{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# California House Pricing\n",
    "\n",
    "*Alex Philip Berger*\n",
    "\n",
    "6/9/2019"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Intro"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, I do Random Forest regressions to estimate (median) housing prices in California. Specifically, I focus on (Scikit Learn's) **Stratified Sampling, Imputers, Pipelines, K-fold cross validation, and Fine-tuning through grid search**. This also means less focus on other parts of the modeling process.\n",
    "\n",
    "Data is obtained from https://www.census.gov/, with a few features removed, as well as a categorical variable (ocean_proximity) added. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Common imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Random Seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Plot configuration\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### 2. Get Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_housing_data():\n",
    "    csv_path = \"data/housing/housing.csv\"\n",
    "    return pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing = load_housing_data()\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### 3. Inspect Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
      "longitude             20640 non-null float64\n",
      "latitude              20640 non-null float64\n",
      "housing_median_age    20640 non-null float64\n",
      "total_rooms           20640 non-null float64\n",
      "total_bedrooms        20433 non-null float64\n",
      "population            20640 non-null float64\n",
      "households            20640 non-null float64\n",
      "median_income         20640 non-null float64\n",
      "median_house_value    20640 non-null float64\n",
      "ocean_proximity       20640 non-null object\n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info() # data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"ocean_proximity\"].value_counts() # distribution of the categorical variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "    </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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe() # distributions of numerical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HAL8J3Al8AXhZqbYKuKLc3lTuU7Z/PjOzlJ9ezt52JLAUuL4/rZCkenM6myRJkqQ6OBzYWNZF2ge4PDM/GxF3AJdGxNuALwMXl/oXAx+NiC1UI5BOB8jM2yPicuAOYDdwdpkmJ0lqwySSJEmSpKGXmV8Fnt+k/B6anF0tM38EnNZiXxcAF3Q7RkkadU5nkyRJkiRJUlsmkSRJkiRJktSWSSRJkiRJkiS1ZRJJkiRJkiRJbZlEkiRJkiRJUlsmkSRJPRcRGyLigYi4raHsf0bE1yLiqxHxmYhY2LDt3IjYEhF3RcSJDeUrStmWiFjb73ZIkiRJ85lJJElSP3wEWDGtbDPw3Mz8FeDrwLkAEXEUcDrwnPKY90fEgohYALwPOAk4CnhlqStJkiSpD0wiSZJ6LjO/COycVva5zNxd7l4LLC63VwKXZuaPM/NeYAtwTLlsycx7MvMnwKWlriRJkqQ+2HfQAUiSBLwWuKzcXkSVVJqyrZQB3Det/NhmO4uI1cBqgLGxMSYmJmZ88snJybZ16mqU2waj3T7bVk+t2rZm2e49ykb1byBJGl0mkSRJAxUR/w3YDXxsqqhJtaT56Nlsts/MXA+sB1i+fHmOj4/PGMPExATt6tTVKLcNRrt9tq2eWrXtzLVX7lG29Yw960mSNMxMIkmSBiYiVgEvBY7PzKmE0DbgiIZqi4Ht5XarckmSJEk95ppIkqSBiIgVwJuA387MRxo2bQJOj4j9I+JIYClwPXADsDQijoyI/agW397U77glSZKk+cqRSJKknouITwDjwKERsQ04j+psbPsDmyMC4NrM/IPMvD0iLgfuoJrmdnZm/rTs5xzgamABsCEzb+97YyRJkqR5yiSSJKnnMvOVTYovnqH+BcAFTcqvAq7qYmiSJEmSZsnpbJIkSZIkSWrLJJIkSZIkSZLaMokkSZIkSZKktkwiSZIkSZIkqS0X1m5hydorm5ZvXXdKnyORJEmSJEkaPEciSZIkSZIkqS2TSJIkSZIkSWrLJJIkSZIkSZLaMokkSZIkSZKktmadRIqIBRHx5Yj4bLl/ZERcFxF3R8RlEbFfKd+/3N9Sti9p2Me5pfyuiDix242RJEmSJElSb3QyEun1wJ0N998OXJiZS4FdwFml/CxgV2Y+C7iw1CMijgJOB54DrADeHxEL9i58SZIkSZIk9cOskkgRsRg4BfhQuR/Ai4FPliobgVPL7ZXlPmX78aX+SuDSzPxxZt4LbAGO6UYjJEmSJEmS1Fv7zrLeu4A3AgeW+4cAD2bm7nJ/G7Co3F4E3AeQmbsj4qFSfxFwbcM+Gx/zqIhYDawGGBsbY2JiYrZtedTk5GRHj1uzbHf7SsVc4ulEp7EPi7rGDfWN3bj7r86xS5JUdxFxBHAJ8HTgZ8D6zHx3RLwF+H3gO6XqmzPzqvKYc6lmSvwU+JPMvLqUrwDeDSwAPpSZ6/rZFkmqq7ZJpIh4KfBAZt4UEeNTxU2qZpttMz3msYLM9cB6gOXLl+f4+Pj0Km1NTEzQyePOXHvlrOtuPaPzeDrRaezDoq5xQ31jN+7+q3PskiSNgN3Amsy8OSIOBG6KiM1l24WZ+beNlactp/EM4F8i4hfL5vcBL6E6sH1DRGzKzDv60gpJqrHZjER6EfDbEXEy8ETg56hGJi2MiH3LaKTFwPZSfxtwBLAtIvYFngrsbCif0vgYSZIkSWopM3cAO8rtH0TEnTSZ2dDg0eU0gHsjonE5jS2ZeQ9ARFxa6ppEkqQ22iaRMvNc4FyAMhLpzzPzjIj4B+BlwKXAKuCK8pBN5f6/lu2fz8yMiE3AxyPinVRHApYC13e3OZIkSZJGXTkD9POB66gOep8TEa8GbqQarbSLmZfTuG9a+bFNnmOvl9kYdtOX9Rg7oPVSH6PY/vm2VIHtHW39au9s10Rq5k3ApRHxNuDLwMWl/GLgoyXTv5NqCCmZeXtEXE6V4d8NnJ2ZP92L55ckSZI0z0TEU4BPAW/IzO9HxEXA+VRLZZwPvAN4La2X02h2cqGeLLMx7KYv67Fm2W7ecWvzn4i9XtZjEObbUgW2d7T1q70dJZEycwKYKLfvocnZ1TLzR8BpLR5/AXBBp0FKkiRJUkQ8gSqB9LHM/DRAZt7fsP2DwGfL3ZmW03CZDUmag2ZZeEmSJEkaKhERVLMe7szMdzaUH95Q7XeA28rtTcDpEbF/RBzJY8tp3AAsjYgjI2I/qpkTm/rRBkmqu72ZziZJkiRJ/fIi4FXArRFxSyl7M/DKiDiaakraVuC/wMzLaUTEOcDVwAJgQ2be3s+GSFJdmUSSJEmSNPQy80s0X+foqhke03Q5jcy8aqbHSZKaczqbJEmSJEmS2jKJJEmSJEmSpLZMIkmSei4iNkTEAxFxW0PZwRGxOSLuLtcHlfKIiPdExJaI+GpEvKDhMatK/bsjYtUg2iJJkiTNVyaRJEn98BFgxbSytcA1mbkUuKbcBziJ6gw6S4HVwEVQJZ2A84BjgWOA86YST5IkSZJ6zySSJKnnMvOLwM5pxSuBjeX2RuDUhvJLsnItsLCcvvlEYHNm7szMXcBm9kxMSZIkSeoRk0iSpEEZy8wdAOX6sFK+CLivod62UtaqXJIkSVIf7DvoACRJmqbZ6ZtzhvI9dxCxmmoqHGNjY0xMTMz4hJOTk23r1NUotw1Gu322rZ5atW3Nst17lI3q30CSNLpMIkmSBuX+iDg8M3eU6WoPlPJtwBEN9RYD20v5+LTyiWY7zsz1wHqA5cuX5/j4eLNqj5qYmKBdnboa5bbBaLfPttVTq7adufbKPcq2nrFnPUmShpnT2SRJg7IJmDrD2irgiobyV5eztB0HPFSmu10NnBARB5UFtU8oZZIkSZL6wJFIkqSei4hPUI0iOjQitlGdZW0dcHlEnAV8EzitVL8KOBnYAjwCvAYgM3dGxPnADaXeWzNz+mLdkiRJknrEJJIkqecy85UtNh3fpG4CZ7fYzwZgQxdDkyRJkjRLTmeTJEmSJElSWyaRJEmSJEmS1JZJJEmSJEmSJLVlEkmSJEmSJEltmUSSJEmSJElSWyaRJEmSJEmS1JZJJEmSJEmSJLVlEkmSJEmSJEltmUSSJEmSJElSWyaRJEmSJEmS1JZJJEmSJEmSJLVlEkmSJEmSJEltmUSSJEmSJElSWyaRJEmSJEmS1JZJJEmSJEmSJLVlEkmSJEnS0IuIIyLiCxFxZ0TcHhGvL+UHR8TmiLi7XB9UyiMi3hMRWyLiqxHxgoZ9rSr1746IVYNqkyTVjUkkSZIkSXWwG1iTmc8GjgPOjoijgLXANZm5FLim3Ac4CVhaLquBi6BKOgHnAccCxwDnTSWeJEkz23fQAUiSJGm4LFl7ZdPyretO6XMk0mMycwewo9z+QUTcCSwCVgLjpdpGYAJ4Uym/JDMTuDYiFkbE4aXu5szcCRARm4EVwCf61hhJqimTSJIkSZJqJSKWAM8HrgPGSoKJzNwREYeVaouA+xoetq2UtSqf/hyrqUYwMTY2xsTERFfbMAzWLNv9uPtjB+xZNmUU2z85OTmS7WrF9o62frXXJJIkSZKk2oiIpwCfAt6Qmd+PiJZVm5TlDOWPL8hcD6wHWL58eY6Pj88p3mF25rRRh2uW7eYdtzb/ibj1jPE+RNRfExMTjOLr2ortHW39aq9rIkmSJEmqhYh4AlUC6WOZ+elSfH+Zpka5fqCUbwOOaHj4YmD7DOWSpDbaJpEi4okRcX1EfKWcBeEvS/mREXFdOaPBZRGxXynfv9zfUrYvadjXuaX8rog4sVeNkiRJkjRaohpydDFwZ2a+s2HTJmDqDGurgCsayl9dztJ2HPBQmfZ2NXBCRBxUFtQ+oZRJktqYzUikHwMvzsznAUcDK8qH8NuBC8tZEHYBZ5X6ZwG7MvNZwIWlHuXMCacDz6FauO79EbGgm42RJEmSNLJeBLwKeHFE3FIuJwPrgJdExN3AS8p9gKuAe4AtwAeBPwIoC2qfD9xQLm+dWmRbkjSztmsilbMZTJa7TyiXBF4M/G4p3wi8heq0mSvLbYBPAu8tRw1WApdm5o+BeyNiC9UpNf+1Gw2RJElS51qdiU0aNpn5JZqvZwRwfJP6CZzdYl8bgA3di06S5odZrYkUEQsi4haq+cWbgW8AD2bm1NL9jWc0ePRsB2X7Q8AhzPIsCJKk+SUi/rRMl74tIj5RplF3PGVakiRJUm/N6uxsmflT4OiIWAh8Bnh2s2rleq/OgtCNU2l2emq7VqexbKbXp8yr62kI6xo31Dd24+6/Osc+rCJiEfAnwFGZ+cOIuJxq6vPJVFOmL42ID1BNlb6IhinTEXE61ZTpVwwofEmSJGlemVUSaUpmPhgRE8BxwMKI2LeMNmo8o8HU2Q62RcS+wFOBnczyLAjdOJVmp6e2m35qy5n0+tSWdT0NYV3jhvrGbtz9V+fYh9y+wAER8e/Ak4AddDhlukxZkCRJktRDbZNIEfE04N9LAukA4Depjvx+AXgZcCl7ngVhFdVaRy8DPp+ZGRGbgI9HxDuBZwBLgeu73B5JUo1k5rci4m+BbwI/BD4H3MQsp0xHxNSU6e827rfTUa2jPMpslNsGo92+frVtECOy5+Pr1uzvPKp/A0nS6JrNSKTDgY3lTGr7AJdn5mcj4g7g0oh4G/BlqtNtUq4/WhbO3kk1LYHMvL1MU7gD2A2cXabJSZLmqXJq5ZXAkcCDwD8AJzWp2m7K9OMLOhzVOsqjzEa5bTDa7etX2wYxIns+vm7N/s69HuEuSVK3zebsbF8Fnt+k/B6qs6tNL/8RcFqLfV0AXNB5mJKkEfWbwL2Z+R2AiPg08EI6nzItSZIkqcdmdXY2SZJ65JvAcRHxpIgIqlM038FjU6ah+ZRpaJgy3cd4JUmSpHnLJJIkaWAy8zqqBbJvBm6l6pfWA28C/qxMjT6Ex0+ZPqSU/xmwtu9BS5IkSfNUR2dnkySp2zLzPOC8acUdT5nW8FjSYo2dretO6XMkkiRJ6iZHIkmSJEmSJKmteTUSqdWRUUmSJEkaVf4OktQtjkSSJEmSJElSW/NqJFI3uM6DJEmSJEmajxyJJEmSJEmSpLZMIkmSJEmSJKktk0iSJEmSJElqyySSJEmSJEmS2jKJJEmSJEmSpLZMIkmSJEmSJKktk0iSJEmSJElqa99BByBJkuaHJWuvbFq+dd0pfY5EkiRJc2ESSZIkaR5olcSTJEmaLaezSZIkSZIkqS2TSJIkSZIkSWrL6WySJEmSJNVQs6nKrjWoXnIkkiRJkiRJktoyiSRJkiRp6EXEhoh4ICJuayh7S0R8KyJuKZeTG7adGxFbIuKuiDixoXxFKdsSEWv73Q5JqjOTSJIkSZLq4CPAiiblF2bm0eVyFUBEHAWcDjynPOb9EbEgIhYA7wNOAo4CXlnqSpJmwTWRJEmSJA29zPxiRCyZZfWVwKWZ+WPg3ojYAhxTtm3JzHsAIuLSUveOLocrSSPJJJIkSZKkOjsnIl4N3AisycxdwCLg2oY620oZwH3Tyo9tttOIWA2sBhgbG2NiYqLLYffPmmW7Z1Vv7IDWdevc/lYmJydr365mr1erNo1Cezthe3vDJJIkSZKkuroIOB/Icv0O4LVANKmbNF/OI5vtODPXA+sBli9fnuPj410IdzDObHIGr2bWLNvNO25t/hNx6xnjXYxoOExMTFDn1xWav7atXqtRaG8nbG9vmESSJEmSVEuZef/U7Yj4IPDZcncbcERD1cXA9nK7VbkkqQ0X1pYkSZJUSxFxeMPd3wGmzty2CTg9IvaPiCOBpcD1wA3A0og4MiL2o1p8e1M/Y5akOnMkkiRpoCJiIfAh4LlUUwpeC9wFXAYsAbYCL8/MXRERwLuBk4FHgDMz8+YBhK15bkmz6QPrThlAJHtqFps0CiLiE8A4cGhEbAPOA8Yj4miq/mMr8F8AMvP2iLicasHs3cDZmfnTsp9zgKuBBcCGzLy9z02RpNoyiSRJGrR3A/+cmS8rR4WfBLwZuCYz10XEWmAt8CaqUzIvLZdjqdbCaLogqiRptGTmK5sUXzxD/QuAC5qUXwVc1cXQJGnecDqbJGlgIuLngF+n/AjIzJ9k5oNUp1veWKptBE4tt1cCl2TlWmDhtKmL1Wh2AAAgAElEQVQMkiRJknrEkUiSpEF6JvAd4MMR8TzgJuD1wFhm7gDIzB0RcVipv4g9T828CNjRuNNOT8s8yqeAHUTbZnsq6Sl7E9+gXrtOTqk8V3NtW6d//050q43z8X+uH+8ZSZJ6zSSSJGmQ9gVeAPxxZl4XEe+mmrrWSqtTNj++oMPTMo/yKWAH0bbZnkp6yt6cNnpQr10np1Seq7m2rdO/fye61cb5+D/Xj/eMJEm95nQ2SdIgbQO2ZeZ15f4nqZJK909NUyvXDzTU99TMkiRJ0gCYRJIkDUxmfhu4LyJ+qRQdT3UmnU3AqlK2Crii3N4EvDoqxwEPTU17kyRJktRbTmeTJA3aHwMfK2dmuwd4DdVBjssj4izgm8Bppe5VwMnAFuCRUleSJElSH5hEkiQNVGbeAixvsun4JnUTOLvnQamvljRbK2bdKQOIZE/NYpMkSZqv2iaRIuII4BLg6cDPgPWZ+e6IOBi4DFgCbAVenpm7IiKAd1MdKX4EODMzby77WgX8Rdn12zJzI5IkSZoTk1ySJKmfZrMm0m5gTWY+GzgOODsijqI6e841mbkUuIbHzqZzErC0XFYDFwGUpNN5wLHAMcB5EXFQF9siSZIkSZKkHmmbRMrMHVMjiTLzB8CdwCJgJTA1kmgjcGq5vRK4JCvXAgvLmXVOBDZn5s7M3AVsBlZ0tTWSJEmSJEnqiY7WRIqIJcDzgeuAsakz4mTmjog4rFRbBNzX8LBtpaxV+fTnWE01gomxsTEmJiY6CRGAycnJpo9bs2x3x/uarbnE2Uyr2IddXeOG+sZu3P1X59glSZIkaW/NOokUEU8BPgW8ITO/Xy191Lxqk7KcofzxBZnrgfUAy5cvz/Hx8dmG+KiJiQmaPe7MHq4bsPWMPZ9vLlrFPuzqGjfUN3bj7r86xy5J3TDMi6BLkqTem82aSETEE6gSSB/LzE+X4vvLNDXK9QOlfBtwRMPDFwPbZyiXJEmSJEnSkGubRCpnW7sYuDMz39mwaROwqtxeBVzRUP7qqBwHPFSmvV0NnBARB5UFtU8oZZIkSZIkSRpys5nO9iLgVcCtEXFLKXszsA64PCLOAr4JnFa2XQWcDGwBHgFeA5CZOyPifOCGUu+tmbmzK62QJEmSJElST7VNImXml2i+nhHA8U3qJ3B2i31tADZ0EqAkSRpezdbIkSRJ0mia1ZpIkiRJkiRJmt9mfXY2SZKkfmk1wskzgUmSJA2OI5EkSZIkSZLUliORJEmSusDRU5IkadQ5EkmSJEmSJEltORJJkiSJ4TjTXGMMa5bt5sxy39FMkiRpGJhE6pJmXzz9widJkpzmJkmSRoVJJEmSVBvTEzJTo3VMyEiSJPWeSSRJkqQB6GT63DBMtZMkSTKJJEmSJEnSiGh14OEjK57c50g0ikwiSZKk2utkbUJH9Uj1FBEbgJcCD2Tmc0vZwcBlwBJgK/DyzNwVEQG8GzgZeAQ4MzNvLo9ZBfxF2e3bMnNjP9shSXW2z6ADkCRJkqRZ+AiwYlrZWuCazFwKXFPuA5wELC2X1cBF8GjS6TzgWOAY4LyIOKjnkUvSiDCJJEmSJGnoZeYXgZ3TilcCUyOJNgKnNpRfkpVrgYURcThwIrA5M3dm5i5gM3smpiRJLTidTZIkSVJdjWXmDoDM3BERh5XyRcB9DfW2lbJW5XuIiNVUo5gYGxtjYmKiu5H30Zplu2dVb+yA1nXr3P5WJicnh65dt37roablyxY9tWn5bF9bGM729pLt7Q2TSJKkgYuIBcCNwLcy86URcSRwKXAwcDPwqsz8SUTsD1wC/F/A94BXZObWAYUtSRpe0aQsZyjfszBzPbAeYPny5Tk+Pt614PrtzFmuBbdm2W7ecWvzn4hbzxjvYkTDYWJigmF7XVu9Vq3+/rN9baFaWHvY2ttLw/j69lK/2ut0NknSMHg9cGfD/bcDF5Y1LnYBZ5Xys4Bdmfks4MJST5I0f91fpqlRrh8o5duAIxrqLQa2z1AuSZoFRyJJkgYqIhYDpwAXAH9WzqjzYuB3S5WNwFuoFkVdWW4DfBJ4b0REZjY9iqzu8YxmkobUJmAVsK5cX9FQfk5EXEq1iPZDZbrb1cBfNSymfQJwbp9jlqTaMokkSRq0dwFvBA4s9w8BHszMqUn+jetVPLqWRWbujoiHSv3v9i9cSdIgRMQngHHg0IjYRnWWtXXA5RFxFvBN4LRS/SrgZGAL8AjwGoDM3BkR5wM3lHpvzczpi3VLklowiSRJGpiIeCnwQGbeFBHjU8VNquYstjXut6PFUEd54cVuta2ThTv7aaZFYP/uY1c0LV+zrJcRdc9MbRsmc3l/zcf/uWav5aj+DXolM1/ZYtPxTeomcHaL/WwANnQxNEmaN0wiSZIG6UXAb0fEycATgZ+jGpm0MCL2LaORGtermFrLYltE7As8lT1P99zxYqijvPBit9rWycKd/TTTIrB1V5u23fpw0+Kt605p+ZD5+D/X7H9oFBcqliSNNhfWliQNTGaem5mLM3MJcDrw+cw8A/gC8LJSbfoaF6vK7ZeV+q6HJEmSJPWBSSRJ0jB6E9Ui21uo1jy6uJRfDBxSyv8MWDug+CRJkqR5pwZjpCVJ80FmTgAT5fY9wDFN6vyIxxZNlSRJktRHJpEkSZLUdUtarKM101pJvdiHJEnqHqezSZIkSZIkqS1HIkmSJKlWWo1QasZRS5IkdY9JJEmSJEnqI6dqSqorp7NJkiRJkiSpLZNIkiRJkiRJasskkiRJkiRJktoyiSRJkiRJkqS2TCJJkiRJkiSpLZNIkiRJkiRJamvfQQcgSZKk+WPJ2itZs2w3Zzac4tzTmkuSVA8mkSRJ0uMsafhxL0mSJE1pm0SKiA3AS4EHMvO5pexg4DJgCbAVeHlm7oqIAN4NnAw8ApyZmTeXx6wC/qLs9m2ZubG7TZEkSVId9TJx2Wrfjn6SJKlzsxmJ9BHgvcAlDWVrgWsyc11ErC333wScBCwtl2OBi4BjS9LpPGA5kMBNEbEpM3d1qyHDyC8tkiRJkiRpVLRdWDszvwjsnFa8EpgaSbQROLWh/JKsXAssjIjDgROBzZm5sySONgMrutEASZIkSZIk9d5c10Qay8wdAJm5IyIOK+WLgPsa6m0rZa3K9xARq4HVAGNjY0xMTHQc3OTkJH/3sSv2KF+zrONd9cRMbZqcnJxTmwetrnFDfWM37v6rc+ySJEmStLe6vbB2NCnLGcr3LMxcD6wHWL58eY6Pj3ccxMTEBO/40sMdP65ftp4x3nLbxMQEc2nzoNU1bqhv7Mbdf3WOXZIkSfXlSS80LOaaRLo/Ig4vo5AOBx4o5duAIxrqLQa2l/LxaeUTc3xuSZLUwvQvmWuW7X5cByxJ0ly43qskmHsSaROwClhXrq9oKD8nIi6lWlj7oZJouhr4q4g4qNQ7ATh37mFLkqTZ8ou/JEmSuqFtEikiPkE1iujQiNhGdZa1dcDlEXEW8E3gtFL9KuBkYAvwCPAagMzcGRHnAzeUem/NzOmLdUuSJEmSpCHlgSm1TSJl5itbbDq+Sd0Ezm6xnw3Aho6ikySpxvyiJQ2vZv+f/m9KkjSzbi+sLUmSasJFOiVJktSJfQYdgCRJkiTtjYjYGhG3RsQtEXFjKTs4IjZHxN3l+qBSHhHxnojYEhFfjYgXDDZ6SaoPk0iSJEmSRsFvZObRmbm83F8LXJOZS4Fryn2Ak4Cl5bIauKjvkUpSTZlEkiQNTEQcERFfiIg7I+L2iHh9KffosSRpb60ENpbbG4FTG8ovycq1wMKIOHwQAUpS3bgmkiRpkHYDazLz5og4ELgpIjYDZ1IdPV4XEWupjh6/iccfPT6W6ujxsQOJXJI0TBL4XEQk8PeZuR4Yy8wdAJm5IyIOK3UXAfc1PHZbKdvRuMOIWE01UomxsTEmJia6FuyaZbublnfzOWbzfNONHTD7ulN6FXM/TE5ODl38nf79O9FJe2/91kNNy9csa15/2P6OMJyvby/1q70mkSRJA1O+3E99wf9BRNxJ9UV+JTBeqm0EJqiSSI8ePQaujYiFEXH41I+EUeTi15I0Ky/KzO0lUbQ5Ir42Q91oUpZ7FFSJqPUAy5cvz/Hx8a4ECnBmq7N3ntG955jN8023Ztlu3nFrZz8RexVzP0xMTNDN17UbZvtazcVHVjx51u3tNI5hfB8M4+vbS/1qr0kkSdJQiIglwPOB6+jz0eNeHbnpxpHmvT0iOZejynUyyu2zbf3Xjc+BVp8nzdo7n46Q91pmbi/XD0TEZ4BjgPunDjSU6WoPlOrbgCMaHr4Y2N7XgCWppkwiSZIGLiKeAnwKeENmfj+i2UHiqmqTsr0+etyrIzfdONK8t0ck53JUuU5GuX22bQBufbhp8dZ1p8x6F60+T5r9Lw/jkfs6iognA/uUEa1PBk4A3gpsAlYB68r1FeUhm4BzIuJSqinRD43yiFb1V7MRxJ18hvTSrd96qPln0ZDEp3oYwt5bkjSfRMQTqBJIH8vMT5fi2h09dtqZJA3MGPCZcgBiX+DjmfnPEXEDcHlEnAV8Ezit1L8KOBnYAjwCvKb/IfeO/ZGkXjKJJEkamKi+8V8M3JmZ72zY5NFjSdKsZOY9wPOalH8POL5JeQJn9yE0SRo5JpEkSYP0IuBVwK0RcUspezNV8sijx5KGwjBPT5EkqZ9MIkmSBiYzv0TzdY7Ao8eSpBHgAQJJo8Qk0gC06kg8oiVJkiRJkoaVSSRJkvrMo9KSJEmqo30GHYAkSZIkSZKGnyORJEmSJEmapxwhrU44EkmSJEmSJEltORJJkiRJ6lCrI/drlu3mTI/qS5JGlCORJEmSJEmS1JYjkYbIkrVX7nH0auu6UwYYkSRJkiRJUsWRSJIkSZIkSWrLkUiSJEmSNKRarb81LDMWhj2+YeDfSKPEJJIkSZIkSR1olRiSRp1JJEmSJEkaAiYmJA0710SSJEmSJElSW45EkiRJkiRJfdFsxJ3rQ9WHSaQh5yJskiRJklTx95E0WCaRJEmSJElDZT6sDzXqbRz19s1XJpEkSZIkSWrCRIj0eCaRasphnJLUe35xlCRJas/vTPOHSSRJkiRJUld1klQYlgPhJkKk9kwiSZIkSVLNmPDQfNarM7w546c9k0iSJEmSJGlgTN7Uh0mkEVPHYaOSJEmSJA0rk1yPMYkkSZIkSRqYfk/Nm3q+Nct2c6bTAqWOmESax3o1j1SSJEmS+snfNqPJtb+GT9+TSBGxAng3sAD4UGau63cMas1hepLqwL5EkrS37Euk+aGXiaj5mLzsaxIpIhYA7wNeAmwDboiITZl5Rz/jUOdMLkkaFvYlkqS9ZV8yPziKZX6Z/no7XbE3+j0S6RhgS2beAxARlwIrAT+sa6qO84lNfEm1Z18iSdpb9iWSeqLfyct+/76NzOzfk0W8DFiRma8r918FHJuZ5zTUWQ2sLnd/CbhrDk91KPDdvQx3UOoae13jhvrGbtz9143Yfz4zn9aNYOarHvUldX5ftjPKbYPRbp9tq6d+tM2+ZC/18XdJ3Yzy/2Yztne02d6Zzakv6fdIpGhS9rgsVmauB9bv1ZNE3JiZy/dmH4NS19jrGjfUN3bj7r86xz5iut6XjPJrO8ptg9Fun22rp1Fu24jpy++Suplv71/bO9psb2/s0+snmGYbcETD/cXA9j7HIEmqN/sSSdLesi+RpDnodxLpBmBpRBwZEfsBpwOb+hyDJKne7EskSXvLvkSS5qCv09kyc3dEnANcTXUqzQ2ZeXsPnqrOw07rGntd44b6xm7c/Vfn2EdGj/qSUX5tR7ltMNrts231NMptGxl9/F1SN/Pt/Wt7R5vt7YG+LqwtSZIkSZKkeur3dDZJkiRJkiTVkEkkSZIkSZIktTVySaSIWBERd0XElohYOwTxbIiIByLitoaygyNic0TcXa4PKuUREe8psX81Il7Q8JhVpf7dEbGqD3EfERFfiIg7I+L2iHh9jWJ/YkRcHxFfKbH/ZSk/MiKuK3FcVhZRJCL2L/e3lO1LGvZ1bim/KyJO7HXs5TkXRMSXI+KzdYk7IrZGxK0RcUtE3FjKhv69Up5zYUR8MiK+Vt7vv1aX2NUdw9Zv7I1O+py66bRfqpNO+606mm3fVked9IHSMGn12dOw/e8iYnJQ8XXbDJ+1EREXRMTXSx/zJ4OOtRtmaO/xEXFz+cz6UkQ8a9Cxdsso9zXNNGnvx8p32tvKd8In9OSJM3NkLlSL4n0DeCawH/AV4KgBx/TrwAuA2xrK/gZYW26vBd5ebp8M/BMQwHHAdaX8YOCecn1QuX1Qj+M+HHhBuX0g8HXgqJrEHsBTyu0nANeVmC4HTi/lHwD+sNz+I+AD5fbpwGXl9lHlPbQ/cGR5by3ow3vmz4CPA58t94c+bmArcOi0sqF/r5Tn3Qi8rtzeD1hYl9i9dOX1H7p+Yy/bM+s+p24XOuyX6nTptN+q42W2fVsdL530gV68DNOl1WdPub8c+CgwOeg4e91e4DXAJcA+Zdthg461x+39OvDsUv5HwEcGHWsX2zyyfc0s23tyed0D+ESv2jtqI5GOAbZk5j2Z+RPgUmDlIAPKzC8CO6cVr6T64Uq5PrWh/JKsXAssjIjDgROBzZm5MzN3AZuBFT2Oe0dm3lxu/wC4E1hUk9gzM6eOmjyhXBJ4MfDJFrFPtemTwPEREaX80sz8cWbeC2yheo/1TEQsBk4BPlTuRx3ibmHo3ysR8XNUP7ovBsjMn2Tmg3WIXV0zdP3G3uiwz6mVOfRLtTGHfqtWOuzbRkXt35cafa0+eyJiAfA/gTcOLLgemOGz9g+Bt2bmz0q9BwYUYlfN0N4Efq6UPxXYPoDwum6+9TXT2wuQmVeV1z2B64HFvXjuUUsiLQLua7i/rZQNm7HM3AHVl2LgsFLeKv6BtiuqaVLPp8pe1yL2MrTvFuABqh/03wAezMzdTeJ4NMay/SHgkAHF/i6qDvtn5f4h1CPuBD4XETdFxOpSVof3yjOB7wAfLkNBPxQRT65J7OqO+fDatXo/19Ys+6Va6bDfqptO+rY66qQPlIbK9M+ezLwOOAfYNPUeHiUt2vsLwCsi4saI+KeIWDrYKLunRXtfB1wVEduAVwHrBhljF416XzPd9PY+qkxjexXwz7144lFLIkWTsux7FHPXKv6BtSsingJ8CnhDZn5/pqpNygYWe2b+NDOPpsq+HgM8e4Y4hiL2iHgp8EBm3tRYPEMMQxF38aLMfAFwEnB2RPz6DHWHKe59qab+XJSZzwceppp20Mowxa7u8LWrmQ76pVrpsN+qjTn0bXXUSR8oDZXpnz3l/Xsa8HeDjaw3mrT3uVRLQPwoM5cDHwQ2DDLGbmrR3j8FTs7MxcCHgXcOMsZumCd9zaNatLfR+4EvZub/6cXzj1oSaRtwRMP9xQzn8Lz7yxQYyvXUkMlW8Q+kXSWD+SngY5n56VJci9inlKlJE1TzfxdGxL5N4ng0xrL9qVTTQfod+4uA346IrVRTal5MlWEe9rjJzO3l+gHgM1Q/gOrwXtkGbCtHZaAa7voC6hG7umM+vHat3s+102G/VEuz7LfqpNO+rXY67AOlodTw2fMbwLOALeX/9kkRsWWAofVEQ3tXUH0X+FTZ9BngVwYUVs80tPck4HkN330vA144qLi6aOT7mmn2aG9E/D8AEXEe8DSq9ZJ6YtSSSDcAS8sq7PtRLTa8acAxNbMJmDp70yrgiobyV5czBBwHPFSGkV4NnBARB0V1do8TSlnPlDmkFwN3ZmZjdroOsT8tIhaW2wcAv0m1dsYXgJe1iH2qTS8DPl/mkW4CTo/qLGhHAkup5pb2RGaem5mLM3MJ1Xv385l5xrDHHRFPjogDp25Tvca3UYP3SmZ+G7gvIn6pFB0P3FGH2NU1dek39kar93OtzKFfqo059Fu1MYe+rVbm0AdKQ6PFZ89Nmfn0zFxS/m8fycyROHtXi/Z+DfhfVEkHgP9MtfB07c3Qtzw1In6xVHtJKau1Ue9rpmvR3t+LiNdRrdX6yqk1vnoVwEhdqFYk/zrVWgL/bQji+QSwA/h3qiz3WVTzM68B7i7XB5e6AbyvxH4rsLxhP6+lWiB5C/CaPsT9H6mG+30VuKVcTq5J7L8CfLnEfhvwP0r5M6mSKVuAfwD2L+VPLPe3lO3PbNjXfyttugs4qY/vm3EeW2V/qOMu8X2lXG6f+r+rw3ulPOfRwI3l/fK/qM6uVovYvXTtPTBU/cZetmXWfU7dLp32S3W6dNpv1fUym76tbpdO+0AvXobp0uqzZ1qdUTo7W6vP2oXAleW73b9SjdQZeLw9bO/vlLZ+hWp00jMHGWcP2j1yfU0H7d1dvs9OfU/a43+6G5coTyZJkiRJkiS1NGrT2SRJkiRJktQDJpEkSZIkSZLUlkkkSZIkSZIktWUSSZIkSZIkSW2ZRJIkSZIkSVJbJpEkSZIkSZLUlkkkSZIkSZIktWUSSZIkSZIkSW2ZRJIkSZIkSVJbJpEkSZIkSZLUlkkkSZIkSZL+f/buPU6ysjz0/e+BEQQUh4u0MIMZjOgJOidqJkB0b09HFLmo4z5RgxKZUbInF4gax8hgzEYFc8ZsETB48ExkAkTCJYhhIkQdLx23OwERRAckhgmO0DBhQC4yEC8tz/ljvS01PVVd3dV179/386lPV73rXauet6p6rapnve+7JDVlEkmSJEmSJElNmUSSJEmSJElSUyaRJEmSJEmS1JRJJEmSJEmSJDVlEkmSJEmSJElNmUSSJEmSJElSUyaRJEmSJEmS1JRJJEmSJEmSJDVlEkmSJEmSJElNmUSSJEmSJElSUyaRJEmSJEmS1JRJJEmSJEmSJDVlEkmSJEmSJElNmUSSJEmSJElSUyaRJEmSJEmS1JRJJEmSJEmSJDVlEkmSJEmSJElNmUSSJEmSJElSUyaRJEmSJEmS1JRJJEmSJEmSJDVlEkmSJEmSJElNmUSSJEmSJElSUyaRJEmSJEmS1JRJJEmSJEmSJDVlEkmSJEmSJElNmURS34qILRHxyg4/x/aIeE4bt5cR8dx2bU+SJEmSpH5hEknzWmY+LTPvBIiIiyLirF7HJEmqLyI+EBGfLvefXU4E7NrB5xv640JEjEXE7/Y6DkkaFD04Fn0yIv6sU9uXZmtBrwOQJEmarcy8C3har+OQJM1f3TgWZebvd3L70mzZE0l9LyJ2j4hzI+Lecjs3InYvy0YjYjwiVkfEtojYGhFvq1l3v4j4h4j4UUTcGBFnRcTXa5ZnRDw3IlYBJwLvLWcT/qF2eU39Hc5KR8SflOe8NyLeXifuj0bEXRFxXzmLsEfnXilJkiRJkjrHJJIGwZ8CRwIvAn4VOBx4f83yZwHPABYBJwOfiIh9yrJPAI+VOivKbSeZuQ64FPiLMsTttc2CiohjgPcArwIOBabO3/QR4Hkl7ueW+P5Hs+1K0qArc9r9SUR8JyIei4gLI2IkIv4xIh6NiC9N7qcj4siI+OeIeDgivh0RozXbOSQi/qmssxHYv2bZkpLoX1Aevy0ibi9174yI36upO+0Jhyb2iYhry3ZviIhfrtnuS8sJikfK35dOeQ1eWfO4dvjDUyPi0xHxw9LuGyNipCx7Rnm9tkbEPeXkR8NhEuWExcMR8cKasmdGxH9GxAERsU9EfC4i7o+Ih8r9xQ229YsYG7zGs4pNknppWI5FUXMSu9k2ImKPiDg7In5Qjk1fj3ISOyJeFxG3lTaORcSvtPJaNXu9NPxMImkQnAh8KDO3Zeb9wAeBt9Ys/1lZ/rPMvA7YDjy/fLH9LeCMzHw8M78LXNzGuN4E/HVm3pqZjwEfmFwQEQH8d+CPM/PBzHwU+HPghDY+vyT1s9+iSrI/D3gt8I/A+6i+fO8CvCMiFgHXAmcB+1Il5j8TEc8s2/hb4Kayzpk0OBFQbANeA+wNvA04JyJeUrN8uhMO03kz1XFnH2Az8GGAiNi3xP5xYD/gY8C1EbHfDLa5osRycFn394H/LMsuBiaoTj68GDgaaDhnUWb+BLi6xDnpTcA/ZeY2qtf6r4FfAp5dnuf8GcRYz6xik6Q+MCzHolrTbeOjwK8BLy1teS/wREQ8D7gMeBfwTOA64B8iYrea7TZ9rQBm8HppyJlE0iA4CPhBzeMflLJJP8zMiZrHj1ONTX4m1bxfd9csq73fjrhqt1cb4zOBPYGbSob+YeDzpVyS5oO/zMz7MvMe4H8BN2Tmt0rS47NUSYjfAa7LzOsy84nM3Ah8EzguIp4N/DrwZ5n5k8z8GvAPjZ4sM6/NzH/Pyj8BXwT+a02VuiccZtCOqzPzG+U4cylV71KA44E7MvNvMnMiMy8D/pXqi3czP6NKHj03M3+emTdl5o9Kb6RjgXdl5mMlCXQOzU9A/C07JpHeUsrIzB9m5mfKyZRHqZJg/9cMYtzBHGKTpF4almNRrUYn0HcB3g68MzPvKceXfy5t/W3g2szcmJk/o0o27UGVbJrNa8V0r9cs26EB5cTaGgT3Up1Bva08fnYpa+Z+qjOmi4F/K2UHT1M/65Q9TpUMmvQsYLzc3zple8+uuf8A1dneF5QdsSTNN/fV3P/POo+fRrVvf2NE1CZengJ8lSpR/1Dp6TnpBzTYj0fEscAZVGdQd6Had2+qqdLohEMz/9FgnaknOCbjWzSDbf4NVTsuj4iFwKephm7/ElX7t1YdWoGqLc1OgHwF2CMijijxvojqCz8RsSdVsucYqt5UAE+PiF0z8+cziHVSq7FJUi8Ny7GoVqNt7A88Ffj3OuvscMzKzCci4m52PGbN5LWC6V8vzQP2RNIguAx4f5njYX+qeYU+3WQdypfjq4EPRMSeEfF/ACdNs8p9wHOmlN0CvCUido1qDqTas7dXAisj4rDyJf2Mmud+Avgrqi6sB0DV9TMiXt0sbkmaR+4G/iYzF9bc9srMtVSJ+n0iYq+a+s+ut9tkJ3QAACAASURBVJGoLrbwGaozqyOZuZCqq37Uq98mkyc4aj0bmDxx8Bg7n4QAoJw9/mBmHkZ1Fvg1VMenu4GfAPvXvB57Z+YLpgukHHOupOqN9Bbgc6XXEcBqqrPcR2Tm3sDLS3m916ZhzK3GJkkDYJCPRbUeAH4M/HKdZTscs8rUGwfz5DFrNqZ7vTQPmETSIDiLqovkd6gy+TeXspk4lWrM8H9Qnfm9jOpLcD0XAoeV4Wd/X8reSTU04WGquZkmy8nMfwTOpToDvLn8rXVaKb8+In4EfInZd1eVpGH2aeC1EfHqkqx/apk0dHFm/oBq3//BiNgtIv4LjYeK7QbsTumBWs4EH93h2K8DnhcRb4mIBRHx28BhwOfK8luAEyLiKRGxDHjD5IoR8ZsRsbTM3fcjqqEJP8/MrVRDH86OiL0jYpeI+OWImMnws7+lGq5wYrk/6elUZ5AfLvM4nVFn3Um3AC+PiGdHxDOA0ycXzDE2Sepng3ws+oVyQmE98LGIOKi05TdKcutK4PiIOCoinkJ1guEnwD+38FQNX6+2NUZ9zSSS+lZmLsnML2XmjzPzHZl5YLm9IzN/XOqMZebieuuV+/dn5vHlbOmvlyrjNXUjMzeX+3dk5otKNv31peybmfmCzHx6Zr41M9+cme+vWX9tZj4rMw/KzPVTtvfjzHxfZj6nPP+vZObHO/qiSdIAycy7geVUE3feT3V280948vvJW4AjgAepkh+XNNjOo1QTfl4JPFTW29Dh2H9I1YNoNfBDqslLX5OZD5Qqf0Z1Nvghqom5axM7zwKuokog3Q78E0/2sD2J6ofId8u6VwEHziCeG6h6Eh1ENRnqpHOp5r14ALiean6+RtvYCFxBddLmJp5MiE1qKTZJ6meDfCyq4z1UJ91vpIr3I8Aumfk9qrmM/pLqePBa4LWZ+dPZPsEMXi8NucisNw2MNBzKELbdqHamv0515vh3M/Pvp11RkiRJkiTtwIm1NeyeTjWE7SCqS26eDVzT04gkSZIkSRpA9kSSJEnzVkTcxs4TZAP8XmZe2u14GomIT1INRZjq05n5+92OR5LUPoNyLJLAJJIkSZIkSZJmoK+Hs+2///65ZMmSjm3/scceY6+99mpecYANextt32Cbj+276aabHsjMZ/YopHmp0bFk2D9/k2zncLGdw6XVdnos6b56x5J+/pwa2+z1a1xgbK0ytum1eizp6yTSkiVL+OY3v9mx7Y+NjTE6Otqx7feDYW+j7Rts87F9EfGD3kQzfzU6lgz752+S7RwutnO4tNpOjyXdV+9Y0s+fU2ObvX6NC4ytVcY2vVaPJV6GT5IkSZIkSU2ZRJIkSZIkSVJTJpEkSZIkSZLUlEkkSZIkSZIkNWUSSZIkSZIkSU2ZRJIkSZIkSVJTTZNIEXFwRHw1Im6PiNsi4p2l/AMRcU9E3FJux9Wsc3pEbI6I70XEq2vKjyllmyNiTWeaJEmSJEmSpHZbMIM6E8DqzLw5Ip4O3BQRG8uyczLzo7WVI+Iw4ATgBcBBwJci4nll8SeAVwHjwI0RsSEzv9uOhkiSJEmSJKlzmvZEysytmXlzuf8ocDuwaJpVlgOXZ+ZPMvP7wGbg8HLbnJl3ZuZPgctLXUmSJEmaVkSsj4htEXHrlPI/KqMdbouIv6gpd3SEJLXZTHoi/UJELAFeDNwAvAw4NSJOAr5J1VvpIaoE0/U1q43zZNLp7inlR9R5jlXAKoCRkRHGxsZmE+KsbN++vaPb7wfD3sZBbt+mex7ZqWzpomfs8HiQ2zcTtk+SNF8sWXPtTmUXHbNXDyIZaBcB5wOXTBZExG9SnZj+PzPzJxFxQCl3dESb1fsMA2xZe3yXI5HUSzNOIkXE04DPAO/KzB9FxAXAmUCWv2cDbweizupJ/V5PuVNB5jpgHcCyZctydHR0piHO2tjYGJ3cfj8Y9jYOcvtW1jkQbzlxdIfHg9y+mbB9kiRppjLza+Wkdq0/ANZm5k9KnW2l/BejI4DvR8Tk6AgooyMAImJydIRJJEmagRklkSLiKVQJpEsz82qAzLyvZvlfAZ8rD8eBg2tWXwzcW+43KpckSZKk2Xoe8F8j4sPAj4H3ZOaNzHF0BDQfIdHPPY47EdvqpRN1y2f7PP36uvVrXGBsrTK2zmiaRIqIAC4Ebs/Mj9WUH5iZW8vD/wZMjk3eAPxtRHyMquvoocA3qHooHRoRhwD3UHUvfUu7GiJJ6l8RsR54DbAtM19YyvYFrgCWAFuAN2XmQ+W4cx5wHPA4sHJybr6IWAG8v2z2rMy8uJvtkCT1nQXAPsCRwK8DV0bEc5jj6AhoPkKin3sczzS22QxRq9eLHnbuSd9Mv75u/RoXGFurjK0zmk6sTTX30VuBV0TELeV2HPAXEbEpIr4D/CbwxwCZeRtwJVWX0M8Dp2TmzzNzAjgV+ALV5NxXlrqSpOF3EXDMlLI1wJcz81Dgy+UxwLFUJyAOpToDfAH8Iul0BtUZ48OBMyJin45HLknqZ+PA1Vn5BvAEsD+NR0dMN2pCktRE055Imfl16mfyr5tmnQ8DH65Tft1060mShlODeSyWA6Pl/sXAGHBaKb8kMxO4PiIWRsSBpe7GzHwQICI2UiWmLutw+JKk/vX3wCuAsTJx9m7AAzg6QpI6YlZXZ5MkqY1GJodFZ+bWySvqUM1ZMXW+ikXTlO9kJlf6HOSx6LNhO4eL7Rxc9eaTGcZ2dlJEXEZ1QmH/iBin6p26HlgfEbcCPwVWlJMQt0XE5OiICcroiLKdydERuwLrHR0hSTNnEkmS1G8azWPRqHznwhlc6XOQx6LPhu0cLrZzcNWbT+aiY/YaunZ2Uma+ucGi32lQ39ERktRmM5kTSZKkTrivDFOj/J28LLPzWEiSJEl9yCSSJKlXNgAryv0VwDU15SdF5UjgkTLs7QvA0RGxT5lQ++hSJkmSJKkLHM4mSeq4BvNYrKW6FPPJwF3AG0v164DjgM3A48DbADLzwYg4E7ix1PvQ5CTbkiRJkjrPJJIkqeOmmcfiqDp1EzilwXbWU02iKkmSJKnLTCJJkiRJkn5hSZ2J4CUJTCJJkiRJklrUKOG0Ze3xXY5EUjeYRJIkSV3hDw1JkqTB5tXZJEmSJEmS1JRJJEmSJEmSJDVlEkmSJEmSJElNmUSSJEmSJElSUyaRJEmSJEmS1JRJJEmSJEmSJDVlEkmSJEmSJElNLeh1AJIkabgsWXNtr0OQJElSB9gTSZIkSZIkSU2ZRJIkSZIkSVJTJpEkSZIk9b2IWB8R2yLi1jrL3hMRGRH7l8cRER+PiM0R8Z2IeElN3RURcUe5rehmGyRp0JlEkiRJkjQILgKOmVoYEQcDrwLuqik+Fji03FYBF5S6+wJnAEcAhwNnRMQ+HY1akoaISSRJkiRJfS8zvwY8WGfROcB7gawpWw5ckpXrgYURcSDwamBjZj6YmQ8BG6mTmJIk1efV2SRJkiQNpIh4HXBPZn47ImoXLQLurnk8Xsoaldfb9iqqXkyMjIwwNja2w/Lt27fvVNYvZhrb6qUTHYuh3vNvuucRRvaAv7z0mh3Kly56RsfimKlheD97wdha08+xNWMSSZIkSdLAiYg9gT8Fjq63uE5ZTlO+c2HmOmAdwLJly3J0dHSH5WNjY0wt6xczjW3lmms7FsOWE3d+/pVrrmX10gnO3rSgad1uG4b3sxeMrTX9HFszDmeTJEmSNIh+GTgE+HZEbAEWAzdHxLOoehgdXFN3MXDvNOWSpBmwJ5IkSUNkyZSzyquXTjDam1AkqaMycxNwwOTjkkhalpkPRMQG4NSIuJxqEu1HMnNrRHwB+POaybSPBk7vcuiSNLDsiSRJkiSp70XEZcC/AM+PiPGIOHma6tcBdwKbgb8C/hAgMx8EzgRuLLcPlTJJ0gzYE0mSJElS38vMNzdZvqTmfgKnNKi3Hljf1uD63NReqpLUKnsiSZIkSZIkqSmTSJIkSZIkSWrK4WySJEmSNCSWrLmW1UsnWOkQNkkdYE8kSZIkSZIkNWUSSZIkSZIkSU2ZRJIkSZIkSVJTTedEioiDgUuAZwFPAOsy87yI2Be4AlgCbAHelJkPRUQA5wHHAY8DKzPz5rKtFcD7y6bPysyL29scSZLUCY0uD71l7fFdjkSSJEm9MpOeSBPA6sz8FeBI4JSIOAxYA3w5Mw8FvlweAxwLHFpuq4ALAErS6QzgCOBw4IyI2KeNbZEkSZIkSVKHNE0iZebWyZ5EmfkocDuwCFgOTPYkuhh4fbm/HLgkK9cDCyPiQODVwMbMfDAzHwI2Ase0tTWSJEmSJEnqiKbD2WpFxBLgxcANwEhmboUq0RQRB5Rqi4C7a1YbL2WNyqc+xyqqHkyMjIwwNjY2mxBnZfv27R3dfj8Y9jYOcvtWL53YqWxqWwa5fTNh+yRJkiRpcMw4iRQRTwM+A7wrM39UTX1Uv2qdspymfMeCzHXAOoBly5bl6OjoTEOctbGxMTq5/X4w7G0c5PatrDO/yJYTR3d4PMjtmwnbJ6kR52CSJEnqPzNKIkXEU6gSSJdm5tWl+L6IOLD0QjoQ2FbKx4GDa1ZfDNxbykenlI+1HrokaRhExB8Dv0t1YmET8DbgQOByYF/gZuCtmfnTiNid6mIPvwb8EPjtzNzSi7iHQTsSNY22IUmSpOHTdE6kcrW1C4HbM/NjNYs2ACvK/RXANTXlJ0XlSOCRMuztC8DREbFPmVD76FImSZqnImIR8A5gWWa+ENgVOAH4CHBOuXjDQ8DJZZWTgYcy87nAOaWeJEmSpC6YSU+klwFvBTZFxC2l7H3AWuDKiDgZuAt4Y1l2HXAcsBl4nOqMMpn5YEScCdxY6n0oMx9sSyskSYNsAbBHRPwM2BPYCrwCeEtZfjHwAaqrfS4v9wGuAs6PiMjMnYZHDzt7AEmS+pnHKWk4NU0iZebXqT+fEcBRdeoncEqDba0H1s8mQEnS8MrMeyLio1QnI/4T+CJwE/BwZk7OPl97IYZfXKQhMyci4hFgP+CB2u3O5CINgz7xeb3J+esZ2WPnSfubbaNe/Zk+Xytm83yN2jLo7+dM2c7BVe8zPYztlCQNt1ldnU2SpHYqw5uXA4cADwN/Bxxbp+pkT6O2XaRh0Cc+rzc5fz2rl07wpgbtbLSNqZP8z+b5WjGb56tXFwb//Zwp2zm46n2mLzpmr6FrpyRpuDWdE0mSpA56JfD9zLw/M38GXA28FFgYEZMnOiYv0AA1F28oy58BODRakiRJ6gJ7IkmSeuku4MiI2JNqONtRwDeBrwJvoLpC29SLN6wA/qUs/8p8nA9p2DhvhiRJ0mCwJ5IkqWcy8waqCbJvBjZRHZfWAacB746IzVRzHl1YVrkQ2K+UvxtY0/WgJUk9ERHrI2JbRNxaU/Y/I+JfI+I7EfHZiFhYs+z0iNgcEd+LiFfXlB9TyjZHhMcRSZoFeyJJknoqM88AzphSfCdweJ26P+bJq4FKkuaXi4DzgUtqyjYCp5eLLXwEOB04LSIOA04AXgAcBHwpIp5X1vkE8CqqIdI3RsSGzPxul9ogSQPNnkiSJEmS+l5mfo0p8+Bl5hdrruZ5PdU8elBdtOHyzPxJZn4f2Ex1cuJwYHNm3pmZP6UaNr28Kw2QpCFgTyRJkiRJw+DtwBXl/iKqpNKk8VIGcPeU8iPqbSwiVgGrAEZGRhgbG9th+fbt23cq6werl04wskf1tx/Vi60fXsd+fT/B2FplbJ1hEkmSJEnSQIuIPwUmgEsni+pUS+qPxKh7gYbMXEc1Tx/Lli3L0dHRHZaPjY0xtawfrFxzLauXTnD2pv78qVcvti0njvYmmBr9+n6CsbXK2DqjP/cskiRJkjQDEbECeA1wVM0VO8eBg2uqLQbuLfcblUuSmnBOJEmSJEkDKSKOobqi5+sy8/GaRRuAEyJi94g4BDgU+AZwI3BoRBwSEbtRTb69odtxS9KgsieSJEmSpL4XEZcBo8D+ETFOdWXP04HdgY0RAXB9Zv5+Zt4WEVcC36Ua5nZKZv68bOdU4AvArsD6zLyt642RpAFlEkmSJElS38vMN9cpvnCa+h8GPlyn/DrgujaGJknzhsPZJEmSJEmS1JRJJEmSJEmSJDVlEkmSJEmSJElNmUSSJEmSJElSU06sLXXYkjXX9joESZIkSZLmzCSSJEnagclvSZIk1WMSSZKkIWdSSJIkSe1gEknqI1N/6K1eOsHKNdeyZe3xPYpIkvpLo4TYRcfs1eVIJEmS5h8n1pYkSZIkSVJTJpEkSZIkSZLUlEkkSZIkSZIkNWUSSZIkSZIkSU2ZRJIkSZIkSVJTXp1NkqQ+Ue/KY16dUZIkSf3CnkiSJEmSJElqyiSSJEmSJEmSmnI4myRJmlfqDRsEhw5KkiQ1Y08kSZIkSZIkNWUSSZIkSVLfi4j1EbEtIm6tKds3IjZGxB3l7z6lPCLi4xGxOSK+ExEvqVlnRal/R0Ss6EVbJGlQOZxNkiRJ0iC4CDgfuKSmbA3w5cxcGxFryuPTgGOBQ8vtCOAC4IiI2Bc4A1gGJHBTRGzIzIe61oo2aTQ0V5I6yZ5IkiRJkvpeZn4NeHBK8XLg4nL/YuD1NeWXZOV6YGFEHAi8GtiYmQ+WxNFG4JjORy9Jw8GeSJIkSZIG1UhmbgXIzK0RcUApXwTcXVNvvJQ1Kt9JRKwCVgGMjIwwNja2w/Lt27fvVNZNq5dONFw2ssf0y3upXmy9fB0n9fr9nI6xtcbYOqNpEiki1gOvAbZl5gtL2QeA/w7cX6q9LzOvK8tOB04Gfg68IzO/UMqPAc4DdgU+lZlr29sUSZIkSQIg6pTlNOU7F2auA9YBLFu2LEdHR3dYPjY2xtSyblo5zXC21UsnOHtTf/YXqBvbpsd2qtftK2b2+v2cjrG1xtg6YybD2S6ifhfPczLzReU2mUA6DDgBeEFZ5/+NiF0jYlfgE1Rjkw8D3lzqSpIkSVKr7ivD1Ch/t5XyceDgmnqLgXunKZckzUDTJFKDsceNLAcuz8yfZOb3gc3A4eW2OTPvzMyfApeXupIkSZLUqg3A5BXWVgDX1JSfVK7SdiTwSBn29gXg6IjYp1zJ7ehSJkmagbn0cTw1Ik4CvgmsLhPTLQKur6lTO8Z46tjjI+pttNnY43Ya5HGIMzXsbRyE9s1lPPrkmPF+b2OrBuH9m4thb1+7RMRC4FPAC6mGFLwd+B5wBbAE2AK8KTMfioigGhp9HPA4sDIzb+5B2BoAXrlIGi4RcRkwCuwfEeNUV1lbC1wZEScDdwFvLNWvozpWbKY6XrwNIDMfjIgzgRtLvQ9l5kxPmEvSvNdqEukC4EyqL/tnAmdTfelvNMa4Xo+nlsYet9Mgj0OcqWFv4yC0b7rx6s1MjhnfcuJo+wLqI4Pw/s3FsLevjc4DPp+Zb4iI3YA9gfcxi0s29yZsSVI3ZeabGyw6qk7dBE5psJ31wPo2hiZJ88ZM5kTaSWbel5k/z8wngL+iGq4Gjj2WJM1CROwNvBy4ECAzf5qZDzP7SzZLkiRJ6rCWkkhTvrD/N+DWcn8DcEJE7B4Rh1CdKf4GVXfRQyPikHKW+YRSV5I0vz2H6kqffx0R34qIT0XEXky5ZDPQ7JLNkiRJkjqs6XC2BmOPRyPiRVRD0rYAvweQmbdFxJXAd4EJ4JTM/HnZzqlUk9btCqzPzNva3hpJ0qBZALwE+KPMvCEizqMautbIjC7NPJP59fpxzqp6c6g1inGm861Nzq027Oq9n7Ntd799Hurpx89tJwxjO+t9HoexnZKk4dY0idRg7PGF09T/MPDhOuXXUU1wJ0nSpHFgPDNvKI+vokoi3RcRB2bm1hlesnkHM5lfrx/nrKo3h1qjOdFmOt/a5Nxqw+6iY/ba6f2c7Zx0gzD/XD9+bjthGNtZ7/NY73MrqdLo4ghb1h7f5Ugk1WppOJskSe2Qmf8B3B0Rzy9FR1H1Zp3tJZslSZIkddjwn5qUJPW7PwIuLXPm3Ul1GeZdmMUlmyVJkiR1nkkkSVJPZeYtwLI6i2Z1yWZpruoNnXDYhCRJ0pMcziZJkiRJkqSmTCJJkiRJkiSpKZNIkiRJkiRJasokkiRJkiRJkpoyiSRJkiRJkqSmTCJJkiRJkiSpqQW9DkCSJDVW77LzkiRJUi+YRBoyU39srF46wWhvQpEkSZIkSUPEJJIkSV1m7yJJkiQNIpNIA8ofIJIkSZIkqZucWFuSJEnSQIuIP46I2yLi1oi4LCKeGhGHRMQNEXFHRFwREbuVuruXx5vL8iW9jV6SBodJJEmSJEkDKyIWAe8AlmXmC4FdgROAjwDnZOahwEPAyWWVk4GHMvO5wDmlniRpBhzOJkmSJGnQLQD2iIifAXsCW4FXAG8pyy8GPgBcACwv9wGuAs6PiMjM7GbAak2jaT22rD2+y5FI85M9kSRJkiQNrMy8B/gocBdV8ugR4Cbg4cycKNXGgUXl/iLg7rLuRKm/XzdjlqRBZU8kSZKkBjzjLfW/iNiHqnfRIcDDwN8Bx9apOtnTKKZZVrvdVcAqgJGREcbGxnZYvn379p3Kumn10omGy0b2mH55L3Uqtrm+F71+P6djbK0xts4wiSRJkgbepnseYaVXLpXmq1cC38/M+wEi4mrgpcDCiFhQehstBu4t9ceBg4HxiFgAPAN4cOpGM3MdsA5g2bJlOTo6usPysbExppZ103T7vNVLJzh7U3/+1OtUbFtOHJ3T+r1+P6djbK0xts5wOJskSZKkQXYXcGRE7BkRARwFfBf4KvCGUmcFcE25v6E8piz/ivMhSdLMmESSJEmSNLAy8waqCbJvBjZR/cZZB5wGvDsiNlPNeXRhWeVCYL9S/m5gTdeDlqQB1Z99HCVJkiRphjLzDOCMKcV3AofXqftj4I3diEuSho09kSRJkiRJktSUSSRJkiRJkiQ1ZRJJkiRJkiRJTZlEkiRJkiRJUlNOrC1JktRBS9ZcW7d8y9rjuxyJJEnS3NgTSZIkSZIkSU3ZE0mSJEmSNNDq9fq0x6fUfvZEkiRJkiRJUlP2RJLmCc/OSJIkSZLmwp5IkiRJkiRJasqeSJIkSbPU6IprkiRJw8wkkiRJkiT1KZPWkvpJ0yRSRKwHXgNsy8wXlrJ9gSuAJcAW4E2Z+VBEBHAecBzwOLAyM28u66wA3l82e1ZmXtzepkjDq9GXB+c0kiRJkiR1y0x6Il0EnA9cUlO2BvhyZq6NiDXl8WnAscCh5XYEcAFwREk6nQEsAxK4KSI2ZOZD7WrIsPLMgyRJkiRJ6gdNJ9bOzK8BD04pXg5M9iS6GHh9TfklWbkeWBgRBwKvBjZm5oMlcbQROKYdDZAkDb6I2DUivhURnyuPD4mIGyLijoi4IiJ2K+W7l8eby/IlvYxbkiRJmk9anRNpJDO3AmTm1og4oJQvAu6uqTdeyhqV7yQiVgGrAEZGRhgbG2sxxOa2b9/e0e23w+qlE3Naf2QP+r6NczHs7+HIHtOvP5u219tOr1+7QXj/5mLY29dm7wRuB/Yujz8CnJOZl0fEJ4GTqXq3ngw8lJnPjYgTSr3f7kXAkiRJ0nzT7om1o05ZTlO+c2HmOmAdwLJly3J0dLRtwU01NjZGJ7ffDivnOJxt9dIJ3tTnbZyLYX8PVy+d4OxNjf9Nt5w4Oqc4ZrN+JwzC+zcXw96+domIxcDxwIeBd5f59V4BvKVUuRj4AFUSaXm5D3AVcH5ERGbWPaZIkiRJap9Wk0j3RcSBpRfSgcC2Uj4OHFxTbzFwbykfnVI+1uJza5aclFlSnzsXeC/w9PJ4P+DhzJzsPlfbe/UXPVszcyIiHin1H+heuJIkSdL81GoSaQOwAlhb/l5TU35qRFxONbH2IyXR9AXgzyNin1LvaOD01sOWJA2DiJi8+udNETE6WVynas5gWe12mw6N7uVww7kOVZ6NZsNih8UgtrOVz998GSY7jO2s9/kcxnb2SkQsBD4FvJDquPB24HvM8orSkqTpNU0iRcRlVL2I9o+IcaqrrK0FroyIk4G7gDeW6tdR7Yw3U+2Q3waQmQ9GxJnAjaXehzJz6mTdkqT552XA6yLiOOCpVHMinUt1YYYFpTfSZK9WeLLH63hELACewc4Xf5jR0OheDjec61Dl2Wg2LHZYDGI7WxlSPF+GyQ5jO+v93190zF5D184eOg/4fGa+oVyMYU/gfcziitK9CVuSBkvTb1uZ+eYGi46qUzeBUxpsZz2wflbRSZKGWmaeTumZWnoivSczT4yIvwPeAFzOzj1eVwD/UpZ/xfmQJGl+i4i9gZcDKwEy86fATyNiOU9OqXEx1XQap1FzRWng+ohYODlVR5dDl6SBM1in7CRJ88VpwOURcRbwLeDCUn4h8DcRsZmqB9IJPYpPktQ/ngPcD/x1RPwqcBPVVT9ne0XpHZJIzYZGd2s4YitDdft5iG83Y5vN+9PPw0uNrTXG1hkmkaR5zEnX1U8yc4xy0YXMvBM4vE6dH/PkEGpJkqD6TfMS4I8y84aIOI9q6FojM5pfr9nQ6G4Nu2xlCHQ/D/HtZmyzGTbcz8Noja01xtYZu/Q6AEmSJEmag3FgPDNvKI+vokoq3VeuJM0MrygtSWqiP9PTkiRJkjQDmfkfEXF3RDw/M79HNXfrd8ttxleU7kHo6jB73UvtZxJJkiRJ0qD7I+DScmW2O6muEr0Ls7iitCSpOZNIkiRJkgZaZt4CLKuzaFZXlJYkTc85kSRJkiRJktSUSSRJkiRJkiQ1ZRJJkiRJkiRJTZlEkiRJkiRJUlMmkSRJkiRJktSUV2eTJEnqgSVrrt2pbMva43sQiSRJ0szYE0mSJEmSJElN2RNJkiSpT9TrnQT2UJIkSf3BnkiSJEmSJElqyiSSJEmSJEmSmnI4myRJkiRp3vOCB1Jz9kSSJEmSJElSUyaRJEmSJEmS1JRJJEmSJEmSJDXlnEiSJEnaQb15QcC5QSRJmu/siSRJ1UhNZgAAIABJREFUkiRJkqSmTCJJkiRJkiSpKZNIkiRJkiRJasokkiRJkqSBFxG7RsS3IuJz5fEhEXFDRNwREVdExG6lfPfyeHNZvqSXcUvSIHFibe3AiTQlSZI0oN4J3A7sXR5/BDgnMy+PiE8CJwMXlL8PZeZzI+KEUu+3exGwJA0ak0iaEZNLkiRJ6lcRsRg4Hvgw8O6ICOAVwFtKlYuBD1AlkZaX+wBXAedHRGRmdjNmSRpEJpEkSeqQRgl4SVLbnQu8F3h6ebwf8HBmTpTH48Cicn8RcDdAZk5ExCOl/gO1G4yIVcAqgJGREcbGxnZ4wu3bt+9U1gmrl040rzTFyB6trdcN/RDbX156zU5lVVw71+3Ge9xMtz5rrTC21vRzbM2YRJIkSZrHTHZq0EXEa4BtmXlTRIxOFtepmjNY9mRB5jpgHcCyZctydHR0h+VjY2NMLeuElS38j65eOsHZm/rzp16/xtYori0njnY/mCm69VlrhbG1pp9ja6b//nslSZLUdiaLNMReBrwuIo4Dnko1J9K5wMKIWFB6Iy0G7i31x4GDgfGIWAA8A3iw+2FL0uDx6mySJEmSBlZmnp6ZizNzCXAC8JXMPBH4KvCGUm0FMDmGaUN5TFn+FedDkqSZMYkkSZIkaRidRjXJ9maqOY8uLOUXAvuV8ncDa3oUnyQNHIezSZIkDRGHrWk+y8wxYKzcvxM4vE6dHwNv7GpgkjQkTCJJA6zeD4Uta4/vQSSSpF4wYSRJkrppTkmkiNgCPAr8HJjIzGURsS9wBbAE2AK8KTMfiogAzgOOAx4HVmbmzXN5fkmSJEkaFiaGJfW7dvRE+s3MfKDm8Rrgy5m5NiLWlMenAccCh5bbEcAF5a8kaZ6KiIOBS4BnAU8A6zLzPE9ISDua/GG5eulES5f7liRJaodOTKy9HLi43L8YeH1N+SVZuZ7qkpsHduD5JUmDYwJYnZm/AhwJnBIRh/HkCYlDgS/z5KSntSckVlGdkJAkSZLUBXPtiZTAFyMigf8vM9cBI5m5FSAzt0bEAaXuIuDumnXHS9nW2g1GxCqqHwaMjIwwNjY2xxAb2759e0e33w6rl07Maf2RPRpvo17bZ/t8vX79hv09nO79a6TR6zGb7XTrNR2E928uhr197VCOF5PHjEcj4naqY8NyYLRUu5hqktTTqDkhAVwfEQsj4sDJ444kSZKkzplrEullmXlvSRRtjIh/naZu1CnLnQqqRNQ6gGXLluXo6OgcQ2xsbGyMTm6/HebaZX310gnO3lT/bd5y4uicn6/eNrpp2N/D6d6/Rhq9J7OJo1vv6yC8f3Mx7O1rt4hYArwYuIEunJDoRpJvricC2qGVZPQgsp3d4UmG1tV734axnZKk4TanJFJm3lv+bouIz1JdQvO+ybPCZbjatlJ9HDi4ZvXFwL1zeX5J0nCIiKcBnwHelZk/qqY+ql+1TllLJyS6keTrh7lrWklGDyLb2R2eZGhdvf3BRcfsNXTtlIZNo8nOvSKy5quWv4VExF7ALmX4wV7A0cCHgA3ACmBt+XtNWWUDcGpEXE41ofYjDj+QJEXEU6gSSJdm5tWl2BMSUh+q92PKH1KSJM0fc5lYewT4ekR8G/gGcG1mfp4qefSqiLgDeFV5DHAdcCewGfgr4A/n8NySpCFQrrZ2IXB7Zn6sZtHkCQnY+YTESVE5Ek9ISJIkSV3Tck+kzLwT+NU65T8EjqpTnsAprT6fJGkovQx4K7ApIm4pZe+jOgFxZUScDNwFvLEsuw44juqExOPA27obriRJkjR/Df/kAZKkvpWZX6f+PEfgCQlJktSnnCtJ89VchrNJkiRJkiRpnjCJJEmSJEmSpKYcziYNmUZdayVJkiRJmguTSJIkSWqZ84JIkjR/mETSUKj3BdYvr5IkSZIktY9JJM3JsCdvPLsqSVJrPIZKkjR8nFhbkiRJ0sCKiIMj4qsRcXtE3BYR7yzl+0bExoi4o/zdp5RHRHw8IjZHxHci4iW9bYEkDQ57Is1jTsAsSZKkITABrM7MmyPi6cBNEbERWAl8OTPXRsQaYA1wGnAscGi5HQFcUP5KkpowiSRhQk2SpF6aehxevXSClWuudeibZiQztwJby/1HI+J2YBGwHBgt1S4GxqiSSMuBSzIzgesjYmFEHFi2I0mahkkkDS3nYpAkSZpfImIJ8GLgBmBkMjGUmVsj4oBSbRFwd81q46XMJJIkNWESqY/YG0aSJElqTUQ8DfgM8K7M/FFENKxapyzrbG8VsApgZGSEsbGxHZZv3759p7K5Wr10oi3bGdmjfdtqt36NrV1xtfszAZ35rLWLsbWmn2NrxiSSJEmSpIEWEU+hSiBdmplXl+L7JoepRcSBwLZSPg4cXLP6YuDeqdvMzHXAOoBly5bl6OjoDsvHxsaYWjZXK9t0Unn10gnO3tSfP/X6NbZ2xbXlxNG5BzNFJz5r7WJsrenn2Jrpv/9eSZIkDS17XqvdoupydCFwe2Z+rGbRBmAFsLb8vaam/NSIuJxqQu1HnA9JkmbGJJLUJn4pliRJ6omXAW8FNkXELaXsfVTJoysj4mTgLuCNZdl1wHHAZuBx4G3dDVeSBpdJJEmSJM079U7+ePGNwZSZX6f+PEcAR9Wpn8ApHQ1K85b7Fg07k0jqOXe0kiSpHq+0qmFlD3ZJg8okkiRJkgaKJ6AkDQP3ZRpEJpHUdp41lCRJkqSKPc80TEwiqWvceUqSpG7z+4ekQVJvn7V66QSj3Q9FqmuXXgcgSZIkSZKk/mdPJEmSJA08exxJktR59kSSJEmSJElSU/ZEkiRJkiSpj3nxIvULk0gaKFN3nquXTrCyB93X7TIvSdLw8UeaJEnTM4kkSVIbmFyWJEnSsDOJ1AP+0OgtX39JkiRJw6Debxt7T6qTTCJJ2ond+SVJetJsTkB5rJQkDTOTSOpL9haSJEmDyO8wkqRhZhJJkiRJkqQh4agCdZJJJEmSJEmS1DLnZuqOfnidTSJJmpNOnunwLIokSRp0DnGUNEyGNok0k5316qUTrFxzrT9IJUmSJEmSmhjaJFK39UO3Mqmf2ItIkiRJ6h/t+H4+dRuTHTNm83xzjUG91fUkUkQcA5wH7Ap8KjPXdjuGbrHrqoaNn2n1i14eS/w/kKThMJ9+l0jT6YfvNp6AHhxdTSJFxK7AJ4BXAePAjRGxITO/2804pppNL6J++AeT5rtO9fzz4DUYunkscZ8vScOpX3+XSJoZRwL1Trd7Ih0ObM7MOwEi4nJgOdB3O2t/OEidUfu/NV33V2kaA3MskST1LY8l0gCYze/y2Qy166RhT2ZFZnbvySLeAByTmb9bHr8VOCIzT62pswpYVR4+H/heB0PaH3igg9vvB8PeRts32OZj+34pM5/Zi2CGRRuPJcP++ZtkO4eL7RwurbbTY8kctelY0s+fU2ObvX6NC4ytVcY2vZaOJd3uiRR1ynbIYmXmOmBdV4KJ+GZmLuvGc/XKsLfR9g0226cWteVYMl/eH9s5XGzncJkv7exTcz6W9PP7Z2yz169xgbG1ytg6Y5cuP984cHDN48XAvV2OQZI02DyWSJLmymOJJLWg20mkG4FDI+KQiNgNOAHY0OUYJEmDzWOJJGmuPJZIUgu6OpwtMyci4lTgC1SX0lyfmbd1M4YpujJsrseGvY22b7DZPs1aG48l8+X9sZ3DxXYOl/nSzr7TpmNJP79/xjZ7/RoXGFurjK0DujqxtiRJkiRJkgZTt4ezSZIkSZIkaQCZRJIkSZIkSVJT8yaJFBFvjIjbIuKJiFhWU/6qiLgpIjaVv6+os+6GiLi1uxHPzmzbFxF7RsS1EfGvZb21vYu+uVbev4j4tVK+OSI+HhH1LuXaF6Zp334R8dWI2B4R509Z582lfd+JiM9HxP7dj3xmWmzfbhGxLiL+rXxOf6v7kc9MK+2rqdP3+5dhExHHRMT3yr5hTa/jaSQi1kfEttrPR0TsGxEbI+KO8nefUh5lP7e57BNeUrPOilL/johYUVNedx/Z6Dk62M6Dy//J7eX/6J3D2NaIeGpEfCMivl3a+cFSfkhE3FBiuCKqCX6JiN3L481l+ZKabZ1eyr8XEa+uKa/72W70HJ0UEbtGxLci4nPD2s6I2FI+V7dExDdL2VB9blVfo89grzXan/aTqfuGfhERCyPiqqi+c94eEb/R65gmRcQfl/fz1oi4LCKe2sNYZvzdpE9i+5/lPf1ORHw2Ihb2S2w1y94TERl9/FtuJ5k5L27ArwDPB8aAZTXlLwYOKvdfCNwzZb3/G/hb4NZet6Gd7QP2BH6z3N8N+F/Asb1uRzvfP+AbwG8AAfzjgLZvL+C/AL8PnF9TvgDYBuxfHv8F8IFet6Nd7SvLPgicVe7vMtnWfry10r6yfCD2L8N0o5o89d+B55R937eBw3odV4NYXw68pPbzUf7X15T7a4CPlPvHlf1cAEcCN5TyfYE7y999yv19yrK6+8hGz9HBdh4IvKTcfzrwb8Bhw9bW8txPK/efAtxQ4r8SOKGUfxL4g3L/D4FPlvsnAFeU+4eVz+3uwCHl87zrdJ/tRs/R4ff13WX/9rnpYhjkdgJbmHJsGrbPrbe673vfHkdosD/tdVxTYtxh39AvN+Bi4HfL/d2Ahb2OqcSyCPg+sEd5fCWwsofxzPi7SZ/EdjSwoNz/SD/FVsoPpprc/wdTjyf9fJs3PZEy8/bM/F6d8m9l5r3l4W3AUyNid4CIeBrVju6s7kXamtm2LzMfz8yvljo/BW4GFncv4tmZbfsi4kBg78z8l6z+Qy8BXt/FkGdlmvY9lplfB348ZVGU217lzOTewL1T1+8XLbQP4O3A/1PqPZGZD3Q4zJa10r5B2r8MmcOBzZl5Z9n3XQ4s73FMdWXm14AHpxQvp/qiS/n7+pryS7JyPbCw7AdfDWzMzAcz8yFgI3BMk31ko+foiMzcmpk3l/uPArdTfWkeqraWeLeXh08ptwReAVzVoJ2TsV0FHFX298uByzPzJ5n5fWAz1ee67me7rNPoOToiIhYDxwOfKo+ni2Fg29nAUH1uVVffHkem2Z/2han7hn4REXtT/ci/EKrfRpn5cG+j2sECYI+IWEDVEaBn3/ln+d2kq+rFlplfzMyJ8vB6evR7t8HrBnAO8F6q7wMDY94kkWbot4BvZeZPyuMzgbOBx3sXUltNbR9Qdd8EXgt8uSdRtU9t+xYB4zXLxumjg+hcZebPgD8ANlEdSA6jHPiGQU1X0zMj4uaI+LuIGOlpUO03bPuXQbEIuLvm8aDtG0YycytUPxaAA0p5o3ZNV95oH9noOTquDGV6MVUvnaFraxnGcQtVT9KNVL0ZHq75glsb2y/aU5Y/AuzH7Nu/3zTP0SnnUn0pfqI8ni6GQW5nAl+Majj9qlI2dJ9b7WQgjiNT9qf9Yuq+oV88B7gf+Osy1O5TEbFXr4MCyMx7gI8CdwFbgUcy84u9jWong7JPejtVr86+EBGvoxpF8+1exzJbQ5VEiogvlbGiU29Nzw5ExAuourj9Xnn8IuC5mfnZDoc9Y+1sX035AuAy4OOZeWdnIp+ZNrev3vxHPc3wzqV9dbb1FKok0ouBg4DvAKe3OeTZxtS29lGdcVkM/O/MfAnwL1QH0J5p8/vXd/uXeaTv9g1t0qhdsy3vmdI77zPAuzLzR9NVrVM2EG3NzJ9n5ouo9m+HUw2F3ala+duudna1/RHxGmBbZt5UWzxNDAPZzuJl5Rh1LHBKRLx8mrqD0B7NTN+/N7PYn3ZNg31Dv1hANdTogsx8MfAY1bCsnivzCy2nGtZ7ENUohN/pbVSDJyL+FJgALu11LFDNTwz8KfA/eh1LKxb0OoB2ysxXtrJe6Vr5WeCkzPz3UvwbwK9FxBaq1+mAiBjLzNF2xNqKNrdv0jrgjsw8d67xzVWb2zfOjt0VF9Pj4V6ttq+BF5Vt/jtARFxJjw92bW7fD6l66EwmWf4OOLmN25+1Nrev7/Yv88g41fjzST3fN8zSfRFxYGZuLcNdtpXyRu0aB0anlI8x/T6y0XN0TEmMfwa4NDOvbhLHQLcVIDMfjogxqrlxFkbEgtKDpja2yXaOlxM+z6DqCj/dZ7he+QPTPEcnvAx4XUQcBzyVarj1udPEMKjtJMtw+szcFhGfpUoMDu3nVr/Q18eRBvvTfrDTviEiPp2Z/ZAQGQfGM3Oy19ZV9EkSCXgl8P3MvB8gIq4GXgp8uqdR7aiv90lRXbDgNcBRZXhwP/hlqsTgt6vR2CwGbo6IwzPzP3oa2QwMVU+kVpRhM9cCp2fm/54sz8wLMvOgzFxCNTHuvw3iD7xG7SvLzqL6svauXsTWDtO8f1uBRyPiyDJPwknANT0KsxPuAQ6LiGeWx6+iGvc+FMoO/h948ov1UcB3exZQmw3L/mVA3QgcGtVVnHajmsh3Q49jmo0NwOTVm1bw5H5tA3BSVI6k6u6+lWqyxqMjYp9yNvNo4AtN9pGNnqMjyvNfCNyemR+rWTRUbY2IZ5ZjFhGxB9UPg9uBrwJvaNDOydjeAHyl7Bv///buP0ySsjz0/vcOCBhEdhdlDu5iFmVjhBARJ0Bi3rwTSADBuJzrkmRziC5kk33zhiQmkshicg5GJYGcKMb462wEXYwKSDTsEfyxAeYY34QfIshPCStsYFkEdRd0NJKs3u8f9Qz0Dj1dM73T3dUz38919TVVTz1VdVdPd1XXXfXUsxFYFdXz/w4BVlA9gLntZ7vMM9065lxmnpuZy8r+bVWJ+/T5tp0RsW9E7Dc5TPV5u5N59rlVW409jnTYnw7cNPuGJiSQKCftD0XES0pRk353PggcG1XP2kEVW9N+8zd2nxQRJwHnAK/JzMY8QiIz78jMAzNzeflObKV6KH7jE0jAguqd7b9S/XOeBB6lOkAD/CnVLYu3tbwOnDLvchree9Jst48q25lUO6HJ8t8c9HbM5f8PGKX6Qfc14D1ADHo7Zrt9ZdoWqquyE6XOZC80v13+f7dTJVwOGPR2zPH2/RjwhbJ91wIvHPR2zOX2tUxv/P5lvr2oekn617Jv+JNBx9Mhzo9TPf/gP8tnZw3Vc1+uBe4rf5eUugG8t2zTHezaS+BvUD2UeDNwZkt5233kdOvo4Xb+XDke3d6yHz95vm0r8FPArWU77wT+Ryl/EVVyZDPVXZd7l/J9yvjmMv1FLcv6k7It99LS8+h0n+3p1tGHz/AYT/fONq+2s6zrK+V112Qc8+1z62va/38jjyNMsz8ddFxt4nxq39CUF9Vd/l8q790/UHpJbMKLqsfir5b9wUf6tQ+fJpYZ/zZpSGybqZ5hNvl9+EBTYpsyfQtD1Dvb5MFIkiRJkiRJmtaCb84mSZIkSZKkeiaRJEmSJEmSVMskkiRJkiRJkmqZRJIkSZIkSVItk0iSJEmSJEmqZRJJkiRJkiRJtUwiSZIkSZIkqZZJJEmSJEmSJNUyiSRJkiRJkqRaJpEkSZIkSZJUyySSJEmSJEmSaplEkiRJkiRJUi2TSJIkSZIkSaplEkmSJEmSJEm1TCJJkiRJkiSplkkkSZIkSZIk1TKJJEmSJEmSpFomkSRJkiRJklTLJJIkSZIkSZJqmUSSJEmSJElSLZNIkiRJkiRJqmUSSZIkSZIkSbVMIkmSJEmSJKmWSSRJkiRJkiTVMokkSZIkSZKkWiaRJEmSJEmSVMskkiRJkiRJkmqZRJIkSZIkSVItk0iSJEmSJEmqZRJJkiRJkiRJtUwiSZIkSZIkqZZJJEmSJEmSJNUyiSRJkiRJkqRaJpEkSZIkSZJUyySSJEmSJEmSaplEkiRJkiRJUi2TSFrwImI8In6zy3lfGBETEbHHXMclSZIkSVKTmESSZiEitkTEL06OZ+aDmfmczPzBIOOSJM1cRHw4It5eU2csIrbO4TozIg6dq+VJkobHTI470rAwiSRJkhpnatJ+rupKktSOxx1pZkwiqVHKDvnciLg7InZExIciYp8y7bciYnNEbI+IjRHxgpb5MiJ+PyLuj4hvRsT/jIgfKdPeEhF/11J3eam/Z5v1vzgirouIb5XlfDQiFpVpHwFeCPzv0oTtTVOXFREvKLFtL7H+Vsuy3xIRV0TEpRHxnYi4KyJGe/VeSpKGg02iJUlTtTtXkZrAJJKa6HTgRODFwI8DfxoRxwF/AfwKcBDwb8BlU+b7r8AocBSwEviNLtYdZT0vAF4KHAy8BSAzXwc8CPxyacL2l23m/ziwtcz/WuDPI+L4lumvKXEvAjYC7+kiRkma16ZJ2r+mJN8fL8+ye+l0dUv5JyLi6xHxRER8ISIO7zKWN5eLClsi4vSW8r0j4q8i4sGIeDQiPhARz26Z/scR8UhEbIuI35iyzA9HxPsj4pqI+C7wCxGxf7nI8I2I+LeI+NOWiyE/Usb/LSIeK/X2L9MmL2acGREPlQswvx0RPx0Rt5f36z0t6z40Iv5PeV++GRGXd/O+SNJ80oTjTpRm1BFxTkR8HfhQKe90If1nI+Lmss6bI+JnW6aNR8TbI+KfS5z/OyIOKBfJv13qLy91IyIuKseYJ8rx4yd3603VvGUSSU30nsx8KDO3A+cDv0aVWLokM7+cmU8C5wI/M7njKy7MzO2Z+SDwrjLfrGTm5szclJlPZuY3gHcC//dM5o2Ig4GfA87JzO9n5m3AB4HXtVT7YmZeU56h9BHgZbONUZLmu6lJe+AfqJL0fwA8H7iG6sf7Xh0S/J8BVgAHAl8GPtpFKP8FeB6wFFgNrI+Il5RpF1Jd6DgSOLTU+R8AEXES8EfAL5UY2jV5+G9Ux7j9gC8CfwPsD7yI6rjzeuDMUveM8vqFMv05PPMixDFlXb9KdQz8k7Lew4FfiYjJY9nbgM8Di4FlZb2StKA17LizBPgxYG2nC+kRsQS4Gng3cADVecvVEXFAy/JWUZ2LLKW6QP8vVMmpJcA9wHml3gnAz1Md1xZRHUu+1UX8WgBMIqmJHmoZ/jequ3peUIYByMwJqh3b0pr5ZiUiDoyIyyLi4Yj4NvB3VCcQM/ECYHtmfmdKHK0xfr1l+HvAPuGtqpJU51eBq0uS/z+BvwKeDfzsdDNk5iWZ+Z1y4eEtwMsm796Zpf9eLiz8H6of678SEQH8FvCH5eLFd4A/p/qxDtWP/Q9l5p2Z+d2y/qmuysz/LzN/CPxn2cZzS8xbgHfw9EWI04F3Zub95fh3LrBqyvHjbeUCxueB7wIfz8zHMvNh4J+Al5d6/0l1cvKCUv+LXbwnkjTfDeq480PgvHLc+Xc6X0g/BbgvMz+SmTsz8+PAV4FfblnehzLza5n5BFWS62uZ+Y+ZuRP4BLseG/YDfgKIzLwnMx+ZZexaIEwiqYkObhl+IbCtvH5ssjAi9qXKuD9cMx9UP6Z/tGXaf+mw7r8AEvipzHwu8OtUTdwmZYd5twFLImK/KXE8PE19SdLMTL2Q8EOqCwdL21WOiD0i4oKI+Fq5ILClTJrpRYFJO0oSaNLkBYrnUx1XbinNHB4HPlvKJ+OdemFjqtbpzwP2mlKv9SLEC9pM2xMYaSl7tGX439uMP6cMv4nquHZTaabRTdNvSZrvBnXc+UZmfr9DHK0X0qceG+CZF7BndGzIzOuo7nB9L/BoRKyPiOfOMnYtECaR1ERnRcSycovmm4HLgY8BZ0bEkRGxN9UV3xvL1dpJfxwRi0uzsjeU+QBuA34+Il5Yrgac22Hd+wETwOMRsRT44ynTH6VqSvAMmfkQ8M/AX0TEPhHxU8AauruVVZIWutak/dQLCUF14eDhNnWhaiq2kqo51/7A8slZZxnD4nLRYtLkBYpvUv34PjwzF5XX/qUJBMAjPPPCxlStMX+Tp+8Qap1ncvu2tZm2k11PBmYkM7+emb+VmS8A/h/gfRFx6GyXI0nzUBOOO1OX2+lC+tRjA+zGBezMfHdmvoKqGfSP88zzIAkwiaRm+hjV8xruL6+3Z+a1wH8H/p7qx/mLebrZwKSrgFuokkZXAxcDZOYmqoTS7WX6pzus+8+oHsz9RFnGJ6dM/wuqB30/HhF/1Gb+X6M6aGwDPkV1O+qm2i2WJE3VmrS/AjglIo6PiGcBZwNPUiXup9aF6oLAk1RXa3+U6sJDt/4sIvaKiP8LeDXwiXJF+m+BiyLiQICIWBoRJ7bEe0ZEHBYRP8rTz5xoqzwn7wrg/IjYLyJ+DHgjVZNqqJ7L8YcRcUhEPKdsz+WlOcKsRMRpEbGsjO6gOmH5wWyXI0nzUFOOO606XUi/BvjxiPhvEbFnRPwqcBidz3XaiqozhmPKtn4X+D4eGzQNk0hqopsz87ByZXd1Zn4PIDM/kJkvzswlmfnqzNw6Zb5rMvNFmXlAZp5dfpRT5j2rLO/QzPzbzIzJH9+ZOZaZHyzDd2XmK8pD8o7MzHdk5rKW5VyVmS8sy/qrzNwyZVlbS2xLSqwfaJn3LZn56y3ju8wrSdrFU0l7quc7/DrVQ6C/WcZ/OTP/Y2rdkuC/lOqW/oeBu4Ebuozh61SJlm1Ud5X+dmZ+tUw7B9gM3FCaLvwj8BKAzPwM1cOtryt1rpvBun6P6of7/VQP2v4YcEmZdglVZwxfAB6g+nH/e11u008DN0bEBFUvoW/IzAe6XJYkzSdNOO7sotOF9Mz8FtXFjbOpkldvAl6dmd/sYlXPpbo4soNqO75F9Rwo6Rkis9MjXqT+iogtwG9m5j/Ocr4EVmTm5p4EJkmSJEnSAuedSJIkSZIkSaplEkmNkpnLZ3sXUpkvvAtJkjQbEfHmiJho8/rMoGOTJM0/Hnc0H9icTZIkSZIkSbX2HHQAnTzvec/L5cuXdzXvd7/7Xfbdd9/6igPQ5Nig2fEZW/eaHF+TY4O5je+WW275ZmY+f04WNmTKM8++Q9Xbx87MHI2IJVS9Jy4HtgC/kpk7Sle6fw2cDHwPOCMzv1yWsxr407LYt2fmhk7r7fZY0vTP5VTG21vDFO8wxQrfh5/oAAAgAElEQVTG242FfCwZlPlyXtKkWMB46hhPZ02Kp0mxwMzi6fpYkpmNfb3iFa/Ibl1//fVdz9trTY4ts9nxGVv3mhxfk2PLnNv4gC9lA/avg3hRJYmeN6XsL4F1ZXgdcGEZPhn4DBDAsVTd2QIsoeq9agmwuAwv7rTebo8lTf9cTmW8vTVM8Q5TrJnG242FfCwZ1Gu+nJc0KZZM46ljPJ01KZ4mxZI5s3i6PZb4TCRJ0iCtBCbvJNoAnNpSfmk5xt0ALIqIg4ATgU2ZuT0zdwCbgJP6HbQkSZK0EDW6OZskaV5J4PMRkcD/ysz1wEhmPgKQmY9ExIGl7lLgoZZ5t5ay6cp3ERFrgbUAIyMjjI+PzzrYiYmJruYbFOPtrWGKd5hiBeOVJGmYmESSJPXLKzNzW0kUbYqIr3aoG23KskP5rgVVgmo9wOjoaI6Njc062PHxcbqZb1CMt7eGKd5hihWMV5KkYWJzNklSX2TmtvL3MeBTwNHAo6WZGuXvY6X6VuDgltmXAds6lEuSJEnqMZNIkqSei4h9I2K/yWHgBOBOYCOwulRbDVxVhjcCr4/KscATpdnb54ATImJxRCwuy/lcHzdFkiRJWrBsziZJ6ocR4FMRAdWx52OZ+dmIuBm4IiLWAA8Cp5X611D10LYZ+B5wJkBmbo+ItwE3l3pvzczt/dsMSZIkaeEyiSRJ6rnMvB94WZvybwHHtylP4KxplnUJcMlcxyhJkiSpM5uzSZIkSZIkqZZJJEmSJEmSJNVaUM3Zlq+7um35lgtO6XMkkqT5pt0xxuOLJGk2PJZIajrvRJIkSZIkSVItk0iSJEmSJEmqZRJJkiRJkiRJtUwiSZIkSZIkqZZJJEmSJEmSJNUyiSRJkiRJkqRaJpEkSZIkSZJUyySSJEmSJEmSaplEkiRJkiRJUi2TSJIkSZIkSaplEkmSJEmSJEm1TCJJkiRJkiSplkkkSZIkSZIk1TKJJEmSJEmSpFomkSRJkiRJklTLJJIkSZIkSZJqmUSSJEmSJElSLZNIkiRJkiRJqmUSSZIkSZIkSbVmlESKiD+MiLsi4s6I+HhE7BMRh0TEjRFxX0RcHhF7lbp7l/HNZfryluWcW8rvjYgTe7NJkiRJkiRJmmu1SaSIWAr8PjCamT8J7AGsAi4ELsrMFcAOYE2ZZQ2wIzMPBS4q9YiIw8p8hwMnAe+LiD3mdnMkSZIkSZLUCzNtzrYn8OyI2BP4UeAR4DjgyjJ9A3BqGV5ZxinTj4+IKOWXZeaTmfkAsBk4evc3QZIkSdJCEBFbIuKOiLgtIr5UypZExKbSQmJTRCwu5RER7y4tIW6PiKNalrO61L8vIlYPanskadjsWVchMx+OiL8CHgT+Hfg8cAvweGbuLNW2AkvL8FLgoTLvzoh4AjiglN/QsujWeZ4SEWuBtQAjIyOMj4/PfquAiYmJZ8x79hE729btdh3dahdbkzQ5PmPrXpPja3Js0Pz4JElaYH4hM7/ZMr4OuDYzL4iIdWX8HOBVwIryOgZ4P3BMRCwBzgNGgQRuiYiNmbmjnxshScOoNolUMvkrgUOAx4FPUO2Qp8rJWaaZNl35rgWZ64H1AKOjozk2NlYXYlvj4+NMnfeMdVe3rbvl9O7W0a12sTVJk+Mztu41Ob4mxwbNj0+SpAVuJTBWhjcA41RJpJXApZmZwA0RsSgiDip1N2XmdoCI2ET1uI2P9zdsSRo+tUkk4BeBBzLzGwAR8UngZ4FFEbFnuRtpGbCt1N8KHAxsLc3f9ge2t5RPap1HkiRJkuok8PmISOB/lQvQI5n5CEBmPhIRB5a6T7WQKCZbQkxXvotetpCYTruWE3N5N3TT7q42ns6Mp7MmxdOkWKC38cwkifQgcGxE/ChVc7bjgS8B1wOvBS4DVgNXlfoby/i/lOnXZWZGxEbgYxHxTuAFVLeV3jSH2yJJkiRpfntlZm4riaJNEfHVDnUb20JiOu1aTsxlq4mm3V1tPJ0ZT2dNiqdJsUBv46l9sHZm3kj1gOwvA3eUedZT3SL6xojYTPXMo4vLLBcDB5TyN1K1SSYz7wKuAO4GPguclZk/mNOtkSRJkjRvZea28vcx4FNUHfU8WpqpUf4+VqpP1xLCFhKS1KUZ9c6Wmedl5k9k5k9m5utKD2v3Z+bRmXloZp6WmU+Wut8v44eW6fe3LOf8zHxxZr4kMz/Tq42SJEmSNL9ExL4Rsd/kMHACcCdPt4SAZ7aQeH3ppe1Y4InS7O1zwAkRsbg8//WEUiZJqjGT5mySJEmSNGgjwKciAqrzmI9l5mcj4mbgiohYQ/UojtNK/WuAk4HNwPeAMwEyc3tEvA24udR76+RDtiVJnZlEkiRJktR4pYXDy9qUf4vqua1TyxM4a5plXQJcMtcxStJ8ZxJJkqQeWd7mAakAWy44pc+RSJIkSbtvRs9EkiRJkiRJ0sJmEkmS1DcRsUdE3BoRny7jh0TEjRFxX0RcHhF7lfK9y/jmMn15yzLOLeX3RsSJg9kSSZIkaeExiSRJ6qc3APe0jF8IXJSZK4AdwJpSvgbYkZmHAheVekTEYcAq4HDgJOB9EbFHn2KXJEmSFjSTSJKkvoiIZcApwAfLeADHAVeWKhuAU8vwyjJOmX58qb8SuCwzn8zMB6h63Dm6P1sgSZIkLWwmkSRJ/fIu4E3AD8v4AcDjmbmzjG8FlpbhpcBDAGX6E6X+U+Vt5pEkSZLUQ/bOJknquYh4NfBYZt4SEWOTxW2qZs20TvO0rm8tsBZgZGSE8fHx2YbMxMTErOY7+4id9ZWKbuKpM9t4B814e2eYYgXjlSRpmJhEkiT1wyuB10TEycA+wHOp7kxaFBF7lruNlgHbSv2twMHA1ojYE9gf2N5SPql1nqdk5npgPcDo6GiOjY3NOuDx8XFmM98Z666ecd0tp88+njqzjXfQjLd3hilWMF4tTMtnccyQpCaxOZskqecy89zMXJaZy6kejH1dZp4OXA+8tlRbDVxVhjeWccr06zIzS/mq0nvbIcAK4KY+bYYkSZK0oHknkiRpkM4BLouItwO3AheX8ouBj0TEZqo7kFYBZOZdEXEFcDewEzgrM3/Q/7AlSZKkhcckkiSprzJzHBgvw/fTpne1zPw+cNo0858PnN+7CCVJkiS1Y3M2SZIkSZIk1TKJJEmSJEmSpFomkSRJkiRJklTLJJIkSZIkSZJqmUSSJEmSJElSLZNIkiRJkiRJqmUSSZIkSZIkSbVMIkmSJEmSJKmWSSRJkiRJkiTVMokkSZIkSZKkWiaRJEmSJEmSVMskkiRJkiRJkmqZRJIkSZIkSVItk0iSJEmSJEmqZRJJkiRJkiRJtUwiSZIkSZIkqZZJJEmSJEmSJNUyiSRJkiRJkqRaJpEkSZIkDY2I2CMibo2IT5fxQyLixoi4LyIuj4i9SvneZXxzmb68ZRnnlvJ7I+LEwWyJJA0fk0iSJEmShskbgHtaxi8ELsrMFcAOYE0pXwPsyMxDgYtKPSLiMGAVcDhwEvC+iNijT7FL0lAziSRJkiRpKETEMuAU4INlPIDjgCtLlQ3AqWV4ZRmnTD++1F8JXJaZT2bmA8Bm4Oj+bIEkDbc9Bx2AJEmSJM3Qu4A3AfuV8QOAxzNzZxnfCiwtw0uBhwAyc2dEPFHqLwVuaFlm6zxPiYi1wFqAkZERxsfHuwp4YmLiGfOefcTO9pXb6Ha9M41lkIynM+PprEnxNCkW6G08JpEkSZIkNV5EvBp4LDNviYixyeI2VbNmWqd5ni7IXA+sBxgdHc2xsbGpVWZkfHycqfOese7qGc+/5fTu1jvTWAbJeDozns6aFE+TYoHexmMSSZIkSdIweCXwmog4GdgHeC7VnUmLImLPcjfSMmBbqb8VOBjYGhF7AvsD21vKJ7XOI0nqwGciSZIkSWq8zDw3M5dl5nKqB2Nfl5mnA9cDry3VVgNXleGNZZwy/brMzFK+qvTedgiwAripT5shSUPNO5EkSZIkDbNzgMsi4u3ArcDFpfxi4CMRsZnqDqRVAJl5V0RcAdwN7ATOyswf9D9sSRo+M7oTKSIWRcSVEfHViLgnIn4mIpZExKaIuK/8XVzqRkS8OyI2R8TtEXFUy3JWl/r3RcTq6dcoSZIkSe1l5nhmvroM35+ZR2fmoZl5WmY+Wcq/X8YPLdPvb5n//Mx8cWa+JDM/M6jtkKRhM9PmbH8NfDYzfwJ4GXAPsA64NjNXANeWcYBXUd0SuoKqN4P3A0TEEuA84BiqLjTPm0w8SZIkSZIkqdlqk0gR8Vzg5ym3hWbmf2Tm48BKYEOptgE4tQyvBC7Nyg1UD7o7CDgR2JSZ2zNzB7AJOGlOt0aSJEmSJEk9MZM7kV4EfAP4UETcGhEfjIh9gZHMfASg/D2w1F8KPNQy/9ZSNl25JEmSJEmSGm4mD9beEzgK+L3MvDEi/pqnm661E23KskP5rjNHrKVqBsfIyAjj4+MzCPGZJiYmnjHv2UfsbFu323V0q11sTdLk+Iyte02Or8mxQfPjkyRJkqR+mEkSaSuwNTNvLONXUiWRHo2IgzLzkdJc7bGW+ge3zL8M2FbKx6aUj09dWWauB9YDjI6O5tjY2NQqMzI+Ps7Uec9Yd3XbultO724d3WoXW5M0OT5j616T42tybND8+CRJkiSpH2qbs2Xm14GHIuIlpeh4qu4wNwKTPaytBq4qwxuB15de2o4FnijN3T4HnBARi8sDtU8oZZIkSZIkSWq4mdyJBPB7wEcjYi/gfuBMqgTUFRGxBngQOK3UvQY4GdgMfK/UJTO3R8TbgJtLvbdm5vY52QpJkiRJkiT11IySSJl5GzDaZtLxbeomcNY0y7kEuGQ2AUqSJEnSQrV8ukdyXHBKnyORpJn1ziZJkiRJkqQFziSSJKnnImKfiLgpIr4SEXdFxJ+V8kMi4saIuC8iLi/NpomIvcv45jJ9ecuyzi3l90bEiYPZIkmSJGnhMYkkSeqHJ4HjMvNlwJHASaXzhQuBizJzBbADWFPqrwF2ZOahwEWlHhFxGLAKOBw4CXhfROzR1y2RJEmSFiiTSJKknsvKRBl9VnklcBxwZSnfAJxahleWccr04yMiSvllmflkZj5A1YnD0X3YBEmSJGnBm2nvbJIk7ZZyx9AtwKHAe4GvAY9n5s5SZSuwtAwvBR4CyMydEfEEcEApv6Flsa3ztK5rLbAWYGRkhPHx8VnHOzExMav5zj5iZ32lopt46sw23kEz3t4ZpljBeCVJGiYmkSRJfZGZPwCOjIhFwKeAl7arVv7GNNOmK5+6rvXAeoDR0dEcGxubdbzj4+PMZr4zpuk9p50tp88+njqzjXfQjLd3hilWMF5JkoaJzdkkSX2VmY8D48CxwKKImLygsQzYVoa3AgcDlOn7A9tby9vMI0mSJKmHTCJJknouIp5f7kAiIp4N/CJwD3A98NpSbTVwVRneWMYp06/LzCzlq0rvbYcAK4Cb+rMVkiRJ0sJmczZJUj8cBGwoz0X6EeCKzPx0RNwNXBYRbwduBS4u9S8GPhIRm6nuQFoFkJl3RcQVwN3ATuCs0kxOkiRJUo+ZRJIk9Vxm3g68vE35/bTpXS0zvw+cNs2yzgfOn+sYJUmSJHVmczZJkiRJkiTVMokkSZIkSZKkWiaRJEmSJEmSVMskkiRJkiRJkmr5YG1JkmZh+bqrBx2CJEmSNBDeiSRJkiRJkqRaJpEkSZIkSZJUyySSJEmSJEmSaplEkiRJkiRJUi0frC1JUp9N93DuLRec0udIJEmSpJnzTiRJkiRJkiTVMokkSZIkqfEiYp+IuCkivhIRd0XEn5XyQyLixoi4LyIuj4i9SvneZXxzmb68ZVnnlvJ7I+LEwWyRJA0fk0iSJEmShsGTwHGZ+TLgSOCkiDgWuBC4KDNXADuANaX+GmBHZh4KXFTqERGHAauAw4GTgPdFxB593RJJGlImkSRJkiQ1XlYmyuizyiuB44ArS/kG4NQyvLKMU6YfHxFRyi/LzCcz8wFgM3B0HzZBkoaeD9aWJEmSNBTKHUO3AIcC7wW+BjyemTtLla3A0jK8FHgIIDN3RsQTwAGl/IaWxbbO07qutcBagJGREcbHx7uKeWJi4hnznn3EzvaVZ6GbeNrFMkjG05nxdNakeJoUC/Q2HpNIkiRJkoZCZv4AODIiFgGfAl7arlr5G9NMm6586rrWA+sBRkdHc2xsrJuQGR8fZ+q8Z0zTS+dsbDl99vG0i2WQjKcz4+msSfE0KRbobTw2Z5MkSZI0VDLzcWAcOBZYFBGTF8eXAdvK8FbgYIAyfX9ge2t5m3kkSR2YRJIkSZLUeBHx/HIHEhHxbOAXgXuA64HXlmqrgavK8MYyTpl+XWZmKV9Vem87BFgB3NSfrZCk4WZzNkmSJEnD4CBgQ3ku0o8AV2TmpyPibuCyiHg7cCtwcal/MfCRiNhMdQfSKoDMvCsirgDuBnYCZ5VmcpKkGiaRJEmSJDVeZt4OvLxN+f206V0tM78PnDbNss4Hzp/rGCVpvrM5myRJkiRJkmqZRJIkSZIkSVItk0iSJEmSJEmqZRJJkiRJkiRJtXywNrB83dXPKNtywSkDiESSJEmSJKmZvBNJkiRJkiRJtUwiSZIkSZIkqZZJJEmSJEmSJNWat89EuuPhJzijzbOOJEmSJEmSNHszvhMpIvaIiFsj4tNl/JCIuDEi7ouIyyNir1K+dxnfXKYvb1nGuaX83og4ca43RpIkSZIkSb0xm+ZsbwDuaRm/ELgoM1cAO4A1pXwNsCMzDwUuKvWIiMOAVcDhwEnA+yJij90LX5IkSZIkSf0woyRSRCwDTgE+WMYDOA64slTZAJxahleWccr040v9lcBlmflkZj4AbAaOnouNkCRJkiRJUm/N9E6kdwFvAn5Yxg8AHs/MnWV8K7C0DC8FHgIo058o9Z8qbzOPJEmSJEmSGqz2wdoR8Wrgscy8JSLGJovbVM2aaZ3maV3fWmAtwMjICOPj43UhtjXybDj7iJ31FafR7XpnYmJioqfL311Njs/Yutfk+JocGzQ/PkmSJEnqh5n0zvZK4DURcTKwD/BcqjuTFkXEnuVuo2XAtlJ/K3AwsDUi9gT2B7a3lE9qnecpmbkeWA8wOjqaY2NjXWwW/M1Hr+Idd3Tf+dyW07tb70yMj4/T7Xb1Q5PjM7buNTm+JscGzY9PkiRJkvqhtjlbZp6bmcsycznVg7Gvy8zTgeuB15Zqq4GryvDGMk6Zfl1mZilfVXpvOwRYAdw0Z1siSZIkSZKknplN72xTnQO8MSI2Uz3z6OJSfjFwQCl/I7AOIDPvAq4A7gY+C5yVmT/YjfVLkoZERBwcEddHxD0RcVdEvKGUL4mITRFxX/m7uJRHRLw7IjZHxO0RcVTLslaX+vdFxOrp1ilJkiRpbs2qvVdmjgPjZfh+2vSulpnfB06bZv7zgfNnG6QkaejtBM7OzC9HxH7ALRGxCTgDuDYzL4iIdVQXHs4BXkV1x+oK4Bjg/cAxEbEEOA8YpXqu3i0RsTEzd/R9iyRJkqQFZnfuRJIkaUYy85HM/HIZ/g5wD1UPnSuBDaXaBuDUMrwSuDQrN1A9h+8g4ERgU2ZuL4mjTcBJfdwUSZIkacHq/snTkiR1ISKWAy8HbgRGMvMRqBJNEXFgqbYUeKhltq2lbLryqevY7Z4+p+uVb3d6/qyzO70ADlsvgsbbO8MUKxivJEnDxCSSJKlvIuI5wN8Df5CZ346Iaau2KcsO5bsWzEFPn9P1ynfGuqtnvayZ2p2eQYetF0Hj7Z1hihWMV5KkYWJzNklSX0TEs6gSSB/NzE+W4kdLMzXK38dK+Vbg4JbZlwHbOpRLkiRJ6jGTSJKknovqlqOLgXsy850tkzYCkz2srQauail/feml7VjgidLs7XPACRGxuPTkdkIpkyRJktRjNmeTJPXDK4HXAXdExG2l7M3ABcAVEbEGeJCne/e8BjgZ2Ax8DzgTIDO3R8TbgJtLvbdm5vb+bIIkSZK0sJlEkiT1XGZ+kfbPMwI4vk39BM6aZlmXAJfMXXSSJEmSZsLmbJIkSZIkSaplEkmSJEmSJEm1TCJJkiRJkiSplkkkSZIkSZIk1TKJJEmSJKnxIuLgiLg+Iu6JiLsi4g2lfElEbIqI+8rfxaU8IuLdEbE5Im6PiKNalrW61L8vIlYPapskadiYRJIkSZI0DHYCZ2fmS4FjgbMi4jBgHXBtZq4Ari3jAK8CVpTXWuD9UCWdgPOAY4CjgfMmE0+SpM72HHQAkiRJklQnMx8BHinD34mIe4ClwEpgrFTbAIwD55TySzMzgRsiYlFEHFTqbsrM7QARsQk4Cfh43zZmDixfd3Xb8i0XnNLnSCQtJCaRJElqiHYnBJ4MSNIzRcRy4OXAjcBISTCRmY9ExIGl2lLgoZbZtpay6cqnrmMt1R1MjIyMMD4+3lWsExMTz5j37CN2drWsmegUZ7tYBsl4OjOezpoUT5Nigd7GYxJJkiRJ0tCIiOcAfw/8QWZ+OyKmrdqmLDuU71qQuR5YDzA6OppjY2NdxTs+Ps7Uec+Y5i6iubDl9LFpp7WLZZCMpzPj6axJ8TQpFuhtPD4TSZIkSdJQiIhnUSWQPpqZnyzFj5ZmapS/j5XyrcDBLbMvA7Z1KJck1TCJJEmSJKnxorrl6GLgnsx8Z8ukjcBkD2urgatayl9femk7FniiNHv7HHBCRCwuD9Q+oZRJkmrYnE2SJEnSMHgl8Drgjoi4rZS9GbgAuCIi1gAPAqeVadcAJwObge8BZwJk5vaIeBtwc6n31smHbEuSOjOJJEmSJKnxMvOLtH+eEcDxbeoncNY0y7oEuGTuopOkhcHmbJIkSZIkSaplEkmSJEmSJEm1TCJJkiRJkiSplkkkSZIkSZIk1TKJJEmSJEmSpFomkSRJkiRJklTLJJIkSZIkSZJqmUSSJEmSJElSLZNIkiRJkiRJqmUSSZIkSZIkSbVMIkmSJEmSJKmWSSRJkiRJkiTVMokkSZIkSZKkWiaRJEmSJEmSVGvPQQcgSZIkSfPVHQ8/wRnrrh50GJI0J7wTSZIkSZIkSbVMIkmSJEmSJKmWSSRJkiRJkiTVMokkSZIkSZKkWiaRJEmSJEmSVKs2iRQRB0fE9RFxT0TcFRFvKOVLImJTRNxX/i4u5RER746IzRFxe0Qc1bKs1aX+fRGxunebJUmSJEmSpLk0kzuRdgJnZ+ZLgWOBsyLiMGAdcG1mrgCuLeMArwJWlNda4P1QJZ2A84BjgKOB8yYTT5IkSZIkSWq22iRSZj6SmV8uw98B7gGWAiuBDaXaBuDUMrwSuDQrNwCLIuIg4ERgU2Zuz8wdwCbgpDndGkmSJEmSJPXEnrOpHBHLgZcDNwIjmfkIVImmiDiwVFsKPNQy29ZSNl351HWspbqDiZGREcbHx2cT4lNGng1nH7Gzq3mBrtc7ExMTEz1d/u5qcnzG1r0mx9fk2KD58Q2DiLgEeDXwWGb+ZClbAlwOLAe2AL+SmTsiIoC/Bk4GvgecMXkxozSF/tOy2Ldn5gbmueXrrm5bvuWCU/ociSRJkha6GSeRIuI5wN8Df5CZ365+47ev2qYsO5TvWpC5HlgPMDo6mmNjYzMNcRd/89GreMcds8qR7WLL6d2tdybGx8fpdrv6ocnxGVv3mhxfk2OD5sc3JD4MvAe4tKVssln0BRGxroyfw67Noo+hahZ9TEuz6FGq48ctEbGx3N0qSZIkqcdm1DtbRDyLKoH00cz8ZCl+tDRTo/x9rJRvBQ5umX0ZsK1DuSRpnsvMLwDbpxTbLFqSJEkaIrW36pRmBRcD92TmO1smbQRWAxeUv1e1lP9uRFxGdQX5idLc7XPAn7c8TPsE4Ny52QxJ0hDqSbNomJum0dM1Y9ydptJzaWpsw9bs0nh7Z5hiBeOVJGmYzKS91yuB1wF3RMRtpezNVMmjKyJiDfAgcFqZdg3Vcyw2Uz3L4kyAzNweEW8Dbi713pqZU69KS5K0W82iYW6aRk/XjPGMaZ5R1G9Tm10PW7NL4+2dYYoVjFeSpGFSm0TKzC/S/oc7wPFt6idw1jTLugS4ZDYBSpLmrUcj4qByF9JMm0WPTSkf70OckiQNjXYdMtgZg6S50v2TpyVJ2j2NbxY9Xc9okqT+s6dPSRq8GT1YW5Kk3RERHwf+BXhJRGwtTaEvAH4pIu4DfqmMQ9Us+n6qZtF/C/wOVM2igclm0Tdjs2hJWmg+zDM7VJjs6XMFcG0Zh117+lxL1dMnLT19HgMcDZzXcnFCklTDO5EkST2Xmb82zSSbRUuSZiQzvxARy6cUr+Tpps4bqJo5n0NLT5/ADREx2dPnGKWnT4CImOzp8+M9Dl+S5gXvRJIkSZI0rHbp6ROYs54+JUnP5J1I05juORg+lE6SJElqvN3u6TMi1lI1hWNkZITx8fGuAhl5Npx9xM6u5p0rk7FPTEx0vR29YDydGU9nTYqnSbFAb+MxiSRJkiRpWPWsp8/MXA+sBxgdHc2xsbF21Wr9zUev4h13DPa0a8vpY0CVTOp2O3rBeDozns6aFE+TYoHexmNzNkmSJEnDarKnT3hmT5+vj8qxlJ4+gc8BJ0TE4vJA7RNKmSRpBrwTSZIkSVLjlZ4+x4DnRcRWql7WLgCuKL1+PgicVqpfA5xM1dPn94AzoerpMyIme/oEe/qUpFkxiSRJkiSp8ezpU5IGz+ZskiRJkiRJqmUSSZIkSZIkSbVMIkmSJEmSJKmWSSRJkiRJkiTVMokkSZIkSZKkWvbOJknSEFq+7updxs8+YidnrLuaLRecMqCIJEmSNN95J5IkSZIkSZJqmUSSJEmSJElSLZNIkiRJkiRJqmUSSZIkSZIkSbV8sLYkSZIkzWOTnTFMdsIwyc4YJM2WdyJJkiRJkiSplkkkSZIkSZIk1bI5myRJ88jylmYKk2yuIEmSpLngnUiSJEmSJEmqZRJJkmCtwwQAAAsWSURBVCRJkiRJtWzONkvtmgmATQUkSZIkSdL8ZhJJkiRJkhYgL5BLmi2TSJIkzXOeJEiSJGku+EwkSZIkSZIk1TKJJEmSJEmSpFomkSRJkiRJklTLZyJJkrRA+awkSVI77Y4PHhskgXciSZIkSZIkaQZMIkmSJEmSJKmWzdnmiLd8SpIkSZKk+cwkkiRJ2oUXRiRJktSOSSRJklTLh3BL0sLmcUASmETqqel2tB8+ad8+RyJJkiRJc8/kkrSwmESSJElds+mbJEnSwtH3JFJEnAT8NbAH8MHMvKDfMUiShpvHkmab7qp0q7OP2MkZpZ5JJ0mD4LGkt2ZzLPA4IA2PviaRImIP4L3ALwFbgZsjYmNm3t3POAbtjoefeOqHcx13qJK0K48l889MTjQmTXdc9I4oSbPhsaRZ3IdLw6PfdyIdDWzOzPsBIuIyYCXgznoas/lhPRfcWUsaAh5LFrDZHBfn8hjaeucUtD9e+lwQaah4LGm4fp8HwTP39b3m8UHDqN9JpKXAQy3jW4FjWitExFpgbRmdiIh7u1zX84BvdjlvT/1+g2OLC4EGx4ex7Y4mx9fk2GBu4/uxOVrOQtavY0nTP5e7aPKxpZ1hj7ccL2dkNnXnyFC9txhvNzyW7L4FeV7StH3vQo9nBseHRr0/GE8nTYoFZhZPV8eSfieRok1Z7jKSuR5Yv9srivhSZo7u7nJ6ocmxQbPjM7buNTm+JscGzY9vAerLsWTY/u/G21vDFO8wxQrGq4FZkOclTYoFjKeO8XTWpHiaFAv0Np4f6cVCO9gKHNwyvgzY1ucYJEnDzWOJJGl3eSyRpC70O4l0M7AiIg6JiL2AVcDGPscgSRpuHkskSbvLY4kkdaGvzdkyc2dE/C7wOaquNC/JzLt6tLrdvvW0h5ocGzQ7PmPrXpPja3Js0Pz4FpQ+HkuG7f9uvL01TPEOU6xgvBqABXxe0qRYwHjqGE9nTYqnSbFAD+OJzKyvJUmSJEmSpAWt383ZJEmSJEmSNIRMIkmSJEmSJKnWvEsiRcRJEXFvRGyOiHV9XO+WiLgjIm6LiC+VsiURsSki7it/F5fyiIh3lxhvj4ijWpazutS/LyJW70Y8l0TEYxFxZ0vZnMUTEa8o27u5zNuum9TZxPaWiHi4vH+3RcTJLdPOLeu5NyJObClv+78uD0i8scR8eXlY4mzeu4Mj4vqIuCci7oqINzTl/esQWyPev4jYJyJuioivlPj+rNMyI2LvMr65TF/ebdy7EduHI+KBlvfuyFLe1++FmqXbz1kP4ujpvnyOY+35vnOO4+35/qoHMe8REbdGxKeHINZG/S6aQbyLIuLKiPhq+Qz/TJPj1XDo57Fk0N+5aNC5xzSxDOy3cjTs3KJDPAN5j6JB5w8dYhno+UL08Pg/0/dmF5k5b15UD8X7GvAiYC/gK8BhfVr3FuB5U8r+ElhXhtcBF5bhk4HPAAEcC9xYypcA95e/i8vw4i7j+XngKODOXsQD3AT8TJnnM8CrdjO2twB/1KbuYeX/uDdwSPn/7tHpfw1cAawqwx8A/t9ZvncHAUeV4f2Afy1xDPz96xBbI96/sj3PKcPPAm4s70nbZQK/A3ygDK8CLu827t2I7cPAa9vU7+v3wldzXrvzOetBLD3dl89xrD3fd85xvD3dX/Xo8/BG4GPAp8t4k2PdQoN+F80g3g3Ab5bhvYBFTY7XV/Nf9PlYMujvHA0695gmlrcwoN/KNOzcokM8A3mPaND5Q4dYPswAzxfo0fF/Nu9N62u+3Yl0NLA5M+/PzP8ALgNWDjCelVQ/Sih/T20pvzQrNwCLIuIg4ERgU2Zuz8wdwCbgpG5WnJlfALb3Ip4y7bmZ+S9ZfSovbVlWt7FNZyVwWWY+mZkPAJup/s9t/9clk3sccGWb7ZxpfI9k5pfL8HeAe4ClNOD96xDbdPr6/pX3YKKMPqu8ssMyW9/TK4HjSwyzins3Y5tOX78XapTGHEt6uS/vQaw93Xf2IN5e76/mVEQsA04BPljGO+2vBxprB438LETEc6lOOi8GyMz/yMzHmxqvhkYTjiV9+ww36dyjaecaTTu3aNr5RJPOH5p4vtDj439X+6n5lkRaCjzUMr6Vzl+IuZTA5yPilohYW8pGMvMRqL6swIE1cfY6/rmKZ2kZnus4f7fcBnhJlNs5u4jtAODxzNw5F7GVWwBfTpWFbtT7NyU2aMj7V263vA14jGqH+bUOy3wqjjL9iRJDT74jU2PLzMn37vzy3l0UEXtPjW2GMfTqe6H+G+SxZCaadmx5hh7tO3sRZy/3V3PtXcCbgB+W8U7760HHCsPxu2jSi4BvAB8qzQU+GBH7NjheDYd+fx6a+J1r1G9nGvBbuWnnFk05n2jS+UMDzxd6efzv6js/35JI7doTdsoczqVXZuZRwKuAsyLi5zvUnS7OQcU/23h6Eef7gRcDRwKPAO8YdGwR8Rzg74E/yMxvd6o6y1h2O8Y2sTXm/cvMH2TmkcAyquz2Szsss6/xTY0tIn4SOBf4CeCnqW45PWcQsalRhvV/2YjPZg/3nXOux/urORMRrwYey8xbWos7rHfg7y3D9btoT6qmL+/PzJcD36VqWjKdQcer4dDvz8MwfecG8Rtr4L+Vm3Zu0aTziSadPzTpfKEPx/+uPjvzLYm0FTi4ZXwZsK0fK87MbeXvY8CnqD78j5Zb1ih/H6uJs9fxz1U8W8vwnMWZmY+WL+wPgb/l6dvrZxvbN6luI9xzd2KLiGdR7VQ/mpmfLMWNeP/axda096/E9DgwTtU+eLplPhVHmb4/1e3HPf2OtMR2UrmlNzPzSeBDdP/ezfn3QgMzsGPJDDXt2PKUHu87e6ZH+6u59ErgNRGxhepW8+Oorkw2MVZgaH4XTdoKbG252nwlVVKpqfFqOPT189DQ71wjfjvD4H8rN+3coqnnE006f2jI+UKvj//dfedzjh7m1oQX1ZWk+6keFjX5YKjD+7DefYH9Wob/mar98P9k14el/WUZPoVdH8B1Uz79AK4HqB6+tbgML9mNuJaz6wPl5iwe4OZSd/KBYCfvZmwHtQz/IVWbTYDD2fUhYPdTPQBs2v818Al2fdDY78wytqBqn/quKeUDf/86xNaI9w94PrCoDD8b+Cfg1dMtEziLXR/+dkW3ce9GbAe1vLfvAi4Y1PfCVzNeu/M561E8y+nRvnyO4+z5vnOO4+3p/qqHn4cxnn6wZiNjpaG/i2pi/ifgJWX4LSXWxsbrq/kv+ngsacp3jgade7SJZWC/lWnYuUWHeAbyHtGg84cOsQz8fIEeHP9n897sEksvdmSDfFE9If1fqdpR/kmf1vmi8oZ/Bbhrcr1U7Q+vBe4rfyc/OAG8t8R4BzDasqzfoHrQ1WbgzN2I6eNUtyH+J1WGcc1cxgOMAneWed4DxG7G9pGy7tuBjey6E/uTsp57aXl6/XT/6/L/uKnE/Alg71m+dz9HdRvf7cBt5XVyE96/DrE14v0Dfgq4tcRxJ/A/Oi0T2KeMby7TX9Rt3LsR23XlvbsT+Due7pGhr98LX816dfs560EcPd2Xz3GsPd93znG8Pd9f9SjuMZ7+EdnIWGng76IZxHwk8KXyefgHqh/9jY3X13C86NOxpAnfORp07jFNLAP7rUzDzi06xDOQ94gGnT90iGXg5wv06Pg/0/em9RVlRkmSJEmSJGla8+2ZSJIkSZIkSeoBk0iSJEmSJEmqZRJJkiRJkiRJtUwiSZIkSZIkqZZJJEmSJEmSJNUyiSRJkiRJkqRaJpEkSZIkSZJU6/8H1Lcac0nPhWYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.hist(bins=50, figsize=(20,15)) # visualise distribution of numerical variables\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### 4. Split data (Training & Test) - Version 1: Random Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20046</th>\n",
       "      <td>-119.01</td>\n",
       "      <td>36.06</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1505.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1392.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>1.6812</td>\n",
       "      <td>47700.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3024</th>\n",
       "      <td>-119.46</td>\n",
       "      <td>35.14</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2943.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1565.0</td>\n",
       "      <td>584.0</td>\n",
       "      <td>2.5313</td>\n",
       "      <td>45800.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15663</th>\n",
       "      <td>-122.44</td>\n",
       "      <td>37.80</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3830.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>963.0</td>\n",
       "      <td>3.4801</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20484</th>\n",
       "      <td>-118.72</td>\n",
       "      <td>34.28</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3051.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1705.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>5.7376</td>\n",
       "      <td>218600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9814</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>36.62</td>\n",
       "      <td>34.0</td>\n",
       "      <td>2351.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1063.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>3.7250</td>\n",
       "      <td>278000.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "20046    -119.01     36.06                25.0       1505.0             NaN   \n",
       "3024     -119.46     35.14                30.0       2943.0             NaN   \n",
       "15663    -122.44     37.80                52.0       3830.0             NaN   \n",
       "20484    -118.72     34.28                17.0       3051.0             NaN   \n",
       "9814     -121.93     36.62                34.0       2351.0             NaN   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "20046      1392.0       359.0         1.6812             47700.0   \n",
       "3024       1565.0       584.0         2.5313             45800.0   \n",
       "15663      1310.0       963.0         3.4801            500001.0   \n",
       "20484      1705.0       495.0         5.7376            218600.0   \n",
       "9814       1063.0       428.0         3.7250            278000.0   \n",
       "\n",
       "      ocean_proximity  \n",
       "20046          INLAND  \n",
       "3024           INLAND  \n",
       "15663        NEAR BAY  \n",
       "20484       <1H OCEAN  \n",
       "9814       NEAR OCEAN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### 5. Split data (Training & Test) - Version 2: Stratified Sampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Motivation: If the *Median Income* is an important variable (in predicting housing prices), we want to ensure that the test set is representative of the various categories of incomes in the whole data set. This helps us to avoid bias when evaluating model results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1156ba518>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing[\"median_income\"].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Median Income* is a continuous variable, so I create an income category attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "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])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of my newly created variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    7236\n",
       "2    6581\n",
       "4    3639\n",
       "5    2362\n",
       "1     822\n",
       "Name: income_cat, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"income_cat\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115679da0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAGIVJREFUeJzt3X+M3PV95/HnKza/6k1tE5I9n+07c6qVOxI3xF4ZV0jRLO7ZBiqMdEFyxIU1cuW7q5umOp8SU4n6yg+FSnXTQq9UvtiNSZxsLBLOPiChrmFbIR0EHCgLcak31AeLfd42azbd2E21uff9MZ+9TpfdnR/fnR/m83pIq53v5/v5zPf9/czsvma+850ZRQRmZpaf97W7ADMzaw8HgJlZphwAZmaZcgCYmWXKAWBmlikHgJlZphwAZmaZcgCYmWXKAWBmlqn57S5gNldddVWsWLGi4fE//vGPWbBgwdwVNEdcV31cV31cV33ei3UdP378byPig1U7RkTH/qxZsyaKeOaZZwqNbxbXVR/XVR/XVZ/3Yl3Ai1HD/1gfAjIzy5QDwMwsUw4AM7NMOQDMzDLlADAzy5QDwMwsUw4AM7NMOQDMzDLlADAzy1RHfxSEWScbfHuMrbueaPl2Tz1wc8u3ae9NfgZgZpYpB4CZWaaqBoCkD0t6ueLnR5J+XdKVko5KOpl+L079JelBSUOSXpG0uuK6+lL/k5L6mrljZmY2u6oBEBGvR8S1EXEtsAY4DzwG7AKORcRK4FhaBrgRWJl+tgMPA0i6EtgNXAesBXZPhoaZmbVevYeA1gM/iIj/DWwGDqT2A8Ct6fJm4JH0qaTPAYskLQE2AkcjYjQizgFHgU2F98DMzBqi8kdH19hZ2g98LyL+QNI7EbGoYt25iFgs6XHggYh4NrUfAz4PlIDLI+K+1H43cCEifmfKNrZTfuZAd3f3mv7+/oZ3bnx8nK6urobHN4vrqk+n1jUyOsbZC63f7qqlC2dd36nz5brqU6Su3t7e4xHRU61fzaeBSroUuAW4q1rXadpilvZ/2hCxF9gL0NPTE6VSqdYS32VgYIAi45vFddWnU+t66OBh9gy2/kzqU7eXZl3fqfPluurTirrqOQR0I+VH/2fT8tl0aIf0eyS1DwPLK8YtA07P0m5mZm1QTwB8Cvh6xfIRYPJMnj7gcEX7HelsoHXAWEScAZ4CNkhanF783ZDazMysDWp6/irpZ4B/C/yHiuYHgEOStgFvArel9ieBm4AhymcM3QkQEaOS7gVeSP3uiYjRwntgZmYNqSkAIuI88IEpbT+kfFbQ1L4B7JjhevYD++sv08zM5prfCWxmlikHgJlZphwAZmaZcgCYmWXKAWBmlikHgJlZpvyNYO8hKwp+O9XOVRMNf8OVv6XK7OLjZwBmZplyAJiZZcoBYGaWKQeAmVmmHABmZplyAJiZZcoBYGaWKQeAmVmmHABmZplyAJiZZcoBYGaWKQeAmVmmagoASYskPSrpLyWdkPQLkq6UdFTSyfR7ceorSQ9KGpL0iqTVFdfTl/qflNTXrJ0yM7Pqan0G8PvAdyLiXwMfA04Au4BjEbESOJaWAW4EVqaf7cDDAJKuBHYD1wFrgd2ToWFmZq1XNQAk/SzwCWAfQET8Q0S8A2wGDqRuB4Bb0+XNwCNR9hywSNISYCNwNCJGI+IccBTYNKd7Y2ZmNavlGcC/Av4G+GNJL0n6kqQFQHdEnAFIvz+U+i8F3qoYP5zaZmo3M7M2UETM3kHqAZ4Dro+I5yX9PvAj4DMRsaii37mIWCzpCeALEfFsaj8GfA64AbgsIu5L7XcD5yNiz5Ttbad86Iju7u41/f39De/c+Pg4XV1dDY9vlmbVNfj2WKHx3VfA2QuNjV21dGGhbc+mU2/HkdGxhueriGpz3anz5brqU6Su3t7e4xHRU61fLd8INgwMR8TzaflRysf7z0paEhFn0iGekYr+yyvGLwNOp/bSlPaBqRuLiL3AXoCenp4olUpTu9RsYGCAIuObpVl1NfptXpN2rppgz2BjXxJ36vZSoW3PplNvx4cOHm54voqoNtedOl+uqz6tqKvqIaCI+D/AW5I+nJrWA98HjgCTZ/L0AYfT5SPAHelsoHXAWDpE9BSwQdLi9OLvhtRmZmZtUOvDl88AByVdCrwB3Ek5PA5J2ga8CdyW+j4J3AQMAedTXyJiVNK9wAup3z0RMTone2FmZnWrKQAi4mVguuNJ66fpG8COGa5nP7C/ngLNzKw5/E5gM7NMOQDMzDLlADAzy5QDwMwsUw4AM7NMOQDMzDLlADAzy5QDwMwsUw4AM7NMOQDMzDLlADAzy5QDwMwsUw4AM7NMOQDMzDLlADAzy5QDwMwsUw4AM7NMOQDMzDLlADAzy5QDwMwsUzUFgKRTkgYlvSzpxdR2paSjkk6m34tTuyQ9KGlI0iuSVldcT1/qf1JSX3N2yczMalHPM4DeiLg2InrS8i7gWESsBI6lZYAbgZXpZzvwMJQDA9gNXAesBXZPhoaZmbVekUNAm4ED6fIB4NaK9kei7DlgkaQlwEbgaESMRsQ54CiwqcD2zcysgFoDIIA/kXRc0vbU1h0RZwDS7w+l9qXAWxVjh1PbTO1mZtYGiojqnaR/HhGnJX2I8iP3zwBHImJRRZ9zEbFY0hPAFyLi2dR+DPgccANwWUTcl9rvBs5HxJ4p29pO+dAR3d3da/r7+xveufHxcbq6uhoe3yzNqmvw7bFC47uvgLMXGhu7aunCQtueTafejiOjYw3PVxHV5rpT58t11adIXb29vccrDtfPaH4tVxYRp9PvEUmPUT6Gf1bSkog4kw7xjKTuw8DyiuHLgNOpvTSlfWCabe0F9gL09PREqVSa2qVmAwMDFBnfLM2qa+uuJwqN37lqgj2DNd0l3uXU7aVC255Np96ODx083PB8FVFtrjt1vlxXfVpRV9VDQJIWSHr/5GVgA/AqcASYPJOnDzicLh8B7khnA60DxtIhoqeADZIWpxd/N6Q2MzNrg1oevnQDj0ma7P+1iPiOpBeAQ5K2AW8Ct6X+TwI3AUPAeeBOgIgYlXQv8ELqd09EjM7ZnpiZWV2qBkBEvAF8bJr2HwLrp2kPYMcM17Uf2F9/mWZmNtf8TmAzs0w5AMzMMuUAMDPLlAPAzCxTDgAzs0w5AMzMMuUAMDPLlAPAzCxTDgAzs0w5AMzMMuUAMDPLlAPAzCxTDgAzs0w5AMzMMuUAMDPLlAPAzCxTDgAzs0w5AMzMMuUAMDPLlAPAzCxTNQeApHmSXpL0eFq+WtLzkk5K+oakS1P7ZWl5KK1fUXEdd6X21yVtnOudMTOz2tXzDOCzwImK5d8GvhgRK4FzwLbUvg04FxE/B3wx9UPSNcAW4CPAJuAPJc0rVr6ZmTWqpgCQtAy4GfhSWhZwA/Bo6nIAuDVd3pyWSevXp/6bgf6I+ElE/DUwBKydi50wM7P6KSKqd5IeBb4AvB/4L8BW4Ln0KB9Jy4FvR8RHJb0KbIqI4bTuB8B1wH9NY76a2velMY9O2dZ2YDtAd3f3mv7+/oZ3bnx8nK6urobHN0uz6hp8e6zQ+O4r4OyFxsauWrqw0LZn06m348joWMPzVUS1ue7U+XJd9SlSV29v7/GI6KnWb361DpJ+CRiJiOOSSpPN03SNKutmG/OPDRF7gb0APT09USqVpnap2cDAAEXGN0uz6tq664lC43eummDPYNW7xLRO3V4qtO3ZdOrt+NDBww3PVxHV5rpT58t11acVddVy770euEXSTcDlwM8CvwcskjQ/IiaAZcDp1H8YWA4MS5oPLARGK9onVY4xM7MWq/oaQETcFRHLImIF5Rdxn46I24FngE+mbn3A4XT5SFomrX86yseZjgBb0llCVwMrge/O2Z6YmVldijx//TzQL+k+4CVgX2rfB3xF0hDlR/5bACLiNUmHgO8DE8COiPhpge2bmVkBdQVARAwAA+nyG0xzFk9E/D1w2wzj7wfur7dIMzObe34nsJlZphwAZmaZcgCYmWXKAWBmlikHgJlZplr/NkYzu2itKPBu852rJhp+t/qpB25ueLs2Mz8DMDPLlAPAzCxTDgAzs0w5AMzMMuUAMDPLlAPAzCxTDgAzs0w5AMzMMuUAMDPLlAPAzCxTDgAzs0w5AMzMMuUAMDPLVNUAkHS5pO9K+gtJr0n6rdR+taTnJZ2U9A1Jl6b2y9LyUFq/ouK67krtr0va2KydMjOz6mp5BvAT4IaI+BhwLbBJ0jrgt4EvRsRK4BywLfXfBpyLiJ8Dvpj6IekaYAvwEWAT8IeS5s3lzpiZWe2qBkCUjafFS9JPADcAj6b2A8Ct6fLmtExav16SUnt/RPwkIv4aGALWzslemJlZ3Wp6DUDSPEkvAyPAUeAHwDsRMZG6DANL0+WlwFsAaf0Y8IHK9mnGmJlZiykiau8sLQIeA34T+ON0mAdJy4EnI2KVpNeAjRExnNb9gPIj/XuA/xURX03t+9KYb07ZxnZgO0B3d/ea/v7+hndufHycrq6uhsc3S7PqGnx7rND47ivg7IXGxq5aurDQtmfTqbfjyOhYw/NVRLW5buZ8FbmP+f5VnyJ19fb2Ho+Inmr96vpKyIh4R9IAsA5YJGl+epS/DDidug0Dy4FhSfOBhcBoRfukyjGV29gL7AXo6emJUqlUT4n/xMDAAEXGN0uz6mr06/Ym7Vw1wZ7Bxr4l9NTtpULbnk2n3o4PHTzc8HwVUW2umzlfRe5jvn/VpxV11XIW0AfTI38kXQH8InACeAb4ZOrWBxxOl4+kZdL6p6P8NOMIsCWdJXQ1sBL47lztiJmZ1aeWOF4CHEhn7LwPOBQRj0v6PtAv6T7gJWBf6r8P+IqkIcqP/LcARMRrkg4B3wcmgB0R8dO53R0zM6tV1QCIiFeAj0/T/gbTnMUTEX8P3DbDdd0P3F9/mWZmNtf8TmAzs0w5AMzMMuUAMDPLlAPAzCxTDgAzs0w5AMzMMuUAMDPLlAPAzCxTDgAzs0w5AMzMMuUAMDPLlAPAzCxTDgAzs0y1/tsszMwuEisKfslSEV/etKDp2/AzADOzTDkAzMwy5QAwM8uUA8DMLFMOADOzTDkAzMwyVTUAJC2X9IykE5Jek/TZ1H6lpKOSTqbfi1O7JD0oaUjSK5JWV1xXX+p/UlJf83bLzMyqqeUZwASwMyL+DbAO2CHpGmAXcCwiVgLH0jLAjcDK9LMdeBjKgQHsBq4D1gK7J0PDzMxar2oARMSZiPheuvx3wAlgKbAZOJC6HQBuTZc3A49E2XPAIklLgI3A0YgYjYhzwFFg05zujZmZ1ayu1wAkrQA+DjwPdEfEGSiHBPCh1G0p8FbFsOHUNlO7mZm1gSKito5SF/BnwP0R8S1J70TEoor15yJisaQngC9ExLOp/RjwOeAG4LKIuC+13w2cj4g9U7aznfKhI7q7u9f09/c3vHPj4+N0dXU1PL5ZmlXX4NtjhcZ3XwFnLzQ2dtXShYW2PZtOvR1HRscanq8iqs11M+eryH3sYrx/Ff2bKuLqhfMavh17e3uPR0RPtX41fRaQpEuAbwIHI+JbqfmspCURcSYd4hlJ7cPA8orhy4DTqb00pX1g6rYiYi+wF6CnpydKpdLULjUbGBigyPhmaVZdWwt+bsnOVRPsGWzs46FO3V4qtO3ZdOrt+NDBww3PVxHV5rqZ81XkPnYx3r+K/k0V8eVNC5p+v6/lLCAB+4ATEfG7FauOAJNn8vQBhyva70hnA60DxtIhoqeADZIWpxd/N6Q2MzNrg1ri+Hrg08CgpJdT228ADwCHJG0D3gRuS+ueBG4ChoDzwJ0AETEq6V7ghdTvnogYnZO9MDOzulUNgHQsXzOsXj9N/wB2zHBd+4H99RRoZmbN4XcCm5llygFgZpYpB4CZWaYcAGZmmXIAmJllygFgZpYpB4CZWaYcAGZmmXIAmJllygFgZpYpB4CZWaYcAGZmmXIAmJllygFgZpYpB4CZWaYcAGZmmXIAmJllygFgZpYpB4CZWaYcAGZmmaoaAJL2SxqR9GpF25WSjko6mX4vTu2S9KCkIUmvSFpdMaYv9T8pqa85u2NmZrWq5RnAl4FNU9p2AcciYiVwLC0D3AisTD/bgYehHBjAbuA6YC2wezI0zMysPaoGQET8OTA6pXkzcCBdPgDcWtH+SJQ9ByyStATYCByNiNGIOAcc5d2hYmZmLaSIqN5JWgE8HhEfTcvvRMSiivXnImKxpMeBByLi2dR+DPg8UAIuj4j7UvvdwIWI+J1ptrWd8rMHuru71/T39ze8c+Pj43R1dTU8vlmaVdfg22OFxndfAWcvNDZ21dKFhbY9m069HUdGxxqeryKqzXUz56vIfexivH8V/Zsq4uqF8xq+HXt7e49HRE+1fvMbuvaZaZq2mKX93Y0Re4G9AD09PVEqlRouZmBggCLjm6VZdW3d9USh8TtXTbBnsLG7xKnbS4W2PZtOvR0fOni44fkqotpcN3O+itzHLsb7V9G/qSK+vGlB0+/3jZ4FdDYd2iH9Hkntw8Dyin7LgNOztJuZWZs0GgBHgMkzefqAwxXtd6SzgdYBYxFxBngK2CBpcXrxd0NqMzOzNqn6fEzS1ykfw79K0jDls3keAA5J2ga8CdyWuj8J3AQMAeeBOwEiYlTSvcALqd89ETH1hWUzM2uhqgEQEZ+aYdX6afoGsGOG69kP7K+ruoIG3x5ryzG8Uw/c3PJtmpnVy+8ENjPLlAPAzCxTDgAzs0w5AMzMMuUAMDPLlAPAzCxTDgAzs0w5AMzMMuUAMDPLlAPAzCxTDgAzs0w5AMzMMuUAMDPLlAPAzCxTDgAzs0w5AMzMMuUAMDPLlAPAzCxTDgAzs0y1PAAkbZL0uqQhSbtavX0zMytraQBImgf8N+BG4BrgU5KuaWUNZmZW1upnAGuBoYh4IyL+AegHNre4BjMzo/UBsBR4q2J5OLWZmVmLKSJatzHpNmBjRPxyWv40sDYiPlPRZzuwPS1+GHi9wCavAv62wPhmcV31cV31cV31eS/W9S8j4oPVOs1v8MobNQwsr1heBpyu7BARe4G9c7ExSS9GRM9cXNdccl31cV31cV31ybmuVh8CegFYKelqSZcCW4AjLa7BzMxo8TOAiJiQ9KvAU8A8YH9EvNbKGszMrKzVh4CIiCeBJ1u0uTk5lNQErqs+rqs+rqs+2dbV0heBzcysc/ijIMzMMnXRB4Ck/ZJGJL06w3pJejB99MQrklZ3SF0lSWOSXk4/v9mCmpZLekbSCUmvSfrsNH1aPl811tXy+UrbvVzSdyX9Rartt6bpc5mkb6Q5e17Sig6pa6ukv6mYs19udl1pu/MkvSTp8WnWtXyuaqyrLXOVtn1K0mDa7ovTrG/e32REXNQ/wCeA1cCrM6y/Cfg2IGAd8HyH1FUCHm/xXC0BVqfL7wf+Crim3fNVY10tn6+0XQFd6fIlwPPAuil9fgX4o3R5C/CNDqlrK/AHbZiz/wx8bbrbqx1zVWNdbZmrtO1TwFWzrG/a3+RF/wwgIv4cGJ2ly2bgkSh7DlgkaUkH1NVyEXEmIr6XLv8dcIJ3vxO75fNVY11tkeZhPC1ekn6mvnC2GTiQLj8KrJekDqir5SQtA24GvjRDl5bPVY11dbKm/U1e9AFQg07++IlfSE/hvy3pI63ccHrq/XHKjxwrtXW+ZqkL2jRf6dDBy8AIcDQiZpyziJgAxoAPdEBdAP8uHTZ4VNLyadbPtd8DPgf83xnWt2WuaqgLWj9XkwL4E0nHVf4khKma9jeZQwBM9+ii7Y+UgO9Rfrv2x4CHgP/Rqg1L6gK+Cfx6RPxo6upphrRkvqrU1bb5ioifRsS1lN+5vlbSR6d0acuc1VDX/wRWRMTPA3/KPz7ybgpJvwSMRMTx2bpN09bUuaqxrpbO1RTXR8Rqyp+SvEPSJ6asb9qc5RAAVT9+oh0i4keTT+Gj/N6ISyRd1eztSrqE8j/ZgxHxrWm6tGW+qtXVrvmaUsM7wACwacqq/z9nkuYDC2nh4b+Z6oqIH0bET9LifwfWNLmU64FbJJ2i/Em/N0j66pQ+7ZirqnW1Ya4qt306/R4BHqP8qcmVmvY3mUMAHAHuSK+krwPGIuJMu4uS9M8mj31KWkv5tvhhk7cpYB9wIiJ+d4ZuLZ+vWupqx3ylbX1Q0qJ0+QrgF4G/nNLtCNCXLn8SeDrSq3ftrGvKceJbKL+20jQRcVdELIuIFZRf4H06Iv79lG4tn6ta6mr1XFVsd4Gk909eBjYAU88cbNrfZMvfCTzXJH2d8hkiV0kaBnZTfkGMiPgjyu86vgkYAs4Dd3ZIXZ8E/pOkCeACsKXZfwiUHwl9GhhMx44BfgP4FxV1tWO+aqmrHfMF5TOUDqj8ZUbvAw5FxOOS7gFejIgjlMPrK5KGKD+a3dIhdf2apFuAiVTX1hbU9S4dMFe11NWuueoGHkuPbeYDX4uI70j6j9D8v0m/E9jMLFM5HAIyM7NpOADMzDLlADAzy5QDwMwsUw4AM7NMOQDMzDLlADAzy5QDwMwsU/8PeriVlPaUYwIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing[\"income_cat\"].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I can now use Scikit Learn to create **Stratified Sampling**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
    "for train_index, test_index in split.split(housing, housing[\"income_cat\"]):\n",
    "    strat_train_set = housing.loc[train_index]\n",
    "    strat_test_set = housing.loc[test_index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, do I indeed have the same distribution of income buckets in *test* as in the full data set?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_test_set[\"income_cat\"].value_counts() / len(strat_test_set) # distribution in test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    0.350581\n",
       "2    0.318847\n",
       "4    0.176308\n",
       "5    0.114438\n",
       "1    0.039826\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"income_cat\"].value_counts() / len(housing) # distribution in full data set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes! Stratified Sampling is complete (with regards to *Median Income*)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "for set_ in (strat_train_set, strat_test_set):\n",
    "    set_.drop(\"income_cat\", axis=1, inplace=True) # delete categorical variable (as it is no longer needed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I decide to continue with the stratified samples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### 6. Discover and visualize data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, I only show a few examples, as it is not the focus of the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.copy() # create a copy, so as not to harm the training data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>286600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>340600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>196900.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>46300.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>254500.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "17606       710.0       339.0         2.7042            286600.0   \n",
       "18632       306.0       113.0         6.4214            340600.0   \n",
       "14650       936.0       462.0         2.8621            196900.0   \n",
       "3230       1460.0       353.0         1.8839             46300.0   \n",
       "3555       4459.0      1463.0         3.0347            254500.0   \n",
       "\n",
       "      ocean_proximity  \n",
       "17606       <1H OCEAN  \n",
       "18632       <1H OCEAN  \n",
       "14650      NEAR OCEAN  \n",
       "3230           INLAND  \n",
       "3555        <1H OCEAN  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I plot the geographical information with information about median house value (through *alpha* and *c*) and population size (through *s*). The big cities (Los Angeles, San Fransisco, San Diego) stand out as bigger and more expensive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x115264cf8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SXS2ob4JV1wzOfc2a5WDbNi+VlQHS0y0UFamib6dTze7ri6Ymk127VO+WadMsJCWdHmE6fhJO1qkUaFmFyfce9bBnhxdTSg4dtrJqpWD16nZ++lMbNlvXgVNiosG0af1voZadbWfZstSI9zeM02nEUEhit4+c9dpnzLDz2mse8vOtun2GNiRsNhtjxowZ6tvQBpjpdtOmU4RRNeICLIAli6GoAI5XwyvPSXwOQaAVzGSQApzxYLfDgklQOGpw7ikry8pjjyXT3BwiPd2Cw9G/IKKlxeSpp7y0t6vh5y++MHjoISfx8SpYSowHiwF1jXDooI/6OgHSJDHBSmOjZO8+CAZMPvtMcuWVYljUfy1dmsJzz9XS0uLDMODmmy+MqdfRMHu2g8mTbTgcQqcHNU0bUPpfuOgakQGWYcCEsTC+GGbOEny5SeJwC6QERwrMHyOYmQfXLRjc+3K5VIrwfBw9auJ2Q0GBOk9pqcmxYyGmTVNf6rQUuOcmKNkDuCW0mZw4bqG11QQERw6HyMuz8t57gpwc6Kb9zVfKy0Ps2hUiK0swa5Z1QIKAMWOcPPZYDg0NAVJSbCQnD99vW9OUlJX5qa4OEBMjGD/eict1fr+2YmJGzoidpmlDw+JykaBThFE1fP9SDQIh4Nf/x+D/ftKkqlJitcHM6QaFhYIlSyA+fqjvsO+s1jNn5Ul5bn1YUb56OzbRTku9n4Y5Tg4f8hJjC5Kc7OSKK1w0NQmamnq+Vn29yTPP+LDZwONRF50zZ2D6WSUlWUlKGt7frj6fyerVjRw+7MNiEYRCEptNcMcdKYwdO/RFrJqmad0x3W7cOkUYVcP7L9YgyM+H3/7GoLVVFZIP1WSO9naJ1wspKec3AjR+vIX8fIOyshAgKCoyGDeu6xGUwkIr4+e42N9icuk4O0lJFrKsFlpaVHA5YULP12pqkpgmZGUZ1NSYHD9uMuc8WlVc6D7/3M3hwz4KCk7PjPB4TF58sZGf/jRLj0Rpmjas6RRhdI34AAsgJka9DZWTJyW//30Qr1dy5ZUWFi/u/7e5wyG47z4HFRWqSX5+vtFtsTpAe8jKlZdBXAxUnIRliyArUS0VFNvLcmGjRhmkpAjKy02E4Ks05EgkpWTTJg85OWeO4MXFGdTXS44d8zF58hB+k2mapvXA4nLh0inCqBq5fxHPQ3k5VFRAejqMH3/+jUAPHjTx+SA7W7Bhg3leARaA3S4oLo7sHJMK4YNN4LSr2rSCHEiLsKl6bKzgwQedHD9ukpQkyMgYuSM0UqpeYlbruT9SUoLfPzyaCWqapnXFdLvp0CnCqNIBVh8dPAjPPQc2G/h8sHQpnG/Qn58vEEJy/Lhg7tzBnSl2xWxISYCGZphUFHlwdUpcnOg2Bfl1sHdvC3/5SzUZGQ7uuCOP+Piua8wMQzBunJOqqgDp6ad/rExTBVbZ2XqtRU3Threv72/yoaEDrD7asgUSEiA1Fbxe2LTp/AOsggKDRx+14fFI8vLOL8CSEvbth8oqSEuDGdN6boJqGDCjl1qrkez110+QmGiloqKdnTtbuPTS7ltEXH11PL/9bT01NQFSU634fCY1NUHmzo0lK0sHWJqmDV+6k3v06QCrj5KT1ShWaiq0takgJhrS0wXp6ec/evX5RnjnbxAXC+0dUFoGK246//sbLKGQ5K23TjBqlJM5c1KG+nbIznZy8KBaoDo5uecgKSfHzkMPpfPpp20cOOAlPt7CTTclMWtWL8VsmqZpQ8xwuYjRNVhRpQOsPrr8cqisVHVYSUlw441DfUenSQkfr4PReVBXG2TPLj87txssmO8gJ6dvwVswCNu3Q309FBXBuHEDdNNn6egIsXVrE42NccMiwLr11lz27GklIcHG+PGuXvfPyrKxcuXQ37emaVpfSLcbv67BiiodYPWRywXf+Q50dKiWDsYwq+s+tazMnt0BHA5BdbXJkSMhcnL69qV+4w3YvEWNhH36Kdx+O1x0ERw7FqC93aSw0EZMjIG7A6wWiIlgzcZIuFxWHn98LE7n8Bisjo21Mnfu0ARMPp9JKASxscPsm0zTtK8li14sIqp0gNUPQvTewmAoCAHXLIY33gZhGFRWBMnLFeTn9+2nxueDbdthTIEK2Fpa4MvNsGGTl7V/CVI8xs+4CYKE8UmU1wkEsGgqLJ5+/jMqAZKTe178eSQ4dMjH6tUtBAJw7bVxLFw4Mha31jRtaBjxLuzRShH+WacIQQdYPXK7JX7/+Tf/HEwXz1ftI8or7HjcVmbOEOTm9m0ExGoFe3iWZEwMtLfDqFHw5zVgtRq0tTvZeDhIYYzJ+HwLoRB8uB2ykmFKwcA8rwtRR4eq00tN7XmiQVfeeKONhAQLTqfggw88zJoVQ1ycHsnSNG1gSLeb0EadIowmHWB1o6bG5He/8+P1wi23WJk588J5qYqLoLio/3NCLBZYsQLWvAzShIREuO46qDgOb74tycvx04AD6TM4dgzy8iDOCZV1pwOsUEiyZUuI8nKT3FyDefMsWK0XTqB6vmpr4ZlnwONRNWx33aUC10g5nYK2NhMhDCyWvgdomqZpfSEAi/4fLqounKhhkJ04IfF4VM3VwYMmM2d2vd/Ro2727XMzZ04SWVnnrrPjdkuCQUhKurCCi0mT4Mc/ArdbdXV3OOAnP3Rw/Tf8NDYZ/Pi/HazZKnDEQV4uzL8MEjvVgP/tb0E2bAiRkADbt4eoqZHcdNPQtioIhSSbNvmpqQkxb56d3NyB+/bfsUO18cjPhyNH4Phx9XGkVqxI5JVXWmhvl6xcmYDTqX/zaZo2gFwurJdGKUX4R50iBB1gdauoyGD0aEFzs2TBgq5fJr/f5IUXqjBNOHrUww9+UHTG46WlJs8/7ycQgOXLrcydG/2XOxCQPS6Fcz7i46EhAL/fooofl00STJ7s4L11UL4TgiGwSjhaDjNmwszw0zdNyRdfhCgoEBiGICVFsnVriGXLrNjtQxdolpT4eeutDlwuwb59AX74w/gBKyBPSVEp1vp6Nfrk6n0C4hmysqw8+mjqgNybpmnaOTxu5N91ijCadIDVjfh4wcMP9zw1zmIRJCXZOHnSR2rquYXZ27aFsFohMVGwcWMoqgGW1ytZsybA4cMmY8YYrFplIzY2usGLxwfPb4EYm5qZ+PwW+PGV0NqmhpKT4sGwgjMFrp6ultsBVehut4Pfr2ZaBgIqPTbUaa7mZhOHQ5CeblBRYeL1DtxkhZkzVYBVWQmzZqk6LE3TtOFKMPS/o79udIB1HiwWwQMP5HPypI+8vHPTg8XFgpIS8HgkCxdG9zt3+/YQBw+q4OrYMZPNm0MsWhTdL6fHD4EQZCeoz8uboN0PCy+GqTPgyGETKYI4TJMkp41TNV9CCG680cqaNUGEkJgm3HyzFcsQzwGeNcvOzp0BKitN5s2zkZw8cPdjGLBgwYCdXtM0LbpcLoxLopQifFqnCEEHWOfN5bJSXNz1yzhtmpXUVAO/HwoKovvHPBQ63RLBYoFgMPqLCafEwqhEKG0ACRSnQaITjFh4+j8lP/6/PGzZ5CNowj/+L8GzzyYwerSqs7roIisZGQaNjZLkZEFW1tDXEKWlWXjssXi8Xkl8/NDfj6Zp2rDhccMXOkUYTTrAGmB9bZEQqenTLWzfHqKiwiQ1VTBnTvS/lFYL3DMH9teoEZmJmep9RUWQp592E2oNUpxnkJRkUFdn8uabHTzyyOlC9sxMg8zMqN/WebHZxIDVrGmapl3Q9P+dUaUDrB54fHD4JBgCJuSAfRi9Wi6X4LvfteN2qwLq/rZA8LRDTA8d6WPsMDPvzG2xsaqOyeMROBwGzc0wc6aBx2P26x40TdO0IeZywcVRShH+RqcIQQdY3erww9ProLZVFXgXZ8A9C9WoTl8dOhSkpcWkqMhKSkr0/kWwWgVJSf0//nAp/PEVmD4J/uH6yI9LS7Nw111x/PKXrWRlQVycwfHjQaZOPbcObSAFAvD6e7B7PxQWwIplammf8+XxSLxeSWqq/ndO07QRwuOGzTpFGE06wOpGRQPUt8GYdPV5ab0KtnKSez5OSsmGDR42b+5g1Cgr2dmxvPuuH4sF4uL8PPJILImJw+MPt9ujZvo1t/b92ORkC/fdF89bb7XT2mpy2WVOrroqJvo32YOSXbB1F+TnwuFjsP7vsPSq8zun223yq1910NpqcvvtTiZPHtreXZqmaYNmePxp+trQAVY37FYwJZimeg/giODVOnzYz9tvt5GTY2XvXh+ffhqisNBJYqJBeXmIkyfNYRNgTZsEqcmQ1s+1jEePtvLwwwnRvak+cLtVOwiLRY1c9SVQdLtDvPNOCx6PyTe+kUhWlgqkPB5JS4tJIAD19dGfOKBpmjYsxblgXpRShOgUIegA6wxSqsaQEti9F5rLob4OMjPg2osgNb73c7S0BLFYICbGIDlZ0twcor7epKNDYrFAWtrwCK5A1V2NHjXUd9F/0ybDxs3w4TpoaYZv3xH5sR991MauXR3ExhqsXt3I44+ravzMTAv/8A9OGhtVKwdN07QRweOGLTpFGE06wAqTEt58E778EqpqARdkFkJtPdz2DZg8JrLzFBY6iIkxKCvzY5pw220JWK02amtNpk2zkZpqEAxCWQUkJUKabkDZbxlpcPE0OH4Mps6ErVtg3my1NmJvQiGJxSKwWsU5LS5mzNCBlaZpI0z/l6/VuqEDrLDGRhVcjR4NDR5YvwXybeBuh9Xr4ScZkBjX+3lSU6089FAqFRUBEhMNCgvP7Qb/1nvwRYmavffIAzrIOh+GgOx0yEyHUo/qnh6JxYvjaWkJ4Xab3HjjecwU6AfTlBiGbhWhadowEueCOTpFGE06wAqz2VTKzO9X9VcNrdC+C8aNA18Q9pTDgkmRnSs11Upqavcv7fETkOCCNg+0tJ4ZYLW3S/761xCLF1vIyfl6/xHetq2Djz/2MG2ak6uvjkOIvj/f2bNh124or4CJE6CgILLjkpKs3HdfWp+vd762bWvl1VdrufjiRJYuTR/062uapnWp3Q3bdIowmoZPQdAQS0iAFSugtRXe/wga6qF6F2z+BP62Eyo9UNOgOqifUlUH+8ogEOzbtb65BJKT4ZK5UDD6zMcMQxVud9eXqr9O1MCWndDYHN3z9peUktdfb8NqFaxf305DQ6jHfUtLTQ4dMs9J5yUnw/cfhZ/+BO68U615OJzt3esmEDDZtq1tqG9F0zTtTEaU3jRAj2CdYdo0sNkk//s3EiMoAIG7Diq+hN9lw+5EmFQIq5bAiQZ46g21Vt+CKbC8D+vOjc6Fh+7v+jGnU7ByZXS/LK1t8PRq8PogOREe+5YasRtKQggKC+0cPOgjLc2Ky9X1T2VVVZA//znArl2CzEzBnDkWVq068/WxWlWAfCG45ppUYmIsTJvmGupb0TRNOy3WBbN0ijCadIDVSUVFkKee8oBXIL2xSKcF4gXeEBzZB6OyYVMJTBgF8SkQDEGsA+pbIr+GxwMvvwzl5XDppXDVVafXFBwoHV7w+SE9BRpb1Ijb+QZYra2weTNMmAC5uf07x6pViVRXB0hPt+J0nhtgHTsW5Ec/auWLLyxYLDBunBXTFKxYIS/Y5W4yMx3ccsswWz9I0zSt3Q3bdYowmvRgXidvv+0lM1Mw7SIDGROCdAnpEDSgvhR2HIG0eHjzXchLg0XTYUw2LJsf+TW++AKOHIHMTPjkEzh+vO/3eeQYvPk3OHw0sv0z0uDqy8CwwA3XQmwf+oGebITfvw1/eBfqOqUXN30Br74Oa//St3vvzG4XFBTYiYvr+ttw3To/W7cKbDbweATV1ZJgMBTV4KqjA3bshIrKqJ1S0zTtwiPQKcIo0yNYqFldpqneCwGLLoONjYAfaAWKgFhIiIE541SDS4sFlszr+7WkVCNWp0atZB97WTY2wXMvqUL8zdvg8YcgpZfu8kLAFZeot7568SPw+lWz1ZfXwSM3qu0TJ8CePTB3bt/PGSm73SAYkqSnmYRMQUGB4NJLuw6uGprgw8/VrMKrLoOUCCcGvvo67NwNDgd877uQkRHFJ6BpmnahiHXBTJ0ijKYRH2DV1QX5wx8aaWoyee5P8dScsKoK8yQBBQIygNEg3OBzQJMH7rhJFaL3x/wd7mduAAAgAElEQVT5UFqqUoSXXw6j+tjo0+8HMwQJKVBbd25bAr8f1n0Bh0ohJxOuvQxcEbSX6E67FxJdqrjf7T29ffRo+OHj/T9vZ921LbjhBgdrXglyYF+Q7CyDmTMtLFrU9Qv/4muqgF9KqGuEh++O7NqtboiJUa+jz38+z0LTNO0C1u6GnTpFGE0jPsDat89LTY3Jr3+TgD9kg1Ppp3YBLQJrEVi9MDoDllwLk7Ng0vj+X8/lggceUIHA396DJ/8JblsJEyeeu6/fDzU1kJ19enZcViZcfQVs3aHeZ3Uq5zFN+H+fho1bYeoEqG1QQce3VqrHDhxQo29FRZAWYYeCGy6FtZ+qkaFbr+z/8+7OZ5+F+PBDk4QEuPNOK1lZpwOt+HjBX9fGsWOHid0uKSgwulwsW0qob4LM1NMfR+rm5fDp56qOLPcC7mqvaZp2Xk6lCLWoGfEBVn6+nc82WPD7QjDaDikCGiTUCwjB1HRoi4XpxaoGyciOznWFULU/bW1w6HDXAdYra2HnLlh4GVy/7PT2Ky5Tb2c7VArvfw6pSbD7oAoYSvbBdYtg6xewaZMK1BwOeOihroOsXbtg40aYM0f1mLqoECaEW0nYOn23HDoEH30M06bCgj7MoOysrk7y3nshcnMFzc3w+utBHnzwzOp7p1Mwf37P7YWFgCWL4J1PVIC1/OrI7yEjA1bc3I+b1zRN+zqJdcF0nSKMphEdYPl80NBsJyAdKrAaK1TNVaEAUwVYsy+CHfVQ3Qo1rbCoIHrXX3Ur7D8AF3dTJO/1qpGnSLuTB0MqwDhWoWrEWjogJKHiBGzZAmPGqOxneTkcPHhugOX3w9q1EB8Pr7+uZgi6XCqwKquA19+RlJWFiHGFeG+daq3w4UZBURFkZfX9+Z/qKWYYalZjIND3c5xy8SyYWKyCrcQLpGWDpmnasNHuhl06RRhNIzbAqq2FZ/8IR0sh4LeSkhGk0ZBgM8AuIEWQlwdNHXD7HFXkbTVgbwUEZ4M1Cms2FRT03Hn81pVQVRV5d/LsNEhxQV0DOGwQZ4UblsLUcbAuBtrbIS4OgkH1/mxWK6Snw4kTkJqqRrpABXjPvgh//9zL9i0+PDJEfJyN5BQn5NnYekQwyw4ZSX1rkJqZCQsWGGzYYNLYLFiyzEIw2P9moUmJ/TtO0zRtxNNrEUbdiA2w1n+qRkzGjIG9h2IYnxNgu4STyQJLUJCbBAW5UJilRoZcTvAH1chQT8vI7T4Jbx8Clx1WToGM8+gn6XKpUaRI+fxw0Ti4biHU1ENqMjx0q3rsttvgpZegoQFmzoQpU8493jDg/vuhshJyctSokmnCX/4G76wLseuLEKH2ANhMGmSIjgQTMhN4Zp1g40GYUgArF6nXKBJCCJYutZKYJvnrO7BhqyArC+bPivw5a5qmaVEQ44KpOkUYTSM2wBICkCoISU8T2B127p4Cr5fAyRZw5sC/fRviEuFPG6HRo4KNm+Z0P0rj9sEreyAtDlq88Oo+eHAA2xicLSMNigvgSCkIQ7UrOKWoCH72MxVUxvTQBysu7sygrrQSNpSokb5QABASHAbYDTraQrR4JG2JIO3w2T6YOwGK+1gsHhcnsIS/Ewe66er5am2HtV9CRgIsnRH9JY00TdOGRIcb9ugUYTSN2ABr0eVwrFQ1+lw4H6bMgNUfgtkiMWsgZMKWEsEtS+GGKWB1QFYyZPXQXykkwQTsFnDaoL0PNUU1NSFaWkyKi61dtiw4m5SSQEA16zzFaoU7b4HqkxAXC6kpZx5jtfY9/dbhlbz8vp9Wp4Q0C9RbwQyClMg4J8GAwRebYU88pDhheV3fA6xpk1TtmGnC9Mmnt5smvP2u6lM1c7qaxTnUAU1pHeyuAKcdFk6ExNi+n0Mi2UgHeVjJp5/9PjRN06JJpwijbsQGWOnpcO2N8JfPwJ4C9iQwQ5IDWyRBGzTXwX/9Gg5VCZwONTp093L4cof6Iz9twrm9sBKdcMUYWF+q6rXumBbZvbS3m/zud620tUlWrYpjxgxHj/tLKVm9OsT+/ZJVqwwmTTr9U2G1qrUOo+WvmwI0N3RAi4CgE/LiIRbwAl6DlgBYHNBcC2RA+1nH+wNQ2wixTkjppkbKMGDmReduP1YKf/8S8kbBhr/DpIkwpiB6z60/ijPh4nGQnqAaz/ZHCNiDHwk6wNI0bXiIccEUnSKMphEXYFXXQpsHstPhtU2qzqnkEOwvh91fQjAcqwQNOHhYErdZ8M2roLYennkF6htAAqUV8A/Lzj3/VUVwcZ4KsBwRvroWi8DhMOjoCOF0njt6FQhIjhzx4XAIxoyxEwrB7t0mPp/k6FHBpEndn3vPXjUCdNkCGJ137uNSgk+CQ5ybnguG4N3DAhxAQIDTgBQLtAEuoAnMw5KUWQIZr2rTClNPH9/qhmffhIZm9ZotvxzmhkeoTtaDux3SkiEpvut7t1rVP1UdHYDof/F7NMU5YeXFZ2471Y0/0vSmFcG3SNT/LGqaNnx0uGGfThFG0zD4kzV49h2BP7+l/tiPzoZACPYdUv2tEmJU/ZGlHUJpQAgsEgJ+yaclgounwclaKBqt2gscLuv+OnF9HJRwOAQPPxyP222SlXXul+Sll5rYv1/1arj++nhSUpy0tPhxu2H8+O6/hF4vrFmritVP1sAPv3/m481B+FM91AQg2wZ3pEFip9O1euFkkhXrLBeBNBMqDJUDBdWQLgCEVADkSAK7HzI7pcw27VaLS4/OUiNZb38OU4th6354Z4MKyCwG3PNNGNNFWrEgH5Z9A/bsg0svgbwojsxFg9cLH6yHku1qxuWiBXDJ3MgCLSvDvNhM07SRRacIo25EBVgHSlWqKiMVyqrhyoXw+W7ITIJ54+GYU9BUCYcbJSFAOCS+1hCuAgv33mhw4Ahs2KrOdVUP6/oFAqq1gasPMwhdLgOX69wCI7/f5OBBH6mpBtXVQXbu9JKYaCM318DjkdTWmowb1/VPhd0O2VlQWaXSa2d7vwUagjDaAcf98EELrOg0AlXVDiJWYIm1EhhjqvxfEypF2IjqGZZmYloFvpAgHfB0nD7e5z/dnNRqVXVtTa3wt40wKl091uqBVz+GH97V9euy4GL11pnPBx4PJPWxLURPqqrg1ddUS4xlSyM779sfwLZdKoUZDMKb74HTCbMiTA1rmqYNG04XTNIpwmgaUQHWhDFQsgdKj0NxHiyaCgfK4WQjuD1QUAivvCRYdbePshYDh0NS32hwU06QotF2xuTC5HFq5CUvp+trNDbCM89CczNceil8Y8n53bPdbjB+vIOXXmri5Mkg8+bFMm2ahf37Q8TGCsaO7f5fDsOA++9RrRkyM899vDEIrvDhLkN93pknqHqvniwDfAZYTLADtSFoleAzETKIud1C+mwHSUkGjZ2KsGZOgJL9UHFSjRbOmXQ64Dr1Ps4JNY2Rvx6NjfD7Z6ClBaZMhltvjU6Q9eln6tzVJ2De3K5fr858PtixR9W7GYZqTZGeBpu2qAArGJSUlaneY2lpkJOjR6w0TRvGvG7Yr1OE0TSiAqxJxfDI7aoGa8woNapyz3WwcY+qN5o/EVITISPRT90JC6bPoMMTornK4LXXYPFi1RurJ7t3Q2uLqnfauEHNVoztx0yzzm69NYn0dAtVVQGuvz6B2FgLTzxhwWJR6cWeOJ3dLyg9zwUvN0BLEPwSrj6rCD1VQGUJZKZBQxu0txnIgBnOgYXACCG9Al9NCF9pgHE3OsjodI7cTHjoFiithvhYmDhG1StlpUJVLSS5oLYJLunDiM++/er1LciHvXuhvl4td3O+pk5VXfXz8yE5ObJjhFDPx90maWqU+IOQkS549VX4+GMTl0t9j0kJkyfDihUGdrugslKtCzlzpmroqmmaNuR0ijDqRlSABZBz1h9jVwxcO+fMbfPnw7ZtEotFYifE/v12EhJUym3p0p7Pn5EBgSBUVKiZio6eJwR2SUrJ3r1BUlIMcnIs2O0GS5acGf3ExvZvRKS1A+rdkJkAM+MgwQIn/JBjhyLnWTsHoCAeZDzE58P6BmjugJCUathGhNe6kQLZGuKaKZCffuYpstLUW2d3LYMPvlAjV1fOgStmR37/aakqGK6qUoFrX9KwPbloCowbq+rVIhkRczhgzgx48x2Tj94N4GmDhGSYdpGV91oEfr8gP1+NYqqvJ2RkSK66SvDHP6oUZ2Ul3HdfdO5f0zTtvDhdMFGnCKNpxAVYkbjhhjg2bPBhGCGSk234/Sqs72px5LNNnAj33avScpMnR97VvLP6epNnn/VQWGjhe9/rZood4POFOHq0ncLCWJzO3i9U3wa//RS8QUhwwoOXQ3EMFJ8dWIWlJ0FRLuw5BP4OMGyQECNorpdq5lwwPP1QmoRMgz+/DqmxsKSXn9EEF9xyVa+326UJE+DOO1TR/kVTzn908BS3O0R7uyQpycBujyzneN2V8Kv/CtHYAK44gbtFsnWLidNpkpEhOHHCSiAANpsgK0vy+luSrFGS7GzB0aOQHaWFwzVN086b1w0HdYowmnSA1YVp0yysWhXDsWMSh8Pgm99UI1N5XbQ56Mr48ed3/dRUg5UrY8jI6PkP/Y4drTzzTAV33DGKyy/vPfo7WqeanxakQlk9VDbC5B6agibHw0/vgpfeh92HVb1UslXw5RcWqsp9dEjAEsRIsCItBklxJpt3G70GWOdrypSul/rpr5KSdt58sxUp1WSDe+5JJjPT1utxdjukJgtSkiAxEaqrJV6vxGIRnDgByclBDMMCCA6WCrbtkzy/Rs02XL48soBd0zRtUOgUYdTpAKsLdjvcf7+gtlbgckFCwsBcx+ORnDwpKSgQWCynU36GIZg/v/fcYlFRLNddl86ECd2PcnWWHg/ShBMtqn4opYsFn882OlMFWe+ug9c+hpwsOFpuocXrJGALEut2YxoGMVYDw6/qri4kdXVBXnuthexsG3a7oLExyJo1zXz/++m9Hww8/LDBwYMmbW0SpxOuuy7Etm2ChASBzRbE67XgcEDFccjOFGRnCcoqYdm1A/zENE3T+sLpgvE6RRhNOsDqhtWqFjyOhN+vUoF9TQeuXRti506TlSstzJ/f938d0tIc3HxzhDcJFKbD3ZdAWQOMz4TsHpb96WzjF/DRx3BwL6xfD0XjYfxEA3+rlbrqOGJMSXyyDV+zQWo/as4GWlkl/OqPIINw580wvVNR/dGKEL6A+GrJoZQUK+XlfoJBidXae53bpZcavPqqjSNHJO++66WgQIAh2bwLfG0W3loPMgQOq0nuaIPGJrjlmwP0RDVN0/rL64bDg5siFEJYgBLguJRymRBiDLAGSAG2AXdKKf1CCAfwAjALaABWSinLwud4ArgftUjGo1LK98PbrwN+iRqXe0ZK+e/h7V1eYyCe34gPsKqr1Uy0CV0sfROJDz9UQUdsLNx5J4weHfmxmZlqoeOUlN73PWXHjhMkJDgpLIxwqttZJmSrt77YtgtysyE+Af7yDmQkQWE+5KQbHDjiwOiQjCsSNHmgZBfccH2/bm1AmCb8j3+Dymq1AHZNNTz+KBxphtoWOFRuYftBSUKSSVaaQV1dkKQkK3V1qlYqEgUFgoICQVqanZfW+Dlaa0U4wBcQHK0WzBwPOTkWfvyIIDEBkrsJbE1TUl0dwG4XZGT0nqLUNE2LmqFJEX4f2A+cyhP9b+AXUso1QojfogKnp8Lvm6SUxUKIW8P7rRRCTAJuBSYDOcBHQohx4XP9GrgaqAK2CCHelFLu6+EaUTeiA6zmZvjt76CjHS69DJb3cWThxAlYt07VZrndsHYt/PCHpx8vKwvQ0mJSWGgjPv7ceqolS6xcdZXEZot8RqDDYcXhGNyfguJCWL9BdbBPTQJpgco6tRxQQjw4YgRfHoD9NZBZBO8fhWuLBvUWu9XWBq1NEqsFbA5VjL52I5QKtX5icYaVoplJVFS3EOgIEhtrobk5hl/+0s+999oYPz7y13r6dFWL1vqSpChfsGOvQdVJyegxgvgYtfh2fDezHk1T8sorTezapTq1Ll+exLx5EeRwNU3TosHhgrGDlyIUQuQCS4F/AR4XQgjgSmBVeJfngSdRwc/y8McAfwF+Fd5/ObBGSukDSoUQR4C54f2OSCmPha+1BlguhNjfwzWibkQHWKGQehNCpfm6IyXs2KFaA0yffrrYPRTuUiDC6+QFAqeP2bXLy0svuQFJWpqFhx9Owuk8N8jqS3AFMHFiZLVB0XT1IhUcNDZCgwn7j4DdASdq4b+fgJQEeOR3UBAHo0bDRwfg8nxwDoPvrpgYKMyErftMJo8x8KaE2JsoaHIbGOmqJ1d2UQzLZzswpInFYvDLX6pliDp/PSOVn2eQkqL6j02dDCfqBIEA3HhL98EVQGNjiN27O8jPt+P3Sz78sFUHWJqmDR6fG45GLUWYJoQo6fT501LKp8/a57+AnwCniohTgWYp5amW11XAqWlYo4BKACllUAjREt5/FPBFp3N2PqbyrO3zerlG1A2DP4FDJzUV7rkbampgxozu9zt4ENasUWsVbtsGjz2mCt9zcmD2bNi6VQVYt912+pj9+wPExxukpVmorAxQXx8iN7frWYE1NZLNm00WLzb63d9qIFmtMGUCPPsSlNeCPUY1XC3Il1QcC/K/ngnx3iYLQb8FYRq4kuGSINy4fKjvXKV9H31E8PzzBi0JIWqv8NLaYeMahx23T/DQHEhxglpcUX19HnzQTmsrFBX1/WuRkgQ3XgNvfqwC8IfvhuVX916f53QKbDZBW5tJe7tJWtqI/tHUNG2wCU79CoyGeilltx0OhRDLgFop5VYhxKJOd3A22ctj3W3v6pn0tP+AGPG/xceOVW89aWxUfyAzM1UDUbdbBViGATfeqFo4HDwEJ05CUZFqVjlunI3t2720tZkkJxukpnb/F7a8XPL3v0tmzIheX6doO3gUqmtg4VTYclgtN3Td7BA//rGPbTvawRSAA1t8LHE2K88/BwsvHR6dyouL4cknBcf9gtU2Sc2JEM46uDIXhISSapiZrZZAAkhKMjhcDrVbYc409fXsi9lTYeoE1RA1Nubcxysq1Khofv7pbS6XhdtvT+H991tJTbXxzW9GOANB0zQtGhwuKB60FOEC4JtCiG8ATlQN1n8BSUIIa3iEKReoDu9fBeQBVUIIK5CIWhH31PZTOh/T1fb6Hq4RdSM+wIrEpEmwaZP6wzh5MmRlnX7syBF4+2218PDhQ+D3wbXXwowZTuLjDZqbQxQX24mJ6f5fg9mz1ZqCycnDb/TqbDnJcO00lR701EkOHPCAGUAtUugl4DHwxcRSVW3w66fhmsUwf25vZx14FguMjrHwqHRhzQV7nnqtN1XCO4ehMBlSwsHQuk3wyQb1b40/AIsu7v683bHb1StytiNH4Jln1Mff+pYK/k4ZO9bJ2LHddH3VNE0bSD43lA7OLEIp5RPAEwDhEawfSSlvF0KsBW5BzfK7G3gjfMib4c83hR//REophRBvAquFEP+JKnIfC2xGjVSNDc8YPI4qhF8VPmZdN9eIOh1gRSApCR59VC1vkpgYXoovrLpa/TFNSVHvP90MHptad29icWTTEg1DRLz+3VCZUKxmEpZXqdGX6xaBr00QCoVQ38vhUVkzSMAvSUsPkpBg5ZU31Gszrrjn8w+WWHFmEDtnFBSnnA6uADo6wGJVMxA7vN2fa8tOOFYB1y6EpMTu9+vM51OpQynB28O5NU3TBlV0U4T99VNgjRDi/wG2A8+Gtz8L/ClcxN6ICpiQUu4VQrwC7AOCwMNSyhCAEOIR4H3U3Mg/SCn39nKNqNMBVoRsNhVonS0/XxVD19RAZS20OiGuFEr2q4WOL7TGm92JiYEHboeTtaqAOyMNWlosTJ1qZ+tWL6YZQhgh4lIMpk9109BosmV3HGU1djqeg3tXwNwIFnXetFeNGl0+fcCfEqBmQqafVUt+5QLwBcDdCnkZKiA6u4YqFILX3gNPO2Snw8L5kV1v4kRYuVIFWJMmRec5aJqmnTe7C8YMfqNRKeV6YH3442OcngXYeR8vsKKb4/8FNRPx7O3vAu92sb3LawwEHWD1oN4DHUHI62F0oqBALdh74AA4TsL+aoiPg3YfNLd9fQIsUCN0o3NPf56YKFi7NolHH23kWGmIxiaTGGcb9Se9tLidVLYK5syEcUV23vpE1SU5e2lE6ooB/xB/VybEw/ypKpV3ZA/Mmwc33HDmPhYLXHkJHDqmRvciZRhqYoSmadqw4ndDuV6LMJoGfEBQCGERQmwXQrwd/nyMEOJLIcRhIcTLQoh+tPccePUe+NWX8JvNsPNEz/uOHQs5xVDdCmXV8NbnkBQPYwZs8ufwkZ9vZe3adH79qyQuW2DDYoRobpSMyrZgtQgqy9s5fChAINj7uQAuKoRZvazlaJqSDz4wOX58wCZ/UFmpRqmys2Hfvq73ueoyeOhuNZqnaZp2QTvVaDQabxowOBnXU51aTznVRXUs0ITqojrstAfAG1QzwdoiaKJ/sByyUuDmK2FiAay8GuK6mEH2dWS3C8aNs+Px2AAbwSC0NFqIEX7aOqy88U6IqkMhPO7oXM80obRU0tQUeYDl9UJtbeTXKCqGRg8cPAJXXNGPm9Q0TbvQGFF604ABThH2sVPrsDI6CW6dAu4AzI1gJGp8Puw6DC0eNXKVNQzaEwym2FgLo0e7yM+3sXWrgz17BHl5EB8ric/yEWdYOHrUEpW2DVar4DvfsVBbCw0NkbWCeO45KCuD++/vvS0HQIsXRA4kJ8DF/ZhFqGmadkGxuyBfL/YcTQNd7dKXTq1nEEJ8G/g2wOi+LPAXRdMjX0eZWRMhOR7a2qEoF+wjaCm54yfg/XXQ0mHF3eTnxAkHDofE44G0tBAyZCE52Up2H9dA7ElJCbz6qprRedddML6XtKLNpvaNdEHu3AyYOgZcNqiohNF5vR+jaZp2wfK7oVLXYEXTgAVY/ejUeuZG1Vb/aYDZs2cPXLFNFBXm9r7PcOb1mhw4ECAQkBQX20hO7j0aCQbh+TXq2C072qmu9CJ8XoqKnGRl2fnBDxxkZtrIzhZkRrHgf88e1ezV71cTDHoLsO66C9rbVZuNSNTXQfUhMEOwbyfceD3MnXX+990XPp/ko48CVFWZzJ9vZdo0PSdF07QBMjSLPX+tDeRv7L52atWGkN8v+eMf3ZSXB7FY1NItDz6YQHp6zz9xwSC0d0B1lY/amgASC8GApLzczzXXOFm82I7VGnkDVSnhaAV4OmBMLiR0s37fvHmwerUakeppmaNTbLbIgyuAzzdBjBPSUlX91kfrzgywjpWr5z628My+aNH0yScBNmwIkpIiePllP+npBjk5usBB07QBYHdBnk4RRtOABVj96NSqDaGysiCVlSEKC1Vu8/jxICUlPpYs6XntHqcTLpkDT34IhrBgBNpxxglsNsmdd9oiCq5MeXqZmvWb4f0Nqp1BfBw8vOp0kBUyobIeXE7VT+qJJ9R+zn40P6+tB5sVkrtZkcZhUwEUQCB4Zsq3vAp+/6KaAHHPP8DkXkbP+qu2VpKYqNphNDVJ2touiIFcTdMuRAE3VOsUYTQNRc5h0LqoDhZTqtHVgRrJGDx9/wMeCMDBAzB2NHQ0ddBgBgHJ/ffHMWVKz02vSj3w1kk43g7FcXDDKNiwDXKzVEBTWgVlx2HqeGhpg2/9C5TsVgHX91bAkgXQ5oa8URDXhzUcN26Gdz4GiwH33gqF+efuc+UiKK2AiiqwWuCuTgt5Sxl+pSJ4uVpb4e13oOakaiy6eLFaPDsSF19s4YUXQrS1hUhPN8jL06NXmqYNIP0rJqoGJcCKpFPrhepQE6w+DKlOuHeiKoqOlGmqDvCxsd2nr0Ihyd69AYJByZQpduz2gYniCgqsjBplpbQ0gGEIHA6YNauXrqBAWxvU1cGCS2LIH21QVxfk8e9bmDix52GlLXXw/c1Qegi8/z977x1lR3mm+/6+qto59e6ck1IrooACIJIRwSBMMNgY2zhjsMdmHIaxZ84dr3Pu3Dt3+ay5Mz6+nmOPsY0Ng42NDZichYRQzlktqdU5h51TVX33j69lSaCsRmC7fmvVoptdu3ZV7e7dj973/Z6nH/Q8/LQKrvaANQalESVkAj51n/75x7Bynao4xZLw2LPw5kqor4LiqPKj8p2hLcbmHVAShUQK9rSeWGCVlsDX74ORUWU8GgodfayxDm6+Sbm9Tz/FikQp4dFHoadHIoTJs89qxFI63jBMa1bbqZg61eCBBzTicUl1tYbP92ev4B0cHD6ouIJQ47QIJxJnavY8WdkLXh26k3A4DrPO0IbAtuE3j8POXaqi8fnPKlf4d7JiRY6XXsoiBBw4YPGxj51FqeYscLsFX/hCkN27C+TzkqlTXRQXn37isbMTtmyCFW9AU7OHT9zlIRpVA+X+d5xqNgtPvQwvHYIVXujbBYVDYBUACcN7YH8xXFkN8xvhhsuhuQ52HYAnXoHBIRjol+hegU9CUEDDIujohr5BaDrDxaaL58FTL6kq2ayWk+/n80HNCUTbUBJebAdLwqQmaHjHez4wAsNDcPgg7N4ticUSDA2Z5POwsyvMlVcZrN8Of/cliITeffxUCl56DYIBuOYqjbKyM7suBwcHh3OmkIRep0U4kTgC6zyZUwxPH4awB6oDp939T8RisGs3NNTD0DCsW39igdXRYVFcrOH1Qnv7GdqhnyNer8b8+aevWh3BNJVVwoeuUtcwOgqjQ/CDH6iK3Fe/qio/Ukq2bjX5w3MWWzoM9rVojG7VKLSruSpigAUgieUFrwtYvgyuWqTc1L/5fTjchtrZBisnabUEjaWCji7weFRF6kxZNB8mN6nB99BJhuhPhaGptiE2uN6hQTfsgideg3WvwsxG2LTZZsTWmF7nJjdskc+b5AoGbpdqUZ6I7bvg7XVqgH/qZGg8QYXNwajxADIAACAASURBVMHBYUL5YIQ9/0XhCKzzZFElTI2qKpb3LO5mIKDER3c3ZHNQcxIz08svd/PII2mSSbj9dh+mCfv3Q0XFmRlsvpdIqSpxfj80BpXo2LYNdLfyxjrS/vzZz0yeftoEt+BA0GakX2L3CyxbQAoQNmgS0hJSkIxo/M+nNZZMg6EBWLmFcQGG+gDIK5F1042Cuc0wtVm18WJxtUskfPpzLz6JIBsZgWj01PN0RX74+tVK75UGIVuAVB6K/bC7TeUpShuSWSivFrR3GOzrsphcYvHZT9g0T4PGGlWhOhF1NRAMQtCvWpUODg4O7zmuIFQ7LcKJxBFYE0DRmRd9/oTbDV/8PGzeAkVFsGD+ifebPNnFd74TxrYlgYDGqlXw1FNQXg4PPvj+Dta7XHDzzfDHPyqxtXgJvPA6rHoNGmrVOa5cCY8/LvH7NTJJQaFKYMZBQwIC3BJyEkwJPgFDNgya9OHmn34KLgEygApVMlECKwNUCBonw9Lx4OStO+GJ59TXH/8IzJ5+9tcTi8ETT8PyG6D6NKao0fH250gK/nMtJHKwoBYunQOHumHhFdBSBnMv0jjQaxDvt5jU4OXyy324T5O+WVsD3/mGah2f6UC8g4ODw3lRSEK/0yKcSJyP7wuIaUI6q+wHhIDSUrju2tM/Tw03KyUVDCpxVlR07uJq3bou1qzp4t57F+D3n/lUfjYr2bVLEokIJk9WL75okbJMsG349ZMQT4MvCLX1qkq3ezdEoxIpJYEADLkFdsAio+sqodKNqk4dWZHXCDRICpbE6xdUB8EXgLwHkOP16ygUfxi6jglZXr0BouOD8as3nJvAikTg1uVQdhbhzfsHIZ6F+ihs6oQbWuAfP69sJ46Io4sxONtftXOxnnBwcHA4Z5wW4YTjCKwLxPAY/OxpZTcwZyrcuUx5OJ0t8+ZBQ8Pxq9rOFk0T6Lp21gLthRds3n4bDMPm/vt16uvVAUIhJR47u2HpEmhqgIoyNUNUXQ0VFWp1Yn8/BNwWoek6nXGBpwdyw0AQyEhoBkok+AxECEbDMLQfZtRCoQE6+kGGwB+EO5ZDp4S0BX4dpjTBa2+N36NZ7z5325YIAeI0F11+lgPlVWH1udQ+CrUR1SY+l/f14YcLbNokefBBw7FjcHBwuPC4glDptAgnEkdgXSA27la2APWVsG0fLJ0LNeXndqzi4vM7l4ULa1i48AwSrN9BOq2qZ6YpyOePf8wwlOHo6vWqsnb1UvX/r78eEgkdj0fg91tcc6PgLb9Oa5/gxQzEOwW5zvF/OjVJcOm4ExrVhqBdQqUJ18yHlAmrDXA3g1sHb1AJK/e4Fll2hWpLgnJXP5bRUclPfpKjvFzjs591oWkT11dtKIavLoXRDDQXn5u4ymQkjz8u6ekRLF5s8alPOQLLwcHhAlNIwoDTIpxIHIF1gQj5IV9QHk6aBr5zmNt6v7npJo1g0Ka0VNB8Ag+nG6+F+XOUCCsZF4F+v8oBvOcejSP155Y0/FKH0WnQHgCzWePwsKRQCoE+iGiChS2Q12AyUFeuBsl3tUPUhuhMqC6CW0rhiFG8psG0ySc+71hMMjoK+byNaXLaGaizpTqitnPF5xN86Usa27ZJrrvOCQNzcHB4H3CyCCccR2BdIBbNgkQaOvth+RVQfB5/kN8viooEt9xy8t9AIaCq8vTHqfLDdS5o7YGCBLcf5tiCrg5ITlKrAQ+NwOw0eLzQ3qfc8j9xBdx9PRjG0XZf3oLOhPpsqAu92zYBoLFR49573ePzax9Ms87lyw3mXgxuZ/bKwcHh/cAIQrnTIpxIHIF1gTAMuP7S9/ss3hsGE/DMWli1Agb3w7w58HcPQPgUdgnd3dBgwMIK6OyDf/wGrN0Mu3thbQYWFMFACm5YDEVhSaEA0xptHnvMZPVqE03TuGSpwcgUF8OmGm6vD8NnZ0B8TL1GWenRhQCTJmkkk/D088r76srL1IzYyejpkfzmNzZ1dXD77Rq6/t4JM8uCnz8Bnb2qunb/3VDu2DM4ODhcSMwkDDktwonEEVgOJ6RQUKLwdIPw8Qz8vy/Bqy9KDu8Ar4DeLkFTA3z+npM/b+4M2LwLOnrh0nmqlfihpXCVDdHnYPdBKApBXbnkd4+bbNtmsnZtlvb2JLlcAXQXDz3iIVwZYvoyHy1zITcVfvwkDLSqitaSBbD8+qPX8MYqWLMBbAsqy2HGKVzct261GRxUgctXXcU5uanva1NZigtmQukpjFDjSSWuGmpU/mJXnyOwHBwcLjBOi3DCcQTWBLF2EB7rgNoA3DcJwse4H6QLoAvwnORuSwm7D0P3IJRE4KJJ76//0dtvw/PPKx+rz33u1CsW++LQ0y05/Lokk5Ik3BJb0xgbPvWgdl0VfPPzkMlB2TFD+7YNn7gJhkZVqHPrfsnhw5LVq00OHwbLMkHTwRPEBsa6M2x/zY0wdEqT0D8ES2eoY63ZBJcsPGrWaZoQG1NGon4/5HKwd6/y82ppOX5Afd48jX37bOrqxDkZuo6MwSN/BKQSWl//9Mn3DQehvhrau8HnVWHXDg4ODhcUIwilTotwInEE1nmSL8BLe+B7W9WMldsPM8NwUzXkLXhyP+wYVPteXQ8fanh3VeiVDfDaZvC5IZuHXW3wyWtP3cJ6r7BtJa6qqlTOYGsrzD+JCSpAcQCsLOSyILwSshLcNvPnnVpgpdPKyfzYqJr1G1ULb2YL3HWHEjwVFYJUyiadFkh5JCroyLEF6IJMLENPRxARgnqfEqxHOHKv+/th03qw0nDdbdBYD488qrIgpYRl1xzvSVZVJfjGN879DdA09doFSwm4U6Hr8LmPQne/8vIqOgMnegcHB4cJxUzCiNMinEgcgXWePLcB/u112L5Thf8KF/yvNBR/AsZGYOsANIbVYy+3QV0Yph5TsUmmYeU2aKiAAx2w5wC8vgbiQ3DVQpg++cIKLU1TmYgHDqivT2cJURqEv78Ldr0iObhDEBY2VyyFpUtP/pxnn4XVq5VB6Sc/efT61m+CgB927IblSUksZuH1Cu67zyCVsnnlFZOREQF2BnIFwACXF13XsArw6aUwOQKvr4JUGuprYCymVjRu3wOb9oHfDUXjhqT79kFToxJ7+/admenrmVIUhi98VLX7Zk05/f5uNzTVTdzrOzg4OJwVTotwwnEE1nkyHFer3qw8uANgCnh7E/z2SpApqPaoSoYhlH9Tf+p4gWWOZ+yNxmDTTti5GUaHYddrcPttcOct8JFl53ZuUqpZqrO1JfjkJ5XAKiqCujP4oz+7QbDpZZ3du8E0dWbMOHmLs1BQLcj6etizR2X/HZlv+tCV8MwLcMVl8JOfJHn66SweD3znO0G+/W03y5ZF+fu/72RkJAvW+CeB4aa8xsXCFtAysPR6VQH70UPq2D97BG67Gd7YAHPmKsPXwYR6TxZcDOvXq8PcvPzs7tGZ0FijNgcHB4cPPHoQip0W4UTiCKzzZPkiWLsPdh5SiS+GCa6Aag3V+yA2BEVeZTOQt6DUd/zzwwGoLYc3tsPWIYj7gHJI74WtG6G88twF1tatasboE584u+f5fDB79tk9R9Ng1gkc1N+JywVz58KWLdDUpOahjjCjBaZNgcd+b/ODH2bQDbV68IFvp/npf5bw+c8bjIyU8i//0k8iIbE1nWi1l5s/7GXeRdDWDS+9aTN3usCyBPU1cLgTevrV8Sc1QWQEhkfU97fcDHPnKDF4IiHZ3Q2jYzBj+rkZiE4Ee/apnMWbrj31qkwHBweH88JKwqjTIpxIHIF1nlQWw39+DTJmhle3GRT8Li5fCp4MfHoGvGjBgTFAwtJaaHnHwLSmwS1XwPP7wc4BSVSZthFyefCeZH5nXwc8vxaaqmD5JSeuGE2dqgbVPwhIKf8UU3PHHXDttWp4/p3nvf8gbNkG2YzJ8IhyeO8dgHv/Ns/X7nXzN38TIRaz+e0TaUQoRGVjmIbxyB6kzS//K8vwlYKLL/KxcRvUVcNli+Bwrxoi1zRYPE/trmlK5J2IeA7+42FIx+C+L8K0qe/JbQFgNAsZU9JXgIhL0JGGta0QdoO1A3ZuV9mKs2a8d+fg4ODwV47TIpxwHIE1AQxrB7n/W2u5fsDF/r7Z1LunszdlsdXM8ZHZLrScC11A+GTu7QIumgJVbnjkV+NtvRKYVAUfuQZ2DMFbvdBSBIsqIZaFrz2sKmaBfdBUAxedwFk9EFDbhSKfl6xbZzE6Kpk1S6e5WSORkDz8cJaxMck993hoaNDRtOMrV0fYuBnWb1bCUncJwADDC4aLrk6L51+DG67R+N73irnhwxF2HtTY06URCSsvqbwpuGSBzty5GvPmwQ3LVGiypsGX71aVrKIw4ILHNsFQCmZXwRWTQB+vUEkJz/fBmhHY0wKlA2cX/nwmjMbg6dehshRmz4V/Xid5ZgziuoXULbzbDJrLdS6rhfkz4SsLVWXPwcHB4T1DD0LUaRFOJI7AmgC2s53D5MmXmPjENlrbp3LIlWCHL06X0LjbW0XgFP80iPjA0GFKC9x1N+w4AGE/fOZSyJfBg29CwQaPDgtKYdcQpDQIWTCYgdYxuGiCr6m3F3buVFWwhoYze87vf6/8qgIBwZo1Fvfe6yaRsOnulgQCsGaNSUPDie9DRyf8/kk1kzY2CsGQxsCAAYYbpE06rdF9KEdrq4v9e01eeqlAMCi4eqmPTbsEQoMbLhdcs8SDlLBqF2xvg5pSuH6eiiZqroe8CT9cBck8hL3wwl44kIVZjTDbD70ZWDUEjQFwtcDrxfAv7fB5HSp8EDbAOM924ba9sLMV9hyCfBGs6oBBt6SQB1eThS502g6BJw03tsDcSef3eg4ODg6nxUpCzGkRTiSOwJoAevHjoR+fLhDFxVzmKrDen2CS20MPOZKYpxRYfg98ajH8ej1UNUPTJLhjroqd+f4GKPJAKg/JDDy/G4YyMKMJpuhKfxj+ib2eQgF+9jPlE/XWW/Dtb5/aC0s9R7Jjh0VTk4YQgt5em717LRYt0gmH1Uq9WbNOfg+krSwihIAbl2ncfXuY++5L0DNcQGoGfrdNRangd0/myVXqdCVdXByyuGEpTJ4K/WMwvVY9f3sbPLseKqKwfr867ty5MJCXCMumLS2YHhW4hGDYgBUdEDLBZ0JLCHo1qJaweVSFSSct+Pp2WFQKZW74bD0UnaR129oLu7pgWpU6nxMxtQmKtsFACHx+aPEKAj7o3KfBQQ+FnE7EDXUBmD/uiWVLSJsQPI3lg4ODg8M54bQIJxxHYE0AFcygnSAaklq9hsuK3Izippscjfgo4fTL+CaVw4M3wCuHIWFBWQX0pJRguKoKVrSBZkMgCH1J6I/DVdMha8KMk7SwbPvchrOl5E+hyLmcOs7pMAy16nBsDCIRSTotKS4WBEIaX/u6DySEQu+2hbdtePllJeQSozBpCnzkJtjX4aVmgcB90KKxxsTMwJKFLl7ZLFk0VVC5SMfnN9jdK/jtavC4YOUe+OoN0DuqRGvQqwxetw3Cyv4Yh/zdtA9rdHQ1UDzsZm5AY4MUjPpAH4VMwGZVzKbIpZHUBHFT4NWhJ6/uSZ0PerOwYghurXr3PRhOwC9XqnPZcAi+fgNUnCBzsrocvvVF+F0Mmt1wRRn0xwX6HMEft0JeB38IZtSDqcGaMfj37bD7LYjE4Tt3wvIFZ/++Ojg4OJwUPQgRp0U4kTgCawK4nGpK8WEjmUEUFxq3UE4WGx8agjPLsWtPwsp+VTVJm3BlDSBVS+rgQRhsh0gUZk6C5jDMrVTiauoJBNaeQ/D4y1BRAp9ZDn7fu/c5GW43fPazsHEjzJwJkTMIphZC8OlPu3nssQJdXZLFiw1mz9H5t6chmRAEMzDYBUsXws03HzXf3LoV3nhDeW/V1kJbG+zYAS/vFDQ3aPS2WnQPQsQn6I+pWatwQFBSpdEfg33dUBSAkhC0DypxNakKVuxUVa1kFprmFDgU6qE9ZdB1MIooSTE0ZPDKkCBRKSAHjNlQL7GkRHgK7LUM5nt0SgzYl4UFxcre1KNByjrxPcjkld9ZcRC6htX3J8OnwT3jc2hf+ZCKHNK8eYxaweCoi4IJ/mr4b9vgrQz0vQrJVlVp+/t/het/eXoDUwcHB4czxkpCwmkRTiSOwJoAvOjM43iVoyHwn6DeKiUMjChzzdKi4x/z6arikjXVCrKGMJT64Y9tSmAVLJuhTkkuaPOlBTq3tpy8PPX6Rgh4ob0H2nqUKDsbGhvVdjZUVWl861sebFuiaQLLUvNl69bB3reUB9UTv1Umo9/9LpSWwuHDyn7giNloNKpElt8LU1pctPfAvt02lgtefNOmcYrO1gMa7a/DzCZoCMJYCnqG4HAfZOIQDcLkMhWHM6kaGpstunLw9u4gmb4AwZIknuY8w6t8UAqMAhEJebVwICcFcWERNnTmRGBWCNYlQBNqGP7Sk+QKVkdh6TTYcBAWT4b6M4zYMXRIB4fZQCsNc3UaB5tY5C/le/ts9uRijNo6YjhESBdU+8BIq4UAjsBycHCYMJwW4YTjCKwLzHNvwZrtIIGblsJlx0yn14bgizMhloPpxapytbwZfrINLJ+NNQTCCxnDJNQyBpw8gTiswxNPQrQYQne855d1HJomSKclmzbZaL0Sa0ijr0/g8gqEUOaeP/oR3HefElmp1FGz0WRSWUtcMw8eelLgC7tJ+W2GkxJykkMvWpDOobsh1gxa2uCiSw3WdcLMamisANOGjgFwGXD1bCg13MzM+6kYcjHiM4n3himqMJB5Ad1ACeATMGxDwMal26RGdDZkYWMXLAwpYdwQhFuboNJ7suuGm+ap7WzZQxd+PLh0N2vtQeL9peyzR7jL+/8h3Aa7ln+DA78JYBTglg+rmCEHBweHCUMLQshpEU4kjsC6gCTTsGYH1FWAZcOLa+CS2UfnpKSERBI2dqoZnoW1UBmBrl1Q6AUkyKzArVts7C2wfT1MqoS7LlNVkGPp2g+zJsOmbfDQr+AfvqEsC45lYBS8bmV2et7XloFHX4V0Dj5+peSp35r09EjCYUFhxEJHx50Hn1uwZAnYtuRXv7K4/XaN5maNtjZ1nNpauOwyVX2KBOGtPeOpOEKoSe+MgJyNlYPD+2FsME9awIeuNP5UBXNrUFsGIwl4fAU8cLvGcnctWyI5XBmNfbqbJqGhNULfBokclEpk1Uh85TYyqaEPuZhWARuHYVsGQjYM+qFy+vnfqxMRJcBhBljb4WdFayWDedA9HmLeEHWuUb681GbSJSoxwAmDdnBwmHDsJKScFuFE4gisC4jLALdLiZGCCSH/8UPorx+Al/dDiV9Va3+9DRZWQ6oXCAilwGww+216t4VoEvCHN4EE3HX98aad0Sj8x68glVBD83X18KVP8qf2Xe8w/OiPEPTBt+5Q53Y+tPVBW79qob3ytqS7W9LYqC6uLCKY3WgzbZpGY6OgpAR27zZ54408iYTgG9/wMTQiQEJjw9HW1+93Qu5IvrME8iiRJWyQYOct8raLja8VWHyxcZzTeQLYGQLfEHQOggzotNT7aamAvjx0F+D+BfDIfJudpk1yEEyPIJfxwKhGVQG0nHLk1zRIW+B+x7B/Mgu/Xg2ZAtx1CZSfwazaybiIRgJ4ecNr0OnyM+aBiBGi1X03hqGzezjElypg3hm2HR0cHBzOmvcpseIvFUdgXUA8bvj0h+GZVWrG6Larjz6WKcCbh6AhetRnKeiBdd0qoDjtFaBLiAMJP1cX67y5BeIjsHUbXDpb+TxJCU/uhydtGNQBAYdb4fv/ZvPzhyzKIvDgtzWmz9HxuCDgUbNFxxKLwcqVKj5m7tx3X0ehAK+8AtXVRx+vK4PSMGRyUBqQtB5zzKIiNWsViUDJuEDI55X1g8ejKk2/fkWtKPzi7apCMxKDAz2on1AT9YtvWWoppa6rZY7CIpvViI8ZvLlKcv214k+5i2NAB1BiwFACnhlQ9zVuwaxKGMnC79thOCGJTMkQacyRFTDc6aekM8D0ABwYgWurwRoBtxdueYeb+94eaO0HjwFrD8BHzmNlnwuDMJVQHqPCTlC+P8otPsH/uLiKzjF49pBa4ODg4ODwnqAHIei0CCcSR2BdYJpr4YETZAMmx+0QjjWx1DXwGXDHcvivNrAKGm4DrmvRCAfg/7gDfvV7tUKwbDxAOl2AJ3fAG3EwFwPPgfRKRvos+nrAWyz4x//b5o1nNf7uYwJDPzpgfoQVK1Qgs66rQfeidwzjDw7Ciy8qA9IjAqsoCN/8qCo0DQ5ovP26RaEgcbkEdXU2W7cKbFuQy0EiAdGowT33aLS0CFq7BMm0ut7DPcrG4JHnoDQI3S5U5QqUejQkGBYkCwCYGRNfpY5pq3DnUEi511cD16AMRd0ukBm1gADgQA6s8cH4uG0jvVl0Q4CAaFWKwoCXsaSOX4f5VXDlDJUnWf2OClVVEfjcyhy1eQIiiUrQuNrtZlmlzrygoDaixO/LO+BQHzw3Bl+75vxfx8HBweFd2EnIOC3CicQRWB8QinzgNVQlyzfeIsuZSnTcsBiqpoDXgrYhWOiCSydDWRi+e78aTzoikgJuSLWrFiQBtQVqIXNQYBULbLekzdT4p0fUH3BGc7jsHJdc4mHGDJXlU1Ojjldermah3klVFXz5y++OuzlyDlVVghtv1HnxRUuNTtmSb3xDw7IEbW2qMtbSAjt3SoaHJUsuFTTXCiwbZk6GbA4GR+HOBcrhfTQtAKkm91OW8rDAAguE20vOrWPmBatXQy5tU+S3mTFH4CvXSWeU+PtUFN4cgIXFEjOU4hHLJKP5SblzpHWJiYYrD4aQuCMWrpDOXBe83QdTQtDggrYuZXtxxPKiphi+eaMSWCWnMWI9E7xo3EGI3UPw4kG4ajK0VEFjCRwchOYJjuxxcHBwOA6nRTihOALrA4IGzC6B5/arWR5NU6vh7pwDrhBsT0DepWJT7m1Sg9xw4pDnUB54HvXueiCcFfhqNURI4tUE/gpBezf89nmL5JDJLK/JoUN5vvMdF6GQxsUXQ3OzqgS5T+CRKoTyxzoVS5fqzJmjMTIiiUYF4TAcOCCZNk3F7/zwhyb5POzZI6moENz3saNlNCmhvBhG4/CpS+HplYKxlMDlBi0sGNxSAKEhNMmkaTbF03VGRyTFEUl/l82IC3p6LcwmjXnzBP97FdyzGL4wCdYxzGZGKa8QlPkthmwfptuHplkUcCHyGgk/7BmTGClB3oRf7IDH90DPAdB0+MGDqhIJEJlgF32AP+6AmGXx7E6dliq4drrNpb4u/HXVOL+yDg4O7wlaEHxOi3AicT6tPyC8sAHe3gFuU1VIFs+AGRVQNT64fX8jxExo9itxNTgCm3crITJ3uhI9R/jyTbDiLRhLgrsIshrE4xqlQUl1vcC2Yfte6JsssCIau1ZYjA1rVNXk+PKXvIDgmWdhYAA+c4+qZJ0L4bAgHFYntnatzZNPSoSAKVOUMEyllJh6p9u8EPDpm+C5VWol4WP/DOUVsKtd46U3fbhvcrF/bx5fWOfH/5ebx5+UPPY7m92HQSIoLpIMJOHmuTB7OiSy8NxOqK0weZMYFXjxmjBsZMgLDU1Y6LaFZtk0ZnvoF1UkZSVpW1DsgSYfvNwJ3jzks7C/46jAei+YPCXPo7kxPueNAi5o3UXg1/8JH/0M2WkX0zag7GpqSiQB35mZ2Do4ODicEpmEnNMinEgcgfUBYXc7VI8PgOeycM2U4x+vH6+UbNkNq7fA+h1QW6Hmvv0+mNZ0dN9LZsMj/x32HoKXXoK3NoAlIBEXfGi2Gk7/6UuQzQsKwkW6L0hPO2zcLnjm2Ry//LmXXbvVsQ8cPHeBdSytrZJoVLUVDx2S3H+/zpo1NqWlGnPmvLsuHQ3Dp25SXx/sh4deVzNUu3sE8xpcLLrSRXEE/rgOVuyz8VfBVfU2V16lIU3JmhGdympBOqvm2pKmzcMyzRp0vLaazRqM+RHFNoUBD7lBN349ix3V8RkmPg2EBvPD4ENlRMpOWDwFLp2j5uUOD6hInsoJHj6/udlgsgwyXYxX9Spr4ZIP0Res5cGfjvHaWh/D3RqR2hz/dL/kq5dMQH/SwcHBwTEanVAcgfUekcvDoU5lf9Bcd7RKY0n4wwDsScPyEvUHHGDhNHhlk/r62pOsRuvuh9+9BOEgHOhQHlbRsPLXOhYh4IqL1dZYBhs2gBuoDEBBg1hWRb6UtkPfToksSJASaWq88obOzl0WV16hMzQEM2dMzP2YN0+wZ49NLCaYN09QW6tx553vFlZjY5KHHrKprIS779bQNMHBfpXvVx2FhfNBy8CsSVBTBX98G65cIli7VXLNfI3P3a6xepfGypfhmbfUasVoiWT6JzKssnJoQmfUznE4qJGMB3Fl8jBkYOgFbBt2dc4kXKQRcQmMDGzaqwbN4274zhfgpvGVhK9vh5c2K/F27w3QMAEi9AgeNOaKY3qPkSjdi27l6u8O0zoWgrgAFwz0uPnvj2a4Yz5UeCbu9R0cHP4K0YLgdVqEE4kjsN4DCgX4xZPQ0aNsm5bOg+XjlgwDedicgHI3vDB8VGBdPRem1Kiva09i0J5Iqf8WR2DBDOjohUvnqcHwk3Hd1fD7n8P//Dm4w7BlFLJ9UBq2GY5ZpF2CmAZYGqCBlPz0IclvHju+7XimHO6C59+EyfWw7LKjwnLWLI0HHhBkMhDLwMNPwNQmuGT+8a8zOAi9vZJYTN1Hj0dFzrxegP4YGG74zDKYXgP7OsZbjLqgrl7Q0gIHe+GZ9XBRDazdCANxiT5okdxtUlpnk84btOk2GU1DjgrSmg9MiW5J0hk/uqUxKaRRyAu6elVMjktCUIM/rAczCZdNUd5aAY/ywhpOTKzAOpZkXx+G18vf/tBP654iCAnIospqo4JU3MVDHfDNZhW15ODg4HBOyCTknRbhROIIrPeA/mHo7IXGGtVKWrMNbrhcTZB/LAAAIABJREFUzR1FDShzw2ABFoWPf97JhNUR6quUHUN7j6pi/T/fhOlnkDFou6GmGRIZSGeUkeeNF5tYBxLMnB9mt9dg7/YC0hZ4A4LKinNfSvLUK5DOwhvrYNZUqK44+lhFhWBoBH76hDr/vQehvAQmNx7dZ9Ik+MxndMJhkFKweRtUV8I9l6tWYXO5ElcAU2rhyotg6wGYPxWGovBvO2G4CLKHocslkbV5NHeB/XtMEktHSeQ95DUN0Wvh7jGRwz7SupdcxoAANFUIbq4WbO+CNzLQZsLMKDSEYE0rVHrUedy1CH63WgmrlvdwHqtj9WpMXzHr916p3FN9QBBIA1mNiy72kbEhYToCy8HB4fyQzirCCcURWO8BAZ+qyiTTynIgOh5mnLDUHNFXaiBmQdlZhvX6fXDfx1WrMBxU4uRMSKTV+Sxogc9OhSdfgbbDLq5YFubeTxus2yDYsNnNmg0WLU2Cv/2a+i3rG1HtsfIzmDGybRhMQlUlbN2p/KjCwXfvly+ofUMBGBlT3x+LpgnmzFFfv/Q6/OIZGDHhf3wFbl7wzn3hxiVqWz0Cf+iDXgG9ARiugMJkS7XTLDfCbdH7SikiKfDWxMl0BrCEjpXQ8Eey5IQPl9QI+zVCLjUYH3SBnoJhC0ZQ9hlFfkjloKoYHvjImd3/82HazTezsU3H8mhQZivTVRdgQv0NOkuuglovFDvBzw4ODueDFgS30yKcSByB9R4QjcDdN8GLb0FJESz/EPwuDtuySuhc5YdlJxAfZ4LPC5Mbjn4fN+GpEVg3AhUGfKUBSt/xx/bKBVBXqfyg/uMR1XLzeQWHu1xs3AnXXaM20NnSDT/dCe0vgC8HUT98ZAksOc0s1os7YdUBFfPzuY9BZfGJA4mryuGqJbBmMyyYpdqEJyKfB7MA7QmoLoUXN8GS6ZAiRYECRRSRzCsB6HdBaxoOmZDxQ3oATJ8kMCdBrs2NHQRrkkGmXSMajFNaNUBsrBg7a2DXSESZxuihMsqKPDSFYY/MMVJSYIlhsP5ND1ZeUB1RPlRuA25bcG7t03NBd7vxeqG8CHIFjVEdXBbcuUyZ0F5lwPW1xxvUOjg4OJw1Mom0nBbhROIIrPeIGZPVBrAureauGl3K6fzVFDS5YdIJPKbOBlvCo0OwcQT2d8FIEkYK8P1pR32y0uToco3gbXShjxVjWRq+8dDnoB+GRo4ebzgBv1yrBuNXd4Puhmsr4c3tpxdYrQMQ8sBwCqLFED7JwjYh4Por1HYikikYGIKnn1LzWLOqINoAi1sgQ4b1rKVAAb1/Pm/sKkcTcNcsyNiwdRSMwnj3zJQEBIgqG1GsYWo6WrUBBZNIKIZvQQaZMCirHyA1WkreHeaKMjf97hTRyhQXeWFv2KJkrp/b/RGCbmUCe9/VsKEPOrtgWhSqx0WkbUM6D0NJ2NUHMyuhcYKMQX1RuPUyeHkdBFzKPX5eLfSOwOQgeJzWoIODw3kiBdjOZ8mE4gisC8CQBQHtaOafjsrEO1/iFvQUICogFYP4IHTXqJZapRsKmKxgNxnyWEgmh6qoKm+gs1eJq5ExybQ6JWSiUcFDz8D6Q5DJwLAJw8PKMuKTl57+XJbPgRd2wMWNKuLmXOjpgx8/Bh1jsGsPfHgJlI3Ctz4KJVFIYWNhI5G82WFTGYS8BS8cgLU6JEfU+Zq2jXXIIhX2YEV1ZE6HnES4LEIyyaWH1jJWEqanuprSkIXh6SBZZrArX0WP7aEWG1dJkrLoGIlqmw3bati8txJXxuDHmzT0oKDCD80NSgA2eGHVRtjUBtu7VStxag381z3n775u2vDSMFx9BURCEEvB1DIlQq+ZCy1153d8BwcHBwBBEGE4LcKJxBFYF4B6F6xMQ1SCKVUVq3wC7rx7XLDVF8HSaZCoBemF/7UDGgJwQ3OOrLtACSEKmPTro9xzewMr1sJYAorckrfflOzYDPVTNVa9LSivgP1D4K2AqxpVxan8DIa4J5XD35wmJy9rqq3I++7HbBt+8AK8EVOtzHgFPHEYLp+F8pgAAgRYyEIKFDhACf1plfk80gvPjkAuDr5wjLKZA9AgGe4tJZUJITw2ht9EM2wqB/ow9ltMqT9AtGSElD6NnDBJFWzSVhapSQayJj7/EBW6l0xeZ1uHQaKQYLQ/CqZESwh6o7A9DxtGIZ8Cfxb6+iCnATlo74dtXecvsAwNPlMLb+6CoRREfHA4Dg/eAp196n0sjpz+OA4ODg6nQpLEsp0W4UTiCKwLwCwPLA/BihS4BHwiAlWuPGPsBjTCzEA7yVth2bC5GwoWXFyrZoCO4NfhhogKAQ5HwRWE3l6oLILuFPyx1yTWkMYkThkuLqORUBBuXqaev349tO2XtHUK2kdhShkUbPj6h+G5gzC5GDpiarD7fEhnYOs+eKINfCH42hKoCEieypt02jZ3uVz8+3MaP3gbcl2ADcEQfPImqGmAn2+Cry4BvxsiFJHOQ7kXntoMO9+Gvk6gEYTPpnTJIPm4G2tAJ+RKkNoTQg5pmBEXWsREr88SL/Ejwza9nioyeHB7C+QKbjqTJeSFSUjvJ5Hwk/NbuEa8JEe9eEIZdJ+FlXKpTFRLCeVYEEZjUO+FgA5jecgZyv9sVvX53bcjhF3KEqJgQTwL5WF4biW8vRU8brjvY1Dp5BQ6ODicJ/Y7YzUczgtHYF0AhIDL/Wo7wjCbGGUz6s+0TRFzT/jcjZ3wxA4QKIPQD7cc//jSMNR5oN8sYJHlmVEfKRmnD4sREeNyKhgjRRqLAMf7QsxeBDVNgn/4Z43t+wQLZsPsqXD5ZIsNh/p4c72LGy8PsbjOd9Jra+2A3iG4ePrREORjsW347r/Axm2QaIapM0AsgTyw2bJISckTfSb/fsAiN+gGVC81GYffrYQHPwtjGVjbazKvYgR7zwq+t/cK1nZV0r4FhraiVtWN2BCykZdIClkX2XgA3WOBrcEYyChYBYPDWjOmdFM8OEy/rKAi0E/KE6Q97yHvzmHaEl3kyJteUiNeitMWbh0KOQ0SAnLK5FN6AFuZvRoGDPZDMAJFcVWBe+jjMGUCvbGWTlE/KYk0XDENfv6EsuwYGFHbEYE1EINH31J5lnddAoYzU+Hg4HAmiBCaNlEtwkcm6Dh/3jgC633CIodAR2JjkTvpfjlTzW7pArKFE+9T67E56NlOjARTJsfZ3l6FDJnUlXcxWphGwi6l1dboElkecNnU6xr7SPMSY3jKNK67tQTz9y6mT4aP3QJpfSctVXsI2xGunZfBy+0nfN1kGn75jLKiyOXg2kvevU93L2zcAvW1MNQLxSF49nEYHhYsvs0g1pjn4XwcUaYDLvBbUDDAhpER2NYDM2tNHu4Z5mB0Nzu3hlnTbSNHO1lcuYlVkcuIj0UhJ5G2YPj5cvSLTawEZHsDEAAagKjA506hhSRdA/UMuCqoDPWQL7jJCj8eI0c6a1LIeRizomTTASzbRUrPMntRP9vXlmObSq0YXsAN4QBM80JjMeyKqxWXxWH40iVwVcu778X54DbgmulHv7/uUnjiFaivhEnHtHD390HPKPTFYNksVc10cHBwOB2SJCar3+/T+IvCEVjvE8UsRGIh0CjiopPut6heVa7y1rvzCY9gYZEiS544oUgPX5/jYrNt8mZO8FZsiC35KvxGnmk++Jmd47s+L1tFGj86CSwWXJ3jk1cf9XYYIcHcuWNcPNfCJH/Sc3O7oCQCA6PKjuJEREJQXgod3WrVY5GEtjYoLobNvzZo/m8pDFvDljpYgEdCzh5/AUFrTlBlWWSFZKBQwWv9FXRuDPI3132f5JiLrvoatu0JKndzAWmPH8LjZZsiqXywJGDaGF4TaQqkBoa/gBSCrO1F18ClFTBNgabZxJIl5PMuikNjFAoa6XLB9OsTlA4VGGuLoMX9FCy4shEWVsKq3aBJiHhgUhRum3b8PZASWtuVAeu0Rv60ivN8mDMVZk1+d1D2zBrY1wNlYdVKdHBwcDhTbO0C+c/8leAIrPcJgwAVnGYqHPC64OaZp97HjYtFTKeVLfhRfbqApdOWjrI3V8dwwsuw9BF3wVDE5H5vjinCzZskcSGoxc2QZfN/JvKssQo06ZP4x1AHEX2Mjryff029ylBqFpeKcu6p0AiO/9S4XXD/naqSVXoSM9JwGP71e/DqW2rVHTl49VVIpiS4JQYwqULydpWJXJBAdrgoZNzIkI6YbhNzCzbHJB9qCBLo9dK6NkwhYTMwHCU7pjNACbgtiJowCjQaqp8KqqVXAGJAKVjoGC6TouIRdGzMgge3JwXCxO/O4HMXkEaWgqWTS4UIEiJoeciaAhHMUZnScNUm8e/2Ux5WFhfP96sK3nWNKsroozOg5h1D5xt2wu9fUa3ixhr48p0T46N1onGJaBC+cPX5H9vBweGvC0EQg6UTdLRfTNBx/rxxBNZfCBUUE2QW+2mjlUOMChtfoYnYWAnSENi6RpY8B+N5fhhaz5VejcuoYRpNRHDxzWSWp60kecOk3XYzEr+eHwR6+VFqAN12ofkP82Q6QqHLy9caxJ8sJ7wetZ2KksY8N9Va9PX42NsLgRk2+608VctM4lLH9vfRNDuOOQfMLh07rtPfUUV9XTvLoito8I4wp7SS15+/jXwhDBnJL395J66ojhkGlo1XvwoSagGvDUkBBaEqYv0SJks8g2kqWnqoKO8nn6zGluV4XTYpzcSnuYlWjjKcDhPyjFEXLNCYK6W23MII2rQJm+l+k8G9QebNUFWplXvUTNRtMyHghq5RVcV6J23dyhespEhFKBVMJU6PEE/Dnk4VaD2rwZmbcnBwuPDYpMiz5v0+jb8oHIH1F0B3HJ7cC8OjFbhDzeh1Wwn7PUQ8FqN2CFmw0Xps8mkdd5HFmgqNSmkySeyjiBgpWclOxqhyjxGTxYyJErJWjAOZJJrMUO9uJ1VoJmFIDqUksYIgOm6dIKXyanKdRBSMkOUpu52VG0Pk2gX9A14OJMBbpnFfUNApTRr1lWRLPRw83MCIFsSstKhvOcxHeYKUGaQnVc1kXx+Tqh6mdPYXyLZ5MXw22VJBPhSFOgmbBMwCDFu55UUkxGxVvRrV4McWzfP2UlIUo2SZn7GoD6HHadJ8NJOjHZNR4KKQmxI09helmGq78WmCUQyuJUSz6eV/7/GyJQ9uLxzuhZAL9vdAdbG6D40niC+aPx12HlDh3wtnHS+usnn46UvK5NW0YEkL3H6CWbb3mlgC2nuhsuTMI5gcHBz+srBwVhGeDCHEUmCKlPIXQogyICilbDvVcxyB9WdO3oQfrIXDo7C+HaLFMzFbJ+PyFQjNN4i6bKwRSTZlIvwWiZEghxPl/MIf5yarl6y+GykFUe1ShkwfIT2Gz8xwH49RIdPUeyvZI6ehaSk8jOGRVX+KZbFs+MUu6IjDp2fAlBO0CVOY9I7odHZ5SA4JRhPKeby/S+fVjTaLrh7CtrIk8pXIUgEBsKVOqbsXI2+R1vyEIyY+vxejOMmcW7ew5gdLEeWCbFsQGgRkdagEKKAXFbALFvhAtrogpsOYTW1TB/Uzh4hYpTyQn8/TviFsstj0MgMdPzpbGKMUFzoRbrBKyCa9uEImV2hhpuFl8zZBYQsc0sBfCc3FalswGUbTymR1y3Z4LQYfv+7oqsopDfDNeyCTfbedwmAMRpPQWK7u59ZDpxZY+4aVmG0+z+H17ftgyx6YORlmTYGfPAFjcWX78LW7HW8tB4e/NiQC6QisEyKE+B5wMTAN1f90AY8Cl53qeY7A+jPGtuGxbbClG3YNqSrISLYM4cmBL4/d7cZVbZPBBlsAGsJrERsJEx8N8XOrhGp3F/5gBuGNMVfbw5BZzjTNxTIjzyOuMCkZICdcjOGlMiu5okgQGv+pSZtwaEyZpx4YU3/4Hz+gqjp3T4UiD1TjpzEXxZX1oksL00jj0zXcwkUmLlhqefnR4BS6XMVolsTtzpPTdXTDJGTEGLWKqPN60QUYDTnEIBhFFnpzHu2whZ0pwBoX9AEDGtatLkSVhhzWoFyqXwNLI+4tp7f1Rhprg1S7bUpIsZNB5iHw4WcaEtvS2Z8fpqi7npdfj6IVNK6YBS1XquvVdQgIWCDh1sVQPQX8HnhlGxzshM4eSPSCkHDNGNQfY1sRDavtnRQFVEVrIAaZPEyqPPn7PZiGn21X5qP/cAkEzzFqaWgUfvuCCtze16ZefywODdXQ3gMjMUdgOTj8taERwM1Elc9/PEHH+cBwGzAP2AwgpewRQpwkEO4ojsD6M2Y0C+sPwpworG9V4chGVgPLg4lBta+dukg76SKDXlcNsUQUGRSk7QCGyyY5FqF/qAZPdQZfc4qoNsw/yEeZ77mXjKwkT4YhyvAIE02TzA6GuPGY9lHIDbdOhs4EXFIFj+xX/78nDev64fp60NG4LlzCi3qKXhmjKGwwlPTg13QenOFh98EKNiUuIhLux+UtIAyJX8/Sk6+i4PFQpSeY4hUEZAzLG6AnWo1nSYbkM0GMehNbs7E6dGSPAcVAQoMhk+CUJFaxQbbCh6wWVPm8zC5zsWQKtPqyDKFTQZZBsmTwI/IafQc0hDDp2xJlS6tGIADd/z977x1l11nmaz7fjienCqdyUKmUrWQF5yQLA7YBg4FuaEwTugHTQHNn7qV73Tt37pqhZwYut7k9dA85NWBsmmAMOMk2thxlWTnnUqlyOnVy2OGbP3YZye5ScJRs72ets1S1z/72/vY5+xz96v3e9/c+6vVm7GmDpUu963McWLbM87+yHdg7AJ31cHwC1l8OOPDzJ6G3BW659MwJ7dEQfGI9PLEXIgG45qLT7xs3YUkDBFSvwfXLxba9ik7T8JZ4oyFYvgC274fuVq8xuI+Pz1sLlyJlNp3vaVyo1KSUUgghAYQQ4XMZ5AusNzAjk7Bjnxf5CFehLCEUAl1YOEaWhU07qQqTqAba/D6cqkp+JI5bMahkFOSoQIYl1lAAM1nhcHo+WwOPc5QNXKd9Ct3ZQck2yNohEiLJO1N72CbBsObTpaaIqrCmGVY3SVz3OVam9rEvU0+2cg1JM4SDy3FGyUUL9F5yhCceXkIhG0AiWLGqwPL5cf7xfoUCSXIDOqFAiVCyjBJTWF2/jcnEJVxOP2kxQTuLeUpeS7EoqcYCsFpD21qlclRDZvGUQpMAW9C4ZozEvAxGnUW+EGWyL43RmWNqcQnZGWEfAerQMWhlhO1kCNE/YFLSpqmvdqE3KUy0Qd8A7B+F7/8WutLw2Q9BLurlWtnS+/BoKlzaC08fgvqo15T6E1+DPSe8SFMyANeuPPP72FoHH7zy7O+3ocJtS175fdPUAG+/Ejbvgusuga4273HjlZ6FhG/m7OPz1sOzvPY//KfhF0KIbwMJIcRfAR8Hvnu2Qb7AegOTjLgsbLfZ0qfREFXoNOAjV0A5dYQD2kbaU0eoqjrjagMbrHW4IUBK3LKKdEBaKm5WoIRqKKpLXEyTGM3SPryXwRaD97csZ40yTMWYiyGOUXNj/H/ZNvqVCTpdyZetvbRXduGGDdzoIS6uT9EWOogUA7THP8kOjtDHGCVZ5kRLnq4rh+nf3YiYshkfVvnYA1mm8m0UrQBWwqBSDlFTC+g1QTi3lr9Lt2IKCMzcpquB6uE8erCI0ulSPRHAnHQpD6gQEhACGhySyydRgy41y0AzbeYuPcii5Qb1cZ1ppYIJTKETIInOApTaOKPlEnVON2ZmPkKBS5dBdxM88BjkHRiahCcPw4ajXkTKduC6eVCswHgRxiuQUeGHW2H3UZgY93KqHt10doF1KmNjnk/Y6tWvrdC5apX3OJXwK2yJ5OPj88ZFIUKAS16lo/3zq3ScCwMp5deEEOuBHF4e1n+VUm442zhfYL1BqWKzI3mMy/6izPV2gPhQPfUBg8UtKhuV7TS5j7EzN5+sFiNBhlZ7iH1iATgCNVIlqU+jBVzGB+swG8osi29nobaHnqPHEAaED/8B2dhMu6ZRZYApEuyvJTmi6SREngPk+FZuD19UiqTGf4swVqMHk7TFkki3Hymm6WeMOqLszOtsGw9x/FgjExtaENKFXtjrukQcGxlRUfIuKCq5oRSJkoZjQbkX4g3e9T48Cg8OQ7AUpZAJYldsnG4Xu9+EegEK0CrReyzUkIsjNSyp42oKbb1Z9ESEmPAcPudjshiNUcqsZD5KeSlPH4TAi5L0ozGv5+Azu+HihbB/8ORzh8rw8C54bguoE1BTQc1ALARTZaiVIRKEzCAviW3b4NHHoLfXM2P18fHxeT3wlgifPd/TuGCZEVRnFVWn4gusNyjDFBinRJsR5pBxArVnJ6lqgUNuO4ZiElQ0OsPHOeDOQxUOES2PZjkIRxLWykSVPFkSROuKNJmDpK1RFCmwohHi2QyVQIRvHF9FRlosbdqOGxGMKIKyMClbFoO5er6Vu4mfG3kWOPP5347fwcoFk1RkNxo6QYIEMJm2amwYNhmxk0w/0AC2QJoaHJA4iyGnC5IBG60rg+paBDWFBVvqWRjUiUW8ay3b8MgodIZhQR00aBqFXQq5Q5KJkKC6SMLlAurAcRRKtTBIEDGXaCKLUe+Ak6JRU0gTYD4hnmQfFWlx1C2zMjgHZSYqdaoH1fFRCNbD2pWwpBVOjMBVyyEYhIcrsO8gHJ8EJ+e5Q3TVQX4CAjXQHUiU4JKzmMS+mKuv9nK9fHHl4+PzeuPbNMyOECKPt4oKYOCVTxWllGfsl+ELrDcoOgogKWORlxlWju6lI7OfiUAI2XotqlFHkz6A6xxmSDYzodajCQtVt3EqKsVKmEIhhobNeDHNgcmFNIY3cnzxfBaVhvj68Be4t9hJBYPfHFnC29sep7lugFZh0VdpwMxYNIcHeXtsA1Fzml+IJZQqezgYqBJX1/NnwmChrPBPI0NMKb1UBoPYBd27NYMSyuCWFRCCuGKzfI7GGl1BBmusX2SxRNExZqrkNAWCKuRtaAp4Ru03r1fY1iY5eAKOmgLLFNhVr9XDdDXF3M59aFGbOkNhgd2JlK3crgZRhMI0Rcquxb6sxuPlEodqsLYHHj8AnamTS3MjGS/PLeRAbgpam6A9Bgta4aFdsGMaSIDqQHnMW2IbPOytVGrA5SvgfTNtHF0X7tkIuw7DDZfCmtMIr0AAmptfyzvHx8fH598jCBNk7fmexgWJlPIFFYNCiPcAa842zhdYb1BaiHIxLRxnmhV2lObMTvLBBlprJlqhyN5UK7YTpoEqE6rOJXIrI+U2BtNpdo8tpJLxGhzHzGkmcmmEIyjbUeYGd1ONNPEHuYwcAcJKiYZghhPVJAvcXbgKjGcSuJE8gYLDtsoqVM1hbt1+dmgNBLMahtzLRHQn/3XzPO566GacHgdXFTOWCQJqElA8x/WKIBhWcVVBOGpRA1o0jVMdCHQFbuuCPwzB6jrYNwKjx2FJi+CS5fDLYzBYBhzISoVcpoFD00FSizNMmDqD5Qg9ukquVSFhQJwQLbVOnirmmOe2cbgIV3WB68DTRzwBJwUUKlBnelYMfWOQseGO56DlMKyfB/cICApQQ6AHIBEEJQLz6mFMATsAhTKYJoxn4Nk90JTyhNaqhSeFXLHsCbDoOdWl+Pj4+Lz6uJQosvl8T+MNgZTybiHE351tP19gvUERCBbTyGIacdUOytqjKJUSpqsRZgmHpqMcdfdiOQFak8fo0StUk0dw7UHuqYzyyMQNqBaMlFrRjQokbbZqi4g6H2GZMpd4egx1MkyIKhEjR85KMpRLMSc2xsaxNYxpzUSVQxiBGi4KW4bXsir1TVb1P8WcHGxqm8u9j99KNa/BHh2xwIIOB/qF14C5ASgpKHFJulnjPXqQLhS6CdDEvzd46gjDZ3phKA9/+B0cGIMWF264Cd7WC5vzkFShYsHuCSATof+BIHK+haIIqsJgVwCubPZeu+VaM48VmhmyvehYQwB6lsOV82A0B4eGIDsMzTqoGgQ1EEnPjmFgCt4TgU8uhnt3QWUMruwApQptvfD4GHS1QygKI+Nei5x4BFIx6B+DcBzu2QTNKeiohx/8zlue/Mwt0OgvDfr4+Jwn/CrC2RFCvPeUXxU801F5mt3/hC+w3gQoSoBwxxcht51NIcmJkMLTx9rpDqnUxAhmZQkikkEwwSKtj470HrL5GPszS4mIGoneKSbi9eTcOAeqi7hC30csUEbGJWpZoGkOil0CaWAoDoZmYZU0MiKFRBA0Sti6il62UB0NW6my/fgcahXNc1IPgNxuEqgvItss7DkajjRBg1BKsDAO7w2YRAic8TotB360Hao2NCZhMOM52PfUwVwD9mZhqgqBMvRvAWdKRVa8foXHA/DLnbAsBVstGLZgSSM0I5kIV3hKd7maIPGQwvAUbNwNwRDs7odVHTC/F+bOg7segrYE7NwFU3uheRQSSa/i0JUwMA6f/jA88ASk6zzzToDJAsTroa8Iz/bB958FqcC1F0Gr7e1TrLy294mPj4/P6VAIE2b1+Z7GhcrNp/xsA33Au882yBdYbxbMemi4nlG2YFOlO1ZmaGoRAXURb0uAwQS7eIIYh0kEQ/zfi/4Hd7uXk5EpNrOGYi1ESplkQtazq9bK+4L7eCzcyGStgYIVpVvtZ3XkGAlUFrSM0bdjDn32HBrtEbR4DbcOdrOWtfp9uKEyObseu6pDCZhxNA9rZaQjUSdtxpPNKIbL/KrCf0lDRDmDG+cMNQeKFly5FHYd9Pr/3X4ZbJ6E5zaCnvHM21sWw7EqoIGsADY4OhwJwOc3QfNciKuQc6HFdJkyyjhI2tFYjMl00VsmdAqgFqA5Cp+4CXbvh3QVigPw84Nw1SVQKMJjz4EuIRCGee2e/cHzPQfVmaT5DdthVz/8YR/kbMhXQTqwrQ8+/SHPQ6vLz73y8fE5TzgUybPlfE/jgkRK+bGXM84XWG8yLmc+o2TprEtRjEJIhYgGk6i4JJkiTTN7qVdjNIg8eRlD2C4kJH4HAAAgAElEQVSOUHFR0LCpyCBxbZJ3qBlccx8hJ0O7NkaDanCRXMkvJpaTUDTKFZWBUhtMCpRxh98kbuZg70rWRYZITUWQgSC4QBUIQmYigWlUsVUFNJc2W/Cp5YJ04PTiqljyHMc1zXMvF3HYMAHpBfC5ebCiAaIl78+JriWw9bAnZAIpF1GrUkHBESopRYGkwlNT8IE8FFwIB+AECnNsA3SblpmPw8I2uOMBeG4rdLXA+ChseAJaG+F5HagoXm/BQ4dBqcGevdDbDe/9iPd88JRgnGXDvFY4MgwVxcspUx2ve5Fbg+VzPUsHHx8fn/OHeN2WCIUQAWAjYOLpkF9KKf93IUQ3cCdeX46twEeklDUhhAn8K3AxMAl8UErZN3Osvwc+ATjA56WUD8xsfzvwT4AKfE9K+f/MbJ/1HKeZ5zc4w1KglPLzZ7pOX2C9yagjQh0REBA2vW01LJ5jJwY6ZS6mQidpnmaRyNLnpLmcxymLMIqAgFpmbm6Aiw9WiCSWM929nGFzlAhzEShsLnVyfDxK0DVwqwoUFCjahFoKjIgUQW2aEauDeCxOxwqbQ0UDdxTP4NTUKBsqhCTdmuBdbQrv6P734mp3v9f65+FH4HgfNDfA1/4T6ClwwvDOCIxUoTsNh8dg3xhMFsGcAFOTTDVU6bj6IMUsjN3fRHZ3nCFcpGVgz4Vv3w1RoCENzYsFK9UIeQs2B+CaJMTDcGk3JCS0NEKtBn2D8O7r4UgGto56VgwPHoBcEd67DhqScHwQqlUgcvJa+sfhJxs9t/3meli7BA4e974JgjrcfLUvrnx8fM4/CiEirDr7jq8OVeA6KWVBCKEDTwgh7gP+A/B1KeWdQohv4Qmnb878m5FSzhVC/BnwFeCDQohFwJ8Bi4EW4CEhxLyZc/wLsB4YADYLIe6RUu6dGTvbOWbjuVdykb7AegtgY+PgECCCQJCnm4t4D73iSS5Tt1Nn3wuKg+lUkQImkg0MhHQizz6DNmVirOwho40zX3bzoz4Tt6pjOzXsCQMqLugahb1xjESNwuIQ1fogJTfI8mtGWRRrY9dWwcSUQCiCcAJWt0BPPVzeBq0zYiSTgVgMRrPw0ydgXwb+uA3ag1Dqh5/eA5/9BBiK98mMm7C3Hx7dBYYGNIKlglxmE09mGC2bHP91NzKvQFJAXjC808WYq0ALxCowNQ3lCXg8BF0RTzzlHXhfI9Q1w3PbQK9BzIIPXgK7RmFnBZpaoEsDzYBDAuJRqFnesqJpeJWDh0YgFoR/exp0DeIhGJmG1a3Qmvbc4GMh+PiyM793Up65l6GPj4/Pq4FLiRzbXpdzSSklUJj5VZ95SOA64EMz238M/Dc88fPumZ8Bfgn8sxBCzGy/U0pZBY4JIQ5z0j7hsJTyKIAQ4k7g3UKIfWc4x2zz/PEruU5fYL3JGWOYKSaoI8YkWRRUVrIYBZMkrdSJQ9S5Nlp5kulglIySwkbDCqrkLxIYTz/LhuFLuW7pOFPlQQqTc1magAf2xFBdC6emQkWAolAb0pnaUU/qigksy+aoWuLWZRZfW2HwwHHYPgE7CtCWhFt64JIWTzxs3wl33AWXroWFF8MgsN2BShhGHDDHYXgUEjp8oh325WFuGH76CLSnPIHVkoCBDNQ6JMWcSuZoHXJC8TLJAeJALzhHJWa3IBuB9gKES9BmQkyDsAq7CnB9DJ7JQmcvZLNg65DqggcPevlYoZkixxXdMDEFOwchrcO7rgfDgC99F06MQl299w1Scr0EfVODa+Pwv14B2So0haEpMvv7VqnAD34BuTx84s+goW72/Xx8fHxeDSQCl1ftr7l6IcSp0Z/vSCm/c+oOQggV2ALMxYs2HQGmpZQzZT8MAK0zP7cCJwCklLYQIgvUzWx/5pTDnjrmxIu2r50Zc7pznBYhRAPwJWARnKzGklJed6ZxvsB6E5Njml1sxaKETZU1rCNOHeaMDUKWATqrOuWKRNUcsloCzbGRjvCsyTVwFzgs3rGB1q+eQOvVSZsaHXNM/sgchClhQPFWuF0BliA7lkLuUFAUyZRU6Y+6zFkJn0l49gb37YbxHOwZhLACS5vAcbxIjeN4tgWOBZYGscUQOAC9XZ5NguNAR9B7SPnC6I4AkLDUVfjXQgwHC1Th1XtIvDlGwNFd9IiKXoHVIUi0Q21Gg01ZkDZgOAdVB1b0eNuzFdg9DrkKdJ7STkcRsGguXNEOV/V4eVe/fhie3QYBDfr7QGmGaAKiQRjLwUQZuhOnvEdFODzgVUW2NXrXZDswPgXHB7xrPjHkCywfH5/XFpUQMV5C49QzMyGlPON6o5TSAZYLIRLAb4CFs+028+9syk+eYftsyWRn2v9s/Ay4C7gR+DTwUWD8bIN8gfUmxsamQpYS41hYHGcnq7nhT89rBLCtPE7VoFoNIaugWJJwtYSsD1GwohxJzWfhkf0crJtHRE/hhJrJ9R+jN17PdFVgWwYEpBeqaZI4QsOqGESbstg1jR+dcGmYO8V1IsQ/fMfkseOCWhlkEUQNpOUJi9tusTDW5/iZWab9Gpj7dIyuxgBreg0mHZunqhW+WZa8PxQiragIAesWwO93QtDw/K8u6YavPaFRHlRg0vA+NiW8nKi5QBXCXZJV7dCkwO0d4Gjwuwk47kKdAbc2QqHgWS640hNRJQvmpLxk9UwZkjM5U1KC7cLc9Mmk9oFhz6x+xyawa3DpTbCkG3IlSEZhw36IPgSXd0IqBHdugM3HPe+tNcu8BPjpGlzVAWtXgVWF+T2v513j4+PzVsShxDTbX/fzSimnhRCPApcACSGENhNhagOGZnYbANqBASGEhrcmMXXK9uc5dcxs2yfOcI4zUSel/L4Q4gtSyseAx4QQj51t0GsmsM5QJbAO+O94CrMA/KWU8vBrNY+3MklSpKijyCgtdGFTQeIiUKhwFMETTBp9RIfGQZFErRylQBhNs4kUi4wp9YR35OjZu5djTTFKdpRd4TADbh39AR1pOCjzLZS8ixJ2UNdWsSaDWDUdWYXaiEmt7PDVhxX+TwOcOhemBAxKGBdIqYB0OJF1+erdZcQBFSeYItjsEk9VGSuX2JMNMR2uYq4u8hMdttQKfMtsxBQqV86Dhqi3NNgQhaN5ePJZF4bxgrj1QAIvilUAVIv6YJkr4gmGKrBh0mvMLCVc3gg3tnmCSovC8hbYPgSagKgJ187xrBW+/ywUa2CqkK/BRU3QfYo56IJuUO7zugHZwqvgbE5AMOHyvcegqsNXnlBYfdAzTd09BG7G+zDsewx6OmDRfDg6DvOa4aNvOw83zivAceD+DTA5BTe93e+p6OPzRsFbIlTPvuOrwMySmzUjroLA9XjJ538EbsWr8vso8NuZIffM/P70zPOPSCmlEOIe4A4hxD/iJbn3As/iRap6ZyoGB/ES4T80M+Z05zgT1sy/w0KIG/FEWdvZBr2WEazTVQl8E3i3lHKfEOJ24L8Af/kazuMti0BhJVcRRKdMnjksQ8xETrNsZB8GtmYRDDoIXdKSHUQfd7F1DTfpMt/dg5U2yK5Nsfz+h3g2HCCbKjNqN1DNm6iqTc/q/TS2jDF4ooOaZjBZMjG0CiG7QrE/hj7toLXZUC5SCodRrpW4NQFjBkQduMQFIbAOBaEoEQoUByQl0ySUrKDaFkIozKvXCEoYEjaj2HTMfBEsaPYeAN+6cybSW8BbEiwCSBh14RjQCoPDQe5QXJIBhUMuLIvDvJDnpXVTG9yTh00lCKThfc2QUry8q6DuRZw+fwVsH/SWDXvqYKQAX9kIugrre2DdJWBZcMevYE4r3HYb/Hyb5FcHJNNZqKoCFJdHYy62ruAmFK8vY50XbOsvwaeSnrA7NOy10FFmC3ZfoPSfgI1PeXOuS8GNbz/fM/Lx8TkXVILEWf56na4Z+PFMHpYC/EJK+XshxF7gTiHEl4FtwPdn9v8+8JOZJPYpPMGElHKPEOIXwF68P6U/O7P0iBDib4AH8P43+IGUcs/Msb50mnOciS8LIeLA/wJ8A4gBXzzboNdMYJ2hSkDOTA68MN+5hOd8XiYmQZZz3Z8iV8/j0ESZARLuGKKkYIbKdA0PIkIOBF3ssk52Koxd1eDdNr82b0bfATtC83DGAlDnEpqXIxIrEY3n6NCOcCLXSWNqEHsM8gdjRIwsU9U0WrBKIZhCSzhE2rMEP1/EfEeV/o1zcdM61IClEoQCNYkScnElWCEFFxUFh+PDCm2RKj3CJiFrIMx/d61Lm4GyBEt4UayqC0NVL59MdyEnsG1IvUPQPs9gsgpbs57hZ1cEJmzYVIZ2HcYdOGbAqhf1Sq8Pw/UzRcAbDsGjx6AtBpYLP98JH7sYbrzGewAcGQA5CdN9YGUFBEBEHayojTOtecuXIcX7islAOQiT4xCJQyTwxhJXAKkkJBNecn5X5/mejY+Pz7niUCbDjtflXFLKncCKWbYfZZYmylLKCvD+0xzrH4B/mGX7vcC953qOs7BJSpkFssC15zroNc3BenGVgJRykxDik8C9QogykMNbd51t7F8Dfw3Q0dHxWk7zLYF4Uc5fI+toRDIp8sTUPqK5HACuInB0jdFaE5rqoI27pBvHyS81+c0zH8B5xvTezaygsDnGeKCB5q4BzIYq7Q39RJw8MmgT3GDh5BRG393CQLSDyliIaimIk9OwUzqJtRnqzFHGD7QgAhJZkpBRkUHh3ZUSsB0C+QrtzcfQVYvr3VE+UTlCTBYgcA0Er/VE2Qy3roDb76lB2fTc449XoVz1fg5pULChzmDfb0v8zf8weGYa+gowVvPsGL51ECoxmBRQdL3o1ZnYPOCJK0Pz8q5iJuwYhvkN3vOlCvzkfijbAiUrkHmgCGrYRg3byLKG+3zguQxCA+0gbFHgopXw0Wte4Zt+HojH4Quf8bzD4vHzPRsfH5+Xwuu1RPgG5CkhxDG8RPdfSykz5zLoNRVYL64SEEIswQurvXNGbP1H4B+BT84y9jvAdwBWrVp1Lln+Pi8BDZ1WuZxqeQfTz+mktTIyqSBLDnpGkmIaqQpkykEddVk99hzfnPrCjP044LjIqEppV4g5qw+SD8UQpRpNd21mxd5DxKOS+95+M8ocSc01sKsGOUUgoxAOFInpOWpLTLIkcXMaQpOohQKOooMpIOOiF6ss6dyGcBQixRJLtCPsUOMU7ChLyxtQlDAETurzmCnonmNx7JjqNZQuVaE0AGUBxTYwHRh3yY84/OEXcMs7vKpFS0KDCZkaBHPQFYUGDa4Mn3y9BqkBklZORs501Utyf741te2Cccr3U6niJcZHTAgKhUAAqhY4wyaBCRs9IHGygmrOc3RPFKCpAG9fCbe+E0YykC16xqdvJIJB7+Hj4/PGQSVEkqXnexoXJFLKXiHEGrylyf/8/FKmlPKnZxr3ulQRnlIl8A5gmZRy08xTdwH3vx5z8HkhfZPH+GNtCyHVQb73PTTteoDg6E7M/VXUdggtKJLNKpRLLnLCILFnGk0tU3UiXm5TENJ1I3y+6//lytxGeo8dYMfDEu2RPMlQmEJDK1oMIrU8TQyhzrMoVoK4AUnCniZilLHCGqGeCiPZDuyCguhw0NM5TM0l91wEZ4eG1mqTKyaJl2pMSck8R+GQDs1uM+nKo2Cu+VMUqwBc1xnlB6sqyD8K0AUEU1BzPfXkSri3QOvCMNUSbNkK03OguxGmbIjrXrXfB2OgnRK9KuLwb0wgkXyCNLGZj826HvjFLi9yZbveY+0pdSupGPS2w+6jkFTATMI0oAuF9y+JcvwgPOVCrRMqNjTk4MZuuPwab2nwV0/BOy6GtfNfp5vCx8fnLYtNmQl2n+9pXLBIKZ8FnhVC/F94gaEfA+dHYJ2hSiAuhJgnpTyIZ2O/77Wag8/s1IpFdv/i50wtVKnWCjStjtG97NNMPHEA/dCXsUc1yopL/2EXJuHo9iANoTGWpn/P03tug5CKUV/jxrrfc0XjEyRC0yglwUXLKxwbNUg/VKb+8ADpa4cJTJQJN+QpRyBZK1ENxcGWlAcdrgw+wg0H9vOz+g9jdaVJ1zVhSJ1DFhxYbHEiY1AsRWlsGqcxWePhvVfzHDrL2w7hhHJgj+EVd3hRpV+egI5uwVVHgjzWZUHBgCkVDtU8EVavQNql+foYve2QjsO2YdglIKx7bhO3pF8orlwJplCYQwAXiUKVClVMQqxsFQQ02DniRa4u6YDmU3K2FAX+fD08vgs622HDRshl4KO3woeuhrslHAlCW73X17BVAz0Cd+6FRAA+fD10+/5XPj4+rxPydepF+EZDCBEDbsGLYPXg+XadNY/rtYxgna5K4K+AXwkhXCADfPw1nIPPLNilEpHBAp3NTUzhsrxyC/t+9nu0kTFits70WARxT5nMcRurDAEFrPwoNwS/RlfDM9xf/Hu0uKBr/jEijXlGzQaGGxsx3Bz1sT7MUhXbLbP04W3svmIZTsklbCaojgjW7r4TbXSM6pODrDGO0Ht1Kx/rWcNDzZME5pRo751PWpXMNW0O35QlNt3D9c79PDu6lHSwTNV12TfUTWPX06AkOLlABxnLi0J1dEDTmMa45eDkFGhXvcz1d2vQAVt1QTuQ1qA8BksXwXgJxgswnoGHjsGiZvh9Ho7XIKUJ3p9MUDN2sIPjgKSeVnq5mEVplUXp2V/nYgV+thH6xkBVIO966WHHx+C/H4UFi+ArC2HDEOzvh4saITJzOSMFeHoYehtnP7YrYeNx6IpDV3L2fXx8fHzOFZUgKS4639O4UNkB3A38H1LKp8910GtZRXi6KoHf4Kk/n/NEqKGBrmvXEdyyhUsvvYK4FWJwfBzXDWE0LGQkmiG6yyZtOQw7gqwsU8lVuNzMsNw8zJqeR/mG8VM2913MwsheUCXZXIRjlUvoW9jIZy7+AQt3HaD9uUG0CYd99c0MlAMcaVrAaDRNb3aUhmo/42OS/Y8Pk8r+kJuW/A1Hjmwh0T6PxoDJDcTp1UIEAoJqVnBkqEjM0ilKlQgWmjsCoQ94Fg+u5EhJEDFgywQctiQLOgWiIcBwt+utHaaAEFADR7g8GFEoD0A+Arbq4kibkWmFH+1W+EFZoa4brr8CtDJsnYKtg1Pc3DPIolgSBEwwQJwGmug+7ev8u80wMAldMyLpnVfA0DhMSairQjQO1zdCVxB+mIOQDuNZr3dhYxgOTp6+F2GxBg8fg5XN519gORLGq1BvvDD65+Pj88bBpsI4e86+41uTOTPOCLMihPiGlPJzL97uO7m/Relat46udesAkK5L5w03UCuNIy8LUbvj12y6wqCmDiD7qmT3gmqAYii0LgtyoLWNwgP13LfrIlLfnSISmGbzgcuYUNO4KDyy7n38S+NHCURrTEzVM62EcKfLiCaX9tE+quOSZsMgfbNGZLDCxKETdCycoGnUJlEw2aNGOA4MCJtVqk5j/IN8oP1R7h4wCSsOH2zZB6F3kzc7eLi8gR8Oxdkz3UK+lqIrkkd0FZgfVNkz0ITQVKSheon5Em9F0YUCkn2NNovmOjwiKlQmTRyjhpbQqNkmR48KyhY01MPYMRiehJ2dc3j74oO8bcUkphKhPONCIpGIF3VgyBZhVz+015/cFo95j5FpWJyHG5Z42xuCnive0RHY0ed1+Fm9AJKh0zd6jprwt2tPRrxeCo4jOXLExrYlXV0aodArU0U/OAKbMrCuGf78rNZ7Pj4+Fyquv0Q4K2cSVzNcPttGX2D5IBSFjuuuw+EEI+wkd1OQ8O8GWfHRGge+AbVukKogJzVSKxqJ9wSJqTnyzzaybfNKGpeMMEU98cQ0A0PtTFhpvl93O5/lf+IuE+zoXkOomOfqR+5AHctiphR6F7fiagF23NbEKmsHovlJdi++msGQJCkd8sJlj+vymFvjY2qQBemb6B0skTCrtDZdS5U/srH2z/zLwE0MlkJElCHGKwEOuCqX1B1m73AHDfOHmZ5uwMqEvEXq58VIDdAdBuoEE5ZEmKA6EgeXcg6cAYmUgu19oCbAGYFIVKdaJ9l8vI6l806gR8boFks4xINYVOhlPTonS+dyZc8VXplFIEUCnsh6nngA1rTCT5/zeiC6eEuE7z9LtL4u9NLfa9eV3HVXkV27aigKpFIqn/pUlEjk5X2xZstw937Py7XF5By8jX18fC5ENILUs+R8T+NNhS+wfP6EQpJ+BsjEY3Ss0pBVk1JIw7q6mbFAEm26TOHyizjY2EPbOwa43NrEjtpiJsfTVO0A2ckE47k0GIItsUv43kdv93qXI0mPjvJxezOFXJh7bvwA1o6nMLQKwwsXsM2oR8kFqHblaJz4J9jocnztO3E6FzIsbL7rSL7kRNk5HCJqhli/pA9LbmRnuZuMo0DNRlRBHXMphsKUmwUx7QiR1EKOtilYRyTIGX+t57tCO2DfD4WYjjJHwhYVt6JBVfOyBzUJcYEzCix1KTwSZSyQpqltin0iTL4yB1016TEmcHGoUXiBwIoGX9jP8FSKFc8e4lRu7IX6ANy11RNct62BuackuI+Ow8/vhnwBbl4Py1/m9+DoqMOePTW6ujSEEPT1WezdW2PNmsDZB8+CocKaAPRX4f3NL29O54LrehWekSBovlWPj8+rjkWZMfae72m8qfAFls+fsFAYxCQTTSOXLKNhX4aBeQbTWYVq2aCl1SHTnmK4fT4Vx6Txs0N8cfQR/m3vBzi8fx55GQdDwIhNfHmWqWiKFm0YgeRo0xK+N/d2rh57nIBRJbK4hFShs26SQ8F51CcnKakaS+ydmFgM9yV4qKWL5UaQg1gMGxZfuNaY8ZkSaAiKMooiaoSUGlXLpFoNIDSwbI14eoyhHXG66xX6FlYp92lQ0EB3aVBg/ABeTtZWBXdMRUk7cMKAMYkmathtOhiKt16XBxQoOSEmyz08/Uwn5QHB/mSSb69bR13EYipXz7ANc2d67yXCsLgDDgxA6ylCybI9e641vS987VUFLuv0HgATE5DLQWymKvHX90KxBPEo/OoP0NMF0chLf4+F8PK6nkfKV+YWHzTgP13lOdnHXp5GOys1C/71QTg2Ag1x+Pg7IPYG8wbz8bnwEf4S4ctn1mQOX2D5/Inj7MbARCiC/aElKMZenD/voTSmA1BYBEONXRRFmKbyCBORRp4OxFnd+AQyLXl66FKmnkmjNNtkr4zQrAlKtTCW0IjpBXYvWEwLQxRkBNvUUBWHgFojJCsI0yHuTmA3KTStneQK+ST9hXamElfhigCTUrIs6s1Tyg403kZXaJSKSDJpB8llojghhWgkSzKQpbh5KSurMbJ9gjopKNW7ZKcdrg0ItiZgfFRAH2BJRIPEHdKhoEDJhhMCNWbhxEwvkqVA5HpY3VVlbNJm97EQRkAj0G9ynxbmL9cLvrsFag7cvho64rD3IDQA04mTVYTuTML6e9ZCR8Pp34fDh+GHPwTThM99DpJJqFa933XdO47jzD72dEnxz5NOq6xcabJlSxUhoKlJZdGil5HIdQpBA071FS1LiQXEzjSRl8DRYTgyBN3NcGwY9vTBpYtflUP7+PjMoBGgkUXnexoXNEKIsJSyOMtT/zTb/r7A8gHApsYEAyikyCoWtquxe+5i6g9O0rdwKZlknGNBgwohDGq8c/pe2tQMqqugSgWrJcmjdevQumtogSpWTGco30YikiNAjbwdoj48ytjiRrKhOI+5l5JyphnX0iTdDFGZpSKC6MJmf9083LLGrZVf8225gqQI0XRKmEUIgSau5jqjyi/rp6gIqAhJSBaY0ziONrWQ8Sc6uXYeBFXJmrxA5uCRQYfJqxWCDS7NisKwCyCQgyocU6EM5DVsVcJBAWk85VACHZehUYOhXAOBjEK4qmKNqPw+I/jk27yqv3wNooa3nHXHb6BQhL/9K69Dz46DMLcLFrR5zuw1G+7eAgeGYdUcL+H9+UucnIRKxRNR+bwnsG5+G/z0V5DJwLWXQ2KWNjSTGfjmz+E918OSebO/z0II3vveEBdfbGBZ0NGhEgi8On+1Okgetgt8t1CmfCTAh7qC/HnKW4p8JYRMQHqFA44L0ZeRe+bj43NmbCqMsP98T+OCRAhxGfA9vO6xHUKIZcCnpJS3A0gpfzTbuHMSWEKIecA3gbSUcokQYinwLinll1+NyfucfxxsXBzGqWNaWoQoIEKS8eUNaFgEhvMY/YJEYJJliV20BsYQZpyiSGLkK1yjH+N+8wQHos0QcyhbEYaqEcp2GBWbVGyMXnEII1AlJSYoaUFUoNUaYEF1P7sDi7B1haBbZrTaiJAKmmKjY7FE6Mw9sAuO7IGexbBwJQBzhMlfmDHuasyhtxTQUYg7rejlOLGow8SQS329wLJgbEySSihsPwDWhKBugYOVs5nqM3H7FC/xHSCA16rHAISEsNeEOhtTqG4yMJc5CNPFGtRRFYGTgVrFi1y58qRNwbvWw0QGWpvgwQ3w1H1w8WdPtr3ZPQBb+qAjBY/tg/lNMGfGzmHFCi9iFQ5D+4wzfE8X/P3nvCXG8GkEhqFDSyNEziJAFEXQ3a2/rPvkTDwrJ/g6/Rx/MEJ2W5wTUYMrv6TSrr4ygdWRhvddDduPwJr5sMhvIu3j86ojEX4vwtPzdeAG4B4AKeUOIcRVZxt0rhGs7wL/Efj2zMF3CiHuAHyB9SZBJ4BBgIJTQXVdNLU24+orSGeGmP/HQQKDeZa3HkZvsKHmUO1M0J9sQy1KLnt4L4+ZT/LVKz/Gz9X1TDrNqJpLppJE0ywaa0MMBdsoiQgxN4twHeZNbmFOpZ+aa7BS7iATirHTXEadlUWRkh3xK1iuJPnM1DTGQ7+EcAyO7oVUI6S9crV3hYZIl4/wdKmNXHUeIVfn0mbB6i9qbHvOZucuF0UXtM9R2TmsELQc9L2C1c0Q7jX4n1vw7BvKnKw0DAACDL2CM6nhRCRuVlBdrOBGoJKQyCmI98FUGb73r/DB90LLKUnea1ae/HnZUrBtaDulwo9cY3AAACAASURBVM51vUV7RfH+PTUvyjDgqlk+uiNZqNSg2/C8sl6MacCtN0As+rJugVeMRBKgBE4MVUqQDqerbd6dgwfHIKzBe5u9XpBnYtV87+Hj4/PaoBMgzYLzPY0LFinliRdF40+TqHGScxVYISnlsy86uP0S5uZzgaOg0MlFHLefYVKRM1tAp0pgMkOwrkqPMYgYL3HgV2UmWhsQTQqp90lkn834fod6OcoHjz7Avr+9jE1KkZpuYogaAa1KfWAcQ6tSlCFsRSVq5dmuXkwuH2dpZQ/JySptR0vIy9LsWrgGITRWqBezXmshbvd7CiQSh0IWLC/cVGGMUfEwc0MR2gNHibkR6pQuTAVAcPU1OgMlOHwC9m6DpXMlyYTCoJTMH9b5+LsUtnS6PJpxYBwvUzspICLAcqgVDURKokUqKCWoZYM4NYFYqKJ1CUoxWD4KxSL84X74q4/N/tq2tb1QXAFc1A4HRrwlwst6ofsMOVkAz+yH327y8qvmtcJHr3thrtVkBr57l1dleP0VcO0lpz/Wa8UaUc9tuPzunSNk5+nc2KbRNotPRaYGdw5CSoexKtw1CH8z5/Wfr4+Pz0ksKgxz4HxP40LlxMwyoRRCGMDnOYc2f+cqsCaEED14f+sjhLgVGH65M/W5MGmki6uchajyAQ6IBBo2rgAZ1gkO5Am7eXIbp7HMCHZvCtmXY/pYDWtpkuKvgwzsbOdR52rWXP842opG9jpLkLqgIThGWC9jCw1XUXHQUVSXuMwxHmxi8miFxcdSCGuSS+8Ls6p7LWZiISHR4k2suROWrIED22Dxau93wKECCHRi2EqRkFLk1EDIweNw4DjMaYXBE7B9r+DylSqi4C3dHc7Brc0K2g54KCphzIKQ6vkAzESylGkXI+cgVBd7ysCd1pHTYMYEqWVQ3Qi5PMybO/tr6rqwZx9MTkF3J3R2eNtNHT582Qv3dRzYuBGGhrwIVvspjaM3H4aGmDe1LYfgXau9ZtLPc6Qfsjnvuh575vwILA2FW7Qm1sfS1FZ4WnW2/KuyO6OXNa9Qc7I2y8F8fHxeVyS+0egZ+DReInsrMAA8CHz2bIPOVWB9FvgOsEAIMQgcA/7i5c3T50KmMfgBbqy6LLf+yLPTZUqmiwi5RKIVAlvzVE2H0FiJadvBkuC4GmapypbPriK3KcbBYhMnnupgftsBFkd2U42bWLqBhY6Gg4oNCKqqga3ohDNZdofqaJUZehTQao0EJ9pRky0nJ6UocN0t3uMUgjQRoIkKo+gkiND1guefTxqX0utPOKcVGush0wxfqYCyHZYK0NsUGnMuYy2Gt5ZXc8BQUUo2sXiWQF2JyfEm3IjqLSOOQbEZ1qVAmwtLe+Gm62Z/PR98GB7ZCMEAPFCDj33k9GJszx647z6Ix+HYMfi7vwNt5hM6txnueQZ2H4WgDj+5D/76PRCcUZTJhGdSuq8fVpznQqCIEKcpWvZoNGBuBA4VAAHvPE2/RR8fn9cPnQDN+OvwsyGlnAA+/FLHnZPAklIeBa4XQoQBRUqZf6kn8nmDIAx08wMkDz3Oul0bcQo5JksGyboUG58sknAhHLdofuwApQVpGvNZ9iSXsbt5CVaDxl57EQP7Wtl7cBGJVI6VCzZRb05QEQYlEcbEReAiBKi2RahawQloTEzuobTiYlqGAjSctSuBh4JBGzdhUUAjjPKi23leB6xcCDsOQGMKbrsJLAH/+dcwlgVUr0pv/TxImIL9E3A0YJAdAyouEhAVmDrWSK2qQ03xjFMLoB+A9l5ILIUbL4XQLInlUsJTm7zIlarCxCQ8t/X0Asu2vWU/04RC4YV5WeuXw65DYBVhaTeMTELfMCzsgtEp+LeNkNdg/xSYZTg8Ci0xKJahLvHKvK5ebTQFPtIGJ8pgqtDyGvln+fj4nDsWVQY5dL6ncUEihPgqXs55GbgfWAb8rZTyp2cad0aBJYT4D6fZDoCU8h9fzmR9LmyECFCefBuy/EeMWAHFsAkuStB4+eUM/XgTzWWLyxdW0ZRhjnSE2W8uwCwXqFXCOCMOifpxwkerTCotHDq4iNUrnkDXFRRcaui4CExZpWZqZJUYyZEx+isCLQDDy4ZZkyxwlpSkk3NFxWAWzwK86M8H1sN7rvGSwoWArz8OYzlQkjP9/6rw5DBobZKlt9XoGlbY/4ROZkphZKfO1HQD/P/svXeYHOWZr32/VdU5Ts5BI2mkUZaQhEASWWAMGLDBAexl8eLs47xnvXvOfpf3nF372vXunk3f57XX+RgwYEy0MUkiCJRQznFynumZ6Ryq6v3+eAdrUJqRsCUBdV9XXx2qq+eplrr7V8/zvL+nCAigcugewAVmCOx+uO8ONTMQlCBqbYUDB6C8HObMUcJmOAblZZDJqlmEp2PePGhvh85O+OhHlefV749FhyXTobcf2nshkQHv+PbXditxVlMBu7qhfxja+uChx5Rr/NK58ME1U3xDzxOGBtMcs1AHh4sKp0R4Wq6XUv53IcTtqBLhncA64NwFFvDmeqRZwDLGlygCtwCvnHusDhc7pQtvYvfOLuJHN1GxoJRg89eYcUMr+c4f0Pnib0nEkqxuKZDNjTKS8uGLZRgbcxFKJrG9Lty+LKWD7SC9VO3pYnRhBS6tgI6JiYE/labo2Cg9gXL2LJmL3nIFQ+EAy9OHMUqf5Uqm4yX6to6hN6Z8k2onjKXpy4FIQyAF5jBkBIwEoC4Cx0Z1ShMC3S2IlEG6QhAfBYZstV7EjxoU6Aa64NkYhN3wlVvgRz+C3/wG+vogGFSr/SqqoHk27N6nHNlXrYArTjkSVOF2w+23n377/Onwk2dgIAaVJcolfloNeN3KV6u6CGaUw9WzQaYgm4P6Sti2D963Cvy+07+2g4PDexsXXqo5jYGew5unu+8HHpRSxqbi73dGgSWl/BsAIcRzwJI3S4NCiG8Bj7ydaB0ublx+P0s++1Vs00QbbwQqW1JEaNp0xhau4NiuXYw8MkB+4xDV83bQPW0OJSN95MxiEjkfpY8/hzcZZ9bCQZY3tHPo6CJ6y2tx+3IIl4Q+myPuJo40z8ZnZ/DIPAOFch4bbKIv/QaN/nXMEmdQG1Pg1b2QLcCfTOiPun42fH8/pHvB2gRSg7gPMuUaCxoEEaC4Aja3wowa2D0EhVENEjaMAb3gCmkUWiWHE5IfHhbc/0PBwEHI5CUFJD63JGAIxuKCK64UTJsOQ/1QKKim93OldxgaKuDKRaoh/vXd8L5LoaoM9nXBa/vgA5fBn14Dx7pgcBS6ByFUBD94FSrCkB9TrujNtXDHlUqcXSzkbXh8EPanocUPt5YxviL0ONkcHOlW2cgZNcqawsHB4e2TJ0cXRy90GBcrTwkhDqBKhJ8XQpQB2cl2mmqTez3HrRgZv914thE6vPN4U1wBeIuKmHvffcT7+ujduxerrJ5cRwdln32Y3C0rydXWssTVz/AG8Ix1Ma15iMr2A/j3a6woHyRmTWegrJxeTxXHwo2MVRSTtvyIFEhdYIdBNOl0j1SzsfBryiOrKRKlJ8WUJ0eaFBGiiDOktG9dcbKguboGvnUFfG87xPqhOAk+TZXgitsErRIGM1DihXIDxsqhpxJkp4Y5BKYN2JJClwkeyUCXwNIMLC8gbBCSdE6QK0iSluSpDo20LsimoDIH056Du+88t3+LoA8sqcxGR5NQGlWZsic2wupFyuh0NKWyWaMpEB5ImbBnGEIJ2HIU8km4Zhbsa4P1xXDdJecWyx+DTWOwLQH1XtieVM3wVxUd374/IfnSj+HYUSi14Y7LBV++UxmsvolpwWt7oGMAmqpgRYvqf3NwcJgc+0yrU97DSCm/KYT4eyAupbSEECng1sn2m6rA+r/AZiHEY6hOlNuBn59ztA7vWCJNTcy5917atmwBr5dISQRP727cv3get0eNfWldPYuMPkiRVSDVaeJ/w6S0LI09N0zaGyDgSTBL20+RPcpv7Jtp1ZvQg15Mw0XQn2QkUMLe2DRCG77JzZf/H9y81TlzF1sZZZjZzKf2DDrfcwqzck3AVxbCp+dA5k4IuqG/F773c9j2KtQ2wYgGdWXQ1QpD3aqXS5aqhncjDvKohLzEH9QwszaWV0IVUIlSQDmwDgsyJmx+zVK/8CHB0WJo+0/wF8Gt10A6C5v2QTQIi5tP3YieTMFvX1TXN1wFNyyD9bugJAJ3Xg25ghJcPo/qKYsl1GO7jqryYawfetpg7T4YFWAEoakAbi+sz4M3Ayu8YFwE36sJC7yaisUnID7Baa/TlPz1btjSDnoxxNPw0MuS21YLZk6ws3h2C7y6B4qCsKcNMrmLS0Q6OFysuPBQy2lW4LzHEUL8yYTbEzedUQdNdRXh3wkhngFWjz90r5Ry+9kG6fDuoG7lSq741rfoWL+e7NgYY8ODVJT2UVoFh45AqZ5nrCKKIYZY2GJRWQljfQJrOIvthcrUAI16G6atUzfaxb+Ev8R+ORdGJRigu8c4Ep5By9CrdPE8TXzwLX/fhQuBwGDycS8DA2rkTOCEhmq/S10AfvEM/Px1SGdg71H40w/BzAZ4aBdUhiGeg3g/1FRBQzl0FQQ9GdDSNqYpoErAdBv6bLCFqtbPk7AZ6BAQtSEnkJqgq0TwH4+C3w27jpgcbLXxh3T8H9ZpaTw5/rXrYcdeNR7nwcfhG5+DayYIBilh0XTYdhQYvx0NwvQa2NMKXcPgFrDjiMrulIdhrQvKq6C+Gh5NWgg0VvouvMJaEoI3EtCRBZeApRMWBBwbF5IiByEbMkEY6Ts5O7XzGNSWgdtQ5c+dxxyB5eAwFfLk6aD1QodxsbJswm0vcC2wjT+EwBJC1ANDwGMTH5NSdpx9nA7vdISmMedDH2L6mjXkk0nM7P/kpc9eR7S4jVQ5WAcHSKyYg5E1cSdy7FqXJ54AeXSAQX8tZcsCCLckX/DQI6uwCwZGziId9xMrdaFjYlgmu+quojn7DNXeVXg5bpY0jyXkyOJn8mVo69ZBYyNceumpt/cMw8MbIVAE/jDEkxD1w9KZcHgmzJdwdACkBZ+7A2ZWww9+LEjEXezbI8nagn3lgpG8AClUfjcrlRu8ADQJfgE5qax5XYLNefjSXxboaMuCnaa6Wuf21VFaGk/+OFrjCTCXoVYKnvRvIeBDK1W8UioBKARcuRj8Xjj4FByKqdfxe5SI7GsHiiDba2PXJ5kpYeVpVmKeTyo98OU6GMxDqQuiE/RzkQbV06CoCob71Vt983zVkzaRsgj0jUBFkSqjNlXh4OAwRSxnFeEpkVL+t4n3hRARVGXvjEy1RPgb+P1YMR8wDTgIzD2LGB3eZbjCAju8C40xrn7gJ+z5wTq0xKNkRnKEnusjeY2XfCDA6LBNpFFgFhmkNwxxZPUcdNtmRuoI68JXk437yCa8aH6JW+RJSz+BQpq9qYVcRh/TOEr1BIGlo09JXAHceutb7Q5OZCQNoQBkM9BzADIj8JQOd9+uSnZFPlgaVMJkUbMSLQE/eFyCq68V5HIwtw5+tVMjlkLZlOtCrTbM228dNmgBOYnLL+joKGDlJB63xkhvnkO70lx5aQjthNEy16yCkbgagXPb+059DJoG0ypPfixSDDPrVaO7nYesCbYJYhSiIxAZgtKAwQ1VU/9StSwl4P5YvloRQ11OpDoPcwpgfgwSPXCDC+5ZLk6K44Or4RcvQHs/VJXALZf9ceJ0cHi34cZDPc7MqimSBmZO9qSplgjnT7wvhFgCfObc4nJ4NyAxSfEgNsNo+BClB1n+Vx8h23Uf7a+8Qt/L/x9dD2+laxoUTJt8TKO/PEIBF725CrrMWo4FpzOSinDZ2k3c3Pc74v4Ia29bzUBVGdawTmllP5YeYZQU1ZOHdEq8k5hYloVhwTx4/FeQ6gfhgze2w0NPwifvgBdeV/ro2pugtFjt86l74aVXVEZp9UrwFIGvHgZHNFoPCjqHIXMgx3AWpUTyqE+aBHQo0yATKTAUz+CyLTRd8usnTHbvS/Hp+3wsmH+87hUJwyc/OvXjtW1VFswV4MgAFAXgjpXwm1ehNwkJAdVlqsw2PaZxe3OAijS8sEv5dC2cpUqHp6KnD350v3KNv+/uyd/bPxTpHPzXWkE8A1WG5DvXC0pP4ydWEoYv3a6O3+N667xGBweH05MnTzttFzqMixIhxFMcTzLpQAvw8GT7TTWD9RaklNuEEMsmf6bDuw2LPAIdySg2QxjUACCRJDnIgdrrOXzX+ym64xouefrfefqubyMtsFw2fi2L58MBfP+5nmTKj3mlxaWJPnrS1fTVVhDQk3zwpSd59eblNFQcY9bQPtyJKgpRL6aWQcONxh92SVhRAG5YDs+8CC63ah6Px6C1Gxpr4b4Pn7xPWRnc+aHj9ze3gZFQPUJLlwoW7gIdg/96JUHe9kG/DkHAB8tqBNcvho/8jY8vfiHNwABI3UvBcLP/GHzjrzP88qdBis/BAsy04MH1sK8TdA0GkzCcASMHdeVwyRwYtuHaeWCnYEYFLKiD//oVDI2oMuTGnfCZD59aZHX1wOioGm49MgpVlSc/54/BWBqSWTUQu21IMJzk9wLLsiCRUplF97hlgxAXl/2Eg8M7AYlTIjwD/zjhtgm0Sym7Jttpqj1YEx3dNWAJMHhW4Tm84xlgO4PsQsdLPSsQCCQ5BB4sUjxJNf1kiEiNtvwYL65cwLx//TB9X/kVMmOT3hAjXOKhbHoxocYC+otjVDT5iFeEyRg+dM2idriTa/bESNYbzH/gVbwLmhico/Pz8p1Ud/dRHmtgTstNeMvefnONZcP9m+BAH8xeAoMDkOiCcBncM0UrhXgGntwFjVGYoUF7DG68HtY9bbBmTZDNmzIkk4Kw5uLeP/Mwby5cfSlUl7t58cVKDh82ue+LWSpLBZYNvX2SQ22wYtHZH88bR2FfBzSO92FZWVi3B2qqARsG2uG798ClExLbHT0wMAwN44KqbxD2HDm1wJo3G3ouU1m1ivM4P7A8ArOq4GAv1BRB/bhzx1gCfv6Ycq73uuHjtypR7ODgcPa48dDAtAsdxkWJlPJlIUQFx5vdpzRTaKoZrInr5E1UT9ajUw/P4Z1OngQD7MRPGXmSDHCQam4hy9NIbAaYSQ/VhGUX+/Iu+m0D0+fDe8f13OrNcewf1qGJOFZ7HHNfHMNrYJuShhFJpGWEeFmUdNBHWWIIs78Evd/GYxVIdccw9G24h2LsijaxvrORTx17hFtu+whEKyYP/Az0x+FwPzSVQWOJyl7Ve+Gea6C5cWqvkTPVmZ97/JMkBJSVw9e+BPsPuujudlFaBtESePC3sO+oMgH9/MegJCpobjZoaDLo6zbxeqBpthePC+JxtfLxbDycemMQ9B4vi9kFKAVWVCurieQIVJ3QuuZxj5+5jjfT5woQOE3pz++H294/tVgKBegdVJmlN0ur54quwcdXKTEb9CrPMoDn1sPwKNRXqSzWA0/BNz9zcc1ddHB4p5AnRyvtFzqMixIhxIeB7wIvobpG/l0I8edSyl+dab+pCqx9Usq3OLcLIe7EcXN/zyDQEQgsCtgU0HDhZh4upiPJ04eHFPvI2QOMIUj6qrFx0+kTbLllIcVGBXXpnUS62unZPcrAEYv60SwFCeXdSYJVPmS1n6TfA78bxsgW6Kp3MV3PoQ32Um4N0NB0lNwSCZ1dsLYVLv8klM89519UQ1PiwpbqJWoq4ENLoblh6q9REoCZZbC/T3lslQahoUTZMCxfCixVz3voGQj6obRIlR87emA0A609EmnmaT9mMmexj1UrDNY+B4+OQFkp/Ok9EJniAr/SEKRy/H6Oo8ut0s3lXtWbpXkgekLvUkUpXLkUXn5DfWtUl8Gy+bwtTBN+9hi0dgICPnYzzHubEzg0DaIniMPYmHpPQV3HRtXfdjvlQQeHs0YikE6J8HT8D2CZlHIAYNzJ/QXgDyKw/pKTxdSpHnN4l+LCTy2r6WcrPsqoHM+UCnwUpI8iaYEGphRYJPDbB+jTIoSx2RMr5ZJoDy9Uvo/r659jec1ucskgPaPVGHv68QXBHQwzJmrIvDxGeaiUaHmG2r5uMjWl2HU+NMui7OggK8pfIl5ZoRK0O34BTVdB843ndExlIbh8Orx+VAkQjw5Rj8ow7ToITXXQMgN+ugG2H4PGSvj0aohOyPBoGty2GILHIGTAynolrk6kphy27xvXghJiaXhoC2x6McPrz5u4sxb61iT6DR4ScZ36Oujqhpdehls/cObjME3YsEk5uNeVQuuAEnu44bMfhKOtEEvCZYtUJutEblgFi1vUSsnOQXh5B8yfCa+3wxUzoDMLeQsur5pa03jfoBJXDTXK9uKVLW9fYJ2KRS3w69+p3rdEUv0NR1w5OJwbHtw0chZnl+8ttDfF1TjDMLkaPaPAEkLciBpuWCOE+LcJm8KoUqHDe4gITUROWMbbVYCfjEJB6iyLNrFNG8FNBlPPs1zsJ593sTe9gIXaHjwyS0ewnoZQnKqsRkkEsvPnkUuEMWQCw55Pkb+dkutvRMYPYY/+lLjU0YRE6DaZsId8yo1bxOmKBqmNNkLbeqi7DHwTusKlDWLyMzEh4KYFsKQBfvU6dPTBfzwN9hiUBOHV7dCahPUvQS4BPh/s/yD85GtvfZ1ftkFbSmXEFtqqn/1ELluker46euD9V0BPCnwuyI5JECDcGvG4JJmUvxcJHjdkMpP/uxw4CE88rW5/9MNwzXxV6quMQnkU1u2AZzbBznaw1sPd1ynXiIliqbwE1u+Ep15Xq++2HQGtDOZUwaOtKva5JUqATkbAr97+0TiMJf94fVHLF6jG/EPHoLwUVjmGog4O54wEbCeDdTp+J4R4Fnhw/P5HgN9OttNkGawe4A3gA8DWCY8ngK+eQ5AO7zK2Z9R0GJ+AeDrKN3w9bKCNHm0faenG5Tbx1GUZGY5wXfJFIvkxQq5+EtOq8OYjhDIR/IUEVjKP67LFDK2fTiE2hCvYiOUtpYQ4XZ468robXzxNZ3ktR8RMDhcGOGbV8e3cYcKpgeMCK7cTUo+A93LwT94wJARUR6GQUyW29gHlGVVeDFsPwxv7IZEAa3yu3+MPwT/cC2XjM/KkhL40lHkkO1slT3RI3r9Uo67urakeXYcrx9sjDx5M88ozSXaPBmhY6GbvIYlM6zS1wLJLdZ75DXR0qmxXSQmsfQlaZkHVeF+/bcO61+BIG6xcpnq1PG4VS1FEubhPZHhMzTEMeFUT/D8+pmYVzq6B21aobQCxuDIjLYuqwdJXXw6/ysOSadCoQWSK2aGiCHziVnh5M8xshBtWT7rLOSEELJmrLg4ODm+PHAWO0nmhw7gokVL+uRDiQ8BKVDfFD6SUj02y25kFlpRyJ7BTCHG/lNLJWDmcRLMbNmYgL6HFAyGtmuXW73ja0hBoZIWfRl8HO6sWkBzyYnssAr5RssE0O4unMbejn+rBYmL1dRyu3Ex37TRK12rMj7ko++jn0bf9GxU7djIcirBh7nI2zViKWxZIl7mIZ3v4++Em/mZoA0bpeA2qsB9kBnK7piSw3uS2FfD0FrhmAWRG4WCr8gX1+6FPqtl9ZgHSKZCF4/sJAbfXS/7m1xYd28CvQdc2i698Raek5OR6WjZr88ADw5SEDRq1MUpKA4RvD1MetfEHNN7YL/jyl2BgEB57HL71bUiNFcjlTP7kbo177nExmtR49mUoK4FfPgHf+Cx88bNKYFWfYvXf6vlwrBcGxiBpQpGE2hI41AO/fBX+bI06jhVz4UA79MXgxstgqwUxCwjC8tP4Tp2OWU3q4uDg8M7ByWCdHinlo5zl4r7JSoQPSyk/DGwXQsgTt0spF5xdiA7vNmZ54auGqhdXGiDlFejWHqK5l4m7I/isOKNtUVIxH8/Mv46QN8UCsYuQHafW6OFwSxnx2cP4CxnaxUKORF3s/dAyZo2a3P3sWhpXfJRM2yPkswbbZi6mPD/MrNBBxrQws8IHyBPh4FiGuWYaDD/41oAIgPvsOrWnV8GXx3udpFT9SD/8DQTDqqc+H1PjcqYvVD5YoDJJBw5bfPdv4zz7ax+mqdEWEixaJFi8GObPhxkzlHjZtMlk/XqLmTMFlgUej6DEZ1EftjDzgppKnVRGrb4rLobOTnhuLXR2pOhqyyCtAv/zryWPPabzjb+MIvDgcY0bxFvHs1unoqIY/vwjsLMVHnkNwuON4bUl0NYPmbzKXJUXw9c/pvy0PG5oLMCePCw7T4aiDg4OFw4PbqZTN/kT34MIIT4I/D1QjspgqfkcUp7x1HOyEuGXx69vftsROrxrKZ3wv0gIFznrcxwYKKO88jXcZpq2YA3FM8ewhQEShikhZQRo5iAhEuSFjsc1zJZCMxutFYzJIspHN9Cjxej2lFNS14z/0CClQyMEa+PkCm66QzVQ0KgIjjKYDYI2Pg9HL4HALWd9DLYNQwklNILj43BuvwKGE3DdGtjTA5Wl8PBfHO9d2rEXvvX3BV5d58XjAV1XPVRvvKHz/POwaRPccgvMmyd58kmT0lLBhg02l10WZdu2MYqLDW69JcT2/bBuoxI1H78NRvPw4BbY1W8Ry2hQ4YahJHY+z5YtBt/9tsl/+0YlrV065ZXw3GZYNEvZFcRSkNTB44MqF3iFEmGapryi5ITTpIKpmuFdE6wgdP24NUSNS10uZlIp1adWUuK4tjs4vB2y5DlM94UO42LlH4BbpJT7z2anyUqEveM3Py+l/IuJ24QQfw/8xcl7ObzXCbndjBz9KDFfMWZ4C0QLaMLGxEVK+MnipYxBTFx4yZGVXu7Pf4xN5gqwNTI5Ly/by5mT30FZLk9RIExvRKPfU8ZoMIzuLmBaLnxWBpc7T7qxhnatlTqmo6EjkQim/mtr2/DQRtjTqfysPnkV1JVAfQV85Q7oGVLN1NOr37oKL+gH27LRkBguHa/PJj4m8Hhg9myBbcO2bbB4sZqHmEhIhIBlJ01skQAAIABJREFUywJ84APHreWuWwWXX6L+hssF/3IQnknBWEhAv60cNS0btWjF4uhRC/Jp9EiIx1+HwTEwdWheBFY1dGUhWKaEYslemG7DJ++E8nJIuWFrB1QGlIfXrctVD137gFr9WH0ODvIXio5O+MnPIZeHpYvh9lvPTWQdHIKyABT7/vAxOji8cxBOifD09J+tuIKp2zSs4WQxdeMpHnNwwOuClTUaew81IC95mZTXwsSFQGJjMEQJZQwwbEWosQcwsNlqLiUgU+RzXvKH/AwLP/fPuIur7HU0DBxjcMVMdpcsxJ9LsFDbQbU1gMtlIV3lFPlLaOMAFlm8dJOmCzdFVHANHiZ3uRxOwp4O5RA+mIBNR5TAAigOq8upaJ4O/8/XBH81lGLHdh/ZrEEoJGlpgXRaNccvXw6BgODee91s365KhFUThivbtioL+if8uGsCvD6JboCFUDVATYClBFY+L3h+bY5CRZCcJbA1GJQw3AuBIsCCwVY1czqTg1odOp+CVTeCPg2sIVhdC9PKoboEfvCKMl2VEj6wEFZMP/d/+/PJhk3KdLSyDrZuh2uuguhZCkTLhicOwvIauKrxjxCkg8M7BA8uZuCMQpjIeGkQ4A0hxEPA40Duze1Syl+faf/JerA+B3weaBJC7JqwKQS8dk4RO7wnWNMMY7kWOnbdQW7xUzTk2sjpXqyCwaC7hA6jlpsKz6K5bLyFHMXEOGDNxk4boAlqQt3EByK8VLua9OIGqn1eFmtd9CWCHDXn48uY1PoKzHdVcnDIz3BBY6O2A08qyrKKCqpDY/TwDA18BG2S84iAR5XP+sfUYOHys2joXr7Mxz//k+Sxx7LEYhZr1niZPl1n0yZVaksm4ZVXLEIhi+ZmwaxZx+tx/QPwswchnoDVl8EN16rHbw7bbGjP0+XW6ClyQcIDmQSq7K9hWbB5M0TnW+AzEK48UuiMRDVGhyWm2wYLfEFB3q0xZgosN1gJCAlY3ARLm5Tlwr4e6BuDxlJl7fD8/neOwKooh23bVakzEFA2GmeLrsHnlirLDAeH9zI5Chyid/InvreY2G+SBq6fcF8C5y6wgAeAZ4DvAN+c8HhCShk7iyAd3mO4DbhrMfTFL6F3ZDH52IuU7/kO/dE821ZewaW5rRjChUEe3dC5OrOeo+Z0ksKDEDb5pAfLdBEJjqH54wxQoMLw8v5oLbutPKMRjXYRps88gJ0PsCcXYlAuIZ3yE9yd48tzN7EmksEijcaZFZPfA392tcpclYVhxYyzO9YFC/wsWOB/y2O7dsHICMRiNv/0T3kyBZNoic09HzP4zKf9aJrghZdVeaumCl5+DRYvgPIyePXZAnO8NnOuhh29go2bAxS6JZmRLJZlUFfnpr7aTSpTwFhymGhtF0V+wdZdy8gKA8Y00DQyYU3VzFyw1y1I9kPEpUqR3z0I8yOwwDPeKG9DKg/hd1BD+6rLVck2NgKXLgXPFDy6TkXoHPdzcHg3IRHYZ9Fa8V5ASnnvVJ4nhPhLKeV3Tnx8sh6sMWAM+Nj4i5QDXiAohAhKKTvOPmSH9xKVYagMa1C9hsL0ZizX01xvCDK5GgJJA82KktUL3Dio8Ug2Q+vQDPScSTA4RlHTEDUV3aSkl4AQmBTolSkyohgh8gRzPnrjFewvNBJ329guDY8/izucZnM+x0y6mMHU0hrVRXD7ssmfNxVsG3bvhsZG2Lq1wNCIoKB7yI3Z/ORhiytWm7S0GLjdgkJBlQiFON7fFYvZhMMCnw9uKJY0GoLGxijPPWdhWRLblriKBIkr0oSbO+npCeKrS1NiD9LdVw3HdOgFkgIKFizVkAFB25CgfLZgfxZuL4Odo1BWAVc2w/ojEPXDh5ee/pjiSTVb8GJxSzcMJbIcHBzePl5cNHMKnxeHqXAnKhH1FqbUgyWEuAX4Z6AaGAAagP2AY/HnMGVcvgbKuJY+fgvuOvTAEsj3Y7jcFIc+y5xjBfqLksQNjTFfiOLqfjLCS3osRK6QR3ry9KSSpA2NRlc5y2I/5mA2SodWxXAkglEwMTQbK6cxUlREki6yDOI/z18amqasFkZGVPuULTQM3UYUsnS2FviP/8iycKGLm24OMjamMTAEt90M4RCMjsGcOTrr1hWoqtJo7YTemEHLEjct8y08hiQYhrUjcGxnDq8BWrFEBCUuKwfDGhwQ0Ibq2zI02GlDo4Buid4gGAhAX1aVBn8xBv+0DK4/w0jHQgHufxIOt0EoCH92h/LgcnBwePeQpcAB+i50GO9UTpn6m2qT+98CK4AXpJSLhRBXM57VcnA4GyLMJsQ0+sUT5L2D4C2miFWEfM0s67N4bns3geIUVc2d+Mhjpwy8dobRgQgelwvXUBW98QgjQ36ixbMobTGZmeylSyuiYLqx81nqgp20uI4wL32AvPcIfu38n5XddRfcfz/4fAYzmnIMpmxivXl0YfL885JNm3KA5LOfVZOcR8fg338Cw6NQWuRi5UrYvceidcBFcVTjO/8gCPl1XJpNzTSb1hQMDkXo1yupuL4HDYuk6YHnLdgnIWuA24ZSAb0CojYYguGksqDYkFRxegX8cBt86VLwnkZgtfco49VptdAzAK9vh1uvOz/vo4ODw/lBlQj1yZ/ocCpO8gmFqQusgpRyWAihCSE0KeW6cZsGB4ezRsNDBbdTYBiBm3i+mIeH4cc7dOLZEN6MIKSnSLWHSBsWrlCBfQdbeOVIOWEtR2lEUl6RZ0tXMzcah/jHlm4e9K/gSWuAYuMgza6jfEDrJGqb+DPPgn/VlGYTngtSKpsElwFFE4YQVlbC178OpqkzNOThhz9O8cjDFm2tNpYPhoYk3/teik9/OoymCTbtgNgo1FdDW5dgyTw399wLe/4XPPc7m1jMIhqRRKMavRkd0y0QaTjw5Bxan52OvNKmEB+Gjd1QCAICChEod6sy4V4BATBsSaxPUmjLURIQLKo02D9k0DMbmk6TlXK71EDpkTHI5lSZ0MHB4d2FF4NZVF7oMN6pvK0M1qgQIgi8AtwvhBjAGfbs8DbQcOGhksEc/GcHbM+aHBaStObGLvjIpH1ohg0eQSIZIZkNk7f9DI36SOVM3JZGbYOHR0dmESnz8vVgmHvx02fuJ5jdiIcQAbEAt20CeVTr4KkZSyg7g8rSsz+OJzbClsOqh+qDl8OSE1bgGQZUVurccpOHX/w8icsFPp/ANJUpaTwuiUYFmnb8FOhNY9BwALZuMunvNJEajFgCdJMKy011naDrABTykOvxwdEMMIT6SAcBGwpx6A2iXS6xvR7YYSK2SGIbksS6C7QnbHaFdSpWRKiLurnvOqh/a68+oFZWDiVh6wFongbLF0I+D+u3QHcf1FTCqmUXT2+Wg4PD2ZPFZD/9FzqMdyqPnOrBqQqsW4EsasDz3UAE+F9/mLgc3ss8PwxH8jZ79AKlTRrJLR7StmD3swtoWN6GXZBksn5IW7jSWQqmh0yPiy5pUVUawGMEWNud5bpZFsUYFGsrkWIbWCaCHBhzQZw55fL9R2E0AV/7OJQWTT32sZQSV3VlyuLgue0nC6w3mTvXzZw5Ll56KcfoqDIcXb7ci9erTnxWLIZDx6CjG6bVwZL5ah5hImZiIPF4VXO7QGDnJStmQ34MtjyugZCoj7INWCBioFlgeNAiNnqLFzvhgtk6qbVZaMsr+3ZTw4xbDOxIsuFwMbFSaEzC/DK4ah54xjNXDz0PC1vgssVwrBsOtMGRI7DnIETDsP8w9A7AXbc5buoODu9knBLhqRFClAGfAhqZoJuklJ8cv/72qfabksCSUqYm3P3ZOUfp4ABITEx6yVqwL1HN4ZxNAYErCA2X2HR1Ggy3VZDYHKZ2cTuJzjB2yk2wPkGyFwpxL8khwcCIxowqi/iIxigWReigFSN8nwNzFxAA1yWTxjOzHvqGIXCWPkoelyqfJdIqy1N5Bk9TwxD8678W8Xd/l6Cry6SiwuDznw8yNiZwuVTz+Oc+oUpwPq9qjn/+WTCEQb5gY6dtQkGToEtDD0JlOfz1R+Cbu6CtXZKOmSAjQBJkDuEC4YkibAsdKBSEOkXqT4OZBwywNchLRKrA7CrJwZ2CTgOG+9U8whXTLA4fMelqE5TMdwEClw6xMdh3GBprlaCKhtX9RFI16gN0dSkfsDPNSDyf2DYMDSm/rEDgQkfj4HDx4cVFC+UXOoyLlSeAV4EXAGuqO01mNJrg1M1bUxp06OBwIpIC/fyYzfYL9Es/Wd8iju37KPFyH4W8jwCSQIskkwJvIAuGRm7AD7bEyuoEqhOM9nmxLTBNiZTqRz40ccSDVg7uqXdh337NuR2L1w2fuBqe2QqlEbj10jM/v67O4J//OcrIiI3PJ3jwlxpdj8LcufDxu1VZ8E1H97Y2OHYMPnOfzgMPmnR2atTVaKxZI/ir/6ERjUBHB9x4reDwYY0dezQ6jpWC7QU0JBoi4MIotsk9qgMSMlnAVqZXVo43jUvttIf2/RZbNgp8BsTCGvteLfA/tg8y2JfH5TM4tCfMZdeG8fsELdNg/etKtOi6uhYcn2HY0Qnf/6Fqe/viZ1Q/2vkiX4DHX4X97TC/CT6wUr2vDz4I+/Ypr6xPfhJqHcNqB4e3kMFkD0MXOoyLFf+J4wKnwmQ+WKEzbXdwOFvi+d08O/gkraWzkWjEQi6uXPksnnyWbruW1wZWYpkefEU59IyNrpnoQQszb6C5baStSmK6DhUlkuG85H2lGr19Bq/HoDIECyrVuJ7zQVMlfOGmqT/f7xf4/TqxGHR1Qk0N7N+nMlb6KbLzZaWCL3zOw8GDki98QWfmTA1Qcw4bGuArXxIMDAgeW+flFz9y0d7mRpqA14Odk+T6NMjbYFpoRh7blKAz4RxMw8zDU99OgoRRt2Coyou/MEZmJItm6OjxHHlPilujXu651UNJFFYvh3WvqwHVuTxcfblanQiqh0zK8crleWbbIdh6EBoqYdM+mFYFVWElrhoaoL9fDeF2BJaDw8lIZxbh6XhaCPF+KeVvz2anqfZgOTj8Qdj70+/SM9NHRBvlqDmLLq2GtMdPaT7GfHMHwpVic9/1NNR00rWtEi1rUTqzj9iRcqycQa7fDRYU19p4IibhtIuiUS/3H4aAGzZ2wpYu+NQy5SZ/IclkLFIpi2jUwDDe+sVVVASXroBtW+GGG04WV42N0Nysep2kFKxZI2huhkOH4OGHIZOBVavUvrW1cLhPMHO2jq4F6B2EvGERCEAiAzKgIS2JPeiDbAAQoMXRDYmlG6AZkPMABuRzFDryjBk2hg4et0HO5WPY8PLCPskdN9iUoHHDlapE2D8E5SUwe4L7fUM9fPZTagzNVLNXqQL0JqE6CP63IY5zBbWi09DV388VIBhUDfiDg2p0Uek5LGZwcHi348VgDmUXOoyLlS8DfyWEyAEFpljFcwSWw3ll6Ol9TL/WxcgbAZhVgXtRDrI2QsDMxDGyXoNj3ceY2zjEFQvW0bm7lpH6MK6KAgN7Kgnm88yar7Oq0U8ibnDdXI1Xe2Fa0fEG69YRODgE8y/giuP29gw//WkXhYJNVZWHP/3TWgKB4x83IeC2W9XlVBgGfOIT0NOjbldVqQHSDzwAkQiUlMBLLykh1tICcxts6ust4nFBTY3OwUEDt1dCRhK3gbyEmA/13WCDjGC5UiBcysZB11WPvOZS13kDE42E4YG0DWnBgddNfh61+PIXfbhcGvsOwJFWmDFNNeZ7J6wlqK+b+nuVt+D7O2EoA+V++MIi1YN/LiyaobJYnQNQWQLzpkHQD/feCxs2QHk5rFx5bq/t4PBuJoPFboYvdBgXJedazXMElsN5JVQyDe/PfkNmbhMzjzxPe00zvgpYnX0FbzDLjFwb2rZH+NQ1OwiIXnoDpQwdirLBey2JBXXU1iTQ8lHyR9Zw1woXgQA8tBeOdILPDQsbwRAwmrmwx/nEE/34/RqRiIe2tgzbtsVZvfr0XfBSSgYHbWxbUlamo+sCw4D6+uPPSafVqr43m7QNA+Jx6O2VfO3rJt09yj3+8stNjva5KAkJvEUQFTYD7ZKMBygIsHWwhWpyDwJeHTwCRizI6iAt1bgkw1DIgGlDHvq32/Qu0OjqsjnWqfHGDqiqgDd2gMsFt9xwbu9VqgBDWagJQU8S0iZEzlFgRYLwxQ9CPAWRwPHxQ/X1b30vHRwcTsZ2SoSnRQhRBMxkguePlPKVM+3jCCyH88q8z/x3tt79NNP3HSJV5uHq3X/FHmsFg+FSJIL37fsdI3PmEOxoQ1iS2mkx6mtzzOp7klcq/wR32KDUZ3HdbInfLXnkaIbtPW4aghq5pMb6/WpVYMNZ2C38MTBNia6rlJqmCUzTPu1zbVvy+ONptm7NA5IZM1zcfXcQt/utngfRqMpkdXSokpemqb6iY8eUp5ZlaqTSgl27LMqLoaoY3EHoymsgDdrTUtXPhgAXyr6iIAi3aMS7bbAtkBoYAoFQvVy2RBNesCTplGDzZkkoJGjvgtJi1TReWgyd3cfjtGw40KMm9cyqOv0Int8flwdWVsPGXlhdA5G3OXzZZUBJ5O29hoPDew0fBvNw6uenQghxH6pMWAvsQE222QCccYmUI7Acziuly1fQ+L6Pkdr+Kyo9WapGepnZ+gT5gB933sCSHjJLBP5+gR3RKJh5wi43ZSVBZs64grhH4idMADc7SfBiIkOgxEtXzk2V5mUoBX9SB/VRiMVVs/XZ/th2keQgY9QTZCbn9kv9/veXcv/9vcRiBSIRF4sXn/51jh412bw5R2OjgaYJDh0qsGtXjqVL3+rfZRhwzz2q1JVOw5Ilqsepo1NQKGgMxyQ+r8Rw61xxGSxcpCwUNhyDcEjjwH6bX/4OrKxE8wk0twvL0qgoyVEcNekclWpxoe1CSglaDmFKNE0ihY2m2fh8HgoFQUszPLsWCiaMjML1Vx+Pc91eeG63eu9vWworZ535vRICbmpSFwcHhwtDGpNdxC50GBcrXwaWARullFcLIWYDfzPZTo7AcjivCE1jxv/+V0Z/OYfhDd8j2W3hjQI58M2+hIHwZYj0LrI1SwgNHMTIWpS4l0DNanDXUDZhIkEMk7Iim2JNw6jM0Jzy4i+BK+vh2z+Dw91QEoJZ9fCR65Qz+mTksHiBblzotJGgFC9FnH1KZfbsEF/9qodEwqK83I3Pd/qaVyajVkVqmjo2t1uQSJx6CV4wCGvWHL+/Zw/87Kc2RRGLQb+gYbpOeZlOXTWsulTS3p4j3ivp6DBoqDdYMEOwJ21jjoDMCUoCFvT2Y2pu6pt9DB02SSbyeAOSWz9s8tJvk8RiUWzbprRUp1DQuP/+DF/9qh+3S/DC2jyZ0Tx+l8GbmfOeEYj4VGtX36iKU44fjmNE6uBwsSKcEuHpyUops0IIhBAeKeUBIcQkp46OwHK4ABjFxQQ+/xdsWbyM3NNPUNWRpuHqFUQW3kjQE4TMw8RG95Io0qjPToPS90PVJSf9Os8nyJH6DMlL0miHwkwrkdx0KXzvccHTG1Q/TjKr/KqefBU+/r6pxyhPPbvzrCgudlN8BvPRN6mt1XG7BYODFroOhYJk5swzL6UbHoNYAp5+0WbdywnyWQuXLhjsDjJ/lo5ZgL/9W5NQKIfPpROPmzSVevjI+9wEpGB4AJIpm5k1o5SETA71QjaX59OfM9jd6mHGvCKuW25w500Rvvf9Au3tGnW1BvV1Jh1jFpsO2Qx25PiX/5Mhawp+8YsU//JPeT7xiTDXzIVfrFdGrKtmw55BeOyAivv22TDPWah0Rmxb/l5sOzicL3zoLGAKX1jvTbqEEFHgceB5IcQI0DPZTo7AcrggvEGSvZe1oM2q4lLKqCxWDsJ+YK7voyS9g7grfURE9WlfoxQXnxRVWHMlIy3wYCHLj2x4yqVRXeEmaghGEhANwYF25dfkmWRenged66njEKPUETyn7NXZUlys86lPhXjppSymKbnzzgC1taf+aMbi8OTrcLhL6c0dRyVdmpuaMhtzOI8QNh6PJBAQ9Pfb9PV5EEJD0yyysQK6x81VVwqyAtp6IJyC2dNCWBvH6O+38Ble/u1/l5IrGDz8APQPe0iFXIwV5egfG2NDvwVVXnpKChzeIkkbXrwBSOXcfP+/EnziE2FqS+Cb46sjk3n4jx1Q6lPxPrwPGi+DoDO38CRM0+aXv9zDgQNDfOADzSxf7ph1OZw/MljsYPRCh3FRIqW8ffzmt4QQ61DjAn832X6OwHK4IETQ0YBgcQlhSt6yzUcYn5jakAAdgY7g0UKWAlCvabirLHrGClhH3USDyrCkdQCeewMubYbySfo4q/FTzSmmHv8Rqa42uOuu4Fsey+bhN1tgT4daEXftfPjtRuXtVF+hBEvpKsGuN6ArpRN0G9x+rU17StC6Da5bI3j0VzbSZbJlv8Gzb7gIh01qZ+h4WwTFpRolNX5i/XFmzgywYIGBEAZP/y7N7jQ88YrOaMf4aJ0mHepdGKEgnvYMezZmMcM+TApk8gIpxCmnPedMsCR4x79pTAlZ0xFYp2JoKM3evYNUVARYu7bVEVgO5xWJcGYRnoAQIiyljAshJqb2do9fB+HMTWuOwHK4ICwgQBVu/GgE/gAf6mFpUyE0ELCiUbBnVDLPhJAf/t+XIF4Dr+6CRa/Cv3wMmt4BS/af3AQ726CmGFI5+M7DUB2B5gm/u6alsWCRl44OC3/YxZYjOgd6QNo2e45pYGQ4vM8LCfUejyYkKdPkptkG168SdI74uetmNyO9eZ5+Ok1VreBHewvseU0ntGyMqqsTjB0oIr1VwEgBc7GBXeSmPwk1UsMf0DBzNm6/TsslYXI5tbJwNAf7Y0pIzSmBvYOAUOXB4rOc+fheobjYR21tmO7uONdc03ihw3F4j+FHZyHRCx3GxcYDwM3AVtTYwIm1ewmccWmOI7AcLhhl/OHm2SzWDTaaJkEhCFZIPrVAZ9iC7a0w2mRSVSfRTYMjBcHDL8A3P/kH+9Nvi6FheGWTckhYuVz5SoFqCt/dAXWlyu4g5IWufph2Qv9Sa7taONDUpLF2AyQyNoQBCw7vy0F+FOxqQIIuwBQU4hYHD+VZnPIQ9ApmNLh4o8/E7Rb0JST7t4cxykz8hRyiV9B41TEOtbdg9hZgbxY7liab1uj0FuN268xq8fChWzTyBUEqo2y2vr8X4nlloXV9HayoUfFOi6rjcTgZt1vnM5+5hHS6QDj8xy9NOzhMJI3FdsYudBgXFVLKm8evp53L/o7AcnhX8D7DRakQDEjJdKExt8mAJvi/r9o83mEyEpREUjo+S5DJXehoFZkM/OhByOTUWJd9h+HL90EkrMp/AY/aFvCCaanLiTMWgwHI55XBaDYn1UI+C3WeZWYhb4LHUo7tJqABXslIXDLSZvP1j2h4XWoQtWnC669rWGkdw7BxRXKkewPkY26EkJDTYcyEvgxkdfJGmnzQz4GUzYYqL2uugHAQ+rKQLEBDSF0fHIVrz8LZ/b2MYWiOuHK4IEgco9ETEUIsOdN2KeW2M213BJbDhSfXD/Ed4JsGweZzeglDCFYYJ2fErmrRmLFZY9Aj8GQFJaNw7WnG05xvhmKQTEHdeHanoxv6B5XAArjtUvj5OslQQlAwoaIIoidYTUxrUEaebW2wr0MZrxu60ljS9oJZAFc/eMtUasljEwxkWVgProyXjg44ugdefNFF1g4xpOUQXoEZcxPviODymgztLef/Z++8w+Qor3z9flXVOU7O0oykUUZZQiJICJGDMcHG2GDweo2x8S7G3ntt73ov+3i9e7138653nRbbYGxMMiYYjEWQhRIgkIRy1mhy7p7O3VX13T+qhSQ00mhGM5oRqvd5+pnp6gpf1UxX//qc8/1OrsUFImWFpdIm4AbdREkbGA6V7k6Tu29R0TQockPAAQ0xK4J1zTmQjrWxOd/xojJ3iL5/H2H+Of/TDSwAtmB9fZ0FvAVccqqNbYFlM7pIA5p+BkYSelZD7QPgKh223dcUw2OfdvLkG5BOw1WXw7zp8M1/gy2b4KK58NXPQ2BInabOjFAQhAKxeL4zjYRw/v4mpWTTmhybn8wRjZtccbXKnZe52XxAoeaYy6MolsgqL7NmFr6/FYwsoINHcTFuQinRvjgdXYcwjSzhqnLmztBYuDCA0yvYcxD2bISqKtjZ7sI5wYFsT4HLQd+2oHUAA/BKcJhwKI51fzEBDTMtMdMGu9Z28eLzHj772SAeDe6dYdVgBV0wrR9X/VgGXj0AYQ8sG2+nDW1sRpskJu8SG+1hjCmklMsBhBC/Bu6VUm7NP58J/MVA29sCy2Z0kRLMNChuMOJY/VmGl4kV8K1PH33+5e/Bb58CTxC27YZ0DP7ur4f9sAMSDMBdt8ILK60eg3d8/OgMx1dfzfEf/5GmuBhKCwQrX9Axs3G8kwK09QjK8s2tDRNyOWjpgc9dn2V3pcH7u1yUFQruuBXq6wNkMpZ6rKqCZ9+A3Q0KLp9KPAkTKmG3tHo9Cw0O7UrDe+0gfVbNlksB6QGnSsjpR5bkcEuBx+3hcFMOaaiAQV9U5atf7WPhQhfTprkIu2BJxcnPfXUDvNVspSVqgjDJtt+xsRl17BThSZl6RFwBSCm3CSHmDLSRLbBsRhdFg8q7oHcN+KaA++S+V2dCIgurDkJrDFbuhmCRFUHqisC2PXww+22kiCVh816YOh6EIWlvNyktEdRPUPjaF09c/7XXsrhcEpCYpiAUUmhr0vnix3W2tTo+8MF6p8VqoXPH9DTvr43idQg+dWOaW24J09UlKC9XKCy0bpq7DsIVF8GMydDVC9MnQP142L8fnnkDEook2ZwE3Qs4rIbQ2QwoCdCDzL1OxXSUMNWj0tRu0NnbTbJPw5JJGvF4ks2bs0ybNvCFLM67YLhVK8plY2MzunhRmcfp2eOch+wUQvwP8BjWDe9OYOdAG9kCy2b08U20HqdAJpPI5iZEVTUoBaa/AAAgAElEQVTCOziPqowOP30POhLWh7m7Bg5HwIxBNg4Tx/dr4TSsrNsGT68Co9fgmR/GyWQM5syW/N13/axYcaLCEMKkq0uno0NHSkk47GbSJHAKyT3XHHVyL98PRQVQHTXZ6RBUVmps356jqSmFlAKHA774RTeaW+F/fgteN/yvOyUOB7jdAtOEHgfMXQIBr2DnWkk7Aisv6MCqipd4XAqhEoWCrOSmGwX/+H0FTXUBOXBYFy+nmvzk8TSLLvIxcfyJ34SllPT1gc8Hi6oEFQHwOo6KLRsbm9EjgclGEqM9jLHK54AvYfUkBFgN/GCgjWyBZXNOYDz+KObBgyjjxqPd++VBbdsQsSJXtflaoD+9Gn4GiF1w3YXw7a+OfI+86eNhSjX8w/djxGMmiiLYtDnL/zycYP58B+Hw8YLk6qsdvPhiEilVpJQUFOgEgwpVVZafVVHIetTnPbEiERcbN6ZobdUpL3cDgpISheZmk61bdS6/3MlFsyRvvNTHkiVJTFPha1/zcudnA/TEYHzeHuLKj/l5fE8vhq6CzAAGihZgxaWCKY4sazbm+GG3xvgJLtQCD2++oZNNmeA00UpL2dqg888Pm9x/p8Lra60i/iXzYdlieOEFg/XrTcaNU/j851XGhU5+0Q0D3t0JTgfMnmz3MLSxORuY2G+0/sj3Ifwh8JKUcvfpbmcLLJtzAhmJIjQN2Td4nxZTHv8B7XHBjUvhnq/CxEHW/gy1aXFNGXxyKfyLMFBVE9WhIJB0dZno/ZSdLVni5qabUrz2WhpVhdpaFzfe6CUYVOnogdIPjTscVnnggUJ0Hd58U2flyhyBgCSTkRQUCDQNytxpHvtpjEgEwOBLX4oxfbpGfZWH9w9Yce+6yW6+9ZcaG99O0dTkIZVyUVGhUV2ts2FDjIJile5uSTSS5Uv3qXQJF1vWKmAo6PtidCX6+MGOCPt2FLBihR+fV/DyGxDwSd56S1JTIzh8WNLVBZWnyAbvOABP/MGaEVkQgPEjkzm2sbHJ40VhAf6BVzwPEUJ8DPhHwAnU5euvviOl/NiptrMFls05gXbn3Zg7tqFMmzHobSsDVquW3hSE3NCTAp8TKgZ5L9l7AJ543oqu3Ho9zJw6uO0Lw7BwsYvVK2MYhsDlhiWLHRQWnqjWXC7Bd75TwKc+lSOVMqmvdxAOqzy5Et7ZDp+9Hi6oP34bRRE4nXDppRrRqMm+fSaXXupg7lzrbX74sE4iIbCklETXJb/4RR//8E8e1u+DSBy+drlgys1FvPxyHy+/3IfLJQmFHBQXawQCCaqqPWzbpbP5vU42vOFk96YaSDkg0QvJbmvfOqx8JoPMFXLDxwoIBhQONQkuuUTw5psmEycqlAzQ8Dngs4SwQwWf7fxuYzPiJJC8TWq0hzFWeQhYBKwCkFJuFkLUDrTRiAksIYQbK0/pyh/naSnlQ0IIAXwX+ARWoccPpJT/MVLjsPloIMrKUcvKh7Rt0A2fnwdPb4emKFQE4BMzwDuIuivThF8/Zxl7qgo89SJMnjC42i2/H/7zn3z8638KGg5mWXG5k3v/1IPSj0eBaUra2nIUFwtKS10frON2Wsd3nsIE3+kU3HLLiXVds2c7CAQkPT1H0pEGEycKvG6YXAWdUXh7L6zfozK5wk1lZZIJE1wYhmTbtiQul4nAINbTS8ivU1sbQssAhg7puKXbcGC9rWHjuj6ymo+acW6WXQiXLNJYtkzidtPvOR9LbSV89dNWAX94FCw0bGzOR4yzNItQCFEDPAqUY3m+/FhK+e/5nn9PALXAIeCTUsrevG74d+A6IAncc8TkUwhxN/Dt/K6/K6V8JL98PvBzwAO8BDwgpZQnO8YAQ9allFExyNTFSEawMsDlUsq4EMIBrBFCvAxMA2qwpj2aQojhMz2ysTkJlUH48yVWim+o9Ty6btkZqIrV2sY0B7+PcTWCf/1/PsB30nWamjL89V83smFDAr/fwec+V8J99xWjKIIbLoUVi4YW1Zk+3c2PfuTngQci9PSYLFokue++CjQVvnwdvLMXXnzHMjNd1awgJGzblmL//izJpOS22/z09WVQFIUFC/x4vQqlSpK4CGAVwx+NjoFKIqPR0wuF5TA5P4fB6z39i1/cj3+WzchiCfs0BQUOPB47wXE+4UNh0dlrcq8DX5dSvieECADvCiFWAvcAr0kpvyeE+CbwTeAbwLVAff5xIVaB+YV5sfQQlgmozO/n+bxg+gFwL7ABS2BdA7yc32d/xzgV24QQnwZUIUQ98OfAuoFOcsTeQVJKCcTzTx35h8SqxP+0lNLMr9cxUmOwsfkwQxVXigK3XAfP/M4SaTdeCW738I7tCI880sGaNUmcTpXe3hw/+lEXy5cHmDbNjaKcWcrsttvC3HZbmFzOQNMUjnwj87igslCi5ySRpMIF9RrPvJLmnXeSOByCefO8HD4s+OQnS5k1K8TLL3dy+HCayuI4kU6NnowH656ZA0B1uaip1li2QqO6wkr52Yx9Xn65hTVruigpcfGVr9TjdJ55I3abc4MEJm+RPivHklK2Aq3532NCiJ1AFXATcFl+tUewUnLfyC9/NK8rNgghwkKIivy6K6WUPQB5kXaNEGIVEJRSrs8vfxT4OJbAOtkxTsWfAX+FFTj6FfAK8LcDneeIfkURQqhYXagnAf8lpXxLCDERuF0IcTPQCfy5lHJvP9vei6U+GTfO7rVhMzgSmBwkRxkaJQzPh8TsGTAlH4kZKXFlGJKOjhwgcLsVhJDoukl39xDCZafA4Tj+mpimZNVL3aR268yZ7+e6+R5e8atMmODC61Xw+azUwfe/30NlpQPwUVurUVHh4IknBJs250hkgigiQ2mJysR6P995yEXFOI3yYvCM0PWyGV4OHkzidqt0dWVJJg1bYJ1HSMRwpgiLhRAbj3n+Yynlj/tbMV/LNBer9UxZXnwhpWw9JsNVBTQes1lTftmpljf1s5xTHONUTM8/tPzjJuBjWC1zTsqICiwppQHMEUKEgWfz9vIuIC2lXCCEuAX4KXBpP9v+GPgxwIIFC+RIjtPmo8ezxDlIDh8K9xHCO0w3jpESVkdQVcFFFwVZsyZFJGJFg+bMCTF+/MgadSWTJgcOZKgpUkm0JQgFfNTUONi/P0smI9E0hcbGHG63YPx4B/G4SiolqK0N4PH0EQrqlHqddPf6UR2Sh77tYfky2xX6XOPjH6/itdfamTo1SDg8wuZwNmMKHwoXMjwzSv4buqSUCwZaTwjhB54Bviql7DtFjVN/L8ghLB8qv8RqjbMNq2bstDgrSXYpZSQfsrsGS0k+k3/pWSxLIhubYSWJxI0gi2T4m++cHq100UYnk6nFN4gb1223FeH3a7zwQpxg0MlttwWpqRnZt6rfr3LVVUG2bElxzTUBNE3wv/93GV1dbfzhD0kOHkxSWKgxZ46HVMqkt9dg5kwX7e2SmhqFvXslhaEMGCnKyxWuuPz4prHJpPVzkB6xNmeZ6movd99dN9rDsBkFEpisl5mzdrx8bfYzwC+llL/JL24XQlTkI0sVwJESoias2u0jVAMt+eWXfWj5qvzy6n7WP9UxTkWnlPKF0z65PCM5i7AEyOXFlQe4AvgH4LfA5ViRq2XAnpEag835y8342USaOhwER6m/1jb2ECWOCyfTObVT/bGoquD668Ncf314BEd3lAMHUqxbF6Ww0MG99xbh8VhpoW3bTFavztHenkII6OvLYJqWZcTUqU6uvz7AunWSvXvdTJqUYf/+LFIKbv90mH/+mTUD8BNXQ3EYvv99q3btwQdH3jXfxsZm8EgE0jxrswgF8DCwU0r5L8e89DxwN/C9/M/njln+lXzT5QuBaF4gvQL8vRDiyJSYq4BvSSl7hBAxIcRirNTjZ4H/HOAYp+KhfKuc17DqsAA4Rhj2y0h+La4AHsnXYSnAk1LKF4UQa4BfCiEexCqC/9MRHIPNeUoJKledYqbe2aCeWpppo4qhTZTduhVqaiA8gjqrqyvLz37WitersmNHklhM5/bby9B1yfPP63R3p9A0gaapgElPT45bbw0yZYqb1aszbNyYQdNUFi4sYOZMg4sv1tjVqeE0IZmCp16B+++wIldSWpMFbGxsxh5+BIsZnsagPxp4lYuBu4CtQojN+WV/iSV6nhRCfB44jGXnBNYswOuAfVg2DZ8DyAupvwXeya/3nSMF71gT6n6OZdPwcv7BKY5xKj4HTMWarHckRSiB0RFYUsr3sQrXPrw8Alw/Use1sRkr1FJJLUO3IDeGaAUxGHp6dKSEoiIHPp9KQ4M1i0hKy0/L4RBEo5BOg2kq5HIKv/99juJiB889lyQSydHebvInf+Lj8st9hMMKf/dDy4EdrPFrGnw5393oWIElJby2Cja/D4sWwNKLR/ZcbWxsTk5cStaaubNyLCnlGvqvkwJY0c/6Erj/JPv6KVZG7MPLNwIz+1ne3d8xBmC2lPKCQW4zSrkTG5sxjClhRxTWdcKh+KnXtYTHyIxjzhwoHGQrn8FSUeHE61VoaEjT1JRhzhzL1VPToLBQ4vGE0XU3hlGElCV4PAU884zBq69m2bUrza5dOUxTsn59H+vXWxfr9mshmQHDhFuvtI6jKCdGr1rb4LU/Wsf6/avQ1T2y52pjY3NyJALTUIbl8RFkgxBi+mA3sp3kbGyOQUr4bRO83QUOBXQTbhsH84uOX0/X4enfwdZdUBCGz94KpcUn7mtdL6zptb6qLS+ChWenrOq0CQQ07ruvij17kgQCGtOmWVXoO3bodHQYTJ/uprnZg67nkFLS2+vj8GGT51/MUnSByjSPg5qwVVe1dm2Cq64KMGm84K++OPCx3S7LuLU3YjnTu05Rm9UTgbWbQBFwyXwI5d3dDcP6W7jOMLPRX4/Jtjad9nYTn08wcaLGKWY42dic8/gRXKQMT4HkCeGkc59LgLuFEAexarAEVmBt9GwabGzONXqzsLEbav2AkCQMg5daFeYXHf+tbPd+2LwD6mqgoxt+vwo+e9vx+9oYhec7oMplJeufbgOvCjPGWOuXwkIHixcfP+vvnXdyFBYK0mkTw7CsX1RVYpo6ra2SPXENpcJNJKQyPptG6hIhBmfkWlgIf3In7N4HM6ZC4CTXRdfhZ89CXwKkCfsb4SufsUTXz56GaAxWXAzLFw/t/N/fBc++Yv1+89Uwayrs2pXlF7+wInKGAYsXu7jpJq8tsmw+ssSlZE1utOZcj3muGcpGtsCysTkGQ1pfTTIizSFaSQmdhOmiHT9lHM3XGaYlJqQA3QmRfu5LW2NQ7AB3vh4ppMH2+NgTWP2Ry1kpvZISECKLYTgxTRMQ6HoardiN1w+9fQadCYNMROeqqwID9hj8MBPqrMexHGq0olvlpfD2dnh7K2zZAxfNsSJYDS2QzsCGTRBPQlUZvLoGLpwN3kHa+MQT8NRLUJL/0z71EkyogeeeS1FcrOLzKZim5J13sixZ4qaszDbetPmIIvmopvfOGCllw1C2swWWjc0xFLlgYsBkZawbl6aSy3mZX9HLRg6znAV4sZxG62uhtxp+mwLDDfNqwNEC1xRAXf5D3qdCswlHYkMZE3wjfP+SEg43WrVhlRUnjwoNxLx5Gk89lWHmTI3nnutG14/0bM/gdErC3Rlun+FD6TVx9Tmpr/czd+6ZmxTqOjz8BJQVwzUr4DdvQIEfWrvhna2W5cPEGssZPhy0hFZnj9WK51QNsE9GNmdFqI6kJw3DWpZOSwIBBdMEIQTZLMRikrKyMz5FG5sxiU8ILtaGRxI8Oix7OfexBZaNzTEoAm6uzdDR1YWRDVLpi1BfkCGCJEHqA4H1iy6T1TU50glJOJuhPa6xrdNHYxr+pBwmeeGyItiTgIYkmAICKiwZ4QbGL74E6zaAooLPC5+/WxIISDweMaj01uzZDnbvNli3LovLpZHJZBEii9Mp0BwKqqETaM5x663+YR2/psGdN1sCqitmRRMLgjB/JuSy0BMH0QK/fhnqy6DCD94gfOwaa9vBUhCCOdOtdK+UMHeGtWxSvZOHf5omnVPZtE8hnfLw7z83+dQtJv/6fxWKRnjygY3N2SYu4c3sCE9bPs+wBZbNeUsMg14MwqgEj+lX6FcdXFAWw4+OhoaJiYnEiRUiadJNvp+J0+oUjC/eS5G7AxFVeXfvFOoYx2NT4aEZUOaC+8fD3oSVTpzig9AQoiynS2cnrH8bxo+z0nsHDup866+6qSw3qKjQuOuuIoLB00tx7dyZo60ti8ulEwoJCgoceDwKXb2CbEZn7wHB4eaRqUc60u8xFAenIvndcyZdPVBQAVcuVXC5BG+8Cb/aC/NmQFcryCuGdiwh4LZrYdFs6/m4SmtZX9JDZY3gid9J9IgDJOQ0nV/80kBKFz/4FwX/8GpLG5vRRYI07RT4cGILLJvzklZyPEUvOhIV+AQFVGLliRxozKaeTexGIJBIJlFNCOsTdW02RywHIqNT6u0gkg4gswoltXtJbq7htdWCT5bD9GIodMKFJ5mYk9Zhdw/EcjAhBJVn+IFtGJY4OGKHcGB/H4ZusniRk6amLK++2scttwwcQnvxxSRf+UqCVApcLhMhDDIZ0DQN0xAEQwoer4YvMLKW7AGvxJ80qQlDbw9s2yzI9kmuu07gMKEvBeXl0NQEbW1QUTG04ygK1FYfvyybFTjcHvRUDjCt0KahgpLjzTUGK1cp3HzDGZ+ijc0YQoBhT+IYTmyBZXPe0cEhXmMbKpIiptFHkPUkuJWjgqGaMkL4SZDGhYMCghiYNNLKIbWdYDhAKuJDSommGODUUQwXgYCgpxve22cJrJMRz8JPtkJn0vrsNoGPT4JF5UM/r5ISqBsH+w9atgXRqM68OZba8vkUolFjwH1IKfnWt9JEIipeL2QyKj6fgdebxeEwcWZUnG4X0y/wseySkb19JBIQjUiSMYVkHNweaGmBDRtgYh3oWWhstFKDlUP3c+2Xa66EP7wKmIaVpxQACpgmmtO6xjbW/0suZ5nS2pzb+AUsdQ1Pkegvh2Uv5z62wLI5r+ikkX1sJEuaNJ10swvJfFTmnLBuAB+BfLsdiWQLu2imnQmKj2pXji6fQkOihgq1Fan7cLbMxJBWsXU2eepxbGyH7jTU5ivgswb87gDMKgb3EN+Vqgp3fQbe2wyxGCya7+fN1b00NBhIKbnxxtCA+zAM6OgErURBUSTpTkku52TSJIV77vFRVq7h9TupqVKprhraOE8Xjwe8XsGhQ5LaMsGWRvCHoa0Lymvgz/8Maoqhro5hLz6fUg/3fwFWrVFJxnSriI4chCXBoMasGcN7vHORVEryyCMmjY2SxYsFN9yg2DYW5zBxE1YPcN+yGRy2wLI5r+ihiRwmbqIouMhiIthPLWXAySuXe+mjhQ4KCSM0wVIlxoTyg7zeMYH3Y/PI9IWZklYpyUGRaTK1OofEgThJs4SmGPiPqcdyqpZFRCw7dIEFVuRqyYVHnnmZPk2loyNHebmTceMGTumZJhRPdLDfqRHPSYxDcZLJHrp7JNE+hb/+doAVl5+dOg1NE9x9t8KaNSa9nSZzxwu8xQqJBHzmBlgwc3C+W4Pl8uVw5SccrHxFkuzNoRQIipb5+fRCwZXLR+645wp79sChQ1BbK1i/XrJ4sRVFtTmHMW2BPJzYAsvmvMKJmygRvDiYhJM4BqWUkqQFOHlYopsICqpVkyUks/3v8seW6XQ5fJhSkknrbNagvMfg9rq9ZOoO8j4hprEQZ37m4bFMKoDt3VCYfymRA68GBSeuekbU1rqorT09m/NsVvLTnxrMmebg8D5JutcAM05GTwGCt9/q4y//0sHFF7soKDhROGazVgTMc4xbg45EO2nLsRPJZKx9eC1DeaqqBD/4gcLDD4OU1n5uvxdmntBhDFpaDJqbDcaP1ygtPfNUh9sNn/0KVC514jWdiCA4/fDVS0dW2J0rHLEAaWsDt1t88DezOTfxK7D0zJ1WADtFeARbYNmcV1QwmV1sIUIvTnKECOIngBxgOxUVPlgrR04kaDKrSEb9yF6FksIOkmkv8awTR7VCkSNMjF4Os5tJzD5hf/NKYWc37ItYzzUF7pxm/RwtDh6UHD5ssmyRxpTxOk8/ZbBbsZrHK4rANDUOHYLWVoOCAoW+OPREQThg1ZuSJ5/UifYZTJlk8I0HFNomJtgmYtTi5SpK0AZofXr4MPz8EcuH6qaPwcIF1vLx4wXf+Ab09kIwaD0+TEeHyQ9/mETXwe3O8sADXkKhM7+YV0yAphR0pEAVcMfU0f0bjSUmTBDceaegsVEya5aCz2erznOZuAGrB+i9ajM4bIFlc17hxseFXMc7rEJFxYOfFAmmMveDdUx0orQAghCVKKiUUshO9mFiItBwKRoKOUr0diZN3gdCAoI+b5j2WDU9KQ2/x0OCvn7H4VThnhnQHIekDtV+8I6ghcPpICUc6ZQR8Cu43eDzBcnmwJQCRfHi9gqadcGm12H7VnirCzZuMelbq0MyCXqODRscPP47Fed1JlXL3Vx7YZrZ1RmqOPXX4zfXWAXr4TD8/pWjAgvA57MeJ6O720TXobZWpaHBoLdXEhq45GxAgi748hyIZKwI42j/jcYaM2cq/UYTbc5RbBusYcUWWDbnHYWUchFX00IDBgalVFLM0Srpg6ynh0NkiCPMQn6fmkCXGWSJoxbpPoSGhiKms6LoLXYYBUSyYSQKMiMoC3TiVbOkpROFGHWMP+k4hIDqUW6b09sLTzwBe/ZBR1bQ1a2w/aDJjDqYN89BQYHO5s1BEkkTpUjFdaOTb+zXiO6BSglb4xl8xYfpMwoho4PUMSJpUhFB6iVBqt3PT3Y6qZ2t8ZUbTz2Wigp4f6tVoD9l6uDOY9w4lZIShYYGg+pqlcrK4QszaQoUD1PqxMZmrOJXYOkpvsQMBjtFaGELLJvzkgAhpnBiI3QretVEhhh99PC6nuDZ7FxMafJGLs0XRSFXapJsajc36wdIesezM+3DNBScJox3Bwn7ein1lFDCDLzZibSZUOKyUkxjjWeftWpoGqPQ0SpYdLFKwlCYPBk+c4PGhg05nntOZ29W0F7voGCmSroD2iSs2QnsUolRA94cJHNWE0MABBxUyXZnyXV4+cbTKl17TP7iPgX/SW7iy5Za0atUCuaeOKnzlPh8gvvv9xKJSAoKBA7HGLzYNjZjmLgBq/sPuNsMEVtg2dgcg4KGnxLa2cV+CthLBSXuJkxT0Jku443sQS5K7eTCVJLDnmKu9DQSdGeJ6l7czgzFziDzi4qZJ5bxRie82g4IqPPCZ8eDc4zV73R2QmEhpA5YRcvppGD8VIE7aBWaX365i0svc/H3u6EjDg0J6OqGnm1AA6ArmDggqEBHjKOmUQKQ0CeRmwTJSsnKVYL6WvjMrf2PRVVh3tz+XzsdnE5BaenQhFUqZfL738fo6tK54ooAdXUja6JqYzMmGdgqz2YQ2ALLxuZDTOBSQGEjB/FKcJppuo0SkrqXA7KSF9xbmKf5Sbc28lafiwQ9lIQ6qA/HKQjEqXJeSSQLr3ZAlcdKMe1LwPYozB3hXoSDZeFCeOUVqwl1QwSmhqGnD6658Og6KQNyJkwOQ1sCelpAN7AWOgBNQEaC0w0pCahYxRzC+j0jIW1iZgSHWz5w7RxTvPFGnLffThIOqzzySA/f/GYpbvcYU8M2NiOIX4Glw1SyYKcILWyBZWPzITRcTOBSaonRoklSope04cHrTOEUWdZmF3NA+zW/Mstoqy3DGdBpNDR6kiorEnGC7sOkZRpwfzDjTAGyA01VPMtEoyaQZcIEmD1bw1eqIZ1QXwMz6o6u59cgoFmSaUEAuoLQ54GoiiWw0kCXCqobay0DcAEaCB10DSVq0t4h8ThMYOxFhxIJE49HEAgo9PWZ6PoY+2PZ2IwwcQNWR0d7FB8tbIFlY9MPCgqT8dJOH+0GeMngUdKYUhAzC3jP46OtppBmxziES8FLgsO+Olqi1cyRPbidMYr9STb1OQgID2FNY8oYag4ci5n88Idx4nGJpkFDQ5p77vExefKJ0+QUATeUw2ONgGkFrMIBiAqsBRnFCtO5Vci4rMgWAnCA1AAdM5elwKvywrM682b4WHrp6XlznS2WLfNz8GCWlhadq67y4/fbTW9tzkPsWYTDii2wbGz6QUHlApag0MxjOYVO3U2J6EQYOlOUVnoUHylc6C43AfpwYKBhkgt42aAqFIsdTKoxccW8KKabm3xTCDuH2UX0DNi5M0ckIqmrs4RENGry+uuZfgUWwIwQfMkBb/XALj8EmkG4FKSBFcESgOKw/CcULFt6QwckTneKXCbBtk0OCgtVvvpgjr/7Wz/XXuvDlLAlBm9FQTdhbhAWhGCYWqKdNqWlGn/xFyXousQ51grlbGzOAn4VlvbjMTcU7BShhS2wbGxOghc/C9Up/Ks3zUPpGL1GObPVGP+QfInfuQXCoSGQiPzXPmn5vNOrq5TQQYlaTUnIQS89xGkF6k59wEESj1utcRxD8GYyDFCUo2kwRbGWnYpxXqh2w/YySLdCuxc6e/NiRMXyAnMqgGE1SQZUh46qpMiZkmxW0tklSaXh+efjTJniZJvfwdpeKHZZu3ixE3Yl4J6qo7MupbRqvhwjfLdSFGE3LR5GslnYutXqFVl48i5UNmOEuA6re0d7FB8tbIFlYzMAyzU3l7n2QfYJS0x5HFyYirMjkKBH9pETLtK4MUyVcZkuymUMTaZB7APvpWiKSpbcwAcaBKtWwcqVlqv5n/4pFBUNbvvJkzWcTkFzs86ePUnefz9LRYULw3Bw552uk34gdsZAF3D9MphQDU+9BD1ZMBKAEChIpCKQMonDqRAKREkmdZAGQgFFKCjkWL8edjbqbKh0UOe10pBg1XvtT1qPMgUe3wjvHgQvMLEIVlwAM8adwYU7TXSyRGmjgGqUARzobfrnwAH40Y/g2mvh5rFoggUAACAASURBVJtHezQ2p4WdIhxWbIFlY3MaiOyzIIpBeEA9wHTXx7mr8wmasmvY4ZnIe/4FBI0uxhld+NwRWlQvJdkIjsx2cp56ikgQ5ee4mImbBQMfcABefx0qK6G5GXbuhEsuGdz2RUUqX/yin2//ny6efyFJNivZty/I22+n2L1b8F//1X8huiqsiJLTAbOnQnEYXl0Le3ZAZj9IXeB0CVKGpKiwl1BI0tkpSSZNnGoWh6ZQVAixmKRbt7KJyoeCRg4F9vbBd9+AvV0Q9IAwrHr6R/8In7wY5k8Y0mWjsRn+7cfQ0AhXLYVP3dJ/6x0FFQcuxBic8XiuUFsLd90FU6aM9khsTge/CkvDw7MvO0VoYQssG5vTRnzw09G3m2kJAbk0WvoQWpvO95N/xm9kkEA4wp1VzzHHtZ+AbGM+t+DkUUzcJHkVJ1NROLOK98mTYds2yzuqqmpo+/D7FV78XYpkog2IA1F0vZxVqzRONtOvOAi1xdASgYowVJXDDVdD9yK47QLIpQVlJSp/XK3y7/+WJZmUhEKSaFSiKFZkzDBMKioczJnuZHc/vc9yEhraYF8XTCyAtr2w722Ttw9LQm6ItCn819cE2iDr0A0DHvw2HGyA0lL4xW+gqwsevP/4BtVgCazgMe7+Z0Jbm87q1SmCQYXLLvOcN/YPbjdceuloj8LmdInrsLp7tEfx0cIWWDY2p4PzJsg+BaYJ6iRIHqTLX0lrWmNH0snatkuJmSEisoBITyH/0nUff1b/c+4MdRHGT5YacuxDpQSBNYMuSifN7MKJh3HMwDlAr75j+eQnYcECK/pSWTm0U9q4SRKJRIEEipJB0o5p9uB2z0LX/WgnuTt8cgk8uR4augABBV548GNQdUxacfLkMNdd6+P11+PEYiZdXZIXXkhhGBAIaDz0UAEzy1XWNUFTGipdlnxtToJmgpKwnvd1QvN2SW+jSSonSEXgyZ+YPHCbwvS6wUWXVr8J770LDhfoGSirhO4eq03Q7AuGdg0HQtclP/95H7mcJJEwMQzJ9dePoemkNjbHYqcIhxVbYNnYnA6OWaDWAikrVaj8PxxmiqSq0pcNkjFdhD09RJIFSMAv4jTkitnt6qYz8wLjlMmMcyzCSSkCBxmS7GItGk5idJEjzTROP8/ndMLUQfbr+zCmKfH5VTJJiZQaUipAmpoaQTpt4vf3H2kJeeELK6Crz5osWBKwiuQ/TFWVg7vuOuqs+oUv6LS3G1RVaZSUWOGnOyuswvZtcWjqgo52mOwFTVqpw64e0PNNqDVpWW1lkpK2Lpjez5wB04SmZitaVVVpXacj7NgBxQHoSkJHJxQWgccNydSZXMVTk8tJYjGT6moNVRV0ddlW2TZjE78KS4dpMoKdIrSwBZaNzemiBIF8wU7hjZR0/oLxejft6HSIElLCizTAqyW5rvoFrvGuxNnrYW1qMmvVXSwNl7PMZ1Vo50gjkbjxYeAiTuSsn87smQpzF1Xy5mttZNOW14LLVUJNDfzgBz186UuFJxVZYKULB0NFhUZFxfG3HL8Gn6qA3gx87zAsrARVgT2dUO+F7hBENagqF7Tut2YTFo8TzJl8YvTKMODXT8PWHXD4sCSbNvn6n8OSJZaYmzcXfvJTiHWBU0AkBdE01OX7cedyVmG2lDBhwlFxtn9/jr17cxQUKMyb5xpUn0OPR+Hyyz28/noKl0uwfLl3cBfNxuYsEddhdedoj+KjhS2wbGyGgn8mmuMBZmUOsje4h0zGSV8shFAkEwv3MN/7Lj6hoqtZ/N6tvJtbTDrSzDxfOyEq8BDER4g+ugFJDTPO+ikUFQl+8h9h/u8/zmfL5i76eiV33VVIcbFKQ0OOhoYcM2acHUNQlwKOfAG9aYLbCTfPh3XbwFkn6HQoOD2WJcW//R8oDJ24j4OHYNsOEFKy6V2deBK+/GcGv/6lZMoUjSlTYdosYBf0JUF1wRXXQHmZJc4eewx27wYhYOJEuOce2Lkzwy9/mcDjEaTTkn37dD79aR9CnL7IWrHCx6JFHpxOcJ1tgy8bm8FgB1iHFVtg2dgMFVclHlclNypLaEq+z7NtCZIVvSwtf4dZrjBZs4u4IelJhelo8NInBb+Lt3J7fQWqojGNS+mjEw0HAYpH5RTq6hR+/N8FSBnme9/rRtPAMCSmCR7P2ZtB53VAtRMeexNMCRdNgmuWwYLx8N5UONQiKA/CsjnWzMX+yOmgqBDpkWSzEAiCaQh279aZMkUjk4G6CXDJxdDcCpMnwk1LrW3b2mDfPityBXDwIDQ1wbp1WZxOhb4+gd8v2b49SzTqIRweXIV9IHBmwqqry+APf0gTj0vmzXOwYMHYcsK3Offxa7C0ZHj2ZacILWyBZWNzBsTj8JOfK3T3TmWW2symlQ7iNwT5r/bLqJ1ZRcqrsn73Arr3l+B2p/h6IshDE3v4zeck00UhhQyxQn2YEUJw550hfvWrKC0tOitWeKmr69/BtDUJm7ssT9FFpRAYhtaCnX3Q2ArXTbVqr3pisLcdplfB9afpajGuGkJByGQELjfE++DCeTkuuMBy0C8rhQVz4N0t4PXClZcd3VbTrOiZYVgRLNO0omVCwPr1Bk6nSjYrqauTaNrZtW5IJk0efjhBJiPxeARPPZXC4RDMnj32ejranLvEc7C6fbRH8dHCFlg2NmfA9t3QE4H68W6i0Yn8ZouJd/x4KsZ3sbNhKgmh0L2nGLUkSybrIm14CNPAPzVofL1WMmuUIlf9UVPj4BvfKMY0JcqHzanytCfhhzusGX66hPd74EvTwX2Gd5JY2hJWxT7reTwFkeTg9uHzwRc/Bxs3CS69SCUUMJhS76Giwoo2KQrcfD2sWApul+WCf4SyMli+3DJwBcteoLISFixw89hjWRwOg1RKUl/vOWVd2kBkMpLnnsuyZ4/BjBkaN9zgOK6mK522xnlscX5Hh0k8Lqmpsc7DMGD79pwtsGyGHztFOKzYAsvG5gzQjaMz6Hp6wS0UGt+Zw5T61xFEScaCOJQcubhGPBskrvvpyRQSzW6nTfqYJcaOwDrCycQVwO6olcKryTsNHI5DSxImnGEPs9KgVXfVHgWHakWTxp3CnT5nQHPUWq/YA+/vgKAfLpgKKy6DvIXpCdsJYUW5+uPKK2HRImuf4XwactYsjVtvDbBzp47Pp/CZz5xZau6tt3Tee8+gvFzw8ssGLhdccYXGq6/mePppk7fe0ejoUigsVPjMZwRf+gJ4vQLDsCwfNE2QSEgKCqxzO9QAkSgUF0H1EP3QbI5i5m0K+psV+1HHr8HS0uHZl50itLAFlo3NGTBlIrz6R2jrsOp/XCqoiQArH7uKghkNBKpakGFoa6jESGmoQQOtLkdJYTN1sgXEEC3JRwmPCrn8h5ApLZuG4eiN7HfDFy6D17ZDJge3LoTqk0wZb+iBX74HiWzeO2svOLqh0A9fCYHbCw1NUFEIlRWDG0foQ8Xzmib48pcdtLc7CAYhGDyz9GBfn0TTYMMGla1bTdatM6ivT9IRVXhnk5dcUgEJXW2S734XVq8XPPmIytVXu/jDH9IIARUVKpde6mL9W/D8S9b/nTTh9ltHzs9rpHj//RjPPtvJtdcWsWhRPzMXziIHDsDjj1s9FG+6CebNG9XhnHXiOVjdNtqj+GhhCywbmzOguAi++FlY85Z1Y/7vf4T170Bj3INeNJVeWcf73gi5iSp0SzzTEijVOWTITxF7kDKJEOfO1P1ZRbCpC96NQg5YXgpVvhPXM03I6lZU6nQpDcIdS069TjoHj24EjwOK8xZbEQ9saodL/PD+Qfj7n0NDM4QV+MZdcOcnT38M/eFwCKqrz2wfR1iwQOP11w327JFkszkmTHCw+4CDxoggpwgowVKNEcglJNt2Cp5/Ce65082sWU4yGUlxsYKiCH7/qhW1cjggmYSXV54osJqaJKtWGWgarFihUlIytlr/7N6dpL09w65diVEXWE89ZbnPh0Lw7LOWz5z33HlrDg92inBYsQWWjc0ZUl4Gt33M+l3XTVYshdaowg/fgGBask1ESHs8SIeKoypHLFNIKinxBoLAuTUbTFPAVWCl8TwCdgnYnYSpx4isjig8ugp6k1BfDp+6eHBC61Qc6LZEVlng6LKZM0DX4KoF8MIfoaUJykIQzcATz8NVy6F0mGZHnSnl5QoPPuimsTFHa6sGKAg3QA68AjJYmc0AkJQImWbHbqtI/0haECwBa0o+6N4kFCu1eSx9fZKHH9bzM0OhoUHnwQetJt9jhauuKqKqysXUqf2o9LNMLgfb90FvFByKpKlZZ+IEyyD2fMCvwdLy4dmXnSK0sAWWjc0ZksuZ7NkTZfXqdpqaEgCEQk6c5eNo6fIhPQpazkT1Z3G6cshghnrVwCnuQohBNtQbZQ6mYVcKZuXrmBIG/LYbvnnM5+PTG6zoleaAV7ZCdRFcMWt4jq+b8CEdgaJAYRkUFVku88JpGYgqEgJe64NzLFFTI/j61xX++Z+zlJcbuLoVYmlBvM8EXbEElg4OR4xcOkIoVA64j9uHosCVy+F3r4AjL6Bu/fjxx+nulug6VFRYAqGxURKNQskYEZsAoZDGRRcNU4fhM2T8RPj9m+DzQDST5m++G+PGazTuuGOY7M3HOPEcrG4Z7VF8tLAFlo3NGZBI5HjssQM0NMQIh12MH+/PL9fJ7t5PESGqQ1WY9S2Y9aBrCnP9Se73fwqF/m0QhoNUSrJqVY6uLsmiRSpTpgzPWz1rWrP9juBRoCdnRU+OeG9GE+BxwZo9lt3CztbhE1jjCqzj5wwrigbW7wKoDMGKFfD6HujphkkOWLbIsmcYayxerPE3f+PkzTez1BuSKTM8rHkPtu0FkRE44h34a3RUFZbM7z9vs/RiqCyH7l4oK4Ha8ce/XlQkcDigs9MSWoHAiTVmNkfxBeCKFVBaDL99XscwVA4fzo72sM4udopwWLEFlo3NEDFNyeOPH6ClJUFtbeC413w+DZ9PI9SbIMxePj2hFsX0c1GlygWBDzI7p42eTpNLpXAFgyjqwFGv55/PsmWLQTAIjz5qcP/9CpWVZ16NXumyHNd7dfAr0JyFef6j4grgoinw0iYocILmgxUzT2/faQO2Ry0RNzVobf9hwh64YTo8vx1UYR1XN2H2JHi4A/b0QXwFZCLQpMA6F6x+yqrXumsOzC87fqyjyZQpTorKnBSHIZuD/Q3wyirYtx+aDweIdHVzwzVeLlpy8ibgkybCpJO8FgwKPv95jdWrrRqs5cvVMZUeHGssnAubt0FLG8yY6WHmRIOrrggMvOFHBL8Dlg6TLZ+dIrSwBZaNzRA5eDDGgQNxamv9J10nU+Hk/alRDof2s7goRLGoHXRasHvvXrb+6leYuRz+8nJm3303rsCpb/wHDphUVgqcTkEsZtLVZQ6LwApr8PlyeK4bIjosDMB1H8qgLJ0OFQXQE4dxxVB5GhmWrAE/PQCNSStC9Xo7fLn+eJGVMeCP7XAwA/UToQzwaeALwbMRECbsTECjLon0gdEA73kFJMGRgCcb4X8thK/NOfVY+hLw4nqr+XR9FVyzyIrIDTdPvwrv7YaF0+ETV1oWExdMhd4I5HQvxYXeM7YLqKoS3HGHfZs/HWqq4IF7obMbSos1CgvGRurybBHPwurG0R7FRwv7nWdjM0TWrevA7z/5W6gt5+BXlS5cwT4yyXae6Yyx9mAx31oaYXI5+KlGcGqxZeo62x5/HFcwiNPno6+piYOvvcbUj3/8lNvNmqXy5ps5XC6BpjEkcZU24N0eCDtgxjGfNTVu+MopPJeEgMmD/CbckobmFNTltWpDAnb1wZK8TZgp4clDsDMKhS6I5SDihC/Vwa/aIGWavJM0aNRzdO9VYb0DuvMbThXkvIL2w/DvYbisEuadJG0oJfzqNWjthqIQvLPbii7dfvngzud06Oi17BU6eo5ffp59ro8pigqtx3mLOdoD+GhhCywbmyHS2JggFDr59LjNOR/NeJib6sHZl6Mo3ES3dPObfS3cWd5FEbMpZdFJtzfJkci2kcum8Pus6T1Ov590JDLg2K6+2kFpqaC3VzJzpkZx8eAF1so2eLPTiijd54DaEZzopQpLCx2p5TKllYo8QiQLu6Iw3me9HnRAQ9wSXFsSOVYmU7TGXUT6HNCqWjlYTUJawG4JMwQSy5Tz1caTC6x0Fpo6oabUOk5NKew4PLhz6ehIs2dPnGzWpKrKw8SJPjTtxOt/xzWw4wDMOLes0Gw+ovgdsHSY7EjsFKGFLbBsbEaITlUh/raP1opqyuvaiPcFcWX7aOguxYVOkuaTbmtmozSkfo3evZHyCe+SaQ2TVC4mHcky4corBzy2pgkWLjyzInr5oZ8jSZUH5hXApl5L2FR7YeZpRHLS0mCLkaAl5iKuqRCVOCekcF+eBAn6PgfptR7MnAOcCqkE/PEw3DsTwh9K+5km7G6F99thfTMEPVDshumnGY3L5UwefbSFTZsihEICTRPkcibhsIO77hpHRcXxtVQlBbBs/mleIBubESaehdUNoz2Kjxa2wLKxGSI1NT4aGxMUF7v7fT2dMaBHcKh9At37y0innHhDSa68fS1ZYlSw9IRtDhNjc7oRseW3VB14kSnJLZguJ7HJRXRkiqm/4a8ov2D2SJ8aAFeVQ5HTShGeKnplYNBKIxoaZVQi+mlR0x86Bh3EyWLgEhoXlfnpUxQyJiwvAvcx2dOwE6aErCjWkRRhQbiBttAWxusKOzuW0KeF8dTGcMk0ak6QMxwYE1Qc47JkGwTSdJBD8FYHLH8B/mEJLC62gl1+J7y4GdbuhQsmwrYDkEhZMyInjoNUFjyn8PKKx+Gb32xj48YoXq8frxcWL5ZUVUFvb5b/+WkDF32+jn0xlZCpsDAsqCsR52VLFpsxjJ0iHFZsgWVjM0SWLCll5869FJ+knWC1mWWjkOgZNzFdQRqClNPHooJrmAg4OL44PofJKprpeOVpWlxtXO3OUaO68HamCBYk0WcKghfUjvh5HcGtwsWn4ZnUSiO72AoI/j97bx4m11nf+X7es9Ze1V3d1fsqqbVa1uod2cYbYGwT2yGQsMQkEJIMkxvIzXKfzJ2byXJDJvfOcDOTEIcQHMwYYhKDAzZ4wULYyLJlW/vaLalbvXdXVddeZ33vH6dBXmQhsCRbpj7PU08/9dY55337nKo63/qtOiZpfvJOo+TZyRg2LiCouvBSVSVOPzGZ5J9n4KNtpwqYKgJ+qT8Icn92BjRhM9ixn4gWoT2aZ1XLEZ4pbsTotlBPAr4kTBXNcamICOpSD/eQDmWoOjCnwZ/vglUysJ798hrYPgwDrcFcXemgZU/IgJPZ4LV3rnr9/+eBBxyee65Evd5OPh+opoVClSuuBSFMntYEX/uGT1NMxRUeA4rC5c0qH7oK0j8/iWoN3sLEDNjSe26O1XARBjQEVoMGPyODg3EGB2OcPFmh6zT9Yq6P1nlMr1KNRnErBhiC1iVwXXfstB88BVA8n3klh+La2NMWetwBFRS7iiejZ20dupAoi4H6CgLlLNY3QYGnGaGJCEkCt9lBC1zXJRceYXZ+CNuO8ZUp+E8DYCxaskwV+sLwpA2mJtifE6zNeLSGLHoTPi8VbGRZJdGUw17QEQ5EUhUqlSia7uJqAurgmFC34OQ85FxYImF+Pojr+lGNL00NHgAdKfjhUbh+5akSDzOz8OA3YT4LQ0tgx44axWKCUkmwUNCxURjJx9jveCiGR6FZI9Xl0PxOnRSCKc9nIqvwxW2C37kFjLfYN7HtwXAOZitBfFxXAvpTr6yB1uDtRdmCbcff7FW8vXiLfawbNLh4UBTBBz84yP33H+PEiaDQaCoV+JEqFZds1uIzzaPMLVvG1oLBpQOSv76+xLTi0UoC81UfPxWFW9QBZi4ZIDd7iIXQIMXZeaIxBy/ej5HYTIjXLwnxCnw/iBg/i5pZZ4u/GI2lvKqKVwddGJgoKDSRPuMxJJLdTJAgjPGy/3/OghMlDVPTUc1x6nY7C+o0n85W2ZBUWKqnkbKZ3UWdBeGxwtjHzIwHiSMkI528p7nIbGWYbfUBXE0lESmBhGw2DSpIGwj7EFZwDVioQ8kCVQZfgrN5aI0F/RCNV50yU4daATw/EF2uC/d9Nfjb1gov7oaxKbBsjWJdwxIanhQgBQtFFeIKjDlkx1V262DnFGQFxqNBM+p17XDT68RijY3Bw9+CShkuvxy2vIPz7lZ8bgK+MxxkkeqLLXhcCU0huHslDDSd3/nPJQsLHiMjFmvXhtH1hjo8I5JGodFzTENgNWjwBohGdT72sWUcPVrk+9+fZnS0DAStct773m4uuaSJaDQINj/MNM8whkCQJMwNrEJ7VZmGVsJ8sv/DfLf/PnKkmBwfYNWITygxSKTnQ4gzlSiVEuo1OLIXnnokEFlX30htaC1urUY4nUYzf/qCTlUcdjPHMAtIJP0kuJQMycU+igKFFs6uXHoJiwJ1Wl8mFOfrMFkKbuaqkKjpI4Ra9xPVbAqKy3c8A812yVjztNRqjNc3knPqxHSdNm+UjYVt5ESa30yoHC1/mqIfpbwQx6upWNUQSsbDGjdRdA8ZdsA3UQWgBomGShziAiZmYaIAA69K069YkIqcsmiVKzC+ALUI7J2HZAxizSFcs44fVfEdEfhXQwoYEjSf3uXjLFt9iENza5iYGQAbRuYEcwX4/ENw1UqIvqqxcLEI//QlCIUhHIZHvwPRKGzetLiuCvzLv4BlwS/9EjSdA+HzgzH41hHojoP5qrtD0YIvvAS/th4GLxKRtX17hW9+s8inP93K0NDF1ffzQhMzYUv/uTlWw0UY0BBYDRq8QXRdYdWqFKtWpXBdHymDsVdznHmSRDDRyFKmRJ0mXutajNLMnfxu8KQb6PRh8hjkctD+yoAdizoVbxpz9gHCzz6FHKnjjOeorbwMy8mx9w+/zIyTJrl6E6n+ftbfcw+x9qDkQy2XY+HECfRIhPTQEOI0ppEqDt+a2EriwH6WmQkKl27gpDHLhL7Ae7XlxPnpujh7+K+RiAcK0KRDXKsxEx3BM2oYvsRTfapCQ8WhuzZGxYgz32nyafu/sj15E+mUhyfKqM4IemgJvbEwQ5UJDkV7yMdSOFJHUX3cOQ1fqmi+hxr1cKs6vqYg1ECTVhUgBtMnYaoKPU1BdXhz8XTMFODWdfD4cXh2Mnj+VB0yNqQjsDcPyTUGthHH2aUg0YLGzXHAhZBeZ0rpodSVpHPdFAvxEpUdCWQdCiXYcwCOn4Q1y195XubmwHEDFyUE9bFGRk4JrJEROHgwMFLu3QtbXpszwciIze7ddVQVNm4M0939+pml2WpguepNnGpD9HISZuAi/Jf98HtXBY2/3+pccUWEdFqlv//8taV6u1C2YNuxN3sVby8aAqtBg3PI6eod/YhW4gwzi4GKhkrkbMXJ9kdhzw8AAe+4A9ZcAb7H1NR3+IH2BG5olv7Zw3Q74wy/o59iqBXXHyN/oI66QaW8Y5RjO2YIPeqz94EHWHXXXSy/8w72/eDrOLKKUhAMXHIdy2+77RXT1nD47xOPcun/uhehOVRliu5nnkRuWstkd5gXeqa5OrYZndPXU5h3YdTxCKk+S3SVkFCIYqAg8PBRUai6MFsX+K5KyZxDTdTR8PE1h6o0sHydmF8kTxLpK9RNk/lkKyujB8mpA7huAVfxqXKYViXCb2d6+NuxFibDPRyvKdSqoBgOqiERNVA1sFWJIkAHHAUyUWiKBg2jx4ow6oBqQFpAnwob+uBIHUamoTMGh0YhloHJPMgydLTDMQm9S0y8vEs2DYux++AI9BabejRJ6USSUrqC0uWBIYKMLQXGFuDhHa8VWPF40MTZtsEwoFh6ZaPmzk5IpYLXBwZee/737atz//1FYjGB78Pzz9f5+Meb6Os7vdh4aTqItzqduPoRMQNOLMCxPAyd2Rv8lqCpSeOyyxq3ubOi4SI85zTeeQ0aXCAupYcIBlVslpDBPNtmz8f3QUsnWDU4cQDWXIG/71u8JO+npquorsvR6ABPXn8t41YbK+UhlpYOUOjPMP/uqzDn8og/2UbxqUkqh2ao3DvN4dFHSX9yKW4miSMj5Hb9gCXOLWj6KdH3XQ5gz+7A7vKI1SqYVp7iQgphVLBSVZ6Wh5jhJINczVLZQ0iGCS9GQT9ZhC9WK4waBfB9lhk+fxJvw583qZc6mErNkkkZeI7g4HQSx3eoG2206g4dqXFQXKa8fmxpMOl1YQob07HITAzz/ecTLNPyZK4bww1BWUQQxCijsjbzBLfMLeGIv5plCcFeV1Ks19FkFZEEz47gqyohA7rDMGIDavDrff0QeAaESkHMU0mBq9fCuhb4+93Qv9goeSwHJR/cGOSArAuagHJBQSZ0zJrE8ggElAElOwEm+DmVuWoGq2gENzIF8MCz4d+eg81r4KYNwRyTk/Dv34LpaXjxRRhcAldeDtdcfept0dICv/d7gSc4dJpKIY89ViGTUYnFAtGfzXo89VSFX/3V0wvig/NBnNVPIqTBSO7iEFjnEteFWg1isbdOP8tzScyELeeo6G3DRRjQEFgNGlwgNFRW8jN0U111GTz3ePCtfvm7AKhPv8jUUJSKY9JsZXEUjVokTGJyHrPNJZtpZ6J/I7bQqXXGEH9yO0rlcaLfP4wfdajrBV5a3U3RbCZuldG3qGxTj3A9qxEIilR5mpeQzSHG+nsZFQpr9h3A0B0WYhYnzE40NUoFl/udHC9aPqad5kZF4+PKI+wqK4yErqCcC7Gp8gyqUuMvmjcR274K/BZ+iGDJdROEtRBlSyXdPEVczRJ1iyTqc4w4gxCVhPwqNRFFUSSJ4WOos1kW6lEODtfZX+wm9dGbuN79R4bsKVzhEtXb+NjmNTx4WDCcg1UpgUeUecuk7ip0tqi8fzXssWGkAh/SAwuSL2F5Cg4CfX2BK+xEHTItUQEZIgAAIABJREFUsGAFxqgxFyZdmAhDyAWlDvky1ICwCn4Jqq4IdlYJLAICsASM+/gZhdrBEJzUA/FVBFRIpCCdgq174cb1wU38n74UBLNfeQVks0F/wnfdAq8OoTPOYAStVn1SqVPmKNMU1OuvXzbW9c4uS1CIIOj954nZWfinLwdWxKWD8CsfOPO5vxgpW7Bt+M1exduLhsBq0OCtzobroXdF4N9qCnxEWnolevkoMcUmoRapZDIsVYbZH11CwYvhNalU1SgpO0fEqzCXSuGt6yf1zCF6kjWmr1tCSSSJs4Ab0mlTSxxUxlnNAG1EeZZJ8jjkmwaoTCVIK/OUKjEK64YY7VqCHYaIbvAlayXH6624lgJumSnhYLg+h5U0hjuF5raTlAU8W3KsVGBTwqNJU9HnkxiTAmtgDl13Uf0KS5SjzFajDCeXEqbORvEinqajug57nDWYUznqsRii7iN6dKLD09h+ij2hD6CEXsCVCoPKRzBFK/esg6kS7JqGbE2wVtVZ2QJDLUGW4MtLtdoe2H7QKuSbc/DDQiA0YiosjwQ6aATIu6B6IFsgHwL3YLCP7gb1smoaIEENA3UCcaUAjoBJFcaBDGACDoQAIwrxKLREg0KmQsCxY1CpQn9fsL50OhBd+w9A5uxyCQBYty7EM89U6evTkRJmZ13e+97XL7rVHodjOQj/BMNq3YVM5MzbvN147MnAFdvXA4ePwMFDcOnaN3tV55iGi/Cc0xBYDRpcDLR0vOKpuvZ9MDpDPbqL6VSGshEj406zxtyNOLZALGxwJLEcqavU1BhFkaSpTefSS32Wbooy8Y4+VM9H8SWKJkATaOhUnBql+SKjyQVqeoi5chu5/jSqtYQXbt1IOqTgRSQDruRJpZ+jMomrKQghUaTErlg84q2glTwukFdi7DbXEnHKlMKdlMuCZEjQ7uok4jHujqbYlRI8OnOcXCiNaK/hqSo3u09iWzrzWgvxSoldUxvYKj+CP+Fj6CVaFk7Qmx5lQMCa0WHCMozedwemaP/xOeqIB4+fhKGeKs1wW0sgqqo+DISC4PtwDAoGKDYkDWjWYDYOZisYeehpDWp0HXMgNAcVE5QQ+DWCWCyfQFQtuiMxoKsbOjyoyyD7L1eAuxbdf/J01iERuAJ/Gm6+OYZlSV56qQ7AdddFufLK8Otuf3lX0CboTGViPT/Qjat/CqH3dsD3XlYeQ7zONbrIiYVgy9Jzc6yGizCgIbAaNLgIUfUQA0s/xH4G6ObfKBMm4ncis1Uc22Fl9QVcPcTu0EYWlASd7iwfyFQY+MxfIV76KssOHuPE5QN4QiFBBYNm9HqUkS/dz4nxeaYG+jn2nuUk1RymqDNW60MxBHrFY3loJ6NGgqzrIBXwPRGU2/IkJTNGqS64VBlml78EQlWOea2o8QyfiDYxMGixa36SDRu3srx5G/vLUcbbe+lsSZOxJ4jGsxzOLUObnceJJXCSbTw5egulaoqalsQ3JTU/iV9zmOjdTPlgmY/Xd6GKOl0t74TEGzuvijhVPf5HSGBNa+AeGi9BUxhsF1atgPAcNKuwvA32ZKHYBi+9ABMalMLgufw41oo06NEgeP3mPuhNwUINEHDXtbBysYr2wEAQU5VfCDIHy+VA2AwOBW68H5WL8BZv+q8XD2QYgrvuSnD77XGECPpTnon+FCxpgrECdJ/mPPoSRgvwzoEg2P3niZtuCFyEo2OwZABWLP/J+1xslOuw7fCbvYq3Fw2B1aDBRcqVNGNyDbNAJ0fo15fRccnNuNUTuNqH6a2/wLULO9HnU6TN6xG33gt6O7L1Eq44/G8wd5i9/f3EZD8RbT19e3L44/OE+vt5vnWQjj0HaV7lcLLQy/XFp7jC2c4Dy9/PZNKgXlSJijKWMKiLMBkxTas6T0mLYdoJJr0WIv4MbjSBZ6kkRJ3veHWiPfOUBi1Uv52HzI+SUnOk1BKqdKh6GqG9BdL/9ijTzjwZZS8T61dh9FfJk8S3VRx0pK6S61lNy/IJTtoOv2J+koxb4dbKPHdG99NT6wVfYibeoNpaJKLCJXE4osKqFih7YAGfzEBtAZJRaEnC947CY4ehLwOP7IDhaSiXAAfUDLR2QUdLIKQ+eV2R1mQdr9hETNNpf1nA+JSE0Hsg9xwUx4JMwY03wp99O1jL79wMk0V4/MXArfjhGwIr2utxtgU2FQEfXAP/ay8cW4CkCXEjEJi5GtRcuLIHbjhNxuLbnY4O+MzvQLUKyeT5L/b6piBp9CI8xzQEVoMGFykagstoBm4/NaiCGl+J5nwWaT5Im2qiDN4JxpUgArOD2HwD7Ztv4A58bsajhksYjbnydoalpGiYzFqSO/tfpI7BZbWtrB8/jBF1WDe1mwPNK3EMDSkg5eZZou2koCZRhKRfjtLdNE74eJVZ/Wbq8RAhswYOyMos00YUA8iHM9iqwYJsol8eY0g5yuDMUYpfPUgiHceJNtO81ian9GOHDZpDeVTDZzzXh6dIpFSpVqLE+0oUcilKToIvn1xg+uAjvGvXTrTsUmKdnSy/7TaSvW+8wdovtsC3c3C4Ckkdbk/DEz+AQ2OBRekTtwYZWHUHHhgBcRX05SF/EtwFaIlBaw9UW2F/c42PHJhnbXyc9S0mn2jfDC9rMfSvk+BFwLsKPtABk2X4v/4HnDgCfgV2PAJDa+GqTVCpw9efht/9hWBfy5KUSoGVzDR/+lS3qAH3rA/KMDw9FhSAVQSsag1ciN2Jt2cG3dlgmq9NMng7EQvBlqFzc6yGizCgIbAaNHgbouo3gH7DGbcRKERQiCyWi+jYuJHZffuYGB3FW92DZrrEJ7L0e5Ok3CJ7EqsZ7eqnJiPohkuzs8AaZTdhaXNc9hOVZVrr80hNUFsWJVQsYOph+sUIIdOi1Z/m2erVJK0y5XCSkGdTVcPk/GZM32bw2C72KirWbIXO1TYjLauoEyGcsmiSWZrFPLZvMJXtAQMqVpTcaIb25BSXZPawRAzj5GD3Nw+yxKsy09fC7IN/zzt/6W4iHSsWo89/Ctw5qO0Dv0BYaebupjXUWyPYOCiuyeGTBr1tMDYDE/PQ3Qr93XD8ODg+mAWwxqCsQvYoHJ6B8HWgjSo4tOCkI5ixw9z3ks2dS0Pky7CyG5bH4IlZmKzC1zx4ZKfHaMWibKgoBZ0DRxVOTsKmSwNx57jBcicnJV/6kk+tFlR+/9VfVejsfK0aKhYle/f6lMuwfLmgv/+V5hhNCUowDKVhft7FMASJxLlrudTgrUm5DtsOvdmreHvREFgNGjQAwIhG2fgbv8ElpRLfLsKukWe5pfVJqiLCoc5+vp+6iiOhpdS9EElvgejYFCtDe5k1euhITqOrLlJXUHyJLwStkWmSWo52MYsnFFKpEhvFC+xkIwIf4UuE6iMErLX3k21ux/LzVKsqe5Zeh9keJU2OmUonlhtC12w6WieYKvVA1EdNCqKixKqWvSzRj9K//RBrn9pFcn6WbZbGQ/9bG6lMgqez/8ofHLeh/eNkyeCGwiREFledR8MgTTfmYkV9B5eiLKKWtpKo7ERBA8WkIBfYbX2NA0oL834cR0L96k62H7+cRHqArg5Bri64+3uSsaqFSNsoOZ2yb+JXlCDIvQKlKTCaVNSQytxYjB0TKxgtmtz/IFzaA3dvgTs2QsGCURXawmDrFiUUpKrgdUo0S+LUBXsPQXc3fPC64Pp9/es+qgo9PYJ8XvKv/+rzqU+9Uhhls5LPf96lVpNommDrVrj2Wo/OToGuC/r64MSJOu3tBrt313niiSL1etCpwLY1UinB5s0qAwOgqq8Ub+PjdQ4dqtDVZbJy5Vn2zFzczzAUMpmLM7DLdSXj40HcXHv7RWzea2QRnnMaAqtBgwY/RlFVQqkU/2d8mN98vp3v1q6lt3WK+VA7o/oQZSdGTqaZrbcQdRIU1O10KVOUZBxX1fGFQPF9PKGRUWfQhI8mfCxpkPSLpPUcXlnh+4nrkbpAQbJEHqPZz1LrMxi4QyU/o/NsOE1nPk9nZhw7HGayGsf3VaQq0DtqhFrKNIVzLDWPYmgWiYUc7mCE4dAQ0S8UiY2MMvTsdsbfdRnDzcv4Pwonaf3m37K7ZTn6coXOphJL1RDLQxnqpe/QNuKiqnOM9McQmoOgRDjezDV2Et93eNCrkS9FCVvzjFstDDtLMUIW4eXPMe9luXHfeuYOKeRUQbjdRnoSLybxdT9oslhfPMF5cFwNN6FgzUkKbRE6aoLZHOyvwUduClxyrSYcW3TPre6TjB5xsYoKsi5QnSCW6zPvh4FuCC+6rfL5U5XeYzGYn3/t9f3e9zwOHYJ6XVkUBD6f/azL5s2gqpJKpYCuV2lt1alWNRYWQjz9tMY3vuHj+xIpJWvXS259j8I9H1UwjEBQZLM2//APEwBYls/HPtbJ0NBr20C9Gsvy+fznJ4lGVf7oj/re6Nv3guN5ki9/2efoUQDJnXcqbNp0cQZoxUKw5RwF7zdchAENgdWgQYPXkFYlf9b8OI99t8jxZJqcMUfpw+uZ1zIowgNTw+lKsc26mXvq9xFzy0yGOwm7NRxNw8CmjklSVAh+GoMubHTh8fHcvayaeI6tsStIeVPcPbSDaLhGRFM5tqGHtrEJLjf2kqoWmWOANmeambkws6lBDnvrWKe/iC8lrmEwJ9tpUbNU7CiFSDNWOI3eUie12aa8vIXokRNUVg9wMjJIx/OPcO2L/86OZbfyvXffzQ/bYHNyJ1dEn2VqqUHNjFB2bebIYAqVKBUO6nnUrI+o27RYRSoiSmtohn2hlZS1KJpjY5TnGT9UpUIMLPCzKpFUGbfTxhkzIecH1UhVBY6ATIPsUogB7SbMpiE0D3Efjh+Ed6yEd2RguAQnK7A+E6LlU4fYudOkdP8gyy4RfPoTsGrJK6/Zpk2wbRs0NUkWFuAd73jl68/tgf/yV5L8PHRmIBaV/PCHHpblsnw5rF+v89hjkuZmSWsrRKNhHn5YUCgIursFuZyg4sLOYUH+YcnyIcl11wYCK5dzcV1JX1+IkyfrTE3ZZyWwTFPh3e9uJhy+OEXJ3BwMD0N/v6BSgW3bJJs2vdmr+tko12HbgTd7FW8vGgKrQYMGr0GnleYWnzve1cYT83Pct+IXOKmuBCExhIdQoKQ08YR3E1db27msuIPnQpsp6zGiqssMLcSoYTh1KlqMkKxh+A6d1UkSJ0a4ee4Y/Tv/nZljDrHP9tIzWGO0uZeIYREbMuh2S3h2iPDwDFZZobfJIPatYdasOMyT3bcQq5QZS3YjVR3w2R9Zw4Q9CKJEdcn12JUw6WKFTOsMVsWnasbYetuHmNiwlsJImspICr2sYa3QORHrxZvTaYnNsLp5L21yiiNiBRYmIadCXKvSbGWZpI1srAVF9VErHtWijlXT8cIKYsIOor/rYIkwlmfCpIAFARMSZkVQa8EHogpsgnI3jM9AIgTqUlgDHBmBbB4WgHeloTUBMU3lkNoOt8Ha204JEc+Tr3DT3XKLQjIpGRuTXHON4IorTr1m2fDZ/wmjJxTqrkfNE4SFx/SEA0i+9rUSCwthlixJcP31CS6/PMTDD0N3t0tTk6C9XUVVQZZgtuIxctzjc//dZcP6GImEQleXSUuLzuho4O5bvvzsK5FeffXpW/dcDEQioGmSfB5KJVi58s1e0RugkUV4zmkIrAYNGrwGnRQRfxCaR2h3Faz2NL4rKJLEUCykVKlYcXwf/jr+aW4Ob2OFdoCSlmBOZlAcFxOHokyQLGfZXHyBtFIgc/wEertCbkZihjziLYL8IwVWf9iizzhJk59DxgDfp+SkqBdiZKbzlCeiWE/Pkzw5zsDlSWaXD+EjyKjTWE6Eo85yNGlTqvaTT0ZJWcdZmEkwWRkgFq6TiOSous0sLG8nOjONFwvT3zFCs5ElV2kjZFYZWVhBXQnTlMgyVu1lYaGZ2kKInupJVpSOcDLRS7hcJdfazEIliX04jKhJqoUI1T3JIBGwieBbVVHAlkH1dv9lcTkqQSPDlxRYgNw68LuhuwW2HfW5tc/miA8Pv2iiKYLfvgKak7CW1zb+e+mlEs3NOoODQfC+pgmuuSaYa2EB9uwBTYOlS2H/IThyALywApqg4EkWfBVfU1Bdj3pdYccOm5UrQ1x3XYRwWGBZDsmkhqaxGNsF8mSdctGnuxVyOcm3v13jgx+MEomofPKT3UxNWaTTBsnkz8etJZEQfPSjKk895bN0aSByL2oaAuuc8vPxKWjQoMFPTXPXr+Md+BOG5EmWucNURIyqG6JCAukrSCSGWUWNaKiRTdyg3Ubz0/+N3dZR9m3uoi4lsecO0vutHbT2Z1lyi4+jeyxMQG5CRfogNAP/RI25sTgt5SKheI3aCOx62MdMZxnq9CjvsTj6tTBlLUL52Xk67vsc4r03Mv/r9xDtLDNtdeAYBprqkauk8ZOCfKmP0HQZf3WC2rSC1ulRW96EcH3yA70MpiZpi88w6XRgayFiokRI1ClqCaQLxZ1NTM53oYUcqivD/GBiC9VsjM7IBIpwcAwDBqB+wmTqqW58T4UoQbV2haCCe1IEQcNZAuuASlAGXQd0Pxg7DAtT4L4TOq+0eXzSojxXJKcZ9Is4mvL6lqCVK6OnLcUwMgL33Rc0J4YgHkvqQQ+943OCkAIgqFXBb9LRizWiEYFuaHz0oybhcHDM1asVnnvOI5cTxBZj1h3bJ6L6REyF7m6F2dlTd+RwWGVw8Oeshw4wOCgYHLz4syxjIdiy6twcqxGDFdAQWA0aNDgtSssAmdV/QeLFr/ArOx9l6rIuVNWn6hfxDYWyiJKI2Gw0q/yG3sUVkwuwx6GvqZubv3KSF5wmZG4QtUklNPUk08+6OO0+u/6hDrYkmjFwahK9w8B+qs6hkIKXrDP8iMf0TpdQRGDqPsU5ME0HWS8iNR0tpdNcmGf2UC97TvYR78pjNUUJeWVqlQREJF5TAq+nhubbUAPPV1GSGtbxBJ6hU6rGyVXW0j40Tt5uYbxoEDaqZMwKoi4ZP96Db6vYUiPf24TtRkGTjBSXYsaq6J0uxMAbUXE8A6ICwkCFUz0IXYLg9tDiCY0sjmcAA+gAYhIGoJzzOTItIB5j98MaN2/6HqsHd5OI3Amc/q4Xjb72pu778NBDQTHM+GKboJmZoH9eJgPNTVApCWwPFKGixiUDGUEyprNqTZT+/lO3hPXrFZ55xufkScncnCAeByE0pLRwXZ94XLBx409Z+qLBW5ZyDbbtfbNX8fbivAksIUQI2EaQoKwBX5dS/ueXvf43wD1SyrPP523QoMEFRaQ7Cd/0v/O+SoXYye/wlYTLhNFEzWwmqrvcoIX5NbWDNCGISjDChPJ1yoUYohYi1dvL8HeP4sdX0nEoh+PMo4Y9hB5Gixms++BSZifLTI1PkMrV2f+ATemkT9iAzrgklwVFDcKXEnGoVh2UpghNtXHe3/kgf+d+ikRLkTKCmojga6D5Lp6jI0wPt6ihqj6a7qK6Hk7NIB2fJy5L1Amjuw7dyVEM26EzNE6VKF5eJWJWsEUIKaE2F8XzdTAENRFCUR10y8VQLWjx8YQKDqfElEsgsmwCl4skaPCXkaALIqkKQvq0WHOMJvohpATNoKUKNlgRkyce3YJ2h8I13Y+CvQrHDZpC/6QK4vV6kE3Y97KEvGhUIggMaJmMoGhAtgxqFHo6NG5e04wC/PIHXnnwaFTwW7+lsWKFx0MPSSYnYdkyg/e8R2VgwOeSSzQuueQndIZucHHRKNNwTjmfFiwLeKeUsiyE0IGnhRCPSimfFUJsAi7eyMYGDX7OENEoN624i2ukZFz6+Eg6hEJCvOymnGyGOz8J+VmOfeMRItEQUkq8usWC2U1ppgtmprA4QNPyAbquXc/AL1xJj10jd2IncwcPU/76Pmr2AqkYuBbU3cVGuxp4OkRboeiohBMGc+U2zCYbuxoiopRpzkxDCrLTGTAE9VIUDYd4ZhYpJRURwy8pJPQSaCA9SOhlEkaetdqLJGWZet5ga/idGAkLK2eiNbnUh8PEmqpUylGiRoV0OotquNSIcNLrhU4BEwTCKkUgqBwgCfQRWLWGCFyDriSl5Cn5CW7ofpIvqr8euAttJRBlBPs6cyYvHlnGJ/bEsR2XoahgKKLysTshHoNKFcKh1wquUChorVMuQ63u8/CTkgNToCVgmZB0xhV0XdDTJ7hiI3z4VhC+QjIB7e28hnhccMcdGnfcAb4v8X3QtIaoejsSC8OWS87NsRouwoDzJrCklBIoLz7VFx9SCKEC/xX4ZeAXztf8DRo0OPeEhWCZOEO8SUs7tLTjG08hfR8hBMn+fnIjI2imiVs3ibdcw+o7byR9hYXLOMLQyCy5k9qhKfTYX2LYCradwzIDzWGaoKaCNjGWA3klxVPLP80zx36FUHeNiFHFquhUq63kc80QF+CDLLn4cYVKNoH/go7tGYStGooi8QXEjBJhrcwSOYzwPDrL49hCYzB2jIloN65nUB0J0ZKYoytxknItzgl/gKlsB5VwjGothDcRgh6Cb7ciYEkwRWAuigHrOBWDZUuwBLN2K/3dxxlL9oIioQSkOZXFtUfB9RVKw3G+n2+lnvd5ekQQmXb56hfg939fZ8dehas2wu03v/L0Kwq8733wuc/B/Q9JylpgqRK+5OCoILnM50/vUdi4WRCPBEHwZ4uiiLdnD76zpFoNsgbfrpSrsG33m72KtxfnNQZrUUy9ACwF/qeUcocQ4neAh6WUU+IMTa2EEJ8APgHQew56iTVo0ODC0bF+PcPf/S5GNErbJZdgJhJYxSJGNIqZSjF49W+gqCoSH4ECKqy6HTS1nRfuvZfs4YO49aMYnSZqwSI9JDCbobx6OVt7fos91q3UjkepPxdGTEsIS0paAn9ARzg+UlfwHQPVcahoSapzceItJW5d+xCJWIG0PseIu5SQaqOoLulsESsUoZyIsEweJSprTFU7sQcMUj05VAGuKrAKESqhKJgCXbXIWWGwRGC5ikhQZeAmHF38blNk4DotCrACdeIqJiOFQYZjy1DabJhT8ceBmhKINBsoqOSebgv8ALrE0zysOZddRclf/N+SpWsNhOuzalAhkRCEw4G1CWDZMrjrbo8vPOijdQs0CdWKgl+Ap2cgXJD847qgknuDs2NsHO69Hz50F6xY9mav5jzScBGeU86rwJJSesA6IUQKeEgIsQX4ReC6s9j3XuBegE2bNsnzuc4GDRqcWzo2bmTi+ecpjo8Ta2+naWCAWj5PPZ9nxe23o6iBFUzwSpPI0K23Emtv59A3vsGhb34dWxsj2hvFbhMM3qKwNXspOTlIZ89YEB81FcOqG9TzEbyUikAiTRBIDNWiLk2UEJhKjUi6yH5jNSG7Rps/TSszyIjC8+JyitGDLFePggDLNVm14gCbL32eWaeNSdmBonh4msY65wUmor2sju3loFjBgt6KX9EWswMFxCRpdRZjiYtUoTDXRE2GoCYDF6ICSJDoICRUNELpOiKqUH8qjFcjEGcLIrBo1QFFICIK0gfHVjhxVBA2LQbTgs99TuI4CgMDGp/5jIKUgocegsefKOP7YNRVvEQYrwjCA+nDySn46/8PlvYE5/zKK2DNmgv33ngrY9sehvFaC20yAWtXQbr5TVjUBSIWhi1rz82xfpKLUAjxReC9wKyUcs3iWDPwNaAfOAG8X0qZF4El5nPAe4Aq8KtSyhcX9/ko8MeLh/0zKeV9i+MbgS8RpJ48AvyOlFK+3hxv/D8+PRcki1BKuSCE2ApcT2DNGl60XkWEEMNSyqUXYh0NGjS4MBjRKBs//nFObN3K5AsvIF2XRG8vq+66i+alZ/64d27cSPu6dVz5u7/Lvz/0G9RyRyBRY+LxOfx9OzBuuwpzsJ2Fa5pxv2fgTJlgC0KhKt68gRvVEK5PXYYhIhBdNo6hURZR1shpruaHLNVGKNoJtlWvohKJMpHoIVqtEa+U0KIOTkTF91UqepikV6CoJPDmNeIH6+Q3xXnhyY0U55MoKRtlqYM7bUBF0NE1wfrwC5RzcYyUhTeo8/Sz78BRDJACFB/qSiCuNBCeRI153HxlkTs2h/jM/6syV5BBDNfLf1Z6CkrMRNhQLEhyWYnrKiQSPiMjQcZgsQj/+I/w7W9DLCHQ0hY1PRkcJiSRBQhrYNcFTz8N6XcFtVG//BX4xbth08bz+IZ4FQcOwN69gbBbvfrCzXsmDhzIs337DPfcsxxFeaV1L5mA99/+Ji3sAlGuwraXLth0XwL+B/DPLxv7Q+BJKeVfCiH+cPH5HwDvBpYtPi4H/g64fFEs/WdgE8Gn5QUhxMOLgunvCDxgzxIIrHcBj55hjvPC+cwibAWcRXEVBm4EPiulbH/ZNuWGuGrQ4O2JmUiw/PbbWXbrrUjPQzXOvpmvoqqEm5u5/gN/yq7j/0Rk/NsM/0ON8FSZ1T94kPnLbsPvizF3eQWrHmL6xS5EVCJqEmFLvIgKvkDEXUKRKp4uMKljlmx6UicZK/cgHYX1yb28VNpM2pyjqCfAVlBLDgP5MeLZMh2t04z3daK4Pt4RnaoZ5sSLS6mOxZFJgXvCRA1bhC6rUD8Spb0ySa0YwVZNXFchGSuRjOTIJlqRNRG4/HAhpYIuEbqHkILlRPjQ1RpHfYf/55hKdXQx6F344AaZlL6u4NtgGi7xuGR6RtLRJvnEJ1S2bFHYulWwsADrb6qyqy1P01EdJz9B9mA70tChTaFah6l56OkSJFPBFJoO3//+mQVWvR70NuzqCkTZG2FuDr7ylSCeafdu+I//8fQB9hea/v44kYj2GnH1c8UFKjQqpdwmhOh/1fAdnPJu3QdsJRA/dwD/vBjX/awQIiWE6Fjc9nEpZQ5ACPE48K5FY05CSrl9cfyfgfcRCKzXm+O8cD4tWB3AfYtxWArwL1LKb53H+Ro0aPAWRFHVoBT4z0BrdAVr+29jxH4Cpd2ktr/I6um8xSkYAAAgAElEQVTnqH41y+hH3s3E2nUUZ5KkOgscLS+nPBfDbLYxXAevRUFmJAgFDRdTrVNJx9htXoovNbqr4+BCNtfMmD7IioEj+HGP1u1ZunPjKGGX9skZmhdy7GtfS60aZyrSBgsKMVFi3m8NvtmEgpAS9biLFQ1hbJjBLWrEuoqodQ+/qGE4LlYE9GqVkFqkZHaCAZ6qIws646Maf3gIymWVzTfCcy+Ck/VQPA+nqOIVBMIDLSbZsAnSSY1SlyS5Uqd/vYqmCVpbwZE+82tHGdmeID38LCw4eGIDOQZBSHxVEk4pzBRgagY620DXoFA583V48EHYtQs+/nFYseJnupQ/xrKCel3JZJDtaFlv7HjnikhEo78//mYv400jFoYt687Nsb4CLUKInS8buncx7OdMtEkppwAWY7Qzi+NdwMmXbTe+OHam8fHTjJ9pjvPC+cwi3AOs/wnbNGpgNWjQ4Ixk7/0elRN7kDYoIWjvsqmOjaDc+29k/sojn0hDv4KzT2NP1wa8hEK0fSFo+txUoEOdYtzpJWpUmFeaWag3kYgUqakmz1pXUXCbUYTgu5WbiJtlPim/wGhTJ+25cWLhOsuPD1NMNVGOJBAKRBIlSjMp/JyCkKCEfNAhvLrGTLGTrvAEHc0TRCIVxuxeqmYYa9YAVJxEDFc1wVSC+CoE3RmFoyXYuQ9SrsJze8FVgQ4BcQUjohJ3fcQ8DCwRfO53DRYkPDoFTSH46hH4o8tg7VqQcYf7HQ//8TJavcKcvxRRc4IALF0llsrRHK+wMN7K/uEIbS0K4xNw3bXw5JM+O3dKrr9ecNllr4yNa28PBFEi8cavZ1cXbNkCL7wA11wTtOA519Tr8OijMDT01nFBvtUpV2Hbi+fscPNSynPV9vp0JkX5M4xfcBqV3Bs0aPCWpTo/z/YvPYAaddFlUBPLrYMhbVr9eVYXtzNd6YWqQrUzyrG5IWrhMGoSms0ivcpx2uUkXkjD1kwcqbIjtBnDtqn5UUYWlqH7HiU/RaFYIaQ7bF17LTft+i4FPU7f/AyiU2OJO8WJ5iV0liepLA2zz78E1bGRvT5KUiKExK3rSE9n7+HN9Pccwy6pKD117KoJphpkaLk6MqFDNGiVoxqCiSqMFEEvwcE8yB8VKw0rIBRs3ycbAbUZlnYLDozAs4eBZVDzIGvDQ6NwTRusHNRoL4DshtpwkoQ3xYLeDUkNJeYQylg4ooyquuyciDHyZAe9K6EwKXnqKz7z84I//4Lk6nWSP/604Oorg+tw001w441v3D0IwTHe/e7gcb6Yn4dt26BQaAisn4o3N4twRgjRsWhZ6gBmF8fHCYqh/IhuYHJx/LpXjW9dHO8+zfZnmuO80BBYDRo0eMsyuXMn5VLQnzCeAdeG7Cg0D6m0fKANL2+ilnz8miAzMMufh/4Tn83+PjNHMwx0HaHHP0G6Pk20p8o+eQmuiBFW6zhhjYoVIhnKYflRopEya5N7UKVDoTPBLvVSrnvscdz5Evb6biK+RWdngab5PdTjBiev60KoNhXiSFcjPFcjbErCYYeOlmm6Oo+TNzMceO5K/OOLrXRCIvhtHSMoLOqBsKEWBUeCPUvwOzsK5AkC3WsEbXXawDOhWJc8dkjQrUNHMzyRg1Vp2JeDA3n4D6tU3m128cCWw0zlVuHP+ZSzgRckolfRRJ2SiLLiloOkO7Ls338Zx2cNnvvnFqZOxH5chf47P5Q89gz89i8J/uD3zk3s1YWkqws+9SlIv7ZHdoPXIRaBLRvOzbF+xkKjDwMfBf5y8e83Xzb+H4QQXyUIci8sCqTvAn8hhGha3O5m4I+klDkhREkIcQWwA/gI8Dc/YY7zQkNgNWjQ4C3L/JEjxLt7KI5PUJx1UTTITUD7+ghhS5KanqdTznHYWMIJ0UdZyVH04zTrWZhwMN0yZnMdYS/QaU4wo7RRFXE8BIPpUYqROHO5DhTdQ8Wl5kUxFIvpji7EDQqRFx08p0pZjdHijqI36eyPa6wyBaMkKeIifIsVUZ8bL/sBR12PY8d7OLF/LbNOgrnhOMITyCpBgrkNxAmEVAL8KEiXILjYIShQahE0GPtRWQeLH7fhydYU9Az80fvgXyfgyjA0mZB1YLQCIyW4tEXj7vQI/62nm4W9KfBVUMGtqmjtLuGOMsvW7GXO7mHosv1sv/cK5ifMIDAKAb4M/njwt/f7CF/nD/5A0Nl54a//z4oQMDj4Zq/i4qJcgW3PX5i5hBAPEFifWoQQ4wTZgH8J/IsQ4teAMYKSThBkAb4HGCb4FN0DsCik/hT40ar/y48C3oHf5FSZhkcXH5xhjvNCQ2A1aNDgLYuqqnRu2EBlZprc0WE0HVDg+H6H5G02pZDJCXMF4ZBDzCzxvZlrqeTChPQqtUSKiVAPbdYYcsKhx9uLMgRHlSTCF7hSRRMeSQrM19PUDZMIFZq8PE0U6NMmMAZUuhameLaQ4Zllm5gKZ1DcBZrd3QgrTLjWxTItxFxd5/sHM2TdOcx4FatoMP1UhmpZIIsEld7hlGiqACXwwiDSoGXBTRMIqTGCwqU/zuhajIdarN7QlQqsDYYCjg9jFuwsw4INzxdhQ0uEe5aF6U99g89O38Dxw124qoHvggh5dLUcZeZEhvlEO/aIianWUSMenquCG2Qt4gA2eCHBN7/t0Nqq8sd//LMlKjS4iLhwWYQffJ2XbjjNthL47dc5zheBL55mfCfwmupuUsrs6eY4XzQEVoMGDd6ytK1bR2l6mvUf+zX2/P3fMHdikpACymSdwpRP34Y6g+IY+XArGg5+RhDOFeF4Hm3yON7USaK9e6ivasUb6GKDs5OUVmBOacWVOrFQkT1iA+7/396dx8lx1ffe/5yq6r17evZ9NKN9s0bW6k2Wd2M7xnEAQ8JmgolZTG4IXAiQ5XkI97mXPOF5SLgJIeRy2eIYGwO2MeBgG9vjVda+WLs0o3X2pWd676o6949q25IsCy0tzaLf+/Wql2aqu0+fPhpNfXXOqXOyAXb3zKc5coiIkWGa3sKQUYW/WjPsg3+f8UdkgjH6x2roCbegDPAXRpm1rZtBp4rB6nbGEmGqAs0kj6bY+usQ2bTpZaMgXnCy8P7/3YV3T5OBN2Q4CuYucHKgc3i9XIN42+c4eEOEeTCjML0a7rnca5trGmBXAnaMQcqBuqDX2aUwqeMO3lMzSuVf7WPT2iOsfbGKAwmFausnE4iSLatCJSAzFERFHWqv7SE/6mewowpn0OetSK9cyEC3z+VXvzL4L/9FU1Y2icYJz0E67TIyoikvV4TDF8f+QNEwrC7RtHTZi9AjAUsIMWHVL17MgeeeA2DF577Irgd+QM+6TSydBfmHu+luW0xiYRNHypoYbYjyEeclDGuIX47miJj7uN33E46mooStPAVfgYSqZbbag6tMClg4jkljzUHodRnO1bA/N5sZ4f0ssTbh9xXoU9U8NfMamkODtCS3ksyX8evAzez2zwafS2G6xeXbDhPItjMM+LTJwW1l3l2AFXhhyQFq8KbpdgE9QMJBlblEl45ijwUwZhjow36cgIEzaEACL5hFAA0hH7xnJfzp9TCtuJp4YxjuWeCwYcDgiUFFPAA31niPKW3gcxxMv83KVSkuvyzP9sEUj+7S3l7UWYfsUBB/ZQYjGSN5KIhpOfhjOTIJyxu39AEZB6cAR4+a9PSYJbmLcCLTWtPRkeORR3Js2GiRyyo+/WmDD3wgwKm2dpsKkinoWDPetZhaJGAJISYsfzTKknvuYev99zN6tJvG626heuXVjCZ2MbflKMbeBL1lYKwqZ1kwwSxW09K+mk8vzJLoXUj32k30+LKsm9HAtKYEKd8gugAL1WaGB4J0m41M376TmQdH6VuwiNS0Rkx/hh+a78eKOljDGdrZRNCnSZTHSDl+bko+Ta+vjqQKoSzNLbdfycPrvalLqSSkQsBib9F2VwEDeGElACzRGIk8UWuMwNwsRiUYsRzOawHsiIORVMxYHCA9qsgWsqTTftIZH0vrFR9dAiumv9k2HQyxNThKdbOfv2usw69Ngq+P4mVehrGf0R69ijWRaroHQmSTEWaXxzmYOYBPZTBrbUKuZmigBkOBFXDRtuENEykFWoNrEywY2LaLZb3dHfBTx+7dNj/6UYbfPOVnoC+A5dN88UsO8+fbLFvm+90FTHYXaIjwYiEBSwgxoUXr6rj8M59huLOTdH8/pt9P+fTp+Ct85OhGsZNyRonSRAMrATDNIJWNcwk3/AmRB79C+WFNLnKYXbfNRQcMzKMJ8o/3UHXjcuwndxEb6KN683YSc2bhvmMOo+XV9HZFeZ/+Hpn5DZg6TYPqQVmakWAFPnIUKCc22E1ldY4VzfDiAehLw1gdWCkgD7YBTq1L2ZEEgaY8+VoTY4kNhkYHLOw4+Cs1ZXUOq29MozsN9m61qcoY+MtyjJXlUN1xloQtlh6zyXAKm62MUk+AbnL0GzlmEn7zCSoEyk+t0cyM/oU8+ZSB6fi5ZqHCmhthb7aHLQNRXv15K0obhENDpI8EvKFM5/XDxjTAUj5aW8d/tXWtNdu351m7NovjwJIlAdrbAyXdtHrt2hwbN2vSaYVhutgFhe0ofvnr/JQPWNEIrF5ZmrJkiNAjAUsIMeEpw6By5kwqZ8487nyY2Uxn9tu8CoIrbqbiaILsM/czevkMlv3jDrpurKG3vgx2JWje8BB2ShHQOdw1WZrv38DW+J9RUVOgqi9LYGGBQq5AJJfGCZkE/XkM08HtdZh1ZD/vfWUr28xWXinUMZqDPsdbWcE1AT8YWYc6o5uZ6U6u7FtDOJfkN7U38dqs2dgmGMrFMvJkgya7A4prr/BxxdVJco5DwMxhpIPMGqlgeS1Ejrm+BzCpwEc3OUwU8RN/lYeWQmABGEEiYxDG602LmHBrRRvQhnsnfF/DI78F142w4qYU3XNdHviVIjlkogsGAcemtVXxmc9Y+HyK5593eeUVTTYLixfD6tUG5eUXplfrP/8zzTPPpKmoMDEMePDBMfbuLfDe95Zu9fVs1ltZvqraYWTYIJ+H+oY8rj31J/gnk9Dx8njXYmqRgCWEmLqUIn7ne/EtvwI3vZ7+63uImz3UPv9bNm7oh0KKdLcmmQbLgcawj8BIhpuzL+GMZGmqH+BI2I+jDMyUQ6oxiP9gN7/X8xgL19vs2V7J4flV1NTBHTMzPP3iEbK1jYzoMK6GSCRFSGdZ2NKDv9LE9ClqnD6CvpnggGHlCdguCqi2BtjptOJTPioyPdy2ZyP1Vhnm3NvAd3yIsFDcST1HyFKBj2pOss+jEQSgrRbefzWMpmH5MTu/GgZ89N3wkT94/fsIAKuW53jw4QJ2TuMzYP58ze23+/jxj122b/d6ssJhbyX2HTtcPvlJ47xPfh8ZcXj++QxtbT5M03uveNxg06YcV10VoqmpNJeyyy7zUVOd48Bhh1jcxTA0kYjm5puDJSl/QtOM90KjU44ELCHElBdubmEBLTjYKBTOH3wJMt/l4BPPMtL9JEQz+JMO5a7BkoE9qFooW5Ij5GaYf3AHXW3TSGdCxHuP0PXoGLFDOTKHG5l7TTsjzeUAWO4Y12U2Mic1zMb5KziYh9Zda7l+28NYMxbi+AocKG+kYFqYjiLkh6AfAuTAgIgFUaXZmSmwvH8nCWXT1Psa2BlY+hHAmxb1017Yn4EPN5rMDkTIOrB1zLuLMOjC42uhbwSWzYJVC7zpVO1tvPH6o/1QWwFW8be/ccxNclprdu/Ic+sNBpblPXDggMO6dS47dhi0tfHGZO+mJjh0SLNuneb6671zfX3ee9TVlfbvb2DAQSneCFfg1UMp6O93zjlgHT2a46WXRgkGDT7xcT8P/7TAkSM2ysjx8Y9VcvnlU3t4ECAahdWXl6as+/+1NOVMdhKwhBAXDbP4K8/AZMV7PkbbgitpWrSCsd5e0tkM5b/4EWWd+0nUtlBnpcnqIOZYgWldh9gVbSL34/3URqO0zU/SqGoY7d7NSLFsq7qG+HXXUBGJcEkYEpke0j97kPK9G0n6TaymRvJ+k0LAhxF2MQ0LhyAj2iSqbWIqhqEgly/QlSlj0FYQcRjpO8oT22BRLcyuho1jkHRgbxqq/fCzw7BxGGIWVB6EvmGoiHhBKx6G9mMmxu87At/6KbzvRrjsbbaQUUqhT9i5rb/fC2In3klXXg579miuv97bDPqhh7zz7343LFtWgr+woljMeEud3nzs3HrPkkmH7363F4B8XjNtWoB/+04tDz44wObNea65xsQwpvbkfigOEb403rWYWiRgCSEuSqbfT/2ll1K3eDF2JoMVDLK7vR3j258gGnNwF5mYlYrdrbX4fA6DQ1Gqb88RCmpCkQD+eftIBm5/ozylFL5ab1sabSWI17+Af4mBGaylORrEGOihY84SdlfPIZJKE4iVYaPw48en/KRcCLmag92K0d4yHnk6wfzFPfw2eA27BuC1fvjyKvhAA7wwCo+n4OUcBAtgKci7cGAAZtaAoSDsh+7h4wNWXQVctxSmN5y8TZRS3HCDn1/+MkdZmSKd1jQ2mrS1maxb99aEk816PVkA27d7vSCvf13KgFVXZzFvnp+dO/M0NloYBhw9ahfrdm69S8PDNrmcF6y01nR1ZYnFFB/6UBU331zGtGmBEn2KCU6GCEtOApYQYkLrZwujdFHHcqKUfr8WpRS+sHcH3tx7P45z2T4OPv4DfM+N0n93I8GoJpkvo6EsRa7CT7Dg4Cofw1VZ6gPPcbhwCwV9/AJR2j+IMiH8nvehjT4Sw9fxeNpmV10aWymiwQw2BiYRKn0KBxhxgKyD7SrKq2owcxaHqheTr76ETD/URsBnwoIovJCBCgcGbbitBuZHoTUMawZh+yGv5ypT8OZfHSsWgXdefer2uPpqH+Xliv37HcrLDS67zAsw4bBmdFTjuppCAcrKIJlUXHaZN5S4fDns2MEbX5faXXdFefbZDGvWZHEczbJlAW68MXLcsOGZchzNvn2aTMbk4MEctu1yySVemeGwyfTpU39y++uiUVh9ZWnKuv8ta6tfnCRgCSEmrAJpetmARZAe1jKL3z/v72nOuofWO9ex+d+2UvnkIPZiE1+gQEaZlJUX8JWbqOE02rWgcASTHIUTylCFcjQKgoPoQpj/tc/P/nQEBiMEZmdQ0QQtwb1op54xp56cC1EfmAEDAprwWkUg3ET9gkYW1hpcOeoFrOK0KJaE4ZERiJowPww1ce9885VQtQ0GRuHWZTC3+cw/v1KK9nYf7e3H9wzdfbfBP/6jTUeHw+CgQ3U1/I//EWD6dC/gzJkDf/EX3hysWOlu7HtDMGhwyy0R3vGO8Bv1PFf79zs8+mgepWJcfbVLLGaybFn0nMudjJJJ6HhhvGsxtUjAEkJMWBYBItSRopdK5h3/YCEP2SSEy8As4a+yyFyM6X9O47seYudvdpAoswhaOXzxArX+UUZ1HJWHhsFD7GpoJ+/E31KEKlRC//VgjfFUZxW7Ej7KKwa5dNoG/OEcI5ShKFBQAwwV6osBTZMLam5v8PPORRVMuzFIfb03PDWj4vjyV0Sg2QfJPPiOGbkLBbxgdToSDqzLQNyEpUFvWPFUWloU118Pu3YVsCwb09TkcopEwiAe95Jf9IRskkq59PQUaGryEQyWZsuZUq6o3tBgsHChRW2twc03T/3V2k9JhghLTgKWEGLCUpi0cTM2OXzHLqQ5OgBP/2/IjEFFPVz/xxAIv31BZyr0e9StjBOqfoyhXJafHk6Tisb5o66f0BzsJjiYo7NiHh3On1J7siUSAFWogEIFG8YctHK4dNYGtK1QA5q503eRVBF6mUfedAiYYCtoyll8uaIC5plsOwR7d8LseqgrP77snix8rwsyjnddvKMBVlae2Uf8YQJ6ClDQ3ir0K0+j+ZYsMWluVgwOambOdNm+3WFkJEd7u5/mZoN4/M2AYtuar3ylky1bBrnqqjr++q9bz6yCF0A0avCxj5Xw52YSi0Zh9arSlHX/j0pTzmQnAUsIMaEpzOPDFcD2DsinoboF+jrh4DaYXaJlqAGUAcHVlM2/gpgzwBVPfYef776U/yeyirbARvJJzZj/ncwKtr9tEWOu5nHSjM7PY3X5iJhJWsoOEg5kqPAN0mz1sdG2OaTnUmubVDk5CsE9PGn72L2+gYH+EGY2zm82mXzsBphW82bZPzviLb/QEvYmtz/WDXNjED/N+d6uhn4bGnzQa8PwaWyRMjAAHS8YfPFLEVJJH0opGhstHnmkwPe+l6elxeDP/uzNCeGFgubVV7sxDJeXXurBdaddFHfjTVbJJHQ8P961mFoujm3ChRBTi2mBW0wFWnuB6HxQPpTVwKIP3kskrbGOxBjatwSz8r3Mq1pOiJMvQHmooPnX2DA76wcJRhPEF/ZTVd5PrHwMK1rA9ZvEVY4F1k4a/Qcot/aQj/6MSOBlto/8Bjv671TPfZ6ypS/hi43RseP48ofyUFb877Hf8HYITNun/7EMBXfEoLcAVhYuPY3/ancegGc64NBhRS6n+da3hnn11QzxuMIwFP4TOvJCIYP3v7+ZaNTij/+4WcLVZKBLdAhAerCEEJPRgmugrwsGDkDTPGhddF7fLtbQwGXvvpXcnhzNFdYbdx2ezIijeTDew3DCwe4KYIYLRBrHqK/sJqnClDFGVI+x15jBdF+WOfo3JINDpFUl2slg+9KUJ8LEM50caaskN2MDicQSsoQJFn9lLyyDtcPQGIKRQnENrJOPVL6t5SEY3gpPvQyPt8I9d576+Ze2w+f+DFqnQTrtzan67ndH+drXqmhv91Nf/9YAde+9bdx7b9spy+3szDAyYtPaGqSycuov6DlRRaOwenVpyrpfNiMEJGAJISajSBxu/TTYefAFvPGy82xxc4DnjwTwnWLKzlHgQWOI3k6T/FAY8oA/RMH101k+g13ZBTjaojIwwO1VT+Czeqm2wpBPkxgJcyA1g3hqhDIGGNIxBsw0+fJBapqD/IooNzCTOEFuq/d6oXaMQUMA7miEwFmsKDA8ArYDvYPFjsBTNKPPB7OLW+34/Sb33VfOf/yHt/p5ff2bPYj5vMujjya4+uoo9fWnDkwbN47x4IN9GIYiHDa4774mKiokZI2HZBI6Osa7FlOLBCwhxORkGOC/cHvENZZBcxkkshA/ydsmgafSOXoO5hjrqkZnDAiCVVZAFzTPVN1Idbwf12dypNDEulQ7KlBg6+GZbD6yGJ+RJ+8PUqCM9RasaEkRrjpAQ9DH7GCENC7rOMwNzCJgwu83claLVrguvLQZXtsH1eVwxzUwt/XMM2pbm48vf7nqLedNU1FbaxEKnXzY1rahqwuqqmDz5iSVlT7Kyy22b0/x3HMj3HRTJZHIxbP+lJi6JGAJIcRpWt0G/77l5AHrINDf00NqfzU6bGAojcq4EFDogEXODDFmlWEZ3mSpvfZsjiYa2b95Lrl0COVqcspPvDFBpNphazrKCuWjKhjCR4AwDmPk3vK+jgs7x8B2YW4ZBH9HNtmwE37RATUV0HUULm+H+urTb4NCQbNzZ47Zs/0nXXrBNBXXXPP2C2E9+SQ8+yzE47BkSYAdO9IMD9usWzeKbTt0dqa5774W/H6ZInwhyRBh6UnAEkKI0zSvGuZVwf4RrzfrWGOAYY/i6DosswAWuHEDHVD4qnKokE1KR1EFjYtB0ElyeLSVkVAlZsHBn86TykepHB5gUdtviUUKWFmDivhyNJoRsszkrT1Gjx2Flwe8IcNZUfjojFOvaXW4F8oi3uEzYf/hM2uDAwcKfP/7Ce6+O057+5n3IKbTXi9aNgsrVsQZGHBZv36U+fNDtLdHOXgwy/Bwgbq6i2SLmgnCGyI8jdtJxWmTgCWEEKfJMuF9i+B7G+BQwgtZrw+txYFgc4Swm6LQ5cOZZhKNJlm0aCPxugQJt4zDhRbydphoaIjBsRpSbow8PnR1kKrkAEvVBtrbNrKnfxZ5oCqQI+Ou53B+FYFcNRGrgVwIAsd07mwchulRMBV0piBlQ+wU05hmNMHLW8A0YXgUrj3DbW2mT/fx8Y+X09Z2hrPqi265BerrobFR89BDSX7xC41tRykv1xw4kKWxMSCT3ceFrDRaahKwhBDiDIR88NGl8PB22N4HQQtqItBiQHmkibJVR/BfkiUTCbGq6gWuyr1CfHSELv80Nsba6bTasHUAPWZip03ciJ/mwEF+r/pXzHV3MT3SScjNciC1hLQKcXAoyxMHW7EjeX4a6aQ5EeSjZZXMitrECTMnZrEl4ZJUBWqDEDYDjGYh70B15K31XzQb3mvDrgNwRTtctfjMPr9pKubMOfvepUgErroKkknNI48UCIVMlIJ0uox77onQ1BTE55PhwQvNGyIszc0iMkTokYAlhBBnKOiDD7TD4VF49TBs6vEW75xvBNhfFsZtsGlQR7kp+zT1Ti9JK8Jl+XVE02McDTdi5TS1dg9Dbg2Gspnt303aCdLr1tHsHuTK0CsMOq0M+UBbw0SrD+IEfLgFP13mEF/J9XCF32Ge38/tLZeQCifZoccor8yxu9DIT1+JkLbhniUw84RRRaVg2QLveJ1twwuveH9efQUELsDoXDCo8PsNUilNoaCZPt1ixozSrKqeTrts2FCgrs5g9mzpDTsd3hDhGSymJn4nCVhCCHEWlIKWuHfcPMsbMszacK0d5//PH8U2NE36KGNmDAeLnOljhtPFgqF1WK/2MkYZ3dX1RONpFjVtIqf8hHJjWKMFWkKHuNR9iR2+dl7ouY7d2SYcLOpr+oiF01iqwP58hGH/GJZ5iJm1QUyy2LiMpm26U7BpALJ++KuVUPk7pkpt3wWP/6f3mUIhuOqy89Nma9cW+PrXMwRDBl/5v0P89/8e4xvfSBEMwhe+cJLutrP07LM5nnkmRyCg+PznY2/slShORYYIS00ClhBCnKNYABbUel8vJcSykV9zUNMAABZkSURBVFG+3fUy/TWVNBe6yRgh4owyQJTatZsJrOmiMufjuits+hZcRt6yaNNd5AN+dpXNwtmbptLtZV3Flex35mH4k+RdxeHhKtrCY/iVwYhjMifqo5cCtUSYTpQIFkvDMV5qADcE/gBsH4FV9aeufzjkzS/TQLR0Oect/vZvM4xlYH+fRf/nciy9xCST99PaqrCs0oWgsjID04RoVOH3ywrypyMaVaxeXZrlMWSI0CMBSwghSmymauUv993Ho5H5OFpRpkcY8sd5xWknn+ljoGku+XAUs8mPz59lf6CNQCFHLf04hxNs3l1F/Zod9H80ggoV0NoAQ+M6Jul0BJ+liAdM5lOBBvaR5EPMwMQLE9dP8/YatBRMi/7u+s6aAZ+8BxwHpp/HPZlNExwbbEfz7A6LX3eakLPgFzb335/m+RciPLPNYNchuPZS7zgbV13lp7XVJB43CIUkYJ2OZNKloyM/3tWYUiRgCSFEqakgIf9Kmnv2srdlOvnODAMjFtlpNkbnCImahextvZqa2hSjTjW+vMMe3yx8IzmCu4+io2GGnDKGdZSCY2E4QWyzAIbGcYL4DZfmkMIqbieb0i4bdZ5LlJ+gUiyuhuYoWAbET/Nmv9aW89geRV/5SphvfCPLQETRnbIgBVRocH3s3WXzne/nSVcGaamBJ9fB0tnechJnSilFS4tc3s6cDBGWkvwECiFEqUVr8detYv5zW0jkutneHyVRWYX720Mkq+rYUncLR8cW4h/eQ/pgkFBjmrGGMrblFlNxSR329hH23XIXASNO0vHjapOAaRPzZag1XeqjLvMCXnLK4TDo+viBm+Ee0+BS5U3qrrpwi9yftsWLLf7iq1Hu+L9cb2VWraGAdyXSinQSjCroGYZICAIyP/2C8YYIS9PgMkTokYAlhBClZpiopR8i5E/SPfIqfRtHyAf6sUYzDH/wGg6mVuKuNenJ1DNtzkHy3T6yRhCtFGOR2ewIL2BUl2HvD2FU5mif6TCazRE3BgkPB7iuzqIcAxfNIDneYdSgCdGGyagNYdMbHpyIAj5orDTYe8CBmIKshsM2punwqT/xkVVwqB8uaYPA2S21Jc6CN0T41p0CxNmTgCWEEOeD5afv0ndwJA1DkRTlG7cTbw1izBrF91IebRuMJCqJ9Y0Rq0gQyBcYSlVwYP0cUkNRyAAFcGuCbMrmKJtm0RKMEbbGOGLbbDPyGEpxqSrnUlVGr23yrX5IuxA24IM1MG0CLoae6IXlVeC0wNYXciR7oCLm8LVvBmhp8aEU1FVCUMLVOJAhwlKSgCWEEOfJa33D2IP9tFmDNN6Yp6tqAb3ZBsKhUcaMKixtM9hfw9HOZqqqezh6ZBqFkaAXrsaUd1tfGhjyM6pMNs9OMyc+xvMarLxDzPUTtEyetobY0FdD0IBqP4w68KM++FwTnGS7wHHT0wvfewDWbrbZ+ppNbcTlsSf9PL0txN4xWPMaZDT8ZrMmYMAnboH6ygnaFTfFeEOEpUnkMkTokYAlhBDngZ3Lsfn//Rats46wovI1Xmy/jT39s3k5vYpsLIq1OEdujx815FBfeZRo2Qh9uxtwXR+ONrxFqUxAAY6GnQaFOh+5Fk3M7ePmwAsYOAw7c3klfxMFXUWt6aWpMhMO2ZCwT68n6Lnn8uzebfPe9wbP65pRqRTsOwxr19s4aYfOQc0fvCfLB//cIhBQ9AzCusOara+4rFsDP/tn+IevGSyYr85oQ+pSyGZdNm/Ok8/DwoU+KitLs4TBROUNEabHuxpTigQsIYQ4D3o2b8b3wmamVabpv66VqvoEh/un4cs7mHkHajRWhU1mLMxYWYDBTa3kRsKYMwo4BGAAqtL9VAYHSasww6ocPWpiGA5LzfWk3ACOClFj7ibltrGfCFkXZhoVuK431BY9zUywZk2BQ4ccVq92z2vAamkGy9A4KcBxIZ9lOO3wq5/5WLQsSEMQLFxefFaRScNYCj7wGc09H1V84j3QXHfeqnYc29Z8//tJDhywMU1FR0eW++6LUV4+lUOWLDRaahKwhBDiPPCHw1TloGerQ+2nw/SHavBrm1hwlLQTRvkd8v4grmuQcCsxKgzcGQZuQxB8LmY0j787R2ooir88y+KWTewYnIcfGxNNHhMbcBVU+ga5rGqAjoE4wypBs67mrmqInCIPuC5k8xAKwIc/HKSvz2XWrPMbIPx+bw0sLANUAUIKxhT7diS5ZlWQ/n6Y1QIkNFFT4VqQyoDteMf51tOT57e/TZDLwb59BrNne91/XV0FOjttliyZugErGjVYvTpUkrJkiNAjAUsIIc6DmoULWfSZT/Hs098GVUWlMUzUTVIeHkLZNol8BY5p4cvkyabD+PIFVKWLDpkQABXVmDpHzJ8m1jRGeSBBKJ3GLljs981gmbEJQ4FSDURVI40Rl/rAEFnbxzutaspP8dt9JAk/eAr6EzCrCd5/jUl9/emFh2zWpasrS0tLgMipElxR1yGIhqG6uCdiJqe9uwcrLUi50OiDesVjQy53Rg1uu8Sg6jKXQ2vBcuGPb1Pc+y5oazyt6p219evH+Ju/OUhZmUl9fYD9+320tfkwTS+MBoNTey6YN0SYHO9qTCkSsIQQ4jxQShG8+1pidx1hwB0iWMgTDoyhC4pVR5/l+bIbSCWigMKXdHD7LVSdg3ZM7+5Bn0F0VpKq/SPEYsMYaahrG2Nl7AgNpp8CK6ikGWgiisMII/gsg5qeFv5tPeQKcNlsuP4SbwX1Y724HQbHYFot7DgIOw9D+/TT+1w///kg69cnmT07yMc/3nDK5yZG4V9+AM2N8Kcf9c7dfbfBMxtcaAlAfw7m+WC/YqhX01mjidYpbrnXYPRWmFMLf3m7Qp3nbDM6avPjHw+Qy7kMDWkaGnxMm+bS02PjuooVKwLMnTvVF+WSIcJSk4AlhBDni+sSLaTJaJdczmJ+eCv7jy5g2KrDLLj4UwXsYB67YBEK2mSdAE4qRyCaR2PTn61lVtU+HGVwOD+TK+eOscCcTgURgsRQ+OgjRxPVtDODzm7FD5+zqItDyA9PbwXDgBsWQTrvHfGQN28efXYfSSnvMIw3U08y6RKJKNQJSSgWhVuvh9pjJqjfepXihtUWT292oDoCwwoccPs1657QfHVYYdYrXD+8752c93D1+mcKBAxaWgLs3JlhZMThU5+qY8GCMLbt7W041UWjJqtXn8a+SqdBhgg9ErCEEOI8mWm0cFRVUdi8k9z0SsojoyyJbePZvmvY68ykRvWQSYbI9QfAMjGHw6R9YaxYjmAsy+D6Sl6puIIKK828lXtYUjnMMm7jBRLEURTIo1DMIkwQH3sOQzjgHQANFbCpE5pq4McbvHnl5SF4dzvs64EDfXBJK8xrPr7eWuu3hKXX3XlnFUuXRmlu9uYnHThg8+1vp7jrriBLlx5/m79hwLVXHv/6ja/B8oWwbS30rnehYEAUDFeTyipeMjQf/kOFaUJhFKgtxd/EqcViFnfdVcVzzyVYvTrOrbeWU18/ARcRO4+SSYeOjsR4V2NKkYAlhBDnSQUxroncw3466f7uM+TaosxRncyLbOKqgZd5+MB7WLt/JYW0n4V/+BoD+Vp0SJNL+zD6IOWP0jjrMFfM2sHyqlGWqSqqCFOHzTB52giynHIq8cJO0A8F+833z9vg98FDG71gFfZDzyh0dMJ9t3uPH7uMg9aaxx4rsHatzapVFrfc8tY1HoJBgzlz3pwMHY8bzJ1rUVt7enO44jFIJkEbBkGfJl9QWBmN5TPIabBsGErAJXNg2qlHIEtq8eIoixeXpgdncpIhwlKTgCWEEOdR3KxjyapvMm/2Rn747TSdmx/jpku+ixNJcWhfC72dDRiOizliUx3uJWf6aJjejR0Co8Lh96vW0arLuVKFSNDMg4zgB3KYhAlRzZshaMVMWLcPDgx4N+rZLtx1BfxkmxeuAMqCMJTyepdOXCNrcFCzZo1Nc7Oio8Nm1Sof0eipx+jKyw0+8pHT35F5ZTugYfs6ly19Cu0DUDjFa/u0Fvi7z0FF3KujuFAkYJWaBCwhhDjfjBChhisxmh0ywev4jzXtrB12WMcy7y66KsXLO1exePFGYuYYpmmzf3gmsVqbuL0IjKNUMIddLCCKpgILB80G0qwm9sbbVEThU++ArQcg78DcRmisgDVH4NCwN/9qIAU3zzt5NcvKFNXVikOHNE1NilBp7to/vikMuHyJ4m8+b/C5L2j27oV0wbu8T5sB//BVRVVF6d9XnJo3BytekrJkDpZHApYQQlwgK1cq/uf/1nRVXs7hoRGsWgfb74OgJuVGWfOLKwhMy1KxbQQ9zWFxW5aMfwlNPkU9JhWMsZ80FUAKlxhvHZYrj8DVC44/98Hl8NQu6EvCyla4asbJ6+f3Kz7xiSA9PS6NjQamef5mmF97rcED/6559HGXzgNw5RUGt96sqKo8b28pTsGbgzU03tWYUiRgCSHEBdLerrj2Jvj7Jxo4Ul+La5sQKI6DpR0wXLLpIEc2NTNnwVEuVU2kgav83nNWEOEIBY5QIIzBHZxej0MkAL/ffnp1DIcVM2ZcmAU158xRfP6zU3fxzslFA+54V2JKkYAlhBAXiFKKD/ye4mhDkH/+ZTfJlyu9a1pBg2HihE0YAbTLjPoGglHFu4IGLcWepDAGf0gFGVwCGFhM7cUvxYUTjVqsXl2asVkZIvRIwBJCiAtIKfjUogibesLoQ90cPVCDM+TzOhAM72hfrviH5SYzAuA7YbkEA0XkJEODQpyLZNKmo2NgvKsxpUjAEkKICyzqhz9d0oiZz7HxWU3Xa4pcQWGGNatXKB75skE4ON61FBcXuYuw1CRgCSHEOLiiEWquC9AxBw4MQciB1a2KK09zyxohSskbIqwqSVkyROiRgCWEEONkVrl3CDHevCHCvvGuxpQiAUsIIYS46MkQYalJwBJCCCEuct4QYU1JypIhQo8ELCGEEOIil0wW6OjoGe9qTCkSsIQQQgiBDBGWlgQsIYQQ4iIXjfpYvbq+JGXJEKFHApYQQghxkUsm83R0HBnvakwpErCEEEIIgQwRlpYELCGEEOIi5w0RNpakLBki9EjAEkIIIS5y3hDhofGuxpQiAUsIIYS46MlCo6UmAUsIIYS4yEWjflavbi5JWTJE6JGAJYQQQlzkvCHCg+NdjSlFApYQQghxkfN6sFpKUpb0YHmU1nq86/A7KaX6gQPjXQ+gGhgY70pMctKG507a8NxJG547acNz87var1VrXZrNAU+DUuoJvDqVwoDW+pYSlTVpTYqANVEopdZprZePdz0mM2nDcydteO6kDc+dtOG5kfab+ozxroAQQgghxFQjAUsIIYQQosQkYJ2Z74x3BaYAacNzJ2147qQNz5204bmR9pviZA6WEEIIIUSJSQ+WEEIIIUSJScASQgghhCgxCVhvQyl1l1LqNaWUq5Rafsz5m5RS65VSW4t/Xn+S1z6mlNp2YWs88ZxpGyqlwkqpXyqldhZf97Xxq/34O5ufQaXUsuL5vUqpbyql1PjUfmI4RRtWKaWeUUollVL/dMJr/qjYhluUUk8opUq1NtCkdJZt6FdKfUcptbv47/ndF77mE8fZtOExz5HrySQlAevtbQPeBXSccH4AeKfWehFwN/CjYx9USr0LSF6QGk58Z9OGX9dazwOWAFcppW69IDWdmM6m/f4FuBeYXTwu9sX+3q4Ns8BfA//12JNKKQv4R+A6rXU7sAX49AWo50R2Rm1Y9JdAn9Z6DrAAeO681nDiO5s2lOvJJCdb5bwNrfUOgBM7ALTWG4/59jUgqJQKaK1zSqko8Fm8C9xDF6quE9VZtGEaeKb4nLxSagNQmt1HJ6EzbT+gEijTWr9cfN0PgTuBX1+QCk9Ap2jDFPCCUmrWCS9RxSOilBoEyoC9F6CqE9ZZtCHAR4F5xee5XOQrvp9NG8r1ZPKTHqxz825go9Y6V/z+q8D/B6THr0qTzoltCIBSqhx4J/D0uNRq8ji2/ZqAw8c8drh4TpwmrXUB+CSwFTiK1/vy3XGt1CRT/LcL8FWl1Aal1E+UUnXjWqnJSa4nk9xF3YOllHoKqD/JQ3+ptX70d7x2IfB3wM3F7y8FZmmt/1wp1Vbiqk5YpWzDY85bwAPAN7XW+0tV14moxO13svlWU34dlnNpw5OU5cMLWEuA/cD/BL4E/LdzredEVso2xLuuNAMvaq0/q5T6LPB14EPnWM0JrcQ/hxfl9WSquagDltb6xrN5nVKqGfg58GGt9b7i6SuAZUqpLrx2rVVKPau1vrYUdZ2oStyGr/sOsEdr/Q/nWr+JrsTtd5jjh1Sb8XphprSzbcO3cWmxzH0ASqmHgC+WsPwJqcRtOIjX6/Lz4vc/Ae4pYfkTUonb8KK8nkw1MkR4hord378EvqS1fvH181rrf9FaN2qt24BVwG75x3Byb9eGxcf+GxAHPjMedZsMTvEz2A2MKaUuL949+GHgTHsfLnZHgAVKqZri9zcBO8axPpOO9lav/gVwbfHUDcD2cavQJCTXkylCay3HSQ7gD/B6BHJAL/CfxfN/BaSATccctSe8tg3YNt6fYbyPM21DvB4XjXdBe/38x8b7c0yW9is+thzvjqV9wD9R3K3hYj3erg2Lj3UBQ3h3aR0GFhTPf6L4M7gFLyhUjffnmIRt2Ip3x9wWvHmU08b7c0y2NjzmcbmeTNJDtsoRQgghhCgxGSIUQgghhCgxCVhCCCGEECUmAUsIIYQQosQkYAkhhBBClJgELCGEEEKIEpOAJcQkp5Qq+WawSqk7lFJfLH59p1JqwVmU8axSanmp6yaEEJOBBCwhxFtorR/TWn+t+O2deHvyCSGEOE0SsISYIpTn75VS25RSW5VS7yuev7bYm/SwUmqnUur+4krvKKVuK557QSn1TaXU48XzH1FK/ZNS6krgDuDvlVKblFIzj+2ZUkpVF7fzQCkVUkr9WCm1RSn1IBA6pm43K6VePmbz3+iFbR0hhLiwLuq9CIWYYt6Ft5feYqAaWKuU6ig+tgRYiLc34YvAVUqpdcC/Aqu11p1KqQdOLFBr/ZJS6jHgca31wwDFbHYynwTSWut2pVQ7sKH4/Gq81edv1FqnlFJ/AXwW+NtSfGghhJiIJGAJMXWsAh7QWjtAr1LqOWAFMAq8qrU+DKCU2oS3/UYS2K+17iy+/gHg3nN4/9XANwG01luUUluK5y/HG2J8sRjO/MDL5/A+Qggx4UnAEmLqeNuuJbw90F7n4P3bP9XzT8XmzekFwRMeO9neWwp4Umv9R2f5fkIIMenIHCwhpo4O4H1KKVMpVYPXo/TqKZ6/E5ihlGorfv++t3neGBA75vsuYFnx6/ec8P4fAFBKXQK0F8+/gjckOav4WFgpNec0Po8QQkxaErCEmDp+DmwBNgO/Bb6gte55uydrrTPAp4AnlFIvAL1A4iRP/THweaXURqXUTODrwCeVUi/hzfV63b8A0eLQ4BcohjutdT/wEeCB4mOvAPPO5YMKIcREp7Q+WY++EOJioJSKaq2TxbsK/xnYo7X+xnjXSwghJjvpwRLi4vYnxUnvrwFxvLsKhRBCnCPpwRJCCCGEKDHpwRJCCCGEKDEJWEIIIYQQJSYBSwghhBCixCRgCSGEEEKUmAQsIYQQQogS+z+1rrkYY1tNUQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.4,\n",
    "    s=housing[\"population\"]/100, label=\"population\", figsize=(10,7),\n",
    "    c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"), colorbar=True,\n",
    "    sharex=False)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value    1.000000\n",
       "median_income         0.687160\n",
       "total_rooms           0.135097\n",
       "housing_median_age    0.114110\n",
       "households            0.064506\n",
       "total_bedrooms        0.047689\n",
       "population           -0.026920\n",
       "longitude            -0.047432\n",
       "latitude             -0.142724\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = housing.corr()\n",
    "corr_matrix[\"median_house_value\"].sort_values(ascending=False) # response vs explanatory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x103f18748>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1155a5940>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x103ca8eb8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x103c9a390>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x115553908>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11557de80>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x115552438>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x103d309e8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x103d30a20>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x103ead4e0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x103eb3a58>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11553efd0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11550c588>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x103e93b00>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x103cff0b8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a18ab5630>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import scatter_matrix\n",
    "attributes = [\"median_house_value\", \"median_income\", \"total_rooms\",\n",
    "              \"housing_median_age\"]\n",
    "scatter_matrix(housing[attributes], figsize=(12, 8))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### 7. Prepare data for Machine Learning algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again, I star with *strat_train_set*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.drop(\"median_house_value\", axis = 1) # Remove column to be predicted\n",
    "housing_labels = strat_train_set[\"median_house_value\"].copy() # Column to be predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "\n",
       "       population  households  median_income ocean_proximity  \n",
       "17606       710.0       339.0         2.7042       <1H OCEAN  \n",
       "18632       306.0       113.0         6.4214       <1H OCEAN  \n",
       "14650       936.0       462.0         2.8621      NEAR OCEAN  \n",
       "3230       1460.0       353.0         1.8839          INLAND  \n",
       "3555       4459.0      1463.0         3.0347       <1H OCEAN  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "longitude               0\n",
       "latitude                0\n",
       "housing_median_age      0\n",
       "total_rooms             0\n",
       "total_bedrooms        158\n",
       "population              0\n",
       "households              0\n",
       "median_income           0\n",
       "ocean_proximity         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "Data Cleaning\n",
    "\n",
    "1. First, NA replacement with Scikit Learn's **Simple Imputer Class** (numerical values):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\n",
       "        408.    ,    3.5409])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.impute import SimpleImputer # import the Simple Imputer Class\n",
    "imputer = SimpleImputer(strategy=\"median\") # impute with median values\n",
    "\n",
    "housing_num = housing.drop(\"ocean_proximity\", axis = 1) # drop non-numerical columns\n",
    "imputer.fit(housing_num) # fit the imputer instance (= calculate median of all columns)\n",
    "imputer.statistics_ # output the calculated median values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = imputer.transform(housing_num) # Apply imputer (NB: All columns)\n",
    "housing_tr = pd.DataFrame(X, columns=housing_num.columns) # Transform NumPy array to DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "longitude             0\n",
       "latitude              0\n",
       "housing_median_age    0\n",
       "total_rooms           0\n",
       "total_bedrooms        0\n",
       "population            0\n",
       "households            0\n",
       "median_income         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "2. Next, I convert text/categories to numbers. Specifically, I handle the attribute *ocean_proximity*. I use Scikit Learn's **Ordinal Encoder Class**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>17606</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ocean_proximity\n",
       "17606       <1H OCEAN\n",
       "18632       <1H OCEAN\n",
       "14650      NEAR OCEAN\n",
       "3230           INLAND\n",
       "3555        <1H OCEAN"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat = housing[[\"ocean_proximity\"]]\n",
    "housing_cat.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.],\n",
       "       [0.],\n",
       "       [4.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [0.]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "ordinal_encoder = OrdinalEncoder()\n",
    "housing_cat_encoded = ordinal_encoder.fit_transform(housing_cat)\n",
    "housing_cat_encoded[:10] # categories are transformed to numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "3. Step 2 is not satisfactory, as the *order* of category values has no meaning. Because of this, one-hot encoding is needed (through Scikit Learn's **OneHotEncoder class**):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "       dtype=object)]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordinal_encoder.categories_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder \n",
    "cat_encoder = OneHotEncoder()\n",
    "housing_cat_1hot = cat_encoder.fit_transform(housing_cat)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 1.],\n",
       "       ...,\n",
       "       [0., 1., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 1., 0.]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_1hot.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "       dtype=object)]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_encoder.categories_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "4. I write my own transformer (just as Simple Imputer was a transformer) based on a transformer function. I do this to work seamlessly with piples and other Scikit Learn functionality. The transformer constructs 2 or 3 (optional) new columns (feature engineering) based on the existing columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "rooms_ix, bedrooms_ix, population_ix, household_ix = [ # get the index of the 4 columns\n",
    "    list(housing.columns).index(col) \n",
    "    for col in (\"total_rooms\", \"total_bedrooms\", \"population\", \"households\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rooms_ix # e.g.:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import FunctionTransformer\n",
    "\n",
    "def add_extra_features(X, add_bedrooms_per_room=True): # add_bedrooms_per_room is a hyperparameter of the transformer\n",
    "    rooms_per_household = X[:, rooms_ix] / X[:, household_ix]\n",
    "    population_per_household = X[:, population_ix] / X[:, household_ix]\n",
    "    if 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]\n",
    "\n",
    "attr_adder = FunctionTransformer(\n",
    "    add_extra_features, \n",
    "    validate=False,\n",
    "    kw_args={\"add_bedrooms_per_room\": False}\n",
    ")\n",
    "\n",
    "housing_extra_attribs = attr_adder.fit_transform(housing.values) # apply transformer: fit + transform variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-121.89, 37.29, 38.0, ..., '<1H OCEAN', 4.625368731563422,\n",
       "        2.094395280235988],\n",
       "       [-121.93, 37.05, 14.0, ..., '<1H OCEAN', 6.008849557522124,\n",
       "        2.7079646017699117],\n",
       "       [-117.2, 32.77, 31.0, ..., 'NEAR OCEAN', 4.225108225108225,\n",
       "        2.0259740259740258],\n",
       "       ...,\n",
       "       [-116.4, 34.09, 9.0, ..., 'INLAND', 6.34640522875817,\n",
       "        2.742483660130719],\n",
       "       [-118.01, 33.82, 31.0, ..., '<1H OCEAN', 5.50561797752809,\n",
       "        3.808988764044944],\n",
       "       [-122.45, 37.77, 52.0, ..., 'NEAR BAY', 4.843505477308295,\n",
       "        1.9859154929577465]], dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribs # inspect output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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",
       "      <th>ocean_proximity</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38</td>\n",
       "      <td>1568</td>\n",
       "      <td>351</td>\n",
       "      <td>710</td>\n",
       "      <td>339</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4.62537</td>\n",
       "      <td>2.0944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14</td>\n",
       "      <td>679</td>\n",
       "      <td>108</td>\n",
       "      <td>306</td>\n",
       "      <td>113</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>6.00885</td>\n",
       "      <td>2.70796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.2</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31</td>\n",
       "      <td>1952</td>\n",
       "      <td>471</td>\n",
       "      <td>936</td>\n",
       "      <td>462</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>4.22511</td>\n",
       "      <td>2.02597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25</td>\n",
       "      <td>1847</td>\n",
       "      <td>371</td>\n",
       "      <td>1460</td>\n",
       "      <td>353</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>5.23229</td>\n",
       "      <td>4.13598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17</td>\n",
       "      <td>6592</td>\n",
       "      <td>1525</td>\n",
       "      <td>4459</td>\n",
       "      <td>1463</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4.50581</td>\n",
       "      <td>3.04785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      longitude latitude housing_median_age total_rooms total_bedrooms  \\\n",
       "17606   -121.89    37.29                 38        1568            351   \n",
       "18632   -121.93    37.05                 14         679            108   \n",
       "14650    -117.2    32.77                 31        1952            471   \n",
       "3230    -119.61    36.31                 25        1847            371   \n",
       "3555    -118.59    34.23                 17        6592           1525   \n",
       "\n",
       "      population households median_income ocean_proximity rooms_per_household  \\\n",
       "17606        710        339        2.7042       <1H OCEAN             4.62537   \n",
       "18632        306        113        6.4214       <1H OCEAN             6.00885   \n",
       "14650        936        462        2.8621      NEAR OCEAN             4.22511   \n",
       "3230        1460        353        1.8839          INLAND             5.23229   \n",
       "3555        4459       1463        3.0347       <1H OCEAN             4.50581   \n",
       "\n",
       "      population_per_household  \n",
       "17606                   2.0944  \n",
       "18632                  2.70796  \n",
       "14650                  2.02597  \n",
       "3230                   4.13598  \n",
       "3555                   3.04785  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribs = pd.DataFrame( # inspect output (more readable)\n",
    "    housing_extra_attribs,\n",
    "    columns=list(housing.columns)+[\"rooms_per_household\", \"population_per_household\"],\n",
    "    index=housing.index\n",
    ")\n",
    "\n",
    "housing_extra_attribs.head() # Two new features have been engineered (in an automatised way)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "5. Putting all of the above transformation together in a **Pipeline** (through Sckikit Learn). The pipeline automates the workflow, simplifies it, can be used in production, and allows for grid search (to come). I create the pipelines in two steps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.15604281,  0.77194962,  0.74333089, ..., -0.31205452,\n",
       "        -0.08649871,  0.15531753],\n",
       "       [-1.17602483,  0.6596948 , -1.1653172 , ...,  0.21768338,\n",
       "        -0.03353391, -0.83628902],\n",
       "       [ 1.18684903, -1.34218285,  0.18664186, ..., -0.46531516,\n",
       "        -0.09240499,  0.4222004 ],\n",
       "       ...,\n",
       "       [ 1.58648943, -0.72478134, -1.56295222, ...,  0.3469342 ,\n",
       "        -0.03055414, -0.52177644],\n",
       "       [ 0.78221312, -0.85106801,  0.18664186, ...,  0.02499488,\n",
       "         0.06150916, -0.30340741],\n",
       "       [-1.43579109,  0.99645926,  1.85670895, ..., -0.22852947,\n",
       "        -0.09586294,  0.10180567]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First pipeline for numerical transformations:\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "        ('imputer', SimpleImputer(strategy=\"median\")), \n",
    "            # Built-in Scikit Learn Transformer (see #1 above)\n",
    "        ('attribs_adder', FunctionTransformer(add_extra_features, validate=False)), \n",
    "            # Custom Build Transformer (#4)\n",
    "        ('std_scaler', StandardScaler()), \n",
    "            # Built-in Scikit Learn Transformer (New: Normalization)\n",
    "    ])\n",
    "\n",
    "housing_num_tr = num_pipeline.fit_transform(housing_num)\n",
    "housing_num_tr # outputs array of transformed numerical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.15604281,  0.77194962,  0.74333089, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.17602483,  0.6596948 , -1.1653172 , ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 1.18684903, -1.34218285,  0.18664186, ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       ...,\n",
       "       [ 1.58648943, -0.72478134, -1.56295222, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 0.78221312, -0.85106801,  0.18664186, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.43579109,  0.99645926,  1.85670895, ...,  0.        ,\n",
       "         1.        ,  0.        ]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, create the 'higher-level' pipeline, handling both categorical and numerical transformations:\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "num_attribs = list(housing_num)\n",
    "cat_attribs = [\"ocean_proximity\"]\n",
    "\n",
    "full_pipeline = ColumnTransformer([\n",
    "    (\"num\", num_pipeline, num_attribs),\n",
    "    (\"cat\", OneHotEncoder(), cat_attribs)\n",
    "])\n",
    "\n",
    "housing_prepared = full_pipeline.fit_transform(housing)\n",
    "housing_prepared # outputs array of all the transformed variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>ocean_proximity</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "      <th>ocean_onehot</th>\n",
       "      <th>inland_onehot</th>\n",
       "      <th>island_onehot</th>\n",
       "      <th>near_bay_onehot</th>\n",
       "      <th>near_ocean_onehot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-1.156043</td>\n",
       "      <td>0.771950</td>\n",
       "      <td>0.743331</td>\n",
       "      <td>-0.493234</td>\n",
       "      <td>-0.445438</td>\n",
       "      <td>-0.636211</td>\n",
       "      <td>-0.420698</td>\n",
       "      <td>-0.614937</td>\n",
       "      <td>-0.312055</td>\n",
       "      <td>-0.086499</td>\n",
       "      <td>0.155318</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-1.176025</td>\n",
       "      <td>0.659695</td>\n",
       "      <td>-1.165317</td>\n",
       "      <td>-0.908967</td>\n",
       "      <td>-1.036928</td>\n",
       "      <td>-0.998331</td>\n",
       "      <td>-1.022227</td>\n",
       "      <td>1.336459</td>\n",
       "      <td>0.217683</td>\n",
       "      <td>-0.033534</td>\n",
       "      <td>-0.836289</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>1.186849</td>\n",
       "      <td>-1.342183</td>\n",
       "      <td>0.186642</td>\n",
       "      <td>-0.313660</td>\n",
       "      <td>-0.153345</td>\n",
       "      <td>-0.433639</td>\n",
       "      <td>-0.093318</td>\n",
       "      <td>-0.532046</td>\n",
       "      <td>-0.465315</td>\n",
       "      <td>-0.092405</td>\n",
       "      <td>0.422200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-0.017068</td>\n",
       "      <td>0.313576</td>\n",
       "      <td>-0.290520</td>\n",
       "      <td>-0.362762</td>\n",
       "      <td>-0.396756</td>\n",
       "      <td>0.036041</td>\n",
       "      <td>-0.383436</td>\n",
       "      <td>-1.045566</td>\n",
       "      <td>-0.079661</td>\n",
       "      <td>0.089736</td>\n",
       "      <td>-0.196453</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>0.492474</td>\n",
       "      <td>-0.659299</td>\n",
       "      <td>-0.926736</td>\n",
       "      <td>1.856193</td>\n",
       "      <td>2.412211</td>\n",
       "      <td>2.724154</td>\n",
       "      <td>2.570975</td>\n",
       "      <td>-0.441437</td>\n",
       "      <td>-0.357834</td>\n",
       "      <td>-0.004194</td>\n",
       "      <td>0.269928</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606  -1.156043  0.771950            0.743331    -0.493234       -0.445438   \n",
       "18632  -1.176025  0.659695           -1.165317    -0.908967       -1.036928   \n",
       "14650   1.186849 -1.342183            0.186642    -0.313660       -0.153345   \n",
       "3230   -0.017068  0.313576           -0.290520    -0.362762       -0.396756   \n",
       "3555    0.492474 -0.659299           -0.926736     1.856193        2.412211   \n",
       "\n",
       "       population  households  median_income  ocean_proximity  \\\n",
       "17606   -0.636211   -0.420698      -0.614937        -0.312055   \n",
       "18632   -0.998331   -1.022227       1.336459         0.217683   \n",
       "14650   -0.433639   -0.093318      -0.532046        -0.465315   \n",
       "3230     0.036041   -0.383436      -1.045566        -0.079661   \n",
       "3555     2.724154    2.570975      -0.441437        -0.357834   \n",
       "\n",
       "       rooms_per_household  population_per_household  ocean_onehot  \\\n",
       "17606            -0.086499                  0.155318           1.0   \n",
       "18632            -0.033534                 -0.836289           1.0   \n",
       "14650            -0.092405                  0.422200           0.0   \n",
       "3230              0.089736                 -0.196453           0.0   \n",
       "3555             -0.004194                  0.269928           1.0   \n",
       "\n",
       "       inland_onehot  island_onehot  near_bay_onehot  near_ocean_onehot  \n",
       "17606            0.0            0.0              0.0                0.0  \n",
       "18632            0.0            0.0              0.0                0.0  \n",
       "14650            0.0            0.0              0.0                1.0  \n",
       "3230             1.0            0.0              0.0                0.0  \n",
       "3555             0.0            0.0              0.0                0.0  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to better see final result, I print the head of the corresponding data frame: \n",
    "pd.DataFrame( \n",
    "    housing_prepared, # [:2, ],\n",
    "    columns=list(housing.columns) + \n",
    "        [\"rooms_per_household\", \"population_per_household\", \n",
    "         \"ocean_onehot\", \"inland_onehot\", \"island_onehot\", \"near_bay_onehot\", \"near_ocean_onehot\"],\n",
    "    index=housing.index\n",
    ").head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "\n",
       "       population  households  median_income ocean_proximity  \n",
       "17606       710.0       339.0         2.7042       <1H OCEAN  \n",
       "18632       306.0       113.0         6.4214       <1H OCEAN  \n",
       "14650       936.0       462.0         2.8621      NEAR OCEAN  \n",
       "3230       1460.0       353.0         1.8839          INLAND  \n",
       "3555       4459.0      1463.0         3.0347       <1H OCEAN  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# before the pipeline, we had this:\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### 8. Select and Train Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, with data cleaned and prepared for ML Algorithms, I can start training ML models.\n",
    "\n",
    "<br>\n",
    "\n",
    "I start with fitting a **Linear Regression**, lin_reg, on training data. I then follow with a **Decision Tree Regressor**, tree_reg, and finally a **Random Forest Regressor**, forest_reg:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(housing_prepared, housing_labels);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor(random_state=42)\n",
    "tree_reg.fit(housing_prepared, housing_labels);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest_reg = RandomForestRegressor(n_estimators=10, random_state=42)\n",
    "forest_reg.fit(housing_prepared, housing_labels);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "How do these models perform? To find out, I use Sckikit Learn's **cross-validation** feature (which splits the training into K subsets (folds), then trains and evaluates the model K times). I measure the model using the MSE metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([66782.73843989, 66960.118071  , 70347.95244419, 74739.57052552,\n",
       "       68031.13388938, 71193.84183426, 64969.63056405, 68281.61137997,\n",
       "       71552.91566558, 67665.10082067])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Linear Regression - Cross Validation\n",
    "lin_scores = cross_val_score(lin_reg, \n",
    "                             housing_prepared, housing_labels,\n",
    "                             scoring=\"neg_mean_squared_error\", \n",
    "                             cv=10) # The model is trained and evaluated K=10 times\n",
    "lin_rmse_scores = np.sqrt(-lin_scores)\n",
    "lin_rmse_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([70194.33680785, 66855.16363941, 72432.58244769, 70758.73896782,\n",
       "       71115.88230639, 75585.14172901, 70262.86139133, 70273.6325285 ,\n",
       "       75366.87952553, 71231.65726027])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tree Regression - Cross Validation\n",
    "tree_scores = cross_val_score(tree_reg, \n",
    "                         housing_prepared, housing_labels,\n",
    "                         scoring=\"neg_mean_squared_error\", \n",
    "                         cv=10) \n",
    "\n",
    "tree_rmse_scores = np.sqrt(-tree_scores)\n",
    "tree_rmse_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([51646.44545909, 48940.60114882, 53050.86323649, 54408.98730149,\n",
       "       50922.14870785, 56482.50703987, 51864.52025526, 49760.85037653,\n",
       "       55434.21627933, 53326.10093303])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest Regression - Cross Validation\n",
    "forest_scores = cross_val_score(forest_reg, \n",
    "                                housing_prepared, housing_labels,\n",
    "                                scoring=\"neg_mean_squared_error\", \n",
    "                                cv=10) \n",
    "\n",
    "forest_rmse_scores = np.sqrt(-forest_scores)\n",
    "forest_rmse_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to print summary of model performance:\n",
    "def display_scores(scores):\n",
    "    print(\"Mean:\", scores.mean())\n",
    "    print(\"Standard deviation:\", scores.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 71407.68766037929\n",
      "Standard deviation: 2439.4345041191004\n"
     ]
    }
   ],
   "source": [
    "display_scores(tree_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 69052.46136345083\n",
      "Standard deviation: 2731.6740017983493\n"
     ]
    }
   ],
   "source": [
    "display_scores(lin_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 52583.72407377466\n",
      "Standard deviation: 2298.353351147122\n"
     ]
    }
   ],
   "source": [
    "display_scores(forest_rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, the random forest model is by bar the best (although it has a typical prediction error of 52,538 USD). Because I run K models, I also get a standard deviations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### 9. Fine-Tune Model - Grid Search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I want to fine-tune the Random Forest model which was the most promising one. \n",
    "\n",
    "To see the hyperparameters, I print the forest_reg object: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "           oob_score=False, random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_reg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fine-tuning means to find the optimal values of hyperparameters. I decide to take an interest in *n_estimators*, *max_features*, and bootstrap.\n",
    "\n",
    "To find the optimal combination, I apply a **Grid Search** (through Scikit Learns **GridSearchCV**). I give as input the hyperparameters it should experiment with, and then it trains and evaluates using cross validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [\n",
    "    {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]}, # 12 combinations \n",
    "    {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]}, # 6 combinations\n",
    "  ]\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state=42)\n",
    "grid_search = GridSearchCV(forest_reg, \n",
    "                           param_grid, \n",
    "                           cv=5,\n",
    "                           scoring='neg_mean_squared_error', \n",
    "                           return_train_score=True)\n",
    "\n",
    "grid_search.fit(housing_prepared, housing_labels);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I try 18 combinations of hyperparameters, and I train across 5 folds of data. That means the above code trains and evaluate 90 models. How did it go?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63669.05791727153 {'max_features': 2, 'n_estimators': 3}\n",
      "55627.16171305252 {'max_features': 2, 'n_estimators': 10}\n",
      "53384.57867637289 {'max_features': 2, 'n_estimators': 30}\n",
      "60965.99185930139 {'max_features': 4, 'n_estimators': 3}\n",
      "52740.98248528835 {'max_features': 4, 'n_estimators': 10}\n",
      "50377.344409590376 {'max_features': 4, 'n_estimators': 30}\n",
      "58663.84733372485 {'max_features': 6, 'n_estimators': 3}\n",
      "52006.15355973719 {'max_features': 6, 'n_estimators': 10}\n",
      "50146.465964159885 {'max_features': 6, 'n_estimators': 30}\n",
      "57869.25504027614 {'max_features': 8, 'n_estimators': 3}\n",
      "51711.09443660957 {'max_features': 8, 'n_estimators': 10}\n",
      "49682.25345942335 {'max_features': 8, 'n_estimators': 30}\n",
      "62895.088889905004 {'bootstrap': False, 'max_features': 2, 'n_estimators': 3}\n",
      "54658.14484390074 {'bootstrap': False, 'max_features': 2, 'n_estimators': 10}\n",
      "59470.399594730654 {'bootstrap': False, 'max_features': 3, 'n_estimators': 3}\n",
      "52725.01091081235 {'bootstrap': False, 'max_features': 3, 'n_estimators': 10}\n",
      "57490.612956065226 {'bootstrap': False, 'max_features': 4, 'n_estimators': 3}\n",
      "51009.51445842374 {'bootstrap': False, 'max_features': 4, 'n_estimators': 10}\n"
     ]
    }
   ],
   "source": [
    "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": [
    "The best model was this one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 8, 'n_estimators': 30}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_ \n",
    "# Alternatively, try these: \n",
    "# grid_search.best_estimator_ \n",
    "# pd.DataFrame(grid_search.cv_results_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### 10. Fine-Tune Model - Randomized Search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The approach is much the same:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise-deprecating',\n",
       "          estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,\n",
       "           oob_score=False, random_state=42, verbose=0, warm_start=False),\n",
       "          fit_params=None, iid='warn', n_iter=10, n_jobs=None,\n",
       "          param_distributions={'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1a1ab4ea58>, 'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1a1ab4ecf8>},\n",
       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "          return_train_score='warn', scoring='neg_mean_squared_error',\n",
       "          verbose=0)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint\n",
    "\n",
    "param_distribs = {\n",
    "        'n_estimators': randint(low=1, high=200),\n",
    "        'max_features': randint(low=1, high=8),\n",
    "    }\n",
    "\n",
    "rnd_search = RandomizedSearchCV(forest_reg, \n",
    "                                param_distributions=param_distribs,\n",
    "                                n_iter=10, # build 10 random models within boundaries\n",
    "                                cv=5, \n",
    "                                scoring='neg_mean_squared_error', \n",
    "                                random_state=42)\n",
    "rnd_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49150.657232934034 {'max_features': 7, 'n_estimators': 180}\n",
      "51389.85295710133 {'max_features': 5, 'n_estimators': 15}\n",
      "50796.12045980556 {'max_features': 3, 'n_estimators': 72}\n",
      "50835.09932039744 {'max_features': 5, 'n_estimators': 21}\n",
      "49280.90117886215 {'max_features': 7, 'n_estimators': 122}\n",
      "50774.86679035961 {'max_features': 3, 'n_estimators': 75}\n",
      "50682.75001237282 {'max_features': 3, 'n_estimators': 88}\n",
      "49608.94061293652 {'max_features': 5, 'n_estimators': 100}\n",
      "50473.57642831875 {'max_features': 3, 'n_estimators': 150}\n",
      "64429.763804893395 {'max_features': 5, 'n_estimators': 2}\n"
     ]
    }
   ],
   "source": [
    "# How did models perform?\n",
    "cvres = rnd_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": [
    "<br>\n",
    "\n",
    "### 11. Choose final model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I choose the best model from the Grid Search approach as my final model. Then, I apply my pipeline on the test data and test performance there:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47730.22690385927"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model = grid_search.best_estimator_\n",
    "\n",
    "X_test = strat_test_set.drop(\"median_house_value\", axis=1)\n",
    "y_test = strat_test_set[\"median_house_value\"].copy()\n",
    "\n",
    "X_test_prepared = full_pipeline.transform(X_test)\n",
    "final_predictions = final_model.predict(X_test_prepared)\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "final_mse = mean_squared_error(y_test, final_predictions)\n",
    "final_rmse = np.sqrt(final_mse)\n",
    "final_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, I compute a 95% confidence interval for the test RMSE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([45685.10470776, 49691.25001878])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "confidence = 0.95\n",
    "squared_errors = (final_predictions - y_test) ** 2\n",
    "mean = squared_errors.mean()\n",
    "m = len(squared_errors)\n",
    "\n",
    "np.sqrt(stats.t.interval(confidence, m - 1,\n",
    "                         loc=np.mean(squared_errors),\n",
    "                         scale=stats.sem(squared_errors)))"
   ]
  }
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