a2-analyzing-cardiovascular-data.ipynb

{"cells":[{"metadata":{"_uuid":"945c0f151b28ca53b79e0f7cf5b107af959a3370"},"cell_type":"markdown","source":"<center>\n<img src=\"https://habrastorage.org/files/fd4/502/43d/fd450243dd604b81b9713213a247aa20.jpg\">\n\n## [mlcourse.ai](https://mlcourse.ai) - Open Machine Learning Course\n    \nAuthors: [Ilya Baryshnikov](https://www.linkedin.com/in/baryshnikov-ilya/), [Maxim Uvarov](https://www.linkedin.com/in/maxis42/), and [Yury Kashnitsky](https://www.linkedin.com/in/festline/). Translated and edited by [Inga Kaydanova](https://www.linkedin.com/in/inga-kaidanova-a92398b1/), [Egor Polusmak](https://www.linkedin.com/in/egor-polusmak/), [Anastasia Manokhina](https://www.linkedin.com/in/anastasiamanokhina/), and [Yuanyuan Pao](https://www.linkedin.com/in/yuanyuanpao/). All content is distributed under the [Creative Commons CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license."},{"metadata":{"collapsed":true,"_uuid":"66409c8728e75df39a1390bcb634ae50c6b37652"},"cell_type":"markdown","source":"# <center>Assignment #2 (demo)\n## <center>Analyzing cardiovascular disease data "},{"metadata":{"_uuid":"5cea59b8adb04c5a07a32c5b70a829319ba0cf2a"},"cell_type":"markdown","source":"In this assignment, you will answer questions about a dataset on cardiovascular disease. You do not need to download the data: it is already in the repository. There are some Tasks that will require you to write code. Complete them and then answer the questions in the [form](https://docs.google.com/forms/d/13cE_tSIb6hsScQvvWUJeu1MEHE5L6vnxQUbDYpXsf24). \n\n#### Problem\n\nPredict the presence or absence of cardiovascular disease (CVD) using the patient examination results.\n\n#### Data description\n\nThere are 3 types of input features:\n\n- *Objective*: factual information;\n- *Examination*: results of medical examination;\n- *Subjective*: information given by the patient.\n\n| Feature | Variable Type | Variable      | Value Type |\n|---------|--------------|---------------|------------|\n| Age | Objective Feature | age | int (days) |\n| Height | Objective Feature | height | int (cm) |\n| Weight | Objective Feature | weight | float (kg) |\n| Gender | Objective Feature | gender | categorical code |\n| Systolic blood pressure | Examination Feature | ap_hi | int |\n| Diastolic blood pressure | Examination Feature | ap_lo | int |\n| Cholesterol | Examination Feature | cholesterol | 1: normal, 2: above normal, 3: well above normal |\n| Glucose | Examination Feature | gluc | 1: normal, 2: above normal, 3: well above normal |\n| Smoking | Subjective Feature | smoke | binary |\n| Alcohol intake | Subjective Feature | alco | binary |\n| Physical activity | Subjective Feature | active | binary |\n| Presence or absence of cardiovascular disease | Target Variable | cardio | binary |\n\nAll of the dataset values were collected at the moment of medical examination."},{"metadata":{"_uuid":"10749a7f9b46749e3a90ff697bc298760045c333"},"cell_type":"markdown","source":"Let's get to know our data by performing a preliminary data analysis.\n\n#  Part 1. Preliminary data analysis\n\nFirst, we will initialize the environment:"},{"metadata":{"trusted":true,"_uuid":"85426734e890fcf72416f29a953da115c1171080"},"cell_type":"code","source":"# Import all required modules\nimport pandas as pd\nimport numpy as np\n\n# Disable warnings\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\n# Import plotting modules\nimport seaborn as sns\nsns.set()\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker\n%matplotlib inline","execution_count":1,"outputs":[]},{"metadata":{"_uuid":"cbcf821e4bea8a7f7fb1ee4a9bf285b6c4b588ee"},"cell_type":"markdown","source":"You will use the `seaborn` library for visual analysis, so let's set that up too:"},{"metadata":{"trusted":true,"_uuid":"b33601a0cadc2fb2d92f211c31bf99a502f71d48"},"cell_type":"code","source":"# Tune the visual settings for figures in `seaborn`\nsns.set_context(\n    \"notebook\", \n    font_scale=1.5,       \n    rc={ \n        \"figure.figsize\": (11, 8), \n        \"axes.titlesize\": 18 \n    }\n)\n\nfrom matplotlib import rcParams\nrcParams['figure.figsize'] = 11, 8","execution_count":2,"outputs":[]},{"metadata":{"_uuid":"89381351e4b95530b355e0e55bc4536c0a813d10"},"cell_type":"markdown","source":"To make it simple, we will work only with the training part of the dataset:"},{"metadata":{"trusted":true,"_uuid":"2bd0005e660693a061577ca5244b48ccebf36719"},"cell_type":"code","source":"df = pd.read_csv('../input/mlbootcamp5_train.csv')\nprint('Dataset size: ', df.shape)\ndf.head()","execution_count":3,"outputs":[{"output_type":"stream","text":"Dataset size:  (70000, 13)\n","name":"stdout"},{"output_type":"execute_result","execution_count":3,"data":{"text/plain":"   id    age  gender  height  weight   ...    gluc  smoke  alco  active  cardio\n0   0  18393       2     168    62.0   ...       1      0     0       1       0\n1   1  20228       1     156    85.0   ...       1      0     0       1       1\n2   2  18857       1     165    64.0   ...       1      0     0       0       1\n3   3  17623       2     169    82.0   ...       1      0     0       1       1\n4   4  17474       1     156    56.0   ...       1      0     0       0       0\n\n[5 rows x 13 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>id</th>\n      <th>age</th>\n      <th>gender</th>\n      <th>height</th>\n      <th>weight</th>\n      <th>ap_hi</th>\n      <th>ap_lo</th>\n      <th>cholesterol</th>\n      <th>gluc</th>\n      <th>smoke</th>\n      <th>alco</th>\n      <th>active</th>\n      <th>cardio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>18393</td>\n      <td>2</td>\n      <td>168</td>\n      <td>62.0</td>\n      <td>110</td>\n      <td>80</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    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>20228</td>\n      <td>1</td>\n      <td>156</td>\n      <td>85.0</td>\n      <td>140</td>\n      <td>90</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>18857</td>\n      <td>1</td>\n      <td>165</td>\n      <td>64.0</td>\n      <td>130</td>\n      <td>70</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>17623</td>\n      <td>2</td>\n      <td>169</td>\n      <td>82.0</td>\n      <td>150</td>\n      <td>100</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>17474</td>\n      <td>1</td>\n      <td>156</td>\n      <td>56.0</td>\n      <td>100</td>\n      <td>60</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    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"_uuid":"636bf459023fbc1d1f1b73863c5246397dc26e6d"},"cell_type":"markdown","source":"It would be instructive to peek into the values of our variables.\n \nLet's convert the data into *long* format and depict the value counts of the categorical features using [`factorplot()`](https://seaborn.pydata.org/generated/seaborn.factorplot.html)."},{"metadata":{"trusted":true,"_uuid":"334ad6a83e7bb58f2c65ec512bfc401a0948c17a"},"cell_type":"code","source":"df_uniques = pd.melt(frame=df, value_vars=['gender','cholesterol', \n                                           'gluc', 'smoke', 'alco', \n                                           'active', 'cardio'])\ndf_uniques = pd.DataFrame(df_uniques.groupby(['variable', \n                                              'value'])['value'].count()) \\\n    .sort_index(level=[0, 1]) \\\n    .rename(columns={'value': 'count'}) \\\n    .reset_index()\n\nsns.factorplot(x='variable', y='count', hue='value', \n               data=df_uniques, kind='bar', size=12);","execution_count":4,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 933.9x864 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"collapsed":true,"_uuid":"cb94ec9ddf78c607cdec5536581b8ec4004ac16b"},"cell_type":"markdown","source":"We can see that the target classes are balanced. That's great!\n\nLet's split the dataset by target values. Can you already spot the most significant feature by just looking at the plot?"},{"metadata":{"trusted":true,"_uuid":"a712d76f8c11588a9aebd4be6a00f01c53a8d80a"},"cell_type":"code","source":"df_uniques = pd.melt(frame=df, value_vars=['gender','cholesterol', \n                                           'gluc', 'smoke', 'alco', \n                                           'active'], \n                     id_vars=['cardio'])\ndf_uniques = pd.DataFrame(df_uniques.groupby(['variable', 'value', \n                                              'cardio'])['value'].count()) \\\n    .sort_index(level=[0, 1]) \\\n    .rename(columns={'value': 'count'}) \\\n    .reset_index()\n\nsns.factorplot(x='variable', y='count', hue='value', \n               col='cardio', data=df_uniques, kind='bar', size=9);","execution_count":5,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1365.9x648 with 2 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"_uuid":"4f4fdb7b979c6594b3060a0b80473fabbdf6acd0"},"cell_type":"markdown","source":"You can see that the distribution of cholesterol and glucose levels great differs by the value of the target variable. Is this a coincidence?\n\nNow, let's calculate some statistics for the feature unique values:"},{"metadata":{"trusted":true,"_uuid":"e9cbba719f93f6b544567638be7332b7385fc715"},"cell_type":"code","source":"for c in df.columns:\n    n = df[c].nunique()\n    print(c)\n    if n <= 3:\n        print(n, sorted(df[c].value_counts().to_dict().items()))\n    else:\n        print(n)\n    print(10 * '-')","execution_count":6,"outputs":[{"output_type":"stream","text":"id\n70000\n----------\nage\n8076\n----------\ngender\n2 [(1, 45530), (2, 24470)]\n----------\nheight\n109\n----------\nweight\n287\n----------\nap_hi\n153\n----------\nap_lo\n157\n----------\ncholesterol\n3 [(1, 52385), (2, 9549), (3, 8066)]\n----------\ngluc\n3 [(1, 59479), (2, 5190), (3, 5331)]\n----------\nsmoke\n2 [(0, 63831), (1, 6169)]\n----------\nalco\n2 [(0, 66236), (1, 3764)]\n----------\nactive\n2 [(0, 13739), (1, 56261)]\n----------\ncardio\n2 [(0, 35021), (1, 34979)]\n----------\n","name":"stdout"}]},{"metadata":{"_uuid":"53507160b237077bf8b19fb987b910669c04ba8b"},"cell_type":"markdown","source":"In the end, we have:\n- 5 numerical features (excluding *id*);\n- 7 categorical features;\n- 70000 records in total."},{"metadata":{"_uuid":"5f9dc83567c83c1ab2eeb4b855d3f8460c1a735e"},"cell_type":"markdown","source":"## 1.1. Basic observations"},{"metadata":{"_uuid":"129e6509399146154e8c7e293a2fd11f5d267fa4"},"cell_type":"markdown","source":"**Question 1.1. (1 point). How many men and women are present in this dataset? Values of the `gender` feature were not given (whether \"1\" stands for women or for men) – figure this out by looking analyzing height, making the assumption that men are taller on average. **\n1. 45530 women and 24470 men\n2. 45530 men and 24470 women\n3. 45470 women and 24530 men\n4. 45470 men and 24530 women"},{"metadata":{"trusted":true},"cell_type":"code","source":"#quantitative vs categorical/binary\n\nsns.boxplot(x='gender', y='height', data=df)\n\n#gender 1 should be female and 2 should be male","execution_count":7,"outputs":[{"output_type":"execute_result","execution_count":7,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7f372ba48c50>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 792x576 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAArkAAAH4CAYAAABOsvXgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzs3Xt4XVWd//H3SYqUUmlKWqBFbjq4RMWfF0BKS1uU+enMM6kW78UigoMDqCgzThlBVNQZEK/DTfAOygzoUGxG/TnDpS3pZRhARFGXxaEUKEibaSqlFEhyfn/sk3JyepImp0nWObvv1/Pk2Wftvc4+3wBNP6ysvVahWCwiSZIk5UlT6gIkSZKkkWbIlSRJUu4YciVJkpQ7hlxJkiTljiFXkiRJuWPIlSRJUu4YciVJkpQ7hlxJkiTljiFXkiRJuWPIlSRJUu6MS11AndoTOBp4DOhJXIskSdLurBmYBvw38MxQ32TIre5o4I7URUiSJGm744GOoXY25Fb3GMCmTU/R21tMXYskSdJuq6mpwOTJe0Mpnw2VIbe6HoDe3qIhV5IkqT4MawqpD55JkiQpdwy5kiRJyh1DriRJknLHkCtJkqTcMeRKkiQpdwy5kiRJyh1DriRJknLHkCtJkqTcMeRKkiQpdwy5kiRJyh1DriRJknLHkCtJkqTcGZfqg0MIRwOnAicAhwCdwErgghjjA2X9lgJzqtzihhjjuyvuuSdwEbAQmAz8Ejg/xnjrKHwLkiRJqlMpR3IXAScBtwDnANcAc4FfhBCOqOi7jiy4ln9dXuWe3wU+Bny/dM9e4GchhBkjX760a7q6NnHxxRexeXNX6lIkScqdZCO5wJeBBTHGZ/tOhBBuAH5FFoBPLeu7Kcb4/cFuFkI4Bng38LEY41dL564Ffg1cAswe0eqlXdTevpg1ayJLltzEwoWnpS5HkqRcSTaSG2NcWR5wS+fWAPcDlSO5hBDGhRAmDnLLtwPPAd8su9824FvArBDCtBEpXBoBXV2b6OhYRrFYpKNjuaO5kiSNsLp68CyEUAD2BzZWXDoCeAp4MoSwPoTwiRBCZe2vAX4XY9xScf5OoAC8ejRqlmrR3r6Y3t4iAL29vSxZclPiiiRJypeU0xWqORk4EDi/7NwfgNvIpjHsA7wH+DxwMPA3Zf2mAY9WuedjpeP04RbT2jrYwLFUu9WrV9DT0w1AT083q1ev4Nxzz0lclSRJ+VE3ITeE8DLgCqADuK7vfIzx9Iqu3wsh3AicEUL4Sowxls7vBTxT5dbbyq4PS2fnlu2jbdJIOvbYmSxfvpSenm6am8dx7LEz2bDhydRlSZJUd5qaCjUNPNbFdIUQwgHAT4BNwDtijL07ecuXyKYgnFB27mlgzyp9x5ddl+pCW9t8mpoKADQ1NTFv3kmJK5IkKV+Sh9wQwiTgZ8Ak4E0xxseH8LaHS8d9y849RjZloVLfufU1FymNsJaWycyaNYdCocCsWbOZNKkldUmSJOVK0pAbQhgPtAMvBf6qbOrBzry4dNxQdu5e4GVVVmB4fen4y5oLlUZBW9t8Dj88OIorSdIoSBZyQwjNwA3ADLIpCqur9NmntItZ5fs+QbbRwy1ll34E7AF8oKzvnsD7gRUxRkdyVVdaWiZz3nkXOoorSdIoSPng2ZeAeWQjufuGEN5bdm1LjPFm4LXA9SGEfwEeACYC7wSOAi6JMT7Y94YY43+FEH4IfKG0Ju4fgPeRbRl86hh8P5IkSaoTKUNu37q1baWvcg8BN5eOK4C3ka2f20u2g9mpMcbvVbnnKcBnS8fJwH3AX8YYV4x49ZIkSapbhWLRJbKqOBR40CXEJEmS0ipbQuwwYO2Q3zdaBUmSJEmpGHIlSZKUO4ZcSZIk5Y4hV5IkSbljyJUkSVLuGHIlSZKUO4ZcSZIk5Y4hV5IkSbljyJUkSVLuGHIlSZKUO4ZcSZIk5Y4hV5IkSbljyJUkSVLuGHIlSZKUO4ZcSZIk5Y4hV5IkSbljyJUkSVLuGHIlSZKUO4ZcSZIk5Y4hV5IkSbljyJUkSVLuGHIlSZKUO4ZcSZIk5Y4hV5IkSbljyJUkSVLuGHIlSZKUO4ZcSZIk5Y4hV5IkSbljyJUkSVLuGHIlSZKUO4ZcSZIk5Y4hV5IkSbljyJUkSVLuGHIlSZKUO4ZcSZIk5Y4hV5IkSbljyJUkSVLujEv1wSGEo4FTgROAQ4BOYCVwQYzxgVKfCcD7gbcCrwQmAmuAa4BvxBh7yu43F7h9gI87Isb4u1H5RiRJklR3koVcYBEwE/ghcB9wAPAh4BchhGNijL8FXgxcBtwKfBn4E/Am4CrgaOD0Kvf9KnB3xbn1o/ENSJIkqT6lDLlfBhbEGJ/tOxFCuAH4FVkAPhV4HDgyxnh/2fuuDiF8GzgthPC5GOODFfddFmO8eXRLlyRJUj1LFnJjjCurnFsTQrgfOKLU3ghsrPL2xWTTGF4GVIZcQggvBJ6OMXaPaNGSJElqCHX14FkIoQDsT/VgW+6A0rFav+vIpjU8HUL4jxDCkSNYoiRJkhpAyukK1ZwMHAicP1CHEMILgI8Cf6D/3NtngR8BPyMLv68C/g7oCCEcHWP8/XCLaW2dONy3SJIkqQ7UTcgNIbwMuALoIBuNHcjlZNMZ/iLG2Nt3sjT9oXwKxJIQQjtwF/ApsgA9LJ2dW+jtLQ73bZIkSRohTU2FmgYe62K6QgjhAOAnwCbgHeXhtaLfx4G/Bj4RY/z5zu4bY/wlcAvwxhEsV5IkSXUuecgNIUwim2IwCXhTjPHxAfqdClwCXBFjvHgYH/EwsO+u1ilJkqTGkXS6QghhPNAOvBR4Y4wxDtDvLcA3gX8DPjLMj3kxsGFX6pQkSVJjSTaSG0JoBm4AZpBNUVg9QL/ZwL8Cy4H3DjKVYWqVc7PIdlTb6dQGSZIk5UfKkdwvAfPIRnL3DSG8t+zalhjjzSGEQ4AlQJFs5YR3hBDK77Eyxvg/pdc3hBC2kj18tpFsG+AzSq8/PZrfiCRJkupLypD76tKxrfRV7iHgZuAwsrm6kK28UOn9QF/IvZlsBYW/BfYBngCuBz4dY1w3cmVLkiSp3hWKRZfIquJQ4EGXEJMkSUqrbAmxw4C1Q37faBUkSZIkpWLIlSRJUu4YciVJkpQ7hlxJkiTljiFXkiRJuWPIlSRJUu4YciVJkpQ7hlxJkiTljiFXkiRJuWPIlSRJUu4YciVJkpQ7hlxJkiTljiFXkiRJuWPIlSRJUu4YciVJkpQ7hlxJkiTljiFXkiRJuWPIlSRJUu4YciVJkpQ7hlxJkiTljiFXkiRJuWPIlSRJUu4YciVJkpQ7hlxJklSzrq5NXHzxRWze3JW6FKkfQ64kSapZe/ti1qyJLFlyU+pSpH4MuZIkqSZdXZvo6FhGsViko2O5o7mqK4ZcSZJUk/b2xfT2FgHo7e11NFd1xZArSZJqsmrVCnp6ugHo6elm1aoViSuSnmfIlSRJNZkxYybNzeMAaG4ex4wZMxNXJD3PkCtJkmrS1jafpqYCAE1NTcybd1LiiqTnGXIlSVJNWlomM2vWHAqFArNmzWbSpJbUJUnbjUtdgCRJalxtbfN59NFHHMVV3SkUi8XUNdSjQ4EHOzu3bH9qVJIkSWOvqalAa+tEgMOAtUN+32gVJEmSJKViyJUkSVLuGHIlSZKUO8kePAshHA2cCpwAHAJ0AiuBC2KMD1T0PQ74AvBa4E/ADcA/xBi3VvTbE7gIWAhMBn4JnB9jvHVUvxlJkiTVlZQjuYuAk4BbgHOAa4C5wC9CCEf0dQohvBq4FRgPnAt8E/ggWdCt9F3gY8D3S/fsBX4WQpgxWt+EJEmS6k/KJcS+DCyIMT7bdyKEcAPwK7IAfGrp9D+SjfLOjTFuKfVbC3wjhPCGGONtpXPHAO8GPhZj/Grp3LXAr4FLgNmj/y1JkrR76eraxNe/fhlnnvkR18lVXUk2khtjXFkecEvn1gD3A0cAhBD2Af4cuLYv4JZcC2wB3ll27u3Ac2QjvX332wZ8C5gVQpg2Gt+HJEm7s/b2xaxZE1my5KbUpUj91NWDZyGEArA/sLF06kiy0ea7yvuVwvG9wGvKTr8G+F1FGAa4EygArx6NmiVJ2l11dW2io2MZxWKRjo7lbN7clbokabu6CrnAycCBwI2ldt/o62NV+j4GTC9rTxukHxV9JUnSLmpvX7x906Te3l5Hc1VX6mZb3xDCy4ArgA7gutLpvUrHZ6q8ZVvZ9b6+A/Wjou+QlHbXkEbFT3/6U6666irOPvts3vzmN6cuR5KGbfXqFfT0dAPQ09PN6tUrOPfccxJXJWXqIuSGEA4AfgJsAt4RY+wtXXq6dNyzytvGl13v6ztQPyr6Donb+mo0XXXV1wG44oored3rZiauRpKG79hjZ3L77bf0a2/Y8GTCipRHZdv6Du99o1DLsIQQJgE/AyYBb4oxPl52uW+qQbWHxqYB6yv6DtSPir5SUrfd9p9A3/9AFVm2zKWcJTWeJ5/s/xjM009vHaCnNPaShtwQwnigHXgp8FcxxljR5ddAN3BUxfteQPYg2b1lp+8FXhZCqIz6ry8dfzlSdUu76vvf/26/9ve+9+00hUjSLrjrrtX92qtXr0xUibSjZCE3hNBMtqHDDLIpCqsr+8QYN5NtFrGwIrwuBCYCPyw79yNgD+ADZZ+xJ/B+YEWM0ZFc1ZHKaTBOi5EkaSSlnJP7JWAe2UjuviGE95Zd2xJjvLn0+nyy7X6XhhC+CbwI+FvgZzHG7ROBYoz/FUL4IfCF0pq4fwDeR7Zl8Kmj/c1IkiSpfqQMuX3r1raVvso9BNwMEGO8J4RwItmuZV8B/gR8A/iHKvc8Bfhs6TgZuA/4yxjjihGvXtoFTU1N9Pb29mtLkqSRkyzkxhjnDqNvB7DTx89LO5x9vPQl1a1CoQnorWhLkqSR4t+sUgJ960oO1JYkSbvGkCslMGHC3oO2JUnSrjHkSgmceeaH+7XPOssdgiRJGkmGXCmBV7ziVTQ3Z1Pim5vH8fKXvzJxRZIk5YshV0qgq2sT5Tuebd7clbIcSZJyx5ArJdDevpienh4Aenp6WLLkpsQVSZKUL4ZcKYGVK+8YtC1JknaNIVdKoG8+7kBtSZK0awy5UgJbtz41aFuSJO0aQ66UgOvkSpI0ugy5UgLbtj09aFuSJO0aJwJKCfT29g7altR4VqxYTkfHstRlJHfJJZ9NXcKYmDVrDjNnzk5dhgbhSK4kSapJ5VSrvfeemKgSaUeO5EqSNAJmzpy9W47snXbagu2vL7vsmoSVSP05kitJkmrWN5p77LHHJa5E6s+RXEmSVLODDjoYgDPO+FDiSqT+HMmVJElS7hhyJUmSlDuGXEmSJOWOIVeSJEm5Y8iVJElS7hhyJUmSlDuGXEmSJOWOIVeSJEm5Y8iVJElS7hhyJUmSlDuGXEmSJOWOIVeSJEm5Y8iVJElS7hhyJUmSlDuGXEmSJOWOIVeSJEm5Y8iVJElS7oxLXYC0YsVyOjqWpS4juUsu+WzqEsbErFlzmDlzduoyJEk550iuJEmScseRXCU3c+bs3XJk77TTFmx//e1vX5+wEkmS8seRXEmSJOVO0pHcEMI04Bzg9cBRwETghBjj0rI+c4HbB7nNBTHGz5f6ngp8Z4B+e8UYt+161dLICOEIABYt+mTiSiRJyp/U0xUCsAh4ALgPOK5Kn98CC6ucXwj8X+A/qlw7H1hXce7Z2suUJElSI0kdcu8GpsQYO0MIbwUWV3aIMf4R+H7l+RDCp4A1Mcb/rnLfn8YY7x3xaiVJktQQkobcGOOTtbwvhHAM8GfApwfpsw+wJcbYW1t1kiRJalSpR3JrdXLp+IMBrt9BNr93WwjhJ8C5McbK6QuSpFFy/fXX8vDDD6UuQ2Ng3brs3/Pustb37uyggw5hwYJTUpcxZA0XckMIzcC7gDtjjA9UXH6K7MGzpcCTZA+0fRR4fQjhNTHGjcP5rNbWibtesDSAPfZoBmDq1BcmrkQaeY8//ghrH/gdB0xsuL9mNEwTyH5huu3xyr+SlSePb+lmjz2aG+rvrEb86fNGYH/gHysvxBh/CPyw7NTiEMJy4CfAx8geSBuyzs4t9PYWd6FUaWDPPdcDwIYNNc3akerac8/1cMDEcbz/VfumLkXSCPjOff/Lc8/1JPk7q6mpUNPAYyOuk3sy0APcMJTOMcafAr8jC8eSJEnaDTRUyA0h7AXMB24prbowVA8DDidIkiTtJhoq5ALzgBcy8ANnA3kxsGHky5EkSVI9arSQuwDYSpX1dAFCCFOrnFsAvAT4+eiWJkmSpHqR/MGzEMIFpZdHlI4LQwizgK4Y4+Vl/fYF/gL4txjjlgFutzKEcDdwD/An4BjgfcDvga+NRv2SJEmqP8lDLlC5sN5ppeNDwOVl598B7AFcP8i9bgD+CngzMAF4tHSPz8QYN49ItZIkSap7yUNujLEwxH5XA1fvpM8FwAWD9ZEkSVL+JQ+5kqT82by5i01buvnOff+buhRJI+DxLd1M3tyVuoxhabQHzyRJkqSdciRXkjTiJk1qYc+nN7rjmZQT37nvfxk/qSV1GcPiSK4kSZJyx5ArSZKk3DHkSpIkKXcMuZIkScodQ64kSZJyx9UV6sz111/Lww8/lLoMjYF167J/z5dcUrnpn/LmoIMOYcGCU1KXIUm7FUNunXn44YeIax6geXxjLdOh4evtaQbggYc3Jq5Eo6lnW2Mtni5JeWHIrUPN41uYcMgbU5chaQRsfejW1CVI0m7JkCtJGhWPu63vbmHLs70ATHyBj/nk2eNbujk0dRHDNOyQG0LoARbGGK8f4Pq7gOtjjM27WpwkqTEddNAhqUvQGHmi9HzBlAP8d55nh9J4f65rGckt7OJ1SVLO+aDd7qPv4dlFiz6ZuBKpv9H43cLBwJOjcF9JkiRpSIY0khtCeAvwlrJTZ4QQTqzSdV/gRKBjBGqTJEmSajLU6QqvBk4tvS4Cs0tflbYAK4EP7XJlkiRJUo2GFHJjjJ8BPgMQQugF3jvQg2eSJElSarU8eHYYsGGkC5EkSZJGyrBDbozRPWdH0ebNXfRs63IBeSknerZ1sXmzS5JL0lir6SdvCGEG2bzbw4FWdlw2rBhjfMku1iZJkiTVpJbNIE4BvgM8B/weWDfSRe3OJk1qYcOfut3WV8qJrQ/dyqRJLanLkKTdTi0juecDETgxxrh+hOuRJEmSdlktm0EcAlxlwJUkSVK9qiXkPgLsOdKFSJIkSSOllpD7deDkEELzSBcjSZIkjYSdzskNIVTubHYX8DbgzhDCFcCDQE/l+2KMy0ekQkmSJGmYhvLg2VKyrXzL9S0Z9s0BrhUBR3pr5Dq5u4fe7m0ANI0bn7gSjaaebV3AlNRlSNJuZygh9/2jXoW2O+igQ1KXoDGybl22r8rBBxmA8m2Kf64lKYGdhtwY4/fGohBlFiw4JXUJGiOXXPJZABYt+mTiSiRJyp9aHjyTJEmS6lqtO54Npgg8TbYT2j0xxu5aCpMkSZJqVcuOZ9+l/8NmfQ+hVZ4rAp0hhPNjjN+orTxJkiRp+GqZrvDnwD3AWuA84K2lr38onbsLOAn4e2AL8PUQwttGoFZJkiRpSGoZyZ1JtuPZkTHGrWXnl4QQrgRWAa+MMX4uhHA18EvgXODfdrlaSZIkaQhqGck9HfheRcAFIMa4hWw6wwfK2tcCR+5CjZIkSdKw1BJy92PwjR7GAfuXtddT24ixJEmSVJNawufvgdNDCF+PMf6p/EIIYRLZSG8sO30Y8ES1G4UQpgHnAK8HjgImAifEGJdW9FsLVFtN/ZIY43kVfVuALwDzgQnAfwHnxhjvHdq3J0mSpEZXS8i9CLgRiCGE75CFXoAAnEo20vtOgBBCE/BuYMUA9wrAIuAB4D7guEE+927gqxXnft3vZtnn/YRsesQXgU7gLGBpCOF1McY/7PzbkyRJUqMbdsiNMf5bCGEB8GWy1RXKPQa8N8bY95BZM/AXwIYBbnc3MCXG2BlCeCuweJCPfiTG+P2dlPd2sqA8P8Z4M0AI4UayIP4pwO3EJEmSdgM1zZWNMd4QQvgR8Dqy6QhQWj4sxthT1u85+k9dqLzPk8P53BDCnkBztYfeSt5ONgf4x2WfsaEUdN8TQtijVJMkSRoBf/zj43R1bWLx4huZP/+dqcuRtqv5gbBSmL2z9DUW/i/wFNAcQvgfsvm411T0eQ1wd4yxWHH+TuAM4M+A3456pZKk3c6KFcvp6FiWuowx19W1CYD29pv5/e8HHNfKnVmz5jBz5uzUZWgQjbLqwX3AHWTTDqYCfw1cHULYN8Z4cVm/acBtVd7/WOk4nWGE3NbWibVVKw3BHntki5RMnfrCxJVIGgn77LPX9j/Xu4v169f3a2/c+ATTpk1LVM3Y2mefvfz5Xed2GnJDCA8CvcDLYozPlUZRd6YYY3zJLldXEmOcV1HTd4AO4JMhhKtijJtLl/YCnqlyi21l14ess3MLvb2Vg8LSyHjuuWxmz4YNw5q1I6lOHXnk0Rx55NGpyxhTp522oF+7s7OTSy+9LFE1Y8+f32OjqalQ08DjUEZyHwKKpS+AdWWvk4gx9oQQvgr8KzAD+H+lS0+T7cZWaXzZdUmSJOXcTkNujHHuYO2EHi4d9y079xjZlIVKfefWV7kmSZKknKllx7N68eLSsXx5snuB14UQChV9Xw9sIVuPV5IkSTlX84NnIYRDgRPJtvD9QYxxbQjhBcABwOMxxmdHosAQwr5AV4yxt+zceODjwJPAqrLuPyJbRuwtQN86uVOAdwA/dvkwSZKk3UNNITeEcAlwLtlmD0WyoLmWbO7rb4AL2HF3soHudUHp5RGl48IQwiyyYHs5MA84v7Qu71qgFXgf8FLgzBjjlrLb/QhYDVwbQvgisJFsx7Mm4NM1fKuSJElqQMMOuSGED5KNov4z8O/Af/RdizH+KYSwBGhjiCEX+GxF+7TS8SHgcuBXwO+AhWTLhz0D3AP8bYzx38vfWHog7S+BS4GPkK2mcCdwSozRqQqSJEm7iVpGcs8CFscYPxpCaK1y/T7gQ0O9WYyxcv5s5fW7yULzUO+3CfhA6UuSJEm7oVoePHsp8J+DXN8ATKmtHEmSJGnX1RJytwF7D3L9EKCrtnKk3UeMvyXG3+6wmLokNYrm5uaKdqNspKrdQS3/Nd4JzAe+VHmhtOrBQmDFLtal3cjuut97uUsuqZyanl/u9y7lx/77H8D69Y+WtfdPWI3UXy0juZcCM0II1wGvKp07IITwJmAp8CLgiyNTnpRPMf520LYkNYLOzo2DtqWUhj2SG2O8JYRwJvA1oO/3rNeVjs8Cfx1jXFX1zVIVM2fO3u1G9qpNUVi06JMJKpGk2rW2Tuk3ktva6iM5qh81TZ6JMV5TWirsHcDLgAKwBrgxxvjooG+WJEm5sHHjxkHbUko1zxCPMT4OXDaCtUiSpAYyZUr/kdwpUxzJVf2odcezGWRr4R5OtgNZ5Vq3xRjjS3axNkmSVMc6OzsHbUspDfvBsxDCKUAH8DaybXzXke1OVv61bgRrlCRJdei442YN2pZSqmUk93wgAifGGNePcD2SJKlBPPPMc/3aPT09iSqRdlTLEmKHAFcZcCVJ2r2tXNl/jfPly29PVIm0o1pC7iPAniNdiCRJkjRSagm5XwdODiE077SnJEmSlMBO5+SGECpX6b+L7KGzO0MIVwAPAjtMwokxLh+RCiVJkqRhGsqDZ0uBYsW5viXDvjnAtSLgSK8kSTk2YcLebN36VL+2VC+GEnLfP+pVSJKkhlMecKu1pZR2GnJjjN8bi0IkSVJjaW4eR09Pd7+2VC9qefBMkiSpX8Ct1pZSMuRKkqSaVI7cOpKremLIlSRJNXEkV/XMkCtJkmpSuZqCqyuonhhyJUlSTbq7uwdtSykZciVJUk1mzjx+0LaUkiFXkiTVpK1t/vaHzcaNG8e8eSclrkh6niFXkiTVpKVlMrNnz6VQKHD88XOZNKkldUnSdq71IUmSatbWNp9HH33EUVzVHUOuJEmqWUvLZM4778LUZUg7cLqCJEmq2f3338fpp5/Mb37z69SlSP0YcqUE3CVIUl5cddVlFItFrrzya6lLkfox5EoJFIu9g7YlqRHcf/99bN36FABbtz7laK7qiiFXSqC3t3fQtiQ1gquuuqxf29Fc1RNDriRJqknfKO5AbSklQ64kSarJhAl7D9qWUjLkSgkcd9ycfu3Zs09IVIkk1e7MMz/cr33WWeckqkTakSFXSuADH/hgv/app/51okokqXYHHnhQRftFiSqRdmTIlRLpG811FFdSo2pvX0yhUACgUCiwZMlNiSuSnlcoFovJPjyEMA04B3g9cBQwETghxri0rE8rcBowD3gZsAfwW+DLMcYfVtzvVOA7A3zcXjHGbUMs7VDgwc7OLfT2pvvnI0lSPTvrrNPZtu3p7e3x4/fiyiu/lbAi5VFTU4HW1okAhwFrh/y+0SpoiAKwCHgRcN8AfWYAnwc6gc8B5wNPAzeGED45wHvOBxZWfD07cmVLkqQZM2Zu38ymuXkcM2bMTFyR9LzU2yzdDUyJMXaGEN4KLK7S537g8BjjQ30nQghXArcA/xBC+GKM8emK9/w0xnjvqFUtjYCurk18/euXceaZH2HSpJbU5UjSsLW1zWfZsttLrSLz5p2UtB6pXNKR3BjjkzHGzp30ebA84JbOFYGbgb3IphbsIISwTwgh9Ui1NKD29sWsWROdwyapYbW0TN4+JxcK/g+76kojh8ADSseNVa7dAWwGngoh/CiEcPDYlSXtXFfXJjo6llEsFunoWM7mzV2pS5KkYbv//vvo6ekGoKen2219VVcaMuSGEPYFPgAsjTFuKLv0FNmDZ2cDJwFfA/4KWBFCmDLmhUoDaG9fvP2hxt7eXkdzJTUkt/VVPUs9J3fYSlMQfgBMAj5Sfq202kL5iguLQwjLgZ8AHyN7IG3ISk/ySSNu9eoV/UY/Vq9ewbnnuoi6pMZSbVvfqVNfmKgaqb+GC7nAZcCbgJNjjL/aWecY409DCL8D3sgwQ65LiGm0HHvsTJYvX0pPTzfNzeM49tiZbNjwZOqyJGlYJkzYu195wlpxAAAaAUlEQVTQnTBhb3+WacSVLSE2vPeNQi2jJoTwKeAs4O9jjP8yjLc+DOw7OlVJw9fWNn/7wxpNTQWfSJbUkNzWV/WsYUJuCOFs4NPAV2KMXxzm218MbNhpL2mMtLRMZr/99gNg6tT9fCJZUkN6xStexYQJewPZKO7LX/7KxBVJz2uIkBtCeBfwz2Rzcf92kH5Tq5xbALwE+PmoFSgNU1fXJp544o8APPHEE66uIKlhnXnmhykUCo7iqu4k3dYXIIRwQenlEcAC4NvAg0BXjPHyEMIxPL8k2CLguYpb/GeM8Y+le60h22DiHuBPwDHA+4AHgGNijJuHWNahuK2vRtF1132735zc2bPnsnDhaanLkiSp7tS6rW89PHj22Yp239/0DwGXAy8HXgBMJQvAlU4A/lh6fQPZkmFvBiYAj5bu8ZlhBFxp1K1a1X91hVWrVhhyJTWkCy/8BI88spZDD30xF174udTlSNslD7kxxsJOrn8X+O4Q73UBcMFOO0qJzZgxk9tvv6VfW5Ia0SOPrAVg7dr/SVuIVKEh5uRKeXPPPXf3a9977y8SVSJJtbvwwk/0a190keNMqh+GXCmBzZs39Wtv2tSZqBJJql3fKG4fR3NVTwy5kiRJyh1DriRJknLHkCslMHlya792a+sOSzxLkqRdYMiVEvjSly7r17700q8lqkSSanfCCScO2pZSMuRKifSN5jqKK6lRvfa1R/Vrv+51xySqRNpR8h3P6tShuOOZJEmDuuCCj7N+/aPb29OnH8jnPndpwoqUR7XueOZIriRJqkl5wK3WllIy5EqSpJpMn37goG0pJUOuJEmqyRlnnN2v/cEPfihRJdKODLmSJKkmBx986PbR2+nTD+Sggw5JXJH0PEOulEhX1yYuvvgiNm/uSl2KJNXsjDPOZq+99nIUV3XHkCsl0t6+mDVrIkuW3JS6FEmq2cEHH8oVV3zLUVzVHUOulEBX1ybuuGMZxWKRjo5ljuZKkjTCDLlSAu3ti+np6Qagu7vb0VxJkkaYIVdKYOXKDvo2YikWi6xc2ZG4IkmS8sWQKyXQ2to6aFuSJO0aQ66UwMaNGwdtS1KjuP/++zj99JP5zW9+nboUqR9DrpTAlClTBm1LUqO46qrLKBaLXHnl11KXIvVjyJUSeOKJP1a0n0hUiSTV7v7772Pr1qcA2Lr1KUdzVVcMuVIC3d09Fe3uRJVIUu2uuuqyfm1Hc1VPDLlSEsWdtCWp/vWN4g7UllIy5EqSpJpMmLD3oG0pJUOuJEmqyZlnfrhf+6yzzklUibQjQ66UgKMfkvLgFa941fafXxMm7M3LX/7KxBVJzzPkSglUPmjmg2eSGtWZZ36YQqHgKK7qjiFXSmDmzOMHbUtSo3jFK17Ft771A0dxVXcMuVICbW3zaWrK/vg1NTUxb95JiSuSJClfDLlSAi0tkxk/fi8Axo/fi0mTWhJXJElSvhhypQTWrVvbb5eghx9+KHFFkiTliyFXSuCaa67o17766ssTVSJJUj4ZcqUE1q9/dNC2JEnaNYZcKYHm5nGDtiVJ0q4x5EoJ9PR0D9qWJEm7xpArJTB9+oGDtiVJ0q4x5EoJnHHG2f3aH/zghxJVIklSPiWdCBhCmAacA7weOAqYCJwQY1xape884NPAy4EngG8Bn48xdlf0awG+AMwHJgD/BZwbY7x31L4RaZgOPvhQpk8/kPXrH2X69AM56KBDUpckSVKupB7JDcAi4EXAfQN2CuEvgJuB/wU+XHp9IfCVin5NwE+AdwOXAX8P7A8sDSG8ZBTql2rW1jYfgLe85W2JK5Gk2nV1beLiiy9i8+au1KVI/aQOuXcDU2KMhwOXDtLvi8AvgDfFGL8RY/wI8E/AWSGEw8v6vR04DjglxnhRjPEKYC5QBD41Gt+AVKv29sUA/PjH/5a4EkmqXXv7YtasiSxZclPqUqR+kobcGOOTMcbOwfqEEF5ONkXh6hhjT9mlK8nqLx8GezuwHvhx2WdsAG4E3hpC2GOkapd2xbp1a7evjbt+/aPueCapIXV1baKjYxnFYpGOjuWO5qqupB7JHYrXlI53lZ+MMa4HHim73tf37hhjseIedwIvBP5stIqUhsMdzyTlQXv7Ynp7s79ye3t7Hc1VXWmEFeinlY6PVbn2GDC9ou9tA/Sj1Pe3Q/3g1taJQ+0qDUu1Hc+mTn1homokqTarV6/Yvs53T083q1ev4Nxzz0lclZRphJC7V+n4TJVr28hWUCjvO1C/8nsNSWfnlu3/hyqNpAkT9mbr1qf6tTdseDJhRZI0fMceO5Ply5fS09NNc/M4jj12pj/LNOKamgo1DTw2wnSFp0vHPatcG192va/vQP2o6Csl09393KBtSWoEbW3zKRQKQBZE5s07KXFF0vMaIeT2TTWYVuXaNLIHzcr7DtSPir5SMlOmTB20LUmNoKVlMvvttx8AU6fux6RJLYkrkp7XCCG3bxOHo8pPhhCmk62ve29F39eFEAoV93g9sAV4YLSKlIajs7Nz0LYkNYKurk088cQfAXjiiSdcXUF1pe5DbozxfuB3wBkhhOayS2cCvUD5IqM/Inu47C19J0IIU4B3AD+OMfo7YdWF446bNWhbkhpBe/tiiqVHV4rFoqsrqK4kf/AshHBB6eURpePCEMIsoCvG2Leu0seBJcDPQwg3AK8EPkS2du7vy273I2A1cG0I4YvARuAssjD/6VH9RqRhmDPnDdx++y3b23PnvjFhNZJUm1Wr+q+usGrVChYuPC1xVVKmHkZyP1v6WlBqn1Zq/11fhxjjvwMnAa1k2/WeBHwO+Ej5jUqbRfwl2eYPHyHbRW0DcEKM0akKqhvLlt22/WGNQqHA0qW3Jq5IkoZvxoyZNDdn42XNzeOYMWNm4oqk5xWKRZfIquJQ4EGXENNoOeus09m27fnFPsaP34srr/xWwookafi6ujaxaNFHee6559hjjxfwhS981YfPNOLKlhA7DFg75PeNVkGSBuboh6Q8aGmZzKxZcygUCsyaNduAq7piyJUSaGubT1NT39qSTa4tKalhtbXN5/DDgz/HVHcMuVICjn5IyouWlsmcd96F/hxT3Um+uoK0u2prm8+jjz7i6IckSaPAB8+qOxQfPJMkSUrOB88kSZKkEkOulEhX1yYuvvgit8GUJGkUGHKlRNrbF7NmTXQbTEmSRoEhV0qgq2sTHR3LKBaLdHQsdzRXkqQRZsiVEmhvX7z9ocbe3l5HcyVJGmGGXCmBVatW0NPTDUBPTzerVq1IXJEkSfliyJUScFtfSZJGlyFXSsBtfSVJGl2GXCkBt/WVJGl0ua2vlIjb+kqSNHrc1re6Q3FbX0mSpOTc1leSJEkqMeRKkiQpdwy5kiRJyh1DriRJknLHkCtJkqTcMeRKkiQpdwy5kiRJyh1DriRJknLHkCtJkqTcMeRKkiQpdwy5UiJdXZu4+OKL2Ly5K3UpkiTljiFXSqS9fTFr1kSWLLkpdSmSJOWOIVdKoKtrE3fcsZRiscgddyxzNFeSpBFmyJUSaG9fTHd3DwDd3d2O5kqSNMIMuVICK1d2AMVSq1hqS5KkkWLIlRJobW0dtC1JknaNIVdKoLOzc9C2JEnaNYZcKYHjjptFoVAAoFAocNxxsxJXJElSvhhypQTa2ubT3DwOgHHjxjFv3kmJK5IkKV8MuVICLS2TOf74ORQKBWbNmsOkSS2pS5IkKVcMuVIibW3zOfzw4CiupIbm7o2qV4VisbjzXomFEL4LvG+QLi+KMT4aQlgKzKly/YYY47uH8ZGHAg92dm6ht7f+//lIkpTKddd9m6VLb2Xu3DeycOFpqctRDjU1FWhtnQhwGLB2yO8brYJG2NXAwoqvU4CtwG9ijI+W9V1Xpe/lY1qtNATr1q3l7LNP5+GHH0pdiiTVpKtrEx0dyygWi3R0LHc0V3VlXOoChiLGuApYVX4uhDALmAD8oKL7phjj98eqNqlW11xzBU8//TRXX305n/vcpanLkaRha29fvP03nr29vSxZcpOjuaobjTKSW80Csi2jrq+8EEIYF0KYOPYlSUOzbt1a1q/PfgGxfv2jjuZKakirVq2gp6cbgJ6eblatWpG4Iul5DRlyQwh7AO8EVsYY11ZcPgJ4CngyhLA+hPCJEEJDfp/Kr2uuuaJf++qrnVEjqfHMmDETKJRahVJbqg8NMV2hijcBrew4VeEPwG3Ar4B9gPcAnwcOBv5muB9SmuQsjbi+Udzy9tSpL0xUjSTV5q1vbeP2228ptYrMnz/Pn2WqG40achcAzwE3lp+MMZ5e0e97IYQbgTNCCF+JMcbhfIirK2i0TJ9+YL+gO336gWzY8GTCiiRp+G6+uZ1CoUCxWKRQKLB48RLn5GrEla2uMLz3jUIto6o01/YtwM9jjJ1DeMuXyH6XcsKoFiYNwxlnnN2v/cEPfihRJZJUu1WrVtC3FGmxWHROrupKw4Vc4K1UX1VhIA+XjvuOTjnS8B188KFMmLA3ABMm7M1BBx2SuCJJGr4ZM2Zu36K8uXmcc3JVVxox5J4MbAGWDLH/i0vHDaNTjjR8XV2beOaZZwB49tlnXFtSUkNqa5tPoZA9eNbUVHAHR9WVhgq5IYSpwInA4hjj1opr+4QQ9qw41wx8AugFbkGqE+3ti7e/LhZhyZKbElYjSbVpaZnMfvvtB8DUqfsxaVJL4oqk5zXag2fvIqu52lSF1wLXhxD+BXgAmEi2zNhRwCUxxgfHrEppJ6qtLenDGpIaTVfXJh5//HEA/vjHx9m8ucugq7rRUCO5ZFMVnqD6qOxDwArgbcCXgU+TjeCeGmM8b6wKlIZixoyZ23/FVyi4tqSkxpTteNYDQE9Pj7+VUl1pqJHcGOOMQa49CLxjDMuRajZnzhu2ry1ZLBaZO/eNiSuSpOFbseKOHdr+Vkr1otFGcqVcWLbsNsp3CVq69NaU5UhSTcaNGzdoW0rJkCslkK0l2bfRiGtLSmpMW7c+NWhbSsmQKyXgfu+S8mD69AMHbUspGXKlBObMeQPlI7nOyZXUiN7znoX92gsWvC9RJdKODLlSAtmc3Oc5J1dSI7rnnrv6te+++85ElUg7MuRKCaxc2TFoW5IaQbXVFaR6YciVEmhtbR20LUmNwNUVVM8MuVICGzduHLQtSY3A1RVUzwy5UgJTpkwZtC1JjcDVFVTPDLlSAp2dGwdtS1IjcHUF1TNDrpRAa+uUQduS1AhcXUH1zJArJeCcXEl5ULlbo7s3qp4YcqUEnJMrKQ9mzJhJc3O2okJz8zh3b1RdMeRKCXR2dg7alqRG0NY2n6ambIvypqYm5s07KXFF0vMMuVICxx03CyiUWoVSW5IaS0vLZGbNmkOhUGDWrNlMmtSSuiRpO0OulEBb23zGjWsGssXTHf2Q1Kja2uZz+OHBn2OqO4ZcKYGWlskcf/xcCoUCxx8/x9EPSQ2rpWUy5513oT/HVHcMuVIic+a8gfHjxzN37htTlyJJUu4YcqVEli27jW3btrF06a2pS5EkKXcMuVICXV2buOOOpRSLRe64YxmbN3elLkmSpFwx5EoJtLcvpru7B4Du7m6WLLkpcUWSJOWLIVdKYOXKDqBYahVLbUmSNFIMuVICra2tg7YlSdKuMeRKCbjjmSRJo8uQKyVQucOZO55JkjSyDLlSAnPmvKFf27VyJUkaWYZcKYFly26jUCgAUCgUXCtXkqQRZsiVEli1agXFYra6QrFYZNWqFYkrkiQpXwy5UgIzZsykubkZgObmZmbMmJm4IkmS8sWQKyXQ1ja/30juvHknJa5IkqR8MeRKiTwfchMXIklSDhlypQTa2xdTKGR//AqFgtv6SpI0wgy5UgKrVq2gt7cHgN7eHh88kyRphBlypQSyB8/GAdDcPM4HzyRJGmGGXCmBtrb5NDVl6+Q2NTX54JkkSSPMkCsl0NIymVmz5lAoFJg1azaTJrWkLkmSpFwZl7qAoQghzAVuH+DyETHG35X1PQ74AvBa4E/ADcA/xBi3jnad0nC0tc3n0UcfcRRXkqRR0BAht8xXgbsrzq3vexFCeDVwK3A/cC7wIuDvgBcDbWNUozQkLS2TOe+8C1OXIUlSLjVayF0WY7x5kOv/CHQCc2OMWwBCCGuBb4QQ3hBjvG30S5QkSVJqDTcnN4TwwhDCDuE8hLAP8OfAtX0Bt+RaYAvwzjEqUZIkSYk1Wsi9jmye7dMhhP8IIRxZdu1IspHpu8rfEGN8FrgXeM2YVSlJkqSkGiXkPgv8CDgHeAvwGeAYoCOE8NJSn2ml42NV3v8YMH20i5QkSVJ9aIg5uTHGlcDKslNLQgjtZKO2nwJOBvYqXXumyi22lV0fstbWicN9iyRJkupAQ4TcamKMvwwh3AK8sXTq6dJxzyrdx5ddH7LOzi309hZrrFCSJEm7qqmpUNPAY6NMVxjIw8C+pdd90xSmVek3jbKlxiRJkpRvjR5yXwxsKL3+NdANHFXeIYTwAuDVZA+fSZIkaTfQECE3hDC1yrlZwAnAzwFijJuBW4CFIYTyMe2FwETgh2NQqiRJkupAo8zJvSGEsJXs4bONwCuBM0qvP13W7/xSn6UhhG+S7Xj2t8DPYoy3jGnFkiRJSqYhRnKBm4GpZIH1CuBtwPXA0THGdX2dYoz3ACeSrbDwFeCvgW8A7xjrgiVJkpROoVh09YAqDgUedHUFSZKktMpWVzgMWDvU9zXKdIWx1gzZP1RJkiSlU5bHmofzPkNuddMAJk/eO3UdkiRJykwD/jDUzk5XqG5P4GiytXd7EtciSZK0O2smC7j/TfWdbasy5EqSJCl3GmV1BUmSJGnIDLmSJEnKHUOuJEmScseQK0mSpNwx5EqSJCl3DLmSJEnKHUOuJEmScseQK0mSpNwx5EqSJCl3xqUuQNqdhBCmAecArweOAiYCJ8QYl6asS5KGI4RwNHAqcAJwCNAJrAQuiDE+kLA0aTtHcqWxFYBFwIuA+xLXIkm1WgScBNxC9j/u1wBzgV+EEI5IWJe0nSO50ti6G5gSY+wMIbwVWJy6IEmqwZeBBTHGZ/tOhBBuAH5FFoBPTVSXtJ0hVxpDMcYnU9cgSbsqxriyyrk1IYT7AUdyVRecriBJknZZCKEA7A9sTF2LBIZcSZI0Mk4GDgRuTF2IBIZcSZK0i0IILwOuADqA6xKXIwGGXEmStAtCCAcAPwE2Ae+IMfYmLkkCfPBMkiTVKIQwCfgZMAmYGWN8PHFJ0naGXEmSNGwhhPFAO/BS4I0xxpi4JKkfQ64kSRqWEEIzcAMwA3hLjHF14pKkHRSKxWLqGqTdSgjhgtLLI4AFwLeBB4GuGOPlyQqTpCEKIXyVbKezdnZcTWFLjPHmsa9K6s+RXGnsfbaifVrp+BBgyJXUCF5dOraVvso9BBhylZwjuZIkScodlxCTJElS7hhyJUmSlDuGXEmSJOWOIVeSJEm5Y8iVJElS7hhyJUmSlDuGXEmSJOWOIVeSRAihGEL4buo6JGmkGHIlSZKUO4ZcSZIk5Y4hV5I0KkIIL0xdg6Td17jUBUjS7iCEcCjwJeDPS6duBz5aOq6NMc6t6H8i8PfAMcB44PfAlTHGr1f0WwusBf6mdP/ZQC/wn8CHYoyPV/R/Ranf8cAzwM+Ajw1S97uADwP/B2gGfgVcGmP8UUW/IvA94DrgM8CrgbuAft+XJI0VR3IlaZSFEFqBO4A24LvAIuApsoC7d5X+ZwD/AUwEPg+cC/wBuCqEcGmVjzgQWAqsAz4OXA+cBFxbcd/DSnUcD1wOXAhMAf7fAHV/DvhX4Engk8B5wFbghyGEs6u85SjgZuBOsuD8g2r3laSx4EiuJI2+RcCLgPfGGPuC31UhhC+QhdLtQgjTgH8G/jXGuKDs0pUhhK8B54YQroox/k/ZtT8D3hVjvLHsPr3AWSGEEGOMpdOfByYDb4gx3l7qdwVwE/CaijpeC5wP/FOM8RNll/45hHAz8E8hhGtjjE+WXXsF8OcxxluG+g9GkkaLIVeSRl8b8BjwLxXnv0hFyAXeDuwJfCuEMKXiWjvwEeBE4Jqy8+vLA27JbcBZwOFADCE0leq4qy/gAsQYi6Ww/daK958MFIHvValjCfAWYAbZiHOfXxpwJdULQ64kjb7DgDtjjL3lJ2OMT4QQuir6HlE6DhYW969o/0+VPp2lY2vpuB/Z9IffVen7myrnjgAKA/QfqI7fD9JXksaUIVeS6kuhdDyFbPS3mspQ2zOE+9VSRxH4i0Huf39Fe2uNnyVJI86QK0mjby3wZyGEpvLR3BDCfkBLRd81pePGEf7V/wZgC/CyKtdeXuXcGuDNwLoY429HsA5JGhOuriBJo68dmAa8p+L831XpeyPZ0l6fCSHsVXkxhDAphLDncAuIMfYA/w4cFUI4oex+BbKlyipdVzr+YwihuUodlVMVJKmuOJIrSaPvEmAB8J0QwjFk81yPB44DNpJNCwAgxvhICOFM4JvAb0MI1wEPAVOBI8keEHs52ejwcF1ANv3g30MIlwGPkD2MNrWyY4zxv0MInwY+DdwbQvghsJ4srL8O+EvgBTXUIEljwpFcSRplMcaNwCyykdTTyELv3sAJZHNfn67o/x2yTR1+AXwQuJJsQ4ZpZOvV9tvgYRh1/IEsXK8o3e8ispD95gH6fwb4K7Jw+1HgCuAMstUfPlJLDZI0VgrFYnHnvSRJ/7+9O6ZBKAiCADotSr4QFOACGwhAAD4IEijpr6RHBMX+kvJyJJv3FFw52cztTrcfifgkuY0xzv9+D0AnJrkAC/zq16YuiCV1gheAiXRyAdZ4bNv2TvJKDRiOqSrAM3UKF4CJhFyANe6p3benJIfUp69rksu++QCAiXRyAQBoRycXAIB2hFwAANoRcgEAaEfIBQCgHSEXAIB2vvRZgZapxthVAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"_uuid":"d6b36cf91cd63fcce92c4177475820bd6b94ec89"},"cell_type":"markdown","source":"**Question 1.2. (1 point). Which gender more often reports consuming alcohol - men or women?**\n1. women\n2. men"},{"metadata":{"trusted":true},"cell_type":"code","source":"#categorical vs categorical\n\nsns.countplot(x='alco', hue='gender', data=df)","execution_count":8,"outputs":[{"output_type":"execute_result","execution_count":8,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7f372b976940>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 792x576 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"_uuid":"8b98389e26edd01374d6217d8dd1e2d5ffff9d1c"},"cell_type":"markdown","source":"**Question 1.3. (1 point). What is the difference between the percentages of smokers among men and women (rounded)?**\n1. 4\n2. 16\n3. 20\n4. 24"},{"metadata":{"trusted":true},"cell_type":"code","source":"#pd.crosstab(df['gender'],df['smoke'], margins = True)\nfemale_smoke = df[(df['smoke'] == 1) & (df['gender'] == 1)]['smoke'].count()\ntotal_female = df[df['gender'] == 1]['gender'].count()\nfemale_percentage = female_smoke/total_female\n\nmale_smoke = df[(df['smoke'] == 1) & (df['gender'] == 2)]['smoke'].count()\ntotal_male = df[df['gender'] == 2]['gender'].count()\nmale_percentage = male_smoke/total_male\n\ndiff = male_percentage - female_percentage\n\nprint(diff)","execution_count":9,"outputs":[{"output_type":"stream","text":"0.20102390309976736\n","name":"stdout"}]},{"metadata":{"_uuid":"c92554c5fe98a07b298cec98eaa2622a3bedbeeb"},"cell_type":"markdown","source":"**Question 1.4. (1 point). What is the difference between median values of age for smokers and non-smokers (in months, rounded)? You'll need to figure out the units of feature `age` in this dataset.**\n\n1. 5\n2. 10\n3. 15\n4. 20"},{"metadata":{"trusted":true},"cell_type":"code","source":"#units of age are in days\n\nsmokers_median_age = df[df['smoke'] == 1]['age'].median()\nnonsmokers_median_age = df[df['smoke'] == 0]['age'].median()\n\ndiff_in_months = abs(nonsmokers_median_age - smokers_median_age)/30\n\nprint(diff_in_months)","execution_count":10,"outputs":[{"output_type":"stream","text":"19.9\n","name":"stdout"}]},{"metadata":{"_uuid":"ac5d59876eb97782eab5f038aa8b2335e014d9bf"},"cell_type":"markdown","source":"## 1.2. Risk maps\n### Task:"},{"metadata":{"_uuid":"9efc6fd8ff26ea2ddf95e7e0b70ef3b802f83af5"},"cell_type":"markdown","source":"On the website for the European Society of Cardiology, a [SCORE scale](https://www.escardio.org/Education/Practice-Tools/CVD-prevention-toolbox/SCORE-Risk-Charts) is provided. It is used for calculating the risk of death from a cardiovascular decease in the next 10 years. Here it is:\n<img src=https://habrastorage.org/webt/ik/gx/hr/ikgxhr3w1vzqihgitgrbdorfb14.png width=30%>\n\nLet's take a look at the upper-right rectangle, which shows a subset of smoking men aged from 60 to 65. (It's not obvious, but the values in the figure represent the upper bound).\n\nWe see the value 9 in the lower-left corner of the rectangle and 47 in the upper-right. This means that, for people in this gender-age group whose systolic pressure is less than 120, the risk of a CVD is estimated to be 5 times lower than for those with the pressure in the interval [160,180).\n\nLet's calculate that same ratio using our data.\n\nClarifications:\n- Calculate ``age_years`` feature – round age to the nearest number of years. For this task, select only the people of age 60 to 64, inclusive.\n- Cholesterol level categories differ between the figure and our dataset. The conversion for the ``cholesterol`` feature is as follows: 4 mmol/l $\\rightarrow$ 1, 5-7 mmol/l $\\rightarrow$ 2, 8 mmol/l $\\rightarrow$ 3."},{"metadata":{"trusted":true,"_uuid":"8caffb39e65c95e4cfebfe448bbe224af4d0766c"},"cell_type":"code","source":"# You code here\n\ndf['age_years'] = df['age']/(365)\n\nlower_cvd = df[(60 <= df['age_years']) & (df['age_years'] <= 64) & (df['cholesterol'] == 1) & (df['cardio'] == 1)]['id'].count()\nlower_total = df[(60 <= df['age_years']) & (df['age_years'] <= 64) & (df['cholesterol'] == 1)]['id'].count()\n\nlower_fraction = lower_cvd/lower_total\n\nupper_cvd = df[(60 <= df['age_years']) & (df['age_years'] <= 64) & (df['cholesterol'] == 3) & (df['cardio'] == 1)]['id'].count()\nupper_total = df[(60 <= df['age_years']) & (df['age_years'] <= 64) & (df['cholesterol'] == 3)]['id'].count()\n\nupper_fraction = upper_cvd/upper_total\n\nratio = upper_fraction/lower_fraction\n\nprint(ratio)","execution_count":11,"outputs":[{"output_type":"stream","text":"1.3332257092665982\n","name":"stdout"}]},{"metadata":{"_uuid":"e71f8859a7fb8f23a78ac84b861d41c8f1b9e11f"},"cell_type":"markdown","source":"**Question 1.5. (2 points). Calculate the fraction of the people with CVD for the two segments described above. What is the ratio of these two fractions?**\n\n1. 1\n2. 2\n3. 3\n4. 4"},{"metadata":{"_uuid":"54cef692e0af3745091c7177d63a2d6c36044474"},"cell_type":"markdown","source":"## 1.3. Analyzing BMI\n### Task:"},{"metadata":{"_uuid":"c52b4b1c20956e3cc420bb13e7bb12d80aa3ef1a"},"cell_type":"markdown","source":"Create a new feature – BMI ([Body Mass Index](https://en.wikipedia.org/wiki/Body_mass_index)). To do this, divide weight in kilogramms by the square of the height in meters. Normal BMI values are said to be from 18.5 to 25. "},{"metadata":{"trusted":true,"_uuid":"5b3134f2597650ae95d0196393b30797518f0498"},"cell_type":"code","source":"# You code here\n\ndf['BMI'] = df['weight'] / (df['height']/100)**2\n\ndf['BMI'].median()\n\n#false, median bmi above normal range of values","execution_count":12,"outputs":[{"output_type":"execute_result","execution_count":12,"data":{"text/plain":"26.374068120774975"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#quantitative vs categorical\n\nsns.boxplot(x = 'gender', y = 'BMI', data = df)\n\nwomen_bmi = df[df['gender'] == 1]['BMI'].mean()\nmen_bmi = df[df['gender'] == 2]['BMI'].mean()\n\nprint(women_bmi, men_bmi) \n#true, average woman bmi higher than an average man","execution_count":13,"outputs":[{"output_type":"stream","text":"27.98758344183312 26.754442357289474\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 792x576 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"healthy = df[df['cardio'] == 0]['BMI'].mean()\nunhealthy = df[df['cardio'] == 1]['BMI'].mean()\n\nprint(healthy, unhealthy)\n\n#false, healthy people on average have a lower bmi than those with CVD","execution_count":14,"outputs":[{"output_type":"stream","text":"26.54817520679475 28.566060627015435\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"healthy_nondrinking_men = df[(df['gender'] == 2) & (df['cardio'] == 0) & (df['alco'] == 0)]['BMI'].mean()\nhealthy_nondrinking_women = df[(df['gender'] == 1) & (df['cardio'] == 0) & (df['alco'] == 0)]['BMI'].mean()\n\nprint(healthy_nondrinking_men, healthy_nondrinking_women)\n\n#true","execution_count":15,"outputs":[{"output_type":"stream","text":"25.872638075460173 26.845406594131518\n","name":"stdout"}]},{"metadata":{"_uuid":"1c2baed24cc5b3abb94b5c6c09d571f29b39213b"},"cell_type":"markdown","source":"**Question 1.6. (2 points). Choose the correct statements:**\n\n1. Median BMI in the sample is within the range of normal BMI values.\n2. The BMI for women is on average higher than for men.\n3. Healthy people have, on average, a higher BMI than the people with CVD.\n4. For healthy, non-drinking men, BMI is closer to the norm than for healthy, non-drinking women"},{"metadata":{"_uuid":"2e059032eee1d57c896e24a98b659a5ab16144fd"},"cell_type":"markdown","source":"## 1.4. Cleaning data"},{"metadata":{"_uuid":"26ccfbcb212c3631b4941a294906c027b974301a"},"cell_type":"markdown","source":"### Task:\nWe can see that the data is not perfect. It contains \"dirt\" and inaccuracies. We'll see this better as we visualize the data.\n\nFilter out the following patient segments (we consider these as erroneous data)\n\n- diastolic pressure is higher than systolic \n- height is strictly less than 2.5 percentile (Use `pd.Series.quantile` to compute this value. If you are not familiar with the function, please read the docs.)\n- height is strictly more than 97.5 percentile\n- weight is strictly less than 2.5 percentile\n- weight is strictly more than 97.5 percentile\n\nThis is not everything that we can do to clean this data, but this is sufficient for now."},{"metadata":{"trusted":true,"_uuid":"483fd26f0aff141810dcb084095c380acafbfbdd"},"cell_type":"code","source":"# You code here\n#df.drop(df.loc[df['ap_lo'] >= df['ap_hi']].index, inplace = True)\n\n#df.drop(df.loc[(df['height'] <= df['height'].quantile(0.025)) & (df['height'] >= df['height'].quantile(0.975))].index, inplace = True)\n#df.drop(df.loc[(df['weight'] <= df['weight'].quantile(0.025)) & (df['weight'] >= df['weight'].quantile(0.975))].index, inplace = True)\n\n#df.count()\n#dropped = 1- (68764/70000)\n\n#print(dropped)\n\nfiltered_df = df[(df['ap_lo'] <= df['ap_hi']) & \n                 (df['height'] >= df['height'].quantile(0.025)) &\n                 (df['height'] <= df['height'].quantile(0.975)) &\n                 (df['weight'] >= df['weight'].quantile(0.025)) & \n                 (df['weight'] <= df['weight'].quantile(0.975))]\nprint(filtered_df.shape[0] / df.shape[0])","execution_count":16,"outputs":[{"output_type":"stream","text":"0.9037\n","name":"stdout"}]},{"metadata":{"_uuid":"f807adaa6459d1c75d202bb973831369d6eeb20e"},"cell_type":"markdown","source":"**Question 1.7. (2 points). What percent of the original data (rounded) did we throw away?**\n\n1. 8\n2. 9\n3. 10\n4. 11"},{"metadata":{"_uuid":"a9f14b459f71ac2304e73145fc199a90131965e7"},"cell_type":"markdown","source":"# Part 2. Visual data analysis\n\n## 2.1. Correlation matrix visualization\n\nTo understand the features better, you can create a matrix of the correlation coefficients between the features. Use the initial dataset (non-filtered).\n\n### Task:\n\nPlot a correlation matrix using [`heatmap()`](http://seaborn.pydata.org/generated/seaborn.heatmap.html). You can create the matrix using the standard `pandas` tools with the default parameters."},{"metadata":{"trusted":true,"_uuid":"a9c19a7bf2dd564b484e8269886340c13967fede"},"cell_type":"code","source":"# You code here\ncorr_matrix = df.corr()\nsns.heatmap(corr_matrix)","execution_count":17,"outputs":[{"output_type":"execute_result","execution_count":17,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7f372adc4208>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 792x576 with 2 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAs4AAAIsCAYAAADiYC+lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzs3XuYXFWVuP+3CQHiIHcdETUo4hIdRRD15wgKo8530BlRYbyjIIwMKgIDGMVBggjeJcow4ogSUEdEVBxABhGI3BQUBXSUxTWAXASRoNyTdP/+2KdIUVR3V19OTnXl/TxPPZXaZ5+zd3V3klWr19l7aGRkBEmSJEljW63pCUiSJEkzgYGzJEmS1AMDZ0mSJKkHBs6SJElSDwycJUmSpB4YOEuSJEk9MHCWJEmSerB60xOQJEnSYIiIjYF9gZcA2wBrAztk5qIez98COArYFngYOA04IDP/2NFvNeBAYG9gY+Bq4IjM/Pb0vJPuzDhLkiRpugQwD3gKcOWETox4CnA+sBlwMPBZ4J+AH0XE7I7uRwCfAn4E7APcBJwUEbtMafbjMHCWJEnSdLkM2CgzNwc+M8FzDwbmANtn5hcz80jgTcBWwK6tThGxCXAA8IXM3Cszv0IJsC8APltlo2th4CxJkqRpkZl/ycy7Jnn6zsD/ZOYtbdf7MaUM401t/XYCZgP/2dZvBPgSMBd48STHH5eBsyRJkhpVZZGfCPyiy+FLKVnnlq2AP2fm1V360dF3WnlzoCRJkkYVEesB63U5tCQzl0zTMBtXz7d1OXYb8MSImJWZy6u+t4/SD+DJ0zSnxzBwnkGW/vH6kabn8M4X/lvTU2jccNMT6BNN/7qqH74PTX8N+sUymv2naTWGGh2/zEH9oB/+Xfj2jac29gNZY5xwGHDoKO3zp2mMOdXzQ12OPdjW597qebx+tTBwliRJ0lgWAAu7tE9Xthnggep5zS7H1uro80CP/aadgbMkSdIgGF5ey2WrcozpDJK7aZVZbNzl2MbAHVWZRqvvdqP0A7h1muf2CH+7JEmSpEZVK2ncSdk0pdOLgcvbXl8OrBMRz+ro95K247UwcJYkSRoEI8P1PGoQEZtFxGYdzd8FXletsNHq90rgWcB32vr9AFgKvLet3xDwr5SNUC6pZdJYqiFJkjQYhvvh9kiIiH+v/rhF9bxrRGxLWYXjP6q2c6rnTdtOPRL4Z+C8iDiasl33QcAVwImtTpn5+4hYABwYEWtRlrB7PaV8482ZWdsXwsBZkiRJ0+nwjtfvrp5vBP6DUWTmzRHxCuDzwCeBh4HTgX/LzIc7un8IuBvYC9idsknK2zLz5KlPf3RDIyONr3CmHrkcXX/oj8/zzWu6zqsfvg9Nfw36hcvR+bPQL/rh34Uml6N7+Nb/q+Uv4xpPfm7zf8n6hH/Xp1lEzI+IcX9wI2IkIuavhClJkiRpGliqIUmSNAj6pMZ5kJlxnn4fp8YdayRJkrqaQatqzFRmnKdZZi4DljU9D0mSJE0vA+dpVtUtH5qZQ9XrNSl3hr6DshXkebStOyhJkjQtato5UCsYONfvOErQ/A3gZ8DfAWc0OiNJkiRNmIFzjSJiS0rQ/MXM3LdqPiYivgk8v7mZSZKkgWM9cu28ObBer6mev9jRvmBlT0SSJA244eF6HnqEgXO95lJuFLyhoz0bmIskSZKmwFINSZKkATBiqUbtzDjX60bKh5Ond7RHA3ORJEnSFBg41+vM6vkDHe37reyJSJKkAWeNc+0s1ahRZl4eEd8CPhAR61OWo3sl8MxmZyZJkqSJMnCu37uBO4G3A28EzgVeC9zc5KQkSdKAsca5dgbO0ywz5wPz214/COxbPdoNrbxZSZKkgefOgbWzxlmSJEnqgRlnSZKkQWCpRu3MOEuSJEk9MOMsSZI0CFw6rnYGzpIkSYPAUo3aGTjPIO984b81PQVOvOzzjY7/pq07FydZ+e5afn/TU+C5szdsegrcO7Ks6Sk07nk8rukpcNdQ83fRX7l8SaPjrz40q9HxAdYcar7y8Q1L12l6Crzpyo81Ov68bQ5udHwNPgNnSZKkQWCpRu2a/4gsSZIkzQBmnCVJkgbAyEjzpVuDzsBZkiRpEHhzYO0s1ZAkSZJ6YMZZkiRpEHhzYO3MOEuSJEk9MOMsSZI0CKxxrp0ZZ0mSJKkHZpwlSZIGwbDL0dXNwFmSJGkQWKpRO0s1JEmSpB6Yce4QEXOBecArgacB9wPnAgdl5uKOvs8HjgZeDNwFHAvcAnwNeHp7/4j4R+BgYEtgWds1r633HUmSpFWCy9HVzsD5sV4E/C1wEvB7YFNgb2BRRDwnM+8HiIhNgPOAYeATwH3AnsBDnReMiN0owfTpwAeBxwP7ABdGxJaZ+Yd635IkSZKmysD5sc7IzFPaGyLiNOCnwM7A16vmecD6wJaZ+euq3/HANR3nrg0sAI7JzH3a2r8N/B+wP/Chet6KJElaZVjjXDsD5w6Z+UDrzxExG1gHuBZYAmzNisD5H4ALWkFzde6fIuKblGxyy6uBdYGTI2Kjtva/AFcA29fwNiRJ0qrGUo3aGTh3iIg5wIeB3YFNgKG2w+u2/XkucEGXS3TWLG9ePZ8/ypDXT2KakiRJWskMnB/raErQvIBSnnEPMEKpeZ7MKiStc94G3Nnl+ANd2iRJkibGjHPtDJwfaxfghMw8oNUQEWsB63X0uxF4ZpfzO9uuq55vy8xF0zVJSZIkrVyu4/xYy3l0eQaUmuVZHW1nAdtFxPNaDRGxAfD2jn4/Av4MHBwRj/mg0lH3LEmSNCkjI8treWgFM86PdTqwa0TcA/wWeCnwKso6ze0+DbwDOCcijqas97wncBOwAaW8g8y8JyLeD5wA/KJaTeMuyjJ3rwNOBf695vckSZIGnaUatTPj/Fj7AidSMsefAzamBM73tnfKzJuBHYCrgI8A+1GC469VXR5s6/t1yuoad1KWnltAKQm5mFI7LUmSpD5nxrlDZi4B3t3l0KZd+l4OvLy9LSIWUILmP3b0PQc4Z9omKkmS1M51nGtnxnkKqqXr2l9vCOwKXJiZFgVJkiQNEDPOU3NpRPwY+B3wJGAPyoYphzc6K0mStOqxxrl2Bs5T80PgjcBelJsBLwPenZmjbXYiSZKkGcrAeQoycx4wr+l5SJIkWeNcPwNnSZKkQWCpRu0MnDUhb9p630bHP/mXX2h0fIAlb9296Slw/NVrND0FfsXDjY4/qw/ubT5/5O6mp8Dskea/Docse1yj418ye874nWp26aNXLG3EGbPva3oKnNLw/xG3L2/++6DBZuAsSZI0CCzVqF3zqQpJkiRpBjDjLEmSNAisca6dgbMkSdIgMHCunaUakiRJUg/MOEuSJA0Cbw6snRlnSZIkqQdmnCVJkgaBNc61M3CehIjYDTgeeHpmLm52NpIkSViqsRJYqiFJkiT1wIyzJEnSILBUo3ZmnPtARDyu6TlIkiRpbDMm4xwR2wOfBf4GuAX4NLAxcGhmDrX12x3YB9gCuA84A/hgZv6hrc9i4HLgqOqazwNuBeZn5okd4z4XOBp4KXAXcGzVt9sc/xE4GNgSWAacCxyUmde29VkErAe8B/g88ELgU8D8iX1FJEmS2ljjXLsZkXGOiK2A/wXWBw4Fvgp8FHh9R79DgeOA3wL7A8cAOwHnRcScjss+CzgZOAs4AFgCLKwC5db1ngScRwmEPwksAN4J7NtljrsB/wP8Efgg8AngxcCFEfHXHd2fAJwO/Ly61s96/VpIkiSpGTMl43wYsBR4WWbeDhARJwO/a3WIiE2BQyjZ5c+1tZ8JXAy8i5Itbtmiut7Fbde7GdgdOLDqMw/YCNgmM39Z9TsBuKZ9chGxNiWoPiYz92lr/zbwf5Qg/kNtpzwZ2CMzvzaJr4UkSdJjWeNcu77POEfELOBVwPdaQTNAVf5wZlvXNwBDwPciYqPWA7gWuA3YvuPSV7aC5up6dwIJPKOtz2uAi1pBc1u/b3Zc69XAusDJHWP/Bbiiy9j3A1/v4e1LkiT1Zni4noceMRMyzk8E5lAC4E7tbZtTPghcP8p1ntDx+qYufe6mlIO0zAUu6tIvO15vXj2fP8rYnXP6fWYuHaWvJEmS+tBMCJx7tRqwHNgRGOly/O6O18tHuc7QKO3jjQ3wNuDOLscfGOe1JEnS1Ix0C380nWZC4HwH8CDwzC7H2tuuA2YB10zjbn43siKb3C46Xl9XPd+WmYumaWxJkiT1kb6vcc7M5cCPgTdWq1wAEBHPpGSXW74PDFNW23iUiFgtIjaYxPA/BF4WEVu3XesJwNs7+v0I+DNwcEQ85sNIVe8sSZJUH2ucazcTMs5Q1ji+GLgoIr5MySy/H/gN8AIoNwtGxEeBj0fEZsBplHWcNwN2Bo6gLFU3EZ8GdgV+FBFfpNzU9x5KJvr5rU6ZeU9EvB84AfhFtZrGXcCmwOuAU4F/n/C7liRJ6lWfBLkRsSbwMUoMtT5loYSPZOY545y3mHJ/WTfXZubmbX1Hq0vZOzOPHeXYlM2IwDkzL4uIHSmblRxOWTbuo5Ql5Z7d1u+IiLgG2I+yhN0w5SbAU4GzJzHubRGxA2UDlA/z6A1QvtrR9+sRcStl2bkPAbOB3wOLgJMmOrYkSdIMtZCStFxAWchhN+DMiHhFZv50jPP2A9buaJsLfJzy2/1OZwHf6Gi7ZBLz7dmMCJwBMvNcYOv2tog4lRKctvc7mbKxyVjX2nSU9u27tP2axy4nB/CYNZirT1JjfprqNoYkSdKU9cHOgRHxYuAtwP6ZuaBqO5FSJfAp4OWjnZuZp3a5Xus39p1LAQNclZmdgXOt+r7GuSUi1up4vTllneVFjUxIkiRJnXahbFr3SHlsZj5I+U39thGx8QSv9zbghva9N9pFxJzOGLFOMyLjXN1wd321a9/1lNrhfwUeptQhS5Ikrdr6o8Z5K0om+N6O9kspS/6+gLIx3bgiYitKWe4Ro3TZE/gAMBQRvwYOzczvT2rWPZoRgTNlzeUfAW8FngQ8RLlZ8COZec1YJ0qSJK0SalrHOSLWA9brcmhJZi7paNsYuKVL31aw/OQJDN1axaxbmcbFwLeBG4CnAvtSdo9+W2Z+awJjTMiMCJwzc4RSWC5JkqSVaz/g0C7th1FWPms3h5Lg7PRg2/FxRcRqlFrpX2Xm7zqPZ+bLOvqfQKmj/nREnFTFjtNuxtQ4S5IkaQz1reO8AHh6l8eCLrN4AFizS/tabcd78QpgE7pnmx8jM++jrHz2FB67Ud20mREZZxX9ULl01/L7Gx1/yVt3b3R8gPW+dXzTU+D5zz246Snwq5V2K0Z3y/vgb8TSPriDffZQ8/mPX67eUwKpNrettrzR8QGW9cFWxyN98Hei6f8jfn7n1Y2OP6iqcozOkozR3EYp1+jUaru1x+u8nRL6TKTs4ubqeTKb3vWk+X9xJUmSNHX9sXPg5cCzI6JzPeaXVM9XjHeBagOVnYFFmdlroA3wjOr5zgmcMyEGzpIkSZoup1A2gduz1VAFwrsDF7UC4Yh4WkQ8u/sleA3lZsSuZRoRsVGXtg2B91KWrqtt4QhLNSRJkgZBH5SPZeYlEfEdyk16GwPXAe+i7AC4W1vXEyl1zENdLvN2yg2G3x1lmPdHxE7A6ZQdojcB3gM8EXj9NLyNURk4S5IkDYCR4eZr7SvvBA6vntcHrgRek5kXjXdiRKwDvBY4IzPvGaXbxcDLgH+h1DPfC/wUOLKXMabCwFmSJEnTptop8KDqMVqf7Udp/zPjLFmXmT+i7O+x0hk4S5IkDYL+2DlwoHlzoCRJktQDM86SJEmDoA9uDhx0MzrjHBHzI2Kk2kN9Oq63OCIWTuHcU6djHpIkSRM2PFLPQ48w47ySVWsWvgVYmJmLG56OJEmSemTg/GhB/TtbPxs4FFgELK55LEmStKrw5sDaGTi3ycyHmp6DJEmS+tOgBM7rR8QXgZ2q198D3peZ97c6RMTuwD7AFsB9wBnABzPzD219FlP2Rd+tre35wNHAi4G7gGOBW4CvAU/vLLeIiFcAnwWeB9wKzM/ME6tjuwHHV13Pi4jWaTtk5qLJv31JkrTKM+Ncuxl9c2Cb71IWy/4QcDJlS8dDWwcj4lDgOOC3wP7AMZQg+7yIGHWR7YjYBDgPeA7wCeAoyjaQ+45yyrOq8c8CDgCWAAsj4rnV8fOBBdWfjwR2rR6/m8iblSRJeoyRkXoeesSgZJx/npl7tV5ExIbAHsC8iNgUOISSXf5cW58zKVs2vouSRe5mHmWryC0z89fVeccD14zSfwvgZZl5cdX3ZOBmYHfgwMy8PiJ+AuwHnG2WWZIkaeYYlIxzZ+B7AbBhtd/5G4Ah4HsRsVHrAVwL3AZsP8Z1/wG4oBU0A2Tmn4BvjtL/ylbQXPW9E0jgGRN8P5IkSRMzPFzPQ48YlIzzTR2v766e1wc2p3xAuH6Uc58wxnXnUoLwTtf2OI/WXNYfYwxJkiTNAIMSOC8fpX2IEjQvB3YEuhXq3N2lrY55SJIk1cfNSmo3KIHzWK4DZgHXTGLDkRuBZ3Zp79bWK3+qJUnS9HPL7doNSo3zWL5P2dTko50HImK1iNhgjHPPAraLiOe1nbMBZWWNybqvep6WbcIlSZK0cgx8xjkzr42IjwIfj4jNgNMowetmwM7AEZSl6rr5NPAO4JyIOBq4H9iTUsu8AZPLHl8BLKOs+LEu8BBwbmbeMYlrSZIkFZZq1G5VyDiTmUcAbwZmA4dRAuIdgVOBs8c472ZgB+Aq4COUZeROoGx+AvDgJOZyJ/Ae4InAV4FvUdaJliRJUh8bGnFh6wmLiAXAXsDamTnaDYHT7s1zX9/4N+u2ZX9pdPzvPmdZo+MDrPet48fvVLNznntw01PghLUm/Llx4Pxp+KGmp8Djhpr/xeErR9ZtdPybZq20f4ZHdcPI/eN3qtlIH9xCc8ey+8bvVKOL77yq0fEBlj18S2MLAtz3iXfV8kPwVx8+wUUOKqtExnkqOncWrDZX2RW4cGUGzZIkSWpW86mK/ndpRPyYsi32kyg7Eq4DHN7orCRJktpZ41w7A+fx/RB4I6U0YwS4DHh3Zp7f6KwkSZLauRxd7Qycx5GZ84B5Tc9DkiRJzTJwliRJGgSWatTOwFkT8tzZGzY6/vFXr9Ho+ADP74MVLV75f0c2PQW+vc1BjY6/eh/c2/w3q/1V01Pg3qHm/6O8mqWNjt8Pv5xe3gcrWuwwvE7TU+C3s9dqdPyzvrZbo+Nr8Bk4S5IkDYLhfvgYOdgMnCVJkgaBpRq1a/53nZIkSdIMYMZZkiRpELgcXe3MOEuSJEk9MOMsSZI0CKxxrp2BsyRJ0gAYcVWN2lmqIUmSJPXAwLlNRCyOiIVTOPfUaZ6SJElSb4ZH6nnoEZZqrGQR8WzgLcDCzFzc8HQkSZLUIwPnRwvq37312cChwCJgcc1jSZKkVYXZ4doZOLfJzIeanoMkSZL604wKnCNia+Ay4LWZ+cOq7eXAT4DzMvPv2vr+HLgpM3euXu8O7ANsAdwHnAF8MDP/0HbOYmBRZu7W1vZ84GjgxcBdwLHALcDXgKd3lltExCuAzwLPA24F5mfmidWx3YDjq67nRUTrtB0yc9Fkvy6SJElugFK/mXZz4BXAX4Dt2tq2o5RXvCQiZgNExNrAVsCF1etDgeOA3wL7A8cAO1GC1zmjDRYRmwDnAc8BPgEcBbwd2HeUU54FnAycBRwALAEWRsRzq+PnAwuqPx8J7Fo9ftfTu5ckSRqNNwfWbkZlnDNzeUT8lMcGzt8DdgG2Bi4BXgrMAi6IiE2BQyjZ5c+1ToqIM4GLgXdRssjdzAPWB7bMzF9X5x0PXDNK/y2Al2XmxVXfk4Gbgd2BAzPz+oj4CbAfcLZZZkmSpJljpmWcoWSRXxQRa0XEapQg+RTgamDbqs92wL3A5cAbgCHgexGxUesBXAvcBmw/xlj/AFzQCpoBMvNPwDdH6X9lK2iu+t4JJPCMCb9LSZKkCRgZHqnloRVmYuB8AbAGpeZ4S2Cdqu1CVmSitwMuycxlwOaU93k9cGfH48nAE8YYay4lwO7UrQ3gpi5td1Oy1pIkSZrBZlSpRuUSYCklOP4zcH1m3hoRFwCfiYg1gJcAn6r6rwYsB3YEun1sunsa57Z8lPahaRxDkiTpscwO127GBc6Z+UBE/JIVgfMF1aELgY2AdwBzqtcA11Hqna+ZxIYjNwLP7NLera1X/lRLkqTpN+yqGnWbiaUaUILlv6UEzxcAZOa1wO2UG/qWAT+r+n6fsurGRzsvEhGrRcQGY4xzFrBdRDyv7ZwNKCtrTNZ91fN6U7iGJEmSVrIZl3GuXAgcCDyeFRnnVvsuwM8z8z4oAXVEfBT4eERsBpxGCV43A3YGjqAsVdfNpykZ7HMi4mjgfmBPSi3zBkwue3wFJbCfFxHrAg8B52bmHZO4liRJUmGpRu1masb5QkrQekdmXt3W3l628YjMPAJ4MzAbOIwSEO8InAqcPdogmXkzsANwFfARyjJyJ1A2PwF4cKITr1baeA/wROCrwLco60RLkiSpj83IjHNm3kWXoD8zvwh8cZRzTqZsTjLWdTft0nY58PL2tohYQAma/zjWuVX79l3ajmfFDoKSJElTZ8a5djM147zSdO4sGBEbUnb7uzAzR1tFQ5IkaaUaGRmp5aEVZmTGeSW7NCJ+TNkW+0nAHpS1ow9vdFaSJElaqQycx/dD4I3AXpS66suAd2fm+Y3OSpIkqZ2lGrUzcB5HZs6jLHEnSZKkVZiBsyRJ0iAw41y7IYu+Z463zn1949+s1Ru+n3QZ7ooEsNbQrKanwH/94jONjr/7Cw9sdHyAWUNDTU+BpSPN/51Y7oaofWGDoTWangL3jixrdPzV+uDv5AmLv9vYJP68x6tr+cu4zlfPbv4L2yfMOEuSJA2AETPOtTNwliRJGgQGzrVzHWdJkiSpB2acJUmSBkHztzwMPDPOkiRJUg/MOEuSJA0Abw6sn4GzJEnSIDBwrp2lGpIkSVIPDJynWUTMj4iRiFivh74jETF/JUxLkiQNuuGaHnqEgbMkSZLUA2ucmzUHaHZ/UkmSNBC8ObB+Bs4NyswHm56DJEkaEJZV1G6gA+eImAvMA14JPA24HzgXOCgzF7f12w04HtgW2A3YBRgCTgH2z8y/TGL49SPii8BO1evvAe/LzPvbxh0BDsvM+ZO4viRJklaigQ6cgRcBfwucBPwe2BTYG1gUEc9pD2IrXwLuAg4BngvsBWwMvHYSY38XuA74ELA1sCdwByWQlyRJmlaWatRv0APnMzLzlPaGiDgN+CmwM/D1jv4PAK/OzGVV39uAwyLiFZn5kwmO/fPM3Ktt3A2BPTBwliRJmpEGelWNzHyg9eeImF0Fr9cCSyhZ4E5fbgXNlWOq5x0nMfyxHa8vADaMiHUmcS1JkqSxuRxd7QY64xwRc4APA7sDm1DqllvW7XLKNe0vMvOuiLibUuIxUTd1vL67el4f+PMkridJkqQGDXTgDBxNCZoXUMoz7gFGKDXPdWfbl4/SPjRKuyRJ0qSNmB2u3aAHzrsAJ2TmAa2GiFgLGG1Xv80pJRWtvhtSMsQ31jlJSZKkKeuTwDki1gQ+BuxKiaOuAD6SmeeMc9584NAuh/6QmU/q0n8P4EDg6ZTf9H8hM4/p7DedBrrGmZL17czw7gPMGqX/XhHR/mHifdXzmdM9MUmSpAG1ENgf+AawLyWkPzMiXtrj+XtRgu7W432dHSJiL+A44NeU2O5nwH9ExAGdfafToGecTwd2jYh7gN8CLwVeRVlyrps5wNkR8V1WLEd3VmYuWglzlSRJmrR+KNWIiBcDb6Hsg7GgajsR+A3wKeDlPVzm5MxcMsYYc4AjgB9k5puq5q9ExGrAoRFxXGbeM5X3MZpBzzjvC5wIvB34HGVN5lcB947S/72UtZcPB95G+cT0plH6SpIk6dF2AZZSssHAIzslfxXYNiI27uEaQxGxTkSMdl/YDsCGwH92tB8DPJ7JrYbWk4HOOFefVt7d5dCmo5xyb2buSdmsZLJjzgfmd2lfSAnE29u8UVCSJE2PmjLOEbEe3e8PW9IlM7wVcFVmdiYpL6WUz74AuG2cIW8C1gb+EhGnAAdm5p86xgD4Rcd5l1G+CltRFoKYdoOecZYkSVoljAzX8wD2A27o8tivyzQ2pntg3Gp78hhv4W7Kimh7Af9MqZF+F3BudcNh+xgPdQTTZObDlHLcscaYkoHOOE+niFib8ulnLHdm5mjL0EmSJM1EC+j4rXmlWx3yHOChLu0Pth3vKjO/0NF0SkT8hlKC8U7gK23XeHiUyzw41hhTZeDcuwPpvkRKu6cDi+ufiiRJ0qPVdXNgVY4x6s16HR4A1uzSvlbb8Yk4FvgM8EpWBM6jjdEaZ6Jj9MzAme71x12cCFw4Tp/bp2M+kiRJM9RtlFKKTq22WydyscwcjohbgA06xlgjIjZoL9eIiDUoNw1OaIyJMHDuUWZeD1zf9DwkSZK66Yfl6IDLgX0jYu2OGwRfUj1fMZGLRcRs4KnAzzvGANgG+FFb+zaU+/cupyYGzjNIf/x9aNasPrifdXkffCdW74Ovw+4vPLDR8Y+/7LONjg+w5zYHNT2FvtD0T+PQY/a5WvlGGGl6Cszqg69D01b5r8FIX7z/UyjlrXtSaqNbOwnuDlyUmbdWbU8DHpeZV7VOjIgnZOadHdc7iFJ+cVZb27nAnyjLCLcHzntTlhyubeM6A2dJkiRNi8y8JCK+A3y6WrP5OsrKGHOB3dq6ngi8gkfv8HxjRJxE2SzlIcp6zTtTSmX/u22MByLiEOCYiDiZEjxvB7wDmDfW5ilTZeAsSZI0APqkVAPKChiHV8/rA1cCr8nMi8Y575vAyyhL0a1BWXDhcOATmbmsvWNm/mdELAUOAHYCbgb2zcwvTuP7eAwDZ0mSJE2baqfAg6rHaH2279L2LxMc5yusWGljpTBwliRJGgAjw31R4zzQmr6nQ5IkSZoRzDhLkiQNgD6qcR5YBs6SJEkDYKQ/lqMbaJZqSJIkST0wcJ5mETE/IppfCV+SJK1SRobreWgFA2dJkiSpB9Y4S5IkDQCXo6ufgbMkSdIAGLGHywPZAAAgAElEQVRQtHYDHThHxFxgHvBK4GnA/cC5wEGZubit327A8cC2lH3Ud6HsnX4KsH9m/mWK81gd+Ahlr/ZNgN8DC4EjM3P5VK4tSZKklWOgA2fgRcDfAidRgtVNgb2BRRHxnMy8v6P/l4C7gEOA5wJ7ARsDr53iPI6jBM0nARcC2wEfowTzE9peUpIkqRtLNeo36IHzGZl5SntDRJwG/BTYGfh6R/8HgFdn5rKq723AYRHxisz8yWQmEBFbUoLmYzNz76r5mIhYAuwVEUdn5pWTubYkSZJWnoFeVSMzH2j9OSJmR8SGwLXAEmDrLqd8uRU0V46pnnecwjReUz1/vqP9qI7jkiRJkzYyPFTLQysMdMY5IuYAHwZ2p9QWt3/31+1yyjXtLzLzroi4m1LiMVlzgWXAdR3t11btc6dwbUmSJMCbA1eGgQ6cgaMpQfMCSnnGPcAIpdZ4oLPtkiRJml6DHjjvApyQmQe0GiJiLWC9UfpvDlzQ1ndDYH3gxinM4UbK13kzHp3R3qxqn8q1JUmSAG8OXBkGPeu6nEeXZwDsA8wapf9e1dJxLe+rns+cwhx+WD3v19G+b/V8xhSuLUmSpJVk0DPOpwO7RsQ9wG+BlwKvoiw5180c4OyI+C4rlqM7KzMXTXYCmXlFRJwAvDci1qcsR7ct8Fbgq5n568leW5IkqWVkxIxz3QY9cN6XknV+O7AWcBElcD5rlP7vpWyAcjglG7+Qx2aKJ2NP4AZWbK5yC/BR4MhpuLYkSZJWgoEOnDNzCfDuLoc2HeWUezNzT0qgO9kx5wPzO9qWAYdVD0mSpGk3Mtz0DAbfQAfOkiRJq4phSzVqZ+Dco4hYG1h7nG53ZubylTEfSZIkrVwGzr07EDh0nD5PBxbXPxVJkqRH8+bA+hk4A5m5kHIj4FhOpKyIMZbbp2M+kiRJ6j8Gzj3KzOuB65uehyRJUjdugFI/A+cZpB92q3kej2t0/PNH7m50fIClfXDb8t+s9ldNT4Grhx5sdPw9tzmo0fEBjvvFZ5qeArfv+C9NT4F5d6zT6PhrDY22p9XK8+BI87e3bLN0dtNT4I7Vm53D8pFGh2/cyCr+/leGfojFJEmSpL5nxlmSJGkAWKpRPzPOkiRJUg/MOEuSJA0AN0Cpn4GzJEnSAHAd5/pZqiFJkiT1wIyzJEnSAHA5uvqZcZYkSZJ6MC2Bc0TMj4iRiFhvOq5XXXMkIuZP1/X6TURsX73H7ZueiyRJmvmGR4ZqeWiFVTLjHBFviYj9mp6HJEmSZo5Vtcb5LcALgAVNT0SSJGk6uKpG/VbVwHnaRcQawHBmLmt6LpIkadXjzYH16zlwjoinAh8D/gFYH/g9cEZm7tvWbf2I+CKwU/X6e8D7MvP+tuusDnwEeBewSXWdhcCRmbm8hzkcUc1hXSCBT2Tmt9r6zAYOBt4BPBW4D/gdcFhmnh0Ri4BXVH1bP2I3ZuamVducan5vq+Z3O3AC8LFWUBwRmwI3AP9GKXd5P/A0YDNgcUQ8Efgk8E/A44HfVu/vlLHenyRJkvpXT4FzRGwCXEoJAr9MCVjnAm8G2gPn7wLXAR8Ctgb2BO4A5rX1OY4SNJ8EXAhsRwnInwb8yxhz2Bj4GfAw8AXgT5QA/b8jYs3MXFh1nQ8cBBwD/AZYD3hRNZ+zKYH32tX896/OubcaYzXgNOAl1fu8GnghJZB+KrB7x7T2BGYD/wkMA/dWgfci4BnA0cDNwFuB70TErpn5jdHeoyRJ0mR5I1/9es04fxJ4ArBNZl7eaoyIf+/o9/PM3Kvt+IbAHlSBc0RsSQmaj83Mvatux0TEEmCviDg6M68cZQ4fpwSnW2XmkqrtSxFxJnBkRJyYmcPAa4GvZOb+3S5SZZ3fB2zUJYh9O7A98LLMvKTtfVwPfDIiPpWZV7X1fzLwzMy8q63vvsAWwFsy89tV25eBS4DPRsS3M3PpKO9RkiRJfWrcVTWqLOxOwKntQTNAZnZW0xzb8foCYMOIWKd6/Zrq+fMd/Y7qON45hyHgjcD/AKtHxEatB/C/wMbAs6ruS4CXRMRTxntvXexCyVJf1zHGOdXx7Tv6f6c9aG57D7cAJ7caMvMh4EvAX1My2JIkSdNqZGSolodW6CXj/ARKicZveuh7U8fru6vn9YE/U8ojllHKOdpdW7XPHWMO6wHvrR6j9bkK+CjwA+CmiPgVJbD+Zmb+tof5b07JFt85xhjtbujSZy5wdZcPFVe1Hf9ZD3ORJEnqmaUa9ZvuVTVGu7lvqt/JVmZ8IfDNUfr8BiAzz4+IzYDXAX8P7A18MCLek5nH9zDOr4APjnL8+o7XD4xzPUmSJA2IXgLnO4G/AH8zDePdWI25GXBNW/tmVfuN48xhKDN/PN4gmfknSpC9MCL+inKz3seAVuA82oIt1wHP7WWMMdwIPCcihjqyztF2XJIkaVq5Gl39xq1xrm64+wHw+ojYqv1YVXs8ET+snjt37WutzHHGKHNYDnwfeHNEROfxiHhC25837Dj3PkqQvlZb832U5ew6nQLMjYh3dRlj7YhYq8s5nX4IPIVSL906dw1K5vsPwGU9XEOSJEl9ptdSjYMpZQ8XRMSxlOXonkrZge9ZY53YLjOviIgTgPdGxPqU5ei2pSzX9tXM/PUYp38I2AH4RUT8F6VmeCNWLDW3adXvtxHxE+AXlCXrXkhZNu+YtmtdBrw9Ij4P/By4NzNPA06s+h4fEa8GfgqsATwHeFN1rWvHeZv/BewFfD0iXsSK5eheAOzqihqSJKkO1jjXr6fAOTNvjoiXUJaEexflZsGbgdMnMeaelJvqdqNkZW+h3NB35DhzuC0iXgwcSgli/xr4I3Al0L4s3hcp9c2vBtaklEYcAnymrc+XKcH2bpS1nG8ETsvM5RHxOuBAygYq/0xZ4/laypJ8t4z35jLzgYjYoer/blZsgPKmzPzOeOdLkiRNhitg1G9oxP0ZZ4y3zn1949+srVi70fHPH7l7/E41Wzoy3PQUePlqGzQ9Ba4eerDpKTTuuF98ZvxONbt9x1H3jVpp5t2xzvidarTW0KxGxwd4cGTMjW9Xir9f9ldNT4E7pnvJgQlq/rsAH77xG41Frxc9aZda4oSX3X6KEXml4R9xSZIkTYfm0zqDb9ybAyVJkiSZcZYkSRoII1PeNkPjMXCWJEkaAMON3wk1+CzVkCRJknpgxlkTctdQs/cszx5p/rPe7KHm53DvUPNphX5YXaRp/bCixZPO/ErTU2DpC/dvdgLN/3VgaR/clvWb2c2vKdH8+iartmFLNWrXfAQgSZIkzQBmnCVJkgaANwfWz4yzJEmS1AMzzpIkSQOg+Ur7wWfgLEmSNAAs1aifpRqSJElSD8w4S5IkDQBLNepnxlmSJEnqgYHzBETESETMb3oekiRJnYZremgFSzUkSZIGgDcH1s+MsyRJktQDM86SJEkDYNiEc+0MnCsRsT3wWeBvgFuATwMbA4dmZtcfxYhYCGyfmZt2tM/vdl5EvAvYB3gO8CDwK2B+Zl4wjW9FkiRJNTBwBiJiK+B/KQHzocAs4KPAndM4xuHAvwMXVM8jwEuB7ao2SZKkSRu2xrl2Bs7FYcBS4GWZeTtARJwM/G46Lh4RmwMHA98B3pKZrZtUj4oIf8olSdKUjTQ9gVXAKh84R8Qs4FXAd1pBM0BmXhsRZwL/NA3DvJ5yI+bH2oLm1jj+nEuSpIEREWsCHwN2BdYHrgA+kpnnjHPeG4E3Ay8G/hq4CTgN+Hhm3tPRd7T4ae/MPHZq72B0q3zgDDwRmANc2+VYt7bJeAawHLhqmq4nSZL0KH205vJCYGdgASWW2g04MyJekZk/HeO8/wJuBb5OCZqfB3wA2DEitsnMBzv6nwV8o6PtkinPfgwGzlMz2qedWSt1FpIkSX0gIl4MvAXYPzMXVG0nAr8BPgW8fIzTd8nMRR3Xuww4obrmwo7+V2VmZ+BcK9dxhjsoK1w8s8uxbm3t7gbW69I+t+P1dZRg+tkTnp0kSVIPhoeGanlM0C6U+8aOazVUmeKvAttGxMajndgZNFe+Xz1v0e2ciJgTEWtNdJKTtcoHzpm5HPgx8MaIeFKrPSKeCew4zunXAetGxPPbztsYeENHvx9QfoNyaEQ86mvuzYGSJKmfRcR6EbFpl0e35OFWlEzwvR3tlwJDwAsmOHwrNvtjl2N7AvcBD0TElRHRGX9NO0s1ivnAxcBFEfFlSnb4/ZRfK4z1DT6J8muH70fEF4HHAXsDVwNbtzpl5jUR8Sngw8CiiDgVWEZZju7XwJHT/YYkSdKqpcbVBvajLNfb6TBKDNVuY8ryvp1uq56fPMGx51HuE/teR/vFwLeBG4CnAvsC34uIt2XmtyY4Rs8MnIHMvCwidqRsgHI4cDNlHectGKO8IjPvqj7dfJ6yYcoNlOB4c9oC56rvwRFxA/A+SqB8H2UDlJ9M+xuSJEmrnBpvDlzAY+uLAZZ0aZsDPNSl/cG24z2JiLcBewCfyMzr2o9l5ss6+p5ASXh+OiJOqmvVMgPnSmaeS0ewW2WGf9/W5zFlFZl5NuWuz07zu/T9CvCVqc5VkiRpZcnMJXQPkrt5AFizS/tabcfHFRHbUeqizwAO6WGO90XEscAngaCmlcxW+Rrnls7C8mrTktcAixqZkCRJ0gQMD9XzmKDbKOUanVptt453gYjYEvgf4ErgzdX9aL24uXreoMf+E2bGGYiI1YHrqzT/9cCmwL8CD1NKMCRJkjS+y4F9I2LtjhsEX1I9XzHWyRGxGfC/lFXPXpuZ901g7GdUz3dO4JwJMeNcLAd+BLwVOJpyY+ClwMsz85omJyZJktSLYYZqeUzQKcBsyooXwCM7Ce4OXJSZt1ZtT4uIR91HVq1u9iNKufb/y8xuK2kQERt1adsQeC9wQ52xmxlnHtn2erem5yFJkjRZNa6q0bPMvCQivkO5SW9jytK976LscbFbW9cTgVfAoyLz/6VkjT9NWfN527Zj17XtOvj+iNgJOJ2yw+AmwHsou0G/ftrfVBsDZ0mSJE2nd1JWKXsnsD6lVvk1mXnROOdtWT1/sMuxE4BW4Hwx8DLgXyj1zPdWx47sYYwpGRoZ6YfPJ+rFP8/dqfFv1r3DDzc6/iHLHtfo+AC/XL3nlXRqc/WspU1PgT+MdFttaOXphzqzoYn/CnPaLa1zAaoe/fdlRzU6/mXPP7DR8QG+tEbzPwt/GWn+34UHhpudwxpDsxodH+AHN53e2A/DiZu8o5Y44Z23fKP5H/A+0Q//90iSJEl9z1INSZKkAdD8758Gn4GzJEnSAGi8nnMVYKmGJEmS1AMzzpIkSQNgErv8aYLMOEuSJEk9MOMsSZI0ALw5sH5mnCVJkqQemHGegohYBKyXmS9oei6SJGnVZsa5fgbOkiRJA2DEmwNrZ6mGJEmS1AMzzpIkSQPAUo36zcjAOSIeDxwOvB7YGLgHuAKYl5m/jIjFwOXAMcCngC2A3wH/mpmXRsS7gI8ATwMuAXbLzBs6xngz8KHq3D8Dp1XX/+M4c3sd8B3gRGCvzByOiNWAfwP2AJ4B/Ak4BfhwZt47ta+GJEmSVoaZWqpxLLA78E1gb+BzwEPAc9r6PJsSvJ5KCZKfApweEXsABwNfpgTVLwGOa794ROwGnAQ8CHwQOAF4O3BeRKw12qQiYmdKQHwc8J7MbH34+ypwBLAI+EA1738BfhARViRJkqQpG67poRVmZMYZeC3w8cz8TFvbpzr6BPCSzLwUICJ+D3wb+AyweWbeVbXPBj4cEU/NzJur15+iZLC3z8yHqn6XAd+iBLxHd04oIt4CfB04JjP3a2vfDtgN2CUzv9vW/nNKcP7/gP+d7BdCkiQJYKTpCawCZmrGeQmwfURsOEafK1tBc+WS6vl/WkFzR/vTq+dtgCdSAuCH2vqdDNxCCdofJSJ2Bb4BHNUeNFd2oZRm/CQiNmo9gPOB5cD2Y7wHSZIk9YmZmnFulU/cHhGXAj8EvpGZN7b1uanjnHuq55tHaV+/ep5bPWd7p6pW+Zq24y2bAwuBEzPzg13mujmwAXDnKO/lCaO0S5Ik9WzY4s/azciMc2aeTLnJ7gPAHcCHgd9GxP9r67Z8lNNHa5/sj9stlKz16yPi+V2OrwbcBrx6lMfnJjmuJEmSVqKZmnEmM28DvgR8qSp9+CXw78BZU7x0K2sdlHKK8qLcxLc58JuO/vcDrwF+AvwoIrbNzGvbjl8H7ABc0FH6IUmSNG28ka9+My7jHBGzImLd9rZqibjfA6OueDEBv6BksfeOiDXa2ncBNgHO6DwhM5cAf09Ztu7HEfGUtsOnAGtQlrZ7lIhYMyLWmYY5S5KkVZyratRvJmacHw/8PiK+S1n54i/A3wEvBQ6Y6sUzc2lEzAOOBxZFxLeAp1LKQn4DfGWU8/4QEa8CLgTOjojtMvOPmXleRBwHzI+IFwLnUH4OnwW8ibLM3Y+nOm9JkiTVa8ZlnCmlEf8JbAnMB46ibFLy3sz8/HQMkJkLgbcCc4DPsmLN6L/LzAfHOO8mSt3yBsBZbdnk91DWm34K8EnK5i3bUoLwy6djzpIkadU2UtNDKwyNjPglmSn+ee5OjX+z7h1+uNHxD1n2uEbHB/jl6nOangJXz1ra9BT4w0izJfv98Kl/aNL3FE+fpX3wi9T/vuyoRse/7PkHNjo+wJfWaP5n4S8jzf+78MBws3NYY2hWo+MD/OCm0xv7Yfjs095RS5xw4E3faP4HvE/MxFINSZIkdXA5uvr1Q9JGkiRJ6ntmnCVJkgZA84Vbg8/AWZIkaQA0fiPUKsBSDUmSJKkHZpxnkNX64A7+1Ru+Y/mS2c2vaHHbaqPt2r7y+Ou4/ljRYq0+uIO/H1JMTa9q8cIrP9vo+ABsc1DTM2BWH/ydaPr/iMcPrTF+pwE23A//IAw4M86SJElSD8w4S5IkDQB/G1k/A2dJkqQBYKFG/SzVkCRJknpgxlmSJGkAWKpRPzPOkiRJUg/MOEuSJA2A4eZXJBx4Bs6SJEkDwHWc62epxgRExG4RMRIRmzY9F0mSJK1cZpwlSZIGgPnm+plxliRJknpgxlmSJGkAuBxd/QycgYiYC8wDXgk8DbgfOBc4KDMX93D+a6vztwaWA/8HfDozT23r837gfcAzgDuBbwOHZOb90/pmJEmSVAtLNYoXAX8LnAR8ADiWEkQviojHjXViROwBnA6sAxwBfARI4NVtfeYDRwM3AgdU/fcHTo0IF4+RJElTNsxILQ+tYMa5OCMzT2lviIjTgJ8COwNf73ZSRKwLLAAuBv4uMx9qOzZUPT8B+DDwQ+AfM3Okal8MfAL4R+C06X07kiRpVWOIWz8zzkBmPtD6c0TMjogNgWuBJZTyi9H8PbA28In2oLm6Zuvn91XAGsCCtjaA/wCWAq+d+juQJElS3cw4AxExh5IV3h3YBGgvn1h3jFOfUT3/Zow+c6vnbG/MzHsj4pa245IkSZPmzYH1M3AujqYEzQso5Rn3UH7jcRJm5SVJkoSBc8suwAmZeUCrISLWAtYb57zrque/ARaP0ufG1iWBm9qu/1eU7PZZk5ivJEnSo3gjX/3MphbLeXR5BsA+wKxxzjsbuBc4OCLWbD/QtlrGj4GHgQ90rKDxXmA2cMZkJy1JktQyUtNDK5hxLk4Hdo2Ie4DfAi+l3NR311gnZeY9EXEA8GXgkog4Cfgz5YbCh4D3ZeadEfEJ4FDgjIg4g5Khfg8l8D69pvckSZKkaWTGudgXOBF4O/A5YGNK4HzveCdm5n8BbwAeAD4KHAlsQVsJRmbOp6wP/QzgKOB1wBeAN3SstCFJkjQpwzU9tIIZZyAzlwDv7nJo045+C4GFXc4/FTi1s72jz9GUmxAlSZI0Axk4S5IkDYARK5JrZ+AsSZI0ACyrqJ81zpIkSVIPzDhLkiQNANdxrp8ZZ0mSJKkHZpxnkH74lLPmULOzuHT8FQJrt2yk+U/0y/sgqzC74Z/IfrgJ5sGR5U1PgaV9UNX4pTUa/q9km4OaHR847hefaXoKvGHrfZqeAmsNjbdvWL3uZ1mj4zet+X8VB18/xGKSJElS3zPjLEmSNACsca6fgbMkSdIAaL5wa/BZqiFJkiT1wIyzJEnSAOiHm6YHnRlnSZIkqQdmnCVJkgaANc71M3CWJEkaAJZq1M9SjQmIiMURsbDpeUiSJGnlM+PcISKeDbwFWJiZixuejiRJUk8s1aifgfNjPRs4FFgELO44FvhzKUmStEoycJ6AzHyo6TlIkiR1MzxijXPdBiJwjoi5wDzglcDTgPuBc4GDOsstImIDSkb59cCTgNuBHwP7ATsDx1ddz4uI1mk7ZOaiiFgMLMrM3SLiRcClwDsy85sdY7wZOAnYLjMvrNqeCxwObA/MAa4ADsnMs6fliyBJklZp/RI2R8SawMeAXYH1KTHPRzLznB7O3QQ4Cvh7yr145wL7Z+YNXfruARwIPB24CfhCZh4zXe+jm0G5OfBFwN9SgtUPAMdSguhFEfG4VqeIeDxwAfBe4IdV368B2wAbAucDC6ruR1K+4bsCv+scMDN/DtwAvKnLfN4E/B64qBr3ecDFwDOBTwAfrPqdGRGvnOR7liRJ6kcLgf2BbwD7Uspcz4yIl451UkSsDZwHbAccQUl0bk2J59bv6LsXcBzwa2Af4GfAf0TEAdP6TjoMRMYZOCMzT2lviIjTgJ9Ssshfr5o/CDwHeF1mntbW/bCIGMrMkYj4CSX7fHZmLhpn3JOB/SJincz8czXu2sCOwLGZ2frwtwC4Fvj/MnNp1e9LwC8pPxjjfgKTJEkay3Af5Jwj4sWURRb2z8wFVduJwG+ATwEvH+P091KSjC/MzF9V555Znbs/8NGqbQ4lfvpBZrYSmF+JiNWAQyPiuMy8Z9rfHAOScc7MB1p/jojZEbEhJVBdQvmk0vJG4LKOoLl1jcn8tJ0MrAns1Nb2T5RSjG9X89kA2AH4DrBuRGwUERsB6wE/Al7UnhWXJEmawXYBllKywQBk5oPAV4FtI2Ljcc79WStors69ipJgbP8N/w6USoH/7Dj/GODxlARmLQYicI6IORHxsYj/v737jpOzqv44/gkhEEA6UekteAQpgiAqoEhRBH9KU7oERZo0C1WE0JsKKC2AUkRCkwhKFRAIiCAgoQSOtBBKgAQILQkt+/vj3MlOJjOzs8nceSaz33de+9rsc5+Ze7fNnuc+555rLwDvAxOA8URwumDZqSsQVy1N4e4PEQH6dmWHvw887+73pY8HA/2IFI3xFW8/J74HizZrTCIiItI3dWX610trAk+6+7sVx+8n4qHPV3tQmi1eHXigSvP9wGfKJhrXTO8rz32QSAtZk0w6JVXj98BuRErEvcBbRI785eS/OLgS+IWZLQR8BGyWxlNS6v9kYhFiNePzDU9ERERk5qUYZ6EqTRPdfWLFscWBl6qcOy69X6JGN4sQd/HHVWkbRwTdiwPPpPfvu/sb5Se5+wdm9nqdPmZZpwTO2wIXu/u0hHAzG8iM3+RngFV7eK7eXlpdCRxOVOn4ABhIStNInk3vp7h7rcBZREREZJZk3GjiQGKhXqWjgaEVx+Yh7v5XmlLWXk3peCOPnYeIuaqZUqePWdYRqRrAx8SVSLn9gP4Vx0YAXzCz/6t8AjMrPf699L7aldUM3H0U4ES6xveBZ9z9wbL214hqHXub2aAq/c5wTERERKS3ptKV5Y24o798lbfTqwxjMjFzXGlgWXs1peONPLZWH6Vza/UxyzplxvnvwC5m9hYwGvgysAnwesV5pwLfA64xswuAh4FBxKLBrYmdAkcRKReHmNmCxJXP7SkAruVK4FDiYu83Vdp/QpTBeyz1+xxxm2ED4htcb4WpiIiISGFSOkZlSkYt44gYp1Lp2Ms1HvcGEXPVemwX3Wkc44C5zGyR8nQNM5uLWDdWq49Z1ikzzgcAlwA7EYHr4kTgPF1ieioZtz5wPlEJ43fAj4lk8gnpnPHAHsAniRWgw4kSdvVcAQwgrn6urGx098eIWtP/AHYnVn3+mJjdPrmXn6uIiIjIDNpkceDDwGdTed5y66b3o6o9yN2nEjWZ167SvC7wlLtPKuuDKueuTcS2D5NJR8w4pyuhH1ZpWq7KuROIOoH71Hm+C+neQbD8+AzPl44/zoypIpXnPA3sXO8cERERkdnc1cRufruTUjnSToK7Afe4+8vp2DLAvKncXPljTzSzNcvqOBuwEXBS2Xm3EzPU+xClfUv2JiZNb8zweQEdEjiLiIiI9HUZFwc2zN3vM7OrgFNSzeZngF2BZYEhZadeAnyN6ScezybuyN9gZr8hUmd/RqRmnFbWx2Qz+xVwlpldSQTPGxATlIdUqfTRNJ2SqiEiIiLSp3V1dWV5mwk/AM5I739HpLNu7u731HuQu78DbAjcDfwKOJZIu/iau79ece7ZRGrt6kQK7HrAAe5+yswMuFGacRYRERGRpkk7BR6U3mqds2GN4y8ShRwa6ed8Yt1ayyhwFhEREekAU3u/kE96SakaIiIiIiIN0Iyz9MpWHy5QaP/XD3iv55My62qD5Rdfn1rs9wFgdP9amza1Rv/6hWxaYu0PBxQ9BB4b8HHRQ+DZrmJ/L9vhZ2GrtfYregiMeOj3RQ+BK1c/stD+X+njUU3xf506Xx//ERMRERHpDDNRc1l6SakaIiIiIiIN0IyziIiISAfQ4sD8NOMsIiIiItIAzTiLiIiIdICZ3KxEekEzziIiIiIiDdCMs4iIiEgHUDm6/BQ4i4iIiHQAlaPLT6kas8jMhppZV8WxLjMbWtCQRERERCQDzTiLiIiIdACVo8tPgXMe8wAfFT0IEREREWkeBc49MLP53P293jzG3afkGo+IiIhINSpHl1/HBM5mtjRwDLAZsDDwInC9ux9gZssChwAbA8sAk4DbgYPcfUzZcwwBLgQ2AHYBtgHeAZZP7esDpwGrAfuLvC4AACAASURBVC8Bp9QYSxdwtLsPLTu2FnAi8JV06B7gEHcfNcufvIiIiPR5StXIryMCZzNbErgfmB8YBjiwLLAdcACwDhGwXk4E1MsBewN3mNkq7j6p4imHAeOAo4ABqY/VgFuA18qOHw282sD4PgfcBbxJBM+k/kea2bru/sTMfN4iIiIi0jodETgDJwGDgLXd/eHSQTM7Iv33ene/uvwBZvY34F5iVvlPFc83HviGu5eXRDwG6ALWc/eX0nP8BXi0gfEdB/QH1nf359NjLwOeTG3bNPJJioiIiNSicnT5zfbl6MxsDuC7wF/Lg2YAd+9K7yeXnT/AzBYFngYmAmtVedrzyoNmM+sPfBO4phQ0p+d9Ari5h/H1B76RHvt82WPHACOAb6ZzRERERKSNdcKM8yAiReOxWieY2TzAYcBuwJJAv7LmBas85LkqfcwDPFXlXAc272F886bzKj0JbA8sRgMpHyIiIiK1TNXiwOw6IXBuxO+JoPl0Ij3jLSLt4nKqz7pPrnJMREREpG0pbM6vEwLn8UTli1XrnLMtcLG7/7x0wMwGAgv1oo/JwEpV2qyBx06qcZ4B7wETGhyHiIiIiBRkts9xTrnI1wJbmtma5W1mVkrJ+Jjp0zMA9iMW7DXSx8dELvPWqYJH6flXJnKfe3rsLemxy5Q9dhlgK+DmdI6IiIjITJtKV5Y36dYJM84AhxML8Eaa2blEPvHSRP7wZ4C/A7uY2VvAaODLwCbA673o4yiiRvQ9ZnYO8bXbD3gcWL2Hxx4BbArcnR4LUY7uo9QmIiIiIm1utp9xBnD3F4B1gb8CuxI5zTsA16dTDgAuAXYCfgMsTgTO7/aij0eI2eUJRGm6HxLB9IgGHvs48FViMeAv09sTwAaq4SwiIiLNoBnn/DplxrlU3m3nGm0TiUC30nIV510EXFSnj7uAtas0Da04rzItBHd/iJgVFxEREWk6bbmdX0fMOIuIiIiI5NYxM84iIiIifZnSKvLTjLOIiIiISAM04ywiIiLSAbo045ydZpxFRERERBqgGWfple8/ckyh/V+91gGF9g/w+seTih4CowcMLHoIvNv1UdFDKNxrcw4oegiN7eKU2eSpHxba/5z9iv8qDGyDMVy5+pFFD6HwvxGHrH14of0XTVU18lPgLCIiItIBtDgwP6VqiIiIiIg0QDPOIiIiIh1AqRr5acZZRERERKQBmnEWERER6QDKcc5PgbOIiIhIB1Ad5/yUqiEiIiIi0gDNOIuIiIh0gKlaHJidZpxFRERERBqgGWcRERGRDqAc5/wUOIuIiIh0AKVq5KdUjczMbL6ixyAiIiIis26mZ5zNbFngEGBjYBlgEnA7cJC7j6k4d3Xg98AXgdeBc4GXgD8Cy5efb2bfBg4H1gA+KnvOpxsc19fTY7Z29xEVbT8GzgM+5+6j07HPAccCGwLzAKOAX7n7P3r7uZrZEOBCYANgF2Ab4B1geTObP/WzJbA48Fbq6xB3f6iRz01ERESkFqVq5DcrM87rAF8BLgf2J4LhjYE7zGze0klmtiTwT2AV4ETgNGAn4IDKJ0yB53XABODgdP4XgbvN7FMNjusO4AVgxyptOwH/LQuaVwP+BQxOfR2czrvRzDbu7edaZhiwInAUcEY6di6wG/BnYG/gN8D7xNdFRERERNrcrOQ4X+/uV5cfMLO/AfcSM61/SocPARYG1nD3R9N5FwJPVTz2E8DpwFnuvl/Z8SuAx4GfAof2NCh37zKzPwMHmtkC7v52ep6lga8Cvyg7/XTgaeBL7v5hOu8c4CHgeOC2Xn6uJeOBb7j71LJjWwDHufupZcdO7unzEREREWmEcpzzm+kZZ3efXPq/mQ0ws0WJIHQisFbZqZsBI0tBc3rsG8TMa7lNgQWBK81ssdIbkeowikilaNQlwEBg67JjOwBdwPA05kWArwNXAQuW9bcQcAuwTmk2uRefa8l5FUEz6dwN02NFREREZDYzKznO8wCHEekHSwL9ypoXLPv/ssDIKk9RmbO8Unp/V40un210bO7+hJk9SKRrXJQO7wTc7u7j0seD05hPTG/VLApM6sXnWvJclWMHAxcDr5jZ/cANwKXu/nyjn5eIiIhILcpxzm9WUjV+TwSSpxMpC28RM7qXM3Mz2aXH7EikOlSaXOVYPZcAv0250YOA1YEhVfo7Gbi1xnOUxtHbz3WGsbr7lWY2klgc+A0iED/czLZ295sb/7REREREZqRUjfxmJXDeFrjY3X9eOmBmA4lUh3LPE7O7lSqPPZPej3P3O2ZhXCXDiQV42wOfJoLZa8raSzPYU9y9VuBc0ujnWlea7T4HOCelhTwEHAEocBYRERFpc7NSVeNjpk9ZANgP6F9x7GZgg1TBApiWX7xTxXm3AG8Ts7AzBPQp0GyYu48Hbkr97ABc5+7vlLW/RqSF7G1mg6r0V36s0c+1KjPrb2bTpXS4+wTgRSIXW0RERGSWdGX6J91mZcb578AuZvYWMBr4MrAJUae53CnAzsBtZvZ7ogby7sBYYBEi5QF3f8vM9iXygB9I1TReB5YDvgP8lZid7Y0/AVek/+9bpf0nRP71Y2Z2AZGbvDhRh3kgUYWjN59rLfMDL5rZX4iFju8AG6Xn+Xm9B4qIiIhIe5iVwPkAYiZ2JyLIvIcIJqdLO3D3F9KmJL8DfknkDZ8FvJeOTSk7909m9jJRdu5QYAAxK3sHkU/cW9cR+cgfErPP03H3x8xsHWAoEcwvBLwKPEh3/eWGP9c6JgFnE7nNWxEz/U8D+7j7Ob39pEREREQqdXVVFvSSZpvpwNndJwI/rNK0XJVzH6Z79hYAMzudCJonVJx7G931k2fVx+ntCnf/qNoJaUfCnes9SaOfq7tfRHcVj/LjHxBVNQ6ubBMRERFphqlKq8huVnKcG5bKuZV/vCixJfXd7v5xxq63IdJBKjcoERERERHplVlJ1eiN+83sVuAJosLFj4AFgGN78yQpAK9WN7ncG8CaRPm5o4B/u/t9vR6xiIiIyGykS+XosmtV4HwDsYvfnsRiwAeBH7p7rc1OatkOuLCHc75O1GveGfgv1VMsRERERER6pSWBs7sfAhzShKe6mdiau55R7j6E6Tc7EREREeloynHOr1Uzzk2RNhAZ1+OJIiIiIn2MUjXym60C576uHYrMHLL24YX2/8rH7xbaP8B/xv+v6CFw8x+HFD0E9jriyUL77z/DnkSt97H+RgEwV7+G9oLKZv5+cxXaP8AkqhZuaqlX2uAvetF/I05+4IRC+5fO1wa/ZiIiIiIyq6Zqxjm7lpSjExERERGZ3WnGWURERKQDdGlxYHaacRYRERERaYBmnEVEREQ6gKpq5KfAWURERKQDqI5zfkrVEBERERFpgGacRURERDqAUjXy04yziIiIiEgDNONchZkNAS6sOPwaMAo43t3vTOctBzyX2g9y919Xea7rgc2B5919ubLjY4CH3X3L5o5eRERE+qLZeQMUM1sIOAXYCpgXuA/4mbs/3MPj5gB2BbYGPg8sQsRmlwG/cff3y85dju64rdK33P2mnsapwLm+XwJjgX7A4sCPgFvMbD13f6DsvPeBHYHpAmczWxTYFJjSmuGKiIhIXzW7pmqk4Pd6YDUilnod2Ae4w8y+4O7P1Hn4vMAfgX8D5xITnV8GjgU2Ajap8phLgZsrjo1qZKwKnOu7ofxKx8wuBV4CtgXKA+cbgK3MzNzdy45/jwiq/wVYC8YrIiIiMrvZFvgKsJW7/xXAzK4E/gccBfygzmM/ANZz93+VHTs/3dk/2sw2dPc7Kh7zoLtfOjMDVY5z77wOfAQMqDg+EniRmHUutyNwLTA5/9BERESkL5tKV5a3FtgWeJmImQBw9/HAlcCWZlYZd1F23gcVQXPJiPR+5WqPM7P5zGyu3g5UgXN9C5nZYmY2yMxWA84DuohvZLku4HJgh9IBM1saWJ/IsRERERGR6tYkZoEro/T7gfmBwTPxnJ9O7ydUaTsWeBeYYmb3mtlXG31SpWrU98+Kj98DtnP3+6qcOxz4hZmtnfKftwfeAP4B7JF3mCIiItLX5cpxTgv3FqrSNNHdJzahi8WB26scH5feLwE80cvnPBh4C7il7NhUIrd5BDHDvRLwC+BWM9vY3Uf29KQKnOvbCyglpC+RPv6zmX2z8ovr7g+Z2f+IWecH0vur3f1DM6U3i4iISF4Zq2ocSOQaVzoaGFp+IC30aygFwt1LxRPmIdaEVSpvb5iZHU4sCtzT3d8q628ssFnFuZcDo4GTgPV6em6latR3n7vfmt4uIb4JbwFn1Dh/OLCdma1C3HYY3qJxioiIiORyOrB8lbfTq5z7VWJtV49vZrZYesxkYO4qzzWwrL0hZrYdcBwwzN3P6+l8d3+ZiNe+ZGbz9nS+Zpx7wd0nmdm/iUT1+aqcMpy4IjubqL7R45S/iIiISDN0ZVrIl9IxGk3JeBLYrcFz30nvxxHpGpVKx15u5MnMbFPgEuBvwE8aHAPAC8Rk8kLApHonKnDuvdLKzk9UNri7m9lDwNeIottTWzoyERERkQK5+yvARb182MPAV8ysX8UCwXWJRXxP9/QEZrYukbv8H2B7d/+4F/2vAHwMvNnTiUrV6IWUHP9l4FWiwHY1hxI5P8NaNS4RERGRqV1dWd5a4GpiLdl3SwdSGsf3gGvd/cOy4yua2YrlDzazlYkNVMYA/+fuVVM7zGxQlWODiXVpd9V6XDnNONe3uZmtmv5f2jlwEWAfd++qtujP3f9BVNIQERERkZ5dTez8d4mZ/ZooIbcPMcE7tOLc29L75QDMbH6iUsbCwKnAFhXx2SPu/kj6/ylmtkJ6jnHAikThB4jqGj1S4Fzf8WX/nww8Cuzo7lr0JyIiIm1ldt1y290/NrPNicB3f6KKxv3AD9y9pzSNRYGl0/9PqtJ+NFAKnG8hAuX9iHzmN9Oxo9398UbG2m92/SL3Rdstu2Xh36wl+w3s+aSM7v3w1UL7B/jP+P8VPQTe+eOQoofAXkc8WWj//elXaP8AK3UV+/sAMLFfb9L48nhy6js9n5TR/P16vflX003io6KHwHpdCxQ9BF6ao9ivw8kPnFBo/wADFluhsBenuQcunSVOeH/KC8W/4LYJ5TiLiIiIiDRAqRoiIiIiHUBZBPlpxllEREREpAGacRYRERHpAJpxzk+Bs4iIiEgHUNicn1I1REREREQaoHJ0IiIiIiIN0IyziIiIiEgDFDiLiIiIiDRAgbOIiIiISAMUOIuIiIiINECBs4iIiIhIAxQ4i4iIiIg0QIGziIiIiEgDFDiLiIiIiDRAgbOIiIiISAMUOIuIiIiINECBs3QsM5vHzI40s28UPZYimdlXzWxQnfbFzOyrrRyTiIjI7GjOogcgnS8FbT8FNgQGAT9w93vNbFHgAOBydx/d7H7dfbKZHQrs3+zn7q0UmD7h7uNrtC8GrOLud2Xo/p/ALsBlNdo3Tm39M/TddsxsVWBzYNl06Hngend/PENfM3VBkunnAAAzWw/YwN1PqtF+KHCnu9+bawypn2/Q/ZrwG3d/0szmB9YBHnb3N3L2n8awBbAF0/8s/N3db8jdd8U4BgKLAuPd/YNW9l02hn7AYunDCe7e1YI+P0+87l1WdmxT4AhgIHCZu5+RexwivaHAWbIysxWBkcCCwChgBWAeAHd/3cy2Iv5w7p1pCA8BK2d67t4oMnjt10P73MDHze7UzI4EuoDj3X1q+rgnXe5+bLPHksYzB3AOsDvxNfkoNc0JnGhmFwB7NTlguIP4GjSqXzo/50XM0cCEOu2rAxsBWe7UmNl8wF9TH1OJO5/DgSeB94nfg2HAUTn6T2OYBxgBbEr8HLyUmjYB9jSzW4Ct3X1yrjGkcXwdOJ64WJgjjef2dCF9NXCyu9+YeQwGHAd8E5gvHX7PzG4CfuXunrH7U4AppNdFM1uG+L68DrwI/NbMJrv7eRnHINIrCpw7SIOBSaVsgUpyKhGUrQK8B7xW0X4dsE3G/n8K/M3MHgEudfemB4gNamnwmv4ALVd26LM1Zj8XAvYkZtqabSgRBJ4MfJA+7kkXkOvn8XDgx0RQdhrwdDq+InAgsBfxdTihiX1+vYnP1SxrAsfUab8XmJnXkkadCKwPbA/cTXfQirt/YGZXAd8mY+BM/IxtSvxMnOnu7wGY2bzAT9IYjwEOyjUAM9sEuBEYDZwO/LzU5u4TzOx94iIvW+BsZqsTExtzEQHrk6nps8CWwDfNbAN3fyTTENYAfl328c7ExdSa7v6GmQ0nfi+bHjib2TW9fEiXu+f8WyWzCQXOnWVolWOl2a7KwK2L7tmtnIHzRsAJ7v58Ss2oNAZYKmP/w4gZpT8CZ5rZi0DlLFKXu3+h2R0XHLzuRgQeXentl+mtUmnm9cdN7BsAd5+j3scFGEKkBVXe3XgK+ImZLQz8iCYGzu5+Z7Oeq4kGUv9Cbk66Zx5z2Bb4vbtfVeM1wYGdMvYPsB0wzN1Pnq5j90nAqWa2AhHYZwucidfd/xAXEYtQFjgnI4nAOaeTgYnAV919utef9Po1EjiJSG3KYUGgPH1tc+AfZWk6txGpNDmsRe/uBmVPXZHZgwLnDlIZmJjZksD1wGPEjEbplttniRm2Vcj3olQyJ/BOnfZFgQ8z9v828BYRHLVakcHr5cDD6bmvIb7/lUFcF3EXYJS717t13ymWAn5Tp30ksHUrBmJm/YEvMH1u7YMtuiMyGvgu8TNROa5+xEzjk5VtTbQI9X8f5yDuwOQ0iHhdrOVR4vc3pzWAg1IaU7WgbBzwqcxjWI80sVHZ4O5jzexc4LCM/Y8j0vcws0WALwL7lbXPT8xAN527L5fjeaXzKXDubGcBT7n7zhXH/wPsZGZXp3O2yjiGUUSu5DmVDSl42A64P1fn7r5hruduQGHBa8pLdAAz2w24y92fa9bzzyoz+wSwMFVmPt19bKZuXyIChRl+FpP1KUsbyMXMdiRuT3+K7s+/C3jVzH7u7sMzD+FM4MJ0G/wYpr+gPor4OuyRsf9niaCxlo2BJzL2XxrDFsDZNdq3SOfkNJmY/a9leWI2OLd6M6m5Z1mvBfYzs7eIhaIfEvnvJWuQ//sg0isKnDvbRsAhddpvI27V5XQicK2ZnQ5clY6Vyp8dDqxG5Bp2nHYJXt394lb3WU2qHHAUkQ5R7RZ9Sa6FcZcAR5nZROA0d38mjauU47w9jeVhzzQz2wa4lLigHMr0OaV7A5ea2fvu3tv8y4a5+8VmNpiYSfw+3bn1/YlA6SR3/0Ou/om0qWPN7FZilh+gy8wGpDF9m3yLhUvOBn5nZtcRF7T/S8eNqMKzGfmr8dwJ7GJmp1U2pMWBuwO3ZB7DvcA+ZvZnd3+xYgxLEd+Hf2Xs/whi9v8I4iJhiLu/mvpfgFj/clbG/kV6rV9Xl9J2OpWZvQbc4O5DarRfDHzL3T+ZeRxDiMVYC9CdVw2RwrGvu1+auf9CyuG1EzNbmbj1vALVZ3q73H3jzGP4I7ArMaM0Eniz2nm5Av10h+MPwA+In8Hyqhr9gIuBH7l7llvDaQwPElUEvl5ZdiwFjncCc+fIua8ylsHE3abl06FngGvd/enaj2pKv/2I4HlXonrCosDL6f3cwAXunnPGuzSOocChwICKpg+BE9396Mz9r0IEpf8jKmicSExkTCEC1rmAddw924yrma1F/MzNAfyF6S8gtiYuqr7q7g/nGkOdsc1BpGpMcvemp/OZWW+/rl3uvmKzxyGzHwXOHSzNZOwPnEEsxnkuHV8+Hd8f+J27/7QFY5mPSNkYTLxIPwPc4u5vZ+63shzeusCm7n57an8UuLvKgrEcYykkeDWzXYALiYDAqR2wZq0AkWZ6r3D3PXP208A4VmfGOs43ZKwcUN73ZCKv9cwa7fsCp7r7PLnHUjQz24CYUVyJ7teEq939jhaOYTGiBF35z8Ktrcr5N7PViBnvDZn+9eAu4Cc5aotXGcMqREm8TYF50+FJxGz3EbkmFVIFk0eBM9z9dzn66KH/qUS6zF3ExUqP3D1nWqPMJpSq0dkOIQraHwgcYGaVM2zDqZ/K0TSp3NOIVvRVoehyeEDDwWtPJetm1lAi13qzNlgE+FDB/ZMC5OxBcg3vUL+KzNLUX0zbMdx9JN2pGtMxs0+4+7stGMMEYi1CIdz9UWDjVNGlNKnwbK2NkjKNYTSwVZrhLe0wOj7nnZfU76SUjpFzcXg9NxIXTV8hcq0vIyp6FFWyVGYTCpw7WLoVvIuZ/ZqYYVsmNT0P3Ojuo3KPIZU0qqeLuNrPtVNV0eXwSoZSXPC6BPDrNgiaryO+H8MKHkeR/g7sb2YPuPvV5Q0p/3k/4oI2mzTT1uPvmrtnyTU3s2H17jqk39ObiE1BsjCz7wKbuPt+Ndp/D9zs7n/POIavuPu/ANz9TWLRdmFSoPxqi7u9hkgXqrVgNxt33yJV8vg+sANRger1VEd8uLvf3eoxyexBgXMfkALk7EFyDWNobGX2FDMbCRzr7vc0sf+iy+GVFBm8Pgos3upO02xSuSOBv5jZ2UTwPJYqm740K32n0QCxQpe753xdPAT4EnCFmb1Ed07pSsQF3JNE3m1OxzDj16U/UXN8S+KOSLaAEdg1LYCcYfGdmS0B3EqkMuV0MPVL7g0kvlc5vw53m9kLwJXAle6ePXC2NtvNk1iwO8zMbiM2ORnDjHX2S3eJmi7Viz4XONfMliYWCG8P7J1q/g8HzistJBYBBc4dJQUKU4F50w5cjQQOuQOFHxG51EsDf6Z7t7aVgB2J2e8LiduUOxPbzX6ziXmOhZbDK1NI8Jr8ggjUrnf3+1rY70Rm/PnrR+xcVy/PuVkzndUCxEK5+/i0IGtP4Ft059Y+QdSYPs/dG8q3nIUxDK3VZmaLA/+mO6DP4fvAVWb2obtP2/QjbTpyG5Gu8LWM/QN8jng9quUh8qdw7UB8LfYBfmZmzwNXEEH0fzP1OZT22s2zlKqzCpHnXakVW9AD4O4vEKl9p6b1KGcTr53vUX+nTeljFDh3llKg8FHFx0VaglgdPtjdp6tJmla13w3M4+4HmtmxwINEybI7mtR/u5TDa1nwWmMr2TeAf1lsPV5tpjfHdrKF/vzVCxAbkXI+lwJeqayAMZPP1x9YEnjX3c8gFu22FXcflza9+BWZUkbc/bpUy3q4mX3g7oelRXK3EBsWbZKCmJz6ExUbalmAeN3Kxt2vIF4T5gO+QwTRBwAHm9kzdAfRjzaxz3bbzTP3JjO9ki5qdyAmVJYiLqCaeQdUOoCqakhW6Vbkb919hlqlqf1nwAHuvmz6+Eii6kC9P2q9HcMQWlwOr0bwakS93qzBq5mNYeZSFFaY1b6bqdmB60z0/ymiTNq0Kiyz+HwDiGoFB9f6fWgHZrY/cHLuyh5mtj3wJ+J2/ZbAC8A33L1yAW+Ovu8kAud1K0udmdlcwH1EGbT1co+lou/yIHozYEDmO4J9npmV7n7uAHyGuNsynMhzznnnRWZT+oWU3Balu8RRNfPSvZIbYgvWplaXcPeL0oKPVpbDW4vqwetYYKH0VqkpV7HeOVvJDgKeI+4IzHLgOpOa9rPo7h+mC8miZ/lqMrNVidSq7AGDu19uZnMCFxHpIZvnLk9Z5hTgb8BtZnYiUCr7tipxJ2p18u6oWkt/oq703MTPSa5KO31emrTZkUgde5GY4R+eMU1GOoQCZ8ntP0QVgRGV9UDN7HNEFYHyHOOVybDtcavL4XVQ8Fq0TgsczgT2MLM/VKYutYqZPUf1i7SFiHrnk4gZ4Gb111MJwilE2cw7zKx0rCvnJjDufr2Z/Zi4E1W+ALAf8C6wl7tfl6v/chbbz3+XSA/YlAiaHTiJCOaa2dfMLJjNVmEFpu0oug0x2bAgM15Ydrn7jzJ0/WviZ28EkWvdBWyQ6otX0+Xuv88wDpnNKHCW3PYD/gk8kqpmlBYHDgY2IHIa94dpL6CbECWKZkoD5e+qcvexM9tnu2uDkoDS7WNiDcIzZnYl1asI5P4DfSczBk9dRG3xZ4idNN9oYn9vV+mvXCFl2Nz9D2V3okppSs8QtXyzz3ybWWlx4DeJKh5PEwtEr8y4GU+1dQdbEYslbyYCdoiUsm8AjxE7fWZhZssSfx+WIxYTL0isx1iImH2fQFzI5DKQ2CFx6wbO7QIUOIsCZ8nL3R9JC38OJf5AfDk1PU+sWj7F3V9M504hbpHOijHMXMpD1lXbBQevYyi2JKB0K89trlVZJOsfaHcfkuu5a/S3YSv7640UIF/d44l5/JlIRTqDvJU0pqlcMGtmewCfBFZ1d69oW5lIkXo545BOJYLlLwHPEhtUbUcsyNsf2Jf4u5HD8j2fIjIjBc6Snbu/TJpVboEfMn2QOAexUn1Z4g9V+YzKjkRQ2YrtXsdQXPBadElA6aY/1gVo0ztRX3T3BzI+fyMOAs6sDJoB3P0JMzuTqHl9fqb+NwLOdvf702YkAP3c/X26y8KdDmzR7I7d/fl67WY2D/CJVu7iKLMHBc7SUdz9ovKPzeyXxO24we7+ekXbUKIc3qdbMLQig9eiSwJK0tMf6xwa3OSiUs5NL0qVK35MBESlWtbPE/nGF2SoojKGNrsT1QZBM0TVmnpf6w/Ju7PqvMT3BrpTehYsa7+XyEXOIpWf25ZYkHmFuz9gZusRdwHWTOe8Cgx19/NyjUNmLwqcJbsCF38A7AWcVhk0w7TNKM4nAtoTM/VfUmTwuhdREnCGxWju/oaZXUDMyp/q7q+b2R+JmSjJxMw+Q2z4MAj4s7uPMbO5iTrPL6UZt2YZOhOPybbpRdpS+3aihvpLwFOpaTWiBNseZrZxtd/ZWVB5J6rl0u9VF7CHu3+cPu5JztdGiBzmfczsUnd/pbwhbYazN7F5Uy5jSYG5u3+UdtP8Et3rXFYhUtiaLtXyv4V4Xf4I2M/MtgMuA94CriUC6i8D55jZRHe/MsdY8hY1+wAAFkFJREFUZPaiwFmyaoPFHz2Vw5snnZNbkcFr4SUBZ1PvAkcTuZdNkTZBOQ8YQndN8XuJWbc5iQ0XTiDKpTVFG2xyUelkol7uDkRu77SA1sy+R9R1PhHYo1kdVt6JKshGxM6ucxCLRDeigZ1dM4/pp8SiwKfM7C9Mfydsa+I1epeM/d9OVBQ5On18EXCYmS1MfJ12IX4ecvglkWO+EfE36Tyirvh/idrtkwHMbCHid/SnxPbo0scpcJbcilz8AVEf9gAz+5u7jypvMLPPpzG0YhvqIoPXtigJOBOaHrgCpHJT3yIu5iBSBG5097vKz0slDI+muY4EdgUOI4KGaV93d38vVXnYiiYGzm3o/4DfpZ3zpuPuV5nZOsTXqGmBczuoLFHZDiUr3f1uM/sSUW3je8REAkSll5uBo5q5c2EVJwHrmNnc6S7LCcTduW2Ji4vLgJ9l6nsN4NfuPg7AzE4lAvVzS0EzgLtPTBMZR2Uah8xmFDhLboUt/kj2JdIdHjSze+i+LbwSsB5Rgmu/TH2XKzJ4bWlJwJ4UFbimernDgc2Ji5I3U9PCxDbHNwHbuXvOOyC7Aue7+ykpZaHS4zSxhnI1DdbynUJsCvFP4i7IM00cwgLELoG1jE3nZFVwClkpVeCJWovPzGwxYJXK34tmS4HxVmmnztLF+3h3n5qz39T3WOL7Xfp4CrB7esvtk8QERUkpVaVaFZGX6b6okD5OgbPkVujiD3cfXVYO71vAF1PT80Q1jVMqc/syKSx4LaAkYFVtELielvoeCpxVqlWcLuj2JWaUTiMWreXyaSJ/vZYPgE9k7B9idvG7RO3eG5n+9vxmRE7r7cTP5m7ADmb2NXd/uEn9PwFsb2bDqmx3PSeRwjG66iObpA1SyEj970LMqlazcWrLskDRzOYlvtdnuPvvUqD8ao6+GhzPQOLO3PgMi0Nr6arxf5GaFDhLboUt/ihx91eJ/LSf5uynhzEUGry2uCRgLUUHrtsSt2GnW/SWxnFMWgy1fcb+Ie4irFSn/cvEJhw5vUzs1PdZd58uDcbMBhN3aEa7+0FmthJxcXsc8O0m9X8ycQH1bzM7i+7tvY1YjLYmETznVHQKGfScijU3ka6QhbtPMrMFiMoZhTGzrwPHA+sQs/6bElWFFiNqbJ/s7jdm6n5pMyu91i6U3q9gZhMqzpupcobSmRQ4S25FLv5oK20SvBap6MC1i0iFqOUxYvFWTsOJ1ftXEQuTSuPCzH5ABIxHZB7DQcSFywy54+7+dApmDwMudPenzGwYsE+zOnf3K1KN3JOAC+ie6etHBLA/bEH1gkJSyFI96eXKDn02pWxUWojYICd3+cJriJz6czL3U5WZbULc9RhNfL1/Xmpz9wlm9j6RtpErcD4hvZUbVuW80kJeEQXOkl2Riz+AaTtg7UZsq7swM870dLn7xjnH0EptWvYKig9c/wJsY2bnVuZvpmoX26RzcjqOmOX8F/H5dhGB2iJEPeNbyJi6lCxFlN+q5SOi3njJc8TsZ9O4+0VmdimwNtPXcX7A3euNrVmKSiHbjbiz0pXefpneKvUjvg85735ATFoMM7PbiKoSY5hxC3g83xbgxxLrP9YHFqEscE5Gki/febdMzysdToGzZFXw4g/MbBdiY5EPiV0D36xyWtNLrxUcvLZj2SsoPnC9kJhZuzvNopbn9u5JLP65qOzWLdDcoMHdp5jZpsQmN9sQAel8xIzbMcAlLViU9Tiwp5ldXLkwzcw+SXwtyi9wVqB74VTTpAD53+mt1YpKIbsceJh4zbmGmGW9s+KcLuA9YJS7V6YMNNvI9H4Voq54pdJMa66NYNYADnL3qWZW7TVoHPCpHB27+8U5nlc6nwJn6XRDiT9Um7Xgj1C5woLXdix7lRQduN5d9v8vMX2KQMnIsv9nCRpS3eI/pbci/IK0KDDV7i3lVA8mavfOTbqwTZuy7EqUJmsqM1uS2IK82l0g3P26ZvdZppAUMnd34gIeM9sNuNPdxzS7n14oetZ1MrGzay3LE4s3RdqGAmfpdEsQtTpbGTS3c/BapKID16KDBMzsWeDAWkGhmX2bqHG8Qq4xuPsdZvYVImjcju4yW1OAW4nthR9K575vZsu4e9MWqaWKFhcQF5NQ/Y5PzllOaIMUMmLhW83Nl1I+9AR3n5RrAG0w63onsIuZnVbZkBYH7k6kL4m0DQXO0ukeBRYvehBFK5s53JCo1Xqwu/83zbBtBfzD3evV1m2GQgPXNggSIBaG1Ss39wm6c36zcff/At9JtXs/mQ6/Vi1NpJlBc3IxsC4RvN5HbG/cUkWnkCVnAF8gqohUM4LI/92rZSNqvSOIfP9/ExcSXcAmZrY+UWFlADO3ZbxINgqcpdP9ArjCzK5391bsEFhTUcFrylu9ndhUZRxxIbFwap5I/PFahfhaZdMmgWs7qJeSsw7V8/CzSIFyK+qYl1sXOMndm70r4+xmE+APddpHALkX7Ba6EUyqs78Bket9InH34dDUfBfwk2rVX0SKpMBZOt2BxMYG/zKzR4hZpsoZtC533ybnIAoOXk8BliRqBJdq1gKRb2tm1xA1a7MGzu2giCDBzA4ADig9P3C6mR1f5dQFiTJklzaz/zY0lqhk0dd9mvoXLa+lc7Jph41g0s6FG6cJhMHE7+SztXZUFCmaAmfpdGsRwcpY4o/BQlXOaUVFiSKD1y2A01PN2mo5lU+Tv+wVUOzsVoFBwivAqPT/5YifxRcrzilVUniQ6nVkO8kJwKFmdqG79+WFX68QVSVq+TzweuYxFLoRjJl91t2fBHD3N4nUFJG2psBZOlobLcorMnidj5jlrtdeGcA2XRvMbhUSJLj7FcAVAGb2T+A4d7+t2f3MLtz94lR+8Gkzu5a4iKh2F+jYGR/dUa4F9jKzG9z9hvIGM9uCSNM4P/MYCtkIpsxoM3uIqDBzedrlVaStKXAWaY0ig9cngPWIDQ6q+T+6Z0RzKnqb46KDBNz967mee3ZhZusQs86LUHvBaBexOUYnG0rkOf/NzB4kNsQBWJVYNPg/4MjMYyhqI5iSPYEdgd8Cvzaz24kgeoS7v5exX5GZln2WSaRoZja3me1hZpeZ2T/MbM10fGEz+6GZLd3TczRBKXitJWfweiawk5kdSFlFBzNbxswuAEqLc3KbFrjSvUNgP3d/391PBW7LPI5GgoT1M/aPmW1sZgdVHNvVzJ43s1fN7LQ0G9vJziUWgW1H3H1YuMrbIrUe3ClSasK6xEXEvMR289sT5QGPB76YtqPPabqNYIDSRjAluTaCIfV5frqYXJbYQfFTRP3sV9Pr9eap8otI29CMs3S0dqkoQQSv55vZf4nV8qXxLUPMKm1ABBJN5+4Xpn5OTW8ANxHpEV3Ar9z96hx9Vyh6dquo3eLKDQWmVU5Js9znE5v0PAXsR+S+npx5HEVaGTjc3a8qeiBFc/d3gV+ltyIUshFMJXd/kVgHcoqZrUrMQu9AvCZOMLPLgT+5+wO5xyLSE13JSacrX5T3eco2W0g7uJUW5WXl7hcSt55PpXvHvJuA54AhZA5eU+mvwcDBxO59fwAOAczdT8jVb4VCZ7foDhJKLgJ+ambnm9kfgJ8Af8vYP0TQWL4AaifiIuJr7r4TEUT/IPMYivYE9WtZS+ucBByfSmVCzH5fTGwE811iI5ift3hMrxKTHK8Rr9dzEb8n95nZ3WZmLR6PyHQ04yydrm0qSrj70WZ2MVGzuVR26Rkin++Zug+eBWZ2FbEj30jgjGqbXLRI0bNb7bBb3HxMv4XwZsBN7j45ffwA8XXoZIcAF5vZje7+YNGDKVKRVWagbTaCwcw+QWz3viPdO0reRMw4X0f8fm5LTDxczPQX3CItpcBZOl1bVJQocfcxZnY+kS4ybfY7pVKU/pA123LAlsTn+a6Z3UdsLnAP8O+yoC23QgPXNgkSXgA+B2BmSxK7xv2urH0R4P0WjqcI+xCz7PcXWVu9aG1QZQYzOx3Y3N0/U6P9SeDv7p4llc3MvksEy98mcrsfJGa4h7v7hIrTLzez+YGzcoxFpFEKnKXTtUVFiTSzdBRRYqrazHdJ0xeGufs6ZjYf8BUil3p94DDiD9WHKe96pLsfVOdpmjGOdghci/ZnYpZ9bmJh2ERiRq1kbaKaQidrl9rqRSu6ygzEHbnL67RfmcaUaw3ICOJi8gzgklJN5zpGEb9DIoVR4CydrrBFeRXOJrbb/iuRMtGybZUBUmmnf6Q3zGwAsDNx2/yLxFbPWQNnAaJawlzA5kTQvGVpE5BUIu9rRBDRsdqotnrRCi+PSKw5eL5O+1ggZ9Whjdz9jl6c/1/gQjNb0N3fyjQmkboUOEtHa6OKElsDF7j7ni3oawapxNlaxGzzBsQs/GLEQpyriWBeMkuLIn+Z3irb3iDzFsvtwMw+D6zi7peVHduUqHAzELjM3Tv64iEpusoMxAV81TSNxIB3cnXey6AZIpXpn8CmxJoJkZZT4Cwdr6hFeVU81MK+pjGzW4m0gHmJkmd3E9U17m7x5y9lzOwzwCDgsT42e3YKUT3lMph292cEsb30i8BvzWyyu9dKr+oU7VAe8WZi98I/ufuj5Q1mtgawF2V36tpEv55PEclHgbP0Ce4+BjitwCFcR9yaHVZA3xsRG45cnd5GuvsrBYxDADP7AbEwcvF0aFPgdjNbDLgf+KW7Dy9qfC2wBtPPpO5M/Hyu6e5vmNlwImDr9MC56CozEPWjNwMeNLMRwOPp+KrEguLXiTsBIpIocJaOZmZT6Xmh0RRipuufwKnNmIU1swUqDh0J/MXMziaC52qVBHD3t2e17yq+RqRnbEDUCZ7fzJ6lu0TdSHd/us7jpUnMbHsiQLqJSB2adjHn7hNSLv4uQCcHzgsC48s+3hz4R9kuebeRN6+3XRReHtHdXzSzLxAb7nwH+F5qeodYNHh42pxERBIFztLpjiFmdT4H3Ej35iMrETMtjxIzP4OB3YAdzOxr7v7wLPY7kRkD9n5E+bF6ec45qmqUAmTMrB8x41fKdT4O+LSZveruSzS7b5nBYUTd5s1TXfHKuyD/IaopdLJxwAowbUHkF4kdE0vmp3tL9o7VLlVm3P1lYJf02jAoHR6fNogSkQoKnKXTvUwsgvusuz9b3mBmg4E7gNHufpCZrUQsyDmOqCs6K46hPUtqDSB2bZsfWCD9vx/wqSIH1YcYUWGllgnEz2snuxbYz8zeAjYEPiSqzZSsQZRnkxZKgfJrRY9DpN0pcJZOdxBwVmXQDODuT5vZWcQs4IXu/pSZDSM2aJgl7j50Vp+jWcxsM7pTNdYhyqF9RCxWHEYsFry7sAH2LW8RlQFq+SxR6aSTHUHMbB5B3JkZ4u6vwrQUp23QJhci0qYUOEunW4oIEmv5iOnrlD4HzJ11RK13A5GzeC9RR3gkcF+6NSytdTOwu5mdWdmQqmzsAfyp5aNqIXd/F9ipRvO7xO/spNaNSESkcQqcpdM9DuxpZhe7e/mCJMzsk0S+8eNlh1cAOq3ixBeAUe7e8Xmjs4HDgfuAx4hKK13ATma2M7ERz3i6qyz0OelntC+V5pPeeYtYi/J4TyeK5NKvq6sd0zBFmsPMNiQWBX4A/IWo3wyxGHBrYnZ5M3e/I22D/Cxws7v/sIDhSh9gZp8mZv63onu76XeIermHqlSg9CVmNgj4KZHvPgj4gbvfmxbPHgBc7u6jCxyiyHQUOEvHM7M1iVm8jYF50uEpwK3AUHd/qOzc/u4+Q5k4kRxS0DAHUcVAdwSkTzGzFYnUsQWBUcRGTZu6++2p/VFio6a9ixulyPSUqiEdz93/C3zHzOYAPpkOv1YtUFHQLK1UmT4k0secStSsXgV4jxmrelxHLBYVaRsKnKXPSIGyboNLy5jZkTPxsC53P7bpgxFpPxsBJ7j78yk1o9IY0rbkIu1CgbOISD5DZ+IxXYACZ+kL5iTy+2tZlKjzLdI2FDiLiGTi7nMUPQaRNjYK+AZwTmWDmfUnKs3c3+pBidSjF3UREREpwonE+pPTic1/ABYzs68C1wOrAScVNTiRalRVQ0SkxcxsVWBzYNl06HngendXfVrpU8xsCHAasADQj0hVgkjh2NfdLy1oaCJVKXAWEWmRVNnlHGB3Ikgo7Wo5JxEwXADs5e56YZY+w8zmI1I2BhN3wp8BbnH3twsdmEgVynEWEWmdw4EfA8OIWban0/EVgQOBvYjZ5xMKGZ1IAdz9PWIDIJG2pxlnEZEWMbOngfvdfcca7ZcB67r7iq0dmUjrmdkyPZzSRWxWNUF3YaRdaMZZRKR1lgJ+U6d9JLEVvEhfMIbunOZ6ppjZSOBYd78n75BE6lNVDRGR1nkJWK9O+/rpHJG+4EfAI8CbwJlEutKBwFnp2MPAAcD5wNrA7Wa2YSEjFUk04ywi0jqXAEeZ2UTgNHd/BsDMSjnO2zNzm6aIzI6WAOYCBrv7xPIGMxsK3A3M4+4HmtmxwIPAUcAdLR6nyDSacRYRaZ3jiOB5H+B/Zva+mb0P/A/4SWo7vsDxibTSXsAFlUEzgLu/QVSZ2Td9/DrwR2LmWaQwmnEWEWkRd/8YGGJmv2XGOs43uPsjhQ1OpPUWBeat0z4vMKjs43FEGUeRwihwFhFpsRQgK0iWvu4/wP5mNsLdR5c3mNnngP2YfsvtldEaACmYAmcRkQKY2SeAhakyg+buY1s/IpGW2w/4J/BIqppRqms+GNgAeBvYH8DMBgKbANcUME6RaVTHWUSkRdIf/6OIagKL1jrP3fu3bFAiBTKzJYBDgW8yferSzcAp7v5iUWMTqUYzziIirXM2sCvwV6Jm85vFDkekWO7+MmlWWWR2oMBZRKR1tiaqCOxZ9EBERKT3FDiLiLTWQ0UPQKRdpPSlbYC1gAWZsUxul7v/qOUDE6lBgbOISOtcB2wEDCt6ICJFM7NlicWBywETicD5DWAhoD8wAXi3qPGJVKPAWUQkEzNboOLQkcBfzOxsIngeC3xc+Th3f7sFwxMp2qlEsPwl4FngNWA74B4i73lfYtGgSNtQ4Cwiks9EoLJ0UT9gTaBenrOqakhfsBFwtrvfb2aLpGP93P194FQzWxk4HdiisBGKVFDgLCKSzzHMGDiLSJgXGJP+/zbxu7JgWfu9wK9bPCaRuhQ4i4hk4u5Dix6DSBsbCywF4O4fmdlLRNpGaZOTVYApBY1NpCoFziIiLWJmpwObu/tnarQ78Dd3/0VrRyZSiNuB7wJHp48vAg4zs4WJ6hq7AJcUMzSR6irLvoiISD5bAFfUab8C+L8WjUWkaCcBx5vZ3OnjE4CLgW2JgPoy4GcFjU2kKs04i4i0zlLEdsK1jAWWbtFYRArl7mOJn/nSx1OA3dObSFvSjLOISOu8CVRN00gMeKdFYxERkV5S4Cwi0jo3A3uZ2WqVDWa2BrBXOkdERNpQv64uVUoSEWkFM1sK+A+wKDACeDw1rQpsCbwOrJtuYYuISJtR4Cwi0kJmtgRwMvAdYP50+B3gWuBwd3+xqLGJiEh9CpxFRApgZv2AQenD8e6uF2MRkTanwFlEREREpAFaHCgiIiIi0gAFziIiIiIiDVDgLCIiIiLSAAXOIiIiIiINUOAsIiIiItKA/wd4KVzZDdPqKAAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"_uuid":"987ff630db004c51cc9e89e0c6cf6d14d619c4c0"},"cell_type":"markdown","source":"** Question 2.1. (1 point).** Which pair of features has the strongest Pearson's correlation with the *gender* feature?\n\n1. Cardio, Cholesterol\n2. Height, Smoke\n3. Smoke, Alco\n4. Height, Weight"},{"metadata":{"_uuid":"01edf6a3a68f47f07164b3e4cb9e6ab5ef1efcd6"},"cell_type":"markdown","source":"## 2.2. Height distribution of men and women\n\nFrom our exploration of the unique values earlier, we know that the gender is encoded by the values *1* and *2*. Although you do not know the mapping of these values to gender, you can figure that out graphically by looking at the mean values of height and weight for each value of the *gender* feature.\n\n### Task:\n\nCreate a violin plot for the height and gender using [`violinplot()`](https://seaborn.pydata.org/generated/seaborn.violinplot.html). Use the parameters:\n- `hue` to split by gender;\n- `scale` to evaluate the number of records for each gender.\n\nIn order for the plot to render correctly, you need to convert your `DataFrame` to *long* format using the `melt()` function from `pandas`. Here is [an example](https://stackoverflow.com/a/41575149/3338479) of this for your reference."},{"metadata":{"trusted":true,"_uuid":"71d38ccddbe26ca51c69fbdba7cc4405ee459de4"},"cell_type":"code","source":"# You code here\ndf_lol = pd.melt(frame = df, value_vars = 'height', id_vars = 'gender')\nsns.violinplot(x='variable', y='value', hue='gender', data=df_lol)","execution_count":18,"outputs":[{"output_type":"execute_result","execution_count":18,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7f3729518e80>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 792x576 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"**Question 2.2. (1 point).** Which pair of features has the strongest Spearman correlation?\n\n1. Height, Weight\n2. Age, Weight\n3. Cholesterol, Gluc\n4. Cardio, Cholesterol\n5. Ap_hi, Ap_lo\n6. Smoke, Alco"},{"metadata":{},"cell_type":"markdown","source":"## 2.3. Rank correlation\n\nIn most cases, *the Pearson coefficient of linear correlation* is more than enough to discover patterns in data. \nBut let's go a little further and calculate a [rank correlation](https://en.wikipedia.org/wiki/Rank_correlation). It will help us to identify such feature pairs in which the lower rank in the variational series of one feature always precedes the higher rank in the another one (and we have the opposite in the case of negative correlation).\n\n### Task:\n\nCalculate and plot a correlation matrix using the [Spearman's rank correlation coefficient](https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient).\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"# You code here\nnew_corr_matrix = df.corr(method='spearman')\nsns.heatmap(new_corr_matrix)","execution_count":19,"outputs":[{"output_type":"execute_result","execution_count":19,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7f372947ecf8>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 792x576 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"_uuid":"2282d0651afdbe69cdb68b266e509f20a865a63f"},"cell_type":"markdown","source":"**Question 2.3. (1 point).** Why do these features have strong rank correlation?\n\n1. Inaccuracies in the data (data acquisition errors).\n2. Relation is wrong, these features should not be related.\n3. Nature of the data."},{"metadata":{"_uuid":"25b4b303156fd6f9178c9b23fba4ff3f220a5977"},"cell_type":"markdown","source":"## 2.4. Age\n\nPreviously, we calculated the age of the respondents in years at the moment of examination."},{"metadata":{"_uuid":"d48bb97cba3968e7f73c790226278f6fa818871b"},"cell_type":"markdown","source":"### Task:\n\nCreate a *count plot* using [`countplot()`](http://seaborn.pydata.org/generated/seaborn.countplot.html) with the age on the *X* axis and the number of people on the *Y* axis. Your resulting plot should have two columns for each age, corresponding to the number of people for each *cardio* class of that age."},{"metadata":{"trusted":true,"_uuid":"fd07f3aab9891fd86228d5880a7a38cc88b282ba"},"cell_type":"code","source":"# You code here\n\ndf['age_years']=df['age_years'].astype('int64')\n\nsns.countplot(x='age_years', hue='cardio', data=df)","execution_count":20,"outputs":[{"output_type":"execute_result","execution_count":20,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7f37293b6e10>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 792x576 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"_uuid":"49557c7bd46d729c27aa8190df65cc25e45e7528"},"cell_type":"markdown","source":"**Question 2.4. (1 point).** What is the smallest age at which the number of people with CVD outnumber the number of people without CVD?\n\n1. 44\n2. 55\n3. 64\n4. 70"}],"metadata":{"anaconda-cloud":{},"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":4}