Final_Project_LononAirbnb.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from matplotlib import rcParams\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3058: DtypeWarning: Columns (61,62,94,95) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(85068, 106)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read data to csv\n",
    "listing = pd.read_csv('D:/airbnb/listings.csv')\n",
    "listing.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>listing_id</th>\n",
       "      <th>date</th>\n",
       "      <th>available</th>\n",
       "      <th>price</th>\n",
       "      <th>adjusted_price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>maximum_nights</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>11551</td>\n",
       "      <td>2019-11-06</td>\n",
       "      <td>f</td>\n",
       "      <td>$105.00</td>\n",
       "      <td>$105.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>11551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>90700</td>\n",
       "      <td>2019-11-06</td>\n",
       "      <td>f</td>\n",
       "      <td>$125.00</td>\n",
       "      <td>$125.00</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>90700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>90700</td>\n",
       "      <td>2019-11-07</td>\n",
       "      <td>f</td>\n",
       "      <td>$125.00</td>\n",
       "      <td>$125.00</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>90700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id        date available    price adjusted_price  minimum_nights  \\\n",
       "0       11551  2019-11-06         f  $105.00        $105.00             3.0   \n",
       "1       90700  2019-11-06         f  $125.00        $125.00            90.0   \n",
       "2       90700  2019-11-07         f  $125.00        $125.00            90.0   \n",
       "\n",
       "   maximum_nights     id  \n",
       "0           180.0  11551  \n",
       "1          1125.0  90700  \n",
       "2          1125.0  90700  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read data to csv\n",
    "calendar = pd.read_csv('D:/airbnb/calendar.csv')\n",
    "calendar['id']=calendar['listing_id']\n",
    "calendar.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <th>neighbourhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Barking and Dagenham</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Barnet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bexley</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   neighbourhood_group         neighbourhood\n",
       "0                  NaN  Barking and Dagenham\n",
       "1                  NaN                Barnet\n",
       "2                  NaN                Bexley"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read data to csv\n",
    "barrios = pd.read_csv('D:/airbnb/neighbourhoods.csv')\n",
    "barrios.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "########### Years from  2019-11-05  TO  2020-11-04\n",
      "*********** Price from  $0.00  TO  $999.00\n"
     ]
    }
   ],
   "source": [
    "#data range\n",
    "print('########### Years from ',min(calendar['date']) , ' TO ', max(calendar['date']) )\n",
    "print('*********** Price from ',min(listing['price']) , ' TO ', max(listing['price']) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "listing_id        10151834\n",
       "date              10151834\n",
       "available         10151834\n",
       "price             10151653\n",
       "adjusted_price    10151653\n",
       "minimum_nights    10151834\n",
       "maximum_nights    10151834\n",
       "id                10151834\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#avg price\n",
    "hist_indicator = 't'\n",
    "calv = calendar['available'].isin([hist_indicator]) \n",
    "CalendarData= calendar[calv] \n",
    "CalendarData.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to clean currency data\n",
    "def clean_currency(x):\n",
    "    \"\"\" If the value is a string, then remove currency symbol and delimiters\n",
    "    otherwise, the value is numeric and can be converted\n",
    "    \"\"\"\n",
    "    if isinstance(x, str):\n",
    "        return(x.replace('$', '').replace(',', ''))\n",
    "    return(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#cleaning data\n",
    "CalendarData['price'] = CalendarData['price'].apply(clean_currency).astype('float')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "##add agrupation for end of month\n",
    "CalendarData= CalendarData.copy()\n",
    "CalendarData['yearMonth'] = CalendarData['date'].str[:7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>listing_id</th>\n",
       "      <th>date</th>\n",
       "      <th>available</th>\n",
       "      <th>price</th>\n",
       "      <th>adjusted_price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>maximum_nights</th>\n",
       "      <th>id</th>\n",
       "      <th>yearMonth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>90700</td>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>t</td>\n",
       "      <td>125.0</td>\n",
       "      <td>$125.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>90700</td>\n",
       "      <td>2020-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>90700</td>\n",
       "      <td>2020-01-06</td>\n",
       "      <td>t</td>\n",
       "      <td>125.0</td>\n",
       "      <td>$125.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>90700</td>\n",
       "      <td>2020-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    listing_id        date available  price adjusted_price  minimum_nights  \\\n",
       "61       90700  2020-01-05         t  125.0        $125.00             2.0   \n",
       "62       90700  2020-01-06         t  125.0        $125.00             2.0   \n",
       "\n",
       "    maximum_nights     id yearMonth  \n",
       "61          1125.0  90700   2020-01  \n",
       "62          1125.0  90700   2020-01  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CalendarData.dtypes\n",
    "CalendarData.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "######Price from  8.0  max  12345.0\n"
     ]
    }
   ],
   "source": [
    "print('######Price from ',min(CalendarData['price']) , ' max ', max(CalendarData['price']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.015165e+07\n",
       "mean     1.533118e+02\n",
       "std      2.697322e+02\n",
       "min      8.000000e+00\n",
       "25%      5.000000e+01\n",
       "50%      9.700000e+01\n",
       "75%      1.750000e+02\n",
       "max      1.234500e+04\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CalendarData['price'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "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>yearMonth</th>\n",
       "      <th>price</th>\n",
       "      <th>priceMax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2019-11</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12345.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2019-12</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12345.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  yearMonth  price  priceMax\n",
       "0   2019-11    8.0   12345.0\n",
       "1   2019-12    8.0   12345.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##calculate max an min price\n",
    "ratings_count_min = CalendarData[['yearMonth','price']].groupby('yearMonth', as_index=False).min()\n",
    "ratings_count_max = CalendarData[['yearMonth','price']].groupby('yearMonth',as_index=False).max()\n",
    "ratings_count_min ['priceMax'] = ratings_count_max['price']\n",
    "ratings_count_min.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-01    1177856\n",
       "2020-03     998347\n",
       "2020-02     961170\n",
       "2020-04     948627\n",
       "2019-12     941610\n",
       "Name: yearMonth, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count of rows\n",
    "months = CalendarData['yearMonth'].drop_duplicates()\n",
    "tag_counts = CalendarData['yearMonth'].value_counts()\n",
    "tag_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##counter by month\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axis = plt.subplots(figsize=(15,7))\n",
    "# Grid lines, Xticks, Xlabel, Ylabel\n",
    "\n",
    "axis.set_xlabel(' Months   ',fontsize=12, color='brown')\n",
    "axis.set_ylabel('Value',fontsize=10, color='brown')\n",
    "\n",
    "X = months\n",
    "Y = tag_counts\n",
    "\n",
    "plt.title('Number of accommodation disponible \\n by month', color='brown')\n",
    "axis.bar(X,Y)\n",
    "axis.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZoYiIHI3UIyoiIiIiSaFEVERERESSQomoiIiIiCSFxoiKiIiISFKoR1REREREkkKJqIiIiIgkhRJREZGKiNqLwXJPsjdRyyZqHyc7DBE59CkRFRGRyova3URtYLLDEJHDmxJRERHZF3fjP21LRGSfKREVERERkaRISXYAIiKHlag1wn8G9yVAGvA5cG/J57lH7Qf4z1R/nZDrn2D/EfjPlW9JyC3fw3E+BjKBbsAfgAsBB0wGfoH/PPb7gVvwn2f/NTA4+Gz42HbqAA8B1wBNgfXAdODhUsePWjfgI+AGwIB7gROBNcDzhNwTMXWLx8q2iBs325KQy46p1yZ4rc4HioC/Ab8g5NaUe94iclRRj6iISOW8j0/8hgLDgU7Ap0TtVABCbi3wV+Aqolav1J5RSwOuBWbsMQndrQ7wIZCPTzrfAq4H/gw8C1wZPIaBZsA7RO2YmOOlANOCfecAvwRexSelXxC1pgmOeTvwSFBvCLAaeJyohWLqDADWAd8E3xd/5cbUOQH4GFgB3AdEg3hfrsB5i8hRQj2iIiKVsxy4ilDwaSBRewuYBTwF/Cyo8yfgKiAEjIjZ9yqgHj6RrIhjgScIuSeD5y8Qtfr4RHIO8CNCbmcQx0J8b2kIGBXUvwHoAjxJyP2qpNWozQDeBR7DJ5CxmgPtCLkNQd2xwTkPxieTEHKvELVHgbWE3CvlxH4i0IeQez3muEXAnUStDSH3TQVfAxE5gqlHVESkcp4oSUIBQu5L/C3n7kQtPSj9G7AMuClu35uAPGBSBY9ViO/xjPV3/K3zF0qS0N3lACfFlF2BvyX+WKkWQu49YB7Qi6jF/x8YV5KE+roF+OEHJ1E5q0olod6HweOJlWxLRI5QSkRFRCpnYYKyr4HqQAuAIFH9M9CRqJ0OQNRa4cd7/oWQ21HBY60m5LbFla0PHpeVKg254vKGMaUt8QnhesrKAo7B97rG+jZB3by4diuivHbiYxSRo5gSURGR/WcJysYCu9jdK3pjUK+it+XB94hWdpuV8/2BOOaBamdf4hKRI5ASURGRymlbTlkhfiyl52eGvwP0C2auXw98QchlVUWQgaVAkzKTprx2wEb8pKN9oU+ZEpH9pkRURKRyfkXUdvfoRa0j0B34gJDbHFd3NFAfeAG/dFJlekMPhEn4v/P3lyqN2s+BM4C/EnJF+9j2ZqDBfkUnIkc9zZoXEamcFsA0ovZX/DJOvwC24pcoijcN30vaH9gCvFZVQQZexPfE/pqoZQKf4icK3QmsBR7cj7Y/B24iar/Fj5stAt4h5LbsR5sicpRRj6iISOX8DJ/ERfDrcn4JXEDIzS9T0/c2jgmeTUjQY3pw+Vn1PYDfA2fh1z3tD0wEzibkVu5H678B3gYGAa/g1x1ttF/xishRx5zTMB8RkYMmar8CHgfOJeT+mexwREQOJUpERUQOFv/JRouALYRc+2SHIyJyqNEYURGRAy1qLYEfAb2AVviP9RQRkThKREVEDrwLgHH4pZGGEXJVPUlJROSwoFvzIiIiIpIU6hGtQscee6zLzMykceMNtGmzCgAzWLToeNasqR9X23HqqStp3DgfMP7zn+NYsSL+k/hEREREDo4vv/xynXPuoK6GoUS0CmVmZjJr1iyeeKIBN9zwLxo3PpW1a+czdmwX7r9/CWa7V9P66quXWbBgPP36TaWgII9Ro87gxhvfoF69zOSdgIiIiBw1zGz53mvtH60jmgRm1di2LR+Abds2kJ5+fKkkFCArawIdO96CWTXq1GlEmzaXk5U1MRnhioiIiBwU6hGtYmZG796v89prvUhNrcP27ZsIhd4rUy8/fwUZGS1KnmdkNGfjxv1Ze1pERETk0KJEtIoVFe1i5szH6Nt3Ms2bd2HFis94440+DBr0Namp6ckOT0RERKTKKBGtYmvWzGPTplU0b94FgObNu5CaWofc3IWccELnknoZGc3Jz19eUhbfQyoiIiKwc+dOcnJy2LZtW7JDOWylpaXRtGlTatSoUeXHViJaxerWbcrGjTmsW7eIY489mdzchWzevIYGDX5Yql67dlczZ85o2ra9koKCPL75ZhI33PBpkqIWERE5NOXk5HDMMceQmZmJmSU7nMOOc468vDxycnJo2bJllR9fiWgVS08/jksuGcnEib1LJij16jWOWrUaMH78xVx44TCaNOlE+/YDyMn5gmefPQmACy54hPr1WyUzdBERkUPOtm3blITuBzOjYcOG5ObmJuf4WtC+6nTq1MnNnj072WGIiIgcMRYuXEjbtm2THUaVys9fybZt6yks3EGjRqdQo0YtCgt3sWHDMgoLtwNGSkpNMjJaUL16DZxzrFv3Dc4VAVC9eg0yMlqQklKzpM2FCxeyePGTzJs3jgce2ERqajrHHmsLBg+mHfDvmMNfFA67vPiYIhFrDbwENATygOvCYbdkb+eiHlERERGRw0haWj3q1PkBeXnflJSZ+buuNWseA/hkddOm76hXLzPo9TyJatV82rd581o2blxJgwYnluy/c2cBkLBXeUM47E6vQFgvAM+Hw+6VSMT6A6OAH+9tJyWiIiIicsRo8cyBbW/5/xy4th555BHOP/98unfvvl/tFCebsapVSylVnpqazpYt/y21vZhzhcQmnYWFu9i2LZ8ePf6PefPGVjqeSMQaAx2BnwRFrwLPRSLWKBx2e7znr0RURERE5CArLCxk2LBhVXIs5xxbtvyXtLR6pcrz8pawc2cB1aql0LDhSSXl+fnLSUurR1paRqLm6kYiNhufub4GPBUOlxnX2Qz4Lhx2hQDhsCuMRGxVUL7HRDRpn6xkZo+bWZaZbTSzVWY22swaxNW5zsyWmlmBmX1hZmfGbKtlZhPNbImZFZnZQwmO0cLMJpnZOjPLM7MRZlYzvl7cPo+a2Vwz22FmMxJsv8jMPjCz783MmVnT/XkdRERE5PCWnZ1NmzZtuP7662nfvj29e/emoKCAzMxMhg0bRteuXZk4cSIDBw7kjTfeAGDWrFmce+65dOjQgbPOOotNmzZRWFjIfffdR+fOnWnfvj2jRo3ap3jy81dQrVp16tRpXKq8YcOT+MEP2lOrVgM2bVoNwNat32NWjRo1aiVoh51A03DYdQJ+DlwF3LRPQZUjmR/xWQj0xw9q7QA0BcYVbzSzrsBI4A6gPvAmMMXM6gZVHPAP4FbgX/GNm1l14B1gZdB2B+BHwNN7iWsp8Ajwp3K2bwFeBgbs7QRFRETk6LBo0SJuvfVW5s+fT926dRkxYgTg1+icOXMmffv2Lam7Y8cO+vTpwzPPPMNXX33FjBkzqFWrFmPGjCEjI4NZs2Yxa9YsRo8ezbJlyyoVR37+SgoLt1O/fquEKwmYGbVrH8vWrX6+0fbtm9i+fSMbN+YwfHgmACNGnEJu7tfs2oULh91/AYLH8UCXBIddCZwQiVh1gOCxSVC+R0lLRJ1zDzrn5jrndjrncoHngG4xVW4B3nLOTXfObQeeBLYDVwT7b3PO/cE59xGQaBXbk4HTgIeCujnAcOAGM0vbQ1zjnHPvAOvK2f65c+4lIKuy5ywiIiJHpmbNmtGli8/R+vfvz8yZMwHo06dPmbqLFi3i+OOPp3Nn/6E1devWJSUlhenTp/Pyyy9z+umnc/bZZ5OXl8eSJXudeF5i48bv2LmzgPr1f1iyRCRAYeFOCgt3lTzfuvV7UlJ8D2i9ei047rgO1K3blLvvzgbgzjuzaNSoHcccQ0okYjUAIhGrDVwGzIs/bpCkzgOuDYquBebubXwoHFpjRC8C5sc87wC8WPzEOefMbG5QXhHFPwGLK6sNtI471kFjZrfie20hAyyidc5EREQOlKk/ncqWVVtiSjod0PZnr9r7sour1q5iZ9HOkrqL1i0if3s+Owp3sGTTEnJX+XxsXcE6ln6/lJ1rd7Jl55YybX+/9XsGDx3Mj7r9qNwYOjXpRH7+CrZuXU9R0U7y8hZTrVp16tf/IZs3ryYlJY116/xs+pSUmjRocCJFRTvZsCGb4iU7q1dPrdDa5K1akQ7MjUSsEKgBvIvvOCQSsU7AsHDYXRxUvx14KRKxR4D1wHV7PQCHSCJqZlfhe0AviCk+BsiPq7oBqEvFfAP8B/idmd0LNAKK575VtI395pz7E8FtfmtiWrRVRETkCLTmuzXMnz2f9p3aM23yNDp07sCify9KWDfzxExy1+aSNS+LU04/hS2bt1AzrSbnXHAOb778Jp27dCalRgrLly6n8fGNqVW79PjNjIzmZGQ0L9NukyaJk/AaNWrTqFG7Cp1HOLw7VfnqKzbMm+cSNhoOu9nAxTHPvwHOrtBBYiQ9ETWzq/FrTV3mnJsTs2kTED99qx5+DOdeOed2mdmlwB+AbOB7YAy+R3WdmZ0HTI3ZpZ1zbsU+nYSIiIgcEt68OjkfHNPypJa8N/E9Hrv/MZq1bEbv63vz+rjXE9atkVqD3438HU899BTbt22nZlpNnp/wPJeHLmf1ytX0/1l/nHPUb1Cfp8Y+VcVnUrWSmoia2Q34yUOXOuc+i9v8FX5NquK6BpwOvFXR9p1z3+BneRW3MQhYBSwOtqXve/QiIiIinlUzHnj8gVJlf/3ir6WeDx0+tOT7U04/hXHvjiPeoAcGMeiBQQclxkNRMpdvugt4CuiRIAkFGA1cGSyXlAoMAdKAt2PaqBlMPKoGpJhZmpkfVBtsP83M0s0sxcy642fD/8YVf8ZV4rhqBG2mANWCNmvGbK8WbC8uqxnUSeYKBCIiIiKHnWQmT8/gx2p+ZGabi7+KNzrnZgJ34hPSfOAa4GLn3MaYNhYBW4HzgHDw/eiY7VcCy4CN+Fv0v3TOvbiXuEYH7fwGuDD4PnaQx/lBWfHnav0neH5+hc5aREREjihNmjVhwocTkh3GYSlpt+adc3udPu6cexm/Zmd52zP3sn8EiFQyroHAwD1s/5hyPoxVRERERCou6ZOVjiZnNjmT2eHkDKIWERE5Ei1cuJC2TdomOwzZRxrXKCIiIiJJoURURERERJJCt+ZFRETkiHGgP8HQhQ/cZ9E88sgjnH/++XTv3r1S+2VnZ9OyZUseeughfvvb3wKwbt06jj/+eG677Taee+65AxZjVVOPqIiIiMhBVlhYyLBhwyqdhBZr1aoV7777bsnziRMncsoppxyo8JJGiaiIiIjIfsjOzqZNmzZcf/31tG/fnt69e1NQUEBmZibDhg2ja9euTJw4kYEDB/LGG28AMGvWLM4991w6dOjAWWedxaZNmygsLOS+++6jc+fOtG/fnlGjRpUco1atWrRt25bZs/2k5wkTJnDNNdeUbH/nnXc4++yzOeOMM+jevTtr164F4K677mLYsGEATJs2jfPPP5+ionKXU69yujUvIiIisp8WLVrEmDFj6NKlCzfeeCMjRowAIC0tjZkzZwLw/vvvA7Bjxw769OnDhAkT6Ny5Mxs3bqRWrVqMGTOGjIwMZs2axfbt2+nSpQs//elP8R8uCX379uW1117juOOOo3r16jRp0oRVq1YB0LVrVz7//HPMjD//+c888cQTPP300/z+97+nc+fOnHfeedx1111MmTKFatUOnX5IJaIiIiIi+6lZs2Z06dIFgP79+/PHP/4RgD59+pSpu2jRIo4//ng6d+4MQN26dQGYPn068+fPL+k1zc/PZ8mSJbRu3RqAn/3sZzz88MP84Ac/KNNuTk4Offr0YfXq1ezYsYOWLVsCULt2bUaPHs3555/PH/7wB374wx8ehLPfd0pEk2Dx4nf56KOHcc7hXBHdug2lbdsrS9UpKipk6tS7WLr0fcDo2vV+Ona8OTkBi4iIyB4V91rGP69Tp06Zus65MvWLy5999ll69OhRqjw7OxuA1NRUzjzzTJ5++mmysrJ45513SuoMHjyYe+65h8suu4yPP/6YoUOHlmxbsGABDRs2LOk9PZQcOn2zRwnnHG+/PYArrvgLt98+jyuvfIVJk67HudLjNRYsGM/69f9h8OAl3HTTP/n446Fs2JCdnKBFRERkj1asWME///lPAF599VW6du1abt02bdqwatUqZs2aBcCmTZvYtWsXPXr0YOTIkezcuROAxYsXs2XLllL7DhkyhMcff5yGDRuWKs/Pz+eEE04A4A/tSJ0AACAASURBVKWXXiopX758OU8//TRz585l6tSpfPHFF/t/sgeQekSTwKwa27blA7Bt2wbS04/HrPR7gqysCXTseAtm1ahTpxFt2lxOVtZEunS5Lxkhi4iIHBYO5HJLldG2bVteeuklbrvtNk466STuuOMOnn322YR1U1NTmTBhAoMHD2br1q3UqlWLGTNmcPPNN5OdnU3Hjh1xztGoUSMmTZpUat9TTjkl4Wz5oUOHcvXVV3PCCSdwzjnnsGzZMpxz3HTTTTz11FM0adKEMWPGMHDgQGbNmkVaWtpBeR0qy5xLzg/saNSpUyc3e/Zsvv32A954ow+pqXXYvn0TodB7NGv2o1J1R448jcsuG8sJJ/jxI5999gQbN+bw85//MRmhi4iIHJIWLlxI27bJ/YjP7Oxsevbsyb///e+kxrE/Er2OZvalc67TwTyuekSrWFHRLmbOfIy+fSfTvHkXVqz4jDfe6MOgQV+Tmpqe7PBEREREqozGiFaxNWvmsWnTKpo39zPrmjfvQmpqHXJzF5aql5HRnPz85SXP8/NXULdusyqNVURERPYuMzPzsO4NTSYlolWsbt2mbNyYw7p1iwDIzV3I5s1raNCg9HIK7dpdzZw5o3GuiC1bcvnmm0m0a3dVMkIWERE5pGmY4f5J5uunW/NVLD39OC65ZCQTJ/YumaDUq9c4atVqwPjxF3PhhcNo0qQT7dsPICfnC5599iQALrjgEerXb5XM0EVERA45aWlp5OXl0bBhw4RLIh2J8vNXsm3begoLd9Co0SnUqFGLwsJdbNiwjMLC7YCRklKTjIwWVK9eA+cc69Z9U7JCT/XqNcjIaEFKSk2cc+Tl5ZGWlsbkyTcyb944HnhgE6mp6TRsSGokYruA2O7ei8JhlxcfUyRirYGXgIZAHnBdOOyW7O1clIgmQfv2/Wjfvl+Z8n79ppR8X61adXr2HFmVYYmIiBx2mjZtSk5ODrm5uckOpcrs2rWNatVS2Lx5Pd9/v5jq1VNxrojCwh2kpPjZ8Fu3/hfn1lC7tl/mybmikg6w7dvXs2zZGurUaQT4ZH7btq+BhIn8hnDYnV6BsF4Ang+H3SuRiPUHRgE/3ttOSkRFRETksFWjRo2STxE62gwffimh0Ls0blx21YCvv36T2bNHct11M8ps++ST37J27Vdcc43/BKeCgjxeeSXMddd9wLx5YysdRyRijYGOwE+ColeB5yIRaxQOuz2+Q1AiKiIiInIEca6I2bNHcvLJl5UqHz/+YlavnkPt2scyYMD0kvIpUwbRrdtQ0tIyEjVXNxKx2fju0teAp8LhMoNKmwHfhcOuECAcdoWRiK0KyveYiCZtspKZPW5mWWa20cxWmdloM2sQV+c6M1tqZgVm9oWZnRmzrZaZTTSzJWZWZGYPJThGCzObZGbrzCzPzEaYWc29xPWomc01sx1mVuZthJkNMbM5ZpZvZmvN7HUza74/r4WIiIjIgTJlymBSU9M566xflCrv128KQ4as4tRTr+XTTx8FICtrItWrp9K6dc8y7eTnsxNoGg67TsDPgauAmw5krMmcNV8I9McPau0ANAXGFW80s67ASOAOoD7wJjDFzOoGVRzwD+BW4F/xjZtZdeAdYGXQdgfgR8DTe4lrKfAI8KdytqcCg4EfACcCW4B399KmiIiIyEE3ffq9fP/9Enr3nlDmUxvBf7pjx443MX/+XwDIzv6IZcs+ZPjwTIYPzwRgxIhTyM39ml27cOGw+y9A8Dge6JLgsCuBEyIRqw4QPDYJyvcoaYmoc+5B59xc59xO51wu8BzQLabKLcBbzrnpzrntwJPAduCKYP9tzrk/OOc+ArYlOMTJwGnAQ0HdHGA4cIOZlfu5Vs65cc65d4B15Wx/zDn3WdDmJuAp4DQzq1/Jl0BERETkgPngg9+wevWX9O07iZSU3TeAt2zJpaBg90T3rKyJNG58GgCXXDKCe+7J4e67s7n77mwA7rwzi0aN2nHMMaREIlYDIBKx2sBlwLz44wZJ6jzg2qDoWmDu3saHwqE1RvQiYH7M8w7Ai8VPnHPOzOYG5RVRnGRbXFltoHXcsfbHRUCOc259oo1mdiu+1xYywCJHx9ISIiIicuC5sGPq1LtYuPAtNm9ew8svd6d27Yb07v06M2f+joYNWzNmzLkA1K/fkj593mbz5jVMnjyQwsKdgKNevZZceeUrez1Wq1akA3MjESsEauDvAD8HEIlYJ2BYOOwuDqrfDrwUidgjwHrguoqczyHxWfNmdhU+6bzAOTcnKFsKPOqci71d/xKw0zl3c9z+HwMznHOPxpSlAAuBvwH3Ao2AyfhE9jzn3My9xDQU6Oqc676HOucC04C+zrn39nqeTcxx295qiYiIiCTmwlWXt1XFZ80n/ZOVzOxqYDRwWXESGtgExE/fqgdsrEi7zrldwKVASyAbnzCODzavM7PzzGxzzFelJhyZ2Xn4dwa3ViQJFREREZHSknpr3sxuwE8eutQ591nc5q/wa1IV1zXgdOCtirbvnPsGP8uruI1BwCpgcbAtfR/j7gFMAG50zlU4HhERERHZLZnLN92Fn+jTI0ESCr6X9Eozu8jMUoEhQBrwdkwbNYOJR9WAFDNLM/ODaoPtp5lZupmlmFl3/Gz437jiz7hKHFeNoM0UoFrQZs2Y7VcBE4H+SkJFRERE9l3SxoiamQN24WfCl3DOpcfUuQ4YChwPLADucM59GbM9G2gR1/RLzrmBwfYw8AugDn5Zpsecc9G9xPUicH1c8XLnXGawfRl+gdb4mfrtnHMr9ti2xoiKiIjIfjjSxogeEpOVjhZKREVERGR/HGmJaNInK4mIiIjI0elQWkf0iHdmkzOZHZ6d7DBEREREDgnqERURERGRpFAiKiIiIiJJoURURERERJJCiaiIiIiIJIUSURERERFJCiWiIiIiIpIUWr4pCRYvfpePPnoY5xzOFdGt21Datr2yVJ2iokKmTr2LpUvfB4yuXe+nY8ebkxOwiIiIyEGgRLSKOed4++0B3HDD32nc+FTWrp3P2LFdaNPmcsx2d1AvWDCe9ev/w+DBSygoyGPUqDNo1ao79eplJi94ERERkQNIt+aTwKwa27blA7Bt2wbS048vlYQCZGVNoGPHWzCrRp06jWjT5nKysiYmI1wRERGRg0I9olXMzOjd+3Vee60Xqal12L59E6HQe2Xq5eevICOjRcnzjIzmbNy4sipDFRERETmolIhWsaKiXcyc+Rh9+06mefMurFjxGW+80YdBg74mNTU92eGJiIiIVBndmq9ia9bMY9OmVTRv3gWA5s27kJpah9zchaXqZWQ0Jz9/ecnz/PwV1K3brEpjFRERETmYlIhWsbp1m7JxYw7r1i0CIDd3IZs3r6FBgx+Wqteu3dXMmTMa54rYsiWXb76ZRLt2VyUjZBEREZGDQrfmq1h6+nFccslIJk7sXTJBqVevcdSq1YDx4y/mwguH0aRJJ9q3H0BOzhc8++xJAFxwwSPUr98qmaGLiIjIIWD69HtZuPBNNmzI5o47FtC48akUFOTx9tsDWL9+KdWr16RBgxPp2XMUdeo0wrkixo7tws6dBQCkpx9Pz54vlFmJZ/LkG5k3bxwPPLCJ1NR0GjYkNRKxXcC/Y6pdFA67vPiYIhFrDbwENATygOvCYbdkb+eiRDQJ2rfvR/v2/cqU9+s3peT7atWq07PnyKoMS0RERA4Dbdpcztln/w/jxp1XUmZmdOnyKzIzuwEwffp9zJhxP716jcGsGv36vU9aWgYAn3/+DNOm3UOfPm+V7L9o0TuAJTrchnDYnV6BsF4Ang+H3SuRiPUHRgE/3ttOujUvIiIichhp3rwrGRml543UqtWgJAkFaNr0nFJzTYqTUIDt2zeWWjayoCCPTz6J0KPH/+1TPJGINQY6Aq8GRa8CHSMRa7S3fdUjKiIiInIEca6I2bNHcvLJl5UqHz/+YlavnkPt2scyYMD0kvIpUwbRrdvQUslqjLqRiM3Gd5e+BjwVDjsXV6cZ8F047AoBwmFXGInYqqA8d0+xJq1H1MweN7MsM9toZqvMbLSZNYirc52ZLTWzAjP7wszOjNl2jpm9Z2ZrzSzfzL40s8vj9m9sZm+Z2SYzyw2OucdzNrMTzWyGmW0xsxwzGxK3/a4glgIz+8+BeC1EREREDpQpUwaTmprOWWf9olR5v35TGDJkFaeeei2ffvooAFlZE6lePZXWrXuWaSc/n51A03DYdQJ+DlwF3HQgY03mrflCoD9+UGsHoCkwrnijmXUFRgJ3APWBN4EpZlY3qNIAmACcGmz/LfCqmXWOOcb44LEpcDZwBXBfeQGZWXXgHWAh0Ai4DPi1mfWJqbYKeAL430qfsYiIiMhBNH36vXz//RJ6955Q5lMbwX+6Y8eONzF//l8AyM7+iGXLPmT48EyGD88EYMSIU8jN/Zpdu3DhsPsvQPA4HuiS4LArgRMiEasOEDw2Ccr3KGmJqHPuQefcXOfcTudcLvAc0C2myi3AW8656c657cCTwHZ8Molzbopz7mXnXK5zrsg5Nwk/q6srgJm1BLoD9znn8p1z3wKPA7fvIazzgRbAA865AufcHPxg25J9nHNvOOfeBL47EK+DiIiIyIHwwQe/YfXqL+nbdxIpKTVLyrdsyaWgYPdE96ysiTRufBoAl1wygnvuyeHuu7O5++5sAO68M4tGjdpxzDGkRCJWAyASsdr4Drp58ccNktR5wLVB0bXA3HDY7fG2PBxaY0QvAubHPO8AvFj8xDnnzGxuUF6GmR0HnBLTRgcg3zm3NKbaHCDTzOo65zYmaKYDsNg5tzlun0GVPJfYuG4FbgUgAyyScEaaiIiIyF65sGPq1LtYuPAtNm9ew8svd6d27Yb07v06M2f+joYNWzNmzLkA1K/fkj593mbz5jVMnjyQwsKdgKNevZZceeUrez1Wq1akA3MjESsEagDv4jsOiUSsEzAsHHYXB9VvB16KROwRYD1wXUXOx1yZ8aZVz8yuwiedFwS9kJjZUuBR51zs7fqXgJ3OuZvj9q8DTAdWOuf6BmUDgv1bxNRrCXwLNHPO5SSI42Ggu3PugpiyC4G/OedS4uoOBB5yzp1Y4fNsYo7bKlpbREREpDQXrrq8zcy+dM51OpjHSPryTWZ2NTAauKw4CQ1sAuKnb9UDSvVkmtkxwFTgv5TOvsvbH2CTmfUzs83FX5U5poiIiIjsv6QmomZ2A34M5qXOuY/iNn+FX5OquK4BpwflxWUNgA/wE4iuds7tiNs/w8xiP47oDCA7GDM63jmXXvwVs0/roIc1dp+vEBEREZEDKpnLN90FPAX0cM59lqDKaOBKM7vIzFKBIUAa8Haw/3HAJ/gZ7v2cc7tid3bOLQNmAE+YWd3gtvyv8YlveT4FlgO/M7NaZnY6cFvsPmaWYmZp+LESZmZpwXMRERERqYRk9og+A9QFPkpwixzn3EzgTnxCmg9cA1wcM8noNvzSTb2B/Jg2How5Rj/8OX4HzAIm45deSsg5VwhcGrSbB0wBnnTOvRZT7SFgK/AnoFXw/dZ9ewlEREREjl6HxGSlo4UmK4mIiMj+0GQlEREREZED4FBaR/SId2aTM5kdnp3sMEREREQOCeoRFREREZGkUCIqIiIiIkmhRFREREREkkKJqIiIiIgkhRJREREREUkKJaIiIiIikhRavikJFi9+l48+ehjnHM4V0a3bUNq2vbJUnaKiQqZOvYulS98HjK5d76djx5uTE7CIiIjIQaBEtIo553j77QHccMPfadz4VNaunc/YsV1o0+ZyzHZ3UC9YMJ716//D4MFLKCjIY9SoM2jVqjv16mUmL3gRERGRA0i35pPArBrbtuUDsG3bBtLTjy+VhAJkZU2gY8dbMKtGnTqNaNPmcrKyJiYjXBEREZGDQj2iVczM6N37dV57rRepqXXYvn0TodB7Zerl568gI6NFyfOMjOZs3LiyKkMVEREROaiUiFaxoqJdzJz5GH37TqZ58y6sWPEZb7zRh0GDviY1NT3Z4YmIiIhUGd2ar2Jr1sxj06ZVNG/eBYDmzbuQmlqH3NyFpeplZDQnP395yfP8/BXUrdusSmMVEREROZiUiFaxunWbsnFjDuvWLQIgN3chmzevoUGDH5aq167d1cyZMxrnitiyJZdvvplEu3ZXJSNkERERkYNCt+arWHr6cVxyyUgmTuxdMkGpV69x1KrVgPHjL+bCC4fRpEkn2rcfQE7OFzz77EkAXHDBI9Sv3yqZoYuIiMghYPr0e1m48E02bMjmjjsW0LjxqRQU5PH22wNYv34p1avXpEGDE+nZcxR16jTCuSLGju3Czp0FAKSnH0/Pni+UWYln8uQbmTdvHA88sInU1HQaNiQ1ErFdwL9jql0UDru8+JgiEWsNvAQ0BPKA68Jht2Rv56JENAnat+9H+/b9ypT36zel5Ptq1arTs+fIqgxLREREDgNt2lzO2Wf/D+PGnVdSZmZ06fIrMjO7ATB9+n3MmHE/vXqNwawa/fq9T1paBgCff/4M06bdQ58+b5Xsv2jRO4AlOtyGcNidXoGwXgCeD4fdK5GI9QdGAT/e2066NS8iIiJyGGnevCsZGaXnjdSq1aAkCQVo2vScUnNNipNQgO3bN5ZaNrKgII9PPonQo8f/7VM8kYg1BjoCrwZFrwIdIxFrtLd91SMqIiIicgRxrojZs0dy8smXlSofP/5iVq+eQ+3axzJgwPSS8ilTBtGt29BSyWqMupGIzcZ3l74GPBUOOxdXpxnwXTjsCgHCYVcYidiqoDx3T7EmtUfUzB43sywz22hmq8xstJk1iKtznZktNbMCM/vCzM6M2XaOmb1nZmvNLN/MvjSzy+P2b2xmb5nZJjPLDY65x/M2sxPNbIaZbTGzHDMbErOtppmNMrMlQZsrzOxJM0s7UK+LiIiIyL6aMmUwqanpnHXWL0qV9+s3hSFDVnHqqdfy6aePApCVNZHq1VNp3bpnmXby89kJNA2HXSfg58BVwE0HMtZk35ovBPrjB7Z2AJoC44o3mllXYCRwB1AfeBOYYmZ1gyoNgAnAqcH23wKvmlnnmGOMDx6bAmcDVwD3lReQmVUH3gEWAo2Ay4Bfm1mfoEoKsA64FKgHnIcfA/F4pc9eRERE5ACaPv1evv9+Cb17TyjzqY3gP92xY8ebmD//LwBkZ3/EsmUfMnx4JsOHZwIwYsQp5OZ+za5duHDY/RcgeBwPdElw2JXACZGIVQcIHpsE5XuU1ETUOfegc26uc26ncy4XeA7oFlPlFuAt59x059x24ElgOz6ZxDk3xTn3snMu1zlX5JybhJ/Z1RXAzFoC3YH7nHP5zrlv8Qnj7XsI63ygBfCAc67AOTcHP+D29uCYW5xzv3HOfeOcK3TOLQfGxsUtIiIiUqU++OA3rF79JX37TiIlpWZJ+ZYtuRQU7J7onpU1kcaNTwPgkktGcM89Odx9dzZ3350NwJ13ZtGoUTuOOYaUSMRqAEQiVhvfOTcv/rhBkjoPuDYouhaYGw67Pd6Wh0NvjOhFwPyY5x2AF4ufOOecmc0Nyssws+OAU2La6ADkO+eWxlSbA2SaWV3n3MYEzXQAFjvnNsftM6gScYuIiIgcFFOn3sXChW+xefMaXn65O7VrN6R379eZOfN3NGzYmjFjzgWgfv2W9OnzNps3r2Hy5IEUFu4EHPXqteTKK1/Z63FatSIdmBuJWCFQA3gX32lIJGKdgGHhsLs4qH478FIkYo8A64HrKnIu5sqMN00OM7sKn3ReEPRCYmZLgUedc7G3618Cdjrnbo7bvw4wHVjpnOsblA0I9m8RU68l8C3QzDmXkyCOh4HuzrkLYsouBP7mnCuTuJvZ3cCDQCfn3IoE228FbgUggzP5ZcVeDxEREZF4Llx1eZuZfemc63Qwj5HsMaIAmNnVwGjgsuIkNLAJiJ/CVQ8o1ZNpZscAU4H/UjoDL29/gE1m1s/MNhd/VeaYwXF/CdwP/DhREgrgnPuTc66Tc64TtRPVEBERETk6JT0RNbMb8GMwL3XOfRS3+Sv8ulTFdQ04PSgvLmsAfACsAq52zu2I2z/DzGI/kugMIDsYMzreOZde/BWzT+ughzV2n69inhf3nA7B9+DGfuKAiIiIiFRAspdvugt4CujhnPssQZXRwJVmdpGZpeITvzTg7WD/44BP8DPc+znndsXu7JxbBswAnjCzusFt+V/jE9/yfAosB35nZrXM7HTgtth9zOxJ4GZ8ErpoH05dRERE5KiX7B7RZ4C6wEcJbpHjnJsJ3IlPSPOBa4CLYyYZ3YZfuqk3kB/TxoMxx+iHP8/vgFnAZOCJ8gJyzhXil2Y6Ff9ZqVOAJ51zrwGYWQvgXuA44KuYY2bt52shIiIiclQ5ZCYrHQ2siTluS3YUIiIicrjSZCURERERkQPgUFtH9Ih2ZpMzmR2enewwRERERA4J6hEVERERkaRQIioiIiIiSaFEVERERESSQomoiIiIiCSFElERERERSQoloiIiIiKSFFq+KQkWL36Pjz56mKKindSq1YBevV6kfv2WpeoUFRUydepdLF36PmB07Xo/HTvenJyARURERA4CJaJVbOvW9UyadD033fQPGjZszfz5r/Dee3fQv//7peotWDCe9ev/w+DBSygoyGPUqDNo1ao79eplJidwERERkQNMt+ar2Pff/4f09B/QsGFrAE466WKWLp1GQcG6UvWysibQseMtmFWjTp1GtGlzOVlZE5MRsoiIiMhBoUS0ijVs2JrNm9fw3XezAJg/fzwA+fkrStXLz19BRkaLkucZGc3ZuHFl1QUqIiIicpDp1nwVS0vLoHfvCUyb9kt27drGiSf+nLS0elSrViPZoYmIiIhUKSWiSdCqVXdateoOwObNa/nHP56kfv1WpepkZDQnP385J5zQGSjbQyoiIiJyuNOt+STYvHkNAM4V8cEHD9Kp0+2kptYpVaddu6uZM2c0zhWxZUsu33wziXbtrkpGuCIiIiIHhXpEk+DDDx9i5crPKCzcQatWP6V7998DMH78xVx44TCaNOlE+/YDyMn5gmefPQmACy54pEyvqYiIiMjhzJxzyY7hqNGpUyc3e/bsZIchIiIih7Hp0+9l4cI32bAhmzvuWEDjxqdSUJDH228PYP36pVSvXpMGDU6kZ89R1KnTCOeKGDu2Czt3FgCQnn48PXu+UGZJyMmTb2TevHE88MAmUlPTOfZYWzB4MO2Af8dUuygcdnnxMUUi1hp4CWgI5AHXhcNuyd7ORT2iIiIiIoeRNm0u5+yz/4dx484rKTMzunT5FZmZ3QCYPv0+Zsy4n169xmBWjX793ictLQOAzz9/hmnT7qFPn7dK9l+06B3AEh1uQzjsTq9AWC8Az4fD7pVIxPoDo4Af720njREVEREROYw0b96VjIxmpcpq1WpQkoQCNG16Dvn5y0ueFyehANu3b8RsdwpYUJDHJ59E6NHj//YpnkjEGgMdgVeDoleBjpGINdrbvuoRFRERETmCOFfE7NkjOfnky0qVjx9/MatXz6F27WMZMGB6SfmUKYPo1m1oqWQ1Rt1IxGbju0tfA54Kh8uM62wGfBcOu0KAcNgVRiK2KijP3VOsSe0RNbPHzSzLzDaa2SozG21mDeLqXGdmS82swMy+MLMzY7adY2bvmdlaM8s3sy/N7PK4/Rub2VtmtsnMcoNj7vG8zexEM5thZlvMLMfMhsRtH2tmK4O4VwfP6x+I10RERERkf0yZMpjU1HTOOusXpcr79ZvCkCGrOPXUa/n000cByMqaSPXqqbRu3bNMO/n57ASahsOuE/Bz4Crgpv9n797Dq6ruff+/vwQjJCEBAjmK3I8CG2nRENRdaIXKOf6KiBSxSCPeryiWLbq9dNflVB9Pvez+8Oxu1M1RKjUbL1suPzFoBCluumt/RUAgBbVcFAQ1BkhIYikk3/PHnIkrKwFiCFkIn9fzrGdljjnGHN+5Hv74MuYYY7ZkrMl+NF8NXEE4sXUw0B2YXXvSzIYDTwG3AJ2AV4FCM8uMqnQGXgIGRecfAuaa2dC4Pgqi7+7AucCPgbsOFpCZpQCvARuArsBY4G4zmxhX7VfAAHfPBP4OSAP+9Rveu4iIiEiLKiq6k127PmLChJdobNzNrA25udexdu1vAdi6dRlbtrzNjBm9mTGjNwAzZ55JScmfOXAAj8X8C4DouwAY1ki324DTgsBSAKLvblH5ISU1EXX3+9x9tbvvd/cS4NfAiLgqNwDz3L3I3fcBjwP7CJNJ3L3Q3ee4e4m717j7AsKVXcMBzKwPMAq4y93L3H0z8Chw8yHC+gHQC7jX3avcfRXhhNu6Nu6+3t0r49rUAP2P4KcQEREROSJLl/6cnTvf4/LLF9C27cl15ZWVJVRVfb3Qvbj4FXJyvgPARRfN5I47tjNt2lamTdsKwJQpxXTtOpAOHWgbBHYSQBBYGuHg3JrEfqMkdQ0wKSqaBKyOxfyQj+Xh2JsjegGwNu54MPCb2gN3dzNbHZU3YGanAGfGXWMwUObum+KqrQJ6m1mmu5c3cpnBwIfuXpHQ5taEvu4Bfg5kAF8Rjuw2FtONwI0AZIEFja5IExERETksjzmLF9/Ohg3zqKj4jDlzRpGWls2ECS+zYsUjZGf349lnvwdAp059mDhxPhUVn7Fw4dVUV+8HnI4d+zB+/AuH7atvXzKA1UFg1cBJwCLCQUOCwPKAB2MxHx1Vvxl4PgjsfmA3cGVT7ueY2UfUzC4lTDrPj0YhMbNNwMPuHv+4/nlgv7tfn9A+HSgCtrn75VHZ5Kh9r7h6fYDNQA93395IHL8ARrn7+XFlI4G33L1B4h5d71rgFXdfm3i+Xt1u5tx06N9BRERE5GA81np5m5m95+55R7OPZM8RBcDMLgNmAWNrk9DIXiBxCVdHoN5Ippl1ABYDX1A/Az9Ye4C9ZpZvZhW1n2/SahQEbwAAIABJREFUZy1330I4p7TwcIugRERERORrSU+czOwawjmYF7v7soTT7xPuS1Vb14CzovLass7AUmAHcJm7/y2hfZaZxb8b82xgazRntMDdM2o/cW36RSOs8W3e5+DaAqcB6YeoIyIiIiJxkr190+3AE8CF7v77RqrMAsab2QVmlgpMB9oB86P2pwDLCVe457v7gfjG0WjlEuAxM8uMHqPfTZj4Hsw7wMfAI2bW3szOAm6qbRNtB3WlmXWMjvsBjwEr3H1vs34IERERkRNQskdEnwQygWWNPCLH3VcAUwgT0jLgJ8DouEVGNxFu3TQBKIu7xn1xfeQT3uenwJ+AhYSJY6PcvRq4OLpuKVAIPO7uL9ZWAa4GNptZJfAW4Ur9Cc3+FUREREROQMfMYqUTgRYriYiIyJE43hYrHWvbNx3XhnQbwsrYymSHISIiInJMSPajeRERERE5QSkRFREREZGkUCIqIiIiIkmhRFREREREkkKJqIiIiIgkhRJREREREUkKJaIiIiIikhTaRzQJPvzwdZYt+wU1Nftp374zl1zyGzp16lOvTk1NNYsX386mTW8AxvDh95Cbe31yAhYRERE5CpSItrKvvtrNggVXcd11/0V2dj/Wrn2B11+/hSuueKNevXXrCti9+y9MnfoRVVWlPPPM2fTtO4qOHXsnJ3ARERGRFqZH861s166/kJHx38jO7gfAGWeMZtOmN6mq+rJeveLil8jNvQGzNqSnd2XAgHEUF7+SjJBFREREjgoloq0sO7sfFRWf8emnfwJg7doCAMrKPqlXr6zsE7KyetUdZ2X1pLx8W+sFKiIiInKU6dF8K2vXLosJE17izTf/gQMH/srpp/+Idu060qbNSckOTURERKRVKRFNgr59R9G37ygAKio+57/+63E6depbr05WVk/Kyj7mtNOGAg1HSEVERES+7fRoPgkqKj4DwL2GpUvvIy/vZlJT0+vVGTjwMlatmoV7DZWVJWzcuICBAy9NRrgiIiIiR4VGRJPg7bf/iW3bfk919d/o2/d/MmrULwEoKBjNyJEP0q1bHt/97mS2b/8j//IvZwBw/vn3Nxg1FRERkRNPUdGdbNjwKnv2bOWWW9aRkzOIqqpS5s+fzO7dm0hJOZnOnU9nzJhnSE/vinsNzz03jP37qwDIyDiVMWOebrATz8KF17JmzWzuvXcvqakZZGeTGgR2AFgfV+2CWMxLE2MKAusHPA9kA6XAlbGYf3S4e1EimgRjx/6fRsvz8wvr/m7TJoUxY55qrZBERETkW2LAgHGce+7PmD37+3VlZsawYf9I794jACgquoslS+7hkkuexawN+flv0K5dFgDvvvskb755BxMnzqtr/8EHrwHWWHd7YjE/qwlhPQ38ayzmLwSBXQE8A/zwcI30aF5ERETkW6Rnz+FkZfWoV9a+fee6JBSge/fzKCv7uO64NgkF2LevHLOvU8CqqlKWLw+48MJfNSueILAcIBeYGxXNBXKDwLoerq1GREVERESOI+41rFz5FP37j61XXlAwmp07V5GW1oXJk4vqygsLb2XEiAfqJatxMoPAVhIOl74IPBGLuSfU6QF8Got5NUAs5tVBYDui8pJDxZrUEVEze9TMis2s3Mx2mNksM+ucUOdKM9tkZlVm9kczGxJ37jwze93MPjezMjN7z8zGJbTPMbN5ZrbXzEqiPg9532Z2upktMbNKM9tuZtMPUi89iu3AkfwOIiIiIi2lsHAqqakZnHPObfXK8/MLmT59B4MGTeKddx4GoLj4FVJSUunXb0yD65SVsR/oHot5HvAj4FLgupaMNdmP5quBKwgntg4GugOza0+a2XDgKeAWoBPwKlBoZplRlc7AS8Cg6PxDwFwzGxrXR0H03R04F/gxcNfBAjKzFOA1YAPQFRgL3G1mExup/ktgS9NvV0REROToKSq6k127PmLChJdobNzNrA25udexdu1vAdi6dRlbtrzNjBm9mTGjNwAzZ55JScmfOXAAj8X8C4DouwAY1ki324DTgsBSAKLvblH5ISU1EXX3+9x9tbvvd/cS4NfAiLgqNwDz3L3I3fcBjwP7CJNJ3L3Q3ee4e4m717j7AsKVXcMBzKwPMAq4y93L3H0z8Chw8yHC+gHQC7jX3avcfRXhhNt6bczsB8D3o+uJiIiIJNXSpT9n5873uPzyBbRte3JdeWVlCVVVXy90Ly5+hZyc7wBw0UUzueOO7UybtpVp07YCMGVKMV27DqRDB9oGgZ0EEASWRjg4tyax3yhJXQNMioomAatjMT/kY3k49uaIXgCsjTseDPym9sDd3cxWR+UNmNkpwJlx1xgMlLn7prhqq4DeZpbp7uWNXGYw8KG7VyS0uTWunzRgFuFobv0NQBvGdCNwIwBZYEGjK9JEREREDstjzuLFt7NhwzwqKj5jzpxRpKVlM2HCy6xY8QjZ2f149tnvAdCpUx8mTpxPRcVnLFx4NdXV+wGnY8c+jB//wmH76tuXDGB1EFg1cBKwiHDQkCCwPODBWMxHR9VvBp4PArsf2A1c2ZT7MW8w3zQ5zOxSwqTz/GgUEjPbBDzs7vGP658H9rv79Qnt04EiYJu7Xx6VTY7a94qr1wfYDPRw9+2NxPELYJS7nx9XNhJ4y93bRsdPRjHcaWYjgCW15w55j93MualJP4eIiIhIAx5rvbzNzN5z97yj2Uey54gCYGaXEY4wjq1NQiN7gcQlXB2BeiOZZtYBWAx8Qf0M/GDtAfaaWb6ZVdR+mtJnNG91NHB/E29PRERERBqR9ETUzK4hnIN5sbsvSzj9PuG+VLV1DTgrKq8t6wwsBXYAl7n73xLaZ5lZ/CuJzga2RnNGC9w9o/YT16ZfNMIa36a2z1GEC58+MbMvgYVAipl9aWYXN+c3EBERETkRJXv7ptuBJ4AL3f33jVSZBYw3swvMLBWYDrQD5kftTwGWE65wz3f3etsoufsWYAnwmJllRo/l7yZMfA/mHeBj4BEza29mZwE3xbX5FXAGYUJ8FnA94er/s6K+RERERKQJkj0i+iSQCSxr5BE57r4CmEKYkJYBPwFGxy0yuolw66YJQFncNe6L6yOf8D4/Bf5EOIL52MECcvdq4OLouqVAIfC4u78YnS939+21H6KNWqPjr47w9xARERE5YTRrsZKZ9SRMAs8g3AM0cSm4u/sFRx7e8UWLlURERORIHG+Llb7x9k1m9iPCR+OphAt7drV0UCIiIiJy/PvGI6JmtgboAoxz95VHJarjVF5enq9cqZ9MREREjn3H6vZNA4AZSkJFRERE5Eg0JxEtAf522FoiIiIiIofQnET0t8ClLR2IiIiIiJxYDv9aynCFfLzfACPNbCHh9ktbCPfRrMfdP2mJAEVERETk+NSUVfNbgcQVTbXbNY05RLuU5gQkIiIiIieGpiSiD9IwERUREREROSKHTUTd/YFWiOOE8uGHi1i27Be4O+41jBjxAH/3d+Pr1ampqWbx4tvZtOkNwBg+/B5yc69PTsAiIiIiR0FzNrS/H5jn7usPcv5M4FJ3f/BIgzseuTvz50/mmmv+k5ycQXz++Vqee24YAwaMw+zrtWPr1hWwe/dfmDr1I6qqSnnmmbPp23cUHTv2Tl7wIiIiIi2oOavmHwC+e4jzg4BYs6I5QZi14a9/LQPgr3/dQ0bGqfWSUIDi4pfIzb0Bszakp3dlwIBxFBe/koxwRURERI6Kbzwi2gTtgANH4brHBTNjwoSXefHFS0hNTWffvr389KevN6hXVvYJWVm96o6zsnpSXr6tNUMVEREROaqalIiaWSbQMa4ou5FtnQA6A/mAMqaDqKk5wIoV/4vLL19Iz57D+OST3/Mf/zGRW2/9M6mpGckOT0RERKTVNPXR/D8Q7he6hXAF/Yy44/jPe8Ao4OkWj/Q48dlna9i7dwc9ew4DoGfPYaSmplNSsqFevaysnpSVfVx3XFb2CZmZPVo1VhEREZGjqamP5n8XfRtwPzAfWJtQx4EK4F13/68Wie44lJnZnfLy7Xz55Qd06dKfkpINVFR8RufO/71evYEDL2PVqln83d+Np6qqlI0bF3DNNe8kKWoRERGRltekRNTdlwPLAcysF/C0u//xaAZ2vMrIOIWLLnqKV16ZULdA6ZJLZtO+fWcKCkYzcuSDdOuWx3e/O5nt2//Iv/zLGQCcf/79dOrUN5mhi4iIyDGgqOhONmx4lT17tnLLLevIyRlEVVUp8+dPZvfuTaSknEznzqczZswzpKd3xb2G554bxv79VQBkZJzKmDFPN9iJZ+HCa1mzZjb33ruX1NQMsrNJDQI7AMTvlHRBLOaliTEFgfUDngeygVLgyljMPzrcvXzjxUrufs03bSP1ffe7+Xz3u/kNyvPzC+v+btMmhTFjnmrNsERERORbYMCAcZx77s+YPfv7dWVmxrBh/0jv3iMAKCq6iyVL7uGSS57FrA35+W/Qrl0WAO+++yRvvnkHEyfOq2v/wQev8fWLM+vZE4v5WU0I62ngX2MxfyEI7ArgGeCHh2vUnO2bADCzfmY22swmm9mViZ/mXldEREREDq5nz+FkZdVfN9K+fee6JBSge/fz6q01qU1CAfbtK6+3bWRVVSnLlwdceOGvmhVPEFgOkAvMjYrmArlBYF0P17Y5G9r/N8Kh1/9RW9RINQfmfNNri4iIiMiRca9h5cqn6N9/bL3ygoLR7Ny5irS0LkyeXFRXXlh4KyNGPFAvWY2TGQS2kjDfexF4IhbzxFe/9wA+jcW8GiAW8+ogsB1RecmhYm3OiOivCZPQp4DLgJGNfA47FAtgZo+aWbGZlZvZDjObZWadE+pcaWabzKzKzP5oZkPizp1nZq+b2edmVmZm75nZuIT2OWY2z8z2mllJ1Och79vMTjezJWZWaWbbzWx6wvnfmdk+M6uI+4xpyj2LiIiIHE2FhVNJTc3gnHNuq1een1/I9Ok7GDRoEu+88zAAxcWvkJKSSr9+DdOYsjL2A91jMc8DfgRcClzXkrE2JxH9H4SLlW5z93nuvryxTxOvVQ1cQTixdTDQHZhde9LMhhMmvLcAnYBXgcJoX1MI9y19ifBtTp2Ah4C5ZjY0ro+C6Ls7cC7wY+CugwVkZinAa8AGoCswFrjbzCYmVH3I3TPiPouaeM8iIiIiR0VR0Z3s2vUREya81OCtjRC+3TE39zrWrv0tAFu3LmPLlreZMaM3M2b0BmDmzDMpKfkzBw7gsZh/ARB9FwDDGul2G3BaEFgKQPTdjSbsK9+cRLQN8H4z2jXg7ve5+2p33+/uJYSjrSPiqtxA+F77InffBzwO7CNMJnH3Qnef4+4l7l7j7gsIV3YNBzCzPoT7mt7l7mXuvhl4FLj5EGH9AOgF3OvuVe6+inDC7aHaiIiIiCTV0qU/Z+fO97j88gW0bXtyXXllZQlVVV8vdC8ufoWcnO8AcNFFM7njju1Mm7aVadO2AjBlSjFduw6kQwfaBoGdBBAElkY4OLcmsd8oSV0DTIqKJgGrYzE/5GN5aN4rPv+TcPTyaLiA+vuTDgZ+U3vg7m5mqw/Wv5mdApwZd43BQJm7b4qrtgrobWaZ7l7eyGUGAx+6e0VCm1sT6k0zszuAncALwBPuvv8w9yciIiJyRBYvvp0NG+ZRUfEZc+aMIi0tmwkTXmbFikfIzu7Hs89+D4BOnfowceJ8Kio+Y+HCq6mu3g84HTv2Yfz4Fw7bT9++ZACrg8CqgZOARYSDhgSB5QEPxmI+Oqp+M/B8ENj9wG6gSQvXzRvMNz1MA7P+wDJgqru/+o0aH/q6lxImnedHo5CY2SbgYXePf1z/PLDf3a9PaJ8OFAHb3P3yqGxy1L5XXL0+wGagh7tvbySOXwCj3P38uLKRwFvu3jY6/ntgI1AODCUcqn7Z3e9t5Ho3AjcCkMUQ/uEb/jAiIiIiEY99s7ztSJjZe+6edzT7aM6I6FOEb1B62cx2ECZ11Ql13N0vaOoFzewywsffY2uT0MheIHEJV0cgfoQTM+sAvA58Qf0M/GDtAfaaWX7Ub23QGYdoUx5X7w9x5941s/uBXwINElF3/zfg3wCsm7Xevx4RERGRY1xzEtG+hNszfRId9zySAMzsGuCfgYvd/fcJp98n3Jeqtq4BZwHz4so6A28QJsRXuPuBhPZZZtY3mh8KcDaw1d3LCEcyC6jvfaCfmaW7e2Vcm0PNi63hILvAioiIiEjjmvNmpd4t1bmZ3Q7EgAvd/U+NVJkFvBE9jv9P4HagHeG77mvnhL5FOIfzWnevNzLr7lvMbAnwmJldS7g6/27iRkEb8Q7wMfCImd0D9AduAn4W9dmRcDHU74BKwsT4AcLV+yIiIiLSRM1+s1ILeRLIBJbF78lZe9LdVwBTCBPSMuAnwOi4RUY3EW7dNAEoi7vGfXF95BPe56fAn4CFwGMHCyhKZi+OrlsKFAKPu/uLUZWTgH+KrldOmID+O408lhcRERGRg/vGi5XqGoZ7eY4ifFQP4aPxt9x9bwvFdtyxbubclOwoRERE5NtKi5UAM7uecF5nBl/PjXSgwszucPdnWyg+ERERETlONedd82MJV4FvBu4n3EAewv07pwL/ZmZfuPtrLRblcWJItyGsjK1MdhgiIiIix4TmjIj+I+HrL89N2PR9qZnNBt4lXBCkRFREREREDqo5i5UGA79JSEIBiOaHPs/Re/OSiIiIiBwnmrtq/lB7ZmrTdhERERE5rOYkou8DV0Wv1KzHzDKAqzn05u8iIiIiIs2aI/oE4ZuNVpnZ/wb+HJXXLlY6HRjfMuGJiIiIyPGqOW9WWmBmtwGPAv/C14/ijfBNQ7e5+8KWC1FEREREjkfN2kfU3Wea2b8D/xPoTZiEbiLc0L6s5cI7/hQV3cmGDa+yZ89WbrllHTk5gwAoLf2QBQuuoqqqlLS0bMaNm0N29hkN2tfUVLN48e1s2vQGYAwffg+5ude38l2IiIiIHLlmJaIA7r4HeLkFYzkhDBgwjnPP/RmzZ3+/XvmiRTczdOitfPe7V7B27QssWnQTV131doP269YVsHv3X5g69SOqqkp55pmz6dt3FB079m6lOxARERFpGU1KRM2sYUZ0aO7uFzQjnuNez57DG5RVVn7Bzp2rmDz5LQAGDZpEYeFtVFaWkJ7etV7d4uKXyM29AbM2pKd3ZcCAcRQXv8KwYXe1SvwiIiIiLaWpI6IjgP3A35pYX1s4fQNlZdvIzDyNNm1SAGjTJoUOHbpRXr6tQSJaVvYJWVm96o6zsnpSXr6tVeMVERERaQlNTUQPEM4DXQLMBha5e81Ri0pEREREjntN3Uf0NOBewq2Z5gOfmtmjZtb/qEV2AsnK6kF5+afU1FQD4YKkvXt3kJnZo5G6PSkr+7juuKzsk0briYiIiBzrmpSIunuJu/+zu38H+HtgIXAj8Gcz+4OZXW9mHY5moMez9PQcTjnlLNavnwvA+vVzOfXUsxs8lgcYOPAyVq2ahXsNlZUlbNy4gIEDL23tkEVERESOmLk3bzqnmbUDLgWuAUYCVcAt7v5Cy4V3fMnLy/OHHvoeGzbMo6LiM9LSupCWls2UKcV8+eVGFiy4iq++2k379p0YN24OXbqEA84FBaMZOfJBunXLo6ammsLC29i8uQiAYcPuZsiQG5N5WyIiInIcMrP33D3vqPbR3ES07gJmw4AYMAp4wN0fbInAjkd5eXm+cuXKZIchIiIi32KN7UleVVXK/PmT2b17EykpJ9O58+mMGfMM6eldca/hueeGsX9/FQAZGacyZszTDbZ+XLjwWtasmc299+4lNTWDLl1s3dSpDATWx1W7IBbz0sSYgsD6Ac8D2UApcGUs5h8d7l6atY+omXUDriR8r/wZwA7gfxEuZBIRERGRo6SxPcnNjGHD/pHevUcAUFR0F0uW3MMllzyLWRvy89+gXbssAN5990nefPMOJk6cV9f+gw9eI1yX3sCeWMzPakJYTwP/Gov5C0FgVwDPAD88XKOmLlbCzE4yswlm9jrwMfAAsBYYA/Ry95+7u/YREhERETmKevYcTlZW/YXK7dt3rktCAbp3P6/e4ubaJBRg375yzL5OAauqSlm+PODCC3/VrHiCwHKAXGBuVDQXyA0Ca7jYJUFTN7T/38BPgU6Eyed04AV339WsiEVERETkqHCvYeXKp+jff2y98oKC0ezcuYq0tC5MnlxUV15YeCsjRjxQL1mNkxkEtpJwuPRF4IlYrMG8zh7Ap7GYVwPEYl4dBLYjKi85VKxNHRG9DWhPmOH+ljCBvdrM7jjI5x+actFoC6hiMys3sx1mNsvMOifUudLMNplZlZn90cyGxJ07z8xeN7PPzazMzN4zs3EJ7XPMbJ6Z7TWzkqjPQ963mZ1uZkvMrNLMtpvZ9EbqXG5ma6M6n5nZz5tyzyIiIiJHU2HhVFJTMzjnnNvqlefnFzJ9+g4GDZrEO+88DEBx8SukpKTSr9+YBtcpK2M/0D0W8zzgR4SL1K9ryVib/GieMBH9KfBEEz9NUQ1cQTixdTDQnbh5pmY2HHgKuIVwNPZVoNDMMqMqnYGXgEHR+YeAuWY2NK6Pgui7O3Au8GPgoO/DNLMU4DVgA9AVGAvcbWYT4+pMBv5f4A4gi3Ce7P/XxHsWEREROSqKiu5k166PmDDhJRobdzNrQ27udaxd+1sAtm5dxpYtbzNjRm9mzOgNwMyZZ1JS8mcOHMBjMf8CIPouAIY10u024LQgsBSA6LtbVH5ITV2sNLKJ9b4Rd78v7rDEzH4N/Htc2Q3APHcvAjCzxwlHZ38MPO/uhQmXXGBm64HhwJ/MrA/hav7T3b0MKDOzR4F/Ah49SFg/AHoB97p7FbDKzJ4BbgZeikZTfwkE7r4karMXWNeMn0BERESkRSxd+nN27nyPn/70ddq2PbmuvLKyBLM2pKVlA+EoaE7OdwC46KKZXHTRzLq6QWBMmVJMamoGHTrQNgjspFjM9weBpREOzi1K7DcW8y+CwNYAk4AXou/VsZgf8rE8NDERdfflTanXAi4gnINaazDwm7g43MxWR+UNmNkpwJlx1xgMlLn7prhqq4DeZpbp7uWNXGYw8KG7VyS0uTX6ux9hlp9hZhsJR2X/f2Cau/+lkZhuJNz8H7LAgkZXpImIiIgclsecxYtvr9uTfM6cUaSlZTNhwsusWPEI2dn9ePbZ7wHQqVMfJk6cT0XFZyxceDXV1fsBp2PHPowff/ht3/v2JQNYHQRWDZxEmIT+GiAILA94MBbz0VH1m4Hng8DuB3YT7q50WEe8j2hLMbNLCZPO8919VVS2CXjY3eMf1z8P7Hf36xPapwNFwDZ3vzwqmxy17xVXrw+wGejh7tsbieMXwCh3Pz+ubCTwlru3jaYL/Cfhnlpjgc8JpyKMBL7j7gcOeo/dzLnpG/woIiIiInE81np5W2tsaP9N5ogeNWZ2GTALGFubhEb2Es7BjNcRqDeSGb1edDHwBfUz8IO1B9hrZvlmVlH7aWKfe6PvJ919S/T4/j5gAOFoqYiIiIg0QdITUTO7hnDT04vdfVnC6fcJ96WqrWvAWVF5bVlnYCnhpvqXufvfEtpnmVnfuLKzga3uXubuBe6eUfuJa9MvGmGNb1Pb5wfAV0Bj/yU5NoaXRURERL4FkpqImtnthI+1L3T33zdSZRYw3swuMLNUwv1L2wHzo/anAMsJV7jnJz4Wd/ctwBLgMTPLjB7L302Y+B7MO4Qb9j9iZu3N7Czgpto27v5XwpX9PzOzHmZ2MuFq/WLgw+b8DiIiIiInomSPiD4JZALLGnlEjruvAKYQJqRlwE+A0XGLjG4i3LppAuGK+NprxK/Gzye8z0+BPwELgccOFpC7VwMXR9ctBQqBx939xbhqdwArCEdJPyVcZX9x1FZEREREmuCYWax0ItBiJRERETkSx9tipabuIyotYEi3IayMrUx2GCIiIiLHhGQ/mhcRERGRE5QSURERERFJCiWiIiIiIpIUSkRFREREJCmUiIqIiIhIUigRFREREZGk0PZNrayo6E42bHiVPXu2csst68jJGQRAaemHLFhwFVVVpaSlZTNu3Byys89o0L6mpprFi29n06Y3AGP48HvIzb2+le9CRERE5MhpRLSVDRgwjquvfoesrF71yhctupmhQ29l6tQPGTr0VhYtanzn+3XrCti9+y9MnfoR1133B373uwfYs2drK0QuIiIi0rKUiLaynj2Hk5XVo15ZZeUX7Ny5ikGDJgEwaNAkdu5cRWVlSYP2xcUvkZt7A2ZtSE/vyoAB4ygufqVVYhcRERFpSUpEjwFlZdvIzDyNNm1SAGjTJoUOHbpRXr6tkbqf1BtNzcrq2Wg9ERERkWOdElERERERSQoloseArKwelJd/Sk1NNRAuSNq7dweZmT0aqduTsrKP647Lyj5ptJ6IiIjIsU6J6DEgPT2HU045i/Xr5wKwfv1cTj31bNLTuzaoO3DgZaxaNQv3GiorS9i4cQEDB17a2iGLiIiIHDFz92THcMLIy8vzhx76Hhs2zKOi4jPS0rqQlpbNlCnFfPnlRhYsuIqvvtpN+/adGDduDl269AegoGA0I0c+SLduedTUVFNYeBubNxcBMGzY3QwZcmMyb0tERESOQ2b2nrvnHdU+lIi2nry8PF+5cmWywxAREZFvscb2JK+qKmX+/Mns3r2JlJST6dz5dMaMeYb09K641/Dcc8PYv78KgIyMUxkz5mk6duxd77oLF17LmjWzuffevaSmZtCli62bOpWBwPq4ahfEYl6aGFMQWD/geSAbKAWujMX8o8Pdiza0FxEREfkWGTBgHOee+zNmz/5+XZmZMWzYP9K79wgAioruYsmSe7jkkmcxa0N+/hu0a5cFwLvvPsmbb97BxInz6tp/8MFrgDXW3Z5YzM9qQlhPA/8ai/kLQWBXAM8APzxcI80RFREREfkWaWxP8vbtO9cloQDdu59Xb3F7vMOuAAAgAElEQVRzbRIKsG9fOWZfp4BVVaUsXx5w4YW/alY8QWA5QC4wNyqaC+QGgTVc7JJAI6IiIiIixxH3GlaufIr+/cfWKy8oGM3OnatIS+vC5MlFdeWFhbcyYsQD9ZLVOJlBYCsJh0tfBJ6IxRrM6+wBfBqLeTVALObVQWA7ovKGb+eJk7QRUTN71MyKzazczHaY2Swz65xQ50oz22RmVWb2RzMbEnfuPDN73cw+N7MyM3vPzMYltM8xs3lmttfMSqI+D3nPZna6mS0xs0oz225m0xPOVyR89plZtZl1aYnfRURERORIFBZOJTU1g3POua1eeX5+IdOn72DQoEm8887DABQXv0JKSir9+o1pcJ2yMvYD3WMxzwN+BFwKXNeSsSbz0Xw1cAXhpNbBQHdgdu1JMxsOPAXcAnQCXgUKzSwzqtIZeAkYFJ1/CJhrZkPj+iiIvrsD5wI/Bu46WEBmlgK8BmwAugJjgbvNbGJtHXfPiP8A/wG86e5fNudHEBEREWkpRUV3smvXR0yY8BKNjb2ZtSE39zrWrv0tAFu3LmPLlreZMaM3M2b0BmDmzDMpKfkzBw7gsZh/ARB9FwDDGul2G3BaEFgKQPTdLSo/pKQlou5+n7uvdvf97l4C/BoYEVflBmCeuxe5+z7gcWAfYTKJuxe6+xx3L3H3GndfQLiqaziAmfUBRgF3uXuZu28GHgVuPkRYPwB6Afe6e5W7ryKcbNtoGzPLJvzfwdPN/BlEREREWsTSpT9n5873uPzyBbRte3JdeWVlCVVVXy90Ly5+hZyc7wBw0UUzueOO7UybtpVp07YCMGVKMV27DqRDB9oGgZ0EEASWRjhAtyax3yhJXQNMioomAatjMT/kY3k4tuaIXgCsjTseDPym9sDd3cxWR+UNmNkpwJlx1xgMlLn7prhqq4DeZpbp7uWNXGYw8KG7VyS0ufUgMV9DOPfh9YPdlJndCIQbfWaBBY2uSBMRERE5LI85ixffXrcn+Zw5o0hLy2bChJdZseIRsrP78eyz3wOgU6c+TJw4n4qKz1i48Gqqq/cDTseOfRg//oXD9tW3LxnA6iCwauAkYBHhwCFBYHnAg7GYj46q3ww8HwR2P7AbuLIp93NM7CNqZpcSJp3nR6OQmNkm4GF3j39c/zyw392vT2ifDhQB29z98qhsctS+V1y9PsBmoIe7b28kjl8Ao9z9/LiykcBb7t42oa4BHwAF7h406T67mXNTU2qKiIiINOSx1svbWmND+6Rv32RmlwGzgLG1SWhkL5C4fKsjUG8k08w6AIuBL6iffR+sPcBeM8uPX3T0TfqMjAT6Av/nYPcmIiIiIgeX1ETUzK4hnIN5sbsvSzj9PuGeVLV1DTgrKq8t6wwsBXYAl7n73xLaZ5lZ37iys4Gt0ZzRgoRFR7Vt+kUjrPFt3qehm4HX3P3Tb3DLIiIiIhJJ5vZNtwNPABe6++8bqTILGG9mF5hZKjAdaAfMj9qfAiwnXOGe7+4H4hu7+xZgCfCYmWVGj+XvJkx8D+Yd4GPgETNrb2ZnATcltjGzHGAcWqQkIiIi0mzJHBF9EsgEljXyiBx3XwFMIUxIy4CfAKPjFhndRLh10wSgLO4a98X1kU94j58CfwIWAo8dLCB3rwYujq5bChQCj7v7iwlVrwW2E85LFREREZFmOCYWK50otFhJREREjsTxtljpWNq+6bg3pNsQVsZWJjsMERERkWNC0lfNi4iIiMiJSYmoiIiIiCSFElERERERSQoloiIiIiKSFEpERURERCQplIiKiIiISFIoERURERGRpNA+oq2sqOhONmx4lT17tnLLLevIyRkEQGnphyxYcBVVVaWkpWUzbtwcsrPPaNC+pqaaxYtvZ9OmNwBj+PB7yM29vpXvQkREROTIaUS0lQ0YMI6rr36HrKxe9coXLbqZoUNvZerUDxk69FYWLWr8FUzr1hWwe/dfmDr1I6677g/87ncPsGfP1laIXERERKRlKRFtZT17Dicrq0e9ssrKL9i5cxWDBk0CYNCgSezcuYrKypIG7YuLXyI39wbM2pCe3pUBA8ZRXPxKq8QuIiIi0pKUiB4Dysq2kZl5Gm3apADQpk0KHTp0o7x8WyN1P6k3mpqV1bPReiIiIiLHOiWiIiIiIpIUSkSPAVlZPSgv/5SammogXJC0d+8OMjN7NFK3J2VlH9cdl5V90mg9ERERkWOdEtFjQHp6Dqecchbr188FYP36uZx66tmkp3dtUHfgwMtYtWoW7jVUVpawceMCBg68tLVDFhERETli5u7JjuGEkZeX5w899D02bJhHRcVnpKV1IS0tmylTivnyy40sWHAVX321m/btOzFu3By6dOkPQEHBaEaOfJBu3fKoqammsPA2Nm8uAmDYsLsZMuTGZN6WiIiItKLGtoKsqipl/vzJ7N69iZSUk+nc+XTGjHmG9PSuuNfw3HPD2L+/CoCMjFMZM+ZpOnbsXe+6Cxdey5o1s7n33r2kpmbQpYutmzqVgcD6uGoXxGJemhhTEFg/4HkgGygFrozF/KPD3YsS0VaUl5fnK1euTHYYIiIi8i32yScryMrqxezZ3+enP11ETs4gvvpqF59/vpbevUcAUFR0F199tYtLLnkWgL/+tYx27bIAePfdJ/n44+VMnDiv7poffPAaGzcuYM2a5xIT0W6xmHc5XExBYG8Dz8Vi/kIQ2BXAtbGY//Bw7fRoXkRERORbpLGtINu371yXhAJ0735evTUltUkowL595Zh9nQJWVZWyfHnAhRf+qlnxBIHlALnA3KhoLpAbBNZwjmECvVlJRERE5DjiXsPKlU/Rv//YeuUFBaPZuXMVaWldmDy5qK68sPBWRox4oF6yGiczCGwlYMCLwBOxWIPH6T2AT2MxrwaIxbw6CGxHVN5wU/Q4SR0RNbNHzazYzMrNbIeZzTKzzgl1rjSzTWZWZWZ/NLMhcefOM7PXzexzMyszs/fMbFxC+xwzm2dme82sJOrzkPdtZqeb2RIzqzSz7WY2PeF8fzMrNLNSM/vSzBaaWe8j/0VEREREjkxh4VRSUzM455zb6pXn5xcyffoOBg2axDvvPAxAcfErpKSk0q/fmAbXKStjP9A9FvM84EfApcB1LRlrsh/NVwNXEE5sHQx0B2bXnjSz4cBTwC1AJ+BVoNDMMqMqnYGXgEHR+YeAuWY2NK6Pgui7O3Au8GPgroMFZGYpwGvABqArMBa428wmxlWbC3xJmOn3AvbG9SMiIiKSFEVFd7Jr10dMmPASjY27mbUhN/c61q79LQBbty5jy5a3mTGjNzNm9AZg5swzKSn5MwcO4LGYfwEQfRcAwxrpdhtwWhBYCkD03S0qP6SkJqLufp+7r3b3/e5eAvwaGBFX5QZgnrsXufs+4HFgH2EyibsXuvscdy9x9xp3X0C4sms4gJn1AUYBd7l7mbtvBh4Fbj5EWD8gTC7vdfcqd18FPJPQ5nTgheh8JfBbwkRaREREJCmWLv05O3e+x+WXL6Bt25PryisrS6iq+nqhe3HxK+TkfAeAiy6ayR13bGfatK1Mm7YVgClTiunadSAdOtA2COwkgCCwNMLBuTWJ/UZJ6hpgUlQ0CVgdi/khH8vDsTdH9AJgbdzxYOA3tQfu7ma2moMkfWZ2CnBm3DUGA2Xuvimu2iqgt5llunt5I5cZDHzo7hUJbW6NO/4lcKWZ/YFwzsTVwPyDxHQjEO6vlAUWWGPVRERERA7LY87ixbfXbQU5Z84o0tKymTDhZVaseITs7H48++z3AOjUqQ8TJ86nouIzFi68murq/YDTsWMfxo9/4bB99e1LBrA6CKwaOAlYRDhoSBBYHvBgLOajo+o3A88Hgd0P7AaubMr9HDPbN5nZpYRJ5/nRKCRmtgl42N3jH9c/D+x39+sT2qcDRcA2d788Kpscte8VV68PsBno4e7bG4njF8Aodz8/rmwk8Ja7t42Oc4F/A84mTETXAhe6++eHvMdu5tzUxB9EREREJIHHWi9vM7P33D3vaPaR7DmiAJjZZcAsYGxtEhrZCyQu4eoI1BvJNLMOwGLgC+pn4AdrD7DXzPLNrKL205Q+zawT8DawAMiIPguA/zSzdk24XRERERHhGEhEzewawjmYF7v7soTT7xPuS1Vb14CzovLass7AUmAHcJm7/y2hfZaZ9Y0rOxvYGs0ZLXD3jNpPXJt+0QhrfJvaPv87YaL6z+7+lbtXAf8MnAH0b8ZPICIiInJCSvb2TbcDTxA+1v59I1VmAePN7AIzSwWmA+2I5mNGc0KXE65wz3f3A/GN3X0LsAR4zMwyo8fydxMmvgfzDvAx8IiZtTezs4Cb4tpsBHYBPzOzVDM7GbiDcMR0U2MXFBEREZGGkj0i+iSQCSxr5BE57r4CmEKYkJYBPwFGxy0yuolw66YJQFncNe6L6yOf8D4/Bf4ELAQeO1hA7l4NXBxdtxQoBB539xej8xXAGOD/AT6LPqOAMQkLnERERETkEI6ZxUonAi1WEhERkSOhxUoiIiIiIi3gWNtH9Lg2pNsQVsZWJjsMERERkWOCRkRFREREJCmUiIqIiIhIUigRFREREZGkUCIqIiIiIkmhRFREREREkkKJqIiIiIgkhbZvamVFRXeyYcOr7NmzlVtuWUdOziAASks/ZMGCq6iqKiUtLZtx4+aQnX1Gg/Y1NdUsXnw7mza9ARjDh99Dbu71rXwXIiIiIkdOI6KtbMCAcVx99TtkZfWqV75o0c0MHXorU6d+yNCht7JoUeOvYFq3roDdu//C1Kkfcd11f+B3v3uAPXu2tkLkIiIiIi1LiWgr69lzOFlZPeqVVVZ+wc6dqxg0aBIAgwZNYufOVVRWljRoX1z8Erm5N2DWhvT0rgwYMI7i4ldaJXYRERGRlqRE9BhQVraNzMzTaNMmBYA2bVLo0KEb5eXbGqn7Sb3R1Kysno3WExERETnWKREVERERkaRQInoMyMrqQXn5p9TUVAPhgqS9e3eQmdmjkbo9KSv7uO64rOyTRuuJiIiIHOuUiB4D0tNzOOWUs1i/fi4A69fP5dRTzyY9vWuDugMHXsaqVbNwr6GysoSNGxcwcOClrR2yiIiIyBEzd092DCeMvLw8f+ih77FhwzwqKj4jLa0LaWnZTJlSzJdfbmTBgqv46qvdtG/fiXHj5tClS38ACgpGM3Lkg3TrlkdNTTWFhbexeXMRAMOG3c2QITcm87ZERESkFTW2FWRVVSnz509m9+5NpKScTOfOpzNmzDOkp3fFvYbnnhvG/v1VAGRknMqYMU/TsWPvetdduPBa1qyZzb337iU1NYMuXWzd1KkMBNbHVbsgFvPSxJiCwPoBzwPZQClwZSzmHx3uXpSItqK8vDxfuXJlssMQERGRb7FPPllBVlYvZs/+Pj/96SJycgbx1Ve7+PzztfTuPQKAoqK7+OqrXVxyybMA/PWvZbRrlwXAu+8+yccfL2fixHl11/zgg9fYuHEBa9Y8l5iIdovFvMvhYgoCext4LhbzF4LArgCujcX8h4drp0fzIiIiIt8ijW0F2b5957okFKB79/PqrSmpTUIB9u0rx+zrFLCqqpTlywMuvPBXzYonCCwHyAXmRkVzgdwgsIZzDBPozUoiIiIixxH3GlaufIr+/cfWKy8oGM3OnatIS+vC5MlFdeWFhbcyYsQD9ZLVOJlBYCsBA14EnojFGjxO7wF8Got5NUAs5tVBYDui8oabosdJ6oiomT1qZsVmVm5mO8xslpl1TqhzpZltMrMqM/ujmQ2JO3eemb1uZp+bWZmZvWdm4xLa55jZPDPba2YlUZ+HvG8zO93MlphZpZltN7PpCed7mdkCM/vSzErNbKaZndwSv4mIiIjIkSgsnEpqagbnnHNbvfL8/EKmT9/BoEGTeOedhwEoLn6FlJRU+vUb0+A6ZWXsB7rHYp4H/Ai4FLiuJWNN9qP5auAKwomtg4HuwOzak2Y2HHgKuAXoBLwKFJpZZlSlM/ASMCg6/xAw18yGxvVREH13B84FfgzcdbCAzCwFeA3YAHQFxgJ3m9nEhPPbomsOBv4e+Ofm/AAiIiIiLaWo6E527fqICRNeorFxN7M25OZex9q1vwVg69ZlbNnyNjNm9GbGjN4AzJx5JiUlf+bAATwW8y8Aou8CYFgj3W4DTgsCSwGIvrtF5YeU1ETU3e9z99Xuvt/dS4BfAyPiqtwAzHP3InffBzwO7CNMJnH3Qnef4+4l7l7j7gsIV3YNBzCzPsAo4C53L3P3zcCjwM2HCOsHQC/gXnevcvdVwDNxbfoD3wH+yd3/6u7bgRnANWbW7sh/FREREZFvbunSn7Nz53tcfvkC2rb9+kFtZWUJVVVfL3QvLn6FnJzvAHDRRTO5447tTJu2lWnTtgIwZUoxXbsOpEMH2gaBnQQQBJZGODi3JrHfKEldA0yKiiYBq2MxP+RjeTj25oheAKyNOx4M/Kb2wN3dzFZH5Q2Y2SnAmXHXGAyUufumuGqrgN5mlunu5Y1cZjDwobtXJLS5Nfq7Nnm3uPNtgDSgX0L8IiIiIi1q8eLb67aCnDNnFGlp2UyY8DIrVjxCdnY/nn32ewB06tSHiRPnU1HxGQsXXk119X7A6dixD+PHv3DYfvr2JQNYHQRWDZwELCIcNCQILA94MBbz0VH1m4Hng8DuB3YDVzblXo6Z7ZvM7FLCpPP8aBQSM9sEPOzu8Y/rnwf2u/v1Ce3TgSJgm7tfHpVNjtr3iqvXB9gM9IhGMxPj+AUwyt3PjysbCbzl7m3NrC3hY/u3gDsJH98vJExgv+/uKxKudyMQbvSZxRD+oRk/joiIiAjgsdbL28zsPXfPO5p9JHuOKABmdhkwCxhbm4RG9gKJS7g6AvVGMs2sA7AY+IL6GfjB2gPsNbN8M6uo/TSlT3c/AFwM9AG2Am/y9TzULxPvzd3/zd3z3D2PtMSzIiIiIieupCeiZnYN4RzMi919WcLp9wn3paqta8BZUXltWWdgKbADuMzd/5bQPsvM+saVnQ1sjeaMFrh7Ru0nrk2/aIQ1vk1dn+6+0d1/5O457j4AqIr6/7A5v4GIiIjIiSjZ2zfdDjwBXOjuv2+kyixgvP3f9u49zIrqzPf490crQgPdyC1IQAGNMkii3BJPJE/I4MSMIUoUowQ1RuINxXhUYmKek52tmcx4mRzMzFETVKIJDzFGhISB2IPBWyZmgmiQHkQC4g1UQGgujYbLe/6oaty9abqxobua5vd5nnr23mutqlr1NjQvtdaqLY2S1Ba4HmgHPJru3xN4kmSofHx6t3K3iHgFmA/cJqksHZa/kSTx3ZungFeBH0pqL+lk4PLCfSR9XFJHSYdJOg34HvDdiNjViDCYmZmZHZKyviN6J1AGLKhjiJx0vuVEkoS0CvgKcEbBIqPLSR7dNBaoKjjGTQXnGE9ynW8CfyaZz3nb3joUETtJht4HkXxX6lzg9oj4ZUGzs4FXSIbr/y/wvyPiZ40LgZmZmdmhqcUsVjoUqJeCy7PuhZmZmR2svFjJzMzMzOwAaGnPEW3VhvYaysLcwqy7YWZmZtYi+I6omZmZmWXCiaiZmZmZZcKJqJmZmZllwomomZmZmWXCiaiZmZmZZcKJqJmZmZllwo9vamYVFTewdOkjbNy4iiuvfJEePQYBsH79y8ya9TWqq9dTWtqVMWMepGvXj+2x/65dO5k37xpWrPgdIEaM+DZDhnyjma/CzMzMbP/5jmgzGzBgDBdf/BTl5cfUKp8z5wqGD7+KSZNeZvjwq5gzp+6vYHrxxels2PBXJk1azoQJf+SJJ77Pxo2rmqHnZmZmZgeWE9FmdvTRIygv71OrbOvWd1izZhGDBo0DYNCgcaxZs4itW9fusX9l5UMMGXIpUhs6dOjOgAFjqKx8uFn6bmZmZnYgORFtAaqqXqes7KO0aVMCQJs2JXTq1ItNm16vo+1rte6mlpcfXWc7MzMzs5bOiaiZmZmZZcKJaAtQXt6HTZveZNeunUCyIGnz5tWUlfWpo+3RVFW9uvtzVdVrdbYzMzMza+mciLYAHTr0oGfPk1myZAYAS5bM4KijBtOhQ/c92g4ceC6LFk0lYhdbt67lpZdmMXDgOc3dZTMzM7P9pojIug+HjGHDhsUtt3yapUtnsmXLW5SWdqO0tCsTJ1aybt1LzJr1NbZt20D79kcyZsyDdOt2AgDTp5/B5z53M716DWPXrp3MnXs1K1dWAHDqqTcydOhlWV6WmZmZtUKSnouIYU16DieizWfYsGGxcOHCrLthZmZmB7G6nkleXb2eRx+9kA0bVlBScgRduhzH6NE/oUOH7kTs4v77T2X79moAOnY8itGj76Fz5761jjt79iW88MI0vvOdzbRt25Fu3fTipEkMBJYUNBuVy8X64j7l8zoeeADoCqwHLsrlYnlD1+IH2puZmZkdRAYMGMOnPvVNpk37zO4ySZx66rfo23ckABUVk5k//9ucddZ9SG0YP/53tGtXDsCzz97JY49dx3nnzdy9/7JlvwVU1+k25nJx8j506x7g/+Vy8Yt8XhcAPwH+vqGdPEfUzMzM7CBS1zPJ27fvsjsJBejd+5Rai5trklCA99/fhPRBClhdvZ4nn8xz+uk/alR/8nn1AIYAM9KiGcCQfF57LnYp4juiZmZmZq1IxC4WLrybE044s1b59OlnsGbNIkpLu3HhhRW7y+fOvYqRI79fK1ktUJbPayHJ7dJfAnfkcnvM6+wDvJnLxU6AXC525vNanZbv+e08BTK7IyrpVkmVkjZJWi1pqqQuRW0ukrRCUrWkP0kaWlB3iqT/kPS2pCpJz0kaU7R/D0kzJW2WtDY9Z73XLOnetF87JN1bR32JpNvT422W9IikbvsbDzMzM7MDYe7cSbRt25FPfvLqWuXjx8/l+utXM2jQOJ566gcAVFY+TElJW44/fvQex6mqYjvQO5eLYcA/AucAEw5kX7Mcmt8JXEAyqfUkoDcwraZS0gjgbuBK4EjgEWCupLK0SRfgIWBQWn8LMEPS8IJzTE9fewOfAr4MTG6gX4uB64Df7KX+28BZ6fF6p2U/b+CYZmZmZk2uouIG3n13OWPHPkRd996kNgwZMoHFi5PUZdWqBbzyyu+ZMqUvU6b0BeCuu05k7dr/YccOIpeLdwDS1+nAqXWc9nXgo/m8SgDS115peb0yS0Qj4qaIeD4itkfEWuDfgZEFTS4FZkZERUS8D9wOvE+STBIRcyPiwYhYGxG7ImIWyaquEQCS+gGnAZMjoioiVgK3Alc00K8fR8RjwKa9NLkMuDUiVkZEFfAt4AuS+jYiDGZmZmYHxOOPf5c1a57j/PNncdhhR+wu37p1LdXVHyx0r6x8mB49Pg7AF794F9dd9wbXXruKa69dBcDEiZV07z6QTp04LJ/X4QD5vEqBM4EXis+bJqkvAOPSonHA87lc1DssDy1rjugokruRNU4CflbzISJC0vNp+R4k9QROLDjGSUBVRKwoaLYI6CupLCL2lmjulaRy4GjguYJ+rZC0CfgEsKqOfS4jSV6hHJSvc0WamZmZWYMiF8ybd83uZ5I/+OBplJZ2ZezYX/HMMz+ka9fjue++TwNw5JH9OO+8R9my5S1mz76YnTu3A0Hnzv04++xfNHiu/v3pCDyfz2sncDgwh+TGIfm8hgE353JxRtr8CuCBfF7fAzYAF+3L9bSI54hKOock6fxsRCxKy1YAP4iIwuH6B4DtEfGNov07ABXA6xFxflp2Ybr/MQXt+gErgT4R8UYDffoZsKPwXJL6AK8B/SPilYLyV4HvRkS9P1X1UnB5fS3MzMzM9i5yzZe3NccD7TN/fJOkc4GpwJk1SWhqM1C8fKszRUPmkjoB84B3qJ19721/gM2SxkvaUrPtY3c3p68N9svMzMzM6pdpIirp6yQPPP1SRCwoqv4LyTOpatoKODktrynrAjwOrAbOjYi/Fe1fLql/QdlgYFU6Z3R6RHSs2falvxGxkeSOaGG/+gNl1J5WYGZmZmYNyPLxTdcAdwCnR8Qf6mgyFThb0ihJbYHrgXbAo+n+PYEngaXA+IjYUbhzOnQ+H7hNUlk6LH8jSeJbX7/aSmoHlAAlktql56/xU+BGSf3SFfy3Ao9FxKoPGQIzMzOzQ1qWd0TvJLmTuKCuIfKIeAaYSJKQVgFfAc4oWGR0Ocmjm8YCVQXHuKngHONJrvFN4M/AbOC2BvpVAWwjebTUxen7ioL6fwF+mx7vTZKE9YIPd+lmZmZm1iIWKx0qvFjJzMzM9kdrW6zUkh7f1OoN7TWUhbmFWXfDzMzMrEXIfNW8mZmZmR2anIiamZmZWSaciJqZmZlZJpyImpmZmVkmnIiamZmZWSaciJqZmZlZJpyImpmZmVkm/BzRZlZRcQNLlz7Cxo2ruPLKF+nRYxAA69e/zKxZX6O6ej2lpV0ZM+ZBunb92B7779q1k3nzrmHFit8BYsSIbzNkyDea+SrMzMzM9p/viDazAQPGcPHFT1Fefkyt8jlzrmD48KuYNOllhg+/ijlz6v4KphdfnM6GDX9l0qTlTJjwR5544vts3LiqGXpuZmZmdmA5EW1mRx89gvLyPrXKtm59hzVrFjFo0DgABg0ax5o1i9i6de0e+1dWPsSQIZcitaFDh+4MGDCGysqHm6XvZmZmZgeSE9EWoKrqdcrKPkqbNiUAtGlTQqdOvdi06fU62r5W625qefnRdbYzMzMza+mciJqZmZlZJpyItgDl5X3YtOlNdu3aCSQLkjZvXk1ZWZ862h5NVdWruz9XVb1WZzszMzOzls6JaAvQoUMPevY8mSVLZgCwZMkMjjpqMB06dN+j7cCB57Jo0VQidrF161peemkWAwee09xdNjMzM9tviois+3DIGDZsWNxyy6dZunQmW7a8RWlpN0pLuzJxYiXr1r3ErFlfY9u2DbRvfyRjxjxIt24nADB9+hl87nM306vXMAvf/CYAAA+5SURBVHbt2sncuVezcmUFAKeeeiNDh16W5WWZmZlZM6rrUZDV1et59NEL2bBhBSUlR9Cly3GMHv0TOnToTsQu7r//VLZvrwagY8ejGD36Hjp37lvruLNnX8ILL0zjO9/ZTNu2HenWTS9OmsRAYElBs1G5XKwv7lM+r+OBB4CuwHrgolwuljd0LU5Em9GwYcNi4cKFWXfDzMzMDmKvvfYM5eXHMG3aZ/jqV+fQo8cgtm17l7ffXkzfviMBqKiYzLZt73LWWfcB8N57VbRrVw7As8/eyauvPsl5583cfcxly37LSy/N4oUX7i9ORHvlctGtoT7l8/o9cH8uF7/I53UBcEkuF3/f0H4emjczMzM7iNT1KMj27bvsTkIBevc+pdaakpokFOD99zchfZACVlev58kn85x++o8a1Z98Xj2AIcCMtGgGMCSf155zDIv4m5XMzMzMWpGIXSxceDcnnHBmrfLp089gzZpFlJZ248ILK3aXz517FSNHfr9WslqgLJ/XQkDAL4E7crk9htP7AG/mcrETIJeLnfm8Vqflez4UvUBmd0Ql3SqpUtImSaslTZXUpajNRZJWSKqW9CdJQwvqTpH0H5LellQl6TlJY4r27yFppqTNktam56z3miXdm/Zrh6R766g/X9LTab937G8czMzMzA6kuXMn0bZtRz75yatrlY8fP5frr1/NoEHjeOqpHwBQWfkwJSVtOf740Xscp6qK7UDvXC6GAf8InANMOJB9zXJofidwAcmk1pOA3sC0mkpJI4C7gSuBI4FHgLmSytImXYCHgEFp/S3ADEnDC84xPX3tDXwK+DIwuYF+LQauA36zl/oNwF3AtQ1eoZmZmVkzqqi4gXffXc7YsQ9R1703qQ1Dhkxg8eKfA7Bq1QJeeeX3TJnSlylT+gJw110nsnbt/7BjB5HLxTsA6et04NQ6Tvs68NF8XiUA6WuvtLxemSWiEXFTRDwfEdsjYi3w78DIgiaXAjMjoiIi3gduB94nSSaJiLkR8WBErI2IXRExi2RV1wgASf2A04DJEVEVESuBW4ErGujXjyPiMWDTXuofi4gZwMrGX72ZmZnZgfX4499lzZrnOP/8WRx22BG7y7duXUt19QcL3SsrH6ZHj48D8MUv3sV1173Btdeu4tprVwEwcWIl3bsPpFMnDsvndThAPq9S4EzgheLzpknqC8C4tGgc8HwuF/UOy0PLmiM6iuRuZI2TgJ/VfIiIkPR8Wr4HST2BEwuOcRJQFRErCpotAvpKKouIOhPNA03SZUDyfKVyUF7NcVozMzNrhSIXzJt3ze5HQT744GmUlnZl7Nhf8cwzP6Rr1+O5775PA3Dkkf0477xH2bLlLWbPvpidO7cDQefO/Tj77F80eK7+/ekIPJ/PaydwODCH5MYh+byGATfncnFG2vwK4IF8Xt8jGT2+aF+up0U8vknSOSRJ52cjYlFatgL4QUQUDtc/AGyPiG8U7d8BqABej4jz07IL0/2PKWjXj+ROZp+IeKOBPv0M2FF8roL6kcD8iNjnZF69FFy+r63NzMzMaotc8+Vtkp6LiGFNeY7MH98k6VxgKnBmTRKa2gwUL9/qTNGQuaROwDzgHWpn33vbH2CzpPGSttRs+3kZZmZmZvYhZZqISvo68BPgSxGxoKj6LyTPpKppK+DktLymrAvwOLAaODci/la0f7mk/gVlg4FV6ZzR6RHRsWY7oBdmZmZmZg3K8vFN1wB3AKdHxB/qaDIVOFvSKEltgeuBdsCj6f49gSeBpcD4iKj1KKWIeAWYD9wmqSwdlr+RJPGtr19tJbUDSoASSe3S89fUl6T1bdPP7dLNkz/NzMzMPoQs74jeCZQBC+oaIo+IZ4CJJAlpFfAV4IyCRUaXkzy6aSxQVXCMmwrOMZ7kGt8E/gzMBm5roF8VwDaSR0tdnL6vKKi/MC17jCRZ3ZZux2BmZmZm+6xFLFY6VHixkpmZme2P1rZYqSU9vqnVG9prKAtzC7PuhpmZmVmLkPmqeTMzMzM7NDkRNTMzM7NMOBE1MzMzs0w4ETUzMzOzTDgRNTMzM7NMOBE1MzMzs0w4ETUzMzOzTPiB9s1I0mZgWdb9aIW6Aeuy7kQr5Lg2Dce1aTiuTcNxbRoHS1yPiYjuTXkCP9C+eS1r6m8oOBRJWui4HniOa9NwXJuG49o0HNem4bh+wEPzZmZmZpYJJ6JmZmZmlgknos3rp1l3oJVyXJuG49o0HNem4bg2Dce1aTiuKS9WMjMzM7NM+I6omZmZmWXCiaiZmZmZZcKJqJmZmZllwoloE5NUIul2SWslbZb0iKRuWferJZF0q6RKSZskrZY0VVKXojYXSVohqVrSnyQNLaofJum/0/oVki4oqu8haWb6M1ibnvOQ+fMvqY2k/5IUknoXlDuujSTpNEnPStoiaZ2kuwrqHNdGkNRT0kPpNW+Q9HtJJxXUO677QNL5kp5Of6fuqKP+C+nv3G2Slkj6fFH9cZLmS9oq6Q1J1xfVl0q6P/0ZbZR0n6T2RW0mS3ozPcZ8Sf2b5mqbT31xlXRG+ud1XRqXpyV9pqiN41qXiPDWhBvwXeBloD9QDjwCzMu6Xy1pA34IDAYOB7oD84DZBfUjgK3A54EjgG8BbwNlaX05sBa4Ma3/B2AL8L8KjvGfwMy0bf/0Z3Jj1tfejDG+HpgPBNDbcd3veI4ENgJj09i0A4Y4rvsd15nptXcB2gK3Aa8Dclw/VBxPB8YBlwA7iur6A9XABWmMx6dx7ZvWlwBLgX8DSoEhwDvAeQXHmAr8F/ARoEf6/u6C+vHpPkPSY/wYWAKUZB2bJozreODLQGeSLwu6Mv3z18dxbSCuWXegtW/Aq8CEgs/HkiQDfbPuW0vdgC8CVQWfHwB+XvBZwGvA19LPX08/q6DNz4Fp6ft+acyPLaifALyS9bU2UzyPB1YAJ1M7EXVcGx/TPwL/spc6x7XxcV0MXFbw+YQ0Ft0c10bFcyR7Jkx54OmisqeBXPr+cySJaseC+luABen79sA2YFRB/ah0n3bp5yeBWwrqO6b1n806Jk0V1720Wwt82XGtf2t1QxItiaRy4GjguZqyiFgBbAI+kVW/DgKjSP5BqnEStWMYwPNpeU39orS8xqKi+qo09oX1fSWVHeC+tyjpsOP9wGSSO3iFHNdGkNQB+CTwnqRF6VDcE5Jqvq7PcW2824FzJHWT1A64DHgmItbhuB4oteKYKo7TyxGxZS/1J5CMADxXVN+e5D+9e5wjPdbygmO0epI+AXQluWMJjuteORFtWjW/3KqKyjcW1FkBSecAlwLfLCjuRP0xbGw9tP6fwzeBtyJiZh11jmvjHEnyu/NS4GKgF1ABzJXUGcd1f/yBZAhzLcmw5tkkcQbH9UA5EHGkqE3N+309RqsmqQfwa+C2iFieFjuue+FEtGltTl/Li8o7k9wVtQKSziWZI3NmRCwqqNpM/TFsbH1NXask6TiSuaFX76WJ49o4Ndc2LSIWR8TfgH8mmeP8aRzXRknv3s8nmbdZTjIH7p+ApyV9BMf1QDkQcaSoTc37fT1GqyWpF7CA5D+n3ymoclz3woloE4qIjSRzlobUlKUr3MqoPfR8yJP0deAnwJciYkFR9V+oHUORzHf8S0H94KJ9BhfVlxetLhwMrIqI4v9dtiYjSBZ/LZG0jmSYB2CxpIk4ro2SXtsqkvmGe1TjuDZWF5J5nP8WEZsi4m8RcS/Jv1On4LgeKLXimCqO0/HpFJS66pcB7xUdYzDJ/MaX6zqHpI7AxwqO0SpJ6ksy33ZeRFxdNE3Ecd2brCeptvaNZNX8MpJfsGXAw8Dvsu5XS9qAa4D1wPC91I8gGaYbRbLK8wZqr5btTDKUNzmtH0Xdq2V/nf4M+qU/k29nfe1NHNdSoHfBdgpJojSMZJK749r42E4G3gAGkqyQ/RawhuRuhePa+LguI1lV3CGN6yXA30hWejuu+x7HEpL5hp8HdqTv25Es8DqWZIHLOJK7+OOoe9X8nSTzE09O43x+wfGnAs+QrOzukb6/p6B+fLrP4PQYU4BKDvbV3fXHdUD6O+EH9ezruNYVm6w70Nq39A/fHcA6ktvqM4FuWferJW0kydH29B+N3VtRm4uAlST/O/xvYGhR/fC0fFva7oKi+h5p7DenP4vbgDZZX3szx7kvBavmHdf9iqWAm4G3SOZoLQBOdlz3O65/B8xJr7mKZGHGWY7rh47jxenf9eKtb1r/hTSB2Za+fr5o/+OAx0kS1tXADUX1pSSLIDem231A+6I230r3rU6PdWxTXW9LiCswLX2/pWgb77jWvym9MDMzMzOzZuU5omZmZmaWCSeiZmZmZpYJJ6JmZmZmlgknomZmZmaWCSeiZmZmZpYJJ6JmZmZmlgknomZmByFJqyQ9kXU/zMz2hxNRM7NmJGmkpCjatkh6TtI3JZVk3Uczs+ZyWNYdMDM7RM0A5pJ8S1Mvkm9tmQKcCFy2D/ufQN3fd29mdtDwNyuZmTUjSSNJvhJ0ckTcUVBeRvJd1EcBR0XE23XsezjJ90q/10zdNTNrUh6aNzNrASJiE/BHkjuk/SV9Px22P1HSjyS9AbwHnAJ7nyMqabCkhyW9Lel9Sa9LmiHp2KJ2p0mqkLRR0nuSFku6oumv1MzsAx6aNzNrASQJOC79uK6gajqwDfhXkqH4NfUcYzTwCLAVuBf4K9ATOB0YBKxI210G3AM8C/xT2v4fgLslHRsRkw/YhZmZ1cOJqJlZNkoldSO5A3oUMAk4CXg2IpYneSkAG4HTImJHfQeTVApMA6qAwRHxZkH1zZLapO2OAn4M/DIivlrQ5i5JdwLXSbonIlbs/yWamdXPQ/NmZtnIA2uBd4C/AJcAvwHGFLWb0lASmjod6Ab8a1ESCkBE7ErfjgWOAO6T1K1wA35L8u/CqMZckJnZh+U7omZm2fgp8DDJcPtW4OWIeLeOdi/v4/E+lr4+30C7v0tf59fT5iP7eE4zs/3iRNTMLBvLI6K+ZLBG9T4er2Ysv6FHodS0u4i9zzdduY/nNDPbL05Ezcxah2Xp62DgP+tptzx9XbePibCZWZPxHFEzs9ahgmS1/fXpgqRa9MHqp18B7wN5Se3raFcu6Ygm7amZWcp3RM3MWoGIqJY0Afg1sERSzeObupMsZPoRMDsi3pB0JcnjnZZK+jnwatru4ySLpQYCq5r/KszsUONE1MyslYiI30gaAdwETAA6AW8DzwAvFrSbJull4AbgcqAzyd3UZcD/Ad5q5q6b2SHKX/FpZmZmZpnwHFEzMzMzy4QTUTMzMzPLhBNRMzMzM8uEE1EzMzMzy4QTUTMzMzPLhBNRMzMzM8uEE1EzMzMzy4QTUTMzMzPLhBNRMzMzM8vE/wcKJufUV3LVDwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##MIN PRICE BY MONTH\n",
    "ax = ratings_count_min[['price', 'priceMax']].plot(kind='barh',\n",
    "              figsize=(10,7), color=['dodgerblue', 'green'], fontsize=13);\n",
    "ax.set_alpha(0.8)\n",
    "ax.set_title(\"Max and min preci of accommodation \\n by month\",fontsize=18, color='orange')\n",
    "ax.set_xlabel(\"Price\", fontsize=18)\n",
    "ax.set_ylabel(\"Month\", fontsize=18)\n",
    "\n",
    "ax.set_yticklabels(ratings_count_min['yearMonth'])\n",
    "\n",
    "# set individual bar lables using above list\n",
    "for i in ax.patches:\n",
    "    # get_width pulls left or right; get_y pushes up or down\n",
    "    #ax.annotate(str(p.get_height()), (p.get_x(), p.get_height()),fontsize=13,color='green')\n",
    "    ax.text(i.get_width(), i.get_y(), \\\n",
    "            str(round((i.get_width()), 2)), fontsize=11, color='olive')\n",
    "\n",
    "# invert for largest on top \n",
    "ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21280c31588>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##koin calendar and neighbourhoods\n",
    "ratings_count_avg = CalendarData[['yearMonth','price']].groupby('yearMonth', as_index=False).mean()\n",
    "ratings_count_avg.plot(x='yearMonth', y='price', figsize=(15,10), grid=True, title='Average to price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(85068, 106)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined=listing.merge(barrios, on=['neighbourhood'], how='inner')\n",
    "joined['price'] = joined['price'].apply(clean_currency).astype('float')\n",
    "listing.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x21280cec8c8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15,7))\n",
    "plt.title('Average , minimun by price \\n by neighbourhood\\n', color='brown')\n",
    "plt.xlabel('Neighbourhood',fontsize=12, color='brown')\n",
    "plt.ylabel( 'Price',fontsize=10, color='brown')\n",
    "\n",
    "##join calendar and neighbourhoods  title='Average to price \\n by neighbourhood_group',\n",
    "avergeCity = joined[['neighbourhood','price']].groupby('neighbourhood', as_index=False).mean()\n",
    "plt.plot(avergeCity['neighbourhood'], avergeCity['price'],marker='.', markerfacecolor='blue', markersize=12, color='green', linewidth=1,label=\"Avg Price\")\n",
    "\n",
    "#minimum price\n",
    "\n",
    "avergeCitymin = joined[['neighbourhood','price']].groupby('neighbourhood', as_index=False).min()\n",
    "plt.plot(avergeCitymin['neighbourhood'], avergeCitymin['price'], marker='.', markerfacecolor='green', markersize=12, color='red', linewidth=1,label=\"Minimun Price\")\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1, 1.02),fancybox=True, shadow=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "##Maximun price\n",
    "\n",
    "avergeCitymax = joined[['neighbourhood','price']].groupby('neighbourhood', as_index=True).max()\n",
    "ax=avergeCitymax.plot(kind='bar', figsize=(15,10), title='Neighbourhood maxium price \\n')\n",
    "for p in ax.patches:\n",
    "    ax.annotate(str(p.get_height()), (p.get_x(), p.get_height()),fontsize=13,color='orange')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Density and distribution of prices for each neighberhood')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZSyLM7KdmttfMni1Y1mJmd5rZZv9vs7/czOx8M9tiZmvN7LUFz/mQv/1mM/vQTMUrIiIitUPXiRCpzky2RFwK/Mm4ZV8C7nLOnQDc5T8GeCdwgn87E/gReEkH8FXgjcAbgK8GiYeIiIhIpTSwWqQ6M5ZEOOfuB7rGLX4vcJl//zLgfQXLL3eeR4EmM1sG/HfgTudcl3OuG7iTwxMTERERkbJoYLVIdWZ7TMQS51w7gP93sb/8KGBHwXY7/WUTLRcRERERkZDMlYHVxaoB3CTLD38BszPNbI2Zreno6JjW4EREROTIooHVItWZ7SRij99NCf/vXn/5TmBlwXYrgLZJlh/GOXehc+4059xpixYtmvbARURE5MihgdUi1ZntJOJGIJhh6UPADQXLP+jP0nQ6sN/v7nQ78Mdm1uwPqP5jf5mIiIhIxTSwWqQ6iZl6YTO7GjgDWGhmO/FmWfo2cI2ZfRjYDvylv/mtwLuALcAA8I8AzrkuM/s68IS/3X8658YP1hYREak5Tz75JNu3b+d//I//EXYokaaWCJHKzFgS4Zz72wlWva3Itg74twle56fAT6cxNBERkcj79Kc/DaAkokJqgRCpzlwZWC0iIiIya9SdSeaCz3/+c5x//vlhh1ERJREiIiIiIiF47LHHufbaa8MOoyJKIkRERKTmqAVCpDpKIkREREREpCxKIkREREREpCxKIkRERCIsuPKyiMhsUhIhIiISYblcLuwQRKQGKYkQERGJsNHR0bBDEJEapCRCREQkwtQSISJhUBIhIiISYdlsNuwQRKQGKYkQERGJMCURIhIGJREiIiIRpiRCJJqiPrOakggRkQqc8+1vc8UVV4QdhojGRIhEVNSTiETYAYiIRNEtt94KwAc+8IGQI5FaF/WCiEitivq5q5YIERGRCIt6QUSkVjnnwg6hKkoiRERERESkLEoiREREIizqtZkiEk1KIkRERCLMzMIOQUQqEPVzV0mEiIhIhEW9ICJSq2KxaBfDox29iIhIjYvH42GHICIVUBIhIiIioYl6QUSkVkX93I129BF13XXXcfbZZ4cdhoiIHAESCV3ySURmn5KIEJx77rnccccdYYchIiJHACURIhIGJREiNejJJ5/kIx/5CMPDw2GHIiJVSiaTYYcgIjVISYRIDbrgggt47rnn2Lt3b9ihiEiVNLBaRMKgJEKkBqkFQuTIoe5MIhIGJREiIiIRpiRCRMKgJEJERCTCoj5NpIhEk755RERERESkLEoiRGqQmQHgnAs5EhEREYkiJREhUgFOwhYkEyIiIiLlUBIRonw+H3YIIiIiIiJlCyWJMLNPm9lzZvasmV1tZmkzO8bMHjOzzWb2SzNL+dvW+Y+3+OtXhxHzTFBLhIRNx6CIiIhUYtaTCDM7CvgEcJpz7uVAHPgb4Bzge865E4Bu4MP+Uz4MdDvnjge+528nItNArWEiIiJSibC6MyWAjJklgHqgHfgj4Fp//WXA+/z77/Uf469/m6kjt8i0UEuEiIiIVGLWkwjn3C7gv4DteMnDfuBJoMc5l/U32wkc5d8/CtjhPzfrb986/nXN7EwzW2Nmazo6Omb2Q0wTze0tIiIiIlEURnemZrzWhWOA5UAD8M4imwZVpMVaHQ6rPnXOXeicO805d9qiRYumK9wZpQYVCYtaIERERKQaYVSFvx140TnX4ZwbBa4D/gBo8rs3AawA2vz7O4GVAP76RqBrdkOeGUoiJGw6BkVERKQSYSQR24HTzazeH9vwNmA9cA/wfn+bDwE3+Pdv9B/jr7/bqRpVRERERCQ0YYyJeAxvgPTvgXV+DBcCXwQ+Y2Zb8MY8XOI/5RKg1V/+GeBLsx2zyJFK43JERESkEompN5l+zrmvAl8dt3gr8IYi2w4BfzkbcYnUGnVnEhERkUqoGlJERERERMqiJEJERERERMqiJEKkBqkbk4iIiFRDSYRIDdNEZyIiIlIJJREiNShIHtQiISIiIpVQEiEiIiIiImVREiFSg4IWCHVnEhERkUooiRCpQerOJCIi1XrhhRf43ve+Ry6XCzsUCYGSCBEREREp23nnncdvfvMb2trawg5FQqAkQkRERETK1tnZCahrbK1SEiEiIiIiFcvn82GHICFQEiFSgzQWQkREpks2mw07BAmBkggREZEIUy2whG10dDTsECQESiJEREQiTEmEhE0tEbVJSYRIDdIgOJEjh5IICdvIyEjYIUgIlESIiIhEmCoFJCzBsaeWiNqkJEJERCTC1BIhYVNLRG1SEiFSg4LZmVSDKRJ9Oo8lbBpYXZuURIjUoKDQoaleRUSkWurOVJuURIiIiESYWiIkbEoiapOSCBERkQhTEiFhCVqzc7lcyJFIGJREiIiIRJiSiMpov00fDe6vTUoiREREIkyF4cpov00f7cvapCRCpAZpdiaRI4fO4+pogonKaZKO2qYkQqQG6Ytf5MihriSVCb4HlYRVLxZTcbIW6b8uUsM0GE4k+pRESNiURNQm/dclkn71q1+xY8eOsMOIrKAFQoUPkejTeVwZtUBMn0QiEXYIEgIlERI5AwMDXHDBBZxzzjlhhxJZwY+nCh8i0afzuDJKIqoXVEgpiahNSiIkcoIuOJs3bw45kujS3N4iRw6dx5VRElG9YB+mUqmQI5EwKImQyNEPpojIQSoMV0YtONNHLRG1SUmERE42mwU0s1A11J1J5Mih87gywX7Tb0n11BJRm0JJIsysycyuNbONZrbBzN5kZi1mdqeZbfb/Nvvbmpmdb2ZbzGytmb02jJhl7giSCNW+VU/7UCT61DpbmSCJ0Pdg9ZRE1KawWiLOA37rnDsZeBWwAfgScJdz7gTgLv8xwDuBE/zbmcCPZj9cmUv0g1k9XWxO5Mih87gywW+JWiKqp+5MtWnWkwgzWwC8FbgEwDk34pzrAd4LXOZvdhnwPv/+e4HLnedRoMnMls1y2DKH6ItfROQgdWeqjPbb9Ekmk2GHICEIoyXiWKAD+JmZPWVmF5tZA7DEOdcO4P9d7G9/FFB4QYCd/jKpUWqCnj7ahyJSq9Q1tnqa4rW2hZFEJIDXAj9yzr0G6Odg16ViilU3H3bGm9mZZrbGzNZ0dHRMT6QyJ+kLv3pqxRGRWqdJOqoX/B7H4/GQI5EwhJFE7AR2Ouce8x9fi5dU7Am6Kfl/9xZsv7Lg+SuAtvEv6py70Dl3mnPutEWLFs1Y8BI+feFXT4mYhG14eJjrr7+egYGBsEOJPH0nViboGqvvQ5HKzHoS4ZzbDewws5P8RW8D1gM3Ah/yl30IuMG/fyPwQX+WptOB/UG3J6lNQY2HfjgrF/xoah9KWO6++26++93vcuONN4YdSuTpPK7M6Oho2CGIRFpYndg+DlxlZilgK/CPeAnNNWb2YWA78Jf+trcC7wK2AAP+tlLDgiRCtUfV0z6UsPT09ADQ2dkZciTRF4vpkk+VCLoziUhlQkkinHNPA6cVWfW2Its64N9mPCiJDPW9rJ6meBU5ciiJqMzw8DCg70GRSumbRyInmAVCTfiVC3409eMpYdN5XD3NjFOZkZERAKzo/C0iMhUlERI56s40fXThPgmLEtnpo9bZygQtEXmn60WIVEJJhESOfjCnjwYWSliCApxUTy0RlRkaGgK8FgklsyLlUxIhkaP+v9NHBTkJS1CA0zFYPSURlSkcExF0bRKR0qk0JpGjPtTTR3P0S1iCQptaw6qnJKIyg4ODRe9L6fR7XNtKTiLMbJWZvd2/nzGz+TMXlsjE1OxcvWAf9vb2hhyJ1KpgPI7G5VRPSURlCitRlERUR7/LtamkJMLM/hnvytI/8RetAK6fqaBqhU66yuTzGgRXrdGsV/vb3d0dciRSq4IaTNVkVk/jxCpTmDioVbY6Oo9rU6ktEf8GvBk4AOCc2wwsnqmgaoX6YFZGFwiqjnOOnm5d6EvCFdSeJ5PJkCOJPiURlenv7x+b3FVJhEj5Sk0ihp1zYyVeM0sAqkavUjCwUMqj5Ks63d3dY/3Q9+zZE3I00aTWsOql02kAUqlUyJFIrRro66cpvcC7ryRCpGylJhH3mdm/AxkzewfwK+CmmQurNqgPZmWUfFVn9+7dALi4Y1fbrpCjiSYdg9XLZDKAatElPH39/TTVeUlEX19fyNGIRE+pScSXgA5gHfAvwK3AV2YqqFqhgkhl+vv7ww4h0tra2rw7S6Bjb4e6h1VAtZbVC5IH9aWWsAwMDNCSaQT0uyJSiVKndMgAP3XOXQRgZnF/mX5Jq6CCSGX0ZV+dIIlwix35tjwdHR0sW7Ys5KiipfDczefzunZJFTTBhIQhl8sxODRIR38XoN8VkUqU+st3F17SEMgAv5v+cI58hdMZqiWiMmNf9ip8VGTPnj3E0jHcAm//Bd2bpHSFSYTOY5HoCX5HBrNDmJm6M4lUoNQkIu2cGzvD/Pv1MxPSka3w6qy6Umtlgi97pRCV6ezsxGXcWLXAvn37wg0oggoTByURItET/I7ELEZ9Mq0kQqQCpSYR/Wb22uCBmb0O0KjgChRenVVJRGWC/aau1JXp6uoin8qDNzmOrhVRgcIZwnQeV0djIiQMhUlEJplWdyaRCpQ6JuJTwK/MzB+RyTLgr2cmpCNbYRKhK7VWZmwgsJoiKtI/2A9Jxs5+zRJWvsJzV+dxdTQmQsIQJA1xi5GI1aklQqQCJSURzrknzOxk4CTAgI3OudEpniZFFM4vr8KHhGF0ZBSXcmPtkLruRvkKz11dM6I6aomQMIy1RMRipOMptUSIVGDSJMLM/sg5d7eZ/fm4VSeYGc6562YwtiNSYa2bauAqM3aFW5U9KhKLxbxWHFfwWMpSuM9UCK6OvgclDEHSELMYmUSavl61RIiUa6qWiP8G3A28p8g6ByiJkFkXXKRKZY/K1NXVYcOGy3s7UFcMLl9hEqGLpYlET2ESkU7UsaevJ+SIRKJn0iTCOfdVM4sBtznnrpmlmI5ohbWWqsGsTENDA6CGiEotmL8A+gC/F9P8+fNDjSeKxlrDgESi1KFlUkjff9PHOaf9WaaDSYSRSdTR36vuTCLlmrIfg3MuD3xsFmKpCeoGUb0gidD0TJVpbGwkNhqD4YOPpTyFiUNhQiESBo2vK19/fz8GxDDSiToGBgfVtU6kTKV2hr7TzD5nZivNrCW4zWhkR6jCxEF90Sszb968sEOItObmZmzIxpKI5ubmcAOKoMLEQUlEZVRgmz5KIso3ODiI19ECMok6crmcJpmogs7n2lRqO/w/4Y2B+Oi45cdObzhHvsLEQUlEZerrdZ3DarS0tJAfymMDNvZYylPYEqHuTNVRi2z1lESUr7+/n7ifRNQl6gDvSvR1dXVhhhVZOo9rU6ml2FOAHwDPAE8DFwCnzlRQRzKNiaheOp0OO4RIa21t9e7s9/4oiShf4WBqJRESNiUR5RsYGBj7DU7HU2PLpDxqgahtpf76XQYcAM73H/+tv+yvZiIokcmo+0h1mpqaALADRiKZUMtOBTS2SeYSFeTKNzAwQMxviUgXtESISOlKTSJOcs69quDxPWb2zEwEJDKVoOZXhbfKjA2k7oUFCxZoP1ZALYoyl+iCh+Ub6B8g5s/xl06oJUKkEqV2Z3rKzE4PHpjZG4GHZiakI1thjZG++CsTFNpU+1aZsYHpAwUzXUnFdBxK2NSdqXz9/f0HWyLiaokQqUSpLRFvBD5oZtv9x0cDG8xsHeCcc6+ckeiOQIWJgwofEobgYn2G0VCvJKISOo9lLlGFVPnUnUmkeqUmEX8yo1HUkMIChwof1VE3ksoUjinR1aoroxZFmUv0W1K+wcFB6swrAqk7k4Ql6uduSUmEc27bTAdSi6J+8IRN+68yhYOCC2cZktKpMkDmEh2D5cnlcgwODZJJe+PDgpaI4CrWIrMl6tcm0YUKJHLUAjF9VPioTGEfdLVEiERL0OIwdp2IeArDlETIrBscHAw7hKooiRCpMYUFYA3IrIxaImQu0TFYnr6+PoCxMRExi5FJpceWS/n0W1KZwsQ1iudxaEmEmcXN7Ckzu9l/fIyZPWZmm83sl2aW8pfX+Y+3+OtXhxWzyJFgdHR07H7Um1LDoiRC5hK1zpant7cXOJhEANQn0mPLpXzZbDbsECKpMNiVOqIAACAASURBVHGNYqtEmC0RnwQ2FDw+B/iec+4EoBv4sL/8w0C3c+544Hv+dpGlAoeEbXh4+OD9keFJtpSJKIkQia4gWYgXjA/LKImoiiqkKnPgwIGx+1E8/kJJIsxsBfBu4GL/sQF/BFzrb3IZ8D7//nv9x/jr32aqdqlpKrRVZ2hoCACXcpGs+RARqUZQcIsXtETMS2bYv39/WCFFnn5LKlOYRBTej4qwWiLOBb4ABCMSW4Ee51zQHrYTOMq/fxSwA8Bfv9/fPpJUgzl9lEtWJkgiSBfcl4rpPJawFc64JlMLkoW4HZydbl6ynv3dPWGFFHn6LalMT09P0ftRMevfPGb2p8Be59yThYuLbOpKWFf4umea2RozW9PR0TENkc4MDWqdPiq8VWZsLvQ61R5VSseezCWqUClPUFgr7M40P9VAj1oiKqaZrSrT3d1d9H5UhFF98Wbgz8zsJeAXeN2YzgWazCy4bsUKoM2/vxNYCeCvbwS6xr+oc+5C59xpzrnTFi1aNLOfoAqF00EqiZAwBImDSztGhkc0RWkFCveZ9p+ETS0R5enq6qIhVY8V1FHOr2ugf6D/kDFjUjolEZXp7u4muFyTkogSOOe+7Jxb4ZxbDfwNcLdz7u+Ae4D3+5t9CLjBv3+j/xh//d0uwtWAhTPjKImQMIz9SNaNeywlK0wcIvx1JEcIJRHl2bdvH4118w5Z1pjyHnd1HVZHKZMIvgs1PW5lurq6aG6GeNwieezNpW+eLwKfMbMteGMeLvGXXwK0+ss/A3wppPimReEMBiq8VSb40lITfmXGjrvUuMdSssIKAE1tKGFTElGezs5OFiQbDlnWWDd/bJ2ULhgLEcVBwXPBvn2dZOpzNDREM4FNTL3JzHHO3Qvc69/fCryhyDZDwF/OamAzqDCJ0JRolQkKcKoBrszYcackomKFLYqF90XCoAqV8nTs7eCE9Ar2DR7sPtKcXuCtm8NjKuca59zYtKRKIirT1bWP5hYYGcmzb9++sMMpm6ovZllhgU1JRGVU81udsUKvX4Wg/Vk+VQbIXKIkonTZbJauri6a0vMPWR4kEXv37g0jrEgaHBwc+/3Q9Ljlc87R1dVDQ72jPpNn377otYIpiZhl6s5UvaAQrB/OyowlDf5gLo3NKZ/OY5lL9F1Yuo6ODvIuz8JM0yHLM4k0mWSaPXv2hBRZ9BS2PiiJKF9fXx/ZbJZMvSNT7+jujl53JiURs0w1mNULkgh1Z6pMPp/3Jk72yx1KIspXODWu5kcXiY7du3cD0DouiTAzWtKNtLe3hxFWJAWJg5kpiahAMNVwfcZRn4EDB3ojN9ufkohZpiSietpv1cnlcljMcOYlYVH70poLChMHJRESNlWolC5IElrTTYetW5huor1NSUSpgpaI+vp6jYmoQJBEpDOQyTjy+YNjTKJCScQs08XmqqckojpjBQ6/JUJJRPnGLtg37r5IGJRElK6trY2YxQ5riQBYWN9MW3ub9meJgsQhk8nQ19en/VamoPUmnXakM+6QZVGhJGKWFZ5kKrxVRrPhVGesO5NPX/zlK+zOpKt+S9hUIVW6trY2WjKNxGPxw9YtyjQzPDwcyVlywhBcGyKTyZDP5/VdWKag1SGdhrq6Q5dFhZKIEGkwXGWURFRnfEuEkojyqSVC5hLNsFa6Hdt3sDjTUnTd4oZWAHbt2jWbIUVWUODNZDKHPJbSBElYXZ2jrs4dsiwqlESESElEZTQbTnWcc4ec+Uoiyjc0NMQ8/wJfqn2TsCmJKI1zjh07drC4foIkwl++Y8eO2Qwrsvr6+ojFYqRSqbHHUrqgAiqV8m4A/f39IUZUPiURsyyROHh9v3j88OZUmZqSiOrk83kM05iIKgwNDZHB24VKIqqjJLZ6+k4sTXd3NwODA2MtDuO1ZppIxOJKIkrU399PMpkcK9coiShPf38/iYQRi0Eq5X0PRq1lW0nELFMSUb2g0KbCR2VyuZx35vtnv2oxyzc0NETSQdJMBTgJnWYIK02QHCypL55ExCzGooYWdu7cOZthRVZ/fz/xeHysXBO1AnDYBgcHSaW82rxk0lsWtX2oJGKWFSYRyeCokbIESYQ6g1VmdHT0kCRCY0zKNzw8TMLlSZpptrAqqVtnZQpbEKNW8AjLWBIxQUsEwJJMK9u3bZutkCJtYGCARCIxViGq47A8Q0NDJJNeZWhQHIxay7aSiFlW2PoQi2n3VyLoM6iWiMoMDw97V6v2D0UVgss3MjxMAkig/SfhKCywRa0fdVi2b99OMp4oOr1rYHF9K7t2tamFtgT9/f3EYjF1Z6rQwMAACT95iMchFoteq6JKsbOssNZNSURlgh/MkZERJRIVGBwcJD+cxzZ7x6Jqj8o3MjKiJGKa6ByuTOFMOJoVpzTbt29ncX0rMZv4t3fpvIVkc1lduboEfX19aomowtDQEInEwRbFVMrUEiGT08Xmqhd8UeWdi1zWPhf09ffh8g7r85II1R6VL2iJiKPuYBKO4Gq34+/LxCab3jWw1B8voXERUwvGRARJhFrEyjMw0E8qeTCJSCbVnUmmUFhrqRrMyhQmDkoiytfV1XXImIioXSFzLhgZGSEOJJxTEiGhKEwcuru7Q4wkGrLZLG3tbZOOh4CD4yU0Q9PUvNmFEpgZyWRSSUSZ+vv7KBwam0pFrzVHScQsK6z1VQ1wZZSIVWcsiTCwlHmPpSwjIyMk8ZIIHYMSho6ODgDMoLOzM+Ro5r729nZyudyUScS8VAMNqXq2b98+S5FFk3NubGA1eJPGqExTnr6+PlJ1B7tzJpO5yO1DJRGzLKgxcrEEXV2qPapEYc2vBr+VZ2BggP6+/rFB1dTD7t27Q40piob97kxJYChizc9yZAiSiAWpJB1794YczdwXdE+aaHrXQovrNc3rVAYHB8nlcockERqbU56+vn7q6g4+rqtz9PYeCC+gCiiJmGVtbW1gMYgl2LlrV9jhRFLh1IYaV1KeXcEx55/5+fo8O3aq2b4czjkGh4aoA1KoH7CEY/fu3dQnk9THY97vikwqSAomutBcocWZFnZs1/fiZIKEIZiqPh6Pq2tsGUZGRhgaGiadPtgSkU7DgQPR2odKImbZiy+9hIslcLEEHXv3RG4QzVygwemV27p1q3fHv1yJa3Ts2rlLF0wrw/DwMNlcjjSQBvpU+yYhaGtroy4GmUSczn37NDZnCrt27SKTTDMvWQ/ALzfcxo7e3ezo3c3/e/xn/HLDbWPbLqpvoXNfp7oqTiJIGIIkIpVKceBAtGrRwxTsq507Yzxwv/eDnM44enqURMgERkdH2bJlCy6ehHgS5xybNm0KO6zIKfyx1A9neTZu3IglbKw7k2ty5PN5XnjhhXADi5CgS2KDf+vZv1/TlMqs2759G/WJOA3JBM45tUZMYdeuXSzMNI9Ns76zdzeD2WEGs8Ns6t7Gzt6D3ToX1TfjnNM0r5MIBvYHSUQymdQsYWUI9tXAgNHR6RXFM2nH0NBwpCr1lETMog0bNpAdHfUSiLh34j399NMhRxU9w8NDBfejc7LNBU8/8zT5lvzBy30v9P4888wzocUUNfv27QNgHjAfGM1m1Re4AmqFrdyBAwfYv/8ADckEDQmvRmCbrrI8qbZdbSya5CJzhRbVe9PAKjGbWFCZkkqlAC+J6Ovr0zjFEgUTmsQLSuEZr5EsUrOtKYmYRY888ghYDBev8/7OW8TDDz8SdliRM9DfT2PKGxehgkjpurq62PrCVtyiglrzNNgC4/EnHg8vsIgJChbNQNO4ZVK64NxVRUD5gm6J85JxGpJeEvHiiy+GGdKcls/n2b1nN62Z5pK2X+gnG2qJmFhQCA6SiLq6Opxzao0oUZAoFF5zuL7e+22O0oyJSiJmiXOO3911N7kFy7w5+YDRpqPZsGG9ZscpU19fLwvTXhKhGuDSPfzwwzjncMsP7XqTW5bjqaee0r4s0Y4dOzC8JCIYoqnpIMsXJA9KIsoXdD+cn0yQiMVoSCXZsmVLyFHNXV1dXYyOjo4lB1NZkJpHMp5QEjGJffv2HXKhuSCZ0HTDpRlriYgfXBYkEWqJkMOsW7eOPbvbybYeN7Ys23o8AHfccUdYYUVSX18/izJ5/3605lQO022/vQ2bb9B46HK3wpHP5bn77rvDCSxitmzZwqJYjCRGKxA305iSCgTdHjSuqXybN2+mLhGnzu8LMS8e4/nnnw85qrlrz549ALSWmESYGS2ZJvZq6twJdXR0kE6nx8aY1PlzlSqJKM2+fftIJCyoUwagocFLIqK0D5VEzJLrrrsOS6TIthwztsyl55NbsJzfXH+9+hGWKJvNMjQ8wqKMNyuTkojSbNu2jXVr15FbnTs4HiLQDNZo3HDjDRogPAXnHM9v2EA+n+dWHAmMJcDGDRvCDi1ygqmaC6dsltKsX7+e+Yk4G3v62NDdy4K6BLt379YUmxMIkoiWdOMUWx7UUrdAvQQm0dnZOTaoGpRElKuzs5N58zjk9ziT8TqqBOPuokBJxCzYs2cP9957LyMLT4R48pB1o0tPZV9nJ/fdd19I0UVL0I96QdJhaExEqa6++mosbrjVRZIEg9xxObZs3sLvf//72Q8uQnbv3k1nVxd5IOjocLRzrF+/XhUBZVISUZn+/n62bdtGYypB70iW3pEsjSnvd2X9+vUhRzc3BUlEc3pByc9pSTeyR0nEhPbs2TOWOIDXncnMxi6CKJPr6OigvuHQKerjcaivj0VqHyqJmAVXXXUVeWB06csPW5drWgn1TVx66WX6MS1BMG93Mu5IxNE83iXYuXMnt99+O7ljct6FDYpwqx2WMS756SVqjZhEMJta4W5cBQyPjLBBrRFlCa7xomu9lOe5557DOUdT3cEKqcZUEjPj2WefDTGyuaujo4N0oo5MYoIvwCKa0gvo7ulR5UARzjm6uroOSSLMjHQ6Hala9DDt2dPO/HmH/9bOm5cbS3qjQEnEDGtvb+emm25idOGJuLp5h29gMYaWvYZt217innvumf0AIyb4Qo8bJGKmL/gS/OAHPyAfy+NOniQ5iEPulBzPrnuWe++9d9Zii5rHH3+cebEYqYJlx+K1SK9ZsyakqKJJA6srs3btWgwOSSISMWNBKsHTTz0VXmBzWGdnJ03pBWP990vRXLcA55wKxUX09vaSzWYPSSLAm+Y1SrXoYclms3R2djF/QZEkYn6e9vZdIURVGSURM+yiiy4i74zR5a+ecJtc6zG4hlZ+/JOfqGZ9CockEYaSiCk88MADPPTQQ+ROzkFm8m3dMQ5rMs47/zzN1FRENpvl8cce47hxLYb1GCvMeOThh0OKLJr27/eu2KopIcuzZs0aGuuSJGKH/nw3p5Ks37BBXTyL6NjbQWOqSCXeJJrS8wH18S9m/PSugVQqpaSrBO3t7eTzeRobD08iGhsdu3fviUzZRknEDFq/fj2/+93vGF56avFWiIDFGF7xevbs3s2vf/3r2QswgoaGvAvNpeOOVPzgYzlcT08P53znHKzZcCeW0EXJIHtalq7uLs4999yZDzBinn32WXr7+ji5yLoTnWPj88+rwFGGtl1ebVt7W7u60JWot7eXjRs30lKXPGxdazpFLpfTBUyL6OjooNlPCkrVVDd/7LlyqCBRGJ9EJJPJSF3jICw7d+4EoKnx8C7sTU2OXC4fmUH9SiJmSD6f59xzz8NS9Ywuf9WU2+eaVpBrWsnPLr1UJ+EkDhzwai/rk46GRG7ssRwql8vxtf/8mtfs/Pps6Wd6M+RflufOO+/k5ptvntEYo+a+++4jacYJRda9zP97//33z2ZIkdXT00NHZwf1qQX09fdFqg9wmB5//HHy+TwLM3WHrWtOe60TjzyiC5gWyufzdHV10VhXZhLhD8JWxcDhglnAkskkmzZtYtOmTYCXVPT29mp85xSCi0U2txxeedLS4u27qFw8UknEDLnjjjvYuHEDQytOg3hq6icAw0e/kaHhYS688MIZji66guy8pS5Pa12WPbt1MaBifvrTn/LkmifJvSZ32HUhpuJe5mAJfPd732Xjxo0zE2DE5HI57rnrLk5wjrrD5siFJRiLYzHuvuuuEKKLnmeeeQaAk5a+HoCn1Je/JA899BCpRJymVOKwdXEzWuoSPPjAAyrEFdi/fz/ZXJamutJnZgJoSGaIx+JKIooIkohUKkVfX9/YVOvJZJJcLkd/f3+Y4c15L7zwAgsWGOki4/xbWx1mRObikbOeRJjZSjO7x8w2mNlzZvZJf3mLmd1pZpv9v83+cjOz881si5mtNbPXznbM5RoYGOCHP/oRbt5isguL1VsW5zJNjCw5lVtvu00zvUzgpZdeIhGDxZk8yxrybN+xIzJ9B2fLbbfdxhVXXEH+mDzumAq6iRjk3pgjl8rxhS9+QbXEeIXcrp4eDp9f7aCX5/OsXbcuMs3QYXrkkUdIJeo4dtGryKTmqfa8BMPDwzz44IMsqktOOEB4SX0dnfv28dxzz81ydHNX0B2pqczuTDGL0ZSerySiiCCJSCQOTWaD60boeiWTe279syxcWLzckkxCczORKQOG0RKRBT7rnHsZcDrwb2Z2CvAl4C7n3AnAXf5jgHcCJ/i3M4EfzX7I5fn5z39OT3c3Q6tOhzJmgwAYXf4aLJnhggu+r37CRWzcuIGV8/IkYrB6fpbR0Wxkmv1mw5o1azjn/54DS8C9torjpw6yb86yv38/n/v852p+oPVtt91G2qzoeIhAMHXC7bffPhshRVY2m+WBBx5k6YJjicfiLGs8lkcfeVSzNE3hkUceYWhoiKX1E09TujhTRzxmuvp8gSAJaC6zJQKgKbVAYyKK6OnpIZlMEhs3uF9JxNS6u7tpb9vN0mUTtxYuXZrluefWRaIMOOtJhHOu3Tn3e/9+L7ABOAp4L3CZv9llwPv8++8FLneeR4EmM1s2y2GXrLOzk6t/8QuyrceSn7e4/BdIpBha/hqefXYdDz744PQHGGHZbJYN6zdw/AJvBqsTGr355detWxdmWHPG+vXr+fK/fxk335F7U27Cs9ueNugBeiB2b8x7XEwjZN+UZfuO7Xz+C5+v2UHsBw4c4L577+UVzpEs0pUp0IxxDMYtN92k7iSTWLt2Lb29B1jR7LXSrmg+kaHhIU2RO4XbbruNdCJBS/rwQdWBRCzGwnSKO+64QzP9+fbu3QuUd6G5QHN6PnvVEnuY7u7uwwZVw8GB1hrXObGgK+eySZKIZcsdvb39Y2Mn5rJQx0SY2WrgNcBjwBLnXDt4iQYQlMCPAnYUPG2nv2z8a51pZmvMbE2YNQdXXXUVo6NZRlacVvFrZBefBJkmLrnkp5HIRGfLli1bGBoe5sQmrxlwYTpPS1pJBHiDsD73+c8xkhgh+4dZmLicgfUYNurfOgzrmaS1bDFk35Bl/XPr+cpXvsLo6Oj0Bz/H/fa3v2VkdJTXl7Dt63Hs3ruXxx9/fMbjiqqHH36YWCzO0sZjAFg0/2iSiToe1hS5E+ro6ODRRx9lWX2K2BSt2ysa0vT29qoSytfR0UHcYiyYbIbECTSnF9DR0anf4XH27t071upQKLhuhFpvJvbkk0+SShmLF098TK1YkRvbdq4LLYkws3nAr4FPOecmm2Kn2DfmYXvfOXehc+4059xpixYtmq4wy9Ld3c2NN97E6MLjcRXUeoyxGMPLXsnWrS/w6KOPTl+AERckC0ESYQYnNI6wbu0zYYYVup07d/LJT32S/mw/2bdkp7weRNlWQP51eR5//HG+9rWv1dQYlGw2y7XXXMPRZiybpBUi8DJgfizGNb/85cwHF1FPrnmSRfOOIuFPOBGPxVk0byVr1sz9H8ywXH/99TjnWDFv6pO7NZ2iPpng19deOwuRzX27d++mOdNIzMov7rSkmxgZHdG1TMbZtWsXmczhx2IqlSIej9PW1hZCVHOfc47HHnuU5Udliccn3m7+fG9cRBQqo0JJIswsiZdAXOWcu85fvCfopuT/3esv3wmsLHj6CmBOHqG33HILo6MjjC57RdWvlW09Hqtr0HUjCqxfv56WDNy6Lc0Vz3tfYCc0Ztnb0Vmzg9/27NnDJz/1SfYP7Cf71iyUX9lWEnesI/+qPPfffz/nnHNOzXTXuffee9m9dy9vLrEmMoFxej7Pmief5Pnnn5/h6KJncHCQrS++SOu8QxuTF847ivb2NhXWihgeHub6669ncaaO+sQkJQ+fmbGyIc26Z5/V7GrA7vZ2WioYDwHQmvGmtmtv1yyAgZ6eHnp6emhoaDhsnZlRX1+vcYoT2LZtG7t372H16ql/P49eleWpp34/5y8eGcbsTAZcAmxwzn23YNWNwIf8+x8CbihY/kF/lqbTgf1Bt6e5xDnHjTfdRH7BMlymecLtUtseITawj9jAPtLrbya1bYJZSWIxhheeyBNPPKHZcXzPPbuW4+ePsK03zrZe78f0+EavVrwWZyPp6uriU5/+FJ09nV4LRBWNX6VwJzryp+a5/fbbOffcc4/4Jv5sNsslF1/M4lhs0gHV470eyMRiXHzRRTMVWmS9+OKLOJenuX4JT22/m6e2ewOAm+u93qsvvPBCmOHNSTfddBO9vb2snl96E+OKeWmS8ThXXnnlDEYWDW1t7bRmmip67kL/t1xJxEHr168HYP784rNdzZ8/n/Xr19dMRVM5HnjgAQBWr85Nue0xq/OMjmZ57LHHZjqsqoTREvFm4APAH5nZ0/7tXcC3gXeY2WbgHf5jgFuBrcAW4CLgoyHEPKVNmzaxu72d0dbjJ90u1r8Py41iuVHivbuJ9U98ifjswhNwznHvvfdOc7TRs2fPHnbv6eCkpkO70qyenyMZPzhYqVb09fXxmc9+hrbdbWTfnIWJ89Zp5V7myJ+U5/rrr+eSSy6ZnTcNyQ033MCutjb+OJ8nVkJXpkAG47/l8zz2+OM88cQTMxhh9Gzbtg2ABZmF9AzspWdg79hj8KZwloOGh4e58soraU6naE6Xdr0h8AZYr2yo4/7776/pxGx4eJh9XftYVN9S0fODJELdcw567LHHiMfjLFhQvNaqqamJ/v7+sWRDDrrnnrtYuswxr4QeA8uPylNfb3O+/BfG7EwPOufMOfdK59yr/dutzrl9zrm3OedO8P92+ds759y/OeeOc869wjk3J6fwuO+++8BiZFtWTdtruvQCXMNC7rnnnml7zagK5pF/ecuhA3sTMTi5aZSHH3rwiK8ZDwwPD/OlL3+JrS9uJfumLCycxTc3cK9w5I/Jc/nllx+x3e3a2tq48Mc/5jiMEyt4/huA1liM75xzDgMDA9MdXmS9+OKLxGMJGuoOvQJiOtlAKplWEjHODTfcQFdXF8ctqC/7uavm15OMx7j44otnILJoCAr/iybpHTCZukSKxvR8du3aNZ1hRVY2m+W+++6jubmZ+ASd+ltbW4nFYiq3jPPSSy+xZctWjj+utDGFsRgce+woDz304Jy+eJ+uWD1NHnjwQXLzl0Bi4jm8KzHadDQbNmyo+b7Cv73tNpbPcyxvOLyJ9A2LR2hr382zzz4bQmSzyznHd77zHdY+s5b8aXlYGkIQ5l2Dwi13nH/++UfchcKy2Szf+uY3yY+M8D4cVkYrRCCJ8ef5PHv27uW8886rmQR3Kps2bWJBpvWwQa5mRmN6IZs2bQopsrmnt7eXSy+9lNZ0itYyWiECqXiMVfMyPPTQQ6xdu3YGIpz7duzwJnZc3NBa8WsszrSMvU6te/DBB+nq6mLZsoln2U8mkyxcuJBbb721ZqcFL+bmm28mFoMTT5q6K1Pg5JflGB4emdPXfVESMQ3a29vZ9tJLZJtWTr1xmXLNR+OcO+IKauV45plnWL9hA28/arDotfvetGSEhqRx9dU/n/3gZtk111zDHXfcQf7UPG5ViAXTGOTfmIcmOOtrZ411U4k65xznnnsuz6xdy586R1MFCUTgaIy34s3v/6tf/Wr6goyo0dFR1q/fQEtD8QJIS8NyNm3aPOcHEs6Wyy+/nL6+Pk5sOnwAa6lWza8nnUhwwQUX1GQf9e3btwOwpEgSMZgdJpPJ8P73v59MJsNgtvjFDpfUt7Jj2/YZjTMK8vk8l156KfX19SxcOHnz98qVK+nv7+e6666bdLtaMTQ0xG9/eyurj8lRX0aj4pIljtZW+M1vrpuzFVFKIqZBMB93rnn6ujIF8vWtWF1Dzc75ncvluOD882hJw1uXF/+STyfgXUcP8OCDD0ViXuVKbd68mR/9+Ee4oxzuZXPgCyUB2T/IMpQf4utnf/2ImPr1qquu4sYbb+QtwKurSCACfwScAvzgBz+Y831bZ9rTTz/N0NAgSxccU3T90sbV5HLZSExrONO2bdvGtb/6FUc1pFmQmuSiL1NIxIwTGjM8//zz/Pa3v53GCKNhx44dNKbnkynSQ2BwdIh3v/vdfOITn+Dd7343g6PFa82XzltIz4H9HDgw2Uz0R77bb7+drVu3snr1amyKa5U0NjbS2trKFVdcQXd39yxFOHfdcsstHDjQx6tfVd5vpBm88lWjbNnywpwt2yiJmAZ33X03NLTg0o1Tb1wuM0aaVvHoo4/R19c3/a8/x1155ZVs2ryF/3l8H+lJZjd859FDLKl3fPtb36S3t3f2ApwluVyOb3zzG7iUI/+6fPGrp4ShHrKvybLp+U384he/CDuaqlxxxRVceOGFvAJ4+yTb3YqjHWgHLsFx6+GXrRkTw/gLvHmpzzrrLO66665pjTlKbrvtNlKJNEsai1e2LJq3gkyqoSYLu4W81rDvETPjhKbq52xeVp+mOZ3iRz/8Yc0VhLdt28bS+uJdmTLJNLfccgvnn38+t9xyC5lk8a7ISxsWjr1Wrerq6uKCCy6gsbGRJUuWlPSc448/nsHBQc4999wZjm5uGx4e5uqrr2LpMsey5Yf+Vjxwf4LODqOzw7juuhQP3J847Pknn5yjocG49NKfgpJTJQAAIABJREFUzcnWCCURVdqxYwfrn3uOkZbJZ2WqRnbh8YyOjtTcQKUnnniCn/3sZ/zB0mFOXzr5lZJTcfjIqb10dnTwjW+cTS5Xer/DKHj44YfZ+sJWcq/MQV2VLzbKIc34VHsR6hXgljmu/sXVDA8Xby2ay5xzXHTRRVx00UW8EvgLmHQ2pnZg2L+95D+eTArjQ86x0jm+/p//yS233DJNkUdHe3s799xzD6taTiERK16zHovFWd36Ch5++OGaHmB955138uSTv+f4BRnq4tX/RJsZJzc1cKC3lx//+MfTEGE0OOfYvm37WBIwXiZRx+DgINdeey2Dg4NkEsW/WJf4SUjQNarW5PN5vvWtbzEwMMDJJ588ZStEoKGhgVWrVnHPPfdwxx13zHCUc9c111zD3r2dnP7G0cO6Y3d0xhgZMUZGjLZdMTo6Dz/f43E47fUjrF27zpvAZ45RElGl6667DmIxsgtnLonINyyChhau/fWv52QmOhNefPFF/uP/fIWjGrL848mlzW5zfGOOvz9xgIcffoQf/ehHR9S+uu4312ENhlsxDZ9plEOa8atOIoD8SXl6D/TO6QFgxYyOjnL22WdzxRVX8Dq8BCI+A808dRgfdI5jHJxzzjlccsklR9TxOZWf/Pgn4IyTlr1h0u1OXPI6ErEkP/zhD2tq/wT279/PBeefT1NdipVTXJ16Q3cvvSNZekeyPL6nmw3dE7fALkglWTUvw80338zTTz893WHPSd3d3fT1902YRJSqNdNEIhav2STiyiuv5LHHHuP4448veoG5yaxatYqmpia+853v1ORUw+3t7VxxxeUce2yOFSsrH5N06qk5WlvhBz+4YM7N9qckogo9PT3cdPPNjLYch0uVPwVfycwYXvJyXty6tSb6C7e1tfGZT3+KZH6Qz76yl8zhLXwTesfKYf545RDXXHMNV1xxxcwFOcs2bdpEbklues7YJIc041N5l+uDFoIljc2bN0/Di82O3t5ePvuZz3DnnXfyNuC9TN4CUa0UxgdwvBa47LLLOPvssxkZGZmx95srHnjgAe6+525OXvoG6lPFL1AVqEvWc8ryP+DRRx/lzjvvnKUI544LLriAA729nNI8b8oa396RLFnnyDpH9/AovSOT97c+rrGB+mSCc7797Ui2GJbr4KDq6pKIeCzOoobWmkwi7rvvPi6++GKWLFnCUUcdNfUTxonFYpx66qmYGV/84hfp6uqagSjnplwux9lnfx3nRvjDt1RXUxeLwRlnDLN3bwfnnXfeNEU4PZREVOGqq65iZGSE0eWvnPH3yrYeB+n5XHTxxUd0Dd2OHTv4+Mf+jeG+br706v0szJSfvf/9iYO8eekwF198MZdddtkMRDm7RkZG6D3QC9M1e3CSQ5rxpyWJMCADe/funYYXm3ltbW185F/+hXVr1/IXwBlYRVO5liuO8T68Add33nknn/3MZ47ofuo7duzgm9/8Ji0NSzl52RtLes4JS17Hwvkr+K/v/FdN1V4++uij3HHHHRw7P8P8VBk1JyVKxIxTmuexq62Nn/3sZ9P++nPNzp07gYPdkaqxJNPCzh07q36dKFm3bh1f//rXaWxsLKsb03h1dXW8/OUvZ9++fXzxi1+cczXpM+WKK65g3bpneetbh5ngunxlWbbc8brXZbntttvm1Ng6JREV2rNnD7++7jpGW4/HVXghm7LE4gwvfw2bnn/+iJ3lZePGjXzsox9huHcf//6aA6yYV1nzX8zgzFMG+MOlw1xyySV8//vfj/T0hslkkpbWFpjL48VzQB8V1VbNts2bN/MvZ55JR1sb/+DctMzCVA7D+P8w3g88u24dH/3Xf6Wjo2NWY5gN+/bt47Of+Sy5Ecfpx76HeKy0gnHMYpx+7J9iLsHnPvd59uzZM8ORhq+/v5//e845zE8lObax8ildp9KaTnFUQ5pf/OIXbNy4ccbeZy5oa2sjbjGa09WX4BbVN9Pe3h7p35FybN26lS984Qskk0le8YpXTHhhuVItWLCAU045hU2bNvGVr3zliG+Bvf/++/npT3/KSSflOPGk6TtmXv+GLMuWOb71rW/y/PPPT9vrVkNJRIUuvPBCcrk8oyteN2vvmV14PDS08KMf/4TR0WnoyD6HPPDAA3zi4x8jPnKAr7x2P0fPr25gdDwGZ546wDtWeF2b/uM//iOy88+bGa977euI74l7o3nnINtpuLzj1a9+ddihTOr555/nk5/4BPT18c/5PKtDnObqVf6A6z27dvHx/7+9846Pqsoe+PfMTCYdQhKkdwghVAFpSleKsqyKgIooFkRUxPZbEQvFCiooNhSxu7sq6sq6CmJBAtJ7ld5rEtJIn7m/P94LTEJCMskkMwP3+/nkkzdv3rvvvDu3nXvPOfeBBy6qwfKpU6cY9+A4EhISubLpDYQFRbh1f4g9nO7NhpCWks6DDz54dufhi5V3332XxMRE4qqFYSnjjG9paV4tjECrhZdefPGi60dcOXbsGJEhEVgt5RsAA0QHVyMnN+eSMMc5dOgQDz/8MA6HgzZt2mC3F7/R4c6dO0lLSyMtLY1169ZdcLPI6tWr07x5c9asWcOUKVMuipDgRbFz506ee24qNWooevc535m6PFitMPDabAKD8njyySd8YvJJKxFl4K+//mLRokVk12iFCnQzBJ8jp2BkHIcbGrlYyKp7BcePHeW7775z77k+isPhYM6cOTz11FPUDspicsfkInelLgsWgdubZzKiWQZL45dw35h7/Xbn0VtvvRXJE2Szr8R2dSEXrJutxDSPoVOnCzvOepNDhw7xyPjx2DIzudvppLoPxMltZCoSSSdO8NC4cRdFeOL9+/czduz9nDh+ku7NbiIqrHaZ0okIuYweMUM5nZjCA/c/wO7duz0sqW+wYcMG5s+fT4PwYCICPWFbeGECLBZiI0LZt38///rXvyr8ed4iISGBqvbyh8gFiAgMP5vmxczRo0cZP348mZmZtG3b1hijXID09HQcDgcOh4Pk5OQSw9DXrl2bZs2aER8fz/PPP3/RKRKHDx/m8ccfxW7P5drrsrF53iqRkBC47tosUlNP89hj3jeH1UpEGXh39mwkIIjc2m3dvlfycgpExpE895b1HBH1cFStw8effOr3+0acOnWKRx55mM8++4xetbN5un0KEYGe9fcQgYENsvm/dmkkHD3A6Hvu9kuHzSZNmjB8+HAs+yzIHu8Pfs/iAOtyK5ItPP7Y4+Ve9q4ocnJymPTsszizsrjL6aSaDygQ+dRDGKkUJ46fYPq0aX7t87R27VruH3s/qcnp9Gw+nOrhdcuVXmRoTXo1v5nMMzk88MADLF++3EOS+gY5OTlMnz6dkAAbTat6ZsBbGi4LDqRmSCCffPyx306slETCqYSzg//yUtVMJzEx0SPp+SLHjh1j3LhxpKam0qZNG7cjMZWWevXq0aRJE3777TdeeumliyYce0JCAo8++gjZ2WkMHpxFBWUfANHVFddem82hQwd54ol/kJVV9EaJlYFWItxkw4YNrFu7lqxabcFW/DJfcSibvUBkHFWGNHLqXUF6Wipff/212/f6CkuXLuXOUXewfcsm7o07wz1xGdgrcPzZOiqP5zolUzfwDM899xwvvvii3zl4jR49mq7dumJZZ0EO+MAg2AmWlRY4ARMmTCA2NtbbEhXLP//5T3bv2cP1HlAgsii4z4Ynmu/6CFej+GPJEr8LkwtGTP558+bx2GOPYVVB9Gl+K5GhNT2SdkRIdfrEjiDIUoUJEybwxRdf+LWi5cq8efM4fPgwsRGhWC2VW6djq4WBcvLmrFmV+tzKIi0tjdAAz0RNDDOjL14MK4VFceLECR566CGSk5Np27Yt4eGeUb6Ko0GDBjRu3JhFixbx8ssv+70ikZaWxuOPP0Zi4kkGDcqiWmTFt0916zm5pl8227Zt49lnn/Haqo5WItzk888/R+zB5NVoUbYErPaCkXGs7isRztBo8qrV5+t53/idnX9WVhavvvoqEydOJFJSee6KZHrULv1qzGd/BXMgzcqBNCvPrwnjs78uvNzqSnSQ4qn2qdzQKJOfFy7grjtHsW3btrK8hlew2WxMnTKVtu3aYlllQf4SLrBZcrGoCIUKMP+qK1REGRLJAWu8FTkijBs3jgEDBrifRiXhdDr5Yf58mgEtPLACkUXBfTY8NQd0JRBpsfDDDz94KMXKISMjgylTpjBr1ixqVmlEn9hb3faBKIkQezi9m99M3YgY3nvvPZ566im/H9AlJiby8ccfUz04kOrB5d1B0n0CrVYah4ewYuXKi26FB4y+JtDqGfMwu5mON2d8K4qTJ08ybtw4kpKSKkWByKdhw4Y0atSIhQsX8sorr/it03p2djZPPjmBAwf2M/DabGrUrLwJjqZNnfTslcuKFSt56aWXvJKHWolwg0OHDrFq1SqyL4uDUkYaqShya7UhPS3Vp0J9lcT+/fsZc+9o5s+fz3UNspjUMcVt/4cDaVYyHRYyHRZ2JAdwIM295QurBYY0yeKpDqnkJB/ngQfu59///rffzGwGBgby6iuv0qtXLyybLMg6ATfbDdVOQQQQAc5eTuOzO6SDbbENa6KVp59+mqFDh7p3fyWzc+dOTiYk0NpD6QVRcJ8NT0XetSC0cjpZv26d3wyQt2/fzp133sXvv/9O6zrdubLpDQRYSx4Qrz/4G8kZJ0nOOMnvO/7N+oMlr77YrHa6NPkb7er15s9lf3LnqDvZtGmTJ17DK/zrX8YO780jKtDuoQTqhwcTarcx9yILHa6UIic3hwAP9dN2c5f1i21/jaSkJMaPH09iYiJt27aliidikbpBo0aNaNiwIT/++CNvvPGG35VBpRTTp09n06bNXH11NvXrV/4gvlUrB1265LJo0SKv7I2llQg3yDczyKse42VJwBlWA4Kr8ssvv3hblFLx+++/c+/oe0g8doAnLk/jlmaZBHix9DWPcPBCp2Quj8zinXfe4amnnvIb86bAwEAmT57MiBEjsOy1YP3Disemw0viONh+tRHiCOG1116jX79+lfTgspO/WuepufEgCu6z4SklAqAq4FTK5wcrubm5zJ071/B/SEqnV/ObaVG7S6ljySdnnCTXkU2uI5tTaYdIzijd/iIiQkzNjvSOvZXMtFzGjRvHO++84/P5VZjU1FS+//57agbbCQ3w3oSURYSGYcHs3LWLtWvXek0OTyMi2Kw2HMozgzqHMsxtbBXhKesl0tPTefTRRzl+/DitW7eudAUin0aNGlG/fn2+++47PvjgA6/IUFa+/PJLFi1aROcuuTSL8d5KSoeODpo3dzB37lzi4+Mr9dlaiXCD5ctX4Ay7DGX33szRWUTIiWjI+vXrfboDVUrx4YcfMmnSJOoFZ/JCp2RaR/lGRIbQAMX4Nme4LSaDP5ctZex9Yzh+/Li3xSoVFouFMWPGMGnSJAJSA7D9aoOKjD6oQP4SrEutNKjTgLkfzKV9+/YV+EDPERhozIz7g+Ffvi4YFORJ1cSzbNiwgbvvuptPPvmEutViuSbujnI7ULtLVFgtro67nUbRbfj3v//NnaPuZM2aNZUqQ3lYtGgR2dnZNKpS9r4kz+ks4JuTV0ZThtqhQQTZbHz//fdllsUXsdsDyHV6pq/JTye/LfF3cnNzefrpp9m3bx+tWrUiIsKz5ofuICI0adKE2rVr89lnn/lNOdy7dy+zZ8+mSRMHHTuWzacjJ7ugf11OGYdyItC7Ty6X1YCXXnqRlJSUsiVUBrQSUUqUUuzbtw+HB3a/9BTO0CiUUhw4cMDbohRJXl4e06ZN4+OPP6Z7rWwmtk+lmoejL5UXERhQP5t/tEvj5JGDjL1vjF/tktu3b19mvzub6LBobIttyMEKcM50gKwSLJss9OzRk/dmv0ft2mUL2+kNmjRpQnBgIMVHMPcddorQsEEDwsIqL1JPaTlx4gTPPfccDz30EKdOnOaqZjfSufG12G3eUXgCrHY6NuxHz+bDSEk6w6OPPsozzzzjF3tK/Pnnn4TZA8q1M3WuUxXwzcl1lq1ttYgQHWhj1cqVF9W+EWFh4ZzJ9czqcnpOhpmm79XLsvD222+zbt06YmNjiYry/phGRIiJiSEqKoqZM2f6vJmiUopZs2Zhtyt69S77XhDZOQX967LLsQefzQZ9+2aTkXGGuXPnlj0hN9FKRCnJzc0lMzMDZfdMtAdPoMzIE8nJyV6W5Hyys7N59pln+PHHH7mhUSb3xmV41XypJFpF5fF0h2RUxmkefOB+n2/EXGnWrBlzP5hL65atsay0IFvL5nBdJNlg/cOK5aCFu+++m6lTp5YYO9zXCAwMpHvPnmy1WMj0WMZ4npMoDijF1ddc421RCpCWlsbs2bO59dZb+e3X32lRqwv94+6kdkQTb4sGQI0qDegXN4pWda5i2dI/GTHiNt58802fbBfBcPTfsH49keVQIAACLFLANyegHNGdooPtZGZlXXCzMH+jRo3LSMr0TAz9pCwjncsuu8wj6XmT+Ph4vv32W+rVq0etWrW8Lc5ZLBYLLVu2JCgoiEmTJvm0X9jmzZtZt24dV3TKoTzdYaC9oH9doPtxdgoQFaVo2TKP77//vtLCEfvwsM63sNvtBAYGIXm+YzqUL0vVqlW9LElB0tLSePyxx1i2bBl3NM9gSJMsj+7aWFHUC3PybMdkqkgGjz7yCMuWLfO2SKUmIiKCmTNnMmDAACzbTIfr8o6XzxgO1LZUG1OnTuWOO+4otc27r3HzzTeToxS+GjxVofgJITQkhMGDB3tbHADOnDnDxx9/zNChw/jnP/9F7SoxDGh1N63rdsfmoag3nsJqsRFXuysDWt1N/WpxzJs3j2HDhjNnzhyfG4xkZmaSm5dHsK18Ma1tFksB3xybpezdeZC5v4uvKl5loUbNmiRknS72+7rhNQm2BRJsCySmWgPqhhcfkjgh00inZk3PhC32FtnZ2bzxxhuEhYXRpEn5JwHy8vIKmtSVM8yozWYjLi6OpKQkPvnkk3LLV1GsWbMGEYiNLV9oWntgQf86uwes5WJbOFBKsWHDhvInVgq0EuEGjRo1IiD1KPhIBAFr6hFsAQE+ZVpy/PhxHhh7H1u2bGJsq3SuqedZpSszTwo0Wpl5nh3URgcpnumQQt3gbJ6aOJFvv/3Wo+lXJAEBATz55JPcdtttWPZakNXlUCQywPaHjWBHMDNnzKRXr16eFLXSadq0KYP+9jdWAQd9cDViI7AbxV133+1V+2QwQrZ+/vnnDBs6jA8//JAIey36tbydzo2vJTTQO86XpSXEHs4VjfrTv+WdVA+uz2effcbQm4by0Ucf+czmnGfOnAHA6kMKef4eFfmyXQw0a9aMpMwU0nOKfqfhLQZSL7wm9cJr8linOxneYmCxaR1MPUZktWpUq1atosStFH788UdOnjxJ06ZNsZRD6cwnLy+vgDmOJ/YqqFKlCjVr1uSbb77h9OnilUBvsnXrVqKiwBddZKpXVwQECFu2bKmU52klwg0GDboOMpKwpB3ztiiQl4U9aQ+9evastLjOJbF161bGjL6HU8cO80S7NLrV9Lx9bUaeFGi0MjysRABUsSsmdkihbVQOr7/+OrNmzfKbzXBEhHvvvZe77roLywELsr4MikQW2JbYCFJBvPH6G7Rt6/7O7L7ImDFjqFGzJl9aLKT7kCJxHMV8Edq0bs0NN9zgNTlycnL46quvGDp0GO+//z5h1upcHTeSq5rdQESIf5lxVAmOomvTwfRreQfVgurw0UcfMXToUL744guvB6KIjIzEbreT7qXNoYriTK7RvtWpU8fLkniO5s2bA7A/pfw+MgfSjtE8NtZvV2LziY+PJzQ0lMjISI+kZ7PZCpjjeCp6Vd26dXE4HKxYscIj6Xma0NBQHA7fLAtKgcOhCAmpHNN7rUS4wdVXX81lNWoQvC8evGnWpBSBe+OxOPO4+eabvSeHC8uXL+ehh8Zhz01lUsdk4iIrpoMMsakCjVaIrWIGg0FWeKRtOgPqZzFv3jyeeeYZv3I6vOOOOxg+fDiWPRZkjxuNnROsK6zYsm28Mv2Vsx3xxUB4eDgvvPgiWVYrX4iQVUZFohYQaP41ND+XlWQUX1gshEdEMGXqVK+FkIyPj+e2Ebfx1ltvESLV6NtiBN1jhnhs1+nC5DqyC6wo5joqpj2NCLmMK5tezzVxt1PFVoP33nuPW2+51av769hsNpo3jyE5O89n4uKfzs7BarHQtGlTb4viMWJjY7FZbfyVtL9c6aRkp3Es7SStWrXyjGBeZNeuXR4N5Wqz2Qqa1Hmo/QoLC8NqtbJ7926PpOdp6tWrR0qKwhcDYyYlCU4n1K9fv1Kep5UINwgJCWHqlClYcjMI2v0bONwfKDtDo1DWAJQ1AEd4TZyhbkZGUIqAw2uxnT7A2LFjiYnx/p4Vq1at4umnJlI3OIfJHZPd3kDOHYJtqkCjFVxBSgSAReC2mExGxmSwdOlSJj37rN/sqikijB07ls5dOmPdaC11+FfZInAKnvjHE7Rp06ZihfQCTZs2ZfKUKRwT4dMyKhLXItTCUB7uRri2jLtgJ6P40GIhLyiIadOneyVKSkZGBi+99BJPPfUUWWkOesTcRM/mw4gKq1gTydy87IKRhSp4UqZaaA26xwyhV/PhODKtTJkyhSlTpnjNX6JPn76k5uSSlO39iYkch5OjGdlcddVVF00IUzD669ZtWrM1sXwD0a0Jxv2dO3f2hFheJSAgwGcU15JQShEQ4Fu+V/n06NEDpxPWrvG9fUNWr7IRFBzIFVdcUSnP00qEm8TFxfHEP/6BLfUowX8tgDz3YnLlNOiKMyQKZ0gUWXGDyGnQtfQ3K4X9wHLsRzdw3XXX+cROwU6nk9dnzuCyoFwmXJ5KuN0/Gih36F8/m1ubZbB02TKWL1/ubXFKjcVi4ZmnnyEqOgrbGlvJO1snguUvC4MHD/aLTeTKylVXXcXU557jqAhzRTjtBdOmwyjmWCzkBgUx4/XXvbLi43Q6mTxpMgsWLKBFrS5c3WIkNas2qpRnB9gCC0YWslXO4PWyKvXp02IErepcxW+//c7EiRO9Yqo4aNAgIiMj2Z1yxuuDuv1pGeQ6nIy6806vylERdOnShSNpJ846RpeFTSd3ElktkmbNmnlQMu/QvHlzTp8+7fOTYUlJSTidTp+YJC2K5s2b079/fzZuDCAx0XfMmvbvs7B3r5WRt93uMZO1ktBKRBkYOHAgkydPJiDjFKHb/4uUo4EqNXnZBO76hYAT2xg2bBj/+Mc/fMI+c9++fRw+cpTetbMIDbj4FIh8+tXLxm6FxYsXe1sUt6hSpQqPP/o4KkUhf12gvCiwrbMRGRXJ/fffX3kCeonu3bszbfp00oODmW2xsLcSFYkNKOaKEBwVxay33iI2NrbSnu3KkiVLWLFyBe3q9aF13e5YLOWLFuQOAdbAAiuKAdbKmwG3iIW42l25omF/Nm7cyMKFCyvt2fkEBgYyZswYkrNz2ZNSNmfmcLsNmwg2EaoFlm3PicSsHPalZjBgwACPROvxNXr27AnAmmNby3R/Zl4WWxJ30btPb5/ob8vL4MGDyc7O5tChQ94WpVicTif79u2jatWqdO/e3dviFMuYMWOoUqUq/50fRGqq98vG8ePCwoWBNG7ciGHDhlXac7USUUZ69+7Na6+9RhWbk9Ct87EmVtwGZZYziYRu+x576mEefPBBHnjgAZ9p0OrXr0+Ny6qz+GgQxzMuzuKU54Tv9wWR4zBmtvyNbt26ceWVV2LdaYViFs7koKCSFQ+Ne6jSHLK8TadOnXhvzhyi69ThY+BXFI4KVCayUXyH4hugVZs2zJk716s26PmzkeFB/h1xpqyEme/trZWAAQMGMGDAAPakZnAq031zrhbVwgm32wi32+hUoxotqrkXYCMzz8HmpDQaNGjAI4884vbz/YHatWvTMq4lq45vLtP9G07sINeRR9++fT0smXfo0qULPXr0YO/evSQlldLGtRJRSrFr1y5SU1N57LHHfNacCSA6OpoZM2aiVDDz5wdSFsvI6tFO7HaF3a6oXcdJ9eiyrRCdOiX88N8gqlevwWuvzahUs8SLc9RXSbRv356PPvqQuBbNCdr9O/a9S8DhQRtXpbAd30LwtvlEhgTw5qxZDBs2zGcUCDBsLB959DGSHCFMWFGVL3cHkZLjO/KVB6eC9Qk2nloVwXf7gune/Sp69+7tbbHKxD333IPKUcjOIn4bBdbtVho3aez3oVzdpV69erz3/vv0HzCAxcAcERIqQJE4iOIdi4UNIowcOZIZM2d6PZRrhw4diI6OZtnu/7D75HocTv+IQFZenE4He09tZsnOeUREVPOarbuI8Oijj9K0aVM2JqaRlFWO7WrdJCvPwdqEVKz2QJ5//nm/20DSHfoP6M+RtBMcKEOUpmVH1lOndh1atmxZAZJVPiLChAkTqFevHps3byYhIaFc6eU7QFutViIiIsq1o7fT6WTHjh0cOXKEW265xS/6oiZNmjB9+ivkZAcz7+tgTpxwb+zTvUce0dUV0dUVN96YQ/ce7vvZ7ttr4dtvgggPj2LGjNcr3bdOKxHlpHr16rw5axYjR44kIGEXoVv/g+VM+SomALmZBO38mcADK+japTMff/QRrVu3Ln+6FUC3bt34/It/0rvvNfx3fzDj4iN4ZX0Yy48HkO2H45IDaVb+uTOYh5ZV47UN4ThCa/Diiy/y/PMveCS2tjdo0qQJV151JdZ9Vij8mxwDlaa4feTtfvt+5SE0NJSJEycydepUUkJCeEeEFSicHlAm8lD8jOIDwB4dzaw332T06NFei8LkStWqVfnwww9p264N6w78wo+b32frkWVk5vjGfgqeJiv3DNuOruDHLXNYs38BsS1i+PDDuURHR3tNpqCgIGbMmEGdunVZn5DK6UpQJLIdhgLhtFh8REvMAAAgAElEQVSZMWMGDRo0qPBnepOrr76aQHsg8YfXunXf8fRT7Dp9gEF/G+RTE3flJSwsjLfeeoumTZuyefNmDh48WObVuJiYGMLDwwkPD6d9+/Zl9mHIyclh8+bNHDt2jNtvv5377ruvTOl4g9atW/Puu7MJC6vOd98GsWtX5fShSsG6dVZ+/NFO48bNeO+9972yZ5j3e7KLAJvNxujRo+nYsSNTn3uOpG3/JateJ/JqxFGWrZotqUcJ2bMYm8rjwUce4frrr/f5Riw6OppnnnmGkSNHsmDBAhb9vJC3tyQSZBNaVcvm8uhc2kbnEhFYvoFZg3AHB9KsZ48bhJdfS8lzws5kGxsSAtiQFMjRdMFqtdClSxf69x9At27dsNvLuR+9D3DTkJtYtnQZcqhgWbLssRAZFUmPHj28JJlv0KtXL1q1asW0l1/mf6tWsU2EG5SiWhmjLx1F8a0IJ5Tiuuuu48EHHyQ0NNTDUpePiIgIZsyYwZo1a5g3bx4rVvzJ1qPLiQ6vTZ2IZtSJaEZYkOdXTCJCLiM54+TZ44rah+JMdgpHTu/mSPIuEtIPo5SiY8cruOmmIXTu3BmrtfL8QIojIiKC119/nXHjxrHu2DHaRoUTHVwx5ggZeQ7WJaSSh/DqK68QFxdXIc/xJcLCwujdpzeLf/mdIc2vIdgWVKr7lhxei9VqZeDA4jeh81ciIiJ44403eOGFF4iPjyc5OZnY2Fiv9HPJycls27aNvLw8HnvsMf7+979XugzlpVGjRrz//hwmTnyShQu2cfx4Ht265VFRzUtODvz6SwB79ljp1asXEydOJCiodOXa04i3I0NUBB07dlRr1qzxyrNTUlJ44YUXWLFiBXmRjchu3AOsBe367AeMCD/nRWZSioCjG7EfWUudOnV4/rnn/NbZzel0smHDBn799VeW/7mMhETD/rJRFSftorJpG51L4yoOLGUYnz2/xlgyfbpj2WdMk7OFTYkBbEgIYPPpQDJzFTarlbbt2tG9e3f69OnjdXMTT6OUYvjNwznuOI6zqmF7qZorrP+zcsftd3D33Xd7WULfQCljL5I3Z81CZedwvXLSspAiMddcpbi7CAVDoVgO/CxC1YgInpgwga5d3YjC5kUOHz7ML7/8wh9/LGHPHiO0ZdWQaGqEN6Rm1YZEh9fFZvGMnfLvO/4NQO9Yz+1143DmkZB+hOMp+zmZtp/TZwxFpWHDRvTs2YO+ffvSsGFDjz3PkyQlJfHYY4+xb+9eWkeGUzO05EHBqhNGUI9ONUr2a0nLyWNdQiq2wEBeefXVi8ZEpzRs27aN++67j1taXEuv+p3Onn9t1UcAPNapYGSqHEcOT/wxky7duzJ58uTKFLVSUUrx7bff8vbbb2O1WomJiaF69epupbFu3TrAMO92B4fDwd69ezl06BC1a9dm6tSpPhuNqbTk5ubyzjvv8M0331CrlqL/gGxKsvD69ltDcbvxxtKtQiYkCAsWBJKaYuG+++5j+PDhlTLJLCJrlVIdC5/3m5UIERkAvAFYgQ+UUi97WaQiqVq1Ki+//DJffvkls2fPxrojjcxm/VD2c86qRYZ1dTqw71tKQMIurr76ah5//HG/dnC1WCy0b9+e9u3bo9Tj7Nmzh+XLl7P8z2V8v2073+1ThNuF1pFZtInKo21UboWGh3Uq2J1iZWNCABuTAtmfaiw5RkVWo0+/bnTt2pWOHTv6dZ6XhIgwoP8APv74Y1QnBcEYPhKKizqkq7uICIMGDaJ9+/ZMfvZZ/r1zJ51RDABsJaxKZKL4FtgBdOvalSeffJKqVatWhtgeoW7duowaNYpRo0Zx9OhRli5dyp9//smmTRvYeWINVouN6mF1qVG1ITWrNKRKcLRXV0mVUqRlJXE8ZT8nUvdzKv0QeY5crFYbrVu34uauQ+jevTt169b1moylJTIyklmzZjFhwgQ2bt5MjtNJ/XDPtEens3PZkJBKWJUqzJg5k8aNG3skXX+hRYsWxMTEsPjwanrWu6LEMrv62BYycjO5/vrrK0lC7yAiDBkyhHbt2vH888+zefNmatSoQUxMTIU6NaekpLBjxw7OnDnD4MGDuf/++y+KvjcgIIDx48cbK9rTXuarLy30659N3bqeCam7Y4eFxb8HUqVKBG+8MZW2bdt6JN3y4BcrESJiBXYC1wCHgdXALUqpbUVd782VCFeWL1/Os89OIlsCyGg+ABVUzGDCkUfQrp+xphzl7rvv5vbbb/d586XykJKSwurVq1m5ciUrVywnOSUVi0BstTw6XZZNx+oXNnsq7UqEwwnbT9tYfdLO6oRAUrMN5aZlyzi6du1G586dadq06UWd14XZs2cPd955J84OTlRjhXWxlYZhDfn0k0+9LZpPkpuby/vvv8+XX35JE4RbUAQiRa5EpKL4RCwkWYT7xo5l6NChF03ZyszMZOPGjaxatYqVK1dx6NBBAILtYdQIb2AoFVUbEWgrvYPu+oO/AXB5/T5uyZKTl3VWaTiRdoCM7FTAiMTTuXNnOnXqxOWXX+63g5KsrCwmTZrE8uXLaVI1lCZVQootR9tPGyFhLhSZ6VRmNhsT06hRswYzZ75OrVrl2WPdf/nxxx95+eWXeeyKUcRENgSKX4l4ccX7SISdTz795KKpwyWRm5vLZ599xmeffYbNZiv1qsTOnTsBSrWKkL/6cPjwYaKjo5kwYUKlbYpW2ezfv5+nnp7I4UOH6do1l8vbO4q0bi/NSoTDAUvjbWzebKNNmzZMmTKl0h2oi1uJ8BcloiswWSnV3/z8JIBS6qWirvcVJQKMCvbwI4+SngsZLQYVWJEAQDkJ3PULtuRDPDlhwkVpf3kh8iMyLFu2jMW//8ahw0cQoHm1PPrUyaLTZbnYCvkplaREHEm3sOhwICtPBpOWowgKtNOlazd69uxJp06dCA93LxTixYRSiqHDhnLCegJnJyfW762MvG0ko0eP9rZoPs1PP/3EtGnTqKMUI5Xid/N8/m7VSSg+sVjIDAjgpWnT3F7a9zdOnDjBmjVrWL16NatXrSYtPQ0RC9Fhdagd0YTaEU09Gjo2PTuZo6f3cCxlD6fSDuFUTkJDQ+nYsSNXXHEFHTt29IpTYUWRl5fHtGnTWLhwIQ3Dg4mJCCvTYPZERhabEtNo3KQJr732GtWqXZrhfMFQzm684UZahDfknrY3AfDl9p8AGN7iXL+7P+UIL62YwyOPPMINN9zgFVm9ya5du3jxxRfZs2cPNWvWJCYmxiOBIFJTU9m+ffvZ1YexY8f6nI+Yp8nIyODll19m8eLFNI910KdP7nl+EvFLjLwtLjJTdjb89KOdw4ctDB8+nDFjxnglMIe/KxE3AQOUUveYn0cCnZVSDxZ1vS8pEWDYYz40fjzZAVXIaDEIXDZ1su9fTsCJrZdsg+WKUor9+/ezePFifl64gCNHjxERCL1rZ9K/fjZh5mZ2RSkRSsGGRBsLDgazNclGQICNHj160rt3bzp16uQ1pyNfZNq0afz0y0/kdszFuszK66+/ftEPej1BfHw8k559loYOJyNRWEwFIgfFbLGQFRrCq6+9RosWLbwsaeXicDj466+/WLZsGUuXLmXfvn0ARIbWpEFUS+pHxhIY4P6qQHZeJoeSdnAgcSuJ6ccAaFC/AVdedSVXXnklcXFxPuEYXVE4nU7eeOMNvvvuOxqEB9PcTUXiuKlAtGjRgldffbVc4TcvFt58802+/eZbpvV8lHB70QPYT7d8z9qE7Xz3n+8u+kFuceTl5fHpp5/y6aefEhgYSGxsbJkV0Px+ff/+/URFRTFx4kQ6djxvLHrRopTi008/Ze7cudSt62TgtTmUdhuH1FT44YcgUlKsTHhiAv37969YYS+AvysRQ4H+hZSITkqpcS7X3AvcC1C/fv0OBw4c8IqsxfHHH3/wzDPPkFO3A7l1LgfAknqM4O3/Y8iQIYwfP97LEvoWTqeT1atX880381ixYiVVAuHWpme4smYOn+80TCZGNs8E4FSmhY//CmFjQgCXVY/i+huGMGjQoIvOMdpTLFiwgBdffBFVW2E9YWXhgoWVujmNP/P999/z2muvcQ3Qw1QivkWxQYQZM2bQoUMH7wroAxw7doz4+HgWLFjA7t27sYiFOtViiKvVhaohJZtHpGYmsf3Ycg6f/guH00HDho0YOHCA3/g2eBKlFG+++Sbz5s2jUZUQYiJKpwiczMxmQ0IqrVq25JVXX/Vb0y5Ps3fvXkaNGsXQ5v25uuH5volZedk88ccM+vTry4QJE7wgoW+xbds2nnvuOY4ePUrjxo2pX7++W4psTk4O27ZtIykpib59+/Loo49espYACxcu5OWXXyYqysHfr88qUZFITYXvvg3G6QzihRde8vpEn787Vh8G6rl8rgsU2DlGKfU+8D4YKxGVJ1rp6NnTmBX//Y8l5EU3Q9lDCT7wJzVq1uTee+/1tng+h8VioXPnznTu3Jldu3bx6quvMHvrDtadCuD+VmfOmjhtSrTxxqZwxBbIAw/cw5AhQ3wiBr8vk2+7KkeFBo0baAXCDQYPHszatWv57Y8/uFwpkoH1wMjbbtMKhEmtWrUYNmwYw4YNY8+ePfz000/Mn/9fFm7dQd1qzWlXvzch9vMHEpm56Ww8uJhDp3dgt9u5/obrGThwIM2aNbtk7NILIyKMGzeOnJwc5s+fT7DVSr3wC/udpGTnsjkxjWZNm2oFohCNGzemRWwsfx5ZT98GXc4rV2tPbCMrL5vrrrvOSxL6FnFxcXzwwQe89NJLLFmyhLS0NOLi4kq1n9CZM2fYtGkTubm5/N///R+DBl1c+224S//+/alSpQoTJz7J/34I5G+DsynOd/3MGZj/fTBOZzCzZr1Js2bNKldYN/CXnaVWA81EpJGI2IGbgflelsltxo4diygnthPbsaYchozTjL7nnot6t1BP0KxZM955513uvfdeVp20M3trKEoZTtMzN1WhXsPGfPb55wwfPlwrEKWgfv36WKxG1W/S2D9DCHsLEWH06NE4gZXAUiAsJIQRI0Z4WTLfpEmTJjz44IN8/fVXjBw5klMZB1i07ROOJu8ucN2JlP0s2vYpx9L2cPPNN/PVV18xfvx4YmJiLumBBxhl7uGHH6ZLly5sP51G4gU2pMt2OFifmEpkdDTTpk/XCkQR9B8wgCNpJzmafvK871Yf20ytmrVo1aqVFyTzTUJDQ3nuuecYM2YMJ0+eZNOmTeTlXXhn5dTUVNavX09gYCBvv/02f/vb3y75egzQtWtXnn76GY4ds/DrLwEUZQjkcMD/fggiK8vOK6+86tMKBPiJEqGUygMeBBYC24GvlFJbvSuV+9SsWZNu3a4kMHEntpN/UaVqBL179/a2WH6B1Wrltttu45577mHFCTurTgYwd0cYNWrWYsbM16lRo4a3RfQbbDYbkVGRAJeceYgnqFevHl27dmWVCDuAQYMH68FaCVStWpXRo0fz4YdzqdegDkt3/YejyXsAOJF6gPhd31CzVnU++GAOY8eOvaQdgIvCZrMxefJk6tSty9bT6eQ6zw8ZqZRia1I6TrEwffr0So/e4i/06tULi1hYc7zgECIt5wx/Je2n79V99YC3ECLCiBEjePLJJ0lOTmbLli04iyiDYKxAbNy4kcjISN59991LzkesJPr27cu9997L7t1Wtm8/36dr5QobJ0/C008/4xfKrF8oEQBKqR+VUjFKqSZKqRe8LU9Z6dGjOyonE9vp/VzZrWuFxmK+GLn11lupV7cOb24O4/gZ4f4HHtS+D2XAHmCElbvssorZKfhip1OnTmQqhdM81pSOevXq8c477xDTrBkr9/5ASmYCK/bON86/+w6NGjXytog+S0hICM8++yw5Difbk9LO+/7ImSxOZWYzduzYS24fCHeIjIykXbt2rD+5vcD5jSd34FROPbF3AQYOHMgTTzxBUlLS2dCuruTk5LBp0ybCwsKYNWsWderU8YKUvs/NN99Mu3ZtWRpvJ90lyOTx48L69TYGDRpEjx49vCegG/iNEnGx4Lo5iC9sFOJv2Gw2+vUfcPazv+wE7GuI6RSsFbCy4brbb1xcnBcl8T+CgoKYPGUyec5cFu/4kuzcLCZPmayjB5WC2NhYbh0xgmMZ2aTm5J4973Aq9qRm0rpVK2688UYvSugfdLuyG8fST5GQefrsuc2ndlE9OpqmTZt6UTLfZ+DAgYwYMYKjR4+SkJBQ4LudO3eSk5PDtGnTqFmzppck9H2sVisTJjyJwyGsW3fOBHvVygCqVKnCgw8WGXjUJ9FKRCXjWrEaNGjgRUn8l/xBm91uv6hDPFYGVapU8bYIfonrhl3alMl96taty5133knTmEaMGDGCJk20b05pueWWWwgLDWV3SsbZcwfTM8jKy2PMffeVyun1UqdLly4AbD1l+OY4nA52nN5Hl65dtSlTKbjrrrto1KgRO3fuxOFwAJCYmMjJkycZNWoUsbGxXpbQ96lduzb9+vVn29YAMjLgxAnh4EELt9xyq1/1Kbq1qWQsFgstW7UmMChIKxFlJN//QXeWZaddu3YA2va8jFyqYQo9yahRo3h39ruMGTPG26L4FWFhYQwbPpxTmdlk5jlQSnH4TDaXt2tHmzZtvC2eX1CvXj2qR0ez8/R+AA6mHiMrN/uS2r+gPAQEBPDQQw+RlZXF8ePHAThw4ADVq1fn1ltv9bJ0/sOwYcPIy1Ps3WNl504rAQE2/v73v3tbLLfQozAv8Nabs/jxf//Ty/dlJH8A17x5cy9L4r+MGzeOt99+m3r16pV8seY89Gylxpvkbzp19EwWydm5ZOTmca0OS1pqRITWbdqwJ+UwAHuSDwHQunVrb4rlV7Rv356YmBiOHj1Keno6ycnJDB8+XPt5ukHjxo2pXbsWe/da2bc3gA4dO/rdBodaifACVqtVV7RyUK1aNcaPH88TTzzhbVH8lpCQEN1hlpMunTszdOhQb4uhuQSpVasWcXFxnMrK4WRmDjabje7du3tbLL+iVatWnM5M4XRWKvtSDlPjshpER0d7Wyy/QUTo168faWlpHDx4EBGhT58+3hbLrxARunTpysGDFlJTFZ07dfa2SG6jg+pr/JIhQ4Z4WwTNJc70V17xtgiaS5gOHTqwffs2nEoRGxvrV3bUvkC+A/WRtBMcOXOSZm31yra75EemO378OE2bNtVKWBlwdeT39T0hikKvRGg0Go1G42e0atUKpSAtJ88v4sn7GvnO/AdSj3IiPVGHxS0D9evXP2uWrctg2XANa92wYUPvCVJG9EqERqPRaDR+Rv369c8e6yAd7hMeHk5E1Qi2JuzGqZwF8lNTOiwWC6+++ioHDhw4G/FK4x6uqzf+GLBDKxEajUaj0fgZ+VHqQCsRZaV2ndps27bNOK5d28vS+CdxcXF6r5xyEBUVhd1up1atmn4ZsEMrERqNRqPR+Bk2m405c+aQkpJSYPNDTempVavWWSVCb46m8QY2m4358+f77Z5XWonQaDQajcYP0WGuy0dUVBRgRMmJiIjwsjSaSxV/DoqgHas1Go1Go9FccuRvthlot2Oz6TlVjcZdtBKh0Wg0Go3mkqNq1armkf/Zoms0voBWIjQajUaj0Vxy5Icn9UN/Vo3GJ9BKhEaj0Wg0mkuO0NBQ40BrERpNmdBKhEaj0Wg0mkuOoKAgAL8MranR+AJaidBoNBqNRnPJka9EnPON0Gg07qDDEWg0Go1Go7nkaNCgAQMHDqRv377eFkWj8Uu0EqHRaDQajeaSw2638+STT3pbDI3Gb9HmTBqNRqPRaDQajcYttBKh0Wg0Go1Go9Fo3EIrERqNRqPRaDQajcYttBKh0Wg0Go1Go9Fo3EIrERqNRqPRaDQajcYttBKh0Wg0Go1Go9Fo3EIrERqNRqPRaDQajcYttBKh0Wg0Go1Go9Fo3EIrERqNRqPRaDQajcYttBKh0Wg0Go1Go9Fo3EKUUt6WweOIyCnggLflKIFoIMHbQvgxOv/Kj87D8qHzr/zoPCw/Og/Lh86/8qPzsHz4Q/41UEpVL3zyolQi/AERWaOU6uhtOfwVnX/lR+dh+dD5V350HpYfnYflQ+df+dF5WD78Of+0OZNGo9FoNBqNRqNxC61EaDQajUaj0Wg0GrfQSoT3eN/bAvg5Ov/Kj87D8qHzr/zoPCw/Og/Lh86/8qPzsHz4bf5pnwiNRqPRaDQajUbjFnolQqPRaDQajUaj0biFViKKQESeEpGtIrJJRDaISOcypNFLRLq5fP5YRG7yrKTnPXO/iERX5DPKioikF/o8SkTe8kRa/oSI1BSRf4vIHhHZJiI/ikiMiNQWkXnmNe1E5FoPPS9QRH4xy/HwQt95vEya5f4HT6bpKUoqN671R0T+LMdzCtR9f8aTdU1EOorIrAt877NlpyyISA0R+aeI7BWRtSKyXERu8LJMg0VkwgW+bygiWypTJvO5M0XkYZfPC0XkA5fPr4nIo26m+bCIhLh5zwXLqKef5w0qov90d+zhjfFRaaiIcljEMyqtjhUukxXx22slohAi0hUYBLRXSrUBrgYOlSGpXkClDSRExFpZz9KUDRER4DtgsVKqiVIqDpgI1FBKHVVK5Tei7QCPKBHA5UCAUqqdUupLD6V50aOUKk/d7UUl1n1/QSm1Rin1kLflqAzMuv4fYIlSqrFSqgNwM1C30HW2ypRLKTVfKfVyZT6zlPyJWWdExIIRN7+ly/fdgGVupvkw4Nagvpxl1O3nXcL9di98s42siHLoFcyy5XaZdBetRJxPLSBBKZUNoJRKUEodFZG+IrJeRDaLyIciEgjnzV52FJHFItIQuA94xJwB7m6m3UNE/jRnpm4y73lHRAabx9+JyIfm8d0i8rx5/B9zJmuriNybL6iIpIvIVBFZCXR1OR8sIgtEZHSF5pSHEJG/ichKM39/EZEa5vkwEfnIzPNNIjKk0H3R5uzedebMxmIRmSciO0TkC7MjR0Q6iMgfZh4uFJFaItJERNa5pNVMRNZW8Kv2BnKVUrPzTyilNiil4vNnJ0TEDkwFhptlZ7iI7BKR6qacFhHZXXjWR0QizXKySURWiEgbEbkM+BxoZ6bVpCQBxeAVU5bNYq5elJC/A8xzS4EbLySTeX6yWYcWm3WhUgeW5u+/xMyTLS710/WadPP/hd772vz3FpFZIvJDUXVfRBqIyK9mPvwqIvXN+z827yvQJvg6IlJdRL4RkdXm35Xm+c0iEmGWoUQRud08/5mIXC0uKw0i0tPMnw1mvQ83kw8rJq+fNZ+1RUTedzm/WIzZwyUisl1ErhCRb80687wXsiefPkBOobp+QCn1phirsF+LyH+BnwFE5P/M99skIlPy7xGR20RklZlP74k56BSj7X9BRDaadauGiFjNciTm7+AUkR7m9fEi0lRcVoDNe74z09go52aGrSIyR4z+5mcRCa6E/FrGuUFlS2ALkCYi1cToa1sA64vKJxEJFZH/me+wRYw28yGgNvC7iPzukmfTxOgHfhGRTnKuDcrvg13LaJHtlBvP6ydG/7TO/L3DzPP7zfK8FBhaCXlbIlJ8HzxZRD4xy8F+EblRRKaLUdcXiEiASzL/Z5bVVSLS1Lz/vLZC3BgfeYHSlMPtYrTj68x8+DucXWHYXlTdEWMMslFElgMP5D9MRFq61O9NYoxD/uFS1maKyG/mcV8R+dw8vsV89hYRmeaSnuuY8CkKlUnzmgLtRrlzTCml/1z+gDBgA7ATeAfoCQRhrEbEmNd8CjxsHu8Hos3jjhizzACTgcdd0v0Y+BpDcYsDdpvnbwZeMY9XASvM44+A/uZxpPk/GKNQR5mfFTDM5Rn7gYbAL8Dt3s7LQvnqMPM1/+8g8Jb5XTXOOfnfA7xmHk8DXndJo5r5Px2oAawErjHP9QJSMGb6LMBy4CogAGN2obp53XDgQ/P4d6CdefwiMK6C8+AhYGYx3zUEtpjHo/Lzxvw8yaW89QO+KeL+N4FJ5nEfYINLvvxQzDM/Bm4qdG4IsAiwmnl8EEOxLi5/8+tGM0CAr/KfdwGZJpu/SSDGTE8ixmpJRZfBdPP/Y8BT5rEVCHepP9GFri3pvRuZ1/3L5b0nU7Du/xe4wzy+C/iPS/6f1yb40l9+PhQ690/gKvO4PrDdPJ4NXAe0AlYDc8zzuzDa1bNl0cyTK83jMMBWXF6b10S6PP8z4G/m8WJgmnk8HjhqltdA4DBmW+mFfLtQXR9lypbfrvfDiM4i5nv/APTAGLD8N79uYPRHt5vHyiUPpgNPm8cLMAY/g8zf4CkzL/a5PDu/3f2Sc+2KFaiK0Q7lca5d/Aq4rZLybL9ZnsZgDDKfw1iRvRJYcoF8GpJf1sx0qrqkF+1yXgEDzePvMBS4AKAtRbSXFNNOleZ55vVLgFDz8xPAsy7X/cPH6nRxffBkYKlLPmUUysPrXd4pv0293SUPi2srJlOK8ZGX8qekcmgDqrj8zrvNMtmQYuoOsAnoaR6/wrm+/k1ghHlsxxjjdQG+Ns/FY4wLAzDGAWMwFIODQHVTlt9cfoeixoSF68B57UZ5/ip1KdUfUEqli0gHoDvGzPGXwEsYjfBO87JPMLTJ191M/j9KKSewzUUDjAceFpE4YBtQTURqYaws5M/QPiTnbGnrYQzYEjEG5t8Uesb3wHSl1BduylbRZCql2uV/EJFRGEoXGIOGL833tgP7zPNXYyhZACilTpuHAcCvwANKqT9cnrFKKXXYTH8DRqVOxhjULBJj8tIKHDOv/wC4Uwwbx+FAJ0+8aAXwIcbv+jrGIPSjIq65CqNzQyn1m4hEiUjVMjzrKuBfSikHcEJE/gCuAFIpOn/TMerGLvP858C9LmkVJ9P/lLHaly0iJzEUlsNlkLcsrAY+NGfR/qOU2lDC9cW9916lVH5Z/Rfn3rswXTm3QvMZRuOdT1Ftgq9zNRBn1ieAKmKsJMRjDOoOAO8C96MVbLoAAArpSURBVIpIHSDJbFdd01gGzBCRL4BvlVKHze+LyuulQG8R+QfG0nwksBVjgA0w3/y/GdiqlDpm3r8Xo71M9Ozru4+IvI1RH3KAt4FFSqkk8+t+5t9683MYRhvfBugArDbzJhg4aV6TgzGIBlgLXGMe5/8GjTD6rdHAHxhlvjB9MAZ8mPU9RUSqYdTn/DqxFuM3qAzyZ4G7ATOAOuZxCsZgvrh8igdeNWdkf1BKxReTfg6GkgVGWclWSuWKyGaKf8ei2qnNpXheF4zB8DLzt7NjKMX5+JppaXF9MMBPLvlkpWAeNnS57l8u/2eax8W1FUXhK21hSeVQgBfFWOVzmt/ny3te3TH7vAiXscpnwEDzeDnwlIjUxWgHd4lhEdHBzKdsYB3GWKk7xpjwCozJ6lMAZhvaA8N8sqgxoSvFtRtlRpszFYFSyqGUWqyUmgQ8iFERiiOPc/kYVELS2S7HYj7rCMYswAAMLTceGIYxW5AmIr3M53dVSrXFaEDzn5NlNv6uLAMGSqEe28d5E2N2rDWGpp3/foKhORcmD6MC9C903jV/HRhaumAMLNqZf62VUv3Ma77BqMyDgLVKqYoebGzFGBS4hVLqEMaAvg/QGfipiMuK+r3LEr/5QuWmqPy90HMuJFNxaVU4SqklGI3uEeAzMc1uLkBx5arMIhSTtr/UWQtGe5Rfp+oopdIw2q/u5t9i4BRwE0abVgBl2OXfgzEwXiEiseZX5+W1iARhzMLfZLYRcyjY1ubf4yx0v5NKLFeF2Aq0z/+glHoA6IsxewhwxuVaAV5yyc+mSqm55vlPXM43V0pNNu/JVeZ0IgXrTzxG/ncCfgQiMGbXl7ghu7fqZr49emuMFfcVGAp4vh16kflkTu51wBjUviQizxaTvmuenS0r5sC1uHc8Ly9K+TzBUBTzZY1TSt3t8v2ZIu7xJsX1wVAwnwrnoWu+qSKOi2srisJX2sKSyuEIjHrcwZwYPcG5/Cquryiyj1RK/RMYDGQCC0Wkj1IqF2MF4U5TlniMCe0mwHYunDdFjQldKa7dKDNaiSiEiDQXkWYup/ILScN8Oz9gJMbsDhg/dv7A0NVmPw0oTuMuzHIMB5h8JeJxznW8VYHTSqkMs6PtUkJaz2LMvL1Tymf7AlUxBnQAd7ic/xlDiQPAnCUDo0LeBcTKBSKNmPwFVBfDYR4RCRCRlgBKqSxgIcasaVGz+57mNyBQXHxVxLDh7lnouqLKzgcY/g1fFdNILMFo3DAVzwSlVGoZZFyC4Y9hFcMPowfGcmpx7AAayTl/i1sqQCaPIiINgJNKqTnAXFwGe26wA2hs2veCsZKVT+Hf70/OraiNwJhZ92cK18t2cFbZjQaaKaX2Yryna1uGyz1NlFKblVLTgDVAbOFrXMjvoBPEsCv3B9+R34AgERnrcq44B8eFwF1yzma+jhj+TL8CN5nH+T5GDUp47kqMwY7TbN82YAwKi5ot/xUYa6ZtFZEqpXu1CmMZxoROkjmRl4ShBHXF6COLzCcRqQ1kKKU+B17lXH12pw8uNaV83grgSjnnGxAiIjGelsWDFNcHu8Nwl//5qy5FthVU0G/jIUoqh1Ux+o9cEekNXLBOKqWSMVb5rjJPjcj/TkQaY6xoz8JYUW1jfrUEo+3MHxPeh2FypzDqeE8xfEKtGH2uq0WGKxWez1qJOJ8w4BMxwm9uwliSnIChFX5tLuk5Mex/AaYAb4hIPIZml89/gRukoONQccRjzHDsxli6iuRco78AYzZuE4Zt3opSvMPDGB3Y9BKv9A0mY+RtPJDgcv55DPOuLSKyEUMbB84uv9+MYeZwf3EJK6VyMAYd08w0NlAwKsQXGErJzx56l2IxG4AbgGvECPG6FePdjxa69HeMJWDXsKzzMcpmccrOZKCjWU5epvQdwXsictj8W45h57oJ2IgxEPqHUur4Bd4pC8OM539iOAoe8IBMFU0vYIOIrMdQ/N9wNwGlVCZwP7DAfO8TGMvdcH7dfwjDbG4TxgTE+PK/QqUR4lI+Doth+vcQ5u8qItswOrh8VmL4k4HRhtWhaKXpYZd6nUnRq2vA2U54DsbM738o2jTHpzDr+vUYnf0+EVmFYQb7RBHX/oxhO77c7F/mYfjpbAOeBn42y84iDH+PCz03G8NXJ7+fiMcYRGwu4vLxGO3nZoyV3ZZFXFOZbMZQQlcUOpeijAAnReYTxozxKtP87SmMfgMM/4mfxMWp1EOU+DzT1GQU8C/zt1vBhRXlyqSoOj2ZovtgdwgUw6F3PPCIea64tsKd8VFlc8FyiDFm6CgiazAUgh2lSPNO4G2zj810OT8c2GKWpVgMf1sw6m0tYLlS6gSQZZ7DNNd8EmOcsBFYp5T6vpjnVlQdOIvesVpzSSMij2M4xj3jbVkuhIh0xHDU9LUG95JFRMJMW3/BsHPfpZSaWdJ9Go1Go9FcDGjHas0li4h8h2Fn2MfbslwI02RrLC7LoBqfYLSI3IHhiLgeeM/L8mg0Go1GU2nolQiNRqPRaDQajUbjFtonQqPRaDQajUaj0biFViI0Go1Go9FoNBqNW2glQqPRaDQajUaj0biFViI0Go1Gcx4icl9JG/GJyCgReauY79IrSK5eIvJDyVf6RroajUZzsaKjM2k0Go3mPJRSs0u+qnIREd1naTQajY+gVyI0Go3mEkBEGorIdhGZIyJbReRnEQkWkSYiskBE1opIvIjEmtdPNvdRyd9ZfZOILBeRV0Rki0vStc37dxXe4FJEXhORdSLyqxg7oCMi7URkhZned2LuRC8ii839UDB3Y91vHo8Ska9F5L+c2xQyTETmicgOEfnC3KsDEekrIutFZLOIfCgigSWcH2CmsRS4sUIyXqPRaC5StBKh0Wg0lw7NgLeVUi2BZIwdu98HximlOgCPA+8Ucd9HwH1Kqa6Ao9B37TB2Xm0NDBeReub5UIzdVNsDfwCTzPOfAk8opdpg7AQ7iZLpCtyhlMrf0+Vy4GEgDmgMXCkiQcDHwHClVGuMlfaxJZyfA/wN6A7ULIUcGo1GozHRSoRGo9FcOuxTSm0wj9cCDYFuwNcisgFjw7xarjeISAQQrpT60zz1z0Jp/qqUSlFKZQHbgAbmeSfwpXn8OXCViFQFIpRSf5jnPwF6lELuRUqpJJfPq5RSh5VSTmCD+R7NzffbWSjt4s7Hmud3KWPDpM9LIYdGo9FoTLR9qUaj0Vw6ZLscO4AaQLJSqt0F7hE30yyuXylpZ9M8zk1sBRX67kwpnlmcnBeSX++2qtFoNGVEr0RoNBrNpUsqsE9EhgKIQVvXC5RSp4E0Eelinrq5lGlbgJvM41uBpUqpFOC0iHQ3z4/EMHUC2A90MI/z73OHHUBDEWlaKO0LnW8kIk3M87eU4ZkajUZzyaKVCI1Go7m0GQHcLSIbga3A34u45m7gfRFZjjGzn1KKdM8ALUVkLdAHmGqevwN4RUQ2YfhT5J9/FcNX4U8g2t2XMM2p7sQwzdqMYU41u4Tz9wL/Mx2rD7j7TI1Go7mUEcMUVKPRaDSaohGRMKVUunk8AaillBrvZbE0Go1G40W0T4RGo9FoSuI6EXkSo884AIzyrjgajUaj8TZ6JUKj0Wg0Go1Go9G4hfaJ0Gg0Go1Go9FoNG6hlQiNRqPRaDQajUbjFlqJ0Gg0Go1Go9FoNG6hlQiNRqPRaDQajUbjFlqJ0Gg0Go1Go9FoNG6hlQiNRqPRaDQajUbjFv8PgJG1vVzpvroAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 936x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#desnity and distribution\n",
    "sub1=joined[joined.price < 1000]\n",
    "plt.figure(figsize=(13,5))\n",
    "viz_2=sns.violinplot(data=sub1, x='neighbourhood', y='price', height=8, aspect=2)\n",
    "viz_2.set_title('Density and distribution of prices for each neighberhood')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#avg price\n",
    "avergeCitymax = joined[['room_type','price']].groupby('room_type', as_index=True).mean()\n",
    "ax = avergeCitymax.plot(kind='bar', figsize=(15,10), title='Room_type average price \\n')\n",
    "\n",
    "for p in ax.patches:\n",
    "    ax.annotate(str(p.get_height()), (p.get_x(), p.get_height()),fontsize=13,color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "##imports lineal regresion\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'listing_url', 'scrape_id', 'last_scraped', 'name', 'summary',\n",
       "       'space', 'description', 'experiences_offered', 'neighborhood_overview',\n",
       "       ...\n",
       "       'instant_bookable', 'is_business_travel_ready', 'cancellation_policy',\n",
       "       'require_guest_profile_picture', 'require_guest_phone_verification',\n",
       "       'calculated_host_listings_count',\n",
       "       'calculated_host_listings_count_entire_homes',\n",
       "       'calculated_host_listings_count_private_rooms',\n",
       "       'calculated_host_listings_count_shared_rooms', 'reviews_per_month'],\n",
       "      dtype='object', length=106)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "listing.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Specify the Prediction Target \n",
    "features = [ 'latitude', 'longitude',  \n",
    "            'minimum_nights', 'number_of_reviews','availability_365']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = ['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2561"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined['id'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>availability_365</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>51.50205</td>\n",
       "      <td>-0.10015</td>\n",
       "      <td>1</td>\n",
       "      <td>91</td>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>51.49865</td>\n",
       "      <td>-0.10284</td>\n",
       "      <td>3</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   latitude  longitude  minimum_nights  number_of_reviews  availability_365\n",
       "0  51.50205   -0.10015               1                 91               353\n",
       "1  51.49865   -0.10284               3                146                 0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x= joined[features]\n",
    "x.head(2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "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",
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       "        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>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   price\n",
       "0   60.0\n",
       "1   69.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = joined[target]\n",
    "y.head(2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "latitude              51.49907\n",
       "longitude             -0.08119\n",
       "minimum_nights         2.00000\n",
       "number_of_reviews    159.00000\n",
       "availability_365       0.00000\n",
       "Name: 2, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.iloc[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=324)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "###linael regresor\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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       "       [165.84437079]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_prediction = regressor.predict(X_test)\n",
    "y_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>846.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>133.419622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>149.608373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>56.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>2000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             price\n",
       "count   846.000000\n",
       "mean    133.419622\n",
       "std     149.608373\n",
       "min       0.000000\n",
       "25%      56.250000\n",
       "50%      99.000000\n",
       "75%     150.000000\n",
       "max    2000.000000"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "144.05523178206013\n"
     ]
    }
   ],
   "source": [
    "RMSE = sqrt(mean_squared_error(y_true = y_test, y_pred = y_prediction))\n",
    "###std ->149.608373\n",
    "print(RMSE)"
   ]
  }
 ],
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