real_estate_proj.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Realestate Analysis\n",
    "\n",
    "**A deep look into the realestate pricing in...***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Donload Dependancies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import tarfile\n",
    "import urllib\n",
    "from zlib import crc32\n",
    "\n",
    "## Saving Models\n",
    "import joblib\n",
    "\n",
    "## Data Visualisation and Manipulation\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.plotting import scatter_matrix                   # Scatter Correlation plots\n",
    "plt.style.use('dark_background')\n",
    "%matplotlib inline\n",
    "\n",
    "## Data Preparation\n",
    "from sklearn.pipeline import Pipeline                        # Automating transformations\n",
    "from sklearn.impute import SimpleImputer                     # To impute numerical features \n",
    "from sklearn.compose import ColumnTransformer                # Automating transformations on columns\n",
    "from sklearn.preprocessing import OneHotEncoder              # To binary encode catagorical features\n",
    "from sklearn.preprocessing import OrdinalEncoder             # To encode catagorical features\n",
    "from sklearn.preprocessing import StandardScaler             # Scaling. Numerical features using pipeline\n",
    "from sklearn.model_selection import train_test_split         # Split data into train and test set\n",
    "from sklearn.model_selection import StratifiedShuffleSplit   # Stratified Sampling\n",
    "from sklearn.base import BaseEstimator, TransformerMixin     # Custom Transformers\n",
    "\n",
    "## Model Building\n",
    "from sklearn.svm import SVR                                  # Support Vector Machine Regressor\n",
    "from sklearn.metrics import mean_squared_error               # Metric to test model performance\n",
    "from sklearn.tree import DecisionTreeRegressor               # A more powerful model\n",
    "from sklearn.linear_model import LinearRegression            # Our baseline model \n",
    "from sklearn.ensemble import RandomForestRegressor           # Ensemble of Decision Trees\n",
    "from sklearn.model_selection import cross_val_score          # Cross-Validation\n",
    "from sklearn.model_selection import GridSearchCV             # Grid Search Cross-Validation\n",
    "from sklearn.model_selection import RandomizedSearchCV       # Randomised Grid Search\n",
    "\n",
    "## Model Evaluation\n",
    "from scipy import stats                                      # Used for sumarising Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>\n",
    "    <span style=\"color:yellow\">\n",
    "    Data Collection\n",
    "    </span>\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/ageron/handson-ml2/master/\"\n",
    "HOUSING_PATH = os.path.join(\"datasets\", \"housing\")\n",
    "HOUSING_URL = DOWNLOAD_ROOT + \"datasets/housing/housing.tgz\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fetches data from a specified url. Extracts and saves data to specified path\n",
    "def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH):\n",
    "    if not os.path.isdir(housing_path):\n",
    "        os.makedirs(housing_path)\n",
    "    tgz_path = os.path.join(housing_path, \"housing.tgz\")\n",
    "    urllib.request.urlretrieve(housing_url, tgz_path)\n",
    "    housing_tgz = tarfile.open(tgz_path)\n",
    "    housing_tgz.extractall(path=housing_path)\n",
    "    housing_tgz.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Call fetch so download data\n",
    "fetch_housing_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loads data set from a specified directory\n",
    "def load_housing_data(housing_path=HOUSING_PATH):\n",
    "    csv_path = os.path.join(housing_path, \"housing.csv\")\n",
    "    return pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>\n",
    "    <span style=\"color:yellow\">\n",
    "    Sumarise Data\n",
    "    </span>\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us begine by sumarising the infoormation in our dataset.\n",
    "\n",
    "We have 10 feature in our raw data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load data set to variable\n",
    "housing = load_housing_data()\n",
    "\n",
    "# Preview data\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a total of 20640 intance in our data, and all our entries apear to be continouse numerical values with the exception of ```ocean_proximity```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           20640 non-null  float64\n",
      " 1   latitude            20640 non-null  float64\n",
      " 2   housing_median_age  20640 non-null  float64\n",
      " 3   total_rooms         20640 non-null  float64\n",
      " 4   total_bedrooms      20433 non-null  float64\n",
      " 5   population          20640 non-null  float64\n",
      " 6   households          20640 non-null  float64\n",
      " 7   median_income       20640 non-null  float64\n",
      " 8   median_house_value  20640 non-null  float64\n",
      " 9   ocean_proximity     20640 non-null  object \n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# Rundown of data\n",
    "housing.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also see the distribution of the numerical features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        206855.816909  \n",
       "std         115395.615874  \n",
       "min          14999.000000  \n",
       "25%         119600.000000  \n",
       "50%         179700.000000  \n",
       "75%         264725.000000  \n",
       "max         500001.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Basic data distribution\n",
    "housing.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot histogrames of the Numerical variables\n",
    "housing.hist(bins=50, figsize=(20,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see above many features tail-heavy and some features (namely ```mean_income```) apear to have been scaled down. However, other features have not. This will likely cause scaling issues for our model, so it may be suitable to rescale (normalise) oour input using:\n",
    "\n",
    "$$\n",
    "X_{normed} = \\frac{X_{original} - \\mu}{\\sigma}\n",
    "$$\n",
    "\n",
    "where $\\mu$ is the mean and $\\sigma$ is the variance:\n",
    "\n",
    "$$\n",
    "\\mu = \\sum{X_{original}},\\space\\space \\sigma^{2} = \\sum{(X_{original} - \\mu)}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us take a look at the distribution of the classes in the catagorical columns (i.e. ```ocean_proximity```)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     9136\n",
       "INLAND        6551\n",
       "NEAR OCEAN    2658\n",
       "NEAR BAY      2290\n",
       "ISLAND           5\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count occurances for each catagory in ocean_proximity\n",
    "housing.ocean_proximity.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a barchart of the catagorical variables\n",
    "housing.ocean_proximity.value_counts().sort_values(ascending=False).plot(\n",
    "    kind='barh',\n",
    "    color=['red','orange','green','blue','purple'],\n",
    "    figsize=(15,5)\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>\n",
    "    <span style=\"color:yellow\">\n",
    "    Train Test Split\n",
    "    </span>\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before progressing with our analysis we will seperate our data into our training and testing sets. To do this we will create a  ```train_test_split()``` sunction that will seperate our data into some specified ratio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seperate given data into a training and testing set\n",
    "def my_train_test_split(data, test_ratio=0.2, seed=0):\n",
    "    # Set randome seed\n",
    "    np.random.seed(seed)\n",
    "    \n",
    "    # Randome permutation of data indeces\n",
    "    shuffeld_index = np.random.permutation(len(data))\n",
    "    \n",
    "    # Preposion which is test set\n",
    "    test_size = int(len(data) * test_ratio)\n",
    "    test_index = shuffeld_index[:test_size]\n",
    "    \n",
    "    # Remaining is train set\n",
    "    train_index = shuffeld_index[test_size:]\n",
    "    \n",
    "    return data.iloc[train_index], data.iloc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our Training set has a total of 16512 examples\n",
      "Our Testing set has a total of 4128 examples\n"
     ]
    }
   ],
   "source": [
    "# Create a train test split on housing data\n",
    "train, test = my_train_test_split(housing)\n",
    "\n",
    "print(\"Our Training set has a total of {} examples\".format(len(train)))\n",
    "print(\"Our Testing set has a total of {} examples\".format(len(test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately if the dataset is updated our random ordering will be changed. To overcome this we can use hashing from the ```zlib``` library. We can create a function that uses the row index as a unique identifier, to create a Hashing for our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Identifies if the hashing ID is intestset\n",
    "def my_test_set_check(identifier, test_ratio):\n",
    "    return crc32(np.int64(identifier)) & 0xffffffff < test_ratio*2**32\n",
    "\n",
    "# Split train test based on hash ID\n",
    "def my_train_test_split_by_id(data, id_column, test_ratio=0.2):\n",
    "    ids = data[id_column]\n",
    "    in_test_set = ids.apply(lambda id_: my_test_set_check(id_, test_ratio))\n",
    "    return data.loc[~in_test_set], data.loc[in_test_set]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reset indexes for housing data\n",
    "housing_with_id = housing.reset_index()\n",
    "\n",
    "# Split into train and test based on ID\n",
    "train, test = my_train_test_split_by_id(housing_with_id, \"index\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nex we have tto make sure that any new entries added to our data is appended to the end of our dataset and that no row ever gets deleted.\n",
    "\n",
    "However, this can become problematic so lets engineer a more stable feature. Using the unique ```latitude``` and ```longitude``` features for a given entry, we can combine these values with our hashing function to create a more stable identifier for each entry. This idea assumes there are:\n",
    "\n",
    "1. There are no repeated entries in our data.\n",
    "2. No two districts have the same latitude and lingitude."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new index column called id\n",
    "housing_with_id[\"id\"] = housing[\"longitude\"]*1000 + housing[\"latitude\"]\n",
    "\n",
    "# Use new indexing to hash data\n",
    "train, test = my_train_test_split_by_id(housing_with_id, \"id\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A far simpler option is to use Sklearns ```train_test_spli()``` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using sklear's model_selection library\n",
    "train, test = train_test_split(housing, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to ensure our  training data is a fair representation of the envirument we are modeling for, we can create stratum (catagories) of income. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD4CAYAAAAAczaOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAUDklEQVR4nO3dbYxcV33H8e/ihOBCoiQNdl3bbVx1RddrKQFvjFGUKMW0Ng/CkdJIjmRsoUhuLbcKohKyeVGEqkh5hcCVnYIwzVo8WBaUxolqwAkgVCmJZxMCjp24dgkJq2zjEOLGiCrE7r8v7kG5DLPeOzs79+7u+X6ko3vm3Ht2/ns8/s3snaeBiECSlIc3NV2AJKk+hr4kZcTQl6SMGPqSlBFDX5IycknTBUzlpZdeiueee25acwcHBzl16tQMV9Q76+qOdXXHurozX+saGRn5OfD239kREbO6tVqtAKbVepnbz2Zd1mVds6fN17oiYqxTpnp6R5IyYuhLUkYMfUnKiKEvSRkx9CUpI4a+JGXE0JekjBj6kpQRQ1+SMjLrP4ZBmq1GRkZo6kuIBgYGGrlezX0+0pekjBj6kpSRKqH/DuDJUnsV+BhwNXAEOJW2V5Xm7AJOAyeB9aXx1cCxtG834N+oklSjKqF/Erg+tdXAr4BvAjuBh4HBtN2Zjl8JbAKGgQ3AXmBB2ncvsC3NGUz7JUk16fb0zjrgv4DngI3AaBofBW5N/Y3AAeA14FmKR/VrgCXAFcAjFB/9ub80R5JUg25fvbMJ+FrqLwYmUn8CWJT6S4FHS3PG09jrqd8+3sm21BgeHqbVanVZZmFoaGjac/vJurozW+tq0sXWY7aul3V1p291dfGFJm+OiJ9HxOJ0+Wzb/lfSdk9EbC6N74uI2yLihoh4qDR+U0Q84JeozJ5mXd21Js3F9bKueuuKGfgSlfcDTwAvpssvUpyyIW3PpP44sLw0bxnwQhpf1mFcklSTbkL/Dt44tQNwCNia+luB+0vjm4DLgBUUT9gepTgFdA5YS/GqnS2lOZKkGlQ9p/97wF8Af10auwc4CNwJPA/cnsaPp/ETwHlgB3Ah7dsO3AcsBA6nJkmqSdXQ/xXw+21jL1O8mqeTu1NrNwasqnidkqQZ5jtyJSkjhr4kZcTQl6SMGPqSlBFDX5IyYuhLUkb85qx5Jnr8JqfpzvebnKS5wUf6kpQRQ1+SMmLoS1JGDH1JyoihL0kZMfQlKSOGviRlxNCXpIwY+pKUEUNfkjJi6EtSRgx9ScqIoS9JGaka+lcCXweeAZ4G3gNcDRwBTqXtVaXjdwGngZPA+tL4auBY2rcb8KMZJalGVUP/c8C3gD8DrqMI/p3Aw8Bg2u5Mx64ENgHDwAZgL7Ag7bsX2JbmDKb9kqSaVAn9K4CbgX3p8q+Bs8BGYDSNjQK3pv5G4ADwGvAsxaP6NcCS9LMeAQLYX5ojSapBlS9R+RPgJeBfKB7lPw7cBSwGJtIxE8Ci1F8KPFqaP57GXk/99vFOtqXG8PAwrVarQpm/a2hoaNpz+2m21tWLfv4+83G9enWx9Zit62Vd3elbXRExVRuJiPMR8e50+XMR8Y8RcbbtuFfSdk9EbC6N74uI2yLihoh4qDR+U0Q8MNX1t1qtoPjLoOvWy9x+tn7W1ZS5ul5zca2nWu/Zul7WVW9dETEWHTK1yumd8dQeS5e/DrwLeJHilA1pe6Z0/PLS/GXAC2l8WYdxSVJNqoT+fwM/A96RLq8DTgCHgK1pbCtwf+ofongi9zJgBcUTtkcpTgGdA9ZSvGpnS2mOJKkGVb8Y/e+ArwBvBn4CfJTiDuMgcCfwPHB7OvZ4Gj8BnAd2ABfSvu3AfcBC4HBqkqSaVA39J4GRDuPrJjn+7tTajQGrKl6nJGmG+Y5cScqIoS9JGTH0JSkjhr4kZcTQl6SMGPqSlBFDX5IyYuhLUkYMfUnKiKEvSRkx9CUpI4a+JGXE0JekjBj6kpQRQ1+SMmLoS1JGDH1JyoihL0kZMfQlKSOGviRlpGro/xQ4RvEF6WNp7GrgCHAqba8qHb8LOA2cBNaXxlenn3Ma2A0MTLNuSdI0dPNI/8+B64GRdHkn8DAwmLY70/hKYBMwDGwA9gIL0r57gW1pzmDaL0mqSS+ndzYCo6k/CtxaGj8AvAY8S/Gofg2wBLgCeAQIYH9pjiSpBpdUPC6A76Tt54EvAIuBibR/AliU+kuBR0tzx9PY66nfPt7JttQYHh6m1WpVLPO3DQ0NTXtuP83WunrRz99nPq5Xry62HrN1vayrO32rKyKqtD9M20UR8aOIuDkizrYd80ra7omIzaXxfRFxW0TcEBEPlcZviogHprruVqsVFHc2Xbde5vaz9bOupszV9ZqLaz3Ves/W9bKueuuKiLHokKlVT++8kLZngG9SnK55keKUDWl7JvXHgeWlucvS/PHUbx+XJNWkSui/Fbi81P9L4CngELA1jW8F7k/9QxRP5F4GrKB4wvYoxSmgc8BailftbCnNkSTVoMo5/cUUj+5/c/xXgW8BLeAgcCfwPHB7OuZ4Gj8BnAd2ABfSvu3AfcBC4HBqkqSaVAn9nwDXdRh/GVg3yZy7U2s3BqyqVpokaab5jlxJyoihL0kZMfQlKSOGviRlxNCXpIwY+pKUEUNfkjJi6EtSRgx9ScqIoS9JGTH0JSkjhr4kZcTQl6SMGPqSlBFDX5IyYuhLUkYMfUnKiKEvSRkx9CUpI92E/gLgh8CD6fLVwBHgVNpeVTp2F3AaOAmsL42vBo6lfbuBgWlVLUmalm5C/y7g6dLlncDDwGDa7kzjK4FNwDCwAdhLcYcBcC+wLc0ZTPslSTWpGvrLgA8CXyyNbQRGU38UuLU0fgB4DXiW4lH9GmAJcAXwCBDA/tIcSVINLql43GeBTwCXl8YWAxOpPwEsSv2lwKOl48bT2Oup3z7eybbUGB4eptVqVSzztw0NDU17bj/N1rp60c/fZz6uV68uth6zdb2sqzt9qysipmofioi9qX9LRDyY+mfbjnslbfdExObS+L6IuC0iboiIh0rjN0XEA1Ndf6vVCoq/DLpuvcztZ+tnXU2Zq+s1F9d6qvWeretlXfXWFRFj0SFTqzzSvxH4MPAB4C0Up2i+DLxIccpmIm3PpOPHgeWl+cuAF9L4sg7jkqSaVDmnv4sioK+leIL2u8Bm4BCwNR2zFbg/9Q+l4y4DVlA8YXuU4s7hHLCW4lU7W0pzJEk1qHpOv5N7gIPAncDzwO1p/HgaPwGcB3YAF9K+7cB9wELgcGqSpJp0G/rfTw3gZWDdJMfdnVq7MWBVl9cpSZohviNXkjJi6EtSRgx9ScqIoS9JGTH0JSkjvbxkU1JmijcD1z9/YMAP5J0pPtKXpIwY+pKUEUNfkjJi6EtSRgx9ScqIoS9JGTH0JSkjhr4kZcTQl6SMGPqSlBFDX5IyYuhLUkYMfUnKiKEvSRmpEvpvAY4CPwKOA59O41cDR4BTaXtVac4u4DRwElhfGl8NHEv7dgN+Xqok1ahK6L8GvBe4Drge2ACsBXYCDwODabszHb8S2AQMp2P3AgvSvnuBbWnOYNovSapJldAP4Jepf2lqAWwERtP4KHBr6m8EDlDcWTxL8ah+DbAEuAJ4JM3fX5ojSapB1W/OWgA8DvwpsAd4DFgMTKT9E8Ci1F8KPFqaO57GXk/99vFOtqXG8PAwrVarYpm/bWhoaNpz+2m21tWLfv4+83G9enWx9ZiP65Xj7atvdUVEN+3KiPheRKyKiLNt+15J2z0Rsbk0vi8ibouIGyLiodL4TRHxwFTX2Wq1guIvg65bL3P72fpZV1Pm6nrNxbWear29fc2P21evdUXEWHTI1G5fvXMW+D7FufgXKU7ZkLZnUn8cWF6aswx4IY0v6zAuSapJldB/O3Bl6i8E3gc8AxwCtqbxrcD9qX+I4oncy4AVFE/YHqU4BXSO4kngAWBLaY4kqQZVzukvoXiidgHFncRB4EGKJ2QPAncCzwO3p+OPp/ETwHlgB3Ah7dsO3Edx53E4NUlSTaqE/o+Bd3YYfxlYN8mcu1NrNwasqlaaJGmm+Y5cScqIoS9JGTH0JSkjhr4kZcTQl6SMGPqSlBFDX5IyYuhLUkYMfUnKiKEvSRkx9CUpI4a+JGXE0Jeki+j0RSR1tH4x9CUpI4a+JGXE0JekjBj6kpQRQ1+SMmLoS1JGqoT+cuB7wNMUX3p+Vxq/GjgCnErbq0pzdgGngZPA+tL4auBY2rcbGOihdklSl6qE/nng74EhYC2wA1gJ7AQeBgbTdmc6fiWwCRgGNgB7gQVp373AtjRnMO2XJNWkSuhPAE+k/jmKR/xLgY3AaBofBW5N/Y3AAeA14FmKR/VrgCXAFcAjQAD7S3MkSTXo9pz+tcA7gceAxRR3CKTtotRfCvysNGc8jS1N/fZxSVJNLuni2LcB3wA+Brx6keM6naePi4x3si01hoeHabVaXZT5hqGhoWnP7afZWlcv+vn7zMf16tXF1mM+rleOt6++1VXxcyAujYhvR8THS2MnI2JJ6i9Jl4mIXan95rhvR8R70jHPlMbviIjPT3XdrVYrKO4cum69zO1n62ddTZmr6zUX13qq9fb2NbO3r6b0+u8YEWPRIVOrnN4ZAPZRnMv/TGn8ELA19bcC95fGNwGXASsonrA9SnEK6BzFk8EDwJbSHElSDaqc3rkR+AjFSy2fTGOfBO4BDgJ3As8Dt6d9x9P4CYpX/uwALqR924H7gIXA4dQkSTWpEvr/weSvp183yfjdqbUbA1ZVuE5JUh/4jlxJyoihL0kZMfQlKSOGviRlxNCXpIwY+pKUEUNfkjJi6EtSRgx9ScqIoS9JGTH0JSkjhr4kZcTQl6SMGPqSlBFDX5IyYuhLUkYMfUnKiKEvSRkx9CUpI4a+JGWkSuh/CTgDPFUauxo4ApxK26tK+3YBp4GTwPrS+GrgWNq3m8m/bF2S1CdVQv8+YEPb2E7gYWAwbXem8ZXAJmA4zdkLLEj77gW2pTmDHX6mJKnPqoT+D4BftI1tBEZTfxS4tTR+AHgNeJbiUf0aYAlwBfAIEMD+0hxJUk0umea8xcBE6k8Ai1J/KfBo6bjxNPZ66rePT2ZbagwPD9NqtaZV5NDQ0LTn9tNsrasX/fx95uN69epi6zEf1yvH21ff6oqIKu3aiHiqdPls2/5X0nZPRGwuje+LiNsi4oaIeKg0flNEPFDlulutVlD8ddB162VuP1s/62rKXF2vubjWU623t6+ZvX01pdd/x4gYiw6ZOt1X77xIccqGtD2T+uPA8tJxy4AX0viyDuOSpBpNN/QPAVtTfytwf2l8E3AZsILiCdujFKeAzgFrKV61s6U0R5JUkyrn9L8G3AJcQ/GI/VPAPcBB4E7geeD2dOzxNH4COA/sAC6kfdspXgm0EDicmiSpRlVC/45JxtdNMn53au3GgFVVipopIyMjFKcD6zUw4FsQJM1OviNXkjJi6EtSRgx9ScqIoS9JGTH0JSkjhr4kZcTQl6SMGPqSlBFDX5IyYuhLUkYMfUnKiKEvSRkx9CUpI4a+JGXE0JekjBj6kpQRQ1+SMmLoS1JGDH1JyoihL0kZaSL0NwAngdPAzgauX5KyVXfoLwD2AO8HVgJ3pK0kqQZ1h/4aikf4PwF+DRwANtZcgyRl65Kar28p8LPS5XHg3R2O25YaIyMjv4yIk9O8vmuAn09z7rRFxFSHXBMRtddVwbTXq8Lv3It5t169mmK959165Xj7GhkZ6bWuP+40WHfoD3QY6/Sv+YXUejUGjMzAz5lp1tUd6+qOdXUnq7rqPr0zDiwvXV4GvFBzDZKUrbpDvwUMAiuANwObgEM11yBJ2ar79M554G+Bb1O8kudLwPE+Xt9MnCLqB+vqjnV1x7q6k1VdA31+gkSSNIv4jlxJyoihL0kZmQ+h/yXgDPDUJPsHgN0Ubwr7MfCuWVLXLcD/AE+m9g/1lMVy4HvA0xTPp9zV4Zgm1qxKXbdQ/5q9BTgK/CjV9ekOxzSxXlXquoVmbmNQPGf3Q+DBDvua+j85VV230Mx6/RQ4lq5zrMP+mV2viJjr7eaIeFdEPDXJ/g9ExOGIGIiItRHx2Cyp65aIeLCB9VqS6iIiLo+I/4yIlbNgzarU1cSaDUTE21L/0rQWa2fBelWpq6nbGBHx8Yj46iTX39T/yanqamq9fhoR11xk/4yu13x4pP8D4BcX2b8R2E/xJrBHgSuBJbOgrqZMAE+k/jmKR9ZL245pYs2q1NWEAH6Z+pem1v7qhybWq0pdTVkGfBD44iT7m/o/OVVds9WMrtd8CP2pdProh9kQJgDvofjz/DAw3MD1Xwu8E3isbbzpNbuWznVBM2u2gOJP7zPAkQ51NbVeU9UFzazXZ4FPAP83yf6m1muquqCZ9QrgO8DjpI+faTOj65VD6Ff96Ie6PUHx2RjXAf8E/FvN1/824BvAx4BX2/Y1uWYXq6upNbsAXE/xSHENsKptf1PrNVVdTazXhyjuhB6/yDFNrFeVupq6fd1IcZ7+/cAO4Oa2/TO6XjmE/mz96IdXeePP83+n+PP8mpqu+1KKYP0K8K8d9je1ZlPV1eSaAZwFvk/xnRBlTd/GJqurifW6EfgwxZOTB4D3Al9uO6aJ9apSV1O3r9/87meAb1LcgZfN6HrlEPqHgC0U95ZrKZ6dn2i0osIf8MY9+BqKf4uXa7jeAWAfxTnzz0xyTBNrVqWuJtbs7RTnUAEWAu8Dnmk7pon1qlJXE+u1iyKUrqX4mJXvApvbjmlivarU1cR6vRW4vNT/S373FX8zul51fwxDP3yN4qVW11DcI36K4h4a4J8p7rE/QPFyp18BH50ldf0VsJ3ioyn+l+KGWMcpgRuBj/DGS8QAPgn8Uam2JtasSl1NrNkSYJTi/PmbgIMUL/f7m1JdTaxXlbqauo110vR6Tabp9VpM8egeijz+KvAt+rhefgyDJGUkh9M7kqTE0JekjBj6kpQRQ1+SMmLoS1JGDH1JyoihL0kZ+X9HaBYf59/+fQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a new catagorical variable: stratums of incomoe\n",
    "housing[\"income_cat\"] = pd.cut(housing[\"median_income\"],\n",
    "                              bins=[0., 1.5, 3.0, 4.5, 6., np.inf],\n",
    "                              labels=[1, 2, 3, 4, 5])\n",
    "\n",
    "# Plot histogram of stratums\n",
    "housing[\"income_cat\"].hist(color=\"w\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To do stratified sampling: sampling from homogenouse subsets of a population (stratums) to generate a fairly representative sample; we can. use Scikit-learn's ```StratifiedShuffleSplit``` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creates an stratified indexing object \n",
    "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=0)\n",
    "\n",
    "# Using the indexes to split housing data into sample that represeent the overall data\n",
    "for train_index, test_index in split.split(housing, housing[\"income_cat\"]):\n",
    "    strat_train = housing.loc[train_index]\n",
    "    strat_test = housing.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set:\n",
      "--------\n",
      " 3    0.350533\n",
      "2    0.318798\n",
      "4    0.176357\n",
      "5    0.114583\n",
      "1    0.039729\n",
      "Name: income_cat, dtype: float64\n",
      "\n",
      "Train set:\n",
      "--------\n",
      " 3    0.350594\n",
      "2    0.318859\n",
      "4    0.176296\n",
      "5    0.114402\n",
      "1    0.039850\n",
      "Name: income_cat, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Take a look at the frequencies in each set\n",
    "print(\"Test set:\\n---------\\n{}\".format(strat_test[\"income_cat\"].value_counts() / len(strat_test))) # Test set\n",
    "print(\"\\nTrain set:\\n----------\\n{}\".format(strat_train[\"income_cat\"].value_counts() / len(strat_train))) # Train set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we must remove the ```income_cat``` column we engineered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "longitude\n",
      "latitude\n",
      "housing_median_age\n",
      "total_rooms\n",
      "total_bedrooms\n",
      "population\n",
      "households\n",
      "median_income\n",
      "median_house_value\n",
      "ocean_proximity\n",
      "\n",
      "\n",
      "longitude\n",
      "latitude\n",
      "housing_median_age\n",
      "total_rooms\n",
      "total_bedrooms\n",
      "population\n",
      "households\n",
      "median_income\n",
      "median_house_value\n",
      "ocean_proximity\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for set_ in (strat_train, strat_test):\n",
    "    set_.drop(\"income_cat\", axis=1, inplace=True)\n",
    "    for col in set_.columns:\n",
    "        print(col)\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>\n",
    "    <span style=\"color:yellow\">\n",
    "    Data Exploration\n",
    "    </span>\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To enssure we do not interfier with the test set, we will rename the train set, \"```housing```\" and operate only on this set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copy training data\n",
    "housing = strat_train.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Geospatial Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot housing location\n",
    "housing.plot(\n",
    "    kind=\"scatter\", \n",
    "    x=\"longitude\", \n",
    "    y=\"latitude\", \n",
    "    color=\"y\"\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let transparancy replect frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot housing location with transparancy\n",
    "housing.plot(\n",
    "    kind=\"scatter\", \n",
    "    x=\"longitude\", \n",
    "    y=\"latitude\", \n",
    "    alpha=0.1, \n",
    "    color=\"m\"\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A better plot\n",
    "housing.plot(\n",
    "    kind=\"scatter\", \n",
    "    x=\"longitude\", \n",
    "    y=\"latitude\", alpha=0.5,\n",
    "    s=housing[\"population\"]/100, \n",
    "    label=\"Population\", \n",
    "    figsize=(16,10),\n",
    "    c=\"median_house_value\", \n",
    "    cmap=plt.get_cmap(\"jet\"), \n",
    "    colorbar=True\n",
    ")\n",
    "\n",
    "plt.xlabel(\"Longitude\")\n",
    "plt.ylabel(\"Latitude\")\n",
    "plt.title(\"Califoonia House Prices\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can calculate the standard correlation coefficient (Pearson's r) between every pair of attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value    1.000000\n",
       "median_income         0.688883\n",
       "total_rooms           0.137469\n",
       "housing_median_age    0.107144\n",
       "households            0.069177\n",
       "total_bedrooms        0.053544\n",
       "population           -0.023797\n",
       "longitude            -0.043236\n",
       "latitude             -0.145570\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate correlation matrix\n",
    "corr_matrix = housing.corr()\n",
    "\n",
    "# Display Pearson's r for median_house_value\n",
    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting atrributes that have strong correlation with ```median_hoouse_value``` we can use pandas ```scatter_matrix()``` plot to illustrate pairwise correlation of the selected features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Osfp9gzBJgzB9ei8Cz17WAruvB2nSS5DG+zYkACy0dx+H5t9vCSXGo/b+S8i0l8au7UI8sxzNTY/kMs7a6TrEcx9BJjRFdu3zdt0qFNUlz9poLlpLtqCd252Ihz+JTG1KEY+9GoHg31mbervORGuU83uniajPN9j7ObUC/05v2oj6Zzi9dy9rrB41Oe7rTYMB4O7141yxop9rU0oppZeEfDEoQcymOeu/MsLmuj7r+kq7/kUigsY4xEjdK/8RpHl+FVqQ7rLj7Xbfc2gbsMR+VyFQNB6BpjKkWdiJNAlz0AIwLKt+CxDLeZKI4rEDMdjLEdM8BTFqr3ulXeuh4fLQ4rsbAdX9yO5xlt3zQbTIPYQ0YAvQguBbsfvRgtxqdTwTAXzXNK+wd6xATBkr6x4y6S4yt6yPJFWgRaqF2NU42jSWEDReRP0xgrDT95COSZtwtxWfhHYZ5qA+GYL6cA8ac1vtexpaSPOQvflKJOi5f8Ij9Da3GU1EqjidIwfAr0ZgZC7q/939Xp3S8UTZczEXPYM28pciAdBNi0YS4G0zwV+SwNb9KuoQr/LIUiB+dTCH5Q1oVyebXrDyyhAAvwIpGR5DPLAaaV5fTfhgXIbG/2RkCpiLFyxC47mLCInpALwezeHpyGSmHikhihDALkfgfC7i89cjQcXXEX/XqcjGux0BeffpGYV4RxHiWfMRWH0F4iMeMvRGpNDYhhQmNUiouAEpR9oRAL8F8ZBHCd+ScVYvh6c/SLz7Dqtntnb6ArT+DEP8fhv9UxdhhojVaxLivzWIZw3NcV9vGgwA/zmyvK9FRkDvQBbqKaWU0nFD9yBmtBUxviT9Bm3XLSRsIpchprQfbf212vkX0FbqOgR65iPGtQqB6RLE6M9FmpSxyEbuXhRLFbR4nE2ArDLEZFvQ4naq1WeH1ftStMi5Y43HI/+t3bsGsZ2JaBFxYD6MAH2TiLjdWH3dubPKPu6M2YG2PhcjG3Xs3XqsHvVI4zILbf0+jRYXB+AbkfbMte+T0GKez7HTSJ9LhEHbQMT1PZp0G2qnc1EfuUYb1NbZ8deTYb9K0AL/GyRAzSUA+B/QQr8EtX+D3duAzJ0eRA5WNQRIOt2ufQSN962o3xYfkTcVuWNzCwePmpLSiUefR/zmKcSv3o7m0frENYUI0HoeBKcOpFA40jQb8Zw84I32KUW89BnEO+9HhgkzEVhfj/jk1hzlOT2IbKdb7P5niN2qv0TAfiMC/KVEHPtTkMLh1cjkZSmac5OQcJyMAPIeu78HCav7UZsVI0GlCGmPhyN+vBHZ449GwP50K7cZ9Y37UVyPNOFuJjQKrXWPIVOcMrS72mjPGo7WqDOQf86zVs9kFBsQPx9PmNI5VSFTuU7E83LZddcgYQF6R0o6OA0GgP87UnXsReqJf2Zg4mVKKaV0zGgPYjYgWfkKxHT+iEDLL7Kun40AzmzEiFcixvxbZK4yFzGoMqQBuQnZ0dXatX9ATpiVyEP9RgTGtiDGNRwxdXfC24wY/Rj7r8vKmIeYbx1ivDORFsMBe7s9d57VZZe910Qrv9Dq6gvBfQQIe8LKG2f//wwtbOcj04Ipds0upIH5CdIxzEXAax0CdVuQhqQULYAbENPehKIplCOAXo1A+e8ZnMPfoZAvtr5DcCyoE/gSipxThhbjYai/kqYjfdFQop1qiJCC3ya26y9Ai2IesYvTZde5cDWZcLgqRMLftw75rfqmO4gkSSkAP/nozQi0zUHJVO7OcY0L2SBesDHHNUeSupEwkI+0w56UrBbxxCFI8C1FfKgOOU6fjYBmBdqxW08454N2J922vBtFIClGLn6P2PNWoR3NRjveQkR2eiMCxzPQPD8H8dVHkYP0BjKjgDSheVOGtOkXoDm/HK05NVb+00iJ85TdV0fvHaznkUBwwNrBtdxXEEEEutAOQaW1wx32jOsRdJ2F1pICe95mBOAXobmd5F3nIf+gbrQGPERv2mv1mowUWx5TPJc9fG8abBSUe0hBd0opnSB0MdI2gjTaubbkaxEDnoOY/AyktTiDsHH20GqFCDgXIrvrNfZ7v93r5iRVaLHaT5gM+OI1GwGqGYh5NdmzGu1Zu+27EzHmvfaMoYjxl9nz1yKQPRU56RQi04YeO65EWpv7kDd7FdISdSIb4/WJulUgpl6KmPAUK3c9WhheRFouj2Rxo/3fSZig1Nszp1mdN9s1RxuALybMNo61acQvEQhwsyPIDDvolEyqAerD+ajd2pDg0oBMgvYjQXAIWvgWE9rnlWRGq0lGJekLGJejPlnDobdPduzn/iLmnOjkUTdeDuESvR89G28pmU7WQwkzi7uJ3bklR7FO09G4dh5XiMbucsQjH0U7jB7ydRVSFDxrx5vQXHkzAoXdSKgoR3OrCc27LqRPnYbe+S1ICTES8bRyxB9LEe+bas+42v5rR3w7H2muv4KUHD0o7OJjVr/bka13JxLSZ6N2nEAkzZpm712BeHUHEh6GkOnA6olxuq0u86zcpxBPL0GOsH8msgkPt/ebiRQs7rt0gz23Cfk+5dJuVxEJkypz/I/997PEbzfDG1iW4MEA8GZiZBbbk9ytNqWUUjqq9EkEcu4gMxzWNBRGqxRt678GMagHEcidYN/vQAz6QbSYvBYx052IabuT5FCkpSi3cioRIJ1s1xYgpuThn+bY/TsQc6u2ez17mZuRdCCmOdU+TQg4rkemJzMQqJ9t79KGWMxQxKj/mCjfzWt+jbTY5yNWdCdarC5FXu5tVq4LDfWIyY9BjH21lfMzFMO1GmlPLiUWwe+RaUcPkaGuAS3eHh/9LHuf0Ujr8yDHhvrbcj4a9EakMXNHIxfi3Ja0P8pDfeBCVhEaK0OBNxGJdHah8fAU2pKegzRMc9A4/AkaQz+wsvoyvbkJbVU3I/OVw6XpKPLNThRPODs04YlMeWg3qwEJnrf3f/lxTyUIiNYhYfFspPW8E+1gTUdztB6ZVjxI5vh9F7JV7kD85VQEPJ9HOy9FaBwORFipJhy1kztV+VbuTsTHPoV403cQf85Hgr/bkw9Hu0R1SDh9i9XffSbm2zOSiX+uQvx/LzI/mW/vsY0QOobap9DaaRQRj/uviLWnFQHTHUjbfaV9ryF2OjsIk51RhD/IaxDvzUdhbovR3K2wejRavUdaOWPtv2rEDy60OnahMeoZe8+yOhcjoL0YzfedSKgYh3Z+X4d41k4kDFQRvime+A6r8360PrTZe6xlYOSZkksGdPVgAHh2xJPXEHFgUnqJKI0j/nKgIuR8kocYXhKAn0c4nr0Fgd8C5Bj5FJL2PVJHFWIkr0QOO0MRs1mPFp8mBKo9gc8IpCnwRDpD0dZaK2IdtcSCtQAxn91WpmfqrEHMqBgxyVWIQd6GNNQgTUY9mQlN8gkgX4g0QNUI8O5GZjL/iQBZt7XB36Ft2LlEAqBtVo8yu6abSNJSjEDemWgB20NEVfHYtNngGwT8T7f2utzKfsCunY62WrNjfTtTPxkc+G5E7TsctaNrfSB3Mh4/n51tzwG790mHfe9Dfe47JUNQ/1+N2q8GCVh7keDUn927a6LcR+BwQwnOJJyGPb7+yULFCNiAwM+JTmOJmNcXIZMmEB8abcezESDdY8d97W54rPsuItSca1b/nOP6bLoJte35yJrXd90uR8qQ/URM7B5iVy8P8a3lhKD5uL3bYqR8ASk1rkJ8bCoKjega8Q+jvh1q5TpAnErM1zprnwq00+RJz7qQQgK7180LsevXILD8DJqnuxBvnI/a8SFro3K0c+DPKyGiU1USCd1a0NpzJRI0zrVPM6H4APXrd4j8D5Ot7OsQcB6Gdj+/htaXnyLBucveocPa8HkywfdZRBjeZ5EgCgPXM/sO4MD4zOFkwvw14W2VUkopHTXqQEz3THoz+wXIBq4E2W1fRzC+cqS1GIcYcDNaaO5DwN23KMfavQUI3Lj0fxHBSNqR1mMV0tg4OC6zOsxBTNydHEGMuojwDC+0ujiT2mPluvbU04ZvRAuBhyIsRposv6YUaafmIMZYhBazRqRFnYoYeTPaPp2BBA6P+NJCbD3PRkzXM5jdQmRP/JVdX4FszV+we/cm+mGp3ddubTMMLQhJOhMtKB3Ijz37/xONfoMWVTf78DCDftwXeVIeX9zdYbOF0Jj9mQDNnqm0HbXrWCQULiFCtI2n/2gnHrVhOUcmjvcTSJjyjKYnE7UhvwXXDJ/o5D4atQjUgYDbU6jvZtr5cgSC7yMTfH8X8ajNaKzWIP7UjEyvihAMyrfj/nZDShEA34z46sVo52oEAogdyBfGd4aeIEw81qNIKcWE0UEp0uTeipQzP0V82WOXVxOKmV/Y8VaUEfMfCTtoz7Gwy9qkHPH5bQiwr7T3OheNiZ3ILvoJxO/djMMdQFutrarRfJtPRASpIYTs1UTkJM8aPMT+X03sFIyzdqtAdtozEK8fjeZ1D5rb7QSwvhath9X2fjWI/26z67dZO+8hjDkchA+z78LEdZ1k2tH3R87/8vu9ymkwAPx1ieN81LJpZoKUUjom9I99nF+CQKdTtuYVZI93KVqQvk8A4Hcj5vQEsq++1L53IQDvads3IMb6NGKSBUSkiz1IqzMVLUz5iEH6/y2IubkGssjKcCDehcD/64nwiAcQY/RFohiZ0ridXjkR97YbaTEK7fyVaJFahRj2rQhQVyKW1YKYrKd894VxCALrN1u9xqM230XEdd1Kbyc/j6gCwdyzabh9FyEwcKID8O/b5yIkxF2ExsBo1A+57MCht3bIvz3uezHaZt6ChJ1taMEtQuPLNXLV9vsOMpOG5KLtHFm3pbXIpvZkpcc5+n4Lx4paEeAEjbt5aKy1I7BZjcZaMxpLbXbNq5BQ97PE/TORoqHb7nfN6EgEpqvQrl5fuzHdhGB5BhL8d6Mx7jtI4wnlQCPiPR5Xv8M+FWjuDUGhTZ+xei9HvHuvvfdViMfOReP1xkRd3mbfcwnfn+8gHlyH7MwX2fFKIob3LiQkNKN152OEED4KZQQF8WqPWr3d2q7Iym2xd9pnxxUIaDvf91wA41CfTUPC0Rak1a9DfPhFO+9Cu5ux/AjFB6mw+p5NxCRfZu2+hEgmtIJMO/P7rQ6+Psy2Z5xKbifMw6PBAPBrE8ediBNdd0Rrk1JKKR0FehAtqu2Jc39CINW3YE9DILUaMdWpiIlVoenehoCWZ3t0gDUUAadtiKkttbJ2oS28WsScPYRXh507ndA6eIILN1XxsHRb7Dkj7NudDUutTguRcLAIMdP3WhkbkWbqy/YON9m7/w4x21MRKP53lLWzAC0Ab0Qaqu8jIP8KwubTE2AMhBrsGUutzkmmfrKkJJ+NgMckBLzriOXkYFpw1w65SYo7OoH6tgMBoBo0DuuImPLuLLsBgZCrUb/usWv93pRSStJQwuTtHGRSMhSBXjcLa0N8yQW+WiIs3RIEFNvtM9qu20FY4k6lf3Oo9YhvjUSC5VYEHhsQGPU05h51YzPihckyRyJQ6T46nmBoFgLjnpFyJwKxnhTreiT4J3dQnwH+hsgZ8Upil9GVLc8jE5bGRD2qEU8vt3s7Ebi9BvHsZPhG0HwttOseQOvEvcBf27nx9vs0tC5dZ+9Zh5QlpWj3621IE38A+AKKjN2D1rZfoP6YbPVvsTrXEdG0nkFmOzsR/+qw5yR3L5qJnc9/Qvytx64fDAA/8iYobx/EtSmllNIxoXykNcxHzLivtMcjEZNeTDDI9XbvRKQJ2ILAdQ2RTr0EaSeHEFuI3YQJQSFiyJVWRiGKirEOaUtuSlzbnbinOHGuxeqd1JD+HjHFOsQE2xHDX4cWhy60oCxBQPxfETP3eOMeenAKMtGpRtqcB60NOpG2ZCcyC6mwc9cgcL6eiBX+O3uHXLGlx6KFy6N5bLX6ViAN01fJZOonC5XZdz3qo/Ic12RrwvOy/uskFvw8IhHPM0hrdzMCQW7icycaF9fZuXH23H0IoL/ayv4xvWP9JqkAAYgtZAqlKZ18dBYanw8i3jEGja9riRCYf4f4xBoiK2KuqDmeqr4Vgb8Cu2aiPSM7K2KStiDN94NEAprhSCvsGvmziNwG/n8eYUI3Epl/ucleDdI6OzjchjTfD6H50ICUIArZjNwAACAASURBVOcSSo41hN9CHqHwuISAg9PQWvIEAsD1aJ75zuF8K9f9a9rQ+rA3cZ1nIG6wtilAPOMua+ufo7C1tYj/fw0JRvciYwvXrC+1dtiL+OlY1Gc1SAhvQ+uNA961hHnhC0joqrVrN1p9N1pbnoI09EkeUIYUTbuR8OWKo6PjuzMQAP5N+ofzf3uE6nJC0uE6QaaU0uHRXMQwQIwsexFwbfMrEFM7BUVvaEZA8RRCAzkBMcrtSMPj9nC+PbgLaQueREyxEQFbNy9pJBjWJgTAJ1r9itHi8oydG06mGctOpIUpQuB1KtrynWblP2/3PW/1y0ML2hiUQGg0AnTuZPMtxDTrEZOuIRxINyCmW4+A8TQkiJQhRr8HAfukvXdfQO1Ce++ZiPl78hj6uedkoCeIGOynktvm0YWp/sxRQMvQftTungzDhUKPA38zAt+bUN8MRXEAPONqWaLs5HE2DUO7GxPIbVKU0slD4xGYBvGapYjvbUA8zDW8+xFfAgn3T9ObJiMzDhfIr0Vj/lYysyL2RTMQX5xGjOtdiM/Ns2+fTz32f5k9YzwC2SBTry2IZz+NNM9dCGzehHjatfaOjQhouv17O5lAcjbwAcKW/QCaT7+3OkxH60nSrtmz/hYivrrB7nMBvMfaY7w9bwkRzWUMWgtKCLvrZnvPa1EfXYuUKe+zeqxD/GW1ledRkiahnYduNP/nISXQXYRQUYxyC8xH0ZKm2WcHmZFPighefQmRUGcVYXb0ApHwLdv342o0PjwJXtKmvH8aCAA/mJFdSiml9JJRcx/HIEbwRcTMi9FUbkQycxOye6xFWpELiJiwuxFTd6bUhQBzO2K2w5E2eS9ijh4NpQcxqP0IiI5BC5xvq3YhzcpPkaZ5AtL0FKGFAyLT3FloAdhiz6uweo1AGp0SO16OwFgPAnDNaDFwh88CxBR3WxnXoIWnE2lbHrPfm6x9CtGisgQB74OFmVuFFoM1hJByq737ioPcOxC60t7jbrQIHS80CTnGjqbvGLmQG3x3Z513O3qPAf9qZN/5OFrIalCEnw7kJOsh2b6JFu8daHxPRKD6WTRmZiGA4lkzX4XGlduBDuPkjun9cqf9xG4dSDsM4g/fRaDp2ax7JqPIJKuRU+Y5aHyNsHJKEJh25cEIIjNuNhUikL8NKRdGonH+aQToViIQ6ZrvjkRdJyEemofm2B40DxbaM0ehMfwp+3+xXTPHynZzkTakEV+Gdor2Juo3F4HTPKvjGjT/dtg5jz3+BOJnixGvd0C8AM25F5FmfxLiow8QIR0/SjheD7HyChHv323vuBr11XQ053cjfrceGV5Msef/PeLhe5FQPsHKvRr1rSuBJqI15WykcNln115kbeffI+i9Y+zt47bl24l8D++1/vkNWiPqrd3m2z0XEOMiGbGlbxoIAP/hgEpKKaWUXgJaiRwv85G2IEnnogWlAEntI4iMXWcihtGBwMsYIothE2KusxFTL0BMrRQx4Fqk4XBwuh4Bz+WIqVcgxvWXaNHwbcp8tHDUEqYLbmLg8aAdDBURKYur7TMWMWx3zAQtiE32u4SIQbsTaZU8Lf1uIra3OwrOsnesIuwgn0Oa7HfaPf9N/9qMxxDom44WkpX2vkciNvcQwr70Io4vAP4+IuqChw2spn/77x7UB27r2kUk/OhEAp4n4SlEY87DSXahnZzfEXGUk1kxr0IAoBEBgJusbvMQkBpPRIXYgPr4CVLwfTLTdqQBrUZ9PoOI8rGFCKf3BmTWdB8ao54mvZawG/8ZGiv7EAC7DPGnBf08/x0IdC9Fwng+AqjrkQCAlV+DeM0SQqvbScTPrkaa3WEIJA+x/ycTGWXbrS4H7B3uRPx4K5pHH7b3/bTdi72LO74XIv5aQMTaH4finP+11XMJ8BE05zrsmmX2fTmy4T4FAVfPj/CsvVsh2mWYTzjYu1N1EZrjJfZ9DrE7O8ruLUP9+RV7D1d6dNk711h5xQhEl9r1nqugwcqbSCRq6yAUQFOsrR6y771W75nWXu4oipXxCcSbfoL6cyzqZ+d//fHBoMHYgA9Dbq8zyEzzc2nuy1NKKaVjQxtynCtFU7OH8Nq/xs6fTmitd5Gp8W5GTKgWMcwVSDO+GzHGUsQMPRpFD2J6jyGGvt2OixEoqiIyYjYjs4ECK8dtw32xcS25Rz9xO0cH6J1o8dmNmOdGBLjututeRNqkLVb/zYm6jrByn0ALlds1uoNoAdKkTye2GD0urzti9UVX2b1zgX87yLWDITfJ8B2H44U8JNsW1HZVhJarr4XHd0iKCOHJwW8e0U/e70VELN861M9noMi3/4T6shCNo33EQltq5btwWEBoPvcjIfVejkZEg5SOR9pKCMP/g+bp2qxr3orm+ZuAzxL+AZ5OvBtpUPMJB0RPD+9h93ynzakA8RyQhtp3BbeQSZPQ/ClGQHajlfM4Eh7zEfD16FMj0S7iSCSMjrT7tyEAPDdxbrLV8c0IqE9G4WfdcdNDuHpyrCJiTrn2fYbV3VPET0OKi2q0PuxA83oK4p2tSFlwPZrP91i98xBILbHfU9GcLrVnzEJKoAmEo+Z2K7/B2qQK7QgeQFFbbiN2N6+3ur9gbToUrXke4esBJDzUI8HCHfI3IeFhDuIp3yB47buJ2CPbEc8oQ+PCQ9rOtnJdqBgcDQaA/xjtrV6NdPF/ycBSQH0YWdWfD/wDEjXXIZfWDjQ6/gaNvpuQ6HEp8DnUm28lgnZ+C/XkX6P9kEbkSVWKYs/8cRDvk1JKJyh5SvhN9G0i4TFRd6Ip8wzS2rgn+iwEcB5EmvL9VtaPEHNtQAz9HARAv4umYB2hmXHqQdPbQ3stIhyN6ohEOA663VQlz+rYSiTGcQ21a8w9aoYn9nFQ3oYA9lordyEyMahDDLYTLbwdhGNQu73PL6yeYxBT32z/rUEanTsQC1pLb/Dtgk0zESu5xc57WMQjRZ3IQbSM0PQeC/LkH9v7eG4Pch7rQgvcp9DCdrDoJ/7tx77966HdxqM+c1OqjQgYua/BLntOPmrv9yAh6S7UZ+eiPmtG4/jVhDZzD9rduPMg757SwSmPSG1+ItE++0A4Jj5MJHxajrS3rkkG8YYmBB6LCDvuM4l2uAEBvq8SPjhdaEzORHPlSwgAN6DoS2MIDa0n3OkiEo5vs4/HzL4Egb4HiNCDbv9dlPjebvdfh+bwPMRfR9k7bbSyWsj0lXBFhwsZa4jIU0mqsvI8Rv/5SPO7F601LQjkz7F6eIblPDSXHYyXWdt5aNYGIpPl+5HJ2T2Ifx+wZ86xTyeCo56Q7gxrC4ezp9hzXo94w3h7j2oik/NXrayVyI/Hhf5i+38/sdPm4RTvtd/FqE9HIw14OeIx2TvQB6fBAPAhyHvrg0iE8k9/VILeErQCXoJ67GNIFfZrBOYvRK31HhQ77JModMEMFIz3b5Bl/o1oBvw3GmEfR3sBi9FoTwF4Si8DuhFtg20j4tRmUwtaXDzL2qXIbKAcabXPsfMLETPzbbmRSHMzBDHFEsT8PI64O10Wkpk8xzNNrkYMbDgRPhDE3Dw9r2tKPfqFO6240xFEpjQH6A6+3YEnDy0qrq1/Dtn8uYYln0gK5NnlPKGGJ9MoRRqTr6OFqQ4JD/uQrXEuupBw0ulGC+8KpHnaj7Q/B2OLg6Fujj3QuQYt3M3IzjrbBOcsxJ6HIjDi9o4D2Xb1a1wjvgH12U4rZw2hOfRMre1oAdyANF832jUeQWESEgR+m3hOM1p4C5GG7If0Do+W0qHRW5GmcgHSJJ5oVIi0nqBx7HHnN6HxmZxvi+y7DI1H50XnEIqB6XbNdYifViPw5qB0n93vwPNqYn7di8bpNnt+PhrvXg+PRjXT6n0d0r5j7zAB8bkpyNHwKtQvdYhXbUfz4kkkhF6N1oQeZErj5iGriMRrBxD4nW/XOCjfjeboCMQTziF2/h4nTPu2E1GtViAtsTvvX2DHm5Ew7SYjLojsR/BvnpX/L/ZunvBshrV5ktc8iWBjqdUnKUjcgbT7N9gzxxAOqtPQWrAZadZXI6HMFVXfQsJXOwLcTu1oFw57lw9YHzzDYGkwANy58GbUi5uIfK590bsQ5/sMEhnvs/N/RNruZchIqNPOfRshhANodD5O7OnWE3vtvt84GwkELmpVkTt39Lvtw9ChQ3P8nVJKJxL5GHatY19BitYhyb8MAZW1if+Sme4+gTQOl9inC2mDJhKZwqaiaeoe76498Zi1hYjRFSJGPoxwgHKQXpD4JEMOukVbMmqGA/QeNK0htnt77H3GoBCAW+zbbRidOVcSYP0AWhxbrP0a7f4WxIhXIQbaF9htINLJ+3ufb3X3rGpdRN8MlM5Cgsv9HBm78SNB/g5VhC1n9v8+BirJjOs9EC24HxejfuhAi/tzCGhUoEWuhrAVfxEJhd9Efey2mlvJHMvFxM7JOgTOl3Mo2qmUclERAn0gnnAiAvBOwm73BSI1fWeOaz0u/TjCmbcezfdCBAxnIWHwUbThPxIBvzPtmvFE5J7nkYB7GdrR+T/EV2oQpJqFeMvDqK2dn3mimTwE7l3wbyR2BF2r7buF6xCArLDr3c+lFM3VRsL3xk0oChEgfruV24Pm2nik5zyVAM7TkDKiiYjV3WHvvokIFdtqZY8hBJEapD0+AwUK+CgaV0vQzms94i2TiSguryR2v862/uq0e1zp4VlFS4hEXlif7CCcTF9p7zQGCf2en2G8fReiPh2Ijtmd0AePLQcDwD+LWu0jiAtWowCafVERWon/CwHwWsLFdA/hgZV9ro5MV113K03GucpP/Ofow+/PBcC/bR927NgxsAjpKaV03NKvkHZiCf1HCF2NmNsYMrWD2bTGypyDtDktiFEnQfIYQoud9PB2rc4BxGQnIXDWiTQ/1UTaeg8T6M6enobezTY8AoqX62mACwmHvU4EuHfavTUImJ1L2GG6k19pouwexOx32/2NBMO8HlnUvYC2P7Mt68qQQ1UxAok/tDa6DIGQFxG49xT2lcRWd39UR4RJK+P48Xe/C2mqVpPJip3uRyCiDLWl943vWgzMAUnkOykj0cI5nNCKdaO+bEIL+plIsHOh8J1kZm0caucmoEXfTYo2DaI+KfVPHUiPdnQyAx47+iGh1b2BSPJVi4TitWicvQZBi1sRSPcwpkk48gziQweQhtq1ueuQtngj4imPWFkXE5GdziYcPt9AgDl3ui5CvKTKymxERgOViAd5ZKoOBNFGIx+fr6E+Wo/mj+8I7kNA2ZUpHpHIcyuW2XPn2fvPIvSe7hSdh+b6eMIkcAjBa6uRAOH+POXEHHflykjEOx4kwgDuJISjBiTwrEa8fSXisbPs2j1EHsgS1GdliBc3o77cj3bMzkbOm59KXD8Xadg3oHCHFyOBxh1sPWmaa/X7yq+xD62tkziU+TAYAP44eus9SE12MHorEnGcdiNRD2Lk77bj5LldiXOQuTpnn0u2it+fUkonOa2ltyNRXzRQc4glyOxiNgKGnYjBO0CuoLejiZuI5CHANAIx6Hy04PiWngNw3171KV6cKKcHsaNWwlSlg5C/QUzVnfMWW51q7ds1RS12rZe9DzHyEvt4drQVaOEosnMe/eBsegsrBQSrLCLa/gUEwBch28zrkdNOM3Lm6UQsrw5pyrKFpRarXyW5U9i/VLQVaeb6on0IXExBoLid2GkYLPUQTrCelc53Vfah/l2PQMwYIorFcwhYJGkMkcCkGbX7aMI5+EiEhUxpYNanxztdhnZbHkQa29ORdvpaJMCdQdhB16Jx9QSRIGY1YYJXjMDsVDQGS5Ad8jpCgfG8lbEaWdu6Jtejf+QTyWuw8ibb+RcQL+lG86MMaYj32fWumPDEVMVod66eSH6zwso8jeDhDYTWe5HVfxhyuDzT7l9kz70MCSHPI0XNJgS3XJFyFwLtexD4/pDV6X5kZuPPONfq9wiyLPZkQsutnGXIjKbd2uZ6ZCY03+rVgtaFIWhudyOl0bX2jNuJrMXPJvpwH2Hj7W242er/SsTL6omIOXsQ7/4L6+Pv2zPLkMCV5OV9xY4/OA0GgD9ib3Mrcj89WGiAaUjMeC9aheejXv0SygryGBoVM9FI9nO+j1KJ9g+WWXk7iRZ39+TFSIxZjFoul7ompZReJjQUTUsP2XYVAos/on9N+YWICbmdYReRhMdBTj2RkdCT3XSgxWE8mn5uStKNmH0joTFxaiVAuztkdhNOmRAmK9uJ1PW1hJd+D1oE3EnQQ0rtt/pWEumPn7a6T7RyVyE2UYBYmjP6EgQ8czHSfUiXMJbMtAivsXevQc5bdXa+knDmeYe9ZwPhxOPURkRmONHskx9B7w9aqAr6uTZJSQ25hyX0UJi1hF3taNQfPg4/hDR8M5H+5zEys13OQ/3jiUFcR1NhzxnIjkRKJzfNRmNkAQKCIKD6dWQ+8iKKFwHiC48QJgtjkBVrF7K1doXCOvuuR7zhPYgXepSU7QgUNyJN+5+IRDzNSPHhfg4/RTka2hAovcLqspvgkU8jSFWBgGAVER1qr13jvNcdLx9BAuxOBK2moDm3wepRgHx8JqG5eQkCr6PsGR7S81rCJ6UDCROTrdzXWp3KkD21m7bkEZGzFqA5O8zKf4VdMxXpd33O70K8oBmtS7XWpm4j7yFPZ1l/rCC08Z7XwkM7TrU2PN3q7Y6iv7A6P4kgpGfsvJwIV7qEUORcgNbTIuQKucielW3a1mX1y2XO1JsGA8Bd3fEm4P8hYPwztLrnoo8ljh9C1vQfs+P1KNdwB9ojeBC1+k12/eeQF0Ar2hsG7R/8DPXo39i5L6EUaWXE/kJKKZ0EdBpiUCuRr3JfNBVFexiJGNaLaEqdhpiVp0neaddfRKRnHobkZA8ReAbhwOKZwhYhZnKB3e9acRCwGUVkHUuGFXRnTSd3uKzIutYBuIMyB/Z7EPP/JmIVHiarx65xa7MGIrNZu/0us+v22nMbrewCK8eTYyyz95ti7fYUCs60DWk8krbPLxB2gk4jiW1VkPblHLTItBARO6Dv7IwtnHjg+wrgPxGYyWfg4Bt624F77PZkGcVobPrOSg9aGH+A+uga5F7UgYB4AxEubBmKi+80GbXxQM1QSgktZ0onD9UhwAUCZ88j3ueZGl0D/n9IG7sJ8UzPlPoBwuVtLqEDHIpAbB3iQcsQgFuAMgFPRyDvcSLU4acQ3Nlu5a+3j8fDd5DrPHwz4WvxKnv2UHuP/Qjgu2ndVMS/xhDxsy9GQHYbEmTfhObOP9gz8xCPbLB76qz+6xCAn4TAZpld7+ELN1s7bbX2w8oaZmU573VB+0YkQOchYcR3PZsQuB2H+POvCXB8HtrR8gykRfaupUTm5WrCXKTDnlGO+PoypKy5x8pqRLz+IWvrpdZ/cxPlXoh4xu2Iv3ufTLJ3PBvxwGqki76PoIKs7/5pMAAc1CsLgM+jOC4/pG8AnqTz7fuL9knSLfQOOfBHekc0WZwox2kjaRzylE5KOhsxkTloKvSlwTsDMa3piNn4Nr6nCl9K5mbVOYhJX0QwiSbErAoJTbRrEH9vZXeiBWAIoaFuQ0y0k3DsyUMAxp15hhGg2e3//BhCO5mMC+7RTkqRC4kvPg7W3TY43+peR9jp1SSuq0Kbb54e2G3M3cd7IxHVwB2S3BSigd4ph7PpIbQAuXPWVjKFpY32ewjhSHQy0E3EYn2oZifJ+8pRH/iYa7PjLQSImIgW0ANo7IIi2D5G2MAWEcDIyaPgJOlcBKb+TKa9/yvsGWuQXud4oJloHC+gbzvUlxu5kmBg6b5FrfYpRXzpt4Svxj/YNVNRGy+x3yVI2FuPwOBNaGz+HjmtF6Px1mDXz0FAtAKNo/8kYkrvIIDyMwhcbyTC39UicD0czY+3E8L7PDKdMcvseyqaIy32vQyZz6xBwr/zwbMQH65H/P9ZpKxYSeyM5hNmZFOR2948ZFryWQKEejQq58O+E/BRBO22Wvuclih3EmG65+9RZ23fZf0yD83105ACI8/e5Wbrg632fmdbG5YQfL/V2qASgWuHtcOJhEvjEQ/B3u+1SIiZYe31KBoXDYSwcQriCV1ICbQSjZ91aDckH62t99GbBsYXBwPAq63Wb0It+isiTVtKKaV0ROkZpPVbRf/b54sRo3oYaTgWo4VpItJQ1qLttllEavfTEAPz+NJNiPk0EkDXo5Ccg6a5x+h2e+0DVoZrUFyL2U4A8yRwhkxnlvzEfTsIbbED8Bq0oLnm2xm3l1eNFr+kk2de4tOZqFMeYevYjbZOlyPmPgaZiXQiu+2Z1kYOqvujH1nb96fBXtTPfycq/RbpPcrpvdAczAkz2wSlh0z7/YVo3O5DQKEZjd1CNA6eI6JAeNivvShe+hAC8Pjuz5vtfAsSmB4nQtAVoRCcTh5ObgKD14TnW723EjGkD5cmIHth0Fz90xEq90SmeuRo63030Og2B1AcBk9q9Wa0O/IYUlZchwSypKneB5E5RScC4K58eC0S7POJSEqj0djyXbphaH6MRgDvrQhI/hlpe09Du4etyDq32erjkGyH3ZuP+t0TzTyPwGQe0tBvRvPjCaTlrrKy77K6b0MmGpeieTIfaXa7kR51sT1jFeL1efYOH0frQS3hYF1B8PQKIqtxvj3jM1answinzAa71q/xaFjLEOguJRIReYK2yUghVINipl+GePZIO++uiK5AOh/N3QIr80UiUVAZkSVzJOGGWIKUV89bn5yBBCJ3Q2xDJksecM+zc3q+B1+jDs/tcDAAfBFS53yGzKCIKaWU0hGnRxnYNHvWPtl0AQKweYhBnW/HuxEjqieSJIwikiX4AuSA9Qoy07dDRL0oSvzuTnw8/J8zyCTgSgJy7PdkMoG/f9x7vpBMrXmenatBzNu96L2OnYhBVxDg29M719j3HGTLdw9aLKvQIvXxHG3pVIyAnzvvtCEg/3KjX6K2/RQCCdlRcQZKucxRZqHtZI+os8k+f0TA6QNIQOpA4/ZSBEhakE6oB0WTmE7ESx6LAM3pRKjJCno7vt6PtOvL6Bt855M72dLriBBtv0AC8eEG3Orq4/jlTOMRXwDxjcGEl3QNbiECq+6jshVpU89A4LQRAUnfOSkggKTH/ncgOglZxJYgfnDArluDzKTqEHi8hdCs/xsSrhqQHfYFaPw+i7SuPWjHzEPIfo/IXvwHZCbXhsaomwMWo7k41O75Fpova4nY22VIqCi3Z3gCoRLCn8IVJxcTChYPJVtk9SxB83M0mlt7kFD0ViQ4P0P4A00kFDunJZ7hfCOfzB0qB/f59pyPo7Vrvr3POOuDB4gwtc4L8tCY2G3ts9fKqiLCIBYiEH8d4jWTkdC/zer8OyIN/VoE/j1BkgcRmIOAewWR6K6UQxG8BwPAJ9I/R/km4o4ppZTSS06r0CLjtsjbkfanCzG/DkLzUEMwZRCD204kRPFon/5xpuzkmunCxPlsVuGgmqz/k8Det0wdjHt0gIKsexxk19p7+MLqQLCTCHHo5g3t9r7laHEBaf7d870bCSZ9UT7adhxC6CJezjSHaO/BhB7MHgNJ4Sz57UmgPPPeVJR/zTNibkX990nUtwvRoriE0GRXAnejsexpwDcjx1dPK3EeGucLkbnWYjSu6gm/Cawu70D9fxsRG8BpmD33TLSL8m9IuDscWo+0vFWcnDsph0IeK94dEgdKZchB0qNYXI76eSTq99GoD69Efbgbjbdm1A9lCIx1II10Exr/5YSW9FK7z3fehqHx0ox40Vx7lsfXz0cmEB7adRsay51oTF5L8L86u+Z8BHJ93pxhdbgQzY18K7sBmU88iZwq6+193azOw7w2Jspx3rvX2sPN8VYjoL0Uueu9DZldFFtdOpBD9ijEa9fbO/julgPtMsJ8CHtOJZqTru1vJ7TtbUh4qLLPYoK3N9mzu63cpI/RQmJ3t8PqU0km7xmP1p5h1gdnWXlfJ3bF3kWEzN2H+FAV2vk8z9r4KSR8jCK3KUr/NBgAfjBx/rxBPz2llFLqg4YQsU1bEAM+D2013oYY5gQEItag7c2JaHH6lV1zjd03HTHfSqTxcOCdRyS5SSbL6UTMxcNeOXUQcZuztdjFZJJr03OBsyTY8qgo1fTWiGazJ7dN9ygsrs3yax24txP25/n27k7JuOP+zM1Ii3MHfVMxWsSgN1CfT6SJPlLmBy8l1aEt1zUI8GTTPARGPVb8YGN/O2WDb9/VKEdjtQuN6SYUHWIIGo87ERAYZsfTiEg4DkR2IC3iArSTVEqYRu23z+lIiz8OCaw/JgBGHop845rBkUiABc2xbAD+W2uXHWSOuYloDi9FYKeawe2arDr4JS8rOsDg7PPPROP5WcIReiShdR2OgHwJ4ntT7LPbnvVlu+4DCMi54+D/orHYiXY/qpCW+/32PDdTcDvl7yJ+vhLxpr2IT61HOyetCKBX2PMuInj0FKtzAeJBw9FceAaNs2FEgjCfS59B2nl3LnQzr/UILDo4duWGCxEQGThdC91JJNO53NrxLKSdLkIA/ZrEM1oJO/GtVrZriX3Oe+4It9s+m0jONZLgBd0Ej3Gb/24krOy080vQ2piHALHvBnzbnnk+mp9uv96J5mkDET53LuIzHq7Wwzp6W12MNOb5yBzIfUdqCLOWaQyWBuuEmVJKKR0Teg2SzE9DDO5sxJzPIMCAa1QaELA+CzGZU9C2faX9904iU2UylXsy7F8yAU5SK50ESUmteC7AlQuIZWu+s6/1iCUeCzpJ+dk3EbbpLgS4c2gyX1clajNfqNwe3OviWvM2wlHnuygJQ1/Uiuwqp5GZcGEsiqIAatf+QPyJQq9GGqL5yNc+2wfhdjIX7EOlXBpwd74qRIJnC5mawXLg79FW8TjkHLULLcZjUX/uQ/3u5kmziYgNSSq1/4uI+OENxLgbSQDgjXY8HAnB2bTR6vUeBCx+ae/xVvt/LGrPRp0VOwAAIABJREFUfGRz+sBB2ialw6fxSKMN4g+3o354CDleNqA+XUuElHM7ZM/A6vSgldWGAKM74f0KgfGRyMbZ+dlYtCPj8a0vQXypHgl970XCbQuR0XEqMR9qEscefjUPKVPa0BifZO/gjpPJTMHV9izXKDugdtMQyIR/VQQPnULMgUoE8CsIExePcOXZL3ejuTWFEJqrrR6vSzxnKqFl7ybCl16QeF4h4ZBfZO13qr2Hg/pCxN9vQ7x8M5r/hfYeDoTfhez1q5D5yX5Cw1+AlC6daKfY7dxfY++4j3Cwd7NK53nn2vXVdv9jyJTlfgZLKQBPKaXjkvYR221uW1iOmGwTWjC6iQxi7iDktskei7qdsOvzCCfJaCK+yHiKYKdcoHk4ucMrJW23k9SX5jv5Xz6ZznwH06S6rXA34UVfl/X8QqTdLEDttRcx9Wa7x8MYtiU+ozk4PUlmHHDQAuoCwckSa9rfw3cnsmk3sXV9OJTsa49U00OAmEr7rkLA9h2oDy9FY/E25Pg2BIGEVVbGK6286WhhHEE42SYTJT+MBK/TEBi6F4ErBwYOtBuQkFWJQFoLuWkPiuV8JXK0S2qvPUEVhJbzeCJ3ADyZfBr2E4DPs0m6GdpKNO+fJ4DxdqQhrkZjfzwCcRuJUIClSBPq5h5u99+GwKgnwilFu5NXop2Y1Wg8bkLa7WForC4keEhyPmQrRPw/D1E4Amnuz7L/2wiflyI0ThvRXE3uVLlfjZtVJNvKNdSdZIaQdUf3WgLkdwL/iITgSQRQheDHWL2cNhDJzzYgUJyHAG/y3fcgHuDRTfYT65ybpjyJbON7EG9wZ1A37yklwpnmo10r9/1oJpwoe1CfDEW8bhgC2K1IiC5FffNnpGUvRIKW71BMRFGz/8Ch0JEE4IeyB5lSSinlpNvQtmkHYVoxDy0YTyOmdysC5uvQVmsF0rhtQ1rwhYhZrAQ+QWzDP0iE3n8D2lbM1kbmAtPZ2Q77At7J/7IjoCRtAJ2yHTMPRm5zDpkRUJLPcrvAVvSehYjpv4AWlPH2ny9Mk5AWayDRT5K0A8Vdr0V9czLQbxAg3UzmAuo0D4GKaYQt6+FSclcmORbKELC5Ce1UXIM0bbuQmYyP8dsRKHC7TrcDT0bdcSEsST8mMxIK9N4JuQCNj3OQLXYd0nrmoguJ+PALgTvtuY8ioF/L8ZfCfSqK0Qzaql/4EtblSNJ2NDerkdLiA2h8FCPwV4cA1FY05kYgYDcB9fMbka/DbMJOvAsJfBcgXnaPPacWCS8r7XlLiFCF7yTi2e9HAN6ji/wHArEeS9tNY1zwdb65wa6/w+pVisbjT5Cj/HcQj3dePNKeUYyA6vsQmPUQsT1kCtGesArCubMAzTMXHivR/HH+W2T3tdvHTQTXWlv1WLtPtP/cryPP2sDr6ppt14y72cpOZPc93trYnfWL0W7SKru+w57jQQZWIh7wNFoPXetdZ9cPsXfstLIeQ+Nhuz2zwz4Ost1p9PvWdvcgkF5GbhO9gdOhAPAKwuAoSV8/rJqklFJKCeqgt51pMpX2RqTpqyeSAUxFAPMixCyXISZ7DqFt2Iq0hj+x68cTKZWTEUicssF4Li12Lsr1X6640QMF3rmEgvKsMt02HGJhcG3lg4g5L0ELajeyoXwOaVYb6TtZzsFoq31OFuqk99hLUjtyMvwfIiLFoZL3f1IIcxDuNqHdaCv7YcI+1KNWbEYLuzvFdqKcb5ejxbsZjfFJCIQsQ1FKBkOr0ZhpRuNpZ9b/UxAYWYZAwWQEXJrINHvJ3j05Xqikj+OTgSYQichORZrOJqS42E/kBKhBAt4rEO84167rRH3uGt4utPviZhKz0K5JHuLJa9B4ewqZEI5BGt1TUdtOJKKLFCLh0oFhduIyJ3cWdz7tgHii1aUE2SXvIbTU0+z6SiQUuv9DcrcwGemng0wb7TY0t9eidaLA2qGWSGjmcz/f2m5yohx3wkyC/Ox4/EkljSdjc02+mwK9iOaxp4Z3O/MRSBjpQJFdbrF2+SnwBSJCjWdGXpeos6ezL7LnXUxo+Teivttlz3mt1W0JAt6edO52QpN/hT0rO9PxwWkwAPxctF9XiUbNHKT7f5/9/4NBPz2llFIaII1BANI1eBPQttoB+56HmOMExDDbkBZ8CloQkg4ojSh6RDXSFLQRoQaTlAv0us11fj/XJKk/0xIPDeggOtvuPLse2c8qJhPAuxbFt1jbiC3cKsSMz0eM/T7g58BHUPvsQJqo1f28S0qieSg+dbbZ0qFSsgwXBJNhJwvQ4jgOLZyevn6p/b+NzNCAW1Gkgo+gOeFaa5BmPFvL7pSPgPZeMsfBQqRVK0RAaE3iv6T2+A4k3C5F8/JECR34LCHMPv4S1+VI0gQinbtrqEsRuPKoSG6i0Y4A2lYk2K0jUo7vQIDW41hvQbzTnRjPtjIOoHF2APHdHiJedRfheOia4h6kzR5HmEI4eW6EPLt/tB1fS8yRNntuKRIg9hIAs5MIm+hOld1o/fBx2UKm1tsd8j0RGog3Juejh5ztIoQC96tpsTptSbzHs0S24LXIb8l9d5y6EuUUISHJhYGHiYRr8xDI9/wUHtbwjSgperG1cYe1x6bEu3QTWZz3Ew60nknT23mM1a8ICTf+7iOsDqB5Mh31yQKrC0R0rYHTYAD415DK7Xb7vQiJVimllNJRpUvQVGtF2dUOIAB0IWGiMteOG5GM7PaOyRimm+x4qpXZQ3i6J0P95QIofj5pm0jWtQ6CkuApeT9Z513DmX2dA+m+gFKSXJPhUVxcMGhFC24l0kp6gochaGvyDrRgDSFsIZMLR0p901i0AGX33WDJ/RAKiVCRWxCI8bTX7uewgrDZnYwAShXKurcQaarGIW3ZBgS+rrZ7diOnRzch6WtMXUhk2fwuEUccQvDdRSYV5zjeZ+/gjnjlCAjuoHeC56NJQxBwXE5vrX02nUzA28nBZgGy7d5DpIn/LOJ7M4h062UI1M1GoPE/kO0vaEx5SMx2BCYdMF5rZe1CgpsnhPJwr2uJJDcVCEp9AO1WNpK56+hjs41MYdT5muc8yEOA0FOyv2jv5gmrnkRmMs12PJeIje27TVvQGM1HPHKIlbvT3tXfzwVtT35WYe/lMc89bO12IgKVP8PNObwsEu/k5H3k5IC4GPHqpxBo/jphA96RKGM4Uqa4uc06JHC5c7WHmK1P1N3NbDrQ7ujFaN62JK5rJNa7YSh5U7mV79k9L0c7qY1EErCB02BNUDZk/T5RRPyU+qCvPHt4OZU+Muucg1+U0mGShz8rRczJt0G3I6ZRSyQvcNBdgBhhM7JxaydMNN5AOLh5zNq+7HiTYBh6A+sk5RO2i7mcNSETiPdl6tJF7kUpeY1rYyCY8T4ieoBnr/sTCivnW7MbkF3uVsKpZjdi3Idnz/fyoVVoq3YSmeDgUMjtPn3HwhNCQeZY2QX8H0rXvQcteDUISN2DAE4pitXsod3cjvVpBDAPBjIL+zjuj5YQDl9efgFy3itHgGw/AsLT0CK9boBlHy79BeIX85ED6suNdqB5PYTMZGUeu9ud9ED93YB2DcsRf/VoGNlOt48hA4BCtPvi/KbRflci/vIeBOYXIB8GB57FSBD0RGge8tUBMGSaW7ggkU9kgaxCY+kyBATHEaDZnZjd5vsiIg73cLQWQNhjQ2iE/Rktdv0LaJ5XIj7ZRYDqrWh8ueDcisb6WQT/93Zyfu1zehcBtDsIjbu3pYP1ViK063C7rxvx6pGEfbxHUpmHdjBq7RoPK+iacHfudt+VcuDvkInbU/Yu70Rr69Nk8idfL/KJiGJ+3iMtDY4GA8A3IDOUHjSC/pZ0xUoppWNA9yBGs5mY5N9DW281SGLfZsd7EBNbbdd2ori5qxFIuYRg1BDRAbIdLJ0Gqsn2/4rITclr2onkDNllJ7Xs2fbBvm2ZdNZLCgeeUAcr4xoE0KYQYbm+iHYCXo80V7VIgzEDbbf+miMPkEagheE5Ms0XTlRaguydPbvqoVLyXrdXrSAzHr2PkUXICWoIASaGEGZUfm4n0vbVoDlzL+r/gaRxvw8t9nsRoJiNwPzB0k1n23Yn4+KXIYFlFgITB9NEp3TkqAoBX7c9dhDuca09kYxf61E3hhJOf29EQP5xNI660Y7Cv6DxO4HILrkaCTtFCPS6w6WXW2HH9WjsehSNXDuGSWiW5Gtb0Rgahxyhr7ZrK+1diggtfScR5akYzYcvIoWE55ZwoDwWgdsitG6MRPz1ABFJqwXNhUqrw8PE2rMfjXVX9Dh/Tmqqkz42yTUoufbkE9k2k/eChMhv2PN+iXi3A3CPrlKM2rYQAWM3R6yzOrqpjpsvdiFYexoSoIbbdwNh756PxsI0e8bTKABCNdpx+2er3zsYLA0GgL8X7QGMQuqPu1EO1pRSSumo0k6k/UvSImR7eiaakmsRs3RP+3UImHSj2KY3o226M4m43+5BvgstOs6M3au8mt6OiX2ZhfRlv53rumxnzOx7k2A9GWmliNAGOcBxDWwy2gV2bQESOn6F3nspYWc5Dgkro9FiO8zKdPvPI0nXo0VhLnJezJXK/ESiCWhxS2rQDoWSdv0+LkB96PaZrah/qtEY/QISlO4kbHD3I1CwnjDD8p2bF+3ev0YL+630nWa+E4EKUPSEiVb214jxdSryuVhACFNVSLBtsvs7kJPzFKRVa0LgbB+54xccLboFgYaXq55sAtIQlyAQ7QDck4k5H3kRzc9dCKi5Wd7bkGbVtb2XIoB5BzJPKUZtfBfaBVmKeHEt4Zw3ktCUOhUQ9sVJx8tkyM/dRNxpNznJs3qOtf/OQ+Dad/8+hwSG++y5nq7+MQQo96G15FdW5tsSz+tE5lLDURQg16w3ovHdjebfk4TJi7dfSeI6j6vvlHz3pMGE++d4ezi5mY7z+xpkY+9hCzfbO52CFBtdSHniO8AjCMVME5rz5Yjv19h77kJmJ5cSIQXHoLVgNxHS9xHCZt3t6wsRP/k66ucXEW9pRKZwZzAYGgwA34GMYFJKKaWXnEoQQyxBDGkN0vJ59st8xGSHoFi0c5HWYB8RSso9zh20uk1uPmLEuezAIbdW3Jlef0C8P+DZn2Y9+dtDYbURkQPaEch4AoX+cqe9NiR83IkElCTdgxJirEbMdivS5izlyJMnhmnhxAffENvyrnE7HBMU35pOLsJJe34PK3g1Ah5vtPNbUd8fQH2/CI1ZDy15tl1zvz1jppU9FWmtDkYueJYQAl4+2qouRtrxf7XnXYrmFwgorEfzMbnb8VJEydlhn5crOdAtIzP2uocnzUMC0RAEfquQYO5ROHqIWPjjCZOmGxG4zkPCuoOzx9Du0DgkiP0OAeEaMqOuXIJ4dCXSpp5tddpLZFD1OeDA281Rqu3eEgRCS+2/KrRrswKBzbV27RZrg5H2HpPQroDbajvf3YkAuKeFd620R40ZauVOtuc12rkyO56G+GcnkSUSguflJY7zCSflfEIz7YJ40j77XQgge3jBMvtMQvzAwxYWE+vPJmvHJ+1dJqI43edbPdchH4+ViN8Ptbo3oTVjk7XPDiKWfCthtjIEuUMORz4ov7VzC5DZ0cBpMAD8S2g/94A9dQ7wIeRunlJKKR0TuhJp4R5AGu0WxBRWIyZyH9IIXExM7woibvJyBFbmEBnfPGRUPpkswYF18nc22HKP+GznTL8+ec4ZbLZ9uJ/3rG3ZlCynC2kdOpBG6xrEGCejxWYzWgB9S7QEMcemzCJZhgB7KWKu3yfs/Y40/czqd6xsf482bUebn59HwPhwQ9clNeHdRAgwTzzlkW3GIM1yIWrLB5HGazICG59DNqv1BIB2s61paHEdaB/8EsVlXkVmCuwdyA69A2kQv0WYlXTQO854Si8dtSEgWkqmINKGNJjXIpOGVyNw9iwCY84DvotA6kbEQ8fZf0+jMZaMjrEVmZs8gPjYPgL8j0U89zIE+sYR9tDO95LmViCYtYtwkJyO5kATERFlNLGb6KEIG5AWvhutAXsJ05JCZN88w57xgn37/TfY9x+JzMTthL32DgTk3aSnmQg56OA0H2mO3QTsNiSwFCKAP8vuTTrbu4YfIlJLJeqn2XZPM4pKMx/x/7XEulVAOHoWW5+UIrM/t0F/feL/0ag/T0G84ceoT7fY9RPseWsIx8otSLAutHum2vkzCZ7Vl99T3zQY1cUVqDevQSNyKsrnmlJKKR0TKiYS7pyLrMD+Fng/mpLlSDtyul1bjxjJHiIyynzELJsIMxS3c8sFnl0j4VqMXOYnnoihM8d/Scr2fs/+zzMh5gL+zrA77X12EFqpYnvvi9ACsQkteOvsvd9OhNhy6kKg+/dIkIHe4Hsu2oLuy659oNSKNGMnEzhbgRLKuNnT4VAy4k0P0l55mvgqtJjXEYvmmQjUPIw0Yb7t79lI/4DGRyUSFCYj29evorEzEGpC8ys7LOX/IqC2FI2NDyOB5IcIjGdHSUnppaOVyJzibjQmqhGQLUJwphbtkDXZZyeyLy5A43o5MjH5CRpjHqq10q4ptHuGIXOj3WiM/RdygE36Apxn981FpgoeotD5qpteOLkpYSsaax7+L0nFRHzvvXbNFKvXhWgOeZhZjzKUpEqC59fbpxIB1yZ79jNorpUgyLcWCRsbiJ3IA4QjcikyybkTge+HiLVnF2Fa5utPnv3XQThi11j7liEwXGF1u4wIyViE5vg2xFvdvOtFwvHSExE5H2mw+o1CpmxXo13Q+cBfoeAE61CfbkC7pHvs9+8QAN+KNN2vQIJFMdJB34dMewZHg9GA+yp0FYp2nnqTpJTSMSU3tZiOAGaSmhCz2okchl6JQPkOJLE7Az0FMbkCxDxc6wi9o4xAZBDsy9bXPcndoSVJA7EHz35eLrOTJPAvRVu27WQKDj32X7t9/wIx59lo4W0kMlUW2jXb7JOLJgDX2bFrhVLKpKfpvagfDvlWezdafH3bHUL4qkH9NxP4DFpky9DivsSufRz4KNKYl6O+fp7eQbz6IrcPzkXtKOvgxVaXKhT14YcDLPtQaTTS0q7g5MlUebSpA/m+gPrpB0iQ+z2a35VoZ+RUBFSrEZBtQ3P+0wiotyAw5v4nvhPXjXiqOwNfikyUCpEWttXKakF81+2HJxP8ag8aU27i4DSaUBpcS/DgmsQ17WhcT0S7eZfb+1WTuSO5Ca0ZPWjnb6YdbyfCICZ3N4ei+eQRT1zJUYKEgXGEedV4JHg0Evz4fUgJ1IMEjnq79jJCYEjyfl+HXBPvZh89hN11DwLidcSOlJshbkO+FuVot6CE0Pj7s7LNLUdYW5agnbwzUD9Nt3caj8D2U3b9BUi5A9pp8Hjws5BQfmi7m4MB4L9FIuEB1MLD6NubJaWUUjoq9HN6m0rkI61gNQKn6xCTWIE0hGvs92VEnNikxrGATJCbCxgfjJLRSw5GScfK7DJyXef/ZTNtZ9KuHXcP/J1oQXgALQBTkUCyzu5/D2K+d9J3dsL2Po5TCrqE3NlND5c8nGWu5B8H0EJZjRbJaiRoLkb9dCkCxC6Muc3s55GWbBlyaM4VMqwM+CYCZ7eQO7fcMLRo/xi9+yikEe8PtB8JuhqBhqlICD/Q/+UpZdEINF5KkFDufiIgoOsmJb9Bms2NCJwPJ7Jh7kFA7nYUNaMA8dbL7biZzB28qsTHI/t4CNkygg8770yaMCSdGUclrkuGKiy2dypFwNEzZHoyGqwecwiN8qUECG1IHGeDYn/mhYl6lKFxOAbxz/sT9yS197OI1PYe5QXCVM0FbL/3/2fvzcPkuqpz71/PLakltaTWaI3W6EGS8YRHwAPYEDAE7DA4jJdASAIkISRkuCHJ/UJuQhguAZIQEi7GgAPYGGzA2MYzeJBsC8mWZNmSNc9SS2qp1XN9f7x73b3rdJ3qquqqHqT9Pk89NZ357LP2u9d+11phKtMaNOu0jGzZUBU+v77F1Mxz21iBZj9r0GDD9PO2r+pgGzbQMPLeie6zyRVN8nYCzZxc4La1Ac061yNvdwu6j/cxGBRDwD+F5vFsruME3kUUERExZEhORZohOxt5BA8iA3oJ8qSYl6aH7IC3pKa7VCJl6+UK2sy1zWJIfb7vJ1EHaOkX96ApWMsR/Vto0HEInb9FzZsXaSHpBHwX8mqOx3tWhws1aAA1GRGE3fkXrzjqkNzi7fgsB+Ui4UYEDqEOzkh4H5oK34GXRbXgq/nVoHRwb3LH14c0rtvc96vcsR5w633a7W+G2/4et/2F7vdX05+ANwMfQt3mY8jzZYPf2xAx+BP6xxuUA/vcsR4hDgiLwZnovm1HXt3pSEbxZkSmzkBk1QIC9yM/4y58RcZut50wAPAh1OYOontS77a7HNmXF1C8jkkm1iA7bMVvjHRbwLxJ8AxhhiHzjGcQyR+f+K8OEd5j7pg7yH4ek/JC+9wY/BbuO8zFb/u22anZeHnjBfhMWaG8ri9YJ/TqW5XOseh+WOBykvzfi2z7i+haT0dE+WJkEyajAYcFrF4UnMvZ7vjr0XMYyh5P4sl1L16H344n51YR+Yj7/C237kLUrzSi5/zd+AFVi7su6ykWxRDw96T8fkvK7xERESVjCjI4m1GnYJ7tMKXYGKRFuxJfeteCdw6iafLLkFGyqX3THObKPZvmmS4UaYS5UII20HLJ/w8iI70PGcbLkIHuRTrGFmTsxyC98A58EQcr6TyV7Kj9EFsLOOahwHzkEQK1gzuG71AABSG+B58popwecCMmMxO/m5b2TLwOcx4+E8I7UUc6B923bahDNM9fH2oLzagjBVXP/CAiRf+EnrVViCjd6ZapQyRtN346HHzn34dkCi3udQ0+pqCc+BGaDj9ArH9XKGYiKdJY5LU2b3QjvqCKEbWJiJhdidrYGfjgwFq3jan42ZjzUdu6G6W2nIGI4xsQEbbBfi0ij88h6ckWNMNipHMe3kZ1kl0t0hAWqQnlgFX4jCFteDnJGHxmlqRNDyUs4J0mR9BgALx0w2afQpoYxgKFspPW4JzC9hk6Zew61uHTBSbjijLoeVqGnu0/Bf4KXb9zyJaWtCOy/Qy+0qjl769GUhsbTHSjZ3g+sg297hiScTk3IYI/Cdm4zXj5ivmdL0L1BXrRvfkgPitNcSiGgIcJDq3k2DNEAh4RUQG8BY2qL0SGYzYi5F9ExhU09flB5HWpQV7gXkTY34E6iUX4gDbzeKd5rMstJShmu4UE8oXb6UXXZDrqhB7Hn5/lh65HnegzKFDGOoYXkVawBk3PjnR99x7UQU7A69iHE1/Dz6JUqs2EU+O2H+usx6OOtD1Yzjr2E+ha3YWIThfw74iYL0K637GIPFztPi9Ane4LqMMP8S63rz3ovH+CusLzEOH+LxTgdxHyjD072BNPQYbCNewRwjwkLwDZSpONvCtYxkjbdCRdewlftGU6nlTOx5O/i/FZP5ahdmUVJi3l4Qlkhyeh+3YzImmvQwPI2fjsIfYMhUHs5sWF7FiI5mAZk9FUu/2Eum+ThFTjSXfoDSax72nB76H3vSbxuS74bPY0SaI7gu8H8CkGW5EnuQpdN9tuchb2LHcMM1H2kkl4LbmlGpwbHFsPuvZGzE0W14K/jq1ue1XuXBuQHbFtVLtzMy95r9vHx9w2P40cN+PJrslhA/wJZM8oFIZiCPhHE98n4v3zERERZYUV7OjAj9It20i4TAc+nWAdIgELkbGajQiHGbhcXu+RgmJIuum9zfvSjU9LZbl6d6DO6QEU8R7qCbfgq6GNhiIl7UibXMvIkB9U2gOb1P6HHbRNHds0eCdq86uQ9rsPxT7choj1IXTv/z80qLVKeDNR25iEru8mNGC9Dnm9zIttpcpb3PtqJHOahjxo01HQ59vw2TMGQiN+YJ3MsmJY4Lb9DCPjno9GbEH6/HFkD9Ym4CtTHkbPlZU4/zGym0+7/60cfWg/rL5CNZotuRbd+z9G7bUT3TurIGmkzmQcpj03+2UIZSChHjwkt0k7aQQySeXC7fbQvxhQch/h8vlSsZqE0bJnGUIyb/nFM2ST+RnB53xk1bTx1Yh4T0UDj93omc8ge2/VPafhZURWfM7SG1q5+2n42Y9m93sN6h/DAkgvoftzHHnQZ7jfX4tkSTaLbOhG195sUXEohoAn0Y4sUURERNlxO/Le7ETGYCm+CpjhAeS9+RAiFy3IyJiRzCADEWoIrSxxiDBQZSQj9NyvxefYfQrl7L0YeTp3I+J1D+r8ksVIjqLqhqMJNugYCXgfCkJsoXIDujA9G3gv20nk4T6MSFAfIjsPo2dlMSJFF6J8xC1IbrQZTWNfjzzdaxHJakZt6L1oQFuPPHATUee7Az1Llg0B1N5m4PPOQ/97U42XyySnud+DpA57UHBostT9JFRQyvJM30lEKdiPBt8TUdt4u/v8I/zsRxfy0k5HJPytiPTNRDNjXchm3odmGq1K8FjUJkMnRwNermC552vd92eRNGIH8qaDD5g0pBHwruA/06pDtrc3SWgtPWIvIpXL8TYkrCJsCKlgUkISDog3u/M64K7BRHy2EsMU916FrqshXCbfoCLsv6bjA1QPo4FpH9LpX+SWtRoYVe5c56Nr/it0P+06W+VnkxHZNd7mttuNZGuWlebM4DgnoPtX5/ax1h3br9zyL5MdiFoYis2CYkdTg6xUJcRuERERdJMd/Pc8euA/iR7DnyNycTkinCfwAT3jUIezCxGREOa9CNP3jVSvuCHsBOyYzXO5Fl2nF1BQ0FH8+S9GHdPb0IAmojzYhGYOrizzdq17Oe7ezasWEvFGdN9b8MGXPahzb0LPQR0+EPIGNCizkuK7EIk3r1eD24YR4g5EsBsRYatC5CmU/mxm4AHc25HUoQfl6d8V/HcFIm52DqcarLjVcKMeDbgmIdnAXETUTqDBU7X7fjc+jeXvu+VmIE9uFSJfF+J142fgifMYfOZ7zMbJAAAgAElEQVSMdkTOJrr3i906LajNWFrCw277lloT8ttgKzhj3vW64Hcj00lbvskd9wm859kytTThM0cZkmTc2uVJfOGdDH7QPR4/6DQ9tmnVG8keJBgshSPuuNIKeLXjZ/vC45ofHOvNSPoDiuU46bY9BwV4Wh/Q6Y7nEF5WshU9f1Y59+vAnyEi/n1UWn6/O8bX4nXti9w+3oBqS4xzy7cjb3laQH86iiHg/xx87nFHu7PoPUZERJSIJYhoTAP+Al9QZwfyAL4BGRyrBmhG15CcqhwN5Btk/A4hozgHkadO5Dmcjjq6Scgktbr/VuO9HueiQgpWTnmggkER+XEWPrdvJWBaTPCdcNhZj8enhJvg/n81vrP+Pmoji1EnOgeRqV3I4/1SsK07kIe8GbWx9W79Kfjno5T2chae6JxHNgF/BnX828hdGKgVxSzMINvzPtJRjYjJbOBBlAZ0OGFBuiB50WT3/XFERK264wpkW41Ymtb5l6jtWOCryTjM64vbzj50rx9025+GDwA23fjZbtsL0ADPAiUt8NFIntnrMPjR8mTjfjMveCvyclvFSpO0GFE2fXIojQgJrc34gE+TaMsY0T/o3usR0ZyIrw1hqEL29iqy5Ry477bsCbyGPfT8J7NxmQSlFj2rS9x29uMHrePwNqEDf+2eBj7g/jsXL+U8iKSZNiCymY1OlFqxDd0L60ObUMyAnZ+lU6xC1/wMd5wXIe/3GHyF0cJRDAF/GPV2Foz5Yp5lIyIiyo7n0FTiZajzmIyM8FhkkJ5HhrAeef7Gk7+K42jwgJv32/K0mravA5FpK9jyblSFztJyrUTTyHXI+/UOvHfkGxU+5mmIwG3k1PRwvhZNQ5e73YRe7jrUEVbjuynztFnhnTF4cjzBrbMbdfLvQ53kHkTmO5D04AC+UwZ16reg9jMVH0R5CKWhnEr/oleF4BZE7E7SPzDz66hD35xcKcDL+GInhcAqER4baMEKogmRb9AAZLgJ+E5ErFqQk+J9qJ3MRNd+FnpGlyBbuQhf2bUPkU3LMrIDn3XjJUTOLAvJdLfM2YjodaM2ZvbVPOcWID4Hn74vDDIO6VjoOLF6B1X4mc4qZPdbUXvfhQYcBktVGGYqyuC92VVk9w2h7MQ887WoPVUje7bV7cMGIvbsGeHvdL8lUxra/sPzCwfUSTtiAw4LjLRrFHrmrXKmySx34/PyW2CqFTyyGd8W/AzG0+g6trr1Z+O9/W9C9/UIfmDQhp+Jawu22Y4GdxPxM3eFoxgC/lvAZ1ECzCoUFfRJskNCIyIiKoZWlH95Okox2IGXooQR5m9FRrmZbJIdGntyfB5pCA3vGGQcOxExuRt5Ev8QGdC97v0ut9wXEcl5yS37KbetUJNYCTSjssa1yNN2b4X3NxxYQWXaTTgYtBRx5h0zEmJT/h1I+7kQ783qQITjGMqyMAd5kTfh9a8h+TZ0o2wmSWx3r1LQiCfes8nO3X4cSafKhWZUXKoBefSHK2/9MeTdXcjwk2/QPRiH91qejQjdAZTSboJbbgc65i1kF/eagCdVR9A97EKxN+PcttqCfVyKrv00ZHsMGbxu/Cjyklv7PuqO0/ZpCJ+verKJp5HCRrz+fE7we5Vbpzr4bAg9zY0pn6vwWV7m4qthjsOnQawJ1rGZg7H4ypQh0U/GcVSRHePRRTYhD4/RBhsW6Gge/n14Cco25O02OmtSl73uuoxHA6Owiulsd45T3avaHf/1+NzwO5C9yKB2EOZHx32vdts+hvrg4lAMAf9L5P222s1TUZRCJOAREUOGPmR89iOj/jIyMBegQKGT+I4nuV4yJ+xIhx1zGOnfjQiHVbh8FpmhrahT3YAv69yHDGMT8EPkqXi+wsdsOYbBd0KnGj6PZmHCFGXlQDL7icUrZIL3WnSN+/Ca7/V4uUkXPrd7tVvmXESubBr5ZnzBqnWUNki6HBG0B8mtdw71tYVkjanF5yD+IcUVW5qBiMoKRCw+hXTGw4GfDdN+c2Eukhc0IKK8BXm9d+GlS7PRNR+D2tBRfNu7Fen1t6H7cxTd113I1hoZtYBZ8PrwhW69qcjLPg/vdQ5TeIapA0PkShGblBSGz1+SyjUH6ySL6Rj6Ep/DbFlhxhj7bQ79n0/D7OC3UJ4WEvuwam5fcMzJWdrwOLaj/s1mIgznBPu/HE/Op5F9rSxX+SvR4PBcFDj5avwgIgySrkNEvBf1JXPc54eRzatGfe5id/xtwL+i/ugO4HcpBsUQcCsTZTjE6EidEBFxCqEKdbY9eInDW5CRmoI8vm1kG6KwMtloemSr8OnCjHyZZ2chIlw70Dm/FxnP1e73NtT5vYy8Fy+izvQ30fV7uELHvBelMpsOPFqhfQw3diFysYTsTngwSOpADebxSlZwtTSE9agD3oLI9y63zoPIX3Q9agddSDZzJuosrSLiAuAJt933ue3diu5jGhah1HOgZ+vnOZYxLXkthXm756PrCSILPyxgHcOLyEPbgYj3WUi/fLqjGT8gtmqKx8ku0w5eJtGLSPZEt8wxdB8sh/QTbplmVJm2Bl+Axjy65nHdijLcvA34MirW04cP+rX9h89PN7mfp/C3kNAmUwGGMKJrz0qubYWkP1/qwXBWKsxQEv5vWu/kwKE25XO+LCjhjK15lXvJ9rjXBeuNw3v7l+BTH56NtylNiL62osF5KJ95DtmKdrLjPaqRnevD5xDPoEGWBd52IDtURylxMcUQ8HuQpfmu+/524KdF7zEiImIQWIYMzHGkY3wZX1IXZEQO4qfnRrrGOxfCY67DlxuvRsS6Dk/MDiAjuAIR6+OIfOxxr7vcdkJ96jIqR8ChcsVYRgpOoDY2l1JSbxUO6/B6UCd4HHmnTrjvE9z/+9FzMA+1hamoQ+1BxLQOdbzbgE+gjtkKdxxBxazW4gPiziGbgE9G7a4bBWldjAjdXnIHURrC0tQT0EC5E5G6ZNrC3fhiSxvzbDMXeoH/wJcgz7f+VcgreD+aSTuV8QKa8RqLvJ/vQu3VCFQDuuY7UJvYC/wtaiPPoswnH3XLfALZVMugYgF3j+K9x0fRta1GM5IzkDzwLxEhP9eta/KU0DkC+Z0jZg/rE7/lknSYzKIm+G4ISXv4exoVzNd32LGH8hkLWm5ILJNEuEyyj+rCz3KtxM9khds6jn/+X8Y7nOwe2Dl142VH17l9viWxPyPQY/He8AwagI3Dz6QasbeKuKbpX+nWuTTlXNNRDAH/JBrOXe6O4msUN0yPiIgYFN6GOv/JyEi9G3nwjiHSsQgZn3qyp/sMoyUg0DoWM4Km6zXCMwkZ5JfQeS9AE3JWqMhy8W5E0683Ic/4E+gaPYw86K9E3o/B6HHrUICnSQdK1QyPJrwb3w2UC6HHLrndPnzGkx4kszKZSS0KeOxEZGcJaivHgDVIr1uDCNiFwfaO4gO3XuXW24YvG254NYq3qEO+p4vwVSlvI72QThIXo3YKIvjJQVo78CU8aSgWh1DcQz7MQOcKOofvlLCf0YS9SC41CxVLuhkf+GgEsAsFttchm3AYn9/9d9CsSS/wJ4i4Wy5pkzz0oWvfgII+z8Hrrmej+2mVMhtRG+gku0x8MQiDKsN1zSEB3psP/W1+KTNWoewk12fIzvGdpjkP09+mDSTAS1KqEdGehM+/PpP+hN8yfpme3LZjjqlaNHCajSfztnwdmtGwYG/LwGT50u3YZgTn0oxPfnDAHd8yNHNWHIotxHM7MaFuRMQwoB55UNqRR2YuIhQNiIxYzuRm0g37aPCEh4bV8vRuRUZzFpqEW4j3VnYj4rQKX5TnMnz6uXe6bc4FPoeXC/wRInQLEeHKNwWbD3PxHq0LOT0I+EeonJQp16Cxzr3Me9eAOkkLamtCs0ET3Ws8eg6+TXYGk9VIp3kj0n6vRdksVrjXJqThDD3D81CnvxQ9g7tQ29tAYfe6AaVFW+SOfS/p2Xsto0OlcBQRmqY8x3AqYT4a1IxDmY92ofNvwwcZNpOtw/4HpO+9BJGsetTu2tBz3oNmf4ycLUI2qobsku+9KEjcZttudv/VkO2BDp+jNHKcViitnew4k5AchwQ8tG35pB+FwNIjJol9dcrnEMmy9iGSgwn7rSH47SCaebKAaiPHYbXnSfjZgG582sRZwT7C4zNJpw0MrGhPLbp3V7t9PEp20OU+fPaZZtSHFD8bWIwVfSsSmx1F7oU2hjfnUUTEaYQuZNA70WN4GSIgNchYhNOBo4FopyGXZ2c+MqQNyNjei4j0PyF95QH32/9Bni7zaL+ElxIcSOzHKhjup3TyDb7qZi+VD/AcKQhrslUa4TR9Bj9F3IxvE1Pcy8h2PRqUWhU8QweSJz2Lnqc7EEGbgtdwLkvs/wHkFd2OBnW/QER9BYo7GAhW8MMyhHyR/m0xibOBj6MAwnLiJPAVFDSWK0vJFNKLo4SoI7us+EjFpYg0z0TxH/vRMa/Ht6lustvXHESkJuKlT33I+w3Z2XnMM1qP7PB5wb6r3X8TEPmz8uwdZKfVTKb/I/gvF4woQnYu7STMI52keGn7yPc8h/+Fg5VC8uOHy5RSyXciPljVJCeQTXbtHmWQvbdl2vDXK1863nDmwOJF+tDg3AZMTWjAY2lFxyHifgDNalxAKUH3xXjA/wklSNxQ9F4iIiLKgLvcaxGabgdPWM2wd5OdAgpGNinPFe1vv4ept7pQB/pTlAk1xOUoKK4T+CryVvwKXzxjFpIMhPge6pj3MzicRIMAm44+HfAEKrhSiTaVbKvWvi39mmVEMPmAkee9SAuaQTrn8SgX/mfILkSyGF/kZxa6Zw8gOdIW+lez2wn8NZJumDzl+mB988KlYTuawZmC2mQhFSKvRAOMC1FbLqefy1I4JnEZyu9+HD1DJ3MsAzrf/4E8kb8G7izjsZUb29x7LfJmW9nyWfhy5ObRtgqZRxDp60Ia8pnoemVQm8qgAZnliN6Ez3Biub+tgM2ZiJyfh8+RbfpmyM4kkkxDmOZRtuVyPXsmm0h6zNPyi4fbSCPjyX2F3unw9078gCAMmB4sOvF5x82zHQZs20DguNun1Q6owpN3SCfgycxLm9F9O4kkbDa7+bL7XIsGNwvQtb4aOXq2UspzWgwB30fx5PuVqGZvL7Jsf4S05G9GT8f7kGW9GdWAPYwiJY6hM/t71PrfjSzhuSgRchWaB12LnqZbUav+axRdEjFE+Ny6x0te9xPLiw9aiJiEtOB1ZOvUqlGnMInsKPKkoRxpsOlSkxeYx7MdealrkIf5buQJfysyBWE1Q8vt3YCIyzG8Z8nyhidh6cTKhdOFfIN0rpXygKe1VdPs2rR0h/utB93vfe61EQ2uFqOO8v2oW3nM/W8V/55DnW0vGtQ+grqOXMU0ehBJN9yFNN1rGfg6dKOCPsXgeeQ93U52irRKwipGNiEymvZs1COPPvig5pGKTjRhb1rda9Gx347uSy1ej20a4NXo2u9DMqVLUfv6NZqZ6EH2yKpOPocI9jiUkfkDeBtmsTgN+PSDYRYT8MQwKUcJgyV7g//CypJVwXJWMTbUQhtxDRF+T8uikgzatO1acZ7kdmw5Qw9e4x1ufyAvtO3Xjr0PZTO6GvVtR1CbS2ZMakDXxO5h6Lix7VYHx2+BmeH64CVF1W57+/HyxF34+24pUC3DTh1qG8UH3xdCwN/q3lejXDp3ku1SuCPPutvQ1etAgrwrkXviCuDPUDjqnUh49yrELD6MCv78T+B16Mz+HBH0/4VEnX1omP5mlPT0r5A1vJtIwCNOKcxC093PIeK5HHnTtqOOYz4+PZRlBwE9IqbXG+mpB8173Io3wscQEbEKbGPx3ohXkE3ALdDuIKeHBnu4cQvwHipbjh6yO1IbnPUiQn0PIsGm77wGLxVYgwZl+1EXMwYFyv4L8iqbPtcGaQ8WcUzLULezm+IL3jSQLXlIw5OIAG5l6KQ+DyLStJf8A9NOlOt7NKQ6bMGTq0Xo2Pe63w8gMr4N2ZVeRNBWIjs6DQVvT0FOjTCrx3h8IN9FeMJrlRct8M8C4cfgSbTljjY5hQ0Akkh6p837e4xsEn8APYfbyc6uEkpb2vD69O34YOCwHSZJtxH5kBgnyXrozQ//S8t7n88RFP7Xjn9W7kGD6U34Altd6D6a/TmJT0s4PtjecfxMcQ96Zme7bS3Fk/Aj6D5n3LbMIXQQzWr0oEHbNehePYForaUh7EYDtKl5zi83CiHgbwo+tyNSbMiQn4CHuZx6EJN4yH2/H3m716OhZo/77WvoLE+ilvMk8L/dOpPxc8nWolYgwZwJBMczdG6DiIgK4x2oSa9AKrBNyDA8gkbkf4gvwx0aXouKH8neb0MGn++7Bhm0A6hznIdI04v4iPM5+DLCIAP6/WB71Wiy7HTSZQ8l2imsuEyxMKIRBqmZttM6xT40EJ3ujmMW6jwnoE779Yigm9Risvt/Oj49GXht6mK8NCEJq4QYEpULEOk4E3lKjaw2ITK2HV9i3sqb27T2DajNWsrANPw2IrgdSEJTiGxlIIxH3tydZKdHNOxDA6tCsMq9Rjp2okFMHaIR16N2sBHdd9As2yK8o2ICan+WZ9483VcigpVBM0Dm4W1GdssKRNnyaZUdqxARHEO2JjopQTHnCfjCYtWo/UwL1rF9j8cHCdpzYghlLhOD/Zi8w/aXKz1hX/A92ZeMJTc5D7XpSflMIWh27w2oAPs09Hwddb/V46+dDWJs2xZ0CZIPWcDti2jgUYVI+D70/J5EcR1vQv3I4/i+owGfNecmZAuOo0FAnTuOTjSYX0QpA9JCCPj7C9zWn6MQ4lxYgYYiR/CW2/KJ2Zxx+NsksgU1dndzCaPCrPC2fpKAf8i9aGlpISJi9KAdGdd2930vmiDKoA7/d5EhNEJh3kILFhoNBLwXH3luj/IdSO89A3m8z0RG9CV0Pa4hvQjvu9BUcAcKejsVy8EPJy7Cd5LlhpHt6uB7OD1fhVIGLkYD0Co0SGtHg9N9qAOdjDrFHkTIn0ca561oIncrmoj9Y7fM35Ht0b4OZcLYicrUW7t81u1vD9nZUm5wx9SH2lwbUk5Odfu3AMDJiFDkm6mZido8aFL4NiSfqEUVGEsJZvsN5PXLoJmA4stmjw7UIqdFPbpud6EB+8/RtX8OtZsz3fd2NECz9HSb0f0x0mue4Gn41IFW9RE0qLPlluCL+oT3KOlpDnNIh+Q2zVbX4L21c4Pfq/Ce+GnBbyahMIQSCztWSKd/SS16LkeOnUd4jIZkFdhcxL4QZJC8x2SVVjIe9GzbNQu93u3B+pbGsBafu93u8xZ034+iAVWd288V+KDaOShLVh+a9bE0hhvxWvvZ+FoTxaPYNIT5cBO5CfhkFKX0W8h9YOHEE/DCngmJ31qD38Df0WTtVMh2xdj6SXzNvTh48OBoSYYccdqjGo2wLb3dz/DFRtYhT5Z54FqQEQkLMIwGmOcnrNa5D3WSzyHidAXqRKwADOQvInId8na04acgDUsREdtCrCNWKm4iv56zVFinbiba2nKD+3wS72kEP8VfizyB4xBJasDrbrtROzjkXhk8+Twfn87ybOQ9ezXqsq5HnfkDbn8WlLgRebAPkd0dWfrAXnyXZMfZgAIwpyAinpYC8A2IGK5DPqv9qKNfiQYDIL9UKRVWjZj0UNlUh8ON6/E67Jmo8u1YdC1nogH9ZkTmahHBCklwM16rbdcsTA9Zhc9HDbLPtv44ZJcmo4w3c/DaYyPcGXx59KSNTgucbEj5HGZESdtWktjXp3xOC/rMF0yZNsMaHvtg5I9V6Pm0c0hW0jQ7YdnAwD/PoHs23R3D+cE6GeTFbkTtYbzbdo37bg6sC/EJDV6BZi4a0UD/KBrM5IovKhzlJOBpd+JWFHi5F81b/R6aS78WiWk2IZdFTfBbOzq7JmQZbc7sMBpyWCUFkPb7Uvc+gZgaMeKUwUzU+TYhvesMvMdjNtKimrcjTLc02saYPXi5gQXWnYXIcg3yetcgMv5vSAV3FjIpL+bY3l7UybajsJAQr8KnrfsVucfrabgUeWSeRNOOpyuWV3j7pj01Mm5t4zh+6rcadbxd+LLQ9vuLaKDajNd7fw54IxqwWuabexHR7UbKyHe57V+Fn3na5rZbj7qcd+JlJWHhjR+hQd0uPHF7AklinkDKya/mOedmNLMAInV/hsj4M2Tn9j+Yc+2B8RNEFvaRO9D0VEGYUtBmG2rQfW5EtOIM/ODNvNoGK7BjBVmsmEs4m2g56UH3w1IybsOnJZyGHAZjEWGfEhyfEd9CMyflI7EhCQ6D7tO2W5XyObmPgTzzhcobB+sISttPOICxe5lczkg16BkKiXw4eK+m/7WwlJEG0+rbvd+K7vXg+oFyEvBcvf5NyKr8o/v+50i8+hhy6X0RWb//QMP6VmQFQRlQ7kNXwRKufhrNK1WhoEwQmb8FPVmfLtvZREQMO/Yj8rkIr3+egJ9KbKD/VD0M3ugNNWoRKTCiMx0Fl74dmQcj2p9FRtDKQL8eDVJ2kx2U+SNkXp6k/3h8A/JA7c7x30C4Fl3vazm9CfhtyJRXEsm2XI0PurLO17IhbEWe6TZEMDPAt1B6yh7g/6KUci8k9rERhQ/1om5mNfKAP4AGeE8iXefH3PK34CUAc8lGF/B04rcr3f6vQF1ePhxDbXIWaqPb8Gn0jqLc3VbIpxT0Is/6qY6fI090HfLt3Yjsyy5EgusRsU4GOdp7d/B5E5pB6yabjIVVhk1uEhK9auQ4eQQNBJ9CtCZfCsEk0pwoYYrBXOQ717rJ7CrVKcvlO5ZcEpRKO3qS269N+RyqkEvxuHfiS9034u/TMbxqeiN+lrkF0dpaRHG/VcI+hUp7wL/rXiEexxNyw7fofxb30z+jyVpkzULsRCGpERGnGLqReutf0fR0LyKfGSSx6MFLAU7gtXAjHWGaRMjWOIKM4HRkng4gj+IP0eCjHulvZyJDeJXblmk7H0LkeznydCU7iscQ0eqk+A7kJaTzzOV1P51gnruhyq4TZpUIf+tBxGgVKsK0FPllTiCfzj2ICG3Ms+0TwefH6E+UL8Of5yw0o3IBGoBdicrUbyB3PMIBJEMYqPAO6Hr+J2rfuYIuC9lGhGykBWRfimYlqtA1vRI5Maz4lmWPyuBJnJU+70ED+cloYLQosZ/Q+2qSBXs3z/oaZIOeQATcPLCF5MgejBMljJmA7Ge1i/RUguU6ljSSX6y9TXryw9gQc9iA+gPL+W12vQqfutSOKbzuyVS9hj3onmfwFU9BfdJ5+IHdeHcs84o8p2yUk4B/f+BFIiIiikcvIpuzUOdglbrMYxMaptEEM4A9yDtnKb16Uee1D3lbDyDP0h+g87wbGcpXIoI1Gx+Rfggp2qa511P0T6tWalaJ7yJPyak8hV8I5lD5WZZkZ53cXzdqA5tRh3kjyhfQiNrNxfhAxrMQ6ZqO4igs1+/P8IU7bkQe8x2oKzPv8zN4va9VojVP8sfduuegQKwwOy/Ip2SzLQPhNcj7vgHlMY8YPDYgsjQF3c8piPJcSO4cDpYm0AjsR1Bg7TnIG2ptcHrweQaeJDaSTcz/yP32GbLjFoolosmgSEPaM2g21ZwaIelOi90I+5BKDa5LsRnh7ER4HUL9+tRg283BZ/NmG5FPBoraOXbiM8c0BfuahJw/GTR4MwfX1fhZuCkMBsUQ8KnA76C54XC9D7j3zwzqSCIiIgJcj6a37kShD+PxU6hmRO3dvDCjRXqS9Er3olRQi5CXez/q2FpRJol/Q2R6DDr/j6AO9ftoAqwZkacMmjJ8wW3rMKVrZtNwupNvGJoMGtZGjqG2H7btPiQjWI882LPwWULs+bgGea8PIRnJeNS2PoBPknXA/XcmSkO2GHksuxDZ3oxI/u0px/gEmoHpQUFdv078340n8gPBypifRXaatYjSYSXox+LTTJq2O80DGmrALYgymVbwOD4474Rbx7zeBvueJGlJ8p3mIQ4/57ProVQrPJeQbJ7Ea92tXgT0L55jv4f7Dgvx5EMp0pZCEGZQCY8hHOw0J343hIOI8LzD7YJPSWiZxGy93agNmEPoArxn3ZaZhOzGAuTsKQ7FEPAfIZ32/VQmCWxERASgse5n0MN9JSIAS5AxGIvXKZphHC3EOwnrNGoQ8diH7+zOR6TmeXT+0/Bj/xfdOrNQAZEG1MG2I43sdkTOzLMRUV68l6Frc8kgOSMR3WimYxwamFkefCOub0ZEdipKHXguynjxEiLWx1F76kRp62aj9rQfdaaWkuyzpHd3T+LJ/lvQzEpSZ54LY1D8QieSyfSigOBXo/YeyXd5cD6eUJ+HzwBjs4e5PKNpGUDC36fg2/9MchPBtGDHpPMh33LFIi0GKCSeaakD08hpoZ7wtCwq5bQTXfgkA4UOVux7A7lRhfpWS2YQyjjD2Y3jaDBfixw7hmpkP6rJrjNQGIoh4GNRaHZERERF0YWfzu5EHXQ44u9DZHU6PhBzNJHwZMDQSWT45qDzsWpxjciDWIevhlaLJzmrkffyXeg6fT3Yx0kiKoUOvAewkrAsBWFb6cHroSeTrfE8jtpBHWpP9YhwnYkkXAcRubgbEbJFKKvJcUTkt7iXZcYpxJPXm/I5Hy5xrxbkQX0Iec/yedBaUDDmqZxCsNxYh+xIE5qdmI+XKFkRGquCaNjhlrNy6IZCiHkyRiEte8hQI5RrhA6JSmnAK9UX2XkUEwBq9yEsbJT83woHhdu1AO8WfJrcl9xvu5CMqQrZDqsIGuYgLwzFCH3uRpFgERERFcVRJL34EvBWNPk0EREf8wAeQvIL+220IOm5aEMd2kpEmupQ59iOAuKeRh7vBxHxehClf7vVrbvIrT+W/pkpIiqDv6nw9vvwwXHJzrwPyVLM433SvXYjb/LdKItJK97buQCRKQuYuh+fxfYEIt0vI9lJK9Jz34u80h9FuaTJcSwgGcqdKFbhpSyKR3sAACAASURBVBz/58Je1N7n0T+nQC5ch4JLP0RhAXwRgl2rLjRwuw8Fa9+G2oYN/kNsQvanFcmPMonlLFDTkJytaHP7CgdTaSXf82EwwYvJfRrxzCX9y7V8vmMZzDENFoV45tOuW/L80q7DNnw63E3ofrahgdp85C1/Bh+HtQ4N2prc8sWhGA/4x4G/QK2zG383J+RbKSLiVMXn1j0+qPU/sfzSPP++BHzBfb4WPXZh7tpzEXkYTcGX1plZntVeZOja8TmdqxEZ+gPU0e1x/z9Pdll5Mz+rEfE+Sf5sFxHlw25038oZw5+EBY4lvYrdZFcfPIxKt78FxQH0oY7wdlT7rQdf6XATaks9qBrm9Yisv9b9dxAVZ7IsNx9FMoZFiMSPRdl4wnaYQd7VSYggFOIF34AGl2PJns4OMQ91rc/hB5YtyFNv6TPrUfBmB1KHjqaBeCVhs4KLkYygFg14DiOZyBREmKqRtM28oxmUPckkTZbysQ85PCa679tQNiobAIal160cfVgALFfKQ0NawGOhcpRCPO3hs5rWX5i8wzTO5m1OSnTCYwq9xSGGIkNSeM7deAlNeK6hPDMZfNqBnr9elMbUqubOws8oL0XtI4NsxAy37vuQvRiH+unFqK96ZdFnUYwFHS05ziJGCSpLYE8VmBzjeWQcrMpfPb6IwGgg4aFuzzqNLkR4bkca8A+jgcZdyOs3GRHwL5DtdXoVIh6bkDcrlJ4MB5rcq9QczaMNY6istr7a7cPaS9jZjkWdZxhs9g40JbzAfT+CL/f+imAbnSiTTS0i169B08kn8IOK3cgrvR1JVxah568WeUWXk03AwZet34vSHxZybb6CvGkbgt9a3HEfw+vsJyFv/FVIux7mrr8MpdoDEcTkcZ2OmI4CbauRfbEBkRUzq0HZcsJsJWHVyDAI04IrQ4mfORAs6D2UBIWBfmemHF/SCz0Y250kumFgaViQKFwmSVYNodTLMsPkk3okdddhYGIpSBtIhDCpB2Q/Y4XsN7ntMLhzXOKzEfcl7nsGPZe2zC4Un2SD4W1owFx8nv1iXRgW8hmGmj5S9F4jIiIKxMeBGxBBHYf37pjBGg3k2xB6vu3zfPd9L5rKX4lI1iJ85oqkmToP76FopPS0guXARJSVpQFJIJ4cxmMZKnyGbF1ppZCrQ7YMKKYPH4MqXFrxlRrULoyEG1nqQt7kjW7ZFrfuQiRb2Y68yG9GXVyT247JWKajDCnfzHFMRrZmuG2eyLFMiHp3TE8EvzUhiUkdkpaFwWPbUDGhJIyMm5QriXPQoP1xTp/sPXPJzg/di671FuS1Ts5SJO2nzayEtrUKBena74vx9ycsfR56ikPvaz4POAX+lwvJAPxwf2nZQ8J9dKX8Hq4TkvHkvtMGEuXsk0IPfJhlpQN/nzvxlDQpO0mTbNm6NfgsSyZlNFh6QxuY2bbNAdaHZsj+HrW74gfAxRDwDyI2MBvlZLkEPdmxCE5EREVQhaa+5uAT/+eKqB8NAZjJTsCCX85Enss1iIzfgzTv70WE5xmyy8VXI23uVSjTyXCSb8gOBJyZb8FRhHOQV3cLGhQlMTnHb0OBHkSeTBLQgAjyWNQuelA7a0Lk4gXUMde65X+N2sxR4MfIm12NUhC2oa7tCCLe57vtHUbTzAfdfre7dS53x/I40ha/BklXCiHfH0FSiEeRBMZ+N8+keRS7gIfzbMs06x2IbIaYDLwN70U/XXKLr0ME2bJVmFfyMLres5CNucD9nrSf4SxdF9mEzJAmD8mlPy7GNpcS7Jhm/9O2FUoxQvXwYAfUNtCxz8WmJEyT0ZzEe6FzXdNkRpRufMICKx8/ENJmCI6gQXUGPdc22LL84jZY/l3336oC9pWNYjXgF+GTny4D/rboPUZERBSIDEqhZkEhs8km4VaRMDnNOFKQNJpVeA/mGGSwG5GO+ynk0duCvHXfRYT2V8H2LkLp23YAn3PbqkJT8VVu2aFOO/gy0vNOQdksTgVchgZ8K9E5HUn8/3VUZKSSSPO61aJZhzYkF9mIZkRqUPuZjtrAAff/JCRVuhdVmgQ9K+MRwZqHOtLpiECvRrKV3YgkH0fe55lIAtKOtJ7md2pHg8dCAzDH49PVhUHDh4E7EEG0gjG1aBD6cp7tpf3Xjc/tXHx2htGLDuA77nMtasszgK8iolSNBmHmHQ3zX4PPitKLt1lJj2tX8DmfRCOXPIRg36V4wwsltKbjtjSvhvCZCjOD5NtuuE4reqYsi0yu4zqBb+NhEaB82vC05z18b3f7zCT+C4+jLvgvlAeF/ZD1pw34+2zYgOxJBg1wr3Wffwy8Dg0G/gv4vDuvhYgKT3Wv4lAMAe/Au5sakOVbWvQeIyIiisA/IM9wNyp3vcD9bmWPRyLxBm/kDiPP46uR2ahChtTK/D6HvIo3oinicagDfdb9F2KFW38u6gQOIbJ0rfu/k1K8EIPHL4Zhn5XEOkQ4t5OtOTa8TOVnXpLbt6wolgcYJM044v6rR8f6BOqWnkbPjKWYm4M60HtR6sF3oe5sq9ueVZh9GrXd16KZgN3u8z/jNbNh5gz7PAYFgzahAM+06peHkFd7Lv3bzTr3uhB5cU9SeiGpNjRQmo4I53DgDHR/BpoVqBSa0GxHJ7KbK5D06Hy8cyD0eIJvZ5At40hLSdiJJ+OhFzgfQjKYRnzTnq3k8qE2PVwnPPZkOXc73278eaWl6UsilG6EOvHwvNOK4fQlPode8rR+LEwRGGrWO/GpUNMkRWYr7BhtPz14x1XyelrxpQx+JgXUflrRM3kt/rqtRHZkDGpXxaEYAm4l5+5Ec26tFFZjNyIiomQcA9YiY9BCdsGRMHBmJMIqdO7AVxvLIAL+LTQlvAOZkanI+7fELXcX/afNH0eZK7YiIgO5yVDE4PAEGsikZfS4IOX3ciETvJLT7Dawq0MBiH2oM6xBhPw85CF/PfI2W9DmPNRJbkDkd4d7/zLqyt6KyNolKDD4FyiYbw79dbRrUVvrxVdYXIgvxHEe+bvGhwY4/9VokNPO4Nr0fvcaDrwGDbrbkfd5OEh4C6Is9ch+mj58mTueJuQgsMwlkF3czFIVJklaUvKQC2GcTkg2k20pDWnp9JJE1faRlGWEy/WQTcgNnfgAxKRH25Ak5lbdMxwIJ88nrdhPUp+d6/c0hDYgg55VG1yn1SUI+8fwPGrJTt97ALUNG5zYOnZtQG1mkftvQrBuD7omtZRSlr4YAm7JUP8GJeOdiMRUERERZUU18ggbEX0Z+GO8DhyGJgiuGCQNpB3nZORt3An8DBHs48iIWenuS5Dn0Qj4bJTp5ClEtg3r6e/N24Byglfj08cNJWpRurspKEXdzgrt5xWI0GwAfl6hfYTIl05vFpUd+IVBZEnycRjvxRrv/reOdg6SA13i1p9EdrByu1v/bjSI+Coi01PwecEt1RzA9xGZ3kJ/opVsay+7bY+j/8xNKTg08CIjGpaybSx61oeDgLeigcBENJO40R3XL1GMQxUi6F1ocNbjvlt7OYmPdwiJZCgfCb3nSW9vdY7fc33PhTSdeYh8+u/QG58mQQnJZvi8h2kIk8eadixp5xQuX5vyudDrEXrDJ5Lt2bZrEQ4kwnOyrDW2/hHUNjuDbdWQPVDZ5/7LIEeYHWc3siX1iAq/wi23nmLjgAoh4BPc3sPIG4tssCFkRERE2TALZT0BefkOoYfddGuFelGGAkkPjHVMfcFvtcgDuQp5Fhej4MuPIcN1DjJo+916TW4dqwY6EDYP6gwGh3nofEBpzipFwK9E1+oSJGEYzuDTi4ZwX2E770HEtwWR707kaZ6FvJgnUFrKSW6ZWtRt1aF290+IVFmFxKUoePEACvydjQi8oQ3N0hSCE8C/FHtypzDuQ7ZgD/0DRIcK1+IHAm9CA/6daPYD+pPopC57bPDd6jDYZ5NZhCQvzWtNAb+Xgnx9QCGFakKpSBP5ddgDIZlFZaDPhSI8DzteGzjZ5wlkk+tc64afq/De6kayiX3o2GpFtqEX+AaqvVGDtOEzUV9Vh+ySBYwWh0II+HdQnqenyR0ynJbwMiIioiTsRyP02Yh0/Q3SU5p2bSRVw7P8r+Y1AnmUWlGH9wpk7Mxj+Qx++q8GkadaNLA4B0lRqlHHeR7D13kXit3IBzGR7JzO5cZziIS/xPBnflmDvPGVGATmIgEZ1H524GdQWpFH83ngZkS6W/FxB9Pcd5OgzMJftxo0K1OHpCPmCV9TpnOoRrnJZyNv+3BpsIcTh5AWfjjxLD5bzhp0vw+49+Pomd2Pr5CaIdu+hl7T4/hsHPvxGUSO48mg6YrTMnoM9LykEfikXjpE2r7C80jbbpI05yKx+dCND/TsIFuyMdD+cn0u5vqEBXdMh5/UiZvDKhdCqVGYFvcYPtCzGfW7GeCmYB9nonOtQdKVajSD1kKxKISAv9G9L8i71CjFYIvBRESUH3XoYW9HhKMRGQKL2k6mIxwuGIlOHssJNH3fg4h0EzLQU1DRnb2ocmE9Cix9nfv/EkTSG1EHdzaSrYxknESez0KrIJaKB5A3Nk1zOpT4FiLgQ4EM6mDXow7OPN3HUHByGz6rzhjg7UhechAVzNiGOsufkj07Y7rRcSjI8jJE5n9QwjEuRwFYT6OB0lT8rMhFnJ4EfCRgE/BXiLr8B/JgzsGnb2xD7cjIuOXFDmUluTzWYeXLsD5hPruc9nso9+gmN2nO50VO81qb7jsZIBl6eNMcOYV66XPp2nNp5g0m9UkiX18Ware7yc6okmsb4fnZ4CAZR5Lc50n6V8qswjuHQM4h+zwHtZk6fBaiFajPuyTPufRHIQR8oNDOZ4raY0RExACYiDx6cxCBeBFNp4aavZGAXME/fcg4XYM8lr3IwNXiy4JvJzvAck3w/258afnJyAuRTIM3ElFJ8m0YCeQb8qfFKyeMKG9BxHsaIg0n0MC0BpHo48jT2YQGbScQ+XocEe8x6DkyZFCayz9D9+0mRNbPQakAi01l+UbU2c9CBPyg299s5IWNGB6cgS8PfhU+ILUd3f+rUUq5r7jfa5E8aQU+A80E1B668MTSCrfYOoYwpWFaCsJwOZvZMdKYpifPZ/PTPOBhOfZCCHXY5pPE3Dzwodc5uVwoD0k79rqU33N9z/V7uN3wOEIvd31imTSvvp1LJrHO5GDZ8D6H17AdnUstfgZlK142WjgKIeCfc++NKD/Sr91RrUBl364oeq8RERF5cAR5zhajNEfdiIwXW7h2qGFTeE0o/dk8RIYOIkP1FPk9Qf+OJAOvQeP+MPNpxMjBnApuO+zwbDC3E5FjIxM22KlHpPwE8mhORh7tF1Gp+SnAh1HHfRfyTI9DQaw9bh3c9k+4l3myi9Hq7kTT0qYt7iV35cqIoUV4D8ejWQ4QqbaBncGcCeYI6MUH+UI64UuTOCSD0kMiGJLjUIddLueKZV7J5eEuNONICCO+tXmWCb+HhXhybacYpJHufPuuCz6nrWMzt+GAId+gIPSij8XXsViIZuimkTtla34U0qNf5d5vQ7VyLQDzXOBPit5jRETEAGjGT7e3IAMx0rKeJGHEqA5f3KAGeZKeQ3rQVvLrbHvRtPBPUBXDfUQCPhJxM5WfibFOtQY9Dz34KfpaRHqt7Hs3IlM70OBvDfJYTsJ3rq9AUgRb5+dI3nQGCmqdhSpH/gY6t2LyyX8bkfsDRZ9lRCWxG4WwTUIyoMXIprYD70ftahnZXmKrchimEQTFG0xCxPYwmt2A7ODM0IucnBkcaqRRu7Rc5aVkJUlqspMl40ksE+YdT6IQjXyIsBhSiPA8Ct1eeLxt+OxJu1DqQZCHe6Jb9gXkJKpBg/5HUd+2g2JRjEttGZ58g3rV84reY0REmTBY/f4nll9apiMpNzrxhqCa4Sv9XQhM82fTtNsQaW5HBusQ6rzuQd7MQtCHJAQRIxO5dJzlhLUlEKFeiDLdHEOzKhPQLNE2NLuScb93AregtjbbLf8Y8lhtRLm+x+Kz5jwV7PMy1MXtongJSh8aLEaMPITpIv8deSr34APtxuKlfTUoB/tkZLfakQOkDxGwFYjA7kIDN1A7teDDsPCLedgXoQHea/GktZBKmOVEJ3pmM/j0eZAtKRnssVjAY74g0cEOSo7hyXEr/rqfwM8kdOG92/n2Yc94huznfTWSsVme/3CZ9W4/T6MZ6hoUfLkd2aHiy+IUQ8A3oNJat7oj+m0qG/YfEXGaIoMCwo4iw7kCTaePBP13GNTShwx6FzJOVuHyy0idVoukA3vx5HsCMlyt9Md4FERXjTzmR3MsEzH8eB4pDyuVBcWy/dSijrAaT4DaUIf7GCJSPwXejdKCNSB9+nSUmx3kAX0ReTtb3fvlaJbFdOF1yJd0GF8NM+LUwxF8PMk9KGDuNuDTePL8ELJjh1AtBnMwnEV29eEe1P678AizoNgsy7No8HcN3qseeqGP4B0soR68UKlImgb8OLKnfWi2aJH73Bksk5ZCMdy2BTjnCv4MEWYfOYkvjBMGXvYllk/LTx7uIzzHVrfdPjS4meX+34iXF+3BJ+YLC/SEA58usgl4G+pf+5A9mYb6sWfRrFkGUd03oXbShSRtc1Hb2Yz6uYdynEN+FEPA3w98BPi4+/4I8K9F7zEiImIAmHfEjCjIcNr0+3AQcTNgncj4LXDHYkS6ChXJXYP3LPbgi+2ADOb7kRfhYWSwQsN9Lt6ztILCczBHDC0upHxtMKmVtfzxlge+FQ3alrvvHWhgNh5JPtagTnYOGuhtBn4PebE2olkYywfeiwj4JFSI5Wtuvz3oWdvP8BRzihh6rEVkbQvyXJ6BBmC9KD/8C6j9nIGkE6sQie1CAZwzkD3+DvBHiFwfxEtTWlCbnYPPET0OT/6MaIep+4qN8cmV3cN+D8nuTLyHf2ywTCH7S8vskvQwh1r2NBlIWkBmobBnGXzJeFBfZNuemjgOuz59aLDTjOzGVLzUyNLi9qEMSlVuX414Z8AsfJGm1yDbsRS1kd9GNsqubeEo5o53AP+GhggvFL2niIiIAlGPpt3r0Sgbhj/1oO37oHs3AzrD/TYXZRyYgYxWLi/iDLfeBaijmoo6M8NmvMfzpTIee0R5sbyM20qSiL7gd6v/lkEd3B3Iez0GtbkeRGAeRIO1HpTZYilKUfg88lz9PiJER5En/QCaNjZkUJq6M8gOzIs4NVGL13NPxeeLP4IcBMsR2f4ByrZzEp97vhfNtGxz26nBe3Pb8G25C18hsR7pgxegdjvdHUcVpRHREGmeY8tTXk02MQyJcr5MK2Fav0L02WEFyfCc0qpfJpE2kAgRZiU5I/gcSjTrg9+Tkpfx+Aq5HfiA27AS5gJ073vdcjZAWuDWMdnJP7p9LcULQULyXxiKCUu9AbkbrPz8ecCPi95jRETEAOhFHpr1KNSiC5+CbiSQ8M+jjqQXPwXbhU/zlZYub617HUM6yimJ//cD/wx8lpFfgOd0RinZDPLB2tFJ1CFaFgqb+q5GhPmbqMLiFtTptiG5APiZlCOI4Bxz/zfjgzifRllRPonvxgyWurCHiFMJVyHtf5ivuwcN5tajCqgb8TN7sxCxakI2aBWa0WtFnvI9bluzUDs7Ey+V6EASqBPIsdCMSN4MRNqOo3aZpj+uVNtLk7OEEpSulGXStpNPIlNI8Z0k0vq1cJ20ypbhNUxKgsLPRs7HIgdPL74fC/fVgU+Da6gFvogG6j9F934KylJ2Fxrs351yDukoxgP+aVRr+SH3fQ1yM0RERJQVe1HBmhlIqvFZNGU+Pd9KZUQvPgezafgyiNx8EnVatUgjuRsZw/9ExqyK9MIj1vFtRJrKJ3MsU2wAXMTQ4zF8cqzBIowl6ETtpxN50drd96dRwq29qG1tRaToRfoThzXIczkTeAIN9FajZ+l+9z3i1EU18Cpkn7a7zyCnwF3Bcs+7F8grvNutswnNlhxD+vCVeD1wg/t9K1Lj1rjlr3PbMadCN/Kqmmfc2jXuvR2fbSUtEDL8nKyEGcoqcmU0qXLHUBeskwvJfdh2LU95KDNJ7q+KbC17uK20YMtkHvHksQykMz+KnxFrRVpt0MDGBusdeO9/PkxH12gcum9NyJacQPeuF93zi93yv0I2Zxwi8HYuVkBuJbJPaceeG8UQ8B5iVFTEKYSRXQX1CffejLwtY6l8CfoMMkp7EUlegKbhjiB5yDfxZKgX+Jhb5yUKj2o3gh6rA45eLCnDNqy99KKu5QjKJPIM6vx3ocCq3YgomWTk2wNsdwwKTbJgKVDAZcTpgRX4Kq11+HLp+VJEHkK2ztrgLPe5DTkbQAF4V+Nna5qC3zuQXZuJ7HUNcjBYRo5OpC9vQsTRggdNg2zowGcSsSxS0J/chjETafIQSwmbQbRtivvchSfmyQqZtp1Q7hgeY7h8Wq5wO8Zcv4frJwcVuWbVbCBhMphdeK/0Vrzkw9LeQnaGpl789TGnUo3bppHuw+i628zb+fjzb0KEPOPW+xyizHe4bVQj+/QRRMxnpFyDdBRDwJ8D3uWOZDHqfX9VxPoRERFF4wQKZCyuxG1hMAMOMsxt7vWC+70eP+6+CxUXeTUy4HXI07C6iP29DUWLP4ZKq0eMTgy2IJQVwbGcwAeRmvHbKPbhLLfMA8hT2Fbgdl+D2uduNCMTcfohLIZyENm1CYhkp2EzatO78RrtVrKldIvxGVCO4oOFLWh3DIpFGO/296xbtgllIbkPpa57EV+x1QKKLVBzn/svg89WAtmkOfROJ3N6G8G0YzM8hmInTqBn6Vz3eyhBCWMxwpLvSWLcgZ7JXry3ODkICAvxpBXDsXWhP5m3dTKIaC9E12AhfgAyF++1T+Yat4QFW1B/Y46l1ch58Ayis+Pd+wy3fQvytuuxBQ2oLCFCg/t9CWpnlhAhl+a8MBRjST8K/KU7yu+gHDv/q+g9RkREFIHXI89KOEVYKsywt+Kn2cxjsB9p3O5DXoBLkNfov5ExbUNG7Vmke+yhOC92Ld7wn0ck4KMZT6CQoGLaYiZ43wV8AfgfaJD3EyRtyuC961WIkE8nd8x/NSI0HfhMO7buLERkiq9MFzHasQVlS65FbWsc0vOeQ3qGm8OI6B1D9m4P0nKH7edW1K7agR8i+9kAfAZ5l2cg6dN33b5+hOQvN6JS9zNRcN/LwJ+itr8e2dJ3Ibv8fVTrsBvN4vwFaud3AO9wnze47dS7bS11x2ckMZRS1CMbvwORe3PmzEb9yV58+fQkITYPtck76tx17Av+vx/lN7f+xMiqDRJw52UD7e1INlKPZlKvRH3KYTRoqXfH8TSSfuxApWem4QNcLaDU4kAy7jrMdL9vdcfS5LazyB3LUeAPkVTkKaSoPoG86L/ED9IeBH7X/XcX3nP+42C7/wL8lfv+c3cMK1AbeQN+EDQwiiHgZ7tXrXu9GVnhFUVsIyIiomA0okj6ZmTILPdsWEY3H5LZJXYjjezL6FGeiQxQPepYJiGj+Av0WB9Exm8OeuSnos6gFO9iD9J8LydOnI12/APwRgqTRGVQZwbqyLsQwZmKOsVNqDM1gv4jlCVnB/Ic7kzZ7iWo85+IAirvRfES1yBJVCTfpy9M51+PspVMQl7PNHwbDd62oDZ5W45l9uMLf5+FSBv4lHVjkEf1WfeqcsttRaRvErKnC5AndhVqp+fhA84zyDHRg2z05/ExEBcikvhfbj+XAH+PnpfJbvlNbrmtiDhf6t7Xo4FIOyKJvWjgeivwPUSQv+i2V4P6iMVunbUo48hE/LM4Bz1fT7t1j6C+5UPoWe9BhbHMq28SjvvdsU9CKWsXu+0+hfqb6W4fL7v9tCE7sMVt98coC/Yxd50muG2bp7sKzWbMcse1A3n/F6HZ2zX4SswNSOufQQM2q6T7ALq3h9CAbb87n/tQ5VzDZtQ3Pkt28Kx5/9MSEWSjGAL+bdQCnyNGSkVEDAE6kP612X1egIyteTbOwOcMt2m7kJR34wl7HzIqPW75tSjjyEeRUbwLGdzpiCQfDbZt04l1btm3uO38kOJKxd9D/+wTEaMPVqhkWsr/1h57UWe63f02CQ0kzYO4DnnkQo12O4Xlf+9z21uJ2v0B1BnGDLkRhi5EvAbCCdR2CsUGFCRfh0iiBWFejNo2ZOfZrkdlUy5D7f0yRB7PR6TuPNR+v418ml3ITh5y+9iEiDKIJN7pPtegnBgLEBluQ33CJvTsnYkPGp2Onr3daAarC/UtS/Bylr0or8Y3gS+hvuZe9JwtRwPcx1Cqxp+hAYQFu25BxLwVeZG/ivqIv0eFsmqRA+aMYJ1b3T6eQA6hvei5/j4K8n7eXbspqD/aiPqpMMtMl9tXHfKOfw24GZ/p5O9Qv5aUsa12x7EVDdhCp9LDwee0RH/b8IW8QmxFMwyF1RMohoAfIDuMOCIiouL4gXuNQyP2v0YkxQLXJiKPzY3Ii3ApnqC/hAjyCrxW7gVkrJqBP0DG8r+Rgfog8rpbMZ1Z7vM8ZFSeQtP+VmnsXIrTgEecGtiD2kuYpssCl07gU3UtQB3uQeSJm42mm7+JyPtJSi/h/oTbXg0iKk35Fx8W1AHvRNfpDrJzj0eMbjzn3i1/+AREEA19iGAuQaT7EL5AWQd6LnYhgvwt93sXyngFajtjkC3fiQj8FGSvQxxDxLUNyVFOIiJ8B16v/iSy48fRIGGBW/dcsus1hAHO70dk/Neo75mLSPZ5yKN+DO/tBzllNrnPa5D3vxdPnMfgtehzEcldh57htWjm9SJE+H+GBhYn3TkfRM/ODHyq23YUhmj4ALIFJ9x6U9216iN3DEktuneN7vxehwY3v86xbDF4EPWbmwtautg0hF9H89NhhMAdRWwjIiKiaLwSGZsD6PFrwBfoMQP3XWTAG5AB3gn8MTJ4NYio70aG7DDyfM9Bncfdbvkv4Mssg59yfBE/ot+KypCbtjDi9MMNiBh0o079KPKSjUXEAyMVkAAAIABJREFUpB2RjV8gAvJONHhcjfd2by3DcdyOyEcDkCuj0XzUqT+HL5YxlJiHJzsXEgn4aEYjImkdSEphIoB25C2upb/sIE1C9Tgivl2I0J6Fr8RqWI5md0B2P0m8QTb4l6idr0HPwUJEbG/Ha9tbg3W2Ig98D/nt91E8GT2Bf34uR4T1EvR8W1/xKJodPUy2pKwaOXsa0fNg6Utfi/y5u933D6JB9HH3/aR7P4SfVRiDrvER+nufw5nYQiSOh9Cgpg9lslmAl74dLmD9NGxGg4bCCskVW4p+GRqahZnkIwGPiKgIGtFY9+1oVD0FadS+g4IzL0bGoh15Il5GU35W7awVGa0lyJh3IsP6Ir5IxDK8BrGPgdVlW1E6Jis8EXH6YS4+Q85OFGRWjR8UWkEd8OW9ayi/LjuDvGpp+E3kmVyKnotCU2WWC7uQ964Zn3M6YnTiUhSPAxr0rQ3+sywbxcBSZK5BZK0Tn7u7D9lkyyOezF1fhZ6/TuAW1MaPopnJH7j196F+IoktSFveS7YftVD8Gjlg1pOtfT6E9+SHOBddO5CNsODOXH1HIYWIylUR+k7k2NqArtsCsrOglIoGRMLHDLQgUBwBX0l5axBHRESk4jUopdp29EBb0Noj7v+focCiVmS4ZgDvc/99Exnws1Du2iPIW9GCAl/WIEIwDXliiiVGJwdeJOIUxn3IY1WPOq4PoIwN30Razt34NnIQ6TInUKhXqHw44PZ7mKEn36Br8BWyC5pEjE4cdO8WS1NOmNd3HtIvd6NgS/Oshx7sGpRBZSaSazyOL89yNyKVA4Xptef5byD8Ai/tKASH8KT7JRR8Opn+A9J3oIHy06RXlLwazSSBBhhbCz3oHAhnJzbiAy4Hc21A7aQW317yoxgCbkr5WEEjIqLisPRQc4Evo6n7nWR7CUL97Jn4dE9nIgK+FC8psSm7bred30Oe8WKCjyIiQG1oLWqjY5E3bT4aHObSUO4ne3p9qHAb0pjuGWjBCiOS79GPdfi84uUm4AZLmVeHyHiuzC3N+JR7Z5EtvQqlgpVEMTk4dqEgyXr8c5jLo28pRJeRTsDNUWQ5yMuFPsoXvP0NNFudlr0pG8UQ8CuA96J5bqutagkQIyIiygpLqbYJGfyBjP46ZLzAT48+iYx1K9IELnDbMUM2kiuBRoxcPI/Idhs+U8/DedcYHvRQHq15RARUfiBnNRY6SY9ZOIS8xPMpLFvQSMBAfVcGBV2uJH+f9CiS/xwlf2XT4UQXxdicYgj49cUeSkRERKkotlx7G5q2DLEHFXQwbCIiYvBoB/5xuA8iIuIUw2GUQWggpHmIRzMewcsr82EoPPxDh6pMZlRNj81Cre9sFDJrdbJtHv2tDBDCeuDAgcy2bbnyN0ZEDC/mzZtHbJsRIxWxfUaMVMS2GTFSceGFF4bFNLJQjAd8JOAwmpf/YfDbOhSxVhC2bdvGRRddVObDKifGooCERpR8vzAx/+mDSSgrSA/SeB7Pv3hOXI/0cw+Tv0JapbAMBcpUofOQdnvVqlUjvG1GnM7w7fODqIjTGKSdXIWChJcg7Wof0mnWu8897vtWlMVnDqpct9+t24mmqY8iH8srUQDj3xJDjiIKQW7bWYf6ikmIMhSmy01HM2rLpfQ5r0Tp/9aiIOXr0PPwDMqhfRD19xbjMw7xgHr3eyjj+E30nP2c8qXXrAV+CwXq34mKBM0vcR9Xo9nYHnQOKxCfuRMFa89E9mMaktz8HCURMAnKdaiuxVMo4NQwG517Kwro/JLbx7vxebfrUZauWSjbyxSUmvFe0jMRXYfKz9s234ISFzwJ/I3bx58i+ctAaMPnhp8CQCaTSSUZo42Ad9A/T8xZSBz0S+DPGfURL8tQBwXKEXr/MB7LSMRKVNULNBHyVJ5lc6ERGUNQWMNwEPCn8QPi0fYIRkT8DpqArEakuwa14wVkl6e3dGEZRMTno8HvWERmpuIzJPwK5QjuRM/1DtQRRgIeUSrmI/IFyp4xGAK+DBHUHlQ1sdgCUlciUn0FIuATUV82EQ0QJuGL3dj+ZrvPK9HAFUTqLOzuUspHwOeiDFmg53C++3x5Cfv4ODovgE8hHymIjJ/jPt+M13FfgQ8svRKR7yZE5Cfis7xciDKoTEZ2ZKr7/d2IKINS854b7MPu0+WkE/AbkT26EHhzsI+lwXG9HgVYDoSxyO41F7Bsilt8lGEx8CrUgt+UssyHUBWI1S0tLUN1XCXiZTTC7iKWVc6FTaiTbqfQalPZ6MDryNblW7CC+FLwuZiI8oiIkYCfoUwofchzt9m9t+K93/a/vayAxlqUkec46hy34/MgP4FI0gFk/0ZiYGfE6MEu1CZ7GTxRPQMRqzrkrS0W1tesR89ABg0yV7vjO4QvSgPiASfQcxDG7rSiZySDr8ZZDux2x9DrjmmH20cpfeQP0PNuecqNz9yLZr36UCpTK0y1Fk+O1yE7AEoPaOQbdA970TX4nttmO6q6a1ib2IfJkvKdx8P4+/FIsI+7EV84QWHFfXDrgs/zPgAymcxofD2UyWRqE7+9PpPJ/MVA665atcpqco/gV1UGqkfAcYzUV7W7RoPZRu0wH//fZeDJDFzx/34fHW0zvk7XV3b7HJeBGzPwpxm4JKPnqTYDkzJQl4GmjNr2hzJwTgYaM1Dv1q11320dEu/VmeF9PuNrtL3y286aMuyjKQNvz8CbM6W3zdqUz2nHl48HlHoMLRn4g4yey6YBrtVgnsHGYP3kedSn7CP8nOvYksdXn+fape0j7VWfsg+zR8Wc+7lZ3zOZzOo0Pjra57/HoSFKL5pjGC6XZplh9y4iN8rhNS6k6lalMA1fUOBK8lfzKx2fWze4NIOfWH7pwAtFnKbowU8nX4z3Wk1B0pENqF1b2x6HpnFPIM9U8vnrSbzHmaGIcqF34EUGxHHgvwe5jZ6Uz2nHl48HlNp/rcC0yb7wTYjwWGwf5yBZxyq8RGYghErh5HmE3uG0a5Kmsw+PL5+XOW0fhSyf6xoUilcg6XCGQqrfjjYJSh0SRa9Eyn1rFY8i4fQPhu/QIiIKxQzgIqR1vXaYjyUiohR0oiniDNJ/fwx1PNcjPel17v0DwA1ooLkC6VaX5dheRERE5bERkeM2CpNwVqPAx2VIH326og64CXgPXt+eC7/nlvtwQVsdbR7wbvozlvOH40AiIkpHk3vvQUGhERGjEf+NMvh8yn1/NfKQTUEZA16J/CJzkJ8E1OYrVUkwIiIiP3ZTXA5/i/OYzvBUsx0pOAs5zEB27d6U5Saj7FBTUv7Pxmgj4BERpwCeRYRkDvB/h/dQIiIGhU7kSVuIZCf3olRiR5DH+2zkbXsIZRzqcP9VAuNQOe/NlJYqLiIioj/+C83a7kLkcglKoXg0zzqnGnYhW1eHAmTT8D3kE/5lQVuNBDwiYshRjaKsx6FJnYiI0YZmlHbwBeBWNJNjuk+rXbANZTrYjbIVtFf4mN6D4isOAV+u8L4iTn1UI5XrIUTATld04TOWvANJy9qAL3D6xKodQudbi+JY0vA8vj7CwBhtGvCIiFMAS9B0/VmoUERExGhCFSqQcQPqkKF/eYZGpIc8D7gEX9tgDArqGluB4xqTeI+IGAyuQfrnD6ACNZXCUnxti5EOe24b8Xn+RzLmoSzVTQMtWAA6yU++QQHo85BtHBjRAx4RMSyYgKazRnpe+oiIJKpQxTlIj2F4A+qIzsQX2elC5RqmoVzfXy3zcX0XWE4h2QciIgaGte1qZKsrgasQQewF/pWRHx/xPZTp4wXKm6noXPTsrkJ1AQaDRjRQ6AB+G9HcuWimrtLYjZwNuwdaEIgEPCJiGDAWGfRaJEOJiBhN6EOd2VJgTcoyvaiwhRXo+TtUJfM5lLLQPFKTkZRlA4OXqOxxr4iIcuA+FEtwkMq1q/HuvYbhmblZhAKpN6HKkS3A7aTrnA+QHoCYC1UoA1IDigPJJbmsQp7jGlR58ouo8NE+Ci5o8//QhMrKjwMexEtkypGKshDcghwMhVVKjQQ8ImLIMQMR8GpKq6oWETHc2OFeafgp8gJNAP43mmKvQh3seuBJ1P4/gDrLlSjYKxfOR2T/l3gt6mAwDckLdqIMthGnH85DUqjHSc9t3YFIXCVxP5I2HELt0VAH/AaaafoJA0sfSsEiRLoBfo1mrEDP23Q0MH6IwQ0+zkFefkivbptBGVZmouxJNyJ5ZimzZFPwTq1pqHz8HFQhs5x4g9v+z8gm2z0U6v2GSMAjIsqIRpT1YRvZU4l1+OyZ96HpMAu/iHrViFMFNSgQ8jzgR8i793rUKVUjL1QV6iCP40t7g5e0JNEIvNEt24ym6QeL1yLysQRNpZ/O6dVOR9QijW4VImxfSlluIrLbB8lNHHPhQkRkH0EEciC0o5ImSSxHxLXGbSccCPwGMB95ol8s8LhyIXzmjiLyOwVlOHmj+70RkdhScQwR7Cp0ru8DpiIvezjw+QYi/XuA33e/TUH3qphiONvQ4H4qumcHKP/sxVxUxwMUy/W9krcUCXhERNlwE9K8ngQ+jzccF6BqgQCHkaejDxmlSng2IiKGA9ORZ6gFebQfRZrvDuQV6kId8hLUyX4D+BYatPahji3p4e5Cz4zlFi8H9iACftwdT8TphR406DLCl4ZrkDYZJMkYaPZlEiLHoEHmLYM4xjFu31Vkk+8pZFdRLoSA16I+6LBbfiV6rtYDdyN5yJPBfuqQLn0Cg3/mtgNfd/uAbC97SMC78TMAd6HA7ecpreLnPSWsUwhmogHJPmQ7mtBgpXREAh4RUTaYR8HkJYaDeC2aecbNKxAJQMSpgtmIOGRQJzURefAshVs3kpGsQB3rPyK9ZyMK7LoUpQ9sDbbZB/wHIvWFT+3mxwNIc36E/tlbIk4P/CfykuYjmDYz0klhuevbkUNlHD4VZ6k4gkhxFdmDhCPu+0xU1bIQXIMIbQYFOZoz6Db6l6IHPaf/huIzypF+0Z7benTsLeSXhPSgAUIp5Bske5mKYk3K9XzPRnK5KjRoWY906oVUE01HJOAREWXD7Whkv5ns4JGXgH93n/chopFB5CIGYUacCliK5CZrEXH5MdJytqMO8SlETr6CZCpXo2DkG1BgJqhzy5XarJPy52GOwZqnN7oZeED3GPJwHqMwR0knIq4tSAoxGDwP/BA5c54Ifu9FA9IGSiOXNcHnfFmoT1L+Z64L+FoBy70Fkf+zUXBoSMRr0TVIyz8+A9kdkAf/xyUdaX9MwNumRcAy9/lq4Pslb3W4CfgVwGI0FzkV+fTzlRk6xbAANfRyTa2ONBSr3xrtOIK8a7kQBmqsQkaklvIElUVEDDVmoBRfjSgQ6TDSpY5FU9lbEIm4EAW6PY88divRNHoH6lwPIY/SLmQHDw/hOUREgEjU5Sijz12J//ZQXAaN45SvCuuqlN8zFEe+f4H6psPIGbQfkeENJRxTPeItO6mcfPIQIuBHyOYPyxC5bkODkFxZk7qRY6saDYjKhfWobx8DrEZyubH0H6QsQzbuaQpJpzicBPzTyDovRQS8DuW2unwYj2kIcSHSi2WQRqpc06sjAbVoumYGiuDONc11OmM6Mh4NlNdIREQMBeYjT9Yi1BntQ7pLC7i6AWUeWOOWO4xv569DneR6NAW+DT0Ljw3Z0UdEZOMCRNjOR4NB866eDbwNSaK+zuiVK/UgOYvhqQLWqUNeX5NMjkOE++0ozukIomtXoGc4LR1pKfgeIrjJWaplyIPfjOQfufTvhxCdbAHWJf6b7rbxHKXlWw8zJn0F+YuTAdxvQ/xnDvDPA25xOAn4byLh3zPu+258UszTAM3uvQo19FOJgE9BGjVQIMloIOCT0dTXCeAOKlsivhvd82p8hcCIiNGCJcgT1ovkJeuQ12kP6pDnIe/4c6ijui1Y91HgNYiAl+KBi4goB65DfdMjiJxehkhkKG04B9lo689Ol8n5WuDD6LwfQ460RYiqnY2u2x6UKWU+ynq0lcJ08oWgBx+gWYUI/wE0cDgDDYjy3YudZKd0NLwHea2Xo1iTwaCd3B74A6itFJZZaTgJeBdq7dbiTzMx7KPo8p+g8GCK0YL9qPOdg6rgjQa8Ek+Gl+J1qZXAUmTYq1DkfETEaMJDwO+gPLgHkR1biKaF34jI+BgkT5kP/IV7Nx1tHSLvv2R4sgCdhTriZylvNb+I0YE6FJQImnD/IiLiSTyJyNRB8ksF69FsZluB+5+ECP9Wiq/aWg28G3mIfwrMQkHNj5L7HEDP5/l4CcpAGI/IN0hucob7vATNZP3/7L13mF1nde//mV6kKeq9jLosyU0uMu4FQrEpwRCCA7kBAoRAyL2QkPLLDSk3N42E3FwChNASigOEYgw22LjhArhbstwkWb1r1KeX+8d3rd96z9E5M2ckjWbG3ut55jln9tnl3Xu/a63vWu8qR9FqgAPudoZvdeC1KFqgHZWL/NQpnKs373M4aD/yspdSgnJkAfg3UGZaM5Lm70IS/GVCnQxfuZyRpn6UkDiWaCNi9OFI+MqnBiKho2KgHTPKaBRSF6pBvBnVw61DRvevo+XoauQd+glSRDchgFKBvFtlyIs1D3nC88ukDSctBN5q3+vIQl9ejtSNVl+WM3A1jq0UrxHu1IC8xfWo9v2TJVz/BgRsV9s1SgXuIPA+376fR4DjCykOwNMqKP/K4DlnBxEvrkIYZSMyVO5FBkk/4v1bkAGxF/H1xZx+HnYHVZ39nQrQ/xJKORzOlbeVqKb62Sg3ZmAaSQD+96gjwhHkEvyfqEtJRhmNAD2PpmQPwxt+Aoo/8zKEw32tjDIaDroFRRE2IKU4zb4/D3yXqAxwk/3eh0B5EzHvfZnZAQIIIOTHflYir9IuTi52M6OM8ukbyJt8qisg04jF+xZKA+AOuDsZuvxvRUbrfFQdpQWBvWIJm/lUqMpQPjUTJQhXIIO6AnnAv4ZWwI4hHeYe9V9ChkUH6oFxukJqb0P1zt1QWYMMhOdO4lyt5MbCDwc9hFYbHipp75GugnIHeiI+jolkafAZjRi1n6Hr7EHLYBXIWh6d9Im1pQmRYvSRVZecppFkNPqoF3mTfYm+DyndaUSy0nIUO7oBeZ22Ia9fF1r8LNWb9XrkjesEvooU8MlWmtgIfAt5LMdCbkpGw0enI/xoE4odn4hCqkqhW1DY6W4G5oEWBH5TsNlPbtm7dUTllnIUr72PXGCfVkEppfzmcaSXJiGe9q6P0+yzkMf+bATW+5PjToXG2zgOIIMe4O0ot6QD+B+cahOc4aGfoRWB0lYBRhKAvw/4c4R6vC1gP4q4zyij00xVqLVvLyqTNpKxn5MR+Pb22hllNNboXCS6jyOFfB1KwNyNIgqfRcvW8xFwPhuBhf+wT4/99nJj9yClXQgg1NvnfOC37HqfIbdhz1BoqHG3GWVUjPpQ6MlQqJfBwyAWIrAJAtg1CNjeTfG8ibcQK0Vpve38KiiDUR8ykmsRr30HGcADVU95DnnI2xnYqXQd0n8/JtfX2ozu+TnUhfNClGiZdhNdTISizGZ0AvDfQGEz+e+gMI0kAP8oMplOtWVURhmVQBehBjggxf3YAPsON6WVT7IY8IzGIj2HlOSjCBTchObyKvt9FZGk5d0xlyIwXoFiJTeg1J9xyDt+b5FreWvqFuSFq0ZK/GQBeEYZjXaqS763IH4BebmLNZeZhxKjvRNzKU6mSxC4v5+oud2E2rIcQ4D+8wxelODHKMTsIMUTVucRVaa7ULUxp3ehELbVhMHdQm4vkW8iD/9+RlZ/D0T+3uoH3MtpJAH4RgrXcckoo2EgV9b9nL5ySSdL9xAx4BmIyGgs0jZUM3k54qeHkDifhWJHl6OkrOeRR6wKJbytBT6MlO1mpOBnMXBs6mHgRwgYvNquV0o1h4wyKkYXI//fA5xcPPFw0zoUhlGNvOXLEFwbSHdNQgB2PVGJqIPc0oplaPWqy76/yrb3EqFjrai+f4ttW4DKDz5G8dCSDuDOQe6p1farLXCeKvusRg1vLkPPIG3Esx74IINXMTmLaEV/pvtsfBXNq1JyAUYWgP8hqlH3c3Kf0u+MzHAyemnTeuALiHlHuub6xQTgmDKSA8koo5OkClQ3fxxqPnEL8Bco1GQV8lSBgPYR+7wGNfJwL9ENCAyUI6/WYI2pDgP/mfx/PvIMPsTwV0/J6KVDlShpsAwZdKMRgENuC/rPot4Rm4rsC8q3aEcrrFeievu7URMhB60XAf/N/v9ucmx+WMsP7LMMQbUqBMQHqwoD8v6+EYH87xHx6EdR/e3x5HaGBvljr0a8/BTFq9MMBr5noHoe9agS25dLGO/ppEtRmE0NxVcqgkYSgH8WmTrexSGjlyWVoZqmexn+JMhtp3j8XASYnyTXMh+MmpEwrECehTcQAHyk86AzyuhkqMY+z0F8MQ1VM2lA3unL0Hz3mt+ewPVHaLn7HlTDuBwB8m7kqbsegfofIoVdgdRD6sUD8c31iI8molrOGWVUCvWgRMUlCH4sRfL55yihshEB0uGsF12MzkZGaL5RsJ+I1p2BVo2eAl6DDN6fIjh1iR270PadjlaOPN56BVqd6kerStsQWC3Wi6QfPYtmSi+XeAPwbsS3h8ktbncBCh+7k4gVL7Nx7UXy5MfJ/ouRkbSFUgCt7neqnXNxieM9nfSb6HlOZ7QD8B6UyprRy5reiITOIWQdn06h14jAwQuc+lLUBFTnuBwJwFtLPG41Ysp5KF72XLTMllFGY5W8U94qBAQqieoL05AXaApS3rXIC/ccSqKsQIBnJ4oBvQjx/lqkMFfbNQ6hVat3o1jULyAw/xYU6vI1O+8MCne9yyijgagSeWirkYd3HqqK/C+o7N0e1JakmD6qRrrgdDaguQgBapB+uRzN+a8Qycn1KNGvCoHscxDAXoPK6P7A9p2Dwks2k5vs2IoM3h5kuHo+0sUol6MQfR7p0Y1525sRr29F/PrLdv5KBIDLyYWYc5GhA5IN306+P4vCbNbnXeNSG+dE5B0frMHNs6ge+lRyPfwnS2ejOVBq4vZiJPtKk0kjCcDvBt6LMmxSdJSVIXxZkYdgNCGv2ulKCygD3oM8cvnZ1CdDXrUEhsY2CxGAqEaC73HgdXnjzCijsUS1iK+m2fdmFGayEnnLJhBJYPWIB/YiBbrM9q1FHnMHMdciQN2HQH0XSsy6EpUiuwsp1Un2txiB8kmU2vY5o4xElWjOtiH94/LcQS1EXftCMddT0dysQDG/m5PfUu9010mMy6kF6USQofsqNNdvt98mIo/4ZBQachgBRR9Lg+1zhNyEzC7btwfx1eV23TQhMp+OIU95J2r+cxkymGej57UagfNm+7sF8X0XWulyOkwYPXvRs5pt5/pPIgRtPJIlG1H8+zxkVJSSL9WDclB6OLEay6ts/A8S8e4gWXS+Hbcl2b4aefO9YXu+cVCIyu0eSsMIIwnA326ff5hsy8oQjlqqRtb2Tk5vp8hbEUO/wOnNya0gYk3Hn8Tx5agcUgWqzrAfuBkJ5vxyTEuQsHyMEz0m99sxj6Mlv/2olFpGGY1VOoYaZFxl/zvfjkMK1ROqepHin2vbW5FymoPCT7qRAVqBwPcS+xyPWnVXI0BfiTxbrUiBtiOg0WPbJpEV08qodOpBpfXOQmEn9yHv94PI830tctoUS3icS4RgtRCgdxwC5pVotedbQxyXx3x3oeTlG5AO+wma93VovrcT1Zs3I32c36DqfMRXC5FB7L9XIh7ste37kK6bQHH6AQLEd6Pwy0ZkGK+13ztRwmQLembPIvDs/P9r6Hl9G7WSb7Tx/HcitvxJZGg8jGKoZ9k9/j0KC+rixDC0QjQLyRbQM0tzQ9YgeXMJuQD8ehQC04ti3I/Y9qnIA9+H3oFTBcplmYpi3F9MfutGcqu0FfeRBOAtI3jtjIZMNyAm7AH+iZNvhJFPO1FXstNNPcDXkcetDQnNYuWRCtG5SMhgxz+EhOLzyT6zkaC91v5vJJdRQcJlIhIebpFn3S8zGuvUipRmL1Lg7pWrIlo6HLfv3oa+FimzI0hJVdnnEaTMetHSbZud8zbkeXvCtm0G/obw5lWg8K4pyChOWz9PQaDiBUpT3Bm9vGgdUVpvC7lVKzzUogwZhQfJXWVZh8Koqsht5lSOdEATJ4ZrFKN65IXej8DwVUg/LEM6C/t9oe37OPIcT0aA/2sopju/LN+TCISvQ8D940jXPohAbh/ijRYb9xEbewO5DrZqFBpTifTcf9r/GxCInYmA+JNEOt8i4Fft+8PEqsJqFPt9xMZebturgN+1+7qQeHb++1DCR3cgGTIFPatpyOBfi+TIuba9GKWyoh0lvfaTu5oxm4gvv4hcAH4cycZitdpzaSQBeBVyBV5h/9+DEjMzdDIqyetVlxOMMdppEwLIV6Jp9lnk4ZiBhMJTSBhUICZzxd6APCLnotivI5xI81BMuNdMLSc3Q73KrnkVMgYmIUCQLZdn9FKg44hvdiBe8HbV3ma+AwGGSvu/yj67kIe7n1iqrUDJWc8hHvw24pXdaAUppTRfv44IYXOwMhlVZXkF4scHGLw8WkYvb/p14G0ovOOfku1XI/DrFUNeh+bvF1DoST71oblcQ+lG37UIKIPmfw3ipQaiVG0nyrEYh+b3s4Q3fDOFG9JcZeedgUDyZTa+x2z8XYgfX2vn6kIpeePQ6sCDdp4eO/8cu+5tCKq1A+9Aeu0qZCi/AhkrdXbeXrunK5HxfRvSq7MQj38F8e3jyJD2pOzvIsPnPoZen6Mbxaxj1/w4khH3oYoohRIjv4/k2A5yE02fQqsk+Y2TdiM9PpkT66Mfs+2ldbgeSQD+aSSV/8X+f4dte8+IjeiM0gRUDeM4mnD5dkcVUebre5yZetETbUxHbUxppY9b0cTbTmFAeqZoPPAmYhlxsCQYT3gss++VSODWIC/Gj9DU60GC6QASXIsX9U1mAAAgAElEQVTQu3mEwgkY45Cw7UBCopNIMutFgvsNdr1jyEPg4DuLsspoLNPFqMRXBVGrt4lIvvIELPeG+3K9f7Yhj9/jSNHWEQ12KhGPL0fLwt9Asm+nnfOtiM++Zue/DxkA3sTnSuSdmosASsNpvfOMxhpNRHNgywD7fBTNuUXkFgLwueONo6bbbwsIL2o5CmGYgObiYaQ/BwOOTUgv+Eqyl8f9DaRTfhvxxXikf3wVaRICsvMonjQJAsDTEV+tJ7y4TcDrEd54gAD4r0Ax0uMRb/7Mrn/cxnI2ESLj1cock7ShsJEb7LcXkDe9Hzmhyu16byGKEExDq1mbbQxz7N7akFf5iO17L9LH28ldfS6FxiGvuyfc/rvd31EErC9Dz/YBtMKdT60EPE2pE0HVQs2OZqJ5Myf/oII0kgD8QhRU7HQXg1cvn4mQ4FnoSfYAv4eQzhZU4HKMeNAvRkwEsq7yLaml6DZ939vPwJjWEJ6k9eQmHbQhATPStJoAsB4zVoiuRdZ/OWLw25GVfhnytu2y/xcjgVCNluMOoESSFiRAihk+G5Ewm4BCW3qQsHKGvAx5TMpQGaZF6H2ut2vPHspNZ5TRKKI/JBItZyDlNR3NcQfZ9fa7e/HcI1hlvzUib9wGJLYPIAP1L1DsZiVSxJchJf0dJAffihTj1chLNxEtATfb+bcisPQUUtrnIVXxVbJqty83moUA1Dhi9XMuChPclezXhebPXnJzeO5AYHg/kvVLkZxPy+W2oDkGAt8PIbB6t233OvVp46irkKG4D7Ur9wTDX7Ex96FVHDcAvKxeBZr7aZm+cQjsbSI3TMLLK+4G/hnFuu9D+s694btQ1ZVqxB+r7BpX2zUXIC/1PXb9/BDOe+25PY307XjE/zuQbu1DPOpNhLYhkF5NLkAts/vot/NtQHp+MypBuNTO9X8o1bMsakNhJ1OQI+1DSKbcbWNtRvLrFwwdNl6DntEd5MbfT0TPoLT+HiMJwHtRHIAH/Cxg8Bp0rehNf8f+n4Jmy2XAx5DL+JunfaTDQi8iG6SLwo1hdiLmryY3xmi4x7QaWXgj3aymGG1G06SX4nW9Z6EpMdX+1iGFX0Uk2FQi71oV8sJ1EQaHx8pBrqBOyVth1yAB8l07bwo09qFlsFrbNtE+p5ZwnxllNFrpHrTCU4Y8QZ+xv6mIL9wLng++25FsOY54bDPw+0gR+tL3VsRzXUi8L7XfVyCeLUcAvs3+X4HUwuXIK/kw0WT57UjRLkAGw1GGXpkio7FLS5HcByXeOXgrJ7cq1vfQPNpK7nxtQyukEHXCIRc27UXzuR4B9avt/NcieFOGPLufTM7tMdFTEMj2GtwNSG/0kcs/O2yMdcgLvQAZEo+gMp3NiJfSpjNVCHwfs/F8P7nmEaQ/ZxIhFxfaZx8CpW5IX4+MjHHII/1V5EjahMJJFtrvd9h9HrRx/Azx2joEeN2DfwkyZtLYeY8VX2T3dCty9h1Fq93YePN7byy3bcWacLWj5z4XAfFb0Lt7AzJ8ViMMMVTwPQfJGx9XCjk9rK608pQjCcB/D72ZTWiWzUPrLwNRB7l3dhFR5+ZOJHELAfD32h+TJ08+6QGfXnoO+Ac0gQolGbQC/4gYqbSA/lyqtXMPpWHMMzYmj9UcjbQF+AQSTh1oCuff4yH0zDwhoh8p/G4kzJYihfxe4IvIO5LSw4h5uym+7LUDAYXVSFB7KTanLyJh14qEUTdRPeW7qOFrVoIwo7FIn0SNciqR0dpExH33EGolzavoRQC5DgGNXuBGpBx7kfHqYKcbLbP3Ik/hdlSJ4ZUo9GWLnWsmAlXjyF1B9Eq2DyMe3IY85ysRkPHSiHdQvONeRmOf7kegeSoChdcgQJ5fxesnSN4/geashxGmtBZ5i4+TqxOOIg9zjR3jIRcdyOM+3a53HQrzWIsW+69Dcz5dYb0Feai70Yrtr9h5t9uYxtlvN9k1Ztn+C+z3Ncjx9JQd58ZHqsvvRvzXhfhjie37j3a+Zruf1Qho3ocA92zE22+z/doIx2CvXfMJG98M1Gm0B+nEX7JrfAvFfk9GXvzX2dh/jAyJ8wgjx3Xp95Ge3YKM6bfbM9tMlPP9OgPraX/fO5EMeBqB/J9SenOhlA6i+VLHiY7Kb6CQmZ/mH1SQRhKA/wTNnqUIiTzL0LulpKjnMMVr6fyr/bF///5RlBI/GLDu4uSA8CLEKJ2oDe1Q4sdPBuyfafIlwDcjhsqvgHAclTtqsH2rkcBYieyzKxGzN6BnVSiMJT8kqBD9GAkbCCt6PNEy+P1FjltBBr4zGrtUhjxAs1H85GTES31oOXYCAjJHEf/1IH7YgbxyHnvpCvQoKjf2QN51HkVhKl5i8PMUJk/wzKe19jcFgYppCLQ/bftfQgbAX8rUBfxB8v+zCDLkt0F/gJh7axBg3IpAuxuQ5yIdUoYA6RPJ8Z0EdPkCURmkH8293cBH7NizkRH4uQLjXYsSIXuIKhudyDvdgYCox6F7XPVKtLLaaeObY/97R8/8xjXHUP6E0zvsXL0oPMRDQL6Jci2eAv43UfbQ8zwqEDjeiEBoWkI4Deu8nCgL6PoXJCMusO9X2PMoFGZ7ITK899rfTPtL9WdNgeMK0RZ0/94kZyjhLCkdQ7kCDZw4lz6I5GJpXbdHEoD/NlrPcAk4AZlBhaLei9EhZI6BZluxwp0vM1qIGKQeMeSZSOA80+Tta0Gxa7fl/e61Up3eg6bKQTTt5iJBV6wFb0qTUeLKEbRM7t6RzXaueiQ8Z9h1ypF9eT9ixhuQ8PgOEmL/jJYovWJERhmNJapHC5bnIxD7PQS8jyBv+CEEGDYhAF2HFHQjUvD1aPHS5/4LCAD9JQJNn7Z9/giB5k+Tq5ybUbRhO+KpwZwU+xB4aUTAapudNwPfLy/q5ETAlE+r7HMumsuuO7ciQ69YyKjTbvtz8hDGvSj04acUz0VoQrqiEy3o70Ug9k60qjMRrdpUIV32NALQ/QgUTkLGwWbEkxOQ0VuNggsOINCc+iD7CX3m7e6rUT3zRrvOdsRrOxFvX4zi2jspXNLvCQS8O5HHfSoCyc8iQD3FnsMOO39+g5tKJF92Ig89do6nk3v9EdFwZy2l0Tai1Gkt8v1u4UTYuAg5BY4hvd+LnqcbWeVoNWQqep5p18s+hlLueCQB+G8iN6XTQds2FAD+MPAB4G/Rms7PBt795UIPI+DdRmkA80xQPQKomzk97eb7EROfR+EM5nzyZjz1SBD93xKvU4eEyQIEGNaR+0w9uaYF2ZTnEfVHQYX8PRb9YcSc5xIWfOYJz2is0XGk/H4FKarVwH8ghbUUAfG/Qq2pFyCP1Wyk9CqQYq+x/b6Clqb/Binn/cirtgkpwjLkKb+LANoXEgnsyxk8dx/gT5F3cyvi30IVDDLK6AEEgp8j13F1qnJ6CjL4Gik+9y5AXmdQSMWnk9/yHUwO8n8f6ZjbUVXnI8j4fRitIPUgPpxrf+vILZe7GvHiz9F9T0POI68zPh2tPM1GYPWDSIfP4MTwzyrCe7/Q7nE8Cm8pQ2D7tcm+/0Z0v0zprXaOVhR65isS99l9ddt1HmRo9CW7n53IcJmHZNY/kls84Vr7/ynCAHiRWCmfg7z5oLAi0Lv7d4QDrrFxD17+dCQBuJencHOsgqgZV4yq0Ew8B5lAf4Teyv3oDX1yWEY65qgVTe7RQuXItmpGceanq/HO3QxciimlbxB1vQvRImTlp/GByxHIuBQJiifITcqsQyw0DZU2rEfT0FtngwT5UmRlexnCc5NzjCQLZpTRyZLHozYiPrkKKaZpSHn/MqpuMguJba8H3kkseR9BPPM+5LmrtvM+h3htnZ17OvAnSAk+ap8X2/lS79NAdITc6hEZ+M6oEE1GhuJUcmPB5xCdW2dSWj+H8QjkbUBG5TkIvBabey+iFaUeBg5huAQB9XuQPvUa1V3IIO6wcTajMIsXEWA8njfuVagbZQXSUb+CeNAb8sxAK1gziYZ2DyKH1FpywfdylNNx2K7t1dTORsaEN+byHJEjaAV7PnKgtSbnmoR06wQE+t0QmYKMhIPI13qTne/L5BpLl9i+d9uYx9n1uonn6oURasg1iDxuvtzO2WtjT3HBHvutGen6P7Z7nmr3PdWe5+gG4D9CqOgz6A7fz+C19rqRpzulnyP3SUajlqqIiT3pDFyvDDFYGoKyk+JLhxciy7wfJU9uQ6xxMRJEnmz2GBIwnkn9SwgA1CEGnoySOxqQhX0zAhLPIuHoTJ4p/4zGOvUj38dcopznXAK8fIyo6e37VyDFto+IC1+GvHlHEbi+mfA03YnkxmwEFty79CUigf1MJIuvQPJhM/LWF0ojqkaAoNn2KS0GNKPRQLPQyuXTCDiBwGcDEZ7wC2RcdlLciZNP70FhJZsRSLybSFmbiUBnWlNiE1pJ6kK6631oXt2MPMqT0GrPq2z/GsQ7ZyMfpIdUbUUG8WV2jc8gI6CTXH5pISJ4W4jqRTUIWB5APPs2pOMWoEZFhQINlhEt7R9HoL+X3GonB1EqXrON+69RWE0LCvHwJMw227aW3NXyS5GMmYNA9TjbnuZxzU6eTxUC4tPQavldBLlx/wC5hsTdSD8fsvt8zP5PY9w70Ap6tT2vo0hO7UZVcJopNS59JAH4x9AM+y10Fz9mdLltMzpt1IliNZdQmHkr7O90KNMKJPimIwWen9hViGYjYTsegehtSJmuRIJlM2LYe2z/ixD49vqkB2zsd9p+N9h+q9ByXn5JoizsJKOxTv+BeOMZpPA+jHhgIVI+tUQVlDL79MpCu5D/ZRwCCecjHluLagt7ycB6O7YeecKa7O8qZNQO5P32Zj5VyPs4WOzvQHSxjeEsBBgOFNhnPuH1O58MgI8lugkBvotQSNR1CAwfQV7rA2iuef5PKXk75Wh+VxH1vD3p7zVEs5l/QXqkFYG4txDAdbrtfy0KFaklkhs90fNi2+cSVB3sXgQef922T0S8UCgP7FE7RzXyhT6NwOo37dotCLh+AoHeJ/KOn4y84VuQgbIQGdc/RMaAVx1LqZHg7wttbB1IDkxD+SCbkayYQG6YSxPy0h9B6YIT7bfnkvMfIzrvdhKhoAvJBeCr0TNfbvfv2KMOORCq0Ts8RmHqI/T6e+0896OyiR3kOv+K00gC8D60tvDpwXbM6KVA6yhcWaQRhafUAf9Jbk3P+URSw1QkWPJB+gKURb0bebj3Iqu+Ggm2B5AQ/RBizL8kVyisQZb3pchLcBUSKDOQZ2EeYuIjiNkvQULvSiRYH0YVHOrt2vW2bzUSmK9DwD2tLlNf8AlllNHYof1Em/g70IrPKgS+fVm33/68SZXzxCKk+L+HvGvTEaBoQ8bwNORFewgZ1E8gpT4JKc6zkPLOB+CTkYzosfFcjrxRD6AmHidTcgy7/iwkh4oltG8jGraUUkEpo5Glc1D87g/QfJqHdMzzREm7VyF534cW53+JaHW+PznX7yE99CfJdveaevO1lC61c+9DnuJzbP91iD/67O9ixEuPIR3nHu9eZBTUImP2GqQ7IUI51tt11qE5OwGBwg6kFz1c5SDiy+PJOUA8Oh7p5W3IeEiTS0GrQhcikHobMjjK7BjvhpvSVJSL0YiSF2vtmDrEN9g4d9pnG7mrxcvsmXmt8v/DiXQIefybkRHVTi749opJG1Dc9hZyMcVlyPAAOReK1RhPaQ9h4Pej51ZaH5WRBOCXAh9HM99r2/ST9el+GdE0gtFBnuP1SPH+DlKg65E1PQcJgR1oumxGCvlVCChPR0D3HjvvLCI56w1I2FbbOT5OWK8XEPGr9cjrPR7Fur2G6Iq5FAngp5HgaENC0GuhTkAg4kmU1DEbWekgpv9uct+DpTpklNFYoHpkJLcjxdeLeCNNMC5D83+8/e7haK+3bQ0IZDchz/EUBDQOokXRZ+33/Si861ykyD+EgMEjdq0b7PjdaJm7BsmJSju+6hTu8zH7y6eL7H4fsGfwqQL7ZDT6qBLJ6InIkfIL9P52kZuW5mEE5QiW1CPwNw/pjSoEIP+AqMv960j/bELz/Hn7/3IEeZ5CPONgdSkC0MeRJ/eXked2M+KJMgQKPaxhJtKB7om/Eem+tyBQO89+PwvxRxMKi7gC6ayf2fVARq57chcQuqw52acSGR9nc2LS4yKkL9sIo7Meeak/ZGN8P9H/YinSwxV2zG6iMs0X0bu4FXmT+22fNNzrTmSsHCT4vhB5HxDQisBjdsyNKJzsYWR4FaoDvgXJmA4b/1+j5/z3lFa2cDZ6ZqXB2JEE4J9HGQCPcnrKYmR0yrQAeYSfprTKAoWoDDHlYQYux7MMMaor6EWIaRqQIJtr22cQnbvOR61pq5HV/Shi6ImISaoRKPd4b2eEuUhI9SHAvgh5qz9qxy5CitRbETcgcP05ZLGPs3NPRsJwG7Kay5DQ/gh6bmsRA96DwIUnnOQz7qksh2eU0WihdyF+Ox8BmGYCdKdURgBhDympRWDDl4y9s2WlnXMmAi6fQ8DDZclu27+b3JUkr4oyy857GDXoqEZ8mSZ5nQ46CxnoEBWZMhobVIHmVw0Cw27Y7ScX8D2MgPZW5DH9FaJDq4d+VBHzvQ6FI9QicHsvWhW6H4WR1BArN+eiOVpPdGl+L9JV/WjldAoC9lUEn7SiWuOzkHH6D3bdKrSatBzxyH67xja7hzeTWwMbpLteifTbvyIDcj4yKHqQLnvYxtdEhNI4vWC/HUfAvtLG90bk0OpHhrEDcG9g02jHLrP7vQ+FpHkjrieJbpvp+/gJ0bzoKEry7kQx8r5fg93HFBSnPc++P2DnBOGTH5LbOG+CPZ8n0ftuR6to/p7XkbtCUIx6iHKVg9NIAvDDnFhbJ6PTSlPQJF9P4bjFfPJC/IsQCB9KF02nq5C13Y9C+tcgj9Wt5DL/FPv0JZstSJFvQda/e6zuQULoesS4i4lyZksQKO6xc29HsWyrkQf9LDvPYQTIJyNA30N4z6cj5d9JZDKfDfwZWnp+DvhzJFC8nulhwlP3LhtzORF3+jG77qeQYt5k192DjACvX55RRmOZGpACXUGAiEL5Da4c0yVp7+zny+ldaHm5Him/KxB4/jfk0bsUKfkvIIDRSq5H7nbkZVyMwMZmpF5+injv7bZtqKXLilEa41la2+mxQZchGXYfubG1LyXqQvNlFQJ9M1CewGQk4y9FQO985HFdjGT9OPtssG2VqAKyx0t/FK22liF++CF6hruRrnoDeq7eqMarAzUTqzTViF/ccVNh51hu+z1J1BqvQLwxFc3tG+2ediDj0+foEiIheh+R5Dkf8YrHq7/Wxv5ZG/tRQqeBVnzuS57jrQgMb7Xnt9G2z0e61cNq/pc9s28T4aEXEf1KXo906BXIi/9TG3MXubjFmyR1I/D8Ztt+3MZVhbzsc2z7a4mqLy3IICpUtvhKhFsOIi/7m+zet9oY+im92tI2BPo3DbYjMLIA/G7g79BbSQtBFlrry+ik6J2Igc6jcLxUPu1CTO5VCi5CzH0vsVTjlQyKJaL4sl0Zmoje2OAV5JYf/IVdqxt5uuajZaFbEVCdh2Kw9qE4vKOIwRyo9xFejF7EKOMQU05HQnUC8GuIiY8TS4Z1yAK/FAnKLUh4vMJ+82Sr44Ti34wEwETkGZhi52pCgvIbCOS/DQnYMvv+BdQEYa5d82v2DDPKaKzTXUhxTUF8U2heexx4scRj58f9SEm2I/6tRGDwbpTgeRTxUwPyRv5PxP/uGfcqD6uI5DWPWf0lJE8WIxm3j+LJVaXSi6juby1RBi6fXkG0Bh+swNdooFrkqQV5Rl+qANzLytWj+fAM0jFPIs/zXDSPKpEe6bDvR4kwqx1ID7YgcLYTzccpyCB9BDlnZiBn1gz0PCchwDcD6ZIXkQ4sR3qqw8a3C+mvKrvm40jXpU6xXjQH1yDg+udIl9Yg3X0VMlT/Fs39g4jf3m3H7kL4wO+j3P5qkN7zqi8bkXF7C/Ju34hA8jcI/k7pa0hHdiBefLttX4n411fJvbNmu53Tw1euRysFW1EojN+zO+3cUHFqQvXJKxB+2GDP91v2eT6qrtJuzz8/lt1Xz7w7Z6V9/xHw/9mzKhWWLrCxLStp75EE4O7bvyDZ1k8EH2V0yjTUsnc+Yffapy+x1gD/RTQrrUFM9mKBc9yFGO8gEjgXIqD9fN5+nUhogBhnOmLOmYhhr0KCrZ8ovF9r+xxHjOSl5HuRNe3JkhOQYKlEgrIPCbtJyJh4EnnWP23nPIbA/wrE5GVICBxDlni7jaUPCTD3Vvhz3Y7sSK917nGEO+ycb7Jt7imbUOC5ZZTRWCJX5Isp7vmGCElxJZ3fhc8rIE0lyn95SlA5Uq49iC8nIrlyCYpgXIc8dfciEH8l8mI+ggCCJ1DtQkq2hli2dk/6qVAh+ZfSJTaui1Gi6miPtOwkuhOW5sEbW1SLZO8+BFB99fRZYv62IgDuq6I9RMfHS5DueQzNp3KkbzxZsh0Bywrk7fZKOLMRj5yNdGKfndd12lS7xl0I/vQhp85Zdo3lSBc3IEfQeMQP/r3bfkuTIM+136ptXA/ZOZfZ7041dswTNpZmlK/0OgKYH0dgeDaqENNMhOPMRSvOW5H+PUysKjt5JaSDhLGx257jClQJ5rMIgB9EMqAJebOrCQB+jz3Hg3Y/U5BOXUfEXE9FIWfbbQwLkOF0DvKMVyFn33eQgbzenvsr0ft6BjnxDhH8nbaqGaxaWzXRhXxwGkkAfvUIXvtlQl9GALLUbphnIWZ6HHmFvZyP10KdS9TeXIyEz3Fyl2C7EKMsRszlguYAscT3LLlGQa/t81Y7/xLEgDUILI9HgvOQjaHCjnfPWi8SKv2I+d0qr0RMV2vfO5C9twrFibrQakSe8u/bdWuR9+K4nXsvElC3oWQXbGz1dt46wmvXijwezUiR/S0SFOMREPgYkfGdUUZjlWYinvVSgX2cWPUgpTQxs6/A9goiHO0wkjvbkUzyWNiNKDzuoF3XV6JAgGoVchwcJUqGgjxZaxEAutjOPZ1TB+CD0eNIsb/A6AffoOf/BSQP89tzp3Qjei/3kfucRzPVoGolc1D1nQ4kt48j7+lq5Dl9MwJ0exAYvhfN8zW2Tw9y1vSjOXkcOZdq0XwdhwzFR5E+WYnA4u8jfdCLnFdvtv2bCN65kQB5byUSmq+xcVci/dOM9OgdCAhPQKDyMQS8txLQro+oMtSI4piXIMDvzqYypBePEQmS7Ta2LXatFqQDn0be8Y025vOQfrzSxnIcecqd7rX7bgL+NzIAJqC47ZvsWb2SkCNNdh8TEH+mYHeyjXOPXdtxRwVaUa9DRslFtn2R3eNUtELRRqyCXW/XXmjj+mJyHY8WmIPiwPsQYH89Athfp7iBWp73OTCNBAD/NVRs838U+f0fzuBYXuLUisInytBk3MfAmbzXI0E1G8VtfQYx7wuI0WYgxvQuUh9EDPdFJJB3IyH2LrRcPA4B70eQJ9wt2sds+zgb4x4ipKXNzvkCAtuvQwy0i6gxXIcmeCViUM9Y9vJHHYhpPGt9HWK8GcnYvQyTeyMm2vjqEKNPRMzcjZavapBy8hAVX3Ivt+f1AvJunG/PrNm277F72oOE40Vk3S8zGvv0IgIhxxBvjB949xOolwDdDsJdaZUhWTULAYtuBAg3o5W444hX1yIQApI9U4l41t9CitK9kDuRw6DJzrkMAaC0nNzJUBnyUHqFppSeQV42b13t119H4WY+o4F8FaIYVRM5LOcy+gH4KgLM3YTmqsvmyQiMrkCro83ovRxGAPUOpO/2oRKDniB5A9KnFSi86BDy/j6PQqYmEC3f+9C8mEQk+n0azY31COzVEInIzgvH7ZxlaN5Uobldj3Si51x02XHdCBzPsnG02v3tRiD0IqQX/x05tbrs/DfYNQ4gHV+NVp8fs+O3oLCkWUiH/TXimwcRGN5mY70cgXzIXTmYiELAKpFRsQPp3cm2f6W9oz6i82inPff8FvXvRo6zLlSVZIXt/1WiKkq5Pdcptu1sojtnuY3LnQC+IlEsnHYBkYt2AeHVXsTpWiEaCSTgLtT8lNqMTokWIAv0MALE7YjBrkfW+1ykhH6b3OWhlI6iRIgdhFJ9E3pllWgyeq3U1XbMOKIUUr9d9w1I6RwhmnF02Tn7kTC4iAD3W2z/5Qjs/hvyFM8jwj287bsnaVXZfXhdTwfhDgS6ELMdQcp2BpH0Uma/VxHe+WO2r1+nHynnz9n/P7fzeuxoN9EkoA0J2IN2jnIkzLFzfNt+ewulWsYZZTR66U1oybiRSFLycLBSycG3/7mHuArxcqd9r7ff2pD8WG/XOYTk3HlIET+IlPJbkWzZjZT1cwhk9SAv2V3ABxCAr0DK+1ToSvvrQ5Uk9iBZdhCBDI+LX25/Zbb97lO87khRF6qM4Z7d0Uxz0ByAAIqeL+Q5QnMQoKxEQPs6FGpyADmg3MDzpjD9SJc6OL4UOYZqkUGy3P5/DAHGBuSRXoh0ZyUKJ/FE4VaiPvcmojPlATvGqZfouuiOqMVozl2N4rE/inRqFQLajyM+mUSETF5IhEBWIz1WhvijCfHxYvu/GTnUvAvnEqTDvOvtXfb8WpEeX0IYI9fbmBcjbFKGgL0ntE63dzLdxnmujanCrtWNdPlSxF9b7dl4xbMZCDf0E7rWybtl3o30by96t5NsrO48O0ZUmSkEwh+3sfeiULfX2bGP2rOro7TShMVpJAD4Z+3zzwbZ7w/R2kBGJdEqNCmmoAn4HEoAmo2stw77fSLFAfhWZLm3ETHVXhqskSj/BQLh1yImcaFRgSb+LMQUnuTSjYTaUjtuIvIiex1hr0Ay3q7ZZfu7rdZl5zxuY3HPurd/9xhU7xLmGetdRLnASqI5iF+vwcZWSwiqnUgQOVifYv9fQMSDz7bz7kKK9gASEm+3+2m2391DXonCbu5DgEwBp2EAACAASURBVGECUew/o4zGGn2A4JEKcptMlUrOiyA+9jC0fjvfM4QBXob4dCMCODfYfh4+5kvX/4TKhn4E8VcTCjm5FIGsSQiktxGNs06VPJ62HMkR7zlwAHk6f27b1xNl0EprUz166Yf2N1LkIHOwbqPdxArLZhRCeA4KzbwBvbtpCGi/EyXweQWNSWh+OcDaiMBrH5qbi9Hc3GfnrEBz8xwiVOQndqw3mnHd9VfIi9qFchXGozmR5gZNSr43I73j8cddaE61orK8Xht7qR3XiLy/82zcHjpajkDrpYSR6/N3IRHWWYG85DXoGe+xMe4gnGdbgD+2/Q4hw3cJUSLQHXLHiFCaRsJR1k2UCvbqKdgY9iGjegOqpnK5vQd3mvXZb9X2fRexIrDQnr0nr+4mwtK+Zud9AiVXLrP3Uiw87Ai5oSlfsc96VKpxCuoI/Itkn9STPziN5rXwt5AB8CHQVjRZNyBhA5rYnpHdhCZeWk6nEjHvXsRoTyMgPxkBym0IbO5EdpOXYKpFCmYlApxHkIBoR8zuLWIdVF+LmH8FUojb7fcOwnPWTHi8LrNPV9KuhD2DGiQ42m2fBiLRA9vmcaZ7ifrgvuztse3YOMYTnvoH7R48OeZ/EWEtO5HQcm/5Dnvee+y5tdgzdau7DwnYlYQweAoJqowyGqv0AFFqtAcpzMYhHJ+GmoB4soboNtuNlGNvso8b5/OQV6oD8ekz5FZDqULyayXiz+cQKGhDYTPtKOlrIgHgrkPL0feTq0xLobuQTDhk4347UszrkQxLq598F4Gs01UK8eVIc1EibRl6ngP1q9iNwHYz0jmXI104n4A+NQjEdiNP8nuRp/ZFNKffgeb39xFw7UZy3Q2/+QjYT0Ox4e9AuqMGra4sRZ7UdyI91Yrmq1cbOZtwDk0jeMPjsMsRv12LdN7zaH77qvJVaM6/xj59NWo6moftCOzXIT14DVElqJvgsWNEmMaLKE5+KTIifp3Q5efY86xDTrLrEJ+9H/FlPwp73Yt4uZ3cnA8vC/oD4J/tPK9ETbfORjHjV9q+822sXv63lWj41YX4yD34n7b7cx6rsnGusGPfiEJmt9i56218njTpYxyPQmh3Ad+kMC20MZaTW+ccwqgp5uTMpdEMwIeynvkSoCaiXM/XKR6HV4tsk2oUD+n7XYkmfVqq6AmiPJcDwpRejUJJ+lDR+k3IQ/QHaNJfSZTY2oOYdBqy9CuQwjyIGON5xCzdtq2BqGKw1a5Vb2NpQBP0CRurF8Jxj1iaQdxHbhe7smTfOsJ75iWU/Ddn/qlI0fbbs2lDQtvrga9Hyt7j1mbZNZyB3KL1a0+0ZzEJWeW3IGH8ETv2GFK6zyIh0UJUdliMjIusCkpGY5luRkvMKwlvXJqEOVDZwZTS/XzpuZcI7XqRSLJ+Dil6r6d/CIVCnGPXvceOfw9aKj6OgMpfIedBp227zs59r123HAErEEArBMAbEDjbj/ja24j/hKheAVpG98oJz3Jiw62TbW6WUZB3h4QTQw8K0Rb7q0PzaCLSO60IoG5H+qEZOVhqkIPkONIdkwhZ34X0m3uTfRyfsM+l5IYmNiKP+rNECcM2pKe9UZuvxvpKKQSI9jrgLyRjvA2B7YlEvHSz/d6OdMwBZAisJNqt+yrxDMKT/3VkSJcRNcP77f4/hHTXeLvvwzbeg7btCFrNXYD4rNrG7LlYDbb/F+16XiLQix94Sd9qZFT/FTKqv4X40EHsYXvex5Cu9UpnoBCfPvQ+Pf58ie3XgGp632DXyc/PWG9j2kNuPsaHEH+793653dOX7fdqe767bVz5lZAcq5S2yjWaAfhozVIZJlqJGB5ktT1QZL8VRMmd8wnh781lFiKG+Rxi+oHq3frrLyNAZpdtn0ksDVUia3Q9sg4vt+v9hx3nS12LCAB7i127CzGbl0jyskJNds/fQQzq3u5+JBh9CSedor5E1kMA7nxl70vYOxGQr7Bx+fFe1WQdEr5HEUNPQ0J5EVLuh1E+8OuRIL0TLbPNRkLsGBIQb0JGTj1RYqkRCSk3HuYgwfAYYSxklNFYJY/pBIHvOk7OX5LPtx6y5R6u/yIM9hftWtPQCt/zKJ//YoLnb7F91iI5+KTt52FzlxFg+6D97iEFy+24QnQtAvoQdcaXImC0NdnPO+H+gqzH3HDROgT4aigtBv0S5PD4mf3vc6wfyf1K4OMobOhR4C+QDrgEJUiuQvrCq2p5GMfz9v+tybU8H8hjtT9vY70R8UwDmh+bkK7uQHqkl9wQLIjEzB6i4tAMhBE8Hv1sYlXYveL9xOpxg50/hVLzCcB/FuKbCgQqO+28L6I5X4ucgh6rvRXpujeglYR6xGd77Z6W2jinI9CLjfkPkL7fSaw+rCFWtquQd7oR1ebYjWTKQaQ3q+yeH0GGdpuNa4Zd4x7btxZ5wA/b83jMxrWAEzvVPmNj2Gxj9lh2r6zSb8/dUxUvQc7KCiSX/gHhlPzzeo5ZFaXQaAbgLzMP+PNICEDUrwXFW7Wg+OFdiPm9McBGxJATERj20nnNCBiuY+AC8rcTVUi8GsB1xNLVIwhIvh41k/kqkRBZB7zPjp2BmGcNwfwXIIbzMkjO9J61XWO/vRqB81TwwIktrVMPfrvdv9cw9Uom3pRnPxKu0xHgf9zGtRgJpFVImLQgpe0lA70tMITX4G3IQLiJaHLQj95Btz3fQ/bnMeV7iLqwrQQzzuLE5JqMMhprdBQpyeXkNsRwKlV0e9hXakT7ilMXArJrEG9fjXhnKZJJXtPZOwcuQUr1rYiPf4bkyl8ipfwQua2nawjj+RuE17IQeRv7HiTTphO9DlLqJReQnSqNQzJ9PPJWbh7i8bOQk+MppDteCtRP6QmsLcibCZLrFehdOjBtRLL6I8gzew9R+7oVPff1aE4+R1TceohwomxHht1EpAscqB9Bc7UM6dMpaK56Xft6osPiItu2gSir+UOEB2oRNvg7IofLV4mO2zk8EdorpXjXy1rbtyn5bTdaqelDfOz6rgrhhYlIJ36MaHE/067VQhQt8JWoHsK55gaDN8Vz3l5mz76WcIp54qN738cjPew86h7wCtvujq1riB4jfn/bEe97lZgLEQA/bO/EMdAaO/5WBPgXI4NgC3JmHkdJrTvtOa1Her+XWI0A8f+dDEylycDRDMCLBeC8RGkfsrjPI0rlNRLNcMahMnj70ZKXl9v6AHqND6EkgzeiOLlFRPjDJhRnNQ9NnM12zk5yW8vOR0vL5yAFNQkx5Hz7+zayhnttbL3Io1SDhP0uZG2W2/UXEBZhGeG9/l0b93SkRL2mZzXhZYATF0EqCIbvIuK+PQHKPfn77Rl4AulcxISVdj+dROzdPGTBVyFgfgwxeBliRJDF/xokCNrsuj9GKQofRsx+Cyq31IJWKT6DlqhX2LFdNq7ZjG62yyijweh+FMLhMsY9WQNRsbCUNBnT+dfDWWYgudVN1AKfYPtPRfW9PQSmHyWF+dL9VCTHjiK+fhMCor6U/jo7/yoEfNLW8vl0H1Ly1yNFvdaufTLJp0OheYQncQVDB+BvR4BkBS/P6r5dKIyyATm1HkTP4l7UNRIk0z+M5lALAmfVRJKjh42cjd5Hnx3zC6SHDqH64o2IL7yO9gykA65GyXufIar9lCEQ63lSHTZWd1SBYrtX2v8fJDzdl9t1FqFwjVUIVH4ZOcm8ilA9UUPbc5z8uwPjGQSoXGz3410wIcr2eZneTcTK9Uak771WeOow60C6rh/x62/adb5uvztYb7Nr7EB6sTp5Pu2E17/Bjl9jz2g2kS/Sh/T7Sjt+vR1Tjvh/A9Fb4C+RXFhDOPqqEE9fYft8GdVKd/pk8iyakGHwCxTTPxUZ9xuS/V3Oldb8cCSRwBT0ZubnjeNd9vlXZ3pAI0+vQwzVi8B4O5pojeQ2jehBXtQb0OM7jibEY6iE39loCckrfsxCnu02pESqkGf9RcQUa5GV6iDTvTzz0NJMI5p8SxDAX2XndUA8G03G2QQjFluCqSLqgnpZJtDk905czsipwq5Ijvd4bg9ZcRDu156PmM4zvL39bzWRoe0x6uW23Zf1jhNdtNYgpb3cznEEMdZuxPR/iph3PDI2epBgakeGzmEkHH9BdE1zQZFRRmOV+pHB34IStEpRI4XAdyE+9/9rkUxbQORujCfkwEG0gvdJVNJtDeLtKUj5tyEg4Mne5yI55u3Gl9v+Bxm4s53TfiJ3YzLDD75BgGcHklVPnMTx7QiIlZIQVoNk1ECGyFgjTz4sQzrrQrSK2YxAWiXSaTXEvKojwhDWo/mzGeVGudx+C5L7lQhcX06UMWxH82MD8qjvR3p5K9KPh5Cn+RykZ6egeelVfZwXliXXW5Rsr0Wry76C4579K5BeK0d88h2UD+HgcIldw4FuP9GCHRuHr16vIip5NCEMMBmFrCy0ca4AfhXx0QIUCusx8huQI7EbhYh5k53XErHiqSNqoo29Bunuh5NnBeJT7wUwB+nVXUQfj0m23Y32K9B7fgZBzGkoVGSFPa8G1BjpN4jO3QeIeuGFqJ8o3jADGVbex2Sj3WMqE0a/B/x76KncydhoE3YGKK0O4FbiZ9AE256372uIEjqH0MtvQEz1AJqYBxEjvBkxThkqi1WBlpXcsl+Kqn3UISGyxc65Ck1Y9yT/IWKWKsRIGxHD+LjdC12MfHnuDUR3St/flVqa9FiIUku7UJpAGVEG0L1rvqJQjUC/N+EZTywR1th4ahFTXorAczVSgv02xl3Ecth69MzcI/840cb4g8jTsgIJgB8SS+QD3V9GGY0Fuh8Z0qV5eoZOFUg2+dL5EWIVrQx5NBciHt2AVrHqkZychQDONiSj7kBytBlVenrMzvcI4vVC6qcSGRg7kVw6gsJaFhIJnClNQIr5jUSN8lMFsx3IoXKy9CUEjjYOst80BEa8LvrmU7jmaKJWorNxM5G7cI397g6cbmLl5fsIzK1Deuo30Kroj5BO6CE6T5YRSX6VCETus/37EPhbgObMbUgP/xTNkXFEm3TXU8WcVt2EM6nb7qWCSID0ih8pL15s1/BwR3dQHUzuOzU8vaQoCPx22nPrsP89SdrDPquQrvPjaogSgcsRpvCKKK6vjyH968fX22cjufp3g23bbsevQpikHvU6OYJWd7yPRxfRhOss278SVcvZgt7XcaLHh4e7PEeEqF5OhOK2oPeSNuk6i1gZaCTKVDYiQ2cmkgvFnAqFaSQBeD0KNMro/6dbEcDbSdQf9Y5QzeRWRtmHJvqzCFRfjJjrBeRlvY7IMp5P1CFdY+eciCZiK/K2zyfiwryEUSMBiCcQpZfKbCwTkPB2yp90+cvO7qmuL7BvJeGV7kv2T5VuIUrBeFpX+Bhh6bun2wF/JVEeaSKReApiqjXIY+ZLkGcRy3gu8LqQIH3OfutEsaQT7XiQAJ9t21ah6hGPIA9cRhmNZXof8lDXDrbjEMl52Gv8diFe9bbUrmjdCzUHxeCus30OE3HiqxA/T0agvAsZxFXIg/+gbb8cGcdpecAbUX5KOYoR3od43T1eKV2AeNoBuydeP5/sMwPJ6KMohnm4DJeUjqEVvMFoDrEkP5+XDgAvJwyzDQgwzkIg+3yk19qRjK9GIO0iNGcWI93WgkDv80QSvzeRgijL6172J4i29O8nEi8Pomd8AZqProeKge6KvO+uA3uSY8qT36rQHJ1q97HSru3FAVxPpv0P65LvKRR0o7fMxv4imiPP2Pk8jNMruEDEtoPmuetuD9txPe7kPTvK7Xm4E68Kef/PsnMutnEuR0nXVXb9KxEveu6XV5/xIgf+/FrQe9xi+9TYuC+0cx4kSjBOR47Nq5Hh8UnkGcf289C4TUgWTECy4Eb7PnQ/8kgC8FvRmsRIVvQ/Q1SNlMQRxKBzENDuyNuvg9zqJ29CVlc9EuyfJ5JplqPJNRNN1IMoO7cWMcBCIk5qO5p8Hhe2HU24LhRjOZ3IDp9FeISrCdDsiR/YtgmIEQay9AqBZz9fIUoFTXrcQMtCadyZx5CCGGQ7eg4rCcvdz7eb8A5UEW1v65CyPIQE2iNIKPeh++9D916PlOkcO1c18nr1IC/bFiS8ziJqmL4NCZuXWYGfjF6CdDmRv3E6yZVcGZGo3UjUMXYl6783Id5vQmDzFrQCNYUAIJ0IQM0nEsFvQcr1VxG/e0iey9cFCDz0I2fGncjgKEMycjpRHanFjmm18W5DINZD7GYDv2XHPWznnohC00pNKBxOWkdUm3h0hMdyOqkdgasKBMTfhuT1ZqK/wzg0bxysX4je02T0HuvRO72AaFXfSnRRfJwI5ehBq6V9yJvqnl33kKedUeFEHZcPukl+y9+vnNBbdUjX34ucPxsRr8zmxDrcadx3eo0eQr93IBzhnu077Ly3o/AO7NoO5suJYgN9yCHoDY122/cyG6fLi/HJdw998XvzJn2zEY+6d3yjjaPDrrfCjq8jjJo6pLsb0Xv1VvR1CEPNQbp5ITKYdhGt6xuI2uZdaJXvens2f2HPoZEo7XyICHHy8Y+dGPAPA3+E7sRNtH6G1tFhFJEnJ3hjlpSuQi8WtPy10vb5R2TV1SLg/SCxbNmEJs8UovrGQjSZ3oiWt1w4VCCh8se231LE9F5qby9abHg3svwvQ5PtuF0j9WKNIxjLJ5EzagocS1W8g3muU0rBd6nndus6jQn3VvW1aKmoh6gZ7h72pUTs33EiE7sKCdt9KPnqiO3TQbSvnUiUe/LQmV773o6e8U5iWdDLE6Zt7FPvQ0YZjSWaggz30z2H8/nek6v9E8RPnShvpQ3JRo9PPURUNPDyopsQLz6OVqQm2zFePnCz3UsZqnjwCRvHN4h60C5PQDJgBRFCcA4KaTmK5OYtCFinDWMOJ+eos/MeQ2CiVAA+AZVB22R/p5M6UAL/S41qkDe1DhlJf2f/f4lcI28CenddhAc1DcmASPQFvVuv4JPywGHkNBtPrvOqjGjSNi05j+uv9H8KfM/vg5F6s3uJEp6PIsPwUVTkwENTDucdX0i/pvea3lM5qoI2h4hJ9+t3Es/BE0grEU5xfJCGuaRVhnx12r366bm9ulEF8rp7SIzXE69DOtorJHkcdzkRCtNtYz6IsNQmhHs88XUJETa0EYFx78zpq+WvJED+O1EsfqWNzePd3Wk6HeG3sROC0jD4LmOJ3oZewqWI0dNJ7kkw04lSff32/14kxBehkkn/jEDzEaQcFiMmOoDin/4OMbODux4EJj0JchJ6+UeRd7wSxTK9FQkOB5zHkCEwjdxGM2kcen6scmmT6uTIBUP+32Ae9vS7x9JNQErYyT0G6TJX2lXThZq3nwcp6l9FMYB9SAB7E58pRJyZZ3PXE8w+Dj3vx4nkrXq0TO3lD8eonZlRRnyFkDOnk1LA4lSe93sZ4rlpyBCeRoSWnYtAaj1SuI+jGsRdyDnhVZi8/CpoIXYp4kcvc/Y8cqR8AIHoc9D93owcH3OIluP3ENUwjqIl7KfIXR3ciOTAg6h60gU2zoFKxObTLyP5vgbpgM6Bdx8xuhwBsHsY+VCW1cR7eA3RMn4OoZ+7iUYyzWjeTEXP1z2zoFVND6fYhXKrQPrZ5+VsIkzzomR7Bbk61mPO+5EebqB0p5MDe5C+8gY7TcBH7V6cB/x66bUP277kXTNfl/pvlYg/Kuy8qc7sSv73MsH9SDf6czs/2WdSco00/ys/DMfBeDmBYeqI/DPQ8/c486fQM60mKr5V2vV2If1chd6P58ltQEb7fvS+G9CzuwutlLUjYH0+gbOusDE9h8o+T0EyppdoTpQ+w8FppOuhTUAzOHXB3ldk3zFChRjpfgSgvYPVBYSz/5j9eWWOm4C/sd+fRWDNvazXoUlUR8Rg+eTy9rAONh0I9qKJ4laml/GrJRItfNxDs94Gp2IA2sF1IS96vjdgIBBeCKT799SK7yPi91LGd0HjSZUO4P33CUSJNX9WCxDD+vJfOWL+jeg9X4tAgSep7EJG1gyivGNpXbIyymh00nCqjYFWzPqQYjyI5KJ32+tBfDaeKG3mFY8WoYoHFyLF+Q3U9TddIv4i4s0LkZfsYgRy25CsBMnnDUjJtqAVzOcRyPw5WuFsRHKgC4ECr/18B7mewB8yeOTlBUhm3IdAk4crevnV0UgNRILjq1BN5ZGk9eg9VaNwktWErN+MANrzaBXY61RvJMr3epm7fmRUuG6aTS6Ad3KdXI3m47zkeAeIfUQdbU/ohKGt/BaiMiI/ybtsVto5U104jeCx/HCY/PO5QdyFdJaXN3S96xXLKogExAPICEvP45R6wAcC/2mcvRs0lcn3fgTMXY+uJPLKVhNhR/7beHJX+sehxMl3oOTad6F32oxCTmvR+/PwFH+HXoVpHOLtqbavr7YNnUYSgL8HhaHMRoHRa1BmzDUDHTR66Wa0XLGBwoz0DLkT+hLb/+vIM/IhxKRdaKIdQZNqGlpufQwx9Hw7XyeyyhcixluLGNw9u55Y6BPZrUqfTN5Mx+MpC4HvwTzQxajQklp6vrK8/dJ982uAp7W++5PffHtf3nFpDGlKvlTnAqMXKfN+JFjcU9KHnn0bUSvVAfxRcp9bGwLdz6OlzaPIw3Udepcv2LU2oKYK5xJei5c+fWJtKZ3qitNHVl0y+E4ZjQB9CXmGXCEOFznPupHbhZRiK7lL2C8g+TgOKdoN9vuFSEl6T4AWFCIyH/G81/lvRaEg5yM+PYZk72Hb/wKia+Z3kHyYj+T2t5CXfIaN8etE3PoOG4uDb6/aMBjQmkokatcho+FbKO9nG7mgbzRRG/IoTkahQCNNjyJDZxYyuq5Gc+RRFIt/PfBZ1DUaJKuno/flNaXn2P8HCR3knTHL0PtYbL/tIxI8/xxV3pmAjAD3OmPfXQ+loNR1Vz6l21I9ljrQ0v3yz9FXYB/InYdpcqeHtICe1y4EOHeje7wA3fdMAoz/EPUpOYAii3/Xtn8dhW94ve8JyTVSSuWIJ1T22fW8wV266vM4ei89iPcvJ3K7jiLc9AVUJvUY0a1zFtLXv4mSrJcRoboHkcyYa39P2XH+HLzk4zSiP0s16o9ycjTSMeAXojozV6Mn8WcjOJ6TpHHoJRwkN4u+EK0n2rmfQ8R7b0U1pZeg5ZPbCEB4AE22p8gt3t+NALxH8pxHxIP3kbtUlQJi98DmLx0VKo13ssp1sOMKjasQQ7rnoCbvOP/uz8IFRzeR/OjHuCfAk7d8Gdqtej9/P9ECey8Smucm+z+LFL13Hj2GSkp1obCjp9DS9jVICHt7YY+/24GYup7odpZRRmORnkb84Z3yhguEe/J3N+JD92YuRuB5Krl5I4sJL+MiAri/SNTs/QRSpNtQC3KPBa+x8+9B4PpPbd/n7M9pHfIVHUJAcy5SxlPsN/fe/SbymG1Apf3ORrk7rcjjvsiulfZ3cGojQKBXvurk5GqBn0nqRV7vJnJLuI0ULUDG0hGkW7vQnD2OvJ+NCIbk65W0s+Nh+zxAJDSmVbzG23mrkFf9PDvvDUQi4/hk/zJy451TCFYsryodX+q8cR3mjqM0fLSD0GtHiPCPVM+n5yrWm8LDW/oR8HyeaFbnyZX9KL/inWi+focoxXhVcp2FyXnz7zV19h2yY9oQf84jDObJRB6Id8JsJFa+epEXvBHJA0/cvBc5xjw055ftXNPRalcN4vNdaBWsDfHuq9Fz3kw4/bz08ARyi2YMnUYSgHcQ62o1COEsHbnhnAx5L6FaZEd4MsBg1IksLu+29XpktT2JLLllRFzYPPt7J1EFACLrF9vPGTG1XkuhkaxJnQLvYkq8WLiGCy83ILz00Dxy7ymtmtBHAIY0KdM92u0IZO9B3jKPAaxE1nMLYsx/QzGOTYjZl6Dlq9cQVvouVLVmPrLCJxFLoq9GijujjMYieeb/cIJvJw+p6yWW/vsQuHInw0Kic28f4sftCCDUIeDwNJK3i5EB3WzbW1E98zchULEAyfXpCCTvsHF4mbEjdiyI/ycQoORB+70aga6zbAzfQd7rMhvj2+xeelAJ2KN2vmvtfD8m+j+kXfbGAnUzOsA3SAZ7kuUO9N4riPCeCvSOU/I441oiwdcbrK1A99dO6OFpBD94kmAtkahbYedMwXHqkR4oDGSw7fXJ93zPtv9B1LjID11KK5t5XLqPMSWvQtKLyv95vkR6T+8n4rR/Dc1/yAX2AzWgy/eAe+jn+UTO1n7EZ92I5/3dPY94qAK9V08I/V0bcxXwJ0guVCDHmjsQ+u2e3oDmyH+hVbDjyEhfZPvssm1VyCjwQhZPIew3DRn6Q6ORBODbkST7LgqSO0isCY4yegWyoh4ltwmDx/qeg4DWXCQ4B6NWZG+sQt7wA0joz0WC2Sevg0ovXl/MaiyUQDHcivFkqFBISn/e/8XCYQqdy5+J75eGq/j5upPvzoy+lJgmZvYi8L0FLWkdRu/UmxEsQ4JoFgo7aULW/Svt91nIcv8M8nY9jJbo3ozmRwNit3GI+TMAntFYpWXkts0eLkpzNPJXzVLPdzthRHcjR8bDyMu1yLZ3IEW5iJAJa1AbcV+RqkAq6Cjie/c4L0UJ8L1oWfubSIk/heTFZCQPjtn5NyBv+GIk619t51iAVjfT6lKughcgpY6N73tIJZ5ueg2SS7cTxsVLjVrQc+1C8fzjkY71Chde/cpXhNMV4GlE1Z1lhKF5DXpH/eTG808g5mYzet9eXzzVL/mx0IXAaDF+SrenOs5zi3y71zPvJ7cpnif8+4rQUKqa9RMebC8F6jkWab+OquS7h9hA7n2WKi+8q6VXavP73Ud4v72pDuT2K9lFYANf3a5Gz8NLCfcgQ7kJ8fqHkP52sD3X9plG5FwcRnyd5s71I4zwPjTHSqm5n0sjCcDfZJ8fRwF4TUgqDJXmoyyYZ9DTetVpGFseXY4e+BXkAvBnUHiIe1yaTzwU0K0uIdrKF2SVtgAAIABJREFU+6TaZ59L0ARwj02apJgPsCnwfz6NRvDtlBoHqUVeRniqi03LYokb/puXkErJl+KqyQ23cY9An+1zFCnTNUiYPJVcr5IA4xXAf0fvvYMAAOMQ0P6aXesAYtgaxNDj7fobkGDPKKOxSucQyYnDTfkOhRSUe6v5PUTpMVfWfWhJeZn9NpUIFexAoR+vQABmMlpi3mv7z7ZjX4lU0wqiRfnZiI9vt3FMRLXAb0IJXxehBh4/QyB9Klr6noy8qI8h4LsfgX0H2QeJsJNCYSmng7zeOch58NVhus5I0mLUJRFkEJ2HZPZRJKerkd5NwwB9TqUryRDx2f0I1LtumZvs4zoA9A4PE0DQE/7HkTuH05WjUhIv033SXhweYlIohDMFxKnRmsZ6p3q2VCjoXnM/l3/vTM6R6vWTSSxNdftx9Pz60ApDI1Eu2PdbTDgpZxFhYDsQrJyC+LkSGZ8vEO/Fyw976cnZRHnnw3aPnn9yse33KHrXk1DunTvTJg75TkcCgHuGYTratfbprRmHSnegdY9honVo2Wpd3vZelKTzFOERcRqHmH8/enHXosmxi4hjmoIm7j22vyc1pJ7dlMFc8YwlyvfGp9/zvQNpTFr+ca5cKyhsYPhv+Ut6tcRStZcSzF9NqEDLWFciq/tq9D7dynevuf+NQ/NhI5qyE4h4dDegZhCtkEHe8Xq0fJUm3mSU0Vgj98KdKSrkgHBPZC9yfDQSivdyBJTd8O5CMtfrAB8nFGsHkuufQYC82o7rQn3i3ogU/yGUaP9hxOMPIcDehJwyHnrmuR87iNjblXatI8hR04bCFVM6CHyKSHobDmq1+/Byey9FSsMy5hOJwvPQey9HMvgs2yfVQanzp8z2n0BULnEHW5oE25Ds755W0Hwj+S39PlTYlT//fcwT87YV8rZDbsz5UD3S+RgkvwtnofOmYaOnIifKiKRlb4Dnej6t7NJEePzPIrDTPCKEdCGRG7YQ8Vk58nKnYcNt6P32IWN8MWGYud72jqc70Rz5rp1z6PHgIwHAv4bSjx/lRJTVj9Z5hkpXo4y4b6PuNqeZfoDK1RQDTps5sebpG9GyZQsSvB6zPRVNIC/h04Ay3g8gQb6YiG+E0sIxRjMNFkZS6m+FgHd+Eqdb42kllDSzuz/5vY9InulGxlEnoXy9xGMdUVGmg7CWy4lKKX7d4zZOP3YdMsBuRzFm61GZoxsHuO+MMhrtdOkZuk6hUDrf1kuUXa1GnqwGQm564hi2zfu9dSCwu9x+m4riw1cgYLbNzvUwAs+XJscfIsCFywuQB+xmBGp3EH0fepAX7QhSdz9KfitEnjA4XNSJQH59ges0IQfEbtRIaKzSWuKdpavJXn2qGwGzfNjhOiT16h4imu74NshtYZI6jTykyPOLClGp4aH5oZmFvqfkK7q+T36o1lCuXYxKNSROJ1bxhFhfxfAmeK6LITdEzfnTw0+9zf0c+608OaeP1Tt/9iFZMAXJgMPEe7/Tto8D/hrp8AY75ilOJvwERgaAX2+fLafpfLtQDEcnCpz7CSc+jffaH5MnT+bkaKhey1rkJZ1ILJMeQJNoL7LGVhBMOwV5WvLjolMaqyA8nwolXhayxVLBUSz+Pf959RD1e92C9mozaXOFdgSsWwmvyTeRlb3Ptk9DSngB0d2smWDKWba9Hynfu+yYVSiR6v8iId4N/D2a8u9n9DbRyCijUiitpT/clC8Lnec9nhPET96hNt2vx37zUqybkbdqDVqRbCQ83vsQjx5FKqQaga9utIL5NCqp1oHk9heJcmV3IlmS31jnKwjo/6uNYzSkOHn8az69ivAKb2X4wmCGm7x7NCgy9UUkoz8LfNC+7yaSYuFEMOZx4R4DXknUv4bcFuSHkRGHHTvD9l+ZN640v8lXagailL/SGPViFcsgtwxhb3KeNFbbK6dA8XjwdLzFVt3z9y+2Mj/QeEuhNNQkNYIOE6sMewijaC9hmB9B+hhOBN0p7UcGaC/i8/HoXb6IZEovCjf+Ozv3i+R65k+eRgKAnz/I70NpDwZR/R7U1mwlJwLwf7U/9u/ffzJBSQPQBBS8/xwh2GoIRnQGLkcTpRIBt1nkxnd7DVI4ubip0UqFrO78+LfB4twH85T7ebrREq83GuqzbeOJEJYOIkFnP/JKr7LvnSgM6AoUJ/ZllEBVhwD5QqSgPQlnCmF99wJ/gVY9Ntrv04gKDB4vmnrlM8poLNLngX9i+EF4MYO8L+//cqIkoe+7hzDEK5EM+DxagWoheLgcKdNWVNGq1c7rLai/iEoSTrXjbk6u43wOESOeyu599nc6aBZy4BSqslWN5E4jWgQuFsIyA4HH9gK/eSx6F1rJG4jmIJ33OFEmcbTQ0eT7dvS+5qIKNe9E7/ZAkWPTGHD3ujq1EqX8nka6uh8BwSlEiKPLdtfvft6BnEmDUX64ZErFeDA9pqqE7/k02Mp7ftx5+r2YgXEyuMZLB/u78fvyOuH95OajNBCVk9IY+YGeuSfeupxoQe/7asI5926kv6sR5nulXXcnAuQLUXGGodFIAPBP2Ke7iJ9ET+hsZLJeVuS4YtRAcN2lqJf7GaTfQNbTZSj6pdm2XU/EEHs3ytl2TNqRsZA3+KXi6YbC95J6A/o5Ubk6A+cD8/xkrPxzHkDKw2O/0qTLNGzkGFKM+5HSOoAE6QvovU1CzOahKV22zUuRuX03mwhnaUbJVj9DoL0CLbo4i92OlqBfRW5L3owyGmv01hG4Zr589H4HHh6QNhprI0BRN1FGbi3RuGwT0c0WonzZFgTe/XpuZN9knw+h1a2UrkFx59sQYD8dDhRvFgaSSasR2P0UJ67GLiQiNy9AfQjy6Vqko47ZOfKB/J3ombSSC2LzqRLV0K5CIPxzA+w7ErQRVaqpREbU7xArzN4teibFdazLa0/y9f+9MgcoP8ipLtnulTHc61yoIdzJ0OnEA6fTuZdvWDgVq20+1PtwA8dDPRqT3zzRGjRnU+elP3cP880fRz6l4b6XEInZRwgjZR4RznKN7ePfPVG0WBGO4jQSAPxq+7wZIRRPwFwJfPQkznc5cj12IhPk56c6wNKpGhXcX4yW7fYjQXguWuLy7F3vSAm5EyQ/hvmlBLwLkcfYuUWalgBMAbcr11KsfV8G6kcM2kAu06UZ29uT63qppFqilXwbErrV9udKtZ6I7d6BFJWXHZpPxIX/Glqq8qXI5bZ9G1r48blR2rLVqXaSzCij4SHn4ZEgl5MeVtJJAFIHTA6KPNFyG0qobEPeYW/HvRaFGXQgo3gn4vU2O2YvAmv/DVUPeYSIO03J21fMQTL/2Cne41uQN34b8AECXDiAzAfI2xBQGY/K2xYiL1c3HsnIQp70UhIzXZZ6PPVopH3o/b+TCD1YTiTkezhJCpSdDiGw3oPmg5ce9O6oTq6rJiTb0tCsMwWah4obTld4a37o53AVh/Byp14+0CnFVC6PHFf4vaTvq9hzyn8eM9H77yWXj9sRAK9AOM9LlW5CVXcmAveVflvJXYwULSPANyhj7dwi+w5EP7S/M0wNqILiEsTkc5FnqAFZS94tq9gjzm+h/lIH3xDP4xhRoaDY8lCh51YsdhzEGDVEFQKvedqHGGY/Ul6zkfLx+DDPnm5CMZCpUnGrtgkJ5k773o0YdSYyvNxTVUmu9/1ZpMzbEPifjeJQLyhwbxllNFboCGfeYZAqytTr5vzaTdRA9t+8KcpMlPv/KVQw60XkNatCILschRXMsn0/guTFzXZOX7lspPAy8z3IE/YCJw++X4FKAz5N1A1fgUrY3obC4jZS2Dt9DIUE5bc2T+kOJJO2c2phMV4LfT4K3xttNB2tQHvSns/TQ/ZbFcUb2MCJTrE0idMpdfK4gyeNtYYTq3GdCq8UO/ZUz5s6uYoB82LXGChk9HRRPn7qTb6n3UWXJd/zK8Ok50rJa3n3kdtp20OH8yutzCSwwmrEcx7O5F07ZzBUGkkA/gxqKfgV9JbdfTjC1ITq3G7gxKSZ5cjifZgoZdOIXpRH1OxBL6vY8lNqIb+UYr0HozTsxBMXC8VnFYoHT4Vifgk0V7QQ4N6fvceM1aMYTq9E44lXR4lYMd/2DFLOE+1aUwmhfcD2eTuyiP8fe+cdH9dV5v2vuqxiFffu2Imd2E5xnOpAOiQEkkCoIYTQlrqwbGEX9t19gWXZXdhlWeBdICydEHpIIwkhvTpxSeLEsRMntuUuS7ZlyVYv7x+/8+y5M5oZjTQjjcr5fT7z0ejOLefee85zfuepli+8GnlSVSHfsB6UlOdOZNotRWaqs9HEGoIwA8YyohPeSCGRK1oePv2gZTUCb/mytKT5yP3CivJsRq4mBSg7yUbkWnIJ3m0sHy3If+j+NiDCab7S1Ujr3OfOtxnJtSl4H+OL0Jhfh+SAoQhlHDGjbR/SsBchHVQlklWdbr9fIu1a/HSdjwL325H8SZUooB4FkWYDptAYjZiHdymYjZ8vLAtKHrFVpOMRLco0Az+3JCPt0exc8T7VidwfsjnvJ5orB0J+ku+prpHOueItCcOB0sj36LPuTfI91XOOplOMLjLsPRtPMURTHJsFxQqAPYe44e/QeEwfuSTg7wc+hhKrgiTMd3LXHMM70MA9D/gqftU1B+/7WIGCOpqQYDN/oEIkgNPNEjARtN6GeB/ORP7fiTKhxB8fP8ijA6gXX2WsGG8qtSj2RDABWuD2rUKT8VT0bt+KXySU4d1lJrvzL3XHzEUasGa3fxnSal2MJsgGFIn/OPJLDAgYq5iTo+smkxUW29GKxl8rGpfmpmJuf7PQAn0x0oKvQAHYR1EK2HVokl+IiPNU5Pv9r2jhXY807e9Cmun9qJKm5Rn/MJIhtyDj7jlIDp1DLAE/F5/Kscntux74hDv/QWRZK0KG4UXI7xo0yVs9itVo0QAiAy8P+ATHP8zP38qXm4tiNCtIKkT7VZTwRanSaIrXysQFJZsKwJGoTxLV2B/Bv59OYokzCb7HI+p5YBbz+Kwm0aJM0dSTIGtKMeIBR5GydvDPIJcEvB1VP7gLpRAZJeiJ/LWVdBuxkfe9iIRH09VZ5bVTRrKxOcZwm6H7iI2oTkbOu/BduQbvp2iacEv1aDlee9FE3YgX0lGSfRLSdOWhwWXBFVvRwF+Mr7xlwr3SfVqRm8le5J40DU2YC911bmHgLAMBAaMZDYxcJcx4WLXc6AK+F42pDnyhlRK0UDiG99e0Yho7EFld6Y45ERHui9w+W5CC5WK3/SjwFSRn5iMyXIvG9qXu+Bp37U53XSPVZ7u/UVi2rD68S8nzeANwG3JrqXd/o56ZVZHvUZN8ZunQxg/akcUAtDjZh+bxTejdleLTyyWKveokNphyIGSa73ukkYygpmpronvJhfU+2oZoIoOKJPukiygZj7qgRr9HUzlW48n5qW6/ImT9HhxyScCvQokVi1Hel9OAf3Lbc4hfI9+7FqScL0Qle7e73y5DwvbLyDxoPr99SOti+UEnAtK5z2SDN1GUeCK/snjyHQ26jKYkimrDLPgqej077jAiEFuRZqkBTWqmLa9112xCwbU2eTcht6QHgE+iSbYPTfaFqE+UowwJdWiSPsEdY35pK5Dma0H8QwoIGEMYzmIxqRCVAQabGKcjQtqNFsLmm3sABUrNR9anF5BbCYiUnYcUKB9EViyrVvlHNCU1Euva8U4kQxYi07NV4TwOkb9H8BXx7qV/xhSQTHkJEfMdbtsRJCuOQzJmXWR/c3MrJDbHwBpE1tvcsQGxeBY912r0Tl6D5oc9+Dic+Dknvhqz4QhejrfiF0LJNMqJyP1owFA04MlcaUeahKeTUSXZ9mQuMqbkMw4Xff9lSb7jjilCAdBzUN94Nsm1kyOXBPzzyPHtIff/s0iq5RhHkZBbhQhVERKK2xGh+gRa/exEgzBKEGvjTxaQkHwbOU6nGEGigW7HmQYomlWmA02Eu5CGqgZfcKAAn2JwPtKO7EITXhvSeJnf+Ap8VoVGZOmYhtxHommsFiASX+nasxARcCPyZcDvkZbsdETmo8EdAQFjDfHm2JFEdNEeXYSDt3TlRf4uRdaoI4j4rseT9sfdPv8HjeNDaCy/6j7fQvJiE15mNaPF+waUV7wcjesaRMj3kjqNXyWKIbF2b43cy8+THNOLnyaj6GMok/7EwelIcdKENJWWAz3e7zeKaIxWdH6K9vmo9Sc+2DL6fSTcMgZL8EeT+0wmiLY9aoUwK3f8PsneRTQGzeLDDNHv0YJAUTJfha+UO5PBYiR6SDJYnd5RijrkarAUEau5KK3RDDQYm90+UQwlKGKiwYRUEbGa6b4EHxL8jRcgpp3qQYPANF6VaDG1B5kV2yLHmL/+NLS4WoLCD77p2lWDBKtFWvchv8w5iDybubsdX6CjDHlTPY7ckKrQRPwCMlO/CU3idfhAroCAsYgXBt5l2BCVDVF524nGuwViRi1sVsTmRlQ8Jop1aNFdgAL4diHS/QNUOfFUJFMMP0WW0O+g6WsvqqD5JJIJ9QO03+oGQH8FxGxEGoeiF6tCIVUfYyj5iMcnNqD30Y6UIPci96LfkRlxjbr+JAt8TXb+iZR4YTjRk2T7YClt/KIpmsUo+m478MHeB/GxY1OQgu4YUtIODrnUgL+AVAEFSLX8KeR4N0pQjYhcF3rIy9FLaEMDeh/KEz2RMRQTm5Hf6GrSNEaVxAZDxGu74q/XhSbByfjMBZVoIrMCHGYqakbvzUyH7fjUhN0o00E3EtAr8SnNDiKt9RT8RPwcmtSnuWsVIbP1b9z/5yCifcxdY7U7vgmVp8/DF84IAjlgrCGXJdVNNsST13Y0ftvRmO1ByhPbtsP9vztyzPEooLICBWg2owl4GVK0LEKWsjvwAY5tSEseHbf7UYrDYjwBn4fSCm7Du6SAZMIPUQ6CpcCbgVuR3Ho/kmPzgNvSehoey/El0ZfHXXOi4iDKp16J+uw9bvtc/FwSP6dEv3fgXQ868XQpld93/P+J3CpzKfOHcu3R6EqTTLs92LL38RrwaFrGKMlvQ+M7H/EAywTVi9zOVqEx+85BXT2XBPyTyPbXgZK0/hEV1BkFKEGFGxYgE+EjKEDnMHox5ehBJ8usMVEw1EEZHTAWMFlD7EBI5B+eKGtKNIuJZSexYjtWxvYYmoS3o3dag9Z/zehdNyIf0BPQJGbV9LYhjdhSNCHXAJ9Dg/EUFAPQ6s7xK6Tp3o1M1kdQwKWlsypEi7YOYn3AR5tgCwgYCJMH3mXYEM1sYmTCKgt3IqJ9DJHoI25bFSLbO5GvdB0ay+9GMT025uvxOcGPR4vzl9B4NQL+WuSq1oK04GZZi7dqXYYW6YtQgGXUb34fvtLlErctWlgkavpOF1vRQj8P79Yy0TENpV6sAb6I3uU89D6sz7QT6xIYXdyVRLZHqVIyN4VEOcXNSptLqhXFUOab0ThHJfMHz7St0Zzg0XdWhvcPXxq5zgyUSakLccbBIZe9Ypn7FLrP1SgAcxSkETHf4VeQEP8UErSFSFtSyuBXWhMR8WQ5mr8XYitYJguQyEvw3WCFNqJaBfMx70PCtQNNvI/iU471oAnTKp7NRIPKcsB2IS1XHX5AFiONyjxkzrzA/Vbh9n8v8tMsQOvI1e6YR9CEuwxNtpcj0h4QMFaxMMfXN81TJxrjlr2oE5GrRciHew4+5mIRIufXAF/Hm5OPokV2IxqXR5Grwn1u31Ji3VasKE8lspK2kRi73fUPkXi834csbWvc/4dR9o7ZwNMDP4J+aECuMAEe16CUj/nA3yEdXx6S9xYjtJnYwmjJyFw68338/BRNEhAwOpHKIhDlJMWR/2vi9mlFcuHRQV89lz3j56j0/AvkPIdSEZ4EGvF+FuWAXo003l14YZ5L1/nRhlQkOV4gWVGEfPwEasENlm4wOiA68JWqEpkLe/GuIlZOvh6Zg4rwAVX/jAJrq1Gu3knIbPx2d80avLuK+ZJudr9tRpqT49x+xwNvw5s1e9CkeQyZpb6FiP6lro2dyBxci9d27Yi7h4CAsYSmHF47XuttWimTE4eRiXgKGvPL0SLZYjZa0HjdC/wYje/FaEFdhjTcz6G84BuQVutSZOHqQwHbhe74fSnaeQ8i7hbEH6+VftJ9oniZkMs7mzCrqGkzLdNVO+onlfSvChpNQ2jZMXDHGgnviny3/W17VGseMHzIlhtPvEtQlBInyrYE/QsQTUNWtGkMFrkk4A3IuS7HKAK+jbQ6x9zncbftcnx0azH9qzAGpPZp64v7vTfyfw+xZaTbEUG2ynW9SEthacUqkJaqB5Fgy3hgRD4fmXn3uXO1IiH7Y1S5FEQcvuq+lyG/z1PxaaU2oIn3oLvmPHe9PSjt5L8hkn0FXuP2edR32tCkfDe+EEQePuf3RmSWbkVloT/p2pxLf9qAgKEgk1LmmSAqZwrQOLfCNVPd729GY+17KKBxFdJC3+COuQr5Ba9HlWp3o7FeizSh5sYCqmpXgqxjte469cDP0mxvj7tWAbKOPTyYm80CipDcnKhB3w8h5UkFckW5Gcn8V1EsThFSikQVO3uRy1EnsoAuxbs6GuL9xI2Ax1dhDFxh+DBcfvTRRVc7foFvCljwCznQO1+BuMibBn21XKch/D5wP7G1uW8Z2WYsQISuGBGnOuB8ZIZsRcSqEWnIJzOxB1WqhPzpJOs3N5Ru92lGq8ZiZGUw95RefNTxY+gd3IZ8M3+K1yT3IBehKve9Br3DYneeh1HEeyK0Al9AgU/vcdcrR0NiB1oM3Icm81Z3/ldRkR7TlncjjdoXUT/a4c59DPWVErzv5zJ3H3lIo2Y+7hY4FRAwVnB5Dq4ZdWEzi1cDmhT3o8V1DRpzr0cLZfPnPQ7FeJSiXNDPE7vwXYjI9S9QILWN2Q1I+bIDkfjBogxP3EY6dWMJyohShabYx0b4+qMBdYiE1yL3gAPuU4hk+TTgRXxAPMggX4H6016khOkk+fuL5o0eiu9+wNCQjhdCuosgkxPxCy2jx5beuDLyv6EAny0lWnskPeS6FP2JqAfb0rGPESXgs5DWYzcadK+iR9IGfAQJsTuQf8/VTGzynQx5SEDFBy30oknLUvrZYLCCExYgWYhf2BS57T3IDWQdmgwPoEn1BNRlcMc2InPxaWiya0aBlhWIGKcTjNSKAimqEMFvRQJ5LRLg0Zy+30Im4vOR+0ozWqi1oewphtPRqhg04W9zn6uRNuoQoS8FjF1UDLzLsMAW50fR+OxBC+JlaCyuQ+4l+a6N2xH57kbj1axSh5ClyvB6JD8mEZuG7Bn6py0cDHa669SiWJCRRDU+49NCckPAFyB5l6vCTbPRuz6EiLRVJO0GvoEskj8iVnPZi4hWJ1p8laGFWws+vaNlxID+1RITVW0O6I+ReD7pXiOa/KEvyfaozLMUymbhbkUcpo7BFtnLJQE/FTnL5hBXIHL9CiLfF6CBVouaNgURvKVMbO13ohSA0d+SFSM4igTVJDwJb0MuPsWInK5F5HQF6thdaEG0CBHrKnyg1W733a7zO5TC69Pu2KeB36IMNV30LwEdhfkHHnafg+6clcicXY6q5P0HPrcwSGO9yt1TL5rc4k3yje5TgE9deD7qWx3ITcaeZwcBAQFRxMeVxG9rQmPLCGYhGo9TUSbbI2hi/C/3OR5f7bYHaYSj6QhfRdPRDrIfkzGUgMpsoB75mM8BHszB9S9B8r0dpWg8mnr3YcF2pBipwQe7gvrL+UjWvw7NFcWob6xGc8k0RLgtw9Y+fAGeZ1CsQB6+kjKk13dC2lkhUxed+Pzd2TgPiJ+U49OdJtrvCOKGIFlURGyMQPrIJQFfg1QXL+bm8lMQ6ZuBNCfn4V1RZiLBBdKSRyNgxysGciExEh5dFdrfaPBk1N+7HBHRV/FahVIk9HqQcK5Bvpj3IMJ8NvLTnIdP81iEVpn73PGW47sJvbvfo65kAvGLyHqRLEPB9Sj/b707ttm1oxkttnpd+/YROwjnus9xSOiWIe3JCmS6NF/z9cj3vBhfwc7MV9b+HtfeXExMAQGZIJofeaQQ1U5NQsGVfWgMT3KfxUhO/F80BlehKc5qALQiq9iNyJq2E93LrYikZqKptSC/0YR7c3jtqe5vKdIe5kLOdaPgWVD/eRvK634vml8mI6VIPeIBzfj5LA/J/zLUz3ajPtOH+o3FM8VrRqMIfuDDh2ylHozXek+KbI+6FJmizPKAW/+eieRKKUNJz5pLAv4aFBmzHd2dPYkRSkN4Hr4Q5314f+Ru15QZeKf78U6+YeDKXfmR7wMFXrbjK75tQsGLTyPy+jtEdouR8OtEKaKeQJ3/AMq5Xur2OYIE3laUpusKRHgtkPPjqMssdtfdgnw5FyOzcxOqamdkvBSl/ypDg+cSRJwXuXPOR24mG9CiIDqp7kZ+gY2or3SigdiLN3Eaeb8r7rn8HjjL3cc+95mCnyAC4vG15+OzRAwOf33yuVlqSUAsnkG5sIcL0Zz/oDHVhZdRVXg/8El4l4BKZAK+CgVw/5XbVoCsnPejKpZvRe4J+1CwJmRWlPkqJFM2MCryCowK3Ivezz7ko58LFABvQfPNE2jRBnI7vQ/J+g3I+lGFLKFl+PieG9H8cgDJd4tReiOenEV9w6OWYotzKkUuMLVuH3OjGuvEfCJo8lvx77cO9Z94V1vQIqwAyZTBIZcEPBeRPBHUIReHfe77FqSMfw2xnjGJ0t+NVZiA6MAHKkbzcscjmrUkerz5YXagLmQmGRNKO9C6ah8iq/ORproOddJqZCK+GxHYcxFRfg74LCLGpfiqc53IReQ8lJFkp2tHA3IJqYjsfzJyQ1mGBsUUZM14xbWtHaUSm+raZ6Wn97s2taB8sXfRv9xtJ/A/7hwfc23egoTrHry2vAT5exeh4NGjrq1/cL+fjzdvXhT/0AMCRjmGa9pIJGdbkQxpwgfCF6Pxbjl49yBMQ23OAAAgAElEQVSCtBMfPzIJjec9aOx9Ek8Ea+L+4o4/AS0uBps15OTIXyPgtUg2vczETDV6mBHPp9APi/CkeymS9XNREO4LyLq9C/hzJNs7kDtkOVLINSJljuWb/z+of72Ed0FowbumtBGrEe9Ec1DUhRFGTyXMTNqRSgk3FAyWY2V67WhAd/Ta0bSS7XgCPh1vGZkZOY8V/IOxFoRZl8NrI0L2Ehpkpv14Avh3RM6iFZHGA6zDdqHXbu4k1vks/Y7dbzsSQpORYLHV/BH3/QE0ub0TaQ+O4lf929BiZjciohchn7k/d8cXub9fQWWZm5AG6QDSYJWiSeuY+zsdBTY+gN7XXLf9JZSvvR4J23IU7V7kjm3DL7CiuNN9QBqPlUgQ70zzWS5Cg7AWBTftInbSXoGCw0Dalofijrdc6PYJCBhLmJfl85k1K5EP5U4kK25GC+mr0fhvQuT7QeTN2IjkVT6SPUeBmxCp/iOxWtjfIK3nc+7/QuSWVoTcEL/H4PAISlloLmjlKIi/GJ/uMGDksBi90z2oH5SjhdAL+BS31e5Tj8h0KVqQ/QLJ7pcRmX4H6n//jvLKd6P+dTqS43vxhKzbnTsfzQdWo6IHn66wGx83lShLWDaQa+30UK4/WJ6Vzv6pSH3UhdZiACCWEpdHvpdGjonuk4+Ug9VIGRcl5wNjApdouhCfUWM7Io2gAVKMLx87lrXf0VVeJ94txMoet7vvtoKLksGNSAv8EXf83Uh7PQ1fyKATCa3J+FLOXXgf6cfQBAh63jMQiT4XaZr2IW35LDT5nYCIc4drSwM+xdgRFLH+E/zi6EOonHS8q8InEEnYx8A5e1tRUGgUxSjV4S4Sm6aLEbm39FSNcb/vxms/EpH6LajPVdOfnAcEjHbMGXiXQSG+XoAhGuT8MSSTypDMMN/tbxGbqQi04D8Tub39IMH1trtP9Ppd+CDwweJRYqvglZDYRSFg+LEEuNZ9vx1lOynGVyM1q+7H0Lyzkdg5/kpEtk5y24vRHHcx/r324hMDEDm+JPJ/GXI9KUP9twUtDA7gXRXy3PbJkeOyAXOBMf4S/1ui79kk7SPBl4ynQWz2maEgPntbIkQXTFG3VNOgd+L9x4d25QkGy+vaQ2zwzc8RgVqAfIvLGTtIZFaxTnMIn+e6EF+m3UxmbZHfLVf3bHywVQvwjyh48SwkvCYhkhutRjkNr13vQlqmOUhrUOP+HkDP/Fx8rlzLmPKqa9MxNKn9AE3CWxHBt2w1IA14PCaj9FKVSOMxFLwNvxiwstVR2LOy4j/xqEfZF/LxhXiiaEe+8KXkLktCQMBQEdUYZQJzZzuGrzBsssvSkVrWo3ykqbSMRd9F7h7x5n2Qd+PpiIhvYuDCQT3IErcQVcbMFIdQzMdc+i/uA4YXpXHfzSobRQHKdlKBTw5gBd2K0RzSjvplNZon1yHLplVKtqDSqD+3WZcLkVJmlrvWInwgcAleS26IT2AwVETzWffFbbfr9cR9L0jwPVH78uK+p9vWZO3IFNHzduADYKPbUy0EzCJh6ZCtXU34ipYHkEzoc9+NK9WjfpPn9i+MfAaHCUzAn0TksQUJ9MkoYKML+A4abP9O7rTfiUyy0dzZ8QGRlhmkHR8UYL+ZlvdifPVICyS0AWu5Le1zO17Ta9k62pHptsRdowEJpnuRkGlF5NdygP8U+BoKdGxBhHkfsjxsRVqIsxGZrUcm3D5EzC0t1LXuONNY3Yvy9vaSWHs8ze0/E29ijkceIv6JyDH4gVZKrIXA0Ova00fy0sPJMrCAd8NZTvJCQQEBoxVPkb0gzF401rvQJNiOXAfWItJixKgQn9v/FjwxT4TDbv965FKSTuXOg+6TLWx0n4CRhVlJClE/TYQ+NEdMd3/PxpPXje7vMXzqyj6kPLLc8ftRn8pHVtJVbrvlkihCfXUWnj/sQDEC9ajvznXnjlpczApj36Pk2AiiEf68yP52jaP4BYWdx4I+iexjsQ9RLXKiDGhRTbrB2tIXd1wXfi6Mnjd6H/VIqZeHxrblVY9eLxWiBD5KtKPtMzlibrWFcfvb82hA76fVtdX2q4q0w1yHQMq8+Xi3owXuvl5Flo3455weJjABB1+5EKQxWei+Ww5nq4443EiUXSRRtHQv6jDH0CCbS6w/cfQ4I+ltqPO86H5bgu7J0itFO75VoLTgg73IDWW6++1qfJGLLtRZn0Da3iakoSpEg+zXeNJ8W+Qeutw9dCK3n0OujU8j95KP4dP/Vbr2zI8cX4vPrV1Lf2xDfqG1xBbbiOIGNIDWIP/QeNyCTNhbSZyn+7v4CP+haLBXIt/CIuDvgWuGcI6AgFyhdeBd0kJ08m5AMmQHig1ZG9mvBLmbTUEa7VTkGzQ+m9AEOlQrWMDYxZoBfu9BRbhPQvJ7IVIsbUd1Hz6MyPtVSP53oWwpRe7/J5GbZSmymLwOzXsvu+3mdlKF4sm2IULehIhnN1IUdSAyX4NfaEYtzqbNj5Jm8BzBXEjz3LFG6ONJsyVd6MNXju7Dp1Y0QmyBpVGXjngSG+UZpkUGzeMz8VnQjIAfw/tPH8TP679F1WnjzxO9plnibTFhlUltuyGqVTfFo7U3nt/ku2ONQ7XhFw/xloOKyLUX4VMHl+PJ9mS04G+jvyvcwJjgBDyKHSjLRhnKhJKPBkw1wx8oZy/dNK3xZVGj2yehF74REV3LP9vm9qtHLh9RX+9pKHtHA+o816DgybnufNYN2t05GtAkOx0JiL2uPVaitwp14Bp37DHUifcDn0Iry2Sm3Pe43w8hn+5W9MzvQsJhDSLch1Dg1VJiJ+PdePN0tJy0oY/+KQCjKMFXq1pCYgLeSHLyjmvbjeiZD8Vs2IoXctkiMwEBI4XjhnhcVHtmi/19KCbiVfd/M/1LQ3QA30ayJh0f7SZEwgMCkmELvnrxt4FzkMXUAnynogJCoHntUbysXoX6cisi7ZZJ67soJmkm8DAib+2IW1ThXVx2uPO3uHMvwQd3no3Gxx48ITZtsVmizf3BXGdMcXczSpFo9TTmuHb+CHivu14dXqG1DZ/dwzLAgea1qEuHzVXxVvlX0fxsSiprRzQfuhF/8Fb2LnfdDnyxOkvzCOIwVW6/Zry1wGLY+tx2s1TvRjIpD8kTO5dZJArc/pPwmnEj6vlo4bQKvatnkXtSj2vXdHzmOCu2U4fe8STETfagBdwPGaxlMBBwQC/v/ahz341cJmajTCmzyV5GlPgVmcG01qbZ6SFW825+bGYqmYpSKNr5WvGuNLORQKhHWtxiFPxoWuVOJDQ2oHRMP3HnXuzu8zT85FiMBukzaKW8HvlfV+M7YCsi1K+6ts5AA2YXEmTxsPsqQ8/7trjf/+D22Yo0DfEBljORAOnBC6jBoANlLTiJoZeHvhJV32wGPoqe9WCwE72POcQuLgICxits4rSApQbk6nc30lLnIZcsSwcaD5u4AwKyjecRiTqM0lWeiOaunyHLSyfqn5fgCzqdiSej5uv9XpRkoNSdcwnq93VoPjwbkb1N+Ll1NZ4TrEVzeyeyLC9z57f0iZaYwOIlOvGL2i7Xtno0fi4DvoAWAj9C8RINyLpk49AyxeSjMbgSkeTHkZunxWZZFfB2xBWq3bUPo/m0FXGPKe68B117Tctu5HiXu98CtIjYgObA25Hl21I2/tQ9y8fcsdPdte9DMWitSAk2yz2fw3h3nE3u2iBOVIRPV/ogUrLeiSzkPe7YA2g+7gA+hzIi1SFOZIk6tuLJvNUsmYpqhfySoSIQcMoRmToFddBNKLp+DnoR0VUX+JWTdX4znZh7SAWJ/am60MuudN+PumOs03YgojsDT5onu9+2IvL7BtfeY6jDTMUHAq5HC4ZzUUd/BJnWZrlr9SGBcInbz9xGjPS/iDq7pc+7FWmCrZ3PuO1GtGcjE90hvCvPZFTkIs/d1030x6/Qs96U4DeQ4JjlPpvw7iYG80HHnSeVtjsZHiSz8syr0XOvdm340yCPX4wEQC9KUxgwHAiFfIYLL+PzK6eCmYqNIFhcxEEkf75HrCk5WcxGQMBw4q2oP9chwmZF4GxOB9VuMA3xZkSWbZ4vcd9LkWV0BpqzZ7rvm1FQ7u2I8LYjxVMHssBa4Z8SfBE7S6Wbh0j1TkT6HgbejHjEc0iRdTyyHJ+AeMl8RO4tGwyoCBWuXefgY6imufP+3J2nBq8FLkLzbx/iHQcRUT/TPaveyOfTwN8g5d5OVFyvz933Fe5caxHHKHbt2Ir84m9Bmvvprt0L8LVFXkYa7WZ3b0fcc6vDZ1h7FHGPEvd8XuOutxWfLrIDvWfD+Sjm70l3zve4c67HK/3+Bb3/PsRFjrnrPIXPXBMfHzY4TGACPglpc7vQC7COvxl1gufwifynIJLZiDppJeqM/wn8KyLCbYiMHkLkPRoEeRRpdich4tqLCOCLKD92E1r1bUUD4ErUOS5GA/MO1Bn+CmmoGxBB/hzqrLcB30Qd8g7UEZ/EB5UedG25EHXy6agTxftSbseb3Q6RGnvpb+btQsRyGp6oJzoukeuIwfJpd5BYE7YRrYKtymYinO3a8TDD4+LxSyTkGhmaFt2ESDmpn0VAwGhEA6mDpsxs3YrId4H7fw/SiP03vihVQECusdD9nY8016bkisb/HI18fwbvm7wRkbxq5FfehLf8/gDNtWZ9js6JNvfejObLI2hs1CBSZ1bSfDSPWezV80gzXorG4XcRiX3Sbb/SfY9ai/KQq0gT8A+I9rWiJBO233LETcrdvf0SKZe+jvjMW9Di4nLkuhO1uB9FwaxXuHNZCkbL9vWMa+8dSDk4z933OxDnOAFp5t/mrvEpfKXqB9B7OYBXXvahxfsGxLu+gnelbXb7He+ezY2ujfGupo/g5+4p7l43EJtVyQo2mcuLcZcKRPqnu/ZFYXIxvYDMCUzALVcsyLRxH3oB0YH2ECK47Uhjc9Adsxy9mHpkjrgYT97b3DmsQmM+Glgb8U79L6JOuQxNSj90++DOeSvqpJWI+P8ECYT/QAOkEQkNcxtZhQZ3POn9IyKjW1z7tiLN8t64+4xiIOKdCpY15TBDz8t5L7qPgyQm4D3u92S+1wvwRVYLGZ7S0M8B12VwfCt6z2a6CwgYS7iBWPJti+X9SL7sQyTgMCIHZyG5cCeSYZnImICAbONuNE9uxLtm1hNLxjbgUwnuRnOp4R8QcX4s7rxdaH5PhR8jEncE+Dw+OUIe4gEFiIfchPhAPZrv5yFXkTX4oNMXEOGN18peiDS+vcjt6+MJ2jEd79oyDwVIWl78T7s2lLnzn484zA7X7l3EWqpbUcyZIVoR1YpcFSB5MRM9y88g/vB2xAFeg+b5FnfPR9CioATxn/uJndujiravR67xE7SASGXx/nu0iLoMadx3ue2/QDypCwXrfgifKjmZgtH8/AcKFBcmMAG3/M1GnBOlqnoBaZ478WRwLd5v90SkhT2IyPUrqCO+iFacS9HAbMBHKK9FK+Ml7hwWVRtFI9KsfwPvcwaaxMyM2+m2mwY/Eb6FBrEJgQfd9S0IMNvowLu1JPL/ThfJOjeI/K5H7Y8P1gINGAuySPZcRgM2oIFal+uGBCRBcGFJhugE34l8TDcjq9Mm5BdpAVbdyNLYjmRPorzdAQG5xCZiramvJNkv2fZ1GV7firUdwbuL1qFxVIS3ktqc+iHEGRLNb4lcIoxf5JO8WMxO9ylCCxAQt7CUvSCO80PEI465dj6LSHJ8rvWB0INIusWR/bXb3gd8FZHtvYjsW8Dq48gakK7bh12jnNQZSowL9dGfF0VJ/tfxBQyToQXdU6L6IP0xgQn4Y2gF1kzqPLGHU/y2Ba3WyvD5Lh9DPlYPo066FBHKZ9CqrgGtoA+gzn2MxAVl2kn9oveiKpXnEbvajKILX+HTkEzznQ20E+sDNxx4AS1MukmsXTjo2lBFcoGZaxxEaZhmIwtKwHjE+CXwZyMNVAHSiH0BkYFpaEyadcrM2/cTEBAwEL6M+EYLqSs4dzM45dL9aEF8CK/djcerqIL0XGJjmiy7ykn4OLAoh0jFjwaCFeEC+FtEtu9F92e85V60ODpE4pTA6VxjoPSAX0buL5tIbbHoYuBA8L9ALrKJKvD2R15fX6bVl8YW1q1b13fmmWcOvGNAwAhj7dq1RPtmpgQuICATxC8A4vtnQMBoQeibAaMVfX1960mSbWHCEfCGhoa+urpg9h9/sBRBgzWFjR4sWLCA0DczQbQCWkC2EfpnwGjF0Pqm1dAICBg+nHHGGdEKRjGYcC4odXV1YaU87lCE8nHXIpeOwaYFHB0IWpxMUI1cskpR3ENIaZdthP4ZMFox+L75Z8gFcCNKlRcQMDzo6+vbkOy34S7xGBAwAqjAl6Wfl8uGBOQMM/A5+UMfCAgISIZCfBGX+al2DAgYVkw4DXjAeMRhFJl9HP3zcgZMDFixqioULR8QEBCQCN0o9eByQhB8QC4RCHjAOEE0sX7AxEMvyjMdEBAQMBCi6YQDAnKDQMADAgICAgaN8ZtmMSAgIGD4EXzAAwICAgICAgICAkYQgYAHBAQEBAQEBAQEjCACAQ8ICAgICAgICAgYQQQCHhAQEBAQEBAQEDCCCAQ8ICAgICAgICAgYAQRCHhAQEBAQEBAQEDACCIQ8ICAgICAgICAgIARRCDgAQEBAQEBAQEBASOIQMADAgICAgICAgICRhCBgOccJcCVwBuAghy3JWB8Ig+4FHgLUJ7jtgQEBASMV5wFvB2YnuuGBIwBhFL0OceZwOnu+wFgfQ7bki7yEZFryXVDAtLCEuA8970NuCeHbUmFSUAP0JnrhgQEBATEYSD5VIMUabbvT0eiUQFjGIGA5xwNQJ/7NOa4LekgD/gAMAd4Erg3t80JSAOHgG403Bty3JZkWAxcC3QBP2BsjIWAgICJgXTkUytwFKhg9MrZgNGEQMBzjpeA7wK9jA3SUYbIN8DxBAI+FtAAfBtpZfbmuC3JcBxywSoA5jE2xkJAQMDEQFQ+zSWxfOpAc/kUYOfINS1gzCIQ8FGBA7luwCBwDHgYWAo8lNumBAwCh91ntGItmtjagU05bktAQEBAFFH59GKK/Y65T0DAwAgEPGAIeIhAvgOyiyPAj3PdiICAgIAECPIpIPvIRRaUFcATwKPAj5BT8WeAx4CfA0Vuv+vcfncCk922i5Hj8YNoOWrnewx4HDhl+Js/nlADvBE4OdcNGWGcB1wOlOa6IeMM5wKXMbqe6yQUGHXeQDsGBARMGBwPvAmYOYRj84ELgdfh6UpAwOCRCwL+ErAaeK37/wzgIuA1wEbgzahXfxQ4H/gZ8BG37z8Crwc+C3zObfsSio54h/sekDbeiB7/W4DqHLdlpHA8Ssl3NnBBjtsynrAYDc1z0OQ0WnABSg12KWpjQEDAxEYB8E5gFfDWIRx/MpIrq9E8EhAwNOSCgHdFvnegHGkPuf/vQzP4EuB5lLrBtpWhHGotwFPAMndMLbAL2ANUDW/Txxua3d8O95kIaEEBryCzYkB2cBT/XEdTekp7x72ojQEBARMbvXg/7eZUOyZBM8paBmEOCcgEufIBvwr4F+Bl1wYbBUeQX0R1gm01xI4Wq1oTXUQkW1B82H2YOnVqhk0fT7gTvYJ6tLaZCKgHvofymG/LcVvGE+y5VgKv5LgtUTyJgpxbGFvBzgEBAcODPuD7KNvSq0M4fjtKRVgI1GWxXQETDbki4Le7z7eQltt8vCcDTe4Tv+1wZBt4dVtvgm3x+J770NjY2JdknwmIXmBLrhuRA9TnugHjFPWMzmc7lEk2ICBg/OIosDmD4/dkqyEBExi5cEEpiXxvRppsc8a9FFiD1LIr3G+2rRVFVFUgp07LBXQIBWTOJtiDAgICAgICAgICRjlyoQG/HPgr930rCqychTKZ7AT+C/mJ/w/KlHIYeLfb/8vAn1Ayzhvcts8Dv0TZVD4x/M0PCMhD3XgKKuseisaMHeQDV6BwkbsY3bnRAwICcoepSM43IjkfEJBdZELAvwr8M3Ievgc4Ffg0cNMAx93mPlF8xX2i+Jn7RHGf+0SxEWVQCRg0ZiHjw6uo0MBoRT6qvlkPdOa4LQALkBEGlKjnlhy2ZbTiVOAkZLzakeG58tD7b0Rr70xwPMp+ABIbd2R4voCAgLGNxcCZKO9DtAjYBcBpaM7ZTPD3Dsg2MnFBeT1yIXkTsBtlLvlMNhoVMFK4AlW0vAIFz41WvB34APDBXDfE4SDyiAIl4AmIRRFwNepbV2bhfFejd/9hfOz1UHEAT+LDuwsICDBZ9Ra02DfUoMX6acQmbwsIyA4y0YBbBvorgF8gX+yAMYV65D5/hMw1i8OJGe7vVNRlu3PYFlBGjW+hzJih2/dHN1qkTCU7QZlWLKMaKCazjD1NwDdRsaDgfhIQEFCPFFAN+PSCINn+NNKAh6LhAdlHJr3qDpRCow34ODCN0c3iAvrhTuBZRJZG8wr/DpQKfhO5J9+GdkJ3TwZL8zWd7GQL+AOqZPky2UmX2Zal8wQEBIx9/BLlcNgft/0BZHFrROFpAQHZRSYE/LPIb7sZ6EGZ7a/ORqMCRhK7E2xbirSN69CrzTW2u0/A2EEH/V08FqKJbgODW7zsQpNkQEBAQLbRQ2J3tCbgN+57HnJHaQdeGKF2BYx3ZELAC1A5+YVx5/nPTBoUMFxYjorPrCN5unSQS8q73PdK+se8BgQkwiTgdDSRJdIWTQauR2Ens4HfjlzTAgICAjLCOSjsDeSS8nIax8wATkDBnSFDckB/ZOqC0o56VypGF5BzLALe5r6XAo+keVyoWRSQLt6M4rB7gK/jSz1H0Rf3NyAgIGCsIR35lQe8D823y4Ebh7NBAWMUmRDwucAp2WpIQDws4Cwb5bP7knxPhN0oprYKuQqMJMpRmrhtKNAxYHhQizTR2cxfPhC5bkZZRc0FJRPUAPNRCEpHhucKCAiY2ChDmurtSE4lwhqk+W5H5UvSQSqZOBfFXoVYlImMTAj43cgmc2+W2hLwv5gJfAh5+fyOzH3OtgO/QgT3mTT2T8e8Nhx4D7r3wyhTRUD2MR/VsMpDftXZete3AiuRC0oi7Tcoj26muXQLUErCcrRQiy8VEBAQEDAYXIcvpP1fSfbpA9YP4px9wI+RQil+/n4DqiPRDPw3o6O2RUAukAkBXwP8HqnSutCM3oecPQMywjR8vuOZZCfoY0sWzjHcmBT3NyD7mI5P/z+D7BHwduDJLJ0rFfKBEvc99JOAgIBMYXKkFE9jsoEDJLZgW1rVye7agYBPVGRCwL8GnIt8wINTZ1axCZmoJgFP5Lgtk4EVwCtkxx0mFX6JKihuGmjHgCHjWTQBFKAct4mQhwIq24AXR6hd6aILuUgdz+A0UgEBAQGJ8CtUbOdFRMJXokDyRBnCsoF7UJXN7YTgzImNTAj4VqSaDeQ76+hFHj6jAe9CJes/gMrxPowyqQwH9tM/F2tAdtGN8r+nwmrgUvf9JuDVNM9tVVV3Idep4RIN29wnICAgYCh4PVIsPQqsBf7otr8b+YN3o2Dy1oRHZ4Z9hLSqAZAZAd8HPISYYjQSKqQhzDoWIneBZ8iNuaoA+Q7vBl7D8BHwgNGBvCTfDaehCSreNeocZDFZjjLtDLfFJCAgIGCwKEIKhlr3fW1umxMwYZEJAbfqKMXuEzAsqMHnT54B3D7C1/8V0hTsQa4LG0f4+gEjjyeQT3cbcj2K4kyk5QalHNwc+e0FYAGwF0X4BwQEBIw29ABzUOBlfNaTW5GCYRfDo/0OCPDIhIB/0f2tRLbmo5k3J6A/8vBayPxUOw4TjgCPu08Bo6MyZsDwopfkVo6oRjy+P65HPuahjwQEBIxmbETKhT1x21vJfdxVwERBJgR8BcoBVuv+bwTeS4igyzIOIT/cmeQ+6CwTYpXN6PKA3GEt6gddJB7q2SDfoa8EBAQMF3qBn6DCYc/luC0BExmZqFS/B/wVsjkvAP4a+J9sNCogHtvwq/KVwNRBHFuDqhSeke1GpYlK4NPAZ1E3CRjbmALMo7/oKEKm2xkZnLsC+Avgc6h6a0BAQEAmmA+cTP9YlmuBjwOXjXiLAgIMmRDwcuDByP8PuW0Bw4ZrgKtQIZJ03e4vR6n93sjgiHu2sBBV1SwGThrma52ItBoBw4c3ov50NXqvhivdtg+iynJDwXxUAbaIgfvKYmDZEK8TEBAw/jETlYO/Brgwsr0UeBNSFrwzy9ecgZRkRVk+b8B4RCYEfBvwj4hhLQT+AQVlBmQdpYjAWgGSInyhnoFwOHJMSaodM0AFsWQsiq0ooOUQ6VXhHCpORcL0Woaf6E9kWH9qRZONFZUodX8LGLxn2yTkyfYqyr97mNTl6hejqqlvR0GhAQEBAfEowmu+SxDdqUBJ215CGcWeQ/JqAQMrtard8clQjhQQV7lPQEBqZOID/gEUiHkL6uWPAO/PRqMmNuagFXsTyhU6H5HKLuDXiHxsQxkq0sEfkWA5F/gS8BjwW3z092ykJd/H0HKPW47wfNferXG/twM/HMJ5B4uSJN8Dsos7kO/3dWjot6Lc7TuAP6A1eHxmgVSYDHwUkfA7gR+lcUx41wEBAQNhF8pqUoNiVz6Kqkzfh4rhnAPci9zezkEZnf5vknMtRQqebuAHQH2CfQqQBbYGKZwCAlIjEwJ+GPgUmkF7CVlQsoQzkDawFpnYl6OBXYC0zPcN4lwlqKJmL3ILOA44BjQgIgUyzc1zn+dQCrkoCpDfbwOJA+Nm4bvRHPoT8JHCWtTWXkJgTaaoRoR4X4LfpqAhfwHS+MxE2u9KtLBLp7T9dLTA7ERuUVYKeh7pBRq/iMh+CbAmjf0DAgImJnYhWVOGyDfIVXEu0oSfA5yPZNpqpEjqjRw/B2hx++chrfosEhPwIrzyoSsLba9Ac1qoljlekQkBPxn4KbFZUG6gf3WOeJyNSkz1oFxnfwl8BjmQ1gU3tj0AACAASURBVCGnrS6kYvsEWkq+G/Xsi4EvI7Xq9agyzArgu2h0fIwxn6j6RfRoW1G+5cmIaKwleVnwFUjD/TyxhOR9iCAdQI+6AuVnjgqPbajy1xESr9rfj4TQi8BvEvz+PN58l8uCBn3Akzm8/njBdODPkGi4nVi3oZnutxKk8Z6L+lMLWo9Hc4Inw+VIBBwCvoM05k8jMfLIINoZikEFBASkwhSk9S4E/oS01ycC9yNF1FJEV/LQ/PkiseT7PFSwpwNRnWmIeiSjOM1IATGF9GRhKphluQD4BblTbAUMJzIh4DeiLCgWiHkhyoyyeoDj6hCRbgd+DrwWuAiVWPw7lLLjVjRyzgfeCnwE+Hfkc/56pBr+HCLoX0I+Gr3AtxGRH8PYCvwrIt6fQo9pCxqEyfB6pIGchU8TB35tVI7Izm3ue1SzuQYJi1b6r9rzkYsKSDuZCF3A71PdUMCYQi1eLMRnNKlBfaILZeVpQpNOHeqfHQyMuZHrlLnjh+L6FBAQEJAKVXhZttB9fwW5cf7E/d+NKMzvkCtdFCb/Sty+A5WP70K6wAokGzNB1LI8l0DAxycyIeBDzYIS7eXdwCnuWJB/xbvRUvR59/t9iNiXIcfnFuAp4N/cMbXIzgTJIwE/7D5MnZqLTCCDRQ/SKN6DtMsPR34rRqauY5Ft21AQ4k5i8zD/Bj1e02I2k9g/N5mJqxf55Z5MMPVPFLyEXHgKgEfjftuCtNTlyNpQhRZ+z5Me+Qb5XF6EAi4H4yseEBAQMBhsAx5AioNHEVWY4raD6AVoztyZ4PgHkMKhMcnvidCNJ995SJE2FBeSqGX56SEcHzAWkAkBtywoP3P/v4fBZUE5BTmANuFZ4xE0Wqrxs7NtqyF2xrY0INFMLsmyunzPfWhsbBwDFT7K0Sr9BbTWMFShdUQleix7kFb7VrSGiR/or9C/lPhgsYHkGSnKgVXIRaUZmfbaM7xeQG5xCVrM7Ufr3RqkgbGsAY+gCP8r0NBvR2vidLETubZcgDKYJHNbWoSsL+sIfSogIGBoiCoRvovmrCNIw3wC8lidg3zBnyPWta0JxbUkwkKkCEulmb4BkehnkMwbCCVI/nYg3WawLI93ZCsLCgwuC0ot8P+Ad+AZHGi52OQ+k+O2HY5sA++s1Ztg2xjHDcjfrAF51RhmIUPALETQK4DTUZn4TE1eQ8HVyE/uRKQRbUNag4Cxi8Xu70y0Dv4QCpJ8GbmZLEVrZ1DA8FDcRy5H2QJORb7kDXG/V6MQkHzXjmSTYEBAQEC66EbkOx/NsSVo7pqMiPkcFAQ+kI7ueCSfoH+cjMFSG4KXqQPhHHxa1QbGfDhbwIAYKgEvQP4Nlw7xmjehwMv9SAX2ceCr7nxr0Gy/wl3HtrUiJlCBfMAtIvEQPtXHOAkXLov7a9iKtOItyJTWg+JQ00EF0lq2ItKUqmT4lUj7eBfeuycRepF20tY92Ui9VOLOlyyK/DzkwrAZ+e2NVRSg7jzakgc9gJ7vVtRXLDeu9cX9SENTjHy/B4M8ZL05hCa8eWjCuStuv140CS5Bi80XkPvLYPAOd/yfiLUiBQQETEy8FqX1fQjJmGL3dwfKNraTWPI9CxXsaUSWZpvnovNysqJj3Uj2rEDxMheiMLfn8BnI4mHzZx++3sJgMRnxgzFg6A8YMgHvQbNzFYMnvVY94yvu/88h7fljaAT8F2Jf/4PsR4eRXzgoA8qfEOu7wW37PIqOyENBmRmigNTkdCTwC+R3/Xzc9h486TR3+2Okh9X4AjU76B/JfSIqJb4badVBMbA/T3HOW5HX0U/Qa0pF1tPBcehVd6FcqwcT7LMKvaMVKBXdWHRPKEauRFOQT3Susrck6utbiTWr/hJpcMxV5BAaosWk78O9EGl3lqBhug71sx4kCp5D7lSGZtSX3ozSYl7M4Ah4Ob6vryIQ8ICAiYpLkBvdOiRHQHKvFbmgbEBG/AfpT3rPQ4qo2UjLvcNtfx4ptIpIHRv1hPsA/I277ulo3kpkrH/etaETZS4bLK5CVTi3o6wtwwULXg3IFJm4oLSjHvMnYlngpwY47hf0T+nxJJ6QG36G9y833Ef/RNgb0dIyQ5Qgc3st8r0aKJvicGIPsYQkEVIR71qUVmkHKpoyCfnUzkOu+4kG91sQqVqMSFYt/f3b5iFyvB+Z8RagYLrGAdqaLhajLlnorpWIgDcjrcRgAv9GG2rRAmsySgmZCwJ+Aios0QJ8H9+f8t3HBGyiOIIKRKi3kTw1ZhR/jrRJc1BA8TloYqpBE2G8+1QNIu17kcZ8sAu7Y6h/XIQyGVwH3IyI+UWofz8+yHMGBASMLSwGLkNzex+au2agoMZVSHu9HCm1Es01e5CbZQOxc2Yfnlini6fwGvBUnrJm0a5COsZi5DAQn6ElimJE2o93/x+H7tM4wLODbGsqvA4p87YAv8rieScmMiHgf3CfcYKZKCYU5OEyHAS8Gmn1WhHJz0ay/kS4EhGYVcio8Bo0IA+irCaJCHg9Ir170LqnlFiSfzKq0NmHNN6rUBhAMaoelo0sKVVIE/88yfOoFuHJUxW58X3PFN2IDJaj+8kWitFCqhRZJ1IZp05CGplqfJBlNSqlXIoIa7KY6qvdMSuRgG9NcZ15yOw7DWl3qtA9n+7al4cWJNbXKlAG0qmo795LcpNtKtzizjsHTUw1aBysdL/vZvAuNAEBAWMHeciqGw2W7ES05wSkQEqlxJmOZEQPkiWJ5Fw+koc1JJ9bQcb8+KxSqWAyC8RHkhHwd6G4nKeQLnQ1mj8vQ0T8VCTHs+Wde7L7eyJBE545MiHgPxng99+hHN5jBLuR6/k0hs9kfTY+MGMz/V1MsgXLStGOBI65Chwl+UD+GVqE7EMCJ17DbsIgDxG1eYhA9iF3kEwJeKk7zxbX/qhgzENa7xmIlFkqqVak0XyFzN1fRhJt6N2Xkt38rsuRYAS5dqSqmroOEeMj+LRc8xEBBrmLJCPg1p/aGHgR2YrutQbFHlSjCWWa+34UjQt7f2VoIdGIxsjtaAKdjvpmKu3RItfu+WhBsRYR+R2I/JuPZTeDy9wSEBAw9mC1/kyeTEHyZgoizkeQDE6GZiTfuvGFe9qJXbgvwgeln0d6mUsWodC27Yg0J8JW1+ZiZMVOhALXJpDsvwfPKaYgAt5Kdt00H0G+9JYlOiATZELAB8KiYTz3MKCH1MVusoHtwFmIFEddTIoRwSxC2r5UGsV0cBtyDbCAuccRAeqkf8YJQxepSewaRI46kdfPHuRWAEo/GI98dF/pDv525M6ymP4CZw7eL/0oPgX8fyBNfIfb9lCa18o1jqHFnlViyxZ2o2dRyMAZQfeiRERRvIzIahmxqSctBX8f0oBUIp/IhxmYgB9ENbtq8TlxOxAZXolIeNSNpde1vR34o9v2ftTXXiJ5MYylSBt0FpogZ6G47uci+zyG+m2yqq8gc/XV+Eqgoy1INiAgID1sR1rpajQ3XIeI6m+RP/gUvF+3oQzJpZ3IL3w3srIuBt7o9vkpXr7uR/NcLemlGgQpjWa5zzoSB1y2I216McndTXuQVv1k+rvU3Ynk6gGy66q5jlCFOHsYTgIewnD/FwUo9VoxSivYgois4WS8aWc/sYV3TkXazGdQiqQopuJ9taOPu4fYoLVliKiChMlQEvt3ohW2oRH4bJJ9S4F/ctf9LfDjBPsUIsI0BWkNdiJfN/Nnm4y0ma+4azUhQfpq5BzTkX/7PBSA0sbYCLibgd7pJBQdf1OWztsAfB31t8no+e4gfetEO/0NWxehYNz9SNi/FfW1GcROOKvQxLWWWNILmgSWu/OA+kOdO+d8pDk6BfWD96NJcDdaiL4PuW1twVdlTQTLRnAILWQb0AS3ndixMdDCZAU+gPMMxs6iLiAgoD/MV3sqkh+HkcWtHBFbi8c5G8mtixA5b8TV7nOIZjuZFPlegTTBh9BclgyLkXLgeTRPnYlk81HkJnOI2GI/K9D8ids3WvOwAlkQd6CsVYlS//YRO1cGjEYMJwEP+F+cgiZzkAB4KO73fWgQ59M/+PJyRGhnEkvArShPERIyf0IkpxRpL5Otf0ZiXTQD73t3BSJ1Rtr6ECFbgM+PeiZe+NjC5D1IyDQB3wD+G91bVCP5DfQMTLM6VrpzJxL6VWTfdca0He9Az3gp0hwPNa2VmThf7843n8QuP29AxH8q/Qn4fGInsD4Uo3AdWmS1ufOd4M4xDWmiF7lr7nDHpdIwPeuu8QAi6+8Frnfbb0txXAV6H9bv9iCtfgHpV78LCAgY3WhGroxz0TzagOaLBuQvXY4I+jwke2ahuWs5mpu/h+bndmItdgXIame+5clwFZqnjkeKh41IzlyFyHYf8B28hTqZHCoB/gLJ2fuRpjtgrGI4GUveMJ57jOEA0krno2DHeOxFZNLSwp2BVq+HkX/uMvqbyibhA/hMW/w2938xsZk1XkQu+UUkLhpQ5M6XKK3cGe73pxi4zlEpEmJ70CJgOfIZs5zO73L7/Ro9k0bkGpPIx83yT9s9dtPfHWA98BGkeS1h7JTsLUNDz0oVDwf2I/LaQvqpKhPhYaQR2osmhBb0Th+K2287mly2xW2/AGn5O90x+9GEYmS7Gb3ro2jyeRVpoAtRMPExd+1fk7qqax/eDFuBT9M5NfHugCa+a9AC4HtoEbcfpVnMJ7ifBASMF1gmlENormtCZLsBzVcr0Bz7EFq4b8anaC10xzfS351jDwoUn01/V0yre9CM5v3JaN6rQzJxC96FL4/YgPx6EsuhamRJLHb3Ewj4WMZwEvC/G8ZzjzHsQRpcW3Engg2yjyBtdwvwn8iFo5r+2T72o8E3A7kGzIr7fToiRC8gAWC+xqvcvo+47aXumtXIxeQpfBq4ArzfWx8DuzJ8AGkvtyP3FAsCARGhPHe9TyBB9LD7PRGxuhkJxXTS3CVaVAwnilB+2R6kcR1s3ngroFRJ4gVZNnAPeucHiXV3SoUzUN+zvgGaiDajCeNa1Kf+AfWvqD/2zSTup1ORBrwP+U6ahace3X8RKhH9DKrHdS7qf8Xucz+KzUi2+JuCgoJ24S1ER1EsxSLk950Mx6E+Webu2zIFZBqDERAQMPpgyQUKkCZ8KiLhP0LKrueQvHzc7VuONN6NiGC/wZ3nJrx7Ry1SUBSiObkFKT4eQ9rthUgZ9St3jv3AF9z1J6P4mCZ33b1uny7EExLJoQ60UKimv7IjYKxhKAT8eRL7MeS57RYSfO9QGzU+ka4LQFHc31RVsaIuKS1Iy12CyMzfoJX+CqTdA5H1N7nvk1Ax0yloMIMIy1qUiq6cWOI2ULBdHj5TyhRE1Kx+0i/ceS17ymxEej6EBNks+qeaO4D3bStBwq8HEUtri5G1hxhZ0nQW8hkECc4NKfZNhP0oTd4cfKDhcCDdKqkgAmqLrVJiy7+fhDTZ1chtpgWR1yiS9dMS9Lw6kEXECPgsNPE0uWv3oDSB09AEudm140S8mRfUdy5A/eZp1C8W49NtWXDlMwy8MHsSTcJHSK1dDwgIGNtoQQqAOcji+jVkKatAyoZTEaH9W7xC5RjK3ASxpUaimuoqPI2ajyx7llp1odt+vDunue1NQfJskjv2Ebd9BYqx6UVxMoncE5tQjcIFkeMCxiqGQsDfNPAuAUPHL9EaZgsSCnORyeqNSIj8mORk07TcNYj4QKwnUCsi1cV4bd8eRI7nI4L1brz5vglpIItIngrJ0IdI2wq0MJgRufYUFCh6F+py1yANqqGY1DgTBeKVuXu4HxHA10fucSRT0qcqGZyHtMUlaAGRTPt8d5LtuUIrWtgU0T9nrL3Hl9Gk0IwWECBf/zlIK/NGNGn9GO/2Uo76US+aNK5317oT9akqRIT7UL853V3jQaRB6kOLHdNkvwGNiSVojBQgf/rdyJVkMGhE2q8o8tH7M+37cOXqDwgIGFnsdJ9SpH0uRgvwlWjOrMTrEePxJJJhHcQmONiOZFUNisWa785vc8RriVVmgALl34N0mUdQQbR9SPbg/taSPD4omolkCXJRterCAWMJQyHgoXrFsMF8dh9ApPXNbvtlaAU9BZGDu0keTHkiCsArRUIjukpuQSavKcRq/O5CJOcKd602JFRuYeAql9Fy5seQZvNclNGiBgm0qKa+G/nzgoTHLAb23S5Agg2kTbgfuRn0uN+yVWQgXWxGWohe+udVPxmf7aON2Iw2V6Ogxvvpn9EmV1iCFhENyBUkvm+A3IB+j3eZ2Y7vj+9A91yKNM6T0OLIFoOTkdXDMtlYZP/L9J+YHnH7vuCuuYr+73c/IuBHEJGvRBPPMQaOUUgHp6BJE3fOwRTPCAgIGL24Ds2Pv0OyqALJvT+iOetF+s+r+Uiu9JC8+mV0jr0ZydRHkRfuLiTzoxnEXgL+0X3/N6T0akOkfD0i+enUCMkH3o5o3Hzgm5HfViBlRR2ydIekdKMRmfiAnwN8C9mni9FMeYzhiyob5zgXaXS7kTvGMqT5rUB+aEXuYxkbkhVZmYtIbwda7ZcjYm/uJXtInAN5N9LaViJTXBsDk+9LkGluMyLVZyFSNgVpRe9JfiggEjYJlbd9LEm7QMTucXT/Jpga0GJiMrlJt7Q3yfYmJOzyiNWOlyFtMWjojAYCbikGe1B6zENosrgGvfuDyC9yO9JWfxL1ozYUNHwEPf8KdK9WNj5qoXkDeh7VqG/3Iq1youd3OnqvCxDR34UWnG93/x9Alo5nXdu6keZoCoPzdU+F6Psbi1VWAwIC+mM+Ku1ehOTVHUge3wF8H8mV+Hick5BLyCHgB/gAzEpElouQe6XFdZWiRANl7nsjsvSmKiN/pjtfhbteO5I9+cCn3Xm+QeJA+l5kjaylv6w627XjJPf7wRRtCMgVMiHg/w/1tt+g6K33IvVkwJBgOUSLgC8iwlKBBm8v0grOROSlEq153otMaL/Fay3XuG3tSJN3JRqAdyNyHl98xbAH+GdEdqYisj4QzN3fslZsQX6+R/AEywLqEgUbVuG1/FWoGmciNANfQfe9z53vAnSPo01DuRP53BcRa0JsRZqPJYhAjgZUur8FSFgfQouDaSgf92bUD/4F3U81er+tiKTvRH30ELJi/MZtvx69lwfQ4mkmIrWnob66B/WLs1G/sMXIJvRet7trHIePGdiFrA4Qm6rzFhQ43EB2tDw7SPz+AgICxi6sImQhIqsbkdx6FCmBTnL7RJVAK/BpUWfjawichtIDFyANs6U5LcWnW61FpH2eO242suhtIjZ48k6krGpG8u7daL5fjWQx7rdk8/YP8C6AUTzrrlnH0FPQBgw3Ms2C8greB+FHJLfRTEAUIW2hmccHwoP46pEr8YEcze4cDyLBMRkJjbPc+TsQyV6LiMhRfHaK36DBWYk04QMNxKPE+sTOcfexI8n+jyOSX4c0BQeRSa0bkaFlKNilCJG4+IIBHUibammhQITure7+f4vPDnM08n21++CuaZlSypE2vYXE1TlHCsk0HsmqOOYK5uPchw8s2oDW0y2IcBcijfRB1MfORBaHFrRYO9Ed+yX0fqrwWXRAi7pjKEXmpah/H0KTUTt6Z0+hQNrpyNVlozv2YTTZNZBcG91H9n0fU2msAgICxg4uQPLoPuDz+MxIH0Wy70ykGT8eWdg+iGTTTqRUmI002dHFeCHe6B+lUE1Io74AX7RsCXIFPQnJ05NRhV7LB34jUnQcRPpMSy9Y4s6dj5ROU5Fsjbf2tqL4KhAPuAbJzbuQQmwH2XHNCxgOZELAW1FveRb1qH346L0JiiUoEG0XWgkfhy8kU+7+30asib4YeAsiobchsnIOcgX4E3qs5otaibR/lyDBshINXKuoeCuxBPoAIsF1SLucLJAyz+13kWvfXYhAvdf9div9i6vMQkLleXdvy932V/CE/RSkFQAJtXgC3o4E0HS8YDkFT95OpX+JXdCipAIJqpbI9vPdMaB38HKCY8c73oT64f30f2egfrkIvedjiAz/PXqm30Qa66PInWMf6r+fcPsWouG+H5lel6KF0BFExregftSHD7LtQJNJEbLylKEA3dlo3d7rtn8EEeklqP8sQBPX06ivDeRmVIS0TXvIbunlgICAsYlFiJSWIllzBz4z0jNIw/00mksnITl0DZqnTae4BxHwcjRPtyNlwf1IHkZT8+YhhcQyNA+uREq0TiQzq5Fs/SCKt6pHbn3mqtmEd/G7GbnkFSL5+hH3/UF3joWIE0TTGn8IKegW4FMtLkMEPbjTjUZkQsCvR8uzPwf+Es1+12SjUWMX56IBsBzvj1qJBsP70CrWVr2GE90HtBqvRaW5OxGBWoNW4x9HROY+NLCWI9P+brSqPoQnn4b78H7c5WgFHe/XnY/yd7/XtdXK5G7HZ7+oSnCvFyIhYGniZiOCfwhpHOagTJTmi5ZM+3uE2CC77fhnl6xseCtKPVdI7JrPhFE3E9PsVoEWOiBinIiA34DI8QFUee0i1GerEImegawr1hc+iBY5eeg9bUALuk7gy+j5V6JJayaaxJ5DfeNtqG/koYmuFPVXM7M+iIh/H1oslaH++T7Uh3oQGd/EwLnWr0MTT/z4CggImJgoQta8EvrXk7gdX1n3b9B8sgnJsNVIDr0WXwl4AT5GazuSS8XEEts5SHYVIblY7rbVI0K9CM3l30Qyt9JdzyzAW5HWvA/4lGtTHpLLeyPXuBSf0vcbketvQbJ7B1KezEfyNdMUvasQx3gKaesDsoVMCPib0dtvR07LoBqp30h6xLjHC2ig7kOR1StRh70MDd6daPW6yP09gAhJOxrMB5AZahYalMVI0/h2tJJ9CZGndkRQ8pG2byPSUManXr/N7XstchFYD/wrsX5ulWhQd7hzFyAytRIRrecQWaogtiJXHdJWTkUCZh9ayZ+AiFknijb/y3QfnsM+4D+Q4EkWVHch0pQXIO3sJ932dUhQtTExCfgxpNleRPIoeluwVLi/j6L3WOa2nYlIeA8SD4fQu2hGWp0GZKFZhfzyP4Oi/c9AC0fLbzsZiYg6lCXgdvf79e58jcil5XfuOt1oYbkPBR+Bz4Ji99Pttlejhd9WfHCRVby0WIqAgICJjUn4tIKT4n4rwSuwzkQyaBly26xD8q4Gab0PIwXSAiQXLS1uIZprlrhz/dz9b1rr/UiDvh/Nr0Ze70YxNi+7Y1e7834HL88OIdmX79r4JyTbNiOSX0z/WgyfQ6R9B5q/K921uxk68pFV31xiAwHPJjIh4DfQn2y/L8G2CYT1yERv2rqdiCxci0zohYg0XI+Iyo2IdH8dkY0bEPnpRQNwN1rbTEECYw3SGn4ADYgWZA570V0n3gW/Fy8Q8hD5sqA5wxFE7K1MbzUyfdUiAbHa/X8asYEgT6AV9/Eo08UxJLiuRhqAEncvQ8FAuZdfRIuBPGJNcMvRYuLJIV4X1PY3IQF3J5mVcU+GPOS1dTpy/3gqS+ftQ65GBe7/N6KJ5278fVieeSPoj6MFXBlKanQELXzWo8wApe4z252jAxHwPDS5/B4RZCtoUYA0M1Xuu/k0rkHaJPP7N3PwIryr0A7392b0LvehxWg96ssnovFyLSLcq1G1WBCRX4X3Hy9FGXoOMfhCSalQhTRjuxneYNpF6B43kN4EWossDu0oI1H78DUtIGBM4Aiao0rpb039KlIWPYLkziIkW9ah+bYOyckePFmfgsbibHzBnSvwmuojSNF1HJqnC/DVqOOxEc1hc911WoldJDyLLNgFro3m6lLk7uV4NG9UIE33q64dVyNOsMy1ZzoKIl2M5krzF08XvUjOzyUEpWcfQyHg1yIb8nF4Gw5I5TWBc928B7mA/B4N5P1o9diNz9rwAF7zmI9WqAcQQXkdIo95SOvdjSbRBWhyPYqISRPS8m5Ag2M70pDjzhNPPp90x5+IhMIuZMIqw6+qTZg8ioTNXLTKn4YGcRsi0zcSS44PIY3rb5HAKkMEZbdr5wuomxwgu0T2EURcZ6HnjbtHC96sQaV/B4u5SKCaH94B9MyyjUuQ51Y+qs62NPXug0YPMhme4f4/gt61oQ71h/eg/vMrtJj7FlpMWYGaqBXBNElnI+2NpZq8EBFlWzSegPrIQbwbi7WnAJ+e60JEGLejrCPRfOm7UL9vQv2mFWmcTkYE3vpgHpqIzkITWjSv+CWR+29A4+l81F/TyfCTDFcgrdUq187hEHnT0LvJQyT8rjSOWYXGA+jdjpZMOwEBuYKlT51J7Lw4CbmLliM59GdIWfEgUjw8jWTdF9Fc0oZIbpnbPhXJu3w0z89Ccm0nmjMqkTztIHH2r1I0t+ejsb4EyeiyyD7PI74wCSm7rkHE/49INk5CfGM6UprVIdk0zd3TXyNKtgOR8ne5a34CvxgpQJb5EnfeaCGzUnfv+1BhtVoGTks8WFQiXrGNiRooOhQC/gQ+LPdrke0tePXTBEMN0moX4wMHJ6EO34HSpx11/9sqtxkfWGYarjo0EH8R+e21KOXRnYhoXIHISgsSIuafXU5it4Mu1652NICWIVcAELGpx1fgqnXnneza1OLavRO98kJiCfhcRMbykQbyNNeeDvfb37pz3Y66SjRgEiRwTkACoTlB21ciErWe/lrMeFJilc26GJrPWwF6h1MQqVvP8FUWq0WajDwkPIcDB9A7LETv7lI0yZyLFoG7keWj2LVhEfLbvgmZGeNT+h2Hgm7X4/2+r0VmT6sO14QmvDmoz3S765WhQCdQ/96ECLXlnT8FjZlSNCFMRxNODSL1r+CDPR9EGt457prXu3Mscue1HN7Wp3tR378K9UnTorcSaz1JBzPcfZzsnl028o4nQl6S76mwFcmHTvTsAwImOhYhGdKB5pE73PYuJJsmI9JZhxQUli7wWmSd24fkTxMi2kVImbAbjbMCJINuQjJ9K3JDqUXWwy8kadftaKzuRC6aFUj2TcNbA5chog3Sd17vPNu0DQAAIABJREFUrjcL755ZgvjGbHeOIvf7ASQLTfP+JXfMJLQYMQK+0p230J3P5tQilCWmCmnZ72FwsrIIKXI6kVtsInJdjAJLy5Gy4LYE+4x/DLUSZh2aWWcgVSlo1k7HVjobscllqNd0I0fSq91534dGyHVouXYI9cBm4GLkYNyOes5uFMr8XdTDPsaILgLmojKyXfiE+DYpz8JnQ1mEr/bYhojyEmSq34U64qlIU/4Nd74T0WD/O2Q+uhhf62gRemwz3N9WRG7nJWjjTKRhLUHBJi8h14c21OlfQISnFK2tOlDWk2JEYp5DxHq7u9ZS9/8GtAY7wV3bAkxPRESuxt1jN9JMP0L/LCjvxudA/XqCtl/u2jHdXc8WHA8Tu1o/3p1rMjLbxZPzPHwRo3hUoEVEhfu0IKHxc1JX2JyKut5mEms5UmE3es5FJC/okynqkUa7BPkxvg8R1Rr0rlvQ8H8ZH2w5Az3LH6NnvwsJ8hORO4cFatbin9dHkTXiedRfVqLMA5OROfdSNFznII36QbTIOYyeQRXq+2vc/2vceatQ35mJ+pGZge9EGhPLpbufWEsSyEVrKRpPv0GTRz0arwXoffcid5zBZMo5HZ9+rA+Ny+HQNB9Ai/Cp+JLTA2EHMqtb1b6AgImOJjSXFtGfQDajcXYUjbUzkSxoRVa+FjTPzEXybgOaD7qQHLKy9cvRXAGiNIuRfDgHzYNLEZk/EHftjXgLXy0+bsncXLrduYqQAsJk22HEEZYh6+9fuG2taC45EV/W3uTA95HM3+OuezWxBD4fn3kMfMIGEF0DbwFNB2/h/7P33mF2XtW9/+dMr9LMaEa9N0u2LDe5V1zABhdKqBecQAgkJOQmcHMTcvMLKeReQi43CYSQkEBIINRACMaAC9jGxrhI7rZkG9kqtmT1PprRtN8f37VY+7w6Z+aMJHtU3vU85znnvGW/+91l7e9aexVhmiHUXqVCAtdTHDN9rMmtDF4upUppOhwb8Dcjb7k7Ue98GgHpbH7pLG1H+8NuO9CFQjFchNDm6xEC/HW0Z/wm1Jt/hfK3vhqNvo8ggP7nSGQdROn8bhj9q1yLpM1bODgN93B0GmFS8jU04DzZyi3I+XI+As8r0aSuQzLEuWhw/tiqXIcmyCK00M9DWj8P5ddPeDT3oYnbjrTay9FEPwMBHTd5OQMxlvMRaH8WMZSpVk6X1Tk7Qb6BNI3noOZtRgDmImur3dZea+368Yh5bbD334m2wbqQ1N2LBJVXWzmfs/r7bkAL6sIBpDmYZu89HU2KR6z+r7brC8iu2akLCR+eB+pHhMlLDQKXk5Ekf7/dfy0CnKvR5Kuy93kEgc6RTGbegdp/GZoGo6FUeHi5UgTXoDa/BNWxFrV3k52bgdr2dgTOX4/aZTICfpPsnkFCW1SLxo0vGlVoTHwGTc1fRX3pUX0uQ9r1Lvvv2pmFaNxuRQLgPGIezUIL1n1op2aiXV+HFsCn0M7KMjTnvk7YiYPmyi/Zdwsh4NyMhMlZiP0UrGwH4GciVrMSLail+mWVtZlrxGqQw2gvsr3fW+KeQ6VnGb295ki+EznldCLRPrTmTkSg1GkAKZE6EBD0oAdn2vWtiE+ehnj8OLRmdiB+uIdwTl+PzFOcl2y18h5H0U7mojXnkwiUP25lvR5pwC9GgL0ZKQ6WIRD4H2jdrkHa+b+ycr+N1rkGwlF0idXjdQT/TmktgmagHTzPyLzZ6ltF8a7xTnvmbATv3oh2/X5K+QzcKbVamUMUm9WktAcpAOdQOtTwK0mXorVqJ3KEfeVA+OEA8D9C6M5Fuy7UOyMB8B6KPYTOQb2M3f8OtMp62IPbEWJrQshlDxrRH7d7OgjvgFLx8kagCUTotksYHQBfh0D2RnuFixEg+R4CwdPsuno0UfrQ1sxZaMLVI5OSdruu1f7XoUk1FU3yAuqqHsIhYhoaKK5lrkLNchECJR3AWxDYWGD3T7U6u23v76Dwg59GTbeBiBXeToRTHGflXWbl9yAt8z4EYN1cBdQVD1j9nEm1IobThzR1WxATdMeQichEYBvSmHYjc4f7UH98DQ2vRfYOaUzoBgQM+63+O62uDqA77J65yA7OMzOeaed77L4rrU+2EbsVw1F/5ns0NAX1l7fN4dAZSFA7gOrtoQfPRAJLF+r7zQgoVqPx0IzG/luI2PQtdv3ZqK0PoLHoQHyVPeM8K8e99KchM6s2Yruxzz5T7f4e1Ofj7J4Ge+Z8tGDUo3n0cWufjYg5X4Sm/ha0wE1B2vkG4A/QGLvTyrmW6E+PQz4PjZchNDY3orFei+ZSrdXzndaWZ6IdkFRj5fQ82jKeZvecQrCcBWhcLUPasptL3J9TTjm9cjQT8f9+tOa+CikefoZ4jvuQbEJ8eC8BRAcRL6xFfK6F8HV5FCkOGpDf1Nft/JcQj92BAP2pds0ypGSbbc9bhHjsXsSr3FdsPgLHg4QzO2hHcQahMPNsxVMRL9qF+E8n4vXjKDZdm4/0pduRsm6mPe8/rZw6AoI53WufKnsPrG7DAXBfh79j79eDlGGXI/7+Y4pzNDxK6VC5rzS5qU8b4uejNU08dDocAF5F8Sq1jTC8HA21EQbAuxDyK3WsnWJDYQ/1kD6z3PPfZx86O7PSoWuzJiOAUYrcm3ljpgqX2yMvIBbi/QQIvhNNRJ/U1yL5YggNzh0ICPl2kWuoH0LauEbUxE0IDC237yVWXoPdO9PqNt3Kabbyqyl2HhlAk+Qxu3cp2kxYYPX2lPUPIybRhAB5A9Jsd9p165Hk/AME6Oci4eWniKlMs3pNtPtcUz/J6jXPnjsRgaAL7He/fbuTXDWSxTyM1Aw0ZK8molBcZWXttHZbR7G39mY7N9/a5UIE7HdZn620cty2rpQZTyn6MhEKL0tziYgcT5c4733jwtXh0JWEvaBHKvk5Glsb0bi+GY3FtWj8Lbbnd9i1d6B2rLNPPeoLB9gOwBtRu3ejtvMtznYik+YgWtD2IoDfQIy9nahvG5DgOBEtFntQ361EQuE8NM+eRMxwur1HFQLBm5Cwuw5pL/pQKMppRKKMnfbcX0Hz5inUV55d7hy0oGxEzsW7rI57Gd6EY4CwsS4g0H7A6vcha/8qjj4APh717Su3uOSU09jSesT/2xDf+mMEOKsIEwyP7NRv31vRXO4mQrYWEF94EvGX24h59CtEyvrrEd+qR/y3xp69CYHvpVYnt01vQhrXPYTTuvPcJgLiLEb8vYD0noP2jIL9nmjl7kV82IMmvAGtQZ1o7k9GvHXIPnWI/7lJolMBmasuAb6ABBb306lC68xmijXFJyPMshvxWt9xeJ2dw+p1Z3LPRMSzn+SVNv0oph+jdXQ9rzR/PBwA/gNkZ/FV+/9WKnPXz9JOQlU8zv57zvX02I7kGISqbbDEsSx9zj5s3bo1s7c8YKfqKR+66/VoMHYDf0Ns9dahhc2dE2vRAPVOnI6kx8VI+p6CQNsu1HQ/QWbrDXaPm8RfYeU1EJO51p71MJIaPZtgAU3uQQS2Z9uxjyLN334i3bsD9hYkyS615/Qi4NmMgMmrrLynkXmFm3BUW11mIBP8A3bd+1DX3Gv1uxAB0ILVfxsCP/eg4XIAdblHWWmwY1VWn05k0vAsoa33yBrj7b2vRWY3HlJxH9KUOyPx8h9H2orrENObgQDn39lzPZyhR/D4TtJ31yOG/C0OBmW7Ka8pf6O15QLgf5c432flFTi8GK0grfAioh18bHqM7O2I2bqQ12N19zjzVyGB5Keo752Bt9k1qZw7xd7JFwsH105u8zcBjZdUwPBnbyBsvE8jxvk+pDU5ycp+DrXvPSgpRRex8DxDmJD0I03/eEKgWGH1Xobm3ni0vXuLlXumfZ5Dc/cGJIQ9j4SUSiObbEBbwxCZPKdz9PmiT0ZZ8qrRWC4VFi2nnI436kEAF8S35iD+4eteNeJ17fZ7PALL1YgPbiIyLt9u3/so5g/OW53PuYPmi4RZXSfia3OIaCldSVmzrF4r0Lo2iMDgGVbuZgTa3VSkQARW8HTzz6J5fobd++fIhGUvWksvRvxqHQLhIP44KfO7E+2kX2PPuRGt/55j5K1ovdmEXO+cTrL6tRGwcsjaY7GVlUbVakAC0UTCyGGs6AXk9/TK0+EA8CGkOroIte7n0N7IaOlBlObxE0gMuQ+tsC5W+jEPlNmCRCpPbbUdrXqDDO81N8KrDBc317XbDloc5HwZgRZPIHIvikt8sVV/KwKCsxAoqSNsn6ci8DMFTXb3yJ5qn1YC0LchANOFgO2/oi3z6YRTmNvsvkCYrLjzZQMCvi1Wrwar07vtWdPRRJtk9dyDAOQAkmqvRqBtr73je+093o2k1057p0lEXNSJqMt2IgnzS9bGBdSldyNwf5bV6yLEuMYT8dPTCbsB2bHNsed71tBP2DvcSYDvdqtbmqHzQfvv4KOfsNk92d57O2Gv7Nt8XUiwKZeV06mKkP+2WPuVC9vUj/q6bphrKqX/Qu07l4jFDmrnS5DAcRHaAai15zkTL6A++ztC070djUUPFziFSMpTgwSZ1ajtlhBac3f+G7J7BwkNDUij04TMNuai8dGL+myrlTfZrqkj4tC76QtIQ+4RDQbQWPwCGg8PEo6+j9l7bUe7Wx75ZQaxeK6y96gmbCK/wOhj3U5Cc8NDeH4TsatGim39x5K6CEFqMjkAz+nEo5eQcN6OFFivQ3OiFfGSKvv4+j6IhPLpaB5Ps3sGEF/xgAIPIwDcjBQ7JyEe1kWAcndy9MyYf4zWzJ8hRdZ8Oz8R8dghu86VNLuJZH1uc95ImFlORGtcO1pbmhCva0O8+Hq7fjbiUc/adY+hncMmq7ubqnpGYS93BgLRjyD+AcFTXDF1P1ortqMdgjWIVz+H+Gy91X2plbsarbsFtJaPJQAfOzocAH4Vcpr8dnLsT+3YcFSLtOenIZXUH6KV8x6EvFzF/E9oVduB9s1B3ou3odH4y3bso0j1WUBOmUeA3oiAwnI0SL+LFvHnKHbO20hx+JwlaHAVEDB/2I65drwfTaIGNKBb7eP24INW/gE0ScYTWut1CKysRBPEIzK4/diA1echNGFvtOscxF9PZKzcikDQa+15a5A8s5Wwn3XnCQ8X50LI2XbOBYZ6q7/boXcTE38Tsf3212jSbkEgusru99BPPpkL9o5uTTTH3vUxBMiGEDhrREzsr5J+cXuyGgJw1CFQ6hFB3H68HgGvDQiUDdg9vnvxNAJ6VYycVXM+0gzsBj6P4rVPo3yEk82EluVIOM7t4WBbulPQO7rm+FTUdqej8fUQ4cwzgQixNUCYlLiTrLMJT8qzzI7V2/XdhBbIFxCIGOBVdm0dYuhnEdpzj+wzRIxVFyj/EWnBL7F3vBvNm0bUth1ox6ILLSAftGd+AI2NxxCofgEJxTuQtuUn1gbbCAecPg4OkzkSXYR2q/YhTdun7N28Hv9AsclandVjD5X5GRwpehIBCY8pnFNOJxr1IH5wMhLWU1+VQcLZ/C7Ey19A65jv8p6KAKZHPzoF8avvI97bYd8euWS//Z+BNNtnIt44iMxWphJaZueFboLi5iHOO5sIHDAH8cZGhC1eQnP7ZLQmN9kzdtrzHCDPtndxc9teYre7irBRb7P2+TICy99Gyr46ZDZ4Ewog8STFu8IbUAwMkBDye9aef0oEhZiDdoQbEd9eYccqcewEte1bEB//IsdDsrFDAeC/gVa4uRTvtbZSmTtrH1KBpnQ/ymud0pfsk9LtHNxbj6GV8AhRLeE88ZtoMN5GxBDNUh1hS/q0VecUNEDPJpzsJiCg4jGv3VO4gdBiu7mJm4uAJlMfkdHqXjQIWwjwvdae/yAapJcRzplN9j0eAex2BMw3EZOqy65ZZ9dvRwzkSntOHRHlZA6h6WxB4MjNWhqRdD5k9R5Ck/1iwt5sEAH2bqvLldZenphok7XVC2iIuTnH2cj+baa17VzEUDqt/CyQvRMxN88ymcb0rkZbfUvsXf4WRfKoRwwK1Je7rd7XIoaEtd25SGBxTeIpaCp1IIb7NJHVsQ0x3Bo0nDchEAlqc9e4HylqQNr7txJOO77DU4P6aMjO+farO1q6AFRDOB9BhKBy4ccdMP2YX9eLFgFnzPVJmb1ojMy37yari2vlL0MLiy80B1C/n47Ggwt130NjeQfSxs+xZ51i929GfXQALXJ9yB77KeQwfb79dyHhLjTud6NFqxTNQIvRYxRryKfZczvR/Pgu4UPQiNo4BeCX2fNB4D91Jn45aZDiqEE55XQiUBPS501GofguRPzmDYj3FhB/2E8olM5DfGYG4iEtiIetQOtTL+Id1xJ8cCnidecj84w2tA4/hniVh2OtszJm2fEmxHvcR8YVEVi9Ggm/JIdqMwlN/US0DrYRzu2eB6Ea8Xw38XOF2hvQeumA3/HJKUgjX0B892ort8eunYh4ay2hMCxHVxF8+WLkD1cg7N492tZ2AgtUQqfae09CuOUk1I/f41iNAHUoAPwriJv/HxSGwMlj8xzj1Icm28UEGJtY5tpqwvlrOwpf5hkg2yi2gy6gwbcZySkOXD3TlmsA3a4VNClda+4T7Fft+AHUfbvtviZkLjKFSD2fAid3yFxndW1AE3whAb4cnFUR3tke9N/NVjxqSTVhUuOOI4sJBrXH3nWSXVeDgMpawr78IRRt0sPcbbVrLrTvp5Dm4BJrj4WEPfpPETDz7bUJiFFNQ/Zvg8DHCDOANyIAtpbYZttAmEpktdy+JTkBMdi5aJPmdMS0TkfmRlvReJmFLKDcVGU8ciicYsc9sssmBLxS++ojRUuRLXIdYqDtybsV0NhsQeOzG2l/G4nQWi32vj5usk6i7sTrxxysOxh3R8zUwdQ9+l17fi0xPrG6dhIAewD1USMyS/Fxtge1+WzE3C8hNOzYO70DOfROteP/085NQGNiChp3aRr5DiRoz0Qa9zuIcInO1N9u9VlMccjJ+5CpUyMaWzcj7XoLkSUWNAdc04+1VbrojLPPy5X4KaecTkR6LVoffHd1CuIzzxBBC562Y22I33QhPlSD1tQexMPcLA/C76oKrUke+3sZMZe7EE+oR87ZzQQWSP12dhFxv51fugmK7wK7HboLDK4pbyVsricTSfV8F9PDALuPzDjCHA9CQVKFeNkKxCsfRKYx45DCYYc9pwqtaYusTA8d7DQL4ZEVaG0ZQOvhhxAP/xqKvDIdBcvzEMf7KK/gTOlha+udiKe6c+d6Ks+XcHTRoQDwXfZ5+xGuy1FE37PPZWhA3lHmuivQNo6bVvwFksr60IB34OExuwcQoJxBOHD2o+acjADMBcSE7CHAr4OaejTIHUTXogE9neIMgD6ZXQOJleFe4V1okrtt7z7EgBrQxJ6BAIEHzJ+LJqKH0HPhwL21XyA8rZuJ4DW1hHPeDmuvvXZsFdJmL0Ya6wZ7/057xl7kVNJs73I2YgD/ZeX/T6TFdnA5ZNf7sD4FmS0UkKPofKuTx5l+3D6ehCHVbvYgWfOXrQ+uQNqROmRLOANtBP2L3fcpium9aKeigMDe/fauBQTavI5zODxqQONoL4o1O5tg1lh9JxACUJX9PhO1qy9OrUmdfBFItdsQW6jZYEM+7lJgnkbd8bGYjk8fm/tRX/YSC5zvCtQlzxiHtknr7bMXjamNSNN0L1o0phOmRichW80ziaybr0OL5p8ggWouAvbVaNfA447XID3D/Qj8u0CQkifOmGHve6k978vJNXOsXgUUX//rVp5HCRiPxlEdCtl1DznllNORoEFiN89324YIZ0PPh+AaZPehAvGmZ9Ca4XysGvHMuYTiYTahDXYNbQ1alxoJJVvqrH6J3TMNKaHcJ8jXwiG0vvtuWh3BW329BvEO5/UDRLSTNrT2OUh37bfjB+ftrhQpWH032Lv9lMgW6jvh0+zd91pb9VKsnLkarcfb0Vr7ESujgwjgMA+ttxsR75yJeP16K3OutfnZiJc+iZzGndYSydfno/V4iOIY75XSBMT3n2UsMwcfjg34CUB3jnC+FnWgm1xMRwPeQ5q12TVu4+UxtQfRQN6LJo2HOPcgLx4qzT21fSuq38qYmJx3kJJOBuwZ24hY06mW2s1KXIPpgGkcITi4BmAgubYDTbx0+8lD1LkW3gHXBLt+m9V7DaFd6LF3+DoCyL+PJtEEZLPbRtho19s7DyDmOIjs1uYh7YbHk34ICUE/ReEhtyDAg73bS0izPoSY5lft2B+ifnsO2aedhaT2B+3YXYTpy37CTv8ppGmdR2nHPbfba0EC02cRiL+WsMdOAemhUD2yCPOsk9NR++9FQuNriaQMzYRNt7eDP7sUoPYFIwXzheRD8j2Y+Z9qv52qStzj0WAOoD7xXZiW5L5BNOY8rKFv+xaQbWEv6sdewpZzJxqzi1BSniut3D3E9ue7ELhehRj7ZDSGpqBxeyoao7OR0/McDnbEPcPqtZ9wMM5SR3J8AgcDbA8PWI90GvOQI2el27JODWh87Ud9PzT85TnldNzT02h+NiJ+7ZG+PARtFZEd122txxG7bq48O4DW0LPQWvYgEWs7BcHOz13J4XOwn2Ko5WZ8HsK4xep1MrGbmIZLThURDqD9Wc5DPceCr+kQPLebUG48Q4SQrU+uPZ8wq7kIrZ/1iJ94KON5SDE5G4H1OmSO+QIC3+dZuW+xcvqsrdy5834iSZv7JnnAh/cQuUhaCKHgu5Q2L/k58usaYPS+O6B1YbLV/y+JKNfHTibME5B8UOxGi/YWNOgWEGDZk530o8nrjpI+SXaiwezp3518wvrE8rBI/ly/ZhzRbeXAt1MTxZrJQUIadicPB/Ye5tDrOogAg08GtyvzMl1LSlLXtB4O8DxFfSoJN9ozTkNMcQnSzK+z9/P4q4NWzyGk0dyMtvk+h0xK3IxjNbKIOgtpl1fatR9A7f0tpJk8zerxIpKw34ik7s2IMfw20tDXIWBbQEBtPGHyswGBrTl27Ewi3vt+wjnxa0iAmIwYxDn2GU+kMYbDSxvu250QYRYLSHP7BaS1uJKIcJLuhPjvFGA7+f+qzLH0niy5qUlK5UCgH69DbehhuNKx5tcNoDbfhdp8PJpXW4kt11Xond+P2ta1WQuJRWUdEs5usPMH0Hi5D/g11FaXofGxE/Xn6ag/V9j/rL3iesQL9iBnpTsz7+naNzdBe46DaZ3ddwmxtX0q4btQKV2I5gaoXZ4c5tqccjoRyHfDHGh3oDnsSXigeH2EWJOr0A6aZ7+8JDn3KoKHp0qG3YiHNRAabPfTcm38QOYeT8JXQPzKee6E5JoUgKf80ZVj7tuTKjlS3u1moSDtv9t9p0ECapPnnGnv0oTsuXcQ5rNLieAOf47A9ktEhLghtM622nu6csLf3RV4qxBPb0VCQRfS2vcgM75LkFnhcLbd5Xx2KqEDyfelyOR4F1KU9Za7qUKqPLhCDsBHRRejyefxLd+GFvFqwsGyi3BULCBwOEA4UnQRINbJt/qzNrelAE915rxv5WcZSRVhuuFbVI1EAoJUm+nba6kU78DBAXn6zLQ+Q8nxtE5p+RMJMO3XNBPmEx4HfBJhK+dZGD3E3UmEc93zhAlMLwIxb0PgZbbV/SwEfvvQltYuu9e1CwsRA/LwipuRNmK6/ffwUFORBuAlpA33tlho9RhHpOp9lkgucxPwZ8gu38Mx+dblNAL4pkLYaGkLkQXybUlZHSj266n2zu74UsquuxwNd032XDktvm91ZgWzdGy4k5OD7yxoLxB+Dj6efUdjC5p396G+dkflIbSweRbOIbt2G4pMMBuNtUXIPKgHLXhz7ZnujDsNjYtfRuOpB22tegRUF4h3WJnZcJVXIAHoLATiL0GCWZbuQnziRnu/NSWuGYnc/WaQkaP25JTTiUA1RLhX34kuIL7glOVlziuriDwbHpXJ+VwH4e/iKUw8ckkj4h++a+31qE1+p87t7iSZNfdLf5db/7PRvlJe6+9RnblnQXLNnExZTr6DXYXWEE/W02D3dyFePJUw53mWwCK9CEwPEDveNXb8dMQn70NKsQLCUn9kz1yINNI/S+rTTpgcjpZuQJr7WykOv/p1xP/XICWNZysfx+El43kX2kG4jZGTwucAfBR0DmpYn8jXoMHpJhge4SGVRH0ApyHWHJxmye8ZTiPpW+xVFD/Dr0lBVlqu3zNETMzsvSmAz07aLMOAkCD70LunWlV/pte5LznvWk0vox8JJ25n50PSGVohudcB5o2EINGHJO7xCAQ9TDg8dhHh5tyZpoGIq96ItIXzESN17cU2pCV3MP5zxCSq0AT12OoO8pcg27Yzie2w+ciU4HnC5Ojf7B3/EkndR4LusndrQExxCJnmZKd2OeBdCRg/VHJTquzzUhDu0WPKmeTUEtFqalB/e5z1TgSo/wD1fRq6qxf1vWv+9yOTnJPs3lmozeYBH0+ObUC7W27a8k3k3OmC4DwCgLv/RruVeyMyMfkRGifnErF53dG5HL2AHDzdX2K09LC1Sy+Hn83tGrQl/AQKI5ZTTsci3YO0qW0IcL2K8HcqRyn/SefrTsSnIBRYEDvKEHbVIHDna2pqw10K8PvxcoqM7HHnlVnzwBSop1rzxuR8P6UjmKTXO15wRZn7rC0k/M5akHLC1/MUk9QTSrftqL3rUZtca9f9MVISzUFr2CcIM9eJaG1dhRRYv452G69DyrQdiDddYu9zd+bdL0Vr/4NEjocLKAbgbrYKUvAstmfsQnhiF4emCPmw1f90cgB+xGgmWpSqEZBagQC5hwJ0DXOqcXYzkhQYQzHQdfLzWW1ylvw5vZQG0uXu9fJTzXR6fLhnZs/VJN8DCKy6pjy91v97GCOX8t32rI+wrXPhxDXf3hapjXsqEHiYxn0IMFUjadzD+s0mwiJWowm1gJDqa63ee4mwTq2E2cwBxDjuImKWX2Dl3oX6ebKdm4wYwC4UIbMLTb5uK5X7AAAgAElEQVSrERC+jQhF9TMr55oybTsa6rD3eQ743eTdINoy7d9Sgt3LTeUWlOwuT6mdHv+f3aGpQ+1eQEzUBS9f+Ny3Yg0R59yT/NQS3vy1aDt1JzI9uhIx9nuQoLQIaX62obG1iHDedXoORcKZh8YXaDzVoP73ufJJZGN5sh3/YZl2OVz7wyMVReW9SIM1H8XbXXuEys0pp1eSehEfmEZETOqj2I7aBXsnF9qHED9wpUY3wZ8ak+vTyCityfGsU3o5yvLC9P5Sv9PrSoHwUs9Ld559RxSKszDXZn6n2ns3dXHbeMc3nQRvdlBfQPzQdyLPBt5s191MhJmdadePQxhhM+qndUihssx+e1SrKWhH+SLEzz+HlB4gAO1RreYB/8Pe0XNxLEbrcDkan9x/A4rw0oN8wkbr4DmByFEyMuUAvCLaR3HGp3MJGy237UrNOlIgkZ0U5QBRORBC5hpPaDIaMOX1Ss1VRkulzBcKaKLVlTjn5AzMr/fJ6rbxDqzdoWSI0FhPp9hZEMLWeIiIqepM05MmOPj29upEDNiTIXUTDq2pqU6vXdeMtJ6DSNt5HQK87lzzWQSiPWvjOhRvFqvTuQhsdSG74g1ExJRdFGtNDoXGIa2Ax9x2+8RSWuRXGnQPRyMJAem5rGbFd3DSa5oJu3CIqCqPI6brUXh6EDP2iC9uk9mPtlDbiGgIi5AAtdzO/QFKRLEdjaNUuzwe7Y64M9FrEZC/AEXqaUf9soOYA5MZnqajMfM8I6dVWIrG2c84siC5F41j92PJKadjkS4Dfgvx7PFo7rYjH6Gldk0WxLpya5BIqleg2ASlnG9MSuUUWyOt76OhrJlKiiHS/6kTaCoYpOaPaVlZ81gXOMZT7HA/Prk+jXY1jnCkfycBRq9Pyu5AORRbEZAeb2VMQCadU5D1wG4iUISbv3QSyQgHKY6UkuaXmICUb9MQP73fjm2huB/uRKB7PXKsn2/HZzJ6AP6C1a0yfpwD8BHJm+hzqOPfhrZROogMlM1UDnqGk4rLTeZyZYyWUjORI0EOpkcC9FnQ7lJ1ti5u01aD2tQ14xBMxeOluhd5IxG/vBmBqF0UO4kOWj2rEejegibKTCKUYh2SrNehST4LMQcPYefA1tPcNxKOs+ej8dGEGMlU+3hc64VW/ttRiDp3nDxUcm/9yxBzca2CM91+is2GjhYardCYbmtm7/cdnTSmeA3q/0sQ6HZNVpo4ooDA8hDSftcjUOy2nLuIxeN9COC7bacnkTofAf3fQgv5DrQrNtvKmU1kJt2FnDy3Wt1uR/HGCyhz3Omov75lZb4aCQPzkCNlOUejWpSl1oXgT1u930QsSqONpOL0HBqzm4g4+keePvn4z0a+aBj68Knnj3xRTicwec6GAjJjc/Do2S9dIE/BpwPJ7E61BwZIASkUB09InSvLreVZBd3hUik8ka1Lal6SaukryUORFVCyJjquDKtPzk8hNOptyfWp6Us1AthNdr8LOzOREOQY4AUic+djSDO+G63hvn6PJxIoPYbW8alo9+MKIjndODQm7kbRVc5DwtijKPE6VpclhOLhq/aev4+sHpqQf1eaYC0l912rzJEzB+DDUh3w31Gj70M2ZMsoBofZiTYWW/2VUjmGcKj3wqGH0ctK2X5skAiFmGUQLml7fdxxJWsT55N+CE0E1+i5Dbdr3xsJiblg5+YRmnnf9agmGFqXleXbmP1ICzmXyJzmoa/227PdlKaKsKE7HNqJGNapaAr3EEzLtfRH8zislIYbW759nAoaPh7cJ8G16OmOkcfyHUQMfDPFTtG+UN+I+rQOMfX7Ub9+D42vAuG8WYtA84tIg96CkvusRYvRHtQ/96Bt1LlWVj8CuaC+fMDKmIH6eDjt87kI7O9EiwhIezPbfi+l2FxmNOTZUT1caa4Fz+lYpBXEzucatHbXII2r765lQ/k2Jr9TEOvHfN47D3clABw+Xz+SdKjrcpbKrSFuGui/PYFbgWIn1xT8Z9dzj5y2IDnm67Ir2TxXRR0yZemyz3sJE5R3o/WwBu0aXm33vAetyZMRX30tWuPb0U7jUsR//xkl+HsGmarsRYqTNyKTlyGUTMjNC/cB3ynTLufbNZeXOV9MOQAvSwUUiu61aBvCncU8nnK5badjHfS8UjQcoK8nNNjlJPzhykqFIk/ckmo0mhBQ2UexNsPNXVKG7J/Unr8lud419i2I2bsNciNiMN3AX9n/2+x/mkB2tHQK0sy62YnXybcZh9thOd7Id0xK9b/vipQ654JKPWLI7ig0gLS/jxJt67HcryPCbY5D7b0HMepNiPGfTjgcTbT71hCOyE0IMPciEN+EeMpGYsvyFmSPuJMIg3g/4Ri1yI5dYfdOQY5mrWib1c241iKAPkBk9K2UTkGaf4/bn1NOxyJtQcCrEe0mzUPzdw2RpToFzVnT0T4ODvkL5c010muOJ6pkLSm3U1nu3nStaipzXWr640oOf85cou9mIn5Vjfiu94NHqKsG/hviqbPQOn0VAvDbEe+8EEW520tYNLjibojYWa1GY6kWKd7+Bq0BTj42KoPWOQAvS+NQx+5GDf0E6ozhJtmJAHpeDsq2W6mdhVLXlgJXWdBVbputlshQ5vd5OMK5xET3CBZu4gEB1J2B70fAuhvZr9+GJvNpduxclFXTbXoPZZychDzHzyJSDDuVA6LHM6V94FROKE7tFrMhMmvRXO9GjPRFZIZ0DRGrfw4CspuQuYnHsa1HpiW1yHlnLpEsajLiFX3Ij6CayMy6wq5db/f/E+GA2YBSWM9HC44f/xnS7JxNcbKOtUjTswyNr0+j8TAFaYyGgC9xcBIhED9r4WA7x8mE8NFZ4nxOOR0LVI3mrPuBtKPx7v48cHD4vRRIpuC8pszvI6VpPpGp3Lrl0eP8mmxb+38PvFDgYEd2X/87CDDvZn/1duwCK2MC4q/XovFyE7HudxAa+/NR4qFqu/Ziu/fFit42pRyAF1ELEbXjRrQQLUUL8VwiqkZOLz8dDpisZCci1RQ7sK4mbABd653tbw8F6ddUIQnes43OR6YAexAwb0DbXW8l7I1H827tSFr/DSvbTR5G2gU43qlSrYz3U2rmlC3HnYKbEQ+YS4SbrEeMdzcComuQU63HJV9A8AWPC/4CYvCe4fJZZL42Dmli1hA24s+g7cpxCGR3IMA/Hy0UO+33z4idlwHkS7AZOZtWI9OZC5M6DyCAvwqNoSwAH4ccg+uRwHhvcm4zWtB6KE7WkVNOxxKtRqCoAwnXnuXyZErbNJcK4ZvT2FEWdKdrcZrMbl5yblpyTTnTYF/XvfwJyfWXEVmszyXs1ucRwsBSAjovRWGFZyHAPjrKAfgv6Byk9dqBFrcOtD0xgbDfPdFAzrFIlfRR1mPcqRZNpOHs3lJQ7trVbEbSeiI+/AFkNrAYAfM2Kt/Wn4CcRU4i0ienvgdH0pnneKOsKU6WGWd/O1BvRuDT7cbdoeYlxGjfhpj8ViSYtyX3Fogst95HE1BklNUICDciAFCHwmVdhrTnexAzvxVp4ycRaa7dBvsHSBDwzKBT0Tb7dLt+JmH/3mPP/wmRICyN4LIQAZJeuzelOYRZ1WS0q5NTTscaTUSCaSuKVuRmD83D3ZTTUUXl1uJsnHOndG1NFVWlYp97+WkIZc807mYnDrrnJGWNy9z/HnvuojLPKE85AP8FeegZ36YaRIBpH5GdMafjg8qZLWQnaSVh87JOoE5p+Dw/N5PRCXILEXBPo3Ckpjn5mDw0ygouqSOn2++734CbIO1AoQnPQZFxFhKal7Tv3f7cEz95Rs42ZIrikXr2owybbah/pxKx7+9DzLyVSMozEWmmf4jY9ofQ1ukawgRqiPDC348AdwHFOQclhniSUDZMRI6hdyRtcTHFdpfTydPa53TsUD3KRuip1T0hzmsI3pnNGumUmqjldHTTcEqywyEXzgrIjM9puJCNfs98Rks5AP8F/QQ15EbC4clTx+cT8sQlHwvD0XDmIKl2dLRbmsuRZtQ1qieanffLReX6wbcj3ZHRQfVqZLvdiniCb0emwlW6oKf84wBhpnIyMvXoR4Db45Zvtet2JfffSoTNfD8KefjHCDC3Eo5lA3ZfFQL9W9C42Yy2UE9BIP9ptKA8ScQi34AivKShDpdRrBFKtUs55XS001Uoh0MB2X+71rsWCal1yDRrYQVl5bz2xKZK+r8Sh9PylAPwX9Be5ND0PJq421BosHwSnph0KBFtUk2ohyhMkw2NlvoQ8LuesP/N6eUl39UAgd+V9qlDWpCridi1Wc03CDzvRf3VRMS7L9ixqcjs41kkWNUjwf9FZIu9AUU9mYIyx30T2XYPoVi0z9u1DxEJH37fyroL+AgC9J3IdGmvlbkS2ZFj1zVZudchgP5jO/dApj0GyCmnY4c8GVsBmY31oPm8EvlmzEVJsn6vzP3l7IZzymkkGv14yQH4L+j1aEF7H9IweQiufAKemFTOdng4j+0UlLl9eDbzaKXjyZ0vzyDXQlZCo2F+5a4dTL57kCD+JOHE+DYEbD1e/QBhI5gmfaqh2HvfM656FtYnUMSSl1Boq+uQvfcP7fhrUJKIbru3z+6fgZL6PIyUAyDt9w57Zho2bSvKvNphz0tjGu9C2d8W2//ZybmfEhp8D7OZU07HCv2IMCFbB7wFCZsb0Y5QI/LJGC1VshOaU+V0POKqXAM+SpqHtvg3o63gOUTmpFKRJnI6+mkkIJba+KWOmJX0tccmT6eNg23XdKaRNwaT44NoTFVqRnI5Akgeszqn4ckFn1Jtm43vmwpJfs8AWrCbUB/1oZjgE5IyPAKOR7jZj0BxakPu2WHTDJJrka32ZGRTfQ/SbPeiUJXNCESfDXwNaemwunzerhtEpiJDaPvcn/kS2iU5DQHzrclzX7BPlmajaCyTkYDxo8x5T+o0xOhjiOeU01jSANJwgyJPtaO5shTNJ8+WW4qyviG7iXTr2aQ8OR0e5bsLcPwA8L9GxosPodSVFdCFyK5yKtrqvReF7LqK4hA3OR0evdITrdyzBojU4w3Jte74thntengoQgiG3E9EwuglgDEIjG1BQOZkZApQbddtRFrMKYiZd9u9jYwMqi+2T/sw73SiUXYsZe2u06x0qQ23n9+HzEAKqE+3oT7dbtc8iDzcT0GmG4+hUIB77fiTyL56DjLV2IpA83bUr0vtmbtR1IU5aAFvQY6NtVbWYmTP/Y8odNVc+7yIeNIUNH7uRFFSViGzkS6kGb+f4FH3INOV4aLitCOtt4OI+dZOG5BZylko45unWH4UsVM3X8kpp2ORLiSiGc0mAiqsRbvdVRycTTdNr96N5r3nHHAaTO6pRDOeg83jl9K+9WR4ldPxAMDPRLPqYuCzaEV8cOTbrkaxeatQWLEbkdaqn5hg+cQ5PEq1jCDm5trihuRcus3tJhvlgFbKMPvsv0cvGczcO0CEAuxGWsd5KApENQLkDyPA46CngBzWXrLyp9i1d6Ht+XMQWHIN4R0os+UCtMV5sh2fjkC9O/dOQULeDGSn2zRMu12KgFia/OdEJxeUqomF08kFnl1oXDWj/hm0Y/vQGGhDpiV7EECuQ8LTbhRKqtfuX4zMT/4GRVHwcKTT7Zp7UUjC3cgk5YcI1G5FDl5P2e9qe+ZkwmlzCzILaUS7HF9EQPzdCCjPRvaq6xGgv8jqe6adex6Ns79CYzptn1J0AxIi1gNfsGMrrCwf727Oci4C8wusbTzsZU45HSvUgoTbWoqTsvSjcT+e4OONaD66U3J2pyzdrUzBVaoNLzfv0ohEe5HA7DRArB9jSZU+/3jHQeXCEqfCVT+BO7opzoa9Aa0PdyEFbuV0PADw84Hb7fftSEVUAQCvQQtoK+EINYi2gluIFKRpytTjhXw7vTpzDIrB6wsIOPogrEp+p9v3gxRnjHQaRIP1XrS9//codNpSxAh7kRA0AQGfzWirfon9d/ONHVZ2L9IwOw0gAD0PMbn7kfZyiZ1/CYVeu5TQVN+BQFOdPesNCBAvRfbWA4hBfwaZIHzM2ulBBOAfQNv3jXbt3Qiwd1sZDyDA9yY0hiajcfYE8A0EAN+DtKPl6GqKI1EcbwwwHTe+K1FDcRjIQcIUohsB4hUoa+R7kAZrMWFS0mjfqxHgBLXh/Qh4Xor6fAayjb4d9f0s1H+9dk8PGlN1SONdQMB7Kxqny9F43GHXv52Im12Pcgg8huy4n0Bz6HIiK98fIX+TZiI5zjoE8qvsmeuRU6Qn7elCAtsgAs4/tnpWQj7OZlgd+63u/2zHuxBQqLF6QGy7VyFQvrrCZ+WU01jT65EODrTD1YPG8VrgP1Eo2J+iTIaDSMOdroUOtIbQmtFu5/sI/rQF7WgV0PxcYMd70ZrQiPjOyUk5npRrAHgcCfbO30o52KdAMPUjyq67pXw0hhBfbbPfvYQf0Xa0/oH4QFeJZ2TLSteeVDmZnvNdQt85aMlcn32nw7WpT/ujhwgF6AnQIASdUlROyboarQl9iI+fT4Ds2XZNN+LHBbTL/SGkgLmFExGAtxErxC6EwLL0PvvQ2emD7yvI/GQ7AkzXoYXoMStuMRqoE5GG3G3JXCvp2lW3FU+1vC4t+7U70SBx8Ojna+28A0xPoFFN2KsVkGb+WSuvDWnQ/H6fiI+gqAeN1gyPosFTb+e3o0myF20799r5Rfa8VXavh2f6HbQl/fd2/78gpnWatdNtSX3vR0zlIwhsYMcfA76KNiY8nrFnAHR6OwIaq5AlUTvK+jgNAZbHgb9DoHoS8EEUFWQI+BzwCbv/ObQd34EmQnNyvhOBsmar9+n23j+wOtxs7XOP1eM+IvnJ/0VM+3H7vxaBl3fbO95qx9fbtR7v+9Wof75q93poOxDwexj1XRoCzunzyEvfx88GNHZK2Z6njDnLpKG0vbv/dy1N1s7cj/k1fm7APnWZa6uJ3Y061Hbb0fzybd1NSCBpQPNrs7VBFWqvQfteZuU+CvwvtIgNAN8iUv0+hjRYq5GwciVa5O5D2twu++wB3osEse9b+esI8PpFtGjs5mA6YPesQZZtu9H8mofGu9OtKKFOAQlp30B9+hKaa1utnEvtfX+ONOstRN/vROnip9izXBj4JhLqnkLj7XQkQH6Gym1Sb0VC75OI72Rpi9WnlmiHx9E830/MkaOPPvn4z0a+qAx9+NTzj2BNcjp6aBXhtOxzqgP4P2g9cz64Fa2la9DaeR1SFA0iP7CdaO34MALydyCgNYiiDrlA/xngO4i3/S8rfyqa8x9E69ataE36AFII/Rjtmt6BhPB/RrzsXCTsDyEh4QzEW19rz2hGAvybEeD7OPC/CZ+V7yB++Djie3+KzNieQ0qIl1BI039HPPAd1kaNVscPWv33IT72KuDXre0Wod3cO+w9foCE+tcgPnsNUo6stWffa8/4OOKzBbQevx4J+B+15y9Au3l/QmCZnyDn9HWI51+A+PH/s3brA94JfBLxtI8gjFGHcIubFn/I+qfV2mE7AshfQ/z3GrTbOB9hi0GEcV6N1qt/sfr3Ir+CL1tbfxrpeWfZM1z/O3oqDA2N9TbIYdNvolXkG8Ab0Qj+VLmLly9fPnT22S4hO3AAdUgDmmyl2sTtOBfb4zahSfwGNBD/HYHdbWhA96OJ/6yVu58AKZ6B7gorx52/utEg2IYG4wUIiN5k9zlw8GtPRYzjc4hhNCDA8jAapAVk3/kwYjbbS7ybCyFe9g1o0C+3/ylwq0YatVo0cFvtPgfXnVbf04F/LdGGo6Eq1OalwNH1iElkncecJiIAVi6BSA0C+lvKnK+EzkF9t7bMeTeFKRfGbT6yUYx2evDBB4mxeRJi9B9HY6gJjacXURt32rMLxA7O+xAzWIfGxx4kjE1GfXc5eudnkMXWf6H+m4aA4STU5s/b83pR3zoYG0CM0JPJNCDQ2UJoVubaMTfnOAf1oZv69Fq9upD2wDUQnj10vH0qTf5Sjcahg1kXGi60Z3aXue9I0mT07k+8As96JagK8ZUHUB+Jisfn4QHgsaYcgB9fVDw2pyMev8b+p+u8Uyfih9/l4N2kTsTPBtFafsDuPw0J1aspdrSvRfxrL5o7TcnvTqRsqJT+Aq0rn8o8AyL6UvpO45Gz6Rfs3GLEy3spT674ywrkb0Prwm9mjlejNi211s22+u4vcc5pIVozHiZ8rLJtPhe5730U8XLfrQPtPO5Ga9EEIvFYM2qDHiLxkivOUqVoNguwv9MMwq/rz4HvISVOOZqM+va5Ya45HQkK7/3FdUNDQysIzVIRHQ8A/Ewk1r0fqWu/yMHBbH9BW7ZsGVq7thxoyimnsaNZs2aRj82cjlbKx2dORyvlYzOno5WWLVuW9eL9BR0PJigPIRHobrRvXRZ8A6xdu7ZIiyOabp9HkCS1BGl+ZqAti2mEXdOVSDJ73h7dgrZ9dtt1LrkVkAZwJ5Ka3IZ5H9LYVRHpxV3T6GYlA0gym2LHtlv5++zauYRpSasd30GYrjyNNOEuJdchKdwz/W1AkrObkLhU3YC0rIOEROtOWDWEfXydveN+wrmkN7m2ytpvi1032+7fiLSfc5D2eo5dO8nep49IYOIOYh76bwdhzzffjt2DpGDXlrszxBRi+/9RtJX/W0gIdbuxzUjLOhNpDGZb+XV230rrg6ut7OXWpich86ULkUZgqx37rPXDu+29diANzN+i8XSK9UUbGjf/bu3/Im5bVqzFeSfaIfkhMnu4FEnX3facmYQtfyMHh0ccC3LzqVS7UmfHPKa1h/x0x8R9SEvUA/wD8Do7vhO1nztFL7Hjj6P+XY7G6vl2/kdEvN+VaPv2AsLp6jw0ph62+5bb737UX1cj7cc9qI9OQv3fivr5nhLv24l2wCYSGp5+q3cjmqfb0JhbhrY0z7FjtxEamk60FfsUkQ0Te+caxJdA474djZ/RJMjxyDsPEzsOZ9h7dlv5qUbqzagv7kP9IYrxeR5it4dLA2g35n6kNWpD8+EcxI9fQuZGTx2BZ+V0PFN2dyannI4WGhoaeqjcueNBAz4qKjZBAS2cv4MWOjermI5sniagBd5DGaVe0Q4yjkcnzSNJwzkQHgnnwqwHc7a8fgRqOkqcy479NKSdCwClIqykDigubG1FQtI8Qtjdh4ShOgKk9RNROlzAEHCOReQsZENYn5TVXKL+xxt5m/q3g8wCB9u3u9NQ6hDUS2wX1xFtljr8uMNwNwJ/65GtYxMSDJbb71YEdl9EguRfcLBs/1kEFhehvnbBfKfVa5LVZ2ryzM1Wv48hu0SQDXmznfusHTsF+CX7/T0kXL7P/t+LAHwlNB/5SGD33IsA9HX2/SQSUL6e3OMZXIfQtvQ3gXR8+vkjRQfQtv5/IKFjFhKytqHIAn+LhOSccipNOQDP6Wil4UxQxlpldhSQ21lBcQi79PhI9+U0dlRJH4y2n7KOi8OVkR0v2XsqKSNLqaCX2rKdaFRpe6VtVKrvyt2TAvv0WHqui/CnKFVGFogOxxeqyvweabxlw3KOZjyXum+4MZqlhmHOHUkqx4PT75xyyimn44dyAM5e5Ak8HW3t1qLIA19GJihXo+3+emRuPg1p0V5CDhzjkDZpAJkqeBSUOvv4tryblvSgRS0NzVNqgdlHhA7aibRzHsbIk4nUWbl9RNxhjzKxkOJ00p5sZIKVN87K6CaiqXgWPwhPctcgViPzkXGEM0gf4dDhZk7+Pvvss4kIgbbZ2nsm0rzNT959AsVaSo+h6trOrUTEDTe/2JC0sUfa8LjL+5AG7THk9fzf0bZ7PeGs8YKV8xzSuvWgcdCHNKM/RP3fjMbGyUjD/U2kPXzRnrkAbdl3Ik/yNuQI+TiKMOEmKINIq7oRedhDaWeZB4A/RJrZ25CJymXI6bbbPt6mUAwEPdNnqTE13I5DNkNo6viTnvc+TncB0uv92gN23ENT9qB50UeErCygcd1m7fBFInqMh/lrQmYkvgv1EwSK3bHYTVDuQPNlA4qEcBqKod2KTCnOsTo9RIQTXGHPvdmee7+V347mz1XIFKUZjYcsfRT5fU9F886dn9dYXeahcduIdjU+j8bNNkL7DXLEXUix4+kTaKzXWJ2H0DhuJxykK6FnUQSZRntfrD36kHZ5n9U7pXcjJ7DliDdm6WoUBSEdZ5WA5HTcgPrtedTun0S85YuojWagefKIvUNOOeWU0/FFYw3AGxGaenpsq7GW8PDdT9h7riYWyquB/w+Bg+1oUViHFokmu38HCqnTh2walyDQ2Up4Cz+AQIMnv/guslWdSnhc70CL414UUaIBbR3fiJrr+2ghfjNa+GqI+MFb0KJfhxbxrVa3VyMziBUo3NHFVv4aBFZ2W10nIoC0Dgkifs1NyP7dbV73IwDbj8IPnW91eZKIQtKDgKYvuK9GYOyH9i5nW/2+jOyqJ6JF+d3WPvORsLAehU58BoUx2mTlvA6B0ScQSGm0tptmz1tv12LPxNrq96x9NljdW5GN9RZk291q7fHXKLRgpXQ68G37/T3U1tVEXNgXk+u6kTBwP6Xpn4lYzdi1J9s79QJ/hsIodaHxWGu/PdJOPxHLHju+B/Vnk5XXQoDlLVYnN5dYhQILfRiNtW1orM9H43sF8nn2JEQulG6wZ/wP1IaL0fjrR3PodMLG+0tonM1AYPoJu8/p99F4n4pYRDNhK+yx11OaRtjyf5fwiB+J7iGS1DjdjkBxGxrXp1t5qY12NwKzqzk4Ws8i1BZuOvEN+36mxPM3UzpSQjZkZ8omJ6J2eZKIQrTQ6pplp9noLEMMD+K/RGng7XQ72lH9pNXBnzscDaLx56FeP075KEbPlzmeU0455XT80FgC8OsQuqlDXlCnI1Rx/RjWqQy1IODp4dxaEOC8DAGZHUiD+ja0GL4VacY6EFB1kNOHAN4iBIL6kWa0G4G0GsIRcxlaiG5AgOtBZFPaikBKJ5GmvAqBaBMukCkAACAASURBVH9Orz3Xtbi7rW5ddqybSLu+zMpaiTTBM5Dz2CykDW5G4OxdVo7HKR1PpF3fiJIcDFo9mxCAfoYAgNcDv2u/FyItZJM9dzWKLfrHaJH+FAJxPchx7SSr13ikxe5EmsoeJJisR4C+3tq8zeryBLIfTYFYPwIX8wnntt9BIan2o5jj3Rya49cjRExt97u4Cgk8gygm6XZ7lgfyv7DCst+A2qEX9fsFCJz2Ic1hC8U2406upa4lkj5UETsdQ3bOExk4kBqPdnxc8+xxvTsQmF+EskH6eNuF2v0UBNY/hQTFOYQDZpuVdzIa70tQ7Nx32jO6UIxc7N3arJ2+hTTILtg+goS/zyfvOQEl6LkQgdm7kXB1OPQxZD89EwmLJyGhzOldhAD06eT4GQQb+yqlQffhUCPwq6gtF6AdmcXAW+z8fyKQ+3JRDeqTWZROBFKKqogEP5ejvr+einKm5ZRTTscUHW6I0hMlTOhYAvA/QfvCd9r/R/BwEEcdpUlv3MQEiu0VW1Bg+RUIhPhxT7DjDn0dhDOeb797WnanIQQ2Zli5Q8gpycGvg+6sLWeVPc+3+FsRaB9PAK/JCFDvRUBugpXZYNc2IqDlkU7cka3erqu3ert5SK3VbSYCPfvtswJpzB9FwH661WcQAWhP6jKViNJwMwI55xLpw59CYBlrq2a79jSkUV5FxKWeZe9VY/WbicDQI3bc6QX7OHla+Fprn/+gdPzxSiirWXSg630Dxdk8R3Jma0Lv32b/u5Bpwzj7n5qBVEI1lAZN2furKDaTGk+MsUGrx97k+btQ/7q5VRdhQlWH+ngeMhXxyC2dyNxgCRJil6BxvxOxgkG0+/IiGj+ziL7ydp2PxmmvPdvNNtKsmvPRWButZnUrEuA+iOatCyM+V90+uj5zX2o3nT13JMjfsYVw+h3NMwtIgNqFtPSjpTpKOzVXSgXUj5cg4awKCeKlEgXllFNOOR2fNJYA3MNTHAO0EwGHV6GFbg3SwPbbudcj8PoRBEpuRqBzCgJbTfYZR2hIdyPN2QQErqoRiNiLslLVEiHZuhEgmYQW/0eQbfAbCLttX5QHiRCADQjkNCEt8yLClvxbKAtloz2jEwHmSVanAmGz22HvM8nKdFt3B+UXI8B1HgIsT6MMXUvsPX+PSP++1+rXjzZBzrLvAgJIHUgrtgNpvx+2+tUijex0JJicgjT1f4CyV51l9XKb12uQpv3XrLy/ozz9LdKiL0Ig771I03kknB9vIcx73BzmbtQmMLKZxI2o3buRucZVhMmGJ4HwnQ/vDwig2E+A5AOE5p3keJY84Y5Hj6kjBADsma51dwGi2u7ZhsbhKjRPVtszf9fq145MPjzT6Uet/D40Xt5oz19lbXML2uU438p5CEXteAQJYa+3538FCU5PEaZbIDOqN9jvQ9VGf82etZLiMfEVq3M2adD9qF36iQyqR5Kcx/wWarerEM/wHZCRbMQvtc8g8l0YbUKqjxGC+WgjGbkN+AGUuuG1qE/vR/2XU0455XRi0JGMJTVaegKpjKvRPuqn0Up7lFIjMo/wWNHbkF3nbEJD7lv7HuO4m2JntTS6hTsNurPaIAIcKxBAaSA0m67NhtAe3oTsS9fZZzcBuLYjwOwZohYjALjdngda8PbaO3kdpxJaWn8WhOOcxwxPIxY4COxAZidvQoKK17eecPjcZnVosnevIZzsuhCgbkZauWcQyD6HcLhbggCW27zWIO3mBUSGxQ4E8FfbuzYiM4jhojnMt2essvdpsLIX2zOX2jstSO5ZgICxXzcXAfjslOpBwtvTyAZ6IeFECiODlyY03k5BQkknAr41xG7MoD0ndXCDiBO+h9i1GOl5aRnNyfHsjktVcqxg1/rOTZ+9o8esXkAICo0I2G8hNNUuJLRaWQ0IoDciG+t+e9fn0Di+HWmnXRveinZOnkBz8vsEqGxCY2d8cr1TNRLczmP48bEfjcmtmeMvWV02Zo4PWl23WRsspnJTjUrpRWRb30u0/X2IhY4kOHo7eC6CLNUB1yJhtBRNsO9DEVC9L/uJ7K6L0FzKKaeccjpxaCw14B9EBqC9SDV1C8oHehTSuQhgVSOQPA9pYn+bADV9CFi8iOyqPdHNAYrBCkTEkE0IVF2OQJU7Ay4iTFdqEIAYSJ7RS9iBV6GFdxbS0nXbs5eiRe5VVsabEOiebe9wAbL+eZtdN5/QRG4hQIcLAuvs+DKKtd/VCBTNsd9LrQ0+j0DnCqSRq7H3AoHwUym2We60d9iNBAE/9hJyXL0QLdjLkUOmO30uRFrwHyInz1akXf5nBKivsLLeSbFTo9MNyAZ8CPhHBJyeRhr0y62eDdYfa5H2b5ydG0SmLPORdv5Ba+ObSzznD6wuBxBo87FQV+LalL6GbJnbkVZ+gDBF8qRPbv5RKiLJELHDQuZ4NpIJhFNvdfKfEr+z/+uRZt61ohej/jrTfu9H4/YvkD11vX0eQoLkTHvuNjSGL7Z6vx1FCfkKEkJS2+YHEFB/DRqXmzk4Vvc+NB+GODj98Q1ot8Ntmv+R0vSrVpe1KErHSDTZynVTjc1IE/7t4W4aJW1Ctt6T0I7AaOjHaBxuR/M6Sx9DfdSN2j9rjvVRYrdstNrvAhFjvwe1VTsaH58ZRVk55ZRTTsc2jSUA70YA/H+NYR0qpFakZTpAaLuhWNs5gMDDerQwlYrzC8UhuE5CC89eYkvXy2+iWEvqJiF77FkdSHvVgwDrGgRGtyKA6BEi3C7W7Xh7EGBbSGjL2ykOObgGmYP0EXbejXatJxTptPYYsjoNJs9qRGD6SSL02Wp7191WRhsC9K4p96RGg/ZpIRwHJyW/PeTiSjvuZhDrkAb0HMKu/Tv2vzt5DtbW0xDY7kzauBYB+RpkCuEJc1wIqbNyFto77rf7XZNbnXlOSh32XYucEH1MjKQZ9agiTUQfpJpHt6ctFT/ZBb1SG12pLXMpquS8+0Sk/9MERW0IlDehMfcS6jPfNdmBdh6+jRwod6N+bERa6SY0jhagvsqGAhxAfb4EzYEzOBiAe4Qi0FgB9dUswrymyurahvpzdfLuHvbSnV0rIR+TPh48EtKRpkN1tOxBQmA5ct5ShdovC8D3IyG1i9Ftog4Q4Uu7Eb9ssfp4QCzvj+fIbcJzyimn45nGEoDfxMEr/C6k4vxHDlZXjSE9hjTObiLh2puNaOF3EOog8uvIusYjU6TAyONajyeimEBx9r9xaJFLF/wqe/ZMBDB3Iftyd6x0jbAvmHPtPgfGVUgD3YuAzOVJOR6f21NTdyH79R5Ce+qLZK/V9UkEKn6CwPp1aOH2+OHvsPq82c59AAGlIWTb/VOkha8jTBMOII3lX6NIMpORtu7vkK2qt9kOBOyfQbsRmxDY3og0ruOQXez5VpcOu9/b8desrGdQCMTx1u5fs2vehIDYEPBv9j0JgQ43aZlk9z6CwOL91kalUpaDQra9B9mob0e23QUODqVXij6GsiJ+Fe1gXIQECwd57ajdByg2cyq18+KgHEoDdv8/lBxLz/sY9+P+2+3QB9DYfYiIy34AjbWT7eMCWz8aB29C82Q7Ch94jZXZTEQaepQI5ZfSKivnbDQWVlMMLj2z5RARmeZdSADbi9q0HtlQvx8JWw8iMxaQprcajYdSOyil6OdWXisSFqegXZtjhVagtlxD5A9IaQDNwX6Kx1ul1ITaaDPiVQdQP7wH7TY+g3b1vjr6queUU045HSM0lgD8OYT0nMu+Fa3YC9Ge+7te+SrNRgtOVuPjyW4gFhuPvLANLdr7iRTY660ct2fNkifqSbWGpSi7sKXgKQXvtZnzaYIWB/UOxvoQyG0iIilsIcIk9iIA69n/XKtdS5hLrEQLaCsK6VeDQO5ZRApuj5IyDUU7uIQw4SkgYPIMArMeV7wfAaiHkAAx0+ozBWlGexAw6LL63mx12GjP6bV7lyHA14rAXi8Czm42M5kw7elH4P0lQuPWZs9aj0B2miznjUSylZWo/7MmJ41W57WEhvhFwsLqd+w77c8stSHzlxXIfOFx1Af+/kuIsG7eTy5spZQ1Iclqz4f774C9usQ1qdmKXzeA5k6P1dt3gjwVfJroaQfhiOwmXJvQOJqE2suTTrUTAZLqkAnOFmsH93/Yac/I2hIPIBv8lNrtAxKMQOPF52rqcNpmdfX6Vko/Q+N3DwH8jxXqRUL1FtRfBzLnx6OxmfqDVEIpP3KBbbV91iAFQT0R+ccjMc1CYL3SuO4puZLipUO4N6eccsrp5aOxBOBnIFTmdBNSp17CwWEFXgG6AmkWe5HGNQ1bt8T+D9h5B00thB12I1r8ZyHNa2rfnI0U4A6bKYDZSzjXNRCOUg6IfNECgYI066RHY2m3+1N74PQ51QjoNNuzmuy3a4CriSQ2/QhwOGjdiYBrgQhhOAntAHwAAdNphEbU69uGdgRcU7Y8KdsTuXhm0O0IED2GkrBcbc9/PwLZL1oZ11rZVwD/hYCYn/8JMllwcHue1WkaSvzxA3vvLqTlfAOyLd6J/IAHrUzXxGUzVd5mbbiZ0iHcqoD32XuvpLTW1uOPe2SSLLVYXaZbGb9tbfZOJKfOtXqmGu/U1GQ405EU8JeKYJFqxUsJh9XJcQfW7nxcRzhu/h4CcM2ovVNNqZsZbEOa7SuRZvy9hCnXNrumDvk27EPz8DzUlwvQbsJzyEbcd2f+c5h3d9qKdko8K22PPe8m1OZ3J9f+kPCHKJUspxxdgHZh+oHPUlqTfLTSErSjsI3SY+lHSLgYLaVhIk9B/GM22vV4AI375wkfmndZHU4j8gOMxixljpVRQDxo1SHUOaeccsrp5aGxBOBdiIu7F9BMwiC3FCp5mckf3YUWhn6kPXkByQo9CKS5Fs/tIyHA3iDFNuIpZSNUpORRLBzEZMFPGs2imjBNScGTRxdwQOS/U5DfizR50yl21HMw77+x+xsQ2HbTFi+rFXVRP9L0LkzOe739eW477XXsR5ryp9FW/49QKLIBZA/sDqhuU+3grgGBLLdnrya0lkuT595uZfehxX0XEhw6UTSK+wlNu8ckh9Aqu0b2bgQICgjwufZ+KwLQDqgmE2MD1P/eP152Su3IrMKplBNmC6Hd9vCN/fa+HdYenkmzkYP7zmkk7WQl2svhAHq5691B1M2XvB8hxp1HqDkPvccmNN8aUTvvJiL7eNx8T+BUQILfPtQ2X0VJgVoJ7fZuu8/7xjO8uh+DgzH3owBpqh9Cfdpm97lVXCqQZ8lDMqYCmUcKqUF9eSwB8PFonA+hd8u+e/tBd4yO3HSpDrX9BNQnt9r5SWhMeF+D2ncu4RNRCaWxyicMd2FOOeWU0ytOYwnAP4wMZlcjLjkHqVKbkUrrFaZbkVZtHMoo52GyJqAknS0IvJ1M8Rb2HgIgplppd2jM2teSOTaEwJSbebj22LWaKTVQbFPu5MlwvMysQxxEmL4pHAyooVizWSA0ihOS+mDvtB6BnQ4Egv7Sfqf2wDuJhEB9hIPlGURyoE+h4DeXonZ/q5XrdfX22Wf1mECkNa9FIOt1CFwtQtrm0wmn0s8isH8K0uidbWV3Wp0eRlruyxAQ/L/2nhusPk/YszxU2gNI6zrH6vRTFHmlH/gcYcrzn8jB9j6KyaNjLE2OlTJReglZYV2MtINuJ/4skQzJ+6qXiC2f7ftSGu5SNNx12XPp/6yGPGtbnoYxBLXTXgS8qlGbfwu15WrUf00oqsmlxM7LM6jv+pHQNt3ufx0ai88iR84PEKEi99mz9qFoP1dYeV9EuyCXINl/R6aOp6PoKP2oD+ZafQ4gP4Ls9W3Ab9g73UrYet9h7bGDYy+1+v8DfgUJI6V2eb6KxvGhRrF1Aa0ZzdWLkAD2Ezv/H0jr7Qm0LkJz/+1oLnyGynwnHkFzvZqDnXNzyimnnMaWxhKAfx+tlovQir2KUEX9zStfnR1Ic1qNFoYGBALnIgC6E22XDyFNaQcClN1okU9Dcg1yMHBxzV9qE+vX7ScyXqagKktZgEPm/2CZ4x6CzeNkZzNvZu2ESe5zzXqD1auPiLpxgIgEknXUe5GwG28n7LCrEfheZnXZjQDneIpjqbvG3+3rh4iERputLNeiuUatC4HtzXbfOuAulARmsdX9ZAT2dlm921G/X4L6oM7eq9nqM5XQNNcj8Ox1vChpi7OQ9t21rDfbuUVIaNiPQEQ1xZlSy0VBuck+KR1AOzJNSCu/j7DZ94RKKTiu1EZ3uGvKjQ2ndMyNFNGlH4GiBUSCJ0/6sx+xgHbClt2jpWxFoLsTbZQ9S4y/OtRHbahPHPT7roGbO/gYWYzMiDwkYAEJpVutPLchr0H9NTEprx21eScyeapHpizuvJxmON2L+s/9DQ4noodrgw8la+Wh0JNEoqhStAPNn8PRhDuf2I7afA4BwH9uHydPGjYFjf1WKgPgA0jAzymnnHI6+mgsAThoJT6JsCMAhZ0YI/qRfW9CC+gCpC17PeHkdSqRWKSAQJuH60tT1GcTl6ROa1nw1UWYsXiM7VSj6WW6trScVjJbfnpdCwKDQwhobLXnlotiMERsEW8k4p83EDbWPQg4dds5DyE4gEDKGiSs1KNFs48As69B0VBuQjbdbnrgzqEei9rNF+qIuMorrdwuIjpLlZXp8dTrEDi9C2k0u5C2dDzSrt4NfBwBqF4kCIxDNqcuQDxu7/MmNB56EQB0m+Qqu2c30uwvRiDyHDQ+ZiLgeC+KZLLS2nJn0jfupFkJ3Y3AyifsHVutXqnNf0qVaCgr1ZKXuzZr5jScJn0P6od6wjnuANJO1iGH3hbUzu6ktxvZ47cjLfgW1GZe3n8ggLYOabrX2PM2ozFyql23Gtllezz579h11yOt9zbkk3CPnd+H+tIFSddk/4a9w0rEui61uv7Qnp/S29BceZHKI6hkqQb5QLQjU5hSMeZfafpXlIXzUKmA5vUWNA7c6fnHHBxq0ulWNC83EBllc8opp5yOXRpLAP5RtPd/MtKGX4NWvzEE4NuBbyb/PXrBE2ihvZoIQ5gCjSoERD2mdg0HA5Hh/qdb+W5eMZyDXDnKmgRkzV/SCASb7b/H8s0CehAA6kXaqDvRtrMnTPEwcnch7fF+AvDtJsxQHEg1EGHLhux/OwI/qc16LyFo7CaitjTbMzxkodtAp+YW1Qigu7Z/PNJM1yDNsUfO2IucZVsJEN2NFvcDqL8PIOHrGgTqXOBoIOyLPQtlrb3LPHsuSFvXZvWbaM84YM/w4zCy1rgKCTP7EZh7jOJdlNRn4FBoNPeVunYkDXk6rnsJ86K25LgLYZ7wqB8B3g0UOywXCA33agTWPkEIMcuR9raKyLQ5DvXzw4Ss74meGpBdOYRt/T7ge6g/h1Bff8uuqSfs+ucgYaja3udZe95ENO4PJM+ZXKJdKqVGQtM8dbgLy9zbhISL0VL6Hlkazsm3UvKdv2oikdTZaDfMI0512bkD6B2+eXAxOeWUU07HKI0lAP8lpEp8GHg3Ws0OVU30MlITWpBPJcwNUtDkQNkjilDiXCXk4NXDtGVtuNP/w4HsUs93c4QeQgt9MqH9Tsv08jy1udvkXonAiGsn/d4z0ULZhIDQgP0+QGThdLDYbM93E5i5yK5+BwJH3q79dv1EJCg4qG4nzDr2IAD2vJXThQSFlUTmw1sQaB1AgOzPkTb81xGA2kTYub6AhAFPcvRlJGxcikxNDiCNeLv9vsmeO4fQeO9BWvVLkGAyF5k/3GxlvsXa7McozFolGvB3oLF3LtLourDi7V8qlfjLRZXYipcao/7bnZRbiDH0DDIJebVdsw/11Uv2mY7a1s2O9gFPIUD2DaL9JiIB8TykDb8d7Tz8Emqz2QiM+45EE7IZn4pA3s0EIHwfAtppPHDQ2L8Zjb97UZKha9E4XIr6twcJSZ9F9vtnc+gJc0Bj6hYk3N01ivtakLa+CQmSo7GBfg1qx+3oPbLmMxdQ2nehEkr5zKlIaKpDAutvo3b8M5Sn4EIkBPw9AcpzyimnnI4PGksA7urMfrQybiayxxwlNA4tDFMRCKglbF79u5T2MWsKkqVS17u97nAaxfT/AMXdly5sWTDtjoyb0WLXRHGEAIj3cW2yO7BNJexnq5Cmr4DA6T4rJzW/6UMgJNVM70EaLgfWrjmuIkxc5hDJeDzySj0C2CdZuW6H7SHs/sjq89+szOUI8AygvnoEgeq77RnPIjDyPsKZ8SMIiPegZEsT7FmPIyDitu+DCNhvQaHvbrMyP4K07J75cRDZFzdSbJJwZdLeldiAO80gMnd6NtBeIpb8K0mV2Ipnr3GTJ98h2YRk7X3ofe4iEk89RaSn30eAPHf8rUG7EQ8iUJqa2EwitMV7EJD7GeorEGjej/q1xq6biPp6JRo72DM9u6k7dKa0gsju+jyyW56JdAjjrP4daI49g0y9dib3NxORVyqN5nEfBzv0urBfKjZ2PZpPHqVpBqMD4NOJnbxmJKCkdCGH7oAJYZvvOziPItY/hOb6dFR/0Jxq5diKIpNTTjnlNDKNJQBfjrjrP6EVbS9Hlav6EmT7exrSxHUQdtgjabWzALjc+ZSymuj0eKlrazL/S2nCUw34OPv4M9xeOxtP3M1fJlDsXDmAwMpkwpmwnogM43Vqts8Bu8ZtxB08byEcLjcjzZ6DchAweQjZU2+mOB75z1E/uMb9RSJduUfW6EBa+VYURWSVHRuHojc4qBqPht8dCID9CC36l1vdz0SbMwesrg1oHDQhQLMOgYKb7P8kq/evI7OGVVa2ay3vs/bsQaDwQkLwGo5uQkLFNGQCldrHw8hj7eWirMY7eyzVentYyRaKHXFBwtPfICHp9+38VASifeeiFfVLAwK9d6JkRtXAV5A5ylOoP4dQe81FgspPUP+0oDbcjfpkgZVZT7FmeR/qq/egcXwqAu3D0ToE9ndanR+x59yABMAXgM8joP4bSGB7FCX8/XmJ8kai6cAvo7b+NyKSK2icvx+14xbifUdDc9GOxEY0f7P0WdRv9SXOjURZX5XT0Nx5AI2TZhRR6FE0T1aRg++ccsrpeKSxBOAfsO9/QB5M4yjeqz2FMUnI4zQLLQidaIFz+8+pBPjJAhAoTlCSHhsOjKdxuL1MNzMYjRmL319Km07mv2t1Pd62x+h2e9ts/fvQQthIgHWPsfwispt+LZFUx6O5rE/epxYBWrfFXkOkot+CgPp6K/NLhJZ+j317P5yOhu4Sq8dmNHwmUJwoyUOQrbV6Tye07JutDg0ImE0mUrq7FrPa6vIcER7x51aXBVb/OxF4r0fgagoCFD9HgP4uwiTnB0ibeFfSFyNpEp+wz/kITKXZL/37SNjkjpbKabzdx8CjmwyitkvnROrj0IjMa9agtvEIM9tRu/m3P2sQCWcO6mcjAF6Dxs9KpNnuQYLhX9q9f4j6xG3Rl6JxtQeNi5TWIEAP4gMjAXCQk+CtmWOz7Xu61W8GGmO1SFiYxaEDcGfdLgw6TSTa60WUVOpQyt+E+mJypnyQMLOfQwPgcHBm1WbE+veg6Eit6P1uR32bU0455XT80VhHQXFaU+LYl5AacozoXqRBnYJkgToCfDo50M1q/rLawVJA3SkF3yllTRPKhQ30c1ktZFofP5c6QXoyF9fAdhPa/dQ22QF5LQK5nk2zHwHTS9GC7GDZI1t0o+E1367fgUDRMwi8tiDbabcJ77d7PI39ZGT7OYiEnlOQPS9EmMI3ov7xZ/hzlxNJWRYRoHwj0mI/jzZcXoUA7aDVxc0PBpDs9wjSal+LwPpTwI1Iw/12BOK/i2TIX7G6b0YRNvYgYD7frl2MhIvlKNPqRVSmAXf6JwROmgj7ae9LGL2g5jTSfeU07KU04B4v3s0L3F8iTe7UiMaCC3/9yG6+F/VzHSGYdaJ22ozGXy/qh6tR6EgHivcAH0Ia7R7U7h5Vxen7COjPtmsfRKBviHCydHoW9f14xAcOlW5BOx0eT/4he363PftQN/weQeC9gHZpUnrOjrWjdjkU+ie0A/AYB4Nv0Bg8FBvwdKw47xxAOwQvIAH1AHq3uwjzoZxyyimn44+OFgBeioZDBR9C6OsiFLD2BqTm/BWOmLfODgSY9hOg4v9n77zD9Lqqc/+brpmRRtJo1ItVrGZZ7rZsyzYGY8AQbNNDgEAuJDeN5AaSm3oTSEJySQKEEEhuIEBIaEkwtimhGGMbN1zBFm6S1aw+KtN7uX+8az17f2fOV6bpG5GznmeemTnfOfvss/f+zn73u9+1Vsz6uYSjglyw7HGzk+H9kiy5R+1IAzhpcpI0QJ12/hCBVY9Zx1FCEhHP8DiEgGI/QXbi13YQHB9rCeDqiP3/DEEn6+nt+5FD4ygCAecjgOwM+hcRAFtsdfPIE87GP4rYbQfv99px13sP2fVDCBg4ozhK0OcPIRnBp1Eq8G1Wvkc3+VdyU1K/186rR4DvlJX3JIq60IfAeA0CUx6q3qU26xDI9wXNw4i5c3s1YUHRjgD5ayidAXfzcl0Lv9mOT1Z6Uuz6eJGQNkbjY86K+kKsl8DGQnAEPobkOYvRYqYOgd0ewsJuBdp9qEft95SV5XHoaxDbPQ+16QbU7+1IlnKfleuJoJ6x+/6i1WUJ8D7GJtbxet5m9+lL+RzU92n669ieIXes9QKft5/JWB9KrZ5mI2hROBm7F32/Dub5fPsEy43HSgfqvwq0IN6FdsE+Rmj3Qjs7nrgsX/9klllmmc1sm8kAPN/btw4hOxAd9mIExH8XBeyewlhVDyKtaSWaxD32twOSWIcbs4AxKwnpjpFJhjspG0krI/5N9Hl8zBl1Z0hjW0iQB/hWv+tmk+U1I0BUS3BAXYgA0izECsf33oHkApcQMm62I2BVR5AXbEYM5warSz8CJr1W7les7GbE9LUjHfSnkB53PgL3a1CfLEDDuNfquByFDnRg14yYvFMIqF1JLij6LiGVuMd4r0ART6jRLwAAIABJREFUMnfbfRejtd0Su28tWpw9i7Je3m91XkfulvnNSEt7sbXNXrtfnx0bDwN+FQKPs9FiIrnzUmiXJbaJMOVpuzTJxZ2bOwePEiK0eN08XGMjcrbzxVI7Yp3PJeyMfAt9zQfts/1oPNyIZBYb7donUPSTTWi8f8/OW4siyAwgmdDL0Rj5CVq4vQ7pmD+DIt4kbTtynD2C9NtxJJA32f2eQP3/02YfRe18FL1ekzpwz/Y50V0XCN+3GtSXNei7+TSSBx1B3720KEFz0HehEcWBf3qCdcgss8wyK5/NZACez96FMkH8KZqp77Ljd6AZNw2A/5L90NLSkvjIMx8mzdmzA4it8Ri8NYRkOUmd9TACV5WoaWuiz/Kx4Q7CYglIVXQ8jnaSNtmlAfIkAx+De2fJY4Aegzf/zEPGeRp5P88T33g9XV7yAuqCYQJQf5zgvNlh5++wdjlKSJLSTtDCPohA9IsRMB5FWt524J2E2OSvQKnIF6LJupGwoPAshr1Wr0OEmNKrEIDrss+fBj6AQNlbrZ5r7B6e8XSzlb0Egb7DyLnvLwk7A8fss9UEZ92NhKQuDyJw+DTwpahvSmHAZyGW3vXytYSdCsiVDBWLqjJRwJSm9Saqg48Xj4XvdU36MTgQ9yguOxCQPUQIMekRU263MgaRY95x1C/bUds+R4jo04fa+Dt2/iaC8+yVhAy3TyFm9yary2XkAvA5aExutOdZghaRcSztc6xeG5g6q0BjzL8v+ayWkPhruuwy+70YLVqTGvi16PszmVCE/v7whdwqtDDejvrf2z3NAXMZ6idQH+xkcplGM8sss8xOv81kAJ6GimuQ6PjjCIDPQ4gJCudG/if74fjx49HMdRVwHQLZnyGwkW9BE89DiNF8pd3Kw751EBhkyGWpXc/s2ukkk03KNSBwMJK4xnW0+XS+SYmKS2XS4pT731UEB7lkwqC4PC8nvn8VAqex46gnH7kWTZgvJoCEiwmOeM/a7zcgIPVxJDPYgAB8G9J9vg2xxhVokp+NgPDDduxJxIZegYDXXivHE7t0o0n7MFoozEaMeCUCaCuQs+QJFMHFteNPIHDsIfOusGe90c59DG3NL7cy3obGSafd6zt27qi1wYAd24HA5X60aPgLa4t1Vt9iDPgNSHG1nFyJRz25/ZW2G1PIJsKex9cmXx1ep2Xkjt9BwoLBrQcBaVAG0Q12rTPmDUhh1oXW18sR4/kgcqZ0n4ylyE/gStTnXUhWshB9bzdbmYcR6P4WAmubrL6zCRkxQXHir0I6/aNWtwfIBd/XEUJCfix/E43LaoBfQyz9HhQV5rmU8y5Gi85WxA5PV2zs76MoK3tJ94N/lsmlofd3o/d3DeqfqxDor0TfnXzRT55HYH0ZchPaghZRaXr1zDLLLLOZaeUG4MuRx01cj3vs9+Up578NCTzd2qwMEF3aNuaKgnaO/V4RXV6DwDdoor4DMUILCEBlAAEvj5Edg+ZhtBaoIZeFTpOhxDYS/bgTG9H5aYyXs+T5NOHxtTEb3oNY40UIhDgoryaED/RoJnEZMVjrtd+eVKeRAGwgJOCpQuzwPtTenkb6eSTd8In8BZSg5g8Iad7vRpNyFQLYA4hh9jCEnjLeM1rWI+3ujwn68qsI2QtdauIp7TdZ3foRmLsdbWn/LBqW68mNA34LGgcvRaDR73kKgb02tAhZheQrSxF4HkHD9E/QuLggaqdCDHgl0rDPs/vcbXW5jvCVccdGt1JBdFLSNF5mPG2MxcedkfQFmGfsHEB99iBqh9WEEJVJeZcz2J5J8jy0mPkxQSN8EPiIff4uQobVlYSY6XPQ+LoLgdY1aKxh5UGIrgPamRlEi75krO5zCenSd9hv97coZv49S1oLWpDNQgB0EwGAx9dsRm20yK45XMI9J2J7ML6C9NfqWUwuDjiMHW8j6Hu0ksCp1JMeBnEIaeAvQj4WNei7mgHwzDLL7MyxcgLwDyIx5VOEGWaUAMDTbCOaJX8Z0R6XIHT8VwgVJbNVFLEfoK393YRJZhAxndeiSfE8Qjp1by6PYx2bO541IpCXpquOgWx8nf8ko1okz4ktZhmTx5IWM+DOXu8msIC1BLbdQwXG9YhjhvuiwkGH19sTg/i2smc09HBr7uC4HIGn30PAqh4BqjsR43UrYj8fQwBoDUH64anFPbxgPdLyuuymFe1mHEHDYjsCKy4r+Dek1V5hdfkeinTj68Bft3vst3bYhQDiCuQ42U1w2mtHY+Y4AvEfRwBwEWLSXdr0h/ZMP0FDeylafCywcvKxmFusHWInxhWE0Ilxf8WW3PGA3LGTj/FOY8BLOZYc25CbKTUOIYgd9wyifs1JchessQ/DOYQwgZ8GfhNJkepRO25EY68V7WL4YgwElPtRnz1CAM4PWZntBM1+P1q83YAY4GdQH9wdPetrCIvMr6BxvBVp/VutfmkbdyDm+hKkT0+GBjyC3kVNVh+PjuJxxB9BGTh9wXrQrpku+wHahYrfi7G1Rn9PRAeetht4CSFJ0Sr0vTuEXu35zPXidYyNBpNZZpllNrOtnAD8ZjR79hc7MbLfjf6+F3i/HXO3/b8dXxWeJt2B53toIrgYseGPoUlmAZrgPYpI0grpvPOd5/8nHTaTjp6F7lXoWGzDiIE+hYDAE2gNtAo9rwPqDjQZOrv9FJoMryHIbg7b554ABzSc2q38byPG+K8JMdXnIsDpcZgPoG77DCET4Z/aj9sIAuAuM7jCynD22RcDriP/OgJOnYSkNR73+yRygnR28Zuof0fQUKwlOG7uQnKG30KbMdUIDN5LiHLyD1au27ujv1eiUG6ViDV9Ei0AutGwjXcK0mwLIVPgvQh4rbPrXAPcyFgmstC4i48VG5OlHovL890VyI2G4pF5+qPjrgPvRoxrP1oI+dg/hfq0hZBd9UnE/tdGZSwjtMMW5LC7HckkWskdS25HkHQlaZ+yH7c7Ep9vtWv70DgC9W0lYq4XoTGdZudFZSQB+Cjw7/aTds15CIDvZupkL4UsGb0laS+O/p6IT0G8QBxCLHcT6ucRwgLsJRQG4L3AZydw/8wym9n2oScfKH5SAXvv1iumqCaZTaeVE4DvJgT3nYhdZb8/SMi2MYX2KAJ99UgL2oXAmjseTcYJKt+1aZreyd4rtioEWJYif9UGtKiIo1VUEMIE+rGthMAzXp+zyHWyA7VNHQKLbybE63bN+QICcG4igKYbEWDutvs1IVDVhRhmT8Hu2vpFiedyR9H1hNByqwnxo5fYz5vs/Buszn2IVXwVAk4eM/woWqD0IPAdR4TZZnW8A8Wvvgqxb/8bsfGL7JkPImDZjMbSOxGb6Wm1Y7Y4zZyl3YyAfy0ClMetDTy843Q6443XKgg7IbE5S1+bOO4OrJvRWHRtcBVi+ucQgPYRtABpIsTkX4nGTAsaW01o3A2gtr8cabj3oc2xcxE470e7Ep8gLKBWI+lRFwL+LlNxh9+9aCF0AbkbbT8kxK7/BbRQ/Rc0Fl+L+uyziFW+jJDG3q0ZRdipQlktj0af3YNIgGS88Fq7ZiFi4h0sX4B2eA6gcJtpcpdS7H1I7bcH6dKT5XwdadanwnxXy4G3h0TtRWFLY7sRPeP9jF0cZZZZZpmdWVZOAN6DUM73yAXhv1Ge6iTtMJINNCGQeBgB8WKMz2RCc023xXKBZajZPbRi0ioQQK0jACMvAzs2bGW4ztbPG0FgfAsCB99A2/cNhDjezmI7yzsX7Sx4nOcq+99T0K+wsjwCQ6PdvweBo7mIuT8bre12IxDcj6QhVYg9f4IQFvF6pFzajyb2boKT598gmUyNHe+yemxAoG4AAToPo3adtcFZCDz0E9j2pVZ/j6RzadSe+XY49qJoKRfZvedZ3e9DUpkme45yu3FM1Fy/fQK1ozPfsbzJw0seQ9KMLtTej6JFiY+9bgTQN6Gx5W3rmWxdOrUYjZNhBJA3EgD4FtSe7svQir7vHtVoLQKerh1324023t6KFggr0CLsPKv/Uvu5l/TEOOsJET02kQvA7yY9jbwvorH7OAA/357zLLQgOTb20pLs5eiZ16F2TkZB2WK/p+o95+8Ufy88Yj9xiMcKgu/ERWQAPLPMMjvTrZyz9+1MPmPEJGw1YkTbEGMVJ3SYhbair0AAoRm99JPRHPLZTGIlk+Z1m20/8bHYKsh17ktq0iEwmvF5EMLwbUOsXxwv2n+c5fYh6LHGXfZSQZC3uO52G7mOrR6NY43V4SzUj6vt86eRBKEHAbEfExLYVCKwfgKxlu0EEHgAge9K+6wZAembCHr3w4SFh+vRW5GcZxViZ69HYMwB4kEE/p5G4A7SWcpq4I8Rg9uOWO9RpMv9WbRA8bafyWOtmK21nzTzxZw7zjaj72Psk+Ayo2Y7/jW0m1GNFnzNVlYTau9KAmC/CI2bH9g5P0K7GVcTwmV+08736CRpSXu2A7+PFhHDKMqKZ/QEAXZPaNOEoovUomQ8I2jhtgUBzh2UZgesPgsJ0i2Qz8QiNGbeavX5XJ56F7IH7Pr9eer0s+R3DJ+o+UK8Cy1y96KF1lzkZPsI6q9h9L7OLLPMMjuzrZwAvMxv0fMJERbOQppRt62I+XEnN98Wn6zn/39HS7aZO6n1E+Q8sVOgT+ouK3FWdAT1RZrTqfePpzfvJmjV1xFSja9BgPsEAm+XWx2W2f93IjZxNLr+Z5CE4auIjfVFQxMhVfYQAnS/hDZw9gOvt/ouQsz8USu/F4GbQgz4EivPx97bUcbOtxKi1vhz/7RZHG0n1pf7zokz3IdRW9aiBcp+pET7Coo8cxWBSY/bDDu3E4Hnz9mxg2iB5hKj4wjQgnYe8tl1hJjVrYRssr6b8mVCRJizCYuCLXa8Bu203UVuuMNCNhTVO7an7Ocagk57I+P2Tedp5FQM2nlJAvhSdgInYh4SdCdaKHv0otfZ770IgP9gzJWZZZZZZmealROAr0eZTM4hN6NDPkpsiu3HaHJqQwArtifRJHSt/V9LYGYzG58l26yOkOwnLSKH/++64ZrE53F5LkHxc+O40+40+6tIeuAAq8+uOU5gkofQsFuA5ASr0e5Ip5V9mR2bZeV7ZJfFVodFVq/fRsz3tQhIdCPgWG3nno9Y2nsQm+3PkLQjiP3bhoDmL1l5zyMA5yEif1rHY5qjZyxXGkWg2mPQ16M+uRW15zBBtuM+CA7s99ix81B/bEL9djUC7Uut3C0oC+6XyI04ciHqu10I7N+BJEVNaLfkCcJOzho0jr5gddiFQHYtAt3DaKE1RHrc74naU1aux98fr3Ug/fqzpEdBeRjtRkyHuV/JF5HO3KU01WhR0Yi+Y0fReDiAnJvXIt36EtQnnyc3OU8F8Eb0vf0vxspqMsvsp8cyJ84zw8oJwD+DAiN/BL1Zf4HTSuntJb+HfR/wf9AL/Ry01eup6KeTBZ/J+nEI2//Fsi3G51ck/vYwiIMEltuT/jigHCAw0BXksqEQtOfPIaDl2mvPIuih7Rz4VqCJ+RbkMPlJ4O8QQB6MyutFIMwdwYaRA95ce+79yOnuegTivR2eQ0BwPgJurWgMfRyxnmvRrko/AhSXU5gBH0JfDZAUZpbd/1dRZJWbCMlg8mn4z3Tz9vdXlGdAdanYcdTvg4SQhUvs/2eQo94xxHJvRcD3QZRW/jWoT2qQ3OF1CBTPR+Om28puJqRFd7sM9ce5SON/P4pFnbQ3W9lrEaj3jLp/nzjvI0y9HWfcAaFyrInAms9lLAh/L2Ls65gaG0ZtcxK101fR9/OjKLJQM8EhdxD16aNoAT2IuJyL0SK5BbHny8iNC+5x/0F9mAHwzDLLrLxWTgBejxwwKxAF/T60t/gnBa6ZRjsPAatdKEzYQQS8lyBmy7dDp5t1nOmsZpwZs5g5iIqzNMYg3nW+SfY7maHTr43LrENgtx0BolkERrwS9d1w9PlJlE1xPmI8dxASwAwhZq0R9f/ldl47ign9YitvLmLRRhG7WoMAYRUCEOvtmm0o5vRzaPxcixjberTr8m2UERSKR6p41OozgkDJOgRGPMmMOxj+NFq8KKtBbeAZYztQnzj4riREzelGIOxaQkx7d+b9AAJis9AYO4Q0+xfYOR69ptrK2YkW4gsIC7iXo92IETSmliJWtgEB/DvRbstaxNQeR1GHFqN3i0uXQE7A5yMgP1HWagMh+sl/UlpSoEI2D+m8d6LxnDRfkEzVu6oSgf55aNdgK4ouO4ja5Tm0I3kxasMfWR3q0Hv7AjSFHLfydjM2SdEpO76aLGZ4ZpllNhOsnADcwwvsRGEdDjI2vtxptCsQSLoAsTsDiMm8E00MLjtIA4expSU/KdVmGgNeyrPEKe1jtrsPTYjfQUDII3acQG3ZSEi+0kuIBOESAgfnDtpd430KMVvnEhIJDSIQ3Y9kI01Wlicc+gFixF5vdWix+3XbvTwa5ilClr+9iAX8HcQ6b0Fygn4EtBzkVCPg903Eop6LGNguAgN/AgE+14RfRmEGPLbv2s9HEcjz0I6P2edHEAN7pkZDKWTJRZc7X3pIyzbUrq4N34HY2behRVMdGoN7EGg7C7WhO0geQ+DOw+C1orHySdT/oAWPq+IuQSDcwwJejMbVFjuvA3gleme4Hhu753r7+zICAK9AbC72e6IAfBsaz5vR+Jpskh6Pdz4HLU6SEr0byI2MNBUW+zXMI4QuPRs5UrcQcrT5zsMWFP5zFfouf5jwvUjaCArNmFlmmWU2M6ycs/b/QkjmN4A/Q1kX3l6eqtyEWLEq5G3fQWArz7P/VxCaKy0iyHjBc7KMGGzkKyf5WaF65NNNJx0eC92rlDrXJP7382qRTOLtieuaUHt2IgbTQXSSCXdzgOzHh9HE64zoALlZFLvs3s6UHkFspMdyd10wiC2fTWCRa1C/L0ATfjvS7661c1sRQ9dH0IcPIbZtBLHsg/bcRxBz7WEK+62uKwiJafz5SrGDVte59r9rhzcSdiVK7duJWpqkqNA5yeNuaU63xe4ZhyasQu1fj4DXSdQ2/cB/EDJnVtvxg4ixvh0xzu7c22vnX43A5gnEIsfs6UEE4ucj3fY6Qtz4u6weB+yeHkZwCdJQd6DkOkftZyG5UUVGkRRiK4UdPYvZk4jZPYKA/HLgW0xcV/4sGrt7CBFcYovj/0/FGEuOgVH0Xemz3y8lRK7ZjxZCW5GW+3m0yN1LWNhsJMRuTyY9yiyzzDKbGVZOAO4hBrqQ/rtMtgCx3m1osvHoApsJ8aJdL5pPFgHp4LHQBJXmaFZsQku7Jt85FQXOSzvHgWzaZ8nrHPg6GIo/H0Vt1mT/O9hxJhsEDA4iRvIcgrbe5SqeEe8EQWbgIeTqont22DnOwO9Ck/a3EXP2NAJZJxFj9jTq6xEEXu9HcoIBBNQeROC+2+q6DmlHZ6Ph+hxBD/tdBOwvtXNvR2ys61K7ECg6HwGaUcSMb7dzSmXA3QYRADwPLS4qrC2STqqF+m+yVmhMjfd4XM8hBPbmE6KEDKO2no0WVL3kRsWpRBFGTiGQ5ZlGVxHkSJ7YqRu13a3Isc+dMv/Iyvh8VK+kfMO1/L4AeJvVcz4aPx+2e3wBjc/FaEwttJ+1qN//MSojtlusXpORjXhWW3c6BiURmygAP4acID2CTFID/ham1hcmOTa6gU+juOtXowXNUiQdaUBtD4EdT7brdrTQvgDFXi81ukxmmWUGk3fizKw0KwcA/1vEfn+NdPrrxtNbHQfey8l1zNmDJmrXhibD3zljm8Y0JzXNhUB5PiawGBhP+3wi1/jxmjzH0xj3CsbGoY7lIu4wFT+//+2A+FG06eFh/WKnu1o0Cc+Lyo7v66B1CE287iS5CfWnl3ExAukjaGve6+xs54sJ2v59iEH05D/1aCGxwq5zZ7pNBA36uYh1O2XHfwUtKo4ipvM4AmkvQ0zdLgTQVlmdXQ9finkm1gXkhsRM6uinyibKbhb6PsSfx78dLK+Kzq0i6PN9YdMblTmIwu2dQmCzAYHsDju/kqCTP4RY6l9E42MYjcF1yOFvOXKu/SNyNc+bgZtRv9+H9NX7kdNmG9oVcRtAY+AQ0mSvQ1IVT02/EPh5O+duBPwH7bPJara9jFOIvV9CkL9MxC5GGvBDaHMyaV9DIHcyILzQe7ECyWqOEpIL9aP++CKS81Sj78LHUZ9/Di2wsf8vQ9+5NsSYvwuNsU/Y8cwyyyyz8lo5ALgL8f6mDPdOsWGUdKeGMCGCJmjf8o4TySTBTvLvUcZOJuQ5nzznuQa6kBViqEuxGBwVYi2TzzLC2MXIKAJKrnuuJcgiYqDVjbbt3wS8HwHUBdF5sYPnEALFq61cZ8BnWx0GUR/tRUBtALHu3WjCbrVr5iGQ3WrlHkAgeACB5R675vvAPyCA5eDvQwjMzUbjozGqn2cS/TYKR/cJBCAbrezHUcSLUeBjBPD4MQIocya3FKslMP1J1ny62e6JXldM3jSIAO8ugkY+vsYzkC4nLJ5+gBbKV9v1i9BiZggxty9B7OkCK+sriOE+C/XZDgJr/TYENmus/G3IZ8HtRWjHYYnV4zHUX0+g7+gGJFlbbv8/R9D5P4uAapeVdRmSUPi4eZQAGKfKhoB/Yuy7bLx2PeqjpUiKcn/i80+h5EPzmbilvQf7CdGRBtBOwuPoe3UULTAeR7tNNchf3zOi7iO05xzUvsNWxxuRLGUUjZsMgGeWWWblt3IA8Eft991luHcB8wmrCoUpm4sYLM+sV0zvWkjLmqadzafVdSCavC7t/3z3GK+Nh0131jb5PNWENN9e/yQYa0AT4FcI6dlj3fgAIeReL2GC99jP7sjpwHWJ/Xi6+G7Efj1r93olmoxbUN6nbcjZ9pid14+Y7jlIevLzCKDdGj1TrZ17ADHQo4hVq7fnvR4x34cJjOgBxED6s8VMaQuKU+zZBEtlP59BOzVLGV9fT4cWvNg90qRZaVZNSD++B7VvnJ21kZAExjX+FYjxXkZYDLrsZxuSBtUQ4qYvQbKTfrvfIdSWNQjUnUDjbACB8V1oV2Op1aeJkI30kJVzBRrn+whs7UpC6LuTaIwcQuPktWist6OFwWHGaqvnoPdOi9XlOXIXA+OxyYBv0Cv6evQcaU6NHQRd/FSa93ElknZ1onZzqVUlausvokXV0+h720mId74ELYzWoCmmDUl0LkRyomemuM6ZZZZZZhOzcgDwJymMVs87XRVJt7PRSx3EtnwKMbYeTzq2JAiFoC+NQWgSiOYDqckyk1YIzJQCstLula8r4uepis7tRRNZBWKVFyOWOanprbbz4iQ5FWjSvhSBpg5CmvYqBHwG7boWBF4WEmQniwhgto8QftAdHL8A/D8Ebv7QrvfrahCbXonY0HvRpO3AqxZtVVcgbfcKBKb+Azm01aNt9yoETOZZudcRWOlXIJA1YO2UZlehhYG3V6kM+PdQwCCXSblW3p1H89l0g++0exS7ZzzGR1AGy++g5/gSYl0bELDyaBy+y1GBdOIegrECLah8IbUA9cEP0Q7GqxFgPkXY5fAELX+OcgEsR2x4LZIl7Ub91IneAZ9DYN6lMn9D8CNYgaRI/UgC8wTa5Thp51+G3ikguct9dm7S+fZCBBovsudZgPwOxptGfirsjUhCs4/cZDZuHg5yKhd3LkXqt/9958n1/I32+0K0gHkEbabeQliwg/qtDy2kvm31v8X+js/LLLPps0xDnVkpVg4A/jP2+9fst0tS3kIuVVgmO4Qm2QYEMH3id7YtDXQTHYtjF8cAN6n1TrLfaYx4Ifa7kD47X3nJcwtZPrlNPSHTpGd5jMuM43vXEUCWM73DCCjFkUtAjPFcBK7m27UeqtAdO10a0oWYy2ZCIp8aBBqus/9dw+9b2ysRqJ9v1y8igOVTiOmcb9c0IdbyAivnnXbvtVbW5wmZMg+jBdsgwWl3CwJ9T6BdlGvQ8O5CAMFjU5fCgF9m16xHY9LHmEeHiSOglMOK7Z4U2yWaB3wWAVYQi+xZLxcQQja6o90laOHWa8dcPuJA3WOEP4cA8c2oXz1k4dvRDoSDyyq0IDyLIDVqQgBuM2J6D5ALmAfs96sQeHeH4SHEnL8Mge0eu+d5CFQ/S/5X3F67/igaQ0ftOUAA/3LCeDodVkimsYaw6J7KsVdFiIjUb//77pfvlLSTu4PQnShjF5KlnCA3HGPyvMwyyyyz8lo5APg++72dEAQXlPLvPuTWXkbrRDGXa5AG9ChiHY8gHaFH90jTuqYB57TP0iauQmX4/5PVlruNRj8Olh0IOtCpSHwen4+d41pvzxzpyU2IyvD42h6qbxbBIdJje/ehCb8HsW8VCHy5s2OvHeslJNf5UxQ3+c/QlvNCBIzejDSrs1A85/V2fD3q22q0DT0bAatWtAZ8gz3Dk0jisNWe4yz7vdLq14dY1R6r052EWOZvtnq9HIHBZYhBfQvBMa+a4KQKhcFLJWLVtyBm1OUv7dZuK4pcfzos7f5p4xrSpSmzUB/PJYytQdSeDWiR53G+fTE2gl4VB5Ck6VE0pu6xMj6F+ngI9fk3Ubs32M91SCcOAsfr7X6+q+BSlufQ+E1jgVeixQB23kfsuqsR432h1fFighwpLaSf237E2I8SnJD9O/lyNF6XIeZ3Kpw2J2N/StDkT9X480WWj4Fe9L31sKFPo92L21CfDqSUAZKb7CR/v2WWWWaZzQwrZxjCRkTt3Wv/X2nHymivQEDuWwiY7UVE/YWoqWZRPMpDKRY7KMbmQDdO954E3ZONTpF0dozvnbxffG1cb/87Xoh4iMAksPQU385adyKnKp9oPZHKCBoOtQhsONPr57hspwoNk/cgUN+BQPEoShDiEoVBApvpbOIKQgi/HhSRYgmSpni66yNWXhMCAY8RnNJcMvMMAtvd9jzu6LkRsZ97ELA7YHXysIMH7X5t9hsKxwEfsc+XEiKCuNQidlic6Dgcr43HVyDtWlIlu6XhAAAgAElEQVTO9X6N+7cC9Z87QPs488/mob7ZaH+vQuzwSxFIbUCvlUrU330IBLsPwF3R/Q/YvU4RFjZ77TNnTVtQPx5Hsae3IO5gDur/XWh8e/xsUCbUSxFbDFqIFYv53me/+6Nj16Lx6bsflyJ5zXisGm081qPQfp3jvD5pd6KF8lRachy5vMz7vA59ZzzZ0Da08LmVEGbwXLQoeozJxVWfKqtA46YZjZssHGJmmWUWrJwA/J2IhvJ4bO0o5WCZbBV6qYMmvf9AwG0lqt48NDHGett87PJ49dgxGB5CE8iFBKlHvuvGY8UYdghSkXjL3497qMBRgrbbAXkH2u6/iLCF7BpZB1ENSIfZQEgV34jA6D7Uvs5YdaJnHyQMjxoEkvz4BrT9fx/SeL6eEI1iPdryn49AVA0CT864t6A2Xmr1mY0WXC+gybIGyRQqkPZ62O55F9Ll/jZiLKvRdreHL1yCAN6n7NzlKGTd8/bsX7Xn/ioBgBf7Cg4j4O5OqmsJET5cN1sKmz4VVoztLnatSwmqUb1dcnE36psbEUj08eOLUdAYa7HPqhEIfgFJV1yWsAyNqVeifjyK+vUraNH0agTcz0Wgdq9d9xHCIsAzm8b2ItTuaxET+yqrZyeKquqhCw9YWaC+egcCXQPICXi8tsDufdye9ylEEvyIXJBezM5B4fxAC4TvTqAusd2HvotTmbg43mVzh2vf+ThBWHi91Y5tIWSZvdXK+Bm7bjkzA4CvRQsm0M7IrQXOzSyzzP67WTkB+KNoVnBNR3vh06fbTiCw1ojAFYhtmUVIbR4nUIF0Vi+NpS7GUMaTTzXhpR1fW6j8fPcoFMkkLi8+N04JHWcfjK+Nt8CrEFC4itwt5ErCNnVldE4VQc7htgGB924EdOcRskx6vSoRoB4kxHj2LJYrEbB1sN+HJudBxFL2IG1/F8GBrx8BMJfN7CY4c3Yj4HzC6n02GhPrEZN52O7Za+XORsO32c5pBn7Tnr8PSU8OECK1/AdiyCvIz4q+BIH0UwhEeqbFudE5FdGPj5/TKUnJ55dQaHz6WHJmew7aYbgb9Zdvgnn/VxNCOMZjtd+uXUHQ4jvjfQx9Zxch4H6SAOaa0YKmBy3aBoFv2GcLrS7HCD4I30R9f65dsxEt1t0J9GV2jrPlDQgkvw0tCH6AFnPxd+ZlVo9vM9bJcjtaHNxl9XDHwzZ77iPkl1/ks8OEHYUXouPXo0XNtwka/FLMGenpstifxHfCOtAOxyD6Png0mVbUjwOoX9cS3t/NSL5znPyLjpVokbMLJeKaSmtFY7ee3HbPLLPMMisvAF8M/AWapW5ANM0VwD+XpzrdKG5zAyG19Q1oom0npE2PQYAzZklLY609Okq+Jk/TzeYDVONhHfMdd8Dm2tp894mjdAwjVrHbji8lJD1Jy+gYg8JqBE6dyW1ErOArEFBqQO3s2l/Xl5+yst3hcwQBIQdd8xCAn2/HBwhp4iutrq1ou/oTCECdRJsvO+06j2zSYNf/mT3rRjQ8BxDwXYD0w4sRoB5CAOvDiHX7OTv3NwiRL3aiyX05muxfQNkKvb3T2m0ZYsz8+k+iJDHn2vkeASYej+XQgpfqd5DPd2GAkNn0EsRSeySaUQSMj6ExcQlB4tSKwNYsNAZOIIfZZ+3v42hBdR4aW9vRWN1ln9+PXjce7egFtCNyHRqj2+2enQj4fgftYtSjhC7H7d59BIe/O62sFyMH3svQgm8Z2vFwW4tec9j1MSs6D4FM0Pj/jD2nLyL/HY2HUpyoY2tFfi3VBJ5jNVL9gd5xt4yjvNcyPQA8Hhut6Ln70DN3IDmPL8IfRYvfOkI/fg3JB13qcS16N2ywMvam3PMGNDbWoRCkyd2PyVgH8HeEOSWzzDLLLFg5Afhn0Qzzh/b/cyg9XJkAOOhl7zrMXkJUjmTEDjdnaIpJUZLRUdIsrZxS9LaFjhWLnOLAOD4/7b7+fyWaFGchQFAKAxr/riQwcVXI8bGJILWZgwC3n1uBgHonIUKFR0oYtWs7ETDyaBhdaJGwkFxH0GUIxK0ihBEcsvJfa9ffhgBbk5XfiSbPETTZb0YT/mKCI+mbrcxDiI1tsbLm2n0eJ4RPdLbza8AvJdo8tjbCbswLCEzWkyvPiJnvfKxzuayUuoyi5/EFXiNi/L3vPZPpAQQWfXfGx+AGtEhajvqrGQH6diTVWITGSTfqkx1oAbQVjY/ZaEfhOdSebyJkXj1m91uHQNQDVhffPalD/boNjal6xJ72IMB8FI3zYQTs4z4+QVhAHbTnfLl9dqfd7yJ71gYE9lejsbSb/I6FDVZOL1owJB01k1FAkvWIbSlihXeTHnXlaXu+WXnqMlmrJMiNalFfnyDsfFUQvqOXojGwD/Xby9GOwa3oubai5zyJdpUWoPbxhYjH1j/J9IQojOeUzDLLLLNg5QTgLYjS+X37f4jiXkqn0YZRBIyDaAKIHSPd0oBvP4G9TZ5XCJQUA1H5wHsac17ofqOJ38VY1PhYJZp03cFxCIGe5AIlrU6VBBlIFQLEriEdQsBjwH48QU4VQbfdhgBQrIv3+ND+PIfQRPx6pG56JUFvvBL14zw79gYE1F6NWLAqK99B1FNo4txo938YAaMLCDKcURRicLGdux8BgsNWl99DwG+RPbfLbi7M01ZuPSjFtjuQbkTMn/sgJJ144z4sl+VjuclzPF5kQQCccYjGFgIod0dJB2WezbTbzulGYPejqL//J+qHNYj59NTlwwikPYzGysNorKy2enwJ9eNmBMqbUNKkZfb5EwiQH0F66grEdl+NpBz3I6b9Y6jP7ku0RTthp63VyrjIPmtFUpxlVs8rrfz9aHwtR6A4za4kpFA4gMZdIeu0Os5GwDW2G9D3ZSPalehIfH4rau85Re4xEYt3zHx8e3jQAwg8N9nnzWgRdRRFMlpAWNQvAN6NHKI97KjvKg0Sdh6+gcbNCWbU9JNZZpmVzSYTx/29W68ofpJZOQF4NyEPOWgmKrMOPGn70URUw1gddD6ZSB2lg6FC5ZRqpbDiMVBLstzF7p+sYw25iXdg7L1iiUt8n9UINHUhgO0SniGChrqWADIrEOBvju6dVr6D/8UIvH4PgdXdCIC5XMTlNosQ6PoYAloLCVE4nDH/O+QU+FoE/p2V82E7QIhBPogAtzOCdfbzBqtDEuDcgoB6PgYcBH7eauV2EiLw9BMiucwUK3VnJ20HJmlxeDuP/FGB+s0Xf+4sXIn6ZgCx1S5R+CYCz5UIWG1AQHgWGne9hKggJ1HipdWonfeiNj6BvvuLkazB9ej7CfGle5BkpdLKW2ZlPIgWcPnMfQFAY2M0+ruHEPP+qLVBlz1jIRnDUftdjRYE85E8qpD1kB6XvB9Ja14gnRX+HdQu02HJMeHfyUUI8Hegth0kjBFfpG0hhPg8TJARgRZvvnsRxwfHzs0ss8xmik02kdF4QHA5rZwA/D3A7WjmvA+hoNeXsT4p9seIKXHWNQleHQSQOF7MHFRUJP4uZPk+L8Q6p/1frJxCx2O2Mmn5gL0zxa4Td2a7E23/HyUkH3k9IR60Oy856+5b6vF9PDNhPSGe9NkEmcZKK3cuCgO2D21DL0Og7HzUt570w2UR+6ycmxGb1oT0wXcjRm251WE5kgicQhkV/wTpfc9FzOI1CLAlVVWvS2mnpL0HSVtWopB6bUi6soGgBZ8pILzYzk4p56V97k7AsWOv6/qr0E7F0wTNfB1it69CrxN3/j2B+uIWtLCpR7sQoyjw0qUois9JQnSRc+z3LNQHX0Y6cAdvjWhcuSzoRgQAn0Bg8RNFntVtN9rtgKBd/nt7FgfV++2Zk0x0bE8iAP9SNHZXonG8v8A1+awWLVr70PMlU9v/Oadn7Pn7MZajnYOy3X4fEQG/ht4V77BrHkLvlS+QK9dpR+08mwxwZ5ZZZjPBygnAH0NCw43o7fosY9/0ZTYHiduiY4V01aWwyGlWKvtdKJrKVN2jFCu1DZylTi5SqhF4WYZY5csIEhBnN4ei8+NJOAbi1WhSdvDs13kGzgb7u9Pu406bNQhcOKDz0JKVdn6L1a0vqk+X3f8s5Dg628rttuPNCOzUoWQp1yLwNxvJVl6NHMF2I5Y1djZMs0MIRHUi8NCIwP/ZietmCgifDksbsx7ppBrJDzxqDgSQ5pIo9zmoRf35c4TINzWEfnVnyotQ9JEOxKouReO0FoHZmDntJ+yMVBB2dZYhEFiFHDKrkLZ7K+rPHyCg34IWCnsQAx9bW+L/UgHjCcRMr0Ov04luKB5HbdBJOgPeQ240nqm2Qu+qBcAvo0VoK0E+9CO0+PHoNcvRgqkC9cNa9F2tRcz+9xm/M2tmmWWW2dRZOQF4FRLprrZ6vMyOf7hcFRprg8hH9AY0scWaVRg7SRRikfNpZB1UdhFiZBeTlEzEphKoeZ0rop+kDROiXECIGhOHKlxBSKnu5QwjgFJh1yYT/sSp16uiaw6jDZWz7ce13jUEh9GthNTzzvBttfM82s0pBKZXAl+0Y48j8PwuFAN6hZXrCX+w+/0biq6yD4GYTYgNfY+VdwUCcdeQPhZi+yOk691t9+pHX5cBBDx6rMz6AmXMZEtbnCUt30LTd0WqEWjuQv10EgE0T1/eg9rbHX09c6gvwIaQnrsFLaqw8+5Huw9VCKwdQ+MlNpdN7bRrvkYYb0fQossT/VYQFvGzgc8jycpZSLe9h8Lsdql2KZJgHEebihMF4F9HjHor6ZzISrtHbcpnU2Fp70f3walB3wuP9b/G6nEB8Dk0HppRoqC/RED9GrTYcdnKA+jZnpym+meWWWaZFbdyAvCvoVnwScqfWzmyJWgiexax36fQZJNP8zgRNjqtDNeOJ8H6RMpLs8lEyxjPNTFjXZc4FjsOVhLifMdM1AgCKT7pxnWAsayVt5nHcN6DwPgQAlbDCIhtJeh/sfN3oom4AU3Ou9HkXYXYsiNIC+7OWQcRUIpDN7pjZK09736r937gNYjN3IPAXzcCRQcRMC/EgA+h1OpuK4GbkGbdQUkh8Ho6bDLjMm2RmXY87Zz4/2FCZsy9qF8hAGxP9uNjqpaQcr4d9dNWgsP1KYLmus3+z5dCfh9BYuW+Cx4GsQuxsA123lkEvwFPGlWBxuZEo2SsR4u8h9FY9Sg7bYxdMBSyq9F34y6CE+zeAucPEBIjTaclfVd80e+7ZXG40kHUTy+xennYwj7Efrve3/1IZpi7UWaZZfbfzsoJwFcQ3PZnkL2G4KT3QTTZPEjYjvatbSgswyBxTiFWu4LibFIxsDMefe1IdKwUAJV2jjPRHlYwbWcgnjhBYHWQwPR3E7IgPozYu1eiLfRqNHk6y+33GyLECPb7+ES8DgGyX0Ds2GV2z9cjFmwAbVUvRgzqXJQwZTmanPcgUNNrP4fJjYzwKeSQ926kNR5ATOMuBOK6kbTkh2gR5+ER/x4l3zmMQNI3yD8u8tmfI93yHLuvx6WPU7afbitW9/GM2eR3Z5jQ9yME513/DvYhkOg7HS5r2oNA7QeQE+tetMafjXZGrrSy9wH/gPrjtWi87EQOvAD/iPw/hsivo34QxaPeiN4PLyDt8U7EoHfYvV5mn21B4+FCJFN6DEkhxptYB3veN1lbrEL65mfQGB2hdNnKOQi0gsbS90u45kLCwmOqd198Qe47Y24VaJHRScg/4DtXzyPfi7WEMI6PoPZ5Ai3A2lB/7iOEKs0ss8wyK5+Vk0L7L4LsZAaZbwV3I6B2gx3rYCwrG1vM8sY2Xp1hzFS7jaeb8jHFsRViXsdrcVml3PMIwSHTpRXHEBD6LpIQeOjBIQLr5SBsGC2QPHpKMo75SQSI3ojCwn0DgTDP6NlDAA9VCLjPQYB6DtLvPolA9WokTfBMqL0IXH3L6lGLJvrbCeHhfPwcRyCxH036D9rzbkeSlmJtdp7Vabb9f9TuOUzIDOlhGs8kKyViSvw9illQ/x44+z+H0AbVqD/qUbtvQDr68xDw/goaZ/2o/R5BYPxFCLjNQ7tebqeQg+dOwntgaaK+3QhYt1q5fQjM/rzVxRn0brvnQcKYGEWOn52k24Xo9diQ53Mffx6Fxe0g43My7CS0cakyGD9vOqVPaSFf6xG73UCITOX6e5CsZxX67s1FHI8v3Fxy0od8Ad5ELsDPLLPMMju9Vk4G/EFEW/j+oc+8TYUumn77dzRhv4CSpcxBE/5O9PKuRRN2Gih2RtIdAcejb01GQ0nTlye36R2oxFuzxWQmDmadYRyPxQ6QDoI8UsWgHXcpTcxkVaE26UOT40IUJm0EsdQ/QSz1KjS5DqNQYwNoi70GTaJPoWiVvt0/bPfy2OF7EWj6BatnLcpq+TdoMp6NnOSctXwOeCcCaaP22bvt2nmEaCXNiNl0ayJkA51l5XzaznW29G/tuQ4ghq4OxZOuQhFV3NL6aRHaicHq/BXki/CYPfd8xO5ewMwG4Mkx62MiuTPi4DpuC38uv74atfsgWkTVoMWay0W6UBt79JsbCJrsK+2zbsSIfhm174VWxmNo/OSTbfycXb8R9avbCRSRo8Xq+Sb78TH/T4SETO4EvNyeoa/A/ZahqCog0HlbnvOGEcifzGv8BcSazyJ/jPGknWL6kvBAeG8mZWtzGPusnfbzatS/dcD/Rt+1NoLmvhOB8A+id84gWpCVwvhnlllmmU29lROAfwh5pT3JjHJHH0ST52VostyE9ILrEfApxB7HgLOYlnWyjpaxljr+v9SmzJfFM189/PgIuQsAd4IsBuir0AR6DopS4OYJPWYTtpWdKfcY7DV2jWu743t7mx9ErOYspONvtHL7CXG0vZ4taCFQTUgGBIHZa0ZM9RwE7LrROFiB1o3t9uMA/KVIdpJkM78b/T2MFhcrEDO7KXqOpPUT2H2PFz2IksSA5FG/Tm7fn0mOmP7MsSNv0pKA3c9xJ9TlBNa7jwC2Owk7VYNoHAwjFnupnf8UArb+fe5Cfb0SjaNVCGw/jsaJAzsfq9sQcN2IdMZfRuPoErvHUTt3CO2iDBHY9Wb0Lnm0QPv0Eb5PyQyWbqOEaCmTdeA8NM7ze1Edp4MrSY7l+O8koeGStCX29wL0fTwfjRFQ++2KrmkjSOfqkN/IU6jfm1HG0zhtvR/bheRN47GFaJH8DBovmWWW2emwycYRP11WTgC+E6Vrm0HgG/Qyfx2aUDehCfciQjiyQs6Mo2himpv4LI259v+TQDrtfD82Srr+PAnC84GxJKOU7z4O/mKLwW4MuocQgPHwfvEzDpNb9ixCQplBpJ92fe51aNLztNMthDZ3jfNWBBYWIADVZcc/jqQmlxKcLRegyfN30cTZgIbcT+x+J9BOxz7E/D1AAMyvRQBvALGPL6B4wxVWry8hAD4fga7taLL9Ivmtytqjk5CRz9s1ae3AJ+1ez6R8fiMhokeaZGmmgPE0MOX/e0QcHyOjKedAcKysI6RZP4AYzkrUVi5VakQh/e5BrPNm1Mc16Hu9BY0nz5K5Ey30XrB7vMyOvdWuWY/ieX8GAe1daGycjQD9JqvrWit/HRrfe5GW/HWIzb4M+Gt7xreg8bwGMeRpdhL1/wK0o5PPPo0A/q4C50yHtaOdhOuY+rGWDFkaL9aGyCU3dqH+jHfmZgFvRqz+Q4xtv4+ihXorChU6F+2E/BXahVuK+uuv0Dsq2Yfj0eu/EX2HLwH+LzNuqssss8zKauUE4IeR2/1/EbJfQNnCEJ6PwPZeNFHPRy9bl5243rDYhJO2NTseB8mpOM8tHyAvVk6SHY9ZyDgsoDPi9YzNAJp2z5jVHEXApwsBq3YEOGpQP3hkkdjWEELIDaO2rkUSg+fs9zpCBAyPB70QgW6XKhyweh9EQHsETdTbCBr1Nfb5DkI2ziYE3K8lRFfxxYpHsliBgBt27IcEyU8nYcHilm9SPkZuBs0L7Pl+iFi8GRQ4KK8lxw8p/yd3i2LW2x0g5xLG1za0yPI2rYvOBS20PBrOfNRnHq+7klzm2+NGHyVkZnU5U439fSXqxza7dx0CvRDGWJddM2x/7yGwxFg57ncwgBb3i5HD8Q8Q896C+tZfhZ6gqpB1Up5Qep6E6HSa+4X4WPFdjlpCOFAfD43oe3gxepf0Ib+NdrQTUYW0/75j521eifr4MOH7lezD8ZhfO4TCILaRhT7MLLPM3MoJwPfYTy2lB5TdBnwEvWkfAX4L5UW+CVGZ72BCyXzWoKyHoBf8MqvS19FEuQa91Ecp7LhTQWmp6D2ih1/jltxuH4+Vwq7EQCUGPnEkk6T+MtbmjhKGjGvA067z8ysRm9dESHxyAgFLZ5N3oEyPHpGkDgFej0TSY/eoQyDa27gzOvelaLIdtvt/z8rebGUss3PXIaD1PNJ0bwbea2X+NQJkDqzd0dETv/gEfyViPpfa+Y8gQP8Ta9O3ocl/JZIZjCLZyhBi5a5BADEGEsVsBfC/UCi7K1BoxHvRQiAeKzPZqSxm6j0lOOQ6VvqCzqN4fJcQ67kG9d0cO7/NyvGkOsN27gVonFyB5CNtCMjei4BzNdqtaUOs5u1ofN5KiLLxz3ZuC+qrWjQGTqHFXB+SfXzWyrqDsItzAklXelDkm01oEeHfuy+jGO/rrax1CEBi9fh6ac1ZVvstQkKo6bb4feIhBEcRmG5G48fDiPYS4vm/Du1MuETpFci/55VoR7MSRUT6F4L23fvEI+Y8S3oflmpfRDt66wiRZtrI5CiZZZYZlBeAv7/I5x8jeMS57UNvsj7kWXM1SnN2FdIa3IzemOO0+MVagSb6AfRiP0aIHVzMCunDp8KK6cqT5yTrUwj0F7LkwiAuMwm8k+XFW8ojCJA/hpymFiPAU4t2HRzQu7ylllyJQlx2AwJBswjZLIdQv9Xa30eQNCCug0dH2IjAcJed04mA+lzEph8hZMJchFhTjyMMAsMLUNKWH1tZW+3e8wlOn10IpA/Z9U8gcDYec5Z9LgJp70NAwXWscHrA0EQtTSYDY8eJn1eJ+mM+2pFqZOzOUg96/kUE7beHrqslRCXpJSRWGiawnZ4kahXqqw7Cq2O1leP6a5dlVdq5IwjUVyLw1m0/saMu0f03oQXaVjQGDiJw6JF9/LmT3MHF9tljzCyr4fQy4PHYcefdGkLG2z6CLK4btaPL4eIMussIjpmu/f9RdJ8eOzZM6Is+9D64CC2095ZY50a7Zh9hgeXjKLPMMsusvAC8mG1PORbngh5C4Svusv/vQOEKJgDAXwD+FbFrl6PJcjV6CV9BAHmlgJzJyEkKXZtPd+4Ws+oQGOokIK9E+sdqtNCYRbrTaFI+EN+7D7FM88gFRgOEJCcgsOGTpDul3YXWSZ4+/A0o3F4NAtWjCLQcQRPlPLtuH5o8zybEgq5AmvBGNDE+aGU6EG6y+lVYXfrtHpeifj5qx56z9lpu9zuB0offZfd5NQJoXUhD+vNo8bAEaZGfBv7Yzj1ivxcQQPjTwDetbruRzng8+tkjiLVfg4b8OYQ+iTOLzlRLyo/i105yp8XbZBPqj2WoT1yC0I/G0geQFr6FEImnFS2MulEbvxuNtatRVJRetFi6DS34ViDp2SzUtvORxvwmq8O3gVvQeO9EfT0fjbfjaNGFlZumw15q12B17UXg7vPI56ATseWL0Hh5Irr2YhRWD7smma6+nPZOpm/MpS3gHXT7bpGTIxWERc4T6D3xDOqjI3ZdI/refhd9F5vQ+70J+M/EvT+ANP47yY0IcyNasA+jKDhdFLfXIOZ7yK45id5f4wkRmVlmmf0020wG4IXsPDTzthEypbhXXJr9kv3Q0uLZ26oQQ9FG7gv3EgR2XDIRsyGnwwqB7FIBWyEWvoIQhSR2miy1PnHZg+QCcJ+UPexgPSFOs+tftxLAt9fBZTs+wbqUpIIgcalFoKUL6Te9Lk8SEpw8hEDPtXZNNxoS/WiRNYRAl2tF5yBWaxcCbqCJfS6a0C9E68DliHF+ELGXO+1ZKtFEfrld049YtH4C4Gyw+u4ghCiMtb3F2n852javRkO8ltzF0UyyUndTSrEK1HZLo/9BfXgcLZIOWnk+vtrtPE9BfynqC19oHkM666/a36+yc32RuIyg756HFjr/RnC8e9bu302IsuNRV9xq0bg5ihZpy+xei+zcYwiExYB9P2OT/fjOiy8uCtkclHJ9N1OTYKYeSXn2k54FtBz+By5Rchlg7EMwisD1IfT8SxDrfIwQ3303mjLOIuxw1th1W9EYaGPsLobfG3Id2EG7Jyvtb0/klHbNCLlMe2aZZZbZmQnAm1FqwTcimmi5HXdvqzT7J/vh+PHj9ga9FilXQNpcn2i+jCazz6IX+ZeB/4OYEW+uYmz0eC0uq5jMpBhLnvZ3bJ7AJOlk6RYnqclXXh0hjnccfjC+xh1X/VrPVriN3G3hbrtuB9LYrrfzFhKkP7WoL34G6bvPQRPrQQTIu6zONxHWYe2ItX4Cge/Po0n3AsSaXY1A860IaFyBwLbLHy4HfgMNt3o0wXos6q8icHiuPf87EODrQfrY/4EWA8vsGbchAPhHVvaKPO2aZn9JkE8stt8u04k1sclypnJ8lmrFFo7+vdmD2EE/7guuWOblcqQl9v9QdH4TWnS1EaQkQ2jMuBNtC/DLVuYAYkYfQTtdrWhH4jDqyxWor6oQaH8Cab9XosXPrXbfb1kdXo7G1nwrL2ZLb0BjzBemvYQY4AutjHyhBWPzRdwIQWaUz96EXoN9KOb9cOHTi9rNSLs8iFxuehOf/zqKTT8dMejz+cRUJT73d487tY6g70cXAtS+cN+Loi39M9rt2I7e5ZVoYXwemkY2IDlZJWMlP7ejHbiDhL6bg3bC1qO+fgpFrvGQjl+1++1nbPtllllmmc3sveu02bwaUVK/g2bahwl06EsJGoRxFh//3WXFHEATUDeaYD3U2XTYTNPvJlnKNNaymrHJUoo9RywzcGbIs2JWIADuic7CbREAACAASURBVEUaCeEH/dxqtGvRRdBFz7Nr29GkuBwB9gbEdnUgdVI9AvB70GQ61+41D7HocxCAbkUM68UEneuo3dNZuGHgcyj0oUfUOIlY2Svs3CcRkGtlrDPreJwlvb08JONwdMzb9Ewyd2pNHksbYy6ZqiRosGvQougXkBOlRy3pQWOgC/XfYtRmDahvZyEZgWfN3IpA8UNoTHQhID7P6tce1cFtFAH3fQRQvQftmq1GnMB5dt9laMw1oHE9QHqWyqUI9CdBdgVic49RvI9LWbyPx+Ly0sr0zJ6n0ypSfjwCUxXqs9moD2vtdwv6Hr5ASHy0HvVjT6Is7HyPYBSbRzOKEyeltVHcVr1oHhlvjPXMMsvsv4vNZAb8oynH3oBmqw/a/7+Pgv7ei6iGv025Jo/dhdjIdtIz0l2PmMtRpPUcRC97t6lmwUuxqbiXJy2BAHyTmstkXHEY6wxZlXJeGgs7SJBNjBIcLD0GdD2aSM9DjmkQlETO9LYj3e6Vds5cK2uFXV+DwMpOxE66vMRDFbahddpCFOv3pJUxiCbntxO2pf8OgbMrEdB4GkVKWI0YMgdmi63MYeBuK+884LcR43YXAmpL0aR+NyFhSinSH7c/RMP8QkJYs1iaM1m50nRbmqZ3WeIclxGk7cp4YiYHqM4sr0Ls+EnU73+LQNXbkX7cwa8D96vQAukf0WbYZsLC6Ao0NjwkpidUugOx5rGtQHrtWuQ78BLEdF6KmHlfjO1BgM5DGN6DgHgMyBrRbkk1WizGmuSfEHaNijlhfhmNveeZPPsNYvwvRK/UNIfhCqY3E2Z8n/jvpEN5LUGi44us1WgRPhv1/XKk7Z6Dvkdb7PzbUJSSfiRR2YAWdJvQIr9Ym3cgLsila0dIl+tklllmmaVbOQH41xhLe7WjGe//IQ1I0r7I2GwnDxAA+ThsCLEasW1GoGwXoWmcWelFL/FSZB4z2Rz8xlEn8lnaZ/FEWMrzJ2OKj6DJahZiG70sZ0aHE+cPEyJZzCHoxV2C0YwAUxcaPv0E3fmw3W+tXeeZK+vsxxcVFyDA1o0crhzcgoDQw1bnk2i7/1EUfOdSxLAdJaRBd8fPH1q9ZxEcww4hkO+yilJsCAGriwjhEP1Zfhqs0AJitMh5rvPfgRZA89Guw1b0nXV5lAPtRrRwWogWRr2ov5rs+nbCWBtAr5bkrle13acDAep5SAIRvxvc16AHLfDm2t/PJcry7+EsBAAbyZWnlOp46Rk859pz7yx8elEbQvXty/P56QDfaZYcA8l3UQVqQ99tqkTftTeixXMd+p4fQgvi40gK1WufLUT97n18A9ohyZfoaC+5UVFWoT6I88utJUhUitkqwo7cTPPvyCyzzKbaygnAd5ObPvBNCMlsQGK6t53e6lyEol2AHHG+gybDV6FkGS9D1T0TQbebOwVO5BmS+tzYISkfkI8dMP2cXtT1bWiXweUFtdG5MUsPAkQ3RWWMRPVpsDL2osnyHBQpxSOv7EOT60EE1HcSJCodCLysQxOuZ9/sR6C5BjGcK9DQ9AyWDYgFrbNzPOxZj93Pmf8Lkd70csSk34dAWFLWkM/mIob0LAJQ9FBmSRnLTB2XpdYrH8CO/47H0QhaHN2GvpvXoHF1CvVLA8HHoA4Bq0b7eQWSBp2NWF5P1tKPomVcYn9fwFgm9CyrwxzEnK5HoM6lDgdQlJB5Vr8nUf+/Bo2vO6OyOpFvwnusrm8hf3bMfHaFPb8vKNpQptZnx1lObDcjMmIAacCTQPw5ygcQY12418F3LFxe5OOjEmm+16LF1JMIkB9C7XM2avPLEfidg/qyA3E661FgrddSPAPmEuQLUoHeDXehyDo+jf0Xej/ls8XR9clxkllmmf00WjkB+IWEOF4gRvweO/aT01OFzehle5BcDeYs9MK9HwE/jym9ljObeUyyilNRXqnmE2cNmmCaCQ6f/rmD6mrU/l5fl7BUJM73hcAQAlwXoEWSf34STbZrEDjxUIee/dCBfy1BKlNpv2PQ5rrypYhpryYwnh4rvB+NkTaC7MHZenco9MRBDVG75GvDtWjyb7F7VdkzOFM/h8CgnsljshRLkzYNIYb7OIGFHkJAKKmn9sgZbi5hWkzYETmG3gM1BAdeL2c26o/no3NBY+Mc+7sWhS08BPwK6qMRtHuyFY1L9xePbTcC7l6P8Zpf44vd+NhEza+P/Txia8lz/HSZvzcqUJ97bG1/T/j3sRb1p8eR9w1W3wWsi67z8KnuF7IBAelRwvsINL4WIEY7fpfG0Yl8hyA5p7hVobknznYbXz/Z/ssss8zOBCsnAF+IaAePv7UKvdmhON0wBXYNkhGMIsXLD9FLeZCQLvgi+70OTagub3DwdybaVDtqJf/2iTEZ2cUn7DlIr+oTJQQgHYcxnEWufjy+V+y014+2iK9EgKrRzhlErHif/VxiZa5DoKfdfvcjBq0Bsd7nWbkr7LMuBOSvIgDqZxAYr0Hg6TYE6p5BkVGeR0P4IXuWx+x+D1t59yO9qUtpkjYX6UuXIlb9uP3uQdvbaxEbnha//afZ4metRiDm/Qgg1yBGc4jctOV1hIg8DsSrETNab+cMIMD9KALyDaivXKL2dvRqakURkzzb4ouQY+/ZiPl+MQLxz6Gx+JdofLyAtMln2/FkqMB/s2d5gvHbvWgM9REygk6knNhuQ/KqvaRHbPke5Xn/eeSTfvvbv+vutA36XrQikDwUXXMniul+CdLn9yKepwH1+wo0Jd2NZGjL0ffwx4TkTc3Au9D4egDtkrrtR9r5efYZ6H3wDfTeeSA69wbk5D2IfE660Bj5qt3j/gm0TWaZZXamWTkB+HvR7PE8eouuAX4VvUn/Zfpv7yxkBcER8D40WZ+DXogNhKgII2iS88QuE7VyhIebrI2nzqWcF7PZhXTmacA+WS/PeLiAEDXFmdFB++0pq73sGjsex2ceia6bbWUdI0Q/cWsnyE167Lw4xm8MrkbQZLoBgXh3wozXl2nPX0tgzCqsDrMJKdBd4vLfDYAnzVnM2J/BF3pD0e/YCbiPkM4+dmJttd+zEIh+htDvDdHvAQTSQPKkatQXPeg9sgbJYPYQuIVDBK1w7MjtdpCJO/ANIYlcmq2xz8eb+rwb1flEns/LST70ou+lO106Q+39707bHm7Vv9fnooVFJ8Fh2uOmtwJfj+7xRruuw8p5Herjp8iVvrmtQO+ENM1+0ok3vrYajU23yS6cMsssszPJygnAv4lEdpsItKKLDccRzWSi9n3CluPe6PgbEUvaaZ9dgyb5A4wNW5XmjJjPcSzWLpbLYnYa0p8jH4OdPC/tfwjMo//t53mWzDhDp0+U88jVlbvznG8x96C+6EaTXaMdP2XnnEMIHziEQPk3EKN0PdLtejSHPjvnNqSfXYqYrz40DvbYPU4iRdR9KHzdIBqqzjTuR0znX1DYrkNs6xDwcXuOWGGVluSpFcUMfz2Kt7wVxV6uQcCh136c4Y3760wF5GkRUEgc8+ccRv3pCzAQsI2j6/SjMddn/zfZ7zbUZn2oneegtuxHi7F5SAcea7+/gPogCZC+iMbcHMIC6yo0nr5OGHP53jXTaVuRdhnEsD8/jmtvsut7UTCq/sTnb0YJh0/XWIsdtesJyZWGGLsb2Qv8O+qHeehdvgxpsV3nPQd9f3cRFsP/Sfhe/hUaG5uR5OTvUF/ejtpyPnovgHajXOd9K6U5zn4TjYWDaNxkNtPsQ08+UPykzDKbpJU7DOHFaG+2mrD3/7nTc+t+tJWaNGfHFqAJuY8glxhKOT85CTkATbJEFdFn5QJJSTATH49Z3tHEbz/HAXJlyrn+uQNs/7sTtVsvmsRcOtGAALVnk/Tru9CkODeq1/OISfJEOqsI8ZQXESKQHEfSgVY0aTajfh60z4/ab8+Qtw9Nzt3200FIqPM4IUNqMwLb8wja4DsRyCrGLvp4qkagoQ2tO73982lpv0lIX7+IENvaFyUVdswzRcYLmDNNHpUMLzfIWG27nzOMxsgpQphK1+67HAHUn+2orbqszH7EmlahMXCcwIp7mvJuQoZNt3wM9WHkXDcbLdyusbKeJzDqkP9dU8ga0EJwDxPLxDsnz9+lWBN6/3WiRV8SgN9i9VrL6TF/p4D6ZbbVrZYQech9Ak4RsqUeQ9OL7xZ5zoBZ6Lu4mtC+Ljk5gMD5+xGwfgch5OpcxIIPIXmi74ZVoHfEUrRIW4d2D07leZ4ucuUr02XzkHTqebKoKpllNvOsnAD8X9Gb6kcEIewopw2A57Nb0ES6DU2mjyDQ9QhSzSwgAMM4GkcMYvPJK4qB7yQLOJVSlxECsPHP43jUyWQoyWgDSb32AAKqnjDH28EdoDxBxjyCk+RCNGntR6x1i5XXTthGPoGAzTDSa3pa7CUEB8T9CBgvJSRXaUIT8mYEjrciiUA9IW5zHZoUlxCip3wE+QMft+s2WXkvQgziu+w5PcJEN2I0H0UgvZh9F42jo4RFw9eAP7D7lJI6/Mtogl+HwNTV1mYrCOAilqOMJ0zk6bTkDgzk1jlewFUlzvVx14fa1P03OgmgfZiQPGke0ss/g3w6PLZ7Mxp3AwTt7WzUrtsRWHkjYjrjDJdptgj4n1a3byOdb63VcUcJ7ZHPKoBftGd43uoyXnsIAc0hSg9n6DaMvjMD5HfHuQcB2NOx2IvfTdXoe9+BIgttQX0/iBbf+xGDvw6NoTb0PI8Df414nlHE/fSh99cdKI/bJQiA/w0C0l+3cl+E3mmfRO+C37R79AJ/ZsfWoPdIrf0eAD6Gxlg5rBFlgq1DvgzfKlM9Mssss3xWTgB+CUJhM2xpfhRpKj26wT2IiTwXTdZrCc5ODiiS4HWiwGcqAVMS6AwTtKruvFQoI2NycQG5IOkoAj+LEIBxbbyDJHd28+uq7bpaQsZCB+8HrbxZdqwDhVLbFJXrgHkAsYvOJHcSIoQMIQDfipzIzkXAug8Bb3ek83B0fWjb+VnUt575dAQlV3H9/yDaKva4zk+h7etSMqN2IZY0tnWE8VOK9QAfsr9XA58hLGxOoEWGZ+SEmeNnkE+3D8FZMj7HdytmR8fi64fRDkWnnVeBxlMHIZJNfO0AcjN5EPVzEwo5Nxj9HEA7Kj2EsIUVqH3dPLPiAStzJWE3xwHoPCvvjqKtUtxcv+zlTsSGmHgou2r0PekjRANKK7+P4Mh6OixeWFajd4JLjTrRwmwpwYfDWfE7Ea/zAMEZ8j2ojQfQYvpVBHZ8DXr+w+g99KVEPXwnqg59744SMp96FCYPdRoD8Gar236m3+oJ0VQmOoYyyyyz6bRyAvAdiIpM5maeAXYEgW7PzviLSP/3PTQRn4VerFXohTpCSOxSQ34wXupE5VpWt3yAKt/xOEyXg2JnqesJC4ZYaz1MrsOaSxxiYNdFiCgxH01EvUiesQZNODX2WTcCh75F7PetQ+36I8ReNaH2bLS/qxGw3k6YXL0tjqG+OQexUi452W33akI7FWcDv4YWB/sRE/VyNOFWI0B2DmJLL0ds148Qa/gmQqjEdsQedqJ+d8Z6m/18lYk5Tu0jOIyOF7x4uL1mQsjDHkJ4Rc80mowe4zYd4LxQmTFoiheF/j2Jr60hN8OqOz7X2nkHUD9djvrO228Y9csJ1Me+QL4MtclL0E7Jfrt+A+rT/yLEfa5GOw1daNzFjrVvRwDrABoPr7LyP4neCU0Ex8ypsGGksd6ExvfptkHCOEvKT9yambxDeqk2anWJM/jOQUzzEGHR9F7CIm6/nX8cLb6Tmt7/tOt3oHfKZ5G/yAkkL7kAjYdnUurzD2j8tKJ437+I2qvdyr0GTWvHomsWIEa6GslPpltjfBzttC0nv5NuZpllVk4rJwBvQVTiQ+S+5W8sT3WS9jAChRsRCJyNXprbCbrwJvv7JCGJS5xlstD2bCFQXSpIyne9+7ImJSZJVnyQwBJ12+8lUR0eQmB2CRoqRwjgtIYAkLoR+3QdQc/dBfwzmow2EyIWOGt5ALXpKgQeawlhwxyIVdt57kD3jNVhqT1PH3JS/BYCUr6W+wMCCBtBjm9/graNPb33UbvGk2+sIUyY7tj4GNrm7kKMOlafxfb3AkqzGqvzIXvGCwg7CQ0FrktaHWrLkwgoVCAAcRFaCPVZ/eaRHnN6Oq1QRJti5/qCMQZZoPHZivrHF8W1aAE3aOfW2HVHURs8g9ppJfreLkLjohL14+P2f4eVdxPagdmHIta41nslauMeQj8viP6uQt+D/VbeRHTahexZJpdMZzJWY/ceJT8DftZprVGuX0CsCY+duV1+NITe3yvRd9odwGPbTy4TfRj5/l+MCIAOcr/fyxHo7rBzP0/o93rUXn1Wl6+l1H8uYWy7JM6TAqX5Fk2FPUauM/FK9O5ICy2ZWWaZnW4rJwB/XxnvXYKtQCzrEjQx/xDJAK5AwPykfVaBXqjH0eTdSAgt5aHxIN3xMamJddAed0uaLryQM2QfYvZWoMVDDQHQ+jVJB8AqNAk8S5CBjKDJ6DYrZyFitj38l4PlOYipW4EmKGfaq1EWxxa7r8sEHLhfHT3PbivvEEESsNruOWLt6tFK/hVNetcRpCi/Zvf7OtpO/qSduxVNQJcStLCvQsDqqN33MAGwbkOOlyfQJLWQEMGi2p7hHkI4ylJZrHcgBnUP2gr/LkHiM54Qce+yclrs/iNogdOMgFI/AqHzCTscsZSjFAfgeAFYkTgO+a8vpj+Po994aDhnrwcIC684fXw/wefC43MPInZ4HRqXG+zzQ4jFvBiNw1egMfkfCJRvRIsV9yc4iNb/g6jft6L2vQOBpUutnL9HrOb5iBU/jMZxl91rjR0bbwbLmWzfQFFE9hCyTMb2SjTGYp+X+PdkLPme9IVqHK4vzq67D+1EvRz1WzMh22kH6rduBMT/nnSw+1JErPj7cSEaT777sN3O6Ufs983o/XQQxYX/CmF85LPd6F0yH7Hm70DvNH8nTLddj3wgulE0pt7TcM/MMsuskJUTgE/lnu00mOcEeh6xoAOI9XHg4JOPAwsHEvuRDKIahY5zkJvPKZOU42nnuBXSDXuYtX+we48SHPeSk2Vclutp3Tt/E5LeuEzijcgx6cXoGWsJUQh6CSyuJ4zpQCDateb9dqwDTZI99vskAtE70MTwOGKzTwK/B7yaEN3E77cEAaIaO28zYqS77Fk99NsfIub8IjT5uPTEZRsHgH+0c68nJPL4MemJWG+P/h6vzncRYsmcNTwXgfh8mQbTrBqBC4/ochi1u2cldCfbJvvb28HBS1r86TRLOhOT8ndsyR2bUnTonjim3n4fQ+NgA0FG4wC8HrGKFWgcH0VpwtcAv0NYEA6gnY4nCDtXj1rZdyLQMw8Btf2o/50Vv58Q0WMRgUmdg9p4l/24OcP5B2gXZ2H02TK0gOtHY7WD0BfTZbMQsJsqNd8x5Iyez7yt+giZY6dqKkl738X+EpWERXwv+k7fjhZDc9H3wR3Bm9B3og71yyYr51lygfhyNDY8nOTzqL8H7TrfTaqze5xNWBTC2PGRz+6J/vYxsyjtRMI48pwArXnOK9V81879X04HAD9d4z+zzM5MKwcAvxfRKx6+wM2p3aYy1CnFnkQMRS0hI95DwLUEB6Aj6GVWiQABCFxdgF7WsYNSGoudPFYKOzmAJg9PQOJWgYDYYsSAO7vok7I7BfkWrjOwPonOIrDSA1b3RuA1aNJZb9e02XN3oRerO7BVIlbIQwy6nMAlOgvQpNKKQNTtVv6P0KR5tbXbZuT05PKCasRiD9rn/8OOL0CAax+aWE+g/voLBNh3ITa7B2mA5yNQ5sl2Phu13X2ESenpPG0/GXsIsauHURvtQu09HgA+hOIM32zlLCVEfvHPGxGIdVlRPQEgj0f7naxTktVOAu6ktMmPu7Qklpd4ohSXjoyg9thL0G1XEaKUHEL9Mmif7UCRJ15h93Hn4ktQ/OfjaGzMRUDqXuDddu9FVkYHiizycwhM7kd8QAtiKevQ7tdeNN7TbA4CbasRAwtaMF6ExvtP0JjuBj7B9IGQWuBX0FhIZmecLvsh+h77wnIqdeBp8iR32B0gSNmG0DjaBLwFvZOWoDEzinaWmgluRstQ3PZ29H78QHSPFrST1G7X7UALtpuR3KkbvReOoff8XPQ++fAknvMWKzstUc+r0Hh22U0V2oksxLAXs28jAuUF9B2ZbnsRmit7EOOegfDMMktaOQD4VfZ7vMFpT7PVoC3I+GU1C72k56CJ/wcICJ1P7qQxh8JZCkcTf5c6gVWgdcsLCOinfe4/7sg5gCanFjRherQIB0jOFs0jAK9YNlNPiHnbjgDvYQSKl9pnVYgl8qgmJ9EE5Yx4m5XvrOY9SB9+Ck1uP2vnePzhFyFm8Wmrtyc5WWj3bLRyRu18ly0sICwAzkIAfJ2VPRdNxp6AYwiB1T1Wx1vztrqeaQGagMcTtKeeEAnhKTu2xO7rjHUpX4Na1J5D9rMXgdIWK2PU7rGUEJXHNclTIQ/It3OTPJaURo2g8dKDFoaH0fhdgPryFCG0o0cYuZiwSDyMwN58wvhsRIvBBtQGvhg8B7XTAjR23Bl1CWGBeTYaR712jsdQPxeNg1sJzOi/F2mTeVbvHfbszQjkYX+vsr8bCbs+47GFVtd8TpBuHr8cwvNMt81GzPNmpt4JM0261IvGhvtvuOPxEbTAX48W0fXou+GSu0ZCRlJfkPYSyJLlhIgz7XaPk0hSAqE9GxBh0IeSEJ0ghCWNbREa0866N5GfuX6a/It9H0dL/397Zx5mZXHl/0/TDc3eLAKCgI1sIoqISNyjJlGjxrgkcSbGUZNMlkliVjOZZDJjtjEZszhZRk0m0ehExzGZRIwLKoq7IKCAIIoCogiC7M3SDXT//vjW+dV7b7/39kJ339u3z+d5+um7vEtVvXWrTp06C/q91IbXByKAb6TpPt2WJNuuNf3fcUqfQpqgjEWzbi1aKk9BxnC5VE4dSG+kVeqLzA0s65lFCemJJt8T0ERpW6KgCcS2+9M0h/Y+TSuZph3P/m4ImVve9l1SMDSN0T4kkIwlCjX7so5ZgaKRVCY+34QGzZ7hvNFEp8dJaILpgR7VFDSBNaAJzzTMtyFb0eFEIWgX2v59BWmy9yLb2Qqi7fx0YjSCt8Mxe8Jx1tYb0cT0AEqWMRkJK79Dz2UEmpzLUezfz4Xrb0ImJguA7yIb4aeBb5NfsL4yXPNF4uTcFH1RH+qNImVYLOaFSEjYh9q3KY17BYqecDZqmzLUfr9F5hSWRdQSF4HqXUnjSDhGLrOmXH2uqUViPhOU6sTng8kMXWe/malo0dULPV+LljMW2WabJt/8LvYgoXsT8H30/FeGc9YiIX4KEtwPRjbgJ6I+cTjqtycgJ8zLgY8hm9yH0GKwObyBFuBj0M7KOLRYqEG/qVXIbvgtWm4acgYx1vt/kjsWN6hNHkYLzkdbeJ/W0g39JtpKh5Lvt2eLG1uw9gyfV6A670C7lQcj86OL0W/VxsS1qI9YUrV9aGw6H/hUuNaD6De6m8yU9GYLn0zS/DB63kNRnoAb0Lh2PoqqsgGFCbVdiceQNr0l3I808rOIpjFPtvAahWY2enbrKMpAZ45TBBRSAP8TkijGIWliJsr5fE4ByxQYiAQokOf4IDTIDiSG4rO40jZ5vIkEgCHkt9tujl1tc6NJJLGteBNqLHyf/ZkgZnGjN4fjXyFTm16LhIZNqL4DiUlyrLuYYLWWKByUEeNlb0eOkI+jRDcjwmdrkABeH8rQF+0eDEKTp5nQmPnJTmKIwd7INOW0cO6daDK9FE2O5qz1HSREmYPWFtS9thEdJ9eG+3Qj2h3bBAvRdt1CTZo2Z3y4xh4k8O4ht4ZyMNE2fhiZgvvEUKfmmKD0Rc/goFBvS7xj905uyyfj0ZuAmyuWe9rCL5uWas/tONtZsc+sXGXEyDxlifc70TOwuPRWxh40DqdoIRYfQ893NvId2Iza+ZDwuhYtsA5Cmu2J4fPeSKAagoToZDbDic2sp/EI6j8jw/sKFB3DuAMJUOYg21zselVIiGvKZOApopKgIxhH7jCXraGMqBAwzBTOxh7rR4bt8uwiJlTai4RoM9uwjKlLwvtnwvsGtOCzXbm+yK9gX+LaA9FCfyaZjqgbUf+x8waGMtgzGxr+bFdiVEp9uxPtzdN4E02FxYolWNuU55iNqP8XBk8l73QGCimAmzr2QhT/6Rc0L7VgB7AWTWgHo4nzC2iwHEAcUHsjQXUMEiDeQZq1k8j02IfGwg9kRoVIRkHJPofEd2maxuTxpnnfR3RWKiM6TnZDQspeNGFsQdpDs58uQ8JcNVFTuR1NNOuQZu+EUPbJSHg2baWlCO8e2uRXSHjehSYUMwEYgbSTFtXiVKQVXhD+H4FsICvC8YOR9vIjaFIahia9cciO8rfI4bQCaUv/PpRjNRLGvoC03NvRQsNMbuqQcLY61Mm0h4cgbWgZshNejTRhZ4U2+iKayE9Dk/1N4drZvI626YfQ2N94GdEZqqkNn61I0BtPzBz6OnHb/V2oTc3uPqnxNuEFMvteWuKoNGEq305M9vtcfTi5+Esu4uzYfsSkSN3JvNZeJFiNJpoB9UU7Ec8hjfPfhrbogbSQw1G/W4F+v2cQd4NWIAfei1HfnxjuvQH13x+mtEFTrEB2vFU01lIeC5yHxocbaX5WxIdRlJ+OstdtKY8hActMOA6U7L4K6gfJXRwL/WomV6bNBvWhhehZDCCmgN+JdsmmIYfdkcg2f214PQL1m6lo3Pg16gcfRWPUKPRbvRMpKoxHiGFZLWzlA0hb/jJSNDwe7pHtsN0d7WgNonXa8WLgSvTsX0C26Y7jtIZCCuB70ex5OfJegvypGdsRsxdMClI2cF4R/g9CE/3+8N6Sf1iyijnhmMPD/x5I2Es2sQkXpjW2pCOmrUzGD8920qwP5bPkP9maWdzJ9QAAIABJREFUdLODNizKRj3S4AzMupfZY5chAXknElgqw3X2EqM5LEXajAXA15HwfXLiehZX9lU0GZYhQbkcmV5sR85uByNBfATRhttsBO9DwvpfQ7uegSbAWiR41iAB2pxAR4Zz70ST3plo235oOP9e4MuhLKPQpNifuGvxaijXKvTMnkDPciRaeNSH81aHeg9Doen6oQndQioOJfYbM5UwbfrTxAydSY4IbWa2903xBFokvBba0NLX34ocAK9Ei6GNxCgow7Ku/Woo32AyE6jkMy9pjn9CckG4jxhb3kxJskNlmpBl9ux2nsWh74na1trnedSPDw7X3I368xwkFJ2L+sEUop24xaJfj/rW6HDsk6ENXkT9/JhQnjtQG6clvSkL19uW+KwKPXOry5PEHRazM4YY9aYPWog1VwB/C5lJFIrs+mXTF7VncqfwQMi302Ljpf2tRwK2HVuLzLj2hzL/M5kZaocAn0B9oycSHOvQ72Areja90cJtGGr7avQ7twWv9R9jA9rRSrbPSqIZVG9ymwP1R/MDRPOsNCpQX0oLA3kgHOh1uxOjwlSnfN8N9Yk0pYTjOEkKKYBfiVQBP0BS0BikcuxgKpAtoMV8fjzr+9nIlvMdJIA1IEHxR2j7+TQkUM4gplM3e2cTMC1urWFZANNicuejkmirnU1yS9gci0wA75M4zkJ67UCTyng0wdSG83YQt4O3oe3bPciD/vLwXVX435+45buVaLfbHU14g1H7zEWCbC3RGbKGmIimAlkejURC5iOhzBb2qw+aNGaEOixBk90PQ9lXoUWBxSt/Bq3tTgn3WIkErL5IWD87tM9jSIAej+zEbwz1sYVWMrbz0+EexxCzd24jOhcdimyJ65ENaDXSmm9BWvKkCcLLaHKvJHNiz8W70WJgINL4WyKk+UTnw22hTc3pb34o7yHoeQ9AGqvTyNR45xPEW6L9biDGf98f6t0/cVy29n0rsW9WoGdjEYW2EpMXnY9+e7bQLUOZMB9GdrY1RDOkmnCtNWg3ZGy45wi0QOmBYvkPRX1wONEk5VjUX7KzFF6C+tVLyIntw6jNX0GC+xXEPmga4T+g/v4E6mebwvvOgEVyWYP6cRrvR22Vpi/J5U+Qi+wdlCQmSFs0JEv6ZcqEJ1CfORopBGqR3fRPwrmXojGrDzFcZQ36XQxBv5mdqF8ch/rtQ0gZMB1p1N9GUYySjEa+Jw2ojczG2YT9CrRbtzKlTpvQgm00mlvSqAQ+Her5ADEK14GSvO4stEPXUvaGcyfT2OypG9pJOBjVMVf9HMeBwgrgy4CrEu9X0bo94AOkiujUOI7GAvgbaJAdhwaYuUgLcysapMcioc3sls02dyMawE9CgmgyMkQ5jdPNG7lscU1ANg/7XCQnMssEaWYoDeF8S7H+erjWEeHzrWgCmhrqsQY9on9AwsUoJNiNCeeahrqGGLHE7tUbme3sR4JYH6RhnIEmw1okpO0J9z0OTWSHIQHmV6G+FtKxjjhprkVtaEk3DiE6a25HgpXZxG9Bz3gLEpgOJdr6rkHC3XiiedEIYsSSAWh3gFDGJaFe1UhTtjmU17Rm9nMaRRT+BoZyJh2RTg5l20+Mz5uPsaGc76DnZrajvdFEvgRp6ceEtnoHJR25APg7oiZ6V7hvmuDUHL+DbOEq+T/7ujvRosnaJBkvvj6UuRY9O1uMWHzwd1DbW9jMfUiDvwgtBE2LdwrqFxtQH3spHDMhtEXyN9YNCQaj0G+zDvX7p8L9bFEzjkwBfFz4PzblfwUx2slU1Dd7oD62Otzn97QN5cTxJRszP8sVaaIPzc9+aPUzs5+0xDUnkdtsqaV24Unh2wRu2x2z3bre6FnuRvWoC8csI2qTJ6Cx+gj0nOuRGU8lerbvEJ23TSDfiNpsMVqAmdnbrcikpZyY6yBJNbFfj0a/R9sxs7F5DBLAk21fGY7LJZiaOZ/53oCeR1sJ4IOyrpsUwM38ay9q73z95VnShfe+qO3t+i6AO04+CimAryJd7XFYymftyCY04VYjzWsuViLv9Okou14dEkK7Ec0epqIBrjsaxCzcmiXqKSM2uWnBIbcGKPm9mUUQrlWRdYy93osEYrNxrsg6vhJNCgchgaUMCa1D0YRzEdF8oBZNJLORFnY78KFw/luh/oegQXcvSlBiUVHKkGnHXjQ52utXiA6SDch0ZDSaTMtRZjmrk5kB9UYTay8kNP8VCTuD0KS3CO1SHIPa/D70HCrDcfOBr4Y63Y4m3IFI+OoR6mPxhF8ihs7LTgU+l5hF8WwkiAwMbbEg1GMfEi4tUU5aFIx54Z4W27gpHkWRYVYRhe9TgS+hPtcdLRT6oF0Ec6zdgQSPqlCW88jMIphmCw6NzZ/KSNeQZ/dZM6MqJwqmEBePye9PQr+9GtRfLBtmPzQE1BCF5yo0uXdDv71zQzvsRn3A0mubnfjPka3+pHD+LtQPKtFzOyS0z27Uf36LHHdPTVzHBM8HkHBm5imz0CJyYTjm4XCte9Cuy1g0VmTb/R8IfdAuXT/k9/Bi4rtypHUdTvoO3jlocbuS5pm1PIQWiC+SO0X6HWgnJW1HBJreOUk7tozYN20xVkEU9O21LazKUSSj7eFzi9rUgLSvthtRjxYtB6HnvT3Uyxb0e5FCZQd6jrNCeXqiNh+Anm3SPWkhMSnbDmRfXofa9+VQ/ufQWHoUGtvmI9vyvai/ZTswdg/3Owj1uflIGZD9PA+EdYnrJvvnEBSVynYZ+tK6mPLb0a7EOPLPpY7jQGEF8OmJ1z3R3u6gHMe2M80ZaOqRn6hNDhaxwwRH07DVEWOA90+ca9ErIAo+9reHmFgnuaWfjGJh1Cauke3EWY+07jORwPVJojOmTYKmVTJP9jlo8u5HNHGxSas3UQNzJxpY348EmtVIK/nBcP52tAhZigQfc6CyyBOWHOb/0I7CfrR9b2YSfZAQOTeUxbTqL4VrDQtlfza0eQNKhGERJqYSM1hWIiH8vvD+OKIWrZqYOMW4GWXOLEcCbHaCjbLQhltQAp8xSCgbTAzzmC3gbA7XtdCV5jwGWnRYnOrskJJprAr3TSbmmEA0oaglmulsQ5P7MCQcfBctns4lCjgWanJvOK8/mdrrZJ/KZ59rux2WedPMSZIx8OtRu1n/7kbMsNobmRxVIsF3UOK7CqImtAH1h91oMXhNqPtlqJ2fR32oItRpbvjO7MtBmvNTkYBjJjKbkdbUov4sQM+qKrzvjoStZLKUZWQKY8kIJGeEch5C40V1SyOhJDmYOJZMIFMA70eM0jOBxgLbhPD/MHJrtJMsJT0TbJJuSGgdQhz7alF7tWR3JU1gt89riUJ3A3F3xITeQeG75G6axXtP5jQoR33TzM8GIUVKNRqndodjng/3sSyoQ4na4glkPvMa4m/9fUS/nFEoUpMxMfF/MzGO+WgaC+CDiNmXJ9A6HwCr8/48x9yb8tmhxMRg49DYPpHWJXV6BBe+Had5FFIAzx6Brkeqi38pQFmayWXI3nAgKv5xSOu6D03kb6BBfCgapHsiocSE7G7EiTS5hdubqN1OTtrJCcgE6PJwb9syz57chiOzg95Eh0oTfs25MnneiFAHiyNtWvQ1SLt3NAop2BdNZJZkaBzR4cic6Hoh7e7BSAC0cHTLQ5uMBP4JCUNvI43sYDQ5jUJrsq8jLeUaZE94NhJwa4hmCFeH+mxGws4CpJUbhLTTSYGpO4qiMhlpphfSmH1Ia/y3aAFTRXS6644WMkPQ5LUAacgfQ8KaabeSmO2pmSAcH8r6X6gf/B6FSxxAZtzhXExFttAWFtGys5oGf1RoD0ud/kqo6+fDdw3E+MllyJzGdj2sH6bZ4mZrvtO05d2Jph5JLWYS24lJXsP69kjU3lWojawfmrBi/bE7WohMRIslSxHfH9nav4GGj7noWV0ZynIr6hML0cKpBkWOKQ/tNBv10cfRwuh19PuaiHQCNaj/70S/qzHh2DQnu9nIWXYRmcLl5UjImUPrNOOrkVA8GPkiJNka6nxYjms/inYb8mm0W0odGjeSz7R3jmOz+1VTuyh2TJ+sY8uJiydzyOyDnt1a1A8qkFCdHOf2EWPlT0S/vXWo72wiji0TQllsEfsU2ikbRv4Qj/PR7/vdaGfkHuRLsh+Nn8ehvrccCf3mNJrN2+G4kUiL3FKGEaM33YIWSM3FlCbd0fg9irbdwXEcJ41CCuDTEq+7IemriLNjWhg9S29cRXRwA03QTyPBcTQyxbBBDTTo9yQ6HUKmGUAD0SHIturNnjCpyduJJo7XkGBmsZL3E8MPmibbNEf1aEA2wbuKaE+5i2gKYVEmViOh83kkwPQlCvR7yHSeS5apDxIy+6EJ7WC0lWlhCD+MJrhxaMKwzKJ1aALqjuw2uyGBpRptC68P7fc9JLxbSMWx4fPJwI+RgGSYnf0QNAEvRpNuTeI7+0/4fEF4PYoogFsGUcJ9FoT634V2Bez5J691fKjLCGSnbAmRhqH+cTBy8Kyk6UQ8IPMbq6/ZuVcCX0OLkXPDtbsTI21sDWUzBzZL+16PhLFeaIFwdKjrQBoL49BYO1mf9bn1ze2ojyR3YyzKSTIWeUPic9NwW0z0WiTsTCImvDK79afDdxaJph/ShA4L5w1E5kVbkSBkITknhnpuQ4LJGcTIHTOJCZJeI4ahK0cmSeXE3/mbxAyKRxAF8OTi5CUaR1Ltg/pyN9SXWyPY7EfhEyHdWfuBPOcuItaxrXgvmUoBI59NeD4tuPWhBjQW9EJ1TiY4s+9BvwHrG7Y7shAtrCpRv9gVXpszuuVuqCE6V/8B7egdT8xwvBuN3U/SeKcMGmuZt6BFuPVL8yfZhMxQkpF1fpt4nRz3jXvy3KcpxhIX2WOJAnhyXMrFbqRxb86xjuO0FYUUwH+SeG35tT9SmKI0RRmKhjCGKLCa4+JOYuzsC9GgX00cQE0QmUiMQJK8blIzlLb+SGoMIdorZz8627pPbgEnYzCbTe5WYpSUocTtRxM8BiCh7F4k9Fi5bHvfoqRYBJQapLnshiaz05AwPRoJZOeG81YThbwapOWxjHIbkObeUi73DsdfTXSo3IC2d9eFcprd+aGojQegLdNHkT3vxHCPR5E2eGxoN8tcZ5rgF5EGdSISjmchLbixPry/MNR5PBL6vh3q/wW02LCMeXciYWk8cfv/kfBdDcrMeRzSlJahBUq+7eYhyI70CLR1vhP1gaXIzvkEJLBWhOsPRAuzS0ObVBAXVxYp59Tw34SApLYx2+Y7+3+aIFWJnneyb5fTWBtu17C+m8zqaiZXoxP3qSWafM1AfcnuV4a2//+A2vlpFMHjEtRXlqHF0zXhHmtQf3iYKJy8F/XZO9AzPRM94xqkVd2I+shK1OefRf3EzDxGoEXfPiT8HYsEwSuIC6EaJPCfhYT852gcVaO5nIIWECtRwKg07XFHcCOK+JNPs52LNKG9gdgnzHype9axyXGtO3qG5txukYx6hfN7o99mGRLW+4XjepC5WDkU+VHMQM/5bvTbPAiN9f9I5oLz71Af3I5+5w8l6tQ/3GMO+RPUgLTcl6Hf5e+IccuN81Ef2oMyoWbHl09jCdHnYUn4/0E0Fswn3fQkyZRw/MZQpub4pjiOcyAUUgA/vYD3biH9kFD9GjEUWg800G5Gg381EmSTdpE2OZkdMEj460G0ia1NHJsvugnEUG+5BCH7PGlvnq3N7BHKXE4UZPYiZ73RieMsKkg5UYioQYLN7tAmbwP/jialfmji2I0Ek9fD/yGhzmORENQPCUuLUXuOCu02D00WEAf/kaH8FrXCYm6b4DwWaSbHIsHzKKQlNdvLo5Dw+39ogrkQaTO3hP/Pou35MUSb7GxhuAFpvceH95PCdW3nYQYxOdPhSDh4DGnV7Hm+ijTm01A/GR7a1RIa5cO03guR0G3b05PQMxqEhPIy9DxMYJiGzDJ2oOc7AC1aetE4Pjc07k+5NOHN0XLmM2MxLXzSTtw+T/o0bCJGYtiMhGxbYNp5vUKdv4na+ifE3+BSJGBdQNy9mov65E1IG31xuM7hqK+VoWe7MBz3PTJjSs8iOumB+kQlegZTiY7H5lQM0bxsE+ovR9F6Afyo8P8wtGhqblzxtqaWOMY0V/A28vUfe53sT2kLQYv5vRW1vZllWeZM2+2x8JX9wjnm9GtMJ9p520K0Ho1hRxGjJ4EE7MNQ3+qFxhMTwIcRd0SyE++kMZE4foyhsQA+LZSrPpSjOQL4DjI17BD7yxSaFsAnExO1DSVGf3Icp70opABeBfwrUseBpJbvkpnxokjYjraWxyE7T7OZXoY0Ht8kxiFehzRhth1oDkJmImKCtoUHNJOTZMjAfNgEle/YiqzvkpPXm6HsPUJ5LPbyQGICFcL1DyaGgysLxwxH2kHLUnkbGrQtHNwxxOgDZipSiTRWw8P7k0O7PYFCHPZCAutONNE1oInOTDTGEG1OeyHhfRKy7e6OBHQr8xQkWE9G2sZPI43WvUggrUTCQ18kNN2Fnt1EMrecRyAtVVXi/SIkjG9H5jR7UGztNUg7uTRcG6RNm4Ymc5uol4fzeyLB2qLj5GNZqFM3omYLtHhZjRzLLPV1Pep/PZCWdlC410D0rJJOgLYAgyj8QjSLIvG+NWSfl9SUlmd91oAECAu1V0E076onOhlWJo63HYxRaIdiI+rb08J1jkTt0y9ccw0SuIahvvEwejanowXk/nCdR9AzXESm8J3GErQzsQ/tTpyGFoCvJY7ZRQyFuR3teFyAnunTNC2wXRTq8mS49nuQ7Xq28H0I0krXIuff9hxGLyYzilNbk6vvJPtQL6LzsY1PSbOV9Wixaz4KO9FzvQo9jwmon21AfeQtlFxnFLLZr0M7JHehhdz1aGyz3UDzVSgnJgKrRztjr6NnfVa4jvklbELPZijStL9K40hLEBNM7UTjxUDkMzIR+AvaxQMtGi8lJlbrjnYJV4Xvn0BzUXMWfHPRb2N9aIt8fAQtNB+ldfbqB0IV8u+oRLtAa/Mf7jhFTCEF8N8hNaaZnVyGRqeLClaivMxMvF6E7EmNKUjAAWmEb0AC5WA0Gc9BpgfvR06G/dDEfCiZE0Y/YpbL5FY9RGGEcO5CJGzY5ANRyE7TMO4J90jGzj2UOJlZ3Gy71zYaa9EbiHbdPcLrgcQYxN2IKexNwC9HGswd4TtLv16N2vSj4R7lRJtwE5LfDueZELaRmKFzauK8eqId+VRkhz4LCe7vC8eNR13O6n1Foi3/icYcQRSUt6BJ7W7i4sOE9cFogsvOorgcmUMk2QVci+zVzwmfNeX2sJ3MhEBGHXIoexNNmDPQAmMd6oMWu3g0mrAsqseLKKHM0cBXiFlJ5yPBYBwSFtIcfCE9pBzhGj1yfGckF42mydwcynZOKM85oczJiEFmStA71Lsy1Pkt9LwtssQeYgz6fkiYehP1oQ1IIJ8WzpuEntFYop34HpRgq7lsJgpD+agjpuxegRZvhLLnE8DLiVrMqSg6Ty57buuvPZGmNtsWvS05j/jbXosWuMlxKI3sfmNCoykfbLxLKhiS409yDLKMmHVIIDMzJcIxu5H2e1jiWrZQPiR8Phz1i3tDfZL0RooHS3J1IjG+tfnJjA7X2hq+ewEJpcNDuZKvT0d9bRj6vY1F40Ud6fG2a4nO2ZvQb/Iw4o7breG8SWTuuq5HizUTwB+j+T4HK9Eioyl6Ek1djqHjBfCxRKXIJFwAdzozaZlgOoqxSAO+Mvx9hw6PAd5WPIsG5efD/7vRhP860U75UKQxWIEGTxMua9Eg2xcNorVoYLZ4tTZR2cRTiyaXzUSN0P7EcRDttG2C24OEv53hfS2aQExgrkMTydpQXosysInMqAOmYT0slHUemtTeRgKMmZz0TVy3lmij3S/c4xWkcXkRCX67wuvZxPTwb4TzbaK3qAbLQt3vCOftQxPPE+H9XDSRX4mEe9uZMAdLQhlWhs+TEUwqUSzef0QLgS1Iqz0UaU1tV2MHEuQm0LoMh9cSwxIuaeLYplgSyrkICZvWB0FtfnEo5zrU7m+ius9BGrgNqE/2QO2xFbVnLbEP2Z9F02lI+atv4n0DjcPwWVSgOrQIuggtzCx2fR0xVOA24q7K1lDW+8L3laEOy0IZd4U6mC9C0v/iyfD9vNBWq9AieQPNi0iT5AwUkeWcJo57Bj2Teei523jxTL6TQpnnhmObylq4mPjsmpNd9UC4NZTJ4mrvIv15J/+gcV+y/pAUrJN9zp6ZmS2ZmVg3tLiy7Lv1aPx5ORxvDpfWh99ADo6voz6yALXRW6QvgOz5bEcLrEvCe7vHdrQbUY802aB+2B8J41uRgN0LjXmPI6H+SKRVn0fj538y6ksXoDHnSDTmlIV7LkG/gUeJQrs98+Xhe9upbU/2oPZrTp9sD15BfXwLqr/jdF4KqQHfjUYdM3A7icYpxzoJ88kU5P4c/gYhJz2QtuC3SCA6EWlmH0MTwN+iwXY0EiRfQZPFOORYNhQJtYuQOcMzyJ55PZqQBqIJYDQSzt8M1x2OtAXL0eRmduiDidv5K4hahMeRRdAYom34M2ii2ooEumnEbcpfEBPD/A3RBrEBCaar0CD5HuIEfR0xPjdEbWAaXyHGBLfoI3OJjlQm2IO0vmY7Popoz/4ambF5QZNymuPjaKK5wyEooYu1BUgjtiwcZ+EMq5Dw1hK6A/8WXq9s4bnZbEDlTGMCEgpeQX1tTuK79cTdhxEo5CPIuey/E8edBXwDaTm3oL7Yj7jj00CMVPI2UdC2ONEWH/9VYrhBMwupRRP6KiRwDCAKZ48hjdz94T43IeGlG+o/n0PDRy8ksDyIdqLMHOl65MAG0dF3Lo0jW/yQ1ifgnY6G0Olk9ulsniFT2Mq2Jc/HA+SPcmK8DfxHM695oPws/P0DGreOQPqUXmicWoaeyZsompLtuJlwvYcYGvI41Ee3IUGzDu0eJqOE9EN97zW0GK9Gz7MXWrhvQAL2EvTcK1E/+ynq+3e0sH5zw99niRlBnwL+Oeu4LyKhd1i4v9XzJTQOWxxtUxrsQr+1WTSOsW196Wg0Jlis90not5O2S9eRzzzJX2n5YrWtqEFjgeN0fgopgH8W7aHbftIWZHxXhLwbRZtYgATgY5Ew8ArpoaqMLWhSORkNzheG45cjU5Rp4bvZaACuQxPPPCT0XU101jTHnyo04Zlnv8UD30q07y5Hk1hfJEhORY6IO8PfNDSwbw/vD0camouIdqqWifCEcK0XkaC/HQlXu5G2eBUyaZgV7n8YWhSY3fdeJJycGNrjuVC+j4Z6WMSDzWgBYFuao8O1RqCoIpbNcS2y49xHDP+1lEyv/XVoATOMlmmE1qCFxVGh3g1IAHwDCZMvhOM2hvtuQpNgPvoj29zuKEzeRtQOJ6OFQi7h+UC5EPWXPqg9LLnKMLTg24ME7bNRJsVj0QJtMVpMLg/Hb0WLkJ6ovauIixTTVFu8+4FIqH4VPael4V79UR/cTszouj3834H66XL0rEehPjERCeXVqA9Xhfv1DNf6YDhmSqK8dcgW9zUkGFagdn8VPSd7fm3FCqS1rEOLm7bMWlhIjkb9YiWygc6mCv0mjyGGi4RoCjYofHciWlSZGcge4q7eXvQ720eMHGVmITtRv1pPjNI0kOjQ3QeNX1tR316DhPnXUD8Yg8bdgahf16Mx6mCkKNiFFuD9w/sR4Z41RDOtv6Kx6lg07qQ5Qs5D5iXLwn1noL5smtnJxCRcNs0tJ93Geh6K27831HddOGcR6u8WReePKec6jtMZKaQA/hJSW45Fo/Q2tP9WhPtKFtHieCSAvwsNkuYJnysaQQPSvg5Ck4cdv41od11NFPQI13oYpQY273jTMA5CA/wkNDmUocF5P5os/wmlij8FCTgjw39LDX8/mrwuQJOAZULcSQzntzsc92i4zng0iUxAWqH1wPeR0DYSCe+DkDB6IppEZoRrb0AaqV1Z7TIKCddmyjIYPfbjiQL4VCSsbkQLH3NWOo0YuWA+UZOcZB+ZNvrNpRYlXfl8KNNU9Lx+l3XcEKLN9zDUxrmYSAy1NxkJahPRT28d6hNtTZ/EdVcTNcEQF3FVSNB6FzGEm4V0O4sogB9PtCm1rK+VRLtTMx2oIpoILEARSQ5C5hn9Uf/rSXSW/DUSnE4L174fCRgfQP27Bu2cPIP62M7welS434eQYL0YCUdbwn3N1OjLobzLgB/QdkloklhYTpB/R6kI4DPQszoCDc1bs74/nmgfbfHWy9AzMzONaWRGftqFXHwsz8BFqF+YqZ2NM+VIO/yjcO/rwzmDEuVYg3YHLXrt1YmyfTbx+uOov9gYdXS4Z3807Vg+hymoj1cThe5ZaHz5ZJ52yt7ZuDHxejpagA5CY+cqJNj/Kce1lhHHuKPJ9Ps4Gz2PycT5w3Gczk4hbcDvRrOt7UfWkO6RUgQsRMLpy2iAPxRNFi/TvFBgyfNrkJDyJJp8XkeD/gvhGDNteJIYuuwt9KjeQULtcjSg2zbvHiQwv4ya8i0k3D1F3PIfH+61CE1g29CEsBoJPi+jSXI5MTmM3WdnOG9zonzPEwV/WyhsCe/fDPd/icbCN0iIfzOU9Y1w3FYys1QuQQLxxlBe46VQnu1kxutuS+x55Sr/EmLUlzeauNarqK13EoXa5ajN99G6dM9pTEOJeSx6wnIy+5NhYSS3oHqYc5rZVa8lM8vjo0jI3IKcZpejZ7IOtYElBtoaXlsM7qOROdNb6DnWhus3oLq/EK6VfJYmXGwK97s3XHd1uN8i1L92IA3l9nDNtEgS9gwX0z7Ct91jLWrP2e10j0Jgv23ru9lY2L8K9Az2ome/GS3S1xAzAZv/yHz03N5C49Lr4ZjZ6NlvD39vof5jTrPLUR9YkTh+TbjXZvKnPc8eo5YmyrmK+Ft4BfW5p0JdlhAd0luLtd16YnSkfLqlbeGc/TTetXt1++zMAAAUZ0lEQVQe9WVrJ8dxSoGyhob2CiXVJC+iPea24GdI5bAQGeblZP78+Q3HHXdcvkPycDLSyoEEp6acqNqCfyZmqry2hecehcJiDUEC77dpndOg0xE899xztL5vmtYeMqOgtCffJjryJqOH9EcaaJAAclNW+a4jfWHjFDOxf16HhlvQ7kK2I+NBKKLsJCRY3kjz7Ngdp3Vkj50/WdIRc7PjNOarR52Q8b6hoWEBccDMoJAa8KeJMbYOhGloz/0UpJZprQTTDF5Bgs0upK3oCCzsWGssc1YjbeNepCVc10ZlcooP6x/WRzvyntl9cwfSMDYkvrN+vAIXvjs7j6IxZT3pYRG3IP3KHrTwfynlGMdxnK5NIWzAlxBzD1+J9gctvWQDLTeKPYGognkYGShmB2VuIzYg7U9H7hrcg7bim0oKksYOZBdeTtMJX5zOzePIbKk1/aS13I36Z/Y9G1Coum6J754ghm5zOjf3IRvpXGPKfuR3cCOZYQgdx3EcoxACeHbWgwNlADH13DbSc3t/Kvwxffr0moaGhjSj0a7EQcigvCtTjG0wraGhIdtouxQoxrZuK7pS3bL7Z2epu5ezbSnGck5raGhYQ/GVqyMoxufRURRd3b/S2Kz70FzHFkIAf72Nr7cVGZ0S/me77INcytPSCXZV5pPDJqkL4W3QcZRyW3flunWWuns525ZiLWexlqu96ar1hk5e90LagLcVzxA9I99LYdJzOY7jOI7jOE6zKAUBfCHy9nkCGZjOK2xxHMdxHMdxHCc35ddcc02hy9AWPICyPOTLB+1ksqDpQ0oeb4OOo5TbuivXrbPU3cvZthRrOYu1XO1NV603dOK6FzIOuOM4juM4juN0OUrBBMVxHMdxHMdxOg0ugDuO4ziO4zhOB+ICuOM4juM4juN0IIWIA+44TtfgyPD3Gu2WndbpQI5FmYYHonwLz6I4vI5TCLw/Op0ad8LsOvQhDlQ1BS5LofA2aH8eAM4GvoTi898LnASsBb5RwHK1BeXABTSe9P8C7CtgudqCAcQkZucRF05/RLnkfwZUAg+jjMP9Ud6F/cBVHV1Yp8vTlftjKY9D+Si5ersAXvqcAXwb2B7++gP9gH9Dg1dXwNug43gEtfdjwOkoNj/Ak8DJhSpUG3EbsBiYTeakfzTwsQKWqy2w53YtEsbvRgunkcCVwOPAqSnn5fq8UBwJfB89m25o8bAV+Ff07IoFL+eB0Vn6Y3tQyuNQPkqu3m6CUvp8FzgT2JX4rA/wIF1H+PQ26DiOAG4FxiIN1e7wec+ClajtqAYuy/rseZQErFQ4EXh3eP0AWkiBtvZvRL8XW8S+ByVCKyZuAD4CrEt8NgK4EzilICVKx8t5YHSW/tgeVFP641Aa1ZRYvV0AL31qgaOAuYnPjkLZQ7sK3gYdx7vC/28TtwX7hvednbuBvwJziJP+u4GZBSxTWzENTWSTiOYo3dCzA/gKcAxwAjAhfP9rNAEWG2VNvC8WvJytpzP1x7ZmJqU7DuUju95VaLfjngKW6YBwE5TSZziyvZ2CbKj2A4uA65BdblfA28BpKw4CZhCF1OeQZqYUnUx7IxOEeYUuSAuYDHwPGIQWEPXAJuAaYEnhitUIL6dzIJyMlEhbkTnGc8BhZCqZShEbf6tQ3aej/tkpcQHccRyneeQK2zoLeF9HFqQdSKtbGTJD6ex1c5xS4ifAUKRIGgx8HNhI9OMoVZ5APggQd2GOAJbSSe3+3QSl9Ml2oqlHK+ZCO9F0JN4GTltQg7zuk5ShnZXOjtWtjMxJrrPVbQTwTWRKU45+68uAHwJvFrBc2Xg5ndYyneinMQW4C7i6cMXpMP6M6nsLMkMBuB94f4HKc8C4AF763ABcAryV+KwYnGg6Em8Dpy14CbgQLd6SPFSAsrQ1pVK325C5WdIkaAbwe+SkVyx4OZ3WUgH0AOqQAulC4L+RuVAp81NU708CnwFuL2xxDhzPhNk1KQYnmkLjbeC0lPOIUV2SdFoNTIJSqVsvtCWdZGn4vJjwcjqt5cvIB8XYApwPfLEwxelQ6oD/RGEHByNfrk6L24CXPu5E423gOF2F01HEnV3ADmR21hPF/J9dwHJl4+V0nC6OC+CO4zhOqdGLGKkmTbNfLHg5HaeL4jbgpY870XgbOE5XoS/waRqnq74JaXCLBS+n43RxXANe+swm3YnmWrqOE423geN0DWYih7SHyUxX/XfABwpYrmy8nI7TxXEnzNLHnWi8DToLc1CILYD7yHQ0OlA+g4QGp7QZDPwR2IziJG8B/oT8P4oJL2dpUQ282I7Xf7odr32gVBPrPh34eeGK0rlwE5TS51sofWu2E00ppAZvLt4GnY9z2vh6N7bx9Zzi5FdoIbeYmK56MoqcUEx4OZ2WcGKhC9BM5oc/pxm4CUrXwZ1ovA3ag2qULfFJZCe6CLgZ+A7K1nYp2m34BUqdXIGiz9yNnsfNKJvZS+Fan0MD+GqkTXkH+AswCi2a/gP4dbh3TXhvIfQ+CLydo5zXhON/jASKuSjCwwDgEyjLWjnwI+AslIzmN6Hc7wnnVSAzps8CtaGMt4frdAc+hcyaxgHXEYX+q4GPAJUomcS/5mxNpy2oACYQ01WvAPYVtETpeDlLh2qUFOZJJCyvRePRRDQO9AZeQ1krt6Ax6GtorDso/K9Gi5ubUbzrbsDFqL1rkD3+aWgsewclmFuAQvI1IKXFT8N3C1Fq+vNylPcaYAwwHD3br6Dx+/2h7B8A9gLHhmv2Dde9AlgXPv8dUmo9Gc47MpTva+G+M4Dr0Ti/G7gSeDlc4/zQJmPRmPj13E3LDcBx4Tp/JI6fuerbh/T5puhwE5TSpy/wVeBWlFThNvQD6VfIQnUw3gbtyzgkCE8BDgc+CpyM2vibaAfiETSIno6E0z5IkN0VzvsBGtTT+Hj4bjpwFdoWJ1zjWeBo4HHg71tQ5go0QXyJOKB/Ck1Kx4Qy/QEJ/begRE42oH82cZ03gBOQAH8L8CE0kX03fH8mMD7ca2qoR6dMm9xJKEeCz5VoYfUJ4AKKb7fXy1l6jEc7BpPRQuViNOf8IxpPltD04vszaCydisa7tCABx6Bx6wgkdJ6ExqmbkCB8MjCkGeUdC5yLnu9/A4+iMW53+Lw7EmQ/RBS4fxDOvRmNxSfkuf5yNNYdA/wLCl1pTCWOqZcgBUsuvoXaYgrKADqF/PXNNd8UHf4jKn1uRz+um8l0ormdruNE423QvqwixlNfipxeG8Jn1cBIpPH4WjimJzAaDc5mL7g4/KVxFcr2Bhqox6M47nXItAikCXpfC8r8f4nzqsPr9yJtlWn3NiPhfhXwSvjs90hLf314PzP8X4IWejvC3x6kXT8z/D0fjusbyv94C8rqNJ9b0LO4g8zf+i1IU1gs3IKXs9RYBbwQXi9AAu4A4LHw2e9R2vh8PIMEyJFojFqRcsw8omD+Ahq/aoCVoQyg5/WpJu51P9JyL0ELrQfC5zZuT0RabcuGW46031VZ9bqN9IRdVajO49F80D3x3Wxi1t1lwKFImZHGR0JdKpDG/gikPM5V3zNJn29eynH9guECeOljTjT14b050XypYCXqeLwN2pfaxOv6xPt6NMbsR9qgl1PObcoG7jQ04Z+AtOVz0IAKmjzs/P20bDyzMibPK0spT1MZU5N1zW6HinD+tUhb47Q/1cBlWZ89j3YoiolqvJylRvL3v5/8TuT7iBYIPROf347M484FZqG06480cR8bZ1pb3noyx9Lk2LWUxlruATQ9boOS3z2KlCfVaOzOvjfkH7vHIEH6ODRv34LaK199y8g93xQVLoCXPu5E421QaGYBXwh/DWhL8nmkBb4UDdJHoq3FbKrQwLsLmbcc347lfBBtAc9BE+QgtI1ajcxsXkXCyGPpp6cyC01Ef0BaqkPQZLehjcrsZHI32hWZg37r/dG29cw85xSCmXTuct5TwDJ1FrahsesUtGBJjh2rkVnHPGTiYRyGNLs/D6+n0FgAT2N5OL46XPuSAyw7SIAdggTwZ5AGewISyrch048n0RieRhWyJwfZfbeG/sDOcL9hSNM+h/z1zTXfFB0ugJc+twP/S9d2ovE2KCzfQyYbi5F2YjVylrkBmQUtRlup81LOfQAJxYvRhPBsO5bzv1AfWYyE5N8Av0T2r3cRnTBbElHlQZQA6pnwvgZt3bsA3j78GGnJZiBN3ZtoG7y6cEVK5TrUJ45CJktWzsMKWagUrkML5AnIBKEGuBOZ9DlNcznRCXMlGktA/fR/kVCeFLAvQePDXmA90ZekKXYD/4DGy3dIH0tbSh1aHPwczZsVaBxfiuphTpizcpz/76hPf4XmLSLSWISE56Wo/Z4Kn+erb675pujwKCilTzlymsnOZPYXuo4A6m3gOF2DXIEFZtEyH4H25icoStB+ZCL3cWAjElTOKGC5svlt+F+HtKFvIU34UJq2MXY6lr5ogVSGdn1XAD8raInal05fX9eAlz634E40t+Bt4DhdgRoa75KUkW7eVEimI1MOUNnuQuEqi41xxHIuIZpLPFqY4jh5+Hukce+BtMal7nfS6evrGvDS5wlkg9bcz0sRb4Ouw7eAD2d9dhcxfJZT2ixAGuRtWZ8/RHFpwJ9CIdLqwvuByKxjOrJ1LRaeQmHuQBGjzPZ7DnKQdoqbK4EvZn32FIrkVGzMRbkSklxGjLBVcrgAXvpcjTQYc8h0onkc2fd1BbLboAqFwHsC2ak5jlMaDCeGqExSQXGZm81AtqlJX4BytHj8n0IUKAeTkcPb/sRnPYCzKT6HUcfpVLgA3jU4GTn7bEWaoeeQs8/cQhaqgzkITXrmhDkdOWs4juM4juN0KC6Alz6dxdmnPXmCGLfU4ocegTyrPSuh4ziO4zgdijthlj6dxdmnPfkzqvstxGQA95OevctxHMdxHKddcQG89KlANnt1KC7mhcjZZ3IhC9XB/BS1wSdRTOnbC1scx3Ecx3G6Mrlipjqlw5fJTIm7BTifxp7RpU4dynz5MWSKs6iwxXEcp0gYgJJ65KMa+GgzrlUNvHiA5XEcpwvgNuCO4zhOV6YapVs/Ms8xpwFfo+mMes25llFskVkcx+lAXAPuOI7jdGV+CIwFXkChWa9DWuwlKDW4HXNKOObLSNB+AlgY/k5s5r2uQH449wAPAoNQRt7FKIGQJQzK9fk1KL33gyiM4UUolOoSlJa7e6K8y8L5P25m2RzH6UDcBtxxHMfpynwDaaynAhcjP5GjUejS51DOhG+QqQHvjRL77AHGoyy705t5vxOQQL0Z+AXK4ncBikp1ayjHd3J8DlosnI4iOT0Tyvx15Gx+bijvhcDhKPpT0gTRcZwiwTXgjuM4jiNORsL0fuBt4DHguJTjugO/QZrnu5Aw3FweQsK33e+28PoR5J9SledzUASnveHe5UjzTXhfjZKN7QH+C2nId7WgbI7jdBAugHdu5hC1LvfRtpqOW4APteH1OpIrgF8WuhCO43Q6ypo+BJAZyttIUz4dRVlqLjubuF9Dns8BasP/eiSINyTem135DOBPSIP+AI7jFB0ugJcO56AMj47jOE7z2QH0C68fR3bf5cAQlKhrXtYxIG30OiT0XhaObw2PA5eG16cB7yANdq7Pm0PfUL77gC8RTVccxykiXADveKqB5Wh78EXgD8B7gaeAFUhz0Qf4HbI/fB74YDi3F/A/yLHmzvDeWI1sFkHOOwtQpsdPJY6pAX6AQvA9CwxroqynAk8DK4na8DLSnZROQ97/xi+RJhrSHYKGIA3Nc+HvpBxl6BbqltTuvxrK/gFgLmqjh3PU5xYyNfk1iddXh3svRjaXjuN0PTah8fdFZJ+9GI2RjyDb6vXhs33h8y+jkKaXo3F0Apla7ZZwDdKgL0bj5OVNfN4c+qGxeDEyoflyK8vmOE474mEIO55qJEAegwTk59Cg/gkUn/tKJKwuQwlzBiANzDHAp5Gz0MeRE89C4HhgPhJSpyNNySBkY9grXP/daJJpCPe4B3nObwe+n6Oct6CFwCXImWcmMI7opHQ20UnpXcBEMp2UfhnKNRM5CiUdgraiZDj/CTwJjAZmAZNylOU/UPSBm8O9foAWLQPDtRpQkp1JwFeR4D8d+Hyox1+BP4Zr1SAN0ZlIMP80WlTMDG3yeI4yOI7jOI7jtAkeBaUwrELaY5AQPhsJkeZEMxIJyl8Lx/REQuqpwM/DZ4vDXxpXIS94gFHIS38TSkZjWuoFyIs/H39BW6zLiNrlXE5KubZHkw5B9ybu/14yHZf6I83NjpRr3An8CxLA/ya8B7XTncBwZIO5qon6JDkz/D0f3vdF7eQCuOM4juM47YoL4IWhNvG6nkynmgok3F4MvJxyblNbFqch4fYE5P0+BwnwkOmws5+mn3+ynGVZ/7PZR6ZJU8/E5zOA9yDh+fMorFa3UMbdTZQBpEEfh8xWLiBq7X+B0szPRPW+polylRGdpcqAa4GbmnF/x3GclnAW8KOsz1YRFSOO43Rx3Aa8OJkFfIEo7B4T/icdc44kJmdIUoXSze9CZh/Ht3HZcjkpvY402pWhDO8Jx+dyCHoQCeNGPkehBhTj9qfAS0ibT7ju2vA6l43kauDY8PqDxEQVs5ApT9/w/hBgaJ4yOI7jNJdZaExL/rnw7TjO/8c14MXJ94DrkYlJGRIizwNuQGYYi5FN9LyUcx9ANtqLkQb92TYu25+R5noREozNSQngf8N9VxBNO/oBdyONeBnRIegq4Ffh+Aok2H8mz33vRPbmVyQ+uwbF4F2L6jkm5bzfhPvPQ6Y+5iz1ILIZfya8rwE+BmzIUwbHcRzHcZwDxp0wHcdxHMdxHKcDcRMUx3Ecx3Ecx+lA3ATF+Rbw4azP7kKh/jqaK4EvZn32FPC5ApTFcRzHcRynXXATFMdxHMdxHMfpQNwExXEcx3Ecx3E6EBfAHcdxHMdxHKcDcQHccRzHcRzHcToQF8Adx3Ecx3EcpwP5f9CnQwu8SlHRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Specify attributes\n",
    "attributes = [\"median_house_value\", \"median_income\", \"total_rooms\", \"housing_median_age\"]\n",
    "\n",
    "# Create plots\n",
    "scatter_matrix(housing[attributes], figsize=(12,8), color=\"b\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that there is a noticable possitive corelation between the median income of a district and the median housing price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot median_income against meadian_house_value\n",
    "housing.plot(\n",
    "    kind=\"scatter\", \n",
    "    x=\"median_income\", \n",
    "    y=\"median_house_value\", \n",
    "    c=\"r\", \n",
    "    alpha=0.4, \n",
    "    figsize=(9,6)\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. We can see a stroong possitive correlation with fairly low dispersion.\n",
    "2. There is clearly a cap of house prices at \\$500,000 - apears artificial.\n",
    "3. Moore of these artificial line apear at: \\$450,000, 350,000, and 280,000."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>\n",
    "    <span style=\"color:yellow\">\n",
    "    Feature Engineering\n",
    "    </span>\n",
    "</h1>\n",
    "\n",
    "Combining multiple features in a relevant manner to improve our model perfomance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The average number of rooms per house for a dsistrict\n",
    "housing[\"rooms_per_households\"] = housing[\"total_rooms\"] / housing[\"households\"]\n",
    "\n",
    "# The average number of rooms per house for a dsistrict\n",
    "housing[\"bedrooms_per_room\"] = housing[\"total_bedrooms\"] / housing[\"total_rooms\"]\n",
    "\n",
    "# The average number of rooms per house for a dsistrict\n",
    "housing[\"population_per_households\"] = housing[\"population\"] / housing[\"households\"]\n",
    "\n",
    "# Group attributes under variable name extra_atributes \n",
    "extra_atributes = [\"rooms_per_households\", \"bedrooms_per_room\", \"population_per_households\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can illustrate the correlattion between house price andour new features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value           1.000000\n",
       "median_income                0.688883\n",
       "rooms_per_households         0.157620\n",
       "total_rooms                  0.137469\n",
       "housing_median_age           0.107144\n",
       "households                   0.069177\n",
       "total_bedrooms               0.053544\n",
       "population                  -0.023797\n",
       "population_per_households   -0.026888\n",
       "longitude                   -0.043236\n",
       "latitude                    -0.145570\n",
       "bedrooms_per_room           -0.255870\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = housing.corr()\n",
    "\n",
    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6FmWC4Mgi6DlUc/Q4+s08Hp6P+5phZmZmwaADoHehwOM0VNz8ZeAXgetJZmVdBXw+PL4+vG4aVaecCdyKhsn2AZegfMVbmvaJx3pjeI8GcAPwWlRxMxMe35D7J8wqvXDnOuApNFCXx29iCg2nNVBGZzWtFweN5xCDphrlWTPMzMyM4ub9/CHwGTSD63HgTWH7vWH7fehy/HaSypS3AR9Dl/UvhhvAR4BPoILpXSiAIjz+AzQpHOA9YVvxmhfurAI/A5xB0hm6V6tR+HgADeytRrPCZpte58VDzcysxCqNRr8d+MbH1q1bGxdeeOHw37iG5qy9hP4XRV1EYeR30OyuKl4Kw8zMSqvRaNyGSl2WcCfoUXAcqtmJ9UD9xKSVcLwXosyOl8IwMzM7ggOgotVQQ8R1aOhqgf6GwBbDcU4kGTxstxSGmZlZSTkAKtoa1BVpH5q63ksjxLR0V+hV4b7VUhhmZmYl5gCoaLFj0TpUiNxvPyBQAfSTKOvTahaYmZlZybkqpEjx29+H6nam6C8AiouqPomWhd2HZ3mZmZm14ACoKDMkhclxHbA8gp+/QY0F0j1/zMzMbAkHQEWI64HFrtCr6b8bdAMFO7tx0GNmZrYMB0BFiOuBNVDtzxT9TX2HpIHi2vC4VfNDMzMzAxwAFWMOWA+8GM3Qmqb/ITDCsa5AGaBF4Ga01peZmZkt4VlgRagCm1CQUiOf4AcUAK1GzQ9ngVeSLIhqZmZmP+QAqAjrUPCzJzzPYzWSdO+fGsoyTYT3MjMzsyUcABVhPwpYjkZF0HkFQOks0hQKsvbncGwzM7Mx4wCoCLPAt9CQVT3c8iiCjsdYi4a+bsaF0GZmZi24CLoo30PdmVehdbs253TcPcCXgF04+DEzM2vDGaCi1IG70CrwG1i6hlevKmhtsUUc/JiZmXXgAKhIB4FnUQfnw+QzE2wCBz9mZmbLcABUpDmU9VmFipb7VUGZpX5XlLfWaijD5oFjM7MVz/+UF2kGOAGFoXn8JuJyGuegAmgviZGfuHZbFc3cuxstO2JmZiuSM0BFqQHnoiBlIdz6tYBmfx1NPhklk/TabXvDfVzI1szMViQHQEWJ63/lmaWZRMNgR5FPQGUS126bD8/nw3MHmWZmK5YDoKLMoeDnKXRBzaMAGnRh3hvuI9eu9GcOBZSxtmoyPJ8r7IzMzKxPDoCKUkd1JAvAAfLrCF0HjiHJAM0AlwIXh/uZHN6jbOLvagpl16bCc9dYmZmtWM4JDFsN9f3ZhDI/O4FTyScDFJfD2IVqgaaA89AU+3mUuTgH+AbFX7xr6PxiJmzU7Ubf20o6ZzMza8sB0DDNAK8DTke5twpHruHVjwrK/FSA89HFejPwfRQAzaPV4qco9gK+UmdUxWVLzMxsxfMQ2LDUgJcDp6EAJQY9eQU/0SRwHMpS7EZBz8noNz0KtSueUWVmZiPAAdCwxPqRQTuECp4rqC/QEyjwmWE0alc8o8rMzEaA/989LDHjMUjp9cTiLLB54DHgdrT0RtFDOOkZVbEuqeislJmZlY4zQMNSB24DHmVpoJLHzK+0aniPCZIZS3ei4KtT8DOsqfKeUWVmZiPAGaBh2g18hmQWWA04C60Fdhr9h6OLKKB4EHiI7mcsDbso2TOqzMysYM4ADVsdeAbYjgKh9cCJJOt49aNKUmRcpbshr6KKkuuMxpCcmZmVkgOgIsSg4zDwZNhWbf/yrsUp9acDP0p3TQ9dlGxmZiXkAKgI6aDjOZLePXmIHaAX6S6T42UezMyshLIGQJcBvxQeH4tyDZbVAvrmp1HhcV7Zlko47mJ4j24yOS5KNjOzEspS6XEtcAHwAuDPUK7gz4FXDOC8xlcsOJ4CtqDOzHkMf0UVYAfqAP0k3WVyXJRsZmYlkyUD9DPA69HSnaDL6/rcz2icpQuOn0H9eRbIpwA6itPqJ9FssBjMLDfN3UXJZmZWIlkyQHMs7WCzNv/TGXPNBccHSNaXymvW1QRaDHUvCrJg5a69ZWZmNiBZMkCfAf4HcAzwK8A/Av9zECc1tpoLjteh30CepeiLqDrrHpLAymtvmZmZLZHlMvjHwI+jy+gLgN8DbhzESY2tWHB8DhqO2oIyMRtJipb7dQCt/3UwPG81zX0UVoQ3MzMrUNY8wI046OnPPuAOlP2ZBk5Ag4p5BEBx8dPDJMXPXnvLzMzsCFkCoH0k9T9T6FJ6gOGscT4eYi3OWtT9eQNaBgPy6QN0GGWU0tPY01mn1SQ1QM7+mJlZiWUJgJpnfL0BuCjHcxlvsRanjmp0YvCTVwPEBrAN+Dow2/QzT3M3MzNbop/y278BfiyvExl7sRanAhxNkkvLcwr8FrSoaiue5m5mZvZDWTJAP5t6PIGaIjbavNaaxVqcGkkzgby/vUngR4BHOTILZGZmZj+UJQD66dTjOrrMXpnr2YyzWItzHnAIZYMOodlgeYnFzutwAGRmZtZBlgDol5Z/iXW0G7gXFUEfh4KgOdRZqd9aoAqaVbaeZI2xXut9arheyMzMxlo3AdCf0nmw5jdyOpfxVwNeBDyLujSvA85ANUF5FUNPAa8DHiHJOmXp+jyortEOqszMbIR0EwBtHfhZlEW6KeEa4Hg0NT0vDfQb3YjWGYtdn79Bd0FHumt07BmUZf92vBSHmZmNmG4CoOsGfhZlEQuhp4GTUNZnFQoupnI4/gSaVbaAGiw+gIKObrs+D6Jr9KCCKjMzsz5kqQE6FngncDZJ+z7wVPjuxSGp89E3uEj+QUAFBUK18B5Zuj4Pomu0l+IwM7MRlKUP0F8A9wOnA7+PZoF9ewDnNN52A19DS1ZsQ4FKHmuAQTK1voKGnSbJ1vU5BmhTqL/3VMb9W2leANZLcZiZ2QjIEgBtBD6C/g//VeCXgUsGcVJjbxb4HnBKeJ7H8Bco8FkA9ofbd9ACJmvoPtcXu0bfEu77rdUZRFBlZmbWpyxDYHEQYzvwk8CTqPewZVVDwc/3SYKCvLJAc8BzJP2Aeik+rpNvgOKlOMzMbMRkyQD9ezRh+7eA3wY+DPzrZfZZBdwK3Ik64Px+2L4BrSr/ULifSe3zLuBhVMJ7eWr7y9El/GHg/SQTx6eBT4ftt7B0MYirwns8FB6PhnRdzCHymwIfj30smmZ/Bgo49pLMCMsS8ubJS3GYmdkIyRIA3QLsAe4BXo0CkuuX2WcWFUmfB7wUuAINm10D3AScGe6vCa8/G3gz8OLw2g+Q5EY+CFwd9jkz/BzgrSjH8HzgfcB7w/YNwLXAxWjR1mtZGmgVJy6JcQwanlrI6biNcKwKCgcrLC0+jjPCzMzMSi5LAPQN4B9QwNFtINFA1SigQZnJsO1Kkun116GV5QnbP4UCp0fQZfwiYDMaLPpm2P/jTfvEY30WeA269F+Osku7UIB0I0nQVJwZdGbnoNlgR5FfVqYSbvuBnbj42MzMrI0sAdCZwO+i7MxtwN8Bv9jFflXgDjQocyPKJB2PaokI98eFxyeh+VHRtrDtpPC4eXvzPnWUpdrY4VjFqQEvA04Nz2dJgpa8VFGIeBAXH5uZmbWRJQAC1fP8Pygrs4vumiQuoOGvLWG/l3R4batQoNFhe6/7pF2Nul1v3bRpU4dTy8EUqoqKZ5Z3PU4cAtuFAqG8Z3SBzjnLrDIzM7MRlCUAOgoVEn8RXU63o4CmW88BX0HDUE+jYS3C/TPh8Tbg5NQ+W9Bss20snXEWtzfvU0OF2rs6HKvZh4ALgAt27tyZ4eP0YA44zNIwrNMqa72oogArDnWli4/7DV5mgEtRVdWljEpFlZmZWWZZAqA7USbnPcBZqCv0bcvscywq9QX1//3nwHdR8XSclXUV8Pnw+HpUBD2NGi6eibJO21FHm0vQ5f0tTfvEY70R+DIKK24AXosu0zPh8Q0ZPm/+6qg3z2PoDOP09MWcjt9AAdYxHDmtvt/gJb2kxSjMKjMzM+tDlsvX8+icr/hT4Nebtm1Gw2RVFGx9BtUOfTM8fivwOPCm8Pp7w/b7ULjwdpI5Um8DPoYCqS+GG6g54ydQwfQuFEARHv8BSbfq94RtxdoNfAH16Hk+cBkK96ZzOHYMTBrh+LNhex7rcXlJCzMzGyNZAqDlBmte0WLbXajst9mzaLZWK/8h3JptpXX90GGSAKrZR8NttNTRTK3jSVZwz8NakoBkf2p7HsHLINYJMzMzK0jWImjLy2pgE/muBTaBBgh3sLS3UB7rcXlJCzMzGyOu4ChKBQU+q3M8ZgNVas2zNLsTg5dzwvvFZTFARdHdLk/hJS3MzGxM5BkA5dnNZvwdBA7kfMwGCn5aZXeag5f1qBh6WOuE1XDgZGZmI6OXIbC1bbb/135OpHTqwNfJbwYY6Le5jvZDU3FKPAx3Rpenz5uZ2YjJEgBdimZn3R+en4fW6oo+ltM5lcc8+WZDGug3um+Z17Uqih7UOmGePm9mZiMoSwD0PrSK1bPh+Z3AK3M/o7KIy2LkuRDqPOq8tG6Z1+ZRFN2tYQZbZmZmXco6BPZE0/O8Lt/lE4uU886ErAMupPMw0zBndA0z2DIzM+tSlsvvE2gYrIEumb9BMhxm3YrFwFU0DT6vRgQVFFzsQQ0QzwO+RvugZlgzutrNQHMhtJmZFShLAPSrqNA5rsz+D6hTs3VrBgUCG9CK8OvJtxPTBAqqYs/uB2m9+lnU64yurDx93szMRkyWAGgn8AuDOpGxF4uBF1EIuUj+bSgXUSZoHVp69iy0zOwoBBzDCrbMzMy6kOUS/J9RxcgkcBMKiH5xECc1luKwV+zWnHfXpDgDrIKCrSfDYxcbm5mZHSFLAPRaNJH5p9AQ2FnAvxnESY2lWAwcszSDCkzigqfx/VxsbGZmdoQsAVCcx/MTwCcZhZXVV5JYDFxDRcpV8m2CWCFZCX4WrS7vYmMzM7OWstQA/S3wXeAQ8H+jjjOHB3FSY2s3cDsKTmZRUfRqktCyHw30m4mLoX4tvIeZmZkdIUsG6BrgR4AL0CDLAeDKQZzUWDuIws4ZtKhIHsEPKPBpoKzSLO7QZGZm1kGWDNBb2mz/eB4nUioNFPg0cj5mFeXo5li6GryZmZktkSUAujD1eBXwGjSg4wAoiylgDQpWqjkedxENsW1EM8Bc/GxmZtZWlgDo15ueHw18IsdzKY+Twv08ygTlNSW+in6jD9N/9id2rHbjQjMzG0P9rER1EDgzrxMpjdWoWHk63PIKfnajBogHUIOCA2FbL2LH6irJ0hW9HsvMzGwEZZ0FFqtWqsCLgM/kfkbjbj/JdPXYDTqPIGh9ON5+4ATgItSuMmv2Jnasjv2EJsPzb/RwrGFyxsrMzDLIEgD9cepxHXgMNUS0LGaBb6J2knnWAFVR8LMHBVUnoGzTvozHiR2r58Pz+XCcLEXVww5GnLEyM7OMskyD/yqaY7QeXXJcZturR1EbyfllXtetBsoi7WPpzLJeMkuxg3Scnj9Jto7SM8ClwMXhfqaHc8ginbHaG+7Pob/BXTMzG3tZAqD/A7gVeFN4fAvwxkGc1NjbiAKLvDJAcQbYIkm25ilUpZVV7Fg9hVZ+m6L7jtJFBCOtMlZVvAaamZl1lOXS9O/QVPhnwvNjgX8EPpv3SY21GipSniLf2V9rUJCyHWWC7qT34afdqOYn6zBWHsNnWaUzVrFmyWugmZnZMrJkgCZIgh+AZzPub6BgYBJlV/JcEb6Kgo1YY9RvDUwdZZCyBC79Dp/1op+MlZmZlVaWDNCXgBvQQqgAPwd8IfczGndzaAmMuHRFXkHQNAqC1pNvcXUWMRg5BwVjsSB50MFIrxkrMzMrrSwB0L8B/gXwCnTZ/hDwuUGc1FirA3cALyHfDFAD5eNmKHYdsN2oOmwdmpXW7YKs/c4cq/e4n5mZlVLW8tS/CjfrxzOoVudE8guCKijwqZKsNt9sGNPTe5mS7mnsZmY2ZFlqeH4WeAh1mtmLSm33DuKkxt4CmrEV7/PQQL+RVSz9rdZQgfQmsk9Pj/t2Gyb3MgvM09jNzKwAWS4z/xn4aeD+AZ1LeVRRgfEE+ZeR7ycJqmJmZQo4GWWdng3vv1x3516yMr3MAiti5piZmZVelsvv0zj4yc9RJFmgPNRRIPEkCq5iZqUKbEEZoHPQAiaxB1KePWgAACAASURBVFG7Xjm9ZmV6mQVWxMwxMzMrvW4yQD8b7rcCnwb+hqUVJn+d90mNtRngcuA48l8KYxr9Rtej31ANOJ5kRlYFFSefAvyA9kFGr1mZXmaBFTVzzMzMSq2bAOinU48PAq9NPW/gAKh7NeB8NBwVL/B5BUEVFPTsQcHELWFbzPIcRPU8E2Hbg7QPMvppLtjLlHRPYzczsyHrJgD6pS6P9S7gP/VxLuNviqQH0Dz5FvrG2WRTaGitCtyFhr9iHc9T4fEPWNrSslm/WZlepqR7GruZmQ1RniW4b8rxWONpDjiA8mYN8psBRuqYZ6MM0xpgB/D3wCPAIZTJeQb4Dkmw0W6mV8zK3BLu+5mWnnU22ShYiedsZmZdG0QOwtqpA7cDG4AzyPfbb5AMd00ALwduAnai/t1rwmsOkQQ/y830yiMrsxJ7/KzEczYzs0zyzAA1cjzW+NoN3AzsIv8hnxhQzQInoKCH8D6xc1M68zPo/jsrscfPSjxnMzPLLM8AyBmgbtTQMhiz5PuNVUi6QR9DstZYO61menWaGt+t9NDRoN5jkFbiOZuZWWZ5/r/2f+V4rPG1DgUI0+SbM1skWQ+shup/DnV4fT8zvdppHjq6fwDvMWiD+F7MzGzkZAmAjgV+BTitab9fDvf/MadzGl+noSYCx5L/iu1VFADV0VT4Wxhu/5300FEMHF4E3IcKs1dKjx/3JTIzK4UsAdDngf8N/CPFrje+Mk0DrwKOZnCDhTW0FthdqPi50+umUE1QXv132jVPPJjjewxL2foSDWORXDOzEZMlAFoDvHNQJzL21pF824MKgBrAYeA8lHlptSL8oGY4dRo6Wok9flbiOffCM97MrKSyFEH/HfATgzqRsbef5II6yPlycyRLXjQb5AynOHQ0hdY5m8JDR6POM97MrMSyBEDvQEHQIZJJ1XsHcVJjaRb4CqrPGeQA4mw4/v4WPxv0DKc8myfa4HnGm5mVWJYAaH14/Wr0f/z14d669Sjw58ADaKhqENajZWvhyE7Gw1h5vY7qfpz5GX3D+PNgZjaisia7Z4AzUaltdHN+p1MCVVQInXcWaB5lf/ahLtMXoKnwB0jqOgY5w8mFtCuPZ7yZWYllCYD+LzQMtgW4A7gE+CbwYwM4r/F1DAojV+d83DgNfhbV/8yiQCuuDv8NdGEbxAwnF9KuXGWb8WZmFmStAboQeAx4NfAylGOwbtVQ/myR/GeCNcIxt4f7WAzd4Mi6jjyHqVxIu/J52NLMSihLAHSYpHJlGvgu8ILcz2icTaHAYB/JTLC8ZoTF7M48CrCmSIKiQdZ1uJDWzMxWoCz/T9+GBnD+BrgRXW6fHMRJja25cGuQbxZoEWVfnkEh7R7UbXoH+g0Psq7DS0eUj+u9zGwMZAmAfibcvxv4J1Rh8qW8T2is1YF7UZHyAgpW8liOdg7l5GLtTQXVAFUZ/EXKhbTl4novMxsT3QRAR6H8wobUtrvD/TpgV94nNdYOAI8Az6G1svJQRb/J1wI/YOnMr2FwIW05tFrvLV1gb2a2gnSTf/jLcH8b6jBzW+q2td1OwckoW3Q/yn28I2zfgIbRHgr3M6l93gU8jLrlXJ7a/nJ0WX8YeD/JANI08Omw/Ra05Gh0VXiPh8Lj4sWhoZPJr/6nir6FGTT0VWf4hcgupB1/rvcyszHSTQD0U+H+dOB54f701PNO6sBvoVzHJcDb0drg1wA3oTlRN4XnhJ+9GXgxcAXwAZJ10z8IXB32OTP8HOCtKAfxfOB9wHvD9g3AtcDFwEXhcTrQKkYdeBx983nVAMVjVVGVVny8mqQZYo0jGyOOk3H/fKPAjRPNbIx0c7k4f5mf397hZ9vDDTT36X7gJOBKtDY6wHVokYh3hu2fQhUsj6CszkWoh/JRqO8QwMeBNwBfDPu8O2z/LPDfUAhwOcouxSG6G1HQ9MllPs9gzQBnoYtHnlPhYxBUQf8jn0I5s/gckgvYSq7baFWA67qU4XC9l5mNkW4CoP833K9C/YXvRJfVc9GQ02VdvtdpqHfQLcDxJIHRduC48Pgk4FupfbaFbfPhcfP2uM8T4XEdzYHa2LS9eZ9i1NBK7RtQQ4HpnI9fDbdTUQBwCIWSm8PPHwo/X6l1G60CnX24LmWYXO9lZmOimyGwV4fbYygbdAHKLbwMZWi6sQ74K+A36byAaqucSKPD9l73Sbsa1TJt3bRpU4dTy0HMzOTdBBH0yeZRvmsXCjdPQ4OFtfDzGvnWbQxz2Kldw8U1uC5l2FzvZWZjIMsk7BeSzP4CuAd4aRf7TaLg5y+Avw7bnibJS2xGHWxAWZqTU/tuQb2GtoXHzdub96mh6fm7Ohyr2YdQUHfBzp07u/g4fUj3Acrz4hGPtx9llk5F2aW4XO0G9M3UyVa30SnAmQEuRRVWlzL46qp2BbgNXJdiZmaZZQmA7gc+jGp3fhT4n2FbJxXgI+F1/yW1/XqSWVlXAZ9PbX8zunyfjvIXt6Jhsn2okLoCvKVpn3isNwJfRpfFG9DE8Jlwe23YVpw6GkDci2oo8poFVkFBymoUAKxGw18NFMBMowHMTSiQ6KZuo1OAU8TyF+0KcA+hzzOFgr1uP5+ZmZValkvWLwFvI5nKfjOamdXJK4B/iS5Jd4Rt/xb4Q+AzaAbX48Cbws/uDdvvQ5ewt5Osm/424GPo8v7FcAMFWJ9Aw3G7UABFePwHwLfD8/cwCj2L9qFMzU5U+bQqp+MuoozIZpb+VveH599DgdItqC6oUzff5fq9tMrGrA7bBxV4dCrAdV2KmZllVGk0MqUhVgOnoB49Y2fr1q2NCy+8cLBvsgaVjW9GJdl5ZU0WSYbYDqDAalV4vA+t3LYOBUDTdJ41tQZlftLVWkeFfQ+Gc76UpQHSFK0Lj/NeNsHLMJiZWQaNRuM2VOqyRJYhsNejLE5c/uKlaPjJuhHraUDf+mryL4auhWOuTr3PYdREIAY7Cyw/fLVcv5eYjVlu2KmXOqHlCqvr4TymOrzGzMxsGVkuIdeinjxfCc/vYGnXZWsnTt9ei8qyN5D/LKUJFBzUUJamihZDjYFMBQUpcap8p+Grbvq9LDfs1MuyCd3083HPHzMzy0GWACj22LEsYiBQRxfvoxnMFO04GwoUHEygzA+o8HoPSYDUzert7QKc5iGodsFM1jqhbgImr0VlZmY5yRIA3QP8n+iydibwG+jSY52kA4G8uz+nxc5Hs2hoawENf9VIgh/I1s23OcDJkn1JD6N1CrSibgKmIoqvzcxsLGWpAfp1tEbXLFogdQ/JjDBrJwYCFZIL9yAsoGBnDg2xHQrPWwU3MbtzS7jvZggp69T3GGitQn25V7U5l6ibdaZGZS0qrztmZrbiZQmAzg63GrqcXUkyxdzaiYFADXgOXbDz6v8TNdAssB3AXWh1tn0owGkX3GTt5jvolcC7Kazutvh6kAHKsBtAmpnZQGS5RPwF8NtoKGxxMKczpmLG5WgUrLwITUnPyyLqpT2LgpJZVHB9DPAs7et3ssg6pBUzRodRMBbrdW4J59jqHLrp57PcawZZJO0aJDOzsZElANoB/O2gTmTsxRLyWZR5yTMAAl2M14b7o1FubzPqoX1reE0/gUHWlcBbZYxmUA+kxQ7n0KmwernXDDpAyaMGyX2MzMxGQtZp8B8GbkKX8eivW7/cjrAe1efkPWwSC6sPhPeYQpmXw2j5i5ehoOMw/QUGWTouN2eMpoFjUb/u2T7OoZNBF0lnzYI18xR+M7ORkXUpjBeif/bjEFgDB0DdqaFAZDO6YE52fnkmE6jQeAfJGmCrULA1gTJOsSM09BcYdMrQNGc30hmjiXB+MXQexAyufgOU5WTNgqV5+MzMbKRkCYDOQ/9kWy+mUFCSZ+FwsxmU5ZlGIWrs+3M0Gn4bVGAQ37tVdiNmjBZQ4fAgz6GfAKVbva47NspT+D0sZ2YllCUA+haaBXbfgM5lvM2RDP0skvTtyUs1dYtZmikU+OxEa4E9n2QV+gdzfO/lshvxopolOOn1ojyMhVG7qVNqNujsVK88LGdmJZUlALoMuAp4BF3KK+hSeu4Azmv81IH7URByDPkHQPE9DqLhpntRNqiBfsvPhNtxwFnAC8K59HPBi0FKN8trQPfBSb8X5V4ClEEbRnYqKw/LmVmJZQmArhjYWZTFM8ADJKuxr+n88swa4dhV4AzgCVT7Ey+0NRT09FMMHYOeNSgfWA3vO0V32Y3lgpNxvigPIzuVxSgPy5mZDViWAOixgZ1FWdTRulwXo0AlTxMo6IjDa4fC+90StsfGgP1c8GJmpgZsQQHWXhSkTKMap36zG+N+UR6l7NSoDsuZmQ2Bm/kPQ7qeZR/wJHA++Q+DrUIXsP0oIJpEQ16nk2RqplG/oEMkQ0ydmhlOpX6ezswsAseH95oP229HF/d+shvpi/ICSc2SL8r5G8VhOTOzIXEANGjpepYplDk5kcEsilpBAckCCnyqqPfO4yhTc1R477hq/FOoSWIcHouztaosHeJaQP17YmZmIrVPjWQl+ixLa7QTL8oXASekznM9Ls4dhFEbljMzGxIHQIOUrmdZQAHFoIKfxXDc9SQZmW0oEDoeBSfHh+2PoCCmijJSMUhbF16/A/UVSg9xnYUCnThc8jRwMsocxKAlr4vnPlRm/31Ur1RlfOqARtEoDcuZmQ2JA6BBStezTKHhp0EEP4Tj1knW3jqApsBvQL/l6XAfh6gWUUZoNQouJtFQGSiI2s/SIa7VqIA7TqVfAL4Y3ifvzMFU+DwHw/NFkjqg+HNnK8zMrA8OgAYpXc9SR1mNQUx/jybC8efRsFGdJFMzGX7+NAooYo1NBf0pOCHsO4uCpXUkw1xxiCtOpR90ANKuOHcNcAHuWWNmZn2bKPoExlocGppCAcVTaHipMcD3ewL4EgoQjiLJ1Hwz3MftU+HcDqIgKM4iq5EUUldJMi9xiCv2GhpE8FMjaQ0Qv7d4rvejIcQ5NCw3RzIjzczMLCNfPgatuch0Cq2qtjHH91hEw10NkjW/WhW2Hgzn07z9TuAklP1ZG46xCPwDSbABCk6yZH6ydHNu1fyw+Xsb5+nxZmY2VA6AhiFdZLoRXbjzNIGGreZJprW3K2xNb48BynMoO3QuSX/vu1C2CnrrzJxln07NDw+mXueeNWZmlhMHQMNUQ/1/qgM49jQKbO5iaYCzmqSgOB0QpZsaVsJ+X+XIjE0vnZmz7tNNdsc9a8zMLEcOgIYp1rRM5nzcBgoEtpFkTGZIeulUge1oOdvdJAFKFU17r6EhsC+ghVObzznr0FPWfbrtSFx0zxqvmm5mNjZcBD1sJzCYYZsqarJ4GWp+eF54r9WoQ/QZaAmOeBGvoWnudZKan/M4MiReIBlig/bBSSxgrrE0oOm0T5QuFk8XaLcbwhtUEXYnM8Cl6Du8NDw3M7MVyxmgYVqNgg3ItydQnMVVRxmdl6KAaD1Lg5ETUZASZ37VUOFzNewbFzWNwUUcJptCGaIdLF1clabXpet97kYB1VHhHO6kc9DSTXanqAzMOC/QamZWUg6Ahik2FayTTDnPy2I47vrUcato+j0o4JkjGS67CwU1a0n6BaWzNOmLfmyquBotrjqbet/m4GAa1Tnd08Nn6NSRuJdC7Lx4BpqZ2djxENgwzaI6nEny7wUUf5OxI/Rd4XGVJPhZJAlwdqCan+2ouWFzUXHzRX827N9cwJ1+3RrglHB7XXjfZ1F36n569qSDrCw9gNLDcv3IOqSX9/ubmVnu/E/zsD2L+vSsyvm4FZR92QfcgbI2D6HMSQyKnmNpALOTI2d+pRdF7aYwOQYH0yijFFdur3DkUhq9Zkx6ycDkmTHqZQZakRkrMzNblgOgYamh4akLUCblMMnaVnlYAB5DHZ9nw7EPoItuXH4j/rY3osBklqXDTs0X7cdRNqfVRT9dj3M3GvZaFT7XNmAzR64WP0dvdTzdzhKLBlGzk2UGmmuGzMxGngOgYYhT0k9Bi5NWyf+br6KC40tIsj67UEASA4jdwBvRcNkicDPwaNi/1UX7FFTzU2XpRb9VduNraAbaIRRYtVotfn2L/brJimTNwAyqZqfbVdNdM2RmNvIcAA1aDc2GOpZkNfbpjnv07hSU9YlFyicD3wduC+/7JnQxPowCnFeiGqCYMUpftBdQRmca1d2kP0+77Mbt4fE0R64WD5o+3rxfqwCrlSwZmKwZo7wV/f5mZrYsB0CDNhVuccr7HJp5lbd4/DjNvR7e9wQUyByDMjezKBPzHMoErQvb0hftSZIV5M9HU9hjpqZTdqNTkLKmxX4zKGu0SHcZoW4zMMPoGt1pKM9dq83MRp4DoEGbI5l+Hqegx5qcvFVIegJNkPQCuhQ4FWVmJtAw1UaUndkf9o0X7fPCa+dR9miepfUry2U32gUpzftNo6zYwygAy7NOJvY36ja7lFU3Bc5Fd602M7OOHAANWh1lUKbDbV3YlvdyGFEF1QIdCo/3oSAodlCO5zCHpuQvkKzyvhsNl8VszmI4Zrp+pdfsRvN+E2gqfhyuy6tOplVwcrDjHtlkKXDuNmNlZmZD5wBoGHYDN6EL/AwqRE7PyspLAwUUcUgr1hodHX62iGZorQKeREXSl5FkKe5EAdNhFEAs0rp+ZTfKrqwjmU3WjXRWZAEtK5FnncwwZl+5wNnMbCw4ABqWOkk2ZpLBtKBcQNmOvSirs4iCnbgifCO87wEU7JyPhsKiaRSotcrwVFEgtT8cu9ceN+msSN51MsMITlzgbGY2FhwADUssmo29d6bIrxYo1hfNk3SYniZZviK+/1MoKHo6nMcJKBhaQIHDCSi4aa5f2QL8NAqeGuHnz9B/lqVVnUw/630NIzhZyQXOXs3ezOyHHAANQ7oupYGGntaQ72KocbgqNleMmY8YGMVC7AXUpPD4NseKAVTM1Eyj6fJxWG01cDoKoqD/LEunRoxZuycPKzhZiQXO7kxtZraEA6BBa65LiXVA8+TbCfo5tPTFAvBilNmpoazNmrA9vdr7JWGftSSZqKdQ8XTaunCMmEU5HO6PQYFcvKD2m2XJq35nWMHJSipwdmdqM7MjOAAatOa6lHUoCFoMt7xqgSaBTWitsQk0RHVUeBwDrRjsxIVN6yjoqZEUQTdfEPeH18aAImauTkRDZk8Bt7bYL6s863dWUnAyDC7cNjM7ggOgQUvXpSygoCHOyMrz21+DMktrUtt2oKLr9ag4epIkmGmgbM9Wkv4+rS6Gs8DXgX8Wjr0eeDAcO05n39fmnLLUnLi4eHD83ZqZHcEB0KCl61LiCvB7SdbrytM6km7Pp6CL3ARJg8QqSaPEHSS9gZZb1fwk4AkU8Owgqf85gLJMrTIJWWtOVnJx8ajzd2tmdgQHQMMQ61JWo0VRzyXfbtCxw3Qsso4F0RMkdT7rUq+fC69d7iKYrh05gDJGz0e1P7F7c6tMQq81JyuluHglzqZaKd+tmdmQOAAahvQF8xmSIag8xYBnkSS4irPDFsPjOBOsTrI4ayfNtSOzJENfccHTVkFUPzUno16/s5JnU436d2tmNkQOgAYtXjDXounnq8i/EWKl6RZrc0CZn9gnCDT8Vg2PzwW+Svv+O61qRw6w/Bpb41pz4tlUZmZjwwHQIMULZh1NG19F0qE5707QMciJNT6L6CJdCe8b1/WKwU/MBk2hwubzWLokxm7a144st/TFuNaceDaVmdnYcAA0SOkLZvqbrpP/N58e7qqRNECM0933oWzUejR1/SkUmCyguqRWS2LU6b12ZKXWnHSq7xnXzJaZWQk5ABqkeMGskFxMYzfmvMVlKirh+Iso+KqhWVuPA6eiIChe5G9FwU67JTH2hmP3WjvSzX6jVFC8XH3PuGa2zMxKyAHQIKUvmM+RrMsVV2fPexp8nAEWsz/p6e8noFlcs2gl+ArKCq1ueaT8i7RbGaWC4m7re1ZqZsvMzJZwADRo6QsmqKHgZlScHFeGz0Mc7jqIan72o8DiQLhfjQKgp8Nr1oVzOoSGwzbSeUmMvOVZUJxHFilLfY9nU5mZrXgOgIYhXjDXkCxFEQuj83yP2K9nDbpwPwdsQ0HNGpR5OiHcniUJGG7lyCLoQV/g8yooziuL5PoeM7NSyXsuUrOPos4396S2bQBuREt33oguYdG7gIeBB4DLU9tfji5tDwPvJxk8mgY+HbbfApyW2ueq8B4PhcfFmwu3ePZ5DoFNowv3o8D16BvcgQKuu8PP0tPh03YDX0vdhjEMlQ44oLeAI51F2hvuz6G3sD4OV06RdLd2fY+Z2dgadAD0MeCKpm3XoDlGZ4b7a8L2s4E3o7XMrwA+QDJp+4PA1WGfM1PHfCu6XD8feB/w3rB9A3AtcDGa43QtSwOtYtRRdmU/cFzOx15EWZ4ngJ0oGIhT36dR4PM9FCA9FH6eXo2+m2Ux8tQu4AB9jm6CmFZZpCpLP1cWcbjylnC/UhocmplZZoMOgG5GCyekXQlcFx5fB7whtf1TaIDoEZTVuQhVzBwFfBNdxj/etE881meB16C8yuUou7QLXcZu5MhArBj7ULbiQZIApV8NklXffxp4PQoE9oX71wBbUOhYIxkuKnp4pzngALgUha2XsnzImkcWqdmwA0EzMyvEoAOgVo4HtofH20lyIXHJzWhb2HZSeNy8vXmfOrAHlfO2O1YrV6M10bdu2rQp40fpwRTqCn0q+QUgsflhLdw2oTza81Be7RgUbB0DnIWKpIcxvFNj+WxODDgg+3CWh63MzKxHo1QE3aoipt1k8VjJ0ss+zT4UbuzcuXPwk78XUMgXV2nP+9gxGIrDXlMkU94PoJlnd6AC6UHKWpzca1G0p6WbmVkPisgAPY2GtQj3z4TH24CTU6/bgjrWbAuPm7c371ND85x2dThW8Taib30tClLyFLtBxyBiOrzXLEmjxNj9eTndZG867Zs1m9PPcJaHrczMLKMiAqDrSWZlXQV8PrX9zeiyfTqqWLkVDZPtAy5Bl/i3NO0Tj/VG4MvoMn8D8FqUh5gJj28Y1AfqWg0NQc2SfzfoGNxMkmRSFlGmJ2aCpuiux8+xwI+ib7xTLU67IKmX4uRhDGf1E9SZmdlYGfSl4JPAq1BVyjY0G+sPgc+gGVyPA28Kr703bL8PXfbeThImvA3NKFsNfDHcAD4CfAIVTO9CARTh8R8A3w7P38ORxdjDF4ejdtC+IqlXcR2wSRRgTZEELttRddRh1JAgBiKtgotNwOtI1hB7mqUNCmPTwTWovqjVEFevPXUGOZw1Sl2nzcyscJVGYxhrHqwMW7dubVx44YWDe4MayqhMoJlOk+SXg1skqQFqoKGnKip4nkeZoNtQIXS7IKCGMj+b0ZBSPL+ngW+h3Nx54ZibgcdQYDWJgpZ0F+d+A4481wiL33u663Tz+VoxRmktODMbS41G4zbggubtHgwYpjrKeV1OvsEPHLkY6nS4zaLMzxzKxd2OiqFbLT0xlTrG8eHxJAqm4qrxJ6AhqtVoOY27UF+j5oLl5bI5nS58nYKnXi6YeXWdtnw5K2dmBXIANEw1NDV9DflXX8VEXhymqqJA5hBJYfRE6n1bBQGxeWIMpuL9Ynjt5nB/GAVG06jU/Pu0HuJqt2bWcgFOuzXC1nfYL+7bKjjyMhejJ8+14MzMelBEEXR5TZEEHYNSIZkRdTA8fw79pmOdELQOAuqoQSMo67MHBRmHUbBTQReutSSNF9eiz9RtF+flZoi1K6Bevcx+M7Rvouh+QaMn7y7eZmYZOQM0TPF/u3FNrjz7AMUMz050Yb8BZUwuQcHDIvBPKDA4iiSD0hwEPINqexZR9ihelJ5DM8heQhJI7Quf6ZsoQLqU5YczlhuOapetqXTYD5bPJrhf0GhxVs7MCuYAaJjqqAbnLJIZYXlYIMnwVFEP7EMomHkGXVz2oHqg5Wpo4npl56AanxjMzKKhrheG4zVQUPEUnYczaHq/5S58MVtzDgpw4vsf7LBftzU+7YbkbPja/Z79+zGzIXEANGz7UHAygQIM6P+3EIe16uHYe1HB8ixJUXQMYroJAlplS2okbStjc4JaeL8GrQOQ49Aytc1ZoeUufO2yNZ32K2s2YSXPonJWzswK5ABo2NahgGUX+od/VQ7HjL/FQ2jYq4pmaz1C+xlfy2kOlGLG6nHUw2givM+D4X2bA5AGynQd5sisUDcXvlaBWrv9yppNGIdZVM7KmVlBHAAN2wbUaTlOMc9jGCweYx2q1Yl1MbHjcx7TvuPQ1TzwvXC8CZQRahWAPAi8gCOzQvHncySLoGbR7oJZtmyCZ1GZmfXFAdAwTQM/gjpBbyAZWspLHO5qoNqcKkl36H6HhNJBTsw43En7ImPQ8Fc6KzQFvJylw3J5ZizKlE1wbyMzs744ABqmdeiitT/c1qFlWvMqhm6gGVn3kPTM6TQk1Kl+pNXPlsuyNAcg6axQ7CuUnl3mjEXvPIvKzKwvDoCGqYYCkzXool8h3+DnWeD+cOzlgpVO9SOdfpYly5I+hw3Aa0gWaf0ByRR7B0DZlbXuycwsJw6AhqWGamK+h7pBr0JNBPNyCPgKS1eZTwcrcSX0BkkTwXbT1vOsLYn7PD8cL2YoTgaepJiMxUqeOZW2EuqexuW7NrOx4wBoWGLX5PVoKvwxJMtX9Cs2JjwVOBHV5uxM/XyGZB0vws8W0QwxOLKpYN61JXEG2RNoBlkFBVYP9nHMXo3DzKm0Ua57Grfv2szGipfCGJYFNPsLlP05jC4MeZgIxzwR9er5idR71dAK7htRwHMAXZiOQ0XZsLR+JF1b0vyzXjXPINuGuk0/08cxe7HcMhyWH3/XZjbiHAANSxXN/qqii0CFfId/JlCm5iDK7pxLMvwQMzsLqduu8PrmtbEGsW5W+pjr0GdPzyAbFq8/NTz+rs1sxPn/Y8Myh7Iv+8LzBgow8hCH0iZQxqYetsXaixhoxYxTUDkTwQAADxhJREFUJZzHN8O25vqMQdSWjEK9imdODY+/azMbcc4ADUvMgkyg7Muq8LjfOqAGGk5bDMecQpmmuO5WXNvrWVSDtDY8vhMtjRFXjm91vu1+1qtBHDPr+3tV+OHwd21mI84ZoGGKWZDjUBF0lf6mwS+SBDp70PDSNOot9K2m972JZBbYIcp7IRqFTFRZ+Ls2sxHmAKgIL0RBUCPc+gmC5tCFZgJ4OtyOBy5BBdF3hp/XUTGqjfbMqXHj79rMRpSHwIZtimRa+Wyfx6qjuooJ4GjUV2dTOO48Cq7OJ5ntZWZmZoADoOGLRclVFAj1OwQW1/3aw9LO0lU0Lf5k4DI09d3MzMwAB0DDVwe+i775fpfBmEAZoI1o6AuUWYoZn0VUIH0I92AxMzNL8SWxCBXymf1VRdOLV6Fan2dQbdGLSTou/wANiU3jdbfMzMwCB0BFOICyM/2okAyBgaa211EQdDBsP4iCH/dgMTMzW8IBUBH2AI8CZ9H7MFgDDYGtCffHAftJ1l6qonXHpvFK4WZmZk0cABWhjlZuPwk1JuwlCIo9gGIm6IVo5tdhks67q4DbKHffHzMzsxZcBF2UOurP0+uw1AQKX2toSK2KskEL4efzKDhawMGPmZlZE2eAirKfpBh6keyhaKwhOhyOcXw43gbUD+gwnet+4kKp7tBrZmYl5AxQUWaBm+l9RtgBlOV5FtX6PIUCn2NQHdCZwOO0Dm5mgEuBi8O9ewSZmVnJOAAq0sPA36IanW6DoJgx2odmfMXf4EaUBXoaLbb6KHAKR+b4aihAmkNLY8zhHkFmZlY6DoCKNIOWrniEpHanGxVU5HwMSd3PJFoOA5QZOojqgqaa9p0K2+dTr231OjMzszHmAKgoMRNTB3agLNByYvZnJwpcnkONDuMwWA3N/PoBCmpa1QDNkQRMoGnysaO0mZlZSXjgoyhTKGg5BgUgDZKFTVtNi2+gIOkwyayv+fD6QygY2o+GtWrhGHeHfdeQFDvXw/ZzUAbqWBSAXRy27871U5qZmY0kB0BFWQ+cQZJ5mWJpQXR8HO8Xwv1q4EHgBFT4fCzK+kwAX0cBTJzdtR4VOcdsUAxwdgO3oEVSHybpFn0O8A08K8zMzMaeA6Ai1ICXoGzNKmBd2LZIMiW+QjIsFut0FsK2BTSD7BRUCF1Ba4HtDK+vs7TYOTZGTAc41fBes6n3WI3XCzMzs1JwAFSEKZTNOYwCkGrYXiEZwpoIP58myQp9L2y/Fc0C20b7Xj6tip3TAU66FigGSF4vzMzMSsJF0EWIAcvT4Xk64xMLnQ+g2p2D4TV1lDXaDPwMcFrYdpDWGZvmYufmACfWAk0BR4V7rxdmZmYl4QzQsMUOzPcBZ6MgKGZ9NqHhsMMoKDpIEgwdi4KjAyiQeSWwnWQIq1m62Hk1rRdE3Y2GxNwR2szMSsYB0DDNkDQdrAD3oKGsmJWJQ1Tno8DmFNTbJ84Kq6NszQ60iOrRqAaonW4CnHqb7WZmZmPMQ2DDEouSq8BxaCjrcjTMFYOQfWgYrAacGO7XkNQIxXqhE9AMr5ew/DIWnYbJzMzMSsoB0LBMoYDmeBSMHEDDW+exNA+3QDLctZdkGOxeFPysQxmh+8PPvIyFmZlZZr50DsscClxqJJmcOgp00lPPq2iI62g0RX5v6ucPoKGvJ1Ov99R1MzOzzBwADUsduAs4CQUxcRZYnJkVi6MXUHZoD0kjxCngdhQ4XUxSE+Sp62ZmZj1xADRMO4AvoGGv2N35blTPky6OfhQFSrH54Z0oEwQa+jo37B9nevWS/YkBl2d/mZlZCTkAGradwFdJgg9Ilqs4Dv1GTgJuIJkhFgOUGeBFJJmh++ht7a44G615iQwzM7OScBF0EdIzs9oVR7+EpcFPemmLPeF1Z5M9hE0fZ2+4dyG1mZmVjAOgoqWLoxc4sjg6arW0RbXpNd3I6zhmZmYrmAOgosXi6AlUHF1DxdFxva5ouaUtupXXcczMzFYwB0CjIBZHb0ednVstW5HX2l1eA8zMzMyVHyOjuTi6VUCS19pdXgPMzMxKzgHQKOlmXa681u7yGmBmZlZiHgIzMzOz0ilDAHQFWkTiYeCags/FzMzMRsC4B0BV4L8Dr0Ndc34+3JuZmVmJjXsAdBHK/Hwflft+Criy0DMyMzOzwo17AHQS8ETq+bawLe1qYCuwddOmTcM6LzMzMyvQuM8Cq7TY1mh6/qFwY+fOnc0/MzMzszE07hmgbcDJqedbgCcLOhczMzMbEeMeAH0bOBM4HbX9ezNwfaFnZGZmZoUb9yGwOvBrwA1oRthHgXsLPSMzMzMr3LgHQKBVtr5Q9EmYmZnZ6Kg0Gq77TdkBPJbj8TahVb7KzN+BvwPwd1D2zw/+DsDfARTzHZwKHNu80QHQYG0FLij6JArm78DfAfg7KPvnB38H4O8ARug7GPciaDMzM7MjOAAyMzOz0qm++93vLvocxt1tRZ/ACPB34O8A/B2U/fODvwPwdwAj8h24BsjMzMxKx0NgZmZmVjoOgAbnCuABtBr9NQWfy7CcDPwTcD9qOPmOsH0DcCPwULifKeTshqcKfAf4u/C8bJ//GOCzwHfRn4UfoXzfwb9GfwfuAT4JrGL8v4OPAs+gzxx1+szvQv8+PgBcPqRzHKRWn/+P0N+Du4DPob8b0bh9fmj9HUS/jdbiTK86Xuh34ABoMKrAfwdeB5wN/Hy4H3d14LeAFwGXAG9Hn/sa4Ca0LMlNjH9A+A504Y/K9vn/K/Al4IXAeei7KNN3cBLwG2iq70vQvwdvZvy/g4+h//iltfvMZ6Pv5MVhnw+g72kl+xhHfv4b0Z+Bc4EH0QUfxvPzQ+vvAPSf4x8HHk9tK/w7cAA0GBehqPb7wBzwKeDKQs9oOLYDt4fH+9CF7yT02a8L268D3jD8UxuaLcBPAh9ObSvT5z8KeCXwkfB8DniOcn0HoC77q8P9GrQI87h/BzcDu5q2tfvMV6J/F2eBR9C/lxcN4RwHqdXn/wf0H0OAb6F/H2A8Pz+0/g4A3gf8DsoARYV/Bw6ABuMk4InU821hW5mcBrwMuAU4HgVHhPvjCjqnYfgT9Bd9MbWtTJ//eaij+p+hYcAPA2sp13fwA+CP0f92twN70IWwTN9B1O4zl/HfyF8Gvhgel+nzvx79nbizaXvh34EDoMGotNhWpul264C/An4T2FvwuQzTT6Hx75GY4lmQGnA+8EEUAB9g/IZ6ljOD/nd7OnAiCgB/sdAzGj1l+zfy36FM0F+E52X5/GvQZ/+9Fj8r/DtwADQY29CYZ7QFpcDLYBIFP38B/HXY9jSwOTzejIKEcfQK9L+dR1Fq98eAP6c8nx/0Z38byvyBiqHPp1zfwT9HKf0dwDz6e3Ap5foOonafuUz/Rl6F/nP0CyQX+LJ8/jPQfwTuRP8ubkFlEicwAt+BA6DB+DYq+jsdmEKFXtcXekbDUUG1H/cD/yW1/Xr0jwDh/vNDPq9heRf6S3wa+p1/Gf3PvyyfH+AplNZ+QXj+GuA+yvUdPI4mAaxBfydeg/5OlOk7iNp95uvR35Fp9O/kmcCtQz+7wbsCeCf6j9HB1PayfP670bDnaeG2Df2H6ClG4DuoDfPNSqQO/BpwA6pq/yiaEjvuXgH8S/SH/o6w7d8Cfwh8Bngruji8qZCzK07ZPv+vowzgFJoI8EvoP1tl+Q5uQZmv29G/Bd8BPoSGhsf5O/gk8Co0zXkbcC3t/+zfG7bfh76jtwMLwz3d3LX6/O9CF/gbw2u+Bfwq4/n5ofV38JE2ry38O3AnaDMzMysdD4GZmZlZ6TgAMjMzs9JxAGRmZmal4wDIzMzMSscBkJmZmZWOAyAzMzMrHQdAZlZWj6J+JcP2FbRSfLdeBfxdm589SjGfwWzFcwBkZsupsLL/ragWfQJmNnpW8j9qZjY4p6HlGz6AOhp/BLgHdfn+ufCaCvBHLba/Cvgq6vL6IOoG/Auozf3daH0gUFfge9A6QTd3OJd/hZZQ+BLwAOouG/1iOO4dwP8gCXb2A+9BXZl/pMOxfz18vruBF4ZtG4C/Ae5CnXvPDdvfDfx2at970Pe0Fvj78DnuIfkeXo6+h9tQV/jNqX3fFM77QeCfhW2rgD8L5/Id4NUtzncjWln+O+HzxgUl252DmbXhAMjM2nkB8HHg36M1zs5DC33+EbqY/yzw0hbbCdveAZyDlkc5C7gI+DAKOkArRF8eXvv6Zc7lIhREvRQFDxcAL0IX+leE7QvhNaCA4B7gYuBrHY67k2T1+hjc/D4KMM5FS7l8fJlzuwIt4nge8BIUqE0Cfwq8EQVCHwX+Q2qfWvhMv0kS0L093J8D/DxwHQqK0q4Nn+dlaC2lUzqcg5l14ADIzNp5DGVALkNr/Cyg1b2/ClzYYTtoQeDtwCzwPZS1AGU3TguPvw58DPgVlh+muhF4FjiEVle/DC0y+vLwXneE588Lr18A/qqLz/jX4f621HldBnwiPP4yyroc3eEYd6MA8L0om7MHBY8vCed9B/C7KIjs9n2/i77/s5re65XAn4fHfw/s7nAOZtaBF0M1s3YOhPtKm5+32w4KfKLF1PNFkn93fhVlaH4SBQkvRUFOK82LFjbC+1+HFpxsdpjuFlaM57WQOq9Wn6uBFmxM/6cxZmceRIHYTwD/CQV7n0OLPbYbfuv2fVtptYBjq3N4T5fHMyslZ4DMbDk3o6GmKnAsykLc2mF7t85ANTq/h4aiTu7w2h9HtTmrgTeg7NFNaIjpuPCaDcCpGd6/nZtJhtJeFc5tL5pxdX7Yfj5wenh8InAQZWb+OPzsAfSdxABoEnhxhvc9Cw1vPdDhNa8DZjqcg5l14AyQmS3nc+hCfifKPvwO8FSH7S9sfZgj/BFwJsp83BSO087X0PDQ84G/BLaG7b+Lsh0TwDyqo3msy/dv592oGPkuFFRcFbb/FfAWlK36Nsq6gGp2/ghlt+aBtwFzKDh7Pxo+qwF/grJC7XwA+P/QcFYdFX/PNr3m99Gw4+1oyPHxDudgZh1UGo1W2VQzs5Hxr1DR868VfB5mNkY8BGZmZmal4wyQmY2Ky9EsprRHgJ/p87ifI6nXid6JevOYWUk5ADIzM7PS8RCYmZmZlY4DIDMzMysdB0BmZmZWOg6AzMzMrHQcAJmZmVnp/P/OCDraTHUhtAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Chosen attributes\n",
    "attributes = [\"rooms_per_households\", \"bedrooms_per_room\", \"population_per_households\"]\n",
    "\n",
    "for attribute in attributes:\n",
    "    housing.plot(kind=\"scatter\", x=attribute, y=\"median_house_value\", c=\"g\", alpha=0.4, figsize=(9,6))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>\n",
    "    <span style=\"color:yellow\">\n",
    "    Data Preparation\n",
    "    </span>\n",
    "</h1>\n",
    "\n",
    "We will creatte a functioon to clean and process our data so we can re-use it for future datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Revert housing data to the predictive features original training set\n",
    "housing = strat_train.drop(\"median_house_value\", axis=1)\n",
    "\n",
    "# Collect the corresponding target features foor housing data\n",
    "housing_labels = strat_train[\"median_house_value\"].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numerical Features\n",
    "\n",
    "For missing values in the numerical features of our training data we can choose to:\n",
    "* Drop examples missing feature: `housing.dropna(subset=[\"total_bedrooms\"])`\n",
    "* Drop feature for all examples: `housing.drop(\"total_bedrooms\", axis=1)`\n",
    "* Replace missing valuse: \n",
    "```python\n",
    "median = housing[\"total_bedrooms\"].median()\n",
    "housing[\"total_bedrooms\"].fillna(median, inplace=True)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate feature median and Save value\n",
    "median = housing[\"total_bedrooms\"].median()\n",
    "\n",
    "# Impute median value\n",
    "housing[\"total_bedrooms\"].fillna(median, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next use tensorflows `SimpleImputer` to impute any other missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an median impute instance\n",
    "imputer = SimpleImputer(strategy='median')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply imputer ONLY to numerical features. As `ocean_proximity` is the only catagorical featture, we will drop that column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10275</th>\n",
       "      <td>-117.87</td>\n",
       "      <td>33.86</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2292.0</td>\n",
       "      <td>531.0</td>\n",
       "      <td>2197.0</td>\n",
       "      <td>509.0</td>\n",
       "      <td>3.4856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20601</th>\n",
       "      <td>-121.57</td>\n",
       "      <td>39.13</td>\n",
       "      <td>30.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>1.5694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20138</th>\n",
       "      <td>-119.05</td>\n",
       "      <td>34.40</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1236.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>1079.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>2.6991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18028</th>\n",
       "      <td>-121.94</td>\n",
       "      <td>37.24</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1484.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>664.0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>4.6750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16289</th>\n",
       "      <td>-121.30</td>\n",
       "      <td>37.92</td>\n",
       "      <td>28.0</td>\n",
       "      <td>3308.0</td>\n",
       "      <td>766.0</td>\n",
       "      <td>3201.0</td>\n",
       "      <td>720.0</td>\n",
       "      <td>1.7694</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "10275    -117.87     33.86                28.0       2292.0           531.0   \n",
       "20601    -121.57     39.13                30.0        442.0           103.0   \n",
       "20138    -119.05     34.40                50.0       1236.0           282.0   \n",
       "18028    -121.94     37.24                35.0       1484.0           244.0   \n",
       "16289    -121.30     37.92                28.0       3308.0           766.0   \n",
       "\n",
       "       population  households  median_income  \n",
       "10275      2197.0       509.0         3.4856  \n",
       "20601       413.0        88.0         1.5694  \n",
       "20138      1079.0       257.0         2.6991  \n",
       "18028       664.0       238.0         4.6750  \n",
       "16289      3201.0       720.0         1.7694  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Numerical features\n",
    "housing_num = housing.drop(\"ocean_proximity\", axis=1)\n",
    "\n",
    "housing_num.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply imputer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(strategy='median')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit imputer to numerical data\n",
    "imputer.fit(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imputers values:  [-118.49, 34.25, 29.0, 2128.5, 436.0, 1167.0, 410.0, 3.5341]\n",
      "Numerical columns:  [-118.49, 34.25, 29.0, 2128.5, 436.0, 1167.0, 410.0, 3.5341]\n"
     ]
    }
   ],
   "source": [
    "print(\"Imputers values: \", imputer.statistics_.tolist())\n",
    "print(\"Numerical columns: \", housing_num.median().values.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replace missing values in numerical columns with the mean imputer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saturate Numerical data with the transform method\n",
    "X = imputer.transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.1787e+02,  3.3860e+01,  2.8000e+01, ...,  2.1970e+03,\n",
       "         5.0900e+02,  3.4856e+00],\n",
       "       [-1.2157e+02,  3.9130e+01,  3.0000e+01, ...,  4.1300e+02,\n",
       "         8.8000e+01,  1.5694e+00],\n",
       "       [-1.1905e+02,  3.4400e+01,  5.0000e+01, ...,  1.0790e+03,\n",
       "         2.5700e+02,  2.6991e+00],\n",
       "       ...,\n",
       "       [-1.1830e+02,  3.4250e+01,  4.4000e+01, ...,  8.5900e+02,\n",
       "         2.9200e+02,  4.5833e+00],\n",
       "       [-1.2191e+02,  3.7310e+01,  4.6000e+01, ...,  1.3730e+03,\n",
       "         5.9000e+02,  4.7287e+00],\n",
       "       [-1.1830e+02,  3.4090e+01,  2.9000e+01, ...,  3.9060e+03,\n",
       "         1.1020e+03,  2.1927e+00]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note top fit the data and then transform it symultaniously we could have used Tensorflows `fit_transform()` method.\n",
    "\n",
    "X is currently just an array. To revert the data back to a numpy dataframe we do the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create dataframe\n",
    "housing_tr = pd.DataFrame(X, columns=housing_num.columns, index=housing_num.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10275</th>\n",
       "      <td>-117.87</td>\n",
       "      <td>33.86</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2292.0</td>\n",
       "      <td>531.0</td>\n",
       "      <td>2197.0</td>\n",
       "      <td>509.0</td>\n",
       "      <td>3.4856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20601</th>\n",
       "      <td>-121.57</td>\n",
       "      <td>39.13</td>\n",
       "      <td>30.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>1.5694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20138</th>\n",
       "      <td>-119.05</td>\n",
       "      <td>34.40</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1236.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>1079.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>2.6991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18028</th>\n",
       "      <td>-121.94</td>\n",
       "      <td>37.24</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1484.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>664.0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>4.6750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16289</th>\n",
       "      <td>-121.30</td>\n",
       "      <td>37.92</td>\n",
       "      <td>28.0</td>\n",
       "      <td>3308.0</td>\n",
       "      <td>766.0</td>\n",
       "      <td>3201.0</td>\n",
       "      <td>720.0</td>\n",
       "      <td>1.7694</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "10275    -117.87     33.86                28.0       2292.0           531.0   \n",
       "20601    -121.57     39.13                30.0        442.0           103.0   \n",
       "20138    -119.05     34.40                50.0       1236.0           282.0   \n",
       "18028    -121.94     37.24                35.0       1484.0           244.0   \n",
       "16289    -121.30     37.92                28.0       3308.0           766.0   \n",
       "\n",
       "       population  households  median_income  \n",
       "10275      2197.0       509.0         3.4856  \n",
       "20601       413.0        88.0         1.5694  \n",
       "20138      1079.0       257.0         2.6991  \n",
       "18028       664.0       238.0         4.6750  \n",
       "16289      3201.0       720.0         1.7694  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Catagorical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10275</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20601</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20138</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18028</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16289</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ocean_proximity\n",
       "10275       <1H OCEAN\n",
       "20601          INLAND\n",
       "20138       <1H OCEAN\n",
       "18028       <1H OCEAN\n",
       "16289          INLAND"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Catagorical Features\n",
    "housing_cat = housing[[\"ocean_proximity\"]]\n",
    "\n",
    "housing_cat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Tensorflows `OrdinalEncoder`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       ...,\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create encoder \n",
    "ordinal_encoder = OrdinalEncoder()\n",
    "\n",
    "# Fit and transform catagorical variables to encodings\n",
    "housing_cat_encoded = ordinal_encoder.fit_transform(housing_cat)\n",
    "\n",
    "# Display Encoding\n",
    "housing_cat_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "       dtype=object)]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Catagories of encoding\n",
    "ordinal_encoder.categories_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This encoding may not be viable as the magnitude of the numerical values may be interprited by the algorithm as holding value. So we can instead use a \"OneHot Encoder\":  `OneHotEncoder`. We'll create the encoder, fit it and then transform the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create OneHotEncoding instance\n",
    "cat_encoder = OneHotEncoder()\n",
    "\n",
    "# Fit and transform catagorical features\n",
    "housing_cat_1hot = cat_encoder.fit_transform(housing_cat)\n",
    "\n",
    "# Display\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       ...,\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Diaplay array\n",
    "housing_cat_1hot.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "       dtype=object)]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Catagories of OneHotEncodings\n",
    "cat_encoder.categories_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>\n",
    "    <span style=\"color:yellow\">\n",
    "    Custume Feature Enginearing\n",
    "    </span>\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "rooms_ix, bedrooms_ix, population_ix, households_ix = 3, 4, 5, 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, add_bedrooms_per_room = True):\n",
    "        self.add_bedrooms_per_room = add_bedrooms_per_room\n",
    "        \n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    \n",
    "    def transform(self, X):\n",
    "        # Calculate rooms in a house\n",
    "        rooms_per_household = X[:, rooms_ix] / X[:, households_ix]\n",
    "        # Calculate average populationo\n",
    "        population_per_household = X[:, population_ix] / X[:, households_ix]\n",
    "        # Add new attributes\n",
    "        if self.add_bedrooms_per_room:\n",
    "            bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]\n",
    "            return np.c_[X, rooms_per_household, population_per_household, bedrooms_per_room]\n",
    "        else:\n",
    "            return np.c_[X, rooms_per_household, population_per_household]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-117.87, 33.86, 28.0, ..., '<1H OCEAN', 4.50294695481336,\n",
       "        4.31630648330059],\n",
       "       [-121.57, 39.13, 30.0, ..., 'INLAND', 5.0227272727272725,\n",
       "        4.693181818181818],\n",
       "       [-119.05, 34.4, 50.0, ..., '<1H OCEAN', 4.809338521400778,\n",
       "        4.198443579766537],\n",
       "       ...,\n",
       "       [-118.3, 34.25, 44.0, ..., '<1H OCEAN', 4.938356164383562,\n",
       "        2.941780821917808],\n",
       "       [-121.91, 37.31, 46.0, ..., '<1H OCEAN', 5.172881355932203,\n",
       "        2.3271186440677964],\n",
       "       [-118.3, 34.09, 29.0, ..., '<1H OCEAN', 2.9446460980036298,\n",
       "        3.544464609800363]], dtype=object)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a Transformer that creates new attributes\n",
    "attr_adder = CombinedAttributesAdder(add_bedrooms_per_room=False)\n",
    "# Create new attributes\n",
    "housing_extra_attribs = attr_adder.transform(housing.values)\n",
    "\n",
    "housing_extra_attribs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use Scikit-learn `Pipeline` class to autimate data transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create pipeline to automate transform numerical data\n",
    "num_pipeline = Pipeline([\n",
    "    ('imputer', SimpleImputer(strategy=\"median\")),\n",
    "    ('attribs_adder', CombinedAttributesAdder()),\n",
    "    ('std_scaler', StandardScaler())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.84450121, -0.82473693, -0.04853356, ..., -0.38320306,\n",
       "         0.11361593,  0.28225234],\n",
       "       [-1.00168564,  1.64070891,  0.11081943, ..., -0.16739639,\n",
       "         0.14811788,  0.30367359],\n",
       "       [ 0.25571729, -0.5721106 ,  1.70434932, ..., -0.25599289,\n",
       "         0.10282589,  0.22665626],\n",
       "       ...,\n",
       "       [ 0.62994436, -0.64228458,  1.22629035, ..., -0.20242628,\n",
       "        -0.0122183 , -0.25527205],\n",
       "       [-1.17133524,  0.78926462,  1.38564334, ..., -0.10505418,\n",
       "        -0.06848902, -0.33912717],\n",
       "       [ 0.62994436, -0.71713683,  0.03114293, ..., -1.03019125,\n",
       "         0.04295583,  2.41512989]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transform numerical data\n",
    "housing_num_tr = num_pipeline.fit_transform(housing_num)\n",
    "\n",
    "housing_num_tr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To automate the transformation process for both numerical and catagorical data type simultaniously we can use the `ColumnTransformer` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list of numerical and catagorical column names\n",
    "num_attribs = list(housing_num)\n",
    "cat_attribs = [\"ocean_proximity\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Pipeline process for all columns\n",
    "full_pipeline = ColumnTransformer([\n",
    "    (\"num\", num_pipeline, num_attribs),\n",
    "    (\"cat\", OneHotEncoder(), cat_attribs)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.84450121, -0.82473693, -0.04853356, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.00168564,  1.64070891,  0.11081943, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 0.25571729, -0.5721106 ,  1.70434932, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       ...,\n",
       "       [ 0.62994436, -0.64228458,  1.22629035, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.17133524,  0.78926462,  1.38564334, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 0.62994436, -0.71713683,  0.03114293, ...,  0.        ,\n",
       "         0.        ,  0.        ]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Call transformer on data\n",
    "housing_prepared = full_pipeline.fit_transform(housing)\n",
    "\n",
    "housing_prepared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.84450121, -0.82473693, -0.04853356, -0.15631878, -0.01220643,\n",
       "        0.67606966,  0.02634094, -0.20234886, -0.38320306,  0.11361593,\n",
       "        0.28225234,  1.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>\n",
    "    <span style=\"color:Yellow\">\n",
    "    Model Selection\n",
    "    </span>\n",
    "</h1>\n",
    "    \n",
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Linear Regression Model\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(\n",
    "    housing_prepared, \n",
    "    housing_labels\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: [172713.58885788  43130.16935351 195014.22158132 268919.62476372\n",
      "  44875.4698183 ]\n",
      "Labels: [142800.0, 57900.0, 181300.0, 245300.0, 73900.0]\n"
     ]
    }
   ],
   "source": [
    "# Let take 'some' data points to test model perfomance\n",
    "some_data = housing.iloc[:5]\n",
    "some_labels = housing_labels.iloc[:5]\n",
    "\n",
    "# Prepate some data\n",
    "some_data_prepared = full_pipeline.transform(some_data)\n",
    "\n",
    "# Make some predictions\n",
    "print(\"Predictions:\", lin_reg.predict(some_data_prepared))\n",
    "print(\"Labels:\", list(some_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68284.24083518104"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate RMSE (randome mean squared error) oof our Linear Regression model\n",
    "housing_predictions = lin_reg.predict(housing_prepared)\n",
    "lin_mse = mean_squared_error(\n",
    "    housing_labels, \n",
    "    housing_predictions\n",
    ")\n",
    "\n",
    "lin_rmse = np.sqrt(lin_mse)\n",
    "lin_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the majority of houses lie between 120,000 and 265,000 -- 68,000 is a fairly large disparity. This may be do to a lack of information being fed to our model. Alternatevely we may infact need a more powerful model.\n",
    "\n",
    "## Decision Tree Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision Trees\n",
    "tree_reg = DecisionTreeRegressor()\n",
    "tree_reg.fit(\n",
    "    housing_prepared, \n",
    "    housing_labels\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate RMSE on data\n",
    "housing_predictions = tree_reg.predict(housing_prepared)\n",
    "tree_mse = mean_squared_error(\n",
    "    housing_labels, \n",
    "    housing_predictions\n",
    ")\n",
    "\n",
    "tree_rmse = np.sqrt(tree_mse)\n",
    "tree_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Support Vector Machine Regressor</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR()"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SVM regressor\n",
    "svm_reg = SVR()\n",
    "svm_reg.fit(\n",
    "    housing_prepared,\n",
    "    housing_labels\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "118292.97659943995"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate RMSE on data\n",
    "housing_preds_svm = svm_reg.predict(housing_prepared)\n",
    "svm_mse = mean_squared_error(\n",
    "    housing_labels,\n",
    "    housing_preds_svm\n",
    ")\n",
    "\n",
    "svm_rmse = np.sqrt(svm_mse)\n",
    "svm_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on SVR in module sklearn.svm._classes object:\n",
      "\n",
      "class SVR(sklearn.base.RegressorMixin, sklearn.svm._base.BaseLibSVM)\n",
      " |  SVR(*, kernel='rbf', degree=3, gamma='scale', coef0=0.0, tol=0.001, C=1.0, epsilon=0.1, shrinking=True, cache_size=200, verbose=False, max_iter=-1)\n",
      " |  \n",
      " |  Epsilon-Support Vector Regression.\n",
      " |  \n",
      " |  The free parameters in the model are C and epsilon.\n",
      " |  \n",
      " |  The implementation is based on libsvm. The fit time complexity\n",
      " |  is more than quadratic with the number of samples which makes it hard\n",
      " |  to scale to datasets with more than a couple of 10000 samples. For large\n",
      " |  datasets consider using :class:`sklearn.svm.LinearSVR` or\n",
      " |  :class:`sklearn.linear_model.SGDRegressor` instead, possibly after a\n",
      " |  :class:`sklearn.kernel_approximation.Nystroem` transformer.\n",
      " |  \n",
      " |  Read more in the :ref:`User Guide <svm_regression>`.\n",
      " |  \n",
      " |  Parameters\n",
      " |  ----------\n",
      " |  kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'}, default='rbf'\n",
      " |       Specifies the kernel type to be used in the algorithm.\n",
      " |       It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or\n",
      " |       a callable.\n",
      " |       If none is given, 'rbf' will be used. If a callable is given it is\n",
      " |       used to precompute the kernel matrix.\n",
      " |  \n",
      " |  degree : int, default=3\n",
      " |      Degree of the polynomial kernel function ('poly').\n",
      " |      Ignored by all other kernels.\n",
      " |  \n",
      " |  gamma : {'scale', 'auto'} or float, default='scale'\n",
      " |      Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.\n",
      " |  \n",
      " |      - if ``gamma='scale'`` (default) is passed then it uses\n",
      " |        1 / (n_features * X.var()) as value of gamma,\n",
      " |      - if 'auto', uses 1 / n_features.\n",
      " |  \n",
      " |      .. versionchanged:: 0.22\n",
      " |         The default value of ``gamma`` changed from 'auto' to 'scale'.\n",
      " |  \n",
      " |  coef0 : float, default=0.0\n",
      " |      Independent term in kernel function.\n",
      " |      It is only significant in 'poly' and 'sigmoid'.\n",
      " |  \n",
      " |  tol : float, default=1e-3\n",
      " |      Tolerance for stopping criterion.\n",
      " |  \n",
      " |  C : float, default=1.0\n",
      " |      Regularization parameter. The strength of the regularization is\n",
      " |      inversely proportional to C. Must be strictly positive.\n",
      " |      The penalty is a squared l2 penalty.\n",
      " |  \n",
      " |  epsilon : float, default=0.1\n",
      " |       Epsilon in the epsilon-SVR model. It specifies the epsilon-tube\n",
      " |       within which no penalty is associated in the training loss function\n",
      " |       with points predicted within a distance epsilon from the actual\n",
      " |       value.\n",
      " |  \n",
      " |  shrinking : bool, default=True\n",
      " |      Whether to use the shrinking heuristic.\n",
      " |      See the :ref:`User Guide <shrinking_svm>`.\n",
      " |  \n",
      " |  cache_size : float, default=200\n",
      " |      Specify the size of the kernel cache (in MB).\n",
      " |  \n",
      " |  verbose : bool, default=False\n",
      " |      Enable verbose output. Note that this setting takes advantage of a\n",
      " |      per-process runtime setting in libsvm that, if enabled, may not work\n",
      " |      properly in a multithreaded context.\n",
      " |  \n",
      " |  max_iter : int, default=-1\n",
      " |      Hard limit on iterations within solver, or -1 for no limit.\n",
      " |  \n",
      " |  Attributes\n",
      " |  ----------\n",
      " |  support_ : ndarray of shape (n_SV,)\n",
      " |      Indices of support vectors.\n",
      " |  \n",
      " |  support_vectors_ : ndarray of shape (n_SV, n_features)\n",
      " |      Support vectors.\n",
      " |  \n",
      " |  dual_coef_ : ndarray of shape (1, n_SV)\n",
      " |      Coefficients of the support vector in the decision function.\n",
      " |  \n",
      " |  coef_ : ndarray of shape (1, n_features)\n",
      " |      Weights assigned to the features (coefficients in the primal\n",
      " |      problem). This is only available in the case of a linear kernel.\n",
      " |  \n",
      " |      `coef_` is readonly property derived from `dual_coef_` and\n",
      " |      `support_vectors_`.\n",
      " |  \n",
      " |  fit_status_ : int\n",
      " |      0 if correctly fitted, 1 otherwise (will raise warning)\n",
      " |  \n",
      " |  intercept_ : ndarray of shape (1,)\n",
      " |      Constants in decision function.\n",
      " |  \n",
      " |  Examples\n",
      " |  --------\n",
      " |  >>> from sklearn.svm import SVR\n",
      " |  >>> from sklearn.pipeline import make_pipeline\n",
      " |  >>> from sklearn.preprocessing import StandardScaler\n",
      " |  >>> import numpy as np\n",
      " |  >>> n_samples, n_features = 10, 5\n",
      " |  >>> rng = np.random.RandomState(0)\n",
      " |  >>> y = rng.randn(n_samples)\n",
      " |  >>> X = rng.randn(n_samples, n_features)\n",
      " |  >>> regr = make_pipeline(StandardScaler(), SVR(C=1.0, epsilon=0.2))\n",
      " |  >>> regr.fit(X, y)\n",
      " |  Pipeline(steps=[('standardscaler', StandardScaler()),\n",
      " |                  ('svr', SVR(epsilon=0.2))])\n",
      " |  \n",
      " |  \n",
      " |  See also\n",
      " |  --------\n",
      " |  NuSVR\n",
      " |      Support Vector Machine for regression implemented using libsvm\n",
      " |      using a parameter to control the number of support vectors.\n",
      " |  \n",
      " |  LinearSVR\n",
      " |      Scalable Linear Support Vector Machine for regression\n",
      " |      implemented using liblinear.\n",
      " |  \n",
      " |  Notes\n",
      " |  -----\n",
      " |  **References:**\n",
      " |  `LIBSVM: A Library for Support Vector Machines\n",
      " |  <http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf>`__\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      SVR\n",
      " |      sklearn.base.RegressorMixin\n",
      " |      sklearn.svm._base.BaseLibSVM\n",
      " |      sklearn.base.BaseEstimator\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, *, kernel='rbf', degree=3, gamma='scale', coef0=0.0, tol=0.001, C=1.0, epsilon=0.1, shrinking=True, cache_size=200, verbose=False, max_iter=-1)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Readonly properties defined here:\n",
      " |  \n",
      " |  probA_\n",
      " |  \n",
      " |  probB_\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  __abstractmethods__ = frozenset()\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.RegressorMixin:\n",
      " |  \n",
      " |  score(self, X, y, sample_weight=None)\n",
      " |      Return the coefficient of determination R^2 of the prediction.\n",
      " |      \n",
      " |      The coefficient R^2 is defined as (1 - u/v), where u is the residual\n",
      " |      sum of squares ((y_true - y_pred) ** 2).sum() and v is the total\n",
      " |      sum of squares ((y_true - y_true.mean()) ** 2).sum().\n",
      " |      The best possible score is 1.0 and it can be negative (because the\n",
      " |      model can be arbitrarily worse). A constant model that always\n",
      " |      predicts the expected value of y, disregarding the input features,\n",
      " |      would get a R^2 score of 0.0.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like of shape (n_samples, n_features)\n",
      " |          Test samples. For some estimators this may be a\n",
      " |          precomputed kernel matrix or a list of generic objects instead,\n",
      " |          shape = (n_samples, n_samples_fitted),\n",
      " |          where n_samples_fitted is the number of\n",
      " |          samples used in the fitting for the estimator.\n",
      " |      \n",
      " |      y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n",
      " |          True values for X.\n",
      " |      \n",
      " |      sample_weight : array-like of shape (n_samples,), default=None\n",
      " |          Sample weights.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      score : float\n",
      " |          R^2 of self.predict(X) wrt. y.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      The R2 score used when calling ``score`` on a regressor uses\n",
      " |      ``multioutput='uniform_average'`` from version 0.23 to keep consistent\n",
      " |      with default value of :func:`~sklearn.metrics.r2_score`.\n",
      " |      This influences the ``score`` method of all the multioutput\n",
      " |      regressors (except for\n",
      " |      :class:`~sklearn.multioutput.MultiOutputRegressor`).\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from sklearn.base.RegressorMixin:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.svm._base.BaseLibSVM:\n",
      " |  \n",
      " |  fit(self, X, y, sample_weight=None)\n",
      " |      Fit the SVM model according to the given training data.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : {array-like, sparse matrix} of shape (n_samples, n_features)                 or (n_samples, n_samples)\n",
      " |          Training vectors, where n_samples is the number of samples\n",
      " |          and n_features is the number of features.\n",
      " |          For kernel=\"precomputed\", the expected shape of X is\n",
      " |          (n_samples, n_samples).\n",
      " |      \n",
      " |      y : array-like of shape (n_samples,)\n",
      " |          Target values (class labels in classification, real numbers in\n",
      " |          regression)\n",
      " |      \n",
      " |      sample_weight : array-like of shape (n_samples,), default=None\n",
      " |          Per-sample weights. Rescale C per sample. Higher weights\n",
      " |          force the classifier to put more emphasis on these points.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self : object\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      If X and y are not C-ordered and contiguous arrays of np.float64 and\n",
      " |      X is not a scipy.sparse.csr_matrix, X and/or y may be copied.\n",
      " |      \n",
      " |      If X is a dense array, then the other methods will not support sparse\n",
      " |      matrices as input.\n",
      " |  \n",
      " |  predict(self, X)\n",
      " |      Perform regression on samples in X.\n",
      " |      \n",
      " |      For an one-class model, +1 (inlier) or -1 (outlier) is returned.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
      " |          For kernel=\"precomputed\", the expected shape of X is\n",
      " |          (n_samples_test, n_samples_train).\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      y_pred : ndarray of shape (n_samples,)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Readonly properties inherited from sklearn.svm._base.BaseLibSVM:\n",
      " |  \n",
      " |  coef_\n",
      " |  \n",
      " |  n_support_\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.BaseEstimator:\n",
      " |  \n",
      " |  __getstate__(self)\n",
      " |  \n",
      " |  __repr__(self, N_CHAR_MAX=700)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __setstate__(self, state)\n",
      " |  \n",
      " |  get_params(self, deep=True)\n",
      " |      Get parameters for this estimator.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      deep : bool, default=True\n",
      " |          If True, will return the parameters for this estimator and\n",
      " |          contained subobjects that are estimators.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      params : mapping of string to any\n",
      " |          Parameter names mapped to their values.\n",
      " |  \n",
      " |  set_params(self, **params)\n",
      " |      Set the parameters of this estimator.\n",
      " |      \n",
      " |      The method works on simple estimators as well as on nested objects\n",
      " |      (such as pipelines). The latter have parameters of the form\n",
      " |      ``<component>__<parameter>`` so that it's possible to update each\n",
      " |      component of a nested object.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      **params : dict\n",
      " |          Estimator parameters.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self : object\n",
      " |          Estimator instance.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(svm_reg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>\n",
    "    <span style=\"color:yellow\">\n",
    "    Cross-Validattion\n",
    "    </span>\n",
    "</h1>\n",
    "\n",
    "We will use Scikitt-Learn's \"K-folds\" cross-validation feature(`cross_val_score`), which will randomly divide our data into \"K\" distinct folds. Thesefoldswill be used to iteratively train and evaluate the Decision Tree model: 10 times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = cross_val_score(\n",
    "    tree_reg, \n",
    "    housing_prepared, \n",
    "    y=housing_labels, \n",
    "    scoring='neg_mean_squared_error', \n",
    "    cv=10\n",
    ")\n",
    "\n",
    "tree_rmse_scores = np.sqrt(-scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_scores(scores):\n",
    "    print(\"\\nScores:\", scores)\n",
    "    print(\"\\nMean:\", scores.mean())\n",
    "    print(\"\\nStandard Deviation:\", scores.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Scores: [70162.32030596 73677.79838172 70982.98313438 70854.05621542\n",
      " 69600.44871852 68626.85785509 73643.36118371 73008.09102743\n",
      " 68000.15170227 69480.51328796]\n",
      "\n",
      "Mean: 70803.65818124461\n",
      "\n",
      "Standard Deviation: 1934.5972329117556\n"
     ]
    }
   ],
   "source": [
    "display_scores(tree_rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Do the same for the Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_scores = cross_val_score(\n",
    "    lin_reg, \n",
    "    housing_prepared, \n",
    "    y=housing_labels, \n",
    "    scoring='neg_mean_squared_error', \n",
    "    cv=10\n",
    ")\n",
    "\n",
    "lin_rmse_scores = np.sqrt(-lin_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Scores: [68816.92050905 69919.6105217  66113.14067776 67981.42224765\n",
      " 68715.57149727 69992.34941025 67163.97848562 70841.91062928\n",
      " 66611.37068257 68651.01821515]\n",
      "\n",
      "Mean: 68480.72928763037\n",
      "\n",
      "Standard Deviation: 1456.394289447671\n"
     ]
    }
   ],
   "source": [
    "display_scores(lin_rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from the above results that the Dessiosion Tree overfits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the regressor\n",
    "forest_reg = RandomForestRegressor()\n",
    "forest_reg.fit(\n",
    "    housing_prepared, \n",
    "    housing_labels\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rmse_cv_scores(esstimator, cv, name=None):\n",
    "    # Returns RMSE of an estimator using cross-validation with cv folds\n",
    "    predictions = esstimator.predict(housing_prepared)\n",
    "    mse = mean_squared_error(housing_labels, predictions)\n",
    "    rmse = np.sqrt(mse)\n",
    "    \n",
    "    scores = cross_val_score(\n",
    "        esstimator, \n",
    "        housing_prepared, \n",
    "        y=housing_labels, \n",
    "        scoring='neg_mean_squared_error', \n",
    "        cv=cv\n",
    "    )\n",
    "    \n",
    "    rmse_scores = np.sqrt(-scores)\n",
    "    if name:\n",
    "        print(\"{} RMSE: {}\".format(name, rmse))\n",
    "        print(\"--------------\")\n",
    "        \n",
    "    print(\"Cross-Validation Scores:\")\n",
    "    display_scores(rmse_scores)\n",
    "    \n",
    "    return rmse, rmse_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest RMSE: 18604.194911538478\n",
      "--------------\n",
      "Cross-Validation Scores:\n",
      "\n",
      "Scores: [49673.09665189 53174.22166486 48198.96000415 50056.48260906\n",
      " 51074.19916858 48291.16422773 49440.34971524 51657.60305677\n",
      " 50434.07199872 51848.96927989]\n",
      "\n",
      "Mean: 50384.91183768834\n",
      "\n",
      "Standard Deviation: 1506.3166627224878\n"
     ]
    }
   ],
   "source": [
    "forest_rmse, forest_rmse_scores = rmse_cv_scores(\n",
    "    forest_reg, \n",
    "    10, \n",
    "    name=\"Random Forest\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>\n",
    "    <span style=\"color:yellow\">\n",
    "    Fine-Tune Your Model\n",
    "    </span>\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Saving Multiple Models</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving models\n",
    "def save_model(model, name):\n",
    "    print(\"-----------------------------------\")\n",
    "    print(\"Saving {} ...\".format(name))\n",
    "    print(\"-----------------------------------\\n\")\n",
    "    joblib.dump(model, name + \".pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dictionary of existing models\n",
    "models = {\n",
    "    \"Random_Forest_Regressor\": forest_reg,\n",
    "    \"Decision_Tree_Regressor\": tree_reg,\n",
    "    \"Linear_Regression_Model\": lin_reg\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nfor model_name in models:\\n    save_model(models[model_name], model_name)\\n    saver = tf.train.Saver()\\n    saver.save(sess, os.path.join(wandb.run.dir, \"model.ckpt\"))\\n'"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "for model_name in models:\n",
    "    save_model(models[model_name], model_name)\n",
    "    saver = tf.train.Saver()\n",
    "    saver.save(sess, os.path.join(wandb.run.dir, \"model.ckpt\"))\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>\n",
    "    Grid Search\n",
    "</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyper-parameters grid search\n",
    "param_grid = [\n",
    "    {\n",
    "        'n_estimators': [3, 10, 30], \n",
    "        'max_features': [2, 4, 6, 8]\n",
    "    },\n",
    "    {\n",
    "        'bootstrap': [False], \n",
    "        'n_estimators': [3, 10], \n",
    "        'max_features': [2, 3, 4]\n",
    "    }\n",
    "]\n",
    "\n",
    "# Create the regressor\n",
    "forest_reg = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestRegressor(),\n",
       "             param_grid=[{'max_features': [2, 4, 6, 8],\n",
       "                          'n_estimators': [3, 10, 30]},\n",
       "                         {'bootstrap': [False], 'max_features': [2, 3, 4],\n",
       "                          'n_estimators': [3, 10]}],\n",
       "             return_train_score=True, scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grid Search\n",
    "grid_search = GridSearchCV(\n",
    "    forest_reg, \n",
    "    param_grid, \n",
    "    cv=5,\n",
    "    scoring='neg_mean_squared_error',\n",
    "    return_train_score=True\n",
    ")\n",
    "\n",
    "grid_search.fit(\n",
    "    housing_prepared, \n",
    "    housing_labels\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the best combinations of hyper-parameters, we can use the ```best_params_``` method like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 6, 'n_estimators': 30}"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get best parameters\n",
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As 6 and 30 were the larges values within their resprective parameters, a suitable sujestion would be to try large values for our parameters.\n",
    "\n",
    "Alternatively, we can get the best estimators directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_features=6, n_estimators=30)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Best estimators\n",
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65021.21109946404 {'max_features': 2, 'n_estimators': 3}\n",
      "55195.12344464695 {'max_features': 2, 'n_estimators': 10}\n",
      "53303.30697249522 {'max_features': 2, 'n_estimators': 30}\n",
      "60637.1105446736 {'max_features': 4, 'n_estimators': 3}\n",
      "52932.65428310139 {'max_features': 4, 'n_estimators': 10}\n",
      "50713.50199936942 {'max_features': 4, 'n_estimators': 30}\n",
      "59766.17186653358 {'max_features': 6, 'n_estimators': 3}\n",
      "52181.997307082805 {'max_features': 6, 'n_estimators': 10}\n",
      "49853.2889937385 {'max_features': 6, 'n_estimators': 30}\n",
      "58431.6293168216 {'max_features': 8, 'n_estimators': 3}\n",
      "52586.16669722744 {'max_features': 8, 'n_estimators': 10}\n",
      "50322.62634449812 {'max_features': 8, 'n_estimators': 30}\n",
      "63130.653262646236 {'bootstrap': False, 'max_features': 2, 'n_estimators': 3}\n",
      "54679.36362904365 {'bootstrap': False, 'max_features': 2, 'n_estimators': 10}\n",
      "60290.02261278914 {'bootstrap': False, 'max_features': 3, 'n_estimators': 3}\n",
      "52732.50125800016 {'bootstrap': False, 'max_features': 3, 'n_estimators': 10}\n",
      "58506.174031104885 {'bootstrap': False, 'max_features': 4, 'n_estimators': 3}\n",
      "51797.077874773895 {'bootstrap': False, 'max_features': 4, 'n_estimators': 10}\n"
     ]
    }
   ],
   "source": [
    "# Evaluation scores\n",
    "cvres = grid_search.cv_results_\n",
    "for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
    "    print(np.sqrt(-mean_score), params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When hyper-parameter search spaces is large its more practical to use a Random Grid Search method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> With hyper-parameters (max_features = 6 and n_estimators = 30) we get error = 49,853.\n",
    "\n",
    "We can also use determin feature importance for random forest models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.09238841e-02, 6.42833578e-02, 4.25994113e-02, 1.84115904e-02,\n",
       "       1.60145258e-02, 1.74009712e-02, 1.65904886e-02, 3.51179019e-01,\n",
       "       6.31933365e-02, 1.04617820e-01, 6.42922141e-02, 1.23821165e-02,\n",
       "       1.51848585e-01, 1.04213310e-04, 2.20500784e-03, 3.95345836e-03])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = grid_search.best_estimator_.feature_importances_\n",
    "feature_importances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Displaying the feature importance beside the corresponding feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.35117901927358136, 'median_income'),\n",
       " (0.15184858511746416, 'INLAND'),\n",
       " (0.10461781968879616, 'bedrooms_per_room'),\n",
       " (0.0709238841205685, 'longitude'),\n",
       " (0.06429221407042549, 'population_per_households'),\n",
       " (0.06428335780716063, 'latitude'),\n",
       " (0.06319333650459649, 'rooms_per_households'),\n",
       " (0.042599411317074634, 'housing_median_age'),\n",
       " (0.01841159040162379, 'total_rooms'),\n",
       " (0.017400971217563246, 'population'),\n",
       " (0.016590488640857572, 'households'),\n",
       " (0.016014525839824576, 'total_bedrooms'),\n",
       " (0.012382116492405764, '<1H OCEAN'),\n",
       " (0.003953458356310837, 'NEAR OCEAN'),\n",
       " (0.0022050078418477505, 'NEAR BAY'),\n",
       " (0.00010421330989907835, 'ISLAND')]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_encoder = full_pipeline.named_transformers_[\"cat\"]\n",
    "cat_one_hot_attribs = list(cat_encoder.categories_[0])\n",
    "\n",
    "all_attribs = num_attribs + attributes + cat_one_hot_attribs\n",
    "sorted(zip(feature_importances, all_attribs), reverse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Impolication: \n",
    "> Only one ocean_proximity category is really useful, so you could try dropping the others"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Evaluating System on Test Set</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 48642.97334623934\n"
     ]
    }
   ],
   "source": [
    "# Final Model\n",
    "final_model = grid_search.best_estimator_\n",
    "\n",
    "# Features and Targets\n",
    "X_test = strat_test.drop(\"median_house_value\", axis=1)\n",
    "Y_test = strat_test[\"median_house_value\"].copy()\n",
    "\n",
    "# Pass Features through Pipeline\n",
    "X_test_prepared = full_pipeline.transform(X_test)\n",
    "\n",
    "# Make predicton\n",
    "final_predictions = final_model.predict(X_test_prepared)\n",
    "\n",
    "# Calculate Error\n",
    "final_mse = mean_squared_error(Y_test, final_predictions)\n",
    "final_rmse = np.sqrt(final_mse) \n",
    "\n",
    "print(\"Mean Squared Error: {}\".format(final_rmse))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a judge of how precise the estimates are, we can compute the confidence intervels for the gemeral errors using the ```scipy.stats.t.interval()``` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46471.28227281784 < x < 50721.766884109726\n"
     ]
    }
   ],
   "source": [
    "# Set confidence interval\n",
    "confidence = .95\n",
    "\n",
    "# Calculate Squared Error\n",
    "squared_errors = (final_predictions - Y_test) ** 2\n",
    "\n",
    "# Calculate the resulting interval\n",
    "interval = np.sqrt(\n",
    "    stats.t.interval(\n",
    "        confidence, \n",
    "        len(squared_errors) - 1,\n",
    "        loc=squared_errors.mean(),\n",
    "        scale=stats.sem(squared_errors)\n",
    "    )\n",
    ")\n",
    "\n",
    "# Display\n",
    "print(\"{} < x < {}\".format(interval[0], interval[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now comes the project prelaunch phase: you need to present your solution (highlighting what you have learned, what worked and what did not, what assumptions were made, and what your system’s limitations are), document everything, and create nice presentations with **clear visualizations and easy-to-remember statements** (e.g., *“the median income is the number one predictor of housing prices”*). \n",
    "\n",
    "In this California housing example, the final performance of the system is not better than the experts’ price estimates, which were often off by about 20%, but it may still be a good idea to launch it, especially if this frees up some time for the experts so they can work on more interesting and productive tasks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ways to deploy model to production:\n",
    "- Save model including preprocessing and prediction pipline. Load model into production envirument.\n",
    "- Wrap model with dedicated webserver. Then application can make queries through REST API.\n",
    "- Deploy model onto a cloud web server. Application can then make request in Jason format.\n",
    "\n",
    "Write monitoring code:\n",
    "- Checks for system performance. \n",
    "- Check in regular intervals as defult.\n",
    "- Create drop trigger alerts for model faliure.\n",
    "- Check for model decay or data rot, (e.g. model accuracy).\n",
    "- Use down stream metrics to measure perfomence (e.g. user retention). \n",
    "- Check for changes in useres seaches, (e.g. location, image format/ qulity).\n",
    "\n",
    "Have back ups of your model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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