{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "423fa464",
   "metadata": {},
   "source": [
    "# Lesson 16 solution: feature engineering\n",
    "\n",
    "This notebook is a worked solution to the lesson 16 feature engineering activity. The goal is to improve linear regression performance on the California housing dataset by engineering better features for the `housing_df` dataframe.\n",
    "\n",
    "The approach uses three complementary techniques:\n",
    "\n",
    "1. Ratio and interaction features that express relationships the raw averages do not.\n",
    "2. Log transforms to tame strongly right-skewed features.\n",
    "3. Geographic distance features that turn raw latitude and longitude into a signal a linear model can use directly.\n",
    "\n",
    "The Model evaluation section is unchanged from the activity so that leaderboard scoring keeps working."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e57dc48",
   "metadata": {},
   "source": [
    "### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "31c2d1e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# Set random seed for reproducibility\n",
    "np.random.seed(315)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e89f5fd",
   "metadata": {},
   "source": [
    "### Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9eef4316",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load California housing dataset\n",
    "original_housing_df = pd.read_csv('https://media.githubusercontent.com/media/gperdrizet/fullstack-2605/refs/heads/main/data/california_housing.csv')\n",
    "housing_df = original_housing_df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcd8e1df",
   "metadata": {},
   "source": [
    "## Task 1: Explore the dataset\n",
    "\n",
    "Before deciding what to engineer, look at the shape of the data, check for missing values, inspect the distributions, and see how each feature relates to the target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2d9dbba7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (20640, 9)\n",
      "\n",
      "Columns: ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude', 'MedHouseVal']\n",
      "\n",
      "Missing values per column:\n",
      "MedInc         0\n",
      "HouseAge       0\n",
      "AveRooms       0\n",
      "AveBedrms      0\n",
      "Population     0\n",
      "AveOccup       0\n",
      "Latitude       0\n",
      "Longitude      0\n",
      "MedHouseVal    0\n",
      "dtype: int64\n"
     ]
    },
    {
     "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>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>MedHouseVal</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>20640.000000</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>3.870671</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>5.429000</td>\n",
       "      <td>1.096675</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>3.070655</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>-119.569704</td>\n",
       "      <td>2.068558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.899822</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2.474173</td>\n",
       "      <td>0.473911</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>10.386050</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>2.003532</td>\n",
       "      <td>1.153956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.499900</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>-124.350000</td>\n",
       "      <td>0.149990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.563400</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>4.440716</td>\n",
       "      <td>1.006079</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>2.429741</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>-121.800000</td>\n",
       "      <td>1.196000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.534800</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.229129</td>\n",
       "      <td>1.048780</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>2.818116</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>-118.490000</td>\n",
       "      <td>1.797000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.743250</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>6.052381</td>\n",
       "      <td>1.099526</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>3.282261</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>-118.010000</td>\n",
       "      <td>2.647250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.000100</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>141.909091</td>\n",
       "      <td>34.066667</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>1243.333333</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>-114.310000</td>\n",
       "      <td>5.000010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             MedInc      HouseAge      AveRooms     AveBedrms    Population  \\\n",
       "count  20640.000000  20640.000000  20640.000000  20640.000000  20640.000000   \n",
       "mean       3.870671     28.639486      5.429000      1.096675   1425.476744   \n",
       "std        1.899822     12.585558      2.474173      0.473911   1132.462122   \n",
       "min        0.499900      1.000000      0.846154      0.333333      3.000000   \n",
       "25%        2.563400     18.000000      4.440716      1.006079    787.000000   \n",
       "50%        3.534800     29.000000      5.229129      1.048780   1166.000000   \n",
       "75%        4.743250     37.000000      6.052381      1.099526   1725.000000   \n",
       "max       15.000100     52.000000    141.909091     34.066667  35682.000000   \n",
       "\n",
       "           AveOccup      Latitude     Longitude   MedHouseVal  \n",
       "count  20640.000000  20640.000000  20640.000000  20640.000000  \n",
       "mean       3.070655     35.631861   -119.569704      2.068558  \n",
       "std       10.386050      2.135952      2.003532      1.153956  \n",
       "min        0.692308     32.540000   -124.350000      0.149990  \n",
       "25%        2.429741     33.930000   -121.800000      1.196000  \n",
       "50%        2.818116     34.260000   -118.490000      1.797000  \n",
       "75%        3.282261     37.710000   -118.010000      2.647250  \n",
       "max     1243.333333     41.950000   -114.310000      5.000010  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Basic information about the dataset\n",
    "print('Shape:', housing_df.shape)\n",
    "print('\\nColumns:', list(housing_df.columns))\n",
    "print('\\nMissing values per column:')\n",
    "print(housing_df.isna().sum())\n",
    "\n",
    "housing_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4ac48da3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness of each feature (largest first):\n",
      "AveOccup      97.639561\n",
      "AveBedrms     31.316956\n",
      "AveRooms      20.697869\n",
      "Population     4.935858\n",
      "MedInc         1.646657\n",
      "Latitude       0.465953\n",
      "HouseAge       0.060331\n",
      "Longitude     -0.297801\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
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fCFe/fn0cO3ZMo9zatWuL9HhSD4dTq1atGho0aCA95trd3R2dO3fGJ598ojVpdevWLen/SUlJePXVVzF48GAMHDhQ42fq1KkAID2pVd176uOPP9bY38qVKzVe29raYsCAAfi///s/XLhwodjjExFZg0ePHmH79u3o1atXkc/SgQMHIiYmBvfv38e3334LGxsbDBw4ELt27cKXX36JJ0+eFHlS3aBBg/D333/j008/1Xqs0jzZevr06bh9+7a0j549e6KgoACrVq3SKLd8+XLIZDL06NFDKgeUrt16Hrm5uXj8+LHGsvr168PR0VFqz6hiY48nolLQNbxArVOnTnjzzTcRHx+PtLQ0hISEwM7ODpcvX8a2bduQkJCAgQMHombNmnjnnXcQHx+PXr16oWfPnjh37hy+++471KhRQ2/1bdCgAd577z3Mnz8fr776Kvr37w+5XI7Tp0/Dy8sL8fHxejsWEVFF0bJlS0RGRmLt2rXIzs5Gp06dcOrUKXz++ecIDw+XJqAFnj6+ety4cRgwYAC6deuGn376Cfv37y/yWd+0aVN07twZ/v7+cHNzw5kzZ/D1118jJiZGKrN69Wq88sor8PPzw5gxY1CvXj1kZWUhJSUFf/31F3766SecPHkSV65c0djuWS+88ALatGmDpKQkTJ8+Hf7+/hgwYABWrFiB27dvo3379jh69Ch+//13AJrDMhYuXIjDhw8jICAAY8aMQdOmTXHnzh2cPXsWBw8exJ07d/R5momITOrbb7/F/fv30adPH63r27dvj5o1ayIpKQmDBw/G4MGDsXLlSsydOxd+fn4aPYsA4I033sDWrVsxbtw4HD58GC+//DIKCgrw22+/YevWrdi/f7/WOQaf1aNHDzRv3hzLli1DdHQ0evfujS5duuC9997DH3/8gZYtW+LAgQP45ptvMGnSJNSvXx9A2dqt5/H7778jKCgIgwYNQtOmTVGpUiXs2LEDWVlZiIiI0MsxyMKZ7oF6ROZJ/Wjt06dPF1vOx8enyCOw165dK/z9/UXlypWFo6Oj8PPzE9OmTRM3btyQyhQUFIi4uDhRq1YtUblyZdG5c2dx4cKFIo/YPnz4sAAgtm3bJi2bO3euACBu3bqltc7p6ekay9etWydat24t5HK5cHV1FZ06dZIeCUtERCV/5nfq1Ek0a9ZMeq1UKkVcXJzw9fUVdnZ2wtvbW8ycOVM8fvxYY7uCggIxffp0UaNGDVGlShURGhoqrly5UuSzfsGCBaJdu3bCxcVFVK5cWTRu3Fi8//77Ij8/X2N/V69eFcOHDxeenp7Czs5OvPDCC6JXr17i66+/FkIIMWHCBAFAXL16VWessbGxAoD46aefhBBCPHz4UERHRws3NzdRrVo1ER4eLi5duiQAiIULF2psm5WVJaKjo4W3t7ews7MTnp6eIigoSKxdu7bkk0xEZEF69+4tHBwcxMOHD3WWGTFihLCzsxP//POPUKlUwtvbWwAQCxYs0Fo+Pz9fLFq0SDRr1kz6Xu7v7y/i4uLEvXv3pHLari/UNmzYIACI9evXCyGEuH//vpg8ebLw8vISdnZ2omHDhmLJkiVCpVJpbFfadkvXsQGI6OhojWXp6ekCgFiyZIkQQoh//vlHREdHi8aNG4uqVasKZ2dnERAQILZu3arzHFLFIhOC426IiIiICEhLS0Pr1q2xceNGDBs2zNTVISIiIivAOZ6IiIiIKqBHjx4VWbZixQrY2NigY8eOJqgRERERWSPO8URERERUAS1evBipqano0qULKlWqhO+++w7fffcdxo4dW+Tx3kRERETlxaF2RERERBWQQqFAXFwcfvnlFzx48AB16tTBG2+8gffeew+VKvHeJBEREekHE09ERERERERERGQQnOOJiIiIiIiIiIgMgoknIiIiIiIiIiIyiAo9gF+lUuHGjRtwdHSETCYzdXWIiExGCIH79+/Dy8sLNja8J8H2gYjoKbYPRbGNICJ6qrRtRIVOPN24cYNPbSEiesaff/6J2rVrm7oaJsf2gYhIE9uHf7GNICLSVFIbUaETT46OjgCeniQnJycolUocOHAAISEhsLOzM3Htng9jMU/WEou1xAEwFrWcnBx4e3tLn4sVXeH2QRtr+t0pDuO0HhUhRoBx6hvbh6JK00YUZm2/l4zHvFlbPID1xWQt8ZS2jajQiSd111gnJycp8VSlShU4OTlZ9JsPgLGYKWuJxVriABhLYRwy8FTh9kEba/rdKQ7jtB4VIUaAcRoK24d/laaNKMzafi8Zj3mztngA64vJ2uIpqY3gQG0iIiIiIiIiIjIIJp6IiIiIiIiIiMggmHgiIiIiIiIiIiKDYOKJiIiIiIiIiIgMgoknIiIiIiIiIiIyCCaeiIiIiIiIiIjIIJh4IiIiIiIiIiIig6hk6gqQ+ag7Y4/W5X8sDDNyTYiIiIiovPidzvLwPSMia8YeT0REREREREREZBBMPBERERERERERkUFwqJ2Vax67H3kFMo1l7LJLRERERERERMbAHk9ERERERGRQx44dQ+/eveHl5QWZTIadO3dqrB8xYgRkMpnGT/fu3TXK3LlzB8OGDYOTkxNcXFwQFRWFBw8eaJQ5f/48Xn31VTg4OMDb2xuLFy8uUpdt27ahcePGcHBwgJ+fH/bu3av3eImI6F9MPBERERERkUE9fPgQLVu2xOrVq3WW6d69OzIyMqSfr776SmP9sGHDcPHiRSgUCuzevRvHjh3D2LFjpfU5OTkICQmBj48PUlNTsWTJEsTGxmLt2rVSmRMnTmDIkCGIiorCuXPnEB4ejvDwcFy4cEH/QRMREQAOtSMiIiIiIgPr0aMHevToUWwZuVwOT09Pret+/fVX7Nu3D6dPn0bbtm0BACtXrkTPnj3x4YcfwsvLC0lJScjPz8e6detgb2+PZs2aIS0tDcuWLZMSVAkJCejevTumTp0KAJg/fz4UCgVWrVqFxMREPUZMRERq7PFEREREREQmd+TIEbi7u6NRo0YYP348bt++La1LSUmBi4uLlHQCgODgYNjY2ODkyZNSmY4dO8Le3l4qExoaikuXLuHu3btSmeDgYI3jhoaGIiUlxZChERFVaOzxREREREREJtW9e3f0798fvr6+uHr1Kt5991306NEDKSkpsLW1RWZmJtzd3TW2qVSpEtzc3JCZmQkAyMzMhK+vr0YZDw8PaZ2rqysyMzOlZc+WUe9Dm7y8POTl5Umvc3JyAABKpRJKpbJU8anL6SovtxXFbmduSorH0jAe82dtMVlLPKWtv94TT8eOHcOSJUuQmpqKjIwM7NixA+Hh4dL6ESNG4PPPP9fYJjQ0FPv27ZNe37lzBxMmTMCuXbtgY2ODAQMGICEhAdWqVZPKnD9/HtHR0Th9+jRq1qyJCRMmYNq0afoOh4iIiIiIDCwiIkL6v5+fH1q0aIH69evjyJEjCAoKMmHNgPj4eMTFxRVZfuDAAVSpUqVM+1IoFFqXL26nvby5T3yuKx5LxXjMn7XFZOnx5Obmlqqc3hNP6okDR40ahf79+2st0717d6xfv156LZfLNdYPGzYMGRkZUCgUUCqVGDlyJMaOHYtNmzYB+HfiwODgYCQmJuLnn3/GqFGj4OLiojHBIBERERERWZ569eqhRo0auHLlCoKCguDp6YmbN29qlHny5Anu3LkjzQvl6emJrKwsjTLq1yWV0TW3FADMnDkTU6ZMkV7n5OTA29sbISEhcHJyKlU8SqUSCoUC3bp1g52dXZH1zWP3a93uQmxoqfZvbCXFY2kYj/mztpisJR51D9CS6D3xZC4TB5JudWfsMXUViIiIiIh0+uuvv3D79m3UqlULABAYGIjs7GykpqbC398fAJCcnAyVSoWAgACpzHvvvQelUildyCkUCjRq1Aiurq5SmUOHDmHSpEnSsRQKBQIDA3XWRS6XF7lRDgB2dnZlvmDUtU1egUxneXNWnnNgzhiP+bO2mCw9ntLW3SSTixtj4kDSn7oz9mj9ISIiIiIqjQcPHiAtLQ1paWkAgPT0dKSlpeH69et48OABpk6dih9//BF//PEHDh06hL59+6JBgwYIDX3a46dJkybo3r07xowZg1OnTuGHH35ATEwMIiIi4OXlBQAYOnQo7O3tERUVhYsXL2LLli1ISEjQ6K00ceJE7Nu3D0uXLsVvv/2G2NhYnDlzBjExMUY/J0REFYXRJxc31sSB2pQ0MaC1TPAF/BuD3Eb7RIX6PIahWeP7YumxWEscAGMpvK2hFTcPoFKpxKxZs7B3717873//g7OzM4KDg7Fw4ULpogLgPIBERJbozJkz6NKli/RanQyKjIzEmjVrcP78eXz++efIzs6Gl5cXQkJCMH/+fI2eRklJSYiJiUFQUJD0+f/RRx9J652dnXHgwAFER0fD398fNWrUwJw5czRGRHTo0AGbNm3CrFmz8O6776Jhw4bYuXMnmjdvboSzQERUMRk98WTKiQNLOzGgpU/w9az5bVUG27exJzu0pvfFWmKxljgAxlLaiQGfV3HzAObm5uLs2bOYPXs2WrZsibt372LixIno06cPzpw5I5XjPIBERJanc+fOEEL3DdH9+7XPcfQsNzc36bNelxYtWuD7778vtsxrr72G1157rcTjERGRfhg98VSYoSYO1KakiQGtZYIv4N/JymafsUGeSvuY8edlrMkOrfF9sfRYrCUOgLGolXZiwOdV3DyAzs7ORZJmq1atQrt27XD9+nXUqVOH8wASEREREVkYkyeeDDVxoDalnRjQ0if4elaeSqZzssLnZexzZE3vi7XEYi1xAIzFXGO/d+8eZDIZXFxcAJQ8D2C/fv10zgO4aNEi3L17V2s7UdJQbG2saZhmcRin9agIMQKMEwDkttp7FpnzUGwiIrJeek88PXjwAFeuXJFeqycOdHNzg5ubG+Li4jBgwAB4enri6tWrmDZtms6JAxMTE6FUKrVOHBgXF4eoqChMnz4dFy5cQEJCApYvX67vcIiIyEQeP36M6dOnY8iQIdLjqg01D2Bph2JrY03DNIvDOK1HRYgRqNhxLm6nvWx5pkkw1lBsIiKyXnpPPJnLxIFERGS5lEolBg0aBCEE1qxZY/DjlTQUW1cdrWWYZnEYp/WoCDECjBMAmsdqny+pPNMkGGsoNhERWS+9J57MaeJAIiKyPOqk07Vr15CcnKyR+DHUPIClHYqtjTUN0ywO47QeFSFGoGLHqWuahfKcj4pwDomIyLBsTF0BIiIiNXXS6fLlyzh48CCqV6+usf7ZeQDVtM0DeOzYMY15SUozDyAREREREekfE09ERGQ0Dx48QFpaGtLS0gD8Ow/g9evXoVQqMXDgQJw5cwZJSUkoKChAZmYmMjMzkZ+fD0BzHsBTp07hhx9+0DoPoL29PaKionDx4kVs2bIFCQkJGkPpiIiIiIjIOEz+VDsiIqo4ipsHMDY2Ft9++y0AoFWrVhrbHT58GJ07dwbAeQCJiIiIiCwJE09ERGQ0Jc0DWNw6Nc4DSERERERkOTjUjoiIiIiIiIiIDII9nkjv6s7Yo3X5HwvDjFwTIiIiIiIiIjIl9ngiIiIiIiIiIiKDYOKJiIiIiIiIiIgMgoknIiIiIiIiIiIyCCaeiIiIiIiIiIjIIDi5uJUoPKG33FZgcTsTVYaIiIiIiIiICOzxREREREREREREBsIeTxamcM8mIiIiIiIiIiJzxR5PRERERERERERkEEw8ERERERERERGRQTDxREREREREBnXs2DH07t0bXl5ekMlk2Llzp7ROqVRi+vTp8PPzQ9WqVeHl5YXhw4fjxo0bGvuoW7cuZDKZxs/ChQs1ypw/fx6vvvoqHBwc4O3tjcWLFxepy7Zt29C4cWM4ODjAz88Pe/fuNUjMRET0FBNPRERERERkUA8fPkTLli2xevXqIutyc3Nx9uxZzJ49G2fPnsX27dtx6dIl9OnTp0jZefPmISMjQ/qZMGGCtC4nJwchISHw8fFBamoqlixZgtjYWKxdu1Yqc+LECQwZMgRRUVE4d+4cwsPDER4ejgsXLhgmcCIi4uTiRERERERkWD169ECPHj20rnN2doZCodBYtmrVKrRr1w7Xr19HnTp1pOWOjo7w9PTUup+kpCTk5+dj3bp1sLe3R7NmzZCWloZly5Zh7NixAICEhAR0794dU6dOBQDMnz8fCoUCq1atQmJioj5CJSKiQph4IiIiIiIis3Lv3j3IZDK4uLhoLF+4cCHmz5+POnXqYOjQoZg8eTIqVXp6SZOSkoKOHTvC3t5eKh8aGopFixbh7t27cHV1RUpKCqZMmaKxz9DQUI2hf4Xl5eUhLy9Pep2TkwPg6RBBpVJZqnjU5XSVl9uKYrczNyXFY2kYj/mztpisJZ7S1l/viadjx45hyZIlSE1NRUZGBnbs2IHw8HCpUrNmzcLevXvxv//9D87OzggODsbChQvh5eUl7aNu3bq4du2axn7j4+MxY8YM6fX58+cRHR2N06dPo2bNmpgwYQKmTZum73CIiIiIiMiIHj9+jOnTp2PIkCFwcnKSlr/11lto06YN3NzccOLECcycORMZGRlYtmwZACAzMxO+vr4a+/Lw8JDWubq6IjMzU1r2bJnMzEyd9YmPj0dcXFyR5QcOHECVKlXKFFvhnl1qi9tpL2/u80/pisdSMR7zZ20xWXo8ubm5pSqn98STevz2qFGj0L9//yKVUo/fbtmyJe7evYuJEyeiT58+OHPmjEbZefPmYcyYMdJrR0dH6f/q8dvBwcFITEzEzz//jFGjRsHFxUXqRktERERERJZFqVRi0KBBEEJgzZo1Guue7anUokUL2Nvb480330R8fDzkcrnB6jRz5kyNY+fk5MDb2xshISEaibHiKJVKKBQKdOvWDXZ2dkXWN4/dr3W7C7Gh5au0gZUUj6VhPObP2mKylnjUPUBLovfEk7mM3yYiIiIiIsuhTjpdu3YNycnJJSZ1AgIC8OTJE/zxxx9o1KgRPD09kZWVpVFG/Vp9XaGrjK7rDgCQy+VaE1t2dnZlvmDUtU1egUxneXNWnnNgzhiP+bO2mCw9ntLW3eRzPBlq/LY2JY3PtoRxlrrGfxcpZyM0/jWERu/t1n5sW+3ly3teLeF9KS1ricVa4gAYS+FtDa244dgAIITA3Llz8emnnyI7Oxsvv/wy1qxZg4YNG0pl7ty5gwkTJmDXrl2wsbHBgAEDkJCQgGrVqkllOBybiMiyqJNOly9fxuHDh1G9evUSt0lLS4ONjQ3c3d0BAIGBgXjvvfegVCqliyGFQoFGjRpJ1weBgYE4dOgQJk2aJO1HoVAgMDBQ/0EREREAEyeeDDl+W5vSjs8253GWusZ/6zK/rcowFSmH5x2jbs7vS1lZSyzWEgfAWEo7Pvt5FTccGwAWL16Mjz76CJ9//jl8fX0xe/ZshIaG4pdffoGDgwMAYNiwYcjIyIBCoYBSqcTIkSMxduxYbNq0CQCHYxMRmaMHDx7gypUr0uv09HSkpaXBzc0NtWrVwsCBA3H27Fns3r0bBQUF0pxLbm5usLe3R0pKCk6ePIkuXbrA0dERKSkpmDx5Ml5//XXpu//QoUMRFxeHqKgoTJ8+HRcuXEBCQgKWL18uHXfixIno1KkTli5dirCwMGzevBlnzpzB2rVrjXtCiIgqEJMlnkwxfruk8dmWMM5S1/jvwuQ2AvPbqjD7jA3yVNq77hpbeceoW8L7UlrWEou1xAEwFrXSjs9+XsUNxxZCYMWKFZg1axb69u0LAPjiiy/g4eGBnTt3IiIiAr/++iv27duH06dPo23btgCAlStXomfPnvjwww/h5eXF4dhERGbozJkz6NKli/Ra/Z08MjISsbGx+PbbbwEArVq10tju8OHD6Ny5M+RyOTZv3ozY2Fjk5eXB19cXkydP1vhu7+zsjAMHDiA6Ohr+/v6oUaMG5syZo/HZ36FDB2zatAmzZs3Cu+++i4YNG2Lnzp1o3ry5AaMnIqrYTJJ4Msb4bW1KOz7bnMdZ6hr/rbO8SlbmbQzlec+pOb8vZWUtsVhLHABjMYfY09PTkZmZieDgYGmZs7MzAgICkJKSgoiICKSkpMDFxUVKOgFAcHAwbGxscPLkSfTr169cw7HL86hsaxqmWRzGaT0qQowA4wR0T81gyqHYnTt3hhC6p4Aobh0AtGnTBj/++GOJx2nRogW+//77Ysu89tpreO2110rcFxER6YfRE0/GGr9NRESWRT2sorjHXGdmZkptgVqlSpXg5uamUaasw7Gf51HZ1jRMsziM03pUhBiBih2nrqkZyjPtgbGGYhMRkfXSe+LJXMZvExERlVZ5HpVtTcM0i8M4rUdFiBFgnIDuqRnKM+2BsYZiExGR9dJ74slcxm8TEZFlUQ+VzsrKQq1ataTlWVlZUpvh6emJmzdvamz35MkT3Llzp8RHZT97jMKe51HZ1jRMsziM03pUhBiBih2nrmkWynM+KsI5JCIiw9J74smcxm8TEZHl8PX1haenJw4dOiQlmnJycnDy5EmMHz8ewNOh1tnZ2UhNTYW/vz8AIDk5GSqVCgEBAVIZDscmIiIiIjIPNqauABERVRwPHjxAWloa0tLSAPw7HPv69euQyWSYNGkSFixYgG+//RY///wzhg8fDi8vL4SHhwMAmjRpgu7du2PMmDE4deoUfvjhB8TExCAiIgJeXl4Ang7Htre3R1RUFC5evIgtW7YgISFBo+csEREREREZh0meakdERBVTccOxN2zYgGnTpuHhw4cYO3YssrOz8corr2Dfvn1wcHCQtklKSkJMTAyCgoJgY2ODAQMG4KOPPpLWczg2EREREZH5YOKJiIiMpqTh2DKZDPPmzcO8efN0lnFzc8OmTZuKPQ6HYxMRERERmQcOtSMiIiIiIiIiIoNg4omIiIiIiIiIiAyCiSciIiIiIiIiIjIIJp6IiIiIiIiIiMggmHgiIiIiIiIiIiKDYOKJiIiIiIiIiIgMgoknIiIiIiIiIiIyCCaeiIiIiIiIiIjIIJh4IiIiIiIiIiIig2DiiYiIiIiIiIiIDIKJJyIiIiIiIiIiMggmnoiIiIiIiIiIyCCYeCIiIiIiIoM6duwYevfuDS8vL8hkMuzcuVNjvRACc+bMQa1atVC5cmUEBwfj8uXLGmXu3LmDYcOGwcnJCS4uLoiKisKDBw80ypw/fx6vvvoqHBwc4O3tjcWLFxepy7Zt29C4cWM4ODjAz88Pe/fu1Xu8RET0LyaeiIiIiMho6s7Yo/WHrNvDhw/RsmVLrF69Wuv6xYsX46OPPkJiYiJOnjyJqlWrIjQ0FI8fP5bKDBs2DBcvXoRCocDu3btx7NgxjB07Vlqfk5ODkJAQ+Pj4IDU1FUuWLEFsbCzWrl0rlTlx4gSGDBmCqKgonDt3DuHh4QgPD8eFCxcMFzwRUQVXydQVICIiIiIi69ajRw/06NFD6zohBFasWIFZs2ahb9++AIAvvvgCHh4e2LlzJyIiIvDrr79i3759OH36NNq2bQsAWLlyJXr27IkPP/wQXl5eSEpKQn5+PtatWwd7e3s0a9YMaWlpWLZsmZSgSkhIQPfu3TF16lQAwPz586FQKLBq1SokJiYa4UwQEVU8eu/xZE7daImIiIiIyLylp6cjMzMTwcHB0jJnZ2cEBAQgJSUFAJCSkgIXFxcp6QQAwcHBsLGxwcmTJ6UyHTt2hL29vVQmNDQUly5dwt27d6Uyzx5HXUZ9HCIi0j+993hSd6MdNWoU+vfvX2S9uhvt559/Dl9fX8yePRuhoaH45Zdf4ODgAOBpN9qMjAwoFAoolUqMHDkSY8eOxaZNmwD82402ODgYiYmJ+PnnnzFq1Ci4uLhodLclIiIiIiLzlpmZCQDw8PDQWO7h4SGty8zMhLu7u8b6SpUqwc3NTaOMr69vkX2o17m6uiIzM7PY42iTl5eHvLw86XVOTg4AQKlUQqlUlipGdTld5eW2otjtzE1J8VgaxmP+rC0ma4mntPXXe+LJXLrREhERERERPa/4+HjExcUVWX7gwAFUqVKlTPtSKBRaly9up728uU98riseS8V4zJ+1xWTp8eTm5paqnFHneCqpG21ERESJ3Wj79eunsxvtokWLcPfuXbi6uhozLIPgJJtEVBEVFBQgNjYWGzduRGZmJry8vDBixAjMmjULMpkMwNObGHPnzsWnn36K7OxsvPzyy1izZg0aNmwo7efOnTuYMGECdu3aBRsbGwwYMAAJCQmoVq2aqUIjIiIdPD09AQBZWVmoVauWtDwrKwutWrWSyty8eVNjuydPnuDOnTvS9p6ensjKytIoo35dUhn1em1mzpyJKVOmSK9zcnLg7e2NkJAQODk5lSpGpVIJhUKBbt26wc7Orsj65rH7tW53ITa0VPs3tpLisTSMx/xZW0zWEo+6B2hJjJp4MmY3Wm1K6iZrTt3ddHW3LfX2NkLjX3NQ3vNqTu/L87KWWKwlDoCxFN7W1BYtWoQ1a9bg888/R7NmzXDmzBmMHDkSzs7OeOuttwDoZ8g2ERkeb6JRafn6+sLT0xOHDh2SEk05OTk4efIkxo8fDwAIDAxEdnY2UlNT4e/vDwBITk6GSqVCQECAVOa9996DUqmULuQUCgUaNWokXR8EBgbi0KFDmDRpknR8hUKBwMBAnfWTy+WQy+VFltvZ2ZX5glHXNnkFMp3lzVl5zoE5Yzzmz9pisvR4Slv3CvVUu9J2kzWH7m66utuW1fy2Kv3sSA+et6uwObwv+mItsVhLHABjKW03WUM7ceIE+vbti7CwMABA3bp18dVXX+HUqVMA9Ddkm4iIjOvBgwe4cuWK9Do9PR1paWlwc3NDnTp1MGnSJCxYsAANGzaUbip4eXkhPDwcANCkSRN0794dY8aMQWJiIpRKJWJiYhARESF9rg8dOhRxcXGIiorC9OnTceHCBSQkJGD58uXScSdOnIhOnTph6dKlCAsLw+bNm3HmzBmsXbvWqOeDiKgiMWriyZjdaLUpqZusOXV309XdtrTkNgLz26ow+4wN8lTa76CYi5K6EJvT+/K8rCUWa4kDYCxqpe0ma2gdOnTA2rVr8fvvv+PFF1/ETz/9hOPHj2PZsmUA9Ddku7DyTBxrTb3lisM4rYexYyxr72191asivJdA8XHqc6JqfZ3HM2fOoEuXLtJr9XfyyMhIbNiwAdOmTcPDhw8xduxYZGdn45VXXsG+ffuknqwAkJSUhJiYGAQFBUnDqD/66CNpvbOzMw4cOIDo6Gj4+/ujRo0amDNnjsYcsB06dMCmTZswa9YsvPvuu2jYsCF27tyJ5s2b6yVOIiIqyqiJJ2N2o9WmtN1kzaG7m67utmXej0qmt30ZSmnPtTm8L/piLbFYSxwAYzGX2GfMmIGcnBw0btwYtra2KCgowPvvv49hw4YB0N+Q7cKeZ+JYa+otVxzGaT2MFWNZe2/rexLlivBeAtrj1OdE1frqEdu5c2cIoTsZKZPJMG/ePMybN09nGTc3txKHTLdo0QLff/99sWVee+01vPbaa8VXmIiI9EbviSdz6UZLRESWZ+vWrUhKSsKmTZukJ5ZOmjQJXl5eiIyMNNhxyzNxrDX1lisO47Qexo6xrL239TWJckV4L4Hi49TnRNXm0iOWiIgsl94TT+bSjZaIiCzP1KlTMWPGDERERAAA/Pz8cO3aNcTHxyMyMlJvQ7YLe56JY62pt1xxGKf1MFaMZe1xre86VYT3EtAepz4nqq4I55CIiAxL74knc+pGS0REliU3Nxc2NjYay2xtbaFSPX1Qgr6GbBMRERERkXFUqKfaERGReevduzfef/991KlTB82aNcO5c+ewbNkyjBo1CsDTmxf6GLJNlqHujD1al/+xMMzINSEiIiKi8mLiiUyOFxZEpLZy5UrMnj0b//nPf3Dz5k14eXnhzTffxJw5c6Qy+hiyTURERERExsHEExERmQ1HR0esWLECK1as0FlGX0O2iYiIiIjI8GxKLkJERERERERERFR2TDwREREREREREZFBMPFEREREREREREQGwcQTEREREREREREZBBNPRERERERERERkEEw8ERERERERERGRQTDxREREREREREREBsHEExERERERERERGUQlU1eAiIiIqCzqztijdfkfC8OMXBMiIiIiKgkTT0RERET0XHQlA4mIiIg41I6IiIiIiIiIiAyCiSciIiIiIiIiIjIIJp6IiIiIiIiIiMggmHgis1V3xh7UnbEHzWP3AwCax+7nHBJEREREVqhu3bqQyWRFfqKjowEAnTt3LrJu3LhxGvu4fv06wsLCUKVKFbi7u2Pq1Kl48uSJRpkjR46gTZs2kMvlaNCgATZs2GCsEImIKixOLk5ERERERCZ1+vRpFBQUSK8vXLiAbt264bXXXpOWjRkzBvPmzZNeV6lSRfp/QUEBwsLC4OnpiRMnTiAjIwPDhw+HnZ0dPvjgAwBAeno6wsLCMG7cOCQlJeHQoUMYPXo0atWqhdDQUCNESURUMTHxREREREREJlWzZk2N1wsXLkT9+vXRqVMnaVmVKlXg6empdfsDBw7gl19+wcGDB+Hh4YFWrVph/vz5mD59OmJjY2Fvb4/ExET4+vpi6dKlAIAmTZrg+PHjWL58ORNPREQGZJKhdsbqSktERERERJYlPz8fGzduxKhRoyCTyaTlSUlJqFGjBpo3b46ZM2ciNzdXWpeSkgI/Pz94eHhIy0JDQ5GTk4OLFy9KZYKDgzWOFRoaipSUFANHRERUsZmkx5MxutISEREREZHl2blzJ7KzszFixAhp2dChQ+Hj4wMvLy+cP38e06dPx6VLl7B9+3YAQGZmpkbSCYD0OjMzs9gyOTk5ePToESpXrqy1Pnl5ecjLy5Ne5+TkAACUSiWUSmWpYlKX01VebiuK3c7clBSPpWE85s/aYrKWeEpbf5MknozRlZaIiCzT33//jenTp+O7775Dbm4uGjRogPXr16Nt27YAACEE5s6di08//RTZ2dl4+eWXsWbNGjRs2FDax507dzBhwgTs2rULNjY2GDBgABISElCtWjVThUVERKX03//+Fz169ICXl5e0bOzYsdL//fz8UKtWLQQFBeHq1auoX7++QesTHx+PuLi4IssPHDigcXO8NBQKhdbli9tpL793794y7d/YdMVjqRiP+bO2mCw9nmd7nhbH5HM8qbvSTpkypUhX2o0bN8LT0xO9e/fG7NmzpQ92XV1px48fj4sXL6J169Zaj1XS3QpzyjrquutR6u1thMa/lqxwLObw/pSXOf2OPQ9riQNgLIW3NbW7d+/i5ZdfRpcuXfDdd9+hZs2auHz5MlxdXaUyixcvxkcffYTPP/8cvr6+mD17NkJDQ/HLL7/AwcEBADBs2DBkZGRAoVBAqVRi5MiRGDt2LDZt2mSq0IiKpeuprX8sDDNyTYhM69q1azh48KDUk0mXgIAAAMCVK1dQv359eHp64tSpUxplsrKyAEC6me3p6Skte7aMk5OTzt5OADBz5kxMmTJFep2TkwNvb2+EhITAycmpVHEplUooFAp069YNdnZ2Rdarn+Jc2IVY85x7qqR4LA3jMX/WFpO1xKPOqZTE5IknQ3Wl1aa0dyvMIeuo665HWc1vq9LPjsyAOhZzv/NTGubwO6YP1hIHwFhKe7fC0BYtWgRvb2+sX79eWubr6yv9XwiBFStWYNasWejbty8A4IsvvoCHhwd27tyJiIgI/Prrr9i3bx9Onz4t9ZJauXIlevbsiQ8//FDjDjoRlY2uBBmRvqxfvx7u7u4ICys+6ZqWlgYAqFWrFgAgMDAQ77//Pm7evAl3d3cAT9tDJycnNG3aVCpT+HukQqFAYGBgsceSy+WQy+VFltvZ2ZX5glHXNnkFMi2lYfYXpOU5B+aM8Zg/a4vJ0uMpbd1NnngyZlfaku5WmFPWUdddj9KS2wjMb6vC7DM2yFNpb8gsReFYzPXOT2mY0+/Y87CWOADGolbauxWG9u233yI0NBSvvfYajh49ihdeeAH/+c9/MGbMGABPH4WdmZmpMTmss7MzAgICkJKSgoiICKSkpMDFxUVKOgFAcHAwbGxscPLkSfTr16/Iccszf4c19ZYrjinjLGvv3+epo6nfT2PM76KPGJ+3R3Zx9BWrqd9LYykuTn3+PhnzPKpUKqxfvx6RkZGoVOnfy5SrV69i06ZN6NmzJ6pXr47z589j8uTJ6NixI1q0aAEACAkJQdOmTfHGG29g8eLFyMzMxKxZsxAdHS0ljcaNG4dVq1Zh2rRpGDVqFJKTk7F161bs2cOEKhGRIZk08WTIrrTalPZuhTlkHXXd9SjzflQyve3L1NSxmPq90Qdz+B3TB2uJA2As5hL7//73P6xZswZTpkzBu+++i9OnT+Ott96Cvb09IiMjpV6t2nq9Pjt5rPput1qlSpXg5uams1fs88zfYU295YpjijjL2vtXHz1iTfV+GnN+l+eJUV89srXRd6wV+W9Tn79PxuwRe/DgQVy/fh2jRo3SWG5vb4+DBw9ixYoVePjwIby9vTFgwADMmjVLKmNra4vdu3dj/PjxCAwMRNWqVREZGanxsCJfX1/s2bMHkydPRkJCAmrXro3PPvsMoaGWe1OTiMgSmDTxZMiutEREZHlUKhXatm0rPaG0devWuHDhAhITExEZGWmw45Zn/g5r6i1XHFPGWdbev8/TI9bU76cx5nfRR4zP2yO7OPqK1dTvpbEUF6c+f5+M2SM2JCQEQhTtreXt7Y2jR4+WuL2Pj0+JybXOnTvj3Llz5a4jERGVnckST4buSkvWS9v8Epx8lcg61KpVq8gNhCZNmuD//u//APzbqzUrK0u6GaF+3apVK6nMzZs3Nfbx5MkT3LlzR2ev2OeZv8OaessVxxRxlrXHrj7qZ6r305jzuzxPjIbsRa3vWCvy36Y+f58qwjkkIiLDsjHVgUvqShsSEoLGjRvj7bffxoABA7Br1y6pjLorra2tLQIDA/H6669j+PDhGl1piYjI8rz88su4dOmSxrLff/8dPj4+AJ4Ok/D09MShQ4ek9Tk5OTh58qQ0OWxgYCCys7ORmpoqlUlOToZKpZKGbhMRERERkXGYrMeTMbrSEhGRZZk8eTI6dOiADz74AIMGDcKpU6ewdu1arF27FgAgk8kwadIkLFiwAA0bNoSvry9mz54NLy8vhIeHA3jaQ6p79+4YM2YMEhMToVQqERMTg4iICD7RjoiIiIjIyEz+VDsiIiK1l156CTt27MDMmTMxb948+Pr6YsWKFRg2bJhUZtq0aXj48CHGjh2L7OxsvPLKK9i3bx8cHBykMklJSYiJiUFQUBBsbGwwYMAAfPTRR6YIiYxI21BswLyGY+uqo7mxlHoSERGR+WPiiYiIzEqvXr3Qq1cvnetlMhnmzZtX7PBqNzc3bNq0yRDVIyIiIiKiMmDiycR4R5GIiIiIiIiIrJXJJhcnIiIiIiIiIiLrxh5PRERERGRyljBHFxEREZUdezwREREREREREZFBMPFEREREREREREQGwcQTEREREREREREZBOd4IiIiIpPjU16JiIiIrBMTT0RERGTVOGk1ERERkelwqB0RERERERERERkEezwRERERmSn21iIiIiJLxx5PRERERERERERkEOzxRFaBd4SJiMjcmNOE6XVn7IHcVmBxO6B57H7kFcgAsJ0kIiIiw2PiiYiIiMjC8IYLERERWQomnoiIiKhCejZ582xvoEvv9zJhrZ6POfWyIiIiIgKYeCIiIiKqsJioIiIiIkPj5OJEREREz6g7Y4/WH32VJ6KiYmNjIZPJNH4aN24srX/8+DGio6NRvXp1VKtWDQMGDEBWVpbGPq5fv46wsDBUqVIF7u7umDp1Kp48eaJR5siRI2jTpg3kcjkaNGiADRs2GCM8IqIKzSSJJ2M1LEREREREZBmaNWuGjIwM6ef48ePSusmTJ2PXrl3Ytm0bjh49ihs3bqB///7S+oKCAoSFhSE/Px8nTpzA559/jg0bNmDOnDlSmfT0dISFhaFLly5IS0vDpEmTMHr0aOzfv9+ocRIRVTQmG2rXrFkzHDx48N+KVPq3KpMnT8aePXuwbds2ODs7IyYmBv3798cPP/wA4N+GxdPTEydOnEBGRgaGDx8OOzs7fPDBB0aPhYiIiIiInk+lSpXg6elZZPm9e/fw3//+F5s2bULXrl0BAOvXr0eTJk3w448/on379jhw4AB++eUXHDx4EB4eHmjVqhXmz5+P6dOnIzY2Fvb29khMTISvry+WLl0KAGjSpAmOHz+O5cuXIzQ01KixEhFVJCZLPBm6YSEiIsu3cOFCzJw5ExMnTsSKFSsAPO0V+/bbb2Pz5s3Iy8tDaGgoPv74Y3h4eEjbXb9+HePHj8fhw4dRrVo1REZGIj4+XuMmBxERmZfLly/Dy8sLDg4OCAwMRHx8POrUqYPU1FQolUoEBwdLZRs3bow6deogJSUF7du3R0pKCvz8/DTagtDQUIwfPx4XL15E69atkZKSorEPdZlJkyYVW6+8vDzk5eVJr3NycgAASqUSSqWyVLGpy+kqL7cVxW5nbkqKx9IwHvNnbTFZSzylrb/JvoEbumEhIiLLdvr0aXzyySdo0aKFxnL2iiUisj4BAQHYsGEDGjVqhIyMDMTFxeHVV1/FhQsXkJmZCXt7e7i4uGhs4+HhgczMTABAZmamxrWBer16XXFlcnJy8OjRI1SuXFlr3eLj4xEXF1dk+YEDB1ClSpUyxalQKLQuX9xOe/m9e/eWaf/GpiseS8V4zJ+1xWTp8eTm5paqnEkST8ZoWLQp6W6FKbKOuu5uPPd+bYTGv5bseWIxtwyytWW2LT0OgLEU3tZcPHjwAMOGDcOnn36KBQsWSMvZK9bycdJtItKmR48e0v9btGiBgIAA+Pj4YOvWrToTQsYyc+ZMTJkyRXqdk5MDb29vhISEwMnJqVT7UCqVUCgU6NatG+zs7Iqsbx6rfZ6pC7HmOQSwpHgsDeMxf9YWk7XEo86plMQkiSdTNSylvVthzKyjrrsb+jK/rcqwBzCi8sRirneJLD2zrWYtcQCMpbR3K4wlOjoaYWFhCA4O1kg8GapXbHmGUVhT0rI4+o7TUDdcnldpbnKUdYiMubHUm1Jl/d3j36Z+h22Z6jy6uLjgxRdfxJUrV9CtWzfk5+cjOztb4+Z0VlaWNHWHp6cnTp06pbEP9cOJni1T+IFFWVlZcHJyKvYaRC6XQy6XF1luZ2dX5gtGXdvkFch0ljdn5TkH5ozxmD9ri8nS4ylt3c1isgtDNCzalHS3whRZR113N56X3EZgflsVZp+xQZ5Ke0NmKZ4nFnO7S2QtmW1riQNgLGqlvVthDJs3b8bZs2dx+vTpIusM1Sv2eYZRWFPSsjj6itPQN1yeV3E3OXTdzDD3mAqztJtS5b2JVJH/NvU5bMtUNyYePHiAq1ev4o033oC/vz/s7Oxw6NAhDBgwAABw6dIlXL9+HYGBgQCAwMBAvP/++7h58ybc3d0BPD03Tk5OaNq0qVSm8DlQKBTSPoiIyDDMIvFkiIZFm9LerTBm1lHX3Q297V8lM/gxjKU8seh6H3UN9fhjYViZ61Uelp7ZVrOWOADGYi6x//nnn5g4cSIUCgUcHByMdtzyDKOwpqRlcfQdp6FuuDwva7pho4u1xajr5hL/NvU7bMtYNybeeecd9O7dGz4+Prhx4wbmzp0LW1tbDBkyBM7OzoiKisKUKVPg5uYGJycnTJgwAYGBgWjfvj0AICQkBE2bNsUbb7yBxYsXIzMzE7NmzUJ0dLT0/X/cuHFYtWoVpk2bhlGjRiE5ORlbt27Fnj0cAkxEZEgmSTwZo2EhIiLLk5qaips3b6JNmzbSsoKCAhw7dgyrVq3C/v37DdIr9nmGUVhT0rI4+orT3G+GWNMNG12sJUb+bT6lLU59Dtsy1jn866+/MGTIENy+fRs1a9bEK6+8gh9//BE1a9YEACxfvhw2NjYYMGCAxhNN1WxtbbF7926MHz8egYGBqFq1KiIjIzFv3jypjK+vL/bs2YPJkycjISEBtWvXxmeffYbQUPPqIU9EZG1MkngyRsNCRESWJygoCD///LPGspEjR6Jx48aYPn06vL29DdYrloiITGfz5s3FrndwcMDq1auxevVqnWV8fHxKHE7YuXNnnDt3rlx1JCKi8jFJ4slYDQsREVkWR0dHNG/eXGNZ1apVUb16dWk5e8USEREREVkOs5jjiYiIqLTYK5aIAN3zNV6eH6KX/Rhr3kciIiJrx8QTERGZtSNHjmi8Zq9YIjIVJqmIiIjKjoknIiIiIqJCdCWZiIiIqGxsTF0BIiIiIiIiIiKyTkw8ERERERERERGRQTDxREREREREREREBsE5nsiqcX4GIiIiIiIiItNhjyciIiIiIiIiIjIIJp6IiIiIiIiIiMggONSOiIiI9I5DnclUmsfux+J2T//NK5BJy/9YGGbCWhEREVVcTDwRERERkdUzZDJU176Z7CIiIuJQOyIiIiIiIiIiMhD2eDISDjkgIiIiIiIiooqGPZ6IiIiIiIiIiMggmHgiIiIiIiIiIiKD4FA7IiIikhQeGi63FVjczkSVISIiIiKLx8QT0TP4VBoiIu0KP5pejZ+PRERERFQcJp6IiIiIiIjMEG+KEpE14BxPREREZqrujD1af4jIMvBvuPTi4+Px0ksvwdHREe7u7ggPD8elS5c0ynTu3BkymUzjZ9y4cRplrl+/jrCwMFSpUgXu7u6YOnUqnjx5olHmyJEjaNOmDeRyORo0aIANGzYYOjwiogrNJIknYzYsREREZDi8sCYifTh69Ciio6Px448/QqFQQKlUIiQkBA8fPtQoN2bMGGRkZEg/ixcvltYVFBQgLCwM+fn5OHHiBD7//HNs2LABc+bMkcqkp6cjLCwMXbp0QVpaGiZNmoTRo0dj//79RouViKiiMclQO3XD8tJLL+HJkyd49913ERISgl9++QVVq1aVyo0ZMwbz5s2TXlepUkX6v7ph8fT0xIkTJ5CRkYHhw4fDzs4OH3zwgVHjIevHbs5ExhEfH4/t27fjt99+Q+XKldGhQwcsWrQIjRo1kso8fvwYb7/9NjZv3oy8vDyEhobi448/hoeHh1Tm+vXrGD9+PA4fPoxq1aohMjIS8fHxqFSJI8yJiMzRvn37NF5v2LAB7u7uSE1NRceOHaXlVapUgaenp9Z9HDhwAL/88gsOHjwIDw8PtGrVCvPnz8f06dMRGxsLe3t7JCYmwtfXF0uXLgUANGnSBMePH8fy5csRGhpquACJiCowk/R42rdvH0aMGIFmzZqhZcuW2LBhA65fv47U1FSNcuqGRf3j5OQkrVM3LBs3bkSrVq3Qo0cPzJ8/H6tXr0Z+fr6xQyIiIj0ozR3vyZMnY9euXdi2bRuOHj2KGzduoH///tL60tzxJiIi83bv3j0AgJubm8bypKQk1KhRA82bN8fMmTORm5srrUtJSYGfn5/GjYjQ0FDk5OTg4sWLUpng4GCNfYaGhiIlJcVQoRARVXhmceu3uIZl48aN8PT0RO/evTF79myp15OuhmX8+PG4ePEiWrduXeQ4eXl5yMvLk17n5OQAAJRKpfSjfq1vcluh930WezwbofGvJTPnWMr6u2LI3zFjspY4AMZSeFtTK+mO97179/Df//4XmzZtQteuXQEA69evR5MmTfDjjz+iffv2pbrjTURE5kulUmHSpEl4+eWX0bx5c2n50KFD4ePjAy8vL5w/fx7Tp0/HpUuXsH37dgBAZmamxrUBAOl1ZmZmsWVycnLw6NEjVK5cuUh9SrqGKI2S2uiyXiuYut22pu9PAOOxBNYWk7XEU9r6mzzxZMiGpbD4+HjExcUVWX7gwAGNYXwKheK54ypscTu977JU5rdVmebABmCOsezdu7dc2xnid8wUrCUOgLE8e8fYnBS+MZGamgqlUqlxt7px48aoU6cOUlJS0L59e4PcmNDGGF8YdF2IlOWYzWPLNm+J3LbQazNO/utTRYizIsQIWEac+vjcKO4zSB+fHc+zzfOKjo7GhQsXcPz4cY3lY8eOlf7v5+eHWrVqISgoCFevXkX9+vUNVp/SXkOUhq42uqzXCuX9Dqpv1vT9CWA8lsDaYrL0eEp7DWHyxJMxG5aZM2diypQp0uucnBx4e3sjJCQETk5OUCqVUCgU6NatG+zs7MoXkA5l/eL/vOQ2AvPbqjD7jA3yVDKjHlvfzDmWC7FlmwvAkL9jxmQtcQCMRU2daDEn2m5MZGZmwt7eHi4uLhplPTw8SrybrV6nzfNcVBjyC4OuC5GyXHDo68aHOSb/DaEixFkRYgTMO059Jg20fQbp47NDzdg3JmJiYrB7924cO3YMtWvXLrZsQEAAAODKlSuoX78+PD09cerUKY0yWVlZACDNC+Xp6Skte7aMk5OT1t5OQMnXEKVRUhtd1muFsn4H1Tdr+v4EMB5LYG0xWUs8pb2GMGniydANS2FyuRxyubzIcjs7O403u/BrfcgrME3CJE8lM9mx9c0cYynv74khfsdMwVriABiLOcau68aEIZTnokKfXxgMecHxvDc+zDn5r08VIc6KECNg2XGW5W+7uM8gXX/35UlWGOvGhBACEyZMwI4dO3DkyBH4+vqWuE1aWhoAoFatWgCAwMBAvP/++7h58ybc3d0BPE3MOTk5oWnTplKZwgk4hUKBwMBAnccp7TVEaejapqzfcc2l3bam708A47EE1haTpcdT2rqbJPFkrIaFiIgsk64bE56ensjPz0d2drZGr6esrCyNu9mGujGhjT6+MBjygkNfCXtzTP4bQkWIsyLECFhmnOX5LNH2GaQr7vLu3xiio6OxadMmfPPNN3B0dJR6qDo7O6Ny5cq4evUqNm3ahJ49e6J69eo4f/48Jk+ejI4dO6JFixYAgJCQEDRt2hRvvPEGFi9ejMzMTMyaNQvR0dHSZ/y4ceOwatUqTJs2DaNGjUJycjK2bt2KPXu0P8GYiIien0kST8ZqWIiIyLKUdGPC398fdnZ2OHToEAYMGAAAuHTpEq5fvy7dreaNCSKyVHVnaE9+/LEwzMg1Mb41a9YAADp37qyxfP369RgxYgTs7e1x8OBBrFixAg8fPoS3tzcGDBiAWbNmSWVtbW2xe/dujB8/HoGBgahatSoiIyMxb948qYyvry/27NmDyZMnIyEhAbVr18Znn32G0FDTDl0jIrJmJkk8GathISIiy1LSjQlnZ2dERUVhypQpcHNzg5OTEyZMmIDAwEC0b98eQMW9MaHrgpWIyBIIUfxk8N7e3jh69GiJ+/Hx8SlxLqvOnTvj3LlzZaofERGVn8mG2hVHnw0LERFZjpJuTADA8uXLYWNjgwEDBiAvLw+hoaH4+OOPpbK8MUFEREREZD5M/lQ7IiIitZJuTACAg4MDVq9ejdWrV+ssY+03Jti7iYiIiIgshY2pK0BERERERERERNaJiSciIiIiIiIiIjIIDrUjeg4V+ekzREREZHrNY/cjr0Bm6mpQGXC4NBFVNEw86RkbEiIiIiLSJ23fL+W2AovbmaAyREREZcTEE5ERNY/dj8Xtit6dZA8pIiIiIiIiskZMPBEZgK6eb3JbI1eEiIiIiIiIyISYeCIyA5wrioiIiIiIiKwRn2pHREREREREREQGwcQTEREREREREREZBIfaEZkxbUPwOPyOiIiIiIiILAV7PBERERERERERkUEw8URERERERERERAbBoXZEFoZPwCMiIiIiIiJLwcQTERGRnulKEBMRERERVTQcakdERERERERERAbBHk9EVsJUQ/A49I+IiIiIiIh0YeKpnDiMgohMSdtnkNxWYHE7E1SGiIiIiIhIB4sfard69WrUrVsXDg4OCAgIwKlTp0xdJSIiMhNsI4iISBu2D0RExmPRPZ62bNmCKVOmIDExEQEBAVixYgVCQ0Nx6dIluLu7m7p6RGZBX0Phnt2PumdN89j9AGRlOq4+6kJUGmwjiIhIG7YPRETGZdGJp2XLlmHMmDEYOXIkACAxMRF79uzBunXrMGPGDBPXjsi8mdNwUc4TRYbANoKIiLRh+0BEZFwWm3jKz89HamoqZs6cKS2zsbFBcHAwUlJSTFgzIjI0JqqoJGwjiIhIG2tpH/hdiIgsicUmnv755x8UFBTAw8NDY7mHhwd+++03rdvk5eUhLy9Pen3v3j0AwJ07d6BUKqFUKpGbm4vbt2/Dzs6u2ONXevLwOSMwrEoqgdxcFSopbVCg0j4UylIwFvNjrDgavLNV+/HLWL44chuBWa1VaPXeduQZ4T05OTNIL/vR9hmkfl9K8xlW2P379wEAQgi91M/UytpGlNQ+aFNcm2HubURZWMvnVkkqQpwVIUaAcRbn9u3bZT5ORW8fgPK1EYU922YYso0oz3tcHmW5brIEjMf8WVtM1hJPadsIi008lUd8fDzi4uKKLPf19TVBbQxvqKkroEeMxfxYSxyAcWOpsdSw+3/eWO7fvw9nZ2e91MWSVLT2oays6e+9OBUhzooQI8A4dXmeNqiitg+AZbURhv6eQUSkTUlthMUmnmrUqAFbW1tkZWVpLM/KyoKnp6fWbWbOnIkpU6ZIr1UqFe7cuYPq1atDJpMhJycH3t7e+PPPP+Hk5GTQ+hsaYzFP1hKLtcQBMBY1IQTu378PLy8vA9XOuMraRpTUPmhjTb87xWGc1qMixAgwTn2r6O0DUL42ojBr+71kPObN2uIBrC8ma4mntG2ExSae7O3t4e/vj0OHDiE8PBzA00bg0KFDiImJ0bqNXC6HXC7XWObi4lKknJOTk0W/+c9iLObJWmKxljgAxgLAqu5kl7WNKG37oI01/e4Uh3Faj4oQI8A49akitw/A87URhVnb7yXjMW/WFg9gfTFZQzylaSMsNvEEAFOmTEFkZCTatm2Ldu3aYcWKFXj48KH0hAoiIqq42EYQEZE2bB+IiIzLohNPgwcPxq1btzBnzhxkZmaiVatW2LdvX5HJAomIqOJhG0FERNqwfSAiMi6LTjwBQExMjM5usWUll8sxd+7cIl1pLRFjMU/WEou1xAEwFmunzzaisIpyvhmn9agIMQKMk0rHkO2DNtb2fjEe82Zt8QDWF5O1xVMSmbCWZ6MSEREREREREZFZsTF1BYiIiIiIiIiIyDox8URERERERERERAbBxBMRERERERERERkEE09ERERERERERGQQFS7xtHr1atStWxcODg4ICAjAqVOnii2/bds2NG7cGA4ODvDz88PevXuNVFPd4uPj8dJLL8HR0RHu7u4IDw/HpUuXit1mw4YNkMlkGj8ODg5GqrFusbGxRerVuHHjYrcxx/cEAOrWrVskFplMhujoaK3lzek9OXbsGHr37g0vLy/IZDLs3LlTY70QAnPmzEGtWrVQuXJlBAcH4/LlyyXut6x/b8+ruDiUSiWmT58OPz8/VK1aFV5eXhg+fDhu3LhR7D7L8zuqDyW9JyNGjChSr+7du5e4X2O/J9bM2s6loT4HzElp2s/Hjx8jOjoa1atXR7Vq1TBgwABkZWWZqMbls2bNGrRo0QJOTk5wcnJCYGAgvvvuO2m9NcRY2MKFCyGTyTBp0iRpmTXEWVIbZA0xVgSW2l6U5jOzc+fORX5Hx40bZ6Ial8za/qZKuv4w9/dHH9897ty5g2HDhsHJyQkuLi6IiorCgwcPjBjFv/RxLaLtPV24cKGRI9G/CpV42rJlC6ZMmYK5c+fi7NmzaNmyJUJDQ3Hz5k2t5U+cOIEhQ4YgKioK586dQ3h4OMLDw3HhwgUj11zT0aNHER0djR9//BEKhQJKpRIhISF4+PBhsds5OTkhIyND+rl27ZqRaly8Zs2aadTr+PHjOsua63sCAKdPn9aIQ6FQAABee+01nduYy3vy8OFDtGzZEqtXr9a6fvHixfjoo4+QmJiIkydPomrVqggNDcXjx4917rOsf2/6UFwcubm5OHv2LGbPno2zZ89i+/btuHTpEvr06VPifsvyO6ovJb0nANC9e3eNen311VfF7tMU74m1ssZzaYjPAXNTmvZz8uTJ2LVrF7Zt24ajR4/ixo0b6N+/vwlrXXa1a9fGwoULkZqaijNnzqBr167o27cvLl68CMA6YnzW6dOn8cknn6BFixYay60lzuLaIGuJ0ZpZcntR2muOMWPGaPyOLl682EQ1Lh1r+psqzfWHOb8/+vjuMWzYMFy8eBEKhQK7d+/GsWPHMHbsWGOFoEFf1yLz5s3TeM8mTJhgjOoblqhA2rVrJ6Kjo6XXBQUFwsvLS8THx2stP2jQIBEWFqaxLCAgQLz55psGrWdZ3bx5UwAQR48e1Vlm/fr1wtnZ2XiVKqW5c+eKli1blrq8pbwnQggxceJEUb9+faFSqbSuN9f3BIDYsWOH9FqlUglPT0+xZMkSaVl2draQy+Xiq6++0rmfsv696VvhOLQ5deqUACCuXbums0xZf0cNQVsskZGRom/fvmXaj6nfE2ti7edSX58D5q5w+5mdnS3s7OzEtm3bpDK//vqrACBSUlJMVU29cHV1FZ999pnVxXj//n3RsGFDoVAoRKdOncTEiROFENbzXhbXBllLjNbOmtoLbdccz/7dWQJr/5sqfP1hSe9Peb57/PLLLwKAOH36tFTmu+++EzKZTPz9999Gq7s25b0W8fHxEcuXLzds5UygwvR4ys/PR2pqKoKDg6VlNjY2CA4ORkpKitZtUlJSNMoDQGhoqM7ypnLv3j0AgJubW7HlHjx4AB8fH3h7e2vc+TS1y5cvw8vLC/Xq1cOwYcNw/fp1nWUt5T3Jz8/Hxo0bMWrUKMhkMp3lzPU9eVZ6ejoyMzM1zruzszMCAgJ0nvfy/L2Zwr179yCTyeDi4lJsubL8jhrTkSNH4O7ujkaNGmH8+PG4ffu2zrKW8p5Ygop4LsvzOWAJCrefqampUCqVGnE2btwYderUsdg4CwoKsHnzZjx8+BCBgYFWF2N0dDTCwsKKfDewpjh1tUHWFKO1srb2Qtc1R1JSEmrUqIHmzZtj5syZyM3NNUX1Ss1a/6Z0XX9Y2vujVprvHikpKXBxcUHbtm2lMsHBwbCxscHJkyeNXuey0nUtsnDhQlSvXh2tW7fGkiVL8OTJE9NUUI8qmboCxvLPP/+goKAAHh4eGss9PDzw22+/ad0mMzNTa/nMzEyD1bOsVCoVJk2ahJdffhnNmzfXWa5Ro0ZYt24dWrRogXv37uHDDz9Ehw4dcPHiRdSuXduINdYUEBCADRs2oFGjRsjIyEBcXBxeffVVXLhwAY6OjkXKW8J7AgA7d+5EdnY2RowYobOMub4nhanPbVnOe3n+3ozt8ePHmD59OoYMGQInJyed5cr6O2os3bt3R//+/eHr64urV6/i3XffRY8ePZCSkgJbW9si5S3hPbEUFfFcludzwNxpaz8zMzNhb29f5AugJcb5888/IzAwEI8fP0a1atWwY8cONG3aFGlpaVYT4+bNm3H27FmcPn26yDpreS+La4OsJUZrZk3tha5rjqFDh8LHxwdeXl44f/48pk+fjkuXLmH79u0mrK1u1vw3pe36w9Len2eV5rtHZmYm3N3dNdZXqlQJbm5uZv+e6boWeeutt9CmTRu4ubnhxIkTmDlzJjIyMrBs2TIT1vb5VZjEk7WKjo7GhQsXSpxzJjAwEIGBgdLrDh06oEmTJvjkk08wf/58Q1dTpx49ekj/b9GiBQICAuDj44OtW7ciKirKZPV6Xv/973/Ro0cPeHl56Sxjru9JRaBUKjFo0CAIIbBmzZpiy5rr72hERIT0fz8/P7Ro0QL169fHkSNHEBQUZLJ6EVmK0raflqpRo0ZIS0vDvXv38PXXXyMyMhJHjx41dbX05s8//8TEiROhUCjM4mEphlJcG1S5cmUT1owqGl2fmc/OpePn54datWohKCgIV69eRf369Y1dzRJZ89+UtusPS3t/KorirkWmTJki/b9Fixawt7fHm2++ifj4eMjlcmNXVW8qzFC7GjVqwNbWtshTCbKysuDp6al1G09PzzKVN7aYmBjs3r0bhw8fLnMPGTs7O7Ru3RpXrlwxUO3Kx8XFBS+++KLOepn7ewIA165dw8GDBzF69OgybWeu74n63JblvJfn781Y1B/0165dg0KhKLa3kzYl/Y6aSr169VCjRg2d9TLn98TSVMRzWZ7PAXOmq/309PREfn4+srOzNcpbYpz29vZo0KAB/P39ER8fj5YtWyIhIcFqYkxNTcXNmzfRpk0bVKpUCZUqVcLRo0fx0UcfoVKlSvDw8LCKOAt7tg2ylvfSmllLe1GWa46AgAAAMLvvSbpYy99Uaa8/LOn9Kc13D09PzyIT9T958gR37twx2/esrNciAQEBePLkCf744w/jVNBAKkziyd7eHv7+/jh06JC0TKVS4dChQxq9Tp4VGBioUR4AFAqFzvLGIoRATEwMduzYgeTkZPj6+pZ5HwUFBfj5559Rq1YtA9Sw/B48eICrV6/qrJe5vifPWr9+Pdzd3REWFlam7cz1PfH19YWnp6fGec/JycHJkyd1nvfy/L0Zg/qD/vLlyzh48CCqV69e5n2U9DtqKn/99Rdu376ts17m+p5Yoop4LsvzOWCOSmo//f39YWdnpxHnpUuXcP36dYuKUxuVSoW8vDyriTEoKAg///wz0tLSpJ+2bdti2LBh0v+tIc7Cnm2DrOW9tGaW3l6U55ojLS0NAMzue5Iu1vI3VdrrD0t6f0rz3SMwMBDZ2dlITU2VyiQnJ0OlUklJNnNSnmuRtLQ02NjYFBlSaHFMOrW5kW3evFnI5XKxYcMG8csvv4ixY8cKFxcXkZmZKYQQ4o033hAzZsyQyv/www+iUqVK4sMPPxS//vqrmDt3rrCzsxM///yzqUIQQggxfvx44ezsLI4cOSIyMjKkn9zcXKlM4Vji4uLE/v37xdWrV0VqaqqIiIgQDg4O4uLFi6YIQfL222+LI0eOiPT0dPHDDz+I4OBgUaNGDXHz5k0hhOW8J2oFBQWiTp06Yvr06UXWmfN7cv/+fXHu3Dlx7tw5AUAsW7ZMnDt3TnrCwsKFC4WLi4v45ptvxPnz50Xfvn2Fr6+vePTokbSPrl27ipUrV0qvS/p7M3Yc+fn5ok+fPqJ27doiLS1N428nLy9PZxwl/Y6aIpb79++Ld955R6SkpIj09HRx8OBB0aZNG9GwYUPx+PFjnbGY4j2xVtZ4LvXxOWDuStN+jhs3TtSpU0ckJyeLM2fOiMDAQBEYGGjCWpfdjBkzxNGjR0V6ero4f/68mDFjhpDJZOLAgQNCCOuIUZvCT2+yhjhLaoOsIUZrZ8ntRUmfmVeuXBHz5s0TZ86cEenp6eKbb74R9erVEx07djRxzXWzxr8pXdcflvD+6OO7R/fu3UXr1q3FyZMnxfHjx0XDhg3FkCFDzC6e0lyLnDhxQixfvlykpaWJq1evio0bN4qaNWuK4cOHmyQefapQiSchhFi5cqWoU6eOsLe3F+3atRM//vijtK5Tp04iMjJSo/zWrVvFiy++KOzt7UWzZs3Enj17jFzjogBo/Vm/fr1UpnAskyZNkuL28PAQPXv2FGfPnjV+5QsZPHiwqFWrlrC3txcvvPCCGDx4sLhy5Yq03lLeE7X9+/cLAOLSpUtF1pnze3L48GGtv1Pq+qpUKjF79mzh4eEh5HK5CAoKKhKjj4+PmDt3rsay4v7ejB1Henq6zr+dw4cP64yjpN9RU8SSm5srQkJCRM2aNYWdnZ3w8fERY8aMKfIl1hzeE2tmbedSH58D5q407eejR4/Ef/7zH+Hq6iqqVKki+vXrJzIyMkxX6XIYNWqU8PHxEfb29qJmzZoiKChISjoJYR0xalM48WQNcZbUBllDjBWBpbYXJX1mXr9+XXTs2FG4ubkJuVwuGjRoIKZOnSru3btn2ooXwxr/pnRdf1jC+6OP7x63b98WQ4YMEdWqVRNOTk5i5MiR4v79+yaI5vmvRVJTU0VAQIBwdnYWDg4OokmTJuKDDz7QuLFsqWRCCPF8faaIiIiIiIiIiIiKqjBzPBERERERERERkXEx8URERERERERERAbBxBMRERERERERERkEE09ERERERERERGQQTDwREREREREREZFBMPFEREREREREREQGwcQTEREREREREREZBBNPRERERERERERkEEw8ERERERERERGRQTDxREREREREREREBsHEExERERERERERGQQTT0REREREREREZBBMPBERERERERERkUEw8URERERERERERAbBxBMRERERERERERkEE09ERERERERERGQQTDwREREREREREZFBMPFEZGE2bNgAmUyGP/74w9RVISIiA+ncuTM6d+6s133GxsZCJpPpdZ9EREREJWHiiazWxx9/DJlMhoCAAIMfq27dupDJZNKPg4MDGjZsiKlTp+LOnTsGPz4REZWNOon/7Of2iy++iJiYGGRlZZm6euWWm5uL2NhYHDlyxNRVISKyKsa8tgAApVKJjz76CC+99BIcHR1RrVo1vPTSS/joo4+gVCqNUgcifalk6goQGUpSUhLq1q2LU6dO4cqVK2jQoIFBj9eqVSu8/fbbAIDHjx8jNTUVK1aswNGjR3Hq1CmDHpuIiMpn3rx58PX1xePHj3H8+HGsWbMGe/fuxYULF1ClShVTV6/McnNzERcXBwBFekzNmjULM2bMMEGtiIgsnzGvLR4+fIiwsDAcPXoUvXr1wogRI2BjY4N9+/Zh4sSJ2L59O/bs2YOqVasarA5E+sQeT2SV0tPTceLECSxbtgw1a9ZEUlKSwY/5wgsv4PXXX8frr7+O0aNHY82aNZg0aRJOnz6Ny5cvG/z4zxJC4NGjR0Y9JhGRJerRo4f0ub1hwwZMmjQJ6enp+Oabb0xdNb2rVKkSHBwcTF0NIiKLY+xriylTpuDo0aNYuXIldu3ahejoaIwfPx7ffPMNVq1ahaNHj+Kdd94xaB2I9ImJJ7JKSUlJcHV1RVhYGAYOHCg1DkqlEm5ubhg5cmSRbXJycuDg4KDxIZ6Xl4e5c+eiQYMGkMvl8Pb2xrRp05CXl1eqenh6egJ4+mX/Wb/99hsGDhwINzc3ODg4oG3btvj222+LbH/x4kV07doVlStXRu3atbFgwQKoVKoi5erWrYtevXph//79aNu2LSpXroxPPvkER44cgUwmw9atWxEXF4cXXngBjo6OGDhwIO7du4e8vDxMmjQJ7u7uqFatGkaOHFkkNoVCgVdeeQUuLi6oVq0aGjVqhHfffbdU8RMRWZquXbsCeHqR8eTJE8yfPx/169eHXC5H3bp18e677xb5nFR/Bh84cACtWrWCg4MDmjZtiu3bt2uU0zXHUmnm7svPz8ecOXPg7+8PZ2dnVK1aFa+++ioOHz4slfnjjz9Qs2ZNAEBcXJw0jDA2Nlbn8csa4/Hjx9GuXTs4ODigXr16+OKLL4o/oUREVsCY1xZ//fUX/vvf/6Jr166IiYkpst/o6Gh06dIFn332Gf766y+NdRs3bkS7du1QpUoVuLq6omPHjjhw4IBGme+++w6dOnWCo6MjnJyc8NJLL2HTpk3S+rp162LEiBFFjlt47kH1dcaWLVvw7rvvwtPTE1WrVkWfPn3w559/Fn9CqcJh4omsUlJSEvr37w97e3sMGTIEly9fxunTp2FnZ4d+/fph586dyM/P19hm586dyMvLQ0REBABApVKhT58++PDDD9G7d2+sXLkS4eHhWL58OQYPHlzkmEqlEv/88w/++ecf/PXXX9i1axeWLVuGjh07wtfXVyp38eJFtG/fHr/++itmzJiBpUuXomrVqggPD8eOHTukcpmZmejSpQvS0tIwY8YMTJo0CV988QUSEhK0xnzp0iUMGTIE3bp1Q0JCAlq1aiWti4+Px/79+zFjxgyMGjUK27dvx7hx4zBq1Cj8/vvviI2NRf/+/bFhwwYsWrRIo669evVCXl4e5s2bh6VLl6JPnz744YcfyvW+EBGZu6tXrwIAqlevjtGjR2POnDlo06YNli9fjk6dOiE+Pl5qJ551+fJlDB48GD169EB8fDwqVaqE1157DQqFQi/1ysnJwWeffYbOnTtj0aJFiI2Nxa1btxAaGoq0tDQAQM2aNbFmzRoAQL9+/fDll1/iyy+/RP/+/XXutywxXrlyBQMHDkS3bt2wdOlSuLq6YsSIEbh48aJeYiQiMlfGvLb47rvvUFBQgOHDh+usz/Dhw/HkyRPs27dPWhYXF4c33ngDdnZ2mDdvHuLi4uDt7Y3k5GSpzIYNGxAWFoY7d+5g5syZWLhwIVq1aqWxn7J6//33sWfPHkyfPh1vvfUWFAoFgoODOfqCNAkiK3PmzBkBQCgUCiGEECqVStSuXVtMnDhRCCHE/v37BQCxa9cuje169uwp6tWrJ73+8ssvhY2Njfj+++81yiUmJgoA4ocffpCW+fj4CABFfl5++WXxzz//aGwfFBQk/Pz8xOPHj6VlKpVKdOjQQTRs2FBaNmnSJAFAnDx5Ulp28+ZN4ezsLACI9PT0Isfft2+fxrEOHz4sAIjmzZuL/Px8afmQIUOETCYTPXr00CgfGBgofHx8pNfLly8XAMStW7cEEZE1Wb9+vQAgDh48KG7duiX+/PNPsXnzZlG9enVRuXJlceTIEQFAjB49WmO7d955RwAQycnJ0jL1Z/D//d//Scvu3bsnatWqJVq3bi0tmzt3rtD21Utdl2c/1zt16iQ6deokvX7y5InIy8vT2O7u3bvCw8NDjBo1Slp269YtAUDMnTu3yHEKHz8tLa3MMR47dkxadvPmTSGXy8Xbb79d5FhERNbC2NcW6muAc+fO6azT2bNnBQAxZcoUIYQQly9fFjY2NqJfv36ioKBAo6xKpRJCCJGdnS0cHR1FQECAePTokdYyQjz9vI+MjCxyzMLtkvo644UXXhA5OTnS8q1btwoAIiEhQWf9qeJhjyeyOklJSfDw8ECXLl0AADKZDIMHD8bmzZtRUFCArl27okaNGtiyZYu0zd27d6FQKDTuNmzbtg1NmjRB48aNpZ5M//zzjzQM49nhDQAQEBAAhUIBhUKB3bt34/3338fFixfRp08fKeN/584dJCcnY9CgQbh//760z9u3byM0NBSXL1/G33//DQDYu3cv2rdvj3bt2knHqFmzJoYNG6Y1bl9fX4SGhmpdN3z4cNjZ2WnUVQiBUaNGFYnhzz//xJMnTwAALi4uAIBvvvlG6xA/IiJLFxwcjJo1a8Lb2xsRERGoVq0aduzYgRMnTgB4Os/Gs9QPkdizZ4/Gci8vL/Tr10967eTkhOHDh+PcuXPIzMx87nra2trC3t4ewNO75nfu3MGTJ0/Qtm1bnD17tlz73Lt3L4DSx9i0aVO8+uqr0uuaNWuiUaNG+N///leu4xMRWQJjX1vcv38fAODo6KizTup1OTk5AJ72rlKpVJgzZw5sbDQv8dVDrBUKBe7fv48ZM2YUme9P2zDw0ho+fLhGXQcOHIhatWpJbQwRwKfakZUpKCjA5s2b0aVLF6Snp0vLAwICsHTpUhw6dAghISEYMGAANm3ahLy8PMjlcmzfvh1KpVKjcbh8+TJ+/fVXab6Mwm7evKnxukaNGggODpZeh4WFoVGjRhg4cCA+++wzTJgwAVeuXIEQArNnz8bs2bN17veFF17AtWvXtD6utVGjRlq3e3Y4X2F16tTReO3s7AwA8Pb2LrJcpVLh3r17qF69OgYPHozPPvsMo0ePxowZMxAUFIT+/ftj4MCBRRo1IiJLtHr1arz44ouoVKkSPDw80KhRI9jY2GDHjh2wsbEp8tQiT09PuLi44Nq1axrLGzRoUOSL+4svvgjg6dxL6jn/nsfnn3+OpUuX4rffftN4lHZxn//FuXbtWpliLNyWAICrqyvu3r1bruMTEZk7U1xbqJM46gSUNoWTU1evXoWNjQ2aNm2qcxv1UPLmzZuXJvRSa9iwocZrmUyGBg0aFDtvIVU8TDyRVUlOTkZGRgY2b96MzZs3F1mflJSEkJAQRERE4JNPPsF3332H8PBwbN26FY0bN0bLli2lsiqVCn5+fli2bJnWYxVO2mgTFBQEADh27BgmTJgg9Rp65513dPZOKu+jWStXrqxzna2tbZmWCyGkfR47dgyHDx/Gnj17sG/fPmzZsgVdu3bFgQMHdG5PRGQp2rVrh7Zt2+pc/zx3gUu7r4KCghK33bhxI0aMGIHw8HBMnToV7u7usLW1RXx8vHQxoe96FVZSm0FEZG1McW3RpEkTAMD58+c15mx91vnz5wGg2ERTeRXXVvG7P5UXE09kVZKSkuDu7o7Vq1cXWbd9+3bs2LEDiYmJ6NixI2rVqoUtW7bglVdeQXJyMt577z2N8vXr18dPP/2EoKCgcl94qIesPXjwAABQr149AICdnZ1G7yhtfHx8cPny5SLLL126VK66lJeNjQ2CgoIQFBSEZcuW4YMPPsB7772Hw4cPlxgDEZGl8vHxgUqlwuXLl6WLAADIyspCdnY2fHx8NMqre7Q+2178/vvvAJ4+IQh42jsIALKzs6WhzACK9CzS5uuvv0a9evWwfft2jWPMnTtXo1xZ2quyxkhEVNGY4tqiR48esLW1xZdffqlzgvEvvvgClSpVQvfu3aV9q1Qq/PLLLzqTVfXr1wcAXLhwodgb3a6ursjOzi6y/Nq1a9K1zLMKX68IIXDlyhW0aNFC5zGo4uFYGbIajx49wvbt29GrVy8MHDiwyE9MTAzu37+Pb7/9FjY2Nhg4cCB27dqFL7/8Ek+ePCnypLpBgwbh77//xqeffqr1WA8fPiyxTrt27QIA6W6Hu7s7OnfujE8++QQZGRlFyt+6dUv6f8+ePfHjjz/i1KlTGuvVj281hjt37hRZpm7MCj9qm4jImvTs2RMAsGLFCo3l6jvVYWFhGstv3Lih8WTSnJwcfPHFF2jVqpU0zE79pf/YsWNSuYcPH+Lzzz8vsT7qu8zP9i46efIkUlJSNMpVqVIFALReNBRW1hiJiCoSU11beHt7Y+TIkTh48KD0pNJnJSYmIjk5GVFRUahduzYAIDw8HDY2Npg3b16ReVnV7UZISAgcHR0RHx+Px48fay0DPG2rfvzxR42n9O3evRt//vmn1vP0xRdfaAwL/Prrr5GRkYEePXpoLU8VE3s8kdX49ttvcf/+ffTp00fr+vbt26NmzZpISkrC4MGDMXjwYKxcuRJz586Fn5+fxt1eAHjjjTewdetWjBs3DocPH8bLL7+MgoIC/Pbbb9i6dSv279+vMTzj77//xsaNGwEA+fn5+Omnn/DJJ5+gRo0amDBhglRu9erVeOWVV+Dn54cxY8agXr16yMrKQkpKCv766y/89NNPAIBp06bhyy+/RPfu3TFx4kRUrVoVa9euhY+Pj9S91tDmzZuHY8eOISwsDD4+Prh58yY+/vhj1K5dG6+88opR6kBEZAotW7ZEZGQk1q5di+zsbHTq1AmnTp3C559/jvDwcGmSWbUXX3wRUVFROH36NDw8PLBu3TpkZWVh/fr1UpmQkBDUqVMHUVFRmDp1KmxtbbFu3TrUrFkT169fL7Y+vXr1wvbt29GvXz+EhYUhPT0diYmJaNq0qdSrFng6RLpp06bYsmULXnzxRbi5uaF58+Za5/Qoa4xERBWJKa8tli9fjt9++w3/+c9/sG/fPqln0/79+/HNN9+gU6dOWLp0qbTvBg0a4L333sP8+fPx6quvon///pDL5Th9+jS8vLwQHx8PJycnLF++HKNHj8ZLL72EoUOHwtXVFT/99BNyc3OlmyCjR4/G119/je7du2PQoEG4evUqNm7cKN08KczNzQ2vvPIKRo4ciaysLKxYsQINGjTAmDFjnvs9ICtisufpEelZ7969hYODg3j48KHOMiNGjBB2dnbin3/+ESqVSnh7ewsAYsGCBVrL5+fni0WLFolmzZoJuVwuXF1dhb+/v4iLixP37t2TyqkfM63+sbGxEe7u7mLIkCHiypUrRfZ79epVMXz4cOHp6Sns7OzECy+8IHr16iW+/vprjXLnz58XnTp1Eg4ODuKFF14Q8+fPF//973+LPHbbx8dHhIWFFTmO+jGn27Zt01iufnT36dOnNZarH7V969YtIYQQhw4dEn379hVeXl7C3t5eeHl5iSFDhojff/9d5zkmIrIEuj4Hn6VUKkVcXJzw9fUVdnZ2wtvbW8ycOVM8fvxYo5z6M3j//v2iRYsWQi6Xi8aNGxf57BVCiNTUVBEQECDs7e1FnTp1xLJly6S6PPu5Xvix1SqVSnzwwQfCx8dHyOVy0bp1a7F7924RGRkpfHx8NI5x4sQJ4e/vL+zt7QUAMXfuXCHEv5/xzxNjYYXrSURkLUx5bSGEEHl5eWL58uXC399fVK1aVVSpUkW0adNGrFixQuTn52vd/7p160Tr1q2lfXfq1EkoFAqNMt9++63o0KGDqFy5snBychLt2rUTX331lUaZpUuXihdeeEHI5XLx8ssvizNnzhT5vFdfZ3z11Vdi5syZwt3dXVSuXFmEhYWJa9euFXdqqQKSCcEZIYmIiIjKq27dumjevDl2795t6qoQEREZxZEjR9ClSxds27YNAwcONHV1yMxxjiciIiIiIiIiIjIIJp6IiIiIiIiIiMggmHgiIiIiIiIiIiKD4BxPRERERERERERkEOzxREREREREREREBsHEExERERERERERGUQlU1fAlFQqFW7cuAFHR0fIZDJTV4eIyGSEELh//z68vLxgY8N7EmwfiIieYvtQFNsIIqKnSttGVOjE040bN+Dt7W3qahARmY0///wTtWvXNnU1TI7tAxGRJrYP/2IbQUSkqaQ2okInnhwdHQE8PUlOTk7FllUqlThw4ABCQkJgZ2dnjOpVKDy/hsNzazjWdG5zcnLg7e0tfS5WdGVpHwqzpt8LU+O51A+eR/2piOeS7UNR5W0jLP33x5Lrb8l1Byy7/pZcd8Cy62+Mupe2jajQiSd111gnJ6dSJZ6qVKkCJycni/uFswQ8v4bDc2s41nhuOWTgqbK0D4VZ4++FqfBc6gfPo/5U5HPJ9uFf5W0jLP33x5Lrb8l1Byy7/pZcd8Cy62/MupfURnCgNhERERERERERGUSZE0/Hjh1D79694eXlBZlMhp07d2qsHzFiBGQymcZP9+7dNcrcuXMHw4YNg5OTE1xcXBAVFYUHDx5olDl//jxeffVVODg4wNvbG4sXLy5Sl23btqFx48ZwcHCAn58f9u7dW9ZwiIiIiIiIiIjIQMqceHr48CFatmyJ1atX6yzTvXt3ZGRkSD9fffWVxvphw4bh4sWLUCgU2L17N44dO4axY8dK63NychASEgIfHx+kpqZiyZIliI2Nxdq1a6UyJ06cwJAhQxAVFYVz584hPDwc4eHhuHDhQllDIiIiIiIiIiIiAyjzHE89evRAjx49ii0jl8vh6empdd2vv/6Kffv24fTp02jbti0AYOXKlejZsyc+/PBDeHl5ISkpCfn5+Vi3bh3s7e3RrFkzpKWlYdmyZVKCKiEhAd27d8fUqVMBAPPnz4dCocCqVauQmJhY1rCIiIiIiIiIiEjPDDK5+JEjR+Du7g5XV1d07doVCxYsQPXq1QEAKSkpcHFxkZJOABAcHAwbGxucPHkS/fr1Q0pKCjp27Ah7e3upTGhoKBYtWoS7d+/C1dUVKSkpmDJlisZxQ0NDiwz9e1ZeXh7y8vKk1zk5OQCeTrqlVCqLjUm9vqRyVD48v4bDc2s41nRurSEGIiIiIiIyP3pPPHXv3h39+/eHr68vrl69infffRc9evRASkoKbG1tkZmZCXd3d81KVKoENzc3ZGZmAgAyMzPh6+urUcbDw0Na5+rqiszMTGnZs2XU+9AmPj4ecXFxRZYfOHAAVapUKVV8CoWiVOWofHh+DYfn1nCs4dzm5uaaugpERERERGSF9J54ioiIkP7v5+eHFi1aoH79+jhy5AiCgoL0fbgymTlzpkYvqZycHHh7eyMkJKTER6EqlUooFAp069bN4h6jaAl4fg2H59ZwrOncqnuAEhERERER6ZNBhto9q169eqhRowauXLmCoKAgeHp64ubNmxplnjx5gjt37kjzQnl6eiIrK0ujjPp1SWV0zS0FPJ17Si6XF1luZ2dX6ovGksrWnbFH6/I/FoaVav8VXVneCyobnlvDsYZza+n1txRsI4iIqHnsfuQVyDSWsR0gImtW5qfaldVff/2F27dvo1atWgCAwMBAZGdnIzU1VSqTnJwMlUqFgIAAqcyxY8c05hxRKBRo1KgRXF1dpTKHDh3SOJZCoUBgYKChQyIiIiIiolKKj4/HSy+9BEdHR7i7uyM8PByXLl3SKPP48WNER0ejevXqqFatGgYMGFDkJvP169cRFhaGKlWqwN3dHVOnTsWTJ080yhw5cgRt2rSBXC5HgwYNsGHDhiL1Wb16NerWrQsHBwcEBATg1KlTeo+ZiIj+VebE04MHD5CWloa0tDQAQHp6OtLS0nD9+nU8ePAAU6dOxY8//og//vgDhw4dQt++fdGgQQOEhoYCAJo0aYLu3btjzJgxOHXqFH744QfExMQgIiICXl5eAIChQ4fC3t4eUVFRuHjxIrZs2YKEhASNYXITJ07Evn37sHTpUvz222+IjY3FmTNnEBMTo4fTQkRERERE+nD06FFER0fjxx9/hEKhgFKpREhICB4+fCiVmTx5Mnbt2oVt27bh6NGjuHHjBvr37y+tLygoQFhYGPLz83HixAl8/vnn2LBhA+bMmSOVSU9PR1hYGLp06YK0tDRMmjQJo0ePxv79+6UyW7ZswZQpUzB37lycPXsWLVu2RGhoaJERGUREpD9lTjydOXMGrVu3RuvWrQEAU6ZMQevWrTFnzhzY2tri/Pnz6NOnD1588UVERUXB398f33//vcYQt6SkJDRu3BhBQUHo2bMnXnnlFaxdu1Za7+zsjAMHDiA9PR3+/v54++23MWfOHIwdO1Yq06FDB2zatAlr165Fy5Yt8fXXX2Pnzp1o3rz585wPIiIiIiLSo3379mHEiBFo1qwZWrZsiQ0bNuD69evSCIh79+7hv//9L5YtW4auXbvC398f69evx4kTJ/Djjz8CePowoF9++QUbN25Eq1at0KNHD8yfPx+rV69Gfn4+ACAxMRG+vr5YunQpmjRpgpiYGAwcOBDLly+X6rJs2TKMGTMGI0eORNOmTZGYmIgqVapg3bp1xj8xREQVRJnneOrcuTOEEDrXP3tHQRc3Nzds2rSp2DItWrTA999/X2yZ1157Da+99lqJxyMiIiIiIvNw7949AE+vCQAgNTUVSqUSwcHBUpnGjRujTp06SElJQfv27ZGSkgI/Pz+Np1qHhoZi/PjxuHjxIlq3bo2UlBSNfajLTJo0CQCQn5+P1NRUzJw5U1pvY2OD4OBgpKSk6KxvXl4e8vLypNfqB3IolUqNqUFKoi4rtyl6LVWW/ZiKuo6WUNfCLLnugGXX35LrDlh2/Y1R99Lu2+CTixMREREREQGASqXCpEmT8PLLL0sjFTIzM2Fvbw8XFxeNsh4eHsjMzJTKPJt0Uq9XryuuTE5ODh49eoS7d++ioKBAa5nffvtNZ53j4+MRFxdXZPmBAwdQpUqVUkStaX5bVZFle/fuLfN+TEWhUJi6CuVmyXUHLLv+llx3wLLrb8i65+bmlqocE09ERERERGQU0dHRuHDhAo4fP27qqpTazJkzNeaazcnJgbe3N0JCQuDk5FTq/SiVSigUCsw+Y4M8leZT7S7Ehuqtvoairn+3bt0s7mm4llx3wLLrb8l1Byy7/saou7oHaEmYeCIiIiIiIoOLiYnB7t27cezYMdSuXVta7unpifz8fGRnZ2v0esrKyoKnp6dUpvDT59RPvXu2TOEn4WVlZcHJyQmVK1eGra0tbG1ttZZR70MbuVyuMV+tmp2dXbku5vJUMuQVaCaeLOmCtrxxmwNLrjtg2fW35LoDll1/Q9a9tPst8+TiREREREREpSWEQExMDHbs2IHk5GT4+vpqrPf394ednR0OHTokLbt06RKuX7+OwMBAAEBgYCB+/vlnjafPKRQKODk5oWnTplKZZ/ehLqPeh729Pfz9/TXKqFQqHDp0SCpDRET6xx5PRERERERkMNHR0di0aRO++eYbODo6SnMyOTs7o3LlynB2dkZUVBSmTJkCNzc3ODk5YcKECQgMDET79u0BACEhIWjatCneeOMNLF68GJmZmZg1axaio6Ol3kjjxo3DqlWrMG3aNIwaNQrJycnYunUr9uzZI9VlypQpiIyMRNu2bdGuXTusWLECDx8+xMiRI41/YoiIKggmnoiIiIiIyGDWrFkD4OnTsZ+1fv16jBgxAgCwfPly2NjYYMCAAcjLy0NoaCg+/vhjqaytrS12796N8ePHIzAwEFWrVkVkZCTmzZsnlfH19cWePXswefJkJCQkoHbt2vjss88QGvrv/EmDBw/GrVu3MGfOHGRmZqJVq1bYt29fkQnHiYhIf5h4IiIiIiIigxFClFjGwcEBq1evxurVq3WW8fHxKfHpb507d8a5c+eKLRMTE4OYmJgS60RERPrBOZ6IiIiIiIiIiMggmHgiIiIiIiIiIiKDYOKJiIiIiIiIiIgMgoknIiIiIiIiIiIyCCaeiIjIKAoKCjB79mz4+vqicuXKqF+/PubPn68x6awQAnPmzEGtWrVQuXJlBAcH4/Llyxr7uXPnDoYNGwYnJye4uLggKioKDx480Chz/vx5vPrqq3BwcIC3tzcWL15slBiJiIiIiEgTE09ERGQUixYtwpo1a7Bq1Sr8+uuvWLRoERYvXoyVK1dKZRYvXoyPPvoIiYmJOHnyJKpWrYrQ0FA8fvxYKjNs2DBcvHgRCoUCu3fvxrFjxzB27FhpfU5ODkJCQuDj44PU1FQsWbIEsbGxWLt2rVHjJSIiIiIioJKpK0BERBXDiRMn0LdvX4SFhQEA6tati6+++gqnTp0C8LS304oVKzBr1iz07dsXAPDFF1/Aw8MDO3fuREREBH799Vfs27cPp0+fRtu2bQEAK1euRM+ePfHhhx/Cy8sLSUlJyM/Px7p162Bvb49mzZohLS0Ny5Yt00hQERERERGR4THxRERERtGhQwesXbsWv//+O1588UX89NNPOH78OJYtWwYASE9PR2ZmJoKDg6VtnJ2dERAQgJSUFERERCAlJQUuLi5S0gkAgoODYWNjg5MnT6Jfv35ISUlBx44dYW9vL5UJDQ3FokWLcPfuXbi6uhapW15eHvLy8qTXOTk5AAClUgmlUlmmONXltW0ntxVFlukqS8WfSyo9nkf9qYjnsiLFSkREhsHEExERGcWMGTOQk5ODxo0bw9bWFgUFBXj//fcxbNgwAEBmZiYAwMPDQ2M7Dw8PaV1mZibc3d011leqVAlubm4aZXx9fYvsQ71OW+IpPj4ecXFxRZYfOHAAVapUKU+4UCgURZYtbqe97N69e8t1jIpC27mksuN51J+KdC5zc3NNXQUiIrJwTDwREZFRbN26FUlJSdi0aZM0/G3SpEnw8vJCZGSkSes2c+ZMTJkyRXqdk5MDb29vhISEwMnJqUz7UiqVUCgU6NatG+zs7DTWNY/dr3WbC7GhZa90BVDcuaTS43nUn4p4LtU9QImIiMqLiSciIjKKqVOnYsaMGYiIiAAA+Pn54dq1a4iPj0dkZCQ8PT0BAFlZWahVq5a0XVZWFlq1agUA8PT0xM2bNzX2++TJE9y5c0fa3tPTE1lZWRpl1K/VZQqTy+WQy+VFltvZ2ZX74lLbtnkFMp1lSbfneR/oXzyP+lORzmVFiZOIiAyHT7UjIiKjyM3NhY2NZrNja2sLlUoFAPD19YWnpycOHTokrc/JycHJkycRGBgIAAgMDER2djZSU1OlMsnJyVCpVAgICJDKHDt2TGNeEoVCgUaNGmkdZkdERERERIbDxBMRERlF79698f7772PPnj34448/sGPHDixbtgz9+vUDAMhkMkyaNAkLFizAt99+i59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467Goymd6dna2OnXqpIiICLNNUlKSCgsLdeDAAbPN5fqptr2vq/L5TT/CHbzpPWDV542VUlJSlJycXOb/YU2O/f3331f37t113333KTw8XN26ddOf/vQnc727zplWufXWW7Vhwwb961//kiR98skn2rp1q/r16+cV8V+sOn8/qQ6nTp2Sn5+fQkNDa1TsFJ6u4H//+58uXLjg8mEkSREREcrPz/dQVLVPaV/Sz9eupKREEyZM0G233aaOHTtK+rF/g4KCzA+gUvTv1du3b58aNGggm82mcePG6b333lP79u3pW0jiXHE1ruZzPj8/X+Hh4S7r69Spo7CwMJc25W3j4n1U1MYbj0VVP9OvpZ8KCwt17ty5WvO+vpbPb/oR7uAt7wErP2+s8s477+if//ynZs+eXWZdTY79yy+/1KJFi3TDDTdo3bp1Gj9+vH7729/qrbfectn3tZ4zrfLYY49pyJAhatu2rQIDA9WtWzdNmDBBw4YN84r4L1adv59Y7YcfftCUKVM0dOhQ2e12c981IfY6btkKgBojJSVF+/fv19atWz0dSq3Spk0b5ebm6tSpU/rrX/+qkSNHavPmzZ4OC0Atx2f6tePzG7g63vZ589VXX+nRRx+Vw+FQ3bp1PR1OpZSUlKh79+569tlnJUndunXT/v37tXjxYo0cOdLD0V3Z8uXLtXTpUmVmZqpDhw7Kzc3VhAkTFBUV5RXx10ZOp1O//OUvZRiGFi1a5OlwymDE0xU0adJEAQEBZe5+UFBQoMjISA9FVfuU9iX9fG1SU1O1atUqffjhh2revLm5PDIyUsXFxTp58qRLe/r36gUFBal169aKjY3V7Nmz1aVLFy1YsIC+hSTOFVfjaj7nIyMjdfz4cZf158+f14kTJ1zalLeNi/dRURtvOxbX8pl+Lf1kt9tVr169WvO+vpbPb/oR7uAN7wGrP2+skJOTo+PHj+umm25SnTp1VKdOHW3evFkvvfSS6tSpo4iIiBobe7NmzdS+fXuXZe3atVNeXp7Lvq/1nGmVSZMmmaOeOnXqpOHDh2vixInmyLOaHv/FqvP3E6uUFp3+/e9/y+FwmKOdalLsFJ6uICgoSLGxsdqwYYO5rKSkRBs2bFBcXJwHI6tdYmJiFBkZ6dLPhYWF2rFjB/18FQzDUGpqqt577z1t3LhRMTExLutjY2MVGBjo0r+HDh1SXl4e/VtFJSUlKioqom8hiXPF1biaz/m4uDidPHlSOTk5ZpuNGzeqpKREPXr0MNts2bJFTqfTbONwONSmTRs1atTIbHPxfkrbeMuxcMdnelxcnPbt2+fyy2bpL6OlX3au1E+19X1dmc9v+hHuUJPfA9X1eWOF3r17a9++fcrNzTUf3bt317Bhw8yfa2rst912mw4dOuSy7F//+pdatmwpyX3nTKucPXtW/v6upYSAgACVlJR4RfwXq87fT6xQWnT6/PPPtX79ejVu3NhlfY2J3W3TlNdi77zzjmGz2YyMjAzj4MGDxtixY43Q0FCXux/gyr7//ntjz549xp49ewxJxosvvmjs2bPHnHH/ueeeM0JDQ42///3vxt69e4177rmnzG0sUb7x48cbISEhxqZNm4xjx46Zj7Nnz5ptxo0bZ7Ro0cLYuHGjsXv3biMuLs6Ii4vzYNTe47HHHjM2b95sHDlyxNi7d6/x2GOPGX5+fkZWVpZhGPQtfsS5wj2f83379jW6detm7Nixw9i6datxww03uNzy9+TJk0ZERIQxfPhwY//+/cY777xjBAcHu9zy9+OPPzbq1KljvPDCC8ann35qPPXUU0ZgYKCxb9++6uuMa+COz/TSW4QnJiYaubm5xtq1a42mTZu63CL8yy+/NIKDg41JkyYZn376qbFw4UIjICDAWLt2rdnG29/X1/r5TT/CXWrqe6C6Pm+qy8V3tavJse/cudOoU6eO8cwzzxiff/65sXTpUiM4ONh4++23zTbuOGdaZeTIkcbPfvYzY9WqVcaRI0eMd99912jSpIkxefLkGhl/Tfn9xN2xFxcXG3fffbfRvHlzIzc31+X/8MV3qPNU7Bej8HSVXn75ZaNFixZGUFCQccsttxjbt2/3dEhe58MPPzQklXmMHDnSMIwfb2X55JNPGhEREYbNZjN69+5tHDp0yLNBe4ny+lWSsWTJErPNuXPnjN/85jdGo0aNjODgYOPee+81jh075rmgvcioUaOMli1bGkFBQUbTpk2N3r17m19aDIO+xU98/Vzhjs/5b7/91hg6dKjRoEEDw263Gw899JDx/fffu7T55JNPjNtvv92w2WzGz372M+O5554rE8vy5cuNG2+80QgKCjI6dOhgrF692rK83c1dn+lHjx41+vXrZ9SrV89o0qSJ8bvf/c5wOp0ubT788EOja9euRlBQkHH99de77KOUN7+v3fH5TT/CXWrie6A6P2+qw6WFp5oc+wcffGB07NjRsNlsRtu2bY3XXnvNZb27zplWKCwsNB599FGjRYsWRt26dY3rr7/eeOKJJ1yKHTUp/pr0+4k7Yz9y5EiF/4c//PBDj8d+MT/DMAz3jJ0CAAAAAAAAfsIcTwAAAAAAALAEhScAAAAAAABYgsITAAAAAAAALEHhCQAAAAAAAJag8AQAAAAAAABLUHgCAAAAAACAJSg8AQAAAAAAwBIUngAAAAAAAGAJCk8AAAAAAACwBIUnAAAAAAAAWILCEwAAAAAAACxB4QkAAAAAAACWoPAEAAAAAAAAS1B4AgAAAAAAgCUoPAEAAAAAAMASFJ4AAAAAAABgCQpPAAAAAAAAsASFJ6AapKeny8/Pr9r25+fnp/T09GrbHwDAGps2bZKfn582bdpULfuLj49XfHx8tewLAAD4BgpP8HkZGRny8/PT7t27r2k7Z8+eVXp6+lV/OXj22We1cuXKa9onAODaues8UF0yMzM1f/58T4cBAABwVSg8AW5y9uxZzZgxo9zC07Rp03Tu3DmXZRSeAABX0qtXL507d069evUyl1F4AgAA3qSOpwMAfEGdOnVUpw7/3QAAlePv76+6det6OgwAAIAqY8QTcAXFxcWaPn26YmNjFRISovr16+uOO+7Qhx9+aLY5evSomjZtKkmaMWOG/Pz8XOZZunSOJz8/P505c0ZvvfWW2fbBBx+UJD344IO67rrrysRR3jxRRUVFmjhxopo2baqGDRvq7rvv1tdff11uHv/5z380atQoRUREyGazqUOHDnrzzTevoWcAwHfs2bNH/fr1k91uV4MGDdS7d29t377dpU3pJXsff/yx0tLS1LRpU9WvX1/33nuv/vvf/7q0LSkpUXp6uqKiohQcHKw777xTBw8e1HXXXWeeD6SyczzFx8dr9erV+ve//22eP0rPGaX7P3r0qMu+Kpon6rXXXlOrVq1Ur1493XLLLfroo4/Kzb2oqEhPPfWUWrduLZvNpujoaE2ePFlFRUWV7kcAAOB7GIIBXEFhYaFef/11DR06VGPGjNH333+vN954Q0lJSdq5c6e6du2qpk2batGiRRo/frzuvfdeDRo0SJLUuXPncrf5//1//58efvhh3XLLLRo7dqwkqVWrVpWO7eGHH9bbb7+t+++/X7feeqs2btyo5OTkMu0KCgrUs2dP+fn5KTU1VU2bNtU//vEPjR49WoWFhZowYUKl9w0AvuLAgQO64447ZLfbNXnyZAUGBuqPf/yj4uPjtXnzZvXo0cOl/SOPPKJGjRrpqaee0tGjRzV//nylpqZq2bJlZpupU6dqzpw5GjBggJKSkvTJJ58oKSlJP/zww2VjeeKJJ3Tq1Cl9/fXXmjdvniSpQYMGlc7pjTfe0K9//WvdeuutmjBhgr788kvdfffdCgsLU3R0tNmupKREd999t7Zu3aqxY8eqXbt22rdvn+bNm6d//etfXDIOAACuiMITcAWNGjXS0aNHFRQUZC4bM2aM2rZtq5dffllvvPGG6tevr//7v//T+PHj1blzZz3wwAOX3eYDDzygcePG6frrr79i24p88sknevvtt/Wb3/xGCxculCSlpKRo2LBh2rt3r0vbJ554QhcuXNC+ffvUuHFjSdK4ceM0dOhQpaen69e//rXq1atXpTgAoLabNm2anE6ntm7dquuvv16SNGLECLVp00aTJ0/W5s2bXdo3btxYWVlZ5ijVkpISvfTSSzp16pRCQkJUUFCgF198UQMHDtR7771nvm7GjBlXvCNpnz599LOf/Uzfffddlc8fTqdTjz/+uLp27aoPP/zQPL+1b99eY8eOdSk8ZWZmav369dq8ebNuv/12c3nHjh01btw4bdu2TbfeemuV4gAAAL6BS+2AKwgICDB/KS8pKdGJEyd0/vx5de/eXf/85z89FteaNWskSb/97W9dll86eskwDP3tb3/TgAEDZBiG/ve//5mPpKQknTp1yqN5AEBNduHCBWVlZWngwIFm0UmSmjVrpvvvv19bt25VYWGhy2vGjh3rcmn0HXfcoQsXLujf//63JGnDhg06f/68fvOb37i87pFHHrEwk5/s3r1bx48f17hx41z+qPLggw8qJCTEpe2KFSvUrl07tW3b1uX8cdddd0mSy2XnAAAA5WHEE3AV3nrrLc2dO1efffaZnE6nuTwmJsZjMf373/+Wv79/mUv02rRp4/L8v//9r06ePKnXXntNr732WrnbOn78uGVxAoA3++9//6uzZ8+W+WyVpHbt2qmkpERfffWVOnToYC5v0aKFS7tGjRpJkr777jtJMgtQrVu3dmkXFhZmtrVS6f5vuOEGl+WBgYEuxTVJ+vzzz/Xpp5+a8xheivMHAAC4EgpPwBW8/fbbevDBBzVw4EBNmjRJ4eHhCggI0OzZs3X48GG37+/SCcRLXbhwoUrbKykpkfTj5X0jR44st01Fc1EBACovICCg3OWGYVi6X3efP6QfzyGdOnXSiy++WO76iy/LAwAAKA+FJ+AK/vrXv+r666/Xu+++6/JL/VNPPeXSrqJf+CtSUftGjRrp5MmTZZaX/oW6VMuWLVVSUqLDhw+7/CX+0KFDLu1K73h34cIFJSQkVCpGAPB1TZs2VXBwcJnPVkn67LPP5O/vX+niS8uWLSVJX3zxhcvI2W+//dYcFXU5lzt/SCpzDinv/CH9OJqp9JI56ce5n44cOaIuXbqYy1q1aqVPPvlEvXv3rvR5DgAAQGKOJ+CKSv9yffFfqnfs2KHs7GyXdsHBwZLK/sJfkfr165fbtlWrVjp16pTLBOHHjh1zmYBWkvr16ydJeumll1yWz58/v0z8gwcP1t/+9jft37+/zP4uvcU3AOAnAQEBSkxM1N///ncdPXrUXF5QUKDMzEzdfvvtstvtldpm7969VadOHS1atMhl+SuvvHJVr69fv75OnTpVZnnppddbtmwxl124cKHMZdbdu3dX06ZNtXjxYhUXF5vLMzIyypyXfvnLX+o///mP/vSnP5XZ37lz53TmzJmrihkAAPguRjwB/8+bb76ptWvXllkeHx+vd999V/fee6+Sk5N15MgRLV68WO3bt9fp06fNdvXq1VP79u21bNky3XjjjQoLC1PHjh3VsWPHcvcXGxur9evX68UXX1RUVJRiYmLUo0cPDRkyRFOmTNG9996r3/72tzp79qwWLVqkG2+80WUS8K5du2ro0KF69dVXderUKd16663asGGDvvjiizL7eu655/Thhx+qR48eGjNmjNq3b68TJ07on//8p9avX68TJ064oQcBwLtVdB5IT0+Xw+HQ7bffrt/85jeqU6eO/vjHP6qoqEhz5syp9H4iIiL06KOPau7cubr77rvVt29fffLJJ/rHP/6hJk2aXHFkUWxsrJYtW6a0tDTdfPPNatCggQYMGKAOHTqoZ8+emjp1qk6cOKGwsDC98847On/+vMvrAwMD9fTTT+vXv/617rrrLv3qV7/SkSNHtGTJkjJzPA0fPlzLly/XuHHj9OGHH+q2227ThQsX9Nlnn2n58uVat26dunfvXuk+AAAAPsQAfNySJUsMSRU+8vLyjGeffdZo2bKlYbPZjG7duhmrVq0yRo4cabRs2dJlW9u2bTNiY2ONoKAgQ5Lx1FNPGYZhGE899ZRx6X+3zz77zOjVq5dRr149Q5IxcuRIc11WVpbRsWNHIygoyGjTpo3x9ttvl7uNc+fOGb/97W+Nxo0bG/Xr1zcGDBhgfPXVVy77LlVQUGCkpKQY0dHRRmBgoBEZGWn07t3beO2119zVlQDgla50Hvjqq6+Mf/7zn0ZSUpLRoEEDIzg42LjzzjuNbdu2lbudXbt2uSz/8MMPDUnGhx9+aC47f/688eSTTxqRkZFGvXr1jLvuusv49NNPjcaNGxvjxo277GtPnz5t3H///UZoaKghyeVcdPjwYSMhIcGw2WxGRESE8fjjjxsOh6PMNgzDMF599VUjJibGsNlsRvfu3Y0tW7YYP//5z42f//znLu2Ki4uN559/3ujQoYNhs9mMRo0aGbGxscaMGTOMU6dOVanPAQCA7/AzDItnugQAAMAVnTx5Uo0aNdLTTz+tJ554wtPhAAAAuAVzPAEAAFSzc+fOlVlWOkdffHx89QYDAABgIeZ4AgAAqGbLli1TRkaG+vfvrwYNGmjr1q36y1/+osTERN12222eDg8AAMBtKDwBAABUs86dO6tOnTqaM2eOCgsLzQnHn376aU+HBgAA4FbM8QQAAAAAAABLMMcTAAAAAAAALEHhCQAAAAAAAJag8AQAAAAAAABL+PTk4iUlJfrmm2/UsGFD+fn5eTocAPAYwzD0/fffKyoqSv7+/E2C8wMA/IjzAwDgWvl04embb75RdHS0p8MAgBrjq6++UvPmzT0dhsdxfgAAV5wfAABV5dOFp4YNG0r68URqt9s9HI31nE6nsrKylJiYqMDAQE+HU618NXdfzVvy3dyrmndhYaGio6PNz0VfVx3nB196j5Jr7eMreUrkyvkBAHCtfLrwVHr5hN1u95nCU3BwsOx2e63/xelSvpq7r+Yt+W7u15o3l5X9qDrOD770HiXX2sdX8pTItRTnBwBAVXGhNgAAAAAAACxB4QkAAAAAAACWoPAEAKg2s2fP1s0336yGDRsqPDxcAwcO1KFDh1zaxMfHy8/Pz+Uxbtw4lzZ5eXlKTk5WcHCwwsPDNWnSJJ0/f96lzaZNm3TTTTfJZrOpdevWysjIsDo9AAAAAJeg8AQAqDabN29WSkqKtm/fLofDIafTqcTERJ05c8al3ZgxY3Ts2DHzMWfOHHPdhQsXlJycrOLiYm3btk1vvfWWMjIyNH36dLPNkSNHlJycrDvvvFO5ubmaMGGCHn74Ya1bt67acgUAAADg45OLAwCq19q1a12eZ2RkKDw8XDk5OerVq5e5PDg4WJGRkeVuIysrSwcPHtT69esVERGhrl27atasWZoyZYrS09MVFBSkxYsXKyYmRnPnzpUktWvXTlu3btW8efOUlJRkXYIAAAAAXDDiCQDgMadOnZIkhYWFuSxfunSpmjRpoo4dO2rq1Kk6e/asuS47O1udOnVSRESEuSwpKUmFhYU6cOCA2SYhIcFlm0lJScrOzrYqFQAAAADlYMRTLXfdY6vNn20BhubcInVMX6eiC2VviXv0ueTqDA2AjyspKdGECRN02223qWPHjuby+++/Xy1btlRUVJT27t2rKVOm6NChQ3r33XclSfn5+S5FJ0nm8/z8/Mu2KSws1Llz51SvXj2XdUVFRSoqKjKfFxYWSvrx1uJOp9NNGbsq3a5V269JyNX7dEwv/7LU/ek/jhisLXleDV/P1RfyBgBYy+2Fp0WLFmnRokU6evSoJKlDhw6aPn26+vXrJ0n64Ycf9Lvf/U7vvPOOioqKlJSUpFdffdXlC0JeXp7Gjx+vDz/8UA0aNNDIkSM1e/Zs1anzU7ibNm1SWlqaDhw4oOjoaE2bNk0PPvigu9MBAFgkJSVF+/fv19atW12Wjx071vy5U6dOatasmXr37q3Dhw+rVatWlsQye/ZszZgxo8zyrKwsBQcHW7LPUg6Hw9Lt1yTk6j3m3FL+8jVr1rg89/Y8K8NXc714xCkAAFXh9sJT8+bN9dxzz+mGG26QYRh66623dM8992jPnj3q0KGDJk6cqNWrV2vFihUKCQlRamqqBg0apI8//ljST5PGRkZGatu2bTp27JhGjBihwMBAPfvss5J+mjR23LhxWrp0qTZs2KCHH35YzZo1Y+4OAPACqampWrVqlbZs2aLmzZtftm2PHj0kSV988YVatWqlyMhI7dy506VNQUGBJJnzQkVGRprLLm5jt9vLjHaSpKlTpyotLc18XlhYqOjoaCUmJsput1c+wavgdDrlcDjUp08fBQYGWrKPmoJcvc/VjHiqDXleDV/PtXQEKAAAVeX2wtOAAQNcnj/zzDNatGiRtm/frubNm+uNN95QZmam7rrrLknSkiVL1K5dO23fvl09e/Zk0lgAqMUMw9Ajjzyi9957T5s2bVJMTMwVX5ObmytJatasmSQpLi5OzzzzjI4fP67w8HBJP/513m63q3379mab8kZmxMXFlbsPm80mm81WZnlgYKDlXzSrYx81Bbl6j/IuyZdUJidvz7MyfDVXX8kZAGAdSycXv3Dhgt555x2dOXNGcXFxysnJkdPpdJnwtW3btmrRooU54SuTxgJA7ZWSkqK3335bmZmZatiwofLz85Wfn69z585Jkg4fPqxZs2YpJydHR48e1fvvv68RI0aoV69e6ty5syQpMTFR7du31/Dhw/XJJ59o3bp1mjZtmlJSUszi0bhx4/Tll19q8uTJ+uyzz/Tqq69q+fLlmjhxosdyBwAAAHyRJZOL79u3T3Fxcfrhhx/UoEEDvffee2rfvr1yc3MVFBSk0NBQl/YRERFXnBC2dN3l2lQ0aWwpT0we62m2AOOnn/0Nl38vVVv7QPKtiUEv5qt5S76be1Xzrq5+WrRokSQpPj7eZfmSJUv04IMPKigoSOvXr9f8+fN15swZRUdHa/DgwZo2bZrZNiAgQKtWrdL48eMVFxen+vXra+TIkZo5c6bZJiYmRqtXr9bEiRO1YMECNW/eXK+//jqjYgEAAIBqZknhqU2bNsrNzdWpU6f017/+VSNHjtTmzZut2FWleHLyWE8pb3LQWd1Lym176WUptZEvTQx6MV/NW/Ld3Cubd3VNHmsY5Re+S0VHR1/V+aJly5ZX/MyKj4/Xnj17KhUfAAAAAPeypPAUFBSk1q1bS5JiY2O1a9cuLViwQL/61a9UXFyskydPuox6KigocJkQ1t2TxpbyxOSxnnbx5KA2f0Ozupfoyd3+KiopO3dD6YShtZEvTQx6MV/NW/Ld3KuaN5PHAgAAALCCJYWnS5WUlKioqEixsbEKDAzUhg0bNHjwYEnSoUOHlJeXZ074asWksaU8OXmsp5Q3OWhRiV+5y2trH1ysNh/ry/HVvCXfzb2yeftiHwEAAACwntsLT1OnTlW/fv3UokULff/998rMzNSmTZu0bt06hYSEaPTo0UpLS1NYWJjsdrseeeQRxcXFqWfPnpJcJ42dM2eO8vPzy5009pVXXtHkyZM1atQobdy4UcuXL9fq1avdnQ4AAAAAAACqyO2Fp+PHj2vEiBE6duyYQkJC1LlzZ61bt059+vSRJM2bN0/+/v4aPHiwioqKlJSUpFdffdV8PZPGAgAAAAAA1A5uLzy98cYbl11ft25dLVy4UAsXLqywDZPGAgAAAAAAeD9/TwcAAAAAAACA2onCEwAAAAAAACxB4QkAAAAAAACWoPAEAAAAAAAAS1B4AgAAAAAAgCUoPAEAAAAAAMASFJ4AAAAAAABgCQpPAAAAAAAAsASFJwAAAAAAAFiCwhMAAAAAAAAsQeEJAAAAAAAAlqDwBAAAAAAAAEtQeAIAAAAAAIAlKDwBAAAAAADAEhSeAAAAAAAAYAkKTwAAAAAAALAEhScAAAAAAABYgsITAAAAAAAALEHhCQAAAAAAAJag8AQAAAAAAABLuL3wNHv2bN18881q2LChwsPDNXDgQB06dMilTXx8vPz8/Fwe48aNc2mTl5en5ORkBQcHKzw8XJMmTdL58+dd2mzatEk33XSTbDabWrdurYyMDHenAwAAAAAAgCpye+Fp8+bNSklJ0fbt2+VwOOR0OpWYmKgzZ864tBszZoyOHTtmPubMmWOuu3DhgpKTk1VcXKxt27bprbfeUkZGhqZPn262OXLkiJKTk3XnnXcqNzdXEyZM0MMPP6x169a5OyUAAAAAAABUgdsLT2vXrtWDDz6oDh06qEuXLsrIyFBeXp5ycnJc2gUHBysyMtJ82O12c11WVpYOHjyot99+W127dlW/fv00a9YsLVy4UMXFxZKkxYsXKyYmRnPnzlW7du2Umpqq//u//9O8efPcnRIAwE2uZlTsDz/8oJSUFDVu3FgNGjTQ4MGDVVBQ4NKGUbEAAACAd6hj9Q5OnTolSQoLC3NZvnTpUr399tuKjIzUgAED9OSTTyo4OFiSlJ2drU6dOikiIsJsn5SUpPHjx+vAgQPq1q2bsrOzlZCQ4LLNpKQkTZgwocJYioqKVFRUZD4vLCyUJDmdTjmdzmvKs6ayBRg//exvuPx7qdraB9JPudXmHMvjq3lLvpt7VfOurn4qHRV788036/z583r88ceVmJiogwcPqn79+pKkiRMnavXq1VqxYoVCQkKUmpqqQYMG6eOPP5b006jYyMhIbdu2TceOHdOIESMUGBioZ599VtJPo2LHjRunpUuXasOGDXr44YfVrFkzJSUlVUuuAAAAACwuPJWUlGjChAm67bbb1LFjR3P5/fffr5YtWyoqKkp79+7VlClTdOjQIb377ruSpPz8fJeikyTzeX5+/mXbFBYW6ty5c6pXr16ZeGbPnq0ZM2aUWZ6VlWUWvWqbObeUXTare0m5bdesWWNxNJ7ncDg8HYJH+Greku/mXtm8z549a1EkrtauXevyPCMjQ+Hh4crJyVGvXr106tQpvfHGG8rMzNRdd90lSVqyZInatWun7du3q2fPnuao2PXr1ysiIkJdu3bVrFmzNGXKFKWnpysoKMhlVKwktWvXTlu3btW8efMoPAEAAADVyNLCU0pKivbv36+tW7e6LB87dqz5c6dOndSsWTP17t1bhw8fVqtWrSyLZ+rUqUpLSzOfFxYWKjo6WomJiS6X+tUmHdN/mvPK5m9oVvcSPbnbX0UlfmXa7k+vvV/GnE6nHA6H+vTpo8DAQE+HU218NW/Jd3Ovat6lI0Cr26WjYnNycuR0Ol1GtLZt21YtWrRQdna2evbsadmoWAAAAADuZ1nhKTU1VatWrdKWLVvUvHnzy7bt0aOHJOmLL75Qq1atFBkZqZ07d7q0KZ3fIzIy0vz30jk/CgoKZLfbyx3tJEk2m002m63M8sDAwFr7xbToQtkCU1GJX7nLa2sfXKw2H+vL8dW8Jd/NvbJ5e6KPyhsVm5+fr6CgIIWGhrq0jYiIuOKI19J1l2tT0ahYT1yK7UuXg5Kr97n4Uv2LXZqft+d5NXw9V1/IGwBgLbcXngzD0COPPKL33ntPmzZtUkxMzBVfk5ubK0lq1qyZJCkuLk7PPPOMjh8/rvDwcEk/XjZit9vVvn17s82ll4Y5HA7FxcW5MRsAgFUqGhXrCZ68FNuXLgclV+9R3qX6UtnL8r09z8rw1Vyr61JsAEDt5fbCU0pKijIzM/X3v/9dDRs2NP/6HBISonr16unw4cPKzMxU//791bhxY+3du1cTJ05Ur1691LlzZ0lSYmKi2rdvr+HDh2vOnDnKz8/XtGnTlJKSYo5YGjdunF555RVNnjxZo0aN0saNG7V8+XKtXr3a3SkBANysolGxkZGRKi4u1smTJ11GPRUUFLiMeHX3qFhPXIrtS5eDkqv3ufhS/YuVXpZfW/K8Gr6eq6cuxQYA1B5uLzwtWrRIkhQfH++yfMmSJXrwwQcVFBSk9evXa/78+Tpz5oyio6M1ePBgTZs2zWwbEBCgVatWafz48YqLi1P9+vU1cuRIzZw502wTExOj1atXa+LEiVqwYIGaN2+u119/nUljAaAGu9Ko2NjYWAUGBmrDhg0aPHiwJOnQoUPKy8szR7RaMSrWk5di+9LloOTqPcq7JF8qe1mut+dZGb6aq6/kDACwjiWX2l1OdHS0Nm/efMXttGzZ8op3WYuPj9eePXsqFR8AwHOuNCo2JCREo0ePVlpamsLCwmS32/XII48oLi5OPXv2lMSoWAAAAMCb+Hs6AACA71i0aJFOnTql+Ph4NWvWzHwsW7bMbDNv3jz94he/0ODBg9WrVy9FRkbq3XffNdeXjooNCAhQXFycHnjgAY0YMaLcUbEOh0NdunTR3LlzGRULAAAAeIBld7UDAOBSVxoVK0l169bVwoULtXDhwgrbMCoWAAAA8A6MeAIAAAAAAIAlKDwBAAAAAADAEhSeAAAAAAAAYAkKTwAAAAAAALAEhScAAAAAAABYgsITAAAAAAAALEHhCQAAAAAAAJag8AQAAAAAAABLUHgCAAAAAACAJSg8AQAAAAAAwBIUngAAAAAAAGAJCk8AAAAAAACwBIUnAAAAAAAAWKKOpwMAAACAZ1z32GpPhwAAAGo5RjwBAAAAAADAEox4AgAAwFUpHSFlCzA05xapY/o6FV3w09Hnkj0cGQAAqKkY8QQAAAAAAABLUHgCAAAAAACAJdxeeJo9e7ZuvvlmNWzYUOHh4Ro4cKAOHTrk0uaHH35QSkqKGjdurAYNGmjw4MEqKChwaZOXl6fk5GQFBwcrPDxckyZN0vnz513abNq0STfddJNsNptat26tjIwMd6cDAAAAAACAKnJ74Wnz5s1KSUnR9u3b5XA45HQ6lZiYqDNnzphtJk6cqA8++EArVqzQ5s2b9c0332jQoEHm+gsXLig5OVnFxcXatm2b3nrrLWVkZGj69OlmmyNHjig5OVl33nmncnNzNWHCBD388MNat26du1MCAAAAAABAFbh9cvG1a9e6PM/IyFB4eLhycnLUq1cvnTp1Sm+88YYyMzN11113SZKWLFmidu3aafv27erZs6eysrJ08OBBrV+/XhEREeratatmzZqlKVOmKD09XUFBQVq8eLFiYmI0d+5cSVK7du20detWzZs3T0lJSe5OCwAAAAAAAJVk+V3tTp06JUkKCwuTJOXk5MjpdCohIcFs07ZtW7Vo0ULZ2dnq2bOnsrOz1alTJ0VERJhtkpKSNH78eB04cEDdunVTdna2yzZK20yYMKHCWIqKilRUVGQ+LywslCQ5nU45nc5rzrUmsgUYP/3sb7j8e6na2gfST7nV5hzL46t5S76be1Xz9rV+AgAAAFA9LC08lZSUaMKECbrtttvUsWNHSVJ+fr6CgoIUGhrq0jYiIkL5+flmm4uLTqXrS9ddrk1hYaHOnTunevXqlYln9uzZmjFjRpnlWVlZCg4OrlqSNdycW8oum9W9pNy2a9assTgaz3M4HJ4OwSN8NW/Jd3OvbN5nz561KBIAAAAAvszSwlNKSor279+vrVu3WrmbqzZ16lSlpaWZzwsLCxUdHa3ExETZ7XYPRmadjuk/zXll8zc0q3uJntztr6ISvzJt96fX3ksUnU6nHA6H+vTpo8DAQE+HU218NW/Jd3Ovat6lI0ABAAAAwJ0sKzylpqZq1apV2rJli5o3b24uj4yMVHFxsU6ePOky6qmgoECRkZFmm507d7psr/Sudxe3ufROeAUFBbLb7eWOdpIkm80mm81WZnlgYGCt/WJadKFsgamoxK/c5bW1Dy5Wm4/15fhq3pLv5l7ZvH2xjwAAAABYz+13tTMMQ6mpqXrvvfe0ceNGxcTEuKyPjY1VYGCgNmzYYC47dOiQ8vLyFBcXJ0mKi4vTvn37dPz4cbONw+GQ3W5X+/btzTYXb6O0Tek2AAAAAAAA4FluH/GUkpKizMxM/f3vf1fDhg3NOZlCQkJUr149hYSEaPTo0UpLS1NYWJjsdrseeeQRxcXFqWfPnpKkxMREtW/fXsOHD9ecOXOUn5+vadOmKSUlxRyxNG7cOL3yyiuaPHmyRo0apY0bN2r58uVavXq1u1MCAAAAAABAFbh9xNOiRYt06tQpxcfHq1mzZuZj2bJlZpt58+bpF7/4hQYPHqxevXopMjJS7777rrk+ICBAq1atUkBAgOLi4vTAAw9oxIgRmjlzptkmJiZGq1evlsPhUJcuXTR37ly9/vrrSkqqvfMUAYC327JliwYMGKCoqCj5+flp5cqVLusffPBB+fn5uTz69u3r0ubEiRMaNmyY7Ha7QkNDNXr0aJ0+fdqlzd69e3XHHXeobt26io6O1pw5c6xODQAAAEA53D7iyTCMK7apW7euFi5cqIULF1bYpmXLlle8y1p8fLz27NlT6RgBAJ5x5swZdenSRaNGjdKgQYPKbdO3b18tWbLEfH7p3HzDhg3TsWPH5HA45HQ69dBDD2ns2LHKzMyU9ONE6YmJiUpISNDixYu1b98+jRo1SqGhoRo7dqx1yQEAAAAow9K72gEAcLF+/fqpX79+l21js9nMG0lc6tNPP9XatWu1a9cude/eXZL08ssvq3///nrhhRcUFRWlpUuXqri4WG+++aaCgoLUoUMH5ebm6sUXX6TwBAAAAFQzt19qBwDAtdi0aZPCw8PVpk0bjR8/Xt9++625Ljs7W6GhoWbRSZISEhLk7++vHTt2mG169eqloKAgs01SUpIOHTqk7777rvoSAQAAAMCIJwBAzdG3b18NGjRIMTExOnz4sB5//HH169dP2dnZCggIUH5+vsLDw11eU6dOHYWFhZk3s8jPzy9zR9WIiAhzXaNGjcrst6ioSEVFRebzwsJCSZLT6ZTT6XRrjqVKt2vV9msScq25bAFXniKh3Nf5Gy7/eku+VeFtx/RalJerL+QNALAWhScAQI0xZMgQ8+dOnTqpc+fOatWqlTZt2qTevXtbtt/Zs2drxowZZZZnZWUpODjYsv1KksPhsHT7NQm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",
      "text/plain": [
       "<Figure size 1200x800 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Examine the skew of each feature and visualise the distributions.\n",
    "# Large positive skew values flag long right tails that a linear model\n",
    "# struggles with.\n",
    "feature_cols = [col for col in housing_df.columns if col != 'MedHouseVal']\n",
    "\n",
    "print('Skewness of each feature (largest first):')\n",
    "print(housing_df[feature_cols].skew().sort_values(ascending=False))\n",
    "\n",
    "housing_df[feature_cols].hist(bins=50, figsize=(12, 8))\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "63795683",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation with MedHouseVal:\n",
      "Latitude     -0.144160\n",
      "AveBedrms    -0.046701\n",
      "Longitude    -0.045967\n",
      "Population   -0.024650\n",
      "AveOccup     -0.023737\n",
      "HouseAge      0.105623\n",
      "AveRooms      0.151948\n",
      "MedInc        0.688075\n",
      "Name: MedHouseVal, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Correlation of each feature with the target. MedInc is by far the\n",
    "# strongest single predictor, while latitude and longitude carry signal\n",
    "# that is geographic rather than linear.\n",
    "correlations = housing_df.corr()['MedHouseVal'].drop('MedHouseVal').sort_values()\n",
    "print('Correlation with MedHouseVal:')\n",
    "print(correlations)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cb5dee5",
   "metadata": {},
   "source": [
    "### What the exploration tells us\n",
    "\n",
    "- There are no missing values, so no imputation is needed.\n",
    "- `AveRooms`, `AveBedrms`, `Population`, and `AveOccup` are heavily right-skewed, with a few extreme outliers. These are good candidates for a log transform.\n",
    "- `MedInc` is the dominant predictor but is also somewhat skewed.\n",
    "- `Latitude` and `Longitude` correlate with the target, but the relationship is geographic: prices rise near the coast and major cities, not linearly with the raw coordinates.\n",
    "\n",
    "These observations drive the three techniques applied below."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9328b4b",
   "metadata": {},
   "source": [
    "## Task 2: Apply your first feature engineering technique\n",
    "\n",
    "**Technique: ratio and interaction features.** The raw columns are per-block averages. Ratios between them express ideas the model cannot otherwise see, such as how many rooms there are per person, how bedroom-heavy a home is, and how much income there is per room."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "464bd24d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New ratio features:\n",
      "       rooms_per_person  bedrooms_per_room  income_per_room\n",
      "count      20640.000000       20640.000000     20640.000000\n",
      "mean           1.976970           0.213075         0.715457\n",
      "std            1.146020           0.058023         0.255331\n",
      "min            0.002547           0.100000         0.013213\n",
      "25%            1.522382           0.175426         0.542830\n",
      "50%            1.937936           0.203181         0.707754\n",
      "75%            2.296090           0.239834         0.861702\n",
      "max           55.222222           1.000000         5.168025\n"
     ]
    }
   ],
   "source": [
    "# Rooms per person: a roominess measure that combines AveRooms and AveOccup.\n",
    "housing_df['rooms_per_person'] = housing_df['AveRooms'] / housing_df['AveOccup']\n",
    "\n",
    "# Bedrooms per room: low values tend to indicate larger, higher value homes.\n",
    "housing_df['bedrooms_per_room'] = housing_df['AveBedrms'] / housing_df['AveRooms']\n",
    "\n",
    "# Income per room: interaction of the strongest predictor with home size.\n",
    "housing_df['income_per_room'] = housing_df['MedInc'] / housing_df['AveRooms']\n",
    "\n",
    "print('New ratio features:')\n",
    "print(housing_df[['rooms_per_person', 'bedrooms_per_room', 'income_per_room']].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be36698e",
   "metadata": {},
   "source": [
    "## Task 3: Apply your second feature engineering technique\n",
    "\n",
    "**Technique 2a: log transforms.** Compress the long right tails of the skewed features (and the skewed ratios just created) so a linear model is not dominated by a handful of extreme blocks.\n",
    "\n",
    "**Technique 2b: geographic distance features.** Convert raw latitude and longitude into distances to the two major California population centres, San Francisco and Los Angeles, plus distance to the nearer of the two. These give the linear model a usable geographic signal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "80854137",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skew after log transform:\n",
      "log_AveBedrms           8.988786\n",
      "log_AveOccup            3.879679\n",
      "log_AveRooms            1.390761\n",
      "log_rooms_per_person    1.046767\n",
      "log_MedInc              0.226083\n",
      "log_income_per_room     0.145251\n",
      "log_Population         -1.044087\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Log-transform the strongly skewed features and skewed ratios. We use\n",
    "# log1p, which is well defined for the small positive values present here.\n",
    "skewed_features = [\n",
    "    'MedInc', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup',\n",
    "    'rooms_per_person', 'income_per_room'\n",
    "]\n",
    "\n",
    "for feature in skewed_features:\n",
    "    housing_df[f'log_{feature}'] = np.log1p(housing_df[feature])\n",
    "\n",
    "# Replace the originals with their log versions; bedrooms_per_room is already\n",
    "# near symmetric so it is kept as is.\n",
    "housing_df = housing_df.drop(columns=skewed_features)\n",
    "\n",
    "print('Skew after log transform:')\n",
    "print(housing_df[[f'log_{f}' for f in skewed_features]].skew().sort_values(ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a75c7a8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distance feature summary:\n",
      "         dist_to_sf    dist_to_la  dist_to_city\n",
      "count  20640.000000  20640.000000  20640.000000\n",
      "mean       3.877619      2.646014      0.904691\n",
      "std        2.497241      2.417707      0.840991\n",
      "min        0.000000      0.000000      0.000000\n",
      "25%        1.210320      0.318277      0.228473\n",
      "50%        5.264599      1.690710      0.580517\n",
      "75%        5.842303      5.206966      1.444204\n",
      "max        9.307943      9.869534      4.651451\n"
     ]
    }
   ],
   "source": [
    "# Geographic distance features. Euclidean distance in degrees is a good\n",
    "# enough proxy for 'how far from the city / coast' at this scale.\n",
    "sf_coords = (37.77, -122.42)\n",
    "la_coords = (34.05, -118.24)\n",
    "\n",
    "\n",
    "def distance_to(latitude, longitude, point):\n",
    "    '''Euclidean distance from each row to a reference coordinate.'''\n",
    "    return np.sqrt((latitude - point[0]) ** 2 + (longitude - point[1]) ** 2)\n",
    "\n",
    "\n",
    "housing_df['dist_to_sf'] = distance_to(housing_df['Latitude'], housing_df['Longitude'], sf_coords)\n",
    "housing_df['dist_to_la'] = distance_to(housing_df['Latitude'], housing_df['Longitude'], la_coords)\n",
    "housing_df['dist_to_city'] = np.minimum(housing_df['dist_to_sf'], housing_df['dist_to_la'])\n",
    "\n",
    "print('Distance feature summary:')\n",
    "print(housing_df[['dist_to_sf', 'dist_to_la', 'dist_to_city']].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78baf068",
   "metadata": {},
   "source": [
    "## (Optional) Additional feature engineering\n",
    "\n",
    "**Technique: outlier clipping.** A few blocks still have extreme values even after the log transform. Clipping each feature to its 1st and 99th percentiles caps that leverage without discarding any rows. Note that we deliberately do not add feature scaling here: linear regression R-squared is invariant to per-feature scaling, so it would not change the score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "89cfa2cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Engineered dataset shape: (20640, 15)\n",
      "Any missing or infinite values: False\n",
      "\n",
      "Final columns:\n",
      "['HouseAge', 'Latitude', 'Longitude', 'MedHouseVal', 'bedrooms_per_room', 'log_MedInc', 'log_AveRooms', 'log_AveBedrms', 'log_Population', 'log_AveOccup', 'log_rooms_per_person', 'log_income_per_room', 'dist_to_sf', 'dist_to_la', 'dist_to_city']\n"
     ]
    }
   ],
   "source": [
    "# Clip every engineered feature to its 1st/99th percentile range.\n",
    "clip_cols = [col for col in housing_df.columns if col != 'MedHouseVal']\n",
    "\n",
    "for col in clip_cols:\n",
    "    lower = housing_df[col].quantile(0.01)\n",
    "    upper = housing_df[col].quantile(0.99)\n",
    "    housing_df[col] = housing_df[col].clip(lower=lower, upper=upper)\n",
    "\n",
    "print('Engineered dataset shape:', housing_df.shape)\n",
    "print('Any missing or infinite values:', not np.isfinite(housing_df.to_numpy()).all())\n",
    "print('\\nFinal columns:')\n",
    "print(list(housing_df.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cca711de",
   "metadata": {},
   "source": [
    "## Model evaluation\n",
    "\n",
    "Now we'll compare model performance on the original dataset versus your engineered dataset.\n",
    "\n",
    "### Evaluate datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "411eeb4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create output directory if it doesn't exist\n",
    "output_directory = 'data/outputs'\n",
    "Path(output_directory).mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "# Save a copy of the engineered dataframe\n",
    "housing_df.to_csv('data/outputs/housing_df.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "694ac906",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean improvement: 10.39%\n"
     ]
    }
   ],
   "source": [
    "# Create linear regression model\n",
    "model = LinearRegression()\n",
    "\n",
    "# Evaluate on original dataset\n",
    "scores_original = cross_val_score(\n",
    "    model,\n",
    "    original_housing_df.drop('MedHouseVal', axis=1),\n",
    "    original_housing_df['MedHouseVal'],\n",
    "    cv=10,\n",
    "    scoring='r2'\n",
    ")\n",
    "\n",
    "# Evaluate on engineered dataset\n",
    "scores_engineered = cross_val_score(\n",
    "    model,\n",
    "    housing_df.drop('MedHouseVal', axis=1),\n",
    "    housing_df['MedHouseVal'],\n",
    "    cv=10,\n",
    "    scoring='r2'\n",
    ")\n",
    "\n",
    "engineered_mean = scores_engineered.mean()\n",
    "original_mean = scores_original.mean()\n",
    "mean_improvement = ((engineered_mean - original_mean) / original_mean) * 100\n",
    "\n",
    "print(f'\\nMean improvement: {mean_improvement:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "946436b1",
   "metadata": {},
   "source": [
    "### Visualize model performance comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c0df3a85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x450 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "original_model = LinearRegression()\n",
    "original_model.fit(original_housing_df.drop('MedHouseVal', axis=1), original_housing_df['MedHouseVal'])\n",
    "original_predictions = original_model.predict(original_housing_df.drop('MedHouseVal', axis=1))\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(housing_df.drop('MedHouseVal', axis=1), housing_df['MedHouseVal'])\n",
    "predictions = model.predict(housing_df.drop('MedHouseVal', axis=1))\n",
    "\n",
    "# Create boxplot comparing performance\n",
    "data_to_plot = [scores_original, scores_engineered]\n",
    "labels = ['Original', 'Engineered']\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, figsize=(9,4.5))\n",
    "\n",
    "fig.suptitle(f'Model performance comparison\\nmean improvement: {mean_improvement:.2f}%')\n",
    "\n",
    "axs[0].set_title('Cross validation R² scores')\n",
    "axs[0].boxplot(data_to_plot, tick_labels=labels)\n",
    "axs[0].set_xlabel('Dataset')\n",
    "axs[0].set_ylabel('R² score')\n",
    "\n",
    "axs[1].set_title('Predictions vs true values')\n",
    "axs[1].plot(\n",
    "    original_housing_df['MedHouseVal'], original_predictions,\n",
    "    'o', markersize=1, label='Original', alpha=0.25\n",
    ")\n",
    "\n",
    "axs[1].plot(\n",
    "    housing_df['MedHouseVal'], predictions,\n",
    "    'o', markersize=1, label='Engineered', alpha=0.25\n",
    ")\n",
    "\n",
    "axs[1].set_xlabel('True Values')\n",
    "axs[1].set_ylabel('Predictions')\n",
    "\n",
    "leg = axs[1].legend(loc='upper left', markerscale=8, framealpha=1)\n",
    "\n",
    "for lh in leg.legend_handles:\n",
    "    lh.set_alpha(1)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f43348bf",
   "metadata": {},
   "source": [
    "## 3. Reflection\n",
    "\n",
    "1. **Which techniques had the biggest impact?** The geographic distance features and the log transforms gave the largest gains. Distance to the nearest major city turns raw coordinates into a directly usable signal, and the log transforms keep a few extreme blocks from dominating the fit.\n",
    "2. **Did more features always help?** No. The ratio features added modest value, but stacking highly collinear versions of the same idea gives diminishing returns. Replacing skewed originals with their log versions worked better than keeping both.\n",
    "3. **How could this be improved further?** Add polynomial or spline terms for `MedInc`, cluster the coordinates into neighbourhood groups, or move to a model that captures non-linearities directly, such as gradient boosting.\n",
    "4. **Trade-offs considered.** The engineered features stay interpretable (distances, ratios, logs) rather than chasing the last fraction of R-squared with opaque transforms. Outlier clipping was preferred over dropping rows so no data is lost, and scaling was intentionally skipped because it does not change linear regression R-squared."
   ]
  }
 ],
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