ML_LAB1_21171.ipynb

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Data.xlsx\")\n","df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":447},"id":"H-t_ieU7QBZG","executionInfo":{"status":"ok","timestamp":1693127514738,"user_tz":-330,"elapsed":693,"user":{"displayName":"Richa Savant","userId":"15667544460440442066"}},"outputId":"8e20b315-6717-497a-e72f-21f0e89e8081"},"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/plain":["  Customer  Candies (#)  Mangoes (Kg)  Milk Packets (#)  Payment (Rs)  \\\n","0      C_1           20             6                 2           386   \n","1      C_2           16             3                 6           289   \n","2      C_3           27             6                 2           393   \n","3      C_4           19             1                 2           110   \n","4      C_5           24             4                 2           280   \n","5      C_6           22             1                 5           167   \n","6      C_7           15             4                 2           271   \n","7      C_8           18             4                 2           274   \n","8      C_9           21             1                 4           148   \n","9     C_10           16             2                 4           198   \n","\n","   Unnamed: 5  Unnamed: 6  Unnamed: 7  Unnamed: 8  Unnamed: 9  ...  \\\n","0         NaN         NaN         NaN         NaN         NaN  ...   \n","1         NaN         NaN         NaN         NaN         NaN  ...   \n","2         NaN         NaN         NaN         NaN         NaN  ...   \n","3         NaN         NaN         NaN         NaN         NaN  ...   \n","4         NaN         NaN         NaN         NaN         NaN  ...   \n","5         NaN         NaN         NaN         NaN         NaN  ...   \n","6         NaN         NaN         NaN         NaN         NaN  ...   \n","7         NaN         NaN         NaN         NaN         NaN  ...   \n","8         NaN         NaN         NaN         NaN         NaN  ...   \n","9         NaN         NaN         NaN         NaN         NaN  ...   \n","\n","   Unnamed: 12  Unnamed: 13  Unnamed: 14  Unnamed: 15  Unnamed: 16  \\\n","0          NaN          NaN          NaN          NaN          NaN   \n","1          NaN          NaN          NaN          NaN          NaN   \n","2          NaN          NaN          NaN          NaN          NaN   \n","3          NaN          NaN          NaN          NaN          NaN   \n","4          NaN          NaN          NaN          NaN          NaN   \n","5          NaN          NaN          NaN          NaN          NaN   \n","6          NaN          NaN          NaN          NaN          NaN   \n","7          NaN          NaN          NaN          NaN          NaN   \n","8          NaN          NaN          NaN          NaN          NaN   \n","9          NaN          NaN          NaN          NaN          NaN   \n","\n","   Unnamed: 17  Unnamed: 18  Candy  Mango  Milk  \n","0          NaN          NaN    1.0   55.0  18.0  \n","1          NaN          NaN    NaN    NaN   NaN  \n","2          NaN          NaN    NaN    NaN   NaN  \n","3          NaN          NaN    NaN    NaN   NaN  \n","4          NaN          NaN    NaN    NaN   NaN  \n","5          NaN          NaN    NaN    NaN   NaN  \n","6          NaN          NaN    NaN    NaN   NaN  \n","7          NaN          NaN    NaN    NaN   NaN  \n","8          NaN          NaN    NaN    NaN   NaN  \n","9          NaN          NaN    NaN    NaN   NaN  \n","\n","[10 rows x 22 columns]"],"text/html":["\n","  <div id=\"df-41fcc265-e2ad-4f69-baf9-c00b5fbc820f\" class=\"colab-df-container\">\n","    <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 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     <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>C_6</td>\n","      <td>22</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>167</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>C_7</td>\n","      <td>15</td>\n","      <td>4</td>\n","      <td>2</td>\n","      <td>271</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>C_8</td>\n","      <td>18</td>\n","      <td>4</td>\n","      <td>2</td>\n","      <td>274</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>C_9</td>\n","      <td>21</td>\n","      <td>1</td>\n","      <td>4</td>\n","      <td>148</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>C_10</td>\n","      <td>16</td>\n","      <td>2</td>\n","      <td>4</td>\n","      <td>198</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>10 rows × 22 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-41fcc265-e2ad-4f69-baf9-c00b5fbc820f')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 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be payments.\n","\n","A = df[[\"Candies (#)\",\"Mangoes (Kg)\",\"Milk Packets (#)\"]]\n","A"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":363},"id":"7hu_aMBbQBjq","executionInfo":{"status":"ok","timestamp":1693127821063,"user_tz":-330,"elapsed":102,"user":{"displayName":"Richa Savant","userId":"15667544460440442066"}},"outputId":"c7c41e25-a355-4e2e-b021-623c43af3549"},"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   Candies (#)  Mangoes (Kg)  Milk Packets (#)\n","0           20             6                 2\n","1           16             3                 6\n","2           27             6                 2\n","3           19             1                 2\n","4           24             4                 2\n","5           22             1                 5\n","6           15             4                 2\n","7           18             4                 2\n","8           21             1                 4\n","9           16             2                 4"],"text/html":["\n","  <div id=\"df-87df1fb7-8afe-46fb-be32-20cf9c36a9a8\" class=\"colab-df-container\">\n","    <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>Candies (#)</th>\n","      <th>Mangoes (Kg)</th>\n","      <th>Milk Packets (#)</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>20</td>\n","      <td>6</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>16</td>\n","      <td>3</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>27</td>\n","      <td>6</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>19</td>\n","      <td>1</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>24</td>\n","      <td>4</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>22</td>\n","      <td>1</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>15</td>\n","      <td>4</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>18</td>\n","      <td>4</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>21</td>\n","      <td>1</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>16</td>\n","      <td>2</td>\n","      <td>4</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" 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Mark all customers (in “Purchase Data” table) with payments above Rs. 200 as RICH and others as POOR. Develop a classifier model to categorize customers into RICH or POOR class based on purchase behavior.\n","\n","df1['Status'] = np.where(df1['Payment (Rs)'] > 200, 'RICH', 'POOR')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iwTt9ExTRU9y","executionInfo":{"status":"ok","timestamp":1693127841626,"user_tz":-330,"elapsed":34,"user":{"displayName":"Richa Savant","userId":"15667544460440442066"}},"outputId":"8590afb3-c9d0-482e-b1f0-98150711cc6f"},"execution_count":12,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-12-e636542820f7>:3: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  df1['Status'] = np.where(df1['Payment (Rs)'] > 200, 'RICH', 'POOR')\n"]}]},{"cell_type":"code","source":["df1[\"Status\"]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lmM-AkgcRdfp","executionInfo":{"status":"ok","timestamp":1693127846310,"user_tz":-330,"elapsed":417,"user":{"displayName":"Richa Savant","userId":"15667544460440442066"}},"outputId":"3988cc4e-1ca3-4678-9b8d-13fc50f76a42"},"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0    RICH\n","1    RICH\n","2    RICH\n","3    POOR\n","4    RICH\n","5    POOR\n","6    RICH\n","7    RICH\n","8    POOR\n","9    POOR\n","Name: Status, dtype: object"]},"metadata":{},"execution_count":13}]},{"cell_type":"code","source":["de = pd.read_excel(\"/content/drive/MyDrive/csv/Lab Session1 Data StockPrice.xlsx\")\n","de"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"id":"SZzpdhhnRdic","executionInfo":{"status":"ok","timestamp":1693128607329,"user_tz":-330,"elapsed":629,"user":{"displayName":"Richa Savant","userId":"15667544460440442066"}},"outputId":"f667321b-215a-4dcd-a3bb-1044784d508e"},"execution_count":19,"outputs":[{"output_type":"execute_result","data":{"text/plain":["             Date Month  Day    Price     Open     High      Low   Volume  \\\n","0    Jun 29, 2021   Jun  Tue  2081.85  2092.00  2126.90  2065.05    1.67M   \n","1    Jun 28, 2021   Jun  Mon  2077.75  2084.00  2112.45  2068.40  707.73K   \n","2    Jun 25, 2021   Jun  Fri  2068.85  2084.35  2088.50  2053.10  475.82K   \n","3    Jun 24, 2021   Jun  Thu  2072.95  2098.00  2098.00  2066.00  541.51K   \n","4    Jun 23, 2021   Jun  Wed  2078.25  2102.00  2111.40  2072.00  809.62K   \n","..            ...   ...  ...      ...      ...      ...      ...      ...   \n","244  Jul 07, 2020   Jul  Tue  1397.40  1410.00  1411.00  1390.05  480.21K   \n","245  Jul 06, 2020   Jul  Mon  1400.75  1405.50  1415.50  1394.00  614.93K   \n","246  Jul 03, 2020   Jul  Fri  1405.10  1415.00  1425.00  1398.00  599.49K   \n","247  Jul 02, 2020   Jul  Thu  1412.35  1440.00  1467.80  1395.30    2.16M   \n","248  Jul 01, 2020   Jul  Wed  1363.05  1363.65  1377.00  1356.00  383.00K   \n","\n","       Chg%  \n","0    0.0020  \n","1    0.0043  \n","2   -0.0020  \n","3   -0.0026  \n","4   -0.0023  \n","..      ...  \n","244 -0.0024  \n","245 -0.0031  \n","246 -0.0051  \n","247  0.0362  \n","248  0.0032  \n","\n","[249 rows x 9 columns]"],"text/html":["\n","  <div id=\"df-4d673d00-1e18-4d13-94a7-877d2a200f4f\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Date</th>\n","      <th>Month</th>\n","      <th>Day</th>\n","      <th>Price</th>\n","      <th>Open</th>\n","      <th>High</th>\n","      <th>Low</th>\n","      <th>Volume</th>\n","      <th>Chg%</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Jun 29, 2021</td>\n","      <td>Jun</td>\n","      <td>Tue</td>\n","      <td>2081.85</td>\n","      <td>2092.00</td>\n","      <td>2126.90</td>\n","      <td>2065.05</td>\n","      <td>1.67M</td>\n","      <td>0.0020</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Jun 28, 2021</td>\n","      <td>Jun</td>\n","      <td>Mon</td>\n","      <td>2077.75</td>\n","      <td>2084.00</td>\n","      <td>2112.45</td>\n","      <td>2068.40</td>\n","      <td>707.73K</td>\n","      <td>0.0043</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Jun 25, 2021</td>\n","      <td>Jun</td>\n","      <td>Fri</td>\n","      <td>2068.85</td>\n","      <td>2084.35</td>\n","      <td>2088.50</td>\n","      <td>2053.10</td>\n","      <td>475.82K</td>\n","      <td>-0.0020</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Jun 24, 2021</td>\n","      <td>Jun</td>\n","      <td>Thu</td>\n","      <td>2072.95</td>\n","      <td>2098.00</td>\n","      <td>2098.00</td>\n","      <td>2066.00</td>\n","      <td>541.51K</td>\n","      <td>-0.0026</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Jun 23, 2021</td>\n","      <td>Jun</td>\n","      <td>Wed</td>\n","      <td>2078.25</td>\n","      <td>2102.00</td>\n","      <td>2111.40</td>\n","      <td>2072.00</td>\n","      <td>809.62K</td>\n","      <td>-0.0023</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","    </tr>\n","    <tr>\n","      <th>244</th>\n","      <td>Jul 07, 2020</td>\n","      <td>Jul</td>\n","      <td>Tue</td>\n","      <td>1397.40</td>\n","      <td>1410.00</td>\n","      <td>1411.00</td>\n","      <td>1390.05</td>\n","      <td>480.21K</td>\n","      <td>-0.0024</td>\n","    </tr>\n","    <tr>\n","      <th>245</th>\n","      <td>Jul 06, 2020</td>\n","      <td>Jul</td>\n","      <td>Mon</td>\n","      <td>1400.75</td>\n","      <td>1405.50</td>\n","      <td>1415.50</td>\n","      <td>1394.00</td>\n","      <td>614.93K</td>\n","      <td>-0.0031</td>\n","    </tr>\n","    <tr>\n","      <th>246</th>\n","      <td>Jul 03, 2020</td>\n","      <td>Jul</td>\n","      <td>Fri</td>\n","      <td>1405.10</td>\n","      <td>1415.00</td>\n","      <td>1425.00</td>\n","      <td>1398.00</td>\n","      <td>599.49K</td>\n","      <td>-0.0051</td>\n","    </tr>\n","    <tr>\n","      <th>247</th>\n","      <td>Jul 02, 2020</td>\n","      <td>Jul</td>\n","      <td>Thu</td>\n","      <td>1412.35</td>\n","      <td>1440.00</td>\n","      <td>1467.80</td>\n","      <td>1395.30</td>\n","      <td>2.16M</td>\n","      <td>0.0362</td>\n","    </tr>\n","    <tr>\n","      <th>248</th>\n","      <td>Jul 01, 2020</td>\n","      <td>Jul</td>\n","      <td>Wed</td>\n","      <td>1363.05</td>\n","      <td>1363.65</td>\n","      <td>1377.00</td>\n","      <td>1356.00</td>\n","      <td>383.00K</td>\n","      <td>0.0032</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>249 rows × 9 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4d673d00-1e18-4d13-94a7-877d2a200f4f')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 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Compare the mean with the population mean and note your observations\n","de1=de.query('Day == \"Wed\"')['Price']\n","print(de1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-coUbaf9RdrC","executionInfo":{"status":"ok","timestamp":1693128634701,"user_tz":-330,"elapsed":7,"user":{"displayName":"Richa Savant","userId":"15667544460440442066"}},"outputId":"478ac6e7-e4ca-4c21-cda1-c66f022c413d"},"execution_count":22,"outputs":[{"output_type":"stream","name":"stdout","text":["4      2078.25\n","9      2089.95\n","14     2071.20\n","19     1917.95\n","24     1894.05\n","29     1858.10\n","33     1746.60\n","38     1723.65\n","43     1795.85\n","56     1709.00\n","60     1757.00\n","64     1760.55\n","69     1824.00\n","73     2018.50\n","78     1886.10\n","83     1675.45\n","88     1698.65\n","93     1618.40\n","98     1469.35\n","103    1438.25\n","107    1452.70\n","112    1461.15\n","117    1483.45\n","122    1434.95\n","126    1411.55\n","131    1423.40\n","136    1617.75\n","141    1349.90\n","145    1353.90\n","150    1365.30\n","154    1371.25\n","159    1300.55\n","164    1333.35\n","169    1331.75\n","174    1338.05\n","179    1356.90\n","183    1384.00\n","188    1372.85\n","193    1367.40\n","198    1348.90\n","203    1359.75\n","208    1369.30\n","213    1361.80\n","218    1386.80\n","223    1336.95\n","228    1352.75\n","233    1372.95\n","238    1351.95\n","243    1390.10\n","248    1363.05\n","Name: Price, dtype: float64\n"]}]},{"cell_type":"code","source":["wrows = len(de1.axes[0])\n","print(wrows)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TpUA6l7GRpKX","executionInfo":{"status":"ok","timestamp":1693128639160,"user_tz":-330,"elapsed":6,"user":{"displayName":"Richa Savant","userId":"15667544460440442066"}},"outputId":"c550b50d-00cf-455e-cd97-b43820044b59"},"execution_count":23,"outputs":[{"output_type":"stream","name":"stdout","text":["50\n"]}]},{"cell_type":"code","source":["wm=de1.mean()\n","print(wm)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"w-XpCPb5RpN2","executionInfo":{"status":"ok","timestamp":1693128639700,"user_tz":-330,"elapsed":13,"user":{"displayName":"Richa Savant","userId":"15667544460440442066"}},"outputId":"ec782d11-bdaf-4eb9-dbea-d63ef92f4b4b"},"execution_count":24,"outputs":[{"output_type":"stream","name":"stdout","text":["1550.7060000000001\n"]}]},{"cell_type":"code","source":["#comparing population mean with sample mean of prices on all wednesdays\n","\n","print(m-wm)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"z2FKLtHQRVAz","executionInfo":{"status":"ok","timestamp":1693128640081,"user_tz":-330,"elapsed":8,"user":{"displayName":"Richa Savant","userId":"15667544460440442066"}},"outputId":"c80d45c4-c3f5-4364-bd02-7fe3764586a4"},"execution_count":25,"outputs":[{"output_type":"stream","name":"stdout","text":["9.957453815261033\n"]}]},{"cell_type":"code","source":["#Select the price data for the month of Apr and calculate the sample mean. Compare the mean with the population mean and note your observations.\n","de2=de.query('Month == \"Apr\"')['Price']\n","print(de2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"84_wtSF-RvW8","executionInfo":{"status":"ok","timestamp":1693128642178,"user_tz":-330,"elapsed":4,"user":{"displayName":"Richa Savant","userId":"15667544460440442066"}},"outputId":"c8da23af-044d-4be1-c5aa-9fc497af5871"},"execution_count":26,"outputs":[{"output_type":"stream","name":"stdout","text":["41    1791.40\n","42    1792.55\n","43    1795.85\n","44    1793.20\n","45    1730.85\n","46    1712.00\n","47    1673.05\n","48    1640.05\n","49    1612.70\n","50    1627.45\n","51    1608.10\n","52    1611.65\n","53    1582.00\n","54    1730.90\n","55    1726.40\n","56    1709.00\n","57    1688.55\n","58    1702.65\n","59    1751.75\n","Name: Price, dtype: float64\n"]}]},{"cell_type":"code","source":["arows = len(de2.axes[0])\n","print(arows)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ijzxJ_8CRvZ1","executionInfo":{"status":"ok","timestamp":1693128642773,"user_tz":-330,"elapsed":8,"user":{"displayName":"Richa Savant","userId":"15667544460440442066"}},"outputId":"218a7584-4c8c-451a-d535-aa4cca103054"},"execution_count":27,"outputs":[{"output_type":"stream","name":"stdout","text":["19\n"]}]},{"cell_type":"code","source":["am=de2.mean()\n","print(am)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YapLp6ClRvcf","executionInfo":{"status":"ok","timestamp":1693128648876,"user_tz":-330,"elapsed":6,"user":{"displayName":"Richa Savant","userId":"15667544460440442066"}},"outputId":"2a13f1fa-2981-4613-ecbd-d38f43236aca"},"execution_count":28,"outputs":[{"output_type":"stream","name":"stdout","text":["1698.9526315789474\n"]}]},{"cell_type":"code","source":["#comparing population mean with sample mean of prices on all Apr months\n","\n","print(m-am)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QOMpPwHzRvfH","executionInfo":{"status":"ok","timestamp":1693128649532,"user_tz":-330,"elapsed":7,"user":{"displayName":"Richa Savant","userId":"15667544460440442066"}},"outputId":"2ef7063d-b4c1-47b9-a44d-a736a1760538"},"execution_count":29,"outputs":[{"output_type":"stream","name":"stdout","text":["-138.2891777636862\n"]}]},{"cell_type":"code","source":["#From the Chg% (available in column I) find the probability of making a loss over the stock\n","# Define a lambda function to filter negative values\n","negative_filter = lambda x: x < 0\n","\n","# Apply the lambda function to the Chg% column and calculate the probability\n","total_rows = len(de)\n","negative_rows = de['Chg%'].apply(negative_filter).sum()\n","probability_of_loss = negative_rows / total_rows\n","\n","print(f\"Probability of making a loss over the stock: {probability_of_loss:.2%}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1O_-cj_aRviv","executionInfo":{"status":"ok","timestamp":1693128652653,"user_tz":-330,"elapsed":690,"user":{"displayName":"Richa Savant","userId":"15667544460440442066"}},"outputId":"e7dfb78d-176b-44ec-f994-06883487f630"},"execution_count":30,"outputs":[{"output_type":"stream","name":"stdout","text":["Probability of making a loss over the stock: 49.80%\n"]}]},{"cell_type":"code","source":["#Calculate the probability of making a profit on Wednesday.\n","# Filter rows corresponding to Wednesdays\n","wednesday_rows = de.query('Day == \"Wed\"')\n","# Define a lambda function to filter positive values\n","positive_filter = lambda x: x > 0\n","\n","# Apply the lambda function to the Chg% column of Wednesday rows and calculate the probability\n","total_wednesday_rows = len(wednesday_rows)\n","positive_wednesday_rows = wednesday_rows['Chg%'].apply(positive_filter).sum()\n","probability_of_profit_on_wednesday = positive_wednesday_rows / total_wednesday_rows\n","\n","print(f\"Probability of making a profit on Wednesdays: {probability_of_profit_on_wednesday:.2%}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nqM9ezPBR4D6","executionInfo":{"status":"ok","timestamp":1693128652656,"user_tz":-330,"elapsed":20,"user":{"displayName":"Richa Savant","userId":"15667544460440442066"}},"outputId":"acec5398-7930-4bac-e293-0b3e895ca6b7"},"execution_count":31,"outputs":[{"output_type":"stream","name":"stdout","text":["Probability of making a profit on Wednesdays: 42.00%\n"]}]},{"cell_type":"code","source":["\n","#Calculate the conditional probability of making profit, given that today is Wednesday.\n","# Filter rows corresponding to Wednesdays\n","wednesday_rows = de[de['Day'] == 'Wed']\n","\n","# Define a lambda function to filter positive values\n","positive_filter = lambda x: x > 0\n","\n","# Apply the lambda function to the Chg% column of Wednesday rows\n","positive_wednesday_rows = wednesday_rows['Chg%'].apply(positive_filter)\n","\n","# Calculate the conditional probability\n","total_positive_wednesday_rows = positive_wednesday_rows.sum()\n","total_wednesday_rows = len(wednesday_rows)\n","conditional_probability = total_positive_wednesday_rows / total_wednesday_rows\n","\n","print(f\"Conditional probability of making a profit, given that today is Wednesday: {conditional_probability:.2%}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"v75-3_0yR4Gf","executionInfo":{"status":"ok","timestamp":1693128655876,"user_tz":-330,"elapsed":400,"user":{"displayName":"Richa Savant","userId":"15667544460440442066"}},"outputId":"1fb53daa-a5ac-4823-bec1-a8766574cd57"},"execution_count":32,"outputs":[{"output_type":"stream","name":"stdout","text":["Conditional probability of making a profit, given that today is Wednesday: 42.00%\n"]}]},{"cell_type":"code","source":["#Make a scatter plot of Chg% data against the day of the week\n","\n","# Create a scatter plot\n","plt.figure(figsize=(10, 6))  # Adjust the figure size as needed\n","plt.scatter(de['Day'], de['Chg%'], alpha=0.5)  # Create scatter plot\n","plt.title('Scatter Plot of Chg% vs. Day of the Week')\n","plt.xlabel('Day of the Week')\n","plt.ylabel('Chg%')\n","plt.grid(True)\n","plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n","\n","# Show the plot\n","plt.tight_layout()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":607},"id":"7gokkwmyR7LK","executionInfo":{"status":"ok","timestamp":1693128660466,"user_tz":-330,"elapsed":693,"user":{"displayName":"Richa Savant","userId":"15667544460440442066"}},"outputId":"29d25d20-b26d-49a9-8913-2db98b73a531"},"execution_count":33,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA90AAAJOCAYAAACqS2TfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACibUlEQVR4nOzdeXycZb3///d9z55M1qZJ27Q0bVMstGUrpbRlU0tbRaSiyOKxUvmhIoiK4hEX1uPhCByWg0XQo4icLx6+uKAodpHlq0LZBSlQaEpDS9ImTbPOZPb7+v0xyTRpkzYpHSaTvJ6PRx/J3HPN5DPpXJn7c1/X9bksY4wRAAAAAAA45OxcBwAAAAAAwGhF0g0AAAAAQJaQdAMAAAAAkCUk3QAAAAAAZAlJNwAAAAAAWULSDQAAAABAlpB0AwAAAACQJSTdAAAAAABkCUk3AAAAAABZQtINAMh79fX1sixLv/jFL3IdSj9r1qzRMcccI7/fL8uy1N7efkie17IsXXbZZYfkuTDy3H///Zo1a5Y8Ho9KS0uH/fje/nDLLbcc+uBGsLH6ugGMfCTdADCCvfrqq/rUpz6lqVOnyu/3q7q6WqeffrruvPPOrP3MBx54QLfffvs+xxsbG3Xttdfq5ZdfztrP3tuTTz4py7Iy/zwej6ZPn66VK1fq7bffPiQ/4+mnn9a11157yBLiXrt379anP/1pBQIBrV69Wvfff78KCwv3+5gtW7boi1/8oqZPny6/36/i4mItXrxYd9xxhyKRyCGNb2/33HOPpk2bpvLycn32s59VZ2dnv/sdx9Gxxx6rf//3f89qHO9VTU1N5v1i27ZKS0s1d+5cfeELX9Czzz6b6/AOaNOmTbrwwgs1Y8YM/fSnP9VPfvKTQds++uijuvbaa9+/4PpIpVIqLi7WWWedtc99t912myzL0uc+97l97rv66qtlWZbeeuut9yNMABgR3LkOAAAwsKefflof/OAHddhhh+niiy/WhAkTtH37dj3zzDO644479JWvfCUrP/eBBx7Qxo0b9bWvfa3f8cbGRl133XWqqanRMccck5WfPZjLL79c8+fPVyKR0EsvvaSf/OQn+tOf/qRXX31VkyZNek/P/fTTT+u6667ThRdeeFCjioN5/vnn1dXVpRtuuEFLliw5YPs//elPOuecc+Tz+bRy5UrNmTNH8Xhcf//733XllVfqtdde228C9l78/e9/1yWXXKLLL79c06dP14033qgrr7xS99xzT6bNT3/6U3V0dOgb3/hGVmI4lI455phMnF1dXXrjjTf00EMP6ac//am+/vWv69Zbb81xhIN78skn5TiO7rjjDtXW1u637aOPPqrVq1fnJPF2uVw68cQT9fTTT+9z31NPPSW3262nnnpqwPsqKyt1+OGHvx9hAsCIQNINACPUD37wA5WUlOj555/fJxlsbm7OTVBZEA6HDzgCfPLJJ+tTn/qUJGnVqlU6/PDDdfnll+u+++7TVVdd9X6EOWy9/0dDSeS3bt2q8847T1OnTtXjjz+uiRMnZu679NJLVVdXpz/96U/ZClV//OMfddppp2VmOBQXF+uqq67KJN3t7e363ve+p3vuuUc+ny9rcRwq1dXV+pd/+Zd+x374wx/qggsu0G233aaZM2fqkksuyVF0+zec902unXTSSVq/fr3eeOMNHXHEEZnjTz31lD796U/rgQce0M6dOzVhwgRJUjKZ1LPPPqulS5fmKmQAyAmmlwPACLVlyxbNnj17wJPvysrKfY79z//8j0444QQVFBSorKxMp5xyitatW5e5//e//73OOOMMTZo0ST6fTzNmzNANN9ygVCqVaXPaaafpT3/6k955553MFN2amho9+eSTmj9/vqR00tt7X9811M8++6yWL1+ukpISFRQU6NRTT91npOvaa6+VZVl6/fXXdcEFF6isrEwnnXTSsH83H/rQhySlk9X9efzxx3XyySersLBQpaWlOuuss/TGG2/0i+fKK6+UJE2bNi3zuurr6/f7vA899JDmzZunQCCgiooK/cu//IsaGhoy95922mmZqbXz58+XZVm68MILB32+m266SaFQSD/72c/6Jdy9amtr9dWvfnWf4w8//LDmzJkjn8+n2bNna82aNfu0efLJJ3X88cfL7/drxowZuueeezL/D70ikYjKysoyt8vLy9Xd3Z25fe2112ru3Lk6++yz9/t76ZVIJFReXq5Vq1btc19nZ6f8fr+++c1vZo7deeedmj17dua9e/zxx+uBBx4Y0s8aqkAgoPvvv1/l5eX6wQ9+IGNM5r5bbrlFixYt0rhx4xQIBDRv3jz9+te/7vf4U089VUcfffSAz/2BD3xAy5YtO2AMd911l2bPni2fz6dJkybp0ksv7besoaamRtdcc40kafz48bIsa9BR7AsvvFCrV6+WpH5LMPb2k5/8RDNmzJDP59P8+fP1/PPP79Nm06ZN+tSnPqXy8nL5/X4df/zx+sMf/nDA19Pbd/v287fffls7d+7UZZddJr/f3+++l19+WeFwuF+fH+rPbm9v19e+9jVNmTJFPp9PtbW1+uEPfyjHcfYbozFGX/jCF+T1evXb3/72gK8JALLCAABGpKVLl5qioiLz6quvHrDttddeaySZRYsWmZtvvtnccccd5oILLjD/+q//mmmzYsUK8+lPf9rcfPPN5sc//rE555xzjCTzzW9+M9Nm3bp15phjjjEVFRXm/vvvN/fff7/53e9+Z3bu3Gmuv/56I8l84QtfyNy3ZcsWY4wxjz32mPF6vWbhwoXmP//zP81tt91mjjrqKOP1es2zzz6bef5rrrnGSDJHHnmkOeuss8xdd91lVq9ePejreuKJJ4wk89BDD/U7/vvf/95IMt/+9reNMcZs3brVSDL33ntvps369euN2+02hx9+uLnpppvMddddZyoqKkxZWZnZunWrMcaYV155xZx//vlGkrntttsyrysUCg0a07333mskmfnz55vbbrvNfPvb3zaBQMDU1NSYtra2zO/xC1/4gpFkrr/+enP//febp59+etDnrK6uNtOnTx/0/r1JMkcffbSZOHGiueGGG8ztt99upk+fbgoKCkxLS0um3UsvvWR8Pp+pqakx//Ef/2F+8IMfmEmTJpmjjz7a9D0FuP/++01BQYFZu3ateeutt8wpp5xilixZYowx5rXXXjM+n8+88sorQ47PGGM+//nPm9LSUhOLxfodv++++4wk8/zzzxtjjPnJT35iJJlPfepT5p577jF33HGHueiii8zll18+rJ/Xa+rUqeaMM84Y9P6LLrrISDIbN27MHJs8ebL58pe/bH70ox+ZW2+91ZxwwglGkvnjH/+YafPTn/7USNqnPz733HNGkvnlL3+537h63/tLliwxd955p7nsssuMy+Uy8+fPN/F43BhjzO9+9zvziU98wkgyP/7xj839998/6O/96aefNqeffrqRlHnf3n///caYPf3h2GOPNbW1teaHP/yhuemmm0xFRYWZPHly5ucZY8zGjRtNSUmJOfLII80Pf/hD86Mf/ciccsopxrIs89vf/na/rykcDhu3220+97nPZY798pe/NIWFhSaRSJiTTjrJfP3rX8/cd/vttxtJmb8JQ/3Z4XDYHHXUUWbcuHHmO9/5jrn77rvNypUrjWVZ5qtf/WqmXe/rvvnmm40xxiSTSbNy5Urj8/n6/V8CwPuNpBsARqh169YZl8tlXC6XWbhwofnWt75l1q5d2++E2RhjNm/ebGzbNp/4xCdMKpXqd5/jOJnvu7u79/kZX/ziF01BQYGJRqOZY2eccYaZOnXqPm2ff/75fRLb3p8xc+ZMs2zZsn1+3rRp08zpp5+eOdabeJx//vlD+h30Jt0///nPza5du0xjY6P505/+ZGpqaoxlWZnEbaCk+5hjjjGVlZVm9+7dmWOvvPKKsW3brFy5MnPs5ptvNpIyifj+xONxU1lZaebMmWMikUjm+B//+EcjyVx99dWZY73JeW+Mg+no6DCSzFlnnXXAn99LkvF6vaaurq7fa5Nk7rzzzsyxM8880xQUFJiGhobMsc2bNxu3290v6U4mk+bss882kowkM2XKFPPPf/7TGJO++POlL31pyLH1Wrt2rZFkHnnkkX7HP/rRj/a7wHDWWWeZ2bNnD/v5B3OgpPu2224zkszvf//7zLG9+0Y8Hjdz5swxH/rQhzLH2tvbjd/v73chyxhjLr/8clNYWLjfCzXNzc3G6/WapUuX9uujP/rRjzLv7169fWTXrl0HfK2XXnppv//HXr39Ydy4caa1tTVzvPdiVd//kw9/+MNm7ty5/f4GOI5jFi1aZGbOnHnAGObPn29mzJiRuf3FL37RfPCDHzTGGPOtb33LzJ8/P3Pfpz71KVNQUGASicSwfvYNN9xgCgsLzVtvvdXvZ3/72982LpfLbNu2rd/rvvnmm00ikTDnnnuuCQQCZu3atQd8HQCQTUwvB4AR6vTTT9eGDRv08Y9/XK+88opuuukmLVu2TNXV1f2mXz788MNyHEdXX321bLv/n/W+000DgUDm+66uLrW0tOjkk09Wd3e3Nm3adNBxvvzyy9q8ebMuuOAC7d69Wy0tLWppaVE4HNaHP/xh/fWvf91nCuiXvvSlYf2Mz3/+8xo/frwmTZqkM844Q+FwWPfdd5+OP/74Advv2LFDL7/8si688EKVl5dnjh911FE6/fTT9eijjw7/hUp64YUX1NzcrC9/+cvy+/2Z42eccYZmzZp1UOuue6uEFxUVDetxS5Ys0YwZMzK3jzrqKBUXF2equqdSKf3lL3/RihUr+hWbq62t1Uc+8pF+z+VyufSb3/xGmzdv1gsvvKC33npLc+fO1R/+8Ac999xzuuGGG9TQ0KAzzzxTkyZN0plnnqnGxsb9xvehD31IFRUVevDBBzPH2tratH79ep177rmZY6WlpXr33XcHnPacDcFgUFK6D/Tq2zfa2trU0dGhk08+WS+99FLmeElJic466yz96le/ykxNT6VSevDBB7VixYr91iX4y1/+ong8rq997Wv9+ujFF1+s4uLirK3XP/fcc/stGzj55JMlKfMeaW1t1eOPP65Pf/rTmb8JLS0t2r17t5YtW6bNmzf3WzYxkJNOOklbtmzRzp07JaWnmi9atEiStHjxYv3jH//ILFV46qmntGDBArnd7mH97Iceekgnn3yyysrKMu1aWlq0ZMkSpVIp/fWvf+0XUzwe1znnnKM//vGPevTRR1lDDiDnSLoBYASbP3++fvvb36qtrU3PPfecrrrqKnV1delTn/qUXn/9dUnptd+2bevII4/c73O99tpr+sQnPqGSkhIVFxdr/PjxmWJTHR0dBx3j5s2bJUmf+9znNH78+H7//vu//1uxWGyf5582bdqwfsbVV1+t9evX6/HHH9c///lPNTY26rOf/eyg7d955x1J6bW2ezviiCMyFwWGa3/PO2vWrMz9w1FcXCypfxI4FIcddtg+x8rKytTW1iYpXZArEokMWAF7sKrYtbW1mjdvnvx+v+LxuL7xjW/ommuuUUVFhc477zwFAgE98sgj8vv9uuCCC/Ybn9vt1ic/+Un9/ve/VywWkyT99re/VSKR6Jd0/+u//quCwaBOOOEEzZw5U5deeumAVa8PlVAoJKn/RY4//vGPOvHEE+X3+1VeXq7x48frxz/+8T7v25UrV2rbtm3629/+JimdTDc1Ne33vSgN/r7xer2aPn36Qb1vhmLv90hvAt77Hqmrq5MxRt///vf36bu9a8sPVLSx77ru9vZ2vfbaa1q8eLEkadGiRUomk3ruuee0detW7dixI9N+OD978+bNWrNmzT7tencF2DvGG2+8UQ8//LB+/etf67TTTjuo3x0AHEpULweAPOD1ejV//nzNnz9fhx9+uFatWqWHHnooc3J6IO3t7Tr11FNVXFys66+/XjNmzJDf79dLL72kf/3Xfz1gMaL96X3szTffPOhWYr2ji736jiwOxdy5c4e07VY+Ki4u1qRJk7Rx48ZhPc7lcg143PQpEPZe3HbbbXK73brsssu0fft2/f3vf9fWrVtVU1Ojm266SdOnT9e7776ryZMnD/oc5513nu655x79+c9/1ooVK/R//+//1axZs/oVJDviiCP05ptv6o9//KPWrFmj3/zmN7rrrrt09dVX67rrrjskr6Wv3t9z74WHv/3tb/r4xz+uU045RXfddZcmTpwoj8eje++9d59ibsuWLVNVVZX+53/+R6eccor+53/+RxMmTBix780DvUd6++43v/nNQQvBHWjbst4k+u9//7sKCgokSQsXLpQkVVRUaObMmfr73/+u7du392s/nJ/tOI5OP/10fetb3xqw3d7bjy1btkxr1qzRTTfdpNNOO63frBQAyAWSbgDIM71Tqnfs2CFJmjFjhhzH0euvvz5o0vvkk09q9+7d+u1vf6tTTjklc3yg6t8DVUDe3/HeKc7FxcUjJvmYOnWqJOnNN9/c575NmzapoqIiMx14sNd1oOftraDe680338zcP1wf+9jH9JOf/EQbNmzIJCzvVWVlpfx+v+rq6va5b6Bjfe3YsUP/9m//poceekhutzszlbx3mnrv14aGhv0m3aeccoomTpyoBx98UCeddJIef/xxffe7392nXWFhoc4991yde+65isfjOvvss/WDH/xAV1111SFNmEKhkH73u99pypQpmS2ufvOb38jv92vt2rX9tkO7995793m8y+XSBRdcoF/84hf64Q9/qIcfflgXX3zxoMltr77vm+nTp2eOx+Nxbd269aD7zXDeuwPpjcXj8Rx0DJWVlZnEurCwUEceeWS/HRcWLVqkp556Su+++65cLlfm/T2cnz1jxgyFQqEhx3jiiSfqS1/6kj72sY/pnHPO0e9+9zu53ZzyAsgdppcDwAj1xBNPDDhq2bseuXeq6ooVK2Tbtq6//vp9Rqx7H9+bFPR9vng8rrvuumuf5y8sLBxwunlvktp3iyNJmjdvnmbMmKFbbrklM3W3r127dg36GrNl4sSJOuaYY3Tffff1i3fjxo1at26dPvrRj2aODfa6BnL88cersrJSd999d2bKtCT9+c9/1htvvKEzzjjjoOL91re+pcLCQv1//9//p6ampn3u37Jli+64445hPafL5dKSJUv08MMP91t/XVdXpz//+c/7fey3v/1tnXLKKVq+fLkkqaqqSpIya/97t13r3X95MLZt61Of+pQeeeQR3X///Uomk/2mlkvS7t27+932er068sgjZYxRIpGQpEzdgZaWlgO97EFFIhF99rOfVWtrq7773e9mElaXyyXLsvptnVdfX6+HH354wOf57Gc/q7a2Nn3xi19UKBTaZz/wgSxZskRer1f/9V//1a8P/uxnP1NHR8dBv2+G894dSGVlpU477TTdc889mYt4fQ2175500kl6+eWXtW7dusx67l6LFi3Shg0b9Le//U1HHXVUZlr/cH72pz/9aW3YsEFr167dp117e7uSyeQ+x5csWaL//d//1Zo1a/TZz372Pc3mAYD3ist+ADBCfeUrX1F3d7c+8YlPaNasWYrH43r66af14IMPqqamJrMHcm1trb773e/qhhtu0Mknn6yzzz5bPp9Pzz//vCZNmqQbb7xRixYtUllZmT73uc/p8ssvl2VZuv/++wdM6ufNm6cHH3xQV1xxhebPn69gMKgzzzxTM2bMUGlpqe6++24VFRWpsLBQCxYs0LRp0/Tf//3f+shHPqLZs2dr1apVqq6uVkNDg5544gkVFxfrkUceeb9/fbr55pv1kY98RAsXLtRFF12kSCSiO++8UyUlJf32Pp43b54k6bvf/a7OO+88eTwenXnmmQMWxvJ4PPrhD3+oVatW6dRTT9X555+vpqYm3XHHHaqpqdHXv/71g4p1xowZeuCBB3TuuefqiCOO0MqVKzVnzpzM//lDDz20332+B3Pttddq3bp1Wrx4sS655BKlUin96Ec/0pw5c/Tyyy8P+JjnnntODz74oP75z39mjtXU1Oj444/XhRdeqIsuukj//d//rQULFgxpZP/cc8/VnXfeqWuuuUZz587NjDD3Wrp0qSZMmKDFixerqqpKb7zxhn70ox/pjDPOyCRozz33nD74wQ/qmmuuGXTf6r4aGhr0P//zP5LSo9uvv/66HnroIe3cuVPf+MY39MUvfjHT9owzztCtt96q5cuX64ILLlBzc7NWr16t2trafr+DXscee6zmzJmjhx56SEcccYSOO+64A8Yzfvx4XXXVVbruuuu0fPlyffzjH9ebb76pu+66S/Pnzx9S4j6Q3vfu5ZdfrmXLlsnlcum8884b1nOsXr1aJ510kubOnauLL75Y06dPV1NTkzZs2KB3331Xr7zyygGf46STTtK9996r559/Xpdeemm/+xYtWqSOjg51dHToK1/5ykH97CuvvFJ/+MMf9LGPfUwXXnih5s2bp3A4rFdffVW//vWvVV9fr4qKin3iWrFihe69916tXLlSxcXFuueee4b1uwGAQyZHVdMBAAfw5z//2Xz+8583s2bNMsFg0Hi9XlNbW2u+8pWvmKampn3a//znPzfHHnus8fl8pqyszJx66qlm/fr1mfufeuopc+KJJ5pAIGAmTZqU2YJMknniiScy7UKhkLngggtMaWmpkdRv+7Df//735sgjj8xsOdV3i65//OMf5uyzzzbjxo0zPp/PTJ061Xz60582jz32WKbNcLZDMmbwfbr3NtCWYcYY85e//MUsXrzYBAIBU1xcbM4880zz+uuv7/P4G264wVRXVxvbtoe0fdiDDz6Y+V2Xl5ebz3zmM+bdd9/t12aoW4b19dZbb5mLL77Y1NTUGK/Xa4qKiszixYvNnXfe2W9bJUnm0ksv3efxU6dO7bdnsjHpPdSPPfZY4/V6zYwZM8x///d/m2984xvG7/fv83jHccyCBQvMFVdcsc99dXV15pRTTjHBYNCccsopmT3aD8RxHDNlyhQjyfzbv/3bPvffc8895pRTTsm8b2bMmGGuvPJK09HRkWnT+z645pprDvjzpk6dmtn6zLIsU1xcbGbPnm0uvvjifnvG9/Wzn/3MzJw50/h8PjNr1ixz7733Zt6rA7npppuMJPPv//7vQ/od9PrRj35kZs2aZTwej6mqqjKXXHJJZm/3XsPpI8lk0nzlK18x48ePN5ZlZeLde7/qvgb6PW7ZssWsXLnSTJgwwXg8HlNdXW0+9rGPmV//+tdDel1vvvlm5ne+97ZejuNk/pY8+OCD+zx2qD+7q6vLXHXVVaa2ttZ4vV5TUVFhFi1aZG655ZbMNoqDve677rrLSDLf/OY3h/R6AOBQs4w5RBVXAABAXlixYoVee+21TOV5DM8dd9yhr3/966qvrx+wijwAAH2xphsAgFEsEon0u71582Y9+uijbKV0kIwx+tnPfqZTTz2VhBsAMCSs6QYAYBSbPn26Lrzwwsx+0D/+8Y/l9XoH3X4JAwuHw/rDH/6gJ554Qq+++qp+//vf5zokAECeYHo5AACj2KpVq/TEE09o586d8vl8Wrhwof793/99SAXAsEd9fb2mTZum0tJSffnLX9YPfvCDXIcEAMgTJN0AAAAAAGQJa7oBAAAAAMgSkm4AAAAAALIk7wqprV69WjfffLN27typo48+WnfeeadOOOGEAdu+9tpruvrqq/Xiiy/qnXfe0W233aavfe1r/dpce+21uu666/od+8AHPqBNmzYNOSbHcdTY2KiioiJZljXs1wQAAAAAyC/GGHV1dWnSpEmy7cHHs/Mq6X7wwQd1xRVX6O6779aCBQt0++23a9myZXrzzTdVWVm5T/vu7m5Nnz5d55xzjr7+9a8P+ryzZ8/WX/7yl8xtt3t4v5bGxkZNmTJlWI8BAAAAAOS/7du3a/LkyYPen1dJ96233qqLL75Yq1atkiTdfffd+tOf/qSf//zn+va3v71P+/nz52v+/PmSNOD9vdxutyZMmHDQcRUVFUlK/7KLi4sP+nmyJZFIaN26dVq6dKk8Hk+uwwFGPPoMMHT0F2Do6C/A0OVDf+ns7NSUKVMy+eBg8ibpjsfjevHFF3XVVVdljtm2rSVLlmjDhg3v6bk3b96sSZMmye/3a+HChbrxxht12GGHDfnxvVPKi4uLR2zSXVBQoOLi4hH7hgVGEvoMMHT0F2Do6C/A0OVTfznQEuO8SbpbWlqUSqVUVVXV73hVVdWw1l/vbcGCBfrFL36hD3zgA9qxY4euu+46nXzyydq4ceOgVyxisZhisVjmdmdnp6T0GyORSBx0LNnSG9NIjA0YiegzwNDRX4Cho78AQ5cP/WWoseVN0p0tH/nIRzLfH3XUUVqwYIGmTp2q//t//68uuuiiAR9z44037lN8TZLWrVungoKCrMX6Xq1fvz7XIQB5hT4DDB39BRg6+gswdCO5v3R3dw+pXd4k3RUVFXK5XGpqaup3vKmp6T2tx95baWmpDj/8cNXV1Q3a5qqrrtIVV1yRud07l3/p0qUjdnr5+vXrdfrpp4/4qRnASECfAYaO/gIMHf0FGLp86C+9M54PJG+Sbq/Xq3nz5umxxx7TihUrJKW36nrsscd02WWXHbKfEwqFtGXLFn32s58dtI3P55PP59vnuMfjGbFvCGnkxweMNPQZYOjoL8DQ0V+AoRvJ/WWoceVN0i1JV1xxhT73uc/p+OOP1wknnKDbb79d4XA4U8185cqVqq6u1o033igpXXzt9ddfz3zf0NCgl19+WcFgULW1tZKkb37zmzrzzDM1depUNTY26pprrpHL5dL555+fmxcJAAAAABg18irpPvfcc7Vr1y5dffXV2rlzp4455hitWbMmU1xt27Zt/TYlb2xs1LHHHpu5fcstt+iWW27RqaeeqieffFKS9O677+r888/X7t27NX78eJ100kl65plnNH78+Pf1tQEAAAAARp+8Srol6bLLLht0OnlvIt2rpqZGxpj9Pt///u//HqrQAAAAAADoxz5wEwAAAAAAcDBIugEAAAAAyBKSbgAAAAAAsoSkGwAAAACALCHpBgAAAAAgS0i6AQAAAADIEpJuAAAAAACyhKQbAAAcFMcxamiLSJIa2iJyHJPjiAAAGHncuQ4AAADkn7rmLq3d2KT6XZ06yS+tfqJONeOLtWxOlWori3IdHgAAIwYj3QAAYFjqmrt071P12tjYoZKAR5JUEvBoY2OH7n2qXnXNXTmOEACAkYOkGwAADJnjGK3d2KTWcFwzK4MK+tOT5oJ+t2ZWBtUajmvda01MNQcAoAdJNwAAGLKG9oi27AppYolflmX1u8+yLE0s8auuOaSG9kiOIgQAYGQh6QYAAEMWjicVTaZU4B24LEzA61IsmVI4nnyfIwMAYGQi6QYAAENW6HXL73ape5CkOhJPyed2qXCQpBwAgLGGpBsAAAxZdWlAM8YHtaMjKmP6r9s2xmhHR1S1lUFVlwZyFCEAACMLSTcAABgy27a0bE6Vygu92twcUiiaHvEORZPa3BxSeaFXS2dXybatAzwTAABjA0k3AAAYltrKIq1aXKM5k0rUEUlIkjoiCc2tLtGqxTXs0w0AQB8suAIAAMNWW1mk6acFta2lS69s2K5LP1irwyqKGOEGAGAvjHQDAICDYtuWqsvSa7erywIk3AAADICkGwAAAACALCHpBgAAAAAgS0i6AQAAAADIEpJuAAAAAACyhKQbAAAAAIAsIekGAAAAACBLSLoBAAAAAMgSkm4AAAAAALKEpBsAAAAAgCwh6QYAAAAAIEtIugEAAAAAyBKSbgAAAAAAsoSkGwAAAACALCHpBgAAAAAgS0i6AQAAAADIEpJuAAAAAACyhKQbAAAAAIAsIekGAAAAACBLSLoBAAAAAMgSkm4AAAAAALKEpBsAAAAAgCwh6QYAAAAAIEtIugEAAAAAyBKSbgAAAAAAsoSkGwAAAACALCHpBgAAAAAgS0i6AQAAAADIEpJuAAAAAACyhKQbAAAAAIAsIekGAAAAACBLSLoBAAAAAMgSkm4AAAAAALKEpBsAAAAAgCwh6QYAAAAAIEtIugEAAAAAyBKSbgAAAAAAsoSkGwAAAACALCHpBgAAAAAgS0i6AQAAAADIEpJuAAAAAACyhKQbAAAAAIAsIekGAAAAACBLSLoBAAAAAMgSkm4AAAAAALKEpBsAAAAAgCwh6QYAAAAAIEtIugEAAAAAyBKSbgAAAAAAsoSkGwAAAACALCHpBgAAAAAgS0i6AQAAAADIEpJuAAAAAACyhKQbAAAAAIAsIekGAAAAACBLSLoBAAAAAMgSkm4AAAAAALKEpBsAAAAAgCwh6QYAAAAAIEtIugGgD8cxamiLSJIa2iJyHJPjiAAAAJDP3LkOAABGirrmLq3d2KT6XZ06yS+tfqJONeOLtWxOlWori3IdHgAAAPIQI90AoHTCfe9T9drY2KGSgEeSVBLwaGNjh+59ql51zV05jhAAAAD5iKQbwJjnOEZrNzapNRzXzMqggv70JKCg362ZlUG1huNa91oTU80BAAAwbHmXdK9evVo1NTXy+/1asGCBnnvuuUHbvvbaa/rkJz+pmpoaWZal22+//T0/J4DRp6E9oi27QppY4pdlWf3usyxLE0v8qmsOqaE9kqMIAQAAkK/yKul+8MEHdcUVV+iaa67RSy+9pKOPPlrLli1Tc3PzgO27u7s1ffp0/cd//IcmTJhwSJ4TwOgTjicVTaZU4B24zEXA61IsmVI4nnyfIwMAAEC+y6uk+9Zbb9XFF1+sVatW6cgjj9Tdd9+tgoIC/fznPx+w/fz583XzzTfrvPPOk8/nOyTPCWD0KfS65Xe71D1IUh2Jp+Rzu1Q4SFIOAAAADCZvziDj8bhefPFFXXXVVZljtm1ryZIl2rBhw/v6nLFYTLFYLHO7s7NTkpRIJJRIJA4qlmzqjWkkxgaMBJWFbtVWBPT6jk4VeQtlyZEkWSYlY4yaO7o1e1KxKgvd9CNgL3zGAENHfwGGLh/6y1Bjy5uku6WlRalUSlVVVf2OV1VVadOmTe/rc95444267rrr9jm+bt06FRQUHFQs74f169fnOgRgxKqWVF0kKbrnWE10iyRpepGkrkatWXNwf2uAsYDPGGDo6C/A0I3k/tLd3T2kdnmTdI8kV111la644orM7c7OTk2ZMkVLly5VcXFxDiMbWCKR0Pr163X66afL4/HkOhxgxHp7V0iPvdGsbS1dWuhv0IZotaaOL9KHZlVq+vhgrsMDRiQ+Y4Cho78AQ5cP/aV3xvOB5E3SXVFRIZfLpaampn7Hm5qaBi2Slq3n9Pl8A64R93g8I/YNIY38+IBc+8CkMs2cUKptLV16ZUODvvTBw3VYRZFs2zrwg4Exjs8YYOjoL8DQjeT+MtS48qaQmtfr1bx58/TYY49ljjmOo8cee0wLFy4cMc8JIL/ZtqXqsoAkqbosQMINAACA9yRvRrol6YorrtDnPvc5HX/88TrhhBN0++23KxwOa9WqVZKklStXqrq6WjfeeKOkdKG0119/PfN9Q0ODXn75ZQWDQdXW1g7pOQEAAAAAOFh5lXSfe+652rVrl66++mrt3LlTxxxzjNasWZMphLZt2zbZ9p7B+8bGRh177LGZ27fccotuueUWnXrqqXryySeH9JwAAAAAABysvEq6Jemyyy7TZZddNuB9vYl0r5qaGhlj3tNzAgAAAABwsPJmTTcAAAAAAPmGpBsAAAAAgCwh6QYAAAAAIEtIugEAAAAAyBKSbgAAAAAAsoSkGwAAAACALCHpBoA+HMeooS0iSWpoi8hxDrztIAAAADCYvNunGwCypa65S2s3Nql+V6dO8kurn6hTzfhiLZtTpdrKolyHBwAAgDzESDcAKJ1w3/tUvTY2dqgk4JEklQQ82tjYoXufqlddc1eOIwQAAEA+IukGMOY5jtHajU1qDcc1szKooD89CSjod2tmZVCt4bjWvdbEVHMAAAAMG0k3gDGvoT2iLbtCmljil2VZ/e6zLEsTS/yqaw6poT2SowgBAACQr0i6AYx54XhS0WRKBd6By1wEvC7FkimF48n3OTIAAADkO5JuAGNeodctv9ul7kGS6kg8JZ/bpcJBknIAAABgMCTdAMa86tKAZowPakdHVMb0X7dtjNGOjqhqK4OqLg3kKEIAAADkK5JuAGOebVtaNqdK5YVebW4OKRRNj3iHokltbg6pvNCrpbOrZNvWAZ4JAAAA6I+kGwAk1VYWadXiGs2ZVKKOSEKS1BFJaG51iVYtrmGfbgAAABwUFigCQI/ayiJNPy2obS1demXDdl36wVodVlHECDcAAAAOGiPdANCHbVuqLkuv3a4uC5BwAwAA4D0h6QYAAAAAIEtIugEAAAAAyBKSbgAAAAAAsoSkGwAAAACALCHpBgAAAAAgS0i6AQAAAADIEpJuAAAAAACyhKQbAAAAAIAsIekGAAAAACBLSLoBAAAAAMgSkm4AAAAAALKEpBsAAAAAgCwh6QaAPhzHqKEtIklqaIvIcUyOIwIAAEA+c+c6AAAYKeqau7R2Y5Pqd3XqJL+0+ok61Ywv1rI5VaqtLMp1eAAAAMhDjHQDgNIJ971P1WtjY4dKAh5JUknAo42NHbr3qXrVNXflOEIAAADkI5JuAGOe4xit3dik1nBcMyuDCvrTk4CCfrdmVgbVGo5r3WtNTDUHAADAsJF0AxjzGtoj2rIrpIklflmW1e8+y7I0scSvuuaQGtojOYoQAAAA+YqkG8CYF44nFU2mVOAduMxFwOtSLJlSOJ58nyMDAABAviPpBjDmFXrd8rtd6h4kqY7EU/K5XSocJCkHAAAABkPSDWDMqy4NaMb4oHZ0RGVM/3Xbxhjt6IiqtjKo6tJAjiIEAABAviLpBjDm2balZXOqVF7o1ebmkELR9Ih3KJrU5uaQygu9Wjq7SrZtHeCZAAAAgP5IugFAUm1lkVYtrtGcSSXqiCQkSR2RhOZWl2jV4hr26QYAAMBBYYEiAPSorSzS9NOC2tbSpVc2bNelH6zVYRVFjHADAADgoDHSDQB92Lal6rL02u3qsgAJNwAAAN4Tkm4AAAAAALKEpBsAAAAAgCwh6QYAAAfFcYwa2iKSpIa2iBzHHOARAACMPRRSAwAAw1bX3KW1G5tUv6tTJ/ml1U/UqWZ8sZbNqaLaPwAAfTDSDQAAhqWuuUv3PlWvjY0dKgl4JEklAY82Nnbo3qfqVdfcleMIAQAYOUi6AQDAkDmO0dqNTWoNxzWzMqigPz1pLuh3a2ZlUK3huNa91sRUcwAAepB0AwCAIWtoj2jLrpAmlvhlWf231LMsSxNL/KprDqmhPZKjCAEAGFlIugEAwJCF40lFkykVeAcuCxPwuhRLphSOJ9/nyAAAGJlIugEAwJAVet3yu13qHiSpjsRT8rldKhwkKQcAYKwh6QYAAENWXRrQjPFB7eiIypj+67aNMdrREVVtZVDVpYEcRQgAwMhC0g0AAIbMti0tm1Ol8kKvNjeHFIqmR7xD0aQ2N4dUXujV0tlVsm3rAM8EAMDYQNINAACGpbaySKsW12jOpBJ1RBKSpI5IQnOrS7RqcQ37dAMA0AcLrgAAwLDVVhZp+mlBbWvp0isbtuvSD9bqsIoiRrgBANgLI90AAOCg2Lal6rL02u3qsgAJNwAAAyDpBgAAAAAgS0i6AQAAAADIEpJuAAAAAACyhKQbAAAAAIAsIekGAAAAACBLSLoBAAAAAMgSkm4AAAAAALKEpBsAAAAAgCwh6QYAAAAAIEtIugEAAAAAyBKSbgAAAAAAsoSkGwAAAACALCHpBgAAAAAgS0i6AQAAAADIEpJuAAAAAACyhKQbAAAAAIAsIekGAAAAACBLSLoBAAAAAMgSkm4AAAAAALKEpBsAAAAAgCwh6QYAAAAAIEtIugEAAAAAyBKSbgAAAAAAsoSkGwAAHBTHMWpoi0iSGtoichyT44gAABh58i7pXr16tWpqauT3+7VgwQI999xz+23/0EMPadasWfL7/Zo7d64effTRfvdfeOGFsiyr37/ly5dn8yUAAJD36pq79OMnt2j1E3WSpNVP1OnHT25RXXNXjiMDAGBkyauk+8EHH9QVV1yha665Ri+99JKOPvpoLVu2TM3NzQO2f/rpp3X++efroosu0j/+8Q+tWLFCK1as0MaNG/u1W758uXbs2JH596tf/er9eDkAAOSluuYu3ftUvTY2dqgk4JEklQQ82tjYoXufqifxBgCgj7xKum+99VZdfPHFWrVqlY488kjdfffdKigo0M9//vMB299xxx1avny5rrzySh1xxBG64YYbdNxxx+lHP/pRv3Y+n08TJkzI/CsrK3s/Xg4AAHnHcYzWbmxSaziumZVBBf1uSVLQ79bMyqBaw3Gte62JqeYAAPTIm6Q7Ho/rxRdf1JIlSzLHbNvWkiVLtGHDhgEfs2HDhn7tJWnZsmX7tH/yySdVWVmpD3zgA7rkkku0e/fuQ/8CAAAYBRraI9qyK6SJJX5ZltXvPsuyNLHEr7rmkBraIzmKEACAkcWd6wCGqqWlRalUSlVVVf2OV1VVadOmTQM+ZufOnQO237lzZ+b28uXLdfbZZ2vatGnasmWLvvOd7+gjH/mINmzYIJfLNeDzxmIxxWKxzO3Ozk5JUiKRUCKROKjXl029MY3E2ICRiD4DDK6zO6pEMqGgxyvLpGSZlCRlvhZ6pJZkIt2uyJPLUIERh88XYOjyob8MNba8Sbqz5bzzzst8P3fuXB111FGaMWOGnnzySX34wx8e8DE33nijrrvuun2Or1u3TgUFBVmL9b1av359rkMA8gp9BhjYkqCkWP9jNdEtme9rg1Ldi42qe3/DAvIGny/A0I3k/tLd3T2kdnmTdFdUVMjlcqmpqanf8aamJk2YMGHAx0yYMGFY7SVp+vTpqqioUF1d3aBJ91VXXaUrrrgic7uzs1NTpkzR0qVLVVxcPNSX9L5wHKN3d4e08fm/ac78kzV5XFC2bR34gcAYlkgktH79ep1++unyeBipA/pyHKOf/X2rXt/RqRnjC2XLUU10i+r9M+TI1pZdYc2eVKzPL57G5w2wFz5fgKHLh/7SO+P5QPIm6fZ6vZo3b54ee+wxrVixQpLkOI4ee+wxXXbZZQM+ZuHChXrsscf0ta99LXNs/fr1Wrhw4aA/591339Xu3bs1ceLEQdv4fD75fL59jns8nhH1hqhr7tLajU2q39Wpk/zS3X+rV834Yi2bU6XayqJchweMeCOtTwMjxdK5k9TQGddbuyKqLvZKkrpiRg2dEZUX+nX6nEny+bw5jhIYufh8AYZuJPeXocaVN4XUJOmKK67QT3/6U91333164403dMkllygcDmvVqlWSpJUrV+qqq67KtP/qV7+qNWvW6D//8z+1adMmXXvttXrhhRcySXooFNKVV16pZ555RvX19Xrsscd01llnqba2VsuWLcvJazxU2M4FAJAttZVFWrW4RnMmlagjkl7P1hFJaG51iVYtruHCLgAAfeTNSLcknXvuudq1a5euvvpq7dy5U8ccc4zWrFmTKZa2bds22fae6wiLFi3SAw88oO9973v6zne+o5kzZ+rhhx/WnDlzJEkul0v//Oc/dd9996m9vV2TJk3S0qVLdcMNNww4kp0v9t7OxZYjRXq2c/F7tbk5pHWvNWl6BVPNAQAHp7aySNNPC2pbS5de2bBdl36wVodVFPG5AgDAXvIq6Zakyy67bNDp5E8++eQ+x8455xydc845A7YPBAJau3btoQxvRNhnO5c+W6XuvZ3LlPKRW/gNADCy2bal6rKAXpFUXRYg4QYAYAB5Nb0cQxOOJxVNplTgHfiaSsDrUiyZUjiefJ8jAwAAAICxhaR7FCr0uuV3u9Q9SFIdiafkc7tUOEhSDgAAAAA4NEi6R6Hq0oBmjA9qR0dUxph+9xljtKMjqtrKoKpLAzmKEAAAAADGBpLuUci2LS2bU6XywnTRtFA0PeIdiia1uTmk8kKvls6uYu0dAAAAAGQZSfcoxXYuAAAAAJB7LOodxWori1RzSqFeqN+lpte265PzqnV8zXi53VxrAQbjOEYNbRFJUkNbRIdVuJkVAgAAgING0j2K1TV3ae3GJtXv6tRJfuk3LzboxW1dWjanipFuYAB1zV1a8+pOvdHQpqUl0s1rNumI6jItnzuBPgMAAICDQtI9StU1d+nep+rVGo6rutgrGakk4NHGxg41dkSYYg7spa65S7f/ZbPeauqSyzhSifR2S1ibd0e0qalLX1sykz4DADhozKQCxi7mGY9CjmO0dmOTWsNxzawMKuhPX1sJ+t2aWRlUaziuda81yXHMAZ4JGBscx+iBZ7bple3tSjmmX59JOUavbG/Xr57dRp8BAByUuuYu/fjJLVr9RJ0kafUTdfrxk1tU19yV48gAvB9IukehhvaItuwKaWKJX5bV/wqqZVmaWOJXXXNIDe2RHEUIjCzb27r1zNZW2ZalcYVeeXvqHnjdtsYVemVblja83artbd05jhQAkG96Zx9ubOxQScAjac/sw3ufqifxBsYAku5RKBxPKppMqcA78OqBgNelWDKlcDz5PkcGjExbW8Jqj8RVWpA+GYonUv2+lhR41BGJa2tLOGcxAgDyD7MPAUgk3aNSodctv9ul7kGS6kg8JZ/bpcJBknJgLLKMFE0k1dge1bvtUUnSu+1RNbZHFU1wgQoAMHzMPgQgkXSPStWlAc0YH9SOjqiM6X/l1BijHR1R1VYGVV0ayFGEwMgyvaJQfq9LDe1RhWMJuV3pEyO3y1I4llBje1R+j0vTKwpzHCkAIJ8w+xCARNI9Ktm2pWVzqlRe6NXm5pBC0fQf8lA0qc3NIZUXerV0dhUVM4Eek0oCKg14lHKM9p7gZySlHKOyAo8mlXChCgAwdMw+BCCRdI9atZVFWrW4RkdOKNLbu0KSpLd3hTR7YhHbhQF72dEZVVmhVxOK/bIsKRJLr+WOxFKyLGlCsV+lBV7t6IzmOFIAQD7pO/vQcRx1RdLJd1ckKcdxmH0IjBEk3aPYO7u79Wx9q7a0pJPuLS0hPbO1Ve/spgIz0Fc4npTXbevISSUK+tyZZRnGGAV9bh0xqVg+t830PwDAsPTOPnTZlta+3qSnt7RIkp7e0qK1rzfJZVvMPgTGAJLuUeqxN5p04583aXNzSKUBrySpNJCebn7jnzfpsTeachwhMHIUet2KJx3VNXfJ63ZpclmBJGlyWYG8bpe2NIcUSzpM/wMAHLyB1i8BGBNIukehZNLRL56qV1c0ocPKAv22pzisLKCuaEL3PV2vZNLJcaTAyDCx2K9YwlF7JKHyAo+CgZ4+E3CrvMCj9khC8aSjicX+HEcKAMgnvVuGpRyjZbOrtGhGhSRp0YwKLZtdpZRj2DIMGANIukehl7a3qX53WOMKvbLt/v/Ftm1rXKFXW1vCeml7W44iBEaWHZ1R+Ty2ygo8autOKJ7s2ac7mVJbd0KlAY+8bps13QCAYem7ZZht2yrquahbFHDLtm22DAPGCJLuUWh3OK5EylHA6xrw/oDXpUTK0e5w/H2ODBiZetd0H3dYmSqL/Iom0iMO0YRRZbFf86aWsaYbADBsfbcMM8b0K6RmjGHLMGCMYIHiKDSu0CuPy1YknlKRf9/rKpF4Sh5XesQbwJ4tXfwel46vKVM4EpfUrhNqylUY8CoUSyqaYE03AGB4ej9fGtu7tbMjpq5ITEfVSM/Vt6oo4NOEEh9bhgFjACPdo9BxU8pUM65Qu8NxOU7/dduOkx7hnlZRqOOmlOUoQmBk6b+li1EonpAkheIJOY5hSxcAwEGpLg2otMCj5+vb1NQZkd+TrlLu91hq6ozo+fo2lRZ4+HwBRjmS7lHI7bZ14eIaFfk92tYWUSianrIUiia1rS2iYr9Hn1tUI7eb/35A2rOlS3c8pV89v11/eT1d3f8vrzfpV89vVySeYksXAMDB6a2RZu31GdJzm08WYPRjLsso9eEjqiRJv3iqXo1t6X26Q7GkPlBVpM8tqsncDyDtnd3d2toSTq+9c6VPgWzLUncypbdbwnpnd7dqK4tyHCUAIJ80tEfUHklofk2ZdnTEFIrEJKVrhlQV+zWh2Ke27oQa2iOaUl6Q42gBZAtJ9yj24SOqdOrM8XqhfpeaXntGP/jEHB1fM54RbmAvvdvsxZIpHVEVlGUcSTHVjCuQsWxtb4/qvqfrdepM+g8AYOh6C6lNr0gvUWru6JbUriMnFqmypECOpPqWMIXUgFGOs8dRzu22NW9quSRp3tRyEgZgAH232XO5XPJ60pX/vR6XXC4X2+wBAA5K30JqL77Trtd3dEmSXt/RpRffadeO9giF1IAxgAwMwJjHNnsAgGygkBoAiaQbAPptszcQttkDABw0CqkBYx5JN4Axj232AADZ0LeQWmWRX9FEOgPvLaQ2v6YsU0gNwOhF0g1gzGObPQBANvQWUptUWqD5NWU6oSZdZ+eEmnIdP7VME0sDiiVTFFIDRjmqNgCA2GYPAHDo9RZS644nVeT3qCjgliJSUcAtY1mKxJIUUgPGAHo4APRgmz0AwKFUXRrQjPFBbWzsUNDn7rd+2xijHR1Rza0uoZAaMMpxJgkAfbDNHgDgULFtS8vmVKm80KvNzaF+y5c2N4dUXujV0tlVsm3KqQGj2Xsa6U4mk7rnnnv05JNPKpVKafHixbr00kvl9/sPVXwAAABA3qqtLNKqxTVau7FJ9bs6Jb/UEUlobnWJls6uUm1lUa5DBEYcxzFqaEsXGGxoi+iwCndeX5x6T0n35Zdfrrfeektnn322EomEfvnLX+qFF17Qr371q0MVHwAAGKFG20kRkC21lUWaflpQ21q69MqG7br0g7U6rKKI/gIMoK65K3OR6iS/tPqJOtWML9ayOfl7kWpYSffvfvc7feITn8jcXrdund588025XC5J0rJly3TiiSce2gjxnnBCBADIhtF4UgRkk21bqi4L6BVJ1WUBzseAAdQ1d+nep+rVGo6rutgrGakk4NHGxg41dkS0anFNXn7GDGux4s9//nOtWLFCjY2NkqTjjjtOX/rSl7RmzRo98sgj+ta3vqX58+dnJVAMX11zl3785BatfqJOUvqE6MdPblFdc1eOIwMA5LPek6KNjR0qCXgk7Tkpuvepej5nAADD5jhGazc2qTUc18zKoIL+9Phw0O/WzMqgWsNxrXutSY5jchzp8A0r6X7kkUd0/vnn67TTTtOdd96pn/zkJyouLtZ3v/tdff/739eUKVP0wAMPZCtWDEPvCdGrDR1y91xJdduWXm3ghAgAcPD6nhTVji+U6Tn3MUaqHV+Y1ydFAIDcaWiPaMuukCaW+GVZ/WeCWJaliSV+1TWH1NAeyVGEB2/YZXnPPfdcPffcc3r11Ve1bNky/cu//ItefPFFvfzyy1q9erXGjx+fjTgxDL0nRNtau9XRHdfGxk5J0sbGTnV0x7WttZsTIgDAQek9KQp4bL3wTpue3tIiSXp6S4teeKdNAY+dtydFAIDcCceTiiZTKhhk3/qA16VYMqVwPPk+R/beHVQhtdLSUv3kJz/RX//6V61cuVLLly/XDTfcQNXyEaKhPaJ/bG/Trq6okimjskD62orfY2lXKCaXbemlbW1qaI9oSnlBjqMFAOSTcDypllBMje0RtYbj8trpC7ht3XE1hRJq7oqpujSQlydFAIDcKfS65Xe71B1Pqsjv2ef+SDwln9ulwkGS8pFsWCPd27Zt06c//WnNnTtXn/nMZzRz5ky9+OKLKigo0NFHH60///nP2YoTw9AVTWjb7m4lko7KC73yutOF7rxul8oLvUqmHG1v7VZXNJHjSAEA+abA41JDe0Q7O6MyxsjTs5e9x23LGKOmzqga2iMq8LhyHCkAIJ9UlwY0Y3xQOzrSny99GWO0oyOq2sqgqksDOYrw4A0r6V65cqVs29bNN9+syspKffGLX5TX69V1112nhx9+WDfeeKM+/elPZytWDFEollQkkZLP4xpwPYTP41J3PKVQjFEIAMDwOMaoM5KQ4xj5Pa5+dUP8HpdSjlFXNCHHsIQJADB0tm1p2ZwqlRd6tbk5pFA0nauEokltbg6pvNCrpbOr8rLy/7DG5l944QW98sormjFjhpYtW6Zp06Zl7jviiCP017/+VT/5yU8OeZAYnqDfnV7zkHAU9Bmpz/vSGKNYwlGB15WpCAgAwFDV7+6WJcnvdSmadDJJd9IxiiaNAl5Xpl1NRTCHkQIjC9u4AgdWW1mkVYtrMltSyi91RBKaW12ipbPzd0vKYWVd8+bN09VXX63Pfe5z+stf/qK5c+fu0+YLX/jCIQsOB6fI59Fh5QXa3tqt1nA8s6Y7nkypLeLI7bY1pSygIt++ayUAADgQt9tWmc+tcCypZColSUqmjAq9bhX4XAozkwroh33tgaGrrSxSzSmFeqF+l5pe265PzqvW8TXj5XYPuwb4iDGsyH/5y18qFovp61//uhoaGnTPPfdkKy68B9WlAR07pUyVRX6NL/IpmkhP8YsmjMYX+VQZ9Om4w8rycj0EACC3plUUqjTgVTThaGKJX5NL00VUJ5f6NbHEp1jCUUnAq2kVhTmOFBgZ2MYVGJ665i7d89e39ZsXGyRJv3mxQff89e287ivDGumeOnWqfv3rX2crFhwiveshGjsi2h2KaWqZT1K7Zk8qUnvU0bigL2/XQwAAcmtKWYFOnFau9W80qbU7ofKe2VSypNbu9FruhdPLNaWM3TGAvtu4JpOOGlrjOrEmvY1r0O9VOJ7UuteaNL0iyHkZoD0XqVrDcVUXeyUjlQQ82tjYocaOiFYtrsnL2SEHtai3s7NzwOOWZcnn88nr9b6noPDe9a6H+PM/d+gf7+yWKqTmzpiOqxmn5XMn5uWbFQCQe7Zt6YITD1NzKKY3d3aqrTu9E0Zbd0K27dLRU0p1/oLDSCAAsY0rMBy9F6law3HNrAzKliNF0vWqZvrTxdXy9SLVQU2MLy0tVVlZ2T7/SktLFQgENHXqVF1zzTVyHOdQx4theGd3t56tb9WWlpAkaUtLSM9sbdU7u7tzHBkAIJ/VVhbpE8dWq6rIr0Qy/VmfSDqqKvbrE8dWc2EX6LH3Nq7qLepvxDauwF4a2iPasiukiSXpZUtdkWS/rxNL/KprDqmhPZKzGA/WQY10/+IXv9B3v/tdXXjhhTrhhBMkSc8995zuu+8+fe9739OuXbt0yy23yOfz6Tvf+c4hDRhD89gbTbrxz5vUFU1oQtArqVulgfQVohv/vEmS9OEjqnIbJAAgL9U1d+nxTc0K+j1aNKNC0jYtmlGhtqijxzc1a+q4AhJvQHu2cfW6LTW2R5VKJaXJ0rvtUblcbvk8Ftu4Aj3C8aSiyZSiCVubdnSpKxLTUTXSc/WtKgr4VFNRoFgypXA8//rLQSXd9913n/7zP/+z357cZ555pubOnat77rlHjz32mA477DD94Ac/IOnOgWTS0S+eqldXNKHDygKSk64s63VZOqwsoG1tEd33dL1OnZnfVQABAO+/vtP/Dq/aM/2vqsSv8SV2Xk//Aw61oN8tl22puSsml2WpwJPuE5YlhWIJdUSNxhX62MYVkFTodSuedPTStjYlUkbBnv5iW1JTV1S7wzFNKS9QoTf/+stBZVxPP/20jj322H2OH3vssdqwYYMk6aSTTtK2bdveW3Q4KC9tb1P97rCCPpd2dMT0bntUUvqq6o6OmII+l7a2hPXS9rYcRwoAyDd9p/9ZVv+k2rKsvJ7+BxxqQa9bLktKOVIs6SjUM408FE0olnSUcozcVrodMNZNLPYrlnC0qyuuaDyp5q6YJKm5K6ZoPKmWUFzxpKOJxf4cRzp8B5V0T5kyRT/72c/2Of6zn/1MU6ZMkSTt3r1bZWVl7y06HJTd4bgi8ZQ6uhMKx5PqPSeyrPS0jY7uhCLxlHaH47kNFACQd3qn/xUMkiQEvK68nf4HHGpGSp+AGaOU48j0rOk2Rko5jmQkY1mZpd7AWLajM6qk4yjpOGqP9M9h2iNJJR1HiZSjHZ3R3AZ6EA7qstott9yic845R3/+8581f/58SdILL7ygTZs2ZbYUe/7553XuueceukgxZOUFHsVT6Tesy7LUnUoXuemOJZWSrZgx8ti2ygs8OY4UAJBvCr1u+d0udceTKvLv+zkSiafkc7vycvofcKiFY0klU0aWZfXMDNmTXvceSzlGYdZ0A+qKJbQ7HFexzyMjo1QqvUTWGKmswCNLllrDcXXF8q/w4EF9In784x/Xpk2bdM899+itt96SJH3kIx/Rww8/rJqaGknSJZdccsiCxPBUFvvlsi2FY45cthT0pCc02Jal7kRKKUfyB1yqzMOpGQCA3KouDWjG+KA2Nnao0OtSd3RPddkCv6UdHVHNrS5RdWkgx5ECuReKJRVLpuSyLTnGkm2lk27bkmxZctmWogkKqQGSFIomFYmnVBRwK+hzy6SSkiKaXOqX5XIrFEuqK5pUKJp//eWgL0NPmzZN//Ef/3EoY8EhEomnFPC4FIol5RijZM9IdzLlyDHpPVb9Hpci8VSOIwUA5BvbtrRsTpXe2Nmpta81ySVHR82Qnt7SopRsHT6hSEtnV1FEDZBU4HMpkTJKOUalAbfcliQlVRzwKmmkzmhKyZRRgc+V61CBnAv63Ap4XIolUgr69k1TY4mUCryuAe8b6Q464vb2dj333HNqbm7eZz/ulStXvufAcPBCsaRctqUSn1ttkYRiTvqqaswxMsZSaU8lTa6qAgDek/6zZdO3AWR0x1LyuCylHEvRpFHQ27tIVYomjDwuW26Xpe4YAyFAkd+jw8YVqK45pLrmkLy2kSZL29siijuWigMeTSsvGHBp00h3UEn3I488os985jMKhUIqLi7uV73UsiyS7hwL+t1KOUYdsaRkpJ7Z5fLYUjIldcSS8npcbE8BABi23i3DUo7RsiOr1B1NSOrUohkVKvB7VLcrzJZhQI+gz61iv0ce25IjKZ5ID3jEE44KvG7Zkgp87rwcuQMOterSgA4rL9DGhg6ZvcoLGhlFEykdVl6Ql8uXDqp6+Te+8Q19/vOfVygUUnt7u9ra2jL/WltbD3WMGKaAx6VIIqVUyshYUs9AtxwjGUtKpdJv2oCHqUwAgOHpu2WYbdsqCqSThaKAW7Zts2UY0EfvyJ3LZSsST8npKV/uGKNIPCWXy9aUPB25A7LCSB6XrbKAV0U9A4RFfrfKAl55XHbeTqg6qKS7oaFBl19+uQoKCg51PDgEmjujiicd2XbPzL8+21NYkmxbiicdNedhuX0g2xzHqKEtnSw0tEXkOGzkAvTVd8swY4y6InsKqRlj2DIM6KN35C6aSMkYI1fP7A+XbcmY/B65Aw61hvaI2iMJTS7za3c4lrl429Ae0e5wXJPL/GrrTuTlRd2DmsuybNkyvfDCC5o+ffqhjgeHQEs4LmOMLFlKmT2TM4zSo91uy5JjjFrYpxvop665S2s3Nql+V6dO8kurn6hTzfhiLZtTpdrKolyHB4wIvVuGNbZ3a0d7VG3hqI6aIT21pUVlhX5NLPWzZRjQV8/IXaHXraDHkhRVVZFfoYRRPOXk7cgdcKiF40ltaw1r044udSdSmSWyKSNFY0m9+m6nEimTlxd1h/yJ+Ic//CHz/RlnnKErr7xSr7/+uubOnSuPp/+UmI9//OOHLkIMmzEmPZW8Z4i7b9K9534rcz+AdMJ971P12tUZkeOkJL/U1h1Tx/ZWNXZEtGpxDYk3oPTIXWmBR4++ukOxpCOfnf4sae+OqymU0NbdYX107kRG7gDtGbmbX1OmHR0xhSIxSZIjqarErwnFvszI3ZRyZpBibPO7bW1uCincs8OSuyfpdttSPCWF4yltbgrJ7z6oydo5NeSke8WKFfscu/766/c5ZllWZiNz5MbUskIZGSWN5LLS/6T0V9uSkkZyy2hqWWFuAwVGiN7CUP/Y1qaGtm4lU0mdWS5teHu33C63qsvSU/8oDAWktYfj6urZJzXgT5/82LaleNxRPOmoo5uZVIC0ZznG9IqgJpcVKByJS2rXCTXlKgx4lTJG9S3hvBy5Aw61xvaIQrGkjCS3tWdDDEvpPCZp0rs0NbZHVFMRzGGkwzfkywSO4wzpHwl37oUT6S3DLKWnY6R6BrR7v0+v67YUTvAHHpDSIxHrXt+puuaQQrGU3D07MrgtS6FYSnXNIa19bWderiECDrXtbd3a1BRSScCjkoBbqZ4PmVQqvQ9xScCjN3aGtL2tO8eRArnXuxyje5CkOhJPsRwD6PHCO21yenKVlJGSPbtSJ509OYxj0u3yzbDG5h9//HEdeeSR6uzs3Oe+jo4OzZ49W3/7298OWXA4OJFESpasQdcIWZIsWYokuEACSFJbJKa3mrqUcoz8blseV/pPo8dly++2lXKMNjd1qa1nWiAwlm1tCas9EleR3y3bsvqNRFiWpaDfrY5IXFtbwrkMExgRqksDmjE+qM3NIT2/tVXP1ad3+XmuvlXPb23V5uaQaiuDLMcA+ujdjXrvJbJWHk82HFbSffvtt+viiy9WcXHxPveVlJToi1/8om699dZDFhwOjjHprSiM0m/O3v9ku+d2uqCaEUu6gbR/vNOmeNKR27b2mT5u25bctqVY0tE/8vDKKpANyZSjXV1RheMpeXoq3Xg8tsLxlFq6okqknBxHCIwMtm1p1sQi7eiIaktLSMmevpFMOdrSEtLOjqg+MKGIpUuApOOmlsru2e7Ytvqv6e57/LippTmN82AMK+l+5ZVXtHz58kHvX7p0qV588cX3HBTem4DHluOY9JvVSm8RJqW/unvWdTuOUcCTf0UIgGyIJXuKDvZcrOq3t732FCXsbQeMZTXlBTJGiiQcBTy23D3Jgtu2FPDYiiScTDtgrHMco007ulTid8vnttUSSs+YagnF5HPbKva79ebOLranBCRNLilQoS+91GKg6eWSFPS7Nbkk/z5fhpV1NTU17VOpvC+3261du3a956Dw3kSTjtwuW7Yl2ZYlT0/W7bFt2ZaVTsZdtqJJRiIASZpU6pfHZcsovTwjmkwvvYgmU4okUjJKTzWfVOrPaZzASGDZlooDHrltS93xlJI9yULSMeqOp+SyLRX5PbIYuQPU0B7RP7a3KRRLyutyqawgfR5dVuCR1+VSKJbUS9vaqBkCSIqmHE0pD2iwjw/bkiaXFSiah7OphpV0V1dXa+PGjYPe/89//lMTJ058z0HhvbFkqdDnUoHXla4m33NClHKMLNtSgdelQp9rP6u+gbFl6awJKi/0KGkks9ffceOkq2WWF3q0dNaE3AQIjCCRRErVpQFVFfvTFct7RrbjCUe2bWlCiV/VpQHqhgCSumIJbWvtVkckoebOiBo7opKkxo6omjsj6ogktL21W12xRI4jBXKvwONSNOHI67LkGqB6ucdlKZpIqcDjymWYB2VYpRI/+tGP6vvf/76WL18uv7//iE8kEtE111yjj33sY4c0QAzf9IpCVQR9aumKyZiUnJ4kwkjy2pZ8bpfGB32aXsGWYYAkud22jpxYrOauFhmZftXLrZ4ZI7MnFcudh/tCAodaodetiqBPFUGvdnRE1R6KSoqqvNCr0qBfE0v8kiyqMQOSQtGkWsNxtXfHlXIkv2vPQEg05SiSjCnpGIWi7CgDOMaoM5KQbVmqLPLKkiMpovJCr4xstUcS6oom5ORhYaphfSJ+73vf029/+1sdfvjhuuyyy/SBD3xAkrRp0yatXr1aqVRK3/3ud7MSKIZuclmBPjChSO+0dstxHJX43JKSCvrc6oillEpIJ00o1uSy/FsPAWRDQ3tExQGv5teU6vWGTiVS6ZOfpDEK+jw6clKRivxeNbRHNIV1qhjjeqsxb2zs0PFTy9QdTUjq1KIZFSrwe1S3K6y51SVUYwYkBdwuhaKJzNrUvtWYjdJrVUPRhALu/Bu5Aw61+t3dsiT5vS5FEo78drrHOCmjqOMo4HVl2uXbPt3DSrqrqqr09NNP65JLLtFVV12VKS5kWZaWLVum1atXq6qqKiuBYnjKAl4V+T2KJVJK9Oydnkg5Cnjc8nr2rCkCIIXjSUWTKU2rKJLjGDW1d0vqVmXQp6rSAtVUBNUZSSg8yD6rwFhi25aWzalSY0dEm5vDKvXbkjfdjxo64xoX9Grp7CqqMQOStraGlUjtf1QukTLa2hrW3Cml709QwAjmdttyG6mlK6qIle47bdGEjLFUUeTP223Dhj1XcurUqXr00UfV0tKiZ599Vs8884xaWlr06KOPatq0admIEcPU0B5ReyShaeMCchwns64ukkgp5TiaNi6gtu4ERTuAHoVet+JJRy++06rd4YTGF6eXz4wv9mt3OKGX3mlTLOkwXRboUVtZpA/NqlRXNK6/1aULqP6tbpdC0bg+NKtStZVFOY4QGBl2h2JyTM8+9nvd13vMMel2wFg3raJQLstSU2dU8b1q7MQdqakzKtuyNC0Pl8ge9BlkWVmZ5s+ffyhjwSESjie1rTWsLbvSV1fTiUJKhV63wgmjTTtDSqQMo3ZAj4nFfsUSjtojCR1WFpCcngJQxqiswKNtbRFVJR1NLKZ6OSBJdc1d+uWGem3a0SXHSX+WdMeSemNH+vjUcQUk3oCkeM9Wk70TP1x9vtpW77aUe9oBY9nEIr9CsaQGmxySMlI4ltTEovw7H6Mq0Cjkd9t6Z3e3IomULJl+I92WjCLxpLa1dstPUShAkrSjMyqfx1bA41Jdc0hv7wpLkt7eFVZdc0gBj0tet60dndEcRwrknuMY3fVEnV6ob1M06SjQMwMk4HUrmnT0Qn2b7nqijn2HAUkzKwvlcVkyRnJZVr813S4rnXV7XJZmVubfyB1wqL2wvVXh2J5BQWuvr5IUiiX1wvbW9zWuQ4GsaxTa2RlVJJFSLOEonDBK9Jz4JByjcMIolkzvpbqTBAKQlJ4dEk86iiWS6oolFeq5UBVKpNQVSyqWSN/P7BBA2tYa1t82t8gYo2K/q19lqGK/S44x+ntdi7a1hnMaJzAS1FYWqao4vQ414Rj1Fl02Jn3bsqSqYj8zQwBJL9S3KZky8rkkr8vKzBCxrfRtn0tKpoxeqG/LbaAHgaR7FNodiiuWcDTYGIORFEs42h2Kv59hASNWgcelrbvCag6lt3Tpe2U15UjNobjqW8J5uS8kcKg9X9+mUCwpl8vS7nBCbZH0Z0lbJK7d4YTctqWuaFLP5+FJEXCoTS4r0PE1ZXK7Bj7l9rhsza8pZ0cZoJeVLtLtcVny9vQbr8uWx2XJsqx9iyPkCaoCjULxVCozuj2YhGMU76lqDox1CcfRrnBMqUG2dEk50q5QTAnHGeQZgLEjmkgpmTKKJdL9wd1zJmEpPQKRTKXksi1FE3zGAFJ6R5myAq9iiWRPzZCU/G5bfq9bPnaUATLm15TLY1uKJ43ctiRX+nMm4ThKpGwlHSOP29L8mvLcBnoQGOkehXYMsSr5UNsBo91L77Qpnth/Qh1LOHrpHUbugBnjC+UYI0fpi1K913gd03NbkmOMZoxnjSrQu6PMnOoiBX3ufhd1gz6X5lQXsaMM0GP+1HJNLgvIaODlGEbS5NKA5k/Nv6Sbke5RqLF9aGu1h9oOGO0a26M60Bi2I/oMIEnqqbqcMntmg0j9v7cH2h8JGIPC8aRaQjHV7w5rVygmy6Q/bWJJR9FQTFaTpZpxhdQMASTZtqWjppTq3fao4kmn30VdSfK6bR09pUy2nX8fMIx0j0KO2f/U8uG2A0Y7r3tof7yH2g4Yzdq6E+l1dfthWZbauhPvU0TAyBXwuLS1JaR32yKKJk3mAq8jKZo0erctoq0tYQWoGQKooT0iS5aOmFAkr6t/tX+vy9KsCUWZdvmGke5RyDvEqz9DbQeMdmWF3kPaDhjNUo6RMUaWNGDBTkuSMUYptgwDlEo62tkZ02DdwTFSU2dUqSQ1Q4BwPKltrWFtbu5SPGXk7XMtKp4yqmvuksdl5+XMkLwb6V69erVqamrk9/u1YMECPffcc/tt/9BDD2nWrFny+/2aO3euHn300X73G2N09dVXa+LEiQoEAlqyZIk2b96czZeQdQcYgBh2O2C0Gx/06UCD2G4r3Q4Y6wq9LhkzcMIt9UwzN+l2wFj37DutSqT2fwEqnjJ69p3823cYONT8LltvNYUUjqcvQrl6zs16v4bjjjY3d8k/yG4AI1leRfzggw/qiiuu0DXXXKOXXnpJRx99tJYtW6bm5uYB2z/99NM6//zzddFFF+kf//iHVqxYoRUrVmjjxo2ZNjfddJP+67/+S3fffbeeffZZFRYWatmyZYpG83ftptc9tBOdobYDRrtCn1t+ryvzR31vtiS/16VCH5ODAGnwhHuo9wNjxVALcFKoE5De7ehWOJYexTZK1w6R9tQQkaRQNKl3O7pzEt97kVdJ96233qqLL75Yq1at0pFHHqm7775bBQUF+vnPfz5g+zvuuEPLly/XlVdeqSOOOEI33HCDjjvuOP3oRz+SlB7lvv322/W9731PZ511lo466ij98pe/VGNjox5++OH38ZUdWr4hrgsaajtgtCv2ezSu0Cev29bevcIlyeexVRH0qdjPti5AVyypAwzcKWXS7YCxrj0cP6TtgNHspXfah/T58tI77e9LPIdS3gzbxONxvfjii7rqqqsyx2zb1pIlS7Rhw4YBH7NhwwZdccUV/Y4tW7Ysk1Bv3bpVO3fu1JIlSzL3l5SUaMGCBdqwYYPOO++8AZ83FospFotlbnd2dkqSEomEEoncF45JJhLyufa8Y3226fe1b7uREC+QawVuS4eVeRWOROWSkbenrxS6jeK25HNLU0q9KnBb9BmMebs7w0P6jNndGaa/YMwbV+AeUn8ZV+CmvwBOakj9RU5qxPSXocaRN0l3S0uLUqmUqqqq+h2vqqrSpk2bBnzMzp07B2y/c+fOzP29xwZrM5Abb7xR11133T7H161bp4KCggO/mCz7gKSbTtj3+A3H71WkI/m2Hn307fclJmCk+1SlpMr+x645rrfPpCS16JUNT+iV9zkuYKSp0BA/Y9o26dFHB/58BsaKhT5p4VD6i7br0Ue3vy8xASPVVA3x86X7LT366FvvS0wH0t09tKnueZN0jyRXXXVVvxH0zs5OTZkyRUuXLlVxcXEOI0v77Uvv6tpHXstUyvTZRjcc7+j7L9iKOelFq7YlXXvmbJ193OQcRgqMDA1tEX3/96/qn+92KJp09ukzfpeto6aU6Iaz5qq6LJDrcIGcWvWLZ/R8fUfm9kCfMZI0v6ZE9154Yi5CBEaMP77coG8/vKeW0GD95T9WzNHHjqnORYjAiPHbl7br6j+8nrk9WH+5/uNH6uzjpuQixH30zng+kLxJuisqKuRyudTU1NTveFNTkyZMmDDgYyZMmLDf9r1fm5qaNHHixH5tjjnmmEFj8fl88vn2rWLs8Xjk8eR+zeeCGePlcXvUGe2/ni7mWIql0m/YEr873W4ExAvkWneqW5uautURM0pveJTW22diKaNNTd3qThn6DMa8Ap9fsdS+Jxl9P2N629FfMNa9uTvcr1/02ru/vLk7rE/QXzDGRVIaUn+JpDRiPl+GGkfeFFLzer2aN2+eHnvsscwxx3H02GOPaeHChQM+ZuHChf3aS9L69esz7adNm6YJEyb0a9PZ2alnn3120OfMB5NLCzS5LCDbSqcPvW/R3u9tS6ouC2hyae6nwgMjQWs4pt2hdBGbvf/U997eHYqrNRwTMNbNm1p6SNsBo9n/29RySNsBo1lqiNvVD7XdSJI3SbckXXHFFfrpT3+q++67T2+88YYuueQShcNhrVq1SpK0cuXKfoXWvvrVr2rNmjX6z//8T23atEnXXnutXnjhBV122WWSJMuy9LWvfU3/9m//pj/84Q969dVXtXLlSk2aNEkrVqzIxUs8JHZ0RjWpNKCKoFe2tafEvlE64a4IejWpNKAdnfm7LRpwKG3c3qHev997F83sve30tAPGuuKCoV3VH2o7YDSLxodWZGmo7YDRrCs6tH4w1HYjSd5ML5ekc889V7t27dLVV1+tnTt36phjjtGaNWsyhdC2bdsm295zHWHRokV64IEH9L3vfU/f+c53NHPmTD388MOaM2dOps23vvUthcNhfeELX1B7e7tOOukkrVmzRn6//31/fYdKOJ5UPOnI73bJ47Jk9aQNtiV5XJb8bpfiSUfhONu5AJLU0DG0C1BDbQeMZrHE0IYYhtoOGM1KCzxS64FnSZVykQqQte/M8vfUbiTJq6Rbki677LLMSPXennzyyX2OnXPOOTrnnHMGfT7LsnT99dfr+uuvP1Qh5lyBx6WG9oh2hdJ/5N0970y3ZSlhpF2hmDxuWwXs0w1IkqpLh1YcbajtgNHMDDGXHmo7YDSbXV2if7wbGlI7YKybUj60pa9DbTeS5NX0cgyNY4xaw3HFk44cY+Sy00m3y7bkGKN40lFbOC7HHGD3eWCMmDu55IB/DO2edsBYZ7uGNsQw1HbAaFZZPLTkYKjtgNGsxO8Z0vlYiT//ZoaQdI9Cb7eEFUs4sqx0oYF4T7WBeMpRyklPyYgmHL3dEs5xpMDIUFLgkecACYLHZamE6X+A/G7XkE6K/G5mUwHzp5bLe4DPF6/L0vyp5e9TRMDIZXTgqeOWtW/9nXxA0j0K7Q7FlTKO7EHetJYlpYyTqdYMjHWdkcQBZ344xqgzkn+FO4BDbUppYEgnRVNYjgFofk25ppTtfxR7SnmB5teQdAPdsZRSB8ioHZNul29IukehskKPZCTHWPK5Lfnd6f9mv9uWz23JmPQlorJCRu0ASWpojchIcmngLcNcSl9VbWiNvN+hASNONOXI2/O5YknqHcRzWXv6j9dtK5qPe7oAh5htWzp6con8bnufwRDbSp+bHTO5RPZgIyXAGNI6hAFBM8R2Iw1J9yhU5HPL7bJlKZ14Oz1XjJyeRNyS5HHZKvLlXR09ICuiSUeWrMy2YX2TCCm9XZglS9EkSQRgW5aK/G4FfS7ZttQ7ScQYyWVLQZ9LRX637HwsLwscYg3tEcmydNLMcaou8avAkz71LvDYqi4J6KSZ42RkpdsBY1xT19B2iRlqu5GErGsUKvJ7NL7Ip50dESVSTuaMKJp0lHAseVyWKop8KsrDIgRANtSOD8ruGaVzuSz1XS3kcVlKpoxsO90OGOumVRSqIuhXe3dcxX63EomkpJSCPrc8HrckS6UFXk2rKMx1qEDOheNJRZMpzZpQollVRarf1SVphxbPqFDN+CIZy1J9S5htXAFJFUVDy02G2m4kYaR7FCryezSxxC/b2jPK3csx6VGKiSV+km6gx4RSvwp8blmWlEyZzHqilJESKSPLkgq9bk0o9ec2UGAEmFJWoBOnlctlWwp43JpUkl67PakkoIDHLZdtaeH0A69jBcaCQq9bfrdLje3denFbh97pWab0TmtEL27r0I72iHxulwq9jIMBtjW01HSo7UaS/IsYBzSx2K940lHKSD63JW/POiGvnV7jnU4kHE0sJoEAJCmWdDSpJ6Heu36H6fk3sdSvGNPLAdm2pQtOPExHTymVbUkd0fTauo5oXLYtHT2lVOcvOIw1qoCk6tKASgMePV/fpuauqPw908v9HlvNXVE9X9+msgKPqik8CGh8kU/uA3x2uG1L44t871NEhw5J9yjU0BFReyQhj8uS3+NSsGdEO+j3yO9xyeOy1NadUEMH64cASSrwuLS7Kz5oxUzHpIt2FHjYAgmQpNrKIi2YVq5Y0lFbOJ10t4XjiiUdLZhWrtrKohxHCIwgPTmEMUbxnou38aQj07P8Lx+3PwKyobLIr9ICj3pqdWaKc/Z+9dhSaYFHlUX5N3BI0j0KbW0JK5JIqbo0oKDPk/ljbpROvCeVBhRNpLSVfboBSVIsldLu8P4rYbaE4oql8m+LCiAbHnujSfdteEfRpKOqnllTVcV+RROO7tvwjh57oynHEQIjQ0N7RO3dCc2aUKRkyqixp2BaY3tEScdo1oQitXcnKKQGSDpuSpmOmFCsgMclT5/9uI0kjyX5PS4dObFYx00py2WYB4Wke5SyjOTz2JpU6tfknmmzk0v9mlTiz0xtApD2+Bu7lOxTAGHvK6uSlHSMHn9j1/saFzASJZOOfvFUvdrCcbltqTOSLgDVGUnKbadHvO97ul5JlmMACseTagnF1NgRkcdta1LPNPJJpQF5XLYaOyJqCcUopAZIcrttLZ1dJdOzo4y750TMbaV3kjGydPqRVXK78y+Xyb+IcUDTKwpVUuBRZ3dCkuTtmRLb+7WjO6HSgEfTqSwLSJI6o/HM1dS+tcuN9vyRND3tgLHupe1t2tzcpUQqpdbuhLp7koXueFKt3Qklkim91dSll7a35ThSIPcKPK50Uh1NalyhV0F/umBa0O/WuEKvQtGkdodiLF8CJDmO0dZdYfk9tjyu/mu708tmbdW3hOXsXSk6D5B0j0KTywp04vRxShlpdyimeDI9JTaeTGl3KCbHSAumj9NkKssCkqTxwT0FOfb+M947Vmft1Q4Yq3aFYuqMJNQdSymRdJTsWZeaNEaJpKPueEqdkYR2hWI5jhTIvXTvsGQ0WHGo9H35l0IAh972tm49s7VVliSPbfWfXm5bsiRteLtV29u6cxfkQWJ/glHIti1dsOAwNXfF9NbOLoWi6aQ7FE3JZds6elKRLqCyLJBx3NQyeWwpsZ/ZsG473Q4Y61KOUSyV3iHDZaW3oZT2fE0ayaQcpfJwJAI41CKJlCqCXlmW1BqOqyyQHu+KJ1NqiziZEe9IgpohwNaWsHZ2RBSKJZV0jHw9w8PGSOGEo1gqoZRjtLUlrKnj8mvGLkn3KFVbWaSvLZmpNRt36o2GNkmdmj6+UEdWl2vZnCoqywJ9FPk98rldSsQHP+nxuV3sbQ9IKnDb6hnclmX1r4Fg9azPMCbdDhjrCr1uVQR9qgh6taMjplAkPQMkmjCqLPZrQrFPksU+3YCklOOoK5pOuC1Jds/HiG1LSkmJlFFXNKmUk381Q+jho1htZZG+fFpQ21q69MqGHbpy2SwdVlHECDewl+bO6JDbTasIZjkaYGRriyTlsiU56e30nJ4M3DFGjrFkW5LLTrcDxrrq0oBmjA9qY2OHjp9aqu5oQlK7TqgpV4Hfo7pdYc2tLmGfbkBSOJZSyjEySl/ITfXk1r1fjdKzrcKx/JsZwmXoUc62LVWXpf+QV5cFSLiBAbSE40oe4Kpp0jFqOcC2YsBYMC7old/tkt9ty20ps799yqQrzPrdtgJul8YFvbkNFBgBbNvSsjlVKi/0qm5XWD2rMGRZUt2usMoLvVo6u4rzM0BSeySemT6191lZ5rbV0y7PkHQDGPMcxyiR6i13s9d02Z7vE46Tl9UygUNtxvigKov8Shkp7vSv9h930sn3+CK/ZoxnVgggpWcerlpcozmTStQRSe8s0xFJaG51iVYtrmHJH9Aj4HYNuG1r39tWT7t8w/RyAGOe32NnEgdb6eJQ6vnas4xIxog97gFJU8oKVFnk05aW8ID3x5KOKot9msIOGUBGbWWRpmeW/G3XpR+sZckfsJfq8oDctiU5RpYl9XYP20oXtHWM5LYtVZfn33IMziABjHnt3ek1qr0JdrInA0+a9G1L6TWq7d2sUQWSSUd1u7r226auuUvJZP4VugGyiSV/wP6VFng1LuiTbVuyZKUTcKnnqyXbtjQu6FNpQf4tXyLpBjDmjQt65bVt2fbA05lctuS1bdaoApLWvrFTbd0J2Rq4v9iS2rsTWvvGzvc/OABA3iryeXTkpGKND/rkclnqXdXnGMntsjS+yKcjJxWryJd/u8kwvRzAmDe9olBej0vRpCOPLfld6TFvv8uWMUYpI/k8Lk2vyK89IYFs2LSzSylnT80Dd0/m7bakRE+bpJNud+bROQoSAJB3qksDOnZKmWIJR1OSSbV0RSWFVFXsV0WRX163W8cdVpaX1f5JugGMebZlqbzQq2gi1bP/8J6CabZty2VJZYVe2RZTAYFinzvTQ9wuq081Zksul6VkKr3dS7GPUwwAwND1Vvtv7IhodyimaeMCkkKaX1Om9qijcUFf3lb7Z3o5gDGvO5FSdWlA1aUBFXhd8rrTfxq9blsFXpcm9dzXnci/fSGBQ2325GK5rPSlqUTKKNkz/y/ZswuAUboI4ezJxTmNExhpHMeooS0iSWpoi7AjBjCAvtX+uyLpWjpdkWTeV/vnMjSAMa/Q61ZF0KeKoFc7OqJqD0UlxVQR9KksGNCEEp8kS4Ve/mQC5YU+FQc8autOTybvu2VYr+KAR+WFvvc9NmCkqmvu0tqNTarf1amT/NLqJ+pUM75Yy+ZU5W0SAWTVXnu45vslKka6AYx51aUBzRgfVCTh6PipZVo0o0KStGhGheZNLVUk4ai2MpiXa4iAQ63Q61bQ5x70BMKSFPS5uUgF9Khr7tK9T9VrY2OHSgLpAlAlAY82Nnbo3qfqVde8/90AgLGkt7+81tipiSXp866JJQG91tiZ1/2FpBvAmNe7hqi80KvNzWGF4+npTOF4Upubwyov9ObtGiLgUDOOUTSRksdtqcAt9W5f77GlArfkdVuKJVMyTJ0F5DhGazc2qTUc18zKoIL+9MWooN+tmZVBtYbjWvdaE1PNAY3u/kLSPcqxfggYmtrKIn1oVqXCsaSer2+VJD1f36pwLKkPzapk+h/Qo761W5YlFXpd8nrcKg6kt9IrDnjl9ewZ4a5v7c5lmMCI0NAe0ZZdIU0s8cvaqxinZVmaWOJXXXNIDe2RHEUIjByjub+QdI9idc1d+vGTW7T6iTpJ6fVDP35yS95OywCyqa65S49valahz6X5NWWSpPk1ZSr0ufT4pmb6DdCH22VrfJFfhV63Uqn0xdxUyqjQ51ZFkU8eF6cXgJSeMRVNplQwyHKLgNelWDKVmWEFjGWjub/wqThK9a6HeLWhQ+6eKbFu29KrDawfAvbWdzrT4VVFmtCzhmhCSUCHVxXl9XQm4FCbVlGo0oBXXdGkJNO3zo1kjELRpEoCXk1jX3tAhV63/G6XuuNJGWP6VWM2xigST8nndlEDAdDo7i/5FzEOqDeB2NbarUQipfpdMZ04Q/rH9naVFvgUjie17rUmTa8IskYV0ADTmfrk1ntPZ5pSXpC7QIERYEpZgWZVBbX29SZJUok/ff3edllq7zlBWji9XFPK6CtAb6HOZ97eraTjKBSJ66ga6bn6VgUDXrltWwtnjKNQJ6DR3V8Y6R6FGtoj+sf2Nr3b1q23mkNq745Lktq743qrOaTtrd16aVtbXq6HALJhNE9nArKhtNCrIr9bXreVmQHiOEZet6Uiv1slBd4cRwiMDLZtadbEIu3ojOrtlrB6xzpsS3q7JawdnVF9YEIRgyCARnd/IekehbqiCW1uCml3KC4jyetO/zd73baMpNZwXHXNIXVFEzmNExgp+k5nGkg+T2cCDrWG9ojauxNaNGOcZk0oVllPgl1W4NURE4q1aMY4tXcnuLALKH0xatOOLk0s8Wv6uEL1rlJyjDS9olATS/x6c2cXy5cAje7+whnkKNQZTai9Oy5jjAIel3rr2bhsSwGPpa5oUm3huDpJugFJe6YzbWzsUNDnVt/rp8YY7eiIam51SV5OZwIOtd6ZIdMrgppcVqBwJC6pU4tmVKgw4FXKGNW3hJkZAmjP8qWZlUEFfe6e/tKuE2rKVRjwKhRLsnwJ6DGa+wsj3aNQJJGSY4xsy5IxUjLlSEp/NUayZckxRpFEKseRAiND/326QwpF08lCKJrU5uYQ+3QDffSdGWJZlooC6ev3RQG3LMtiZgjQB8uXgKHr218G+nzJ5/5C0j0KWUq/KSWj3aGYWnvWdLd2x7U7FJOs9B95SyQQQK/ayiKtWlyjOZNK1BFJzwLpiCQ0t7pEqxbXsE830KN3ZsiOjqiM6T/Fr3dmSG1lkJkhgPZcpGps79bz9W16rr5VUrow1PP1bdrRHuEiFdCD6uXIK9MrClXkc6utO6GEI/XuLZ90pLgjmURS44MFms52LkA/tZVFmn5aUNtauvTKhu269IO1OqwiPwt2ANnSOzOksSOizc0hVRen13SHokk1dMaZGQL0UV0aUGnAo/VvNMnrtlUecEmS/B5bzV1RvdvWraVHVnGRClCf6uVbdyuZdBSK9qle7vfK7ba1cDrVyzFCTCoJKJ5ylEgNXGQgkZJiKUeTSvLvDQtkm21bqi5L943qsgCJAzAAZoYAw9D7MWKM9uxJaXpu99ulEhjTMtXLO9LVy1NOeolsynH0dktYOzvyt3o5I92j0LbWsHaF4pnb1l5fJaklFNe21rCmc2IEADgIzAwBDqy32v/8mjLt7IipKxKTJEUTRlUlAU0o9mWq/edbYSjgUOutXl7idyuWSGpXV7q/7OqKye/zqtjv1ps7u/TBD1Tm3WcNI92j0F/eaFY86cjrkrwuq98ed16XJa9LiiUd/eWN5twGCgDIa8wMAfavtzDUpNICHV9TphNqyiVJJ9SU6/ipZZpYGsjbwlDAodbQHtE/trcpFEvK67I1sWca+cTSgLwuS6FYUi9ta8vLLSlJukehzmhCMuktwrxuW353z/oht0tety2XbUlGbBkGDMBxjBra0n/MG9oiebkXJABgZKDaPzB0XbGEtrV2K5kyGhf0qcjf01/8bo0L+pRMGW1v7VZXLP9yGHr4KHT4hCK5bCmZMnLZ6jfSLbPn+OETmFoO9FXX3KW1G5tUv6tTJ/ml1U/UqWZ8sZbNqWKNKgBg2HoLQ21s7FDQ5+631K+32v/c6pK8LAwFHGqhaFKReEpF/vRFqb4VDyzLks9jqyuazGztmk8Y6R6Flh8xQeOLfEoZKZ501DtQ5/TcThmpssin5UdMyG2gwAhS19yle5+q18bGDpUEPJKkkoBHGxs7dO9T9apr7spxhACAfNNb7b+80KvNzaFMshCKJrW5OUS1f6CPoM+tgMelWCI14JaUsURKBV6Xgr78Gzcm6R6FvF6XLjmtVgVet1KOUSyVkiTFUimlHKMCr1tfOq1WXq8rx5ECI4PjGK3d2KTWcFwzK4MK9kxnCvrdmlkZVGs4rnWvNTHVHAAwbFT7B4amyO/RYeMK5HHbag3HFU+mc5h4MqXWcFxul60p5QUq8ntyHOnw5d9lAgzJZxfWqLkrpl8+Xa94Iv0H3nHSScTKhTX67MKa3AYIjCAN7RFt2RXSxBJ/ejpTn9zasixNLPGrrjlEdVkAwEGh2j9wYNWlAR07pUyxpJPZp1tKV/sfX+ST22XruMPK8nI5Bkn3KFXX3KXWcFwnTitXyklKatbi2nFy2W61huOqa+7iyirQo7e6bIF34D/iAa9LTZ1RqssCAA5ab7X/V0S1f2AgvcsxGjsi2h2Ka2q5X1K7Zk8qVnvU0bhg/i7HYHr5KNR3quwHJhZrTnWpJGlOdak+MLGYqbLAXvpWlx0I1WUBAO8Vu2MAB9a7HGNudYlSPX0k5RgdNTm/l2NwBjkKMVUWGB6qywIAsondMYChG43LMRjpHoX2TJUd+JpKwOtSLJliqizQg+qyAIBsYXcMYPh6l2NIo2M5Bkn3KMRUWWD4qC4LADjU2B0DgMT08lGJqbLAwRmN05kAALnDkj8AEiPdoxJTZYGDN9qmMwEAcoclf8DBGW2FBxnpHqV6p8r2Fu2Qf89U2aWzKdoBAACQbX2X/BX5Pfvcz5I/YF+jsfAgPXwUY6osMHx7X1k9rMJNnwEAHBSW/AHD01t4sDUcV3WxVzJ7Cg82dkTyts4O08tHOabKAkNX19ylu56o081rNkmSbl6zSXc9UUdlWQDAQWHJHzB0o7nwICPdAKB0wn37XzbrraYuuYwjlUhvt4S1eXdEm5q69LUlM/PyyioAILdY8gcMzWguPEjSDWDMcxyjB57Zple2t8vrtlUS2HNltTWS0ivb2/WrZ7fpu2ccyWgEAGDYWPIHHNiewoMDL7cIeF1q6ozmZeFBppcDGPO2t3Xrma2tsi1L4wq98rrTfxq9blvjCr2yLUsb3m7V9rbuHEcKAMhXLPkD9q9v4cGB5HPhQZJuAGPe1paw2iNxlRZ40tOZ+rAsSyUFHnVE4traEs5RhAAAAKNbb+HBHR1RGdN/3XZv4cHaymBeFh4k6QYASZaRjAYrzJF/BTsAAADyyWguPEjSDWDMm15RqJICjzq7EwNeWe3oTqg04NH0isIcRQgAADD69RYenDOpRB2RhKQ9hQfzdbswiUJqAKDJZQU6cfo4rX+9SbtDMZUXuCRJ8WRKrd0pOUZaMH2cJpflV6VMAACAfDMaCw8y0g1gzLNtSxcsOExHTymVy7YViqYkSaFoSi7b1tFTSnXBgsPy+o89AABAvhhthQcZ6QYApa+qfm3JTK3ZuFNvNLRJ6tT08YU6srpcy+awjyoAAAAODkk3APSorSzSlzPTmXboymWz8n46EwAAAHKL6eUA0Mdom84EAACQbxzHqKEtIklqaIvIcfJ7JxlGugEAAAAAI0Jdc5fWbmxS/a5OneSXVj9Rp5rxxXm93I+RbgAAAABAztU1d+nep+q1sbFDJQGPJKkk4NHGxg7d+1S96pq7chzhwSHpBgAAAADklOMYrd3YpNZwXDMrgwr605Oyg363ZlYG1RqOa91rTXk51ZykGwAAAACQUw3tEW3ZFdLEEr8sq39NHcuyNLHEr7rmkBraIzmK8OCRdAMAAAAAciocTyqaTKnA65YxRl2RpCSpK5KUMUYBr0uxZErheDLHkQ4fhdQAAAAAADlV6HXL73apsb1bOzti6orEdFSN9Fx9q4oCPk0o8cnndqnQm38pLCPdAAAAAICcqi4NqLTAo+fr29TUGZHfk55i7vdYauqM6Pn6NpUWeFRdGshxpMNH0g0AAAAAyL3eGml7renuvb3X0bxB0g0AAAAAyKmG9ojaIwnNrylTZZFf0UQ6A48mjKqK/ZpfU6a27kReFlLLvwnxAAAAAIBRpbeQ2vSKoCaXFSgciUtq1wk15SoMeJUyRvUt4bwspMZINwAAAAAgp3oLqXXHk7IsS0WB9PhwUcAty7IUiacopAYAAAAAwMGoLg1oxvigdnREZYzpd58xRjs6oqqtDFJIDQAAAACA4bJtS8vmVKm80KvNzSGFoulp5KFoUpubQyov9Grp7CrZdv6VUyPpBgAAAADkXG1lkVYtrtGcSSXqiCQkSR2RhOZWl2jV4hrVVhblOMKDkzdJd2trqz7zmc+ouLhYpaWluuiiixQKhfb7mGg0qksvvVTjxo1TMBjUJz/5STU1NfVrY1nWPv/+93//N5svBQAAAAAwgNrKIl1y2gxd+sFaSdKlH6zVl06dkbcJt5RHSfdnPvMZvfbaa1q/fr3++Mc/6q9//au+8IUv7PcxX//61/XII4/ooYce0v/7f/9PjY2NOvvss/dpd++992rHjh2ZfytWrMjSqwAAAAAA7I9tW6ouS6/dri4L5OWU8r7yovTbG2+8oTVr1uj555/X8ccfL0m688479dGPflS33HKLJk2atM9jOjo69LOf/UwPPPCAPvShD0lKJ9dHHHGEnnnmGZ144omZtqWlpZowYcL782IAAAAAAGNGXox0b9iwQaWlpZmEW5KWLFki27b17LPPDviYF198UYlEQkuWLMkcmzVrlg477DBt2LChX9tLL71UFRUVOuGEE/Tzn/98n2p5AAAAAAAcjLwY6d65c6cqKyv7HXO73SovL9fOnTsHfYzX61VpaWm/41VVVf0ec/311+tDH/qQCgoKtG7dOn35y19WKBTS5ZdfPmg8sVhMsVgsc7uzs1OSlEgklEgkhvvysq43ppEYGzAS0WeAoaO/AENHfwGGLh/6y1Bjy2nS/e1vf1s//OEP99vmjTfeyGoM3//+9zPfH3vssQqHw7r55pv3m3TfeOONuu666/Y5vm7dOhUUFGQlzkNh/fr1uQ4ByCv0GWDo6C/A0NFfgKEbyf2lu7t7SO1ymnR/4xvf0IUXXrjfNtOnT9eECRPU3Nzc73gymVRra+uga7EnTJigeDyu9vb2fqPdTU1N+12/vWDBAt1www2KxWLy+XwDtrnqqqt0xRVXZG53dnZqypQpWrp0qYqLi/f7enIhkUho/fr1Ov300+XxeHIdDjDi0WeAoaO/AENHfwGGLh/6S++M5wPJadI9fvx4jR8//oDtFi5cqPb2dr344ouaN2+eJOnxxx+X4zhasGDBgI+ZN2+ePB6PHnvsMX3yk5+UJL355pvatm2bFi5cOOjPevnll1VWVjZowi1JPp9vwPs9Hs+IfUNIIz8+YKShzwBDR38Bho7+AgzdSO4vQ40rL9Z0H3HEEVq+fLkuvvhi3X333UokErrssst03nnnZSqXNzQ06MMf/rB++ctf6oQTTlBJSYkuuugiXXHFFSovL1dxcbG+8pWvaOHChZnK5Y888oiampp04oknyu/3a/369fr3f/93ffOb38zlywUAAAAAjBJ5kXRL0v/5P/9Hl112mT784Q/Ltm198pOf1H/9139l7k8kEnrzzTf7zau/7bbbMm1jsZiWLVumu+66K3O/x+PR6tWr9fWvf13GGNXW1urWW2/VxRdf/L6+NgAAAADA6JQ3SXd5ebkeeOCBQe+vqanZZ6svv9+v1atXa/Xq1QM+Zvny5Vq+fPkhjRMAAAAAgF55sU83AAAAAAD5iKQbAAAAAIAsIekGAAAAACBLSLoBAAAAAMgSkm4AAAAAALKEpBsAAAAAgCwh6QYAAAAAIEtIugEAAAAAyBKSbgAAAAAAsoSkGwAAAACALCHpBgAAAAAgS0i6AQAAAADIEpJuAAAAAACyhKQbAAAAAIAsIekGAAAAACBLSLoBAAAAAMgSkm4AAAAAALKEpBsAAAAAgCwh6QYAAAAAIEtIugEAAAAAyBKSbgAAAAAAsoSkGwAAAACALCHpBgAAAAAgS0i6AQAAAADIEpJuAAAAAACyhKQb+P/bu/P4mu98j+Pvk0VkkWgqQhLEWktVLdUaRhLTKqW3G0Xta6vWWIvaadBWCDpUZ3A7ttqqqqgS0lItehmxlRR1bbE1e2T73j9MzsigTd0ev0Rez3843/NbPuc88s3J+3y/v+8PAAAAAByE0A0AAAAAgIMQugEAAAAAcBBCNwAAAAAADkLoBgAAAADAQQjdAAAAAAA4CKEbAAAAAAAHIXQDAAAAAOAghG4AAAAAAByE0A0AAAAAgIMQugEAAAAAcBBCNwAAAAAADkLoBgAAAADAQQjdAAAAAAA4CKEbAAAAAAAHIXQDAAAAAOAghG4AAAAAAByE0A0AAAAAgIMQugEAAAAAcBBCNwAAAAAADkLoBgAAAADAQQjdAAAAAAA4CKEbAAAAAAAHIXQDAAAAAOAghG4AAAAAAByE0A0AAAAAgIMQugEAAAAAcBBCNwAAAAAADkLoBgAAAADAQQjdAAAAAAA4CKEbAG6Rk2N07nqaJOnc9TTl5BiLKwIAAEBh5mJ1AQBQUJyMT9KW2Es6fTlRTYpL86JPKtjPW88+6q8qpUtYXR4AAAAKIUa6AUA3A/eiXacVez5BPu6ukiQfd1fFnk/Qol2ndTI+yeIKAQAAUBgRugEUeTk5RltiL+laSoaqlvaSV/Gbk4C8iruoamkvXUvJ0JeHLzHVHAAAAL8boRtAkXfulzTFXU5WWZ/istlseZ6z2Wwq61NcJ+OTde6XNIsqBAAAQGFF6AZQ5KVkZCk9K1sexe68zIV7MWfdyMpWSkbWfa4MAAAAhR2hG0CR51nMRcVdnJV6l1CdlpEtNxdned4llAMAAAB3Q+gGUOQFlnRXZT8vXUhIlzF5r9s2xuhCQrqqlPZSYEl3iyoEAABAYUXoBlDkOTnZ9Oyj/vL1LKYT8clKTr854p2cnqUT8cny9Sym5rX85eRk+40jAQAAAHkRugFAUpXSJdS9cbAeDfBRQlqmJCkhLVO1A33UvXEw9+kGAADAPeECRQD4lyqlS6hSqJd+vpKkg9+eVb+wKipfqgQj3AAAALhnjHQDwC2cnGwKfOjmtduBD7kTuAEAAPD/QugGAAAAAMBBCN0AAAAAADgIoRsAAAAAAAchdAMAAAAA4CCEbgAAAAAAHITQDQAAAACAgxC6AQAAAABwEEI3AAAAAAAOQugGAAAAAMBBCN0AAAAAADgIoRsAAAAAAAchdAMAAAAA4CCEbgAAAAAAHITQDQAAAACAgxSa0H3t2jV17NhR3t7eKlmypHr27Knk5ORf3efDDz9UaGiovL29ZbPZ9Msvv/whxwUAAAAAID8KTeju2LGjDh8+rK1bt+rzzz9XTEyM+vTp86v7pKamqkWLFho9evQfelwAAAAAAPLDxeoC8uPo0aPavHmz9u7dqwYNGkiS5syZo+eee07vvfeeAgIC7rjf4MGDJUk7duz4Q48LAAAAAEB+FIqR7m+//VYlS5a0B2NJevrpp+Xk5KTvvvuuwB0XAAAAuFVOjtG562mSpHPX05STYyyuCMD9UihGui9evKjSpUvnaXNxcZGvr68uXrx4349748YN3bhxw/44MTFRkpSZmanMzMx7rsdRcmsqiLUBBRF9Bsg/+gvw2366nKxtR+P185UkNSouzY/+UeVLldBfapRWJT8vq8sDCqTC8PmS39osDd1vvfWWpk+f/qvbHD169D5Vk38RERGaOHHibe1ffvmlPDw8LKgof7Zu3Wp1CUChQp8B8o/+Avy6QEmBxW/+v1Hxc1KydGzvMR2ztCqg4CvIny+pqan52s7S0D106FB169btV7epVKmSypQpo/j4+DztWVlZunbtmsqUKXPP57/X444aNUpDhgyxP05MTFS5cuXUvHlzeXt733M9jpKZmamtW7fqmWeekaurq9XlAAUefQbIP/oLcHc5OUZ/++aUjlxIVGU/TzkpR8HpcTpdvLJy5KS4yymqFeCtHo0rysnJZnW5QIFSGD5fcmc8/xZLQ7efn5/8/Px+c7tGjRrpl19+0f79+1W/fn1J0vbt25WTk6Mnn3zyns9/r8d1c3OTm5vbbe2urq4F9gdCKvj1AQUNfQbIP/oLcLuz11J18kqaSvt4SE4uMiZbkmRszpLNWaV9PHTicpriU7JUzrfgzpYErFSQP1/yW1ehWEitRo0aatGihXr37q3vv/9eu3btUv/+/dW+fXv7CuPnzp1T9erV9f3339v3u3jxog4cOKCTJ09Kkg4dOqQDBw7o2rVr+T4uAAAAcC9SMrKUnpUtj2J3HudyL+asG1nZSsnIus+VAbifCkXolqSlS5eqevXq+stf/qLnnntOTZo00Ycffmh/PjMzU8ePH88zr37+/PmqW7euevfuLUlq2rSp6tatq88++yzfxwUAAADuhWcxFxV3cVbqXUJ1Wka23Fyc5XmXUA7gwVBoerivr6+WLVt21+eDg4NlTN5bL0yYMEETJkz4fx0XAAAAuBeBJd1V2c9LsecT5OXmoluv2jbG6EJCumoH+iiwpLtlNQJwvEIz0g0AAAAUJk5ONj37qL98PYvpRHyyktNvjngnp2fpRHyyfD2LqXktfxZRAx5whG4AAADAQaqULqHujYP1aICPEtJu3tM3IS1TtQN91L1xsKqULmFxhQAcrdBMLwcAAAAKoyqlS6hSqJd+vpKkg9+eVb+wKipfqgQj3EARwUg3AAAA4GBOTjYFPnTz2u3Ah9wJ3EARQugGAAAAAMBBCN0AAAAAADgIoRsAAAAAAAchdAMAAAAA4CCEbgAAAAAAHITQDQAAAACAgxC6AQAAAABwEEI3AAAAAAAOQugGAAAAAMBBCN0AAAAAADgIoRsAAAAAAAchdAMAAAAA4CCEbgAAAAAAHITQDQAAAACAg7hYXcCDwBgjSUpMTLS4kjvLzMxUamqqEhMT5erqanU5QIFHnwHyj/4C5B/9Bci/wtBfcvNfbh68G0L3HyApKUmSVK5cOYsrAQAAAADcT0lJSfLx8bnr8zbzW7EcvyknJ0fnz59XiRIlZLPZrC7nNomJiSpXrpzOnj0rb29vq8sBCjz6DJB/9Bcg/+gvQP4Vhv5ijFFSUpICAgLk5HT3K7cZ6f4DODk5KSgoyOoyfpO3t3eB/YEFCiL6DJB/9Bcg/+gvQP4V9P7yayPcuVhIDQAAAAAAByF0AwAAAADgIITuIsDNzU3jx4+Xm5ub1aUAhQJ9Bsg/+guQf/QXIP8epP7CQmoAAAAAADgII90AAAAAADgIoRsAAAAAAAchdAMAAAAA4CCEbgAAAAAAHITQDQAAAAAo8M6cOSNJKmxrgRO6AQDAPcvKyrK6BKDQKmzBAbDSF198oYoVK2rbtm2y2WyFqv8QugEAwD0ZPny41qxZY3UZQKFls9m0Z88evfHGG3yBBfyGhg0bqmvXrnrxxRe1ffv2QhW8Cd1FSO4P5enTp7V3714dPnxYiYmJFlcFACissrOzVb16dUmM2AH3IicnRzt37lRMTIzCw8MJ3sCvKFWqlObOnasuXbqoVatW2rVrV6EJ3jZTGKrE/5sxRjabTWvXrtWYMWOUmZkpf39/ubi46OOPP1b58uWtLhEosHL7T+6/t7YBRdF//vxv3rxZly9fVps2beTu7m5hZUDhk5ycrAULFmjlypVq0KCBoqKi5OLiYnVZQIGSk5MjJycnffPNNzp27JjefPNNeXh4aP369QoJCSnwf5cx0l1E2Gw2xcTEqGvXrho4cKCOHz+uN954Q19//bXWrl1rdXlAgZX7S3zz5s3q1q2bunXrpg0bNhToX+yAo9lsNuXk5Ei62Ue+/vprde3aVevWrVN6errF1QEF308//WT/v5eXl9544w21adNG+/fv16BBg5SdnW1hdUDB4+TkpHXr1ql169Y6c+aMevbsqerVq6tly5aKjo4u8CPehO4iZOfOnercubP69u2rS5cuacyYMerXr58GDx4sSUpNTbW2QKAAyf3FbbPZtGXLFr388stKSUnRuXPn9MILL2jWrFnWFghYzMnJSatWrVLnzp01depU9e/fXz179tTq1asJ3sCvOH78uNq1a6cxY8bY2zw9PdW3b1+1bNlS69at06hRo5hqDtwiKSlJ06dPV79+/TR58mT99a9/1YoVK9S2bVu1atVKMTExBTp4E7ofcLf+4F25ckUeHh46e/asGjZsqJYtWyoqKkqStGHDBi1ZsoRf8MC/5I5kX7lyRRcuXNB7772n1atXa/369Zo9e7aGDRum9957z+Iqgfsjd1Q7OTnZ3nbixAlNmjRJjRs3liRFRUWpR48e6t27N8Eb+BW+vr5q0KCBdu7cqcmTJ9vbS5QoofDwcLm7u2vhwoUaNmyYhVUCBUtmZqYuXryoChUq2NsqVKig8ePH65FHHlGbNm3si6sVRITuB9yVK1ck3QzfgYGB2r17txo3bqznnntOCxYskCRlZGRow4YN+umnn5jOBNzixx9/VOnSpTVp0iT5+PhIkjw8PDRgwABFRkZq5MiRioyMtLhKwLFyr6Pbv3+/6tSpo+TkZB08eFCLFi1SvXr11KtXL3vAnjdvnj14r127VmlpaRZXD1jLGJNnACQnJ0d+fn6aMGGCnnjiCX3++ed5gndWVpaeeuopjRgxQkOHDrWiZKBA8vX1VcOGDfXpp58qISFB0s0BkkqVKumxxx5TQkKCXnvttQI7c5fQ/QA7cuSIHn/8ca1atUo2m039+vVTamqqEhISFB4eruzsbN24cUMTJ07Uxo0b1atXL7m5uVldNlBg+Pv7a/To0Tp37pzOnTsn6d+zRwYMGKC5c+dq6NChmjNnjpVlAg6TG7gPHjyosLAwtW7dWl5eXho/fryioqJ0+PBhubq6qnjx4srIyJB0M3j37t1bnTp10oYNGyx+BYA1cj8zsrOzZbPZtHXrVg0ePFjh4eHau3ev/fOlcePG+vzzz9W/f3/Fxsbq3Xff1YULF9SnTx+VK1fO4lcB3H+3flGVkJCgS5cu2b/YbdOmjeLj4zVz5kwlJSXZ9/H09NSSJUsUGxsrDw8PS+r+TQYPjMzMzDyPDx8+bDp37mwqVqxoVq1aZYwx5tSpU6ZChQqmZs2apnbt2ua5554z/v7+5ocffrCiZKDAS0hIMCNHjjROTk5m+fLltz2/cOFCc+TIEQsqAxwrOzvbGGPMwYMHjYeHhxk9erT9ufT0dNO2bVtTpUoVM2vWLPu2N27csG8zbNgwc/To0ftbNFAAfPrpp8Zms5mYmBhjjDGfffaZcXd3N88++6ypX7++cXZ2NitWrDDGGHP58mUTERFhqlatasqWLWsqVqxo9u/fb2X5gKVycnKMMTf7UWhoqAkMDDQvvviieeedd4wxxkycONE0bNjQhIWFmWnTppnOnTubUqVKmRMnTlhZ9m/ilmEPiNjYWK1Zs0ajRo1SsWLF7O1Hjx5VZGSkNm3apNmzZ9sXg1q8eLEuXryoypUrKyQkRBUrVrSwesB65l+rlO/fv19xcXG6evWqWrdurTJlysjV1VXDhw/XzJkztWzZMrVr187qcoH74uzZs6pXr56aNWumlStX2tuXLFmibdu2KSkpSVeuXFH37t3Vo0cPSTcvWbr1cwgoKnJnhpw7d04TJkzQJ598os2bN2vPnj3y9PRUnz599Msvv2jatGl6//33tWTJEr322mvKyMjQ9evXderUKVWsWFH+/v5WvxTgvsntNzdu3LDPuN20aZNeeeUVTZkyRc2aNdPf/vY3ffDBB9q2bZtCQ0O1fPlybdq0SYcOHVLp0qU1ffp0Pf7449a+kN9icejHH+DAgQPGZrOZBQsWmL1795qvv/46z/OHDx82ffr0MUFBQWbdunXGmH9/iwTg31atWmV8fHzMU089ZTw9PU2tWrXMpEmTTEpKijHGmOHDhxs3NzezePFiiysF7o9Tp06ZJ554wvzXf/2X+eabb4wxxrzzzjvGw8PDHDx40Fy5csW88sor5s9//rNZtGiRtcUCFsqd7XHkyBEzefJkExcXZzp27Gjc3d1N/fr1zYYNG+zbZmRkmJEjRxpnZ+c7zqACipqzZ8+amjVrmjNnzhhjjHn11VfNxIkTjTHGXLt2zQQGBpoBAwbcll8SExNNenr6fa/3XnBNdyF35MgRNWrUSGPHjlWXLl30zjvvqFu3btq9e7d9m5o1a6pfv34KCgpS//79tW7dugK7sh9gldjYWA0cOFAzZ87UV199pYSEBDVv3lxffvmlIiMjlZWVpXHjxql3794aMmRInmuJgAdVcHCwli5dqoyMDM2YMUN9+vTRrFmztG7dOj322GN6+OGHNXfuXJUtW1YzZ87U0qVLrS4ZuO9uXfugVq1acnV1VaVKlTRz5kz16NFDP/zwg33hp5ycHLm6umrKlCkaOXKkXnvtNa1du9biVwBYyxij9PR0jRs3TllZWUpJSVGtWrX0v//7v6pdu7ZatWqlqKgo2Ww2rV+/XtHR0ZJurvhfWNajInQXYrGxsQoJCVFwcLAmTpyo4sWL64033lCDBg305ptvateuXfZtH3vsMT366KPKyMjQ+PHjlZSUVGDvYwfcD6dPn9Znn31mf3zq1Cm5u7vr2Weflaenp5ydnTVlyhTVq1dPn3zyiZKSkuTl5aWIiAgdPXpUJUqUsLB64P6pWrWqZs+erbS0NP3jH//QiBEj1Lx5c0k3V1ouU6aMIiMjVbduXTVp0sTiaoH7Kzdw5w6CjBs3TiNHjpQklS5dWmPHjlXXrl3Vp08f7d69W05OTjLGyMXFRRMmTNCECRNUo0YNi18FcH/9ZwYJCAjQ66+/rv3792v9+vVydnbWli1bFBoaqpYtW2r+/PmSpKtXr2r16tU6ceKE/VaWhQXXdBdSBw8e1J/+9Cc1bNhQP/74o15++WX7CsrR0dGaN2+efvrpJ33wwQd66qmnJElDhw5V1apV1bZtWz388MNWlg9Y6vz586pTp478/Pw0evRoderUSdu2bVPXrl21Y8cOValSRZmZmXJ1dVVKSop8fX21ePFidejQwerSAcvExcXpzTfflLOzs0aPHm0P2Ll9JTs7W87OzhZXCdw/uYE7NjZWYWFh8vPz05EjRyT9u19I0uXLlzVkyBCtW7dOX375pf70pz/Z1xEBiprcfnP9+nU99NBD9vaEhAT9+c9/Vr169dS3b1/95S9/UdWqVfU///M/9m3GjBmjlStX6ssvv1SlSpWsKP+eMdJdCO3bt09PPPGERowYoa+++krjx4/XihUrNGDAAElSWFiY+vXrpypVquill17S0KFD1b17dy1fvlwtWrQgcKPI+/HHH3Xt2jV5eXlp9erVWrFihRo3biybzaYJEyZIkv2PpZSUFNWsWVN+fn4WVgxYr3Llypo7d66MMZoyZYp9NlVuXyFwoyi5dUr5k08+qUcffVQJCQkaNGiQpJv9IisrS5Lk5+enyMhIvfTSS2rVqpV27txJ4EaR5eTkpLi4OFWrVk0vvvii4uPjlZqaKh8fHy1cuFBLly7Vt99+q0WLFungwYNq166dunfvri5dumjevHlatWpVoQvcEqG7UEpNTVXfvn01fvx4OTs7q127dpo6deptwXvMmDF6/fXXtX37dsXHx+uLL75QcHCwtcUDBUBoaKi6deumzMxMubi46K9//au2b9+uVatWacuWLerQoYMOHDigkydPas6cObp06ZKqVatmddmA5apWraqoqCi5urpq2LBh2rNnj9UlAZZwcnK64yDIsmXL7MHbxcXFHrxLlSqlyMhIhYSEqGPHjkpLS7OyfMBSOTk5ysrK0meffabOnTtr4cKFio2N1ZNPPqkBAwZo2bJlCg4Otn9Bdf36dfn7+2vPnj2qW7eu1eXfE6aXF3K505MSExO1YsUKjRkzRu3bt7dPNZekpKQkubi4yN3d3cJKgYIh95YUX3zxhVatWqUOHTpowYIFunLlivr06aNHHnlE7du3V1pamn1xjtWrV6tevXoWVw4UHMeOHdPYsWP1/vvvq3z58laXA1giJiZGa9as0ezZsyXdnB67cuVKjRkzRq+99pq9PSsrSy4uLpJuXpN648YNBQQEWFY3YIXc2SG5/SEqKkqnT5+Wh4eHrl69qv3792vSpEny9fVV165d1a5dO02YMEFpaWlyd3e3719YEbofILcG706dOikyMtLqkoAC4ezZs9q3b59eeukle9vly5fVtGlT9e/fX23btlXfvn119epVjRw5Uk8//bT++c9/KiUlRVWrVlXZsmUtrB4omLgfN/BvdxoEuTV433qNN1CU5PaN5ORkeXl52dt37typadOmaeDAgQoNDdXf/vY3jR07VuPGjdOWLVu0Z88excTE6LHHHstznMKq8H5dgNt4e3urffv2ioiI0OzZszVq1CirSwIsd/bsWdWtW1evvPKKWrVqpU8++UQ//vij/Pz8NGPGDK1cuVI2m01TpkzRww8/rHfffVfLly9X/fr11bRpUwI3cBcEbuDfcsNA7t9iU6dO1bJlyzRkyBBJInCjyLLZbLp48aJq1qypMWPG6Oeff5YkhYSEqHHjxurSpYuuXbum/v37a8OGDYqNjZWLi4sSExP19ttvKzs7236cwszF6gLwx/L29lbbtm3l6uqqRo0aWV0OYLmcnBxVrFhR1apV08WLF7V161aNGDFCo0ePVsmSJeXj46N9+/apZcuWmjx5sgYNGqTVq1frhRdekI+Pj9XlAwAKmdzg7eTkpD59+sjNzU0RERFWlwVYpnjx4urVq5dmzpypPXv26Pnnn9fgwYP19ttv6+TJkxozZoxmz56tJk2aqGrVqva7AEydOvWBWaST6eUPqMI+BQP4I504cUJvvfWWcnJy1KVLF9lsNs2ePVslS5bU+vXr1bBhQ8XExKhYsWI6fvy4PD09FRQUZHXZAIBCLCEhQZ9++qkaNWrEYpyApCNHjmj8+PE6cOCAgoKCNH/+fP3zn//Uxo0b1alTJz399NP2bR+0LEPoBlAkHD9+XOHh4crOztacOXMUGBioQ4cOaerUqWrXrp06der0wP2CBwBYi88VIK9r167p22+/1dixY5WQkKBXX31VW7duVf369bVgwQKry3MYQjeAIuPEiRPq37+/JGncuHFq3LixxRUBAAAUTeHh4Tp27JgOHTqk8+fP68MPP1SvXr2sLsshCN0AipQTJ05o4MCBMsbo7bffVpMmTawuCQAAoMi4dQbIjh07tHnzZn3wwQf6/vvvVb16dYurcwxCN4Ai58SJExoyZIiuXLmiyMhIPfXUU1aXBAAAUGT856UXiYmJ8vb2trAix+KWYQCKnKpVq+rdd99VUFCQAgICrC4HAACgSPnPtQ4e5MAtMdINoAjLyMjgXsMAAABwKEI3AAAAAAAOwvRyAAAAAAAchNANAAAAAICDELoBAAAAAHAQQjcAAAAAAA5C6AYAAAAAwEEI3QAAAAAAOAihGwCAIm7Xrl2qXbu2XF1d9eKLL+Z7v8WLF6tkyZIOq+t+Cw0N1eDBg60uAwDwgCF0AwDgQN26dZPNZpPNZpOrq6v8/f31zDPP6O9//7tycnKsLk+SNGTIED3++OM6deqUFi9efMdtgoODNWvWLIfXMn/+fJUoUUJZWVn2tuTkZLm6uio0NDTPtjt27JDNZlNcXJzD6wIA4F4RugEAcLAWLVrowoULOn36tDZt2qSwsDANGjRIrVu3zhMurRIXF6dmzZopKCjI8pHrsLAwJScna9++ffa2r7/+WmXKlNF3332n9PR0e3t0dLTKly+vypUrW1EqAAD5QugGAMDB3NzcVKZMGQUGBqpevXoaPXq01q9fr02bNuUZWZ45c6Zq164tT09PlStXTm+++aaSk5MlSSkpKfL29tbq1avzHPvTTz+Vp6enkpKS7njuGzduaODAgSpdurSKFy+uJk2aaO/evZKk06dPy2az6erVq+rRo4dsNtsdR7pDQ0N15swZhYeH20ftb7VlyxbVqFFDXl5e9i8YbvXRRx+pRo0aKl68uKpXr64PPvjgru/VI488orJly2rHjh32th07duiFF15QxYoVtWfPnjztYWFhkqScnBxFRESoYsWKcnd3V506dW57r2JjY9WyZUt5eXnJ399fnTt31pUrV+5ay8aNG+Xj46OlS5fedRsAAH4LoRsAAAs0a9ZMderU0dq1a+1tTk5OioqK0uHDh7VkyRJt375dI0aMkCR5enqqffv2WrRoUZ7jLFq0SG3atFGJEiXueJ4RI0ZozZo1WrJkiX744QdVqVJFzz77rK5du6Zy5crpwoUL8vb21qxZs3ThwgW1a9futmOsXbtWQUFBmjRpki5cuJAnVKempuq9997Txx9/rJiYGP38888aNmyY/fmlS5dq3Lhxmjp1qo4ePap33nlHY8eO1ZIlS+763oSFhSk6Otr+ODo6WqGhoQoJCbG3p6Wl6bvvvrOH7oiICP33f/+35s+fr8OHDys8PFydOnXSzp07JUm//PKLmjVrprp162rfvn3avHmzLl26pFdfffWONSxbtkwdOnTQ0qVL1bFjx7vWCgDAbzIAAMBhunbtal544YU7PteuXTtTo0aNu+67atUq8/DDD9sff/fdd8bZ2dmcP3/eGGPMpUuXjIuLi9mxY8cd909OTjaurq5m6dKl9raMjAwTEBBgZsyYYW/z8fExixYt+tXXUaFCBRMZGZmnbdGiRUaSOXnypL1t3rx5xt/f3/64cuXKZtmyZXn2mzx5smnUqNFdz7Vw4ULj6elpMjMzTWJionFxcTHx8fFm2bJlpmnTpsYYY7Zt22YkmTNnzpj09HTj4eFhdu/enec4PXv2NB06dLCfs3nz5nmeP3v2rJFkjh8/bowxJiQkxAwaNMjMnTvX+Pj43PV9BQDg93CxOPMDAFBkGWPyTNX+6quvFBERoWPHjikxMVFZWVlKT09XamqqPDw81LBhQ9WqVUtLlizRW2+9pX/84x+qUKGCmjZtesfjx8XFKTMzU40bN7a3ubq6qmHDhjp69Ogf8ho8PDzyXFNdtmxZxcfHS7o5JT4uLk49e/ZU79697dtkZWXJx8fnrscMDQ1VSkqK9u7dq+vXr6tatWry8/NTSEiIunfvrvT0dO3YsUOVKlVS+fLldfjwYaWmpuqZZ57Jc5yMjAzVrVtXknTw4EFFR0fLy8vrtvPFxcWpWrVqkqTVq1crPj5eu3bt0hNPPHHvbwwAAP9C6AYAwCJHjx5VxYoVJd28vrp169bq27evpk6dKl9fX33zzTfq2bOnMjIy5OHhIUnq1auX5s2bp7feekuLFi1S9+7db7vG+n5ydXXN89hms8kYI0n269EXLlyoJ598Ms92zs7Odz1mlSpVFBQUpOjoaF2/fl0hISGSpICAAJUrV067d+9WdHS0mjVrluc8GzduVGBgYJ5jubm52bd5/vnnNX369NvOV7ZsWfv/69atqx9++EF///vf1aBBA0vfWwDAg4HQDQCABbZv365Dhw4pPDxckrR//37l5OTo/fffl5PTzSVXPvnkk9v269Spk0aMGKGoqCgdOXJEXbt2ves5KleurGLFimnXrl2qUKGCJCkzM1N79+793fejLlasmLKzs3/XPv7+/goICNBPP/30u6+LDgsL044dO3T9+nUNHz7c3t60aVNt2rRJ33//vfr27StJqlmzptzc3PTzzz/bA/p/qlevntasWaPg4GC5uNz9z5/KlSvr/fffV2hoqJydnTV37tzfVTcAAP+J0A0AgIPduHFDFy9eVHZ2ti5duqTNmzcrIiJCrVu3VpcuXSTdHN3NzMzUnDlz9Pzzz2vXrl2aP3/+bcd66KGH9PLLL2v48OFq3ry5goKC7npeT09P9e3bV8OHD5evr6/Kly+vGTNmKDU1VT179vxdryE4OFgxMTFq37693NzcVKpUqXztN3HiRA0cOFA+Pj5q0aKFbty4oX379un69esaMmTIXfcLCwtTv379lJmZmSdIh4SEqH///srIyLAvolaiRAkNGzZM4eHhysnJUZMmTZSQkKBdu3bJ29tbXbt2Vb9+/bRw4UJ16NBBI0aMkK+vr06ePKkVK1boo48+yjPyXq1aNfvibS4uLvfl/uQAgAcXq5cDAOBgmzdvVtmyZRUcHKwWLVooOjpaUVFRWr9+vT3s1alTRzNnztT06dP16KOPaunSpYqIiLjj8XKnnPfo0eM3zz1t2jS98sor6ty5s+rVq6eTJ09qy5Yteuihh37Xa5g0aZJOnz6typUry8/PL9/79erVSx999JEWLVqk2rVrKyQkRIsXL7ZPq7+bsLAwpaWlqUqVKvL397e3h4SEKCkpyX5rsVyTJ0/W2LFjFRERoRo1aqhFixbauHGj/TwBAQHatWuXsrOz1bx5c9WuXVuDBw9WyZIl7TMLbvXII49o+/btWr58uYYOHZrv1wsAwH+ymdwLrwAAQKHw8ccfKzw8XOfPn1exYsWsLgcAAPwKppcDAFBIpKam6sKFC5o2bZpef/11AjcAAIUA08sBACgkZsyYoerVq6tMmTIaNWqU1eUAAIB8YHo5AAAAAAAOwkg3AAAAAAAOQugGAAAAAMBBCN0AAAAAADgIoRsAAAAAAAchdAMAAAAA4CCEbgAAAAAAHITQDQAAAACAgxC6AQAAAABwEEI3AAAAAAAO8n/1Q1iCHGDwvQAAAABJRU5ErkJggg==\n"},"metadata":{}}]},{"cell_type":"code","source":[],"metadata":{"id":"IY7bjujnR7O4"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"8EVCbQGaR7R5"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"Cqf0F7GXR4J9"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"kuruFRXwREfu"},"execution_count":null,"outputs":[]}]}