Forbes_31_01_23.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"yyFLgL7gY4W-"},"source":["## Fuente:\n","\n","[Forbes World's Billionaires List 2022](https://www.kaggle.com/datasets/prasertk/forbes-worlds-billionaires-list-2022)\n","\n","Es la lista Forbes de personas más ricas del mundo de 2022."]},{"cell_type":"markdown","metadata":{"id":"FFZg1it8ebFg"},"source":["---"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qIWiNgaqquY6","outputId":"85ed7991-c9e4-491f-e20a-9b6bb5605b28","executionInfo":{"status":"ok","timestamp":1675876154434,"user_tz":-60,"elapsed":4490,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: factor_analyzer in /usr/local/lib/python3.8/dist-packages (0.4.1)\n","Requirement already 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pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from pre-commit->factor_analyzer) (6.0)\n","Requirement already satisfied: identify>=1.0.0 in /usr/local/lib/python3.8/dist-packages (from pre-commit->factor_analyzer) (2.5.17)\n","Requirement already satisfied: nodeenv>=0.11.1 in /usr/local/lib/python3.8/dist-packages (from pre-commit->factor_analyzer) (1.7.0)\n","Requirement already satisfied: virtualenv>=20.10.0 in /usr/local/lib/python3.8/dist-packages (from pre-commit->factor_analyzer) (20.19.0)\n","Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn->factor_analyzer) (3.1.0)\n","Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.8/dist-packages (from scikit-learn->factor_analyzer) (1.2.0)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.8/dist-packages (from nodeenv>=0.11.1->pre-commit->factor_analyzer) (57.4.0)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas->factor_analyzer) (1.15.0)\n","Requirement already satisfied: platformdirs<4,>=2.4 in /usr/local/lib/python3.8/dist-packages (from virtualenv>=20.10.0->pre-commit->factor_analyzer) (2.6.2)\n","Requirement already satisfied: distlib<1,>=0.3.6 in /usr/local/lib/python3.8/dist-packages (from virtualenv>=20.10.0->pre-commit->factor_analyzer) (0.3.6)\n","Requirement already satisfied: filelock<4,>=3.4.1 in /usr/local/lib/python3.8/dist-packages (from virtualenv>=20.10.0->pre-commit->factor_analyzer) (3.9.0)\n"]}],"source":["!pip install factor_analyzer"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"O9fb76AQXMXr","outputId":"828262fe-d965-4e73-9ffd-a994b0f07ed5","executionInfo":{"status":"ok","timestamp":1675876158461,"user_tz":-60,"elapsed":4035,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Using matplotlib backend: agg\n","Populating the interactive namespace from numpy and matplotlib\n"]}],"source":["import pandas as pd\n","from pandas.plotting import scatter_matrix\n","import numpy as np \n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","\n","from scipy.stats import shapiro\n","from sklearn.preprocessing import LabelEncoder, StandardScaler\n","from sklearn.model_selection import train_test_split, GridSearchCV\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.tree import DecisionTreeRegressor\n","from sklearn.neighbors import KNeighborsRegressor\n","from sklearn import metrics\n","from sklearn.metrics import confusion_matrix, accuracy_score, recall_score, precision_score\n","\n","from sklearn.cluster import KMeans\n","from sklearn.datasets import make_blobs\n","from sklearn.cluster import AgglomerativeClustering\n","from scipy.cluster.hierarchy import dendrogram\n","\n","from sklearn.decomposition import PCA\n","\n","from factor_analyzer import FactorAnalyzer\n","from factor_analyzer.factor_analyzer import calculate_kmo\n","\n","from keras.models import Sequential\n","from keras.layers.core import Dense\n","from tensorflow.keras.optimizers import SGD, Adam\n","\n","%pylab\n","%matplotlib inline\n","\n","%config InlineBackend.figure_format = 'retina'"]},{"cell_type":"code","source":["from sklearn.metrics import confusion_matrix\n","from sklearn import metrics\n","from sklearn.metrics import r2_score\n","from sklearn.metrics import mean_squared_error"],"metadata":{"id":"7lTSmEIo2ZXd"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":733},"id":"0Dee1wTFW9SF","outputId":"e2e9b479-3ce6-49a7-c5b6-ada3df154cbc","executionInfo":{"status":"ok","timestamp":1675876158461,"user_tz":-60,"elapsed":86,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   rank                personName   age  finalWorth  year  month  \\\n","0     1                 Elon Musk  50.0    219000.0  2022      4   \n","1     2                Jeff Bezos  58.0    171000.0  2022      4   \n","2     3  Bernard Arnault & family  73.0    158000.0  2022      4   \n","3     4                Bill Gates  66.0    129000.0  2022      4   \n","4     5            Warren Buffett  91.0    118000.0  2022      4   \n","\n","                category              source        country       state  ...  \\\n","0             Automotive       Tesla, SpaceX  United States       Texas  ...   \n","1             Technology              Amazon  United States  Washington  ...   \n","2       Fashion & Retail                LVMH         France         NaN  ...   \n","3             Technology           Microsoft  United States  Washington  ...   \n","4  Finance & Investments  Berkshire Hathaway  United States    Nebraska  ...   \n","\n","  selfMade gender   birthDate             title philanthropyScore  \\\n","0     True      M  1971-06-28               CEO               1.0   \n","1     True      M  1964-01-12      Entrepreneur               1.0   \n","2    False      M  1949-03-05  Chairman and CEO               NaN   \n","3     True      M  1955-10-28         Cofounder               4.0   \n","4     True      M  1930-08-30               CEO               5.0   \n","\n","                  residenceMsa numberOfSiblings  \\\n","0                          NaN              NaN   \n","1  Seattle-Tacoma-Bellevue, WA              NaN   \n","2                          NaN              NaN   \n","3  Seattle-Tacoma-Bellevue, WA              NaN   \n","4                    Omaha, NE              NaN   \n","\n","                                                 bio  \\\n","0  Elon Musk is working to revolutionize transpor...   \n","1  Jeff Bezos founded e-commerce giant Amazon in ...   \n","2  Bernard Arnault oversees the LVMH empire of so...   \n","3  Bill Gates turned his fortune from software fi...   \n","4  Known as the \"Oracle of Omaha,\" Warren Buffett...   \n","\n","                                               about  Unnamed: 22  \n","0  Musk was accepted to a graduate program at Sta...          NaN  \n","1  Growing up, Jeff Bezos worked summers on his g...          NaN  \n","2  Arnault apparently wooed his wife, Helene Merc...          NaN  \n","3  When Gates was a kid, he spent so much time re...          NaN  \n","4  Buffett still lives in the same Omaha, Nebrask...          NaN  \n","\n","[5 rows x 23 columns]"],"text/html":["\n","  <div id=\"df-0e7d09e1-ac65-4d9d-822b-3fbcd66ce6e2\">\n","    <div 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>rank</th>\n","      <th>personName</th>\n","      <th>age</th>\n","      <th>finalWorth</th>\n","      <th>year</th>\n","      <th>month</th>\n","      <th>category</th>\n","      <th>source</th>\n","      <th>country</th>\n","      <th>state</th>\n","      <th>...</th>\n","      <th>selfMade</th>\n","      <th>gender</th>\n","      <th>birthDate</th>\n","      <th>title</th>\n","      <th>philanthropyScore</th>\n","      <th>residenceMsa</th>\n","      <th>numberOfSiblings</th>\n","      <th>bio</th>\n","      <th>about</th>\n","      <th>Unnamed: 22</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>Elon Musk</td>\n","      <td>50.0</td>\n","      <td>219000.0</td>\n","      <td>2022</td>\n","      <td>4</td>\n","      <td>Automotive</td>\n","      <td>Tesla, SpaceX</td>\n","      <td>United States</td>\n","      <td>Texas</td>\n","      <td>...</td>\n","      <td>True</td>\n","      <td>M</td>\n","      <td>1971-06-28</td>\n","      <td>CEO</td>\n","      <td>1.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>Elon Musk is working to revolutionize transpor...</td>\n","      <td>Musk was accepted to a graduate program at Sta...</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>Jeff Bezos</td>\n","      <td>58.0</td>\n","      <td>171000.0</td>\n","      <td>2022</td>\n","      <td>4</td>\n","      <td>Technology</td>\n","      <td>Amazon</td>\n","      <td>United States</td>\n","      <td>Washington</td>\n","      <td>...</td>\n","      <td>True</td>\n","      <td>M</td>\n","      <td>1964-01-12</td>\n","      <td>Entrepreneur</td>\n","      <td>1.0</td>\n","      <td>Seattle-Tacoma-Bellevue, WA</td>\n","      <td>NaN</td>\n","      <td>Jeff Bezos founded e-commerce giant Amazon in ...</td>\n","      <td>Growing up, Jeff Bezos worked summers on his g...</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>Bernard Arnault &amp; family</td>\n","      <td>73.0</td>\n","      <td>158000.0</td>\n","      <td>2022</td>\n","      <td>4</td>\n","      <td>Fashion &amp; Retail</td>\n","      <td>LVMH</td>\n","      <td>France</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>M</td>\n","      <td>1949-03-05</td>\n","      <td>Chairman and CEO</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>Bernard Arnault oversees the LVMH empire of so...</td>\n","      <td>Arnault apparently wooed his wife, Helene Merc...</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>Bill Gates</td>\n","      <td>66.0</td>\n","      <td>129000.0</td>\n","      <td>2022</td>\n","      <td>4</td>\n","      <td>Technology</td>\n","      <td>Microsoft</td>\n","      <td>United States</td>\n","      <td>Washington</td>\n","      <td>...</td>\n","      <td>True</td>\n","      <td>M</td>\n","      <td>1955-10-28</td>\n","      <td>Cofounder</td>\n","      <td>4.0</td>\n","      <td>Seattle-Tacoma-Bellevue, WA</td>\n","      <td>NaN</td>\n","      <td>Bill Gates turned his fortune from software fi...</td>\n","      <td>When Gates was a kid, he spent so much time re...</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>Warren Buffett</td>\n","      <td>91.0</td>\n","      <td>118000.0</td>\n","      <td>2022</td>\n","      <td>4</td>\n","      <td>Finance &amp; Investments</td>\n","      <td>Berkshire Hathaway</td>\n","      <td>United States</td>\n","      <td>Nebraska</td>\n","      <td>...</td>\n","      <td>True</td>\n","      <td>M</td>\n","      <td>1930-08-30</td>\n","      <td>CEO</td>\n","      <td>5.0</td>\n","      <td>Omaha, NE</td>\n","      <td>NaN</td>\n","      <td>Known as the \"Oracle of Omaha,\" Warren Buffett...</td>\n","      <td>Buffett still lives in the same Omaha, Nebrask...</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 23 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e7d09e1-ac65-4d9d-822b-3fbcd66ce6e2')\"\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 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\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","    [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-0e7d09e1-ac65-4d9d-822b-3fbcd66ce6e2 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Puesto en la lista\n","# personName - El nombre de la persona\n","# year - El año (siempre 2022)\n","# month - El mes\n","# birthDate - Fecha de nacimiento\n","# bio - Texto biográfico\n","# about - Texto informativo\n","##\n","forbes.drop(columns=['rank', 'personName', 'year', 'month', 'birthDate', 'bio', 'about'], inplace=True)"]},{"cell_type":"markdown","metadata":{"id":"rG52NJGedzWG"},"source":["### Comprobamos que el dataset sin las columnas anteriores cumple las condiciones:"]},{"cell_type":"markdown","metadata":{"id":"G0qxZsxjgHeo"},"source":["#### Condición 1 - Al menos 30 filas X 10 columnas"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"i7kv4Jqmdx7b","outputId":"b21aae13-175a-4dc5-903a-df9e79020304","executionInfo":{"status":"ok","timestamp":1675876158462,"user_tz":-60,"elapsed":81,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(365, 16)"]},"metadata":{},"execution_count":7}],"source":["# El dataset tiene 2668 filas X 15 columnas\n","forbes.shape"]},{"cell_type":"markdown","metadata":{"id":"ZuM2k8eIgPQI"},"source":["#### Condición 2 - Una columna de tipo object debe tener 2 opciones"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"D6kIM3sHeG8_","outputId":"4cd9edb5-f378-4c18-ce5f-b26c0e73e12f","executionInfo":{"status":"ok","timestamp":1675876158463,"user_tz":-60,"elapsed":80,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["age                     float64\n","finalWorth              float64\n","category                 object\n","source                   object\n","country                  object\n","state                    object\n","city                     object\n","countryOfCitizenship     object\n","organization             object\n","selfMade                   bool\n","gender                   object\n","title                    object\n","philanthropyScore       float64\n","residenceMsa             object\n","numberOfSiblings        float64\n","Unnamed: 22              object\n","dtype: object"]},"metadata":{},"execution_count":8}],"source":["# Comprobamos los tipos de dato\n","forbes.dtypes"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MhV12QdEaE3r","outputId":"2eab1fed-2728-4f99-988c-dee0e46c63b4","executionInfo":{"status":"ok","timestamp":1675876158463,"user_tz":-60,"elapsed":78,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[ True False]\n"]}],"source":["# La columna \"selfMade\" (que determina si el rico en cuestión \"se hizo a sí mismo\") tiene 2 opciones, pero es de tipo boolean\n","print(forbes['selfMade'].unique())"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cJycRpllfVWJ","outputId":"ed9e6050-5a48-4835-9b95-47d594cb2d4b","executionInfo":{"status":"ok","timestamp":1675876158463,"user_tz":-60,"elapsed":76,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['M', 'F', nan], dtype=object)"]},"metadata":{},"execution_count":10}],"source":["# La columna \"gender\" (género) es de tipo object, pero contiene valores nulos.\n","forbes['gender'].unique()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1plaOjh9hmMl","outputId":"f3ea56a7-450a-4463-faad-d570fc9826ed","executionInfo":{"status":"ok","timestamp":1675876158463,"user_tz":-60,"elapsed":74,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["6"]},"metadata":{},"execution_count":11}],"source":["# Queremos eliminar las filas que contienen estos valores nulos, pero antes comprobamos que no sean demasiadas filas para no cargarnos el dataset\n","nan_gender = forbes[~forbes['gender'].isin(['M', 'F'])]\n","\n","len(nan_gender)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ruZLAKjwe02j"},"outputs":[],"source":["# Como son solo 16, las eliminamos\n","forbes.drop(nan_gender.index, inplace=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4nx38ajYhhYA","outputId":"d1a5197c-23e1-49bc-ee9e-43dc4a3edfaa","executionInfo":{"status":"ok","timestamp":1675876158464,"user_tz":-60,"elapsed":72,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['M', 'F'], dtype=object)"]},"metadata":{},"execution_count":13}],"source":["# Ahora sí tenemos una columna tipo object con 2 valores\n","forbes['gender'].unique()"]},{"cell_type":"markdown","metadata":{"id":"DWS5PSuliInW"},"source":["#### Condición 3 - Otras 2 columnas de tipo object deben tener 3 o más opciones"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mA2eyL67iOtC","outputId":"970eadf4-9143-4454-e4f8-8bd4fc264548","executionInfo":{"status":"ok","timestamp":1675876158464,"user_tz":-60,"elapsed":70,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['Automotive', 'Technology', 'Fashion & Retail',\n","       'Finance & Investments', 'Diversified', 'Media & Entertainment',\n","       'Telecom', 'Food & Beverage', 'Logistics', 'Real Estate',\n","       'Metals & Mining', 'Manufacturing', 'Gambling & Casinos',\n","       'Healthcare', 'Service', 'Energy', 'Construction & Engineering',\n","       'Sports'], dtype=object)"]},"metadata":{},"execution_count":14}],"source":["# Probamos con 'category' (que determina la industria a la que pertenece el rico en cuestión)\n","forbes['category'].unique()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ujkE0NMWinwp","outputId":"0fcc2739-df81-4388-d0f4-0fe0c5241a1e","executionInfo":{"status":"ok","timestamp":1675876158464,"user_tz":-60,"elapsed":68,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['United States', 'France', 'India', 'Mexico', 'China', 'Singapore',\n","       'Spain', 'Canada', 'Germany', 'Switzerland', 'Belgium',\n","       'Hong Kong', 'United Kingdom', 'Australia', 'Austria', 'Italy',\n","       'Japan', 'Bahamas', 'Indonesia', nan, 'Chile', 'Russia', 'Sweden',\n","       'Czechia', 'Monaco', 'United Arab Emirates', 'Nigeria', 'Denmark',\n","       'Thailand', 'Malaysia', 'Brazil', 'Colombia', 'New Zealand',\n","       'South Korea', 'South Africa', 'Philippines', 'Egypt', 'Taiwan'],\n","      dtype=object)"]},"metadata":{},"execution_count":15}],"source":["# Probamos con 'country'\n","forbes['country'].unique()"]},{"cell_type":"markdown","metadata":{"id":"ODg7YvDFi9t1"},"source":["#### Condición 4 - Al menos 2 columnas deben contener nulos."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"N2-VHBl0jAVj","outputId":"64138c13-6268-48d7-e1bd-39e6a60443aa","executionInfo":{"status":"ok","timestamp":1675876158464,"user_tz":-60,"elapsed":66,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["age                      True\n","finalWorth              False\n","category                False\n","source                  False\n","country                  True\n","state                    True\n","city                     True\n","countryOfCitizenship    False\n","organization             True\n","selfMade                False\n","gender                  False\n","title                    True\n","philanthropyScore        True\n","residenceMsa             True\n","numberOfSiblings         True\n","Unnamed: 22              True\n","dtype: bool"]},"metadata":{},"execution_count":16}],"source":["# Comprobamos si hay nulos en las columnas, vemos que hay unas cuantas\n","forbes.isnull().any()"]},{"cell_type":"markdown","metadata":{"id":"QawdwVe5jgOP"},"source":["## 2.- Transforma las columnas object a categóricas y posteriormente a numéricas. Una columna categórica de dos opciones a columnas dummy y elimina dicha columna categórica. Una columna categórica de mas de dos opciones mediante cat codes. La otra columna categórica de mas de dos opciones mediante LabelEncoder."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qFwMVlBhjqSp","outputId":"a77e0950-88cb-45d4-9d9c-96d56bc3f5f6","executionInfo":{"status":"ok","timestamp":1675876158465,"user_tz":-60,"elapsed":64,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["age                     float64\n","finalWorth              float64\n","category                 object\n","source                   object\n","country                  object\n","state                    object\n","city                     object\n","countryOfCitizenship     object\n","organization             object\n","selfMade                   bool\n","gender                   object\n","title                    object\n","philanthropyScore       float64\n","residenceMsa             object\n","numberOfSiblings        float64\n","Unnamed: 22              object\n","dtype: object"]},"metadata":{},"execution_count":17}],"source":["# Volvemos a mostrar los tipos de dato\n","forbes.dtypes"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HeJjnkP4jwAG","outputId":"ada34a9c-a317-4682-819e-d71700532251","executionInfo":{"status":"ok","timestamp":1675876158465,"user_tz":-60,"elapsed":58,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Index(['category', 'source', 'country', 'state', 'city',\n","       'countryOfCitizenship', 'organization', 'gender', 'title',\n","       'residenceMsa', 'Unnamed: 22'],\n","      dtype='object')"]},"metadata":{},"execution_count":18}],"source":["# Estas son nuestras columnas object\n","cat_cols = forbes.select_dtypes(include=\"object\").columns\n","\n","cat_cols"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_g4daBrZkSCt","outputId":"ca478a3c-6c5a-4851-dee0-860460cb528f","executionInfo":{"status":"ok","timestamp":1675876158465,"user_tz":-60,"elapsed":55,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["age                      float64\n","finalWorth               float64\n","category                category\n","source                  category\n","country                 category\n","state                   category\n","city                    category\n","countryOfCitizenship    category\n","organization            category\n","selfMade                    bool\n","gender                  category\n","title                   category\n","philanthropyScore        float64\n","residenceMsa            category\n","numberOfSiblings         float64\n","Unnamed: 22             category\n","dtype: object"]},"metadata":{},"execution_count":19}],"source":["# Transformamos todas las columnas object a categóricas\n","for col in cat_cols:\n","  forbes[col] = pd.Categorical(forbes[col])\n","\n","forbes.dtypes"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":374},"id":"isxk9GN9kuXM","outputId":"4680a482-fc0e-48be-b7d5-4eb744b9b343","executionInfo":{"status":"ok","timestamp":1675876158955,"user_tz":-60,"elapsed":543,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["  gender             title  philanthropyScore                 residenceMsa  \\\n","0      M               CEO                1.0                          NaN   \n","1      M      Entrepreneur                1.0  Seattle-Tacoma-Bellevue, WA   \n","2      M  Chairman and CEO                NaN                          NaN   \n","3      M         Cofounder                4.0  Seattle-Tacoma-Bellevue, WA   \n","4      M               CEO                5.0                    Omaha, NE   \n","\n","   numberOfSiblings Unnamed: 22  gender_F  gender_M  \n","0               NaN         NaN         0         1  \n","1               NaN         NaN         0         1  \n","2               NaN         NaN         0         1  \n","3               NaN         NaN         0         1  \n","4               NaN         NaN         0         1  "],"text/html":["\n","  <div id=\"df-1ac02c8f-9834-44a6-99c2-38cc2ca3d320\">\n","    <div 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 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Melinda Gates Foundation</td>\n","      <td>True</td>\n","      <td>Cofounder</td>\n","      <td>4.0</td>\n","      <td>Seattle-Tacoma-Bellevue, WA</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Omaha</td>\n","      <td>United States</td>\n","      <td>Berkshire Hathaway</td>\n","      <td>True</td>\n","      <td>CEO</td>\n","      <td>5.0</td>\n","      <td>Omaha, NE</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b27954f6-600f-4d4e-8d13-53001062e408')\"\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 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\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","    [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-b27954f6-600f-4d4e-8d13-53001062e408 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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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","  </div>\n","  "]},"metadata":{},"execution_count":21}],"source":["# Eliminamos la columna 'gender'\n","forbes.drop(columns=['gender'], inplace=True)\n","\n","forbes.iloc[:, 6:].head()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"U1hoeeuEm5Ne","outputId":"79e985b4-61e8-4500-b5d6-7d38bdc7b016","executionInfo":{"status":"ok","timestamp":1675876158956,"user_tz":-60,"elapsed":76,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0     0\n","1    16\n","2     4\n","3    16\n","4     5\n","Name: category, dtype: int8"]},"metadata":{},"execution_count":22}],"source":["# Una columna categórica de más de 2 opciones mediante cat codes\n","forbes['category'] = forbes['category'].cat.codes\n","\n","forbes['category'].head()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"K5EWCFlbnOmY","outputId":"19ae4a2b-f2d1-4708-bf4c-c5b9fd878c50","executionInfo":{"status":"ok","timestamp":1675876158956,"user_tz":-60,"elapsed":66,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0    36\n","1    36\n","2    12\n","3    36\n","4    36\n","Name: country, dtype: int64"]},"metadata":{},"execution_count":23}],"source":["# La otra columna categórica de más de 2 opciones mediante LabelEncoder\n","label_encoder = LabelEncoder()\n","\n","forbes['country'] = label_encoder.fit_transform(forbes['country'])\n","\n","forbes['country'].head()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eQxNcOZXnzie","outputId":"b0974c3a-e5bd-416a-b458-4cd9767125fd","executionInfo":{"status":"ok","timestamp":1675876158956,"user_tz":-60,"elapsed":54,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["age                     float64\n","finalWorth              float64\n","category                   int8\n","source                    int64\n","country                   int64\n","state                     int64\n","city                      int64\n","countryOfCitizenship      int64\n","organization              int64\n","selfMade                  int64\n","title                     int64\n","philanthropyScore       float64\n","residenceMsa              int64\n","numberOfSiblings        float64\n","Unnamed: 22               int64\n","gender_F                  uint8\n","gender_M                  uint8\n","dtype: object"]},"metadata":{},"execution_count":24}],"source":["# Ahora vamos a pasar el resto de columnas a numéricas también con LabelEncoder\n","cat_cols_ = forbes.select_dtypes(exclude=['number']).columns\n","\n","for col in cat_cols_:\n","  forbes[col] = label_encoder.fit_transform(forbes[col])\n","\n","forbes.dtypes"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":287},"id":"Or8ZYSB1okRW","outputId":"a26f8ab9-3297-40a2-da0a-58b84a3b534c","executionInfo":{"status":"ok","timestamp":1675876158956,"user_tz":-60,"elapsed":51,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["    age  finalWorth  category  source  country  state  city  \\\n","0  50.0    219000.0         0      31       36     25     8   \n","1  58.0    171000.0        16       2       36     27   140   \n","2  73.0    158000.0         4      22       12     30   123   \n","3  66.0    129000.0        16      24       36     27    96   \n","4  91.0    118000.0         5       6       36     17   119   \n","\n","   countryOfCitizenship  organization  selfMade  title  philanthropyScore  \\\n","0                    39            92         1      0                1.0   \n","1                    39             4         1     21                1.0   \n","2                    12            65         0      8                NaN   \n","3                    39            15         1     15                4.0   \n","4                    39            14         1      0                5.0   \n","\n","   residenceMsa  numberOfSiblings  Unnamed: 22  gender_F  gender_M  \n","0            45               NaN            1         0         1  \n","1            39               NaN            1         0         1  \n","2            45               NaN            1         0         1  \n","3            39               NaN            1         0         1  \n","4            28               NaN            1         0         1  "],"text/html":["\n","  <div id=\"df-9d9a007f-3c6c-4367-b020-d2bb0289fe16\">\n","    <div 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>age</th>\n","      <th>finalWorth</th>\n","      <th>category</th>\n","      <th>source</th>\n","      <th>country</th>\n","      <th>state</th>\n","      <th>city</th>\n","      <th>countryOfCitizenship</th>\n","      <th>organization</th>\n","      <th>selfMade</th>\n","      <th>title</th>\n","      <th>philanthropyScore</th>\n","      <th>residenceMsa</th>\n","      <th>numberOfSiblings</th>\n","      <th>Unnamed: 22</th>\n","      <th>gender_F</th>\n","      <th>gender_M</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>50.0</td>\n","      <td>219000.0</td>\n","      <td>0</td>\n","      <td>31</td>\n","      <td>36</td>\n","      <td>25</td>\n","      <td>8</td>\n","      <td>39</td>\n","      <td>92</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1.0</td>\n","      <td>45</td>\n","      <td>NaN</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>58.0</td>\n","      <td>171000.0</td>\n","      <td>16</td>\n","      <td>2</td>\n","      <td>36</td>\n","      <td>27</td>\n","      <td>140</td>\n","      <td>39</td>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>21</td>\n","      <td>1.0</td>\n","      <td>39</td>\n","      <td>NaN</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>73.0</td>\n","      <td>158000.0</td>\n","      <td>4</td>\n","      <td>22</td>\n","      <td>12</td>\n","      <td>30</td>\n","      <td>123</td>\n","      <td>12</td>\n","      <td>65</td>\n","      <td>0</td>\n","      <td>8</td>\n","      <td>NaN</td>\n","      <td>45</td>\n","      <td>NaN</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>66.0</td>\n","      <td>129000.0</td>\n","      <td>16</td>\n","      <td>24</td>\n","      <td>36</td>\n","      <td>27</td>\n","      <td>96</td>\n","      <td>39</td>\n","      <td>15</td>\n","      <td>1</td>\n","      <td>15</td>\n","      <td>4.0</td>\n","      <td>39</td>\n","      <td>NaN</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>91.0</td>\n","      <td>118000.0</td>\n","      <td>5</td>\n","      <td>6</td>\n","      <td>36</td>\n","      <td>17</td>\n","      <td>119</td>\n","      <td>39</td>\n","      <td>14</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>5.0</td>\n","      <td>28</td>\n","      <td>NaN</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9d9a007f-3c6c-4367-b020-d2bb0289fe16')\"\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 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 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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","  </div>\n","  "]},"metadata":{},"execution_count":25}],"source":["forbes.head()"]},{"cell_type":"markdown","metadata":{"id":"xTthUv4ro7D6"},"source":["## 3.- Rellena los nulos"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WDLCFALXo9iu","outputId":"8b06b4ab-3224-4bb8-ddb8-5dc9f11ddf26","executionInfo":{"status":"ok","timestamp":1675876158957,"user_tz":-60,"elapsed":51,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["age                      1.671309\n","finalWorth               0.000000\n","category                 0.000000\n","source                   0.000000\n","country                  0.000000\n","state                    0.000000\n","city                     0.000000\n","countryOfCitizenship     0.000000\n","organization             0.000000\n","selfMade                 0.000000\n","title                    0.000000\n","philanthropyScore       71.309192\n","residenceMsa             0.000000\n","numberOfSiblings        94.707521\n","Unnamed: 22              0.000000\n","gender_F                 0.000000\n","gender_M                 0.000000\n","dtype: float64"]},"metadata":{},"execution_count":26}],"source":["# Comprobamos la proporción de nulos en cada columna\n","forbes.isnull().sum() / forbes.shape[0] * 100"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"E3Z9cIQGqPgX","outputId":"b168347d-927b-408d-daf4-3f90eca0fb8e","executionInfo":{"status":"ok","timestamp":1675876158957,"user_tz":-60,"elapsed":48,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["age                     1.671309\n","finalWorth              0.000000\n","category                0.000000\n","source                  0.000000\n","country                 0.000000\n","state                   0.000000\n","city                    0.000000\n","countryOfCitizenship    0.000000\n","organization            0.000000\n","selfMade                0.000000\n","title                   0.000000\n","residenceMsa            0.000000\n","Unnamed: 22             0.000000\n","gender_F                0.000000\n","gender_M                0.000000\n","dtype: float64"]},"metadata":{},"execution_count":27}],"source":["# Como vemos que hay un par de columnas con una proporción muy alta de nulos, vamos a eliminarlas\n","# Escogemos un umbral de 50% de nulos, si en una columna hay más de eso, la quitamos\n","forbes.dropna(axis=1, thresh=forbes.shape[0]*0.5, how='all', inplace=True)\n","\n","forbes.isnull().sum() / forbes.shape[0] * 100"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"B24LeQ8arFf8","outputId":"5f7cd7e3-dab1-4a1a-8216-b14f64d3f2ca","executionInfo":{"status":"ok","timestamp":1675876158957,"user_tz":-60,"elapsed":44,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["age                     0\n","finalWorth              0\n","category                0\n","source                  0\n","country                 0\n","state                   0\n","city                    0\n","countryOfCitizenship    0\n","organization            0\n","selfMade                0\n","title                   0\n","residenceMsa            0\n","Unnamed: 22             0\n","gender_F                0\n","gender_M                0\n","dtype: int64"]},"metadata":{},"execution_count":28}],"source":["# Ahora rellenamos los nulos de la columna 'age' con la media de los valores de esa columna (la media de edad)\n","mean_age = float(int(forbes['age'].mean()))\n","forbes['age'].fillna(mean_age, inplace=True)\n","\n","# Ya no hay datos nulos en todo el dataset\n","forbes.isnull().sum()"]},{"cell_type":"markdown","metadata":{"id":"hzUaMdT0y3kk"},"source":["## 4.- Dibuja el histograma de cada columna"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"3kl1fYW138K5","outputId":"72bc2987-9042-4769-a3f2-0e2407799118","executionInfo":{"status":"ok","timestamp":1675876167372,"user_tz":-60,"elapsed":8456,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n","  warnings.warn(msg, FutureWarning)\n","/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n","  warnings.warn(msg, FutureWarning)\n","/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n","  warnings.warn(msg, FutureWarning)\n","/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n","  warnings.warn(msg, FutureWarning)\n","/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n","  warnings.warn(msg, FutureWarning)\n","/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n","  warnings.warn(msg, FutureWarning)\n","/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n","  warnings.warn(msg, FutureWarning)\n","/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n","  warnings.warn(msg, FutureWarning)\n","/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n","  warnings.warn(msg, FutureWarning)\n","/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n","  warnings.warn(msg, FutureWarning)\n","/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n","  warnings.warn(msg, FutureWarning)\n","/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n","  warnings.warn(msg, FutureWarning)\n","/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n","  warnings.warn(msg, FutureWarning)\n","/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n","  warnings.warn(msg, FutureWarning)\n","/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n","  warnings.warn(msg, FutureWarning)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 2160x2160 with 15 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GfQG9fqDVnVNUBky8zJREffSK6e78J2sPKkC3MgAA/sKQz+8uDEvwOMpMjr19cZITvCrICBbLBYzU0jtkOREAAAAAAJFvzd4mffSRje77BrlpifrY1eVaXpE/rgVl4a6cWaD//oflKswMbhgz7A/o009t0Sv7ms9yJAAAiDehjmFLJT2qYEHXFyXNkPRjScuNMefU8jR0vxWSHpQ0M3SeZZIekbQk9Djj8tgAzqype1BvHGxx57fOL1F6DF7bBIBosmJGgVuhf6C5Vy09rMcAToeiMgCII5tq2tU75JMkTclNVWURLbYBG+67slxpSV5J0t7GHr28l4vMAACcrDO0k58k5aQlTdiisIlWmp3qjus7By0mAQAAAAAg8v15Z6M+/qvNGvIFJElFmcn69T+sUFl++oRnmTcpW8984gqVFwQfe8Rv9MDjm7Wxun3CswAAgMhmjDlmjLnPGFNqjEkyxpQZYz5njOk4xX0dY8wpL4oYY9qNMZ8NHZ8UOt/fGWNqx+Kxz3COj4ZyvXSuxwDxwhij57bX6/h+0tPz07SkLNduKACA8tKTNLsk052/dZhaeuB0KCoDgDgSXrhyfVVRzC7MBSJdbnqS7glr8f3Q2oMyhm5lAACEa+8bdse5aYkWk4yvkuwUd9zYRVEZAAAAAACns+5gqz7z1Fb5Qqs1p+Sm6ukHrlBlceZZjhw/U/PS9OTfL9PknOCmMYMjAd3/6NvaWddlLRMAAACAibOnoUeHWvokSY6k91wymTV5ABAhVszId8dbjnZoaMRvMQ0QuSgqA4A4sjasqOy6qiKLSQD8/dUVSkoIPhXbdqxT6w6yEwYAAOE6+sOKytKTLCYZX5PCisoaugYsJgEAAAAAIHJtO9apj/9qk4b9wQ5l5QXpevqBKzQtP81ysmAX8sc/tkwFGcmSpJ4hn/72Fxt1rL3fcjIAAAAA48nnD+j5nQ3u/PLyPJVkpZzhCADARJpZmKHC0Ps1Q76Athw950atQFxJsB0AADAxalr7dLg1uCtKaqJXKyryz3IEgPFUlJWiu5dO0eNvHZUkPbT2gK6qLLCcCgCAyNHZP+KOY7tTWao7bqBTGQAAAAAgBjy54egFHfehZdNOefvB5l599JGN6hsO7iZdkpWix+6//ITu37aVF6Tr8Y9drr/+r7fUNTCi9r5h/f2vNum3n7hC6cksywAAAABi0ZuH2tTeF9woMyXRoxvmFFtOBAAI5ziOls/I17Pb6iVJ6w+3a1lFvjx0lAROQKcyAIgTL+1pcsdXzsxXSqLXYhoAkvQP18yQ1xN8gfLW4XZtPtJuOREAAJHj+AUYScpNi+FOZTl0KgMAAAAA4HRae4f0kV9sVEdo85mctEQ9dv/lmpJrv0PZyapKsvTzjyxVkje4DGNvY4++8Ot3FAgYy8kAAAAAjLWewRGt3dfszldWFbOhBABEoMVTc5ScEHyvprV3SIeaey0nAiIPz2AAIE6s2Rv2IjZOd0W50J0xjzvdDpnAhZqal6b3XTpZT2+ulSQ9tOagHrnvcsupAACIDJ39YUVl6bFbVFaSFV5URqcyAAAAAACOG/L59cBjm1XXGdyEJS3Jq0c+epkqizMtJzu9pdPz9O075+tLv90uSVq1q0k/fvmAPn/jLMvJAAAAAIyll/Y0acgXkCQVZiRreUW+5UQAgFNJTvRqcVmu1h9qkyStP9wW0e8tATbQqQwA4kD34Ig2Vo92QLpudpHFNADCfeJdM3S8m/LafS3aWddlNxAAABGivW/EHeemJVpMMr4m5aS644ZOisoAAAAAAJAkY4y+9rud2nSkQ5LkONJ/fPBSXTot13Kys7v7sqn6uyvL3fmPXz6gVbsaLSYCAAAAMJaaewa1qabDnd+2oFRej2MxEQDgTFaUjxb+7mvsUXvf8BnuDcQfisoAIA68tr9FvoCRJM2fnKWS7JSzHAFgoswozNBtC0rd+U/WHrSYBgCAyHFCp7K02O1UVpSV7I6bewbl8wcspgEAAAAAIDL8/I1q/WZzrTv/X7dWaeWcYouJzs9XbqvS1ZUF7vxLT293O64BAAAAiG4v7m6SCY1nFmZodgkdbwAgkhVkJmtWcYYkyUh663Cb3UBAhKGoDADiwJo9ze54ZVX0XHAD4sUn3zXTHf95V6MONvdYTAMAQGRoj5OisuQErwoygt9fwEjNPUOWEwEAAAAAYNe6g636zvN73PkHFk/R319dYTHR+UvwevTQBxdrcqhDedfAiD7z1Fb5A+YsRwIAAACIZMfa+7Wrvtud3zSPtXgAEA1WVIx2K9t0pF3DPjb8BY6jqAwAYpw/YLR2X1hR2Zwii2kAnMrcSVlaWRX8f9MY6eG1hywnAgDAvo7+EXecmx67RWWSVJqd6o4bugYtJgEAAAAAwK6m7kF99v9t1fHaq8XTcvSd98+X4zh2g12A7LREPfjBS+X1BLNvPtKhl/Y0WU4FAAAA4EIZY7RqV6M7nz85W1Ny0ywmAgCcq8riTOWH1p4MjgS09ViH5URA5KCoDABi3NajHe6C3KLMZM2flG05EYBT+eT1o93K/rCtXkfb+i2mAQDAvs4TOpUlWkwy/kqyU9xxQ9eAxSQAAAAAANjj8wf06ae2qrU3+J5AQUayfvrhJUpO8FpOduGWlOXqizfNcuev7W/RgeYei4kAAAAAXKgDzb063NonSfI40k1z6FIGANHC4zhaHtatbP2hNhlDR3lAoqgMAGLey3tHu5RdX1Ukjyf6dnIE4sHiabm6YkbwRYs/YPTT1+hWBgCIb+19o0VleTHeqWxSWFFZI53KAAAAAABx6ocv7tfG6nZJwQWaD37wEhVlpZzlqMj3wDUzdHVlgSTJSHp6c636h312QwEAAAA4LwFjtDqsS9nSsjwVZCZbTAQAOF9LynKV5A2WzzT3DLmFwkC8o6gMAGLcy3ua3PFKdkcBItqnrhvtVvb0ploWlQMA4taIP6CeweDiKo8jZaXEeqeyVHdc38nffwAAAABA/NnX2KOHXxndbO3zN8zSFTMKLCYaOx6Pox/efYkKMoKb5vQM+vTc9gbLqQAAAACcj9313aoPreNJ9Dq6vqrIciIAwPlKSfTq0mk57nz9oTaLaYDIQVEZAMSwY+392t/UK0lKSvDoypn5ZzkCgE0rZuS7L1qG/QH939cPW04EAIAdHWFdynLTkmK+2+6knLBOZd0DFpMAAAAAADDxeod8enpLrTu/ZlahPhm2CVssKMxM1nffv9Cdv3OsUzvruiwmAgAAAHCuAgGjNXub3fnyinxlpcb2ppgAEKuWV4yuo97T0K2O/uEz3BuIDxSVAUAMC38xe8WMfKUlJVhMA+BsHMc5oVvZkxuOqr2PFy0AgPjTFvb3Ly89yWKSiVGSNVpURqcyAAAAAEA8McbomS216hsKdiwvzEzWj+5eFJMbzNw4t1iXTh3dDfv379SpN/R9AwAAAIhcq3c3qrF7tEvZ1ZWFlhMBAC5UcVaKZhSmS5KMpA2H6VYGUF0AADHspT1N7njlnGKLSQCcq+urijSnNEt7Gro1MOLXz984rH+6ucp2LAAAJlR7nBWVTcpJdcf1nXQqAwAAAADEj4017drb2OPOf/BXi5SfkWwx0fi6Y+EkHWrpVfegT/3Dfv1+a53uWTZNjhN7RXSIHE9uOGo7AgAAQNQKBIz+/aUD7nxFRb4ykll6DQDRbEVFgQ619EmS3q7p0Mo5xUr00qsJ8Yt//QAQo3qHfNpwuN2dX19VZDENgHN1creyX715RJ20WAYAxJnwTmX5GbFfVFaSnaLjG7A39wxpcMRvNxAAAAAAABOguWdQz+9ocOf3XTld18yK7R3/U5O8ev/iKe58d0O3dtZ3W0wEAAAA4ExW7Wp0N8JI8np0FV3KACDqVZVmKjctUZI0MOLXtmOdlhMBdlFUBgAx6o0DrRr2ByRJc0qzNDms+wGAyHbL/BLNLMqQJPUM+fTzN6otJwIAYGK19w654/z02N2d/LhEr0el2XQrAwAAAADED3/A6DebajXiN5Kk4qxk/c9bqiynmhizijN12fQ8d/7stnoNDLPBDAAAABBpAgGjH7882qVsOV3KACAmeBxHy8rz3fn6w20yxlhMBNjFsxsAiFEv72lyxyvpUgZEFa/H0WdWVuozT22VJD2yrkb3X1WunLTY79QCAIB0YqeyvPTo+fv35IajF3xsond035/ajgFVFGaMRSQAAAAAACLSq/tbVBfaVMXrcXT30qlKSfRaTjVxbp1for2N3eoZ9Kl3yKcXdjac0MEMAAAAgH2rd5/cpazAciIAwFhZOj1XL+9t0ojfqKFrUDVt/SovSLcdC7CCTmUAEIMCAaO1+5rd+co5FJUB0eb2BaVut7JeupUBAOJMeFFZfkb0FJVdjNy0RHdc20GnMgAAAABA7GroGtDavaPXsW6cU3xCB+94kJLo1XsWTXLnm4506HBLr8VEAAAAAMIZY/STtYfc+fKKPLqUAUAMSUtK0KIpOe58/eE2i2kAuygqA4AYtK22U629wYW4+elJJzzxARAdjncrO+6RdTXq7B8+wxEAAMSO9t7o7FR2MXLDvs+6zn6LSQAAAAAAGD/+gNFvN9fKb4wkaWpuatzu9j9vUrbmlma5899trdOIP2AxEQAAAIDj3jjYqh11XZKk5ASPrpwZn69bACCWrZiR745313epa2DEYhrAHorKACAGrQnb3fG6qiJ5PI7FNAAu1Mndyn72Ot3KAADxob0vDovK6FQGAAAAAIgDr+5vVn3XoCQpwePoA0umyOPE73Ws9yyapOSE4LKNtr5hvbKvxXIiAAAAAJL0n6+Mdim7e+lUZaYknuHeAIBoVJqdqun56ZKkgJE2VNOtDPGJXqwAEINe3jNaVHbDnCKLSQBcDK/H0WdXVurTT22VJD36Zo3uv6r8hE4mAADEora+IXecn55sMcnEyUkb/ftOURkAAAAAIBY1dg9q7d7Roqkb5hSrKDPFYiL7slITdcv8Ev3hnXpJ0usHWrR4Wo7yM+Lj/RAAAAAgEr1zrFNvHgoWFng9jj5+TYVeP9BqORUQXZ7ccNR2BOCcrJiRr5q2PknSppoOXV/FmmvEHzqVAUCMqe8c0O6GbklSktejqyoLLScCcDFuW1CqyrBuZT9/g25lAIDYF5+dysKLyvotJgEAAAAAYOwFjNHvttTKb4wkaWpuqq6qLLCcKjJcNj1PU3JTJUm+gNGz2+tlQj8nAAAAABPv4bUH3fF7Fk3S1Lw0i2kAAONpbmmWslKCfZp6h3zaXd9tOREw8SgqA4AYs2bvaJeyZRV5ykimKSUQzbweR59ZWenOH1lXrY6whfYAAMQanz+gjv4Rd56blmgxzcTJTk2UExo3dQ9pyOe3mgcAAAAAgLH01uE2HQt15vZ6HL1/8RR5HOcsR8UHj+PoPYsmue8L7G/q1Z6GHquZAAAAgHh1oKlHq3c3ufNPvGuGxTQAgPHm9Ti6bHqeO99Q3W4xDWAHRWUAEGNe3jP6onYlbViBmHB7WLeyvmG/fvbGYcuJAAAYPycXlCV44+OtC6/HUXbqaAFdfeegxTQAAAAAAIydzv5hrd41ev3qXbMKVZyVYjFR5JmSm6bLykcXMD23o17DvoDFRAAAAEB8+umro2tybphTrFnFmRbTAAAmwtLpefKEdvupbu3T/iY2+0F8iY+VWQAQJ/qGfFp3qM2dr5xTbDENgLHi8Tj67A2j3coeXVdDtzIAQMxqD/sbl5eeZDHJxMtJG/1+azv6LSYBAAAAAGBsGGP0h3fqNewPFkgVZSbr2lmFllNFppvmFistyStJ6uwf0av7WywnAgAAAOJLU/eg/ritzp3TpQwA4kN2aqLmlGa588ffOmIxDTDxKCoDgBjy2v4Wd9fCqpJMTc1Ls5wIwFi5bf6J3cr+7+t0KwMAxKa2viF3nJ+ebDHJxMtNG+1UVtsxYDEJAAAAAABjY0ddl/aFdnd2JL3v0slx05X8fKUlJejmeSXu/LUDLWrrHTrDEQAAAADG0i/frNGI30iSlpblaklZruVEAICJsrwi3x0/s6VOfUM+i2mAicW7tQAQQ1bvbnLHN4VddAIQ/U7uVvbLN2tO6OQCAECsiOdOZbnpdCoDAAAAAMSOwRG//rSjwZ0vq8hTWX66xUSRb0lZrqbkpkqS/AGjZ7fXyxhjORUAAAAQ+/qHfXpiw1F3/rGrKyymAQBMtIqCdBVkBDc+7h3y6ffv1J3lCCB2UFQGADFixB/Qy3vCisrmFltMA2A83Da/VLOKR7uV/derhywnAgBg7J1QVJYRZ0VldCoDAAAAAMSQl/c0qWcwuKtzZkqCbprLhohn43EcvXfRZDmh+f6mXu1p6LaaCQAAAIgHv9lUq66BEUlSWX6abmTtHQDEFcdxtKw8z50/tv4IG/0gblBUBgAxYmN1u7pDF+Ym56Rq3qQsy4kAjDWPx9Hnbpjlzh99s0aNXYMWEwEAMPbaekeLyvLjrFNZTlp4pzKKygAAAAAA0au+c0BvHmpz57cvKFVKotdiougxOTdVl4ctYnpue4OGfQGLiQAAAIDY5g8Y/WJdtTu//6pyeT3OGY4AAMSixdNylegN/v7f29ijLUc7LCcCJgZFZQAQI1bvanTHN84tluPwwhaIRbfMK9H8ycGi0SFfQA+uOWA5EQAAY6utb8gd58VZUVnuCUVl/RaTAAAAAABw4QLG6I/b6nV8L+eZhRlaMDnbaqZoc+PcYqUlBYvwOgdG9Or+ZsuJAAAAgNj14u4mHWkLXpvLTnbhkf0AACAASURBVE3UXUumWE4EALAhNcmrRVNy3Plj649YTANMHIrKACAGGGO0eneTO7+J9ttAzPJ4HH3p5ip3/t9vH1N1a5/FRAAAjK32vrBOZRnJFpNMvOzURB3fGqKpe0hDPr/VPAAAAAAAXIgtRzp0tD24INPrOHr3oklshnie0pISdMu8Enf+2oFW1XAtAAAAABgXP3v9sDu+Z9k0pSUlWEwDALBpWUW+O35+R6PaeofOcG8gNlBUBgAxYGddtxq6BiUFF6JeVp5nORGA8XR1ZYFWhF68+ANGP1i9z3IiAADGTltvWFFZnHUq83ocZacmuvP6zkGLaQAAAAAAOH8dfcP6865Gd371rAIVZsbXpjFjZXFZrqbmpkoKXgv49p92W04EAAAAxJ5txzq16UiHJCnR6+gjV0y3GwgAYNXknFRdMjXYrWzYH9CvN9VaTgSMP4rKACAGrN49enFuZVWREr38egdimeM4+tIts935c9sbtLOuy2IiAADGTninsrw4KyqTpJy00e+5tqPfYhIAAAAAAM7f91btVf9wsPN2Tlqi3jWryHKi6OU53uUtNH9pT7PW7mu2mgkAAACINb98s8Yd37FwkoqzUuyFAQBEhHuXl7njJzYckT9gLKYBxh9VBwAQA1bvanLHN80rtpgEwES5dFqubpo7+v/7v62iWxkAIDaEF5XFW6cyScpNG+1UVtsxYDEJAAAAAADnZ8vRDj218Zg7f/fCSUpKYEnCxZiSm6YlZbnu/FvP7tawL2AxEQAAABA7WnqG9Nz2Bnf+UbqUAQAk3b6wVDmhtRu1HQN6dT+b/CC28Q4uAES5mtY+7WvqkSQlJ3h0zaxCy4kATJT/cfNseUJblL66v0XrD7XZDQQAwEXyB4w6+keLynLjsKiMTmUAAAAAgGjk8wf0td/tdOdzSjI1pzTLYqLYcdO8EqUkBpd2VLf26ZF11ZYTAQAAALHhqY1HNewPbtpw6bQcLZqaYzkRACASpCR6dffSqe788beOWkwDjD+KygAgyr2ws9EdX11ZoLSkBItpAEykWcWZev/iKe78e6v2yhhaLQMAoldn/7ACoT9lWSkJSvTG39sWdCoDAAAAAESjx986ot0N3ZKkRK+jOxZOspwodmQkJ2hlVbE7f/DlA2ruHrSYCAAAAIh+w76AHn/riDunSxkAINw9y6a547X7mnWsnU2BEbvib3UWAMSYF3aOtuC+dX6pxSQAbPjcDZVKCi2433q0Uy/ubrKcCACAC9feN9qlLD8j2WISe8K7s1FUBgAAAACIBs09g/rB6v3u/LrZRXHZfXw8La/IV2VRhiSpb9iv//3nvZYTAQAAANHthZ0Nau4ZkiQVZSaz7g4AcIKy/HRdO6tQkmSM9MQGupUhdtHOBgCiyJMnPSnp6BvW9touSZLXcdTZP/IX9wn3obDKeQCxYUpumu5ZPk2PrKuRJH1/9T6tnFMsr8exG+wkZ/rddC74/QUA8aEtvKgsThef5aaFF5Wx0xUAAAAAIPJ9f9U+9Qz5JEkVhem6amaB5USxx+tx9PV3z9OHf75BkvTMljrds6xMS8pyLScDAAAAotMv36xxx/csK1NSAj06AAAnund5mV7d3yJJ+vWmY/r8jZVKTvBaTgWMPZ4FAUAU21Xf5Y5nFKUrNYknK0A8+uR1M5Ue+v9/f1Ovfr+1znIiAAAuTHinsrw4LSrLSk3Q8drwpu4hDfn8dgMBAAAAAHAG22s79ZvNte786++epwQvyxDGw1WVBbplXok7/8YfdykQMBYTAQAAANFpe22nthztlCQleh02OgYAnNJ1VUWanJMqKbie5YUdjZYTAeODd3MBIIrtrO92x/MnZVtMAsCmgoxkfezqCnf+wxf3a3CEBegAgOhzQqeyjPgsKkvweFSSleLO6zoGLKYBAAAAAOD0jDH61rO7ZUJ1TTfMKdK1swrthopxX719jpJDHRR21HXp15uOWU4EAAAARJ/H3zriju9YOEmFmckW0wAAIpXXc2Lh8WNhfz+AWEJRGQBEqa6BER1t75ckeRxpTmmW5UQAbPrY1eVuR5e6zgE9+maN3UAAAFyA9l46lUnS1Lw0d3wk9JwfAAAAAIBI8+z2Bm060iEpuLv/V2+fazlR7Jual6YHrp3hzr+3ap+6BkYsJgIAAACiS1f/iP64rd6d37uizGIaAECku3vpVCV6HUnS5iMd2h3WDASIFRSVAUCU2lXf5Y4rCjKUnpxgMQ0A2zJTEvWZ62e684fWHFRLz5DFRAAAnL/2vtG/XXnp8bsjYHlBujuuae2zmAQAAAAAgFMbGPbru8/vcef3XVl+wutZjJ8Hrp2hyTmpkqT2vmH9+0v7LScCAAAAosczW2s1OBKQFNzE/dKpOZYTAQAiWWFmsm6dX+rOH99AtzLEHorKACBK7awbrXafN5kuZQCke5aXaUZh8KJ975BPP3yRC8kAgOjS2jfaqSw/jjuVleWPLsI70kanMgAAAABA5Pnpq4fU0DUoSSrISNKnwjY9w/hKTfLqq7fPcee/Wn9E+5t6LCYCAAAAooMxRk9sOOrO71k2TY7jWEwEAIgGH14+2tXy91vr1D1I13jEFtraAEAU6hkc0ZG2YMcCR9LcUorKAEiJXo++dsdc3ffI25Kk/377qO5dXqa5k/gdAQCIDu29o0VleXFcVFZekOaOq+lUBgAAAACIMHWdA/qv1w6583+6ebayUhItJoo/t84v0YqKfK0/3CZ/wOibz+7S4/cvY0EsAAAAcAbfeX6vDjb3SpKSEjwa8QX0ZFiRWaSJ5GwAEMtO/v1rjFFxVrKauofUP+zXV5/ZoRUzCiylA8YencoAIArtqu+WCY2nF6Qrkwt1AEKum12ka2cVSpICRvqX53bLGHOWowAAiAztfRSVSSd3KqOoDAAAAAAQWf73C3s1OBKQJM2blKW7lky1nCj+OI6jr79nrryeYBHZuoNtWrWr0XIqAAAAILJtqG5zx5dMzVFyotdiGgBAtHAcR8vK8935hup21mQiplBUBgBRaHttpzueTwciACf52u1z3AvJ6w+36cXdTZYTAQBwbtrCisoKMpItJrGrLH+0U9mxjgGN+AMW0wAAAAAAMGpjdbue3Vbvzr/+7nnu+9GYWFUlWbp3eZk7/5fn9mhg2G8xEQAAABC5eod82lXX7c6XledZTAMAiDaXTM1RkjdYetPcM6Qjbf2WEwFjh6IyAIgynf3Dqgk9GXEkzZ+cbTcQgIhTWZype5ZNc+ffeX6Phn0sRgcARLZAwKijf7SoLDc9frvxpiUlqDgrWFTnDxjVdQxYTgQAAAAAQPC1+7ee2+XO71hYqstZiGnV52+Y5XZ7r+sc0H+9dshyIgAAACAybT7SIX+oq8zU3FSVZqdaTgQAiCYpiV4tmprjzjfWtFtMA4wtisoAIMrsqOtyxzMKM5SZEr+LbQGc3udumKWslARJUk1bv361vsZqHgAAzqZ7cET+QPBCTmZygpITvJYT2TU9P90dV7f1WUwCAAAAAEDQ05trtTO0s39ygkf/67Y5lhMhOy1R/3TzbHf+n68c0lF2ygYAAABOEDBGG6vb3Pmy8nyLaQAA0Sq8y+WOui71DfkspgHGDkVlABBltteOFpUtnEKXMgCnlpeepM/eMMud//jlA2rrHbKYCACAM2vrG+1SlpeRZDFJZAgvKjvSSlEZAAAAAMCunsERfW/VXnf+D9fO0OQcdvaPBHcvnar5k7MkSUO+gP75jztlQh0YAAAAAEgHm3vV0T8iSUpN9GoBa+4AABdgUk6qpuYG3w/zB4y2HO2wnAgYGwm2AwAAzl1r75DqOgckSV6Po3mTeIEL4PTuXV6mJ946osOtfeoZ9OlHL+3Xt+9cYDsWAACn1NYbVlSWTlHZ9ILRorIadhgHAAAAgIjy5IajF3zsh5ZNG8MkE+ehNQfVGnrtXpqdogeurbCcCMd5PY6+fecCve/hdTJGemVfi57f0ajbF5bajoZTuNDfH9H6uwMAACASbKxud8eLp+Uo0Us/DgDAhbm8PF/HOmolBf++XDmzQB7HsZwKuDg8MwKAKLKtttMdzyrOVGqS12IaAJEuKcGjr9w2x50/ueGo9jX2WEwEAMDptfeNdtTMp6hM5QVp7rimjU5lAAAAAAB7qlv79It11e78y7dWKS2J/WsjySVTc3Tv8jJ3/s1nd6lncMRiIgAAACAydA2MaG9jtzu/vDzfYhoAQLRbMDlbKYnBEpy2vmEdbmE9B6IfRWUAECWMMdp+rMudL6INN4BzsHJOka6aWSBJChjpW8/tkjHGcioAAP5SS1inssLMZItJIkNZflinslbehAQAAAAA2POvf9qjEX/wfeUlZbl6z6JJlhPhVP7HzbPd91Sae4b0g9X7LScCAAAA7NtU065AaJlMRUE61yEBABclKcGjxdNy3fmG6jaLaYCxQVEZAESJ3Q3daukNdm9I8npUVZJlORGAaOA4jr52xxx5Qh2W1x1s0x+31dsNBQDAKbT0jHYqK8zgYk5Z/minstqOAY34AxbTAAAAAADi1esHWvTSniZ3/vV3z5XjOBYT4XSyUhL1z3fMdee/XF+j7bWd9gIBAAAAlvkDRm/XtLvzy8vzLKYBAMSKy6eP/j3Z09Ct7gG6xSO6UVQGAFEivAikqjRTSQn8CgdwbqpKsvSRK6a782//aY+6B3khAwCILCcUlbFDoNKSElScFfw5+AJGdR0DlhMBAAAAAOKNzx/Qt57d7c7/askULZySYzERzuaOhaW6ZlahJMkY6Su/2yH/8bYMAAAAQJzZ19it7kGfJCk9OUFzJ7GJOwDg4hVlpai8IF2SFDDSpiMdlhMBFyfBdgAAwNkFAkbPvjNaVLboAi/YPbnh6FhFikv8/HAxbP/7+cKNs/T8jgY1dQ+ppWdIP1i1T9987/xzPt52fgBA7KOo7C9Nz09XU3fw51LT1qfpoTclAQAAAACYCE9sOKoDzb2SpPQkr/7pltmWE+FsHMfRv7x3nm760Wsa8gW0s65bv1pfo/uuLLcdDQAAAJhwG6pHu5QtLctVgodN3AEAY+Py8jxVt/ZJkt6uade7ZhfK4ziWUwEXhmdIABAF1h9uU33XoCQpLcmryuIMy4kARJvMlET98x3z3Pmv3jqi7bWdFhMBAHCill6Kyk42PX+0iKwm9GYkAAAAAAAToX/Ipx++uN+df+r6ShVlplhMhHNVlp+uT18/053/YPV+NYauMwIAAADxoqNvWAdDm2Q4ki6fnmc3EAAgpswrzVJ6kleS1DUwov2NPZYTAReOojIAiAJPb651x4um5LBrCoALctuCEl0zq1CSZIz01d/tlD9gLKcCACCoNbxTWQaL1CSd0Jmspq3fYhIAAAAAQLx5aW+zugZGJEll+Wn6u6um2w2E8/Lxa2ZoZlFwk8reIZ++9dwuy4kAAACAibX5aIeOr4ipLM5QbnqS1TwAgNiS4PVoSdlowXJ4d0wg2lCVAAARrnfIpz/vbHTni8tyLaYBEM0cx9G33jNPSQnBp4A76rr0xIYjllMBACAZY9QSVlRWkMlFHUmanp/mjmva6FQGAAAAAJgYjd2D2ljd5s6/ctscJSd4LSbC+UpK8Ojbd85358/vaNSavU0WEwEAAAATJ2CMNh/pcOfhi/4BABgrl5eP/n3Z39Sjjv5hi2mAC0dRGQBEuOd3NGhgxC9JKs5K1qRsujYAuHDTC9L1yXfNdOf/9ud9au4ZtJgIAACpe8CnYX9AkpSRnKC0pATLiSJDeKeyI3QqAwAAAABMAGOMnt/eoEBoS/8rZ+brprnFdkPhgiyvyNddS6a486/9bqe6B0csJgIAAAAmxsHmXrfzclqSV3NKMy0nAgDEorz0JFWGOsUbSW/X0K0M0YmiMgCIcE9vrnXHi6flynEci2kAxIIH3lWhitAi9Z4hn7793B7LiQAA8a6ld7TAuTAz2WKSyFIW1qnsWHu/fKHCOwAAAAAAxsvexh4dbOmVJHkc6Z/vmMe1qSj2ldvmKC892BG+vmtQ332e6wEAAACIfeFdyi6dmqMED0ulAQDjY1lYt7JNNR3yH9+pCYgiPFMCgAh2tK1fG6uDletej6NFU3MsJwIQC5ITvPqXO+e78z9uq9cbB1otJgIAxLvmniF3XJhBUdlxaUkJKs4K/jx8AaO6zgHLiQAAAAAAscznD+hPOxrc+YeXl2l2CTv6R7O89CR9673z3PlTG4/ptf0tFhMBAAAA46t/yKfdDd3ufMn0vDPcGwCAizO7JEtZKQmSpN6T/gYB0YKiMgCIYL/dMtql7JrKAmWlJFpMAyCWXDmzQO9ZNMmd//MfdmrI57eYCAAQz1rCi8roVHaCsvx0d1zd2mcxCQAAAAAg1r15qE3tfcOSpNRErz5/wyzLiTAW7lg4SbctKHHnX/7tdvUMjlhMBAAAAIyfrcc63S4xU3JTVZKVYjkRACCWeT2OloYVMG+obrOYBrgwFJUBQIQKBIye2TpaVPaBJVMspgEQi752xxxlhnbJONzap5+sOWg5EQAgXlFUdnrlYUVlNRSVAQAAAADGSc/giNbsa3bnK+cUKTc9yWIijKVvvXe+8kL/Peu7BvWd5/dYTgQAAACMPWOMNh/pcOdLy+hSBgAYf5dNz5MTGh9u6TthDQwQDeKmqMxxnCmO4/zCcZx6x3GGHMepcRzn3x3HyT3P8+SFjqsJnac+dN5TVns4jnOX4zj/4TjO647jdDuOYxzHeXxsvisAsWz94TYdax+QJGWlJOiGOcWWEwGINUWZKfrSzbPd+cOvHNKu+i6LiQAA8aqll6Ky0ykvHC0qO0xRGQAAAABgnKze3aRhX0CSVJSZrGXl+ZYTYSwVZCTrW++d586f2nhML+1uspgIAAAAGHt1nQNq7B6UJCV6HS2ckm05EQAgHmSnJqqqNMudv13TbjENcP7ioqjMcZwZkjZLuk/SRkk/knRY0mclrXcc55zeEQ/db33ouEOh82wMnXez4zgVpzjsa5I+JekSSXUX950AiCdPbjjqju+8dLJSEr0W0wCIVfcsK9PSsmCNvS9g9KWnt2vEH7CcCgAQb1q6w4rKMigqC1dZlOGODzT1WkwCAAAAAIhVtR392hK2m//tC0rl9ThnOALR6I6Fk3T7glJ3/qXfbldzaMEtAAAAEAs21Yy+rlkwOZv1dgCACbOsfLQ75uYjHazBRFSJi6IySQ9LKpL0GWPMncaYLxtjrlewKGy2pH89x/N8R9IsST80xqwMnedOBYvMikKPc7LPh47JkvSJi/w+AMSJlp4hrdrV6M7vWVZmMQ2AWObxOPreXQuVnBB8Wrirvlv/32uHLacCAMQbOpWdXmVRpjs+0ExRGQAAAABgbBlj9KftDTKheVVJpiqLM894DKLXv75vvkqyUiRJ7X3D+uJvtikQMGc5CgAAAIh8w76AttV2uvMlZXlnuDcAAGNrZlGGctMSJUkDI37trOuynAg4dwm2A4y3UJeymyTVSPrJSV/+uqSPS7rXcZwvGmP6znCeDEn3SuqT9I2TvvyQpC9IutlxnApjjLsS2xizNuwcF/6NAIgrv950TL7QBZylZbmaXcLFOwDjp6IwQ1+4cZa++8JeSdKPXzqgm+YWs3AAADBhWnooKjudybmpSkn0aHAkoNbeIXX0DSs3Pcl2LAAAAABAjNhe16Uj7f2SJK/j6LawTlaIPTlpSfrhXy/SPT/bIGOk1w+06pE3a3T/VeW2owEAAAAXZVd9l4Z8wa4w+elJmp6fZjkRAMSWJzcctR0honkcR5dPz9Oq3U2SpA3V7bp0Wq7lVMC5iYdOZdeFPq82xpzQR9AY0yNpnaQ0ScvPcp7lklIlrQsdF36egKRVJz0eAFyQQMDoqY2jT74+tGyaxTQA4sX9V5Vr0ZRsSdKwP6B/enq7/OxOCgCYIBSVnZ7X42hGYYY7p1sZAAAAAGCsDPsC+vPORnd+xYx8FWTwujzWXTGjQB+/psKd/58X9rJ7NgAAAKLepiMd7nhpWS5NIAAAE27J9Dx5Q39/jrb3q6FrwHIi4NzEQ1HZ7NDn/af5+oHQ51kTdJ4L4jjO5lN9SKoaj8cDYM9rB1pU2xF8IpGTlsiOkAAmRILXo+/dtUiJ3uCLmneOdeqRddWWUwEA4sGIP6D2/mFJkuNIeXTh+guVReFFZT1nuCcAAAAAAOfutQMt6hoYkSSlJyfouqoiy4kwUb5442zNn5wlKbjR3D8+scX9twAAAABEm7beIVW39kmSPI50aRmdYQAAEy8jOUHzQu+3SNLG6naLaYBzFw9FZdmhz6fbWuv47TkTdB4AOKPwFrEfWDxFKYlei2kAxJPZJZn69PWV7vzfVu1z33QDAGC8tPcNy4SaY+alJSnRGw9vVZyfyuJMd3ygiU5lAAAAAICL19k/rNcPtLjzm+cWc00qjiQlePTg31yqjOQEScHds7/463cUCBjLyQAAAIDzF96lbFZxprJSEi2mAQDEs8vL89zxO8c6NeTzW0wDnBtWakUJY8ySU31I2ms7G4Cx09g1qJf3NrvzDy2bZjENgHj0iXfN0JzS4G4ZQ76A/udvt3MRGQAwrlp6htxxYWayxSSRK7xT2cFmisoAAAAAABfvz7saNeIPvvc7KSdFi9nJP+5UFGboe3ctdOcv7WnWT187ZDERAAAAcP78AaMtR0eLypaW5Z3h3gAAjK/y/HQVZgTXvgz5Atp+7HT9jIDIEQ9FZcf/T8w+zdeP3945QecBgNN6csMR+UPFGysq8jWjMOMsRwDA2Er0evRvdy2U1+NICrZgfmLDEcupAACxjKKyswvvVLa/qcdiEgAAAABALKhp7dP22tEFLbcvmCSP41hMBFtuW1Cq+68qd+ffX7VP6w62WkwEAAAAnJ8DTT3qGfRJkjKSEzS7JPMsRwAAMH4cxzmhW9mG6jYZw6b+iGzxUFS2L/R51mm+Xhn6vH+CzgMApzQ44tdjb40Wbnx4eZnFNADi2fzJ2frEtTPc+Xdf2Kv2vmGLiQAAseyEorIMispOZVpempISgm/hNPcMqat/xHIiAAAAAEC0Chij57bXu/MFk7NVXpBuMRFs+/KtVVoa6lQXMNI/PrFFh1volA4AAIDosOnIaJeyS6fluJsoAwBgy+JpuUoI/T2q7xpUXeeA5UTAmcVDUdna0OebHMc54ft1HCdT0pWS+iW9dZbzvCVpQNKVoePCz+ORdNNJjwcA5+WZLXXqCC0OnZyTqpvnFVtOBCCefXrlTFUWBbsl9g/79dsttQqwYwYAYBy09NKp7Gy8HueELsYHW+hWBgAAAAC4MJtrOlTfNShJSvQ6unV+ieVEsC3R69FP7lnsvi/TNTCi+3+5SR1sNgcAAIAI1zM4or2N3e58SWizBAAAbEpN8mrhlBx3vqG63WIa4OxivqjMGHNI0mpJ0yV98qQvf1NSuqTHjDF9x290HKfKcZyqk87TK+mx0P2/cdJ5PhU6/ypjzOExjA8gTgQCRj9/Y/TXx31XTleCN+Z/RQOIYMkJXn3/rxa5OzhVt/Zp/aE2y6kAALHohE5lFJWd1vFib0k60MRu4QAAAACA8zcw7Neq3Y3u/JrKQuWkJVlMhEhRnJWin/3tUqUkBq9PVrf26YHHN2vYF7CcDAAAADi9d451KhDaH7ksL01FmSl2AwEAELKsPM8db6/t1MCw32Ia4MzipWLhHyU1S3rQcZzfO47zXcdx1kj6vKT9kr560v33hD5O9pXQ/b/gOM7LofP8XtKPQ+c/uWhNjuPc6TjOo47jPCrpy6GbVxy/zXGc74/FNwggur26v0WHWoK1rRnJCfrry6ZaTgQA0qKpOfrEtTPc+erdjWoNW/gPAMBYoKjs3JxQVNZMURkAAAAA4Pyt2duk/tAClpzURF1dWWg5ESLJoqk5+tHdl7jzDdXt+vIz2xU4vkoXAAAAiCDGGG2q6XDnS6fTpQwAEDmm5KaqNDtY7DziN9p6rOMsRwD2xEVRWahb2VJJj0paJumLkmYoWAy23BhzTm03QvdbIelBSTND51km6RFJS0KPc7JLJH0k9HFz6LaKsNvuuqBvCkBM+VlYl7K/uWyqMlMSLaYBgFGfWVmpqpJMScEXN09vqVXAcAEZADB2Tigqy6Co7HQqi0eLyvY39VhMAgAAAACIRs3dg1p/ePSy+K0LSpWUEBfLBXAebl1Qqi/dMtudP7OlTt96brcM1wUAAAAQYY6296ulN3idMSnBo/mTsy0nAgBglOM4ujysW9nG6nbeX0HEipt3iY0xx4wx9xljSo0xScaYMmPM54wxf1H2aYxxjDHOac7Tboz5bOj4pND5/s4YU3ua+3/j+PlO8zF9jL9VAFFmd3231h0MXsTzONJHr5xuNxAAhElK8OgHdy+SJ/TM6Gh7v9440Go3FAAgphy/2CPRqexMZhZluuODdCoDAAAAAJwHY4z+tKNBxxtOlReka/6kLLuhELE+ce0M/c1lU935o2/W6Aer91tMBAAAAPylTUdGl/4unJyt5ASvxTQAAPylS6bkuJs6NfcMqaat33Ii4NTipqgMACLVz14f7VJ264JSTclNs5gGAP7SvEnZur6qyJ2/uKdJTd2DFhMBAGLJCZ3KKCo7ren5aUr0Bqu8G7oG1TM4YjkRAAAAYo3jOFMcx/mF4zj1juMMOY5T4zjOvzuOk3ue58kLHVcTOk996LxTTnP/GsdxzGk+GsfmuwPi297GHh0IbVDiSLpjYakc55R7rAJyHEf/+r4Fun1BqXvbQ2sP6idrD1pMBQAAAIwa8vm1o7bLnS8tO6+3LgAAmBDJiV5dMjXHnW+obrOYBji9BNsBACCeHWnr0x+21bvzj11VbjENAJzetbOKtKehR3WdA/IHjJ7eXKsHrp0hr4eFBwCAC9c/7FPvkE+SlOT1KDs10XKiyJXg9aiiIEP7mnokBbuVbTchSAAAIABJREFUXTqNC2QAAAAYG47jzJD0pqQiSX+QtFfS5ZI+K+kWx3GuNMac9Yq34zj5ofPMkrRG0v+TVCXpPkm3O46zwhhz+BSHdkn691PcTpte4CL5/AH9aUeDO7+8PE+l2akWEyEaeD2OfvTXl2hgxK81e5slSf+2ap/8AaNPXz+TokQAAABYtaO2S8P+gKTgppVT89jEHQAQmZaV52ljdbskaVddt3oX+pSRTAkPIgudygDAoofWHJQ/YCRJV87MZ1EogIjl9Ti6a8kUJYSKyOo6B/TK/mbLqQAA0a61Z9gdF2YmsyDpLGYWZ7jjA02srQUAAMCYeljBgrLPGGPuNMZ82RhzvaQfSZot6V/P8TzfUbCg7IfGmJWh89ypYHFaUehxTqXTGPONU3x8/+K+LQDrDraqvS/4+js10asb5xRbToRokZTg0cP3LNaKinz3th++uF/ffHa3AqHrmwAAAIANm450uOOlZblcYwQARKzS7FRNzQ1u8OQ3RlvC/oYBkYKiMgCw5Ghbv57ZWufOP7tylsU0AHB2xVkpuiFswcHavc2q7xywmAgAEO1aegfdcUFmssUk0aGyKKyorLnHYhIAAADEklCXspsk1Uj6yUlf/rqkPkn3Oo6TfpbzZEi6N3T/b5z05YckHZF0s+M4FRefGsC5aOoe1Np9Le78hjlFSmMnZJyHlESvfvaRpbq6ssC97dE3a/SFX7+jkVBnCADSsC+gA009WrWrUb/fWqffbq7Vr98+pme31WtHbZd6BkdsRwQAIGY0dw/qaHu/JMnjiE3cAQARb1n56IY9G2vaFTBs1oPIwjvGAGDJT9aOdilbUZGvy8vzLCcCgLO7qrJAuxu6dbS9XwEjPb25Vv/4rhlK8LJXAQDg/LX0DLnjwgyKys5mVnGmOz7QTKcyAAAAjJnrQp9XG2NOqBAwxvQ4jrNOwaKz5ZJePsN5lktKDZ3nhF0QjDEBx3FWSfp46PEOn3RssuM4H5Y0TcGitO2SXjPG+M/1m3AcZ/NpvlR1rucAYs3/eWGvhkOFP8VZybo8bAELcK7SkxP0s48s1Rf+e5v+tKNBkvT7d+rV0DWohz60WIVsFIQ41D04ojcOtOqVfc3adKRDR9r63Wv/p1OUmayrKguUlujVzKJMJSVwbQ0AgAuxOazDy5zSLGWwcQYAIMItmJKt53bUa3AkoPa+YR1q7lVl2PoPwDaeTQGABcfa+/XbLbXu/LM3VFpMAwDnzuM4umvJFP3HmgMa8Rs1dg9qzd5m3TSvxHY0ADhnjuPcJelaSZdIWiQpU9ITxpgPWw0Wh04oKmMB0lmFdyrb20CnMgAAAIyZ2aHP+0/z9QMKFpXN0pmLys7lPAqd52Qlkh476bZqx3HuM8a8eobHBHAaW4526Jmtde78joWT5PU4FhMhmiUnePXgBy9VTlqinthwVJK0obpd7/6PN/TwhxdrMd0hEAcGR/xatatRv9lUq7cOt8l3liKykzX3DOmZLcHfy4leR/MnZevaWYUqykoZj7gAAMSkEX9AW451uvMlZTwPBQBEvkSvR0um5WrdoTZJwfdUKCpDJKGoDAAs+Mnag+6bzMsr8rS8gp0hAUSPgoxk3TyvRM9tD+5I+ur+Fs0pzdLUvDTLyQDgnH1NwWKyXkm1Ytd6aygqOz/lBelKSfRocCSgxu5BtfYOqYAObwAAALh42aHPXaf5+vHbc8bpPI9Iel3SLkk9kiokfUrBrmYvOI6zwhiz7SyPLWPMklPdHupgtvhsxwOxJBAw+uYfd7nzeZOyNKMw4wxHAGfn9Tj69p3zNSknVd9fvU/GSI3dg/rr/1qvr90+V/cuL5MnzgoX+4d96h30qX/Yr75hn3z+0SIjj+MoNcmr9GSv0pISlJmcEHc/n1hxrL1fj6yr0TNba9XZP3La+03OSdWMogzlpCYqwePI43HU2T+imrY+HW3rdztHStKI32jrsU69c6xT8ydn67qqIpVQXAYAwFmt2dusviGfJCkrJUGVRSzIBwBEh8vK89yisr2N3eoaGFF2aqLlVEAQRWUAMMGqW/v09OawLmUrT7UpKwBEtuUV+dpV363q1j4ZSU9vrtWnrp+pRK/HdjQAOBefV7CY7KCCHcvW2o0Tv1p6KSo7Hwlej6pKsvROaAfGXfXdunZWoeVUAAAAwMUxxnzzpJt2SnrAcZxeSV+U9A1J75voXEA0e3pL7f/P3n2Hx1WfaR+/z8xIo957l+Xeu42xsQlgSghgIBBKNpAESEhdyEuSzbsJkDfJpocldUMCG0IJmBqK6c0Y914ky7Z6771MOe8fIx/JDu6yjsr3c12+dH5TztwmjqQ583ueRzvKA3WcLoehS6en2pwIo4VhGPrK+eM1PT1aX39im1q6PPL4TP3gxT16dXeVfnbNLGXFj44GdKZpqr3Hq9q2HjW096qps1eNHb1q6fKordujH760V10e30mfz+UwlBDhltNhKCo0SPHhwYqPCFZChFvJUSGKcLN9Z7gprGnTH949qBd2VMr3MVPJZqRHa8WkRC2fmKipaVEKCz72/4Y+v6k9lS16fU+Nntpcptq+ZlumpF0VLdpd0aKl4xN04dRkPmsDAOA4ntpUZh3PzYplGjMAYMRIigxRbkK4iuo75DelzSWNumByst2xAEkUlQHAkPvJK/usKWWLcuN0Th5TygCMPA7D0LVzM/TA24Xq9fpV196jN/bW6LIZbE4AMPyZpmkVkRkGHzTYqaZ1QFEZE7dOyrS0gUVlLRSVAQAAYDAcniAWfYz7D9/ePETnOeyPChSVnXeSjwcgqa3bo5+tKbDWSyckKC482MZEGI2WT0zUS19bqjse3aK9Va2SpPWHGnXxb97X3SsnKtjlkMsxcgpjTNNUc5dHZY2dKm/qUnlTp2pae06paOxEvH5T1a3dkqSK5q5/uT8yxKXU6BClx4QpOz5MmbFhCg12Dtrr4+QV1XfoF68X6OWdVf9yX0ZsqD6zIFPXzMtQanToSZ/T6TA0MyNGMzNilBYTqrLGTr2dX6uCmjZJgeKyDw7UK7+6TdfOy1Bm3OgozgQAYDDVtHbrnYJaaz0vO9bGNAAAnLpFuXEqqu+QJG0ubtKKiUkUSGNYoKgMAIbQ+kMNen1vjbX+3ien2JgGAM5MbHiwLpueque3V0iSPjxQrympUcpNCLc5GQBgpKgcsIEmLSbExiQjx/T0/v25eypbbUwCAACAUeRw9cnEY9w/oe/r/iE6z2F1fV+52AScggffPqD6vsngKVEhWjExyeZEGK0y48L07J1L9MBbhfqf9w/J5zfV5fHp/728T3HhwbpwSrJmZkTLMQybOrV1e7SzvEXvFtSqrLFTZU1dau/xnvJ5gp0ORYW6FBbsUliwU0FOhw7/dQ//9+js8am9x3vC87d1e9XW3a79Ne2SJENSclSIJiZHaEJypHq9fgW7Rk6h3khU29atB986oCc2llpNYg87d3y8bj8vT8vGJ8gxCBv+MuPC9LklOapo6tKaPVU6WBfYVFjX3qM/vndQF01N1vKJiTRFAwBggNVbynX4R3RuQrjiaVgJABhhpqZFKdztUkePVy1dHu2vadOU1Ci7YwEUlQEYWo9vKLX19W9clGXba/v9pv7fy3ut9ao56ZqZEWNbHgAYDAtyYrWnskWFte0yJT2ztVxf/8QEPtgEMCYYhrHlGHdNHtIgI9jh7sySlBJNUdnJmJbWf0FxT0XLcR4JAAAAnLTD05xXGobhME3Tf/gOwzAiJZ0rqVPS+hOcZ72kLknnGoYRaZpm24DzOCStPOr1TmRx39dDJ/l4YMw7WNeuhz8sstbfvWyyOnoGb9IScLSQIKe+fclkXTo9Rfes3qn86sC3/saOXj21uUzv76/T0vEJmpERrSCnPZ8beH1+7a9p17ayJm0vbdb2smYdqGuXaZ74ucEuh5Ii3UqMcCs2PFhxYcGKCQ9SlDtItyzNUaTbddJFP90en+raevTY+hI1dXnU0N6rxo4e1bb1qKa1Wx7fkYFMBa6dVbd26/3Cej2xsVRL8uK1fGKilk9MUlY8k6wGS4/Xp4c/LNaDbxWqo/fI75kXTknSneeP19ysszMJJT02VJ8/N1cbixv16q5q9fr8MiW9vrdGdW09WjUnXS6b/r8DAMBwYpqmnt5cZq3nM6UMADACuRwOzc+O1Xv7A/3UNhQ1UFSGYYGiMgAYIs9vr9DuisAkAbfLof9z8SSbEwHAmTMMQ1fPzdADb+1Xt8evxo5erdlTpStmpdsdDQAwzHX1+tTc6ZEkBTkNJYTTTfBkTEyOlMthyOs3VdzQqbZujyJDguyOBQAAgBHMNM2DhmG8rkDR11ckPTjg7vsUmBT2J9M0Ow7faBjG5L7n5g84T7thGI9Kul3SvZLuHnCer0rKkfSaaZqHBpxniqTSgefuuz1H0m/7ln8/o78gMEaYpql7X9xjFabMz47VFbPS9MTGshM8EzhzMzNi9OJXl+qRdUX63TsH1dIVuOZT3dqt1VvL9fKuKs3LjtXszBilRoectelLpmmqorlLu8pbtL2sWdvKmrWrvEVdnhMXV7pdDmXEhiozNkyZcWFKjQ5RdGjQMbNGneL1mJAgpzLjwpQVH66j26D6TVP17T2qbO5WaWOnShs7VN3SrYHDsjp7fXpzX63e3FcraY8mJkfo8plpunxmqsYlRpxSFvR7J79W97+0V0X1R/wqokW5cfrOpZM15ywVkw1kGIYW5cZrQlKkVm8pU3FDpyRpW1mzmjp7ddOibIW72d4FABjbNhQ1Wj8j3S6HpqVF25wIAIDTsyAnTu/vr5MpqbCmXY0dvYoLD7Y7FsY4rjoAwBDo6vXpZ2sKrPVty8YpLSbUxkQAMHiiQ4N0+Yw0rd5aLklaf6hRU1OjNT6JDzEBjG6mac77uNv7JpjNHeI4I05VS5d1nBwVIofj7GwmGm1CgpwanxRhdf7eW9mqRePibU4FAACAUeBOSesk/bdhGBdI2idpkaTzJe2X9L2jHr+v7+vRv8j/h6QVku4yDGO2pI2Spki6UlKtAkVrA10v6W7DMN6XVCKpTVKepE9KCpH0iqRfnOHfDRgTXt1drQ8K6yVJDkO678ppZ61wB/g4wS6Hbj8vT9cvyNI3ntymDw/UW0WOXR6f1h6o19oD9YoKcWlSSqTyEiOUFhOquPBgOU7j32p7j1cF1W3Kr25VflXf1+o2tXV7T/hchyFNSolShNulzNhQZcaFKTHSfVo5BoPDMJQUGaKkyBDNzoyRJPV4fDpU36H9NW3aX9Ompr7mTIftr2nXr97Yr1+9sV9TU6P0yZmp+tTMNCaYnaSi+g798KW9eju/9ojbJyZH6LuXTdGKiYlD/j00LjxYX1g6Ti9sr9DmkiZJUnFDp/743kF9cdm4Ic0CAMBw89Sm/mYZszJjFOxikicAYGSKCw/WhOQI7a9plylpU3GjLp6WYncsjHEUlQHAEHjw7UJVt3ZLkhIi3PrSijybEwHA4JqTFaM9lS3a17fB/dmt5fr6BRMUEuS0ORkAYLiqbum2jlOjQ2xMMvJMT4+2isr2UFQGAACAQdA3rWy+pPslXSLpMklVkh6QdJ9pmk0neZ4GwzDOkfQDSVdJWiapQdLDkr5vmmb5UU95R9IkSXMknavAVLRmSWslPSrpUdM0TQE4rs5er3740l5r/dnF2XTuh22iQ4O0cmqKluQlaEtJkzYWNRxRENXa7dWm4iZtKg78aHG7HEqKdCsqNEj51a1KiHAr2OWQy2HIYRjq8vjU1u1VW7dHDe29qmzpUmVzl+rbe086U2p0oFjr8J8ZGdEKC3bp8Q2lg/73HyzuIKempEZpSmqUTNPUkvEJeq+gVu8X1mvdwXp1e/zWY/dWtWpvVat+/lqBZmZE6+o56bpydrpi6XT+L9p7vPrt2wf0l7WHrKJHSYoKcemuiybq5sXZcjnt26TudBhaNSddCRFuvbanWqakho5e/WXtIV0zL11JkVxHBQCMPS1dHr2yu8paL8iOszENAABnblFuvPbXtEuSNpc06YIpSTYnwlhHURkAnGV7K1v1p/cPWetvrZyoCDfffgGMLoZh6Ko56Sp+s1BdHp+auzx6ZVeVrp6bYXc0AMAwVXlEURlTfE/FtLQord4SON5T2WpvGAAAAIwapmmWSbr1JB97zNEdpmk2SvpG358Tnec9Se+dbEYAH+/Btw+oqu99dnx4sO5aOcnmRIAU4XZp+cRELZuQoMKadm0va9L+mnZ1eXxHPK7H61dZU5fU1DUo1zmiQ4M0JTVSszJjNCczRrMzY5UywhsaGYah3IRw5Sbk6pZzc9XR49Vb+bV6aUel3t1fp15vf4HZzvIW7Sxv0Y9fydeFU5N07bwM+fymnI6xPbnQ7zf17LYK/WxNvmrbeqzbDUP6zIJMfWvlJMVHuG1M2M8wDJ03MVFx4cF6clOp/KZU396rm/68QU/cvlgJwyQngFNjGEaG+pt4xCvQxON5nUITj77zxEn6vgJNPFIVaOKxRh/fxOOUX9swjHRJVyvQaGRK32u0S9oq6Q+maT57slmBwfLijkqroH5KapTSYkb273YAAExMjlR0aJBaujzq6PFqL/s+YDOqGgDgLPL6/PrOszvl8we6nC3MjdN18zNtTgUAZ0dkSJCunJ2mJzeVSQp00ZiWFq1JKZE2JwMADEfVLV3WMZPKTs3AbvN7KltsTAIAAAAAsNvBunY99EF/c8PvXDpZ0aFBNiYCjuQwDE1KidSklEj5/KbKGju1v6ZNFc2BiWMdvb4Tn+RjOB2G8hLDNTklSpNTIzWl72tKVIgMY3QXUIW7XbpiVpqumJWmtm6P3txXo5d2VOn9wjpr+lavz69XdlXrlV3VigxxaU5mjOZmx47JSVdbS5t034t7tKP8yOtoc7NidN8V0zUjY3hOdpyeHq3PKMsqLCusbdfND23QE7ctZgodMMIYhpEnaZ2kJEkvSMqXtFCBRhyXGIZxrmmaDSdxnvi+80yU9LakJyVNVqA5yCcNwzjHNM1DRz3nVF/7a5K+LalIgcnS1ZKyFSg0u9AwjF+bpnnXaf2HAE7TPzb1T5f9zILMUf+7HgBg9HM6DM3PidVb+2olSRuLGm1OhLGOojIAOIse/rBYO/suTge7HPrJ1TPkGONd4ACMbjMzYrS7slW7KwLf+57bVq5vXDBRocFOm5MBAIabqgGTykZ6t+ihNjUtyjourG1Xt8enkCB+1gIAAADAWGOapu59cY9VRDIvO1bXzM2wORVwbE6HoZyEcOUkhEsK/Btu7faqob1HbT1eTUyOVEN7j7x+U76+P26XQ5EhLkWGBCkmLEjpMaFKiwlVUqRbLqfD5r+R/SJDgrRqToZWzclQS6dH/9xZqae3lGtHWbP1mLZur94vrNf7hfXKjA3V3OxYzcqIGfXXk6pbuvXTNfl6blvFEbcnRbr1nUsna9Wc9GG/KX16erSuMzP1j01lMiXlV7fpS3/foke/sEjBLv79AyPI7xUo6vq6aZoPHr7RMIxfSfp3ST+S9KWTOM+PFSgo+5VpmncPOM/XJT3Q9zqXnOFrb5S0om+qtAY8foqk9ZL+3TCMx0zT3HISeYEztruiRbsrAtNbgl0OXTU7XS/vqrI5FQAAZ25+dpzeya+V35QO1XfoQG27xidF2B0LYxRFZQBwlpQ2dOqXbxRY629cMEF5ifzABzD6XTkrTUX1Hero8aq126t/7qxkSiOAYcUwjKskXdW3TOn7eo5hGI/0HdebpvmtIQ82xgwsKkuNDrUxycgT4XYpNyFcRfUd8vlN7a9p08yMGLtjAQAAAACG2Ku7q/VBYb0kyWFI9185jeaGGFEMw1B0aJA1Xe/GRVk2JxrZosOCdPPibN28OFuFNW1avaVcz26rUF1bj/WYsqYulTV16eWdVZqWFqV52XEalxguxzAvrjoVXb0+PfTBIf3+3YPq8vRPwgt2OXT7snH68oo8hbtHznapmRkx8vlNPb2lXJK0oahR33tul3527cxhXxQHwJoUtlJSsaTfHXX3DyTdLumzhmHcbZpmx3HOEyHps5I6JN171N2/lXSXpIsNwxh3eFrZ6by2aZrPftzrm6a5zzCMf0i6TdIKSRSVYUg8tbnMOr50eoqiw5jKDAAYHaJDgzQ5JUp7qwLF049vKNX3PzXV5lQYq2hbAwBngdfn17ee3qFuj1+SNDklUrefN87mVAAwNMLdLq2anWatt5c1a29li42JAOBfzJb0ub4/F/fdNm7AbdfalGtMObKojEllp2ragGllhzs0AgAAAADGjs5er3740l5r/dnF2ZqWFm1jIgDDyYTkSH33sin66Duf0L8tzta0tCg5BxQgef2mdpS36K8fFunnrxXojb3VamjvOc4Zh79er19/+6hY5/38Hf3yjf1HFJRdOj1Fb921XN+6eNKIKig7bE5WrO65ZJK1fnpLuf743iEbEwE4Bef3fX3dNE3/wDtM02yT9KGkMEmLT3CexZJCJX3Y97yB5/FLeu2o1xvM1z7M0/fVe5KPB85It8en5wdMHL1+Ac2MAQCjy6LcOOt49ZYydQ94HwsMpZF3pQQARoDfvFmojcWNkgKdIX96zUwFOanjBTB2TE2L1pzMGG0ra5YkPbe9Utnx4SPygzoAo49pmvfqX7s4YohVtXRZx6kxFJWdqmlp0XppZ5UkaQ/F2wAAAAAw5jz49gGrYUt8eLDuWjnpBM8AMBa5nA5NTo3S5NQodfR4taO8WVtKmo5o+NTS5dE7BXV6p6BOOfHhmpcdo6mp0QoNdtqY/OT1eH16bmuFfvvOAZU3dR1x3+SUSH3/U1O1JC/BpnSD58vL83SorkOr+yaW/XRNvnITwnXJ9BSbkwE4gcO/pO0/xv2FCkwTmyjprTM8j/rOM9ivLcMwoiRdI8mU9PrxHjvgOceaZjb5ZJ4PrNldrdbuQA1jVlyYFufG25wIAIDBlZcUobjwYDV29Kq126uXdlbp2nkZdsfCGMSuXgAYZB8U1ul37x6w1v9+4UTNyoyxMREA2OPymWk6WNeu1m6vOnq8emFHpW5cmGV3LADAMNDV61NzZ6ChpcthKCHcbXOikeeISWWVTCoDAAAAgLHkYF27Hvqgf0LNdy6drOjQIBsTARgJwt0uLclL0JK8BFU2d2lLaZN2lDWrs7e/E3pxQ4eKGzr0vKNSE5MiNDMjRpNTI+V2Db8Cs/Yer57YUKqH1h5STeuRU9aSo9z6xgUTdd38DLlGSfNXwzD041UzVNrYqY1FgQa333p6hyalRCo3IdzmdACO4/Ao2WN1hzt8+4k2Fp3OeQbltQ3DMCQ9JClZ0u9N09x3/KjA4HhyU6l1fP2CTDkcxnEeDQDAyOMwDC3MidOaPdWSpMc2lFBUBltQVAYAg6i2tVvffHK7TDOwXjo+QXeeP97eUABgk9Bgp66em6FH1hVLknZXtGhnebNmZlBoCwBjXXVrfyfk5KgQPgQ6DQOLyvKrWuX1+UfNBhkAAAAAwLGZpqkfvLBHHl/gw6h52bG6Zi6bTQCcmrSYUKXFhOrSaSnKr27TlpIm7a9pU9/H3PL5Te2rbtO+6jYFOQ1NSonSzPRoTUyOVLDL3mtQeypb9MTGUj2/rVLtPd4j7osNC9KdK8brs+dkKyRo+BXCnalgl0N/unmervzdhypt7FR7j1dfeWyrnr1zyaj8+wIYNn4p6dOSPpB018k+yTTNeR93e98Es7mDEw2jVXF9h9YfChRROwzxngcAMGrNzY7VG3tr5DNNbStt1p7KFk1Liz7xE4FBRFEZAAwSj8+vrz+5TQ0dvZKkxEi3fn39bDnZIAtgDJuYHKn52bHaXNIkSXphe6VyE8IVGULXXAAYy6qau6zjtJgQG5OMXPERbqVGh6iqpVs9Xr/217Rr6oBCMwAAAADA6PTijkqtPVAvKbC58v4rp9GsBcBpczkdmp4erenp0Wrt9mhHWbN2lreoYsD1O4/P1O6KFu2uaJHLYSgvMUKTUiI1MTlSceHBQ5KzvKlTa3ZX6587KrWj/F8H7iRGuvWFpbm6aVHWqP8MKjY8WL+/aa6u/v069fr82lvVqvtf2qsfr5phdzQAH+/wN61j7Qw+fHvzWTjPGb+2YRg/k/Tvkt6X9EnTNHuO9VhgMD21ucw6Pn9SklKi+TwRADA6RbhdmpYepZ1973Uf31CqH/H+DkOMojIAGASmaeq7z+6yOqQYhvTA9bOVGOm2ORkA2O+yGak6UNuu5i6Pujw+PbetQp9dnC3DYKMDAIxVVS39k8pSokNtTDKyzcmKUdWuaknS1tImisoAAAAAYJRr6fTohy/ttdafW5JD52IAgyYqJEjLJiRq2YRE1bf3aFdFi3aWN6umtb9+wOs3VVDTpoKaNklSTFiQxiWEKyc+XBmxYYP2+XhXr09bS5u0/lCD3t9f97GFZJI0LjFcty0bp1Vz0sfUpK7p6dH6z8un6D9f2CMpsOlwUW6crpydbnMyAB+joO/rxGPcP6Hv6/6zcJ4zem3DMH4t6ZuS3pF0uWmanSfICAwKr8+v1VvKrfV1CzJtTAMAwNm3KDfeKip7fluFvnvZFEW4KfPB0OFfGwAMggfeKjzizezdF03UkvEJNiYCgOEjJMipa+Zl6C9riyRJ+dVt2lbarLnZsTYnAwDYpbq1v6gsjc6Cp21uVqxeOVxUVtKkmxdn25wIAAAAAHA2/fS1fNW390qSUqJCdPfKSTYnAjBaJUS4df6kJJ0/KUk1rd3aWd6iPZUtqm07ckBNc6dHW0ubtbU0MOTG5TD0zNZy5SVGKDs+TNnxYUqJClF0WJBiQoMV5g4Ufpl+yev3q6nTo8aOXjW096iooUOFNe0qrG1TQXWbPD7zY7MFOx26ZHqKblyUpUW5cWO2ieHNi7O1oahRL+2skiT9x7O7NCczVlnxYTYnA3CUd/q+rjQMw2GKJVIoAAAgAElEQVSapv/wHYZhREo6V1KnpPUnOM96SV2SzjUMI9I0zbYB53FIWnnU6532axuBb6y/lXSnpDckXWmaZpeAIfJuQZ31O0dChFufmJxkcyIAAM6unPgwjU+K0IHadnX0+vTC9grdtIj9Hxg6FJUBwBl6enOZfvNmobW+bn6GvnL+eBsTAcDwk5cYocXj4rX+UIMk6aVdlcpLilB0aJDNyQAAdqhs7v/sMYWistM2b0CB9pbSJhuTAAAAAADOti0lTXp8Q6m1vveKqXQsBjAkkqNCdNHUEF00NVmNHb0qqG5VQU2bius71evzH/FYr9/U9rJmbS9rHtQMLoehc8cn6LIZKVo5NUWx4cGDev6RyDAM/eTqGdpT2aqi+g519Pr0rad36InbF8vpGJuFdsBwZJrmQcMwXleg6Osrkh4ccPd9ksIl/ck0zY7DNxqGMbnvufkDztNuGMajkm6XdK+kuwec56uSciS9ZprmoTN8bUPS/0j6oqRXJV1tmma3gCH0j81l1vG18zIU5HTYmAYAgLPPMAzdtChL9/1zryTp7+tLdePCrDHbRAVDj6vMAHAG1uyu1nef3WWtl01I0I9WzeAHOQB8jEumpaiwpk0NHb3q9vj17NZy3bIkh++ZADAGVbf0f/6YSlHZaZuWFq1gl0O9Xr9KGjpV19ajxEi33bEAAAAAAIPM4/Pre8/1fx51weQkXTwtxcZEAMaquPBgnZOXoHPyEuTzm6po7tKhunaVNXWpsrlLLV2eQXutCUmBhoWLxsVp2fhERYfRqPBokSFB+s31s3X1H9bJ5ze1sbhRf1l7SLefl2d3NABHulPSOkn/bRjGBZL2SVok6XxJ+yV976jH7+v7evQH6f8haYWkuwzDmC1po6Qpkq6UVKtA4diZvvb3FSgo65K0XdJ3Pubz/O2maT5/3L8xcJpqW7v1dn6ttb5ufoaNaQAAGDpXz8nQT9fkq9vj176qVm0va9acrNgTPxEYBBSVAcBpemF7he56aod8flOSNDklUr+/ae5xu6MM7CAJAGNNsMuha+dl6H/ePyRTUmFtuzYVN2lhbpzd0QAAQ6zyiKKyUBuTjGzBLodmZURrU3FgStnW0iY2FQIAAADAKPTwh0XKr26TJIUGOXXfldNo1gXAdk6Hoay4MGXFhVm3tfd4NTU1SsUNHSpt7FRJQ4caO3rV3OlRc5dHnT1eOQxDMiSHYSgmLEhx4cGKD3crPSZEE5IjNSEpQhOTI5lGdpJmZcboq+eP1wNvFUqSfvHafi2fmKRJKZE2JwNwWN/EsPmS7pd0iaTLJFVJekDSfaZpNp3keRoMwzhH0g8kXSVpmaQGSQ9L+r5pmuWD8Nq5fV9DJX33GFH+VxJFZTgrVm8tt/biLcyN07jECJsTAQAwNKLDgvSpmWl6ekvgV7rHNpRSVIYhQ1EZAJyGpzeX6Z5ndsoMvIdVbkK4Hrl1oSJD6I4GAMeTHR+uc8cnaO2BeknSK7urNCEpgg8GAWCMqW7pso6ZVHZm5mbH9heVlVBUBgAAAACjTXlTp379RqG1/uaFE5QRG3acZwCAfSLcLi2dkKClExLsjjKmfPUT4/V2fq12VbSo1+fXv/9ju57/yrkKdh27IS6AoWWaZpmkW0/yscfsHmCaZqOkb/T9ORuvfYukW0723MBgMk1TT2/ur428fn6mjWkAABh6Ny3OtorK/rmjUv/5yalM7caQ4OoBAJwC0zT15/cP6f+s7i8om5gcoX/csVgpbIYFgJNy0dRkJUa4JUm9Xr9e3FEp8/A3VQDAqNft8amp0yNJcjkMxff9TMDpmTegM9WWkpNqZgoAAAAAGCFM09S9L+5Rl8cnSZqcEqnPL809wbMAAGNNkNOhX18/yyoi21vVqt+/e8DmVAAAnJp1BxtUVN8hSYp0u3TZjFSbEwEAMLRmZURrWlqUJKnH69czW/9lEC1wVlBUBgAnqdvj011P7dCPXtln3TY1NUpP3n6OkiIpKAOAkxXkdOjaeRk63F6toKZNuytbbc0EABg6VS3d1nFyVIicjmM23MRJmJvdX1S2s6JFPV6fjWkAAAAAAIPptT01enNfrSTJMKQfrZqhICcf8QMA/tX4pEjdc/Eka/27dw6osKbNxkQAAJyaxzaUWMdXz01XaLDTxjQAAAw9wzB08+Jsa/3YhhKa9WNIcMUZAE5CZXOXPv3Hj/TctgrrtnnZsXritsWKCw+2MRkAjEyZcWFaNC7OWr+0o1JdvWyCB4CxoKqlyzpOZdrvGUuIcCsnPkxSYALoHgq1AQAAAGBUaO/x6t4X91jrGxZmad6AxiIAABzt1nNzNTszRpLk8Zn6zrO75PezAREAMPzVtnbr9T011vrGRdnHeTQAAKPXFbPSFOF2SZIO1nVo3cEGmxNhLKCoDACOwzRNvbC9Qpc+8IF2VbRYt9+wMFOP37ZI0WFBNqYDgJFt5dQURYYE3gC19Xj1+t5qmxMBAIZC9YBJZakxoTYmGT0GTivbUtxkYxIAAAAAwGD5xWsFqm4NvIdOiAjWty+ebHMiAMBw53QY+uk1M+VyGJKkLSVNR0x9AQBguHpqc5m8fYXQC3JiNSkl0uZEAADYI9zt0jVz0631wx8W2ZgGYwVFZQBwDPXtPfry37fqG09uV0uXR5Lkchj64VXT9eNVM+R2MWIbAM5ESJBTn5qZZq03FjWqtKHDxkQAgKFQNbCojEllg2Jgp/otJRSVAQAAAMBIt6WkUf/7UbG1/s/Lp9LoEABwUialROrOFXnW+qdrClTZ3GVjIgAAjs/nN/XExjJrffNippQBAMa2zy3JsY7fyq9VcT17KnF2UVQGAEfx+U09tqFEK3/9vtbs6Z+akxEbqsdvW6zPLs6WYRg2JgSA0WNaWpQm93WYMiU9t71Cvr7uUwCA0amqpX8DQ0oURWWD4YiistImmSY/SwEAAABgpOr2+HTP6p06/Nbu/EmJumJW2vGfBADAAF/5xHjlJYZLktp7vPrBi3tsTgQAwLG9W1Crir4C6LjwYF0yPcXmRAAA2GtcYoTOn5QoSTJN6ZF1xfYGwqhHURkADLDuQL0++d8f6HvP7VZjR691+w0Ls7Tmm+dpYW6cjekAYPQxDENXzEpTsDPwa2lNa4/WFtbZnAoAcDZVNfdPKkuLoahsMExIilSk2yVJqmvrUXkTnYcBAAAAYKT63TsHdLAu0H04PNipH62aQbNDAMApcbuc+q9rZlrrN/bW6J2CWhsTAQBwbI9tKLWOPz0vQ26X08Y0AAAMD7eem2sdr95SrrZuj41pMNpRVAYAkjYXN+rmhzboxoc2KL+6zbo9PSZUj9y6QD+5eoYi+jZpAgAGV0xYsC6ckmSt38qvPaKwFwAwulS19BeVpUSH2phk9HA6DM3OirHWm0sabUwDAAAAADhdeytb9Yd3D1rr71w2RWkxvHcGAJy6BTlxum5+hrW+78U96vH6bEwEAMC/Km/qPKLw+YaFWTamAQBg+Fg2IUHjkyIkBSZQP7253OZEGM0oKgMwZpmmqXUH63XzQxt07R8/0toD9dZ9oUFO3X3RRL1193KtmJR0nLMAAAbDOXkJSosOTKvx+k29sL1CpmnanAoAcDZUtw6YVBbNpLLBMj+7f6ry+oMUlQEAAADASOP1+fXtZ3bK6w9cF12YE6eb2FAJADgD375ksqJCAs1zixs69dAHRTYnAgDgSE9sLNXhrSHLJiQoJyHc3kAAAAwThmHoliU51vp/PyqWz89+SpwdFJUBGHO6PT49talMlz7wgW7884YjiskchnTN3Ay9860V+toFExQSxDhtABgKToehq+aky+hbF9a2a2d5i62ZAACDr73Ha02jDHIaio9w25xo9Dh3fLx1vPZAPcXZAAAAADDC/GVtkXZVBK6JBrsc+sk1M+RwGCd4FgAAxxYf4dbdKydZ69++fUAVzV02JgIAoF+v169/bOqfunLTomwb0wAAMPxcPTfdahRS0tCpt/NrT/AM4PRQVAZgzGjt9uhXrxfo3P96W/c8s1P51W3WfU6HoWvmZuitu1fol9fNUgoTEwBgyGXEhmlxXv+G+Jd2Vamr12djIgDAYCtt6LSOM2PD5GRz3KCZlRmjCHfgYmJFc5eKB/y3BgAAAAAMb0X1HfrVG/ut9TcvnKC8xAgbEwEARoubFmVpckqkJKnL49OPXt5rcyIAAALe2Fuj+vYeSVJylFsXTEmyOREAAMNLWLBLNyzKstYPf8j0aZwdFJUBGPUqmrr01OYy/XxNgf777QNq6JuMIEmhQU7dvDhLb921XL+8bpZyGaENALZaOSXZ6q7R0ePVm/tqbE4EABhMpY39hU5Z8WE2Jhl9gpwOLR4XZ63XFtbZmAYAAAAAcLL8flPfeWanerx+SdK0tCjdtmyczakAAKOFy+nQD6+abq1f2VWttYX1NiYCACDg7+tLrOPrF2QpyMl2ZgAAjvZv5+RYDZvXHWxQfnWrzYkwGvFbGIBRyec3tbuiRX96/6B+9+4BbS9rls80rfvTokP03Usna/13L9D/u2qGcigmA4BhwR3k1Cdnplnr9YcaVNncZWMiAMBgKm3ssI6z4ygqG2znjk+wjtceYGMIAAAAAIwET2wq1YaiRkmS02Hop9fMZDMlAGBQLciJ06o56db6By/uVm9fMTMAAHY4UNuujw41SJIchnTDwkybEwEAMDylx4Tq4mnJ1vqRD4vtC4NRi6vRAEaVrl6fPiis0y/fKNDjG0tV0tB5xP3zs2P1+5vm6v17ztcdy/MUHRZkU1IAwLFMT4vS+KQISZIp6cUdlfL7zeM/CQAwIgz8/TyTorJBt2xCf1HZuoMN8vrYGAIAAAAAw1lFc5d+8kq+tb7jvHGanh5tYyIAwGj13UsnK8LtkiQdrOvQI+uKbE4EABjLnthYah1fMCVZqdGhNqYBAGB4u/XcXOv4uW0VauzotTENRiOKygCMCs2dvfrnzkr9dE2+Xt1dreZOj3Wfw5BmZ8bozhV5Wv3lJbpsRqpcdHgEgGHLMAx9amaanEZgbHNpY6dWby23ORUAYDCUNvYXlWXHMy14sOUlRig5yi1Jauv2aldFi82JAAAAAADH4vebumf1DrX3eCVJ4xLC9fULJticCgAwWiVFheibF/b/nHngzULVtHbbmAgAMFZ1e3xavaV/D8hNi7JsTAMAwPA3PztW09OjJEk9Xv8RxdnAYHDZHQAAzkRjR6/e21+nrSVN8plHTrEJC3ZqYW6cFufGKyo0MJHs8Q1n9oP0Rt7EAsCQSIx0a+mEBL23v06S9F+v5uviqSlMmASAEW7gpLLseCaVDTbDMLR0fKKe6SvGXltYrzlZsTanAgAAAAB8nL9vKNGHBxokBRok/vzTsxQS5LQ5FQBgNPvckhz9Y1OZCmvb1dHr049f2acHPjPH7lgAgDHmpZ1VaukKNIzPjAvVeRMSbU4EAMDwZhiGbl2Sq7uf3iFJevSjEt1+3jgFMWAFg4R/SQBGpLZuj17YXqFfvVGgTcWNRxSUJUe5dfWcdH37kslaOTXFKigDAIws509KUkzf9/DGjl794vUCmxMBAM6Ex+dXRXOXtc6Ko6jsbFg6Id46Xnug3sYkAAAAAIBjKarv0I9f2Wet71iep3nZNAUBAJxdQU6H7rtimrV+YXulNhxqsDERAGCsMU1Tf/uo2FrfuDBbDodhWx4AAEaKy2elKiHCLUmqbu3Wq7urbU6E0YSiMgAjSo/Xp7f21eiXr+/XhqJG+QcMJ8uKC9MtS3L09U9M0PycOCqwAWCEC3Y59MmZqdb67xtKtKu8xcZEAIAzUdncJV/fL/DJUW66r58l545PsI63ljaps9drYxoAAAAAwNF8flN3P7Vd3R6/JGlScqS+eeEEm1MBAMaKJeMTjvj87f6X9lrXbQEAONu2lTVrZ9++j2CXQ5+en2FzIgAARga3y6mbF2dZ67+sLZJp8l4Og4OKCwAjgmma2l3Rol+/sV9v5deq1+e37suJD9cXlubqjvPGaWJypAyD7iUAMFpMTY3SxOQISZJpSv/5wm75+WALAEakkoZO6zg7LtzGJKNbUmSIJiVHSpI8PlMbihptTgQAAAAAGOjPHxzS1tJmSZLLYeiX182S20XjFQDA0PneZVMUEhTYMranslWrt5TZnAgAMFY88mGxdXzFrDRr4goAADixmxZlK7hv4MqOsmZtKm6yORFGC4rKAAx7De09+t+PivX4xlK1dvd32U+KdOtz52TrtmW5ykuMoJgMAEYhwzB0+cw0683Q9rJmPbWZD7YAYCQqbewvKsuKD7Mxyeg3cFrZh4X1NiYBAAAAAAxU3dKtX72+31p/44IJmp4ebWMiAMBYlBYTqtvPy7PWP39tv9q6PTYmAgCMBTWt3XplV5W1vmVJjn1hAAAYgRIj3bp6brq1/p/3D9mYBqMJRWUAhi2/aerDA/V64K1C7a9pt24Pd7t09Zx0ff2CCZqUEkUxGQCMcgkRbt2xfJy1/umafDV19NqYCABwOgYWlWXHUVR2Ni2b0F9U9u7+OhuTAAAAAAAO8/r9Wr2lTL0+vyRpVka0vrwi7wTPAgDg7PjS8nFKiQqRJNW39+h37xy0OREAYLR7bEOpvH5TkrQgJ5YGGwAAnIYvLsu1jt/cV6MDte3HeTRwcigqAzAsNXb06i9ri/TyrirrzaQhaVFunO66cKLm58TJQTEZAIwZd64Yr/SYUElSU6dHP3+9wOZEAIBTVdLQYR0zqezsWjwuXiFBgUs+B2rbdbCOi4gAAAAAYLd38utU2dItSQp2OfTL62bJ5eTjegCAPcKCXfr2pZOs9V/XFqm0ofM4zwAA4PT1eH16fEOJtb5lSe5xHg0AAI5lfFKkLpySZK0f+oBpZThzLrsDAMDRtpU26YXtlVanRklKjQ7Rqjnpyohl8ykAjEWhwU794FNTdfujWyRJT2ws1Q0LsjQjg85VADBSlAzYkJDFpLLjenxD6RmfY1xChPZWtUqSXttTrTtXjD/jcwIAAAAATk95U6fe219rre+5eJLGJ0XamAgAAOnKWen633Ul2l7WrF6fXz95dZ/+cPM8u2MBAEahl3dWqb69V1JgH+DKack2JwIAYOS6/bw8vbkvcK3x2a0VumvlRCVFhticCiMZrc8ADBu9Xr+e2Vqup7eUWwVlDkM6f1Kivrwij4IyABjjLpqarBWTEiVJpin95wu75e+bZgkAGN5M01RpY39RWXZ8uI1pxoapaVHW8et7amxMAgAAAABjW4/Xp6c2l+nwpcyFuXH6/Ll05QcA2M/hMPT9T0211q/urtb6Qw02JgIAjEamaeqRdcXW+ubF2QpiajMAAKdtQU6sZmXGSJJ6fX79bV3JCZ4BHB+/mQEYFuraevSH9w5oS0mTdVtCRLDuOC9PF01NkcvBtysAGOsMw9APPjVNwX0XF7eXNWv1lnKbUwEATkZ9e686e32SpEi3S7FhQTYnGv0mp0TK6TAkBX5mVrd025wIAAAAAMamgR35g50O/eLaWXL0vV8DAMBuc7NiddXsNGt9/z/3ykdTRwDAINpU3KSd5S2SpGCXQzcszLI5EQAAI5thGLrjvHHW+tH1Jero8dqYCCMdVRoAbFdQ3arfv3tANa091m2zM2P0lfPHKzOO6WQAgH65CeG67bz+Lr4/XZOvlk6PjYkAACejtLHDOs6KD5NhsHnubAsLdmnxuDhr/cbeahvTAAAAAMDYtKuiRZsHNFS8YlaasuL57AsAMLzcc8lkhQQFtpDtrWrV05vLbE4EABhN/uf9Q9bxNXPTFRcebGMaAABGh4unpSi77zpjS5dHT2wstTkRRjKKygDYxjRNfVBYp799VKIer1+S5HIYWjU7XZ+elyG3y2lzQgDAcPSV88crLTpEktTQ0atfvVFgcyIAwImUNHRax9lsnhsyF09LsY5f21NjYxIAAAAAGHuaOnv13LZyaz0zI1pzsmJsTAQAwMdLiwnVHeflWetfvF6gtm6aOgIAztyhuna9ld//GdUXlo47zqMBAMDJcjoM3T5gWtmfPzikHq/PxkQYySgqA2ALr9+vZ7ZW6NXd1TL7bosODdKXludpQW4ckwsAAMcUFuzS/718qrV+dH2J9la22pgIAHAipY39RWVZceE2JhlbVk7tLypbf6iB6Z4AAAAAMER8flNPbSpTtyfQVDE2LEhXzU7n8y8AwLD1peV5Su1r6ljf3qvfvXPQ5kQAgNHgL2uLZPZtDvzE5CSNT4qwNxAAAKPINXMzlBTpliTVtPbo2a0VNifCSEVRGYAh1+3x6W/rSrS1tMm6LSsuTHeuyFNaTKiNyQAAI8Wl01O0dHyCJMlvSj94cbfMw1ciAQDDTimTymyREh2iWZmBLvhev3lEJ0gAAAAAwNnzbkGtSvoarDgM6fr5mQoJctqcCgCAYwsNdurbl0y21n9dW6SShg4bEwEARrqG9h6t3tI/vfmLy3JtTAMAwOgTEuQ84ufrH987KK/Pb2MijFQUlQEYUq1dHv35g0M6UNdu3TYvK1ZfXJqryJAgG5MBAEYSwzB07xXT5HIEOvtuKm7S89vptAEAw1XJEZPKKCobShdPS7aOX9tTbWMSAAAAABgbius79HZ+rbX+xORkZcUztRsAMPxdOTtNc7ICTap6fX795JV8mxMBAEayv68vVY83sLF9WlqUzhkXb3MiAABGnxsXZSs6NLD/vqShUy/vqrI5EUYiisoADJmDde3643sHVdXSbd120dRkXT03XS4n344AAKdmfFKEvrC0v9PGj1/JV1u3x8ZEAIBjKWmgqMwuF09LsY7f21+nzl6vjWkAAAAAYHTr6vXpqc1lMvvWOfHhWjEp0dZMAACcLMMw9P3Lp1rrNXuq9dHBBhsTAQBGqm6PT4+uL7bWty0bJ8Mw7AsEAMAoFeF26ZYlOdb6D+8elGmax34C8DGo4gAwJPZWtuq6P36k5q7AZn+HIV0zN0PnT0riDSMA4LR97YIJSo5yS5Lq2nr0mzcLbU4EADhaR49X9e09kqQgp6G0mFCbE40teYkRmpAUIUnq9viZVgYAAAAAZ4lpmnp+e4X1WVhokFPXzc+Qg8/BAAAjyJysWK2ak26t739pr3x+NiQCAE7NM1vLVd/eK0lKjQ7RJ2em2pwIAIDR65YlOQoLdkqS8qvb9Na+WpsTYaRx2R0AwOi3tbRJt/x1o1q7Ax3xg5yGblyYrUkpkTYnA4Cx4/ENpXZHOCsi3C79x2VT9I0nt0uSHllXrOsXZGpiMj9jAGC4KG3sn1KWERsmp4PNdENt1dx0/WxNgSRp9ZZyrZqTYXMiAAAAABh9NhU3aVdFi7VeNSddMWHBNiYCAOD03HPJJL26u0rdHr/2VbXqqc1lumFhlt2xAAAjhNfn15/eO2Stbz03R0FO5l8AAHC2xIYH68aFWXpobZEk6b/fLtQFUxj6gpPHb2oAzqp1B+t180MbrIKykCCHPn9uLgVlAIBBc8WsNC3KjZMk+fymvv/CbkY4A8AwUlzfYR1nxYXZmGTsunpOhg7X8q072KCK5i57AwEAAADAKFPR1KV/7qy01gtyYjU9PdrGRAAAnL7U6FB9aXmetf7FawVq7fbYmAgAMJK8vKvKajoZHRqkGxdl25wIAIDR7/bzxsntCpQG7Sxv0TsFTCvDyaOoDMBZs6m4UV94ZLM6e32SpLjwYH1x6Thlx4fbnAwAMJoYhqH7r5xuTb5Zf6hRL+2ssjkVAOCwgpo263hCUoSNScaulOgQnTs+QZJkmtJzW8ttTgQAAAAAo0dnr1ePbyyRzx9odJUSFaJPzkizORUAAGfmjvPylBodIklq6OjV7945YHMiAMBIYJqm/vDuQWv9uSU5inC7bEwEAMDYkBQVopsGFHL/5s1CGvPjpFFUBuCs2FHWrFsf3qQuT6CgLDnKrafuOEdpMaE2JwMAjEaTUiL1uXNyrPWPXt6njh6vfYEAAJb9A4rKmFhsn2vnZVjHq7eUc/EQAAAAAAaB3zT19OZyNXUGpre4XQ7duChLwS4+hgcAjGyhwU5959LJ1vrhtcUqaeiwMREAYCR4O79W+dWBzwbDgp26dUmOvYEAABhDvrT8yGll7xbU2ZwIIwVXswEMun1Vrfq3v25Ue99m/oQIt564bbHGM5UAAHAWffOiCUqIcEuSqlu79eDbdEwEgOHg8AdHEkVldrp4Wooi+zpBFjd0aktJk82JAAAAAGDke39/3RETuq+dl2FdowQAYKS7Ylaa5mTFSJJ6fX79+JV9NicCAAxnpmnq9wOmlN2wMEux4cE2JgIAYGxJigrRjYuyrPVv3txPw2GcFIrKAAyqA7XtuvmhDWrpCnRkjA0L0mNfXKRxiRSUAQDOrqiQIP3HZf0dE/+y9pAO1rXbmAgA0O3xqbg+0L3WMKQJSRSV2SUkyKnLZ6VZ69Vbym1MAwAAAAAj34Hadr2xt8ZaLxufoGlp0TYmAgBgcBmGoe9fPtVav7anRusO1tuYCAAwnG0sarSaGgY5Dd22bJzNiQAAGHu+vDzPmla2g2llOEkUlQEYNKUNnbrpofVq6OiVJEW6Xfrb5xcxjQAAMGRWzUnX/OxYSZLHZ+reF/fQbQMAbHSwrl3+vm/D2XFhCg122htojLt2XoZ1/PLOKnX1+mxMAwAAAAAjV0uXR//YVKrDVx5z4sO0clqKrZkAADgb5mTFatWcdGt9/z/3yufnszcAwL968O0D1vE1czOUEh1iYxoAAMamo6eV/eoNppXhxFx2BwAwOlQ2d+nGh9arprVHkhQW7NQjn1+gGRl0ZAQADB3DMHTfldP0qQfXym9KHxTW67U91bpkeqrd0QBgTCqobrOOJybTbMJuc7NilJsQrqL6DrX1ePXq7ipdPTfjxE8EAAAAAFg8Pr+e3Fiqjr5GHRFulz6zMEtOh2FzMgAY2R7fUHrazx24Yf8S9S8AACAASURBVA6D755LJmnN7mp1eXzKr27TPzaV8d8cAHCEjUWNWnsgMM3S6TD0peV5NicCAGDs+vLyPD2+oVQ9Xr92VbRoze5qXTqD/ZM4NiaVAThjdW09uvmhDSpv6pIkuV0OPfS5+ZqXHWdzMgDAWDQtLVo3L8621j98aR+TWADAJgU1/UVlk5lgbDvDMI6YVvbXD4voSAUAAAAAp+i/Xs1XSWOnJMmQ9JkFmYoKCbI3FAAAZ1FqdOgRxQE/fy1fTR29NiYCAAw3v35jv3W8ak66chLCbUwDAMDYlhQVoluW5FjrX7xeIK/Pb18gDHsUlQE4I00dvbr5oQ06VN8hSQpyGvrjZ+dpSV6CzckAAGPZ3RdNUnx4sCSporlLv3/3gM2JAGBsOmJSGUVlw8INC7PkdgUuB+2uaNXGokabEwEAAADAyPHMlnL9ZW2RtV45LUXjEiNsTAQAwNC4/bxxSo8JlSQ1dXr0s9fybU4EABgu1h9q0EeHGiQFppR97RPjbU4EAAC+tDxPkW6XJOlgXYee3VZhcyIMZxSVAThtrd0e/dtfN1rTB5wOQw/eMEfnT0qyORkAYKyLDgvSty+ZbK3/9N4hFfcVQAMAhs7+AUVlk5IpKhsO4sKDdfXc/mllAzdDAgAAAACObUtJk7777C5rPSU1Sssm0GQRADA2hAY7de8V06z1k5vKtK20ycZEAIDhYuCUsmvmpis7nillAADYLTY8WLedN85aP/BmoXq8PhsTYTijqAzAaeno8erWhzdpV0WLJMkwpF9+epYumZ5qczIAAAKunZeh2ZkxkqRen1/3/nOPTNO0ORUAjB0tXR5VtnRLkoKdDuUk8AHScPGFpTnW8Rv7alTSQOE1AAAAABxPZXOX7nh0i3p9fklScpRb183LkMMwbE4GAMDQuWhqsi6YHGgybJrS/31+t3x+PnsDgLFs3cF6bShqlCS5HIa+9okJNicCAACHfX5pruLDgyVJFc1demx9qc2JMFxRVAbglHV7fLrtb5u1paS/69SPV83QVXPSbUwFAMCRHA5D9185TYf3dbxbUKeXd1XZGwoAxpDCmv4pZeMSwxXk5BLEcDE+KVLLJyZKCmz+ePjDYnsDAQAAAMAw1tnr1W1/26z69h5JUmxYkD67OEfuIKfNyQAAGHr3XjFNblfgWu+eylb9fX2JzYkAAHYxTVO/eaPQWl87L0OZcWE2JgIAAANFuF36yvnjrfXv3jmgtm6PjYkwXLGjC8Ap6fX69eW/b9G6gw3WbT/41FTdsDDLxlQAAHy8mRkxunlRtrW+98W9aunkjREADIWCAUVlk1MibUyCj/OFpbnW8VOby9TSxc9HAAAAADia32/q7qd2aE9lq6RA5/0/3DxPcX0dfgEAGGsy48L01QGbEn/xeoHq2npsTAQAsMs7BbXaWPz/2bvv+Krq+4/jr3N3dkIWEEjCCmHLEhAXTqx71ap1j2prtdX+Wtv6a7Wttr9aW7e2tWqrolXrlipuBJUNMsIIEAIBsve48/z+uMlNgmySnIz38/HI49zvOfec80nOTXK+5/v9fL+ts5S17bQuIiIi3cPl0zPJSIwCoLzexxOfbrY4IumOlFQmIgctEAxx20sr+GRDaWTd/5w+kmtmDtnPXiIiItb6n9kjSY93A1BW5+UP7+VZHJGISN+wYXdrUlmOksq6neNGpJCTHgtAgy/IS4sLLY5IRERERESk+3noo038d83uSPm3541l+tBkCyMSERGx3o0nDGVISgwAtU0Bfj9XbW8iIn1NMGTyf//dEClfNi1Ts5SJiIh0Q26HnZ+cnhMpP7VgKzsqGyyMSLojJZWJyEEJhUx++urX7RrObpk1XCOMiIhItxfvcXLPOWMj5RcXb+erLeX72UNERDpC26QyzVTW/RiG0W62sr9/vpUGX8DCiERERERERLqXd77eyUMfbYqUrz4mm0uPzrQwIhERke7B7bBzzzljIuXXVhSp7U1EpI95fUURG4rDbYExLjs/PGmExRGJiIjIvpw7IYPxgxIA8AVC3P/+hgPsIX2NkspE5IBM0+SXb6zhtRVFkXXXHTuEO07L2c9eIiIi3cfssf05fUx6pPyL11fT5A9aGJGISO9mmmakIQkgJ11JZd3RuUdlMCDBA4Rn83xmYYG1AYmIiIiIiHQTq3dU85NXVkXKx41I4a4zR1kYkYiISPdyfE4qZ44bECn/6s01+IMhCyMSEZGu0uQP8ud5rZ3Rbzh+KKlxbgsjEhERkf2x2QzuOnN0pPzmyp2sKKy0MCLpbpRUJiL7ZZomv30njxcXF0bWXTYtk7vOHIVhGBZGJiIicmjuOWcssW4HAFtK63n8080WRyQi0nuV1nqpavADEOt2kJEYZXFEsjcep51bT24dOfKvn22muvm6iYiIiIiI9FXbKxq45tklNPnDHeOHpMTw6KWTcNjVtC4iItLWXWeNItplB2BjcR3PLNxqcUQiItIVnvtyGzurmwBIiXVx/XFDLY5IREREDuToIf2YPaZ/pPy7d/MwTdPCiKQ70ZNvEdmvB+Zt5Ok2D/4umJjB784dq4QyERHpcfonePjZ7JGR8hOf5rOpzSw6IiLScdbvbjtLWazqD93YRZMHMSQlBoCapgB/+1xJ1yIiIiIi0neV13m58unFlNV5AYjzOHjqqikkRDstjkxERKT7GZAQxY9OaR206sEPN7GzqtHCiEREpLNVN/h59JP8SPnWk0dEBvcVERGR7u3OM3Jx2sP9d5Ztq+Sdr3dZHJF0F7qbE5F9euyT/HaVwG+N688fLxqPzaYOoSIi0jNdPi2LN1buZNm2SvxBkztfW80r35uh/20iIh1sY5uk3ZH94yyMRA7Eabfx41NzuPXFFQA8vaCAq47JJi3OY3FkIiIiIiIiXaveG+DaZ5ewtaweAJfDxlNXTmFYaqzFkYn0HXMWFR7WfpdNy+zgSKSv6Emfue4a6zUzh/Dqsh1sLK6jwRfkrjfW8I+rpmigMRGRXurBjzZS3egHICs5mu9M1X2YiIhIT5GdEsNVM7J5akF4spl7383jpNw0YpQg3udppjIR2aunF2zl/vc3RMon5abx4CUTcdj1Z0NERHoum83g9xeMazfixpzFh9cIJyIi+7ZuV03kdU66ksq6u7PGDWDUgHgAGv1BHv9Es5WJiIiIiEjf4g+G+MGc5azaUQ2AYcDD3zmKaUOTLY5MRESke3Pabdx7/rhI+eP1Jby1aqeFEYmISGfZVFzLv77cFin/bHYuLof6EoqIiPQkPzx5BCmxbgB21zTx8MebLI5IugPd0YnIN7y0uJDfvLMuUp45PJnHL5+kSqCIiPQKOelx3HTCsEj5//67nuKaJgsjEhHpfZZvq4y8Hj8owcJI5GDYbAb/c3pOpPzCom3kl9TuZw8REREREZHewzRNfv7aaj7dUBpZ95tzxzJ77AALoxIREek5pmb348oZWZHy3W+tpbzOa2FEIiLS0UzT5J631xEMmQBMH9qPM8b2tzgqEREROVQJUU5+8a3cSPkfn29V/xBRUpmItPfGiiJ+/vrqSHlKVhJ/v3IKHqfdwqhEREQ61g9mDWdoSgwAtd4Av3x9NaZpWhyViEjvUFbnpaC8AQCXw8bYDCWV9QSzRqYxNTsJAH/Q5Bevr9H/RhERERER6RP+NG8Dry7bESnfMms4V0zP2s8eIiIisqefzs4lIzEKgMoGP3e/ve4Ae4iISE8yb10xC/LLALAZ8Ouzx2AYhsVRiYiIyOE4f2JGpH9IIGTyqzfXqn9IH6ekMhGJeGvVTm5/eSUt/xfGD0rg6WumEu1yWBuYiIhIB/M47dx3wbhI+cO8Ev6zvMjCiEREeo9lbWcpy0jA7dAAFT2BYRj89ryxOGzhBsDFWyt4pU2nShERERERkd7on18U8NgnmyPlb08ZxB2n5exnDxEREdmbWLeDe88fGym/vWonH6wrtjAiERHpKE3+IL97tzVZ+PJpWYwaEG9hRCIiInIkDMPgN+eOxd7cP+SLzeW8/fUui6MSKympTEQAeOfrnfzopRU0z1BNbv84/nnN0cR7nNYGJiIi0kmmD03mqhmtIw7f89ZadlY1WhiRiEjv0DapbHJWkoWRyKHK7R/PdccNiZTvm5tHeZ3XwohEREREREQ6z5sri7j77bWR8km5adx3/jiNti8iInKYThyZxgWTMiLln7+2mop6n4URiYhIR/j7/C1srwj3pUiIcnL7qRqIQ0REpKcbNSCeq2ZkR8q/eXsdVQ2qv/VVSioTEeau3sVtL62MJJTlpMfywvXTSIpxWRuYiIhIJ/vZGblkJ0cDUOsN8LP/fK2pnEVEjpCSynq2204ewaCkKACqGvzcN3e9xRGJiIiIiIh0vPfW7Ob2l1fR8ijwqMGJPHrZRBx2NZ+LiIgcif89czSpcW4Ayuq8/PL11Wp7ExHpwbaU1vHIJ/mR8h2n5ahPoYiISC/xo1NHkB7fWn/73bt5FkckVnFYHYCIWOu9Nbu59cUVBJszyoanxfLC9dNJjnVbHJmIiEjni3Y5+NPFE7j4r19imvD5pjKeX1TIFdOzDryziIh8Q5M/yOod1ZGyksqsNWdR4WHtd3JuGv/8chsA/1m+g7MnDODEkWkdGZqIiIiIiIhlPt1Qwg9fXB5pG8tJj+Xpq6cS7VLTuYiIyJFKinHxxwvHc82zSwD475rdvLGyiPMnDrI4MhEROVSmafKL11fjC4QAGD8ogcunqS+FiIhIbxHvcfK788Zxw7+WAvDqsh2cM2Egx+ekWhyZdDUNtSbSh81bu5tb5iwn0NxoNiw1hjk3TIuMGiUiItIXTMnux43HDY2U73s3j/ySOgsjEhHpudYUVeMLhhuWhqTEaLCKHmpk/3jGZiREyne8vIqSmiYLIxIREREREekYX2wu43vPLcMfDLeNDUmJ4fnrptFPI+2LiIh0mFm5aVx6dGak/Ks317KzqtHCiERE5HC8snQHX22pAMBuM7jv/HHYbYbFUYmIiEhHOnV0OmeNHxAp//y11dR7AxZGJFZQUplIH/XhumJ+0CahbGhKDC/eMJ20OI/FkYmIiHS9H5+aQ056LACN/iC3vbQCbyBocVQiIj3Psm2VkdeapaxnO2fCQOLc4VH6y+t93PbSysgo/iIiIiIiIj3RF/llXPvsErzNo+xnJEbxwvXTSItX25iIiEhHu+vMUWQlRwNQ2xTgJ6+sIqTniyIiPUZprZd75+ZFytcfO6TdgIQiIiLSe9x9zhgSo50AFFU18sf31lsckXQ1JZWJ9EHz1u7m+y8sj4zCmJ0czZwbpqvRTERE+iyP085D35mIyxG+PV67s4Y/vb/B4qhERHqepW2SyqYoqaxHi3U7+PbUwbSMN/nllnIe+XiTpTGJiIiIiIgcrgWbyrjm2SU0+cMJZenxbubcMI2BiVEWRyYiItI7xbgdPHDxBFomtPliczlPfLbZ2qBEROSg/eaddVQ3+gEY3C+KH52SY3FEIiIi0llSYt38+uzRkfI/v9zGwvwyCyOSrqakMpE+5j/LdnDzC8vxBcONZlnJ0bx443T6JyihTERE+rZRA+L5+Rm5kfLfP9/K/I2lFkYkItKzmKbJ8rZJZdlKKuvphqXGMis3LVJ+6KNNfKEHhyIiIiIi0sPM31jKdf9snaFsQIKHf984g6zkGIsjExER6d2mZPfjB7OGR8oPzNvAkoIKCyMSEZGD8faqnby9amekfO9544hy2S2MSERERDrbeUdlcFKb/iF3vLyKqgafhRFJV3JYHYCIdJ1nF27l7rfXRcrZydG8cMN0BiRoFEYRERGAq4/JZv7GUj7ZEE4mu/3lVcy97VjS4pR8LSJyIAXlDZTXhx8oJUQ5GZoSa3FE0hFOyk2jwRfgqy0VmCbc9Pwy/nPzMYxIj7M6NBERERERkQOat3Y3t8xZERlscWCChxdvnG5pQtmcRYWHtd9l0zI7OBLpK/SZExEr3XbyCL7aUs6SgkpCJtz64grm3nocSTEuq0MTEZG92F3dxF1vrImUL5o8iONzUi2MSERERLqCYRj84cJxzH7wcyrqfeyuaeIXr6/mscsmYRiG1eFJJ9NMZSJ9gGmaPPThpnYJZbn943j5phlkJCqhTEREpIVhGNx/8QRSYt0AlNV5ufXFFQSaO52IiMi+LW0zwuzkrCRsNj1U6g1shsFD35lIalz4f2NNU4Crnl7M7uomiyMTERERERHZvzdWFHHzC8sjCWUZiVG8pBnKREREupTDbuOh70wkMdoJwK7qJv7n1VWYpmlxZCIisifTNPmfV1dR3egHwnWoX5892uKoREREpKukxXn444XjI+W5q3fz6rIdFkYkXUUzlYn0MIc6klzINPnv6l0s3FweWTcpM5Fnrj6ahOaHdp15/t6mr3//IiJW6Yi/vwc7qmpKrJu/XDKBK59ejGnCV1sqeOCDjfxsdu4RxyAi0pst21YZeT05K8nCSKSjpcd7eObqqVzy1y+p9wXZWd3E1c8s5uWbZhDvOfR6pYiIiIiISGd7YdE27npjDS391bOSo3n+umkM7hdtbWAiIiJ90MDEKP500QSu/9dSAD7MK+HJz7Zw84nDLI5MRETaeu6rbXy+qQwAw4A/f3sCcWoHEhER6VNOGZ3OZdMyI/01735rLVOy+zEkRQN19WaaqUykFwuGTF5bXtQuoey4ESk8f/20w0ooExER6SuOG5HKj07OiZSf+HQzH6wrtjAiEZHub6mSynq1sRkJPP7dyTiaZ6Bbv7uWG/65lHpvwOLIREREREREWpmmyUMfbuKXr7cmlI1Mj+OV781QQpmIiIiFThmdznXHDomU//j+ej7bWGphRCIi0taG3bXcNzcvUr7xuKFMG5psYUQiIiJilbvOHMXQ5iSyel+Qm59fRqMvaHFU0pmUVCbSS/mDIV5aUsjywtaOnWMGxvPUVVOIdmmSQhERkQP54UnDOSEnNVK+/eWVbCuvtzAiEZHua3tFA/kldQC47DYmDEq0OCLpDCfkpPKHC8dHyou2VnD5U4uoavBZGJWIiIiIiEhYIBjiF6+v4S8fboysmzAogZdunE5avMfCyERERATgzjNyOTq7HwCmCT+cs1xtbyIi3UCdN8DNLyyjyR8CILd/HLeflnOAvURERKS3inY5ePjSibjs4VSj9btr+eUbqzFbRvGSXkdJZSK9UJ03wFOfb2HtzprIuslZSXxnaiZuh93CyERERHoOm83gwUuOIiMxCoDapgDX/3MptU1+iyMTEel+2s7mOHN4MlEu1Tt6q4smD+LnZ+RGyiu3V/Gdv31FSW2ThVGJiIiIiEhf1+gLctPzy3lxcWFk3bHDU3j++mkkxbgsjExERERaOO02Hrt8Ev2bk71rmgJ877llNPgCFkcmItJ3mabJz/7zNVtKw0m+UU47D186UX0MRURE+rixGQncc+6YSPm15UXMafPsVXoXJZWJ9DIltU088Wk+2ysbI+uOHZ7CBRMzsNsMCyMTERHpeZJiXDx++SRcjvBt86aSOn744goCwZDFkYmIdC/vr90deX3amP4WRiJd4XsnDOM3bR4ert9dy7ef/DIyW52IiIiIiEhXKqlp4pK/fcmHea0Dnpw/MYOnr55KnMdpYWQiIiKyp9Q4N09eMTnS9rZ+dy0/emklwZBGvBcRscI/vyjg3a93Rcp/uHAcOelxFkYkIiIi3cV3pg7mosmDIuV73lrHyu1VFkYknUVJZSK9SH5JHU9+tpnKhvAMKgZw1vgBfGvcAAxDCWUiIiKHY8LgRP7vwnGR8qcbSrlv7noLIxIR6V4q6n0sKagAwDDg5FFpFkckXeHKGdn8+dsTIoOXFJQ3cO6jC/jv6l0H2FNERERERKTjrCmq5tzHFvL1jurIuptOGMYDF0+IdFYXERGR7uWowYn87ryxkfK8dcX89p11mKYSy0REutKybZXcOzcvUv7u9EzOPSrDwohERESkOzEMg9+dN5ZRA+IB8AVD3PivpeysajzAntLT6Em6SC9gmiYL88t4ZuFWmvzhmVOcdoPvTs/imGEpFkcnIiLS850/cRA/mDUsUn564VbmLNJ0ziIiAB/lFdMyiOykzCTS4jzWBiRd5oJJg3j88kl4nOHHS/W+IDe/sJzfz83TrJ4iIiIiItLp3luzm4uf/JJd1U0A2G0Gvz1vLHeekYvNpsEWRUREurNvTxnMDccNiZSf/aKAfyzYamFEIiJ9y/aKBm7811L8wXAj3/hBCfzvWaMtjkpERES6G4/TzpPfnUS8xwFASa2Xa59dQp03YHFk0pEcVgcgIkfGHwzx5soilhe2TicZ53Fw5fRsMpKiLIxMRESkd7nj1JFsLqnnvbW7AbjrjdWkxLo4bUx/iyMTEbHW+2uLI69PH5NuYSRihdPH9Oe1m2dy0/PLKKxoAOCv87ewcHMZf7xwAqMHxlsc4f51ZJL4ZdMyO+xYIiIiIiKyb8GQyV8+2Mijn+RH1sV5HDx++SSOG5FqYWQiIiJyKH5+xiiKqhqZuzrc9nbv3DwyEqM4Y9wAiyMTEendqhv9XPvsEsrrfQD0i3Hx2GWTcDvsFkcmIiIi3VFWcgxPXjGZK/+xmEDIZP3uWm6Zs5ynrpyCw645rnoDXUWRHqyi3sff5m9pl1A2OCmKH5w4XAllIiIiHcxmM/jzJRMYmxHuHB8y4ZYXV7BoS7nFkYmIWKfBF+DzTaWR8qmjlWjbF40eGM/btxzLSblpkXVrimo459EFPDBvA95A0MLoRERERESkN6lq8HHts0vaJZRl9ovm9e8fo4QyERGRHsZmM/jzt49iclYSAKYJt720kvkbSw+wp4iIHC5/MMQtc5azqaQOAJfdxt+umMzgftEWRyYiIiLd2THDUvj9BeMi5U83lHL322sxTdPCqKSjKKlMpIdat7OGRz/ZRFFVY2Td5MwkbjhuKPFRTgsjExER6b2iXQ6eufpospLDD1R9gRDX/3Mpa3dWWxyZiIg15m8sxRsIAZCTHsuQlBiLIxKrJEQ7eerKKfxsdi4uR/hxUyBk8sjH+Zz65/m8ubKIUEgPE0VERERE5PB9vaOKsx9dwGdtOpofNyKFN38wk+FpcRZGJiIiIofL47Tz9yunRJ4t+4IhbnxuKV9pUEcRkQ5nmiZ3vb6GzzeVRdbdf/F4pmT3szAqERER6SkunjKYW2YNj5Sf/6qQP83bYGFE0lGUVCbSwwSCIeau3sXzi7bR5A933rQbBmePH8AFkzI0jaSIiEgnS41z89y100iNcwNQ6w1w1dOLyS+ptTgyEZGuN29tceT1aZqlrM+z2QxuPnEYc289jinNowsDFFY0cNtLKzn70QV8uqFEI1WJiIiIiMghMU2Tpz7fwoVPfMH2itbBFr9/4jCeveZokmJcFkYnIiIiR6pfjIvnrjuajMQoAJr8Ia59dgnLtlVaHJmISO9hmia/fSePfy/dHln3o1NGcO5RGRZGJSIiIj3N7afmcM6EgZHyY59s5rFP8i2MSDqCsk9EepD8klqe+GwzC/JbRwtJjHJy4/FDmTEsBcMwLIxORESk78hMjuZf1x5NnMcBQFmdj+/87Ss2FiuxTET6Dn8wxEfrSyLl08coqUzChqfF8vL3ZvCbc8eQ0GYm7bU7a7j6mSWc8dDnvLx0O95A0MIoRURERESkJ6io93HDv5byu3fz8AfDA1TEuh08+d1J/HR2Lnab2sZERER6g0FJ0bxw/TTSmgd1bPAFufqZxSzbVmFxZCIivcMD8zby9MKtkfIFkzK47eQRFkYkIiIiPZHNZvCniydwcm5aZN3972/gmTb3GdLzKKlMpAcwTZN/fVnAmQ8vYFd1U2T9yPQ4bjlpOIP7RVsXnIiISB81akA8z1w9lWiXHQgnll36t69Yv7vG4shERLrG/I2lVDf6ARiQ4GFsRrzFEUl3YrMZXDkjm/k/ncXNJw7D42x9BLV+dy0/ffVrZv7hY377zjrWFFVr9jIREREREfmGj9cXc9pf5vNhXuuAJuMyEnj31mOZPXaAhZGJiIhIZ8hOiWHODdNIbp6FtLYpwHefWsz8jaUWRyYi0rM99kk+j7aZQeRb4/rzxwvHawB7EREROSwuh43HLp/EMcOSI+vueXsdT32+xcKo5EgoqUykmyssb+DypxbxqzfX4g2EAHDYDM4aP4ArZmQR7XJYHKGIiEjfNSW7H/+69mhi3eH/x+X14cSy1TuqLY5MRKTztR3N8KzxA9TwJHuVEOXkZ7Nz+fQns7hqRlYkGRvCCdn/WLCVsx5ZwKl/mc/9769nSUEFgWDIwohFRERERMRq9d4AP3/ta659dilldd7I+mtnDuHVm2eQlRxjYXQiIiLSmYanxfH89dNIiQ0nljX6g1z3zyXMXb3L4shERHoe0zR5YN4G7n9/Q2TdSblpPHjJRBx2dR0WERGRw+dx2vn7lVOYnJUUWfe7d/P487wNGlS4B9KdoUg3FQqZPLtwK6c/OJ8vNpdH1veP9/D9WcM5ZlgKNnXaFBERsdyU7H7889qjiWtOLKts8HPJ377kkw0lB9hTRKTnWrezhoX54XqKzYCrjsm2NiDp9voneLjn3LF8eefJ3HlGLgMSPO2255fU8dgnm7n4yS+Z+NsPuOFfS3nsk3wW5pdR0+Tv1NhM0yQQCtHkD9LoC+L1B/EFQoT0oFNEREREpMt9sqGE0/4ynxcXb4+sS41z88zVU/nV2aNxO+z72VtERER6g1ED4nn5ezMY2PwM0R80uWXOcp77ssDSuEREepJgyOR/31zDIx+3zlB2zLBkHr98Ei6Hug2LiIjIkYtxO3jmmqlMaZNY9vDH+dzz9jpCIfW36Ek0xZFIN7SmqJq73ljDyu1VkXU2A244figDE6JwaqQQERGRbmVyVhLPXT+NK/+xiJqmAA2+INf/cyn3nT+WS6ZmWh2eiEiH+8eC1lnKzhg3gEFJCuKNPgAAIABJREFU0RZGIz1JQrSTm04YxvXHDuGLzeW8saKI99bupsEXjLyntinAB+uK+WBdcWRdSqybISnRZCXHkBrnJjnGRb8YF9EuBy6HgdNuw8Cg0R+k0R+kyReMvG7wBalt8lPd6KemsWUZoLrRT0WDD38gxL4eZ3qcNqKcdqJdDhKjnSTHuEiKcZEe52FAokcdWkVEREREOkhZnZffvrOON1fubLf+zHED+N15Y0mKcVkUmYiIiFhhaGosr9x8DFc8tYgtZfWETPjfN9eyqaSOX501WjPsiIjshy8Q4o5XVvH2qtb61ayRqTx++WQ8TrVriIiISMeJ9zh57rpp3PT8Mj7bWArAs18UUFrr5U8XTyDKpXuPnkBJZSLdSHWjnz/P28BzX22jbYJuTnos9180gQmDE5mzqNC6AEVERGSfjhqcyH9uPoarn1lCUVUjwZDJz/6zmm3lDdxx2kjsNs0wKiK9Q0lNE2+tKoqUrz92iIXRSFfozHrolOx+jB+UyMbi2shXTVPgG+8rq/NSVudlSUFlp8WyN03+EE3+EJUNfoqqGtttM4CUODeDEqMYkhLDcSNSGNxPCZYiIiIiIociGDKZs7iQB+ZtoKqhdZbipGgnd58zhnMmDMQw9FxNRESkL8pIjOLlm2Zw7bNL+HpHNQD/+nIbW0rreeyySSREOy2OUESk+ymt9XLz88tYuq21PeXcowbyp4snaCB7ERER6RRRLjt/v3IKP/73St5dvQuAd1fvoqC8nr9fOYWBiVEWRygHoqQykW7AHwzx4uJCHvxwExX1vsh6l93GzScO4/uzhnXY6OdKShMRkb7qSP8HXjbtwDOOjUiP4/XvH8M1zy5h7c4aAB7/dDOri6p56DsT6acRlUWkF3juq234g+FRMCZnJTExM+kAe4jsn8thY2xGAmMzEjBNk5JaL9srGthR2cj2ygZKar0EQ/uaS6xj2AwijammCSYmgaC5zxnMAEzCjbOltV5WbK/itRVFZCRGMSs3lZNHpTNjaLJG/BQRERER2Y/FWyv49VtrydtV0279+RMzuOvMUSTHui2KTERERLqLlFg3/75xBj95dRXvfh3unLggv4yzH13AI5dOZMLgRIsjFBHpPlbvqObG55ayq7opsu7KGVncffYYbBoEV0RERDqRy2Hj4Usnkhrn5tkvCgBYu7OGcx5dyF+vmMzkLPUt6s6UVCZiIdM0+SivhPv+m8eW0vp2247PSeWec8YwJCXGouhERETkcKTFe/j392Zwy5zlfLohPKXz55vKOPuRBTzx3UmMH6TGLRHpuZr8QZ7/alukfJ1mKZMOZhgG6fEe0uM9TMkOrwuZJtUNfsrqvVTU+6j3Bqj3Bqn3BfAHTYKhEIGQCWb4QaXT3vJlRMoehw2P006Uy05Um6XHacdpt+11RtGQadLkD9LoC1LnDVBR76OiwUd5nY+dVY2U1nq/kXRWVNXI818V8vxXhUS77Bw3IoWTR6VzUm4aKeoQKyIiIiICwObSOv70/gb+u2Z3u/WD+0Xx23PHcuLINIsiExERke4oymXn0UsnMiItlgc/3ARAYUUDFz7xBT+bnct1xw5RsoSI9GmmafKf5UX88vXVeAMhAAwD7pydy43HD9XszyIiItIl7DaDu88Zw4j0WH795loCIZOyOi+X/PVLbj8th5uOH6a6WzelpDIRC5imyYL8Mh6Yt5GV26vabctIjOKXZ47ijLH9VaETERHpoWLdDv5x1VQe/HAjj3ycD4Q7mV/4xBf86JQcvnf8UBzNs6GIiPQkLy/dTmWDH4BBSVGcNjrd4oikL7AZBkkxLpK6eMZPm2EQ7XIQ7XKQHOsmK7n9oC++QIhd1Y0UlDewpbSOoqpGGnzByPYGX5D31xbz/tpiDAMmDk7klNHpnDIqnRFpsarzi4iIiEifU1zTxEMfbeLfS7a3m43Y47TxgxOHc8PxQzXbr4iIiOyVYRj86JQcRqbH8dNXv6bWGyAQMrl3bh4L8sv4w4XjGJAQZXWYIiJdrrrBzy/eWB2ZzREgzuPgkUsnasAOERERscTl07IYmhLLzS8so6rBTyBk8sf3NrAwv4w/f/so0uM9Vocoe1BSmUgXakkme+TjfBZvrWi3Lc7t4PuzhnPNzGw1mImIiPQCdpvBHaeNZMKgRH788kpqm8Kzqdz//gY+zCvmgYsnMDQ11uowRUQOWnFNE/e/vyFSvvqYbCXISp/mctjISo4hKzmGE3JSuWjyIJZtq+SjvGI+zCumoLwh8l7ThOWFVSwvrOKP720gs180J49K49RR6Uwd0g9nN/9dmrOosEOOc9m0zA45joiIiIj0LLuqG3ny0828uGQ7vuZR81ucPWEgPz8jl4GJ6gQuIiIiB3bGuAGMGZjAD19czqod1QB8trGUU/88nzvPyOWyozM18r2I9Blf5Jdxxyur2FXdFFk3LDWGv185RX0RRERExFIzhiXz9i3HcutLK1hRGJ6AZ2F+ObMfnM9dZ47mgkkZGoi3G1FSmUgXCIZM/rtmF09+tpk1RTXttrnsNi6blskPTxpOcqzboghFRESks5wyOp13fngsP/r3ykgFaUVhFd96+HNumTWc64/TCMwi0v2ZpskvX19DbVMAgOzkaC6flmVxVCLdi8thY8awZGYMS+aXZ45ic2k9H+UV81FeCUu3VdBmIgYKKxp4ZmEBzywsIM7j4LgRKRwzLIVjhiUzJCVGD09FREREpFfIL6njHwu28p9lO/AF2yeTHTMsmTvPyGX8oESLohMREZGeKjM5mlduOoY/zdvA3+ZvAaDOG+CuN9bw1qqd/ObcMeT2j7c4ShGRzlNW5+W+uXm8tryo3fpLjx7MXWeOJsatbsEiIiJivcH9onn5ezN46MNNPPZpPqYJlQ1+7nhlFa+t2MG9540jOyXG6jAFJZWJdKqqBh//XrKd577axo7KxnbbHDaDb08dzC2zhmv0RRERkV4uKzmGV743g7/O38KDH27EHzRp8of407yNvLJsB78+ezQn5aZbHaaIyD69/fUuPswrjpT/cOF4olxKiBXZF8MwGJ4Wy/C0WL53wjAq6318sqGEj/JK+GxjKXXeQOS9tU0B5q7ezdzVuwFIj3czcXASEzMTmTA4kVH940mIdlr1rYiIiIiIHBLTNPlyczn/WLCVj9aXfGP7hMGJ/PiUEZyQk6rBFEREROSwuRw2fvGtUZycm8bPX1vNlrJ6ABZvreBbD33OJVMzuf3UHFLjNLiziPQegWCIl5Zs54/vraemqbWdISnayR8uHM/pY/pbGJ2IiIjINzntNn5y+khmDk/hJ6+soqgqnE+xML+c0x6cz/XHDuGmE4cR71GfCCspqUykg5mmyfLCKl5esp03VxXR5G8/8qLbYeOSqYO54bihDO4XbVGUIiIi0tUcdhs/mDWcWSPT+Mkrq1i3Kzx76bbyBq59dikzhydz+6k5TM7qZ3GkIiLtldd5ufuttZHy5dMymT402cKIRHqepBgXF0waxAWTBuENBFm0pYKP8or5MK8k8tC0RXGNl/fW7ua9tbsj69Lj3eSkx5GVHE1GYjQZSVGkx7lJiHYS73GSEOUk2mVXp1wRERERsUxVg49Xl+3gxcWFbC6t/8b2yVlJ3HryCI4fkaL7VhEREekw04YmM/e243j4o038df4WgiGTkAkvLi7krZVFXHfcUK6dmU1itMvqUEVEDlsoZPLe2t08MG/DN+pb3xrXn7vPHkNavMei6EREREQObMawZOb9+Hj+8sFGnl64lZAJvkCIxz/dzIuLC7nlpBF8d3ombocGuLaCkspEOsiu6kbeWrmTl5du32tjWWK0kyumZ3HVMdmkxGokJBERkb5q9MB43v7hscxZtI37398QGUFsYX45C/O/5IScVG47ZQSTMpMsjlREJDzi4Z2vraai3gfAgAQPd56Ra3FUIj2b22Hn+JxUjs9J5e5zTDYW17Ewv4wvNpexaEsFtW1mMWtRXOOluMbL55v2fVyHzSA+ykm8x4HHacflsOGy23Dabbgc4aXbYcNpNyJll8MWeZ/LbsPpaF2622zPL6kjxm0nxu0gxuXAblMnYBEREREBfzDE/I2lvL6iiHnrivEF2g+0aBhwcm461x07hOlD+ymZTERERDqFx2nnp7NzOfeoDO6dm8f8jaUA1PuCPPzRJv7x+Ra+OyOL648dqpnLRKRHCYZMPswr5pGPN7GmqKbdtsx+0fzm3DGcODLNouhEREREDk2M28FdZ43m3KMy+OUbq/l6RzUAlQ1+fvvOOv4+fwvXHzeE7xydSaxbaU5dST9tkSNQUtvE+2t28/aqXSwuqNjre8YMjOeqY7I5Z8JAPE5lz4qIiAjYbQZXzMjmW+MG8Kd5G3l56XaCIROAzzaW8tnGUiZmJnLtzCHMHtsfp91mccQi0hcFQyZ3vLKKD9YVR9bdd/444jTlvEiHMQyDkf3jGNk/jmuPHUIwZPLghxvZUdFIYWUDO6saKa31Emi+T9ifQMikot4XSQLtTFFOeyTJLM7jJM7jIH4vS4/T1ikdh+csKuyQ41w2LbNDjiMiIiLSl/iDIRZtqeC9tbuYu3r3Xu8/Y90Ozp+YwTUzsxmaGmtBlCIiItIXjewfx7+uPZpPN5Rw39w8NhbXAeHksr9+toVnFhTwrXH9uWJGFpMyk5TwLiLdVr03wH+W7+DpBVspKG9oty3O7eCG44dy4/FD1RdRREREeqRxgxJ44/szefvrndz//gZ2VDYCsLumid+9m8fDH23iu9OzuPToTAb3i7Y42r5BSWUih8A0wyOIf5hXzAfrilm5vWqv74tx2Tl7wkAunjKYSZmJehAlIiIie5Uc6+b3F4zjxuOH8vBHm3hjZRFmc5/xFYVV/LBwBenxbs6fOIgLJ2UwIj3O2oBFpM8Ihkz+55VVvLlyZ2TdNTOzmZWr0Q5FOpPdZjAgIYoBCVFMHdIPCP8+Vtb7KK5torLBT1WDj6oGP/XeAI3+IE3+II3+IP7ggRPPOkpj8znL6vafwOawGcR5WhPPNhbXkhrnJt7jIMbtINrlINbtINptJ9btwOOwY2JG7odMws9iTCAQNPEFQngDQTaX1hEImgRCIQIhM/I62OZ1eNn8FQwRMk1shoHDbuCw2bDbDEpqm3DabUS77CRGO0mMcpEQ7SQxyklitIt4jwOHkvtFREREqGrw8fmmMj7ZUMJHeSVUN/r3+r5xGQlcNi2TcyYMJEYjyYqIiIhFThyZxnEjUnnn65089kl+JLnMFwzxxsqdvLFyJ7n947hgUgZnjR/IwMQoiyMWEQk/C1+6rZJXlm7n3a93Ue8Lttvudti4emY2Nx0/jKQYl0VRioiIiHQMm83g3KMymD22P89/VcgTn26mrM4LQE1TgMc/3cwTn23m2OEpfGdqJiePSlNCfSfS03yR/TBNk6KqRpYWVPL5pjIW5JdSXOPd63ttBswcnsI5EwZy5vgBRLv06yUiIiIHZ0hKDH+55Ch+MGsYT3y6hbdX7cQXDAFQXOPlyc828+RnmxmbEc8ZYwdw2uh0hqfFKnFdRDpFoy/IL19fzWsriiLrLp+Wya/OGm1hVCJ9l91mkBLnJiXOvd/3BYKh5iSzEIFQiFNGpeMLhvAFQpGlf4+lNxDC35ys5Q+G9vr+Jn+QDbvrqPcFwolsviAHm74WCJlUNvipbAh3Ol67s+YIfxod67ONpQd8T0KUk5RYF8mxblJj3STHukhps0yJlN3EuOy6PxMREZFeobrBz7LCCpYUVLJoSzkrt1exr8lzByR4OPeoDM6bOJDc/vFdG6iIiIjIPtibOyiePX4g89YV89f5m1lR2Dpw9Prdtdw3dz33zV3P0UP6MXtMf07KTSM7JcbCqEWkrwmFTFZsr2Le2t28t3Y32/aYlQwg3uPg8ulZXHNMNmnxHguiFBEREek8boed644dwuXTMnlteRF/m785MlOracLnm8r4fFMZMS47J49K51vjBnB8ToryNDpYn/lpGoYxCPgNMBtIBnYBbwD3mKZZeQjH6Qf8CjgPGACUA+8BvzJNc0dnnls6nzcQZE1RDSsKK1m2LfxVUrv3JDIIP4Samp3EmeMGMHvsAFIP0MFLREREZH+Gp8XxwLcncOcZubywaBvPf1UYGYEDYE1RDWuKarj//Q1kJ0dz4sg0ZgxLZvqQZBKinRZGLtLzqJ62d1/kl3Hna6sprGhttLr06MH89tyxSpQQ6eYcdhtxdhtxzW3KEwYndtix5ywqjLwOmSYNviD13gB13gC1TQFqm/zUNgWoaVk2+qn1BvAFQh0Wg1WqG/1UN/rZXFp/wPd6nDaSY8IJgCkxeySftayLc5Mc4yIp2oXNpr+rIiLSfagdre8KhUy2VTSworCSJQWVLNtWEZnNY18GJng4bUx/Zo/tz9HZ/XRfIyIiIt2WzWYwe2z4vmVNUTUvLNrGGyt20uhvnQFo8dYKFm+t4DfvrGNoagzHj0hl+tB+HD0kmX6aDUg6SE+rcxmGMRq4GzgRiAe2AS8BfzBNs/Fg45Vv2l3dxML8MhZuDneQLt1H38RhqTFcOSObiyYP0kzQIiIi0ut5nHYum5bJJVMH82FeMS8uLuSzjaWYzYOd1fuCvLVqJ2+t2onLbuPoIf04PieFmcNTyO0fj13PqI9In7jbNAxjGPAFkAa8CawHjgZuA2YbhjHTNM3ygzhOcvNxcoCPCVeUcoFrgDMNw5hhmuaWzji3dCzTNNlZ3cTG4lo2FdeysbiOTcW15O2qjcwKsi/xHgfHjkjhlFHpzBqZpumkRUREpMOlxrn50Sk5/GDWcOZvLOW15UV8sK643X1KQXkDz35RwLNfFGAYkNs/nqMGJ3LU4ATGD0pkaGoMboemfBbZG9XTvmlrWT1PfJrPy0vbtzdeMmUw9543Th0ERSTCZhjEuh3Euh2kH+C9Xn8wnGTm9VPbGGBEeiyltV7qvIHmpLQgDb5AJEHNGwhhGGBgNC/BMAwMwGE3cDlsuOw2qhr8OOwGdpsNh80IfzWXnTYDu715nc3WvN7AbhgETZNgyCQQNAmETEYPiMMbDFHvDVDdGAgnkDX4qGr0U9Xgp6bJH3lIfTCa/CGKqhopqjpwnwqbAf1i2s505iIhykm8x0l8lKN52Vpu2RbnceCw2w4+KBERkYOgdrS+wTRNSuu8bNhdG/na2NxG1rZT9d4YBhw1OJETclI5KTeNcRkJGnhEREREepyxGQn8/oLx/Pxbo5i3tpg3VxaxML+s3YysW0rr2VJaz7NfFAAwIi2W8YMSmTA4gXEZCQxPiyXOo4Ee5dD0tDqXYRjTmo/vBF4FtgMnEU5mO9kwjJNN09z3KO0SUe8NsKG4lq+3V7FiexUrCqvaDey4pzi3g7OPGshFkwcxcXCi6l0iIiLS59htBqeP6c/pY/qzo7KBl5fu4J1VO9lS1joIrC8YYkF+GQvyywCIdTuYmJnIxMwkxmUkMGZgPAMSPLqXOgR9IqkMeJxwxehW0zQfaVlpGMafgR8D9wI3HcRx7iNcKfuzaZp3tDnOrcBDzeeZ3UnnlkPU6AtSVNXIjsqGcIeeynCnnsKKBvKL66j1Bg7qODEuOxMzkzh6SD+OG5HC+EGJymYVERGRLuG02zh5VDonj0qnusHPR+uL+WBdMZ9tLKXB19rZxzQhb1cNebtqeHFxeJ3NgMx+0QxLjWVoagzDUmMZlhZLVr9okmPdup+Rvk71NKCmyc+nG0p5aXEhX2xu31YZ73Hwv2eN5qLJg/SQRaSHaju7mFXcTjtup52U5pndL5uW2SHH7arvrWVWtjpvgLqm1uS3lq925aYAgdDBZ6CFTCir8zbPSlt7SHG5HDainHY8Thsep735tb35dXjdrJFpe01Oi/M4e+x9oL85AfCFRYV4AyG8/mB4GQjhC4RfN/lD+AIhgqEQgVA4eTCcSBhqfd28DIZMEqOdhMxwJ3sIX3PTbF4CmGAS7khvN8IJiraWpc3AboQbNsrqfNiM8P27wx5ObnTabTjtRrhsNyLbXHYbboet+ZqFr6W7+fWVM7It/An3PQf7tyRkmvgCodavNoN9mMAZY/tHyoYBbocdt8OG22mLvHbZbUrSl+5O7Wi9QDBkUlHvo7TWy47KBrZXhtvItleElzsqG6k7yLYxh81gzMB4pmT3Y0pWEtOHJmuQRREREek14j1OLpo8iIsmD6KktokP15Xw8fpiFuSX0eRvPxD1ppI6NpXU8Z/lrQOypcW5GZoaw9DUWIamhNvgspKjSY/3aDYh2ZceU+cyDMMOPANEA+eapvlW83ob8DJwYfN+fziYb7wvaPQFm+tgDRSWh+ti28ob2Fhcu98Eshb9YlycOiqd08emc8ywFDxODRwrIiIiAjAoKZrbT83hx6eMYP3uWt79ehcfrCtmQ3H79vU6b4DPN4VngW2RFO1kaHNdLTs5JrLMTo4hIVoDheyp19dkm0fbOA0oAB7bY/OvgRuBKwzDuMM0zXr2wTCMWOAKoJ7w1M5tPQrcDpxuGMbQlhE/OurcfV0gGKLeG6TW66e2qbWjTk2Tn5pGP2V1PirqfZTXeymv81Fe76O8zktlg/+wzpedHM2krCQmZSYxOSuJnPS4HtvZRkRERHqPhGgnF0waxAWTBtHkD7JoawVfbi7ny81lrC6qZs8+zCEzPJtZQXkDH61vv81uM0iJdZEe7yEtzkN6vJu0OA9JMc69zpAR53HgcdjVCVJ6hb5YTzNNk9JaLwXlDWwrr2ftzhqWFFSQt6vmG387INwx+p5zx5AW5+n6YEVEupG2s7IRv//3ms1JJ3V7JJ61nZWtbWLagWYD2Z+WxJbq/UyI9t81u/e5LdbtIN7jaJds1vra+c1tzTOkhZOkwskxDntr0pRhGJhtkrFCZmtyljeS7NV+2eALUtvkp64pQG1TgFpvgNqm5md/TQFqva3bapoC1Hn93+jU1REOZla5rnTvu3mR++84T/O18LSUHcS6ncQ2v45zh98T63FErmlsH79vN00TbyBEoy9Io79lJsQg9b4ADS1LX5B6b3i5pKAi8vvkbU4Way0HIwlk/uD+E0Yf+yT/oOJzO2zEuB1EOe3EuO1EuxxEu1qXMW47UU5HeOmyE+NyRJbh99nD+7fZFu2y49TshXKE1I7WvQRDJvW+QOT/YJ3XH/5f2Nw+1vL/s6YpQGWDL5ykXhtuI6uo9+21jncwUmJdjB6YwNSsJCZnJ3HU4ESiXb2+GVlERESEtDgPl03L5LJpmTT5g3y1pZyvtlSwaGs5q3dU73UQoZJaLyW1Xr7aUvGNbbFuB2lxbtLi3c3tcG6SYlpnqE+ICj9/SWiu/7cMFqR+Sb1XD6xznQCMAua3JJQBmKYZMgzjp4STym4yDOP/zJaRmnqJUMik0d/+WVKjL0htU4Dyeh8VbfomVjT3T9xZ3URp7aFN2uZy2JiSlcTM4SnMHJ7CuIwE/Q0QERER2Q/DMBg1IJ5RA+L5yekj2VXdyOcby5i/qZQlBRUU13zzfqyywc+ybZUs21b5jW1xHgepsW5SYt2kxrlJiXWREusmJc5NYpQz0gbbtk3W47T16kG5+0JrwKzm5TzTNNv1PDBNs9YwjIWEK0/TgY/2c5zpQFTzcdqlNzZXmt4nXNGaBbRMI91R5+62tpbV89ryHQRCJqE2o/2GzNYRf4Mhk2Cbcsg0CQTDy/CowqFIB5M9Rxn2BoIHbLQ/XAlRTnLSYxmRHkdOWiw56XGM7B9Hcqy7U84nIiIi0lE8Tjsn5KRyQk4qEJ5taPWOalZur2LV9irW7aqhqKqRfT3GD4ZMimu8zRWq6oM+r8seHm2/ZVYFt6N5hgxHuMHL7QiPvu+IzNzQ+rrtOrst/NWyzmaAgYFhhCuBp4xKY/ygxA74SYnsVa+vp9320gpKarxUNoQbtSobfAesV9kMOCk3nStnZHF8898WERE5eIZhRGZmO5hnS4FQeBCl+jaJZ43+IE3+IE3+UOR1ZOkLr2/yBznSJ2UtyW47q5uO8EjS0byBEKW13kPuiLInl8OGJzIbWuusdh6HPXI/77SHZ11re7/eMhPb3u7hHTYjPA3XgRxkXyKTcPJhIGQSDJp7zCS3x8xywdb1/mC4c0+jL0hTIEhTcwJZY/PvTncWft7t6/Djuuw2ot12op12ot2tCWjRzYlnzj3qYnvWy1pn4AvPyGdrLhsQqaOlx3u4aPKgDo9dug21o3Wy11fs4I0VOyNJq942Ca0tSawt7WLBw80KO0gxLjsj0uPI7R9uExuZHkdO/zhS1DYmIiIigsdp58SRaZw4Mg2ABl+ANUU1fL2jitVF1azbWcO28oZ2M1jvqeW5y5ayQxsTwWk3muvtberxbdrgHHYDh611VvSfnj6Swf2ij+j7lS7T0+pcJzUv39szANM0txiGsZHwbGlDgc37ibfb2lXdyC1zVkQGHmoZmOhIBgLbF7vNYGhKDKMHxjNxcCITM5MYNSAel0ODBImIiIgcrgEJUXx76mC+PXUwpmmyo7KRZdsqWbWjirU7a8jbWUOtN7DP/WubB3U7lHqb3Wa0a39179F38rQx6Vwzc0hHfHuW6AtJZSOblxv3sX0T4cpRDvuvmB3McWg+TkefG8Mwlu1j04S8vDwmT568v907TZ03QMEhPgjpMgaR0ZMjS4cR7gztCD9w2UK4Fv2+1bEegor6ju94ICIiIgf2QIzL6hAOSVKbmSHaJu77g53fQelIPZIYRT+Lft55eXkA2ZacXLpKr6+nrd9dS2A/jdpteZz28Gw00U5WzLOx4uFODq4DqW4k3U1H3Sv05s+2fkYdJDIjWPvZwcw2s4Q57bbwAFBm+wGfQt38PvBAbLaWRJfwgAS2yMAE4ZnljLaDFTS/D2gtE37RkiwTH+UMb29zjn2NMBfOlTJpXkSS+0ygttGPSfgamM3vbfe6eYeWRKqW67bn9ZN8NoQ+AAAgAElEQVTuyzDaf67afkr2HEm65dq2vd49/fpGuez8PjXWknOrjtYlekU7GnTfOlpJrZeSmq5J5rbbwp2MnXajuV0s3D7W8tphM9gObAc+6JKIei8r7kkP9376SGLt6nMeSZ2hq6+JrkfnnPNw6Xp0zjkPl65H55zzcPW09q3uLhbwtxkgoGXAAF8wRCBodln97+O0WDxOe5eca0+qpx2ynlbnOph9cpq/9ptU1l3raP6gyYbdNR13wDZ9FF2O1vqXxxHuo1hhwALCX9Ix+nwbgYiI9Eqqu3UcFxAfbB3craXO1lLujHrbZzEuHk2M6vDjHoyOqKP1haSyhOblvqZgaFl/oKkQDuc4HXXu/Qk2NjZWL1++vOAIjtHd5DYv1x/pgXyE5/yWXq/DPjPSp+hzI4dKn5k+ruDwdtPn5jAUFB/2z7sjZAMd2Iog3ZDqaW34CH/gd1gdyL7p72jf1eOufYHVAfQABQd+S4+77tJhLLn2RV15MtkX/d73AD5g+fYOP+zBXvtsVEfrbL29HQ06t46mv2N9kyXXvaArT2bRObv6fIchcu0LLDh5V5+zq893JAo69/B7/Z3v5HN+Q1ef70gU9I5z7vdvfSecr9MUWB1Az9Jj7u3WFVt6+mxUTzsUPa3O1dvb0Trl91x9FLtUj/lbLful69jz6Rr2fLqG3UzB4e2m69hN7Gr+OgwdcQ2zOcI6Wl9IKusVTNO0ZmgOC7SMUtKXvmc5MvrMyOHQ50YOlT4zcjj0uRHp3fS73fn0d7Tv0rXvm3Td+y5d+75L177v0rWXzmDF50mf5b5J173v0rXvm3Td+yZd975J112kY3XH3yX9nvd8uoa9g65jz6dr2PPpGvYOuo49X3e5hjYrT95FWkbHSNjH9pb1VZ1wnI46t4iIiIiIiHQM1dNERERERERaqR1NRERERESk8/S0OpfqaSIiIiIifUxfSCrb0LzM2cf2Ec3LjZ1wnI46t4iIiIiIiHQM1dNERERERERaqR1NRERERESk8/S0OpfqaSIiIiIifUxfSCr7pHl5mmEY7b5fwzDigJlAA/DVAY7zFdAIzGzer+1xbMBpe5yvI88tIiIiIiIiHUP1NBERERERkVZqRxMREREREek8Pa3O9XHzcvaeARiGMZRwstk2YMsB4hURERERkR6i1yeVmaa5GZgHZAM/2GPzPUAM8JxpmvUtKw3DyDUMI3eP49QBzzW//+49jnNL8/HfN01zS5t9DvncIiIiIiIi0nlUTxMREREREWmldjQREREREZHO0wPrXJ8BecDxhmGc0yYmG/B/zcUnTdM09/Nti4iIiIhID+KwOoAu8n3gC+BhwzBOJlzxmQbMIjwV8y/3eH9e89LYY/0vgBOB2w3DOApYDIwCzgVK+Gbl63DOLSIiIiIiIp1L9TQRERH5f/buO1ySqkz8+PclSw6CIKijKAiimFBBxRkwYQJRFBMMinHVxYThp4KrrmtcVNZVQRlRURQTuiQFBhUEdMWEK5JGRAmS8zDA+/vjnHZ6errvdPfcDvf29/M856l7q05VnepK/fapc0qStJT1aJIkSZI0ODMm5srMuyPiAMoby46LiOOAy4DdgccCZwL/2cO2S5IkSRpzMSmdRkTE/YB/o7yaeRPgCuB7wAcy8/qWvAmQma2BGRGxMXAIsBewBXAtcCLw/sy8fGXXLUmSJEkaPOM0SZIkSVrKejRJkiRJGpyZFnNFxPaUt5nNA9YD/gJ8A/iPzLy9l22XJEmSNN4mplGZJEmSJEmSJEmSJEmSJEmSJEmSJAlWGXUBJEmSJEmSJEmSJEmSJEmSJEmSJEnDY6MySZIkSZIkSZIkSZIkSZIkSZIkSZogNiqTJEmSJEmSJEmSJEmSJEmSJEmSpAliozJJkiRJkiRJkiRJkiRJkiRJkiRJmiA2KpMkSZIkSZIkSZIkSZIkSZIkSZKkCWKjMkmSJEmSJEmSJEmSJEmSJEmSJEmaIDYqkyRJkiRJkiRJkiRJkiRJkiRJkqQJYqMyDU1EbBIRB0bE9yLiooi4PSJujIifR8SrIqLt8RgRu0TECRFxXZ3ndxFxUESsOuxt0OhFxMsjIms6sEOe50TEwnp83RIR50TE/sMuq0YrInav15srI2JxRPw9Ik6OiGe1yet1RkTEsyPilIi4vB4Hl0TEtyNi5w75PW4mQES8MCI+GxE/i4ib6v3nayuYp+djw3uXJC0rIraKiC/X73CLI2JRRBwWERuNumyTqH7+2SFd2WGeodwPI2L/iDi35r+xzv+cKfKvGhFvqeW5vZbvhIjYpftPZHYZ5+87w9i/EXGviPhARFwQEXdExNUR8a2I2G6qss0Gvez7iJgzxXUgI+KbU6xnLPdjRGxc7y2LYunvBl+OiK06zTMbxBB/o/W8Hy+97nvPe6k/K3PuaPyFsepEij5iYs0MMaR4WOOll/3ufX326DUeaprPc16aBYb5G4cGx3hsZugnfvJ+OxrDiofC56EGZlixjffCwRlmnOK5OBi97sNxPhcjM6drWdKUIuJ1wH8DVwCnA5cB9wH2BjYAvgPsk00HZUTsWcffARwLXAc8F9gWOC4z9xnmNmi0IuJ+wO+BVYF1gVdn5pEted4IfBa4lnLM3Am8ENgK+GRmvn2ohdZIRMTHgHcAlwMnAtcAmwKPAX6SmQc35fU6IyLio8DBlGvH9ynHzIOB5wGrAftl5tea8nvcTIiI+A2wI3AL5ZryUODrmfnyDvl7Pja8d0nSsiJia+AsYDPgB8CfgMcB84ALgCdm5rWjK+HkiYhFwIbAYW0m35KZn2jJP5T7YUR8Angb5R59HLAGsC+wMfCmzDy8JX8A36rLvQD4Yc37YmAt4AWZ+YMVfiCzzLh+3xnG/o2INYFTgScCvwJOA+4H7FPLuFtmntP+k5v5etn3ETEHuBT4LSVmavWHzDyuzXxjuR8jYhPKvWabmv+Xdfv3BK4Gds7MS9ps54w3rN9oPe/HT6/73vNe6k+/547Gn7Hq5Oo1JtbMMYx4WONnGLGwxo/PK0mTbVi/cWhwjMdmjmHUKWp6jGv9oLo3jvV86s0419mpO+Na99aXzDSZhpKA3SgXrlVaxm9eT6KkVBA3xq9PqcxdDDy2afxalCAhgX1HvV2moR0/AfwEuBj4eN3/B7bkmUO5UV4LzGkavxFwUZ1n51Fvi2ngx8qr675eAKzRZvrqTX97nTE17kN3A1cCm7VMm1ePg0s8biYz1WPgIfU+NLfu3691yNvzseG9y2QymZZPwMn1+vemlvGfquM/P+oyTloCFgGLusw7lPshsEsdfxGwUcuyrq3Lm9Myz0vqPGcCazWN36mW92pgvVF/3iPYv2P3fWdY+xd4d53n2zT9XkVpYJDA+bT8jjWbUo/7fk6dvqCH5Y/tfgS+UKd9smX8m+v4k0a9fwa43wf+G63n/XimPva9573J1Efq59wxzYyEserEJnqIiU0zKzHgeNg0nqnH/e59fZakPuIhz3mTaRalYf3GYRroPjQemyGpl/jJ++3I99XY1Q+aBroPvReOYRpGnOK5OHb7cGzPxbavxZMGITNPy8wfZuY9LeOvBD5f/53bNOmFlDcLfTMzf9WU/w7gvfXf1w+uxBozb6ZcfA8Abu2Q55XAmsDhmbmoMTIzrwf+vf77ugGWUSNWeyn+MOVm/JrMvLM1T2YuafrX64wAHgCsApyTmVc3T8jM04GbKcdJg8fNBMnM0zPzwqzfxFegn2PDe5ckNak9DT6dUuHwXy2TD6HEAq+IiHWGXDR1b1j3w8b/H675GvMsohw7a1Lix2aN9b63lqcxzy8pPXJtWss/Ucb0+87A9299M05jPQc3/16V5Y04PwO2B57CLNXjvu/HWO7HiFgXeAXlnnJoy/oPB/4CPCMiHtT9ps4cQ/qN1vN+DPWx7/sxlvtx0s97SSvPWFWanYYQD2sMDSEW1hjyeSVJfejnNw4NgPHYrOb9doTGtH5QPRjTej71YIzr7NSlMa5765mNyjQuGo087moat1sdntQm/0+B24BdaiMSzWIRsR3wH8CnM/OnU2Sd6pg5sSWPZqenUb40fRe4JyKeHRHvjIh/jYid2+T3OiOACymv9H1cRNy7eUJE7AqsR3lTYoPHjTrp59jw3iVJy5pXh6e0+dHlZsobLNYGnjDsgok1I+LlEfGe+v16XkSs2ibfsO6HPc0TEWtRenC6jfKwebfr0bJm0/7dGrg/8OfMvLSHsk26+0bEa+u14LUR8Ygp8o7rfnwCcC/gzHpv+ad67zm5/juPyTNdv9F63s887fZ9g+e91J9ezh2NP2NVdRsTa/aybmiyeV+f3XxeSZocA/uNQwNlPDbzDLJOUaPh81Czh/fCmWOUdXaaHiOpe+vXaiu7AGllRcRqwH713+YDfts6/HPrPJl5V0RcCjwMeBDwfwMtpEamHh9fpbx56j0ryD7VMXNFRNwKbBURa2fmbdNbUo2JnerwDuA8YIfmiRHxU+CFmfmPOsrrjMjM6yLincCngD9GxPcpr4XdGnge8GPgtU2zeNyok36ODe9dkrSsjtfF6kJKb4TbAKcOpURq2JwSmzW7NCIOyMwzmsYN/H5Ye5vcErglM69oU9YL63CbpnFbA6sCl2Rmux/t2s2j5c2m/dvN9aZ1HpXOXJ7WPCIiFgL7Z+ZlTePGeT+679uY5t9oPe9nkCn2fYPnvdSfrs4dzRjGquo2JtbsZd3QZPO+Pkv5vJI0cQb5G4cGx3hs5hlknaJGw+ehZg/vhTPAKOvspqP8GnndW198U5nGwX9QGn6ckJknN43foA5v7DBfY/yGgyqYxsL7gUcB8zPz9hXk7faY2aDDdM18m9XhO4AEnkx5y9QjgFOAXYFvN+X3OiMAMvMwYG9Kg/tXA+8C9gH+CizIzKubsnvcqJN+jg3vXZK0LO+z4+koYHdKJdA6wMOBLwBzgBMjYsemvMO4Hw5yHR5bU5tN+9djoje3AR8EHgNsVNNTgNOBucCp9UfthnHej+779qbzN1rP+5ml0773vJf60+u5o5nB68hk6yUm1uzldWAyeV+f/XxeSZoMw/iNQ4Pj/phZBl2nqNHweaiZz3vhzDLKOjtNj1HWvfXFRmUaqYh4M/A24E/AK0ZcHI2ZiHg85e1kn8zMX4y6PJoRGve1u4DnZebPM/OWzPw98HzgcuApEbHzyEqosRQRBwPHAQsovWSvQ/nidgnw9Yj42OhKJ0mSNFqZ+YHMPC0zr8rM2zLzD5n5OsqbXu8FHDraEkoatMy8OjPfn5m/zswbavoppQfYc4AHAweOtpTql7/RTq6p9r3nvSZZRCyKiOwhfa0xr+eONPsYE0uTy/v67GYsLM0sxmnSzGD8JI0n74Uzh3HKzDdT695sVKaRiYg3Ap8G/gjMy8zrWrKsqPVrY/wNAyieRqy++vFoyis339flbN0eM51a7Grma1wPzsvMRc0T6qtZGy2+H1eHXmdERMwFPgocn5lvzcxL6g8bv6Y0Rvwb8LaIeFCdxeNGnfRzbHjvkqRleZ+dWT5fh7s2jRvG/XCQ6/DYmtps2r8eE9MgM+8Cjqz/Duta4L6fRgP6jdbzfgboYt+35XmvCXExcEEP6e8rWuAU545mBq8jaqddTKzZy+uA/sn7+szn80rSjDTMOM1rwHhxf8wO01WnqNHweahZynvheBmTOjuthDGpe+uLjco0EhFxEPBZ4A+Uk+bKNtkuqMNt2sy/GvBAytuILhlUOTVS61L2/XbAHc29yQCH1DxH1HGH1f+nOma2oLx56PLauEizU+MY6HSDvL4O79WS3+vMZHtOHZ7eOqFeL86lfGd6VB3tcaNO+jk2vHdJ0rI6Xherh9Thn4dQFq3YP+pwnaZxA78fZuatlIb/69bprdodJxcDdwMPquXoZh4tbzbtX68302e5a8GY70f3fTXA32g978dcl/t+Kp73mtUyc/fMfGgP6eAuF93u+7NmBq8jasdzerJYN6RWXgNmKJ9XkmamYcZpff7GocExHpsdpqtOUaPh81Czm/fCMTAudXbq3xjVvfXFRmUauoh4J/CfwG8oJ83VHbKeVofPbDNtV2Bt4KzMXDz9pdQYWAx8qUM6r+b5ef3/F/X/qY6ZPVryaHY6FUhg+4hod4/boQ4vrUOvMwJYsw437TC9Mf7OOvS4USf9HBveuyRpWY1G3k9v/T4XEesBTwRuA84edsHU1hPqsPlHyWHdD3uaJzPvAM6q639yD+vRsmbT/r0YuAzYJiIe2EPZtLx21wIY3/14NnA78MR6b/mneu95ev13uY5HZpMB/0breT/Getj3U/G8l/rT6dzR+DNWVTue05PFuiG18howA/m8kqQ2puU3Dg2U8djsMF11ihoNn4ea3bwXjtgY1tmpR+NU99a3zDSZhpaA91EafPwK2HgFedentLpcDDy2afxalIrmBPYd9TaZhp+AQ+v+P7Bl/AOBO4BrgTlN4zcCLqrz7Dzq8psGfnz8oO7rt7SMfzpwD+VtZRvUcV5nTAAvqvv6SmDLlml71OPmdmATj5vJTsDcun+/1mF6z8eG9y6TyWRaPgEn1+vfm1rGf6qO//yoyzhJifL26HXajJ8DXFj3yXuaxg/lfgjsUsdfBGzUUq5r6/LmtMzzkjrPmcBaTeN3quW9Glh/1J/5iPf3WHzfGdb+Bd5d5/k2sErT+D3r+PObx8/m1MW+f3S7zwLYve6PBHaZKfsR+EKd9smW8W+u408a9T4Z8P4e6G+0nvfjm3rc9573JlMfqZ9zxzQzEsaqE5noMSY2zdzEAOJh0/inLva79/VZlHqMhzznTaZZlIb1G4dpoPvQeGwGpF7jJ++345MGEQ/h81Djtg+9F45pGnSc4rk4dvtwbM/FqAuVBi4i9gcWAHdTXu93Y5tsizJzQdM8ewHHUQ74bwLXAc8Dtq3jX5QexBMnIg4FDgFenZlHtkx7E/AZyoXyWMqbhV4IbEV5YODtwy2thi0itqJ8Qbof5c1l51G+GO3F0i9N32nK73VmwtWejE4GngrcDHyP0sBsO+A5QAAHZeanm+bxuJkQdV/vVf/dHHgGpTeIn9Vx1zTfW/o5Nrx3SdKyImJryve5zSgdBvwf8HhgHuWV7btk5rWjK+FkqfHX24CfAn+hfF/aGng25cfJE4DnZ+adTfMM5X4YEZ8E3gpcXpe7BvBiYBNKxeLhLfkD+FZd7p+AH9a8L67b8oLM/EHvn9LMNq7fd4axfyNiTUqvXbtQfuQ9Fbg/sE8t426Zec5Un99M1su+j4iFwEMo1+fL6/RHALvVv9+XmR9qs46x3I8RsUndlm3qvOdSYsA9KQ1YdsnMizt9djPZsH6j9bwfP73ue897qT/9njsaf8aqk6mfmFgzxzDiYY2fYcTCGj8+ryRNtmH9xqHBMR6bGYZVp6jpMa71g+reONbzqTfjXGen7oxr3VtfVrZVmsnUbWLp26WmSgvbzPdEyhfK6ylvivk98BZg1VFvk2nkx9KBHaY/FziDEpjcCvwS2H/U5TYN9RjZlHKD/gvlC9A1lIZCj+uQ3+vMhCdgdeAg4GzgJuAuygNFPwKe7nEzuamL7y+LpuPY8N5lMplMyyZKBwFHAVfU73N/AQ6jqdcd09D2xVOAb1Ae8r4BWELp/erHwH5QOixqM99Q7ofA/Jrv1jrfGcBzpsi/Wi3H72u5rq/lnNhepcf5+84w9i+wNvBvlF4yF9fj+9vA9qPeN+O074FXUeKjRcAt9bO6jFLx8OSZuB+BjYFPs/S3gyuALwNbjXrfjHi/T9tvtJ7345V63fee9yZTf2llzh3T+CeMVScu0WdMbJoZqYvvR4vazGPd0AxPvex37+uzJ/UaDzXN5zlvMs2CtDLXc3r8jcM00P1oPDbmqd/4yfvtyPbXUOIhfB5qLPah98LxTMOMUzwXx2MfjvO56JvKJEmSJEmSJEmSJEmSJEmSJEmSJGmCrDLqAkiSJEmSJEmSJEmSJEmSJEmSJEmShsdGZZIkSZIkSZIkSZIkSZIkSZIkSZI0QWxUJkmSJEmSJEmSJEmSJEmSJEmSJEkTxEZlkiRJkiRJkiRJkiRJkiRJkiRJkjRBbFQmSZIkSZIkSZIkSZIkSZIkSZIkSRPERmWSJEmSJEmSJEmSJEmSJEmSJEmSNEFsVCZJkiRJkiRJkiRJkiRJkiRJkiRJE8RGZZIkSZIkSZIkSZIkSZIkSZIkSZI0QWxUJkmSJEmSJEmSJEmSJEmSJEmSJEkTxEZlkiRJkiRJkiRJkiRJkiRJkiRJkjRBbFQmSZIkSZIkSZIkSZIkSZIkSZIkSRPERmWSJEmSJEmSehYRb46IP0bE7RGREXFQHS4cQVnm13XPH/a6ByUiFkZEjrockiRJkiRJkiRJkiRpdrJRmSRJkiRJkqSeRMS+wKeBO4DDgA8AZ4+0UE0iYtWIuCEilkTEem2mb1EboWVEHNBhGWfU6bsOqIwL6vLnDGL5kiRJkjRodoYhSZIkSZIkzWyrjboAkiRJkiRJkmac5zSGmfn3xsiI2A64bTRFWioz765vTNsT2BX4n5YsuzeyArsBRzVPjIi1gScAtwK/GGhhJUmSJEmSJEmSJEmSRsA3lUmSJEmSJEnq1X0BmhuU1f//lJmXjaZIyzmtDndrM2034HbgBGBem+lPAtYAfpaZSwZTPEmSJEmSJEmSJEmSpNGxUZkkSV2KiPkR8Z2IuCQibo+ImyLizIh4eYf8O0XEKRFxc837k4jYOSIOjYiMiLlt5nloRCyIiL9GxJ0RcVVEHBMR2w58AyVJkiRpBRrxDLUhVo1tso5r/L+w3TwRMTciXhgR50bEbRFxXUR8MyK2bLOex0TEpyPitzXfHRFxYUR8MiI26rK4p9Zhp0ZlZwInA1u2ibka8zSWQUSsEhGvi4hfRsQtEXFr/fv1EbHc76yNzyIiNo+IIyPibxFxd40tE9i/Zr206XNc1GY5q0XEe+r2L67x4kcjYo0uPwdJkiRJIiIeFxHH1thkcURcUeuxXtSUp6u6sIiYU+Oap9T/syktbMm7VUQcXpe5OCKujYjjI2KnDuXcIiKOioiraxl+ExH715gyI+LQNvM8JCKOrtt2Z0T8vf7/kDZ5m2PUl0bEOTXGW1Tr6TIiTp/ic/x9RCyJiC1W8JFLkiRJ0kqLiOdFxKk1hltc450zIuINLfl6iYsW1NhnTptpbWOvWueVEbFGRLw/Ii6o5VnQlGeriPhMrdO6vdbxnRsR72uznp5iRUnS4Kw26gJIkjSD/DdwPvBT4ApgE+BZwFcjYtvM/GfwExG7AqcAqwLfBS4GHg6cztLe8pcREc+seVcHfghcBGwF7A08OyLmZeavB7NpkiRJktSVhXU4H3gA8IEe5n0D8DzgeOAM4PHAi4EdI+KRmbm4Ke+rgefXfD+hdI71GOCtwB4R8fjMvHmqlWXm+RFxVV3+Jpl5LUBEbF3L/gVKjAawO3BB0+y71+GpTeO+CrwU+CtwJJC1jJ+jvNnsZW2KsTFwNnALJd67B7iK8rntBewIfBq4oea/oc0yjgGeDJwI3ESJQw8GNgMOmOozkCRJkiSAiHg1pZ7rbkpMdiElpngsJVb7Vs3abV3YDZS4Zj7Lx4aLmtb7aEp92caUTj2+C9ybEg/9PCKen5knNOXfDPhFXeZPgbOAzSlx1ykdtm0nSty4Xt22PwIPBV4O7BkRT83MX7aZ9W3A0yh1cqcDG2Tmn2qDsnkRsU1m/rllXbsAOwDfycwr2pVHkiRJkqZLRLyGUp91JSV2uYYSyz2CUkf0uZqv37ioH98BdqLUW30fuLqW4bGUuG9jSjz3XWBtYHvgUOCDTdvVU6woSRosG5VJktS9HTLz4uYRtWf4E4F3RcTnM/NvUXqo/xKwJvCszDyxKf/rKBVytCxnI+AbwG3Arpn5x6ZpO1AeQjwSePT0b5YkSZIkdSczFwILo7x5+QGZeWgPsz8T2Ckzf98YERHHAC8B9mTpQ4wAHwH+JTPvbl5ARLyKEhu9AfhoF+s8rS5/HnBcHdd4C9nplIcl/1HHNSreNgAeBVwL/KaOewmlQdl5lJjtljr+vZSGby+NiP/JzGNa1v9wSmO0V2bmXU3jT6y9P+4IHJaZi6bYhq2Bh2XmdXWd/w/4LbBfRLw7M6/s4nOQJEmSNKEiYntKvHMT8OTMPL9l+lZN/3ZVF5aZNwCHThUbRsRqlDhvXWBeZp7RNO2+wC+BL0XEnKZORj5CaVD2scx8Z1P+w4Bz26wjgKOB9YGXZ+bXm6a9GPgmpUHc9pl5T8vsuwE7Z+Z5LeM/R4khXwO8vWXaa+rwC61lkSRJkqQBeC1wJ7BjZl7dPCEi7l2HKxMX9eMBlNjxmqb1rAF8m9JI7GWt9WXNcWefsaIkaYBWGXUBJEmaKVor0eq4O4H/ojTUbvRkvwvwYOD05gZl1ReBP7O8/YANgUOaG5TVdfwBOAJ4VK34kyRJkqSZ6DPNDcqqI+rwcc0jM/MvrQ3Kqi9THoR8RpfrbLxpbLemcbsBNwO/ysykvH1tbq10A5hLeev06XU6wCvr8F2NBmW1nLcCjQcdD2yz/juBt7c0KOvVOxsNyprW+XXKb7uPXYnlSpIkSZoMr6fUY32wtUEZQGZe3vR3t3Vh3Xg2pZOMzzY/JFiX+XfgY5S3kO0O/3wI8SXAjcCHWvL/lvKQZKtdKL3v/6L5wck6z7HAz4FtKW+XbvXFNg3KoPS0fwUwPyLWbIyMiA2BFwEXU94AIEmSJEnDcBewpHVkU6OulYmL+vG+5gZl1XOBOcDxbTpgXCbupMdYUZI0eL6pTJKkLkXE/SkPC+4O3B+4V0uWLevwUXX489ZlZOY9EXEWsE3LpJ3rcMeIOLTN6hv5t6O8nlqSJEmSZppftRn31zrcqHlkRKxO6X1xX2B7YAOW7SBrS7pzWh02NyqbB/ysqaHX6YRc5g0AACAASURBVMA+lLeG/aYp76lN8zwauIfSAK3VGcDdLI0Fmy1q7TmyD11/bpIkSZLUxhPqsLUjxOX0UBfWjUbd1wM61H09pA63A06gPOR4L0oHIDe3yf9zlu/M49F1eBrtnUZ5cPJRwE9bpi335jOAzLwrIo4A3g+8AGg8EPmKWr4vNnVAIkmSJEmD9HXgk8AfI+KblDqpMzPzH015ViYu6ke7WKrruJPeY0VJ0oDZqEySpC5ExIMoAdFGwM+AUyg9Jd5N6WVjf6DRW+EGdXhVh8W1G79JHb56BUVZt7sSS5IkSdLYuaHNuEbDrlVbxh8LPB+4BPgBcCWwuE47iKXx15Qy89KIuBTYNiK2pMR092HZirWFdbg7pVFZo+fD5p7nNwCuqz30t67jroi4BtisTRGu7KacU8nMXj43SZIkSWq1YR3+bapMPdaFdaNR97XPCvI16r76qV9rzHNFh3ka4zdsM22qeO2LwP+jdHbSaFT2GsrbqI+aYj5JkiRJmjaZ+alaB/UG4M2UOrKMiDOAd2Tmr1i5uKgf7WKpruLOqtdYUZI0YDYqkySpO2+lBDQHZOaC5gkR8RJKRVrDTXV4nw7Lajf+xjrcMTN/txLllCRJkqQZLSIeS2lQ9hNgj6Y3ihERqwAH97jI04BXUd5A1niz1+mNiZn5fxFxJbBbRHwVeBhwWWZe1LSMG4GNI2L1zFzSUt7VgHuzNBZsZu/1kiRJkkat0VHFlsCfpsjXS11YNxp1X3tm5vFd5F+Z+rXNO8yzRUu+Zh3jtcz8W0QcDzw/Ih4KbAzsABzb8kYASZIkSRqozDwaODoiNgR2odShvRI4ucYr/cRF99Rhu3YEUzY+6/Dm5ua4c0V6jRUlSQO2yqgLIEnSDPHgOvxOm2lPafn/vDp8UmvG+gDkLm2WcXYdPrmv0kmSJEnS7NGIv45vblBWPQ64V4/LO7UOd6vpesobyZotpMRjz2iZp+E8ym+pu7ZZ/q6UN4b9usdy3V2Hvm1MkiRJ0iA16qD2WEG+XurCGu4GiIh2cU2vdV9/Am4HHhER67WZvly9G0vr5OZ2WOa8Ouw1XgP4XB2+lvKWMoAv9LEcSZIkSVppmXlDZp6Qma8GFlA6v9iV/uKi6+vwfm3yP7aP4nUbdzbn9TlJSRoTNiqTJKk7i+pwbvPIiHgGcGBL3jOBi4F5EdEaKL0G2KbN8o+i9NhxSEQ8rnViRKwSEXOXm0uSJEmSZp9FdTi3eWREbAb8Vx/LO60On0p5EPKMzLynJc/pwHrAO+r/rY3KvlyHH4mItZvKtDbwH/XfL/VYrmvr8P49zidJkiRJvfhv4C7gfRGxfevEiNiq/rmoDue2TG9XF9YwVVzzA0p92b9ExLPazRwROzdirMy8EzgW2AB4b0u+HYH92iziTOAC4EkR8cKWeV5IeUjxz8DPO5R/KqfWefcHXgRckJmnTz2LJEmSJE2fiJgXEdFm0mZ1eBv9xUXn1uGrW/I/HPjXPor6Q0pM+bz6putlNMWd0GOsKEkavHavrZQkScv7HHAA8O2IOA74O7AD8EzgW8CLGxkz856IOBA4CTg+Ir5DCYQeATwNOJHSK8c9TfNcW4O47wFnR8SpwPlAUnoE2RnYBFhrwNspSZIkSaP2S0oF2N4RcRalkus+lDjqAko81rXMvCoizgceVked1iZb48HAh7fLk5nHRMSelAcJz4+I71Pitb2ABwLHZubXeykX5QHFdwBH1LjxZuCGzDy8x+VIkiRJUkeZ+ceIeAPweeC8iPgBcCGl3mkn4CZKz/Vd14U1ORXYB/huRJxAedPYXzLzq5m5JCL2Bk4G/qfGd7+hPPR4v7ruBwFb1HEA76K8YfrgiHg8cFad/iLgBEoM1ly/lhGxP/Bj4Ni6bX8Ctq15bwb2a9OxSDefW0bE54FP1VFf7HUZkiRJkrSSvgfcEhFnUxptBaWR2E7A/wI/6TMuasSFL6kNvs6hdBayZ532ol4KmZl3RsQ+wCnAMRHxWsobydYCtgN2p7ZZ6DNWlCQNkG8qkySpC5n5O0qF2lnAs4HXA+sDe1Mq4VrzL6T0gL+w5n8zcK+6jEtqtpta5jmV0vDsc8Ac4HXAqygVdqcB+07nNkmSJEnSOMrMu4HnUXrTvy8lnnoScCTwDGBJH4ttfvPYcj3LZ+aFwN/qv3/MzCvaLOMlwL9QeuJ/LSVmux54I/DSXguUmScDb6Nsz0HAB4G397ocSZIkSVqRzDyCElf9iPImsndQ4q5/UN8I3WtdWHUk8BHK28UOpsQ1r2pa7++AHYGP1jwH1OU+BjgPeAVwTVP+q4BdgKMpHYO8BXgU8Aag0ZFHa/3aOZSHDo+hdNL4jrqMbwA71en9WkBpxHYH8JWVWI4kSZIk9eNdlM4YH02Jiw4AVgfeCczLzCXQe1yUmXdQGnp9i/Js4hspDbleSqmf61lm/gp4ZJ3/AcBbKTHfhsD7W/L2FCtKkgYrMnPUZZAkaaJExJnA44ENMvPWUZdHkiRJkiRJkiRJGmcR8WHgPcAzaycdw1jnXErHJF/LzFcMY52SJEmSJEnSMPmmMkmSBiAi1o6IDduMn0/pBeQUG5RJkiRJkiRJkiRJS0XEfduMezjlLdbXAWcMsTgH1+HhQ1ynJEmSJEmSNDSrjboAkiTNUvcHzouIHwMXUe65jwKeBNwAvG2EZZMkSZIkSZIkSZLG0a8i4iLgD8CtwEOAZ1M6TX5tZt4xyJXXBmzPAR4D7AH8KDPPGeQ6JUmSJEmSpFGJzBx1GSRJmnUiYiPg48BTgM2BNYErgZ8AH87Mi0dYPEmSJEmSJEmSJGnsRMQhwF7AHGA9SmeNZwOfyMyFQ1j/fOAo4CbgZOANmXnNoNcrSZIkSZIkjYKNyiRJkiRJkiRJkiRJkiRJkiRJkiRpgqwy6gJIkiRJkiRJkiRJkiRJkiRJkiRJkobHRmWSJEmSJEmSJEmSJEmSJEmSJEmSNEFsVCZJkiRJkiRJkiRJkiRJkiRJkiRJE8RGZZIkSZIkSZIkSZIkSZIkSZIkSZI0QWxUJkmSJEmSJEmSJEmSJEmSJEmSJEkTxEZlkiRJkiRJkiRJkiRJkiRJkiRJkjRBbFQmSZIkSZIkSZIkSZIkSZIkSZIkSRPERmWSJEmSJEmSJEmSJEmSJEmSJEmSNEFsVCZJkiRJkiRJkiRJkiRJkiRJkiRJE8RGZZIkSZIkSZIkSZIkSZIkSZIkSZI0QWxUJkmSJEmSJEmSJEmSJEmSJEmSJEkTxEZlkiRJkiRJkiRJkiRJkiRJkiRJkjRBbFQmSZIkSZIkSZIkSZIkSZIkSZIkSRPERmWSJEmSJEmSJEmSJEmSJEmSJEmSNEFsVCZJkiRJkiRJkiRJkiRJkiRJkiRJE8RGZZIkSZIkSZIkSZIkSZIkSZIkSZI0QWxUJkmSJEmSJEmSJEmSJEmSJEmSJEkTxEZlkiRJkiRJkiRJkiRJkiRJkiRJkjRBbFQmSZIkSZIkSZIkSZIkSZIkSZIkSRPERmWSJEmSJEmSJEmSJEmSJEmSJEmSNEFsVCZJkiRJkiRJkiRJkiRJkiRJkiRJE8RGZZIkSZIkSZIkSZIkSZIkSZIkSZI0QWxUJkmSJEmSJEmSJEmSJEmSJEmSJEkTxEZlkiRJkiRJkiRJkiRJkiRJkiRJkjRBbFQmSZIkSZIkSZIkSZIkSZIkSZIkSRPERmWSJEmSJEmSJEmSJEmSJEmSJEmSNEFsVCZJkiRJkiRJkiRJkiRJkiRJkiRJE8RGZZIkSZIkSZIkSZIkSZIkSZIkSZI0QWxUJkmSJEmSJEmSJEmSJEmSJEmSJEkTxEZlkiRJkiRJkiRJkiRJkiRJkiRJkjRBbFQmSZIkSZIkSZIkSZIkSZIkSZIkSRPERmWSJEmSJEmSJEmSJEmSJEmSJEmSNEFsVCZJkiRJkiRJkiRJkiRJkiRJkiRJE8RGZZIkSZIkSZIkSZIkSZIkSZIkSZI0QWxUJkmSJEmSJEmSJEmSJEmSJEmSJEkTxEZlkiRJkiRJkiRJkiRJkiRJkiRJkjRBbFQmSZIkSZIkSZIkSZIkSZIkSZIkSRPERmWSJEmSJEmSJEmSJEmSJEmSJEmSNEFsVCZJkiRJkiRJkiRJkiRJkiRJkiRJE8RGZZIkSZIkSZIkSZIkSZIkSZIkSZI0QWxUJkmSJEmSJEmSJEmSJEmSJEmSJEkTxEZlkiRJkiRJkiRJkiRJkiRJkiRJkjRBbFQmSZIkSZIkSZIkSZIkSZIkSZIkSRPERmWSJEmSJEmSJEmSJEmSJEmSJEmSNEFsVCZJkiRJkiRJkiRJkiRJkiRJkiRJE8RGZZIkSZIkSZIkSZIkSZIkSZIkSZI0QWxUJkmSJEmSJEmSJEmSJEmSJEmSJEkTxEZlkiRJkiRJkiRJkiRJkiRJkiRJkjRBVht1ASRJmhQR8RDgZcBi4FOZuXjERZIkSZIk9ckYT5IkSZJmFuM4SZIkafIYB0iSNDUblUmSNAQRsQZwLPAoYL7BqSRJkiTNXMZ4kiRJkjSzGMdJkiRJk8c4QJKkFVtl1AWQJE2/iJgbERkRi0ZdllYRsbCWbX7L+LEtczsRcWgt74IuZ/kYJTj998z8yuBKJkmSJGm2Ged4yRjPGE+SJEnS8sY5JjKOmzlxXETMqduYoy6LJEmSVmycv1MbB8ycOEDjI4o3RsRvIuK2RnwWEXOmafnz6/IW9jn/jDp/p0tELKrbPbePeRfUeQ+d/pJJM5dvKpOkMVKDnf1bRt8F3ARcD/wBOBf4RmZeOqQybQgcBJCZhw5jnbNNRDwLeDNwHPDeERdnVvN4lSRJ0jgxxpudZmqMFxEHARsCCzJz0QCWvxfwSGBhZi6c7uVLkiRJw2AcNzvN1DhuKvXhubnAbzLz+6MtjSRJ0sxmHDA7zcY4YFyN4/EaEasBrwCeT6m/2gy4DfgrcCpwZGb+cQWLeQ/wofr3HcBV9e+726xvW2A+sDvwAGAj4FbgUuBM4OuZeXYP5Z8PzAG+n5m/6XY+SeqHbyqTpPG0hPIF9CrgOmBtYGtgT+DDwMUR8a2IuHeH+W8DLgAunoaybAgcUtN0uIxSthunaXmjcg1lO66YKlNEbA4sAH4F7JeZ9iQ4WNN9vEqSJEnTwRhv/E1CjHcQZb/PGdDy96rLnzug5UuSJEnDZBw3/iYhjltC2cYL2kybSzkm9hpmgSRJkmY544DxNwlxwEw0Vs/sRcQjgfOBLwPPBe5HaRS2DvAI4C3A7yLi4xExVVuKf63DtwJrZ+bmNf21aV2rR8Rn6vreBewE3Bu4pa7vUcAbgV9ExCkRsV7T8m+kHM+XtVn3fMrn+cgpyjed15xJcQXlM7tm1AWRxolvKpOk8XRWZs5tHlF7c3gC5cviPjXtEhFPyMzLm/Nm5rnAQ4dT1N5k5n6jLsN0yMzDgcO7yHclpZcLSZIkSZPLGG/MGeNJkiRJamEcN+YmIY7LzL8xpseRJEnSLGUcMOYmIQ7QyomIx1LeRLY+cCXwPuC4zLwhIoLSSOttwMuAtwP3rX+3LmczYNP67xHtGibWt6H9CHh6HXUs5fg8JzOX1PVtTXlb2luBpwGbADcDZOb3gO/1u63jfM0ZV5n5buDdoy6HNG58U5kkzRCZeUNmnpSZ+wLPpvScsCXl9cySJEmSpBnEGE+SJEmSZhbjOEmSJGnyGAdIM0d9C9ixlAZlFwGPycwjM/MGgCzOy8yXA++os700It7QZnH3avyRmbd0WOUHKQ3KEpifmftm5s8zc0nT+i7KzI9TGpctWPmtlKTpZ6MySZqBMvMkSi8JAI+PiOc2T4+IuRGREbGodd6IWCUi5kfE6RFxbUQsiYh/RMT5EfHliHhmU96FwKVN/2dLOrQ5bx03PyI2jIiPRsSfIuK2iLihXb6ptjEinlvLeH1E3BIRv4iIl3bIO6dRpimW1/EzacqzXUR8PiL+3Ch3RPw+Ij4TEY9pyXtoXd6CDstaJSJeFRFnRMR1EXFHRFwaEV+MiAd3U8aIeGJE/CgiromI2yPitxHxxtqDRV8iYp2IeHtEnNVUrksi4viIeFlErN5mnvXr9v627otbIuJ3EfGBiNigw3oWtB4jbfK0PRbqMZT1+Gs+Fm6o6z47Il7SbnlM0/Faj6l7ap4dptiGdWuZMiKe3imfJEmSNBVjvOXyGuN1ISKeEhHHRcTlEXFnRNwYERdGxPcj4rURsUrztgEPqLOe3rLfFzYtc9WI2CMivhAR/xsRV9Vl/z0ivhcRu3XaTmD/OuqQ1mOrw+f5ioj4cT1eG+s4NiIe38/nIUmSJA2TcdxyeY3juhQ91NW1+1wb44BD6qj92xwXcyJiv/r3lVF6z+9Unnk1323Rod5PkiRJhXHAcnmNA7rUSxzQNI/P7HXehk7P7L0OeBBwD/CyzPx7p2Vk5ieAE+q/h0bEWnXZjXqvRR226dA6bgvK28cA/iszv9JpXXV9t2XmAcBlTctd5jNvHgc8pY46qmX9zeVqe341HQcrSgtayxl91OG1npcRsX9EnBMRN0fETfU4elqnzyYidoyIoyNiUUQsrvNdEhEnRcRBEbH2FPNuHBGfquf54oj4W0QcUfdPu/xtz5FouZ7F0uvAP+rx+Zso1wHb3mhW6vjDkSRp7B0BvJ/ymuaXAj/scr6v1vwNN1J6Zrg3sH1NJ9Vp1wHX1GkAV7Usq10PDJsC/0v5cr4YuLPLcv1TRBwE/CelB4cbKb0+PAF4QkTskplv7HWZXazzTXWdq9ZRt9b171DTI4C5XS5rbcpriRsByxLgNmAO8GrgFRGxb2b+YIplzAeOpDQAvwlYq5bhs8CDgYO63bamZW4P/E8tB8Bdddn3Ax4IPBc4k2UDogcDP2Hpw4e31eHDa5ofEU/NzAt7LU+XZX4f8G+UQO9mYB3g8cAxEXGfzDysKfu0Ha+ZuSgifkJ55fQBlFdet/PiWqbLKJ+TJEmS1C9jvGk022O8iHgN8IWmUbdRtvXBNe0JfIXSY+otlH29aV3/9Sy7H69r+ns7llagUct6J7AFsBewV0S8JzM/0pTnzrr8Dep23Ur7Y6lR9vWA7wJPraOSEu9tAbwIeGFE/GtmHj7lhyBJkiSNnnHcNJrtcVxdZs91dW3cTTkO1qXUUd1B2Ueteb4NfAa4D7AHnY/PV9bhdzOzdTmSJElannHANDIO8Jk9pveZvdfU4U8y89wVbCbAh4Fn1fLsDRzD0nqvVTtsU2N7DgDWoOzP5nqzKWXmPSvIcntd38bA6pRj5fam6f/oYjU3svx+aHZvlp5z/zQddXgRcSTwKkpceivlOjcX2DUiXpSZ32nJ/yzg+5RthXI83EM5Nx4IPINybfxTm9VtRXn72wMo50gC9wUOBJ4aEY/OzOun+Bw6bcMLgG9S2tncUMu2I+U6sHtE7JOZd/W6XGmc2VpSkmaozLwTOLX+++Ru5omIXSnB6d3AW4D1M3NDSvBzX2A+8POmdewN7NT0/+Yt6RNtVvN+ypeoPYC1M3N94LE9bNqmwMeAo4EtMnMjypfYT9bp/xIdej/pV0TsQ6lUWZXyavLtM3Pduu5NgJdTgphufYoSnC6m9H6xXv2ctwUWUj7vYyJimw7zb0p5OPC/KZ/BhsBGlC+lAG+OiIf1UB4iYmPKl+s5lJ5B9gLWycxNgLWBJwFHUYKcxjxrAN+hfOn+a92mdWt6KiUouz/wvYhYs5fydOmRlF4W3wdsUj+HzVn6+viP1O0CBnK8HlmHL4/OPTgeUIdf6SLgkyRJkjoyxps+sz3Gq5Wijc/vy8D9M3OdzFyXsn17AN+gVLiQmZ/IzM0pcR3A3i37fe+mxd9Zl/kMYIPM3KAu9z6U2Oxu4MPNPRFm5ll1+cfWUZ9oPbZaNuFoSkz567qetTNzA0rl2HvrOj4dEU/s9jORJEmSRsE4bvrM9jgO+quraycz/1rjrMa+P7bNcfHXzLyd8kAkLK3Pai3T+pQHJ6HEgpIkSVoB44DpYxzgM3tM4zN7EbElpeEflEZKK5SZZ7G0kdbcxrgac3Xapsb2zKvD/80p3ojWq8w8tq7/rDrqX1vWv9NU89dltM7TXGf3SqDx1r2TWmZd2Tq8PYGXAa+nXOc2oDQc/Cmlzcpn2+zTwynHwo+AbTNzrTrfBsCulIa8d3RY32cpnWnukpnrUM6RPSkNweYA7+74IU3tS5TGig+q16MNgYMpda971b+lWcVGZZI0s/2+DreMNq9AbuMJdfjjzDwsM28GyOKKzPxKZr59ivm7sSbwrMw8qfGFPTMv6mH+tSlB3PzMvKrOf30tV+MVwR+I6P910s3q5/af9d9vZOY+mfl/jemZeV1mfj0zO/V60bq8OZSeTaB8of9CZi6uy/oz8GzgYsp2vrfDYtYGjs7MNzV9Bjdk5psp+zyAF/SwmQDvovRucg3w5Mz8Qf2Rg8xckplnZuYrM/PypnleTOlpZQlln/44lzqV0kvHEuBhlGBgum0AHJKZH8rMG2pZrwL2owRzawHPWcl1THW8fp/yeW1G2W/LqD8wPJHSw8VRK1kOSZIkCYzxVtqExHg7UCpFbgVek5mNxmKN7TspM1/aiPl6kZl/zsxXZeYpmXlT0/irM/NDwAdqeV/X67IBIuKplMqWC4Dd6nruqOu4PjM/TKlIXIX+K3okSZKkYTKOW0kTEsdBf3V1K+uIOnxORGzaZvq+lG29FDh9GtcrSZI02xkHrCTjAJ/ZY/qf2du+6e/f9lCW39Xhdj3M05y/l3WNVERsS+l8ZBXgY5n5zaZp01GHtyFwYGZ+PjNvq/NeCryE0rHlFsAuTevcjPI2Mup8f25My8ybMvNnmfmazFzUYX2Lgadm5i/qPHdl5vHAh+r0F3bzubRxObBXLTuZeWtmfhz4YJ3+ztoJqDRr2KhMkma25lezbtwx11KNB8I2i4hB3QNOzMw/rOQyPpKZ2Wb8h+vwwZTXyU6H3YEtKb0ovGMalvd8yv31Spb2mvFP9cvyx+q/e0fEcq8Rrjq9Ernx+u0deizXfnX4icz8W5fzNL5U/6DdPs3M81naA8mLeixPN+4ADmsdmaVnxZPrv71+Dq06Hq81gD+6/vvKNlkaPZ4sbAQQkiRJ0koyxlt5kxDjNfb76pSeOofph3XY71vE9q/DIzLzxg55vl6H86b4PCVJkqRxYRy38iYhjoP+6upWSmaeR+lhfnXKWx5aNeq6FnTY35IkSWrPOGDlGQd05jN7/T2z13wuXttDWa6pw17r3Br5r+txvpGIiA2A4ymNBk9k+YZh01GHdxlL35j9T1ne5HZu/bf52LmF8vYvKA3OevXFzGy3rxtvqntgRKzTx3I/2WiU2uJTlPNjfcobBKVZw0ZlkjRZTqW0+H80sDAiXh4R953mdfxiJedfApzZbkJmXghcUf999Equp6HRE8xvp6kCp1Gun2Xm3R3ynFaH61Ber93qusy8pMO8jTJu1G2Bak8s96n/ntDtfCzdlql6Jmxsy3Ttj2Z/zMxbO0zr+XPoYEXHa+NHhmdFROMzpAZFjaD/SytZBkmSJKlfxnjLm/UxHnBhTWsAv4iIt0TEQ6exd9B71WUujIirI2JJRGREJHBezdbvcdboffC9EXFluwT8suZZm+E3mpMkSZIGzThuebM+jluJurrp0KjrOqB5ZERsR/ns7wEWDLlMkiRJk8Y4YHnGAZ35zJ7P7P1/9u483raquhP9b5ArRFFBVNQXnxIbxCQ2EaMosSHkIdHSYPeJ0RBDbMpSoxJMpUp9EUzpixVjRKOVaEQiim1UUmUqYhR8KmJK1IpWoWBzFcSOKz2IGkb9sdYOh8M55+7T3Hua/f1+Pvszz15zzbHmbs79nHnXGmusqTGZ9R1JDsxQiew3JxXi5liLc3ifWeKGJTf67ozJnh8bn36oql5SVfddxk0n/8ci2+f+m7LvlLHmOnOhjd19ea4/V7orfvdg3UgqA9jc5v5xvtM7HowLvH+X5JokD0lySpJvVdXXq+q/VNUvrsGcvr/K8RdPSjwvYvIH321XeZyJyaLjm2sUbzKvpRa7c8tVL/Q6rlhi7A/HdprS6RO3m/Pzcl7ncl7LrdfqAsY51vp9WMiS39exrPpZSbblhndwPDLDRZSXJXnfKucAAAAT1nirt+XXeOPJ0CePc7pLhrvinZvk4qp6T1U9ZqXrs6q6Q5LPjzEfluH1XJvhe/DdXH+3xpXc1S+5/i6D+2b4rBZ7TNxshccBAIDdxTpu9bb8Oi4rP1e3Fk5NcnWSe1XVwXO2T+74/4/dvbvnBACw2VkHrJ51wOJcs7eya/bm/i4u56aFt1lg/DQmFbKmqVa43v4kya8luTTJYxapRLYW5/BW8t15eobznPsn+eMMSVuXVtUHxwTcbUvEXPB43f3DOU9X8l1d6ndvrf8thA1BUhnA5navsb2wu388zYDuPinJzyZ5QYayzDuSHJDkWUnOqaoXrXJOi93pY9b89HpPYA1tpdcy3zTf1zeN7dw7OE5+fsdY2hsAANaCNd7GtaHWRd39mSR3z3Ai7a1JvpbhpNUTMnwPPriMu/jN9ZoMdyn8WpLHJ9mvu2/e3ft39+1z/Z1DV2ry/9GP7e6a4rF9lccDAIBdzTpu49pQ67j1Ml4s+J7x6TFJMl6Ud/S47aT1mBcAwCZnHbBxbaV1wFZ6LfPtimv2zp3z832WMZd7j+3/XsaYucdbzrF2u6p6cpI/yPCeP6m7z1tk13U5hzdWB7x3kscmeWOG9/XmSR6ZIQH301V187U4FrA4SWUAm1RV7Znk8PHpx5cztru/290ndvdRGTLmH5Dk/UkqyR9X1b2XDLBr3WZ8bYuZlP6ee7eKn0x+qKrFFlP7LLL9u2N75+mmt1OTed1piX3uuMD+u9J35/y8nNe5nNeyY17p4slnstTidrHPZKN5d5LLk/x8Vf1SVd0myaPHPifaAABYE9Z4rwcUhQAAIABJREFU1njL1d3XdPfbu/up3X3XDFXL/r8kneFug89aTrzxc/r18elTuvt93X3JvN1ul9WZfD5LvZ8AALApWMdZxy3DSs/VrZW/HtsnV9VeGS7Ou12GO/F/YB3mAwCwaVkHWAcsg2v2VmdZ1+x194VJvjI+PWqaA1TVg3N9xamPLXN+Z4ztwVX1fy255zoZq1VP1oN/2N0fWmL3dTuH190/6e4PdPe/7e6fy1A17Q8yVDe7X5KX7uYpLfV5LvRvIWx6ksoANq9nZCj5miRvX2mQHvyPJE/MUBZ5jyS/PGeX6yY/7IJSyQu5SZIHLdRRVXfL9X+UfXZO16Vzfp67+JvrlxbZfvbY3ruqfmbaSS5hMq8HVtVCJX6T5FfG9qokX16DYy5pvCvEd8anj1zG0MlrOWyJfSav5bPztk8+kwU/j6raO8k9lzGXaa3597W7r07yjvHp7yZ5SpI9k3xx/N0BAIC1YI13PWu8Fejur3f3i5K8a9z0sHm7TD77xT732yTZa/z5c4vs86tLTGFn8ZPkU2P7a0vsAwAAm4V13PWs45awinN1S5lmDTY5/ieSfCnJrTJcXDm5u/+p3X3tGs0HAGBWWAdczzpgCa7ZW50VXrP3xrH91ap6wBSHmVQIvDjJ+5Y5xZOT/CjJtiT/YdpBy3h/pl7zLXKc22W4ichNk5zS3X+2kyEb5hxed3+nu1+V5DXjpvnnPHe1BY9XVbfIkOSW3Ph3DzY1SWUAm1BVPSLJn45PP9XdH5xy3KJ3E+nuf0kyKce915yuy+f8vO9y5rkK/3GRP57/49ie392fn2zs7iuTbB+f/vr8QVV16yRPX+RYH0nyrSQ/levf09V4X4Y/6G+d5JkLzOVmGe6ikCTvG9/33eGUsT1uGQvx947tr1XVL87vrKqfT/KE8em753V/YWyPWORONMfmht+ztbKrvq+TctpPyvCfQ0ny5jWMDwDADLPGs8Zbjp3cKTRJrhnb+WuuyWe/2Od+RYYqZ0lyrwWOe4ckv7fEcXcWPxlOsCXJI6rqyCX2S1Xdaql+AABYT9Zx1nErsJJzdUuZZg021+Tu9L+f5FHjz851AQAsg3WAdcAKuGZvdZZ7zd5fZvhO7pHk7eO5rQVV1Qtz/drohO6+ZrF9F9LdF+X6pKfnVtVTl9q/qm5WVW/J9FXrlrvmm3usPZP8bYbkwn/KAr8TCzh5bHfbObyquslOkuwWO+e5qx23yL/bL8hQBfDyJKfv3inBriWpDGCTqKp9quoRVfWOJH+f4Q4CF+T6BcI0XlFV762qo6pqvzmxb1dVr03ysxkuIPvwpK+7L01y0fj0mOx6V2coEf7mqtp/nN++VfXKDHecSJLjFxg3WSC9pKoeU1XbxrGHJPnHDHepuJHu/nGS48anv1lV766qgyb9VbVfVT1jfH92qru/kevvePEnVfXMqtprjHVgkg8mudv4Ov/TNDHXyCszLMRvk+Tj43u05zivm1TVw6rqnVU19y4l70ryz+PPH6iqX538EV9Vh2f4Ht4kyf/Kje+8818z/FF/2yRvnfNZ7lNVL87wGV621i9yV31fu/ucJJ/PsEj7+Qx3GXnbWsUHAGD2WONZ463CI6vqU+Pr+NcTT+PJqGdkuFNjknxo3rj/Nba/udCJxO6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\n"},"metadata":{"image/png":{"width":1738,"height":1691},"needs_background":"light"}}],"source":["plt.figure(figsize=(30, 30))\n","n = 0\n","for i, column in enumerate(forbes.columns):\n","    n += 1\n","    plt.subplot(4, 4, n)\n","    try:\n","        sns.distplot(forbes[column], bins=30)\n","    except:\n","        pass\n","    plt.title(f'Distribución {column}')\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"dfCAMwoM53QI"},"source":["## 5.- Aplica el test de Shapiro"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VjjOS2I9586Y","outputId":"fb854a53-df84-4334-bd9c-c3c086a15b4d","executionInfo":{"status":"ok","timestamp":1675876167373,"user_tz":-60,"elapsed":20,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Valor de test=0.9911260009, p-value=0.0296579823\n","La variable age proviene de una distribución normal (no rechazamos H0) \n","\n","Valor de test=0.4999439716, p-value=0.0000000000\n","La variable finalWorth NO proviene de una distribución normal (rechazamos H0), por lo tanto H1 \n","\n","Valor de test=0.9201605916, p-value=0.0000000000\n","La variable category NO proviene de una distribución normal (rechazamos H0), por lo tanto H1 \n","\n","Valor de test=0.9652686715, p-value=0.0000001558\n","La variable source NO proviene de una distribución normal (rechazamos H0), por lo tanto H1 \n","\n","Valor de test=0.8229792118, p-value=0.0000000000\n","La variable country NO proviene de una distribución normal (rechazamos H0), por lo tanto H1 \n","\n","Valor de test=0.6174182892, p-value=0.0000000000\n","La variable state NO proviene de una distribución normal (rechazamos H0), por lo tanto H1 \n","\n","Valor de test=0.9581612349, p-value=0.0000000137\n","La variable city NO proviene de una distribución normal (rechazamos H0), por lo tanto H1 \n","\n","Valor de test=0.8306889534, p-value=0.0000000000\n","La variable countryOfCitizenship NO proviene de una distribución normal (rechazamos H0), por lo tanto H1 \n","\n","Valor de test=0.6112900972, p-value=0.0000000000\n","La variable organization NO proviene de una distribución normal (rechazamos H0), por lo tanto H1 \n","\n","Valor de test=0.5845513344, p-value=0.0000000000\n","La variable selfMade NO proviene de una distribución normal (rechazamos H0), por lo tanto H1 \n","\n","Valor de test=0.6432428360, p-value=0.0000000000\n","La variable title NO proviene de una distribución normal (rechazamos H0), por lo tanto H1 \n","\n","Valor de test=0.6188226342, p-value=0.0000000000\n","La variable residenceMsa NO proviene de una distribución normal (rechazamos H0), por lo tanto H1 \n","\n","Valor de test=0.0276352167, p-value=0.0000000000\n","La variable Unnamed: 22 NO proviene de una distribución normal (rechazamos H0), por lo tanto H1 \n","\n","Valor de test=0.3969585299, p-value=0.0000000000\n","La variable gender_F NO proviene de una distribución normal (rechazamos H0), por lo tanto H1 \n","\n","Valor de test=0.3969597220, p-value=0.0000000000\n","La variable gender_M NO proviene de una distribución normal (rechazamos H0), por lo tanto H1 \n","\n"]}],"source":["# Como todas las columnas son numéricas, no necesitamos filtrar ninguna\n","for col in forbes.columns:\n","    stat, p = shapiro(forbes[col])\n","\n","    print('Valor de test=%.10f, p-value=%.10f' % (stat, p))\n","\n","    # Una alpha muy pequeña nos muestra que la variable 'age' proviene de una distribución normal (se ve en el histograma como se parece a dicha distribución)\n","    alpha = 0.00005\n","    if p > alpha:\n","        print(f'La variable {col} proviene de una distribución normal (no rechazamos H0) \\n')\n","    else:\n","        print(f'La variable {col} NO proviene de una distribución normal (rechazamos H0), por lo tanto H1 \\n')"]},{"cell_type":"markdown","metadata":{"id":"p9To2PsU9vo8"},"source":["## 7.- Normaliza los datos numéricos y divídelos en train y test"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"AxHpUfwI-BC-"},"outputs":[],"source":["# Antes de normalizar, vamos a separar la variable target de las features\n","# Nuestra variable target va a ser selfMade (si el millonario \"se hizo a sí mismo\", es decir, empezó de la nada)\n","features = forbes.drop(\"selfMade\", axis=1)\n","target = forbes[\"selfMade\"]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WFCYiwRj9y0F"},"outputs":[],"source":["# Normalizamos las features con StandardScaler\n","scaler = StandardScaler()\n","\n","features_normalizadas = scaler.fit_transform(features)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"y4xxN3vY-8Fp"},"outputs":[],"source":["# Dividimos el dataset en train y test\n","X_train, X_test, y_train, y_test = train_test_split(features_normalizadas, target, \n","                                                    test_size=0.25,\n","                                                    random_state=0)"]},{"cell_type":"markdown","source":["#Importar pickle\n"],"metadata":{"id":"QsrcBP6X-DcT"}},{"cell_type":"code","source":["import pickle"],"metadata":{"id":"I9khrBTi-FY_"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"4GFyycdO_u3J"},"source":["## 8.- Aplica el modelo de Regresión Logística, umbraliza los datos y matriz de confusión"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"N6TV5vjb_yoo"},"outputs":[],"source":["# Instanciamos el modelo y los parámetros que usaremos en el GridSearchCV\n","modelo_lr = LogisticRegression()\n","lr_params = {\n","    \"C\": [0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09], \n","    \"class_weight\": ['balanced', None]\n","}"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YX9d1mhzAy3H","outputId":"ac1f444d-73dd-4caa-b458-1bea6b240bf8","executionInfo":{"status":"ok","timestamp":1675876167890,"user_tz":-60,"elapsed":530,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["GridSearchCV(cv=5, estimator=LogisticRegression(),\n","             param_grid={'C': [0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08,\n","                               0.09],\n","                         'class_weight': ['balanced', None]},\n","             scoring='roc_auc')"]},"metadata":{},"execution_count":36}],"source":["modelo_lr_grid_search = GridSearchCV(estimator=modelo_lr,\n","                                     param_grid=lr_params,\n","                                     cv=5,\n","                                     scoring='roc_auc')\n","\n","modelo_lr_grid_search.fit(X_train, y_train)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WJJvlMURxejH","outputId":"848b2cd2-3c44-48eb-b113-47ab4e02758a","executionInfo":{"status":"ok","timestamp":1675876167891,"user_tz":-60,"elapsed":33,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["LogisticRegression(C=0.01)"]},"metadata":{},"execution_count":37}],"source":["# Instanciamos modelo con la mejor alpha obtenida en el GridSearchCV\n","reg_log = LogisticRegression(\n","    C=modelo_lr_grid_search.best_params_['C'],\n","    class_weight=modelo_lr_grid_search.best_params_['class_weight']\n",")\n","\n","# Entrenamos\n","reg_log.fit(X_train, y_train)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"tGRG5Jctybka"},"outputs":[],"source":["# Obtenemos las predicciones de y_test\n","lr_y_test_pred = reg_log.predict_proba(X_test)\n","\n","# Umbralizamos\n","lr_umbral_pred = 0.6\n","lr_y_test_umbralizada = 1 * (lr_y_test_pred[:, 1] > lr_umbral_pred)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kTrA5T0Ax7c0","outputId":"cd72eacb-1624-4aef-cc8b-ddd6acdd17ee","executionInfo":{"status":"ok","timestamp":1675876167904,"user_tz":-60,"elapsed":41,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Matriz de confusión\n"," [[15 13]\n"," [ 1 61]]\n"]}],"source":["# Obtenemos la matriz de confusión\n","print(u\"Matriz de confusión\\n\", metrics.confusion_matrix(y_test, lr_y_test_umbralizada))\n","\n"]},{"cell_type":"markdown","source":["R2"],"metadata":{"id":"WcMhiFrt3uAs"}},{"cell_type":"code","source":["lr_y_train_pred = reg_log.predict(X_train)\n","lr_y_test_pred = reg_log.predict(X_test)\n","print('La R^2 en los datos de train es: {}'.format(np.round(r2_score(y_train, lr_y_train_pred),2)))\n","print('La R^2 en los datos de test es: {}'.format(np.round(r2_score(y_test, lr_y_test_pred),2)))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"k-ceEZz43JjN","executionInfo":{"status":"ok","timestamp":1675876167905,"user_tz":-60,"elapsed":39,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}},"outputId":"59ba7217-333d-4d94-add1-9552c355ffaf"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["La R^2 en los datos de train es: -0.17\n","La R^2 en los datos de test es: 0.27\n"]}]},{"cell_type":"markdown","source":["MSE"],"metadata":{"id":"1ZQS5NIx5_x6"}},{"cell_type":"code","source":["print(mean_squared_error(y_test, lr_y_test_pred))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ACo-Uaby6ApJ","executionInfo":{"status":"ok","timestamp":1675876167905,"user_tz":-60,"elapsed":37,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}},"outputId":"313faaeb-d213-4947-99f8-b72b33935757"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["0.15555555555555556\n"]}]},{"cell_type":"markdown","source":["Pickle"],"metadata":{"id":"GJhuhVLU-eqN"}},{"cell_type":"code","source":["#Crear modelo pickle\n","pkl_filename = \"pickle_model_reg_log.pkl\"\n","with open(pkl_filename, 'wb') as file:\n","    pickle.dump(reg_log, file)"],"metadata":{"id":"Qzkk4Yb3-hGP"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"V99KmgAn0b_E"},"source":["## 9.- Aplica el modelo de Arboles de Decisión, umbraliza los datos y matriz de confusión"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ax3RN-Up0gTX","outputId":"5149a3cc-afe5-40a7-c48c-657a51ed8233","executionInfo":{"status":"ok","timestamp":1675876167906,"user_tz":-60,"elapsed":36,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["El mejor estimador es: DecisionTreeRegressor(max_depth=4)\n"]}],"source":["# Instanciamos modelo de árboles de decisión y parámetros (profundidad del árbol)\n","modelo_dtr = DecisionTreeRegressor()\n","dtr_params = { \"max_depth\": range(3, 7) }\n","\n","# Sacamos el mejor estimador probando los diferentes parámetros\n","dtr_optimizado = GridSearchCV(modelo_dtr, dtr_params, cv=3)\n","dtr_optimizado.fit(X_train, y_train)\n","\n","print(f\"El mejor estimador es: {dtr_optimizado.best_estimator_}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7KcQ5nyT1NoF"},"outputs":[],"source":["# Obtenemos predicciones de y_test\n","dtr_y_test_pred = dtr_optimizado.predict(X_test)\n","\n","# Umbralizamos\n","dtr_umbral_pred = 0.6\n","dtr_y_test_umbralizada = 1 * (dtr_y_test_pred[:] > dtr_umbral_pred)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qk3LzMes2ktI","outputId":"6ed0673a-5515-46c7-881e-0ce5ff142a90","executionInfo":{"status":"ok","timestamp":1675876167906,"user_tz":-60,"elapsed":30,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Matriz de confusión\n"," [[13 15]\n"," [ 0 62]]\n"]}],"source":["# Obtenemos la matriz de confusión\n","print(u\"Matriz de confusión\\n\", metrics.confusion_matrix(y_test, dtr_y_test_umbralizada))\n","\n"]},{"cell_type":"markdown","source":["R2"],"metadata":{"id":"-FCg-FbV3yGP"}},{"cell_type":"code","source":["dtr_y_train_pred = dtr_optimizado.predict(X_train)\n","dtr_y_test_pred = dtr_optimizado.predict(X_test)\n","print('La R^2 en los datos de train es: {}'.format(np.round(r2_score(y_train, dtr_y_train_pred),2)))\n","print('La R^2 en los datos de test es: {}'.format(np.round(r2_score(y_test, dtr_y_test_pred),2)))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Z0kbn80F3zOW","executionInfo":{"status":"ok","timestamp":1675876167907,"user_tz":-60,"elapsed":28,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}},"outputId":"f2f80914-e0ca-4f04-d966-bebd3151ac2d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["La R^2 en los datos de train es: 0.43\n","La R^2 en los datos de test es: 0.39\n"]}]},{"cell_type":"markdown","source":["MSE"],"metadata":{"id":"FUKTLPGt7RJu"}},{"cell_type":"code","source":["print(mean_squared_error(y_test, dtr_y_test_pred))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wyf737yI7RtT","executionInfo":{"status":"ok","timestamp":1675876167907,"user_tz":-60,"elapsed":26,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}},"outputId":"5bed50dd-2785-4f4f-d07f-87cb89ac504f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["0.13081089987038358\n"]}]},{"cell_type":"markdown","source":["Pickle"],"metadata":{"id":"Em2oX3am_53X"}},{"cell_type":"code","source":["#Crear modelo pickle\n","pkl_filename = \"pickle_model_dec_tree.pkl\"\n","with open(pkl_filename, 'wb') as file:\n","    pickle.dump(dtr_optimizado, file)"],"metadata":{"id":"VLtaP81s_64g"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Q27WYf7P27nn"},"source":["## 10.- Aplica el modelo de KNN Vecinos , umbraliza los datos y matriz de confusión"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_VP7TPIf2-zE","outputId":"6c4f951a-1a69-4779-a4a8-7e9f54f51992","executionInfo":{"status":"ok","timestamp":1675876167908,"user_tz":-60,"elapsed":24,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["El mejor estimador es: KNeighborsRegressor(n_neighbors=8)\n"]}],"source":["# Instanciamos modelo de KNN vecinos y parámetros (número de vecinos)\n","modelo_clf = KNeighborsRegressor()\n","clf_params = { \"n_neighbors\": range(1, 10) }\n","\n","# Sacamos el mejor estimador probando los diferentes parámetros\n","clf_optimizado = GridSearchCV(modelo_clf, clf_params, cv=3)\n","clf_optimizado.fit(X_train, y_train)\n","\n","print(f\"El mejor estimador es: {clf_optimizado.best_estimator_}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wa6fIckK4CPF"},"outputs":[],"source":["# Obtenemos predicciones de y_test\n","clf_y_test_pred = clf_optimizado.predict(X_test)\n","\n","# Umbralizamos\n","clf_umbral_pred = 0.6\n","clf_y_test_umbralizada = 1 * (clf_y_test_pred[:] > clf_umbral_pred)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jtPCjZRv4Lgd","outputId":"caa1e356-0074-4ddd-8968-d43d72a295bc","executionInfo":{"status":"ok","timestamp":1675876168332,"user_tz":-60,"elapsed":15,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Matriz de confusión\n"," [[16 12]\n"," [ 9 53]]\n"]}],"source":["# Obtenemos la matriz de confusión\n","print(u\"Matriz de confusión\\n\", metrics.confusion_matrix(y_test, clf_y_test_umbralizada))\n"]},{"cell_type":"markdown","source":["R2"],"metadata":{"id":"lIVy8wml4HlN"}},{"cell_type":"code","source":["clf_y_train_pred = clf_optimizado.predict(X_train)\n","clf_y_test_pred = clf_optimizado.predict(X_test)\n","print('La R^2 en los datos de train es: {}'.format(np.round(r2_score(y_train, clf_y_train_pred),2)))\n","print('La R^2 en los datos de test es: {}'.format(np.round(r2_score(y_test, clf_y_test_pred),2)))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zFNzrGVC4IZm","executionInfo":{"status":"ok","timestamp":1675876168332,"user_tz":-60,"elapsed":10,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}},"outputId":"790734e8-acde-45ac-d2df-a9e63fcec336"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["La R^2 en los datos de train es: 0.36\n","La R^2 en los datos de test es: 0.24\n"]}]},{"cell_type":"markdown","source":["MSE"],"metadata":{"id":"8xxlxD1x7VaG"}},{"cell_type":"code","source":["print(mean_squared_error(y_test, clf_y_test_pred))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SBGBob8X7WBC","executionInfo":{"status":"ok","timestamp":1675876168332,"user_tz":-60,"elapsed":7,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}},"outputId":"3dc9d033-ffc6-446e-fe79-85915a021bb1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["0.1623263888888889\n"]}]},{"cell_type":"markdown","source":["Pickle"],"metadata":{"id":"JMct67T7ARFy"}},{"cell_type":"code","source":["#Crear modelo pickle\n","pkl_filename = \"pickle_model_knn.pkl\"\n","with open(pkl_filename, 'wb') as file:\n","    pickle.dump(clf_optimizado, file)"],"metadata":{"id":"mYDUr9gDARvf"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"T3dR9_iaQltO"},"source":["#11.- Aplica el modelo de Random Forest, umbraliza los datos y matriz de confusión"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YznZZ0TO4YN8"},"outputs":[],"source":["from sklearn.ensemble import RandomForestRegressor"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3EgCbQSnQy1O"},"outputs":[],"source":["# Defino un modelo\n","model = RandomForestRegressor()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lCXyB_5O4Rna","outputId":"367e7293-e781-4633-cc33-6b2f7cd3ba8c","executionInfo":{"status":"ok","timestamp":1675876168902,"user_tz":-60,"elapsed":574,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["RandomForestRegressor()"]},"metadata":{},"execution_count":56}],"source":["# Ajusto el modelo a los datos\n","model.fit(X_train, y_train)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Pq8YzUdZ5K5v"},"outputs":[],"source":["# Obtenemos predicciones de y_test\n","y_test_pred = model.predict(X_test)\n","\n","# Umbralizamos\n","umbral_pred = 0.6\n","y_test_umbralizada = 1 * (y_test_pred[:] > umbral_pred)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SdrMtGqe6BB8","outputId":"89ac857e-eb46-40a4-c3ae-55306b12c12d","executionInfo":{"status":"ok","timestamp":1675876168903,"user_tz":-60,"elapsed":22,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Matriz de confusión\n"," [[18 10]\n"," [13 49]]\n"]}],"source":["# Obtenemos la matriz de confusión\n","print(u\"Matriz de confusión\\n\", metrics.confusion_matrix(y_test,y_test_umbralizada))\n"]},{"cell_type":"markdown","source":["R2"],"metadata":{"id":"P_hKT_qb4WoU"}},{"cell_type":"code","source":["y_train_pred = model.predict(X_train)\n","y_test_pred = model.predict(X_test)\n","print('La R^2 en los datos de train es: {}'.format(np.round(r2_score(y_train, y_train_pred),2)))\n","print('La R^2 en los datos de test es: {}'.format(np.round(r2_score(y_test, y_test_pred),2)))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rJy80XMy4Xct","executionInfo":{"status":"ok","timestamp":1675876168903,"user_tz":-60,"elapsed":18,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}},"outputId":"32c1776c-2255-46a2-e032-db8fc482477d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["La R^2 en los datos de train es: 0.91\n","La R^2 en los datos de test es: 0.35\n"]}]},{"cell_type":"markdown","source":["MSE"],"metadata":{"id":"IBIElgKy7acu"}},{"cell_type":"code","source":["print(mean_squared_error(y_test, y_test_pred))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kSr6Iw2m7b0N","executionInfo":{"status":"ok","timestamp":1675876168904,"user_tz":-60,"elapsed":16,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}},"outputId":"5681e314-9e19-4210-ed1a-1d7eb64fb885"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["0.13879555555555556\n"]}]},{"cell_type":"markdown","source":["Pickle"],"metadata":{"id":"dpCzRyxcA2k7"}},{"cell_type":"code","source":["#Crear modelo pickle\n","pkl_filename = \"pickle_model_r_f.pkl\"\n","with open(pkl_filename, 'wb') as file:\n","    pickle.dump(model, file)"],"metadata":{"id":"rNdChVawA3y2"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"v6E6HfTbQzS-"},"source":["#12.- Aplica el modelo de XGBOOST, umbraliza los datos y matriz de confusión"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"VwF4L4Cq6ShO"},"outputs":[],"source":["import xgboost as xgb "]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vk_cKtA56Sad","outputId":"c8be1975-3249-4e62-e46a-551e57045f1d","executionInfo":{"status":"ok","timestamp":1675876172641,"user_tz":-60,"elapsed":3749,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[17:09:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n","[17:09:29] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n","[17:09:29] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n","[17:09:29] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n","[17:09:29] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n","[17:09:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n","[17:09:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n","[17:09:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n","[17:09:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n","[17:09:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n","[17:09:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n","[17:09:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n","[17:09:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n","[17:09:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n","[17:09:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n","[17:09:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n","[17:09:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n","[17:09:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n","[17:09:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n","[17:09:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n","[17:09:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n","[17:09:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n","[17:09:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n","[17:09:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n","[17:09:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"]},{"output_type":"execute_result","data":{"text/plain":["GridSearchCV(cv=3, estimator=XGBRegressor(),\n","             param_grid={'eta': [0.3, 0.5], 'lambda': [0.5, 1.0],\n","                         'max_depth': [3, 7]})"]},"metadata":{},"execution_count":62}],"source":["# Obtengo el mejor parámetro de profundidad del árbol entre 3 y 7 usando \n","# validación cruzada y gridsearch\n","regr = xgb.XGBRegressor()\n","params = {\"eta\": [0.3, 0.5], \"lambda\":[0.5, 1.0], \"max_depth\": [3, 7]}\n","regr_opt = GridSearchCV(regr, params, cv=3)\n","regr_opt.fit(X_train, y_train)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1v2PTfSn6SR9"},"outputs":[],"source":["# Obtenemos predicciones de y_test\n","y_test_pred1 = regr_opt.predict(X_test)\n","\n","# Umbralizamos\n","umbral_pred = 0.6\n","y_test_umbralizada1 = 1 * (y_test_pred1[:] > umbral_pred)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PvNSWcNm6_3o","outputId":"1a2a1624-58a5-4deb-e62b-60136df5991a","executionInfo":{"status":"ok","timestamp":1675876172642,"user_tz":-60,"elapsed":40,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Matriz de confusión\n"," [[18 10]\n"," [12 50]]\n"]}],"source":["# Obtenemos la matriz de confusión\n","print(u\"Matriz de confusión\\n\", metrics.confusion_matrix(y_test,y_test_umbralizada1))"]},{"cell_type":"markdown","source":["R2"],"metadata":{"id":"HDcoPQMm4l67"}},{"cell_type":"code","source":["y_train_pred1 = regr_opt.predict(X_train)\n","y_test_pred1 = regr_opt.predict(X_test)\n","print('La R^2 en los datos de train es: {}'.format(np.round(r2_score(y_train, y_train_pred1),2)))\n","print('La R^2 en los datos de test es: {}'.format(np.round(r2_score(y_test, y_test_pred1),2)))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yYl0fMwL4mm5","executionInfo":{"status":"ok","timestamp":1675876172643,"user_tz":-60,"elapsed":37,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}},"outputId":"9c2cdd6e-e687-4a2d-f4a8-f7ce09e48098"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["La R^2 en los datos de train es: 0.81\n","La R^2 en los datos de test es: 0.31\n"]}]},{"cell_type":"markdown","source":["MSE"],"metadata":{"id":"8l6Y7WZo7fsi"}},{"cell_type":"code","source":["print(mean_squared_error(y_test, y_test_pred1))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0Qq8hTwi7gVT","executionInfo":{"status":"ok","timestamp":1675876172643,"user_tz":-60,"elapsed":32,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}},"outputId":"2c16b2a3-cb8a-46bf-898e-a64e6d179ea5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["0.14789022035394478\n"]}]},{"cell_type":"markdown","source":["Pickle"],"metadata":{"id":"x42Zy2CIA_-Q"}},{"cell_type":"code","source":["#Crear modelo pickle\n","pkl_filename = \"pickle_model_xgb.pkl\"\n","with open(pkl_filename, 'wb') as file:\n","    pickle.dump(regr_opt, file)"],"metadata":{"id":"qyVQ6SmlBA6Y"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"tNo1FqXVRHoy"},"source":["#13.- Aplica el modelo de SVM kernel RBF , umbraliza los datos y matriz de confusión"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5W3jrHh3RYGP"},"outputs":[],"source":["from sklearn.svm import SVR"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"koIsXFNe7tOU"},"outputs":[],"source":["# Ajuste del modelo de regresión\n","svr_rbf = SVR(kernel='rbf', C=100, gamma=0.1, epsilon=.1)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"m-7UJkNf8AJx","outputId":"f8a0a259-b784-4f57-91e4-a8feb38a8f56","executionInfo":{"status":"ok","timestamp":1675876172644,"user_tz":-60,"elapsed":30,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[LibSVM]"]},{"output_type":"execute_result","data":{"text/plain":["SVR(verbose=True)"]},"metadata":{},"execution_count":69}],"source":["svr_rbf = SVR(verbose=True)\n","\n","svr_rbf.fit(X_train, y_train)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cwmaXgAN8MfQ"},"outputs":[],"source":["# Obtenemos las predicciones de y_test\n","y_pred = svr_rbf.predict(X_test)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"OYAgw37l8Slw"},"outputs":[],"source":["# Umbralizamos\n","umbral = 0.6\n","svr_y_test_umbralizada = 1 * (y_pred[:, ] > umbral)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JPiEKShR8ZFA","outputId":"62c6f2f5-a200-4b5d-97c7-d486ca611fef","executionInfo":{"status":"ok","timestamp":1675876172644,"user_tz":-60,"elapsed":27,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Matriz de confusión\n"," [[17 11]\n"," [ 7 55]]\n"]}],"source":["# Obtenemos la matriz de confusión\n","print(u\"Matriz de confusión\\n\", metrics.confusion_matrix(y_test,svr_y_test_umbralizada))"]},{"cell_type":"markdown","source":["R2"],"metadata":{"id":"S_kBB3JP40i4"}},{"cell_type":"code","source":["svr_y_train_pred = svr_rbf.predict(X_train)\n","svr_y_test_pred = svr_rbf.predict(X_test)\n","print('La R^2 en los datos de train es: {}'.format(np.round(r2_score(y_train, svr_y_train_pred),2)))\n","print('La R^2 en los datos de test es: {}'.format(np.round(r2_score(y_test, svr_y_test_pred),2)))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lsnsOmKb41JL","executionInfo":{"status":"ok","timestamp":1675876172645,"user_tz":-60,"elapsed":26,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}},"outputId":"7587529b-11a2-443e-9796-e0b87dd721f9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["La R^2 en los datos de train es: 0.51\n","La R^2 en los datos de test es: 0.3\n"]}]},{"cell_type":"markdown","source":["MSE"],"metadata":{"id":"fccOIpPT7lmL"}},{"cell_type":"code","source":["print(mean_squared_error(y_test, svr_y_test_pred))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VIdpwte77mIS","executionInfo":{"status":"ok","timestamp":1675876172645,"user_tz":-60,"elapsed":16,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}},"outputId":"c75a727c-cb49-480e-a361-d2dc7545b99a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["0.14962864261723371\n"]}]},{"cell_type":"markdown","source":["Pickle"],"metadata":{"id":"StUlaumSBUlo"}},{"cell_type":"code","source":["#Crear modelo pickle\n","pkl_filename = \"pickle_model_svr_rbf.pkl\"\n","with open(pkl_filename, 'wb') as file:\n","    pickle.dump(svr_rbf, file)"],"metadata":{"id":"CFsQLnBfBV42"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"FVFRaFFLRdl2"},"source":["#14.- Aplica el modelo de SVM kernel linear , umbraliza los datos y matriz de confusión"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0ytJ7dlhR6O6"},"outputs":[],"source":["svr_lin = SVR(kernel='linear', C=100, gamma='auto')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vkyt4KvA8xA1"},"outputs":[],"source":["svr_lin.fit(X_train, y_train)\n","\n","y_pred_lin = svr_lin.predict(X_test)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"19wfpKKE86RX"},"outputs":[],"source":["# Umbralizamos\n","umbral = 0.6\n","svr_lin_y_test_umbralizada = 1 * (y_pred_lin[:, ] > umbral)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_ot_PNgT9MDm","colab":{"base_uri":"https://localhost:8080/"},"outputId":"c4b437f4-9934-4dd4-e83b-409de417d392","executionInfo":{"status":"ok","timestamp":1675876173047,"user_tz":-60,"elapsed":18,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Matriz de confusión\n"," [[14 14]\n"," [ 0 62]]\n"]}],"source":["# Obtenemos la matriz de confusión\n","print(u\"Matriz de confusión\\n\", metrics.confusion_matrix(y_test,svr_lin_y_test_umbralizada))"]},{"cell_type":"markdown","source":["R2"],"metadata":{"id":"K-8RJbMf498I"}},{"cell_type":"code","source":["svr_lin_train_pred = svr_lin.predict(X_train)\n","svr_lin_test_pred = svr_lin.predict(X_test)\n","print('La R^2 en los datos de train es: {}'.format(np.round(r2_score(y_train, svr_lin_train_pred),2)))\n","print('La R^2 en los datos de test es: {}'.format(np.round(r2_score(y_test, svr_lin_test_pred),2)))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ww-fzPSY4_CH","executionInfo":{"status":"ok","timestamp":1675876173047,"user_tz":-60,"elapsed":17,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}},"outputId":"29caca5d-7da6-43b3-a2e8-0484f3a82db3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["La R^2 en los datos de train es: 0.03\n","La R^2 en los datos de test es: 0.37\n"]}]},{"cell_type":"markdown","source":["MSE"],"metadata":{"id":"VEQLU78q7pgm"}},{"cell_type":"code","source":["print(mean_squared_error(y_test, svr_lin_test_pred))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"A0JX8m257p8T","executionInfo":{"status":"ok","timestamp":1675876173047,"user_tz":-60,"elapsed":13,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}},"outputId":"7a57f9d7-d7c9-49c7-9357-f0b735eae021"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["0.134465779744339\n"]}]},{"cell_type":"markdown","source":["Pickle"],"metadata":{"id":"roZyRGGEBhzA"}},{"cell_type":"code","source":["#Crear modelo pickle\n","pkl_filename = \"pickle_model_svr_lin.pkl\"\n","with open(pkl_filename, 'wb') as file:\n","    pickle.dump(svr_lin, file)"],"metadata":{"id":"hPsgVWcMBinW"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Bs_rZ1hzR7qY"},"source":["#15.- Aplica el modelo de SVM kernel polinomico , umbraliza los datos y matriz de confusión"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"RusfzJDlSF2Y"},"outputs":[],"source":["svr_poly = SVR(kernel='poly', C=100, gamma='auto', degree=3, epsilon=.1,\n","               coef0=1)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qta-iQdA9lik","colab":{"base_uri":"https://localhost:8080/"},"outputId":"5e64429c-60ec-4a1c-f8df-b758f0ee0963","executionInfo":{"status":"ok","timestamp":1675876174157,"user_tz":-60,"elapsed":1120,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[LibSVM]"]}],"source":["svr_poly = SVR(kernel='poly', C=100, gamma='auto', degree=3, epsilon=.1,\n","               coef0=1, verbose = True)\n","\n","svr_poly.fit(X_train, y_train)\n","\n","y_pred_ply = svr_poly.predict(X_test)"]},{"cell_type":"code","source":["# Umbralizamos\n","umbral = 0.6\n","svr_ply_y_test_umbralizada = 1 * (y_pred_ply[:, ] > umbral)"],"metadata":{"id":"HFVrQGVBV8OS"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Obtenemos la matriz de confusión\n","print(u\"Matriz de confusión\\n\", metrics.confusion_matrix(y_test,svr_ply_y_test_umbralizada))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9dZNfnTCWIPX","outputId":"2753fe9c-1412-4f04-aae5-1ca4a7a7693c","executionInfo":{"status":"ok","timestamp":1675876174158,"user_tz":-60,"elapsed":16,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Matriz de confusión\n"," [[16 12]\n"," [22 40]]\n"]}]},{"cell_type":"markdown","source":["R2"],"metadata":{"id":"H_ErVKKE5LSR"}},{"cell_type":"code","source":["svr_poly_y_train_pred = svr_poly.predict(X_train)\n","svr_poly_y_test_pred = svr_poly.predict(X_test)\n","print('La R^2 en los datos de train es: {}'.format(np.round(r2_score(y_train, svr_poly_y_train_pred),2)))\n","print('La R^2 en los datos de test es: {}'.format(np.round(r2_score(y_test, svr_poly_y_test_pred),2)))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qFWuXnvG5MFS","executionInfo":{"status":"ok","timestamp":1675876174158,"user_tz":-60,"elapsed":15,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}},"outputId":"5e731848-3b5d-4e2b-cd7a-6f1a7dcc34e0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["La R^2 en los datos de train es: 0.79\n","La R^2 en los datos de test es: -5.63\n"]}]},{"cell_type":"markdown","source":["MSE"],"metadata":{"id":"7YbsUiN67vOP"}},{"cell_type":"code","source":["print(mean_squared_error(y_test, svr_poly_y_test_pred))"],"metadata":{"id":"AmXF_1M77wzQ","executionInfo":{"status":"ok","timestamp":1675876174159,"user_tz":-60,"elapsed":11,"user":{"displayName":"Carlos Germán Sellers Mendo","userId":"10483787242844398475"}},"outputId":"567edd78-20f1-44e6-8125-712f959a19bd","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["1.421727752012451\n"]}]},{"cell_type":"markdown","source":["Pickle"],"metadata":{"id":"JgehavDdBokn"}},{"cell_type":"code","source":["#Crear modelo pickle\n","pkl_filename = \"pickle_model_svr_poly.pkl\"\n","with open(pkl_filename, 'wb') as file:\n","    pickle.dump(svr_poly, file)"],"metadata":{"id":"V2k33ZNPBpRE"},"execution_count":null,"outputs":[]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"},"accelerator":"GPU","gpuClass":"standard"},"nbformat":4,"nbformat_minor":0}