aapl_stock.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyN9wiMHvKBHzvd0gJXnBIoU"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9zeVfoFFkLU4","executionInfo":{"status":"ok","timestamp":1703919853468,"user_tz":-180,"elapsed":18413,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"e6e9611f-a82a-49a9-c85c-743f6ba1fe8b"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","source":["dosya_yolu = '/content/drive/MyDrive/Colab Notebooks/Veri Bilimi/AAPL.csv'"],"metadata":{"id":"gDKL7O6ZkROs","executionInfo":{"status":"ok","timestamp":1703919857351,"user_tz":-180,"elapsed":360,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":["# gerekli kütüphaneler\n","import matplotlib.pyplot as plt\n","import pandas as pd\n","from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n","import plotly.graph_objects as go\n","import numpy as np\n","import seaborn as sns\n","import statsmodels.api as sm\n","from scipy.stats import shapiro, normaltest, anderson"],"metadata":{"id":"opX8KvTEkaPW","executionInfo":{"status":"ok","timestamp":1703919861476,"user_tz":-180,"elapsed":2925,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}}},"execution_count":3,"outputs":[]},{"cell_type":"code","source":["# Veri setini yükleme\n","df = pd.read_csv(dosya_yolu)"],"metadata":{"id":"RQtrE5iHkb0O","executionInfo":{"status":"ok","timestamp":1703919862333,"user_tz":-180,"elapsed":859,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["# Veri setini görüntüleme - ilk 5 veri\n","df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"qMvINv9-kfpW","executionInfo":{"status":"ok","timestamp":1703919862333,"user_tz":-180,"elapsed":12,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"1f980366-f5f5-4288-8fc5-df7187c6c5c7"},"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["         Date       Open       High        Low      Close  Adj Close  \\\n","0  2018-01-16  44.474998  44.847500  44.035000  44.047501  41.651932   \n","1  2018-01-17  44.037498  44.812500  43.767502  44.775002  42.339878   \n","2  2018-01-18  44.842499  45.025002  44.562500  44.814999  42.377689   \n","3  2018-01-19  44.652500  44.895000  44.352501  44.615002  42.188580   \n","4  2018-01-22  44.325001  44.445000  44.150002  44.250000  41.843418   \n","\n","      Volume  \n","0  118263600  \n","1  137547200  \n","2  124773600  \n","3  129700400  \n","4  108434400  "],"text/html":["\n","  <div id=\"df-f7ba72db-c81e-428a-950a-63b93ac8ef7f\" 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>Open</th>\n","      <th>High</th>\n","      <th>Low</th>\n","      <th>Close</th>\n","      <th>Adj Close</th>\n","      <th>Volume</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2018-01-16</td>\n","      <td>44.474998</td>\n","      <td>44.847500</td>\n","      <td>44.035000</td>\n","      <td>44.047501</td>\n","      <td>41.651932</td>\n","      <td>118263600</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2018-01-17</td>\n","      <td>44.037498</td>\n","      <td>44.812500</td>\n","      <td>43.767502</td>\n","      <td>44.775002</td>\n","      <td>42.339878</td>\n","      <td>137547200</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2018-01-18</td>\n","      <td>44.842499</td>\n","      <td>45.025002</td>\n","      <td>44.562500</td>\n","      <td>44.814999</td>\n","      <td>42.377689</td>\n","      <td>124773600</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2018-01-19</td>\n","      <td>44.652500</td>\n","      <td>44.895000</td>\n","      <td>44.352501</td>\n","      <td>44.615002</td>\n","      <td>42.188580</td>\n","      <td>129700400</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2018-01-22</td>\n","      <td>44.325001</td>\n","      <td>44.445000</td>\n","      <td>44.150002</td>\n","      <td>44.250000</td>\n","      <td>41.843418</td>\n","      <td>108434400</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\" onclick=\"convertToInteractive('df-f7ba72db-c81e-428a-950a-63b93ac8ef7f')\"\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: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-f7ba72db-c81e-428a-950a-63b93ac8ef7f button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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<tbody>\n","    <tr>\n","      <th>1486</th>\n","      <td>2023-12-11</td>\n","      <td>193.110001</td>\n","      <td>193.490005</td>\n","      <td>191.419998</td>\n","      <td>193.179993</td>\n","      <td>193.179993</td>\n","      <td>60943700</td>\n","    </tr>\n","    <tr>\n","      <th>1487</th>\n","      <td>2023-12-12</td>\n","      <td>193.080002</td>\n","      <td>194.720001</td>\n","      <td>191.720001</td>\n","      <td>194.710007</td>\n","      <td>194.710007</td>\n","      <td>52696900</td>\n","    </tr>\n","    <tr>\n","      <th>1488</th>\n","      <td>2023-12-13</td>\n","      <td>195.089996</td>\n","      <td>198.000000</td>\n","      <td>194.850006</td>\n","      <td>197.960007</td>\n","      <td>197.960007</td>\n","      <td>70404200</td>\n","    </tr>\n","    <tr>\n","      <th>1489</th>\n","      <td>2023-12-14</td>\n","      <td>198.020004</td>\n","      <td>199.619995</td>\n","      <td>196.160004</td>\n","      <td>198.110001</td>\n","      <td>198.110001</td>\n","      <td>66831600</td>\n","    </tr>\n","    <tr>\n","      <th>1490</th>\n","      <td>2023-12-15</td>\n","      <td>197.529999</td>\n","      <td>198.399994</td>\n","      <td>197.000000</td>\n","      <td>197.570007</td>\n","      <td>197.570007</td>\n","      <td>128256700</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\" onclick=\"convertToInteractive('df-a5635372-bfd9-4d0f-a465-1c87880001f4')\"\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 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kontrol\n","df.loc[[10, 100, 1000]]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"qsjKNczplcoL","executionInfo":{"status":"ok","timestamp":1703919866948,"user_tz":-180,"elapsed":3,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"a73a4bf0-67d8-4319-de8a-0740f8a65dd8"},"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/plain":["            Date        Open        High         Low       Close   Adj Close  \\\n","10    2018-01-30   41.382500   41.842499   41.174999   41.742500   39.472294   \n","100   2018-06-08   47.792500   48.000000   47.442501   47.924999   45.678795   \n","1000  2022-01-04  182.630005  182.940002  179.119995  179.699997  177.669968   \n","\n","         Volume  \n","10    184192800  \n","100   106627200  \n","1000   99310400  "],"text/html":["\n","  <div id=\"df-cbe94236-d16c-49e2-92d8-4a03e498a740\" 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>Open</th>\n","      <th>High</th>\n","      <th>Low</th>\n","      <th>Close</th>\n","      <th>Adj Close</th>\n","      <th>Volume</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>10</th>\n","      <td>2018-01-30</td>\n","      <td>41.382500</td>\n","      <td>41.842499</td>\n","      <td>41.174999</td>\n","      <td>41.742500</td>\n","      <td>39.472294</td>\n","      <td>184192800</td>\n","    </tr>\n","    <tr>\n","      <th>100</th>\n","      <td>2018-06-08</td>\n","      <td>47.792500</td>\n","      <td>48.000000</td>\n","      <td>47.442501</td>\n","      <td>47.924999</td>\n","      <td>45.678795</td>\n","      <td>106627200</td>\n","    </tr>\n","    <tr>\n","      <th>1000</th>\n","      <td>2022-01-04</td>\n","      <td>182.630005</td>\n","      <td>182.940002</td>\n","      <td>179.119995</td>\n","      <td>179.699997</td>\n","      <td>177.669968</td>\n","      <td>99310400</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\" onclick=\"convertToInteractive('df-cbe94236-d16c-49e2-92d8-4a03e498a740')\"\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: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-cbe94236-d16c-49e2-92d8-4a03e498a740 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","source":["# tarih kolonun da unique olan verilerin kontrolü\n","df[\"Date\"].unique()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MTMopyNmmcSM","executionInfo":{"status":"ok","timestamp":1703919867776,"user_tz":-180,"elapsed":2,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"74ef06ca-a5c1-46e4-f59b-5648a81dd0a3"},"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['2018-01-16', '2018-01-17', '2018-01-18', ..., '2023-12-13',\n","       '2023-12-14', '2023-12-15'], dtype=object)"]},"metadata":{},"execution_count":13}]},{"cell_type":"code","source":["# tarih kolonun da unique değerlerin sayısı\n","df[\"Date\"].nunique()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sSd54wB9nDAO","executionInfo":{"status":"ok","timestamp":1703919868502,"user_tz":-180,"elapsed":2,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"ef12c7c5-2b7c-40f1-ef2b-753b1ed6bb55"},"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1491"]},"metadata":{},"execution_count":14}]},{"cell_type":"code","source":["# 2023'ten kaç tane veri var ?\n","len(df[df.Date > \"2023-01-01\"])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xPh8WSaHpl26","executionInfo":{"status":"ok","timestamp":1703919868780,"user_tz":-180,"elapsed":3,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"1268a88f-4096-471e-ff5c-36c8f6a88d7d"},"execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/plain":["241"]},"metadata":{},"execution_count":15}]},{"cell_type":"code","source":["# kolonlarda boş değerler var mı\n","df.isnull().sum()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5gzTBEVkkpnj","executionInfo":{"status":"ok","timestamp":1703919870744,"user_tz":-180,"elapsed":2,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"68f7dbd7-a375-4b21-d1a7-beda7e35a590"},"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Date         0\n","Open         0\n","High         0\n","Low          0\n","Close        0\n","Adj Close    0\n","Volume       0\n","dtype: int64"]},"metadata":{},"execution_count":16}]},{"cell_type":"code","source":["# ortalam\n","df.mean()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XMrXW0f_qeTM","executionInfo":{"status":"ok","timestamp":1703919871872,"user_tz":-180,"elapsed":3,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"a3cdc36d-5b0f-41ea-a90a-c4f810f238e9"},"execution_count":17,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-17-eb23926447c3>:2: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n","  df.mean()\n"]},{"output_type":"execute_result","data":{"text/plain":["Open         1.102194e+02\n","High         1.114863e+02\n","Low          1.090474e+02\n","Close        1.103260e+02\n","Adj Close    1.087356e+02\n","Volume       1.077971e+08\n","dtype: float64"]},"metadata":{},"execution_count":17}]},{"cell_type":"code","source":["# medyan\n","df.median()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bw2hza1DaPIJ","executionInfo":{"status":"ok","timestamp":1703919872832,"user_tz":-180,"elapsed":2,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"e41eee38-ecc0-4cd3-cdf8-f1aac68b9e2c"},"execution_count":18,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-18-091d7e1128bb>:2: FutureWarning: The default value of numeric_only in DataFrame.median is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n","  df.median()\n"]},{"output_type":"execute_result","data":{"text/plain":["Open         1.236600e+02\n","High         1.249800e+02\n","Low          1.221400e+02\n","Close        1.235400e+02\n","Adj Close    1.216024e+02\n","Volume       9.329200e+07\n","dtype: float64"]},"metadata":{},"execution_count":18}]},{"cell_type":"code","source":["# mod\n","print(df['Open'].mode())\n","print(\"High: \\n\" + str(df['High'].mode()))\n","print(\"Low: \\n\" + str(df['Low'].mode()))\n","print(\"Close: \\n\" + str(df['Close'].mode()))\n","print(\"Adj Close: \\n\" + str(df['Adj Close'].mode()))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_sawy9l4a5QI","executionInfo":{"status":"ok","timestamp":1703919906113,"user_tz":-180,"elapsed":277,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"345a87ee-d3d9-4687-cd4b-a459c73aefcd"},"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["0    127.820000\n","1    142.699997\n","Name: Open, dtype: float64\n","High: \n","0    151.570007\n","Name: High, dtype: float64\n","Low: \n","0    133.770004\n","1    142.649994\n","2    147.679993\n","Name: Low, dtype: float64\n","Close: \n","0    145.860001\n","1    163.759995\n","2    175.839996\n","Name: Close, dtype: float64\n","Adj Close: \n","0      41.017090\n","1      41.809361\n","2      44.832893\n","3      54.248791\n","4      89.133003\n","5     119.666565\n","6     125.046890\n","7     140.444199\n","8     143.569794\n","9     147.433731\n","10    163.098389\n","11    167.184952\n","12    170.244827\n","13    171.611877\n","14    173.102188\n","15    175.608643\n","Name: Adj Close, dtype: float64\n"]}]},{"cell_type":"code","source":["# standart sapma\n","df.std()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"x-Wy_RZNaaIo","executionInfo":{"status":"ok","timestamp":1703919908420,"user_tz":-180,"elapsed":272,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"e9f748a8-b30e-4cac-be40-7440170085be"},"execution_count":22,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-22-797d75bfc3ab>:2: FutureWarning: The default value of numeric_only in DataFrame.std is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n","  df.std()\n"]},{"output_type":"execute_result","data":{"text/plain":["Open         5.062023e+01\n","High         5.114602e+01\n","Low          5.013299e+01\n","Close        5.067006e+01\n","Adj Close    5.102660e+01\n","Volume       5.521541e+07\n","dtype: float64"]},"metadata":{},"execution_count":22}]},{"cell_type":"code","source":["# varyans\n","df.var()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RIYBFn9ecoIY","executionInfo":{"status":"ok","timestamp":1703919909891,"user_tz":-180,"elapsed":298,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"765fa873-be08-41c5-8e71-9464960a4fa5"},"execution_count":23,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-23-bac5d9bb4bd6>:2: FutureWarning: The default value of numeric_only in DataFrame.var is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n","  df.var()\n"]},{"output_type":"execute_result","data":{"text/plain":["Open         2.562408e+03\n","High         2.615915e+03\n","Low          2.513317e+03\n","Close        2.567455e+03\n","Adj Close    2.603714e+03\n","Volume       3.048742e+15\n","dtype: float64"]},"metadata":{},"execution_count":23}]},{"cell_type":"code","source":["# aralık\n","print(\"Open: \" + str(np.ptp(df[\"Open\"])))\n","print(\"High: \" + str(np.ptp(df[\"High\"])))\n","print(\"Low: \" + str(np.ptp(df[\"Low\"])))\n","print(\"Close: \" + str(np.ptp(df[\"Close\"])))\n","print(\"Adj Close: \" + str(np.ptp(df[\"Adj Close\"])))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-_77UJKYc7-R","executionInfo":{"status":"ok","timestamp":1703919910708,"user_tz":-180,"elapsed":3,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"659af96f-40aa-4c5a-c811-80bf4637d842"},"execution_count":24,"outputs":[{"output_type":"stream","name":"stdout","text":["Open: 162.025005\n","High: 163.18999499999998\n","Low: 161.5\n","Close: 162.5625\n","Adj Close: 163.99111200000002\n"]}]},{"cell_type":"code","source":["# çarpıklık\n","df.skew()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"A5xf48fmdmfY","executionInfo":{"status":"ok","timestamp":1703919911552,"user_tz":-180,"elapsed":2,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"df819401-8d66-494a-8915-d6288e63346f"},"execution_count":25,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-25-83ebc6e063f4>:2: FutureWarning: The default value of numeric_only in DataFrame.skew is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n","  df.skew()\n"]},{"output_type":"execute_result","data":{"text/plain":["Open        -0.061898\n","High        -0.069493\n","Low         -0.052948\n","Close       -0.061569\n","Adj Close   -0.051804\n","Volume       1.968944\n","dtype: float64"]},"metadata":{},"execution_count":25}]},{"cell_type":"code","source":["# basıklık\n","kurtosis_values = df.kurtosis()\n","\n","# Basıklık değerlerini görselleştirelim\n","plt.bar(kurtosis_values.index, kurtosis_values)\n","plt.xlabel('Sütunlar')\n","plt.ylabel('Basıklık Değerleri')\n","plt.title('Basıklık Değerleri')\n","plt.xticks(rotation=45)\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":568},"id":"AcLeLOGefAU_","executionInfo":{"status":"ok","timestamp":1703919917579,"user_tz":-180,"elapsed":568,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"6542f05a-739a-4d36-9d38-bd25f837cf90"},"execution_count":27,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-27-a225a8ae0bbb>:2: FutureWarning: The default value of numeric_only in DataFrame.kurt is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n","  kurtosis_values = df.kurtosis()\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["# yüzdelik %25, %50, %75\n","print(\"%25\\n\", df.quantile(0.25, numeric_only=True))\n","print(\"%50\\n\", df.quantile(0.50, numeric_only=True))\n","print(\"%75\\n\", df.quantile(0.75, numeric_only=True))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XaGqyB3AfNW2","executionInfo":{"status":"ok","timestamp":1703919916009,"user_tz":-180,"elapsed":285,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"16d24bef-2c6d-45bf-c764-5d17a26169da"},"execution_count":26,"outputs":[{"output_type":"stream","name":"stdout","text":["%25\n"," Open         5.425375e+01\n","High         5.494750e+01\n","Low          5.366250e+01\n","Close        5.413250e+01\n","Adj Close    5.199073e+01\n","Volume       7.108655e+07\n","Name: 0.25, dtype: float64\n","%50\n"," Open         1.236600e+02\n","High         1.249800e+02\n","Low          1.221400e+02\n","Close        1.235400e+02\n","Adj Close    1.216024e+02\n","Volume       9.329200e+07\n","Name: 0.5, dtype: float64\n","%75\n"," Open         1.522400e+02\n","High         1.542500e+02\n","Low          1.504000e+02\n","Close        1.524400e+02\n","Adj Close    1.511226e+02\n","Volume       1.284988e+08\n","Name: 0.75, dtype: float64\n"]}]},{"cell_type":"code","source":["# veri tekrarı\n","df.duplicated()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6QOF1HYirCPY","executionInfo":{"status":"ok","timestamp":1703919921439,"user_tz":-180,"elapsed":267,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"e66004ad-f8e2-42fd-843c-69fdb045c23e"},"execution_count":28,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0       False\n","1       False\n","2       False\n","3       False\n","4       False\n","        ...  \n","1486    False\n","1487    False\n","1488    False\n","1489    False\n","1490    False\n","Length: 1491, dtype: bool"]},"metadata":{},"execution_count":28}]},{"cell_type":"code","source":["# kolonlara ait istatistiki bilgiler\n","df.describe()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"id":"Usn5M8CqknCC","executionInfo":{"status":"ok","timestamp":1703919923227,"user_tz":-180,"elapsed":295,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"af0c384b-f788-45a5-89b4-b135f3af2e94"},"execution_count":29,"outputs":[{"output_type":"execute_result","data":{"text/plain":["              Open         High          Low        Close    Adj Close  \\\n","count  1491.000000  1491.000000  1491.000000  1491.000000  1491.000000   \n","mean    110.219390   111.486263   109.047396   110.325951   108.735566   \n","std      50.620231    51.146017    50.132989    50.670056    51.026600   \n","min      35.994999    36.430000    35.500000    35.547501    34.118889   \n","25%      54.253749    54.947500    53.662501    54.132500    51.990730   \n","50%     123.660004   124.980003   122.139999   123.540001   121.602386   \n","75%     152.239998   154.250000   150.400002   152.439995   151.122581   \n","max     198.020004   199.619995   197.000000   198.110001   198.110001   \n","\n","             Volume  \n","count  1.491000e+03  \n","mean   1.077971e+08  \n","std    5.521541e+07  \n","min    2.404830e+07  \n","25%    7.108655e+07  \n","50%    9.329200e+07  \n","75%    1.284988e+08  \n","max    4.265100e+08  "],"text/html":["\n","  <div id=\"df-7bca56ee-b05c-433c-b922-3c37b347bb21\" 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>Open</th>\n","      <th>High</th>\n","      <th>Low</th>\n","      <th>Close</th>\n","      <th>Adj Close</th>\n","      <th>Volume</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>1491.000000</td>\n","      <td>1491.000000</td>\n","      <td>1491.000000</td>\n","      <td>1491.000000</td>\n","      <td>1491.000000</td>\n","      <td>1.491000e+03</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>110.219390</td>\n","      <td>111.486263</td>\n","      <td>109.047396</td>\n","      <td>110.325951</td>\n","      <td>108.735566</td>\n","      <td>1.077971e+08</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>50.620231</td>\n","      <td>51.146017</td>\n","      <td>50.132989</td>\n","      <td>50.670056</td>\n","      <td>51.026600</td>\n","      <td>5.521541e+07</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>35.994999</td>\n","      <td>36.430000</td>\n","      <td>35.500000</td>\n","      <td>35.547501</td>\n","      <td>34.118889</td>\n","      <td>2.404830e+07</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>54.253749</td>\n","      <td>54.947500</td>\n","      <td>53.662501</td>\n","      <td>54.132500</td>\n","      <td>51.990730</td>\n","      <td>7.108655e+07</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>123.660004</td>\n","      <td>124.980003</td>\n","      <td>122.139999</td>\n","      <td>123.540001</td>\n","      <td>121.602386</td>\n","      <td>9.329200e+07</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>152.239998</td>\n","      <td>154.250000</td>\n","      <td>150.400002</td>\n","      <td>152.439995</td>\n","      <td>151.122581</td>\n","      <td>1.284988e+08</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>198.020004</td>\n","      <td>199.619995</td>\n","      <td>197.000000</td>\n","      <td>198.110001</td>\n","      <td>198.110001</td>\n","      <td>4.265100e+08</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\" onclick=\"convertToInteractive('df-7bca56ee-b05c-433c-b922-3c37b347bb21')\"\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: 50%;\n","      cursor: pointer;\n","      display: none;\n","      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Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-523b01db-e154-4db4-8a0c-4c71e3adff85\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-523b01db-e154-4db4-8a0c-4c71e3adff85')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-523b01db-e154-4db4-8a0c-4c71e3adff85 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Select only valid columns or specify the value of numeric_only to silence this warning.\n","  df.corr()\n"]},{"output_type":"execute_result","data":{"text/plain":["               Open      High       Low     Close  Adj Close    Volume\n","Open       1.000000  0.999749  0.999695  0.999364   0.999311 -0.467459\n","High       0.999749  1.000000  0.999633  0.999695   0.999626 -0.461596\n","Low        0.999695  0.999633  1.000000  0.999711   0.999685 -0.475142\n","Close      0.999364  0.999695  0.999711  1.000000   0.999949 -0.468849\n","Adj Close  0.999311  0.999626  0.999685  0.999949   1.000000 -0.470461\n","Volume    -0.467459 -0.461596 -0.475142 -0.468849  -0.470461  1.000000"],"text/html":["\n","  <div id=\"df-b93d7b8f-086c-4bfe-b0bc-9b8b7bb3a59f\" 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>Open</th>\n","      <th>High</th>\n","      <th>Low</th>\n","      <th>Close</th>\n","      <th>Adj Close</th>\n","      <th>Volume</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Open</th>\n","      <td>1.000000</td>\n","      <td>0.999749</td>\n","      <td>0.999695</td>\n","      <td>0.999364</td>\n","      <td>0.999311</td>\n","      <td>-0.467459</td>\n","    </tr>\n","    <tr>\n","      <th>High</th>\n","      <td>0.999749</td>\n","      <td>1.000000</td>\n","      <td>0.999633</td>\n","      <td>0.999695</td>\n","      <td>0.999626</td>\n","      <td>-0.461596</td>\n","    </tr>\n","    <tr>\n","      <th>Low</th>\n","      <td>0.999695</td>\n","      <td>0.999633</td>\n","      <td>1.000000</td>\n","      <td>0.999711</td>\n","      <td>0.999685</td>\n","      <td>-0.475142</td>\n","    </tr>\n","    <tr>\n","      <th>Close</th>\n","      <td>0.999364</td>\n","      <td>0.999695</td>\n","      <td>0.999711</td>\n","      <td>1.000000</td>\n","      <td>0.999949</td>\n","      <td>-0.468849</td>\n","    </tr>\n","    <tr>\n","      <th>Adj Close</th>\n","      <td>0.999311</td>\n","      <td>0.999626</td>\n","      <td>0.999685</td>\n","      <td>0.999949</td>\n","      <td>1.000000</td>\n","      <td>-0.470461</td>\n","    </tr>\n","    <tr>\n","      <th>Volume</th>\n","      <td>-0.467459</td>\n","      <td>-0.461596</td>\n","      <td>-0.475142</td>\n","      <td>-0.468849</td>\n","      <td>-0.470461</td>\n","      <td>1.000000</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\" onclick=\"convertToInteractive('df-b93d7b8f-086c-4bfe-b0bc-9b8b7bb3a59f')\"\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: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-b93d7b8f-086c-4bfe-b0bc-9b8b7bb3a59f button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":30}]},{"cell_type":"code","source":["# Korelasyon matrisi\n","correlation_matrix = df.corr()\n","\n","# Korelasyon ısı haritası\n","plt.figure(figsize=(8, 6))\n","sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\", linewidths=.5)\n","plt.title('Korelasyon Isı Haritası')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":600},"id":"m6tjqoOwl-GZ","executionInfo":{"status":"ok","timestamp":1703919925942,"user_tz":-180,"elapsed":672,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"2b2b1096-60cd-4269-fa0a-570bfd76363e"},"execution_count":31,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-31-8a577b0c9e72>:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n","  correlation_matrix = df.corr()\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 800x600 with 2 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAn0AAAIQCAYAAAD0GdhJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB+ZUlEQVR4nO3dd1QU19sH8O+CsCC9NxXEgmLBimIvGFTsxm7sXdRINJEkijXYNbHE3hJ97TVGDWLF3itiw4aCItJEVmTn/cOfqyugzDorLHw/58w52Tt3Zp99XMjlmTt3ZIIgCCAiIiKifE0vtwMgIiIiIu3joI+IiIioAOCgj4iIiKgA4KCPiIiIqADgoI+IiIioAOCgj4iIiKgA4KCPiIiIqADgoI+IiIioAOCgj4iIiKgA4KCPKA8ZP348ZDJZbodBGjp06BBkMhkOHTqU26EQEWXCQR8VWKtWrYJMJsPZs2fV2hMTE+Ht7Q0jIyPs3bs3l6LL/94NkDZv3qyV8zdo0ADly5fPct+9e/cgk8kwc+ZMrbz3h9atW4e5c+dq/X2IiD6nUG4HQJSXJCUl4ZtvvsHly5exbds2NG3aNLdDIh1Sr149vHr1CoaGhqq2devW4erVq/j+++9zLzAiIrDSR6SSnJwMPz8/XLx4EVu2bEGzZs2++JwvX76UIDLK69LS0qBUKqGnpwcjIyPo6fFXKxHlPfzNRAQgJSUFTZs2xfnz57Flyxb4+/ur7b9w4QKaNWsGc3NzmJqaonHjxjh58qRan3eXiw8fPowhQ4bA3t4eRYoUUe3fs2cP6tatCxMTE5iZmcHf3x/Xrl37bGwrV65Eo0aNYG9vD7lcDk9PT/z555+Z+p09exZ+fn6wtbWFsbExihcvjj59+gAABEGAm5sbWrdunem4tLQ0WFhYYODAgaq2p0+fom/fvnBwcICRkRG8vLywevVqteM+vES6ZMkSlChRAnK5HNWrV8eZM2c++7mykpycjO+//x5ubm6Qy+Wwt7dHkyZNcP78eVWfXr16wc3NTaPzf0p8fDxGjRqFChUqwNTUFObm5mjWrBkuXbqk1u/dZen169fj119/hYuLCwoXLoykpKRMc/oaNGiA3bt34/79+5DJZJDJZKrYX79+jXHjxqFq1aqwsLCAiYkJ6tati4MHD2aKbf369ahatSrMzMxgbm6OChUq4Pfff88UE+cSEtGn8PIuFXgvX75Es2bNcObMGWzevBktWrRQ23/t2jXUrVsX5ubm+PHHH2FgYIDFixejQYMGOHz4MGrUqKHWf8iQIbCzs8O4ceNUlb6//voLPXv2hJ+fH6ZNm4bU1FT8+eefqFOnDi5cuPDJQcyff/6JcuXKoVWrVihUqBB27dqFIUOGQKlUYujQoQDeDtK++eYb2NnZYcyYMbC0tMS9e/ewdetWAIBMJkP37t0xffp0xMfHw9raWnX+Xbt2ISkpCd27dwcAvHr1Cg0aNMDt27cREBCA4sWLY9OmTejVqxcSEhIwYsQItfjWrVuH5ORkDBw4EDKZDNOnT0e7du1w9+5dGBgYiPq3GDRoEDZv3oyAgAB4enri+fPnCA8PR0REBKpUqSLqXACQkZGBuLi4TO0vXrzI1Hb37l1s374dHTp0QPHixREbG4vFixejfv36uH79OpydndX6T5o0CYaGhhg1ahQUCoXaJd13fvnlFyQmJuLRo0eYM2cOAMDU1BTA26kEy5YtQ5cuXdC/f38kJydj+fLl8PPzw+nTp1GpUiUAQGhoKLp06YLGjRtj2rRpAICIiAgcO3Ys078FEdEnCUQF1MqVKwUAgqurq2BgYCBs3749y35t2rQRDA0NhTt37qjaHj9+LJiZmQn16tXLdL46deoIb968UbUnJycLlpaWQv/+/dXOGxMTI1hYWKi1BwcHCx//WKampmaKyc/PT3B3d1e93rZtmwBAOHPmTLafNzIyUgAg/Pnnn2rtrVq1Etzc3ASlUikIgiDMnTtXACD8/fffqj6vX78WfHx8BFNTUyEpKUkQBEGIiooSAAg2NjZCfHy8qu+OHTsEAMKuXbuyjUUQBOHgwYMCAGHTpk2qNgsLC2Ho0KGfPK5nz56Cq6vrJ/sIgiDUr19fAPDJbcaMGar+aWlpQkZGhto5oqKiBLlcLkycODFT3O7u7pn+bd7tO3jwoKrN398/y3jfvHkjKBQKtbYXL14IDg4OQp8+fVRtI0aMEMzNzdW+Ux/L6n2JiD7Gy7tU4MXGxsLIyAhFixbNtC8jIwP//fcf2rRpA3d3d1W7k5MTunbtivDwcCQlJakd079/f+jr66teh4aGIiEhAV26dEFcXJxq09fXR40aNbK8nPchY2Nj1X8nJiYiLi4O9evXx927d5GYmAgAsLS0BAD8888/SE9Pz/I8pUuXRo0aNbB27VpVW3x8PPbs2YNu3bqplor5999/4ejoiC5duqj6GRgYYPjw4UhJScHhw4fVztupUydYWVmpXtetWxfA28qZWJaWljh16hQeP34s+tisuLm5ITQ0NNP2999/Z+orl8tVc/EyMjLw/PlzmJqawsPDQ+3y8js9e/ZU+7cRS19fX1UdVCqViI+Px5s3b1CtWjW197O0tMTLly8RGhqq8XsREQGc00eExYsXw9DQEE2bNkVkZKTavmfPniE1NRUeHh6ZjitbtiyUSiUePnyo1l68eHG117du3QIANGrUCHZ2dmrbf//9h6dPn34yvmPHjsHX1xcmJiawtLSEnZ0dfv75ZwBQDfrq16+P9u3bY8KECbC1tUXr1q2xcuVKKBQKtXP16NEDx44dw/379wEAmzZtQnp6Or777jtVn/v376NUqVKZbkYoW7asav+HihUrpvb63QAwq0uonzN9+nRcvXoVRYsWhbe3N8aPH6/R4PEdExMT+Pr6Ztpq166dqa9SqcScOXNQqlQpyOVy2Nraws7ODpcvX1bl+UMf/ztrYvXq1ahYsSKMjIxgY2MDOzs77N69W+39hgwZgtKlS6NZs2YoUqQI+vTpw6WEiEgjHPRRgefp6Yl///0Xr169QpMmTTIN4sT6uPqjVCoBvJ3Xl1XVaceOHdme686dO2jcuDHi4uIwe/Zs7N69G6GhoRg5cqTaud+td3fixAkEBAQgOjoaffr0QdWqVZGSkqI6X+fOnWFgYKCq9v3999+oVq1aloPanPqwqvkhQRBEn6tjx464e/cu5s2bB2dnZ8yYMQPlypXDnj17NI4vp3777TcEBgaiXr16+Pvvv7Fv3z6EhoaiXLlyqjx/6EuqfMDb3Pfq1QslSpTA8uXLsXfvXoSGhqJRo0Zq72dvb4+LFy9i586daNWqFQ4ePIhmzZqhZ8+eX/T+RFTw8EYOIgDe3t7Yvn07/P390aRJExw9elRVjStcuHCmCiAA3LhxA3p6elleFv5QiRIlALz9n7evr6+ouHbt2gWFQoGdO3eqVdSyuyRcs2ZN1KxZE1OmTMG6devQrVs3rF+/Hv369QMAWFtbw9/fH2vXrkW3bt1w7NixTAsHu7q64vLly6olSD78vO/2a5OTkxOGDBmCIUOG4OnTp6hSpQqmTJkiyRI6n7J582Y0bNgQy5cvV2tPSEiAra2txufN7gkrmzdvhru7O7Zu3arWJzg4OFNfQ0NDtGzZEi1btoRSqcSQIUOwePFijB07FiVLltQ4NiIqWFjpI/qfxo0b4//+7/9w+/ZtNG3aFElJSdDX18c333yDHTt24N69e6q+sbGxWLduHerUqQNzc/NPntfPzw/m5ub47bffspxv9+zZs2yPfVdF+7BqlpiYiJUrV6r1e/HiRabK2ru7Pz++xPvdd9/h+vXrGD16NPT19dG5c2e1/c2bN0dMTAw2bNiganvz5g3mzZsHU1NT1K9f/xOfVnMZGRmZLqPa29vD2dk502fQBn19/Uw53LRpE6Kjo7/ovCYmJlleHs7q3/bUqVM4ceKEWr/nz5+rvdbT00PFihUBZP63JSL6FFb6iD7Qtm1bLF26FH369EGrVq2wd+9eTJ48GaGhoahTpw6GDBmCQoUKYfHixVAoFJg+ffpnz2lubo4///wT3333HapUqYLOnTvDzs4ODx48wO7du1G7dm3Mnz8/y2O/+eYbVZVn4MCBSElJwdKlS2Fvb48nT56o+q1evRoLFy5E27ZtUaJECSQnJ2Pp0qUwNzdH8+bN1c7p7+8PGxsbbNq0Cc2aNYO9vb3a/gEDBmDx4sXo1asXzp07Bzc3N2zevFlVFTQzM9Mgs5+XnJyMIkWK4Ntvv4WXlxdMTU2xf/9+nDlzBrNmzdLKe36oRYsWmDhxInr37o1atWrhypUrWLt2rdoNPJqoWrUqNmzYgMDAQFSvXh2mpqZo2bIlWrRoga1bt6Jt27bw9/dHVFQUFi1aBE9PT7VL8v369UN8fDwaNWqEIkWK4P79+5g3bx4qVaqkmmdJRJQTHPQRfaR3796qhXo7dOiAbdu24ejRowgKCkJISAiUSiVq1KiBv//+O9Mafdnp2rUrnJ2dMXXqVMyYMQMKhQIuLi6oW7cuevfune1xHh4e2Lx5M3799VeMGjUKjo6OGDx4MOzs7FQLLwNvb+Q4ffo01q9fj9jYWFhYWMDb2xtr167NdMOBoaEhOnXqhIULF6rdwPGOsbExDh06hDFjxmD16tVISkqCh4cHVq5ciV69euUsiRooXLgwhgwZgv/++w9bt26FUqlEyZIlsXDhQgwePFhr7/vOzz//jJcvX2LdunXYsGEDqlSpgt27d2PMmDFfdN4hQ4bg4sWLWLlyJebMmQNXV1e0bNkSvXr1QkxMDBYvXox9+/bB09MTf//9NzZt2qS2yHL37t2xZMkSLFy4EAkJCXB0dESnTp0wfvx4PvmDiESRCZrMtiYinTZy5EgsX74cMTExKFy4cG6HQ0REXwH/TCQqYNLS0vD333+jffv2HPARERUgvLxLVEA8ffoU+/fvx+bNm/H8+XM+wouIqIDhoI+ogLh+/Tq6desGe3t7/PHHH6q7e4mIqGDg5V2iAqJBgwYQBAGxsbEICAjI7XCIiPKNI0eOoGXLlnB2doZMJsP27ds/e8yhQ4dQpUoVyOVylCxZEqtWrdJ6nBz0EREREX2Bly9fwsvLCwsWLMhR/6ioKPj7+6Nhw4a4ePEivv/+e/Tr1w/79u3Tapy8e5eIiIhIIjKZDNu2bUObNm2y7fPTTz9h9+7duHr1qqqtc+fOSEhI0OqztVnpIyIiIvqIQqFAUlKS2ibVU3BOnDiR6bGcfn5+mZ7IIzXeyEFEREQ6abeBh9bOfeaXLpgwYYJaW3BwMMaPH//F546JiYGDg4Nam4ODA5KSkvDq1SsYGxt/8XtkJU8N+rT5j1dQ+KdHMo8SYS6lw1xKg3mUjn96JOq2PprbYeQLR3fUze0QtCIoKAiBgYFqbXK5PJeikUaeGvQRERER5ZTMQKa1c8vlcq0N8hwdHREbG6vWFhsbC3Nzc61V+QDO6SMiIiL6qnx8fBAWFqbWFhoaCh8fH62+Lyt9REREpJP0Cmmv0idGSkoKbt++rXodFRWFixcvwtraGsWKFUNQUBCio6OxZs0aAMCgQYMwf/58/Pjjj+jTpw8OHDiAjRs3Yvfu3VqNk5U+IiIioi9w9uxZVK5cGZUrVwYABAYGonLlyhg3bhwA4MmTJ3jw4IGqf/HixbF7926EhobCy8sLs2bNwrJly+Dn56fVOFnpIyIiIp0kM8gbtat3TzzKTlZP22jQoAEuXLigxagy46CPiIiIdFJeubyrK/LGEJmIiIiItIqVPiIiItJJ2lyyJT9ipY+IiIioAGClj4iIiHQS5/SJw0ofERERUQHASh8RERHpJM7pE4eVPiIiIqICgJU+IiIi0kmc0ycOK31EREREBQArfURERKSTZPqs9InBQR8RERHpJD0O+kTh5V0iIiKiAoCVPiIiItJJMj1W+sRgpY+IiIioAGClj4iIiHSSTJ+1KzE0GvS9fPkSU6dORVhYGJ4+fQqlUqm2/+7du5IER0RERETS0GjQ169fPxw+fBjfffcdnJycIJPxmjoRERF9Xbx7VxyNBn179uzB7t27Ubt2banjISIiIiIt0GjQZ2VlBWtra6ljISIiIsox3r0rjkYzICdNmoRx48YhNTVV6niIiIiIckRPX6a1LT/SqNI3a9Ys3LlzBw4ODnBzc4OBgYHa/vPnz0sSHBERERFJQ6NBX5s2bSQOg4iIiEgcPntXHI0GfcHBwVLHQURERERapPGqhgkJCVi2bBmCgoIQHx8P4O1l3ejoaMmCIyIiIsqOTE9Pa1t+pFGl7/Lly/D19YWFhQXu3buH/v37w9raGlu3bsWDBw+wZs0aqeMkIiIioi+g0VA2MDAQvXr1wq1bt2BkZKRqb968OY4cOSJZcNpiXacaqm37E43vH4V/eiQcWjX+/DH1vFHn9FY0TbmCBhH/oUiPtpn6uA7uioa3wtA0+TJqHdsIi+oVtBF+nsJcSoe5lAbzKB3mUnp9u7pi+8oa2L+xFuZMLI8iTkafP+h/urUvgqM76mJYX3dVm6O9HEd31M1ya1DLVhsfIU+R6cm0tuVHGg36zpw5g4EDB2Zqd3FxQUxMzBcHpW36JoWRdDkSV4dPyFF/Y7ciqL5zMZ4fOoXwaq0RNW81KiyeDNsmdVR9nDo0Q9kZQbg1eQHCvdsi+fIN1Ni9HIZ2+Xs9Q+ZSOsylNJhH6TCX0urargja+ztj5p+3MHD0RbxKU2LW+PIwNPj8AKNMSVO08nPC7agUtfancQq07nlSbVu+7j5SX73BqfPx2voopKM0urwrl8uRlJSUqf3mzZuws7P74qC07dm+I3i2L+cVSdcBnfEq6hEifpwGAEi5cRfWtaqi+IheiAsNBwAU/743Hi7fiEertwIArgwJhn2zBijaqz3uzFgq/YfII5hL6TCX0mAepcNcSqtjSxes2fQA4affDsamzI3EjtU1UbemLcKOPsv2OGMjPYwL9MD0BbfQs0NRtX1KJRCfkK7WVremDQ6Ex+FVmlL6D5HH5Nf19LRFo0pfq1atMHHiRKSnv/2iyWQyPHjwAD/99BPat28vaYB5gWXNSog7cEKt7VloOKxqVgIAyAwMYFGlHOLCjr/vIAiIO3AcljUrf8VI8z7mUjrMpTSYR+kwl9lzcjCCjbUhzl5KULW9TM1AxM1klPMw++SxIweWxIlzL3Dug2OzU7qEKUq7m2L3/rx/1U0KvLwrjkaDvlmzZiElJQX29vZ49eoV6tevj5IlS8LMzAxTpkyROsZcJ3ewhSI2Tq1NERsHAwsz6BnJYWhrBb1ChaB4+vyjPs8hd8z/cyrEYC6lw1xKg3mUDnOZPRurtw8xeJHwWq09PuE1rK0Msz2ucV07lHY3xeI1UTl6nxa+Drj3MBVXbyRrHizlWxpd3rWwsEBoaCjCw8Nx+fJlpKSkoEqVKvD19c3R8QqFAgqFQq1NLpdrEgoREVGe06S+HUYNLqV6/dOka6LPYW9riOH93BE47gpepwuf7W9oqAffevZYvfGB6PfSVfl1aRVt0WjQ906dOnVQp06dz3f8SEhICCZMUJ8YHBwcjOpfEowWKWLjIHdQ/ytU7mCL9MRkKNMUeB33Aso3byC3t/mojw0UMep/9RZ0zKV0mEtpMI/SYS7fCz8dj+uR7x9JamDwdnBiZWmI5y/ez8GztjTErY9uznjHo4QZrC0NsWxOFVVbIX0ZvMpZoJ2/Mxp/Gw7lB9P2GtayhZFcD/sOPpX401B+ofEQOSwsDC1atECJEiVQokQJtGjRAvv378/RsUFBQUhMTFTbgoKCNA1F6xJOXoRNo5pqbbaNa+HFyYsAACE9HYnnr8G2kc/7DjIZbBr6IOHkha8Yad7HXEqHuZQG8ygd5vK9V68yEB2TptruPUzF8/jXqFrRUtWnsLE+ypY2w7XIrC/Fnr2cgB7DzqHP9+dVW8StZIQefoo+359XG/ABgL+vA46diUdCUnqW58uPOKdPHI0GfQsXLkTTpk1hZmaGESNGYMSIETA3N0fz5s2xYMGCzx4vl8thbm6utn3Ny7v6JoVh7lUG5l5lAACFixeBuVcZGBV1AgB4TA6E18ppqv73l6xH4eJFUSZkNEw83OE6qCucOjRD1O+rVH2i5q5E0b4d4fJdG5iWcUf5BeNRyMQYD/93h1p+xVxKh7mUBvMoHeZSWht3RaNnx6Ko7W0Nd9fC+PX70nger8DRk++rnHMnVkC75m/z++pVBqIepKptaWkZSEx+g6gHqWrndnE0glc5C+z6r2DcwEGa0ejy7m+//YY5c+YgICBA1TZ8+HDUrl0bv/32G4YOHSpZgNpgUbU8fML+Ur32nPkzAODhmq243DcIcic7GP/vlxoAvLr3CGdaDYTnrCC4DeuBtEcxuDLwV9USBADwZNMeGNpZo3TwcMgd7ZB0KQKnW/TD648mLOc3zKV0mEtpMI/SYS6ltW7rIxgb6WP0kFIwNSmEKxGJGDXhmtp8PWdHI1iYG4g+t7+vA549V+DMxRdShpzncckWcWSCIHx+duhHTE1NcfHiRZQsWVKt/datW6hcuTJSUrKen/A5uw08NDqO3vNPj2QeJcJcSoe5lAbzKB3/9EjUbX00t8PIF47uqJtr732tdSOtnbvcjgNaO3du0Xidvm3btmVq37FjB1q0aPHFQRERERF9Duf0iaPR5V1PT09MmTIFhw4dgo/P2wm5J0+exLFjx/DDDz/gjz/+UPUdPny4NJESERERfYBLtoij0aBv+fLlsLKywvXr13H9+nVVu6WlJZYvX656LZPJOOgjIiIiygM0GvRFRb1dGTwu7u0dR7a2+XsldSIiIsp78utlWG0RXRdNSEjA0KFDYWtrCwcHBzg4OMDW1hYBAQFISEjQQohERERE9KVEVfri4+Ph4+OD6OhodOvWDWXLlgUAXL9+HatWrUJYWBiOHz8OKysrrQRLRERE9A4rfeKIGvRNnDgRhoaGuHPnDhwcHDLt++abbzBx4kTMmTNH0iCJiIiI6MuIury7fft2zJw5M9OADwAcHR0xffr0LJdyISIiIpIal2wRR9Sg78mTJyhXrly2+8uXL4+YGD4ChoiIiCivEXV519bWFvfu3UORIkWy3B8VFQVra2tJAiMiIiL6FK7TJ46obPn5+eGXX37B69evM+1TKBQYO3YsmjZtKllwRERERNnR05dpbcuPRN/IUa1aNZQqVQpDhw5FmTJlIAgCIiIisHDhQigUCvz111+fPxERERERfVWiBn1FihTBiRMnMGTIEAQFBUEQBABvn7zRpEkTzJ8/H0WLFtVKoEREREQfyq83XGiL6CdyFC9eHHv27MGLFy9w69YtAEDJkiU5l4+IiIgoD9N4BqSVlRW8vb3h7e3NAR8RERF9dTI9Pa1tYi1YsABubm4wMjJCjRo1cPr06U/2nzt3Ljw8PGBsbIyiRYti5MiRSEtL0zQVOcLbXoiIiIi+wIYNGxAYGIjg4GCcP38eXl5e8PPzw9OnT7Psv27dOowZMwbBwcGIiIjA8uXLsWHDBvz8889ajZODPiIiItJJeWVx5tmzZ6N///7o3bs3PD09sWjRIhQuXBgrVqzIsv/x48dRu3ZtdO3aFW5ubvjmm2/QpUuXz1YHvxQHfUREREQaev36Nc6dOwdfX19Vm56eHnx9fXHixIksj6lVqxbOnTunGuTdvXsX//77L5o3b67VWEXfyEFERESUF2jz7l2FQgGFQqHWJpfLIZfL1dri4uKQkZGR6RG1Dg4OuHHjRpbn7tq1K+Li4lCnTh0IgoA3b95g0KBBvLxLRERElBVt3sgREhICCwsLtS0kJESSuA8dOoTffvsNCxcuxPnz57F161bs3r0bkyZNkuT82WGlj4iIiOgjQUFBCAwMVGv7uMoHvH1Erb6+PmJjY9XaY2Nj4ejomOW5x44di++++w79+vUDAFSoUAEvX77EgAED8Msvv0BPS4+XY6WPiIiIdJI2b+SQy+UwNzdX27Ia9BkaGqJq1aoICwtTtSmVSoSFhcHHxyfLuFNTUzMN7PT19QFA9eALbWClj4iIiOgLBAYGomfPnqhWrRq8vb0xd+5cvHz5Er179wYA9OjRAy4uLqrLwy1btsTs2bNRuXJl1KhRA7dv38bYsWPRsmVL1eBPGzjoIyIiIp2kySLK2tCpUyc8e/YM48aNQ0xMDCpVqoS9e/eqbu548OCBWmXv119/hUwmw6+//oro6GjY2dmhZcuWmDJlilbj5KCPiIiI6AsFBAQgICAgy32HDh1Se12oUCEEBwcjODj4K0T2wft+1XcjIiIikopMe0u25Ed5oy5KRERERFrFSh8RERHpJG0uzpwfcdBHREREOimv3MihK5gtIiIiogKAlT4iIiLSSby8Kw4rfUREREQFACt9REREpJM4p08cmaDNh7wRERERaUnM6O5aO7fjjL+1du7ckqcqfbsNPHI7BJ3nnx7JPEqEuZQOcykN5lE6/umRqNc2PLfDyBeObKuTa+/NOX3isC5KREREVADkqUofERERUU6x0icOB31ERESkm3gjhyjMFhEREVEBwEofERER6SSZjJd3xWClj4iIiKgAYKWPiIiIdBIXZxaH2SIiIiIqAFjpIyIiIp3EJVvEYaWPiIiIqABgpY+IiIh0E+f0icJsERERERUArPQRERGRTuKcPnE46CMiIiKdJJPxgqUYzBYRERFRAcBKHxEREekmXt4VhZU+IiIiogKAlT4iIiLSSXwMmzjMFhEREVEBwEofERER6SQu2SIOK31EREREBQArfURERKSbuE6fKBz0ERERkU7i5V1xOEQmIiIiKgBY6SMiIiLdxCVbRNFo0JeRkYFVq1YhLCwMT58+hVKpVNt/4MABSYIjIiIiImloNOgbMWIEVq1aBX9/f5QvXx4yGa+pExER0dfF8Yc4Gg361q9fj40bN6J58+ZSx0NEREREWqDRoM/Q0BAlS5aUOhYiIiKinOOcPlE0ytYPP/yA33//HYIgSB0PEREREWlBjit97dq1U3t94MAB7NmzB+XKlYOBgYHavq1bt0oTHREREVE2uE6fODmu9FlYWKhtbdu2Rf369WFra5tpX15nXacaqm37E43vH4V/eiQcWjX+/DH1vFHn9FY0TbmCBhH/oUiPtpn6uA7uioa3wtA0+TJqHdsIi+oVtBF+nsJcSoe5lAbzKB3mUnp9uhTDtuXeCF3vg9njy6OIk1GOj+3WrgiObKuDYX2KZ9pXzsMMcyeWx77/88GetTUxb3IFGBoWgEufMj3tbflQjit9K1eu1GYcX5W+SWEkXY7Ew1VbUG3zgs/2N3Yrguo7F+PBkvW42GMUbBr5oMLiyUh78gxxoeEAAKcOzVB2RhCuDg1GwulLKD68J2rsXo5D5Zri9bN4bX+kXMNcSoe5lAbzKB3mUlpd27qgvb8zQv64icexaejX1RUzx5VHj+Hn8Dr909OlypQ0RatvHHE76mWmfeU8zDBjbDms3foIc5feRUaGgJJuJhCUnIJF6grk4szP9h3Bs31HctzfdUBnvIp6hIgfpwEAUm7chXWtqig+opfqF1nx73vj4fKNeLT67aXtK0OCYd+sAYr2ao87M5ZK/yHyCOZSOsylNJhH6TCX0urQwgV/bXqI8NNvB7dTfr+J7StroE4NGxwIj8v2OGMjPYwd6YHpC2+hR4dimfYH9C6OLbsfY+3WR6q2h49fSf8B8iJe3hVFo/pl5cqVUaVKlUxb1apVUbt2bfTs2RMHDx6UOtZcY1mzEuIOnFBrexYaDqualQAAMgMDWFQph7iw4+87CALiDhyHZc3KXzHSvI+5lA5zKQ3mUTrMZfacHOSwsTbE2UsJqraXqRmIuJWM8h7mnzx25IASOHE2HucuJ2baZ2lhgHIe5niRmI6FIRWxfaU3/phcARXKfvqcVDBpNOhr2rQp7t69CxMTEzRs2BANGzaEqakp7ty5g+rVq+PJkyfw9fXFjh07pI43V8gdbKGIVf8rTBEbBwMLM+gZyWFoawW9QoWgePr8oz7PIXe0/Zqh5nnMpXSYS2kwj9JhLrNnY2kIAHiR+FqtPT7hNawtDbI6BADQqI4tSrubYsnf97Lc7+zwdk5g787FsCs0BqMnXsPNOymYM0HcfEFdJZPpaW3LjzS6vBsXF4cffvgBY8eOVWufPHky7t+/j//++w/BwcGYNGkSWrdunel4hUIBhUKh1iaXyzUJhYiIKM9pUs8OPwx6v57tT1OuiT6HvY0hhvd1R+D4q9nO+Xt3dXPnvhjsOfAUAHArKgpVK1qieWMHLPn7vvjgKd/SaNC3ceNGnDt3LlN7586dUbVqVSxduhRdunTB7Nmzszw+JCQEEyZMUGsLDg5GdU2C+QoUsXGQO6j/FSp3sEV6YjKUaQq8jnsB5Zs3kNvbfNTHBoqY7OdpFETMpXSYS2kwj9JhLt8LPx2P6zcvqF4bGLytHFlZGOL5i3RVu7WlYZY3ZwBA6RKmsLY0xLJZ7y99F9KXwcvTHG2bO8O34zE8f/G2cnjvUarasfcfpcLBtgAUUzinTxSN6pdGRkY4fvx4pvbjx4/DyOhtOVmpVKr++2NBQUFITExU24KCgjQJ5atIOHkRNo1qqrXZNq6FFycvAgCE9HQknr8G20Y+7zvIZLBp6IOEkxdA7zGX0mEupcE8Soe5fO9VWgaiY9JU272HqXge/xpVK1qq+hQ21kfZUma4GpmU5TnOXU5EzxHn0TfwgmqLuJWM0CPP0DfwApRK4MlTBZ49V6CYs7HasUWcjRHzTJHleang0qjSN2zYMAwaNAjnzp1D9epv63NnzpzBsmXL8PPPPwMA9u3bh0qVKmV5vFwuz9XLufomhWFS8v0dUIWLF4G5Vxm8jk9E2sMn8JgcCCMXB1zq/RMA4P6S9XAd0g1lQkbj4aotsG1YE04dmuFMq4Gqc0TNXQmvFdOQcO4qEs9chtvwnihkYoyHq/P3QtXMpXSYS2kwj9JhLqW16Z9o9OhQFI+evMKT2DT07eqK5/GvEX7q/RzHORPK4+jJ59i65wlepWUg6oF6BS9NoURScrpa+/rt0ejduRhu33uJ21Ev0bShPVxdjDFuxo2v9tlyi4yPYRNFo0Hfr7/+iuLFi2P+/Pn466+/AAAeHh5YunQpunbtCgAYNGgQBg8eLF2kErKoWh4+YX+pXnvOfDtQfbhmKy73DYLcyQ7GRZ1U+1/de4QzrQbCc1YQ3Ib1QNqjGFwZ+KtqCQIAeLJpDwztrFE6eDjkjnZIuhSB0y364fVHE5bzG+ZSOsylNJhH6TCX0lq3LRpGRvoYNbgkTE0K4UpEEkZNUp+v5+xoBAvz7G/syMqmfx7D0FAPw/q4w8y0EO7ce4nACdfwOCZN6o+Q98h4eVcMmZCHHqC728Ajt0PQef7pkcyjRJhL6TCX0mAepeOfHol6bcM/35E+68i2Orn23qkrgrV27sJ9Jny+k45hXZSIiIh0k56e9jaRFixYADc3NxgZGaFGjRo4ffr0J/snJCRg6NChcHJyglwuR+nSpfHvv/9qmokcyfHlXWtra9y8eRO2trawsrKC7BMl1fj4/P0oHSIiIqJ3NmzYgMDAQCxatAg1atTA3Llz4efnh8jISNjb22fq//r1azRp0gT29vbYvHkzXFxccP/+fVhaWmo1zhwP+ubMmQMzMzPVf39q0EdERESkdXlkLDJ79mz0798fvXv3BgAsWrQIu3fvxooVKzBmzJhM/VesWIH4+HgcP34cBgZv53C6ublpPc4cD/p69uyJpKQkKBQKtGvXTpsxEREREemE169f49y5c2pLz+np6cHX1xcnTpzI8pidO3fCx8cHQ4cOxY4dO2BnZ4euXbvip59+gr6+vtZiFXX3rqWlZY4qfBkZGRoHRERERJQT2lyyJbunh3285FxcXBwyMjLg4OCg1u7g4IAbN7JeNufu3bs4cOAAunXrhn///Re3b9/GkCFDkJ6ejuBg7d2cImrQd/DgQdV/C4KA5s2bY9myZXBxcZE8MCIiIqLckt3Tw8aPH//F51YqlbC3t8eSJUugr6+PqlWrIjo6GjNmzMg7g7769eurvdbX10fNmjXh7u4uaVBEREREnyXTXqUvKCgIgYGBam1ZPVjC1tYW+vr6iI2NVWuPjY2Fo6Njlud2cnKCgYGB2qXcsmXLIiYmBq9fv4ahoaEEnyAzLtlCREREuklPprVNLpfD3Nxcbctq0GdoaIiqVasiLCxM1aZUKhEWFgYfH59M/QGgdu3auH37NpRKpart5s2bcHJy0tqAD+Cgj4iIiOiLBAYGYunSpVi9ejUiIiIwePBgvHz5UnU3b48ePdRu9Bg8eDDi4+MxYsQI3Lx5E7t378Zvv/2GoUOHajVOjR7D9iEu3UJERES5QabFy7tidOrUCc+ePcO4ceMQExODSpUqYe/evaqbOx48eAC9D246KVq0KPbt24eRI0eiYsWKcHFxwYgRI/DTTz9pNU5Rg76Pl2pJS0vDoEGDYGJiota+dWv+f3A2ERER0TsBAQEICAjIct+hQ4cytfn4+ODkyZNajkqdqEGfhYWF2uvu3btLGgwRERFRjunxaqMYogZ9K1eu1FYcRERERKRFXzynj4iIiChX5JE5fbqC2SIiIiIqAFjpIyIiIt3EFURE4aCPiIiIdJMWn72bHzFbRERERAUAK31ERESkm3gjhyjMFhEREVEBwEofERER6SYuziwKK31EREREBQArfURERKSbOKdPFGaLiIiIqABgpY+IiIh0ExdnFoWDPiIiItJNXJxZFGaLiIiIqABgpY+IiIh0Ey/visJKHxEREVEBwEofERER6SYu2SIKs0VERERUALDSR0RERLqJd++KwmwRERERFQAyQRCE3A6CiIiISKy0fcu1dm4jv75aO3duyVOXd3cbeOR2CDrPPz2SeZQIcykd5lIazKN0/NMjUafl4dwOI18I31U/996cN3KIwmwRERERFQB5qtJHRERElGNcnFkUVvqIiIiICgBW+oiIiEg3cckWUZgtIiIiogKAlT4iIiLSSQLn9InCSh8RERFRAcBKHxEREekmrtMnCrNFREREVACw0kdERES6iZU+UTjoIyIiIp3EGznE4RCZiIiIqABgpY+IiIh0Ey/visJsERERERUArPQRERGRbuKcPlFY6SMiIiIqAFjpIyIiIt2kx9qVGMwWERERUQHASh8RERHpJK7TJw4HfURERKSbuGSLKMwWERERUQHASh8RERHpJIGVPlGYLSIiIqICgJU+IiIi0k28kUMUVvqIiIiICgBW+oiIiEgncU6fOMwWERERUQGg0aCvR48eWLlyJe7cuSN1PEREREQ5I5Npb8uHNBr0GRoaIiQkBKVKlULRokXRvXt3LFu2DLdu3ZI6PiIiIqKsyfS0t4m0YMECuLm5wcjICDVq1MDp06dzdNz69eshk8nQpk0b0e8plkaDvmXLluHmzZt4+PAhpk+fDlNTU8yaNQtlypRBkSJFpI6RiIiIKM/asGEDAgMDERwcjPPnz8PLywt+fn54+vTpJ4+7d+8eRo0ahbp1636VOL9oTp+VlRVsbGxgZWUFS0tLFCpUCHZ2dlLFRkRERJQtQSbT2ibG7Nmz0b9/f/Tu3Ruenp5YtGgRChcujBUrVmR7TEZGBrp164YJEybA3d39S1ORIxoN+n7++WfUqlULNjY2GDNmDNLS0jBmzBjExMTgwoULUscoOes61VBt259ofP8o/NMj4dCq8eePqeeNOqe3omnKFTSI+A9FerTN1Md1cFc0vBWGpsmXUevYRlhUr6CN8PMU5lI6zKU0mEfpMJfS69vNDdtX10TY5jqYO6kiijgZ5/jY7t8WRfiu+hjer4Rau7WlAX4NLIMda3wQuqkOls+tgvq1bKUOvcBRKBRISkpS2xQKRaZ+r1+/xrlz5+Dr66tq09PTg6+vL06cOJHt+SdOnAh7e3v07dtXK/FnRaNB39SpU3Hnzh0EBwdj/fr1mDNnDlq3bg0rKyup49MKfZPCSLociavDJ+Sov7FbEVTfuRjPD51CeLXWiJq3GhUWT4ZtkzqqPk4dmqHsjCDcmrwA4d5tkXz5BmrsXg5DO2ttfYw8gbmUDnMpDeZROsyltLq1L4pvW7hg5sJbGDDqAl6lZWD2xAowNPh8ValMKTO0auqE21Epmfb9GlgGxVyMMWbSVfQMOIsjx+Mw8UdPlHI31cbHyFu0OKcvJCQEFhYWaltISEimEOLi4pCRkQEHBwe1dgcHB8TExGQZdnh4OJYvX46lS5dqJS3Z0WidvgsXLuDw4cM4dOgQZs2aBUNDQ9SvXx8NGjRAgwYNULp0aanjlNSzfUfwbN+RHPd3HdAZr6IeIeLHaQCAlBt3YV2rKoqP6IW40HAAQPHve+Ph8o14tHorAODKkGDYN2uAor3a486Mr/uP+jUxl9JhLqXBPEqHuZRWh1YuWLPxPsJPPQcATJ5zAzv/qoW6NW0RdvRZtscZG+kh+IcymD7vJnp2cs20v3wZC8z68yYibiUDAFZvfICOrYvAo6Qpbt3NPEiknAkKCkJgYKBam1wu/+LzJicn47vvvsPSpUtha/t1K7IaVfq8vLwwfPhwbN26Fc+ePcO///4LQ0NDDB06FGXLlpU6xlxnWbMS4g6ol2ifhYbDqmYlAIDMwAAWVcohLuz4+w6CgLgDx2FZs/JXjDTvYy6lw1xKg3mUDnOZPWcHI9hay3Hm4gtV28vUDFy/mYTyZcw/eWzgoFI4fjYeZy8lZLn/6o1ENKprDzPTQpDJgMZ17WBoqIcLV7Lun58IkGltk8vlMDc3V9uyGvTZ2tpCX18fsbGxau2xsbFwdHTM1P/OnTu4d+8eWrZsiUKFCqFQoUJYs2YNdu7ciUKFCml1OTyNKn2CIODChQs4dOgQDh06hPDwcCQlJaFixYqoX7++1DHmOrmDLRSxcWptitg4GFiYQc9IDgMrC+gVKgTF0+cf9XkOE4+vMzlTVzCX0mEupcE8Soe5zJ61lSEA4EVCulr7i4TXqn1ZaVzXDqVLmKJ/4Pls+4ybdh0TfvTEnv+rjTdvlEhTKPHzb9cQ/SRNmuDpkwwNDVG1alWEhYWpll1RKpUICwtDQEBApv5lypTBlStX1Np+/fVXJCcn4/fff0fRokW1FqtGgz5ra2ukpKTAy8sL9evXR//+/VG3bl1YWlrm6HiFQpFpMqQUJVMiIqK8oEl9e4we+n6q048Tr3yid9bsbeUY0b8kRo67jNfpQrb9+nUrDjOTQhjxyyUkJqWjbk1bTPzRE0PHXMTd+y81il9X5JXHsAUGBqJnz56oVq0avL29MXfuXLx8+RK9e/cG8PahFi4uLggJCYGRkRHKly+vdvy78dPH7VLTaND3999/o27dujA3/3RJOjshISGYMEF9YnBwcDCqa3Q27VPExkHuoH7dXe5gi/TEZCjTFHgd9wLKN28gt7f5qI8NFDHqf/UWdMyldJhLaTCP0mEu3ws//RzXb55VvTY0eDs4sbI0wPMXr1XtVpaGuJ3NvDuPkqawtjLE8rlVVW2F9GXwKmeBdi1c0KjdETjaG+Hbli74bugZRD1IBQDcvvfybR9/Z8xcmM8fmpBHBn2dOnXCs2fPMG7cOMTExKBSpUrYu3ev6uaOBw8eQE8v92PVKAJ/f3/VgO/Ro0d49OiRqOODgoKQmJiotgUFBWkSyleRcPIibBrVVGuzbVwLL05eBAAI6elIPH8Nto183neQyWDT0AcJJ/P+EjZfE3MpHeZSGsyjdJjL9169ykD0kzTVFvUgFXHxClTzer/KRWFjfXiWNsfVG0lZnuPspQR8N/QMeg8/q9oibiXhv8NP0Xv4WSiVgJFcHwCgVKofm6EUoJc/nySWZwUEBOD+/ftQKBQ4deoUatSoodp36NAhrFq1KttjV61ahe3bt2s9Ro0GfUqlEhMnToSFhQVcXV3h6uoKS0tLTJo0CcqPv3lZyOnkSG3RNykMc68yMPcqAwAoXLwIzL3KwKioEwDAY3IgvFZOU/W/v2Q9ChcvijIho2Hi4Q7XQV3h1KEZon5fpeoTNXclivbtCJfv2sC0jDvKLxiPQibGePi/O9TyK+ZSOsylNJhH6TCX0tq0Mxo9OxVDbW8buLua4NfAMnger8DRk++rnHMnV0Q7f2cAbweOUQ9S1ba0NCWSktJVVb37j1Lx8HEqRg8thbKlzODsaITObYqgeiUrHDn5PMs48pO8sjizrtDo8u4vv/yC5cuXY+rUqahduzaAt2vOjB8/HmlpaZgyZYqkQUrNomp5+IT9pXrtOfNnAMDDNVtxuW8Q5E52MP7fLzUAeHXvEc60GgjPWUFwG9YDaY9icGXgr6olCADgyaY9MLSzRung4ZA72iHpUgROt+iH10/z9w8dcykd5lIazKN0mEtprd3yEEZG+vgxoDRMTQrhyvVE/BB8RW2+noujMSzNDXJ8zowMAaPHX8WgXsUxbWx5GBvrI/rJK0yZewMnz8Vr42OQDpMJgpD97NBsODs7Y9GiRWjVqpVa+44dOzBkyBBER0drFMxuAw+NjqP3/NMjmUeJMJfSYS6lwTxKxz89EnVaHs7tMPKF8F25t2pH/OWjWju3dcWv8zzcr0mjy7vx8fEoU6ZMpvYyZcogPp5/WRARERHlNRovzjx//vxM7fPnz0fFihW/OCgiIiKiz5LJtLflQxrN6Zs+fTr8/f2xf/9++Pi8vQvrxIkTePjwIf79919JAyQiIiKiL6dRpa9+/fq4efMm2rZti4SEBCQkJKBdu3a4du0a/vrrr8+fgIiIiOgLCTI9rW35kUaVPuDtzRwf36V76dIlLF++HEuWLPniwIiIiIg+RUD+vAyrLflzKEtEREREajSu9BERERHlpvx6GVZbmC0iIiKiAkBUpa9du3af3J+QkPAlsRARERHlXD5dWkVbRA36LCwsPru/R48eXxQQEREREUlP1KBv5cqV2oqDiIiISBSBs9REYbaIiIiICgDevUtEREQ6SeCcPlE46CMiIiKdxCVbxGG2iIiIiAoAVvqIiIhIJ/ExbOKw0kdERERUALDSR0RERDqJc/rEYbaIiIiICgBW+oiIiEgncckWcVjpIyIiIioAWOkjIiIincS7d8XhoI+IiIh0Em/kEIfZIiIiIioAWOkjIiIincTLu+Kw0kdERERUALDSR0RERDqJc/rEYbaIiIiICgBW+oiIiEgncU6fOKz0ERERERUArPQRERGRTuKcPnE46CMiIiKdxMu74nCITERERFQAyARBEHI7CCIiIiKx7ty9q7Vzl3B319q5c0ueury728Ajt0PQef7pkcyjRJhL6TCX0mAepeOfHom6rY/mdhj5wtEddXM7BMqhPDXoIyIiIsopQeCcPjE4p4+IiIioAGClj4iIiHSSwNqVKMwWERERUQHASh8RERHpJK7TJw4HfURERKSTOOgTh5d3iYiIiAoAVvqIiIhIJ7HSJw4rfUREREQFACt9REREpJNY6ROHlT4iIiKiAoCVPiIiItJJfAybOKz0ERERERUAHPQRERGRThIg09om1oIFC+Dm5gYjIyPUqFEDp0+fzrbv0qVLUbduXVhZWcHKygq+vr6f7C8VDvqIiIiIvsCGDRsQGBiI4OBgnD9/Hl5eXvDz88PTp0+z7H/o0CF06dIFBw8exIkTJ1C0aFF88803iI6O1mqcHPQRERGRTsorlb7Zs2ejf//+6N27Nzw9PbFo0SIULlwYK1asyLL/2rVrMWTIEFSqVAllypTBsmXLoFQqERYWJkVassVBHxEREemkvDDoe/36Nc6dOwdfX19Vm56eHnx9fXHixIkcnSM1NRXp6emwtrYWnQMxePcuERER0UcUCgUUCoVam1wuh1wuV2uLi4tDRkYGHBwc1NodHBxw48aNHL3XTz/9BGdnZ7WBozaw0kdEREQ6SRBkWttCQkJgYWGhtoWEhEj+GaZOnYr169dj27ZtMDIykvz8H2Klj4iIiOgjQUFBCAwMVGv7uMoHALa2ttDX10dsbKxae2xsLBwdHT/5HjNnzsTUqVOxf/9+VKxY8cuD/owvrvSlpaVJEQcRERGRKErItLbJ5XKYm5urbVkN+gwNDVG1alW1mzDe3ZTh4+OTbezTp0/HpEmTsHfvXlSrVk0r+fmYRoM+pVKJSZMmwcXFBaamprh79y4AYOzYsVi+fLmkARIRERHlZYGBgVi6dClWr16NiIgIDB48GC9fvkTv3r0BAD169EBQUJCq/7Rp0zB27FisWLECbm5uiImJQUxMDFJSUrQap0aDvsmTJ2PVqlWYPn06DA0NVe3ly5fHsmXLJAuOiIiIKDt54e5dAOjUqRNmzpyJcePGoVKlSrh48SL27t2rurnjwYMHePLkiar/n3/+idevX+Pbb7+Fk5OTaps5c6ak+fmYRnP61qxZgyVLlqBx48YYNGiQqt3LyyvHd6oQERER5RcBAQEICAjIct+hQ4fUXt+7d0/7AWVBo0FfdHQ0SpYsmaldqVQiPT39i4MiIiIi+hxBEP+4tIJMo8u7np6eOHr0aKb2zZs3o3Llyl8cFBEREdHn5JXLu7pCo0rfuHHj0LNnT0RHR0OpVGLr1q2IjIzEmjVr8M8//0gdIxERERF9IY0qfa1bt8auXbuwf/9+mJiYYNy4cYiIiMCuXbvQpEkTqWMkIiIiykSbizPnRxovzly3bl2EhoZKGQsRERERaYlGlb6HDx/i0aNHqtenT5/G999/jyVLlkgWGBEREdGncE6fOBoN+rp27YqDBw8CAGJiYuDr64vTp0/jl19+wcSJEyUNkIiIiIi+nEaDvqtXr8Lb2xsAsHHjRlSoUAHHjx/H2rVrsWrVKinj0wrrOtVQbdufaHz/KPzTI+HQqvHnj6nnjTqnt6JpyhU0iPgPRXq0zdTHdXBXNLwVhqbJl1Hr2EZYVK+gjfDzFOZSOsylNJhH6TCX0uvb1RXbV9bA/o21MGdieRRxMsrxsd3aF8HRHXUxrK+7qs3RXo6jO+pmuTWoZauNj5CncE6fOBoN+tLT01XPn9u/fz9atWoFAChTpozaitN5lb5JYSRdjsTV4RNy1N/YrQiq71yM54dOIbxaa0TNW40KiyfDtkkdVR+nDs1QdkYQbk1egHDvtki+fAM1di+HoZ21tj5GnsBcSoe5lAbzKB3mUlpd2xVBe39nzPzzFgaOvohXaUrMGl8ehgafH2CUKWmKVn5OuB2l/piup3EKtO55Um1bvu4+Ul+9wanz8dr6KKSjNLqRo1y5cli0aBH8/f0RGhqKSZMmAQAeP34MGxsbSQPUhmf7juDZviM57u86oDNeRT1CxI/TAAApN+7CulZVFB/RC3Gh4QCA4t/3xsPlG/Fo9VYAwJUhwbBv1gBFe7XHnRlLpf8QeQRzKR3mUhrMo3SYS2l1bOmCNZseIPz028HYlLmR2LG6JurWtEXY0WfZHmdspIdxgR6YvuAWenYoqrZPqQTiE9QfilC3pg0OhMfhVZpS+g+Rx+T/TygtjSp906ZNw+LFi9GgQQN06dIFXl5eAICdO3eqLvvmJ5Y1KyHuwAm1tmeh4bCqWQkAIDMwgEWVcogLO/6+gyAg7sBxWNbkYtUfYi6lw1xKg3mUDnOZPScHI9hYG+LspQRV28vUDETcTEY5D7NPHjtyYEmcOPcC5z44NjulS5iitLspdu+P+cKIdQMv74qjUaWvQYMGiIuLQ1JSEqysrFTtAwYMQOHChSULLq+QO9hCERun1qaIjYOBhRn0jOQwsLKAXqFCUDx9/lGf5zDxcAe9x1xKh7mUBvMoHeYyezZWBgCAFwmv1drjE17D2sow2+Ma17VDaXdTDBh1IUfv08LXAfcepuLqjWTNg6V8S+N1+vT19fHmzRuEh78t2Xt4eMDNzS1HxyoUCigUCrW2d3MEiYiIdF2T+nYYNbiU6vVPk66JPoe9rSGG93NH4LgreJ0ufLa/oaEefOvZY/XGB6LfS1fl16VVtEWjQd/Lly8xbNgwrFmzBkrl2yvq+vr66NGjB+bNm/fZal9ISAgmTFCfGBwcHIzqmgTzFShi4yB3UL8LSu5gi/TEZCjTFHgd9wLKN28gt7f5qI8NFDHqf/UWdMyldJhLaTCP0mEu3ws/HY/rkedVrw0M3s6msrI0xPMX7+fgWVsa4tZHN2e841HCDNaWhlg2p4qqrZC+DF7lLNDO3xmNvw2H8oNJbQ1r2cJIrod9B59K/Gkov9BoTl9gYCAOHz6MXbt2ISEhAQkJCdixYwcOHz6MH3744bPHBwUFITExUW0LCgrSJJSvIuHkRdg0qqnWZtu4Fl6cvAgAENLTkXj+Gmwb+bzvIJPBpqEPEk7mrCRfUDCX0mEupcE8Soe5fO/VqwxEx6SptnsPU/E8/jWqVrRU9SlsrI+ypc1wLTLrS7FnLyegx7Bz6PP9edUWcSsZoYefos/359UGfADg7+uAY2fikZCUnuX58iPO6RNHo0Hfli1bsHz5cjRr1gzm5uYwNzdH8+bNsXTpUmzevPmzx8vlctVx77aveXlX36QwzL3KwNyrDACgcPEiMPcqA6OiTgAAj8mB8Fo5TdX//pL1KFy8KMqEjIaJhztcB3WFU4dmiPp9lapP1NyVKNq3I1y+awPTMu4ov2A8CpkY4+H/7lDLr5hL6TCX0mAepcNcSmvjrmj07FgUtb2t4e5aGL9+XxrP4xU4evJ9lXPuxApo1/xtfl+9ykDUg1S1LS0tA4nJbxD1IFXt3C6ORvAqZ4Fd/xWMGzhIMxpd3k1NTYWDg0Omdnt7e6SmpmZxRN5iUbU8fML+Ur32nPkzAODhmq243DcIcic7GP/vlxoAvLr3CGdaDYTnrCC4DeuBtEcxuDLwV9USBADwZNMeGNpZo3TwcMgd7ZB0KQKnW/TD648mLOc3zKV0mEtpMI/SYS6ltW7rIxgb6WP0kFIwNSmEKxGJGDXhmtp8PWdHI1iYG4g+t7+vA549V+DMxRdShpzncU6fODJBED4/O/QjjRs3ho2NDdasWQMjo7erib969Qo9e/ZEfHw89u/fr1Ewuw08NDqO3vNPj2QeJcJcSoe5lAbzKB3/9EjUbX00t8PIF47uqJtr7x1+/aXWzl3H00Rr584tGlX6fv/9d/j5+aFIkSKqNfouXboEIyMj7Nu3T9IAiYiIiLKiFF22Ktg0GvSVL18et27dwtq1a3Hjxg0AQJcuXdCtWzcYGxtLGiARERFRVnh5VxyN1+krXLgw+vfvL2UsRERERKQlOR707dy5M8cnbdWqlUbBEBEREeVUfl1aRVtyPOhr06ZNjvrJZDJkZGRoGg8RERERaUGOB33Kj1eBJCIiIspF4tcfKdhELc584MABeHp6IikpKdO+xMRElCtXDkeP8hZ4IiIiorxG1KBv7ty56N+/P8zNzTPts7CwwMCBAzF79mzJgiMiIiLKjhIyrW35kahB36VLl9C0adNs93/zzTc4d+7cFwdFRERERNIStWRLbGwsDAyyfzxMoUKF8OzZsy8OioiIiOhzePeuOKIqfS4uLrh69Wq2+y9fvgwnJ6ds9xMRERFJRRC0t+VHogZ9zZs3x9ixY5GWlpZp36tXrxAcHIwWLVpIFhwRERERSUPU5d1ff/0VW7duRenSpREQEAAPj7cP/r5x4wYWLFiAjIwM/PLLL1oJlIiIiOhDfAybOKIGfQ4ODjh+/DgGDx6MoKAgCP+rf8pkMvj5+WHBggVwcHDQSqBEREREpDnRz951dXXFv//+ixcvXuD27dsQBAGlSpWClZWVNuIjIiIiypIyn8690xbRg753rKysUL16dSljISIiIiIt0XjQR0RERJSbuGSLOKLu3iUiIiIi3cRKHxEREemk/LqenrZw0EdEREQ6Kb8+I1dbeHmXiIiIqABgpY+IiIh0Ei/visNKHxEREVEBwEofERER6SQu2SIOK31EREREBQArfURERKST+Bg2cVjpIyIiIioAWOkjIiIincS7d8XhoI+IiIh0ksDFmUXh5V0iIiKiAoCVPiIiItJJvJFDHFb6iIiIiAoAVvqIiIhIJ/FGDnFkgsCUERERke7ZdFKptXN3qJn/LobmqUrfbgOP3A5B5/mnRzKPEmEupcNcSoN5lI5/eiTqtDyc22HkC+G76ufae+elstWCBQswY8YMxMTEwMvLC/PmzYO3t3e2/Tdt2oSxY8fi3r17KFWqFKZNm4bmzZtrNcb8N4wlIiIi+oo2bNiAwMBABAcH4/z58/Dy8oKfnx+ePn2aZf/jx4+jS5cu6Nu3Ly5cuIA2bdqgTZs2uHr1qlbj5KCPiIiIdJJSkGltE2P27Nno378/evfuDU9PTyxatAiFCxfGihUrsuz/+++/o2nTphg9ejTKli2LSZMmoUqVKpg/f74UackWB31ERESkkwRBe5tCoUBSUpLaplAoMsXw+vVrnDt3Dr6+vqo2PT09+Pr64sSJE1nGfeLECbX+AODn55dtf6lw0EdERET0kZCQEFhYWKhtISEhmfrFxcUhIyMDDg4Oau0ODg6IiYnJ8twxMTGi+kslT93IQURERJRT2ryRIygoCIGBgWptcrlce2/4FXDQR0RERPQRuVyeo0Gera0t9PX1ERsbq9YeGxsLR0fHLI9xdHQU1V8qGl3eTUhIwLJlyxAUFIT4+HgAwPnz5xEdHS1pcERERETZUQra23LK0NAQVatWRVhY2Pu4lEqEhYXBx8cny2N8fHzU+gNAaGhotv2lIrrSd/nyZfj6+sLCwgL37t1D//79YW1tja1bt+LBgwdYs2aNNuIkIiIiypMCAwPRs2dPVKtWDd7e3pg7dy5evnyJ3r17AwB69OgBFxcX1ZzAESNGoH79+pg1axb8/f2xfv16nD17FkuWLNFqnKIrfYGBgejVqxdu3boFIyMjVXvz5s1x5MgRSYMjIiIiyo4gyLS2idGpUyfMnDkT48aNQ6VKlXDx4kXs3btXdbPGgwcP8OTJE1X/WrVqYd26dViyZAm8vLywefNmbN++HeXLl5c0Px8TXek7c+YMFi9enKndxcVF63edEBEREeVFAQEBCAgIyHLfoUOHMrV16NABHTp00HJU6kQP+uRyOZKSkjK137x5E3Z2dpIERURERPQ5eekxbLpA9OXdVq1aYeLEiUhPTwcAyGQyPHjwAD/99BPat28veYBERERE9OVED/pmzZqFlJQU2Nvb49WrV6hfvz5KliwJMzMzTJkyRRsxEhEREWWSF+7e1SWiL+9aWFggNDQUx44dw6VLl5CSkoIqVapkepwIERERkTbx8q44Gi/OXLt2bdSuXRvA23X7iIiIiCjvEn15d9q0adiwYYPqdceOHWFjYwMXFxdcunRJ0uCIiIiIsiMI2tvyI9GDvkWLFqFo0aIA3q4eHRoaij179qBZs2YYPXq05AESERER0ZcTfXk3JiZGNej7559/0LFjR3zzzTdwc3NDjRo1JA+QiIiIKCv59YYLbRFd6bOyssLDhw8BAHv37lXdwCEIAjIyMqSNjoiIiIgkIbrS165dO3Tt2hWlSpXC8+fP0axZMwDAhQsXULJkSckDJCIiIspKfp17py2iB31z5syBm5sbHj58iOnTp8PU1BQA8OTJEwwZMkTyAImIiIjoy4ke9BkYGGDUqFGZ2keOHClJQEREREQ5oVTmdgS6RaN1+u7cuYO5c+ciIiICAODp6Ynvv/8e7u7ukgZHRERElB1e3hVH9I0c+/btg6enJ06fPo2KFSuiYsWKOHXqFDw9PREaGqqNGImIiIjoC4mu9I0ZMwYjR47E1KlTM7X/9NNPaNKkiWTBEREREWWHlT5xRFf6IiIi0Ldv30ztffr0wfXr1yUJioiIiIikJXrQZ2dnh4sXL2Zqv3jxIuzt7aWIiYiIiOizlIL2tvxI9OXd/v37Y8CAAbh79y5q1aoFADh27BimTZuGwMBAyQMkIiIioi8netA3duxYmJmZYdasWQgKCgIAODs7Y/z48Rg+fLjkARIRERFlRdDqpD6ZFs+dO0QP+mQyGUaOHImRI0ciOTkZAGBmZiZ5YEREREQkHY3W6XuHgz0iIiLKLbx7V5wc3chRuXJlVKlSJUebLrCuUw3Vtv2JxvePwj89Eg6tGn/+mHreqHN6K5qmXEGDiP9QpEfbTH1cB3dFw1thaJp8GbWObYRF9QraCD9PYS6lw1xKg3mUDnMpvb7d3LB9dU2Eba6DuZMqooiTcY6P7f5tUYTvqo/h/Uqo2hzt5QjfVT/LrWFtW218hDxFqdTelh/lqNLXpk0bLYfxdembFEbS5Ug8XLUF1TYv+Gx/Y7ciqL5zMR4sWY+LPUbBppEPKiyejLQnzxAXGg4AcOrQDGVnBOHq0GAknL6E4sN7osbu5ThUrileP4vX9kfKNcyldJhLaTCP0mEupdWtfVF828IFU+bewJPYNPTr5obZEyug+5AzeJ3+6ZJVmVJmaNXUCbejUtTan8Yp0Oq742ptrZo6o2vbIjh5Ln/nk8TL0aAvODhY23F8Vc/2HcGzfUdy3N91QGe8inqEiB+nAQBSbtyFda2qKD6il+oXWfHve+Ph8o14tHorAODKkGDYN2uAor3a486MpdJ/iDyCuZQOcykN5lE6zKW0OrRywZqN9xF+6jkAYPKcG9j5Vy3UrWmLsKPPsj3O2EgPwT+UwfR5N9Gzk6vaPqUSiE9IV2urV9MGB8Kf4VVaPi1XfYCXd8XJ8Tp9L168wLx585CUlJRpX2JiYrb78gPLmpUQd+CEWtuz0HBY1awEAJAZGMCiSjnEhX3w15YgIO7AcVjWrPwVI837mEvpMJfSYB6lw1xmz9nBCLbWcpy5+ELV9jI1A9dvJqF8GfNPHhs4qBSOn43H2UsJn30fjxKmKF3CDP+ExnxpyJQP5XjQN3/+fBw5cgTm5pm/nBYWFjh69CjmzZsnaXB5hdzBForYOLU2RWwcDCzMoGckh6GtFfQKFYLi6fOP+jyH3DH/z6kQg7mUDnMpDeZROsxl9qytDAEALz6qyr1IeK3al5XGde1QuoQpFq++m6P3afGNI6IevMTVG/mzCPMxLs4sTo4HfVu2bMGgQYOy3T9w4EBs3rw5R+dSKBRISkpS2xQKRU5DISIiytOa1LfHfxvrqLZChcSv+WZvK8eI/iUxcdaNz875AwBDQz341nPAblb5KBs5XrLlzp07KFWqVLb7S5UqhTt37uToXCEhIZgwYYJaW3BwMKrnNJivTBEbB7mD+l+hcgdbpCcmQ5mmwOu4F1C+eQO5vc1HfWygiFH/q7egYy6lw1xKg3mUDnP5Xvjp57h+86zqtaHB2xqLlaUBnr94rWq3sjTE7bspmY4HAI+SprC2MsTyuVVVbYX0ZfAqZ4F2LVzQqN0RtbtMG9a2hZFcD3sPxEr8afIuzukTJ8eVPn19fTx+/Djb/Y8fP4aeXs5OFxQUhMTERLXt3dM98qKEkxdh06imWptt41p4cfIiAEBIT0fi+WuwbeTzvoNMBpuGPkg4eeErRpr3MZfSYS6lwTxKh7l879WrDEQ/SVNtUQ9SERevQDUvK1Wfwsb68Cxtnu2l2LOXEvDd0DPoPfysaou4lYT/Dj9F7+FnMy0r0qKJE8JPP0dCUnqW5yPK8aCvcuXK2L59e7b7t23bhsqVczYRVy6Xw9zcXG2Ty+U5DeWL6ZsUhrlXGZh7lQEAFC5eBOZeZWBU1AkA4DE5EF4rp6n631+yHoWLF0WZkNEw8XCH66CucOrQDFG/r1L1iZq7EkX7doTLd21gWsYd5ReMRyETYzz83x1q+RVzKR3mUhrMo3SYS2lt2hmNnp2Koba3DdxdTfBrYBk8j1fg6Mn3Vc65kyuinb8zgLcDx6gHqWpbWpoSSUnpiHqQqnZuFycjeJWzwD//Pfmqnym3CUpBa1t+lOPLuwEBAejcuTOKFCmCwYMHQ19fHwCQkZGBhQsXYs6cOVi3bp3WApWSRdXy8An7S/Xac+bPAICHa7bict8gyJ3sYPy/X2oA8OreI5xpNRCes4LgNqwH0h7F4MrAX1VLEADAk017YGhnjdLBwyF3tEPSpQicbtEPrz+asJzfMJfSYS6lwTxKh7mU1totD2FkpI8fA0rD1KQQrlxPxA/BV9Tm67k4GsPS3ED0uf19nfDsuQKnL7z4fOd8JJ+OzbRGJoh4WvEvv/yCkJAQmJmZwd3dHQBw9+5dpKSkYPTo0Zg6deoXBbPbwOOLjifAPz2SeZQIcykd5lIazKN0/NMjUafl4dwOI18I31U/1957+hbtrUX4Y/scXwzVGaKevTtlyhS0bt0aa9euxe3btyEIAurXr4+uXbvC29tbWzESERERZcIbOcQRNegDAG9vbw7wiIiIiHSM6EEfERERUV6g5KQ+UfLfBWsiIiIiyoSVPiIiItJJnNMnDit9RERERAUAK31ERESkk1jpEydHg74qVaogLCwMVlZWqFy5MmSy7B8cbWpqinLlyuHnn39G0aJFJQuUiIiI6ENKjvpEydGgr3Xr1qrHpLVp0+aTfRUKBcLCwtC9e3ccPsyFL4mIiIjyghwN+oKDg7P87+zcuXMH5cqV0zwqIiIios8QtPdAjnxJKzdylChRArGxsdo4NRERERFpIEeVvs/N4/vQ+fPnAQAWFhaaR0VERET0GQLn9ImSo0Hfh/P40tLSsHDhQnh6esLHxwcAcPLkSVy7dg1DhgzRSpBERERE9GVEz+nr168fhg8fjkmTJmXq8/DhQ2mjIyIiIsqGknP6RBE9p2/Tpk3o0aNHpvbu3btjy5YtkgRFRERERNISPegzNjbGsWPHMrUfO3YMRkZGkgRFRERE9DmCIGhty49EP5Hj+++/x+DBg3H+/Hl4e3sDAE6dOoUVK1Zg7NixkgdIRERElBVl/hybaY3oQd+YMWPg7u6O33//HX///TcAoGzZsli5ciU6duwoeYBERERE9OU0evZux44dsxzgXb16FeXLl//ioIiIiIg+R2CpT5QvXpw5OTkZS5Ysgbe3N7y8vKSIiYiIiIgkpvGg78iRI+jRowecnJwwc+ZMNGrUCCdPnpQyNiIiIqJsCYL2Nm2Jj49Ht27dYG5uDktLS/Tt2xcpKSmf7D9s2DB4eHjA2NgYxYoVw/Dhw5GYmCj6vUVd3o2JicGqVauwfPlyJCUloWPHjlAoFNi+fTs8PT1FvzkRERFRQdKtWzc8efIEoaGhSE9PR+/evTFgwACsW7cuy/6PHz/G48ePMXPmTHh6euL+/fsYNGgQHj9+jM2bN4t67xwP+lq2bIkjR47A398fc+fORdOmTaGvr49FixaJekMiIiIiKSh1bE5fREQE9u7dizNnzqBatWoAgHnz5qF58+aYOXMmnJ2dMx1Tvnx5tXWQS5QogSlTpqB79+548+YNChXKef0uxz337NmD4cOHY/DgwShVqlSO34CIiIhI1ygUCigUCrU2uVwOuVyu8TlPnDgBS0tL1YAPAHx9faGnp4dTp06hbdu2OTpPYmIizM3NRQ34ABFz+sLDw5GcnIyqVauiRo0amD9/PuLi4kS9GREREZFUtLk4c0hICCwsLNS2kJCQL4o3JiYG9vb2am2FChWCtbU1YmJicnSOuLg4TJo0CQMGDBD9/jke9NWsWRNLly7FkydPMHDgQKxfvx7Ozs5QKpUIDQ1FcnKy6DcnIiIi0pSg1N4WFBSExMREtS0oKCjLOMaMGQOZTPbJ7caNG1/8eZOSkuDv7w9PT0+MHz9e9PGi1+kzMTFBnz590KdPH0RGRmL58uWYOnUqxowZgyZNmmDnzp2igyAiIiLKS8Rcyv3hhx/Qq1evT/Zxd3eHo6Mjnj59qtb+5s0bxMfHw9HR8ZPHJycno2nTpjAzM8O2bdtgYGCQo9g+pNHizO94eHhg+vTpCAkJwa5du7BixYovOR0RERFRjinzyDNy7ezsYGdn99l+Pj4+SEhIwLlz51C1alUAwIEDB6BUKlGjRo1sj0tKSoKfnx/kcjl27twJIyMjjeL84sWZAUBfXx9t2rRhlY+IiIgoG2XLlkXTpk3Rv39/nD59GseOHUNAQAA6d+6sunM3OjoaZcqUwenTpwG8HfB98803ePnypWrJvJiYGMTExCAjI0PU+39RpY+IiIgotwh5pNInxtq1axEQEIDGjRtDT08P7du3xx9//KHan56ejsjISKSmpgIAzp8/j1OnTgEASpYsqXauqKgouLm55fi9OegjIiIi+kqsra2zXYgZANzc3NQGsw0aNJBscMtBHxEREekkXVucObdJMqePiIiIiPI2maCLF8SJiIiowPt+XorWzj13mKnWzp1b8tTl3bqtj+Z2CDrv6I66qNc2PLfDyBeObKuDOi0P53YY+UL4rvr8+ZbA0R11+Z2USPiu+tht4JHbYeQL/umRufbeAi/visLLu0REREQFQJ6q9BERERHlVF5ZnFlXsNJHREREVACw0kdEREQ6iXP6xGGlj4iIiKgAYKWPiIiIdBIrfeKw0kdERERUALDSR0RERDqJhT5xWOkjIiIiKgBY6SMiIiKdxDl94nDQR0RERDpJ4OLMovDyLhEREVEBwEofERER6SQlL++KwkofERERUQGg0aDv6NGj6N69O3x8fBAdHQ0A+OuvvxAeHi5pcERERETZEQRBa1t+JHrQt2XLFvj5+cHY2BgXLlyAQqEAACQmJuK3336TPEAiIiIi+nKiB32TJ0/GokWLsHTpUhgYGKjaa9eujfPnz0saHBEREVF2BKWgtS0/Ej3oi4yMRL169TK1W1hYICEhQYqYiIiIiEhiogd9jo6OuH37dqb28PBwuLu7SxIUERER0eew0ieO6CVb+vfvjxEjRmDFihWQyWR4/PgxTpw4gVGjRmHs2LHaiJGIiIgoE2U+veFCW0QP+saMGQOlUonGjRsjNTUV9erVg1wux6hRozBs2DBtxEhEREREX0j0oE8mk+GXX37B6NGjcfv2baSkpMDT0xOmpqbaiI+IiIgoS/n1Mqy2aPxEDkNDQ3h6ekoZCxERERFpiehBX1paGubNm4eDBw/i6dOnUCqVavu5bAsRERF9Dfl1EWVtET3o69u3L/777z98++238Pb2hkwm00ZcRERERCQh0YO+f/75B//++y9q166tjXiIiIiIckTJOX2iiF6nz8XFBWZmZtqIhYiIiIi0RPSgb9asWfjpp59w//59bcRDRERElCNcnFkc0Zd3q1WrhrS0NLi7u6Nw4cJqz98FgPj4eMmCIyIiIsoOb+QQR/Sgr0uXLoiOjsZvv/0GBwcH3shBREREpANED/qOHz+OEydOwMvLSxvxEBEREeWI8NGycfRpogd9ZcqUwatXr7QRS67o29UVLZs4wtREH1duJGHWn7fx6Elajo7t1r4IBvUojo07ozFv+V0AgKO9HJuWemfZf+y0CBw6HidZ7HlNny7F0NL3XS6TMXuxiFy2K4KB37lh065ozFsRpbavnIcZ+ndzRdlSZlAqBdyOeokfJl7D69f584e9bzc3tPzGEWYmhXAlIgkzF97Coyc5+5nr/m1RDOrpjo07HuGPZXdU7daWBhjSpwSqV7JCYWN9PIhOxZqND3A4H38fAf58S0nq76WjvRybl9fMsv/Yqddw8Fj+yqV1nWpw/6EvLKqUh5GzPc62H4LYnWGfPqaeNzxnjoGpZymkPXyC2yF/4tGabWp9XAd3hXtgX8gd7ZB0+QaufT8JiWeuaPOjkA4TPeibOnUqfvjhB0yZMgUVKlTINKfP3NxcsuC0rWu7Imjv74zffo/Ek9g09O3mhlnjy+O7gHN4nf7peQJlSpqilZ8TbkelqLU/jVOgdc+Tam2t/JzQpa0LTp3Pv/Mdu7Z1QXt/Z4T8cROPY9PQr6srZo4rjx7Dc5jLbxxxO+plpn3lPMwwY2w5rN36CHOX3kVGhoCSbib5dpJtt/ZF8W0LF0yZewNPYtPQr5sbZk+sgO5Dznw+j6XM0Kpp5u8kAPwaWAamJoUwZtJVJCalo0l9e0z80RP9As/j1t3M/fMD/nxLRxvfy6dxCrT67rhaW6umzujatghOnst/udQ3KYyky5F4uGoLqm1e8Nn+xm5FUH3nYjxYsh4Xe4yCTSMfVFg8GWlPniEuNBwA4NShGcrOCMLVocFIOH0JxYf3RI3dy3GoXFO8fpb/cpgVLtkijui7d5s2bYoTJ06gcePGsLe3h5WVFaysrGBpaQkrKyttxKg1HVu6YM2mBwg/HY8791MxZW4kbKzlqFvT9pPHGRvpYVygB6YvuIXklDdq+5RKID4hXW2rW9MGB8Lj8Cotf1amAKBDCxf8tekhwk/H4+79VEz5/SZsrA1Rp4bNJ48zNtLD2JEemL7wFpJfvsm0P6B3cWzZ/Rhrtz7CvYepePj4FQ4ej0P6m/z5g96hlQvWbLyP8FPPcefeS0yecyPH38ngH8pg+rybmb6TAFC+jAW2/BONiFvJeBybhtUbHyDl5Rt4lMy/z8zmz7d0tPG9zCqX9Wra4ED4s3yZy2f7juBm8FzE7tifo/6uAzrjVdQjRPw4DSk37uL+wrWI2bIPxUf0UvUp/n1vPFy+EY9Wb0VKxB1cGRKMjNQ0FO3VXkufgnSd6EHfwYMHcfDgQRw4cEBte9emK5wcjGBjbYizlxJUbS9TMxBxMxnlPD69DuHIgSVx4twLnPvg2OyULmGK0u6m2L0/5gsjzrucHORZ5/JWMsp7fLryO3JACZw4G49zlxMz7bO0MEA5D3O8SEzHwpCK2L7SG39MroAKZXWnmiyGs4MRbK3lOHPxhartZWoGrt9MQvkyn/7MgYNK4fjZeLV/gw9dvZGIRnXtYWZaCDIZ0LiuHQwN9XDhStb9dR1/vqWjze/lhzxKmKJ0CTP8E5p/cymGZc1KiDtwQq3tWWg4rGpWAgDIDAxgUaUc4sI+qJYKAuIOHIdlzcpfMdLcJQiC1rb8SPTl3fr162sjjq/OxurtZekXCa/V2uMTXsPayjDb4xrXtUNpd1MMGHUhR+/TwtcB9x6m4uqNZM2DzeNsLN/m60ViFrm0NMjqEABAozq2b3M5+mKW+50djAAAvTsXw8JVUbgd9RJ+DewxZ0J59BpxPsdzs3TFu+/di4R0tfYXOflOljBF/8Dsn3s9btp1TPjRE3v+rzbevFEiTaHEz79dQ3Q+y+E7/PmWjja/lx9q8Y0joh68xNUbSZoHm4/IHWyhiFWf16iIjYOBhRn0jOQwsLKAXqFCUDx9/lGf5zDxcP+aoZIOET3oO3LkyCf316tX77PnUCgUUCgUam1yuVxsKKI0qW+HUYNLqV7/NOma6HPY2xpieD93BI678tl5LABgaKgH33r2WL3xgej3ysua1LPDD4NKql7/NEWDXNoYYnhfdwSOv5ptLvX+txrQzn0x2HPgKQDgVlQUqla0RPPGDljyt24vEN6kvj1GDy2tev3jRPGTr+1t5RjRvyRGjrv8ye9kv27FYWZSCCN+uYTEpHTUrWmLiT96YuiYi7h7P/NcSl3Dn2/pfM3v5Ttvc+mA1Rt0+2eavr78Or9bW0QP+ho0aJCp7cO1+jIyMj57jpCQEEyYMEGtLTg4GEBjseHkWPjpeFyPfP8Xp4HB2yvbVpaGeP7i/V+w1paGuJXFRHgA8ChhBmtLQyybU0XVVkhfBq9yFmjn74zG34bjw7vHG9ayhZFcD/sOPpX40+Su8NPxuH7zfSVElUuLzLnM6uYM4O1lMWtLQyyb9f4yRCF9Gbw8zdG2uTN8Ox7D8xdvqzT3HqWqHXv/USocbLX7R8LXEH76Oa7fPKt6baj6ThqoPvvb14a4nc3NFh4lTWFtZYjlc6uq2lTfyRYuaNTuCBztjfBtSxd8N/QMoh68zeXtey9V39uZC29p4+N9Vfz5ls7X+l6q5bL221zuPRAr8afRXYrYOMgd1OdMyh1skZ6YDGWaAq/jXkD55g3k9jYf9bGBIiZ/3fn8KRz0iSN60PfixQu11+np6bhw4QLGjh2LKVOm5OgcQUFBCAwMVGuTy+UI63habDg59upVBqJfqQ9In8e/RtWKlqqBSWFjfZQtbYbte59keY6zlxPQY9g5tbag4aXx4FEq1m59hI+XC/L3dcCxM/FISFK/LKLrXqVlIDomm1ze+yCXpbLP5bnLieg5Qv2yz5iAUngQ/Qrrtr3N5ZOnCjx7rkAxZ2O1fkWcjXHqvPr3UBdl9Z2Mi1egmpeV2nfSs7Q5tv/7OMtznL2UgO+GnlFr+/l7D9x/9AprNz+AUgkYyfUBINP3M0MpqKqpuo4/39L5Wt/LD7Vo4oTw08/zXS6/RMLJi7Brpn7lzLZxLbw4eREAIKSnI/H8Ndg28nm/9ItMBpuGPri/8O+vHC3pCtGDPgsLi0xtTZo0gaGhIQIDA3Hu3LksjlInl8u1fjk3JzbuikbPjkXx6Mmrt8sQdHXF83gFjp58/1fS3IkVcORkHLb++wSvXmWoKiXvpKVlIDH5TaZ2F0cjeJWzwOiJ4i8z6aJN/0SjR4f3uezb1RXP418j/NT7+SZzJpTH0ZPPsXXPE7xKyyKXCiWSktPV2tdvj0bvzsVw+95L3I56iaYN7eHqYoxxM258tc/2NW3aGY2enYrh4eP/fSe7u2X+Tk6uiCMn4rB19+NsvpNKJCW9z+P9R6l4+DgVo4eWwoIVd5GYnI56NW1RvZIVfpx49at+vq+JP9/S0cb38h0Xp//lckL+XltO36QwTEoWU70uXLwIzL3K4HV8ItIePoHH5EAYuTjgUu+fAAD3l6yH65BuKBMyGg9XbYFtw5pw6tAMZ1oNVJ0jau5KeK2YhoRzV5F45jLchvdEIRNjPFy99at/vtyiFPLfnd7aJHrQlx0HBwdERkZKdbqvYt3WRzA20sfoIaVgalIIVyISMWrCNbU5KM6ORrAwz/5mhOz4+zrg2XOF2h1v+dm6bdEwMtLHqMEl/5fLJIyadPWLc7npn8cwNNTDsD7uMDMthDv3XiJwwjU8jsmfNyCs3fIQRkb6+DGg9Ns8Xk/ED8Hqc8xcHI1hKSKPGRkCRo+/ikG9imPa2PIwNtZH9JNXmDL3Rr5cD+0d/nxLRxvfy3f8fZ3w7LkCpy/k71xaVC0Pn7C/VK89Z/4MAHi4Zisu9w2C3MkOxkWdVPtf3XuEM60GwnNWENyG9UDaoxhcGfirao0+AHiyaQ8M7axROnj428WZL0XgdIt+eP3RzR1E78gEkfclX758We21IAh48uQJpk6dijdv3iA8PDybIz+vbuujGh9Lbx3dURf12mr+b0DvHdlWB3VaHs7tMPKF8F31+fMtgaM76vI7KZHwXfWx28Ajt8PIF/zTc6/g0zZAe3OSt80v9flOOkZ0pa9SpUqQyWSZ1rCpWbMmVqxYIVlgRERERCQd0YO+qCj156Lq6enBzs4ORkZGkgVFRERE9Dm8e1cc0YM+V1dXbcRBRERERFqUo0HfH3/8keMTDh8+XONgiIiIiHIqvz4uTVtyNOibM2dOjk4mk8k46CMiIqKvQvnxoo/0STka9H08j4+IiIiIxIuPj8ewYcOwa9cu6OnpoX379vj9999hamr62WMFQUDz5s2xd+9ebNu2DW3atBH13noaxqx6c5ZWiYiIKDcISkFrm7Z069YN165dQ2hoKP755x8cOXIEAwYMyNGxc+fOVXv0rVgaDfrWrFmDChUqwNjYGMbGxqhYsSL++uuvzx9IREREVEBFRERg7969WLZsGWrUqIE6depg3rx5WL9+PR4/zvqxhu9cvHgRs2bN+qLl8UQP+mbPno3BgwejefPm2LhxIzZu3IimTZti0KBBOZ77R0RERPSlBEGptU0bTpw4AUtLS1SrVk3V5uvrCz09PZw6dSrb41JTU9G1a1csWLAAjo6OGr+/6CVb5s2bhz///BM9evRQtbVq1QrlypXD+PHjMXLkSI2DISIiIsoLFAoFFAqFWptcLodcLtf4nDExMbC3t1drK1SoEKytrRETE5PtcSNHjkStWrXQunVrjd8b0KDS9+TJE9SqVStTe61atfDkyZMvCoaIiIgop7Q5py8kJAQWFhZqW0hISJZxjBkzBjKZ7JPbjRs3NPqMO3fuxIEDBzB37twvyNRboit9JUuWxMaNG/Hzzz+rtW/YsAGlSuW/59QRERFRwRMUFITAwEC1tuyqfD/88AN69er1yfO5u7vD0dERT58+VWt/8+YN4uPjs71se+DAAdy5cweWlpZq7e3bt0fdunVx6NChT77vh3I86Lt69SrKly+PiRMnomPHjjhy5Ahq164NADh27BjCwsKwcePGHL8xERER0ZfQ5l22Yi7l2tnZwc7O7rP9fHx8kJCQgHPnzqFq1aoA3g7qlEolatSokeUxY8aMQb9+/dTaKlSogDlz5qBly5Y5iu+dHA/6KlasiOrVq6Nv3744cOAAlixZgu3btwMAypYti9OnT6Ny5cqi3pyIiIhIU0ot3XChLWXLlkXTpk3Rv39/LFq0COnp6QgICEDnzp3h7OwMAIiOjkbjxo2xZs0aeHt7w9HRMcsqYLFixVC8eHFR75/jQd/hw4excuVKjBo1CkqlEu3bt8ecOXNQr149UW9IREREVFCtXbsWAQEBaNy4sWpx5g8fd5ueno7IyEikpqZK/t45HvTVrVsXdevWxbx587Bx40asWrUKDRs2RIkSJdC3b1/07Nnzi24jJiIiIhJDm5d3tcXa2hrr1q3Ldr+bm9tnH3yh6YMxRN+9a2Jigt69e+Pw4cOIjIxEhw4dsGDBAhQrVgytWrXSKAgiIiIi0i7Rd+9+qGTJkvj555/h6uqKoKAg7N69W6q4iIiIiD5JUOrWnL7cpvGg78iRI1ixYgW2bNkCPT09dOzYEX379pUyNiIiIiKSiKhB3+PHj7Fq1SqsWrUKt2/fRq1atfDHH3+gY8eOMDEx0VaMRERERJno4py+3JTjQV+zZs2wf/9+2NraokePHujTpw88PDy0GRsRERERSSTHgz4DAwNs3rwZLVq0gL6+vjZjIiIiIvosQcfW6cttOR707dy5U5txEBEREYmi5OVdUUQv2UJEREREuueLlmwhIiIiyi1cskUcVvqIiIiICgBW+oiIiEgncckWcVjpIyIiIioAWOkjIiIincQlW8RhpY+IiIioAGClj4iIiHQS5/SJw0EfERER6SQu2SIOL+8SERERFQAyQRBYG80BhUKBkJAQBAUFQS6X53Y4Oo25lAbzKB3mUjrMpTSYR9IGDvpyKCkpCRYWFkhMTIS5uXluh6PTmEtpMI/SYS6lw1xKg3kkbeDlXSIiIqICgIM+IiIiogKAgz4iIiKiAoCDvhySy+UIDg7mhFoJMJfSYB6lw1xKh7mUBvNI2sAbOYiIiIgKAFb6iIiIiAoADvqIiIiICgAO+oiIiIgKAA76SKtWrVoFS0tLUcf06tULbdq00Uo8VHDJZDJs3749t8PI88aPH49KlSpl+1qq8xLg5uaGuXPn5nYYVIAUqEHfw4cP0adPHzg7O8PQ0BCurq4YMWIEnj9/ntuh6aTsBmeHDh2CTCZDQkICOnXqhJs3b3794HQUB7yai4mJwbBhw+Du7g65XI6iRYuiZcuWCAsLy+3QctWJEyegr68Pf39/jY4fNWpUjnK4ZcsWNGjQABYWFjA1NUXFihUxceJExMfHa/S+eV3Lli3RtGnTLPcdPXoUMpkMly9f/spREX1agRn03b17F9WqVcOtW7fwf//3f7h9+zYWLVqEsLAw+Pj45NtfTLnN2NgY9vb2uR0G5XP37t1D1apVceDAAcyYMQNXrlzB3r170bBhQwwdOjS3w8tVy5cvx7Bhw3DkyBE8fvxY9PGmpqawsbH5ZJ9ffvkFnTp1QvXq1bFnzx5cvXoVs2bNwqVLl/DXX39pGnqe1rdvX4SGhuLRo0eZ9q1cuRLVqlVDxYoVcyEyouwVmEHf0KFDYWhoiP/++w/169dHsWLF0KxZM+zfvx/R0dH45ZdfALwtt0+aNAldunSBiYkJXFxcsGDBArVzJSQkoF+/frCzs4O5uTkaNWqES5cuqfa/u4zx119/wc3NDRYWFujcuTOSk5O/6mfOC7K6vDt58mTY29vDzMwM/fr1w5gxY7K87DNz5kw4OTnBxsYGQ4cORXp6+tcJOo86fPgwvL29IZfL4eTkhDFjxuDNmzcAgH/++QeWlpbIyMgAAFy8eBEymQxjxoxRHd+vXz907949V2LXtiFDhkAmk+H06dNo3749SpcujXLlyiEwMBAnT57M8pgrV66gUaNGMDY2ho2NDQYMGICUlBTV/kOHDsHb2xsmJiawtLRE7dq1cf/+fdX+HTt2oEqVKjAyMoK7uzsmTJig+vfIK1JSUrBhwwYMHjwY/v7+WLVqVaY+U6dOhYODA8zMzNC3b1+kpaWp7f/cZdnTp0/jt99+w6xZszBjxgzUqlULbm5uaNKkCbZs2YKePXtmeZxSqcTEiRNRpEgRyOVyVKpUCXv37lXtf/36NQICAuDk5AQjIyO4uroiJCREtf9zv4e1rUWLFrCzs8uU05SUFGzatAl9+/bFli1bUK5cOcjlcri5uWHWrFnZnu/evXuQyWS4ePGiqi0hIQEymQyHDh0C8P4qyr59+1C5cmUYGxujUaNGePr0Kfbs2YOyZcvC3NwcXbt2RWpqquo8SqUSISEhKF68OIyNjeHl5YXNmzdLmQ7SEQVi0BcfH499+/ZhyJAhMDY2Vtvn6OiIbt26YcOGDXi3ZOGMGTPg5eWFCxcuYMyYMRgxYgRCQ0NVx3To0EH1Q3bu3DlUqVIFjRs3VqsW3rlzB9u3b8c///yDf/75B4cPH8bUqVO/zgfOw9auXYspU6Zg2rRpOHfuHIoVK4Y///wzU7+DBw/izp07OHjwIFavXo1Vq1Zl+T+sgiI6OhrNmzdH9erVcenSJfz5559Yvnw5Jk+eDACoW7cukpOTceHCBQBvB4i2traq/1m8a2vQoEEuRK9d8fHx2Lt3L4YOHQoTE5NM+7OaU/ry5Uv4+fnBysoKZ86cwaZNm7B//34EBAQAAN68eYM2bdqgfv36uHz5Mk6cOIEBAwZAJpMBeHv5rkePHhgxYgSuX7+OxYsXY9WqVZgyZYpWP6tYGzduRJkyZeDh4YHu3btjxYoV+HBp1o0bN2L8+PH47bffcPbsWTg5OWHhwoWi3mPt2rUwNTXFkCFDstyf3Zze33//HbNmzcLMmTNx+fJl+Pn5oVWrVrh16xYA4I8//sDOnTuxceNGREZGYu3atXBzc1Mdn5Pfw9pUqFAh9OjRA6tWrVLL6aZNm5CRkYGyZcuiY8eO6Ny5M65cuYLx48dj7NixkvweGz9+PObPn4/jx4/j4cOH6NixI+bOnYt169Zh9+7d+O+//zBv3jxV/5CQEKxZswaLFi3CtWvXMHLkSHTv3h2HDx/+4lhIxwgFwMmTJwUAwrZt27LcP3v2bAGAEBsbK7i6ugpNmzZV29+pUyehWbNmgiAIwtGjRwVzc3MhLS1NrU+JEiWExYsXC4IgCMHBwULhwoWFpKQk1f7Ro0cLNWrUkPBT5b6ePXsK+vr6gomJidpmZGQkABBevHghrFy5UrCwsFAdU6NGDWHo0KFq56ldu7bg5eWldl5XV1fhzZs3qrYOHToInTp10vZHynU9e/YUWrdunan9559/Fjw8PASlUqlqW7BggWBqaipkZGQIgiAIVapUEWbMmCEIgiC0adNGmDJlimBoaCgkJycLjx49EgAIN2/e/Cqf42s6deqUAEDYunXrJ/t9+DtgyZIlgpWVlZCSkqLav3v3bkFPT0+IiYkRnj9/LgAQDh06lOW5GjduLPz2229qbX/99Zfg5OT0ZR9GYrVq1RLmzp0rCIIgpKenC7a2tsLBgwdV+318fIQhQ4aoHVOjRg21n8fg4GC11x9r1qyZULFixc/G8vF5nJ2dhSlTpqj1qV69uiqeYcOGCY0aNVL7zr+Tk9/DX0NERIQAQC2ndevWFbp37y507dpVaNKkiVr/0aNHC56enqrXrq6uwpw5cwRBEISoqCgBgHDhwgXV/hcvXqid/+DBgwIAYf/+/ao+ISEhAgDhzp07qraBAwcKfn5+giAIQlpamlC4cGHh+PHjarH07dtX6NKly5d8fNJBBaLS946Qw4eP+Pj4ZHodEREBALh06RJSUlJgY2MDU1NT1RYVFYU7d+6ojnFzc4OZmZnqtZOTE54+fSrBp8hbGjZsiIsXL6pty5Yty7Z/ZGQkvL291do+fg0A5cqVg76+vup1fs1fTkVERMDHx0dVaQKA2rVrIyUlRTWnqH79+jh06BAEQcDRo0fRrl07lC1bFuHh4Th8+DCcnZ1RqlSp3PoIWpPTn+sPRUREwMvLS60yWLt2bSiVSkRGRsLa2hq9evWCn58fWrZsid9//x1PnjxR9b106RImTpyo9jugf//+ePLkidpltdwUGRmJ06dPo0uXLgDeVqY6deqE5cuXq/pERESgRo0aasd9/PvvczTJf1JSEh4/fozatWurtdeuXVv1u7ZXr164ePEiPDw8MHz4cPz333+qfjn9PaxtZcqUQa1atbBixQoAwO3bt3H06FH07dsXERERWX6+W7duqaZhaOrDuYIODg4oXLgw3N3d1dre/b68ffs2UlNT0aRJE7VcrVmz5qvmivKGQrkdwNdQsmRJyGQyREREoG3btpn2R0REwMrKCnZ2dp89V0pKCpycnNQum73z4WUMAwMDtX0ymQxKpVJ07HmdiYkJSpYsqdaW1cRmsQpK/qTUoEEDrFixApcuXYKBgQHKlCmDBg0a4NChQ3jx4gXq16+f2yFqRalSpSCTyXDjxg1Jz7ty5UoMHz4ce/fuxYYNG/Drr78iNDQUNWvWREpKCiZMmIB27dplOs7IyEjSODS1fPlyvHnzBs7Ozqo2QRAgl8sxf/58WFhYSPI+pUuXRnh4ONLT0zP93H6JKlWqICoqCnv27MH+/fvRsWNH+Pr6YvPmzTn+Pfw19O3bF8OGDcOCBQuwcuVKlChRQqOfNT29tzWYDwfR2c1j/jDPMpnsk78v381T3b17N1xcXNT68bm+BU+BqPTZ2NigSZMmWLhwIV69eqW2LyYmBmvXrkWnTp1UVZSPJ36fPHkSZcuWBfD2F1FMTAwKFSqEkiVLqm22trZf5wPpMA8PD5w5c0at7ePXlFnZsmVx4sQJtf8hHDt2DGZmZihSpAiA9/P65syZo/qfzrtB36FDh/LlfD4AsLa2hp+fHxYsWICXL19m2p+QkJCprWzZsrh06ZJa/2PHjkFPTw8eHh6qtsqVKyMoKAjHjx9H+fLlsW7dOgBvfw9ERkZm+h1QsmRJ1f+8c9ObN2+wZs0azJo1S60Kf+nSJTg7O+P//u//ALzNw6lTp9SOze7Gl+x07doVKSkp2c4FzCr/5ubmcHZ2xrFjx9Tajx07Bk9PT7V+nTp1wtKlS7FhwwZs2bIF8fHxeer3cMeOHaGnp4d169ZhzZo16NOnD2QyGcqWLZvl5ytdurTaVYx33hUdPqwof3hTh6Y8PT0hl8vx4MGDTLkqWrToF5+fdEuBqPQBwPz581GrVi34+flh8uTJKF68OK5du4bRo0fDxcVFbQL2sWPHMH36dLRp0wahoaHYtGkTdu/eDQDw9fWFj48P2rRpg+nTp6N06dJ4/Pgxdu/ejbZt26JatWq59RF1wrBhw9C/f39Uq1YNtWrVwoYNG3D58mW1SxMFXWJiYqZf9gMGDMDcuXMxbNgwBAQEIDIyEsHBwQgMDFQNMqysrFCxYkWsXbsW8+fPBwDUq1cPHTt2RHp6er6t9AHAggULULt2bXh7e2PixImoWLEi3rx5g9DQUPz555+qS4bvdOvWDcHBwejZsyfGjx+PZ8+eYdiwYfjuu+/g4OCAqKgoLFmyBK1atYKzszMiIyNx69Yt9OjRAwAwbtw4tGjRAsWKFcO3334LPT09XLp0CVevXlXdXJOb/vnnH7x48QJ9+/bNVNFr3749li9fjkGDBmHEiBHo1asXqlWrhtq1a2Pt2rW4du2aqJ/HGjVq4Mcff8QPP/yA6OhotG3bFs7OzqplserUqYMRI0ZkOm706NEIDg5GiRIlUKlSJaxcuRIXL17E2rVrAQCzZ8+Gk5MTKleuDD09PWzatAmOjo6wtLTMU7+HTU1N0alTJwQFBSEpKQm9evUCAPzwww+oXr06Jk2ahE6dOuHEiROYP39+toNjY2Nj1KxZE1OnTkXx4sXx9OlT/Prrr18cn5mZGUaNGoWRI0dCqVSiTp06SExMxLFjx2Bubp7t3dWUT+XifMKv7t69e0LPnj0FBwcHwcDAQChatKgwbNgwIS4uTtXH1dVVmDBhgtChQwehcOHCgqOjo/D777+rnScpKUkYNmyY4OzsrDpPt27dhAcPHgiCkPXE5zlz5giurq7a/ohfVXY3HbybbJzVjRyCIAgTJ04UbG1tBVNTU6FPnz7C8OHDhZo1a37yvCNGjBDq168v/YfIY3r27CkAyLT17dtXOHTokFC9enXB0NBQcHR0FH766SchPT1d7fgRI0YIAISIiAhVm5eXl+Do6Pi1P8pX9/jxY2Ho0KGCq6urYGhoKLi4uAitWrVSTYLHRzdzXb58WWjYsKFgZGQkWFtbC/379xeSk5MFQRCEmJgYoU2bNoKTk5NgaGgouLq6CuPGjVPdNCMIgrB3716hVq1agrGxsWBubi54e3sLS5Ys+ZofOVstWrQQmjdvnuW+dze+XLp0SRAEQZgyZYrq57Fnz57Cjz/+KOpGjnc2bNgg1KtXTzAzMxNMTEyEihUrChMnThRevHiR5XkyMjKE8ePHCy4uLoKBgYHg5eUl7NmzR7V/yZIlQqVKlQQTExPB3NxcaNy4sXD+/HnV/s/9Hv6ajh8/LgDIlPPNmzcLnp6egoGBgVCsWDHVjVbvfHgjhyAIwvXr1wUfHx/B2NhYqFSpkvDff/9leSPHu5wKgpDl79iPc61UKoW5c+cKHh4egoGBgWBnZyf4+fkJhw8fluLjkw6RCYIGs3DzMTc3N3z//ff4/vvvczuUAqNJkyZwdHTMt4u4EumyoKAgHD16FOHh4bkdChF9oQJzeZfyhtTUVCxatAh+fn7Q19fH//3f/2H//v1q6yASUe4TBAF3795FWFgYKleunNvhEJEEcn/GMRUoMpkM//77L+rVq4eqVati165d2LJlC3x9fXM7NCL6QGJiIjw9PWFoaIiff/45t8MhIgnw8i4RERFRAcBKHxEREVEBwEEfERERUQHAQR8RERFRAcBBHxEREVEBwEEfERERUQHAQR8RERFRAcBBHxEREVEBwEEfERERUQHAQR8RERFRAfD/T/qeGRLdJzoAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"code","source":["# Shapiro-Wilk testini uygulayalım\n","statistic, p_value = shapiro(df['Volume'])\n","\n","# Test istatistiği ve p değeri\n","print(\"Shapiro-Wilk Test İstatistiği:\", statistic)\n","print(\"P Değeri:\", p_value)\n","\n","# P değerini alfa düzeyiyle karşılaştırarak hipotez testi\n","alpha = 0.05\n","if p_value > alpha:\n","    print(\"H0 reddedilemez, örneklem normal dağılımdan gelmektedir.\")\n","else:\n","    print(\"H0 reddedilir, örneklem normal dağılımdan gelmemektedir.\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"40Rc_8XvhbdO","executionInfo":{"status":"ok","timestamp":1703919954872,"user_tz":-180,"elapsed":302,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"d8640b60-c889-4bc9-bb01-c911e5733159"},"execution_count":32,"outputs":[{"output_type":"stream","name":"stdout","text":["Shapiro-Wilk Test İstatistiği: 0.838366687297821\n","P Değeri: 1.9755886903233567e-36\n","H0 reddedilir, örneklem normal dağılımdan gelmemektedir.\n"]}]},{"cell_type":"code","source":["# normaltest() fonksiyonunu uygulayalım\n","statistic, p_value = normaltest(df['Volume'])\n","\n","# Test istatistiği ve p değeri\n","print(\"Normaltest İstatistiği:\", statistic)\n","print(\"P Değeri:\", p_value)\n","\n","# P değerini alfa düzeyiyle karşılaştırarak hipotez testi\n","alpha = 0.05\n","if p_value > alpha:\n","    print(\"H0 reddedilemez, örneklem normal dağılımdan gelmektedir.\")\n","else:\n","    print(\"H0 reddedilir, örneklem normal dağılımdan gelmemektedir.\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Tcw7yCkbio-1","executionInfo":{"status":"ok","timestamp":1703919956958,"user_tz":-180,"elapsed":279,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"c9426714-8f87-43f0-9a64-6c59960b55d3"},"execution_count":33,"outputs":[{"output_type":"stream","name":"stdout","text":["Normaltest İstatistiği: 623.3088146892537\n","P Değeri: 4.469002827144171e-136\n","H0 reddedilir, örneklem normal dağılımdan gelmemektedir.\n"]}]},{"cell_type":"code","source":["# Anderson-Darling testini uygulayalım\n","result = anderson(df['Volume'])\n","\n","# Test istatistiği ve eleştiri değeri\n","print(\"Anderson-Darling Test İstatistiği:\", result.statistic)\n","print(\"Eleştiri Değerleri:\", result.critical_values)\n","\n","# P değerini alfa düzeyiyle karşılaştırarak hipotez testi\n","alpha = 0.05\n","if result.statistic < result.critical_values[2] and all(result.statistic < result.critical_values):\n","    print(\"H0 reddedilemez, örneklem normal dağılımdan gelmektedir.\")\n","else:\n","    print(\"H0 reddedilir, örneklem normal dağılımdan gelmemektedir.\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7xwPwAIBi_Zd","executionInfo":{"status":"ok","timestamp":1703919958923,"user_tz":-180,"elapsed":277,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"7661b9b0-81aa-468b-8ed0-b65ba6ab3bb6"},"execution_count":34,"outputs":[{"output_type":"stream","name":"stdout","text":["Anderson-Darling Test İstatistiği: 54.28376299113188\n","Eleştiri Değerleri: [0.574 0.654 0.785 0.916 1.089]\n","H0 reddedilir, örneklem normal dağılımdan gelmemektedir.\n"]}]},{"cell_type":"code","source":["# Son 5 gözlemin Open ve Close değerlerini kullanarak line plot çizme\n","plt.plot(df['Date'].tail(5), df['Open'].tail(5), marker='o', linestyle='-', color='red', label='Open')\n","plt.plot(df['Date'].tail(5), df['Close'].tail(5), marker='o', linestyle='-', color='blue', label='Close')\n","\n","plt.title('Line Plot: Open vs Close (Son 5 Gözlem)')\n","plt.xlabel('Date')\n","plt.ylabel('Value')\n","plt.legend()\n","plt.grid(True)\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"gdd6W30zsREs","executionInfo":{"status":"ok","timestamp":1703146036854,"user_tz":-180,"elapsed":768,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"1b7fb71b-ac41-4850-b359-cd722c177976"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["# Son 90 gözlemin High ve Low değerleri ile scatter plot çizme\n","plt.scatter(df['High'].tail(90), df['Low'].tail(90), color='green', marker='o')\n","plt.title('Scatter Plot: High vs Low (Son 90 Gözlem (3 Ay))')\n","plt.xlabel('High')\n","plt.ylabel('Low')\n","plt.grid(True)\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"UXjYDFloveFl","executionInfo":{"status":"ok","timestamp":1703146038679,"user_tz":-180,"elapsed":816,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"cda0d985-4f31-41e1-e8ab-88af93730c57"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["# normal dağılım ve görsel normallik testleri (histogram)\n","for column in df.columns:\n","    plt.figure(figsize=(8, 6))\n","    plt.hist(df[column], bins=20, color='skyblue', edgecolor='black')\n","    plt.title(f'Histogram - {column}')\n","    plt.xlabel(column)\n","    plt.ylabel('Frekans')\n","    plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"6tLP_ajpgv23","executionInfo":{"status":"ok","timestamp":1703147713592,"user_tz":-180,"elapsed":13437,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"3b4c1f5d-44e1-4e54-81be-a01254dbc8cc"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 800x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}}]},{"cell_type":"code","source":["# belirli bir zaman diliminde açılış, kapanış, en yüksek ve en düşük fiyatlar\n","figure = go.Figure(data=[go.Candlestick(x=df[\"Date\"],\n","                                        open=df[\"Open\"],\n","                                        high=df[\"High\"],\n","                                        low=df[\"Low\"],\n","                                        close=df[\"Close\"])])\n","figure.update_layout(title = \"Apple Stock Price Analysis\",\n","                     xaxis_rangeslider_visible=False)\n","figure.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":542},"id":"2TCHhHwfzRGk","executionInfo":{"status":"ok","timestamp":1703919971273,"user_tz":-180,"elapsed":399,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"789c8dfb-4ca4-4f00-aa66-5accd110917f"},"execution_count":35,"outputs":[{"output_type":"display_data","data":{"text/html":["<html>\n","<head><meta charset=\"utf-8\" /></head>\n","<body>\n","    <div>     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\n"},"metadata":{}}]},{"cell_type":"code","source":["# Aykırı değerleri belirleme\n","Q1 = df['Volume'].quantile(0.25)\n","Q3 = df['Volume'].quantile(0.75)\n","IQR = Q3 - Q1\n","\n","lower_limit = Q1 - 1.5 * IQR\n","upper_limit = Q3 + 1.5 * IQR\n","\n","outliers = df[(df['Volume'] < lower_limit) | (df['Volume'] > upper_limit)]['Volume']\n","print(\"Aykırı Değerler:\", outliers.tolist())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Nln9UzZfkkS_","executionInfo":{"status":"ok","timestamp":1703919976225,"user_tz":-180,"elapsed":273,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"07258106-80ce-471e-d981-93be7c284112"},"execution_count":37,"outputs":[{"output_type":"stream","name":"stdout","text":["Aykırı Değerler: [346375200, 290954000, 272975200, 217562000, 282690400, 243278000, 261964400, 266157600, 224805200, 246876800, 271742800, 249616000, 384986800, 233292800, 365314800, 264654800, 243204000, 271300800, 248104000, 259092000, 382978400, 234330000, 365248800, 234428400, 219111200, 244439200, 259309200, 229722400, 277125600, 216071600, 221652400, 275978000, 216229200, 222195200, 230673600, 320605600, 426510000, 341397200, 319475600, 219178400, 226176800, 286744800, 285290000, 255598800, 418474000, 370732000, 322423600, 324056000, 300233600, 271857200, 401693200, 336752800, 287531200, 303602000, 252087200, 215250000, 240616800, 264476000, 374336800, 308151200, 338054800, 345937600, 225702700, 257599600, 332607200, 231366600, 287104900, 240226800, 262330500]\n"]}]},{"cell_type":"code","source":["# Aykırı değerleri silme\n","df_cleaned = df[(df['Volume'] >= lower_limit) & (df['Volume'] <= upper_limit)]\n","\n","# Temizlenmiş DataFrame'i yazdırma\n","print(\"Temizlenmiş DataFrame:\")\n","print(df_cleaned)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"o_HcJ7kek-iu","executionInfo":{"status":"ok","timestamp":1703919977748,"user_tz":-180,"elapsed":3,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"7feb9b50-6a4c-4d1a-fb5e-c1c64447bdfe"},"execution_count":38,"outputs":[{"output_type":"stream","name":"stdout","text":["Temizlenmiş DataFrame:\n","            Date        Open        High         Low       Close   Adj Close  \\\n","0     2018-01-16   44.474998   44.847500   44.035000   44.047501   41.651932   \n","1     2018-01-17   44.037498   44.812500   43.767502   44.775002   42.339878   \n","2     2018-01-18   44.842499   45.025002   44.562500   44.814999   42.377689   \n","3     2018-01-19   44.652500   44.895000   44.352501   44.615002   42.188580   \n","4     2018-01-22   44.325001   44.445000   44.150002   44.250000   41.843418   \n","...          ...         ...         ...         ...         ...         ...   \n","1486  2023-12-11  193.110001  193.490005  191.419998  193.179993  193.179993   \n","1487  2023-12-12  193.080002  194.720001  191.720001  194.710007  194.710007   \n","1488  2023-12-13  195.089996  198.000000  194.850006  197.960007  197.960007   \n","1489  2023-12-14  198.020004  199.619995  196.160004  198.110001  198.110001   \n","1490  2023-12-15  197.529999  198.399994  197.000000  197.570007  197.570007   \n","\n","         Volume  \n","0     118263600  \n","1     137547200  \n","2     124773600  \n","3     129700400  \n","4     108434400  \n","...         ...  \n","1486   60943700  \n","1487   52696900  \n","1488   70404200  \n","1489   66831600  \n","1490  128256700  \n","\n","[1422 rows x 7 columns]\n"]}]},{"cell_type":"code","source":["# Bağımlı değişken (Y)\n","y = df['Adj Close']\n","\n","# Bağımsız değişkenler (X)\n","X = df[['Open', 'High', 'Low', 'Close', 'Volume']]\n","\n","# Sabit (intercept) ekleyerek regresyon modelini oluşturalım\n","X = sm.add_constant(X)\n","\n","# Regresyon modelini uygulayalım\n","model = sm.OLS(y, X).fit()\n","\n","# Modelin özetini görüntüleyelim\n","print(model.summary())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VsLKcrlem4s0","executionInfo":{"status":"ok","timestamp":1703919980170,"user_tz":-180,"elapsed":274,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"a4fc4c6e-e33a-4eaf-c110-4219b819aae9"},"execution_count":39,"outputs":[{"output_type":"stream","name":"stdout","text":["                            OLS Regression Results                            \n","==============================================================================\n","Dep. Variable:              Adj Close   R-squared:                       1.000\n","Model:                            OLS   Adj. R-squared:                  1.000\n","Method:                 Least Squares   F-statistic:                 3.058e+06\n","Date:                Sat, 30 Dec 2023   Prob (F-statistic):               0.00\n","Time:                        07:06:19   Log-Likelihood:                -1090.2\n","No. Observations:                1491   AIC:                             2192.\n","Df Residuals:                    1485   BIC:                             2224.\n","Df Model:                           5                                         \n","Covariance Type:            nonrobust                                         \n","==============================================================================\n","                 coef    std err          t      P>|t|      [0.025      0.975]\n","------------------------------------------------------------------------------\n","const         -2.1094      0.061    -34.860      0.000      -2.228      -1.991\n","Open          -0.0220      0.017     -1.276      0.202      -0.056       0.012\n","High          -0.0046      0.020     -0.234      0.815      -0.043       0.034\n","Low            0.0501      0.018      2.714      0.007       0.014       0.086\n","Close          0.9833      0.016     60.345      0.000       0.951       1.015\n","Volume     -1.496e-09   3.25e-10     -4.607      0.000   -2.13e-09   -8.59e-10\n","==============================================================================\n","Omnibus:                      566.741   Durbin-Watson:                   0.042\n","Prob(Omnibus):                  0.000   Jarque-Bera (JB):               76.098\n","Skew:                           0.069   Prob(JB):                     2.99e-17\n","Kurtosis:                       1.902   Cond. No.                     5.66e+08\n","==============================================================================\n","\n","Notes:\n","[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n","[2] The condition number is large, 5.66e+08. This might indicate that there are\n","strong multicollinearity or other numerical problems.\n"]}]},{"cell_type":"code","source":["# Tahminleri elde etme\n","predictions = model.predict(X)\n","\n","# Tahminleri DataFrame'e ekleyerek inceleme\n","result_df = pd.DataFrame({'Gerçek Değerler': y, 'Tahmin Edilen Değerler': predictions})\n","print(result_df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"f0YYnRxMpAiU","executionInfo":{"status":"ok","timestamp":1703920249788,"user_tz":-180,"elapsed":302,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"48e0122d-e6da-4007-8d69-6798595f0fc0"},"execution_count":42,"outputs":[{"output_type":"stream","name":"stdout","text":["      Gerçek Değerler  Tahmin Edilen Değerler\n","0           41.651932               42.045882\n","1           42.339878               42.728756\n","2           42.377689               42.808325\n","3           42.188580               42.598563\n","4           41.843418               42.270595\n","...               ...                     ...\n","1486       193.179993              192.199822\n","1487       194.710007              193.726607\n","1488       197.960007              196.993228\n","1489       198.110001              197.139830\n","1490       197.570007              196.575414\n","\n","[1491 rows x 2 columns]\n"]}]},{"cell_type":"code","source":["# kalıntılar\n","kalıntı = model.resid\n","fig = sm.qqplot(kalıntı, line=\"s\")\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"id":"LW2vqrhnki6k","executionInfo":{"status":"ok","timestamp":1703920925454,"user_tz":-180,"elapsed":343,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"4ca16709-6127-491f-ea29-998a205514c6"},"execution_count":48,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["# Ortalama Kare Hata (MSE)\n","mse = mean_squared_error(y, predictions)\n","\n","# R-kare (R-squared)\n","r2 = r2_score(y, predictions)\n","\n","# Ortalama Mutlak Hata (MAE)\n","mae = mean_absolute_error(y, predictions)\n","\n","# Ortalama Mutlak Yüzde Hata (MAPE)\n","mape = np.mean(np.abs((y - predictions) / y)) * 100\n","\n","print(\"\\nPerformans Metrikleri:\")\n","print(\"Ortalama Kare Hata (MSE):\", mse)\n","print(\"Ortalama Mutlak Hata (MAE):\", mae)\n","print(\"Ortalama Mutlak Yüzde Hata (MAPE):\", mape)\n","print(\"R-kare (R-squared):\", r2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"leH-GjreprPj","executionInfo":{"status":"ok","timestamp":1703920936715,"user_tz":-180,"elapsed":278,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"5b7050c9-3df3-4273-e275-1b42722b1066"},"execution_count":49,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Performans Metrikleri:\n","Ortalama Kare Hata (MSE): 0.25270195956878855\n","Ortalama Mutlak Hata (MAE): 0.43903864846230395\n","Ortalama Mutlak Yüzde Hata (MAPE): 0.46906144776419506\n","R-kare (R-squared): 0.9999028804357568\n"]}]},{"cell_type":"code","source":["# Gerçek ve tahmin edilen değerleri alalım\n","y_actual = y\n","y_predicted = model.predict(X)\n","\n","# Scatter plot çizimi\n","plt.figure(figsize=(8, 6))\n","plt.scatter(y_actual, y_predicted, alpha=0.5, color='blue', label='Gerçek vs Tahmin Edilen')\n","plt.plot([min(y_actual), max(y_actual)], [min(y_actual), max(y_actual)], color='red', linestyle='--', linewidth=2, label='Doğru Denklemi')\n","\n","plt.title('Gerçek ve Tahmin Edilen Değerler Arasındaki Scatter Plot')\n","plt.xlabel('Gerçek Değerler')\n","plt.ylabel('Tahmin Edilen Değerler')\n","plt.legend()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":564},"id":"8UD0Eb9xoewc","executionInfo":{"status":"ok","timestamp":1703920939362,"user_tz":-180,"elapsed":931,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"632f6561-8828-4e33-bcbe-ff0612b9f403"},"execution_count":50,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 800x600 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAArcAAAIjCAYAAAAZajMiAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC480lEQVR4nOzddXhT1xvA8W9Sb6lQoLS4u+uw4TasA0aR4TZcNmwMHzJsDIcxvLgPd2cMLVAKQ4q7tbSlmvv74/4aCEkNWmrv53nytDnn5OTcNEnfnLz3HI2iKApCCCGEEEKkANrEHoAQQgghhBDxRYJbIYQQQgiRYkhwK4QQQgghUgwJboUQQgghRIohwa0QQgghhEgxJLgVQgghhBAphgS3QgghhBAixZDgVgghhBBCpBgS3AohhBBCiBRDgluRaowePRqNRsOLFy8SeygJ6kseZ4cOHciRI0eC309caDQaRo8erb++dOlSNBoNd+7c0ZdVq1aNatWqffGxfQllypRh2bJlhIaGcuHCBRwdHQkICIj3+zH1uIrPc/jwYTQaDYcPH47zbTt06ECaNGlibJeSn/uJJSm+D6Z2EtyKOPH19aV3797ky5cPW1tbbG1tKVSoEL169eLSpUuJPbxkq0OHDmg0mhgvHTp0SOyhJqjIf+5RXdasWZPYQ/wsd+7cMTgeCwsL0qdPT8WKFfn555+5d+/eZ99H79696dSpE1ZWVpQqVYrmzZvHKuhJLubOnYtGo6F8+fKJPZQULTQ0lD/++IOSJUvi4OCAk5MThQsXplu3bly7di1B7vPRo0eMHj2aixcvGtWtWrWKGTNmJMj9RqVatWoGr1dnZ2fKli3L4sWL0el08XIfEyZMYMuWLfHSl3jPPLEHIJKP7du34+Hhgbm5OW3atKF48eJotVquXbvGpk2bmDdvHr6+vmTPnj2xh5rsdO/enVq1aumv+/r6MnLkSLp160aVKlX05blz506M4UXpzz//jLc3+Q/17duXsmXLGpVXqFAhzn21bduWli1bYmVlFR9DixetWrXim2++QafT8fr1a86cOcOMGTP4448/+Ouvv2jZsuUn992hQwcqV67MpUuXyJw5c4oLAj09PcmRIwf//vsvN2/eJE+ePIk9pGRl7969sWrXrFkzdu3aRatWrejatSthYWFcu3aN7du3U7FiRQoUKBDvY3v06BFjxowhR44clChRwqBu1apVXLlyhf79+8f7/UYnS5YsTJw4EYDnz5+zfPlyOnfuzH///cekSZM+u/8JEybQvHlz3N3dP7sv8Z4EtyJWbt26RcuWLcmePTsHDhzAzc3NoP63335j7ty5aLXx82VAYGAgdnZ28dJXclChQgWDwO3s2bOMHDmSChUq8P333yfiyKJnYWGRIP1WqVKF5s2bx0tfZmZmmJmZxUtf8aVUqVJGf9e7d+9Sp04d2rdvT8GCBSlevPgn958nT54EC/oS6rUZm359fX05efIkmzZtonv37nh6ejJq1KgY+w4PD0en02FpaRlfw022YvMYnDlzhu3btzN+/Hh+/vlng7rZs2fz5s2bBBrdl6XT6QgNDcXa2jrKNo6Ojgav1e7du5M/f35mz57NuHHjEuw9UHweSUsQsTJ58mQCAwNZsmSJUWALYG5uTt++fcmaNatB+bVr12jevDnOzs5YW1tTpkwZtm3bZtAmMnfvyJEj9OzZExcXF7JkyaKv37VrF1WrVsXe3h4HBwfKli3LqlWrDPo4ffo09erVw9HREVtbW6pWrcqJEydiPK67d++SJ08eihQpwtOnT0222bBhg358H1uwYAEajYYrV67E6Zg/xaVLl+jQoQO5cuXC2toaV1dXOnXqxMuXL022f/PmDR06dMDJyQlHR0c6duxIUFCQQRuNRkPv3r1Zv349hQoVwsbGhgoVKnD58mX98eXJkwdra2uqVatmlF/5ca5Z5NfuU6dOZeHCheTOnRsrKyvKli3LmTNnPvsx+FBISAgDBgwgQ4YM2Nvb07hxYx48eGDULra5oSEhIYwaNYo8efJgZWVF1qxZGTx4MCEhIQbtIh+zLVu2UKRIEaysrChcuDC7d+/+rOPJnj07S5cuJTQ0lMmTJxvUvXnzhv79+5M1a1asrKzIkycPv/32m9Gs+cuXL2nbtq3+a+T27dvj5eWFRqNh6dKlBm3j47Vpyq5du6hSpQp2dnbY29vToEEDvL29DdpE5ofeunWLb775Bnt7e9q0aRPjY+Tp6UnatGlp0KABzZs3x9PT06jNh8/BGTNm6J+DV69eJTQ0lJEjR1K6dGkcHR2xs7OjSpUqHDp0yKifNWvWULp0af37TtGiRfnjjz/09abyY6tVq0aRIkW4evUq1atXx9bWlsyZMxv9PQEePHiAu7s7dnZ2uLi4MGDAAKPnGsCxY8f47rvvyJYtm/55OWDAAN69exfj43Xx4kUyZMhAtWrV9HnXscm5vXXrFgCVKlUyqjMzMyNdunQGZQ8fPqRz585kypQJKysrcubMSY8ePQgNDQXg1atX/PTTTxQtWpQ0adLg4OBA/fr18fLy0vdx+PBh/bc1HTt21KcCLF26lGrVqrFjxw7u3r2rL//wfSeur11PT08KFy6MlZVVnF+3tra2fPXVVwQGBvL8+fMo2wUGBvLjjz/qX7P58+dn6tSpKIpiMJ7AwECWLVuWalLPvhSZuRWxsn37dvLkyROnrzi9vb2pVKkSmTNnZujQodjZ2bFu3Trc3d3ZuHEj3377rUH7nj17kiFDBkaOHElgYCCg/nPt1KkThQsXZtiwYTg5OXHhwgV2795N69atATh48CD169endOnSjBo1Cq1Wy5IlS6hRowbHjh2jXLlyJsd369YtatSogbOzM/v27SN9+vQm2zVo0IA0adKwbt06qlatalC3du1aChcuTJEiRT7pmONi37593L59m44dO+Lq6oq3tzcLFy7E29ubf/75B41GY9C+RYsW5MyZk4kTJ3L+/HkWLVqEi4sLv/32m0G7Y8eOsW3bNnr16gXAxIkTadiwIYMHD2bu3Ln07NmT169fM3nyZDp16sTBgwdjHOuqVat4+/Yt3bt3R6PRMHnyZJo2bcrt27djNdPx9u1bkyfEpUuXTn+cXbp0YeXKlbRu3ZqKFSty8OBBGjRoEGPfpuh0Oho3bszx48fp1q0bBQsW5PLly/z+++/8999/Rjlxx48fZ9OmTfTs2RN7e3tmzpxJs2bNuHfvntE//rioUKECuXPnZt++ffqyoKAgqlatysOHD+nevTvZsmXj5MmTDBs2jMePH+vzEHU6HY0aNeLff/+lR48eFChQgK1bt9K+fXuj+4mP16YpK1asoH379tStW5fffvuNoKAg5s2bR+XKlblw4YJBQBIeHk7dunWpXLkyU6dOxdbWNsbHx9PTk6ZNm2JpaUmrVq2YN28eZ86cMZnCsmTJEoKDg+nWrRtWVlY4Ozvj7+/PokWL9F+1v337lr/++ou6devy77//6r8K37dvH61ataJmzZr614uPjw8nTpygX79+0Y7x9evX1KtXj6ZNm9KiRQs2bNjAkCFDKFq0KPXr1wfg3bt31KxZk3v37tG3b18yZcrEihUrTL621q9fT1BQED169CBdunT8+++/zJo1iwcPHrB+/foox3HmzBnq1q1LmTJl2Lp1KzY2NjE+vpEiU8s8PT2pVKkS5uZRhwqPHj2iXLlyvHnzhm7dulGgQAEePnzIhg0bCAoKwtLSktu3b7Nlyxa+++47cubMydOnT1mwYAFVq1bl6tWrZMqUiYIFCzJ27FijdKyKFSuSOXNm/Pz8ePDgAb///juAPo88rq/dgwcPsm7dOnr37k369Ok/6USw27dvY2ZmhpOTk8l6RVFo3Lgxhw4donPnzpQoUYI9e/YwaNAgHj58qD+GFStW0KVLF8qVK0e3bt2ApJd6lmwpQsTAz89PARR3d3ejutevXyvPnz/XX4KCgvR1NWvWVIoWLaoEBwfry3Q6nVKxYkUlb968+rIlS5YogFK5cmUlPDxcX/7mzRvF3t5eKV++vPLu3TuD+9XpdPqfefPmVerWrasvUxRFCQoKUnLmzKnUrl1bXzZq1CgFUJ4/f674+PgomTJlUsqWLau8evUqxsegVatWiouLi8H4Hj9+rGi1WmXs2LFxPuaYnDlzRgGUJUuWGBzTx1avXq0AytGjR42Os1OnTgZtv/32WyVdunQGZYBiZWWl+Pr66ssWLFigAIqrq6vi7++vLx82bJgCGLRt3769kj17dv11X19fBVDSpUtn8Lhu3bpVAZS///472uM+dOiQAkR5efz4saIoinLx4kUFUHr27Glw+9atWyuAMmrUKH1Z5PPrw3FXrVpVqVq1qv76ihUrFK1Wqxw7dsygv/nz5yuAcuLECYPHzNLSUrl586a+zMvLSwGUWbNmRXt8kY/PlClTomzTpEkTBVD8/PwURVGUcePGKXZ2dsp///1n0G7o0KGKmZmZcu/ePUVRFGXjxo0KoMyYMUPfJiIiQqlRo4bRc+lzX5sf1kU+rm/fvlWcnJyUrl27GrR78uSJ4ujoaFDevn17BVCGDh0a3cNl4OzZswqg7Nu3Tz/eLFmyKP369TNoF/kYOzg4KM+ePTOoCw8PV0JCQgzKXr9+rWTMmNHg9dKvXz/FwcHB6Jg/FPlcPXTokL6satWqCqAsX75cXxYSEqK4uroqzZo105fNmDFDAZR169bpywIDA5U8efIY9WnqdT9x4kRFo9Eod+/e1Ze1b99esbOzUxRFUY4fP644ODgoDRo0MPgbR47xw+e+KTqdTn8sGTNmVFq1aqXMmTPH4P4itWvXTtFqtcqZM2dM9qMoihIcHKxEREQY1Pn6+ipWVlYG75+m3vciNWjQwOC9JlJcX7tarVbx9vaO9vgjVa1aVSlQoID+/5uPj4/St29fBVAaNWqkb/fx++CWLVsUQPn1118N+mvevLmi0WgM3jvs7OyU9u3bx2o8IvYkLUHEyN/fH8DkGdfVqlUjQ4YM+sucOXMA9WuogwcP0qJFC/0s3IsXL3j58iV169blxo0bPHz40KCvrl27GuRG7tu3j7dv3zJ06FCjnKjI2buLFy9y48YNWrduzcuXL/X3ExgYSM2aNTl69KjRV7dXrlyhatWq5MiRg/3795M2bdoYHwMPDw+ePXtm8BXkhg0b0Ol0eHh4fPIxx8WHMy/BwcG8ePGCr776CoDz588btf/hhx8MrlepUoWXL1/q/56RatasaTB7ETk736xZM+zt7Y3Kb9++HeNYPTw8DB7XyFmY2NwWYOTIkezbt8/o4uzsDMDOnTsB9cSzD33qySbr16+nYMGCFChQQP93e/HiBTVq1AAw+tq6Vq1aBjMsxYoVw8HBIdbHF53I19nbt2/1Y6tSpQpp06Y1GFutWrWIiIjg6NGjAOzevRsLCwu6du2q70ur1epn5CPFx2vTlH379vHmzRtatWplME4zMzPKly9v8qv/Hj16xPpx8fT0JGPGjFSvXh1Q3wM8PDxYs2YNERERRu2bNWtGhgwZDMrMzMz0Oac6nY5Xr14RHh5OmTJlDF5DTk5OBAYGGsygx1aaNGkMcjQtLS0pV66cwXNj586duLm5GeSV29ra6mfvPvTh6z4wMJAXL15QsWJFFEXhwoULRu0PHTpE3bp1qVmzJps2bfqkEyk1Gg179uzh119/JW3atKxevZpevXqRPXt2PDw89Dm3Op2OLVu20KhRI8qUKWOyHwArKyv9+RgRERG8fPmSNGnSkD9/fpPvXXER19du1apVKVSoUKz7v3btmv7/W8GCBZk1axYNGjRg8eLFUd5m586dmJmZGb0//fjjjyiKwq5du+JwhOJTSFqCiFFkgGNqrcwFCxbw9u1bnj59avCGfvPmTRRFYcSIEYwYMcJkv8+ePSNz5sz66zlz5jSoj8z7ivzK35QbN24AmPzqNZKfn59BoNWoUSMyZszInj17Yr1EUmQ+79q1a6lZsyagpiSUKFGCfPnyAZ92zHHx6tUrxowZw5o1a3j27JlBnZ+fn1H7bNmyGVyPfAxev36Ng4NDlO0cHR0BjPKnI8tfv34d41iju+/YKFq0qMHqER+7e/cuWq3W6Cu8/Pnzx6r/j924cQMfHx+jYCjSx4/3x8cH6jHG9viiE/k6i3zd3bhxg0uXLsU4trt37+Lm5mb09f7HJ5bFx2vTlMjXYmRQ8bEPn3Og5unHlL8bKSIigjVr1lC9enV8fX315eXLl2fatGkcOHCAOnXqGNwmqjEvW7aMadOmce3aNcLCwky279mzJ+vWraN+/fpkzpyZOnXq0KJFC+rVqxfjWLNkyWKUIpQ2bVqDpRIjc/0/bmfq+Xvv3j1GjhzJtm3bjJ5fH7/ug4ODadCgAaVLl2bdunXRphPExMrKiuHDhzN8+HAeP37MkSNH+OOPP1i3bh0WFhasXLmS58+f4+/vH+17NKhB8B9//MHcuXPx9fU1+DDyOWk8EPfXbmyeyx/KkSMHf/75JxqNBmtra/LmzYuLi0u0t7l79y6ZMmUymBwAKFiwoL5eJCwJbkWMHB0dcXNzMzhpKlLkbN7HJ+tEzpb+9NNP1K1b12S/H//TjUtO2Mf3M2XKFKOlYyJ9HMA2a9aMZcuW4enpSffu3WN1P1ZWVri7u7N582bmzp3L06dPOXHiBBMmTDAaS1yOOS5atGjByZMnGTRoECVKlCBNmjTodDrq1atncjmuqGbalA9OaIiuXWxvH9+3TQw6nY6iRYsyffp0k/UfB/oJeXxXrlzBxcVFHwzqdDpq167N4MGDTbaP/HAVWwn12ozsd8WKFbi6uhrVfxxofTibF5ODBw/y+PFj1qxZY3KtY09PT6Pg1tSYV65cSYcOHXB3d2fQoEG4uLhgZmbGxIkT9R+mAVxcXLh48SJ79uxh165d7Nq1iyVLltCuXTuWLVsW7Vjj87kRERFB7dq1efXqFUOGDKFAgQLY2dnx8OFDOnToYPS6t7Ky4ptvvmHr1q3s3r2bhg0bxvk+TXFzc6Nly5Y0a9aMwoULs27dOqMTFKMzYcIERowYQadOnRg3bhzOzs5otVr69+//2UsJxvW1G9f/M3Z2dtF+0BZJkwS3IlYaNGjAokWL+Pfff6M8QetDuXLlAtSloj71jSFyVu7KlStRBoWRbRwcHGJ9P1OmTMHc3Fx/MlDkiWkx8fDwYNmyZRw4cAAfHx8URdGnJED8HHNUXr9+zYEDBxgzZgwjR47Ul0fOlqU22bNnR6fTcevWLYPZruvXr39Sf7lz58bLy4uaNWsazaZ9SadOneLWrVsG34Lkzp2bgICAGJ9T2bNn59ChQwQFBRnM3t68edOgXUI9TyNfiy4uLvH+/Pf09MTFxUWf9vShTZs2sXnzZubPnx9j4LJhwwZy5crFpk2bDP7OppYTs7S0pFGjRjRq1AidTkfPnj1ZsGABI0aM+Oxl1rJnz86VK1dQFMVgHB8/fy9fvsx///3HsmXLaNeunb48qnQJjUaDp6cnTZo04bvvvmPXrl3xuhuZhYUFxYoV48aNG7x48UL/IczUxMeHNmzYQPXq1fnrr78Myt+8eWNwIm90r72o6pLKa/dD2bNnZ//+/bx9+9Zg9jZy84sP14JPKmNOaSTnVsTK4MGDsbW1pVOnTiaXzPp4VsLFxYVq1aqxYMECHj9+bNQ+uiVUItWpUwd7e3smTpxIcHCwyfsrXbo0uXPnZurUqSbTJkzdj0ajYeHChTRv3pz27dvHepmuWrVq4ezszNq1a1m7di3lypUz+IorPo45KpGzQR8/zl96x56kIvKs85kzZxqUf+rj0aJFCx4+fMiff/5pVPfu3btoVwiIL3fv3qVDhw5YWloyaNAgg7GdOnWKPXv2GN3mzZs3hIeHA1C3bl3CwsIMAghFUZg3b57BbRLqeVq3bl0cHByYMGGCwdf9n9vvu3fv2LRpEw0bNqR58+ZGl969e/P27dtYvY5NvY5Onz7NqVOnDNp9vLyeVqulWLFiACaX64qrb775hkePHrFhwwZ9WVBQEAsXLoxxvIqiGCxJ9jFLS0s2bdpE2bJl9atnxNWNGzdM7pb35s0bTp06Rdq0acmQIQNarRZ3d3f+/vtvzp49a9Q+ctxmZmZG713r1683yu2OXOfY1Dq6dnZ2JtOvksJr92PffPMNERERzJ4926D8999/R6PR6N+/QD2ulLJucFIiM7ciVvLmzcuqVato1aoV+fPn1+9QpigKvr6+rFq1Cq1Wa5BDN2fOHCpXrkzRokXp2rUruXLl4unTp5w6dYoHDx4YrHFoioODA7///jtdunShbNmytG7dmrRp0+Ll5UVQUBDLli1Dq9WyaNEi6tevT+HChenYsSOZM2fm4cOHHDp0CAcHB/7++2+jvrVaLStXrsTd3Z0WLVqwc+fOKHMFI1lYWNC0aVPWrFlDYGAgU6dONWrzuccc3WPx9ddfM3nyZMLCwsicOTN79+41yD9MSY4dO2b0gQbUE7eKFStGiRIlaNWqFXPnzsXPz4+KFSty4MABo1nK2Grbti3r1q3jhx9+4NChQ1SqVImIiAiuXbvGunXr2LNnj8kTZj7V+fPnWblyJTqdjjdv3nDmzBk2btyIRqNhxYoV+kAKYNCgQWzbto2GDRvSoUMHSpcuTWBgIJcvX2bDhg3cuXOH9OnT4+7uTrly5RgwYAC3b9/WLwUWmXP44QxRQjxPHRwcmDdvHm3btqVUqVK0bNmSDBkycO/ePXbs2EGlSpWM/tnHxrZt23j79i2NGzc2Wf/VV1+RIUMGPD09Db5JMaVhw4Zs2rSJb7/9lgYNGuDr68v8+fMpVKiQwYfjLl268OrVK2rUqEGWLFm4e/cus2bNokSJEvq8yc/RtWtXZs+eTbt27Th37hxubm6sWLHCKF+6QIEC5M6dm59++omHDx/i4ODAxo0bY8zttrGxYfv27dSoUYP69etz5MiRGPNiP+Tl5UXr1q2pX78+VapUwdnZmYcPH7Js2TIePXrEjBkz9IH3hAkT2Lt3L1WrVtUvxfX48WPWr1/P8ePHcXJyomHDhowdO5aOHTtSsWJFLl++jKenp/5bhEi5c+fGycmJ+fPnY29vj52dHeXLlydnzpyULl2atWvXMnDgQMqWLUuaNGlo1KjRF3/txkajRo2oXr06w4cP586dOxQvXpy9e/eydetW+vfvb3CuQOnSpdm/fz/Tp08nU6ZM5MyZM8XtKpgovuTSDCL5u3nzptKjRw8lT548irW1tWJjY6MUKFBA+eGHH5SLFy8atb9165bSrl07xdXVVbGwsFAyZ86sNGzYUNmwYYO+TeSSQqaWklEURdm2bZtSsWJF/XJQ5cqVU1avXm3Q5sKFC0rTpk2VdOnSKVZWVkr27NmVFi1aKAcOHNC3+XApsEhBQUFK1apVlTRp0ij//PNPjMe/b98+BVA0Go1y//59k21ic8wxMbUkzoMHD5Rvv/1WcXJyUhwdHZXvvvtOefTokdHSV6aOU1FML4kFKL169TJoF9VyVZFLH61fv15fFtVSYKaWuvp4nKbEtBTYh7d/9+6d0rdvXyVdunSKnZ2d0qhRI+X+/fuftBSYoihKaGio8ttvvymFCxdWrKyslLRp0yqlS5dWxowZo1+WK6rHTFEUJXv27DEu6RP5+ERezM3NFWdnZ6V8+fLKsGHDTC61pCjqMlvDhg1T8uTJo1haWirp06dXKlasqEydOlUJDQ3Vt3v+/LnSunVrxd7eXnF0dFTatm2rHD9+XAGUNWvWGPT5ua9NU4+roqh/w7p16yqOjo6KtbW1kjt3bqVDhw7K2bNn9W0+XLYqJo0aNVKsra2VwMDAKNt06NBBsbCwUF68eBHtc1Cn0ykTJkxQsmfPrlhZWSklS5ZUtm/fbvQ83rBhg1KnTh3FxcVFsbS0VLJly6Z0795dvxRd5HFiYimwwoULG93vx/0riqLcvXtXady4sWJra6ukT59e6devn7J7926jPq9evarUqlVLSZMmjZI+fXqla9eu+qXnPnx/MPWYvnjxQilUqJDi6uqq3LhxQz/GmJYCe/r0qTJp0iSlatWqipubm2Jubq6kTZtWqVGjhsn3sbt37yrt2rVTMmTIoFhZWSm5cuVSevXqpV92LTg4WPnxxx8VNzc3xcbGRqlUqZJy6tQpk2PZunWrUqhQIcXc3NzgGAMCApTWrVsrTk5OCmDweH7uazcqUf09P2bq7/v27VtlwIABSqZMmRQLCwslb968ypQpUwyWrFQURbl27Zry9ddfKzY2Ngogy4LFE42iJNEzPIT4yNu3bylSpAjnzp2LcsMFIYShrVu34u7uzvHjx03uOCWEECmN5NyKZMPe3p5SpUrFy1a2QqREH2/JGhERwcyZM3FwcKBUqVKJNCohhPiyJOdWJAtTp07F3t6ef/75R7+IuxDCUJ8+fXj37h0VKlQgJCSETZs2cfLkSSZMmPBJS+0JIURyJGkJIlmoVq0ap06domTJkmzfvl3SEoQwYdWqVUybNo2bN28SHBxMnjx56NGjB717907soQkhxBcjwa0QQgghhEgxJOdWCCGEEEKkGBLcCiGEEEKIFENOKEPdm/rRo0fY29vLVnhCCCGEEEmQoii8ffuWTJkyodVGPT8rwS3w6NEjsmbNmtjDEEIIIYQQMbh//77Bjqgfk+AWdf1UUB8sBweHRB6NEEIIIYT4mL+/P1mzZtXHbVGR4Jb3e647ODhIcCuEEEIIkYTFlEIqJ5QJIYQQQogUQ4JbIYQQQgiRYkhwK4QQQgghUgzJuY2liIgIwsLCEnsYQqRYFhYWmJmZJfYwhBBCJHMS3MZCQEAADx48QHYqFiLhaDQasmTJQpo0aRJ7KEIIIZIxCW5jEBERwYMHD7C1tSVDhgyyyYMQCUBRFJ4/f86DBw/ImzevzOAKIYT4ZBLcxiAsLAxFUciQIQM2NjaJPRwhUqwMGTJw584dwsLCJLgVQgjxyeSEsliSGVshEpa8xoQQQsQHCW6FEEIIIUSKkajB7cSJEylbtiz29va4uLjg7u7O9evXDdoEBwfTq1cv0qVLR5o0aWjWrBlPnz41aHPv3j0aNGiAra0tLi4uDBo0iPDw8C95KEIIIYQQIglI1OD2yJEj9OrVi3/++Yd9+/YRFhZGnTp1CAwM1LcZMGAAf//9N+vXr+fIkSM8evSIpk2b6usjIiJo0KABoaGhnDx5kmXLlrF06VJGjhyZGIck4iBHjhzMmDEjsYfxSapVq0b//v3jvd/Ro0dTokSJeO83Nj4+po//PhqNhi1btnzxcQkhhBBxkajB7e7du+nQoQOFCxemePHiLF26lHv37nHu3DkA/Pz8+Ouvv5g+fTo1atSgdOnSLFmyhJMnT/LPP/8AsHfvXq5evcrKlSspUaIE9evXZ9y4ccyZM4fQ0NDEPLxE9+TJE/r160eePHmwtrYmY8aMVKpUiXnz5hEUFJTYw0tUOXLkQKPRRHnp0KFDoozrp59+4sCBA5/Vx9KlS00ek7W1dZz6OXPmDN26dfussQghhBBfWpJaLcHPzw8AZ2dnAM6dO0dYWBi1atXStylQoADZsmXj1KlTfPXVV5w6dYqiRYuSMWNGfZu6devSo0cPvL29KVmypNH9hISEEBISor/u7++fUIekp9PBvXvw9i3Y20O2bKBNwI8Wt2/fplKlSjg5OTFhwgSKFi2KlZUVly9fZuHChWTOnJnGjRt/Ut+hoaFYWlrG84i/rDNnzhAREQHAyZMnadasGdevX8fBwQEg0VbGSJMmTbys8+rg4GCU4hPXE7YyZMjw2eMQQgghvrQkc0KZTqejf//+VKpUiSJFigDqzKOlpSVOTk4GbTNmzMiTJ0/0bT4MbCPrI+tMmThxIo6OjvpL1qxZ4/loDPn4wKRJMHIkjBun/pw0SS1PKD179sTc3JyzZ8/SokULChYsSK5cuWjSpAk7duygUaNG+rZv3ryhS5cuZMiQAQcHB2rUqIGXl5e+PvKr8kWLFpEzZ079DOCbN2/o3r07GTNmxNramiJFirB9+3b97Y4fP06VKlWwsbEha9as9O3b1yDl5GOLFi3CycnJ5Mylv78/NjY27Nq1y6B88+bN2NvbExQURGhoKL1798bNzQ1ra2uyZ8/OxIkTTd5XhgwZcHV1xdXVVf9hysXFBVdXVywsLPjhhx/InDkztra2FC1alNWrVxv1odPpGDx4MM7Ozri6ujJ69GiDeo1Gw4IFC2jYsCG2trYULFiQU6dOcfPmTapVq4adnR0VK1bk1q1bRo91pA4dOuDu7s7UqVNxc3MjXbp09OrVK8bd8jQajf74Ii8fvk4CAwNp164dadKkwc3NjWnTphn1EVPayP3792nRogVOTk44OzvTpEkT7ty589ljF0IIIT5Hkglue/XqxZUrV1izZk2C39ewYcPw8/PTX+7fv59g9+XjAzNnwoULkD495M+v/rxwQS1PiAD35cuX7N27l169emFnZ2eyzYezeN999x3Pnj1j165dnDt3jlKlSlGzZk1evXqlb3Pz5k02btzIpk2buHjxIjqdjvr163PixAlWrlzJ1atXmTRpkn590lu3blGvXj2aNWvGpUuXWLt2LcePH6d3794mxzN58mSGDh3K3r17qVmzplG9g4MDDRs2ZNWqVQblnp6euLu7Y2try8yZM9m2bRvr1q3j+vXreHp6kiNHjrg+fAQHB1O6dGl27NjBlStX6NatG23btuXff/81aLds2TLs7Ow4ffo0kydPZuzYsezbt8+gzbhx42jXrh0XL16kQIECtG7dmu7duzNs2DDOnj2LoihRPiaRDh06xK1btzh06JA+p3zp0qVxPq4PDRo0iCNHjrB161b27t3L4cOHOX/+fKxvHxYWRt26dbG3t+fYsWOcOHGCNGnSUK9ePYN0oIQYuxBCCBEtJQno1auXkiVLFuX27dsG5QcOHFAA5fXr1wbl2bJlU6ZPn64oiqKMGDFCKV68uEH97du3FUA5f/58rO7fz89PARQ/Pz+junfv3ilXr15V3r17F/sD+r+ICEUZP15RmjdXlJEjFWXUqPeXkSPV8gkT1Hbx6Z9//lEAZdOmTQbl6dKlU+zs7BQ7Oztl8ODBiqIoyrFjxxQHBwclODjYoG3u3LmVBQsWKIqiKKNGjVIsLCyUZ8+e6ev37NmjaLVa5fr16ybH0LlzZ6Vbt24GZceOHVO0Wq3+scyePbvy+++/K4MHD1bc3NyUK1euRHtcmzdvVtKkSaMEBgYqiqL+3aytrZVdu3YpiqIoffr0UWrUqKHodLpo+/nYoUOHTD7PPtSgQQPlxx9/1F+vWrWqUrlyZYM2ZcuWVYYMGaK/Dii//PKL/vqpU6cUQPnrr7/0ZatXr1asra3110eNGmXwfG7fvr2SPXt2JTw8XF/23XffKR4eHlGOdcmSJQqg/1tHXurVq6coiqK8fftWsbS0VNatW6e/zcuXLxUbGxulX79++rLIv8+Hx7N582ZFURRlxYoVSv78+Q0e65CQEMXGxkbZs2fPJ439c15rQgghvpyICEXx9VWUS5fUn/Edx0QlunjtQ4mac6soCn369GHz5s0cPnyYnDlzGtSXLl0aCwsLDhw4QLNmzQC4fv069+7do0KFCgBUqFCB8ePH8+zZM1xcXADYt28fDg4OFCpU6Mse0Efu3YNr1yBrVvg43VGjgSxZ1Jnbe/fgEyYY4+zff/9Fp9PRpk0bfc6xl5cXAQEBpEuXzqDtu3fvDL4uz549u0EO5sWLF8mSJQv58uUzeV9eXl5cunQJT09PfZmiKOh0Onx9fSlYsCAA06ZNIzAwkLNnz5IrV65ox//NN99gYWHBtm3baNmyJRs3bsTBwUGfk92hQwdq165N/vz5qVevHg0bNqROnTpxeIRUERERTJgwgXXr1vHw4UNCQ0MJCQnB1tbWoF2xYsUMrru5ufHs2bMo20SmBRQtWtSgLDg4GH9/f32+78cKFy5ssGOXm5sbly9fjvYY7O3tjWZiI/OIb926RWhoKOXLl9fXOTs7kz9//mj7/JCXlxc3b97E3t7eoDw4ONjgefMpYxdCCJF0+fjApk3w9NBVrmkLkSYNlC4NTZvC//+1J7pEDW579erFqlWr2Lp1K/b29vocWUdHR2xsbHB0dKRz584MHDgQZ2dnHBwc6NOnDxUqVOCrr74CoE6dOhQqVIi2bdsyefJknjx5wi+//EKvXr2wsrJKzMPj7VsIDoYoMgOws4OHD9V28SlPnjxoNBqjE4oig8cPT5YKCAjAzc2Nw4cPG/XzYa7zx+kNMZ1wFRAQQPfu3enbt69RXbZs2fS/V6lShR07drBu3TqGDh0abZ+WlpY0b96cVatW0bJlS1atWoWHhwfm5urTuFSpUvj6+rJr1y72799PixYtqFWrFhs2bIi2349NmTKFP/74gxkzZlC0aFHs7Ozo37+/0eobFhYWBtc1Gg06nS7KNpGpIKbKPr5dXO/nY1qtljx58kTb5nMEBARQunRpgw8vkT78EPQpYxdCCJE0+fjAuBGh1DowjOFvpvOD62YOO7rz339w+TKMGpU0AtxEDW7nzZsHqOtrfmjJkiX6pZh+//13tFotzZo1IyQkhLp16zJ37lx9WzMzM7Zv306PHj2oUKECdnZ2tG/fnrFjx36pw4iSvT1YW0NgIJialAsMVOs/mvz6bOnSpaN27drMnj2bPn36RJl3C2pA+OTJE8zNzeOUn1qsWDEePHjAf//9Z3L2tlSpUly9ejXGAKtcuXL07t2bevXqYW5uzk8//RRt+zZt2lC7dm28vb05ePAgv/76q0G9g4MDHh4eeHh40Lx5c+rVq8erV6/0J43FxokTJ2jSpAnff/89oAae//33X6J/ExBfcufOjYWFBadPn9Z/0Hj9+jX//fcfVatWjVUfpUqVYu3atbi4uEQ54yyEECLl0Olg0c+3+fHvlpSOOAPAxKedqKmtwBurjPz7LyxcCNOmJexqULGR6GkJMbG2tmbOnDnMmTMnyjbZs2dn586d8Tm0eJEtGxQooJ48VqiQYWqCosCDB1CqlNouvs2dO5dKlSpRpkwZRo8eTbFixdBqtZw5c4Zr165RunRpAGrVqkWFChVwd3dn8uTJ5MuXj0ePHrFjxw6+/fZbypQpY7L/qlWr8vXXX9OsWTOmT59Onjx5uHbtGhqNhnr16jFkyBC++uorevfuTZcuXbCzs+Pq1avs27eP2bNnG/RVsWJFdu7cSf369TE3N492c4Svv/4aV1dX2rRpQ86cOQ2+Wp8+fTpubm6ULFkSrVbL+vXrcXV1NVptIyZ58+Zlw4YNnDx5krRp0zJ9+nSePn2arIJbRVFMrhbi4uJCmjRp6Ny5M4MGDSJdunS4uLgwfPhwtHF4N2rTpg1TpkyhSZMmjB07lixZsnD37l02bdrE4MGDyZIlS3wejhBCiC/s4yVMX/+5nlFbu+CgqMunhmDJdIcx3A9xwUIBCws4fBju3IEYsgwTXJJa5zal0Wrh22/h/n24elXNsbWzU2dsHzxQV01wd0+YTzi5c+fmwoULTJgwgWHDhvHgwQOsrKwoVKgQP/30Ez179gTUr4l37tzJ8OHD6dixI8+fP8fV1ZWvv/7aaIm1j23cuJGffvoJDw8PXr9+TZEiRZg0aRKgzuweOXKE4cOHU6VKFRRFIXfu3Hh4eJjsq3LlyuzYsYNvvvkGMzMz+vTpY7KdRqOhVatWTJ482WgXOnt7eyZPnsyNGzcwMzOjbNmy7Ny5M05BG8Avv/zC7du3qVu3Lra2tnTr1g13d3f9OszJgb+/P25ubkbljx8/xtXVlSlTphAQEECjRo2wt7fnxx9/jNPx2dracvToUYYMGULTpk15+/YtmTNnpmbNmjKTK4QQyZyPD2zerJ43FBH4jk6XB1Lzxnx9/R3z3PROv5YrVqWxVSAoSI1lXr6E//5L/OBWo8Rm+jSF8/f3x9HRET8/P6N/zMHBwfj6+hqs7xpXHz5JgoPVVISCBdXANinkpsSHhg0bMnXqVAoUKJDYQxHJVHy81oQQQnw6nQ7274cZM+D5cyjncI1hXh5keXlJ32azVUtGuCwgQPs+XgoPV+MbKyv46y+oVy9hxhddvPYhmbn9AgoWVNe3/ZI7lH0pz549IzAwEEtLS3bt2iXBrRBCCJEM+fjA/PmwahW8eaOWjdQNIYtODWzfaWwYoJ3JMjrjotHw4SJQWq0a3KZPD1EsovRFSXD7hWi1X2a5ry/Ny8uLJk2akD59ekaNGpXYwxFCCCFEHHl7w5AhcPq0GtiamamXPuHzKR96ipeaDHS0Xct1iyJEBKoTdba2apuICDUtQaOBihWTRqwjwa34LLVr1yYoKCixhyGEEEKIOIpMQxg1Sl3KK/xdGOE6CzQaMDeH19ZuNNLs4yZ5CFbssERNPQB1plajUfuIiICcOeHHH5PGt9JJYAhCCCGEEOJL8vGBgQOhWzc4d1bh+8AFXNQVxYnXhIXBu3dqLq2PZXHeae0wM1MDWRsbNZBNl06dvbW2VlMvJ0yAwoUT+6hUMnMrhBBCCJFK6HSwdy9MmqTO1mrf+rEivBserANgMZ1oyiYURUNwsBrMgjoja2YGefJA7tzw6pVaVrAgtG+fdAJbkOBWCCGEECJV8PFRZ1g3blRnZstwhjW0JDe39W0ekAVzwglX1B0mQ0PVIDYsDJydYfRo9aSxpHyCvAS3QgghhBApnI8P9O8Phw5BWJhCf2bwG0OwJAyA1zjRmb/YTFP9bRRFTU0wNwdHR2jcGGrVSnrB7MckuBVCCCGESKF0Orh5Ezp1UldDSKu8ZAkdaczf+jb/UJ6WrOEuOYxur9GAgwNUqwbduyf9wBbkhDIhhBBCiBTJxwc6dICyZeGff6CicpyLlDAIbH9jMFU4FmVgmyaNOmM7enTy2XhKglsRK9u3b6djx468e/eOtWvX0rx588QeUrypVq0a/fv3j7K+Q4cOuLu7J/o4hBBCiNjy9oYffoD168HfXy0rjDdZeQDAc9JTn50M5TfCsTC4rbm5eiJZwYLqagoLFyafwBYkuE2xOnTogEajQaPRYGFhQcaMGalduzaLFy9Gp9PFub/atWvj6+uLra0tvXv35scff0yAUavie+zJxaZNmxg3blxiD0MIIUQyFrkaQsuWcPSouh5tpIV0Yx3fcYhqFMeL3dQ32YeNDWTODFWqqOkM5sksiVWC2xSsXr16PH78mDt37rBr1y6qV69Ov379aNiwIeHh4XHqy8rKisOHD+Pv78+zZ8+oUKFCtO1DQ0M/Z+jxOvbkwtnZGXt7+8QehhBCiGTKxwcGDIBWreDKFcjNzY9aaOjIEmqxn8dkMtmHpSW4ukLDhtCvX/KasY0kwW0KZmVlhaurK5kzZ6ZUqVL8/PPPbN26lV27drF06VJ9u3v37tGkSRPSpEmDg4MDLVq04OnTpwZ9/frrr7i4uJApUya6du3K0KFDKVGihL4+8qv78ePHkylTJvLnzw+ARqNhy5YtBn05OTkZ3P/njP3Nmzd06dKFDBky4ODgQI0aNfDy8tLXjx49mhIlSrBixQpy5MiBo6MjLVu25O3bt1He944dO3B0dMTT09NkvU6nY+LEieTMmRMbGxuKFy/Ohg0b9PWHDx9Go9GwZ88eSpYsiY2NDTVq1ODZs2fs2rWLggUL4uDgQOvWrQ12d5O0BCGEEJ/Kyws6d1ZTCPxehTOOX7hOfhqw3aBdEHboMDPZh5kZ1K8Pc+fCtGnJM7AFCW4/3fTpkCVLzJfGjY1v27hx7G47fXq8D7tGjRoUL16cTZs2AWqg1qRJE169esWRI0fYt28ft2/fxsPDQ38bT09Pxo8fz2+//ca5c+fIli0b8+bNM+r7wIEDXL9+nX379rF9+3aj+vgeO8B3332nDxrPnTtHqVKlqFmzJq9evdK3uXXrFlu2bGH79u1s376dI0eOMGnSJJP3sWrVKlq1aoWnpydt2rQx2WbixIksX76c+fPn4+3tzYABA/j+++85cuSIQbvRo0cze/ZsTp48yf3792nRogUzZsxg1apV7Nixg7179zJr1qx4eGSEEEKkZnPmwNdfw6lTkC74AYeozi+Mxwwdy2hPRp7E2IeNjRqebNiQPJb7ik4yy6JIQvz94eHDmNtlzWpc9vx57G4bmQEezwoUKMClS5cANSC9fPkyvr6+ZP3/WJcvX07hwoU5c+YMZcuWZdasWXTu3JmOHTsCMHLkSPbu3UtAQIBBv3Z2dixatAhLS8sEGffHYz9+/Dj//vsvz549w+r/m11PnTqVLVu2sGHDBrp16waoAfzSpUv1X/m3bduWAwcOMH78eIO+58yZw/Dhw/n777+pWrWqyfsPCQlhwoQJ7N+/X5+akStXLo4fP86CBQsMbvfrr79SqVIlADp37sywYcO4desWuXLlAqB58+YcOnSIIUOGxNfDI4QQIhXR6WD8eHVjhuBgaMB2ltGedKgTPOGY8RtDeIZLtP24uKhLfY0enfzya01JAYeQSBwc1GzrmGTIYLosNrd1cIj7uGJBURQ0Gg0APj4+ZM2aVR/YAhQqVAgnJyd8fHwoW7Ys169fp2fPngZ9lCtXjoMHDxqUFS1aNEED24/H7uXlRUBAAOnSpTNo8+7dO27duqW/niNHDoNcVjc3N549e2Zwmw0bNvDs2TNOnDhB2bJlo7z/mzdvEhQURO3atQ3KQ0NDKVmypEFZsWLF9L9nzJgRW1tbfWAbWfbvv//GdMhCCCGEEW9v+PNPmDkTzJVQpjKMH3n/je9dstGSNfxD1OfImJlBpkzq+rVNmybfNISPSXD7qQYOVC+fYtu2+B1LHPn4+JAzZ85479fOzs6oTKPRoCiKQVlYWNgn38eHYw8ICMDNzY3Dhw8btXNyctL/bmFhuMSJRqMxWnWhZMmSnD9/nsWLF1OmTBl9AP2xyNnqHTt2kPmjDyiRs8em7jdy5YeYxiGEEELEZNs29WSvO3cgJ7dZQ0vKcUZfvxl3OrGYN6SNso+cOaF0aTWwrVEjeachfEyC21Tm4MGDXL58mQEDBgBQsGBB7t+/z/379/Wzt1evXuXNmzcUKlQIgPz583PmzBnatWun7+fMmTPGnZuQIUMGHj9+rL9+48YNg5OoPmfspUqV4smTJ5ibm5MjR45P6jNS7ty5mTZtGtWqVcPMzIzZs2ebbFeoUCGsrKy4d+9elKkLQgghRELZtg06doRXr6AW+9hAcxxR0xhDsOQnpjKb3oDpSRqAXLnUk8++/TblzNZ+SILbFCwkJIQnT54QERHB06dP2b17NxMnTqRhw4b6QLVWrVoULVqUNm3aMGPGDMLDw+nZsydVq1alTJkyAPTp04euXbtSrlw5KleuzOrVq/Hy8iJ37twxjqFGjRrMnj2bChUqEBERwZAhQ4xmMD9n7BUqVMDd3Z3JkyeTL18+Hj16xI4dO/j222/144+tfPnycejQIapVq4a5uTkzZswwamNvb89PP/3EgAED0Ol0VK5cGT8/P06cOIGDgwPt27eP030KIYQQsXX5MgwerAa2ADfIi/L/IPYGefBgLRcoFeXttVr1VKB585L/SWPRkeA2Bdu9ezdubm6Ym5uTNm1aihcvzsyZM2nfvj3a/z+jNRoNW7dupU+fPnz99ddotVrq1atncBZ/mzZtuH37NgMHDiQ4OJgWLVrQsWPHWOWLTps2jY4dO1KlShUyZcrEH3/8wblz5+Jt7Dt37mT48OF07NiR58+f4+rqytdff03GjBk/6THLnz8/Bw8e1M/gTps2zajNuHHjyJAhAxMnTuT27ds4OTnplysTQggh4ltoKMyapV7u3XtffpccdGQJ37GeH5jPW6I+V8fcHAoUgIkToU6dLzDoRKRRPk6ITIX8/f1xdHTEz88Ph49O4goODsbX15ecOXNibW2dSCNMemrXro2rqysrVqxI7KGIFEJea0IIYWzePHU1hAcPoDnr2U09Aojbhj92dvDdd/DTT1C4cAIN9AuILl77kMzcihgFBQUxf/586tati5mZGatXr2b//v3s27cvsYcmhBBCpFjz5qkBqSYogKX0oj3LWcH3tGM50eXUfihHDvjlFzVPN6WmIXxMglsRo8iv/8ePH09wcDD58+dn48aN1KpVK7GHJoQQQqQoOp2aevD8OYwYAbmDLrGOFhTgOgBtWckCunOCyjH2lTYtbNkCxYsn8KCTGAluRYxsbGzYv39/Yg9DCCGESNF8fGDzZvjnHzj9j0LzlwuYQX+sCQHgLWnozoJYBbb29rB0aeoLbEGCWyGEEEKIROftrZ7sdeUK+N/3Y87rrjRnvb7+PCXxYC03yRttP1otFCmi7lzWsGFCjzppkuA2luS8OyESlrzGhBCplbc3DBigLvWV+9UZNoV6kAtfff1M+jCIKYRiZfL21tbw9ddQsSJUrQqVK6eMbXQ/VSo+9NgxMzMD1O1VbWxsEnk0QqRcoaGhwPvXnBBCpHQ6Hezf/37GtmjoOXaHVsISdSfP1zjRicVs4dso+8iQAVq3hunTU88JYzGR4DYG5ubm2Nra8vz5cywsLPRrrAoh4o9Op+P58+fY2tpinpqnG4QQqYa3N0ydCn//Df7+EBEBR3Ql2UdtGrCTf7Vf8Z1uDffIHmUflpZQvbq6ha6EJ+/Jf5EYaDQa3Nzc8PX15e7du4k9HCFSLK1WS7Zs2dBoYre8jRBCJFfbt8OwYXDzJoSo54qhZmZpac8yemrmMctmKMERFhBsug8rK2jRQu0nJW6h+zlkEwdityiwTqfTf20qhIh/lpaW8s2IECJFCw+HVatgzBh4dD+CQRETOa6rxDHz6oSHG7a1sAAbG3j3Tp3VVRT1YmUFJUpA166pa+1akE0c4p1Wq5Vdk4QQQgjxSbZvhxkz4NQpsA96wt98Ty0O8Ag3SkVc5KW5i0GAGxamBq4aDUTGcenTq2vftm6duk8Yi0kqiveFEEIIIb68bdugVy84fhwqBe3Di+LU4gAAGXlKdeUAOp3xLGxoKJiZqTO4+fLB779Du3YS2MZEHh4hhBBCiATi5QU//ADPH4czmlEMYyJa1IzQR7jRmlUcoRr8P7jVaiPzb9UdxooWhXLloH17KFw48Y4jOZHgVgghhBAiAWzbpq5fa/b4PgdpTRWO6+t2Up/2LOMFGQA1/UCnU+vMzdXAdtw4qFsXsmVLXbm1n0uCWyGEEEKIeLZtG3TrBuWf/c1iOpCOVwCEYc7PTGAaP6J8kB2q0agztubmam6th4d60pgEtXEnwa0QQgghxCfQ6eDePXj7Fuzt1RlWgL171Rxb3bMXrFRaY08AAHfITkvWcJqvTPal0YCLC1SpImvXfg4JboUQQggh4sjHBzZvhmvXIDhY3QLX2RlevoQ9e+D5c4D09GYOy2jPJr6lM3/xhrQm+7O2hsyZ1eW9mjaVtWs/hwS3QgghhBBx4OMDM2fCixeQNau6msHZs2oqQnCQDkXzfsp1haYdjxQ39lMLMN6kRqtV0xC+/lqdra1RQ2ZsP5cEt0IIIYQQsaTTqTO2L15AoULw339w8CD4P3vH7wxAh5ZezAXUNAOA/dQ22ZdGA66u6k5j3brJbG18keBWCCGEECIaH+bW+vmpM7dZssDRo+ratbnDr7GPFhTjMgCHqM4GvkNR1AA28mSxyJ+gzs42aAB9+8psbXyT4FYIIYQQIgof59a+e6eWhYbC/fvQjmXMpSd2BAEQhA3WBBv0ERnUKoq6KYO5OXzzDaxbJxsyJAR5SIUQQgghPhIaCrNmgacnhIdD6dLqLmGnTsGtW2BHAMvoSTtW6G/jTSFasI6rvN9tIXKmFtTA1tYWKlWC8eMlsE0o8rAKIYQQQnxgzhz49Vd48uR92bVr6nJfr15BMbxYiwcFuK6v/5Mu9OMP3mFr1J9Go6YdODtDy5bQo4fk1yYkCW6FEEIIIf5v5EiYNAnCwgzLw8Lg1SuFH5jP7wzAmhAA3pKG7ixgNa1N9mdpCQ4O6slnw4dDrVqSX5vQJLgVQgghRKqn06nr006ebBzYRtKiw4O1+sD2PCXxYC03yRtlv46O0Lq1usyXzNZ+GRLcCiGEECJV8/aGxYth+XIICYm6nQ4z2uDJBUqyhpYMYgqhWEXZPlMm+OsvqFNHZmu/JAluhRBCCJFqbdumpiLcuwdv3hjWadDhyhMek0lf9ojMFOIqL0kfbb/Zs8Ps2VCvXgIMWkRLPkcIIYQQIlXatg169YLr1yEoyHBlg3S8YBuNOUElHHljcLvoAlszM2jSBHbsgIYNE2jgIloS3AohhBAi1fH2VmdsX76ENGkM6ypzjIuUoCE7yMkdFtItVn3a2cHcubBpExQuHHN7kTAkuBVCCCFEqhEeru4s9vPP6iYMVlZqPmxICGiJYDi/cphqZOEhAM/IwGI6xdivvT2sXKluoyv5tYlLcm6FEEIIkeLpdLBkCSxaBHfvquvVhodDRIRan5EnrOR7anFAf5tDVKMNngY5t6akTQtLl0Ljxgl4ACLWEvWzxdGjR2nUqBGZMmVCo9GwZcsWg3qNRmPyMmXKFH2bHDlyGNVPmjTpCx+JEEIIIZKqy5fVE7t69oSzZ9UTx3S694FtLfbhRXF9YBuBlpGMoRb7Ywxss2eHY8cksE1KEjW4DQwMpHjx4syZM8dk/ePHjw0uixcvRqPR0KxZM4N2Y8eONWjXp0+fLzF8IYQQQiRxs2ap293u26duqRseDu/evV/LdiRj2ENdMvIMgEe4UZMDjGMkOsyi7NfCQt1p7PZtya9NahI1LaF+/frUr18/ynpXV1eD61u3bqV69erkypXLoNze3t6orRBCCCFSr/Bw6NABPD2jb/ecDGhRl0nYSX3as4wXZIj2NjVrwvbtYG0dT4MV8SrZpDw/ffqUHTt20LlzZ6O6SZMmkS5dOkqWLMmUKVMIDw+Ptq+QkBD8/f0NLkIIIYRIGbZvV9MFYgpsAebRg9W0ZBCTacj2GAPbevVg/34JbJOyZHNC2bJly7C3t6dp06YG5X379qVUqVI4Oztz8uRJhg0bxuPHj5k+fXqUfU2cOJExY8Yk9JCFEEII8YVt3w5t2oCpeStLQqjDXrbT6INSDa1ZBWii7Vejga5dYcGCeB2uSAAaRflwyeLEo9Fo2Lx5M+7u7ibrCxQoQO3atZk1a1a0/SxevJju3bsTEBCAlZXpLfFCQkII+WB/PX9/f7JmzYqfnx8ODg6ffAxCCCGESDzh4ZAnj7oawsdycYu1eFCGc9RnJ7uJOi3yY4UKqct8lSwZj4MVcebv74+jo2OM8VqySEs4duwY169fp0uXLjG2LV++POHh4dy5cyfKNlZWVjg4OBhchBBCCJE86XRw5w4MGGA6sG3BWi5QkjKcA+BPumJJiHHDj1hYQO/e6moLEtgmH8kiLeGvv/6idOnSFC9ePMa2Fy9eRKvV4uLi8gVGJoQQQojE5OUFkyapP69fN6yz5h0z6E93FurLbpCHFqwjFNPf7kbKmBHmz4covlAWSViiBrcBAQHcvHlTf93X15eLFy/i7OxMtmzZAHUKev369UybNs3o9qdOneL06dNUr14de3t7Tp06xYABA/j+++9JmzbtFzsOIYQQQnx5I0bA9OkQFGRcV5CrrMWDolzRl62kDT2YRwD2Ufap0UCjRrBxI5gniylA8bFE/bOdPXuW6tWr668PHDgQgPbt27N06VIA1qxZg6IotGrVyuj2VlZWrFmzhtGjRxMSEkLOnDkZMGCAvh8hhBBCpDw6nbrG7MKFpmoV2rOMOfTCDjXqDcKGXsxhKR2I7sQxrRZWr4YWLRJi1OJLSTInlCWm2CYoCyGEECJx+fhAgwbg62u6/mfGM55f9NevUBgP1nKV6HdaMDeHadOgb9/4HK2ITynqhDIhhBBCpG46nbq+bOXKUQe2AKtphR9q4LOQrpTj3xgDW1tbGDZMAtuUQrJJhBBCCJGk+fjA+vUwbx68ehV9W19y0ZElWBHCGoxTGj9WsKCaihCLc9ZFMiHBrRBCCCGSLG9vGDoULl6EJ08M6xx5wwjGMZKxBGGnL9+M4YZPpmi10KoVLF+u/i5SDgluhRBCCJEkeXmpu4399x9ERBjWleVf1uJBTu7gzCs6sSTW/ebJA5Mnw7ffxvOARZIgn1WEEEIIkaTodDBuHHz9tTpzGxamlgFo0DGQaZygEjm5A0ATtpKZBzH26+gIEyaoaQ4S2KZcMnMrhBBCiCTDxweGDIHt2+Hj9ZzS8YKldKAhO/RlJ6lAS9bwkCzR9ps/P0ydCg0bJsSoRVIiM7dCCCGESBK8vKBjR9OBbRWOcpESBoHtRIZSlSPcJ1uUfdrYQP/+cOWKBLaphczcCiGEECLRzZ4Nw4eDv79huZYIhjGRMYzCDDU34RkZaMsK9lI32j6LF4cVK6Bo0YQatUiKZOZWCCGEEIlq5EgYONA4sAVoyRp+ZYQ+sD1IdUpwMdrA1txc3Zr3/HkJbFMjCW6FEEIIkSh0Oti2DX77TT1pzJTVtGIPdYhAy0jGUJt9PCZTlH3a2sLvv8PYsbLEV2olaQlCCCGE+OK8vKB3bzh58v1KCCoF0HxwTUtbVlAQH45S1agfMzM1P1ejATc3Nb2hSZMEH75IwuQzjRBCCCG+qJEjoXx5OH7cMLDNyj0OU40qHDVo/xwXk4GtuTmkTQs5ckDbtrB7twS2QmZuhRBCCPGFhIfD4MEwY4bxagiN2MZSOuDMa1bRmhJc5CXpTfZjbQ0ZMkDnzmqQnC+fGuBKGoIACW6FEEIIkcB0Oli8GGbNgkuXDOssCeE3htCfP/RlYVjgyhOTwa2FBZQsCT//LEt7CdMkuBVCCCFEgvHxUWdrd+0y3kI3F7dYiwdlOKcv20hTOvMXfjgZtDU3hzRpoGxZmDtX3UJXCFNkAl8IIYQQ8U6nU3NgGzdWN2X4OLBtwVouUFIf2IZgSU/m0JwNRoGtpaW6dW6hQlC9OuTK9YUOQiRLMnMrhBBCiHjl46MuxbV+vXFQa807ZtCf7izUl10nHx6sxYsSJvtLn17NqS1WDNzdJbdWRE+CWyGEEELEm8uX1VzYe/dM12fnLm1Zob++kjb0YB4B2Bu0iwxgXVzU2drChdXAtmDBBBq4SDEkuBVCCCFEvNi2TV2Sy9ROY5GuU4BezGE2venNbJbSgQ/XtQV1zVobG8iSBSZOVE8gy5ZNZmxF7EhwK4QQQojPtn07dO1qHNim4S2hWBKKlb5sKR3YR20eksWoH60W7OygQAF1PVxZEUHElXwGEkIIIcRnCQ6Gfv3g2TPD8uJc5CxlmMKgj26hiTKwzZgRvvsOliyRwFZ8GgluhRBCCPHJtm6F3Lnh9u0PSxV6MJd/+Ir8/EdfZuHO5mj70Wigfn1Yvhz+/FPNsRXiU0haghBCCCE+yfDhMHUqhIa+L3PkDYvoQnM26svOUprLFI2yHzMzta9RoySvVnw+eQoJIYQQItZ0OrhzR936dsIEw8C2HKe5QEmDwHYG/ajECW5hetcFS0sYNgzGjJHAVsQPmbkVQgghRKxcvqxuobt7N9y//75cg46BTGciw7AgHIBXpKUjS9hGkyj7c3NTdxtzd0/ggYtURYJbIYQQQkRLp4Px49XA9sULUJT3dfb4s5pWNGCnvuwEFWnFau6TzWR/9vbQqZOa0mAukYiIZ/KUEkIIIUSUvL3hl1/UNWx1OuP6IGxx4P36XxMYxijGEI6FUVsrK/j+e3VlhaJRp+AK8VkkuBVCCCGESdu2qSd6XbkSdZsIzGnNKvZQl/7MYB91jNpoNJAmDQwapPYnubUiIUlwK4QQQggDOh0sWgQ//wwvXxrWufKYdLzEmyL6sgdkpQhXUEycp67VqjuNDR0KPXok9MiFkOBWCCGEEB/w8VHza9evN1wJAaA2e1nJ97zFnlKcxx9Hfd3Hga1WC6VLQ6tW0KuXuiqCEF+CfDEghBBCCEBdDaFdO1i1yjCwNSeMCQxjL3Vx4Tm5uc0Efo6yHxsbdamwkydhwAAJbMWXJTO3QgghhGDrVujQAd68MSzPyj1W04pKnNSXbacBoxhjsh97eyhYUD1pTFZCEIlBZm6FEEKIVG7ePGjd2jiwbcxWLlJCH9iGYc6PTKUx23hJeoO25uaQPr0a2I4YIdvnisQjn6mEEEKIVMzLSw1Gg4Lel1kSwm8MoT9/6Mt8yUFL1vAv5Y36MDODMmWgShVo314CW5G4JLgVQgghUilvbzU39sMVETToOER1KnJKX7aBZnRhEX44GfVhZQWlSsHy5ZArlyzzJRKfPAWFEEKIVCY8XA1GPTzg3DnDOgUta2gJQDBW9GQO37HeKLDVaMDCQg1of/4Z8uSRwFYkDTJzK4QQQqQiW7aowaiPT9RtZtGHnPiyjPZ4UcKoXqNRc2wLF4Zx46BhwwQbrhBxJsGtEEIIkYLpdHDnDly7BvPnw44dhtvoFuQq1TnEXHp9cCsNA/ndZH8WFuosbfv28OOPsiKCSHrkKSmEEEKkUD4+sGAB7N4Nt29DWNiHtQodWcJsemNNMP+Rj/3UjrIvGxs1v7Z+fVi6VNauFUmXZMcIIYQQKZCPD4wZA2vWwI0bhoFtGt6ygrYspjO2vEOLwhB+i7IvOztImxby5YPhwyWwFUmbBLdCCCFECqPTwcaNcPYsPH9umIZQnIucozTf46kvW0A3GvG3yb7MzdWNGbJkkfVrRfIgaQlCCCFECqHTwb17an7tkSPq7+8DW4WezGU6A7FC3VvXH3u68ifr8DDZn6UlFCsG1avL+rUi+ZDgVgghhEgBfHxg82b159WrcPHi+8DWkTf8RWeasUnf/iylackabpHHZH+OjmpaQ5MmkC2bLPMlkg8JboUQQohkTKeDgwdh4UJ49Ei93L1rmIrwcWD7O/0ZyiRCsTLqT6OBdOlg0SI1sBUiuZHgVgghhEimvL1h8WJ1ea+HDyEgwHS7IfxGbfYRjjkdWcI2TEet5uaQOzdMngyNGyfgwIVIQBLcCiGEEMnQtm0wcqQ6U/v6tbrrWFRukYfmbOAaBbhPNpNtMmaEb75R166V3FqRnElwK4QQQiQz27ZBz57w4gVERBgGtl9zhOGMx50tvMNWX76POib7cnJSA9rWrSFHDsmtFcmfPIWFEEKIZOTyZejfX52xDQ19H9hqiWAkYzhIDeqwj5n0jbGvQoVg0CB1O95cuSSwFSmDPI2FEEKIZCA8HJYvV3NhfX1BUdQLgBuP2EdtxjAaM9QzyXLiixXBUfaXPj1UqQLffitBrUhZ5OkshBBCJHHbt8PXX8MPP8CdO4Z1ddjDRUpQg0MARKBlBGOpw15CsDbZn60ttGkD/fpBwYIJPHghvjDJuRVCCCGSsG3bYOBAuH9fTUOIZE4Y4xjB0A+2zX1IJlqxmmN8bbIvjUZds3bECOjYUWZsRcokwa0QQgiRRF24AF26qFvofigbd1lNKypySl+2nQZ0YCkvSW+yL60WSpSApUuhaNGEG7MQiS1RP7MdPXqURo0akSlTJjQaDVu2bDGo79ChAxqNxuBSr149gzavXr2iTZs2ODg44OTkROfOnQmIaqE/IYQQIhkIDYXOnaF8eePAFsCDtfrANgxzfmQqjdkWZWCr0UDdumrOrgS2IqVL1OA2MDCQ4sWLM2fOnCjb1KtXj8ePH+svq1evNqhv06YN3t7e7Nu3j+3bt3P06FG6deuW0EMXQgghEsScOeqas4sXQ1iY6TbT+JH91MSXHFTmONP5ESWKf+nm5tCwIUybJuvXitQhUdMS6tevT/369aNtY2Vlhaurq8k6Hx8fdu/ezZkzZyhTpgwAs2bN4ptvvmHq1KlkypQp3scshBBCJITgYHXlgt27jevS8JYA7PXXdZjRmlWEYokfTlH26eysLhs2fLjk14rUI8k/1Q8fPoyLiwv58+enR48evHz5Ul936tQpnJyc9IEtQK1atdBqtZw+fTrKPkNCQvD39ze4CCGEEIlBp4PevdXNFEwFti1ZzV2yU5ETBuXPcYkysNVqoUMHePxYPXlMAluRmiTpp3u9evVYvnw5Bw4c4LfffuPIkSPUr1+fiIgIAJ48eYKLi4vBbczNzXF2dubJkydR9jtx4kQcHR31l6xZsybocQghhBCmXL4MuXOrqQghIYZ1NgSxkK6spjXOvGY1rXDmpemOPmBlpW7KsGQJWFom0MCFSMKS9GoJLVu21P9etGhRihUrRu7cuTl8+DA1a9b85H6HDRvGwIED9df9/f0lwBVCCPHF6HQwahT8+qvp+kJ4sxYPiuCtLztMNUKwirbfwoXVPt3d43GwQiQzSTq4/ViuXLlInz49N2/epGbNmri6uvLs2TODNuHh4bx69SrKPF1Q83itrKJ/gxBCCCESgrc31KwJT5+aqlXoyBJm0xtb3gEQiC09mcty2kfZp50d/PIL/PSTegKZEKlZkk5L+NiDBw94+fIlbm5uAFSoUIE3b95w7tw5fZuDBw+i0+koX758Yg1TCCGEMGnbNihWzHRgm4a3rOR7FtNZH9heoihlOBttYOvqCqtWwdChEtgKAYk8cxsQEMDNmzf11319fbl48SLOzs44OzszZswYmjVrhqurK7du3WLw4MHkyZOHunXrAlCwYEHq1atH165dmT9/PmFhYfTu3ZuWLVvKSglCCCGSlG3b1HQBRTGuK8olNtKMvLz/nzif7gzgd4KxibLPQoVgzRpZu1aIDyXqzO3Zs2cpWbIkJUuWBGDgwIGULFmSkSNHYmZmxqVLl2jcuDH58uWjc+fOlC5dmmPHjhmkFHh6elKgQAFq1qzJN998Q+XKlVm4cGFiHZIQQghhQKdTV0Hw8DAd2ALo0JKFBwD44UAL1tKD+dEGtkWLwoYNEtgK8TGNokT1Uks9/P39cXR0xM/PDwcHh8QejhBCiBQgMqidMAFOn4bw8Ojbd2UhXfkTD9biS64o25mbQ/fuMHOmLPElUpfYxmsS3CLBrRBCiPjl4wM//ADHjpmerS3Jea5QhDA+XKtLwZxwwrGIst9mzWDlSrC2jv8xC5HUxTZei9NnvrCwMDp16oSvr+9nD1AIIYRIiby8oFIlOHrUOLDVoOMnpnCa8kxiKB/XRhXY2tqqM7UbNkhgK0RM4hTcWlhYsHHjxoQaixBCCJFsRa5dW6IEvH5tXJ+e52ynIVMYjAXhDOR3qnI4xn4LFIDVq6FPn3gfshApUpyzddzd3dmyZUsCDEUIIYRInry91aB27FjT9VU5zEVK8A27ANChYTw/c5zKUfap0UC7duouZo0bJ8CghUih4rwUWN68eRk7diwnTpygdOnS2NnZGdT37ds33gYnhBBCJHXbtkH79vDmjXGdlgh+4VdGMhYzdAA8xYXvWcl+akfZp1YLv/8O8i9ViLiL8wllOXPmjLozjYbbt29/9qC+NDmhTAghRFzpdPDXX9CvH7x7Z1zvxiNW8j01OKQv209NvmclT4l6F800aWD5cvj224QYtRDJV2zjtTjP3MrJZEIIIVI7Hx+YNw8WLIDQUOP6AvhwhKq48ByACLSMZCyTGIoOM5N9arXqmrW//goNGybk6IVI2T55h7LQ0FB8fX3JnTs35rLfnxBCiFTC2xuGDIFDh0wHtgA3ycNN8uDCcx6QmVas5jhVouzTyQmmT4e2bWULXSE+V5xPKAsKCqJz587Y2tpSuHBh7t27B0CfPn2YNGlSvA9QCCGESCq8vNSdxvbuhaCgqNuFY0ErVuNJa0pwMdrA1s0Nli2Djh0lsBUiPsQ5uB02bBheXl4cPnwY6w8W26tVqxZr166N18EJIYQQiU2ngzt3YPhwqFFDnbkNCzNs04QtlOCCQdk9svM9nrwkvcl+zc2hQgVYuFBWQxAiPsX5M+KWLVtYu3YtX331FRqNRl9euHBhbt26Fa+DE0IIIRKTjw9s3gwbN8KFC8abMlgSwhQG0ZdZ3CAPpThPAPYx9psrl7ombuvWMlsrRHyL80vq+fPnuLi4GJUHBgYaBLtCCCFEcubjAzNmwK5dcP++cX0ebrCGlpTmPAB5uUl7ljGH3tH2W60azJ0LBQvG/5iFEJ+QllCmTBl27Nihvx4Z0C5atIgKFSrE38iEEEKIRKDTwe3b8OOPsGSJ6cC2Jas5Tyl9YBuMFd2Zzxx6RdlvhgwwZgwcOCCBrRAJKc4ztxMmTKB+/fpcvXqV8PBw/vjjD65evcrJkyc5cuRIQoxRCCGE+CIi0xAWLID/ny9twIYg/qAfXVmkL7tGfjxYyyWKm+zT0lJd3qtfP/V3IUTCivPMbeXKlbl48SLh4eEULVqUvXv34uLiwqlTpyhdunRCjFEIIYRIcD4+8Mcf6vq1pgLbQnjzL+UMAtultKcMZ6MMbK2s1CW+Bg2SwFaILyXOO5SlRLJDmRBCpG46HQwcCBs2wMOHxvWOvOEOOXDCD4BAbOnJXJbTPso+06eH0aOhV9SZCkKIOIjXHcr8/f1jfccSHAohhEhuDh5U0xFMBbYAfjgxlpFM50e8KIYHa7lOAZNtzc2hSRN1NYSiRRNw0EIIk2IV3Do5OcW4EoKiKGg0GiIiIuJlYEIIIURCi1zD9vff4dGj6Nv+zgCCsWYJHQnGxmQbW1t1hYXOndXtdIUQX16sgttDhw4l9DiEEEKILyry5LEzZ+DwYQgPj6xR6MUcnHjDeH754BYa5tEzyv5sbcHTE9zdE27MQoiYxSq4rVq1KgDh4eFMmDCBTp06kSVLlgQdmBBCCJFQvL1hwgR49gzevVMvAE685i8605TN6NBwigocpGaM/Tk4wIoVstOYEElBnL40MTc3Z8qUKYS//3grhBBCJAuRKQibN8MPP6jrzZ4+DadOqTuPlecfLlCSpmwGQItCBU7F2G/58nDypAS2QiQVcV7ntkaNGhw5coQcOXIkwHCEEEKI+BeZgnD6NFy8qObXRs7TaNAxiKmMZzgWqIUvcaYDS9lOI5P9aTRQoQIMHw716kl+rRBJSZyD2/r16zN06FAuX75M6dKlsbOzM6hvLB9dhRBCJCE+PjBzJjx/Di9egL//+8A2Pc9ZTjvqs1vf/hiVac0qHpDVZH/W1up6uF26SFArRFIU53VutdG8kpPragmyzq0QQqRMOh1MmgQXLkCmTLB+PTx+rNZV5TCraE0m1AIdGibwM6MZTYSJuR+NRu1j+HDo0eNLHoUQAuJ5ndsP6XS6zxqYEEIIkdAi82uPHVNza7NkUVdEiAxsNeiYxo/6wPYJGfmelRyglsn+ZO1aIZKPOAe3HwoODsba2jq+xiKEEEJ8lvBwWLkSFiyAmzchNBQCAtRg90MKWlqxmvOU4hQVaMsKnuJqss80aWDIEPj5Z0lDECI5iPPLNCIignHjxpE5c2bSpEnD7du3ARgxYgR//fVXvA9QCCGEiI0tWyB3bujYEf75531+bWRga0WwQfsb5KMCp6jLHpOBrYUFZM+uLvH1yy8S2AqRXMT5pTp+/HiWLl3K5MmTsbS01JcXKVKERYsWxevghBBCiNgYMQI8PODePeM6c8KYxBD+4SuseWdQd4WiKCb+FdrYqKsg/P23bMogRHIT5+B2+fLlLFy4kDZt2mBmZqYvL168ONeuXYvXwQkhhBAxmT0bfvtNTUH4WHbucJSvGcJkSuDF7wyIsT+tVg1sf/tN8muFSI7inHP78OFD8uTJY1Su0+kICwuLl0EJIYQQMdHpYPdudfUCU/9+3NnMYjqRljcAhGLBdfIDCqAx2aeZmboZw7p16klkQojkJ84zt4UKFeLYsWNG5Rs2bKBkyZLxMighhBAiKuHhsHSpuolC06ZqXu2HrAhmJn3YTFN9YHubnFTiBDMYQFSBraUlfPstjB8vga0QyVmcX74jR46kffv2PHz4EJ1Ox6ZNm7h+/TrLly9n+/btCTFGIYQQAlBPGuvTBx48MF2fhxusxYNSXNCXraUF3ViIP44mb6PRqCsifPstDB0KBQsmwMCFEF9MnGdumzRpwt9//83+/fuxs7Nj5MiR+Pj48Pfff1O7du2EGKMQQohUTqeD3r3VmdqoAtuW/1/aKzKwDcaK7synJWuiDGyzZoW2bWHDBliyRAJbIVKCT/ripUqVKuzbty++xyKEEEIY8faGTp3g33+jb5eXG9gTAIAPBfBgLZcpZrKthYW6ZNiwYZAtmyzzJURKIllFQgghkqzt26F7d3j0KOa24xlONQ5zj2z0ZjaBpDFqo9Gou5X98YeahiCESHniHNymTZsWjcY4GV+j0WBtbU2ePHno0KEDHTt2jJcBCiGESJ0uX4YOHeDlS1O1CkW4whXer9Wlw4wG7CAYmyj7rF0bpk+HwoXjfbhCiCQiVl/EfPXVV2zbtg1QdyLTarU0aNCAMWPGMGbMGBo0aIBGo6Fnz57ky5ePHj168OeffybowIUQQqRcXl5QtarpwNYefzxpw3lKUZ5/DOqiCmy1WmjSBHbskMBWiJQuVjO306dPp379+rx584YTJ07w66+/8sMPPxi0+fPPP9m9ezcbN26kWLFizJw5k65duybIoIUQQqQ8Op26w9iiRTBjBgQGGrcpyXnW4kFebgKwhpYUxCfa2VqNRl02bOJEWeJLiNRAoyiKElOjgIAAHB0defv2LRkyZMDLy8toI4c7d+5QpEgRAgICuHXrFsWKFSPQ1DtTEuTv74+joyN+fn44ODgk9nCEECLV8fFRN07w9IQbN0y1UOjDLKYwCCvUrcj8cKALi9jAd1H2a2YGDRrApEmyEoIQyV1s47VYpSU0a9aMli1bYmtrS7p06fj777+N2mzduhVnZ2cAAgMDsbe3/8ShCyGESE18fKBbNxg71nRgm5ZXbKIpM+mnD2z/pSwluWAysDUzU1dDyJ0btm2DzZslsBUiNYl1WkLh/ycpjRgxgh49enDo0CHKlSsHwJkzZ9i5cyfz588HYN++fVStWjWBhiyEECKlCA6GZs3UANeUrzjFGlqSnXv6smkMZBgTCcNSX2ZhAQ4OEBQEefJA0aLw88+SXytEahSrtISPnThxgtmzZ3P9+nUA8ufPT58+fahYsWK8D/BLkLQEIYT48ubNU3cE+3j73EgdWcxCumFOBAAvcaY9y9hBQ4N25uZgb68Gyo6O0LMnNG8us7VCpDSxjdc+KbW+UqVKVKpU6ZMHJ4QQInWbMwf69lVPIovKOUoTjjnmRHCUKrRmFQ/JYtTOwUGduXV2Vjdl6NhRNmUQIjX7pOD21q1bLFmyhNu3bzNjxgxcXFzYtWsX2bJl06cvCCGEEB/T6WDnTujXL/rAFuASxenHH2ThAWMYRUQU/7LMzKB4cejTBxo2NNlECJGKxPmz7ZEjRyhatCinT59m48aNBASoWx16eXkxatSoeB+gEEKIlMHHB9zd1fVmIyIM67RE0J35WPz/hLFIC+nOSMZFGdimSwdr1qjr10pgK4SATwhuhw4dyq+//sq+ffuwtHyfzF+jRg3++eefaG4phBAiNdLpYP9+aN1a3U734xnbTDzkADWZTw8m8HOs+tRo1DzbceOgRg1Zv1YI8V6cg9vLly/zrYkNuV1cXHjx4kW8DEoIIUTK4O0NP/6onuB18SJ8fApzPXZxkRJU4wgA/fiDnNyOtk8zM8iaFX77DXr0SKCBCyGSrTh/1nVycuLx48fkzJnToPzChQtkzpw53gYmhBAiedu+HXr3hrt3jevMCWM8wxnMFH3ZfbLQitX4ksuovVYL1tZQqJA6A9yrF3zw5aEQQujFObht2bIlQ4YMYf369Wg0GnQ6HSdOnOCnn36iXbt2CTFGIYQQyUh4OKxcCQMGwJs3xvXZucMaWvIVp/Vlf9OQDizlFemM2qdJA/nywfjxUKeOrIQghIhenN8iJkyYQIECBciaNSsBAQEUKlSIr7/+mooVK/LLL78kxBiFEEIkE9u3Q6VK6nJcpgLbb9nEBUrqA9tQLOjP7zRmm8nAVqOBr76C5cuhXj0JbIUQMfukTRwA7t27x5UrVwgICKBkyZLkzZs3vsf2xcgmDkII8fm2bYN27cDPz3R9E7awhffnbNwiFx6s5RxlouwzY0bYt0/dcUwIkbol6CYOANmyZSNbtmyfenMhhBApyOXL0KqVuv1tVHbyDacpR3n+ZS0t6MZC/HGMsr2lJcydK4GtECJu4vQFT2BgICNHjqRIkSKkSZMGe3t7ihUrxtixYwmK7h1NCCFEihQeDkuWQIUK0Qe2AGFY0pI1dGUhLVkTbWBrbQ1DhkDTpvE8YCFEihfr4DY0NJSqVasyefJk8ubNS58+fejVqxc5c+Zk/Pjx1KxZk7CwsDjd+dGjR2nUqBGZMmVCo9GwZcsWfV1YWBhDhgyhaNGi2NnZkSlTJtq1a8ejR48M+siRIwcajcbgMmnSpDiNQwghRNxt2wa5c0OnThAYaFhnSyDz6U4xvAzK75CTRXQFNABkyqTuLpYlC6RNq27KULasujHD2LFf6ECEEClKrNMS5s2bx4MHD/Dy8iJ//vwGddeuXaNatWrMnz+fPn36xPrOAwMDKV68OJ06daLpRx/Pg4KCOH/+PCNGjKB48eK8fv2afv360bhxY86ePWvQduzYsXTt2lV/3d7ePtZjEEIIEXdz5qhb6H680xhAYa6wjhYUwodqHKY05wgkjUEbjQZq1oQZMyB/fvjnH3j6VM2x/eor2ZRBCPHpYv32sWnTJkaMGGEU2AIUKFCA4cOHs2HDhjgFt/Xr16d+/fom6xwdHdm3b59B2ezZsylXrhz37t0zyPe1t7fH1dU11vcrhBDi023cqK5fa0yhC4uYSV9sCAYgMw8pyQWOU0XfSquFkSNhxIj3qx9Urpzw4xZCpA6xTku4evUq1apVi7K+evXqXL16NT7GFCU/Pz80Gg1OTk4G5ZMmTSJdunSULFmSKVOmEB4eHm0/ISEh+Pv7G1yEEEJETaeD27fhhx/U3cY+Zo8/q2jNn3TTB7ZeFKM054wC27VrYdQoWdZLCJEwYj1z++bNG9KlM16DMFK6dOnwi2r9l3gQHBzMkCFDaNWqlcHyD3379qVUqVI4Oztz8uRJhg0bxuPHj5k+fXqUfU2cOJExY8Yk2FiFECIl8fGBefPUE8cCAozrS3GOtXiQh1v6srn04EemEYyNQdsuXUwHx0IIEV9ivc6tmZkZT548IUOGDCbrnz59SqZMmYgwlYAVm4FoNGzevBl3d3ejurCwMJo1a8aDBw84fPhwtGubLV68mO7duxMQEICVlZXJNiEhIYSEhOiv+/v7kzVrVlnnVgghPuLjA507w6lTpmoV+jCLqfyEJeoJxX440Jm/2IhxBFu4sLrJQ44cCTpkIUQKFe/r3CqKQs2aNTGPIss/plSATxUWFkaLFi24e/cuBw8ejDH4LF++POHh4dy5c8dkfjCAlZVVlIGvEEIIlU4HbdvCuXOm6wtwjWn8iAXq+/+/lKUla/All0E7jUZdBeGbb0CWRxdCJLRYB7ejRo2KsU2zZs0+azAfiwxsb9y4waFDh6JNi4h08eJFtFotLi4u8ToWIYRITXQ6cHePOrAFuEZBfmYCUxjMVH7kZyYQhqVBGxsbsLdXZ2vbt5c8WyFEwovX4DauAgICuHnzpv66r68vFy9exNnZGTc3N5o3b8758+fZvn07ERERPHnyBABnZ2csLS05deoUp0+fpnr16tjb23Pq1CkGDBjA999/T9q0aeN9vEIIkRpcvgxdu8Lp04blGnRoUNBhpi+bxo+coBKnqGjUj1YLdnZqYDtihJqWIIQQCS3WObcJ4fDhw1SvXt2ovH379owePZqcOXOavN2hQ4eoVq0a58+fp2fPnly7do2QkBBy5sxJ27ZtGThwYJzSDmKbwyGEECmZTqdunDBpEnxwWgIALjxlOe04QSXGMTLafjQasLJSt8/18FDXw5XAVgjxuWIbryVqcJtUSHArhEjtvLygZUu4ds24rjoH8aQNbjwhAi01OMhRqprsJ21acHZWg+M6dWDBAtmQQQgRP2Ibr0n2kxBCpHLDh0OZMsaBrRnhjGEk+6mFG2pa2HMyoMH0nIiTk7rDmI0NVKoEP/0kga0Q4suTtx0hhEjFunSBv/4yLs/EQ1bRmqoc1ZftpTZtWcEzMhq0tbGBYsXA0VHNsS1dGpo2hYIFE3r0QghhTIJbIYRIhXQ6dWmuPXuM6+qzk+W0Iz0vAQjHjF/4lckMRvngCz8XFzU/t00bePQI3r5VV0bIlk1WRRBCJJ5PCm4PHDjAgQMHePbsGTqdzqBu8eLF8TIwIYQQCePCBTUf9sULw3ILQhnPcAYxVV92j6y0YjUnqWTQtmVL8PR8H8TKxgxCiKQizp+tx4wZQ506dThw4AAvXrzg9evXBhchhBBJ1w8/QKlSxoEtgBkR1OX9VO42GlGSCwaBrZkZTJ8Oq1fL7KwQImmK88zt/PnzWbp0KW3btk2I8QghhEggzZvDxo1R1wdjQwvWcZKKjGUkf9AP0Ojrra1h8mTo0yfhxyqEEJ8qzsFtaGgoFSsaL9YthBAi6QkPh+PH1ZULPt5tzIpg0vGSR2TWl12nADnxxR9Hg7ZZssCcOdC48ZcYtRBCfLo4f6nUpUsXVq1alRBjEUIIEY+2boUCBaB6dePANi//cYoK7KABVgQb1H0Y2FpYqJsw+PpKYCuESB7iPHMbHBzMwoUL2b9/P8WKFcPCwsKgfvr06fE2OCGEEJ/ml1/gt9/UmduPtcaT+fyAPQEA/MYQ+vOHUbv8+WH9eihaNKFHK4QQ8SfOwe2lS5coUaIEAFeuXDGo02g0Jm4hhBDiSwkPh44dYeVK4zpbAplFHzqxRF/mQwEW0cWobd26sHOnnDQmhEh+4hzcHjp0KCHGIYQQ4jNt367O2Hp5GdcV5grraEEhfPRlS+hAb2YThJ1B27p1YffuhB6tEEIkjE/+TH7z5k327NnDu3fvAFAU09sxCiGESHhbtkCvXnD58sc1Cl34kzOU1Qe2AdjRluV0YolRYDtkiAS2QojkLc4zty9fvqRFixYcOnQIjUbDjRs3yJUrF507dyZt2rRMmzYtIcYphBDChPBwNbd2wgQICvq4VmEFbfkeT33JRYrjwVr+I79BS60W1q5VlwsTQojkLM4ztwMGDMDCwoJ79+5ha2urL/fw8GC3fNwXQogvQqeD+fPBzU1NRTAObAE0+JJTf20OPfmKf4wCWycn2LxZAlshRMoQ55nbvXv3smfPHrJkyWJQnjdvXu7evRtvAxNCCGGatze0bg2XLsXcdgyjKMYlltOOTTQzqDM3h6pV4Y8/oHDhBBqsEEJ8YXEObgMDAw1mbCO9evUKKyureBmUEEII0zZvVldD8PMzrkvLKypznL95vyBtBOa4s9WgnYUFNGwIkyZBnjyyIoIQImWJ81talSpVWL58uf66RqNBp9MxefJkqlevHq+DE0II8d7w4dCsmenAtgInuUgJNtCcsvxr8vYaDZQooW7osGkT5Msnga0QIuWJ88zt5MmTqVmzJmfPniU0NJTBgwfj7e3Nq1evOHHiREKMUQghUrXQUKhfHw4eNK7ToGMwk/mVXzAnAoDZ9KY8p4H3a49ny6YGx126SEArhEjZ4vwWV6RIEf777z8qV65MkyZNCAwMpGnTply4cIHcuXMnxBiFECLVmjMH0qUzHdhm4Bm7qM8khukD2yN8TVM2ERnY2trC+PFw6xZ06yaBrRAi5dMoskAt/v7+ODo64ufnh4ODQ2IPRwgh0Omgf3+YPRtMvUtX5yCetMGNJ2p7NIxjBOMYQcT/v5RzcoLFi+Hbb7/cuIUQIqHENl6LVVrCpdickvt/xYoVi3VbIYQQxnx81BnbOXOM68wIZyRj+YVf0aJGvY9x5XtWcpCa+nYODuratxLYCiFSm1gFtyVKlECj0cS4C5lGoyEiIiJeBiaEEKmRjw+MGgU7d5quX0g3OrFEf30vtWnLCp6RUV+WPj3s3w/Fiyf0aIUQIumJVXDr6+ub0OMQQohULzQU+vWDY8cgONh0m1n0oQ2emBHBL/zKZAajfHD6RKFCsG6drFsrhEi9YhXcZs+ePaHHIYQQqdq2bTBwoHriV3QuUpKu/MktcnOSSgZ1DRvC5MlQsGACDlQIIZK4WAW327Zto379+lhYWLBt27Zo2zZu3DjaeiGEEIbmzYNhw4zXr82BL4OYQj/+IBwLffkK2hm0MzNT83O7dpXVEIQQIlbBrbu7O0+ePMHFxQV3d/co20nOrRBCxJ5Op+bWDhoEgYGGdU3ZyF90xgk//HDkZyaa7MPeHlasgCZNvsCAhRAiGYjVZ3ydToeLi4v+96guEtgKIUTs+PhAu3bQtKlhYGtFMLPpxUaa44Q6lducDdgSaNRHxoywapUEtkII8aE471AmhBDi0+l06oYMQ4bA5csQFva+Li//sY4WlMBLX7aalnRnAUHY6cvMzcHDA5YuVX8XQgjxXqzeFmfOnBnrDvv27fvJgxFCiJTMxwc2boQlS8DX13BzhjasZD4/kOb/M7TvsKYPs/iLzkTuNuboCL16wYgRYG2dCAcghBDJQKx2KMuZM6fB9efPnxMUFISTkxMAb968wdbWFhcXF27fvp0gA01IskOZECKhXb6sLvPl4wPPnqkzuAC2BDKLPgZr116lIC1YhzdF9GUZM8LChSDn7AohUqvYxmuxyrn19fXVX8aPH0+JEiXw8fHh1atXvHr1Ch8fH0qVKsW4cePi7QCEECIl0Olg0SKoUweOHjUMbAH6MtMgsP2LTpTljEFga28P8+dLYCuEELERq5nbD+XOnZsNGzZQsmRJg/Jz587RvHnzZLnhg8zcCiESgre3mkKwZw8EBallWq1hcGtBKCeoREF8+IH5ePK9QR+yGoIQQqhiG6/F+VSEx48fEx4eblQeERHB06dP49qdEEKkSNu2wfDhcP264Uljik7Hh1+ahWGJB2sxJ5wb5DPoI3dumD5dZmyFECIu4rzcd82aNenevTvnz5/Xl507d44ePXpQq1ateB2cEEIkNzqdmhvbqRNcvWoY2JbmLF4UpwiXDW7jSy6DwNbcHNzdYetWCWyFECKu4hzcLl68GFdXV8qUKYOVlRVWVlaUK1eOjBkzsmjRooQYoxBCJAs+PtC+PfTtCy9ffph+oNCXPzhJRYpyhbV4mFy3VquFdOlg5Eh1VYXChb/o8IUQIkWIc1pChgwZ2LlzJ//99x/Xrl0DoECBAuTLly+GWwohRMrl7Q09e8KpU4aztWl5xRI60oT3W5f744Ajfvq1a83N1aC2USM1MC5a9EuPXgghUo5PXv47X758EtAKIQRqYNu1K5w9axjYVuQEq2lFNu7ryyYziOGMJxwLAKys4OuvYfBgqFFDnb0VQgjx6WL9NlqoUCFevXqlv96zZ09evHihv/7s2TNsbW3jd3RCCJHE+fjAhAlw5YqahqDVghYdQ5nIEarqA9vnpKc+OxnCZH1ga24O9evDH39ArVoS2AohRHyI9VvptWvXDFZJWLlyJf7+/vrriqIQHBwcv6MTQogkLDwcFi9WTxyLiAAzM3DVPGU39ZjIz5gTAcARvqYEF9lNfQA0GnW3scaNYf16KFgwMY9CCCFSlk9OSzC1PK5Go/mswQghRHLh7a3OuO7cCaGhEBKilufmFtU5CIAODeMYwThGEPH/t1sLC8iXDypVgv791dlbIYQQ8UfeVoUQIo62b4exY+H+fXj7Fiwt1XKdDo5TkVGacfRSZtFW48lBpbr+dhYWULEi1K2rLvUlM7ZCCBH/Yh3cajQao5lZmakVQqQmOh3s3w/DhsHz5+DqCunCnvDaPAPvLMwIDQVFgd8Ywl/m3XiuS4eG92kIo0apO41lyyb5tUIIkVBiHdwqikLNmjUx//93aO/evaNRo0ZY/n/KwtSuZUIIkVL4+Khrz65aBXfugK0tlHy0g2mv2zPfegCznIbz8qWae6ug5ZUmHaDO1ubIAVOmyIYMQgjxJcQ6uB01apTB9SYmNjpv1qzZ549ICCGSGB8fmDkT7t6F4GCwtwplTPDP/BA4DYAhQSM5YVGN0w6VCAxUZ2+trcHODurUgZ9+kg0ZhBDiS/nk4FYIIVIDnQ42b4YXL9R0gndXfZka0JLS4f/q2+y3bshDh4Jog9UVE9KkUZf4atNG1q4VQogvTU4oE0KIaNy7B9euQdasUPT6Bibf74KDzg+AUCz41XEqCyz7kMNNQ6YQePpUTT+YM0dWQhBCiMQgb71CCBGNt28hIjAYj6sDKX9unr7c1yw3XdOswceuDBEh8O4dvHqlzu727SuBrRBCJBZ5+xVCiGg4vb3PhEONyP7aS1+2N60HgxwX8ircgZB36hq3L19CliwwYoTk1wohRGKS4FYIIaKRuVg6Xlqoq8GEmVuzq95M9mTtQvrrGnTPICwM0qeHRo2gQwcJbIUQIrFJcCuEENHQprHF/8+1BHVpz8wSS9DlKUpaOyhaFP77T92IoVs3OXFMCCGSik8Kbg8cOMCBAwd49uwZOp3OoG7x4sXxMjAhhEgUly6pi9jmyaMvyt24MD6Hz5Bui4Zr1+DhQ3WprypVZKcxIYRIauIc3I4ZM4axY8dSpkwZ3NzcZJcyIUTKoCiwcCH07w+FCsHJk2Blpa8uWEhD/gLq6glv34K9vew0JoQQSVGcg9v58+ezdOlS2rZtmxDjEUKIL8/PT80tWLdOvX7+PMyYAUOGGDTTatXdxoQQQiRdcZ5zCA0NpWLFivFy50ePHqVRo0ZkypQJjUbDli1bDOoVRWHkyJG4ublhY2NDrVq1uHHjhkGbV69e0aZNGxwcHHBycqJz584EBATEy/iEEKnAmTNQqtT7wBagVy/o1y/xxiSEEOKTxTm47dKlC6tWrYqXOw8MDKR48eLMmTPHZP3kyZOZOXMm8+fP5/Tp09jZ2VG3bl2Cg4P1bdq0aYO3tzf79u1j+/btHD16lG7dusXL+IQQKZiiwO+/Q6VKcPu2WuboCBs3wuzZalKtEEKIZEejKIoSlxv069eP5cuXU6xYMYoVK4aFhYVB/fTp0z9tIBoNmzdvxt3dHVBnbTNlysSPP/7ITz/9BICfnx8ZM2Zk6dKltGzZEh8fHwoVKsSZM2coU6YMALt37+abb77hwYMHZMqUyeR9hYSEEBISor/u7+9P1qxZ8fPzw8HB4ZPGL4RIRl6+hI4d4e+/35eVLw9r1kjegRBCJFH+/v44OjrGGK/Feeb20qVLlChRAq1Wy5UrV7hw4YL+cvHixc8ZswFfX1+ePHlCrVq19GWOjo6UL1+eU6dOAXDq1CmcnJz0gS1ArVq10Gq1nD59Osq+J06ciKOjo/6SNWvWeBu3ECKJCwhQ0xA+DGwHDYJjxySwFUKIFCDOJ5QdOnQoIcZh5MmTJwBkzJjRoDxjxoz6uidPnuDi4mJQb25ujrOzs76NKcOGDWPgwIH665Ezt0KIVCBNGmjTBiZOVHdfWL4c6tdP7FEJIYSIJ6lyEwcrKyusPljiRwiRyowdC+/ewU8/QebMiT0aIYQQ8ShWwW3Tpk1ZunQpDg4ONG3aNNq2mzZtipeBubq6AvD06VPc3Nz05U+fPqVEiRL6Ns+ePTO4XXh4OK9evdLfXgiRyh04AHfuQOfO78vMzdWTyYQQQqQ4scq5dXR01G/W8GGuqqlLfMmZMyeurq4cOHBAX+bv78/p06epUKECABUqVODNmzecO3dO3+bgwYPodDrKly8fb2MRQiRtOp0av16+rP7U6YDwcPjlF6hdG3r0gA/eJ4QQQqRcsZq5XbJkicnfP1dAQAA3b97UX/f19eXixYs4OzuTLVs2+vfvz6+//krevHnJmTMnI0aMIFOmTPoVFQoWLEi9evXo2rUr8+fPJywsjN69e9OyZcsoV0oQQqQsPj6weTNcuwbBweoKXmXdHtB5fytszx9XG4WFqbuPLViQuIMVQgiR4BI15/bs2bNUr15dfz3yJK/27duzdOlSBg8eTGBgIN26dePNmzdUrlyZ3bt3Y/3B+pOenp707t2bmjVrotVqadasGTNnzvzixyKE+LJ0Ojh4UI1ZAwIgf371XLHsl7fTYm0HbENfqg3NzGD8eHVFBCGEEClenNe5ffnyJSNHjuTQoUM8e/YMnU5nUP/q1at4HeCXENt104QQiU+ng/37YeVKOH4cgoIgQwbIlD6Uwa+HUdPr/Vrbfk7ZsP97NdrK8bOrohBCiMQT23gtzjO3bdu25ebNm3Tu3JmMGTPqc3GFECKh+fjAhAmwfbsa1EZEgKUlZI+4zeQbLSkeckbf9nKuJswts5ghWZzJkXhDFkII8YXFObg9duwYx48fp3jx4gkxHiGEMOnyZejSBS5cUINaMzN1Fjc0RGFe8HcUV84DEKqxZH/dqZws1ZuXNzS8fZvIAxdCCPFFxXmHsgIFCvDu3buEGIsQQhjR6dTzwGrUgH//Vc8N0+nUAFejATQafmABoVjga56HtrlPsTd/HwKDNFhbg719Yh+BEEKILynOwe3cuXMZPnw4R44c4eXLl/j7+xtchBAivvj4gLs79OkDL158WKOg06lBrqLAeW0Zmmq3Ut3+HOcpRXAwPHgABQtCtmyJNHghhBCJIs5pCU5OTvj7+1OjRg2DckVR0Gg0RERExNvghBCpU+RJY0OHgpfX/9et/b/vWUFrVtGIv4nAHJ0OtFrYqdTHNhQyhMO9e5A9uxoYa+P8EV4IIURyFufgtk2bNlhYWLBq1So5oUwIEe98fGD+fFiyBIN8WTsCmE1vOrAMgNGMZgS/AmoAGx6upizY2EDlyvDtt+rMrRBCiNQlzsHtlStXuHDhAvnz50+I8QghUjEfHxg4UF3iKyDgfXlRLrEWDwpyTV+WkaeAAmhQFPUEsxw5YMYMqFVLZmyFECK1ivPbf5kyZbh//35CjEUIkYrpdOqM7T//wPtzVhW6sYDTlNcHtm9JQ2s86cafgPrNkaJA+vQwZQrUqSOBrRBCpGZxnrnt06cP/fr1Y9CgQRQtWhQLCwuD+mLFisXb4IQQKZ9Op+bIXr0Ku3ZBaKg6C2sX4cdCuuHBOn3bC5SgBeu4SV59mUYDefKogW3jxolxBEIIIZKSOO9QpjUxJaLRaJL1CWWyQ5kQiePyZZg1C65fh9ev4cYNNdgtrTvDivCW5Oa2vu0sejOIKYTwfvttc3No2BB+/RUKF06MIxBCCPGlJNgOZb6+vp81MCGEAJg3T91t7NWr/69XizprC9BSWakPbF/jRGf+YjNNDW5vbQ0//wzDh0saghBCiPfiPHObEsnMrRBf1tat0KmTuhqCnZ0aqIaFqYEugKUSzEkqEoYlHqzh7kcb6KZNC3/9pa6IIIQQInVIsJlbgBs3bnDo0CGePXuG7sMFKIGRI0d+SpdCiFTi8mXo0UNNQwDw9wfLgFeEWDtjbQ3BwRCCNd+wEz+zdERoLSBMbWtmBrlzS36tEEKIqMU5uP3zzz/p0aMH6dOnx9XV1WCdW41GI8GtEMIknQ4OHlTTCJ4+VVc4sDTXMVg3iZ/Cf6Ny0CluWBTC3Fxds/aF1hWtVr2dmRnY2kLt2jB2rOTXCiGEiFqc0xKyZ89Oz549GTJkSEKN6YuTtAQhEk7kbmOennDsGDx6BCEhkJEnrNS0pZayH4ArFKGi2Wl01raEhICjI+TMCRYWkC8ftG4t69cKIURqlmBpCa9fv+a77777rMEJIVIHHx8YPx62bVPXrtXp1EtN9rOS73FVngIQgZZNZs0JwQrC1FUQ6tSBIUPUIDdbNglqhRBCxE6c/11899137N27NyHGIoRIQby9oV07WL1aPXEsPBw0unDG8Qt7qYMramD7CDfqmR9gvNkowhUzwsLUgHbYMCheXN11TAJbIYQQsRWrmduZM2fqf8+TJw8jRozgn3/+MbmJQ9++feN3hEKIZMfbG9q0UU8eizznNDMPWE0rqnBc324X9WjPMl7qXOD/s7qWltCrFxQtmkiDF0IIkazFKuc2Z86csetMo+H27dsxN0xiJOdWiPjj7Q1Dh6q7jUXu6VJPs4eVSmvSoa71FY4ZPzOBafyEotHqZ2YtLaFZM1i2TGZrhRBCGIrXnFvZuEEIERve3tC1K1y48D6wBQhUbHDiDQB3yUZL1vAPFdBo1FUQbG3VPNvKldWNGSSwFUII8ak+aZ1bIYT4mI+POmN7+bJx3TG+ZjSjKcV5OrGYN6TV12m1kDkzVKsG3bpBwYJfbsxCCCFSnk8Kbh88eMC2bdu4d+8eoZH7Zf7f9OnT42VgQojkQ6eDjRvVAFejgbqWh/g7uCq6D85ZHc9wFDTq2tj/T4bKnBl++UVdv1ZOHBNCCBEf4hzcHjhwgMaNG5MrVy6uXbtGkSJFuHPnDoqiUKpUqYQYoxAiCQoPh5Mn1VSEoCA4ehQsI94xM2wAHYIX8LPZJCZGvF8PW/l/oBuZ5W9nB7Nmgbt7IgxeCCFEihXnTRzKlStH/fr1/9fefYdHVSxuHP9uEhJCqkASEoVAQIoQqhoRAYVIUSnKlSIWqqChClIUpHgRBAU7RRRUQMErTRCVLlKlhBIxQAzFC4EfYEiDtD2/P85lYUmhmGST8H6eZx+yc2aH2cMh+2YyZ4Zx48bh5eXF3r178ff3p2vXrrRq1YqXXnopv/qab3RDmcjNWb4cxo6FQ4cgPd0cra1q/YMFmR2pZTXnJWTgTF3nA0RlVrd7rcUC3t7m+rcREQ7ovIiIFEk3mtdu+peABw8e5PnnnwfAxcWFixcv4unpyfjx43n77bdvvcciUiRMn26uXxsZaY7YpqdD59Qv2JrewBZsU3BnqOdMTnlXw83NDLRgTjuoWRO+/FLBVkRE8sdNT0vw8PCwzbMNDAwkJiaGmv/b6P3s2bN52zsRKVT274cxY+DCBTOwelmS+MiI4Dm+tNWJ4h76+S3iqEdNXFLM6QfOzld2HZs/31zyS0REJD/c8Mjt+PHjSU5O5oEHHuDXX81F2B977DGGDBnChAkT6NGjBw888EC+dVREHMtqhXHj4PLPsLWMfWy33stzxpVg+ym9uI/f2J5UE29vKFfODLelS0OrVjB+vIKtiIjkrxuec+vs7MypU6dISkoiKSmJ2rVrk5yczJAhQ9iyZQt33303U6dOJTg4OL/7nOc051bk+tasgWeegf/7P3iEdfzAY5QkFYBEPHnZaSYLeAarFUqWBF9fM8iWLq1lvkRE5J/L000cAC5n4JCQEFuZh4cHM2bM+AfdFJHCymqFo0fNm8asVli40JxfC7CD+4mlEjX4g93UozML+dNyNxjmvNp77oEBAyAgAKpW1TJfIiJScG5qzq3l8l0hIlKsHTwIM2fCxo1w/ry57FdSknnMyQmSrZ50ZiHdmcNrTpNIxQ3Dai7z5eoKjzwCzz2nQCsiIgXvpsJt1apVrxtwz58//486JCKOdfCgObd2xw7zRrByAQbt//qQ+elPcDgzBCcncyR3H7V5xTINjCtr11osEBwM3bsr2IqIiGPcVLgdN24cPj4++dUXEXGwjAz47DPYudMMp9X9zjEyuhsPnl9BK5eveNR9M4mprpQoAZmZZsi9WlAQTJ1qLvclIiLiCDcVbjt37oy/v39+9UVEHOjgQfj8c1i0CP7+Gx523sTMo88QlPkXAPUydtLcWM1P7o9jsZijuikpZsh1dobq1eGLLyA01MFvREREbms3HG4131akeLJaYd06mDULTp0CZzIZmTmR4UljcMYcmj3v4sfYSl/yS2IrrMlwxx3QsCGkppqrJwQHw2uvacRWREQc76ZXSxCR4uFyqF2+HNauNTdmqFgyjrlxz9I0fa2t3q8lHmZIwHxKBgbh7w4nT5pzbBMToUwZuP9+aN9ey3yJiEjhcMPh1nrt5DoRKbIur4bwww9mSE1JgcdKrOaj089SJuMMAJk48Z7PGN73eJ3UNGcqXjKXAgsIMDdkeO458PGBChV085iIiBQeN739rogUbVFR8NZbsH27eQNZYCA4/XmEeeda2aYhnLIE8oLLAnY6PUxJwwy1p05BiRJw333Qs6dGakVEpHDSeIvIbSQqCgYPhg0bzLmySUlw5gzEOldhhudQANa7t6ZLjb3s9n4YZ2dzZDc1Fby8oG1bGDNGwVZERAovjdyKFHNWKxw/Dnv3wpw5EB0N3t5mYHVzg0uXIC0Nxjr/m8O+9/BNieeo4OGEvz/UrQsnTkCVKvD66xASoikIIiJSuCncihRjBw/CkiXmn5GRcPYsOGWkMTJpOFFpdzO/5Mt4eJjLeV3KKME8txewZphzcA0D4uOhVi1zK90qVRz9bkRERK5P4VakGMrIgK++MjdUuHAB/P3N0dnaHjFMjO1MfetOUnFlh8uD/OldFw8PuHjRnFN76RKcOwflysFDD8GTT2oagoiIFB0KtyLFzIoVMGQIHDp0pezECXiaRcymN94k2MrvIYoDKXVxdTW3zi1RwpxbW6cO9OkDzZppGoKIiBQtCrcixciyZdC1KyQnXykryUWmMZi+zLSVxThVoZv7Is6Wq4fHRUhIMEdsvb3h8cfhxRc1WisiIkWTwq1IMWC1wgcfmCO2Vy9JXY0/WERHarPfVjafZxjnP4P4TC98M6FUKXOpr+rVYexYCA/XaK2IiBRdCrciRdyePdC8Ofz9t33583zBJ7yMBykApOBOPz5iDt0plWDhzjvN1RDi46FePW2fKyIixYPCrUgR1revudPYtdxJYSxjbcE2invoyCJ+x0yvqanmaghubvDII9o+V0REig+FW5EiyGo1N1RYuTL74xcpRWe+4VceYi7dGMj7XKSU7bi3NwwbBo8+qu1zRUSkeFG4FSlioqJgxIhrg62BB8kk42kr2UEYtTjAIaplaaNNG3MLXYVaEREpbvTRJlJEWK0we7a5msGKFVfKfYjnW55mBU/gRKbda7ILthUqmKO2CrYiIlIc6eNNpAiIioIOHeDll+HYsSvl97GDPdTjX3zHw2xkNG/m2o6fH3z8sW4cExGR4kvhVqSQW7rUHK1dutRcsgvAgpVXeJfNNKISRwH4G1/2UifbNpyczA0Z1q+HJ54okG6LiIg4hObcihRiH39srl2bmnqlrAxnmUs3nuDKpNutPEBnvuE4wVna8PODL7+EFi00FUFERIq/Qv9RV7FiRSwWS5ZHREQEAA8//HCWY3379nVwr0X+ueXLYehQ+2DbmF+IpK5dsJ3EcJrwS7bBtkYNiIuDVq0UbEVE5PZQ6Eduf/vtNzIzr9wkc+DAAR599FGefvppW1nv3r0ZP3687XmpUqUQKaqsVvjzTxg0yNwS12TwOhMYxxicMbcgO4Mfz/MlP9Eq23aaN4c1awqkyyIiIoVGoQ+3fn5+ds8nTZpE5cqVadq0qa2sVKlSlCtXrqC7JpLnoqJgzhxYuxZiY68+YiGIk7Zgu56H6cp8ThGUbTu9e8OsWfnfXxERkcKmSP2iMi0tjXnz5tGjRw8sFoutfP78+ZQtW5ZatWoxcuRIUlJScm0nNTWVhIQEu4eIoy1fbq6IMHOmGXKv9QpT2U093mAc4azJNthWrAjffadgKyIit69CP3J7taVLlxIfH0+3bt1sZc888wzBwcEEBQWxb98+hg8fTnR0NIsXL86xnYkTJzJu3LgC6LHIjVmyxNxUIT4eLBZwIYP67GU3DWx1UinJA2wjHdcsr3dzMzdmmD8fXLMeFhERuW1YDMMwHN2JG9WyZUtcXV35/vvvc6yzbt06mjdvzpEjR6hcuXK2dVJTU0m96i6dhIQEypcvz4ULF/D29s7zfovkJC0N+vQxQ+nlZb7Kc5yv6UJdImnALqKpnmsbnp7QujWMG2feQCYiIlIcJSQk4OPjc928VmSmJRw7dow1a9bQq1evXOuFhYUBcOTIkRzruLm54e3tbfcQKWjTp0NQEMydeyXYtmE5kdSlEVvwIIV5PAvk/POnpyc89ZSCrYiIyGVFZlrCnDlz8Pf35/HHH8+1XmRkJACBgYEF0CuRWzN9OgwfDomJ5nNXUnmb4QzifVudowTTj48AS5bXWywQGgpTpkB4uJb5EhERuaxIhFur1cqcOXN44YUXcHG50uWYmBgWLFjAY489RpkyZdi3bx+DBw+mSZMm1K5d24E9Fsme1Qp//AFjxlwJtiHEsJBO3MsuW73FPElPPiOeO2xlFgt4eECFCjBwIPTqpVArIiJyrSIRbtesWcPx48fp0aOHXbmrqytr1qzhvffeIzk5mfLly9OhQwdGjRrloJ6K5CwqCl5/HX766cr6tZ34hlm8iDdm0k3FlVeYyie8zNUjth4e0LSpOdr74IPgUiT+54qIiBS8InVDWX650QnKIrciIwPefRcmTTJXQ7jsTUYxigm254epQkcWEUk9u9e7ukKzZjB1qubViojI7avY3VAmUhStWAGNGsGIEfbBFmAtzbH+b3R2Hl2pz+4swdbJCZ5+WsFWRETkRumXmyL5ZOlSc17suXPZH9/AI7zGW5zBnzl059obxywW6N/fDLaaWysiInJj9JEpksesVnOHsE6drgRbD5IYxDQs/9s+97K3GcEcenBtsHV2NjdlULAVERG5ORq5FclD+/fDkCGwdq0ZcgHqEMlCOlGNQzhhZSpDcny9xQJ33GEu7zV2rIKtiIjIzdJHp0gemT7dvPFr9erLwdagL9PZxgNU4xAAr/EWXiRk+/qSJeGJJ8xwPHas5tiKiIjcCo3civxDGRnwzjvmLmGXl/jyIZ7Z9OJffGert4v6dGIhiWS9w7NkSXM1hXbtzHVsNWIrIiJyaxRuRf6BpUvNUPu/jfEAuI8dLKQTlThqK3uPgQznbdJwy9KGmxu8+qq5MYOIiIj8Mwq3Irdo9GiYPBnS0sznFqwMZhqTGEEJMgA4zx304HOW0T7bNipXNkd922d/WERERG6Swq3ILfjwQ3jrrSs3jQEM5H3eZajt+RYa0plvOEGFbNsID4eVK81NGkRERCRvaGafyE367jtzGoHVflUvZtOLaKoCMJERNGVjtsHWYoEGDeCDDxRsRURE8ppGbkVuwkcfmasZXJ6KcLUkvOjEQgI4zc+0zPb1JUpAx47w+utaDUFERCQ/aORW5DoyMuCXX8xNGQYPNoNtOU7xHzoQfNVNYwB7qZtjsK1fH5Yvhy+/VLAVERHJLxq5FcnFihXm3Np9+yA52Sx7lJ/5iucI4Ax38l8as4kMSuTYRokS0Lq1udSXQq2IiEj+0sitSDasVpgxA557DrZvN4OtMxlM4DV+piUBnAGgPCeoRGyO7dStC598AkuWKNiKiIgUBI3cilwjKgr69YMNG66Ulec4X9OFRmyxlf1Aa17gC87il6UNZ2dzbu6ECeCi/2UiIiIFRiO3IldZvhwefNA+2LZhOZHUtQXbdFwYyhSeYEW2wdbdHbp2hYkTFWxFREQKmj56RTCnIXz6KUREQGamWeZKKm8znEG8b6sXS0U68w07CMu2nTJloEMHGDRIW+iKiIg4gj5+5bZ38KA50tq375VgC9CIzXbB9j90oB57cgy23t7mHN1BgzS/VkRExFE0ciu3tagoeOwxOH4867H1NONdXqEfHzGYaUznJcCSbTs+PvDNN9CihUZsRUREHEnhVm5bUVHmFrhxceZzV1JJw5WrA+xIJvI5Pfidmtm24eoKAQHm5g6tWhVAp0VERCRXGmOS205aGkybZh9sa/A7O7mXF5llVzcd12yDrZOTGWqbNjWX+mrbtiB6LiIiItejkVu5rXz8MUyefPU0BIMX+IKPicCDFN5nIFtpyH5q59jGXXeZy3zdey888IBWRBARESlM9LEst4033oC33zZHbgE8SeQTXuY55tnqHOZu0nPZbczDA374AUJD87u3IiIicis0LUFuC3v3wjvvXAm2dYhkJ/faBduZvMj97OAPsl/qwMsLFixQsBURESnMFG6l2LNaoX9/uHgRwOBlPmYbD1CNQwAk4EVnvqYvM7mEe7ZtVKxoBlvNrRURESncNC1BiiWr1ZxXm5gI587Brl3gQzyf0ZMOLLbV20kDOvMNMVTJth0XF3Njh3fe0dxaERGRokAf11LsHDwI330Hu3dDcjKcOAEpKXAHVu5lp63eNAYxgkmk4ZZtO1Wrwn/+o2kIIiIiRYnCrRQrUVEwYgT8/jukp5sjuGfPmsf+pjSd+YaltOdFZrGcdtm24eRktvHmm9qQQUREpKhRuJViIyoKevWCPXvMG8dKG2dxJpNUAmx1ttGQSsRykVLZtuHtDQMHwvjxBdVrERERyUsal5JiYc8e6NgRtm+H1FRobGxkL3VYwDM4kWlXN7tg6+ICnTrBL78o2IqIiBRlCrdS5I0aBWFh5lQEi5HJKN5kHc24k5M0Zx1DeNeuvsVihllnZ/NRqhT8+9/magh16jjoTYiIiEie0LQEKbIyMuBf/4Jly8zn5TjFPJ6lOetsddbSjC953u51FguUKGE+SpaEVq3g1Vc1v1ZERKQ4ULiVImnFCvOGrx07zOct+ImveA5//g+ATJwYwzgmMhIrznav9fKC2rXNMFutGgwapGArIiJSXCjcSpFitcKcOTBhAvz1F7iQznjeYCSTbHX+4k6eYQGbaJJtGxYLlC0L990H7dtDjew3JBMREZEiSOFWioy9e81Q+8MP5m5jrtaLrCGcRmyx1VnB43RjLucom20bTk7QrBlMmWLuOqYRWxERkeJFH+1SJIweDQ8+CN9+a27MYLXCJdyJoiYA6bgwhHdoy/Icgy2YI7Zjx0JIiIKtiIhIcaSRWyl0rt4618sLZs+GiRPN8msN4j3KEccEXmcHYbm26+1tBlvtOCYiIlJ8KdxKoXLwICxZAn/8YU49OHcONm0yg21ljlCVQ6ziMVv9i5SiHctzbfPyjWOTJkHbtvn9DkRERMSRFG6l0IiKMkdoz5wxR2zPnYPdu80lvzrxDbN4EQsG9dnNEe6+bnsWC9x9N4wcCc8+a65tKyIiIsWbPu7F4axW8yaxV16BU6fMjRUyMsxw6pyawiwG0pvZtvoTeJ1OLMqxPVdX8PGB+++Ht9+GmjUL4l2IiIhIYaBwKw4VFQU9elxZr/ZqNfidRXSkFlG2sq94lgg+ybYtFxdzXm358vDII/Dii1rmS0RE5HajcCsOs3SpOV0gOfnaIwbdmcNH9KMUFwFIphQRfMwXvABYsFjAMMzaFguEh0P//uauY1WrapkvERGR25XCrRQ4qxXGjIF//zvrMU8Smc5LPMt8W9l+atGRRfzBlWHYy8EWoF49eP99jdKKiIiIwq0UsL17oXVrc25tVgYreIKm/GIrmUEfBjONS7hnqe3sDI0bwyefKNiKiIiISb+4lQLz2mtQt25OwRbAwpuMxoqFC3jTkYW8xAxbsHV3Bzc389GoESxfDmvXKtiKiIjIFRq5lQIRHm4G0etZSzh9mMk6mvEnle2OeXmZqyAEB8PMmeYuYyIiIiJX08it5Cur1Qyh2QXbMLbxMS8Dhl35bHpnCbZOTmY7lSpBs2bmDWMiIiIi11K4lXyzd6852hoba19uwcpQprCJxrzMdAby/nXbCgoyHyEh0L69VkIQERGR7CkiSL4YNcqcX5uSYl9elv9jBU8whWGUIAOAdizDgjXHttzcoHp1uPdeGDBAc2xFREQkZ5pzK3mub19zTuy1mrCRBTzDnZy0lb3FSMYwDiOHn7MCA6F5cxg3TmvXioiIyPUp3Eqeycgwt9C9Ntg6kcko/s0bjMf5fyO0p/HnOb5iNS1ybK9yZfNGtIEDdfOYiIiI3BiFW8kTS5bAkCFZ59cGcpJ5PEsz1tvK1tKMZ5lHHIHZtlWihLnUV4sW5vxaTUMQERGRG6VwK/9IRgZ06GCuOZud0bxpC7aZODGGcUxkJFacs61///0wYwbccQdUqKBpCCIiInJzCnV0GDt2LBaLxe5RvXp12/FLly4RERFBmTJl8PT0pEOHDpw+fdqBPb69rFgB5cvnHGwBhvM2R6jMfwniEdYzgVE5BtunnoLt283tdDW/VkRERG5FoR+5rVmzJmvWrLE9d3G50uXBgwezcuVKvv32W3x8fOjXrx9PPfUUmzdvdkRXbyvLl8MLL0B8vH25MxlkXnVZJeJNG77n//DjHGWzbcvJCXr1yv4mNBEREZGbUejDrYuLC+XKlctSfuHCBT777DMWLFhAs2bNAJgzZw41atRg27ZtPPDAAwXd1dvG/v3Qo0fWYNuOpbzDUJqxjhNUsJX/Qc6TZhs3hvffN0drRURERP6pQv+L38OHDxMUFERISAhdu3bl+PHjAOzatYv09HTCw8NtdatXr06FChXYunVrrm2mpqaSkJBg95Abs38/tG0L585dKXMllfcZwFKepAoxfE0XXEi/blujR8OGDQq2IiIikncKdbgNCwtj7ty5/Pjjj0yfPp3Y2FgaN25MYmIicXFxuLq64uvra/eagIAA4uLicm134sSJ+Pj42B7ly5fPx3dRfCxebN7wdfTolbLKHGELDzKAD21lJwnCjdQc27FYoFMnGD9e82pFREQkbxXqaQmtW7e2fV27dm3CwsIIDg5m0aJFuLu733K7I0eO5JVXXrE9T0hIUMC9jtGj4d//ti/rzNfMpA/eJAJwCTcGM40Z9AUs2bbj5QVvvmmuXSsiIiKS1wp1uL2Wr68vVatW5ciRIzz66KOkpaURHx9vN3p7+vTpbOfoXs3NzQ03N7d87m3x8f779sHWnRTeZyC9mW0ri6YqHVnEPurk2E6dOrBjB7i65mdvRURE5HZWpH4pnJSURExMDIGBgTRo0IASJUqwdu1a2/Ho6GiOHz9Ow4YNHdjLos9qhSNHYM4ccyOFQYOuHLuHKHZwv12w/ZLnaMCuXINthw4QGalgKyIiIvmrUI/cDh06lDZt2hAcHMzJkycZM2YMzs7OdOnSBR8fH3r27Mkrr7xC6dKl8fb2pn///jRs2FArJfwDUVHw2muwejVcvJj1eGViqEUUAMmU4mU+4UteyLE9iwXeecfclldEREQkvxXqcPvXX3/RpUsXzp07h5+fHw899BDbtm3Dz88PgGnTpuHk5ESHDh1ITU2lZcuWfPLJJw7uddE1bx50727uOpaT72nL+wzgEdbTiYW5LvN1xx0wd665uoKIiIhIQbAYhmE4uhOOlpCQgI+PDxcuXMDb29vR3XGI2rXNZb6uVYk/iaUSV98g5koqTli5RM439TVpAmvXgkuh/vFJREREioobzWtFas6t5L20NPD2zi7YGvTjQw5Sgx58bv8a3HINtq+9Bhs3KtiKiIhIwVO4vY29/z64uUFion25L3+zmKf4kAG4kcaH9Kcaf1y3vTJlzJvGJkzIn/6KiIiIXI/G1m5T4eHmtIFrPcBWvqYLFTlmK5tJn/9NTchZq1awcqU2ZRARERHHUhS5DVWsmDXYWrDyKpPZRGNbsD1HadqwnFeYRhrZrwtcogRMnQqrVinYioiIiONp5PY2YrVCrVpw7Jh9eVn+jy95ntb8aCv7lUZ04Wv+Iued28LDzWAbGppfPRYRERG5ORpru00cPAiPPWb+ebW67GEvdWzB1oqFCbzGw2zIMdjecQf89JP5ULAVERGRwkQjt8VcWpp549iHH8KJE1mPH6cCmTgDcBp/nmUea3g0x/Z69oRZszQFQURERAonRZRiKiMDBg8GLy8YNiz7YAtwnjJ04Wt+pCV12JtrsB0+HGbPVrAVERGRwksxpRhasQJCQuC998yR26s9ys/4c9qubDMP0ZofOU25bNuzWKBvX5g0KZ86LCIiIpJHFG6LmRUroHPnrCO1LqQzkRH8TEu+4jksWG+ovTvvhF27YPr0fOisiIiISB5TuC1GLl2Cf/0LkpPtyytwjI00ZQRvA9CC1XRk0XXba90ajh+HevXyo7ciIiIieU/htpj47jvw8IDUVPvydiwlkro8yFYA0ijBK7zLQjrl2t6LL8IPP2h+rYiIiBQtWi2hGOjUCRZdMxDrSipTeJUBfGgr+5NKdOYbfuP+HNvy9IQ5c8wRYBEREZGiRuNyRVyvXlmDbRUOs4UH7YLtt/yLeuzJNdg+8AB8/bWCrYiIiBRdGrktojIyoG1bc9vbq5XnOLupjxdJAFzCjUG8x0z6AJZs27JYYPx4GDECXHRFiIiISBGmkdsiaPFicHfPGmwBTlCBRXQE4A+qEcZ2ZtKXnIKtszOMGmU+FGxFRESkqFOcKWJeeglmzMi9zgA+4DQBvMVrJOOZY7277jJ3LmvfPm/7KCIiIuIoGrktQoYNuzbYGnTnc/7Ft3b1UvDgdd7KNdj++98QG6tgKyIiIsWLRm6LiBdfhE8/vfLck0Rm0JeuLCABL3ZTnz+pfN12XF3h22/N+boiIiIixY1Gbgs5qxX69bMPtvXYzW7q05UFAHiTyJMsuW5bDRrA7t0KtiIiIlJ8KdwWYgcPQv/+8PHHl0sMIviIrTTkbo4AcAFvnmYR7zI017Zefhl27ICaNfO3zyIiIiKOpGkJhdT+/dC9O+zaZT735W8+oydPXTVC+xv30omFxBKSYzvu7jBlCkRE5HePRURERBxP4bYQWrIEunS5spVuGNv4hs5U5Jitzru8wkgmko5rju08+SR88405z1ZERETkdqBwW8iMHm2uZHCZK6n8h39xF/8F4Byl6cZcVtAmxzZ8fWHtWqhfP587KyIiIlLIaM5tIfLhh/bBFiANN3ryGQCbeIi6ROYYbC0WqFIFvvpKwVZERERuTxq5LSSWLIEBA8yvLVgxrvq542da0oKfWEczMnP4JwsIgMcegyFDdNOYiIiI3L4UbguBZcvg+efBiUxG8yY1iaIji7h6y9zVtMj2tXfcARMmQMuWULEiOGksXkRERG5jCrcOtmyZuSqCV9JJltOVR9gAQD8+4iP65/raUqVg/XqoU6cAOioiIiJSBGicz4GWL4eePeH+v39kL3VswTYTJ9y5mOtrLRaYNEnBVkRERORqGrl1kKgoeO3VdIadG8UwJtvKT3AXXfiazTyU6+v79DE3eBARERGRKxRuHcBqhaXvHeXzI124n2228u95gm7M5TxlcnxtyZIwfz489VRB9FRERESkaFG4LWBWK0RPWkL/OT3wtsYDkEYJhjGZ9xnI1TeRXeu++2DLFnDRv5qIiIhItjTntgAdPGjOkz370Td4Z8YDEEslmjht5n0GYbFkH2ydnGD4cNixQ8FWREREJDeKSgXk4EH44AM4exZONp9FxW9+Y7v1PvpYZnEBHywWMIysrwsJgf/8B+rVK/g+i4iIiBQ1CrcFwGo1N2k4exbuuQfAh4Fh29gQ5UfKRQuWTHNE1jAgM9P802KB2rXN+bXalEFERETkxmhaQgE4fhz++APKlzdDq8UCgXX8CQyy4OoKzs5mvZIlwc0NPD2hcWMFWxEREZGbpZHbApCYCJcugYfHlTI/P3j4Ydi1C2JjISHBDLf+/vDoo+ZSXzVqOKzLIiIiIkWSwm0B8PIyg2tyMnh7Xyn38zO3zT1xAo4dM7fgbdxY2+iKiIiI3CpFqAJQoQJUr26G2OxuGktMhPBweO458wYyBVsRERGRW6MYVQCcnODJJ6FsWfj9d7hwATIyzD9//90sb99eoVZERETkn1KcKiA1asCAAeaSXufOwaFD5p/165vlml8rIiIi8s9pzm0BqlEDqlUzV09ITDTn4laooBFbERERkbyicFvAnJzMG8ZEREREJO9pzFBEREREig2FWxEREREpNhRuRURERKTYULgVERERkWJD4VZEREREig2FWxEREREpNhRuRURERKTYULgVERERkWJD4VZEREREio1CHW4nTpzIfffdh5eXF/7+/rRv357o6Gi7Og8//DAWi8Xu0bdvXwf1WEREREQcqVCH240bNxIREcG2bdtYvXo16enptGjRguTkZLt6vXv35tSpU7bH5MmTHdRjEREREXEkF0d3IDc//vij3fO5c+fi7+/Prl27aNKkia28VKlSlCtXrqC7JyIiIiKFTKEeub3WhQsXAChdurRd+fz58ylbtiy1atVi5MiRpKSk5NpOamoqCQkJdg8RERERKfoK9cjt1axWK4MGDaJRo0bUqlXLVv7MM88QHBxMUFAQ+/btY/jw4URHR7N48eIc25o4cSLjxo3LUq6QKyIiIlI4Xc5phmHkWs9iXK9GIfHSSy+xatUqfv31V+66664c661bt47mzZtz5MgRKleunG2d1NRUUlNTbc//+9//cs899+R5n0VEREQkb504cSLXLFgkRm779evHihUr+OWXX3J9MwBhYWEAuYZbNzc33NzcbM89PT05ceIEXl5eWCyWvOv4bSYhIYHy5ctz4sQJvL29Hd2dYkPnNX/ovOYPndf8ofOaP3Re80d+nVfDMEhMTCQoKCjXeoU63BqGQf/+/VmyZAkbNmygUqVK131NZGQkAIGBgTf89zg5OV03NMuN8/b21jeJfKDzmj90XvOHzmv+0HnNHzqv+SM/zquPj8916xTqcBsREcGCBQtYtmwZXl5exMXFAeYbc3d3JyYmhgULFvDYY49RpkwZ9u3bx+DBg2nSpAm1a9d2cO9FREREpKAV6nA7ffp0wNyo4Wpz5syhW7duuLq6smbNGt577z2Sk5MpX748HTp0YNSoUQ7orYiIiIg4WqEOt9e71618+fJs3LixgHoj1+Pm5saYMWPs5jPLP6fzmj90XvOHzmv+0HnNHzqv+cPR57XIrJYgIiIiInI9RWoTBxERERGR3CjcioiIiEixoXArIiIiIsWGwq2IiIiIFBsKt3LTxo4di8VisXtUr17ddvzSpUtERERQpkwZPD096dChA6dPn3Zgj4uGihUrZjmvFouFiIgIwFwS79pjffv2dXCvC59ffvmFNm3aEBQUhMViYenSpXbHDcPgjTfeIDAwEHd3d8LDwzl8+LBdnfPnz9O1a1e8vb3x9fWlZ8+eJCUlFeC7KHxyO6/p6ekMHz6c0NBQPDw8CAoK4vnnn+fkyZN2bWR3jU+aNKmA30nhcb1rtVu3blnOV6tWrezq6FrN6nrnNbvvsxaLhSlTptjq6FrNauLEidx33314eXnh7+9P+/btiY6OtqtzI5//x48f5/HHH6dUqVL4+/vz6quvkpGRkad9VbiVW1KzZk1OnTple/z666+2Y4MHD+b777/n22+/ZePGjZw8eZKnnnrKgb0tGn777Te7c7p69WoAnn76aVud3r1729WZPHmyo7pbaCUnJ1OnTh0+/vjjbI9PnjyZDz74gBkzZrB9+3Y8PDxo2bIlly5dstXp2rUrUVFRrF692rb194svvlhQb6FQyu28pqSksHv3bkaPHs3u3btZvHgx0dHRtG3bNkvd8ePH213D/fv3L4juF0rXu1YBWrVqZXe+vv76a7vjulazut55vfp8njp1is8//xyLxUKHDh3s6ulatbdx40YiIiLYtm0bq1evJj09nRYtWpCcnGyrc73P/8zMTB5//HHS0tLYsmULX3zxBXPnzuWNN97I284aIjdpzJgxRp06dbI9Fh8fb5QoUcL49ttvbWUHDx40AGPr1q0F1MPiYeDAgUblypUNq9VqGIZhNG3a1Bg4cKBjO1XEAMaSJUtsz61Wq1GuXDljypQptrL4+HjDzc3N+Prrrw3DMIzff//dAIzffvvNVmfVqlWGxWIx/vvf/xZY3wuza89rdnbs2GEAxrFjx2xlwcHBxrRp0/K3c0VUduf0hRdeMNq1a5fja3StXt+NXKvt2rUzmjVrZlema/X6zpw5YwDGxo0bDcO4sc//H374wXBycjLi4uJsdaZPn254e3sbqampedY3jdzKLTl8+DBBQUGEhITQtWtXjh8/DsCuXbtIT08nPDzcVrd69epUqFCBrVu3Oqq7RU5aWhrz5s2jR48eWCwWW/n8+fMpW7YstWrVYuTIkaSkpDiwl0VPbGwscXFxdtenj48PYWFhtutz69at+Pr6cu+999rqhIeH4+TkxPbt2wu8z0XVhQsXsFgs+Pr62pVPmjSJMmXKUK9ePaZMmZLnv44sbjZs2IC/vz/VqlXjpZde4ty5c7Zjulb/udOnT7Ny5Up69uyZ5Ziu1dxduHABgNKlSwM39vm/detWQkNDCQgIsNVp2bIlCQkJREVF5VnfCvUOZVI4hYWFMXfuXKpVq8apU6cYN24cjRs35sCBA8TFxeHq6prlAy0gIIC4uDjHdLgIWrp0KfHx8XTr1s1W9swzzxAcHExQUBD79u1j+PDhREdHs3jxYsd1tIi5fA1e/Y318vPLx+Li4vD397c77uLiQunSpXUN36BLly4xfPhwunTpgre3t618wIAB1K9fn9KlS7NlyxZGjhzJqVOnmDp1qgN7W3i1atWKp556ikqVKhETE8Nrr71G69at2bp1K87OzrpW88AXX3yBl5dXlqlzulZzZ7VaGTRoEI0aNaJWrVoAN/T5HxcXl+3338vH8orCrdy01q1b276uXbs2YWFhBAcHs2jRItzd3R3Ys+Ljs88+o3Xr1gQFBdnKrp5HFxoaSmBgIM2bNycmJobKlSs7opsiWaSnp9OxY0cMw2D69Ol2x1555RXb17Vr18bV1ZU+ffowceJEbX+ajc6dO9u+Dg0NpXbt2lSuXJkNGzbQvHlzB/as+Pj888/p2rUrJUuWtCvXtZq7iIgIDhw4YHe/TWGiaQnyj/n6+lK1alWOHDlCuXLlSEtLIz4+3q7O6dOnKVeunGM6WMQcO3aMNWvW0KtXr1zrhYWFAXDkyJGC6FaxcPkavPbu3auvz3LlynHmzBm74xkZGZw/f17X8HVcDrbHjh1j9erVdqO22QkLCyMjI4OjR48WTAeLuJCQEMqWLWv7P69r9Z/ZtGkT0dHR1/1eC7pWr9avXz9WrFjB+vXrueuuu2zlN/L5X65cuWy//14+llcUbuUfS0pKIiYmhsDAQBo0aECJEiVYu3at7Xh0dDTHjx+nYcOGDuxl0TFnzhz8/f15/PHHc60XGRkJQGBgYAH0qnioVKkS5cqVs7s+ExIS2L59u+36bNiwIfHx8ezatctWZ926dVitVtsPFJLV5WB7+PBh1qxZQ5kyZa77msjISJycnLL8al2y99dff3Hu3Dnb/3ldq//MZ599RoMGDahTp8516+paNZdR7NevH0uWLGHdunVUqlTJ7viNfP43bNiQ/fv32/1QdvkH4XvuuSdPOytyU4YMGWJs2LDBiI2NNTZv3myEh4cbZcuWNc6cOWMYhmH07dvXqFChgrFu3Tpj586dRsOGDY2GDRs6uNdFQ2ZmplGhQgVj+PDhduVHjhwxxo8fb+zcudOIjY01li1bZoSEhBhNmjRxUE8Lr8TERGPPnj3Gnj17DMCYOnWqsWfPHttd+5MmTTJ8fX2NZcuWGfv27TPatWtnVKpUybh48aKtjVatWhn16tUztm/fbvz666/G3XffbXTp0sVRb6lQyO28pqWlGW3btjXuuusuIzIy0jh16pTtcfkO6C1bthjTpk0zIiMjjZiYGGPevHmGn5+f8fzzzzv4nTlObuc0MTHRGDp0qLF161YjNjbWWLNmjVG/fn3j7rvvNi5dumRrQ9dqVtf7HmAYhnHhwgWjVKlSxvTp07O8Xtdq9l566SXDx8fH2LBhg93/8ZSUFFud633+Z2RkGLVq1TJatGhhREZGGj/++KPh5+dnjBw5Mk/7qnArN61Tp05GYGCg4erqatx5551Gp06djCNHjtiOX7x40Xj55ZeNO+64wyhVqpTx5JNPGqdOnXJgj4uOn376yQCM6Ohou/Ljx48bTZo0MUqXLm24ubkZVapUMV599VXjwoULDupp4bV+/XoDyPJ44YUXDMMwlwMbPXq0ERAQYLi5uRnNmzfPcr7PnTtndOnSxfD09DS8vb2N7t27G4mJiQ54N4VHbuc1NjY222OAsX79esMwDGPXrl1GWFiY4ePjY5QsWdKoUaOG8dZbb9kFtdtNbuc0JSXFaNGiheHn52eUKFHCCA4ONnr37m23hJJh6FrNzvW+BxiGYcycOdNwd3c34uPjs7xe12r2cvo/PmfOHFudG/n8P3r0qNG6dWvD3d3dKFu2rDFkyBAjPT09T/tq+V+HRURERESKPM25FREREZFiQ+FWRERERIoNhVsRERERKTYUbkVERESk2FC4FREREZFiQ+FWRERERIoNhVsRERERKTYUbkVERESk2FC4FREpxCpWrMh7773n0D7UrFmT48ePc/LkSUJCQkhMTMyTdi0WC0uXLs2TtkRELlO4FZHbWlxcHAMHDqRKlSqULFmSgIAAGjVqxPTp00lJSXF0927ahg0bsFgsWCwWnJyc8PHxoV69egwbNoxTp07dUpt9+/alSpUqlC9fnk6dOuHl5ZXHvRYRyTsuju6AiIij/PnnnzRq1AhfX1/eeustQkNDcXNzY//+/cyaNYs777yTtm3b3lLbaWlpuLq65nGPb1x0dDTe3t4kJCSwe/duJk+ezGeffcaGDRsIDQ29qbb69+9Pnz59sFqtlCxZ8h/3La/OjaPPsYgUThq5FZHb1ssvv4yLiws7d+6kY8eO1KhRg5CQENq1a8fKlStp06aNrW58fDy9evXCz88Pb29vmjVrxt69e23Hx44dS926dZk9ezaVKlWyhcD4+Hj69OlDQEAAJUuWpFatWqxYscL2ul9//ZXGjRvj7u5O+fLlGTBgAMnJyTn2efbs2fj6+rJ27dpc35u/vz/lypWjatWqdO7cmc2bN+Pn58dLL72Upb0aNWpQsmRJqlevzieffGJ3fMuWLdStWxdvb28eeughli5disViITIy0lbnwIEDtG7dGk9PTwICAnjuuec4e/as7fjDDz9Mv379GDRoEGXLlqVly5bZ9vnEiRN07NgRX19fSpcuTbt27Th69KjteLdu3Wjfvj0TJkwgKCiIatWq5XoOROT2pHArIrelc+fO8fPPPxMREYGHh0e2dSwWi+3rp59+mjNnzrBq1Sp27dpF/fr1ad68OefPn7fVOXLkCN999x2LFy8mMjISq9VK69at2bx5M/PmzeP3339n0qRJODs7AxATE0OrVq3o0KED+/btY+HChfz666/069cv2/5MnjyZESNG8PPPP9O8efOber/u7u707duXzZs3c+bMGQDmz5/PG2+8wYQJEzh48CBvvfUWo0eP5osvvgAgISGBNm3aEBoayu7du3nzzTcZPny4Xbvx8fE0a9aMevXqsXPnTn788UdOnz5Nx44d7ep98cUXuLq6snnzZmbMmJGlf+np6bRs2RIvLy82bdrE5s2b8fT0pFWrVqSlpdnqrV27lujoaFavXm33Q4KIiI0hInIb2rZtmwEYixcvtisvU6aM4eHhYXh4eBjDhg0zDMMwNm3aZHh7exuXLl2yq1u5cmVj5syZhmEYxpgxY4wSJUoYZ86csR3/6aefDCcnJyM6OjrbPvTs2dN48cUX7co2bdpkODk5GRcvXjQMwzCCg4ONadOmGcOGDTMCAwONAwcO5Pq+1q9fbwDG33//neXYqlWrDMDYvn27rf8LFiywq/Pmm28aDRs2NAzDMKZPn26UKVPG1hfDMIxPP/3UAIw9e/bY6rdo0cKujRMnThiA7X03bdrUqFevXpb+AMaSJUsMwzCMr776yqhWrZphtVptx1NTUw13d3fjp59+MgzDMF544QUjICDASE1NzfUciMjtTXNuRUSusmPHDqxWK127diU1NRWAvXv3kpSURJkyZezqXrx4kZiYGNvz4OBg/Pz8bM8jIyO56667qFq1arZ/1969e9m3bx/z58+3lRmGgdVqJTY2lho1agDw7rvvkpyczM6dOwkJCbnl92YYBmCOSCcnJxMTE0PPnj3p3bu3rU5GRgY+Pj6AOW+3du3advNs77///izvYf369Xh6emb5+2JiYmzvvUGDBrn2be/evRw5ciTLzWqXLl2yO8ehoaGaZysiuVK4FZHbUpUqVbBYLERHR9uVXw6P7u7utrKkpCQCAwPZsGFDlnZ8fX1tX187veHqNrKTlJREnz59GDBgQJZjFSpUsH3duHFjVq5cyaJFixgxYkSubebm4MGDgLm8WFJSEgCffvopYWFhdvUuT5u4EUlJSbRp04a33347y7HAwEDb1zlN/bi6nQYNGtgF/cuu/oHheu2IiCjcishtqUyZMjz66KN89NFH9O/fP9fQVL9+feLi4nBxcaFixYo3/HfUrl2bv/76i0OHDmU7elu/fn1+//13qlSpkms7999/P/369aNVq1a4uLgwdOjQG+7DZRcvXmTWrFk0adLEFhaDgoL4888/6dq1a7avqVatGvPmzSM1NRU3NzcAfvvttyzv4bvvvqNixYq4uNz6R0r9+vVZuHAh/v7+eHt733I7IiK6oUxEbluffPIJGRkZ3HvvvSxcuJCDBw8SHR3NvHnz+OOPP2wjmOHh4TRs2JD27dvz888/c/ToUbZs2cLrr7/Ozp07c2y/adOmNGnShA4dOrB69WpiY2NZtWoVP/74IwDDhw9ny5Yt9OvXj8jISA4fPsyyZcuyvaHswQcf5IcffmDcuHE3tKnDmTNniIuL4/Dhw3zzzTc0atSIs2fPMn36dFudcePGMXHiRD744AMOHTrE/v37mTNnDlOnTgXgmWeewWq10qdPHw4dOsSaNWuYMmUKcOVmu4iICM6fP0+XLl347bffiImJ4aeffqJ79+5kZmbe2D8E0LVrV8qWLUu7du3YtGkTsbGxbNiwgQEDBvDXX3/dcDsiIgq3InLbqly5Mnv27CE8PJyRI0dSp04d7r33Xj788EOGDh3Km2++CZhB7ocffqBJkyZ0797dtrzWsWPHCAgIyPXv+O6777jvvvvo1KkTISEhDBs2zBb6ateuzcaNGzl06BCNGzemXr16vPHGGwQFBWXb1kMPPcTKlSsZNWoUH374Ya5/b7Vq1QgKCqJBgwZMmjSJ8PBwDhw4wD333GOr06tXL2bPns2cOXMIDQ2ladOmzJ07l0qVKgHg7e3N999/z549ewgNDWXEiBG88cYbALZ5uEFBQWzevJnMzExatGhBaGgogwYNwtfXFyenG/+IKVWqFL/88gsVKlTgqaeeokaNGvTs2ZNLly5pJFdEborFuHyHgYiI5KsnnniCd955h+rVqzu6K7ds/vz5dO/enQsXLlx3TrGIiCNozq2ISD47c+YMycnJuLq6smrVqiIVbr/88ktCQkK488472bt3L8OHD6djx44KtiJSaGlagohIPtu7dy81a9Zk586dNGvWzNHduSlxcXE8++yz1KhRg8GDB/P0008za9YsR3dLRCRHmpYgIiIiIsWGRm5FREREpNhQuBURERGRYkPhVkRERESKDYVbERERESk2FG5FREREpNhQuBURERGRYkPhVkRERESKDYVbERERESk2/h8/xRCVt6bI7AAAAABJRU5ErkJggg==\n"},"metadata":{}}]},{"cell_type":"code","source":["# Örnek bir yeni veri seti oluşturalım (uygun veri yerine kendi verilerinizi eklemelisiniz)\n","# Adj Close: 194.71, 193.18, 195.71 166.67\n","new_data = {'Open': [193.08, 193.11\t, 194.20, 170.37],\n","            'High': [194.72, 193.49, 195.99, 171.38],\n","            'Low': [191.72, 191.42, 193.67, 165.67],\n","            'Close': [194.71, 193.18, 195.71, 166.89],\n","            'Volume': [52.696, 60.943, 53.377, 70.625]}\n","\n","new_df = pd.DataFrame(new_data)\n","\n","# Sabit (intercept) ekleyerek yeni veri setini hazırlayalım\n","new_df = sm.add_constant(new_df)\n","\n","# Modeli kullanarak tahminler yapalım\n","predictions_new = model.predict(new_df)\n","\n","# Tahminleri ekrana yazdıralım\n","print(\"Yeni Veri Seti Tahminleri:\")\n","print(predictions_new)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kRIUfQF8qz8z","executionInfo":{"status":"ok","timestamp":1703920941303,"user_tz":-180,"elapsed":3,"user":{"displayName":"Hüseyin ADIGÜZEL","userId":"13500403158658555803"}},"outputId":"56a28574-731e-4ec1-900e-0671e4630550"},"execution_count":51,"outputs":[{"output_type":"stream","name":"stdout","text":["Yeni Veri Seti Tahminleri:\n","0    193.805413\n","1    192.290976\n","2    194.855863\n","3    165.752779\n","dtype: float64\n"]}]}]}