upd6cRhZvZ6opXDxlKMBweWXX8DVV5+yV9svkUgOPMpXGqYobGmcoigqTkMjmuuiFeSm08Hao06FgIZYQjqBDiRZqZzzXv2707dvN9BVNFXBt4MbpaqqBAKdH1IrcrIYWJiPvh3nw+WXX87MmTOZMmXKDrfppptvprikgFjUJJm0aWiIkJ+fRSi07fDkkN/bFqFqNDbW4zN8BAJB2tpaqavbRMAfQFE0FMUmO8ePq+mgKgS36makpibloWCYrMxsIm0tqYcv0SmSxrHtdE0gAbS1bZnwiT2pB7Q9Uk4dAN1n4A+FaGtqAdVzFNSuWkP3/v0Ipd68q0KAsiUt5avt7feUVZmgnlTBZ0eNZuGQc1lXXcWaNSswHRufrqPHvdSWJRs2bGc3UuHursu6FoWcnAyGDCnvtF4sFuWxJ+9lyNEjOfGEM9PLmxMmA4vyuWhwf/rm53JO/757vU87QlVV8vIyt/nZhPGnEo9HycxdAIr3Fqt7qYbjehPtM8+YSP/+3pv9Sy4Zv0e///9OPYElP7w2/aDYzqSTJvObX97HKd/6Jr9943nOOWcMqqry1/u9t2ZXjxjCf668EHWvvJF7xuBBwykp6cHb777W6bPGxs37tTB0VkE+AK2bt9Q/qHx/Jv3GjqLXiKE4VQ1cMfxCit0MWj/6FyUZYX7/3idomsqJJw7hnbc7RwG188EHi2iMLmTSqZsZMKAHN3z/QUzHwnBVEnoIRbgMyumftrkRI8ZhmgkWLpqzZfv8Pvrk5TBvU0dH09rmVspzsvFrasd0MMchIzeHQRPHM/OZF9Prz7JdqnNzmTl2MHGR5MKzr+TLDV59o+zWTZxYtZKShgbKPq3luGqHpBnDf4BraZUPPZp4a4S5b8yg5+CBh8wD1/4kJ+CnX34uX6acQAADC71z9sRUAfO3V61hUu/yvXuRcIDwb1UTCI4cJ1B7JFBFT093quI5WLfuDPb1YYM5tW8Fd3z4WYfvWpbFulQHzM6RQN790Wvl3pxe3mqaBAydVY3NzFxTzbhYG4bPYHO9Z/OWU0MoaHDFhAwys+IUJg2K+1Skv791JJCmKuSLMI5wqZg4hs/fegsAM2EQj3k2GolHcFQfphkjFq9hxPDDywl0/BWXUDrgKAZOOO5gb8pO0QyDYcePwp25jnXxOA15FZTWryQ3GGDJ5o6RQBmZQRQErc0t+PwB2iLenH75Npp07A4PfDaHb//nzXSKRjvTlq7k2lfe2On3t04HA/jwny8Qzs3h9O9/C4BIQxOm7dAcT+Bva2NjTGdgv0JUPcKGDQr3/fXb9OvXfXvDSySSLohn8wLDaJ/7pYIpUulgRalULyU3E8NvdOgOBlAfi1Eo08H2LT6fj6KibuTlFqSLt7aTFfCDrhI3LQJBH4XdvRSo7aWEqapC//6lDBpURji8xSkT0DVygl4KV7dt1DYIh8OMHz+ea6+9Nu0EUlWVP/3pTyxYsID58+dzww9+wC233Ub37t34x1Mv8PjjzwNQWbkay7Lp3aeESy65hCeeeAKAs88+m88++4wZ78/k/55/kVBmJvUNtZhJEwRs3lxDpK0V0/QermrrG7ARWL4QbeEs8vOLME0TVwEldcIWFBSjKAqNjfU019YR30YHBMeyMQIBkqqfpriyVT2g/ZcOFsrOBgTRllZcvHrR8956m8z8PC782qUMW7Gcnq7n3GovUKuo6l7XAwKosROoE8pZ0ZYERWHmR6kJZCoSyJf0jtG81au2vR+utx/CdVnX6u3PCdtJCXvzrX+zctUSvnv9renokfrUG+fZuQ5uhsH1x+7fMPVnn/spq1Y/Qo8eHWt3KIrC+PGnMHvOJwwf0ZPgwpfoPvNRtGiMHj1CJJMmp5xyPNXVm/noo8VccukEFEXhwgvH8djjP8Tv33l0T0EoyHdGD6NbZgbfHj0MgHHjBvKLX1zJjd//JRNPOIMrr/ou448pwufT+et90xg1agi3/eg6/t+pJ3DmUb05b8C+dZIdd+xJ/PRHt5OdnbvddU6ZdA6JRJyPP3mn02cNDXXk5xcypLiAc7exbcFgmJ57UU+pPW2zPRIIoPK9meg+Hzl5BYwvHUlLSxMrs+oZLZqYsXINH6/bwKhR/cjODm8zFawdzTD4OFLCB5uDXPf/ZvLww29S17qZhObQEoqgAMcltxSFHjViHAsXfYllJdNjDC3xzuPOTqAWRpWWMKSoAAHEU7W82ruDtTU28dEz/0qvP70txqsnTWLNylU88/TfOWbIKJQS77hVVH1G2LL4eUMr9eP6kV2fZKRoIRAIURAKMqxk1yOxfjR+NE9ffPYuRaN9lYqhR7NuYSVr5i3sUJT7SKQgFOSUPuWMK/M6XH65oTbtBGovDj2xoieL6+p5bsESijPCHH0IdA5rbwe/dU2gI6FFfGlxT/JjPi664FiqqmopLOyOAumi0KVZGfz5zEl8uKaaBz6f0+n7q9csJ5lMYttWh+Xt98f3n3yWv33ju+nlEdNb79fvzOTvX8yjwHUZaJrU1npRjEm3jryAy7D+BeQVRPAlDY4avOW+2u4E+kGfnqz72c2Ua5nUZMZJJuK8/uDDuK5LIqHjOJ4DryXeiu73sWFDA0KpY0D/IYRCO2/KcShQMewY8lOpcmMvOu+A/KamKrtVy1BV1fTxHjjhOPo3NEJ9jOUFA9hU0BcjFUm2ZHM9LZE40XiSsy4/i2OG9gYF2lqaycnOw9CORtdtNm5at1fbv6iunifnLNr5ituh/bxuZ828BSz96DOOOm4MQDp9uy4aI9/n4935jczdpFDr07GTuWyeH+Sje2/m/r9ez+mnj9jhb2VmBnniyZv4cs69neZtEonkwNHu+DXSPoSO6WDtDh7L7yMQCmJZVromEMDmaPyw6A62S0V1Tj/9dO677z40TePRRx/lzjvv7LTOfffdx1lnnUUsFuOb3/wmc+fO3em455wzhuOPH8Rf//oak048h9zcAnJy8kDAyHOvIrO4O8lkEkXx6gGhKcRMC9d1CYUCqLbLaMfBFl71bl1XQVGwLAddU9E0FSEExwlv2foVlax+/VHvx30aBUqImlgi3eUCvFSwN998kxUrVtDY2MiIESMYM2YMvXr1Yvjw4TiuS98hg0nYNjd89ztcf/3XWbWxIR0mVlVVx1FHdadbt1wCAYOMjABLlizlyiuuJMNJctHFF/ONa7/J3Jvn0dzcQCTaSnZpCb5AgJbGZoxknEB+IU3xJFmKAa6Oqig0tzWRkec93La3qmuLRkgmTZJJM91adWss0ySYlUkimqC+sanjh3sQCCSAr7aCh5QTKFUDJ5STTaItimvbOEKgCLjtzEFM2Dyb4vkb0C0bGx1RviVtTlGUve4MBhBp9Zww1dWb6F0BH86cDnhpYkFDp35pJQzvy8q6bafUeK3uvUigNlunqmo9E04YzP33v4aqqtx11zc55dRh3HP3Kzz77Afcdfdt/OXuf3LPXU9x26++w5dqLn/dHOfmX56B+34VJ7Yl6ZmdSXVLZK/37atceOE4Jp81iXjMx1/u/Q6XXPyH9GeDBg6jIL+IspMGE162nBGtSVxV4Dw+l8uHD6K06hPyc3ReqaxFDxRx1dWlPDHr31zYzyIc8qPrGt+46s+dflNVFFRFwXZdvjd2OCGfQW0wxI9POJZXq9fyn2m/oK21gjWrctncWk9ZRhY9zbXUxTN48vWVDBtSxNVDTiZ/3Wzq2mL85PgxvLJkxT45Hrm5BfzsljvJzMxm9Kjj+d3tN7Oo0nvA+d71Uxl33CTuuOtWTpx4Jp9/+jaFPo118Y5jtDTUcHz3DC79zlXoqsrPpn/APR/PAiAczuTee/5JRXlffv27G/nk03d3exszU5FAkYYtTqC1CxaRiLQw2ijCCAYYHPgcdU6EPJ/Bb9/z6vVMmeJ1ZHrvvQUoqsp5t/yQvmNG8vQtv6JmpffWfvR5Z2FnFfPfmUsY9/Urmfv2RyxYt4jygpOw1jdCdjkFbQ1El0XIyyukV6+jmP72fzps36hUUeB5NbUdlr+5vIoR3Uqoy2mjLWdLEdn2GmTvPvbPdH0ygI+eeZENS5Yz93/T8Rk+Ljn3Kn75u4eJf/kCQbOVd1at5YF//48LfnouBfWNnLqsgZVWE4t//F0yVDjzqX/xwZrqTsdPV1V652YT9vmYesJYLhh0lHdc/T4ueu5V7F2sKeYPhSjp25tF737I2vnew0PZMYPZsHT5Ln3/cMLQVP5z5YWM7tEtvWzOplqaEwmiSYvBRQXoqsr4slL+Ma+Sd1IpFyf3qWBhbf32hu0StDdMMFPt4QuCLn377riQ6766F+1PDEMnNzeDurrmDstLBx7F0JNO5MpTvkZma5j8XmN46MHn6D/xO6xLJjHNBN0yw7z+tYvRFIXrXn1zm+91Xnv9BTZsWNtpefv90YzFOtj7a0tXoirwr8pl6KpKCzC+YTPP1zYBo7GVRjQll7ZXlyBmr8cYdy1TThjFilOGsXp1DaHsLPJaW5kyZACaomBVf8igk3rzi7+8S6SxkaQVwUzoaKrneGiONFPsL2LjxkYys3SwdY45ehSfffHBvjvIB4kRk08jGU/w+b+nMX7KRWQXF9JSu3m//V7/gjz+efFkjsrP44bXZ/D0/J1H5dz6kzs46cSzeOe91wlVZDPos0riWXn4B57M7x74PY+P601G0iQwcgQ//vMfiStBThrVnfeW+BFAS0MDof5hYvEmikrmdmhMcDBoP6+35s0HHmHA8V6EWVvKCVTbFqUoI8QL//2C2NcvY/b0fzF5wDjGVH2Bz4pz+eBj+N7/zubSS+7l7Xfm0NLSRDDo5957v8UZZ45k3rwqBg8uo6yskEQiybvv3c5JJ97Ghg171hmtnbKy3vTrM4h33nu9w/K/nX0KFww6ir9++iX/98XcdJ0/iUSyVWHoVGCFonh/uq6XDtYeCdQQjdA90A0XF8PwMe64SfTtM5CGeDUDU3P6Q5mdOoFUVeWBBx7g1FNPZf369cyaNYtp06axZMmWVqNnnnkm/fr1o1+/fowdO5a///3vHHvsjkN0s4sLufaX3+XskflcctG1rK0qwhbrCATjuI6LmUyQqxuoqoZrexcvR4hU5pGKadoEdRWfruMz2nP5XEDBZxgIAY6zxWGh6yrZWdmEFA0n6CMKZCUdjirvRm0sgdkaIRQ0uPrqb/DYYw/Ru3cJH374Nj/4wfcoKenOk08+RUl+HqFwAMOKo9tJVEXBikHQMujTswJVUSgr6kGiyUV3dEI+P72LS+jTpy8//flUCguL0Hw+1m7aSPf+/cgqLCCQEcYfDGKZJhmp0PtEW5SG9RswuvUkFMrAxCazxEtTMWMxnIR3MjY27ngSHqlvoK2hsfOkVoE98gJth/a6RKHsLHTDSE9azFSprSlWLsxcC6WZmGcPZM3mOnr3KdmyOYq610WhARYvXseyZev54MP30bWMdPh7XVuM3GCAW4ZUALC+ddtOmXQ6mOOgqAoffLCIyy6bwFOv/IFe5QUcP7yUNWtqeeofP+LWqRdzzdX38bvbb+COPzzC009OT48z+7MI61Z9zHko/PjU8dz88puoitKhAOfW+HUNBSXdCvyr5AYD+DWN+qRFYVkPhg8Zzs9/9iNmf5YPKBTnXcGjfypk/QfTGT+gjMFHDUEsf5djwk246xpZW5DLVfc/wTvXXswlegGicRU0wJWBTFZ370XldD+T/ZvxP74cM5Jgysn9afrzzfztkX9jJuCKHiVcOmQA/QsLsFSFu2Yv4MbhR7O2ewmV5d0569M5vP9/t6BrAdYt9TOg4QNGblxLIGHiztZZMqAHt/zsj6hJP6UbF9CYV8YT82dw69D+nFDRk7mbaqnIyaI+YWJlZaH7/SQibbTWbUZTFUaPOZEe3XrQu2k1fbIz+PsXc5mz0XNUZPgMHr5kMqf3H0h045c8+O57DD7nWu6955888tif0TSNSy6+mkQixl//8izhRAtft9fy0x9+i5/P/IxHZs8lGonTLz+Xb/kaKKuzWBIoZuGcj7jjtInkBgO8u3YDl3/n15T37ENtTTV/veGnfNinlI821GGH86jo1Y9gOEzVlx/SWr2Cz9aspTlhpvU3vFsxvfNzEd2LQQjs1jZOPelsLpvybcp69iJTSaBXR+m39nVCra24IYM3CwsY9stb+e3JR3F6H4eX/zsfLTufK37yI0ZMPp1ENMoPnn6Yf/36Dha9/xGTrr2KtQsqeejbN/Ljl/7Jtx++FzPqFb/++OOljDpjKKYvhPrGCp696nK6bVzExmVbogDG9ujGL04cx5yNNelOKO28sGgpLyxaypnXFTG0+5aWzHVr1lKzqopPXvx3h/UbN2yiccMmABJOnKf++TfOnnwpkRUrOKOsmNs/+JREW5Q//fElnvrtuQTXfM7A1R+SCGeTEIJ/XXkRxz/4FGPKyxjTq4IW26Uw6Ofs8hKKUnUlBApfHjWUBt3mTOCfF0/m+6/NoDHuPeiO6FaM4fcTMQzembOgw/aVDRmEqmmsmb+Ipk01RGvrGDxkMJ+++Mo27W975PcoJadbMatnz92vjoP8UJDy/Dw2mAKfz4/wa8TiURo3esdYVTXy8gooLCzBcRxWrKhECJGOkIqlOkXmBgPkhgIEhxxN9769GJAR4YqiMkZaPt51sti0YgE1DZtpSqX7La6rZ3BRAaNKS8jw+/igah11cZNNScENZ05m2qYIq6uWMaAgj96FhfSddDF9+gzikcfuSTtgDybtqV9O0uTnP7+UX1/eG11TWffbK7njjn8zbNggBlZ04yzVYGzPUjaNPh669+bZx+5h1oyPWFxXT4uZZPiwseTlFfHhR2/huG668+aBJLMgn/NvvYmBw47inB4tHNWnkEcfmc7vf/88NTVNjJh8Gt+47ccMatYwNB+ZOW3ULda4cNhkvmjYhBPM5pz+fbjrjJMoDoe48LlXqGrq/OIIYP6CL5i/4ItOy7+aNtPOjFVrmLFqDeDVEPufDZe3NPNDn0Plh8+Qf1wO7vRVBBZsprtukLXsTarzTuV/b/wBRXGY/ulmhs1ZhND9rA3nU16zFlGTy12XH8P5pbdQUKRTv9nk6MGjcF2X1mgzpX4fGzc2Mu64EqoWtjJx5Fi+/HLmTuu/dGU0XWfYGadQ+d6HzHz6RSZceSmjzz+btx96otO6YZ/B704+nhHdivnNux93cJr7gkFySoqoX7c+nWavqCplQwYRCIdZO2sOE8pKOW/kMXx9QB9iSYuqWBuPX3gWv/3G5dwz40Meeuvt9HzFMHTCIT/NLVFOO+U8Tjv1fObN/5wTJ5zB8KoP0WyHyj4ncfdfbuONt17l8nG/4MSMDE791a3UV29g0ZxlnDiuL42m95D17tuv0dbQxGNP/IVodN+/INtdtnVeVy9azKL3PqT3yGFph2ddNMawbsXEpv2XuZ99zvTZ87jy4hWofSuoLSinuLKKVZF8fnD9/XznGpPGD55k4pBc8k/ow/wEDD/uGCxT5+6n1xFrjHDzyT358tHb+N59z/PvN2cxpLiAipxs5m6q2+4ctZ3Skjx++4crGTRoCK2No/D7wwwYcAz/9+AdCCH4zaTxfHv0MBbV1vP7UyZw68RxPLtgMR9VrWVsz+7kBPy8sXw1/1u+Ol1ndXfxaRp+XevkXMrICJGTk8v69R3LLpRmZdA9M4PZG2u2l1QgkXRgX76U0TQdv99PLOaVx2gPXDAMHcdx0DQV20qmrweFGZ4TaPXq1YwY353+Y4dRECrm9t/9HQAx/RF6GEn6F+SxrH7bdWYVdd882+4uiqLQf/xYsosKmfO/6Ttel514A4499lh+85vfcMYZZwAwdepUAP74xz+m13nwwQd5//33ef55Ly1q6dKlnHjiidTU1Gx33I2RCPfNeJfMgMqQbgUQ86HFGvjejTeS4SQhaYPwTgDVFQgFEoFMtk5l8iXjqK7ltVMH2NqbLwSKcLwdVBQUvNbkQgHTn4FlO4RdE8X1OlUpArJzcnhn1hc01td7v6tpIASVCxfy3FP/4POPPuqwD2998jGXTT6b5iYvyubzJYsZO9B7SDr7ggs4dsLx/OJHP+aJF1/giUcfZdrb7zBy7Fh+etMPOfeii/nalVcy9Jgh/PDGm7AtC38ohGboxFsj3iQ+lEEoFKYl0owR8JOMJ3CSSQyfn4A/QGtk25O4nRHKysEfyqBm9e51ZcgqKCaUlUtTzfoOy8O5OekOQa7j0rRxE0IIsrPDPP2PJ5nzzjtsqm4gUpLBaWeM5OgB3WhpiXHNNfcBMPzk8+gzbBy3XzZ2j/ZnZ+iqyvkD+3HNyCE4ruCcp1/e5npnXHsLI0+/iLUrPmTIyRMRbc2M7KlRmuk5s+bVacx4dxG9igOcOqaUzJBBPGEjnDDNzT5yc02spEH1uixUVMqqZxOKehcHBXBUDVfVAC9CyvvTRXNdXEUh4Q8TDQSxNQ1FuARNk6AZx5dygsZDWURDeSiuQHNtbGHixKPk46CnotkszQeqhqvY+AMKbpafNwr7YusGx+XGKDYj1Fg5NK9xKd+8jMBW6UCxYDa2qpEVbcT0hbH8QQJmBD1pYmZnEw9mYUQjhCPNAKzvO5KoL5eytbPwxyMk/JlpB6nSM5uonoWoi5MRrcfSfCi46I7Nur5jiGuZ9Fk1EwDVsVFT6YACBdswQFXQk0kECvFgNrqdxJ+M4qgqmusSCYRJ+sNkxiMYyQRt4XxCZjOabRM3VJozitG1IKrr4DeShAMm0VaNYEsj6ApuThCjLgJFYSyfjloTwRWwpLAQI2coCbuRspoV5LdtsTFH00ARaPaWi3rSCGIZAVTXJpiIbLXch6Pp6LaNYXd+A+eoKmYwE0I+lDaTQLyVpoTJu3WbuPCmM3FUnagFeUHBxojCl40Z6e4Fs6e9wcpZX3LKdd+goKwntmWhGwYzHnqC6sol5BQXMWji8WQXFxJtbiGyMU6fYeMYtupzTvZF8ZtRDNt70F9QU8eSzQ2c0a83dW1RTn7iBWratt1St90+Xrr71m1+vjP65edyTHEhLy/eEnFzwgWnccPJA9E3tGD270HduiDdl81Fc1MpZ7TfpBQiGYW0ZRbhGCoJw4dt5IHqUtCymoINq3E0jVggTEY0grrVbc0MBolk52DZIFDRNANFVUkm2vAnY+Ql4miOQ1swg9ZAJgIHBRc1dcRtRcFRFGxFQbigOTaGpuEL+lAdFyPaBmYSU9dJ6gaOroOiYggX1XWxVBWEIGzbGLaNo4CNitD8uKpBQhXYuPhcgYGCpnvbhwDDjJPR1ogqXCw9QDSci6P5cFUNV9dwUVBcUF0XRbgYtknAbE7dG10EkAiEEZpGMBZBEYJEZhaJ7Fx0O0lGzSYac3pS260/QqjYtBAICnyGQt6qVWQ2NuCoGrpjs37kcJJONlnVayloqML0hbAMjYzUg5zpC9OcWYAuQNgxXEVBoOAoDjbCuw8LQcBOorkuSU3HUQ0UzYeChovrdcUUDi4OLi5uut6ciqrpqKqCJgSqAFdRcFGwhYvAxee4qVE0hK6hhYIEFUFWcwO67RDxh7BD2Si2gut4Z1V2yyZ0O4Gj+dAci2hGHuHU8QZI6n5sI4CjGqg4CM0laRhYmoHAj6Lo6D4Hn2bhNMUQtkPSl4GiQGZbA7qdpC2YQZs/jCMECBefcNCENxcRQsERAtdrnA2qgqL7UHUdXRMYmoLmKgihklQDKLqKT7VImiY+R0XYYGkGPitOZnOtp3NVQdFVSHoOgAV5fanK7MZPFj5HQyzOuU+/zKwN25+bbY9dtf8hp5zIaSdNoHTtWnq2RdBSk/j/rljNktoGfnL8aKKhPExfEFV1USxBdqSG2pJiisbmkvzvMvz2FvuNGX7asnsiVJX11auJqCZlPUspbm0knLBgq5crlmEQzcrCNHwEW1rRLItWf4CI7kMIFzUVtaypoCkqCgJhWdiug6mqWKkKCaqqYvh9aKqKsB2E66C0T5fT/wZNVTEMA1VVELaNsB2vwKii4fq8+5jPtVFch7grSDguQkm9hVYUFFVF1Q1QVVwBgawwTRs24ZgJCku7Y+g+7KSNYztYloljJfE5Nt0ScQKOTVLV8LsODb4AEb+BEwwRCgdp73rsuC6uUFFUDdV1CcSjZDY0oDkOrqoSy83BHFBGzMwksGYDeY1rUvMRFUs3EIqKYVsouJjBIKaehePTyMiMobaYGA1REgNKcXvn8LWv3w54te96DBrAphWraNpUw7XXnsrks0fz7EyBESzmN+cN2+1zb3+yvfPaCPjJyMulaaNnK9eOGMLk/p3Thh9fsITa3r25prQbxS2NOD4dFx0jmcBVVVTXJeELkvRlYPlCuKpOIN5CZtR7iesqKrbhx5fcEpps6TrxUAaWP4jlODjCRTMMAq5FuKUVw0ri6Dq25sc2/KgGEHdQRRKhKAQSMVryurGxaDD+RBt5TWvJaq1FFS6OquKiYLgOjqLQEgjR6jPQab+uqjiaiqNqKLqB4fejal7DFDVpEUzE8cdj+M04ihBEQhlEDQPdtgk6NqGk6S3PzKLZ50dzFcKJONmxVhQgrhs0h0IYCmiKgh0IYhs+rGQS23JQVT+oOpadwHVMBC5iKxtXVBVVUzB0DU0xUEQIVdFI2jEsO45AePM+x0IVgpimkdC2pPwo3h9oqrIls0EAuN6fmoquavgMFVVVSSZdhKthaEFUdBzXQrFjBOOt6I5DIhAm4QtiuUksx3OoGb4AQX82mqugJ9swzDZCZgzdsbA0nYSmk/BlkvQFUEhiCItw0kZ1XEwjgKUbuK6D6zrYjoWbmg+5gK0qOIqGrmjedcO1EMKhY5kPBTd1D1NQURU15UwR3n+p54/2r+i6gT8URNU1knET2zRT5T9E+//eWEJgCOGdRyipezA4bLkfe/+r3rH1mkR7z98pcYU3q9N03Wu8I1zwZjLoqo5u+NFUHVUFFRfDMtGTJrppgnCJ+XzEUHBd17t36gZC0dBT13dXKDguOMJFd21ynSR+AbYvC8sIYSkWSTfOhnBP6vRsLmr8gKKAjrNyI1nYvJp/NCtKBjNs/mv0zs3hD4sX8LNfXUZ9bS6NmzOI2jUI16XQCVFaPRtVCGpCGSQRXqAKoOo6vnAYzafj2ha2YxO3BabtgPDOPZ+heZkVSQvLsr3j064XvDmvUDydp2vuqhpBQ0dXFWzbxXFdFF0D1ZtrCVQUVcEXCKD7dFRXoJoJssrLGD16dKdrF+xCJFBpaSnV1VveMqxfv56xY8fudJ3S0tJOTqDrrruOb3/bKw4rbJWQ1gvHgnmplODhfhU7qdJMALZVp9PdcoAAEnoA2IOCogLQdFq0jrs/+bwLeemVadz6y9+ml7307JPMXbKCC7/xTWbMWYTjOORkZ9Pc0kJrLI6dV0xTzDPQuoZG8gcNZdXqKiacdTbRaJQmfybB7BxW17cijDBXXDoFVdMJhnPRVB+qopNd0K3DdgRDOR02NSuvOLW8427kZ+w4tH1HOPbue/9dx0FVVPK7de4OI1LDKajklWzpNpVwDMzjriLvOMgDlsWgPQDhip//Nb1eS/3uT0p3Fdt1ealyGS9VLtvhevFIMz5/kH5Hn06iFqCET77iJyvvMxEXeGtbUdOb2hXkXdQqi4ft7abvG1J+jA8bMoBUzZUALOy5B0WQQ0D711wg4bKweOT21y/exjIXcAULy47f/d/fG3JT0s7WJcG2PqWTNpDFqqKR6cO1X9m6tvcg+Pe8Ha9+9NgLOHrsBQCp8zSADUw4/7twfsd1c7OAMs++bnv8QSaecDoLFn5JpHoZ5/QrZ0J5T8b06M6qxiYueu7V7TqAYIt9bG23e8IVF3T896ub8KrTtWcGVpyw80EEKT0B/grYk7bl+yqde3+Xmdlftcp9QO+B3t9N75oFYWj3WWYfA1vfYtozirN7eXKoUPCV+l5blz/Jqui8/p4e769Ghufs4Ti7wrZKuOQM3O7qVksDxz/yDJV19UT38M3/7tj/phhsKixk9tbHsvckKoCXtvWFwlSE4UKgbAdFdgd6zQzqYed2dyiWf3KAEm8f0w3PfSnZE7YOcNHw7nlfLYW5KfVnVsW27WF7ZKckCSyl03kxzMtiJg68NBt8wf07z9tTdvW8jrPtczer9ySygJnQ+RqwPbJ2axN37bvbKoWYdEENQf5AT/YGDe+eGdzOb+0te9bsef+zve3K2cXvH9ieF3vPtq4RBxON/XYMM5NR+lSvx1F1WrK7scQSbKyrxe0xjDkjL2QOMKD3JF5JBzbbgFfLa60GsytO3PmP7KznyP4qZ9eeWGLAWZjbXW2nTqDt1YDZ3XUAHnnkER555BEA5i1YyIM3X9bh87v/8Cs2V6/e2SbtN846dRJ/uvvuDtvw4vPPM2BAf1YsW8pbr76AZVk89vgT/P3vD/LQgw/y1EP3s2lTDaedfgY/u3Uqj//9PtZXr6dy8WIywmE2V6/m17/6Ff93711s3LCRz7/4gpLCfDZXr6a1sY54W8+Dss+Os/sTwbamehLRSLsvfZeING7mwV/8YKfrtTbU7nSd/c3Mlx5j2awPOuWH7ys0zfOOt3do+iqKAprPwElaO0wfO5LQ/QbCFZ4IFxQFXddRdQ3H9t6QinSBcQVV09B9OlkZIZpbYwjXwXUEwrF3GoKsagpZmSEcx8F2vLclritwhcCx7Q5vo6A9QMF7Q6aoSqfPuxKtDbVEWxpZsXKL93JJTS13zeyc9rE99rd9bI2qawjX3e4xVTUF19n2Z4qmousamuKlCDtesCeqouDzGyQTJooXA4AjwO/z4wqB61rk5IQRAgzD36EDlq5q+FUFVVWJI7AdF10x0HUd27bRfQaWbZJImGDbYNskUgVHDU3DMHSicdPbJ0UQzs7C79fRNS0d2dKasIi2RUnE4tjWjs9Xr06eQcDnJ55MougqbYkkvkAA13ZwHAfh2Ph8BmaqcK+iqSh4nRiDhk7cslGULfX+FVVJtUK1txxHnw9hWRiGRsK08AWCqIaGroImXBzHoS2WJJSdjXBdwn6DpOOk7xBeII/AMAx8qWNuuV7QrpdGjKcrXcPQveOhKAo+3SDgD2DoBrgOrm2TjEWJJxK0xC1sFHwqBAyFzGAQvy9IcyJB3LIRjo0uXEI+nbaYTUM0jusKotEIPk3DEV6E0baOr64pKLqB7vfjODaK4+C6rlcPTVVQVYOkZeHYFkG/D7+h4VoumuEn6trk5WUgkkkUAU1Rk2BmNq5jkRU0CAd8uAg2NzRjxpNouobPb2D4DPyBAKoeQNg2ZrQNO5nEtrw064aWZhQFcgIBQoZOzLYxXYil2rsbqRa3O0qFarf/veFA2v9XCeh6+g22QGC77nbtw9A0MgI+Egp061HsFT72B7zoOkUlmTC9aFnAa2GqEgyHyTBUMnSvm6pt2SRi8dQ1wrsHuEJ41xJdQ/UZ2I6NmTBJmBau4+I4LnogQCIZxXUszOY2SDq0OS6+YIjiony6FeYiHHfLfcm2sRNxNFXBH/Tj03UcR2CaFrV19SQSCRTAn5lBZlYGGTlZRCwLVwjspI3r2N4bdU1HR8FnOTQ0tZK0HXRNQxWCpJnAFTaKzyCatGluieAKl0AgSCB17YsnYlhJEyMY8Ma1bfx+I7Xf3rXBtV0CAT+xaKzDdWN36ArzvK9yoM/r9syzr2aJKAre9bY9EkCAqqhohk4wYKC4gmTSpq2tPZ1FRUl9jnCxzOR29eLz66iqhuH3YQQDaLoBwsV1XQzdICsYIp60cWwb27XxAX4FVMfBcVxsx8FxbGKW1Tl9TFEIZ2WhqAq26Z07qqqiqhp+VdCWNNH8PlRFIRgMoSpg2QLbcrAtC9e2yc8MYmga/mDAi9JTVVRNJRjKRNU0VF1FVTQvMtdMYpkmzS1R4mYCVRNkZAbRdd27dmsaQlExUdA0jbCmkaVr6fgKBLiui2laOLaDm8r60HQVRQEzbhKPxrEsGwFYjkU47MdyHMCbW9iKIKEp6LpBvC1Gtq4TCvjJyMhEFQqRlmZqNm3GtCwirk0gHMC2HVzHC1wMZmXgVxxyfAaRhIVpq2yqqwUhyMgI4Pf7SFoOgYwwWZkZZOfkACqaCiHVmzPEEzEc1yEQCOH3+XFcUlE1IhXppKGqCpaVxHbs1POHhqp5kUHtCFdgJS2cpEn7lEr3GanuvQqoCmoqwscGEqkIY1VVvIgpVcFQNQxNRVe8YytcF9dxEcKbuztCpJ5nvCgugcCKedcRVyi4LiQSCWKxGJG2CJbl4LguScelzXIIBIKp+6+GqmlkBP2EMzMI+n3odhIcm5akjSkgYKgE/AaZ4SCuorK5JUEkGiMWj4KiENR0crKyUFSI1FbzaNNmahubicYskqmMiqLyR7d5PfCHQ2i67t27fTr5+YXYjoWmq4RDYYL+MJoGrmVC0ka4LpHmKD5NIyczSFZmEFCwbIeW5ihJyyIzJ4NQZhg3lankpoKfVUVBw4tYVRUv8slMmjS0tJF0bFThPVPaCRNckZpPqt6c13ZxbBvTtqlvi3PWA51rvG6N2JEce+yx4s0330z/e+rUqWLq1Kkd1nnwwQfFlClT0v9eunSpKCkp2eG4s2bN6rTsH//4xw6/I+XQE6lTKVKkSJEiRYoUKVKkSJEi5cDJtvwt7bJT1/esWbPo168fFRUVGIbBlClTmDZtWod1pk2bxlVXXQXA2LFjaWlp2WE9IIlEIpFIJBKJRCKRSCQSyYFlp+lgjuNwww038NZbb6FpGo8//jiLFy/m+uuvB+Chhx7if//7H2eddRYrV64kFotx9dVX7/cNl0gkEolEIpFIJBKJRCKR7Do7dQIBvPHGG7zxxhsdlj300EMd/n3DDTfsu62SSCQSiUQikUgkEolEIpHsUw58hb8dIIRA03ZWSltyqKBp2jYLhEskEolEIpFIJBKJRCI58HQpJ9CaNWuYPHmydAQdBmiaxuTJk1mzZs3B3hSJRCKRSCQSiUQikUgk7GI62IHi3nvv5aabbuKiiy7aZtt5yaGDEII1a9Zw7733HuxNkUgkEolEIpFIJBKJREIXcwI1NTXx61//+mBvhkQikUgkEolEIpFIJBLJYUeXSgeTSCQSiUQikUgkEolEIpHsH6QTSCKRSCQSiUQikUgkEonkCEA6gSQSiUQikUgkEolEIpFIjgAU4KD08G5ubmbFihX7bfzs7GxaWloO2fELCgqor6/fb+Mf6sfnUB9f6vfwHl/q9/AeX+r38B5f6vfwHl/q9/AeX+r38B5f6vfwHl/qd9+OX15eTlFR0XY/FwdDHnroITn+DmTWrFmH9PbL8aV+j+TxpX4P7/Glfg/v8aV+D+/xpX4P7/Glfg/v8aV+D+/xpX4P3PgHLR3stddek+MfRA7143Ooj7+/OdSPz6E+/v7mUD8+h/r4+5tD/fgc6uPvbw7143Ooj7+/OdSPz6E+/v7mUD8+h/r4+5tD/fgc6uPvbw7147Ovx9+vHispeyb72xMqRepXitSvFKlfKVK/R5pI/R7eIvV7eIvU7+EtUr8HTmRh6C7Kww8/fLA3QbIfkfo9vJH6PbyR+j28kfo9vJH6PbyR+j28kfo9vJH6PXActMLQEolEIpFIJBKJRCKRSCSSA4eMBJJIJBKJRCKRSCQSiUQiOQKQTqADyGOPPUZtbS0LFy5MLzvmmGP45JNPWLBgAdOmTSMzMxOAK664grlz56bFcRyGDh0KwKWXXsr8+fNZtGgRd95550HZF0lndke/uq7z5JNPsmDBAhYvXszUqVM7jfef//ynw1iSg8u+0q+0367J7ujXMAwef/xxFixYwLx585g4cWKn8aT9di32lX6l/XY9evTowbvvvsvixYtZtGgRN954IwC5ublMnz6d5cuXM336dHJyctLfmTp1KitWrGDp0qWcdtppncaU9tt12Jf6lfbb9dhd/ebl5fHuu+8SiUS4//77tzmmtN+uw77Ur7Tffc9BL0x0pMiECRPE8OHDxcKFC9PLvvjiC3HCCScIQFx99dXid7/7XafvHX300WLVqlUCEHl5eWLt2rWioKBAAOLJJ58UkyZNOuj7JmX39Hv55ZeL5557TgAiGAyKqqoqUV5env7eBRdcIJ555pkOY0k59PUr7bfryu7o93vf+554/PHHBSAKCwvF7NmzhaIo6e9J++16si/0K+23a0pJSYkYPny4AERGRoZYtmyZGDhwoLjzzjvFrbfeKgBx6623ij/+8Y8CEAMHDhTz5s0TPp9PVFRUiJUrVwpVVdPjSfvtWrKv9Cvtt2vK7uo3FAqJ8ePHi+uvv17cf//9ncaT9tu1ZF/pV9rvfpGDvgFHlJSXl3e4MLW0tKT/3qNHD1FZWdnpO7fffrv4wx/+IAAxatQoMWPGjPRnX/va18QDDzxw0PdLyu7pd8qUKWLatGlC0zSRl5cnli1bJnJzcwUgwuGwmDlzphg4cKC8iXUx2Vv9Svvt2rKr+v3b3/4mrrzyyvRnb7/9thg9erQAab9dWfZWv9J+Dw159dVXxSmnnCKWLl0qSkpKBHgPIkuXLhWAmDp1qpg6dWp6/TfffFMce+yxAqT9Hgqyp/qV9ntoyM702y7f+MY3OjmBpP12fdlT/Ur73fci08EOMosWLeLcc88F4JJLLqFnz56d1rnssst47rnnAFi5ciUDBgygvLwcTdM4//zzt/kdSddge/p96aWXiEajbNq0iXXr1nH33XfT1NQEwO9//3vuueceYrHYQdtuya6xu/qV9ntosT39zp8/n/POOw9N06ioqGDkyJHpz6T9Hjrsrn6l/XZ9ysvLGT58OJ9//jnFxcXU1NQAUFNTQ1FREQClpaVUV1env7N+/XpKS0sBab9dnb3Rr7Tfrs+u6HdHSPvt2uyNfqX97nukE+ggc8011/D973+f2bNnk5mZSTKZ7PD5mDFjiMViVFZWAtDc3Mx3v/tdXnjhBWbOnMmaNWuwbftgbLpkF9iefseMGYPjOHTv3p1evXrx4x//mF69ejF06FD69u3Lq6++enA3XLJL7K5+pf0eWmxPv48//jjr169n9uzZ3HvvvXzyySfYti3t9xBjd/Ur7bdrEw6Hefnll7npppuIRCLbXU9RlE7LhBDSfrs4e6tfab9dm13V7/aQ9tu12Vv9Svvd9+gHewOOdJYtW8bpp58OQL9+/Zg8eXKHz6dMmZKOAmrn9ddf5/XXXwfguuuuw3GcA7Oxkt1me/q94oorePPNN7Ftm82bN/Pxxx8zatQo8vPzGTlyJFVVVei6TlFREe+99x4nnXTSwdwNyXbYXf1WVVVJ+z2E2J5+HcfhRz/6UXq9jz/+mBUrVjBx4kRpv4cQu6tfkPffroqu67z88ss888wzvPLKKwDU1tZSUlJCTU0NJSUl1NXVAV5kyNZvkHv06MHGjRs57rjjpP12UfaFfkHab1dld/S7PaT9dl32hX5B2u/+4KDnpB1J8tWaBIWFhQIQiqKIp556Slx99dXpzxRFEdXV1aJXr14dxmj/Tk5Ojpg7d67o16/fQd8vKbun31tuuSVdeDQUConKykoxZMiQHY4l5eDLvtCvtN+uK7uq32AwKEKhkADEKaecIj744IOdjiXl4Mu+0K+0364pTz31lPjLX/7SYdldd93VofDonXfeKQAxaNCgDoWDV61a1aEw9LbOFSmHh36l/XZN2R39tsu2agK1i7TfriX7Sr/Sfve5HPQNOGLk2WefFRs3bhTJZFJUV1eLa665Rtx4441i2bJlYtmyZeKOO+7osP7EiRPFp59+us1xKisrRWVlpbjssssO+n5J2X39hsNh8eKLL4pFixaJyspK8ZOf/KTTePIm1rVkX+lX2m/XlN3Rb3l5uVi6dKlYvHixmDFjhigrK+s0nrTfriX7Sr/SfruejB8/XgghxPz588XcuXPF3LlzxZlnniny8vLE22+/LZYvXy7efvvtdPMFQNx2221i5cqVYunSpeKMM87oNKa0364j+1K/0n67nuyJfquqqkRDQ4OIRCKiurpaDBw4sMOY0n67juxL/Ur73beipP4ikUgkEolEIpFIJBKJRCI5jJGFoSUSiUQikUgkEolEIpFIjgCkE0gikUgkEolEIpFIJBKJ5AhAOoEkEolEIpFIJBKJRCKRSI4ApBNIIpFIJBKJRCKRSCQSieQIQDqBJBKJRCKRSCQSiUQikUiOAKQTSCKRSCQSiUQikUgkEonkCEA6gSQSiUQikUgkEolEIpFIjgCkE0gikUgkEolEIpFIJBKJ5Ajg/wPDCweUELUQEgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_df.plot(figsize=(20,5))\n",
    "plt.title('Recession Forecast with XGBoost predicted')\n",
    "plt.legend(loc='lower left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "pred_df['2019':].plot(figsize=(8,5))\n",
    "plt.title('Recession Forecast with XGBoost predicted')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_df['1981':'1984'].plot(figsize=(8,5))\n",
    "plt.title('Recession Forecast with XGBoost ')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_df['2007':'2009'].plot(figsize=(8,5))\n",
    "plt.title('Recession Forecast with XGBoost')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "4f7527b0034a603a2f6ced09004f8a888be406f0f25f6fb9b2bc8cc71cf9bc4b"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}