{
 "cells": [
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-11T02:51:57.789440Z",
     "start_time": "2025-02-11T02:51:57.208223Z"
    }
   },
   "cell_type": "code",
   "source": "import pandas as pd",
   "id": "6a10e07f5f498bc6",
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-11T03:14:30.972240Z",
     "start_time": "2025-02-11T03:14:30.857326Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# source_df = \\\n",
    "#     pd.read_csv(\"C:\\\\Users\\\\lanyuanxiaoyao\\\\SynologyDrive\\\\data\\\\Tushare\\\\日线行情 1990-2024\\\\分组行情\\\\000001.SZ.csv\") \\\n",
    "source_df = pd.read_csv(\"/Users/lanyuanxiaoyao/SynologyDrive/data/Tushare/日线行情 1990-2024/分组行情/600519.SH.csv\") \\\n",
    "        [[\"trade_date\", \"vol\", \"open_qfq\", \"close_qfq\", \"high_qfq\", \"low_qfq\", \"macd\", \"macd_dif\", \"macd_dea\"]]\n",
    "df = pd.DataFrame()\n",
    "df[[\"date\", \"volume\", \"open\", \"close\", \"high\", \"low\", \"macd\", \"macd_dif\", \"macd_dea\"]] = \\\n",
    "    source_df[[\"trade_date\", \"vol\", \"open_qfq\", \"close_qfq\", \"high_qfq\", \"low_qfq\", \"macd\", \"macd_dif\", \"macd_dea\"]]\n",
    "df[\"datetime\"] = pd.to_datetime(df[\"date\"], format=\"%Y%m%d\")\n",
    "df[\"datetime_text\"] = df[\"datetime\"].apply(lambda x: x.strftime(\"%Y%m%d\"))\n",
    "df.sort_values(by='datetime', inplace=True)\n",
    "df"
   ],
   "id": "5c40c0d27e1dacd9",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "          date     volume        open       close        high         low  \\\n",
       "2187  20010827  406318.00     4.23675     4.36443     4.63820     4.03295   \n",
       "2188  20010828  129647.79     4.29568     4.52525     4.54244     4.24902   \n",
       "2189  20010829   53252.75     4.53999     4.46632     4.54244     4.43195   \n",
       "2190  20010830   48013.06     4.45405     4.55472     4.60505     4.41967   \n",
       "2191  20010831   23231.48     4.56086     4.54367     4.61856     4.51789   \n",
       "...        ...        ...         ...         ...         ...         ...   \n",
       "2182  20241225   17123.39  1538.80000  1530.00000  1538.80000  1526.10000   \n",
       "2183  20241226   18286.51  1534.00000  1527.79000  1538.78000  1523.00000   \n",
       "2184  20241227   20759.32  1528.90000  1528.97000  1536.00000  1519.50000   \n",
       "2185  20241230   25129.82  1533.97000  1525.00000  1543.96000  1525.00000   \n",
       "2186  20241231   39354.45  1525.40000  1524.00000  1545.00000  1522.01000   \n",
       "\n",
       "       macd  macd_dif  macd_dea   datetime datetime_text  \n",
       "2187  0.000     0.000     0.000 2001-08-27      20010827  \n",
       "2188  0.021     0.013     0.003 2001-08-28      20010828  \n",
       "2189  0.025     0.018     0.006 2001-08-29      20010829  \n",
       "2190  0.037     0.029     0.010 2001-08-30      20010830  \n",
       "2191  0.042     0.036     0.016 2001-08-31      20010831  \n",
       "...     ...       ...       ...        ...           ...  \n",
       "2182  5.430     5.063     2.348 2024-12-25      20241225  \n",
       "2183  4.192     4.968     2.872 2024-12-26      20241226  \n",
       "2184  3.295     4.931     3.283 2024-12-27      20241227  \n",
       "2185  1.993     4.529     3.533 2024-12-30      20241230  \n",
       "2186  0.880     4.083     3.643 2024-12-31      20241231  \n",
       "\n",
       "[5591 rows x 11 columns]"
      ],
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>volume</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>macd</th>\n",
       "      <th>macd_dif</th>\n",
       "      <th>macd_dea</th>\n",
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       "      <th>datetime_text</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2187</th>\n",
       "      <td>20010827</td>\n",
       "      <td>406318.00</td>\n",
       "      <td>4.23675</td>\n",
       "      <td>4.36443</td>\n",
       "      <td>4.63820</td>\n",
       "      <td>4.03295</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2001-08-27</td>\n",
       "      <td>20010827</td>\n",
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       "    <tr>\n",
       "      <th>2188</th>\n",
       "      <td>20010828</td>\n",
       "      <td>129647.79</td>\n",
       "      <td>4.29568</td>\n",
       "      <td>4.52525</td>\n",
       "      <td>4.54244</td>\n",
       "      <td>4.24902</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.003</td>\n",
       "      <td>2001-08-28</td>\n",
       "      <td>20010828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2189</th>\n",
       "      <td>20010829</td>\n",
       "      <td>53252.75</td>\n",
       "      <td>4.53999</td>\n",
       "      <td>4.46632</td>\n",
       "      <td>4.54244</td>\n",
       "      <td>4.43195</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.006</td>\n",
       "      <td>2001-08-29</td>\n",
       "      <td>20010829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2190</th>\n",
       "      <td>20010830</td>\n",
       "      <td>48013.06</td>\n",
       "      <td>4.45405</td>\n",
       "      <td>4.55472</td>\n",
       "      <td>4.60505</td>\n",
       "      <td>4.41967</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.010</td>\n",
       "      <td>2001-08-30</td>\n",
       "      <td>20010830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2191</th>\n",
       "      <td>20010831</td>\n",
       "      <td>23231.48</td>\n",
       "      <td>4.56086</td>\n",
       "      <td>4.54367</td>\n",
       "      <td>4.61856</td>\n",
       "      <td>4.51789</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.016</td>\n",
       "      <td>2001-08-31</td>\n",
       "      <td>20010831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <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>2182</th>\n",
       "      <td>20241225</td>\n",
       "      <td>17123.39</td>\n",
       "      <td>1538.80000</td>\n",
       "      <td>1530.00000</td>\n",
       "      <td>1538.80000</td>\n",
       "      <td>1526.10000</td>\n",
       "      <td>5.430</td>\n",
       "      <td>5.063</td>\n",
       "      <td>2.348</td>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>20241225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2183</th>\n",
       "      <td>20241226</td>\n",
       "      <td>18286.51</td>\n",
       "      <td>1534.00000</td>\n",
       "      <td>1527.79000</td>\n",
       "      <td>1538.78000</td>\n",
       "      <td>1523.00000</td>\n",
       "      <td>4.192</td>\n",
       "      <td>4.968</td>\n",
       "      <td>2.872</td>\n",
       "      <td>2024-12-26</td>\n",
       "      <td>20241226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2184</th>\n",
       "      <td>20241227</td>\n",
       "      <td>20759.32</td>\n",
       "      <td>1528.90000</td>\n",
       "      <td>1528.97000</td>\n",
       "      <td>1536.00000</td>\n",
       "      <td>1519.50000</td>\n",
       "      <td>3.295</td>\n",
       "      <td>4.931</td>\n",
       "      <td>3.283</td>\n",
       "      <td>2024-12-27</td>\n",
       "      <td>20241227</td>\n",
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       "      <th>2185</th>\n",
       "      <td>20241230</td>\n",
       "      <td>25129.82</td>\n",
       "      <td>1533.97000</td>\n",
       "      <td>1525.00000</td>\n",
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       "      <td>3.533</td>\n",
       "      <td>2024-12-30</td>\n",
       "      <td>20241230</td>\n",
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       "      <th>2186</th>\n",
       "      <td>20241231</td>\n",
       "      <td>39354.45</td>\n",
       "      <td>1525.40000</td>\n",
       "      <td>1524.00000</td>\n",
       "      <td>1545.00000</td>\n",
       "      <td>1522.01000</td>\n",
       "      <td>0.880</td>\n",
       "      <td>4.083</td>\n",
       "      <td>3.643</td>\n",
       "      <td>2024-12-31</td>\n",
       "      <td>20241231</td>\n",
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     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-10T13:23:42.952570Z",
     "start_time": "2025-02-10T13:23:42.938745Z"
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   },
   "cell_type": "code",
   "source": [
    "daily_df = df.copy()\n",
    "daily_df = daily_df[daily_df[\"datetime\"].dt.year >= 2024]\n",
    "close_median = daily_df[\"close\"].median()\n",
    "daily_df[\"close_to_median\"] = daily_df[\"close\"] - close_median\n",
    "macd_max = max(abs(daily_df[\"macd\"].max()), abs(daily_df[\"macd\"].min()))\n",
    "close_max = max(abs(daily_df[\"close_to_median\"].max()), abs(daily_df[\"close_to_median\"].min()))\n",
    "ratio = close_max / macd_max\n",
    "daily_df[\"macd_close\"] = daily_df[\"close_to_median\"] / ratio\n",
    "\n",
    "# daily_df[\"macd_sign\"] = daily_df[\"macd\"] > 0\n",
    "# daily_df[\"group\"] = (daily_df[\"macd_sign\"] != daily_df[\"macd_sign\"].shift(1)).cumsum()\n",
    "\n",
    "daily_df[\"macd_diff\"] = daily_df[\"macd\"].diff()\n",
    "daily_df[\"macd_diff\"] = daily_df[\"macd_diff\"].fillna(0)\n",
    "daily_df['macd_trend'] = daily_df['macd_diff'].apply(lambda x: 1 if x > 0 else -1 if x < 0 else 0)\n",
    "daily_df[\"macd_trend_group\"] = (daily_df['macd_trend'] != daily_df['macd_trend'].shift(1)).cumsum()\n",
    "\n",
    "daily_df[\"pre_macd\"] = daily_df[\"macd\"].shift(1)\n",
    "daily_df[\"point\"] = (daily_df[\"macd_dif\"] > 0) & (daily_df[\"macd_dea\"] > 0) & (daily_df[\"macd\"] > 0) & \\\n",
    "                    ((daily_df[\"macd_dif\"] > daily_df[\"macd\"]) & (daily_df[\"macd_dea\"] > daily_df[\"macd\"])) & \\\n",
    "                    (daily_df[\"pre_macd\"] < daily_df[\"macd\"])\n",
    "daily_df.loc[daily_df[\"point\"] & daily_df[\"point\"].shift(1), \"point\"] = False\n",
    "daily_df"
   ],
   "id": "f6ca89b1047f1eb0",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "          date      open     close      high       low   macd  macd_dif  \\\n",
       "3576  20240102   8.57646   8.41205   8.60386   8.41205  0.107    -0.162   \n",
       "3577  20240103   8.39379   8.40292   8.42119   8.35725  0.098    -0.155   \n",
       "3578  20240104   8.39379   8.32072   8.39379   8.29332  0.080    -0.153   \n",
       "3579  20240105   8.31158   8.46686   8.62213   8.28418  0.087    -0.139   \n",
       "3580  20240108   8.43032   8.35725   8.49426   8.32072  0.076    -0.135   \n",
       "...        ...       ...       ...       ...       ...    ...       ...   \n",
       "3813  20241225  11.86000  11.92000  12.02000  11.84000  0.050     0.080   \n",
       "3814  20241226  11.92000  11.86000  11.93000  11.78000  0.051     0.087   \n",
       "3815  20241227  11.87000  11.83000  11.90000  11.66000  0.044     0.089   \n",
       "3816  20241230  11.78000  11.95000  11.97000  11.78000  0.052     0.100   \n",
       "3817  20241231  11.93000  11.70000  11.99000  11.70000  0.020     0.086   \n",
       "\n",
       "      macd_dea   datetime datetime_text  close_to_median  macd_close  \\\n",
       "3576    -0.216 2024-01-02      20240102         -1.50113   -0.339145   \n",
       "3577    -0.203 2024-01-03      20240103         -1.51026   -0.341208   \n",
       "3578    -0.193 2024-01-04      20240104         -1.59246   -0.359779   \n",
       "3579    -0.183 2024-01-05      20240105         -1.44632   -0.326762   \n",
       "3580    -0.173 2024-01-08      20240108         -1.55593   -0.351526   \n",
       "...        ...        ...           ...              ...         ...   \n",
       "3813     0.055 2024-12-25      20241225          2.00682    0.453394   \n",
       "3814     0.062 2024-12-26      20241226          1.94682    0.439838   \n",
       "3815     0.067 2024-12-27      20241227          1.91682    0.433061   \n",
       "3816     0.074 2024-12-30      20241230          2.03682    0.460172   \n",
       "3817     0.076 2024-12-31      20241231          1.78682    0.403690   \n",
       "\n",
       "      macd_diff  macd_trend  macd_trend_group  pre_macd  point  \n",
       "3576      0.000           0                 1       NaN  False  \n",
       "3577     -0.009          -1                 2     0.107  False  \n",
       "3578     -0.018          -1                 2     0.098  False  \n",
       "3579      0.007           1                 3     0.080  False  \n",
       "3580     -0.011          -1                 4     0.087  False  \n",
       "...         ...         ...               ...       ...    ...  \n",
       "3813      0.019           1                66     0.031  False  \n",
       "3814      0.001           1                66     0.050  False  \n",
       "3815     -0.007          -1                67     0.051  False  \n",
       "3816      0.008           1                68     0.044   True  \n",
       "3817     -0.032          -1                69     0.052  False  \n",
       "\n",
       "[242 rows x 17 columns]"
      ],
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       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.107</td>\n",
       "      <td>False</td>\n",
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       "      <th>3578</th>\n",
       "      <td>20240104</td>\n",
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       "      <th>3579</th>\n",
       "      <td>20240105</td>\n",
       "      <td>8.31158</td>\n",
       "      <td>8.46686</td>\n",
       "      <td>8.62213</td>\n",
       "      <td>8.28418</td>\n",
       "      <td>0.087</td>\n",
       "      <td>-0.139</td>\n",
       "      <td>-0.183</td>\n",
       "      <td>2024-01-05</td>\n",
       "      <td>20240105</td>\n",
       "      <td>-1.44632</td>\n",
       "      <td>-0.326762</td>\n",
       "      <td>0.007</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.080</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3580</th>\n",
       "      <td>20240108</td>\n",
       "      <td>8.43032</td>\n",
       "      <td>8.35725</td>\n",
       "      <td>8.49426</td>\n",
       "      <td>8.32072</td>\n",
       "      <td>0.076</td>\n",
       "      <td>-0.135</td>\n",
       "      <td>-0.173</td>\n",
       "      <td>2024-01-08</td>\n",
       "      <td>20240108</td>\n",
       "      <td>-1.55593</td>\n",
       "      <td>-0.351526</td>\n",
       "      <td>-0.011</td>\n",
       "      <td>-1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.087</td>\n",
       "      <td>False</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3813</th>\n",
       "      <td>20241225</td>\n",
       "      <td>11.86000</td>\n",
       "      <td>11.92000</td>\n",
       "      <td>12.02000</td>\n",
       "      <td>11.84000</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.055</td>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>20241225</td>\n",
       "      <td>2.00682</td>\n",
       "      <td>0.453394</td>\n",
       "      <td>0.019</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>0.031</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3814</th>\n",
       "      <td>20241226</td>\n",
       "      <td>11.92000</td>\n",
       "      <td>11.86000</td>\n",
       "      <td>11.93000</td>\n",
       "      <td>11.78000</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.062</td>\n",
       "      <td>2024-12-26</td>\n",
       "      <td>20241226</td>\n",
       "      <td>1.94682</td>\n",
       "      <td>0.439838</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>0.050</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3815</th>\n",
       "      <td>20241227</td>\n",
       "      <td>11.87000</td>\n",
       "      <td>11.83000</td>\n",
       "      <td>11.90000</td>\n",
       "      <td>11.66000</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.089</td>\n",
       "      <td>0.067</td>\n",
       "      <td>2024-12-27</td>\n",
       "      <td>20241227</td>\n",
       "      <td>1.91682</td>\n",
       "      <td>0.433061</td>\n",
       "      <td>-0.007</td>\n",
       "      <td>-1</td>\n",
       "      <td>67</td>\n",
       "      <td>0.051</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3816</th>\n",
       "      <td>20241230</td>\n",
       "      <td>11.78000</td>\n",
       "      <td>11.95000</td>\n",
       "      <td>11.97000</td>\n",
       "      <td>11.78000</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.074</td>\n",
       "      <td>2024-12-30</td>\n",
       "      <td>20241230</td>\n",
       "      <td>2.03682</td>\n",
       "      <td>0.460172</td>\n",
       "      <td>0.008</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "      <td>0.044</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3817</th>\n",
       "      <td>20241231</td>\n",
       "      <td>11.93000</td>\n",
       "      <td>11.70000</td>\n",
       "      <td>11.99000</td>\n",
       "      <td>11.70000</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.076</td>\n",
       "      <td>2024-12-31</td>\n",
       "      <td>20241231</td>\n",
       "      <td>1.78682</td>\n",
       "      <td>0.403690</td>\n",
       "      <td>-0.032</td>\n",
       "      <td>-1</td>\n",
       "      <td>69</td>\n",
       "      <td>0.052</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>242 rows × 17 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 16
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-10T13:23:46.230728Z",
     "start_time": "2025-02-10T13:23:46.054326Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 根据macd、macd_dif、macd_dea的值绘制macd图像\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(20, 6))\n",
    "plt.plot(daily_df['datetime_text'], daily_df['macd_dif'], label='DIF', color='red')\n",
    "plt.plot(daily_df['datetime_text'], daily_df['macd_dea'], label='DEA', color='green')\n",
    "# plt.plot(daily_df['datetime_text'], daily_df['macd_close'], label='Close', color='gray')\n",
    "plt.bar(daily_df['datetime_text'], daily_df['macd'], label='MACD')\n",
    "point_daily_df = daily_df[daily_df[\"point\"]]\n",
    "# plt.scatter(point_daily_df['datetime_text'], point_daily_df[\"point\"], label='Point', color='blue')\n",
    "for i, row in point_daily_df.iterrows():\n",
    "    plt.axvline(x=row['datetime_text'], color='blue', linestyle='--')\n",
    "\n",
    "for group_name, group_data in daily_df.groupby(\"macd_trend_group\"):\n",
    "    first_row = group_data.iloc[0]\n",
    "    last_row = group_data.iloc[-1]\n",
    "    x_start, y_start = first_row['datetime_text'], first_row['macd']\n",
    "    x_end, y_end = last_row['datetime_text'], last_row['macd']\n",
    "    plt.plot([x_start, x_end], [y_start, y_end], color='cyan')\n",
    "\n",
    "# plt.xlabel('datetime_text')\n",
    "# plt.ylabel('MACD')\n",
    "plt.axis('off')\n",
    "plt.title('MACD')\n",
    "# plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval=15))\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n",
    "point_daily_df"
   ],
   "id": "25cd7f58d7b8c044",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ],
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "          date      open     close      high       low   macd  macd_dif  \\\n",
       "3645  20240418   9.66336   9.86430  10.07437   9.64509  0.017     0.043   \n",
       "3650  20240425   9.59029   9.69076   9.69989   9.57202  0.017     0.057   \n",
       "3652  20240429   9.64509   9.87343   9.95563   9.60856  0.039     0.075   \n",
       "3658  20240510   9.83690   9.99217  10.01044   9.82776  0.040     0.111   \n",
       "3662  20240516   9.90997  10.20224  10.23878   9.89170  0.046     0.133   \n",
       "3808  20241218  11.58000  11.65000  11.74000  11.57000  0.005     0.048   \n",
       "3811  20241223  11.64000  11.73000  11.84000  11.64000  0.008     0.049   \n",
       "3816  20241230  11.78000  11.95000  11.97000  11.78000  0.052     0.100   \n",
       "\n",
       "      macd_dea   datetime datetime_text  close_to_median  macd_close  \\\n",
       "3645     0.034 2024-04-18      20240418         -0.04888   -0.011043   \n",
       "3650     0.048 2024-04-25      20240425         -0.22242   -0.050251   \n",
       "3652     0.055 2024-04-29      20240429         -0.03975   -0.008981   \n",
       "3658     0.091 2024-05-10      20240510          0.07899    0.017846   \n",
       "3662     0.110 2024-05-16      20240516          0.28906    0.065306   \n",
       "3808     0.045 2024-12-18      20241218          1.73682    0.392394   \n",
       "3811     0.045 2024-12-23      20241223          1.81682    0.410468   \n",
       "3816     0.074 2024-12-30      20241230          2.03682    0.460172   \n",
       "\n",
       "      macd_diff  macd_trend  macd_trend_group  pre_macd  point  \n",
       "3645      0.055           1                23    -0.038   True  \n",
       "3650      0.004           1                25     0.013   True  \n",
       "3652      0.022           1                27     0.017   True  \n",
       "3658      0.010           1                29     0.030   True  \n",
       "3662      0.026           1                31     0.020   True  \n",
       "3808      0.001           1                64     0.004   True  \n",
       "3811      0.013           1                66    -0.005   True  \n",
       "3816      0.008           1                68     0.044   True  "
      ],
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>macd</th>\n",
       "      <th>macd_dif</th>\n",
       "      <th>macd_dea</th>\n",
       "      <th>datetime</th>\n",
       "      <th>datetime_text</th>\n",
       "      <th>close_to_median</th>\n",
       "      <th>macd_close</th>\n",
       "      <th>macd_diff</th>\n",
       "      <th>macd_trend</th>\n",
       "      <th>macd_trend_group</th>\n",
       "      <th>pre_macd</th>\n",
       "      <th>point</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3645</th>\n",
       "      <td>20240418</td>\n",
       "      <td>9.66336</td>\n",
       "      <td>9.86430</td>\n",
       "      <td>10.07437</td>\n",
       "      <td>9.64509</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.034</td>\n",
       "      <td>2024-04-18</td>\n",
       "      <td>20240418</td>\n",
       "      <td>-0.04888</td>\n",
       "      <td>-0.011043</td>\n",
       "      <td>0.055</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>-0.038</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3650</th>\n",
       "      <td>20240425</td>\n",
       "      <td>9.59029</td>\n",
       "      <td>9.69076</td>\n",
       "      <td>9.69989</td>\n",
       "      <td>9.57202</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.048</td>\n",
       "      <td>2024-04-25</td>\n",
       "      <td>20240425</td>\n",
       "      <td>-0.22242</td>\n",
       "      <td>-0.050251</td>\n",
       "      <td>0.004</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>0.013</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3652</th>\n",
       "      <td>20240429</td>\n",
       "      <td>9.64509</td>\n",
       "      <td>9.87343</td>\n",
       "      <td>9.95563</td>\n",
       "      <td>9.60856</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.055</td>\n",
       "      <td>2024-04-29</td>\n",
       "      <td>20240429</td>\n",
       "      <td>-0.03975</td>\n",
       "      <td>-0.008981</td>\n",
       "      <td>0.022</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>0.017</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3658</th>\n",
       "      <td>20240510</td>\n",
       "      <td>9.83690</td>\n",
       "      <td>9.99217</td>\n",
       "      <td>10.01044</td>\n",
       "      <td>9.82776</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.091</td>\n",
       "      <td>2024-05-10</td>\n",
       "      <td>20240510</td>\n",
       "      <td>0.07899</td>\n",
       "      <td>0.017846</td>\n",
       "      <td>0.010</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>0.030</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3662</th>\n",
       "      <td>20240516</td>\n",
       "      <td>9.90997</td>\n",
       "      <td>10.20224</td>\n",
       "      <td>10.23878</td>\n",
       "      <td>9.89170</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.110</td>\n",
       "      <td>2024-05-16</td>\n",
       "      <td>20240516</td>\n",
       "      <td>0.28906</td>\n",
       "      <td>0.065306</td>\n",
       "      <td>0.026</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0.020</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3808</th>\n",
       "      <td>20241218</td>\n",
       "      <td>11.58000</td>\n",
       "      <td>11.65000</td>\n",
       "      <td>11.74000</td>\n",
       "      <td>11.57000</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.045</td>\n",
       "      <td>2024-12-18</td>\n",
       "      <td>20241218</td>\n",
       "      <td>1.73682</td>\n",
       "      <td>0.392394</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "      <td>0.004</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3811</th>\n",
       "      <td>20241223</td>\n",
       "      <td>11.64000</td>\n",
       "      <td>11.73000</td>\n",
       "      <td>11.84000</td>\n",
       "      <td>11.64000</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.045</td>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>20241223</td>\n",
       "      <td>1.81682</td>\n",
       "      <td>0.410468</td>\n",
       "      <td>0.013</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3816</th>\n",
       "      <td>20241230</td>\n",
       "      <td>11.78000</td>\n",
       "      <td>11.95000</td>\n",
       "      <td>11.97000</td>\n",
       "      <td>11.78000</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.074</td>\n",
       "      <td>2024-12-30</td>\n",
       "      <td>20241230</td>\n",
       "      <td>2.03682</td>\n",
       "      <td>0.460172</td>\n",
       "      <td>0.008</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "      <td>0.044</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-10T15:07:41.915428Z",
     "start_time": "2025-02-10T15:07:41.902660Z"
    }
   },
   "cell_type": "code",
   "source": [
    "daily_df = df.copy()[[\"datetime\", \"volume\", \"open\", \"close\", \"high\", \"low\"]]\n",
    "monthly_df = daily_df.groupby(pd.Grouper(key='datetime', freq='ME')).agg(\n",
    "    {\n",
    "        \"volume\": \"sum\",\n",
    "        \"open\": \"first\",\n",
    "        \"close\": \"last\",\n",
    "        \"high\": \"max\",\n",
    "        \"low\": \"min\",\n",
    "    }\n",
    ")\n",
    "monthly_df.reset_index(inplace=True)\n",
    "monthly_df.set_index('datetime', inplace=True)\n",
    "monthly_df"
   ],
   "id": "584f50c57e061148",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                 volume      open     close      high       low\n",
       "datetime                                                       \n",
       "1991-04-30       116.00   0.38158   0.34183   0.38158   0.34183\n",
       "1991-05-31      1760.00   0.47921   0.42275   0.47921   0.42275\n",
       "1991-06-30       247.00   0.41856   0.37479   0.41856   0.37479\n",
       "1991-07-31        58.00   0.37104   0.32572   0.37104   0.32572\n",
       "1991-08-31     27573.00   0.32407   0.30716   0.33267   0.29185\n",
       "...                 ...       ...       ...       ...       ...\n",
       "2024-08-31  19711097.70  10.03061   9.94253  10.32419   9.65874\n",
       "2024-09-30  27848355.05   9.90339  11.94866  12.03673   9.40431\n",
       "2024-10-31  39329408.65  13.14254  11.38000  13.14254  11.24000\n",
       "2024-11-30  31330015.78  11.38000  11.38000  12.04000  11.14000\n",
       "2024-12-31  25547709.02  11.39000  11.70000  12.02000  11.31000\n",
       "\n",
       "[405 rows x 5 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>volume</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</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>1991-04-30</th>\n",
       "      <td>116.00</td>\n",
       "      <td>0.38158</td>\n",
       "      <td>0.34183</td>\n",
       "      <td>0.38158</td>\n",
       "      <td>0.34183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-05-31</th>\n",
       "      <td>1760.00</td>\n",
       "      <td>0.47921</td>\n",
       "      <td>0.42275</td>\n",
       "      <td>0.47921</td>\n",
       "      <td>0.42275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-06-30</th>\n",
       "      <td>247.00</td>\n",
       "      <td>0.41856</td>\n",
       "      <td>0.37479</td>\n",
       "      <td>0.41856</td>\n",
       "      <td>0.37479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-07-31</th>\n",
       "      <td>58.00</td>\n",
       "      <td>0.37104</td>\n",
       "      <td>0.32572</td>\n",
       "      <td>0.37104</td>\n",
       "      <td>0.32572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-08-31</th>\n",
       "      <td>27573.00</td>\n",
       "      <td>0.32407</td>\n",
       "      <td>0.30716</td>\n",
       "      <td>0.33267</td>\n",
       "      <td>0.29185</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>2024-08-31</th>\n",
       "      <td>19711097.70</td>\n",
       "      <td>10.03061</td>\n",
       "      <td>9.94253</td>\n",
       "      <td>10.32419</td>\n",
       "      <td>9.65874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-30</th>\n",
       "      <td>27848355.05</td>\n",
       "      <td>9.90339</td>\n",
       "      <td>11.94866</td>\n",
       "      <td>12.03673</td>\n",
       "      <td>9.40431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-31</th>\n",
       "      <td>39329408.65</td>\n",
       "      <td>13.14254</td>\n",
       "      <td>11.38000</td>\n",
       "      <td>13.14254</td>\n",
       "      <td>11.24000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-11-30</th>\n",
       "      <td>31330015.78</td>\n",
       "      <td>11.38000</td>\n",
       "      <td>11.38000</td>\n",
       "      <td>12.04000</td>\n",
       "      <td>11.14000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-12-31</th>\n",
       "      <td>25547709.02</td>\n",
       "      <td>11.39000</td>\n",
       "      <td>11.70000</td>\n",
       "      <td>12.02000</td>\n",
       "      <td>11.31000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>405 rows × 5 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 43
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-10T15:07:44.088406Z",
     "start_time": "2025-02-10T15:07:43.838889Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import mplfinance as mpf\n",
    "\n",
    "mpf.plot(\n",
    "    monthly_df,\n",
    "    type='candle',\n",
    "    volume=True,\n",
    "    ylabel='Price',\n",
    "    ylabel_lower='Shares\\nTraded',\n",
    "    style='charles',\n",
    "    show_nontrading=True,\n",
    "    returnfig=True,\n",
    "    figsize=(20, 6),\n",
    ")"
   ],
   "id": "2a4da021e0d5fc44",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 2000x600 with 4 Axes>,\n",
       " [<Axes: ylabel='Price'>,\n",
       "  <Axes: >,\n",
       "  <Axes: ylabel='Shares\\nTraded\\n  $10^{6}$'>,\n",
       "  <Axes: >])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x600 with 4 Axes>"
      ],
      "image/png": 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rV6+omwB4gzwBesgToIc8ATrIEqCHPAF6yBN8RTHBQbNnz466CYA3yBOghzwBesgToIMsAXrIE6CHPMFXFBMc1Lp166ibAHiDPAF6yBOghzwBOsgSoIc8uWnRhDejbgKyQJ7gK4oJDuratWvUTQC8QZ4APeQJ0EOeAB1kCdBDntzcjJliQmEiT/AVxQQHzZs3L+omAN4gT4Ae8gToIU+ADrIE6CnmPE1bNN8cf/c19vwbvxxlVn0xK7LNmKUd0h4UtmLOE/zWLOoGoL62bdtG3QTAG+QJ0EOeAD3kCdBBlgA9xZyn6q1bzMKKZfZ85eIlkbYlaAcKWzHnCX5jZoKDysvLo24C4A3yBOghT4Ae8gToIEuAHvLkHpklsWTiJHuKwkKe4CuKCQ5asGBB1E0AvEGeAD3kCdBDngAdZAnQQ57cI7Mk1i9cFPlsCWSOPMFXFBMc1KFDh6ibAHiDPAF6yBOghzwBOsgSoIc8bdO6ezfTtIRVwdE45Am+opjgoLKysqibAHiDPAF6yBOghzwBOsgSoIc8bXPoA3ebDrvvZly0aMKbUTcBaSJP8BXFBActWrQo6iYA3iBPgB7yBOghT4AOsgToIU/19SrvYnbp1M2euoBiQuEgT/AVxQQHderUKeomAN4gT4Ae8gToIU+ADrIE6CFP9Y0bNdYcNmAfewpkgjzBVxQTHFRaWhp1EwBvkCdAD3kC9JAnQAdZAvSQJ0APeYKvKCY4aPHixVE3AfAGeQL0kCdAD3kCdJAlQA95AvSQJ/iKYoKDunRxYy0+wAfkCdBDngA95AnQQZYAPcWWp/GfToy6CfBYseUJxYNiAgAAAAAAAIrK+GkUEwAgUxQTHLRs2bKomwB4gzwBesgToIc8ATrIEqCHPAF6yBN8RTHBQd27d4+6CYA3yBOghzwBesgToIMsAXrIE6CHPMFXFBMcVFVVFXUTAG+QJ0APeQL0kCdAB1kC9JAnQA95gq8oJjhoxYoVUTcB8AZ5AvSQJ0APeQJ0kCVAD3lyQ6/yLqZkh2ZRNwONRJ7gK4oJDurZs2fUTQC8QZ4APeQJ0EOeAB1kCdBDntwwbtRYM6jnLlE3A41EnuArigkOWr9+fdRNALxBngA95AnQQ54AHWQJ0EOe3DF80LCom4BGIk/wFfOmHLRq1aqomwB4gzwBesgToIc8ATrIEqCHPEXn+LuvMUvWrIx9P3wvigmFjjzBV8xMcFDv3r2jbgLgDfIE6CFPgB7yBOggS4Ae8pRf0xbNt0UEsbBimaneuiXqJkEReYKvKCY4qKKiIuomAN4gT4Ae8gToIU+ADrIE6CFPudW6ezdT1qunPRVSPJAiAvxEnuArigkOWrt2bdRNALxBngA95AnQQ54AHWQJ0FNMeZIZARNmTInNDIjX87BD1PcvOPSBu023YfvbU1GyQzPTq7yLynXDPcWUJxQXigkO6tOnT9RNALxBngA95AnQQ54AHWQJ0FNMeZIZAfNXLInNDIjv2K9TTMjR/gWDeu5ixo0a26jrWDThTbX2QFcx5Skq77zzjhk+fLjp3r27adKkiXnhhRfq/P7MM8+0Pw9//fCHP4ysvb6gmOCgpUuXRt0EwBvkCdBDngA95AnQQZYAPcWcJ42O/caQQoYUNBpaGmnVF7PMG78cFfs9xQR3FXOe8qWystIMHjzY3HPPPUkvI8WDJUuWxL6efvrpvLbRR/VfqeBEGADoIE+AHvIE6CFPgA6yBOghT9GRQsZ5T9xa7+eyJNLEK0ebYTeOsd/XVG8xlYuX1LucFBXCsykQPfKUe0cffbT9SqV58+ama9eueWtTMWBmgoP69esXdRMAb5AnQA95AvSQJ0AHWQL0kKfcy1WHPzMU3EOe3PDWW2+ZHXfc0ey2227m/PPPNytXroy6SQWPYoKDFi5cGHUTAG+QJ0APeQL0kCdAB1kC3M7T+E8nql9nIctmY2dmHBQmjk+N27w6/LV58+asrkeWOHriiSfMhAkTzM0332zefvttO5Nh69at6m0uJhktczR58mTTuXNne37ZsmV2g4uqqiqzYsUK07NnT7N+/XqzatUq07t3b1NRUWEfcNlwRNYJk+k9UpWTMG3atMn079/fzJ0711RXV5uBAweaGTNmmJqaGrvW1dSpU+3/SHS+adOmZsCAAWb69OmmpKTE9O3b18ycOdO0aNHC9OrVy8yePdu0bt3aTmGZN2+eadu2rSkvLzcLFiwwHTp0MGVlZWbRokWmU6dOprS01CxevNh06dLFqdsk1/vRRx95dZt8fJy4TYVxm8gTt4nbRJ6K7XHiNhXGbSJP3CZuk85tWr58udl55529uk0+Pk7cpuLN0zMfv24Gtuvm3OMk1y8qN2wws2bNMps2brLH5Xw+TocP3Nv2swW3afk3y20b4m/T0rZl5pvJk+1tqtxQaZo2aWL/p9wm+X9r1qyxj53cP4X63CNPhXGbcvk4SfuHDBlif96tWzezYcMGE7j22mvNddddZzJ1yimnxM7vueeeZtCgQfb+kdkKhx12WMbXh22a1NbW1n57Ho6QFwMJG4DGI0+AHvIE6CFPgA6yBLidJ9kH4MHTL3Nubf8hY84zUxbOMfv02tVMHv1gvXZGQWZxDN8r9WyF8cNH2tPh45+3p8F+CuF9FeAGjk/Zk6JI/L4H8pVKkyZNzLhx48yIESNSXk4GyV9//fXml7/8pUpbixHLHDlIKoUAdJAnQA95AvSQJ0AHWQIKK0/J1vZnzX/TYCEBhYXjU/ZkdkP4q6FCQrq++uoru2eCzHxA9igmOEimHAHQQZ4APeQJ0EOeAB1kCfAjTxQT4BuOT/mZ/fHpp5/aL/Hll1/a87K0k/zud7/7nZk0aZJdckn2TTjuuOPsMk1HHXVU1E0vnj0TkB+ydhkAHeQJ0EOeAD3kCdBBlgA95AnQQ55y7+OPPzaHHLJ92bRLL73Unp5xxhnmvvvuM9OmTTOPP/64Wb16td2b4cgjjzRjx45Vm+lQrJiZ4CDZBAWADvIE6CFPgB7yBOggS4Ae8uQnZn1Egzzl3sEHH2xkK+D4r8cee8y0bNnSvPrqq+abb76xGzzL7IQHH3wwtvkzskcxwUGymzoAHeQJ0EOeAD3kCdBBloDCzZNsOOzK9Q4f5O9+BRQTosHxCb6imOCgwYMHR90EwBvkCdBDngA95AnQQZaAws3T+GkTnbneQtn8uHX3bvZLvPHLUWbVF7OibhKS4PgEX1FMcNDUqVOjbgLgDfIE6CFPgB7yBOggS4Ae8uS+Qx+4236JysVLTE31lqibhCTIE3xFMQEAAAAAAACRkBH2SyZOsqcAALc1i7oBqI+pUIAe8gToIU+AHvIE6CBLQOHnSUbYr1+4yJS2aRPJ/wdygeMTfMXMBAcxFQrQQ54APeQJ0EOeAB1kCSjMPB1/9zVmwowp9hTwEccn+IpiAgAAAAAAAHIqvGHwwoplZv6KJfY0n8Z/mptNn6Mm92t4mahFE96MtD2+3s8AKCY4ialQgB7yBOghT4Ae8gToIEuA23matmh+bPaBCxsGj5/mZye33K9y/zpTTPD0fs4Exyf4imKCg5gKBeghT4Ae8gToIU+ADrIEuJ2n6q1b8j77oFi07t7NfqWaAYLocHyCr9iA2UFNm1LjAbSQJ0APeQL0kCdAB1kC9JCnwnLoA3fHzjctaWZadu5sz4dnKCA65Am+4pntoAEDBkTdBMAb5AnQQ54APeQJ0EGWAPfyxHr5+Tfogl/VKS4gehyf4CuKCQ6aPn161E0AvEGeAD3kCdBDngAdZAlwL0+urZdfDMWNnocdEnUTEIfjE3xFMcFBJSUlUTcB8AZ5AvSQJ0APeQJ0kCVAj695cq24EaWoN2YuJr7mCaCY4KC+fftG3QTAG+QJ0EOeAD3kCdBBlgD/8lQMMwlySTZllj0UguLBimnTY0WEKIoJxVrAcCVPgDaKCQ6aOXNm1E0AvEGeAD3kCdBDngAdZAlwO0+9yrvYr2xnEhRrR3RjyN4JHXbfLbb8UadBA9NaBknrvj7+7mvMhBlT7OkbvxxlPhx7kz0tNhyf4CuKCQ5q0aJF1E0AvEGeAD3kCdBDngAdZAlwK0/hjmQxbtRY+9XQSPqyXj3tabxcFROkwLFLp24ZFzoK3aovZiXt2Ne6rxdWLDPzVyyxp5WLl5j1CxfZ02LD8Qm+opjgoF69ekXdBMAb5AnQQ54APeQJ0EGWALfyFO5IzmQkfbdh+9vTXAoXOqTAcdiAfRosdPimpnpLrGOf5aRyi+MTfEUxwUGzZ8+OugmAN8gToIc8AXrIE6CDLAGFmaf4mQHpLMOTDSkcTFs0P2GhY/igYaaY3fDSk7EZJDJbQWYtQD9Pct+OHz6yKJd6gp8oJjiodevWUTcB8AZ5AvSQJ0APeQJ0kCWgcPIU3hg4fmZArooJUjio3rol4e+G71XcxQS5X4LCisxWkFkL0M+T3LcVn88oyqWe4CeKCQ7q2rVr1E0AvEGeAD3kCdBDngAdZAlwI0/pLJkjyxgNuuBXse/zPTOgWPdJQDQ4PsFXFBMcNG/evKibAHiDPAF6yBOghzwBOsgS4Eaexk9Lb/398AyEfM8MKNZ9ElySqw21XcTxCb6imOCgtm3bRt0EwBvkCdBDngA95AnQQZYAPeQJuVZMxQTyBF9tW6wOTikvL4+6CYA3yBOghzwBesgToIMsAXqKIU/Fvuky8qcY8lSMPvnkEzNz5kxTWVlpzj33XFOMmJngoAULFkTdBMAb5AnQQ54APeQJ0EGWEJV09ggoNMWQp2LcdDlXm1sjtWLIUzH5+OOPzZ577mm++93vmtNOO82cf/75ZtOmTbZo1KxZM/PWW2+ZYkExwUEdOnSIugmAN8gToIc8AXrIE6CDLCEqqfYIKNRCQ5CnQm0/EqOYEA2OT/744osvzKGHHmpmzJhhamtrY18tWrQwI0aMMDU1Nea5554zxYJigoPKysqibgLgDfIE6CFPgB7yBOggS3BRupsRu5qnQm0/4BKOT/647rrrzPr1603Tpk3NAQccUOd3++23nz199913TbGgmOCgRYsWRd0EwBvkCdBDngA95AnQQZaA6PKUixkMx999jZkwY4o9Fau+mGXe+OUo9f9TrJqWNDOtu3ez50t2aGZ6lXeJukne4vjkjzfffNM0adLE3HTTTeaWW26p87vevXvb06+++soUC4oJDurUqVPUTQC8QZ4APeQJ0EOeAB1kCYguT7mYwbCwYpmZv2KJPRU11VtM5eIl6v+nWHXYfTdz6AN32/ODeu5ixo0am9f/X0zFIY5P/lizZo093Xvvvev9rrq62p5u2LDBFAuKCQ4qLS2NugmAN8gToIc8AXrIE6CDLAHFlSf2c3B3/4RFE96s9zOZBVHWq2dsNkQxFYcKIU9IT9euXe3pa6+9Vu93wV4JO+20kykWFBMctHjx4qibAHiDPAF6yBOghzwBOsgSUFx5Yj+HwiomyCyIbsP2j82GKCaFkCek54gjjrAbLt96663moosuiv1cNmX+29/+ZpdAOvLII02xoJjgoC5dWLMO0EKeAD3kCdBDngAdZAmuS9TB6kOeZE+DaYvm19vnQNbg36fXrnlZiz983xbS/YzEfHsMOT7546qrrjLt27e3BYVPP/3UFg/E22+/bU/ld1dccYUpFhQTAAAAAAAAcsC3DtKGyBr8k0c/mJe1+Ckm+DUbwrfH8PVZnzR4O1m2qzDIJsuvv/662WOPPWxBIfw1cOBA+7uePXuaYkExwUHLlm3baAhA45EnQA95AvSQJ0AHWQKiyZMUC2QD3+D8YQP2USkgpJrZEJ4NgcYbPmhYo68j6AyXTZWXTJyUcHPlXC6t5Cp5rt725j8bLiawbFfB2Geffcxnn31mPvnkE/Pss8/aLzk/bdq0hBsz+6xZ1A1Afd27d4+6CYA3yBOghzwBesgToIMsAfnLk3QcD9+r8R3QqaQqSCysoHioSeOxlM5wuR7ZVHn9wkWmtE0bY3p0NMVOnqtNdmD8to8GDx5sv4oZz2wHVVVVRd0EwBvkCdBDngA95AnQUUxZSrUchm/Lg8DNPN3w0pN2xLULUo2ER/617t7NlPXqaU9lVskunbadNsTn1y5ZAgd+uPfee+1my2eccUa9351++un2d3KZYkExwUErVqyIugmAN8gToIc8AXrIE6CjmLKUajkMnzvkCk0hL8UjeQpvphyveusWZ2YHBCPh5TQV6dDOx2bQPkvn9eXQB+423Ybtb08zWfbK59euLVu2NHgZea1wpUCH5B5++GG72fKgQYMSLn/01ltv2csUC4oJDiqmTTuAXCNPgB7yBOghT4AOsgTXSGe7dLoLGTW/6otZppDyJO2fv2KJM0WDbIQ7aKVDOx+bQbtGc5+CZB3+qQpnGvsxFLrSkpIGL+NSgQ7JzZ07154mKibssccedS5TDCgmOGj9+vVRNwHwBnkC9JAnQA95AnSQJbgiUYerjJqvqW54dLIrfMkTHbT52fQ4XDiLl+u9NQpBTU1N1E2A8iyTRYsW1ftd8LN0ZqL4gmKCg1atWhV1EwBvkCdAD3kC9JAnQAdZgit8WK4l0zwx+hxR7BdTKFnbsnVr1E2Akt69e9s9MMaOHWtmz54d+7mcv/7662OXKRYUExxUTE9AINfIE6CHPAF6yBOgo1izVCidafA7T66MPmczZl2yfNGQMec5s5Z//H4xhfL617y0tN7m1NL2FdOmx25DyQ7N2NOjABx77LH2dOHChWbgwIFm9913t19yfsGCBaZJkyaxyxQDigkOqqioiLoJgDfIE6CHPAF6yBOgo1izVCidaSis0d+J8uTqcy3cQZvuZsxITu5H+Qqs3rA+40JNrpZWKtRNird8uwRUeHNquY86DRoYu6+uOubUotzTo9Bcfvnldk8ZmZ2wZcsWM2fOHPsVLG200047md/97nemWFBMcNDatWujbgLgDfIE6CFPgB7yBOgolixJR9qEGVNiHWqyqS+jsKE9+jtRnlwtJoQ7aNF4cj8G96V0bh82YB97mkmhJp1iQtOSZrGihTy3qtety2gPjELa1Hzr1poG7xtXZvcgtQ4dOpj33nvPHHPMMaZp06a2qCBfcl5+9u6775ry8nJTLJpF3QDU16dPn6ibAHiDPAF6yBOghzwBOoolS9KRNn/FEtO+VZn9Xjb1ZRR2tILR0tLhKh2cG5cvtz+XJUuWrFlpii1PUe6fIJ3TVevWxTqpZemYbu06RtYeJNdh993MsBvHxDrYMy1WFdLrXvPmzRMWE/KxOTb0yeyD8ePH271l5s6da3/Wt29fW2goNsxMcNDSpUujbgLgDfIE6CFPgB7yBOggS4hKeLR0mBQXBvXcxfiQp0yWuIlyhHX8LAWWjnFXso50H/e92FJdnfDnFBMKW4cOHczQoUPtVzEWEgQzExxUWVkZdRMAb5AnQA95AvSQJ0AHWYILpBN74pWjjW95Cpa4KW3TJrI2ybI4LTt3zmjWB0vHuCtZR7oLzzVtW2u2L3OEwnL22Wfb06uuusrO2Aq+T6VJkybm4YcfNsWAYoKD+vXrF3UTAG+QJ0APeQL0kCdAB1kClPP0snF2WRyZbXDeE7fGfsfSMf6S5Y8K/TFtEVrmCIXlscces8WBX/ziF7aYEHzfkIeLpJjAMkcOWrhwYdRNALxBngA95AnQQ54AHWQJaDzZ+0H2gJA8yej/fXrtak9dRzHBX65u/J2JqqqqqJsARcGmy7VJvooJMxMctGnTpqibAHiDPAF6yBOghzwBOsgSXBTlZsTpGv/pxNhyQMHeD5Kn8F4D418dGVn7AFfzkq6aIutg9smbb24rZu255551vsc2FBMc1L9//6ibAHiDPAF6yBOghzwBOsgSXFQIa/aPn1a/c7QQ8lQIhRof5Ot+LpQZJYny0pCWLVrkrD3IrR/84Aex85s3b44tcdSjRw+77FGxY5kjB82dOzfqJgDeIE+AHvIE6E29J0+ADrIENJ4saVSyQ7OCyFMhFGp8ENzPrbt3M2W9etpTF4sJMmPAVZs2b466CVBQWlpqDj30UHPIIYeYSZMmRd0cJ1BMcFB1dXXUTQC8QZ4APeQJxSz8YVWjmECeAB1kCa4olBHWicjSRoN67kKeUM+hD9xt9r3mD/bUxb0MZMaAq4ptHX1fyawEmZEgOnbsGHVznEAxwUEDBw6MugmAN8gToIc8oZhpf1glT4AOsgRXFFoxQTZclo2Xw8gTGnpuT7vnfvPGL0dF2p5C0bIlyxz54txzz7XFoaeffjrqpjiBYoKDZsyYEXUTAG+QJ0APeUKxks6WCTOm1Ot0aQzyBOgo1iw1LWkWW3ZEOvZWfTEr9jvXRg7DTdVbt8Q2Xk6Wp/glbmQpJFkSCcWrpnqLqVy8RP165TlWvscAeyqvaUsmTiq4okX8a++mjZsia0uxeOedd8zw4cNN9+7d7QyCF154oc7vpQAwevRo061bN9OyZUtz+OGHmzlz5mT8f2Rmwi677GKefPJJc9BBB5lbbrnFPP744+aJJ56o81UsKCY4qKamJuomAN4gT4Ae8oRiJZ0t81csqdfp0hjkCdBRrFnqsPtusWVHpGNPOvgCFBPc5fpjE58neY51G7Z/7LkmSyHJkkiANnmODR//fOy5FpasiCVLUH60YFZsKcps8tXYPRdSLYPJIke5V1lZaQYPHmzuueeehL+XTv8777zT3H///eaDDz4wrVu3NkcddZTZtCmzQs8555xjvvzyS3v+vffeM3/4wx/M2Wefbc4666zYl3xfLCgmOEiCAEAHeQL0kCdgGxkB3NjRcuQJ0EGWUEhcLiYMHzTMyTwV2vJR0N+n4apjTo0VsWTmgszMCjaJHtp7N3sq78s+HHtTxu/P0lnGUmamyrJgQq5//PCRsf+T6u9btWyZUVuQuaOPPtpcf/315vjjj6/3O5mVcMcdd5irr77aHHfccWbQoEF29sDixYvrzWBIh1xfQ1/FYlsC4ZSpU6eaoUOHRt0MwAvkCdBDngC9Kf7kCdBRrFmigxXapEP2o48+qpenqJ9rUf9/RP+4y3MzIAWGcFFOimBC3petX7jIlLZpo/L/ZcZB8H/DM1Pl/1R8nt7yehs2blRpSzFau3Ztne+bN29uvzIhMwmWLl1qlzYKtGvXzuy3337m/fffN6ecckra13Xttddm9L99l1ExYfLkyaZz5872/LJly+yaVFVVVWbFihWmZ8+eZv369WbVqlWmd+/epqKiwj74ffr0sQ+eTD3p16+fWbhwoZ1O0r9/fzN37lxTXV1tN/mRtflkSp1UwuUNoUh0vmnTpmbAgAFm+vTppqSkxPTt29fMnDnTtGjRwvTq1cvMnj3bTlvp2rWrmTdvnmnbtq0pLy83CxYsMB06dDBlZWVm0aJFplOnTqa0tNRWpLp06eLUbZL/Lwdxn26Tj48Tt6kwbhN54jZxm8hTsT1O3Cb927R161b7t5UbNtjLbty4wdTU1pqNGzeSJ4ceJ25Tcd6m5cuX2//p++Mkrz9iy9at9rWj7U7d7W2X27R582b79/JzuU0bKivteddvU6E/TtJR2KJ585Sv5bVt25jatevsZeQ2bdywIfY4RX2bZBSt3Abp50mVp6Vty8y6GTPsbVr+zXIza9asgnqcfHzuRXmb5DZsrdlq1qxZY98HyfshuWw+b5M8JzfPnWtvU9fqEttpXF1dZa9bboPc1+neps2bNtv2xj9O/5z2julpWtnb1KN9JzN/6dc2u8Ho88oNlea4u64yk+d/YQ5eOMq8dvlt5qvJk80/Tz7VHP3YQ/Y1e0v1lqI4PmndJmn/kCFD7M9lj4MN3x73gs786667zmRC7gMR/K+AfB/8Lh1Tpkwx7du3t23cc8897TJJsj9DMWtSW0zzMAqEPEElYAAajzwBesgTitWQMeeZKQvnmH167Womj37QTm8XsrZvtsgToKNYshT/OhQW/5o08crRZtiNYyJpp49kFHSi0fHymIj4xyNe8HjYjbJnzTYddusXG10d5aj7RO1vKE/nPXGrefD0y/LSPrgp/HqTbgby1S6ZMSCbOGfy/izZczr+58H34Rz/rkdVndfll0442bTs3NnmW5ZGWrx6hfng6vvUbmMxyWZmgnTwjxs3zowYMcJ+P3HiRHPggQfa4oUUJwInnXSSveyzzz7bYDt+8YtfmEcffbTOz4YOHWpefvllWygpVuyZ4KCgCgig8cgToIc8AXrIE6CDLKFQ9jkIlmEJlslzcf8E8oRiFs6k7JEgBYF44U3JZVPoXTp1i20O3WH33WL7PMgeD91btM1j6/0isxvCX5kucSRkpkQw+yFMvg9+l8ojjzxiv+L3Rfjoo4/MJZdcYooZxQQAAAAAAApUeENSGLX10hNxsQCQT8H69IDrsslq+G+qt26ps1dCoue+FAwOG7BPbHNouGXnnXe2RYMJEybUmfHwwQcfmAMOOKDBv5dCQvi6ZNknmdFQW1trZzXIEoPFimKCg+QJCkAHeQL0kCdAD3kCdJClbSNlZUSskCU4lkycZE+RvfHTCq+YkKwAopmn8Ea4gMvSyarMPJgwY4o9bei1M/zcT7Y8WfzPyzt2zLjdyIzsGfHpp5/aLyH7Z8h52SdCOv4vvvhic/3115sXX3zRfPbZZ+b000+3ezQESyGlIntEyHWce+65di+ITz75xDz22GOxJeHmzJljihXFBAcxtRDQQ54APeQJaFi6HU3kCdBBllIvpQPd1/cV06bHXudLdmgWW97E5QJIJsgTfLHqi1kNFlVl5sH8FUvqzEAIpMp3uGgQnrEQX0yoWLkyi5YjEx9//LHZe++97Ze49NJL7fnRo0fb7y+//HJz4YUXmvPOO8/udSDFh1deecVuHp3uvg0nn3xy7Gfh8+vWrTPFimKCg2SndAA6yBOghzwBesUE8gToIEvIF+ko7DRoYKzDcFDPXSJd3kRjJkI88oRCXu6trFdPeypqqrdkVFQN74WQSb5TzdaRUe3IrYMPPrjengbyFcwgkMdgzJgxZunSpWbTpk3m9ddfN/369cvof4QLD+EN6mtra02xYmFFBw0YMCDqJgDeIE+AHvIE6CFPgA6yBNclWxJFYyZCso5MKWwn+r9SgAj+RkZed2tXdxkW8oRCJUWAiVeONsNuHGNc0b5Dh6ibAAU33nij2XHHHRv8eZMmTczDDz9sigFlZwfJulwAdJAnQA95AvSQJ0AHWYL2KH9ZP33aovn2vCyTIsul5LqYkM1eDNJGaWsm13fDS0/G/ibRyGvyhEKWq8Jdtga26Rp1E6Dg5ZdfNo8//njsq8m3M07ifx7MhigGFBMcVFJSEnUTAG+QJ0APeQL0kCdAB1mC9n4Dsn569dYt9rwskyLLpeRaNsUEaWOitd5TbSSb7G8C5AnFXEzQLkYcumvqDc3hvkRLKNUm+SomLHPkoL59+0bdBMAb5AnQQ54APeQJ0OF7lsLL0iD/ZP31jcuXN7jxatRktsGSNSvrbMJd2qZNyr9J1H7f84TGa1rSzLTs3Nmed2kDcg3axQTyVNiuvfbaqJvgLIoJDpo5c6bdZRxA45EnQA95AvSQJ0CHL1lKusZ9inXxkb912BMWExQfF1lKSWYSBJu/ZirVbINkErXflzwhdwZd8KtYDqLcgDzXNIqF5KmwUUxIjmWOHBTeKRxA45AnQA95AvSQJ0CHL1nKZpkbFN4sh7JePe2pqF63Lva4y1JKMqMgWzJCXDZUbixf8oTi2ZegsXukJKNRLCRP8BXFBAf16tUr6iYA3iBPgB7yhGIlnTS7dOqmOp2fPAHFnaV0OrOKuT0+dp7KrINuw/aPzT4oadNG7bplhLhsqBxPZjuMHz4y4f4JPuUJyHaPlFwiT/AVxQQHzZ49O+omAN4gT4Ae8oRiJZ00hw3YR3U6P3kCijtL4c4s6eiVZW5caU8xmrZovt13wLeR2DLboeLzGWnPeijUPAEuIk/wFcUEB7Vu3TrqJgDeIE+AHvKEYpCv0bnkCSjuLIU7r6WjV5a5yfcMKWxXvXVLbN8BrSWDclGYyPUxqlDzBMST5cSCJcWkYJvu7BzVNpAneIpigoO6du0adRMAb5AnQA95QjHI1+hc8gQUd5bCndfJSLFhwowp9jTbGVLsxaC3ZJB2MSFVYSHZ45bOMSp+b4ZiyBMQT5YTC5YUk4JtY/YkyRZ5gq8oJjho3rx5UTcB8AZ5AvSQJxSbdEeAZjNSlDwBOnzOkhQb5q9YEis6DB+U+YagFBPclU0xIZu9GTLhc56A+CJtrpEn+IpigoPatm0bdRMAb5AnQA95QjGRD5kXP3N3Wh82s5nNQJ4AHcWUpeF7JS8maK7rz2bMhbm3g8bzoZjyhOIhs3SaljTL+/Jx5Am+opjgoPLy8qibAHiDPAF6yBOKSfyIYG3kCdBBlnJQTCjyzZijIB2d6SxLFF4eS/Z1SNYJmu3zgTzBRzJLp8Puu9nz4SXjsl0+Ll3kCb6imOCgBQsWRN0EwBvkCdBDngA95AnQQZbgA+noDJYlko1iV30xq8G/kX0dtDtByROghzzBV/Xn+SByHTp0iLoJgDfIE6CHPAH1yXITS9aszPjvyBOggyzVJaPbq9aty2rzXWyXzf4UjRGeSRDFRrEB8gToIU/wFTMTHFRWVhZ1EwBvkCdAD3kC6pPlJmTZiWA06ZKJk+xpQxtokidAB1nS23wX6e1P4foyVY1BnlBsclk4JE/wFcUEBy1atCjqJgDeIE+AHvKEYphlIJtbJtqUL50PmzKadP3CRbFRpamKCeQJ0EGWkosvcMIv8ccljU5R8gRfhQt24azksnBInuArigkO6tSpU9RNALxBngA95AnFNMsgflO+ZB82pdggm2BmijwBOshS8k6zTAqc4aLqhBlT7KmQQgTFiGiN/zTxhtjxxyWNTlHyhKIoJuRp5hF5gq8oJjiotLQ06iYA3iBPgB7yBNQnxQbZBDNT5AnQ4WOWknUeN3bJnHSKCVJUnb9iiT0VUojI1Rr+6d7O8OXSuQ2+GT+tcc+HYs8TEBXyBF9RTHDQ4sWLo24C4A3yBOghT4Ae8gTo8DFL+eo8Djrp42cjuHY7w5crtmJC/GMjM+GC5ffSIRtxl/XqGduQu6G/9zFPQFTIE3xFMcFBXbqk/+YAQGrkCdBDnuCLcGdUY0cAZ4s8ATrIUvaCTvrwbIT4/WJc2ssm1R4Qvs5eiJ8pIjPhguX3stmQu6G/J0+AHvIEX1FMAAAAQFEJdzTd8NKTsRGf6e5/0LSkWWyUJwA0lryeyOtKQxvB54N0NN9xyqiMOqwbO+JeigPjh49MWCQI72UTvwdE+Loufubu2Gv5tHvuT2ufh6iKyfmWbNkrAACyQTHBQcuWbRt1AKDxyBOghzzBR9JJFYz4THf/gw677xYb5Zkt8gTo8CFL8noirysNbQSfL/nYnDQ84l6KAxWfz8h6b4b40fs11VsavK74AkSmywcV7Mazg4Z5nyfAFeQJvmp46BXyrnv37lE3AfAGeQL0kCdAD3kCdJCl7Egn+pI1K+156URfvWG9M53pMmOgoYKGtD9Ylimb9gcFiPatyuz3Vx1zal6KKFFr6DaSJ0APeYKvKCY4qKqqKuomAN4gT4Ae8gQk1tBIz0SKMU/pdBACxZqlYPS4dIgHnfy5FIziFzLz4bwnbjUPnn5ZTv9nMAtA/l/dAkDd2yvLzz3y7sspZ2RI+6csnGPPTx79YJ3XF1kyqmXnzvZ8sNyRzP5IVYBw6bVJlr3auHx5JIUeX/IEuIA8wVcsc+SgFStWRN0EwBvkCdBDnlBs0i0SZNMJVYx5CjZ7BTT5kqWgmJDucmvpdkqX9erpzB4v0pEfXlYuWMIpVTvDe9mELyc/36fXrgmLAYMu+FVsKTpZ7ihY8igoQMhpVEtIZbrsVb7b6UueABeQJ/iKmQkO6tmzZ9RNALxBngA95AnFVkDI5UjVQs5TeHSvix1xKC6FnKW0XoeymPkU7pSeeOVoM+zGMRn9z3SFZwPIxvbZbvQr7Qz/fXjJIXmNCTZKDl/u0BTXF25HqlH+jblvfd002cc8AVEhT/AVMxMctH79+qibAHiDPAF6yBOKQb6WuijkPIVH98oSIuOHj4wtJQLkWyFnKZ3XIc3XpFVfzIplNTziP9v/E55tJJ38mQp35tfZJDiuLeHvM+1klwKEzFRINMrfpaWN4iW7nbkugPiYJyAq5Am+opjgoFWrVkXdBMAb5AnQQ56A9DuzpMNuycRJsY67+I42X/Iky4dUfD4jtoxIuoKRxkBj+ZKlfKip3hLLqnSoywyAXEi3sJCvzvyoRvnnQq7vM/IE6CFP8BXFBAf17t076iYA3iBPgB7yBB/Ed/JrdkylWvc7vnOt2PPE/gnQUuxZiqpjWpY7m7Zofuz1bcW06fZUXls/HHtT7DVWLhdsvBxsfAx3kSdAD3mCrygmOKiioiLqJgDeIE+AHvIEH8io3PULF2U8kj7d5TT2veYP9lS+ug3bP7YJaHh5EUGeAB1kKX1NS5qpbcYsy5xVb90SK7B2GjQwYaF1yZqVsfMyG4J9VjKXz70dyBOghzzBVxQTHLR27dqomwB4gzwBesgTkP2shfDyIoI8oRhls65+Qwo1S7JfQb5H6cveAUGBM1evffGFVFlKyaUCgsubLieTz70dCjVPgIvIE3xFMcFBffr0iboJgDfIE6CHPAF6yNN2MmODDZyLQy6KCYWSJVnqZ8iY82JL/gzquUveO9kbu3dAqscv6YbBjm1y7Fp7XFMoeQIKAXmCrygmOGjp0qVRNwHwBnkC9JAnQE+x5Sm8vrpsvvzRglmxTZhlxkayZafYqBm+ZEmWBZqycI499b0Y5NOGx8WmUPIEFALyBF9RTHBQZWVl1E0AvEGeAD3kCdBTbHkKr68uI4OH9t4trRHC4Y2aczGqHYWvULNUKMvthPdZiN/7JRmKCYWrUPMEuIg8wVcUExzUr1+/qJsAeIM8AXrIE6CHPGWOYgJ8ylKhLLfTYffdYvsfxO/9Av8Uap4AF5En+IpigoMWLlwYdRMAb5AnQA95AjKTanQueQJ0SJZYDgvQwbEJ0EOe4CuKCQ7atGlT1E0AvEGeAD3kCdApJkjHZzhPPo+4b0wnb3ifBVlaRZZYQWGRxzDYcDhXj6FkieWw8q9XeRdTskOzqJsBZbzXA/SQJ/iKYoKD+vfvH3UTAG+QJ0APeQJ0SMdnkCfpYJ12z/3G18JAuJO3MWRpFVliBYW3V0aw4XCuHsP4Y1N8MYHigl5RNLx/wrhRY81Vx5waYcuQC7zXA/SQJ/iKYoKD5s6dG3UTAG+QJ0APeQJ0yGj7Y/9yZcF1kmdaGAjPLMhmJLt0Vg7quUtGf4/iERS35Ngkz7PgeRO/SXDUxYRCX4IpXEwYdMGvYvsnFNK+D0gf7/UAPeQJvqKY4KDq6uqomwB4gzwBesgToKN66xazeF2F8VG441RGpMttTWT4oGENjmRPdTkgKG7JsUmeZ8HzJrxJsAvLY2nNzsnF3i1RXhfcxHs9QA95gq8oJjho4MCBUTcB8AZ5AvSQJ0BPy5YtjI+SdZzGr6+e7ohmRj4j02NTeCmeXM78CRfO0pn9IBnYpVM3e5pPFACQCd7rAXrIE3xFMcFBM2bMiLoJgDfIE6CHPAHZC3dwik0bC2tTvvCSRdl0iqZaXz1830jBId+drXBgb41GLAUUf2zqsPtudZbiyZV0Nn2W3EyYMcWeSgYOG7CPPQVcxXs9QA95gq8oJjiopqYm6iYA3iBPgB7yBGQv3MEpHeZdytqbQhJeskg6Q+84ZVTGnaLJZhmE7xvZI4HO1sKWzbI+6XTMu3ZsSrVPQzg381csqbN0F+Ay3usBesgTfEUxwUGDBw+OugmAN8gToIc8oZAFI59lBHxZr551Zgnkm3SYv3b5baaQsfwQ0ulkTzXTJCgahEfv27+/5/6EHfPpHpvytaxPsn0aXNj0GcgW7/UAPeQJvqKY4KCpU6dG3QTAG+QJ0EOeUMiCkc8yAn7fa/6Ql2VQUiFPibHhcuELd7KnmmkSdLjHj96P75jPNEsu7BGQrJjA8xuu49gE6CFP8BXFBAAAABSVcGcja/TnTvymy+k8HslmPMhMEtlbAYhSY/Z2EMzoAQAAhY5igoOYCgXoIU+AHvIEH0W1Rn8x5CnVpsuZjiSXmSSyt0KAZWQKT7ggJMsYLZk4KaPljLSzlM1zKNl+EOFNxOU2yR4KQCEqhmMTkC/kCb6imOAgpkIBesgToIc8oVDFr8nucp4KvZM8vF5+rkZiS2fth2NvalRHdGNHmCNz4YKQLGO0fuGijJYzyubYFD+jJZwvzawNuuBXCZdOk9k5u3TqxuwnFAze6wF6yBN8RTEBAAAAXotfk91lhV5MCK+XnysaHdHJRpgjt/K9n0G4gNHYIlR8UTK8RFr4dsn/lOJCMDvnjlNGRTL7CQAAIBcoJjiIqVCAHvIE6CFPgH6ecrX0i49y1RFd6AWcYnwMwx37kqV09j5pbBEqviiZaom0dPYBAVzEez1AD3mCrygmOIipUIBunugkAHRwfIKPhg8aFmmecrX0iwbXlgKimFB4cpWvcMe+ZCmXe5+4lgMgl3ivB+ghT/AVxQQHNW3KwwJo5olOAkAHxyf4KF+jhuM7wgshT4WyFFA6nb0y22P88JE5mfVBZ3Pm+ZIZOWW9esY2LW4M7SzFP57JchBVIRLIpUI4NgGFgjzl3nXXXWeaNGlS52v33XePulne45ntoAEDBkTdBMAb5AnQQ54AvWJCOE/5XkfeN+kUPWS2R8XnMxLO+lj1xazGbeYc9/8ZxNAwmZHTbdj+9lSWJ9qn165Zb1IsWUrVsR/kK90CRvjxlGWUZFPxRJsps3wRfMR7PUAPecqPPfbYwyxZsiT29e6770bdJO9RTHDQ9OnTo24C4A3yBOghTyhU8Z2AruWpkIsJ8R20ch+7dD8nIx3EwUa6NdVbGrW0VPi6UhUTmMFQV/C8l+WJJo9+MK1lihLdh5KlVB37wf+RwsW+1/zBnqa7P4ksoySbigftZDNl+I73eoAe8pQfzZo1M127do19derUKeomeW/bbm9wSklJSdRNALzKU3XUjQA8wfEJhSTo3JWOP/mSTkiXRhIXWp6SFQikY1Y6z8Mdw4VAOoiDjXQ1r0s6pzcuX550xLtLz8GoZVNES3QfZpKlZP8zKCrI8zkoDslzWZ73S9asjF2Oxw++K7RjE+Ay8pS9tWvX1vm+efPm9iuROXPmmO7du5sWLVqYAw44wNx0002mV69eeWppccqomDB58mTTuXNne37ZsmX2waqqqjIrVqwwPXv2NOvXrzerVq0yvXv3NhUVFfbB79Onj1m6dKmprKw0/fr1MwsXLjSbNm0y/fv3N3PnzjXV1dVm4MCBZsaMGaampsbudh5sUpLovKw5JlOFpMInwezbt6+ZOXOmfdLIk2X27NmmdevWtho1b94807ZtW1NeXm4WLFhgOnToYMrKysyiRYtspaq0tNQsXrzYdOnSxanb1K1bN/PRRx95dZt8fJy4TYWTp0nLl5vPPvvMm9vk4+PEbSqM28TxidtUSLdp5ldf2pFKX3/9tb1Ng7vsZNvlwm1auWJlwjzJ9YvKDZVm1qxZkT9OUxfOMwdfP8r86agzzLPnXWNvh7Q5/jYtbVtmVn76ad6ee1u2bIndT3LbVq1abduV6jZt2LDB/k1tba29bOWGDaZVy5bfnq80rVq2suezee5JezZXVdn/t3nzZlNZud58+eWXZuZ115u1X31t2vToYfa8/lqz/Jvl9vmY6+feu488ZtoM2Tuy1wi5D7Zs3WqWL1+e1W0KPx6i08pVZkXHDvb81q019ufB4ymPo/xvuR8zuU0D/ni1mfHCi2bno46wj8k3c+eakmYltl2r1601lZs22sfwyv2ONX+Z/IptYyG87vn4Ws5tyu9tktfITPPk+m3y8XHiNhXGbSJPmd0maf+QIUPsz+V9cnCsF9dee63dHyHefvvtZx577DGz22672SWO/vjHP5rvf//7tq1t2rSpd3noaFIrz244Rd4gDx06NOpmAN7kqXrceDPsxjFRNwUoeByfUEiGjDnPnsryKa4574lbzbn9f1AvT7JBsKzrX77HADN8/PMmaq7ehzKKfNWs2abDbv1M5fk/M6NffNSMOfaslKPG4+/b8G2T34ls7/P4+2nilaPt+47w/3zkqAFm2lfzzKCd+tgR7+HZHBrCM2+C/1+oz5v4xyN8eyQ7D55+2ba9DL69P6XDv7HHppdOONm07NzZzkzY/4Zfm27tOsZm2bg2qwnIJd7rAXrIU35mJoStXr3afOc73zG33XabOeecc3LYwuLGngkOkuoeAB3kSQfrPEOQJyC6POVrU99CeL0Pb94rnbxDe+/mZGdvqg1/p91zf531+sOPb/gxSOdxl471G1560p6P3wugEB7PdMntnDBjSmwJomD/Ao1jU4fdd7PPJ3HVMafWWa7LxecWkCu81wP0kKfsyeyG8Fc6hQTRvn17OxtDZmAgdygmOIi1vQA95EmHrFEMkCcgv3nKtFO5mF7vc7lpdfi+zmYz5fCGv0HRQzqnDxuwT8I9JaTjX4oLQjrKL37mbnsqP/9w7E1JNwkO2hDeJFg2kl6/cFFsQ+nw45mv51CuyO2cv2JJbH+KoJNf+9hE8QDFjPd6gB7ylH+y7JMstSTLJCF3KCY4SNYbA6CDPAF6yBOgY/igYWnlKV8d+4U4ej1cTJD7M1OysW6yTaXTKiakeGySFTrC7QyPhJeO/5rqLfU6zOMLA8lmI8jtKNmhWcqR/IlmQ/iCYxOghzwBeshT7l122WXm7bfftvszTJw40Rx//PFmhx12MD/96U+jbprXKCY4SDYuAeBOngqxkyWXCn1kI7LH8QnQG/nsUp6CjvH4zudUHe4uSWckefySQzJDIJgl0LSkWezndj+GL2ZF1s50j7/h2QhyO2RpnoZG8kvBIlFhQkP4vZIUNhrzvAk/HunQyFIuZ7oAhcSlYxNQ6MhT7n311Ve2cCAbMJ900kmmY8eOZtKkSaZz585RN81r9YewIHKyAzoAvTytVuhkKeYp79KptGTNyjqbXs75+/OxEZUoHhyfgPzlKdhgNlgfXjq45TVY67U32FQ2/H/iJVqSp1DJ/ZZs0+NBF/wq9vNkne1y/0invHSSy/2yZkNl7D5sbMFFOs43Ll9uz8t1rd6w/tvr3HbsDcjMgmTH3yjfpwTPoUfefdneN4N67mI3Sc6WzNoINlyW53xw3+Ty2EQxAdiG93qAHvKUe88880zUTShKzExwkKzvBUAHeWq8VGsxo7iQJyB/ecr1qPJgNkL4/6Ra198HyTqMwz+Xjn0ZGR+/mbHcP1MWzok9Hqfuf0SsAz88yyHd/xkmxQEpaATXlewxSOc5EJ6BIQWJXTptO000A0NryaP456qm8BJQcjv26bVrveINxyZAD3kC9JAn+IqZCQ6SncoB6OWp7ri+xks2shHwHccnILo8SUdwy2+nbAedwMwQy90MhnABvbRNG2N6dMxqJkA6BYz474O9FYKO/+A0/ByQDvVg1mCi9sv1HRqagdLQDAzN9zbZ7GGRzqyNZAUbjk2AHvIE6CFP8BXFBAeVl5dH3QTAqzyFP2qHP1Rn++GZYgKKFccnILo8hZd+YXZYbgXHeOnIrlq37ttR/h3sz7T2kEj1PiJ4nxJfLAo/B6RjPdmeTnUKE6H3PPEzMIJOenlf88ntd9nzsoySPL/k99kWqzSXXJI2TLxydMrLcGwC9JAnQA95gq9Y5shBsgs5gNzkKVjWIRjZKesPI3vhjgw2ZvYfxye4KFmHaiHmKdUSNfmiOaq80ElH9r7X/MGeSuf95NEPRroEVHwBojGd9nKbpDgRXG+nQQPtqRQSKj6fEWnBKtMBGxybAD3kCdBDnuArigkO6tBh28gnAI3vXFr6l3tj6x2LaYvmxzaZDK/Dm0r4b+wGxF/Miv2u2DvQw8WZYr8vigHHJ7hYQCjU16FEeYrvvI5i/4IoN/J1kUszEXPZFpdvZ0Nt49gE6CFPgB7yBF9RTHBQWVlZ1E0ACpp0/F/8zN32tHrFyjobBstGwpluEJjqb8IdV8U2Sl/u3wkzpsQKLVJk0drMEW7i+ARXhAsIYYX02pssT+musQ80Vvg5lez5pblRc7Yaeu5zbAL0kCdAD3mCrygmOGjRokVRNwEoaNLxP3/FEntaVV2let3hZQHiZykU6uhYjftZyCwP1vH2G8cnILo8UUyAtkTPKVliq3yPAbFNn+W47vqxnWMToIc8AXrIE3xFMcFBnTp1iroJgDeaNctun/lUxYDgw3eyZZKkyBBeWqlYNC1pFut8iL8Pi6G4Ugw4PgF+5ElmlMkSfkCiQRPDxz+f1ubLrhzbOTYBesgToIc8wVcUExxUWloadRMAbzRt0iTp76TjWzrAhXT8jx8+MlYASKeYkGzJHykyhJdWcvGDdy4MuuBXsc6H+M2tfb7dxYTjE5DfPOVqM2SZUSZL+AGZcnFJR45NgB7yBOghT/AVxQQHLV68OOomAP5o19aU9eoZGzFfskMz06u8S70li6Tjv+LzGRlN5ZfrDI/ED/88/D/D6w278sE7F8JFlnQ3t0Zh4fgEV8ioeinexu/dUr1uXex1Vl7rg9f7Qs1TPjZDlvtol07dnL6v4MasQykkPDnpP87NwuTYBOghT4Ae8gRfZbf+B3KqSxc+zAFavvunm8xX9//VDLtxjP1+UM9dzIOnX1bvcvJBuWrdOntq90KYNdueSsFBChDd2nWs9zfhJQDGjRprznvi1th1S2dWeDkkX4Tvp17lHczqDesTdkDJ7zcuX27Px9+fKFwcn+AKGVUvo+vjX39K2rSJvfbK67LLXMmT3E/SSZyPwgUKjwy6CN5DxT9HStu0saeSw2TvB4opS4APyBOghzzBV8xMAIBvCwPdhu2fsLNbChCZdkqFR+mHR/XJhs3hUXyFNlMhfD/JfXLYgH0S3jfye1n2SKRa9ims0O4LANFL9TqE9FFIQLpLOwbPlfDeCpK/O04ZRQ4BAACKAMUEBy1btizqJgBFnadUhYVsR/Uluq74fQUKUar1vBPtLZHJ2ssUF9zD8QnQQ55QCNI5lkddkCJLgB7yBOghT/AVxQQHde/ePeomAEWTp3Q+JKe7AWY6lwtvUuzDvgLZdB6EiwTjp01M73KhogOiw/EJvnR8FmueeC2Fjzg2AXrIE6CHPMFXFBMcVFVVFXUTAK/ylKpzKa1iQpod5skuF/4f4fPhDZxllsL44SOd2MhQm9zG8j0GxPaj+HDsTRnfzlRFhzqXo6Mspzg+wXWFVEyIIk/Ba6msbS/7AQE+4NgE6CFPgB7yBF9RTHDQihUrom4C4FWe0u1cylUnVLLrlRkK4VkKFZ/P8Gqz5kB4XeVU+yfE7ycROP7ua8yEGVPsaXzRJb54EC465GqZpPj/WUzLMXF8govSnT3mU56kOCv78WRCXkOnLZpvz8va9lcdc2rW/x9wCccmQA95AvSQJ/iKYoKDevbsGXUTgIKUaFR6fJ4au8Y/dO8n6diSDi4hSz4FRQbpoK9et67e5eOLLuHiQbijLJMZEJkWA+JnSSRbjsnHIgPHJ7ioUDcPbkyepDgr+/GIVEXW8PmFFctM9dYtBX+/AfE4NgF6yBOghzzBVxQTHLR+/fqomwAUpERL4cTnKd3Ok2IvLEjHfNDJH78chuZ9Ix1b0sElZJRtsOyT/I+SNm1iI2jvOGWUPZXfl/XqGbtclzYdEhaRUs2AiJdpp3+4ACKddzKjQsjPLn7m7tgMinAxo1AKCw0VQzg+AXq08pSqyJruEnFAIePYBOghT4Ae8gRfsViqg1atWhV1E4CCIx24S9asjHV+B6fZ5inqYoJ05EbZhqCDX0gnvtZeBFIEqFq3LlYMkCJFt3YdY5tTJ9tfIigCyWjc8H0z9vhzErYzuP5gnwbpZJPz8vdBIUAuH54BEX+5dAogyQTFjNJvCyJRP57x5H4K7tPw+RteetI88u7L9r6Zds/9Zs7fty1PFeD4BOghT4AOsgToIU+AHvIEX1FMcFDv3r2jbgJQcOI7vwPLly83hSi+Izedzuhwp7A2reuNLwbImt3BdcffvmS3N9X9EC46BKRIYAsYZluBQYpOQQEjfF3B6N5E92ey+z+4PeFihvzNG5+NqlPUCPaDSFWkyCcZrSzttEtDfTUvVkAICy87FeD4hCjl8jUuCuQJ0EGWAD3kCdBDnuArigkOqqioMJ07d466GYAXCiVP4ZH08aQTeuPy5Q0XE77tIHZdohkHuSQd+BOvHG2G3TimXgEjvj2BcCf7hZ+tMqtmza43Sj/R3ycqZiTrmHdhFk+8+PumUPME/8QXvsKzmgoVeQJ0kCVAD3kC9JAn+IpigoPWrl0bdRMAbxRKnuI7n2VjzaDzO74T2rUlcwpBOgWMZAWd+CWLCn3kdfwsnvOeuNU8ePpl9vuG/neh5An+keft/BVLTPtWZfb7QT13iT1vCxV5AnSQJUAPeQL0kCf4ig2YHdSnT5+omwB4w4c8See2bE4czFKQJZAaIpcLNv9FentgSEFn+Phtsw9Sbfose3EE+3KkK7y5dKrHJrxZar42bR4+aFiDbQ7aE+SpsW3T2oMDKObjUzqva+EN4+V1S2Z0AL7x4b0e4AryBOghT/AVxQQHLV26NOomAN7wIU/SuS0zFeJJh/T44SPtqXQWTZgxJdZpJKPpXVlWp1CFlyza95o/xGaPSIEhfn+Bhsjm0sHfJ3ts4h/D+S+8GOu0z2VxKNlshHCbgyJWkKdGFxNCRROgWAtPjT0+pVNMCG8YL69bMqMD8I0P7/UAV5AnQA95gq8oJjiosrIy6iYA3ijUPCXbjFg6d6WTN7xhcDpFg3yNcvdVY5eVSvX3wWMTLOMSdPyVtGkT+7soikOJ2ix5ksLCkomT1IobPncWo3GksHbDS0/GRtXv0qlbbFZQshk1xX58ii9KykyE8EwqH+43wJf3eoCLyBOghzzBVxQTHNSvX7+omwB4o1DzlKyYED4fXn5HRpweNmCf2Ih5+Vl4WZ3w0kjJCgsUHPK/bJXc5zIDIVFnqUuCIpbkKdhDItviRrizU74ufubuWMcny3MhTAprMrJexL/GFcJm81Ecn+KLkrKpengmlQ/3G+DLez3AReQJ0EOe4CuKCQ5auHBh1E0AvOFznuKX3wmPOJWfhTd1btm5c8LCQnhUOMWE/AjPLpHCkMxASNRZGi4gxe+bESxvlc9R/tIeyVO4iJWqLel2doaxPBdS8W1UvdbxKZzJejM4KB6gCPj8Xg/IN/IE6CFP8BW7sDlo06ZNUTcBcIp0cme7zIzveQrfL8k6jcJFhfiO59EvPmrPt77vKbNq1mzbKZzs8tCTaKZJfGdp+OfymATFnmB5q2R7EQTPA3l8w8+JxuQonCdpy8QrR5thN46xhYRwW4IZBlIQCf+/8M+DTk45le/j2wkk49vzROv4FLw+SN4OTZB9wHe+v9cD8ok8AXrIE3zFzAQH9e/fP+omAJFLZ8R8OqOwyVPyUfHS2TTm2LNinU6l346QT7TONqItCMVfLh2y1nx4+aDwjJRsBXlK1haZbRDMOAhvIC2WrFlpT6WAMHn0g2ktV5Or2TLh1w72bEBUNI9P6b6OAD7ivR6ghzwBesgTfEUxwUFz586NuglA5GSEdSLBOuviyUn/iXUESse3dIDHI0/pdTpJkWH4+OdjsxIG9dylzjrbcFfQ4R6/8WoykpVsC0VBntIpbOwy4tjY5eS5JM+phmSz10djX1/iiy7s2YB84fgE6CBLgB7yBOghT/AVyxw5qLq6OuomAJFItDSDdOyFl98Jr7PerlXr2OWD5VLikafs+LY2uU+ks71q3brYngUbly/ftp/Bt3sRtG9VZi8nnfcPnn5ZvWWSGlMkis9TuC3x4gsO6Tynki2xFbwOzPn7toJXY5ZsksLBtK/m2dP4+4L9GqJXTEv0cHwCdJAlQA95AvSQJ/iKmQkOGjhwYNRNAHIm1ZIiwWjh8Ahr6dxbv3BRWp18iTqgyFN2iqUzrxBJZ3q3YfvHOt6DzbVltsE+vXbdvvFqXOd9Y/dLSJSn+E3AUy2PlelzKrwkV3gZLiksfDj2pqxnEMRvAH3VMacmLbCwQbk7s9J8xPEJ0EGWAD3kCdBDnuArigkOmjEj8caaQKGqsz55iuWLpi2an9H1pjPSmTzBZ9LhHnTkZ7IXQbYS5SlcpEjVMZ+N4LrDy3DFFxjT7eQPL4m2S6du24suSe4neU26+Jm77WljCxhInxwHgmWnki1f5wuOT4AOsgToIU+AHvIEX1FMcFBNTU3UTQByUkCIX9P9k9v+krAjUDoj7zhllD2V5VPKevWMLaMSHvmcTmcpeYKPNGYZZKOhPOVjRou8FpTvMSD2miD7KoQ7+RNtrhwuDIRfXxJdd9OS+p3XmcyQKvYZDI3d0Lp665bYrBF5jKRA5SuOT4AOsgToIU+AHvIEX1FMcNDgwYOjbgLQaMkKCMF67mLjipV1NmgNdxqFNwYOL+mS6chn8gQfRVVMcCFP8ZuF11RvqdPJH579FJyPX9ooWdEjvLRSqqJmqoJBsRcTwhtaa/B5yTUX8gT4gCwBesgToIc8wVcUExw0derUqJsANEqyJYvil2FJt9Mo3HGaaccSeQL0uJ6n8GuPjJD/78pl9jR+P4lUEr3exO8NEZ4NIf8z6DyXQsKKadPtqfx+/PCRRbc0UnhmQTqFFrnvhow5r87SRuk8Tj5wPU9AoSBLgB7yBOghT/CVvwvRAohMuCNJCgfSmdeY0aVRjcIG4L5gtkA8ec0JXnc0RreHX4eCTa+TXUZOP73jblPxuf/rpEoRQF7zpQAgr/eyFF23dh3t74JCSlCEkWJC/Ou5/O2UhXNi32vuuQEAAAAA0EUxwUFMhYJv0plxkCvkCfA7T0FHdbh4me//mc7rWqKO9Ew0tiibK/HFAFmKLmjnxuXLY4UXKSzI967fnmLPE1CIyBKghzwBesgTfMUyRw5iKhQKnYxQldGpDclHMYE8AcWVJ1c6qOP3WZj/wouxZX6kYz3T5Y/Ce0FEtU9DOoWa8P3fYffdYoUX2ddC9reI3xA7kyWofFMIeQIKAVkC9JAnQA95gq+YmQBARbDetYzSzdfoYABwlXSih2cj7DLi2IQFVCkqSEe7FB3kb7IZsZ/NrIdM/0Ze45esWWnbFm6z6ZH8b8LXH78cVftWZfaUZY0AAAAAoHBQTHAQU6FQiOI33HRldDB5AvSQp8yEO9PD58PLJEmnfLC3gt1A+qt55pF3XzYXfraqTpFBNpaW30vne/xeBGHhYkS4yBv+Gzm/atZsM+fvz9e7jvDfyHV9+OVMM/b4c+zPgr0QwoIZBYlmFiS7zRQQtiFPgA6yBOghT4Ae8gRfUUxwdCrU0KFDTSELd2aERz+yRjLyzYc8Aa4gT/qCEfvBaTBiP1xkENVbt9Qr2gbH2Op162LH2NEvPmrPp3usjd9AWWYfBEWD8HWECwDhwsChGd5ebEeeAB1kCdBDngA95Am+opjgoKZNC3sri/BSCNLJ8cntd9mff9qxubn7zXH2fLBMQrKRlYCWQs8T4BLypC9Zx/z4V0em9Tfh0f9B539wmrQAEFqC6a5vZyIk2kA5fF3QR54AHWQJ0EOeAD3kCb6imOCgAQMGmEIWHjkZdHLIac+4TgkZdQnkWqHnCXAJeYpuxkKqDYoTFRQaEvxN/JJDFA/yhzwBOsgSoIc8AXrIE3xFmcxB06dPV72+YCNcmQkwfvhIeyqzB4aMOS+2NrL8LJgpEP6bdMn1BNclHR7hTo9MN4VE4Qg/T0p2aJays8uXPAHFjDzlj8weGD5++54Gweb28Ad5AnSQJUAPeQL0kCf4ipkJDiopKcnJkkOiat0609psG+W4esP6WOfvxuXLTcvOnev9jRQYwhtAxl+3iO/cSLezo2lJs9j/TLXkUXjPBZcV234Q4Y1C5TEf1HMX8+Dplxmf8wQUO/IE6CFPgA6yBOghT4Ae8gRfUUxwUN++fRv19/Gd/MFGitJRP/HK0WbYjWPsuszhzu9BF/wqaYd9UICQTv2ZT/yv6X/6z+1lw5s0ZjNaMtn/jC9gFEwxYVpxFRNkOav5K5bENgsdPmiYl3kCsB15AvSQJ0AHWQL0kCdAD3mCr1jmyEEPvvx8ozt5g30LUi2LEO74DnfWy+VlA0Yhnfn7XvMHeyqXKevRPXZZuUxjllwI/0+5/vCsBClgCCkkrJg23Z4mWo4p+Hk2SzNBl6uFlJkzZ0bdBMAb5AnQQ54AHWQJ0EOeAD3kCb6imOCIcEf4xK9nJ9yLQC5z3hO32lPpRJdZBsk62ZNJd4R/skJDNps8ZkqKCt2G7R8rYOx9yYUJ220LDVOn2fNyn3z45cx691P8faNdcAiuT07/u3JZwv+pjaJJZlq0aBF1EwBvkCdAD3kCdJAlQA95AvSQJ/iKZY4UpNpXIJu153fu2iPh+vvyfbJO/vCeB6k2wW3sckH5Wm4oWQEjfP/Kz4Pfhe+b+PspLCg4BPtBxF9nIJ2llcJ7S8T/z/DjERR85Pri/2f4cU52Pr4t6SynVGz7N6TSq1evqJsAeIM8AXrIE6CDLAF6yBOghzzBVxQTHCgsxK89f1KfoWboXkPt+XSXEQrvP9CYpYdcoVm0CD8OY48/J+FlgpkN8nvpvP/k9rsSFgDCHfvBUlINPR7Jbov8z7vfHBf7PjgvRYBw0WP+Cy/Grkf+pkubDvb78B4Wn3Zsbv9+1CHH1/v7cDEj/Ddz/v58Ws/V8B4c8v9tMWPQsFjRKlXxygWzZ882Q4duyxOAxiFPgB7yBOggS4Ae8gToIU/wFcWELIU7aMMdsfFL26QalZ5M69atM25PIWxQ7Jrw45Boxkei+zRYVkl+l80MkPj/Gf6/4fPhoscuI45NOAMj3M6ecdcR/vtEszzkVIoJYfFFh0S3I34GSCHIJk8AEiNPgB7yBOggS4Ae8gToIU/5c88995g//elPZunSpWbw4MHmrrvuMvvuu2/UzfJWk9ra2tqoG1Es4ke4B6PcL5r5hh3lLp3TMvq7oqLClJeXR91cwAvkCdBDngA95AnQQZYAPeQJ0EOe8uPZZ581p59+urn//vvNfvvtZ+644w7z3HPPmVmzZpkdd9wx6uZ5iWKCgz766COmQgFKyBOghzwBesgToIMsAXrIE6CHPOWHFBDkfr777m0rgdTU1JiePXuaCy+80FxxxRVRN89LTaNuAOpr27Zt1E0AvEGeAD3kCdBDngAdZAnQQ54APeQpe2vXrq3ztXnz5oSXq6qqMpMnTzaHH3547GdNmza137///vt5bHFxyWjPBHmAOnfubM8vW7bMdO/e3T5wK1assFWf9evXm1WrVpnevXvb6TzygPfp08euWVVZWWn69etnFi5caDZt2mT69+9v5s6da6qrq83AgQPNjBkzbPVI1raaOnWq/R+JzsuTYsCAAWb69OmmpKTE9O3b18ycOdO0aNHC7pQuG5zIumRdu3Y18+bNs+GVaUULFiwwHTp0MGVlZWbRokWmU6dOprS01CxevNh06dLFqdsk7ZQKpk+3ycfHidtUGLeJPHGbuE3kqdgeJ25TYdwm8sRt4jbp3Cb5W/mfPt0mHx8nblNh3CbyxG3iNpGnqB4naf+QIUPsz7t162Y2bNhgAtdee6257rrrTDy5H7Zu3Rq7voB8/8UXX9S7PHSwzJGDmAoF6CFPgB7yBOghT4AOsgToIU+AHvKUPSmKhDVv3tx+xZPCRI8ePczEiRPNAQccEPv55Zdfbt5++23zwQcf5KW9xSajmQnID6ncAdBBngA95AnQQ54AHWQJ0EOeAD3kKfdLRMlshx122MHOcAiT72WmBHKDPRMcJFOAAOggT4Ae8gToIU+ADrIE6CFPgB7ylHuybJIsjTRhwoTYz2TpJvk+PFMBuigmOEjWEgOggzwBesgToIc8ATrIEqCHPAF6yFN+XHrppeahhx4yjz/+uN3L4fzzz7f7RZx11llRN81bLHPkIJmmA0AHeQL0kCdAD3kCdJAlQA95AvSQp/w4+eSTzfLly83o0aPtxtN77bWXeeWVV+ptygw9FBMcnaYDQAd5AvSQJ0APeQJ0kCVAD3kC9JCn/Bk1apT9Qn6wzJGDZDdyADrIE6CHPAF6yBOggywBesgToIc8wVcUExzEVBxAD3kC9JAnQA95AnSQJUAPeQL0kCf4imKCg5o25WEBtJAnQA95AvSQJ0AHWQL0kCdAD3mCr5rU1tbWRt0IAAAAAAAAAADgLspkAAAAAAAAAAAgJYoJDpo8eXLUTQC8QZ4APeQJ0EOeAB1kCdBDngA95Am+opjgoLKysqibAHiDPAF6yBOghzwBOsgSoIc8AXrIE3xFMcFB5eXlUTcB8AZ5AvSQJ0APeQJ0kCVAD3kC9JAn+IpigoMWLFgQdRMAb5AnQA95AvSQJ0AHWQL0kCdAD3mCrygmOKhDhw5RNwHwBnkC9JAnQA95AnSQJUAPeQL0kCf4imKCg1hXDdBDngA95AnQQ54AHWQJ0EOeAD3kCb6imOCgRYsWRd0EwBvkCdBDngA95AnQQZYAPeQJ0EOe4CuKCQ7q1KlT1E0AvEGeAD3kCdBDngAdZAnQQ54APeQJvqKY4KDS0tKomwB4gzwBesgToIc8ATrIEqCHPAF6yBN8RTHBQYsXL466CYA3yBOghzwBesgToIMsAXrIE6CHPMFXzaJuAOrr0qVL1E0AvEGeAD3kCdBDnoDizVK/6/uZxWu3dTJ1b9vdzL56dtRNAgo2T4CryBN8RTEBAAAAAIA8kUJCZUll7DwAAEChYJkjBy1btizqJgDeIE+AHvIE6CFPgA6yBOghT4Ae8gRfUUxwUPfu3aNuAuAN8gToIU+AHvIE6CBLgB7yBOghT/AVxQQHVVVVRd0EwBvkCdBDngA95AnQQZYAPeQJ0EOe4CuKCQ5asWJF1E0AvEGeAD3kCdBDngAdZAnQQ54APeQJvqKY4KCePXtG3QTAG+QJ0EOeAD3kCdBBlgA95AnQQ57gK4oJDlq/fn3UTQC8QZ4APeQJ0EOeAB1kCdBDngA95Ck/vv76a3Pqqaeajh07mpYtW5o999zTfPzxx7Hf19bWmtGjR5tu3brZ3x9++OFmzpw5kba50FFMcNCqVauibgLgDfIE6CFPgB7yBOggS4Ae8gToIU/5uY8PPPBAU1JSYl5++WUzY8YM8+c//9l06NAhdplbbrnF3Hnnneb+++83H3zwgWndurU56qijzKZNmyJteyFrFnUDUF/v3r2jbgLgDfIE6CFPgB7yBOggS4Ae8gToIU+5d/PNN9vlpB599NHYz3beeec6sxLuuOMOc/XVV5vjjjvO/uyJJ54wXbp0MS+88II55ZRTIml3oWNmgoMqKiqibgLgDfIE6CFPgB7yBOggS4Ae8gToIU/ZW7t2bZ2vzZs3J7zciy++aL773e+aE0880ey4445m7733Ng899FDs919++aVZunSpXdoo0K5dO7PffvuZ999/Py+3xRT7zITJkyebzp072/PLli0z3bt3N1VVVXaHcqkEyXpgMsVEqm8SGnnA+/TpYx+4yspK069fP7Nw4UI7laR///5m7ty5prq62gwcONBORampqTGDBw82U6dOtf8j0fmmTZuaAQMGmOnTp9tpLH379jUzZ840LVq0ML169TKzZ8+2U1a6du1q5s2bZ9q2bWvKy8vNggUL7DSXsrIys2jRItOpUydTWlpqFi9ebCtSLt0muV75Pz7dJh8fJ25TYdwm8sRt4jaRp2J7nLhNhXGbyBO3idukc5uWL19uevToUVC3Kd5HH33k/ePEbSqM21SIeSrGx4nbVBi3iTxldpuk/UOGDLE/l/0NNmzYEDtOXnvttea6666rd/ycP3++ue+++8yll15qrrzySns8veiii+z/OeOMM+z9IIL/F5Dvg98hc01qZc4HnCIBkuABaDzyBOghT4Ae8gQUb5bKLi8zlSWV9nzr6tZm/S1s0gk3FGKeAFeRp+xJUSSsefPm9iueFA1kZsLEiRNjP5NighQVZOaB/Fz2VJAChhQoAieddJJp0qSJefbZZ3N8S/zEMkcOojoG6CFPgB7yBOghT4COYslSv+v72SKEfMl5IBeKJU9APpCn7MnshvBXokKCkAKBzJoIkxkYMhtDyGyJYAZEmHwf/A6Zo5jgIJmKBEAHeQL0kCdAD3kCdBRLlhavXWxnM8iXnAdyoVjyBOQDeco9mXUwa9asOj+TZZe+853vxDZjlqLBhAkT6sx6+OCDD8wBBxyQ9/YW5Z4JyA9Z0wyADvIE6CFPgB7yBOggS4Ae8gToIU+5d8kll5hhw4aZG2+80S5d9OGHH5oHH3zQfglZyujiiy82119/vdl1111tceGaa66x+zSMGDEi6uYXLGYmOCiYjgOg8cgToIc8AXrIE6CDLAF6yBOghzzl3tChQ824cePM008/bTeVHjt2rLnjjjvMz3/+89hlLr/8cnPhhRea8847z15eNq1+5ZVX7AbSyA4zExwku6wD0EGeAD3kCdBDngAdZAnQQ54APeQpP3784x/br2RkdsKYMWPsF3QwM8FBslkIAB3kCdBDngA95AnQQZYAPeQJ0EOe4CuKCQ6aO3du1E0AvEGeAD3kCdBDngAdZAnQQ54APeQJvmKZIwdVV1dH3QTAG+QJ0EOeAD3kCdBBlgA95AnQQ578UlNTY5dMatKkif1+0qRJ5oUXXjCbN2+2mz8fcMABSf/27LPPzvj/yf95+OGHjYua1NbW1kbdCNS1ceNG07Jly6ibAXiBPAF6yBOghzwBxZulssvLTGVJpT3furq1WX/L+pz8DVAMeQJcRZ78ccUVV5g//elPZo899jDTpk0zEyZMMD/84Q9tgUE0bdrUvPbaa+aQQw5J+Pfy+6AIkQ7pqpfLb9261biIZY4cNGPGjKibAHiDPAF6yBOghzwBOsgSoIc8AXrIkz/ee+8928F//PHH2+/vvfde29EvP5MvOX/LLbekvI7gsg19FQKWOXJQUNkC0HjkCdBDngA95AnQQZYAPeQJ0EOe/Nr/QmYKDBw4MFZckO+fe+458+qrr5qHHnrIfPzxx0n//s0336zz/ZYtW8xvf/tbe72XXHKJ2Xfffe31ydJJf/nLX0yPHj3MXXfdZVxFMcFBgwcPjroJgDfIE6CHPAF6yBOggyxt99FeB5jNS5fa8827djVDP30/6iahwJAnQA958kdFRYU97dy5sz3/zTffmPbt25sTTjjBnkoxYc2aNUn//gc/+EGd78eMGWM+++wzWzD49a9/Hfv5j3/8Y9O9e3dz4YUXmrfeesscccQRxkUsc+SgqVOnRt0EwBvkCdBDngA95AnQQZa2k0JC+YZN9isoKgCZIE+AHvLkj2Dviw8//NB28ovdd9/dnlZWbtvPqG3btmlfnxQfRM+ePev9bqeddrLLHT322GPGVRQTAAAAAAAAAACIs+eee9rTP/zhD+bEE0+0SxLtt99+9mcLFy60p7I0UbpWrlxpT//4xz+aWbNmxX4u58eOHWvPr1q1yriKYoKDmAoF6CFPgB7yBOghT4AOsgToIU+AHvLkj4svvtgWEIJNkktLS815551nf/fSSy/Z0wMPPDDt65M9EsQnn3xiBgwYYGc1yJecnzJlSp1ihYsoJjiIqVCAHvIE6CFPgB7yBOggS4Ae8gToIU/+GDlypHnjjTfspslXXHGF3Wy5f//+9ncHH3ywnU1w7rnnpn19d9xxhy0eBMWJ9evX26/g+zZt2pjbb7/duIoNmAEAAAAAAAAASOCggw6yX/Euv/xyk6m99trLFiSuvvpqO7NBCgmidevWdhNmKU707dvXuKpJrZQ84JSqqio7ZQZA45EnQA95AvSQJ6B4s1R2eZmpLNm2YWPr6tZm/S3rs/6bj/Y6ILbZcs2GjaaTaWLPV7RqYb639Msc3gr4qBDzBLiKPPnn66+/Nn//+9/NzJkzzYYNG8wjjzxiJk2aZH+3//77Z/V4S7f8N998Y8/vuOOOdokj17HMkYOYCgXoIU+AHvIE6CFPgI5iz5IUEso3bLJfhnGCaCQf8tTv+n62+CZfch6Iig95wnb333+/nS1w2WWXmb/+9a/m6aeftsWDs846yxxyyCHmhRdeyOp6ZaPlefPmmWnTpqVdSKisrDTV1dWx79977z1z1VVX2S85n2sUExzUtCkPC6CFPAF6yBOghzwBOsgSoMeHPC1eu9jO4pEvOQ9ExYc8YZtXXnnF/PrXvzabN2+2MwnCjj/+ePuz559/PqPr/O9//2uOOeYYOxvh+9//vjn66KPNpk2bzB577GH69OljJk+enPRvZbmlWbNm2fNS2JA2rFu3zi6X9JOf/MQ8/PDDJpd4ZjtIdu8GoIM8AXrIE6CHPAE6yBKghzwBesiTP26++WZ72q1bN1tUCNtzzz0znokiyyUNGzbMFilqampiGy+3aNHCDBo0yHz55ZfmmWeeSfr3c+bMMQMHDrTn//znP5sJEyaYO++80/zlL3+xG0XfeOONJpcoJjho+vTpUTcB8AZ5AvSQJ0APeQJ0kCW4pNCX2CFPgB7y5I8pU6bYJYhuueUW89Of/rTO73baaadYgSBd1113nVmyZIktIPTu3bvO7773ve/ZUykKJNO+ffvY/5NlknbbbbfY72RWw/Lly00uUUxwUElJSdRNALxBngA95AnQQ54AHWQJLin0JXbIE6CHPPkj2J+gY8eO9X63YsUKexq//FEqL7/8si1O/P73vzd/+9vf6vwuKC589dVXSf9+1KhR5owzzrAzGC699FJzwQUX2MvL129+8xtz5JFHmlxqltNrR1ZkQw8AOsgToIc8AXrIE6CDLAF6yBOghzz5Q0b7z5gxw9x7773mkksuif18w4YNdnkh0a9f+rPRgpkDhx9+eL3f7bDDDvZ0zZo1Sf/+8ssvN61bt7azGKQNctlHHnnEbgjNnglFaubMmVE3AfAGeQL0kCdAD3kCdJAlQA95AvSQJ3+MHDnSzjx46aWXzI9+9KPYz2UPhUmTJtlZBtKJn65ghsPHH39c73f/+c9/7GmXLl1SXofMRli0aJHdq+G9996zGzavXLnSznRo166dySVmJjhINtwAoIM8AXrIE6CHPAE6yBKghzwBesiTP373u9+Zf/7zn3YfjM2bN9vigVi3bp09lU2TwzMWGvKDH/zAPPvss2b06NHmiCOOiP387LPPNo8//ri9/kMOOaTB62natKnp1auX/RKyj8Kbb75pFi9ebDZu3Ghatmxpunfvbvbaay/To0cPo4VigoOCJwGAxiNPgB7yBOghT4AOsgToIU+AHvLkD1lS6N133zVXXnmlefrpp+2mx6JDhw52Q+YbbrjBdtynS67nhRdeMFVVVbH9E4QUEmQGhBSiZCmjdE2cONFe/v3330+4d4Nc//777283kD7wwANNY7HMkYNmz54ddRMAb5AnQA95AvSQJ0AHWUIU+l3fz5RdXma/5LwvyBOghzzl1//8z//YTvOLL7449rNNmzbZ5YBkWaGysjK7XNGyZcuyuv62bduau+++2264LNchX3JefpbpskJ77rmnnenQqVMn2/kf/urcubN5/vnnzYABA9K6rtdff90cfPDBtj1S1JDvP//8czNv3jx7Kt+PHTvW7tNw6KGH2u8bi5kJjla8AOggT4Ae8gToIU+ADrKEKCxeu9hUllTGzvuCPAF6yFP+fPTRR+aBBx6wyw2FydJDss/Bc889Zzv8R40aZU444QS7x0C6ZINjKUTIkkK33Xabueiii2yHf2MdffTRZsGCBea1116LFZ5kE2dZ9qhVq1ZpX8/VV19t9t13XzNhwgTTvHnzer/v37+/LSJcdtlldukkuXyijZ8zQTHBQV27do26CYA3yBOghzwBesgToIMsAXrIE6CHPOXH+vXrzc9//nPz0EMPmeuvvz728zVr1piHH37YPPXUU7YzXTz66KO2c102TZZlf9IhHfvl5eV2aaN0ZwukS5ZGOu6440xjTJs2zdx5550JCwlhpaWl5swzzzS/+c1vTGNRTHCQTEWRJyqAxiNPgB7yBOghT4AOsgToIU+AHvKUvbVr19b5XjrKk3WWyzJGxxxzjB1tHy4mTJ482VRXV9cZhb/77rvbvSxkb4F0iwni2GOPtfsZyN8dnsWo/ieeeMJk4/TTT2/wMrJvw9y5c9O6PrmcXD6vxQR5IIKpHLIWk+wILZtFyBpRPXv2tNUgqdT07t3bVFRU2Ae/T58+ZunSpaaystJO11i4cKFds0oqQXIj5IEdOHCgmTFjhqmpqTGDBw82U6dOtf8j0XmZViKVINlBu6SkxPTt29fMnDnTbk4hTwiZGiJTiaQCKMGVNa0kvDJ1RO4wmZqyaNEiuy6VVGVkh+suXbo4dZukXTJFx6fb5OPjxG0qjNtEnrhN3CbyVGyPE7epMG4TeeI2cZt0bpNcv/zPQrpN8eS1oKHHqWZrjTEl2y7/0P+2NP/vsd7GNDGmduMm2Vqx3nXK5aVNrjxOvj33wo+HnP/ss8/qPU5yftasWQVzmwo1T/Hn4x+DTz/91Kvnno958vU2+ZCnfD5O0v4hQ4bYn3fr1s0uLxS49tprzXXXXVfvWPfMM8+YKVOm2ONoPLkP5P+1b9++zs/lf8vvMnHhhRead955xxYrqqurzY9//GN7PcHGyQ1tui0zAuIv2xC5fDrFhFNPPdXcfvvttj3nnnuuvb/jyfPkwQcfNHfccUedPSWy1aQ20TbPiJRsiqGx/hYA8gRoIk+AHvIEFG+WZNPeYL391tWtzfpb1mf0N6/c29n03LqDPb+itsZ0atK03vmKVi3M95Z+mcNbUdySPYbZPLYuKcQ8xSv0xwD+kDwtOOJYs/nbjuvmXbuaoZ++H3WzvJmZIAWK7373u+Y///lPbK8E2Yh4r732sp3msrzRWWedZTZv3lzn72R/Adk74Oabb067PVJQkc596UJvkqQoID/fsmVL0r/PlFzf1q1bG7ycFGLOOOMM8+yzz5pmzZrZApIUY+T+ktu+ZMkSW/CRtp144onmb3/7my2yNAbLHDlIqneFfgAHXEGeAD3kCdBDngAdZAnQQ54A3TxJIaF8g8wgM6Yiw9HwxUxmN6Szes4333xj9tlnn9jPpPNdZhDcfffd5tVXX7Ud7atXr64zO0FmQzRmP4vaLMbky8yKeOPGjbP7HRx44IG2wCHFgw8++MBuDr3rrrvafSDSIYWBp59+2m42/Y9//MPOxpICwsaNG+2eDDLz40c/+pH5yU9+Yv+PBooJDtJYvwrANuQJ0EOeAD3kCdBBlgA95AnQzRPlg9w57LDD7BJzYTITQfZF+P3vf2+XgZJlmSZMmGBGjhxpfy9Lz8myTwcccEBG/+uggw7KeJmiVMWE5557zvzxj380l156qbn11lvr/O6yyy6zyxZ95zvfMZmQQoFWsaAhFBMclGh9KwDZIU+AHvIE6CFPgA6fs9Tv+n5m8drFpnvb7lE3BUXC5zwB+UaecqtNmzZ274cw2buhY8eOsZ+fc845tsNe9nCQ2Q6y94EUEjLZfFm89dZbqm0fM2aMLU4k2sxZfnbbbbeZW265xS5f5KLMF21Czsm6XwB0kCdAD3kC9JAnQIfPWZJCgqz9LqdAPvicJyDfyFP0ZIS/bJYsMxNkdoEsb/TPf/4z6mYZ2QhbPPnkk3YT7PASSvIzMX/+fPX/u27dOjszo7GYmeAg2eEcgA7yBOghT4Ae8gToIEuAHvKEYhKe/TX76tk5ydPX6teKTGYQtGjRwtxzzz32q7Fk/wUpRHz88cd2H4ZwEUDITIOHH344revq06ePmTlzpt3r4O2337abRgvZ72Dx4sX2uuQy2u68804zevTotDZ2ToVigoMau6s2gO3IE6CHPAF6yBOggyxBq0NR5KpTsVCQJxSTXM/+Ik/+WLlypfnBD35gCwCJyIyCTIoJ0qH/05/+1J6X4oF8xV+XXMZVFBMcJE+iHj16RN0MwAvkCdBDngA95AnQQZag1aEYnC9m5AnpogjXsHAHMQqbbJY8Y8aMhL9rksXGzCeddJL9O9nP4euv685fkddg2ZRZLpOOJ554Iu3/+8knnxgNFBMc1KVLl6ibAHiDPAF6yBOghzwBOsgSoIc8IV0U4dLLU+NXp4cLXnnlFdv5f9ppp9nOezkvmyRv3LjR3HDDDWbvvfe2mypn4sQTT7R7OUyePDm2P8Iuu+xihgwZYpo2TX+L4zPPPNO2R2Y0pCOb4kc8igkAAAAAAAAAACTZTPvkk0+OzQQYOnSoGTZsmGnVqpW55JJLzMSJE83BBx+c0fVK0UCuR76y1aFDB7vnwi233NLgZWUZpgceeMA0FsUEBy1btsz06tUr6mYAXiBPgB7yBOghT4AOsgToIU+Abp7ghx122MGelpWVmebNm9vNmJcsWWJ/tuuuu9pZAffff7+58sor876h87777mu++OILO6MhnRkWGigmOKh79+5RNwHwBnkC9JAnQA95AoorS+H1xTdUbTCmJOoWAYWbJ6BQ8vRl1I2Aio4dO5qvvvrKVFZW2sd1wYIFdoNkKRg98sgj9jJr1qyJZENnKSa8+uqr5ptvvjE77rhjysu2b99epWCc/iJMyBupTgHQQZ4APeQJ0EOegOLKUrC+uHylu64xkG+Fkicgn4XgssvL7JeczwR58kf//v3tqRQPDj/8cHscl9kAF154od3UWDr+pVM/0w2d5XrivzJ1+eWXmy+//NIud9SQCy64wF62sSgmOGjFihVRNwHwBnkC9JAnQA95AnSQJUCPb3mSWUDZdgSjuHy01wHm3a472y85n6gQnOlG04nyFPyf8P+A+0466SRz5JFH2vPXXHON6dGjR50CQNeuXc2dd96Z8YbOp59+uv1ezt9+++3mxhtvtHswfO973zMTJkxI67pat25tvvOd75iSkvxNeWSZIwf17Nkz6iYA3iBPgB7yBOghT4COQs/SQ0+1NO8+sbM937xrVzP00/ejbhKKWKHnKZ508kknsMi0IxjFZfPSpaZ8wyZ7vmLpUrU8zU3yf7T+B/Lj7LPPtl+BmTNnmnHjxpmvv/7aduQPHz7c7qcQ9YbO+UIxwUHr16+PugmAN8gToIc8AXrIE6Cj0LNUXtnElG/V7cAC8pmn8H4g3dt2N7Ovnp2DlgGNF/9czbVCPz4hubKyMnPaaac5taFzPlFMcNCqVauibgLgDfIE6CFPgB7yBOggS3BpdknNTzYa084UVZ6CZWCC84Cr8v1c5fjkl5qaGrvR8dy5c83q1asT7m8gmzJHsaFzvlFMcFDv3r2jbgLgDfIE6CFPgB7yBOggS3Bpdokp8M21yRNgzE9/9JVZdXlZbN8NU5J9nmbpNg0RmTZtmjn++ONtp38q6RYTZENnKSYEGzo/9NBDsQ2dRaYbOucbxQQHVVRUmM6dO0fdDMAL5AnQQ54APeQJ0EGWAD3kqfCXcJKNfWVd/ob2YSnE25YvK1tuNRu/ncFgNjcuT/DDr3/9a/Pll1+mvEyTJk3Svr4TTzwxdnnZ0Pn//u//7P4LgW7dumW0oXO+UUxw0Nq1a6NuAuAN8gToIU+AHvIE6CBLKHbpdh6ngzxFsyyOZsd+uhsJszyVyfnjWbO1xrxQ1caUm6ZRNwmNNHnyZNv5v9NOO5kLLrjALlPUrFn2XernnHOO/dLa0DnfKCY4qE+fPlE3AfAGeQL0kCdAD3kCdJAlFLt0O4/T6cjuUtbFzBs6T72NSI2OfU8fzxJZ+czdDmGkT2ZsSUe/zBY47rjjGnVdGzZsMKNGjbLnR4wYYY499thGb+icb5THHLQ0izcAABIjT4Ae8gToIU+ADrIETbI+etnlZfZLOtmLqeNTvpasXWJcm3Xxbted7ZecB4AonHvuuXbDZdl8ubFatWplnnnmGfP444+b5s2bm0LEzAQHyW7eAHSQJ0APeQL0kCdAB1mCJuks0hoh/tBTLc27T+yssvxQvmzasskWUlxZR7+xsy58XtKqtqrKNCktLajnF+pi3wp3vfPOO3W+P/DAA+2myVdddZVZvHixOeigg0yHDh3q/Z38PB2DBw82H374YcHuq0ExwUH9+hXHCAggH8gToIc8AXrIE6CDLMFV5ZVNTPnWwuoI1yimBB2k2XaOhjvMazZslG1Ns2qHj8LFlRW1NabjlpqCen6hLpa3ctfBBx+ccENleY2844477Fc8ufyWLVvSuv5bbrnFHHXUUea6664zQ4cONX379jWFhGKCgxYuXGj23HPPqJsBeIE8AXrIE6CHPAE6yBLgZgdptp2jdTvMa6WHTrmFANAwKRxk8vNMXHvttaa8vNzMmTPHznjYddddTZcuXeoUMOT8hAkTjIsoJjho06ZtB04AjUeeAD3kCdBDngAdZAkAdGU6uyTYa0SwXA98cMYZZ+Rs6aS99trLvPXWW7ZYIF9bt241s2bNsl/hgkWimRGuoJjgIKlKAdBBngA95AnQQ54AHWQpveViWFPdncdDHgvBY1PYCmW9+/DzLtnzLH5/j8VHZja7JN3lsXhNQqF49NFHc7J0UtOmTWNFhfAMB43ZDvnUNOoGoD6N3cEBbEOeAD3kCdBDngAdZKnh5WLkK+jAQ/SPh5zy2BQe2+HedWf7JZ3iwXJOjVnSKd/Pu5T7e+Th+ejb814KSjIjQ77kPPLvpptusnsOtGnTxuy4445mxIgRdUb4BzMYL7jgAtOxY0dTVlZmRo4caZYtW5b1/1yzZo25+uqrzY9+9CO778E111xjvvnmm7T+NigafPnllw1+zZ8/37iKmQkOqq6ujroJgDfIE6CHPAF6yBOggyyhWBTKSHhfFeKG2r5xbWYDGyhH7+2337aFAikoyObHV155pTnyyCPNjBkzTOvWre1lLrnkEvPSSy+Z5557zrRr186MGjXKnHDCCea9995Led1jx461X7K3wYIFC0yLFi3Mhg0bzHe/+906Hf2vv/66ncnw0UcfmW7duqXV7u985zumkFFMcNDAgQOjbgLgDfIE6CFPgB7yBOggSygWdFw2TqGu6x+/PFYhFwBqNmyUbWWzLhKEN+emoAPxyiuv1Pn+scceszMUJk+ebA466CA7i+Dhhx82Tz31lDn00EPtZaTjX5ZInDRpktl///2TXrcUB6RAcdxxx9lCgrj//vvNvHnz7H4G4aWJlixZYm688UZz1113NdjmTz75xF5vOuQ2uIhigoOkgjZkyJComwF4gTwBesgToIc8ATrIErCddNbKMjyujNx2Sfy6/uGZHlVbqkxps9K8FhrC/18KHabEpOxAL9TO83ABYIV0vibYVJYiAeKtXbu2zvfNmze3Xw2R4oGQ2QRCigoyg/Hwww+PXWb33Xc3vXr1Mu+//37KYsLMmTNt0WDfffeN/WzcuHGx8zK7QTZqliWPPvvsM/Pqq6+mddsuuuiitC4n/zvdooPTxQR5EDp37mzPy/pS3bt3N1VVVWbFihWmZ8+eZv369WbVqlWmd+/epqKiwj74ffr0MUuXLjWVlZWmX79+ZuHChXa9KqkCyfqW8qDKaBJ5E1hTU2MGDx5spk6dav9HovOyWcWAAQPM9OnTTUlJienbt699gKVKJE+G2bNn26ksXbt2tdWitm3bxqakdOjQwa6PtWjRItOpUydTWlpqFi9ebLp06eLUbZLrkgqYT7fJx8eJ21QYt4k8cZu4TeSp2B4nblNh3CbyxG3iNuncpuXLl9v/6fptSkvttpGQweNUs7VmWwdjrdl2HUk6GxORv5WOiNhV19baNZh57tW/TXL/JiOPR6LbFHts6j+EDT4e9R6bmlrbrkS3af/79jffrP/GdGvbrc7zSM5L2+Q21Xl+1dZu77j9erG9TEOPU7LbItcrHXMNPU51NhGtqd32fcm2872u7mVWblppf9exRUfz4agPG3yckt6Jtdv6pBp67iW7PfGPx1ervzIbm2/8tjfbmOqW25ZM+3r117Yt8tyT25CwKbW1dX4n5+W+zuS599Wqr8zGFtv/f7ht8hwIblP45/L8S3bfyP+Xxym8j2u4nXJeLhPkKenjvrUm9pjKeXmNbczjZP9PAvJzuVwsT6GfS8dsoteI+Ps8eJzSfY0I/73NjXxbsu18uA2pcpzosQnI/WsfoxT3TZBXuYvDj4Gcl+eF68fcXL6WS/uDwQGyXJAsKRS49tprzXXXXZfkjt3+mF588cXmwAMPjM1alPtB/mf79u3rXFb+v/wuFXnuC7lvhdxvwTGhadOm5r777rN95NLOn/70p/Y2pqPQNltOpEmtD7fCMxIiebIDaDzyBOghT4Ae8gSkFl724mfHLjEV7ZvFRg0LGdEr56dfPr0gsiTLqwSjos16+cG2s6/c29n03LqDPV/RqoX53tIv6/1N6+pt6z4Hfx/+mxW1NaZTk6b1zst1idho37jrRsOPjdzv629Z3+DfhB+P7562zGxsX5vwsRV2hHkGj02y50C4bTITYfvI77rPgXQe82xuf7K/D7czVZtTydXtib9t4baFf/fU451Mzy3bvjnyJwvNxnY1CR/PH56+POPbFl6y6JAjP0vrfg/uj/jnTbL7Jnz/xT/vEr2+pLpt0pY3X9sz4RJE6T5v0nk849uc7HFO93LZPFdfeaJz7LrDOU5128LXl+7zJvx4ZPMcKhbZzEw4//zzzcsvv2zeffdds9NOO9mfyfJGZ511ltm8OVSxM8bONjjkkEPMzTffnPT65P/JzIB//vOfdqkj2WPh+9//vi0G77333ubjjz+2l3vrrbfsEkpSNIlvd5gUIORvpZCRziwLIYMAXMQyRw6Syp9sHgKg8cgToIc8AXrIE5DB0hglVWZjybaOgGBZEOmAkfNkqXHY1Lfw1///Z1VrU26SjFhv5BI7rm14m++NlusM81dQiEsWZbMEUfj5lez5+dMffWVWNeI5XEzPm2IksxsyIZsq//vf/zbvvPNOrJAgZLaEDOBZvXp1ndkJMiNCfpeKzJiQWR333nuvnY3xpz/9Kfa7Q7/df0HITIvgf6XjH//4hxk2bJgpZBQTAAAAAAAoQmzq69Y+B7VVVabJtzNt7Ia7Rza8/n9tbausCgi1rb/t8Kw7YLcg17Kvs8nvTzYa0y7qFhW38OtKsufnypZbzcYMn8PpylUR7KGnWpp3n9i5wXwif+S18MILL7R7GcgMgZ133vb4BGTZJFmaacKECWbkyJH2Z7NmzbJFggMOOCDldcs+C7J58+uvv24GDRpU53cnnnhi7Pzbb79tT2V5qWJBMcFBsi4ZAB3kCdBDngA95AnQQZb8VTSzJsL7HNTWmI5banLSeR/u4E1VQChE4aIHo8L9ks4sh5Qz2+Z/GSvWNbbQFJ594HJxrZhccMEFdimjf/3rX6ZNmzaxfRDatWtnWrZsaU/POeccc+mll9p9HGTGgxQfpJCQavNlIXs0vPTSS/X2Vvj5z38emxEp+1I899xzdvmi8CbPvqOY4CCm6gJ6yBOghzwBesgTikkul0pxOUtBJ1iwzwMS3zfJigTMmkCyZXG8Li4h41kO6RbrKDT5RzZBFgcffHCdnz/66KPmzDPPtOdvv/12u1+BzEyQvROOOuoou3RRQ3r06GE++eQTc9ddd5kpU6bYYoUUDM4555zYZeTnxxxzjD0/YsSIlNcnm1VL0UE2ri50FBMAAAAAADmTy6VSRv5rpKl4rsKef/SZNqZbVQtn1ncPOsEKqSM8nU7+Yr1vil24M79qS5UpbVba4J4LqZaIyTSf4WVxeN4kXypLzhvTJOomAXlb5qgh0nl/zz332K9MyWbJ119/fdLff//737df6ViwYIHxBcUEBzFVF9BDngA95AnQQ54AHRVVFbFRq23XtWIJikaik1+v89s34c58WSapumV17Hw6WCImX0tl1RrThGICgNwp7O3SPSVTdQHoIE+AHvIE6CFPgI6ardvWl0d6I5flS5adQpqd3xs22a9gmS7ozXIou7zMfslsGOSHZD94Hdg2gyH715Fs/j7TdvJaBbiJmQkOkrW8AOggT4Ae8gToIU8Aohq5zKhwRC1+yaLwvirFvkyPLBslRRYhy4094tJG1XnafyBoJ69VgJsoJjhowIABUTcB8AZ5AvSQJ0APeQIKtzAX7Csg2Ai2eLChdu6EO7kLcZme+AJAY14TZA34uhuPd1ZrJwBoYEiUg6ZPnx51EwBvkCdAD3kC9JAnQEdNTU1k+wqwt0BxYT8JNFQA4PmRPyyVBUSHmQkOKikpiboJgDfIE6CHPAF6yBN8FMmIfYcGMIeXapENe82RmXeOrQqNbnZVFI9zupshxz8GyS7HLAOgsLFUFhAdigkO6tu3b9RNALxBngA95AkonDyxDAuiHLkdnM+Hpk2aptwotKFOZc1Obvmfnb7twMpmre/4zjFXRfE4282Qtza850N4uZzw5eJfE4tllkGdDtafbDSmnfHuttnCnaPqFRiRM4W+VBZQSCgmOGjmzJlm6NChUTcD8AJ5AvSQJ6Bw8pSssy/coVa1pcqUNivNqOBAkQLawiPEs3k+JV3mKM0Nh8OdfT87dompaL/tI3K67Ql3chdKB1Z4BoSs9W6KYKJUFAUQFzR6w12HFcImvcmKWwBQyCgmOKhFixZRNwHwBnkC9JAnoPDzFO5QM5uNqW5ZHft5OgUE6XisbV1bdB1y0BM/Y2DxkdtHiGe1TEWTxo1orjOataTKbCzZ7P3zOzwDQl4HAAAA0kUxwUG9evWKugmAN8hT4zACFWHkCS7w5XUpn3mSAoBsUJjtKOT4AoRPz6FHn2ljulW1yHopHF+ej3mVYsZANstUJFvmqNBHNLsg3f0HACQ+5v6zqrUpN5m/RgGAyygmOGj27NksI4GC5dqHavLUOMU6JRyJkSe4wJfXpVzkKdmGorW1tc4WA6LorAw/h9qua5XWOuy+Px8LuUMt6TJHcGqJlvjPCICvwsfc2tpWUTcHANRRTHBQ69ato24CkDXXPlSTJ0APeQLczlMUG4o2thjAetL+yluHWp62KEhWrENhfkYAAADZoZjgoK5du0bdBMAb5AnQQ54APb7kiWIAopbuMkfhfRIKpVhXCB56qqV594mdGzW7qLFFyXCh55GM/zsAAMgExQQHzZs3z5SXl0fdDMALnw87zDRZvdqeZ61XoHE4PgF+5yncIRf1MoUur9sevyF1pntQIJpljtgnITfKK5s0aqmw+KLkUft8bFalscdLoo3ht33fOeFm3zU/2WhMu6yaBwAAQigmOKht27ZRNwHwR8UqU75p2wLRfHgEGofjE/Il0drauV5eJN97/riYp1yNvG7sfRs/+yE8wjzdwkKdUelHmkbxbUPqgi+uNMn9ng8sbZRd8e9nxy4xFe2bZfTcWNlyq9mYRr7SymFos285DwAAGo9igoNcG6UGNOZDZOSjHPO0ji5QDBp7fHJtg3YU1tramXRyp/tcSzSyNfw/czmy3sf3ez/90VexEcXh+117rfRsRpgzKj13oi6uNGnSJOd7PrC0Ufqj/MPFvxUlVWZjybdPCgpvAAB4gWKCgxYsWGA6d94+PRMo5A+RUa8vW1vDKCTAleMTmy8il8Id9l/9ZKHZ2K6mwedavjpBE+0r4Pr7vWxGm4dHFKe639NZ3zz8//9Z1dqUm/TWxUfx4b1enjDKHwAAUExwU4cOHaJuAlBQwh0OVVuqTGmzUnteOikeztFotWKkscEeChvHJ7g88yTcYZ+qoyvqJVmC0fvSAdrj1R6Nup/iZzw0dvmefBVa6g406Nzg/6+tbVUw+ylELZsloApdrmYmAAAAoD6KCQ4qK9s2PRwoxo6m+Mv87z86NthJEN/hUd2yOvZz06RTzttcLDQ22EO0Gvt85viEVCPMo5h5Ei5yyhIc6axtl6tO8nQ7tdMdvZ/tjIdio3kfhJdxscWZb69fo2M+/Pr76DNtTLeqFrH/05jrjn9df2RpdfEt50QtAQAAIG8oJjho0aJFpuu3H2AAnyTraEq1ZvXmbz8Ui2w+GDd26jvLssAn8c/nTPc08eH4VIgFQpfbrLmUXTYzBsJFzhUyGyHJCOXwcz3bjVcTPQZ1luJZ2NrstGXbUjxF1ZGbxn2Y7uMZ3nMhko18Q8u4BI9h0DGfznMoXNyqraoyTUpLYwWDxUduf/1tu66VWnG+/vuUhpfO8m02B8scAQAA5A/FBAd16tS4kdRAocnlUgpMfS88Lnecuty2fHQE+3B8Cr/ezF0xt06HsauPZyEWNbNZFi1/y+pkt/Fq8P0f719l3r1753p7M6Raiiebzuef/2RlwtebcEdwurMxohC+D9N9PMOzNlzbrDWd51Dd4laN6bilplEFg8YuWZRsNoQ8bzp9+7zxofDFez0AAID8oZjgoNJvP0iicGQ6uhfZCY9YrPOh+CcbjWmX5I+aNPz3PozK86lj3eWOU5fblo/H07fjU6IOY+hkPd1l0aLevyAb4dvW2E1IU3U+L167POHzM7ysT6rZGCgM4aWVfnbsElPRvlnCJYuyyWH4mBWeDRF+3iRb2ilRgYv3SgAAAKCY4KDFixebHj16RN0MRLTMQ6FobAdQnXWuUxUDkoxYDH8oTtWZE576nuzvfRiVF8Vjq9mxXmekbdzzgWJdfqTzeKY6PmXT0eXbTI98S7VUieZ9G35uhEflp7vhb3xnZbiduZyNELXwqHIgnaWVVpRUmY0lmxMuWZSzYnaSpZ3iC1xH7fNx0gEZmht/Z1sYBgAAxeuee+4xf/rTn8zSpUvN4MGDzV133WX23XffqJvlLYoJDurSpUvUTUAD4jtpilFjO4A0R3Y2dup7qg0Xwx+QUy1Hkc5ovUJZozibxzabJU2SrTUe/3xIVqwL/33VlipT2qw0r53S2dzmVBpbNNEsuiS7bXJ8SrbsRrp7ooSLU5qdY6meD+HrT1Yga+zjGUW+U208m6oAkKxtye7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JQF3//e9/TXl5uS0QhDs6ZSBWon0Q+vTpY2fJSScOhQRgu+bNm9tBIfKeToraMlDkxRdftAMaFy5cGLucFBGCQkLQLwGgPtnzQFYckfdsAcmY9PPJcehXv/qVPQ0KCYJCAvKFZ5pn5IC8du1aM3ToUPtCEnRiymiAr7/+2o4EDZPfy5d04sgIHADbyRtd2RchmJlw+umnm3vvvbdeQUEEB3mZhihrFUonjYwGpbMG2E6WX5FR1EKOUUGnjcyKk2PQOeecY79v2bKlXf9TRuMMGjQo0jYDrpIlJGQkqIxQC45Zkinp0JHsyLR32WxZyPFowIABFBKAOJILWS5MjjkiyIbs3xPe8FJIcUGWnJDZpwMHDoykvYCrJDMjRoywg7CEFNsSFRRkoIgUvQUdn0ByskeCLJssM03DBQVZheTMM880X3zxhV2SD4gCr94eHsQfeOAB2/kZfHgUMuJGficfIoODthQXAKQmI9AOOOCA2AdMOXDL0hLhgoKMGpCp8cGo6gDrfQKmzsgzmSUnS0pceeWVdTo/hWzKLMcleWMcYENLILmePXuaCy64wC4FJsvriWC0p3z4lKKcTIOPPx5RSAC2kdmkcpyRJfiC74PPSVI4qKioiF32z3/+sy3cyfFMsgWgPtlUOciS9ENIQUFmmEpB4YorrrBLWMrSRzI4C0BqsgeC7CsiM0tl1YOAvKeT/j6ZnSr7KABR4NOEZ+TALRuxCHmzG3x4lDfGsgRSQDpyZFqvbNAXHPQBNEwKcjJDQfz617+261JPmjTJbnIky4UBqC/onJFZBjJ7R9b7lM4Y2ccn6PzccccdYwVwAA2/35P3b9IJKksXSceMvM8LjkMyiKRDhw6M+gRSCGaXBntbyVcwc0eWjmjfvr39/dVXX20LCdKZQ6aA+uL3h5Pz0g8heZLZPO+++67dQFaK37LcZTBLFUBiQeH6qaeeMvvuu6/td7j11lvNQQcdZH8vn5lk31MpNgBRoJjgmWANdzkNv9mVn1VWVtrz0nkjGyG9//77FBKAFIL9EOI/bErnp4wGkPXfZaS1vEmeNm2azRybHgGJyTJ7sp77RRddZPdEeP75582XX35p9yKRpSTGjx9vWrdubTe1BJC8kya895V0ysiI6bFjx5r/+Z//Mf/5z3/s8itS5JZZP6eddlqELQfcJx0y0ukpg0Pkc1Ewc0d+JrO6JVuSsQ8//NB23ACov8mrHI+Cn8mARSnESUFO8iTHMBlhLccryZTsNyI/Z8k9oOF+CBkcMmXKFFuMu+yyy2wx4Xvf+5556623zNSpU2MrKAD5Ro+XRy80cgCXN8RyMJe1CBcvXhy7jPxMNmaRZVn+9Kc/2TfEsq8CAJNwA7BwZ42MnpkxY0adzhzJ2P/+7//aTc4nTpwYW4eaQgJQn2RDMrJgwQKbF+mckU3DZC1dWY7lyCOPtMu0PPnkk3ZTZgDbBceeYIZpsBSLFOR22203my0ZOf3EE0/Yot38+fPtPj+ffPKJ7RBlxg+QmORFMiKdnyeeeKL5+OOPY7+Tovc999xji3QyqprNlgFTbzmws88+23ZwCvmZ7IF1zDHHmKVLl8YuL4XtE044wS4L+8Ybb1BIADI4Np166qn2fdzbb79tCwoyIFiWDPvoo49snmRJZiAK9Hp5INjMSMiLjnTWyEhpqWCGP4jKRkcyDV5OZXQAgO2CN8WvvPKKLcQFG5iPGzfOdnROnz69TqFBpunKJpcy+pM3xUB9UrSWjhn50CnZkDfEspGYjJyWabvnnXeezdXjjz9u9yGRLHFsAhK7+eab7czS4L2eHJvOOOMMu9eIjP6UItwRRxxhXn31VfP3v//dPPjgg7EiN/v3oNjJhsnBhq8yECRYDkwyIjPkZDNL2YNEPj8FunTpYnbaaSfbYUMhAag/U076GqTQ9sMf/tB+L4UEOS6df/75ttAdkM9Uo0aNskvyBcclPjMBxvY5SN+dkL6F66+/vs6xadiwYaasrMx+yaweWV1EZiTIYKzXXnvN7LXXXhHfAhSzJrXBsHYUpOAN8COPPGI7PKWwIJ0xI0aMsPshBAf7efPmmYMPPtj83//9H1N0gQTkpVA2BTvqqKNsZ6fkSEYAyBtk2fRIOj7DZMSNrPEub5B5UwzUzZJkon///va489e//tUsX77crvcp+ZKituQmfukWAMnJe7qrrrrKHpd69OhhR37K/j3nnntu7DLxmSJjwLZR0YcddpgdACKjOmWW9v3332/f18ngEFkiYtdddzV/+9vf6uRFlj2SDZiloACgLul7kOORLE1522232bzInoySpQsuuKDBZcWAYrdp0yZbdBs8eLD9fHThhRfaAVayPKUct+TYJMtWPvbYY3WWMgdcQTGhwMlUdpn6dPLJJ9tRNjK6U0Z6yhrUwYtNcNDevHmzfSMNIDkZ2Sl7Irz00kt2eSPJjXTaJMOBHUhMRtjIqGlZukg6Y2QktbxRJi9AaomKAnPmzDFnnnmm7QCVUxnhKSOpATRs5cqVtqAtg7D+8Ic/mBtuuCH2u9WrV9vZPfH7krBsJZCYDKi6+OKL7Uy4H/3oR3a2j1i7dq0dPQ0g/SxJAU6W1ZMZqLIXY0COV7JMOZ+b4CreJRU42cjo+OOPN6NHjzZff/21nVYYLiSIoPovHaQAEu+RIEUDIaNpZBS1FOUOP/zwlIUEwQEeSEz2E5HOTtloT04lW+QFaFiQk8rKytj3sgeCTGcfM2aMHbFGIQFomBTi5P2ebKwsOZJZPZ999pkdUR0IFxKC94YUEoDt4seedu3a1XZ6Hnfcceaf//ynefbZZ+3PpZDAOFUg/VncrVu3tu/pZIUDWVJPBgoHwoUEcgUX8U6pAAUfLgOyiaWs8SlLR8iLUrLOGjpxgO2CD4oy+0AEs3Zk2bBVq1bZ6bsA0icdNLJ2bkA2BJMRazLSZsmSJUxrBzIgex7IxpbS6Snv7cQf//hH06FDBzvlXfDhEmh4ho8MDpFZ2nPnzrUbV8poT9lUOSgohD8fUUQA6pICW5CRFStW2AFXYujQoXb26UknnWSuu+46O/tUBMuxAEh9bJJjkRS6ZUbP559/bldF+N3vfhcrKISPTfTjwUW8Yyown3zyienevbu56aabYhuJtWnTxq6pJvshBAdvDuJAw2TzIlkuQqYXPvDAA/YDp6z9eeONN9qNmGWEAIDU5HgjxYJLL73U/PjHP7ZvhIOiwuWXX243rrznnntMdXV11E0FCoYUtaVjU/bDOuuss+yAETk+SZFOjl2CD5dA6s4a6eA8+uijzRVXXBHb/+D555+3s7llI0uZOSdkrXc5hgGoKyiwyV4jsueIbAh76KGHmg8++MDujSXZ2n///c3VV19tXnjhBXtZjk1Aw8emY4891uZKBgrLe7uJEyfaTZXls5Psdyqkz0/6JQAXsWdCgb3wyDQoedH59NNPbWFB9kqQmQl9+/Y1gwYNMiNHjoztAg8g9TrU69ats2sUSma++OILuwHzZZddZkd+Pvfcc3ZU6M9//nM2CwPSIGu6z5gxw65H3b59e7tBuWzKd+utt9rRN9KBIyNwANSVan32N954w/zrX/+yxyRZ810KCo8++qgdQPLDH/4w720FCoXMPJXOGilmS1FOljgKsibHq1NOOSW2zNHHH39sO3GkUxRA3ePSww8/bD8fyUzTli1bmoceesju2yMFuRNPPNH2S8gyy1JMkGWPDjrooKibDzhLBiyOGDHC3HnnnfZ9XK9evWJ5k1neUrSTQl2nTp3MhAkT7Cw6GZgFuIZiQoF0fsp67rLnQdARunjxYjtqWkYBtGjRwo60kRecWbNmmb/97W/mO9/5TtRNB5x9UywFhE2bNtmiQeCrr76yB2yZoVBWVmY/hO622252lED4cgC2H5sWLlxoli1bZjtpJDeyXq7MUvjwww/th07Jmqzt/u9//9vccccd5qKLLoq66YCzxybJicxIkPd8v/jFL2KXkVk9a9asscscybFKigsykERmK7BRLFCf5OLCCy+056WYEORElgyTwSHBEhOycawsMSGzf6TzBkBdsvSKZEUGiZx66qmxn8vyRvJeTzo6pd9h0qRJdqaPjKpmABaQmPQ/yPFGCgjyOSn4PCUDF+VUjlMzZ840t99+u/1ejmMDBw6MutlAQhQTHBa8uLz88svmvvvus292ZUSazEyQTk5Z4106cWT9T1lbV6qc0ukpI6w7d+4cdfMBZ4Q7W2S6oCzBMnXqVLthuYxWk3U/A9JR880339iDuLxBvuGGG8xpp51Ghw0Qd2yS0WeypJF0fJaUlNiOGMnX4MGDY5eV0WwyykbWeJfC3IABAyJtO+DqbDmZ0fPEE0/YTpnZs2eb/fbbz05tl1mn4Rl1GzZssEXvq666yh7HZJk+AHVJ0eDAAw80e+65p/nrX/9aL28rV660n6mkA0fe27EsC1CfvH+THK1fv94Wr6WILZ2hMpBR7L777uaII44wd911V52/Y0Y3kJisMiJ7nQ4fPtz2MSQ7Nsnl5NgkGzMDrqJnzGHyoiKj1GQalKyjJlOeZEqhfC/TCOVg3qVLF/u9THeXKfAyTZdCAlBXUASQzhcZHS2bwspmYbLJskzRDTbhEzLLZ5999rEzfKQz58knn6xzHUCxk2OTFOSkyHbxxReb//znPzZP8sFRRqrJJmKBc845x2ZOZjBQSADqCj48/vnPf7aFhBdffNEW3WTquwwkkXxJwSAgHzhlqbBLLrnEHqdkhgJQ7GSwhxQP5HNRQDpg9t57b7thrMyWC28MKxsxy+hp2TchmKUAYJvwOFMpbsvxSEZRyzFJSCEh2ANLit3S6RmPQgKQmORF9j9dvXq1HYwVFBLkVAaSyGwFGdQoK5JQSIDr6B1z+I2xjAL405/+ZN/wyihpmd4u0wllFLWMYFuwYIG9bHAQl/0Sdt5554hbDrhFRscIKcz94x//sJ01F1xwgR2tJhmSdT4lZ9KBEwgyJVMLpYAnsxUAbM+TFOBkNJpkRGYknHHGGWb06NF2/x4pLMjyRuEPpLL8EYDtswsC8qFx/vz5trAta+LKjB85RslxSY5RUjiYMmVKvT1/5ENo+HqAYiSdL7/5zW/M97//ffO9733PHHPMMea9996zn6Nk5KfM2paBI1I4EJIjGSQSfs8HYFuWpPAWPs7Ie7ef/OQnZsyYMfZ93+mnn15ngJUcu4JZCgC2k6K1DGKUFRCOOuooc+6555r//ve/dklYGXh1//33m2eeeca+lxOSOxnI+M4770TddCBtlLscIuvkyptfmdokB2n5kjVyZS1qIS82srTRs88+a/baay9buZRN+KRyCaAumaUj0whldIx0gMqMHZmeK7MNZP1PWfdT8tO7d297kJdlWn75y1/ajZCCTMlaupK7Nm3aRH1zgMjMmzfP7tEjm1UGo81kBKjs0ROe7i6FbunIkVHW0skpm/QBqEtmkcrsUim6yRJFsjyl5EY2V5bitgwgkcEjsr+I7IUla+ueeeaZdgPzYEmjDz74wM5YkE5SoFhNmzbNHHroobazRk7XrVtnZ8rJ+zjZU+73v/+9XX7lyiuvtLPp5Jgkn61kT6y33nor9vkKKHaywoFsVi4zEOT4I8tVyqwDIZ2fsgqCFOLkuCTv9WTGgnxukiWY5T0fgLrHpsMPP9wuESYDfZcuXWoHNUr/gwwQPvvss+2MbZm9LUU6+RxVWVlpxo0bZ4sJO+64Y9Q3AUgLxQRHVFRU2I5PWTZCRnrKB0iZyi4vLvKmVzpBpZAQFBTkxSmYtgugLumoOeGEE2zlX6r+0gEq+4zIcmHyxvfWW2+1HzKDETb9+vWzo9RktoJ8CA2TgkK7du0iuiVAtGTpCCm6yYdFmbETZGaPPfawnZvScSMdOXJcEjKyOjimSWEcQN3CnGysLJ2awXJFsvyXZEims8tgESlw/+xnP4uNVJO9fWSpll122SV2PXLMkhFu3bp1i/DWANGRmQbyPk9Ge8pePQGZ1S0FOBkRWl5ebn8vHTNSqJPZ3bKR5dixY9lsGQiR2aRS1JbBVY8//rgd3CifmaQQJ0sqywwFyZuQ/MgSLfIeMCg4yHtFlmQBjC0SyKw4KRTIvlfBTJ8ZM2aYUaNGmV//+te2n08GlPTs2dP8v//3/+zysPK+TvZBlc9XQKFgmSNHyBteGSl9zz332A0rpYIpZGSNjAi94oor7PdBh43MYpDR0jLimj20ge0kEzNnzjStW7e2IzePO+44+/P27dvbg7e8YZZCXDAiTTY6kg5QyZ4c2IW8iRayyeX+++8f4a0BoiUfDqWAIG+A5UOlFBaEzFKQDMnPZESbzKKT3Mioa8meZA1AXVJgkw4aGdEpxx5ZykiWlpCcBWu5y/s/OY7JKGvJk3R+SpEhmGUnZDYDhQQUM1nKSDLw29/+1n4v+ZFjkIyiltwcffTRdoS1jPaU94HXXnutHRX6P//zPxQSgDjBzDjp3JQR1JIXKX5LMU6WsZTPU3LckvPXXHONnX0qeykE2HcE2EaW1pMlX+XzUbAXgpCBI7KMkfxOBpIIKThIv5/MlJMNzikkoNA0qaUnOnIy2jNYVkXe5MoIG3kBkiVXpHhw33332SlRMuVQOja//PJL8/e//91MmjSJFx0gATkon3zyyTZDcl6KdTJbQcyZM8f89Kc/tZtXSp5kHwUZySbTDOWgL501bBwG1CW5kWORdGpKB42MrhYyik32FJFigsz++eSTT8yECRPsxpcAtguOLa+99prdH0FmwslyRZIb2QdLRqVJ543MUpVRoFJgkMLc5MmTbScOgO1kjx75LCTLSYSXe5WCgixlJMWGww47zM4ulT3l4vccAbBNkA3p1JT3eDKQSpZmkcFXwawE6auQpcRkZQSZLffUU0/ZAY8HHHCA/RsA28jMg88++8zOOIgnx6enn37aFhHks5LkiWMTChkzEyIkm+6FCwlCZiBcdtlldlqUdNzIC4y8KMkBXkYByJJHMppNlmShkADUJdNsxcEHH2w/PMq0QVlrWjYIk++FrDktm/VJp+dtt91mMygH9GD0AIUEFDspWEuRbfr06bGfSW5kdoJsxCcj1h588EH781dffdWOrpZRNvI7mUlHIQHYLtgkOTi27LTTTnagyPHHH29HVctxSgaSyIw6mbUgI0BlA2Z5LygbL0shITi2AdhGZiAsX77czuAJzygNNoaVGT3B3nOCzhogsSAb8t5NlqiU2Txi2LBhduCVLNsim8XK0nyydKwUumXJIykmyPGKZZeB7eT93aJFi+yeciI8bluOSbKXgvQ9yPKVgmMTChkzEyIinZsy3Uk2MJIXFTlgS3U/WBP3L3/5i13vUw7UMsUwWHta3izLh0o2XQbqbrYs09ZlFGdANjF67LHHzB133GF/Lx2gMgJUfi7kQC97kgQbnrPeJ2DMsmXLbJZkPVw5zsime7LetBQSunbtao9BUuyWEaFyjJLjE4DEZE1pGcEpa7jLvgjBezfZZFmmwkux4Mknn7QjQSV3v/vd7+xpeKQas+WA7YJsyNrSUpCTL3mfJx041dXVtvgmmZE9RWSGqizFIp+vADRMCtkyYFE+E8msbpl1IO/9gsK47PcTHJuk6CCXY185YPuxST4fyUBg2ZtRBlpJ30LQxyDHJpm1IMvHSmFO+gKBQsbMhIh88cUX9g2vLB0hHZkXXXSR+fGPf2y+//3v21kIsvmeTHuXN8GyvposIyHkshQSgO3+9a9/2bU+JTuy74EsayRkjVxZe1o6aU488URbmJMlJIIZCrIuaOfOnW2mpIOUQgJg7F48sgm5dGgGhQTp8DziiCPsRnuypqd02sjxSvYYkY5SAPXJTAMptkkBW5bWkxlxUtQWUjSQ2aVvvPGGnT132mmn2feDssSlFLrDI9UoJACJR1HLMkayhKVsCCudNMFyYJKZRx55xHZ+ynItANIj/REyO0EKCFIMDwoJQmYmSP6CWUAygItCAlD32PSjH/3IDl6UJcplUGN4sKIcm6TYIMcq+XwFFDqKCRGRN8AyGk02hZWOGenkvOuuu0z37t3t8hG9e/e2P5POUPnQKW+Wg4M3gG1kmqB0wEhRQKbhytJhMhLt4osvtqPWbrjhBlu4k9HWMnpNlhF799137YjqsGBaPFDM5BgjHxblGLTXXnvZ5SFkHXfJzIsvvmhnJ8gaoPLmWNb8lKntMvJG9hwBUJe8j5O1pWUjWHnP16tXLzviU7IlS0bIMmLSWSNkFJssGyEfLmVjcwDJyQwEmVkqxe0hQ4bYZWEPOeQQuzyf/EyOVTIYSz5nhTtDgWIVLEQRXpBCzgff///2zjNGC2pbw/skNybGrthRIqJiwwJiVwQRFVBJDIpiwwKiWAFRwYLtqGiMYuxR7IIFRUUFLKhgQbCBgiBYsIYYsKF/zs2z5uz5vm9mmKHMnAG/50kmODNwMvcme9be633Xu9jDyJ4RovbY50NvokmTJtX+DfhmEllybSKCjzcTPT76DRhG3nvvvRARLrjgguj3YRzO50tkVcaYo0b+hYNI0LNnz3hwMqYLNGYo6OTocjFmVBf3GiqniJSCUDBq1KiIXiFKgqLNYj6EuJkzZ0ZDFPc0kwosE6MZSmNHt6dIdfLyykWLFsW4O2Icy5Y5W0zFUZ/IAX3hhRfSjBkzwlFNNrWIVD9HOKMRtVm0jJP6uuuuC8c0Z4dmJ0I4I+/ZoZbH5PO/Fyl3qi6nzLFfmEdwe9L0JBqWKVXMJYgH1CTugdYmkVQZScQkwV9//RVnhhrDueKD6TnucrylEL/Zg8A7iWlvDFoiUp2q97Rcm9htSoQycbEYgtnLyMQpU3ItWrRI119/fQh2Iv8EFBNWEkGB7LSTTjop3GpVf1Hxy4jcQhGpGZbwPfjgg+nKK6+MIo04x9d4TBLRgtN6l112Kfk35lCL1EzOnWaxJQ5PRDmi9zhXOUZCRJZeUGDB8osvvhgRYsSD0QQlko/GZ8uWLUvqUdXmqUi5Tp4iYCNeM4XAueCccHYwWbFrjvsehqx81mbPnh0T3sCUnYikmCRFKOBd1LRp03TGGWfEJA+MHTs2de7cOaZ7evfuHeeMvsOBBx4YJiwixESkAIuT81RBvrvlP+fNm5f23XffOE9HHHFEvKcQ8BC6mValfhEnK/JPQTGhkcj5afkCzOgTjupevXql4cOHlzR0RKRuuCSPGDEiBIX+/ftXZlNzKWbU0AaNSN3kCzGZuYjYCApMKHARRvRmSsG6JLJs54mGKI3P559/Ppo0NGhYZAnWJpFSmIjjvHz33XchuOHuzFMG7JDD1ckuLHZiFZ8dz5JIKexd7NOnT0zFcZ9DcCP+i6kDphE+/vjjNGvWrPjvYkhEoFZpuhIpwFlp06ZNOvbYY8OoWHzPY/pg9913j31z+Xu5Hlmb5J+KM9SNQLGzhsswl2WWwtIIpcAzoQA2bETqJu8SWW+99SoXXLL0CPcnICRw5iziInWL3NlZQ7NmzJgx4aDh0Yl7mlgW9iWISO3kLGrOE9NxiHPsF2ExHzGW1CkEBrA2iRQg9oupA6JYyG8nyvLmm28OgxV3ObKnMV4xyV317HiWRApQd8hmJ0aZxcrktw8ZMiSMIv369Qv39J577lkiJGSPafv27Ssd1yJSARGVGEGoU0zyAOeESTr2JHCWcm0qrkfWJvmnopjQgOSCTIOmGH7p5BFdHDebbrppfI3le0wljB8/PvLeRaQA5wIHDXC5zc0aJnso4CywRFgg2514I84SbjbQWSNSYO7cuXHZZRHYuHHjYmQXELlx1uyxxx7R9OzSpUucKQQFMqnbtWuXOnbs2Ng/vshKBYYQ8ttZSA6cmexCe/rpp9MBBxwQkz1EtQwaNCiijli8fO+99zb2jy6y0tWmbt26xX2OGsUy5b333jvOEgYrBAXeSjfccIM7RUTqgD1xn332Wcki5ebNm8c+BMyMTHHztiqmatPT95NIAURuTIpMH0yePDmmfoBIPmLBEL49M1JOeBNrIFieTCOGiBUaNFlQoJCTn9upU6fUtWvXaHjmws0vnx49esR4LyKDiFTAPoRDDjkkGjH5rGTVn6YM52avvfaKIs/SIyLDcN0gPpjkJlIAN81+++0XAhyRK5wTFsIi0NGoyc4aRnQ5XzRs+N7aa68djR0yP0WkAqZJeUASB0GDhkXLnBk+WFJ+9NFHp2HDhsX3ERlwtNHAueSSS2IHiYgUeP/99yNvGhd1ZtGiRWnKlCmpbdu2IWa//PLLcQf0bidSO9zb2Bc3ceLEMI1wp3vyySdj6nTgwIGpe/fuca+ranoUkZphart169YR+Yp5EUEBY9app54adz7eUSLlhDsTGshZw6OSqAiy01D9c9RKVit5cHIxduxJpHbIxGUJLC4aRDpGdskr5PL79ddfpxYtWkQMS9XGDA9QHNWcMbMKRVJMxNGM4SwRA4bT8+KLL47HJSIDzumFCxemddZZp7F/VJFVQkjAlcbUDq600aNHx9JXMqqpOU888UQs3svRlVB8D6zpc5FyBgMWsUbbbbddfH7jjTfGpCkLzKlPH330UdwBERdo6ohIdYrrCrtHqE28iVq1ahV1iTNEI5TvEV354YcfhhlLRGoHQzDTckSTb7vttumRRx6JdxSRYtQn6pL3Oikn/q+xf4B/4i8ZsgkREbj8kt2OI+2NN94IQYFMNR6dZBSKSO3gjkZIwEVDVMQWW2yRnn322RATmPhhXJfpg7yYr6ojBxQSRCoel5yd3XbbLaYRckTEeeedlx599NFYKsZDUyFBpG5+/vnnqE/EreBIg/nz50cjlA/ueizoq0rVB6YPTpFC85N3Eh/AGSIejGm5HK+HWYs6RvNTMUGkFO5xNDg5SzikMYywq2ebbbYJEyN7el5//fV4T8Eaa6wR06buaBSpG84UohvpIb/99luYRyZMmBBfx9hIdCXmEu91Uk4Yc1TP8IuFxiYLlRkfZLydryEo4LhBSHCZkUjdPPPMM+H6HDVqVGRMc464FOP6xEWdqUlIKEYhQaSiaYlQQIQEF+F82eV84FjDVVMVBxdFaoaJg5kzZ6aNNtqo8muvvfZa1C1ixHbdddeIDwPPkUjN8C6CqoteOTO8l9ibgJBARFiuV+yZw1giIgUee+yxMIQwdQoIBAhywJ4RphAwOCIkcL74HlOpnCX3j4hU34VFFCx7r6ZOnVp5pqhVxBzNnj07nXjiiREhhuHx3HPPjb974YUXNvaPLvI/xcmEBnDWnH766ZVfI8cd5xrZhMUTCixFYslys2bNLOIiNcCDkeYM5yZPF9CkueOOOyJX11FCkWWjOG4lnykmeBAXEOsyOEGZYLBhI1IgiwKcGxZaIsxdeumlkUU9cuTIWF7++OOPx1l65ZVXInqPvGoeniJSCothO3funHr27JmGDh0adzlEA95E2QSS/8zvpHvuuSciLlu2bNmoP7vIygRvJfbw0HNgjxznhXgwBDnOC5Pc+TzxOU3SW2+9NS1YsCB2JoBT3CIVYFhkwTL3vC+//DKmdzAzsgMLmE7o1atXfJ2zRBoJQh7njp2oIuWEXex6ijbiAlx16QoFm8sxDVAcAXlC4aeffgpxAUWTkUMRSdUcnHvssUeJkAD77LNPat++fbrmmmvi7CgkiCwZ9vbQfCEf96WXXqrx7+RlsfmM8SDt3bu3bmqRKnBG8jkhvx0n2v777x9RLN9//32cM5o5PCppkCLSffrpp439Y4usdCC8HXfccdHkZJoHMQGoRXkKAfJ5YwqIJZeYSciq5myJSEWvgaXkTBywY4SdWEwpXHnllfF9zlie+uE8ccb4nKk6HNd8n/8NhQSRlObMmZMOP/zwEA4whfB22nHHHdPYsWMrF5VjIsEswv4RhATeSxiFeTsRvyxSTjiZsILwUDz//PPjFwwjUfw3kSyolbmA0/DkwcmEAktaNt9881A1X3311RI3qEi5Q9Y0Y4ScnZzdnhco88ElGDcAmZ88QHv06KGbRmQJzpqDDjoosnLJd+dskeFO04apn3xmEMP5PmL41VdfHWPwb775Ztpyyy0b+/8EkZUGHpVvvfVW3Oe23377iLFE7Obj66+/jiV8CAwZGqLUsPXXX79Rf26RlQ3ubDQ7N9tss9jZ8/bbb8fngJs6NzuzWWT69Omx7Jzzx90PsU5EKuC9xDmiH4EJi4lS6g9TcnD55ZfHWcpvJc4XUcx8QPHkgkg5Q/QXkzoYF6+66qqY7GGyFKEOEzB9vA022CDOy+23317576pO0ImUE1aPFQA3Gi7p448/PsbdufD27ds3sgnJTGPbe/HYLkUe5ZJYCTLWUDpFpALcZiwv58LL9A7CHGOGNEOLxQKWl+NK46KMmKCQIFIKi8FwyOD8ZJSdSD0WVlKrvv322zR8+PC09dZbx9/lUty0adNw2rzzzjvRsDGWRaTA/fffH0vL27VrF8Lbv//97xhtZ0KOxg3iHDsSyMvljsdSS1xriAk43ESkAHc2JrM33njj2IdAFBggKHD/y83P/HbircSkD3fC4h0lIlIBb6I8rcOf3P+gWFBgPwlO6jPOOKOk6amQIFIBNYf3EIYRhIQswCEurLnmmtUSSPK/UUSQcsYKsgKgSnbo0CGcnMBIFLESFGt++QwaNCiaMtllw4b3cePGpXfffVchQaQImjBERnCWdthhh3Cq9e/fP02aNCkNGDAgCnnO+uTii4OaRyjniT9FpABnhAWxiNz5ccnE3OTJk+NrnC0W79Gw4XLM8uWvvvoqxITc2BGRijiW6667LkQ5puJ+//339N5770WUBKI3sStbbbVVxBu9+OKL6bbbbosaxhnENJIXyxrJJ1KAmpR3+CAQ1NT85O3E3RCDlm8mkQLc5ag/v/zyS/QeELB5I9HYzEvKEQ34b/b5MIHKvyHCha+LSHWYLsXEyJ2uGEwipCYUiwnTpk2L3XIKCVLuKCasAPPnzw9nDfz6669prbXWSi1atIhIIyImiGFBTEDZ5CHJ38UdiuIpIhUgECDAnXXWWemUU06pnD5gWTnNGAo4jgBG27ODhqgwnKJMBolIKTQviTUiZzrDJXjbbbdNEyZMCHGOBungwYPTeuutl/r06RMXaJdaipSSM9zz2SCakvgwhDfOETnu3PWuuOKKdPDBB0fkEdOnhx12WNz7jJAQSbFTBGGO5ifnJItrVZufWVDg3bRw4cIwYTFNRySSiFQsIcesyDuIeGXiWLjLHXHEERHBkneOcGa42yGAs0uBdAQMj9Q042FFSmsTpqpDDjmkUkgoNoFQi6hdGeL4mPAmoYQoS8+SlDP/+o9bFpcbHpFcevllgiuAKAkcabgAZs+eHVFHNHMYmRKRmsFBzZJy3DUXXXRR+vPPP9Pqq68e4gKRErhAiYzgezWNE+r6FKnOzTffHJM+OKe7dOlSKSggzhHPMmbMmPjYcMMNG/tHFVlpWbBgQUSCscyS6bnic/TBBx+EeYTvMUFXFWuTSEoff/xxNDrZFYfIjXBAM6ZTp07RiMmCAh80du68885okOIGZfrU2D2RCjAkdu7cOSbg2NdD7wERjlrEG+rss8+OO10WCxASyHsHUhHysmUFbpG6a1M+R7NmzYo+BT09Ukmuvfba2C1nbRJJydmcFYCFRyiYOAFwofHgpJjjumEEHscn0REismQo4kSrsPQI8QAh4dlnnw33Dc1QGjg0Rmnq1DROaLNGyh0aMIhuL7/8cjQwgWgI9vawMIzlsUADFJo0aZIWLVoUD1ERWTLc7zCG4EIjxgjyuDsPydNPPz2Wwi5evLjy7GWsTVLucKc75phjYl/P2LFj04wZM+K+h1hAdBjfL77X0cyZO3duTHq7v0ekFPYeIASwo4faRCzLgw8+mLp27RqRYI888kgYtHLkEft9qEtM0ikkiCxbbcoTBwjbGIMxNvJ9hQSRAooJSwlq5MUXX5xOOOGENGzYsFAzt9xyy2je4EpD+b/77rvjAxh1ZxyeBXwiUh0utRnOVrNmzSIKjFHc7t27h6OaMV6cn1x+uUSLSCnUIkQDahMXY7KlmZjj7AwcODBqECPwOYuaJuiXX34ZOdVVm58i5Q650ojZDzzwQCwyhx49ekTM3vXXXx93vmJhDvcaedSI4ooHIqXQkEFoQ9xu3rx5RK9Qi3CD0vzknHF+ctPmvvvui4k5BDomvUWk9N3EB4IBcLYA0YA+RI4FA0Q6zFhMLVCvFBJElr02AWZGpoKee+65mPBRSBApoJiwFKBWslyPxUVkt6NYsjjsrrvuis+JX+EDdTPDLyRcnywZE5ECo0aNit0I7dq1iyYnjhmmeng83nvvvXH5paFz6qmnxt8npxCRgYkFEandWYNjjeWV7ETYeeedY1k5F1/EBr7Hg5Opn1tuuSXcnyJSAeeCSLAhQ4bEknIEBB6UGEfIqEaYQ/h+6KGHQojj/FGrEMLNzBWpDuI1TczclCHGMjc/2T2CaYRY2Aznb+rUqbHYUkRKIfmA1IPzzz8/PmcyIQsLxK/wTmKiG6hRTKEicjOloJAgsny1iTPXt2/fqE28o0SkgDsT6gBnGtFFjD7hSsuLl/mcwkw+IQ3RDKNPNEt5gLI81l86IgUefvjh1Lt37xAMuPSOGDEiHJ0UaXYkQN6LwEWY4s754++MHj3aho1IEYgH5Oc++eSTJU4ZGp/PP/98iHbs9uGy/Mknn6Tx48dHnm6HDh1SixYtGvVnF1kVsqjff//9dOyxx8aZYj8WggORfFtssUVabbXVoo4RMYbz06WWItVp27ZtGK9effXV+JzmJ3c6YBKVWvTYY4+5Y0SkCixY/vvvv6Me5f1WkyZNSkcddVTq2LFjxBoVv5swlzB1Sh0TkfqpTcAUA+KdiJTiZEIdUJzZ8J5FAZoyxEe0b98+7bTTTuEG5aP476N04lZTSBAp8M0338QILlFgLC/C/cnZmTdvXgh1LIXNZwjHAOOE7B5hZJdmac4AFZG6nTUIBjwoERF4iDJdh/CNmKeQILJ0WdRHHnlkGjlyZCyF5c530003hfDAxA/7SKZMmVIZIaGQIOUOC19//fXX2MmTYYp7+vTp6bjjjovPadbkmEsm5fg3oJAgUgChgEkdXNLbbbdd1COgRvGWYhcWES2YHmmCImYTsUxzVETqrzaBQoJIzSgm1AKFmSLNJAIfwB4Empv88jnxxBPj+2SrZfbdd99YFktutYiUQiHH0cnZwm1DvBEFm8sxyy2Z7AGaM4znkgVP4yY3a2pawCxSrjAhx8JKYo0Al3QeeeexiZONuCMRWf4saiL5uNcRdYngwP2OxyfZujRAcVQbISHlDpNyNDeZ7Nl+++0rXdP8N/Vo3LhxYRBBBM93uZ9++inEbs6eg/IiFXB2+vTpExPbxOoxvY0R5PPPP48+BJMJiAvErnAPxODIe2nhwoWxIFZEClibRBoOY45qoOqoLTmE/fr1i8gIFrSQNc1CPhzWOKYHDBgQ7jTydH1QitTM3LlzIwcXNyfxEfDUU0+lyy67LPaQ8LVu3brFgvOqOP4uUuGsYTqHsr322mvH16ZNm5YOPfTQmER49NFH42t50d6FF14Y0SxM+YhI7TB50LRp0/TSSy9VG3lnGSwNm+HDhzfyTymycjZrMIZgsmrTpk0sfWUyjmWV3PuYnpswYUI0RXFOt2zZMmLCXnjhhdibxdkTkZQ+++yzdPLJJ8feuPxWAs4VkXvs88lQozhnTKXinGaXAnc/ly2LVGBtEmlYrDRVmDVrVhozZky4znB8wplnnhm/YBAVyC8knmXgwIHxvR9++CEWs6y//vqOuItUoThjcKuttgoHNW4b8txx1xAfQQY1jVAcOGQT8m+4BBdfhBUSpNzhQsxDkaWvP/74Y4hyLF7OzhoEb5w1CAr5vBQ7a/iaNUpkyVnUGERwfHKucK4hJOQsapaZe35EqkMULLWJc8MED/CGwjV9//33R8OG+17Xrl1jyoeIMP4Nd0P2jSDUiUgFOaaIBijkXTzE73H3y1/jgxpVLC6Ak3IiFVibRBoeq00RbG1nTPCXX35JCxYsiMWVRK3wkDzppJNisREFPTvVYObMmRHVkh1sPjZFKmBq56OPPool5RtvvHF87bzzzoudI0888UREspD5efDBB8elGGFuk002MZdQZCmcNUzKcdHlMkzcCk1RnDWtWrWq5qzxYSlSAKGA3Qfc9YiFYNqUs5WzqM8555wYiSdGAhGOmkQWdfPmzRv7RxdZ6SAagp0jRx99dHyeBTgMJDRmipufa621VuzIKv57IlKAKFjik3kr5fPFfY5kBN5NQK+BD84XZsZiNF+JVGBtEml47DAUxUeQLU1Thg3uNEBxczKBgKAAxWIBuYUsbhkxYkR6++23bYCKFDF69OjUvXv3+G/OEXErnCPOD1/PhT0Xa6YR2I1AI1RECuisEan/LGoEBJZaEmlEFnXbtm1DhGMyYd11142JVLKoN9poo3hosrTPLGqR6mAWefjhh9M222xT6Yzmbkcz9KuvvippfnKOckSf5iuRmslCArWHnXH5XGF0zF8n8qhjx47ptNNOa9SfVWRlxdok0vAoJvwXfrm0bt06xgiZQKDxSaGGLCjkXy4skWVZC1nVEydOjPF3ESnERzzwwANp6NChUbDJ/aSAFwtzuXgz0YPLmr0JjO+yhLl4rFek3NFZI1J/WdTs52EqgboE++23XwgKRO8hJuD8POyww2Lq1CxqkaUjN2uoO7n5SU0iai+DYQtTFpM/nCHveCK1U3xGqD2ZLl26xOQ3jVIRWTLWJpGGxRfRf+EBSZQRURGAe5pfNixa5s9BgwaF0EBTlMclrrWePXvGvgQRKcAicpoxZLkTzYJrmnNUkzDHWZo8eXLEG40dOzaKuMuWRQrorBGpH8yiFmlYqE3FZpAsaGMYYWoOE5ZnSGTpycYQ7nbUMAyPX3zxRZo7d240RxW4RerG2iTSMHhqishCQm7WULD5xUOkBL98yHsfNmxYFHCWXCokiNR8jhDmcvRX8TkqFubIqsZxjeMzN0O9FItUR2eNyIpjFrVIw5MbNtQhzhzvphtuuCFNmTIlosNEZOnJTU/eRzfeeGNEW06fPl0hQWQZsTaJ1D9WoBrgwcgvHBo3RB3xi+eEE05Izz33XJozZ07kUOeHp4hUJwsJnCHOT44MQ1DIC8379esXTdI777wzvseZ81IssmR01oisGGZRizQsuS5xvu65555wVL/11ltp9913b+wfTWSV5eSTT45o5XfffdfIPZHlwNokUv/86z+8nKRG8v9raNx06NAhFsS+/vrr7kgQWQZybARFfOTIkbFIlukFFltmd42ILNvI+xVXXJG+//77EOQGDx6cJk2a5IVYZDlA5GbS9Pbbb0+dO3eOLOocISEiywduTxabf/rpp2mHHXZo7B9HZJUnm0kUEkSWH2uTSP2hmFAHONYGDBiQbrnllhATWrVq1dg/ksgqR7Gbeuutt45Yiddee013jchycs0116QhQ4aEs2b8+PGpTZs2jf0jiaySwlyfPn1i18+8efNiuscICZH64ffff6+MkBWR+n1PicjyYW0SqR8q5n2kVnbcccc0depUhQSR5YSL7x9//BHREYsXL1ZIEFlBOnXqFH8ykaCQILLiWdSzZ89WSBCpR2zWiNQvCgkiK461SaR+cDJhKdAFILLisOzymWeeSd26dbNZI1IP6KwRWXHIzO3Vq1eaMWOGIreIiIiIiEgdKCaIyP8cmzUiIrKyYBa1iIiIiIjI0qGYICIiIiJljVOoIiIiIiIidePOBBEREREpaxQSRERERERE6kYxQUREREREREREREREakUxQUREREREREREREREakUxQUREREREREREREREakUxQUREREREREREREREakUxQUREREREREREREREakUxQUREREREREREREREakUxQUREREREREREREREakUxQUREREREREREREREakUxQUREREREREREREREUm38P/hW0FwEPuaWAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 44
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "现在有一份股票的日线行情数据导入pandas表中，列datetime,open,close,high,low分别表示交易日、开盘价、收盘价、最高价和最低价，现在需要进行如下计算\n",
    "1.计算行情月线以及月线的MACD，根据月线的MACD，找出当DIF和DEA均在0轴上方，且MACD值大于0并且小于DIF和DEA的月份\n",
    "2.在上述筛选出的月份中，找出那些该月份的前一个月的MACD值小于0的月份"
   ],
   "id": "8a98fd3bbe6cfffb"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-11T03:14:36.513645Z",
     "start_time": "2025-02-11T03:14:36.474163Z"
    }
   },
   "cell_type": "code",
   "source": [
    "daily_df = df.copy()\n",
    "import pandas as pd\n",
    "import talib\n",
    "import numpy as np\n",
    "from scipy.signal import argrelextrema\n",
    "\n",
    "\n",
    "# 假设daily_df已经导入，列名为datetime, open, close, high, low\n",
    "# 代码中会直接使用daily_df\n",
    "\n",
    "# 步骤1: 计算月线行情和月线的MACD\n",
    "# 首先需要将日线行情转换为月线行情\n",
    "def calculate_monthly_data(daily_df):\n",
    "    \"\"\"\n",
    "    将日线行情转换为月线行情\n",
    "    datetime: 转换为日期的月初\n",
    "    open: 每个月的第一个交易日的开盘价\n",
    "    close: 每个月的最后一个交易日的收盘价\n",
    "    high: 每个月的最高价\n",
    "    low: 每个月的最低价\n",
    "    \"\"\"\n",
    "    # 将datetime列转换为日期格式\n",
    "    daily_df['datetime'] = pd.to_datetime(daily_df['datetime'])\n",
    "\n",
    "    # 按月份分组\n",
    "    monthly_df = daily_df.groupby(pd.Grouper(key='datetime', freq='M')).agg(\n",
    "        {\n",
    "            'open': 'first',\n",
    "            'close': 'last',\n",
    "            'high': 'max',\n",
    "            'low': 'min'\n",
    "        }\n",
    "    )\n",
    "    return monthly_df\n",
    "\n",
    "\n",
    "# 计算月线的MACD指标\n",
    "def calculate_monthly_macd(monthly_df):\n",
    "    \"\"\"\n",
    "    计算月线的MACD指标，包括DIF, DEA和MACD柱状图值\n",
    "    \"\"\"\n",
    "    # 获取收盘价序列\n",
    "    close_prices = monthly_df['close'].values\n",
    "\n",
    "    # 计算MACD\n",
    "    dif, dea, hist = talib.MACD(close_prices, fastperiod=12, slowperiod=26, signalperiod=9)\n",
    "\n",
    "    # 将结果添加到数据框\n",
    "    monthly_df['DIF'] = dif\n",
    "    monthly_df['DEA'] = dea\n",
    "    monthly_df['MACD'] = hist\n",
    "\n",
    "    return monthly_df\n",
    "\n",
    "\n",
    "# 筛选出符合条件的月份\n",
    "def filter_monthly_macd(monthly_df):\n",
    "    \"\"\"\n",
    "    筛选出满足条件的月份：\n",
    "    DIF和DEA均在0轴上方，且MACD值大于0并且小于DIF和DEA\n",
    "    \"\"\"\n",
    "    # 定义筛选条件\n",
    "    condition = (monthly_df['DIF'] > 0) & \\\n",
    "                (monthly_df['DEA'] > 0) & \\\n",
    "                (monthly_df['MACD'] > 0) & \\\n",
    "                (monthly_df['MACD'] < monthly_df['DIF']) & \\\n",
    "                (monthly_df['MACD'] < monthly_df['DEA'])\n",
    "\n",
    "    # 筛选符合条件的月份\n",
    "    filtered_months = monthly_df[condition]\n",
    "    return filtered_months\n",
    "\n",
    "\n",
    "# 步骤2: 进一步筛选符合条件的月份\n",
    "def further_filter_months(filtered_months, monthly_df):\n",
    "    \"\"\"\n",
    "    在步骤1筛选出的月份中，进一步筛选：\n",
    "    1. 该月份的前一个月的MACD值小于0\n",
    "    2. 该月份前几个月MACD值经历了从下降（至少连续下降超过2个月）再回升的第一个月\n",
    "    \"\"\"\n",
    "    # 合并所有月份的数据\n",
    "    merged_df = monthly_df.merge(\n",
    "        filtered_months[['DIF', 'DEA', 'MACD']],\n",
    "        left_index=True, right_index=True,\n",
    "        how='left', suffixes=('', '_filtered')\n",
    "        )\n",
    "\n",
    "    # 条件1: 前一个月的MACD值小于0\n",
    "    condition1 = merged_df['MACD_filtered'].shift(1) < 0\n",
    "\n",
    "    # 条件2: 前几个月MACD经历了从下降超过2个月再回升的第一个月\n",
    "    # 使用rolling窗口检查是否连续下降超过2个月，然后回升\n",
    "    # 创建一个标志，表示是否处于连续下降状态\n",
    "    merged_df['macd_diff'] = merged_df['MACD_filtered'].diff()\n",
    "    merged_df['descending'] = merged_df['macd_diff'] < 0\n",
    "\n",
    "    # 使用rolling窗口计算连续下降超过2个月\n",
    "    merged_df['consecutive_desc'] = merged_df['descending'].rolling(window=3).sum() >= 2\n",
    "\n",
    "    # 检查前一个月是否在连续下降，当前月是否回升\n",
    "    condition2 = (merged_df['consecutive_desc'].shift(1)) & \\\n",
    "                 (merged_df['macd_diff'] > 0)\n",
    "\n",
    "    # 筛选满足条件的月份\n",
    "    further_filtered = merged_df[condition1 | condition2]\n",
    "\n",
    "    # 返回筛选出的月份的索引（日期）\n",
    "    return further_filtered.index.tolist()\n",
    "\n",
    "\n",
    "# 步骤3: 在日线行情中判断峰值\n",
    "def find_dayline_peaks(further_filtered_months, daily_df):\n",
    "    \"\"\"\n",
    "    在步骤2筛选出的月份中，从下一个月开始，在日线行情中找到前两个峰值\n",
    "    \"\"\"\n",
    "    # 确保日线行情已经按日期排序\n",
    "    daily_df = daily_df.sort_values(by='datetime')\n",
    "\n",
    "    # 将日线行情的索引设置为datetime\n",
    "    daily_df.set_index('datetime', inplace=True)\n",
    "\n",
    "    # 初始化结果列表\n",
    "    result_dates = []\n",
    "\n",
    "    for month in further_filtered_months:\n",
    "        # 获取下一个月的起始日期\n",
    "        next_month_start = month + pd.offsets.MonthBegin(1)\n",
    "        next_month_end = month + pd.offsets.MonthEnd(2)  # 可能需要调整范围\n",
    "\n",
    "        # 截取日线行情数据\n",
    "        period_df = daily_df.loc[next_month_start:next_month_end]\n",
    "\n",
    "        # 找到收盘价的峰值\n",
    "        # 使用scipy的argrelextrema函数找到相对高点\n",
    "        peak_indices = argrelextrema(period_df['close'].values, np.greater, order=5)[0]\n",
    "        if len(peak_indices) >= 2:\n",
    "            # 获取前两个峰值\n",
    "            first_peak = peak_indices[0]\n",
    "            second_peak = peak_indices[1]\n",
    "\n",
    "            # 比较两个峰值\n",
    "            if period_df['close'].iloc[second_peak] > period_df['close'].iloc[first_peak]:\n",
    "                result_dates.append(period_df.index[second_peak])\n",
    "\n",
    "    return result_dates\n",
    "\n",
    "\n",
    "# 步骤4: 输出结果\n",
    "def main(daily_df):\n",
    "    # 步骤1: 计算月线行情和MACD\n",
    "    monthly_df = calculate_monthly_data(daily_df.copy())\n",
    "    monthly_df = calculate_monthly_macd(monthly_df)\n",
    "\n",
    "    # 步骤1: 筛选出符合条件的月份\n",
    "    filtered_months = filter_monthly_macd(monthly_df)\n",
    "\n",
    "    if filtered_months.empty:\n",
    "        print(\"没有满足步骤1条件的月份\")\n",
    "        return\n",
    "\n",
    "    # 步骤2: 进一步筛选符合条件的月份\n",
    "    further_filtered_months = further_filter_months(filtered_months, monthly_df)\n",
    "\n",
    "    if not further_filtered_months:\n",
    "        print(\"没有满足步骤2条件的月份\")\n",
    "        return\n",
    "\n",
    "    # 步骤3: 在日线行情中查找峰值\n",
    "    result_dates = find_dayline_peaks(further_filtered_months, daily_df)\n",
    "\n",
    "    # 输出结果\n",
    "    if result_dates:\n",
    "        print(\"满足条件的日期:\")\n",
    "        for date in result_dates:\n",
    "            print(date.date())\n",
    "    else:\n",
    "        print(\"没有找到符合条件的日期\")\n",
    "\n",
    "\n",
    "main(daily_df)"
   ],
   "id": "46bd60b709f637a2",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "满足条件的日期:\n",
      "2005-09-16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/7h/w0cmp4zj6mn9br_6nyj310m40000gn/T/ipykernel_60768/2739124595.py:26: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_df = daily_df.groupby(pd.Grouper(key='datetime', freq='M')).agg(\n"
     ]
    }
   ],
   "execution_count": 10
  }
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