rganeeva-ds1-solution.ipynb

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     "text": [
      "/kaggle/input/real-estate-price-prediction-moscow/sample_submission.csv\n",
      "/kaggle/input/real-estate-price-prediction-moscow/train.csv\n",
      "/kaggle/input/real-estate-price-prediction-moscow/test.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
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   "id": "tribal-church",
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.preprocessing import StandardScaler, RobustScaler\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import r2_score as r2\n",
    "from sklearn.model_selection import KFold, GridSearchCV\n",
    "\n",
    "from datetime import datetime\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "beginning-highlight",
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    },
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   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bored-caribbean",
   "metadata": {
    "execution": {
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   "source": [
    "matplotlib.rcParams.update({'font.size': 14})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "lonely-florida",
   "metadata": {
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    },
    "tags": []
   },
   "source": [
    "Функция для подсчета метрик"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "swiss-aquatic",
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   "source": [
    "def evaluate_preds(train_true_values, train_pred_values, test_true_values, test_pred_values):\n",
    "    print(\"Train R2:\\t\" + str(round(r2(train_true_values, train_pred_values), 3)))\n",
    "    print(\"Test R2:\\t\" + str(round(r2(test_true_values, test_pred_values), 3)))\n",
    "    \n",
    "    plt.figure(figsize=(18,10))\n",
    "    \n",
    "    plt.subplot(121)\n",
    "    sns.scatterplot(x=train_pred_values, y=train_true_values)\n",
    "    plt.xlabel('Predicted values')\n",
    "    plt.ylabel('True values')\n",
    "    plt.title('Train sample prediction')\n",
    "    \n",
    "    plt.subplot(122)\n",
    "    sns.scatterplot(x=test_pred_values, y=test_true_values)\n",
    "    plt.xlabel('Predicted values')\n",
    "    plt.ylabel('True values')\n",
    "    plt.title('Test sample prediction')\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "brilliant-titanium",
   "metadata": {
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     "start_time": "2021-06-28T14:43:11.011466",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Пути к директориям и файлам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "rising-sampling",
   "metadata": {
    "execution": {
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     "status": "completed"
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   "outputs": [],
   "source": [
    "TRAIN_DATASET_PATH = '../input/real-estate-price-prediction-moscow/train.csv'\n",
    "TEST_DATASET_PATH = '../input/real-estate-price-prediction-moscow/test.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "utility-school",
   "metadata": {
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    },
    "tags": []
   },
   "source": [
    "**Загрузка данных**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "raised-gothic",
   "metadata": {
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     "duration": 0.076823,
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   "source": [
    "**Описание датасета**\n",
    "\n",
    "* Id - идентификационный номер квартиры\n",
    "* DistrictId - идентификационный номер района\n",
    "* Rooms - количество комнат\n",
    "* Square - площадь\n",
    "* LifeSquare - жилая площадь\n",
    "* KitchenSquare - площадь кухни\n",
    "* Floor - этаж\n",
    "* HouseFloor - количество этажей в доме\n",
    "* HouseYear - год постройки дома\n",
    "* Ecology_1, Ecology_2, Ecology_3 - экологические показатели местности\n",
    "* Social_1, Social_2, Social_3 - социальные показатели местности\n",
    "* Healthcare_1, Helthcare_2 - показатели местности, связанные с охраной здоровья\n",
    "* Shops_1, Shops_2 - показатели, связанные с наличием магазинов, торговых центров\n",
    "* Price - цена квартиры"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "choice-dryer",
   "metadata": {
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    },
    "tags": []
   },
   "source": [
    "Признаки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "focused-waste",
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    "execution": {
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       "      <th>Ecology_1</th>\n",
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       "      <th>Ecology_3</th>\n",
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       "      <td>52</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>37.038420</td>\n",
       "      <td>9.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>5</td>\n",
       "      <td>B</td>\n",
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       "      <th>9998</th>\n",
       "      <td>14561</td>\n",
       "      <td>21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.330292</td>\n",
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       "      <td>3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1969</td>\n",
       "      <td>0.194489</td>\n",
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       "      <td>9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1975</td>\n",
       "      <td>0.127376</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
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      "text/plain": [
       "         Id  DistrictId  Rooms     Square  LifeSquare  KitchenSquare  Floor  \\\n",
       "9995   1260          61    2.0  49.090728   33.272626            6.0      3   \n",
       "9996  16265          27    2.0  64.307684   37.038420            9.0     13   \n",
       "9997   2795         178    1.0  29.648057   16.555363            5.0      3   \n",
       "9998  14561          21    1.0  32.330292   22.326870            5.0      3   \n",
       "9999   7202          94    1.0  35.815476   22.301367            6.0      9   \n",
       "\n",
       "      HouseFloor  HouseYear  Ecology_1 Ecology_2 Ecology_3  Social_1  \\\n",
       "9995        12.0       1981   0.300323         B         B        52   \n",
       "9996         0.0       1977   0.072158         B         B         2   \n",
       "9997         5.0       1958   0.460556         B         B        20   \n",
       "9998         9.0       1969   0.194489         B         B        47   \n",
       "9999         9.0       1975   0.127376         B         B        43   \n",
       "\n",
       "      Social_2  Social_3  Healthcare_1  Helthcare_2  Shops_1 Shops_2  \\\n",
       "9995     10311         6           NaN            1        9       B   \n",
       "9996       629         1           NaN            0        0       A   \n",
       "9997      4386        14           NaN            1        5       B   \n",
       "9998      8004         3         125.0            3        5       B   \n",
       "9999      8429         3           NaN            3        9       B   \n",
       "\n",
       "              Price  \n",
       "9995  119367.455796  \n",
       "9996  199715.148807  \n",
       "9997  165953.912580  \n",
       "9998  171842.411855  \n",
       "9999  177685.627486  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.read_csv(TRAIN_DATASET_PATH)\n",
    "train_df.tail()"
   ]
  },
  {
   "cell_type": "code",
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   "id": "manual-clinic",
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   "outputs": [
    {
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       "Id                 int64\n",
       "DistrictId         int64\n",
       "Rooms            float64\n",
       "Square           float64\n",
       "LifeSquare       float64\n",
       "KitchenSquare    float64\n",
       "Floor              int64\n",
       "HouseFloor       float64\n",
       "HouseYear          int64\n",
       "Ecology_1        float64\n",
       "Ecology_2         object\n",
       "Ecology_3         object\n",
       "Social_1           int64\n",
       "Social_2           int64\n",
       "Social_3           int64\n",
       "Healthcare_1     float64\n",
       "Helthcare_2        int64\n",
       "Shops_1            int64\n",
       "Shops_2           object\n",
       "Price            float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "narrative-guyana",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:12.270826Z",
     "iopub.status.busy": "2021-06-28T14:43:12.270127Z",
     "iopub.status.idle": "2021-06-28T14:43:12.315401Z",
     "shell.execute_reply": "2021-06-28T14:43:12.315904Z",
     "shell.execute_reply.started": "2021-06-28T14:36:15.520203Z"
    },
    "papermill": {
     "duration": 0.126753,
     "end_time": "2021-06-28T14:43:12.316081",
     "exception": false,
     "start_time": "2021-06-28T14:43:12.189328",
     "status": "completed"
    },
    "tags": []
   },
   "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>Id</th>\n",
       "      <th>DistrictId</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Square</th>\n",
       "      <th>LifeSquare</th>\n",
       "      <th>KitchenSquare</th>\n",
       "      <th>Floor</th>\n",
       "      <th>HouseFloor</th>\n",
       "      <th>HouseYear</th>\n",
       "      <th>Ecology_1</th>\n",
       "      <th>Ecology_2</th>\n",
       "      <th>Ecology_3</th>\n",
       "      <th>Social_1</th>\n",
       "      <th>Social_2</th>\n",
       "      <th>Social_3</th>\n",
       "      <th>Healthcare_1</th>\n",
       "      <th>Helthcare_2</th>\n",
       "      <th>Shops_1</th>\n",
       "      <th>Shops_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>10379</td>\n",
       "      <td>29</td>\n",
       "      <td>2.0</td>\n",
       "      <td>43.177521</td>\n",
       "      <td>30.339945</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1962</td>\n",
       "      <td>0.069660</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>31</td>\n",
       "      <td>6119</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>16138</td>\n",
       "      <td>38</td>\n",
       "      <td>3.0</td>\n",
       "      <td>93.698122</td>\n",
       "      <td>94.521465</td>\n",
       "      <td>10.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.060753</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>15</td>\n",
       "      <td>2787</td>\n",
       "      <td>2</td>\n",
       "      <td>520.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>3912</td>\n",
       "      <td>101</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.656723</td>\n",
       "      <td>19.003259</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1966</td>\n",
       "      <td>0.038693</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>28</td>\n",
       "      <td>6533</td>\n",
       "      <td>1</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>5722</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38.635155</td>\n",
       "      <td>20.976257</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.089040</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>33</td>\n",
       "      <td>7976</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>11004</td>\n",
       "      <td>21</td>\n",
       "      <td>2.0</td>\n",
       "      <td>67.122742</td>\n",
       "      <td>33.944344</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.194489</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>47</td>\n",
       "      <td>8004</td>\n",
       "      <td>3</td>\n",
       "      <td>125.0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Id  DistrictId  Rooms     Square  LifeSquare  KitchenSquare  Floor  \\\n",
       "4995  10379          29    2.0  43.177521   30.339945            5.0      6   \n",
       "4996  16138          38    3.0  93.698122   94.521465           10.0     21   \n",
       "4997   3912         101    1.0  33.656723   19.003259            5.0      2   \n",
       "4998   5722          10    1.0  38.635155   20.976257            9.0      8   \n",
       "4999  11004          21    2.0  67.122742   33.944344           13.0      9   \n",
       "\n",
       "      HouseFloor  HouseYear  Ecology_1 Ecology_2 Ecology_3  Social_1  \\\n",
       "4995         5.0       1962   0.069660         B         B        31   \n",
       "4996        27.0       2018   0.060753         B         B        15   \n",
       "4997         5.0       1966   0.038693         B         B        28   \n",
       "4998        14.0       1970   0.089040         B         B        33   \n",
       "4999        17.0       2009   0.194489         B         B        47   \n",
       "\n",
       "      Social_2  Social_3  Healthcare_1  Helthcare_2  Shops_1 Shops_2  \n",
       "4995      6119         4           NaN            1        2       B  \n",
       "4996      2787         2         520.0            0        7       B  \n",
       "4997      6533         1        1015.0            2        5       B  \n",
       "4998      7976         5           NaN            0       11       B  \n",
       "4999      8004         3         125.0            3        5       B  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.read_csv(TEST_DATASET_PATH)\n",
    "test_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "enclosed-barrel",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:12.475139Z",
     "iopub.status.busy": "2021-06-28T14:43:12.474191Z",
     "iopub.status.idle": "2021-06-28T14:43:12.481574Z",
     "shell.execute_reply": "2021-06-28T14:43:12.480690Z",
     "shell.execute_reply.started": "2021-06-28T14:36:15.570554Z"
    },
    "papermill": {
     "duration": 0.088119,
     "end_time": "2021-06-28T14:43:12.481771",
     "exception": false,
     "start_time": "2021-06-28T14:43:12.393652",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Строк в трейне: 10000\n",
      "Строк в тесте 5000\n"
     ]
    }
   ],
   "source": [
    "print('Строк в трейне:', train_df.shape[0])\n",
    "print('Строк в тесте', test_df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "capital-geneva",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:12.643867Z",
     "iopub.status.busy": "2021-06-28T14:43:12.643205Z",
     "iopub.status.idle": "2021-06-28T14:43:12.646015Z",
     "shell.execute_reply": "2021-06-28T14:43:12.646580Z",
     "shell.execute_reply.started": "2021-06-28T14:36:15.579142Z"
    },
    "papermill": {
     "duration": 0.085988,
     "end_time": "2021-06-28T14:43:12.646759",
     "exception": false,
     "start_time": "2021-06-28T14:43:12.560771",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape[1] - 1 == test_df.shape[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ruled-explosion",
   "metadata": {
    "papermill": {
     "duration": 0.077563,
     "end_time": "2021-06-28T14:43:12.802834",
     "exception": false,
     "start_time": "2021-06-28T14:43:12.725271",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Приведение типов**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "human-rocket",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:12.962659Z",
     "iopub.status.busy": "2021-06-28T14:43:12.961991Z",
     "iopub.status.idle": "2021-06-28T14:43:12.970054Z",
     "shell.execute_reply": "2021-06-28T14:43:12.969518Z",
     "shell.execute_reply.started": "2021-06-28T14:36:15.594005Z"
    },
    "papermill": {
     "duration": 0.08921,
     "end_time": "2021-06-28T14:43:12.970235",
     "exception": false,
     "start_time": "2021-06-28T14:43:12.881025",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                 int64\n",
       "DistrictId         int64\n",
       "Rooms            float64\n",
       "Square           float64\n",
       "LifeSquare       float64\n",
       "KitchenSquare    float64\n",
       "Floor              int64\n",
       "HouseFloor       float64\n",
       "HouseYear          int64\n",
       "Ecology_1        float64\n",
       "Ecology_2         object\n",
       "Ecology_3         object\n",
       "Social_1           int64\n",
       "Social_2           int64\n",
       "Social_3           int64\n",
       "Healthcare_1     float64\n",
       "Helthcare_2        int64\n",
       "Shops_1            int64\n",
       "Shops_2           object\n",
       "Price            float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "political-freight",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:13.148203Z",
     "iopub.status.busy": "2021-06-28T14:43:13.147507Z",
     "iopub.status.idle": "2021-06-28T14:43:13.165057Z",
     "shell.execute_reply": "2021-06-28T14:43:13.165534Z",
     "shell.execute_reply.started": "2021-06-28T14:36:15.606422Z"
    },
    "papermill": {
     "duration": 0.115927,
     "end_time": "2021-06-28T14:43:13.165733",
     "exception": false,
     "start_time": "2021-06-28T14:43:13.049806",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_df['Id'] = train_df['Id'].astype(str)\n",
    "train_df['DistrictId'] = train_df['DistrictId'].astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gothic-rings",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-23T13:09:12.931077Z",
     "iopub.status.busy": "2021-06-23T13:09:12.930669Z",
     "iopub.status.idle": "2021-06-23T13:09:12.936318Z",
     "shell.execute_reply": "2021-06-23T13:09:12.935165Z",
     "shell.execute_reply.started": "2021-06-23T13:09:12.931045Z"
    },
    "papermill": {
     "duration": 0.100212,
     "end_time": "2021-06-28T14:43:13.348415",
     "exception": false,
     "start_time": "2021-06-28T14:43:13.248203",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **1. EDA (Разведочный анализ данных)**\n",
    "Делаем EDA для:\n",
    "\n",
    "* Исправления выбросов\n",
    "* Заполнения NaN\n",
    "* Идей для генерации новых фич"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "square-guidance",
   "metadata": {
    "papermill": {
     "duration": 0.081056,
     "end_time": "2021-06-28T14:43:13.509918",
     "exception": false,
     "start_time": "2021-06-28T14:43:13.428862",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Целевая переменная"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "right-grounds",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:13.678670Z",
     "iopub.status.busy": "2021-06-28T14:43:13.677899Z",
     "iopub.status.idle": "2021-06-28T14:43:13.968970Z",
     "shell.execute_reply": "2021-06-28T14:43:13.967818Z",
     "shell.execute_reply.started": "2021-06-28T14:36:15.654870Z"
    },
    "papermill": {
     "duration": 0.372358,
     "end_time": "2021-06-28T14:43:13.969162",
     "exception": false,
     "start_time": "2021-06-28T14:43:13.596804",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (16, 8))\n",
    "\n",
    "train_df['Price'].hist(bins=30)\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('Price')\n",
    "\n",
    "plt.title('Target distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "genetic-spray",
   "metadata": {
    "papermill": {
     "duration": 0.081311,
     "end_time": "2021-06-28T14:43:14.134433",
     "exception": false,
     "start_time": "2021-06-28T14:43:14.053122",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Количественные переменные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "considered-activity",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:14.301444Z",
     "iopub.status.busy": "2021-06-28T14:43:14.300741Z",
     "iopub.status.idle": "2021-06-28T14:43:14.363528Z",
     "shell.execute_reply": "2021-06-28T14:43:14.364031Z",
     "shell.execute_reply.started": "2021-06-28T14:36:15.918635Z"
    },
    "papermill": {
     "duration": 0.150399,
     "end_time": "2021-06-28T14:43:14.364229",
     "exception": false,
     "start_time": "2021-06-28T14:43:14.213830",
     "status": "completed"
    },
    "tags": []
   },
   "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>Rooms</th>\n",
       "      <th>Square</th>\n",
       "      <th>LifeSquare</th>\n",
       "      <th>KitchenSquare</th>\n",
       "      <th>Floor</th>\n",
       "      <th>HouseFloor</th>\n",
       "      <th>HouseYear</th>\n",
       "      <th>Ecology_1</th>\n",
       "      <th>Social_1</th>\n",
       "      <th>Social_2</th>\n",
       "      <th>Social_3</th>\n",
       "      <th>Healthcare_1</th>\n",
       "      <th>Helthcare_2</th>\n",
       "      <th>Shops_1</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>7887.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>1.000000e+04</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>5202.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.890500</td>\n",
       "      <td>56.315775</td>\n",
       "      <td>37.199645</td>\n",
       "      <td>6.273300</td>\n",
       "      <td>8.526700</td>\n",
       "      <td>12.609400</td>\n",
       "      <td>3.990166e+03</td>\n",
       "      <td>0.118858</td>\n",
       "      <td>24.687000</td>\n",
       "      <td>5352.157400</td>\n",
       "      <td>8.039200</td>\n",
       "      <td>1142.904460</td>\n",
       "      <td>1.319500</td>\n",
       "      <td>4.231300</td>\n",
       "      <td>214138.857399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.839512</td>\n",
       "      <td>21.058732</td>\n",
       "      <td>86.241209</td>\n",
       "      <td>28.560917</td>\n",
       "      <td>5.241148</td>\n",
       "      <td>6.775974</td>\n",
       "      <td>2.005003e+05</td>\n",
       "      <td>0.119025</td>\n",
       "      <td>17.532614</td>\n",
       "      <td>4006.799803</td>\n",
       "      <td>23.831875</td>\n",
       "      <td>1021.517264</td>\n",
       "      <td>1.493601</td>\n",
       "      <td>4.806341</td>\n",
       "      <td>92872.293865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.136859</td>\n",
       "      <td>0.370619</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.910000e+03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>59174.778028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>41.774881</td>\n",
       "      <td>22.769832</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.974000e+03</td>\n",
       "      <td>0.017647</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1564.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153872.633942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>52.513310</td>\n",
       "      <td>32.781260</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.977000e+03</td>\n",
       "      <td>0.075424</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>5285.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>192269.644879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>65.900625</td>\n",
       "      <td>45.128803</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>2.001000e+03</td>\n",
       "      <td>0.195781</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>7227.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1548.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>249135.462171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>641.065193</td>\n",
       "      <td>7480.592129</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>2.005201e+07</td>\n",
       "      <td>0.521867</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>19083.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>4849.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>633233.466570</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Rooms        Square   LifeSquare  KitchenSquare         Floor  \\\n",
       "count  10000.000000  10000.000000  7887.000000   10000.000000  10000.000000   \n",
       "mean       1.890500     56.315775    37.199645       6.273300      8.526700   \n",
       "std        0.839512     21.058732    86.241209      28.560917      5.241148   \n",
       "min        0.000000      1.136859     0.370619       0.000000      1.000000   \n",
       "25%        1.000000     41.774881    22.769832       1.000000      4.000000   \n",
       "50%        2.000000     52.513310    32.781260       6.000000      7.000000   \n",
       "75%        2.000000     65.900625    45.128803       9.000000     12.000000   \n",
       "max       19.000000    641.065193  7480.592129    2014.000000     42.000000   \n",
       "\n",
       "         HouseFloor     HouseYear     Ecology_1      Social_1      Social_2  \\\n",
       "count  10000.000000  1.000000e+04  10000.000000  10000.000000  10000.000000   \n",
       "mean      12.609400  3.990166e+03      0.118858     24.687000   5352.157400   \n",
       "std        6.775974  2.005003e+05      0.119025     17.532614   4006.799803   \n",
       "min        0.000000  1.910000e+03      0.000000      0.000000    168.000000   \n",
       "25%        9.000000  1.974000e+03      0.017647      6.000000   1564.000000   \n",
       "50%       13.000000  1.977000e+03      0.075424     25.000000   5285.000000   \n",
       "75%       17.000000  2.001000e+03      0.195781     36.000000   7227.000000   \n",
       "max      117.000000  2.005201e+07      0.521867     74.000000  19083.000000   \n",
       "\n",
       "           Social_3  Healthcare_1   Helthcare_2       Shops_1          Price  \n",
       "count  10000.000000   5202.000000  10000.000000  10000.000000   10000.000000  \n",
       "mean       8.039200   1142.904460      1.319500      4.231300  214138.857399  \n",
       "std       23.831875   1021.517264      1.493601      4.806341   92872.293865  \n",
       "min        0.000000      0.000000      0.000000      0.000000   59174.778028  \n",
       "25%        0.000000    350.000000      0.000000      1.000000  153872.633942  \n",
       "50%        2.000000    900.000000      1.000000      3.000000  192269.644879  \n",
       "75%        5.000000   1548.000000      2.000000      6.000000  249135.462171  \n",
       "max      141.000000   4849.000000      6.000000     23.000000  633233.466570  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "resident-guess",
   "metadata": {
    "papermill": {
     "duration": 0.079693,
     "end_time": "2021-06-28T14:43:14.523925",
     "exception": false,
     "start_time": "2021-06-28T14:43:14.444232",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Номинативные переменные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "contemporary-assurance",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:14.688563Z",
     "iopub.status.busy": "2021-06-28T14:43:14.687813Z",
     "iopub.status.idle": "2021-06-28T14:43:14.695855Z",
     "shell.execute_reply": "2021-06-28T14:43:14.695307Z",
     "shell.execute_reply.started": "2021-06-28T14:36:15.992819Z"
    },
    "papermill": {
     "duration": 0.090927,
     "end_time": "2021-06-28T14:43:14.696007",
     "exception": false,
     "start_time": "2021-06-28T14:43:14.605080",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Id', 'DistrictId', 'Ecology_2', 'Ecology_3', 'Shops_2']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.select_dtypes(include='object').columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "level-filter",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:14.866618Z",
     "iopub.status.busy": "2021-06-28T14:43:14.865948Z",
     "iopub.status.idle": "2021-06-28T14:43:14.873162Z",
     "shell.execute_reply": "2021-06-28T14:43:14.873662Z",
     "shell.execute_reply.started": "2021-06-28T14:36:16.004049Z"
    },
    "papermill": {
     "duration": 0.096016,
     "end_time": "2021-06-28T14:43:14.873838",
     "exception": false,
     "start_time": "2021-06-28T14:43:14.777822",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27     851\n",
       "1      652\n",
       "23     565\n",
       "6      511\n",
       "9      294\n",
       "      ... \n",
       "209      1\n",
       "205      1\n",
       "196      1\n",
       "117      1\n",
       "207      1\n",
       "Name: DistrictId, Length: 205, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['DistrictId'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "contemporary-turning",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:15.039442Z",
     "iopub.status.busy": "2021-06-28T14:43:15.038765Z",
     "iopub.status.idle": "2021-06-28T14:43:15.047512Z",
     "shell.execute_reply": "2021-06-28T14:43:15.047977Z",
     "shell.execute_reply.started": "2021-06-28T14:36:16.024755Z"
    },
    "papermill": {
     "duration": 0.092883,
     "end_time": "2021-06-28T14:43:15.048176",
     "exception": false,
     "start_time": "2021-06-28T14:43:14.955293",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    9903\n",
       "A      97\n",
       "Name: Ecology_2, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Ecology_2'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "tough-tribune",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:15.212737Z",
     "iopub.status.busy": "2021-06-28T14:43:15.212030Z",
     "iopub.status.idle": "2021-06-28T14:43:15.220724Z",
     "shell.execute_reply": "2021-06-28T14:43:15.221241Z",
     "shell.execute_reply.started": "2021-06-28T14:36:16.041741Z"
    },
    "papermill": {
     "duration": 0.092491,
     "end_time": "2021-06-28T14:43:15.221417",
     "exception": false,
     "start_time": "2021-06-28T14:43:15.128926",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    9725\n",
       "A     275\n",
       "Name: Ecology_3, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Ecology_3'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "flying-agriculture",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:15.386792Z",
     "iopub.status.busy": "2021-06-28T14:43:15.386132Z",
     "iopub.status.idle": "2021-06-28T14:43:15.395907Z",
     "shell.execute_reply": "2021-06-28T14:43:15.395290Z",
     "shell.execute_reply.started": "2021-06-28T14:36:16.054480Z"
    },
    "papermill": {
     "duration": 0.09352,
     "end_time": "2021-06-28T14:43:15.396049",
     "exception": false,
     "start_time": "2021-06-28T14:43:15.302529",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    9175\n",
       "A     825\n",
       "Name: Shops_2, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Shops_2'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "proud-decrease",
   "metadata": {
    "papermill": {
     "duration": 0.081137,
     "end_time": "2021-06-28T14:43:15.558715",
     "exception": false,
     "start_time": "2021-06-28T14:43:15.477578",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **2. Обработка выбросов**\n",
    "Что можно делать с ними?\n",
    "\n",
    "1. Выкинуть эти данные (только на трейне, на тесте ничего не выкидываем)\n",
    "2. Заменять выбросы разными методами (медианы, средние значения, np.clip и т.д.)\n",
    "3. Делать/не делать дополнительную фичу\n",
    "4. Ничего не делать"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "modified-exchange",
   "metadata": {
    "papermill": {
     "duration": 0.080723,
     "end_time": "2021-06-28T14:43:15.720411",
     "exception": false,
     "start_time": "2021-06-28T14:43:15.639688",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Rooms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "exciting-attendance",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:15.888141Z",
     "iopub.status.busy": "2021-06-28T14:43:15.887414Z",
     "iopub.status.idle": "2021-06-28T14:43:15.893544Z",
     "shell.execute_reply": "2021-06-28T14:43:15.894050Z",
     "shell.execute_reply.started": "2021-06-28T14:36:16.074204Z"
    },
    "papermill": {
     "duration": 0.092967,
     "end_time": "2021-06-28T14:43:15.894250",
     "exception": false,
     "start_time": "2021-06-28T14:43:15.801283",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0     3880\n",
       "1.0     3705\n",
       "3.0     2235\n",
       "4.0      150\n",
       "5.0       18\n",
       "0.0        8\n",
       "10.0       2\n",
       "6.0        1\n",
       "19.0       1\n",
       "Name: Rooms, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Rooms'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "spare-society",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:16.062522Z",
     "iopub.status.busy": "2021-06-28T14:43:16.061904Z",
     "iopub.status.idle": "2021-06-28T14:43:16.111210Z",
     "shell.execute_reply": "2021-06-28T14:43:16.110666Z",
     "shell.execute_reply.started": "2021-06-28T14:37:03.952691Z"
    },
    "papermill": {
     "duration": 0.13463,
     "end_time": "2021-06-28T14:43:16.111353",
     "exception": false,
     "start_time": "2021-06-28T14:43:15.976723",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>DistrictId</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Square</th>\n",
       "      <th>LifeSquare</th>\n",
       "      <th>KitchenSquare</th>\n",
       "      <th>Floor</th>\n",
       "      <th>HouseFloor</th>\n",
       "      <th>HouseYear</th>\n",
       "      <th>Ecology_1</th>\n",
       "      <th>...</th>\n",
       "      <th>Ecology_3</th>\n",
       "      <th>Social_1</th>\n",
       "      <th>Social_2</th>\n",
       "      <th>Social_3</th>\n",
       "      <th>Healthcare_1</th>\n",
       "      <th>Helthcare_2</th>\n",
       "      <th>Shops_1</th>\n",
       "      <th>Shops_2</th>\n",
       "      <th>Price</th>\n",
       "      <th>Rooms_outlier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11809</td>\n",
       "      <td>27</td>\n",
       "      <td>3.0</td>\n",
       "      <td>115.027311</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.075424</td>\n",
       "      <td>...</td>\n",
       "      <td>B</td>\n",
       "      <td>11</td>\n",
       "      <td>3097</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>B</td>\n",
       "      <td>305018.871089</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3013</td>\n",
       "      <td>22</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.832524</td>\n",
       "      <td>23.169223</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1966</td>\n",
       "      <td>0.118537</td>\n",
       "      <td>...</td>\n",
       "      <td>B</td>\n",
       "      <td>30</td>\n",
       "      <td>6207</td>\n",
       "      <td>1</td>\n",
       "      <td>1183.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>B</td>\n",
       "      <td>177734.553407</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8215</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>78.342215</td>\n",
       "      <td>47.671972</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1988</td>\n",
       "      <td>0.025609</td>\n",
       "      <td>...</td>\n",
       "      <td>B</td>\n",
       "      <td>33</td>\n",
       "      <td>5261</td>\n",
       "      <td>0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>282078.720850</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2352</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.409907</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1977</td>\n",
       "      <td>0.007122</td>\n",
       "      <td>...</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>168106.007630</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13866</td>\n",
       "      <td>94</td>\n",
       "      <td>2.0</td>\n",
       "      <td>64.285067</td>\n",
       "      <td>38.562517</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1972</td>\n",
       "      <td>0.282798</td>\n",
       "      <td>...</td>\n",
       "      <td>B</td>\n",
       "      <td>33</td>\n",
       "      <td>8667</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>B</td>\n",
       "      <td>343995.102962</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id DistrictId  Rooms      Square  LifeSquare  KitchenSquare  Floor  \\\n",
       "0  11809         27    3.0  115.027311         NaN           10.0      4   \n",
       "1   3013         22    1.0   39.832524   23.169223            8.0      7   \n",
       "2   8215          1    3.0   78.342215   47.671972           10.0      2   \n",
       "3   2352          1    1.0   40.409907         NaN            1.0     10   \n",
       "4  13866         94    2.0   64.285067   38.562517            9.0     16   \n",
       "\n",
       "   HouseFloor  HouseYear  Ecology_1  ... Ecology_3 Social_1  Social_2  \\\n",
       "0        10.0       2014   0.075424  ...         B       11      3097   \n",
       "1         8.0       1966   0.118537  ...         B       30      6207   \n",
       "2        17.0       1988   0.025609  ...         B       33      5261   \n",
       "3        22.0       1977   0.007122  ...         B        1       264   \n",
       "4        16.0       1972   0.282798  ...         B       33      8667   \n",
       "\n",
       "   Social_3  Healthcare_1  Helthcare_2  Shops_1  Shops_2          Price  \\\n",
       "0         0           NaN            0        0        B  305018.871089   \n",
       "1         1        1183.0            1        0        B  177734.553407   \n",
       "2         0         240.0            3        1        B  282078.720850   \n",
       "3         0           NaN            0        1        B  168106.007630   \n",
       "4         2           NaN            0        6        B  343995.102962   \n",
       "\n",
       "   Rooms_outlier  \n",
       "0              0  \n",
       "1              0  \n",
       "2              0  \n",
       "3              0  \n",
       "4              0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Rooms_outlier'] = 0\n",
    "train_df.loc[(train_df['Rooms'] == 0) | (train_df['Rooms'] >= 20), 'Rooms_outlier'] = 1\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "postal-congress",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:16.282896Z",
     "iopub.status.busy": "2021-06-28T14:43:16.281865Z",
     "iopub.status.idle": "2021-06-28T14:43:16.288198Z",
     "shell.execute_reply": "2021-06-28T14:43:16.287643Z",
     "shell.execute_reply.started": "2021-06-28T14:37:16.171331Z"
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     "exception": false,
     "start_time": "2021-06-28T14:43:16.193718",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_df.loc[train_df['Rooms'] == 0, 'Rooms'] = 1\n",
    "train_df.loc[train_df['Rooms'] >= 20, 'Rooms'] = train_df['Rooms'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "generous-respect",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:16.456861Z",
     "iopub.status.busy": "2021-06-28T14:43:16.456174Z",
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     "start_time": "2021-06-28T14:43:16.370751",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0     3880\n",
       "1.0     3713\n",
       "3.0     2235\n",
       "4.0      150\n",
       "5.0       18\n",
       "10.0       2\n",
       "6.0        1\n",
       "19.0       1\n",
       "Name: Rooms, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Rooms'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "inner-flooring",
   "metadata": {
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     "duration": 0.083319,
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "KitchenSquare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "incorporated-brake",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:16.808233Z",
     "iopub.status.busy": "2021-06-28T14:43:16.802051Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0       2460\n",
       "8.0       1306\n",
       "5.0       1169\n",
       "10.0      1075\n",
       "6.0       1038\n",
       "9.0        843\n",
       "0.0        697\n",
       "7.0        609\n",
       "12.0       249\n",
       "11.0       233\n",
       "13.0        67\n",
       "14.0        51\n",
       "4.0         39\n",
       "15.0        31\n",
       "3.0         22\n",
       "16.0        16\n",
       "20.0        14\n",
       "17.0        12\n",
       "19.0        11\n",
       "18.0         6\n",
       "2.0          4\n",
       "22.0         3\n",
       "112.0        2\n",
       "51.0         2\n",
       "58.0         2\n",
       "37.0         2\n",
       "32.0         2\n",
       "41.0         2\n",
       "43.0         2\n",
       "30.0         2\n",
       "25.0         2\n",
       "39.0         1\n",
       "23.0         1\n",
       "123.0        1\n",
       "40.0         1\n",
       "48.0         1\n",
       "21.0         1\n",
       "72.0         1\n",
       "27.0         1\n",
       "63.0         1\n",
       "1970.0       1\n",
       "29.0         1\n",
       "66.0         1\n",
       "2014.0       1\n",
       "31.0         1\n",
       "78.0         1\n",
       "35.0         1\n",
       "42.0         1\n",
       "73.0         1\n",
       "60.0         1\n",
       "84.0         1\n",
       "26.0         1\n",
       "54.0         1\n",
       "36.0         1\n",
       "53.0         1\n",
       "96.0         1\n",
       "62.0         1\n",
       "75.0         1\n",
       "Name: KitchenSquare, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['KitchenSquare'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "significant-martin",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:16.987920Z",
     "iopub.status.busy": "2021-06-28T14:43:16.987248Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13.0, 0.0)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['KitchenSquare'].quantile(.975), train_df['KitchenSquare'].quantile(.025)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "continuing-denmark",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:17.167403Z",
     "iopub.status.busy": "2021-06-28T14:43:17.166768Z",
     "iopub.status.idle": "2021-06-28T14:43:17.175535Z",
     "shell.execute_reply": "2021-06-28T14:43:17.176072Z",
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     "exception": false,
     "start_time": "2021-06-28T14:43:17.075545",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "condition = (train_df['KitchenSquare'].isna()) \\\n",
    "             | (train_df['KitchenSquare'] > train_df['KitchenSquare'].quantile(.975))\n",
    "        \n",
    "train_df.loc[condition, 'KitchenSquare'] = train_df['KitchenSquare'].median()\n",
    "\n",
    "train_df.loc[train_df['KitchenSquare'] < 3, 'KitchenSquare'] = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "alleged-century",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:17.346928Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0     3183\n",
       "8.0     1306\n",
       "6.0     1227\n",
       "5.0     1169\n",
       "10.0    1075\n",
       "9.0      843\n",
       "7.0      609\n",
       "12.0     249\n",
       "11.0     233\n",
       "13.0      67\n",
       "4.0       39\n",
       "Name: KitchenSquare, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['KitchenSquare'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "otherwise-grove",
   "metadata": {
    "papermill": {
     "duration": 0.083718,
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     "start_time": "2021-06-28T14:43:17.438564",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "HouseFloor, Floor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "nasty-rogers",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:17.695280Z",
     "iopub.status.busy": "2021-06-28T14:43:17.694641Z",
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     "exception": false,
     "start_time": "2021-06-28T14:43:17.608357",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,\n",
       "        11.,  12.,  13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,\n",
       "        22.,  23.,  24.,  25.,  26.,  27.,  28.,  29.,  30.,  31.,  32.,\n",
       "        36.,  37.,  38.,  39.,  40.,  44.,  45.,  47.,  48.,  99., 117.])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['HouseFloor'].sort_values().unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "modified-niagara",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:17.877483Z",
     "iopub.status.busy": "2021-06-28T14:43:17.876828Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33, 37, 42])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Floor'].sort_values().unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "headed-unemployment",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:18.060326Z",
     "iopub.status.busy": "2021-06-28T14:43:18.059642Z",
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     "exception": false,
     "start_time": "2021-06-28T14:43:17.971417",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1825"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(train_df['Floor'] > train_df['HouseFloor']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "efficient-estimate",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:18.242619Z",
     "iopub.status.busy": "2021-06-28T14:43:18.241935Z",
     "iopub.status.idle": "2021-06-28T14:43:18.249214Z",
     "shell.execute_reply": "2021-06-28T14:43:18.249792Z",
     "shell.execute_reply.started": "2021-06-28T14:36:16.250424Z"
    },
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     "exception": false,
     "start_time": "2021-06-28T14:43:18.151925",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_df['HouseFloor_outlier'] = 0\n",
    "train_df.loc[train_df['HouseFloor'] == 0, 'HouseFloor_outlier'] = 1\n",
    "train_df.loc[train_df['Floor'] > train_df['HouseFloor'], 'HouseFloor_outlier'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "organic-system",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:18.425454Z",
     "iopub.status.busy": "2021-06-28T14:43:18.424694Z",
     "iopub.status.idle": "2021-06-28T14:43:18.430631Z",
     "shell.execute_reply": "2021-06-28T14:43:18.431181Z",
     "shell.execute_reply.started": "2021-06-28T14:36:16.263257Z"
    },
    "papermill": {
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     "exception": false,
     "start_time": "2021-06-28T14:43:18.335960",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_df.loc[train_df['HouseFloor'] == 0, 'HouseFloor'] = train_df['HouseFloor'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "decreased-explosion",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:18.606806Z",
     "iopub.status.busy": "2021-06-28T14:43:18.606175Z",
     "iopub.status.idle": "2021-06-28T14:43:18.614195Z",
     "shell.execute_reply": "2021-06-28T14:43:18.614717Z",
     "shell.execute_reply.started": "2021-06-28T14:36:16.276920Z"
    },
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     "exception": false,
     "start_time": "2021-06-28T14:43:18.516767",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([  17,   19,   21,   25,   26,   33,   34,   37,   39,   44,\n",
       "            ...\n",
       "            9913, 9921, 9930, 9931, 9938, 9953, 9960, 9968, 9970, 9975],\n",
       "           dtype='int64', length=1610)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "floor_outliers = train_df.loc[train_df['Floor'] > train_df['HouseFloor']].index\n",
    "floor_outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "signed-dylan",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:18.791359Z",
     "iopub.status.busy": "2021-06-28T14:43:18.790662Z",
     "iopub.status.idle": "2021-06-28T14:43:18.800721Z",
     "shell.execute_reply": "2021-06-28T14:43:18.800124Z",
     "shell.execute_reply.started": "2021-06-28T14:36:16.291651Z"
    },
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     "exception": false,
     "start_time": "2021-06-28T14:43:18.700057",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_df.loc[floor_outliers, 'Floor'] = train_df.loc[floor_outliers, 'HouseFloor']\\\n",
    "                                                .apply(lambda x: random.randint(1, x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "configured-terry",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:18.980013Z",
     "iopub.status.busy": "2021-06-28T14:43:18.979376Z",
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     "start_time": "2021-06-28T14:43:18.887637",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(train_df['Floor'] > train_df['HouseFloor']).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "numerical-reader",
   "metadata": {
    "papermill": {
     "duration": 0.089513,
     "end_time": "2021-06-28T14:43:19.159297",
     "exception": false,
     "start_time": "2021-06-28T14:43:19.069784",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "HouseYear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "satellite-solution",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:19.332538Z",
     "iopub.status.busy": "2021-06-28T14:43:19.331916Z",
     "iopub.status.idle": "2021-06-28T14:43:19.339498Z",
     "shell.execute_reply": "2021-06-28T14:43:19.339995Z",
     "shell.execute_reply.started": "2021-06-28T14:36:16.320996Z"
    },
    "papermill": {
     "duration": 0.095684,
     "end_time": "2021-06-28T14:43:19.340204",
     "exception": false,
     "start_time": "2021-06-28T14:43:19.244520",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9163    20052011\n",
       "8828        4968\n",
       "7721        2020\n",
       "9122        2020\n",
       "7955        2020\n",
       "          ...   \n",
       "7610        1916\n",
       "6250        1914\n",
       "2066        1912\n",
       "2795        1912\n",
       "6794        1910\n",
       "Name: HouseYear, Length: 10000, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['HouseYear'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "weekly-wireless",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:19.514764Z",
     "iopub.status.busy": "2021-06-28T14:43:19.514155Z",
     "iopub.status.idle": "2021-06-28T14:43:19.518963Z",
     "shell.execute_reply": "2021-06-28T14:43:19.519465Z",
     "shell.execute_reply.started": "2021-06-28T14:36:16.335276Z"
    },
    "papermill": {
     "duration": 0.0935,
     "end_time": "2021-06-28T14:43:19.519648",
     "exception": false,
     "start_time": "2021-06-28T14:43:19.426148",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_df.loc[train_df['HouseYear'] == 20052010, 'HouseYear'] = 2008"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "recreational-trader",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:19.696149Z",
     "iopub.status.busy": "2021-06-28T14:43:19.695497Z",
     "iopub.status.idle": "2021-06-28T14:43:19.700436Z",
     "shell.execute_reply": "2021-06-28T14:43:19.700929Z",
     "shell.execute_reply.started": "2021-06-28T14:36:16.344001Z"
    },
    "papermill": {
     "duration": 0.09462,
     "end_time": "2021-06-28T14:43:19.701121",
     "exception": false,
     "start_time": "2021-06-28T14:43:19.606501",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_df.loc[train_df['HouseYear'] == 4967, 'HouseYear'] = 1959"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "casual-possibility",
   "metadata": {
    "papermill": {
     "duration": 0.136439,
     "end_time": "2021-06-28T14:43:19.923675",
     "exception": false,
     "start_time": "2021-06-28T14:43:19.787236",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **3. Обработка пропусков**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "naked-indonesia",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:20.110580Z",
     "iopub.status.busy": "2021-06-28T14:43:20.101388Z",
     "iopub.status.idle": "2021-06-28T14:43:20.118710Z",
     "shell.execute_reply": "2021-06-28T14:43:20.120428Z",
     "shell.execute_reply.started": "2021-06-28T14:36:16.355479Z"
    },
    "papermill": {
     "duration": 0.110174,
     "end_time": "2021-06-28T14:43:20.120641",
     "exception": false,
     "start_time": "2021-06-28T14:43:20.010467",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                       0\n",
       "DistrictId               0\n",
       "Rooms                    0\n",
       "Square                   0\n",
       "LifeSquare            2113\n",
       "KitchenSquare            0\n",
       "Floor                    0\n",
       "HouseFloor               0\n",
       "HouseYear                0\n",
       "Ecology_1                0\n",
       "Ecology_2                0\n",
       "Ecology_3                0\n",
       "Social_1                 0\n",
       "Social_2                 0\n",
       "Social_3                 0\n",
       "Healthcare_1          4798\n",
       "Helthcare_2              0\n",
       "Shops_1                  0\n",
       "Shops_2                  0\n",
       "Price                    0\n",
       "Rooms_outlier            0\n",
       "HouseFloor_outlier       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "serial-enterprise",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:20.325458Z",
     "iopub.status.busy": "2021-06-28T14:43:20.324609Z",
     "iopub.status.idle": "2021-06-28T14:43:20.327956Z",
     "shell.execute_reply": "2021-06-28T14:43:20.328421Z",
     "shell.execute_reply.started": "2021-06-28T14:36:16.377121Z"
    },
    "papermill": {
     "duration": 0.104374,
     "end_time": "2021-06-28T14:43:20.328605",
     "exception": false,
     "start_time": "2021-06-28T14:43:20.224231",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Square</th>\n",
       "      <th>LifeSquare</th>\n",
       "      <th>KitchenSquare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>115.027311</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.832524</td>\n",
       "      <td>23.169223</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>78.342215</td>\n",
       "      <td>47.671972</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.409907</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64.285067</td>\n",
       "      <td>38.562517</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>62.528465</td>\n",
       "      <td>47.103833</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>33.938750</td>\n",
       "      <td>21.772930</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>50.391814</td>\n",
       "      <td>32.893256</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>46.887892</td>\n",
       "      <td>44.628132</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>79.867477</td>\n",
       "      <td>75.085125</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Square  LifeSquare  KitchenSquare\n",
       "0  115.027311         NaN           10.0\n",
       "1   39.832524   23.169223            8.0\n",
       "2   78.342215   47.671972           10.0\n",
       "3   40.409907         NaN            3.0\n",
       "4   64.285067   38.562517            9.0\n",
       "5   62.528465   47.103833            6.0\n",
       "6   33.938750   21.772930            9.0\n",
       "7   50.391814   32.893256            8.0\n",
       "8   46.887892   44.628132            3.0\n",
       "9   79.867477   75.085125            3.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[['Square', 'LifeSquare', 'KitchenSquare']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "devoted-weather",
   "metadata": {
    "papermill": {
     "duration": 0.087358,
     "end_time": "2021-06-28T14:43:20.503213",
     "exception": false,
     "start_time": "2021-06-28T14:43:20.415855",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "LifeSquare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "offensive-tunisia",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:20.681036Z",
     "iopub.status.busy": "2021-06-28T14:43:20.680402Z",
     "iopub.status.idle": "2021-06-28T14:43:20.690698Z",
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     "shell.execute_reply.started": "2021-06-28T14:36:16.394562Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_df['LifeSquare_nan'] = train_df['LifeSquare'].isna() * 1\n",
    "\n",
    "condition = (train_df['LifeSquare'].isna()) \\\n",
    "             & (~train_df['Square'].isna()) \\\n",
    "             & (~train_df['KitchenSquare'].isna())\n",
    "        \n",
    "train_df.loc[condition, 'LifeSquare'] = train_df.loc[condition, 'Square'] \\\n",
    "                                            - train_df.loc[condition, 'KitchenSquare'] - 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "selected-heritage",
   "metadata": {
    "papermill": {
     "duration": 0.088568,
     "end_time": "2021-06-28T14:43:20.867351",
     "exception": false,
     "start_time": "2021-06-28T14:43:20.778783",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Healthcare_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "moral-holocaust",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:21.046048Z",
     "iopub.status.busy": "2021-06-28T14:43:21.045409Z",
     "iopub.status.idle": "2021-06-28T14:43:21.051944Z",
     "shell.execute_reply": "2021-06-28T14:43:21.052429Z",
     "shell.execute_reply.started": "2021-06-28T14:36:16.412595Z"
    },
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     "duration": 0.097725,
     "end_time": "2021-06-28T14:43:21.052634",
     "exception": false,
     "start_time": "2021-06-28T14:43:20.954909",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_df.drop('Healthcare_1', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "differential-burke",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:21.230235Z",
     "iopub.status.busy": "2021-06-28T14:43:21.229521Z",
     "iopub.status.idle": "2021-06-28T14:43:21.245180Z",
     "shell.execute_reply": "2021-06-28T14:43:21.245709Z",
     "shell.execute_reply.started": "2021-06-28T14:36:16.424911Z"
    },
    "papermill": {
     "duration": 0.106304,
     "end_time": "2021-06-28T14:43:21.245893",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class DataPreprocessing:\n",
    "    \"\"\"Подготовка исходных данных\"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        \"\"\"Параметры класса\"\"\"\n",
    "        self.medians = None\n",
    "        self.kitchen_square_quantile = None\n",
    "        \n",
    "    def fit(self, X):\n",
    "        \"\"\"Сохранение статистик\"\"\"       \n",
    "        # Расчет медиан\n",
    "        self.medians = X.median()\n",
    "        self.kitchen_square_quantile = X['KitchenSquare'].quantile(.975)\n",
    "    \n",
    "    def transform(self, X):\n",
    "        \"\"\"Трансформация данных\"\"\"\n",
    "\n",
    "        # Rooms\n",
    "        X['Rooms_outlier'] = 0\n",
    "        X.loc[(X['Rooms'] == 0) | (X['Rooms'] >= 6), 'Rooms_outlier'] = 1\n",
    "        \n",
    "        X.loc[X['Rooms'] == 0, 'Rooms'] = 1\n",
    "        X.loc[X['Rooms'] >= 6, 'Rooms'] = self.medians['Rooms']\n",
    "        \n",
    "        # KitchenSquare\n",
    "        condition = (X['KitchenSquare'].isna()) \\\n",
    "                    | (X['KitchenSquare'] > self.kitchen_square_quantile)\n",
    "        \n",
    "        X.loc[condition, 'KitchenSquare'] = self.medians['KitchenSquare']\n",
    "\n",
    "        X.loc[X['KitchenSquare'] < 3, 'KitchenSquare'] = 3\n",
    "        \n",
    "        # HouseFloor, Floor\n",
    "        X['HouseFloor_outlier'] = 0\n",
    "        X.loc[X['HouseFloor'] == 0, 'HouseFloor_outlier'] = 1\n",
    "        X.loc[X['Floor'] > X['HouseFloor'], 'HouseFloor_outlier'] = 1\n",
    "        \n",
    "        X.loc[X['HouseFloor'] == 0, 'HouseFloor'] = self.medians['HouseFloor']\n",
    "        \n",
    "        floor_outliers = X.loc[X['Floor'] > X['HouseFloor']].index\n",
    "        X.loc[floor_outliers, 'Floor'] = X.loc[floor_outliers, 'HouseFloor']\\\n",
    "                                            .apply(lambda x: random.randint(1, x))\n",
    "        \n",
    "        # HouseYear\n",
    "        current_year = datetime.now().year\n",
    "        \n",
    "        X['HouseYear_outlier'] = 0\n",
    "        X.loc[X['HouseYear'] > current_year, 'HouseYear_outlier'] = 1\n",
    "        \n",
    "        X.loc[X['HouseYear'] > current_year, 'HouseYear'] = current_year\n",
    "        \n",
    "        # Healthcare_1\n",
    "        if 'Healthcare_1' in X.columns:\n",
    "            X.drop('Healthcare_1', axis=1, inplace=True)\n",
    "            \n",
    "        # LifeSquare\n",
    "        X['LifeSquare_nan'] = X['LifeSquare'].isna() * 1\n",
    "        condition = (X['LifeSquare'].isna()) & \\\n",
    "                      (~X['Square'].isna()) & \\\n",
    "                      (~X['KitchenSquare'].isna())\n",
    "        \n",
    "        X.loc[condition, 'LifeSquare'] = X.loc[condition, 'Square'] - X.loc[condition, 'KitchenSquare'] - 3\n",
    "        \n",
    "        \n",
    "        X.fillna(self.medians, inplace=True)\n",
    "        \n",
    "        return X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "under-manhattan",
   "metadata": {
    "papermill": {
     "duration": 0.086003,
     "end_time": "2021-06-28T14:43:21.418366",
     "exception": false,
     "start_time": "2021-06-28T14:43:21.332363",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **4. Построение новых признаков**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acceptable-summary",
   "metadata": {
    "papermill": {
     "duration": 0.086287,
     "end_time": "2021-06-28T14:43:21.591610",
     "exception": false,
     "start_time": "2021-06-28T14:43:21.505323",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "engaging-duration",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:21.768391Z",
     "iopub.status.busy": "2021-06-28T14:43:21.767760Z",
     "iopub.status.idle": "2021-06-28T14:43:21.794378Z",
     "shell.execute_reply": "2021-06-28T14:43:21.794877Z",
     "shell.execute_reply.started": "2021-06-28T14:36:16.445016Z"
    },
    "papermill": {
     "duration": 0.116891,
     "end_time": "2021-06-28T14:43:21.795066",
     "exception": false,
     "start_time": "2021-06-28T14:43:21.678175",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "binary_to_numbers = {'A': 0, 'B': 1}\n",
    "\n",
    "train_df['Ecology_2'] = train_df['Ecology_2'].replace(binary_to_numbers)\n",
    "train_df['Ecology_3'] = train_df['Ecology_3'].replace(binary_to_numbers)\n",
    "train_df['Shops_2'] = train_df['Shops_2'].replace(binary_to_numbers)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "furnished-enforcement",
   "metadata": {
    "papermill": {
     "duration": 0.087565,
     "end_time": "2021-06-28T14:43:21.970749",
     "exception": false,
     "start_time": "2021-06-28T14:43:21.883184",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "DistrictSize, IsDistrictLarge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "synthetic-elder",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:22.148844Z",
     "iopub.status.busy": "2021-06-28T14:43:22.148161Z",
     "iopub.status.idle": "2021-06-28T14:43:22.162183Z",
     "shell.execute_reply": "2021-06-28T14:43:22.162636Z",
     "shell.execute_reply.started": "2021-06-28T14:36:16.486227Z"
    },
    "papermill": {
     "duration": 0.104325,
     "end_time": "2021-06-28T14:43:22.162816",
     "exception": false,
     "start_time": "2021-06-28T14:43:22.058491",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <td>294</td>\n",
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      "text/plain": [
       "  DistrictId  DistrictSize\n",
       "0         27           851\n",
       "1          1           652\n",
       "2         23           565\n",
       "3          6           511\n",
       "4          9           294"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "district_size = train_df['DistrictId'].value_counts().reset_index()\\\n",
    "                    .rename(columns={'index':'DistrictId', 'DistrictId':'DistrictSize'})\n",
    "\n",
    "district_size.head()"
   ]
  },
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       "      Id DistrictId  Rooms      Square  LifeSquare  KitchenSquare  Floor  \\\n",
       "0  11809         27    3.0  115.027311  102.027311           10.0      4   \n",
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       "4  13866         94    2.0   64.285067   38.562517            9.0     16   \n",
       "\n",
       "   HouseFloor  HouseYear  Ecology_1  ...  Social_2  Social_3  Helthcare_2  \\\n",
       "0        10.0       2014   0.075424  ...      3097         0            0   \n",
       "1         8.0       1966   0.118537  ...      6207         1            1   \n",
       "2        17.0       1988   0.025609  ...      5261         0            3   \n",
       "3        22.0       1977   0.007122  ...       264         0            0   \n",
       "4        16.0       1972   0.282798  ...      8667         2            0   \n",
       "\n",
       "   Shops_1  Shops_2          Price  Rooms_outlier  HouseFloor_outlier  \\\n",
       "0        0        1  305018.871089              0                   0   \n",
       "1        0        1  177734.553407              0                   0   \n",
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       "3        1        1  168106.007630              0                   0   \n",
       "4        6        1  343995.102962              0                   0   \n",
       "\n",
       "   LifeSquare_nan  DistrictSize  \n",
       "0               1           851  \n",
       "1               0            78  \n",
       "2               0           652  \n",
       "3               1           652  \n",
       "4               0            89  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
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    }
   ],
   "source": [
    "train_df = train_df.merge(district_size, on='DistrictId', how='left')\n",
    "train_df.head()"
   ]
  },
  {
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   "id": "approximate-joyce",
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    {
     "data": {
      "text/plain": [
       "True     5069\n",
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       "Name: DistrictSize, dtype: int64"
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     },
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   "id": "fitting-proportion",
   "metadata": {
    "execution": {
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    "train_df['IsDistrictLarge'] = (train_df['DistrictSize'] > 100).astype(int)"
   ]
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    "                            .rename(columns={'Price':'MedPriceByDistrict'})\n",
    "\n",
    "med_price_by_district.head()"
   ]
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   "id": "killing-briefing",
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       "      <td>1</td>\n",
       "      <td>305018.871089</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>851</td>\n",
       "      <td>1</td>\n",
       "      <td>213530.788443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3013</td>\n",
       "      <td>22</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.832524</td>\n",
       "      <td>23.169223</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1966</td>\n",
       "      <td>0.118537</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>177734.553407</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>146032.829834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8215</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>78.342215</td>\n",
       "      <td>47.671972</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1988</td>\n",
       "      <td>0.025609</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>282078.720850</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>652</td>\n",
       "      <td>1</td>\n",
       "      <td>244005.272380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2352</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.409907</td>\n",
       "      <td>34.409907</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1977</td>\n",
       "      <td>0.007122</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>168106.007630</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>652</td>\n",
       "      <td>1</td>\n",
       "      <td>146735.671740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13866</td>\n",
       "      <td>94</td>\n",
       "      <td>2.0</td>\n",
       "      <td>64.285067</td>\n",
       "      <td>38.562517</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1972</td>\n",
       "      <td>0.282798</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>343995.102962</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>89</td>\n",
       "      <td>0</td>\n",
       "      <td>201584.441255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id DistrictId  Rooms      Square  LifeSquare  KitchenSquare  Floor  \\\n",
       "0  11809         27    3.0  115.027311  102.027311           10.0      4   \n",
       "1   3013         22    1.0   39.832524   23.169223            8.0      7   \n",
       "2   8215          1    3.0   78.342215   47.671972           10.0      2   \n",
       "3   2352          1    1.0   40.409907   34.409907            3.0     10   \n",
       "4  13866         94    2.0   64.285067   38.562517            9.0     16   \n",
       "\n",
       "   HouseFloor  HouseYear  Ecology_1  ...  Helthcare_2  Shops_1  Shops_2  \\\n",
       "0        10.0       2014   0.075424  ...            0        0        1   \n",
       "1         8.0       1966   0.118537  ...            1        0        1   \n",
       "2        17.0       1988   0.025609  ...            3        1        1   \n",
       "3        22.0       1977   0.007122  ...            0        1        1   \n",
       "4        16.0       1972   0.282798  ...            0        6        1   \n",
       "\n",
       "           Price  Rooms_outlier  HouseFloor_outlier  LifeSquare_nan  \\\n",
       "0  305018.871089              0                   0               1   \n",
       "1  177734.553407              0                   0               0   \n",
       "2  282078.720850              0                   0               0   \n",
       "3  168106.007630              0                   0               1   \n",
       "4  343995.102962              0                   0               0   \n",
       "\n",
       "   DistrictSize  IsDistrictLarge  MedPriceByDistrict  \n",
       "0           851                1       213530.788443  \n",
       "1            78                0       146032.829834  \n",
       "2           652                1       244005.272380  \n",
       "3           652                1       146735.671740  \n",
       "4            89                0       201584.441255  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.merge(med_price_by_district, on=['DistrictId', 'Rooms'], how='left')\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pressing-citizenship",
   "metadata": {
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    "MedPriceByFloorYear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "hundred-scout",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:23.931980Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def floor_to_cat(X):\n",
    "\n",
    "    X['floor_cat'] = 0\n",
    "\n",
    "    X.loc[X['Floor'] <= 3, 'floor_cat'] = 1  \n",
    "    X.loc[(X['Floor'] > 3) & (X['Floor'] <= 5), 'floor_cat'] = 2\n",
    "    X.loc[(X['Floor'] > 5) & (X['Floor'] <= 9), 'floor_cat'] = 3\n",
    "    X.loc[(X['Floor'] > 9) & (X['Floor'] <= 15), 'floor_cat'] = 4\n",
    "    X.loc[X['Floor'] > 15, 'floor_cat'] = 5\n",
    "\n",
    "    return X\n",
    "\n",
    "\n",
    "def floor_to_cat_pandas(X):\n",
    "    bins = [0, 3, 5, 9, 15, X['Floor'].max()]\n",
    "    X['floor_cat'] = pd.cut(X['Floor'], bins=bins, labels=False)\n",
    "    \n",
    "    X['floor_cat'].fillna(-1, inplace=True)\n",
    "    return X\n",
    "\n",
    "\n",
    "def year_to_cat(X):\n",
    "\n",
    "    X['year_cat'] = 0\n",
    "\n",
    "    X.loc[X['HouseYear'] <= 1941, 'year_cat'] = 1\n",
    "    X.loc[(X['HouseYear'] > 1941) & (X['HouseYear'] <= 1945), 'year_cat'] = 2\n",
    "    X.loc[(X['HouseYear'] > 1945) & (X['HouseYear'] <= 1980), 'year_cat'] = 3\n",
    "    X.loc[(X['HouseYear'] > 1980) & (X['HouseYear'] <= 2000), 'year_cat'] = 4\n",
    "    X.loc[(X['HouseYear'] > 2000) & (X['HouseYear'] <= 2010), 'year_cat'] = 5\n",
    "    X.loc[(X['HouseYear'] > 2010), 'year_cat'] = 6\n",
    "\n",
    "    return X\n",
    "\n",
    "\n",
    "def year_to_cat_pandas(X):\n",
    "    bins = [0, 1941, 1945, 1980, 2000, 2010, X['HouseYear'].max()]\n",
    "    X['year_cat'] = pd.cut(X['HouseYear'], bins=bins, labels=False)\n",
    "    \n",
    "    X['year_cat'].fillna(-1, inplace=True)\n",
    "    return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "informed-experience",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:24.120657Z",
     "iopub.status.busy": "2021-06-28T14:43:24.119521Z",
     "iopub.status.idle": "2021-06-28T14:43:24.128734Z",
     "shell.execute_reply": "2021-06-28T14:43:24.128141Z",
     "shell.execute_reply.started": "2021-06-28T14:36:16.695384Z"
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     "exception": false,
     "start_time": "2021-06-28T14:43:24.024545",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       2\n",
       "2       0\n",
       "3       3\n",
       "4       4\n",
       "       ..\n",
       "9995    0\n",
       "9996    3\n",
       "9997    0\n",
       "9998    0\n",
       "9999    2\n",
       "Name: Floor, Length: 10000, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = [0, 3, 5, 9, 15, train_df['Floor'].max()]\n",
    "pd.cut(train_df['Floor'], bins=bins, labels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "furnished-scheme",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:24.330146Z",
     "iopub.status.busy": "2021-06-28T14:43:24.323453Z",
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     "shell.execute_reply.started": "2021-06-28T14:36:16.718899Z"
    },
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     "duration": 0.113936,
     "end_time": "2021-06-28T14:43:24.334807",
     "exception": false,
     "start_time": "2021-06-28T14:43:24.220871",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         (3, 5]\n",
       "1         (5, 9]\n",
       "2         (0, 3]\n",
       "3        (9, 15]\n",
       "4       (15, 42]\n",
       "          ...   \n",
       "9995      (0, 3]\n",
       "9996     (9, 15]\n",
       "9997      (0, 3]\n",
       "9998      (0, 3]\n",
       "9999      (5, 9]\n",
       "Name: Floor, Length: 10000, dtype: category\n",
       "Categories (5, interval[int64]): [(0, 3] < (3, 5] < (5, 9] < (9, 15] < (15, 42]]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = [0, 3, 5, 9, 15, train_df['Floor'].max()]\n",
    "pd.cut(train_df['Floor'], bins=bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "fitting-olympus",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:24.525878Z",
     "iopub.status.busy": "2021-06-28T14:43:24.523786Z",
     "iopub.status.idle": "2021-06-28T14:43:24.565887Z",
     "shell.execute_reply": "2021-06-28T14:43:24.564922Z",
     "shell.execute_reply.started": "2021-06-28T14:36:16.739843Z"
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     "exception": false,
     "start_time": "2021-06-28T14:43:24.426385",
     "status": "completed"
    },
    "tags": []
   },
   "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>Id</th>\n",
       "      <th>DistrictId</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Square</th>\n",
       "      <th>LifeSquare</th>\n",
       "      <th>KitchenSquare</th>\n",
       "      <th>Floor</th>\n",
       "      <th>HouseFloor</th>\n",
       "      <th>HouseYear</th>\n",
       "      <th>Ecology_1</th>\n",
       "      <th>...</th>\n",
       "      <th>Shops_2</th>\n",
       "      <th>Price</th>\n",
       "      <th>Rooms_outlier</th>\n",
       "      <th>HouseFloor_outlier</th>\n",
       "      <th>LifeSquare_nan</th>\n",
       "      <th>DistrictSize</th>\n",
       "      <th>IsDistrictLarge</th>\n",
       "      <th>MedPriceByDistrict</th>\n",
       "      <th>year_cat</th>\n",
       "      <th>floor_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11809</td>\n",
       "      <td>27</td>\n",
       "      <td>3.0</td>\n",
       "      <td>115.027311</td>\n",
       "      <td>102.027311</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.075424</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>305018.871089</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>851</td>\n",
       "      <td>1</td>\n",
       "      <td>213530.788443</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3013</td>\n",
       "      <td>22</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.832524</td>\n",
       "      <td>23.169223</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1966</td>\n",
       "      <td>0.118537</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>177734.553407</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>146032.829834</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8215</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>78.342215</td>\n",
       "      <td>47.671972</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1988</td>\n",
       "      <td>0.025609</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>282078.720850</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>652</td>\n",
       "      <td>1</td>\n",
       "      <td>244005.272380</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2352</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.409907</td>\n",
       "      <td>34.409907</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1977</td>\n",
       "      <td>0.007122</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>168106.007630</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>652</td>\n",
       "      <td>1</td>\n",
       "      <td>146735.671740</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13866</td>\n",
       "      <td>94</td>\n",
       "      <td>2.0</td>\n",
       "      <td>64.285067</td>\n",
       "      <td>38.562517</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1972</td>\n",
       "      <td>0.282798</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>343995.102962</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>89</td>\n",
       "      <td>0</td>\n",
       "      <td>201584.441255</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id DistrictId  Rooms      Square  LifeSquare  KitchenSquare  Floor  \\\n",
       "0  11809         27    3.0  115.027311  102.027311           10.0      4   \n",
       "1   3013         22    1.0   39.832524   23.169223            8.0      7   \n",
       "2   8215          1    3.0   78.342215   47.671972           10.0      2   \n",
       "3   2352          1    1.0   40.409907   34.409907            3.0     10   \n",
       "4  13866         94    2.0   64.285067   38.562517            9.0     16   \n",
       "\n",
       "   HouseFloor  HouseYear  Ecology_1  ...  Shops_2          Price  \\\n",
       "0        10.0       2014   0.075424  ...        1  305018.871089   \n",
       "1         8.0       1966   0.118537  ...        1  177734.553407   \n",
       "2        17.0       1988   0.025609  ...        1  282078.720850   \n",
       "3        22.0       1977   0.007122  ...        1  168106.007630   \n",
       "4        16.0       1972   0.282798  ...        1  343995.102962   \n",
       "\n",
       "   Rooms_outlier  HouseFloor_outlier  LifeSquare_nan  DistrictSize  \\\n",
       "0              0                   0               1           851   \n",
       "1              0                   0               0            78   \n",
       "2              0                   0               0           652   \n",
       "3              0                   0               1           652   \n",
       "4              0                   0               0            89   \n",
       "\n",
       "   IsDistrictLarge  MedPriceByDistrict  year_cat  floor_cat  \n",
       "0                1       213530.788443         6          2  \n",
       "1                0       146032.829834         3          3  \n",
       "2                1       244005.272380         4          1  \n",
       "3                1       146735.671740         3          4  \n",
       "4                0       201584.441255         3          5  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = year_to_cat(train_df)\n",
    "train_df = floor_to_cat(train_df)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "third-seattle",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:24.757347Z",
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     "status": "completed"
    },
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year_cat</th>\n",
       "      <th>floor_cat</th>\n",
       "      <th>MedPriceByFloorYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>380359.112959</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>309690.981584</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>425423.585714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>274992.472366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>467230.539057</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year_cat  floor_cat  MedPriceByFloorYear\n",
       "0         1          1        380359.112959\n",
       "1         1          2        309690.981584\n",
       "2         1          3        425423.585714\n",
       "3         1          4        274992.472366\n",
       "4         2          1        467230.539057"
      ]
     },
     "execution_count": 57,
     "metadata": {},
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   ],
   "source": [
    "med_price_by_floor_year = train_df.groupby(['year_cat', 'floor_cat'], as_index=False).agg({'Price':'median'}).\\\n",
    "                                            rename(columns={'Price':'MedPriceByFloorYear'})\n",
    "med_price_by_floor_year.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "tested-alarm",
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    "execution": {
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   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>DistrictId</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Square</th>\n",
       "      <th>LifeSquare</th>\n",
       "      <th>KitchenSquare</th>\n",
       "      <th>Floor</th>\n",
       "      <th>HouseFloor</th>\n",
       "      <th>HouseYear</th>\n",
       "      <th>Ecology_1</th>\n",
       "      <th>...</th>\n",
       "      <th>Price</th>\n",
       "      <th>Rooms_outlier</th>\n",
       "      <th>HouseFloor_outlier</th>\n",
       "      <th>LifeSquare_nan</th>\n",
       "      <th>DistrictSize</th>\n",
       "      <th>IsDistrictLarge</th>\n",
       "      <th>MedPriceByDistrict</th>\n",
       "      <th>year_cat</th>\n",
       "      <th>floor_cat</th>\n",
       "      <th>MedPriceByFloorYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11809</td>\n",
       "      <td>27</td>\n",
       "      <td>3.0</td>\n",
       "      <td>115.027311</td>\n",
       "      <td>102.027311</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.075424</td>\n",
       "      <td>...</td>\n",
       "      <td>305018.871089</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>851</td>\n",
       "      <td>1</td>\n",
       "      <td>213530.788443</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>164803.070010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3013</td>\n",
       "      <td>22</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.832524</td>\n",
       "      <td>23.169223</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1966</td>\n",
       "      <td>0.118537</td>\n",
       "      <td>...</td>\n",
       "      <td>177734.553407</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>146032.829834</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>192736.759561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8215</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>78.342215</td>\n",
       "      <td>47.671972</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1988</td>\n",
       "      <td>0.025609</td>\n",
       "      <td>...</td>\n",
       "      <td>282078.720850</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>652</td>\n",
       "      <td>1</td>\n",
       "      <td>244005.272380</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>204940.082609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2352</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.409907</td>\n",
       "      <td>34.409907</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1977</td>\n",
       "      <td>0.007122</td>\n",
       "      <td>...</td>\n",
       "      <td>168106.007630</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>652</td>\n",
       "      <td>1</td>\n",
       "      <td>146735.671740</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>183575.193416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13866</td>\n",
       "      <td>94</td>\n",
       "      <td>2.0</td>\n",
       "      <td>64.285067</td>\n",
       "      <td>38.562517</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1972</td>\n",
       "      <td>0.282798</td>\n",
       "      <td>...</td>\n",
       "      <td>343995.102962</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>89</td>\n",
       "      <td>0</td>\n",
       "      <td>201584.441255</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>171931.149112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id DistrictId  Rooms      Square  LifeSquare  KitchenSquare  Floor  \\\n",
       "0  11809         27    3.0  115.027311  102.027311           10.0      4   \n",
       "1   3013         22    1.0   39.832524   23.169223            8.0      7   \n",
       "2   8215          1    3.0   78.342215   47.671972           10.0      2   \n",
       "3   2352          1    1.0   40.409907   34.409907            3.0     10   \n",
       "4  13866         94    2.0   64.285067   38.562517            9.0     16   \n",
       "\n",
       "   HouseFloor  HouseYear  Ecology_1  ...          Price  Rooms_outlier  \\\n",
       "0        10.0       2014   0.075424  ...  305018.871089              0   \n",
       "1         8.0       1966   0.118537  ...  177734.553407              0   \n",
       "2        17.0       1988   0.025609  ...  282078.720850              0   \n",
       "3        22.0       1977   0.007122  ...  168106.007630              0   \n",
       "4        16.0       1972   0.282798  ...  343995.102962              0   \n",
       "\n",
       "   HouseFloor_outlier  LifeSquare_nan  DistrictSize  IsDistrictLarge  \\\n",
       "0                   0               1           851                1   \n",
       "1                   0               0            78                0   \n",
       "2                   0               0           652                1   \n",
       "3                   0               1           652                1   \n",
       "4                   0               0            89                0   \n",
       "\n",
       "   MedPriceByDistrict  year_cat  floor_cat  MedPriceByFloorYear  \n",
       "0       213530.788443         6          2        164803.070010  \n",
       "1       146032.829834         3          3        192736.759561  \n",
       "2       244005.272380         4          1        204940.082609  \n",
       "3       146735.671740         3          4        183575.193416  \n",
       "4       201584.441255         3          5        171931.149112  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.merge(med_price_by_floor_year, on=['year_cat', 'floor_cat'], how='left')\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "empty-secret",
   "metadata": {
    "execution": {
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   "source": [
    "class FeatureGenetator():\n",
    "    \"\"\"Генерация новых фич\"\"\"\n",
    "    \n",
    "    def __init__(self):\n",
    "        self.DistrictId_counts = None\n",
    "        self.binary_to_numbers = None\n",
    "        self.med_price_by_district = None\n",
    "        self.med_price_by_floor_year = None\n",
    "        self.house_year_max = None\n",
    "        self.floor_max = None\n",
    "        self.district_size = None\n",
    "        \n",
    "    def fit(self, X, y=None):\n",
    "        \n",
    "        X = X.copy()\n",
    "        \n",
    "        # Binary features\n",
    "        self.binary_to_numbers = {'A': 0, 'B': 1}\n",
    "        \n",
    "        # DistrictID\n",
    "        self.district_size = X['DistrictId'].value_counts().reset_index() \\\n",
    "                               .rename(columns={'index':'DistrictId', 'DistrictId':'DistrictSize'})\n",
    "                \n",
    "        # Target encoding\n",
    "        ## District, Rooms\n",
    "        df = X.copy()\n",
    "        \n",
    "        if y is not None:\n",
    "            df['Price'] = y.values\n",
    "            \n",
    "            self.med_price_by_district = df.groupby(['DistrictId', 'Rooms'], as_index=False).agg({'Price':'median'})\\\n",
    "                                            .rename(columns={'Price':'MedPriceByDistrict'})\n",
    "            \n",
    "            self.med_price_by_district_median = self.med_price_by_district['MedPriceByDistrict'].median()\n",
    "            \n",
    "        ## floor, year\n",
    "        if y is not None:\n",
    "            self.floor_max = df['Floor'].max()\n",
    "            self.house_year_max = df['HouseYear'].max()\n",
    "            df['Price'] = y.values\n",
    "            df = self.floor_to_cat(df)\n",
    "            df = self.year_to_cat(df)\n",
    "            self.med_price_by_floor_year = df.groupby(['year_cat', 'floor_cat'], as_index=False).agg({'Price':'median'}).\\\n",
    "                                            rename(columns={'Price':'MedPriceByFloorYear'})\n",
    "            self.med_price_by_floor_year_median = self.med_price_by_floor_year['MedPriceByFloorYear'].median()\n",
    "        \n",
    "\n",
    "        \n",
    "    def transform(self, X):\n",
    "        \n",
    "        # Binary features\n",
    "        X['Ecology_2'] = X['Ecology_2'].map(self.binary_to_numbers)  # self.binary_to_numbers = {'A': 0, 'B': 1}\n",
    "        X['Ecology_3'] = X['Ecology_3'].map(self.binary_to_numbers)\n",
    "        X['Shops_2'] = X['Shops_2'].map(self.binary_to_numbers)\n",
    "        \n",
    "        # DistrictId, IsDistrictLarge\n",
    "        X = X.merge(self.district_size, on='DistrictId', how='left')\n",
    "        \n",
    "        X['new_district'] = 0\n",
    "        X.loc[X['DistrictSize'].isna(), 'new_district'] = 1\n",
    "        \n",
    "        X['DistrictSize'].fillna(5, inplace=True)\n",
    "        \n",
    "        X['IsDistrictLarge'] = (X['DistrictSize'] > 100).astype(int)\n",
    "        \n",
    "        # More categorical features\n",
    "        X = self.floor_to_cat(X)  # + столбец floor_cat\n",
    "        X = self.year_to_cat(X)   # + столбец year_cat\n",
    "        \n",
    "        # Target encoding\n",
    "        if self.med_price_by_district is not None:\n",
    "            X = X.merge(self.med_price_by_district, on=['DistrictId', 'Rooms'], how='left')\n",
    "            X['MedPriceByDistrict'].fillna(self.med_price_by_district_median, inplace=True)\n",
    "            \n",
    "        if self.med_price_by_floor_year is not None:\n",
    "            X = X.merge(self.med_price_by_floor_year, on=['year_cat', 'floor_cat'], how='left')\n",
    "            X['MedPriceByFloorYear'].fillna(self.med_price_by_floor_year_median, inplace=True)\n",
    "        \n",
    "        return X\n",
    "    \n",
    "    def floor_to_cat(self, X):\n",
    "        bins = [0, 3, 5, 9, 15, self.floor_max]\n",
    "        X['floor_cat'] = pd.cut(X['Floor'], bins=bins, labels=False)\n",
    "\n",
    "        X['floor_cat'].fillna(-1, inplace=True) \n",
    "        return X\n",
    "     \n",
    "    def year_to_cat(self, X):\n",
    "        bins = [0, 1941, 1945, 1980, 2000, 2010, self.house_year_max]\n",
    "        X['year_cat'] = pd.cut(X['HouseYear'], bins=bins, labels=False)\n",
    "\n",
    "        X['year_cat'].fillna(-1, inplace=True)\n",
    "        return X\n",
    "            \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "freelance-providence",
   "metadata": {
    "papermill": {
     "duration": 0.092857,
     "end_time": "2021-06-28T14:43:25.507051",
     "exception": false,
     "start_time": "2021-06-28T14:43:25.414194",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **5. Отбор признаков**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "accredited-marshall",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2021-06-28T14:43:25.701278Z",
     "shell.execute_reply": "2021-06-28T14:43:25.701774Z",
     "shell.execute_reply.started": "2021-06-28T14:36:16.870560Z"
    },
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     "exception": false,
     "start_time": "2021-06-28T14:43:25.599650",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Id',\n",
       " 'DistrictId',\n",
       " 'Rooms',\n",
       " 'Square',\n",
       " 'LifeSquare',\n",
       " 'KitchenSquare',\n",
       " 'Floor',\n",
       " 'HouseFloor',\n",
       " 'HouseYear',\n",
       " 'Ecology_1',\n",
       " 'Ecology_2',\n",
       " 'Ecology_3',\n",
       " 'Social_1',\n",
       " 'Social_2',\n",
       " 'Social_3',\n",
       " 'Helthcare_2',\n",
       " 'Shops_1',\n",
       " 'Shops_2',\n",
       " 'Price',\n",
       " 'Rooms_outlier',\n",
       " 'HouseFloor_outlier',\n",
       " 'LifeSquare_nan',\n",
       " 'DistrictSize',\n",
       " 'IsDistrictLarge',\n",
       " 'MedPriceByDistrict',\n",
       " 'year_cat',\n",
       " 'floor_cat',\n",
       " 'MedPriceByFloorYear']"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "absent-colon",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:25.895269Z",
     "iopub.status.busy": "2021-06-28T14:43:25.894269Z",
     "iopub.status.idle": "2021-06-28T14:43:25.897354Z",
     "shell.execute_reply": "2021-06-28T14:43:25.896822Z",
     "shell.execute_reply.started": "2021-06-28T14:36:16.890877Z"
    },
    "papermill": {
     "duration": 0.102749,
     "end_time": "2021-06-28T14:43:25.897496",
     "exception": false,
     "start_time": "2021-06-28T14:43:25.794747",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "feature_names = ['Rooms', 'Square', 'LifeSquare', 'KitchenSquare', 'Floor', 'HouseFloor', 'HouseYear',\n",
    "                 'Ecology_1', 'Ecology_2', 'Ecology_3', 'Social_1', 'Social_2', 'Social_3',\n",
    "                 'Helthcare_2', 'Shops_1', 'Shops_2']\n",
    "\n",
    "new_feature_names = ['Rooms_outlier', 'HouseFloor_outlier', 'HouseYear_outlier', 'LifeSquare_nan', 'DistrictSize',\n",
    "                     'new_district', 'IsDistrictLarge',  'MedPriceByDistrict', 'MedPriceByFloorYear']\n",
    "\n",
    "target_name = 'Price'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "consecutive-headquarters",
   "metadata": {
    "papermill": {
     "duration": 0.092633,
     "end_time": "2021-06-28T14:43:26.083257",
     "exception": false,
     "start_time": "2021-06-28T14:43:25.990624",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **6. Разбиение на train и test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "atmospheric-prophet",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:26.276867Z",
     "iopub.status.busy": "2021-06-28T14:43:26.276215Z",
     "iopub.status.idle": "2021-06-28T14:43:26.329549Z",
     "shell.execute_reply": "2021-06-28T14:43:26.328919Z",
     "shell.execute_reply.started": "2021-06-28T14:36:16.902392Z"
    },
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     "duration": 0.152367,
     "end_time": "2021-06-28T14:43:26.329700",
     "exception": false,
     "start_time": "2021-06-28T14:43:26.177333",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_df = pd.read_csv(TRAIN_DATASET_PATH)\n",
    "test_df = pd.read_csv(TEST_DATASET_PATH)\n",
    "\n",
    "X = train_df.drop(columns=target_name)\n",
    "y = train_df[target_name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "native-examination",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:26.523599Z",
     "iopub.status.busy": "2021-06-28T14:43:26.521929Z",
     "iopub.status.idle": "2021-06-28T14:43:26.529601Z",
     "shell.execute_reply": "2021-06-28T14:43:26.529024Z",
     "shell.execute_reply.started": "2021-06-28T14:36:16.994967Z"
    },
    "papermill": {
     "duration": 0.106535,
     "end_time": "2021-06-28T14:43:26.529750",
     "exception": false,
     "start_time": "2021-06-28T14:43:26.423215",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.33, shuffle=True, random_state=21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "attached-sudan",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:26.723503Z",
     "iopub.status.busy": "2021-06-28T14:43:26.722397Z",
     "iopub.status.idle": "2021-06-28T14:43:26.805524Z",
     "shell.execute_reply": "2021-06-28T14:43:26.805003Z",
     "shell.execute_reply.started": "2021-06-28T14:36:17.011497Z"
    },
    "papermill": {
     "duration": 0.18289,
     "end_time": "2021-06-28T14:43:26.805679",
     "exception": false,
     "start_time": "2021-06-28T14:43:26.622789",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((6700, 22), (3300, 22), (5000, 22))"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessor = DataPreprocessing()\n",
    "preprocessor.fit(X_train)\n",
    "\n",
    "X_train = preprocessor.transform(X_train)\n",
    "X_valid = preprocessor.transform(X_valid)\n",
    "test_df = preprocessor.transform(test_df)\n",
    "\n",
    "X_train.shape, X_valid.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "corrected-virgin",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:27.000904Z",
     "iopub.status.busy": "2021-06-28T14:43:27.000139Z",
     "iopub.status.idle": "2021-06-28T14:43:27.122158Z",
     "shell.execute_reply": "2021-06-28T14:43:27.122653Z",
     "shell.execute_reply.started": "2021-06-28T14:36:17.105645Z"
    },
    "papermill": {
     "duration": 0.22331,
     "end_time": "2021-06-28T14:43:27.122836",
     "exception": false,
     "start_time": "2021-06-28T14:43:26.899526",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((6700, 29), (3300, 29), (5000, 29))"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_gen = FeatureGenetator()\n",
    "features_gen.fit(X_train, y_train)\n",
    "\n",
    "X_train = features_gen.transform(X_train)\n",
    "X_valid = features_gen.transform(X_valid)\n",
    "test_df = features_gen.transform(test_df)\n",
    "\n",
    "X_train.shape, X_valid.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "french-local",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:27.314561Z",
     "iopub.status.busy": "2021-06-28T14:43:27.313907Z",
     "iopub.status.idle": "2021-06-28T14:43:27.325170Z",
     "shell.execute_reply": "2021-06-28T14:43:27.324609Z",
     "shell.execute_reply.started": "2021-06-28T14:36:17.229951Z"
    },
    "papermill": {
     "duration": 0.109066,
     "end_time": "2021-06-28T14:43:27.325331",
     "exception": false,
     "start_time": "2021-06-28T14:43:27.216265",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train = X_train[feature_names + new_feature_names]\n",
    "X_valid = X_valid[feature_names + new_feature_names]\n",
    "test_df = test_df[feature_names + new_feature_names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "transparent-space",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:27.516911Z",
     "iopub.status.busy": "2021-06-28T14:43:27.516148Z",
     "iopub.status.idle": "2021-06-28T14:43:27.525439Z",
     "shell.execute_reply": "2021-06-28T14:43:27.524883Z",
     "shell.execute_reply.started": "2021-06-28T14:36:17.248279Z"
    },
    "papermill": {
     "duration": 0.106921,
     "end_time": "2021-06-28T14:43:27.525584",
     "exception": false,
     "start_time": "2021-06-28T14:43:27.418663",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0, 0)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.isna().sum().sum(), X_valid.isna().sum().sum(), test_df.isna().sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "certified-perfume",
   "metadata": {
    "papermill": {
     "duration": 0.094046,
     "end_time": "2021-06-28T14:43:27.713241",
     "exception": false,
     "start_time": "2021-06-28T14:43:27.619195",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **7. Построение модели**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "romance-antibody",
   "metadata": {
    "papermill": {
     "duration": 0.094836,
     "end_time": "2021-06-28T14:43:27.902290",
     "exception": false,
     "start_time": "2021-06-28T14:43:27.807454",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Обучение"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "martial-mauritius",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:28.098819Z",
     "iopub.status.busy": "2021-06-28T14:43:28.098142Z",
     "iopub.status.idle": "2021-06-28T14:43:33.145406Z",
     "shell.execute_reply": "2021-06-28T14:43:33.144877Z",
     "shell.execute_reply.started": "2021-06-28T14:36:17.261752Z"
    },
    "papermill": {
     "duration": 5.147116,
     "end_time": "2021-06-28T14:43:33.145556",
     "exception": false,
     "start_time": "2021-06-28T14:43:27.998440",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(random_state=21)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_model = RandomForestRegressor(random_state=21, criterion='mse')\n",
    "rf_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "forbidden-henry",
   "metadata": {
    "papermill": {
     "duration": 0.09507,
     "end_time": "2021-06-28T14:43:33.339403",
     "exception": false,
     "start_time": "2021-06-28T14:43:33.244333",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Оценка модели"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "demanding-matter",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:33.541037Z",
     "iopub.status.busy": "2021-06-28T14:43:33.540409Z",
     "iopub.status.idle": "2021-06-28T14:43:34.201368Z",
     "shell.execute_reply": "2021-06-28T14:43:34.201873Z",
     "shell.execute_reply.started": "2021-06-28T14:36:22.707086Z"
    },
    "papermill": {
     "duration": 0.76582,
     "end_time": "2021-06-28T14:43:34.202065",
     "exception": false,
     "start_time": "2021-06-28T14:43:33.436245",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train R2:\t0.966\n",
      "Test R2:\t0.706\n"
     ]
    },
    {
     "data": {
      "image/png": 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U0iKE+C6UBNBua3n3F9bX+W/WP8D5mafVQoi/QSnBfktK2aVz2meg7OTwrBBiJpSEzrVQtpP8SQDr84mIKIpJKWuFsi30rwEUCSHegNLofgyUbYnXQdnFz5t4AVCuPVcJIe4DUA+gSUq5xcXTPyiE+DKU69lxKB/Qvwvlw/oG6zF/hVLt8lchxF+hXKP/n3UMMwJ46a6cgjKhUQilV9q1UBre3u/QdNfRrwF8DcCbQog/Qan8SYZSjXoDgKnwXKnh73Orfmw9frsQ4hkolUjZUN6n66BsqQ0o1/ylAP4khLgYwEkA34ISM7klpTwmhPgZlJ+JD4QQr0GpUJkFZbm5mjCqAvD/hBAPQ6ka7pRSvuXitA9AWaL2gTWOU7eFzoJNUogoFjDhQhS9bgGwCsB/AkiA0n1/HkJfWbAHSrXFAigXNzOUC/S1Uso3AUBKeVQIcS2Uprm/grJN3wtQyn/fCdG4boaylaK6beMfAdzr6UFSyqVCiE+gBGw/h5JkOAGlR46rgC8oz219fosQYhGA70OZmfkGlJmpWigl0nttDv8PKKW0t1qP2wJltyDHxn56z/MPa0LnYSgNDOOsz/F/Noe9BmWXoZutfwSUANop4SKlNAshroSy9vxWKEFODZSttB13vyIiokFESvmEEKIayvXkASiVFvVQrkuvWA/zGC9Y/QDAH6B8IE+Fskufq+vvm1C2pb4dSpVlC4DtAH6mLmGWUv5JCDECSuXoVVCuTT+AMskwI7BXrqsNSiJpNZRtiE8DWC6l/JW7B1n7zXwZwE+gTLDcBmWJTzWAR6EkU0Ly3DZjaBJCXAbgQSjJmv8CcAZKJdK9Nsd1CyHKADwFJfHSDWX51t/gfqmy+vhHhBCfQakA/hnO/yzYjvNnAC6E8jOVAWUyUTfhIqU8ZN2c4RcAlkP5+asC8B0p5VZvXjtRtBD+7xhKRBR6QoifQkki5EspvQlOBsVzExERUWRZq41HSikneDp2MD03EQUPe7gQEREREREREQUZEy5EREREREREREHGhAsRERERERERUZCxhwsRERERERERUZCxwoWIiIiIiIiIKMi4LXQIDBs2TI4ePTrSwyAiIooqu3btOi2lHB7pcQwVjEeIiIichTMeYcIlBEaPHo2qqqpID4OIiCiqCCHqIj2GoYTxCBERkbNwxiNcUkREREREREREFGRMuBARERERERERBRkTLkREREREREREQcaECxERERERERFRkDHhQkREREREREQUZEy4EBEREREREREFGRMuRERERERERERBxoQLEREREREREVGQMeFCRERERERERBRkTLgQEREREREREQUZEy5EREREREREREHGhAsRERERERERUZAx4UJEREREREREFGRMuBARERERERERBRkTLkREREREREREQcaECxERERERERFRkDHhQkREREREREQUZEy4EBEREREREREFGRMuRERERERERERBxoQLEREREREREVGQxUd6AEREROQbi0XieEsXGtvNyMswYnRuKgwGEelhERFREPF3PVHsY8KFiIgohlgsEpsOnMI9L++Guc8CY4IBK2+cgfmTRwYtEGeQT0QUWeH4XU9EocclRURERBFgsUjUNndi+7HTqG3uhMUivXrc8ZYuLQAHAHOfBfe8vBvHW7r8PqfjuDYdOIUFldtw8zM7saByGzYdOOXXuYiIyD/uftcTUexghQsREVGYBTJz2dhu1gJwlbnPgsZ2Mw6f6vDrnLYVLSmJ8Vix6ZBTkD+hohSm4WmBvXAiIvKKq9/1TR1m/i4miiGscCEiIgqzQGYu8zKMMCbYX76NCQakJMbpnvPj42fcVrs4VrTctGY7biopRH6mUTtGDfKJiCg8XP2uH5FudPEIIopGTLgQERGFmbuZS09G56Zi5Y0ztEBcrWTpHbDonnNbzWm3y4L0kj+VW6qxaNYo7RgG+URE4eXqd/3o3NQIj4yIfMElRURERGGmzlzaJkg8JTVsl/1Myk/H23eXornTjBHpSlPb4y1duueU0nlZkO25evoGdBM1cYbz52CQT0QUXgaDwPzJIzGhohRNHed/17NhLlFsYcKFiIgozNSZS8d+K66SGt70fNE7Z8W8YqzdUQfgfAXN6NxUu3MtKxurm6gpmzACV1yUyyCfiChCDAYB0/A09mwZpLgj4NDAhAsREVGY+Tpz6arni20jW9tz1rV04dPPz2Ltjjo0tCnLlNQKGsdzvVxVj2VlxVi1udoumTO1IIuBHxERUQj42jyfyZnYxYQLERFRiLkKlLydufR2twr1nKNzU9HTZ0Frdy8A+2VBOz9rsTtXQ5sZL2yvw5/uvBQSkhUtREREIebNRIoqkJ0NKfKYcCEiIgqhYARKvvZ8cVdBo3eu1u5eDE9PYtk6ERFRGPiy7bcvyRmKPtyliIiIKIQC2QJa5c9uFWq1y2zTMJiGpzn1euHOF0RERJHhy7bfgexsSJHHChciIqIQ8mUWy5Vg7lbBnS+IiIgiy5fm+f7sbEjRgwkXIiKiEApWoBTM3Sq48wUREVFgAmlk68vkh687G1J0YcKFiIgohMIRKHH3AiIiovAJRn82byc/WJka25hwISIiCqFQB0rcvYCIiCi8wt3IlpWpsYtNc4mIiELMVQPbYAi0Ka/FIlHb3Intx06jtrkTFosM2tiIiIgGIzayJW+xwoWIiCiGBdKUl9UxREREvmMjW/IWK1yIiIhimC9bSzoKxpbVREREQ43an029/rKRLbnCChciIqIYFkhT3mBsWU1ERNGBDdTDJ5D+bPw+DS1MuBAREcWwQII+lkQTEQ0OXCIafv40suX3aejhkiIiIqIY529TXpZEExENDlwiGhv4fRp6wppwEULkCyH+JIRoFkKYhRAHhRBfsrlfCCF+KoT4QgjRI4R4Xwgx2eEc2UKItUKINuuftUKILIdjpgoh/mk9x0khxENCCOFwzGLr85+z/n2dw/0ex0JERGQr1nb8UatjNlaU4qXyy7CxopSzbEREMYi75sQGfp+GnrAtKbImRf4F4AMAXwXQDMAEoMnmsB8BuBfAHQCOAHgIwD+EEOOllB3WY14EUAhgvvXrPwJYC+Br1ufJAPAPAFsBXAJgAoDnAHQB+I31mMsBrAPwMIDXACwC8IoQ4t+klDt9GAsRERGA2C0T9qckmoiIoksol4iy50jwcCnv0BPOCpcfAWiQUn5bSvmRlPIzKeVmKeUhQKkoAfB9AL+UUq6XUu4HcDuAdAC3WI+ZCCXRUi6l3C6l3A7gPwEsFEKMtz7PrQBSANwupdwvpXwVwAoA99hUuXwfwHtSyp9LKQ9JKX8O4H3r7V6NhYiIyBbLhImIKFJCtURUnUxYULkNNz+zEwsqt2HTgVNRX8EZrbiUd+gJZ9PcawFsEkKsA3AlgC+gVKc8LaWUAMYAGAngHfUBUsoeIcRWAFcA+AOAywF0AvjQ5rz/glK9cgWUSpTLAWyTUvbYHPN3AI8CGA3gM+sxTzmM7+8Allr/7c1YiIiINJ52/OEMIRERhUogDdTdcTWZMKGilJWRfgjV94miVzgTLiYA/w/AkwB+CWAGzic9VkNJcABAo8PjGgEUWP89EkCzNUEDAJBSSiFEk83jRwKo1zmHet9n1r/1nsf2HJ7GQkREpHFXJhzK5UZM5BARERCaJaKeJhPId1zKO7SEc0mRAcAnUsqfSCk/lVI+B6ASwPfCOIaQEUKUCyGqhBBVzc3NkR4OERGFmbsy4VAtN2Kpt3/YxJ+IyDvqZIIt9hwh8l44Ey4NAA463HYISgNcADhl/TvP4Zg8m/tOARhuG6xY/z3C4Ri9c8CLY045HOfuGDtSyjVSyhIpZcnw4cP1DiEiohjg705D7nb8CfauBOoY3z/ahCOn2pGdkqidk31j3LNp4i+gNPGfCOBu6DfxvxtKA/4mKI3z022OeRHALCi95eZb/73W5nnUJv6N1nMsA/BDAPfYHKM28f8zlMrfP0Np4n+Zj2MhIgoJ9hyJHrG2EyIpwrmk6F8AxjvcNg5AnfXfn0FJZlwN4GMAEEIYAZRCCVAAYDuANCg9WNQ+LpcDSLX5ejuAFUIIo5RSjWSvhtIz5rjNMVcD+LXNWK62OYc3YyEiokEm0KU/rsqEg7krgd4YK+YVY+2OOjS0mdk3xjOtib/NbZ+p/3BsnG+97XYoiY5bAPzBpon/HGsDfwgh/hPANutuhkdg38S/B8B+IcQEKE38V1qXR38f1ib+1qf/uRDiSuvtN3szlqC+M0REDthzJDrE6k6IFN4KlycBzBZC/LcQYqwQ4gYAFQCeBpReLAB+C2C5EGKREGIKgOehNMl90XrMIQCboAQ7l1tnhv4AYIM1uIH12G4AzwshpgghFgH4MYCVNr1fVgGYJ4T4sRBighDiJ1Aa+f7W27EQEdHgE6qlP8GcIdQbY+WWaiyaNUo7d3JCHPr7LVxupO9aADuFEOuEEE1CiN1CiKU21bO6jfMBqI3zAc9N/NVj9Jr4XwClib96zDuw93ebc3gzFiKikFInE2abhsE0PI0f8COAOyHGrrBVuEgpPxZCXAvgcQAPAjhh/ft3Nof9CkAylCRMNoCdAK6RUnbYHHMLlGa7f7d+/Vec310IUso2IcTV1nNUAWgF8BsAK22O+VAI8U0AjwH4GYBjAG6SUu70cSxERDSIhKo5YDBnCF2NUQho1S4VL32KR78xlTtL6GMTfyIiiilsXhy7wrmkCFLKtwG87eZ+CeCn1j+ujmkF8C0Pz7MPwFwPx7wK4NVAxkJERINLMJf+OArWrgSuxjghLx1L5pi0pUVVdWcYnOkzAKiSUv7E+vWnQohiKE38V0duWMEhhCgHUA4AhYWFHo4mIiJ/hHvJbijjEwqtcC4pIiIiimqx0BxQb4yPXzcVP994CE+/V4OGNqV9mUWCO0voYxN/IqIwCKTJazQ3iI3EDoGxEJ+QvrBWuBAREUWzWGgOqDdGgwBau3vtjntrz0msWDwNy9fvtWuwx+CMTfyJiEItkCav0d4g1lU/lVAu2Y2F+IT0MeFCRERkI1hLf0LJcYwWi8TKG2fYBafL50/ENRPzMLUgk8GZvScBfCiE+G8oWzLPhNLE/35A68XyWwD3CyEOAzgK4AE4NPEXQqhN/Mut59Vr4v8wlCb+j0FJ6vwYwCMOTfy3CiF+DOANANdBaeI/x9uxEBFFo0CSEpFIaPgiUv1UYiE+IWdMuBAR0ZAX69snu5v5YnBmj038iYhCL5CkRLQ3iGU/FfIFEy5ERDSkRVvpcqDJHxk9y9yjFpv4ExGFViBJiWhPaKj9VBzjBi7ZJT1MuBAR0ZAWTaXL/iZ/oi1pREREQ1sgSYloT2j4008l1itpyX9MuBAR0ZAWTaXL/iZ/oilpREREFEiT11hoEOvtkl2LReKz01041NCO6qYOvFxVj9buXk6KDCFMuBARUdQL5cxQNJUu+5v8iaakERERERBYk9fB0INMr/q0Yl4x1u6o46TIEGKI9ACIiIjcUQOWBZXbcPMzO7Ggchs2HTgFiyU4zUrU0mVjgnJJjGTpspr8seVN8sffxxEREVFo6FWfVm6pxqJZo7RJERr8mHAhIqKo5mq5zPGWrqCcXy1d3lhRipfKL8PGilJcMzEPx1u6sP3YadQ2dwYtueOJv8mfaEoaERERkevqUyE4KTKUcEkRERFFtXAsl7EtXY5kA1p/163Hwnp3IiKiocTVkmWDACdFhhBWuBARUVQL93IZXypqLBaJ2ubOoFbCqMmf2aZhMA1P8zpp4u/jiIiIKPj0qk8fv24qFs0sYMPcIYQVLkREFNXCvT2ktxU14aiE4TaSREREsYnVpwQw4UJERFEu3AGLt7sWhXor5kgubSIiIqLADYbdligwXFJERERRLxjLZbxd/uNtA1p3lTDBEOpmwUREsS4UyzrDIVbH7Y+h9FqJ9LDChYiIBj1fqkW8rajxthLGX56WNnG5ERENZbFaBRir4/bHUHqtRK6wwoWIiAY9X6tFvKmoCfVWzO6aBatB7ILKbbj5mZ1YULkNmw6c4swhEQ0ZsVoFGKvj9sdQeq1ErjDhQkREMcmXMuVQLP9RK2E2VpTipfLLsLGiNKizdu4SOgxiiWioC/WyzlDxd9yxuDQnVr9HRMHEJUVERBRzfC1TDtXyn1A2w3O3tMnbnZSIiAarUC/rDAWLRSIlMd7nccfq0pxY/B5RcHDZ83mscCEiopjja4VHqJf/hIqrpU3ulhsREQ0FsfZ7XU2aVLz0CSrmFfs07litaoy17xEFB5c922OFCxERxRxfKzyCubV0ILM2wZrxUYNYx9lOBrFENFQE8/d6ONgmTdbuqMOSOSbEGYCyCSMwtSDL7bhjtaox1r5HQ1EoKlFcJQgnVJRG9c9rqDDhQkREMcefMuVgLP8JpKw7mCXhDGKJiEK7rDPYbJMmDW1mPP1eDQDgiotyPf7ujuWlObH0PRpqQrVULVYThKHCJUVERBRzIlWmHEhZty+P9aY5ojc7KRERUXQIZCkol+ZQKIRqqZqrn3UBETMNn4OJFS5ERBRzIlXhEcisjbePjdXmiERE5FogS0FZ1UihEKpKFL2f9WVlxfj+ut1o7e4dcjENEy5ERBSTIlGm7K6s29M6aG9Lwrn2mYho8Ak0acKlORRsodzBUf1Zr2vpwqefn8UL2+vQ0KZsBz7UYhouKSIiopjnzRKcYHBV1l2YneKxI7+3JeHuZpyIiCh2cSno4BeueCQYQrlUTf1ZNybEoXJzjZZsAYZeTMMKFyIiimnhXILjaobSU1WKWv0yPD0R68pno7t3AHkZRhRmpzhVxcRyc0QiIqKhKtaWBIdjqRpjGla4EBFRjAtm0zd/m9W6q0pRA7AFldtww+934KY1O9Da3YfC7BS8c6jRqSqmMDuFzRGJiIhiTKia0IZSqKuu2PCZFS5ERBTj9JId2SmJaO4457Kfip5AZqbczeC4CsDWlc/WvX1jRSmbIxIRUVB56jNGgeN2yM7Y8JkJFyIiClCkgzjHZEd+phHfvrwItz/3kVPiBIDLsQbSrNbd7hM7P2vRDcAa2twHZmyOSEREwRBrS11iFZfP6BvqDZ+ZcCEiIr9FQxDnmOy4oWQUVm2udkqcjL+7FEcaO1yONZCZKXczOK4CsPzMZAZmREQUctz9LjwC2fqbBi/2cCEiIr9Fw3plNdmxsaIUL5VfhhkXZukmTk6ccT9WNTFiy5cEiKt10K7WL0/Ozxjy65qJiCj0uPtdeDjGI+oSYVYRDW2scCEiIr+Far2yr8uUbMtVa5s7dStHUhLj3Y41VDNT7qpf5k8eifF3l+LEmS6kJMYjLyMpoOciIqLoF+6luFzqEj5DffkMOWPChYiI/BaKIC7QZUquEid5GUluxxrKxm7uAjB3y5yIiGhwicRSXC51IYocIaXzlpcUmJKSEllVVRXpYRARhVwoAsfa5k4sqNzmlBjZ6MNac3X20DZxAiDi/WYcBeO1xhIhxC4pZUmkxzFUMB4hij6R+r2vd10M57Uv0g32iWyFMx5hhQsREfktFFUhwVim5KqiJNq2JuQWkkREQ0ukfu9HcqlLNDTYJ4oUJlyIiCggekFcIDNZoVxrHm1rq7munohoaBmKv/e5SxINZdyliIiIgkqdyVpQuQ03P7MTCyq3YdOBU7BYvFvC6mpXn2CuNe/vt2DP563YtL8Be+vP4rPmTmw/dhrHmjpx/LTy79rmTq/H7K9wvFYiIooeQ/H3PndJIn9YLBK1zeGLyUKFFS5ERBRUgc5khbJ5LaAkW97YcxIPvLFfK21eVlaMF7bXobW71+7foS55DvVrJSKi6DIUf+8PxaoeCsxgWobGChciIgqqYMxkqUt/ZpuGwTQ8zeuLqzezIQca2rRkizq2VZursWjWKKd/3/Pybhxv6fJ63P7w97USEVFsGmq/94diVQ8FxtXkXahjslBghQsREQVVpGayvJ0NaWjTTwgJof9vNrAlIiLy31Cs6qHADKZNBVjhQkREQRWpmSxvZ0PyM5O1samMCQZIef7f4/PSkZ9pdJko0qukGSxrjYmIiIJtqFX1UGDUyTtbsboMjRUuREQUVKGcyXK3+1FjuxnZKYlYNGuUVqGyfle902zI5PwMPHbtFN0eLsYEAyrmFeOJdw7j25cXoTgvzSlR5KqSJjFeYOmLn8b8WmMiIiKiSFIn7xxjrVhchiak5AxcsJWUlMiqqqpID4OIaFDxtGTo+OlO/G3/KazaXG2XSPn3KSMxeliaXbJmZIYRHeY+NLSZkZ9phEEIvHOoEQMW4LVP6tHQZoYxwYC37y7FRSPsS1drmzuxoHKb05Kp8rkmVG6usbttI7e8tCOE2CWlLIn0OIYKxiNERBSr1LgtFMvQwhmPsMKFiIhigqfdjwYs0JIt6v2rNlfjmkkjPSZrth87bZcsUR/f3Gl2Sri4WlfsuIIoVtcaExERUexwV/0by9RlaLEeR7GHCxERhZ0//U487X7U1KF/f3On2WN/F1/WCrs61jG2idW1xkRERBQb1AmlBZXbcPMzO7Ggchs2HTjFPnJRhAkXIiIKK3+DA09JEXf3e0rW+NLo19Wx00ZlcstLIiIiCpvBtH3yYMUlRUREFDBfylk9LQ1yxVMDNXf3SwndraqHpynJGl8a/bo6FgA2cstLIiIiCpPBtH3yYMWECxERBcRTfxRH/gYHnpIi7u6PMwDLyoqdGurGGezP7+1aYVfHDoa1xkRERI4Ga5+QWKdW9zpOKHFJc/RgwoWIiALia8VKIMGBp6SIq/sb2sx4YXsdlswxQQhASuCF7XWYWZiF0cNClyBhgEpERLHMYpH47HQXDjW0o7qpAy9X1aO1u9ftxAqFz2DaPnmwYsKFiIgC4mvFSiSCg7wMI1q7e/H0e/bbNodyBsjXyh8iIqJool7HVmw6hIXTChBnAB5aOAn/836NV0uByV4oJmF8WRJNkcGECxERBcTXipVIBAeRSPL426uGiIjCjxWJzo63dGHFpkO4qaQQlVvOL8l9cOEkrN5Swz4hPgjlJMxg2T55sGLChYiIAuJPMiOcwYEaRGenJGBd+eXoGxhATmpSyINpNrIjIooNrEjU19huxsJpBVqyBVCuY49uOIjyuSb2CfEBJ2GGLiZciIgoIO527alt7nQ5WxiO2URXQfSswpyQB9FsZEdEFBv4YVhfXoYRcQboTh6My0tnnxAfcBJm6GLChYiIAuZYseJptjBcs4m2QXR+phGLZo3C4VPtKMhKxtSCTO25QpH8YSM7IqLYwA/D+kbnpuKSohzdyYOJIzOGdPWPrzgJM3Qx4UJEREHnabbQ39lEXxMjahCdn2nEbbOLtLLoNVtrtQQPAJfJH/W1NLYrlTtxBmXHI2+em43siIhiAz8M6zMYBC435WLF4mlYvn6v3TWyKCfFbRUr2eMkzNDFhAsREQWdp9lCf2YTLRaJLUcasbe+DRYJxAlg6qhMzBuf5zLIU4PoRbNGOa1BVxM8AHSTP5OWleJgQ4ddcLSsrBgvbK/zektMNrIjIop+/DDsWny8AV+bdgGmFmRqkweF2Sl451Aje974gJMwQxcTLkREFHSeZgv9mU08caYL1Y2dWLO11i4BMnZ4GkYP009oFGanYM1tJWho68FdpSas31WPhjYzgPMJHin116c3tp9zSsSs2lyNJXNMePo9bolJRDRY8MOwe46TB7XNnUOm500wlxxzEmZoMkR6AERENPios4XGBOUy4zhb6Ol+PY3t57Bqc7VTAqSx/Zzu8RaLxDuHGlG+tgrL1+/DH7fV4rbZRcjPNGrPOSLdqCV/bBkTDOjq7ddNxAhx/t9NHWYf3xkiIopG6ofh2aZhMA1PY7LFDXdVqoOJ2m9uQeU23PzMTiyo3IZNB07BYpGRHhrFEFa4EBGR31zN/HiaLfR2NtH2/J3n9BMg3b39umPT6xNTuUWpUHn2g1q7BI9eKXlRTqpuFY6U5/891Nf3ExFR7PK3eiPWe954+7qjefeqcOz0SMHBhAsREfnF005DnkpnPd3veP4fzx+vG+AV5uhXxbiagZtWkIGNFaV2wYmrba0dEzFqDxeu7yciCg9+sAyNQHYLjOWeN7687mjdvSpcOz1ScDDhQkREfgn1TkOO5//T9jrcc/U4rPzHUS3AeOzaKSjKSdF9HlczcMV56U7jc5X8sU3EDE9TdimaWZjF9f1ERGHAD5ahE0j1hrsq1WhPkPnyuqO1ksfVaygon43u3oGofN+HMiZciIjIL/7uNOTvzFJDmxnP/es4fn39dBxp7ICUwFNbqnFhdgqGpydpM2tqoJefaQx4Bk4vEeOqQS8REQVXNC/piHWBVm/oXR9jIUHmy+uO1koeV69h8+EmVG6uicr3fShjwoWIiPziz8xPoDNLrd29OHyqA0+/V6Pdtq3mNP64rRarb5mJ3n5pFxitvmUm3r67FM2d3HWCiCjWROuSjsEgFNUbkUyQeVtZ48vrjtbdq1y9hgHrl0xMRhfuUkRENARZLBK1zZ3Yfuw0aps7/eq4799OQ/rBc2O7884GeudfVlaM1z6p145Rm9ia+yzYW9/mFOgtffFTCAHuOkFEFINc7SIX6SUdg4E/13BPIrV7kS+7Cfn6uqNx9yq911Axzz4+Goy7RsUqVrgQEQ0xwSr59WfmJyUxXndWJiUxzuP5h6cZ8VlLJ1q7e7XHVcwrxtoddcrrsiZeVPmZRiyaNQpHGzsAICpmpYiIyHu+LOmI9t4h0SYU1RuR6nniS2VNtFat+MLxNSQnxKHipU/R0HY+wcLEZPRgwoWIaIgJZsmvp52GHPUODKBiXjEqt1RrwXPFvGL0Dlh0j3c8/5hhqdhYUYq6li58+vlZrN1RpwUYcQJaoJefacRts4vsnsc2qcTAnIgo+nn74TgWeodEI1+v4Z5EqueJr0vPgv26I8H2NVgsEsvnT4y6XjOkYMKFiGiI8TUwCWZyIjc1CeuqTmDJHBOEAKQE1lWdwJSCTFgs0uN51QBjdG4qevosdtUuU0dlaoHeolmjtGSL+vrUpNLo3FQG5kREMcKbD8dsrhsdIlU9Eq27CYXLYKjaGcyYcCEiGmJ8CUyCPWs4OjcV91w9HsvX77WrcHnwzX147o5LvQ6MXQUXADChohRHGzvcriNnYE5ENHiwuW70iET1SLTuJhROg6FqZ7BiwoWIaIjxJTAJ9qyhwSBwQZbRrsJFXRakFxi7q65xFVyoX+sllfLSjWjqOIe7Sk0AgPW76tHQZmZgTkQUw4Z6hcNQF+kKDy5TJneYcCEiGmJ8CUxCMWuYm5qEZz+odQqMR2YYUdvcqQUshdkpeOdQo1fVNY7BTmF2ilNS6f5/n4BdJ1rxwBv7tdse/tpk5KQmorqxA8kJ8V4tayIioujCCgeKVIVHJPsHMdETG4SUvm8FSu6VlJTIqqqqSA+DiCigi7HFIrHvZBtuWrPdKTmyMYDlN3rByepbZqK3X2q3FeUm45GvT8H+k20w91u0ShS953YV7EwcmY7Xd5+ERSqVNMkJBqx+r8bptZTPNaFycw17uYSBEGKXlLIk0uMYKhiP0FCiXu/Yw4LCqba5EwsqtwU1TvIGG0UHJpzxCCtciIgGqUAuxupjV2w65LSrUKCzhmqFzaRlpWhsP4eu3n7kpiTipmd2aDsM3VRSiO/+3y67Pi+ulh65Wvb0u1tnoXJzjXbc0nljdat1LBJ2j2MvFyKi2MMeFhQJkeofxEbRsYMJFyKiQcqbi7GrChjbx67dUYclc0yIMwBlE0ZgakGWz7Mnekt+DjZ0aM9RUXY+GaK3w1DllmosmWPCsx/UYnia0e6cRxs7cFepSauCUR+TmhTvtKa/KDcZC6cVQFiH/9aek7At9GQvFyIiIvJWpPoHsVF07GDChYhokPJ0MXZXAWP72IY2M55+T6kUueKiXL+SLY7Ps2LxNKz8xxHtOSzyfJNbIaA77jgDsKysGHEG/XPaVsEU5SYjOd6AJ66fjuqmDrxcVY8dx5rxvSuL8dCb9j1c/rKzTnseNlkkIiIib0WqfxAbRccOJlyIiAYpTxdj2yqW/EwjFs0ahcOn2lGQlYz8TM8Xcm/7w+hV2ixfvxdL5pi0RM76XfXa0iX1uRyfe+yIdPxi4yHMLMyCRTpv7axWwWzYexJ3zyvGjWt2aMHP49dNxdjhqdpt6mMeeesAyueasPdkO5ssEhFRyLDB6eAUqh2SPP28sFF07AhbwkUI8VMADzvc3CilHGm9X1jvLweQDWAngO9JKQ/YnCMbQCWAr1tv+iuAu6WUZ22OmQpgNYBLAZwB8AcAj0qb7sBCiMUAHgVwEYBjAP5bSvm6zf0ex0JEFC7+BmmeLsZqFUt+phG3zS7SlvGs2VqLlTfOwOpbZmLpi5/qPtaX/jCuKm3iDOe/bmgzY13VCawrnw0pJS4aloqfvL7PrnrlFxsPobW7FyPSjS7POa0gA9dMGoGbHBIr97++D7+7dZbuY6YWZOIv37mMATAREYUEG5wObsHuH+TNz0ukt8Im74W7wuUIgC/bfD1g8+8fAbgXwB3W4x4C8A8hxHgpZYf1mBcBFAKYb/36jwDWAvgaAAghMgD8A8BWAJcAmADgOQBdAH5jPeZyAOugJFReA7AIwCtCiH+TUu70YSxERCEXSJDm6WKsVsDo9Uy55+XdePvuUmx08VhX/WEKymeju3fALnnhqtKmpChHu92YYMA9V4/HxLwMvHukCb/dfFTrGzNxZAb+5/0atHb32iV99M5ZnJfuMhmj19PFmGDARWyySEREIcQGp+QLb39e2Cg6NoQ74dIvpTzleKO1ouT7AH4ppVxvve12AE0AbgHwByHERCiJljlSyu3WY/4TwDZrIuQIgFsBpAC4XUrZA2C/EGICgHuEECutVS7fB/CelPLn1qf/uRDiSuvtN3szliC/J0RELgUapLm7GKsVMIdPtesmKJo7zZhtGqb7WFdJjSOnOnCitQdv7TmJ5fMnYv7kkS4rbWaPzsGa20pQVXcGAxZg5T+OIDslUTtOXW5kTDDgT3deiuHpSVoSx9U5C7NTcLK1R7dZbmpiHB6/birut6mcYfktERGFmqtrZmM7G5zqGerLr9gQd3AJd8LFJIT4AsA5KMt07pdS1gIYA2AkgHfUA6WUPUKIrQCugJLkuBxAJ4APbc73LyjVK1dAqUS5HMA2a7JF9Xcoy4dGA/jMesxTDuP6O4Cl1n97MxYiorAI5UVXrYApyErGmq21PjVec1W1UnemB89+UIuKecVYsekQJoxMh2l4mm6lzfGWLpSvrbI7R1XdGbueMupuQnEGOM3quDrnA2/us9vKuig3GUuvLMb1v9+O7JRElM81YVxeOiaOzMCYYUMriCMiovBLSdSvsExJjIvgqKITl1+xIe5gY/B8SNDshLJEZz6A70BJanwohMi1/hsAGh0e02hz30gAzba9WKz/bnI4Ru8c8OKYkQ7HuTuGiCgs1IuurWBedA0GgakFmVh54wztedxVflgsErXNnWjpOocVi6fZPaZiXjFe+6Rea2C7cFoBmjrM2vOYhqdpFTMGg7DrIfO9K8di6byxKB6RjqLcZNw2uwjPflCL1Vtq8MdttTh51gyLRdqNxdU561p6tK2sl84bi3uvmYAHrTsTNbSZUbm5Bve9sgdCYMgEb3SeEOKnQgjp8OeUzf3CeswXQogeIcT7QojJDufIFkKsFUK0Wf+sFUJkORwzVQjxT+s5TgohHrJW0does1gIcVAIcc7693UO93scCxFFv96BAVTMK3a6ZvYNWDw8cuhxVdl7vKUrwiMLH7WK15u4jKJf2CpcpJR/s/1aCLEDQC2A2wHsCNc4QkUIUQ6lyS4KCwsjPBoiGizC0YXe28ZrjrNOJUWZ+J9bZ6G54xzqzvRoWzIDSoCU7EWVTFFuMm4qKbSrRnn4a5Px//78idOuRlMLMrUqFscyY7X82CAEjAkGu62sK8rGsjSXHLGnHAVkqC95IN/kpiZhXdUJLJljghCAlMC6qhOYP4VzuY4CqewdLP8v2RB3cInYttBSyk4hxAEAxQDesN6cB+CEzWF5ANRZp1MAhgshhFrlYp0pGuFwTJ7DU+XZ3OfumFMOx7kbi97rWQNgDQCUlJRIV8cREfkiXBddbxqvOW4j/e0rTPj087MoHpGODXtPaskWQJmNubgo2ykxZBsMjUg34peLpuHO5z/Wgqu6lh4c/EK/p0xjuxmHT3U4JZ+umZiHdw414p6XdyM7JRHLyoqxanO1dswlNs15bcc3PI2luUMYe8qR37jkgXw1OjcVy+dP5Ba+XvB3Oc1g+3/JhriDRziXFNkRQhihzPg0QOmtcgrA1Q73l+J8z5btANKg9GBRXQ4g1eGYUutjVVcD+ALAcZtjroa9q23O4c1YiIjCRm/pTLioS4i2HzuN5o5zyE5J1LaR/tGre1C5uQY/fHUPvjt3LIpykwGcL5WOjxN2Y1WDoQWV23DzMzvx1ae2oaHNjOyURLvnNPdbdJdRpSTG6ZYZH2ho025vaDPjhe11KJ9rwnN3lGBjRSkKc5OxrMy+lHtZWbHdttQ05Jisy3Q+E0K8JIQwWW/X7eMGpVLlCutNnnrKqcfo9ZS7AEpPOfWYd2Dv7zbn8GYsFAFc8kC+UidPNlaU4qXyy7CxojRmEwGh5u9ymlj8f2kbY9U2dzotnabBIWwVLkKIJwC8BaVqZASAB6EkS/4kpZRCiN8CuF8IcRjAUQAPQAloXgQAKeUhIcQmKLNL5dbT/gHAButsEqzHPgzgeSHEYwDGAfgxgEdser+sArBVCPFjKJU11wG4EsAc6/N4HAsR0VCgN1u0rKwYAxbptI30IxsO4FfXT0dNUwfGjkjHb9457FQqrRcM3f/6PpTPNaFyc4123Ft7TmLF4mlYvn6vtszoka9PQVP7OdxVasL6XfV2S5ca2uzLj9U+LS+VXwbT8DRsP3YaL2yvsyvlfmF7HWYWZmH0MM4cDUFqT7nDUOKRB6D0lJsM933cCqz/1u0pJ4Rw7ClXr3MO9b7PEFhPuQJQxHAHEfIHKxa8429lb6z9vwxGRc5gWUI12IVzSdEoAH8BMAxAM5S+LbOllHXW+38FIBnA0wCyoQRE1zisUb4Fyg5Df7d+/Vec310IUso2IcTV1nNUAWiFslZ6pc0xHwohvgngMQA/A3AMwE0266W9HQsRDQFD+WKmlyBZtbkaj3x9sm5QU9PUAWN8HH7zzmEsnz/RaTbKVTA0Li9dKx82JhjwzUsKMTw9AWtuK8HhhjakGhPw3f/bpd1fMa9Y6xdjTDAgP9N9+XFehhGt3b1aTxfH+2loYU85ChR3ECEKLX+SU7H2/9JVRc6EilKvXvdgW0I1mIWtoFpK+U0p5QVSykQpZYGUcrGU8qDN/VJK+VMpZb6U0iil/JKUcr/DOVqllN+SUmZY/3xLSnnW4Zh9Usq51nPkSyltq1vUY16VUk6wjmWilPI1h/s9joWIBj/HJTALKrdh04FTQ6bk01WC5MKcZN0lP3OLh+PSMdl47o5LdS/4rnZcKsxORvlcZUehJXNMeGF7HXbUtuLBN/dhZFYKHt1w0C4gqdxSjUWzRmnBxeR897ssOZYnF+UmY81tJWhsN7OElyCl7ASg9pSz7eNmS7ennHpnGHrKuTpG7/WskVKWSClLhg8f7uowCgB3EKFoZbFIHGvqxJbDjdhZ24Ljp4fONS7W/l+6q8jxRrQvoeJyqfMi1jSXiCjaBTr7EOtczRZdkJmsu3PSrMJst7MqrnZcOjdgsVtSBAApiXG4qaQQNU0dugHJtIIMbKwo1SqO3JUf295/puscTp41o3xtFWeECIBdT7n3YN/H7WOb+0sB/ND6ENuecmofF72eciuEEEYppRo9u+op92ub4bjqKedqLBQB3EGE3IlUZayrZcCTLkjHqKxUNHUM7krdWPt/GWhFTjQvoWL1jT0mXIiIXIjmi1k4FGanYM1tJaiqOwOLVHqrLJ8/EYU5qSjMSXUZ1LgKNl0FQ8dbupyCjlFZKbjv1T24q9SkG5AU56XbfQ88lR+r9wPAt579aMgm0Yg95Sg42I+D9ETyg6arZcBP3jgDX31qmzaeFYun4YIsI3JTk6I6IeGPWPp/6WoSytuKnGheQjXUJywdMeFCRORCNF/MQs1ikdpWy7ZB2jUT87TgTC+o8RRs6gVDtkFHdkoibigZBQnlAr1+Vz0q5hVrTXoDLREe6kk0AsCeckQUIpH8oOnq+nboVLvdeJav34slc0x49oPaIV11EGmBVuQEmrAJJcZa9phwISJyIZovZqH22WnnoHH5+r2YWpDp9mLpT7CpBh2TlpXikxNncf/r+7TKloY2M9buUHYYijMAc4uHQ0KpoPFnZm4oJ9FIIaX8pof7JYCfWv+4OqYVwLc8nGcfgLkejnkVwKuBjIWIokckP2i6ur4N2A8H5j4LhGDVQTQIpCInmpdQMdayF7amuUREsUa9mG2sKMVL5ZdhY0XpkJgJslgkDjW0uwwa3fG3CZzBIGCRwP2v77OrbFGTLs9+UIvCnBR8UncG/zx6Gm/uPoktRxp9bsIWa031iIgodrhqDh+OD5p617eHFk7Chr0nncajLmz0pUkrRR81YTPbNAym4WlRE58y1rLHChciIheG6pbQx1u6UN3U4dfsRCCzGrbJGtvKlqkFGbgg04jdn5/FynfPLy1aVlaMscPTMHqY9zND0TwjREREsS2SlbHq9W383aU4caYLKYnxGJmZhKyURLvxVMwrxtodygrKoVx1QKHDWMseEy5ERDqGcof1xnYzXq5y7p3y+HVT3QaNFouElMAT109HdVMHXq6qR2t3r9fBZkpivJasyc80YtGsUYgzALmpiTjddQ4tXb3ITklEQ5tZawY4qzDbp4QLEFtN9YiIKHZE+oOmwSBw0Yg0XDTi/PXtwmylyX1juxl9AxIPvrkPDW3mIV91QKHFWOs8JlyIiHQM5Q7reRlGtHb3ahUmQgAGAcwqzHIZNOolqB6/bipmFWahMMe7YLN3YAAV84qxruoEbiop1JI9a7bWomJeMd7YfRK3zS7C2h11WtKlu7c/2C+fiIjIb9H2QdN2PBaLxHN3XMqqgwgaqtXTQxl7uBAR6fC3F8lgoJZEt3b34un3avDHbbWYMDIDhTmuZ8H0ElT3v74PFgmvAgmLRcIiJYQAHl44WUu2qOeq3FKNhdMKULmlGotmjQKglELnpCQG4RUTERENftHa82OoUCenFlRuw83P7MSCym3YdOCUz/3oKLawwoWISMdg6rDu62yKY0n08DQj4gzAzs9aXD6+sd2M7JRELJo1CsJ61/pd9XY7M7gbx+etXTh+uhurNlfjrlKTbrJL3VVBCGjr0PssDtsvEBERUVi5ur6zmsPeUK6eHsqYcCEi0jFYtoQOtBdNnEFg/xdtWL5+r9vH52ca8e3Li7Bqs31T25EZRrfjuGZiHk60dqO6sQNfnO1BtrViRS/ZJaXy9/i8dCyZY8K6qhOYP2VkkN8xIiKi6GKbuEhJjEfvwAByU5OiIoHh7vr+zqHGIdkLz5VIbhtOkcOECxGRjkg3vgsWf2ZTbIOnZWXF6OkbwF2lJgBK1Yre4wcs0JIt6vOs2lyNf7soVwsU1XGoDXEPn2pHdkoCfvzaXtS19GhVK5v2Nzg17FV7uywrK8bjGw/51IyXiIiiCysfvKeX0FCvicvnT9QmLmzfSwBhe39dxRnrymezmsPBYKqeJu8x4UJE5EK0Nb7zhz+zKWrwlJ2SiIzkBLuqFXU7ScfHN3XoP8+7h5tQf9aM7JQELdly2+wip4a4aiPcyi3VWDLHhLU76lA+14SpBZkYkZ6EvgEL/n3KSHSY+zBmWCryM5MxOT+DAToRUYyJ9V0Ag50sslgkPjvdhbozXUhNjEdeRpJds3m9hIZ6rVyx6RD6Bix2Vairb5mJ3n4ZtvfXVZyhNrd3vH0wV3N4+tkYLNXT5BsmXIiIBjFvZlMcA4SWrnPITknETxZMRE1TB+4qNWH9rnotIVI+1+Q0G+PqeQYssM50XQ5jggGLZo3SbYi7ZI4Jr31Sj0WzRqEoNwU3lozCRSPSMHZ4GoqsgUgsB+hERKTwpfIy2iphgp0s0jvfsrJiFOelYd74PBgMwmVCQwhg4bQCLdmi3r63vg1rttaGrbLE1fU/PzM5LNUc0fIz4s3PxmCpnibfMOFCRDSIeZpN0QsQfnvTDNz5b6Pxo1f3OFW2NLSZMS4v3Wk2ZnRuKlbfMhN769tgkUCcAHJSEvF7a9DXNzCAlTfOwOFT7bqBY1K8wa7yxZhgwA+uGoffvHMEy+dPxPi8dJYmExENAt5WXkZjJUywm57qnW/VZmViwzRMqbB1ldCQEogzwOm9tEjn24JRWeIqseEqzpicn+Ex/gg0URJNPyPe/mwMhupp8g0TLkREg5in2RS9AOFgQ7vT7JhahfLsB7WYOFJ/KU9vv9QepyZMAOv2zalJmFWYg4KsZLtzq/ePHpaqJXjU53zy3aNYMseEe17ejd/dOmvIlSYTEQ1G3vaxiMYdXYLd9NTV+SwS2jn1EhpqD5fHvjHV6b2ME/qN5wOpLPGU2HAVZ7i6PViJkmj6GWFDXHKFCRciohjnaZbI3WyKXoDganYszgCsvHEGxgxzXmusF/Q8+e5RlM81YcLIDG1MUwsynQLHBxdOgrRY3G4FnZoUz0ZzRESDgLd9LKLxA2ywm566Op9BQDunbeJC2aUoDn0DFsyfMhKF2SlO7+XUUc7X2UD7hHhKbLiKM1zdfrylCys2HcKSOSYIa7iyYtMhTBiZ7tP3Npp+Rlx9LwUEaps7uXRoCGPChYgohgU6S6QXILiaHSubMAJTC7J0z6sGPeoOREnxBowZloqM5DhkGROx87MWLRl0zcQ8PHfHJdhe24LiEel44p3D+Nr0ArdbQeelJ7HRHBHRIOBtH4to3NEl2E1P9c6n9nCxPae7iRO99xJAUPuE+JrY8DQR1NJ1DjeVFDrtRnim65xPiZJAfkaC3fvF1ffy++t2azsrsu/c0CSklJEew6BTUlIiq6qqIj0MIhoCaps7saBym1OwsdHLclq9hI0/OxzUNnfizuc/cgqgHl44Gb/fWqNt+/z4dVMxMT8N246eRpt5AOnGOKQbE7Bm6zHd4Evd9nL+5JEAlFkxNpqLXUKIXVLKkkiPY6hgPEKxLJr6cziOK5jXInWXohNnupCis0tRNPAl1vDm+7bn81bctGaH0/nWlc/G9AuzvR6Xvz8jofrZUn826lq68OnnZ/FKlbLhgPr6vI3NKPTCGY8w4RICDHCIKFy2HzuNm5/Z6XT7S+WXYbZpmFfn0AseAd+SG/39FnxcdwZ3Pv+xUwC1ZI4JT79Xo3399C2zcLanF//9+n6Y+ywoyk3G8vkT0djWg/H5Gdh/sg1TCzIRHyeQm5rExMogwoRLeDEeoVgX7OQG+ceXBIU3yRlXsctfvnMZLr/Iu9jFdmy+/oz4O1nlbVVMMGIzCq1wxiNcUkREFMOCUXLtqlTZ2y76FovEO4caXe5AJIT913vqz2r/BoC6lh7c8/JurL5lFs719eOqiXkMqomIiDu6RAlftjP2ZvmRq9glL8P35WL+/Iz40/vFl6RTNC6Ho8gxRHoARETkmcUiUdvcie3HTqO2uRMWi1KdWJidghWLp8GYoPw6j0Rvk89OK830LNZ+K7bUPiy2Xw9YlMa8tsx9FtQ2d6IoN01rwEdERETRQU1szDYNc3udVpMNthyTDWq/E9vYZfUtMyElnOKcUPBmjI5cNQ4+3tJld5zFIiEl8MT107GsbCzyM43sOzfEscKFiCjKuZpVuWZiHt451IiV/ziCJXNMiDMAJUU5uMKUG9aERd2ZLpj7LFi/qx4V84rt+rA88vXJ+N3755cTqX1ZvjGjwO4cxgSDtpsRERERxSZvGgs7VsyMzDDiYEMHvvrUtpD267FdEvTMbSV44M19Wo85TwkRb6pi9OK1x6+bilmFWVHXl4fChwkXIqIo4G5dsKtZlXXls7XbbXukhLspW2qismVzQ5sZa3fUYckcE5ITDJhVlI361i48/LXJ2P35WQxYgHVVJ3DLpUUwxhu0cltjggErFk8Le6KIiIiIPPNlRx9vlx/ZLgWqbe50u+10sF6DYzJkxeJpKMgyIseLfnHeLBPSi9fuf30fNlaUMr4ZwrikiIgowtQgYEHlNtz8zE4sqNyGTQdOaeW0rmZVGtpcz7YEe3x6y5lUqUlxWFZWrCVdnv2gFnEGgf31Z/GjV/ejprEDACAEsHBaAZ7/8Dj+98PP8Kvrp6OibCz+b8ll+Nq0CxAf7/slydPYiIgo9vF3feR4ilH0eLv8SOWueiRY9JIhy9fvRU5qkldj1FsG5VgVE47XQbGHFS5ERBHmqoJFndlxNauSn5nsd1M2b2erPDWJs1gkunsHUJCdjCeun47a013ot1iQlhSPSfkZeKn8Mq1U2PYcFfOK8YuNh9DQZsbk/Ay/Zn6idctQIiIKHv6ujyy1T1soq0/C0WTWn0a5tryp3GGzXNLDChciogjzNCPialZlcn6Gx9kWPb7MVjkmg7JTEnH4VDveP9qE46c7senAKXz7fz9CxV92475X9yA3LRHJCXH44we1yE5NxGzTMIwelob5k0fi/5ZchoqysVhWVgyLlFh88SgsKxuLguxkv943bxvYERFR7OLvev8FWhlksUgcatDfgTCYVRveVI8Eyl2jXG/fJ0+VO+F4HRR7WOFCRBRhnmZE3M2qeLtNoy13FTWjc1PtKl9sk0H5mUbcNrtIa4pbUTYWa7bW2p3n0Q0HUT7XhAcXToLFIrHlcCNSE+ORl5GEKSMzYO7rR92ZHjy64aA2UzkuLwOT8jN9nqkMdLaKiIiiH3/X+ycYlUHHW7pQ3dQR8qoNf+MZX7hq5luYnRK0CqpwvI5g8aUvDwWGCRciogjztqO/2lzOlqvb9agX16ONHchOScSiWaMgrNfW9bvq0dhuxuFT9kt/nrmtRAu0Fs0apSVbAGVrZ70g+NLR2WjrGcDC1R9o53n4a5ORmhSHmqZOpyTNva/sxsR830uTWbpLRDT4+fq7nh8kFZ6WK3ujsd2Ml6ucdyB8/LqpQa/a8CWe8ff8esmQYLxPjs8TytcRDFymF15MuBARRVigMyK2weWIdCPiDEBDm32gaXtx/f5Vxfj25UVYtfl88LSsrBjpxnjc+fzHdkHHA2/uw4rF07B8/V4I4Zxg0QuC040JuOuFXXbneeStAyifa3KZpPFnptKbRBUREcU2X37X84Pked5uY+wuOZWXYURrd6+2A6EQgEEAswqzYvL91EuGDMUKqmAnmcg9JlyIiKKAvzMiesHlsrJivLC9Dq3dvVqgaXtx7R+QWP1ejd2FdtXmalxclI27Sk0AlIqXhjYz6lp6UJBlxMaKUjR3nsMft52vTlm/qx7LyortEjcrb5yB7t4B3eBFXRIdrKqUWCrdJSKKZtFcFeLL73p+kDzPU2WQN8kp22TX0+/VnF+GkzN4JjaGYrXsUEwyRRITLkREMUwvuFy1uRpL5pjw9Hs1WqBpe3E191t0L7Qf1JzG6i012i5Ca3coSRt1y8TC7BSsua0EVXVnYJHAW3tOojgvDW/fXYrmzvNB8Genu3SDF4MAXtEpTQ6kKiUWSneJiKJZLFSFePu7nh8kz/NUGeRNcmooTGwMxWrZoZhkiiQmXIiIooC/s4uN7WbdfiyFOcnIzzRieFoiTnecQ1tPH5aVjcXLVfUA9KtMBqxfmvssqNxSjfK5JkwYmYHRuamwWCTeOdRoF5CsWDwNXy4eAYNBQAhlLIBSbuxY+fLDr4zHsLRErTS5fK4JFw1Pw+T8DN1O/0REFB6DqSpkMH2QDLTqyFOyxNvk1GCf2BgKSSVHQzHJFElMuBARRVggs4v5mUbdfixt3b34QdlYDEDg2899ZHff3/Y1OCVE1IoWlbnPgpkXZqF07HCcONOFpvZzOHyqHXeVmrTlRsvX78WUCzJxpNG+0e4T10/HC9vPr/eWEujtt2DlP45qtw1YgJX/OILn7rh0UAc1RETRbjBVhQyWD5K+xgWukjPukiXeLDmK1mVmwTbYk0qOhmKSKZKYcCEiijBfZhcdAyCLRWqJE/WxqzZXY+mVY5GSlID7Xt3jdN/zd16CvgEL1n1nNrr7BpCSEIeKdZ+ioc2sPY8xwYALs1PwfnUTqhs7dZMzDW1mnDjjPPbqpg60dvfitU/qtcqbC3NS0Nsv8fR7NXavJxYDeiKiwWQwVYUE44NkNCQafI0L/Jm0cZecirZlZtHwPRlshlqSKZKYcCEiijBvZxf1AqAnrp+u+1hzvwVd5/p17/vwWAsqN59vfnfBBUZ885JCpyoZc/8A9ta3OW3jXLlF6RHz7Ae1SEmMd3qOl6vq8fPrpqCp/ZzTOV/YXqcldmI1oCciGkwGS1WIKpAPktGSaPCl6sjfJWHuklO1zZ1Rs8wsWr4nRP5iwoWIKIQ8zcpYLBIpiXGoKBsLizy/O5BeMuJ4SxdWbDqkLcsBgC/OduvOTEoJpBrjPfZquefl3fjTnZc6LQF6YXsdxgxLhUVCt0dMnAFYeeMM5GUkOT1Ha3cvUhPisWrzfqfqmvK5JrtkT6wG9EREgwWXF5wXLf1sfKk6cpWcqWvp8vh9dJWciqZlZuH4nrCChkKJCRciohDxNCujd3/FvGKsqzqB5fMnOiUjWrrO4aaSQrsdfn5w1Tj85obpuPeVPU7nuHTMZDx27RQ88MZ+t71aunv70drda7fcx5hgQH6mESdaunR7xJQWD8Ok/EwA0J0ZHZBSN1ibeWEWXiq/bEgH9ERE0YbLCxTRkmjwperIVXLm08/PoqfP4nUliG3SISUxHkW5yahr6bE7ZySqUkP9PWEFDYUaEy5ERCHiaVZG7/7KLdVYVz4bUwuytAu9GgT19lu0ZIt6/JPvHsXL5bOx0br1c0piHPoGLJg/ZaS2Drt4RBpOtZmRm5aEe1/Z7dSrpTBHP7CbnJ+JjnP9uOtPVU6VKtdMOh+I6M2Mutoauig3dcgH9EREFJ2ipZ+NL1VHesmZBxdOwrqPTmDN1lqvKkH0kg6PXTsFT22pRl1Lj7YzYUvXOe05w5WMCPX3JFqqmmjwYsKFiChEPM3KuLq/p2/ALtmiBkF3lZp0j+/qHcC0C7NhGp5mN0MFKEHR9AuzMbVA4rPTXfjhNRNQ3dSBl6vq0drdi5U3zsCYYakYMyxVN7CLE0L3OZs7zbhohBKI6M2MFuWkYMXiaVi+fu+g6AlARESDk+11Mz/T6JS8WH3LTEgJbD92OuTLTfSWtnj60G8wCFwzMQ//862L8emJVgxYgDVbj+GmkkI076jzqhJEL+nwwBv7sa58Nrp7B9A3IPHgm/u05EswKkC8XcYT6h5D0VLVFCgui4peTLgQEYWIp1kZb2ZtHIMgvePV3YpOnOnCJyfO4v7X99kFJddMzMM7hxrtgpXHr5uKWYVZKMw5f0HWKyd3NcbhaUbUNnfqXtgtFol3DjVi5T+OYMkcE+IMQElRDq4w5fLiT0REUUOvsmP1LTPx9t2laO40Y2SGEQcbOvDVp7aFfLlJIEtbTrR247/+b5fdtbpyi9I3zZtKEHcTQHkZRiyo3BbUChBfXmuwegy5SkhES1VTILgsKroZIj0AIqLBSp2VMSYov2odZ2U83Q/YB0Hrd9WjYl6x3fGPXzcVozKT8UHNaew/2a4lW4DzQdGBL9qcZq7uf30fBixKQmf7sdOobe6ExSK9fg2ftXRiQeU23PzMTiyo3IZNB05pj1eTRHUtPXj6vRpUbq5B+doqnGjtDvp7TERE5C+9yo6lL34KIYDZpmGwSOguNzne0hWWsXj7XK4SJuPy0r2qBFGTDrbUpIO7ChB/+fpa1Ura2aZhMA1P8yvZsunAKd24xZtYLNoF8rNDoccKFyKiEPE0K6N3f2F2ijYDMyLdiNSk8zsNNbSZsXZHHcrnmlCQmYyTbT24uCgLfzt4CsvX73W55OhEa4/u7YdOteM+m2a7erMhemM0CGD+KtezXYHsmEBERBQu/i79DcVyk0Cey1WVxsSRGV5dcz0t2wl2BUi4l/F46tMS67t0hfv95PIl3zDhQkQUQp52frC9X68k9P5/n4AfXDUOT757FOY+C1q7e2GMj8Pv/lmD5fMnon8AWp8UQD8oEi5uP9rY4VOJsLQWwDR3nnN7YQ/WjglERESh5Hi9ys804oaSUejuHUBtcydGpIdvuUkgS1tcJUzGDNOv0tD7wOwq6RCKHirhXsbjKSER67t0hfP95PIl3zHhQkQUJRxnYLJTEnG6qxcXDUvFr6+fjoa2HswqzAYgtV2Idn7W4rTkyHbb6AcXTsKZ7nN49BtT8OCb57eHfvy6qfj134/YPb/ebIjehXXF4mlut4vUC87U7ahbu3vZ+Z+IiLwS6pl02+tVdkoivn15EVZtPn8NXX3LzJA2bHU1Fl+fy5c+J+4+MOslHYLVQ8XX1xrM7/1g6NPiTqgbC9virk6+E1I6r9mnwJSUlMiqqqpID4OIYsz2Y6dx8zM7ASizbLfNLrJLnlTMK8alY7JRMjpXe0xtc6ddMzt1dm5cXjqS4gx4bONB1LX0oKQoExVl49HYbsaYYakYnp6Ir/xWeVx+phGLZo1CnAEom5CHqQWZWlDjeH4AKMpNxi8XTcP22hZYJPDWnpNYPn+i3eyGxSLx8fEz2FZzGlICr31Sr21H/VL5ZZhtGhaW95SiixBil5SyJNLjGCoYj1As6++34O39DU673QV7Jl39YN/ccQ63P/eR04fyTctKYZEIy3ITi0XZUfDEmS6kJMYjLyPJrrl9MOhd140JBmwM8wdm9X23fV8B5QN9S9c5fHHWHLTv/VCoytB7P0Px2mxjVVuxFtuFMx5hhQsRUYjozc4AcDljY1u6vGjWKC3ZAigzCJVbqrGufLbdc4zOTbXbflldcvTrvx/GNy8pRG+/RH6mEfMmjET52iq7QGP1LTPx+/dr8B9zLsLhU+2wSKDipU/w4MJJGJObhqYOMwxCIDslUUuW5GcacVNJIe58/mO7ipdrJuY59X4Znp6EP26rHbQzSkREFBoWi8SHtS12S2ZDNZOuLidxtezkVLtZa9YaDkcaO0KaGIiWbZAdl/HYJkWWzDHh2Q9qg/a9D0WVTrQJ17KowV4tFApMuBARhYCrrSZ7+6XL7ZnjDMCysmKs2lwNIaAbEHX3DtjdZjAI/PukkRiZYcTxFmVG7Jmtx1DX0oNVm6uxZI4JAJySN/e8vBuv/ufl+NbsMXbj+cn8Caht7sLSFz/VbltWVowXttehoc2smwhavn4vphZkOl3kw1niSkREg8fxli5U1Z0Ja2IgWB8kA1kKE47lGtH6gdn2tbuKgQL53kdjn5ZYbD7L2M53TLgQEYWAXtC0t74Na7baz9jc//o+lM81YcLIDAxPT8QL2+uwZI4J4/PSdQOivAz7gMhikXj3SJNTv5Tmzl40tJkhrNdtvcDlizYzak934q5SJSmzflc9Wrp7nca4anM1yueaULm5BnEG74OgYM8oxWJgQkREvmtsN8Mizzd8t136mpwQD4tFBv1aEowPkoEuXQlH9Um0fmB2fO3RmBQKJn9/ViIdCw2FaqFgY8KFiCgE9IImi9RPVlgkcM/Lu7GufDZau3vx9Hs1yM80OjXA1QuI9BI7lVuqtXJcg4Bd0Koqyk3GWZvkipqoMbhIqMy8MAsvlV+G5IR4u4QM4D4ICtaM0lBYf01ERIq8DCPe2nMSFfOKsa7qBG4qKdSuh2u21vr9+9/TtSTQD5KBVqiEo/okEh+YvUkS2L52vU0A/E0KRTpB4Yo/PyvREgtFY7VQNDNEegBERIORGjjYihNwus2YYICU55cLrbxxBowJBjS0mbGu6gSevHEGnr51Jt6+u9TpgqoGEXoJkjgD8Ph1U7FoZgG+NG4Yfn7dVO25jQkGLJ8/EQ/99YBTomZUVoruGItyUzHbNAxTCzK1Mar3hWNmzFVgcrylK6TPS0RE4Tc6NxXL50/EuqoTuPeaCbrLYv35/e/pWqJ+kFR7tvj6IdZdhYo31OqTUF9jA32dvlCTBAsqt+HmZ3ZiQeU2bDpwChaL/cYttq9djYFW3jgD91w9DuVzTZiUn+53gs3Tc0eCPz8rjIViEytciIhCQK9kd+qoTJfbJavLhfIzjSifa4JFAlICP9twEK3dvdhYUeqUbNl04BSOnGrXnQ0rKcqGsFa3DE9Lwn2v7MGvr5+O6qYODFiAmqZO3Qu9BPCbG2bg3lf0S40jVUoaLU3+iIgo9LRrzch0HG3sCNrv/1BfSwKtUBmMyzW8reRQX3vunZdqOxw+uuGg1rT/iotyMXqYb9+jaN7C2J+fFcZCsYkJFyKiEHAVNAHA+LtLcehUO442dmDtjjq0dvdqSY2dn7WgcnON0/nUi6la1XK8pQtHTrXjvcNN+MFV4/Dku0e1BMnDX5uMB9/cj7qWHhgTDHjs2ino7Zd4fOMhfHeuCS3dvRgzLFX3Qp9gEJiQn46NboK9SJSSRmuTPyIiCg31WgMEr59HqK8lweiPMtiWa/iSJAj2DofRnKDw52eFsVBsYsKFiCgE3K0ZvmhEGsYMS8Wk/AxccVGuXVLD3cW0v9+Ct/c3aNtkqhUyb+/9AkuvHIvhaUkozE3Bj1/bi7qWHgBKYPHAG/tRPteEV6rq0WeRWLO1FtkpidqOSLbnOn6mC8PSE5GTmgQpvXs94RCtTf6IiCi0Av39b3v9ys80hvRaYjAIXDMxD+vKZ6OhTXm+yfmZXl0vI32dDRVfkwTBvN5Hc4LCn2omxkKxSUgZ+TVsg01JSYmsqqqK9DCIKEQ8BUXeNDVzdQ5Xj71mYh4+rG1B+doqp8BhyRwTnn6vBsYEA566eSa+88IupzE/cf00NHee0xIsAJCfacQNJaNQmJOCxPg4/Oadw7j10kLkZSZj+fq9yE5JxA0lozB2eBqyUxPxwBv7tKqZSDRpU9+zwVJmPRQJIXZJKUsiPY6hgvHI4DVYP5y74u/vf71r6upbZmJMbhqaO92fy5/3OJCdZ6KhGWoo+PPagnW9D/f7Go7/l4yFgiOc8QgTLiHAAIdo8PLm4l3b3IkFlduQnZKIRbNGQQilYe7c4uHISklEfBywq+4s7n99n+459C6mx1u68Mbuk7rLjVYsmoqTbT2YODIDp9p7sGLTEaekzJM3zsCARWLpXz51+fjkhDhMvzAT//F8FbJTEnHb7CK7HQLUfjMNbWYYEwzYGAVroCm2MOESXoxHBqfB/OEcCO6HVvV67HhN9HT98vc99vf5/H1cKIQiaeBPkiBY4whXgmKw/78cbMIZj3CXIiIakiwWidrmTmw/dhq1zZ1ed6z3pkN8Y7tZS1g8+0EtVm+pwR+21uJwYwf+408f4eAXHVqyxfEctgGGbWDQ2G7Wtne2ZUww4GRbDyo31+AHL++GAQL3XD3OboeDinnF+OWmQ0gzxus+Pt0Yj8tNufjy+GFo6eyDuc+CRbNGOe0KUbmlGotmjdK+9nbXBSIiCp7BvFNJsHeVcdXDo7Hd7DYG8Pc99mXnGds4pLnjHLJTEr16XChZLBJbjjTijd0n8a9jLXhz90lsOdIY8K4+vu6KFMyfg3DtyDSY/19SYNjDhYiGnEBmIbxpwJaXoSzVcUxYPLrhIJbMMeHQqXaXAeDhUx2648rLMOKtPSdRMa/YrupkWVkxXthep53jF5sOK7f9x6X4oOY0BizQqlJ6Byxa3xZ1uVBhTgpy05Lw87cP4ssTRkBAScIIAd0xFuYkIz/TiNbu3qhYA01ENNREcyPQQAV7VxlXPTz6BqRWUaIXA/j7HnvbM0QvDlGv5+quPN72GglmRcqJM12obuzEmq21duMaOzxN2yEoHMtmonl3IVcG8/9LCgwTLkQ05ARyIbcNpvIzjVg0axTiDEByQjwsFgmDQWB0birGjUjXvfCqWzXrBWQWKXHkVDuyUxLR0KZcuFdsOoSCLCO6ewfw2DemYtXmI1gyx4RMYxwmF2RiV91ZLL54FNbvqtce09M3gM5z/U7Lj2qbO5GSEId7ripGqjEBj244aLdcyGAA/vRhHSrmFeNc/4DuGE+e7cG3Ly9CcV4aCrNTUNvcqTUiHLAouykNhX4CRESREs2NQAMV7A+tek1GVyyehgffdK4ytY0B/H2PvW1qqheHrNpcjfK5JlRurvG6GWqwl7E0ttv3elPHNaswG6OHpYVt2UwsJi8G8/9LCgwTLkQ05ARyIVeDqRWbDuGmkkKt2mTN1lq7oGNifobuhXd8Xjoaznbj4a9NxiNvHbCbQbrvlb1o7e7VeqUMT0vETZcW4qY1O7TjfnHdVHSd64MwGLDkT1Xa7Q8unIQOcx/+8tEJlBTloCAr2en5e3oH8JePTuDeaybgR6/ucVou9Ovrp6O1uxdrd9ShvHQMfvb1KXjor/u1apgLs1PQ1GHGSx+fwLPfvgTvHGrEPS/vRnZKIr59eZHdjkdct0xEFBqDeaeSYH9o1dsJpqXrnLaTn8oxBvD3PfZ25xlXccjMC7PwUvllXvcaCXYlSFdvv+64unv7Q/J8rsRi8sKfn5mh1vx6qGLChYgGLVcXskAu5GowVZBl1BIhgHPQMWaY84V3WVkxHt94CK3dvfjvBRO1rZxPtvXYlRFXbqnGsrJijM5NxQ8cApufvL4Pz91xCe58/mOn5Urlc01YemUxZo/OQXy8wen5c1IScculRahp6tANqD473aUtOXpkwyGUFGXi+TsuwbHTXU7VMKfae7RzL5o1ymlGLNpLf4mIYpU/28nGilAkk9QeHrbXI08xQCDvsd7zOXIVh+SkJmJqQZbX38tgV4IU5aTqjqswJzUkz+dKLCYVff2ZYZPdoYMJFyIalNxdyAK9kBsMAt29A26DDtsLb11LFz79/KxdUuXnGw9hyRwTPj/bg9VbapzOMzo31WWvl7aePt3bLRJ48M39GJeXhu7eAYzPS8emZaX47LTy/L/fWgsAuH/BRN2Aqt9iwaQLMvH23aXadpkdPX1askV9nsot1Xjhzku121z1e4nm0l8ioljmzYf6WBSOZJK3MYC377E/VQqF2Sl47NopeOCN/XaTGRUvfYrl8yd6/aE7GJUgtuPPzzTiNzfMwL2v2L83Y4alBuX5vH2vIpVUDLTixJf/l7HYp4b8w4QLEQ1Kni5kgV7IvQk61AtvY7vZqZ+Kuc+COIPrfi4GN71e8jOdlwsZEwyQUjnv5sNNdmvAr5mYh54+C1q7e2Hus+CJdw47BXqPXzcVFxdloX/Avg/Lzs9adJMpXb39dmOItdJfIiKKTqFOJgXzw7y/VQonWrvx1JZq/Or66ahp6rBrcO/Lh+5AJ5D0xr/6lpl2Ey+2700gz+frexXupKI/38tAEjSx2KeG/MOECxENSp4uZIFeyH0JOlwlZ8aOSMfZ7nNYfcss7K0/C4sE3tpzEsvnT0RWSqLurkSPXzcVk/MznJ5b7ftiTDAgMc6gvd4Vmw5hRHoSspITsK78cvQNDCAnNQmF2SmYVZitBZuF2SlaTxbb1zM+L91lebE6hvW76rWlSLFS+ktERENXsD7M+1ul0NhuRl1LD442duhWuXr7oTvQ5JHe+Je++Ck2VpRitmlYUJ8v2is6fB1foEuCYrFPDfmHCRciGpRCfSHzJejQS84sKyvGH7cew/wp+Vj64idaY9p7rh6P4hFpSIwX+OYlhXjp4xNYMseEOAMwcWQGJl2Qjvh4A+ZPHonxd5fiYEMbqps6sXZHHVq7e7GsrBgpCXHIz1Re500lhfjWszvtgoFZhTlasDk6NxXHW7qw60SrbqDxt4pS3cTSmGGpGDMsVXv9IzOMuGbSSN0ZMSIiosEo0O2jgcArRNXdEdXxAPD6GuzP+P1NVgVa0RHqBrO+ji/QBFIs9qkh/zDhQkSDUjguZN4GHY7Jmbx0I0629eDC7BTc9+oeZKck4rbZRU6VLOPy0vCNGQWwSOU8CfECF2anaue8aEQaznb3orqpE4svHgVjvBK8tXT34v4FE1Hf2q3bzHbSslJYpBJc9A9IPPDmPnxteoFuoHGgoR3zJ43ERheJJcfXf9GIyM9SERERBZveB/5At49esemQUyWrr7GKWmmxYtMhLJxWgDgDcElRDi435SLeGhe4Es4qi0CeKxwNZn0dX6AJpMHc/JrsMeFCRINStF3IbCtK1KDhrlKTtsuPGmwBygX7/tf34alvzrQ7h5TAiTNdaGhTgr3C7BSc6e5F5eYa5GcacdvsIrz08QksnFaA6qYOTB+VhXEj0rD3ZLt2juyURHxy4izuf32f3XIkCakbaBxt7MCk/IxB2ZyRiIjIG64+8F8zMS+w7aNHpuNM1zmsK5+N3gELEuMM6O4dwPGWLp+W6qzYdAg3lRTaJW5WLJ6Gr027wO05wlllEchzhWM5kq/jC0ayarA2vyZ7TLgQ0aAnpfv7Q12massxaDAmGFzu8nOkUWmklxRvwJhhqfisuRPn+i34+dvK1tKPXTsF5/oGYEwwYNGsUVhXdcIp4Hpw4SQ0b6nRdke6oWSUlmxRn0fdhtpxlk3tC3PFRbkMBoiIaMhy9YF/Y0VpULaPDqSCo7HdjIXTCpwmbpav34upBZlur9/hnJxy91ye4rBwNJj19b3gkiDyFhMuRDQoeRu8hKNM1ZZt0LB+Vz0q5hXjXP+A7izJ9Auz8MAb+1DX0gNjggE/uGocvmjtxj1Xj0NThxlPbanG/QsmYVlZMeIEcO81E1DT1IG7Sk1Yv6seDW1mPLrhIMrnmrRdi8aOSHOx69AA1u+qR/lcEwoyk3GitUfrC8MGbkRE0SeckwVDnacP/IFWKQRSwZGXYUScQX/ixpuEhKsqi1D8fOk9lzdxWLiWPvlScRJtldQUvZhwIaJBydvgJVRlqnqBCgCkJMShomwsLFJJuKzdUYc7ryjCz74+GQ/99YBddckDb+zDTSWF2laRT757FOVzTXj8b3u1Y8y9/fjbvgbcdvlo/OjVPXaVLR3mPvzpwzqMykrG07fMRLoxAQe/aHO5pXRrdy8Kc1Lw1JZqLcnD2RoiougT7smCoS7UH/gDqeAYnZuKS4pyUJSbjIXTCiCs3/639pzEiHSjX4mTcP58eROHRWs1CZcEkTeYcCGiQcnb4MXdceoOPo5BiqvgRb29pescvjhrxvL1e7XAYPUtM9HbL522cl5XdQJZKYl491ADfnX9dNQ0KcuI1CRL5ZZqLJljwtPv1cDcZ9Ea6KpLgf5w28X496n5WP2ecpwaaK3ZegzfmFGAb19ehJauXpzt7kVn7wDiDQY8eeMM/HLTIS2psmLxNBRkGbF4VoHTdtGcrSEiijzH645BIKq32B1s/PnA70uiI5CEjsEgcNnoHCy9shgPvrlfG99j107BqMxkLXGi7oY4bkQ6JuZnYMww1+PxdzLKn+SON/Eaq0koljHhQkSDkrfBi6vjhqcZnWZ3Vt8yExcNS8Onn9s3nVUb571zqBH3vLwbS+aY8OwHtdo5s1MSYe614GhTB75/VTH6ByTM/Rac6x/A/Qsm4fGNB/HDayag61w/KjfX2I3P3GfRkihqJYrtfSesjfUce7dUzCuGwQD89t1qLP/KeCQmxGHlu/a9XTrMfZh5YTYuGZ1jF7RwtoaIKHroVRs8ft1UZKckav25gOD3tKDzfP3A72uFSKAVHPVtPVqyBVB+Fh54Yz+KR6RpyRbH3RAdx2ObLOnpG/C54sbfqhhv4zVWk1Cscr9XGBFRjFKDF2OC8mvOVfDi6rg4g/3sYXZKIqobO/H67pNOTWfveXk3DjS0acfbNsFVdw+679U9eKWqHgYhsPq9GqzeUoM/bK3Fma5e9PZLSEh80dajjUOlJlmMCQYsKyvGa5/U2913YY7yehyb5VVuqcYFWSkw91kwLN2IRzcctLv/0Q0H0d07gOHpSZwhIiKKYnrVBve/vg83lIyyOy5U2/nGGotFora5E9uPnUZtcycsFg+d872kfuCfbRoG0/A0t9dOVxUi+06e1R2PmtDZWFGKl8ov05rxent9dlUl0tBmdrkb4j0v78bxli4A55MlCyq34Z6X96D73AAqysZi6byxyM9UfqY8/Xy5es3qc7jibbxGFKtY4UJEg5K3s1Gujtv5WYtd8LJo1iis2lytbeVsyzaoUamzNbZBzq2XFWLlP47aBSOPvHUAy8qKMXFkBqYWZKIoN9Wueubx66ZiRHoirpk0G/WtPWjt7tXO/5P5E3CooR0FWcm6Yzp+ugvGBAO6e/t17x+Xl86Ahogoyrn6MD0uL1271vBDqiJaetu4+p5tPtyEk2fNuuPxt4LDYpFISYzXrRLJzzS63Q1RrVhRkyVqJcx9Nj3h1OXPy+dPdPvz5W8fGi4XosGOCRciGrS8DV70jnMscbUNVtwFNeY+i7b7UOWWarvHDU9L0g1GRuemIj5OoKHNjFmFWdiwdA6ONHVAWoD6s92oa+nCrKIsJMQpVS6jslNwrn8AaUnx+MXfDuFr0wt0x9RvseDx66Zi7Ig03fsnjsxgQENEFOVcLbmYODIDG/kh1U6oGuH7ytX3bMCCoI5HTTCt2HRIiztsE02T8zOx8sYZOHKq3e2yHTVZolcJU7mlGuvKZ2NqQVZQlgbp4XIhGsy4pIiISIdjiWucUAIHNZniWPqqBjXGBAMa2szYcvgUnrvjEswek6Mdm5IUr7tkKM0Yj9c+PYl/HWvB7c99hJrmTpxs7cF9r+7Bnz6sgwRwpqsP8XHKY3746h7c98pefH/dbtxUUoitR5qcxvTQwklIS4zDrMIsTLkg06lcd8XiaTjTfS6o5dZERBR8rpZcjBmW6vUSl6HCXZVFOOl9zyrmKcuCgzkeNcFU19KDtTvqsGSOCRVlY7GufDbmTx6J+HgD5k8eietmFuDx66a6XLajJktcVcL09A14/Pni0iAifULKyATaQoifAHgcwNNSyqXW2wSAhwGUA8gGsBPA96SUB2welw2gEsDXrTf9FcDdUsqzNsdMBbAawKUAzgD4A4BHpc2LFUIsBvAogIsAHAPw31LK123u9zgWV0pKSmRVVZUvbwcRRYCnbvrq/U0dZozMMOJgQ4d9p/+8dEwceb7Tv3r8ma5zOGndpSg7JRHfvrwIqzZX4/tXFUNKYNXm8zNQy8qKESeAx/92RAvI4g3AynernZrcLZ8/XnusyphgwNIrxyLOIDAqOwVxBuCLsz149oPjaGgz46XyyzDbNMzutfYNSDz45j67rZ+5lSiFgxBil5SyJNLjGCoYjwwettcjVrO4VtvciQWV25yukxsjsHuTxSKx7+RZbD7chAEL8Non9WhoM3scjy87/Ww/dho3P7PT6Xb12q93Xr2fIbVS5sipdvxha63f7x9/TilWhDMeiciSIiHEbCiJjL0Od/0IwL0A7gBwBMBDAP4hhBgvpeywHvMigEIA861f/xHAWgBfs547A8A/AGwFcAmACQCeA9AF4DfWYy4HsA5KQuU1AIsAvCKE+Dcppfpby5uxEFGUc7eFs6d13o4lroU5qW7XGBsMAqNzU9Fh7sOx5k7cVWrC+l31eGF7HcrnmpCfYUS/lCifa4JFAgYBpCbG4X/+WQvgfOnukzfOwJI5JhRmJ+OLth5tJ4quXv1dA0ZmGPHUe9VYOK0AcQZg4sgMDE9LRGt3r1bKq74WAHbBKLcSJSKKflxy4Z1Ad/sJJoNBYGpBFk6eNXs9Hm970KixTU/fAJaVjcXLVfXajlXqTouu6M21q31UJuWnO/WS8+X9U+MgQKk2AsCkCw15YU+4CCEyAfwZwH9ASXiotwsA3wfwSynleutttwNoAnALgD8IISZCSbTMkVJutx7znwC2WRMhRwDcCiAFwO1Syh4A+4UQEwDcI4RYaa1y+T6A96SUP7c+/c+FEFdab7/Zm7GE4r0houByF7gEss7bVWGg3vP94KpxeP7D46jcXIOl88Zi/a56PLRwEtp7+jA8PQn//cZ+p209z/UPaNtKq1Uva3fUAdDvH9Pcec5pW+gHF05CXkaSU5Dkb1M7IiKiaBfMBqy+VJp4Gs+kZaVobD+Hrt5+FOW4Tl54E5voxRrLyorxwvY6tHb3YllZMT5r6dSqb109Rm+SafSwNBTmpGLGhVl+vX/R0rTYk2B8b4m8FYkeLmsAvCqlfM/h9jEARgJ4R73BmjDZCuAK602XA+gE8KHN4/4FpXrF9pht1seq/g7gAgCjbY55B/b+bnMOb8ZCRFHO3RaFvq7ztt0y8eZndmJB5TZsOnDKrv/JZ6edn+/Jd4/i1ssKte2dE+MFznT34uG3DuCTz89quw6pjAkG1J7ucmpYt2jWKKzfVY9lZfa9Wh67dgoA522hH91wECPSkpxeh7pO2/E5uZUoDVVCiJ8IIaQQYrXNbUII8VMhxBdCiB4hxPtCiMkOj8sWQqwVQrRZ/6wVQmQ5HDNVCPFP6zlOCiEesk7q2B6zWAhxUAhxzvr3dQ73exwLEZ3ny/bNrnhzzffFwYYO3P7cR/iP56vw1adcn8ub2EQvtlm1uRr3L5iIJXNMeGF7HZa++Knddsy+bNkcyPvn79bQroRii2/HLbDf2H0SG/c14FgTe9pRaIQ14SKE+A6AsQAe0Ll7pPXvRofbG23uGwmg2bYXi/XfTQ7H6J0DXhwz0uE4d8cQUQT4cvF1F7j4mnjwJoioO9Ol+3wj0pPwk/kTkJeeiEe/MQUJBoEfXjMeI9IS8eDCSQ4JlKl4pare6RxCAK3dvSjOS8Pbd5fipfLLsLGiFGNyU9HTp7/U6N3DTU5BHZvaEZ3nxRLnu6EsT26Csqw43eaYFwHMglJ5O9/677U251aXODdaz7EMwA8B3GNzjLrE+c8AZlj/fkUIcZmPYyGiIFF7rwQrceBLEkKNTfIzjfjelWOxdN5YLCsbi5EZ52OTxnYzslMStfuXzhuL7JREHGnswNPv1aChzeyUpAlXM+FgPk+wk14qxy2w12ytxdK/fOo2EUYUiLAtKRJCjIfSJHeOlLIvXM8bLkKIcihBGwoLCyM8GqLBwbHkszA7Be8cavSqVNVikegfkC63KPR1nbc3S3FSE+N1n68wNwVNHedwsrUH5Wt3ac93z9XjsGFXPcrnmjB2eBomX5CJOAO0qpf8TCMWzRqFOANQUpSNjKRijB2ehtHD0nDRCOU5j4tOTMzP8Hr7yWCWWxPFMi5xJiJH6of8w6fag7b81pelvKNzU7H6lpmobuy0a7A/fmQGCnOUa3V+plFrxm+7pMiW4wRSIFs2+yKYzxOqLb7dbYHNnnYUCuGscLkcwDAAB4QQ/UKIfgBfAvD/rP9usR6X5/C4PACnrP8+BWC4bTmu9d8jHI7ROwe8OOaUw3HujrEjpVwjpSyRUpYMHz5c7xAi8oHezMaHtS1ezxIdb+nCA2/uc9ouecXiaVqCYf7kkdhYcb5axN0aY08VMRaLRGpSnNOSn2Vlxeg614+apk67HYbMfRas/MdRlI4bgcrNNfjR+r2IMwADFuCJ66fjx/PH47tzTXj2g1pUbq5B+dpd6LcApzvP2Y2hMCcVKYkGp0qZH1w1zuX2k8EotyYaBLjEmSjKhWJJiTvqh3yLRNCW3/pSUWswCIzJTXOKF+55eTc+Pn4Gtc2d6B+QTvev2lxtd27HCaRwVbcG83lCVZXjaQvscG8hToNfOJvmvgHAcW/C5wBUQ6l8OQolmXE1gI8BQAhhBFAKpQQXALYDSIMSoKhBzuUAUm2+3g5ghRDCKKVU/8dcDeALAMdtjrkawK9txnK1zTk+82IsRBRCejMbVXVnvJ4lamw3o66lB2t31GHJHBOEUJrdFmQZXe5C5I4aRKzYdAg3XHwhhqcnIT/TCAGgv9+Cdw41YsWmQ7hrjsluF6KclAR09w7AIvUv7Gr6ODslEZ+cOGu3M8CysmJthyK1l8u68tl25zAYBErHjsCJM1149vYSnGztQVPHOTz/4XFt+0n2ZyGyZ7PE+Vs6d7tbVlxgc4zTEmchhOMSZ/v1gfZLnD9DYEucC0A0iEWiAav6IX/9rnr84KpxePLdowHvdqRXUbti8TQUZqfoHt/UoZ9o2FZzGn/cVosnrp+ue/+Ekel4qfwyl7soelvdGkhD2WBW0YaqKkf9fhw51R6Wqh+isCVcpJRnAZy1vU0I0QXgjJRyv/Xr3wK4XwhxGEoC5gEoM0gvWs9xSAixCUo5b7n1NH8AsMFavgvrsQ8DeF4I8RiAcQB+DOARm8BoFYCtQogfQ0kEXQfgSgBzrM8jPY2FiEJLb2ZDnXHy5uKoXqgb2sx4+r0a7djFs/z7jGIwCFwzMQ/9Axb8aP1eu6TIjAuztPXAjrNvKUnxONnagzihP3b1t9INJaO0ZAtwfsZqyRyTNn5znwVnunphsUjn7aitOwtsOnAKD/31QMS3wySKVlziTBQbQrWkxB3bahRjvMFuAiUx3r8kjxo/rLmtBFV1ZzBgAVb+4whSk+IwJjdN6yunJiZcJRqkdeKmuqlD9/6i3FS790UvceJpkikYSa5gbWEe6BbfrhJHwdoCm8hbYd8W2oNfAUgG8DSAbAA7AVwjpeywOeYWAE9BKbkFgL8CWKreKaVsE0JcbT1HFYBWAL8BsNLmmA+FEN8E8BiAnwE4BuAmKeVOH8dCRCGiF3C8teckfnHdVPzEi4tjoBdqPSdau7VkC3A+KfK7W2bhrlITjPEG/GLTYacgaFlZMXJSErGsrNhuzfU9V4/Dc/86rvR5yUlxWwGjnuvTz8+ip8+iG/ywPwuRV2yXOKu3xQGYK4T4LgB1B6A8ACdsHqe7xFmdzAnDEmdXY7EjpVwDZbkUSkpK2P2RIs7figlfep8Eixo7HD7Vrns93+hnsudEazfK11Zp58vPNKK6sRNLX/zULka5ZmIeDAJ4/LqpdomAinnFWLujDgDw3uEmp/tX3jgDBgFsP3ba5553tiKR5FLp/Zz4G9N4ShwZDAKFOakYsAC/u3UWUpPikZeepPXJIQqmiCZcpJRfdvhaAvip9Y+rx7RCvwTY9ph9AOZ6OOZVAK+6ud/jWIgodPQSJnfPK8afdx7HkjkmayPZHFxhytW9OPqafPAmIHQV/H3y+Vn8cVstHv3GFN37e/oG8ML2Onz78iL8+vrp6OntR3ZKImqaO7H44lGQUjm33oyVOgRjgtKnpcPchyOn2jEpPx2jhzkHP8GaWSIaxN4AlzgThUUgFRPhavRqS40dHJ8XCCzZ4xg/LJo1yqkPy4pNh9A3YMHy9XuRnZKI8rkmFI9IQ31rD17YXoeGNjPyM43496n5WLX5qF0sJGHB/FXb7JYsrfzHEZ8TJ5FIcgHuf078iWk8JY5cPV9hDqtbKPiircKFiEhLfgxPT8S68tno7h1ASmIcKl76FHUtPaiqawPgebbJ2+RDf78Fb+9vwHKbpUJ6AWFehhFFuclYOK1Aqzx5a89Jrcy3vrVbNzicNioLa7bWYsWmI1ri5LGNB1HXcr6XZlFuMlYsnmY3hh9cNQ4WKVFRNhYlRTl48M19qGvpgTHBgAtzlLXfnI0h8g2XOBOFTyAVE6GoVPWGwSBgGpaKirKxUFcJr99Vj9buXr+TPY7JI72GrQunFWgxQEObGZWba2BMMGDNbSXa7oU3lJxP1Nguly6fa7J7j5ev32u3JFm93VPiJBJJLiD4lTWeEkeRrOShoYcJFyKKKq5mHQDYJSgA/2ddbKtZRqQb0dRh1oIc9bx6F97C7BTcPa8YD7yxXxvbwwsn4y8fKWW+L1fV47Frp9jd/8jXJ+P371drM1EzRmXhpY+P46aSQm07QmOCAd+8pBAzLszEn+68FNtqTkNKaI1vAaCibKz2+s19Fjzwxn6UzzVhwsiMkDYQJBqiuMSZKAgCqZiI1DJZi0XiYEMH1myttevXVpyX5neyxzF5pNfXLc6g31w/IU5go/U96O4d0D3GcfMmc58FcQ570XqTOIlUkivYlTWeEkeRquShoYkJFyKKKq5mHdaVXx6UWRe9hM6DCydh3Ig0lI4boVWurN9V73ThPdHarSVT1LE9suEAll45FntPtqO1uxcTRqZrTfbG5aXjN+8cRm+/EglZJLCn/iy+O7cYH9Wdwa+un47jp7twrt+CF7bX4ZLR2ejtt+CP22qdXueAfVygBVickSEKHJc4E4VGoBUTkVgmqxeHrNpcjbfvLvU62eOpH8nIDCPG5WXg3lfOxyIT8zN036u8DKP2HtQ2d7pdfmx7W0lRjnast4mTSCS5LBaJlMT4oFbWeEocRaqSh4YmJlyIKKq4mnXoGxgIyqyLbSCVn2nEolmj0NrVi2VXjcPPNhzQluw8uHAS+vot2PP5WfQODCA3NQmn2vTHdkFWMopyk3HP1ePR1GGGRQJbjzRh4sh03HxpITKTE/HIW+d3DirMTcVfPjrhtKQIENhTfxZP3jgDv9x0SBvLL66bht9uPmL3vLY7FnBGhoiIolGkKiZ84ZgccRWHNHeacdEIz9daX/qR3HNVMfKzUtB9rh9ftPbg/n+fgMf/dtjle+Xq/UyMF07JlStMuVpljC+Jk3AmudT3asWmQ6iYV2xX+RvIz4mnxFEs/FzS4OFVwkUIMQnAgLou2VoiezuAAwB+JaUcCN0QiWgocTXrkJOahFmFOQHPuqiBVH6mEbfNLrK7uKu7ADS0mfHohoNYMseEZz+oRcW8Ymw5fArfu7JYd2zJCXF44obpuO+VPahr6UFRbjK++6WxuPeVPVh65Vgt2aImeOpaurR+Lerxd88rxree3WlXddNh7kOHeQBjhiVj+fyJdoGBOlbOyNBQwniEKLZE++55/f0WfFjbgr31Z1GUm4p99WcxdVQWinKT7SZF3F1rHRM20lp9qrdMeXRuqnZsv8WCfgvww1f32PVu+/OSy9Bnsei+V3rvZ2F2CurPduNPd16K7t5+FOakYsywVJ8SJ97uJOXvjlOuznO8pQtHTrWjt19i7Y46bfl12YQRmFqQBYNB+P2c7l5/tP9c0uDibYXL/wL4LYAjQogLAbwJ4H0A3wOQAeAnoRgcEQ097mYdgjHroiZ0Fs0apSVbACUgqtxSrTWZU7dkVm//1fXT8dO3DuDBhZPw6IaDdomPX246hB9eM0ELzpb82xikJ8VjxaKpyElNwl2lJqQlxUFA4Ml3j8LcZ1Ga6C6ehoIsIxLjDLhxzQ67sdgmfBbPKsC0UdkYf3cpDp1qx9HGDqzdUYfW7l7OyNBQw3iEKMY4XrstFona5s6AP7QHymKRTg3zK+YV48ev7cXSK4ux+r1qrdLU1bVWr5rlieun61bINLabcfhUh3bs6ptnOsUhT757FOvKZ+Pi0cNcjtv2/XRVTTNmmPdxgbc7SQWy45Sn51MnkdQmv1dcpOxA6e2mBv7gro4ULt4mXCYA+MT67+sB7JRSLhBCXAllK0UGOEQUFKGedVATOvVnunUDIrWHi7pkR72951w/6lp60GHuw5I5JggBSAmtIqbrXD8AYFpBBpIS4vDrdw7jppJCLH9tH8x9FlSUjdUa8KnnXL5+L96+uxSHGtq129UqGCGACSPTsfqWmdrrv2hEGsYMS8Wk/AxccVEuZ2RoKGI8QhTDgvWh3dNzeFMRcbyly6lhvjrx8uCb+7GufDZ6+gbcXmv1+r1UN3XoVsOmJMbhzuc/1ipee/r0G+B293pfqBeM3Xa8PUewdvbRO4/thJdaTWSxSHxY2+LVpgZE0czbhEscgF7rv8sAbLT++xiAvGAPiojCL1hlosEQylkHg0Hgmol5+OTzVt2ASErYzbaot6ckKQ3dOs8N4NkPnJvajshIwlM3z0RWSgK+80IVlswx2c1cWaT+7gMnznShuqkDRbnJ+OYlhchITrCroFF3aHL33kTT944oxBiPEMWwUG/H60tCx1WvFrW6tadvALNNzpUmttdcgxDITknUdhQElB0LH79uKu5/fR+yUxJxQ8kojBuRjnP9FmSnJAIAbptdhPrWbpdNcr0VjN12vD2HL8/lLi5x977bVhMdb+lCVd0Z7iZEMc/bhMt+AP8lhNgAJcBRZ5AKAJwOxcCIKHzCMeMUDSwWiRNnuvDJibOIM8CpQduDCyfhgqwklBZfgr2ft2HxxaPw1p6TuKmkEH/6sBYPL5yM32+t0X3ckcYO/OWjE/j+VePsAjZb+jNe8XjvcBO+O3csGtp7sGpztVMgOmlZKSwSuoHLUPneEVkxHiGKYa4+bDe2m7X7/Z04sFgk9p08i8On2nFXqQnrd9Wjoc2Me17ejdw7L8Xw9CS787rqGadOvOj1bNG75i4rK8YL2+u0pEtrdy9mFWZh07JSfHLiLO5/fZ/dsQMWiXVVJ3DDxRfi0W9MQX1rN16uqvdrmXAwdtvx9hzeHucpLnF1ntKxw7BoZoH2PWpsVzYh8PY5OfFE0crg+RAAwHIA34GyTvov1m0OAeDrAD4KwbiIKIxczTgdb+mK8MiCw2KROH66E2/sPonXPj2J+1/fB4MwYF3VCSyZY8LSeWOxZI4Jr3/yORrOnsMdz32Mx/92GH/cVotlZeNw0fAU3H6FCX2WAdxw8YWINwB/vusyPHXzDJTPNWH1lhr89t1q3FRSiOZ2M4wJyq9W9W9A2WZ6WVmx3X0rb5yBvIwkfHnCCDyy4YBuFUx2SiI+OXEWCyq34eZndmJB5TZsOnAKFouy3mmwf++IHDAeoYCo/UO2HzuN2uZO7XcphYf6YduWMcGAvgHp8jrnDfVD/k1rdqBycw3+uK0Wt80uQn6mEeY+C7bVnHY6r7rE2Pa6XDGvGBv2nnSZ+HC1ZfQNJaO0c6y8cQYKc1JhkdCSLbbHXpiTjJtKCrH6vRr88NW9+MPWWiydNxZ/XnKZz5Mleq/B16SNt+fw9jhPcYmr81wyOgem4Wl2CbG39pxExTz72GnF4ml2z6l+7wP5+YkV/P0Vm7yqcJFSbhVCDAeQIaVstbnrDwC6QzIyIgqbYJSkRiv1Qnz4VDvWbK3FXaUmmPsseGbrMXx37lg8suH8ds1P3jgDP3AIEu5/fR+WlRVjxaYj2uyUaUQaEgwCP3x1r937VrmlGkuvHIuffX0Knn6/2q4SprW7F6mJcVh65VgAwPRRWTD3D2BgQGLKBZnaeRxncm4oGeUUsNmWXw/m7x2RI8YjFAhWBEaeXmP8FYun4cE3XV/nvOGuL8izH9RCSufz2vaMa2w3IyUxDn0DFsyfMtJlhYSra+7MC7PwUvlldv1eXB2bl2HE8vX2r/fRDQexrny2259DV1Ucgfa98/Yc3h7nKS7x9jyjc1OxfP5ErNh0SNu9qKQoB1eYcu2ODfUytWjB31+xy9slRZBSDggh4oQQlwHYLaU8J6U8HrqhEVG4BKMkNRJcBR/q0qHG9nNoN/fhREsX4g0Gu6TG3pPtwEd1+NX10yGlxNHGTnT19usGCTkpiZhWkIG75l6Ent5+pBsTsKPWfl2x2ux2eHoSclIT8MQN09HQZsYfbrsYZzp7cardjK7eAcTHCaQbE/CdtVV2wWZRbjLW76p3Wq40bkS628AlVr93RP5iPEL+GiofzKKZ3oftlq5zdlswA8HrQ6IuH960vwHfu3IshACaO89p8YKvPeNcXXOLclOdzuHq2J5e35vl6n3YXrF4Gr46JR/x8YaA+955+z54c5w3cYk359F+VkamB5TgCZZgLlvy51z8/RW7vFpSJIRIF0K8AqAJwIdQ1kpDCPF7IcRPQzc8IgqHYJSkhpurEtL+fgu2HGnE3/afwo/W78Huz9vQ3TeAmYVZKMpNxtYjTVh54wxUlI3F3PEj8Jt3DiPDGI9nP6hFUnycXalzfqYRFWVjkZGcgHuvGY9TZ7vx23ercfv/foSLRqRpx+ZnGnHb7CI8+0Etlq/fh6V/+RSfnjiLk609+M+1u5BkXRO+anM1OswDWlNc4PxuRY98fQpau3uxdkcdyueaUPnNGXjujktQmJOiW36tBi6x+L0j8hfjEQqEuw9mFD7qh+3ZpmEwDU/D8LQkVJSNxdJ5yp/8TKPffUhsGRMMmHJBJhLjBW67fDTiDcry3tv/9yO/l5zoXXMfv24qDAJO59M7dsXiaRiWlqQ71pTEOJfPq/dhe/n6vfiwtsXuef1ZchLsZSrBjEscf1b0khKuvvfBnHgK5rIlf8/F31+xS0jp+QdFCPE7ANMBfA/ABwCmSSlrhRALAfxcSjk9tMOMLSUlJbKqqirSwyDyiZptD8VWzKFQ29yJBZXbnGZQ1pXPxubDTXhzt9Ls1rZaZMXiqWhsP4eV/zhqN0P0lQl5+PvhRjS29WDAmhjJTknEty8v0prYqsuJUhLi8PuttUiMF1h6ZTEefHO/VrLsOJbf3TpLaaALaEuVls4bi9Vbapxez33XjMO0UVnoOtcHQOCXmw6hrqUHRbnJuHteMR54Y7/LEtJY+97R0CWE2CWlLAng8YxHfMB4xJ6r68ZGzhBHjKsmtMV5aZg3Ps/ra5neeVbfMhNnu/vsrp/qDoSt3b1+f98tFonPTnfh0Kl2HG3swCs2DW8dl3f091vwYW0LqurOYMACbNh7Ej/6ygTUt/bgyXfPxyIPLpyECSPTMfPCbN3XvP3Yadz8zE6n2yvKxuLaGQUwDU/za8lJqJapBDMu8VQNEo6lNsH83eHvufj7K7gCjUd84e2Soq8DuE5KuVsIYZuhOQTAFPxhEVG4hXIrZl94c2E93tKFo40ddrsQAEqmv6FN6Wq/cFqB3bbM5j4LjjV3Yc1WJTGiLgE61tyJvZlGzLgwE5aCTNQ0d+KJ66cjJTEO/+/FT5ya3ZXPNWHRrFF4+r0aJMYLLJljwri8NN1Zh+b2c/jdP2vwo69McLpAOpUY91lQvrYKT1w/Hfe9en4Wq66lB09tqca68tno6RvQDVyi5XtHFAaMR8hvev1DWBEYWa6a0L59d2nAfUikBL764ja7c6t9XZ5+r8bvJScGg4AQwH2v7LG7lust7zjR2o1y6xJi1b2v7ME9VxVj6ZVjMTLDiBOt3Vi9pQat3b12y4RsuVqmM2CB9jr8WXLi6jEF5bMxtSDL74RFsOISb5Ipwehj40kwly35ey7+/opd3iZcsgG06NyeDsD1gkMiIh94urDq3a/OVjW0KbsD5WcacaihHVJnW2Z1FyB1CZCakFmztRbLyoox6YJ09PZL3PfqHq25ri1znwUWCQihBDqJcXHYsPckHvn6FN1AaMzwVPz8uqnoNPdr9+v1aVFfg7nPottHpq6lBz19A5htGha6N58oNjAeIb+F44MZ+cbVh8/mTjMuGuHbB1nHD/nbj53WPbd6DQ9kyYm3H5pdHTd6eBp6+yy471X7pM3y9XuRnZKIOWOH2f1cjs5NxYrF07B8/V672GFd1QksnlUAi0WiueMc7ipV8s7qZJSnD/Kuxrf5cBNOnjVHvCGrt0mkUE88BbNfnr/n4u+v2OVtwuVjKLNKv7V+rc4q/SeUNdRERAHzdGH1tAvByhtnYHJ+Jpo7z6FvQDpd0OKsQdaiWaOcql9Wba7Gc3dcgntfOV/VondBVK9ry8qKYe4fwP0LJuHhv+7HD64aZ1ce/ItF0zDLWhp84kwXHr9uKu5/XdnBVgjgNzdMhxBAXUs3Xth+PmGUmhjPJrhErjEeoYCwIjC6BPpB1rYqNiUxHr0DA8hNTcLo3FSX5zYIBFwZoJ47OyURi2aNghBKjDEyw6h7nO0YinKT0dM7gGPNndrtatWtEFCa/Z/pwuhh9gmFr07JR3ZKorY8aV3VCSyfPxGF2SkuJ6Nau3vdvpfuKmeioSFrtOzEGMzqkkDOxd9fscnbhMv9AP4uhJhsfcw91n9fCmBuqAZHRLHN1y7sni6sru6fVpCBjRWl2vnnjc/D563nkxzqBW3qqEysvHEGDp9qtwuSAGU26MSZbu3863fVOyVRHlo4CRdkJSFOxKGh3YyEOAMS4wXqWnrw9t4vtHMPWIDfvnsESfEGXDMxDxYJXJiTjFe/ezkOn+rEA2+cH9OysmIA55vKpSYpt9n2jmHJKJGG8QjRIOLqw2dhdgpqmzvdxg+uql7VRMQ1E/Oczv34dVMxqzALhTmBVQaMzk3F6ltmorqx0+56PX5kht259V7fo9+YivK1Vbir1KQlbWyrbtVxOo4xPt6AK0y5yEyOR0ObGddMysPk/AycaO3WnYwqn2vChJEZbuMHvfHZVt2GO7HhKFp2YgxmdQkrVYYerxIuUsoPhRBXALgPwDEAZQA+AXC5lHJfCMdHRDHKnyZmni6sru4vzkt3Ki0tyk3DhdmpmHFhlt0FDQAKc5KRnBDn1BA3IzlBO39Dmxk7jp3GM98uwRdne5CSGI+39nyOson5ePivB7TH/ewbU1BSlInLTMPtAh5AmR1ac1uJtn67omys1kMGOF9Z8+vrp2NEehIuGZ0DACjKScWswmx09/ajMCcVY4bxQkwEMB4hGmz0PnwWZqfgnUONHuMHd1Wv97y8GxsrSr36YOvPFr0Gg8CY3DQsffFTt8td9F6fOnmkLjE29w84Vd3e//o+zCrMgkVCG5er9yU7JUF3MmrmhVn40rgR2pJsvdeojq/AuuHAgAV2y7QjXV0bTX1LglldwkqVocXbChdYA5nbQzgWIhpE/Gne5unC6uuF19UFzRh/Ptmijm3V5mos/8p4raolOyURV00eie+8UGX3XI6v6aE39+NPd16Kf7lYK36qrQfjRqShdNwIFGQm6x5T3dSByRdknJ8RG5aG0cPStABp52ctXgeBRIMd4xGiwcXxWl3b3OlV/OCq6lUI++pY9Y9e0gGA3zvcNHV4t9xFLxYxJhjQ0GbG2h11+H5ZsdN5slMS8cmJs1qVblFuMh75+hTUNnfabRhwz8u7sa78ct3JqCJrzOBpAsxgEJhakIWTZ81RkdiwxWoQGgy8SrgIIXLc3S+lPBOc4RDRYOHPultXM12267PzM5Owrnw2unsHvEpCqAFWS9c5JMYZ0N07AAmJ7JREbXcjdWwt3X3ISIpD+VwTikek44c2zezMfRYcPtWun1RpN+NLxcPtqlcAJeCpP9uDmy8twu+31uBr0wt0g6KSohynoMaXCiF/ZueIYhHjEaLBz1P8oF7zDELoXlOldF524uqaOj4v3efJIZW/y11G56ZqS54b2sz4oq3H6Tw3lIzSki35mUbcVFKI7/7fLm3sDy6chA5zHzrPDUBKi9vJKG8mwKI5scFqEIp13la4nMb5xnR64oIwFiIaRALpwm47G+VuffZlY3LdJh/yM4042NCBFZsO4aaSQrv10cvKirVmterYDALITk3Ccx8e1Z1xskj9RrrHmjvR2NaDhxdOxiMbDtiNVW1at2SOSXeHol9cNxWzR+c4vQ5vK4T8WbpFFMMYjxAFWbQl7fMyjCjKTcbCaQVan7W39pzEiHSj3TUvOyXRqeeZGiM4Vme4uqb+7tZZfjdl1au6XX3LTEip7JDk6r00GARmFWahfK7JGlfE4b5rxuOJd44gOyURN5SMQvGIdNxVasLWI024a+5F+JHDBNCjGw5qGwYUj0jDV6fkY6OLZIm3E2DRktiItp9HokB5m3C50uHrBAAzAfwXgAeCOiIiGhSCse7W0/ps2+SDxSLx2ekuHGpoR3VTB16uqscNJaOwZmstlswx6e5KVD7XhMrNNTAmGPDz66agICsZrV29+NXiaejq7XdKrry15yQeu3YKHnhjv1NSZfHFo/Dnj2rwu1tm4ZPPz0LK8+ugASAp/nz58JI5JhTlJKPuTA9+u/kokhLinBIk3gZI/izdIophjEeIgigak/aF2Sm4e16x3bX2sWunaBWv6lgb2sx4YXsdyueaMPPCLOSkJqJvwIL5U0Y6fUi3vaba7giUlhSPotxk1LX0aMd627vEsSpkZIYyyfPVp7Z5fC8Lc1IxYWSGljj6ry+ZcM9VxUg1JuDRDQe1ZUTfnTsWNU0dbpdOLV+/F1MLMl0mS4LReDYYSRBvztHfb8GHtS2oqjsDi1TiLrUB8onWbiZhKCZ52zT3nzo3vyuEqAVwF4AXgzoqIop5wShP9XZ9tl7A+Og3piA9Kc7ueMfzjMpKxtJ5Y5GRFAeLBbjjuY+1x/9k/gQ8tHASfmYNfIwJBpTPvQhv7/kCT1w/HYcbO7SkSmt3L6QE6lp60NU7gD9uc15aNHZEGvIzjWhoM+PZD5Qk0NPv1QDQ33rRVYA0PM0+QIqWLROJwoHxCFFwRWPS/kRrt5ZsUcf0wBv7Masw2+ma19BmRuXmGrxUfhmmX5jt8py22zjb7gj0x221eOzaKXhqSzXqWnp0J4e8SRRICbT19GPFpkNev5fj89Lxu1tnIS0pHt/+34+wZI4JK989Pzm0cFoBHtlwQNvNSG/plPo87q75gU6A+ZOUc3zPvGmEbLFIvL2/AcvX77Wb1PrfD46hb8Bid3ukk4JEvvC6aa4Lu8FtGInIBcflQZ4CFsdjRqTrJx0c12frBYwPvrkfq2+ZhaLcZO1xjucZPzIdhbkpSEuKxzsHG3FXqQmAsiX0LzYdxqpvztRKfqUEVm+pQWt3L264pFBLqthWuRgTDKhv7dYtcV6x6RAWzRqFZz+oxYMLJ2H1lhptLHrBkl6AtKysGJ+1dNrtWhQtWyYSRdhuMB4h8lk0Ju3djSmQvikrb5yBw6fanSpeH3hjP9aVz0ZP34DT5JC7ZAPg3HBXjQfU6la999LxnBVlY3Unh9Sv9ZYjq88DAEW5ys6LrpYxBToB5mtSTu89W7F4Glb+44jbcxxv6dKSKuoxlVuq8avrpzstqYp0UjASuNQqdvmdcBFCpAH4PoDPgzYaIhqUvJkd0TvmD7fNworF05xmOxzXZ7sKzvbWn8VPvzYZP33rgFOwsmLxNEwtyAIAvLH7pNbw1jaQMff1Iy/DqJX3qkmP379fg6VXjsXIDCNOtHZrVS4V84rx0scnUFFWjKVXjoW532K3tKgwJxnlc03oMvfZNezVCxYNBoFJ+el2CZ8XtivPs9EmyIimLROJIoHxCJH/ojFp725M/l7z1KSD43kBJV7o6RvAbNMwp8e5SzYAcLnsWa1g1XsvHc+p9odTj3d83bbLkeMMwL9dNAw/Wr8HDW1mFOUm4+55xbhpzQ7dhJDjB3R/EhS+JuX03rPl6/favS9653D1PD29/VGXFAy3aFz6R97zdpeiDtg3qRMAUgB0Abg1BOMiokHEm9kRx2OyUxJx8IsOvPTxCSy9ciwKspKREGdA/dlufPOSQmSnJGjbJbuqhBmwANVNnVg+fyLqz3Rh1TdnYmDAAgiB8SPOP6+6E4A6tsotSn+Xo41d2LD3JP7n1lk40tiJ/gELUhLicLSpE3tPHkVRbjIeWjgZaUnxGJWVjAEpceulhUhJiEfvgHK+1z5Rtm40Jhhw8mwPJozMQGL8+Z0V3AWLaqm0I9sgI5p3FiAKNsYjRMEVjUl7d2Nyd83zVAFgMAiMzk31mGCyPY9BCN1dDZs6zJBSf7lynOH8efXeS8fEglrBsq7qhN3k0Ft7TmrN+NXlyBXzitE3MICHvzYZmckJkBLYWt3stFX0+LtLcaSxw+MHdG+qJnxNyrlKnKjvi6tzuHqewpwUl0usa5s7h0TFRzQu/SPveVvhcjfsAxwLgGYAO6WUrUEfFRFFLX9KGr2ZHXE8ZtGsUdqynJ4+C35iTYpMK8jAd788FttrW7SGaj/7xmT8/Lqp+G/rMbaVMAunFWDDnhp8b14x2rp78dBfD9gFH8PTE2Hus9g10QOA4hFpeHzjYTS0mfFff/5Em5kpKcrEC3deiubOc8hJTcRv3jmMqro2AMC0ggzcdGkhfqCzq9KysnGYVZiFwhwl8HK1m4Atb4OcaNlZwBOWw1IQMB4hCqJAkvah+p3uaUx61zxvKwA8JZj0zqO3q6F6Hda7RpdNGIErLsp1+V7qXduFAH54zQTkpiXgxSWXoc3ch5yURDz69gEsmWOCEEql67qqEzD3F+CVqnp8+/Iip+XLakXtiTOeP6AH6z1z5Cp2KSnKcTvZpPc8KxZPw8UXZus+/2ctnVj64qdDouIjGpf+kfe8bZr7fIjHQUQxwN+SRm8SB47H2K5lVv+dn2nETZcWOq2XPtLQgXcOntLWZw9YlKCkomwc/rLzOOZPyceBL9q0ZUPA+eBjXflsFOUmO20b/dDCSdrY1LXVJUWZ+Oalo/Ht5z6ye/6TZ88BgO7WjZVbqrGufDamFmTZvUfeJEiicebRXyyHpWBgPEIUfP4k7UP9O93XMXlbAeApmaN3HsddDW2vw3rXaMfrvaPRual4/LqpuP/1fchOSdRNnGw5fAr3XjMBt1w2WqvCtU3+2E5KqeNUlzM9+0Et4gzC4wf0YL1neq9P7325wpTrdrLJ3fM43m4QwPxV24ZMxUc0Lv0j77lMuAghcrw9iZTyTHCGQ0TRzN+SRm8SB47HxAn7mSNjggGLZo3CoxsOIjslUatGOdc/gIsuyERVXRtOnj2o3b5wWgFGpCWgomw8ytdW4a5Sk27w0d07gEe/MRXla6vsXtfPNhzUqlqMCQZcNiYHFxdm4b/+P3vnHh5Vee3/z57JTGYm9wQSYkKCQ8IlNyBERA/JqUQ91MYLF8HqQW2xafsTodpWWytSRLGoxYrYVqq1ldYjtngpFKkKtmAFa0C5XxICCQkJCbknk8lMZvbvj8nemcnsSQIEDPB+nqdPYbLvE3nXXuu7vuvPu/wCnOdnj6fe5gg4urHd6TqrAPRSahcScljB2SLiEYFg8DHY/k0/EwVAb8mcQOOjJ1ujyUudTHRIsM86PC19GKMfyKW8vg2LMYi48OB+Xa/FqKcwz0pqbBg/1ijUrJg9nm/94XOiLEYK86yMHhZGpNnAj/6yh6ome8Dpi3odPHFLOoeqmvt8QR+oZ9aT3mKXvo4RaJuen28/evqyUnxcSgW4y5HeFC6n8ZXtaiF1baMfsCsSCASDlrOVNPYncaBsM/qBXA5WN3OywcaD14/i+Y+OsG5nBQvzU2l3uvxGOibHmBmXGMmPbhyFvdOt9jAnx5gZEWPhRIPNJ2nTM/gI0knU2xya9yV1JX0W5qdiMej51/F6ze1a7E6WbjgQcHTjuVQgLpZ2ob4QcljBOSDiEYFgkHG+/k0/2zalgVIABB4f7XnBzU6K9rueY3Wt7Klowi2DXoLMxAimjo4LeN3H69r4wVrPi/P8qSmaz/FQdTN2p5uqJrvaPnTE6aLB5vC5v573Oyo2DEOQjuc/KvYbFrBseqbPC3qgZ2Y26HG75XMq7pzv2OVyU3xcSgW4y5HeEi7XXbCrEAgEFwXnssD1t7IhSfCjv+xWW4gUV/7c1CF0+dCqAUR8hIk5OUn8vzd2qQHFooI02uxORsaGcv8bX6hJEK2xik/elsHh6maGx4SwID8Ft4xPwmbSiCjGxI3jVLOd020d6iQBv+DEGITdqT26UVQgPFxuwZFgQBHxiEAwyDgf/6afS5vSQCkAehsfraXgKa9vo/hUq8+kw4X5qaQMDWXEkFDNBFLPZFUg038FpX1oVGyoaqKrFKK8W5EW5qdS19pBe6cnMaNMNpIk0EmQneTb6qT1zBZMTWXBm1/wyLSxg7rl93JUfFwqBbjLEUmW+yoaCc6UnJwcuaio6Ku+DIFgwLkQPhzbj57mm7/7zO/zNwuvZtKIGDburWL+/30BwP3XpfDqJ6V+gUphnpXhURZWfHgEQK1SRVmM3J6TyMihoURZDJTXtdHmcPP8R0f8eqdnZiexZEO3we5T0zNp73Ai6XQ+Y6IXTE1Fp4NffdSdBJqRnYheB/ljYvvs5b5cEB4uAgBJknbKspzzVV/H5YKIRwTni/Pxb3ppbSs3rdzmt6Zv7Gebktstc+x0m097T1L0mRsAx0eYKK1t49t/9P9v583Cq33GR39WWsc9Xb5u3tf8x29N4qoR0ZrPKC0+TPUfUcz2veOKRQVprN56lLK6dgDmT01h1ZYS7r8uhQ17KinISkCSIMSoJzhIxxWRZtxumbJ6G69vLwPw84Xp+d0o91vX1oGExI7SOtqdbp/Jiv197l8Vyj0IxYfgbLiQ8Uh/pxSpSJI0DDB6fybLcvmAXZFAIBi0XAhJY29VM51OYmx8uPrzQD3MbhkWvbdPNblTqjx6HVx9ZTSPvrOXsrp2FuSn+BnprtxSzMtzJ/LdNTt9Pv/ZO3uZN8XKhj2VrJg9ntLaVsYNj+Sxd/fi6JRVZYsyurGncd7lPqFHyGEFA42IRwSCr47z8W/6QLQpnc0o5KQoC/8uraWl3UVbRyd1bQ6GhQcHHEXsTZujM4A/XGdAn5u/P5DLitnjWb7pINMy4lm99SjzplgxG3RMTI7yFHPyR6mTFxVPO0mCsrp2Xvq4xOd8C/JTyEmO9kmwpMaF8vcHcqlt9f9uvJNlSiFqeJSFmhb/0dfeE40GWwwjFB+Ci4V+JVwkSYoAVgKz6RHcdCF6pgWCy4TzvcApMtHlmw5SkJWAXgdXJUeTFGUB4Moh3TJS0JbiyrInWEiKspAcY1aPk5McxeHqFmZmD+fKISGArBkoNdqcmp8HB+koyErgUHUzOclR/H5bqTrdaM2OMh66PpUx8eF0umSSvWStQt3hQQRHgnNFxCMCweBhoP9NP9c2pf4Y+Wqtxy/MGU9ju5PFf+tWtS6+OZ3f3DWB7/+5e+zwwvxUjtW1cuWQ7mRDcnSI5jUnRfu3DinXVNNiZ1r6MBIiTcxZvQO7060mUUwGHWsLJ7Ny8xG1Hchk0PPQDaNo6+gM2H5kd7pYWzgZm8PlkxAZGev/3SjPqadPjfdo6QabQ33uIoYRCM6N/ipcngPGAbcBbwPfBhKAhcAPz8uVCQSCyxKdTuLGsXE4XW4eWbdHc3Gflj6MhMLJfFZax6KCNHVykXeVJjnGjE4nM/+6VBa9t48oixGzQe9TAVp6awbJMWZVtgue4CXaYtQMalJjQ3nQK+B48PpR/H3PSeZNsRJm0jMkzERhlzLG+5oH2zQHgeAiRsQjAsElyrn6cvRHIaO1Hu+vavZTuy5Zv5/Xvz2JwjwrbhlkGV7f7klEeLfaeBeBvK/5yiEhtHolSLzbjY16HQA2h0vzequa7H5KlvgIE0tuSeeJW9J53CsxtGBqKmuLPMI+RV179ZUxvaprlec0IzvRz6dm5RbPCOwxw8LV5y5iGIHg3OhvwuXrwDdlWd4mSZIL2CnL8lpJkqqA7wJ/PW9XKBAILjvKG2xqsgV8F/cRMSEcr2vD4XIzLMLMLz88zCP/M5oQk4HH39unBiFLbklnSGh3AkQxnfM+5qL39vkFSg9eP4omu8PPjO7nN6fzi00HffZ//qMjzL8uhec+OMKC/BQe7ZL/9rxmMaFHIBgwRDwiEFyinGubUn8UMlrrsVvWbk+uaelg5Wbf9h3AZ+32vub6tg4Meh02h4vjdW24ZTcPXj+KN/5Tpqph7U43q7eWql4uPa83OcZMTIjRz8i/weZgd0UT247UqKa+LjesLSpnTk4Sa3aU+SVCAilTRseFqS1KWvc9YXgk/z0qVn3u/Y1hBmPbkUAwGOhvwiUSKOv6cxMQA5QA24FXBv6yBALB5Uygxb2+rYPS093jF0ONep6ZlUWnS2beH4t8kh2L/7af52aNUz+Lthg0j1nRYFNlu7IMEjI/+sseoixGH3f/hEiTjxJG2X9oaLA6ilHr+GV1bVw5RFtyLCb0CARnTCQiHhEILlnOtE3J+yU/NszEqjsnMP+NL3wSDH2NQlY8Unqu0cP62eKk00mMiAnhULWvf8wzM7OIsgTx4xvH8KO/7g7o5aLskxxj5v6vpTL39//xU7B8N28kL3cZ6S7dcEBVy/zwxjE8vfEgVU129dhKIiSQMmXTQs95D1c3a95fco9ESWxY389BtB0JBIHpb8LlKGAFyoGDwB2SJP0HmAHUn6drEwgElyFut4zFqOfRr48mPtLCsdMeNcv63ZWYDHqKjzf4jV8cM0w72dHm8Mh5oyxGMhLD1YoReKpGDTYHY4aFU7imSD3e0zMyVUmvt5z3t/+brRlwXBFlZuOCXOQAI6O/ONFIu9PdZxAoEAj6hYhHBJcsQiFwZgR6yd+0MJfqZm2FjFbbUmZiBE/elsFj73arZJ+8LYOsKyL63eKkldx4eN2erpYk7SJSbavHy2X0A7kcrG4m3BSkqnKVbVZuKeaZWeN4ZetR7rgqiRc2d5vzL5+ZxS8/OKQmW8A3ERKoeFXd7DlvWnwYyTEhqjpX6/7cbpljda1+qt+e24m2I4EgMP1NuPwByAL+CfwC2ADMB3R4+qYFAoHgrPEeT3iy0c6KDw8zJyeJH3dVhBS/FbvT7dcW9MLmYtZ8e5JmsqO+zcGCqakE6aC01uaXqLEY9BytaeHFOybQZHdSXm/jZGO75rFCjEF+AcfC/FSGR5kZMcQj3e0ZmHmbz/39gVw2igk9AsG58gdEPCK4BDnfCoFLMZkT6CV/44Jcn9HN3gRqW3K7ZVJjQ6lusjMswkR6fARBQbp+tzgFSm4kRJqxGIN6nb4oSfCjv+xmyc3pmsdotHVwX95I7E4Xr397Eka9RLjZSFKUBYNeFzAh1NfUxxFDQkmKDmH88Ej1/pKiLD6/JzoJ5r/xhZ/qNy0+zOc5iNZpgSAw/Uq4yLL8vNeft0iSNAbIAYplWd57vi5OIBBcemiNY/zg4CkeeutL5k2x8uonpcybYvUzclv03j5+d3eO34IeZTFS3Wz3S4Y8cUs68ZEmdp9oIj0h0q/l6IXNxSzMTyX9inD2VDSx6uMS1dhOGfHsnTg5cqoFi0GvGujpJEiN8wQr0B3ExXxrEttKTiPLsGZHmVp5qm21M9k6RAQeAsE5IOIRwaXK+VQIXKrtHv15yQ+UaOrZtqTTSYwbHsW44d3HOpMkVaC2m/L6djbsqWRxQTpLNuzXTIwo92EJ9k/M5CRHEByk52GvAtQTt2Zw9ZXGPj1v+mNC7P0stH5Plk3PJMpi9FP9XjsyhhFDup/fuU6YEgguZfo7Fnq8LMtfKn+XZbkcj5xXIBAI+k3PxTw5xsyy6Vkcqm7mvlwrocF67E53QCO3U812dcyz1BXzhAfr+fFfPZ4r869LIS7cxNCwYFrsnXzndY80d0F+iubxEqMsPPePw+SNjlV/XtVkZ82OMuZNsZIUbaa8vp21ReUUZCXw9q4KZmQnIknwXyOHcNWIaJ/gS6eTGBoWzCvbSkXQIRCcB0Q8IrhUOZ8KgUu13aOvl/zeEk1Ar8mUM0lSBWq7UVSuVU12fru1pMtXzkVtawfGoO5jKPfxu61H/RIzC/NH8501vgWjx9/bx+q5EymuaWXq6LiAnjdnakKs9Xvy6Dt7Kcyz+pgHa8U05zphyvtZXmpKLIGgvy1FuyRJOgCsAd6QZfnEebwmgUBwiXLsdPdiHh9hYk5OEvP++Lm6OC8qSCM5xgxo+6HEhhlZXJDOlxWNuGVYv7uS+69LJcpiBMCg17HovX3Mm2Jlw55KVf6aGhumOf45xKinye70O5/SH60obhYVpNFidzJzYqLq/TJ9fIJmEDBQQYdAINBExCOCS5LzqRC4WNo93G6ZY6fbKKtvI8QYRFx4MEnRgV+4+1pvAyWa0hbmUlLbbcCvlyA7OZLEyBBqWjwv+rJMv5NUx+vafNpulGKNt8q1rK6dto5OftXlwWIy6NTx0klRFl65J4fKhnaiLEZeuzeH+jYniVFmKhvbtX1YmuzUtTlIGRrqozTpyZmYEAf6PRkV1z1NKVBMc64TpuDCK7H6Su6I5I9goOhvwmUMcBcwD3hKkqRP8AQ7f5Vluel8XZxAILi0KKtvUxfzGdmJrNxSTJTFqKpGaprtLCpIY+mGA2pbT5TFyO05iVw5JASXG5Zs2E9ZXbtaPXrp42KWz8ykrcPFg12LdJhJ7zOC0WTQsbggnd9uLfHZd8mG/TwybSzLNx30ayN6enomIcFB/O7uHF746DCVjR3cnpPID28cRaTZQEWjZ/pQz8V3IIIOgUAQEBGPCC5Jzmey/nwkcwb6ZVTrZXthfiqpcaFMHR2neey+1ttACYS6VgfFp1p9fN0W35zO4+91xxfeUw6999VKUinnUdpu5k9N4dVP/JWu5Q3tftOERsSEqG3VSryTFG0hvstHBiTN785kDOKFv+0nOymq14TLmRDo92TssPB+edCd6YSpnlxIJVZfyZ1LtQ1P8NXQXw+XI8BiYLEkSVfjCXaeAl6UJOnvsizffh6vUSAQDELOJtgK8TKOkySP/8rcyck+iY5nZmbx7KxxtDmcvDHvakrr2nwmB3hLdFduKWbeFCtFZQ0kRVvURToh0qIa7oJn0V6yYT8vz53I58cbfPxVSmtbefTraYDMr++cAJJEW4eLIaFGTjS0sf+kndkTh9PQ3snzHx3xCc72VDTS7nT53f+5Bh0CgUAbEY8ILlXOZ7J+oJM55+NlVOtl+4XNxRTmWbEOCbye9rbeBkog2Dtdfgb8S9bvZ94UKy91+bkV17Ro7js01ERpbatP7NPzPOt2Vmia7L++vcznWLFhJvW+teKh5TOz+HraML8JSosL0nll61HsTjc2R6f6nZxrAizQ74lSXDrfMc2FVGL1ldy5VNvwBF8N/VW4qMiy/BnwmSRJfwZ+i2cUo0AguIw422ArLjxYDUIAbs9J9DHHjbIYqWxs5+F1e1TvFaUCBd0jEr2DIr0OXG44UW9TA55jp7uVNPERJlVB09bRybqdFWqFyWTQkZUYgcPlprrRjl6vZ8n67t7pJbek8++SWuZNGcnj6w/4BWdKX7OofAgEFx4RjwguNc5Xsl5J5ox+IJfy+jYsXe06Z8tAv4wqyQKtl223zFm/cAdKIHQ43ZrnkryW77eKKlg2PdNvZPKxulbmv/GFz2c3jo3zOU+DzUFqXCh/fyCX4poWSmpasRj0NNgcAKoZ7YiYED47Vofd6VZVv97P9JF1e8hMiOC2cQmMiAmhtLYVkzGIV7YeZU9lMyaDjqTokAFLgH3VCt0LabzbV3LnYmnDE1wcnFHCRZKkK/FUk+4CUoCtwH3n4boEAsEg5myDraToEFLjQlmYn0pilJlOt6y2FIWZ9IyND2dvRRP35VpZt7MCt6xtnqsERSaDjvT4CEpPtyJJsPTWDBa9tw+HyxNwaFWMlCpTg83BklvSWbn5CEVlTaz65gR+1EMVs/hv+3lm1jgOVjcHDATP5P4FAsHAIOIRgeDMOXyqZUBUKQP5MqokCw5XN2u+bOskzvqFW6eTuHFsHGsLJ1PVZCc+wkx6fDhlXgUa73PJcve+DTYH2UmRPq00Ognuee0/qj8cwPJNBxkzLCxgokKS4Adrv2RUbCjPzBpHe0cnIaYgMhPC0ekkNckQaFiA8kyzk6KoaenQVJ+cSwJMSxnzVSl0L6QHXl/JHTF1STCQ9HdK0f14gpqrgX3A7/GY1VWex2sTCASDlN6CrRExIQFlrTqd5HHUHxJKXVsHekni7muSefPzcubkJPHdNTvVRfbB60eRFGPR7l0O0qkmuzJu3vy8nLK6dnKSI/j1ndmU1LSwqCCNU812v4rRC5uLeXbWOA5Vt/Drf5ZQkJVAUVkTbR2dmvfU7ujELWub+HoHZ6LyIRCcf0Q8IhCcHQOpShnIl1HvlpqeXmqKh8u5tD4p/ig9FSnPzMxS1bRKm/Bv/1Wi3suy6ZmqYa/yfD4/XufnD7dgair1bR1qkqLnsxwRE8LLc7OpaLD7jHZeMXs8w6NC1CRDoIST8kx7U5+cbQJssPmUXEiFTV/JHTEAQTCQ9Ffh8gjwf8B3ZVneex6vRyAQXAQECraGhZv6XLy9g5d/Hq7hhc2eFqGeiZHnPzrCQ9en8uD1o3y8UxbmpxLb1Zq0aksJDTaH2mKUP3aYOsHIDQyPsmgGIYdPtfDSx57ASqlSWYKDNO/JbAxi/e5Knpqeyc+8pMWB+rEFAsF5RcQjAsFZMJCqlEAvo0lRFj9/k75elr0NZ9fsKFPVI1dfGc3wKHOvU4r6IlCSaeOCXOIjTeq5ZBn+77MyCrISSIr2TAaKj/Bfz416nV+ssnJLMW8VTvbZzls1Eh9hQkLH0g0HAia7pqUPY+ywMIZHh/DYu3sDvuAHajk72wSYd7JLab0+XN1MWnzYgBnxnikXygOvP8kdY5BEYZ4Vtww6CZ9R3gLBmdDfhEuyLHvXcgUCweVMoGDL5e7/GMW6tg4iLUa1RUgrEIywGLF1dKoLniyjtgPNm2JVvVgkyePVEm42+JjUPT97vGYQMioujPlTU9BLYDLoAfjd1qMsvjndx8NlcUE6r39ayp2Tkvnjv4+x5tuTONXSwYl6m18/tqh8CAQXBBGPCARnwUCqUrReVpOiLJpqkr7UEhYvM31lyo/JoOOGsbHn/NIfKMlUVteGyaj3myR0pKaVhfmpmIL0/OTtPbx27yQf1W67w6V5vPKGdjISIjWn2yzIT1G367mfkuzS6SSuHBpKckwI2UmRPs/0eF0bdW0dGPU6bA5/k363W0Ynoek301dMcqrZrtl6nRwTck6JrouF3pI7yqjvnv+9bBSt44KzoL9TikRwIxAIVAJVBhTzN28CtRqFBusJN3kCLdBu2alsbGd4lIUlGw76XUNwkI77r0tBr4PU2DDuujrJr4L0i00H1THT3ka4v/zgkDr68aEbRhEfYeJITStx4Ube/M5kTja1ExocxPG6Nq62DuUPnx6nweYgJjSYicnRHK9ro76tg7WFkzUDIIFAcH4Q8YhAcHYMdItEz5fV0trWfhVcvNUfsWEmGmwdfq1EC6am4nS5Nc97JgRKMn1xohGzQa85SWjc8Ah++NYeqprs1Ld1cKi62/dmYX6K5vGOnGohLT5cc7qN4vWmqaA16Nl+9LRPDKE8UyVxs3zTQb82JiWRBajJnSiLkcI8K6Piwhg7LFydLNQbsWEmv+EFdqebR9/Zy/jhkZd1YkGY5goGkjOeUiQQCASgXRkIFNwMDfVvNVo2PZNWu5OF+am8+Xm5ZsC1ZkcZs3MSNY+ZEhvqc7ylt2b4LY5lde2Em4KYf10K9k43o+LC1GQLeBbPFR8e4blZ4zhY3cKS9Qd4ZNpYbhw7jA8OnmLZxkN+gakY+SwQCASCi43z7Y/RnxdULc+QxwvS2HKo2qe9Z21ROdMyhp3zNWklmZR24JkTE1m3s8LnvK9vL+MHllSqmuyYDDoMep1P8uStIv9xz0qscu3ImIDTbdbvrvSLcZ6ansmCN79Qiz891UBK4kar5Xr5poMkRJqoa3NwuLqZKIuRqia7OjVx44Jcv+9VyxxXr4OkaO3W68s9sSBMcwUDiUi4CASCAUMruFk+M4sgvW+rUZTFyOkWO4lRFn7z94PMyE5Ep4OX505k94kmOjrdrNlRRlWTnbeKKnjytgwee3efT3Jl+aaDPgFIRYP21AGdJGHvdLNuZwW35ySqyRYFu9PNIS9PF6W/+6scjSgQCAQCwbnS3wk0Wtud6XrXnxdULU+VJzYc8IsblJHJA0FafBh//NYkbI5OQoKDWPjml2o7coPNoa79yvXWtnaoCZC2Dt8WoqomO69vL+O5WeM4dKoFWYY1OzxtzkNDTZTWtqKTJJ/nsG5nhTocYN4UK3odTBgeRWVDG45OWX0OPdVASuKmZ8t1fISJOTlJzFm9wy/pU9Vk10yWBDLHHRpm5FSzXSQWNBCmuYKBRCRcBALBgARb0D2CcfXcHIrK6nG5YcWHh1mYP0qtwMRHmPhenhWb08XhUy0+Ac/8qSm8ss23p7rB5mB4lIlf35XNlycacbmhoa3DL3HyVlGFT/tQcoyZn0wbS3FNK3oJvv/fVhKizGc0bUgoWQQCgUBwMdLfCTQDNammPy+ogVQwJTWtqtJEJ0F2UuQ5Fzi07mv5zCzV+HTdzgo/1ckzM7O4ItLETZm5jIgJYW9lk1/M0GBzEGoKUmMV5T6P1bUy/40viLIYfVQwDTYHFoOeuZOTiQ0zUVLbyvMfHuZrY2J56IZR1LTY+dOOcqqa7D6JEiWBFRqsZ0F+SndrUpC2ca8yPKBnssTtltlb2ajZ7vWneVcjy/D87PH8YtNBVW2zfGbWZZ9YuJATkwSXPmeUcJEkaQgwEvhSluWO83NJAoHgQjLQYwHLG2wUrinyCVAefWcvhXlWVm4uYUZ2InU2B6u3lvqNgdxxtJYnb8v0celfMDWVh9ft5a5JSbjcXQa5kf7johtsDprbnRTmWclKjOBUcwcP9pARn2q2a46dfH17GfERJmZkJ6LXgdkQ5DGiEwurQDAoEfGIQNA7/R0DPVDjovvzghobpq2C6XS71WTBitnjSYo+t5f9QEmGR9btYfXcHArXFFHVZGdtUTmr5+Zg0EuaxSaHy6XZ7hxpDmKj133qJJj2wjbV+Pf17WUU5llJiDBT3tDOb7eWcntOIs99sEfTpHbB1FTWFpX7JEpGxISw6s4JlNa2sXprd3Lnydv826cVJUzPJJcS3x2qbtbcZ2txrdqGtKggjTa7k7SECBIizHx2rO6y96cTLeSCgaJfCRdJksKAV4FZgAykAqWSJP0WqJZl+efn7QoFAsF5ZaCCLYVAFaxRcWGYDDokyWMipwQm3mMgr7XGsK+y0aenWpHJNne4VCVMfIRJox86A4shiGa7k+AgvZ+B7gubi3l21jhe3HKUwjwrE4ZHkhQdwrG6VhIig5mePVzdZ/XW0nNKOgkEgvODiEcEgv7RX9PPgTQH7e0F1e2WOVbX6ueB8svbx5N+RRjXjozRTNKcqQK3rySDQS/5JEt6O15MSDBri8r9/GUmJkcBMGlEDDqdxPajp/3O5XIDkqdIZAySGBUbht3pZka2v0ntyi3FrJ6b4zcC+sqYUJ9JOXanmxP12u3TuSlDmDEhwed+lPjuvlyr5j6KL7Hd6WbphgO8VTiZikY79/1x24AU4AQCgYf+KlyWAwlANvCJ1+cbgKeAnw/sZQkEggvFQDuxB+rjThsWxp/mXU1tSwcuWSY5xkxZXbvPGMgRMRbcQIRJT3ykBVtHJ/87OYm3ik4QYtSrx1MqU7+7O4eGNgeNNgeOTpmfveNJHD0zM1PznmwdnTTYHIyOC+e/R8Wi00kkR1sI0un43p92+iWdRj+Qy8hYUdkQCAYRIh4RCPpBf00/+7PdQLQdK2N2oyxGn/ah9CvCGDEkVHME9NkocI/XtbF800F+dOMYzfuKCzf5JIXcbpnS2lbNexsRE8Ij08b6me7+YO2XNNgc6rV4P8P4CJOfgmX5zCzSrwhXi06BEkE976mmxT8+69k+rTyTq0ZE++2vxHda7VOK74v3NdS1Oc6pADdQ7ekCwaVGfxMutwDTZVn+UpIk75GMBwHrwF+WQCC4UAy0E3vPPu7kGDO/mJHFzvJGFr3XbXy7+OZ0fvuvErVneGF+KkNDjdidLlrsnfz4r7vV/RffnI7N4eIn00bzx+0eg7o5OUk89u5evptnJXlICDvLGrgv1/PPUYu9U/OeIswG1n5nMulXRKhBgcWoZ1d5g2YAVF7fJhIuAsHgQsQjAkE/6K/pZ1/bBUp63Dg2jvIGW79frpWXf6XIonDtyJiAyZa9lY0cqm7mvlwr63ZWUNVk7zMBUNfWwZycJJ774JBfkqHn/feV0FHapEY/kEvp6Vb2Vjbx+nZPkmLeFCuHqptJiDSTHh+uPkMtBcsj6/awYf4Uls/MorS2NWAiqCda8ZnSPq0krXJThmgmW7z391YT63WQlzqUH/6l2zxYuQaLMeisC3AD3Z4uEFxK9DfhEgXUaXweBrgG7nIEAsGFZiCc2HtWNW4cG8fGBbnUt3VQ2Whne2md2oMMngV8yfr9PDdrHBWNNq6ItHD8dBsdnTK2jk5e2FxMlMXIXVcnERduYveJRt4qqqDB5mBRQRrN7U5e3+5pNYoJNVHR0O7T4/zTaWP40Y2jee6Dw+pnS25JJyhIIv2KCD44eEq9X8WMTisAshiFr7hAMMgQ8YhA0A/6a/rZ13ZabcfLNx3E6fIkEvr7cn0mxR2tl3fvSTxaU3iUGMSo17G2qJyyuna/JMP4xEifWEWW6Zei4/CpFg5VN7Nyc4mfgkVpQb5+dCxrCydzoqFdM2lx6FQLKz48zB1XJbH45nSWrN/fZ8ylFZ8tmJqqxj8mg44ZExICPnPv/aua7Lz6SSnLZ2aRdUWEn3JnxezxxIUHn3UBbqDb0wWCS4n+vk18jqeq9KuuvytVpe8Cnw7wNQkEggtIf4MyLakoQHl9G7vKG3n0nb2qImXprZkY9BJhpiBONraTEGHWDEBONdtxu+HhLjWLYtw2KjaUaRnxmvLXpRsOMG+KVQ02zAZ/v5anNx3ioetTeWbWOI6cakEnQXKMhWHhJioabT5BgVuG9bsr/SphiwrSiAsPvgDfgEAgOANEPCIQ9JP+mn72tp1W23FBVoKabIH+vVyfSXFH6+VdmcTz6ielfu1OvSVnFDVNbuoQn2KLyaDjuVnj+lR09PRB0VKwPPTWl6oZbyC/lCOnWiira+f17WV8L89KYZ4Vt+xpq1ImJ2l9L0p8dqrZjtMls+i9vWr801dxLNDkSINepxbGvOM+4KwLcAPdni4QXEr0N+HyKPAPSZLSu/Z5qOvPk4C883VxAoHgwtBXUBZIKmoMkthT0aSqS+IjTMzJSaJwTRFRFiN3X5PMC5uLAwYgo+LC+I7XRCPFuG313IkUdbUIKTJi77GHwUE6Najy7nFWJg1JEowaFs6KDw6zp7IZgIQIM/e+9jnLpmeqI6rBMxpy7uRk1RhPr4Oxw8KxBOvOeVKCQCAYcEQ8IhBcQLSUKXqdtg9Jby/XZzJmN9DLu16HXwKgt+TM27sq1OmDOiSWbzros11xTUufio6ePij2TpfmtVU3tXNfrpWth2v8CjjLpmfy7D8OAzAjO5GnNx3yO+fGAMkq7/jM7ZZ57d5J/TL8VYpkx+va2H2ikb8UVahxz0Nvfamer+c5z3YU8kC3pwsElxK6/mwky/KnwLWAETgK5AMngWtkWd51/i5PIBAMBgJJRfdUNKkThwCfys+M7ER1EoESqJgMnn9yFAXJwSrtKQJFZQ2s3FzCK9tKmTs5mfgIE3Zn99jDtCvCWZjvqWCdavZUehSZ76uflLJqSwnf+9NOpmXEEx/hCQJqWzuwO908+s5ebs9JVM+nGPCunDOBnORI/mvkEMbGh5GbEttnkKGY7W0/eprS2lbcbrnX7QUCwbkh4hGB4MKiKFO81++rkqPVvyv05+VaSR5Mtg7BOjRUXWN7rqXK+Oiex88fE6u2LSn7HDnVohlHRJj0akywcnMJd736GXNykoiP6L7Gt4oqWDY90+feeiZ0evqgpMaGaV5bWX07r2wrZVpGPJv2VTFvipWX/zebjQtyyU6KpMHmACA4SKd5vSU1rb3GEd4q4/4kWzbtr+amldv49h+KeHlrdyylnK+mxa65b6DvqC+0fk/OtD1dILhU6bdBgSzLe4F7zuO1CASCQUqgapMSFyhVDW/3feXPiuokOEjH7+bmsLuikXanm9Vbj/LYTWl9jir0lhHrJFgwNZWlG/ZTkJVAVZOdP+0o50c3jqbZ7tQctViYZ2VYuIkXt5SonysjqpXq0yPTxpKZGHlGxm7CIE4g+GoQ8YhAcOHQUqYkRph58rYMHnu32wj/ydsySIqynPHxtdbSVXdO0GxtyUyIVJMtyj6BFLTZydH876ufaSpflDajBpuDsfGhrP3OZKqa7cRHmIm0BPHZsTq1dTopyqK25Lhl+OOnpX73rrQw9YxZFBWJ2y2r93PlkBDN67V3urlp5TZNQ+KkKItfO1Rv8UYg1c+zs8axbONBGmyOAVeenImCSSC43OhXwkWSpOjefi7Lcv3AXI5AIBiMBJKK6iT456EaVswez6HqZlJjw9RxzwDJMWbm5CT5ebG8vcsjbT12upWF+amqSe7tOYkMj7LQ4XTx8tyJNLY5sAQH0WTr8DPLTYo2Ex9h6qo6Hecn08ZqJoVGxYWx+l9HVSmtyaBj7LBwtXd5WLgJlxufAKs/AYIwiBMILjwiHhEILjw9245La1t5sSuxIEkgy/DilmKyk6LOeP3TWkvnv/EFmxbm+nmMaBn5ao08XjF7PE6XO2BbEqAa7G8rrmPFh0fUfRfme0xpG2wOVt05AUen7JPoWD4zi6+nDSM7KYojp1rYW9ms+sV4n8Nb3eGdjDhe16o5ovlkoy2gIfHquTlnFG8EKpIV17Rw9zXJJEaZzyo51hf99QwSCC43+qtwOU23MZ0W+gG4FoFAMEgJZHYXEuwxrfX+fOmtGaz6uJh1Oyt4vCCNBwP0Vr/0cQnNHS7W7axgYX4q4WaDan7bM+h54pYM3t97ko+PnAY8gVJlY7vqvTInJ0ltN+qZFArW6zhS0wqgGvrWtHiqRjlJ0WrVSEn4jIoNY2x8OFcO6d+IS2+EQZxAcN4R8YhA8BVzqtlOWV27z3hnoNfpQYEKGoHW0upmu9rWonV+ZR/vkcdZCeGkxoUxIiaE43VtmjFBXupQAFxuaOnoZNXHJT4xygubu2MUb4865eePrNtDZkKEel0/WPul5jnG91DNKskIgCXrD/gkq9YWlVOQlQBoGxIXldWfUbwRqEjmcsMLm4tZmJ9KeYNNxCoCwQWivwmX63r83QBMAL4PPDagVyQQCAYdWlLRpCgL+082qT4t4AkAFr23j9fuvYoT9TYAoixG1cgWPCa1yp/1kkfS29rh8juOd9Dz+N/28dyscXx85LTq/9Jid/J//ynnhzeO4fVPSxkZm8KigjSfpM3jBWm88VkZhXlWMhIiaHe4KOwy6VUqVSs+PEyUxegz5nGgR1wKBIIBQ8QjAsFXhJJA0UlSn+tff9tu+1pLtZI2PfdRRh57G88mRVk02570Oli52ZMomj81RTORocQo3h513j9XEh2Bxjb/4v0DLMgfjUEv+SWaRsSE+I1kVlqSQNuQ2C1rF5QCxRuBrktpe2pzuERxSCC4gPQr4SLL8r80Pv5IkqRS4D7gjQG9KoFAMGjoGexMGhEDwKb91Ryq1ja93V5ax8rNJfz+3onqpCJv5UqEOYgF+SmMjA3lydsyKK+39Rr02J1ukODFb45HQqL0dBsut5tvX3slzbYObs9JYuGbXxBlMVKYZyUp2kJ1k52Xtx5lYf4ospMicbnhGy9u86tUzZtiBdAc8zhQIy4FAsHAIOIRgeCrwTuBEmUx8ujXx3C6zYFb9hRPMhMj+pwepLWueq+l3kpTWYbOTrePd4miUg3SSfxubg6PvbeXsrp2zfW3vMGm2fa08o4JPskLrUSG3KWh02soZ5NjzJgNerYfPU1cuIkbx8axtnAymw/V4HLDpn1VTMuI9ynueCea+hr1rBgSe59z/e5Kls/M8mkz6i3eUM6R4HVdStuT0g4uikMCwYWj36a5AfgSMYZRILhkCVShGh0X1qtZndmg5/7rUrA73NidLnUMs6Jc+f29V/HEhoNqALV8ZpaPbFc5juxlyhsTYuTLE41+yZtJI6K5q8sYr6rJzsrNJZgMOuZNsVJW186j7+xV+8AD9XP3VcXSQhjECQSDii8R8YhAcN7omUDxmN+X+sQG3vS37VZZS9MW5rKrvJFH39mrHvOZmVnquGWzQUeoMchPpZoQaSI6JNhv/Q3U9mRzuNQEj9LS7B1XKAra5BgzmYkRPoWV5BgzD0xNZc7qHT73PTTMqKpm7r8uRbOAM/qBXEbG+scTCZFm/vitSVQ3d6uHexZzHpk2lhvHxpGZENHveEOnk8hMiKSy0e5zrIX5qaTGhYrikEBwATnrhIskSaHAD4ATA3Y1AoFgUBGoQvXru7JVs7qebTxPTc/A0Snz5uclQAJ6HTxekMZv/lnCnkqPIqbCS9FSVtfOc/84xBO3ZvD4e/t8goLXt5dhMuhYXJCOo1O77Wj13Il9qmMUdY5WcignOZqdZfVn1R4kDOIEgq8eEY8IBOcf7wTKjOxEnv/oSK/qlTNpu9XpJNwyarJFOebD6/bw0PWpyDJ0dLqxDjUxKjZUjSUeWbfHZxJQaW2rut4ro6V7nj8u3MTVV8aoxZL4CBPXWGM40dCOBKzeepQjNa0sn5nF11Jj0ekkdVuzQc+c1Tt8WqUPVzeTFB2nOa1Rwe50U17fxsjYUM1C1vKZWXwjI56gII+jb6BizpnGG0oya/QDuZTXt2ExBhEXHkxS9KVTHOqPT5BA8FXT3ylFLfia1EmABWgD7joP1yUQCAYBgSpUIcFBmAw6qprstNidLMxPZWhoMOUNNqoa7fxl5wm/6USLCtKo3VJCg82Byej7T09RWRMP3hDMm9+ZTGVjO0PDgjHoJIZHWYgKMXCstpUme6cqDQaPF0xVk512h6tXSbDJoMNi0AdsAbrWGkNStJnkmBCfyppoDxIIBh8iHhEIvhq8EyiBkgre6pUzbbvVijeiLEZCTAZWfOQfSyiq2ZoWOyNiQvo9Wto7eaG134PXj+LrsszR2lb2VzWrBrnWoaFsP3pa0/MtOSaEVXdOYP4bXwDabUoWYxBut8zeyka/QtYj6/YQbjYQpIMrIixcOSRkwIo5Op3EyNhQTXXNxU5/fYIEgq8aSZZ7M/vv2kiS7unxkRuoBT6TZbnhfFzYxUxOTo5cVFT0VV+GQHDOlNa2ctPKbX6Bw6aFuRyoalF7rr2nEc2fmoIsw6uf+LcIFeZZSYg085eicorKmoiPMDEjOxG9Dq5Kjsbm6GR/VTNBOh2pcaH84v2DlNW1kxxjZsHUUfzs3e6EyIKpqawtKufJ2zI4WNUScKzjgqmpZCWGMyU1Vq2EaElye/uZQCAYGCRJ2inLcs457C/ikTNAxCOCgcL75fa+XCuvbPNf4zf28Gc5k3VVK9549OujaXW4cHe9qqzbWUGDzcEzs8Zx5FQLegmmT0jALeO3b3KMmRfvmMDpNgchwUHEhXUrO5Trqm3p4J7X/qMZqyjtyd4v8KW1rbz7ZaVmC/TawslIEpxstFPX5vBR/j50wyj+Jz2O/SdbOFTdrLYfebMgPwWX2xM7DVTS4FJXfwSKUXv+HgoEWpxrPHIm9KlwkSQpCAgB3pVl+eT5vySBQDBYCFShSooOITHSwtrvTKa8oR23LPsseFou+3anm9TYMIw6mZkTk4BypmcP9wlKnrg1g1Cjntc+7U6WrNlRRkFWgppsUY61cksxz88ez2Pv7uOOq5JU9UuIUY91aCgzJyaq4xYnJo8Dem8BEu1BAsHgRsQjAsHZc64v396+ZfVtHaTGhmqauHqfJz7ChCxDP2q7fvFGcoyZyJBgVny0z6fQsmZHGSU1Laza4kmIjB4WztAwo0/MER9hYk5OErN7eK0kRYf4JY60JikqCZ6erVIjYkIYFRumGd9sPlRDXLiJ1VuP4uiU1ZhEJ0FmQgSdLnr1vnO5UZVDfZn294fLQf3RX5+gwcClnvwS9E6fCRdZljslSXoW+PsFuB6BQPAV470oxIaZGDssjF/fle1TIQJ8JgcszE9RA4h1Oyt4vCBNM6CIMAdxqrkDZ6eL738tlfvf2OWTRHn8vX0U5lmZOzmZNTvKWNk1YSA4SKe5qBbXtFJW106bw+VjjLcgP0UNxhbmpxIXHnwBnpxAIDifiHhEIDg7Burl27swke2W/UxcAZ9JRj2nFK66cwJXxoRS0+L/0ultnnuquYPmdif7Tjb5mO6v3FJMYZ4VV1c4oCQn1hZO9ok5ZmQnBpw8CKjPITRYrzlJ0RvFB846NBSdTmJsfHjAhMnSDQeYN8XKSx+X+MQk11hjVON+Le87RbFbkJWgnvNckwbH69pYvumgTyv28k0HGTMs7JyTEYMleXAmPkFfJZdD8kvQO7p+brcDmHg+L0QgEHz1KIvCTSu38c3ffcY3XtzGpv3V/Oydfdzz+/9woKoF8DfT/fhQDYu6kixVTXZe/eQoT96Wgcng+SfGZNDx85vTWfTePn781z2cbnOwu6JRM4nilj0jmmdkJ2J3ugkO0pEaG6oeS8Fk0HHlkBCSY8w+1TOTQceo2DDmT02hMM9KalyomiQSCAQXPSIeEQjOkEAG+Mfr2s76mEryZbJ1iJqMKK9v41B1M/flWllUMJY3Py9XzxllMVJ8qpVvvOiJL25a6YkvOjvdlNa2sv3oaY6dbqOktpV7XvsP31mzk5e3ljJ3cjLxESb1upOiLLy9q0K9DrvTrU4eUuKEQCrbmha7jyqi0yVrmvG73N1Bhcd/Re9JMpxupdHm4MnbMn3imwVTU3l7VwV2p2fyoTeKUa+SHKhqsrP2P+WsmD2eBfkpzJtiZW1ROXNyktT7Mhl0SHhamNzufsiDNKhr62BOThKvflLKqi0lvLKtlDk5SdS3dZzV8RR6xonK93i213kuKKoo7+9iMPrvnY///gQXF/2dUvQ74DlJkpKAnXjM6VRkWd410BcmEAguPFqLwgubi9WKzfJNB0mINFHVZOe+XCvrdnqCg2kZ8azeepR5U6zodTB2WDjOzk4K86y4ZRgTF8azHxyirK4dQJXrBjK7VUz5TAYd44ZH8Ph7+1gwNdXPhPe5Dw7x/76Wwl+KytX9l8/MIiMhnCFhRuHFIhBceoh4RCA4Qy5E64XbLbOrvNFnVLTSAlTVZGdGdqJfcmP5poM4XW6f1qSF+al+qhYlBjEZdNS2dlDVZFfPqygavCcPmQ1Bmj4rivJBiT3snW7N5+LoktAo9+Bwudly+BTFp1p5YXMxURYjz80ax5GaFlxu1Hs0GXSMGdatgDEZdCybnqkmAJZNz+TRd/ayp7KZ5ZsO8si0sZTUtPLUbRnsrWxm5sRE9BLEhBj5wdovabA5zloJYdTr/FQ+K7cUs7Zw8hl+s74ESh6cawvU2eDd5jaY/fcuptYnwfmh14SLJEm/xzNq8Y2uj1ZobCYD+oG9LIFA8FUQaFGQJMhKCOd7X0th86EagoN0hAfreeiGUYQE63nord3YnW5VQuttOgcwf2qKmmyJjzAxOi6Mqkabpqx2zQ7PKOgxcWE8P3s8J+raKKtrZ82OMlUaK8vQYndSVtfO4r/t55lZ47g2pYWc5GiutcYQFKRjxBCxiAkElwoiHhEIzp4zab0423aR43VtfmOdvZMlWpONCrIS1GSLso93kUf5TCnALL45HWenyyehsTA/Fb3Ot93J7ZZZMXs8yzcdpCArAbNBx8TkKE63dmAy6Pnl7eP44V92q8+h53NJ6VLJKj5w/5USw56KJjWJU9Vk56mNBzXbkX77zxIK86wkRVmobe1gVJwnFtHpJLKTItVClCx7WpCMQRJDQo0+x3nw+lHqvZ9tMsPmcGnGczaH64yO05OBSB4MZEvSxeC/d7G0PgnOH30pXO4BfgJceQGuRSAQfMUEWhQizUHMmZTk03+6MD+VFR8e4facxICtQVkJ4dyXNxK3W2bVnRNYV3SCySOH8OO/ehI0OckRvHJ3DnVtDvQ6idX/OkqDzcHC/FSe2niQBpuDRQVpJMeYKatr90nozJtiVc9l1EvcNj5hUFY2BALBgCDiEYHgLOnviOaz9ZpQXqC1YgGlxUYv+Sc3ArX+SF6nUgow86ZYaWp38MdPfYsvr28vY0JSpE+RRaeTuHFsnJ96RvFKuXNSMg9dn8qIoaEsn5nls83SWzP4ZZciV7l/h8sT03hfa1WTnde3l7F67kSKyhow6nXoJbj72hFUN9l58eNi5uQk8fO/7WNB/mgMeonYMBNZiRHMf+MLn+fbUzHy/EdH1KTT2SohAsVzceHn9pJ/rsmDy9HP5ExHpAsuPfpKuEgAsiyXneuJJEm6H/guMKLro/3Ak7Is/73r5xKwGCgEooDPgPtlWd7vdYwoYCVwS9dHfwMekGW50WubTGAVMAmoB14Glspe868lSZoJLAVGAkeBn8my/I7Xz/u8FoHgUsPtlpFleG7WOIprWnirqEJNfowYEqIGCOBbhXLL2hWiYWFGvjkpmYe7kismg44nb8vgxS6Ja3yEialjhnHf60U+gU5DWwevfVqmSoaXbjjAi3dM4IE3v/AJmtbsKFPPFR9hGtTVDYFAcM4MWDwiEFxu9Lf14mzaRZQX6MPVzb2qRUKMep6ZmcXDXsmNscO0DWiVy1JeTI1BEq9+Usp9uVYabA4fQ9pAL/sVjTaO1rZyX66nOLNuZ4WquFESGgv+7ws2LczltXuv4kS9DbMxiPW7T7D01kwMeklVXxyva9NMGDXYHCREWkiMslDf1oFBr+NUcwfRFiOP3pTGm5+VMXXMMArXFPm8aL+/IJf9Vc0cOdVCSU1rr0mns1VCBHrJ10mw/ejps1aWnGvyYDC1JF0oLpbWJ8H5oz8eLgPlglQBPAIU4zHrvQd4V5KkibIs7wEeBn4I3AscBh4HPpQkabQsyy1dx3gDSAKmdf39FWANcDOAJEnhwIfAVuAqYAzwGp4e7192bXMNsBZPQuVtYAbwF0mS/kuW5c+6jtufaxEILhr6km96VxyiLEZuz0nk4WmjMRn0/ObjEtqdsQEDgnU7K3jw+lE8/9ERdfF96rZM4iNNfPsPn/ssqo+9u0+t2mhNEVj0nufn3v3ZdqebZruTlXdM4FB1CymxoSzfdFDtl14wNRWny/faBALBJcmAxCOiACS4HOmr9aI3lUpvCgvlBTrKYvTzWluYn8rTXWrVZdMzGTc8grXfmczmwzW43PCbf5b47fPMzCwSIs1MGB5JUnQIVw7xvMhvDDCOetWdE5Bl3yQCENBPRmltkiSPke/eymaKa1pwyx4Vzn+NjGXRe3t57d5JarLlVLOdSVdGExNiZNn7h3wSDcr1Hapu8VMB3zU5mfn/94VfcuH1b00iPT6cjCvCOdXSEdDP7lyUED1f8oeGmjhW18q0F7adk7LkXJMHg9nP5HxOX7oYWp8E54/+JFyqJan3XzZZlvvsmZZl+b0eH/1MkqTvA9dIkrQXT2/2L2RZXgcgSdI9QA1wJ/CyJElj8SRapsiyvL1rm+8C27oSIYeBuwALcI8sy+3APkmSxgAPSZK0oivI+QHwsSzLT3Vdx1OSJF3X9fk3u4KbXq+lr3sVCAYT/ZFvegdMcycn+wQ/C6amotP5V3aSY8yMigvjm5OSGDk0hIeuT6W5w4VOgla7k5ONcq/yYq1+7kAO/2X1Nox6HSs+PEJ8hIkZ2YlIEkxMiuSlj4uZljFs4B+cQCAYbAxIPIIoAAkEPvSlUulNYaG8QFc12X281nJThhAbFszwaAtHTrXw7D8O02Bz8Nyscaq/G0Btq4N5U6yMS4zAZNCz6L29Pu08Vw4JCTiOeli4iQNVLXzjRd8kwui4ME0/GWWktJLQuP9rVoK8Xqjf/bKSO65K4o6rPNN8eiZRnp6RxdrvTKbe5lCTQTqdZ5qQ1sCBJbeka8Y5nxw9jdmgJzUulK+lxvopRpbPzCI52syNabHYHC6O17X1++VfK2lgHRpKaW2rn1L5bJUl55I8GKx+Jpdjq5PgwtGfhEsh0DiQJ5UkSQ/cDoQCn+LpyR4GfKBsI8tyuyRJW4Fr8SQ5rgFau7ZX+Dee4OVaPIHINcC2rmSLwj/wVI9GAMe6tnmxxyX9A5jf9ef+XItAcNHQH/mmEjBpqU5Wbinm+dnjVZM5u9NNcoyZ7/13ik+70IKpqazbWaEqT169O0dzUc1KjPQZ4Rfo5z2rUrfnJAKevmllWoEuz8oPbhgt+mAFgsuDAYlHRAFIIPClN5VKXwoL7xdo7/V5xoQEZOBHXXGDQnFNi8/aX9Vk59VPSllbOJk5q3f4xSqjH8hlZKyvP4vysq+V6HjorS/59Z3ZmomOETEhNNoc/PL2cQQbdDS3d/Kg1wv2gqmpvPl5OT++cQwGvc7v2D99ew+r5+bw36NifV7CA6k2LMYgzTjH5YYXNnsSQNYhoX6KkaQoCx8cPHVWXjqBkgaDRVkyWP1MLsdWJ8GFoz8Jl/WyLNcMxMm65LXbAROe5Ml0WZb3SpJ0bdcmp3rscgpI6PrzMKDWW4ory7IsSVJN18+UbSo0jqH87FjX/2udx/sYfV2L1r0V4gkGSUpKCrSZQHDB6c8iqwRMgVQnLXYnxiA9L94xAXuniyCdTg1SlG28pxFEWYw43C6evC2Dx97dpy6qP785HWSZwjwrFqNec0rRyUabj4v/mh1lNNgcARMxo+LC+v0szqdcVCAQnHcGLB5REAUggaB3lcpVI6J7XSd7e4H+7FidX0zxVlEFy6Zn8sZnx7n7Wivtjk6Soi24ZW1V7MHqZlVJEui6e+4TpJcCDAAwqMqXBfkpPqOjveMYvU4KOOWnqKyexCizz0t4INVGVaONp6Zn8rOuc3rHLnanx4hXicW8FSNKIinKYlQVvYerm0mLD+t1AmOgpMHoB3IHjbJksPqZDJaElODSpK+Ey0D5tygcBsYDEcAs4I+SJH1tgM/xlSDL8mpgNUBOTs5APzfBZc65JAr6s8iOiAlh+cwsSmtbNbeta3PQ2uFCr4OshAhaOzoDerrER5hYcks6h6paefPz8q7gBbISI4kJCWLxewf42phYosxG6m0OfjV7PJ2yzJFTLaoRrlZbU6BEzJFTLaTFh/e5IAq5qEBwUTOg6+qlXAASCM6U3lQqfa2PgV6gASxGPQvyU3DLqArYBpuDnBGRuNy+pvpLb80gJzmCorIm9dgmg67XNT5QfHOoqtlv+tCigjSWbNivbttz6hB0tzWnxoai02knbVxu/F7CR8SE+BkCL8xPxWTQk5McydrCyWw+5PGtWbOjTFUC6yQYGuqf8DjVbNds8U6OCSEpOnD8FyhpcLC6mWlpw9TEmOLVNyo2DFn2xEcXMg4ajH4mgyUhJbg06deUooFClmUHoDRu7pQk6SrgQUCR08YB5V67xAHVXX+uBoZKkiQpQU6X3Da2xzZxPU4b5/Wz3rap7rFdb9ciEFwwzjVRoFV9Wj4zi6Qoi7qNTieRFG3GZNCz9NYMFr3XrUpZcks6jk5PP7LSTvR4QTrJMWYKshKQJDAbdBh0ElEhwTx+cxput6xu7z3K+bf/O5EmuxNTkJ4/f1bG18bE0tTuxOlykRwTQoPNgd3pZm1ROc/PHk9xTSsdnW7W7CjDGCRRmDfSTxGzZkcZ146M6XPhFnJRgeCiZqDfBi7ZApBQ3ArOlHNt8+j5Aq0VtygjmR+ZNpaGVqcaZ0C3af6r91zFvD9+3u81PinKwpO3ZfLYu90Kkp9OG0NLRydmg461hdfQYncgSRIdLhdlde0++2u9YOckR6vn6pm0Ue5hZrZvvlOnk7gpI57oUCMnG9oxG4NoaOsgNS6Mk4124iNMjBzqa/a7MD+VEKMevc6/qBYbZuL2HP8W70ff2cv44ZEBY5ZASQMlaTUtfRhpC3PZVd6oKn1E8cnDYG11Elwa9JpwkWVZ19vPBwAdEIyn0lMN3AB8DiBJkgnIBX7cte12PJLfa+iW8V4DhHj9fTuwXJIkkyzLypiTG4CTwHGvbW4AnvW6jhu8jtGfaxEILhjnkihQFvFIs4HX7r2KvRWNNNldrPjwMAa9Tl1g3W6Z8vp2Vnx4mPumWFUliU6CIL3Er//ZLbstyErgN/8s5nt5KWq1SAkeVnx4hAabg6dnZAZoTerkxzeOYfXWo3w9M54XNhcTZTFy9zXJ/O6TY6oaZuywcFrsDn79zxLfQOc/5RTmWUmIMGMJDmJZ1/SD/lQg+pKLinYjgWDwMtDxyKVcABKKW8GZMtBtHlpxy8otxawtnExmQiQfHKjWXI+b2j2mum2OTsJMQZxu6eD2nETMhiA/FUZnp5tPS+sINwepMUuIUY+9082qj0t8YpPXt5cxOyfRJxmxbmcFC/NT1eKQyaBj2fRMkmPM6jnS48N5fvZ4XLLMiXobb37uSRhpvYQHBemYPCKGA8FNVDfbSR4Swt6KRl771KPGXXnHBOZfl8LQ0GAswUFUNNj4zb9KSbsinANVvua8q+6cwKjYsDNucRkRE8Ky6Zk+yZSeSSu3jJ+h8PksPl0ssdVgbXUSXBr0x8NlQJAk6RfA34ETQBgew7evAd/okuL+CnhUkqRDwBHgMTwy3zcAZFk+KEnSJjyGdYVdh30Z2NBlUEfXtouBP0iS9CQwCvgJsMRL+vsCsFWSpJ8A7wLTgeuAKV3n6fNaBIILydn2lQaqML29yyPrVRbYETEh7K1s5JF1e5g3xcpTGw/6VUcUbxbwTBe62jrUR5prd3oUMMp2x0+3aVZZTjW3kxBpoTBvJD/qkhLPyE7UVMOsuH0cS25JZ2hoMIdPtfD6do8Md09lMwvyU3C5ocHmYMXs8SRFWSitbe11Qe9NLirajQSCyx5RABJc1gxkm0eguKW+zcFnx+oYGhqsuR5HWYx86w+fqy0vSdEWgjs6WfDmLh6ZNpb0K8KoburA4XJxusXBT9/ZqxZtlBjk1U9KNWOTt4oqeOiGUaz48Ah2p5sGm4MQo8eb7vCpFkbEhPBUVxFn1Z0TcHTKPjHBsumZ/PFbkwK29Ljdsp/R7cL8VL6XZ+W3W0vZf7KJl708Y5R71jLnnf/GF7z13WvOuMVFp5PITorUbL9W9ruQXiUXW2w1GFudBJcG51vB4s0w4E94ZLyb8YxJ/Losy+93/fwZ4HngJaAIiAdu7DH28E5gNx5TuX90/Xmu8kNZlpvwBCZXdB3jJTzjF1d4bfMpcAeeEYt7gLuBOV4jGPt7LQLBBUFJFHjTn77SQBWmGdmJ6t+b2x2s33OSzYdqsDvdBAfpNBfinqOa9Trt3mdlYutbRRU8XpDmM41oyS3pmIJ0RIUYcMky9+VaiY8wBTTqPXSqhUfW7eX/vbEL2atGazLoyE6K4rrRQ9i4IJcbx8bxwcFT3LRyG9/83WfctHIbm/ZX43b7FnYVuaj3NSly0UAqouN1bb0+Y4FAcPEhSdIvJEnKlSRphCRJmZIkPY2nAPTnruLMr4BHJEmaIUlSBvAHehSAAKUAdE3XeGetApANTwEoQ5KkGXgKQCt6FICmSpL0E0mSxkiS9FM8BaBfdZ2nz2sRCAYKt1umtLaV7UdPU1rb6reG9ne/zk636t0yf2oK8REm4iNMLMhPoaalg38frePlrcU8cWuGz3q89NYMfvnBIaIsRr6XZwXgeJ0NR6ebb197Jb//5CiflNRxz2v/YUdpAz/tUmlUNdl5fXsZhXlWUmNDA8YmVU12Xvv3cf40bxK/uSu7S0nj4smNBwg3BXG8ro2ZExO5L9dKaY3/9KNH39nrUf4GSBRoxREvbC6mzuZgRnaiahbcMwYJZM7r6HQFjFl6Iyk6hDHDwnllWykvfVyiFqaU/c42pjwbRGwlEHi4YAoXWZbv7ePnMvDzrv8F2qYB+N8+jrMXyOtjm78Cfz2XaxEILhRn21caqIoxKjaEF785gY5OFzaHm0fW7eG+XCsmg44rh4RoVlTGDgtXP1+/u5LHC9I1t1NeIxpsDpranRTmWRk5NJSTje38eUcZX8+M597XfPuzdTrtPmqXu/ualckBr35SyvKZWUwZOYSgIE/AEGgsZE95bG9yUeFOLxBcVigFoGFAE57iy9dlWf5H18+fAcx4ii5RwGdoF4BexFP8Afgb3dOFkGW5SZKkG+gu3DSgUQCSJOkO4EngCeAo2gWgvq5FIDgnzlaJ0HO/5BgzP785nf0nm3HLsH53Jd/Ls9LhcqvKEmXt/+jASf4072rqWjsYFmHCLcsUlTXxyLTR2JwudYKQYnj70I2j+enbe9UEiveaXdVkZ+XmElbdOaHP2CQ6JJjspGiO17UxJMzIpBHRFJU1qG1IyTFmFhWk8/++loLD5VbNfvuKCQLFEW7ZowxusDnITopk44JcTjXbsRj1OFxujHq95jUr16kVs/TWptNXa8yF9Co5m9jqYmlBEgjOhAuWcBEIBGdHf/tKey5S8RH+LTTJMWZaHS6Wbugei2h3egKKBVNTqWq08eD1o3j+oyM+gc5v/lmijomUZfjT9uN+RnVKn7SyT0yIER0ST71/gLK6du6/LkX1bLnr6iSGhgYTYgrCZNDx6NfHsOz9Q349xwp2p5v0K8LY2NUC5X3vZ7KgB5KLCnd6geDyQRSABAJfztYr7tjp7v3iI0zMyUni+3/epa7lD14/ivhIMwve/MJPbTtvipVOt5v/yYgHUKckJkZZ+HFXy7Gy/dINByjMszInJ0mNDbTW7LBgPU/elsFj7+7TjE2UxIJ3LLD7RIMa8yj3MP+NXX7xSF9+cYHiCCVcWTF7PEnRnqTGoeoWvvWHz9UET89r1rpOhf4kx3prjbmQXiVnGltdbC1IAkF/EQkXgeAioK++0kCL1Ko7JzD/jS/Uz34ybSwPegVVbtmz+FU12Vmzo4xHbxrLcx8c8jF2CzboOFLTyp7KZuIjTMzITiRzeCTx4cH87u6JVDXaabY7cbtlHp42GnOQnqAgiSCdRE2LQ50KIEkQZTFy77UjfBI6C/NTSYwy8/L/ZtNs7yQ2zMTD63ZT1WRX789k0BEaHESLvdPPOG8gkiXCnV4gEAgElytnq/Isq29T95uR7T9V5/mPjvDcrHEBW5W912llHW61OzW3T4g08+t/ljAjO5F1Oyv8ikML81Mpr2/nvS8rWFs4mXani6GhJvQ6mJAUGTCx4N3So3UPK7cUU5hnZcyw8F794pKiLKyem0NRWb2q7rnjqiSSoi2kXxGuer/0VOWW1bXzYpehcLvT1WcCZCAmLl4or5Izja3ENEnBpYpIuAgElwCBFqlX7slhYX4q0RYjQ8OC2Xey2SeQUZQtK7cUU9Vk57kPDnH/dak87jUW+qfTxvCzm8YiyzImg57H/9Y9mWjB1FS2HKrm21NGUlLTyqkmO1nDI9hV1oh1aAiy26UmQ0KD9fzsprGqWa5ynS9s9gQzk0ZEEx9hotHm5I6rklQ1jGKct7eyibeKTvDA1FRuG5egthQNRLJEuNMLBAKB4HJFq3CRHGPGYtCz5dApQoxBhATraXe6iAkJVtfHEGOQul8gP7Y2R2fA8csjYkJ81LkR5iCMeklz+8rGdubkJBGk87QQScg+5rCvb/eoUOZNsVLVZGdUXJh6nSOG+L+sK+fVSVKf95CZEMF/pwz1M8VdNj2TCcMj0elgZ5nvqOVl0zOZmBzJ8Ki+Vbllde20O11Mtg7p87u6mFqgzzS2CnRvp5rt6s9Fm5HgYkQkXASCS4BAi9SX5Y3oJInF6/dzX64VveQrw61qsvPliTpeuSeHqkY7w8JNrNlRyrOzxnH4VAuyDP88XMO0zHgqG9vVnmrl+Cu3FPslOxbmp/Lnz8ppsDlYemsGj359DK/++xgSEkdqWgL2OO84Vs8r20pZmJ/K+3urWJifSrjZwNINB3wSPC9uKWZETAjZSVHodNKAJUuEO71AIBAILkd6Fi6SY8w8MDWV2at3+KztFoOe3396jEemjeXGsXGEBOtZVJDG0g0HAO02n9qWDrWwoxxr+cwsrrXG4HbLfFpaR1FZPcFBOgw6icRoi1+LjXdbz8tzJ7IgP4X4SAvLNh5kRnYikgQzJ3qUL3qdp9XpQFUzo2LDGBsfzpVDfGMCb1VwlMWojocOdA97K5sIDtJrGukW5lkZOyzcb9Tyo+/sZeOCXL9YRDEQVjyJ1+2s6LNdyZuLrQX6TGKrQPfmdMnctHKbaDMSXLSIhItAcAkQF24iOcZMQVaCOilo/e5KkmJC+OUHh5g3xYpRr2PssDAfv5TkGDM3jxvOf455JLAVDTb+a2QslY02XtlWSpTF6DGre+tL7su1aiZLDlU3+wQZ3uOhF723j/nXpfDDG8fw8F93q+a8Wj3OLrfv/q0dLnVctHJspe+7tLaVmpYOdcEVyRKBQCAQCM4O78JFfVsHIPG/r36mqUYtyEpg+aaDOF0e0/0oi7Gr5SYsoH8KwLwpVvQ6yEsdSnZSFADr95zkkXV7fLZfuuEgxiCJl+7MZv/JZjo63azZUaa2Ge8+0cTKzSWs+fZV6jho7/3TrgjjUFWrj+nusumZZCdFqm093qpg7ylHk0ZEkRqb5XNN3p5ygQpGB6ubNX/WU3XidsscqGph9dZSVcH7wxtHER9hIinKEvD78VYBxYaZ/NrFL5UWaC3F8vKZWSx6zzeZJdqMBBcbIuEiEFwCJEVZeGBqqk+gs/TWDBxOJ3NyknwqS0/elsHv7/UoWhKjzOwqb/QJTBbmp2IK8gQZ9k6XT0Klt2lCCoosV/mzvdPNkVMeZYt3C5N3u9CQUCPl9TbiI0weqbDX/j2PrdeByRgkFlyBQCAQCAYInU5iREwIh6pbfNZ9BSW5IElQkJWgJiWUCUFK/FCYZyUpykK7w0VMqGedd8uglyAlNpTxiZGql4lyDMUfrt3p4tGbxrJs40Huf2MXhXme4o2CyaCjo9MTq1iMQbQ7XdyX6xkhvW5nBS9sLuaFOeNVbxfluhUlyphh4UxLH+anClbu4c3Cq/lGRjxDQo1sL61HllGTPYrnXc8YSJbBjfbPhob6qk6URE+Uxcjcyck+sVkg1UYgj75NC3Opbr40WqC9E0pp8WH8/YFcals991bX1qF6ASoM1hYqgSAQur43EQgEg53yBpuabAHPYrTovX0Mjw71M4B7cUsx9a1OLMYgZFADlvgIk1rFGhJmYs2OMhIizWqQoSRLTAbPPxtKUmfDnkqfa/Eewdjzz4o578L8VOZPTWH11lJ+9Jc9fO9Pu2jrcPG9PCvJMWbV1V85l/exxyVG8srWo+qCKxAIBAKB4NxREgLKuu+NokaVZYgw6Zk3xcr8qSnMn5qixg/RFiNuGU402JCROdlkZ/XWUlZtKeHlraU4XTIVjTbcbllNesRHmJg7OZlXPyll5eYSfvzX3cyfmsKo2FBGxIT4xBwLpqbyWWktf/z2VRyvt6nHfmVbKXMnJxNlMdLS0amZLArS6XjorS85Xtemtq70vL9h4SY+OHiK/3S1OL/0cYmqrFm/u5LlM7P8ruftXRWs313JooI0n589XpBGkN73+Sr3PCM7kbVF5eozvC/XyvJNBzle1xbwO+mp8HDLMNk6BOvQ0Is+2bJpfzU3rdzGN3/3GdNe2MbhUy1MGhGDdWgoMSHBmt/VYG2hEgi0EAoXgeASIJCHS4PN4fO5MvLw2Q8OMScniQff6q6uLCpIY+1/ytlT2Yyto5MGm4Nwk4H1u0tUVcqaHR7Z7ZVDQjxqlEYbhXkjfXxWvEcwLi5I57dbS3B0ymqPdFWTnXanf7uQIldeemsmSdFmTrd2kBob6iPtXXprBn/afpw9lc1iwRUIBAKBYABRYolAU4AsBj3r91TyzatHsOKjboPYBVNTWVtUTmVTO6u2eNQuq+7MVscrg7/SZHRsKCaDTnMy0NINB3h+9niCDRLv/L9raG530Wx3MiTEyMihITS0OflZD88UZZpQXJh2i3XK0BC1UDNpRIym2b7LjapA6ek789ANo/l62jAyrojgYHUzR061qL4yiwrS+Gh/NStmj+dQdTMuN7y89Sgmg97HNFdJ9ISZ9H7q4wVTU6lv6/BTbVxMJrlnQ1+TicQUScGlgEi4CASXAIGMxixGPavunMDqfx1lT2WzGtjMm2LVDHBWzB7P8k0HiTAbeHbWOF795Cj3fy2Vl/5ZrPZfjxkWzm//WcI9147gh3/ZS3KMmT986yo+PVqHUa9DL8Ej08ZQ1dTO//2njB/fOIYjNS0AzL8uBXunm6Roi2YAkRBhxuboxC1DdlI02UmQmRDBqWY7TpfMovf2UlbXLhZcgUAgEAgGGCWWqGqy84dPjzP/uhRiw4JJiDITYTZgd7oYnxTJnC4zXehOdjw/ezxPdJnn2p1u9lQ0BmxLeuitL3nt3qtYmJ9Ku9Olud3R2lZGDg3l+Ol2DlU381ZRBbfnJGI26APuMyouDJ1O5nt5KSzZ0D1RcXFBOjqpWxkRyGz/s2N1apvUmh1lzJtiRZJgdFwYz31wCINex7T0YVw5JIS0+HCuHRnD0FAT7c5OTjXbfRIHAI++s5fxwyPVxIiSPHC7Zb+JjSu7RkMH+k4uFpPcM6WvhJKYIim4FBAJF4HgIkbpe61r62D5zCw/87lH1u2lweZg8c3p8FkZel23x0ogA9yf35zOo+/sA+B7eVbiwo3cOj4Bt+wxtl264QANNgdVTXZMBh13XJVEc3snKzf79lm/ek8OI4aEEKzX+fwsPsLE413S254BRGVTO4+8vdenn1kxw3W7ZV67d5JYcAUCgUAgOA94qwmqmuys+riEFbPHc411iLrebj96WjN+KK5pVdtv4iNMpMaGBfQ8sTvdbC+t4y9FFTx601jN7axDQ3nQS9WwYGoqOh0MCQ3meJ1Nc5+xw8Kpt3VQ1dzu4+2yZMN+Vs+d6FOo0TLb905uVDXZeeljj1pn3hQrZXXtPsoL733dbpnjp219KlGU5MG/jtRobmtzuHr9Ti5FhUd/EkpiMILgYkckXASCQYi3gVhcuHZyoaeRWnKMmdVzc+hwuth7sonXt3e7+i9Zv5/X7r0Kp8vt02OsZYDrkmXV5C4+0sSLW4q5IS3eR1r85G0ZGHQS86ZYeX17GT/IT/W5tiiLkfo2J8U1LUy2xpAcY1ZNz2ZkJ/KLTQf95LqLCtJYtcWTmNFyoRcLrkAgEAgE5w+dTuLGsXGsLZxMVZOd+AgT6fERPvFHoBfkTrfn74ony3MfHPJb55WJP0q8UdVkZ9lG7Xhg+aaDfgqQZ2eNI8IcxItbiv32WTY9k+GRZr440eAzCEA5p83h4saxcb0WarSSGz2nFGm18uh0EmPjw/ulRNHpJJK7vGl6bhsX7q9aOVeFR3/iya+SSz2hJBCASLgIBIOOQI70Pd3rj5327Xstq2uncE0Rz88e76MoAU+QcKLexvMfFbMwP5U3Py9nUUGaj/fKgqmpbDlUjXVoiF+w8vc9J1VprSyDQSdRXNumVn/qbQ71XPERJu6+Jpkfd8llV28tZckt6fz6nyWU1bWj13mu1VuuK8vQYneqCSLlmi+VHmWBQCAQCAY7brfMBwdP9Rp/aL0gPzMzi0abw8+TZdO+Kp6fPR57p4tjp9tUz5PlM7NY8eFhALV9pzDPSlK0hWOnbbTZnZqTaSob27EYQ5iTk6Sazup1kHlFBJEhBj4vr/cbIKB4uxw73UZ5g63XmMI7uVFW18YXJxp9RlL31spz5ZD+Jw7ONMlwtgWn/saTXyWiZUhwOSASLgLBIKMvAzGFsvo2P0lqlMWI2ajXrJyEBgcxIzsRSYIf3ziGUJOeVXdms6eiEZcbthyq5sfTxvDvkjofKa7i+aKMZjQZdDw3axyy3D2pKMykZ0F+Cm4ZxsSF+fUmL/7bfp6bNY6QYD3RIcGs3lqqynWVYxbmWX3u5VLqURYIBAKBYLDTM/6Ishg5VO0xqR8RE6K+CPd8Qa5r6+Dp9w8xb4qVpCizOn1oWkY8D3aZ0N6ek8jPvjGWscPCSY62YNDr1HM12ByYDXpMBj2j40KItBjVOEYZGa3XQfoV4RyrbWVtUblqims26DlW18aKN49wX65Vs1UnKdrCLz84woSkyF6TFt5qkKToEFyyzOqtpQD9Sor0N3FwoZIM/Y0nv2qEgllwqSMSLgLBICOQgVhZXZvPghxiDPJLrNyek8jiv+3zk9o+eVsGkSFBpMeH4ZIhSC9h0Ov4479Luf2qJCrq2/jm1cmU1LRpSnH1XRP5PE79o4gONTB2WCjPzBrHht0nyB8br+63ID9F2x/mVAv/NTKGzIQIzcqOMUhS72cwSEoHuwxXIBAIBIKBxDv+UFqDvGOJp6dnEmEJIibERHp8uM8LcoPNwUsflzB/aoqf0qWqyc7KzR5F7N8fyKW8wUZcWDCFeVbcskfl+vp2j/pl3hQrb+86rE4+6jnN58HrR3HfFCtPbTyoxhxK/AHa7dLVTXYabA7iwkyU1rZqruuB1CCbFuZS3dy/pMiZJA4uRJLhUp9wJBBcLIiEi0AwyAjUH/3FiUbanW5VChoXHqyOWlaCg6Roi2a7TkyIgd0nmn22feiGURSMv4Llmw5SkJVAq8PmE7R4S3FHxYbx3Kwsals7GBoWzKmmdp75h2fE8/3XpbD4b/vV/dyydsCjk8DpkgE0KzsAGweJpPRikOEKBAKBQDCQeMcfd12d5DfN8Kfv7GXeFCuvfvIFT96WwW3jEggK0vm0yKzbWaFOH4qyGFVlLXhUsweqmvjxX/dwX65V9W3zRpI8bUab9lXxxK0Z7Cxr4IGpKegkiTaHC5ujE6NeUmOcsfHh6jWu21nhV3B6vCCNl7ce5dGvj2FvZRMPew0X8F7XA6lBNi7IZbJ1yDk/285ON/urmrq8ccykx4cTFKQ75+P2xqU+4UgguFgQCReBYJDRm2lbg82hSkGTokNIjQtVK0Q6yVORUkY6erfrjB02Tk22gCeQWPHhEQrzrKosN0in06yEJEVZeGrjQbXy9NO391KYZ2VGdiIvfVxCcJDvfloBz8L8VEKDg1j03l5eu3eSn8O/wmCRlF4sMlyBQCAQXD6ci/KyP/sq8cfyTQeJCzdpxgTKlMPH3t1Hamwo44ZH+ZntxoYFo9dJmA16n0LPwvxUqhrbsTvdmA06n2RAfISJb12bTGpcGC/PnUiL3cl31+z02XfdzgoabA4WFaSx7UglXxsTS2hwt9rX2w8mIcJMZVM7I4ZYKMhKoNneybL3D/mt6zHfmuQpJJ1HNUhnp5t3d1eq/jKK8lhJWJ0vhCGtQDA4EAkXgWCQofT2xnxrEttKTiPL+Ji2KYu/TicxdXQc1iGhqiokMcLMk7dl+CzqiwrSKD3t7/did7pxy6DXgcWoD+iaX91sV8+tBFpuGZKizcyfmkJKrO9+VU121haV89q9V9Fgc2Ay6Ck73cav/3mUqib7RSFlFTJcgUAgEAwmzkV5qTXVcOmtmRj0kk/yRYk/EiJNbDlUE3CsM6AmOMYN1zbbXT03x6/Q88LmYuZfl0J8hIlQY5Cq0h0VG8o9/zWCmuYOvv/nXV0qmlK/fRU/uaUbDvD87PE8+NaX/KWowkft22BzYArS8+t/lVCYN5I9FU28vauCH1yfqrmubys5zSvbSvnd3JzzpgbZX9XkZ+brnbA6XwhDWoFgcCASLgLBIESnkxgaFswr20p7Xfx79gCX1rby5n/KeHnuRE412TEbg6hrteNyuwO2+YwZFk5ZXRu/eN9/NOPjBWm8uKVbKaMY5eokKK9vZ8OeSq4akcHSWzOoaLDxVpGn+lSYNxKdBL//pJSisqaA168w2PxShAxXIBAIBIOJc1Feeu8bH2FiTk4ShWuKNBM3Op2EzeHirSJ/tar3iGSTQUeExaCu3z2vraisXjPBcUWkmUdvGutpeW6x88QtaQwJM/HliUa1rVkp7vTcV2lNsjvdHKxuVpM+r2/vnnJkDNJzstHGHVclEWMxYtTrePzmNMpOtwVMINmdbh57by/LZ2bxSI+Wo4FQg1Q1aRdxqrsSVucTYUgrEHz1iISLQDBI6Y8U1O2WOXa6jbL6NkKDg+h0uZg6ZpiPDPen08YwYkiIn9/LQzeMYsSQECrqbcRHmLl5XAKb9lUxb4qV4CAdI4aEIMtuZk5MZP3uSnUM48L8VCwGPet2VTAnJ8nnXE/ckkGjrYNVW0posDl48rYMaluLKatrDxi8DEa/FCHDFQgEAsFg4lyUl977epvZKsd46K0vGf1ALpIETe0OgnQSt+ckotPB/OtSCNLrSIkNZemG/VQ12TEZdCy+OZ2jp1qICzNpXlsgP7fjdW2qge5DN4zCGmGmqKwBt4zftoHUNSaDDpcbdYKRJHnOd7q1g1+8f5j4CBN3X5PMD7zW8J9OG8OD14/i+Y+OaCaQyuraSYg0nRcvufgIs+b9DIsQRRyB4HJAJFwEgkFKX1JQLYnwsumZfoHU05sO8cIdEzAF6Xh57kTaHS4sRj1IMlWNHTzzj8M+wcemfVVMy4jn4a7RziaDjqemZxJq1PH0jEyabJ0U17TynbyRPPeBbz/043/bx7wpVrUF6bF397G2cDLtTlfA4GUw+qUIGa5AIBAIBhPnorz03jeQemT/ySZe336MmdlJLNmw3ydR0eFys3TDfgqyEtDrYHxiJBt2V3JFdAg1LXbNa1u/u5Klt2aw6L3uFueF+am8vr1MPeeKD4/w3KxxnvZmqTvJEsgL7vXtZWpM0t7hZP7UFJZuOODTQh0fYeL2nES/dqanNx1iYX6qarY7Ji6MpzYeVOMVk0FHdEjweVGDpMeH+7V7P3lbBunxEQN6HoFAMDgRCReBYBDTUwrqdsuU1rZS19aBDomH3vqSKIuRu65OIi7cRGVDu0/Ao1R/7E4Xw6MtlJ1uU03jeo5SVKYSPTNrnJpsUT7/2Tt7+dXs8ZSdtrHEK7hRqkNKwOIt+VX+3u50aTr8KzLkI6dauC/XyrqdFT7H+ar9UoQMVyAQCASDhTNVXnq36saGmVh15wTmv/EFoK0eKalt5e5rrX7rf53NocYK3mb8q+dOpKrJjtkQRFKUhWXTM3n0nb3qtd1xVRLtzk41wTE6LoxlXQkOb2WKxahnx9Fabs5KUJW4ihfcitnjKa1tJTU2jNqWdh6/OQ1k+EXXdMWePi9LNxzgU4xa3QAA/FdJREFU2VnjsHV0aiaV2hwuXvrYo65ZdWc2DTaHej/LZ2adk4q1t9booCAdt41LIDU2lOomO8MiTKTHR5z3KUUCgWBwIBIuAsFFgqJoWb7pIHdOSqbN0UmUxcj38qzU2RyU1dsYMyyM5BgzZXXtxEeYmDs52a9CFGUxUtVk95PvQlfCBFnzc6dbVpMtymcrt3Sb2IGv5Ff5eyDPlp5tRN7JG+GXIhAIBAJBN2eivAzUqrtpYS6nWztIjQ318SpZVJDGqi0l/CDf31g2UKxQ3WTnkXV71WNPTI5UpyYqyZWZExPVpMj8qSk02ByascnignTW7Sonf+wwVs+dSFFZAy43LN1wQI0J5k2xkhQTQmFXG3Mgpc7hUy1A4JYkJRaqbrQxb4oVvQ5ykqO51hoT8Fn25THXn9booCAd44ZHnXfPFoFAMPgQqVWB4CLA7ZbZV9lIi93J4oJ0JGQsRj3fujYZm9PF6q2lrNpSwo/+spvv/XcKyTFmzT7tFzYXMyM7UT2uyeD7T4DJoEOvkzQ/V47hjd3pRq/r3mbxzels2FOp/j1Q9U2rjWjlFs+1Cb8UgUAgEAj8UZSXk61D1GmFWgRq1XXLkDMihpuzrmDjglx++7/ZzJtipcXupMHmwNI1YtkbpdXHG5NBh8kY5HPsTpfHhP+VbaVUNtq4PScRo17H87PHkxxjZt1OzzSh23P8Y5MlG/Zz97VW2p0uWu2drNxcwksfl/ioXvU6aOtw+SVRel6XLKOeS/m5klQKM+kpzLNiHRLClNSh5KbGcNv4BKakDNFUmyiJlJtWbuObv/uMm1ZuY9P+atxu2We7QM/7eF1bL9+mQCC4XBAKF4FgkNPZ6Wbjvioe9qpGLcxPJTQ4iMRoC9//007fwGX9flXq25vLvxKQeBvpLsxP5bf/POr3+aKCNIxBOs2K0bUjY0iPjyA+MphwkwHrkHHYHJ0kRYdw5RDt6lsg87+shHA2LsgVfikCgUAguGwY6El9gdbYsro29dhKu+wP1npakxdMTeWPn5ayuCDdx8Ml2mL0M5tdXJDOK1uP+hy7ttXOtPRhpC3MZVd5o0970aKCNIaGBZM6NJSS2lbN1me3WyY1NozTrR2asca4xEiGhgb36vPy0A2jeO3fxzEGSWQmRvDCHeNxdMqcbrHT3O6kzeFCliEm1MiIIaGMGNL/6U7KfWp5zJ2JofFgm8ooEAjOPyLhIhAMUjo7PSZ2jXanmmyBbqVKYZ6VoWHBfot8lMVIp0vGYgwKOApaMZVLjrHwh3uvoq7NQXm9DYCvjYklNS6UH904Cr0kMSY+nMrGdmRZ5hczMvnJ291B1IrZ45k0wleG21cAA4HN/1LjwoRnikAgEAguGwZqUp/3i3yg9f+LE420O93qsb19YdbsKOP2nESuiArmj9+aRGVjO+FmA0s37MfRKVOYZ+XKISEMCQ3mhY8Os6ey2efYQ0KCOV7XRm1Lh5psgW5vlb8/kMuVQ0Op9UqoaLUXPTJtDM/MyuLhv3YXmZbckk50iIEIS5DqFePt81JS00qn28211hiykyKpbLRz3x+7x14/eP0o9VqDdDAkNFjzucVHmHC5UY2A+5tI6a+h8WCcyigQCM4/kizLfW8lOCNycnLkoqKir/oyBBcxnZ1u3t1dyWPv7uO+XCurtpT4bbN8ZibDwoP57p92qYu8Mgrxhc3FRFmM3H1NMm9+Xq5OFkiLD6e53YGzqzdaWfAf/foYUuPC+PfROkKMelxumSCdjtS4UJ7YsF8d67zklnSGhhoBiRFDQs66MiOCDoHg8kSSpJ2yLOd81ddxuSDikcFPaW0rN63c5veyvvEMJvVpTS18YGqqz1QcxSetwebwObaScKhv68Cg12FzeKYK6nWeMcvenzW1O6hsbKe1w8WS9fvVcz12UxpOtwzIuNwyh6pbVTXt1sM15I6KJTMhnFFxYXR0drKrvImlGw4wb4rVx/hWufdfzR6P0y1z7HQbHZ1uPiut5eFpY/mk5DTBQToyrgin2e7C1tHJ6bYO/rSjnKomO6/dm0NyTIjm8yzMs6rjqJV4A1Cfm1bMdI01hp+8vYeyuvZev5v+xjQD8V0LBIKB4ULGI0LhIhAMQvZXNamBEmibv1U2tqOTJF66awJPrD9AWV27zyjEqiY77++tojBvpN/YxNVbj/pUn5a9f4jXvzWJHUdrmZ493Gd7bzPbxX/bz6v35FDf5iAxwhxQFtuXZFaMXRYIBAKBYGDaUXq2vpTVtfPilmJevSeH7aX1yDI+EwW9j60oXQ5Vt/QrYfDgW1/y6E1pFOZZsRj1RJiNPPDmFz6JHiWJkhxj5nt5KT4tSk/emsEVkSYW5qcSE+qv0rU73eyrauaVbaU8eP0oPjpQzbSMeO7+/X/UYw79WiqP/803mbS2qJzQ4CCf5+k9DWl8YiRZCeHUtjo4VN2MyaAjJiSY5ZsOYne6mZGdyJuflzMnJ0lV3KzeWspTt2VS12qnucPF+t2VPDJtrJ/HXH9jmjP5rgW9I1qzBBcTIuEiEAwy3G6ZysZ2VW4bYtSzqCBNMwnSYHNQmGflu3kjCTMFYXP4GsrljopV94Nuaa/3ZCHl86Z2J/dfN4rv/9nXE8Z7EpHd6eZ0q4NH1u0h7H8NfK/LPyZQxai3wM177LJYOAUCgUBwOeLdjqIkCPQ6MBuCcLtln0JGoLVV60W+rK6dIL2OV7b5K0h6trr016tkREwIj0wby/6TTazcXML916Xwq4/2q/sVZCX4FIsKshLUZIty3Mfe20dhnpX4cDP1Afxa5K7JSM9/dIQVt4/job/s9jmmkmxRnpe908WTt2ZSb3OSMjQUk0FHlMXoPw3p5nT0yDy+3j+ekiTPsXsa+v7s3b2qEmf5zCxuHBunGZ94xzT9+a57+z7OhcshnhIqacHFhphSJBAMIpRFxC1DTnIEiwrSaHe6aLE7WXH7OOZPTWHeFKtaqbI73bhleGLDAYprWjnR0O7j2h9obKK+x3/5JoMOk0FPbUvvRrsmgw6DTsLudLOrvEHTkf9M3fr7OwVAIBAIBIJLDcVHJTnGzNzJybz6SSkrN5cwZ/V2n7Wwt7VVeZH3xmTQERcWzIrZ432m9WhNAexNeeGNouTIHxOHyaDzizH6+rtyXLcMSzbs58qhoSy+Od3n+hZMTeXtXRXqtk6XrHlMxf9FeV7fWVPE6dYOmtsd/PquCfzsprH+05DW78cSbPArKinTG/U67etVzvnIuj2UN9g0vsX+oXzXfX0fZ8vlEk+JqVCCiw2hcBEIviK0qhDKInLNldHcMWmET/b++dnjNStVSiXI3TUK0du1Xxnn2HOfscPC1c+V6UQPr9vD7JzEgNUmxcPlt/86ismgw+Ubk6jBmXI9Wj/TkkfvrWzsV2VNIBAIBIKLif6oDZQkRkKkiTmrdwRcC3tLikwaEaOa33pX/JOiQ0iKDumz1SU2TFt5MTTUX3mh00lkJkSwfGYWpbWtmvv19ffRcWHcl2tFlt1Ehxh4btY4XG4ZvU5i9dajauuTyaBjaJhR8xgzsv3HSy/dcMCjnokwU9faofm82hydfp/pdfCXogoevzktYAzk/bzPNjY53+3U/VUq9ZfBqpYRrVmCiw2RcBEIvgICySGjLJ7Ky8QR0Tz2rq/L/y82HQzYWqRMH6pqsrNmRxnzpljR6yD9igi/cY5PTc+kxe5pRUqIMFPZ1M7r2z2KmbeK/EdFL701g7q2DgrzrDg63TTZnTw1PZOVm4/43JO3LFYrYJGQKK1tVRds5Rkcqm4WC6dAIBAIzisX+uXxTNoedDrJryUYfNfC3tpR+nqR76t9V6/Db+1fmJ/qp4b1vt6vpw3j8/J6hkWYVQPd9bsrWXxzesC/K6Obl208SIPNwctzJ3LgZLPfeWtbHTTYHCzMT8XpcvvEMet3V7LklnS19brn83LLsGT9fp6dNU7zeYUYfV99TAYd+WNiuXZkDOYgPY8XpPGERpzl/bzPhf60Hp0tA5mIGMxtOxeiNUsgGEhEwkUguED0HNmoGLVBdxVibeE1JMeYiQs3afZjt9mdPD97PG0dnZQ32FQfl4duGEWwXofJoKOqyc6rn5Sy+OZ0jtZ6JgU8M2scx7vc/ts7nDS2d5IQYWZoWDC/2lysVpOqmuy8vr2MZ2eNo7imhbHDwvnNP0vU8Y8mg45X7s4hLtzEooI05r/xhc9CrMhie1baFuan8oO1X9Jgc6gLtlKJuS/XKhZOgUAgEJw3voqXxzNVG/T1Euk9wjnKYuT2nERGxYYhy/i0jChqjJ7JlaQoCx8cPKX5DJS1f94UK2EmPVdEWjh+uo1TzR1IElQ3ddDm6CQ5OoTkaAtl9TYOVjVT0WBjdHwYr9ydw8nGdsJMBlyym8I8K24ZQox6rogM5pe3j6O1o5P6Ngcut6zGHC3tnWqyRXlGL2wu5td3ZvNlRSMJUWYe/1v3WOpRcWEkRZlxyzIpQ0NZvTWw6rey0eaj+FUKSGFmvY/Cd8Xs8WQmRKLTeYpCL289yrwpVoKDdKTEhrJ800GqmuyYDDqWTc8csPaf88G5JCJ6/r7IMoNWfez934JWDCoQDDZEwkUguABoBXve039A6VV2sfTWTHaW1WsumimxYSzZ4Ak+bs9J5OFpo0mMMnPwZDM2p4vX7r2K7aV1uNzQ1O7wCWQAkmPMFOaNZFWXAa6SDFEULgANNgcAqbFhPLHhgPq5co07jtXzyrZSVswez6aFuVQ3+1fTlEpbWV0bX5xo9Dm+smArlZiebVBi4RQIBALBQDLQrRb94UzVBn29RCoqlrSFuewqb+TRd/b6bGcMknyKIE/elsGLW4opq/N4u62emxPwGcSFm2iwOdh2pIbvfS2FQ9XNuGV4eN1uvvffKfz2XyXqcZ64NYOXPu4+7qKCNN7ZdYKpY4ZR0diuJkEUj5XvrtnlE/fovFQzbR2dPs9IMcFtc7iYbI0hMcrEMzPHYXN0ktSV7ClvsHGq2U6YKYgf/89onv3HYU3Vb4vdxdu7KlTFb05yFCs3H+F7X0vh7w/kUtvqH7sopsDe47UfmTaWkppWOt1uspMie03QfdUtOGebiNCKUZ+bNW7Qqo/FpEvBxYZIuAgEA0Bfi6xWsOc9/QeUXuVgik+18lZRBT+6cTTPfdAdSCy5JZ3TrXYe+Z8xIMHplg4qG9o5drqNcYkRHDvdRlWTnbHx4by67ShfGx3Hk7dlcKLexltFFTTYHPz8lgy+/yffKUQvbC6mMM/Kys0lagJm2caDzJyYqCZfFLyrRx5FzmQmjYjRlEcrPecrN5f4/ExZsJVKTM82qPwxsWq1SSAQCASCc+Wr8Hw4U7VBf14idToJt4yabFHu46G3vqQwz+rz2WPv7vOZMFhUVt+rB8yqOydwqrnDrzD023+VUJCVoB7n8fe6jxtlMXKq2c6911pptHVwZWiIeg4tj5WVW4p5ZtY49fwNNofPhCatqUIrNxfTYHOw6s4JHD7lO7r6p9PG8MKcCbTYnT6q38UF6fx2a4mq+F2Yn8r+k80UlTUx/40v2Lggl8nWIQG/g9EP5HKwupkjp1pYuuGAqs5Nig6cuBgMLThnm4jQilGLa1oGtfr4fLZmCQQDjUi4CATnSH8W2VPNdqIsRmZkJyJJEBqsp9MlMzQsmPlTU1i/u5Inbk1nT0UTEhLfujaZ2PBgVZYbHqwnLtzEkeoWzEYDTW0dWIKDWPb+IaIsRoaEGGm2d9LY3olegnuutVLVaON4nY0dR2t5anoGjTYnDW0OzYArIcLM/KkpmIJ0hBj1NNgcrNvp7+fi3ctsd7rZfKiGykZ7wICit4DTuxKjBEXe0l6BQCAQCAaCr8LzISnKwpO3ZahjkhXVSVKUJeA+/XmJDJQ86jmIxu7snjAI4Ja1/dUUD5grY0JVhYyyv1IY8j6OctyshHBVDVNW30ZYcBCHqpvVcwSaUFTeNUnGZNAxNDxYLS5pJWgUL5ZlGw+yp6LJp4XI7nTz9KZDPDtrHC9+XExBVgIzJyYiy7BuVzk/vHEMR061IMvw+vYyZk5MVPfzTrRpFcxGxoZy5ZAQ0uLDuXZkTL8SF1+FikqLs0lEaP1OvVVUwbLpmX5KKqE+FgjOHJFwEQjOkf4ssvERJu6+JpkXNhcTZTFy9zXJPm09z8zMItJkpLbFwYl6Gwa9jof/usen6vO9LmWKIuF9sSswufuaZNocLjUQUVQqIUY9rQ4X/zv5So5Ut/DH7WUBpxCVN7SrSpv4CBMvz51Io82J2aDnRzeOIsxkoLKx3acFSplU1FtA0Zu8dTBJQr9qGbBAIBAIzh9fhedDeYONF70SFrIML24pJjsp6pxewAMlj3ouWd7TdQDW7670Md5PjjGz9NZMTjV71vR6m+9UH6W9JynajMUYRHyESfUyCTHqmTMpyc+r7eNDNWqLsHINPa8zKSaE+VNTGBMXxokGG+/vreKlO7M5HWCqUHFNC3MnJ6MLMLK5qtHGnJwkH2XMwvxUnt540CdeUZ6Fd6Ktr4LZmSQuLubJOVq/Uw02B9lJkWwcBDGaQHCxIxIuAsE50p9F1uVGVYrMyE70M4l7eN0en7aepbdm9CrLXbrhAM/OGkd5fRtjhoXx/T/v8vn5m5+XM/+6VFa/f8hHnbJpX5WfauXRr4+h2d7J/KkpgCcoa+9wERKsx9Ep89wHR9QkkdJi5K126S2g6CupMhgkoYNBBiwQCASC88dXkeA/1WynrK67mKFwri/gWskjb+N85TPvaYImg45vX3sl7Y5Ols/MJCHSzPG6dgrXFPkUfh79+miaO1yYDTpCjUE8vck3hlhbVM738lJobHeoiRvobk+eN8WqtgiHButZNj2TFzYfoSArAb0OxiVGUtloQy+B2ajj9e1l3J6TyP1v7ApooG/U61hbVM6Tt2ayID8FtwzrdlaoyZ+rrTHIskzaFRPZVd6AUd+t1FWOoXjV9Uy0nYkqpa/CzMU8OSdQQjIpOkSN0wQCwdkjEi4CwTnSn0W23tahVrmGR5p7lQPbnW4qGmx9ynIPn2rhlW2lPHe7v7FZQVYCi97bpykNfn17GavnTsTmcNHY1gGSjlUfdwdVi29OJ0gv8bXUWHQ6SQ1Qh4WbuNYaw+bDNbjcqGqXvgKKwZBU6Y3BIgMWCAQCwfnjQq9F5+sFvGfySELiB2u/BPBR0yRFm3lm5ji2lZwmxKjH3unmjf+UMScniaO1bX7tOUrhZ9WWbj+3KIuRqia7GkO8PHci7Q4XYeYgbTVMlJmZExNZt7MCY5DEozeNpTBvpJqcMRl0PHj9KN79spKESDOLb06jqd2J3altoK+odefkJPHYe3vVxM2Tt2Ww9vPj3DYhicyESI7XtXHH7z5Tryk+wkRhnpWECDMJUWZiQg1kJ0Wpk5YU+qtK6U9h5mKenDOYFMcCwaWIru9NBAJBbyiLrMng+c+p5yLrdsvUtTrQd/3XFmIKUrdV6Cn9fauogkUFaT7H1Nre7nRTUW/z+7k+gPQ2OEhHg81BUVkDx063ER9l4efr9/v1TO+pbKK8waYGqJOtQxgxJJTMxEjGDAvn1U9K1WTLxRJQBKK3gEsgEAgEgrOhr9jgXPBem4eGBdNgc1DVZOftXRXIsicGMOr1xIUH88q2Ulo7XDz/kUdpsnJLMW5ZO0bwLvy8sLmYGdmJPj///HgDP1j7JXqdpN6X0vb86ielPPL2Xl7ZVsrd1yTz1G0Z7D/Z7KeEef6jI9w+cTgVje00tTuxGIP8DPQX5KfwzKxxvL69jKiQYNYWlTMnJ4lXPyll5eYS7n9jFzemJ5B+RRg6neSzjivJH7fsibeGR5s5drqde177D9/+QxHfeHEbm/ZX43bLalLMGyUp5nbLlNa2sv3oafZWNmoWZo53+dEo38m09GFsXJDLm4VXs3FB7nlVynpfX2ltq89o8LPB+3fKOjRUJFsEggFEKFwEgnOkr8pAeX0bx053V5OSY8wsuSWdxX/b71PFeX17mXrMBpuDNruTFbPHU9XY7me8521e+8ftZSy+OZ0l67uPNy4xUrOylhobyk+njeG3W0tpsDlYckt6wKBLqfD0lNHeODbukurpvZhlwAKBQCAYnFwo1cCImBBW3TmB0ppWQkwGNcHx3peVLL01k1/ePg6j3hNnRFuM3JdrxWzQaa573oWfnqa73oWeX7x/UPWD0Wp7fmFzMau+OYHU2DDuy7UC3W1AdqebYeEmHutS4SbHmFlckM6SDftVA/0FUz0eLA02BzZHp5oo8j7HY+/u5Q/fuorkmFB1HY+yGP0mHfU2DjtgK02UxUfRsiA/pV9KmAulohKt0ALBxYVIuAgEA0DPRVapPJxqtuOWZR/PlrK6dn79zxKemzWOQ6daMAXpsBh8+42X3JLO8GgzFQ12EqMt2BydPDdrHDZHJxU9zGsbbA4abQ51rPJVI6J54aPDftLcBVNT+cWmg9w+cbi6r1JZ0jLfUyo8gRb1S6Xd5mKWAQsEAoFg8HKhXsBtHS7Ao9hUEiqhxiAfj5aF+an8qmvE8k+njeHB60fx/EdHAhZ+lFggPsLE7TmJXDkkBLNBz0++Ppo/flqGy+3m9/deRU2zv9ltlMVIfZuTx//mXyhqsDkob7D5xES/3VrCK3fnUNfm4Ghtq7rdE7dmkBRtprKxXTPhcbrVQWenG1mG52aNAwme/cchn+RKb+OwrUNDNZNiPVuNe5vwdKHwLn5ZjEEs33RQtEILBBcJIuEiEAwwPZMUz92e5bfYl9W1Y+908co2j+rlxrQhrJk3ibYOF3ani+gQI1+WN+B0y0SZDfzqoyP8v/9O4c+flTFnUpKmGZwxSOLnN6ez+0Qjk0cOZdO+KjUJkxIbpjr22zvd6r5VjTafqQUmg44nbkknOtSIToJ/HqnhcHWzTx/3pbaoi95lgUAgEJxvznYaXqD9Ojvd7K9qorKxHZNRT7jZyIqPfJWz3mu3Ymr70sclPL3pEAvzUynMs5IUbSEs2EB1U7tPbPHg9aMYNSyUKyLMauuxYtD706+PIiTYyI7SOlJjw/ySEbfnJKrJFuj2kSvMszIs3MSLW3yNhMvq2vnP8Xr+UlTB3dck88i0MZSebuX5D49gDJJ4vCBdM+ExNNTIe3tO8jOv0cVKYkcpLPWVLNFKivVsNd56uMYvVrqQhRmt4lfP++xtgIFAIPhqEQkXgWCA8a6MxEeYCA3WVpFUN9mZN8VKYmQwIcEGio43+EwP+tGNo3nrP8f50Y2jmZOTRFO7g69nxrN661E1kZJ5RQR6HTx60xhaO1zqtKKei/H8qSmq54osdydqTAY9a/9Trhrt6SRIHmKhxd7JtBe2aR7rUlzUB7uxr0AgEAguXs62BSTQftePjuVve0+qrcYL8lP8THC9EyzKZ0qLkN3pZkhoMLHhwfxk3V5mTkxk/e5KH9PdDw9UkXbFaD+ftxUfHmH13IkUlTXglqGywcbTMzL56dvdSY+kaIumqiQtPhydTlITOwomgw6XG6qa7LR2uPjJ23t89v/NP4tZemuGOgxAKQ7ZnS412aKcQxkQoNz3+t2VLJ+ZxSPr9mgmS7QSWt6txvERJqZl+MZeOcnRXGuNuWCFGS1z/573KVqhBYLBi0i4CAQDjHdlZEZ2Ik+/f9BPurv45nRWbi4GYPnMLIrK6v2Cpec+OOwJfpBYuaWY+delsOrjEuxOt88CO/+6FBwut9/+ymL86ielapJlUUEaQ0KDeW7WOIaEGjlyqoUjNa3sqWz2mggQxD2//zzgwi4WdYFAIBAI+s/ZTsM7dlp7vz/Nu1pNtgABTXC1PFiUPw8NC+ZEXRu35yRiMep9pgnlJEfw/a+l0mhzah632d6pxhwmg44f/89oXrs3h/L6dizGIGqa7QFHPP/qoyMszE/1KTB5+9JpTWYsKmvi+jTPtMcxw0IJNxs41dTOzvJGzetThhQkx5hZemsmZqOOtYWTsTlcfiqhv++r8knGLJ+ZxRWRJn43N0edjKS0Z3vHXhsvoNI3kLm/cp/eSaSzVVIJBILzh0i4CAQDjHdlRJLA0SljCtJRmGfFLXtUJGEmPXddncTQUM90gd6CpdLTbdidbuydbs1tlBahQIvx4pvTaWl38Pzs8dQ021m28QB3XZ2MzeEkeUgIz80aR5ujk/o2B6lxobQ7XURZjMzITlSDtXU7K5CkgZ2yMNgRQYtAIBAIBoL+jh/2xu2WOVjVrLmfojb1JpAfm/Jnpf3YZNDx9PRMJidHs76lg/e+PEZBVgKdLpnf3Z2DLLs51ezg/jd28eyscZrHxStmsTvdPPuPwxTmWVm52VOUCeQRU1LTwszsRNyyzK/vzKbN4SJIJ/GLTQfV1phA99Jid/HqJ6XqeeZPTQm4bf6YWPJSh1DZaPfxsVkxezxXX+lRprjdMp+W1qnJFuVeHlm3Ry1WLZ+ZhbnH8fvz3Q00gcz988fEcu3IGLUVGhBmugLBIESMhRYIBpieoyBvz0nk6U2HWLm5hFVbSvhLUQUn6ttZ9XEJv9pcTHSIEb2kPfrZFKTDOjSkz/HQgfYfFRtGo83BsvcPI0lwRZSZh/9nDOMSw2ls7+S7a3Yy//++YPHf9hMXbuJrqbEMDQ3m7ms8Ix5XbSlRRzxeN2roeR9zOFhQZNw3rdzGN3/3GTet7B4jKRAIBALBmdDb+OFAHK9ro7imRXO/cHOQz+frdlbw0A2jfGKFH//PaK6xxvCbuybwq9njsQ4N5cHrU3nlnhy+kRHP4doWVn1crI5bfu6DI3zn9SJqWhys+tij6KhstHW1H3cfd2F+KicabD7XZHf6jpR+etMhJGSemzWO+VNTKMyzYjHo2bCnCpvTxR+2H2dneSPFNS0Yg3Q8Mm2Meo71uytZfHO6zzkXTE1lw55Klt6aQWZChM+2C6b6Xt/ymVlkJkQSHRLsl0zxHuV8vK4toKGuJHmMf4/WthJqCjrj726gCTRiPDMh0meMcyAllff4aoFAcOGRZFm8QAw0OTk5clFR0Vd9GYKvEEUdUd/WwckmOwv+70v1Z/dfl8Krn3ikuPdfl8JnpbXcdfUIqpvtPhLbR6aNQS/Bq/8+xpycJLYcqmZ69nAf07afThuDzekiMcqM3en26W9WJLozJybyyrZSfnNXNjUtHZxoaCfEqPeZnATdEllZhm+8uM3vZ39/IJeRsZeHx0lpbSs3rfR/BhdSQiwQXIpIkrRTluWcr/o6LhdEPDI4OBsPl+1HT/PQW7v9xhwvmJrK58fquDE9niUbPP4qyTFmHrphFEdr21QlbYzFyO8/PcaSWzL43p92qvuvunMCjk6ZQ9XNuNyo8YiCyaBj3hQrb++q4K6rk7gi0owxSEdFgw2bw8WouDCe/cchyura/fZRWm4Anrs9i4RIE/8+Wo8sw9u7KpiRnciGPZXMyUnyuacnb8sg0mzA6ZIJDdYjSTKdbol2p4tIs4HTrR3ISLz+aSm/nD2eU00dlDfYiLIY+c0/i7naOrTLWyWK4VEWqpvttDtdfPsP/r/7bxZezWTrELYfPc2/j9apwwu872X+dSnoJE87d5TFyN3XJPvEZwOpGumvmlbZrjdz/+1HT/PN330W8J4FAkE3FzIeES1FAsF5wNuE9eipFh/ztaSu5Ah4epWLyppwdB7j/qmp/PrObGxOF7FhwbjcburanDxxSwY6nUxseDAvdnmpmA06rroyivK6dp7edEgNuJ6fPZ7imlY6Ot3qWEWdBAvzUyk+1cKy9w9jMuhYemtGQImsHKC9qbbVftkkXM5G/i0QCAQCgRZnMw0vLtxEg83Bmh1lzJtiJSnaTHl9u2pgX9fmaRV2uNzoJYkf/XW3ZuKkrrWD+3KtrNtZQVWTnT0VTazeWsp9uR4DWK21LsKk90v0PHlbBraOTurbOrhzUnKfI6VtHZ2AxIgYC2EmAxlXhON0u5l0ZQaHq1p4dtY4KhtttHa4eHFLMQVZCbz6SSlP3JJOu8PlE9s8Mm0sJTWtXDcmjn2VzfzwL7vVcy8qSKPF7kQHNNqcFK7xFEsW5qf0Op0oLtykKmR6JrQkCTXBUtVk5/XtZRTmWZkwPJLoECMOl5vjdW3n3Gp8Jom4/pj7B2o9Er57AsFXi2gpEgjOI263zLH6Nhbmp5IcY2bu5GRONrX7yFNNBh17Kpv57pqd/OzdfdS3drCzrIFv/aGI+W98wXf/tJOKhg5e3FJMWV07b++qQCdJ/LukTlW0gMcr5mB1M3HhwUgSGIMkltySTmyokRCjntc+9QRDdqebigZbQIns2Uif3W6Z0tpWth89TWlt60XfenM2z0AgEAgEgkAoL8zeLSC9obSRNNgcvPRxCScb23n1k1LV62RPZTMPvvUlJxvbcbndzJtiZf7UFOZPTSE+wqT6uEWYDazfXcncycnER5hUz7h1OytIiw/XXOvSEiLUJAR4tn/s3X1EhgQTrNcjyzIL81M957suhRCj3mek9FPTMwg26PnWHz7nlx8cobS2lV9sOkhFfTvfXbOTZe8f4sd/3Y3b7WkLmpOTRLTFgN3p5vG/7cfmdKlFqjsnJbN800E6Ot10dLrR6SRGdRV/7E43SzccwOZwMSY+goe9WojeKqrwa4fy9qAbERPCI9PGsrbIM6nxRzeO4jd3ZbO2qJw2h8snaVHVZGfl5hLaHC7mrN7B7b/dMSCtxgPdAhSo9ehy8N0TCAYzQuEiEJxHjte1Mf+NL4iyGPn5LensP9lEkE7H87PH84tNB1m3s8LHrf/2nEROtzn8Jg4tWb9flevOyE5k5ZZi7su1qtvER5j8qlGLCtL4S1E5D08by8I3v/QxpHurqIJFBWk+7Unei/KK2eP9Ki6BFuyzHXd5plxIE1slaOnvMxAIBAKBYCDxVsXUt3VgMuhJjgnh0Xf2+qgx3t9bxZxJSWprkPL52qJyxg4Lp7S2lZ9MG8svNh3k7muSGR5lYUF+Cm4ZmtsdfuOWHy9I43BVi6bypaSmRTXGXVSQhiRBu8OFKcjThmPvdKOTIC7MxH2ve8xqZ2QnqiOqeyZxlAmIK7cU8/Lciern0RYjAHddncQb/ynza0FaVJBG7ZYS1Tx4wvBIDHrJL0ny+vYy/vitSbhlGYtR76dMmZY+jDHDwlTVUVKUhdfunURta4dmq9GRUy1+yZG+Jk31xkCrac9GSSUQCM4/IuEiEJxHlMV0aKiRDmcnAA6Xm0PVzSzIT6W2pYNRcaH85n+zqW3uQJIkyuptvY7/U8zcRseFqUFTT08Wpeozb4qVmpYOtfKk0GBzYB0Swt8fyKW21XdRdrtljEGSz1QlY1Dgxfpsx12eCRcqqaMgghaBQCC4+LjUpsvpdBIjYkI4VN3CQ299SZTFSGGelVFxYVwZY2Fr8Wm+kzeSH3u1EymJjFV3ZrPyoyPkjY6lxe7k/v8eSUyYid0Vjbhl2HG0luzrUnG7ZV66M5vdFY243PDy1qP8ZNpYzdYUV9dfoyxGTjXbSYgw0+5ox+GW+fNn5TTYHCwqSKPaK5EQZtJ7WqKizD6tTcq1KmOgm9udzJ+agl6CKyJNxEeYGBoa7DOWWdlHiW9e+tiT/EnuKob0vOYGm4O48GAOVLXwrT98rhk/KG063r87sWEmVt05gflvfKHus2x6Js/+47DP93OmyZGev5/xEQPfAtSf1qOLiUvtv2nB5YlIuAgEfXAm/9i73TLHTrdRVt9GiDGIIL1EcoyZBdenUnyqVVWuKD3PYcF6LMYgPj1aR2psGFWNNvQSJMeYKchKUMcyr99dydhhHulvaLCeu69JVgMspdoTZTH6qFiUJM2QUCPLZ2apbv2Ki//E4VFUNLXT0zdbUeX01zD2QvidXIikTk8utaBFIBAILmUudGL+QuG9/imtLR6T3NG8sNlX7apgd7o5cLKZIzWtFOaNJMSk52Sjnfvf2KU+m8UF6SxZv1/1TvE+xi82HfRTwSpG/IEUtS98czztjk4OnGwhMcqCyaAjymIkzGTgVx8V+x2nqsmuTlo0GXRISKza4kmgDLs5ne//t5VQU1BAnxlJ6m6ZSYqyUFZv47lZ4yiuaeGtogoabA5WzB6Py02f8UOg351NC3Op7krA6CT8ildnkhwJdI6eiR2hpu3mUv1vWnD5IRIuAkEvdHa6+bS0jqKyetyyJ/GxMH8U2UmRJEV3J16URMvBqmafxf6hG0bx5G0ZNNk6/RQob35ezgNTR3Hva5/7VFBSQwwMizCzZP3+7sDo5nScnZ3Mvy6F5OgQHuwRPCzdcIDCPCsrN3dPCDAZdIxPjCQ+wsSkETFkJkT4yGY/OHhKcxE70wRKf0zazrVCIUxsBQKBQNAbX0Vi/kLQc/2LjzDxwxvHUFLTwn25HhN9rTW40+1mwdRUnv3gED+/JUNNnkBXq/IGT6uyojDxpqyunRa7k8I8KwkRZsLMQSzfdIiqJjv3X5eiqTgpzLMSH2Hmzc/LcXR6PF7anS6/8yptRK9+Uqq2Pi2+OZ3VW492X9v6/RTmWclIiECnkzTvb3RcGIV5VsYOC/OLZ5ZNz1TjtM+O1fUZPwT63dm4IFed7uN2y6y6cwJ7Kppwy6CXIDMxot/JkUDn+PsDuWwUalpNLtX/pgWXH8I0VyAIgNst8/d9VRSuKWLl5hJe2VbKnJwkXth8hLe/qFTN0jo73azfc5JvvLiN+f/3BS9vLWXu5GSiLEZWfHiETpdMW0en34JfkJXAY+/u9VlIHn1nL3aHW022KJ8vWb+f8gY7z31whP1VzZrBQ3K0xcco7cnbMgk16xgeFaLKkmPDTJxqtrO/qimgUduZGsb2ZdKmVChuWrmNb/7us7MymhMmtgKBQCDojd4S8xcDgcznvde/+AgT9147gof/uluNS8JNBh68fpTPGvx4QRpmg55N+6ooyEqgptnOfblW4iO610xFJaLs443JoMPmcGEK0vPnz8o43dLBHVcleZQoGgkau9ONW0ZVzCj+KQmRZs1t0+LDeP3bk0iJC2XZ9Ez+77My9lQ2+x3P5ujEYvQoaLzv78HrR7Fs40FWbi7RfCl/9J29npZonRQwfhgaalKf9/G6tn797jg6ZVZvLWXVlhJe3lqKo7P/cUyg38/aVvsZmSlfTlzs/00LBAoi4SIQBOB4XZvahgPdlZmCrATcskeiWl7fxqeldZrb3XV1EvOmWGnrcBETavRb8APJZJvtTs3PwRNsjRkWphk8VDXZKcyzsnxGJvOmWHlxyxFiLGbVl8U76bH5UE3ARexMXe4Vv5ONC3J5s/BqNi7I9ZF7DoQLv3DeFwgEAkFvXMyJ+d4KE0lRFp68LQOTQcddVyep45jBs54+tfEgRr2krv+FeVaa2p28vr2MaRnxvPpJKY+s28sr20rVSUWA2s6zbmeFX8Jm6a0ZZFwRwaZ9VXxtTCzL3j/E69s946kzrtCebCTLvkmcqiY7JxraNbc9UNXC3b//DyWnWtlf2cSRmla/bcbEhXHgZDPtDjdjh4Xxyt05PDMzk/nXpfCHT4+rLUkWY1CvL+WB4odjda3q8959orHP353y+jYOVTdzX65nGlSUxXhGsczF/Pv5VSGemeBSQSRcBIIABMqs63WogUVtSwdFZfWa2w0LN/HqJ6XM/78vWPr3Ayy9NcNnwc+4IkJzIbEYgzQ/n5AUyfypKTz7j0MsmOo76vDB60fx58/KWbm5hBON7bz0cQllde1qwNEz6eGWtStasWGmPhMoWvQ27rK/FYreRkufzTUJBAKB4PLhYk7MBypMlNa2Ut5g48WuNpzkmBDN9TQkOAhTkJ5fbS5m5eYS2p0udaJhz2LQjOxETAYdS25J57PSWqqa7Eh4WoCeuz2LwjwrKz48woI3v2DOpCTS4sNV/5i3d1VwqsnuN275wetH8fauCjXxorB+dyVPTc/02XbB1FTe3lWhXo/DJfPQDaP8tnn2g0PISDz9/kE2H6rlZ+/uRaeTWPVxiZpsWT4zi6CuliNvvF/KteKHtPgwH6+6vkZIu90yu8obVXWLkryKshj7rba4mH8/vyrEMxNcKggPF4EgAIG8ScYMC2fphgOYDDoabU41edFzu/KG7mlDZXXtrPq4mOdmjaP0dBspQ0OQkVl8c7qPV8vjBWnUtdr52U1jeWrjQR+D3U63rPZCr9nhqTTpdTAqNowTDTYfAzrlGixGPeCf9Fi3s4IFU1N9TO+8F7GBNIztr8dLX8ZowsRWIBAIBIG4ENPl+utH1tt2Wj8LVJjYX9XM0NBgyuo8hZTf35OjuZ4OCQ3m+Y+K1ThgQlIUX5Q3aB4zKdrMwvxU6tscfPu/rCzID6LT5abV4Rn7rNQ6FG+W1+69Sj3njOxEnt50iCiLUfV/0UnglmUabA6W3JL+/9k78/CoyrP/f85smZnsCSTExASHJAQS9qjoD7AStdRGUQSxWmx98U03DJVqrVakgHUvCGqrqK2VLqKlbrxKFdCCLS5BZQ8kBBID2ffMklnO+f0xOYeZzAQSJCzyfK6LCzJz5pkzM2Ge77mX783vPyzXzmt2fjp//+QQy24cS0VDJ7bBUTz7YXnQlCKXV+avn1Txxx9cyMcHm/DJaKa6yzfs156nssnJU5vKWFM0EYfbh8ensPDNnZpfzCufVVE4OhW9Di7MSCA93qq97p76YeuBxqD3JnCEtIKi/e4AVDR00tDRpY3jVs975aYyiqbY+lxtIaYf9h/xngm+KYiAi0DQC2oZ7/1v7Apy4n/2w3LN/d5s1PH29sMhwYuFhSN5elN50HqVTU58ikyEQceC17YTbzXxk8tszC/IYnBURHcWy7/2omtyWXBFFu1dPhQFXt5ayaz8NG2zr2lz8cwH/vXnTc0E0J63w+VhfkEmCVYTnu4Zjj2DHjVtLtaUVLGmaCJOj29ANzE1Q9EzmBKYoRDGaAKBQCD4ugxkYL6vE1PCHffoDaP5bl4KOp0Udo2RKdFhAykHGjpJi7No93lkf3BBNeFXEzLl9R1asGV+QRYv/+cgRZfZWLU5dM2Gji50kqRploxECz/7ViYPvLU7SMN0uDx0dvloc7o1jaP6twRqEIAnZo3m6ZvH8+yHZVrQI2dIDM9+WM6Ow+3sOvIlcyfZWPCq/2/Vr0VNErU43LQ5PUHG/3C0qlgdR13Z5NQ0y3ef2qK9tnd31lA0ZVjQVKVjTbMJlwhqcbgZHB0RdnJRb5OgspOj+1VtIRJH/Ud9z4YmRnKoyc4nB5vEeGjBWYcIuAgEvRBYxitJYDbosLs83HllNhmJkeh1UN7QyU0XpvPKZ1U8NnMM5fUd+GTocHkwGSR+dnlm0GhnvaTT+q9njE/joXdLmTvJxoqNu4I288Vv+6cHBAqa3ippdBLkJEfzh+9P4Ddv7aKyyd8zveDKbAZFRQDhgx73TBvBqNS4EKH4daYJhaMvGQoxhUggEAgEZzJ9TQyEO+6etTuIt5pIjbP0OqkmXIJnw+5aLr4gQRt3XNfuJNKkp2iKDVmBSJOe4UOiaLZ7ePrmcViMOlZsKAOgtr0rJDhz11XDMeklGu1ubp9sA/xrqMEW9ZyWrtujTRJ66PpRxJgNPD5zDEZ9+IlBQ2LN3P7nElwemZLKNu12NbiieruoART1/juvyOZvn1ay4Mpsbcxzb1XF6s9J0eYQk9vJ2Ukh05COlbQ5kURQuHMbMSRGXPSfAsR4aMHZjgi4CAS9UNfu0sp4A3ml6GJsg6P4pKKJJW/v4X8uvYDpY1NxeXxadmZ0agw/npLJ4nUBo50Lc2m2u7UNWxUfvTn+63s4LL29/TAPTs/j/jePCrL5BVlYjXoqmx0hY6eXvb+fgpxkLYgSbzWypugSPD4fCZERIUGPgdzQjpfV6UvbkUAgEAgEp4u+JgZ6O66kshmvHNfrpJrc86JZfuNY9ta245Ph9c+/4oYJ6cz9cwnxVhOz8tOIMZtIT7Dg8ck0dLqJNOn5zdu7tUTLA4UjmTkhjbjICH75j+0hrT+ZSZEcbnWxanNFUGAn3mrS2nzUc1K1yX2v72TuJBsPvrOXe6flsODKbJa9v197/IPX5bHncFvY1xU4BUnpThplJ0Uzb2omOgkGRZu45aJ0hg2OZMWGspBq4d9eP4o/fnRAq95RgyINHV1BmqE3HdVb0qY/iaCUWDNmg99IuLrFwasl1VqV8wWDhJfIqUBUQQvOdkTARSDohXBBgIxECxajnq0HGlFQcHsVnt1cwb1Xj2BQpFE7fnJ2khZsge7Rzut289ycCUFrBhqB9Qw2jE6L0243G3UUTRlGYrSJl//nIqqaHUQY9FS3OHh2c0VQu5GKKjb21XWEBFHGpyeEBFFO54bWl2yTQCAQCASni5RYM8UFmZrHydpt/gvvnomB3hIIPhm/uW0vyQVZVrgzYA++66psFr+9m3iriR9PsdHkcFPb7sTh9rIkoHWmeGqW5nmyZN0enr55vJbc6dn689JtF4ZUgixdt4eiKbagdp5AP7jA4MvbOw5z97dH8PjMMUSa9ERb9LTYvUQY9JiNOuKtJmaMTyParCctzorL69NanN/ecZjlN46lrHsi0WvdgYu5k2y0ODxMGZ6ETgfzLs/E5ZWZnDmICenxjDs/LiQokhwTEVS9o++lOuZYSZu+JIIyEi3Mzk8PCgI9eN0o8jPiSE8QLS2nClEFLTjbEQEXgaAXegYB8jNi+dnlWWwsrUdW/BUnt16SwctbKznS6kDCEtLnHIjLI9PY4ebeaTk0OdwYdDqWzx7Li1sOhGR1iqdmUdvq0DJTigKrNh/g3mkjcHs9NHR0BZUJj0iJCSs2rCY9t730WUgQJbVoYkg70enc0IQxmkAgEAjOVGRZYU9NR1BlyPyCLLKSo0ISA0MTI3n0htHcs3ZH0J6+pqSKWRNSe00ufHKwSduDU2LNJMeYcXlkbr0kA4fHx6rNFcydZOPJDaGTh9QWZJdHZkd1K/kZ8WE1gcvjC7vPDxscFZTgUYM46uPMBh2XZw9i2qgUfvCnT7Xjllyby6slVRxu7eK+7+Rgd/t45bMqZuenc9c/tmvHLb42lx/+P5sWUAp8Dr0OqpodPL2pXLv97e2HmTEuFYNBFzYokp4QSVZyVFBrVc+WrK+btBmaGMnS6aMoWl0S9H7f/8ZO3imeLPTJKURUQQvOdkTARSDohcAgQLvTTUWjg5/89fMgsfDKZ1XcdmkGkWYjh1ucrCmp4rk5E+jyykGZsM376vlWThKREXrirEb++N+DuL0Ks/LT+P7ECzgvLoI//fBCqlucmI16/vzfCuZOGsaD75Rqz3f3t4djMEis3FDGDePTNKGhk6DD4Q4p833o+lG4fXJYcbWxtJ7Dra6gdqHTvaEJMzmBQCAQnImEqwBdsbGM/7sj9MJbp5P4bl4K8VYTJZXN+GRYU1LFPdNGkJ4QSXpCZFByIT3eyqEmO51dXuYXZPJqSTUzxqdR3eLAbNSRFm/l7u7gRW/JnMDWHZ8MDreXxdfmsijACHfJtbkcarSH3eePtDp5YuYYHG4vKbEW7n9zZ5AJr9mg47ZJtpDgwwNv7eapm8bh9MooikJClMSciRk88d7+oOMWvbWboim2kEBR0RQbo9PiWLlhv+Z51+X18ciM0b0GS9Q26UiTgYKcZK1NOj3eyvj0+JOWtNHpJIx6SVRWnAGIKmjB2Y4IuAgEvRBoIAuwYuNRARFvNeHy+vjpZZmkJ1r5038OcNOFQ1lw5XCMOolau1vLhGUkWvjxZZlB45/vvCIbs0GnjVe89ZKMoIqVRdfkMjjayLJZY+hweWl2uEmOMdPc2cWU4Ul0eX2MT49n1+E2nB6ZZ/59gJ8XZHPXVdm0Or3oJBifHter0a5PRqt0cbh9JMf4RZ/Y0AQCgUBwLtAfk/jeKkAbOl0MSwq98DYYdEzKHERavIX6Dhc3jE/V1pe7MzFKd8Lkvb11Qfvu/IIsIiP0tDu9LJ2eR6RJT7zVBMDw5PDTjFR/lEXX5NLhdGM1GYiM0POnH15IY2cXSdFmfrl2O26vwsLCkUHTfIqnZvHy1kpumJDGsMGRPP6vUgpHp2rVtS9vreRX04bT5vSEfQ8cHl9QNc8jM0aHPU5NQAXedsGgSJrtLqblpQRV+WYcI9gSzmtObZM+2Umb052IEvgRVdCCsx0RcBEIwhBuU1fLX3OSo7jlkqHsqG7lq1Ynr22rYlZ+Oj/92+fEW038+uoRWlYJoHB0qhZsAb/IWL5hv5btmTE+jRUby7TeZ0mC2jYnydEmEqMi8Phkhg6KRCfBA28dFUlLp+dx4QXx1Ld3MSlrNL/7VykllW2aAElP8AuWnkEU9XWolS4rN5Zrj7lqRDLviA1NIBAIBN9g+msSfyIX3uECAD2f955pw0MM71/5rIr5Bdk8FFDhet93cnD7FJ54rzSkBXnRNbm0Od0UTbHR4XTz0Lv7gtqYFl+bR1Wzg8omJ+CfoqhWyCoKrP64khaHG50EZoOe/fWd2thmgKtGDiLGasLe5Q37HuglSTPddXlkDjZ29jpRMRCzUUdtm4vz462s3HTUVybeauJQk50P99czNDEySIf05jUXmDzqi27pa7DtVFdWDMSkyG8KogpacDYjAi4CQRjCbeorN5Vx33dyiI80saO6FVkBvQTfuziDv39SyZJrRzIo2kxDRxe3T7axdls1AOnxlmNmeyTJLzDmTMwIElEPFI7kuc17mJ2fzpqSKm65OIP5BVnY3T4Anv6gjEWFuTzx3j7++IOLeGzm2LCBkmm5Q0gtmsjG0np8Mpq5nlrpop7Pgle/5J1ug1yxoQkEAoHgm0p/TeLDXXg/esNo0uOt/XreqmY7pbXt3D7ZhsWoIzWMPigcncp9r+8MOje726cFZlZ/XMncSTb0OshOiqaps4s//9e/r8+bmklKrJkZ49NweX3cdVUO1c12Lhh01KPlz/+t5IeXDmX5hqMtyPMLskiKiaCu3cmSa0dS3epCViAx0sigKDM/+cs24q2mkDHTxVOzePy9UuZMzNC0xasl1Sy5NlcbNa0miJzuowEbs1HHgiuziY7Qa68R/N41PbVQYCCst0qjnskj9fhwAQygz8G2U1lZIUYfCwTfXETARXDO0ZcMQpO9SzOsBf80gpo2F1nJUXz5VVuIcd6Cq7I50trFTwM8XtS2oSNtzuNme2blp2kCA/wCYsm6Pcy7PFMzxFv2vr8qJtBYrrPLQ+HoVBo6XUy0Dep1/OGo1DgOt7rCVrqoiL5kgUAgEJwL9NckXqeTuGpEMqvm5Gu+LMve34dRr+vzBbEsK3xe1RqkHx4oHElGokWrPgHQ60J9WhKsJu22wMlD86Zm8sKWCh6fOYYn3ivFbNAFBSwyEi385tpcnF4ff7hlPF981UqEQUekScdz359AZ5eXKLOBfTXtPPGv/ZgMEj/9VmaIxlErWF7eWskTM8ewv74jKIETaNzb4nCTlmDhsZljqGqyMzQxEotJx4qth4JGVA8bHInd7dO8atSK355aKDAQdqwJUD2PH5oYGTaAMTIlul/BtlNVWSFGHwsE31x0p/sEBIJTiZpBuHrlFr73/CdcvXIL63fXaj3V6jFHWl28+FEFT28q54UtFcyZmEFGogWPTwkp/12xsQy9pNfc8dXbl2/YT5PDzasl1RRPzQoaAb3omlwyEq2YjTrWbqsmPcEaVvwNjorA5ZGJMOiCqmLUiptYqwm9juP2E6tZmneKJ/NK0cWsKZrImpIqatpc2jGiL1kgEAgE5wLqhXsgx9sDq1ocFK0uYeXGcp75oJzKJicLXv2SQ032Pj3noSZ7SOXKknV7uGfaiCB9oE4dDCTSbAh7voriX6esvoMfX5ZJjNmgBSxSYs3Mzk/nN2/tZn9tJz/56+es3FjOU5vK6ejy8at/7uQXr21nz5F2/tRdIVM4OjWoJVrVODPGpwH+YE9pXYf2HqgaQjXuVScSPfJOKcV//6LbPNfHknV7mJydxD8/r0ZRQFbAqNfxu/f28eetlZpG6s0UuL7D/zxqpVHg+1U8NYt/fl4dcnxvAYy69q5jPsfporcgYF27i4qGTrYeaKSioTNIrwoEgrMDUeEiOKc4VgZhaGIkh5rsNHR0aQZw6jErN5Xx+1vG0+Hy4vLIjE6N4fYpw3B2ebFGGGjoDL+By4pfoKglwJIE48+Pw+72Mjg6gruuyiY6wsj58ZbwY50j/CJr6KBITVwFrt/Y0UV+RkKf+okDszSyrHDPtBHCIFcgEAgE5xwn4s3R36qYQNTK2nCPL6/v1PRBTnI0f/ignDuvyA5q+bGYdGHNbld/XKlVeCx+ezdPzByj+cHlDInm7n9sZ+4kW0jVyIqNRytSAv99vClI4G+lDqdXxp8fx/Nz8vnjRxWaB4zZqKO23UVlk5Nosz6kXUh9DapG6s0UWA2E9WzxsRj1FL/yRdjkUW+fl8Md3otmIBJO/fFk6a16x+NTuHrlFtFmJBCcxYiAi+Cc4lgZhNLaDha8+iW3T7aFPabD6SE+0kR+Riw3jE/nl91jGs1GHb+/Zfwx24bUEmCzUcfcSTZe/KiC3988ngf/rxSzUcfzcyaE9EbPL8iiptXBnVdkU9vqYGHhSJ7eVB60flKMmUsuSOz3xisc3wUCgUBwrtJzDxwSY8YnwycHm3q9MA53QZyRaMFi1LP1QGPQ4wIvtJOizRxs6mRndVtYneCVZU0fLL9xLPvrO/mOogQZ2654v4z/nWJj3uWZZCRakZCoaLRzY34aCVYTz3a3ABn0kjb18PbJNuKtpl595NQgSs+AyrG0jNmoIzMpml9Ny+GR9UdNfRcWjmRwdAR1HS62HmzWjg0MCqXFWbmrWzepzxvYivTMB+VkJFp49IbRQVOPegbC+pM8Cvda0hNOjRFufz1ZevMJWvhmcFWUaDMSCM4+RMBFcE7RWwbBatJz20ufabeHO0bS6fjLxwe588oc5v75s6ANcPHbu/nt9aP4dXe5sNo25PXJQSZxatDE5ZFpsrs1QXKgoZOkmAhNYOkkSIqJoKmjCwmF3NRYjrQ6aXG4tfN5ZMZoLrkgEYPhxDoDheO7QCAQCM5V1D2wN6+PnhfGPS+IMxIt3DE1i9mrPg563FUjksOOev6gtD5kwtCdV2QjKwrzpmYyPDmaFz86QPHULJweHys3lgedb1Wzg9e2fcXNF2UEVb/ceUU2cLTFSE3cREXo+cllNq0dqaemUStmAwMqb28/zKJrcln89m7irSZm5aeROTiKxCgTz35/PLKs8OG+WgpGpPDEzDHY3V4iTQZ0Omh2dPGtrCT+747J7K1tZ39dhzYBadmNY7H0OAfw6ye97uh53DNtBFeNSGZUamyfkkHHSh71VsV0waBILhgUOeAJp4ON4Suqh98xOewo8XCvpcneFeTvo64j/PYEgrMLSVFEL+DJJj8/XykpKTndpyEIQ28Zh8HRJmY9+zEQ3iW/eGoWUWY9ep0Ok17H3f/YEbL2s98fj9un4HR7GRwVwa/f2AWgjXpWFIg263mke2Tj87fm89mhZl4rqcZkkLj3OyPYebhdO/afn/uNelfcNJY3Pq+mtK6TGePT0OvgElsiE86Pp7rNKcYHCgSCswZJkrYpipJ/us/jXEHokeNT0dCptWyomI06bWpfIGrlitrOogZbAh+3pmhi2NvnTrLxz8+rNU1wiS2B+17fqV1Qj06N4XsXZ/Dsv8u566oc7g6oBgF/Nc1vrsnlJ93m/IFrF02xkRZvJdZi5Ddv7aamzcWiwhG4vP4x07Pz04M0zYIrs/nTfw7R4nDz8IxRxJgNONwysRYDtW1OXB4Zi8nAwjd3BQWN9BLkpMRQWtvBsvf3B62HonDFyCFa5Yla4WM16XH7ZEx6PbNXbdX8ZVQ9k58Rj8srM2xQFBcMOrk6JvDzOtWVvJtK6/ifl0L/7/3ph/lcnpPcpzX687spEAj6x6nUI6LCRXBO0Vs25GCjXcsAqZ4r8wuyyEi04pPhcKuD1DgL26vbyM+ID5stijQZ+PnqEuKtJu67egSz8tOQlaMTjlTBZTbquPvbw9HrFHwy3HlFFpXNTmranLz4UUXIuu0ODx/sbwTQJhNMzhrEhn31IaWn58WZSYyMEMEXgUAgEAj6QH+8WQIrQ7ceaAz7uJq28OtJUnB78fVjU4PaYb6Vk8Sz/y6ncHQqLY4uHps5mvL6TmTF75syKCqCzi5v2LXT4izEWozMf+UL7vtODu0uLxmJkfz0b/7gTM9R0h5ZZvH0XA41dOLyyNz7zy+CEkyyovDQu6Uhvi/Pz8lHktCCLep9y97fz+Mzx2jvmVphUlrboVUP52fEsmR6Hs98UBYSAFIrT46nW/rjidLz8zrVRJrCVxZZTX2/9DoRryGBQHDmIaYUCc451A04cIxydaudhYUjNed7k0EizmpiwavbuePvX/D3T6tosntYtbmCJ/61j0XX5Aa55C+ZnoesyDx2Qx63XpLB3f/YzsqNwROOHrp+FBcOjeXp740jOymK8jo7L35UQWWzk39+Xo3LIwedg9mo46HrR/HCfyqCzt9s9FfZ9CxVvWftDj7c1xh28lJPZFkRrvcCgUAgOOc5kYlFx3pcSqwl7O2BPihqgCFweuDY8+OobPLrAUWG2jYXqzb7pyU+t7kCg15HY2dX2LWjzEYONnYSbzVhd/t4+oNyGjqOmvmrgZ6VG8sprevg3n/uRJFhSJyVxW8HTyVaualMm5AYiMsjc6TVyeFWZy9DAhQsRoOmJ3oOKbjYNphnPijjF1flhB39fLxpT32ZMnkmkRwTwfyC4AmV8wuySI6J6PMaPSdMvlM8WRjmCgRnIaLCRXDOU9VsZ8+RDl75rErLAF00NIHbXy4Jcft3eWR2HG5nptfHUzeNw+HxcaChk+Xv76fF4eah60fxymdVIeLlhVvzeXLDPq7KTSHObKDJ4WFwdARP3TSOv31SyZyJfpO7eKuJoik2LhgUSazZyCufHWLBlcNDDOQcbl+vGbTjmar1x8itv9kkgUAgEAjOJk60iqC3x41IjubB6/K4/42j7TgPXpfHhPR4Lh2WGNLaolZgVDR0YjbquPWSDBKjIkIMZhe/vZvnb53A0ul5Qa0+CwtH8sePDvCDS23cd/UITas02rt69W5xeWTcPpkjrQ5tQhIcrchVJySGVPJGGLB3eZlfkMmrJdXadCCzUcf+uk7uWbtD87Fp6Oji9sk2bV1JgsomJ/vrOvpcURTIsaZMBj7uTNEt6QmRZCVHBXnzZSVHkZ7Qv+oU4bcnEJz9iICL4Jynrr1LM5lTW3buuiqbeKtJ83IJnFyUEmtmcLQZvV7inleOjo8enRqDSa/jziuGo9dJPL/5ADsOt2tZobmThvH6F1VcnpMSNI1oYeFIVm0+oJUir9xYrmVCrso9j++MHBJiIHeoyR5WDGUnRzNvaiYAzfausBt0f0RLfxz21cecCUJHIBAIBIK+cKJT+wIf12zvwqjX4XD72FvXwSufVmqBDEWBpzaV8acfXsRE2yDt8ep+2WTvwqTXYe/ysWrOBBo6u9hfHz4o8cnBFj4+0MAzN49ne3UrPhlWbT7A3P93AZVNdgZHR/DzK7Lw+hT0OolFhSNZ3GOU9PpdNRQXZGIy6EiMjODJDcHBG7vLg9PtCRs0eum/FZRUtmka5eWtflNcdRqRqidWzcmnaHVJ0PPqdARVe/TUL8erKOpL69eJ6JaBQqeTmDo8GdugKDENUiA4xzllARdJku4FZgDDgS7gY+BeRVF2BRwjAYuAIiAe+AT4maIouwOOiQdWAtd23/QWcIeiKK0Bx4wCngYuApqB54ClSoBDsCRJNwBLgWHAAeDXiqK83p9zEXwzcPt8QRmezfvqGZMWh14naYER8AsCNQhzZ/f4aLUCJjrCQFZyFEvW7aayyemfUlSYC59Wsr++E4vJQGltO7dMvIAfrd4WFOxYum6PNhZRxeWRSYw04ejycqTdGZLdCJdZW1SYy+/eK9WePyspivGyErK597Vfva+BGZUzSegIBAJBbwg9IujJiVYRBHqVBO59agBCrQABwgYGHl2/N8TPZH5BFhEGXa/VKSWVbSxZt5t7vp1Dh8vLLRel4/TIPP1BOfFWE7deksHTH5Rrk5T+8P3xNHe6qWx2sH5XDd8Z5U/6zJ1kC/KNU/VI0RQb8ZERvLf7CMtuHEtpbTs+2R80mp2fzuHWLr+h/8YynpszgU8PtgS9VpdHpqSyOaTSd35BFg9el8dTm8pCpjX1paKotymTgYGa/uqWgUZUpwgEAji1FS7fAn4PfAZIwBJggyRJIxVFae4+5pfAL4AfAvuAB4D3JUkarihKR/cxfwPSgWndP78ArAauAZAkKQZ4H9gMXAjkAH8C7MDvuo+5BFiDX8D8E7/wek2SpP+nKMon/TgXwVmKLCscbLRzpM1BQ4dbEx1mo47F1+ay+uMKrh6Vpm3aa7dVUzw1C5fXx5oSf+vRqNRYIk36oGqVQJG1eN1unpg5hq9aHDz0zl5aHG4WFo4k3moKEmGBYxFVzEYdlc1+E92882Kpa2/C7vaSkRCpGcupmbW6dv9av/rnDm3agerpMio1NmSjT44xk5FooXB0qhZkenv74ZDsUn+MBOHMEzoCgUDQC99C6BHBSSLc3rdyU1lQIsVs1GEx6tl6oJHkGDOKAgte/ZK5k2whfiYrNvqDEz2DEqq+AH9rjsPto6GzC6dH1jTMjPFpQYmiyiYnP/nL5/z+lnFUNsP/ThmmtRypLciB+L1YYOGbu3hs5pig1wUEvS6XR6Yr4LlVzEYdvuBlcXlkcoZEMzlzMOPT42m2d/Fq0USa7G6sJkOffE360vrVX90iEAgEp4JTFnBRFOXbgT9LkjQHaAP+H/B2dwbn58AjiqKs7T7mB0A9cDPwnCRJI/ALm0mKomztPuZHwJZu4bEPuAWwAj9QFMUJ7JIkKQdYIEnSsu6s0s+BDxRF+W336fxWkqTLu2//Xl/O5WS/P4JThywrbNpXx47qNrKSornv9Z1BYmfRW7t5bOYYDtR3BGVTZEUh97wYIk0Glm/Yz7zLM7UskvrYnmLEpyi8vPVo5kfNHq3ceLSaxWzUMWJIjPZcalnv05v82aq9dR0sXbeHeKuJWflpZCdFMyIlhgsGRQZNS1CDLSqqyFBbkNQ2n7RYC3dMzQopFU6PtwY9vi/ZpECE0BEIBGcDQo8ITia97X1qIkXdY4tf+UKrQH1i5phjBj0GRUXQZO9i3uWZJEVHUN3qDKoiMRt1VLU4mGhL4OOKo9Ukva3n9ipkJUUjy0rInt6bx4uzl4lIaqLGbNQxJCaCh64fpemowDbpQMxGHRmJkRgMOn9bjQSfV7UGPe54FbF9af3qr24RCASCU8Hp9HCJxj8lqaX75wuAIcB76gGKojglSdoMXIpfVFwCdAL/DVjnP/izRZfiz/xcAmzpFjcq/8JfrjsUONh9zFM9zudfwLx+nIvgLKWq2U5ZXSerNlcEebOouDwyTreXV0v8VS1rSqqYnZ/OmpIqHrp+FMs3+MchurzyccWIJEkh1SxZSVGa4VyLw82Sa/No6HQx7/JMZAVsgyLxyTI3TEgjJkKvBVtUP5lw4qQ3kTE4yhzS5rNqTr4WbFHP6f43dnF+vJXB0UdHSvfXSFAIHYFAcJYi9IjghOlt7yvISeLSYYlERRjYUtbINWNSAX/FbFl3Qkc9tudj69pduH0y6QlWBkeZ0OkkWhxu7X612iUpOoLx6fFBa6jtzzPGpyFJ/pHS8VYj++o6GBQVoR2rVu6Gq6IxG3VEW8Ib5yrK0SDSHa98gdurUDTFRnq8lQ6XB6fby7zLs6hucWg6R9UOaitVaW07qzYHtzOpFbE9k0SBQZXjteiIMcpnH8L7T3AucDoDLiuAL4Gt3T8P6f67rsdxdUBqwDENgb3PiqIokiTVBzx+CFAdZg31voPdf4d7nsA1jncuQUiSVIS/v5r09PRwhwjOEAJNciG82EmJNTMrPw0FhfuuHsn8V75gceEIABZfk4s1wkCLo/cJAGpr0sv/DR3pXFrbyYsfVbDk2jxcHi9Ndv/53Dsthy6frE0mMBt1LLk2TxNOqihKiTUzY3wapbXtpMZZGJUa26vI0OsIKXUO7K1WcXlktpQ38sKWiqBATn+MBIXQEQgEZynfKD0i+Hr09wKwt71vVGocAG98eTjEKN8nyzx8/Sie3LifhYUjWRpgbHvnFdmYDToeXl+q3fbIjFHc950ckmMtGHUSTXY3P7nMRkZiJE2dXSyfPZZH3t3L2m3V3PedHOxuX9BzxliMJESa+ONHFdrz1bS5WFNSxTM3j6fd6eFgk53VH/tNcOcXZNHc2RUSkPntdaOIsRh48saxPLx+r1ZZu3JjORmJFoqmDAt6LQ9eN4r8jDjSE/zvYUVDJ4+u38tdV+UETTCqafNXCdW1u0L8cPrjBdcf3TJQF/oigNB3hPef4FzhtARcJElaBkzCX4rrOx3ncLJRFGUVsAogPz9fOc7hgtOI3e3VghhREfoQsbP42lwWvrmLyiYn+Rmx5A+N58+35VPZ7GLun4+67i+6Jpcl14zkgbePPva314/CqJd4bs4Ealsc/PRbWSwOMNINdPJ/4K1drJqTr418bnK4QzI+D7y1SxspqAZbAitdVm0+GiAJJzI+OdgUElyRlWOXEQf6rvTH8O1EJz0IBALB6eKbqEdEAujEOd4FYODFdEqsGZ/sN8MdmRLN/90xmYbO4L2voqEzpG1ZbS0ePiSau6/KwaiXeHzmGLq8Pg42OpAVRQu2qI9ZvmE/d12VQ0VDJys2lmnmuP/7cklQIKfD5WFwtJmHus9fTdC0OT2cF2fhmtGp/qlGk2zodTAuPZ5DDZ2s2nKQGePTuGFCGooCL2+t5L6rRyDTxZM3jqXLJ3OgoZMn3ttHi8PNomtycXuDpW7h6FRNS6nnff8bO1lTNFE7psnexez8dM1HJlAXtTjcWE16bnvps35XvgTSF90yUBf6J3PdMylwM1DnIrz/BOcKpzzgIknScuAm4HJFUQLT/7XdfycDVQG3JwfcVwsMliRJUrNK3f3NST2OSe7xtMkB9x3rmNoexx3rXARnIMfbFGRZISk6gp9cZqPR7qazywcKPPf98bQ6vZj0Oh7pztqMTo3hlolD2VfTwdBBkTzwZnAbzuK3d7P8xrE8ddM4vIqCz6cQbdbT7vJpk4hUERQVoWd/nT3Eyb/F4eaGCWms3VatBVUATSRJEow7P46yOn/5cWCli7pGoBgB/whKlXClzm9vP8yjN4zWAj09zfi+ju+KcOQXCARnC99UPSISQCfOsS4AhyZGahfTasAjsIok3IV1b/4uVpOe8np7ULJnybW5xJr1yAT7sKTEmrnv6pEYdRKJkSbumJqJohBUqasGcuYXZOGVFW6fbMNi1BFlMgRVyswvyMLtVYLMfJ+fk0+Lwx00KdFs1FFW34HFqEcfI3HPqzuCzmnx27tD/Oj0uvD+MRtL6znc6uKqEclISLi8Pm6fbNMqW1ZuKqNoio2cITG4feFbtb9u5Ut/P+cTDS6crADCmVT5MZDnIrz/BOcKuuMfcvKQJGkF8D1gqqIopT3uPohfPFwZcLwZmMzRHumtQBT+nmeVS4DIHsdM7n6sypXAEeBQwDFXEsyVAWv05VwEZxjqpnD1yi187/lPuHrlFtbvrkWWlaD7P69swe72sWpzBU9vKufJjWXsqemgw+lGAa1E9seXDaOh3YXd7dPKXQNxeWQ8Ppkmu5s713zJvL9/wY/+8jnVLQ7irSbtmKXr9qCTdLz4UUWQn4vq8fLClgrmTMwgyqTX2pnmTMzgxY/85/eTv37O4Ggzj88c06ugqWt3hX3t6fFWlt04NqhX/J5pI/huXgrvFE/mTz/Mp2iKLcSMbyB8V2RZoaKhk60HGqlo6NQ+F4FAIDjVCD0iCMexLgADL6Z7TgNSL6wPNdmDHqsmPQIxG3WMSIkJqQZ54K3ddLp9JEZGkJFoAfzBlp9+axgVDZ389G+f88u1O3lqUzlDYs0h5xlvNRFrMXL3P7bz9KZyntpUjsPjC9IjKzaWMWN8WtBrK6/vYGHhyCCdsLBwJFaTHpfHh9PrC/ueDB8STXFBJvOmZpKRaNHM/1VSYs0UF2SSGmuhqsnOhtI6vv/iJ6zcWK7pnpTu1zHu/Dim5Q4hMTIi7PtlNenDBjJ6vt99pbfPuTct1Ve9cqzfn/7QW+DmRF/v12Egz6W3/x/C+0/wTeOUBVwkSXoGuA2/q36LJElDuv9Egb/3GXgSuEeSpBmSJOUBL+E3pftb9zF7gfX4JwRc0j1O8TlgXfdEALqPdQAvSZKUJ0nSDOBXwLKAXusVwFRJkn4lSVKOJEn3Apd3P3+fzkVw5nG8TUG9Pz4yIkQordhYxvmJkZiNOu3L3+1TOD8hkhUby7CaDGE3hUHREfzm7d0ha/UUNIdbnRRPzQoSNIsKc3lh8wFcHv90o6zkKB68Lo9Z+aFVLPe+vpORKTEU5CT3S4xUtTiYljuE9fMn89qPJvLU98aRGGmiutXB0MRILstOImdITJAZ30D4rhwvGCYQCASnCqFHBL1xrAvAwIvp3qYB9bywVv1dAvf++QVZbP+qLezj1ZHM90wbQUaihYXfHYHJoMPVI3DyVbMj5Dxn5aexpEcQZ8XGMu69egTzpvoDI/FWk2bsr55PxqBIVm0+wOMzx7DgymyW3TiWVZsP8Mi7+3hucwVur6wFgAIft6+2QwueFE0ZxqufVWk6JyXWzK2XZLBqcwX3/HMnT24s42CjPeg1rNzk10rqBKNAs/7A92vZjWN7rXzpbyBDpefnrAaHOru87KttDzrP/gQXTlYA4WQFbk4GA3kuvX3ewvtP8E3jVLYU/bT77409bl8M/Kb7348BFuAZIB74BLhKUZSOgONvxu/o/6/un9/iqJs/iqK0SZJ0ZfcaJfinDvwOWBZwzH8lSboJeBBYAhwAZiuK8knA8/TlXARnEMcrTVTvd/Qy6nBndRtZydEsnZ7Hwjd3kRxjora9C5dH5vnNB1hUmMvidbuPergU5lJR3xF2rZ6C5rw4C797r5Snbx5Pq8PN4OgIfvevfew43K49RidJXDcmlc3lDWHXbOh0cdHQxLDmfMcSI0MTI9lTE1yKO78gi6zkKKYOTz4lviuiT1cgEJxBCD0iCEtvBrj+CTzekGlAPb3Qel5Yq95m2fMmsb++g9LaDl7eWskNE9KO6aVW0+qkaMowFrwW6nVS0+bi1ZLqEP+5YYOjwuqA8voOnt5Uru39+oBJir+9fhSvfHqIwtGpSBJ4ZTlkr77/jV0sv3Esdwa8JwuuzOZP/zmkHbN03R7mTrKxpqSK5+ZMoMPp1QYAqMes2FjG3Ek2rXXJ5fGPzg68wO7NC+5Qk/2kTkEM/JzDtYcFvtf9aXE5WcMDzqSpj0nRA3cuwvtPcK5wygIuiqIc939PdybnNxwVPOGOaQG+f5x1dgJTjnPMP4B/fJ1zEZxZHG+DGhLjz2DEWo1hjxt7fhxHWh2kJ0by4g/yMeh0HGr0b/I7DrfDp5U8NnMMLreX9EQrXV4fFmP4sYm6AEFTPDULUCgcncrCN3bR4nDzxMwxTBmexJThSazd5h+bmJEYicGgY2h3pU2413EiYiRcsGPFRn/PtG1QlOa5MpCBD9GnKxAIzhSEHhH0Rrg9Nj3eynt763h0/V5tas/abdXML8gK8XAJd2Gt00ndBrp2zfNk7bbQgEngSObM5CjNCw6OVoQ8NnMM++s60EtwXpyZeZdn4vbJZCdFY9BLYXWAr/tHde9/9vsT+OMP8jEZJI60OhmXnojd7aO6xUFGYmTYvbqsvpO5k2xIEugk8MlKUIu0GjyZnZ/OSx8dZPq4tD4lowpykhiVGhd0gR3OC+5kT0EM/JwbOrr4wZ8+DXmv1eBQf4ILJyuAcKZMfZRlhYNNnX3+XT8RhPef4FzgdI6FFghOKsfKTB1s6GRbVQurNlcQbzX5y2bf3x8kdF7eWsHNF19AdYuTQVERKIqPD0rrNYG143A7v/zHduYXZLF8QxktDjePzxwdstaCK7PxyQrzuo3t1pRUMX1sqrZxL52ex+PvlWqTi9RqE3XzOt5G218xEm5SkVq6fKoCHmdStkYgEAgEgt7oucdWNHRqe+vqjyu1CT+TswZx1cghIZOJVAJN/E16HWPOj6O4IBNZ8QdcOlwe5hdkkRRtprL56Ejm4qlZ7Kxu06YpqkGKtduqqW11oCigSNDlkXlvdy07Drfz6IxRxEeZeg3iqKg+Jb//sJybLkzXql3WbqvGZJBYfG1e2L26yysHmeoWF2QGvWdmo47spGh++85eZoxPo7bNedxklDo6+2SPe+4r6ufcW0JIkk6sxeVkBBDOlMqPQ0125v3tC+KtpqCA28iUaFGFIhD0A0lRhIfCySY/P18pKSk53adxTqIKnMDM1Idl9bjcMvvrOzShk5McxY+/lUllkx2LycA/t33FVXkpLH77aMvQ/IIsIk16/rGtmsnZSaTHWzjc5uS1kuogg9lVcyaw+0g7CZEmmu1urEZ90FSAB6/LI8FqxOGRSYuzMH/NF5oxr7rGunmTyEyO7vV19GWj7e0xFQ2dXL1yS4jwKZpi47qxqack4HImOe4LBILThyRJ2xRFyT/d53GuIPTI12frgUa+9/wnIbe/UnQxE22Dwk5HBHqdaJSRaOGeaSOobnaQmxoDQLPdgyRBQ7sLl1fGNjhKGwEdmMxJjDLx69d3abctLBzJ659/xa2X2jjUaGeiLZ5tla3Y3T6GJ0fzRHdyR0Xd+80GPWtKqrRW6cLRqQCs23GY2fnpmo+cqoVe3lrZQ/fkU7S6JGg/Nxkk5v3tC20C0ZyJGUHrPHT9KManx1Hbfma1jvSmkf5820UMjo44Y87zdHC8332B4GzmVOoRUeEiOKs43tjnnpmFgw2dlNV1Bgmdpdfl0urwaCWkqljore3mWzlJrNxYTnFBZtAIRPW4HdVtPPHefm260B//e1DLgI0YEoNB7z+n9IRIPtxfHyR+1DW+anEEBVxOJEPS22PCVb/0rKoZaM6UbI1AIBAIBP3hWBWavSUThidHh51olBJrZnZ+esh+/PJWf3XLosJcXtpazqwJ5/P0B+VBmmTZ+/spmmILum3V5gPcMTWLX/7jqNfLkul5vPJBGWu9Sq/eJC0ON3Mn2Wjs7GJ2fjo6HXR2+ahscmpVPJIEZoOOtHhriLH+pbZE3umxnwO8UzyZhs4uXthSEbSOToLx6XEMHRTF0EFfL8lzPB3YX3qrEL5waMI5r1FEdbJAcHIQARfBWUN/qyS8XpnadhdOj4/bJ9vYvK+eaXkpQQEY8IuW0tr2Xttuzo+zMG9qJhMvSODNRItmLgfw9vbDOLsfV9PmYvXHlcwYn0Z6goXBURGU13eQEmchLc6KTicRaQrv+WI1ndh/xb4IDzXYMfyOyVQ127GaDCTHRJCecGoDHqJPVyAQCARnG8dqV95T04YsKyy+JpdGexd/+biKBa9+ye9vGa/t84ETjWaMD51CuGJjGfMLsujs8lHT7uSuq3I43OroVZMEUjg6lfvf2BW03gNv7uKFH+Tz6cFmAP7w/Qlsq2xBUdCMYAH0Oqjv6OLpD8o1bxizUUdNm0trHzIbdbxw6wSKptiQFX/gRFEUbdJhz/3cNjgq6P1SW6mX3TiW9ISvn+AZiGrZMyUhdLIDSSeDM8VLRiA42xEBF8FZQ38m3ciywju7avjl2h0hpbc3XZQRJGRSYs3kDInutd+4qsXJix9VcH5sBD+ekhk0qWjJtbm8WlKlPaamzcWLH1VQNMVGVbNTExvpCVbGnB9PckxEiPnY/IIskmMi+v1+9Ed46HQSw5KiGJYkgh0CgUAgEPSV3ox0PyyrD0rgmI067rwim5f+eygkuaL+O9w46XiriRiLMWidRYUjyUi0hLQD9bz+1uvCj6f+9GCzVpH77PfH88KWihB9MyYtTgvWHGq08/b2wyyZnscDbx5tWVo6PY9fv7ErbFtSzpCYXvXGQAUwBmriYV8TQgMVFDlT267PlGCUQHC2IwIugrOG/ky6OdRk14It6nGrNh9gyfS8oNGOahvQo+tL+fXVI3C6vaTEWXG6vSREmvApCs9vPsCdV2TT4fbxxHt7gzNJb+3mqZvGseuVL4ICKFERBn7/4QHtuK9anLg8MkNiI8hKjgrKFmUlR51Q5meghMeZmGURCAQCgeB0Ec5Id0d1G6s2VwTtwcs3+Nt+dDqFpdPzWPjmrqCJRhA6TnpWfppmdKuus3jdHp6cPZafr/F7wMzKT2PY4CgiTXotEGM26hifHh9+6mJaHHd/O5t/7aqloaMrJNHz4HV5/GXrIWraXJiNOmyDIllYmEur3cXcSTYsRh3j0uPocHnDtkHLCsfUGwNV0arqwJRYc5ChcLO9a8CrZwcyKDJQeu5kIKqTBYKvjwi4CM4a+tNLGhicSYk1c8vF6STHmPm8soUPSuu1yUIzxqexpqSKmy5MJ8psoLPLy90BvdCLr83lrqtyeOK9UmZfmBE24CMrCn/64YXUt3cRH2lEVhT21nQEGcztreng8R2l3H/1SHwoTMochNvrIyXWygWDTiygMRCjls/ULItAIBAIBKcbNSFR3m3CH24Pzkiw8klFCzFmg5ZcAZh3eSYmvaQFYtQ9Nj3BGnYdh9vHA4UjMOh0PPDW0crahYUj6XB5cLh9VDfbWXxtLoveCjb8//Ubu2hxuFl+41ju7DbuDfRTcXR5+WB/o6Zz1m77ioomO0um53Gkzc2wwVHc9/pOrhmTGlZ3KcrX1xsnQnKMmYxES4ixb1ZSFONlZUB1ykAGRQZCzw0UIiknEPQfEXARnDX0p5c0KdofnIm3mkKc8ounZhEboadoio1hgyKZnZ+O0+PjYKM9JFu16K3drJozgbmThuFTlPD+KxEGbnvps6CSYnXUovp863fVcPNFGdzRoxLG5ZW5YNCJ9cIOhJnZmZxlEQgEAoGgv5ysC8TAhMTjM8egl0KrVcxGHUNizShAtNlIXYcbSQK728fmffVMzk4iOVbi+Vvz6XB50CFh0Eu97OURyIqJotXbgvbkpev2MHeSjWc+KGfe1Eze3n6Yx2aOweX2Ut3qDJootLfbny7QlwXgqe+No7ggkwkZ8Tzw5i7u+XYOh5oc/Kj7uQK1S/HUrBANtfrjytNinjo0MZKl00dpE5LU9+SetTsYlRo7oDplIIMiZ4s5rUjKCQQnhu50n4BA0FfUXtJ3iifzStHFvFM8matGJHOoyc7WA41UNHQiywper0y7y83vbx7Pz6/IosvrI95qAvy90i6vj8QYM1lJ0Rj0Orq8Pqwmfa/Zqto2F0vW7eHxf5Wy6JpczEb/fxs1aFLf4dLWV0uK89LiWHBlNo/PHMPqjyuZnJ3E8g37Q4zydlS3cajJfkLvhxqACjyf3gJQsqxQ0dAZ9D6F41iC4mTR13MRCAQCgeDroF4gXr1yC997/hOuXrmF9btrT2jfCUxIHG51kGA1Mb8gK2gPXnxtLve9vpPfvbefdqeHFz+q4OlN5by9/TC3TMzgxY8qmP/Kdv735RIaO7pQJCir7wxZp3hqFr9+YydftTg1faGiesGolSaVTU7213VQ1eJk5cZyLdgCICto66qYjToUYOXGcmpbXVQ2OfH4lBAz35WbypicncTqjyuZd3kmy2ePYcEVWciKwqz8NJ6fk096vLXf7+PXQaeTMOqlAdcp4VCDIoGcrKBIf/Tc6aS3pNyJ6liB4FxBVLgIzioCe0nDRdqfvnkcXp9Ck92t9USr4uWzg01ckTuE1z//iuQYc9D9CwtHghI+W1Xd6mTOxAxWf1zJs/8u54mZYyit60BR0EY5qtkm8G9AJYda+P2H5RRNsVHT5gprlKf2QZ9odqSvZmb9yUgMdJZFZEcEAoFAcKror9n+sSphAhMSnV0+/v6pvx358ZljcHR5ibEYeWT9XiqbnPzs8kytbQj804TUth/1PB56t5Tls8fi9Ph4raSax2aOoby+A598dJrQ0nV7KJpi0wxw4ah5bmCliaKAQReqYd7efpiFhSOD9M6Sa3N5YfMBzEYdDZ1d/mrgSGNYjSJJ/mEAT39Qzv1X5zA42sy9r+884f37ZFQbna5qkIGc2HO2mNOeTa1PAsGZhAi4CM5aegqpeKsJl1tmf30HqzZXEG81aaZqOgl+OOkCf+nstBEs6O5pVu+vb3cxPiMuxFhOnTgQGFQprevg6U3lQeciBeyJZqMOryxz97eHkxhpCspYhJuC9HVEQl/MzPojOAd6BKBoWRIIBALByaI/QRKVcBeIfUkGBF7or91WzZyJGbzyWRWFo1PR6/z3x5qN3HXV+aTFW7l9so2126qPmXRxe3yMSImhxeFmfxht4fLI2AZFac+rTg5yur3Y3T5m5acxJi2OP3xYxu2TbSy6JpfFbx/1c/nxZZm8t6smyL9lSKyZwtEppCdG8sKWAyy+NhezSderV4tazTt0cBS3/7nkhPfvk5VwOV2jigc6KHI2mNOeLa1PAsGZhgi4CM5aehrjzpmYwf5uI7tw3i0LC0dy04XplNa2h73/gcKRREfoefb7E6hrc9HQ2cVL/z2kleeqJbw991azUUekSa/9e8m1uaTEmtl1pJ0/fnSQeZdncn68hQevy9NGMKoCJis5asBFQn8yEgMtKER2RCAQCAQng/4GSVTCXSD2JRkwNDGSR28YzT1rd1DT5mJTaS3zC7K5r7viIyPRwk+/lRlkYKtWoajP2/M8zEYDde1Olk7Po7rFEfaYWKshaLKhx+cj0mzgoXdLted58LpRJEaZePjdUi24oijw7L/LKRydGuTfUlyQiU+Gn6/5kqXT81AUmbtf28GSa3ODzHl/e/0okqNNTMm6iOSYCGravt7+fTITLiaDFPSemAynphLkbAiKDCSnOtglDHoF3xREwEVw1hIopGaMT2PlJn+GJyZCz+Jrc9l1pI3bJ9sAWLutmqXr9rDypnF0eWVm5aeF9CsvWbeHx2aOYfeRNp7aVB62GmXBldlE6HVB2ab5BVkMGxzFE7NGE2UysHjdHm6YkMYLW/wGvDsOtwOQkWjh1aKJNNndWE0GkmMiSE8Y+M2jvxmJgRQUIjsiEAgEgpNBX4MkfblA7EsyQKeT+G5eCvFWEyWVzeSlxFK85gvtcbMmnB/SNrRyUxlzJ9l4e/thFhXmsnjd0YDGgiuz6fL5eOjdUlbcNI5Iuz6k/eeh60fxm7d2B41mNht1FE2xBVXpftVsJ9psoLLJGRRcgdAKXJ+MVnGz8M1dPH9rPo/cMJrf/auUe749nIzESOxuH0a9xPkJVjIS/a9f7qXtuq/7d127K+icwa/N+ptwOdRkZ97fvgg5j3dEpeyAcypbn0QLuuCbhAi4CM5a0uOtrJqTT0llMyNTYphfkIVJ758Q0NjZpU0cMht13DstB48s4/T4qGl1MjQxMnx5r9eHXieFiJ4HCkfS5fGRlRzFriPtPDZzDIca7XR5ZV7eWsmvpuVQ1+5i0HkxzMpPw6DTsXz2WB5519/PbTbquGfaCPJS4075RnG6ym/P9HMRCAQCwdlLX4MkfblA7GsywGDQMSlzEGnxFg41dWrVJBajjrQ4S9DjU2LNzBifRlZSFEun5/Gnjw5SNMVGerwVr89HxqAotlY0cftkG9XNDrwyrNp8gLmTbOh1kDMkBqtJFxRsUV+j1aQPqdJdMj2PjERLSHBGfalqxc2aEn8blLqW1ydj0OlxexXMRgM//dvnQWumxloxGHT93r97ViecF2fm1ksygtq25xdkMSSmfwkXUSl7ejlVVT6iBV3wTUIEXARnFeoG3mTv4kiri3vW7iDeamJWfhq558UQYzbS1OlmSXewBPztRQ6PL2iTf3L22LDiqqrZwcqN5WQkWlh+o9/Mrq7dhccr89LWQyydnseTG8pCHre/vpMXP6rgsZmjsRj12N0+Smvaufc7IxgUZSIhMuK0lUKeSWZsZ9K5CAQCgeDspa9Bkr5cIPYnmKDTSQxNjOTLr1p58aMKrZ3ogcJc7XzUNueggMi1ucRaTbzySSUXXpDIb176TLtv+Y1jeWT9XgpHpxJt1nNenJWKhk4uHJoQNoiSFmflrn9sD7oYfeDNXSGvYen0PJKiTVob0ZqSKmbnpwe1OaUnRCJJUDRlWNg1hydHMeb8+H7t3+GqEx69YTSvfFYVtP6KjWVcNXJIXz9yQFTKniuIwJrgm4QYCy04awgc7/jhvkYt2DJnYgb/LW+gudPDbS99RmldR9CX9IzxaVqwBfxf2A+/u5eFhSODDG0XFo7EatIzb2ombq/Cna9+yZFWJ4+u38fD60u5Z9oInB4fd101PGR845b99cwvyKLD5WXFxjKe3lTOc5srONhoJzEyAtvgqNMaVFAF50TbIHEuAoFAIDjrOZmjdNVgwjvFk3ml6GLeKZ58zNaFQ012zbsF/FOIlqzbTfFU/3hntc05KHjx1m52H2lj1oXpIfc9sn4vRVOGsW7HYWQZfvmP7Tzx3n5+8KdP+dm3MslItGivcdE1ubh9ctiL0fL6Toqm2Hh0xijmTrLx9Adl7Dzcjk+GESnRLL42jzUlVdS0uTAbdfxu1lguGBTJ0MRIFMIb+9YGjJnu6/4drjrhnrU7tMqawPUbOvs3zvl4n7ssKxxq7OSTiiY2ldZxoL7zhMaAC04vAzmGWyA41YgKF8FZw8HGoxu4JPkrV+69egTl9R3cUZDNj1Zv0zb3wOxHz+kAKbFmCkenEmM2sGrOBNqcHhIiTSx/fx8llW1BRncu71GxUNHQyciUGJJjInhi5hicHh+Hmhys31XDtLwUnAFVNOpjVmwsY3RaLBeIaLxAIBAIBCeNk10x2Z9WiZ7Zd0mCyiYnqz+uZO4kG+nxlrDBC1mBiobOkPsqm5zYXR4eun4Uc3tMAnrgrd08MXMMLo+P2nYXbU439OKnYhsUSVNnF09uLNMM/wdHRXC4zUlKjJnOLg/Tx6ZqZrOSBNurW4i1mEhPsIZds68tP4EtRDpJIt5q0s5BfS36Hmne411A92aa2tvnLssKm/bVUVbXGVTVLLw/zj5EC7rgm4QIuAjOCmRZYW9NuyYEoiL03HpJBr/sLn8tLsjU7lu7rZriqVlaBkkvccwy3+KpWTzx3j5m56dzuLWLmjYXKzeVUTTFhq9bd5iNOoYNjmJfXYe2ic+bmsmLH1Uwd5JNM+wNJ7A6Xb5+v1bhyi4QCAQCwbE5HVNjZFnB1G2er5rADk+OxmzUUdPm4pkPypk3NbPXMcsZiZFh7xuVFsfhVmdYHVFa18ELW/x645N9DRQXZId4zd15RTaPv1fKzRdlBK17uM3JayXVAKQnWIkw6PjLx0erXJbfOJafr/mSJdNzWTo9j4VvHp2muOiaXFpdbmRZCdIhPXVKeryV9/bWBV0czy/I4uWtlVrQxWzUkZ+REDR04HgeMMcyTQ33uR9qsrOjuk3z8FPfP+H9cfYhWtAF3yREwEVwVnCoyU5ZfYe2UXt9Ck9/cHSSUKB7fk2bi9UfV1I0xcaIITEY9BKLrsll8du7w5b5qlME1L+f6V43PcHK797br41c/MOH5UwZnhQS2HF5fWEra9SfU2Ij+vw6hSu7QCAQCASnhv4kOGRZ4WCjnb017RxpdfDrq0fQ2eVvI463mlhwZTbL3t+PyyP7pxJ1647A5M7qjyu57dKMoKSQGpxY8Op2bsxPC6sjhidHE281+StmB1kpWr2NeKuJoik2Luiuannxo0PUtLlYvmE/cyfZePGjCuYXZPHuzpqwiabVH/uDIXtr2ykcncqPVn/O6z+9hCdmjsHu9mIxGXhh8wH213eyfv5kZMVf2ZMUbeZgU6c2Kchs1LFqTn5IC9GKjf7E1cqN5ZqWudSWyDt9vIA+EdPUunYXshK+NUp4f5x9nOtjuAXfHETARXBGo4qh/XUdxJiN/Pb6PH79+i5c3uD+5c376oOyPS0ON5EmA42dXfz2nb2aMMkcHBV2I1bbjqQAN/8ok4FfXJXNkBgzBh3sr+9kyvCkkMDOr787ArNRF1JZ4w/U5DEyJbbPr1e4sgsEAoFAMPD0J8EReGy81cRtl2aQmRTFbd3GtwARev+4ZqtJT1qcFUmCP/7wQpo6uyir72T1x5W0ONycnxDJI+v3aq1Hh9ucWiXIqyXVzC/ICmqH8VfhlnLrJRlIwP1v7NKe0yf7262zkqK1c3V5ZDISLPz+5vE8uWE/k7OTek00vfhRhTYmOt5qorHTQ2ldB+BPKtW0uUiJNfN5VavmWaMGiNSWIZdHpqSyOay2Gnd+HK8UXRwUXDnRti11zWMFTpJjzEFVzSrC+0MgEJxORMBFcMYSTgwtuDKb+76Tg21wFC8EtAlNy0vh9c+/4vk5+bQ6PcRaDNS0OpCQWHBFFilxVhxuL4OiI8I6/itK8N/FU7NYvG4PLQ43T8wcg1eWWXBlNn/9pDIoqNLicGvu+/es3aFV1mQOjmJoopWRKbEYDH33phau7AKBQCAQDDyHmuw82h34UJMtj67fy/DkaIYlRQVNRZSQKK1t546pmQyONOHyyjR1url9sg0As0HHw+tLNSN/ddqP2pZjMeq5YUIaigJ/+LCc2fnpWivy05vKtXOqaXPx8tZKnr81n88ONeOT0SpRVmws43ezxhyzPVoN6lQ2O3ngrd0UTbH1WvGRkeCfxvjiRwf41vBkbr0kg/99uSRkvVn5aUEGwWr1iloRDP4q44xEC4WjU7X38u3th8lIjDxh7XIi04iGJkYyKi02JGglvD8EAsHpRARcBGcs4ao9lr2/n2U3jmXPkTZ+e30e9e1dpMVbqWl1MC3vPP53dUlQBiYx0oiCxN0B4mfJtXk882EZlU1OTVSsKanit9ePoqHDxdxJNk3gANi7vCx6ezfP3DyO6WNT0engiZljkHQwPCmaQ812lr2/j7mTbOh1kJ+RwKW2xH4FWlTEuEOBQCAQCAaeJnuXFvgIDDIcaOwkI8HvSfLo+r0hx/z2+jxcLm9QUGXp9DxcHjls2/Lit/2Bj8DASpvLwxMzxxBjMYTs+S0ON+1ODys3lgedr8sj09nlDZmClBJrZsb4NFxeH7/+7giaOrp4ttvD5IJBkRxqtIfVFZXNTl78aDeLrsnF5/Px4DulIVUwRVNsZCdF91oZrPLxgQbmXZ4V5P/y4HV5pMdbQ973vrZxnYhpqk4n8a2sJIbEmMlLjcXl8XFBd9BHtGULBILThQi4CM5Yeqv2KK1tJ9ZsxOtTtAxGcUEmqzaHTgh6YuYYHnhrTw/H/108NnMMhxrtDB0USW2rg7uvyiEpNoJfB2RxwC9KGjq7cHlkmu0eLEY9nV0+9td3MGOc3+lf7WNWMz1mo453TrAFSLiyCwQCgUAwcKgX/IpM2FabJ2aOYXdNGwte/VLzdws8prLJEWLKWt3iwGzUhUxFVO9Pjz86ASgj0cLCwlxaHW5kpxLSirywcCRfNTvCBkkaOrqYX5CF0+PrtdLlziuyteObOrswG/UhBrvaJMbugNCLP8jXDIDVQMrabdWMOz+O9ITwJr+6gBbs4oLhFK0Onq50/xu7GJ8eH6SF+tPGdSKmqbKshJj3LrtxrKgQFggEpxURcBGcsSTHmENKVD8+0EBmUjRxFgNFAWOgeyuZtXd5w96+v64jKNs0b2ombp8cJHwyEi385ppc9tV2Mr8gk1iLkagIA3/4sJwdh9u5dFgiykk2ZxOu7AKBQCAQDAyBF/w/L8gOu39XNNox6KUgf7egNXrs+ymxZox6HY/OGI1eL4UNTkRGGJhfkEVavIUOl5d5f/scl0dmfkEm/ylv4LGZY3C6vVhNBho7XPx5a2XYQMyqzQe47+oRWI36kEoX9fyXb9hP0RQbkSaDZqSbEmvWzHCrmp1BVbwuj4zbK3PrJRlBbTjzC7K4YFAk6QnhE0EjU6K5dFgiSdHmPrdD99enrr+mqcIHTyAQnImIgIvgjCU93hpSorrk2lx+914p14xJDdrc4y3GsP3DkebQcl3VqyXw50iTniGxZmRF4aXbLqS6xUGXV+Enf/08KGv0t08rKS7Ior69C4fbR2JkeE+YY7UAHa+c9uu4souR0gKBQCAQhEe9II+3mshItIbVDUa9xKCoCIoLMhkxJDpkjw80Ze1ZYZKRaGHJtXk88FbwaOWvmu3YBkUSazVSVt/J7ZNtrN1WzQel9Xzv4gx++Y/txFtNzMr3j5i+MT+N9btqNH8ZnQRJ0RH88JKhdLi8PPTO3pApiSouj0xanIXlG8q0oEqLw83e2g4kCV78qCJEEyVYTVqwRV1jxcYyrho55JiJoKGDooLW6a0dOnAAgvraAwM+J8unTvjgCQSCMxERcBGcsRxqsmvBFlDbgXYzd1K3SV335j46NYaMRCs//VYmi946OoJx8bW5xFr0PHjdKO5/Y2fQ7b//8Gj7z4Ir/ZOI/qd72kBgJqln1mh+d7AlMAv04HV5PLXpqCfMsVqABnLssxgpLRAIBAJB79S1u4i3mrj36hG8+NEBfjwlk8XrgnVDnNXI91/8RAuA3HnFcGrbnPx5q9+QNjHSxF1XDeeJ9/Zxy8XpQRUmlU1OnvnQ35bk8vhod3mINuvpcEKj3c38NUf35+KpWUgSLH57t2a2G1jRct93cmh3eXH7ZEakxFDT6mRwtJm7uoMzsqKQlRTN/IJMXi05GsAwG3UkREXQ4nBrP995RTayoiBJhLQXLbtxLG5ZDhuoaOh0MSwp6riJoGO1Q4fTJoFjqU+mT53wwRMIBGciIuAiOGM52GTv1agtcATzz6Zm4fEpWrBFPW7RW7v5wy3jibMYWHBFFkkxFmranPz140puujCd9ESrVpZ720vBvcdL1+0JcuBXb0+Lt2oGvOpt97+xizVFE3F6fMdtARrIcldRSisQCASCc5WeFZ7p8VaqWhxBP0ebDcybmkl5fQcX2wZrwRY4qhuKptjCBkCWTs/D6fZid/uQJFhx0zgc7tC25comJ6V1HbywpYIXfpDPzuo2nB4fyzaE+sWoU4d6tgbFW03Y3T6e/qA8qMJXr5PCntv8gixe7g4IPXhdHhIKcyfZiDDomJARx4H6Th56t1Srwll+41jMRh0ZiZEMTYzkUFN4Y92+BiqOVQVT0dAZok0Cx1KfTJ864YMnEAjORETARXBG4fXK7K5po6bNRazFGFS2O2N8GnodZCVFMzjKn91ZfG0uMWYDWyuawgZntlW18sKWCoqnZrF8wz4eun4U58WaOdBo58F1e2lxuHl4xqiwj9X3GDJkNupw9OIJ4/T4mGgbdNzXN5DlrqKUViAQCATnIuGqKHpWnz54XR4NHf4K1dsn+6cKhtszZYWw3igL39zFshvH8uL6vRSOTqXV4SYywhC2rdhs0DF3ko0WuweXx4fVpA/SMmoLk9GgC2u2O2N8WkiLzwNv7ebPt13ErPzQc1OHBFgj9Pz+gzImDhustQ7d/90cnnhvf1AVzp2vfsmrRROpa/dXxaTFWlg1J5+SymZkxd9adc+0EccMVMiywsFGO5XNdiJNBpJjIhgaZgx0b9pkdGoM7xRPPqmtz8IHTyAQnImIgIvgjMHrlXlj+2Huf2OXloVZdE0uz/67PGQso9oWVNnkpLggE1kJ3z+smtqq2ZS69i4e/9c+ZuWn8fOCLA63ORkcFRH2sTlDYrTb1RLYFoc7bBDIqNPh9crHHQU9kOWuopRWIBAIBOci4So8739jl1apqv78+Ex/RcnabdUsumZkr9N3ZMIHY6qb7SF6ZFFhLs9uLg8K7DjdPp7+YG9Qm3J+RixTc4YEP/aaXO6dlkNzgLYAep12tOdIG8MGR4W9r6LRzu8/LGfuJFtQFXCrM3yi6OOKJtpcPmLNehKjzdqURrNRx6M3jOaqEcm9BirCBbjmF2SRlRzF1OHBj+tNm2QlRw9IMujr+OAJBALBQHDsq0OB4BRSWtdOVbOD2yfbuPvb2fzwkqFEReh5fOaYkGzOord2Uzg6FUDLxiws9IsnQAuQ/PPzau0xeh0027v8QRODnt//u5zUOAtPbtjHb68bFfTYB68bRWKkgb/OvZj7rs7hsZlj0OlAARZcmU1GooU5EzN48aMKVm4s55YXP+GN7YfxeuXQFxaAWu4a+Fwnq9x1INcWCAQCgeBMpbcqCkkK/VndI9sdbpZOzwvaM++8IptEq0kzxg3EbNSRFm8N0SOL1+1m6fRRFBdkMneSjebOLn77zt6QNuX5BcNDH/v2bjq6vNroZvU5e3v+5FgLkSZ92PuGDorE5ZGJMOioaXOx+uNK5k6ycfHQ+JDjMxItRJqNvPhRBW0unxZsUc/rnrU7qGpx9Pp+hwtwrdhYxo7qNg412YOOFdpEIBCc64gKF8EZgSwrlNZ2sGpzRVC25PnNFdx66dBjCqm126qZMzGD1z//imU3jqXT5aG6NXjsodmoY3RaHFERel78QT6dXT5+cMlQ2hxuCkYMISXWxJ9vu4j6ji5iLQb+9NFBth5s5qHrR/Gn/xzS1gH/CMgVs8dy658+DcmmZSVFMeb8+F5f50CWu4pSWoFAIBCci/RWRdFzIuFXzQ5tus8Db+/h51dkUTTFhkHnD1gcaXUgozDRlkByjJklAeay8wuy6PKFN5ctqWxm5Ua/59u8qZlhj2lxuMPe7vLKPL2pnJRYM0VTbGQlRVPT6uDOK7JZvmG/9vy/vX4U1c12kmPMISOji6dmcaTVgdmoIzMpipRYMzVtLl78qIJYcxYPXpenVQ+bjTp+NW0Ed3YHTHqrpjlWO3JvAS5ZIeRxQpsIBIJzHRFwEZwRHGqya2IA/Bv3K59VsXR6HtsqW44ppGraXKwpqeLB6aPYW9NGxqAovLIS5NC/sHAkR1ocxFpNzH/lS80Ar8XeRWqcmQMNDl74qILC0anodXDjhek02d3c9/pOiqbYNCEF/vGKjfausGKjts3FqNSBG/t8PEQprUAgEAjONcKZpaoeLnC06vXlrZUA/LwgC5dH5s//reTHU2w4PD5+2W2IrwZXIk16iqbYkBVQFHh3Zw1Flw0Lq0d8PYpbwx0TZzWGvV2VBy0ONymxFpKijXR5I3iquxVar4OxaXH8/sMySirbmF+QyRtfHtZGRisKrCmp4u6rclg6PY+qJju3XpLBio1lFE/NYt2OGmZOSNNei04CT4/AUX/bkXsLcPnHV4c+TmgTgUBwLiMCLoIzgp7ZkpRYM7Pz0ympbOG1kuqQbI7q7QL+Tf6OqVnc/+ZOzbhOzRSlxlo43ObE65X5438P8vCM0Tw6YxTNDjep8WaiIgzUtjl5aeuhkL7shYUjeXpTOdnJ0ZqwyEi0sHT6KAy68AIlJdYsRjMLBAKBQHCKGZkSzZ9vuwiH20t6QiQZCVbGp8dT3+HCYtRT/MoXWrXqV61+v5WaNhcdXV5tGhAcbY95fOYYHnhrD+DXFHMmZvD4v0pD9MjS6Xk8/UGZdh5rt1UzvyBLM701G3UsuDKbJ/61L+T2Jdfm0upwM29qJjoJTAaJ0ppO1n5eTeHoVCQJMpOiWbxuN5VNTlJizZiNeoqmDAsa7bz42lwef69U85FZcm0eK24ax8HGTv53yrCg6YoA8wsyNQ0T6PfS18k+4QJcqofL12kV6jlp6myphDlbz1sgEJwaJCWw3lJwUsjPz1dKSkpO92mc8QRuUFaTgdmrtmqC4GeXZ/LiRxXcPtnGC1sqiLeauOXidIbEmGl3eUiKiWB/XaeWrclKiuKOv38Z8hyP3TCKymYn63Yc5seXZeLx+njo3VIevn4Uf/3kECWVbcybmomioDn6q5iNOp6YOYbzE6zEWgw0dnZxuNXFPWt3EG81aRmkwGzahPR4vrNyS8g674QZzSw2aIFAcK4hSdI2RVHyT/d5nCucC3oknIFrz0RH4DHq/n1enIWKhk70Oh3L3t8fsu6jN4xi0Vv+sdGqJulpmJ+ZFE1UhI5mu5f73zhqOnvvtBwcHh8JVhOH25wkWk08u7kCgFn5aeSmxOLxyTR2unB6ZOxuHzoJLrEl8Kt/7sTtVZgxPo1os56RKTF8+VUbeh1EmQw8vL6UeKuJWflpDBschdmg56F394RMSlKrc4sLMoOqdMEfQJo3NVML2qjJJKNe6rMeUacUVTXbsXZPKUpPOHEd05fP8UzkbD1vgeBc51TqEVHhIjgt9Nygrho5iN9eP0ozblPHNW7eV89TN42j2eHGajLg6HKTnRzF7S9v6zVbo2I26rhgUCR6nY67r8pBp4PmTh+r5uRTWtPOzy7P4pnurFRv4yFL6zq46x/bWXbjWIYnR/P9F/2+LTVtLl7eWknRFBu5KbEMiY0gNyWWzyqb+9QLLTZogUAgEAi+PuEMXBe8+iU5PRIdI1Oi+evciznY5AgKjjxz8/iw+qHZ7tYqPwJ9TmraXDzzgT+AUVyQyYghMXQ43UEtPs9urqCmzcWCK7N5raRam4z4VauT10qqMV2s44n39mutTm9v9yeFHG4fC787klanh8aOLiwGPUWrtwVVkcRbTdS0uVi5sRyzUcfyG8cGBVvU90DuzqeGm+LY4nDT7vTw59suQkHpl69Kz2TRZdlJX0u3qOsdarKzr7Zde329fY5nGn39/RMIBOcuIuAiOC0EblCjU2OYfeFQalodPDdnAg0dXZiNejISLXx39Hnc8coXQWLDYOjSNmSVV0uqWVg4MqjEdn5BFjXtLgZFGymr6+StL48wLS+FotUl2jEPXz+KCwZZaXN6wwqu4cnR3DE1k+pmO6AE3a8KnleKLmZUahyHmuw4PT7mF2Tyakl1kGFvz55msUELBAKBQPD16c3AVU10BCY45k6yBVWzujwyS9btDtEP90zLISs5irLaDl6eexFdbh8vhNEII4bEYHd7GRRt5tF/7QvVEEOimTMxI6hdZ35BVtB5rtxUxmMzxxChh2a7h4Vv7gobYFFbneYXZNHZ5dMGB8RHhveGUQvY126rZlHhSBYHvL7iqVm88lkV384d0i/NcbKTReHWK56apQ09OJ5575nA8X7/BAKBQIyFFpxyZFmhoaOL2yfb+NV3hnPLxAx+9rfPqWl3s62yhfvf2MVv/28vv5o2QnPoh6N91ZVNDmblpwWtqWZr5k6yUVyQyao5+by7s4Zf/mMHBp2OB/+vlMnZSSEjGe99fScxFhP/b9ggHr1hdNDYwvkFWTz0zl6e2lSOT4GDjXbt/pRYMz+7PJPigkxMeh3rdhzh6pVb+J+XSnhucwW3XpLR3Wsdvhf6WBu0QCAQCASCvqEauAYSmOgITHCEm8hT2eQkyqTn8ZljePSGUTz7/QkYdRJxZj15abFUNjq4/81dFE/NCtIICwtH8ocPy4kw6KlpdYTcXzw1C1lWQnTHio1l+OSj7fwuj0x5fQdmk0ELtgQeO2P8Ub0TbzURY/GPc356UzkvbKmgqdPNnVdkh+iXf35erT1Or4PlN47VRlevKaninmkj+u230luyqOco6K+z3spNR1/z8cx7zwSO9/t3tiDLChUNnWw90EhFQyeyLCwnBIKThahwEZwS1JLRJnsXR7p9UFwemeKCTJ7csFsTQrJyVAw5Pb5exw4OTbBqGZ1Ag9sWh5uFhSMprWnjO6NS2LF+Hy0OD0Cvow/r2v1BjvPizKwpmkh9exc7j7Tx8tajY6XVrFLx1CzWlFQFGewC2jhrdc0VG8v4820XMTg6ImyZbm8O/2fbBi0QCAQCwekknIFrYKKjZ4Ij3N57oNGutegsLBzJxr116PU6atqc2v6++uNKbWpQZlI0D7+zlxaHm+oWB9nJ0SxZtztkctBPLws/ItrlDX7+MWlx7KxuC3usFCAfZuWnaZU46v2/eG07vykcwWMzx6DICiaDjg6nW5vUOCs/jQfe2kO81cSM8WlIEkwfm8rIlOh+V6Wc7GqO3taTJPpk3nsmcLzfv7MB0eZ+9iN8Ic9sRMBFMOAEfpHPuzwzaBqAQafTTOiGJ0f7szxGHTPGp1HZZO917GC7y6MJnzFpcTS2O/nFldlUtTi0wMsDhSPJSLSQGGnCbNRhMerCridJcHW30a3ZqOPJ2WNZubFcq2JRxY4kwctbK7n36hHa+EgIDhKp+ANDSq8C5JuwQQsEAoFAcLrR6SSm5Q5h+B2TgwxcVQITHIETeVTj2fQEK3XtLlJizdS0uVi6bg/PzZnAj1Zv4/bJtrDeLfOmZmIySCy7cSwVDZ1EmnT85ppcvviqlQiDDqNOYsGVw0mKjjjmKGizUcdD14/iDx+WMXHY4OMemzk4KqzeiLGaWPDq0bHWd16RzfyCLOxuH6mxFs17Tj1/gEuHJTJ0UP+CJCc7WdTbejnJ0awpmsio1Lgz/qJR/f3LKZ5MfYerX344Zwqizf3sRgTMznxEwEUw4Khf5PFWE8kx5qCN9YJBkeRnxPI/k4ZR0dBJ3nmx3PedHBrtbl4tqWbRNbksfnt3UD9zpElPVnIUVc1OIk0GLCYdh9u7gqpMAJas28Pvbx6PySAxd5KNMWmxISMZ5xdk0eWVtR7peKsJk15HRqIl7JhogPL6jhDBE04wWE36Xt+Tb8IGLRAIBALBmcK+uo6gC46nbx7HBYlR1He4eH5OPve/uZPKJidrSqp4bs4E6tq7eCDAL+XOK7KRFQW724fTfbTCNtz+PjlrEEMTrZq20eukEG3xyLulmAwSiwpzWbxud9DzjEyJZsGV2XhlmcgIAyWVbRxu7QoZz/zgdXlEm438/pZxGHQ6ZEUJez6yTNDF8vIN+5k7ycYzH5T3OlTgRIIk/UkW9SXjPjQxkoeuH8V9rx81MS6emsVv39nL8tljzhpNpNNJ2AZHnbXBiZNVuSSqLE4PImB25iMCLoIBR/0inzE+jeoWR9DG7/R4uOmioUGb96JrcpmclQiAtduBv7PLi9mop7rFwR/+XcGs/DSt/PfRG0aRnmANu1nY3V7WbT/MP7+sIT1+FC9vrQwq+X15ayWz8tOYMT6Nf35ezZyJGZTXd/CraSO4s8eX19J1eyiaYmNESkzQa1i7rTokkFM8NQuPL/h8enK2b9ACgUAgEJwJ9LzgiLeaKKvrZN7fjpruP3rDaCwmPbsOt7Gzui2o2lYNUhRNsbF5Xz3jzo/DbNQFVcSo6yy+NpcOp4f739ilaRt1/1fXWrGxTAt4PLu5nMdmjmF/XQfDk6N54r1Spo9NxWLUk5EYiVEnYTbqqGlzsfrjSuZdnklqnAWzUc8j6/dS2eTEbNRx11XDMRkk5hf4DW8LR6ei18GIlBga2oP93wLbckalxZ60itq+Jov6mnHX6STGp8dRNMWGrPh12eqPK2lxuM/KFuuBDDgM5Nono3JJVFmcPoRx85mPCLgIBhz1i1yS/NOEAsVLSoyV/+2eGgT+L4hn/13OHVOztIqVnq71ZqMOk16nHX/P2p08OXts2M2irL6TlHi/qLBGGGhxuINKas1GHT7Z3y40Y3yaVmZ897eHh/3ySo218OqnVUEZmRaHm0iTnnmXZ+Lyylrv9rS8Icd9b0Q2QCAQCASCr4d6wZESa2bG+DRyhkRzd0Drr18r7KBoio2VG8uZNzW8t0pqnIUfXzaMJet2a1pl9ceVFE2xMWxwFHFWI15ZpsXu0R7fmz+c2o5c2eRkf10HL2ypoGiKjdn56VpQ4ZffHs6RVpc2JammzcXTH5Tz1E3jtAmN6npPvLeP5+dMwNHloWjKsKCpSguuzNZaoqC7CidzEDPGpWqBlZNVUduXZFF/Mu7pCZHkDIk561usBzLgMNDBjHCVSw9dPwqd5H/uvjyHqLI4fQhfyDMfEXARDDhDEyN59IbRVDR0YjJIWIw6nr91ApIkBYkWlVkTztcyR3DUtV4d5xg4UlG93+uTQ6pMlkzPw+XxEm+NICXWzPObD/DgdXna2mogZ01JFdPHpiJ3r1XT5qKmzRn2y6uhs4uLbIms2Lhf85AZlx6Posj89K9f9EssiGyAQCAQCARfD1lWsJr03HVVNsMGR/HI+r1IUmqvAZV5UzOJt4QfpXy41cmwwVFUNjk1k9wIg47MpCgeDag2+cMt48lItFA4OpXhydHHHMus+rAsnZ5Hp8vDqi0HtcBIpMlAZASkJ1hYfuNYDjR0Mi49jm2VrWHP/7PKFtITrKzafCBIIy17f78WTFK1xIVDE4K0xKmsqK1rdwWZ9IK/Gjhcxv2b0mI9kAGHgQ5mBPog7a1tZ39dB4//ax8tDnefdelAVlmI5OSxEb6QZz4i4CIYUNQvSYtRx6WZCYzPiKehowtFgV2H28gZEk1GooVZE85ncFQEUWYDsRaD5qmi4vLIpCdYmDvJxstbK7lhwtExiWajjqQYM0danTw+cwyOLi8NnV0880EZhaNTeeidUi2wYjJIPHPzeLZXt+KT0UYjjkyJpq6jixe2+Ktq/vJxVUgZcfHULAw6WLbBf5taKWM26vi/OybzTj/FgsgGCAQCgUBw4oRLXBRPzUKnC++9UtXs5MWPKnju++NDkjSqh4tOkphfkMmrJdU880E5P7s8M2SvPtzi4MeXZbL47d3EW01h/eFe3lp5NPnj9tJi76LN5QuqQjnc5tSCJPMLsshJiWZIjAWf3Bz2/H0y3P/GLq1dScXlkRl3fhyvFF18RgQsUmLN3HpJRsh7MiQmfMb9m9BiPZABh1PRMqLTSUgS3PXa9qDn6qsuHagqC5GcPD7flKDlNxkRcBEMGIFfktlJUdx66VCe2lTGTRem0+X19zhnJ0Xxs8uzgozr5hdk8ZPLbPzh3xVBwqSq2ckzH5SHuPYvvjaXndVtPPTuvpBzUEt9V24q47GZY3j4nb2Af0xifkYcN4xP1b6U0hOORohr2lysKali1Zx8jHoJq0mPxyfT7vKG3fQaOl1MtA3q18Ynei4FAoFAIDhxwiUuVm4qY97lmSFJkwcKR/LKp1XML8ii2eHh5a2VPD5zDPvqOjAbdJgNOh5eXxoSNNHrgluGUmLNJERGUFrXwe2TbazdVs3LW/1tR+kJVmrbXOgl+OW04ZgNel7/oorrxqVTXt/JiJRoUmLNmAwSv5o2grL6TuZNzWTttmpWbCxj3bxJ6HWQnRwd1htu9ceVuDwy3V3VGmajjozEyCDtcDqrAnwyYX1trhp5/Fbrs5WBbOs4VS0jX0eXDlSVhUhO9o1vQtDym4wIuAgGjMAvydunDON375UyOz8dp8enbcSTs5O0YAsc3ZSLptiCjHGXXJvL8g1lWl9pp8vDvKmZ5KXEUNVsJyXOesySXpdHpry+Q/OAyRkSw2XZSSHmbceLEFc0dPZr0zuW4BE9lwKBQCAQnDi9XSC6vP4R0EVTbKTGWqhqcRJtNnDD+DSaHG4ONdppcbjZ1+2tMneSjac/KA3RIk/OHku02aDt1SmxZuZMzGDBa9tDAiErN5bz7PfHc36ClQSrkcomO9YYA1eOPC/oInTp9DzMRr1mzB+4xr76Dn7x6nbirSZuuzSDP992Ef850IhPJsjHbnx6vHZO4S5sT3dVQH1H+M+lodPFsKRv5gXhQLZ1nKqWka+jSweqykIkJwXfBETARTBgBH5Jur0+brownUiTnpHnxbD4mlycHi8xFlPYL1JZgfO7e611kj+j9Kvv5BCh11Hf6SIhMoIhsRY6XF6WbSgLW9KrChjwbxgFOUlcOizxmJvA8SLE/R2JeCzBI3ouBQKBQCA4cXq7QFQUaHG4MRv0PLmxDJNBYsSQHCLNBi4YHElZXSdP3TSOunYnCwtHhniOREXo8foUurwyRo+P316fx69f36WZ6/fmMbenpl1LFBVPzWJYkp75PcxvF765i6IptpA1iqbYUOSjXnIPvbuP/IxYZl+YwcKAKuAHr8vj0gsSj9nGfLqrAs7FhNJAtnWcqpaRr6tLB6LK4lz8XRJ88xABF8GAkRTt/5K85IIEspKiGBQVQWNnF19UtfBBaT0zJ6QRE5A5UlFbhqpanJpJbmSEgT9+dJBbLx2Kwy0zKEriQEOnFmCpaXNpJb3nx1uJjDDw6Pq9WjZo2Y1jGZUa97U3p75sempVy6EmO/tq2zU/mp6CR/RcCgQCgUBw4qTHW3n4+lHc2z01UG0dirMaKZpiY/XHlZgMEj+eksld/wiuSnnwnT3ccnEG67ZXc+/VOViMelZs9Cdwbr0kQxsbrU4Bml+QRYI1fJJIr0NrQVJvW7mpjBduze81qdTztgsGReKTZa3FqKbNRUllG/d/N5I1RROpbXMxJNZMbkosBoPumBe2p7sqID3eyqM3jOaetTvOqYTSQLZ1nIqWkTNRl4rkpOCbgAi4CAYEWVY42NTJkmtHEmMxUlZv5/43jgqihYUjUWSZhg5XSGXKgiuzSYwy0dDRRdEUG9bu0tubLkynvs3JeXEWSmvbSY21BAmKmjYXKzeWU1yQyWsl1czKTyNzcBS558VywaD+bxi9tQMda9PrzcBPLQXuKXhEz6VAIBAIBP1HlhXe21vHkwFTA3OGxPDHjw5w97QcAG6YkEZ2cjS/7DEiWq1KWfb+fuZdnkmrw6vpkBnj00L8R9QpQI32rrBJoosvSODRd0tDzP5dHl+vSaVAzEYdtW0uHl2/L0g3tDjcxFhM2AZHMeb8vr83fa0KGAifF/VzWfb+Pu1zyc9I4FJbokgonQWcabr0TAwCCQT9RXf8QwSC/nOoyc6zH5ZzweAooiKMWrAF/CJk6bo9pCdGUtvexbs7a5g7yca8qZkUTbGRnRRFfXsXDrePzKRont1cQWWTkxUby4ixRvDLf2xn5cZyjnSPbg7EbNSRmRTNr77jz1aNTotlWFKU9sUsywqHGjv5pKKJTaV1HKjvRO6ZauJo4OTqlVv43vOfcPXKLazfXRv22J6vO5yB34zxadr5iTJIgUAgEAi+Hup+6/b692VZgaomOw8U5tLY4cZi1PPClgr213WErfZQTfUHR0VQVt+pHaPe3vP4jAQrmUl+M1tVe6jmuvYuH0WXDSMl9uj+bjbqiLUYWXbj2KDjH7p+FBcMigxZo2d1zKz8tGNm8mVZoaKhk60HGqloCNYyalVA4HP05vPSX51zPNTPpbLJP+hg5cZyilaXUNXi+FrrCs48jvU7eDJRg0DqcAoRbBGcbYgKF8GA0OHyUFyQzaFGB7KihBUvnx5q4YUtFUEVIACPzhjFio1lFE/N4uF39mq3uzwye2vbtbVeLakO69vyu/dKmT42lfyMBNITgsXFpn11lNV1Bj0mnJHcifY/91bGK0nhBY9AIBAIBIL+U9fuIjspih/8v6FUNjmQFXB6fBxqdvC79/YRazby2MwxxPbSuqwo3dN9BlmJthgoLshEVsBi1IU93mLU02p38/LWSuZOsiFJoCjw8tZKbpiQxgtbKrTASYvDzcLCkXR5fVp2vq7dhcensPDNnbi9CvMLsshIjCTabOCX/9gRUh0z7vw4JmcODluBcjyPuL5UBQyUz8vpbmcSnBpOtzGzQHA2IQIugpOO1ytzoMHvYbJqcwWPzxzTq9gJLO1VRz4Pjo7guTkTeODNXUECxGzU4QvYw1Xflj/cMp4vvmrFJ8Oakipm56ez+uNKxp0fFyIudlS3sWpzxXEFRm+CYX9dB0Cv5YwpsWZNtAGs3VZNi8PN5MxBzBiXKsogBQKBQCA4QQJbYKLNBu6elk2n6+he/caXh/nRlGH8dIqNJoeXl/9bwfwrhvPgdXl81ezg1RL/nlw8NYs1JVXc950cKpscLHprt3bReO+0HBZcmc2y9/cHJXN8isKRNictDjfPfFCuPWegnlmxsYzHZ46hrL4Du8tDSmyilp0HuHrlFk1bqO1Da4ouocXhDnqdZqOO9IRI3ttbp13QZiRaWDp9FEa9hNVk4I8fHdACP/719pIzJLrPLcsDFRgRJqfnBqfbmFkgOJsQARfBSWdPTRsrNu7nl9/O4fbJNkwGHYuvzQ0SNIEThAIrQOYXZCGj8OS/9jM7P12bBuB35h/FU5v2Bz1Xi8NNZIQBi1GP3e2jcHSq1vdsNRmCxJlOkjDodH0SGL0Jhp2H2/n5mi/DRvFlWWFPTYcW0FFfT1ZyFBcOTRCBFoFAIBAIThCvV2ZDaR17atqRFRidFku708P9b+wK0hbPbT7AosJcNu47xA3j0/nfl0uCDHUToyI42NjJXVflUNlk5+l3g8dBP7y+lJU3jWPe5Zm4vDKKAptKa/nhpTb0Oh3LbxzLI+v3UtnkDKtnyuo7sBj1pMVbuWDQ0YrWnpOQwJ+U8fh8YU1BjQa/rlh8TS4xFgMdTg9Fq4++lkWFuTy7uTzoPJrsXX2+2A2nczISLViMerYeaDxhTxdhcnpuICqZBIK+IwIugpOKLCvUtLmYnZ/OL147OhHgsZmjmXd5JhmJkZTVdwS1EJmNOoYnR1M0xUZqvIWVG8rYcbidhk43cyfZyE6OIiHSxOqtFfz0W5lBgZuHrh/FY+v3cuXIFF7YcjTQseiaXIbERoSUOy6fPbZPmZdwgkEVVb1F8cNF+1dsLOP/7pgsgi0CgUAgEJwgsqzwWWUzBxvtWlKjuCAzpGJVrZj9srqVWyZewI9Wb9Puj7eaqG13oZckUMBk0DE4KiLsRWO706NNKcpItPCLK4ezv74DWYHS2naKC7Iw6CQONNhD9MyEjHj2HGlncExE0N6fEmvm1ksyeOWzKgpHp6LXwQPXjGRwdATj0xOC2n/SYi28seMIDwSMg150TS7ZSVHsOOxvrV68brdWHay+9r/MvbjP72lPnZORaOGOqVnMXvXx12oRESan5waikkkg6Dsi4CI4qVQ22bFGGLTKFPCLl1/+YwdFU2wcbnVgMeq18ll1YpEsy/hkqG11suNwO+BvGXrxowr++MMLKTnUQvaQOF4rqeKxmWM41Ghn6KBIjDqJy3OSibcYKJpiQ1ZAJ0FkhB6PVwkJgDzy7l4WFo5k6bo9x8y8BAqG/XUd7DzcHiSqwkXxe4v2N3S6GJYkov0CgUAgEJwIh5rsNHW6g6YHyUp4c1u9DnwytNo92v0psWbmTMwIqpqdX5CFSR/er6XV6dYm7EzKGsS2Qy0h1auRJj3pCdYgPXPXVcP51dqdtDjcvFM8OejcfDK88llVSPXuozeM5vz4SK39R5YVPq9q0YIt6uta/PZunpszgV+t3alNPZQCYhguj0yzPbg16Vj0DIxYjHot2KKud6ItImfapBvByUdUMgkEfUcEXARfG1lWONhop6rZDoC9y4vLI5MSa9ZKZy1GHWPSYvnyqzZyhkSz4Ios2rt86CT/4x9Zv48Wh5sVs8eRkWjRsj9j0+JY9l4pJZVtWpVJq6MLg07Sxjyq4ue1kuqgLNPvbxkfIsYqm5xkJFj4820X4XB7SU+I7HVkdGDf9c/XfHncKL6I9gsEAoFAcPKpa3dp2iKQcHtuzpAYHl2/l0duGK3dP2N8WkgiaMXGMlbcNC7EfH9+QRbnJ0TS4WpjaGIkje1dIWOiV2ws4w+3jMfp9vmnFLn9esaokzAZpLAXnvUdLgpHp4acxz1rdzAqNVYLtqzfXUuHyxM2mLStsoUfXjoUWVFwenxkJUWTEmumps2F2agjOSaiX+9rYGBk64FG0SIi6DOikkkg6DtiLLTgayHLChtL63hz+2G+anEQbTZgNRnISLQwZ2IGL35UwdObynlqUzk7D7fz10+qmPf3L4g0G4mK0GtrmAwSS6fn0eXxUDRlGC9+VMHKjeX89G+fMzVnCCmxZq1kNiMxMqxwUkcvq7eZuycNBGI26jgvzsrFtkQuz0kOGhndG30Zr9if474Op2oEn0AgEAgEZwrJMWZaHO6gPX3ttuqQEc0LC0fyxhdV/ObaPNqdHh68bhRmo67XUc+tjqNTh+ZNzWTuJBsvb61k5+E2nttcQUNnFw6PL+xjGzvdLHhtO51dPp7e5B9/rPq/hGvDSY4xo9eFP4/6Dn+yqKrZTmltOymx5rD6xSfD8g37cXp8rNxYzt3/2M6ciRlkJFp48Lo8clNiv9Z7HO45RdJI0BtiXLNA0DdEhYvghJBlhapmO812NzWtTpKiTAyKjsDjUyiv7+CBwlx+9rfPQ4Iiar/x0nV7giYTPTFzDINjTPynvKnXnmy1T7nDFZrl6llaazbq2FXdxoPX5QUZ6vUMgASa6vZmENfXKP5AR/vFCD6BQCAQnIsMTYwkMzkqaHpQi8NNrMXAXVdlkxxjJs5i4kiLnStHnsdP/rJN8yV5+ubxmPRS2GoYq8lwzKlDy97fz7Ibw09aVNuIe2vrUfVFk70Lk16HvcvHJbZE3vzyMJVNzqC1JCQONXbyeVUrqzZX8GFSFIuuyWXx26HDBlweWZuEGOjdMjYtDoPhxPOookVEIBAIBgYRcBH0G1lW2LSvjiMtTrKSo/wlvCkxlNZ28vM1n+HyyDwxa/QxgyI9/+2RZdqd3l57stVj/dmWiLDiRxdwTPHULP7030pS4yL4y9yLaezsIiXWTG5KrBac6E8Ao6/9yAPZtyxG8AkEAoHgXESnk5g6PJkhMW1kDh5Pi8NNnNWISa+jo8uHLCvc/+ZO7pk2ImifrGxyMu9vn7PypnEUT80K8XB5fvOBkJainlOHqpod3HlFNss3HB0TfecV2bz030NacEbFbNTxxVetdHllHG4fKzaGTlxcOj2Ppz8o06YLzS/I4udrvmRWfpqWcNpxuB0+qWTVnAmUVLbgk9F85Ho+p8sj45XlrxVsUd9j0SIiEAgEJx8RcBH0m0NNdmpbnQyONuNTFJJiLLQ6PEEGbxEGfdigiCoSev57UFQEeh3opfA92YpydGy0xagPEUgLrswmPyMeWQFFQRNLU3OG8P0XPwkbUDnbAhhiBJ9AIBAIzlV0OokOl5cnN+znfyZdwP66Tk0H3HVVNoWjUymtbQ8yylV95KLNBjaV1jJ3kg1JArNBh16CoinDSIgy8tT3xiHLCrtr2kOmDjncPl4r8bcvnRdn4UBDJy/99xAtDjeLrsnl2X+Xa8eqwRo1eDJ3ki2kBXrhm7v4y9yLaep0s7umjZe3+p+vZ8Jpx+F27lm7kwVXZrMwYFrR/IIsXt5aqR13Mtt+hNmtQCAQnHxEwEXQb5rsXZhNBh5Zv5d5l2ex8M1d3D7ZFiQUmjpdPFA4kiUB04BUkRDu37WtTpocblJjzSFltEun55GeYGFQpJFVWw7ikxWiI4KnEtkGR5IQadJGQwP87PLMEKETGFA5WwIYalmyTgpfEi36qwUCgUBwLpAcY+ZbOUlUBIyHTok1M2xwFHtr25G7kzPxVlPQVKIXtlSw+Npcfv9huVZZcucV2TR1dnFXtwF/RqKFn30rM2jq0IPX5ZEWZ8FqMuBwe4mz+mXzDRPS0EmgR2HJ9Dw+O9SCosD6XTXMGJ9GaqyF2yfbiIrQh9UZm8sakBV4elN50H099/gWh5v8jHje6a46GRxl5mBTZ9A5noq2n760XwsEAoEgPCLgIug3EQYd97+xi7mTbLy/5wjPzZmA3eVlfkEmr3ZPCnJ6ZKwmPctvHMve2nZMen826Z5pOTR2uvD4FH5+RRaHW53EWgws21DGjflpNCn+sYnzLs9kSIyZqhYHy97fT4vDzZJrc1k8PRdFgWc2lfGdUSmcH28lwqjjgsQo0uKsPH3zOHZUt2HQ6cg7L4Z/Wk1apgr8QqeyyU5DRxcKygkHME6V+Ahse4q3mkIqe0R/tUAgEAjOFYYmRpKdFE1pXYe2d996SQaHmuxkJ0VT3eLg3mk5NDncIQmXRW/tZvXci+h0eXF6fMSYjTy5YV9Q+9EzH5bz59suoq7dRWKUieXv79OmJC6dnscDb+4O8V95bs4EXthSERLkUU18MxItIY/xyfgrbQI0iGoC3HOPz+jWF2oi6IJBkVoA5lS0/Qj/OIFAIPh6iICLoF/IskK708tPv5XJRFs86QkWfrR6W0gVi9PjY/mGSm69JIOspGgcXV4aOruobXfyyLv7MBt1/OH7Ezjc6uSpTQdocbgZOigSRfGLHqdH5v6AFiWAB97aTdEUGxajnln55yPLipaZMht1PDl7LD5Z0bJeaqvRn/5zKKg82OWR+clfPz3hAIbqYbOjug1Z8bdBjUqLZerw5BDx8XUDM4FtTzVtLl7eWknRFBvjzo8jIzFSZJkEAoFAcM6g00kMHxJNWX0HGYkWbrowncRIEw+8tTto3x82KDKksiTeauJQoyOoPWdRYS5ub6XfMwW//mi2uznS5qS2zcnPrxyOXpLw+BQO1HcEBU7AH8gpr+ukeGoWLq8vJMizdN2eECPaQI+YQF+ZFoebrOQo/u+OyTR0Htug/3htPz21R3q8laoWxwlpkd7ar4ffMRlJ4pRUvYgKm7MD8TkJBOERARdBn5Blha9a7Hxe2cq9r+/E5ZF5+nvjWNQtcuDoJKInZo5BwV8K++j6fdoaZqOOuZNsWpluQ4eT0WlxAPhkeHLDfhYV5h5zhKOswIqNZRRNsZGdFM3tk22s3eavqjnYaNeCJ+rxy97fz7JZY9hT28Hb2w9TNGUYj6zf+7UCGFXNdsrqOoMCO/MLssgcHMXQQUcF0MnICvVse6ppc7FyYzmvFF18RrU9CQQCgUDQV/p6YRYucPBVi50JGXGcF2fhcKtTC7aAf9//6yeVPHrDGIoLMpEVNI0wKz9NC7aoxy5et5vHZo6h+O9fAH6dsre2HbNBz99Lqrhn2gim5Q5BlhUiTeG96ZodHtbtOMz9V48Mq1tA0cz7B0dF8PC7e7Qk0OqPw2uQYUknvr/31B4ZiRbumJoVMrGxr1qkt/brvbXt3PXa9hPWNyf6ekSFzZmJ+JwEgt75epbmgnMCtaJjf22nFmwBsHeFH8/sUxSeeK+U4qlZmI3+XzG1tDbarKdoio2MRAtmo5Ed1a3ICvzz82oqm5yU13ewsHCkZp4bSOCoRlmB0roOXthSwZyJGaTEmhkcHRH2fPbU+o8rLsjGJ8tBGSo1gGEx6bENjuqj+OgKCeys2FhGQ0dX0HG9ZYUONdn78rYD/n71cO+D8G0RCAQCwdmIemF29cotfO/5T7h65RbW765FlpWg47xemY/KG3njy8P850ATt730Ke/sqqG62cnuIx0semt3iNFsSqyZ2fnp/PBPn7JyY7mmETISLVwQpurF5ZFxub2Af2/97fWjeK2kmpWbyigcncqj6/fyxVctrN9dy2eHmrl3Wk6QrimemsW6HYe5Z9oIrBH6sPt1VISR77/4CT/+y+fc8uIn3HSR/3zAn5jKGRLDZdlJfdYgx6On9igcnaoFW9TXvODVL9l5uJWtBxqpaOgMee8D6U2H7A9o6zoRfXOir2cgn0tw4ojPSSDoHVHhIjguh5rs7KhuIz3BGiRW4iJNWgYJ/FmkFoebI61OlkzPo7yuk8dmjkEnQX27i6c3lfu9WKbnUdvWxS//sSOkxLa9y4cMRJr0LCwcydIA0131GLNRR0yEniFxVm6fbKPL6+Mnl9mIt5p6nXDk8sj8+vWdLL9x7Nc2nrW7wweaattdyLKiCaaTYco7NDEypBxZ+LYIBAKB4GzleBMCZVmhqtlOSWUrT23aT+HoVPQ6+NW0Ebz40QGuGjmEQVFHEyyBe/qM8WkhbT0rN5Xx2MwxmPThjeeHxJopLshk3PlxNHS4tOqTCIOO2fnp3PLC0UmHd16RzfyCLJweH1OyBgMK0/KGoJPgB3/6NGT09MPXj2LhmzuDzuf+N3axpmgiTo9vQDxYemqP3iqGN5bWs3Jj+XErEcLpkIeuH8Xj/9oXsmZdu0s7h5PVUnK2DDg41+nL5yRajgTnKiLgIjgmsqzQ0NHFsEGRxFiMmvv/rZdk0GJ3h7TVWI16nt1cwaz8NMwGPb97r5SbLkwnNd6qufrHWYwUv/JFiCCaO8mGXoK/f1rF7Px0Vm0+wLzLM0mJNVPZ7GD1x5W0ONzc950cTAY9vwzwb1k6PY8/fFgWInaWXJtLs8NNSqyZmjYXXllmwZXZLHt//wkHMDISIsOKtrL6Tg412bWNRc0KfZ3gjk4nMS13CDmn0CBPIBAIBIKB4lgXZkMTI9m0rw6XW+apTfuZnZ/Oyk1lxFtNzMpP45aJQxkSY2ZndStmo46126qD9n29LnxwobbNSZzFwJJr83jgraOtNYuvzeVQkx2fDA63j+Ubjo54HjooUtMZ6jrLN+xn7iQbz3xQzqXDEploGwTA1gONVDY5Wf1xpTZ6WlEg1moI6/vi9Pi0x/aVvl6s9qY9ev7sk4+eT2DAqyfhdIhOQpuUFLimx6dw9cotJ7Wl5GRoKcHAc7zPSbQcCc5lRMBF0CuBX45zJ9n4pKKBR28YRZvDQ0JkhGZYC0fbauZd7h+p6JNh5aYylt04lqXr9nDDhDRe2FLBnVdkU9HYGVYQ6XWQYDVxy8UZ/PWTSgpHp+L2ySREmnB0+bjziixSYi3Yu7z8vEd2bOGb/qlJqz+uDJpwtHxDGS0ON8VTs1hTUkVZfSdXjUxmTNpFONxe0hMiuWBQ/wIYFwyK5KHrR3Ffd3tVYPXNpcMSNcFysqpT+mKQJxAIBALB2cCxLsxUjzSnx0fh6FQt2NJz+s+ia3J57IZR/HLtTs0HJT3eSnKMWTPTTYu34ujy0uJwMzY9js8ONlPZ2MHLt11ETZuLuEgjL310kA/2N2pJo5o2l9YCXdvqCKtV1OlCFqNeq2pVX1NNm4tnPjgatLlq5CUnJVjQn4vVntrj7e2HefC6vCAPl0DjXvV1HatipKcOkWUlRN88esPokGqeYwVy+oqo9D07ON7ndLzKNoHgm4wIuAh65WDj0S9HSYKLbYNRZJnB0WbaXZ4gw1rwf3m6fbK2kbs8MuX1nbQ43Fw0NJ44SzYvfnSIGyakhRUgmUnRPPzOXgAenjGKzi4v8VYjOw+30dnlY+22akwGiV9cNbzXgI06krrnhKPA4E9gVupE0OkkxqfHUTTFhqz4s1hq9U2giBLVKQKBQCAQBHOsC7PPDjWzYmMZt0+2adUq4dqEFr+9m3mXZ2qBltp2F797fz8/+ZaNnxdkU9vu4u6AKtjF1+byQWkdN4xP59Y/fRoUeKhosnPLxRn4ZMXfWpQej9vj5fx4S1itopP804WKX/lCM9Xt7TXlpsR8rWCBWtXS0NHV54vVcNojPd7K+PR46jtcWIx6il/5QtNu6uv6utW3TfausNU8X7f1R2ips4PjfU6iNUxwLiMCLoKwyLJCbbuTn34r018BIkFSjInKJhe/CjPeUM0KqUET9WevLHPnFdksfHMXs/PTAb/XS89xzMVTs4Iet7emncSoiCAHfPW5zosNnx3Lz0g45oQjNfhzMspQ0xMiyRkSc1wRJapTBAKBQCA4yrEuzFSPtLXbqnmgcOQx93SXV+bpTeXaBMSaNhcJ1gj213do7c7qsYve8k8j6tkitHJTGctvHMuhJjtOj4+cITFIKMxfs514qylEqywsHEm708PLW/26JzDo0dtrOpFggSwrHGy0s7emnbL6Diwmfdj3oLKXFqNw2kP9WZYV7pk2YkCqbweq9UdoqbODY31OojVMcC4jAi6CEMKVri4sHIlBp+P+N3aGiJW5k2y8+FEFi6/N5XfvlWpBkyXX5tJkd/PSfw/5pwEFHBtp0vP7m8fj8vowG/QsXrdbe9zvZo1hSKyZf+9vCKqiWbmpjAVXZNHY6Q4RQctuHMultkTeKZ5MQ2cXL2w5KrZSYs3Myk8jOSaC5+fkkx5v/drvkci4CAQCgUBwYvR2YZYRb9Vac/7wYTkLC0dS3+7SLtRSYs3MGJ+GXgc5Q6I1fza1zUeSCJlcBN2+Kb0Y3ntlhegIPSPPi2FndRtDB0USbzVR0+bi5a3+dqXclFiizHruem1HUGVIYIa+t9fU32BBbxosI9ESVEFiNur44qvWPhnf9jyfablDGDl/MnXtXdjdXjISvn57jmj9ERwL8fshOJcRARdBCD37LOOtJuraXcSYjWHFSkaCheU3jqWhw8WiwlzaXR5q2lw02d08un5f0LE5Q6J5YuYYvmpx8Os3dlHT5iIj0cLKm8bh9PgYEmNmT01H0FSAwCqanJQYilZvI95q0ozpdBKMTInGYNBhGxwV9KWuGvz2DM6cDJMukXERCAQCgeDkIMsKB5vtmrH9jsPtmD7/inu+M4LUOAvPfFiumegG6oM1JVVMSI8je+YYEiJN6KXwlRZWk0Ez/p8xPg1JAr0ENa0OYq0mHnhzF5VNzhDdsXJjOfOmZqLvxSh2SIyZiobOkzZ5JZzXxdJ1e0IuVucXZPHy1krtmP76Yeyp6TipBqYiESU4FuL3Q3AuIwIughAaO7u0YEZUhB4JieUb9nP7ZFtYEZMYFUF1s4M//vcgS6fnUd/uIsZiZNXmA0Hrmo069BLY3T5cHp922/yCbEalxqHTSVQ0dIYIjcDKGK9PweWRg4zpAC4dlsjQQX6REfil3tDRxQ+6+7XV9YRJl0AgEAgEZxaHmuzM+9sXzC/IomiKDatJT7TZyJwXPyXeauLXV48IMetfuamMP9wyHqNex5HWDow6iYQwrUBLp+fx5/9W8PD1o6htdwXdd+cV2fz+w3IKR6fyzAflQbrjmQ/81SOKAq+GaYd++uZxJz1w0ZvXRXl9J0VTbIw7Pw6rycDP13zZa7VNX97rgTAwFYkowbEQvx+CcxURcBEE4fXKVLc4efEjf0tOcUGm1gvdc/yi2ajjwetG0dTp4o//PciPL8vE5fVxfkIklU0OfjVtBI+s36tljJZOz+PRf5VqPy8sHInd5WF8etxxTbUyEiw8MXMMiVERfeoBVb/UhUmXQCAQCARnPup+bXf7eHpTOT+7PJMnN5RpSZbSuo6w+/m2qlZtCqIkwR//e5CbLkzn8ZljcHR5aXa4SYs384urcujy+rj39eDWaHXUsyQFr6u2KQVO9PHJCk/OHkt8pInk6Ah8Mnz3qS0nHLgIN+q5N68LryyTMySGy7KTONRkD1tt01c/DKGNBIKzi76OhRecmYiAiyCIPTVt2rhjCO6FrmlzsfrjSuZOspEzJIo4q4moCD3VLU4eun4UNa0ODjY4WL5hvz9IkmjhN9fk4vLIJEVH8PC7e7T+Y7VEdtWcfNIDeod7ExrVrU5WbiwnI9ESMt7wWD2gva1nMerZeqBRfGkJBAKBQHAGoO7XcNSPpWdQINx+rihHAycPFI7gx5dlsvjt3ZpGWHRNLm1ODw+/W8pPL8vsdcqhTw5+nktsCegktGBLz9HUj94wmmhzeDPbvgQuehv1fNWI5JD2oYeuH8X49DjSE/x65ev6YZwOA1NxwSgQnBj9GQsvODPRne4TEJxaZFmhoqGTrQcaqWjoRJaVoPuPtIVmPVQBBP6gy4sfVaAg8dmhZjw+BaNeh93tI9YSoQVbUmLNzM5P5yd//Zyf/PVzbnnxE6bmDCEl9uhm7vLIGPVS0JeFKiICRdf8giw+LK3nZ5dncs2YVJo7u3i1aCKvFF3MO8WTj/mFE269B6/Lo/iVL/je859w9cotrN9dG/I+CAQCgeCbiyRJUyRJekuSpMOSJCmSJP2wx/2SJEm/kSTpiCRJTkmSPpQkKbfHMfGSJK2WJKmt+89qSZLiehwzSpKkf3evcViSpAckSZJ6HHODJEl7JEnq6v77+v6eyzcBdb9+e/thiqdmaV4sKuqEw8D9vHhqFv/8vBroDpxIOv7+SSWr5kzgqe+N448/uJC0uAgeftdfXWuNMAStqa4zNi2OdTsOaz8/dP0ozk+wkDMkhhaHO+xo6nvW7sDeJYddry+Bi97aeqpaHEzLHcI7xZM1nXPd2FSGDooKmkLU85j+XHyF00YDaWCqXjBevXKL0F4CQT/p7bviUJP9NJ+ZoK+c0oCLEDinl75seDHmYDGyeV89C7tHMwJatuh375WycmM5t/7xUyoa7Kz6dzkeWda+DMKJk5Wbyrjv6hFa0MVs1JEcE9oKFCgi/nzbRby7s4ZpeSm8+FEFT28qZ9mGMsob7Fw0NFGbDNAbPddbUzSRpzaVBVXaiC8tgUAgOOeIAnYB8wFnmPt/CfwCuAO4EKgH3pckKTrgmL8B44Fp3X/GA6vVOyVJigHeB+q615gP3A0sCDjmEmAN8FdgbPffr0mSdHE/z+WsR92vH7thDGkJFnLPi9VGQ4PfsDYpJoLlN47lqe+No2iKTTO2hW5j3AgD++s7+eRgCwAGAzQ7PNqef7jVERK0WVg4kg5nF/dMG0FxQSbzLs+kscPFpwdbiLcaeLd4MqNSY8JWshxstFM8NavfgQtZVmjo6OL2yTbmTc3UdJFaHaNWsSRFm6lrd3GoyR4SnFBbpyfaBh1XC/X2Xp9owKa/iAtGgeDEOVYLoODs4FS3FKkC5+XuPz1RRcUPgX3AA/hFxXBFUTq6j/kbkI5f3AC8gF/gXANBAmczfmGSA/wJsAO/6z5GFTiLgH8CM/ALnP+nKMon/TiXs4reNrzhd0xGkqDN6SbeamTJtXk88NYu4q0mvjMqhVWbDzB3kg29DkanxfHsh8EBi5Wbylh241j21bZrJarhSoFdHpmy+g7mTMxgTUkV90wbEVaUqCJiaGIkOw+38a2cpJDgzX2v72Ts+XH96jVWFGiyu4PGKqrrib5lgUAgOHdQFOUd4B0ASZJeCryvO0Hzc+ARRVHWdt/2A/yBjpuB5yRJGoFfh0xSFGVr9zE/ArZ064R9wC2AFfiBoihOYJckSTnAAkmSlimKonQ/zweKovy2++l/K0nS5d23f68v53KS35rTik4nMTg6gkNNdor//iUpsWYenzmGw61OspOjWPz2biqbnGQkWvjxlEzNx0Sthq1pdWiTiyCVu/9RETRS+c//reTHU2wUTbEhK/4ph2ajjiPtXazYuJt4q4k5EzNYtiG4dSjvvNiwLThdXpm/f17N3Ek2RqfGkJUcfdxWmXDtAapPTIvDTVK0+ZS0EPQ0MFUroAei5Ud4xggEJ87paAEUnFxOaYWLoijvKIpyn6Io/wCCvnl7igpFUXYBPwCi8YsKAgROkaIoW7tFzo+AQkmShncvFShwdnU/16P4BY66c/ycboGjKMrebqHzYfftfTqXs5HeNry9Ne1s2FPLVy1OGjs9PPOh353/vqtHsGKjP7jyzAfl/tGIf/uci22DQ9YorW3n1ZLqkExPIGajDp8MKzeV8btZxxYOqtgofuVzzo+3hj3vuvbjR3Z7VvVs/6r1hMt/BQKBQHBOcAEwBHhPvaE7YLIZuLT7pkuATuC/AY/7D/7kTuAxW7ofq/Iv4DxgaMAx7xHMvwLW6Mu5fKMYmhhJeoKF4oJMbpiQBoAOhZ/+9XMtYVLZ5OTZzeU8MXMMj84YRdEUG+f///buPD6q+t7/+Os72Sb7BiQxkEAk7CBCxOUKbUEtrbQquLT602r1crso3HrttVZxv1qrtZXibWu1deltxdbdulWwRStqcWETZAkkggmEELJPtjm/P87MZNYsZCAJeT8fj3mQmTlz5szXmPOZz/l8P9+sREZlJeO2LG5eMInMpFiuml3E/jqXr1K3otZu8j8hNw1nrIOSwiyGpcTT3NaBq80dcepQjIOQKTje6UzeqdbFOak9qjQJd/Fr+ertXFAy0lcdczQqQvynmO8+0HBEp/z49+fxUuwl0jNHewqgRN9AapobNqgwxniDit/QfYDzKZEDnDuwA5xdnm1+GfT+rwFX9+JYBp1IGdIYh6HDgspDzTgwlFU388yHe/jxVydy1ewiwJ47XeHp7xITlKbzJlL8m+qmJMRwxzlTWPb8ppArOK42NwcbW7tsnuYfbOyvd4U97rYOC7fb6jK4CQ5anloXuqyj/miJiIifXM+/+4Ie3wfk+21T5alSAcCyLMsYs9/v9bnAnjD78D63y/NvuPfx30d3x3LM2V/f6lsh0bvKYWZSfMAyyGXVzbS53dQ0tzIsOR5Xm5ubnttAZlI8l51aGHCev/Vrk1k6r5gOt8XMwkxiYwwTcvN85/6MxAQeWlMasTq3ss7F/Mm5TFgym311Lto6LJav+pSFM0YS44CTCrMoyEzq0WeLdPHrxFEZfGHcCBwOc8QrQoIraPxXpPS+VzSWifbqa5NfkaHMOwVwwpLZ7K93MSJVTacHm4GUcBnUAY4xZjGwGKCgoCDcJv2uIDOJexZN4/qn7YDkgpKRjB2ewsHGFiYdl0p7B1Q3tPCj+eNJjIvhh39ZH7bcdUJumi8B4p3//NCanYCddHnwzR044xw8evlJ/OLC6XxSWUeHG99ca2ecg/hYB19d/lbEUln/YOMP75azbMEk7njpk4DjWfb8Rn5/+awug4HgoKWi1sXja8t47IpZWFj6oyUiIseUwRCPtLe72VxRS0Wti7z0RCbnpREb66C93c3Hew5x/dMbAr78L3t+E4vnFLF81Q7fPpxxDg41ttLU2sGE3DS+84cPfFUq3mSL9/W3vriZJ749i+yUhLDn/Kn56SFTo/3fZ0SqM2AKTnu7m5qmMb7jDI5julqRJ9LFr8IebDM8xRkw7acgM4nymqZeTwMKvhjlvyKlV6QEz+GsNqQvjH2jFZ4keAqgDC4DKeEyqFmW9RDwEEBJScmAa7vudlu8vmUff92wh4cuncneQy7fsoklhelcfPJo33LQ3rnQ3qtJ3nLXxXOKSI6P5dd/3+Hr6XLq8dnUN7exdN64kNfvb2jh4TWlzJ+SF7CU4p3nTuGWFzZ1eSXFP9ioqHVR72rjytOLMMbuxeJN3pRVN0Y88bjdFknxMSyZNxa31VmlU9PUyvDUBP3REhGRcCo9/+YA5X6P5/g9VwkMN8YY70Ugz3TkEUHb5ATtO8fvua62qQzarqtjCTBQ4xHvl8ba5lZ27G8MqIC989wpLJicx0ubKyg/2BT2y39BVlLAxZ77LjiB5tYO7nntU66aXeR7TaQqlXpXO9kpCby3qzrkS6s3ITAxN5VRWcnc9NzGgERKcCVGeU1TSFLIG8eMzk7usv9KT6o9Im2zq7qBq//4UcC4eRcC6E2fl3AVND3pEdGX3jL6wnh4tCSwyOA3kBIugzrAGei8VzN+9f9mYDBU1DZz1ewiNu85xJVzirjqsXUBgcMDq+w+Lg++ucP32MiMRKobW9mwt44Ne+sAGJmRiGXBmOFJLJ1XTGNrB5YFj6+1q2G8qwh4kyUOA2OykymrbiYv3cnCGSPxdtY52NjiOxEHBxvNrR088nZpSDDw0WeHaG5zh5x4IjWl66pZr4iICHYlbCVwJvAvAGOME5iNvcoQwFrshQBOpXOa86lAst/9tcA9xhinZVneuTBnAp8Du/22ORO41+/9z/TbR0+OZcDzPyf/9PwTfMkWwDMVaBMFWUnc9NwmrppdFPbLf7ozLqDZbXJCDNf9eb1vO//XhHu9w2G44tH3fU137zhnKnExJiD5MmZ4CoXZycwoyOiyEqO7VUPC9V/xXlTqSbVHuG0cBuY/8FbIuHljtd5MAwquoPEuud3ddOtIvWWiNfVIQmnMRQa/gZRwUYATRcHlh9WNLYwbkUJVXQs3v2BXthRmJ3LN3GL21jSHDRz8F9J2xjkoO9gc8tieQ80sX7XDl9DwVpF4jctJpaap1TfN6P4LpzMsNZ4ff2U8yc64gGlCxSNSmOHpyRIcbOSmORmfmxaxq3/wiSdSU7qVi09han6GrgqIiAxhxpgUYKznrgMoMMZMBw5allVujPkF8GNjzFZgG3ATdg+5PwJYlrXFGPMq9opFiz37+Q3wkmeFIjzb3gI8aoy5ExgH/Ai4zW9q9APAGmPMj4DngPOALwGne97H6u5YBgP/c7LldvsuwkBn9WmlJ4nx9Ad2A37/ytg7zpnCb9bsYO+hFhbOGIkbqKprCUgYeF8TLnmwdF4x2yrrWDAtn2c+3MNFJQUsfmJd2IqBnlRidLVqSE/6r/TkPYK3WbvzQLexWk/7vARf1KppaqU4J4W/XjObqobDSzTpy/+RoTEXGfyOasJFAc7REa66455F0/jeF8dy96tbfIHO+JxU7nt9K9edNSH81SDT+bO3OuSc6fm+x5bOK+bxtWUAZCbF42rv4MazJ5LujKOprYMOt5v89ER+e9lM4mNjyE1LwO2GD8oO0dDa4Vt2ETpXAphyXDoAZQcbSY6PJSctgVmjs3E4DAVZyWRfMYu3dhwImFYEsL/e5evsv6/OhcOYkAZ7rjY3zW0dACFLHwIR58dq7qyIyDGnBHjT7/5tnttjwOXAT4FE4EEgE3gPOMuyrHq/11yM3YD/Nc/9F+hsvo9lWbXGmDM9+1gH1AA/A+732+YdY8w3gDuB24GdwEWWZb3n9z49OZYBy+22qGls5d7zT8DtdtNu4atY9Y8vRqQm+FYSenVTBfdfOJ2tnh5wK97czqWnjCbOYbj71a242twsnTeWwuxEFkzLt6cbY3HtGcVkpySQk+b0Vd06DIxIS6CqvoVUZ0zASkTeStutlXXkZyQyNT895PweLgboblpQV9NzDjemiJTkGZeTytVz7dD6xfV7e7TyT1dVNsePOLxE05Ey1GMwLQksMvgZv/6zR/7NjPkigQGO12OWZV3umR50C/ZSz96g4vueZZm9+8jEDnC+7nnoBeBqy7IO+W0zFTswmYUd4PwauN2/2a4x5nzsAKcIO8C50bKsZ/ye7/ZYIikpKbHWrVvX3WZHTGlVg68hrZczzsH/XnIiWysaAq4aLZlbTIozhsaWjoCrQdeeOY6iYckcbGyl7GAzL23Yy8WzCnFbFo2tHYzPSeWul7d4Gt45ufy00fz8jW0hAdQlJxcyPDWBRlcbx49I5WBjKxbgauvguj9vCDn2FRef6CsR9iZ1inNSmDs+B4fDsHN/A99+7H1fgAV2gPHYFbP4pKI+IPhZOq+YVzZWMHvcCIyBGAPnTs9nS2V9SJAUH2sC5kV7r3YBmjsrIhIlxpgPLMsq6e/jGCr6Ox5xuy1Wf7qPz2uaOdDYytT8dK7500ch8cn9F07nuY/KOWPicdz8gj1N5pG3S8lMivdNPY4xkBwfw12v2NfXpuWn8c2TC3396JxxDm5ZMJk/vV/GVXOK2FvTzPCUBMprmvjzuj3UNLWybMEk6l1t/OSVT8lLd3LpKYUBMVHw+b2r/hlgX6jxJiz8G9i2d1jc9PzGkN4qcHgxhdttUX6wkQ/LDwX0y7v9nCk8+GZnD5c7z53CuSfkExvriLivvmhvd/NOaTXryg7ituz46/r5E49YTDTQ+5ccjWTQQB8DkcHqaMYjRzXhMlT0d4CzducBvvnb90J6pMweO4xv/f79kEDn6i+NJTUhluqmVt/c6OykeH73zi5+NH8irR1uPj/UzONrOytKls4by288Swhed9Y4VnjmDwfvt7XDTdGwZHLSnezcV096UgJl1Y2cMCqDm57bSGu7FTaY8h57jMOeljTluDQKspJ5a8d+Pj/UEhBg3XnuFKaPzGDBirdDjuHnF07nB34nqf85byrV9S5+/07nZynMTuSG+RNpae8gIzmeqvoWDtS3MH9KLm6LsMmrl3s5d3aoX6EREQElXI62/o5HSqsaeOOTSjosePJf5fznGeP4wcr1Idtde+Y47v/bNn7z/04kMT6WQ81t3P3y1pCEyO1fn8wf3i1jw946vv+lsWF7uy2eU0R8jIOi4SkBU4u9z997/gn88C/rfUmd4Odfuvp039LMSfExXPTQu93GAJEqi/MznGQld66MFOmCWFcxhf++vStMjstJZVRGIktWfkRZdXPIvvwrfnsSc/QkRon0Gc+eknfEEjw79zdw9i9Dx+uv18zushLnaAg3HnefN5X0pFiyk52+lbd6ItKqXf7v5Z/cUwwp0ndHMx4ZSD1cJEpy0pwUZify7dPG+JIoKfEx1DS1hswDzUyKZ3R2MqUHGhmXk0qMgb2HmvndO7u4Zm4xu6sbiY9xMCwlgZqmVsA+2RUNT+a2r0/mlhc2MzwlIez80vzMRG54ZmPAlaf7Xt/quxJzz6Kp7Ktr4f6/dVbG/OCMcXxp3DAuOKmArZV1uC2497WtLJ03DrCnIz20pjNA8jaN+9UlM8Iew86qhoBtb3x2I4vnFHHpKYU88a49HeriWYXc/eoWLiop4Hq/4x2R5mRUVqLv9f4JrKqGlh6f8HR1QkREjiWRvqCH6x+Xl5HkS3DsPtAYdnrEmGHJFGYnkuyMw9XWQWpCLBeUdE79AfscfvMLm7n/wunc8dInEVcjOn54Co+8VcpZk3PDPp8QaycKdlY1BFTQgN0LZnNFHff/7VPKqptZMm9sxP4Z/kmNpPiYkL5x1z+9ISSR0pN+HMFjaFmdTXgral2+vnm//OaJAckW77721bnYGqaSN1LM0dMYJVxvvOuf3sDU/PQj1kuk7GBj2PEqP9jY7wmXcONxw7MbPYm8j3pcbdTe7ua59Xu56bnAVbv8X6sVnkQGNyVcjkGjs5P5ycJpfPxZZ3LCGefgFxdNDwh08tKdXHZqIdf9pXMKz7VnjiM3LYE7z5lCRV0zT/6rnB+eNYGEOAcPXTqT/fUtJMTG8Ng7pcybmMviOUXkpoefX7rrQGPAiei2lzYHdNPfWdUYkjz5+Rvb+NUlM/ju/30YMD3pgVXbuPf8E8hPTww5+WYmxZOUEBv2GAqCOuy72ty4LVi+2l6FCeDnb2zjytOLQgK7Hz+7kT9ceTLOOAeZSfEBV9oefqu0x0kTdZgXEZFjRaQv6GdNzOH1LftCKiDaOty+RMef3i8PaYi7bMEk7nt9K9/74lgKshIZmZHMlspaCrKSwn7ZLq1qsCtgTfheKQkxDm48exLOuJiw1bdjR6QwOjuZTypqSYyLCWmuu6+2mQXT8nnwzR24LULipgtKRtLc2sE/dxzwTRvqKjHjf57vrh9HuGqW4hEpYfcdY0zYfaU6Y1lbWs1Vs4tISYgh1mGod7Xx7q4DHJeeSEFWcreJlHAxSqRk0b46l+/5aFfwJseHj+2SE2JxexZZ6C/VjS1hmz97E4E3PbeJ4hEpnDAqs8v9bK6o9SVboHevFZHBQQmXY5DDYXAY4wsiwP4DfvcrW7jla5N903EuKBkZss39f9vG4jlFTMuPpbqhje99cSz3vr6Vi0oKQnq/ZKfE4YxLwmE6y4H9A6gVq3cEHJerLbCbvtsKf3Xqo88OBRyTNzmyr87F57XNYZNG68trQoK4H5wxDmesw9dM7ukP7DncltV5LA7T+XO4Y3m3tJo7z51C+cGmkIRMT5Mm6jAvIiLHikhf0FcuPiVsBcRjV8zyVbHUNLXyxLtlvi+qDgN1zW2UVTdzywubeeyKWRRkpTAxN51DTW1hv2wXDU/BAZQfbOQHZ4wL6B93xzlT2N/gIiM5jqn56V02tj3U3BYSAz2wajtXf2lswJdob2yRmRTPZacWBiRovKslBidmvMca3Nh0dHYyKy4+kQ17anFb9lTqqSPTGZ2djNttsXHvIbZW1nHN3LGkxMdy96tbIy6TvWN/PbcsmMxtL3VOsV5x8Yls29fAQ2tKfcd73+ud4xPcFw+6TqT0JFnU1mH5pkn1pJqmN1OdctIS+PFXJnCgsdU3XllJ8fzwL+u59szxR2w6U7jjhM7FFfLSnXx+yBW2+bO3U4OrzU1lrYsTRnX9XhW14cff/7XRnJauKe4iR58SLseoxpb2kD/gZdXNJHnmN7stwlaLeCtAmts6KBmdyaWPvB+2+mP56u386pKZfPf/PmDciBSuOH0M919wAp9U1mNZUO9q801B8nLGOfBvGZQSHxP25F08wu64771a4GpzE+OA7fsb+PO6PSxbMMm3nLQ3aXTV7CJeXL/XF8Q5Yx0kxjlY8uRHAYFGUlwMv15TijPOweyxw3DGxfDQmlLfewcfS3Obm1+u3s5NZ0867KSJOsyLiMixItIX9EhfHB0O+OmiaVQcavIlLx58c4fvS6p3tUNXm5um1nbfa49LT+Su86YGNIldMreYe17dwu3nTPH1e/Oe9y0LahpbSHbGkZvm7HIlntKqBt7fdTDs8bZ2uH2xSkWti5Xrylm5+BRcbe6APnj+F4TCLWV9z6Jpvi/q/lrbLV9C5IKSkTS1drCzqoGyg40BzfuXzismMyk+7L6XLZjEyvfLiY81/ObSmVQecjFmeDLDUxL4iif5sXBG6EW1B1ZtZ/GcIoanJDA1PwOHwzAiNXIixb+KJNzKTPcsmsay5zf26GLU4UyvHpmRRFJCLA+9sjXgYlpru8X1T28gMyme08cOi2rCINJx+i+usGTe2JAK7eWrt/umu3nHMDe9+zgvLz0x7Ph7XxvNaema4i7SP45Mlyvpd8M8yyv6c8Y5yEiO58/r9rBi9Q4+O9QcdhuHscs499W2dFn98dFnNbja3MweN4IbntnIJ5X1PPxWKQ++uYPH3iljydxi3/69PVxe2rAXsBvV5mXYyzb6b3PtmeO46+UtPPxWKZeeUkieZ7rS9FEZDEuOZ9HMkaTEx3Dl6UVcPXesL2n09Ad7uKikgEfeLmXF6h20dri5y3OC9h7vA6u2U9/STk1TK/dfOJ2TRmf5roC9uH5vyPH+4IxxPPPhHsqqm0lLjAs7Vj1JmniDFP99+19lExERGSy8X9D9OeMcvvN18OPZyQksmHYcMwuzWLmunCtPL+KeRVNZPKeIJ97tbGDvjHNQkJVMe7ubt3cc4NmP9xIXY3zn+ytPt7cvq27mQH0Lzjh7CekH39zBitU7eOTtUjosOyFUWtVIaVUDAEXDUzilaJhdGeNX1eGtSgk+3hNHZfhiFWecg+vnT2RqfgZuywobCxnTmZj56fkncPXcsfYU6AxnyJdYb3WQd5ryQ2tKWfKnj/naircprWokMynet98HVm1n4YyRActkL5lnj8NDa3ayaMZIzpyUx3888QHXP7ORy373Ph99dsi3j0ixm9uCVVv38+rmStxuixgHLFswKSBGWTK3mGXPb2TXAXsc1+48wO7qRs6amMPLS2bz5OKTeXnJbI7LcIbtI7O/3hXyexOpMmp3dWPItl7lNU0h021+/sY2Fs4YiavNzbqyg12+/nBEOs4Ne2p9j0Wq0C6taqCi1uVbpGFyXnq37zc5L407z50SMP53njvF99rDGbfefrZoj6GIBFKFyzGqpb0jpNR2ydxibn5+E5edWsjja8vCXjVZOq+YkZmJNLS24/DMD4bw1R8dbntKT0FmZ9LDuz9v8PG/l8xgV1Ujw9OcHGpq4b/OmsDuA43MLMzgysfWkZkUH1Ba3OG2fMHX8tX2lZhRmUl8XtPEgcZWVqzewdJ5nSsTXD13rC/o8pYpxzhgYm5a2JNh8YgUXwd/byA0f3Iu43NSqaht4tErTmJvTTMVtS4efWe378SZk5rQZWlyV7q6yiYiIjJYuN0Wu6obWDqvOGBqzf0XTmdyXuQpPA6H4YSRGVwzt5ibntvkm+7i34z//gunU5iVxF83VXD90xtwtbkDzvdezjgHyfGx3HnulIBGozfMn4Cr3R3Quy7c1Xu326K9w/JdaPGPge4+byr/VjSM318+K+R8Hala1fIkbi4qKeDul7dQ09TK4jlFTD4uLWT8vNVBC2eENgS+/2/bfH3uvI/5VpkcNyJkxaXqptaQKosfexYGWL5qh+/4go/XjrXwVaLsq3NR72oLqBTyJsK2VNZx3Z/Xh4ynf/VKTyt4D2d6daTXGNMZh0Z7enak93QHLeoaqW/g1XPHYllQkJXYo+lOsbEOzj0hn+IRKVTWushNdzI5L9332mhOS9cUd5H+oYTLMSJ4TmZWUgI799fyyLdKeG/XQTrcnSdQb0np8lU7WLmunF9cNB2AjKR4YhxQ29TG1X/6iP88o5gff2UCrraOgGk83nLWZz/8jEtPKfT1VQlOepQUZrG1opb739geclK645wpvhJkb3AB+PqtgH0SGJmRyIGGFhpbO3yPP7Vujy/Ye/qDzp8rau35tEvmFpOcEGG6Uk5qyEnF4TAcPyKF40ek2OWWjZW+RnveAKMgK5mCrOTDTpqow7yIiAx2uw7Y016CL5ZMzE0lNtbR5cUF7xfLCbmp1DS14Xa7Wfnvp9DU1uHrJbG7utGXbIHA8733nHzXeVOZkJfKyIwkikeksGrrfjrcUN/SHtAkN9L0lt3Vjdz0/EYuKinwVdzEOGBGQSanHz+M2FhH2PN1uCk1Ny+YRKoz1letU9PU6uuTEu6CjDdpE6n6xL/PnTc5AhDjCN0+UpXFuJxUnHGOgPgo3NRq7xftnDQnza0dYRNb2/bVdzme4cYk0sWow5leHek1DoOvZ8qiGfkRX384unpPr3Bju2RuMXe/vMV3oa43xxUb6+CEUZlh+71Ec1q6priL9A8lXI4B4eZk/uyCE1g4s5B3dh7wXenwsis9Uln+jenExziIizFkJMbR1OrmvlWf8q3TirhqdhFxDgdpiXHc9cpWMpPiWTyniOOHp1BV72Ll++V854tjfaWx/pUt3qTHfa9t5StT80KSNUvnFbO/3hXxSpH//RRnHA/+fQfnTM+nw7NpRa2Lx9eW8ci3Sqh3tVNe3cjVXxqLq92ed71yXTkzR08LewWuu4qU7qpRlDQREZGhyO22KD3QEPZiyYmjMhjjmbIT7jzZ3u5mc0Ut1Y0t1DZ1cMOzGwLOzSePycbhMCFX4L3n+/vOP4HkhBgKs5MDzslT8zPYe8jFtU99zFWzi8ImIIKbv+6rc1FW3cwT75b5loTucGOv6BPriNhU1OEwnDUxh4e/VUJlrYvyg038cvUO4mMNd5wzlRNHZZAUH0tOWkLISkBeo7OTuWfRNEqrGrr8Um/HcdMpzE5kcl4aw1ISAqpZgIirNE3MTeNlTwyTm+bkzIk57KpupKXNzWc1Tfx6TakvKeCNcaaOTA+b2Lr3tU9DxtO/GqInFbz+4/nbS0t8Kzv1JC4Ll9BZtmASdc1tPPmvcq6fPzHq07MjJZHiYztXhappaqU4J4W/XmN/7rYOi2XPb/SNazSnjfcmqXU09yUiPaeEyzEg3JzM//rzeu49f1rEzvkxBu54eavv5HDdWeN44t0yvjNnLD/0Wyba27StotbF8lU7fI9t2FtHaVVn4OW/6sDMggxufXEzZdXNbNvfwI1fncjSecUUZCeRkhBL2YFGHvnnrpBS3tvPmcKDb273HeOyBZP43ds7+cZJBSTHx/Crf5T6PkNNUyt56YmcWpQctgHYzFFZ5KQ6mVGQSVNrOwVZyYwZ1rOKFFWjiIiIdPJe2Pm0si5sTJEUHzmcbG3t4PkNn7Ps+U1ceXpRQCVFcNVEuCvwNU2tJDtjGZOdFDaRMSkvlceumEWH5ebht7pv/up9D/+kkbciobumouU1TVzlmQ69cMZIFs0cicPY00dGD+s+ZnA4DGdPyeNfZQcZkeYMuBj1swumM/m4VE47PjsgcTElPwO32wr5ojx1ZPgpXN5Yxz+GKRqewqubK8NehHI4DHPH5zB2eEpAzBTjIOziB8HVEF3FTOHG855F08jPcJKVnNBtpXBwQmd4ipMYB1TWufjy5NwjMj07UhIJ8CWy/P/7eKujw01DO5LHczj71xR3kf5hLMvqfivplZKSEmvdunVH7f3W7jzAN3/7XsjjT3/nVN7ffZBhKQnsqWniqXX2ssjeJZu9vVIA7lk4lfKa5rAlpf5zigEe+MZ0MpNiSYqPY832KtwWvhWFnHEOnlp8Cs1t9moD2cnxtHa4fSdWt9ti6746DjW10e52kxQfS2OLvd3tL23m5KLhJMQ6GDs8mXa3m+yUBI5LT2TngYaA7v3+AZD36olOHiIiA5sx5gPLskr6+ziGimjFI6VVDXx1+Vu+Zq/Bvd++MiU3JOHgdlvsOtDI9v31bKmo46l1e1g0cyQrVu8I2f/vLy/hC+NGAIR8QfeuyLNtfwN3nTeVGQUZFGQlh2xbmJ3IknnjuDFoVaOV68r5/eWzfAmBrpIqu6sbfUsceznjHLzsSQhFireeXHwypxQN6/F4ut0W5Qcb2VfX0uOLQuFiHaDH8U9vY6VorGjj/b2JNJ4iIv3laMYjqnA5BoS7IlRSmE75waaAqxnLFkyiICuRB97YFpBsccY5SEqI7fGc4n21zTS1xnPbix+GBDVL540jLTGOKfnhT+QOh2FSXnrISfyu86bS2m4FJHbADmLGDE+hMDs57JUF7z5VkSIiInJkeKf6eCta7Qb7SQBkJccxMiMpYPtwX9aXzC3GwgpbIfPRZ4dobnMzf3Ku7wp8WXUjH312KOACkbcp7ITcNMbnpAZU95ZVN1Nd7wrb/LWn02C6ayoarR4YDodh9LCUHlXF+L8mXKzT0/int7FSNKoh1KRVRETLQh8T/Jcdzkt3cv388Vw9dxw3eK7ygH2Cu+OlT2h3w5Wzj6cwOxHAd3VqT02T776/4DnFS+YW09phcduLmwP2vXz1dn40fyL3vvYp8x94y7fcYDjhpkD9+NmNfPeLY8lL7wxa/IMYb6AQvLSjiIiIHFneRIOXZcEP/7Kea/70EVc+to7Xt+wLOOeHO88vX70dhzEsmVscsgTxn9ft8S1P6z3fO+NiWL4qsBrXu1rMtU99TNnBxpAv83UtdvPXFat38OCbOwJ6lfiLFFMEf07vMXpf7x9veZ87lntg9DX26m48jyS32/ItaV1a1RAxJhUROdJU4TJIeUt1yw42khwfy8TcVF5ZcjobP69jx/4G9tWGv6rwQVkND79Vyi1fm0xbRwdZSQm0dripqG3m3Z1V3HHOFJY9vymg8uRAvcu3zN0T75ZxQcnIsPtu95zMXG1u7nl1C/kZTppaOwKazkHkKx5bKuu4oGSkr1fMsRzEiIiIDBb+zTbDLWkcvHpN8Hk+L93JwhkjyUqKp6apld9eNpPNn9dT72r3VaFA4BK/XS3D7Gpzk5wQG/L8i+v3cs+iab6VjnobS3TXVDRaPTAiNeY91vRXk9ZoTIcSEYkWJVwGIbfb4pVNFfz0ta0smJZPjAMm5qUxIjWevTXNPLSmlKtmF3UZqNz24mYevHgG3/9j57SgO8+dSklhhq/rOsDPXt/K3Am5vLh+Lwum5XNByUhOKcri+Y8TKatu9u27MDsRA1x75jjqmtuIdRgueujdgBPdpLxUKmpdJMXHUpgd+HpnnIMON0zLT+fJxSerF4uIiMgA4XAYzhg/gkevOMkz1bgI6OzfFjxNxD9Zkpfu5PLTRvPzN7b5YoKfLJxGXnoC+RmJfOu0Qh57x15S2b/yIdyX9SVzi3ni3TKccQ5yUhNCnr9+/kTOmpjD1Pz0sAmR7hIdPUmo9HUa81BKBvRXk9ZwFVbhlggXETkalHAZhHZWNfDT17ZyUUlBQOO6/zlvKk/+q5zMpHiS42NClmP2Bipgn3zW7zkUcDK66bnOudGT8lJ55qO9rCurpbXdYvGc4337emiNg1u+Nplf/2MHZdXNFGYn8p05Y7kuwupG3hPd4jlFvuqVO86Zwoo3t/uWBvT2gDlr0ghOGJUZ8pn9g6Sk+FhaOzrI7kGHexEREekbt9ti/eeH2HWgyTel2D+uCE6WFGQm+SpmLzm5wJdsATve+NEzG7j6S2NZ8eYOls4r5rtfKOK4zMSAygfvl/Xx18xmS2Ud2/bV+97r/gunU5CVTEFWctgv8+ESIj1NdBzpvnBDLRnQH3321DtGRAYSJVwGobLqRhZMyw8p6b3x2Y1c/aWxOIzhgVXbyUyKZ/GcIo4fnsLnh5p5fG1n2a63osSf/9zox66Y5VtSeva4Eb5ki3e7217czCPfKmFPTTOpCbFc++f1Ac8/sGp7wOpG3n17f172/CYe+VYJ7+06SIcbVq4r55q5xUzOSw/5vOGCpGULJvHsh1v49unHd3tVaKiU7oqIiBwJu6sbafGc+4P7sngv1PgnS8prmqhpbGHxnCIKs5PDfvkdnpLgixcWzyni38YODzk3e5fdHTMsmUl5aSFLJkPPm8YOlESHkgFHXrSaG4uIRIOa5g4y7e1ukhJiiHGEX1FoRGqCLxFTUeti+aodXP/0BkZnJ1PT1ArYJ52bF0zipQ17A17vP+WoqbWdF9fvZcnc4ojvtbb0ILe8sJnG1o4erW7kvwK5q82NwxjmTRhBSWE6P7tgOselJ1Je0xTS2CxckHTHS5/w7dOP555Xt7C7ujHieHmTNV9d/hbf/O17fHV51w19RUREJFB1YwvVDa1hz/WTj0sLuPDhdltU1bfgareYmJtGjCN8Q/6khFjfPtwWVDW4iCQajfO7SnQcTf3ZSHaoGGrNjUVkYFOFyyDidlu8U1rN1opaZhZkhs/epznDBhRuLJbOK6axtQOHgVgHfO+LY7nlhdDSYGecg9HZydxxzlS2VtRSUhj+vbzJmT01TWGf91/daOm8Yh5fWxbwvLfa5NXNlXz3/96LWOIbKUjaWlnHgmn5XV4VGihXtERERAar+BgHpQcawp7rE2JjKKtupLLORV66k08q6gMqUh+6dCZL5xXzwKrOKdDBqyM6DEc84dDTqocjXRXbX41kh5L+6h0jIhKOEi6DRHu7mw17D1Hb3IYzPoZaV1tIAHPtmePYHSEgisEwJT+dA/UtxMY42FPTxCsbK/j5hdNpbGmnvKbJNzd6xcUnsqWyM2AqzE4MWb3Ivx/MU+v2hDz/gzPG4bYsrp47ltljh1Hf0hZQYeMNLnqSEIkUJHW4IcbRdZCm0l0REZG+aWrt4Kl1e8L2hrvlhU3cMH8imyrqSI6P8cUl0DmF+L/OGs/iOUW4LXAYSI6P4Vf/KPUlX4pzUo54wqEniY6j0dB2oCQDjvXp1v3RO0ZEJBwlXAaB9nY3b++swmEMBpiQk8Zlv3+fzKR4rjy9CGPsAKbDbfGbf+5mydzigGa6S+cVc9tLn1DT1OpLlFTUunDGOdj0eR3PfLiHhTNG8s1ZBZwyJgsMXPa7930BU1l1Myve3M4frjyZelcbH312KGAZx5qmVkZlJfLby0r41267J8uj7+z2vcfCE/M5aXQWL4cJLnqSEBmdnRyyzKO3ye4d50ztMkjTPF4REZG+yUlzUtPUiqu1wxd3WBa+WKChtZ2H3yrljnOmhJzTy6qbSXd2hpvxMQ4aWzu4oGQkpx8/jBFpCRRkHfkv+z1JdBytqtj+TgZ0lVgCjulEjIjI0aaEyyCwp6YOC8O6shrcln0i9PZo8TalBbh67lgqal088W4ZV55exMS8VLbtqw9olrt8td3M9pG3S33TfCpqXTzz4R4uO7WQy37/Pv95RrEvoAJ72cey6mbWbK9i2sh0JuSmBVSreJMh6c44LppVEHD1y3v1KFJw0ZOEiMNhOHtKHplJ8awr62yye+2Z4zmtKLvLQECluyIiIn3jvfAR4zDc+/qnIefs4akJXU4xHpmZTGOrO+RcXDI6K+Ac7l91kZfupMMN++uj98W/u0THUKmKjZRYGn/NbD7dVz8klqwWETlalHAZ4FyudtaVN3DTcxt9J7+fexqBBQc0zli7OVhFrYtH3i7l8StmcfUfPwrcX5ubwqxEVv77KVQ1tvgSJxeUjPStbJTqjOMXb2wPqSbpcMPVf/yIV5fODqhWqW5soay6GWimavUOX7Jm9thhnDQ6C4DSqoawV0t6mhCJjXVw+thhjMxMZH+9i0Uz8nsUfA2U0l0REZHBLCMxFle7FVJFu2RuMYca7Vgi3LSj+y+czphhyYwZFn4JZy//qovMpHguO7UwYNr00fjiP1SqYiMllsoPqu+diEi0KeEywG2sqPUlW8A++f3k1S3cvGASt/sFNEvnFZMcH0Neul32e8vXJpOaGBM2cMhNT2TqyAwAX+KkybPS0MIZI0OWgF6+ejv3Xzjd93hlncu3UoD/fv2rbrxTiYAu50P3JiFyuCW4/V26KyIiMlh5EyGfVtaRGBfDynXlAdOKVq4r54KZowB7inFuWgJPLT6FpraOXi3h7F91sXDGyJBeMEfji/9ArYqNdr+VSImlpPjYIVHhIyJyNCnhMoC53RaVdS1h50OnJMT4GtBZFjy+1m54+78Xz6ChtYOH1+ykeMSkkMDh7vOmkhjvYHd1I6Ozk33BT2mV3WzXmPBLQO/Y3+DryRJ8pSdcgLLi4hOxLPjX7oPdXi1RQkRERGRg8iZCMpPi+c6cIi45uZD7/7bNd77/4ZfH8/Bbu3DGOVi2YBKxMYYp+Rm9Tgj4V11EikWO9Bf/gVgVeyQa+UZKLOWkJQyJCh8RkaNJCZcBrPxgI3np4U9+aYnxLF+1PuQ1H352iBWr7QqTrOQEZhRkMWHJbPbVuWjrsFj2/EbKqptDTtjek++nlXVh36+lPfKVnuAAJTfNXhby7F++xVWzi3S1REREZJDyJkIqal38ek0p3/1CEb+5dCZ1ze2MSI0nJzWBomHJJMXHktOHBrjBVRf99cV/oF0EOhKNfCMlloABWeEjIjKYKeEygNU2t1FR6wpZ/vnmBZMor24MG4xYVuCyy97AAeCry9+KeMJ2OAxnTcyhICuRUVlJ3PRc5xLP9yyaRn6Gs8u+Kf4BSmlVQ0BwoKslIiIig1NwIqShpYP/eOKDqPdW8a+6ePqDPSGxz1D94n+kGvlGSiz1psLnWF9aWkQkGpRwGcCaWjv44V82hCz/XNvcxuNry7jjnCkse74zMbJswSQKsxJZNGN2yEmvuxO2223x+pZ9vrLhxXOKGJeTysTcNMYM690J1P+9nv5gT0iDvaEaNImIiAw2/omQhTNG+s7nEN3eKuGqZc+alEtVw8CY2tNfjnYj355W+ByJqU4iIsciJVwGsOqG1ojLP9c0tZKf6fT1cXEYyElL4LTjh4c90XV3wvYvWa2odbF8lT0t6eUls3t94vR/L+8y1YvnFHHiqAwKs5OHbNAkIiIy2PgnQrbtqz+i04TDfdk/fsTAmNrTXwZqI98jMdVJRORYpITLAJYboX+Lw8BPF02jZFQWuWmJPSr77O6EHc2S1eD3qmlqZUJuGl8YN0KJFhERkUHGf3qypgkfXQOxkS8cualOIiLHGiVcBrApuenc/vUp3PxC57ShO86ZwoyCDMYMS+lVY7fuTtjRLFkdqMGBiIiIHL6BWm1xrBtojXzh6E91EhEZrIxlWf19DMeckpISa926dVHZl8vVzsaKWvbVtZCTlsDUvHSczujnyTQXV0REjjRjzAeWZZX093EMFdGMR7y8jVJ1QWVoU9woIoPZ0YxHVOEywDmdsZw0JvuIv4+qUkRERKQ7A7HaQo4+xY0iIj2jhIv4KIgSERERkZ5Q3Cgi0j1Hfx+AiIiIiIiIiMixRgkXEREREREREZEoU8JFRERERERERCTKlHAREREREREREYkyJVxERERERERERKJMCRcRERERERERkShTwkVEREREREREJMqUcBERERERERERiTIlXEREREREREREokwJFxERERERERGRKFPCRUREREREREQkypRwERERERERERGJMiVcRERERERERESiTAkXEREREREREZEoU8JFRERERERERCTKlHAREREREREREYkyJVxERERERERERKJMCRcRERERERERkShTwkVEREREREREJMqUcBERERERERERiTJjWVZ/H8MxxxhTBZT193H0wTDgQH8fxCCjMesdjVfvaLx6T2PWO0drvAotyxp+FN5HOCbiEdD/y32l8esbjV/faQz7RuPXN5HG76jFI0q4SAhjzDrLskr6+zgGE41Z72i8ekfj1Xsas97ReMlApd/NvtH49Y3Gr+80hn2j8eubgTB+mlIkIiIiIiIiIhJlSriIiIiIiIiIiESZEi4SzkP9fQCDkMasdzRevaPx6j2NWe9ovGSg0u9m32j8+kbj13caw77R+PVNv4+feriIiIiIiIiIiESZKlxERERERERERKJMCRcRERERERERkShTwmUQMsbMMca8YIzZa4yxjDGXBz1vjDG3GmM+N8Y0G2P+boyZHLRNpjHmCWNMref2hDEmI2ibqcaYf3j2sdcYc7MxxgRts8gY84kxpsXz73m9PZYjzRhzgzHmX8aYOmNMlTHmRWPMlN4e51AZM2PM940xGzzjVWeMWWuMObs3xzdUxiocz++bZYxZ0ZvjHEpj5nl/K+hW2ZtjHErj5TmOPGPMY8b+G+byHOsXenOcQ23M5Mgzikf6xCg+6ROjeCWqjOKXXjOKZ/rMDIX4xrIs3QbZDfgqcBdwPtAEXB70/PVAPbAImAI8BXwOpPpt8wqwGTjVc9sMvOj3fBpQ6XntFM971QP/5bfNqUA7cCMw0fNvO3Byb47lKIzXa8AVnvefCjzr+WxZGrOw43UO8BVgLDAO+B+gDZimsep27E4BdgHrgRX6/Yo4TrcCW4Fcv9twjVfE8coASoHHgVnAGGAeMFFjplt/3lA80tfxU3zSt/FTvBK9sVT8cnjjdiuKZ/oyfhkMgfim3/8H163Pv6gN+AU4gAEqgBv9Hkv0/HL8h+f+RMAC/s1vm9M9j4333P8uUAck+m1zE7CXzmbLK4G/BR3PG8Cfenos/TRmKUAH8DWNWY/H7CDwHxqrLscoHdgJfAn4O56ARWMWdqxuBTZFeE7jFTomdwH/7OJ5jZlu/X5D8Ug0xlDxSd/HUPFK78dM8cvhj92tKJ7py/gNifhGU4qOPWOws6uvex+wLKsZWAOc5nnoVOzA6B2/1/0TaAza5i3Pa71eA44DRvtt8zqBXvPbR0+OpT+kYk+nq/Hc15hFYIyJMcZ8AzsIfKeHxzckxwp72bm/WJb1ZtDjGrPwijwlmbuMMU8aY4p6cYxDbbzOBd4zxqw0xuw3xnxsjLnarxRWYyYDkX4ve0/xyWFSvNInil/6RvHM4TuXIRDfKOFy7Mn1/Lsv6PF9fs/lAlWWJzUH4Pl5f9A24fZBD7bJDdquq236wwPAx8Baz32NWRDPPMcGoAX4NXCeZVkbe3h8Q2qsAIwx/45d0nxTmKc1ZqHeAy4H5gP/7nnvd4wx2Wi8wikCvodddvtl7L9hPwG+73leYyYDkX4ve0/xSS8pXukbxS99pnimb4ZEfBPb1ZMixxpjzP3YZWanW5bV0d/HM4B9CkzHLjM9H3jMGPPFfjyeAcsYMx67JPJ0y7La+vt4BgPLsl7xv2+MeRf7ZPst4N1+OaiBzQGssyzrBs/9j4wxxdgByYrILxORwULxyWFTvHKYFL/0neKZPhsS8Y0qXI49lZ5/c4Iez/F7rhIY7t+Z2fPziKBtwu2DHmxTGbRdV9scNcaYnwPfBOZallXq95TGLIhlWa2WZe2wLOsDzx/Bj4Ef9PD4htRYYZcgDgM2G2PajTHtwBeA73l+rvY7Ln9DecwCWJbVgN3grBj9joVTAXwS9NgWoMDzs8ZMBiL9XvaQ4pPDp3ilTxS/RJnimV4bEvGNEi7Hnl3Y/9HP9D5gjHECs+mc27YWe47rqX6vOxVIDtpmtue1Xmdid2Le7bfNmQQ6028fPTmWo8IY8wCdwczWoKc1Zt1zAAlorMJ5Dnt1iel+t3XAk56ft/XgOIfamAXwHMME7BOvfsdC/RMYH/TYOKDM87PGTAYi/V72gOKTqFO80nPPofglqhTP9NrQiG+66qir28C8eX6ppntuTcDNnp8LPM9fD9QCC7GXrHqS8MtnbaRz+ayNBC6fle75pXrSs4+F2N2d/ZfPOg17uawfYf9xuQF7Ob7g5bO6PJajMF4Peo59LvYcO+8tpTfHOVTGDHvu5GzsJlJTgbsBN/AVjVWPx/DvhC6rqDHrPIb7sK+ijQFOBl7yfJZCjVfY8TrJc1w3Ys+1v8BzTN/X75hu/XlD8Uhfx0/xSd/GT/FK9Mf07yh+6c14KZ7p2/gNifim3//H1u2wfjm/iL3UVfDtUc/zBnuZsgrABfwDmBK0j0zgD55ftjrPzxlB20zF7rzs8uzrFjxLZ/ltcz72+vOt2CVgC4Oe7/ZYjsJ4hRsrC7i1N8c5VMYMeBQ7s9yC3XDqDeDLGqtejeHfCQxYNGaBx+A9QbViL8n3NDBJ49XlmJ0NrPccwzZgif9n0Zjp1h83FI/0dfwUn/Rt/B5F8Uq0x/TvKH7pzXgpnun7GB7z8Y133WkREREREREREYkS9XAREREREREREYkyJVxERERERERERKJMCRcRERERERERkShTwkVEREREREREJMqUcBERERERERERiTIlXEREREREREREokwJFxE5Kowx5xtjLL/7lxtjGvrpWF4yxjx6hN+j3z6fiIiIhKd4RESOJiVcRIYwY8yjxhjLc2szxpQaY+4zxiQfhbdfCRT1dGNjzG5jzHVH8HhERESkHygeEZFjVWx/H4CI9Ls3gEuBOGA28DCQDHw3eENjTCzQYVmWFfxcb1mW1Qw093U/IiIickxQPCIixxxVuIhIi2VZlZZlfWZZ1h+B/wPOBTDG3GqM2eQpR90JtADJxph0Y8xDxpj9xph6Y8w/jDEl/js1xlxmjCkzxjQZY14CcoKeDylxNcZ81RjznjGm2RhTbYx50RjjNMb8HSgE7vVeAfN7zWme928yxuw1xvzKGJPm93yS58pZgzFmnzHmx10NhjEmzfP+Xwt6/CzPVbcRnvs/McZ86tl2tzHmp8YYZxf7vdUYs6kHY/A1Y8wHxhiXMWaXMeZ/jDHxfs8vNMZs8LzvQc9nDxhbERGRQUjxSOAxKB4ROQYo4SIiwZqxry55jQEuBi4ATsAOcv4K5AMLgBOBNcBqY0wegDHmZOBR4CFgOvAicHtXb2qMmQ+8APwNmAl8CfgH9t+phcAezz7yPDeMMVOB1z2vO8Gz3XTgd367vg84E1gEzPMc75xIx2FZVp3neC8JeuoS4G+WZe333G8Evg1MBL4HfAO4savP2B1jzJexA8wVwGTP/s8H7vI8nws8CTzmed85wBN9eU8REZEBSvGI4hGRwc+yLN10022I3rCDkJf87s8CDgArPfdvBdqAHL9t5gINQGLQvj4G/tvz8x+xgwH/5x+2/+T47l8ONPjd/yfwZBfHuhu4Luixx4FHgh6bDljACCAFOyC7xO/5FOAQ8GgX7/V1oAlI9dxPBOqAi7t4zXeAHV18vluBTUGvCd5mDbAsaJtzPeNtgBmez1bY3787uummm2666Ratm+KRiO+leEQ33Qb5TT1cRGS+p4w0FvtK0vPANX7P77Esa5/f/ZlAElBljPHfjxM43vPzROyrMv7WAld2cRwnYgdcvTETGGuMucjvMe9BHY8dpMR73hsAy7IajDEbu9nvK57XnocdRH3ds9/nfG9izPnAfwJjsYOmGM+tL2YCs4wx1/s95sAOsHKB9dhz3DcZY173/PwXy7Kq+vi+IiIi/U3xSCjFIyKDnBIuIrIGWIx95ehzy7Lagp5vDLrvAPZhN7QLVhf9w+uSA/tK1c/DPLcXGHc4O7Usq80Y8xR22e7jnn+ftSyrCcAYcwp2Ke1twA+wr1B9HbtcOBI3ncGXV1zQfYdnn38O8/oqy7I6jDFnAacAZ2EHjHcbY75gWdb6nn9CERGRAUfxSBDFIyKDnxIuItJkWdaOXmz/IXbDObdlWaURttmCfRL2F3w/2EfYc5p/G+H5VkKv2HwITI50/J7Gem2e9y71PJYMTAF2dnM8fwDWGGMmAfOx54d7/Ruw17KsO/zeq7Cb/VUBOcYYY1mWt8ne9DCfZ0JX/z08r10LrDXG3A5sBi7CvtokIiIyWCkeCU/xiMggpoSLiPTWG9jzm583xvw3sBW7vHQ+8IZlWW8By4F3jDE3AH8BvohdDtuV/wFeNMbswJ5zbbCvmvzGcyVnNzDbGPMH7JUMDgD3AO8aY34N/AaoByYAX7Ms6z885bqPAPcYY6qAz4Gb6UGprWVZ7xhjyjzHcgBY5ff0NiDfGHMJdrDxZeCb3ezy70AW8GNjzJPYY3J+0Da3Ay953vcpoB07GJtlWdZ/e65knQG8hn1V70RgFPBJd59HRETkGKN4RPGIyICnVYpEpFc8VzS+CqzGvvrzKfbJeDx2AIFlWe9il5d+F9iA3a3/1m72+zJ2EPQV7KtL/8BeGcDt2eRm7JP5TuyrM1iWtQG7M/5oz/brgbuxT/5e1wFvAs96/t2EXbbcE/+HvdrAk5Zldfgd64vAvcAvPJ/vTM/xdfX5tmCPx2K/19wVtM1rwNmez/2+5/YjoNyzSS321ayXgO3Az4A7LMv6Qw8/j4iIyDFB8YjiEZHBwHRWkomIiIiIiIiISDSowkVEREREREREJMqUcBERERERERERiTIlXEREREREREREokwJFxERERERERGRKFPCRUREREREREQkypRwERERERERERGJMiVcRERERERERESiTAkXEREREREREZEoU8JFRERERERERCTK/j+Rn5EmpA0R+AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_train_preds = rf_model.predict(X_train)\n",
    "y_test_preds = rf_model.predict(X_valid)\n",
    "\n",
    "evaluate_preds(y_train, y_train_preds, y_valid, y_test_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "greater-kingdom",
   "metadata": {
    "papermill": {
     "duration": 0.099393,
     "end_time": "2021-06-28T14:43:34.400147",
     "exception": false,
     "start_time": "2021-06-28T14:43:34.300754",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Кросс-валидация"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "alike-knitting",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:34.605282Z",
     "iopub.status.busy": "2021-06-28T14:43:34.604586Z",
     "iopub.status.idle": "2021-06-28T14:43:44.972364Z",
     "shell.execute_reply": "2021-06-28T14:43:44.972880Z",
     "shell.execute_reply.started": "2021-06-28T14:36:23.397611Z"
    },
    "papermill": {
     "duration": 10.473898,
     "end_time": "2021-06-28T14:43:44.973065",
     "exception": false,
     "start_time": "2021-06-28T14:43:34.499167",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.77171415, 0.72773527, 0.75580829])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_score = cross_val_score(rf_model, X_train, y_train, scoring='r2', cv=KFold(n_splits=3, shuffle=True, random_state=21))\n",
    "cv_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "checked-optimum",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:45.183396Z",
     "iopub.status.busy": "2021-06-28T14:43:45.182388Z",
     "iopub.status.idle": "2021-06-28T14:43:45.186672Z",
     "shell.execute_reply": "2021-06-28T14:43:45.185933Z",
     "shell.execute_reply.started": "2021-06-28T14:36:34.395771Z"
    },
    "papermill": {
     "duration": 0.111101,
     "end_time": "2021-06-28T14:43:45.186829",
     "exception": false,
     "start_time": "2021-06-28T14:43:45.075728",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7517525687418313"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_score.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "together-packaging",
   "metadata": {
    "papermill": {
     "duration": 0.101921,
     "end_time": "2021-06-28T14:43:45.391714",
     "exception": false,
     "start_time": "2021-06-28T14:43:45.289793",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Важность признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "occupied-vacuum",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:45.606122Z",
     "iopub.status.busy": "2021-06-28T14:43:45.605404Z",
     "iopub.status.idle": "2021-06-28T14:43:45.646962Z",
     "shell.execute_reply": "2021-06-28T14:43:45.646439Z",
     "shell.execute_reply.started": "2021-06-28T14:36:34.404254Z"
    },
    "papermill": {
     "duration": 0.150997,
     "end_time": "2021-06-28T14:43:45.647133",
     "exception": false,
     "start_time": "2021-06-28T14:43:45.496136",
     "status": "completed"
    },
    "tags": []
   },
   "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>feature_name</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>MedPriceByDistrict</td>\n",
       "      <td>6.497812e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Square</td>\n",
       "      <td>1.088107e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LifeSquare</td>\n",
       "      <td>3.416604e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>HouseYear</td>\n",
       "      <td>2.311110e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>HouseFloor</td>\n",
       "      <td>2.198612e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>DistrictSize</td>\n",
       "      <td>2.043867e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Social_3</td>\n",
       "      <td>2.019519e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>MedPriceByFloorYear</td>\n",
       "      <td>1.973094e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Floor</td>\n",
       "      <td>1.652959e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KitchenSquare</td>\n",
       "      <td>1.579934e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ecology_1</td>\n",
       "      <td>1.529669e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Social_2</td>\n",
       "      <td>1.462371e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Social_1</td>\n",
       "      <td>1.392159e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Shops_1</td>\n",
       "      <td>8.517929e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Helthcare_2</td>\n",
       "      <td>5.760082e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rooms</td>\n",
       "      <td>5.203031e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>HouseFloor_outlier</td>\n",
       "      <td>1.920245e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Ecology_3</td>\n",
       "      <td>1.085497e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>LifeSquare_nan</td>\n",
       "      <td>1.039745e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Shops_2</td>\n",
       "      <td>1.026837e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>IsDistrictLarge</td>\n",
       "      <td>7.006784e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ecology_2</td>\n",
       "      <td>3.040273e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Rooms_outlier</td>\n",
       "      <td>5.097261e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>HouseYear_outlier</td>\n",
       "      <td>3.855468e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>new_district</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           feature_name    importance\n",
       "23   MedPriceByDistrict  6.497812e-01\n",
       "1                Square  1.088107e-01\n",
       "2            LifeSquare  3.416604e-02\n",
       "6             HouseYear  2.311110e-02\n",
       "5            HouseFloor  2.198612e-02\n",
       "20         DistrictSize  2.043867e-02\n",
       "12             Social_3  2.019519e-02\n",
       "24  MedPriceByFloorYear  1.973094e-02\n",
       "4                 Floor  1.652959e-02\n",
       "3         KitchenSquare  1.579934e-02\n",
       "7             Ecology_1  1.529669e-02\n",
       "11             Social_2  1.462371e-02\n",
       "10             Social_1  1.392159e-02\n",
       "14              Shops_1  8.517929e-03\n",
       "13          Helthcare_2  5.760082e-03\n",
       "0                 Rooms  5.203031e-03\n",
       "17   HouseFloor_outlier  1.920245e-03\n",
       "9             Ecology_3  1.085497e-03\n",
       "19       LifeSquare_nan  1.039745e-03\n",
       "15              Shops_2  1.026837e-03\n",
       "22      IsDistrictLarge  7.006784e-04\n",
       "8             Ecology_2  3.040273e-04\n",
       "16        Rooms_outlier  5.097261e-05\n",
       "18    HouseYear_outlier  3.855468e-08\n",
       "21         new_district  0.000000e+00"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = pd.DataFrame(zip(X_train.columns, rf_model.feature_importances_), \n",
    "                                   columns=['feature_name', 'importance'])\n",
    "\n",
    "feature_importances.sort_values(by='importance', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "casual-contributor",
   "metadata": {
    "papermill": {
     "duration": 0.120197,
     "end_time": "2021-06-28T14:43:45.869272",
     "exception": false,
     "start_time": "2021-06-28T14:43:45.749075",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Идея более сложных моделей:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "proper-recruitment",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:43:46.097509Z",
     "iopub.status.busy": "2021-06-28T14:43:46.094522Z",
     "iopub.status.idle": "2021-06-28T14:44:12.644919Z",
     "shell.execute_reply": "2021-06-28T14:44:12.644260Z",
     "shell.execute_reply.started": "2021-06-28T14:36:34.450405Z"
    },
    "papermill": {
     "duration": 26.671171,
     "end_time": "2021-06-28T14:44:12.645103",
     "exception": false,
     "start_time": "2021-06-28T14:43:45.973932",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StackingRegressor(estimators=[('lr', LinearRegression()),\n",
       "                              ('rf', RandomForestRegressor(random_state=21))],\n",
       "                  final_estimator=GradientBoostingRegressor())"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import StackingRegressor, VotingRegressor, BaggingRegressor, GradientBoostingRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lr = LinearRegression()\n",
    "gb = GradientBoostingRegressor()\n",
    "\n",
    "stack = StackingRegressor([('lr', lr), ('rf', rf_model)], final_estimator=gb)\n",
    "stack.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "buried-classics",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:44:12.856246Z",
     "iopub.status.busy": "2021-06-28T14:44:12.855091Z",
     "iopub.status.idle": "2021-06-28T14:44:13.624754Z",
     "shell.execute_reply": "2021-06-28T14:44:13.625272Z",
     "shell.execute_reply.started": "2021-06-28T14:37:02.853853Z"
    },
    "papermill": {
     "duration": 0.878177,
     "end_time": "2021-06-28T14:44:13.625456",
     "exception": false,
     "start_time": "2021-06-28T14:44:12.747279",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train R2:\t0.94\n",
      "Test R2:\t0.698\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_train_preds = stack.predict(X_train)\n",
    "y_test_preds = stack.predict(X_valid)\n",
    "\n",
    "evaluate_preds(y_train, y_train_preds, y_valid, y_test_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "frank-vitamin",
   "metadata": {
    "papermill": {
     "duration": 0.10564,
     "end_time": "2021-06-28T14:44:13.837617",
     "exception": false,
     "start_time": "2021-06-28T14:44:13.731977",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **8. Прогнозирование на тестовом датасете**\n",
    "1. Выполнить для тестового датасета те же этапы обработки и постронияния признаков\n",
    "2. Не потерять и не перемешать индексы от примеров при построении прогнозов\n",
    "3. Прогнозы должны быть для все примеров из тестового датасета (для всех строк)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "medical-rabbit",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:44:14.054622Z",
     "iopub.status.busy": "2021-06-28T14:44:14.053957Z",
     "iopub.status.idle": "2021-06-28T14:44:14.056996Z",
     "shell.execute_reply": "2021-06-28T14:44:14.056396Z",
     "shell.execute_reply.started": "2021-06-28T14:37:03.655405Z"
    },
    "papermill": {
     "duration": 0.112816,
     "end_time": "2021-06-28T14:44:14.057162",
     "exception": false,
     "start_time": "2021-06-28T14:44:13.944346",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 25)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "known-maker",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-28T14:44:14.281475Z",
     "iopub.status.busy": "2021-06-28T14:44:14.280432Z",
     "iopub.status.idle": "2021-06-28T14:44:14.317593Z",
     "shell.execute_reply": "2021-06-28T14:44:14.316941Z",
     "shell.execute_reply.started": "2021-06-28T14:37:03.661875Z"
    },
    "papermill": {
     "duration": 0.152881,
     "end_time": "2021-06-28T14:44:14.317749",
     "exception": false,
     "start_time": "2021-06-28T14:44:14.164868",
     "status": "completed"
    },
    "tags": []
   },
   "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>Rooms</th>\n",
       "      <th>Square</th>\n",
       "      <th>LifeSquare</th>\n",
       "      <th>KitchenSquare</th>\n",
       "      <th>Floor</th>\n",
       "      <th>HouseFloor</th>\n",
       "      <th>HouseYear</th>\n",
       "      <th>Ecology_1</th>\n",
       "      <th>Ecology_2</th>\n",
       "      <th>Ecology_3</th>\n",
       "      <th>...</th>\n",
       "      <th>Shops_2</th>\n",
       "      <th>Rooms_outlier</th>\n",
       "      <th>HouseFloor_outlier</th>\n",
       "      <th>HouseYear_outlier</th>\n",
       "      <th>LifeSquare_nan</th>\n",
       "      <th>DistrictSize</th>\n",
       "      <th>new_district</th>\n",
       "      <th>IsDistrictLarge</th>\n",
       "      <th>MedPriceByDistrict</th>\n",
       "      <th>MedPriceByFloorYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>36.847630</td>\n",
       "      <td>19.094182</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.036122</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>171145.169069</td>\n",
       "      <td>187599.514240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>42.493907</td>\n",
       "      <td>42.568133</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.072158</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>131107.271962</td>\n",
       "      <td>178912.369081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>59.463678</td>\n",
       "      <td>47.463678</td>\n",
       "      <td>9.0</td>\n",
       "      <td>19</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1977</td>\n",
       "      <td>0.211401</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>580.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>165993.292450</td>\n",
       "      <td>171931.149112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>49.646030</td>\n",
       "      <td>33.893825</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1965</td>\n",
       "      <td>0.014073</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>372.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>172778.615365</td>\n",
       "      <td>180434.337043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>53.837056</td>\n",
       "      <td>47.837056</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1977</td>\n",
       "      <td>0.309479</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>164116.183543</td>\n",
       "      <td>192711.526271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43.177521</td>\n",
       "      <td>30.339945</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1962</td>\n",
       "      <td>0.069660</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>183575.963167</td>\n",
       "      <td>187599.514240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>3.0</td>\n",
       "      <td>93.698122</td>\n",
       "      <td>94.521465</td>\n",
       "      <td>10.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.060753</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>425037.641820</td>\n",
       "      <td>192903.132839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>1.0</td>\n",
       "      <td>33.656723</td>\n",
       "      <td>19.003259</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1966</td>\n",
       "      <td>0.038693</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>156266.487610</td>\n",
       "      <td>180434.337043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>1.0</td>\n",
       "      <td>38.635155</td>\n",
       "      <td>20.976257</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.089040</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>185704.766694</td>\n",
       "      <td>192711.526271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>2.0</td>\n",
       "      <td>67.122742</td>\n",
       "      <td>33.944344</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.194489</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>182602.286983</td>\n",
       "      <td>227478.687998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Rooms     Square  LifeSquare  KitchenSquare  Floor  HouseFloor  \\\n",
       "0       1.0  36.847630   19.094182            5.0      5         9.0   \n",
       "1       1.0  42.493907   42.568133           10.0      7        17.0   \n",
       "2       2.0  59.463678   47.463678            9.0     19        19.0   \n",
       "3       3.0  49.646030   33.893825            6.0      2         2.0   \n",
       "4       1.0  53.837056   47.837056            3.0      8        17.0   \n",
       "...     ...        ...         ...            ...    ...         ...   \n",
       "4995    2.0  43.177521   30.339945            5.0      4         5.0   \n",
       "4996    3.0  93.698122   94.521465           10.0     21        27.0   \n",
       "4997    1.0  33.656723   19.003259            5.0      2         5.0   \n",
       "4998    1.0  38.635155   20.976257            9.0      8        14.0   \n",
       "4999    2.0  67.122742   33.944344            6.0      9        17.0   \n",
       "\n",
       "      HouseYear  Ecology_1  Ecology_2  Ecology_3  ...  Shops_2  Rooms_outlier  \\\n",
       "0          1970   0.036122          1          1  ...        1              0   \n",
       "1          2017   0.072158          1          1  ...        0              0   \n",
       "2          1977   0.211401          1          1  ...        1              0   \n",
       "3          1965   0.014073          1          1  ...        1              0   \n",
       "4          1977   0.309479          1          1  ...        1              0   \n",
       "...         ...        ...        ...        ...  ...      ...            ...   \n",
       "4995       1962   0.069660          1          1  ...        1              0   \n",
       "4996       2018   0.060753          1          1  ...        1              0   \n",
       "4997       1966   0.038693          1          1  ...        1              0   \n",
       "4998       1970   0.089040          1          1  ...        1              0   \n",
       "4999       2009   0.194489          1          1  ...        1              0   \n",
       "\n",
       "      HouseFloor_outlier  HouseYear_outlier  LifeSquare_nan  DistrictSize  \\\n",
       "0                      0                  0               0          76.0   \n",
       "1                      0                  0               0         164.0   \n",
       "2                      0                  0               1         580.0   \n",
       "3                      0                  0               0         372.0   \n",
       "4                      0                  0               1          65.0   \n",
       "...                  ...                ...             ...           ...   \n",
       "4995                   1                  0               0          56.0   \n",
       "4996                   0                  0               0          26.0   \n",
       "4997                   0                  0               0          54.0   \n",
       "4998                   0                  0               0          40.0   \n",
       "4999                   0                  0               0         117.0   \n",
       "\n",
       "      new_district  IsDistrictLarge  MedPriceByDistrict  MedPriceByFloorYear  \n",
       "0                0                0       171145.169069        187599.514240  \n",
       "1                0                1       131107.271962        178912.369081  \n",
       "2                0                1       165993.292450        171931.149112  \n",
       "3                0                1       172778.615365        180434.337043  \n",
       "4                0                0       164116.183543        192711.526271  \n",
       "...            ...              ...                 ...                  ...  \n",
       "4995             0                0       183575.963167        187599.514240  \n",
       "4996             0                0       425037.641820        192903.132839  \n",
       "4997             0                0       156266.487610        180434.337043  \n",
       "4998             0                0       185704.766694        192711.526271  \n",
       "4999             0                1       182602.286983        227478.687998  \n",
       "\n",
       "[5000 rows x 25 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df"
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   "cell_type": "code",
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   "id": "weighted-screen",
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    {
     "data": {
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      "text/plain": [
       "     Id     Price\n",
       "0  4567  200000.0\n",
       "1  5925  200000.0\n",
       "2   960  200000.0\n",
       "3  3848  200000.0\n",
       "4   746  200000.0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
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    }
   ],
   "source": [
    "submit = pd.read_csv('/kaggle/input/real-estate-price-prediction-moscow/sample_submission.csv')\n",
    "submit.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "second-limit",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([161829.69828708, 123815.90751118, 139599.03931645, ...,\n",
       "       149021.4212487 , 189463.74337778, 227996.2968221 ])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = rf_model.predict(test_df)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "european-wichita",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
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   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2</th>\n",
       "      <td>960</td>\n",
       "      <td>139599.039316</td>\n",
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       "      <th>3</th>\n",
       "      <td>3848</td>\n",
       "      <td>171354.866790</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
       "      <td>746</td>\n",
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      "text/plain": [
       "     Id          Price\n",
       "0  4567  161829.698287\n",
       "1  5925  123815.907511\n",
       "2   960  139599.039316\n",
       "3  3848  171354.866790\n",
       "4   746  175321.795799"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submit['Price'] = predictions\n",
    "submit.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "disabled-tours",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "submit.to_csv('rf_submit.csv', index=False)"
   ]
  }
 ],
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