covid-symptom-severity-classifier.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Covid Severity Classifier\n- A Machine Learning system to predict the severity of a patient's Covid-19 based on their symptoms\n- Created with the [Kaggle Covid-19 Patient Symptoms](https://www.kaggle.com/bitsofishan/covid19-patient-symptoms) dataset\n- It preprocesses the data\n- Defines a Deep Neural Network model in Tensorflow\n- Trains the Machine Learning model\n- And outputs the model and some required data to be used in a Django web application","metadata":{}},{"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport matplotlib.pyplot as plt\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2021-08-07T19:27:57.431640Z","iopub.execute_input":"2021-08-07T19:27:57.432032Z","iopub.status.idle":"2021-08-07T19:27:57.450029Z","shell.execute_reply.started":"2021-08-07T19:27:57.431995Z","shell.execute_reply":"2021-08-07T19:27:57.448569Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"/kaggle/input/covid19-patient-symptoms/COVID-19.xlsx\n","output_type":"stream"}]},{"cell_type":"code","source":"!pip install openpyxl\n!pip install missingno","metadata":{"execution":{"iopub.status.busy":"2021-08-07T19:25:19.863753Z","iopub.execute_input":"2021-08-07T19:25:19.864114Z","iopub.status.idle":"2021-08-07T19:25:37.026163Z","shell.execute_reply.started":"2021-08-07T19:25:19.864082Z","shell.execute_reply":"2021-08-07T19:25:37.024752Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"Collecting openpyxl\n  Downloading openpyxl-3.0.7-py2.py3-none-any.whl (243 kB)\n\u001b[K     |████████████████████████████████| 243 kB 596 kB/s eta 0:00:01\n\u001b[?25hCollecting et-xmlfile\n  Downloading et_xmlfile-1.1.0-py3-none-any.whl (4.7 kB)\nInstalling collected packages: et-xmlfile, openpyxl\nSuccessfully installed et-xmlfile-1.1.0 openpyxl-3.0.7\n\u001b[33mWARNING: Running pip as root will break packages and permissions. You should install packages reliably by using venv: https://pip.pypa.io/warnings/venv\u001b[0m\nRequirement already satisfied: missingno in /opt/conda/lib/python3.7/site-packages (0.4.2)\nRequirement already satisfied: scipy in /opt/conda/lib/python3.7/site-packages (from missingno) (1.6.3)\nRequirement already satisfied: matplotlib in /opt/conda/lib/python3.7/site-packages (from missingno) (3.4.2)\nRequirement already satisfied: seaborn in /opt/conda/lib/python3.7/site-packages (from missingno) (0.11.1)\nRequirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (from missingno) (1.19.5)\nRequirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.7/site-packages (from matplotlib->missingno) (8.2.0)\nRequirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib->missingno) (1.3.1)\nRequirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.7/site-packages (from matplotlib->missingno) (0.10.0)\nRequirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.7/site-packages (from matplotlib->missingno) (2.8.1)\nRequirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib->missingno) (2.4.7)\nRequirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from cycler>=0.10->matplotlib->missingno) (1.15.0)\nRequirement already satisfied: pandas>=0.23 in /opt/conda/lib/python3.7/site-packages (from seaborn->missingno) (1.2.4)\nRequirement already satisfied: pytz>=2017.3 in /opt/conda/lib/python3.7/site-packages (from pandas>=0.23->seaborn->missingno) (2021.1)\n\u001b[33mWARNING: Running pip as root will break packages and permissions. You should install packages reliably by using venv: https://pip.pypa.io/warnings/venv\u001b[0m\n","output_type":"stream"}]},{"cell_type":"code","source":"import openpyxl\nimport missingno as msno\nimport tensorflow as tf\nimport tensorflow.keras.layers as layers\nfrom sklearn.model_selection import train_test_split\n%matplotlib inline","metadata":{"execution":{"iopub.status.busy":"2021-08-07T19:25:37.028921Z","iopub.execute_input":"2021-08-07T19:25:37.029254Z","iopub.status.idle":"2021-08-07T19:25:44.184850Z","shell.execute_reply.started":"2021-08-07T19:25:37.029219Z","shell.execute_reply":"2021-08-07T19:25:44.183755Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":"# Load the Data","metadata":{}},{"cell_type":"code","source":"raw_df = pd.read_excel('../input/covid19-patient-symptoms/COVID-19.xlsx')\nraw_df.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-07T19:28:01.516630Z","iopub.execute_input":"2021-08-07T19:28:01.517035Z","iopub.status.idle":"2021-08-07T19:28:02.059779Z","shell.execute_reply.started":"2021-08-07T19:28:01.516996Z","shell.execute_reply":"2021-08-07T19:28:02.058681Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"   Sno  age  gender  body temperature  Dry Cough  sour throat  weakness  \\\n0    1   20       1              98.6          0            0         0   \n1    2   19       1              99.0          1            0         0   \n2    3   55       0             102.0          1            1         1   \n3    4   40       0             100.0          0            0         0   \n4    5   33       1              99.2          0            1         0   \n\n   breathing problem  drowsiness  pain in chest  ...  \\\n0                  0           0              0  ...   \n1                  0           0              0  ...   \n2                  1           1              1  ...   \n3                  0           1              1  ...   \n4                  1           0              0  ...   \n\n   stroke or reduced immunity  symptoms progressed  high blood pressue  \\\n0                           0                    0                   0   \n1                           0                    0                   0   \n2                           0                    0                   0   \n3                           0                    1                   1   \n4                           1                    1                   0   \n\n   kidney disease  change in appetide  Loss of sense of smell  Corona result  \\\n0               0                   0                       0              0   \n1               0                   0                       0              0   \n2               0                   1                       0              1   \n3               0                   1                       0              2   \n4               0                   0                       1              2   \n\n   Unnamed: 21  Unnamed: 22  Unnamed: 23  \n0          NaN          NaN          NaN  \n1          NaN          NaN          NaN  \n2          NaN          NaN          NaN  \n3          NaN          NaN          NaN  \n4          NaN          NaN          NaN  \n\n[5 rows x 24 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Sno</th>\n      <th>age</th>\n      <th>gender</th>\n      <th>body temperature</th>\n      <th>Dry Cough</th>\n      <th>sour throat</th>\n      <th>weakness</th>\n      <th>breathing problem</th>\n      <th>drowsiness</th>\n      <th>pain in chest</th>\n      <th>...</th>\n      <th>stroke or reduced immunity</th>\n      <th>symptoms progressed</th>\n      <th>high blood pressue</th>\n      <th>kidney disease</th>\n      <th>change in appetide</th>\n      <th>Loss of sense of smell</th>\n      <th>Corona result</th>\n      <th>Unnamed: 21</th>\n      <th>Unnamed: 22</th>\n      <th>Unnamed: 23</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>20</td>\n      <td>1</td>\n      <td>98.6</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>19</td>\n      <td>1</td>\n      <td>99.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>55</td>\n      <td>0</td>\n      <td>102.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>40</td>\n      <td>0</td>\n      <td>100.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>33</td>\n      <td>1</td>\n      <td>99.2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 24 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"# Exploratory Data Analysis\n- Analyze the data to figure out what needs to be done to it before it can be used for machine learning","metadata":{}},{"cell_type":"code","source":"msno.matrix(raw_df)","metadata":{"execution":{"iopub.status.busy":"2021-08-07T19:28:04.428420Z","iopub.execute_input":"2021-08-07T19:28:04.429070Z","iopub.status.idle":"2021-08-07T19:28:05.244315Z","shell.execute_reply.started":"2021-08-07T19:28:04.429018Z","shell.execute_reply":"2021-08-07T19:28:05.243106Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1800x720 with 2 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAABcAAAAMNCAYAAAC/FdeyAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAD8DklEQVR4nOydZZgeRdaG7xMngsviLK6LLIsuLO6y6Ie7L+7u7rK4++IOiy0s7r64B/dgSQiZ8/04p5mel5kQApnumXnu6+orMy1vqqa766166tRzzN0RQgghhBBCCCGEEEIIITob3aougBBCCCGEEEIIIYQQQggxOpAALoQQQgghhBBCCCGEEKJTIgFcCCGEEEIIIYQQQgghRKdEArgQQgghhBBCCCGEEEKITokEcCGEEEIIIYQQQgghhBCdEgngQgghhBBCCCGEEEIIITolEsCFEEIIIYQQQgghhBBCdEokgAshhBBCCCGEEEIIIYTolEgAF0IIIYQQQgghhBBCCNEpkQAuhBBCCCGEGK2YWff8t5uZ9ay6PEIIIYQQousgAVwIIYQQQggx2jCzHu4+3Mz6AWcCG5vZ+FWXSwghhBBCdA0kgAshhBBCCCFGC2bW3d1/NLMBwCPArMAYwKBqSyaEEEIIIboK5u5Vl0EIIYQQQgjRSTGzPsA9wPfAFsC77j4sxfHh1ZZOCCGEEEJ0dnpUXQAhhBBCCCFEp2ZxYAJgdXd/A8DMFgFWM7NewMPufkFlpRNCCCGEEJ0aCeBCCCGEEEKI0Ul3oD8wvplND2wM7AY8DkwJLGJmb7r7fRWWUQghhBBCdFIkgAshhBBCCCF+F8ysm7s3NeweCHwBnA38CPQCNnT3S81sbsIbfML2LakQQgghhOgqSAAXQgghhBBC/GbMrEcmvOwBTAT0AT5y96fNbENgYUIIf9jdXzaz7kA34BXgm8oKLoQQQgghOjVKgimEEEIIIYT4TRQJLc1sAHA5MD0wNvAZYXdyl7sPLZ3fC5gJOBUYBizeSuS4EEIIIYQQvxkJ4EIIIYQQQojfjJn1I3y9PwcuBRxYElgF2Ak4290Hm9mEwD+AJYCewILuPqwQ0aspvRBCCCGE6KzIAkUIIYQQQgjxqylFfZtHVM0uwHfAZu7+Sp4zFFgV+MbdB+el0wJLAU8D2xe2Ke7+YwXVEEIIIYQQnRwJ4EIIIYQQQoiRxsxmcPdXimhtb15SOivwFfB2nrc2cB6wl7ufb2bjAD3c/SEzWwX42N09hXSJ30IIIYQQYrTQreoCCCGEEEIIIToGZvZH4L+Z1LLY1zN/nBAY6u5DzWwtwgZlb3c/KhNe7gccbma93P2jFL9NtidCCCGEEGJ0IgFcCCGEEEIIMbIMArZ29wszkSXuPiyPXQMsYman0Sx+H5nHZgPmAD5y9x+KD3MlJBJCCCGEEKMZJcEUQgghhBBC/CIlr+8i6vsu4Dt3Xy73zQmcAswLXOPua6VIPiNwFjAMWDQ9v03itxBCCCGEaA8kgAshhBBCCCF+kSJRpZn1cfchZnY6sApwt7uvm+csB+xNRHzfCowNTAA0AQu6+7AieWY1tRBCCCGEEF0NCeBCCCGEEEKIEVJEbJvZAOA+YA/gEcLXeyPgLndfO8+dC5gfWBl4F3ge+Ke7Dy9E9CrqIIQQQgghuiYSwIUQQgghhBBtUkRsZyLLq4EBwK7u/oyZjQfsSYMIPqLPaZdCCyGEEEIIkSgJphBCCCGEEKJNUvweA5gG+Bo41t2fyWOfA0cAFwBLmNklxXXpE46ZWfE57VtyIYQQQgghJIALIYQQQgghWqEQrvPfy4GXgWWBD4v9ZtbN3b+gpQh+C4C7D8t/teRUCCGEEEJUhgRwIYQQQgghBNAiartHen73TAH7n8CdwHjAnBDCtrs3NYjgN+b1GmcIIYQQQohaIA9wIYQQQgghBGY2FrAa8J6735EJL+8CDgZuAxYBjgYmAlZw92dL13ZLMXxM4JsUz7u5e1O7V0QIIYQQQogSiswQQgghhBBCAEwIrA2cYmZrAs8C3wJPp5B9L7Ab8CVwg5nNXlyY4re5+9cpfpvEbyGEEEIIUQcUAS6EEEIIIYQAwMyWAk4EpgZeBOZ396GlCO9uwMLAycAAYCV3f76yAgshhBBCCPELKAJcCCGEEEKILk6R8NLd7wB6Aj8CfYElcn+TmXXPqO77gO2Br4DHzWyaSgothBBCCCHESKAIcCGEEEIIIQQAZtYd2AwYBuwMOLCXu9+cx4tIcAMWBTYFNnD34VWVWQghhBBCiBEhAVwIIYQQQoguSkZ1D8+fe7j7j6VjqwCH5K97uftNuX9iYB53v6G1zxFCCCGEEKJOSAAXQgghhBCiC1KI1mbWD9gXmBR4BrjJ3V/Lc1YDDsxLDgMeAS4BmoC/uQYTQgghhBCi5kgAF0IIIYQQoouS4vcTQB9gCDAd8ABwqLvfleesCuwD/An4NLe53X2YmZlEcCGEEEIIUWckgAshhBBCCNGFKEV+G7AesBawDSFszwn8G3gV2Mfd/53XLAjMBIwFnOTuPzZapgghhBBCCFFHJIALIYQQQgjRxTCzvsCewMzAK8C+RSS3mc0B3E+I4Hu7++2tXC/PbyGEEEII0SHoVnUBhBBCCCGEEO3ORoTv91+BV93dzax7RnU/AywETAscYmYrNl4s8VsIIYQQQnQUJIALIYQQQgjRyTGzHuXf3f00wtd7QmAXM5s1Re3hJRF8YWBuYJX2Lq8QQgghhBC/F7JAEUIIIYQQoguQCS8Xc/ebSvv2AXYHbgMOdPeX0xu8e/p8Twe8Ja9vIYQQQgjRUVEEuBBCCCGEEF2DvYEbzGyDYoe7HwacACwDHGhmM6YX+PD0+X6tSHhZUZmFEEIIIYT4TagjK4QQQgghRNfgEmAy4FwzM3e/EMDdD4ygb7YHmszscHd/AfjJ51sR4EIIIYQQoqMiAVwIIYQQQohORkZvt0hU6e4vmdmhQHdCBKdBBHfgAOA14IV2L7QQQgghhBCjAQngQgghhBBCdDLcfbiZjQFM5e4vlfa/ZmYH5a/nmtlwd78kjx1kZu8BF1ZQZCGEEEIIIUYLSoIphBBCCCFEJyMTWV4LLAgs4+5PNRyfFbgA+BOwnrtf2XC8h2xPRFeltRUUQgghhOi4KAmmEEIIIYQQnYxMZHks8ClwoZn9ueH4C4RA3gP4l5kt33Bc4rfosuQKir5mtmrVZRFCCCHEb0cR4EIIIYQQQnRwzKybuzc17OsOzEUkvxwGbAA8U5yXfuDjAi8Dp0n0FqIZM7sKmNndZ6m6LEIIIYT4bSgCXAghhBBCiA5M2pU0mVkvM5vJzBYws8ncfbi7Pw6sB/QCLgYWMrPxzWxqYH7gBXc/2d1/NDPlBxKimQuAqc1staoLIoQQQojfhiLAhRBCCCGE6KAUXsVmNoCwNJkemBQYBJwAXObub5rZnERyyz8C7xKC+LfAXxT5LUT45ntpcGxmfwRuAh5y9y1aW2UhhBBCiI6BBHAhhBBCCCE6MGbWF3gM+BI4BxgKLAlsDFwKHJAieD/gQGBM4Ctgn4z8VsI/0WVpfP7LQriZ7Q3sC8zq7m9WVUYhhBBC/DYkgAshhBBCCNEBKYQ6M9sJ2ApY3d2fLx3fBTgG2Nvdj2zjMyR+iy5PTiKdCZwFvObuH+X+aYG7gCuBvYAm1wBaCCGE6HDIA1wIIYQQQogOSEmImxIYg7A2wcy65fHjCNuTnc1s4jY+Q+K36FJkctjy7z2AdYE/A7cDV5nZDmY2nru/TgjgK0K8c2Zm7V1mIYQQQvw2JIALIYQQQgjRQSjE7Qa6AX2AcVo5757cP8noL50Q9cbMxgHWM7MF8ve+wNXAG+4+M7AZ8AZwNHCfmZ0E3A1MR6yyQBHgQgghRMdDArgQQgghhBAdADPr4e5NZtbHzFYxs5nz0NlEBPgBAO7eVErW1xMYCHzd/iUWoh6UorZ7AdsCp5vZssALwMTAiwDufhnhnT8LcCuwOHAJ4MASZtZDEeBCCCFEx0Me4EIIIYQQQtScwqvbzAYQlgzfALcAJxPR37sQCS4vyn+/JqxRzgI+AlZS5KoQYGbTAA8C/YBXgJXd/X0z65YTTMW/3YEmYGtgYWBVYAl3v6+ywgshhBBilJAALoQQQgghRAcg7RoeBj4F9gGedvcf8tjEhH3D7sBg4If8dxAwv7sPK4S9SgovREWY2fzACcD88JOP95fAWMDzwLbufn+ea8VEUcPPUwHXAY8A2xHJMPUuCSGEEB0EWaAIIYQQQgjRMdiMiFrd3d0fdfcfSgkvPwSOA+YDLgYuJUS/eVP87iHBTnQ10q5kZuB+T/LQtsAKwITACWb2t7LgDS29vt39beBNYHZ3/1HvkhBCCNGxUAS4EEIIIYQQHQAzOx+Yw93nbNhfWDb0dPdhrVzX3d2Ht1tBhagROfnzY66gOAvYsHgfzGwuwkrofWAXd/9v7p+I8AF/GBgC9AX+BfQGVnH379q/JkIIIYQYVRQBLoQQQgghRMfAgX5mNlaLnSF+jwHsaGYzFfuLZH0Sv0VXxt1/zB+XBtYBHkx/b9z9KSISfBLgGDNbycyKBJg7AEMyEnw+YHlgN4nfQgghRMdDArgQQgghhBAdgxeBaYHlzKxXw7E5gGWAqYsdSnopRAv+TdgITQc8WhLBn6RZBL8GuJsYJ69evEPufjfwB3d/toqCCyGEEOK3IQsUIYQQQgghKqbRf7i1YxnRfTshdu8I3OLug8xsHuB44EdgcUV8i65OW7Y/aYOyLnAk8BbhkV/YoUwIbEokjj0rbVN6kAkvR/SOCiGEEKLeSAAXQgghhBCiQszsz4TFwnnuPriNcwoRfBrgNGBJ4G3ge2AA8AmwQCa8lOe36LI0eH6vAYwP3AG85+5fjkgEb/gcvUdCCCFEJ0ECuBBCCCGEEBWRNgzHA9sB2wPnuPuQkbhuB6Dw+34eOMPdhxfi32grsBAdADMbADwETEQI4N8DZwCnuPs7JRH8COB1YEGJ3UIIIUTnpUfVBRBCCCGEEKKrkqL1oUS//ASgh5md5e7ft3Z+EQnu7ie1cqy7xG/RVWmI2D4AeI+wNHmdELo3AcY0s8Pd/W0zuxRoAs4GziT8wYUQQgjRCZEALoQQQgghREWYWTd3/9TMDgC+JWwZvjezS939u8bzR+RBrAhW0ZXJyaS+wDrAOMBFwOP5zmxpZoOBDQBKIvgVhH3QrVWVWwghhBCjHwngQgghhBBCVEARsZqi3Y7AmEAvIhK8p5m16QkuhGhJJok9BtgI+AY4Ln3zx3D3we6+Y5zC+kCTmR3r7q8DN+X1sg8SQgghOindqi6AEEIIIYQQXZEUv/sBzwILAO8DB+bvJwCbm9kY1ZVQiHqTHvrAT6sjTgfuBSYEtsn9g82sd/68I3AhsAWwavmzJH4LIYQQnRdFgAshhBBCCFEdB+a/m7v7GwBmdjGwD3AsMNzMLmjNDkWIrk5OIo0BTAy87e4vZILYbsCaZvaBux/u7kPNrLe7D3X3nc3sHeDUSgsvhBBCiHZDEeBCCCGEEEJUx6TAd8CHxQ53fws4HHiK8ARfJyPFhRAlzKwbcB1wBzBjeuq/DuxAvD9bm9k+ACmC98mfT3L3H81MAWFCCCFEF0ACuBBCCCGEENUxDOgHDIHwIQZw9zeBO/PYmcDyVRVQiBrjhF1Qd+BcYKYUwV8lRPD/EQkw9wJw9yEtLpbtiRBCCNElkAAuhBBCCCHEaCYjVVvjHGAK0o6hQZBz4J/AlsC1o7WAQnQAGt+j9P2+A9gYmIyWIvgrhAj+PHCwmW3Q3uUVQgghRD2QAC6EEEIIIcRoxMx6uHuTmfU2s7nNbHkzm8LMegKPACcDG5nZGWbWw8wGmNmMwGLAZ+5+tuwaRFfHzCzfo15mNlGxP0Xw/wLrE5ZC5xAiePcUwXcHTgEuraLcQgghhKgei/6CEEIIIYQQ4vcmRbjhZjYAuB2YHJgQ+IEQ6k4HPgX2BLYDPgK+B/oDXwNzyaZBiCAngR4HngP2cff3SscMWBq4EngY2AV4yd2Hl87pXv5dCCGEEF0DRYALIYQQQggxmkjxewzgPsLnewtgLmBfwp7hGGAocBiwBHAP8BhwCSl+m1n3KsouRN3IyaAbgXWA3cxs8tIxB/5DeOcvSQjhkzdcL/FbCCGE6IJoGaUQQgghhBCjgbRscGA1ot+9O/BU2jhMnKf9292/z58fyq38GT0UAS66Kunl3ZQ/mwcHmNm3wFG5+xh3Hwjg7j+Y2RdE4tixgYFVlV0IIYQQ9UER4EIIIYQQQvyOFIn6vNlrcAZCjHs1xe91icR9+7r76WY2tpktZWb90sbhJyR+i65KyTu/p5lNABSTRrj7MYRt0LbA7mY2dV4zIzAVcJO7r50rMCpdQZFlF0IIIUSFKAJcCCGEEEKI34iZ9QJ+JHTvJjPrDfRz9y/yFHf3r81sLeBiYG93PzIF702BuYFn3f27SiogRI3IyO8f0zv/emBaYJiZne/uhwG4+9Fm9iNwLDCvmb0DTEf4699efFaVtidmNhNwjZmd5+7HVlUOIYQQoqujCHAhhBBCCCF+A2bWn4hEXd3d3cz6Aa8Ay+UpVwGTmtnthLd3WfyeEVgJ+AL4pP1LL0T9yEmkXsBNxJj1YuAZ4BAzO9PMeuZ5xwOrA98SEeLPAAvUIfI7+RroDmxuZttWXRghOhrFuy6EEL8VRYALIYQQQgjx25kOONbMxgV2Bd4D7k6R+1XgSGBr4FHg+IxsnZ2IXu0G7JDiuZWsU4ToUpQ9v4GJgGHAru7+rJmNDzwOHAJ0N7Nt3P0Hd7/WzO4Avi/5hVfunW9m3d39fTNbDLgW2DHrd3KV5RKio5Arqe4ws8fcfbeqyyOE6NhIABdCCCGEEOI34O7fmtmuRATqScBrwHru/mGeMsTMzgMM2AN4AugPfEVEri6edg/dq7RrEKJKCtE6Iz7HB6YA3nf3ZwHc/TMzOxtoAg4HmsxsW3f/AfiumDjKSaQ6eOcbQIrgmwM3ApuZ2Y/uflq1RROiQzAp8B2wkZl94+4HV10gIUTHRQK4EEIIIYQQo0gpYnUIMBbgwNTAwmb2vrsPA3D3N8zsYOAKYEVgOPACcGvaNVQesSpEVRSida6MuBWYjBDAB5nZX9z9cQB3/8LMzs3LDgbGMbO1yhNHdVhB0eBhfjvwNtCPEPYPyoUep1dZRiHqSimR9JtmtguwL7GCAongQohRxWrQPxBCCCGEEKLDUURsZ8TqOIRoNwQ4EFge2Ab4l7sPzfNbtTdR5LfoypTeox7AA4Ttyd2EBcqWhP/3Ye7+aumacYEdgb8Bi5ZsU2pD2jc8SESw7gZ8CkwAXAb0BI6UCC5ES8ysL3A2cB5wT+YDmBnYh8ircYJEcCHEqKAIcCGEEEIIIX4lJbuG/sBZwJvAle7+opntmKedFqfaJRndPbGZLQnc6O5fFp8l8Vt0VXJSaHiKxeMD/wOOcfeX8/hA4FBguJkdWYjgGQl+LHBAeud3q6EIvgAwObCVuz+W+94ys0WBe4GDs/6yQxGimSmA/wOmBXY1swfze/WwPL6TIsGFEKOCBHAhhBBCCCF+BSV7g/7AY8DnwF1Eskvc/QMz2ylPP5mwaXiRsGwYk4hoFaLLk+J1T+Bm4M/AB8BHpeOHm5kDhwGeIvhreexr+ElEr5v4DWGHNAERAU4mxO3u7gPNbF2izdjMzMZy9yMqLKcQtcHdXzazuYk24QRC8JYILoT4zUgAF0IIIYQQ4leQS7J7ABcBXwMbAm+VI1Ez8d1OhCXKcYT9wZvAn/L6Vu1QhOiiPEFYCI0H9IawEHH3oe5+hJk1AUcA45nZdu4+sLiwxu/Rl8AgYBkze8TdvzazYrXHF0TbMB0wp9oDIZpx92fMbEUkggshfke6VV0AIYQQQgghOiCTAjMDl7v7m4V4leJ2kcDrfXffAFgJ2Ar4q7sPS/sUiV2iS5KR0D+RiWIPBC4E+gPXm9kAdx+a1ii4+1HA4cDEwPvtW+IRY2bdW9vv7s8Sft9bAiua2Zil93584BZgDmDtnDyz1j5HiK5GTiQ/TeTSmBg4Efhr5gt4kbBFuhXY1syOrq6kQoiOhJJgCiGEEEII8Ssxsz8DDwEbu/tljR7EaY8yg7s/2XCdEl6KLkvJO78HMBbwdQrgRdLInYHtgZeAld39myISPM+xOnl+l+rTjyj79MRqj+fd/fw852ZgMSKx311AP2An4FtgiayP2gXRZRnR829mcxKTRR8S782DmTdgRuBoYDbgL+7+WbsVWAjRIZEALoQQQgghxK/EzKYGngEucPftc99P4hywCTAlcJy7f1VZQYWoCYVobWYDgKuJBJHdiMjuO939QzPrQwjJ2wEvAn9PEbxnSSivhV1I6X0fADwONAEfE/UaANzn7mukx/nRwPrAuMAnwGvAYrkipBb1EaIKyuK3ma0FTEK8P5cCA3MlyJyEHcpHtBTBpwe+dfcPKiq+EKIDIQFcCCGEEEKINviFyLSjgV2BDdz9ktL+6Qh/8JeATSVuCRGYWS/gPkIsfpiwAFmYEIjPyASRhQi+NeGjPZ+7f19NiUdMRrJfTUSzb1Ek6DSzG4EVgKXc/a7cNxsh7BnwcJFLwN1/rKb0QlRLeSWHmV0FzJOHegF9gWOAi939HTObg4gEfw/YG7inDqtAhBAdByXBFEIIIYQQohUa7A12A6YihLvrgP8AJxM+4BeZ2fxEFOj4wHp53haFt69EcNFVabArGZ+M4nT3t/L4mcBeQE8zOyVF8OMJsXgWYGgV5R5JxiZsT04D3gYws1WAZYFd3f0uM+vv7t+6+/PlC3NyTeK36LKUxO/jCPF7Q+BNd383J5H2AZ41s4GZGHM54BFgX8KCbHBFRRdCdEAkgAshhBBCCNFAinY/luwNhgGfAZMBSwP3EoktdySsUP5B2J68CfwPWCevl7ev6LKUJpF6EuL3zEQC2U+Lc9x9SzNrIlZTuJn9M0XwA4BhNffI/gMwIyHaDTOzdYBLgH3c/Xgz6wvsbWb3uvsd5QtrWh8h2hUzmwBYEDgdeMjdfzCzqYD5iNUVd+VqCXP3Z81sHmCIu0v8FkL8KiSACyGEEEIIAZjZhO7+CURkWtobXEqIdRsRIpeb2X3A34Hz095gXzM7h4hY/Qp4L8+TvYHosjRMIl0PTAt8DfxARm6aWR93H+LuW5uZEystxjKz/Yt3MYWvysXiQoRvWNHxGTHptbCZTQKcRUStHpnH/wrMS0SrCiF+Tn9gJuDcFL9nIOyR7gS2dPfBZrYdcAfwSuNKCiGEGFm6VV0AIYQQQgghqsbM/gZ8lNFlBRMDUxMepG+kqL0SIWrtn/YGfcxsDHd/292fd/eBRSJMid+iq1JKeNmLSF43ALgA+ByYE7gCwN2HpOc37r5N7p+dlhHildsH5WTW8Izo3tPMVgRw948IO6TdgTOBg939CKBb5gLYH/gOuLWiogtRd74FvgcmNLMxCfH7LmAzd//ezGYB1qLZH1wIIUYJCeBCCCGEEKJLk0LV5cDp7v6YmXXPQ2MB0wGD8ry1iEjWfdz9uPQG3xX4c+NnKjmX6IqYWbeMkC5WUMxErIrYzN0PIBJDngAsZmaXwc9E8LWBBQvv/Gpq0ZLCqzsj2R8AlgFmKpV5C8KqwYB+ZrY4YYl0MRHdumr+PTT2FiNF+dnvLM9N6Xu1kc+AC4GDiYmva4nE0t+Y2fhEQtw+wD3tUlAhRKelUzSmQgghhBBC/Ab+BAwHTjGz3sBVZjYTYdfwHTCNma0LXEZLe4P5iGR3Y7V/kYWoD2n/gbs3pXjdE3iMsBDqT/ji4+7fAocD5wHLNIjgvYrPyAjyyiO/Iby6M/L7PkKg2w44McvcLc9Zk6jTcoRVwzrAK8DcKZ730KSYGBlywsVLwnffSgv0O1D28Dezrc1sFzPbEn5a4XE1cDuRPPoVoK+ZLQ2cBKwKbOTu71VTeiFEZ0Ee4EIIIYQQoqszmEjMtwnwf8QA/BN3/9zMziAi07oBe7n7UQBmNj1wIGHpcFsVhRaiDpjZbMC1Zra5u9+bu3sSkZx7EPYnMwPPA+R7dQTgwEZm9m93X8bdfyg+s4Zi8QZEFOqOwMspUP4FmNnMxgL+6e6bmdmEwLjAQOD7uuUCKKxpWtlvdZlw6MqUfOb7A8elH/aYZnYV8C93f6viIo4SJfH7SmBx4t0fx8zWBFZ39yfM7HDgY2KCbFfCGuVj4G/y/RZC/B5IABdCCCGEEF2WFH5uNbMTgJ2A94Dd3f3zPOUcYHxgU6CPmS0HTAFsBnQHFi1FrNZNtBOivTjF3e8txN707v0nsYriOOAfZravu38GLUTwsYDJO8D7MxXQ3d1fMrPJzGwd4ABiAmwyYClghUzc+UlxUbYvdRG/e2Q0em9i1UtfYJC7PyPxu3qKZK8pfj8JfAG8CvxITLYub2YHu/sdFRZzlDGzZYicGssRtkh/Aw4BbjWzld39ATN7kmgvpgPeAN4vfRcLIcRvQgK4EEIIIWpBYwSaItJEe5ARmr2ABQkv0imB1c3sQ3f/2N3fNLOj89g/iCjQF4GXgI1L9ga1ELmEaE+ynX4eeD49sa82s0fc/VB3/8rMLgB6EbZBw8zsoJII/oWZ7Qp8U0ocW7kIXorC7Qb0cffviUSXu5rZ40T06ozALrl/AeA8M/uLuz9e/qy6fIc1+JjfQUzq/RH4zswuAg7LhJ6inWjs4+Q70B04kbDa2djdX8tzPyFWU/SsoqyjQtn2JPkReBN4Nu2DBgJfAqcAN5jZ3939Y+CF3IQQ4ndFArgQQgghKqcUmfbTgLAuwoHoEowDnArcSVgc7E3oE6e6+wfu/jqwd9qh9CHE8C/rZm8gRHvT0E5PTqyOmN7MvnH3k9z9azM7M48fCXhGsRYi+NfwkxhYB/G7+C7qR0Snvm5mVwAPAWsDawFPA1u5+1N5zXzEhNgnbXxs5aSgPwbhYz6I8DEfQgj5pwFjmNlO7v5NhcXsavQlJiDKQnF3YA7gnpL4vS6wG7C3u9+S97HJ3YdWUeiRodHzG5iYiOoe6O5DAHKVyE15ySlE7o213P2DSgothOj0SAAXQgghRKWUItP6A6eZ2djEAH0PDYTE6KAU4WkefAxcnIf3NDMD9iLEulPd/UMAd3+34XNqY28gRNW4+2sp1p0I7JDvx4nu/k2K4E74+45jZtu6+6DStZVPeDZESd9P5Ab4HxGh/gMh0F0DdMvzehKWDpsReQMGVlX2keT/iAm8jYHn0rppyjz2ksTv9sPMZgQeTuuP+zLyG2ACIh/Fh3ne+sCFhPh9ZK6yOAn4N+GxX0saPL9XICaNJwMGm9lt7n5XnjckRfAm4HJiJcXyDZHjQgjxuyABXAghRhElEhLi96EUmfYQIZAMAuYBFjSzTYD76hAZKDoHJfG7HyF2Twk8Ctzv7s/laXvlv3sTIvg/UyRvgdp6IZop7FDMbCdCBN/ezCiJ4GcRCTEXA2ontma70Ae4hxDstgXecPdh0CJKvcnMxgXWISLC+wGLef1zAcxIRBi/mGVdCzifSO57XNZpQXe/aYSfIn4PJgA+AG4xs6Xd/aHc/76ZvQCsZWbvARcA+7v7kXl81tyebO8CjwwNkd+zE1HfSwHvArMD1wP7mtkQd38AfhLBbwXWICZiJH4LIUYLpn67EEL8ehoSCc1DDOi+KnVghRC/QHmyyMwWB3YlItO+AWYiLCkmATYilgPXVVQQHYxcbfAoMAYwjPDCfQw4xN1vz3O6EdGqOwNnAAe6+xfVlFiI6snJooEjaouLdt3M/kSI4FMBJ7n7SXm8LzC4Tp7fZczs78R7vybwvyznIoSINyFwqbvfY2bbEBHVHwDre81yAbTiv4yZHQhs6+7jm9lKhBhZRBZ3AzYHVgE2dff327vMXQ0zW4iwBZobWMTdH877sBphSzMesRLumDx/ZiIp83fAMnUWii0S3BowEbBlrqDAzJYCbgMeAPYpRHAhhGgPulVdACGE6Gi0skT2POBG4A4zu9zMpqq0gEJ0AFIo8BzsQQiR37v7R+7+HRHdtDHwPhEBtWjpXCF+NaUl5hACw3vAsu4+A7A+MDZwpJktC5DC3N7AucBcRLIuIbokZjYR8Bxw6Yja4mzXLVdT7Ai8BWxrZvvm8e9L59RK/E76ANPkz5OZ2WFEboDlgaWB681seuBKwkd73ewTdq+L+A3NK6vMbK7S7keBIWZ2FyF+7wIclcdmIiLaXyNEfTGasEi6jLvfDxxLJIa8y8zmznfiLuBs4GMiEnw9MzuGsEIZA1gu72/31v+HajGzBYANgG2AH9z9BzPrme/IHcCywF+Bg8xs0SrLKoToWigCXAghRoG0a7ifiFQ9JP+dmvCvuxbYzN2/qqyAQtSYkgVFfyLKaXIiGdRr7r5ew7kzEwLkxMRg6t81FU1EDWmMMM3o02OIIJCB7n546diqwMFERPje7n5b7jdoIeyp8yy6HGY2DtEG7030dbYYyUjw2YBLCY/sNev0/pTbh9L30jzACcDMhKf3eMAewFXAosDVwNplm5CaRrJ3J8r6J2Ajd78/PcvPA9YlInCXI6KJ5yOi9R34qzckpBa/H6XnbGziXoxNeH5PRyQlXcrdH0g7miWALYnxxTtEYMAeNVxt8LPn38w2IiZYpiGeqafMrAfxVTrczJYEbgduBdZw98HtXW4hRNdDHuBCCPErKA0IVgbGBLYCns7O3Ix52qMSv4VomwbP7z6EMPJn4C9m9oC7n1E690Uz2xS4Cdjc3W+tpNCiw5FRaDOa2aXuPjR3rwtsmD9vk+eN4e6D3f3a1LoPBA4xsz7ufl3JpkeCkOiyuPuXZnYS8D1wKPCjRSLLVkW40oTR82a2CvB2nSaRSlZ2PYio2gmAN939MTPbHfgb8AWRg+LFvOZbYlVSC//yuonf8NP37L+B6YFjzGyvtG7ZlLCmWJhI8Pl1/v4FsHgpmr229hodicbn3Zt95u8lfOaPAh4hvpu2IiLBF3f3B83sane/0swmdPdPSp9Zm9UG1tLze3ngM3d/1N0vMLMhxKTy1Wa2mrs/bWY98po70/ruI4nfQoj2QhHgQggxEpQiNop/DwA2A6bzSN6yNhHhtJe7H5WRG8u6+6WVFlyIGlFECWVE7XTA8cD27v6mmU1LWAn1AI5293Marp2KiNjVoFz8IvmMXU2IbruU9vclPL13Ipb5/yXb8F4lj9JVgDOBW9x94/YvvRD1wsx6uvuwfK+WI/IyrEZESu8+su1yXYTVUl9uAHAJMAMwGfAgcAqx0ujH0nm9gWmBs4gJgKXrJno3RLP39ObEnesD+wJfEX3U/2Qk+OLExDPAy8D1WdfaRBZ3BkoTLY05T64lEqjeURKQlwKOI6KmF3b3J/Kds9K9rcUEEvxM/L4QmI2w1jm5CAQys/WAPYlghzUKERxoqts7JITo/EgAF0KIX6Ak2o1JCCcnET6JRwHjAMsANxDJXI6w8MbcAFiPiFh9q6KiC1E7MvLpEcJ/+VNgk9KgcCZimXkv4Ch3P7eV62shoIj6UxLt+hIJ7P6bUaxjEAL4bsSS8r+7+7cNIvjfgAf0rImuTkMf6D+EAPwDMAcwLpGUb6uOJmaZWT/gceATIinfx4Rn+ZTA0cR3UFMGNGxEJLzsBiyQ7UptbE9KdjO9Cc9lb2jPChF8ELCru9/Xxufo+/V3xMzmBbYlkkB+X9q/DjHxMoW7v9dwr7YFTga+BVZ09/9WUPRfhZn9C/gLMUZ62N0/aRD8NyRshLoRCWMfr660QoiujJJJCSHECCgN/PoAjxGZ2scjEkF9BtxDiN+7uPsRedmMRPK+14G3273QQtSbaYjl40sAfXKg3iOjpF4C1gCGArua2Q6NF2twLkaWIgKSsGu4FljFzMbO5dYnEJF2sxBJ7fp5JOrqndf+12ucZEyI9iL7QD2JyM4hwNbuvgThG30osAlwRgd8V3YnxPytiVVHFxABDmMB75XE7SWBRYCngflT/O5RF/EbfrKb6Un4Kd+dfdcfrDnZ4sWEqD8XcJyZ/bWNz9H36+/LGsC7ZfE7eQgYTDx7lO8VkVz1NeLZPAia81DUETNbF1iAaAduSfF7HGB2ywSX7n4hcDhhHXmmmfWuc52EEJ0XCeBCCNEGJfG7B+EN+TKwm7u/7pG5/Qai0/c4cIWZdTOzhYDziaV+2xZ+l1XVQYi64e7/I5bD/hv4PzP7v1xu3dQggo8HzK/3R/wO7AdcRyR5W60kgh8HnEoku7vWzPp7s1c4IEFIiGRcYnL/lmzDcffXCbuQIwhLuKOyv1RLWhHo5yT8h1/Kvtr6RDT7Pu5+sZkNMLMp3f0KYAdC+C9sUWphEdLw/dgbeJGwc7myFRH8XOBfwBTAhWY2R3uXt6vh7ru6+z5m1tfMDjezCfLQd8DNwOpmtnGe+0M+o/MSCS/XAhbLY3Vesj8N8HVGqnc3s4UJgb+YjDkdwN0vId6jNdx9aM3rJITopNS2kyKEEFWT4ndv4C5CjBtEdEqL4zvk4GMF4Jk8/gOxnHYpVyIh0cVpfP6LJbEeyZ2cGLBfbmbDPBMQpgj+spn9hYjCq03SNNExcffv0of0ckIEx8yucfevzOw4oImIZD2MGKALIZLs54wJ/IFY+UZh2eDun5rZRcA/CPuDyc1srbq119bs5T0eMKW7PwX0Az7P4+sCFwJ7u/uRGU29H/C1mR1RWNnld1Et+nSlOvUAxnb3z8xsXyKp5WbAVWa2RklY7QYMICLZPwWer6zwXYCyrQmwBWEBMrmZ7ZzvzZHEM7eXmU0PnE14aO9BjDX+m/2f2owj2uiLvQ5MY2anAX0J4f4qYsXIn4B9zexid3/I3a9q1wILIUQDEsCFEGLETEwkDpqaiHD4HsDMemcEw/ZmdiXRae0DvALc7kokJLo41pz4qT+wFxE9+IWZPefup7j7QxbJZAGuzoH6NSmCd3f3d/JzajP4Ex0Xdx9skaz4MkIENzO7OkXwE4D3CU9WIbo0Dd69hc/1a2Z2F2FNdUPaHBQi+Gtm9iwhvI4LGFArATz7ZGMATxGC3eLA/cCeZrYPYTWxv7sfmZfMCMxPKUFhfk4t6tXw/XoaMNjMDnf3d1JYdWBzwvrp71n/KYCeRNLS5/Jz9P36O2KRb6K7u3+TEw99iYmjk4BJCXH4ZDPbwSMZ5AaE4L1d/vsN8D9gg9Lkf6X3xyJR7PLu/q82nv/7idVUaxM5NbZx9/Py2u6EFeRH7VRcIYQYIUqCKYQQJUoRNd3gpyjwGYG9iaSWJ7r7znluObqj1c9pt4ILUSMKASUHTk8QKyPeI1ZSzEQkUlvb3b/PSO+DiCSFG+UyWSFGCymCXUYIYDsB17n7F6XjartFl6UkrP6U+NLdv85jaxM+0k8RCb4/yf2zE1YoB7n73bmvFgkiy4EIZrYdsDyRs+V/ZjYVcCkhdJ/s7jvmeXMSovKPwCJ1aw9K92YA8DARlX8h8C8PayesOWn7P4jViY8AfyY83OfOfq5WVv2OWOQK2oFYWXA0MJyYbDnL3Q/KlRTHEslU7wd2yImk8YAxCOuTT4EH6xJEkxMszxJ5j9Z29yFtnNedqHc3d/8q940PHElMJq3s7p+3S6GFEGIESAAXQogGMmLjvNzuzo7odMD+wN+JgdI+ea7EEiGShsjBHoTlxATAFu7+au6/BFgHWMbd78h98xICyjB3X7CSwosOTWnS5RdFnRTBLwZWJQbmN7VLIYWoMaUAgP5ExOqMRELIG4HT3X2gme1P2GsMAU4GxgdWJBL6LZzCbK2EVTPrR0yyDiBW8u2W+w1YGdgV+AtwLjAZEak7HFjQI+Fl7fp5FvZ8dxCTy1sC7+S9601EIH+f9V4G2JSo++vExIXs+UYTZrY9scLoLOJv/xbR3/k0/+6NIvj27v5pK59T+f1JUftsYuJkIXf/2symcfc3RuLapYh6r0y0C7LbEULUAgngQgjRQIrdLxD+iLsAD+TAYgZgX6JDd7K775vn1yLSSYiqMLN+7v5d/lyO/n4YuIhYOfGDma1OiOJ7ufuxFkkHv83rZgZe1rskfg2NQsHICuE50bkfsF/VUXZCVE3pvelPrNr5gugH/QBsRIinO7n7PWa2DrA+sWrnfaKv9PcUi2vXHzKzvwH35K+nufu2DcenBjYkhL5BRP1PScGy8ijc1rBIYHkF8A93vyv3LUcIq9MBF7v76aXzy9+1taxTZ8HMDiZs3z4AVnf3x3N/z3xHChF8TeC/wM7Faoo6kRHttwFfuPtqZnYNsZJvL087yDau25KwQxlArOqT+C2EqA3yABdCiBI5CHzNzOYGbiEiOXY0swfc/RUzO5TwVtzGzMZy9+3qNtgToj3J5e+Hmdlx7n5PiijdgEmAmYFXU/xel4i63SfF7zGAI8zsbne/3t1fzM+rnYAi6knJrqEvsBUwLdBkZpcTS/7bjKDLAfxe5c9pjzILUUdK7faxRGLIjdz9NQAz+xQ4ABgnz70MuCyF46+Bz/P6WrxHrUTPPkYkKz8VWNrM5nD3Z4qD7v4mcECjrV1+TuX1gVaTD/YnhG5PMXwDYEfgXsK65Z9m9ra73wZQEr+tLnXqxExFvBeTA+ub2UfuPjDF70IE35VIvrwzMbl0YGWlbQN3H2JmNwHHmtnjRL2WHpH4nTwCfAw87u7vj+ZiCiHEr6Jb1QUQQogqSZuGYinsT4PAjFhYgfAsPhH4aw6GXgEOBR4CZiiuE6ILMxOwNLC7mS0E4Z1PRAo9B6xkZlsQ4ve+7n5EXjcfMAsNk/ESv8XIkO30j7nS4HEi4duShE3V/cAhZjbpyHyWBCEhgEheOSfwZEn8XpdYKbGPu19rZv3SWgPgbXf/rNRvqsV7lCv2+prZnik4DiYiwLcnRPxjzGza4vwU/gGGNX5OuxV6BGTf082sl5nNkH/rB4CrgDuB64GVgNXcfTFgXSLp4GyNn1Una5rOQivjgO2BaYgVo9sCu1kkIKVkqePA7kTyy0Pas7y/knOBgcTqiIvd/alfusDdnwVukPgthKgjEsCFEF2aFFD6E2LJrLmvKQcYzxEi+ATAMcDCGeH0KrAN4WHsEsFFV8bd/0Xz8vH9SyL4d0Q02oZEQrHD3P1wM+ueNkOHAt8D11VScNGhyXa6JyECfQasQXgW/41IvLUnsVLH1EYL8XNaeS8mIlbufJTH16c0cZmWCCcQYmuLyco6TFymZ3HBnoQAeXT22wYTntmbAHMBp+f30E9lr6M4nGUvfNmvA/YAFsrDWwKrEZN/S7t78V06PvAN8HY7F7fLkffHzaxHTg5N6e5f5XY4sXJiW2DX0oTshGZ2CDClu5+W97d7m/9JBZTahg2JFQXXEathdxqZ6+v4LgkhBMgCRQjRRWlYqrs8sDcwqZkd5e4vl0VwM9uFGATuAfQ2szvc/d38HNk1iC5L8fy7+2UZRXc8IYIf5u73uvuOZjY5sAowuUVipDnz9z7A33Lwp/dIjAqTETY7R7j7CwBm9qa7753P417Aze7+cJWFFKJuWHPCyx7AuO7+ibt/YGZPAeuY2bvABcD+7n5kXjYzsWrn2WpK3TbWMoHnpkQ5BwM7EDYhe7j7UDO7HdiYiGw9xcx2cveXqit52zSscnkU+ITwZH4MwN2/Aq4r1b0PMD3wT8KC4ppqSt41KCxy8v5cBMwATGZm/yGer1vd/RAzayKivMcxs/8Sz+cElGxParTawDzJXRcQz1Fv4vk7Ls85vqoyCiHEb0ER4EKILoOZjWlm4+SS2B/NrL+ZzePuVxBLFjcE9jazGaFFRNPLwIdEwqd16hb1JERVFBNF+fMlhJ/l7MA+ZrZY7l8NOIWIzr2ZsKh4AZjbm5OM6T0Sv0hhWVViIkIE/zqP9yqtyjmXiGRdI48pClwIfhZVfDGwl5nNn4fPIazfLiIivw/Na2YhhNXBwBkVFHuEZH36EeLw8sDThPj9LJHE87js+w0FChF8KSKKupaUVrlcRqxy2RC41t0Hm9m42actxO/xCZH1PKA7sEQdI4s7Ew3P3LjApcCuxCTEecBOKRYfBuxCWNMcTFjtzFRM/ldT+p9TsmYho9knB7539/c9fPJPAM4iPMF3rrKsQggxqigCXAjRJUjhZEPgr8B2ZjYEeIeInnna3f+ZHdET8/yj3P1/efkkhHB3JJHVXYgujZWSjDVMCF2SA+5jCFEFd/+Pu+9gkahwYuBddx+Wn1OLpGmivpjZuMBE7v5STpiMBazn7qcSHvNvAv8HXOqRbLVX/vsuMJTwNdaSbCFoToKY4vdjhLB6M/BMnnIXYVm1BbCBRQLM2YAFiHFjsWqnMdFkHdiNSA65I/BSTobdBpwOrA8MK0WC3wksDNR9dcgkhJ/0we7+DoCZrQrsRExUfGRmyxKTgd0Jr/O9SpPLlX+/1vRZ+b3YC/iOmEh5JZ85iEmij4vvHXc/wczuIla+PZmTG7W4P9DyHpnZ8cT7PjvwhJn9290Pc/dXzey4vORYMxvu7idVVWYhhBgVJIALIboEORh4ifAdvprIZv4EsA+RiR13Pzk7ricCY5pZEXWzLzDQ3QdCp+/MC9EqhU1JMWhLQXtLQtR+G3jA3Z9z9wsz2vZoQgRvSjuU783srUIwL4SYyiokak9OSi5MJK3blYjcfBF4x8wuAb4lItKOMLMT3X1Hd/8hL5+Z8C59o4KiC1FLCr9iwtrgayI6+q1if0YXH0tETm9OWL+9A/yHSIRZG2G1Ff4IfO3uL8JPK0K+NLOtCWF4J6C7me2WIviDpXrXsT4Qk3g9gcXM7EtiRcsmROT+S8CqhAXUzmZ2iLt/Cc32HFUVuqDUX+hHRORPDXwJPOju11dauN+HOYH33P1l+Mk3/3TiXbk4J5omcfdX3f354qK63J+Ckvh9JTA/YXtyI7Fi70Azm97dN3T317J9GA6cYGbD3P20qsothBC/FgngQogug7vfZWabAlcCnxMRNR9Ccyc9RfDBhCf48oQA/iYRPVSIdhK/RZfCzOYCFjCzf7n7Z+l5+QQwJjGBNAHwqpld4O7HuvsFZuaECL5nDvbubogWV0SuGCE54fISYWdwJSGcPANs4u6DAMzscmA6YmXPNHneWESb/TUhRgghmpkQmAm4MK0NgJ8CBYqEkTcCN5rZBO7+aXFO3YQ7aPYtJia7ljWzGT1yuRQrQr40s5OAY4HlgE/N7PBSdG4t6tNGcMXXhNi9GZF89C1geXe/LUXlucjxfEn8rkU/1Vp6mD+euz8nAlBWN7PV3X29ygr4G8hJ/j7EioP3ct+6wIXA3u5+ZNrXHAh8nBO0w4rr63B/GjGzNYFFgLWA+919WAYCbQnsbmYfufse7v66mZ1ATM7cW1mBhRBiFKiN75QQQrQTUxIRhN2Ag81s+tKS4GIQcTawMrAC0fFbODuCPSTaiS7KtsDJwLoWXqMHAu8CSxCRtnMAXwHbWySNxd0vJDzBlwJWbPcSi06Bu78CHEVEQY4P3OjuH8BPAstA4CBgdyIJWfHcfQjM5/LBFaKRcYFpgU8bDxSCpZktmLs+K47VSVgt/17ql70AjANsYmZ/yGM/pFg5JXAHISCvS/wNaoM1+7L3MbMlzWx9C1/2IcDhwDJEn3S1FL97EPYoRnwX/0Rd+qnFijHgciIp5wruvqC7T0qsKljHzJaotJAjSfmZKyZccqLoUWAlM9ud+O4pJ42dCZgP6FUWv2vM1ERk9xM55unm7m8DpxI2Satb+ILj7q8DuxerLYQQoqNgNfmOFEKI0UIpMqj4fSzCJ3FeorP6IiFyv5pLYbsB3Rs7q7I9EV2ZjGQ6m4gM2oEQvv8LnFq8X2Y2KRF9OyawlqeHvpktBdyt90f8Wor228Ljdj1C3FoC2NDdL284x4DewCzAJ8Sy9LrbGwgxWmnt+TezqYGnCDu4bUq2QYXQty7h/3uku39GjShZavQiBMYJgE/c/bk8fiQxGXYccJ67v2RmsxO+5mcTK0ieApZz939XUYdGrDmR5QDCqmVCoq3rS1hRnO3ud5bO70tYbxwN9CIn+tq/5L+MmU1CTDycBJyf924l4DpgVw9v7D7uPqTSgo6A0jPXg7gn/UqrR6ck6jIHcLy775r75yBWHw0DFq3r/SmTIv6RwJTuPtCaE5w3ZT/u38AC7v5IleUUQojfgiLAhRCdlhxUuAW9U8T72t2/AO4mvC9nBs4Epk4BZXzgHDObu/xZHaHzKsToIieENicGeqcDSxO++MX71d3d3ycmk2YEFixde4eicMWoUJq8LNrrnYEbgAvNbJ2Gc8Zy9yHu/qS7F89mN4nfoiuTwl0/MzvRzP6Y+94E/knYBK2bYnLBNMA2RILFz9u9wCOgsF9Jofhu4CZCXL3fzG5IIXVPYsXIDsADZvZKntMHuJTIWfEF8FEllWiFIvKb8Fn/GliHmMhbDlgd2MfMpoKfgjgOIwTlYYQgWefv1wmJfvb7ee/WBq4H9k3xux+wm5ktXGUh26LBxqVIFvuGmV1uZst5JCbdj5hU2Trfs2uBcwlrmsVrfn/KvAB8D+xqZuN5WNYV369jEitGvqyqcEII8XsgD3AhRKekFFHTn/B9nJlY2ne/hffjEDO7G9gQOJ+IXL2BGHBMAWxaUdGFqCW5JHYjYgC0FbChmT3m7h+aWeHt/VEen6yV6zWJJEaJUoTqy2Z2QP58gZkNA64FJgJuMrMz3f2s0nVNCCHmB7YHpjCznVK0O4uwQTkTWNjMHiECANYk+kqbFxOcNbLUGG5mYwAPECL2roT9x1pEX+4eM1vU3ffK+sxM5Af4H3BSCpkbAR/kVjkpsDYBSxLRxTsCD2fU7bR52lVpRVHwJXAFEXE8vC6rXEr97vIz8w1hozOrmU0InEckny9sQv4KLA481u4F/gWyHk05OXE/MJj4u38DbAf8xcymcfdTzOwFwipuNmAQYftyotc7aWwL3P3WFO+3Br41s1Pc/SMzm5jwn/+IkiWS+H2pU1srfplS2y06GLJAEUJ0OoovpRS/HyYiGm4kIpvWAO4C1nX3782sN2GHch5hjfIKsGKKfbI9EV2Wtjp3+c6cDmxADGQvdvcPcgXFn4A7gYPc/dR2LbDo8Ixsm2tmMwEHA6sRE5fTEOLRTB3Ea7XDokF6xyOtG1YmolLvA7Z29/ct/Hw3A/4B9ANeI2zh1qurcGdmGxIR0CsBz2Rfb0zCH/s04B53X6WV6xYCNgFWIfK6PNeOxf4ZjX9bM9uZiCSeOSeV1wEuISKlD8/I76WBq4AeRTtXl35qySakL7AFYSt4ax47iwgqMcIj+9C015iG6Ht/DqxaJzGpNI7oBixPrD7ayiMnBWY2AzF5NCmwXWGnY2a93X1o6XNqcX9+iXI5zexK4n36DHiZWA3yR8LK5dnqStk2ZtbX3b+vuhyjQrbP3d19aGf5fs0V15MQCW/fzknXDktOgi1C5JL4yt2vyP2d4n51NSSACyE6JSnSXU/4I67t7p+Y2dXAYnnKw8DqHklsCk/FKYGXM+qpdgM/IdqL0mC2N/Bnwo/0Y3d/Io93Ay4G/g+4JX+eEvg7MACYW++PGBUywnNWd398RIOLtHPYgmjTXwU2rqto11loEEn6EZPGP+SKKg0Ea0Bbz3+KLKsA5xAi+FZpW1X4GPchIlc/rlMfqBRV3DMDEw4mIm0nyeeuOD4mcCjhX/5Xd3+p9BkTEhHvUwHru/vzFVTlJ0ri6njA0e6+qZltRURF/4FIGn0FsLe7H5mTy1sQov92aWFTG6ylh/m9wLfEqspz8x6ND5xC9Bf2JRJHTkNMvvQi+wt1i6hM0etW4r3o7e7L5f7iWZyByIVyp7uvn8c6bDtYfufNbEsiOGhy4DngrEL8r5K0klke+MLdH8h9ZxOBTVfV6fkZGTJQ6zzgQeDMzvBdmnX6F5GUfBpgIHCZu+9VacFGkWzXbiXa5qmJoLrb3H3NSgsmRhkJ4EKITkO502BmiwK7EQOIZ8zsGmAeIgL8/wh/yOuISKfBDZ9Tq064EO1Jw2D2VkLYnhRoIhLHnu3uj6YIfjawMTFAvB14m4hY+7GjRD51VDr6IKk1Uuj5DzC4EBtG4pqxgUF1Eu06Iw3i93FEksRpCXHkNK9JQsGuTNEm5OTE2sBN7v5x6Xghgp9H+GLv6u5vtfI5tegDlYTiMYkVHycCixKR7Au6+8PZZnTL76yFCEFyEXe/r+GzJiZSBlTq/V26R72J1VLjENG2g4no+/cIG4093f2YrF8RbfwaaU1TTenbJoNIHiZsMvYCXvCWyVXHIu7fkoRl1VPA60RC41pOXKYAfhvwN6JvM7+7f5z3pEeK4AcR0eHTEslY63hv2uyLNfYjGs9NwbmpLvXKfumFRBLYDQgbpL9Rg1Udv5Z8vv4NLAy8RaxsubQjR4Lnd8+jpOgNvAnsDcwF7OHuF1VYvF9NivmPAu8TSYffJFZMrQms5O5P53kd8n51VZQEUwjRKcjOs5vZmGY2DfGFdQvwnJntRWRoX8Mje/m+hG/kMsB/cyDyE3UY+AlRFd7ss3o/kQBpW2ApYH9iwHGkmc2T78k2xLLzsYBL3H3P0mBW4vdoIAdNnZIcQNwAzGdm843kNV9l2291E1A6EyXx+yrCc/lxIglhH+BWM9uiwuJ1WczsL2a2AcT7k+LcNkTE8yYZfUse/5FI5Hc8IYTvZ2ZTN35mHfpAJfG7F+H5PSPQk7BkeBfY38xm9GB4TshOCbxDg09xtg0f1kj87k70SYcC6wEfuPsnhMg/DiF0X5iR64sSgl8/Imrfs651Y1NCV9gOeNbdfzCzuc1sbTPb2t0HufvGhNg3B9H/rpXVTr47xc/d3X0IEXV/Cbl6wMzGzmeusNrqD3wIfFdHAaxh4nINM9vDzLY2s0WgRRJp8vfhjb/XqV7u/g0RwPQD0VeYH1iqA4rf3Yi8DJMRE0bfEALruhY2Ol5+HjsCOcF6PPAxIRJf6u4PEkEyw4AlKizerya/ey4m8kVsQ9hrvQlcQLTTRZ3piPerK6MkmEKITkF2ovsDjxBLLw/z9CA2s78SS34L7zojOk8PEpGr8owVghaRf2sRViabedqeAHeb2bOEtdCmwGMZqbIL4eF5W36GhMjfGTMbh/B//be7f2WxdP4qM9vc3d+ouHi/N48QEy/zA4+M7EqCOg3SOysWCQT/QqyiejQjIJcjBrZ/qouQ1VXIybAdgHWy7b4gB+LHARMDhwDdLZLDfgrg7oPN7F5iQL8R0XbvVkkF2qBB/B4feIno070GvGbhKb0rcGJG4D4LzEpM1r5OiOQ/UZe2Ie9NLyJSeiix0qXsqXw50T/dl4iQ7kVEHn4BLOP1Xlk1FdDT3V81s4nMbH1i0vxrYBIzW8Hdl/cG+5a69Bes2fatiOTsBgx3928s7GnGBg4CupnZhRkJPithwfU/4LvKCt8GWZfyxOVfiZUG/YC+ZnaCu+9fZRlHhlxdcDlhc/aFuw80sw+JBLfvAOOU2oyOEonbh5gM+tjdjwKOMrNngKMAzKwjRoJPSVgm3kz4fjeZWS8PC9JbgL+a2YCcxOgITASMCVwNlPvZTYQovj8wmZkNJCYnP7GarJ4SI6aOM8hCCDHSZCRNwWbAp0S0TOFj15+IHOqXAz8jvqRfJwZ9a3pzohshuiSlKIai4zYFkfjo4zxu2RG/lYhS29jM5sxrhrr7ARmF16MDddY7EjMAuwA3mNmkhEjckxAXOiQNbfdPv+cqneuAncxs3JqKPV2V6Yno2v+l+D01ER15KbB7Ckh/rLSEv0ARpdUZorUyQvU4IjHieWa2ee5vIgTiUwnRbgszm6h06cR5zbzAnu1a6JGgJH7fCTxGiNvvlo4fTkRLTk8EMrxHtBkGLFfzPl0hgM8LTGeR0BeAnKT4J1Hfg4kk0zsCS+b7VpuVVaX3qPg7PwBMZGaPEYLRgURk69LATsCSRZ+hTB36CzmpUATRnGZmtwLXmtlaGfH9PWF5cBfh0/6Ymd1OrH4bSowjaheZX/xtzexYYG5itcFswEzAtcC+ZrZsdSUcaRYGlgMetLA8AzifsLQcBpwMLNWR+p/5TP2DsD8qmJ9oy46iFAleRflGkaFEVP7JxVjCm22Q3gcmJPqtHQJ3H0jcowtz1UdhXXUtkf/kfWLydUHgP2bWR+J3x6BWDbUQQvxaUnTra2YHEtENt7r7W8VyJHf/luikrm5mJxODwvMJy4bnS+fpS0t0STJi4UczG9fMrk7hYRAwBpH0BVr2F54kOn/jNX5WHSK5OinP0pxU6GWi470kERnY4Sgi08xsDDM7LEXTsiB+LdCbsGmgbsJCV6AsEJd+ngLo6+5fmtkUwBOESLm1u39vZhsCB5REitpQmmDx8r8dWQjP9+hpwjv2fOBMM1sXfhLBdyIE1YOAXc1sQTObG9iKiG59vJi4rKgKI6If4bc6jBBNfoCfEpzj7kcSouQmwLHEBOECJaG4Fn26xucr+6QHEQk7JwM2sPA4L2hy94/d/Sx3P9vd/1sI+nX4fm3lPSr+zo8S/esPiESYi7j7qe7+P2Ki9lUiQKV25DvQj2jP/gb0Jfo3lxGrDGbPyaa1icm+yYl6HuXu8+Uz17Muz1yZrNdfgYuAh939O2J13wrE5OV/KyzeyHIXsD5hN/O4mY3j7he6+zWEMG7AScQkSzeIZNpmNn/Du1Ub8n1+J79Lu+UkzGBgAZpF8PXMrGeeP7mFJVJtcff3gFPc/YvSfSj6bh8TK/t+6udZg/1oHXH3l7NvU9TjIOArYqJ1W3ffjLB9mgHYuqJiil+JBhRCiM7ArsAexFLEV+CnLO3FzPm1RLTQpsQS2a+IznkxqOhIM+xC/G4Ukz8pel9FRNRNTiw3fR84vog6K70nYxAJbgZVUuguRrZRg4nEb8OJv39PYrl5XcWrNrHmfA0DiCjHjYBngLPM7O952m3EsvKymCfaiRyMFwJx/9K7/wCx3HxX4p7dAWzh7t+a2eTAssAQUqysC0UblpPl25nZkWZ2sJlN1VG//0vv0RjAQoRgDHCxma0H8d64+w5E1OrOxP0qfNt3ys+piwVFo1D8JRGdfj2RYPDi3D+0JII/4WH7cri7/yvvcfc61Ada3KNuZtbdwsqhiPQ+kYje3x3YvhDqiqCMxs+qQxtYeo/GMLPNzexwM9svo6Q/cPdz3f3vwCHu/oSZ9TSz6YlJipeIPkVtsJarkDYhRLqV3X0Rwnt9SyLvyY5mNmZG7W5DPJMrEtYufQC82RO8NuRzNBFhW/ViCnkzAU8TovJWuW93M/tblWVti9LEz9XEOG8MIgJ/bAB3fx1YnhDBjwdWyMnZk4BzqIndb/H+m9k05Qm6og9earsKEfx9ot1eL+/ZuYTtXW0iqBvq1AsiJ0tDUFnx/fol0W/tldcOAE41swvau9xt0XiPSvvL9bkQWNrdXyldehvwPTFBIzoA1kH7fUII8RMZ4XAMEdX0EJEQ5Xsr+ZFmR3BSYuD3Rg4y5FcquizZqfPs6P2J6GzvBTyTnfHNiAHFc4RY8ikREX4yEdG1VB0G5Z2ZHBAVCd6mICbxehCi8UvA3939647WluXg50rgbmKyZX1iELsgEZF2PTGgOBbY0N2vr6SgXRBrmTTtEMLbd093f9/MpiTu2dTEfVo8J9AmJiJalyDsGl6tpvQ/x5q9YQcQUarFJFIvIhJyf+Byd/9sBB9TSywsG54B3gJeJESGzYBJiPwN55XOXSr3G3CRN1tWVd5uWLP/cjegt4ddXXHfJiKE8I2BG9x9w/I1VZZ7RJTa7v5EFP6MRJt2PxE5/H32XQ8ivl8PAE7ymvrjluozgMipU1ix9CW+k1bJaO/i/LGJCP2NiPftL8U9rlO/Ie/BUYSv7/fuvmfuL56/LYAzCJuTq/NYX2JF1kLEfTvPI7K/UqwVf/icLHoWuJ1IjHsfIX5v5uFvPgcxEX01kci8TvemRX1S/F2DCGgaTDxTX+Wx6YAbiSCON4kI/qXd/fH2Lncj2QacAcwOzEJ8h57t7le2cm7xnvUmrO4mIfrekxPft080XlMFv6ZOef6axCqE6YBPiAmK9YGF3P2pdin0CPil+hTjpVauM2A+YrJld3e/pa1zRX2QAC6E6FC01sHL/X2JaJq1CS/Ibd39u7YGSXXrhAtRBTmgeJRYlvgNsHApMqU/kexub2LyaCiRMO1T4K8ey371Ho0mSqLQGMRA4U53fysjbXYmkt+9RAgPg7IjPgEwZkZF1YqGCckLiIHqep5J0cxsXML7dmfCp/SPRCTxCe6+lwYVo5/y+2yRNO1PxATFxZ7JVs1sFsL2ZHD++y1xv+YkJsWeqaDoIyTfmZsI0fsfxAqWwYSd03jEcubnqivhqGFmpxBR90u4+9u5bz6izV4B2MDdL2nj2lokU2wQis8mJlcA/gOc4+5v5ATLHsCGwI0lEbzW3z8prj5GiKsvE1H6SxIi5O7u/mQKxXsTXt8nAgdmpHHtyH72XcQqjw09khH+B1iEsG1Yyd2fyUn1XYBlCLFr3fwuq92khZktTfgW9wJOc/dtc78Rk0W9icm+gUR/yPN57QvcCkwDzFYIsVXRMHG5HpGn4em8F2cTEes9iX7E6lm/sYkghz8Rke/vVVP6EWNmqwGPu/u7vyCCDyDepSHEpGblE7HZrj1GvB//JXJoHEC8F+Uk8+VriuCUWYDniYnNRdz9+fYreduMYp1WJoIe5iZWVmxMjCOebq9yt8WvrU9DP2lcYgLtz8AK7v5Be5ZdjCLurk2bNm0dYgN65L99iEHE5sTSvqlyfz+io/cB4YfZL/d3r7rs2rTVcct36UDgO6LTN3fD8e7A+ITYuguwTvE+Fe+jttFyX4q/8QCiY35f/v175v7eRLT+B4RQNCERIfQIcFvV5R9BvfoRUYGnAqs3HLPSOZMDRxAC5SBg5qrL3pU24BQiUfQChOc35PLl/HkaYiXIw4QtyonA9FWXewT1mQl4I5+9brlvLcJfeo/83cr/doSNEPX/kz93L+2fD/iQEF7XqbqcIyh/8Tcfg7Cvez77bndk+V8H5shz/kCIdZ8CN1dd9hHUqXwflsq2e4b8vTexSuJDIsKwT+4fh4g+vL/Ozx9h/3EXME3+fhUhDG9KrEB4E/hTHpucEIWK962W/fD8vvm/fP5eA6ZrON4t27jrW7l2DGCyGtSh/MxdlPfiDGIyHKIf8WS2B4cA4xLe2RcSlpCzVV2HEdRt/Sz34cCkua8HEew0MNuIcRquqcU7lN+Zl+c7M2WpvZs3v3t2GsG10+T79S016v/82jqVji9ECPkPEBMXc1Vdl9/hHi0FXJD1qu07pK2Ve1d1AbRp06ZtZLZSJ3oA4V83kJjl/44QgJbO432BMwn/tHOA/lWXXZu2umytDQyIwfeO2dk7AxhvROfn/loOZjvTlgPz5wgxaG7CGqA8oOhNWAN8TETmv5Ln96y67G3UxwhhtSnb7QVzf5sTKUTyroHAduW6axut92lCQizZsbTvj8QKq2uIhE89G66p9X0Bls72bbb8fd18DvfM38cilmSPV1UZR7FelwGvl37vUfr5UGIFRRMRmVt5eRvKXkzydSfsj/4NTFE6vjYhiL8KTJv7JgDOy3O7VV2HEdStiJQ+HzijleNL5fO4f2lfP2o8CZPt9+rAJvn7ScDbhGVAdyLytoh0n7fh2lrcq7bKQXj3rgV8BDxIaTKPmDx7nViJVHkdfqF+lxJ2SEsCf8h9ReDQmMAtwLvAj4TY/yAdQLgDTstn6whywoEQwdfJ/sFLwLhVl7OVcs9DWFRtWXq3iyCGh4Fr82druK4b4Un/FjURin+HOi1Ec9/vT1XX43eoz1rE5OYzHeEd0tZyq0ViACGE+CU8vPj6EEuuPyOSWT5JDGzPBs4xs7+6+ztmtiOReGMLIiLl8GpKLUR9KFlqdCeW+/Z09689stCfQ0QzHQoMNrND3f1z90jg5Q3LzL0GS+e7ALvlv1sBb+W9mBmYNJfI3mFmxxFt3LxEm7en13SpeZb/KmAyYGWi7X7QW/GFLX539wfM7CMiIdkp7jHyEKOV/oSlyVdm9kciYvU4miedTyLski6oqoAjog1rjzcI0ecvZjYJkVBxX3c/Mo//hfCfn5MQLmvFCKw+bgOWMrPDgf282UfbiZU71wBPEVYNtcKbbSROJdqE74GBxf1z98vTpuEEYEMz29/dPzWznYGvvTmJeR0tUP5CeN0uRibvbGiT7wPuBRY0sz7uPsTdv8vzamH11FiObL9vzmOTESL+ocBzeexqwu/7D8B+hAVPcW3l96jU/+lNJFWdgrB/+84jie/NeeqJwG1mdjeR62RBor3bLT+nFvenETNbnRAZN3D3e3PfOMAcZtbb3f9tkWR6CkLUfxH4yt2/qKjIP6MVz+/e7j7U3bfJtmCP3H+qu79nZlcSoup5wJ1m9hfiUa3L/ekGPAFcVpTJm5OlDgSmbK2vlm3bTcDt7l6rxLGMYp0Ie5EziDwHr1AfRrU+zxLawvM1vEfiF5AALoToSMxHLN3bB3jC3YfmYG9cYLcUv3t5JFDahRCGjquwvF0CM+tZ6jCIGpIDix/TI/EsYgA01MweBw5w989TTIUY1HqK4F/UYfDa1cjB3jTAe+7+ppmNa2brEnY1w4Hxzewwd9+P8FW8snRt9zqI321MnNxnZsOICMl9zewDdz+jUcwqfifE2MHA98VguN0r0okpizmlSYc3zexCQlQYSIwVTiJ8LocSiaaXoCSA10VwKIlcfQm/6JuJgfcnRG6Q44ho7x3c/ZS8ZgbivXqHWE1WK0p16gXMTNhWfe7urxHJYlcCNiAidPcixO9piTb+Mnc/s/w5FVRhRMxE+ioDN+VzNLzoU7j7ZWa2OZH8bT8Ab/b7rav4jbv/18x2IFborGJm/3T3x4t74O5DzOxbYpLih4ZrK3+XSs9c4UVctA1D8vgUxKqQ70rlnY+IlF6LiIqsDQ39n+uJxL5TERG255jZmRkIUIjgBxHJZI8movjPr+vEcolJiAnKRy3yhixA9PX6AhOZ2SXAxh65HN6orpht480e5ou4+705xuvl7j+4+9Zm5oQI7mZ2uocH/TXE5OazdWsP3P0RM3vdI9lokeugeIY+Jiw3fnrfy2Mpd/+0omKPkFGtk0f+ml3cfXBlhW+FUahP8Ty+ZGYv16G9Fr+eblUXQAghfgUzEl9Gj2bHaF0iwmk/dz8hO7ebmtmk7v6dux+dX2bdKy11J8TMupvZzGY2TtFhM7PNM2JQ1AxvjrZ7mBAdniKWL28A3GFmC7v7D4Sv795EorhjzWzMioo8QlIg7rRkp/o5YJkUI68BjiFEyFWAY4FdzWyKFIrL11YenZ8DiCYz62Vm05jZAhk1iLs/TAgM/waOMLMtc39TuS45mF2SsEE5SuL370sO9sqDtzFKP29JLMHeA/g/d9/P3b8l7FGGAy+0X0lHjgaR6x6iDVsewN2/JizR3iRWkJHvznqEZ24/op5Nje9TlTTU6S7gWsI7+kkz25cQvbcg2vXNzOxtwmP1bsLa6tz8HKubcJdlehJYnPD1XtPMNoKIwCv12z4kRPHe5evrJnYVFN9N7n49ESE4ELjVzObx5iTAswCzAC/WrR6lZ64/cLZFkssnzewgixVIEALql8DqZraQmS1M+IC/5+5P5XtUi353PmfDLRKSPkiIWRsR7/xYwNbA7mY2drZxtxAJ8AYSK0Kuy+exzuI3RH9uWqIPdylwI7HSYB2ijusRK8VqjZkdCNxY6hf8kJN/uPs2wBXATsDmZjZViqtX54RgbSi1A5/lv0W/rPjO/ZqYnOie5/cHDrdYwVxLfkOdds7zayV+j2J9DjOznfJ8id8dFEWACyE6Ep8SEWhTmNkcxNLSfdz9iPwiW50YTN1dvqgOglAnZCHgMOB6i8jhm4CpCVFL1ARruaR0dsIregt3fyWFnrmJwdLxZrZkRkGdRvhFLkwk4KkV5TqZ2UTu/nHVZfotWOuWDRCC3XiE/cfjwF7u/khe81dChPyqxgLKAGIQPi0wKfCBmd3l7hu5+0MWtg17A0dmoOFZrdTlRsIX+L32rUXnpuEd2oeI3pzWzK4DTsu/9wUN10wB7EtEGl7VviX+ZUoi18OEl+9WwE8RWu7+nxQX/kFMJB1G+OG+SCSKrF2EZ9ZpDELU/ormXA1/IvxwJ3L37cxsE0LsX44YsD9EtBc/jqB9qZSMLDZ3f9jM1iYSke2W+84HMLNpCDuRB+o0AWZmEwBDc2KlBaV6ubvfmFGrRwJ3mdktxHfqrIStxj/y82phq1ESi/sTk+SDCKvB8QghdQMzWzujJjcnJo9WIYSiV2lZn1o8c3k/uhOTxu8DG7r7J2Z2GfEuvQHsDDSZ2THu/lXeJwgx+cqs80eVVKCBtt7nfNb2ADYnkmFv7e4X5TU9iGj3b9q1sCOB/Xwlx2XEqpad8jk6I0XwPh4rEM4A/k58F/1osRKuFs9ambbe5waRdUygZ96f46n5JEVnq1Nnq48YeSSACyFqxwgGbI8Rfm+XEtEz+6b43Y2wC9iY6MzWKhKgM+Lu95rZIGIQviERSbOsuw+stmSiTElA+Q8h9rzj6b+Xg47HMgrybsIGYAePpYCHEAP8Vj3Aq6JBuDsCGMfMLnX3+ysu2ihhzUvN+wH7E5NIw4Hbgavcfc8Ukn/wWPXSk1gFswKRbKx2A1pvXm3wEBFtuz8RebslsJqZTe3uC3v4ex9CiOBnmNnH7n5Dw2cNIywsxO9Evs/FO3QVMAeRW+M+QhiezMxOdPenStfsQUx6zgUs57GMvo7sTkySb0xEo7qZzUasehlECMlrE+/ZpDS3iV438bvU7m5ERHpv6+7P57FiZc4rKRJ9A/wrt/Jn1KpOjTSI4OsTQQ3nmtnyRIRuL8ICaXOoh1BsscrtSeBQMztnJETwmzJAY2/C7uWfwK7Ak3WbdCm+7wkf7C+JVRFvA5jZWYQtyPTAI+5+s4Xn8mLE5My13tI+oE70JMTuG1P8voDw9l7I3d8ws6eJKOluZnashyXcDcR443LimVyx6n5QQ/9nV8KG5lvCpuV1dz8m79NgjxV9xWTNmsAXxGqK2tBQn5mAj939VTNbjVj1touZkSL4kLxsPMKe6zvgmjqK3yOi1IZ9l7sGEH3vdYC/Fm18R6Kz1amz1Uf8HAngQohaURKExiCiuSciBhufePi9bQ2cTgjdd+dAcAFiyWIvYPM6i3adAWv2g1zOwsdyWsJrcGAer3yQKlowG9GBW52I2O8GdCsNUp8gBLA/m1lfd//em70+rUbvUVm4u4KIDDyB8O7tcOTftlhq/iThBfsWIXDPTyR+W8Xdv8zzxyaiPLcmvLE3LgstlVSibdYDxs5/X0hh5DniWTvYzM5z901S+DoKeIVYei5GM8X7bGZHE5HE62dE5055ylrABGa2l7s/Y2aTAxMQky2Ler0SWDUyHiHGfQ4MMLNNiUjBJsIS5CTgQG/wwc22pRainTUngSza3ZkJy8o38/haRJTkXu7+TzMbLyeVHm/8rDrU6Zfap1Ibdr9FJPi/iEj2W4Fj3f3u/Jy6CKvvEVYaBwBDcgJ2UONJDSL4jRmFvA8hRp7i4QNeRLXWicJr/p6S+L06IX7v7u4X5YTtWK28R7XIP9FI/q0PBL4zs8WJVVVbExHhEGLrPoS1xrvA6R65hG4G1iC+wyrvB5X6P1cTdfiQCP5ZBTjAzK4pP4tmtgKwGhExvbCn1UMdaBC/TyMS4V5pZld5+EUXIvhOZjaGh83lH4AVCe/53asr/W/CiMjiQfnz6cAywILlSecORmerU2erj2hAArgQojaUBKEBxABjCmL50VDgcTPb1t0vj2AaziASv41LDAw/Bpb3mi35LQn6fYjEYd8TkWmvVly0UcbD37EnMDExgPiCWG7+npmd7WGjUZsJiK6Ouz+W4tYeROTwku5+e0loGZ4TGZMR0cfla2sjrJaEu5OJJYhrERnYv0txoalO5f0lUiDpTkRvfUoIkW8BZPTZioQNzQ0Wyy+3IQaz7wKL1Cl6sJX3fUbA3P3ZPN4j24XziXu3mJlN7O4fuvsDRGRunUSuTk1GsU4HHJ3i966EX/FqxIDvcmAfi0S4z5rZfkB3D4/cOjOYsJc4jxDt5yP85q8khPD1CSuEFisn6vJdZc1JuMYmJvOPIe5H72znViHE773d/aiczNwM+JuZre/un1dX+p9Tqk8PoE9bz09JLH7YzNYhVvlNSkxaFNSiT+fhB70KcCGxJN7N7Ap3/6KVc8si+HVm9iOxyuK/ZraEu7/YzsUfGfoQfYEiOe7/Ee3B3u5+bPZldwPeNrOLy33tuvS7W6O4Pxb2ib2B+0uTDwOA0wgrl7NK13xP+O5XSoNY/Bfi/ixNjH36EgEMxwM9zOxf+YyuSSRh/ZGIdK9N3oZ8J8qrkOYkEo7e47HSrVuK4GsQ/aN9zGx7YmJzauBvVZX9t1L6rhlC3Mexgfnd/ZmqyvRb6Wx16mz1ET+nNolehBBdm+zgFcsvzyAE7VUJgXt/Qgi/x8xmc/fLiajWbYHtieQ7y3hzoppKO+FmNouFdyUlQf8+wtP338AtZrZqlWUcFcyaEw96JJ55F5jR3ecH7gAOBbawSIzZZEFPM5uwqjJ3dYp75u53EnY1TwPXmdkypXNmIaKp/+c18lltjYwAmhs43t0fSVFoamLwerOFH31HYkxCiLyWSGKFmf2dEL93dfcbLFbDNBG+y7sAq9VB/LawOQF+mhQbo/Suvw9MZGZz5vGivF8S7eAUxMCiBRK/243PCLuJ681sSUIo2ZLwXL+TmIBeDTjBzGZ198F1Er+tjQR77r4bsZJgHMIKbVGPZNhvA48Sk0e1xVt6fi9iZpMQ+TUmMLPbiIjI3dz9yLxkFiIy7VViIro2FH0xixUulxJ2BlO0dX5JLH6QsFWbGNjTItl5LSZjU8iHEIf3IgTIvYD/y0mLn1HUK3++iRCPuxETmz3L/ar2pniPzKybNScZLWyn5jSz7UjxmxAoIXKJLElMiNVW8B4BHxD2OivCT/2fhYiEpAflM1uLBJ4FJbF4R8Le6SOivF945GuYm3j/jwbWyvL/h7CEWrlO4jc0v8tmtj8xIb4ecKG7v5/9ir5mNoHHCoN1gEOIMdTDhBBZOwuK4j3+Fe/zW0Sy5nnqKqx2tjp1tvqIUcdq0J8QQggAMrJkBaKDdyZwS6njtwKRtOpLYC1vJSFNK1GI7Y6FJctDhHC/sLu/bmY3Eh3uIwjRZ31i+eK6KebXnoYIlPEIT8XB3nK55S3EwGhvIgLvS+A4Qrzb29OTULQvRQRa/rwY8R7NRUxaDCJ8JPsA86ZQWRtLjcZ3OgXWF4lJshOJwdERhIXGIGIge4S779f+pf31ZCTuS8A27n6eNdsbFMl9+xKR+4+4+22l6ypt61LU2hAYz90PzonLF4HL3f0gM1uaEOyvBg725qX03YmI1T2JKPYOaV/TkWjrWTGzfjmBdCwxAbaip5+xmV1M2I/NSEQP1uY+WUubtBUJC65niYSXb+Q53YGeHtYHPYiowQuJSaZ16tK+FRT3KAfmMwKnAP/wSFY8DnAqsDLwuLsvkmLln/I8J+5RbdruUn0GEAn5PgeuAM4pT7K2Vt5in0Wi31uBZ4jVfZXmOyjVaUzgNiJaeFJiMg+i39OqHUpeX/4eXgZ4zSv00reW+ScOIYThs919kJktC1xP9PMOd/d985oZgXOJlYzLdEQB3MymI8YXcxC2fWMSfdV56jwBa2bzEWOLT4Dr3X2r3N87o6b7EXmSxiLseS6seX16EX2E9919m9w3C2FrNwExZlrPYwVj8e7VYoVYMUHS+Pxn/3pa4CIfCWsjMxurrfaivelsdeps9RG/M+6uTZs2bZVvRETMzYQVwEBg8tzfu3TOHsQS51mqLu8I6tGDyGD+IvA/wp/vXELsKc6ZmxhcNAFrV13mkahT99LPxxDRae8AjwNzNJx7C7Fs7Oas43Bg9qrr0EqdrOoytGd9yscJb/278/k7mxBSeuexHlXXpVTO8nP3R2BA/nwo4Zf9MRGhcRBhEzA+cC9wRdVlH8n6dSNExrcIb+LN8p7sRXi0AywC3E9EfVde5lLZ+xJR918QItxbROTw5KVzjsj6nF+0E9n2PUIIW53qHazj1vAOTUFEDE/WcM75+X01Qf4+Wd6fJQkRufJ6NNaHsCx4mIjyfim/c64mIr7L549PrCR7CHiqaN/q+OwRk5BP5XfoFQ3H/khE4n5GiP1PEsLwA8U9Kt/rOmyEzcQ9+V3zx9Lfvm/5HhRtXcO1xbEFgGmrrktDnR4molHnJQIdFgCuI0ThbYCxR3B9LZ67hvfo2fyO2ZywqSG/T7ck7AdvJyYsj8jn86m6PXO/9u9KiN8HEX7zx5SezVrUZwTlXiO/c78hks4X+3vlv/2IiYxXgDGrLu8v3TNi5dsjwLKEaD+UWCV7SrZvjxKCfvG8Vv7+ZBvwELHyuEdp/4AsexMxmfK7Pa+qU9euj7bR8IxUXQBt2rRpKzbC1+3V/HLat7S/EOcmzGPrV13WX6hHD2JZ8ptE5NPrwDQN58xJswi+VtVlHkFdysLpFYTwvT0RefsBEY2yRMM152bH9X5gtqrr0EqdyqLQ2FWX53eoTyGW9hxRfRru5dKEWPwdMF/u6z26yvgb79EphMCwQv4+EbAU8H/lTiyRAO9e4LCqy99KfcqdcGs4ViTpawIOKO2fjhC4bqGGA3MiWdo1xJL5t4FJcn/P0jmHE0kJh2V78R4xcVYIKD8Tv7T9bven/A6dCzyXz9grwBmlY5sTE8vnATsT1igDgamqrkMb9eqbz9DdwAy57wng29z319K5RxK2J9fQLHLVZpKvoV6zEYP2oYQVjRF9iUL8GT/bvZOJiN11S8dqVydgHqIPtGhp39KE6HgTcCAjmGBpbCfrsBErJT4jVhGU9w8g+nNfEvlQxqm6rCNRlz75Ht1BrDr42b0gVlg8SEw03UGs6KvVe1QqT3ciMWf52Eh/v9SlPkVdRnBsTSJQ6L8N71YhgvelYbxR9dZWfYhxxJPE5NFjhO1bcez0bA9r1Q4Q49C7iX7N2rTs280HbFp1Gbt6nTpbfbSNhmek6gJo06ata27ljikthbk/E1nl36IkdBPRkssQNgdLV13+kahTL2C57MANBpZr5Zw5iAiIJiIxYeV1GEHdDiAi7QqxdNfstL5CLAVuFMHHBfpXXe5W6lEWhQ7MAd3sVZfrN9SnEL97EJFZhwITjeD88ru2GDFRMYiw66m8Pq2U8Spi8L0dDZGrDddMTUSzf0BNIgaBMYp7k//2z+ftinyfZi7duxOyHTiFSO65MyHqPVO6vhZiMSHMFcLbPYQf6cfAcaVzyit3FicmzQ4HNqLGol1n3IBLCBF4K0I8OSCftVtpFk0Ozfv4OfB8XdvEfPYOAO4CJs591xETs/tkvf5btGdEv2GpcjtZdR1+oX4LAjdQmhjPOrT57lPDybEs1wLAh4RV0jxEkr7iuXuGyBOwZtXl/JV1mp9YbbBm49+esN8aRPRddwP6VV3eUtkmb2Xf8kSfbvbS+7EoYZF2CTB37utPCPzlvmstnrnSd0l/YuLuMaJPvcEvlZWW/YzaiKwNz9TKwBZEJOvMpf3rEcEnrYrgddoa6rM4sDqxKqdYCTJltg/Tl84bh0hcfCkxUVOn+9MNmIRYcfQ9IbCO0cp5faoua1etU2erj7bR8IxUXQBt2rR1vY1mQac3EXnyp4bj8xMi+NtEAszpiIRcDxKCXS063w1lLgYQvYA/5M99CBH8ReBlUphr6HjPQ0Sp1XZgTvjxnQhslb/vQlhQ/B8xyH2HiEhZpOqyjsw9yp+vIKLTdqaVwWFH2BreoymJyYiPiMH3BCO4rvz8LZrXfVDDgcbB2QbMR/PS7L5Z33FL521LLNMeSIMlT4VlnyMHcGUR4aVs154mRJTHCB9ViOj9/Qix/2si8vtc6hdt173h35kJW40r8hk6vnRum4PxOrbhnXEjJrnezu+h4h36cz5/F5MiRO6flfDGbLPtqMNGTBDtnz+fTgiOc+bv+xO2WzeTk86l62oxgdRGncptcpFUuonwZf+p7NRXtGvNxmQCQqB7P9vml0krJyKa/Stgh6rL/ivrOTEx2XdBaV/RFk5ETB69S6xEqsX9ISaHf6QhcIRYQdCUdZqSZmuxZ4mJi0G0Eklco3oVNjl9CbvBZ4GLsr17j/Aub3GP6r7Rso96Zb47H+T9ewE4pHR8LUIEv6vx3tZla6jP5dkOfJlt9KOUJipK581BJMr+Apip6jo0lK28um2GbN8+IiaWy5P+jZaDPxNf67J1tjp1tvpoG03PSdUF0KZNW9faSp3WIjnSZ8Rg/BJixrYY6BUieFN2Zq8nBKFiEF+bDi0tLSjuzY7eFLmvN+Fv9xohhP9MBC99Ti1ErtbuGeFrPiHhe/kusHnp+Gk0WzgsXnV5R6I+hWiyABmlTo3FkTbqUDxzxXt0G+FN+h6x4mAvYPwR3dPSzwsDf6y6Tg3l602I2ieV9s1ELJ1/nLDXmR8Ym4iePptSBFHVG7BDvg/XEAO61QlRbnJiomF2YtLoJdLaJa+bKM8pC5O1aBdoGcl+OiHYT5j7JqB1EXxSQliptajaWTci4v4zmqOlpyeivC+jOQJvharK9yvrUo4k7EkIe68QEcZFv2DFbP+agJOrLnMb9Si3vW2thJuP8NQvi+C1EB5bqU95InZBQnQo/OQnIaJVlyXF1Lx3sxOWPLW0f2PEFhTb533Zs2F/kbRzYpr7uZXfM8L7f9XyvcqfpydWGQ0lhPsPCVF8ADEx/h01jdCn5AlNTMDeTPZhiOjhqwix9ejSNR2mj0esBHuXyAEyCdFneIgYK61SOm8tmif8+lZV3pGoz5lZn6Xzfk1GiPvv0TI/0p7Z7r1KzVYhNbTV/yT63IWt2OdElHHPhmv2osa2nZ2tTp2tPtpG39YDIYRoR9zdMzvzpUQE0F6ESLJD/vsPM3vZ3R82s/8jRJXBwI3ufh40Zz2vpAINFFnJzaw3IQJNBkwF7Glmh7n7+2b2H2LQdDJwg5mt5O5vmJm5xzcwgNcju3k3d28q78sy3pjHVyWiUe4vnfIdMUHxDdGprS1mNiURdX+Yuz+U+6YCtjWzyYCH3f2kCos4Urh7k5n1Igbcw4C9iUHD1MCOhEcsZnaOu3/ayvVePH/ufl/7lfxX0QRMbWbLEYk6DySeuxeIe7i3u69oZocCg939+8pK2oC7n2RmhV3DcMAJsfu9fJ+eNbP5iMmLo/O9u9HdPy5/Tt6jurQLP5rZAGIlziBiIuLrLOOnZrY9MXD/PzMbC7iQSJzWl/g7iNFIa213MjYwyMwmJp63O4Et3P37fLe2NLPX3P2VdizuL2Jm3d19eOl7sgfxLuHuw8xsOmJ12GvuPiQvG5eYkL2ZSFRYK0r9hTGBH0rlbmyTHzGz/Yh241ozW8fdr6qs4G1QtE/ZLtxBJP3uCdxnZge4+zNEcENxfm/C6/xEYqVLHetU3KN+ROL1GYg6XUO8O+cQdTjczGYm6t0d+AfRB/o472Vb72O7YGbjuvsX7v4u8K6Z9QQeNbM73H1PIihjayL68RPgEXd/Ma8tAk8+qqj4P8PM+hTvS7YLfYj+zw/EJN+7+Tx+aWb/IL6L1s5Xao/sM1V6T0YGM5uI8Jo/ibgnQ8xsPEI4vphI4geAu//LzIYBz9ep/1Mm2+l5iRV997r70Gz/BhD5AB4tnf4N0cfb0t3fbPfCjoDiuTGz84gE0bsSqw7mIiaaz4rDdpW7D8vL/ktE8j/6sw+sAZ2tTp2tPmI0UrUCr02btq6x0XJmdgxiZvav+Xt3Yon2R8SgdWaaI1wXJKwqngXWqLoerdWJ6Mg9R3gPPk0IdE1ElOTkeU4RCf4yMRM9adXlb6U+5Qi72YiB0YyUPKUJH99BNCcfG4sYxO5KPf0HGxMOTk1E0WyZ921LYiD+P8KXtInofFde9pGo26zEwHWTcn2Jwfp5xMTR7ozAE7wOG21EZhG+3y/k/Xka2KN07GLgwarL3ka5y+/RHvm+fw5sU75H+fM0xJL654HVqy77L9SrD80C6vQ0R34azV7SEwMXEJObXxGrEpTwcvTfm/IzN13p578SkZ0X0hz5PSCPTURMRF9PzZIB83Pf/JuJZLBb0rxqZ3oiwfRV2a7PQ0zOnNna36UuG9CP6Acc2fj9lMfLkeDzErkdHqi63CO6V/kM3Q2sARxDrN57GZirdN4EwKl5j8rtQm3uUfG3J/oGL+X3zyNE/2Aw0cebjJjU2zbfrcHE9/A9dWnriL7BY8BGpX0TE+L9MGC/hvPLFn4zExZcd1Rdj1L5FiK+8yco7VuA6Hd/AZxV1KN0DyYkBNa3KSX9rftG9LmHkRGpxCTfF4RIV6zaWY/Mx1O3rfF9Jlbq/UBzEvPpCBuUcn3WpHk1We3GEaW6TEtMDO1U2tctv39uynqtSclOgxoll+8Kdeps9dE2mp6TqgugTZu2zr/Rconsn7MzewUt/bi6EcvjPiJmZMsi+PzEUueB5FLOumyE2HgPMaCbERgz911IRDOcTordWf9Vsu61GfQVf//Szxfk37qwNfkPzV7FkxI2B08Syxpvys55LRIPNtSpLApNkn//blnuJkpekYS4NwUhtJ5dddlHsn5zEtH3G5brSwiScxDC6mfATnUdVDTco78RiblWLu2bnbADKCeAGpeIxjs/72nly8wb60PLpeY75fP2CukJnvvLIngTcGnV5S+VbepW9i1OiFp/LrXNSxAWNDcAy5buz3zEpGbtEl7W6Xn5nepTfodOIsSrlUv7zsnn65nivhKDxHOJ79u6+awWImT/fGcez+frJkIYupZYEWLZdr9H2Dh8SFg61OZZK9Wp3B6cSkwitfl3p6UIPgs1ESJbe+by9wuAxUq/r0+Ixm/Q7M8+KSGUH0fNchs03qt83h7Jtrl37j8i35draLZ4mSjbwzlLbWLldSIiHj8l+mlrlfZPkX//JmDfhmvGIVZkPprvXC3E/CzDesAp+XP53Vg627sm0l8+95dF8DuJRLm1a/db+9sSE3ufEpN9MxD96ytonrich/D9Xqnq8rdS9vK9OZsQHecgEhEumm1AY30WIsZ8HcE+cRZCzN+s4TnrRvSFhhA5kTakpn3uzl6nzlYfbaPpOam6ANq0aesaGxFR8yQRKVMIq2s1nFOI4O8Tftl/pHkwvDAhTv5MmKm4XjMS3nb/aOXYRTRHghee4D1LdaqVCJ5luqDUOfgzkfDyRUL0XinPmZcQGl4lorlmq7rcrdSjLAqdkPdi8fz9j8A6wGbAQqXzJiImMvZsz7L+hjqOSayOuKGx3kRk/qPEIP4HYP7cX5tBIC0nXS4jfNm/IcSsB4AVaBATiMi2cwhhv27CXSHq9CWi11ctHdueiIi+nlKizlLnfNK6tAeESPIDJUEh96+c7dksxMD2SMIO6XFiOf1QSgJ/4zNZh62hXejwvuS0FByuIgTHzWiYkMz278v8Dr2DiA59h5okjW3tPhHR6f+lZUR7MXm5VOm8FYlJpm2o4YRLqez9iAnwUyit2hmZe5u/Vy5Elv+2xKTxPIQP8a1kH6d03rqECP4apWTA5XtcdV3aqN+kxETfno33Idu8wbSxGrEO94hmIf7P+bd/BlivdLwsgu9T2r82IUyeSU0nKPIdOpxSn5MQtR4ixhZlf+yiDuOU/iZ16v+Uv4uWpeXk+KXZXn9L9I2KuoxL9H8epWarSGnZnzue6O8smb/fTYzrBuV30Ri5f3xiIvYB4A9V12Ek6tifGO9dVNrXq/TzE3nfPgDGrLq8XbFOna0+2kbTc1J1AbRp09Z5N5oHo92IhHZ3E8LqjoRY8kjRQSpd040QWm4qX5//9qm6Tq3UcSoiomH30r5C1OpDDKTeISLzai24ENG27wKbNnTOlyeWYT9JihF5n8YC+lVd7lbq0SgKvUYI+ZON4JopiYiVgdRvkuWnyO5Wjq1HiJDHNeyfN9+3OfLe3USIRbUZAJbKelY+d8sTgsqMxGD2ZWCe0nkHE0LL69QvQVJxjwYQgsO9+cyVl1nukh3v68vlp2V0aOWCA2FVUERz9yztn4JY7dKU92YgIX71yuftK3I1Qh23hjbtqHwn/lR1uX6nuh1GiN/z0Byx2puWlgHrZr0vJfyKa5X4tqE+fQn7if1o/v5fm4gAL4TJVr97qK+wuh9tTP53lI2WFiFPEfZUnxNC3TqN7VfuezaPz9j4OXXciD7dIEo2IQ1t9MvAFVWXcwTlL7fZixArJB6m7UjwvXNft9xf5wCN5bPMFzc8T0sT3tGfAn8v7S+3+ZVPTrRRrvPzHdkFGCf3zUrYRA7Pd2gyIlL6UmK8Ubugk1J9JiNyBq1R+i5alph0/Z6wrhlA+JxfnO3HrFWXu63708qx3fIZ3KNh/8zEaoMFqaHtYGerU2erj7b23ZQEUwgx2vBIVNMXGI+IIr7C3R8BMLP3iUio/c0Md78zr2kysxvd/YY8r7u7D8+PrDTxZSkpVzdikDCcsKD4AFjGzP7l7u96c3KNvoQ4+TmwMREtfWVdEvGUkj0VScbGIzqvT2Y9e7r7MHe/xcz+QAjE0xBJx5qIQWLtyLpgZkcRney1gGfdfXAmThpKCJPf53lbEVGEcwLLeY2S75Seuf7AcZkM6TtgL3f/gEiIdDyws5lNT4h6BmxBPHfP5b89S+9RbTCzWQmLo32BO939BzObkJg8eoAof8HX+ft27v5Guxd2BHhzUq67abadeSnfrx7u/qO7Hxd5MdkbOMDMjnD3x72U5NJrkPDSI2nqbZk07REze9bdN3H3d81sO2I1zlfAU+7+MkRWIcIe4MOqyj0iso0bnj9fRUwMnUUMyDsMmWxwMeDfnomg8z7NBtzi7o/lvlmBQ4EpzexDYDd3v5QQUGpNfr9OSqwyeCX7BOsTtmJ7u/uRZjYGcI6ZnVf0HQrq2M4lVxDCz07AomZ2TamvUHsa+j9nEe3cccAfiGSKexKWNU8W17j7ZdkHXISYiC72ezsW/dfyHWEftpSZXeTu7xT9JOJ76VsiCrx25D0alkkGryOibsck+kGHZFfvimzLTyKSrB5kZmO7++7ERHSL9rJOZF90A0I07m5mB7v7y+5+u5k50Y843cz6uvtl5TrUoc9dUPouugKYmxDsHnT3L/OUF8nJPyJCegjNfvOLuPvz7V7okcDMziYmwwGeKb6jCMuWAUQ97yYmKorxw6Lu/kK7FnQElMecZrYZERwzPvHd+TTx7P0ZONTMJiXy7YxD2D5NDLzuDcnMq6az1amz1UdUQNUKvDZt2jrvRnTebiBmYt8Cpsn9RYTJasRA4z5giarLO5J1GoOI4FyR5kjvlQmh+xRgytK5sxKC5MREBM5/qi5/qWzlKOmDCc/eeYgO9p40R92Vo4m+BQ6ouuwjWb+xiIigA0v7piVEiHvyuZwamJwYzF9KJvas20ZMpLxEiL+P5DvzLjFwgJi4WJeIjP6WiJ6+nYgA7U8slz2NGkaAE1FNg4v3n+YESVfQnCBpjdK7VttkNdkOvEMIlC2WXNMygnDXbBOPrLrMv1CfcYiEdkOBExuOFfXqne3cA8TAtnZRgw3lPj7fk/loXobds87PVUP5LyWSQZafpzGIqNQricmkfQhh/37gn9ke1DlitdUVLsRS5ZuB/yMiIct2DfMRtgcbVl3+NurUVmLf6bMtbgJ2rLqco1CvPsBKRHLBxYr7RiRbfZ2IZP3zL93rOmy0jMIt2rOi3V6H6NOdDExVOm8GQuTfp73KOYr36Bmin/O3bJ9Xye/VF4D/K507BWGp8WDd+ga/UMf18/5cRstI8KWIvtItVZdxJOvwFjGhXPRvxiUmK8rWfEsBqxK+7uNXXe4R1Kc7EXjxcbZvq9JynFFMIK0NbEVY19TK9qShvFcTq9yez7btOyIIaNJ8bw4lAgGG5b/vUUNLsc5Wp85WH23VbJUXQJs2bZ17y4HEvcSAvBC5yn5cqxJRKi8Df6m6vCNRn8mIJYj/I5Zd9sr9u2aH/F4iCmVnYhDycB6/HHik6vJnWcpefadkx2DO7Hw/l3VbmGZhojsRMfk+HWTpNuEV+TCxxHK+vB+DCU/ZG4iBx/l57sSUvEnrsNHs+WiEp++thFjfj4hseICICFqidM2YRCRokeiuP+Hp/jkwfdV1aqOefyME1jmBsWk9QdIjwN+qLutI1GUfYFDpd2v4t+x/+zO7gKo3WhFAiCRi+xEC5AkNx8bJtu6x3IpBfG1ErobyjpnvTdnaYDpiwHQXYSMyRVXlG8k69CetP4hIu+I9WZmIEhxERA/uUbrmVEIsrqOFWNHO9SMmJ5YjJiS6ZZv9OSGm7JXnGWGR9BBhEVC7Z42WSb/nJfoJC5SOT0lMunYoETzvyW0021MNKB0zwtrgdaLfM1dr7UldttI96k/Y091O9N2OACbOY/sSfboHgAOzfX86t1q13Q11W5EQIZdu2F/YVL1KSzuUiRq/qzrCRtsi+Dx1bBdaKf/+wMv5cw+iz/0SsaK0Cbik6jKOQp36ECsuv8q2YsqqyzSK9TiCCDJZEJgw912Y92Wj/L13tuXr5/dWmxaLddg6W506W320te9WeQG0adPWeTbajnpajRBVP6Y5Crwsgq9HyfO7rltpkDB1dlRfIaIzCqF4JWLw91l+Md9MRhcSYux5VByFS8vZ8z8RAumqNItXsxKiw5PARsSgd3aa/bFrJxCN4Lk7iFh2PYiIfCpHEN4C3FZ12X/hOeuXnbxzgKPKx4lItJ9EcEqR+nnOEoQ9yvvUIOKhrXebiG6/j+YESeeTQjHNCZL+Swfw6yMSXX5HaSKvdC97AXtQ8ifN/ZUJKaWyGc2CUDdKwlbum5hWRHDg70RCq39S06RpDfWYKJ+xfYGZiESl3xJexoXfau0iO4kI72JSq4hQLVYRrENzJPvURAT4DKVrxyMmz86p273h5775TxD2QMV30TjE99MneX+WIESjR/P8HuXPqcPWUKcHiRUhTURS2auByfP4lESyweHA9lWX+1fUb2ki8XITsGXDMcvn7xXie6muK6p+mpAkxOAXgGuJidavCS/94j6tRfRdvyCCA66o43PXUL9V8v7MVZSz1G6sls/iA8AWDdfVxiP7V9R1/azPxTT4Ytfp/tAQCZ3/bkSME84j+tffZz3+TvT7vgbmrbrso1DXXnlfviNWJU3e2t+hrlu23f8hkq0WAU5TEOPXi2iegO5bdVm7ap06W320tf9WeQG0adPWObbSoKAnEak6LaXlbdmpe4XwiS1E8J6tfE6dOq0/ReGW9hUDiWmIKKhXCRG8+BIeL+s/Rf7ej5iV/pwaDQiJpb1PZwd8hvL9ICKM3yYic4cQ0dK1XDpGy2XMcxMRwwuW9i0MLFkuOxHpfiMRGdmjDp1yQjg4pWHfQjQnTTuilWtmIMTjgURyqJ+Wcee1+1GDyO+Ge7QksCg5OM996xJRq18TkeDd898iQdIsVddhJOs5X96rE4HxSvuLSaT7aRAdKi7vmPlv8d4XKwYeJlasrFM6tyyCH1/aP0npuatT293WpNj+eY8+yjZu/1L9b6VmE7H5LlxLDOzKUY7jElHrnxIi+IBWrp2emEBqcW3VGy1XII1BCN93Z3mL79FCSB6TSJr9FCFCPkxYiNR2wiXr9AwxcbcKYQ9yUN6rB8koNKKPdHo+j2tWXe5fuE/lnxci+g3PA6s2XGOE3/eVdXqPWqsPETn8GC0tTjYk+jtv0BwJXvTp/lBq62r33DXcn++ICddicqx4n+Yivmd/AC6suqytlL01oXiE/TMigKaJmtnzEd+n643g+FSEh/6L2cZvUjq2BvH9NG3V9RjFuvekpQjeYSJviSTgH5ArxfJ7qViZWNjybUMHWJXYWevU2eqjrf23ygugTVtX3ajxAG4U6lKOerqLiI4eQkTMbF06byVCNC6L4LWuPyFgHwfMXdpXiOBT0xxBtCwNPrLEkqv7icHiHFXXpaFsexHRkE1E4sfGezk+4b26N+HZV+vIb2LW/43scA8FrqEkhJfOm4mItvmEmkxI5GDhn8DRrRxbnBCBXwPma+X49Pm+3djKsVq9W4R/8ad5f74Fdi4d25IQjQYTE2XPEwPD2asu90jWrRis753v1MnEkvMeRNTkg4R4VwtRiLCReJuMSCcG6//Lv/vlxITXu8DBpWsmJqKnh9IgnlCDSaRSWcoTLtMQExOT0iyuLpttc7lNn4gQLI+oU12ybNvkfXmB0mQQkefgzmwf1qUU7ZTt+0PZJs5edR2yTJM2Pv/ABvm+z1TaNz8x2bIvJT9pImq67H1ei3eplXqum+/On0vtQt985j4FriqdOy2RGK5ubXU5oGECwlqrB82TRYsT0e1PAauN4HMqv0f5bhf9mqLv1jfLfmJjOYnI3K+J3Cit2ULVqn1oo87XEVH4S5EieOm+nU1MyFZ+b9p45rrlNuavuHbpOr1DxPfpm8AljMB6ipgs6wuMW9o3AREV/gClifSOtmXbsR5hh3I7MGnVZRrJco+V30lnElHFXxAifmE3NiPhr79VR2gLOmOdOlt9tLX/VnkBtGnriltpUDQ2EeX4x47eSGcn7n+E4LshsCmx7LoJ2LN03kp5XlNH6BARkbVNwFWUhITSQGoFmr2lV6alKDsF4RtZaRQuLaNqygO9TYlIoIdpadtQm4HESNbvPEJwWJVIQLgYMQHzKPCn0nn7EbY0b1ETUahUtrHy376k323p2NKEYHw7MGcr105OzQaz/vNnbS9isujvRHTTefleHVo6Z1JCfNgt36uJq67DKNS5H5FEdni2C98SYvID1MgjO5+pF4nVA8sAMxO2QH/M4xMSyX4HAoeXrvsDcFS287VbMt/Q/l6Qz1xT1uM6MolYQ5v4R0IUeh+Yruo6lMpVLuNGhGD3P1qKxWPTIIITosoGwNHkRHPVGyG4vQks3rD/H4RIP322Ywdk2/1S3rcHaLB/afzb1G3L75mvaPYlLYuuB2e78DNrA2ryvUvLgIababZxeSrbtkJkWCKPPQmsUnW526hLkbRy31K9LN+bT4DTWvv7A48Dd1Vd/t9w7/5AWLp8RPRB5yO8wR+hlCCSGnwXNZS7PzFueCjvwQnExOtIfdfU4R0iVu1cQKywLFZZTTmS1y5OrBj9klLftQ7bqLS5hAi+WX631ioKfETPPjHpPDy388ngJmJy4lxiQrqOQUGdqk6drT7a6rNVXgBt2rraVhoM9SAErWfIAV5H3ojlls/R0n90O1pJ9ET4Kl5Wl873SNStWGJ5LQ3CKTHT/Hoev7KVaysdqDcM6rrT4IkGbJud7ZtoGRFZW4Ghofx/IYS8NWlpQzOYiA7oXTr3SCLhVW2XlRJCcRPwz4b9yxJi6h20IoIX97fq8rf27BBRuMfks1a0f5Pl/WghgneU7Zfej3wut6RZzC8G+JUP0EtlXIaY/PqQWDFxPhF5V0zQTppt3kDgsNJ145XOqZ0InuW6kIhw35iIKN6NEInfJf0h87y9CQF5IPVbpVOeQJqDEIYGAc82fM+OTbMIvnapHfyZxViFdRkf2Cl/7lFqB5bJtvoZYsD6KSGY9Mz3pglYqOry/8q6bkoMyufN37uV6rto1mmxqsv5C3Xol9+r9wK75HP1MJHf5FyaRfBFiYmN96jhcvNsq54h+mi7lNrhPoQn+9vAwqXzi+M3ATdUXf426tSj4fdWv4uIScxriGj2JkLwf5jmidha9PFK3yX9icmKh4EzcvuUmGBZvMoy/tr6ECthb8jfbwSO5ReSEBNWT/cSllCzVl2PhrKVv4t60ZCThRH0A7ItH+lo/naqT3kydVeiL3oAzZZBUxCJiofkvZuVCLD5FzWcnOiMdeps9dFWr63yAmjT1pW2UkevFzHT/29icF6Ljuio1KX0+0XAvaXf18pO9275+zjA/K18Ti1Eu18qCxGBV4jgZf/iJQih9Q91qQutC91HE0vCHs26lDsX2xNLyG4u160jbIQw/F3xbAHT8XMvuLLFywgHIVVvRBTkKYTQcFrDsWWBb4iEcLVLjkREOM7UsK8Q9AcCKzUcm5hmEfyAhmO1aRN/7XvdVtlr1D6UI4uXI1avfAOcWpST5uXokxIiylv83KO+NveooVzzZHlXKw2WZiYmkC4r7RuHiI48hxpFfrdyj64hLHSeyfa7ibBDmbl0ztjZLjQBa1Rd/lK5GpPz9iG81g8sPWOrZpu3C5nIjhCNlyIi3jvad9JsRAT7E5QmWwlhbE1CMK51nYiAhtcpCXFEMu/z8rtp99L9Wyqf0Vq0b6XyFmL2eESujHfyGSvKvTwhntxGSbwnbGleoZR4um4bIRbvMJLnzpd1XZoaTsRmeboR/ej7y20xIXg1EStHa/l901APy+/P3bPcTxM5GObiFyaLiWCaNYBJqq5HQ7nK4vfR+S59R1jaLV++h1WXdRTqdkW2Z28S1m5PkhPhxKqkg7OunxArle6mZpMTnb1Ona0+2uqxVV4Abdq60padox5EBNcLwJOlY7XvPNDgW05pcEvMuj6dP69Gyfok67wrscx8nKrr8Qt160ssrTqWWMq8cOnYxlmvO/LnpYmlmleXPqfSQSAhMDxARN8XQs8lROTj1dl5bcqO7Pil67bPjvr91CwS8hfquwoRQfgHIsrwi3wWiwi1pQjx6M9VlrONsrf6rBDR0acREZ2NIvgyef9OqLr8DeXqmc/OSbScXJmZEEeasqPaODEzMXBYHt+rPcv8a+4RERF5BBFxsistPTtrPzBvpV5lgXUFmoXV5YvjtBTB7yUsRGpX18bvTkJk/I602CKEhc+zXSgmxVbNZ7YHJY/cum3A4cTAbj5g7Ny3CzHQe4GWiTHHyXtUl9wGsxEi8B9L+8YmxOE3acP7Ou/LLNme3NZ4fzvCBuxETPo9QeQC+EP++ygxQK9VnRrfa2IFxXOl34tVBb2zDo+38Tl1FcHHp1kE/+m5I6yChhCR4GcQibGfIfrotRKJG+q1M7HKYLZfqnvd71GpXI8SuTOKe7Y28COwR/7er6qyjUJdxstnqomWNjsj/NvX7fuVlv2EK4hJsf0IK7tvCEud9Uvn1Kpd+4W6zUOM3+Yi+txLEhNfL9HSEnJKYAEi59NYVZe7K9Wps9VHW322ygugTVtX3GiODmgCFq26PCNZ5rGATYAl8vcxiaWKq+bvOxJZmc/Oeu1G87Lf2XPwUUurg1I5BxDLsN8kBq/fEt51x9A8AFwrv4B/IKwD7qdGS0oJAf8RQgjeNDsQd5LLfIFxiWX/PxL+imURfI+s++RV16Ote9TK/nGyM/R81vlCmsXvwgvuLmCCquvQUO7Cr64PsYz873nvimdpctoWweejhoNzYPXS337y4r4RPqy35f1ZsbHshMB6AKWI1jpthPhdiHav5rt/Bx3QMqihXuXB7dLAY4Rn7HLFcZqFoglK7WTldc2yNSZU/EP+uywRKTQdzQmSrgD65/FliMnANsWjOmyEEHwL4cXek5YTS/8AhvFzO5TK703DM/U54R09RWl/WYzclZYRhhPmvgcI8bhoDzuEsNJwj3akOd/JIKJP8Uhd6kSsROzW8PtE+fPpWd7y5EXRB9og6zRT1XUYyXoWbdj4RH/tHaIPXtyH5YiVIR/k/TmXhmCPum3AQkT/dLM6PEu/sS5FotVPgV1zX2E7WATR9CYijjepurwjWaftsj7XZz32LR3rcPcKOCjbsvny902zXh8QCdrXrnv9+Hl/YUHgPzT3C7oR46WXif7evI3X1G3rbHXqbPXRVt+t8gJo09bZN1qKDOWI6aIDcQMwS9XlHIl6TE8IqS8TQtebRCTTxHl8XCJypgn4V+m62Qmh/IHSoKI2g/RSOXsRFiH3E9FnYxIi5M2EKHR0qfwzEJHhS9MsCtVmoEQsj72ZiBw8irDa6Vs63peYoBhOiODjlY7VLkKflgLJAsDcNCdG60l4S79NLJObLPfPQojhn9bl/SIEg7Idy9iEOPQlzbYG65Y6e4UI/hkNnuB1eub4eQTuUcSExBzFcUKM/E/ej9ZE8Fp1YsttFbEi5FYiymQCIgrlUyIyZb6qy/oL9ZiIEUQ483M7lIeJlSDLlupffv8qH9zm87QasE1p37+A44mI7v9v777Dbbnq+gF/Jr0nJKEjvbfQO4FQjCCdUKQjKE2awo+iIgqKNCkiVUACAekgRUogQKSHIl1KKNIDhFAlwJ3fH9812XP2Pefem5Bkz5m87/PMk7Nn73Oz1pnZM2t9Zs2aK7Tv1Gvad+eVWdwNc3Dqjpi3ZjSKf4pL6lb6D2f0ML6sbUO8IYsQ4lJndvl2oPx7pC42fDk1n/QFRu+NQ/Dx3My3SnVsX5IJhpBL35d1Lwhlbah8qdRDwR+eujNhElNQpAY0PCjJndrr3dq+drf2+g5t33pE2vlo9LuPTIVek/z+ZBvtzHb8HkLw8Ujw3VNtvj2W/50pLUv738vadphcm207dRi+N8th12tTIev9Um3Tvxy9d9V2vPizVZd/e9tltO5cqXbPMCjor5f/Bpthad+ZV6c9yyl1d8vJ7dh+SOoOii8kucuqy7qNOozbMA9LtauPTPKv63x2CFg/lQpgJ9dfnWOd5lYfy7SXlRfAYpnzMmpc79w6GOdeev/BrWH08oxCuqkezFuD53OtwfPxLEaxjkcJ/nfqgUjvSY2S/FTWjnqaWtA1NMYvlwpR77b0/gGpsOSbWWcO8ynVKW2EVvt5j1TwPQSrByx9dgjBf9ka6AdNcd/L2g7fkalw7pftv7dt6/dOdcq/ngr9P5GaJ+5LWXpo6arqkOpcfzl198AwwvYVqZHRN01y/VQA8aNUB3AIwc+X5FltOz5s1XXZ3jZqr/8qNUXD0VmE4F3WhuB/mAkGDEv12DsVzh2V5DFL++I1Wj3enwnOx97K+HupUYKPydLUMxttv9To6Q+k7ny59arrsFF5U2H3j1O3Yw8XKa88+szD2nfmS2nzLaem43lh226TvNtgeZskeVw7pt1q9N5wvn1patT+MUkusuoyL5V/GC28Z2qKnR+278pGI8HHczNfbFT/SZxbx2UZlW3D50lk2w+FW3mdUm3St7fjw5+lBjS8L6P5h1MBxMmpc+uF2rortPPUa5eP+yuuzx5Lr/dJjWJ/a1vuOOx7qbsMhhB8zR0Io9+fRN3G+9zo56Et/UfjY8O29rmpLKPvzu6p9sG9R+/dJdXO3pLkCaP99FLtnHT0FL47G22j9vNeSS649P6lUs+ZWA7BJ1eXbdTx6qm7qa6eOtf+SRb9vyem7nD5UpI7rLqs26nHv7dj3meyuAv7rut87qqpPsaHtnWcn8IytzrNrT6WaS4rL4DFMtdl1FjdN3X1/FOpcOuotNF17f0HZRGCT7JTnrUByZdT86t+fqkeQ4f3wFSwf2RqPsX7ZSKjnloZLtQa2s8ZGnBt/TXbdrjNaN3QIT9/kl+k3Y45paV1EC68tO6eo33v31u9HpQ2PcXoc3umpp74QZae6j6FZalj8bepUPVOSe6Tmhrgt0nuO+x/qZH5j0jy+FSH93yrrsNSfW6eusjy4Va+Vya54ej9nVIh0UlZG4JfMKOAaErL0jYaz4v9wFSo8u5sHYK/o+2Tf7Dq8o/Ke2iWRjC1/WxLKmi9+2j90Im/Rhbz5l931XVYp04HpOYs/2XqYteOhuDDqN03rboO26nf69u56MfD339pf3xEaqqnT2dxUewr2VzPOLhC6vkNxyY5fLT+bO34cack+626nEtlHs73+6dC+v9MTaexJclHs3UI/t52rHhsJna3wagsQ1tgn9RzDt6RCuT+IptsztGl7/pnUm2bz6YFd6Pj20GpNtzwEOPPtu308UxkGpdWhiul7vYYLnTtngrjvpC6S/FjqUDlFWkXxFODNd7X6vM3U6jHNuq3VzvWPT7tuQbD374d2/5z1WXcwXqMBwT9QdtG308byNDee3zqwsRnU3fJPjPVXvpEJjiIZul49dR2fDs5dcfro0fvjUPwR5/Z5TwV9dloqsHhmPCQ1CCoc4ze+5f2Xfpwpnchdnw3zoVTF4yvkxogdPXUufWTSW6/zu9eaWr1mWOd5lYfy+ZYVl4Ai2XOSxZzx74v9QC4hyT5amp03YNGn/uz1jB6W0ZzLk5tSd1efp/WMP1sKtQfPwV812387sobramQ+zOp2/w/luSKo/cuk+okPT9bj5Y+W9tmj1t1Hdap06GpuVLv0l6/OTX/7TAVyL5t3UmpOdyXb2feMxO9lXlUxvO2Rva9RusukeTFqRD8Pqsu43bK32Vxp8EfpEY5fSo1GnwIuce3Xr8/NYXDfbIUbmVCIXi2vkDxuKydF/tBWT8Ev2Rq6qeLr7oOrUx7pS5SPmed9/68HZs/mNE0E1l0CK/e3n/uquuxQd0OSE1z9NucuhD8GlM4Zm9QziEIeWkqbPhh2/eGaU7G++VNUyHlc1PTC11g1eVfqsu2RgoP+9iNUhcpv5gKhB6e5E2p4H+S7YV2XvlMKiS+fSoA+vvRsW85BP98ktdlIiNvN9gO+7Rj9keSvKodM/4vFfBfbdXlPJV1GkZvfjt1keiHqfbBVm241JR3z0jyvFQbdlJT0yS5ZRbTCV6+lfHo1IXj4Vjxt6k23MuzeD7Fwakwb5L73ah+52v73P+07/zTktyyvXev1EX1w1dVvh2sw3hA0Fvb8evLWcyPP36Q4l1TFzT+t23H8fSDk9jn1qnfq1LB/dPa+eZj7Zj98tFnLtG+Q5O8my9rz5vXSo2wXR5c87B2zLtIe32OVvebZ6LthVbO57Xzz0symhIuNX3G8ak7l7cKWKe8zK1Oc6uPZdrLygtgscx1SQU9T0yNCLhEFgHYMOL7TlkbODwqdQV9kiNRlsuV5DZZPwQ/V5Lbrbq865T/GqmRTs/KKPhe+sxTU6Ml75XRvIqpee6+llEAO5UlFSa+vu1Tx6VGOF5l6TP7tE7HSa1u+6yirKexfv+YCrm+lOR6S+9dJIsQ/F5L7628Qzv6zg//HTrjv58KHrZk9FCnrL0j4X3t/alOQzEetfHvrYH68IxuoW/vPSSLEPyQYdtkGxfLVlSf87b/7j0+nrV1j2jb4l+TXHR5H0tdPJty5++A7HgIvnycn0y9lr/TqQfBXSUVpnwvNXpw7/beJIOSoR4ZTVfV1l1wW3VOzXn5ilbP76TaFZdfdV22Ucc7pO54u/aoDnsluXUqiFweCb5/lqYYmdKSGrV6ZGq0+sVG65/Rjg2/P8Vyb7Q/jV5fs51HP5UK7O6dRTg+6Wlclspzq1Qw97IkL0ryjOU6pAah/DzJEaN1k9vv1vu7Z3H31N+mRuD/OhU8Pi918WJ4eOQk6rBBvfZoZX9XO3bvm7oT7qOpaanuuvT5A5deT2qfG5XriFb+G2cR1J+jHRt+mORJo89eoq2fxHNpNqjPkamLD79NBfkPGL13WOrOis8keXpqcM0PMpHBDBvU55KpC11bkryxrdtl9L0fAtbjlvfBqS5zq9Pc6mOZ/rLyAlgsc1iSnH2D9e/I2gdC3qU1Kh7VXu+d0e07WXQUJxmCj8vYfh5C8P9OcrvUQ+L+KzUn6WQa4qm5cD+RmvZkv9H65YDybK1T8cvUSPDbp0ZFfbSdeKfaAL94a4BvSfLY5fq1n4cQ/AdJHpAWFE1tWf4bp0bYDmHwg7J1cHSRLG4tvduZVc4dqMcBSe6Zxcjnfdp345rt9WGpEPyTSW4++r1xCP7cTDjIG5Xx66kRQ/st73ft9Z+nRq99PMllV13mpbKNj2ddkqe0fWl5OpS/zjZC8PbzJLbVesfe1HQGOxSCT3FZ57gwfhjkbqkpkYYQfBgJvndqNOGFz6xy7kA9dmrnyocN2yA1kvi52WAeyyzaBbulQqPzZGk6q6ktqWnQ/i/tglgWwdDeqYuaW9pxfXmE4VTPsbun2jmPy6K98EepIPKR7fWk5yHNth8OuUdqOo0hBB+Ch/Om2kDnOLPK+TvU77apQQ5bkvzzaP34WPHfWQQs4xGvk9jvsvahnNdKcsMkV136zAVSF1zem7ozaUtqKpGLndnlPZV1OzR1UezmS3/7G6Ta1ydl7cWJ8XabTH9inXo9KnVRcjiej5+J9LbUnTtnH31+ahf/x9viyalBNPdoy3vbvvVXo8/cO8k7U4OC3pfkcquuww7U8capZx6cnHa3RCpgHY7l12r737FTP7fOtU5zq49l2svKC2CxbPalNUZ/nuTPR+t2SgVeH0p7gnHq9ustWXSWdkuNOH7Q1Bp6WQpysnWgtRyCfyLVEfxWqoOx6/LnVlSPITi4Zerq8rV34HcOaI3AE7N4iNpbM835B4f6XT91q+h7WpnvPvrMuHG7T6rD9PVMfN7S1Mjh86VG3l05NRLl+NYIWh7FdvHUFCmXWkVZNyj/eVtD7bOp0U5fTt3hce7RZw5PTQnw4awNwZcf6DWJYHWdOl60fffH+9vvtePai7L2gU+Pap+9wKrLvZ06XSUVSG714J3Uwz23pC6OXXLVZd2g/EPne+g07Dt6b//U6LNNFYIvHcMekxoJfVzqosS12vo9sngY5pNTd+08JxVMTGqfS43i/Fkr51tbGa+8A7+38rbBqajjoe27cufRumHfPCQ1r+eWjKYImNIy/h4l2TX1HJCfpl1kTV1YGbfn9mz75c1XUd4dqM8QaO+TumvqmNSDBf90OJa1Onw6dSHpYanR4R9OtSsmOyhjqZ5/mOQnqRGs1x6t3ykVKv9Xktevupzb2Ub7tm3zlVSg/3+pi5eXXfr8fqmphZ7Y6vwnQ11XXZcN6nfL9p25cHs9fmj7ndp7P8jEH6S4Tr0elpp28BKjdUN/4bqtXpN7Rsg69TgodbfbnbNoP1yuHddOTPKY0WcPTgX8+6+63Et12NZdKzds36v/S3Kdtm4csF49o8ENU1nmVqe51ceyOZeVF8Bi2exLap7Bl7cG0P2X3nt2amTug1PzLI6voh+Smuv3Eauuwwb12jvJ07fx/jgEv1aqI/WwTHCuvtS8qV84lb9zmVQYdqksgubJ1GmprLumOraXT/La1uC+x3hbZfGQ0l0zuvV8KkvWhlz3SoVC4wtDV07ddvnFrB+CT25UTZLLpub8/2kqSBhGQ3ajfWqYE/zDWZp6Y2pLth6Fe6HUnKQPTV04uk+r62dSt9T/NslfjD5/tjOzvL9DPQ9JzQu7Xgj+lxkFX1Nasjbkem7qAswHUiNuD27v7Z26dXlTheCt7K9t35VXJvmPVED01bQHF6cCrtekwuUfpsLw7QbLK6rLC1OdvJ8kudGqy3M6161Lcu5UuH98khssvXeX1MWKay4fU1a9ZG27Zp9U+DPMGX1sKjge7uT7y9Fnr55qz01umrRRGfdq58/PpKYNenfqbrc3ZfEQ2T1T800Pc+t/JBMZ0HAq6nmLVq+3pYUobf1F2zHj6asu4zbKvmfqQvF7UiMir5saibsldVF5OI4vt3/ekuTjqy7/BnUa2joXTgXcT8timp2hXXpQO5Z/sO2fV1lFWU9j/W7Wts8Ts3akd5eaCuqETHiKkFbWx7c6fCvtweyj7Xbp1DR3Pxof86a2ZG0f4srt+3PprL2r8gZtH1s3YJ3aMrc6za0+ls27rLwAFsscltSox+HhJvcZrb9MakTNliT/MFp/SDvAvzcT6wCOyjiMWL/BNj6zbodoanVqDe7jxyfZdT4zXGG+TZam2Ri/P7VlnY7QFbIU3qU6vi9L8verLu9SWYdyDfMvDwHeQ1KdwJ2zNpC4UhYh+DU22v+msqQCuW+2bfGZrH3o6k6jDsbhqVH5X80O3KWw6iUVeF+6bb8Xpzp4X0+N6vyH1N0tF2x1fsaqy3sq6jXe17YVgt8jE7sYNtqXhgf1fSgVsr4oNXrrQ1mEXAck+afURdu/29ZxcSpLOyb8b/veD7eaDw+P/seMApXU1FX3yISmPmllG3f+nt+2y09SFyQOausneZ45jfW9Rar989VUaHzBJNdLjcJ94WifXXl7IaORjFlcxH9Qks+M1j8gi6nG/nrYXqn5Sz+Qun175XVZqtcp054kuV9q/uULZHGufVA7dr8xbQRr+w7doX2Pdh7/O5tlSc01/8vUNGPPSQ1GeV/qwuzk6jL6Ltw9ddfYuK1wu7bPPXid3xvarY9ux/2VT1Wz0XcgFe6/NjV1xj1Hx+ydUyM/39Lqf1Im/mDzder27NRdsH+ddsdR6i7Af0tNc3fwqsu4nfLfph0bTk67aydrB6BcKtVW35LRoIapLOucW7+cCuy3pKa1O2T0/hCw/iTJYasu+1mlTnOrj2VzLysvgMWymZcsRi/slORGqUB7S5J7tvW7pDp+/50aTfOE1Gjxj6Vu4Z7ctBqjul041dl7Qns96bBxgzoMnYoHpMKeI7bz+X3aCfchqy77BuVbHoE7blAsh3fDSPAXtZ9/meRqq67DUvnvlLrK/4Ek5xqtf1SSt2zwO1dKheMnTK0+65R1v1SIcMvUyLNPZDQidWmb3TzJG6Z4LFiq02EZ3e2Sush3zyT3TXLo6HNnb9t1ciOlt1O/jULwu6zz2UkFKe089K+pkarjB/UNo7vG0+wc2I4Nx26GY3sqMH1D2vRAWYwmPDKLQHxyd7ZsUJenpcKESyd5SWsbPCMbjO6c4rL0PVm+CLt8XHtz2/9+nbpj5KOZ0Kji1J1T709y+6X1f5kKr4aQca8sptV5b6vb41N373wii7B5Usfw1F0fT2vflee1dePngzw4dR7+4w1+f1L1ORX1vkUqTN3Sjh/3zwTvUFwq8+NTbYXheRp3zNqpdg5KG6E7+p2dUg9C/3xGz7hZUfmHCyZ7p6bTeFoqwB+mPTmolfNr7Zh3qdTc7R9M8pr2meOTvGjV22IH6zv+Hg3Pohke+n1cKuA7ZNXlXCrzRgOXbpbFXOyHtHW7jN6/XKvjJc7I8v2OdXtZ27dunbro99bUNEIvzeih0al27OfbsXzPKZyHzip1mlt9LJtzWXkBLJbNumTRKdo/dVvse1Mduy1teUB7f9fUVAjPaZ97XSrgm0xDfPnEMqrb36ZChkuvuoy/Y/3OnQoZ3pbkguvVu52Ib5bqzF5j1WXeaH9rPz8zyTm3tR1TweQzUlfZP5gJPqgm1XF7YGoE2keyGAn+/CRvbz+f8iTw9nrnJFdLjWi9yJld5u3UZ6ORT7ukpm05PhWojEPw82Rp1PdG/85U6tQasN/KBqOaUiM9X5Aa/T6pUbg7WOflEHyYE/zwVZdte+Vu56Cnjo7hf5SafusR7fXeo8/vN/rcZDsX7Tt/TJL/bK8vnAoWXjnUJ/Vgrv+X9gDMKS1Lx69/TN0pcb3RuiOzCMEPbOv2anWa1ENjx/XJ2nBk+Tkh4/PVOdvx7z6pkYaTGlWceobGT9s55VZL2+qY9vMwWnWvds46NnUR9v2p8/Fk2nPr1O8qWbRLXzpaP95+xyZ5b/t5kseC9cq1vbImuWmr93i6msmcX9cp7+OTfLf9fIusDb+7tu+9O206tbb+aqkpU654Zpd3gzrskxrF/o3UAxR/lboIdtX2/kGpc+r3W/1+nLorZPdUe+j4jJ6ptOK6rNf+2XCfS/InqQvLb08942FSYfHSuWjfJOdIcsBo3U1TA7Z+nBZGptqvpzyIedV12Ebd7pNqXw8Pmn9E6sLei9t+9rK0h9K39w/NqD84xWVudZpbfSybd1l5ASyWzby0BtsHW+Pz6m3djVPzpZ0Sgo8+v9fS65U3xLMYxb5zWniQRShyw9Q8qvebSnlPQ/2GzvY9W0P8qCw9RCPVsbhIqjP7xkzsNvSsDeSemW3MG7v02V1SnY3Jzb082se61Ai0IQTfLcljk7xruT7t9dnbfyc1bUMWAcieqXm9H9i+Pwe09bsmuXZqdNdx7b1DUiMH37nq8m9rG7Wfxw/vPF/qwspr1vmdP0nNz/ytcUN2Csu2jl/r7Gfj79FVUlO7TCrcWq5P2y4/SvLA9vouWRug7J66A2l5SpfJHO/W2Q47teXFqVDlZlmE3/u3z1yg7XN/n2l30C+Umnf9vuvsY0emLjY/tx0nntNeX2jV5V5v+6QeOvqGrJ3jf92HZS9v07ZuEm2JURkPSwV2H0tyu7bu2UnesI3fPUeWLs6uuj7bqN+12jH5hxm1HbJ4yOfb0y46T2VZ+n6cMro+dY7d8OLLOv/O9Sd47F63zO27/71UILwlyUNH7102dSHwX5b+NjtlFGKuqD7j7fGwtj9dpr3+i1R74b1Z9JP2bMftm6Xu6utaPV6SGvG58gvnS9/tW2cbD7Jc59h3yhR3U1mW6vPU1EWvH6X6PQ8YvXeTLELwyy1v36kuSe6W5Knt5/sn+XmSO7bXT0kNBHh+Ntf88rOq09zqY9m8y8oLYLFs5qU1SH+U5O5L6y+e6qBv9TDCVZd5VO47jl7vk5of8cWtAT5uzP5Ham7ByYQkp7HOZ0vyyNRUIO9NPWjxoCTnT813/sGM5oecSn2XOjrnT90udqvsQNAzlf1tG+VbDsG/lQog3jv6+XOp2+C+lArJP5zpPXl+uMiybyvfZ1NBw+dSI5/O2d4fQvAvtmPDt1sdJ/UAz3Xq9/rUQ9/ulMUD4R6WGql1m9HnLp0KWF+WiY58yuLW7GckOSKjOymWvy/rfX8ysY5gakTqvUav35y6qPIn2fpBfVdNdXrvf2aXcwfq0WUbYWJqmophOoM3ZDGFxjlSt2V/ORO7I2Sp/I9uZf9JWsC6vD+1epyUmhv820mutOpyL9VhHEIempqT/QtZ28aZxHnzVNRp/EDiG2YRgt8wNTf7u1LtuQukLmBcrP183ST7jv+dVddl2DbbeO+6qfbqB5LceLT+ou2c9JxVl3+pvMPURsPo+31Scyp/MPVw0ruMPrvdiw9TOXaPvke7py68XDWLB2Tvn7oIdlJqQMAuqYtN10y1LT48+v1T9t0pLKlz62Pb92Z58M/9Uxf/35s2Enzp/bumHmD8/UzgwvnSuejIVCD8N5lY2/M01u3VqbvznpG6y+Uj7dz01NFn/jA1UGNL2oWMqS6j4/eBqan3zpl69sQjR8eQP0gNgNqSOs9O9kL5HOs0t/pYNv+yS4DfxYGpB4p9I0m6rtu97/tf9X3/xa7rXpSa++5fu647qO/7p/Z936+wrOm6rks1Up+T5GJd13V9378iddL5TpJLpBqor++67ti+75+Zmsrg6amQ+KXtd1Zaj9Oi7/sTu657TuqBXE9L/Q2elOqEfCUVsN6x7/vfdF23S9/3v1ldaReGv3XXdS9MzZe4e5JP9H1/8va2xRS3U9d1O/V9vyVJRv/tu657ZmrUzL1Scw2+JBUk75UaFbBXap64t/R9f9Iqyr6Rvu9/23XdXqnRWSelLoh9oeu6T6dG07yu67rb9n3/3a7rPpgajfcnSX6WCh0mtc+tY59UZ/tJSd7Vdd3bUyOI7pzkXl3XvbHv+9/2ff+5rusemuQXfd//dIXl3UrbRvukAoSzZTH9zqu6rntS3/cfb/vhKd+p9b4/U9lGo3I+M8nvd133hr7vf5ia7/8fUg9l/vu+7/++67qdUsf2Z6RG3DxvVeVe1nXdzm3f6VOBfbque3ySy3Zdd7Yk70jyxr7vP9V13X1So3LPkeRPu67bI/VQxWunHtb8ldXUYoccl7o4cZPUFC7DsfA3o7/Bvbuue13qWHdc3/dfW11x12pl/E3XdfumLkCckPoOXTzJE7uu27Xv+xf0fb9lfIyfuuE7375P7+q67p6pgQBPSHKuLKa42yN17t011V76SOpuv1P+nTO98EuG/ajruuEiX5J8o+/7f02Svu+P7bru1qkLmq/uuu5tqXPqxVK3oT+w/Tsrb+N1XXepJO/ruu7mfd9/qOu6/VIjVX+baqtdLclhXdddqO/7x7V679z3/W83+jencOxuf9vhe/T21J2H+yT5n67rHtT3/X91XfeY1HfrZqkLez9P1fvE1Oj932yvrityvyR/ldqX7pgkXdft2ff9L/u+f3Z1P/LQJE/quu5Rfd9/aPS7faqdd72+7z9/Jpd7K8Pftuu6V6T2tYck+dBy23MzHeuSpOu6O6UuhN0hyfvbvnSRVLv7kV3Xfb/v+yf2ff+Wrut2Tt2xtPLvzdjyvj9qr/2ovX/11Pfq033f/6J9bO/Ucf2DqXPryWduqbdtbnWaW32Yn+GKDHAqDB2Erut2S41O/Uzf97ds7+3e9/2vuq7bNXUg71Kjjq+76k7FoOu6G6fm9z4wyT/0fX9kW3+u1IiUP05yjVTdPpVqLL247/sHrabE2zfq/A3bZsOGadd1B6VCkwulQsiPJ/nv1nmfRBA57oR2Xbd7kgelnjC/Z5Kb9X3/9lWW77QYN4q6rjt/quP3jb7vf9bW7ZTqIN0ndfv/rfu+/96qyrujWrkfk/rO3Lvv+292XffKJNdJ3Qly9ySfSY38/P46vz+Zzux6Zem67oapEVwnpjrjt0ryltRIyRekpnR4/plc1B0yPi6kAp7DU/vYCan5Ll+aejbAY/q+P679zsoDoB3Vdd11UgHdn/V9/7y2Lz4hNVr/B6kLFVdM7Yu7pUbfTSJA6bpuz9TdAu/o+/55bd1rU6OLj0uFjxdP3Yp9177vP9h13fVSt8run3qo4sdT57CVhyaDpWP3+Jh3WOoYft0kN+n7/ujhPDWF7bE97YLDB1Lb49Gpi8lXSu1je6S2w7+2z046GNrW37vrusNTx7V9U6NSX54KIPdKjU77ZZIPjNsbZ1Kxt6uF3x9JbY/9U4H9+/q+v/noM9dNTdN37tRo44/1ff/C9t5U2j83SN0Kf2DqAfO/TY1WfWDf91/uuu7iqRG5N03yjL7vH9t+b7L73ehctEvq/LlbanDJRVPH60ul2nbv7rpu/9SdmjdKBcpfSl0I/O1UttGy1n94eKqtcGzf97/f1u/e9/2v2s/3TR2/X9L3/QOWjpV7jcKwleu67ojUse0eqfr8pvUdLp6acuY/2+cmv8+NXj8m1b6+Qt/3J4zWnz91Mf3KqYeZf7Wt37vv+5+fycXe0NL59G6p5818Jskn+74/vq2/auq4/drUHQl7pgZv7NX3/e1WUOxtmlud5lYfZqqfwDD0s/qSjR+ctqluJd3Bum7KOmVx+/xw0WiYumHP1IiH7yd5ytLvXCc1N/g1R7+38tsVs7h98nqpzuxnsvWcsGdLjQp6eWoE3vAApeuvuvzbqdueqSlCNrwVfqPv23i7rnpZrxypzuy9UyO2XpnRnMybYcnaW0qfkbr97cTUrZi3T3KOoe6pgPJ/2/55vlWXfUe2V+oBfPdvr5+besr5Jdvro9r35z1pD/qc+pKa8/J87eeDUyPyX5yahudqqYtjn2774wmZ2HQNS3XZKzVly6tSHfTxtEK3btvmrdlk8w62/W7PVEfiuLT5otv6eyR5U2oqjWNSocOkHtSX5JLtGPDp1C3wh6RGeV49iylObp8K9L6Tdmt8Ktw7W2rE0KSmD1o+v2TpNt7UfMTHJjk5NWp9MuedHajb9VNtnZtn7fMBrt6OB99Lcs/x/rnqMm9Qj+F7sFdq6q1npi4a3TCL56DcKHVn37FJ/mBHtvWK6zS0MR+auqB34dRULX+Zusj/7qXPXyc1Tdeb0+ZlntrStsfHUtMGvSDJs5bev3jqAtqPkjx2qvtd1k6zs3vqzoKXpj0Irq0fHxcO28a/NYl9bun7Pz6fnj3JP6UuEo0fuLr76OdbZ217cOX9og3q+PAkx4+222GpCxHfSbUZXrHqMp6KbXTn1AW9v2nbZv+2fjwF1x1avSbblhuV9ZXte39CK/M7U8F9sphL/nupdvhx7bOXW3W5z0p1mlt9LPNaVl6As/qy1Ai4UarTeqckF1h12U6Huu3ZTqiPaP+9QFs/ycbO9rZRqrP9T6kw6x+TXKytP19qPsLvpwKHP0hNb/ChVEdkCMtX3ijPouO3T2ok5FvbSecbSW6/Tp271FPZ/zQ1mvDvplKXDer3B6nb9YaHWG1Yzs2wH6aeIv/40et9U6NrTk7NkXbOVZXtVNZj3EH699TIwXul5oz+fGq+74ckOXjYbqkR7z9LcnQm0ulbr06j7/fu7Zh3mVa/O2YR4t0oFfT/PMnzV132HajbPdrf/tNJbtjWXSQ1vcvwQMVdUuHKx1IjQicR7Kemlzlsad3fpRrg30zy+23drqNtOITgb0pyrVXXYYN6bevC3d1T0zLcdJ33zr6j/86ZXJ/hb3+F1DzSH2/HtGHk97jzfvPUfPMvTo2anOSxO2vbc49qx7rPJvmzjB68nLr4PIRd12/rJnlOXarfTdr35Irt9S6j7XjT9t4XM5qTfmrL6Hi9b+ri/5fbvnViqg33zCT7tM8cnsXDme+86rJvUJ9dll7/aepuluH1/lmcS5dD8MNavY9Jcp1V12V5G422wbFt33p2Wzc+dl88FSafkOSfVl32pXqcN4ugsUvNnf/e1EXjT2VpEEPqzpBj27H8lKBo1fXYaJ9r2+G8w/Fg9P45UtMM/ihrQ/A9lj43iXPRcllG9btZqs32ilS/75ftHHST1IM9t2QbFytWXJ9xm/u1qfPrIak+0o9TU0AOF/uG/t7tUhfML7Xq8m9n+9ws1W+4Yaove+/Uc6o+kvZsg9S56ZGpZwY8LxN7Js0c6zS3+ljmvay8AGflJWsbea9oJ55fpDpFP011avdYVfl+x7rtmwpPvphqYH8vddX88FWX7TTWZ+92MP9MW76bCu2u1N4/b+qW4C+1RtEPU52KIQBbeSM2iw7DPm27vCM1wvuZqVssv5xRJy9bd6yenuog7ntmlfk01vODSd676nKcDvU4VyoM+lqS/zdav0+SB2QRgp9j1WU9FXX6f+24cPX2+uFt33t/++9DsnYk+P3SLjRNZcl2Om2pByv+Kq0T0erxsNSDh26/vd9fUZ3We9jjHZO8sR3Pnpeal/0W7TgxjFztUgHEeVZdh1aevVOjTJ6yznvPbnU5OosHju08Oi7eqr2/1e9OZUmNWH15aoqQ5fDkmCQfTRtxPKrXZEfaZRFGXinVOTopdav58P6uo59fmLpQu/uZWcZTW5f286vaOfZFqZH3v06NYL3S6DPXa9tsS2p6tJXXYRt1G/aly7Y2wJOT7NfWDWHR+VPtvE+1/fBqqy73NuqzazsO/Fdq2ol9UiNXX9Pq8Iy0tneSG6TOtS9adbnXqcd4cMbjkjwr9YyDhy99bt9UCP7TJEcvvXdo2wffkgn0N0b70/i49YepYPgXaRcos/biy8Vb+d82lWNcagT+19u+tH9bt1eSe6YeqPijUV3Gx47rpu4U25IJPAxyG/vcPqm55L+YGqH/4bYvDQ+0G4fg/7bqcu9IndrP90m1BfZvx4RHpNqsL0s922X43C1SF86mGBaP96dztu/OYe31Lqm84XvtOD6E4OdItS0+meTAVddhG3V7QOqunX/M2tHrd0r10T+a0QN+l7fvFJe51Wlu9bHMc1l5ASyndMy/kXbLeWokzatSje77tM+sPEA9FfXZPTXK4ejUfGK7pa48f701hi6w6jLuYD3GB+4HpB5Yc9FU6HPr1MjHnya5cvvMnq1xcY3Urd07Lf87q15SYdwLU2HDhUdl/P1Ux/XLWTsSfKcsOhn3Sk1LceFV12Mo29Lr4WLDMPftzVddxt+hbsPf/BKpUfpfSxt529YPIfjPU7eZnX0V5TyVdRqmC/qr9vohqdD79kn2S/Lutt0ekORcqy7vBnUYOud7p26Zf0XqgXA3TxuNnwqJfpXk79t2ulI7Hj529O9MprG3XJa0DlH7ede2PY5PdXRfkbpl/kmZ6IWwJL/X/rtXar7r8XvPTYWszxptr3EIfuiUjtfr1O0W7Vjw01Sn9o+yCFfukrrwesv2ehJB0A7UaQhUDkl1jrYkefDo/eE791epEfyTvuCXmjP2S1lc5LtvFlOIvTqj0ZKphyi+NRMLUbZ1fEqFxD9M3VI/hOA7ZfG8g1umRko+aNX12EYdLpV2F9LS+r1SoyW/lzYCt62/8pSO2UtlHgZnfCf1MO/fpEKsQ5Y+t0/qToQtSf65rRuOe9fOhEbftbL+XUYBcGrk6nFZ2+Yeh+C/l0V7dhLHviT/mRoo84QkZ2vr9ktdXP5W6gLM2dv6cWh5o1S/cFLnoqwdRPO5VJvttkmukrrI97FU32gcqj617XN/veryb1Cn8d/9Zam2zl8mOWh4P9V23W/0ubOnLmh+KO2uxSkuqXbO61IDTA4erR8uXpyYOle9udXlxOXjxpSWtm/9OtXveVRbt8fo/SFg/WBG01ZN5XhwVqjT3Opjme+y8gKc1ZfUyOEvZut5Sc+Tmh/pl0kuu+pynso6XSnVubh52mitVEf9V0ke0V5PqqG6jbrsnbqK+aThYD5678ZZhODjTu14O07qwkVryL0vyauHso62xeGpiy5fS/JHS7+3c5IjU432g1Zdj1G5dk27XXm07gKtnM9cdflORT2WQ8idswiGLpEa2fS14fvT1u+dGln8/WyC+cDbvnZIqlN0qVRH4wFZjFh9eBZB0f0n+N0ZOn/7pi4gfTw12viDqZFpL09N6bJLKvz+bWpk1A/acWJSndnl/a79/V/Zjg+PT015MtT5GkmemMXcl7/M0m3Pq16WjrtdatT6Vrcop0bl/ijJv2QRgu+y9PuT2FbL58fRMeG+Sf6j1e/Y1Ly/+6fOu0euutzbqdO2pqW6XPtufSnt4n9bf7ZUUPyh5eP9lJbUhe+j0+6kat+pX6cCrfu37XVUWoDXPrPnKsq6jTqMRxU/IhWiHJE2hUv7bn2ofYf+KTX/9xGpEaBvap/5XEZTH6x6yeIiyhDMXbltizut85mzpy6SDdO9jY8LkwjBR9toGIzx5tSDxi6Qmr7q5NTFlksv/d5+bVtN4vi23jZqP9++bZ9nZ9T/SbVRlweeLN+puPJ2Q9aeV1+ZumP0iVmE4PukQvDvp863p9z1tq2/yxSWVJv7Zak7SIcp616VanN/IYvBXHu3986duvtgUvVYp14vTbWxD8s2Qu3U9CcvSYXFl191ubdRzgunnqFzQpIPjtYPffI9U9MlvTp1J9KzMqGLYBvUab9U2/T7qYt8w3Fwt9Fn7pi6gPnutLsRprzMrU5zq49lvsvKC3BWX1KjBbdk0WE65fbl1IPGfpjkHzLxoHipTn/U6nRge33n9nq4Grhvq9OkR3K1st4ii1BuCO+X523/aGpOtcne8rtUp/9IzcM1nJh2ziIEf3Y7MZ2QtVdnL5ka+bXy0CuLjuouqVtJj0kFwbuOGnePToWPk3+Yy1LdnpTkeu3nnUbb6OJZjJD+89Hn907rVE1pyQZBwWg/O6LV5Qqj9x6cml/xyVnquE9lafvca1Ijty4y2t/e1o4Bw/zSB7Vjw+NSIf+kHj7YyjIOdl6TGl37jiTvSgUo/50a3TVss71TAf/7UwH4hg+ancKSCube3rbLDZfe+9d2bv3nTGT6lnXKP3z3h7///kvv75V6cNpbWh0/nOQT7Vx1g1WXf1t1aj9fNXUReXnbXCEVpPw6FR79W2ou7R9nwoFDK/seqfbP2VN38v0wFUjulLoT7phUePemKdclFdB9th0Tvt32qTdk7ajoIdjb0o7l/5U6B58zNRL5Easq/1Jdhot4+6UurNw+daHlxFToddDS5/dPBWFPWnXZt1OvvZI8v22XJy6990ft+/PabHAundi5aDjW7duOzY9v5T851Sa49Oizv59Fm3uqz2sYh/kXaN+l41MjwQ9o6/dJPRvpe1kbgk+6r5cKtJ+T5Ij2+uWp0PsyqcEa30y1HW6drQenTGafWyrXjVID0W48WneOJH+cmoZvuJvnT9ox/CPZBAPTklwziwvl437Dbut8dlL7XTbuQ+yT5LGpZxq8NYs2+DhgvW0m2D6dW53mVh/LWWtZeQHO6ks7UHw7o1FbSweJryR5warLeSrrdMXUiMibt4PcliSPbu8NI1benqXb06e4pDpDd0tN/fGRrDNSI/WQh29kaW7FVS8bNWhSnYufpy5MjPe1XVIjOd7SGrjLo5JXPvIuizDooNQt/7dPjX76ceq2qqenRj5cOzWdy4Pb5ycxcms7dbtKqvPwlSTXGOqbRefwsqlQ/2tJHrfq8m6jHuOQ68apTsQtklx8tP4uqQDiju31gamQ68mrLv869RkHxedLPQPggaN1R6RGez+8vd4968ylOtV9MDUv+7dSI5+GoP5Kbd3ns+j8jR/2OamHry5to/HPV06NyD0pWwetz2/npr9YdfnXqc94qp3npqbQ+XLqItF5lj57QCo0flWr5wemuK8tHRdelJpX9aRU++clWTv1zuVTo4h/mwq7/igTmXprVMZ1R5pmMXf0P6cujO0zeu91qTtHfpLkfKuuwza2z/1T0zcMzzH441TI/c60h3a29Zdqx/grZxE0H5m6U2TlnduMLiKl2gYfSHLJtu6xqYD1gRmF4Klw/KtJ7rfq8m9rn0ty1/b9OSHJA4b6jrbDcNflqzLhaQ1G9dmrfeePSbUPbpO6S2dLOz6MQ/Abp6ZUnFSbe7yNUmH+J9t3/iupPsRvs3Y6lH1Sbdhvp861kxvMsE79dk8Nztq17WPHp9oOQ71f3bbZzzK6YDblJTWq+yepds8uqX7rd1Lt8Z+lwvGLp+6yuEkmdsdltnG+T92997bUhdf7jn8nEwu916tPK//hqQuYwwDBYYqk76X6q8P0l5N8Psgc6zS3+ljOesvKC3BWWTY6QaU6uP/UTr7LD665cKqT+P/a66merPZKPThkuOXtgqkRAF9NNcBPKX9rRPxXatTAym9V3MFtdLbUKK4fp65m7t/WjzsjV9lWI2QFdRmPkj5325fOMXr/uFSD/C5ZNFwvkXr4zu+P/yZT2e+y6Njtnhoh+OlWvv1b/V6Umsf8F6nb+f4vdbvspjnhpjpDx6U6Fdccb8v284dSF1s+kQlNRTMq3/g78cpUB+LE1O3y309yh/begakg+RupUOWd7TOTnAs3iwc7XTQVOty1vb5T1t7dsnfq7pZrrrrsO7KN2uvXpkZ+n3JHSPvvJVPnpRds9LtTWLI25NopW486u0o2DsEfmYmNSsvaAOVzqfD771J3E2xJPVhs3XAx1REZfn8y56OlMh7Zvvd/mJoC7nWtXq/LaF75VBD5/VQgtt+qyrutfa79/HtJLpQ29/xo/StTodawPc6fGo13kUz0tt9UW+5xqSkOHpa1F5Pu2rbHOzIKwUfv3z11Mfp7mdDD+1K3+t861Xa7WxYd9P1So4t/nRoJfvdUO/a4VLthUt+fjNo/o3UPSp03/3f4m6fa2cNn79i+W3+/6vLvQL3+PBVqX37p/Ye1Orw4yeVG6682tW00KttuqX7O+1MDF87elren2kPjkeB7p/oXb55afTYqz+iY9tRUX2/v0Xv/2s5X/zy1+oz3t3H9Us/++ETqYuu7s5gi7QKpNvlPsrhLexL9ofW2UTtGPzY1Bd8dRuuvncWdYuMQfHLtuaW6HdW+L1tSfYn7ZzEIbd+2n30/9bD2rUa1T3GZW53mVh/LWWdZeQHOCsvSCeqPU53ue40aQJdLBUAnpJ4YfqHUqOIXtnUrH0mznfo9I3V1+TFZhOA3TnUsvpEaJbBv6qr6B1MjoIaAdhIn4FF59myNiEelbn0bHlCzV6qRelI2CMGXt/Wq97f2N39zKiw+OaORq6nblI9LhcWfaCenr6ZGrAx/i8k09LI25Lp1akTDpdf5+x+YGiX51ixu3b7HVOuzwXt3bNvkK0muPVp/wdQo6RtmYqMH16nDs9r+dNPUraSXSwVZW7II9s+VGi10bNtPL7Pqci/VYTzn97faMftsqZFBT0lyq1afR48+e93UVBS3XXX5t7ffDeeV1KjId2Xt8wCG0RpPa9+j803p+7Ncn9RokyNbXb6dOhddfvS5IQT/cdaZHiTTC8F3b8ewd2Yxz+orU3fubEmFDBcZfX7XjbbzlJb2HfrvtJGBqbnL/y81X/vPUhdjxg8bOyQTa/8sfYeel2rP/Kwd7x6VFuKnHvL7nVQ77p6pUPlbmfBDwNux7JepDutN2rrxA6zumgq435atLybdtB3PV34RM2tDrte378zXsxj9Pb7I9DepdvZvUiM93zw6/k3qe5Qa0HBcRlOepKbY+kbqQtnlh/pncU660ZSOb6lpGW66fD5JXXj5fhajo8ffs+em2rDPXd6/Vr2Nss7DoFN35Hw7Sw9Zbe+9I4uR4MM0keO7Xyaxz2UxbcEeqYuVd0i12caDa56eaqcO87FfItXHu93U6rNeWdY5th2Z6s/eerT+4u24cMtVl3+d+ixPY/f91HnoF+04/p+j94cQ/IQkD1112be3fVJ3Jn4p1R86PHWB/NdJ/jqLZ7cMx+/fJnnlqst/VqjT3OpjOesuKy/AWWlJ3Yr4ndTV5R+nOk7nau9dMTXC4SepxvoJqYdBXWHV5d5GffZJXe17Uapj/sN24NunvX/j1Ijc77WT8WdTI6Am1bnI2s7Qp1Oj7r7fyv3d1K2YXRYh+ImpOTwne7tialTJ51OB0MNSo2ve0/atfxh97m/aSeudGT11firbZqlOe6Y6p69K8prR+lM6e6N150zdRv/lJK9fddmXyjZuQNw+FTo8IsltltZ/rO1/R6Q6sf/ajgn7r7oO26nfudv3/iFZPHTswu2497K2HU+ZTmPYtqsu93rbKHWHwQtS4elV2rq/SHXGt2TtVCiXTI34eksmcmFvG/V7Szsed6nRQienzZeftXccPKV9hya1fVrZhn1on9TFvWNTIyIflzofvSajabZS0zS8o223K6+izDtSn/bzoakpKIaLRa9KjfK8TDue/zbJMzOaVmhKS+pc+oKsvetot1QAPkwVdL9Uu+CI1IWl52Qx3cGkRnxvUMeXt21y/9RDSf++lf8FWbRxnpcKvU9q37dDVl3upTosB0K7pwKgLakRrPu39buOPjM80+Vp6/x7W039dCbWZdel/w4jvfdOhXJb2ndmuJtnHB5dJNVeuFgWwfFkQuNROQ9OzRf9H1l7t8SDU1OjvS+LEHx5cMDK69P2r7dnac7y9t7DUm3ra4zWDefh4blCv049lH635fqtqD7XSJ3zL7S0/tKpEPJP19k/z5UaKfnFVKC/VYC+wvpcNmvD6wNSg2J+0P7+x6fadcN36NqpwU+fSvWJ/js1inqK/Ydxu/vR7Tv0sbYNLjF6bzwt5HlS7e7PJznvquuwjbo9MXUuOjTVT90/NfL2Z0nePfrc1dpx/Wtt205mUMPS8fi6qbsL7r/0maNSbZ9xwLpf6sLz5NpCc6vT3OpjOWsvKy/AnJelE+7dUh2gw1Kj6e6XulL79bT5xFIjJS+aClkPzcTmGVuq2xCwvrsd6P4sFRz/NHUL1hCCXzA1+vNWqdEBQ2ix8sb4Un12T41QPTo1emP/VvbjW8NiGC25T+pW2S1JnrLqcm+jPo9JhaUXHa27SDthbUmbM3Jc/9HPK9026zXKUkHd2VJB15bUKNutQrksOq9Dx+lOqRGGk3gY5lID4lWpC13fTAVBW5K8aPT+LVIj7YaHjH0jEwtQxn/r0etDWpmHUZ6XSoXfr8yi4/TQtNF4U11SHYlrpoKuO472rYulwrpfpQKVm6UCiONSI/cndXdLK8s4wLpdquN3eGoU10VSHddvZG1H8JypB6y9KaNbnKe0tOP2G7N2pPSLUx2/n6QumF159PlrpgK+qZ1/huPVAa1Oe6XudNkpdXHsa1mE4RfKYnqxV2aCD/FMPf/jHVma6iN1Tj1P6iLZp1MXk4ag8qpZBC2vzoQ65+vU7w9TbYMbZRFsXTGLAHyv0Wcvn2pTTGre/FH59kp1Zk95UFX7jpyQmhpk/7Z+fAw5PBMKuVIXXP48i4cQ75+68HD79nrPdsw7KXUX5rDPrXscmNKxe52y3TkVMFx3af2D2z55TCZ4gW9UzoOW9rvhfHnO1KjpN6XdHTv6nXukphZ7TKqtNIkHELZj1j+2n8dtu/On+navW/7et2PfV1P9ijdN4TiXal/vmxow873R9+aotLs9UuH4e9s2emwWfbwbpS5qHJt6YPHkBtFk65HS30zdGfLGVDvhq6kR4OPw+1apOy5PyATb3aNy7pG68PX8pfLvm2o7/CKjBxKn5jn/vTO7nBuUfc+M2pxt3d+n+gtfSXKdtm7cPx0C1kdnkZ2s/Ds01zrNrT4Wy7CsvABzXbJ2NNdtUyOEHp9FR3eXVBDx1VTwcK5Vl/lU1K1LXXH+Ska3I6Y67u9MhQ9/nQ1Ck0ywc9EaBV9NhY5Dh/ZOqZGRjxzq3f57QOoWzkmFKEv1eXlqJEaXtfviRVtj6RNZZw7pKZykUh3wvZNcKxX2nG303sGpkZFbUqMJtzkyNRVU/DBt9O5UltTI2q+nOg97pELIv0uFWi8bfW6/JNdJcoNM+IJYK+sjU6ObDkzd4fKgtv2G8HuYGuBqqekdbrnqMm+nPsNDEk8cGnmj9y6WCu++nRql/+HUVAen3B48gfLvka0brn+c5C9Td+0MFyOHaYU+37bbU5I8KRV+n5SJhA0b1PFqqY738NDYV6U6t+dr22dLqpN79XV+d+XbaKk8e6fuPHresF3af9+W5Pmjz+2auvD8xtRorsmcT9s+d7H28xAy/lm2HiF55bav3X607s6pqXjuMPwbU11SAxi+lRYkpM6rP0qdd4eLfNdYVflOZV2e274nNx1ts91Sc+B+O3XXzn7jbTr63Ul8h1LzK7+01eP2qdG1709dbBmOyXulLrp8P2tD8Ml8f5bqtHxheajH2VMPZH9lq9P4jp0HpgLif1l1+XegfsMdH+P97nap/sObU6OLz56avurDqYsyF2v1u/8qy75OXfZKTZ9xvSz6Cfdt9XtMRv271IWy17R9czjGr7zd3cpx0/a3/p/UoK0XJfn9UZ32TN09thyC75e1/YxJHBfWqd9Dsnjg9/C3v0hq8NY3klyvrTuifceOzujhqysu+x6pc+OTW/mH78zBqXPpk4Z9KYucYb/UFJivXXX516nPTm1fOuX5BW39lbIY6PSE0fpxwPqS9v7DM63nVM2qTnOrj8UyXlZegDkt7Ut+vqV1N2kHgS1JHtvWjYOHIQT/cjZXCP6qJB8dvR5GDw0PKPxBanqNYQqESR38snXn4g6p2yuHq5XDbb5D+L1/a3jst/R7U23ovaSdtLYa6ZQaFfDLJOdfdTnXKfdlsphX8LepqQw+375HQ2P74FTw873UyNx1Q/C2Lz41FWBO5vbFVCfiw6mHBI1H1R3Uts1WI/SnvqRGPG1Ju7U5Fdodn0X43bXlwNQooQ9m+oH+JVOB1pbUtBpbXdBr2+zCGY1Ym8IxIXWB9U2tkXqFtu7qo3PRkUuf71Kj2V6UmqbrK6mG72TD71HZb50KhYeHqF1r9N6HUp3192UCcxOvt51Gf/+/T13cG89dvk87VrwxixDiaq0+4znAVx7itX3uzUv73NXa/vaGjEadpUKtX6dGCJ09NfrzBanR+5Obbmeduj4yyU/az+dux7l/H52jbpsaiXuBVZd1B+py8Xa8PiF1wXg5BP9Wqj1xwKrLup16XDgVWJ2cusB/4Oi9YVDDEIJ/LzWqeNIPyG7lPSxL054l+adUkD88o2b5Dp/JjL7dRt3W7Hdt3Z6p8PFbqVDvh+34/fF2jLxK6oLzH06g/OPA97LtOPf+VHA/HKufPDr+/VlquqSPpUZa77T876ywLuMR0jduZfyfth2GPtEew39TbYNvpcL95YdOT6qvt1S2I1N9h+EYNwTF50q1e147+uxV0+4qW/WSGs39vlR75jOpO8SHc81OqQEln0tr42Tt81xe33535e3Sdep1w9Qc0h9LXRga2kOXbMfpbyT549HnxwHr8zPNNt2s6jS3+lgsw7LyAsxlSY3eelbqyv54DrVzpG73PyHVOD+grR+H4LdNjbT7VCZ+pSyLht0z20HxQqP3hhD84alpJ76c5E8zgQbeBnXYI4uHIl0p1SG/Zer27S1JHj36nZu1htO1z+zybqcu6/5tU527n6RGcu46/mwqdPh0WudpKktqeoLjUyHKE9qJ9x9TUwf9MjVydXi69MGpzsb3Uhcv1psO5eypUOXyZ2Y9trfvpUb+/CrJo9q68cWJ87Xv1UsmfhxYvoDUpTpD30x1HC6W6rz+Isl92mcOTQVcJ2Ziwep636NWp4ulOnsnpm6J3WV7v7PquozK8ojUSMhjsggkj0hdGDs+G0wLlLqYsXcmNu3JNo51w/H8tUmOGq0fwuNjUh3ESZ2HRuXcO/WQxBck+Yt13n9KO5Y/N3Vu/VCr06RGD25jn7tDKsx6Q9aG4I9LnWf/J9V5/9HUjwujfe3QVGDyb63cR2UxVci5U0HLazKx5zUsH7dH64fw+AepEHw8Hco/t+30mFWXf1vbKHUB5v2ptsLPsxSSZm0I/t+tTisPUrdTt1elHs756dSgjMu09QelAsinb7RtN9rWq9xG66y/aOqu0R8kudlo/dlTU7r8TUZ9iNRFpi9kxQMaRuXZI+0iVxbh/IfSpqtq6/801Rf6eerce3SW2uSrXtY5zt0kFXhtyWi0fRbB8R6pObS3JLnnqsu/I/VL9a3fkWoTnDKH/Ghb/G2qzTqpeYpT7Zj/Sd0ddZ2s85yF1N0GJ6ee43Sx0fpztv3x2VPZ15bK3bU6fTV1kWscsF421S74QjYIWKe4zK1Oc6uPxTIsKy/AHJbU1dlPtBPNE7P1KOGDUw94+VWqk7tmjth2cr5lRvM1T3HJ2hEPt22Nn0dl6SEurSHx7Pb3+OR6J+xV16E1hl6f6lhcIYt51L6RGnn80NHvXCJ1m/0rptKIaOUf9qPdUw/juUrWzlX+xlRA/ORU53Dn1FXbj6c651MKTq6bCkufsfw9SM0hf2TqAsUjsuicn71tl2+nAqTd1vl3t1p3Zm+nDda/J3XBaxhdMx7B9c5UQ30S+9o6ZR+PFhqPVr1K+z69rL2+Yqvjt1MB3hfbvjelCxLb/B619RdKdT5+kKUQfIrL0vZ5QPu7vyeLh6PdPnXB9c1JLjj67OQC1VHZ1ttGFx6XvW2j40brLte+S+cZf24CdTnlFuX2+s+yGJn/gNH68Wf+LRWufCc1SnxqAcr29rk7pp4P8oa0O4/ad+9eqTtE/jkTeiZA25/Gf/+LpcKEYSqQfVLzlG9Jhan7t/UXTN1F8e2J1Wd84W6PrDMqKxWCvzOLkeDDiM/dU3dXTCZQHZV5/LDiLjWl2A1SI4t/llGoOv47pC46vXRqdRrVZzgW750Kvo9q358vpEZ/XyM1v/TbMrGBDMv1Gf3Nd0vdhXTJLEaud1kbgp9yB8LSv3ODdpz4UVY4H/PSMWH3VHvnLqN1V021uT+cGtAxXDA7bzuGXHa0bSfRjsjac+uho/XXb8e2rye542j9EILvmepbTOo7tJ26DtOi3XB5G6SmdPl6JnTRMtVv+/fUufSCo/VbtdVSF8q3pKZHu12Su7TjxomZ1rloq4vK2ThgvVwqYP2fJPdYddnPKnWaW30slvWWlRdgsy+p0SQfaQ24y44OCsujMc6RGr3166wTgk95STVid8/SqIvU9BK/To3MvXBbd/lU8H3X1BzOW5LcaNV1aGUb/uZ7pOYg/UDqNtL3pEbeXjPV+ftGqtNxQGrE5IfaQX/l2yxLoxNSF18+1Orxf6lOxMNTHfj9UoHDT1KjhT6emtrgE1Ooy6gO10xdHHpyRrdTZm0ofJ5UY+4XGc3HnLq49D9J3rzqeqxTr3Fn6aFJbjN6/RdtWz0niydlD6PDP5rq5K5822ynfs9NdUiflkUgd/f2nb9Fe33+VGf9T1Oh5SQeBLeD36M/T7uNPhWCH92OD6c8J2CKy9J+d/7UNC7fST3A6tJt/R1SgcqbsjQ/89SWLEKE9bbRQ7MIUx7c3ntr6m6ET7TP7zz+d1ZYj72XXu+VerBv144Hv07dbTAO7MfHwIumQspJBSinYp8bQvA3Zu1I8Mkc57L+6LrnpoLH76TCiOFhpAdmMRXAB1N3ILw/dRfMFVZdl1bGi49+3iXVLjg2NTDhiut8/jKpzuzxSf4gS3dWZUJhV9aG2c/O2vPr5bNOCJ46v/7Rev/Oqpel+vzLOuU8NDUi+oTUA5dPTJ1rj1h12be137XXwxQOw8XwN4++R+MQ/PtZer5OKjz+69QdmCu5Q2T5e9T+e47UiO7LtdfDcXkIwT+U0XQoS//eJI55WZwb903dtfPuJPcevX+jVL/hC1kbgu++3r+z4rrsmXaH6DY+c752/Dsxo2c0pC5uvjY18GTfM7Kcp7JO50sNIrnvRn/jrD33PiyLuZm/244TkxhwkrVTY6x3B+k4YD0ki3bfZVMXYr6f5M6rrsec6zS3+lgs21pWXoDNviT5f6nA6nIbNHQOHv18UPv8r1OdqsmGKKMy75MaffvxVMP7X4bGYKoDP1x1Pj7Vqfpm6ta54cFqP8gEHiKStSHK51IN8GNSTy/fkuqonycV2r8v1VH/afvsG7MI+FbW0Es1uH+Y5COjdR9u5b1j6qGdL0jdMvuidsLaJxXmv7Atj8i0HtR3QCo02JLt3AGR6lh8o22rvUfbdP9VbpcNyjq+W+LlqRGRL106Hrykbc93peb5vFNbd2KWHl44tSUV3B2VGkn8zdTImSPad+i5bTtdYNXl3KDsp+Z79IK0h7CmOulva/vqdVddjw3qNh4R9NpUZ+/TqY7QcJwbOuxDCP76jEa8T2nJooO+63a20R7tWPePWQR4/5GJjJROnSsfnORpw3ZKhUH/1F7vn7qQ/NvUXWQHjH53q+P0quvzO+xzQwj+2kzswksqIP5gkk+M1r2wHcse3Y5rX2nH8huMttu9UyP0X506v174zC77BvUZjnMfWlr/B1mcd7aaBimLeYu3ZDSf/pSWrA3uPpkazPCPWduBv2KqffeT1ICM66YGq3w5E7vLZRv12S1bBxFnS91l8bK2jY5OXYyZRJ2y/vn1/e04cOfU1EdfSE31dr32/hCCD+3xay39mwdm9DD0CdSnS/X5fpp1psxItVW/0+p9vVVvk+3Ub+/UNIPvTj3Ec/mi142zCMHvsOryblCHnVNts69lO9PjpB7q+ZEs7sh+dqo/eFLaeWoqS2qwxZZsZwR31s75fUDqruaLrOo7s075dku13d443mbr1OE6bRt+IKMLEamw9UOZUDt1bnWaW30slu0tKy/AZl9St+W9c5319051iD6dGiU0jII6OIvbsJ6x6vJvp277pB5AeEySB2UxuvPVGXWcUkH3U1JB319lEbC+OhWGT+IWzdZI+o9WpgtlEY48IhVGvDc12mSPdjC/Rep2zUmMuEt1jh6Rmk/1na2h86okVx19ZrfULfW/yTrzyY7/FqveHq0cw5PNj08FJttrvP5bKpBYb6TeJOq0Tnm/mup8H9TWjUd1/k0qUNmSusD0iazwFt9Ts7+kgpRvpsLIf0h13F+XGhF+bJLHZ4JzwZ2G79FDRusvkXpI6+T2taU6PrHtT9fKYoT0X6VGq74ni7lkb9/2vVes+vi2Th2GC1y7t230yu0d69q6g9vxffj9ldcrNTrtsamR60e1493bMrpIlDrf/k3WCcE3w3Ia9rmjprBtRuXfKxV0n5Tk6Lbu+RnNE526tfyjqQ7gDVZd5u3UZ3yce8fSe4e1er4r9fyT8UWMf0iF4I+b0vZZp357poKso1PTS6z3wO/LZDFX8XdSF9GGdt8kAuMdqM+GF7tSfYlfZEIPGsvW59c9U8/+uOLoM0ek2ntfy9oQ/JKZ2Pl1qT5Hj9ZfK3WHwfKUfcPFjKu2/e75q67Ddur3pNTozfHc0TstHRNu3I57P06bPmRKS+oC+R1Tbe2PJjnfOp8Z1+eyqT7rV1PB/uvSzk9TWlJT7v0qi2dVrffcmaF/eunUtDWTOq61sp2tHQNOSPJvo/XrBayHt+/VM5bqt9LpLOdep7nVx2LZ3rLyAmzmJTVi6LWpK2EHtQPDxVOdii2p6QE+nmqgfiVtDq/UiIIHZUKN1nXqtltqZOA70katpm7//VHqYS5HZ20YMW5cXCMV/J2Uidx+Nfq7fy7JP7TX41G6j8xitNp6twZPYsRdqjH+4NSDnr6Rmv5jeWqa3VMjwL+X0bxxU13avnar1O2kx2X9xuvQaf2ztl+dfyrbZBv1OjTVwB4HKGdP8sdJ7p/W4UiNIrx2qtN70KrLvZ063TFrQ7tnpjqxe6VClWelHsazpR3zJjP34FI9Tu33aKvRqploOJQa0fXOVGDcLR2bHz46zl22rbvNhLfTLqlRJT/fgW201cjbKR0jUhf7nt3+/t8cnVfHtzAPIfhvUkHkgasu91lpnxsdF/4vdev5J7LUTktd8B/Cu0Pbum7836kso/r8IksDNVLzKp+UGvl5ndR5+GLt9b1Gn5vqce4OqVHE4+dQXCs1XcZj09qn7W9w29SDwYdwcnJ12k59HpO1D1Yc6rFHKrB47KrLv8F+98v2PflYRne/tc8M36OvLn+PpraNRvX5eZJ3t3WXT7VxNryTNxVKTibM36CM70jymnXWd0uvb5k6106yPtmxfsRymLdHqg2x16rLv0GdLt72uadstF1G69+a5CWrLvM26nL21OCYE8flXGeb7JZqR/xX1k6JOalz6xzrNLf6WCzbWlZegM2+JLlaqnP3rlQY/r3U7XLPSIXiu6VGEv88yQtGvzeZjvkG9bpMq8+N2utXtobFpVrD9eT2/tWXfu8CqdGEn870bifbJ9XBeOFo3Xi00LtSnYm3Z8JTULTG+ENaXX6QNho/a4OUe6Q6H5O5ALGdOu1I43WX1Jzmk5vve4M63aJtn0unrq7fKjUK7X+zeHjaJG6X38H63KCV+/1JHtbW7ZXqQD29bZ9dktwkNSXKT9MeeDfFZY7fo1bmITR++2jd+K6DN6RGcn0yK5pT9VTUZe/UKOLPz2EbtfPoV1PB47iDMT4P7ZMKvbYkud+qy7yD9ZrTPjccF76SuuA/jFwfT69x61bfn2Si04Qs1WejEPzQVGf326mRk5/L6HknU15anb6Zuhh+kdSFo1+1OmxJha5bPXMi0w3wtlef45Kca+l3zpNqTzxi1eXfYL8bvkffTLtwntEowfY9+nA7fm818GRKy9L36M1Jbt5+/ou2/gFt+fPU6OKrbYJ9brd2TH7daN34IsS+Se6zzu9NuT63yjr9iKV6XTTVR7/6mV3GU1mf/VPTUnwryS1H65cfUnip1KC0B5xZZTuN9TlHqq+wvYD1xW37Te4O0rnXaW71sVg2WlZegDksqekNvph6AMC/pzoVe43e3ys1CuKlqy7rqajTHqnb3nZujbqvpj18MHU7+jC/50ey9QNvLpTk3Kuuwzp12i01LcvxWfsgxZ1afY9NjWL7dtqImkz0imZrGD0k1UF/3zonp7ulHoJylVWX9VTUafeNGq/t/eu0/e2Bqy7rDtbn4qmO3QdSV8p/nBohfbHUHH1bktxl1eU8lXW6XOrBid9MTY10xVRA+basvSNkkseAdeozx+/RTm0/+06Sw0brh1GDr00Fyh/JROdpn+s2SnKuLKbRWe5g7LL02T9ZXjfVZcb73IlJ3jNaPw7v7pgaLb3NZ1dMYcm2Q/DzJ/nX1MCFpyfrP8h9aktqBO4vU23vz6YGL9wzddFsGJgyiQewnxH1SV10emBGUyRMbRl9j36cNnK6rR9/j+6Ummt/0vtbK+vwPfpx6iLmd1MXWr6T6jd8KzXH/Ac3w7E71b87qpX/euu8f1gqWL3mmVmu37FO2xxMk7q49Lp2LJzk92apvIekppv4WJKbr/P+2dr35382ybl13YB19P75U1OmPXszfIfmWKe51cdiWW9ZeQHmsqRC7vVGm3StYfupLEZNTjJUXa/s7b+vTD0IcnwF/Y1Jnpe67WryDddRuS+dGpn6zoweZJcKJd+ZmoPwqNSolUnPZ5Vkv9YY/1kqGLpqagT+tVKjud6Tid9psE6dlhuv523rL5gKkY/eZPvboalw+IlJbjtaf5nU3IOHr7qMp6FOB6fmwv1U6sLYs9o++IJVl+001meO36NLpDro78zaW+cPSl2kPTybaI7puW2j1K2mW3UwUs+g+Ods5yGYU1xmvM/9PGvn/h2Hd/uc2eX6HeqzrRB8+aLSpjjHJrl6kuekBmkMD1rdOXUX0ucz8VHFv2t9kvxhRnM3T3HZke/RaN3k97tWn4emgu6PZ3GRb9+0Z9Nk0XfaDPW5/HBMyCgET/WV3p/q422ac2sr+/JgmnO39UP4/bMkV1h1OU9Fff6glfnrSf4uyTlTD4a9ZZKXtvPuIasu56mozzhgfU3aw35TI/NflLr4N9m7sM8KdZpbfSyW5WU4SXM66rput77vT24/ny3JU1MPpzis7/uvr7Jsp1bXdTunbl8+MMkt+r7/Ydd1h6Su/D2k7/uPDp/r+/63qyvpjuu67vdTI9J+mBr1fUKqg/HLvu+v1HXdE1JzlF6p7/ufr66k29d13b6pOaX/JkmfqtPHk5w7NVro113X7dT3/ZYVFvNU6bputyQ3TQVB303N+/3U1GiiK7U6bab9bU1Zu647d2p7XS/1QKFvr6xwv4Ou63ZKbaPLpEa075fkrn3fH7XKcp0WM/0e3Tj1HIdvpjqxX0tyo9SdFFfu+/6rqyvdqbe0jbosHhp77tT3aFNto67rzpF66OI9UndTPC819cm5UndVbYrj29iM97l/SPLBvu9v1Naf0sbbTEb1eUKSY/u+P7yt37Xv+1+3n7t+k3YMuq7bNXXn1bNTod4fbpbjwXo2qs9m20ZL36MP9H1/47Z+l77vf7PSwp0GXdftnxqd/49J/mt0XBh/jzbTuegmqT7RT1MXlH+buqD5i9Q0IZvq3JokXdftnurXPSvVj3hw6m6Em6TuAP7kygp3GnRdd+XUQ5kvl7obpE/dcfCt1NQnn1lh8U61ruvOntomD0yFrD9M1emcqZHun1xd6U6budVpbvWBMQH4GajrusNSndtbJLl+3/f/vdoSnTZd1101NRLgP1Mjow9N3XZ56GbspCdJ13WXTk3dcKXUA8c+nbqV/repucBPSnLHvu//b2WF3EFd1+2X6lzcNzUn5HmS/Lzv+34TdzB2T416+KfUdBqfzSL83pR1SpKu6+6emkv7ZqkLYp9acZFOk3EHvIVet0h9f67S9/2XVlq402im36PLpi4eXTY1euNbSe65ife7YRs9IDUFwGWHi5SbcRu1DsZDU9Od7Jya7/ewzXaRb2yG+9wQ3v1tki/1fX/VFRfpd9Lqc88kj0/y5b7vr7TiIp0uuq47KMn9Unca7JUK7n6z2YK7wQzrM97vvtL3/RVXXKTfyVJ9ju/7/gqrLdHvpuu6y6XmL79s6llWn0rymLbPbbpza7JmMM2TU6O/f5668/eTqyzXadWOCRdMDTjZOfUcim/2ff+jFRbrNOu6bp/U/na31ACazyR55Wa7UD42tzrNrT4wEICfAdpJ9x2publ+nuRPN9vV2WVd1103yQtSJ93PJLn9Zu6kJ6eMrtk1dZviT7uuOzDVcb9Vkmv3ff+5VZbv1GgjUh6Y5Iap0ZCbbpTQshaC3zYVGN93MzfEk6TrumumRkz/LDVi47MrLtLvZHn/6rrugL7vf7zCIv3OZvo92isVoOyb5Ed935+04iL9Tkaj756QGn03jCbclOeiFqScN8n5khzT9/1vN/NxLpnlPrdv6qLLA1PTu3xjxUX6nbT6PCh1br3xZgxUl7U7+x6bmkP73jNoL8yqPsn89rsZ1men1HQnvxmt25Tn1UHrR9w6yX2SPKjv+0+vuEgArJgA/AzSdd3Vk1wlyes36xQHy9qVwN1THdpNOypyPV3XHZ4ayXFQkltvxtH6LXT4Zds2m7rROliaTmhT72+tc3Gx1PfnhFWXh/XN8Xs0N3MblTtmn5um1v7Zte/7E1ddltPD0nFuU44qHuu6rktdRPrmHI7dc6vPYIb73azqM0dd1+2ROnb/dNVlYa2lu0k39WCTwdzqNLf6QCIAP0PN+UAxx4Ze13V/knpS/VdWXZbfxZz3Oziz+B5N29xG5cIqzPE4N7f26dzqk8xvv5tbfQBgrgTgnOVpuAJsPnMblQsAAMAZQwAOAAAAAMAs7bTqAgAAAAAAwBlhUgF413VHdF33z13XHdt13U+6ruu7rnvZqssFAAAAAMDms8uqC7Dkr5IckuRnSb6Z5JKrLQ4AAAAAAJvVpEaAJ3lokosn2S/J/VZcFgAAAAAANrFJjQDv+/6Y4eeu61ZZFAAAAAAANrlJBeCnh+tf//r9qstwenr605+eJHnIQx6y0nKcXuZWn2R+dVKf6ZtbneZWn2R+dVKf6ZtbneZWn2R+dVKf6ZtbneZWn2R+dVKf6Xv605+eK1zhCqsuBmc9cx3hOvn8cTh+DcezCTvD95GpTYECAAAAAACnCwE4AAAAAACzJAAHAAAAAGCWBOAAAAAAAMySABwAAAAAgFkSgAMAAAAAMEsCcAAAAAAAZmmXVRdgrOu6WyW5VXt5rvbfa3Zd92/t5x/0ff+wM7lYAAAAAABsQpMKwJNcIcndl9ZduC1J8vUkAnAAAAAAALZrUlOg9H3/2L7vu20sF1x1GQEAAAAA2BwmFYADAAAAAMDpRQAOAAAAAMAsCcABAAAAAJglATgAAAAAALMkAAcAAAAAYJYE4AAAAAAAzJIAHAAAAACAWRKAAwAAAAAwSwJwAAAAAABmSQAOAAAAAMAsCcABAAAAAJglATgAAAAAALMkAAcAAAAAYJYE4AAAAAAAzJIAHAAAAACAWRKAAwAAAAAwSwJwAAAAAABmSQAOAAAAAMAsCcABAAAAAJglATgAAAAAALMkAAcAAAAAYJYE4AAAAAAAzJIAHAAAAACAWRKAAwAAAAAwSwJwAAAAAABmSQAOAAAAAMAsCcABAAAAAJglATgAAAAAALMkAAcAAAAAYJYE4AAAAAAAzJIAHAAAAACAWRKAAwAAAAAwSwJwAAAAAABmSQAOAAAAAMAsCcABAAAAAJglATgAAAAAALMkAAcAAAAAYJYE4AAAAAAAzJIAHAAAAACAWRKAAwAAAAAwSwJwAAAAAABmSQAOAAAAAMAsCcABAAAAAJglATgAAAAAALMkAAcAAAAAYJYE4AAAAAAAzJIAHAAAAACAWRKAAwAAAAAwSwJwAAAAAABmSQAOAAAAAMAsCcABAAAAAJglATgAAAAAALMkAAcAAAAAYJYE4AAAAAAAzJIAHAAAAACAWRKAAwAAAAAwSwJwAAAAAABmSQAOAAAAAMAsCcABAAAAAJglATgAAAAAALMkAAcAAAAAYJYE4AAAAAAAzJIAHAAAAACAWRKAAwAAAAAwSwJwAAAAAABmSQAOAAAAAMAsCcABAAAAAJglATgAAAAAALMkAAcAAAAAYJYE4AAAAAAAzJIAHAAAAACAWRKAAwAAAAAwSwJwAAAAAABmSQAOAAAAAMAsCcABAAAAAJglATgAAAAAALMkAAcAAAAAYJYE4AAAAAAAzJIAHAAAAACAWRKAAwAAAAAwSwJwAAAAAABmSQAOAAAAAMAsCcABAAAAAJglATgAAAAAALMkAAcAAAAAYJYE4AAAAAAAzJIAHAAAAACAWRKAAwAAAAAwSwJwAAAAAABmSQAOAAAAAMAsCcABAAAAAJglATgAAAAAALMkAAcAAAAAYJYE4AAAAAAAzJIAHAAAAACAWRKAAwAAAAAwSwJwAAAAAABmSQAOAAAAAMAsCcABAAAAAJglATgAAAAAALMkAAcAAAAAYJYE4AAAAAAAzJIAHAAAAACAWRKAAwAAAAAwSwJwAAAAAABmSQAOAAAAAMAsCcABAAAAAJglATgAAAAAALMkAAcAAAAAYJYE4AAAAAAAzJIAHAAAAACAWRKAAwAAAAAwSwJwAAAAAABmSQAOAAAAAMAsCcABAAAAAJglATgAAAAAALMkAAcAAAAAYJYE4AAAAAAAzJIAHAAAAACA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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"for i in raw_df.columns:\n    print(f\"column name: {i}, unique values: {len(raw_df[i].unique())}\")","metadata":{"execution":{"iopub.status.busy":"2021-08-07T19:28:06.325983Z","iopub.execute_input":"2021-08-07T19:28:06.326494Z","iopub.status.idle":"2021-08-07T19:28:06.345481Z","shell.execute_reply.started":"2021-08-07T19:28:06.326460Z","shell.execute_reply":"2021-08-07T19:28:06.344326Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"column name: Sno, unique values: 127\ncolumn name: age, unique values: 48\ncolumn name: gender, unique values: 2\ncolumn name: body temperature, unique values: 29\ncolumn name: Dry Cough, unique values: 2\ncolumn name: sour throat, unique values: 2\ncolumn name: weakness, unique values: 2\ncolumn name: breathing problem, unique values: 2\ncolumn name: drowsiness, unique values: 2\ncolumn name: pain in chest, unique values: 2\ncolumn name: travel history to infected countries, unique values: 2\ncolumn name: diabetes, unique values: 2\ncolumn name: heart disease, unique values: 2\ncolumn name: lung disease, unique values: 2\ncolumn name: stroke or reduced immunity, unique values: 2\ncolumn name: symptoms progressed, unique values: 2\ncolumn name: high blood pressue, unique values: 2\ncolumn name: kidney disease, unique values: 2\ncolumn name: change in appetide, unique values: 2\ncolumn name: Loss of sense of smell, unique values: 2\ncolumn name: Corona result, unique values: 3\ncolumn name: Unnamed: 21, unique values: 1\ncolumn name: Unnamed: 22, unique values: 1\ncolumn name: Unnamed: 23, unique values: 1\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Data Preprocessing\n## Missing Values\n- all columns are good, with no missing values\n- Except for `Unnamed: 21`, `Unnamed: 22` and `Unnamed: 23`\n- We'll remove them because they won't help the final model\n\n## Relevant Columns\n- `Corona result` is the severity of the patients covid symptoms\n- `Sno, gender, and body temperature` are not relevant for our model or difficult to ask\n\n## Scaling\n- All columns we will use are categorical except for age\n- So leave all columns as they are except for age\n- Normalize age with min max scaling\n- Store minimum and maximum so web app can scale age appropriately as well\n\n## Conclusion\n- So we will drop the columns `Unnamed: 21, Unnamed: 22, Unnamed: 23, Sno, Gender and body temperature`","metadata":{}},{"cell_type":"code","source":"less_data = raw_df.drop(['Sno','Unnamed: 21', 'Unnamed: 22', 'Unnamed: 23', 'gender', 'body temperature'], axis=1)\nless_data.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-07T19:28:07.541918Z","iopub.execute_input":"2021-08-07T19:28:07.542268Z","iopub.status.idle":"2021-08-07T19:28:07.560929Z","shell.execute_reply.started":"2021-08-07T19:28:07.542238Z","shell.execute_reply":"2021-08-07T19:28:07.559778Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"   age  Dry Cough  sour throat  weakness  breathing problem  drowsiness  \\\n0   20          0            0         0                  0           0   \n1   19          1            0         0                  0           0   \n2   55          1            1         1                  1           1   \n3   40          0            0         0                  0           1   \n4   33          0            1         0                  1           0   \n\n   pain in chest  travel history to infected countries  diabetes  \\\n0              0                                     0         0   \n1              0                                     0         0   \n2              1                                     0         0   \n3              1                                     1         1   \n4              0                                     1         0   \n\n   heart disease  lung disease  stroke or reduced immunity  \\\n0              0             0                           0   \n1              0             0                           0   \n2              0             0                           0   \n3              0             0                           0   \n4              0             1                           1   \n\n   symptoms progressed  high blood pressue  kidney disease  \\\n0                    0                   0               0   \n1                    0                   0               0   \n2                    0                   0               0   \n3                    1                   1               0   \n4                    1                   0               0   \n\n   change in appetide  Loss of sense of smell  Corona result  \n0                   0                       0              0  \n1                   0                       0              0  \n2                   1                       0              1  \n3                   1                       0              2  \n4                   0                       1              2  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>Dry Cough</th>\n      <th>sour throat</th>\n      <th>weakness</th>\n      <th>breathing problem</th>\n      <th>drowsiness</th>\n      <th>pain in chest</th>\n      <th>travel history to infected countries</th>\n      <th>diabetes</th>\n      <th>heart disease</th>\n      <th>lung disease</th>\n      <th>stroke or reduced immunity</th>\n      <th>symptoms progressed</th>\n      <th>high blood pressue</th>\n      <th>kidney disease</th>\n      <th>change in appetide</th>\n      <th>Loss of sense of smell</th>\n      <th>Corona result</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>20</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>19</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>55</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>40</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>33</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# Min-Max Normalization\nage_min = min(raw_df[\"age\"])\nage_max = max(raw_df[\"age\"])\ntarget_min = min(raw_df[\"Corona result\"])\ntarget_max = max(raw_df[\"Corona result\"])\nprint(age_min, age_max)\nprint(target_min, target_max)","metadata":{"execution":{"iopub.status.busy":"2021-08-07T19:28:09.396689Z","iopub.execute_input":"2021-08-07T19:28:09.397045Z","iopub.status.idle":"2021-08-07T19:28:09.403839Z","shell.execute_reply.started":"2021-08-07T19:28:09.397014Z","shell.execute_reply":"2021-08-07T19:28:09.402770Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"18 80\n0 2\n","output_type":"stream"}]},{"cell_type":"code","source":"def min_max_scaling(min_val, max_val, df, col_name):\n    def min_max_scale_val(val):\n        return (val - min_val) / (max_val - min_val)\n    return df.apply(lambda x: min_max_scale_val(x) if x.name == col_name else x)","metadata":{"execution":{"iopub.status.busy":"2021-08-07T19:28:09.575920Z","iopub.execute_input":"2021-08-07T19:28:09.576269Z","iopub.status.idle":"2021-08-07T19:28:09.581936Z","shell.execute_reply.started":"2021-08-07T19:28:09.576238Z","shell.execute_reply":"2021-08-07T19:28:09.580976Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"scaled_data = min_max_scaling(age_min, age_max, less_data, \"age\")\nscaled_data = min_max_scaling(target_min, target_max, scaled_data, \"Corona result\")\nscaled_data.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-07T19:28:10.926639Z","iopub.execute_input":"2021-08-07T19:28:10.927003Z","iopub.status.idle":"2021-08-07T19:28:10.971665Z","shell.execute_reply.started":"2021-08-07T19:28:10.926971Z","shell.execute_reply":"2021-08-07T19:28:10.970632Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"        age  Dry Cough  sour throat  weakness  breathing problem  drowsiness  \\\n0  0.032258          0            0         0                  0           0   \n1  0.016129          1            0         0                  0           0   \n2  0.596774          1            1         1                  1           1   \n3  0.354839          0            0         0                  0           1   \n4  0.241935          0            1         0                  1           0   \n\n   pain in chest  travel history to infected countries  diabetes  \\\n0              0                                     0         0   \n1              0                                     0         0   \n2              1                                     0         0   \n3              1                                     1         1   \n4              0                                     1         0   \n\n   heart disease  lung disease  stroke or reduced immunity  \\\n0              0             0                           0   \n1              0             0                           0   \n2              0             0                           0   \n3              0             0                           0   \n4              0             1                           1   \n\n   symptoms progressed  high blood pressue  kidney disease  \\\n0                    0                   0               0   \n1                    0                   0               0   \n2                    0                   0               0   \n3                    1                   1               0   \n4                    1                   0               0   \n\n   change in appetide  Loss of sense of smell  Corona result  \n0                   0                       0            0.0  \n1                   0                       0            0.0  \n2                   1                       0            0.5  \n3                   1                       0            1.0  \n4                   0                       1            1.0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>Dry Cough</th>\n      <th>sour throat</th>\n      <th>weakness</th>\n      <th>breathing problem</th>\n      <th>drowsiness</th>\n      <th>pain in chest</th>\n      <th>travel history to infected countries</th>\n      <th>diabetes</th>\n      <th>heart disease</th>\n      <th>lung disease</th>\n      <th>stroke or reduced immunity</th>\n      <th>symptoms progressed</th>\n      <th>high blood pressue</th>\n      <th>kidney disease</th>\n      <th>change in appetide</th>\n      <th>Loss of sense of smell</th>\n      <th>Corona result</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.032258</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.016129</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.596774</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.354839</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.241935</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"print(scaled_data.columns)","metadata":{"execution":{"iopub.status.busy":"2021-08-07T20:59:30.422399Z","iopub.execute_input":"2021-08-07T20:59:30.422809Z","iopub.status.idle":"2021-08-07T20:59:30.431161Z","shell.execute_reply.started":"2021-08-07T20:59:30.422771Z","shell.execute_reply":"2021-08-07T20:59:30.430087Z"},"trusted":true},"execution_count":97,"outputs":[{"name":"stdout","text":"Index(['age', 'Dry Cough', 'sour throat', 'weakness', 'breathing problem',\n       'drowsiness', 'pain in chest', 'travel history to infected countries',\n       'diabetes', 'heart disease', 'lung disease',\n       'stroke or reduced immunity', 'symptoms progressed',\n       'high blood pressue', 'kidney disease', 'change in appetide',\n       'Loss of sense of smell'],\n      dtype='object')\n","output_type":"stream"}]},{"cell_type":"code","source":"scores = {\n    \"age\": 7,\n    \"Dry Cough\": 2,\n    \"sour throat\": 1,\n    \"weakness\": 4,\n    \"breathing problem\": 7,\n    \"drowsiness\": 3,\n    \"pain in chest\" : 8,\n    \"travel history to infected countries\": 5,\n    \"diabetes\": 6,\n    \"heart disease\": 8,\n    \"lung disease\": 10,\n    \"stroke or reduced immunity\": 10,\n    \"symptoms progressed\": 7,\n    \"high blood pressue\": 6,\n    \"kidney disease\": 7, \n    \"change in appetide\": 2,\n    \"Loss of sense of smell\": 1,\n}\nprint(len(scores) == len(scaled_data.columns))","metadata":{"execution":{"iopub.status.busy":"2021-08-07T20:59:57.926186Z","iopub.execute_input":"2021-08-07T20:59:57.926772Z","iopub.status.idle":"2021-08-07T20:59:57.933809Z","shell.execute_reply.started":"2021-08-07T20:59:57.926736Z","shell.execute_reply":"2021-08-07T20:59:57.932823Z"},"trusted":true},"execution_count":98,"outputs":[{"name":"stdout","text":"True\n","output_type":"stream"}]},{"cell_type":"code","source":"for key in scores:\n    scores[key] /= 10\nscores","metadata":{"execution":{"iopub.status.busy":"2021-08-07T21:09:19.699939Z","iopub.execute_input":"2021-08-07T21:09:19.700298Z","iopub.status.idle":"2021-08-07T21:09:19.706273Z","shell.execute_reply.started":"2021-08-07T21:09:19.700268Z","shell.execute_reply":"2021-08-07T21:09:19.705547Z"},"trusted":true},"execution_count":118,"outputs":[{"execution_count":118,"output_type":"execute_result","data":{"text/plain":"{'age': 0.7,\n 'Dry Cough': 0.2,\n 'sour throat': 0.1,\n 'weakness': 0.4,\n 'breathing problem': 0.7,\n 'drowsiness': 0.3,\n 'pain in chest': 0.8,\n 'travel history to infected countries': 0.5,\n 'diabetes': 0.6,\n 'heart disease': 0.8,\n 'lung disease': 1.0,\n 'stroke or reduced immunity': 1.0,\n 'symptoms progressed': 0.7,\n 'high blood pressue': 0.6,\n 'kidney disease': 0.7,\n 'change in appetide': 0.2,\n 'Loss of sense of smell': 0.1}"},"metadata":{}}]},{"cell_type":"code","source":"def heurestic_score(data):\n    score = 0\n    for i in range(len(list(scores))):\n        this_symptom = list(scores.keys())[i]\n        score += data[i] * scores[this_symptom]\n    return score","metadata":{"execution":{"iopub.status.busy":"2021-08-07T21:09:25.252143Z","iopub.execute_input":"2021-08-07T21:09:25.252754Z","iopub.status.idle":"2021-08-07T21:09:25.257711Z","shell.execute_reply.started":"2021-08-07T21:09:25.252717Z","shell.execute_reply":"2021-08-07T21:09:25.256701Z"},"trusted":true},"execution_count":119,"outputs":[]},{"cell_type":"code","source":"heurestic_scores = []\nfor i in range(len(scaled_data)):\n    this_data = list(scaled_data.iloc[i])\n    this_score = heurestic_score(this_data)\n    heurestic_scores.append(this_score)\nheurestic_scores[:10]","metadata":{"execution":{"iopub.status.busy":"2021-08-07T21:09:29.161812Z","iopub.execute_input":"2021-08-07T21:09:29.162312Z","iopub.status.idle":"2021-08-07T21:09:29.193108Z","shell.execute_reply.started":"2021-08-07T21:09:29.162270Z","shell.execute_reply":"2021-08-07T21:09:29.192114Z"},"trusted":true},"execution_count":121,"outputs":[{"execution_count":121,"output_type":"execute_result","data":{"text/plain":"[0.02258064516129032,\n 0.21129032258064517,\n 3.1177419354838714,\n 3.948387096774194,\n 4.269354838709677,\n 3.8725806451612903,\n 8.599999999999998,\n 1.4838709677419353,\n 4.095161290322581,\n 1.3064516129032258]"},"metadata":{}}]},{"cell_type":"code","source":"max_heurestic_score = max(heurestic_scores)\nmin_heurestic_score = min(heurestic_scores)\nscaled_heurestic_scores = []\nfor i in range(len(heurestic_scores)):\n    def scale_heurestic_score(score):\n        return score / max_heurestic_score\n    scaled_heurestic_scores.append(scale_heurestic_score(heurestic_scores[i]))\nprint(scaled_heurestic_scores[:20])","metadata":{"execution":{"iopub.status.busy":"2021-08-07T21:15:01.507948Z","iopub.execute_input":"2021-08-07T21:15:01.508332Z","iopub.status.idle":"2021-08-07T21:15:01.515414Z","shell.execute_reply.started":"2021-08-07T21:15:01.508297Z","shell.execute_reply":"2021-08-07T21:15:01.514268Z"},"trusted":true},"execution_count":125,"outputs":[{"name":"stdout","text":"[0.0026256564141035263, 0.024568642160540142, 0.3625281320330084, 0.4591147786946738, 0.4964366091522881, 0.4503000750187548, 1.0, 0.172543135783946, 0.47618154538634677, 0.15191297824456118, 0.10802700675168794, 0.29538634658664675, 0.38972243060765205, 0.21661665416354092, 0.4039759939984997, 0.4866841710427608, 0.6275318829707429, 0.059077269317329345, 0.39759939984996256, 0.7477494373593401]\n","output_type":"stream"}]},{"cell_type":"code","source":"target = scaled_data.pop('Corona result', in_place)\ntarget.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-07T19:30:07.762145Z","iopub.execute_input":"2021-08-07T19:30:07.762520Z","iopub.status.idle":"2021-08-07T19:30:07.773189Z","shell.execute_reply.started":"2021-08-07T19:30:07.762478Z","shell.execute_reply":"2021-08-07T19:30:07.772290Z"},"trusted":true},"execution_count":21,"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"0    0.0\n1    0.0\n2    0.5\n3    1.0\n4    1.0\nName: Corona result, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"scaled_data.insert(0, \"heurestic score\", scaled_heurestic_scores)","metadata":{"execution":{"iopub.status.busy":"2021-08-07T21:20:18.374132Z","iopub.execute_input":"2021-08-07T21:20:18.374842Z","iopub.status.idle":"2021-08-07T21:20:18.381897Z","shell.execute_reply.started":"2021-08-07T21:20:18.374802Z","shell.execute_reply":"2021-08-07T21:20:18.380875Z"},"trusted":true},"execution_count":126,"outputs":[]},{"cell_type":"code","source":"shuffled_data = scaled_data.sample(frac = 1)\nshuffled_data.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-07T21:24:08.683085Z","iopub.execute_input":"2021-08-07T21:24:08.683452Z","iopub.status.idle":"2021-08-07T21:24:08.703788Z","shell.execute_reply.started":"2021-08-07T21:24:08.683418Z","shell.execute_reply":"2021-08-07T21:24:08.702983Z"},"trusted":true},"execution_count":130,"outputs":[{"execution_count":130,"output_type":"execute_result","data":{"text/plain":"     heurestic score       age  Dry Cough  sour throat  weakness  \\\n109         0.447487  0.354839          0            0         1   \n125         0.316767  0.177419          0            1         1   \n105         0.208177  0.129032          1            1         0   \n55          0.258440  0.032258          0            1         0   \n110         0.188860  0.177419          0            0         1   \n\n     breathing problem  drowsiness  pain in chest  \\\n109                  1           1              0   \n125                  1           1              1   \n105                  0           0              1   \n55                   1           0              1   \n110                  0           1              0   \n\n     travel history to infected countries  diabetes  heart disease  \\\n109                                     0         1              0   \n125                                     0         0              0   \n105                                     1         0              0   \n55                                      1         0              0   \n110                                     1         0              0   \n\n     lung disease  stroke or reduced immunity  symptoms progressed  \\\n109             0                           0                    1   \n125             0                           0                    0   \n105             0                           0                    0   \n55              0                           0                    0   \n110             0                           0                    0   \n\n     high blood pressue  kidney disease  change in appetide  \\\n109                   1               0                   1   \n125                   0               0                   1   \n105                   0               0                   0   \n55                    0               0                   0   \n110                   0               0                   1   \n\n     Loss of sense of smell  \n109                       1  \n125                       1  \n105                       1  \n55                        1  \n110                       1  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>heurestic score</th>\n      <th>age</th>\n      <th>Dry Cough</th>\n      <th>sour throat</th>\n      <th>weakness</th>\n      <th>breathing problem</th>\n      <th>drowsiness</th>\n      <th>pain in chest</th>\n      <th>travel history to infected countries</th>\n      <th>diabetes</th>\n      <th>heart disease</th>\n      <th>lung disease</th>\n      <th>stroke or reduced immunity</th>\n      <th>symptoms progressed</th>\n      <th>high blood pressue</th>\n      <th>kidney disease</th>\n      <th>change in appetide</th>\n      <th>Loss of sense of smell</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>109</th>\n      <td>0.447487</td>\n      <td>0.354839</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>125</th>\n      <td>0.316767</td>\n      <td>0.177419</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>105</th>\n      <td>0.208177</td>\n      <td>0.129032</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>0.258440</td>\n      <td>0.032258</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>110</th>\n      <td>0.188860</td>\n      <td>0.177419</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"# Tensorflow Data Pipeline\n- Create a data pipeline to transform the data so it can be used to train the Machine Learning model","metadata":{}},{"cell_type":"code","source":"dataset = tf.data.Dataset.from_tensor_slices((shuffled_data.values, target.values))","metadata":{"execution":{"iopub.status.busy":"2021-08-07T21:27:00.925947Z","iopub.execute_input":"2021-08-07T21:27:00.926328Z","iopub.status.idle":"2021-08-07T21:27:00.933985Z","shell.execute_reply.started":"2021-08-07T21:27:00.926289Z","shell.execute_reply":"2021-08-07T21:27:00.932886Z"},"trusted":true},"execution_count":131,"outputs":[]},{"cell_type":"code","source":"train_dataset = dataset.shuffle(len(shuffled_data)).batch(1)","metadata":{"execution":{"iopub.status.busy":"2021-08-07T21:27:10.452487Z","iopub.execute_input":"2021-08-07T21:27:10.453010Z","iopub.status.idle":"2021-08-07T21:27:10.458911Z","shell.execute_reply.started":"2021-08-07T21:27:10.452978Z","shell.execute_reply":"2021-08-07T21:27:10.457856Z"},"trusted":true},"execution_count":132,"outputs":[]},{"cell_type":"markdown","source":"# Define Model\n- Define the architecture and training strategy of the deep learning model\n- We are using a straight-forward deep learning model with 21 layers\n- Training will use accuracy as the main metric","metadata":{}},{"cell_type":"code","source":"def get_compiled_model():\n  model = tf.keras.Sequential([\n    tf.keras.layers.Dense(10, activation='relu'),\n    tf.keras.layers.Dense(10, activation='relu'),\n    tf.keras.layers.Dense(1)\n  ])\n\n  model.compile(optimizer='adam',\n                loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n                metrics=['accuracy'])\n  return model","metadata":{"execution":{"iopub.status.busy":"2021-08-07T21:27:12.775744Z","iopub.execute_input":"2021-08-07T21:27:12.776094Z","iopub.status.idle":"2021-08-07T21:27:12.781698Z","shell.execute_reply.started":"2021-08-07T21:27:12.776064Z","shell.execute_reply":"2021-08-07T21:27:12.780871Z"},"trusted":true},"execution_count":133,"outputs":[]},{"cell_type":"markdown","source":"# Train Model","metadata":{}},{"cell_type":"code","source":"model = get_compiled_model()\nhistory = model.fit(train_dataset, epochs=15)","metadata":{"execution":{"iopub.status.busy":"2021-08-07T21:27:14.716666Z","iopub.execute_input":"2021-08-07T21:27:14.717017Z","iopub.status.idle":"2021-08-07T21:27:17.821958Z","shell.execute_reply.started":"2021-08-07T21:27:14.716986Z","shell.execute_reply":"2021-08-07T21:27:17.820861Z"},"trusted":true},"execution_count":134,"outputs":[{"name":"stdout","text":"Epoch 1/15\n127/127 [==============================] - 1s 1ms/step - loss: 0.7215 - accuracy: 0.2579\nEpoch 2/15\n127/127 [==============================] - 0s 1ms/step - loss: 0.6954 - accuracy: 0.2639\nEpoch 3/15\n127/127 [==============================] - 0s 1ms/step - loss: 0.6814 - accuracy: 0.2500\nEpoch 4/15\n127/127 [==============================] - 0s 1ms/step - loss: 0.6879 - accuracy: 0.2385\nEpoch 5/15\n127/127 [==============================] - 0s 1ms/step - loss: 0.6796 - accuracy: 0.2139\nEpoch 6/15\n127/127 [==============================] - 0s 1ms/step - loss: 0.6727 - accuracy: 0.3041\nEpoch 7/15\n127/127 [==============================] - 0s 1ms/step - loss: 0.6648 - accuracy: 0.3296\nEpoch 8/15\n127/127 [==============================] - 0s 2ms/step - loss: 0.6725 - accuracy: 0.2933\nEpoch 9/15\n127/127 [==============================] - 0s 1ms/step - loss: 0.6689 - accuracy: 0.3273\nEpoch 10/15\n127/127 [==============================] - 0s 1ms/step - loss: 0.6660 - accuracy: 0.2974\nEpoch 11/15\n127/127 [==============================] - 0s 1ms/step - loss: 0.6513 - accuracy: 0.3201\nEpoch 12/15\n127/127 [==============================] - 0s 1ms/step - loss: 0.6748 - accuracy: 0.2384\nEpoch 13/15\n127/127 [==============================] - 0s 1ms/step - loss: 0.6644 - accuracy: 0.3333\nEpoch 14/15\n127/127 [==============================] - 0s 1ms/step - loss: 0.6640 - accuracy: 0.2907\nEpoch 15/15\n127/127 [==============================] - 0s 1ms/step - loss: 0.6553 - accuracy: 0.2995\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Analyze Model","metadata":{}},{"cell_type":"code","source":"test_dataset = model.evaluate(dataset.batch(1))","metadata":{"execution":{"iopub.status.busy":"2021-08-07T20:50:20.455825Z","iopub.execute_input":"2021-08-07T20:50:20.456232Z","iopub.status.idle":"2021-08-07T20:50:20.726960Z","shell.execute_reply.started":"2021-08-07T20:50:20.456195Z","shell.execute_reply":"2021-08-07T20:50:20.725956Z"},"trusted":true},"execution_count":92,"outputs":[{"name":"stdout","text":"127/127 [==============================] - 0s 1ms/step - loss: 0.5153 - accuracy: 0.4409\n","output_type":"stream"}]},{"cell_type":"code","source":"plt.plot([i + 1 for i in range(15)], history.history['loss'])","metadata":{"execution":{"iopub.status.busy":"2021-08-07T20:09:02.732420Z","iopub.execute_input":"2021-08-07T20:09:02.732838Z","iopub.status.idle":"2021-08-07T20:09:02.924876Z","shell.execute_reply.started":"2021-08-07T20:09:02.732803Z","shell.execute_reply":"2021-08-07T20:09:02.923861Z"},"trusted":true},"execution_count":47,"outputs":[{"execution_count":47,"output_type":"execute_result","data":{"text/plain":"[<matplotlib.lines.Line2D at 0x7f1904395550>]"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"min_pred = float(\"inf\")\nmax_pred = -float(\"inf\")\nfor i in scaled_data.values:\n    this_pred = model.predict(tf.reshape(list(i), (1, 17)))\n    if this_pred < min_pred:\n        min_pred = this_pred\n    elif this_pred > max_pred:\n        max_pred = this_pred\nprint(min_pred, max_pred)","metadata":{"execution":{"iopub.status.busy":"2021-08-07T20:20:59.094310Z","iopub.execute_input":"2021-08-07T20:20:59.094733Z","iopub.status.idle":"2021-08-07T20:21:04.155960Z","shell.execute_reply.started":"2021-08-07T20:20:59.094688Z","shell.execute_reply":"2021-08-07T20:21:04.154209Z"},"trusted":true},"execution_count":73,"outputs":[{"name":"stdout","text":"[[-3.1807234]] [[2.8405762]]\n","output_type":"stream"}]},{"cell_type":"code","source":"scaled_data.values","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pred_range = (max_pred - min_pred)[0][0]\ncategory_size = (pred_range / 3)\nprint(pred_range, category_size)","metadata":{"execution":{"iopub.status.busy":"2021-08-07T20:28:43.771914Z","iopub.execute_input":"2021-08-07T20:28:43.772286Z","iopub.status.idle":"2021-08-07T20:28:43.778207Z","shell.execute_reply.started":"2021-08-07T20:28:43.772254Z","shell.execute_reply":"2021-08-07T20:28:43.777351Z"},"trusted":true},"execution_count":81,"outputs":[{"name":"stdout","text":"6.0212994 2.0070997873942056\n","output_type":"stream"}]},{"cell_type":"code","source":"def predict_severity(inp):\n    '''\n    - given a list containing a Patient's symptoms\n    - predict the severity of their disease\n    - from 0 as least severe, to 2 as most severe\n    '''\n    this_pred = model.predict(tf.reshape(list(inp), (1, 17)))[0][0]\n    category = -1\n    if this_pred < min_pred + category_size:\n        category = 0\n    elif this_pred >= min_pred + category_size and this_pred <= max_pred - category_size:\n        category = 1\n    else:\n        category = 2\n    return category","metadata":{"execution":{"iopub.status.busy":"2021-08-07T20:32:49.663230Z","iopub.execute_input":"2021-08-07T20:32:49.663709Z","iopub.status.idle":"2021-08-07T20:32:49.670236Z","shell.execute_reply.started":"2021-08-07T20:32:49.663663Z","shell.execute_reply":"2021-08-07T20:32:49.669165Z"},"trusted":true},"execution_count":83,"outputs":[]},{"cell_type":"code","source":"for i in scaled_data.values:\n    print( predict_severity(i), end=\" ,\")","metadata":{"execution":{"iopub.status.busy":"2021-08-07T20:33:24.233040Z","iopub.execute_input":"2021-08-07T20:33:24.233415Z","iopub.status.idle":"2021-08-07T20:33:29.391005Z","shell.execute_reply.started":"2021-08-07T20:33:24.233381Z","shell.execute_reply":"2021-08-07T20:33:29.389662Z"},"trusted":true},"execution_count":84,"outputs":[{"name":"stdout","text":"0 ,0 ,1 ,2 ,2 ,2 ,2 ,1 ,1 ,0 ,1 ,1 ,0 ,0 ,0 ,1 ,2 ,0 ,1 ,2 ,1 ,0 ,0 ,1 ,1 ,1 ,1 ,1 ,2 ,2 ,2 ,1 ,2 ,1 ,1 ,1 ,1 ,1 ,2 ,1 ,0 ,0 ,1 ,1 ,1 ,2 ,1 ,1 ,2 ,2 ,1 ,2 ,1 ,0 ,1 ,2 ,0 ,2 ,2 ,1 ,0 ,0 ,1 ,2 ,1 ,2 ,0 ,1 ,1 ,1 ,0 ,0 ,1 ,1 ,0 ,1 ,2 ,1 ,0 ,1 ,1 ,1 ,1 ,1 ,0 ,0 ,1 ,2 ,0 ,2 ,1 ,2 ,1 ,2 ,0 ,0 ,0 ,0 ,1 ,1 ,0 ,1 ,1 ,1 ,0 ,1 ,1 ,1 ,1 ,1 ,1 ,1 ,1 ,1 ,1 ,2 ,1 ,1 ,1 ,1 ,0 ,1 ,0 ,1 ,1 ,1 ,2 ,","output_type":"stream"}]},{"cell_type":"code","source":"model.save('/kaggle/working/model')","metadata":{"execution":{"iopub.status.busy":"2021-08-07T20:36:52.399523Z","iopub.execute_input":"2021-08-07T20:36:52.400305Z","iopub.status.idle":"2021-08-07T20:36:53.074338Z","shell.execute_reply.started":"2021-08-07T20:36:52.400247Z","shell.execute_reply":"2021-08-07T20:36:53.073593Z"},"trusted":true},"execution_count":87,"outputs":[]},{"cell_type":"code","source":"import shutil\nshutil.make_archive('/kaggle/working/model', 'zip', '/kaggle/working/model')","metadata":{"execution":{"iopub.status.busy":"2021-08-07T20:38:26.009484Z","iopub.execute_input":"2021-08-07T20:38:26.010039Z","iopub.status.idle":"2021-08-07T20:38:26.019831Z","shell.execute_reply.started":"2021-08-07T20:38:26.009993Z","shell.execute_reply":"2021-08-07T20:38:26.019042Z"},"trusted":true},"execution_count":89,"outputs":[{"execution_count":89,"output_type":"execute_result","data":{"text/plain":"'/kaggle/working/model.zip'"},"metadata":{}}]},{"cell_type":"markdown","source":"# Conclusions\n- We were able to get a pretty good model with very little data\n- It is twice as good as guessing randomly on this problem\n- However, we need more data to create a good enough model for any practical use\n- This is just a proof of concept","metadata":{}}]}