(4) Trail Info EDA and Create Pandas DataFrame.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "import mysql.connector\n",
    "from mysql.connector import errorcode\n",
    "from secret import mySQL_password # Note: secret.py file\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import chi2_contingency, chi2, fisher_exact\n",
    "pd.options.display.float_format = '{:,}'.format\n",
    "from pandas.plotting import autocorrelation_plot\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "#plt.style.use('fivethirtyeight')\n",
    "plt.rcParams['figure.figsize'] = (12, 8)\n",
    "plt.rcParams['font.size'] = 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<mysql.connector.connection_cext.CMySQLConnection object at 0x7fefe529d8d0>\n"
     ]
    }
   ],
   "source": [
    "# Create or get a MySQL connection object\n",
    "cnx = mysql.connector.connect(user='root', password=mySQL_password,\n",
    "                              host='127.0.0.1',\n",
    "                              database='trails')\n",
    "print(cnx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiates and returns a cursor using C Extension\n",
    "cursor = cnx.cursor()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis on `trail_info` table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def null_counts(col):\n",
    "\n",
    "    sql = \"SELECT COUNT(*) FROM trails.trail_info WHERE {} IS NULL\".format(col)\n",
    "    cursor.execute(sql)\n",
    "    result = cursor.fetchall()\n",
    "\n",
    "    for row in result:\n",
    "        print(row[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10146\n"
     ]
    }
   ],
   "source": [
    "sql = \"SELECT COUNT(*) FROM trails.trail_info\"\n",
    "cursor.execute(sql)\n",
    "result = cursor.fetchall()\n",
    "\n",
    "for row in result:\n",
    "    print(row[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Trail ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10146\n"
     ]
    }
   ],
   "source": [
    "sql = \"SELECT COUNT(DISTINCT trail_id) FROM trails.trail_info\"\n",
    "cursor.execute(sql)\n",
    "result = cursor.fetchall()\n",
    "\n",
    "for row in result:\n",
    "    print(row[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "null_counts('state')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('California', 10137)\n",
      "('', 7)\n",
      "('Oregon', 1)\n",
      "('United States of Amer', 1)\n"
     ]
    }
   ],
   "source": [
    "sql = \"\"\"\n",
    "SELECT state, COUNT(*)\n",
    "FROM trails.trail_info\n",
    "GROUP BY state\n",
    "\"\"\"\n",
    "cursor.execute(sql)\n",
    "result = cursor.fetchall()\n",
    "\n",
    "for row in result:\n",
    "    print(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "sql = \"\"\"\n",
    "SELECT * \n",
    "FROM trails.trail_info\n",
    "WHERE state != 'California'\n",
    "\"\"\"\n",
    "cursor.execute(sql)\n",
    "trail_info_list_not_CA_sql = [i[0:4] for i in cursor.fetchall()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('', 'Santa Rosa', 'Creekside Trail to Big Leaf Trail Loop', 10018817),\n",
       " ('Oregon', 'Ashland', 'Pilot Rock Trail', 10027241),\n",
       " ('', 'Twentynine Palms', 'Wall Street Mill Trail', 10036072),\n",
       " ('', 'Tehachapi', 'TMTA Lehigh Trail Loop', 10302436),\n",
       " ('', 'Stonyford', 'Snow Mountain Wilderness Loop', 10309312),\n",
       " ('', 'Cedarpines Park', 'Camp Seely to Cedarpines Park Loop', 10309399),\n",
       " ('', 'Ojai', 'Ojai to Ventura Bike Path via Downtown Ojai', 10318649),\n",
       " ('', 'Sunnyvale', 'Baylands Bike Path Loop', 10318863),\n",
       " ('United States of Amer', 'Calexico', 'Pista el Alacrán', 10652811)]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trail_info_list_not_CA_sql"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = tuple(i[3] for i in trail_info_list_not_CA_sql)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "sql = \"\"\"\n",
    "SELECT url, trail_id\n",
    "FROM trails.trail_urls_ca \n",
    "WHERE trail_id IN ({0})\n",
    "\"\"\".format(', '.join(['%s'] * len(values)))\n",
    "cursor.execute(sql, values)\n",
    "trail_urls_list_not_CA_sql = [i[0:2] for i in cursor.fetchall()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#trail_urls_list_not_CA_sql"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: The trails above were confirmed to be in California after visiting websites except for trail_id IN (10027241, 10652811) which will be removed from the `trail_info` table.  Those confirmed will be set to 'California'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sql = \"\"\"\n",
    "# UPDATE trails.trail_info \n",
    "# SET state='California' \n",
    "# WHERE trail_id IN (10018817, 10036072, 10302436, 10309312, 10309399, 10318649, 10318863)\n",
    "# \"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### City"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "null_counts('city')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "sql = \"\"\"\n",
    "SELECT * \n",
    "FROM trails.trail_info\n",
    "WHERE city IS NULL\n",
    "\"\"\"\n",
    "cursor.execute(sql)\n",
    "trail_info_list_city_NULL_sql = [i[0:4] for i in cursor.fetchall()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('California', None, 'La Jolla Heights Preserve Walk', 10541176),\n",
       " ('California', None, 'Plantation Trail to Grand View Point', 10577579),\n",
       " ('California', None, 'Carrington Point Trail', 10581562)]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trail_info_list_city_NULL_sql"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = tuple(i[3] for i in trail_info_list_city_NULL_sql)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "sql = \"\"\"\n",
    "SELECT url, trail_id, city\n",
    "FROM trails.trail_urls_ca \n",
    "WHERE trail_id IN ({0})\n",
    "\"\"\".format(', '.join(['%s'] * len(values)))\n",
    "cursor.execute(sql, values)\n",
    "trail_urls_list_city_NULL_sql = [i[0:3] for i in cursor.fetchall()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#trail_urls_list_city_NULL_sql"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Trail Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "null_counts('trail_name')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Trail Difficulty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "null_counts('trail_difficulty')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('hard', 1928)\n",
      "('easy', 3108)\n",
      "('moderate', 5110)\n"
     ]
    }
   ],
   "source": [
    "sql = \"\"\"\n",
    "SELECT trail_difficulty, COUNT(*)\n",
    "FROM trails.trail_info\n",
    "GROUP BY trail_difficulty\n",
    "\"\"\"\n",
    "cursor.execute(sql)\n",
    "result = cursor.fetchall()\n",
    "\n",
    "for row in result:\n",
    "    print(row)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "null_counts('stars')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Decimal('0.0'), 102)\n",
      "(Decimal('1.0'), 25)\n",
      "(Decimal('1.5'), 10)\n",
      "(Decimal('2.0'), 43)\n",
      "(Decimal('2.5'), 74)\n",
      "(Decimal('3.0'), 228)\n",
      "(Decimal('3.5'), 655)\n",
      "(Decimal('4.0'), 2744)\n",
      "(Decimal('4.5'), 5582)\n",
      "(Decimal('5.0'), 683)\n"
     ]
    }
   ],
   "source": [
    "sql = \"\"\"\n",
    "SELECT stars, COUNT(*)\n",
    "FROM trails.trail_info\n",
    "GROUP BY stars\n",
    "ORDER BY stars\n",
    "\"\"\"\n",
    "cursor.execute(sql)\n",
    "result = cursor.fetchall()\n",
    "\n",
    "for row in result:\n",
    "    print(row)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Number of Reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "null_counts('num_reviews')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Trail Region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "null_counts('trail_region')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "null_counts('distance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Duration Mins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "409\n"
     ]
    }
   ],
   "source": [
    "null_counts('duration_mins')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "sql = \"\"\"\n",
    "SELECT trail_id, trail_name, duration_mins\n",
    "FROM trails.trail_info\n",
    "WHERE duration_mins IS NULL\n",
    "\"\"\"\n",
    "cursor.execute(sql)\n",
    "trail_info_list_duration_mins_NULL_sql = [i[0:3] for i in cursor.fetchall()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(10001403, 'High Point Truck Trail', None),\n",
       " (10005408, 'Main Divide Truck Trail to Santiago Peak [CLOSED]', None),\n",
       " (10006296, 'Bee Canyon Truck OHV Trail', None),\n",
       " (10006938, 'McIvers Road OHV Trail', None),\n",
       " (10007150, 'Pilot Rock OHV Trail', None),\n",
       " (10007329, 'Wildomar OHV Area [CLOSED]', None),\n",
       " (10007673, 'Soquel Demonstration Forest Flow Trail', None),\n",
       " (10010256, 'Rockhouse OHV Trail', None),\n",
       " (10012063, 'Old Spanish Trail Highway', None),\n",
       " (10013046, 'Mother Grundy Truck Trail', None)]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trail_info_list_duration_mins_NULL_sql[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = tuple(i[0] for i in trail_info_list_duration_mins_NULL_sql)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "sql = \"\"\"\n",
    "SELECT url, trail_id, trail_name\n",
    "FROM trails.trail_urls_ca \n",
    "WHERE trail_id IN ({0})\n",
    "\"\"\".format(', '.join(['%s'] * len(values)))\n",
    "cursor.execute(sql, values)\n",
    "trail_urls_list_duration_mins_NULL_sql = [i[0:3] for i in cursor.fetchall()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#trail_urls_list_duration_mins_NULL_sql[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: The trails above were confirmed to not have a duration minutes parameter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Elevation Gain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "null_counts('elevation_gain')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Route Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "null_counts('route_type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Out & Back', 4634)\n",
      "('Loop', 4885)\n",
      "('Point to Point', 627)\n"
     ]
    }
   ],
   "source": [
    "sql = \"\"\"\n",
    "SELECT route_type, COUNT(*)\n",
    "FROM trails.trail_info\n",
    "GROUP BY route_type\n",
    "\"\"\"\n",
    "cursor.execute(sql)\n",
    "result = cursor.fetchall()\n",
    "\n",
    "for row in result:\n",
    "    print(row)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Trail Tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "null_counts('trail_tags_str')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the `trail_info` table from the database and create Pandas DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>state</th>\n",
       "      <th>city</th>\n",
       "      <th>trail_name</th>\n",
       "      <th>trail_id</th>\n",
       "      <th>trail_difficulty</th>\n",
       "      <th>stars</th>\n",
       "      <th>num_reviews</th>\n",
       "      <th>trail_region</th>\n",
       "      <th>distance</th>\n",
       "      <th>duration_mins</th>\n",
       "      <th>elevation_gain</th>\n",
       "      <th>route_type</th>\n",
       "      <th>trail_tags_str</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>California</td>\n",
       "      <td>Angelus Oaks</td>\n",
       "      <td>Forsee Creek Trail</td>\n",
       "      <td>10000006</td>\n",
       "      <td>hard</td>\n",
       "      <td>4.5</td>\n",
       "      <td>116</td>\n",
       "      <td>San Bernardino National Forest</td>\n",
       "      <td>21.9 km</td>\n",
       "      <td>449.0</td>\n",
       "      <td>1,331 m</td>\n",
       "      <td>Out &amp; Back</td>\n",
       "      <td>DogFriendly Backpacking Camping Hiking Forest ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>California</td>\n",
       "      <td>Pescadero</td>\n",
       "      <td>Franklin Point Trail [CLOSED]</td>\n",
       "      <td>10000015</td>\n",
       "      <td>easy</td>\n",
       "      <td>4.5</td>\n",
       "      <td>25</td>\n",
       "      <td>Año Nuevo State Park</td>\n",
       "      <td>1.1 km</td>\n",
       "      <td>19.0</td>\n",
       "      <td>16 m</td>\n",
       "      <td>Out &amp; Back</td>\n",
       "      <td>KidFriendly Hiking Walking BirdWatching Beach ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>California</td>\n",
       "      <td>Thousand Palms</td>\n",
       "      <td>Indian Palms Trail</td>\n",
       "      <td>10000044</td>\n",
       "      <td>easy</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20</td>\n",
       "      <td>Coachella Valley Preserve</td>\n",
       "      <td>1.9 km</td>\n",
       "      <td>33.0</td>\n",
       "      <td>34 m</td>\n",
       "      <td>Out &amp; Back</td>\n",
       "      <td>KidFriendly Hiking NatureTrips Walking Views W...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>California</td>\n",
       "      <td>Sierraville</td>\n",
       "      <td>Mount Lola Trail</td>\n",
       "      <td>10000118</td>\n",
       "      <td>hard</td>\n",
       "      <td>4.5</td>\n",
       "      <td>82</td>\n",
       "      <td>Tahoe National Forest</td>\n",
       "      <td>17.5 km</td>\n",
       "      <td>366.0</td>\n",
       "      <td>772 m</td>\n",
       "      <td>Out &amp; Back</td>\n",
       "      <td>DogFriendly Backpacking Hiking HorsebackRiding...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>California</td>\n",
       "      <td>Santa Rosa</td>\n",
       "      <td>Cobblestone Trail Loop</td>\n",
       "      <td>10000133</td>\n",
       "      <td>moderate</td>\n",
       "      <td>4.5</td>\n",
       "      <td>103</td>\n",
       "      <td>Trione-Annadel State Park</td>\n",
       "      <td>7.9 km</td>\n",
       "      <td>141.0</td>\n",
       "      <td>173 m</td>\n",
       "      <td>Loop</td>\n",
       "      <td>Hiking MountainBiking NatureTrips Running Lake...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        state            city                     trail_name  trail_id  \\\n",
       "0  California    Angelus Oaks             Forsee Creek Trail  10000006   \n",
       "1  California       Pescadero  Franklin Point Trail [CLOSED]  10000015   \n",
       "2  California  Thousand Palms             Indian Palms Trail  10000044   \n",
       "3  California     Sierraville               Mount Lola Trail  10000118   \n",
       "4  California      Santa Rosa         Cobblestone Trail Loop  10000133   \n",
       "\n",
       "  trail_difficulty  stars  num_reviews                    trail_region  \\\n",
       "0             hard    4.5          116  San Bernardino National Forest   \n",
       "1             easy    4.5           25            Año Nuevo State Park   \n",
       "2             easy    4.0           20       Coachella Valley Preserve   \n",
       "3             hard    4.5           82           Tahoe National Forest   \n",
       "4         moderate    4.5          103       Trione-Annadel State Park   \n",
       "\n",
       "  distance  duration_mins elevation_gain  route_type  \\\n",
       "0  21.9 km          449.0        1,331 m  Out & Back   \n",
       "1   1.1 km           19.0           16 m  Out & Back   \n",
       "2   1.9 km           33.0           34 m  Out & Back   \n",
       "3  17.5 km          366.0          772 m  Out & Back   \n",
       "4   7.9 km          141.0          173 m        Loop   \n",
       "\n",
       "                                      trail_tags_str  \n",
       "0  DogFriendly Backpacking Camping Hiking Forest ...  \n",
       "1  KidFriendly Hiking Walking BirdWatching Beach ...  \n",
       "2  KidFriendly Hiking NatureTrips Walking Views W...  \n",
       "3  DogFriendly Backpacking Hiking HorsebackRiding...  \n",
       "4  Hiking MountainBiking NatureTrips Running Lake...  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read SQL query or database table into a DataFrame\n",
    "state_trails_df = pd.read_sql('SELECT * FROM trails.trail_info', con=cnx)\n",
    "state_trails_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examine the DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows: 10146, Cols: 13\n"
     ]
    }
   ],
   "source": [
    "# Return the number of rows and columns (dimensionality) of the DataFrame\n",
    "print('Rows: {}, Cols: {}'.format(state_trails_df.shape[0], state_trails_df.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10146 entries, 0 to 10145\n",
      "Data columns (total 13 columns):\n",
      "state               10146 non-null object\n",
      "city                10143 non-null object\n",
      "trail_name          10146 non-null object\n",
      "trail_id            10146 non-null int64\n",
      "trail_difficulty    10146 non-null object\n",
      "stars               10146 non-null float64\n",
      "num_reviews         10146 non-null int64\n",
      "trail_region        10146 non-null object\n",
      "distance            10146 non-null object\n",
      "duration_mins       9737 non-null float64\n",
      "elevation_gain      10146 non-null object\n",
      "route_type          10146 non-null object\n",
      "trail_tags_str      10146 non-null object\n",
      "dtypes: float64(2), int64(2), object(9)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# Print a concise summary of a DataFrame including the index dtype and column dtypes, non-null values, and memory usage\n",
    "# Note: Useful to quickly see if null values exist \n",
    "state_trails_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['state', 'city', 'trail_name', 'trail_id', 'trail_difficulty', 'stars',\n",
       "       'num_reviews', 'trail_region', 'distance', 'duration_mins',\n",
       "       'elevation_gain', 'route_type', 'trail_tags_str'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Column names (which is \"an index\")\n",
    "state_trails_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sort a DataFrame by multiple columns\n",
    "# Note: ascending: sort ascending vs. descending; ascending=True is default\n",
    "#       inplace=True changes the original DataFrame\n",
    "# state_trails_df.sort_values(by=['X', 'Y'], ascending=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "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>state</th>\n",
       "      <th>city</th>\n",
       "      <th>trail_name</th>\n",
       "      <th>trail_id</th>\n",
       "      <th>trail_difficulty</th>\n",
       "      <th>stars</th>\n",
       "      <th>num_reviews</th>\n",
       "      <th>trail_region</th>\n",
       "      <th>distance</th>\n",
       "      <th>duration_mins</th>\n",
       "      <th>elevation_gain</th>\n",
       "      <th>route_type</th>\n",
       "      <th>trail_tags_str</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>California</td>\n",
       "      <td>Angelus Oaks</td>\n",
       "      <td>Forsee Creek Trail</td>\n",
       "      <td>10000006</td>\n",
       "      <td>hard</td>\n",
       "      <td>4.5</td>\n",
       "      <td>116</td>\n",
       "      <td>San Bernardino National Forest</td>\n",
       "      <td>21.9 km</td>\n",
       "      <td>449.0</td>\n",
       "      <td>1,331 m</td>\n",
       "      <td>Out &amp; Back</td>\n",
       "      <td>DogFriendly Backpacking Camping Hiking Forest ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>California</td>\n",
       "      <td>Pescadero</td>\n",
       "      <td>Franklin Point Trail [CLOSED]</td>\n",
       "      <td>10000015</td>\n",
       "      <td>easy</td>\n",
       "      <td>4.5</td>\n",
       "      <td>25</td>\n",
       "      <td>Año Nuevo State Park</td>\n",
       "      <td>1.1 km</td>\n",
       "      <td>19.0</td>\n",
       "      <td>16 m</td>\n",
       "      <td>Out &amp; Back</td>\n",
       "      <td>KidFriendly Hiking Walking BirdWatching Beach ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>California</td>\n",
       "      <td>Thousand Palms</td>\n",
       "      <td>Indian Palms Trail</td>\n",
       "      <td>10000044</td>\n",
       "      <td>easy</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20</td>\n",
       "      <td>Coachella Valley Preserve</td>\n",
       "      <td>1.9 km</td>\n",
       "      <td>33.0</td>\n",
       "      <td>34 m</td>\n",
       "      <td>Out &amp; Back</td>\n",
       "      <td>KidFriendly Hiking NatureTrips Walking Views W...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>California</td>\n",
       "      <td>Sierraville</td>\n",
       "      <td>Mount Lola Trail</td>\n",
       "      <td>10000118</td>\n",
       "      <td>hard</td>\n",
       "      <td>4.5</td>\n",
       "      <td>82</td>\n",
       "      <td>Tahoe National Forest</td>\n",
       "      <td>17.5 km</td>\n",
       "      <td>366.0</td>\n",
       "      <td>772 m</td>\n",
       "      <td>Out &amp; Back</td>\n",
       "      <td>DogFriendly Backpacking Hiking HorsebackRiding...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>California</td>\n",
       "      <td>Santa Rosa</td>\n",
       "      <td>Cobblestone Trail Loop</td>\n",
       "      <td>10000133</td>\n",
       "      <td>moderate</td>\n",
       "      <td>4.5</td>\n",
       "      <td>103</td>\n",
       "      <td>Trione-Annadel State Park</td>\n",
       "      <td>7.9 km</td>\n",
       "      <td>141.0</td>\n",
       "      <td>173 m</td>\n",
       "      <td>Loop</td>\n",
       "      <td>Hiking MountainBiking NatureTrips Running Lake...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10141</td>\n",
       "      <td>California</td>\n",
       "      <td>Fremont</td>\n",
       "      <td>Bayview and Meadowlark (Accessible)</td>\n",
       "      <td>10740073</td>\n",
       "      <td>moderate</td>\n",
       "      <td>4.5</td>\n",
       "      <td>46</td>\n",
       "      <td>Coyote Hills Regional Park</td>\n",
       "      <td>2.4 mi</td>\n",
       "      <td>59.0</td>\n",
       "      <td>118 ft</td>\n",
       "      <td>Loop</td>\n",
       "      <td>DogFriendly WheelchairFriendly KidFriendly Str...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10142</td>\n",
       "      <td>California</td>\n",
       "      <td>Idyllwild-Pine Cove</td>\n",
       "      <td>Deer Springs Trail to Round Valley</td>\n",
       "      <td>10740648</td>\n",
       "      <td>hard</td>\n",
       "      <td>4.5</td>\n",
       "      <td>8</td>\n",
       "      <td>Mount San Jacinto State Park</td>\n",
       "      <td>16.0 mi</td>\n",
       "      <td>565.0</td>\n",
       "      <td>4,819 ft</td>\n",
       "      <td>Out &amp; Back</td>\n",
       "      <td>Backpacking Camping Hiking NatureTrips Forest ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10143</td>\n",
       "      <td>California</td>\n",
       "      <td>Big Pine</td>\n",
       "      <td>Birch Lake</td>\n",
       "      <td>10740792</td>\n",
       "      <td>hard</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Inyo National Forest</td>\n",
       "      <td>13.0 mi</td>\n",
       "      <td>495.0</td>\n",
       "      <td>5,055 ft</td>\n",
       "      <td>Out &amp; Back</td>\n",
       "      <td>Backpacking Camping Hiking NatureTrips Lake Vi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10144</td>\n",
       "      <td>California</td>\n",
       "      <td>Lee Vining</td>\n",
       "      <td>Bloody Canyon Trail to Silver Lake via Rush Cr...</td>\n",
       "      <td>10741536</td>\n",
       "      <td>hard</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ansel Adams Wilderness</td>\n",
       "      <td>21.8 mi</td>\n",
       "      <td>706.0</td>\n",
       "      <td>5,285 ft</td>\n",
       "      <td>Point to Point</td>\n",
       "      <td>Backpacking Camping Hiking NatureTrips BirdWat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10145</td>\n",
       "      <td>California</td>\n",
       "      <td>Sausalito</td>\n",
       "      <td>South Rodeo Beach</td>\n",
       "      <td>10741577</td>\n",
       "      <td>easy</td>\n",
       "      <td>4.5</td>\n",
       "      <td>53</td>\n",
       "      <td>Marin Headlands</td>\n",
       "      <td>0.6 mi</td>\n",
       "      <td>13.0</td>\n",
       "      <td>108 ft</td>\n",
       "      <td>Out &amp; Back</td>\n",
       "      <td>KidFriendly Fishing Hiking NatureTrips BirdWat...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            state                 city  \\\n",
       "0      California         Angelus Oaks   \n",
       "1      California            Pescadero   \n",
       "2      California       Thousand Palms   \n",
       "3      California          Sierraville   \n",
       "4      California           Santa Rosa   \n",
       "10141  California              Fremont   \n",
       "10142  California  Idyllwild-Pine Cove   \n",
       "10143  California             Big Pine   \n",
       "10144  California           Lee Vining   \n",
       "10145  California            Sausalito   \n",
       "\n",
       "                                              trail_name  trail_id  \\\n",
       "0                                     Forsee Creek Trail  10000006   \n",
       "1                          Franklin Point Trail [CLOSED]  10000015   \n",
       "2                                     Indian Palms Trail  10000044   \n",
       "3                                       Mount Lola Trail  10000118   \n",
       "4                                 Cobblestone Trail Loop  10000133   \n",
       "10141                Bayview and Meadowlark (Accessible)  10740073   \n",
       "10142                 Deer Springs Trail to Round Valley  10740648   \n",
       "10143                                         Birch Lake  10740792   \n",
       "10144  Bloody Canyon Trail to Silver Lake via Rush Cr...  10741536   \n",
       "10145                                  South Rodeo Beach  10741577   \n",
       "\n",
       "      trail_difficulty  stars  num_reviews                    trail_region  \\\n",
       "0                 hard    4.5          116  San Bernardino National Forest   \n",
       "1                 easy    4.5           25            Año Nuevo State Park   \n",
       "2                 easy    4.0           20       Coachella Valley Preserve   \n",
       "3                 hard    4.5           82           Tahoe National Forest   \n",
       "4             moderate    4.5          103       Trione-Annadel State Park   \n",
       "10141         moderate    4.5           46      Coyote Hills Regional Park   \n",
       "10142             hard    4.5            8    Mount San Jacinto State Park   \n",
       "10143             hard    0.0            0            Inyo National Forest   \n",
       "10144             hard    0.0            0          Ansel Adams Wilderness   \n",
       "10145             easy    4.5           53                 Marin Headlands   \n",
       "\n",
       "      distance  duration_mins elevation_gain      route_type  \\\n",
       "0      21.9 km          449.0        1,331 m      Out & Back   \n",
       "1       1.1 km           19.0           16 m      Out & Back   \n",
       "2       1.9 km           33.0           34 m      Out & Back   \n",
       "3      17.5 km          366.0          772 m      Out & Back   \n",
       "4       7.9 km          141.0          173 m            Loop   \n",
       "10141   2.4 mi           59.0         118 ft            Loop   \n",
       "10142  16.0 mi          565.0       4,819 ft      Out & Back   \n",
       "10143  13.0 mi          495.0       5,055 ft      Out & Back   \n",
       "10144  21.8 mi          706.0       5,285 ft  Point to Point   \n",
       "10145   0.6 mi           13.0         108 ft      Out & Back   \n",
       "\n",
       "                                          trail_tags_str  \n",
       "0      DogFriendly Backpacking Camping Hiking Forest ...  \n",
       "1      KidFriendly Hiking Walking BirdWatching Beach ...  \n",
       "2      KidFriendly Hiking NatureTrips Walking Views W...  \n",
       "3      DogFriendly Backpacking Hiking HorsebackRiding...  \n",
       "4      Hiking MountainBiking NatureTrips Running Lake...  \n",
       "10141  DogFriendly WheelchairFriendly KidFriendly Str...  \n",
       "10142  Backpacking Camping Hiking NatureTrips Forest ...  \n",
       "10143  Backpacking Camping Hiking NatureTrips Lake Vi...  \n",
       "10144  Backpacking Camping Hiking NatureTrips BirdWat...  \n",
       "10145  KidFriendly Fishing Hiking NatureTrips BirdWat...  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the first 5 rows and the last 5 rows of the DataFrame\n",
    "state_trails_df.head().append(state_trails_df.tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examine Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city               3\n",
       "duration_mins    409\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the missing values in each column of a DataFrame - sum() works because True is 1 and False is 0\n",
    "null_counts = state_trails_df.isnull().sum()\n",
    "null_counts[null_counts > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a vertical bar plot by number of missing values per column using matplotlib\n",
    "# Note: figsize: a tuple (width, height) in inches\n",
    "state_trails_df.isnull().sum().plot.bar(figsize=(10, 8));\n",
    "plt.title('Number of null values per column');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examine the Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California               10137\n",
       "                             7\n",
       "Oregon                       1\n",
       "United States of Amer        1\n",
       "Name: state, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_trails_df['state'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Categorical column frequency\n",
    "# Returns counts of unique values in descending order (first element is the most frequently-occurring element)\n",
    "# Note: Excludes NA values by default\n",
    "def value_counts(df, var, bar):\n",
    "    value_counts = df[var].value_counts(dropna=False)\n",
    "    df_ = value_counts.rename_axis(var).reset_index(name='counts')\n",
    "    df_['percent'] = round(df_.counts / sum(df_.counts), 3)\n",
    "    \n",
    "    if bar:\n",
    "        # Create a vertical bar plot\n",
    "        # Note: figsize: a tuple (width, height) in inches\n",
    "        #       rot: rotation for ticks (xticks for vertical, yticks for horizontal plots)\n",
    "        ax = df[var].value_counts(normalize=True, dropna=False).plot(kind='bar', figsize=(10, 6), rot=0);\n",
    "        ax.set_ylabel('Percent');\n",
    "    \n",
    "    return df_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>state</th>\n",
       "      <th>counts</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>California</td>\n",
       "      <td>10137</td>\n",
       "      <td>0.999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>7</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>United States of Amer</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   state  counts  percent\n",
       "0             California   10137    0.999\n",
       "1                              7    0.001\n",
       "2                 Oregon       1      0.0\n",
       "3  United States of Amer       1      0.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Categorical column frequency\n",
    "# Returns counts of unique values in descending order (first element is the most frequently-occurring element)\n",
    "# Note: Excludes NA values by default\n",
    "value_counts(state_trails_df, 'state', bar=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>state</th>\n",
       "      <th>city</th>\n",
       "      <th>trail_name</th>\n",
       "      <th>trail_id</th>\n",
       "      <th>trail_difficulty</th>\n",
       "      <th>stars</th>\n",
       "      <th>num_reviews</th>\n",
       "      <th>trail_region</th>\n",
       "      <th>distance</th>\n",
       "      <th>duration_mins</th>\n",
       "      <th>elevation_gain</th>\n",
       "      <th>route_type</th>\n",
       "      <th>trail_tags_str</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>761</td>\n",
       "      <td></td>\n",
       "      <td>Santa Rosa</td>\n",
       "      <td>Creekside Trail to Big Leaf Trail Loop</td>\n",
       "      <td>10018817</td>\n",
       "      <td>moderate</td>\n",
       "      <td>4.5</td>\n",
       "      <td>220</td>\n",
       "      <td>Shiloh Ranch Regional Park</td>\n",
       "      <td>3.8 mi</td>\n",
       "      <td>117.0</td>\n",
       "      <td>616 ft</td>\n",
       "      <td>Loop</td>\n",
       "      <td>KidFriendly Hiking NatureTrips Walking BirdWat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1203</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Ashland</td>\n",
       "      <td>Pilot Rock Trail</td>\n",
       "      <td>10027241</td>\n",
       "      <td>hard</td>\n",
       "      <td>4.5</td>\n",
       "      <td>187</td>\n",
       "      <td>Soda Mountain Wilderness</td>\n",
       "      <td>2.8 mi</td>\n",
       "      <td>101.0</td>\n",
       "      <td>810 ft</td>\n",
       "      <td>Out &amp; Back</td>\n",
       "      <td>DogsOnLeash Hiking NatureTrips RockClimbing Bi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2234</td>\n",
       "      <td></td>\n",
       "      <td>Twentynine Palms</td>\n",
       "      <td>Wall Street Mill Trail</td>\n",
       "      <td>10036072</td>\n",
       "      <td>easy</td>\n",
       "      <td>4.0</td>\n",
       "      <td>196</td>\n",
       "      <td>Joshua Tree National Park</td>\n",
       "      <td>2.4 mi</td>\n",
       "      <td>62.0</td>\n",
       "      <td>88 ft</td>\n",
       "      <td>Out &amp; Back</td>\n",
       "      <td>KidFriendly Hiking NatureTrips Walking BirdWat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6207</td>\n",
       "      <td></td>\n",
       "      <td>Tehachapi</td>\n",
       "      <td>TMTA Lehigh Trail Loop</td>\n",
       "      <td>10302436</td>\n",
       "      <td>moderate</td>\n",
       "      <td>4.5</td>\n",
       "      <td>28</td>\n",
       "      <td>Tehachapi, California</td>\n",
       "      <td>10.7 mi</td>\n",
       "      <td>310.0</td>\n",
       "      <td>1,272 ft</td>\n",
       "      <td>Loop</td>\n",
       "      <td>DogsOnLeash KidFriendly Hiking MountainBiking ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6572</td>\n",
       "      <td></td>\n",
       "      <td>Stonyford</td>\n",
       "      <td>Snow Mountain Wilderness Loop</td>\n",
       "      <td>10309312</td>\n",
       "      <td>hard</td>\n",
       "      <td>4.5</td>\n",
       "      <td>7</td>\n",
       "      <td>Snow Mountain Wilderness</td>\n",
       "      <td>38.6 mi</td>\n",
       "      <td>1,261.0</td>\n",
       "      <td>8,090 ft</td>\n",
       "      <td>Loop</td>\n",
       "      <td>Backpacking Camping Fishing Hiking NatureTrips...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6574</td>\n",
       "      <td></td>\n",
       "      <td>Cedarpines Park</td>\n",
       "      <td>Camp Seely to Cedarpines Park Loop</td>\n",
       "      <td>10309399</td>\n",
       "      <td>moderate</td>\n",
       "      <td>4.0</td>\n",
       "      <td>17</td>\n",
       "      <td>San Bernardino National Forest</td>\n",
       "      <td>2.8 mi</td>\n",
       "      <td>68.0</td>\n",
       "      <td>849 ft</td>\n",
       "      <td>Loop</td>\n",
       "      <td>Hiking NatureTrips Walking BirdWatching Forest...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6932</td>\n",
       "      <td></td>\n",
       "      <td>Ojai</td>\n",
       "      <td>Ojai to Ventura Bike Path via Downtown Ojai</td>\n",
       "      <td>10318649</td>\n",
       "      <td>moderate</td>\n",
       "      <td>4.5</td>\n",
       "      <td>58</td>\n",
       "      <td>Ojai, California</td>\n",
       "      <td>14.9 mi</td>\n",
       "      <td>365.0</td>\n",
       "      <td>134 ft</td>\n",
       "      <td>Point to Point</td>\n",
       "      <td>DogsOnLeash KidFriendly Hiking RoadBiking Runn...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6939</td>\n",
       "      <td></td>\n",
       "      <td>Sunnyvale</td>\n",
       "      <td>Baylands Bike Path Loop</td>\n",
       "      <td>10318863</td>\n",
       "      <td>easy</td>\n",
       "      <td>4.5</td>\n",
       "      <td>75</td>\n",
       "      <td>Sunnyvale Baylands Park</td>\n",
       "      <td>1.2 mi</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6 ft</td>\n",
       "      <td>Loop</td>\n",
       "      <td>KidFriendly NatureTrips Walking BirdWatching R...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9893</td>\n",
       "      <td>United States of Amer</td>\n",
       "      <td>Calexico</td>\n",
       "      <td>Pista el Alacrán</td>\n",
       "      <td>10652811</td>\n",
       "      <td>easy</td>\n",
       "      <td>4.5</td>\n",
       "      <td>8</td>\n",
       "      <td>Calexico, California</td>\n",
       "      <td>2.8 mi</td>\n",
       "      <td>nan</td>\n",
       "      <td>164 ft</td>\n",
       "      <td>Loop</td>\n",
       "      <td>MountainBiking</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      state              city  \\\n",
       "761                                Santa Rosa   \n",
       "1203                 Oregon           Ashland   \n",
       "2234                         Twentynine Palms   \n",
       "6207                                Tehachapi   \n",
       "6572                                Stonyford   \n",
       "6574                          Cedarpines Park   \n",
       "6932                                     Ojai   \n",
       "6939                                Sunnyvale   \n",
       "9893  United States of Amer          Calexico   \n",
       "\n",
       "                                       trail_name  trail_id trail_difficulty  \\\n",
       "761        Creekside Trail to Big Leaf Trail Loop  10018817         moderate   \n",
       "1203                             Pilot Rock Trail  10027241             hard   \n",
       "2234                       Wall Street Mill Trail  10036072             easy   \n",
       "6207                       TMTA Lehigh Trail Loop  10302436         moderate   \n",
       "6572                Snow Mountain Wilderness Loop  10309312             hard   \n",
       "6574           Camp Seely to Cedarpines Park Loop  10309399         moderate   \n",
       "6932  Ojai to Ventura Bike Path via Downtown Ojai  10318649         moderate   \n",
       "6939                      Baylands Bike Path Loop  10318863             easy   \n",
       "9893                             Pista el Alacrán  10652811             easy   \n",
       "\n",
       "      stars  num_reviews                    trail_region distance  \\\n",
       "761     4.5          220      Shiloh Ranch Regional Park   3.8 mi   \n",
       "1203    4.5          187        Soda Mountain Wilderness   2.8 mi   \n",
       "2234    4.0          196       Joshua Tree National Park   2.4 mi   \n",
       "6207    4.5           28           Tehachapi, California  10.7 mi   \n",
       "6572    4.5            7        Snow Mountain Wilderness  38.6 mi   \n",
       "6574    4.0           17  San Bernardino National Forest   2.8 mi   \n",
       "6932    4.5           58                Ojai, California  14.9 mi   \n",
       "6939    4.5           75         Sunnyvale Baylands Park   1.2 mi   \n",
       "9893    4.5            8            Calexico, California   2.8 mi   \n",
       "\n",
       "      duration_mins elevation_gain      route_type  \\\n",
       "761           117.0         616 ft            Loop   \n",
       "1203          101.0         810 ft      Out & Back   \n",
       "2234           62.0          88 ft      Out & Back   \n",
       "6207          310.0       1,272 ft            Loop   \n",
       "6572        1,261.0       8,090 ft            Loop   \n",
       "6574           68.0         849 ft            Loop   \n",
       "6932          365.0         134 ft  Point to Point   \n",
       "6939           29.0           6 ft            Loop   \n",
       "9893            nan         164 ft            Loop   \n",
       "\n",
       "                                         trail_tags_str  \n",
       "761   KidFriendly Hiking NatureTrips Walking BirdWat...  \n",
       "1203  DogsOnLeash Hiking NatureTrips RockClimbing Bi...  \n",
       "2234  KidFriendly Hiking NatureTrips Walking BirdWat...  \n",
       "6207  DogsOnLeash KidFriendly Hiking MountainBiking ...  \n",
       "6572  Backpacking Camping Fishing Hiking NatureTrips...  \n",
       "6574  Hiking NatureTrips Walking BirdWatching Forest...  \n",
       "6932  DogsOnLeash KidFriendly Hiking RoadBiking Runn...  \n",
       "6939  KidFriendly NatureTrips Walking BirdWatching R...  \n",
       "9893                                     MountainBiking  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_trails_df[state_trails_df.state != 'California']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Drop non-California observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Return elements, either from `x` or `y`, depending on `condition`\n",
    "state_trails_df['non_california'] = np.where(state_trails_df.state.isin(['California', '']), 0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10144\n",
       "1        2\n",
       "Name: non_california, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Categorical column frequency\n",
    "# Returns counts of unique values in descending order (first element is the most frequently-occurring element)\n",
    "# Note: Excludes NA values by default\n",
    "state_trails_df.non_california.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Excluding non-california values\n",
    "state_trails_df = state_trails_df[state_trails_df.non_california == 0]\n",
    "\n",
    "# Remove columns by specifying directly column names\n",
    "# Note: inplace=True changes the original DataFrame\n",
    "state_trails_df.drop(columns=['non_california'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assign `state` to 'California' for the following `trail_id`s that were confirmed to be California trails"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "761     10018817\n",
       "2234    10036072\n",
       "6207    10302436\n",
       "6572    10309312\n",
       "6574    10309399\n",
       "6932    10318649\n",
       "6939    10318863\n",
       "Name: trail_id, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_trails_df['trail_id'][state_trails_df.state != 'California']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10018817, 10036072, 10302436, 10309312, 10309399, 10318649, 10318863]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = state_trails_df['trail_id'][state_trails_df.state != 'California'].values.tolist()\n",
    "values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Return elements, either from `x` or `y`, depending on `condition`\n",
    "state_trails_df['state'] = np.where(state_trails_df.trail_id.isin(values), 'California', state_trails_df.state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    10144\n",
       "Name: state, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Categorical column frequency\n",
    "# Returns counts of unique values in descending order (first element is the most frequently-occurring element)\n",
    "# Note: Excludes NA values by default\n",
    "state_trails_df.state.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows: 10144, Cols: 13\n"
     ]
    }
   ],
   "source": [
    "# Return the number of rows and columns (dimensionality) of the DataFrame\n",
    "print('Rows: {}, Cols: {}'.format(state_trails_df.shape[0], state_trails_df.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### City"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>city</th>\n",
       "      <th>counts</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Yosemite Valley</td>\n",
       "      <td>213</td>\n",
       "      <td>0.021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>134</td>\n",
       "      <td>0.013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>131</td>\n",
       "      <td>0.013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Oakland</td>\n",
       "      <td>125</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Mill Valley</td>\n",
       "      <td>110</td>\n",
       "      <td>0.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>844</td>\n",
       "      <td>Goodyears Bar</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>845</td>\n",
       "      <td>Ione</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>846</td>\n",
       "      <td>Daggett</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>847</td>\n",
       "      <td>Mather</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>848</td>\n",
       "      <td>Pinole</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>849 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                city  counts  percent\n",
       "0    Yosemite Valley     213    0.021\n",
       "1        Los Angeles     134    0.013\n",
       "2          San Diego     131    0.013\n",
       "3            Oakland     125    0.012\n",
       "4        Mill Valley     110    0.011\n",
       "..               ...     ...      ...\n",
       "844    Goodyears Bar       1      0.0\n",
       "845             Ione       1      0.0\n",
       "846          Daggett       1      0.0\n",
       "847           Mather       1      0.0\n",
       "848           Pinole       1      0.0\n",
       "\n",
       "[849 rows x 3 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Categorical column frequency\n",
    "# Returns counts of unique values in descending order (first element is the most frequently-occurring element)\n",
    "# Note: Excludes NA values by default\n",
    "value_counts(state_trails_df, 'city', bar=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>state</th>\n",
       "      <th>city</th>\n",
       "      <th>trail_name</th>\n",
       "      <th>trail_id</th>\n",
       "      <th>trail_difficulty</th>\n",
       "      <th>stars</th>\n",
       "      <th>num_reviews</th>\n",
       "      <th>trail_region</th>\n",
       "      <th>distance</th>\n",
       "      <th>duration_mins</th>\n",
       "      <th>elevation_gain</th>\n",
       "      <th>route_type</th>\n",
       "      <th>trail_tags_str</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>9040</td>\n",
       "      <td>California</td>\n",
       "      <td>None</td>\n",
       "      <td>La Jolla Heights Preserve Walk</td>\n",
       "      <td>10541176</td>\n",
       "      <td>moderate</td>\n",
       "      <td>4.5</td>\n",
       "      <td>48</td>\n",
       "      <td>La Jolla Natural Park</td>\n",
       "      <td>1.2 mi</td>\n",
       "      <td>43.0</td>\n",
       "      <td>344 ft</td>\n",
       "      <td>Out &amp; Back</td>\n",
       "      <td>Hiking NatureTrips Walking Views WildFlowers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9314</td>\n",
       "      <td>California</td>\n",
       "      <td>None</td>\n",
       "      <td>Plantation Trail to Grand View Point</td>\n",
       "      <td>10577579</td>\n",
       "      <td>moderate</td>\n",
       "      <td>4.5</td>\n",
       "      <td>8</td>\n",
       "      <td>San Bernardino National Forest</td>\n",
       "      <td>7.7 mi</td>\n",
       "      <td>227.0</td>\n",
       "      <td>1,010 ft</td>\n",
       "      <td>Out &amp; Back</td>\n",
       "      <td>Hiking Running Forest Views Wildlife</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9372</td>\n",
       "      <td>California</td>\n",
       "      <td>None</td>\n",
       "      <td>Carrington Point Trail</td>\n",
       "      <td>10581562</td>\n",
       "      <td>moderate</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2</td>\n",
       "      <td>Channel Islands National Park</td>\n",
       "      <td>8.8 mi</td>\n",
       "      <td>253.0</td>\n",
       "      <td>987 ft</td>\n",
       "      <td>Out &amp; Back</td>\n",
       "      <td>Hiking NatureTrips BirdWatching Running Beach ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           state  city                            trail_name  trail_id  \\\n",
       "9040  California  None        La Jolla Heights Preserve Walk  10541176   \n",
       "9314  California  None  Plantation Trail to Grand View Point  10577579   \n",
       "9372  California  None                Carrington Point Trail  10581562   \n",
       "\n",
       "     trail_difficulty  stars  num_reviews                    trail_region  \\\n",
       "9040         moderate    4.5           48           La Jolla Natural Park   \n",
       "9314         moderate    4.5            8  San Bernardino National Forest   \n",
       "9372         moderate    4.5            2   Channel Islands National Park   \n",
       "\n",
       "     distance  duration_mins elevation_gain  route_type  \\\n",
       "9040   1.2 mi           43.0         344 ft  Out & Back   \n",
       "9314   7.7 mi          227.0       1,010 ft  Out & Back   \n",
       "9372   8.8 mi          253.0         987 ft  Out & Back   \n",
       "\n",
       "                                         trail_tags_str  \n",
       "9040       Hiking NatureTrips Walking Views WildFlowers  \n",
       "9314               Hiking Running Forest Views Wildlife  \n",
       "9372  Hiking NatureTrips BirdWatching Running Beach ...  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Detect missing values\n",
    "state_trails_df[state_trails_df.city.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note: The city names were found for the following trails after searching the internet for the trail name\n",
    "state_trails_df['city'] = np.where(state_trails_df.trail_id == 10541176, 'La Jolla', state_trails_df.city)\n",
    "state_trails_df['city'] = np.where(state_trails_df.trail_id == 10577579, 'Big Bear Lake', state_trails_df.city)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Trail Difficulty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Map values of Series using input correspondence (i.e., map existing input values to new outputs values)\n",
    "state_trails_df['trail_difficulty'] = state_trails_df.trail_difficulty.map({'easy':'Easy', 'moderate':'Moderate', 'hard':'Hard'})\n",
    "state_trails_df['trail_difficulty_ord'] = state_trails_df.trail_difficulty.map({'Easy':0, 'Moderate':1, 'Hard':2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_b23b56e0_0f7f_11eb_9680_acde48001122row0_col0 {\n",
       "            background-color:  #ffa500;\n",
       "            color:  #000000;\n",
       "        }    #T_b23b56e0_0f7f_11eb_9680_acde48001122row0_col1 {\n",
       "            background-color:  #fff6e5;\n",
       "            color:  #000000;\n",
       "        }    #T_b23b56e0_0f7f_11eb_9680_acde48001122row0_col2 {\n",
       "            background-color:  #fff6e5;\n",
       "            color:  #000000;\n",
       "        }    #T_b23b56e0_0f7f_11eb_9680_acde48001122row0_col3 {\n",
       "            background-color:  #ffebc5;\n",
       "            color:  #000000;\n",
       "        }    #T_b23b56e0_0f7f_11eb_9680_acde48001122row1_col0 {\n",
       "            background-color:  #fff6e5;\n",
       "            color:  #000000;\n",
       "        }    #T_b23b56e0_0f7f_11eb_9680_acde48001122row1_col1 {\n",
       "            background-color:  #fff6e5;\n",
       "            color:  #000000;\n",
       "        }    #T_b23b56e0_0f7f_11eb_9680_acde48001122row1_col2 {\n",
       "            background-color:  #ffa500;\n",
       "            color:  #000000;\n",
       "        }    #T_b23b56e0_0f7f_11eb_9680_acde48001122row1_col3 {\n",
       "            background-color:  #fff6e5;\n",
       "            color:  #000000;\n",
       "        }    #T_b23b56e0_0f7f_11eb_9680_acde48001122row2_col0 {\n",
       "            background-color:  #fff6e5;\n",
       "            color:  #000000;\n",
       "        }    #T_b23b56e0_0f7f_11eb_9680_acde48001122row2_col1 {\n",
       "            background-color:  #ffa500;\n",
       "            color:  #000000;\n",
       "        }    #T_b23b56e0_0f7f_11eb_9680_acde48001122row2_col2 {\n",
       "            background-color:  #fff6e5;\n",
       "            color:  #000000;\n",
       "        }    #T_b23b56e0_0f7f_11eb_9680_acde48001122row2_col3 {\n",
       "            background-color:  #ffd78c;\n",
       "            color:  #000000;\n",
       "        }    #T_b23b56e0_0f7f_11eb_9680_acde48001122row3_col0 {\n",
       "            background-color:  #ffa500;\n",
       "            color:  #000000;\n",
       "        }    #T_b23b56e0_0f7f_11eb_9680_acde48001122row3_col1 {\n",
       "            background-color:  #ffa500;\n",
       "            color:  #000000;\n",
       "        }    #T_b23b56e0_0f7f_11eb_9680_acde48001122row3_col2 {\n",
       "            background-color:  #ffa500;\n",
       "            color:  #000000;\n",
       "        }    #T_b23b56e0_0f7f_11eb_9680_acde48001122row3_col3 {\n",
       "            background-color:  #ffa500;\n",
       "            color:  #000000;\n",
       "        }</style><table id=\"T_b23b56e0_0f7f_11eb_9680_acde48001122\" ><thead>    <tr>        <th class=\"index_name level0\" >trail_difficulty_ord</th>        <th class=\"col_heading level0 col0\" >0</th>        <th class=\"col_heading level0 col1\" >1</th>        <th class=\"col_heading level0 col2\" >2</th>        <th class=\"col_heading level0 col3\" >Total</th>    </tr>    <tr>        <th class=\"index_name level0\" >trail_difficulty</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_b23b56e0_0f7f_11eb_9680_acde48001122level0_row0\" class=\"row_heading level0 row0\" >Easy</th>\n",
       "                        <td id=\"T_b23b56e0_0f7f_11eb_9680_acde48001122row0_col0\" class=\"data row0 col0\" >3107</td>\n",
       "                        <td id=\"T_b23b56e0_0f7f_11eb_9680_acde48001122row0_col1\" class=\"data row0 col1\" >0</td>\n",
       "                        <td id=\"T_b23b56e0_0f7f_11eb_9680_acde48001122row0_col2\" class=\"data row0 col2\" >0</td>\n",
       "                        <td id=\"T_b23b56e0_0f7f_11eb_9680_acde48001122row0_col3\" class=\"data row0 col3\" >3107</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b23b56e0_0f7f_11eb_9680_acde48001122level0_row1\" class=\"row_heading level0 row1\" >Hard</th>\n",
       "                        <td id=\"T_b23b56e0_0f7f_11eb_9680_acde48001122row1_col0\" class=\"data row1 col0\" >0</td>\n",
       "                        <td id=\"T_b23b56e0_0f7f_11eb_9680_acde48001122row1_col1\" class=\"data row1 col1\" >0</td>\n",
       "                        <td id=\"T_b23b56e0_0f7f_11eb_9680_acde48001122row1_col2\" class=\"data row1 col2\" >1927</td>\n",
       "                        <td id=\"T_b23b56e0_0f7f_11eb_9680_acde48001122row1_col3\" class=\"data row1 col3\" >1927</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b23b56e0_0f7f_11eb_9680_acde48001122level0_row2\" class=\"row_heading level0 row2\" >Moderate</th>\n",
       "                        <td id=\"T_b23b56e0_0f7f_11eb_9680_acde48001122row2_col0\" class=\"data row2 col0\" >0</td>\n",
       "                        <td id=\"T_b23b56e0_0f7f_11eb_9680_acde48001122row2_col1\" class=\"data row2 col1\" >5110</td>\n",
       "                        <td id=\"T_b23b56e0_0f7f_11eb_9680_acde48001122row2_col2\" class=\"data row2 col2\" >0</td>\n",
       "                        <td id=\"T_b23b56e0_0f7f_11eb_9680_acde48001122row2_col3\" class=\"data row2 col3\" >5110</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b23b56e0_0f7f_11eb_9680_acde48001122level0_row3\" class=\"row_heading level0 row3\" >Total</th>\n",
       "                        <td id=\"T_b23b56e0_0f7f_11eb_9680_acde48001122row3_col0\" class=\"data row3 col0\" >3107</td>\n",
       "                        <td id=\"T_b23b56e0_0f7f_11eb_9680_acde48001122row3_col1\" class=\"data row3 col1\" >5110</td>\n",
       "                        <td id=\"T_b23b56e0_0f7f_11eb_9680_acde48001122row3_col2\" class=\"data row3 col2\" >1927</td>\n",
       "                        <td id=\"T_b23b56e0_0f7f_11eb_9680_acde48001122row3_col3\" class=\"data row3 col3\" >10144</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fefe67d5550>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute a frequency table of two factors\n",
    "# Builtin-Styles - https://pandas.pydata.org/pandas-docs/stable/style.html\n",
    "orange = sns.light_palette('orange', as_cmap=True)\n",
    "pd.crosstab(state_trails_df.trail_difficulty.fillna(' NaN'), state_trails_df.trail_difficulty_ord.fillna(' NaN'), margins=True, margins_name='Total', dropna=False).style.background_gradient(cmap=orange)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>trail_difficulty</th>\n",
       "      <th>counts</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>5110</td>\n",
       "      <td>0.504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Easy</td>\n",
       "      <td>3107</td>\n",
       "      <td>0.306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Hard</td>\n",
       "      <td>1927</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  trail_difficulty  counts  percent\n",
       "0         Moderate    5110    0.504\n",
       "1             Easy    3107    0.306\n",
       "2             Hard    1927     0.19"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Categorical column frequency\n",
    "# Returns counts of unique values in descending order (first element is the most frequently-occurring element)\n",
    "# Note: Excludes NA values by default\n",
    "value_counts(state_trails_df, 'trail_difficulty', bar=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>stars</th>\n",
       "      <th>counts</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5580</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2744</td>\n",
       "      <td>0.271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>683</td>\n",
       "      <td>0.067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>655</td>\n",
       "      <td>0.065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>228</td>\n",
       "      <td>0.022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>102</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>74</td>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>43</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   stars  counts  percent\n",
       "0    4.5    5580     0.55\n",
       "1    4.0    2744    0.271\n",
       "2    5.0     683    0.067\n",
       "3    3.5     655    0.065\n",
       "4    3.0     228    0.022\n",
       "5    0.0     102     0.01\n",
       "6    2.5      74    0.007\n",
       "7    2.0      43    0.004\n",
       "8    1.0      25    0.002\n",
       "9    1.5      10    0.001"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Categorical column frequency\n",
    "# Returns counts of unique values in descending order (first element is the most frequently-occurring element)\n",
    "# Note: Excludes NA values by default\n",
    "value_counts(state_trails_df, 'stars', bar=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Number of Reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>num_reviews</th>\n",
       "      <th>counts</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>252</td>\n",
       "      <td>0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>240</td>\n",
       "      <td>0.024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>239</td>\n",
       "      <td>0.024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>237</td>\n",
       "      <td>0.023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>228</td>\n",
       "      <td>0.022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>724</td>\n",
       "      <td>923</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>725</td>\n",
       "      <td>1051</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>726</td>\n",
       "      <td>1083</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>727</td>\n",
       "      <td>1163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>728</td>\n",
       "      <td>1967</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>729 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     num_reviews  counts  percent\n",
       "0              5     252    0.025\n",
       "1              3     240    0.024\n",
       "2              1     239    0.024\n",
       "3              2     237    0.023\n",
       "4              6     228    0.022\n",
       "..           ...     ...      ...\n",
       "724          923       1      0.0\n",
       "725         1051       1      0.0\n",
       "726         1083       1      0.0\n",
       "727         1163       1      0.0\n",
       "728         1967       1      0.0\n",
       "\n",
       "[729 rows x 3 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Categorical column frequency\n",
    "# Returns counts of unique values in descending order (first element is the most frequently-occurring element)\n",
    "# Note: Excludes NA values by default\n",
    "value_counts(state_trails_df, 'num_reviews', bar=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>trail_region</th>\n",
       "      <th>counts</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Angeles National Forest</td>\n",
       "      <td>255</td>\n",
       "      <td>0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Yosemite National Park</td>\n",
       "      <td>254</td>\n",
       "      <td>0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Tahoe National Forest</td>\n",
       "      <td>201</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>San Bernardino National Forest</td>\n",
       "      <td>198</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Los Padres National Forest</td>\n",
       "      <td>195</td>\n",
       "      <td>0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1890</td>\n",
       "      <td>Freedom Park</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1891</td>\n",
       "      <td>Santa Maria, California</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1892</td>\n",
       "      <td>Garcia Wilderness</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1893</td>\n",
       "      <td>Fullerton Arboretum</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1894</td>\n",
       "      <td>Mojave, California</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1895 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        trail_region  counts  percent\n",
       "0            Angeles National Forest     255    0.025\n",
       "1             Yosemite National Park     254    0.025\n",
       "2              Tahoe National Forest     201     0.02\n",
       "3     San Bernardino National Forest     198     0.02\n",
       "4         Los Padres National Forest     195    0.019\n",
       "...                              ...     ...      ...\n",
       "1890                    Freedom Park       1      0.0\n",
       "1891         Santa Maria, California       1      0.0\n",
       "1892               Garcia Wilderness       1      0.0\n",
       "1893             Fullerton Arboretum       1      0.0\n",
       "1894              Mojave, California       1      0.0\n",
       "\n",
       "[1895 rows x 3 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Categorical column frequency\n",
    "# Returns counts of unique values in descending order (first element is the most frequently-occurring element)\n",
    "# Note: Excludes NA values by default\n",
    "value_counts(state_trails_df, 'trail_region', bar=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>distance</th>\n",
       "      <th>counts</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.8 mi</td>\n",
       "      <td>145</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3.3 mi</td>\n",
       "      <td>130</td>\n",
       "      <td>0.013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4.1 mi</td>\n",
       "      <td>129</td>\n",
       "      <td>0.013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2.5 mi</td>\n",
       "      <td>127</td>\n",
       "      <td>0.013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2.1 mi</td>\n",
       "      <td>125</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>497</td>\n",
       "      <td>46.0 mi</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>498</td>\n",
       "      <td>63.3 mi</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>499</td>\n",
       "      <td>77.8 mi</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>76.9 mi</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>501</td>\n",
       "      <td>27.5 mi</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>502 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    distance  counts  percent\n",
       "0     2.8 mi     145    0.014\n",
       "1     3.3 mi     130    0.013\n",
       "2     4.1 mi     129    0.013\n",
       "3     2.5 mi     127    0.013\n",
       "4     2.1 mi     125    0.012\n",
       "..       ...     ...      ...\n",
       "497  46.0 mi       1      0.0\n",
       "498  63.3 mi       1      0.0\n",
       "499  77.8 mi       1      0.0\n",
       "500  76.9 mi       1      0.0\n",
       "501  27.5 mi       1      0.0\n",
       "\n",
       "[502 rows x 3 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Categorical column frequency\n",
    "# Returns counts of unique values in descending order (first element is the most frequently-occurring element)\n",
    "# Note: Excludes NA values by default\n",
    "value_counts(state_trails_df, 'distance', bar=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: Distance in kilometers is contained in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10120"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Distance containing 'mi' for miles\n",
    "state_trails_df[state_trails_df['distance'].str.contains('mi')].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Distance containing 'km' for kilometers\n",
    "state_trails_df[state_trails_df['distance'].str.contains('km')].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>trail_id</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10000006</td>\n",
       "      <td>21.9 km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>10000015</td>\n",
       "      <td>1.1 km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>10000044</td>\n",
       "      <td>1.9 km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>10000118</td>\n",
       "      <td>17.5 km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>10000133</td>\n",
       "      <td>7.9 km</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   trail_id distance\n",
       "0  10000006  21.9 km\n",
       "1  10000015   1.1 km\n",
       "2  10000044   1.9 km\n",
       "3  10000118  17.5 km\n",
       "4  10000133   7.9 km"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_trails_df[['trail_id', 'distance']][state_trails_df['distance'].str.contains('km')].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Return elements, either from `x` or `y`, depending on `condition`\n",
    "state_trails_df['distance_km_flag'] = np.where(state_trails_df['distance'].str.contains('km'), 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10120\n",
       "1       24\n",
       "Name: distance_km_flag, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Categorical column frequency\n",
    "# Returns counts of unique values in descending order (first element is the most frequently-occurring element)\n",
    "# Note: Excludes NA values by default\n",
    "state_trails_df.distance_km_flag.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Converting `distance` to numeric measurement as `distance_numeric`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_trails_df['distance_numeric'] = state_trails_df.distance.str.replace(',', '').str.extract('(\\d*\\.\\d+|\\d+)', expand=False).astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Converting `distance_numeric` to numeric measurement as `distance_miles`\n",
    "Note: Records flagged as distance in kilometers will be converted to miles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Return elements, either from `x` or `y`, depending on `condition`\n",
    "# Kilometer to mile conversion factor\n",
    "conv_fac = 0.621371\n",
    "state_trails_df['distance_miles'] = np.where(state_trails_df.distance_km_flag == 1, round(state_trails_df.distance_numeric * conv_fac, 1), state_trails_df.distance_numeric)\n",
    "\n",
    "# Remove columns by specifying directly column names\n",
    "# Note: inplace=True changes the original DataFrame\n",
    "state_trails_df.drop(columns=['distance', 'distance_km_flag', 'distance_numeric'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>distance_miles</th>\n",
       "      <th>counts</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>145</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>130</td>\n",
       "      <td>0.013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>129</td>\n",
       "      <td>0.013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>129</td>\n",
       "      <td>0.013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>125</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>475</td>\n",
       "      <td>33.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>476</td>\n",
       "      <td>64.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>477</td>\n",
       "      <td>35.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>478</td>\n",
       "      <td>48.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>479</td>\n",
       "      <td>52.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>480 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     distance_miles  counts  percent\n",
       "0               2.8     145    0.014\n",
       "1               3.3     130    0.013\n",
       "2               4.1     129    0.013\n",
       "3               2.5     129    0.013\n",
       "4               2.1     125    0.012\n",
       "..              ...     ...      ...\n",
       "475            33.5       1      0.0\n",
       "476            64.5       1      0.0\n",
       "477            35.2       1      0.0\n",
       "478            48.5       1      0.0\n",
       "479            52.4       1      0.0\n",
       "\n",
       "[480 rows x 3 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Categorical column frequency\n",
    "# Returns counts of unique values in descending order (first element is the most frequently-occurring element)\n",
    "# Note: Excludes NA values by default\n",
    "value_counts(state_trails_df, 'distance_miles', bar=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Elevation Gain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>elevation_gain</th>\n",
       "      <th>counts</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>26 ft</td>\n",
       "      <td>48</td>\n",
       "      <td>0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>13 ft</td>\n",
       "      <td>48</td>\n",
       "      <td>0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>6 ft</td>\n",
       "      <td>48</td>\n",
       "      <td>0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>16 ft</td>\n",
       "      <td>44</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>9 ft</td>\n",
       "      <td>44</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1742</td>\n",
       "      <td>68,221 ft</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1743</td>\n",
       "      <td>6,105 ft</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1744</td>\n",
       "      <td>4,603 ft</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1745</td>\n",
       "      <td>7,723 ft</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1746</td>\n",
       "      <td>4,393 ft</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1747 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     elevation_gain  counts  percent\n",
       "0             26 ft      48    0.005\n",
       "1             13 ft      48    0.005\n",
       "2              6 ft      48    0.005\n",
       "3             16 ft      44    0.004\n",
       "4              9 ft      44    0.004\n",
       "...             ...     ...      ...\n",
       "1742      68,221 ft       1      0.0\n",
       "1743       6,105 ft       1      0.0\n",
       "1744       4,603 ft       1      0.0\n",
       "1745       7,723 ft       1      0.0\n",
       "1746       4,393 ft       1      0.0\n",
       "\n",
       "[1747 rows x 3 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Categorical column frequency\n",
    "# Returns counts of unique values in descending order (first element is the most frequently-occurring element)\n",
    "# Note: Excludes NA values by default\n",
    "value_counts(state_trails_df, 'elevation_gain', bar=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: Elevation Gain in meters is contained in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10120"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Elevation gain containing 'ft' for feet\n",
    "state_trails_df[state_trails_df['elevation_gain'].str.contains('ft')].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Elevation gain containing 'm' for meters\n",
    "state_trails_df[state_trails_df['elevation_gain'].str.contains('m')].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>trail_id</th>\n",
       "      <th>elevation_gain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10000006</td>\n",
       "      <td>1,331 m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>10000015</td>\n",
       "      <td>16 m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>10000044</td>\n",
       "      <td>34 m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>10000118</td>\n",
       "      <td>772 m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>10000133</td>\n",
       "      <td>173 m</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   trail_id elevation_gain\n",
       "0  10000006        1,331 m\n",
       "1  10000015           16 m\n",
       "2  10000044           34 m\n",
       "3  10000118          772 m\n",
       "4  10000133          173 m"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_trails_df[['trail_id', 'elevation_gain']][state_trails_df['elevation_gain'].str.contains('m')].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Return elements, either from `x` or `y`, depending on `condition`\n",
    "state_trails_df['elevation_gain_m_flag'] = np.where(state_trails_df['elevation_gain'].str.contains('m'), 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10120\n",
       "1       24\n",
       "Name: elevation_gain_m_flag, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Categorical column frequency\n",
    "# Returns counts of unique values in descending order (first element is the most frequently-occurring element)\n",
    "# Note: Excludes NA values by default\n",
    "state_trails_df.elevation_gain_m_flag.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Converting `elevation_gain` to numeric measurement as `elevation_gain_numeric`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_trails_df['elevation_gain_numeric'] = state_trails_df.elevation_gain.str.replace(',', '').str.extract('(\\d*\\.\\d+|\\d+)', expand=False).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Converting `elevation_gain_numeric` to numeric measurement as `elevation_gain_ft`\n",
    "Note: Records flagged as elevation gain in meters will be converted to feet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Return elements, either from `x` or `y`, depending on `condition`\n",
    "# Meter to feet conversion factor\n",
    "conv_fac = 0.3048\n",
    "state_trails_df['elevation_gain_ft'] = np.where(state_trails_df.elevation_gain_m_flag == 1, round(state_trails_df.elevation_gain_numeric / conv_fac).astype(int), state_trails_df.elevation_gain_numeric)\n",
    "\n",
    "# Remove columns by specifying directly column names\n",
    "# Note: inplace=True changes the original DataFrame\n",
    "state_trails_df.drop(columns=['elevation_gain', 'elevation_gain_m_flag', 'elevation_gain_numeric'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>elevation_gain_ft</th>\n",
       "      <th>counts</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>48</td>\n",
       "      <td>0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>48</td>\n",
       "      <td>0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>44</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>44</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1731</td>\n",
       "      <td>8057</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1732</td>\n",
       "      <td>13136</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1733</td>\n",
       "      <td>8041</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1734</td>\n",
       "      <td>10062</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1735</td>\n",
       "      <td>6105</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1736 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      elevation_gain_ft  counts  percent\n",
       "0                    26      48    0.005\n",
       "1                    13      48    0.005\n",
       "2                     6      48    0.005\n",
       "3                     3      44    0.004\n",
       "4                    16      44    0.004\n",
       "...                 ...     ...      ...\n",
       "1731               8057       1      0.0\n",
       "1732              13136       1      0.0\n",
       "1733               8041       1      0.0\n",
       "1734              10062       1      0.0\n",
       "1735               6105       1      0.0\n",
       "\n",
       "[1736 rows x 3 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Categorical column frequency\n",
    "# Returns counts of unique values in descending order (first element is the most frequently-occurring element)\n",
    "# Note: Excludes NA values by default\n",
    "value_counts(state_trails_df, 'elevation_gain_ft', bar=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Duration Minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>duration_mins</th>\n",
       "      <th>counts</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>nan</td>\n",
       "      <td>408</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>88.0</td>\n",
       "      <td>54</td>\n",
       "      <td>0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>93.0</td>\n",
       "      <td>53</td>\n",
       "      <td>0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>68.0</td>\n",
       "      <td>52</td>\n",
       "      <td>0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>75.0</td>\n",
       "      <td>52</td>\n",
       "      <td>0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>996</td>\n",
       "      <td>547.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>997</td>\n",
       "      <td>2,529.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>998</td>\n",
       "      <td>562.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>999</td>\n",
       "      <td>1,005.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>9,855.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1001 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      duration_mins  counts  percent\n",
       "0               nan     408     0.04\n",
       "1              88.0      54    0.005\n",
       "2              93.0      53    0.005\n",
       "3              68.0      52    0.005\n",
       "4              75.0      52    0.005\n",
       "...             ...     ...      ...\n",
       "996           547.0       1      0.0\n",
       "997         2,529.0       1      0.0\n",
       "998           562.0       1      0.0\n",
       "999         1,005.0       1      0.0\n",
       "1000        9,855.0       1      0.0\n",
       "\n",
       "[1001 rows x 3 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Categorical column frequency\n",
    "# Returns counts of unique values in descending order (first element is the most frequently-occurring element)\n",
    "# Note: Excludes NA values by default\n",
    "value_counts(state_trails_df, 'duration_mins', bar=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Route Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>route_type</th>\n",
       "      <th>counts</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Loop</td>\n",
       "      <td>4884</td>\n",
       "      <td>0.481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Out &amp; Back</td>\n",
       "      <td>4633</td>\n",
       "      <td>0.457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Point to Point</td>\n",
       "      <td>627</td>\n",
       "      <td>0.062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       route_type  counts  percent\n",
       "0            Loop    4884    0.481\n",
       "1      Out & Back    4633    0.457\n",
       "2  Point to Point     627    0.062"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Categorical column frequency\n",
    "# Returns counts of unique values in descending order (first element is the most frequently-occurring element)\n",
    "# Note: Excludes NA values by default\n",
    "value_counts(state_trails_df, 'route_type', bar=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Trail Tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>state</th>\n",
       "      <th>city</th>\n",
       "      <th>trail_name</th>\n",
       "      <th>trail_id</th>\n",
       "      <th>trail_difficulty</th>\n",
       "      <th>stars</th>\n",
       "      <th>num_reviews</th>\n",
       "      <th>trail_region</th>\n",
       "      <th>duration_mins</th>\n",
       "      <th>route_type</th>\n",
       "      <th>trail_tags_str</th>\n",
       "      <th>trail_difficulty_ord</th>\n",
       "      <th>distance_miles</th>\n",
       "      <th>elevation_gain_ft</th>\n",
       "      <th>trail_tags_str_lst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>California</td>\n",
       "      <td>Angelus Oaks</td>\n",
       "      <td>Forsee Creek Trail</td>\n",
       "      <td>10000006</td>\n",
       "      <td>Hard</td>\n",
       "      <td>4.5</td>\n",
       "      <td>116</td>\n",
       "      <td>San Bernardino National Forest</td>\n",
       "      <td>449.0</td>\n",
       "      <td>Out &amp; Back</td>\n",
       "      <td>DogFriendly Backpacking Camping Hiking Forest ...</td>\n",
       "      <td>2</td>\n",
       "      <td>13.6</td>\n",
       "      <td>4367</td>\n",
       "      <td>[DogFriendly, Backpacking, Camping, Hiking, Fo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>California</td>\n",
       "      <td>Pescadero</td>\n",
       "      <td>Franklin Point Trail [CLOSED]</td>\n",
       "      <td>10000015</td>\n",
       "      <td>Easy</td>\n",
       "      <td>4.5</td>\n",
       "      <td>25</td>\n",
       "      <td>Año Nuevo State Park</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Out &amp; Back</td>\n",
       "      <td>KidFriendly Hiking Walking BirdWatching Beach ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>52</td>\n",
       "      <td>[KidFriendly, Hiking, Walking, BirdWatching, B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>California</td>\n",
       "      <td>Thousand Palms</td>\n",
       "      <td>Indian Palms Trail</td>\n",
       "      <td>10000044</td>\n",
       "      <td>Easy</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20</td>\n",
       "      <td>Coachella Valley Preserve</td>\n",
       "      <td>33.0</td>\n",
       "      <td>Out &amp; Back</td>\n",
       "      <td>KidFriendly Hiking NatureTrips Walking Views W...</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>112</td>\n",
       "      <td>[KidFriendly, Hiking, NatureTrips, Walking, Vi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>California</td>\n",
       "      <td>Sierraville</td>\n",
       "      <td>Mount Lola Trail</td>\n",
       "      <td>10000118</td>\n",
       "      <td>Hard</td>\n",
       "      <td>4.5</td>\n",
       "      <td>82</td>\n",
       "      <td>Tahoe National Forest</td>\n",
       "      <td>366.0</td>\n",
       "      <td>Out &amp; Back</td>\n",
       "      <td>DogFriendly Backpacking Hiking HorsebackRiding...</td>\n",
       "      <td>2</td>\n",
       "      <td>10.9</td>\n",
       "      <td>2533</td>\n",
       "      <td>[DogFriendly, Backpacking, Hiking, HorsebackRi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>California</td>\n",
       "      <td>Santa Rosa</td>\n",
       "      <td>Cobblestone Trail Loop</td>\n",
       "      <td>10000133</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>4.5</td>\n",
       "      <td>103</td>\n",
       "      <td>Trione-Annadel State Park</td>\n",
       "      <td>141.0</td>\n",
       "      <td>Loop</td>\n",
       "      <td>Hiking MountainBiking NatureTrips Running Lake...</td>\n",
       "      <td>1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>568</td>\n",
       "      <td>[Hiking, MountainBiking, NatureTrips, Running,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        state            city                     trail_name  trail_id  \\\n",
       "0  California    Angelus Oaks             Forsee Creek Trail  10000006   \n",
       "1  California       Pescadero  Franklin Point Trail [CLOSED]  10000015   \n",
       "2  California  Thousand Palms             Indian Palms Trail  10000044   \n",
       "3  California     Sierraville               Mount Lola Trail  10000118   \n",
       "4  California      Santa Rosa         Cobblestone Trail Loop  10000133   \n",
       "\n",
       "  trail_difficulty  stars  num_reviews                    trail_region  \\\n",
       "0             Hard    4.5          116  San Bernardino National Forest   \n",
       "1             Easy    4.5           25            Año Nuevo State Park   \n",
       "2             Easy    4.0           20       Coachella Valley Preserve   \n",
       "3             Hard    4.5           82           Tahoe National Forest   \n",
       "4         Moderate    4.5          103       Trione-Annadel State Park   \n",
       "\n",
       "   duration_mins  route_type  \\\n",
       "0          449.0  Out & Back   \n",
       "1           19.0  Out & Back   \n",
       "2           33.0  Out & Back   \n",
       "3          366.0  Out & Back   \n",
       "4          141.0        Loop   \n",
       "\n",
       "                                      trail_tags_str  trail_difficulty_ord  \\\n",
       "0  DogFriendly Backpacking Camping Hiking Forest ...                     2   \n",
       "1  KidFriendly Hiking Walking BirdWatching Beach ...                     0   \n",
       "2  KidFriendly Hiking NatureTrips Walking Views W...                     0   \n",
       "3  DogFriendly Backpacking Hiking HorsebackRiding...                     2   \n",
       "4  Hiking MountainBiking NatureTrips Running Lake...                     1   \n",
       "\n",
       "   distance_miles  elevation_gain_ft  \\\n",
       "0            13.6               4367   \n",
       "1             0.7                 52   \n",
       "2             1.2                112   \n",
       "3            10.9               2533   \n",
       "4             4.9                568   \n",
       "\n",
       "                                  trail_tags_str_lst  \n",
       "0  [DogFriendly, Backpacking, Camping, Hiking, Fo...  \n",
       "1  [KidFriendly, Hiking, Walking, BirdWatching, B...  \n",
       "2  [KidFriendly, Hiking, NatureTrips, Walking, Vi...  \n",
       "3  [DogFriendly, Backpacking, Hiking, HorsebackRi...  \n",
       "4  [Hiking, MountainBiking, NatureTrips, Running,...  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_trails_df['trail_tags_str_lst'] = state_trails_df.trail_tags_str.str.split(' ')\n",
    "state_trails_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Backpacking',\n",
       " 'Beach',\n",
       " 'BikeTouring',\n",
       " 'BirdWatching',\n",
       " 'Blowdown',\n",
       " 'BridgeOut',\n",
       " 'Bugs',\n",
       " 'Camping',\n",
       " 'Cave',\n",
       " 'CityWalk',\n",
       " 'Closed',\n",
       " 'CrossCountrySkiing',\n",
       " 'DogFriendly',\n",
       " 'DogsOnLeash',\n",
       " 'Event',\n",
       " 'Fee',\n",
       " 'Fishing',\n",
       " 'Forest',\n",
       " 'Hiking',\n",
       " 'HistoricSite',\n",
       " 'HorsebackRiding',\n",
       " 'HotSprings',\n",
       " 'KidFriendly',\n",
       " 'Lake',\n",
       " 'MountainBiking',\n",
       " 'Muddy',\n",
       " 'NatureTrips',\n",
       " 'NoDogs',\n",
       " 'NoShade',\n",
       " 'OffTrail',\n",
       " 'Ohv/OffRoadDriving',\n",
       " 'OverGrown',\n",
       " 'PaddleSports',\n",
       " 'PartiallyPaved',\n",
       " 'Paved',\n",
       " 'PrivateProperty',\n",
       " 'PubWalk',\n",
       " 'RailsTrails',\n",
       " 'River',\n",
       " 'RoadBiking',\n",
       " 'RockClimbing',\n",
       " 'Rocky',\n",
       " 'Running',\n",
       " 'ScenicDriving',\n",
       " 'Scramble',\n",
       " 'Skiing',\n",
       " 'Snow',\n",
       " 'Snowshoeing',\n",
       " 'StrollerFriendly',\n",
       " 'Views',\n",
       " 'Walking',\n",
       " 'WashedOut',\n",
       " 'Waterfall',\n",
       " 'WheelchairFriendly',\n",
       " 'WildFlowers',\n",
       " 'Wildlife'}"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identifying unique trail tags\n",
    "unique_tags = set(itertools.chain.from_iterable(state_trails_df.trail_tags_str_lst))\n",
    "#unique_tags = state_trails_df.trail_tags_str_lst.explode().unique()\n",
    "unique_tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Backpacking</th>\n",
       "      <th>Beach</th>\n",
       "      <th>BikeTouring</th>\n",
       "      <th>BirdWatching</th>\n",
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       "      <th>CityWalk</th>\n",
       "      <th>...</th>\n",
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       "      <td>1</td>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Backpacking  Beach  BikeTouring  BirdWatching  Blowdown  BridgeOut  Bugs  \\\n",
       "0            1      0            0             0         0          0     0   \n",
       "1            0      1            0             1         0          0     0   \n",
       "2            0      0            0             0         0          0     0   \n",
       "3            1      0            0             0         0          0     0   \n",
       "4            0      0            0             0         0          0     0   \n",
       "\n",
       "   Camping  Cave  CityWalk  ...  Snow  Snowshoeing  StrollerFriendly  Views  \\\n",
       "0        1     0         0  ...     0            0                 0      1   \n",
       "1        0     0         0  ...     0            0                 0      1   \n",
       "2        0     0         0  ...     0            0                 0      1   \n",
       "3        0     0         0  ...     0            0                 0      1   \n",
       "4        0     0         0  ...     0            0                 0      1   \n",
       "\n",
       "   Walking  WashedOut  Waterfall  WheelchairFriendly  WildFlowers  Wildlife  \n",
       "0        0          0          0                   0            1         1  \n",
       "1        1          0          0                   0            1         1  \n",
       "2        1          0          0                   0            1         1  \n",
       "3        0          0          0                   0            1         1  \n",
       "4        0          0          0                   0            0         0  \n",
       "\n",
       "[5 rows x 56 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating trail tag flags\n",
    "tag_flags = state_trails_df['trail_tags_str_lst'].str.join(sep='*').str.get_dummies(sep='*')\n",
    "tag_flags.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>city</th>\n",
       "      <th>trail_name</th>\n",
       "      <th>trail_id</th>\n",
       "      <th>trail_difficulty</th>\n",
       "      <th>stars</th>\n",
       "      <th>num_reviews</th>\n",
       "      <th>trail_region</th>\n",
       "      <th>duration_mins</th>\n",
       "      <th>route_type</th>\n",
       "      <th>...</th>\n",
       "      <th>Snow</th>\n",
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       "      <th>StrollerFriendly</th>\n",
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       "      <th>Wildlife</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>California</td>\n",
       "      <td>Angelus Oaks</td>\n",
       "      <td>Forsee Creek Trail</td>\n",
       "      <td>10000006</td>\n",
       "      <td>Hard</td>\n",
       "      <td>4.5</td>\n",
       "      <td>116</td>\n",
       "      <td>San Bernardino National Forest</td>\n",
       "      <td>449.0</td>\n",
       "      <td>Out &amp; Back</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>California</td>\n",
       "      <td>Pescadero</td>\n",
       "      <td>Franklin Point Trail [CLOSED]</td>\n",
       "      <td>10000015</td>\n",
       "      <td>Easy</td>\n",
       "      <td>4.5</td>\n",
       "      <td>25</td>\n",
       "      <td>Año Nuevo State Park</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Out &amp; Back</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>California</td>\n",
       "      <td>Thousand Palms</td>\n",
       "      <td>Indian Palms Trail</td>\n",
       "      <td>10000044</td>\n",
       "      <td>Easy</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20</td>\n",
       "      <td>Coachella Valley Preserve</td>\n",
       "      <td>33.0</td>\n",
       "      <td>Out &amp; Back</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>California</td>\n",
       "      <td>Sierraville</td>\n",
       "      <td>Mount Lola Trail</td>\n",
       "      <td>10000118</td>\n",
       "      <td>Hard</td>\n",
       "      <td>4.5</td>\n",
       "      <td>82</td>\n",
       "      <td>Tahoe National Forest</td>\n",
       "      <td>366.0</td>\n",
       "      <td>Out &amp; Back</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>California</td>\n",
       "      <td>Santa Rosa</td>\n",
       "      <td>Cobblestone Trail Loop</td>\n",
       "      <td>10000133</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>4.5</td>\n",
       "      <td>103</td>\n",
       "      <td>Trione-Annadel State Park</td>\n",
       "      <td>141.0</td>\n",
       "      <td>Loop</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        state            city                     trail_name  trail_id  \\\n",
       "0  California    Angelus Oaks             Forsee Creek Trail  10000006   \n",
       "1  California       Pescadero  Franklin Point Trail [CLOSED]  10000015   \n",
       "2  California  Thousand Palms             Indian Palms Trail  10000044   \n",
       "3  California     Sierraville               Mount Lola Trail  10000118   \n",
       "4  California      Santa Rosa         Cobblestone Trail Loop  10000133   \n",
       "\n",
       "  trail_difficulty  stars  num_reviews                    trail_region  \\\n",
       "0             Hard    4.5          116  San Bernardino National Forest   \n",
       "1             Easy    4.5           25            Año Nuevo State Park   \n",
       "2             Easy    4.0           20       Coachella Valley Preserve   \n",
       "3             Hard    4.5           82           Tahoe National Forest   \n",
       "4         Moderate    4.5          103       Trione-Annadel State Park   \n",
       "\n",
       "   duration_mins  route_type  ... Snow  Snowshoeing  StrollerFriendly  Views  \\\n",
       "0          449.0  Out & Back  ...    0            0                 0      1   \n",
       "1           19.0  Out & Back  ...    0            0                 0      1   \n",
       "2           33.0  Out & Back  ...    0            0                 0      1   \n",
       "3          366.0  Out & Back  ...    0            0                 0      1   \n",
       "4          141.0        Loop  ...    0            0                 0      1   \n",
       "\n",
       "  Walking  WashedOut  Waterfall  WheelchairFriendly  WildFlowers  Wildlife  \n",
       "0       0          0          0                   0            1         1  \n",
       "1       1          0          0                   0            1         1  \n",
       "2       1          0          0                   0            1         1  \n",
       "3       0          0          0                   0            1         1  \n",
       "4       0          0          0                   0            0         0  \n",
       "\n",
       "[5 rows x 71 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenate pandas objects along a particular axis with optional set logic along the other axes\n",
    "# Note: axis: the axis to concatenate along; {0/'index', 1/'columns'}, default 0\n",
    "state_trails_df = pd.concat([state_trails_df, tag_flags], axis=1)\n",
    "state_trails_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove columns by specifying directly column names\n",
    "# Note: inplace=True changes the original DataFrame\n",
    "state_trails_df.drop(columns=['trail_tags_str', 'trail_tags_str_lst'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10144 entries, 0 to 10145\n",
      "Data columns (total 69 columns):\n",
      "state                   10144 non-null object\n",
      "city                    10143 non-null object\n",
      "trail_name              10144 non-null object\n",
      "trail_id                10144 non-null int64\n",
      "trail_difficulty        10144 non-null object\n",
      "stars                   10144 non-null float64\n",
      "num_reviews             10144 non-null int64\n",
      "trail_region            10144 non-null object\n",
      "duration_mins           9736 non-null float64\n",
      "route_type              10144 non-null object\n",
      "trail_difficulty_ord    10144 non-null int64\n",
      "distance_miles          10144 non-null float64\n",
      "elevation_gain_ft       10144 non-null int64\n",
      "Backpacking             10144 non-null int64\n",
      "Beach                   10144 non-null int64\n",
      "BikeTouring             10144 non-null int64\n",
      "BirdWatching            10144 non-null int64\n",
      "Blowdown                10144 non-null int64\n",
      "BridgeOut               10144 non-null int64\n",
      "Bugs                    10144 non-null int64\n",
      "Camping                 10144 non-null int64\n",
      "Cave                    10144 non-null int64\n",
      "CityWalk                10144 non-null int64\n",
      "Closed                  10144 non-null int64\n",
      "CrossCountrySkiing      10144 non-null int64\n",
      "DogFriendly             10144 non-null int64\n",
      "DogsOnLeash             10144 non-null int64\n",
      "Event                   10144 non-null int64\n",
      "Fee                     10144 non-null int64\n",
      "Fishing                 10144 non-null int64\n",
      "Forest                  10144 non-null int64\n",
      "Hiking                  10144 non-null int64\n",
      "HistoricSite            10144 non-null int64\n",
      "HorsebackRiding         10144 non-null int64\n",
      "HotSprings              10144 non-null int64\n",
      "KidFriendly             10144 non-null int64\n",
      "Lake                    10144 non-null int64\n",
      "MountainBiking          10144 non-null int64\n",
      "Muddy                   10144 non-null int64\n",
      "NatureTrips             10144 non-null int64\n",
      "NoDogs                  10144 non-null int64\n",
      "NoShade                 10144 non-null int64\n",
      "OffTrail                10144 non-null int64\n",
      "Ohv/OffRoadDriving      10144 non-null int64\n",
      "OverGrown               10144 non-null int64\n",
      "PaddleSports            10144 non-null int64\n",
      "PartiallyPaved          10144 non-null int64\n",
      "Paved                   10144 non-null int64\n",
      "PrivateProperty         10144 non-null int64\n",
      "PubWalk                 10144 non-null int64\n",
      "RailsTrails             10144 non-null int64\n",
      "River                   10144 non-null int64\n",
      "RoadBiking              10144 non-null int64\n",
      "RockClimbing            10144 non-null int64\n",
      "Rocky                   10144 non-null int64\n",
      "Running                 10144 non-null int64\n",
      "ScenicDriving           10144 non-null int64\n",
      "Scramble                10144 non-null int64\n",
      "Skiing                  10144 non-null int64\n",
      "Snow                    10144 non-null int64\n",
      "Snowshoeing             10144 non-null int64\n",
      "StrollerFriendly        10144 non-null int64\n",
      "Views                   10144 non-null int64\n",
      "Walking                 10144 non-null int64\n",
      "WashedOut               10144 non-null int64\n",
      "Waterfall               10144 non-null int64\n",
      "WheelchairFriendly      10144 non-null int64\n",
      "WildFlowers             10144 non-null int64\n",
      "Wildlife                10144 non-null int64\n",
      "dtypes: float64(3), int64(60), object(6)\n",
      "memory usage: 5.7+ MB\n"
     ]
    }
   ],
   "source": [
    "# Print a concise summary of a DataFrame including the index dtype and column dtypes, non-null values, and memory usage\n",
    "# Note: Useful to quickly see if null values exist \n",
    "state_trails_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summarize the DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>trail_id</th>\n",
       "      <th>stars</th>\n",
       "      <th>num_reviews</th>\n",
       "      <th>duration_mins</th>\n",
       "      <th>elevation_gain_ft</th>\n",
       "      <th>distance_miles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>10,144.0</td>\n",
       "      <td>10,144.0</td>\n",
       "      <td>10,144.0</td>\n",
       "      <td>9,736.0</td>\n",
       "      <td>10,144.0</td>\n",
       "      <td>10,144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>10,264,364.52</td>\n",
       "      <td>4.218</td>\n",
       "      <td>96.336</td>\n",
       "      <td>240.325</td>\n",
       "      <td>1,514.581</td>\n",
       "      <td>8.142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>184,246.512</td>\n",
       "      <td>0.647</td>\n",
       "      <td>205.405</td>\n",
       "      <td>885.314</td>\n",
       "      <td>4,814.886</td>\n",
       "      <td>28.717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>10,000,006.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>10,039,832.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>341.0</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>10,273,475.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>36.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>862.0</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>10,350,852.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>97.0</td>\n",
       "      <td>281.0</td>\n",
       "      <td>1,791.75</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>10,741,577.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5,135.0</td>\n",
       "      <td>81,440.0</td>\n",
       "      <td>429,832.0</td>\n",
       "      <td>2,650.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           trail_id    stars  num_reviews  duration_mins  elevation_gain_ft  \\\n",
       "count      10,144.0 10,144.0     10,144.0        9,736.0           10,144.0   \n",
       "mean  10,264,364.52    4.218       96.336        240.325          1,514.581   \n",
       "std     184,246.512    0.647      205.405        885.314          4,814.886   \n",
       "min    10,000,006.0      0.0          0.0            2.0                0.0   \n",
       "25%    10,039,832.5      4.0         12.0           80.0              341.0   \n",
       "50%    10,273,475.5      4.5         36.0          152.0              862.0   \n",
       "75%    10,350,852.0      4.5         97.0          281.0           1,791.75   \n",
       "max    10,741,577.0      5.0      5,135.0       81,440.0          429,832.0   \n",
       "\n",
       "       distance_miles  \n",
       "count        10,144.0  \n",
       "mean            8.142  \n",
       "std            28.717  \n",
       "min               0.1  \n",
       "25%               2.7  \n",
       "50%               5.1  \n",
       "75%               9.3  \n",
       "max           2,650.0  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describe numeric columns\n",
    "# Generates descriptive summary statistics of the central tendency, dispersion, and shape of the distribution\n",
    "# Note: By default only numeric (int64) fields are returned\n",
    "#       Excludes \"NaN\" (missing) values\n",
    "round(state_trails_df[['trail_id', 'stars', 'num_reviews', 'duration_mins', 'elevation_gain_ft', 'distance_miles']].describe(), 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Greater than 95th percentile of `elevation_gain_ft`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "507\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>trail_id</th>\n",
       "      <th>trail_difficulty</th>\n",
       "      <th>duration_mins</th>\n",
       "      <th>elevation_gain_ft</th>\n",
       "      <th>distance_miles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2249</td>\n",
       "      <td>10036219</td>\n",
       "      <td>Hard</td>\n",
       "      <td>854.0</td>\n",
       "      <td>4803</td>\n",
       "      <td>27.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8744</td>\n",
       "      <td>10463428</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>nan</td>\n",
       "      <td>4803</td>\n",
       "      <td>24.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8817</td>\n",
       "      <td>10480470</td>\n",
       "      <td>Hard</td>\n",
       "      <td>680.0</td>\n",
       "      <td>4803</td>\n",
       "      <td>20.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9033</td>\n",
       "      <td>10541113</td>\n",
       "      <td>Hard</td>\n",
       "      <td>497.0</td>\n",
       "      <td>4806</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4086</td>\n",
       "      <td>10260190</td>\n",
       "      <td>Hard</td>\n",
       "      <td>811.0</td>\n",
       "      <td>4809</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8491</td>\n",
       "      <td>10414459</td>\n",
       "      <td>Hard</td>\n",
       "      <td>7,249.0</td>\n",
       "      <td>43057</td>\n",
       "      <td>227.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8490</td>\n",
       "      <td>10414374</td>\n",
       "      <td>Hard</td>\n",
       "      <td>7,565.0</td>\n",
       "      <td>45246</td>\n",
       "      <td>237.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>190</td>\n",
       "      <td>10004781</td>\n",
       "      <td>Hard</td>\n",
       "      <td>6,774.0</td>\n",
       "      <td>46030</td>\n",
       "      <td>203.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4879</td>\n",
       "      <td>10271383</td>\n",
       "      <td>Hard</td>\n",
       "      <td>9,855.0</td>\n",
       "      <td>68221</td>\n",
       "      <td>293.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2649</td>\n",
       "      <td>10040362</td>\n",
       "      <td>Hard</td>\n",
       "      <td>81,440.0</td>\n",
       "      <td>429832</td>\n",
       "      <td>2,650.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>507 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      trail_id trail_difficulty  duration_mins  elevation_gain_ft  \\\n",
       "2249  10036219             Hard          854.0               4803   \n",
       "8744  10463428         Moderate            nan               4803   \n",
       "8817  10480470             Hard          680.0               4803   \n",
       "9033  10541113             Hard          497.0               4806   \n",
       "4086  10260190             Hard          811.0               4809   \n",
       "...        ...              ...            ...                ...   \n",
       "8491  10414459             Hard        7,249.0              43057   \n",
       "8490  10414374             Hard        7,565.0              45246   \n",
       "190   10004781             Hard        6,774.0              46030   \n",
       "4879  10271383             Hard        9,855.0              68221   \n",
       "2649  10040362             Hard       81,440.0             429832   \n",
       "\n",
       "      distance_miles  \n",
       "2249            27.3  \n",
       "8744            24.1  \n",
       "8817            20.1  \n",
       "9033            12.5  \n",
       "4086            25.5  \n",
       "...              ...  \n",
       "8491           227.8  \n",
       "8490           237.2  \n",
       "190            203.1  \n",
       "4879           293.4  \n",
       "2649         2,650.0  \n",
       "\n",
       "[507 rows x 5 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_95 = state_trails_df.elevation_gain_ft.quantile(0.95)\n",
    "above_p_95 = state_trails_df[['trail_id', 'trail_difficulty', 'duration_mins', 'elevation_gain_ft', 'distance_miles']][state_trails_df.elevation_gain_ft.gt(p_95)].sort_values(by=['elevation_gain_ft'], ascending=True)\n",
    "print(len(above_p_95))\n",
    "above_p_95"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the distribution of `trail_difficulty` for trails with `elevation_gain` > 95th percentile?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trail_difficulty</th>\n",
       "      <th>counts</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Hard</td>\n",
       "      <td>405</td>\n",
       "      <td>0.799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>88</td>\n",
       "      <td>0.174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Easy</td>\n",
       "      <td>14</td>\n",
       "      <td>0.028</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  trail_difficulty  counts  percent\n",
       "0             Hard     405    0.799\n",
       "1         Moderate      88    0.174\n",
       "2             Easy      14    0.028"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Categorical column frequency\n",
    "# Returns counts of unique values in descending order (first element is the most frequently-occurring element)\n",
    "# Note: Excludes NA values by default\n",
    "value_counts(above_p_95, 'trail_difficulty', bar=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Greater than 99th percentile of `elevation_gain_ft`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "102\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trail_id</th>\n",
       "      <th>trail_difficulty</th>\n",
       "      <th>duration_mins</th>\n",
       "      <th>elevation_gain_ft</th>\n",
       "      <th>distance_miles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>7084</td>\n",
       "      <td>10323769</td>\n",
       "      <td>Hard</td>\n",
       "      <td>1,112.0</td>\n",
       "      <td>9419</td>\n",
       "      <td>30.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9741</td>\n",
       "      <td>10611496</td>\n",
       "      <td>Hard</td>\n",
       "      <td>nan</td>\n",
       "      <td>9452</td>\n",
       "      <td>46.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1705</td>\n",
       "      <td>10031436</td>\n",
       "      <td>Hard</td>\n",
       "      <td>1,478.0</td>\n",
       "      <td>9458</td>\n",
       "      <td>53.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7513</td>\n",
       "      <td>10346286</td>\n",
       "      <td>Hard</td>\n",
       "      <td>1,604.0</td>\n",
       "      <td>9478</td>\n",
       "      <td>50.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7358</td>\n",
       "      <td>10339460</td>\n",
       "      <td>Hard</td>\n",
       "      <td>1,916.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>63.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8491</td>\n",
       "      <td>10414459</td>\n",
       "      <td>Hard</td>\n",
       "      <td>7,249.0</td>\n",
       "      <td>43057</td>\n",
       "      <td>227.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8490</td>\n",
       "      <td>10414374</td>\n",
       "      <td>Hard</td>\n",
       "      <td>7,565.0</td>\n",
       "      <td>45246</td>\n",
       "      <td>237.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>190</td>\n",
       "      <td>10004781</td>\n",
       "      <td>Hard</td>\n",
       "      <td>6,774.0</td>\n",
       "      <td>46030</td>\n",
       "      <td>203.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4879</td>\n",
       "      <td>10271383</td>\n",
       "      <td>Hard</td>\n",
       "      <td>9,855.0</td>\n",
       "      <td>68221</td>\n",
       "      <td>293.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2649</td>\n",
       "      <td>10040362</td>\n",
       "      <td>Hard</td>\n",
       "      <td>81,440.0</td>\n",
       "      <td>429832</td>\n",
       "      <td>2,650.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      trail_id trail_difficulty  duration_mins  elevation_gain_ft  \\\n",
       "7084  10323769             Hard        1,112.0               9419   \n",
       "9741  10611496             Hard            nan               9452   \n",
       "1705  10031436             Hard        1,478.0               9458   \n",
       "7513  10346286             Hard        1,604.0               9478   \n",
       "7358  10339460             Hard        1,916.0               9550   \n",
       "...        ...              ...            ...                ...   \n",
       "8491  10414459             Hard        7,249.0              43057   \n",
       "8490  10414374             Hard        7,565.0              45246   \n",
       "190   10004781             Hard        6,774.0              46030   \n",
       "4879  10271383             Hard        9,855.0              68221   \n",
       "2649  10040362             Hard       81,440.0             429832   \n",
       "\n",
       "      distance_miles  \n",
       "7084            30.2  \n",
       "9741            46.2  \n",
       "1705            53.3  \n",
       "7513            50.5  \n",
       "7358            63.3  \n",
       "...              ...  \n",
       "8491           227.8  \n",
       "8490           237.2  \n",
       "190            203.1  \n",
       "4879           293.4  \n",
       "2649         2,650.0  \n",
       "\n",
       "[102 rows x 5 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_99 = state_trails_df.elevation_gain_ft.quantile(0.99)\n",
    "above_p_99 = state_trails_df[['trail_id', 'trail_difficulty', 'duration_mins', 'elevation_gain_ft', 'distance_miles']][state_trails_df.elevation_gain_ft.gt(p_99)].sort_values(by=['elevation_gain_ft'], ascending=True)\n",
    "print(len(above_p_99))\n",
    "above_p_99"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the distribution of `trail_difficulty` for trails with `elevation_gain` > 99th percentile?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>trail_difficulty</th>\n",
       "      <th>counts</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Hard</td>\n",
       "      <td>83</td>\n",
       "      <td>0.814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>17</td>\n",
       "      <td>0.167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Easy</td>\n",
       "      <td>2</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  trail_difficulty  counts  percent\n",
       "0             Hard      83    0.814\n",
       "1         Moderate      17    0.167\n",
       "2             Easy       2     0.02"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Categorical column frequency\n",
    "# Returns counts of unique values in descending order (first element is the most frequently-occurring element)\n",
    "# Note: Excludes NA values by default\n",
    "value_counts(above_p_99, 'trail_difficulty', bar=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of the trails that are greater than the 99th percentile of `elevation_gain`, 83% are greater than 10,000 feet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.83"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(above_p_99[['elevation_gain_ft']][above_p_99.elevation_gain_ft > 10000].count().iloc[0] / above_p_99[['elevation_gain_ft']].count().iloc[0], 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of the trails that are greater than the 99th percentile of `elevation_gain` which are greater than 10,000 feet, 82% are hard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>trail_difficulty</th>\n",
       "      <th>counts</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Hard</td>\n",
       "      <td>70</td>\n",
       "      <td>0.824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>13</td>\n",
       "      <td>0.153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Easy</td>\n",
       "      <td>2</td>\n",
       "      <td>0.024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  trail_difficulty  counts  percent\n",
       "0             Hard      70    0.824\n",
       "1         Moderate      13    0.153\n",
       "2             Easy       2    0.024"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value_counts(above_p_99[above_p_99.elevation_gain_ft > 10000], 'trail_difficulty', bar=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the majority of the trails that are greater than 10,000 feet are hard, the analysis will focus on trails <= 10,000 feet where there exists more variation in `trail_difficulty`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Greater than 95th percentile of `distance_miles`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "506\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        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>trail_id</th>\n",
       "      <th>trail_difficulty</th>\n",
       "      <th>duration_mins</th>\n",
       "      <th>elevation_gain_ft</th>\n",
       "      <th>distance_miles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>8936</td>\n",
       "      <td>10503969</td>\n",
       "      <td>Hard</td>\n",
       "      <td>706.0</td>\n",
       "      <td>4248</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>968</td>\n",
       "      <td>10023120</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>nan</td>\n",
       "      <td>4763</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>10024025</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>nan</td>\n",
       "      <td>3631</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2172</td>\n",
       "      <td>10035754</td>\n",
       "      <td>Hard</td>\n",
       "      <td>890.0</td>\n",
       "      <td>8763</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6600</td>\n",
       "      <td>10309913</td>\n",
       "      <td>Hard</td>\n",
       "      <td>737.0</td>\n",
       "      <td>5019</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8491</td>\n",
       "      <td>10414459</td>\n",
       "      <td>Hard</td>\n",
       "      <td>7,249.0</td>\n",
       "      <td>43057</td>\n",
       "      <td>227.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8490</td>\n",
       "      <td>10414374</td>\n",
       "      <td>Hard</td>\n",
       "      <td>7,565.0</td>\n",
       "      <td>45246</td>\n",
       "      <td>237.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4879</td>\n",
       "      <td>10271383</td>\n",
       "      <td>Hard</td>\n",
       "      <td>9,855.0</td>\n",
       "      <td>68221</td>\n",
       "      <td>293.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8754</td>\n",
       "      <td>10467025</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>nan</td>\n",
       "      <td>34665</td>\n",
       "      <td>349.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2649</td>\n",
       "      <td>10040362</td>\n",
       "      <td>Hard</td>\n",
       "      <td>81,440.0</td>\n",
       "      <td>429832</td>\n",
       "      <td>2,650.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      trail_id trail_difficulty  duration_mins  elevation_gain_ft  \\\n",
       "8936  10503969             Hard          706.0               4248   \n",
       "968   10023120         Moderate            nan               4763   \n",
       "1000  10024025         Moderate            nan               3631   \n",
       "2172  10035754             Hard          890.0               8763   \n",
       "6600  10309913             Hard          737.0               5019   \n",
       "...        ...              ...            ...                ...   \n",
       "8491  10414459             Hard        7,249.0              43057   \n",
       "8490  10414374             Hard        7,565.0              45246   \n",
       "4879  10271383             Hard        9,855.0              68221   \n",
       "8754  10467025         Moderate            nan              34665   \n",
       "2649  10040362             Hard       81,440.0             429832   \n",
       "\n",
       "      distance_miles  \n",
       "8936            22.1  \n",
       "968             22.1  \n",
       "1000            22.1  \n",
       "2172            22.1  \n",
       "6600            22.1  \n",
       "...              ...  \n",
       "8491           227.8  \n",
       "8490           237.2  \n",
       "4879           293.4  \n",
       "8754           349.1  \n",
       "2649         2,650.0  \n",
       "\n",
       "[506 rows x 5 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_95 = state_trails_df.distance_miles.quantile(0.95)\n",
    "above_p_95 = state_trails_df[['trail_id', 'trail_difficulty', 'duration_mins', 'elevation_gain_ft', 'distance_miles']][state_trails_df.distance_miles.gt(p_95)].sort_values(by=['distance_miles'], ascending=True)\n",
    "print(len(above_p_95))\n",
    "above_p_95"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the distribution of `trail_difficulty` for trails with `distance_miles` > 95th percentile?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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",
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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>trail_difficulty</th>\n",
       "      <th>counts</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Hard</td>\n",
       "      <td>318</td>\n",
       "      <td>0.628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>151</td>\n",
       "      <td>0.298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Easy</td>\n",
       "      <td>37</td>\n",
       "      <td>0.073</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  trail_difficulty  counts  percent\n",
       "0             Hard     318    0.628\n",
       "1         Moderate     151    0.298\n",
       "2             Easy      37    0.073"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DBw+uW7EAAJtlqnB2SpL7l7UdSrLjONu9PLMaf32lld19XXfv6e49u3btethFAgBstqnOOVtIsnNZ284kh4+2QVX9SGbnnl3Q3Z/cwNoAAIYx1czZgSTbqurMJW3n5OiHK38wyVVJvqm7PzxBfQAAQ5gknHX3kSQ3JrmmqrZX1flJLkxy/fK+VfW9SX42ybd09wenqA8AYBRT3oT2iiQnJ7k3yQ1JLu/u/VV1QVUtLOn3yiRflOTPq2ph/vrVCesEANg0k93nrLvvS3LRCu23ZXbBwOLyU6aqCQBgNB7fBAAwEOEMAGAgwhkAwECEMwCAgQhnAAADEc4AAAYinAEADEQ4AwAYiHAGADAQ4QwAYCDCGQDAQIQzAICBCGcAAAMRzgAABiKcAQAMRDgDABiIcAYAMBDhDABgIMIZAMBAhDMAgIEIZwAAAxHOAAAGIpwBAAxEOAMAGIhwBgAwEOEMAGAgwhkAwECEMwCAgQhnAAADEc4AAAYinAEADEQ4AwAYiHAGADAQ4QwAYCDCGQDAQIQzAICBCGcAAAMRzgAABiKcAQAMZLJwVlWnVtVNVXWkqu6qqkuO0u8bquqPqupQVd05VX0AACOYcubs2iQPJjk9yfcmeUNVnb1CvyNJ3pzkyglrAwAYwiThrKq2J7k4ydXdvdDdtye5Ocmly/t293u7+/okH5yiNgCAkUw1c3ZWkoe6+8CStn1JVpo5W7Wquqyq9lbV3oMHDz6sAgEARjBVODslyf3L2g4l2fFwdtrd13X3nu7es2vXroezKwCAIUwVzhaS7FzWtjPJ4YneHwDgEWGqcHYgybaqOnNJ2zlJ9k/0/gAAjwiThLPuPpLkxiTXVNX2qjo/yYVJrl/et6oeVVWPS3LSbLEeV1WPmaJOAIDNNuWtNK5IcnKSe5PckOTy7t5fVRdU1cKSfs9K8kCSdyY5Y/71H0xYJwDAptk21Rt1931JLlqh/bbMLhhYXP7jJDVVXQAAI/H4JgCAgQhnAAADEc4AAAYinAEADEQ4AwAYiHAGADAQ4QwAYCCT3ecMAE50u6+6dbNL2HLufPWzN7uEdWfmDABgIMIZAMBAhDMAgIEIZwAAAxHOAAAGIpwBAAxEOAMAGIhwBgAwEOEMAGAgwhkAwECEMwCAgQhnAAADEc4AAAYinAEADEQ4AwAYiHAGADAQ4QwAYCDCGQDAQIQzAICBCGcAAAMRzgAABiKcAQAMRDgDABiIcAYAMBDhDABgIMIZAMBAhDMAgIEIZwAAAxHOAAAGIpwBAAxEOAMAGMhk4ayqTq2qm6rqSFXdVVWXHKVfVdVrqurj89drqqqmqhMAYDNtm/C9rk3yYJLTk5yb5Naq2tfd+5f1uyzJRUnOSdJJ3pXkQ0l+dcJaAQA2xSQzZ1W1PcnFSa7u7oXuvj3JzUkuXaH79yd5XXd/uLv/Icnrkjx/ijoBADbbVIc1z0ryUHcfWNK2L8nZK/Q9e77ueP0AAE44Ux3WPCXJ/cvaDiXZcZS+h5b1O6Wqqrt7acequiyzw6BJslBV71+nelmd05J8bLOL+HzUaza7Ah5BjHO2AuN8ek8+2oqpwtlCkp3L2nYmObyKvjuTLCwPZknS3dcluW69imRtqmpvd+/Z7DpgIxnnbAXG+VimOqx5IMm2qjpzSds5SZZfDJB52zmr6AcAcMKZJJx195EkNya5pqq2V9X5SS5Mcv0K3d+a5MVV9eVV9WVJfiLJb0xRJwDAZpvyJrRXJDk5yb1JbkhyeXfvr6oLqmphSb9fS3JLkr9O8jdJbp23MR6HlNkKjHO2AuN8ILXCqVwAAGwSj28CABiIcMYkqmp3VXVVTflUClgV4xMYiXBGkqSq7qyqb17W9vyqun2zaoKjmY/XB6vqtGXt/3sesnZvTmUrW+nzBRtlPt4eqKqFJa9f3uy6WD3hjHVn9oGJfCjJ9ywuVNXXJPmCqYsw3hnUc7r7lCWvH9nsglg94YxVqaqrquqOqjpcVe+rqucuWff8qvqTqvrFqvp4kpdX1aOr6uer6mNV9cEkz9686jlBXZ/keUuWvz+zW/EkSarq8VX11qo6WFV3VdVPV9Wj5uuOOT7n276pqu6pqn+oqldW1aPn61Ya70+rqndX1cfn+/zNqnrCvP/1Sc5Icst8BuMn5+3PrKo/rapPVNW+qvr6jfuvguRY43S+/qfm4/1wVb2/qr6pqr6kqv6pqr5oSb/z5p+rkzbnOznxCWes1h1JLkjy+CSvSPK2qvrSJev/ZZIPJjk9yauSvCDJdyT5F0n2JPnOSatlK/hfSXZW1VfOg9N3J3nbkvWvz2y8PjXJv84syP3AfN3xxudvJHkoyVfM+3xrkh9esn75eK8kP5fky5J8ZZInJXl5knT3pUn+Tz47k/HaqvryzG4T9MokpyZ5SZL/WlW7Pu//DTi+o47Tqnp6kh9J8ozu3pHk25Lc2d0fTfLHSb5ryX4uTfJb3f2pySrfYoQzlvpv87/iP1FVn0jyK4sruvt3u/sj3f2Z7v7tJB9I8rVLtv1Id7++ux/q7gcy+yD/5+6+u7vvy+wHAqy3xdmzb0nyt0n+Yd6+GNZe2t2Hu/vOJK/L7JdKcozxWVWnJ/n2JD/W3Ue6+94kvzjf36LPGe/d/ffd/a7u/mR3H0zyC5kFwqP5viTv7O53zj9T70qyd/6+sB4+5+d5Vb3gOOP000kem+Srquqk7r6zu++Yr3tLZmM28z+Evicr30SedeJcCZa6qLv/cHGhqp6f+WxBVT0vyYuT7J6vPiWzB+UuunvZvr5sWdtd61wrJLNfEO9J8pQsOaSZ2dg8KZ877u5K8uXzr481Pp883/aeqlpse9Sy/p8z3ueB7pcym13eMe//f49R95OT/Luqes6StpOS/NExtoG1+Jyf58mxx2l3/31V/VhmM2lnV9XvJ3lxd38kyTuS/GpVPSXJ05Mc6u73TvadbEFmzjiuqnpykjdmNuX9Rd39hMye3lBLui2/m/E9mU2ZLzpjQ4tkS+ruuzK7MODbM3tE3KKPJflUZiFo0Rn57Mzascbn3Uk+meS07n7C/LWzu89e+tbLSvnZedvXdPfOzGYZjvX5uDvJ9Uv2/4Tu3t7drz7OtwwPxzHHaXe/vbv/VWafm07ymnn7Pyf5nXn/S2PWbMMJZ6zG9sw+qAeTpKp+IMlXH2eb30nyoqp6YlV9YZKrNrZEtrAfSvKN82f4Lvp0ZmPwVVW1Y/4Hxovz2XPSjjo+u/ueJH+Q5HVVtbOqHjU/kfpYhyl3JFlIcmh+PtmVy9b/Y2bnvi16W5LnVNW3zS9OeFxVfX1VPXHN3z2s3lHHaVU9vaq+saoem+SfkzyQ5DNLtn1rkucn+bcRzjaccMZxdff7Mjtf588y+yXzNUn+5DibvTHJ7yfZl+Qv87mzGrBuuvuO7t67wqoXJjmS2Yn7tyd5e5I3z9cdb3w+L8ljkrwvs8M+v5fkS3N0r0hyXpJDmZ3ov3x/P5fkp+fn/ryku+9OcmGSl2X2R8/dmf2i9DOZ9bJ4dfDi66Yce5w+NsmrM5t1/miSL07y0sWV3f0nmYW1v5zPWLOBPFsTADiuqnp3krd393/Z7FpOdMIZAHBMVfWMJO9K8qTuPrzZ9ZzoTKEDAEdVVW9J8oeZ3V5GMJuAmTMAgIGYOQMAGIhwBgAwEOEMAGAgwhkAwECEMwCAgQhnAAAD+X9BLOtSRnhdowAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Categorical column frequency\n",
    "# Returns counts of unique values in descending order (first element is the most frequently-occurring element)\n",
    "# Note: Excludes NA values by default\n",
    "value_counts(above_p_95, 'trail_difficulty', bar=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Greater than 99th percentile of `distance_miles`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "102\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trail_id</th>\n",
       "      <th>trail_difficulty</th>\n",
       "      <th>duration_mins</th>\n",
       "      <th>elevation_gain_ft</th>\n",
       "      <th>distance_miles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>9461</td>\n",
       "      <td>10587702</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>nan</td>\n",
       "      <td>4829</td>\n",
       "      <td>48.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8704</td>\n",
       "      <td>10456943</td>\n",
       "      <td>Hard</td>\n",
       "      <td>1,763.0</td>\n",
       "      <td>14452</td>\n",
       "      <td>48.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8657</td>\n",
       "      <td>10449452</td>\n",
       "      <td>Hard</td>\n",
       "      <td>1,505.0</td>\n",
       "      <td>8116</td>\n",
       "      <td>48.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4266</td>\n",
       "      <td>10262809</td>\n",
       "      <td>Hard</td>\n",
       "      <td>1,696.0</td>\n",
       "      <td>12749</td>\n",
       "      <td>48.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8420</td>\n",
       "      <td>10407993</td>\n",
       "      <td>Hard</td>\n",
       "      <td>1,493.0</td>\n",
       "      <td>7391</td>\n",
       "      <td>49.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8491</td>\n",
       "      <td>10414459</td>\n",
       "      <td>Hard</td>\n",
       "      <td>7,249.0</td>\n",
       "      <td>43057</td>\n",
       "      <td>227.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8490</td>\n",
       "      <td>10414374</td>\n",
       "      <td>Hard</td>\n",
       "      <td>7,565.0</td>\n",
       "      <td>45246</td>\n",
       "      <td>237.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4879</td>\n",
       "      <td>10271383</td>\n",
       "      <td>Hard</td>\n",
       "      <td>9,855.0</td>\n",
       "      <td>68221</td>\n",
       "      <td>293.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8754</td>\n",
       "      <td>10467025</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>nan</td>\n",
       "      <td>34665</td>\n",
       "      <td>349.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2649</td>\n",
       "      <td>10040362</td>\n",
       "      <td>Hard</td>\n",
       "      <td>81,440.0</td>\n",
       "      <td>429832</td>\n",
       "      <td>2,650.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      trail_id trail_difficulty  duration_mins  elevation_gain_ft  \\\n",
       "9461  10587702         Moderate            nan               4829   \n",
       "8704  10456943             Hard        1,763.0              14452   \n",
       "8657  10449452             Hard        1,505.0               8116   \n",
       "4266  10262809             Hard        1,696.0              12749   \n",
       "8420  10407993             Hard        1,493.0               7391   \n",
       "...        ...              ...            ...                ...   \n",
       "8491  10414459             Hard        7,249.0              43057   \n",
       "8490  10414374             Hard        7,565.0              45246   \n",
       "4879  10271383             Hard        9,855.0              68221   \n",
       "8754  10467025         Moderate            nan              34665   \n",
       "2649  10040362             Hard       81,440.0             429832   \n",
       "\n",
       "      distance_miles  \n",
       "9461            48.5  \n",
       "8704            48.7  \n",
       "8657            48.7  \n",
       "4266            48.8  \n",
       "8420            49.4  \n",
       "...              ...  \n",
       "8491           227.8  \n",
       "8490           237.2  \n",
       "4879           293.4  \n",
       "8754           349.1  \n",
       "2649         2,650.0  \n",
       "\n",
       "[102 rows x 5 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_99 = state_trails_df.distance_miles.quantile(0.99)\n",
    "above_p_99 = state_trails_df[['trail_id', 'trail_difficulty', 'duration_mins', 'elevation_gain_ft', 'distance_miles']][state_trails_df.distance_miles.gt(p_99)].sort_values(by=['distance_miles'], ascending=True)\n",
    "print(len(above_p_99))\n",
    "above_p_99"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the distribution of `trail_difficulty` for trails with `distance_miles` > 99th percentile?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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>trail_difficulty</th>\n",
       "      <th>counts</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Hard</td>\n",
       "      <td>76</td>\n",
       "      <td>0.745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>21</td>\n",
       "      <td>0.206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Easy</td>\n",
       "      <td>5</td>\n",
       "      <td>0.049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  trail_difficulty  counts  percent\n",
       "0             Hard      76    0.745\n",
       "1         Moderate      21    0.206\n",
       "2             Easy       5    0.049"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Categorical column frequency\n",
    "# Returns counts of unique values in descending order (first element is the most frequently-occurring element)\n",
    "# Note: Excludes NA values by default\n",
    "value_counts(above_p_99, 'trail_difficulty', bar=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of the trails that are greater than the 99th percentile of `distance_miles`, 94% are greater than 50 miles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.94"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(above_p_99[['distance_miles']][above_p_99.distance_miles > 50].count().iloc[0] / above_p_99[['distance_miles']].count().iloc[0], 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of the trails that are greater than the 99th percentile of `distance_miles` which are greater than 50 miles, 74% are hard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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>trail_difficulty</th>\n",
       "      <th>counts</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Hard</td>\n",
       "      <td>71</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>20</td>\n",
       "      <td>0.208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Easy</td>\n",
       "      <td>5</td>\n",
       "      <td>0.052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  trail_difficulty  counts  percent\n",
       "0             Hard      71     0.74\n",
       "1         Moderate      20    0.208\n",
       "2             Easy       5    0.052"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value_counts(above_p_99[above_p_99.distance_miles > 50], 'trail_difficulty', bar=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the majority of the trails that are greater than 50 milies are hard, the analysis will focus on trails <= 50 miles where there exists more variation in `trail_difficulty`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10025"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating dataframe where elevation gain <= 10,0000 feet and distance <= 50 miles\n",
    "state_trails_df_0_10000ft_0_50mi = state_trails_df[(state_trails_df.elevation_gain_ft <= 10000) & (state_trails_df.distance_miles <= 50)]\n",
    "len(state_trails_df_0_10000ft_0_50mi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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>trail_id</th>\n",
       "      <th>stars</th>\n",
       "      <th>num_reviews</th>\n",
       "      <th>duration_mins</th>\n",
       "      <th>elevation_gain_ft</th>\n",
       "      <th>distance_miles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>9,646.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>10,263,976.364</td>\n",
       "      <td>4.216</td>\n",
       "      <td>97.25</td>\n",
       "      <td>211.675</td>\n",
       "      <td>1,323.287</td>\n",
       "      <td>7.032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>184,179.952</td>\n",
       "      <td>0.642</td>\n",
       "      <td>206.348</td>\n",
       "      <td>199.022</td>\n",
       "      <td>1,470.165</td>\n",
       "      <td>6.545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>10,000,006.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>10,039,825.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>10,273,357.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>37.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>849.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>10,350,386.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>98.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>1,745.0</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>10,741,577.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5,135.0</td>\n",
       "      <td>1,566.0</td>\n",
       "      <td>9,776.0</td>\n",
       "      <td>49.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            trail_id    stars  num_reviews  duration_mins  elevation_gain_ft  \\\n",
       "count       10,025.0 10,025.0     10,025.0        9,646.0           10,025.0   \n",
       "mean  10,263,976.364    4.216        97.25        211.675          1,323.287   \n",
       "std      184,179.952    0.642      206.348        199.022          1,470.165   \n",
       "min     10,000,006.0      0.0          0.0            2.0                0.0   \n",
       "25%     10,039,825.0      4.0         12.0           79.0              334.0   \n",
       "50%     10,273,357.0      4.5         37.0          150.0              849.0   \n",
       "75%     10,350,386.0      4.5         98.0          276.0            1,745.0   \n",
       "max     10,741,577.0      5.0      5,135.0        1,566.0            9,776.0   \n",
       "\n",
       "       distance_miles  \n",
       "count        10,025.0  \n",
       "mean            7.032  \n",
       "std             6.545  \n",
       "min               0.1  \n",
       "25%               2.7  \n",
       "50%               5.0  \n",
       "75%               9.1  \n",
       "max              49.4  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describe numeric columns\n",
    "# Generates descriptive summary statistics of the central tendency, dispersion, and shape of the distribution\n",
    "# Note: By default only numeric (int64) fields are returned\n",
    "#       Excludes \"NaN\" (missing) values\n",
    "round(state_trails_df_0_10000ft_0_50mi[['trail_id', 'stars', 'num_reviews', 'duration_mins', 'elevation_gain_ft', 'distance_miles']].describe(), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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>Backpacking</th>\n",
       "      <th>Beach</th>\n",
       "      <th>BikeTouring</th>\n",
       "      <th>BirdWatching</th>\n",
       "      <th>Blowdown</th>\n",
       "      <th>BridgeOut</th>\n",
       "      <th>Bugs</th>\n",
       "      <th>Camping</th>\n",
       "      <th>Cave</th>\n",
       "      <th>CityWalk</th>\n",
       "      <th>Closed</th>\n",
       "      <th>CrossCountrySkiing</th>\n",
       "      <th>DogFriendly</th>\n",
       "      <th>DogsOnLeash</th>\n",
       "      <th>Event</th>\n",
       "      <th>Fee</th>\n",
       "      <th>Fishing</th>\n",
       "      <th>Forest</th>\n",
       "      <th>Hiking</th>\n",
       "      <th>HistoricSite</th>\n",
       "      <th>HorsebackRiding</th>\n",
       "      <th>HotSprings</th>\n",
       "      <th>KidFriendly</th>\n",
       "      <th>Lake</th>\n",
       "      <th>MountainBiking</th>\n",
       "      <th>Muddy</th>\n",
       "      <th>NatureTrips</th>\n",
       "      <th>NoDogs</th>\n",
       "      <th>NoShade</th>\n",
       "      <th>OffTrail</th>\n",
       "      <th>Ohv/OffRoadDriving</th>\n",
       "      <th>OverGrown</th>\n",
       "      <th>PaddleSports</th>\n",
       "      <th>PartiallyPaved</th>\n",
       "      <th>Paved</th>\n",
       "      <th>PrivateProperty</th>\n",
       "      <th>PubWalk</th>\n",
       "      <th>RailsTrails</th>\n",
       "      <th>River</th>\n",
       "      <th>RoadBiking</th>\n",
       "      <th>RockClimbing</th>\n",
       "      <th>Rocky</th>\n",
       "      <th>Running</th>\n",
       "      <th>ScenicDriving</th>\n",
       "      <th>Scramble</th>\n",
       "      <th>Skiing</th>\n",
       "      <th>Snow</th>\n",
       "      <th>Snowshoeing</th>\n",
       "      <th>StrollerFriendly</th>\n",
       "      <th>Views</th>\n",
       "      <th>Walking</th>\n",
       "      <th>WashedOut</th>\n",
       "      <th>Waterfall</th>\n",
       "      <th>WheelchairFriendly</th>\n",
       "      <th>WildFlowers</th>\n",
       "      <th>Wildlife</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "      <td>10,025.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.503</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.155</td>\n",
       "      <td>0.346</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.905</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.189</td>\n",
       "      <td>0.208</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.251</td>\n",
       "      <td>0.134</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.172</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.533</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.553</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.579</td>\n",
       "      <td>0.586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>0.335</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.238</td>\n",
       "      <td>0.343</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.299</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.362</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.213</td>\n",
       "      <td>0.496</td>\n",
       "      <td>0.294</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.299</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0.481</td>\n",
       "      <td>0.391</td>\n",
       "      <td>0.406</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.466</td>\n",
       "      <td>0.434</td>\n",
       "      <td>0.341</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.233</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0.219</td>\n",
       "      <td>0.185</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.377</td>\n",
       "      <td>0.203</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.499</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.228</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.217</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.318</td>\n",
       "      <td>0.497</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.184</td>\n",
       "      <td>0.494</td>\n",
       "      <td>0.493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Backpacking    Beach  BikeTouring  BirdWatching  Blowdown  BridgeOut  \\\n",
       "count     10,025.0 10,025.0     10,025.0      10,025.0  10,025.0   10,025.0   \n",
       "mean         0.128    0.073        0.005         0.503     0.012      0.003   \n",
       "std          0.335     0.26        0.071           0.5     0.108      0.056   \n",
       "min            0.0      0.0          0.0           0.0       0.0        0.0   \n",
       "25%            0.0      0.0          0.0           0.0       0.0        0.0   \n",
       "50%            0.0      0.0          0.0           1.0       0.0        0.0   \n",
       "75%            0.0      0.0          0.0           1.0       0.0        0.0   \n",
       "max            1.0      1.0          1.0           1.0       1.0        1.0   \n",
       "\n",
       "          Bugs  Camping     Cave  CityWalk   Closed  CrossCountrySkiing  \\\n",
       "count 10,025.0 10,025.0 10,025.0  10,025.0 10,025.0            10,025.0   \n",
       "mean      0.06    0.136    0.016     0.018    0.099               0.006   \n",
       "std      0.238    0.343    0.125     0.133    0.299               0.076   \n",
       "min        0.0      0.0      0.0       0.0      0.0                 0.0   \n",
       "25%        0.0      0.0      0.0       0.0      0.0                 0.0   \n",
       "50%        0.0      0.0      0.0       0.0      0.0                 0.0   \n",
       "75%        0.0      0.0      0.0       0.0      0.0                 0.0   \n",
       "max        1.0      1.0      1.0       1.0      1.0                 1.0   \n",
       "\n",
       "       DogFriendly  DogsOnLeash    Event      Fee  Fishing   Forest   Hiking  \\\n",
       "count     10,025.0     10,025.0 10,025.0 10,025.0 10,025.0 10,025.0 10,025.0   \n",
       "mean         0.155        0.346      0.0    0.031    0.048     0.44    0.905   \n",
       "std          0.362        0.476    0.022    0.173    0.213    0.496    0.294   \n",
       "min            0.0          0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "25%            0.0          0.0      0.0      0.0      0.0      0.0      1.0   \n",
       "50%            0.0          0.0      0.0      0.0      0.0      0.0      1.0   \n",
       "75%            0.0          1.0      0.0      0.0      0.0      1.0      1.0   \n",
       "max            1.0          1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "\n",
       "       HistoricSite  HorsebackRiding  HotSprings  KidFriendly     Lake  \\\n",
       "count      10,025.0         10,025.0    10,025.0     10,025.0 10,025.0   \n",
       "mean          0.027            0.099       0.005        0.364    0.189   \n",
       "std           0.162            0.299       0.072        0.481    0.391   \n",
       "min             0.0              0.0         0.0          0.0      0.0   \n",
       "25%             0.0              0.0         0.0          0.0      0.0   \n",
       "50%             0.0              0.0         0.0          0.0      0.0   \n",
       "75%             0.0              0.0         0.0          1.0      0.0   \n",
       "max             1.0              1.0         1.0          1.0      1.0   \n",
       "\n",
       "       MountainBiking    Muddy  NatureTrips   NoDogs  NoShade  OffTrail  \\\n",
       "count        10,025.0 10,025.0     10,025.0 10,025.0 10,025.0  10,025.0   \n",
       "mean            0.208    0.031        0.681    0.251    0.134     0.021   \n",
       "std             0.406    0.173        0.466    0.434    0.341     0.143   \n",
       "min               0.0      0.0          0.0      0.0      0.0       0.0   \n",
       "25%               0.0      0.0          0.0      0.0      0.0       0.0   \n",
       "50%               0.0      0.0          1.0      0.0      0.0       0.0   \n",
       "75%               0.0      0.0          1.0      1.0      0.0       0.0   \n",
       "max               1.0      1.0          1.0      1.0      1.0       1.0   \n",
       "\n",
       "       Ohv/OffRoadDriving  OverGrown  PaddleSports  PartiallyPaved    Paved  \\\n",
       "count            10,025.0   10,025.0      10,025.0        10,025.0 10,025.0   \n",
       "mean                0.033      0.057         0.008            0.05    0.035   \n",
       "std                 0.179      0.233         0.091           0.219    0.185   \n",
       "min                   0.0        0.0           0.0             0.0      0.0   \n",
       "25%                   0.0        0.0           0.0             0.0      0.0   \n",
       "50%                   0.0        0.0           0.0             0.0      0.0   \n",
       "75%                   0.0        0.0           0.0             0.0      0.0   \n",
       "max                   1.0        1.0           1.0             1.0      1.0   \n",
       "\n",
       "       PrivateProperty  PubWalk  RailsTrails    River  RoadBiking  \\\n",
       "count         10,025.0 10,025.0     10,025.0 10,025.0    10,025.0   \n",
       "mean             0.014      0.0        0.006    0.172       0.043   \n",
       "std              0.119    0.014        0.077    0.377       0.203   \n",
       "min                0.0      0.0          0.0      0.0         0.0   \n",
       "25%                0.0      0.0          0.0      0.0         0.0   \n",
       "50%                0.0      0.0          0.0      0.0         0.0   \n",
       "75%                0.0      0.0          0.0      0.0         0.0   \n",
       "max                1.0      1.0          1.0      1.0         1.0   \n",
       "\n",
       "       RockClimbing    Rocky  Running  ScenicDriving  Scramble   Skiing  \\\n",
       "count      10,025.0 10,025.0 10,025.0       10,025.0  10,025.0 10,025.0   \n",
       "mean          0.017    0.164    0.533           0.01     0.055    0.003   \n",
       "std           0.129     0.37    0.499          0.099     0.228    0.059   \n",
       "min             0.0      0.0      0.0            0.0       0.0      0.0   \n",
       "25%             0.0      0.0      0.0            0.0       0.0      0.0   \n",
       "50%             0.0      0.0      1.0            0.0       0.0      0.0   \n",
       "75%             0.0      0.0      1.0            0.0       0.0      0.0   \n",
       "max             1.0      1.0      1.0            1.0       1.0      1.0   \n",
       "\n",
       "          Snow  Snowshoeing  StrollerFriendly    Views  Walking  WashedOut  \\\n",
       "count 10,025.0     10,025.0          10,025.0 10,025.0 10,025.0   10,025.0   \n",
       "mean      0.05        0.019             0.037    0.886    0.553      0.024   \n",
       "std      0.217        0.137             0.188    0.318    0.497      0.152   \n",
       "min        0.0          0.0               0.0      0.0      0.0        0.0   \n",
       "25%        0.0          0.0               0.0      1.0      0.0        0.0   \n",
       "50%        0.0          0.0               0.0      1.0      1.0        0.0   \n",
       "75%        0.0          0.0               0.0      1.0      1.0        0.0   \n",
       "max        1.0          1.0               1.0      1.0      1.0        1.0   \n",
       "\n",
       "       Waterfall  WheelchairFriendly  WildFlowers  Wildlife  \n",
       "count   10,025.0            10,025.0     10,025.0  10,025.0  \n",
       "mean       0.073               0.035        0.579     0.586  \n",
       "std        0.259               0.184        0.494     0.493  \n",
       "min          0.0                 0.0          0.0       0.0  \n",
       "25%          0.0                 0.0          0.0       0.0  \n",
       "50%          0.0                 0.0          1.0       1.0  \n",
       "75%          0.0                 0.0          1.0       1.0  \n",
       "max          1.0                 1.0          1.0       1.0  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describe numeric columns\n",
    "# Generates descriptive summary statistics of the central tendency, dispersion, and shape of the distribution\n",
    "# Note: By default only numeric (int64) fields are returned\n",
    "#       Excludes \"NaN\" (missing) values\n",
    "# Remove columns by specifying directly column names\n",
    "# Note: inplace=True changes the original DataFrame\n",
    "pd.set_option('max_rows', None, 'max_columns', None)\n",
    "round(state_trails_df_0_10000ft_0_50mi.drop(columns=['trail_id', 'stars', 'num_reviews', 'duration_mins', 'trail_difficulty_ord', 'elevation_gain_ft', 'distance_miles']).describe(), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.reset_option('display.max_rows')\n",
    "pd.reset_option('display.max_columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>state</th>\n",
       "      <th>city</th>\n",
       "      <th>trail_name</th>\n",
       "      <th>trail_difficulty</th>\n",
       "      <th>trail_region</th>\n",
       "      <th>route_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>10025</td>\n",
       "      <td>10024</td>\n",
       "      <td>10025</td>\n",
       "      <td>10025</td>\n",
       "      <td>10025</td>\n",
       "      <td>10025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>unique</td>\n",
       "      <td>1</td>\n",
       "      <td>846</td>\n",
       "      <td>9895</td>\n",
       "      <td>3</td>\n",
       "      <td>1889</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>top</td>\n",
       "      <td>California</td>\n",
       "      <td>Yosemite Valley</td>\n",
       "      <td>Dry Creek Trail</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Angeles National Forest</td>\n",
       "      <td>Loop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>freq</td>\n",
       "      <td>10025</td>\n",
       "      <td>202</td>\n",
       "      <td>4</td>\n",
       "      <td>5087</td>\n",
       "      <td>254</td>\n",
       "      <td>4850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             state             city       trail_name trail_difficulty  \\\n",
       "count        10025            10024            10025            10025   \n",
       "unique           1              846             9895                3   \n",
       "top     California  Yosemite Valley  Dry Creek Trail         Moderate   \n",
       "freq         10025              202                4             5087   \n",
       "\n",
       "                   trail_region route_type  \n",
       "count                     10025      10025  \n",
       "unique                     1889          3  \n",
       "top     Angeles National Forest       Loop  \n",
       "freq                        254       4850  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describe object columns (e.g. categorical, strings, or timestamps)\n",
    "# Generates descriptive summary statistics of the count, unique values, top values, and frequency\n",
    "# Note: \"top\" is the most common value; \"freq\" is the most common value's frequency\n",
    "#       Excludes \"NaN\" (missing) values\n",
    "round(state_trails_df_0_10000ft_0_50mi.describe(include='object'), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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>stars</th>\n",
       "      <th>num_reviews</th>\n",
       "      <th>duration_mins</th>\n",
       "      <th>elevation_gain_ft</th>\n",
       "      <th>distance_miles</th>\n",
       "      <th>trail_difficulty_ord</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>stars</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.159</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>num_reviews</td>\n",
       "      <td>0.159</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.084</td>\n",
       "      <td>-0.065</td>\n",
       "      <td>-0.097</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>duration_mins</td>\n",
       "      <td>0.054</td>\n",
       "      <td>-0.084</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.918</td>\n",
       "      <td>0.987</td>\n",
       "      <td>0.601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>elevation_gain_ft</td>\n",
       "      <td>0.054</td>\n",
       "      <td>-0.065</td>\n",
       "      <td>0.918</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.827</td>\n",
       "      <td>0.655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>distance_miles</td>\n",
       "      <td>0.037</td>\n",
       "      <td>-0.097</td>\n",
       "      <td>0.987</td>\n",
       "      <td>0.827</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>trail_difficulty_ord</td>\n",
       "      <td>0.096</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.601</td>\n",
       "      <td>0.655</td>\n",
       "      <td>0.499</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      stars  num_reviews  duration_mins  elevation_gain_ft  \\\n",
       "stars                   1.0        0.159          0.054              0.054   \n",
       "num_reviews           0.159          1.0         -0.084             -0.065   \n",
       "duration_mins         0.054       -0.084            1.0              0.918   \n",
       "elevation_gain_ft     0.054       -0.065          0.918                1.0   \n",
       "distance_miles        0.037       -0.097          0.987              0.827   \n",
       "trail_difficulty_ord  0.096        0.017          0.601              0.655   \n",
       "\n",
       "                      distance_miles  trail_difficulty_ord  \n",
       "stars                          0.037                 0.096  \n",
       "num_reviews                   -0.097                 0.017  \n",
       "duration_mins                  0.987                 0.601  \n",
       "elevation_gain_ft              0.827                 0.655  \n",
       "distance_miles                   1.0                 0.499  \n",
       "trail_difficulty_ord           0.499                   1.0  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the correlation matrix using the default method (Pearson correlation)\n",
    "state_trails_df_correlations = state_trails_df_0_10000ft_0_50mi[['stars', 'num_reviews', 'duration_mins', 'elevation_gain_ft', 'distance_miles', 'trail_difficulty_ord']].corr(method='pearson')\n",
    "round(state_trails_df_correlations, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure and a set of subplots\n",
    "# Note: figsize: a tuple (width, height) in inches\n",
    "fig, ax = plt.subplots(figsize=(10, 8));\n",
    "\n",
    "# Return an array of zeros with the same shape and type as a given array\n",
    "mask = np.zeros_like(state_trails_df_correlations, dtype=np.bool)\n",
    "# Return the indices for the upper-triangle of array\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "# Plot the correlation matrix (color-encoded) using Seaborn's .heatmap() function\n",
    "# Note: vmin, vmax: values to anchor the colormap, otherwise they are inferred from the data & other keyword arguments\n",
    "#       annot: write the data value in each cell\n",
    "ax = sns.heatmap(state_trails_df_correlations, vmin=-1, vmax=1, annot=True, mask=mask, ax=ax);\n",
    "\n",
    "ax.set_xticklabels(ax.xaxis.get_ticklabels(), fontsize=10);\n",
    "ax.set_yticklabels(ax.yaxis.get_ticklabels(), fontsize=10, va='center', rotation=0);\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a simple linear relationship between two variables using Seaborn scatterplot with regression line\n",
    "# Note: data: DataFrame name\n",
    "#       height: height (in inches) of each facet\n",
    "#       aspect: aspect ratio of each facet, so that \"aspect * height\" gives the width of each facet in inches\n",
    "#       fit_reg=True estimates and plots a regression model relating the 'x' and 'y' variables\n",
    "#       ci: size of the confidence interval for the regression estimate\n",
    "#       truncate: if 'True', the regression line will be bounded by the data limits\n",
    "#       scatter_kws=alpha: blending value, between 0 (transparent) and 1 (opaque)\n",
    "sns.lmplot(x='distance_miles', y='elevation_gain_ft', data=state_trails_df_0_10000ft_0_50mi, height=8, aspect=10/8,\n",
    "           fit_reg=True, ci=95, truncate=True, scatter_kws={'alpha':0.2});\n",
    "plt.xlabel('Distance (Miles)');\n",
    "plt.ylabel('Elevation Gain (Feet)');\n",
    "plt.title('Elevation Gain by Distance');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Note: data: DataFrame name\n",
    "#       hue: variables that define subsets of the data, which will be drawn on separate facets in the grid\n",
    "#       height: height (in inches) of each facet\n",
    "#       aspect: aspect ratio of each facet, so that \"aspect * height\" gives the width of each facet in inches\n",
    "#       legend: if 'True' and there is a 'hue' variable, add a legend\n",
    "#       legend_out: if ''True', the figure size will be extended, and the legend will be drawn outside the plot on the center right\n",
    "#       fit_reg=True estimates and plots a regression model relating the 'x' and 'y' variables\n",
    "#       ci: size of the confidence interval for the regression estimate\n",
    "#       truncate: if 'True', the regression line will be bounded by the data limits\n",
    "#       scatter_kws=alpha: blending value, between 0 (transparent) and 1 (opaque)\n",
    "sns.lmplot(x='distance_miles', y='elevation_gain_ft', data=state_trails_df_0_10000ft_0_50mi,\n",
    "           hue='trail_difficulty_ord', height=8, aspect=10/8, legend=True, legend_out=False,\n",
    "           fit_reg=True, ci=95, truncate=True, scatter_kws={'alpha':0.2});\n",
    "plt.xlabel('Distance (Miles)');\n",
    "plt.ylabel('Elevation Gain (Feet)');\n",
    "plt.title('Elevation Gain by Distance');\n",
    "plt.legend(title='Trail Difficulty', labels=['Easy', 'Moderate', 'Hard']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dag3EPwz8hRjj71KePwxwCri9nsWEEN4fQvhGCGExhHAyhHB/5fFvDSEcDyEshRAmQwi3Vl2zI4TwGyGEuRDC2RDCh9b9nhteK0mSJNUaiLuBhcrtlUC8s+qxLQshfDvwUeBHgF3AA5SPhh4BPgP8DLAXOAb8x6pLPwzcBdwKTAD/IITw3srvudm1kiRJSrhaA/F/A34lhLADIIQQgI8Af1DHWn4O+PkY4xdjjKUY48sxxpeB7wGejjH+XowxSzkA3xtCOFS57oeAj8QYZ2OM3wD+PeWONjVcK0mSpISrNRB/CLgBuAjsptwZvpU6zSEOIXQDh4HREMJzIYSXQggfCyH0A/cAT648N8a4CJwE7gkhDFfqerLqt3uycg1XuvYyNXwwhHAshHAsnU7X42VJkiSpDWy2DzEAMcY54C+FEMYoB+EzMcazdazjOsqn4X0vcD+QB/4L8NOUp2asT6gXKU+r2Fl1f/0Ym1y7RozxE5QXDXL48OG4flySJEmdqdYOMSGEfcCfBSZijGdDCDeGEG6uUx2Zysf/J8b4aoxxBvgV4Dsod6OH1j1/CJjn0hzmocuMscm1kiRJUm2BOITwIDAF/FXKC9SgvJDt4/UoIsY4S3kbt+rO7Mrtp4F7q2oZBO6gPDd4Fni1erxy++nNrq1H3ZIkSWp/tXaI/xXwl2OM7wUKlcceB95Wx1r+A/B/hhDGKnOD/x7wh8BngdeHEN4XQugDfhZ4qurkvE8BPx1CGK4slvsA8JuVsc2ulSRJUsLVGohvizH+r8rtlc5tjhrnINfoI8CXgRPAN4AngF+IMaaB9wG/AMwCbwfeX3XdP6W8UO5F4GHgl2KMnwOo4VpJkiQlXIhx8/VjIYTPU94S7Y9DCOdjjHtDCH8G+CcxxiONLnK7HT58OB47dqzZZUiSJKl+wkYDtXZ4fwr4wxDCHwH9IYR/B3wn8F11KE6SJElqmpqmTMQYvwi8kfJitN+gfGzz22KMX25gbZIkSVLDXbFDHEK4fmW/4RjjK8C/2JaqJEmSpG2yWYf4RPWdEMJnGliLJEmStO02C8TrJx8faVAdkiRJUlNsFog9wliSJEkdbbNdJnpCCBNc6hSvv0+M8aFGFSdJkiQ12maBeJryrhIrzq27H4Hb612UJCXRhaUcp2YWmcvkGepPcWBkkD0Dvc0uS5I63hUDcYzxtm2qQ5IS7cJSjidOzzLQ28PwQC+ZfJEnTs9y3/5hQ7EkNVitRzdLkhro1MwiA709DPT2EEJYvX1qZrHZpUlSx9swEIcQvhxC+L4QwmVbEyGE3hDC94cQHm9ceZKUDHOZPP2p7jWP9ae6mcvkm1SRJCXHlaZM/BDw88DHQwhfBaaAeWAXcBB4M/AQ8MMNrlGSOt5Qf4pMvshA76Vvy5l8kaH+VBOrkqRk2LBDHGN8Jsb4vcDrgd8CMsAIsAR8CrgnxviXY4zf2JZKJamDHRgZZClXYClXIMa4evvAyGCzS5OkjrfZLhNUjm7+rW2oRZISa89AL/ftH+bUzCKzSzmG+lOMX++COknaDpsGYknS9iiHYgOwJG03d5mQJElSohmIJUmSlGgGYkmSJCVazXOIQwh/BngTsLP68Rjjz9a7KEmSJGm71BSIQwgfA74fmKS87dqK2IiiJEmSpO1Sa4f4B4B7Y4xnGlmMJEmStN1qnUM8A1xoZCGSJElSM9TaIf5l4HdCCP8ceK16IMb4fN2rkiRJkrZJrYH445WPf2Hd4xHorl85kiRJ0vaqKRDHGN2eTZIkSR3JoCtJkqRE27BDHEL4XIzxvZXbj7LBFmsxxgcaVJskSZLUcFeaMvGpqtu/1uhCJEnqFBeWcpyaWWQuk2eoP8WBkUH2DPQ2uyxJG9gwEMcYP111+5PbU44kSe3twlKOJ07PMtDbw/BAL5l8kSdOz3Lf/mFDsdSirubo5uuAtwEjQFh5PMb4Gw2oS5KktnRqZpGB3h4Gesv/xa58PDWzyH37DcRSK6r16ObvBn4beBa4B3gaeD3wGGAgliSpYi6TZ3hdJ7g/1c3sUq5JFUnaTK0d4n8G/EiM8fdCCLMxxvtCCD9CORxLktQ2Gj2/d6g/RSZfXO0MA2TyRYb6U3X7MyTVV63bru2PMf7eusc+Cfz1OtcjSVLDrMzvzRVKDA/0kiuUeOL0LBfq2L09MDLIUq7AUq5AjHH19oGRwbr9GZLqq9ZAPF2ZQwzwQgjhncAdeEqdJKmNVM/vDSGs3j41s1i3P2PPQC/37R+mt6eL2aUcvT1dLqiTWlytUyb+PfBu4D8D/xKYBErALzeoLkmS6m675veWQ7EBWGoXtR7d/NGq258KIRwFBmOM32hUYZIk1ZvzeyVdzjUd3RxjPG0YliS1G+f3SrqcKwbiEMK3hxD+ZdX9TAihWPXrWxtfoiRJ9eH8XkmXs9mUib8L/GrV/WXK+w8DvBP4EPC/GlCXJEkN4fxeSettFojfCDxUdT/GGE8ChBBeBH6xUYVJUpI0em9cSdLGNptDvGfd/durbgdgb33LkaTk2Y69cSVJG9ssEL8MvGnlToxxtmrsPuCVRhQlSUmyHXvjSpI2tlkg/jTw8RDCDdUPhhBuBD4G/HajCpOkpJjL5OlPrT3nqD/VzVwm36SKJClZNptD/M+Bw8BzIYTHgbPADcBbKR/O4RxiSdoi98aVpOa6Yoc4xpiPMf5F4LuAx4HFyse/FGP8zhhjYRtqlKSO5t64ktRctZ5U9yfAnzS4FklKpJW9cU/NLDK7lGOoP8X49e6NK0nbpaZALElqrGbvjeu2b5KS7JqObpYkdQ63fZOUdAZiSUo4t32TlHQGYklKOLd9k5R0Nc0hDiEcAH6B8iEdO6vHYoz7G1CXJGmbuO2bpKSrdVHdp4GTwE8BS40rR5K03Q6MDPLE6fJBpP2pbjL5Iku5AuPXDze5MknaHrUG4nuAd8UYS40sRpK0/dz2TVLS1RqIHwHuA77SwFokSU3S7G3fJKmZag3ELwCfCyF8lvLxzatijD9b76IkSZKk7VJrIB4E/hBIAbdUPR7rXpEkSZK0jWo9uvlHGl2IJEmS1Aw1H90cQrgL+CvATcDLwP8XY3y2UYVJkmrjscuStDU1HcwRQvhOygvqDgHngXHgWAjhLzawNknSJjx2WZK2rtYO8S8C3xVjnFx5IIRwBPgY8F8bUJckqQbVxy4Dqx9PzSy6a4Qk1ajWo5tvBh5d99hjlcclSU3iscuStHW1BuL/TfmUumofqjwuSWqSlWOXq3nssiRdnVqnTPw48AchhL8DnKG89doS8J2NKkyStDmPXZakrat127XjIYTXAe8EbgBeAR6PMfozOUlqIo9dlqStq3nbtRhjgW+eRyxJajKPXZaSLcbyOWkhhCZX0r42DMQhhG/EGF9XuX2GDU6lizHub1BtkiRJ2sByoch8tsBCtsAtewfoNg9fsyt1iD9QdfuvNboQSZIkXVmMkYXlAnPZAsvrFtTq2m0YiGOMj1XdHYsx/t7654QQvrchVUmSJGlVrlBiLptncblAsXTZH9prC2rddu3XN3j8E/UqRJIkSZfEGJnP5nnlQoaXZpeYy+QNww1yxUV1IYTbKze7QggHgOrZKbcD2UYVJklSo1xYynFqZpG5TJ6h/hQHRgbdmUMtI1coMZ/Ns2A3eNtstsvEc5QX0wXg5Lqxs8CHG1CTJEkNc2EpxxOnZxno7WF4oJdMvsgTp2e5b7/b1al5Yows5orMZfJknRu87a4YiGOMXQAhhIdjjA9uT0mSJDXOqZlFBnp7GOgt/xe48vHUzGJHb19nV7w12Q1uDTXNITYMS5I6xVwmT3+qe81j/alu5jKde9bUSlc8VygxPNBLrlDiidOzXFjKNbu0RFrZKeLVi+W5wRedG9x0NR3MEULoAX4CeBAYoWoucYzxgcaUJklS/Q31p8jki6udYYBMvshQf6qJVTVWUrvirSZfLDGXsRvcimrdZeJfAj8KPAK8BfjPwBjwUIPqkiSpIQ6MDLKUK7CUKxBjXL19YGSw2aU1TBK74q2iuht85rzd4FZVayD+HuDPxRh/FShUPn43MNGwyiRJaoDyUdfD9PZ0MbuUo7enq+MX1K10xat1ele82fLFEucXc5w+v8T0XJZMzoVyraymKRPAAHCmcjsTQhiIMR4PIdzXoLokSWqYciju3AC83oGRQZ44PQuUO8OZfJGlXIHx64ebXFlnKf/EochcNm8AbjO1BuJvAG8FvgQcAz4cQpgDXm5UYZIkqT5WuuKnZhaZXcox1J9i/PrO7opvp3yxxHy2wEK2QKFUanY5uga1BuK/A6y81fkQ8HFgF/DBRhQlSZLqK2ld8UZb6QbPZ8tz0NXeagrEMcYvV91+Fvi2hlUkSZLUogrFEnN2gztOrduuPQn8NvC7McYzmz1fkiSpkywuF+wGd7Bad5n4MOU5xN8IITwcQvjREMLexpUlSZLUXIViidnFHKfPLfHaXNYw3MFqPanuszHG7wduAH4D+EvAmRDCf21kcZIkSdttKVfg7MUsp88vMbuUc2pEAtS6qA6AGON8COHTwAWgF/iOhlQlSZK0jQqVnSLmnRucSLXOIQ7Ae4AfoNwdfhH4NPBDjStNkiSpsZZy5RC8uOx0iCSrdQ7xK8D/W/n4rhjjfTHGX6r3ArsQwl0hhGwI4berHvuBEMKLIYTFEMLvV89dDiHsDSF8tjL2YgjhB9b9fhteK0mSkqlQLHFhKceZ80ucvZg1DKvmQPxdMca7Yow/E2P8RgPr+TfA6hZvIYR7gH8H/CBwHbAE/Nt1z89Vxv4q8PHKNbVcK0mSEmQpV+C1uSxnZjOcX8yRLzo1QmW17kP8pRDCIeD7gOtjjP9H5X5vjPGpehQSQng/5bnJfwrcWXn4rwJ/EGN8pPKcn6G808UuoAS8D3h9jHEBeKyyyO8HgX90pWtjjPP1qFlSY1xYynFqZpG5TJ6h/hQHRgY9UUvSNSkUSyxUtkwzAGsjNXWIQwjfBzwK3EQ5cALsBH6lHkWEEIaAn6d8Cl61e4AnV+7EGE9S7ggfrPwqxBhPVD3/yco1m117uRo+GEI4FkI4lk6nt/aCJF2zC0s5njg9S65QYnigl1yhxBOnZ7mwlGt2aZLaSCZXtBusmtU6ZeLngW+LMf4Yl45wfhK4t051fAT49RjjS+se3wlcXPfYRcrHRu8E5jYY2+zabxJj/ESM8XCM8fDo6OhVli+pXk7NLDLQ28NAbw8hhNXbp2YWm12apBZXLMXVucGvXsywuFwgxtjsstQGat12bQxYmRoRqz5u+V9ZCOFNlI+Cvu8ywwvA0LrHhoB5ylMmNhrb7FpJLWouk2d43fSI/lQ3s3aIJW0gkysyn82zmCsagHVNag3EX6E8VeJTVY+9H/hSHWo4AtwGnC7v7sZOoDuEcDfwOaq60CGE24EdwAnKgbgnhHBXjPHZylPuBZ6u3H76CtdKalFD/Sky+SIDvZe+PWXyRYb6U02sSlKrKZYiC9kCc9m80yG0ZbUG4r8N/I8Qwt8EBkMIf0x5Lu6fqUMNnwB+t+r+36cckH+ccmf6CyGE+4GvUp668ZmVRXEhhM8APx9C+FvAm4DvAr6l8vv8zpWuldSaDowM8sTpWaDcGc7kiyzlCoxfP9zkyiS1ArvBaoRad5k4XtlV4i8AfwicAf6wsrvDlsQYlyhviQZACGEByMYY00A6hPBjlMPtPuBPgB+puvwnKB8lPQ2cA348xvh05fd9epNrJbWgPQO93Ld/mFMzi8wu5RjqTzF+/bC7TEgJZjf4yi5m8nzu66/y5994Y7NLaVvBd1ff7PDhw/HYsWPNLkOSpETL5ovMZewGX87CcoE/fW6Gh6bSfOXFWYqlyKP/YIJb9g40u7RWFjYa2LBDHEJ4lBoWzcUYH7jGoiRJktawG7yxTL7IF06eY3Jqmi+dOk++eCmmDfR2c/zsvIH4Gl1pysSvbVsVkiQp0bL5InPZPIvLdoOrLeeLPP7CeSaPp/ni8+dYLlx6k9Db08U7bt/LxPgY3/eWW9jZV+vSMK234WcuxvjJ7SxEkiQlS6kUmV8uMJexG1wtVyhx7MXzHJ1K8/nnzpHJF1fHeroCb71tL+85NMo779i3uiNPf1WDh+4AACAASURBVG93s8rtCFd8KxFC+Ncxxr9ddf9vxhh/ver+f44xvq+RBUqSpM5iN/ibFUuRr56eZfJ4mseem2FhubA61hXgLbcOMzE+xrvvHLET3ACbfUZ/mPKWayt+Cfj1qvvfXu+CJEnt4cJSjlMzi8xl8gz1pzgwMuhuINrQSjd4PpsnV7AbDOUQ/LWXLzI5Nc0jJ2a4mMmvjnUFeOPNe3jPoVHuv3OU3QPuxd5ImwXi9avxNlydJ0m6pNPD4oWlHE+cnmWgt4fhgV4y+SJPnJ7lvv1ukae17AavFWPkmVfnmDye5uETac4trj2F8w03DXFkfIwHD46yd9Cvpe2yWSBe/y/Xf8mSOla9QmwSwuKpmUUGentW5y+ufDw1s8h9+zvjNera2Q1eK8bIidcWmJya5uhUmun55TXj49fvYmJ8lCMHRxkb6mtSlcm2WSDuCSFMcKkzvP6+M7gldYR6htgkhMW5TJ7hdZ+X/lQ3s0u5Da5QEmTzReazBRaXC5QS3g2OMfL8zCJHp9JMTk3zyoXsmvE7RgeZGB/jyPgoN+7pb1KVWrFZIJ6mfBLcinPr7k/XvSJJaoJ6htgkhMWh/hSZfHH18wTlPVKH+p3nmDSlUmQhV94pwm4wnD63xOTUNJNTaU6fX1oztn/vABPjo0yMj7F/n/sFt5IrBuIY423bVIckNVU9Q2wSwuKBkUGeOD0LlD9PmXyRpVyB8euHm1yZtovd4EteuZBZ7QSfTC+uGbthdx/vOVTuBN8+MkgILsdqRe7bIUnUN8QmISzuGejlvv3DnJpZZHYpx1B/ivHrO2eOtC5vpRs8ny2wXLU3bhJNz2U5eiLN5FSaqbPza8bGdu3gSKUTfPC6nYbgNmAgliTqG2KTEhbLr7OzXpMub7lQZC5jN/j8Yo6HT6Q5OjXN116eWzO2b7CXBw+OcmR8lLtvHKLLENxWDMSSRP1DrGFR7S7GyMJygbmEd4MvLuV55NlyJ/iply5Qqno/sLs/xQMHR5gYH+MNN+2mu8sQ3K4MxJJUYYiVyt3g+WyBhWxyu8EL2QKPPjfD0alpvvLi7JoQvHNHD+++c4SJQ6O8ef+wIbhDGIilDtPpB0JIqj+7wbCUK/CFk+d46HiaYy+eJ1+8lIL7U9286859TIyPcfi2YVLdXU2sVI1gIJY6SBIOhJBUP7lCiblsPrHd4Gy+yOOnzjM5Nc0Xnz+/Ztu4HT1dvOP2fUyMj/L2A3vZkfLohU5mIJY6SBIOhJC0NSvd4PlsgWwCu8G5Qokvv3Ceo1NpPn9yhmz+UghOdQfedtteJg6N8c7b99HfawhOCgOx1EGScCCEpGuTK5SYz+ZZWC5QLCWrG1wolnjizAUeOj7NY8/NsLh86Y1Ad1fgLbcOMzE+yrvuHGHnDqNREvm3LnWQJBwIIal2Se4GF0uRp166wNGpNA+fSDOXLayOdQV40y17ODI+xv13jbDb75GJZyCWOkgSDoSQtLmkdoNLMfLMK3M8dHyaR56d4fzi2p+OveGm3UyMj/LAwVH2DjqNTJcYiKUOkpQDIdxJQ/pmMUYWc0XmMvlEdYNjjEy9Ns/k8TRHp9KkF5bXjB+6fhcTh8Y4cnCU0V07mlSlWp2BWOownb6XrjtpSGslsRscY+T59CIPTU1zdCrNqxeza8bvHN3JxKHyqXE37O5vUpVqJwZiSW3FnTSkS93g+WyeTC453eAXzy0yOZVm8vg0Z2Yza8Zu3TfAe8bHODI+yi17B5pUodqVgVhSW3EnDSVZvlhiPltgPptPTDf45QsZjk5NM3k8zfMzi2vGbtrTz8ShUSbGxzgwMtikCtUJDMSS2oo7aShpktgNfm0uy9Gp8pzgqdfm14xdN7SDIwdHmTg0xl1jOwnBo5O1dQZiSW3FnTSUFEnrBp9bWObhE2kmp9I8/crcmrF9g708OD7Ke8bHeN0NuwzBqjsDsaS2kpSdNJRMMUaWckXmswWWcoXNL2hzF5ZyPPLsDEenpnnyzEWqY/+e/hQPHBxlYnyU19+0m+4uQ7Aax0Asqe10+k4aSp6VbvBCtkChVNr8gjY2n83z2LMzTE6l+erpWaqb37v6erj/zhGOjI9y3/5hQ7C2jYFYaiL305WSbbFyilynd4OXcgX+9OQ5Hjo+zbEXZilUpeCB3m7edecIE+OjvOXWYVLdXU2sVEllIJaaxP10pWQqrM4N7uxucDZf5IvPn+fo1DRfPHWeXOHSa+3r6eKdd+xjYnyMtx3YS2+PIVjNZSCWmsT9dKVkWcoVmMt0djc4VyjxpVPnmZya5gvPnyObvxSCU92Btx/Yx8T4KO+4Yx/9qe4mViqtZSCWmsT9dKXOl4RucKFY4tiLsxydSvP552ZYrNoarrsrcPjWYSYOjfGuO/YxuMPYUW+p7i4GertxuvXW+C9TahL305U6V6d3g4ulyJNnLvDQ1DSPPTvDXPbS6+wKcN8te5g4NMa77xzxe1qddYXAQG83fb3dDKS66XHOdV0YiKUmcT9dqbMUiiUWKovk8sXO6waXYuTrL19kcirNIyfSzC7lV8cC8IabdzMxPsr9d42yd9BpX/W0I1UOv/293fQ51aQhDMRSk7ifrq6Vu5O0lqXcyk4RRWLsrAM0YowcPzvP5NQ0R6fSzCysndJ19w27ODI+xoMHRxndtaNJVXaenq4u+nvLAbg/1e32c9vAQCw1kfvp6mq5O0lrKJYi89l8R3aDY4ycTC/y0PFpHj6R5tWL2TXjd43tZGJ8lCPjY1y/u69JVXaWEAL9qXL47e/tdteNJjAQS1IbcXeS5srkisxn8yx2YDf4hXOLHD2e5qGpaV6azawZOzAyyJHx8qlxNw8PNKnCzrKyGG6gt4e+VJfHUTeZgViS2oi7k2y/Tu4GvzS7xORUmqNTaU7NLK4Zu3m4vxKCxzgwMtikCjtHd1e5C+xiuNZkIJakNuLuJNunU7vBZ+eyHD0+zeRUmmenF9aMXTe0g4nxMSbGR7lzbKddyy3qq5oG4WK41mYglqQ24u4kjVUsRRayBeay+Y7qBs8sLPPwiTSTx6d55tX5NWMjO3tXO8GHrt9lCN6CVHcXfaluBiqL4bpcDNc2DMSS1EbcnaQxsvkic9k8i8ud0w2eXcrxyIkZJqem+dpLF6l+VcMDKR44WJ4T/PqbdtNlCL4mq4vhKgHYxXDty0AsSW3G3UnqoxO7wXOZPI89N8PkVJonTs9SqkrBQ3093H9XOQTfe8set/K6Rr09XfSnXAzXaQzEkqRE6bRu8OJygc+fPMfRqWmOvTBLoSoFD/Z28+67RjgyPspb9g+7kOsarCyGW+kC+znsTAZiSVLHK5Ui88sF5rN5coX27wZn8kW+ePIck1NpHj91jnzxUgju6+ninXfs4z2HxnjrbXv9Mf5VCiGwo6e8JVpfysVwSWEglpQonvKWLNl8kflsgYXlQtt3g3OFEo+fOs/RqWm+cPIc2apgn+oOvOP2fUyMj/L22/fRb4i7KqnurtUOsIvhkslALCkxPOUtGUqlyEKuwFym/bvB+WKJr7w4y+RUms8/N8NSrrg61tMVOHzbMBPjY3zLHfsY3OF/6bXqCqG8JVpveUeIlNMgEs+vHkmJ4SlvnW2lG7y4XKDUxt3gYinyv89cYPL4NI8+N8N8trA61hXgzfuHmRgf5d13jbCrz/2na9Xb08VAb0/5cAwXw2kdA7GkxPCUt86z0g2ezxZYzhc3v6BFlWLkay9f5OjxNA+fSHMhk18dC8C9t+zmyPgYD9w14k8zatTdFVanQQz09rirhq7IQCwpMZp1ypvzlutvuVBkLtPe3eAYI994dZ6HpqZ5+ESacwtr35jdc+MQE+OjPHBwlJGdO5pUZfsIIdCX6lrdEWJHj/OoVTsDsaTEaMYpb85brp8YV3aKaN9ucIyRZ6cXmDw+zdETaV6bW14zfvC6nUyMj3FkfJTrhvqaVGX7WFkMN9DbTV+Pi+F07QzEkhKjGae8OW9565YLlZ0isu3bDT41s8hDx6c5OpXm5QuZNWO3jwyuHp1803B/kypsD12hMg2iMhXCxXCqFwOxpETZ7lPenLd8bWKMLFS6wdk27QafOb/E0ak0D01N8+K5pTVjNw/3857xMY4cGuW2fYNNqrA97EitzAPuZkePi+HUGAZiSW2l3ebjNmvecrvKFUrMZ/MsLBcoltqvG3z2YpbJqWkmj6d5Lr2wZuyG3X2rneA7RgebEuzms3nOzGZYzBYY7OvhluH+ltupoqeri77eSztCuBhO28FALKlttON83GbMW2437d4NTs8vc/REmqNT03zj1fk1Y6M7d5RD8KFRxq/b1dTu5nw2zzOvzNGX6maoP0U2X+SZV+a4+8ahpoTi6nC+d2cv49ft4rrdfS6GU1MYiCW1jVrn47ZSF7kZ85bbRTt3g88v5njkRJrJqTRff/ki1dUPD6R48OAo7zk0xt03DtHVIj/iPzObKR9GkSp/3ax8PDOb4e4btjcQZ/JFXji3yJ7+Xm7Z00+2UOJkeoGh/pSBWE1hIFZHaqVApPqpZT5uK3aRt3veciuLMbKYKzKfzZPJtVc3+GImz6PPznB0apr/feYC1Rl+qK+H++8qd4LvvXlPS/6YfzFb+KapOn2pbuaq9jxulPWL4b7+8kWuH+qvenNbXhznYlM1i4FYHacVA5Hqo5b5uO7q0JryxRLz2QLz2XxbdYMXlgt8/rkZJqfSfOXF2TW1D+7o5t13jjAxPsab9++hp8V3PBjs6yGbL652hqF8ut9gX2OiwI5UNwOrewKvXQznYlO1GgOxOo6BqHPVMh/X/2hbR4yRpVyRuTbrBmfyRb5w8hyTx6f50gvnyRcvheC+VBffcscIE+OjvPW2vfT2tHYIrnbLcD/PvDIHlDvD2XyRbL7I7aP12eXiahbDudhUrcZArI5jIOpctczH9T/a5lvpBi9kCxRKpWaXU5PlfJHHXzjP0eNpvvD8OZYLl+ru7eniHbfvZWJ8jLcf2Etfqj3nuO7qS3H3jUOcmc0wl8kz2NfD7aOD17ygbuVkuIFUD329XVc199fFpmo1BmJ1HANRZ9tsPq7/0TbHSjd4PltgKVdodjk1yRdLHHthlsmpaT7/3DkyVTtc9HQF3nZgLxPjo7zzjn1rvp+0s119qS0toEt1dzFQNRf4WnfNcLGpWk1nfIVLVQxEyeZ/tNursDo3uD26wcVS5KunZzk6lebRZ2dYWL4U3rsCvOXWYSbGx3j3nSPsbNDc2nZSvRhuINVd13nSLjZVK/GrXR3HQJQMV9pJxP9oG28pV2Au0x7d4GIp8rWXLzI5Nc2jJ2a4ULWrQgDuvWUPE+OjPHDXKLsH/EnSlRbDSZ3KQKyOZCDqbO4k0hzt1A0uxcgzr8xxdCrNwyfSnFtcu4bgnhuHmBgf48GDI+zbuaNJVbYGT4aTDMRSXbjv8fZyJ5HttZQrh+DF5dbuBscYOfHaApNT0xydSjM9v7xmfPz6XUyMj3Lk4ChjQ31NqrL5trIYTupUBmJpi+xWNsaV3mRc7U4ivmG5eoViafU45XyxdbvBMUaen1nk6FSayalpXrmQXTN+x+ggE+NjHBkf5cY9/U2qsvnqtRhO6lQGYmmL7FbW32ZvMq5mJxHfsFydTGXf4KVckRhb9wCN0+eXmDxe7gS/eH5pzdj+vQNMjI8yMT7G/n0DTaqwuRq5GE7qRAZiaYvc97j+NnuTcTU7ifiGZXPFUmQ+m2/5bvArFzKrneCT6cU1Yzfu6VvtBN8+MpjIDmj1Yrh23StZahYDsbRF7ntcf5u9ybianUR8w7KxTK7IfDbPYgt3g6fnsjx8Is1DU2mmzs6vGRvbtYMjlU7wwet2Ji4E93R1rXaBXQwnbY2BWNoi9z2uv1reZNS6k4hvWNYqliIL2QJz2XzLdoPPL+Z4+ESayePTfL1y1PCKvYO9PHhwlInxUe6+cYiuBIVgF8NJjWMglrbIfY/rr55vMnzDUtbq3eCLS3kefS7NQ8fTPPXSBUpVJe7uT/HAwREmxsd4w027E9UJdTGctD1CK35jbLbDhw/HY8eONbsMXYG7BnS+ev4dJ/XfS6t3gxeyBR57boajU9Mce3F2TQjeuaOH++8a4cj4KG/eP5yYENwVAgO93fS5GE5qhA2/kRiIL8NA3Nqqdw2o7vi5a8D2S2rQbHXZfHmniMXl1usGZ3JF/vTkDJNTab78wnnyxUv19ae6eded+5gYH+PwbcOkEhIGXQwnbZsNA7FTJtR2mrlrgAHwErczay2lUmR+ucBcpvW6wdl8kcdPnWdyapovPn+eXOFSfTt6unjH7fuYODTK22/by44EBEIXw0mtx0CsttOsXQNaIQC2UiC/mjcmrVR3p2nVbnCuUOLLL5zn6FSaz5+cIZu/FIJT3YG33raXifExvuWOffT3dnYIrl4M19/bTW9PMjrfUjsxEKvtNGvXgGbvZ9sKgbxarW9MWq3uTrDSDZ7P5td0W5utUCzx1dMXmJya5rHnZlhcLq6OdXcF3nLrMBPjo7zrjhF29nX2fz8uhpPaS2d/R1JHatauAc3ez7bZgXy9zd6YrHSFv3p6lh09Xdw+Ut4nttl1t7Nsvsh8tsDCcuGy3eD5bJ4zsxkWswUG+3q4ZbifXX2NfaNYLEWeeukCk1NpHjmRZi5bWB3rCnDvLXuYGB/j/rtG2N3BW915MpzU3gzEajvN2uas2fvZNjuQr3elNybVXeEuoIvA1Nl5xq/fxa6+lAdjXIVSKbKQK88NvlI3eD6b55lX5uhLdTPUnyKbL/LMK3PcfeNQ3UNxKUaeeWWOyak0D59Ic35x7d/lG24aYmJ8jAcOjrJ3sHPf9LgYTuocBmK1pVoPZainZu9n2+xAvt6V3pishOGB3h529aXIF0v0pbp55UKG8evLryMEeOL0rPOKN7BcKDKXKbC4XKBUw9zgM7MZ+lLd9KfK/z5WPp6ZzXD3DVv/NxJjZOq1eSaPl0Pw9PzymvHX3bCLI+NjHDk4yuiuHVv+81qRi+GkzmUglmrU7AM4mh3IL2ejNybV3ewb9/QzdXaOHT1dLGQjS7kC0/NZArCjp9t5xVVWusHz2QLL+eLmF1RZzBa+6c1RX6qbuUx+w2s2m2IRY+T5mUUmj08zOZXm1YvZNdffObqTiUOjHBkf5Ybd/VdVbzvwZDgpOQzE0lVoRmd67Z/dHifiVXezd/WlGL9+iOfTC5Qo0dvTxe7+FDt6ultmPnSzLRcqc4OztXWDL2ewr4dsvrjaGYbynOPBDRavXWmKxfnFHJNTaY5OpTl9fmnNdbfuHaiE4DH27x24plpbmYvhpGQyEEttpJmB/Gqs72Z3dwVuGu5f7QA/PDVN/7o5l0mbVxxjZGG5wNw1dIMv55bhfp55ZQ4od4az+SLZfJHbRwcv+/z1UywWskW+8PwMH5t8jpcvrO0E37SnnyPjo0yMj3JgZLBtQmItiwxdDCcJDMSS6mxld4mlXJHpuWV29vVw457+Nd3sVpsPvZ1yhRJz2fyWusGXs6svxd03DnFmNsNcJs9gXw+3jw5uuKBuMVugUIo8+uwMX3lxlhfXdYLHdu1gYnyUiUNj3DW2s21C8IordcBHdvW5GE7SGgZiSXVTvbvELcMDq/Oc1y+Ya8X50I200g2ezxbI1qEbvJFdfalNF9CdW1jm4RMz/LevvcrzM4trxob6enjzrcN875tv5nU37Gq7EFytugPe3RXYt7OHUimSL0Zu2tN5850lbY2BWFLd1LpXcjvNh96KXKHEfDbPwnKBYql5p8hdXMrzyLNpJqemefLMRaor2bmjhzfv38Mbb97NDbv7eP1Nuxu+d3GjhRDIF0pct6uPvt5uUpVpEDHGRE3LkVS7lgjEIYQdwL8Fvg3YC5wE/nGM8b9Xxr8V+DfAfuBx4IdjjC9WXftx4HuBJeBfxBh/per33vBaSfV1NXslt8t86KsVY2QxV2Quk29oN3gz89k8jz13jqNT03zlxVmq8/jOHT3cf9cI77h9L3sGesnmitt2kEejrF8Md2EpR65QWg3DkJxpOZKuXksEYsp1nAEeBE4D3wH8pxDCG4AF4DPA3wL+APgI8B+Bd1Su/TBwF3ArcD0wGUJ4Jsb4uRDCyCbXSqqjpM8NbnY3eClX4PPPnWNyappjL8xSqKpjoLebd905wsT4KG+5dXhNUGxH3V2B/lQ3fRsshkvatBxJWxMud/xnKwghPAX8HLCPclf3WyqPDwIzwH0xxuMhhFcq4/+jMv4R4K4Y4/tDCB+80rUb/dmHDx+Ox44da+TLkzpS9Rzi6hDSqfsLr3SD57N5MrnmdIOz+SJffP48k1PTPH7q/JrT7Pp6unjH7fuYODTG2w/spbenvUPw1Z4Mt7LA08NfJFVsuDCiVTrEa4QQrgMOAk8DPw48uTIWY1wMIZwE7gkhvAbcUD1euf3dldv3bHQtsGEglnRtkjI3OF8sMZ8tMJ/NN6UbnCuU+PIL53no+DRfeP4c2fylEJzqDrz9wD4mxkd5xx37vml7u3ay1ZPhOnVajqT6a7lAHEJIAb8DfLLSAd4JpNc97SKwC9hZdX/9GJXxja5d/+d+EPggwP79+7fyEqS6aLfu1vp6771lT0vXe7VijCzlisw1qRtcKJb4yulZjk6leezZGRaraujuCrz1tmGOjI/xrjv2Mbij5b6116T6ZLj+3u6272hLah8t9V0zhNAF/BaQA36y8vACMLTuqUPAfGVs5X523dhm164RY/wE8AkoT5m45hehltJuoXJF9dSDdjjauN3qvRor3eCFbIFCqbT5BXVULEWefOkCk8fTPPpsmrlsYXWsK8B9t+xh4tAY775zpG3naW/lZLh2/fqW1HpaJhCH8nfBXweuA74jxpivDD0N/FDV8waBO4CnY4yzIYRXgXuB/1l5yr2Va654bQNfilpEO4e0WrcvaxXtVm8tFiv7Bi/lChs+p5aT0K5WKUa+/vJFJqfSPHIizexSfnUsAG+4eTcT42M8cHDkm3b0aAddITDQu/FiuFq189e3pNbTMoGY8tZprwO+LcaYqXr8s8AvhRDeB/wR8LPAU1WL4j4F/HQI4RjlMP0B4EdqvFYdrJ1D2tVsX9ZItXbgWqXerSqszg3evBt8pZPQrjYUxxg5fnaeyalpjk6lmVlY+3m7+4ZdHBkf48GDo4zu2nHVr6vZrnYxXC3a+etbUutpiUAcQrgV+FFgGThb9SOzH40x/k4l0H4M+G3Kewm/v+ryf0o5TL8IZICPxhg/BxBjTG9yrTpYO4e0Vti+7Go6cK1Q71Ys5QrMZa7cDV6v+iQ0YPXjmdnMpqfFQTkEPze9wORUmqNTac7OZdeM3zW2k4nxUY6Mj3H97r6reDXNt9XFcLVo569vSa2nJQJx5aCMDb9jxhj/BDi0wdgy8Dcqv67qWm2/7Zzz184h7Vr3UK3n5/dqOnDtuOfr1XSDL2cxW/imf0t9qW7mMvkNrih74dwik8enmZxK89JsZs3YgZFBjoyPMjE+ys3DA1ddU7M0YzHcdn99O19Z6mwtEYiVDNs9568dQ9qKa9m+rN6f36s/da49tltbyq3MDS6ylX3YB/t6yOaLq51hKO8JPNj3zd9WX57NMDlVDsGnZhbXjN083L/aCT4wMnjN9Wy3rSyGq4ft/Pp2vrLU+Vr2YI5m8mCOxnji9Cy5QmlNR2cpV6C3p4v79jcmpCapq1Pvz28z/r4apViKzGfzzGcL5Iv12Smieg5xX6qbbL5INl9cnUN8di7L0ak0R6emOfHawpprrx/qW+0E3zm2c9vD5LXY7GS4Ztiur+9O+lqQEq69DuZQZ2rGnL8kbcxf789vvTpwzXxTkqmcIre4xW7w5ezqS3H3jUOcmc0wl8kz2NfD7v4e/vjp1zg6Nc0zr67d3XHfzl4mxkeZGB/j0PW72iIEN2IxXD1t19e385Wlzmcg1rZp5zm967Vi57nen996TINY+VFzqQTnF5c5fnaer7x4nm+/+3pu3deY6QGN6AZvZFdfiht2Rx55Lc3kl9J87aWLVMfu4YEUD9w1ysShUV5/0266WjwEb8diuHbUSd+7JF2egVjbpp3n9FZr1fmEjfj8brUDd2pmkVLp/2fvzZrjOu80z9979sw8uSCRCQLgTkoELcmSbMm7yxZrxtV9MTHRHdGXE/MV5itMxHyPuZiriYmYmK2jo6proVpul0tlbaa1ENxAACS2BHLPsy9zcZBJAARAAARFgHx/YZp2LifzHCRxnvPk8z5/WGg6WLpK3TbpugF//+0q/+GDc8d6vF6kG7yTrhvyX++tc/P2Gl8uttk6vbloafzVmzX+emaC985XTrSolJPhDsar8rtLIpHsjRTEku+N07Twaj9Oav/pXscXsgzky3Czu25Ic+CPcrYApZxBo+8dy/GKk5S+F9H1whfuBg/8iD/c3+Dj2TU+e9gi2qKCC4bKr96o8dFMnQ8ujqGfgHztXrzsxXCnkVfld5dEItkbKYgl3yuvQqb3JOcJdx7fl+1ml3I6t1d61O0nwyT8KKaaN55ZT7YfXhjT9UIG/ot1g90w5tMHG/zT7Qafzm0Qxk9ey9IUfnF1nBszE/z0cvXEuqsncTHcaeRV+N0lkUj2RgpiieSQ7MwT9ryQB+t9/Cg5Ngf2uDLKL9vNvlwr8Pl8k64bUMoZ+FHWxHChWjh0/vL7coODKOFf55rcnF3jj/c38KInr6Wrgp9fyUTwz69UT+RCMzj5i+EkEonkpCEFsURySLbmCaM44dbjDoKUH56tEETJczuwx+nqHqebfRSRXskb/O6tSf7+21UafY9q3uBCtYCicODO3e/DDQ7jhM/nW3w82+AP99YZBPHoPk0RfHhpjBszE/zy6jgF8+T92pSL4SQSieT5OHm/2SUnjpPYqPAy2Zon/GapTdHUmChaLHc8+n6MpmZfU//m2sSRtn+cYelofAAAIABJREFUru5xrY5/HpF+cbzAf/jg3KE+Q0mS0vMjel5IEL0YNzhOUr5abHPz9hq/v7dOz3sytlkR8OMLY9yYqfPrN2sUrZPVJiBEFoPIbbrAJzWuIZFIJKcFKYgl+/KyM6gnlWGesOuGaIrgzmofS1cpbU4v+2qhzbvnKi98QtyzOK7V8c8r0ofHa3hx9efF9q7C2Atjel5E349eiBucpCl/edzh5u0Gn9xp0N6SYxbAu+fK3Lg+wW/erJ24z7ehKeR0lbyhYemKXAwnkUgkx4gUxJJ9GQqhOEm5s9qj70doinguB/RVopTT+Waps61FQQioFowj53RLOZ1Gz6flBPT9CNvMLkaq9uG3dVyr449DpO91cfXe+QqaqtB1X4wbnKYp3y33uDm7xsd3Gmz0t7/nt6ZK/PX1Or+5Vqe2ZfHfy2a4GG4Yg5CL4SQSieTFIQWxZF+eOKC9TQdUxwsjvlpoHdkBfZW4XCvw+ztr1GyTNFVGi8aunSkeuUWhWjD4ZHaNck6naOn0vJDHLYd/9+NzR9recayOP47oxU6XWVMUgijhXx5scH2y9FzvbydpmnJ3rb85OrnBStfbdv+1MzYfzUxwY6bOmZJ1rK99VIQQmFpWibb1AksikUgkLx4piCX7UsrpfPN46IBmHxchFKq2+dJ7d08ClbzB+xfGWNhw6HohtqlxcbyAqgjy5tEcveYg4J2zFR63Hb5b6QKCiZLB/MbghU13exbHEb3ouiHlnM7AjxgEEWGUkgI9N3rmcw/K3Pogc4JnGzxqudvuu1IrcON6nY+uTXB2LHdsr/k86KoycoBzuooiF8NJJBLJS0EKYsm+ZA5oY9MBTfGjZNMBtbc5oK/zwrt3z1WIk5S8oT0lFo9yXLpuiKUrxAlcrRcxNeW5c8nPy/NGL/woJklT5jccTO2J8+mFMQXr+X4NLTYdPp5tcHN2jYcbzrb7zo/luDEzwUfX61x6SRcTW1GEwNqMQeQN9UQP8JBIJJLXCSmIJfuSOaAVFpoOXS/CNlUujhe3OaCv+8K7Z02I2+24AHsK5efNJb+oi5PDRi/SNKXvR3S9CD+MqRYMVjoeSZpi6SpemMVLrtQPL1RXOh43Z9e4Odvg3lp/231TZYuPZurcmJngar3w0hefGZqyGRVRMTW5GE4ikUhOIlIQS57Jfg4ovPzhDyeB3cTiUAzvPC63HrVHx3O3C4ij5pLbTsCtR22+WmhTLWTbOY5e5MPiR5tNEV5EsqUpomjpvDVdYrHl0nVDCpbGlXrhwJVmjZ7Px3cafDy7xnfLvW331W2Tj2bqfDRT5/pk8aWKTk1RsIwnjRCyE1gikUhOPlIQS57Js74uP8mjjF8mex2XPz3coJIziJIU29SYruTIG9roAuIoueShS/+47VKzTYSAO6s9ZiZL27b9ohi6wT0vwgvjPR9XtHTemjr4QrzmIOCTOw1uzjb4+nGHrUVsY3md317LnOC3z5ZQXpIIFkJg6cqoEWJrJEQikUgkpwMpiCUHYr+vy5+ngeBVzh7vdlwaPZ/HLYdq3qBk6fhRzOxKl2tnituE5MXxAndWe8QJFIwUN4hRFPZcxDa3PiBJ4OH6AAEUTI2ipbPUdrl2pshiK8vWHvdxDqKEnhfS9yPi5Hh6g7tuyO/vrnNzdo2vFtts3WzJ0vjNtTo3Zuq8e67yXO5rzwtZbLkMvIiCpXF+LHdgt9oNY9a6HmGUUCuaXKnbr8znViKRSF5HpCCWPDdHbSB41bPHux2XuY0Bl+s2QiibzuKTeMnbZ8tAdlweNPpcHrdpDnyaTkDHi/jdW2f2PC5LbZfVjouhZtuN4pTljksQJ4zlDZY7mXN8HMf5oG7wYej7Ef98b52bsw0+m29tE9cFU+XXb9S4MTPBjy9UDt3Hu5vwBfh2qZtVCeZ0vDDm26Uub02XdhXFihCj0ch+GDO/kcWEcgX1lfvcSiQSyeuIFMSS5+aoDQQnKXv8Ipzq3Y7LVNmiktO5s5otBDM1hTTNogGXa9nisq3HZWKzI9cJIpqDYM/atb4XoQjBdCXPfHOAqaqINKU9CDMRPm4/93E+bjfYDWL++GCDm7fX+NeHTcL4yTYtXeFXV2t8NFPnJ5eqRx5N3PPCXYWvomy2PWxekAz/Xmy5o0iHqavkRzGIJ4vhvlzrn5jPrUQikUiOBymIJcfCUYY/vMzs8VYBLET2XupF69id6t2OSxAlzEwWWWq7dL0ITYX3L1SeK5NtWxqDIEJVBBeqeZbbHoMwZrpiMlW2qBe3T2A76HFO05RBENPzQtzg+d1gP4z59GGTm7cb/MuDDfwtk+kMTeHnl6vcuD7Bzy5Xj2UwxWLL3VX43lvr8c7ZyrbHFgwNL4qZKFnkdHXPOIbMzEskEsmrhxTEkpfGcUw/Owo7oxq3HrfpeyHVgokQ4oU6fsMYRd7QuHamOIqXvHvuiTg7ynGZruSwtEyU+RFcqRcYy4+Nxj0fdnthnNDzInpe+NxucBgnfPawxc3ZNf5wbwN3S8xCUwQ/uVTlxvU6v7w6vu09HgcDL3pqPy1dJSVrw6jkDExNxdQVwjihrpnY5v7v4WV9biUSiUTy4pCCWPLSOI7pZ0P2izzsvK/nhdu+8o5iKG0uQJuZ1DdvS/hmqf2Cunz3j5cc5bhcrhVoOwHnq/ltzxnGMA6yvTRNcYKY7jG4wXGS8sVCi49nG/z+7jp9/8k0OkXAe+cr/Lc/OMOv3xg/8EK2o1CwNLwwHjnDmipIU/jRhQrqMBd8yM/ecX5uJRKJRHIyEGl6PCvDXyU+/PDD9LPPPnvZb+O14DBCdi9RutXx3SpQhgMwdt736YN1PrhYpZTLtjW70iWIEoI44YOLVXpeyJ8ftSmaGu+eq2zbXiVvHOp9HTWXvPO51YJBcxDsu62jHsuhG9z3IqIkeeq9HJQ4Sfn6cYd/ml3j93fWaW/pTBbA1Qmbn14a4weTJTRV7LmA7TgZ+BH31npU8gaVnEEQJ9s+G8f183mV2lEkEonkFWbPaiIpiHdBCuKXz14i90rdfkoYzq0PCKJk21fYThBhaAo9L2Sh6RDFYJsq05UcD9b7kMJ75zNRtFMADyMU754bGwm24fa2Rh5yukqj5zO3MWCqbDFdyY2E0X4i/bDC6Ti3NWToBve8CCeInv2EPUjSlG+Xunw82+C/3GmwMdieo31nusT1qRLvTJeYLOVGt7thhK4pvDVVOvJr78XWxXCWru4pXqWolUgkktcOKYgPgxTEB+OwguIwj/9yofWUyF3resxtDPjh2TI5XWVufcCXiy3Wez6Xxgu8e77CVDkTXWmasthyuL/Wp2abWLqKHyV4Ycx0xWJ2pcfProyPBOZaz6Oc00lTuNfoc/1MceQgD7c3jDgM31fPC5ld6Y56fy+MF0ZCdT+RPnQnD8pux+Io22o7AffW+qx2PBRFMF2xjuTQpmnKndU+N2fX+Hi2wVrP33b/zGSRGzN1PrpWZ6Jk8ae5JqWcjtjyeyglpeuG/ORy9dCvv5PhZLjhBcNBuolfxEWGRCKRSE48e54gZIZYciQO2yF82Md33RBNEcyudOn7MbapMvBD4iQThsttl0/uruEGMV034EGastJx+Zt3ppgq53DDmL4XUS0YCDGcJpa1FjT6Pu9fqGBoykjk/tWb9dH7GIrerQwXTW1tGFhqZw0GpqbS9aJti/GO2kSw20XDcbQaLLdd/vG7Fdb7AVGcoKkKS22HDy9VDySK0zTlwfqAj2cb3JxdY6ntbbv/Sr3AX89M8NuZOmcruW337czxAnhhTME62q+f4WS4vK5hGcqRJsOdpMo/iUQikbx8pCCWHInDCorDPl4I+HSuSRSnRHFCmCTca/S5WM0xWzD582KLgRdRtDLnUQhouwGfPljnb96ewgkibEvb7PztAWBqKmma0Oz7/HfvTu/pBO63aGpufTBqGOj70ea0uQTbVEePH4psN4yJk5Sltkvfj9AUwYXx/LbX2ln/1nFDJnbUv6mKOFKrQTRqioj444N1FpoOtqljWwZBFLPa9bi90uMnl/Z2aRc2nJETPN90tt13fizHjesT3Jip79mPPHzct0tdIGt48MIYL4y5Ut/7OTvRVYWcoZLfXAQnnnNM82EuMmS0QiKRSF59pCCW7Ml+QmCroOh5YSb6vIiEZFfBcFiXs+9HLHccxnImQoHlpkvfDYnjHGGc8u1yh7OVPEKIbGiDgKYT8+VCm9/OTGyLLcxMljY7fzPX+f1nfC0+bIK49ajN10sdBClvTBQBqBYM/v7bFeIk22/Hj0CAbWp8Pt9CU+FCNc/lWoH/erfBo5ZDydIxVIWOF9JxQ9pO8FTOeCxvcOtRm54fMb6j/s2P4lHO9yCtBk6QieDBlmaH+Y0Btqlhbrrk2d8p8xuDpwTxcsfl5u3MCb7fGGy7b6ps8dfXJ/jppTEUReD48aineC+nuWjpvDVdYrHl0nVDCpbGlXphX2d662S4nK6iH3I63bM4aHXaqz5NUSKRSCQZUhBLduVZQmCrAzq70sPSVaIkodEL+N/++SHvX6jw7rnKthjCYVzOlY7L9TMlen7Ew40BtqVTK1o0ui6QYmga64OA4bfztqlRty3COB715h6k83c/4iTlnenySIT+/m4DAaORym4Yc3etR71oUckZQErXC+lutiuUcjq2qxPEYJsK752roCqCufUBl2vwd1+v0HYDxgsm05UcUZJS3lH/ltt0VJ9V1RbFyWicchg/3RQhtvz31luHtzR6Ph/PrnFztsHtld62R9Vsg3fPVXh7qsi1ySJjeYPFpnPgscewKYqn9ne0TT0Tv/kdk+FeBAetTpPRColEInk9kIJYsivPEgJDQfG47WJqCk4Q8aDR5+pEkZyucnu5x53VHlPlHNOVHNWCwYNGNq54PwEydKXvrvUp53TOjRVwgoSCqRKEMQgLXVU4O2Zxd6VP2dIpmCphnNL3I35yqUre0Dbf59iRRkrvtf8dN4QULp63mShZXJ8q88cHChu9gCBOsE2Nd8/ZI9GbpvDu2co2YTdc7Nd2AtpuSK1gEsQJsyvZOGFI6ftPOoCHFw17TQJ0N3uDnSBmvwWyF8YL3FvrA5mjHkQJqz2PvhfxP/3vX/KXx91tj68WDD66Vuenl8eIk5S8oY3iDn+4t865sfy+Y48Pgqo86QHOG9qBFsMdFwcdNy6n0kkkEsnrgRTEkl15lhAYCooH6wMUoO2EXJ0oYuoq8xt97qwOuDJRQFMENdvkQaM/qkzbS4AMXekkgYKhcnupy8KGy4RtQprgRwnXJorMTJYYyxtMFJvcWenxqOkTp2DnNNwwZmFjQN7URm72TiE5FN3DmIdtadsq0/ba/yhKSdkuOnVFYaqc44OLT2IHWxspdnPF+15EzTYZLxgEcYq1KSiDOKHrhdiWTpqme140xElKzwv3dIN34/pkkb4Xstbz+WKhz/1Gn0ctd9velHM6v3mzxo3rE/zwbBlVEXy73CWMkm3iN06h42TO9hBLV0fO+F4IITA1JcsBG+qRFsMdJwcZNy6n0kkkEsnrgRTEkm0MxeL9Rg9DVblSt0dfg+8UApW8wY8vjBFECd8td9EUwfz6gLVeQNXWsTSVu6t9rp0pkTc0moNg35qwufUBSQILzQFT5RxdN2ax5bDY7DNRtHjjTJGrdRsniHDDiCs1mzurPXKmhqkqTJQsBn5Eo+9jeE+yujv3byi6V7seCjDwQywt66vdGglp9HxaTjBquQiSmLy+/Z+MripPJRGGx2mvr+UVBRY2BjT6ARt9n+lKjkpOp+uFnBvLU8rpu140uJtZ3cEz3OCd9L2IP9zb4B++W+WrxTZbJzHbpsav36hx43qdH52voO3I6u42+rhiabS97eJ3r9aI4WK43GYcQvkeXeDjQE6lk0gkktcDKYglI7bmhmfOlLj1uMOtRy1+eDYTSrsJgaFg0BTBo7aDQOBHMdPlAoLMYVtqu1w7U3zm18xdN6Q58LF0lTgFU1OYLlt0DJWzlRy2qdF2Q4qWRkomwn5+eZwvRq6yRpykbPQDfnm1tmvOcxiFWGw65PRhDCCitTn2ePicasHgk9k1yjmdopWNe272A8o1HSeIRuKoktdJYdttw+O029fyk2Wb+40+mhBMlixMTbDUduh5OlPlHL/eUv8GmRvccUK6XnhgNxgy8fzP99e5OdvgTw+bhPETFZzTVX71xjgfzdT58GI1W5S4B7tVppXzBj0/uyjZ2RqhbNbbDUXwfts+DRw0WiGRSCSS040UxJIRO3Oz752r8GC9z+3VHj++MLarEBgKBlURfD7fZLJkMVE0Weq4+FHMxWqeRt/nfDX/zK+ZSzmdLxfbpEnKfHNAkgjSNMU2tc2pZmWqdvb6pqay0Q+oFkxqtsXAD1nueFwcz2PqKvWiuasAH0Yh+n5MadPRzHqEw22RkOYg4J2zFVpOQM+PsC2dn1QL6JrY1l/86zfro2N3EME0vzHg8rjNQtPBjxLG8iaqEEQp/Jt3JkfP88KYrns4N9gPYz6da/JPs2t8+qCJv6VL2dQUfn5lnBszdX52uTpqm3gWu1WmKSL7bNxb69Mc+EyULH5xdZxrZ0pY+otdDPcyOEi0QiKRSCSnGymIJSN25maLls67m6Jwv6hDJW/wm2sTANxe7tLo94jihOlKniRJmVvvE8cJl+s2yx2XlY6L48fUixY/vzo+6rCtFgyW2y45TcEPE9pOSJSmXJsoYKgK880BXhRjm1nzhW1qtJwAP4rxwgRdE5RyOuXN3OduAnyYCbXNbHJdNsEu2+bW53TdkHrRZKJkjZ47zAbvdix2E0y7NXV8tdDig4tVZiaLm1VwEQVTI29qFC390G5wECV8Nt/k5u0G/3x/Azd8siBPVwU/vVTlo5kJfnl1nJxx+MzubpVpZ0oWq12Xt8+WqOQMgjihOch+Dkd5jeNAdgVLJBKJ5HmQglgy4nkXEL17rsKd1R7vTJdRVYXltstaz0MRCoauoCmCf/huBV1V+cFkETeI+L+/eMS/+/E5Lo4XmN8YUM0bfLHQZLnnUzY1JksWfT/iB9MVoihbkDZdyY3e1xfzTUxNoU9KGKbcW+3yizfqe+Y8hxGPsbzBfHOAH0YkacrEuL3tOcexmGq3poqqbTK3PuC982OjarW2ExAmCQtN50BucBQnfLnY5ubtBr+/12CwpZVCVQQfXBzjxkydX71Rwzaf/5940dL58QUrywEbKt8td5mu5Ef7Ncwdv6wqMtkVLJFIJJLnRQpiyYidC4gaPZ+5jQFTZWt0/7MEhhfGtAYBQgjOV/NMli00RSGIE75+3GEsZ2JoKhuDgIvjNgD/+N0Kb0wU+T/+tECSpLxxxs4GbfR92m5I1TZRBQRpim1po/e51vVGzRWDIKaa1ymYGm4Q8975yqZj2N7mGG7NhHqbAtu2NKq2sW3/jmMx1W5NFZfHC3w+36Lvh6QpbAx8+l7EW9OlfcVwnKTcetTm5myDT+406HpPhm4oAt47X+HGzAR/9WaN8jE0IGjK9slwWxfD7dyvnhfyuOWw2s3GOX/f7qzsCpZIJBLJ8yIFsWTEVrG42HJY7rhcHrepF83RYIpyTqfnRU/VlQF8udCikjeo5k2EyMSxFyWULYFtatxb61GzsylsQ1dTVQR/XmhjaCopKR0/ouWG5HSNy7XM7XPDCF1VmBi3qdpbRW0fTVG4Urf59Zt1ipZO1w34crHN//PlY6qFTOQ2+wGfzzdHnciXa4VRR/FWhk7j8Gv3Z9XEPYvdXOYkhUu1As1BQN+LKFjangMtkjTls4dN/tPXK3w5n02x28oPz5a4MTPBb67VqRaeT/gJIUZNEDlj/8VwW/er54XMrvQQpJwpWQRR8r27s7IrWCKRSCTPixTEkm1U8gaXa3BnpUecQMsJiJOUla7L1487WLrKRMnENjQGQTSqK1OVbNTwlZrN7EqPJE1Z73ncXeujqYJrZ0r4YZY1LVkGikiZ3+jz3VIXVRHcWuzwcH2AH6bomoKpxUBKOWdwfbLE+WqeRs+j5wn+y+wapZzOGxNFwijL9d5Z7aOIlI2+jxclXKwWEAL+vNgGAXldxfGjPQXbbl+7P2j0dxV2B82rDl3mJE1J02yhXs8L953olqYps6s9bt5u8E+za2z0t4u6c2M5/uatM/ybtyepF81dt3FQdDXrBM6Gbhx8MdxW9/xxy0FstjOfHcu/FHdWdgVLJBKJ5HmRgliyjaEwbLsBtYJJ2w35Yr6JpavUbJOFDQchBJUzWTvCsK7s66UOP7lYRQjB2UqOT+c2cIKIjhcQx7DSWaOS0+h5EdNjecqWhhfE9PyYsbzGV/NN2m6IoiioYUxO1ynnVRCC9V7Anx42SVN4a6o0cqyX2y5z6wMmSxZFS+fLxRZLLZdizsDSFOqlHH0/ysSRpXOv0UcIwVjeGI1PHgrblY5HzTaf+tr91qP2pvOcid9qweAvj9q0nWzhm64qPG45T9WlQdbKcKGaZ3a1R8/d2w1O05QHjQE3N0cnL3e8bfefH8sGf3x4cYyCqaJrypHEsCIEeUPFMlTyuvpU5/Dw5/8ssb/1m4TVrseZksXZsfxov75vd1Z2BUskEonkeZGCWLKN4XCMvhfzcL1Jo+eTphAmCW9O2GhaFn9Y73lcqBboeln/riAduXRdL+SNiSJ/edQmTQRTZRMnjHCDFCHgcWvAuqaTMxSSNGa1E9Hzs4EVqiKIkoQ4iSgYJmEY86s3ajQHPgM/YqE5IGeoFC2dMEmp2ga2pTPfdLi72sM2dXp+QGug4wQDel5IywnQq4LxgkkYJ8xv9NkY6LSdgCSB5sDni4UmBUPj51dqTFVyQLZ47auFFj+7Uhu5xv/fV48Jk5TJkkXeMPCjmEcth1uP2vzm2gRJktIPIrpuSBAlCCG4Plna9VjPbwy4Odvg49kGC01n230TRZNL43luzNS5XCuObk9JnzkRbiumnonfnKFiPaNq7TCL07ZWkQVR8lLdWdkVLJFIJJLnRQri15SdTmC1YNAcBPzDt6u4YURe12j2fbpeiCKgPchE3tlKHi+MCOOstsw2Vb5+3OGL+Rb/8O0qEyWLCdtiomTy5aMWliJY7/tomooiMtd0tetTGTMJk4TVrk+cpsRpQk7XsxHAXkicCqI44cJ4gYmSxWLLpZTLBOhS22VmMnNtN/o+UZwJ+aptUNA1+l7Eat/jjG2x3HEpmBopgnrJwtI1/DDm4YbDeMFkoTnA0lUmSzk6TsCnc+v8Nz+YBOCTO+u4YcRi02G6kqNoZdPrcqY6Grds6RppmvLdcpcfTJUZ+FlrxV48brt8vOkEP2gMtt03Vba4PlnkJ5fGuFyzmV3p0nRCJoKQgpEJzL0mwg3RFAXLUMgbGjldRT3EZLijLE47Ke6s7AqWSCQSyfMgBfFryE4nsNHz+WR2jXfOVhACkjhlceBQL+Xw45THbRddFRiqYK3rsdJ1uFK3mQxNBn7KP363yqVagbOVHIstl0/urnG5XkQTghiBH6VsDFzCOCFNIIxjnDDrAI6TFD+MCeKUIAmp2gaTJZMozdzkS+MFZle6PGo5GKpgupLDjyKW2y63FtukAnpehONHGLpCz48o5vRs/PHAp+eFWLpC349odF38nIEQ2bTl4VQ8S9eob4599sKIO6vdrC3DCfjh2RJhnDC70mVmsoSmCjw/6whO0xRvsy95EET0vN2d29Wux8ebTvDsam/bfRNFkxszdW5cnyCME6I4HU2FO1/NM7vSZbHlMHOmtG0i3BAhBJaujBbDmdrRe4CPsjhNurMSiUQieRWQgvg1ZKcT2HICdFXhq8UWK12fIIzp+yGWqlCyNNY1hTBKSdLssaoiuN8YUM7pfLfcQ1EEyx2P+WZKXlcp53QWNwbEaULfizbHOSd0vZAkSUGA3h6gqZkb7IUJeV1BSaHjhERxxNlKgbyl4YQxYZxSLxr8aa7J7++uo6uCJEkoWDpnKzl6XkwQxWgKCFWhZhvESUqKoFYwMXWN1Z7LctulmNP55dVxLlTzNJ2Aup1VyhUMjalKjpYTMLva4/K4TTmncb/hUMlnQzOW2i5nShYPmw7rfZ8kTfHDhJ4f8saEve0Yb/R9/sudBjdnG3yzOeltyHjB4LczdW7M1HlrqjRazPanuea2qEHB0Ll2psiDTSe/YGlcqReoFszRaOSdlWjPw1EXp0l3ViKRSCSnHSmIX0N2OoFrPZ/mICBK4GzZpOVkuduVnk/B0NBVgQCcICZnaliqoOeF/GmuyaOWm7mahsrF8TyugEdNB8tQGMtbLLVc/DABAVEMhgZxCsttH1UFVWTjmYMooWjpFEyVJIUrEzaXawU2+j5OEHJ/rZ9Nueu6CBRMVaFg6ZsOqSCMVQZhwnnb5NqZEitdlwfrA67WbOY3BuR1HdWEvK7w1WKb/+HnF/l0rknXDTajGAmKEPzo/BjfrXTRVMFkOcdyx8PxE7rugPWBxnTJYqpkMQhCoihF0wSTJZPrk0XaTsAnd9f5eHaNPy922BqcKOd0fnstE8HvnC3vGmUoWBpeGI8cYgBVUXh7uswHF6sjEbxfJdrzcND4g5wKJ5FIJJJXDSmITzFHESZtJ2Cl43F7pcd4wcimvgURUZxgGSpelLLW87G0rIZLVUBJYSxvEEQJYZKy2HLoOgFxCnECpg5JkPLtco+iqeHHCYGfMlVWEYogBpIYNBVUVRAGmduspNkiMV1TEGQ54oKpYqgqNdsgb2hMn83x8ewad9f6uGHC2UqenhcjSNnoB6N9GLeh7fiMF0wafY9G1+fNCRtTUzk/nqfvxfT9iDiF6YpFlKT87HKV//OLR7hBj3rR5M2JIooCBUNFAOWCiaoIHrdcNvoBBSvl376d5YsXWy4DL0IosNh0+F/+43d8sdAi2aKCbVPjr96s8dFMnR9fGHtmnvf8WI5vN93k4YS5nKHws8vjjBWJL+EUAAAgAElEQVSer2LtIBwk/iCnwkkkEonkVUQK4lPKUYTJ8Dk122Tgh/S9kNsrWX3YRt+nG0REcUoYxaQp2JZOKaehaoLFlksQxfSDCD+ICWNIgQRwQlBIUIAwjElS0DVYbjmEUQrZf1AEeGFKlKUmiGKIgLwKiiIIYuh6MVMlnYfrDgM/4a2pEilZj3AYp1mONohIEfT9iNWuzpmSRTVv4PghlqGgq4Ja0cA2NeY2XMbyOiXLIE1TWm7A2UqOu6s9ul5IJWdgmwmaImj0Pd6/MMnAj1jcGOCGMUIoFC0NQ80zXjQpWjpOELHUdvmn22t89rBFtEUF5w2VX71R48ZMnQ8ujqHvUm22G4oQTJVzVAsGq92sUeNluK/Pij/IqXASiUQieRWRgviUchRhsvU5cZryl0cd1nputpgsivHCFF0VFC2dFEbZ0TgB0qxOLEkSvGzIHFv9zmTzT5ytN0NJYN0NsAwFL0xIgGBz0Fq6+UcFhAJelKAKsDQFUuh5AZqSJ0oSvl3ubDrTMT03IIhTbFPHCSOiEJoDj66Xp5TTee98hUs1m5yucutRm0bfJ4pjBh6oqsJyy8WNIsZyBqsdl2uTRS5U8/hRghfGTJct5tYHKIpCKW/SdUP6fkjByDLEi22H//n//YZ/mWsSRMlo3y1N4RdXx/loZoKfXa4eONJgbmaA8zsq0c5XC/s86+Uip8JJJBKJ5FVECuJTymGFSdsJ+MO9Bq1BQNeP6TkBY7bJmaLFQuTQaLmcLeeo5A2iJKXt+PTckI4bUStkzujKA3ckagF2KxdTRZYRDmNI/RhTVxEKkEC847GKApamZmI8BktLMTWFKAFDUzhTtGg6AZdrBf78qE2cgqmBqgn0VEEgIBVoioJtatRsa3RhcKVu0/dDJooWq12Xx22PvK7wznSFtb7PIIwJNjuJO25MnCQ0Bx7nxgpcnyqy3HbYGAR8u9Rhoemw0vW3OcG6Kvjp5So3Zib4xdVxcvt0/Pa8kMWWi+NH1Iom184UmSrnDlWJdlI47qlwMo8sOW3Iz6xE8moiBfEp5TDCpO0E/N3Xy3yx0KLjZN29mqIw5gSsmRp9P8I2NZwgwtQ1TE1BV1WaA5fljo8QKX0/RhFPi9qdKAqwGacQCvT8vZ8hUnCjGJFmbjEiJU6gYCo4QQJki/X+/Y/PsdB0+HKhRRDFBFGCJgSlos4bdZs3J4r4m+J2OLSjaOlcrtl8+qCJFyWU8wYXq3mmKjk0TYE05dO5FtPlHGES0+wHfLsc8NNLKet9n//0lxVmV3vbRLAi4EcXKvzurUl+dXWcgrn/Px8hBH4Ys9B0GMsbXBjL44bx5gARjUreOHUn1+PsHZZ5ZMlpQ35mJZJXFymITymHESa3HrX5fKEFKbhBjBcmuEFAyw2p5jVURaPnuVQLBlfPlNAVwV8et+m6WR1baxDiRwkqCoaaEOyjitMkywUDKPF2F9lQIEkh2rxR0wRBlKIqYCgCUoW2G6CqBgsbA8YKOjOTRebWB4zb2eK5ME4pmJkbW7J0bFPDtjRsoO+Fo6EdPS9kbr3PlYkCUZTH0AReGDO2KUL7QYQThHiRTrPvsz4IWO36/K//3MXfEocQwGTZ4mwlR71o8NHMBD+5VN1z/zVFIWdkMYicrvLnR20mitau0ZbLNU7dyfU4e4dlHlly2pCfWYnk1UUK4lPKfsJkq+soBPzd18vMrfWxDI0oTUnSlJgE101w/YiL43kAllsuf/v1CkEUMfAiDE2Q0zVW/YgoSAiBZ6VjhwvmFCDcFL4Km7nhNItTDEmSNItYJKAoKZqa3eb4EZW8TscJ6LkR44WEH50fo+dF3F3rkdNNum6AH8dMWhbTlRx9L+Lb5Q4DP84WzzkBKYIrNZvFpkPXi/DDmDurPSZKJq4foSsKn841We54hPH2AEg5p3FtwuZqrUjOUElJ6TghCxuDbYJ4OBgjr2vkjKcr0ZbaWVRiEMTYpsZ0JYdtarQ2f0an8eR6XL3DMo8sOW3Iz6xE8uoiBfEpZjdhsvUrPU0R/OvDJrdX+rQHAX7XxwtjBn6IH2WL4IRIWe35QObYdhwPP0qziXJJihsGhHH2WHjy916IzT/plv+fAJoAVYVwSwbZi5+IZy8ETU1QiEhIKZk6OV3jQWPY9pCJZRL482KTJBVMFC1+fqkGZCORa7ZJXo9pOgFzjT6/uFrDjxI0VdBxAnRVML/R49O5Jv/6sImzw+qeKlv8YLLIzBmbrx61uTJewNic/BbGKaahkAK6+sQFHo6B3i3y0HYCljtuFu/YHDs9u9LlQrVA1TaO9eR62qIXcPx5ZInkRSM/sxLJq4sUxK8Ytx61edxyiZKURy2Hvh9RyWusdl3cIKbnRQRbVG0UQ8uJyOsKXSdBVSAZLoBLsg/Is0TwVrY+diiOIYtKxLtsKCV7LX1TRXtxwripoSiCzxeyyW1JmrDU9mg6AbahcqVe5EzJ5P5ajz/OrfP2VAlBJibfOVtGEVmzxdzGgHOVhK8fd/lysc29tT6DHSK4nNO4VC3ww3MlKjmDy/UCXphkXcteSCkHoBDEMeO2wYeXxjhfzRz1thPw1WJ7z8jDrUdtkgTubvQp5wymKhYCmNsY8MGlzN0/jpPrac01HmceWSL5PpCfWYnk1UUK4leIthPw1WbPsKoIvlvq4gRZg0KKwI9jwp2idFOIDjbviHbcH3F0hvVqQ7a+tsYTMTx8H0IoqAIURSGOU7pehCIUel7EvUafjhtStDQuVgsYmsbViSJLHZfZ5S4/mC5TKRg4flYApyrwt1+vsthy6bjb92K6bHGmZFLMqfhhQscJ+W65xwcXxvDCBEXAv//ROT6bb9FxAhAJeV1FkDVatJ2ASt7YN/JwuQZfLbSp2SbXp0ost11uL/e4dqbAVMmikjdGGWJ4vpPr6Y5eHE8e+TRyGl39153X/TMrkbzKSEF8ipnfGPAv9zdo9DzqRQtFgUEQs/y4Q8sNWWq5tL2AIEoxNYGpavQJgSymkLA90/siUdkhrrfmKsjc4yiOEZqCG8Ss9lw0BdwwoueHRFFMksR03QRdK7LccRnL6VTzBpWCiW3ptAYhn82v8dnDJksdb9vrj+V1Prg4xltTJR40egiyHuD1foDICxQhWOl5lPM6//aHk1ytF/ngUpVbj9p8tdCiaptcHi+gqcrIfd0v8jC3PqBaMBACbEPnzTM6XhgTpwnTlRxwfCfXg0YvTqIAO6488mnjtLr6ktf3MyuRvOpIQXwKaTsBf7y/wd99s0ytYHBxvMBG3+f3dxv8+OIYLSfgcdtlww0QaYqqCuIEgjhEIxOmh4lBPC9D7btVA8ebVWtDYZ4CRdNAiIQ0TWkOAkqWztlqHseP0VWVJI1I05S1rks5Z9AcBJwbs5hrDPi/vnzMUnu7CC5ZGj+/Ms6HF6ucH8shhOBBo8fGIODKuE3PjyjnDMYLAkTKWN7gw0tVupuOciVvULR0fnalti3WAJn7ul+esOuGXK4VuLPaA8DUVNI0odn3uVx7MnjjOE6uB8k1SgF2sjitrr5EIpG8qkhBfMoYCpuvFtrUCwamrrPQclEQ2JbGN0td+kFEqx9kC9pSMHUFP4xxnyf/8BwMRbCusC2/rGzmlRUgZ0AQRyhCMFm2cMOYRj9gupJDkFLMaThhRJKkNHo+Kx2Pnh/zyd3tO5U3VN6aKvLhxSqQcr5aYLxgAqCqAi9KyBsqbhix3HERpBhaFuDIGRo5XWWx5QCZ83q/0WPmTGnbawzd1/fOV/aMPMytDwiihJnJEnfXetxv9AmihEu1/LEf34PkGqUAO1nItgKJRCI5WUhBfMoYCptBEFKzTRSR1XzdX++jKoJGz8fQskVg6mbfWZqAF3yfnvDTWDqUCyatno87bJcQ2eQ5TREIoRDEKXlDYaXnoZCiCsHsao/mICCKE8IkJYxTgh05j7G8zg/Plnn3XJm3psooIlvK1xz4rPZcpsoWqgKLLZfFtkMUJaz3A/wwRgiBEwTZ6OQUHm4MWOt51GyTsbyBoarcetzhvXMVilbWb/yg0SeIMwf2St2mOQieijwM88FJAnGccK6SIwEuVgvH7sweJHohBdjJQrYV7M1JjPZIJJJXHymITxlDYTOWN3H8CNsy6Ach91Z6pKRYuoIfJnhRQhglWc2ZSEbDML4vBFC2NPwoyvqHE0jjFENXSUVMmkIpb4wGWPhBNkq550eEcYJtqKDAUttjEMRPjYnWFMFbU0X+x19c4r3zFb6Yb1HK6RiqgqmrhHGMpmQtGvMbA9Z6PmcrOd6s29xP4cFaD0MXOAGkSUI5ZzJdsfhyvsX7F8ZYbDo0+j5LLZeHzT63HrV572wFN4ywDI0rtQLfLHVo9n3evzDGe+crT520VUXwyd01kiTlat3m2kSRoqXjBNGxO7PPil4chwCTQuX4kG0FuyOjPRKJ5GUhBfEpYyhsfniuzH/+epn76wMW1vs4UYIqBCkw8LPe3lFE4nsWw7rIXrJS0MlpFut9l64X03ZD8qZKJW9iahrnx3IoiuD2ao+BG6JpCkGYNWE0wuipty0A29SYKlkULIVa0eL2coe2E1C1TTQhaDkBjb7Pet9nvGBwaTyPHyeUcxpX6jZ3Vnu8f2GMrhfiBTETRQNDFeiaSt022ej7bAx80hSWWg6PWy5eENN3Q9a6HufH8vz22gRLbQ9LV6nZJgsbDnGSjk7aW0/qF8bymJq6bfrdy3Bmn1eA7RQqjZ7P5/MtpsrZYJTjFseHEd+nUajLtoLdkdEeiUTyspCC+JQxFDZuEJMKuN/o4QYxAsEgiikYKnGS0HdfXkQiTLMxzTlNycRgAtW8TpykKEIhTuBq3UaIlEEYE4UxiqLQGkRP18KRRSsUAQVdoWrrTFdM7q31uVSzuTheYHaly99+s4xAMDNZIq8L4ijlUdPljXqRpY5HydJZarvYpkYYJ7w1VabjRrx3voIXRuiqwvlqnrkNBwVoeyFtNyRIUkxdo2CAFyY8brl8PLvGu+crWLpKmip0vZC8oY1O2ltP6kVLJ4wTLF0djZUeDhr5cqH1lIh7UeLueQXY1n3qeSELzQGaAMePCKLkWF28w7iEp9lRlG0FTyOjPRKJ5GUhBfEpxAsT/uOfH6MoCgVDR0VkwjKKaYbRS1s8t5UggfWBjyoCFFK8OEUTYBoqhip4sN4np2ssNB1aTriria2SCWEE5AwFVVVoDwIKhsZUJc+EbXFnrU/bjTE0lThOaPYD5vyQt6dKnB+36HohtqkSRAl9P+LamSKzK11MTUERKR3HJ0lTJsZtnCDici1Pz83EcNcJ0YQgjBMGcULR0nD8iKWuR73tYuoqqshc660n7a0n9elKbvR6fS/djEv0We/56JpKNa8TxSltJ6Bmm3w6t0G8eQExvP0o4m4vYX1UAbZ1n5baLpauYmoqXS86dhdvp0sYJymP2y4P1gf8+MLYtouE18FRPI0O+FGR2WqJRPKykIL4FDF0wzb6PtNjOWxT585qFz9MCKIEN0yJn72Z741mP4s9TBQNVEWh43i0BhGpsvvUOk0BVQgEKUGcxS5UBXRNoAqFNEkxdQXHj7lat1ntuTR6PpamZIv0FMGVus1Sx0FTBIamcHulS7VgstxxOV/NYZsaF6oF5jYGXJ8qkaZgWxpV2+ByrcDc+oBmP6DlRDhRTNHUUIGioVOyNLwwRkUghOBx26FoaNiWxh8fbFDJ6bQ33dfhSb1o6cxMlnjQ6JOQ4EcxjZ5P0dQ2xzknzK52CaOEr5faXKjaXKoViBJYaDpcqOYPLe6exzXdS3xt3ae+H1GydPwowTaz0dbH6eJtFd89L2R2pZddwMBTbvSr7iieZgf8KMhs9ffH63ShJZEcBCmITxFDNyxKUkxNYbXn0fciem5IkHy/3cIHQZC9p7VegBBbhoBseaOGKjKxFQSIVCFvqBibgiaJEwxdxTZ1wjgmiFMsXWV6zELXBH6YkAIdL2Sl42PpCnPrfZI04cF6n/trPTRVQVcywfywMaDjRFyo5vndW2e4OF546j1frmUnivfPV2gOPFY7Hk4QM102CeOUyZJFwdQY+CGxl2JXNZIkRVegZpt8udDiSt3mQaMPZCd1VRGcHcuNIguGplDKGQghiNOU1Y5H1wshFVi6wsLGgIs1G0tXaQ58NFU89T7346iu6X7ia6tQKRgqXTcgRXBxvAgcr4u3VXwP3WhIKVr6U/vyqi8WfB0c8K3IbPX3w+t2oSWRHAQpiE8RQzdMCFhqedxp9GgNwucar3zcbCYcRmOZR+Obd2QiFAEVS4XNbPB0uchaz0cRYGkK9aJFx/HJmRrjeZ2WJ4i8EE1TMTSFvh8jgJ4bEm26yYoQbPR9bFOl0Q+o2gYTJRM/ihkEEVdrNvWiSbVg8vffrjBVzj21IGzrCfn9C2N8Od8mRRAlCWdKFnXb4v0LFdwg5vOFFpoqsC2d6Upu1CDRHARcqdvbpgj+/Or4pqPZppo38KMYS9dY73nYpsbjjpdNtkPB1ATrPY8L1QKNvs8bZ4qH+hl03RBNEcyudOn7MbapMlW28ML9vz/YX3yNjY5L3tToeCGXxwvYpoYTRMfq4m0V330vwtAEfpSMLmC2OsDHvVjwRQuDw4rvV90B3w2ZrX7xvG4XWhLJQZCC+BRRyuk0ej5fLrT4bL5J349OlBiGLQJ4DwTZIA5dBT9OUQVECQgBl2o2jh8SRCkVS6NeMFjueKx0vWxh2mYLxUrHo2iqmLpGQjbQ42zFQgFSAS03oFowuDCWZ6xg8HDDIU1TOm4IQtD3YzQh9lwQNjwh/+jCGL97a5Jbj9r8y4MN4jjbuweNPpW8ztvTJc6P5RHiiYM7HOzRdgIu1Qr8YKqEG8Y8aPQp53RKuSwbvNAcADDwI0CQ01QuVPN03AhDUxj4EV03QFXYNtnuIAyCiD8+2EBFoZxTiRODRt/n7enSvs97lvjaelyGwu5FuHhbL0oSEpJUZWayRNHKXN+tDvBxLhaEFysMjiK+X4dM7Ul26F9VXscLLYnkWUhBfAoYnjD+eH+d//zNCnMbfaIowQlf9js7HEPZqCgQRhCLhJyhIISg0QspGHFWHSfA1BS8ICZnCAbdhDBN8MOEgpkN4ohT8KOE//69af680KHjBqiK4PpUiZbjU8npzK07FC0DAWiKwv1Gn8u1AmP1Iqb2pB0C9hZAlbzBu+cqdNyQjhsSRSls1sqVLG1XsdL3Imq2uavIqhYMPp9v0vciNgYBXS/E0FX++voEbTckZ2g0+h5+lHVH/+6tyUOJg7YT8GCtTxInlPLaKItcyT9bQB1GfL1oF2+4/aEDrCqCNE13dYCPa7HgkL2EwfMKt6OI760OeBQnWcZ9EPD+hQptJzj1wlF+df9yeB0utCSSwyIF8QlneMLouhFfzreI4hQvSEaLzk4yGk9iE2z5O0yefPBSITAUMAyNziDAiWKMzdyvu+kGx0mKpois5gwI4hQvijBVlfYg5FItz8A3eHOyhCrAbKu0HB9Ty0b1mZpC348wNQU3ijE1BT+Ksc3sXTzLGZlbHzBRtLg0bo9uc4IIP8rE2XAbQ7FmW1nrxFa2OseXx22aA5+mE2BqeepFk+mxHBMlk7mNAXld5ZdXa7x77ulhH89ibn2ArqlcGC/woNGn60VYusqZkkn6jA/MfvGDl+XivehM6UGFwXEIt6O4csP9v/WozVcLLaq2yQcXx9BU5ZUQjvKr+5eDXLwokTyNFMQnnFuP2txd6/HJ7DorXZdG3x9lZk8iKoyaLqI9bmfzf4/nshOhqipYmsJ6kmDrGqoKbTek4wRoSrbwTCiCJE2xNIWeF3G2kkNTFVa6Hj+YKlIwNaIoJkhT3jxjc/O7AT+YLBEmCUIIgijh3bNlWm5A1w0YhJkg/ny+SRDFRAnca/QRpEyWszaKNM0E01Lb5fxYftt+5nSV9b5P0dL4ZinLGb85YY/E27Oc44mSBTwR1oam4IUxb0+Xn0tsdt0QSxM8WHep2zmmy4IgTni4PuCds+V9n7uX+AReqot3FAf4oAL+oMLgOITbUV25St6gaOn87Ept23MP+/onEfnV/ctBLl6USJ5GCuITTNsJ+OP9DVbbLo9bA9b7Hr0Tfp7YKnoNBZI0a5fYuZxLASJScoaS9fwG2aI4TRPoqkrbCQjiBE0VJIAXJAxNV01N0TVB3Ta5OlFAUwUdL2Jj4FEtmFTyOr97Z5LmICCK4Xy1QMnSedjs43UTbj3uoKmCaxM2IPh6qYtIU354rkIK/O3XK+R0lYvjeUqWTssJsDR1JGIBGj2f5Y5HzS7z4cXxkZCCvUXWXs6xF8b86MLxODOlnI4fpU+umERWXdZzQ2496lC09nd3dxOfn9xZ43HLJUpSbFNjupLbNojkZbCf4D2Mm3tQYXAcwu15XLnjFo4nJbcrv7p/ecjFixLJdpSX/QYke3PrUZvP55v8/ewqS+2TL4Z3kiRAut3NHuaIY8APYxoDHyEEishqtQZBRMv1SZL/n707D4/squ/8/z61L9pb3e3eu93ebby2F7ANZCEhQ0hIIAnMACEEk4QsE7Z5MvNjksxkewgxcQKTBDIQMIHJEAZIAgwJBMbYOAa3jW3c3rvddu+SWlJJtdete35/nKqWWm611Kor1XI/r+fhKdW9urdulcr0p059z/f4JGLGtTSLGlJx17+iWK3jW59SxaNc9dg8mKbq+fQlY/zQJedx/c511H3L5ZsH2TKU5tJN/Vy0sd9NbBvLN7pMxElEIzw7UeLETJnRbIKNAynyFY/xfJVqtU6l5lGp+UQjBs+3PHYsR7HqYW1jcY2TeTb0Jzk0WeTB56c4NFnE992IXTNkJWIRpopVErEI12wfZvNQmtKCTg/zV62768kxvvf8FNNLhJxm4DvT7+8azVKueWwbSRONwPFcieO5EpdtHqQvETs1iXCpx5j/WA89P0XEwEBj1b0nj8/g1X1mSu0pYm8+/6rnM5xJvOA5zR/NNcac+vnZicIZz9f8e73s4g2Ljno3g9t85xrcFntfLCeIBvH4TUu9fmtp12j2VJeS5n9bboGcc5tIKiLSKo0Qd6jpYpWvPnqMJ4/n8c60nnEXOFMHjPnhuFQDbJ10zKdU9IgYQ77ibtOJKFEilCp1otZigEwyChiGMwni8RiXbxli37EZNg+6EctjuTIXn+c6KUwWqqdG/g5NFXno+Sn6UwmOTpd4eixPXzLGlVuHqHg+0QhkkjGKVZ+x2RJ9qRh1C6WaTyoeY2N/kiO5EgcnCjw/WcQC47MlHnp+mv5UnA39Seq+ZaZUpeylT4Wca7bPLcX88KFpjHEjfev7U6dGCMdny1ggGYsuqxxhsdHP89f3MVmoMlOq0ZdyvaqHswl8CxdscG3bThYqPH5shljEEI0YXnrRhlPnXDhaCC5Yfu/5SQqVOtmkTzphSMXnSgUuX6IEY7VGIZcqX1iNiXILR3fHZys8e7LApsHUqf3LeW4rHZULsuazk+p29dW9iHQKBeIO9exEgWfHC5RrHl698xbdWIlmj+LTnosPdb9OzXMLb9Tr4GPxfY9qHRJRSMYj+D5Ua5bzBhIMZRNcuL6PLcMZJhohMBGLsH+8QL7ikU1EySRjjd657h/WZDzKsxN5Mok4o9kEs9U6ew9Osn1dmvV9KYoVj0wyBhaKXp1SrY7FkklEiBnDZL7KtdtGKFRqlD2f7z5bYCAZJxYxFCoxitU6w9k4+fLcx4AzhVdLjYpXp9wYGR6frVCtWyo1/1QvY1g8nJwpzOTLHv/40BGGMwlqdR+vbpkqVNmzcx3WWnzfsn88z+4NfQyk4pRrHg89P8WVW4eAF9YH3/P0eGOFwRQRIqzvT7B/bBY2wHAmgbXuA8fZRvGWE9xXGpKXCrxn+hp+fLbCRL7CXU+OnRb6m9cYixj2Hc1x91NjXL19+AUTGucHt0NTRY7lSuxa5/paB1lTvVhADzI4dlrdrr66F5FOoEDcgaaLVb7++AmePD5LpZPWYl6hCC4En6lHcdWHfKWGMREyySjFiofx3dLO8Qhg3M+xaIREzBCPRelLxuhPubfuYCrGWL7CdLFKNhmj6tV54OAUs+UaJ2ZKXL9zHQ8+P8X3j0wBhv50glQyxni+4hZ9iBpGskmmilU2DKTIxCPsb5RWbBxIUaz4PD0xw9Vbh5kqVkknYsyUywym4tR8MMaQr9QYSCWYmK2ctvrdmcLrhv4UiVjk1Ihfruzh132OTZd5/NgMN+5ax3mDqUXDyZnCzOGpAocmi40Je27Rj0LF47nJPBbD2GyF3Rv6GMm60UxjIoz0JU+VECy8xulijWK1TqXmc3i6RCJq2DyUYbpYJRqJEIvC1dvP3gFjseD+tceO86ItQy1Nzmv2454qVslXPPqSLnSP9LlznGk099Ej01yx4HGjEVdOUfctT53Ik4pHGe1L8vzJInXfvuC65ge3xdrqtRLsmh8ifB8mCxWeOD7DA89NnVpVMajgqLrdtdcpNdtB69XnJeGkQNxhpotV7n56nP1jbgniXhCLQvUMwT6CqyUuVsHik4m7emEiUPYs0UhjQp7vk4pHKVfqHKoWyCQijOUrwIwLPNMl6ljqPhydLjGYiXPJxn4OjBd55FCOXaN9xCIRvLrl8MkC0aghlYji+5aahb5kjAs39JMr1zCRCOdv7GMkk8D67toHkzEG0zHyFY+BVJxitc5oX5JcuUYiGiFX9hhMxylU6xyfKfOtp8YoVOp87fHjFEoeJgLr+5NcuKGf3ev7KNfqbtEJ3014iwLD2TiFcp3vPHuSl+wePRXuFjpTmDnQ6G/cLGdIxWNsHEhR8nxee+1W7rz3IOl4DGstFc+nXKtz0ca+UzXACwP2TLnG4aki/alhdqzL8MxYnnzF47zBFJdu6qdY9U6NLi/mTMF9slCh7tNykBzJJvjWk2MMpuP0p+LMlmscmSrymmu3AmzXLIoAACAASURBVC/8Gn4iX+GKLUOnJkU2H/fRozmu3zHCUydmScWjrq2fnetP/chhVxKz8B/71Rphbb4nnp8skIpHWd+XYqZU5WuPHed1120LLGio5dba6tVey736vCS8FIg7zLMTBY5NlynX6lS87g/EUSAejRAzlrJnT5VLxCONSXfMlVBUPEvEuBKJqHEBNxJx4TUWMXhR4+qpjeGqLYM8d7LAwZMFynWfcs1nPF8mZtw80ROzFVf7mopyYNyN/hGDiWKFQslnOJtgx+YBNvSnuWHXulMTnO56coxYxPDMeJ5Dk0Wqdctof5LjuQrxaISDJ4uUqh4YQzIaZTAdZzATJ192ZRtbh9I88NwUB8fzTJWrFCs+sYibHOjVLScLVa7bMcxMqcZkocLmwTTHcyVqnk8mGWFitsqzJ/Nct3PbGV/PM4WZqldn+4K2cGAwWIYyCa7ePsTzk0Vmyh59ySg71vW70dGke60WBuxc0SPTCIgQ5cIN/RycmGVi1k0Em/9V/WIjRGcK7pPFKiMBBMnJQpUrtgwxVawyW6nTl4qzbSTLZKF6aoR+/mjqXU+OnTHAlqp1Hjk8zRPHZ1iXTbB+IE3UuA9IXt3noeenuPH80Rf8Y79aI6zN94QL5+7cA2m3ymCQ9b2q211bnVSzHaRefV4SXgrEHebodImDE3lmixUqnbYu8wq4bhI+iQhkkhGKFd+VT1i3Yh2++51oY1vdQrHi059y7cjiUdeXIhIx1OuWWCTCiZkyT53Ic+HGPvrTce7df5LzBlNUPJ9U3FCqWQ5PlUjEo2wfSXNipsJgJsaBiSK1ms9QNsbOkQyeb3nR1sEX1J9O5qv4vmX3+j7qvuXhw1M8dnSSyzYPkIwZChWPJ4/PMJCOM1GoMJhJsC6b4NYL13M8V+LQZJHj+SqlSo1MIk4qHqXuW4rVOpOFucd54vgM6/tSJONRJmbLTBdrDGWTbBpMM5RJLBo2F4aZWy/awHMni5RrdTzf59h0iYl8hUs3DTBdrHLl1iHqviWTiJ1xRHBhwI5EYCCVoFzzSMaiRCOweSjNxsH0ae3hzjZCtGs0yz1PjzNdrFGr+8SjEaqez0g2edr7YyVB0k1MTJ7WBs9au2iwXqym2FrLbMWjP+lG/Z85McOGgRRXbxvmwHiekUXKIlZrhHX+e6Kp4vmMZOKBd/RQ3e7a6bSa7aD06vOS8FIg7jD5skehUueJsdmemEgH4Fnw62DqPhYXfqMRqM2bLGiZa8lmDFTrbtJZueZjaj7Fike9DrEY5Cse3zs0xeHpIjXPUqx4nARKNY9a3bVJ8+o+6/oSjM2WmSrUSCfc6O1kvkKpasFE2L4uxf7xPA8cnKI/HWMgHWckm3BBrlSjWvM5WahQ9Xwu39xPzbfM5muMzVRY358gGjHEI4ZDE0W2DqU4MF7gOwcmKFQ9StU608Wqq5tutJMqVDz6UlFmy67k4IHnppgpVRlIJ9g4kGYwnWD7SIaRvsSSX0fODzO7ilnq/jjHpsscnMiTSUTZsS7L9pHsqWPONiK4cN+Ld6+j5rmAOVOu0ZeMsWFd3wvKOM42QrRrNOvqxY0bq8bAhv4kxZprq9VKkDzXEdozBdhnTxa4bNMg6USUZ8ZmefJ4nlQiQiYRJRoxTDZG8udr/mO/WiOsu0azp70nmuUt20cyqu/tYr1as92rz0vCS4G4w5RqHt99dpJCj33I9mmMAuNGhOvzaoqbnSfibqVlrIV4NEoUw0zZI2LmjvM8SMUss+Ua1bqPV/eJADXfEjXua3nftwylE1RrPvvHC2wZSJOKR5kpuk4SfWnD2GyJ9X3u6+hCxSNfNtz1pOs7fGy6xGA6wdFCiXy5zky5Sn8yykAmSa3mk03GGUhFOZmvcvGWfhKxIg8dmma0z02YG8/XKZQ9UokYWMPxXIn+TJxYNMpMyePAeJ6bLxjlFZdt5GuPHWc8X2EkE2f7SIZIxAWjc/k6cijjRqf/+dHj7BjtIx13pRCHpkrEopxqsbbYiODCgN0M49tGMqcF14VdJc42QnS25a6bfXhXGiRXMkIbjZjTVhTcNJhifX8SYwzXbB/hgg39HJkqcmKmTCIW4ertQ8Sip7dpn/+P/WqMsA5lEmd9T0h36tWa7V59XhJextpOXQS4ffbs2WP37t27po85XazyyOFp7vjakzx4aGZNH7vTxAys64tTrNQpVf1T/YybgToSgQ19Sde+zKuTjEbIJOOkY1FiMcOJXIloNMK24TQzJY+6D57vsy4b58ptw+SKVWaqHsVynUwiiudbSlWPcs3Hx5KIRLj5wlH2TxToT8Q4PlNiuuiRjEeYLtYYzcaJRw35it9Yec4yU/HYOpyhWPEo1jymCjViEbcc82ylyob+lBv9zbqv+vfsHOalF21YtCyiWfdqjDn1ujTLAl528YYzvm7N+ueHD+XIVzy8ugv4GPill+0+p+C5nNnjzcUd5o8QFaseiVjkVFg+l+s/F8ud3T5/pH3+P9rRiCEZi57x2q/ZPrzocWsxYUgz93tPr/5Ne/V5SU8zi+5QIH6htQ7E08Uq9zw9zj3PTPBPDx2m0J4FwDpKDLewR7N38fySCr/xc1/ckEzEqHk+dXwMhoFUHAMMZV1JQ8QY6nWfE7Nl6hZ2jWQpex5bhrNgIVeqMjFbperXySaiRCMRciWPRASGMknSySjFSo3ZiusscSJXIhoxWGDTUAowPH+ySDJu2DCQZrpY44INfVQ9nxMzJY7lysSMYf1Aiut2jLBtOEPEQNnzeOstuxd9/mcLm4st8/y956e4/+BJjkyV6U+60F6oeJS8Oq+4bOOphTiCcrbQ+OxE4bTrny3XODCeJ1eqMtqXoi/lloBe7X9AF3sdK179jHXV8wOv/rEXEek5iwZiLd3cAR45PM39Byf52qPHFYYbmmHYMtfD2Of0muOiZ6k1VppLxWJkGu3GYtEI5WqdgVScaMQw1agHrtd9juVKTBZq5Cs1MokIx3JljIFkLEbEGGLRCJFGR4xsMsrJfIWi57NzJIMxkE7E8K0hFo1S8yyHJ4uuHVsySSJqSMUj9Cdj7FyX5Yado1yxeZCXXrSeWy4Y5ZLzBsgmY4DFLv7fJLCyJW13jWZ5djxPImqIxwy1uo/FsnMkwzNjswH8VU53tqWI51//TKnKI4enGJutAIZS1eNErsRkvrrqSwbPlGqNUfw56XgUa1lyGeXlLOksIiK9oedriI0xI8DHgB8BJoD/bK39THuv6nR3PTXG3U+Nc7LUA20lAtTsU7wYYyFXqZOIQTpqMbEI1oJX95mt+Nxy4XqePDFDzLgFPXzrU61bRjIJxnJlJvJVpos1UjG3VHTdj5Cv1bG+xUQM/ek4qYSbBBeLRdmYjnPxxn6K1TpPj89yaKpEIgbnr88SwXIyX+Xa7cMczZUo19xEvGRskMNTBfpT8UYf4Doz5RqXbZ5b9ni53SSWqrcdyiTYMpxpTMz0yCSinDfURwRLqcUWfmdfQe2F1zT/+h89mqMvFSeTsMSjrp1bueYxVayybSSzqm2azjbxR50WRESkqecDMfA/gCqwEbga+LIx5mFr7b72Xtacf9t/kvGZSrsvo+PUgUQEPN+NDMdwHSiscWHY4G5jQN23lGp1popVRrJustzhqQL1usUYQyYZBRuhLxmnbi2zxTpDacO6bJyJfJVKo99wOhalYnwwUKh6DKeTbBlK42PY0JcEY5nMV9g+nGUonaDmWfpTMaIR8C1EjOGKLUNcuXUQa931njeYolb3yZWqxKMRtg5nTi1scS7dJJbjyq1DPHY0x2A6QTIWpeLVyZVOD+DnaqUN+JvX36wnfvD5aZIx96VUMhZlplxb9TZNmvgjIiLL0dOB2BiTBV4LXGGtzQP3GGP+EXgT8Fttvbh5xmYqRKNAyMolmkUDzSr2hIFq406zNVsmHqWOpVL1iUZhMB0nHnMhyliI+z6xWBRrLelYlETMLfF8w651zJZqzJRrDGXiJKIR6taSiEU4WagQjxou2NjPur4kdz05xmy5RiwC8UiESNywe0MfF583QDoRYd+RaYazSa7aNki55vOYn+P4TMW1acvEScfjVOquPVaxWueGXSMv6Ne7WC1q0M3tr9w6xEypxnSxdsYAvhKtXmNzlLYvGW30inZBvS8ZW/U2TVqEQkRElqOnAzFwEeBZa5+at+1h4GULf9EY83bg7QDbt29fm6triEcjRE5VyfauZk1wDEgmItTrbiQ2E49Sqfuuhtf3ObVidcT9fn8ixs6RBGOzFfqTcSyNYzyfwWySmuc6QyTihngsQjIe41VXbsZay/954BCz5ToYuGi0j1ypRrnisWUw5UZ+rWH3+ixTpRpYGO1LMpCOccnGASIRwyXnDbAu61Ze83zLSF+CN714p1vh7Ngsz53Mk4xF2T6SoV539cwL63zPNtIbdHP7oUyCWy5cH+hksFavsTlKO5xJ8NxkgUrNw7eWDev61mS0VqURIiKylF4PxH3Awh5mOaB/4S9aaz8KfBRcl4nVv7Q5l2zqZ9/R3Fo+5JqIAI2F5ojHYCSTZNNwhol8BQPEI4a+lPsaPhGNMFmsMTlb5mSxiolE6E9ESCdjnD/az6uu3MTBk3m+8fgY08UqG/qTjPQlGEglGUzFePzEDMVGJ4gXnz/KpsE0xarHG1+8k1ypxrHpMmMzZSYLVQb73KjhliEXXKMRqNZ9Lj1vgIvPG3AdESbyVDyfRCzCLReuf0GgbK7+tnt9H5OFCpPFKtFIhFdcdt45hc/VaG4fdABs9Rrnj9KWvTr5skdfKsZIX0KdG0REpCP0eiDOAwMLtg0AwU+5b8Ftt57PH3z5ccpVj5lK+9rgxZhbJGPhZLZEY7Q2GY8QNcZ1MohGiUYN8WiEuu9jrVtFLh4xuDUNDMYYLt3Uz7XbR5gp1zhvwC2x/OjRHBOzFW65cJSXXrSBQ1NFvv7YcbKJCNftGiYZi1Ks1tk2kuUnrt7MjnUuvL7hhh08cnia+w6cPBWsM8kYF27oo1yrs3O0jxdtGTjV4aBZutAcMTUGjk2XmCzWKFU9wJVRWGsZziSw1hKNGLYMpc9aIzs/5MUa5RcrCXfdUOMaxDU2Q/piLeNERETaqaf7EDdqiKeAy621Tze23QkctdYuWkPcjoU57n1mnM89eJjvHjjJ4elgJ9gZIBvH1doCXq2O1/izpxJRzutPcdF5A+xYlwZgquCWHI4Yw3SpRs3zyaZiXLllkKu2DZ8qCTjT1/LPnSzwr48fZ9/RWSo1jx2jWW7ctY4rtw6RK9W4b/9JxmfLrO9PcdPudaeCLswtTvL0WB6D5YIN/Vy5dWjRBRceOTzNI4enmSxUGMmmOH99lr5kDGtZcrGGRw5P88zY7KmVy3asyzJZqLal52w39LvthmsUERFZQngX5jDG/B1ucPNtuC4TXwFecrYuE+0IxCIiIiKyqkK9MMc7gDQwBvwv4Fc6qeWaiIiIiLRXr9cQY62dBF7T7usQERERkc4UhhFiEREREZFFKRCLiIiISKgpEIuIiIhIqCkQi4iIiEioKRCLiIiISKgpEIuIiIhIqCkQi4iIiEioKRCLiIiISKgpEIuIiIhIqCkQi4iIiEioKRCLiIiISKgpEIuIiIhIqCkQi4iIiEioKRCLiIiISKgpEIuIiIhIqCkQi4iIiEioKRCLiIiISKgpEIuIiIhIqCkQi4iIiEioKRCLiIiISKgZa227r6HjGGPGgefa8NCjwEQbHldWh/6evUN/y96iv2fv0N+yt6z233PCWvvKM+1QIO4gxpi91to97b4OCYb+nr1Df8veor9n79Dfsre08++pkgkRERERCTUFYhEREREJNQXizvLRdl+ABEp/z96hv2Vv0d+zd+hv2Vva9vdUDbGIiIiIhJpGiEVEREQk1BSIRURERCTUFIhFREREJNQUiEVEREQk1BSIRURERCTUFIhFREREJNQUiEVEREQk1BSIRURERCTUFIhFREREJNQUiEVEREQk1BSIRURERCTUFIhFRHqcMWanMcYaY2LtvhYRkU6kQCwi0mGMMQeNMVVjzOiC7d9rBNud7bmyM2tc7w+3+zpERFZKgVhEpDM9C7yheccY8yIgs9YXoVFlEQkDBWIRkc70KeDN8+7/PHBn844xZtAYc6cxZtwY85wx5n3GmEhjX9QY8yfGmAljzAHgVfNP3Dj2Y8aYY8aYI8aY3zfGRBv73mKM+bYx5k+NMSeB3zXG7DbGfMMYc7Jxzk8bY4Yav/8pYDvwT8aYvDHmPzW232SMudcYM22MedgY8/LVe6lERFqjQCwi0pnuAwaMMZc2wurrgb+dt/9DwCBwPvAyXHj+hca+24AfB64B9gCvW3DuTwAecEHjd34EeNu8/TcCB4CNwB8ABvgjYDNwKbAN+F0Aa+2bgOeBV1tr+6y1f2yM2QJ8Gfh9YAR4D/B/jDHrV/xqiIisIgViEZHO1RwlfgXwOHCksb0ZkP+ztXbWWnsQuB14U2P/zwJ3WGsPWWsncWEWAGPMRuDfAb9prS1Ya8eAP22cr+motfZD1lrPWluy1j5jrf2atbZirR0HPogL4Yt5I/AVa+1XrLW+tfZrwN7G44qIdBzVhomIdK5PAd8CdjGvXAIYBeLAc/O2PQdsafy8GTi0YF/Tjsaxx4wxzW2RBb8//+dmiP4z4Fagv/H7U2e57h3AzxhjXj1vWxz45lmOERFpGwViEZEOZa19zhjzLG5k9Rfn7ZoAarjg+Vhj23bmRpCP4coamLev6RBQAUattd5iD73g/h82tr3IWjtpjHkN8OGz/P4h4FPW2tsWe24iIp1EJRMiIp3tF4EftNYW5m2rA58F/sAY02+M2QG8i7ka488Cv2GM2WqMGQZ+q3mgtfYY8C/A7caYAWNMpDFp7mwlEP1AHsg16oPfu2D/CVwtc9PfAq82xvxoY4JfyhjzcmPM1nN+9iIia0CBWESkg1lr91tr955h168DBdzkt3uAzwAfb+z7a+CfgYeBB4HPLzj2zUACN7o8BXwO2HSWy/hvwLVADjdZbuH5/gh4X6OjxHustYeAnwT+CzCOGzF+L/o3R0Q6lLF24TddIiIiIiLhoU/rIiIiIhJqCsQiIiIiEmoKxCIiIiISagrEIiIiIhJq6kN8Bq985SvtV7/61XZfhoiIiIgExyy2QyPEZzAxMdHuSxARERGRNaJALCIiIiKhpkAsIiIiIqGmQCwiIiIioaZALCIiIiKhpkAsIiIiIqGmQCwiIiIioaZALCIiIiKhpkAsIiIiIqGmQCwiIiIioaZALCIiIiKhpkAsIiIiIqGmQCwiIiIioaZALCIiIiKhpkAsIiIiIqGmQCwiIiIioaZALCIiIiKhpkAsIiIiIqGmQCwiIiIioaZALCIiIiKhpkAsIiIiIqGmQCwiIiIioaZALCIiIiKhpkAsIiIiIqG2ZoHYGPNrxpi9xpiKMeYTC/b9kDHmCWNM0RjzTWPMjnn7ksaYjxtjZowxx40x7wrqWBERERGRtRwhPgr8PvDx+RuNMaPA54H/CowAe4H/Pe9Xfhe4ENgB/ADwn4wxr2z1WBERERERWMNAbK39vLX2i8DJBbt+Gthnrf17a20ZF2KvMsZc0tj/88DvWWunrLWPA38NvCWAY0VEREREOqKG+HLg4eYda20B2A9cbowZBjbN39/4+fIAjhURERER6YhA3AfkFmzLAf2NfSzY39zX6rGnMca8vVHjvHd8fPycnoCIiIiIdK9OCMR5YGDBtgFgtrGPBfub+1o99jTW2o9aa/dYa/esX7/+nJ6AiIiIiHSvTgjE+4CrmneMMVlgN642eAo4Nn9/4+d9ARwrIiIiIrKmbddixpgUEAWixpiUMSYGfAG4whjz2sb+3wYesdY+0Tj0TuB9xpjhxmS524BPNPa1cqyIiIiIyJqOEL8PKAG/Bbyx8fP7rLXjwGuBPwCmgBuB18877ndwE+WeA+4CPmCt/SpAK8eKiIiIiAAYa227r6Hj7Nmzx+7du7fdlyEiIiIiwTGL7eiEGmIRERERkbaJtfsCRERERKTLFCdhcj+Uc5AahJHdkBlp91WtmAKxiIiISC9Z7bBanIQjeyGehfQI1Eru/pY9XRuKVTIhIiIi0iuaYdWrurDqVd394mRwjzG534XhRAaMcbfxrNvepRSIRURERHrFWoTVcg7i6dO3xdNue5dSIBYRERHpFWsRVlODrkxivlrJbe9SCsQiIiIivWItwurIbqgVoFoEa91treC2dykFYhEREZFesRZhNTPiJtDFElCadLddPKEO1GVCREREpHc0w+rkfhdWU4Ow4dLgw2pmpKsD8EIKxCIiIiK9pMfC6lpQyYSIiIiIhJoCsYiIiIiEmgKxiIiIiISaArGIiIiIhJoCsYiIiIiEmgKxiIiIiISaArGIiIiIhJoCsYiIiIiEmgKxiIiIiISaVqoTERERWSvFSbescjnnllUe2a1V5TqARohFRERE1kJxEo7sBa8K6RF3e2Sv2y5tpUAsIiIishYm90M8C4kMGONu41m3XdpKgVhERERkLZRzEE+fvi2edtulrRSIRURERNZCahBqpdO31Upuu7SVArGIiIjIWhjZDbUCVItgrbutFdx2aSsFYhEREZG1kBmBLXsgloDSpLvdskddJjqA2q6JiIiIrJXMiAJwB9IIsYiIiIiEmgKxiIiIiISaArGIiIiIhJoCsYiIiIiEmgKxiIiIiISaArGIiIiIhJoCsYiIiIiEmgKxiIiIiISaArGIiIiIhJoCsYiIiIiEmgKxiIiIiISaArGIiIiIhJoCsYiIiIiEmgKxiIiIiISaArGIiIiIhJoCsYiIiIiEmgKxiIiIiISaArGIiIiIhJoCsYiIiIiEmgKxiIiIiISaArGIiIiIhJoCsYiIiIiEmgKxiIiIiISaArGIiIiIhJoCsYiIiIiEmgKxiIiIiISaArGIiIiIhJoCsYiIiIiEmgKxiIiIiISaArGIiIiIhJoCsYiIiIiEmgKxiIiIiISaArGIiIiIhJoCsYiIiIiEmgKxiIiIiISaArGIiIiIhJoCsYiIiIiEmgKxiIiIiIRarN0XICIiPaA4CZP7oZyD1CCM7IbMSLuvSkRkWTRCLCIirSlOwpG94FUhPeJuj+x120VEuoACsYiItGZyP8SzkMiAMe42nnXbRUS6gAKxiIi0ppyDePr0bfG02y4i0gUUiEVEpDWpQaiVTt9WK7ntIiJdQJPqRESkNSO7Xc0wuJHhWglqBdhwaXuvq5toUqJIW2mEWEREWpMZgS17IJaA0qS73bJHgW65NClRpO00QiwiIq3LjCgAr9T8SYkwdzu5X6+pyBrRCLGIiEg7aVKiSNspEIuIiLSTJiWKtJ0CsYiISDuN7HaTEKtFsNbd1gpuu4isCQViERGRdtKkRJG206Q6ERGRdtOkRJG20gixiIiIiISaArGIiIiIhJpKJkRERGRtaWU+6TAKxCIiIt2s28Jlc2W+eNatzFcrufuaSChtpJIJERGRbtWNyz7PX5nPGHcbz7rtIm2iQCwiItKtujFcamU+6UAKxCIiIt2qG8OlVuaTDtQRgdgYs9MY8xVjzJQx5rgx5sPGmFhj39XGmAeMMcXG7dXzjjPGmPcbY042/vd+Y4yZt3/RY0VERLpeN4ZLrcwnHagjAjHwF8AYsAm4GngZ8A5jTAL4B+BvgWHgk8A/NLYDvB14DXAVcCXwauCXAJZxrIiISHfrxnCplfmkA3VKIN4FfNZaW7bWHge+ClwOvBzXCeMOa23FWvvngAF+sHHczwO3W2sPW2uPALcDb2nsW+pYERGR7tat4TIzAluvhwt+2N12+vV2suIkHL4fnvm6u+3kCZUdrFMC8R3A640xGWPMFuDHmAvFj1hr7bzffaSxncbtw/P2Pbxg39mOPY0x5u3GmL3GmL3j4+MtPyEREZE1oXAZXt3YZaRDdUog/hYuqM4Ah4G9wBeBPmDhzIAc0N/4eeH+HNDXqCNe6tjTWGs/aq3dY63ds379+haeioiIiMga6MYuIx2q7YHYGBPBjQZ/HsgCo7ia3/cDeWBgwSEDwGzj54X7B4B8Y1R4qWNFREREulc3dhnpUG0PxMAIsB34cKPW9yTwN8C/A/YBV87vHIGbPLev8fM+3IS6pqsW7DvbsSIiIiLdqxu7jHSotgdia+0E8CzwK8aYmDFmCDdZ7hHg/wF14DeMMUljzK81DvtG4/ZO4F3GmC3GmM3Au4FPNPYtdayIiIhI9+rGLiMdqu2BuOGngVcC48AzQA14p7W2imur9mZgGngr8JrGdoCPAP8EfB94FPhyYxvLOFZERESke3Vrl5EOZE5vwiAAe/bssXv37m33ZYiIiIhIcMxiOzplhFhEREREpC0UiEVEREQk1BSIRURERCTUFIhFREREJNQUiEVEREQk1BSIRURERCTUYu2+ABEREZG2K07C5H637HFq0C1uoX6+oaERYhEREQm34iQc2QteFdIj7vbIXrddQkGBWERERMJtcj/Es5DIgDHuNp512yUUFIhFREQk3Mo5iKdP3xZPu+0SCqohFhERkXBLDUKt5EaGm2olt30+1Rn3LI0Qi4iISLiN7IZaAapFsNbd1gpue5PqjHuaArGIiIiEW2YEtuyBWAJKk+52y57TR39VZ9zTVDIhIiIikhk5e/lDOedGhueLp12Alq6nEWIRERGRpTTrjOc7U52xdCUFYhEREZGlLKfOWLqWArGIiIjIUpZTZyxdSzXEIiIiIsuxVJ2xdC0FYhERkVaoN62EmbVgfYhE230lLVHJhIiIyEqpN62EWbUA08+BV2n3lbRMI8QiIiIrNb83LczdTu7XKLH0rroHxQmo5Nt9JYFRIBYREVkp9aaVsClNuW9ArG33lQRKgVhERGSlmr1pmyPDoN600ptqZSiMubKgHqQaYhERkZVSb1rpslmICQAAIABJREFUdX4d8mOQO9yzYRgUiEVERFZOvWmll5Vn3KS58ky7r2TVqWRCRESkFepNK73Gq7ryiFq53VeyZhSIRURERMSV/RQnoTzdc5PmlqJALCIiIhJ21QIUxl1LtRBSIBYREREJq7rngnC10O4raSsFYhEREZGwsdaVRvRgT+GVUCAWERERCZNayY0K93AbtXOlQCwiEqTipFu2t5xzizOM7FYHAhHpDH4dChNQmW33lXQc9SEWEQlKcRKO7HWjLukRd3tkr9suItJO5ZzrKawwfEYKxCIiQZncD/GsW8bXGHcbz7rtIiLt4FXcKnP5cfD94M/v1+HZu4M/7xpTIBYRCUo5B/H06dviabddRGQt+b4rj8gdXr0FNo5+D/7+zfC/fhYOfnt1HmONqIZYRCQoqUE3WSWRmdtWK7ntIiJrpZJ3k+b8+uqcf/Y43Pvn8PQ/z207eA/svHl1Hm8NKBCLiARlZLerGQY3MlwrQa0AGy5t73WJSDjUa42ewsXVOb9XhgfvhAc/4UoxADZcDj/2fth16+o85hpRIBYRCUpmBLbscTXDpUk3MrzhUnWZEJHVZS2Uptz/VqOnsLWw/1/h23fA7DG3LbMOXvzrcMmrYHBr8I+5xhSIRUSClBlRABaRtVMtNpZcrq3O+Seehrs/AEcecPcjcbj638OeX4REdnUesw0UiEVERKQ16r+99la7p3BpCr7zl7DvC2Ab3Sl2vQxu/k0Y2r46j9lGCsQiIiKycs3+2/Gs679dK7n7W/YoFK+W0jQUT65OeUS9Bo9+Dr77kbmwPbwLbnkX7HhJ8I/XIRSIRUREZOXm99+GudvJ/QrEQfMqkB+bm9AWtOfvg3tuh8kD7n6iD278JbjiZyAaX53H7BAKxCIiIrJy5ZwbGZ4vnnYTSyUYvu9GhFerp3nuENzzp/DsXY0NBi7/KbjpHZAeXp3H7DAKxCIiIrJy6r+9uiqzrlZ4NXoKV4uw92Pw0KfBb0zK23wN3PpeWH9x8I/XwRSIRUREmjQ57Nz1Wv/tTnkPeFXXPaJWCv7c1ocnvwL3fgiKE25b30Y3Ye6CV7il50NGgVhERAQ0OWyleqn/die8B1a7p/Dx78PdfwInHnX3o0m47i1wzZteuPR8iCgQi4ishU4ZdZLFaXLYyvVK/+12vweqRSiMQd0L/tyFcfi3D8MTX5rbduGPwEt+A/o3Bf94XUaBWERktXXCqJMsTZPDpF3vgbrnShcq+VU4dxUe+oyrFa41lnQevRhufQ9suTb4x+tSCsQiIqut3aNOsjyaHCbteA+UptyH5qDLI6yFg9+Cez4IucNuW2oIbvpVuOwnIRIN9vG6nAKxiMhq08hjd+i1yWFy7tbyPVAru/IIrxr8uScPwN23w6H73P1IFF70c3D9bZAaCP7xeoACsYjIatPIY3fopclhsjJr8R7wfVceUZ4J7pxN5Rm4/6PwyGfBNtq0bbvJlUeM7Ar+8aIx92F//v+3dSkFYhGR1aaRx+7RK5PDZOVW8z1QnnFh2PeDPa9fh8e+CPf9BZSn3bbBbW655Z23Bt9GzRj3GqWGeqZFmwKxiMhq08ijSLitZk/hIw+4NmoTT7n78Qxc/za46g0QTQT7WMa4//9KD/dcDbICsYjIWtDIo0j4WOsmzJWng580N3MM7r0Dnvn63LZLXg0v/jXIjgb7WADJPsisg2g8+HN3AAViERERkaBVC25UeGFP4fI05A65JZmT/a60ITW0/PPWSvDgJ+HBO6Fecds2vghe+l7YeHlw198UT0Fm1N32MAViERFpPy1cIr2i7rkgXC28cF952q0QF0tDchC8sru/8YqlQ7G18MzX4Nt3QP6E25YZdQtrXPxjYCLBPo9o3I0IJ/uCPW+HUiAWEZH20sIl0gusdYH3bD2Fc4dcGG4ukdy8zR06eyAef8LVCR/9nrsficM1b4Tr3hp8h4dIpBGEB3pmwtxyKBCLiEh7aeES6Xa1khsVXqqncGXWjQzPF0tBJXfm3y9Nuc4R+74ANEL2+S+Hm98Jg1tbvOgFjHGT5VJDLhSHjAKxiIi0lxYukW7l16F4cvk9hZP9rkyiOTIM7n6y//Tfq9fg+38P3/0IVBvLOY/shlvfDdtuDOba50sNuP8Go+GNheF95iIi0hm0cIl0o3LOheFz6Sk8uM3VDIMbGfbK4JVg3e6533nuXrjndpg66O4nB+DGX4YrXguRgGNbIuPKI2LJYM/bhRSIRUSkvbRwiXQTr9LoKVw+92NTQ24CXe6QK5NI9rswnBqC6efhng/Cwbvd75qIC8E3/DKkz6ELxXLEEm5CXg+sMBcUBWIREVnaanaB0MIl0g18370/y7nWegqnhk6fQFfNw7f/DB7+DPiNFm1broNb3wujF7Z2zQtFom5EODUQ7Hl7gAKxiIic3Vp0gdDCJdLJKnk3KuzXgzun9eHxL8F9H3alFwD9m+Hm34TdPxhsh4fmhLn0cKg6R5wLBWIRETk7dYGQsKrXGj2Fi8Ge9/j34VsfgLF97n4sBdf9gmulFgtwAQxjXA1yZqTnlloOmgKxiMi5CtsiEkF1gQjb6ybdy1rX8qw0FeySy/kx+LcPwZNfmdt20Svd4hp9G4N7HIBEtjFhLhHseXuUArGIyLkI4yISQXSBCOPrJt2pVnLBtV4L7pxeBR76NDzwcXd+gPWXuOWWN10d3OOA6xiRHT29tZssSYFYRORchLF8IIguEGF83aS7+HUoTLjFM4JiLRz4pltueeaI25YegRf/Klzy6mDLGKKxxgpz/Uv/rryAArGIyLkI4yISQXSBCOPr1qRSkc63kp7CSzn5jFtu+fD97n4kCle+Aa5/W7ChNRKZW2FOE+ZWTIFYRORchHURiVa7QIT1dVOpSGfzKq48wqsEd85yDr7zV/Do51wnCYAdN8Mt74LhncE9jjHuv5/0SCiXWg6aArGIyLkIahGJsI0ahnXxDZWKdCbfbyy5nAvwnB7s+zzc91du0Q2AoR1uueUdNwf3OOBGmDMjEI0He94QUyAWETkXQZQPhHHUMKyLb4S5VKRTVWZdrXCQPYUP3+/KI04+4+4nsnD9bXDl64MNrfG0qxOOB9iaTQAFYhGRc9dq+UBYRw3DuPhGL5eKdNu3HPWaK49odnkIwswRuOcOOPCNxgYDl/0E3PSrLrgGJRp3nSMS2eDOKadRIBYRWWsaNQyPXi0V6aZvOVajp3CtBA/8DXzvU1Cvum2brnLLLQf5t41E3evZCx+gOtxZA7ExJgb8BPAq4CpgCJgGHgb+L/BFa6232hcpItJTennUUE7Xq6Ui3fItR7XoVpoLqqewtfDUV+HeP4fCmNuW3QA3/0e48EfPvctDeRpyh1wZR7IfBrfNdYtodo7QhLk1sWggNsb8MvBfgMeBu4AvAbNAP3ApcBvwQWPMH1pr/2oNrlVEpDd0y6hht30l3ql6sVSk07/lqHtQnIBKPrhzjj3ulls+/rC7H03AtW+Ga9+yskUwytNw4lGIpSE5CF7Z3d/+Yhje7foKy5o526t9AXCDtfb4GfZ9AfhDY8wm4N2rcmUiIr2qG0YNu+krcVl7nfwtR2nadZAIqjyieBLu+x/w2D8CjXPu/iG4+TdhYPPKz5s75MJwM0xnhiGyESozCsNtsOgrbq19z1IHW2uPAUv+noiILNDpo4bd8pW4tEcnfstRK7syBq8azPnqNXjk7+D+v4ZqwW1bdwHc+h7Yen3r56/MupHhaBzSQxBLNeqdO2SUPWSW9RHEGDNprX3B/wMaY8astRuCvywREWmrTv9KXNqrk77lWI2ewgfvhrs/CLnn3f3kINz0K3D5T0EkoNHb1CDEMtA3OretU0bZQ2i5f9UXNNEzxsSBABfhFhGRjtHJX4lLZ+iEbznKM65WOKgll6cOwj0fhOe+7e6bKFzxOrjxl4J77zcnzO24BY4+4Cb+dcooe4gt1WXiblzBTMoY860Fu7cC967WhYmISBt14lfiErxunTjpVV33iKB6CldmXWnEI383t2DH1hvcKnPrLgjmMYyB5IB7fSON8cROGWWXJUeI/ydggOuBj83bboETwDfOdJCIiHS5TvpKXFZHN06ctNZdd3k6mElzfh0e/0c3aa405bYNbIFb3gm7Xn7ubdQWk8i6hTpiidO3d8IouwBLBGJr7ScBjDH3WWufWJtLEhGRjqB/rHtbt02crBYaPYUDWv7g6PfccsvjjXgTT8N1b4Wr/wPEksE8RizpVphbSVs2WVPLrSF+0hhzG/AGYNRae6Ux5qXAedbaz67e5YmIiMiq6JaJk3XPBeFmp4dWzR53C2s8/c9z2y5+Fbz416FvfTCPEY25EeFkfzDnk1W33ED834FXAHcAzUU4DgN/CigQi4iIdJtOnzhprSuNKE4GUx7hleHBO+HBT4BXcds2XOaWW950ZevnB7eqXLqx1HJQ5RayJpa7HuBbgB+31v4dp7pS8yxwfpAXY4x5vTHmcWNMwRiz3xhza2P7DxljnjDGFI0x3zTG7Jh3TNIY83FjzIwx5rgx5l0LzrnosSIiIquiOAmH74dnvu5uix026gpuAl2t4LocWOtuawW3vd1qJbdwRSGABTasdX+HT78OvvsRF4Yz6+CHfgd+5pPBhOFm54ihna6nsMJw11nuCHEUaK5/2Hxn9s3b1jJjzCuA9wM/B3wX2NTYPgp8Hngb8E/A7wH/G7ipcejvAhcCO4DzgG8aYx6z1n51GceKiEgn68YuCN0yWa0TJ0769UZP4ZlgzjfxNNz9ATjygLsfibka4T1vhURfMI+R7HcBW6vLdTVjl/HJyxjzP4Eq8E7gGLAOVy6RsNa+I5ALMeZe4GPW2o8t2P524C3W2pc07meBCeAaa+0Txpijjf3/0tj/e8CF1trXL3XsYteyZ88eu3fv3iCeloiIrNT8YDm/9VunBcuFDt/v2oLNL0WoFl2HgSBWOOtVQfYULk3Bd/4S9n0BbON8O1/qukcMbW/9/ODek9nR4CbgyVpYdOh+uR9n3gV8EsjhFunIA/8CvLnlSwOMMVFgD/CPxphngBTwReC9wOXAw83ftdYWjDH7gcuNMSdwI8kPzzvdw8BrGj8veiygrhkiIp2s27ogNHXLZLVO4VXdksu1cuvnqtfg0c+50ojKrNs2vBNueTfseEnr5we31HJ21LVSk56xrEBsrZ0BfsoYswFXmnDIWns8wOvYiAvarwNuBWrAPwDvw5VmjC/4/RzQ39jXvL9wH0sce5rGaPLbAbZvD+jTo4gsrRu/Epe10a3BstMnq3UK33d/y3IumElzz98H99wOkwfc/UQf3PBL8KKfcSG2VZGo+/8m/R170rILXowx64AfBTZZa//YGLMZiFhrDwdwHc2lZj5krT3WeLwP4gLxt4CBBb8/AMwyV8M8AJQX7KOxf7FjT2Ot/SjwUXAlEyt9IiJyDrql1lLaox3BMogPaN22yl87PpRW8q6VWnNVuFbkDsE9fwrP3tXYYODyn4Kb3uEmurWqOWEuNeS6SEhPWtZf1hjzMuBJ4D8A/7Wx+ULgL4O4CGvtFK6N2/wg2vx5H3DVvGvJAruBfY3jjs3f3/h531LHBnHdItKi+V+JG+Nu41m3XWStuyA0P6B5VfcBzau6++faIaI5WS2WcCOgsUTnfsgL6jkvV70GM0ddL+BWw3C1APd+CD79M3NhePM18HOfhh/4/4IJw6kBGNrRWG5ZYbiXLXeE+A7g56y1/2qMaaxtyHeAGwK8lr8Bft0Y81VcycQ7gS8BXwA+YIx5LfBl4LeBR+ZNirsTeJ8xZi+u9OI24Bca+5Y6VkTaqVu/Epe1sdZdEIKsWe6WVf7Wqk7bWjfRrTQVQBs1H578igvDxQm3rW8j3PybcMErgml5lshAZvSFSy1Lz1puIN5prf3Xxs/Nd3L1HI5fjt8DRoGncOUPnwX+wFpbbgTaDwN/iwvir5933O/gRqqfw5VevN9a+1UAa+34EseKSDup1lKWspbBMowf0NbiOddKkB9zo8OtOvEofOsD7hYgmoRrfx6ufXMwyyPHkq6F2vz/T5JQWG6gfcwY86PW2nnrHPLDwPeDuhBrbQ14R+N/C/d9HbhkkeMqwFsb/zvT/kWPFZE267ZaS+ksQde+hvED2mo+Z78OhYm5bg+tKIzDv30YnvjS3LYLfwRe8hvQv6n180djjRXmFk47krBYbiB+N/AlY8yXgbQx5iPAq4GfXLUrE5He14kLA0h3WI0JmWH8gLZaz7mccwtstNpTuF6Fhz4Dez8GtaLbNnox3Poe2HJta+cGV16RGXET5rS6XKgta2EOgEZXiTfSaLsG/G1AHSY6jhbmEBHpcKu1+EUY2wAG+Zy9iiuP8CqtXZO1cPBbcM8HIdeIGqkhuOlX4bKfdC3QWmGMe67p4dbPJd1kZQtzGGPOa/YbttYeBf444AsTEZFu1O7guFq1r0HXLLf7dVqOIJ5zs6dwabr165k8AHffDofuc/cjUXjRz8H1twVT0pDsayy1HEBvYukZS5VMPMW8Pr7GmM9ba396dS9JRCQg3RBGulEn9I/uhnrfM71OB77pRjqxvfOerMy6WuFW26iVZ+D+j8IjnwXbONe2m1x5xMiu1q8znnKdI+Kp1s8lPWepQLxwaPnlq3QdIiLB6oTQ1qs6YUnlbqj3Xfg6+R5MHYLUtOuX2+3vyXrNTXarFls7j1+Hx74I9/0FlBsjzINb4ZZ3wc6Xtl7bG427EeFk39K/K6G1VCDWim0i0p06IbT1qk5oT7bcCZkr+ZYgqG8WFr5OM4fd+fzq3EI00H3vySB7Ch95EO7+AEw85e7HM3D92+CqN0C0xR7AkUijc8SgJszJkpYKxDFjzA8wN1K88D7W2m+s1sWJiKxYJ4S2XtUp5QpL1b6u5FuCIL9ZWPg6VfIQSZw+Utlt78lq0Y0Kt9pTeOYY3Ptn8MzX5rZd8mp48a9BdrS1cxvjylLSw1pdTpZtqUA8Bnx83v2TC+5b4PygL0pEpGWdEtoW6oW65m4oV4CVfUsQ5DcLC1+nSAwqOVh3zdzvdMJ7cjn8ugvClXxr56mV4MFPwoN3Qr3RiWLjFfDS97rbViX7GxPmglw3TMLgrO8Ya+3ONboOEZFgdWJo65W65m7pH72SbwmC/GZh4es0vNN1YYjEXKlBJ7wnl6M07XoKt1IeYa0bDf72HZA/4bZl17uFNS56JZgWR3LjaTeyHEu2dh4JLX2EEpHe1ImhrRvqmpc7gr2WSyqv1Eq+JQj6m4WFr1Pz9e2U9+TZ1MpuVLjVnsLjT8DdfwJHv+fuR+JwzZvgul9ofYnkWMJ1jtBSy9KiRQOxMeZ+XN/hf7DWVs+wPwG8Bni3tfbG1btEEZEV6rTQ1ul1zb0ygt20km8JVvubhU57T56J77sR4XKutfOUplzniH1f4NQc/fNfDje/03WRaEUk6kojtNSyBORsI8Q/D/x34C+NMQ8CTwKzQD9wEXAt8A3gLat8jSIivaFT65qbumEE+1ys5FuCTvxmYS0F0VO4XoPv/z189yNQbdQcj+x2/YS33dDa9RnjJsulh9U5QgK1aCC21j4GvM4Ycx7wCuBFwCgwBdwJvMlaO7YmVyki0gs6sa55vk4fwV6JlYzIdsMobtC8qiuPqJVaO89z98I9t8PUQXc/OQA3/jJc8VpXO71SxrhzZUa01LKsiiXfnY2lmz+1BtciItLbOn30sdNHsCV4QfUUnn4e7vkgHLzb3TcRuPynXRhOD7d2jVpqWdaAJtWJiKylTh597PQRbAlWtdDoKey1cI483P8xePgzbiU+gC3Xwa3vhdELW7s+LbUsa0iBWEREnE4fwZZg1D0oTrTWU9j68PiX4L4Puwl4AP2b3IS53T/YWn2vllqWNlAgFhGROZ08gi2tK025biKtlEcce8Qttzz2mLsfS7kWate80f28UlpqWdpIgVhEulsvrPwmstpqZSiMuclzK5Ufh3/7c3jyK3PbLnqlW1yjb+PKz6ullqUDLDsQG2N+BLgaOO07DGvtbwd9USIiy9JrfXOldfqAdDq/3ugpPLPyc3gVeOjT8MDH57pQrL8UXvoe2HR1a9enpZalQyzrHWiM+TDws8A3geK8XS185yIi0qJe65srrdEHpNOVZ1ytsO+v7Hhr4cA33XLLM0fctvQIvPhX4dKfaG255UTGBWEttSwdYrkfyf49cJW19tBqXoyIyLI0RwH3/z8Y2ASD2+ZWrOr2vrmycvqA5HhVVx5RK6/8HCefgbtvh8PfdfcjUbjy9XD9bW5Ud6W01HJn0DcpL7DcQDwBTK/mhYiILMv8UcCBTW6m/NhjsOEyF4rVNze8enFhkXNhrfvvozy98klz5Rx85yPw6OfANlar23Ez3PIuGN658mvTUsudQ9+knNFyA/HtwKeNMX8EnJi/w1p7IPCrEhFZzPxRwMFtjZnuBnKHILJr7fvmtnukpd2P30nCvLBIqz2FfQ/2fR7u+yuo5Ny2oR0uCO+8ZeXXpaWWO4++STmj5Qbiv2zc/viC7RbQGooicu5WGuTmjwKmBtzI8P/P3puHuZHX575vad+7W73abtvttseexZ4Zz8YMs8AwgQQCJJwEAgGSEw45CctNQoB7z33uuTd5cu/JueeQQEIgCwkkhCVcEg55CGcGyEwGxrPbM57F42289+JepZZarV2q+8dbNVWtlloltaSW1N/P8/hxS62q+lVJlt96f+/v+41NAPGrwOCB8nVzmyUaN9tp2ezjtxtbsbFIIU8hnF2pfx+TR4Ejf8SYBAC4/IxG3Pje+rvDKQr/rXn7pNVyu7HVZ1IqYEkQq6oqdVAEQWgcGxFypS6gJ0RnePAAMHp7Y49Vjc12Wjb7+O3GVmos0oiWy/Fp4PHPARf+TXtCAa7/OeDOjzLeUC/Sarm92cozKesgdU4EQWg9GxFytbqAzRSNm+201HP8bo9YbIXGIrkUkJgDCrn6t3/ub4HjXwMKWl3ibTex3fJG3HRptdwZbMWZFAtUFMSKovxAVdWf0X4+ggol1lRVva9JYxMEoVvZiJCs1QVspmjdbKel1uNLxKKzKRaAlQUgs1zf9qoKnP0B8OTnWYUCAPxDwN2/DVzz0/VnfKXVcmexlWZSamA9h/jvTT//TbMHIgjCFmKjQrIWF7CZorXVTkupu+vtByLnrB9fIhadSzrGBhv11hSeOwU89hlg5kU+truAwx9ky2Wnt7592myaEA7JgrlOYyvMpNRIRUGsquo3TT9/tTXDEQRhS9BKIdnMY7XSaSnn7kbOAeF9QGpx891yoTnkM1w0V29N4eQi8NQXgVPfw2sTvXsfAO7+HSC0vb596pUjPL3SalnoGmpp3TwM4A4AAwBeuxVUVfUrTRiXIAjdTCuFZLOP1SqnpZK7m1osv5iwHJsd8RCsUyzy85qO1bdorpADXvwH4Ojf8AYQAPr3Afd+yvrnpRyeEG+qpNWy0GVYbd388wC+DuBVADcAeAXAQQCPAxBBLAhC7bRyyq4bpgcb4e7KYprOIJOgK1ws1Lf9pSPAkc8CsSt87O4B7vwIcMO7AFudQtbl44I5h6u+7QWhzbH6L+P/AfBrqqr+o6IoUVVVDyuK8mugOBYEQRCaTSPcXVlM094UchTC8WnW1s4ss01yz07GE6oRvQQ8/lng8hN8rNiBQ78I3PEb9c8CONzMCUurZaHLsSqId6mq+o8lz30VwAyATzV2SIIgCMIaGuXudoNb3m2YawqnosDsCcDhpbObT/Px8MHKojizDBz9a+Clbxmu8ugdwL2fZEyiHuwObcFcsL7tBaHDsCqI5xRFGVZVdRbAJUVR7gKwAOlSJwiCYJ2N1AAWd7c7ySa1lstaTeHYBMWwXvlB/zs2sVYQFwtcLPf0FymkASC0A7jnE8CeN9ZX+cFmMxbMSeUIYQthVRD/NYB7AHwHwOcAPAqgCOCPmzQuQRCE7qIRNYDF3e0eKtUUzizTGTbj8ACZ2Ornpo+z3fL8aT52eoFbPwTc/H7GHGpFWi0LWxyrrZv/m+nnv1cU5ccA/KqqnmrWwARBqINu70LWyUgNYEEntcRyaOWqR7iDjEmYawPn00Z0YXmGjTVe/aHx+wM/C9z1cSAwVN94pNWyINTXullV1SuNHoggCBuk27uQdbrY7+YawJ3+3rSKfIYtl/OZyq/p2cnMMEBnOJ8G8imgZ5Q54ef+js8BwNANwH2fBkYO1TceabUsCK+xriBWFOXNAN6mquontMcpAOaaK29RVfWRJo5PEASrdLMD2Q1iv1trAOvvTbEIJBeAmZPAxDPA/rcB/eObO652EenFIh3hdKz6az29XEAXm2BMwhUAFs4Bj/wB9wEw1vD63wKufTug1NEYw+4E/AOAy1/7toLQpVT7l/Q7AB4yPc4A2K/9+VUAv9ukcQmCUCvp2NoWrE6vtf+E2x2z2FcU/u308/lOIbyXVSGySU6VZ5N8HN672SPbGJHzFHzRi0AhDwQGAcUBnH2IonQz0EV6PssbqHyWjzdjPJllYOlybf8OPb3A8CHANwg88SfAkf9OMazYgWt+GnjDfwL23Fe7GLbZ+f707RYxLAglVItM3Ajg30yPVVVVzwOAoiiXAfxhswYmCEKNdKsDCXRH3KBbq0SkY3SGHV5j6t3TA6zMbd7sRDvMluSzWsvlVO3bpqLAM38BvPJdQC3yue2HgZt+GQiOcJ/lqk5UQlotC0JVqgni0n9t5vkvBUCHf5MLwibRjOncbu5C1i1iv92rRNTzufT0MCYRGDSey2e4SGuzZic28wbKXFO41pbLhRxw4jvAs39pVJ8IDAOHPwhsu8l4XbmqE5WQVsuCYIlq/0KmANwM4BgAqKoaNf3uMIDpJo1LELqXZuVhu9WBBDZH7LdTBrUV1Pu5DO9lZli/TvkMF4EFxzbvhmWzbqCyK1pN4Xzt2048wzJqkQt87ArXx1OfAAAgAElEQVSww9zwdYykmDFXnaiEy8+bEmm1LAiWqCaIvwngLxRFeaeqqlf1JxVF2Q7gCwC+3szBCUJX0szp3HZ3IOulVOxDYZ5y+vnmiNVuWMRXK/V+Ln1hLqA7+xBjEr5+imGbffPy0a2+gSrkGRvJJGrfNjYBPP454OJPtCcU4IZ3AXd+lDGH9FL5qhP9Fa6tw80Fc6XrCQRBWJdqgvi/ArgNwDlFUZ4BWzVvA3A72JxDMsSCUCvdkIfdDHSxbxarutixKlatuL7JCHD6+0ByCQj0A6FRTjsD3VGxoxIb+Vz2jwM3va/xjnq9Ln2rZktUlYI1Gak9HpFdAY59BXjhG0BR61K3/TBw76eBwQPG60qrTriDFMOl+WFptSwIG2JdQayqag7AOxVF+SkADwAYAPAMgD9UVfVfWzA+Qeg+uiUPu1nU62RacX3116SigH+QC6PmTgJD11NodPNNy0Y/l42endioS9/s2ZJcivGIfLa27dQicOZB4Mk/o6sMMCd89+8A+95cvl2yp7fyAjpptSwIDcFqp7qHATzc5LEIwtagmxe/tYJ6nUwrQlp/jX+AQkevmhCfBHrHuvumpd0+l+1QKaIcxYJWUzhe+7azJ4DHPmNEIOxu4JZfBW75ldojDq+1Wg5L5QhBaACy7FQQWk03L35rBfU6mVaEtP6a0CidYQCwu4DEAh3jbr5pabfPZTtGi9IxiuHSRW7VWJkHnvoCozg6+94M3P3bQHBb7eNwB/m+SKtlYbPposXHIogFYTPo1sVvraBeJ9OKkNZf4wkxJhGfpJjx9XX3gjqddvpctlO0KJ/Ragqna9uukAVe+CZw7MtALsnnBvYzJ7zjltrH4fRy9sLhrn1bQWg0Xbb4WASxIAidRb1OphUhbX6NO8iYhH+wY7/gO9q9aYcIR7HIz1hqqbbtVBW4dAR4/LNcDAcw43vnx4Drf44VOGrB4eKCOekuJ7QT7RprqhMRxIIgtC+VBF09TqYVId3q2EAzBWunuTflrsVmRjgyy8DKAjPDtRC5ABz5Y2DiaT622YFDvwTc/utGtRKr2Ow8327OrgudSzvGmjaAJUGsKMoeAP8FbNIRMP9OVdVdTRiXIAhbHatVIWoRlFaEdKtiA80WrJ3k3qx3LUZvb+1YCjnGI7LJ2rZLx4GjXwJe+jagaiJ6553AvZ8Cwntq25featnb19zKEZ08gyBsPu0Ua2oAVh3ibwI4D+CTAGr8lhAEQaiDaoKu0xzQUholWCuJmk5yb9pBvNfbcrlYAE7+M/D0n7MmMQD07ATu+V1g7N7aBK2iAO4Qz7nWWEWtdPq/H2HzaYdYUwOxKohvAHC3qqo1Lq0VBEGok2qCrh1E1EZohGBdT9R0knuz2eI9m2SXvVpbLk89Dxz5DLBwlo+dfuD2DwM3vZfVSWqh1a2WO/3fj7D5tFtlmg1iVRA/BuAwgOeaOBZBEASDaoLOqohq12nhSucHBZg8CsQmmWN1BYHe0fLjXk/UdJJ7s1HxXu97XG/L5fhV4Mk/Bc6Z+lNd+w7gro+zCkQtbFar5c2+CRG6g3aqTLNBrAriSwB+oCjKd8H2za+hqur/1ehBCYIgVBV0VkRUO08Llzu/xAyggjnW+FVAsVGsOT2cyi8d93qippPcm42I93re43pbLudSwPN/Dzz/VaCQ4XPDh4D7Pg0M32B9P8Dmt1rupBkEQWgBVgWxH8D3ATgB7DQ9X2PzdkEQBItUE3RWRFQ7TwuXOz9PL+DwAEuXAKePQjiX5u97x9aOu5yoScxyUdi5h627pZvtom9EvNf6HtfTcllV6QY/8Se8vgDgG2Bjjf0/wxsXq9hsFO6ens1ttdxJMwiC0AKstm7+tWYPRBAEYQ3rTcdZEVHtPi1cen7nHub4MgkurgI4pZ6Jlx93qahJzAJXXwS23WzdLW0XF73eqVer73G9LZfnTwNH/giYPs7HNidw+APArR9afSNSDUXhDY+3rz1aLXfSDIIgtADLdYgVRbkGwPsA7AAwBeAfVFV9tVkDEwRBqEo1EdVp08L6eN0Bdkdzevi3O1B+3KWiZmWeYjg4zN9bccTb2UW3gpX3uJ6Wy6koK0e88l28Nhk6/kbg7k8APaO1jdEdZDzC3mal/7so/ykIG8VqHeJ3APgGGJu4DOAAgGOKonxQVdXvNXF8giAI9dNp08L6eL39bPCQTwNqEQjuqTxus6g593Dtjni7u+jVWO89rqflciEHvPxt4NkvAdmEcYx7PwnsfF1tY5NWy4LQMVi9Xf1DAD+nquqj+hOKorwRwBcAiCAWBKE9addp4fU68OnjzaeMKhP+AWu53noc8U5z0Usp9x4PHuCNRGyytkVzl58EHv9jIHqJj90h4HW/CRz8BcBWg7vrcDFjXEukQhCETUVRLXxZKIoSBTCoqmre9JwDwIKqqr1NHN+mcNttt6nHjh3b7GEIgtCNmDO7ZkezEZndevbdzPFsBvW0XF66Ajz+OeDSY3ys2CiC7/hNwFvDf3E2O6MRtbZoFgShVVRcyWr1lvcFsEvdfzM997va84IgCIJVmpnZrccRb1cXvVbqabmcTQDHvgK88A2gqPk9O24F7v00EBgEYhPA/ElmgHt2clFcOVrValkQhKZhVRB/BMC/KIry2wAmwNJrSQDvaNbABEEQupJmZ3brWSjVyYur6mm5rBaBU98Hnv4CF9sBQHAbF8ztfROQiQGzJwCHF3D3MMs9ewIYPrhaFLey1bIgCE3Fatm104qiXAfgLgDbAEwDeEZV1VwzBycIgtC21Fu7t9Mzu+1ENklXuFDDf0VXX2K75bmTfOzwALf+e+DwB/kzQGfY4TW6x+l/xyYMQdzqVsuCIDQVy6sEtPzwkSaORRAEoTPYSO3eTqt80Y7U03I5MQc89WfAmQeN5/b/DPD63wICw6tfm1mmM2zG4aFzvFmtlgVBaCoVBbGiKKdUVb1O+3kCFbrSqaq6q0ljEwRBaE82kgPulszuZpGK1tZyOZ9hRvi5r/DmAwAGr2W75W03l9/GHWRMwix6i3mgbwzo3Vl+G0EQOpr1HOJfN/38gWYPRBAEoWPYaA64kzO7m0UuDazMWW+5rKrAhUfZbjk+xee8YeCujwHXvXP9dss9O5kZBnizY3PRGR4+uLFzEAShbakoiFVVfdz0cEhV1X8sfY2iKL/YlFEJgiAA9ed0N7ptNSQH3Drqabm8eJ7tlief5WObHbjxfcDtH6b7Ww1PLzB8CEhHuQDP22d8fpr5uRIEYdOwWoc4rqrqmsKKiqJEVFXtum8CqUMsCG3ARurjNru2brfV7m1X0nFmha22XE7HgGf+CjjxT4Cq1SHefTdwz+8y7mCVSq2W5X0XhE6nvjrEiqKMaz/aFEXZU7KjcQA19MMUBGHLUo+rVimnO32cjQ/W21cza/0CkgNuNrW2XC7mgVf+B/D0X3LhGwD07qYQHrvH+nGrtVpu9udKEIRNo1qViXPgYjoFwPmS380A+P0mjEkQhG6i3ooM5XK6hRww9Rxdv/X21exav4DkgBuJfsOUWmJtX0+f9fjJ5FHGIxbP8bHLD9z+68CN7wXsTmv7cLjoCLv867+uFZ8rQRA2hXUFsaqqNgBQFOUnqqq+oTVDEgShq6jXVSuX042c14RLlX1ZzfhKHnTz0W+YYOOCuWwCiE2ubYJRSnwKeOJPgfOPaE8owPU/B9z5UX5GrGCz8/22Kr4lOy4IXYvVxhwihgVBqI96XbVy9XqTi8DoHevvKxlh9nTqOQqj8F46haW1fjdSS1hoHAtnV0cjyjXBMJNLAc/9LXD8a0BBqzix7Sa2W7Zay7neVstSQ1oQuhZLglhRFAeAjwJ4A4ABmLLEqqre15yhCYLQFdTrqvnCQHgfcPlxYHkWCA4DA/vXToOb92UWuaN30P2dfBbYcetaoSt50M1Fb7m8+CrgCq0WpnoTjNLXn30IePLPWH4NAPxDwN2/DVzz09aFrSfEG6V6Wi1LdlwQuharneo+B+BNAL4E4L8A+D8AfATAt5o0LkEQuoV6XbVkBIic4/bDB7ldYgZYngGCI+X3VSpyd9zC9r4O11rREpsEsiv84w4AoVFWF9iMPGgroxvtEBPJpbhoLp8FXIG1TTDy6dXl0eZOAY99Bph5kY/tLuCWXwFu+ffWO8a5fIBvYOOtliU7LghdyTqVyVfx7wC8VVXVPwWQ1/7+eQD3N21kgiB0B7qr5nAB0csUY9kV/p1cR3yaxa2i8O/ACODt5b5SEf5tdn7TsbUCyenl82aSESA+zda/7hCQzwFzJ9net9V5UN3VzmcZ3chn+Xi9a9MJxypHsUC3PzZlNNjo2QnkUxTJqsq/8yk+n1wEHvkD4NsfNMTw3geA938HeN1HrIlhhxvo2QGEtm9cDAuC0LVYdYh9ACa0n1OKovhUVT2tKMrhJo1LEIRuQhesqSgQGDLc3fUyu5Wyx/kUMHp7+eNYjWdEzgPhcSB6iSW+HG66kpFzwM6SjHKzaWV0YzNjIukYBW5pTWFPL2cAYhOMSbiDQO8u4OT3gKN/wxkAAOjfB9z7ybUZ8krYHYxGWGnEIQjClseqID4F4HYAzwI4BuD3FUWJA5hq1sAEQegyahVj9WSPrcYz0jEgMAw4fUB8EsjEGZtw+ls/Hd7KUl6bUTbMSk1hT6+xgO7S48C//h6wdJmP3T3AnR8BbngXYLPwX5aiaJUjemtbMCcIwpbGqiD+bQBa2x/8LoC/ABAE8B+bMShBEDqQatnUWsWYWdwWclrEYpEL5JKR8sK1dNETFECxA9PPrx6TLrY9IcBzPbfVs8atppWlvFp5rGKR70E6xihENaIXgcc/B1x+go8VO3DoF4E7fsPa+BSFr/P21bdgThCELY2l1s1bDWndLHQVrVhEZaWl7eRRbRGVSYzpIrRSBCIZYWe6ciXUqpVHW29MQPu04G1lO+Bqx2rUZyWToCtcLFh47TJw9K+Bl75lvH70DsYj+vdZO547oLVattiIQxCErUrFaSNLglhRlBcBfB3At1RVnaj2+k5HBLHQNbRKbFkRu/WOpR4hbWW7dqi2oNMOVSYa8Vkp5CmEsyvVX1ssAKe+Bzz9RWbLASC0A7jnE8CeN1qLOzg9rBzh9FgbnyAIW52KXyxWIxO/D+B9AH5PUZTnAHwTwD+qqir9KgWhnWnVIiorcYh6a7jWm3uttl07lc9q5VgqHWsjnxW9pnAqai0eMX2c7ZbnT/Ox3QVc907gtv/ARZfVsDu1BXOB6q8VBEGwgNVOdd8F8F1FUYJgCbb3AfisoiiPqKr6zmYOUBCEDdCqRVRWs6n1CL96c6/SZrc26v2s5FIsV1fIVT/G8gzw5OeBV39oPLfzdcBN7+MCx8WzdPArtWy22ThGT099C+baaVZAEIS2wqpDDABQVXVZUZRvAlgC4ALwtqaMShCExtAqUdjMlrb17lva7NZGrZ+VYgFYWWAGuBr5NFstP/d3/BkA+vYAh94DjBxc/dpyLZsVhc95+yiK60FadQuCsA5WWzcrYKe6XwbwLgCXwdjErzZvaIIgbJhWicJmtrStd9/SZrc2avmsVKopXIqqAucfAZ74E2D5Kp/z9QN3fVyr9tG3+vXlWja7g9qCuZr8m7VIq25BENbB6jfMNIAE2Kr5blVVTzVjMIqiXAPgZQD/pKrqB7TnfhnAfwUwAOBfAXxIzy4rihIG8GUAbwGwAOB/V1X1m6b9VdxWELYErRSF9eZg9UoS82e43GHgALD98Op91bvvdsoJtztWPitWagrrLLwKHPkMK4QArCF88/uB2z7Eds2zL6/fstnpBfwDbJrSCDajBrMgNBqJ/TQNq4L451RVfbapIyFfBHBUf6Aoyg0A/grAzwJ4HsCXAPw5gPeaXp8FMAzgZgD/U1GUF1VVfcXCtoKwNWiVKKz2RV3u9wBw4VEgOmFMzc+8DKSWgL33Wx+3/CfRGCp9VmqpKZyKAs/8BfDKdwFVc5DH7mP1iN5dxut6dgKzJ/izw0MxnE9RhIe2r45uNALJlAudjsR+morlOsSKolwL4N0ARlRV/Zj22KWq6ksNGYiivBdcsHcSwD5VVT+gKMofAhhTVfWXtdfsBbvm9QMoAogCOKiq6lnt918DMKWq6n9ab1tVVdcNvUnZNUGoESv1bcv9XrGzfbJiN0pn5dKAWgC23bh+WTX9uOXqFCdmtByqKgJ5o1itKVzIASe+Azz7l0auuG8MuOeTwO7Xl98mvaS1bF7m+7TtZqBvd0OH/xqtrPcsCM2g3hKUgpmNlV1TFOXdoLv6HTBH/DEAAQD/L4Cf2vDoFCUE4A/AnPKHTb+6AcCT+gNVVc8ripIFsB8UxHldDGu8COANFrZ9bqNjFgTBRLV8ZqXfz7wIwAZ4TF/wDjedyHRsfedXFzhLE4BvkAuv5k/ReYxOAJ4lRi/ERamPWmoKTzzDMmqRC3zsCrDD3KF3r98sQ18op/9pZqtlyZQLnY7EfpqK1cjEHwD4KVVVX1QU5Ze0514EcFODxvF/A/iyqqqTyuovxACAkhUWiIFtowsA4hV+V23bNSiK8h+htaLetWtXuZcIglCJal/UlX6vAnA4mU3VHeJ8RhNRyvrTg7rILuYBd8gQU1dfAII7gGKWz1VbPFVP3KKbIxq11BSOTbDd8sWfaE8owPU/D9z1MQrc9VAUvm++cPlWy824xpIpFzoZif00Fav1a4YA6NEI1fT3hvs+K4pyM+gyf67MrxMAQiXPhQAsV/ldtW3XoKrql1RVvU1V1dsGBwetn4AgCMYXtRnzF3Wl3w8eALy9zAxnU/yTXjLKbumusi5snX6KJIBCyellc4Z8hs853BRSwOqmDU4vX1+K7jLnsxTd+SwfJ9dxXOrZplPIpZjhvvgT4PITXPiWXlr7umwSeOoLwDfebYjh7YeBX/o68Kb/XF0Mu/x08gODlcVwt15jQaiX8F7GfLJJ3qxmk3ysr8cQNoRVh/g5AB8E8Pem594LoBEL7d4IYAzAFc0dDgCwK4pyPYAfwORCK4oyDsAN4CwYmXAoinKNqqqvai+5CcAr2s+vrLOtIAiNpFrJrkq/33EbnzNXmRg5RHE1/fzqCgT6trrrrIvs0Cgwd5LPqSqn6zMxoP+wsV0lF6WeUlzdWL5Lrykcm+BCN4cXcPdwodvsCWD4IG9S1CJw5iE210gucNvAMHD37wD73lw98uBws3JE6ftaSidc426eJRDaE4n9NBVLi+q0BXQ/AnARwJ0Afgxmcd9iEqP1DUBRfFjt5H4KFMgfAZ3pp2BUivgrAA5VVd+rbfst0KX+MFhl4kEArzdVmai47XrIojpBqIN6qkys90VebQGJeZFUMsLXJ2aB/v10Hvv3Vl88de5hOpBmIZeKafv1lS8DV24bVeV/UPs2vKSi9ZhrCs++zGvu9ALZBBcnJqN8vwIjrB6hV4awu4FbfhW45VeqC1y7Q2u1XDaxthar13izRKks0BOETmVji+pUVT2tieK3A/g+gAkA31dVNbHRkamqmgSQ1B8ripIAkFZVdR7AvKIovwngG2BliYcB/Jpp848C+AqAOQCLAD6iquor2n5fqbKtIAiNpFo+s9b8ZjXXWXdLpo9zMV3fbmDvm4wqE3oZr0ouSjICxKeBmZNAoJ9OMwBceRJYWaQrCqwtA9ctOb5yNYUzy3SGswlg8Rxg9wA2J/DyP/Ea61zzFuD1vwUEt61/DJuN8QlPb20L5qxc480sQdUJDrYgCDVhuezaVkIcYkFoE6w4gPWUItLFVLEARC4Cio1xgGIeiF4B+vfQ0QTWloHrdHdwvZrCukO8PAVk04ytXPwJUMjy9727gPv/T2DHLesfQ1H4fnnD9bVatnKNN7MEVbfNEgjC1qF2h1hRlCOwsGhOVdX76hyUIAjC+lhxlespRWR2+Jw+ID7JDG18hk0hzPszl4HTx9SpOb5qNYV7dtIRn3yOi+pSUT7vCrCE2vabq4thd0BrtbxOubVqWLnGm1mCqltmCQRBeI31IhN/07JRCIIg1IunB0jMAalFCj53APD2A/7+ytuYxZQnBHiup8M38TSzseXKwJnFTqeV7yrkKPir1RRORoHjXzeiKooNGH8jcOg9/Nnhqryt0wP4BozrtlGqXePNFKXV4jyCIHQcFQWxqqpfbeVABEEQ6sLbD5x7hDlVdwhIx9ms49B7Km9TSUwNHGCZsegEoGrCKhOjc9qJpY1WFlmXOT7FBW09O42SdmbSceDol4CXvs14CAAMXgvc+D4gvMfIY/eXuQZ2p7ZgLrD2d81kM0VpqYMNhd0Wp5+XihOC0KGsu6hOUZTPq6r6W6bH/0FV1S+bHn9HVdVfaOYABUFocza7/FRqEdh2E4VJZhnwBLnALrUIYLz8NvWUgWu1wCl3XQFrz/nCQGwauPBvXBRXroQawOoSL3yDLZez2hrpnlG2Wx45xChJJkYx3b93tZi22eiye3qa22GuEpsdXdEd7HJ5Z+mMKAgdx7qL6hRFiauqGjI9jqiqGq70+25BFtUJgkXaYYFZvQucminkN7rvctd1eYYCPTBiPJeY4UqPoOm5TBzoGwfmTxol1HRyKcYehg8BFx9ju+X4FH/ncLN6xJ0fBfxDlcemKEbL5XoWzHUbm7m4TxCEWqm77FrphptgAwiC0La0Q/mpdlvgVKkcWHgfXWsrIrncddU7xoXHjedS2nP9uhNeBDIrFMN6CTUzDg8QvQgc/wZw7l+N58fuZcTE4WYt50qC2B3UFsxZ7em0BdjMxX2CIDSMat9qpfax1GgTtg6bHQVoNM04H7MYSMc5xZ5eBlDc2P5rGWs9WdJm1rAtJ2Yzy8DZh4BtN1s7XrnrOnOC59e/jwsBAS6WA1gWLbXEBYCFDDB3js/bXYyPuLT21if/GTj7Q6CYM67dLb9iiGxVZUSiFKeXHeYc7o1dm26k3W7IBEGoi2qC2KEoyv0wnOHSx2Wa0AtCF7CZRf+bQbPORxcDxTzbJzu8rNKAYv37r3Ws9WRJm+lsl3MMkwu8RlaPV+66ekKcmp87CQxdz8c2O6fnE3MUs9kEMHcKcPlZM3juFDDzCpBPAq981yij5h8Ebvh5YPutfK1OPr26m5zdSSFsfo2wGqk4IQhdQTVBPAd2gtNZLHk81/ARCUI70A5RgEbS6PPRHdylSTZxKBYBlyakUhGKqtnTzL1e+/bKxyjnBNcz1lrLoNUyzV2rs17OMVyJGGXgzE56ZpmPoa7ety6ylibYLQ6gAFYBQAFiE0BhhILZ5tAyqx4gepmv1V1hdxA4/jW+HuACu8MfBG79NaCY5SK7nI3bmitJ2OxGVz5hfTZ7cZ8gCA1hXUGsqupYi8YhCO1Ft+UC6z2fSpUOdAe3bzfrzp55COgdAzwBzh/ZXRRkyfnK7m4lJzi7AvTurn2stWB1mrseZ72cY2i3MXubjhuObzHPvO7Vl9nwIp9dve8dtwEL51j/1xM0KmBEL7EChstvLNqKTTDqUMxSjBWLwLGvABd+jNeSbuNvBO7+BKtIAAB8rDihb+sOAgPXAH1jXDQnC+as02l1qQVBWIOsjBCEcnRbLrCe86kkBhX7agc3OMKyZwDFsc3Fv3NpTrc7/eXd3VInuJinI7r4Ko9tzso2+tpbneau160udQz3vw2InAMWzxmO7/IU0LeHwnZ5ijEI8759YWDn7UYFA1Wlo+z0slvc8CHjmHo5NKcXOPsj4PT3gVySzwW3AYc/ANz43rVj9fQa23pCfJ9lwZwgCFsQ+eYThHJ0Wy6wnvOpJAavvgjsunPt/iefpdjzD1AM51P8OXoRiF/l67z9RqWFhbPAoCYCdefU7gF8QxR+U8fpnNqdjb/2Vqe563XWyzmG3t7Vjq9vyHhNJl5+3/r7ls8w0pBZ5nUNH1x7zMtPAo99Bohd0fblB657B7DzDuOGpRwuHzvMrdeFThAEocsRQSwI5WhELrCdqlTUcz6VxKCCtW6z3QnsuJU1bZPzFML+AS27qgChbWwdfO4RVloIDHHx3dUX2PQiPskYAQCEhoHQKN3UuZN0SZuRybQyzd3omQJ3AEhqpdLcfgpd/fly+/b0AD27TWXUyjTIWLoCPP5Z4NIRPtbbLe97M697pe50DheFsPncBEEQtigiiAWhEhvJBbZjlYpaz2e99sa5FT4ubcax/bBx3tGLoHpWKcrikxRmqUUgOMxIxNRxCt9CjgK5kGarYE+I+0pFNre5QaNmCvTPg38QyCS4oC6bYLk0h49OeDa5et/pGDvJ2eyr4xE62QRw9MvAi99k3ATgTcm9n2YWuBI2O/PMehxFsE473eQKgtBQRBALQjPohioV1dobV3Kbw/uAy48Dl57gAq7thym+5hOAO0SnE9BE7810gQEARaOcGNAeme1GVRAwfx6cPt4cFDJ8bnA/oOYBh5/7dvmB2CRjJ+VQi8Cp7wNPf4GCGWBO+O7fAfY+ULmNsqKwu5y3b3NaLXc67XiTKwhCwxBBLAjNwEr2tN3dpmpisNxYkxEuHgvv5dR9JsHYhDvIWEA6zvysjt3JSIQuvm0OLh5rp8x2IyoImD8PnhDgud7UXvoBPl8s8nFskr8rx8zLzAnPvcLHDg9LqB3+AH8uh6LwRsQXpjss1Ec33OQKglAREcSC0AyqZU8b0d63FdQqBs2ioWen5v5qdXN9/awi0Te2VvQ2o5ZrO91wVPs8ZBLAyjwd39iEkRfW87+JeeCpzwNnHjS23/8zwOt/CwgMVz6uO6C1WnY257y2Et1WilEQhFWIIBaEZlAte9qI9r7tSKkTOnQ9BV78KjB4ADj0Hgr+So5zo86z3aa3K30e+vcB8Wnmh9NLbJTh8ALuHlaVmD7Om4oX/4HbAMDgtcC9n2IUpRJODxfMOSu4xkLtdFspRkEQViGCWBCaQTXHsxHtfduRUtHgCQG2PRTDry2OG2/+OGqd3m62m1z6eaQLqHYAACAASURBVHCHgOA+LozT4xGxCYphp5fPzb4CvPANfi4Afl7u+hgwdi+wPA1ceXK1iwzQCfb1G1UrWnmO3U63lWIUBGEVIogFoVms53hWa++r02lTsmbRUMhRgCUXWf0gGWl8BAIoL/Jqbc3cCjdZ/zzkUkBijtfHTGaZznBsAjj+DSMnrNiAm34ZuP3DgFpY6yLPngBGbtKqc/SUXzDXbo55JyItmgWhqxFBLAitwizooHCKPDCytr2vmU6bktVFw/RxYOo5ns/oHXQuNyLAygm6C4+yK3FwZK3Iq2V6u1WLpYoF1mLWq2yUoijAc18BLv7EcI2HDwKH3w/sewsfz75suMj6WN0htmz2lqk1rNOoc9zqLrO0aBaErkUEsSC0gnKCTgUdvnxqdXvfbLKzp2R9YUYldt+9tulDvSKznKBLaQ0u+seN5/TX1jK93YrFUnpN4WJx7e+KeeCV/wE8/eeGWA6MAIfeDQzsoygGeAN19SU6xm4/0DcOhHawlXa1sTbiHMVlFgShixFBLAitoJygC46wW5i58YS3tzumZGuNLFRzHcvtrzRyYD5GLdPbViqC1OuK5jOMR+QzFLSlFSQWXgWO/BGbkwBau+W3szW2t8/IB+sL7uwulldzBYHlq3yNzVF9rI1YECZlxwRB6GJEEAtCK7AqEDtlSraaSLQqwKy6juX2V66UmPkY+rXUxzr9fPmxrucm1+uK6jWFdRe7tIJEbBI48llg5iVtAwW47p1cNFcamwG0Ws49wI7tPBebg87w4jmgd2f1sTZiQZiUHRMEoYsRQSwIraCbSjaZhZfi4DT++Ue5cG77YQpFqwLMqutYbn/eXsZO9IhJYhaIXABC241tgOqCdj03efJo7a5oZplZ4WLBeE6vIKEowInvsJ5wUXO4R24C7vt0ZXFqd1AAh3Zwe4ebne7SywCKxrmsN9bR2ze+IKybPsP1sNXz04LQ5YggFoRW0E0lm3QRW8wD86co9HyDQPQSqyDoAs2KAItNAtkV/nEHgNAo4wSpCAXI9HFg/gygAAhqeVk9cz1+vzGe6GVgeYqNTQJDhvBV7NYEbSVnvhZXtJBjc41sssx+4qwn/NL/B6SifM7bp7nC/0v5yhCKosUderlfXYzqne6ySUZu1ivlZx7rRmcfuukzXCuSnxaErkcEsSC0gm4q2aQLL10MOz2sipCJUzDoYrNUgCUjdDF1h83bz6YUip2P8xmKxr49jENceBSIThgOZPQiM7V7718rZgEK4VLhO/MisPPO1ePXRaIVx8+KK6qqFLmpaPmWy3OngCc/b+SEbU7g2p8F9j7ANtalYlhRtOvTZ7RatiJGm+3gdtNnuFYkPy0IXY8IYkFoFVYzre2OLrwyCZb8Aihm3YHaav2efRDwD7PxRD7DKEA+zUobg9cxf+vtNbqtKVqpunIipJI7qqK8SIRizfGrJkSzSbq35Rb4JReBp74InPoeOBAA228Bbn4/XfB8igLfTKVWy1bEaCsc3E7JuDcayU8LQtcjglgQWkk3TL3qwsvmAHJpCtV8io0h1qv1WywAS5c0IR1g1tbTy/bO8Uk6zO4Arw1UIBUDCmlD0PoGGdNIx9buv5I7OniAohBYLRKtRCn0G5fsCrA8w8oOvaMUmJ4ePpdJrB1LIcdWy0f/xjh2/z7gdb8J+Ad43g4X0L/X6DBnpdVyNTG6lR3cZrPV89OCsAUQQSy0L924iKUbpl514aXYjeYbg9dpArmCI7k0yTJhTq2RRD4DpOKAOgkM38BMLGDkYtNxtie2e9mwJHoFmH6RjurANWv3X8kd3XEbnysVidPPG80tdMyOn/nGpXe3sb/wXtYBjl4qH4+4dITVI2JX+NjdA9z5EeCGd/H6lLJeq+V62KoObrPZyvlpQdgiiCAW2pN2cVJLF3YNHDAqKdTDZky9NuPGwhcG9j3Aa6Hv2+Gv7EhmlykkdQfU6QH8g8DKbPlGJNPHeZ2WZ+gcKy4Adr4mvbS2DXQ1d7TWbHC5G5d8FrjyJDBw7drzi14CHv8scPkJPlbswMFfBF73G+VdRJuN51ep1bLQXoj7LghdjwhioT1pByc1GVm7sGvmZWZbSxd2WaXVU6/NvrGw6ki6g4wX5NJaVjhDUTxykI7wGpGhArtfD5z7Vwpim8qOdL4wu7iVizbogn/7LdXHVM3xM9+4qEU+ziwDmZK4RmYZOPrXwEvfMsqsjd4B3PtJxiRKURTGJLx9FMVC5yDuuyB0NSKIhfakHRaxRM7XtrDLCq2aetVF4sRRCtD+fRRv8UnWyF2eAa59e+3nUK/b3DPKihSpRSMrHBwH/P3ch77PyHm+3tNDR7Z3F1sXK4ompp2Vow21CH6z47d0mdfGFVx9/FyKP6eXKHbzaQp7gI9PfQ94+otGGbXQDuCeTwB73lje9XUHtQVz8rUrCILQbsg3s9Ce1OukNjIekI5xgZTHNAaHm8+XW9hlhWZPveoRDz3bm0tTiE4eY+TD28/FaSsLtTvF9YrPxQvAxDHg6gtAYJhNInxh3gh4+8vvM7yP1SaqLdzbyEyC/vtUFPAPGTcoU8eAnt3A4vMAbGyVnE/z+P17eX2P/BEwf5rbO73ArR9i9QiHe+1xnF4uptN/14psfDfm7wVBEJqICGKhPanHSW10PMDTw0VP+vQ+wJ/tzo1FHJo19aqf/9IERa+isCSY080ML0AXM5cGAv2rawaX21epoKpHfC5eAF7+NmMCO25jLeHT/wIceBuw577K+0wtrl645/AAdjdFtc0B7H8rX7fRmYTp47xexbzmWu8ACgVg/iTQfw07zGVidHfdQeAn/x149YfG9gd+Frjr46yBXIrDxZsSl3/1dW12Nr5d8veCIAgdhAhioT2px0ltdO44vBdYusIMsaoJ4EyMlQ70tsCV2AyHztxBzh2iIO4ZZUyiUGBDiFzacFrLNaiAwvjAwlmKufBeRhemjgGZFaBv9+pjVhOflx/XMrNaebFtNzKGkonzelz4CW909E517h5GFOJX+frth9mo4+yDvBnx97M8WeQc92l1JqHc+wGwKoSqavlflU00Bq9jxz1PL//k08DxrwHP/R1/BoChG4B7PwX07aJojpyjYO7Zyevm62dHuUrvUT2fUaufqXbI3wuCIHQYIoiF9qVWJ7WcW1jIsVtZPcLUF2Z7YHOViZFDa6tMlAoVbz8FUqsdOv383QHD1faFgUKWsYBcihnc8B6KtWwSqxpUKA46sEuTQHiM7uz8KdYJdvqZO641xrI8S1caoOhdmQPSCSCXAHbfw3bLioP7SEWAiWeB3jEgtI2xjolngWwK8PUBw/sMkZlN8ppbmUmo5Jgm5oCFCxS/Ti9gd/FmAjZgx2EK5fOPAE/8CUvGARS6d32c+etMHJg9wUiKu4fXeekyBXw5MWx+j8xYcbRrcX3bIX8vCILQYYggFrqHUrcwHafAcwfrF6Z6ebF9D5T/faUObOG9rXfo9PMPjbIFMkBR5wlxcZoCVmhweikoSxtUzJ2kI2q7SrHnH+A+4pN0Td1Bo9FEIcfzSS4CO25dWwZNJzjMfdmdQOQCYPcAUCggSzvVpWOMRSTnWGs4eonjSy5yzGd/wKxvcIjRhnzK2kxCOcc0EwdO/0/AO0BRvrIIFLMcV3aZtZH/+TcY1wAY07j5/cBtHwJcWs3g2ATFsMvHWIReXzl6gU72eu9Rrdn4WlxfaSIhCIJQMyKIhe6h1C1cPMeOuYFhOp2ZBIWNYq8scGulnFApFCnigiPG69ZrabyRaEVp3CG1xOMOXrdasG4/bIy3UoMKvRWzp8dYNOhwUzzmUoxfhPeuXrQ3egfFbqUbjd33MEOcjlPs5rNAbhnY92bmiwsZo1PdygLddZeP0RSHl8cvZNnhzu6mCM7neKMzcojHqDaTUM4xXZ7hTYE7z8+I3Q7AwbhGdIHiWy3ytWP3sXpE767V+8gs8wbD22u0Wi4XQzG/r/VWGanF9ZUmEoIgCDUjhTCF7kF3C/W6tvkMqwLEJiii3CEANoq55Aamj5MRYPIocO5h/l3Irf69P0whaqZSrnXqGEWiN2xkda2OrXR7h4cu8Mo83d5khKXEMnGjnNjo7azTC1AMx6cZHQCMqIXdyWjD1HFg7jSf07u0+cJ0nHffDey4BfD2UMDqC/RK6R8HDr2HTnV6CXC5KYZ7dhjXyROiKB69lc5qcIji3FyvWFU4rmyS+y3TJK4i5hJqxQKvy+xJwObi4rl8EvAEGemYfYm5cbUI9I0B7/gz4O2fWyuGHW4gPE5nWBfDgHYcpfL7WvoZdbiszViYz8F8rHKub73H6FRK/z1u5N+2IAhbFnGIhe7C7BZ6eoCrL9FpNNcR9vXXH18ojUjolQ+2HzZyo74BOnrlOrCZ2ejip3Lb68I0PA5ELvJ841e1GsBRo5yZPv6C5rbiZsYQrjzJDHHPHrq2yXmgZxcwdq8xplozqv3jwKF/R2FonsYvvU7eMCs+9I1pAjoGQOXr3EEgNsnHDiew/WYgHaUAquauh/cC5x+lK5xeosiOXGAb6XyOrvr083we4LU68Fbgvv91tdgFWEPY18/xuPzlnVhzDKXc+1pPlZFaXd+t0kRCKmoIgtAgRBAL3YsuhHyDFFj5DKfcB6+rv45wqQjt30cndfEcRXEuxQ5k+9/K0mHRy8ykuk1NH6wKy2pxinLbJxe4MCwVoeBzelhZIrXIxWqXHzfyzem4cayJZxlB8PQBfTYuhuvZDuy8g/u78iQrTPjC9WVUvf3AiX8CEvOMrYS2cbGdfp1SEWaWD72Hj/UWzeF9PKdMgnnkoet547E8S3fbP1RdCBWy3H8mTud3+SpQ1LrPLU/xODo77wQOvZsO8MJpxiLcQV678J7VrZbL5ZeD24BXfwDARtc5NMrxbnRR21ZtHVzt34BU1BAEoUGIIBa6F1+Y+dnoJaM7WngPBZnDX3XzspSKUE+IbuXcybVCJdlLVzZQ0vRBF22lwjI2RWGamANefQSAysVl5tJnZsFXTpiuRBg70PPAgJEDdnopJIcPUgzPnaQb2rOL+V23H8AIhVxghGI6u8Lzik8Dp7/P6grV3MpSEaM42IJ5/iyg2AAodJ9D08zfllbtwDijHfp+8inuKzxOcZpN0uWutnBRVbmPK0/xWgSG+Xz0CpCYZuUQPXvhCnL/N76HNxCFDKtOeHr5eVmZ5Q1BaQc6sxOru5V2NwAb3ee5kxTxNocsaqsVK+6vVNQQBKFBiCAWupvth7WyWv7GLDAqJ0LzaYrtUqq5V7qwzMSB2dPApcconPyDFKC5NKfnC1mj9JlZ8IX3Ahce5ZR/Icfp/YK2jaIY+dt8huPLpeiy5lIUpHqUxNyoY3kGSC4BgUGK4cgFAAqdz+SSIUgquZWlIiYxB5x5ELDZKbwLaZYmC2yncIxe4vtTztnVxebo7XThT/0LEJ+isxwYMgSujnlB2+wJZsedPp5TcDvd4ctPsjFIXsvjugK8aXL6eRORinK/dhdvLDw9fE/0Mm9W6mD37zNuNuwezh707tzYoratGA2w4v5KRQ1BEBqECGKhu2n0VHOpO5qYowPp66dosju5KGv8/urulS/MSMDZB4HpF/haBRRrigJ4QxSPO24zSp+VOl+li8sCI0AuyX1FLlKsq0UgOE5hqNiAl75Np7h/H6MLNpvRqMOtLcJLx7RjKTyIV2uuoYtyvf2ymWSELnIqyvhDaJTxB5udjnRgGEgv0jFenuTfxTwQ2l5ebOoO8dxpYPJZoG8c2PMGjm/uJLPFgUFeG72CiKcXePVHHLczwLjF9AtA7mlg+piWQwavQ3gvs9EAkE2w1FpgiGK4b/fqNsxWXEf9/VYUo3JGepnXf6PCdStGA6y4v1JRQxCEBiGCWOh+GrnAyCywly4DV57lwjNFoQBU7OxsN33cmnuVWgS2HWYJMv8gHVObQ6sOoOV8zaXPzNtGzrPEWv+48Vw2SRHsCdH51fPLdiebYaxEGcPIpZiRza2wBJ3eqKNnFBi5CTj7EF3q4DYKYcXG81uvfNzUMbrI/kFGPOZOai73AJCMagvYYszlFnPcn8NJ4Z5LU2SX7s/p5zVxeCluPSGOp2+MXebCe1g7WHFS5C+8yvPzD1LkLpzhosH508a+hw9RsOtNS+wOCuu+3WxI4vBw/GasuI7m99sTAjzX85o6XBv//G3FaICVfz9bNVstCELDEUEsCLWi/2ebihrCDnYgepE5VE8P86k3vbe6e6ULHV8YyCQZYchorYxXFph9XVkEXN7K25pxeukwj96+WmBOHgWiec3l9QC7Xscx2rTau94+Y/++MHDT++j2Jpe4jb44LJssLwx1BzPQTzHp9HB/i6/y98UcbxyyCf7eZqfQ6dnJuEd2ufz+XD7NcR4ECnmWg3PtoRM+f8bULKQIhHczepJeolN84p+Ai0coegE61LvvAYavB/r3A1efZ81of5hl1dwBtmQG6nMdm+lWbsVogNXruVUqagiC0FREEAtCPeiCzaEtoEKBDtXlWdbYVRx8TWaFUQV3kO5rcBufn35eEzMK/6PfdjMXnhULLHtms9P5DA6xNu6Bt62ddi/XmW/xHDPDpSvy0zEKT48+5e4HBvYzIxy/CgweWO2s+cJcQKe7tHp3u+UZCuRzD68+hi7O9S55uRVgaUpreuEABq+nIC5kgGIaGDzI4yl2QM3w+pgxi339ZsHj540CQMfc0wv07mZERMcbAqaeB575S6O+stMH7H0TcPDddPIzMYri3lGKbbXIczJfr3pcx2a6lVsxGiDuryAILUQEsSDUgy7YencDMy/T+XR4KWiX57hQbGWB0/C6ePH2r64BnEtxQZzeUnnvTwGnvgc47HzcP85ogDdsTPGbMYskvZ6wCla9KK1K4elhbEJfaAfQHe7fC2y7cbWbrKNnnC8/zjiCKwC4fYxplFbN0MW53mTj0hE61T3buY/Zlym8YaNL27Od18jhZb65tNWxWey/drOgCf3UEpCY4fXR2zoDrNLx0rcZkwAY89h2GNh1JzBykFnpXIri29OjVRyxl39/63Udm+VWblVxKO6vIAgtQgSx0F1stBWyVXTBNngAmDtFYZVPMS/q8gChnRQuwRHDwTXXAAb4d3CEmV+Hi7GIHYeBwfezA5yOqrKeMbD2vHSRNPMihV7/PqNBCLC6osXSFeabVW3fmRgjC+G95c8xGTHKmw0fZC56aYqiWlUZMfCGtUYgJnHuDvKPV4sizJ9hfnhIWxS4MseFa7tfr2WbV9aOwby/0HaK6KnntFrSaWDsDSwPN3uCrvHZh+ha6+2Whw8C+94CKCqrWzj9Wqc3Fdh1FzPDnYaIQ0EQhKYhgljoHlpZmkoXbE4/RWU6RoE5dD0FWmCYi8d0zDWAzZgzvwDFbumCrsQsG0gEKjShMEcWzHVySytajN9PUTt/hq70yKEyNYBNlFY2SMcZMShkeSOQz6xeEGd2ML19bJqRWtSaWwQAKKxq4dYaa8ydBHbeXt7p1MX+9HGK/WKRTUJ6dzEqAdCNj1wEjn7ZyCCHdgD3forVIxSFeeLYBNtZK2A1kNgEBbmIS0HYPFplXgiCRUQQC62lmV+CrSxNZXZnXR7mZHt30DnNrXCBHVQ22nAHGJfQawCvtzBqTVm3WeDCj5lxXbpkLG4rPa9yi64Wz9O9vvQkH/fvAQav5WI/K9ejdNFeboVCMrvCyg+5JBe6mTu3lTapWFnQaiS7mR3uGWemV7EDA/vKRzX07S8+Bpx5iELc5WMmOH6VCwIXLwBH/siIRzh9wO0f5mJAu8vYj3+QNyfzp1bXom7WjZL8Jy8I1dmKdbWFtkcEsdA6mv0lGJukWMuuUISGRjl1v15pKl3ALE0aJcp6Rq0JGV0AevuBl79NIabYKdqmjzPL6x9mLdqlCWDnXcz5ZuIUknYHj7f/bav3qQvt6GU6w54+Tvsno8DVE1pTikEujNMFZamQXjzPery9u7kYTs0DE0eBQgGYeAYI7uCisvXOs1Rku/x0dtNxXl/FSZGbmON1NO9HP4/lGWDhHF1ld4ivdYeYY65UISEZYcORVx8Blue5cDG1xGu2PMvqEdPPG6+/9h3AXR/n9Vw4Q0fa08Pycb27jM9cs2+UGvH5FkEtbAW2Yl1toe0RQSy0jmZ+CSa19sKKXYsdZDgl37dn7YIt8zZTx7SFcFe5CCujLY5LRa0LmdQiF34tXQHmXqFw234LS43Nn2RDjfBeYO5lwD/CKftchkIvvI85XW/v6goPvrAWUYgAmWlg5iU6rQ4fIxaZBEWTLkRLF11NPU+xvXSZi+eCw3R0T36XDS6SUS1Ssc55lopsd4gOeGgbG2q4fEBgD8+j3HvoCzP7u3ie9YdtdlbBSEYY1Ri/v/z1XDzHBXKJWS7iszuBHHhtIxe4GA9g/OS+T2utqJeYJ3ZqpdxsDmDxLN30VtXw3ejnW1yzxiA3Fe3PVqyrLbQ9IoiF1tHML8HIedYAjl6iGHa4ufgqco7Z00rbOP2MIjh9Rhvj1CLQO2ZdyKRjdG11YRw5z/3Fp1ku7OJjFJP+QWZrB641jlXMGt3f9DGlYwAU4OLjdDjD48CFIwDyFNDpGPcTHl89Rl0YJyPAS//IdsWZBCMNixeBlQhLnAWGKCCjF3nDUHqeZkGh2Hkd8ylWvFg8q8UfCrzO9hRz0+nY2m09PRS2vn5GJ66epfAf2M/xl94IAHSzF89xe5uDAjw2ySoVOb3dsh+4738DDryVNzGAlgv2cKwLZ42YSuR8bTV8zeOHnsdWrQmrjX6+xTXbOHJT0RlsxbraQtsjglhoHc38EkzHmBV1+rRWvtq0vtNf+T9CXcBkEhSsgNEVbr2ObKXuk35e+n6cPiA2DUQvsEKC7jino6wmER5fe6zoZb5G/498+jiQjQP5JAVlcIhRgKXLzN4OXV85DhI5z2tRyLNyRSFPQZuY4fEvHmGFBt8gu7/lU8b1gMJ4QnDEEBS5FQoKgJUeUkuGEFXB7f0Da8VIYg448yAwciNrM3vCrPrQN0aXV78R8IU5xuQCr6HTa4jhS0e4WBHg2Pr3Aa/7TWD8jcb5unx0z1MRXnt3SFvwd4Fj2/MGazV8zeNXHOuXsSvHRj/f4pptHLmp6Ay2Yl1toe2xbfYAhC1EeC+/9LJJip1ssnzJrXrQxYiZZIQZ1nMPs1NbMlJ+G3eAAgrg3+6Ati+F2+nbL17gl3g+S+GiiyRvP8/D5qDr6w5p9Ya9Wqxhjs6luweIT60+lqJQ/E4+w5xxUVuklo7Tnb38JNsOOzyA288YhstP0b94ni506fmlY8wW5+KAzQmkloHFS0DiqrZ/B/c3/Txw9SXuQz+n6CW6rfo4XD5DuEbOU9z6+5nXLeRZB3jmBN9DsxhRFDrmvgEK3VyKjTXsLjq+7oBxfSIX2RXv1R/RCVYV4PwjwLkfGmLYHWJN4e23Gp8Xh4s3HKHtvL6Kjc67omh/23gTocdJHC6tJbarvLA1j395itUsvL382XwdKrHRz3e5z7C4ZpVJRlb/+0xG+HnS61Lr6J8zoX2w+m9SEFqIOMRC62hmcwFvPxdbRS9ThLn8FHe9oxSA5Rw+3aXwhinK8mnWsXUNUojmksYCu3wWOPvg2jrCAIXfjtsoYKeeo6PrG2AsoZhnptUdAOKzFJK5JI+VSXCxmcvHEmWwMffcs5Puqs0FBLbxGPEpOsjhPVyop5ctu+Yta6eGPT0Uq717OJ74FIWZb4jHUgsUCcUCMHca2Hu/cS7FvCbcJwHP9XzO7FI6PHRNVe2625xGp7hShzOTYCRj/qRRTk4Fr0tolLnixDwFssNLwXn6QeDUvxiutSvIEm99Y+zyp9iAK08Atnsozl97L4I8Xi6txWUy2nsZND571T5n5vGbZw0y8bXXoRwb/XyLa2adStEIxS5T8Z2C1NUW2gwRxEJracaXoN5AwuHh1H0+DSxcAQZv4OPlKUYMgLWZW13A5NJaLVsFWJmlqPIN0G2cP8XtC0U6osEROrjxSVaQQJFiZt8DXCw2fZy/L2aBwHbuI7vC1yk2LkrLLGuiK8Aau7OvsAJEaIQVL3pGKe49AWDgAMWk08tqE7MntAVnPYxh6E6ufn6ZJPD833F/UICBaziW0CjgcHLbmRM8P4eb8Qr9fKKXuYjN02tcM7OguPqSJqazQLYA2BSjOUdpZMAd4PUZ1ATd/CkK34EDPP/kAgWMwwssvgoc/wawPM3XOn1sV33gZ3kTkU3QEff0cXGezbn6Bqd3lK5wKmLEZYJ7+P5bxTx+86yBO7D2OlRiI5/vrdqNrh4qRSPyad5EAHJTIQhCTYggFjof/T9Hh5t1dhUFuPIUF42ll7TKCBVKsOkCRi9fNnmU4nP2hDH9DmjlvGJ8fu4UqzQEhygqnV5DnAF0YLffTJGbuArY3Jx6Vwus9rDnDczhPvV5LnaDNr5CkrGJ7DJjAMFhjjkT5zkEtwMocHoxnwVsSWDqBQpMT4jjmHuF5dX8Q9xm4Yy2vxEeJ5ukAHUFKLrTcQrN5AKfD49r3d8SdHDtTsZOvL0cx8SzWlOLQUZEMiuAM0CXV8/q6mJ3aZI3I2P3sUV070662jYbox/DB1lj+dT3gavHee0UBdhzP3DNm3njkM9SxGfibLqh2Pi4NBsa3ksHvXdstRCqJY5jdmiDO1ZniPX4w0aElZXqB+KaWaNS3jqfkpsKQRDqQgSx0N5YERH6f466q6cW6EwWcnQ/UdRKsI1VdwzN+0pqbuPKIoVt7x6Ky6U0AIUObyoK7Hvz6nyp088mHYvnKCZzcxTmO26msDz2t+wWtzzLaX27HYhdBvqvMYRoYpbVMTIxHsfu5KI4gLGGXNJoWDF/hh3fcikKbJud551PA6l+ILnE65GM8PzdAcDupfgc10Rsn1Y+LQ9GNuwuRh0GDrDYgsPDyhB6Dtrh4fswpEUfrr5M8Z5NH8uEFAAAIABJREFUscqDw0XXtn8vHXeAY93zBrrP2QRw7CvA8a8bZdQGrwUOf4Bi3uHiOCLntXJvU7zm+RRjI8DaTnwbFULmfeRT7OQHsIazw8/IRuQ8s9e1lvOS6geNZb0FjHJTIbQbUgqwIxBBLDQfK18G5cpdpeN0GMP7WCqsoohQjJjCyhydUW+Y7XqzyxR1xTyrDux83fpjfW2hXQ/dUGeIjqfi4PZ9e4CFU4xYFNIcSyZGR1cXZ4qD2+TTFIkODwVgbJLiMZemUMyucJ+hEcDmZYmygWvocqoFNrTw9DBL7PAwVuAKsimHt5evL7q56G74BmOq2DegXYt5CjllmZlkl59Rg5UIYx877wIG99OVdgeMqMHobYab7gnx2LrwCG0DoHKBX98Y37elS3SvvWFGLmxOlp/zhLQcrx8oZPg+qEXg1PeAp77A+AlAwbv7bh7XqdVZHr6BFSX00nJqEUCRMQ69U19phKERQqjSPjYqaKX6QWORvLXQKcjNcMcgglhoLla+DErLXU2/oLmSXj6OXuR/euVaFicjdF/Ty3Qe7U6WFfOFgR2HKbDUAoWeqlZ3+MJ72SVt+kWgqLJCxMoMMHwjBWZiBvBv4/jyaUYfbA6KWHPO1tvPWMLKAp1Yb0ir3ZvSGlPcRLc2GwfSKyyDlk+zPJnDo0UFXMzqugPA2N3cbyHN/bgDnMpfmacLvTLPDnkLp1mlQS1QaLp9QEwTpQEtRrHjFgraTJzvR3iMQt/stmWTWh3hku5/bj/Qt5uNTDJxZpj7xhjvUBTeeHi0RXn2MaPDXGaZ4z/yGbr1AOsZ73uArnFuBUjGeH2uexdvMGZfNm6i9v8Mc+J6OTYrAqiRrsxGBa2UVGsskrcWOgW5Ge4YRBALzcXKl4H5NXMn6X4CwMKrwMhBxiD0qgelIiJyHgiM0BWNT1JADh6g2Nxzr/G65RltKjxb/S5dheYSBwFHmOMKjfC42TzQO0Qn1KWVd0pFV4uz849SiKcWmZVVHIDNw4iEf4BiOrsC+PqAZB+Qn+ex7P1APkf3FjYKTz3DDFAkxqaZpbW7WKHCEwb8TrrEnl5gx+uAS4/xWOFxCkw1y65w2QQXrbmDFJVXX2QJtd33UGwCq9224Da65Obuf5lldtkLj3MB4cXHuegtNKoNUuF1XpkHhmb53qTjwMl/Zq5b55qfBva/hY6308tcsTvE816ZBRLTq2+iIuc4U5BatCaAGu3KbFTQSiOCxiPRCKETkJvhjkEEsdBcrHwZVCp3pcDoOqeXvioVEfq2imKUCRuIAZPP0uV0epnHvfBjVihYukTxZnab9b/TMdbk9Q8x75vPcWFdMkKnFKpWf7dAMWt3AbkEs65moTVwgKK0UGDuNrMMpBYoztxBZnyTCxSDNjvd3NgEMHoHsO2QUali+rhWBi3AMYfHKPjVIsWtzQH4+wx33NsLeMFc8Ll/Y9Z36ACvZ2KGx+u/hovSUkssD+fpBfrHuW2p21au+5+q8HrancCVp/l3eIzXMxVjZCU2pS0GTAFnvkpHvpDltRm8Frj3UzzHK09qzn+QY1RsFOoTLwEjN5Uvb6cvfqxGo12ZjQra8F7eKKWXmG3XK3nsrdC+WhCE7kBuhjsGEcRCc/H0ML+aWjTaCOcyXEhW2umttNxV7246nPk0ny+30r/cl43dyfbAkfNA5BK3cbjZBjmfNWr9ppfYbMPfT+EXGNYqQ8xRbEYvA94ewK2J2uSSVnVhmkJZn7YN7+ZxJ49SOF95lq5oYJji0dsP5m6DFIb61P38Ge539DYKRG8P/wZMMZAeIJelOPZq1TCK+dWiavYV4yYCYDRi/H7g/MOs1avYgbM/ogjeflhrxZzlOae1Gw1z2+fIeeDiT4CJZ1i72B3guFNRRkiC2xh1yGnd77JJbjd7QqvoMcIbi1P/bDRE8PYBd34M2HMfr9+VJ3lsb5jnoJOYBaITAGwUyvrNS62OSqNdmUZkVpUqjwVB6D4k794xiCAWmou3Hzj3CEWPYtfq7WaB/W81mmWE9xlT9sEdRoZ4280UxJELzMA6XGunyct92SRmGHsI79W6lSVYxSEVpfjNrrC5g2+IUYp0lK7m7tdrNXEv0jEdvoEL2678iJGM8DiweJbn1L+fU/vnfkSRF72idZC7ykV2WU3IJ+YojP1aTeNthwD/IEX5+Bu4cEx3q7NJzamOU7xC5aJCp59OcmKerY9XFjUxfgsFbm6FDrrXLCxn6MZ6tIYVQ/t5/rMv0yUevoHCenmKYlYXw+cfpbMcucBxZFaAoWt5HV1+wDnGfeq1j32DdIUzcZ47VGaw589wHIqd1+eB36MTPnuCi+6KBbrcr/7QKMuWmGWMI7SN+eLkEnD1BAW+J8SsslUa7cpsNLOqR3v0tt2A8X7LtL8gdC+Sd+8YRBALzSW1CGy7iV8EM69QtPkHuZisd9R4jbnc1TZTuSv/ACtDVPryKPdl4+k1KiNkV/icOsq6uC4/xWYqxlrC6RhQCABYZrvg3t10XnMpvhagG9wzwv317qJgvPBvzPraPcCFn/DnohZlWJ6hsA5t17LEUUYbnAGKu+Qij5uKMfcMkwu6dJnZad8g95nP8PWpZQrGHbdRkKdjdJEBZoBf/jZ/doc0cTrNChIzJ3lz4R8wWjsX8zyuOwgM3UD32RMCzv+Yx1AcdHQdPiB6HljyMwYyd5Ilz/S8cGaZ48gluZ/Fr1FQq0X+fuRGNtewOynElyb5niajdMNDoxS+U8cYRcks8ybI6QUmjzEHbXfxPBWF75su3quxnitTrqIJ1OoL7zaSWZUcoSBsXSTv3hGIIBaaSzpGhzQ4sn473HJfGLpwqVb3tXTbcw9zv4ARwfCGjSYPiassfeYKMA5hA7Ox8asAFC1KsMQxJudZCs3upFj3DVCgrkQ1Qeeg8ErFGAPxDVBorixQawV38HhLV4Ce3cDyPJ1lp5+NH5b6APcJYNfdRt7Y108BqCgU49llOtC9u4yFfIpCV/v09ym8B69jFCE+xfMKbaej/NrCxYt0ff1aK+hsmmPOpjmeA2+lcLd7gNgloG8vRSvG6QD3jTFGEtbqFSdmeT0zCS68++F/pjMO8P2++QOsmDF/mg04vP3AlWeAdALo2Q7AzuohfXu4oK5ndHUe3B3keRfyFPDbD/P4pZnv9RpclHNlgPIVTbbdXL69d6OQHOHG6LQ6rp02XkEQRBALTabedrgbqRJgPmZolM5mPs36vb1jFH5zZ+iaqkWKSCisPbyywPbIY/fSNY1eBmDjvlQw7rESoSh0uHg+Ngdg9wGZJbqfSpGRgNgksDTFShOZBPO1s68YtXYVcFtXkIv+Rg5psQgbkJrn2L1hTq1n4hSNAF3v6CV2zAtuo+AspLmvg79gVLdY0BqDFPM8x1Sc106x8TlngI60f8BYOOfpAVwhRj3CY7yxCIxwbP17tWxznNtfPMLIg+5yOv1srKE3CUnMc6GgfwC4/ATd88QMkE8A3gHGKYoFVhLRhcNrolHVWlZneBPjCfH6RbXoi5XPRbmbrMmj5SuaVGrv3SgkR1g/nVbHtdPGKwgCABHEQrMpbYdrduTWa4cbOc8IwtIlzVkO0GW0IlbMx3QH6W6ac8j73wbMvAzMnaDbm0lq1RLGue++nUa92+AIMHca6NFyrfNngXwSCI5SrBWyrEqRXmbONxUBYGdONqPVIO7fp0UcEswrF/N0gXNJimanTxvHMt3VwBDLqU0dY2yhkOW44pPM9KYjQC7HbnPpOK/JwH7u9+yDLMtWyDOzm9CakyRmtX3lGaco5immU1F2xLM5GDPJJCj2F86ygoaqACPX0ynv2U0X++IR4Pmv0uEFACjMM9/wC8wX2xyMuUQvadfsFM/NP0yRno7RSVfz7Oa341bDRdPfN5dfizSoRme6XIrnEhhqTD3g9WYsGo3kCOun0+q4dtp4BUEAIIJYaDal7XDN+WCHv7IoiE0ywuD0UbTkMxS1+ZRReqvStGSp+CjNIScjFIcLzzGS4A7SqUzMAde8mRUaIufpRto1d3J5ltnk3lGKu2KO7nLfGMdazFMQ+waA+AQXhGWTFL6ZBKMO+RSFcjLKhhm5JI87d4pCNRnRFvVpndlsDkDJAe5e7jOf11o5J4H0Im8whq/jNsl5ut+JeY5veYaRiOAQkHJSVBeyFHyKAkDhtRnYr4lhN69TLgHMnOI1d3i1phoJwNXD1z33NYrhYp7Xsv8autJZbWHfwD6K4KXLRsTB4aUTG70C9O3i+5qY1XLKbuDS48Z7o79vuiAOj2vxCe3myR004jA69dYDrmXGohFIjrA+Oi1/3WnjFQQBgAhioRXUIwQyy5zad3r42Olh7CE+Y5Q3WzhHQWV3UbhGr7Cuq368SseMnNfysHuZHy4WAKiAaqPjqm+XigKBG5nPjZynizt8E3Dt27VW0drivGzSaOQR3qc1zTirjdtF8WwPMaOaS1LULjv5upUIawt7+wAorMKQXKAg7R2jGM2tAIpTixvk6MLah3iN7E5GCrIrFHf+MN2p5Bz/A84leE42BwAbb0TC19Jpzq0Au+9iDeRkhI8Tc3SWff10jP39dK1ffQg48V3miQGe65776O7m0qzx7HTRvQV4TfJpXjPfIP9Mv8h9jt5BgZ9Z5nsQGObz+rTy6O38o9/wlNZG3mg94HpmLITNo9Py1502XkEQAIggFnTabRGISysXlktT9OYzzKBmEhSWy1oDCIeLLqdip7CbPs4aueudTzpGh3PoWuZ+9WPYnDwGwP0sTRiNMfr3sYyZw8VGFgBw9ofAwhkKumt+mtGAK08ye7vzDo4vMUvHNJ9mBKSYYyZXVSmo8yt0Q7NJusIZVVtQ5wD+f/beNEay7LwSO/ctsW8ZuS+1Zm1d3c3uYjfJJqmxRJEjDUnZI8ge2zBgeGzYMzAwhn8PYNgD+JdtwL+8AGMYhmc81kgejyCJEiWIQ3JkihS7el9q6aqsLfct9uXFW/3j3FcvMityq8yqjMi8B6jOrIi33Ld0vfPOPd/5IFVnAWmbGCbpTI+TCD/+JfeRP8tc4s27sl2z9MWmiiTgAVj8N3we8DpUq1ol2kGsCl8yNu7yPNpNktjaIj3LnSrwwT8hGQdIrCe+BJz/FY4ntGCMvsJmJSFMqYhPv0XrRODxGgRSWU4O0ftspkjoe00rb3+paZVI7hff576LszIR5AAk9nlnLBSOD4Pmvx608SooKABQhFgB6M8ikMIMVeF2iVPx8QwV1cIZkqfqEz5srAobSAxfZDHYxl0mEux2PIm8bF/sAdkp7s/tcLnsuDwf71PVFDqwfg+Yv0nSlx3jA690n+R44ku0Kyy8y1bLiRzVzvwZmdoQ498371MJFiaL99w2UF8mUdaTJKxCSOIMwOywTbNuUimvr3Js8RzQ2CRBn3hNKqibJNCFM0DSJOk2M0BcAKmr0qs7x9iyi7/K9ZL3qQa3NrnvM1/ly0bpPkmvmeI5CBVhAJj+Cgmu75Hcayb90+0SSW9ILoFIESvOcr9WhWpzbYnXJD1OK4XbjjzCu00rd9+jM1/lcS+8y/Ec9D5V1oXBwqD5rwdtvAoKCgAUIVYA+rMIpDhLQlk4H6ks1UUSUICqbmtTjlXQX9t8REJ65wf08GaGn23TnCpy28OXgPm/plKrmyRsiTwzfUtzUdFbfZmkNlGkLzZwqR6baRL1xgYJb7JA4hfL8CGYm2IyQ3YSWPmYiujIJebx1pdJyIUh/bA+lVuA6rfvAX6W6nS7ErWuTg3zO00DGks8tsw4m4B4HZLG0BMby9CSAI/r5KZ5jPEUjy+MZBu+xGg0oTP71/PZZrq6wBcGgMd35W/RK7zyCV88jJhs+lFndnNjOSpE3K6IhVG/ZirqPhh4PO78Gb7ElB5R5S5eBKa+/Ox9t/0enf4yVXUjpojGacCgvcQM2ngVFBQUIVbA8ReB7GRv6FZZILNplz8h0QU4Xe+50mcc0O7g2yTA6dGoTfPYda4bHk+7QsJmVVlAFkuyecj136bSfP8Bx/DFn8vItBqJn2dTfd64C5x5h/YCIQB4PIZ2RUa5adIOkJTk+eskorUlwJ4lWXUsjttp8Y+ZJJnUdG4zPQI010l+EZBYBy7HIjTaHTYfkIj7NpVju0ESbSRkO+RAKuAByWpqmITSlBYNaOwM57R5fqvzQOkeVWqA6+TOkHRPvRE1JvE87jOMKbNbgHuJ5HS7IrZws3eHNtciQV/+hC8ITkv6oNvA7R8Ar/zWVkJxVPdov1mDFBQUFBT6AooQKxxvEch+7BpLH3L63kiQlFp1WWimR0kUTodEMTNJMunaUUFebYFKcyJPEvnp7wNajGplbprkbPrLtD60JPkuzdEu0NwE4ml6mjNjTJ6oLlDNbJW5ri1j28w0x2TE+LnbpmI79SZJ7tpttl5OjfCYjLjs6uYD0KlEmzEmYATgOPQELQ1J6QfWDF4nPUWbQmeILwCNFR5/epiFd75NUhxLsflFeL7CDn7xNMfvWPQ5z/14a4xabgY4/00q4JoGjL9OQq2ZHLvQmKnc2uSYrnw38lZ3X9v5m1w2kY3U+tBfnChwFqC5AaQK9Cz7HhX1/DS94CGO4h7tR2uQgoKCgkJfQBFiheMtAtnNrgFwXJV5+nndFqPXOnX+tGokgLq0HjgNFnblZjj9b9dJDj2bqmZ+Gvj0X5Kchrm7Rpzb27jLuLUwPcKqk0AmCkyiiKdJnksPZIqEQzJXfkzircepXPs2PcIAcOk7JGyuzeMafYXEvrFAP3BqmIV4nkc/bWqIyrbT5DWYeBWAYMvj+hpgGsDkVyW5vkM12A8Au8N849Qwm28kUlSQYxm+MGQneSyLHwC5SZJBzSQRnn+X5zhst5wcolc4MyY7/OXo2TVTUsGW5Lz8kNdFM7jN5Q+pVndH2y2+x/MLjdchVOudNl826qsk1IXzJMQAgIDna+PuVkJ8FPdoP1qDThOUOq+goNDHUIRY4XiLQMKpcKsmG080mLoQS/N7M03iJ3RJhquyY5xLC4GZoupYfcLPVz8DIKimArQ8ADy2zBiV20QB2LzHzmsA99VcJ8m9/xdAZooWB93kdkRCNs8okUim8tyHHo+KzmIZKtOmVJNv/aEsBszxuMI8X93k7/EC2yn7Ae0c8TwAF3jl3yRxrS3SatBYI7n1PRJh36df1+1Q4W6X6R9OFmg5QBMYvcLr6bS5j1Bdb2zw/NhNKsNL70fnKTUMvPo7tEfUlkk6R6+wY1+yEOUy23WS8eo8I95SQ7R5lLsSPsLzbabpUV67RcKvJ6RFo0WLitC4TmMZMONSOXZoYQm23SdHcY8etzXoNEOp86cb6mVIYQCgCLEC8TKKQHr9o5jIMwHh8c9J7oQBJLNR+kPhHBMmNu6RSMXz7KJmxIH4JIliq0KiKzSg9ASoPCGBHrlCJRY6ievDvyR5rC1QnSw/Yle65AiJ6/JH0pOcBDbm2N5ZgGSytUGia6ZYXBdLs8gvlmUWsWuxYO7yb5Is2g3aBbITtFjkZ6hI5s8wL9mqSguCYFFcdpTH7DSpRo9dk55aWxbDZRhP5ttUqIsXeOyBKwvpNB7/6FU2F3HawJNfyGxdi0V0gUNVuPwwipfTDODq99hgRAhaG8avk7iHZCWMO9u8x3OxcY8vAHaLJHtTNtJorNJjnSpG5FMIqsK1BarutWXg4r/B82JKJbu6xEYi2SnAqTPTePTq3vfOQe/Xo7QGqQf8waDU+dML9TKkMCBQhFjh5WCnfxRTo8C9v4gaYnguCVIsw3zc9FhkgXAs2T2tQ1KYzJMEug6Jml0noautcBnPBcTrtBOkx6kWx/PAFz9kG2hotCKsfgGMXYkaWyzeBDYfAvUlel31BG0RMdnNLJ4ESo+5D98HhE/F17WBJ++S0CeH6RleeJctm3OTJIYCwNmvAau3qMDqcdoGWhtUZ9tlRqZt3qVVIlGgmpocYjKDI7vfuQ7HFy8AuRQL+oYvsjFJ4JL06nEgCAsCLXpzu9XQ4izbLVfn+UKQnaQaX3ki213LqLbP/gWw8jlVW012uEvkaKGoPKIn2u3wJSV80PUin16HHmpDdppL5IDZXwfu/4jXLFVkpnJ+isR6r3tnPw/UbuIKwePJThzOGqQe8AeHUudPL9TLkMKAQBFihZeDnf5RXP2URCpZIOGLJYHMCImuLbu0mWlg5BrzhhsrtC0IUBmOZ7ickZJRbCX+9BzaDsL1oZOM1Rbp0/VdqqBBwGzhygLHs/Q+txu2YtYTsjjOIgnNjNCzW31MgpwoAIHBznAw2VI59RWmJwQyNs1zOK5UkWpxLEcSNf8+SWIsRZJrVZnyAANorgLQAKsEbAZMaciOM6lCM0iiIQBdJ0nOTwHf+C+jwrbV2xHxXP0kao4BkMCe+wZV2ESe6m1+hmquLv9JaG3ymOd+yhcU3+GfjXme+/IT2iUCn+exXWImsJnmtQ49v50abRtC43XLTlOJn7pBQpyfBq78Ji0ruaneauv2e8d36V/euA+c+crO6mwv4ioQFTw+rzVIPeAPDtW97fRCvQwpDAgUIVY4PEIVrrJAlTaeJcHa3h2u1z+K9VUSSzNBlRcAEHCbmfHIN5oZo2pZeUwlc2MOEB1O2/sOCY4AyWcsCTTqzNQVJi0JddnRrbnJh7DfpPIceByb0wbMLOAvkUT7nvS1Jmg18HwSz9oKkyCCgNs1U9xG4ANawLEvf0gyqsljEeCfpY9J+tolkvh4jsfYXOdnzTWqw15LeoM9Eoiww12ywA21y/yTP8sxBj7zmZMFHt+yTOVol4ClD2QeMUhIc1PAuW+SiOamaWNIDrGIzZf7S41Kr7ZPD7MpVXkIqvJ2C7ArgC44XrfDazJ6NXrQhZ7fOz+gUh9mQgPA4odsVDJ1g+dd09kOO7RnlOY47pAcd987Vo2eZD0hZwfsZ9XZcBsLN6mSD18i2Y+l6Ms2YmwN/bxQD/iDQ3VvO71QL0MKAwJFiBUOh1CF8z3myQqNxWOeR0U3O82ucxC9/1GMZehvXbtNcqkZJLcAVcxuxW3zPolI4AMjs9I3rJME6wawfpfktLXJbRhxEnT4bJIhNEaINdejbne6RhVXNwFfRrdpOvchNFozEFCVDCThjcnCOafOsWsalWLX4nftCkmzr8tGFRaP35fbrC3RD4yAyRjJIUDL8DgSJv200Ji8oEuPsZdmQVxiiMdXfsRzlhljNnB6FLj3Ix5DfY3XpDrP8yY0+nNjadmiWirf2UmeC6EDnRa9y44DrHxGwhzL8ry0StyvVZYNSZpALM8XmOwESfK5b1LxtVvRgy5VJAEff03mNUtMvUlSu704bicrgtCje6e20GW5yD6rznZvAxr/hOkWYeTbYYmresAfHKp72+mFehlSGBD0BSEWQsQB/C8AvgOgCGAOwD8MguCH8vtvA/ifAZwF8EsAfzcIgsdd6/6vAP4dAC0A/30QBP9j17Z3XFfhCBBOH1cekdCaCalSvg/kz/EfvuYGSZbTipRjXRK/eIrKplWJ8n3Tw8DwZZLbzQdsJ2ymqTynRuiHHbtO5XPpQ6ZPtDa5bnOd44plOZbGJpBIcz27yf0v3CRxMlMANCDoAFnpKU7k2QrZ8wDbIqG1WhEhDsyIUBkp6c8NAK0GJEepuoYk0ohxOd2UEWk2i8oAqtGeBzRLwMQb8oViieco8Em2RAA4Np6qwlYZwDzgNuV4PPqTPYdFiY9/RjV2/heSvIPjGXuNpDjMJrbqJNcCJJtmksTWqvKPbgAjl0mKq/M8v8lhku7mOonw0FlaOPIztHNkxmUB37YHXS/yqJu0OmxXaXeyIrgWtwtw7Hqc90Cvls/d20hkpb88SSKduH40xFU94J8Pqnvb6YR6GVIYEPQFIQbHMQ/gVwE8AfA9AL8vhHgdQAPAvwTwnwL4YwD/LYDfA/COXPcfAbgM4ByACQA/EULcCoLgz4QQI3usq3BYhNPHnQbJVfkh7Qx2C4ARFWClx0i6ABaaTb9FIpydYA6t0yRhbZdIMp0mUxzqa8Dkl54lOIsfAhVZOJadZtZwY5UqLEBLhACV2sQQleK2tGGYGWmLcGRKg0PSXXlIcjdyhWS9sgB0HG4zcMEWywIIWjxWPSYVV43bhE+Ps+fTRmAmIH0TUWSaYQBGmgqxmaR6vPwJ2zqnx3jeAp9pG0aSxyV02Vo6x3NdKnO5WJpFbs1V/n3zfuQTzk4Br/0OvcnLn0Sto/U47RGewxcMpwMYsn11ehgonuP5DFwSv/ITxsnZbUnUM8DkDWDmLX5enN39QXcQ8riTFcFtRw9UyEYmoeILbCW53dvIzUT2Cqvem7A/D/r5AX+S0i9O0rGcdqiXIYUBQF8Q4iAImiCxDfEDIcRDAG8BGAbweRAE/w8ACCH+EYANIcS1IAjuAPiPQNW3DKAshPjfAPxdAH8G4Hf2WFfhsAgVQAjGocWy/HunQXJsJKharn5Kgnf+G/S7JnJ80JlJEmDX4nKaQW9tIs9s2/Z6RGDCtIlOjZFm6VEW1aEFBIIkz5Pe38DjGMwMPcClh1RhK0tAp0J1NztOT6q9SiVWCCYsxDLs7iZ0qsW+Ry9y4NNSIDSO25d+5ZCEew736zlUYn2ZPRxLAolhEnykaHOoLHA9M0nVWNOBM9IH67sco90E8ucBu0pSGAQ8NgGOxbOoKFcecR2AJP31vyObguToGy4/5u/1FSrN8RyPsfyYzT9Kj4C1X0ZJESOXgdFrJCNC47nYlFFrI5dp0dD0iKDs9qA7CHnsVpPDXOrmBsn81JepKIcEWzOkHWUbwe7eRiJH4rx5n+fLiB0dce3HB/xOlpPiJd7Hg0QsVZKHgoLCS0ZfEOLtEEKMA7gC4HMA/zmAj8PvgiBoCiHmALwqhFgFMNn9vfz9t+Xvr+60LoAthFgI8fcA/D0AOHv27FEf0slFSFBci00mXFtm6WZJxpwGAAHzCzJ2AAAgAElEQVR4AAwh1dgHbJMcBCS47SptFr5DohWSPyPBbZTm2FoZiArnvHAqfEmS6Q7bMXttKrZGSnpjqwBcKq5C47ZDn3Jzg/s0EgACkkGhRT7o7Bi/77SZ5iA0AA1pP5Bd4vQEAJuEy3dpnzCSJMSdMn3AsSQJnR4j+W+sksgLQRtDdpQeZ7sFDF8hkTPiLDgUAOoucOFXaYmor1LNFRptIiERBkgULv0mcPk3WDwWeCTB4ctGIstzX1viMZtJwJ4Fnvw1FeCRy1SsH/wll7n6XWDoXFTQmCzwPB2UVO2XPO6UTJEe20qGdiPY2xVpzQAKZ04HkeplOenUgC/+lKr+follPyizKslDQUHhJaPvCLEQwgTwzwD8n0EQ3BFCZACsb1usCiALINP19+3fQX6/07pbEATBPwbwjwHg7bff3t4nS2EnhARl4z6zdt0W/bpBQCK1fgfQk/SlCsGuZJV5kr9r32cBWuDz71YZJKZpYO1zEiE9xuI8QDbNSAOFFLflduiFNRPSYmCwkC0xxO+DgApxPEOV1/dk22Kdub9WOdpmokC/LQKOtb4K2ElJcjuAK9XceAaA3HcgCbxIAehqkGHEmWdsbZKkN0rA0kck6ELjODSdy8eSbIwxepUFeue/TgJy5wck9abskue5jJ5b/ZREuL0RXQMzxbEXZ2ljiGf5IpLIk8SOv8YCQqtGRdluk/TkpoA7f8TjSQ0B8NmqOVTX2yXZAlo20HiedIb9kKvuZYTO37uTKcJivZAM7Uaw96NI9wPhexHoZTlpbdLCs19i2S/KrEryUFBQeMnoK0IshNAA/FMANoB/ID9uAMhtWzQHoC6/C/9ubftur3UVjgqpIoukXJsP3LVbzPJtrpMQCo1T/XaLCQW2xeVaGyRvQpAgejYfgnpM+j2fkLBkxji9X30CjL/BIjkzBSy8L/NxTaYxQNA769tMTdANru+2qZh6Fm0KnSpVXKvBZhOOxXFYJcB1SVjhkZS1ZfyYJ4vbhM51DchiOekvjmX4u2txWc/msduyKUZ1gVP/iQL3b5V5DDGZ29suk8xWFkg6c1OMVVuXSRLr9xk5V7ofqcJC5/ENXQDGXwcmpcqcyHEbrRLw8T+nmu67JNzQqVIL6Xte/4Jjnf0W9wnIcy9tLyF2IiO7kcv9kKtey9h1YOarTK/Ya/873Y+7qZ/9QPheBHoVMLY26Q0HIhuKVec93etFoF+UWZXkoaCg8JLRN4RYCCEA/O8AxgF8LwgCR371OegTDpdLA5gFvcFlIcQygDcA/IVc5A25zq7rvsBDGVwcRjnrnqrOTrOTW6cOFKTq2lijchxPs5NbZlzGZwVcvllmDq/vcpo8gGyqkaEPVDPk+O6T7GXGqcS2ykCwwcYU2QmSWAQkn7rJB6obJxnQDZmSEKMKmizQkmDV6LGETpXZabMDnPCkx1n6hsM2yX6HxFYzJHls8Wd2QnZjM2mLQECCrMUAeDyW+gqVz+Qw1d9Og2MKArapri/KmLM8VWXPJUme+xFTKAAS4TNfYwpESradHr4oO/nF6a+uLtD24Hs8F0YcaPycJLxwRmYTu7y+7Sp9xGaC460t8TygKyqtFxnZi1yG5Mp3Sew7DZ4zoQOXvs1t9CJgqeGtNpmd9v886BfC9yLQq4BRM3iPhPnNRpKFlfB7vwj0izKrkjwUFBReMvqGEIPRaa8A+E4QBO2uz/8AwP8ghPi3AfwJgP8awCddRXH/BMB/JYR4DyTT/xmA/3if6yqEOKxy1j1V3Vgj+Rw6R0JXX+KDePw1Gbe2JovZZAOK2jKw+jnbKuvxiBQOnWdnuLh0xsTSwOYXzBsuzcnmHWNUQF2HKrHXIcHTdBLf0CbRkO2cQzOMZ5NQei4tBJ0al9dSbPjhtlm0Zwr6gK2qVH3jbNUcy1BlQ5Nk2Ujwe9cGDAeA4HFCAwKL6zRXSA5bVSBtAGuPSFR1k2Q2lqTaW5qjXWTlU2DxA9okQoxeo5/43DeBJ7+QGb0trt/eZGpEdpwFeUIHfEs2yhhmg4qN+5HFInCpwnZqMhrvNq+jEeO5bm3wXOpmbzKyF7m0qrSyLL0v4+lk97/qPJtyhMtsJ2DFWSaR2K2jJ0P9Qvj2g4O+oPayi1z5Ll8iN+9Lvzui2ELNePZFoF+U2X5O8lBQUDiR6AtCLIQ4B+DvA+gAWBFRiP/fD4Lgn0lC+z8B+L/ALOF/v2v1/wYk048BtAH8d0EQ/BkABEGwvse6CiH2q5zt9pDunqrOjEXbsGrA/X8FLL5LW0FthQqtmSJxC3wAOgBJGvMyy9d3SJ4W3pdKZoUEq12KEhEaq1RkzZgkhFLdNROyw5tHq4VrkzTqBreTHpbEOfxfQCepdtoADCqljXWKpOkRdm9L5DjlHMtTPW7XosYgjgVoRZL3wJcxZQ63q+kANI7ZlfYFTRbvxWWnN6sK5M9QtV67A2z8kbQ7dOT5HAcu/jpj4XLjzAF22sDdP5Hb9Nn9LvA43sY6bSCdOs+/25bFeAEAIcmqxnM1UWCTj06FBLl4kefbsZjZPPtrvclISHjXblH9jWeo9oeNVRJ54PEvgNoqxxJPAFaT3y99SJV4p5zi6bdIzI+aDPUL4dsLz/uC2ssukiyQEENjYWXxAu/lIHj2RaCflNl+TPJQUFA4segLQiwbZYhdvv8RgGs7fNcB8J/IPwdaV6EL+1HO9hvrVFmgOhwikaMf9uFPuU2RoCq5+YDEWReAmScxTORkvJkNNB7JTOA1Et/FD1j85Vpcv1Wmyum0gSBJv6SepKVBywBeQ6ZXyJbE2XFZMLdOUq7pYAKGLIYz42x64ToAAlmUJ8ca84H4hCzma/EzzZDbkNm4Kdnco1WiT9oPeGxCRsIFcjlfSLVZo6IbS/P3239C8r78IV8UAJ7rV38buPr9yHqQHieZb6wAo69QfS/P0Xpx5hvc1+J7JJ6pcW4/N8Ms46vfB+79BfebGaXSLTSS0HgBGDojPdJxEuLW+i7KpACWP6J9JJ7j9Vv+iIkZANf74J8CsRy379ok5EPnqfJf+jaXmfsJx+Y5XC5RoKf5RZChnQhfdpLkv18K7Y7S2pEq0mYUevxD9HoRUMqsgoLCKUVfEGKFPsB+lLP9xDo1VoEHPyX5i+dIjHNTwMO/BLIzJJ/NNU6Hpwqy/W4WqD0hcTLitFAYMaqibgdY+5QqlybJbH1NNtbwqYiGcWqeT3uC3aBCmhqiBcNvyyl76QV2WkBgMwoOGkhoPWYMQ8jPAkkoJQGvL/NYfV92TZPvb7E0SbZm8LiETluGpnPfmsZFfQ9P/Rph1z27GRXT2RZw+w+jjmwQVPLe+S/Yptp3gMQYyWZ7k+QtnqUNYvkjjsNzmEkcS4OKt0vi69tSiQd9zpe/DazcihptJPJ8sejUSYbNBJevzPPnnR8A136rNynansfS/fdUkZnFlmy0Yqao/kNjPF6I7a/CO74aHwF6Eb7sZNQNMXzRe/ATEvPniZk7Chy1teMgyq9SZhUUFE4hFCFWIPbzwNwr1smqAau3qH7aTXqC1+8BK58wwWH0Oi0Hjuz05nRIfocTJFK+zRzf3AQV4uJ5pktoJm0OjiWJlEZLgtCk6gqQ3QaAZ/BnIMdrxNnNzZeWBaFRQWZ3C5AMdyOQ2wIJbacRkW4fUaavmeF2mpvRsqLDAjYhi+30gKkXnSrVXddlgoYuu9xBKuK1RaC1Fg0hbEQRaCymSw+zGBHgi8HwxciusH6bhDNZ4HlZuEl/rhHnn06JZH3tc/pJEdCHnBolsQ6tDpkx4NHPqIp3GkyfEAEV+nZ5h+n6AJh6k57vTo3bmXpTdvWTmLpBL3SiwPG4HarBoYpcmmMCSPFitE53xNqLwHbCt3Bz64ue77IpTKLC8R9HEsVRWzuU8qugoKCwKxQhPu3YngHrWlRGtz8wWyWmD6x+TkUzzIftjnWqLTAyKzPJn/E0lc1GndPxtSU+4D2fxV6uE7VjNtMAfGb3Dl0EUiY/DxVdx5KENiBhEQagBZK7dqmvCMmY4BSx55CsurKb3tPl9hE1rZvct5mSiRabkc3CbtBiYSRos/BtZiAbMjfYrgKuIGE3E4CXAUSL43ZkkoPvssApJPV6nNYSoTFL2feA9c+Aj/5vYPbX6Rtul0lsEnm2ZDaSVNpdly8ymTGO2SpTJS/MyOxljdd54nW+/LTLbJnd/fIz+y0eY+kBEE/R0yx0ID7C69OrAMu1WaAVor5Kpfz+j6JIuHaFJNiSBXr5MySaQH8UuW0fQ22BY/dl98LjSKJ4EV7e/Sq/JzWnWUFBQWEXKEJ8mtHLE+w0n1XCWiVOIberwOptElMzTiLk2ox1Aqgsei5JT6rIKf8gALRP2TK5sQx0pEJqW7JBhQHAp5XCTABDl7n9lvQk+57sgueS1IYZwQDVYEP6fp+BVHoDSI7sg1aI/c7HCyrYQcBjci3+7geyWM4HAlPuR5NWCR9ortLjmyzKRh0eFU9N7ttpSwLcReKFxtQHIyH9tjpfKKqLYG5wDVj6gCR8/DrJSui9NVLMXN68xwSM7BQJMUB7RCLP4/A6QFp+vpNaCMhOcU2SsMo8iWzhPK+tVeMyIUHaTtoaa7RvTL4R3U+l+yS/O7UOPogS+qKI2vYxdBqMygvTTcLjKz/m7y+DKB5W0X3ec3WSc5oVFBQUdoEixKcZ+y3cWfqQU8iGSYXUqgDlGm0LZ98hibZb9K4GPglVYYjtd9sVElmrRD9p9QmL4aw6CbBV4T7So+zE5rdpIUiNSNvDE0mebfp3QzIMgN3huqbne0KQdPseDmxODVMiPIckFQG9xyGRDYknEKViOJZsOJLjuQoEf9dlFnLgYIs6LQyqwhOvM12itckWytVFnpv8WVooPIf+7FgmeuEYuQLc+xGJshbjtjfucAypInON7QbgtejfNpMsZtvNFzv9NrA5R2KbGaVaX5vn/sde4QvQTm2Um2skw9kJbiu8n9qbO3e4e4ZUr1KhDpuEhGN8kUStV7vnThUYvhEt01ilNSQz9vKI4vN6eQ9zrk5yTrOCgoLCLtCOewAKxwirSgLQDTPJz7uxfpcEqr5K8pubAsauyvSEjmybHJMP4ALV4/I8ibHvkyQnCiRNQUDi+FQZdkimA8HYr7HrLLyyG1TqYhl6gMOGGwe+ZQPuX8TwtHjuGfQiyhr3HUuTBLt2tL2n68gIM+jSxqHJqDeXlgQ9Gfmpq4+l/7lrfTMDmFme09amjIpzmRbhu7SlmDFaMBJZjqd0n9di8wHw4F8z47fyhNtoyyi1wjme78oT2lsufgsYucToLSMu1WtJbFvbrAkh0R65TGXYaZDM6ybHHEtF9olw+ZmvAJe+w/vCSDKGbf5d/vScZ++n7fubfpv3T/lxpH4Xzm0dYzdREz3GcRh0j6Et86/zZ3iPBoH0ND9gmsqL2P9R4zDnar//JigoKCicMCiF+DRjv9PVAiRcq5/JNsYOQ/51k4Vz9UXg1b8dtQu++X8A5U9YjFY4R4LcKjFT2PdJfjUZRaZJZbX8EGhvUE1slqlGx7NAsyNbCPcisvvEdlX2KXRs9R93HbCZ5P5bFeYcB0+9F4iSKTQ5fleSJ7CgLGxA0anRy2vXuor/BMmJa9NmoemydbVgEWFuQnqLHarkns2/hy2fq8vA4vssXqw84eduhw038pNArMB1Zr7Ocyo0EurlT3hMw5d298W2SsDGXY63tsCix/w0kD7P6wns4vHdI4ZtJ+yUX909xm6f735aEB8U29XYkISHdoXcFMfWjX5t6HEYX/ag5DQrKCgoHDEUIT7N2G/hTnYauPfnVIMN2UiitQGMv8HvQz7ZKtFesXmX5C5/hus8+jnQ2CDBKz1gOoMeI9F0LRI0OICXBpY/p1KHgN+3N9CbzIaQqRK7Euad1t9BLTYzsi2zRWXWd0m+RJyEXpM2DEO2XhYaj0GAxNlp0zNs10n2u8dqJKiomxqJq2cB2Uvcb2uDucJn32HmslVndrJm0j5RXQZmvsxiOSPBl4fEEPOPAy/yWVsNvnykxqLiOrcJzP5NFkKG2ClnWo9T3U4WqY6GbaoNqRw21rYWznUT0t1i2PbCdiJn1XgctWWeB08WDu6nBfFh0cuuMChE8TCktp8acygonCaoYtZjhyLEpxn7LdyJZzh9b7eA+ko0fV56wEK51Ajww3/IrF7dZKGX71Gh9BySw8Yy2xHrOr+HR4tAYEqbgUMyZ9eBji+VV5/e4Wei0boRYGfWFSq5XRBSyUW397g7fUKnrSCe4T9QjvQRx9NSre1IUmzIojnZwMNzogYc8LZybc3kNoROkuE70tMsx6NLUp8/C4xelQWGSWYgG0me05oApsaBs18Hbv0hPdd6AqiuAMmm7Ky3BqRsrhv4LLSLpbj9kVfkfrqwU8708KWIdOam+HnuDDA8Czz8GWPUJl7jeLs9xfuJYdsN3UTOqgEL79FPblWiboPFCyxaBPgykT/D4r+N+8CZr7y4h8ggEcXDjFXFsykovHyoYta+gCLEpx37KtwJgHPfoB91/pdsjBHL0vfqJOlnTeSoJDoWEFQAjFPFXLsNZKZYoCV0pjQInRzUlnnEcCWvtaVq3IyI5Z6FcDspwwI9ifQzZPjphxKutDqENgfZpEMIme0bk0Tdl5nCplR6O1Lp3sKESUj0JMei6bKZSQPwPMAw6AuO50mSWyU2xwh8Zg2nhvnSkR2XecFZYP0OyW9tkYRYkx3z7JYs8vPpey49IkG++KtUnMMECIANT7aTpFYJmL8pI9+yJJqdKuAajNErnmfmcacBTH6J416/Tc936E/tFcNmtwAjvcc1lOgmcut36Hlulfi5GaeHfeEmcPabbF+dHqGCrCdkcxb7xT1EBokoHnasqjGHgsLLhSpm7QsoQqywN0KiM3oVqDwm8ao8AVwPgC+/t/i5bpIsuzKyzPeZHTz1FomeFqP/07UAQ5BUWnUqwY4ksbohCTFwsDn3bsiCNy0mbQshOd6HWum7iIi4z3UCjccbki9dJzn2RFer5+0E3MfT1tDJAsmxa9FDbYYNQ3wSy1gOSHuSVAckpImc9OJa/KzyWO47RmtKcphKsZDto3NnqDybkoBnJ6kS15ZIbgOQ9E68znzikCSF6oQRB6DxeKx5EtuCLJgEuK/VzzgmIc9PbYE2j3aJzUQOo6J2E7nVWyTT8TStIW6C91mnCSTzHFuoYgOy6PAFP0QGiSgeZqxq6lZB4eWiH/LYFRQhVtgHirPMIS7P0wtsNYBWjcTEbksbQkAi69pAQmfKgpkiEbRbJNPJIRlpVeKUu+eR2OkmyZMXVrL3sDrsCankPk1+kL/7Pg5OqiWRFQZ/BnI84eY9C0CShN61AHT7hLWubYCEUMgGH4HMVLZl+oYu7RKxFIsInRaw/CkweolEs5OnXeDhX8nYN402BNeilcNfo4UgnpMtojWq8pNforIsNMbePfornv/CWaC1TpLZTXJKcyTSHWlT0EyqyJ7LNJGxV5iDnCzK/cvMaCPOdULrxVGoqCGRm/spo87iw/IFyQXaS9LKssl7yqrLOLsS1fP5d6mOm+mdY94UdoeaulVQePlQxax9AUWITwsOo/qkirKwalFOUZtAZoRKp10n8QI4pa2bAOSUvNDZqSw9TNKiGcDoFU63D18APvxdEhwrjFQLsR8y3O377WqM8UxqhMvvD5xSETbgAKL/TXwZneYDXhNobW8Iokvvs7V1nJpGK0mnyRQJ1+5q7iEVWbvF/N7MKNdpV0nwCufoW05NsulH6QHXi6WARAaYfJNk0EgAS58AhRzHrmkkjGE2tJngGNI9us5VFvj3+hpJc7tMNdqqANe+L6+//Ac7N0NlFpBNVwz6ypOF3kV2z4tYBlSrZYIHwBeodAqYfkuq1j7vvwCcCTDivL+tCu93ReAODjV1q6Dw8jFINQonGIoQnwYcieoTcNnRyyR2QpO5twZzc1PDjPxCwJSJi78OzLxNAm0kZaevJ/SG2g2Su+Y6VeR2mQTOD9CbDEsl1bO2jmfL8LaT6u7lDhHZBiCyWRh7bCuIiuUAkLRrXePWZF6x4PH4Dgm2mZfRajm+UNRX2ZijVaKX2UxRXW6s0WqRm5GkdZ4+28wYleLSA3qQGxu0VbgWz3UAYPEjjmPsOu0objsapl3nvuJZEs1EAUg2aOlobwK4GP2DbaZpkVj5hJnIqVGuO/Fa5E0+CkUxNwGMvwJs3AMsm4Q/OSQL9W5EnfI+/l0eqxGPihyLlxSBe168qKnbzQfA45/x3s6OA+d+hT55BQWFwapROMFQhPg04CCqz05KciIPrH7OCn+zQUJn10ng0jI71ogDE6+SJI9cYn5tPMvp9oc/JwnOTVGlbKzTIlBfYxGdL/3IO0GPdRXaAVsV4h0K6I4cvfzHMfm5IUl99zIhwQ/HqkXtrY04F/FlFrNmkvAlCkB1iWQ2NSxzijUqtr4v3SQuFfHcJNBpA5373G5uWmYHL5BQTr7JosbWOgsWp96W/uOPgMmubOB4ltfCiMvEDBsQPpAY3tqQQejA8se0fCAArnyPUXGdBlB+JBuIyFi3wxLS/AxfpDLjPPYgoII+/mq03VSR95PdjFItihd4PP3ovTsKb+6L9ve+iKnbzQfAp7/Pezs3zWv16e8Dr/+7ihQrKIQYpBqFEwpFiE8D9qv67KYkF2fp62xuyKI4h/5N0aBqefZrjASLZ4Ev/hy49cckY+kRKpmxFOAPkXjNv0eVuV0G3Ibcud5j4BoAme4gID29mrRGSI9vYPdY72VARqXFUzwmockW0Ta2eqC7iXogFVtB0gmQCHuyILG2xGUy4/TJWjWeWzPF867LJiCBz3843Q5V9uIFEmcjRcJbW+b1yY7RelBf5LVyGiSs24M78jMkm5sPqRwncrRqJPMkQt33xdl36Pvu1HkP2U1ZVNnhfZG4fjSKYnGW98f4q5xpCKcQp25sXS4/QwtKN4GzW/3nvTuKWZqX4e99EVO3j38mZx0K/Hv48/HPFCFWUFDoGyhCfBqwX9VnJyV56UP+bG8C9SWu6/skRbEcMHweyE6xw5ndJpEx4iR+T35Jj6ldB5qbJHS+S+IWdFscelkRfAA61VJf3qpCp31AxEgCPXuHdfcLA/tKntgCTa4jqI5qsjGHE2Ym9yD3IkbC7LXwNJMY4LlwHCApldWlj4DUOOC2qL4OnScJTA7xWFPDnHLOzQCPf87t6HLbRpyJHvEUSZIpz/HIVTbSaG4AQ+eoHHdnAyeHeU1NmTvsOUDpobRizD57X/guo+JqC1GRXVhgBxxNMcj2KUQIHuvSB1uV0UHx3h2FN/dl+HtfxNRtfZXKcDfiOUYHKigoKPQJFCE+DdgvaejVHre2yinr4UvA8FWS3NoiAMEp+2SRJKy6wGnz9btUS5MjVEPLsjirVUYU02BIUqsDWkLGou1EauXnoTc3AABdJlrI3N3nhix4O/AmwhWkP9l3JWGXZFg35bDDhAuDhYV2C/CkBUIYMjkCLHgrXuBLhtsGGitUd/NTPHfrt5kSYaY5Xs9hpnCnymvidoBkUhbrBREZ7TQkYXX4wjJ0jh7i7dnA7U0qv5V5Xi8BLmu3SECXPmKUngC3F0audRrA6DUW2bkWv7NbR0dIwynEbmU0vH+7ldFB8N4dhTf3ZUUzHfXUbXacL0uhMgzw79nxo9uHgoKCwiGhCPFJR+g57DSp1MaznGbuRRpCJdl3o4zXTlU2epgDxl8jES1eYkGVmSR5MxLAwvuc5g8CTrk3l4BaQC9qqwzpeQCVVYf5vfClDeKgCq8HuPUjODlhLFsIHZHvdz/QEKnFLuPV4hkeUzt8AdCZgOC0aC8w4vwszPYNbRbCAMrynAZOl6dX8GXj8m+QlM7/NV9EMuMABGDXZLpChbaIwCVBfvBjXr/0CNXhRB648Gu9CatV5fayE+z2ZtXoF/ccjqu2DAQLvP6uw5kBp0NCE88ySaR0n9e+NMdxlua47aMgVnspo4PgvTsKb+6gRjOd+xV6hgEqw50a79fLv3G841JQUFDogrb3IgoDi1BZc20qfsVZktvkMMnE/R+x81dLKkzFWZKlzftsQAHw4VW8SA9gbYF+VrdNVdFuUO1c+YQELjkEwJcdzmxGrdXXEBHebX5auEDQnRxxHOi2S+xR2NcNPU4CvOUznWTUacioMAOADvgtNufQpP85jIfzLOYEC50vFp0qPcEhYW5uUAU307RFLL5HMpzIs3HH5BtRC+lYhteqE/qZTZlSUeF36VEqza71rOc0JFohagvcRmaY/uPhi1FDFSMux2PQztEuUf2+8j0S5OIs77Wwa1zrCNRLqyqbjXTBTG4t+Ot3hP9v2S2+OIQvJsXZl7uN48DwRRbQxVKcXYqlVEGdgoJC30EpxCcZvZS1Tg344k+ByRucfm2ssR1zdprdy4qXSIghW/iOvkLSlZ+hajh8icVysRyJmFXlspoZdVSzbXqJA9nlDWFL4+ftOvcyIahw+p60ZOwwZs/HM+Q50GRRnezAJ0Di7ElrhGZSddUFrSKeRzXYd0luAL5suG1aGhIOfcOhv9jrAO0akJmgzQKgart+hy8o8RxV6HiWLyfZAEik6Pdty1i12uKzhWnJYeCLH3IcqWEq0vEcfcrrd3ifjMbpKw7THDLjwKVvR9tYuNn7XrvzA/qSnycRIZzd2PiC53H4UpRicRhl9Dg6sR1V05JBsIf0wvBFRYAVFBT6GooQn2T08hy2NknmYilOjZcfclq8scaHbGtTToOfp+Jn1WQjBgGMXSM5FhqnyeNp4NHPSZztJqfSAxdsONFtg+hqoPFS4tGeFwKATrKlG4CelIlpAY85jCSDPMbtVg/PYvFc4PBlITMWEWSvTYU4M8ZtuG0AAWBmZFyxIZXaFl8sWjLb2e2QsBomMP46sPJZFNkGud7Um7Q1XPo2ianQ+ZITAKjatE3oBnPLaZQAACAASURBVDB0kapzt/+2VaLdoTjLIr5miap1UZJPIegLd9ryBUneA6HlI8T2e82qkUB7Lkn7QRMRun3Do9fpmV78kMeqm8/vUz7OTmxHYe143m2odswKCgoKu0IR4pOMXp7D1ianuAE5BZ6UnuHPgYk32GjBrpOA4E0SuKHzbPqQnaJSPHSevmHf5eelh4wH8zr0vAZhd7hu9DsZBqjsxvG0OE74svOehCebfxiygK17vbBgMIyBM1K0l3gGoHmAnicRjqcBZHjuPJeKb3KITSgam/Qe+y7Jd26K1flamF/cAXQtIjVuh8Q6e55qvNPmWNa/kHYO6UVurFKd82zaILo71XXPIoRFTvUVfh7PkNi2KzwPiQJJ6dAZ4OK3nj13Sx/KBIpM1LwlM0xSvVMiwk4NG7bPbkzd4MzF2i36nJ9XGd0tSSWRO5mEUbVjVlBQUNgTihCfZPRKl9AMIDXCzzoNqokLN0m2GsskxEYcGLnMYizdoMJ45mvRw7NVAh78BFiTMWutComZHyqnQNSQYsAgQJ91apQ2hLAtte/SRhHLSC+wIAn1pU1EmGABHaRdQufyvixCSxRkV78NWifiWf6xytyO22FcWuDyxSM9wvbYlQVg/Do9vEEbGLnG7Xkd2R1whC8liZxMnmhwPLE00FiSiRY6CbzbZppFdzJBr1mEzDjbPDfXSITHr8svAq6bKGwlUqFX2arLlzCbvvKh81tV3O2JCLs1bNg+rkSOpLhdAma+8vzXt9fxeg6w+D5w7psnkzCqdswKCgoKe0IR4pOMXp7DK9/lFLndoqi5+hlJwshVwHWZJTx8maRIN4FL34mmWx/+a6qHnSawKbfpWkBqCCjX8awCHOzwe7/CJyE1EsDwBaAzRhtB4AEwWCQYNsnwJBEOEUiPtJ5kEWLlCW0ksRQQEzzffoer5Isk3E5LduDr8Gciz+I33aA6LAQV5dIDXsvJN6kmuxYJYmWBRW/FS1TyG2vA3R9yGwJAqkDPcGhzGbvO9bobV+yUXFCY4X0x/loUswZQud4e81Wao685NcJZh06DxFKPRZ7fcLvdvt/dGjaMvfJiEhV6HW9pTr5cnCDC2G2RWP+C174bLyKuTUFBQWGAoQjxSUcvz2GyIKPY6mxuMXReNooIqHwKROQjnG71fWBjjoRHM6kIagZVUM0EtABb2ylvxyAQYtAD3CrTqxs23dBioAosiWFIfp9ZF4AfUPHNTHA5TbZ0TuZIWMMue50a7RmFczyXvkeSMnSeaunUDeCzf0H/rJmggrxxl57jTg2Y/TWSzcxYROCy48Dkl/j79Jf506rRDhAq0ttj13bLqC7N7Y+UhqqrEOxUBwAjVWDhXe5vp+zr3Ro2XPutF9Nwo9fxtjaBma9uXW6QCeN2i4QRZ5b09I2jKUpUUFBQOIFQhPi0oFdRjVUFxNus4l+7FU1db9yjb3TkimyzXAHW77O1sBGTTSbqQGaSZK7ymF3oBoX07gqXCmy1q3mFZtMXO3SB50nosm10d2Sbz5QJePRUp0eoyhoJqrrJIdoWfJsvItkJFsq16yQpIVmZ+UrUHS4l/bdCkFzXVgF9kxaG5ibV4MI5ICc71yVyXLebiGoGI9qShd7JBHslF+yHlPZSXXUTmH6L98tOiQi7NWx4UYkKvbY7/dZWrzgw2IRxu0Vi+BJfijbv80WrX7v5KSgoKBwjFCE+aehFfAF6ftsV+iU9G7jzQxZwaQb9wmfeAVY+BR79lFPfk18GVm8BC+9R1WysUD1OjwBr94DWGjubuTaVz74vmNsn3K48Xs+h8u3rjEhrlankerbsGKdja46xoMIcS8mGGQmgusmfdgOIJQHLZQpFu0JrQ6rArn5taVuZ+Qqv4fxN+njb6yS7VpUWBKfJF5fyQ/qEO1XALZKoj13vTURnv7U7kdwpuWC/pHQnlXkvD+5eDRteVMON7dsNFdXt4x9UwtjLfz35JrB+a/Di2hQUFBReEkQQnARV72jx9ttvB++9995xD+Pg6NXiNgzyLz2iEue7wMrHQHWJiRF6TBZi+SRY0IHieapJgUufaqtE4tsuSQuByYQEXQCNEuC1jve4jwwaqHIL/q7rTIvwHZLTWJoeWviy+1yTFgk4XE8YTKBIFniOfJvrQZM+XtAvLDTaJdJFLjt6nSR7+kZkFajMcz2nxVzgTo12CcPgNu0Gr127BJz7BscJHyic2ZmIvsjorYNsu3vZToORcXZja8rEy8ZJiiVbuMn/X7sVe7vFl6TDFCQqKCgoDD52rPZXCvFJQjhV6rvsStZp0Hs6/0taIbw2UyF8l9m2Toue0/l3ZayXQ3IVzwCP/ookza7R05nI0UpgN0n8zBgjxXznuI/6COFjSzyc55H4h0kTnk21F4ZM1fBBMgwwYSJOa4PTBrJDsrGHiM6Z71Jt91wS2CAAYkP0ZadH6fENr+HwJaq+sTStK2u3pU3lIlD+ENDTgFPnetUFFunFEr3JcKvEKfPF92nDKM5GneSOKklhv2ruM/7WJInbcSc6DEL75/1iN1+4goKCgkJPKEJ8kmBVSbzWb5NoCJ1KY20ZyJ1hikT5IQCNJMRzSbjSo7RJhA0iyo9ol3BaYIthJyLXgUfluNPGYGQLb4eOZxpqbEH48hgelxc1GdFTTH0wM0DlkdxOuD0Zwybkun6H57IwRmuKJZuWmDK2rXCGql17g+c/O0HFvrtAbew6ybJVp9qcn2Fb6FguGlM8x1SLwOPLTS8yHCrOqVHZaOM2t92dR/yycBIiwPpdTR7kjnYKCgoKxwRFiE8SEnlg+ROSYTNB8mvEgcJZoL7EODXNYP6ra7M4bvUWVV6hA4XzVBvnfkw1NPBkK2GXKmfg42kDCgCDR4aB3ckwZEc6gP8JFWPBz32XKnCnShLbaZLk+r7MYAZJs5GU0WNDsoBuCvAXpeXCJTEx4ty2bgDjr1KFryxQBfZc2gcSOQAzgHOf40kNswAylmahY2aS1yQImJQQ+sW7CVttCUiPyYYZuSgpo7bAttwvO0mhVw7wy0x0OCyZHZQmFydJ8VZQUFB4CVCE+CShOAvM/YRKYBCwcEsYwNSXgfU7nDZtl+lHjWW4zpO/ZiHW8CyJc+kRya+mAVpKEuM2BpP87hcyLk5PANDZDc5u06OrxciJbYuk2OvwBcL3ZYMOA4AnkyACID1EQhxP8/xD8GWkvszzrBu8PpNfYuxYa1N2kpsFhs7x97BLoJFgXJYAfcYIaFXJzvCFpzIvLSs+C+nCVszdhG31cxkfZ1DhN2WHwZV7zFNODnGdoyRPu5HOnXKPX0aiw9MIQY/nffVz2oWufHf/vuWToHArKCgoKDyD7f11FQYZqSKJEXyS3ngWyE/LWK836Q2OpRnblZsiSTKSJEVminFhq59FVgojJlXh0wCN6Q1DUzxfySKQGWVhXQCeC90kmYzlmRgRz8hoM0HinB1n/vDYZeC1v8NzalXYpCPwSJjNNNBcZ2yamaEqXzgDTL5BUp2d4O/NNRmFl2VU1tg1UCWWnmHPYQbx+b/B9adu8DC6CZsQUfybANX+1iY7DCJg4V96jCSxdUQKbUg6XZvnMPQqh9svzkaFnkEQ5SKH6vaLRGmOZLj8iPd4eowzI1/86f6P36rymnfDTPJzBQUFBYWBhVKITxqmbpB8mWmSpuWPSEqm3qQSFxZvaTGSPARAY4NT+0aMmbWrn1El1mMyJeGkk+IAQIwvEZlhIJkADJmckRmXGcs+i9ZGr4Oq7yIj1gIXqNSp/GbGmAlsZljpr5v0ZseHgMBmkwyh8/Plj+njzU0xEqu7o1vYJRCI/MQAl6/OU/GNpXgd08NbFdjtloTcDND+HLBtqtJzP2ax3+gbwOjVqHPdUSmceymox+lvtap8IQgtRQD331jf//Efp8I96Oh377WCgsKphiLEJwHPPGguAe1NKoITr3OZwKUSnG0Dq3dIfmNJkhe7TmWzvkKPqictAV4Te3puTwwcwBfMGs6Ms4CtvkaLRDJPC0SyCBQuMBtYO89zXCuzyG3sKoltGGP35M/4wG+s0p4SH2HUWrvEjGcNwKt/m22ZXQtAF6HqJljd5CuRA7QLJLI7xWdtJ2yJHFC8QFU6cIHcBDD76zymEEfp4d2PR/i4/K2JPG0S6bHoM7fD+Lv9KrwqweH5MCjeawUFhVMLZZkYdPSaoi7dZ6xaIg8gICma+jIzXp12lKtbmqeHuL5KP+z8eyzuMhPSKnEayLABIEavdTLHB3YATudf+VvMBs5OAcLk+awtAK4D2FUgUQQSGWDoLFVh35XZxA6n5mOytbLv0rvty5eSkYu0rWQnmBBRuh9ZCOorVPWrC1SU6ysHsxf0siRoOvONL32HRPpFdmULCfmL2v5hUJylTciq8tw4Fl8aUyP7H1+ocIeNT4yYInX7wXYrTywVpZwoKCgo9AGUQjzo6DVF3akDX/yQimW3GiN04OzXqVQ++DEAnUSuUwXu/YgEzG4x4gsBBjNWbb8QPB9ajIWDAIkuaiyG62SY3Zw/I5txaJKgLpDojl7ly4ie4B8zRsKbn2aKx+hVkq3sJF9SILjd/DQV+bPvcJ+ZcRIzIwaUH9OKUbxEIu20pfdXErf92Av2siTsV+F83untflRQu48lN81ZENemMpy9wJfBg3iYVYLDwXHc6SIKCgoKe0AR4kFHrwdNa0MWf6VI4moLTCRYu0sLhd0EipeB2hOqmXaLxKWxLtPGToNvWKeXNFQzNZPELfCoIrZrwOIHLLRLDQHZMRLn3DSXLT8iuU0WgOYKVUbPpdqeyDHS7N6fAa0aLSiOPMf5aeDS3+RPgJ8VZiILhJmkFaPymEV7yWFu7yAdxkKyFpLAUIXbr4d3P9PbOxHmfsvA7dUIxPd43RAoL+vLgvJeKygo9DkUIR5UhIRk4wsqvsOXosKsZonFVlaNSQVBQILn28wmri+ysYamczuNdZJAzwbgHuthHS1knBoAQJOKsM7PhcwWFoIk2LNpJRACcDqyE59NDzA8thbu1JkkMXyJ5zE1zAd8bpLtrq06AJ8Wi9VbLGCsr3L3mXFg/BqvxfxNdo7TTRbGXfkul6kusImKmWJmsNuRHuP2wQjxXoR2L4Vzr8K4w26/e5wvusiq17FkJ7a2MW6VWAR5Goq9jquwrR9nDo4DqrBQQaFvoQjxIKKbkOTOAA9/Cix8CEy9DhTOMUc3NUxl2EgCjWWSweIsp+U7TRaMOS3AswCrgafd1raQyEHHtuMIAlokABJjTQPiYySyrkyVgABicZJlu8niQsPg+XPaQLtKhTg7yWSJxioVYDPFF5LUMLA5Byy8y22NXJEvHhtcLjvF75MF5gwXL0nPd4GEW2hRAoKZoF2iU996HHs9VA+blbvX9PZRZPG+rCKrvY7lNBV7Heex9tvMwXHgNN1rCgoDCEWIBxEhIfFdkt7iRaCxBmzc42dnvwm01tl4ITVK76pmAOlxRqqVHlJ1DFVieNjage4koJvYC6Y/eA7g1ADIArqhC4w9W/oIsATPUeBRcU/kWdAmNKDTYpGhmeJ5qy3LxIgySXPhDDvCtcrAxl2gPA8YKTbjcFrA0Hluz26RhGfGWKznWGzxHBYXxbJMqHAsdrJzO9xvLBsd1n4eqof1a+41vX0UftDnIdXPo67tdSynqdHGcR/rafdeH/f5V1BQ2BUqZWIQETYHCBXg1DAweg0YucxCusAlQUoOkRTHsyRw8zdZ8KUZQDwPQDaLOHHQsJXcB1TCQxU8WeD5aG4AK5+SfCaLfDnwbUaSaZpUlHWSWtdiA45EntPtyQLtFcULvBbxNKeAHZsKvNC4XukhvcjVBarN9VWS6NXPSaLrayTq8zdJoH2XVo1ODTBMbr8wEx1K98vQ+m2+4FTmacEIcdikh72aZxxFksRBG1zs1fDjeY/lNDXaOE3H2o9Q519Boa+hCPEgIiQknQbJnN2UyuRjelsrC1Qcrv0Wfasj10ieWpuc5vcDSSR0IHBwspRh4GkXtp2+c9rsxKebPE+ZESrDhRkgOQq0KkBtBfB8kttOk6RVGEydiOfpP03k2VVu/FVg5CrQWOFLiGEAG7ep8GoxWdj2EFi7TSIsIPOf20D1CfD457yOY9dJmp0Wt1c4T0JenI18rnd+CNz5E+Dj3+OMgNABaMDi+0fXDW6vaLGj6DZ3UFL9vLFdex1LP8fEHTVO07H2I9T5V1DoayjLxCAiLFDRDJLcymMgEMDoFZJkq0JyFJKBOz/gtHt1iZ+HMV7wQXYW2iZOCgKmZwAADKq1gR197fvywSRIRBN5YPwVrpMep6fX8wB3lYRPNyMPse8B6bxUcJN8EfG9qFjPky8YegIQPiCkNSWRZwpFskCrhZGgSg2NRY7pEW4rluL1Wb9F0h0WHS2+F427tsR9ubJIMjfFWYKj7Aa32/T2UWz/oEVWh7Fp7HYsp6nY6zDH2suuAvCzygKjBONZNrRRhWK9cZruNQWFAYQixIOIkJAIHbj9R/SrjsimA74DpCeAT36PZE6AHtjMGPDkFyRhdhP8AqA6POhkeLdCQJcvC93LCkFy6jtAPEFFNnWOm6mvsnAOgg+t8MHlOXzZaK0Do5fpI9Y0wGpGKr2ZYgEjNKZEWBX6gVMjfOhVnjDGLZ6OItgq81Si9VjkG/ZdFu2FKQgLN6mGVh6RcFQXAD3J5It4DqguApd/Y+vU64v2ax42SeKgpPpFxXadpmKv5z3WXr71Bz+RHc+lV15ocsYqyRc+VSj2LE7TvaagMIBQhHhQkSoCl75NpdJu8o+RpNK4dofK4fTbXLYxByx8xIeWG5JhDYNPhEPsZfnY7ifuAB2fOb8TN/hAX3qfxFgzOK1uVblaPEMC63b4MiEkoR69zM+sGlWe9DCgnSEZ8Byq8bGkLKIbBxqb3FZ+hk07OjXgzFeBz/6AavFuyRKhOtpp8LoXLzAqr11hhJiZpIptpI/4vB4SRxXPBhydurYbQT8NeJ5j3e5b7zT4EpYe4X1vpnjfOhYztAvnVaHYTjhN95qCwoBBEeJBRfhgr8vEg9FrjP1auxV5hZ/8XBZq+UB1XpItF2R6JyFveCdlWNoXdjtGz+UDfvU20F5nTrMeFsvpkQfZqpAM6DEgMwEMTQPTX6Fnd+gcG3bkZnjug4A+7liG18Cx2P1uc47jHHmF08pWleQ4TJ2IF6NkiXaZSrKZojJcnI3U0XiGJHzoHJfPjQOZSQD+VnLYL1mnR1lVfxTqmoq9ej5YVfrn12/zpTueA5w5ZmQHHv+/AHj/dmqqA52CgsJAQhHiQUT3g330OrD8EbD4ITD1JpXI5grQKAHwuIznysixJqiQhqR4kCGoiGoavbS+1fVdIBtu9FhHi5Mrex5gd4DmKlhc6NJS7TkyRSIv/dY2oKeZUlGa43Kf/wGL8kaukPCu3WJBnGawMC+RZeHdp/9cKsoGu925bRIvp8nrYsSA83+DqRLtEovyGuu0t2TGoiSFp1nFwyQhQka3GTFaOKbfAqZu7K9pxvPgeQn2UbfrPay6pmKvng+JPLD8CclwOJMRSwGewZkptyNftjt8aVOFYgoKCgMIRYgHEdsf7JkJYO5fAY//PyCWo12iU2XCQbxNAmwYQLNKMnUS1GEtAeQngOw0UFskUXzabtpnfi8XlJ/LwsHApQJsmIDrkQCnMoAjH/S+Cxiyra8up4HtKslnepIqcGMd0CvA4k0WK5oJnuOxq1Qtlz5kY47MJBMojCSVM6tKz/A3/sHWFsiL73GaOQi4LAKmWYTXt70ZqaNum3aKzDjJ91E35diOwxDsfmvXe9QE/bSgOAvM/YSZ5kEgiW8WT1+uw1jCwAeyF1WhmIKCwkBCEeJBRPeDfe0ucPuPqUQaKaqNlXlOX2o+i788W1oAZJvikwAzDpgZngu7RcLmNBElZ0h5WI+RALsWAFNmMKeZ/ACPCRF2kw/6wAU8jfnEsThtEIkhEuIgIIlul4D0GImz3aInuF2hgnvt+9xnbQHYvE/S4HRoX8lNAhdu0ILRTSRTRSrAj38GPPorbm/qRtSGOyRsoTq6Vwvn5yF9uynA+yXYvbbRb1X1/UbQBwWpImchyo/4YhfPADNv89w11/hSGKZMpIdVyoSCgsJAQhHiQUT4YPdd4LP/lx3VHFlYFwgqNa2KbEYhyaHn4kQowwAAkx7b0IMbeCxMiyWp+Lq2jJWTjTQgSIiFz3g0M0VftSej2TwbiCVoofB9wKsB6fNcLvAAO+A2Whv0B7crJLklC0iPkmAnZ6jklh8CzTJQOEtfcmAD7QaL3oJbzCkOI/EASSTvk0SElfrVeZKLRO7ghO2gpG8vBXg/BHu3bfRTVX2/EfRBwtQN/r9gpqNzp2nMOlfkV0FB4QRANeYYRISNEZ78Ali/xylLP5DT+3XZ7awJwKMiCh+As8dGBwjZySg1ThgysizGv6eGZeW7fHDHMrQ0pEdpJzGSJJ7xHJAukvRqpvQO6yShmSk2xhAyjUNo/N5uyJzgFs+1ptM+EXqNKwvA+l0Sv+m3gHgSiBVInpsrJBRjr2ztsNatwObPgMq2ICl+noYXB22asVfDi/00E9htG6Gqfek7/Hmc5GmvJh0KO0OdOwUFhRMOpRAPIsKH04e/S5VGM6hExlIkK5bM0YVsRTzwBXTdiAGpPNCqkuiGHt76UmSP0OMsbItlqJjH84Au1cDxayxOa6wwn3njbkSQjaTsWHeWanB6hCkdmgGIDhDoVN6hRV3u3CbQ9rmvejoi6plR4Mw7wIMfc1pZN4HiRRbDaUZEFrsV2ESOxXnVeVpdRq8+X0ON7apsdpJ/X/rgWUtEuH+rRqtHp8GCwViaBHY/quogeXNV7NXzQ507BQWFEwxFiAcVqSKQzAHDs0BjNQrFt+qgImxQmfROkDIMAAhIFnWThYO+SxtCZhyoLgP2Ji0MwmQChNumdcR3GVdWOEcC3Frn3+MZWk6sMs9jssCc4naFSRSxLBDLA401uS2b+yrOkgx7DtBc59B8Hzj3Dr2WQjb20OLST3uRTTnWbgGjr0hLB561OCRygHaBZHgvvzCwd67uXpaIhDy28sMoUsuq8k93t8PdbA9H4c3tl6g4BQUFBYVTCUWIBxkj11gAlpshIXaaXS2LfekhPkmQiRFhcwxhsOK9sUbV1UwBcKgUQ8hz4ZOoxQtUiuvLwJmvsUXz+m3pPU6T7JnJaJvJIfqO/YCRacPvkCgnCrRKOC2gJFs7JwvcXuByv8kii+pKc9xGZhwono8iq0pzwOSX+Pth2+nulQCxV1FccRaY/yWPO+yUh4AEvrsV9G7k9LDeXJUPrKCgoKBwzFCEeFDRKgG5Kbb97VTZsWzzvlSEw6ixQYYJFgGGdg+dRDQQ9O8GPpA/K/OV20ydcJpsWx3PULW1XSA5CsRTLJpzLSA9xe2MnGHzDLEG1B0qzmaK200NAUMXgI17JIZnvkqi2N5kakQ8Azz5JZAoUoXPjPF7YQB3/5SqfXqE8Wi5GK+Pa8ntB9xv6Ok9qMWhG/tJgNjLzpAqMrrOaUYJAsULLOrbr+XhsE0zVD6wgoKCgsIxQxHiQUSoqKVHgOv/FnD7D4Hlj2Xx0wmJVXtK6HX+0HREDTdcIDlOBdx1SaCy4wA0xpaVH7Ljmy5JtdOhaux0yK9zU1wucZeqr2uTaNsN2QY5zuK8xDpV0+Ya/z52nUVqyQKweovkOZGXhYsdFue5Dm0HCEgK9RjTP9plLqcZLLjbHr22X4tDN/bj3d2PnaEww3PQvYzdOpjl4TD+0kHyICsovCgo25CCwrFCEeJBRKiotctsAOE7gJ4EvOpxj+wIoAPQWMUeTt/DA3xNRqzJFAbPZpScLklme5Pf1xaoxmoArAagW/QK6zGS58ClbSL0ypbmqCaHqnPcppe4PsLs4C/9e8Dqp/QtBz5w7leA4YtUfx/+jH7kdkmef5vqciLPsfsy/s1MkdCPv0YldurGzod/ELV0P2R3P3aGlxFHttvDvl/zgQeVoAzquE8zlG1IQeHYoWLXBhFWlVaBR38F1Jb4p7Eio9YGGToJrm5Q3dVjiJRih+qwLr2uTovfZSekXaJDUmvVSFJdV2b/uvzMtqgGlx9xP60SkymamySrhinbzzrA5gNg6WOS39Y6CcWlb/Nn6T6/9z0Z25aQ7bJXWRA3epVk2IiTvI9dl8fg7y+qyqqSlHbDTMrkkG3YT8TafuKyti/jWkzrWPoAWLgZRcQ9L8KHvWvzYR+2pA63e9CouJeBvcbcrxjUcZ927BV/qKCg8MKhFOJBRCIPLH/CPNzyIyYi+Ceg6YYRZ7KDLn3CfreHGDJCLUPF1W3Tsxu2Ys6M00OdHmebat+johtLs+CwsSLV5QLw5Of83ojz4ZOb4blsbABmjNtPjQDVxyRl29Xaxz/j5+kRKtKeHGdmjIR47RZJZTxD9bpwZv9Kz15q6TPq3yWq47t5d/djZwiX6VaqQrX4sErVXqr3YT3ILwKD6mse1HGfdijbkILCsUMR4kFEcRaY+wlgt/kPpt2SecODDKkOC53xZb6NrYWBAoBH37Aepwo89SaQnwbWvyDBzYwCQ2dZEFd6wD9Oi1aGMIKuuU6i7Nm0TdhtoDgB2DK72HdZLGfE+HtrQ/qTJcwkUF+l/UEIIHGdhHr1c5L4eBYYOs99x9LczkHI3W72hV7TqqX7Rzut+iII1X4e9v2WcTuoBGVQx33a0a+2IQWFUwRFiAcV8SxQfsIH3UlQh+FRyX2GBAs8zVXWDP6aHSehtWpUczsNwKrQYlFdAhwHqD0GWmXGpMU9oCkJQbLAffkeI+vMONMVfBuAxpg1aEDhPAvpmtuIhNPm/rdnBxcvkGy3S1SOz3xtd4LXy+cJ8DO7yWzkWJYFbyGhXrj54tW/F0GoBvFhP4hjBgZ33Kcdqq24gsKxQxHiQUOoEqZGaBcIdDz1qA4cBLZ20dt+YdJe7wAAIABJREFUDN2Ra7IjX1z6dM00ldrlj0lqAaZE+C59k0In+RU6rRKaxv35LtCxmAjhd4DiK/QW12u0ZBRmmEF87uv0FlsVKvCeQ+LZ2gRGrpCwZieih5emA9d+aysx3Yn0Ln0ILL5Pwl2c5Xgf/ISHm51g85Dwgdirq1w3tpPVwxZUvQhCNYgP+0Ebc3jdqwusKShepI2o38etQPSjbUhB4ZRBEeJBQ2kOaNeAW38ElB4CQee4R3QIdJNhTf5dB4ly2GBE0CIB6QlO5IDRKyS0jRV+lpsGrCZQecRGG06TLwydKsmuVaXqKmTL5VB5jhfo+515C1i5xfSJwll2iEsOcTtXvscYt5DAznyV22isUBGuLXFzI1e3Hlove8PcT7hsuwKkRmm5WL/Nwrt2hesNX+TPXurvfvzFh61UfxFEcBAf9oM05u7rXjjHjoOl+7TwdM8wKPQ3+s02pKBwyqAI8aBh/Q7w+R8BC+/JNs0nAQIRIdYQkWGDHlyIqIVyfhoIPMD1GH0W+EDpMVdL5GmlgEXPMATJgmZyc67DYrfsJNMh4jkS2Rv/Ib/vVle7vb/tTeDcN7cS0bACvHiRqvHqZ7w2V75LUtvLi2tJ0uu73LcQ/HttoXeL7e3q715k9Sj8vy+KCA7iw35Qxrz9umfHaakyYvtr/62goKCgoAjxwGHhA2D5MyYbaNpgOiWeQQAeiCYbb8g0wJhspRxPkwwaMcDMsOit8oiKbixNcis06QlOchuVeRJnTRJtIwEYAS0UVp3rjFwBchPRMHYiQL2sCq0NEvLyIypy6TEu98WfAm/8B9E6Vo2Et9MASo+A9CiQG2c0m5lg0kWnJpXrbdhuVdiLrB6V/3dQiKACoQrpFBQUFA4NRYj7Hd2qJQSw9BFglel39QfZLtEFzWSRmxAUi6ExssxIkMBe/T7/3tqg8mWV2VZ55m1aITp1/nRbXCaW5facFgvtdA1ImDIKLUvrw/j/z96dR8l51ee+/+6aq6t67tYsty0PwpYtT63kMAYHchlCggNcDicDhBWGkBAb29jmZmU4a+XkJJZtiA3BhJkQOEkgJEBCyABm8gEieZIt2fLcstSSuqvHqq656r1/7KpWq91DjV3T81nLS1ZXvVW76m11P7Xf3/7ty2zQ9vesP76VShUWpu2sridog23xfrFJe74CvRCbsOUWnuCZGeH5E7bkI/m8PcZxbG10sM9+LkjH1y5VWCusNuuCKm0UUV/Net5FRFqIAnEzW1obaDw2DMcLQYxW7CxRWBiHsa3VvP5CSO2xG2eYQjj0h20Nb/dWSBfKQpy8rY/cMWr7Dfdst4vrxh+EqeIHBgf8vXa2NRu3m234usAXtjOyOQeCg7D9KhvKinW76ym2uUvO2vfe7bXt31xeO8NblE3ZzTqSc7DtKnj+p/a8efz2tq5BW9McOw3DF59ZpLf96jO711VTqtBsC8Hi0ysvINQOXLXVbOddRKQFKRA3s6W1gacesQu54pO0ZhgGWxJRWDTn9UH3JtsH2FAodXBB93a76YbLay8Db73S3rdr0JYlTD0Fz/+X/Xtixs4iF0smnKxtwxbotTPD0XG7fbNj7GxxsAuC3XYW1uO1fYydEt9Ls+zv3ZshOnFm1jObsuPuPs/+vWvAvpbMgi2J8IdtmHccmDxin3fr3hfOllYTEptpIVjxw9zs8y9cQFisv25UIG63GetmOu8iIi1KgbiZJedsH93oaXjoyzYIOrlGj6pMhSTp6gJPoWNEJm4DbDphN9PIpu3rik3Y/w/0QSgM5KF/J2zZC8//BB7/JoS32JlhDJw4AOHt0L3Nhhy3C/p3wewxW2/s8tqewMVd67xh2PkSOzO96ZJCMA6t/xKKH0yyhd3zvAE70+zvtfXBsUk7M9x9nq1ZLrZX69thX8/SS9npuF3oVFzsFJ+2/YWL4Sw4aBfxVRrWmqX+t/ierbSAcPjixtW31qITRzNqlvMuItKiFIibmXHB/V+Aw/94pnQAF825kq7QKcLtLwTQwsyrJ2hLBtxeOzuaTRRKPlw2PBo35DO2O4THD8Fee3wgbINTcg6O3WcfZ+eLbbBKTNnH8vfZWeLkdGEGeId9vP4RG0RDw3bXOAykZu3Xoidhyx4bTEu9rDx3HOZP2tlnf48Nc6ceAU8XnPvSwp2cFwbY9S5lLw9nsQl46juw9fIzPWRXCmutMMNZXOjlD79wAWEj61u1tbGIiKxAgbgZpWLw7A/hP/4Ipp6wXzMG3F2QXWjs2DC2LtYphNpiqUIxDPu6ClsvZwqtnwKFrZNdhdnSkH2MfKEFmq/LvrboxJlyCl8QXD7bR3jyBPbbNAeBfggXZmaLC9EmjthAOD9uF9QtnLbdIwIhGDwf5o7Z8DV8sX2+qSfL31I5FbWv0xuws8GnHoJkHLr67WtzuVeeYVzvUvbycJaYsrPjiWm7QcdKYa1VZjiLC716dthzBGcWEDayvlUdGUREZAUKxM1m8ijc+6d2443ixhXBAfCEIT5Bw2eI3QE7LKewO56Tx9YEF0Ks2w+pwgxu0Gu7P+SSkMvZUoXgQKEn75ydcY1N2KAb7LHHevy2fGL6GTuL63bbRXG47UYYHp+tC/Z32ZnmXMb2/80kbTDN520oDu2BTS+yC+iKbc+8Qbj4l+GCV5X3mn3d9vj4NJx8yL5mX8jWIc88Z2ehV5thXOtS9vJwlorZGejUkv7Sy8Naq8xwFmfHvaGVFxA2aqzqyCDSmlrhypi0NAXiZpHPwU8+Dt/7c7sFMdiw1D9iN48YfxASTVAu4Q3Zy94Ua5nd9g9PwC5+y6bsphhO3nZUMMYG38w05AM2SBoX9J8Hvh6YfMSWR3QN2s4Q6ZgNz4lpO/PcNWRnTYuzyNk4dG22M78evx3PwoR9Xl+XXUxnCht6TD8DW6+wgSx22v49NW9rdsv5Ydq3w84On7jflmaEhm1I93fbUo741Mp9hNezPJz5w7ZvcaD7zH2Wh7VazHCu94ulFr94ls6OZxMrLyBsBHVkEGk9rXJlTFqaAnGzMC548t9tIPT3wu7XQHDIzm7On7Cr9bPxxo7REwKyhZlYr/2TwnbILrfdPS6ft+He7bXB2NtlA2ouU6gl9tgfaqEhGyqd3TZU5rN21tfJQnQMMNB7ri2fmH4aenZCaDPMPG23rA5vtUE5NWvv6/bay/Fuv511Pn7Q1uJmkzYIn37UtnJLL8DClK09LvWH6cD59v7+bth86ZkWceFN9jUtTBQW+pVpeTgLDtrz3D9iZ9hXCmvVznCu94ullr94mnGhlzoyiLSeVrkyJi1NgbhZGAOvvc3OEr/4/XD6CNz/eZh43F5CzyUaODi3DZwGW5rg9hY2kigEYMexIbF7S6E8Il18UXbm1pWxPYUzCegZsTPC8QhMPlFofVaoOc6m7XNt2g0uv53lzSXsD7x01M40hrfY8JzLQi4Oxg/pmSVBPGEXu/l7bQhPztpAbNy2JGHqKTtb3HsOPP7P0LNt/VnQYoiKnrKvwcna2lhvofTD5TnTWaIcy8NZaBAue6utJV4trFU7w7neL5ZO+MXTjEFdRFan2n/ZAArEzWTzJfBLd8HMmN3UYe54IQwnWawnrjmz5LFXKclwe+1t3rANpdkMZKdt6UCo3443n7WhNrNQ6CiBDWteHxC0j7tlD5i8rZNOztqwlU3ZGdHpp2HwAltDm8+wOOs7ewJSC0DOzgr3brOLtHxdhU0wovaHYjZmA2/v9jOBPdgPcyfsh4vNe2yAj562HSLcD8CWy2wwPnnIbryxVn1r1wC86A02jObzNtDHJu1iwYteX3nAWjGc7Vr7/tXMcK73i0W/eESk2aj2XzaAAnGzcblh7Ec23OVyhTBcx7phlx/yyeJfOBOQi8/pKWyp7LV/phP2Ph5PoXfwyUK3iLBto+ZQeIwcZDJ2IV2g1wbg0LD9MxAutFjrh4UIBHpsyEzO2ef1hWHoAvv0uZTtaBGfsWUWgW47S5zL2LZrySgMnW/LJBYi9qmHd9uQHeyDU4dtfXM2aW8vhvuFQkeK4i5qXcN2gZyTW708YGkYdXvs8zSiLraaGc71frHoF4+INBvV/ssGUCBuNvFpeO7/wthPbG1qvRfR5TNL/pID3IW2ainAaxeTgZ3p9fhtwF0sk4jZPz0B6Nls//R1w9yYLWlwe8EdtAvR+s+1s93dW+2Oe+EtgLHbJ6fmoXenfbxgf2Grard9vuGLbX11zzY7y2uMvX9gwNYDhzfbMbp90LfT1g37CjPZPTvgxIO2lCN2GvDYIJtN2A8ebr8dU+92+zpS8+vvotbql9vX+8VSyi8erfYWkY2k2n/ZAArEzSQ+bXdjm34OEhFbI1tvxm23Nvb4bfkCeTvL6+u1XQ+cnG1/5nLb2uBAj52VNW7bKi0Vs/dPx+2MrMsHM0AuDz1bbL2tA3i77Sxu73bAbRepAYQLO8kZY2uGe3ec2Vo5NQ9dfXDuK2zLtdkxW2/sCdt63q5BuPiXbPCOnbJjijxhg9zwxba2N7zZziZnk/bYZBQwMHyRvT1f2EAkm7Kv1xu0JSBQfeCrV3Cs5nHX+8Wy3u3lLrpTeBaRWmj1yQhpegrEzWT8QTh2wIa3TKqOT7SkbthV2FCja9h+zdt1ZnvoXLrQ39drg6NxQc6xfy7+Z+zsqttvA2c2Zcsa/GFb75uK2svviWm7kYXj2O4Q8+OFXe0idjypebsFc2IeIkft1sqhAdt5weWy/YRPPmhnlHMZ+xzZpA3WfTtg1zVnuiQUA5gnBJe+2R6XTdrX0jVoZ4yzCdutwheC2eN2IVtok+1AkU3aDhLVdFmoV5ugWjzuer9Y1rq9nEV3apUkIiItQoG4mZx40M6CLkyCLwCp9PrHlMt47CxuLg547ayqt1D/27PTtnbzFDbQcBzb9i0dt+UQTg6i43aWOJu2i92cvK3lDfZBaKs9Ppey9b7hzbZmOJ0CYva47s02YGfjNvSnYnb22bhsjXHkCRtyZ5+zvYbDm+GCV9u66vis3alu8AL7WOm4nVXese/M61spzAX7bAg/cX+hnjh9pt45PgXH/wt2jNr7nH7UhuZ81ob9YuAbf7AwO75kphNWn/2sV7eG1R53pfHVI3SWs+iuEzpWiIhIW3A1egCyRHzKlhWk5gqdFWrFYD/7GGy3iIAtO/B4CzuuuW13B3+XDcvdW23rs4EROO9lMHCe3Qa571wYuMDW65K3AXLzZdAzDC4DmZgNm0MXweBFtl7X5YV8GoLDNmjnsjD5aGHWsO/Mrm/dW2Dsx3Z22F3YcGPzpXY2+Kn/tCE5NGQ7XEwcsRtYeIOFhXjr6Bqwu9Pte5d9TrfPBvM918K5L7MBOzFr66R7d9jXP3/8zPG5jA3T2bQNg9m07UrxzL1nf+3EQTsrCoWd+IJnj6PU8a5lpcddaXxLx1JLxUV3S6226K5e74GIiEiNtf0MsTFmAPgM8P8AEeD/cxzny40d1WqMvXyfS3JmJ7haPKwX272h1y44Cw3agJlasEHGGBsUDbYvcCZ+ppewN2iPmXveBvaebTB8oR1nMordmMNX2ASjUMrgDdiSiOCg/c/fY8sQ0nE7Szi8pxCKjN2UI3ba7h6XTcGWy225xeyzMHCunW1OTMP2K23QKy7ymz9uA3o53Q+6BgqL8y61rxkAp7DZRtSWaQBk0nbmumj6aTtrvHSmMzlr/39g15mvLd53oH7dGlZ63JXGt3QstVTOam91rBARkRbRCTPEfwmkgc3ArwH3GGP2NHZIK5h6Bk4+VFj85WDTaSWWn1Lvmb68vpC92ReGnu3QV+jZO3wx7LgaLv9VO3OaTxcW0nntTOzEYzbgutyFrZWzttTCZSC7YOuP47M2xGYScMEv2MdPR23d7tAFMHShnZHdfpV9ae6AXdSWmAS32wZVb8DWEqdjNhwf+4ldYJjP244R2URhYxAfxKZsECt3Q4xArw3gE0fg+f+y5SmxUzaEg32eVGGzDcex70N86oXPk8vY/856q5fMfg6cb8eXjp95nErGu9xKj7vS+Oo1E1tcdOfx2Q8qHt/qNcH1eg9ERERqrK1niI0xIeDNwKWO48SAHxljvgH8BvChhg5uqaln4JG/L8ymBW1wWGsjDuOzpQ0UQ5ljZ1qzGVsG4e8p9AyOQ7DXdpHwBe1//efZYOwO2ONCg7ZOd9uV9vmzR+zitmCvDVSnnrSlEG6v3RgjMWM3DHH7balBcrZQd+yxm1wEeyE+aRezJabO1LTOHbcL1cCWQ2QS9pK+p8v+6fLaUJWJ2+OGLirsSJexAR3sDO78cRtiu/orW5wVHISnvmPLPfyFOuSJI7btm+PYINy7036AKHZZ2H51oX/xEsv/DmfPftarTdBKj7vS+Oo5E1vqam+1ShIRkRbR1oEYuAjIOo7zxJKvPQz8XIPGs7KxH9mA1tUHiU2QP1VogVYsm3BsmMhjZ2k9ftuD11vYPtnlsSEjlzvTraF3mw2/8ydsizJ/D2y9zF7iN8bOtHq80L0dJo+cCSyDF9pewNETZ9qSbd5rg6/HZ++TdtsQ3D8CyT5b2xvss9slB3tt4E1Mnb3YDc5cPu/eZGd5547bmcy+HYXXa2y7OeMq9Df22dplXHbL5W1X2jKJ0HDlnQoSU7D1CvtnKmrH0ltYTFh8D86/5uzHLnZLgDNlAoFCiUk6vnrpQL3aBC1/3JXG1yxN69UqSUREWkC7B+IwML/sa3NA9/I7GmPeA7wH4Jxzzqn/yJaKnrYlBgMX2nCTmrOzuvlMYR2cC3w9dhY40A3+EMyftju+ZVJ25rV/xNbYzh2HLZfabZKLNbHR03aWzhOwQdgYW34wcJ4NvTv2nR1es+kzxz77Qzsb3LPNhu/QkC1hWDhtnze0CfrOsTXHxc0wVuo6sLT2tHu73fa4a9jWCWNg6gk7Kzx51I7RF4LNF8PQbhuOJ47UZpYxOWfH3b35zNccxz72Ba9e+ZiVZjrPv8be1gyzn5qJFRERqUq7B+IY0LPsaz1AdPkdHcf5JPBJgNHR0TXqFeqge7Od2d2029bk5vO2vVnWZWd2hy60LbUySRtIvYHCQrUUeFI2gKYK9ZnhzYWa4UEb9DIJG6gveh3MPHtmq+LixhXLZxKXL5rq3grTz9htit1eiE7akoih3XDZWwvt0CbtuAbOO9MObfnl+qWhLZuALZfZ3esiT9jxnP9quzOfMfa2rXvtY4F9vJ37XjjjXIlKF3qtNtPZLKFTM7EiIiIVa/dA/ATgMcZc6DjOk4WvXQ4cbuCYXmjkZbaGONAHW66wM7KTT8KOq2yP3+mn7Ozwrp+zrcymn7FdF+ZPFXoDT0A8YmeOd78etuy1JQHLZwsHd9myg6UbVyyfSVw+23juS+3iObCL6br6oHsYrnqHfbxg35nNF7zBMwunVrpcv1JoW7qRRs9eu8HG9FNnFrXV+vJ/OV0SREREpCMYx9nYydCNZoz5W+wKtXcBVwDfAl7iOM6qoXh0dNQ5ePDgBo2wYOoZO9saPW1njDdfZrc9LrYnAxZriYubLmzUtrjLxzbyMhuGi2o9jnq/Lm0nLCIi0olWbeHVCYF4APgs8AvAFPCh9foQNyQQi4iIiEg9rRqI271kAsdxpoFrGz0OEREREWlOnbAxh4iIiIjIqhSIRURERKSjKRCLiIiISEdTIBYRERGRjqZALCIiIiIdTYFYRERERDqaArGIiIiIdDQFYhERERHpaArEIiIiItLRFIhFREREpKMpEIuIiIhIR1MgFhEREZGOpkAsIiIiIh1NgVhEREREOpoCsYiIiIh0NAViEREREeloCsQiIiIi0tEUiEVERESkoykQi4iIiEhHUyAWERERkY5mHMdp9BiajjFmEhhrwFMPAZEGPK/Uh85n+9C5bC86n+1D57K91Pt8RhzHee1KNygQNxFjzEHHcUYbPQ6pDZ3P9qFz2V50PtuHzmV7aeT5VMmEiIiIiHQ0BWIRERER6WgKxM3lk40egNSUzmf70LlsLzqf7UPnsr007HyqhlhEREREOppmiEVERESkoykQi4iIiEhHUyAWERERkY6mQCwiIiIiHU2BWEREREQ6mgKxiIiIiHQ0BWIRERER6WgKxCIiIiLS0RSIRURERKSjKRCLiIiISEdTIBYRERGRjqZALCIiJTHGnGuMcYwxnkaPRUSklhSIRUTahDHmOWPMq5d97TeNMT9q1JhERFqBArGIiLyAZoFFpJMoEIuIdAhjzIeMMU8bY6LGmCPGmF9ZcttvGmPuM8Z8xBgzBfxPY4zbGHOHMSZijHkG+MXGjV5EpH40AyAi0jmeBl4OnAL+X+BvjDEXOI5zsnD7zwJ/C2wGvMC7gTcAVwILwD9s+IhFRDaAZohFRNrLPxljZov/AR8v3uA4zlccxxl3HCfvOM7fAU8CP7Pk2HHHcT7qOE7WcZwE8FbgLxzHed5xnGngzzb0lYiIbBAFYhGR9nKt4zh9xf+A3yneYIx5uzHmoSVh+VJgaMmxzy97rG3LvjZWt1GLiDSQSiZERDqAMWYE+BTwKuDHjuPkjDEPAWbJ3Zxlh50Edi75+zn1HaWISGNohlhEpDOEsIF3EsAY807sDPFa/h64zhizwxjTD3yovkMUEWkMBWIRkQ7gOM4R4E7gx8Bp4DLgvnUO+xTwb8DDwAPA1+o5RhGRRjGOs/wKmYiIiIhI59AMsYiIiIh0NAViEREREeloCsQiIiIi0tEUiEVERESkoykQi4iIiEhH08YcK3jta1/rfPvb3270MERERESkdsxqN2iGeAWRSKTRQxARERGRDaJALCIiIiIdTYFYRERERDqaArGIiIiIdDQFYhERERHpaArEIiIiItLRFIhFREREpKMpEIuIiIhIR1MgFhEREZGOpkAsIiIiIh1NgVhEREREOpoCsYiIiIh0NAViEREREeloCsQiIiIi0tEUiEVERESkoykQi4iIiEhHUyAWERERkY6mQCwiIiIiHU2BWEREREQ6mgKxiIiIiHQ0BWIRERER6WgKxCIiIiLS0RSIRURERKTuHMdp9BBWpUAsIiIiInUVS8eYSk41ehir2rBAbIx5vzHmoDEmZYz5/LLbXmWMedwYEzfG3GuMGVlym98Y81ljzLwx5pQx5sZaHSsiIiIi9eM4DpFEhEgiohnignHgfwGfXfpFY8wQ8DXgD4EB4CDwd0vu8j+BC4ER4BrgFmPMa6s9VkRERETqJ5vPcmrhFLF0rNFDWdeGBWLHcb7mOM4/Acvny98EHHYc5yuO4ySxIfZyY8yLCre/A/gTx3FmHMd5DPgU8Js1OFZERERE6iCeiXMydpJULtXooZSkGWqI9wAPF//iOM4C8DSwxxjTD2xdenvh//fU4NizGGPeUyjpODg5OVn1ixIRERHpRLPJWSbiE+ScXKOHUrJmCMRhYG7Z1+aA7sJtLLu9eFu1x57FcZxPOo4z6jjO6PDwcFkvQERERKTT5fI5Ti+cZjY12+ihlM3T6AEAMaBn2dd6gGjhtuLfk8tuq/ZYEREREamBVC7FZHySbD7b6KFUpBlmiA8Dlxf/YowJAedja4NngJNLby/8/+EaHCsiIiIiVYqmo5xaONWyYRg2tu2axxgTANyA2xgTMMZ4gH8ELjXGvLlw+x8BhxzHebxw6F8Df2CM6S8slns38PnCbdUcKyIiIiIVKrZUm0pMNXVLtVJs5AzxHwAJ4EPArxf+/w8cx5kE3gz8KTAD/CzwtiXH/TF2odwY8H3gdsdxvg1QzbEiIiIiUplMPsPJhZMt0VKtFKbVE309jI6OOgcPHmz0MERERESaTjwTJ5KIkHfyZR0X8oYY7mpo4wKz2g3NsKhORERERFrATHKGudTyBl+tT4FYRERERNaUy+eYTEySzCbXv3MLUiAWERERKZhLzTE2P0Y0HaXb181Izwi9/t5GD6uhktkkk4lJcvnqNtoYmx+j19+Lz+2r0chqpxnaromIiIg03FxqjkOTh0jn0/T6e0nn0xyaPNSWJQKlmkvNcTp+uqow7DgO//bcv/He/3gvH77/wzUcXe0oEIuIiIhgZzCD3iBBTxBjDEFPkKA3yNj8WKOHtuHyTp7J+CQzyZmqWqrFM3FuP3g7H3voY6Tzab73/PeasjOFSiZERKRl6fK21FI0HX3B90/AHei4GeJMLsNEYoJMLlPV4zw58yT7D+znVPwUAD+34+f43y//34R94VoMs6YUiEVEpCUVL28HvUF6/b0kc0kOTR5i7/BehWKpSLevm2QuSdATXPxaMpek29fdwFFtrIXMAlOJqbJbqi3lOA7fePobfP7w58k6WbwuL+++7N28+cI30+PrqeFoa0eBWEREWtLSy9vA4p9j82PsHd7byKFJixrpGeHQ5CHAzgwnc0kSmQQXDl/Y4JHVn+M4TCeniaajVT3OfHqeux+4m5+e+ikAO8I7uGXfLZzXex7GrNoGuOEUiEVEpCXp8rbUWq+/l73DexmbH2MuNUe3r5sLhy9s+ysO2XyWyfgkqVyqqsc5PHWYOw7eQSQRAeDnd/48v335b581496sFIhFRKQl6fK21EMxFHeKRDZBJB4h51TeRSLv5PnqE1/lS49/ibyTJ+AO8L7L38fPn/PzNRxpfSkQi4hIS2qny9taHCiNMJeaYyY5U9VjzCRn+PD9H+ahyYcAOLfnXG7ddys7unfUYogbRoFYRERaUrtc3tbiQNlouXyOSCJCIpuo6nEemniIO++/k9nULACvP+/1/Nalv9WUG2+sR4FYRERaVjtc3tbiQNlI6VyaifgE2Xy24sfI5XN8+fEv85UnvoKDQ8gT4veu/D1euv2lNRzpxlIgFhERaSAtDpSNEk1HmU5OV7XRxmR8ktsP3s5j048BcFH/Rdw8ejNbQltqNcyGUCAWERFpIC0OlHpzHIep5FTVO8T99ORPueuBu4hmbGu2X7ngV/iNS34Dr8tbi2EwKdoOAAAgAElEQVQ2lAKxiIhIA7XT4kBpPpl8hsn4JOlcuvLHyGX4/OHP841nvgHYD3E3XnUjo1tGazXMhlMgFhERaaB2WRwozaPYtSSSiJDNZ9ka2lrxFYeTCyfZf2A/T80+BcCewT3cPHozg8HBWg654RSIRUREGqwdFgdKcyh2Lck6WXL5HOl8mqMzR9ndv7vsUPyD4z/gYw99jEQ2gcHw33f/d962+224Xe46jb5xFIhFRKSjqOdv++vkc/zs3LMkcjbAYsDv9gMwHhtn98Dukh4jmU3y6Uc+zb+N/RsAA4EBbrr6prb+0KZALCIiDbPRwUU9f9tfJ5/jVC7Fc3PPEfKGwJz5ut/lJ5qOlvQYx+aPcduB2zgWPQbAVZuu4oarb6DP31ePITcNBWIREWmIUoJLrQOzev62v048x3OpOY5MHeHkwkki8Qh9gT76A/2Lt6fyKRuS1+A4Dv957D/5xKFPkM6lcRs3b7/k7Vx7wbW4jKvqMRpj6PJ2Vf049aJALCIiDbFecKnHTJ96/ra/TjvHM8kZ7jtxHxjo9naTDWR5evZpzu87n35/P6l8imQ2yUj/yKqPEc/Euefhe/je8e8BsCm4iZv33cyLBl5UkzH63X6GgkN43c3bnk2BWEREGmK94FKPmT71/G1/nXSOM7kMD5x+4Kxa4f5AP+f3nc9schaP8RDyhhjpH1n19T89+zT7D+xnfGEcgBdvfTHXXXkdYV+4JmPs9ffS5+/DGLP+nRtIgVhERBpiveBSj5m+dur528kLx9bSzOe4ludsIbPAVGJqsVXfUv3+fjzGw1Wbr1r1eMdx+Jdn/4XPPPoZsvksHpeHd136Ll5/3utrEl49Lg9DwSECnkDVj7URFIhFRJZQyNg4Iz0j/Hj8x8yl5hZ/Iff6e3nxthcD9Znpa5eev528cGw9zXqOa3XOHMdhOjm9uEgu5A2RyqcWZ4hh/ZrhWDrG3Q/ezY9P/hiA7eHt3DJ6C7v6dlX46s4W9oUZCAzUpPZ4oygQi4gUKGQ0hikshzecPStVr5m+duj524kLx8rRjOe4Fucsm88SSURIZpOLX9sW3sbRmaOA7SaxXs3w49OPs//AfiYTkwC8cscred/l76vJgjeXcTEYHFx3AV8zUiAWESlQyKhMpbPqY/NjDHUNsbNn5+LXEtnE4vvdrDN9zaDTFo61g2rPWSKbIBKPkHNyZ32929fN7v7djMfGiaajq9YM5508X3vya3zxsS+Sd/L43X5+e+9v86pzXlWTEomAJ8BQcAiPqzWjZWuOWkSkDhQyylfNrHop73czzvQ1g05aONYuqjlnc6k5ZpIzaz72WptuzKZm+cj9H+GBiQcAe/Xl1n23srN756rHlMoYQ5+/r+U/qCoQi4gUKGSUr5pZ9Xq9351QB97MC8dkZZWcs7yTJ5KIEM/EK37ehycf5s6DdzKTsoH6tee+lndd9q6zao4r5XV7GQoO1eSxGq11qp1FROpspGeERCZBIpvAcRwS2QSJTIKRntX7d3a6aDpKwH32KvKAO1DSrlj1eL+LM9bpfJpefy/pfJpDk4fabpa/OHPuc/mYS83hc/lU697kyj1n6Vya8dh4xWE4l8/xpce+xB/e94fMpGbo8nRxy+gt/O4Vv1uTABv2hdkW2tYWYRjAOI7T6DE0ndHRUefgwYONHoaINEAnzC7WUjF8Lp3lTWQTi7/s11Pr97va8Yg0g1g6xlRyikoz2lRiitsP3s7hqcMAXNB3Abfuu5UtoS1Vj81t3AwGB5t617k1rFosrZIJEZElVLNanmov3df6/VYduLQyx3GYSk4RS8cqfowDpw7wkQc+sniV5o3nv5F37HkHXlf1u8QFPUGGgkO4Xe6qH6vZKBCLiEjFmq0ThOrApVVl8hki8QipXKri4//68F/zT0//E2C3cf7AVR/gZ7b+TNVjM8bQH+inx9dT9WM1KwViERGpSjPNqmuxmbSieCbOVGLqBS3VSnVq4RT7D+znydknAbh44GJuHr2Z4a7hqsfmc/sYDg7jdVc/w9zMFIhFRKRtNNuMdT2p3r09zCRnqirp+dGJH/HRBz9KPBvHYHjrRW/lf7zof9SkrKHX30ufv68mfYqbnQKxiIi0lWaasa4X7arY+nL5HJOJybN2nStHKpfiM498hn997l8B6PP3cdPVN3HFpiuqHpvb5WYoOHRW6VG7UyAWERFpMdpVsbWlcikm4hPk8pWVSDwffZ79B/bz3PxzAFwxfAU3Xn0j/YH+qscW8oYYDA7iMp3VmVeBWEREpMWom0brmk/PM5Ocqbil2neOfYd7Hr6HVC6Fy7j49Yt/nTdf+OaqA6zLuBgIDBD2hat6nFalQCwiItJi1E2j9VTbUi2RTXDPw/dw7/P3AjAUHOLm0Zu5ZPCSqscW8AQYDA7WpDVbq1IgFhERaTG16qahhXkbI5PLMJmYJJ1LV3T8M7PPsP/gfk7ETgDws1t+luuvur7qD0DGGPr8fTrnKBCLiIi0nNW6aYDdra+UgKuFeRsjnokTSUTIO/l17xtNRxmPjbOQWSDkDbE1tJUfnvghn3n0M2TyGTzGwzsvfSe/tOuXqu784HV7GQoOtc3Wy9XS1s0r0NbNIiLSapYG3KWzxqsFXG1zXV+O4zCTmmE+NV/S/aPpKEdnjhLwBPC7/MymZ/nK0a/w2PRjAGwNbeWWfbdwQd8FVY8t7AszEBjouIVzaOtmkdrSZUYRaTbldp7Qwrz6yeazRBKRslqqjcfGbRh2+xmbH+OLR77IdHIagFdsfwW/e8Xv0uXtqmpcbuNmMDhY9eO0IwVikTLpMqOINKNyA64W5tVHMptkMjFZdku1YpnEvcfu5V+e/RfyTh6vy8vrzn0d77rsXVWXSAQ9QQaDg3hcin4r0bsiUib1/xSRZlRuwNU217U3l5pjNjVbUUs1x3H45COf5ImZJwDY3LWZt73obewM76wqDBtj6A/00+PrqfgxOoECsUiZdJlRRJrR8oAbSUQ4Nn+MzaHNi7cv/dnVSdtc11veyTOVmGIhs1DR8Y9EHuGuB+9iJjUD2C4Sr9/1evJOnm3hbRWPy+f2MRwcxuvu3HZqpVIgFimTLjOKNI7q91e3NOCOx8Y5tXCKkZ4RBoODq5Z2dcI21/WWyWWYSEyQyWXKPjbn5Pj7o3/P3z7+t+TJE3AHeNOFb2J3/25CnhDbwtsq/t3S4++h399fdalFp1AgFimTLjOKNIbq99e3NOAOBAdU2lVjyz+QDQeHyeQzJbVUW24qMcWd99/JI5FHANjVu4tb991a1YwwgNvlZig4dNakjayv4/ptiFSr+AvH5/Ixl5pbbFGkX8gi9bW0ft8YQ9ATJOgNMjY/1uihNZ1oOkrAHTjrawF3gGg62qARtb7iB7J0Pk2Pr4dIMsJ94/dVVC53/+n7ue7e6xbD8Bt2vYE7XnFH1WG4y9vFttA2heEKaIZYpAK6zCi1oMv/5Wn1+v16ne+VHlelXbVX/EDmc/mYTk2Ty+cIeAKMx8bZPbC7pMfI5rN88cgX+dpTXwMg5A1x/ZXX8+JtL65qbC7jYiAwQNgXrupxOplmiEVEGmDpbFOvv5d0Ps2hyUMtE+4aoRjylmqVkFev873a4/b7+0lkEiSyCRzHIZFNkMgkGOkZqdEr6jzRdBQXLiKJyOIWzH6Xv+SFdKcXTvOhH35oMQy/aOBF3H3N3VWHYb/bz9bwVoXhKikQi4g0gC7/l2+kZ6RlQ169zvdqjzuTmlFpV425jIvxhXFyzpn+wql8ipA3tO6x9524j+vvvZ6jM0cBeMuFb+HPXvZnbOraVPF4jDH0+fvYGt6K16UuEtVSyYSISAO0+uX/RmjlNmH1Ot9rPa5Ku2oj7+SJJCKEvWFOxE4AdmY4lU+RzCYZ6V/9A1k6l+Yzj36Gbz37LQD6/H3ccPUNXLXpqqrG5HF5GO4axu/2V/U4coYCsYhIA6jGszKtGvLqdb6b5fuoXevhl7ZU6/Z1s7t/N+OxcaLpKCFviJH+kVXf6xOxE9z2X7fx7PyzAOwd2stNozcxEBioakxhX5iBwAAuU95F/nY9R7WiQCwiJdEP09pS+77OUq/z3QzfR+3aDm8hs8BUYuqslmrdvu6SFtB999h3uefhe0jmkrhw8asX/ypvuegtuI274vG4jZvB4CBd3q6yj23Xc1RLppLtBdvd6Oioc/DgwUYPQ6RpLP1huvSXrn6YVkcfMjrLRnaZ2Mjvo+KivqWz1IlsYrFuudU4jsN0crqiFnXJbJJPHPoE3zn2HQAGA4PcPHoze4b2VDWmgCfAUHAIj6uyecx2O0dVWHWXEs0Qi8i6li7cATX5r5VWvfwvlanX+W7091E71cNn81kiiQjJbHL9Oy/z7Nyz7D+wn+Ox4wDs27yP66+6vqoPJ8WFc9V+wGmnc1QvCsQisi79MBWR1TRLHXO1EtkEkXjkrC4SpXAch28/920+/cinSefTeIyHd+x5B288/41VbZvsc/sYCg7hc/sqfoyidjlH9aRALCLr0g9TEVlNM9QxV2suNcdMcqbs4xYyC3zsoY/xoxM/AmBz12Zu2XcLF/VfVNV4evw99Pv7qwrUS7XDOao3BWIRWZd+mIq0vnrVGrdyO7xiS7V4Jl72sU/MPMHtB27nVPwUAC/b/jLef8X7V+1LHE1HGY+Ns5BZIOQNsS287QWTCm6Xm6HgUEVbL691flv5HG0ULapbgRbVibxQoxfuiEjltDD2hdK5NJOJSTK5TFnHOY7D15/+Ol84/AWyThafy8e7976b14y8ZtUZ3Wg6ytGZowQ8gbN6GO/u370Yiru8XQwGBnG7yu9EofNbMi2qE5HqNHrhjohUTgtjz7ZSS7VSzKXmuOuBuzhw+gAAO8I7uHXfrZzbe+6ax43Hxm0YLmykUfxzPDbOxYMX0x/or6oETee3egrEItLRNPMtnUALY61qWqodjhzm9oO3M5WcAuDV57ya9+59LwFPYN1jFzILLwi8fpefZC5Zk62XdX6rp0AsIh1LzeqlmdTzw5kWxlbeUi3n5PjqE1/ly499mTx5Au4Av3PF73DNzmtKfoyQN0Qqnzprq2Wv28umrk1Vh2HQ+a2F8vb9ExFpI0svMxpjCHqCBL1BxubHGj00WUPxg8x9J+7j0OShtpgFK76mdD5Nr7+XdD5d09c20jNCIpMgkU3gOA6JbIJEJsFIz0hNHr/ZJbIJTsZOlh2Gp5PT/NF9f8TfPPY35Mmzq3cXf3HNX5QVhgG2hbeRzCZJ5VK4jZuQN4TbuNcttShVp5/fWlAgFpGOFU1HCbjPvtwZcAcqupwqG6PewbFR6v3hrLgGwOfyMZeaW9yhrBOuhMyl5ji9cLrs/sIPTDzA9fdez6GI7bDzi+f9Ire/4na2h7eXPYZuXze7+3fT4+vB6/IS9oZr+v538vmtFZVMiEjH0mXG1tOui4c2oga00xbGVtpSLZvP8qXHvsRXn/wqACFPiOuuuo6XbHtJxWNxGze7+nat2pKtFjrt/NaaArGIdCz1V25+y+tqTy6cPGuGLpaOcTJ2kon4BEDLLorUh7PaqrSl2kR8gjsO3sFj048BsLt/NzeP3szm0OaKxxLwBBgKDuFxKXI1M5VMiEjH0mXG5rZSecTphdNMJewq/1g6xlMzT7GQXWBT16aWLp9QDWjtxNIxTi6cLDsM/+TkT7j+3usXw/CbLngTf/7yP684DBtj6A/0syW0RWG4BegMiUhH02XG5rVSecQ5PecwNj9GyBfiZOwkGNtKa2t4a0uXT2gnseo5jsNUcopYOlbWcZlchs8d/hzffOabAPT4erjh6hsY3Txa8Vi8bi/DwWF8bl/FjyEbS4FYRESa0kp1tUPBIdK5ND6Xj4n4BJu6NrE1vJWwLwy0du/VWnw469S+2pl8hkg8QiqXKuu48dg4+w/s5+m5pwG4bOgybrr6JgaDgxWPpdvXzUBgYNVd66Q5KRCLiEhTWq2udktoy2JwTOfTqrst6NS+2vFMnKnEVNldJL5//Pv85UN/SSKbwIWLt73obbx191txm/K3Tga7cG4wOEiXt6ui46WxFIhFpC46daaqkdrtPV9v0aMWRZ6tXTtwrGU2OctsarasY5LZJJ965FP8+9i/AzAQGOCDV3+Qy4Yvq3gcQU+QoeAQbldlYVoaT4FYRGquU2eqGqkd3/P16mpVd3u2Slq3teqHqFw+x2RisuyNNsbmx9h/YD/HoscAuHrz1dxw1Q0Vv2ZjDAOBgY69KtFOFIhFpOY6caaq0dr1PV+vrlaLIs8ot3Vbq36ISuVSTMQnyOVLL5FwHIf/GPsP/uqRvyKdS+M2bt5+ydu59oJrcZnKGm753X6GgkN43dVvvSyNp0AsIjW3EZsMyNn0nku5JSSt+CFqLjXHbGoWx3FKPiaeifPxhz/O949/H4BNwU3csu8Wdg/srngcvf5e+vx9Tb1wrlVn/xtFfYhFpOaKM1VLdfJip42g91zK7avdSluX5508k/FJZpIzZYXhp2af4gPf+8BiGH7Jtpdw1zV3VRyGPS4PW0Jb6A/0N30YbsctzutJM8QiUnNa7LTx9J4LlFdC0iq742VyGSYSE2VttOE4Dt985pt87tHPkXWyeF1e3nXZu3jdua+rOMiGfWEGAgMVl1hspFac/W80BWIRqTktdtp4es+lXK3wISqWjjGdnCbv5Es+JpqOctcDd/HTUz8FYHt4O7fuu5Xzes+raAwu42IwOEjIG6ro+EZQCVX5FIhFpC602Gnj6T1vvFaq22zmD1GV7jp3ZOoItx+8nUgiAsA1O6/hfZe/76xZ8HIEPAGGgkMtt/VyI2b/W+l7fyWmnFqcTjE6OuocPHiw0cMQEZEWsrRrw9IZ12bv2tAIa4WnSnadyzt5/uHJf+BvHvsb8k4ev9vP+y5/H68651UVjc8YQ5+/r2XP20Z/L7bQ9/6q9TLNXwgjIiLSApbWbRpjCHqCBL1BxubHGj20prLWgq94Js6p2KmywvBMcoY//r9/zF8f+WvyTp5ze87lI6/8SMVh2Ov2siW0pdmCXFnKXWBZrXb43m+tawAiTajVLxOJNKtW+7elus3SrLbg69HIo2wPby/rsR6aeIg7779zcbe61537On7rst/C7/a/4L7RdJTx2DgLmQVC3hDbwtteUELQ7etmIDDQ1B0kSrWRJVTt8L2vQCxShVZtbC+VabWA1spa8d9Wq3RtaLTl4Snv5FlILzCVmCo5EOfyOb78+Jf5yhNfwcGhy9PF7135e7xs+8tWfc6jM0cJeAJ0+7pJ5VMcnTnK7v7ddPu6cRs3g8FBurxdNXmNnaYdvvebomTCGHOuMeZbxpgZY8wpY8zHjDGewm1XGGPuN8bEC39eseQ4Y4y5zRgzVfjvNrPkY91ax4rUQjtcJpLStEJfz+IY7ztxX9ONrVyt+G9rpGeERCZBIpvAcRwS2QSJTIKRnpHF+7TTOarU0p7Z6Vyayfgk0Uy05C4OkUSE37/v9/n7J/4eB4cL+y7krmvuWjUMA4zHxgl4Anbm2Nhd5gKeAOOxcYKeINvC2xSGq1DK936za4pADHwcmAC2AlcAPwf8jjHGB3wd+BugH/gC8PXC1wHeA1wLXA7sBX4JeC9ACceKVK2VGttLdZo9oLVCYC9HK/7bWq9us93OUaWK4SmSiBBJRIhn4ySzSbaFt6177H+d/C+u++51HJk6AsC151/Lba+4jS2hLWset5BZwO86u4zC7/bjMi42hzbjdrkrf0Gy4TXL9dAsJRPnAR9zHCcJnDLGfBvYA7wSO8a/cGw7jLuNMR8Efh74NvAO4E7HcY4DGGPuBN4NfKKEY0Wq1g6XiaQ0zV4jV2oj/lYp+2jVf1tr1W1qswSr29fNtvA2npp5arGed6R/ZM1zm8ln+MLhL/D1p7+++Bg3XHUD+7bsK+k5Q94QqXxqsbbY6/IS8ATo8mhWuFZave1js8wQ/wXwNmNMlzFmO/A6bGjdAxxyzu4Nd6jwdQp/PrzktoeX3bbWsSJVa4fLRFIag+FI5AgPTzzMUzNPEUvHmiqglTKj2kozlO34b6sVZ71rLZ1Lc3LhJG7jZvfAbq7afBW7B3av+e/o1MIpbv3BrYtheM/gHu6+5u6SwzDAtvA2ktkkqVyKkDdEyBsik8u09PeT1FazBOIfYIPqPHAcOAj8ExAGlv+kngOK/3KW3z4HhAt1xOsdexZjzHuMMQeNMQcnJyereCnSSdrhMpGsby41x3x6nlg2htflJZ1LczhymEg80jS/UJfWZRYtD+zNXvaxVDv+2yrlHLWacmqiY+kYJxdOlrUF8w9P/JDr772eJ2efxGB42+638acv/VOGgkNljbPb180lg5ewuWszjuPgd/tb/vtJaqvhJRPGGBd2NviTwEuwQfazwG3ASaBn2SE9QPHjdGzZ7T1AzHEcxxiz/Lblx57FcZxPFsbA6OiodiuRkrX6ZaJ2VcvSgLH5MYa6hugP9HNy4eTiZd4eX09Zj1nPcoVStuGtd9lH8fWdXDjJQnqBsC/MltCWil9nu/3baoWtkstRaieQSnadS+VSfPqRT/Pt52yFY7+/n5tGb+Ly4csrGmvIG+KcnnNwmWaZB5Rm0wzfGQPAOdga4pTjOFPA54DXA4eBvUs7R2AXzx0u/P9h7IK6osuX3bbWsSLSpmpdGlC81B32hbmw/0Ku2HQFe4b24FD6Z+d6lyuUMqNazxnK4uubSc4wuTBJIpdgYmGC6eR005ZlbLR2m/Uemx8j5+Q4Hj3Ow5MPczx6nJyTO+uKQyaf4dTCqbLC8PPR5/ng9z+4GIav3HQld//83RWFYZdxMRQcYrhrWGFY1tTwGWLHcSLGmGeB9xlj7sDOEL8DW+/7PSAHXGeM+QR2wRzAdwt//jVwozHmW4AD3AR8tHDbeseKSJuq9eKlWizw2ogFVevNqNZzhrL4+k5ETxDw2vZWqWyK2dQsO7p3dNzCsdW006z3qYVTTCxM2N6+3m7SuTTH54+TzqXZO7yXeCbOVGKKnJMr6fEcx+E7x77DJw59glQuhcu4+I2Lf4M3XfimisJswBNgMDiI1+Ut+1jpPA0PxAVvwi6suxUbYr8L3OA4TtoYcy3waeDPgceAax3HSReO+ytgF/BI4e+fLnyNEo4VkTZV69KAWgTJZuhSUQxjY/NjzKXm6PZ1c+HwhTWZoSy+voWMLZUA8Ll9RDPRpurGIbUTS8cwLoPfYzs3+D1+UvkUsXSM2eTs4u5xpYhn4tzz8D187/j3ABgODnPz6M1cPHhx2eMyxtDn71vz+7pVuq3IxmmKQOw4zkPYNmkr3fYgcPUqtznALYX/yjpWRNpXrVt21SJINksbsXrNUBZfX8gbIp1L43f7SefShLyhll84JisL+ULEF+Kkcil8bh/pXJpcLkfGlSkrDD8z+wy3HbiN8YVxAP7b1v/GdVdeV9H3jNftZSg4tOLWzUWtuAui1F9TBGIRkVqqR2lAtUGy3RZULVd8fX3+Pp6ff55ULoWTd9jRtaOtXqecsTW0lYA7wGxqllg6hs/tI+QLEfaGSzrecRy+9ey3+PSjnyabz+JxefitS3+LXzzvFzl7+U9pwr4wA4GBdcsr1A9aVqJALCJtp56lAe00plpa+vqSuaTtMhG0AUWXo9vTSM8Ic6k5tndvJ5fPMZmYLHnHuVg6xt0P3s2PT/4YsOH61n23cn7f+WWPw23cDAYHS956uRnKl6T5mLP3rRCwbdcOHjzY6GGIyBqqqQFU/aBIbcwmZzkUOUQkHiHkDbEtvG3dUofHpx/n9gO3M5GYAOCVO17J+y5/X8mBdqmgJ8hgcBCPq/T5vWK3l6XlS4lsYrHrh7S1VS89aIZYRFrOejWAawVe1Q+K1EYmlyGejXNO9zmc033OuvfPO3n+8al/5ItHvkjOyeFz+/jtvb/Nq895ddklEsYY+gP99PiWbzewvnYvX5LKKBCLSMtZqwaw+MtutcCr+kGphK4qnC2eiRNJRMg7+ZLuP5ea48P3f5gHJh4AbCi9ZfQWzulZP0gv53P7GA4O43VX1k6t3cuXpDIKxCLSctaqAVwv8LZa/aCCWOPpqsIZjuMwk5phPjVf8jGPTD7CHfffwXRyGoDXjLyGd132LgKeQNnP3+Pvod/fX9Giu6XaqR+01IYCsYi0nLVamK0XeJul/VkpFMSag64qWNl8lkgiQjKbXP/OQM7J8XeP/x1/d/TvyJMn6Any/ivezyt2vKLs53a73AwFh876dytSSwrEIgK01kzkWjWAxS4HqwXe5cdGEhGOzR9jc2jz4u3N8roVxJpDq11VqIdkNslkYpJcvrRd56YSU9xx8A4enXoUgAv6LuDm0ZtL6kCxXMgbYiAwgNvlLvtYkVJpY28RWZyJTOfT9Pp7SefTHJo81LS/8IuXO30uH3OpucXV4b3+XkZ6RkhkEiSyCRzHIZFNkMgkGOkZecGx47Fxjs0fY6RnhO3h7U33uqNpu8vbUgF3gGg62qARdabiVYWlmvWqQj3MpeY4HT9dchg+ePog19173WIY/uXzf5n9L99fdhh2GRdDwSGGu4YVhqXuNEMsIi05E7laDWApC2aWHjsQHGja191K5R3trFO7EuSdPJFEhHgmXtL9M/kMXzzyRf7xqX8EIOwN84GrPsDPbv3Zsp/b7/Yz1DWE11XZwjmRcikQi0jbXRIudcFMs7/uTg1izaYTuxJkchkmEhNkcpmS7n9q4RS3H7ydJ2aeAODigYv54OgH2dS1qaznNcbQ6+ulL9BX9phFqqFALCIdOxPZ7K+7E4NYs+qkrgQLmQWmElMlt1S778R9fPTBj7KQXcBgeMtFb+HXXvRrZZc5eFwehruG8bv9lQxbpCoKxCLSsTORrfC6myWItdKiS6mM4zhMJ6dLrlFP59J8+tFP86/P/isAff4+brz6Rq7cdGXZzx322W2+XUZLm+KtCVAAACAASURBVKQxtHXzCrR1s3SiTg08nfq6y7G0/dvSDw7ltH/T+9zcsvksk/FJUrlUSfc/Hj3O/gP7eXb+WQAuH76cm66+if5A/wvuG01HGY+Ns5BZeMH2zm7jZjA4WNG2zSIVWLWBtQLxChSIRUTOKHYgWVpaksgmFrt7rKcWgVrqJ5FNEIlHyDmldZH47rHvcs/D95DMJXHh4lcv/lXectFbcJsXlkhE01GOzhwl4Angd/lJ5VMks0l29+9muGuYoeAQHpcuVsuGWTUQ67tQRETWVO3iw1bsYtIp5lJzzCRnSrpvIpvgEw9/gu8+/10AhoJDfHD0g+wZ3LPqMeOxcRuGC3XBxT9jmRiXhS6rcvQitaNALCLSYZaXL/T7+5lJzaxazlDt4sNm7+ZRD81eIpLL54gkIiSyiZLu/+zcs+w/sJ/jseMA7Nu8jw9c/QF6fD1rHreQWTjr+8Tr8jIUGCKeLa2Vm8hGUSAW6VDN/gtb6mP5dtCRRIT7TtzHJYOXMBgcXHF76GoXHzZ7N49aa/Ytt9O5NBPxCbL57Lr3dRyHbz/3bT71yKfI5DN4jIff3POb/PL5v4wxq159XhTyhkjlU/jdfsLe8OL3Qruee2ldWs4p0oFabWe6jVR8b+47cV9bvidLyxeMMcymZunx9zCbmsUYQ9ATJOgNMjY/tnjMWjsDlmK93QPbzfL3eKX3tFGi6SgnF06WFIYXMgvcduA2Pv7wx8nkM2zp2sL+V+znjRe8saQwDLAtvI1MLkOXt2sxDLfzuZfWpRlikQ6kms6VNfvMXi0sL19YyCwQ9oZZyCwsfm2lcoZq2r91Wj/lZiwRcRyHqeQUsXSspPs/MfMEtx24jYn4BAAv3/5yfveK3yXkDZX1vJtDm9ke3s7x2PGOOPfSuhSIRTpQM/7Cbgad8EFheflCyBsilokR9oYX71OPS9rN0k8Z6l8u1GwlIpl8hsn4JOlcet375p08X3/q63zhyBfIOTl8Lh/v3vtuXjPympJnhQFcxkV/oH/xNQ8EByoev7SHZi/TU8mESAcq/sJeSnV99oNCwB0462sBd6DkjQpawfLyhT5/H/Opefr8fS1ZzlBuictGlAs1U4lIPBPnZOxkSWF4LjXHn/zkT/js4c+Sc3Ls7N7JnT93J68997VlhWG/28/W8NaO/3kiZ7RCmZ5miEU6UCvs0FatSmYjmm1mrx6Wly8MBAZ4w643MJOaablL2s/PP8+9z99LzsnR7+8nk88wl5pbs8RlI64CNEuJyExypuTA8WjkUe44eAdTySkAfuGcX+A9e99DwBNY58iz9fn76PX3lhWgpf21wtU3BWKRDtQsv7DrpdJa4E74oAArly/sZGeDRlOZudQc9z5/L26Xmz5vH+lcmuPR4+zo3rHmL9mNKhdqZIlILp9jMjFJMptc/75Ojq8c/Qr/5/H/Q548QU+Q37n8d3jlzleW9Zwel4fhruHFPsMiS7VCmZ4CsUiHaqaazlqrdDai3T8otJOx+TFyTo4+Xx8Gg99jg9hschavy7vqce1+FSCVSzERnyCXX3/XuenkNHcevJNDEfshcFfvLm7Zdwvbw9vLes6wL8xAYACXURWmrKwV/t0pEIvIupp9McRy1cxGtPMHhVaz1vddNB2l399POpdenJX0uX1MJafY1bdr1cds56sA8+l5ZpIzOI6z7n0fOP0AH77/w8yl7b+JN5z3Bt556TvxuX0lP5/buBkIDpTdeUI6Tyv8u9PHORFZUysshlhOiwZb33rfd92+bvoD/aSyKVK5FA4O0UwUt3GvuXit2p7KzSjv5JmMTzKdmF43DGfzWT5/+PP88Y//mLn0HCFviN//md/nvZe/t6wwHPAE2BreqjAsJWmFf3emlE+SnWZ0dNQ5ePBgo4ch0hSKoWTppa5ENrH4A60ZLa0hXjob0Ww/gJdqtVn4elvv+654jvNOnpnkDDOpGdzGzTU7r2FnT2vVQ1cjk8swkZggk8use9+J+AS3H7ydx6cfB2B3/25u2XcLm7o2lfx8xpjFhXMiLWjV1Z4qmRCRNbXCYojlWq0WuFYbgrRTqF7v+27pOfa4POzq29XSr7cSC5kFphJT5J38uvf98fiPuevBuxY3YHnzhW/m1y/+dTyu0mOA1+1lODhc1kyySKtQIBZpI/UIRK2wGGIlrVQLXIuWRO22y14p33etdI5ryXEcZlIzzKfm171vOpfmc49+jn9+9p8B6PX1csPVN3D15qvLes5uXzcDgQG1U5O2pRpikTZRr1rfZtpkoF3VYkOQpaHaGEPQEyToDTI2P7bqMeVuarGR9H23smw+y+n46ZLC8HhsnJt/cPNiGN47tJe7rrmrrDDsdrnZ1LWJweCgwrC0Nc0Qi7SJejU+b7Xyg1ZUi1n4cktbmn1GuZ2/7yq9kpPMJplMTJbUUu17z3+Pjz/8cRLZBC5cvO1Fb+Otu9+K27hLHmeXt4vBwCBuV+nHiLQqBWKROtrIms561vp26qXpjVKLlkTlhupW2DmqHb/vKv0gMpeaYyY5s+7jJ7NJPnnok/zHsf8AYCAwwAdHP8hlQ5eVPEaXcdEf6G/6siiRWlLJhEidbHS7MrUaa121aElUbolBLco0pHzllrbknTwT8YmSwvDY/Bg3fv/GxTA8unmUu6+5u6ww7Hf72Rraqp8b0nE0QyxSJxs9A9cKjc9lddXOhpZbYrDajLLBcGjyUFt0qmhG5VzJSefSTCYm122p5jgO/z7273zy0CdJ59O4jZt3XPIO3njBG8vaPa7X30ufv0+1wtKRFIhF6mSj25W1c81lO6pHOU05oXqlD1CReAQAn8fXlHXF7aDU0pZYOsZUcmrdjTbimTh/+dBf8oMTPwBgU9cmbhm9hd0Du0sek8flYSg4RMATWP/OIm1KgVikThrRrqwday5bRTkBtxkWtK30AarH14PP42vquuJWt96VHMdxmEpOEUvH1n2sp2afYv+B/ZxcOAnAS7e9lPdf8X7CvnDJ4wn7wgwEBsqaSRZpRwrEInWiEobOUW7AbZYFbcs/QN134r4V64qbqR1bq1vrSk42n2UyPkkql1rzMRzH4ZvPfJPPPfo5sk4Wr8vLuy97N68997Ullzu4jIvB4GBTbr3cThvMSOtQIBapE5UwdI5yA26z7v7Xqpuw1Fq9A9lKV3LimThTiSlyztot1ebT83z44Ie5f+J+AIaDw9x49Y1cOnRpyc8f8AQYCg6VtUvdRmmGqyfSmZrvX4NIG1EJQ2coN+CWGzw3asZMVzUaE8hmk7PMpmbXvd/hqcPsP7Cf6eQ0YLtIvOH8N5DMJRe/N9ZijKHP39fUwbJZrp5I51EgFhGpUrkBt5zgWauAVkqobvarGhvxwWAjA1kunyOSiJDIJta8X97J89UnvsqXHv8SeSePz+XjzRe9mX1b9gGQyqUYj42vuZDO6/YyHBzG5/bV9DXUWrNePZH2p0As0mRUP9d6yp1ZLSd41iKglROqm/WqxkbN3G5UIEvlUkzGJ8nms2vebyY5w4fv/zAPTT4EwOauzbxzzzvZFNq0eB+/y79m/+huXzcDgYGWaKemsh1pFAVikSai+rnGq+QDSSUzq6UGz1oEtGpCdbN8QNuomduNCGTRdJTp5PS6LdUemniIO++/c7Gc4vXnvZ6Xb385LMu1qXxqxcVxbuNmMDhIl7erZmOvN5XtSKMoEIs0EdXPNVY1H0jqNbNai4BWaahupg9oGzVzW89AVmpLtVw+x5cf/zJfeeIrODiEPCF+78rf46XbX0o0HeXozFHAzgyn8imS2SQj/WfvSBj0BBkKDuF2uase90Zq9rIdaV8KxCJNpN3r55pltnE1zfiBpBYBrdJQ3Uzvx0ZdSq9XIMvkM0zGJ0nn0mvebzI+yR3338GRqSMAXNR/ETeP3syW0BbAvg+7+3czHhsnmo4S8oYY6R9ZfB+MMfQH+unx9VQ13kZq1rIdaW8KxCJNpJ3r55pptnE1zfiBpBYBrdJQXe/3o5wPSBt5Kb3WgSyeiRNJRMg7+TXv99OTP+WuB+4imrH1wNdecC1vv+TteF3es+7X7etecQGdz+1jODiM1+19wW0isjYFYpEm0s71c80027iajfhAUk2NcqUqDdX1fD/K/YDUqpfSZ5Iz636AyOQyfP7w5/nGM98A7Pt+41U3MrpltOTn6fX3/v/svWt0XGl55/t7a1ftqlLdL9bNsuW25ava7b64Ybp7IN1DgAxNgBN6pWGGRcjkkJOVDOHefJkz5wyZJIsOlwEyAxNCOJCBDExImAkQJgOYCTHdod0X2y233b60ZNmSLJVUqvt113s+lKtcJdVNUlW5JL2/tbQkVe29691v7ar9389+nv+D1+rdFIVzCkUvogSxQtFDbNaTfiv0YvR1JZ2+ILmdUfL1iOqNzEcz4b+eC6TNdCvdKBgspBZI59MNl5tNzPLkM09yafkSAOOBcT56/KME7IGWXkczaQTtwaqLFoVCsXaUIFYoeozNdNJfC5shHaTTFySbIUpeyXrnoxXhvxkukNZLOp9mIbWAUWjcde6n137K51/4PKl8CoHgHQffweOHHkcTrRXCOSwOAvYAJmFqx7AVim2NEsQKhaIrbJZ0kE5ekNwuEbiRYsb1zEcrwn8zXCCth0gmwnJmuaGlWjqf5j+d/k+cmD4BgFt387573sc/GfonLb2GSZjw2/w4dWdbxqxQKEBdVioUiq5QEla6SSeSiaCb9J4qqOsGJRFYSadFYClamy1k8Vg9ZAtZziyc6agIj2Vj2DRb1WM2zVbVPGLUPUoqlyKVTyGlJJVPkcqlGHWPrtzcpqAgCywkFwinww3F8NXoVT74kw+WxfAh3yHed8/7MJvMDZtrlLCZbQw5h5QYVijajIoQKxSKrrGeaGOvW7WthXpR8gH3AGcWznRkH29HmkYr0d/bnS/fzuMqZ+SYT82TM3J1l5FS8sOrP+SLZ75I1shiEibedMebeHjXw5iEqWn7ZSEEXqu354/9rfR5VWwvRLNOOduR48ePy1OnTt3uYSgU257KXNRKAbmZI8srBYPP6mMyOtmxfTx5/SQeq6fKfUBKSSQT4aGdD214+7Xo9fetneNL5BIsphYbWqolc0m+cPoL/OTaTwDwWr28+8i72ePZc2shWYys3ztw76r1LZqFoD2IVbPW3JdeEaC9/r4rFKzq83gLlTKhUCh6lsrophACu9mO3WJnKjp1u4e2bkqR0Yd2PsRdO+4inAl3dB9vR5pGr6fHtOO4klKylF5iIbnQUAxfXr7MB3/ywbIYfmDoAT5074cYcg5VLVev/bJTdzLkGKorhrudDtOIrfh5VWwfVMqEQqHoWbayE0Epsvf0zNP09/WX80Lj2Tiz8Vnmk/MAG474daOYsV6UshedM2Djx1W+kCeUCjW0VJNS8r1XvseXX/wy+UIes8nMb9z5Gzx6x6PEc/Gm7Zc1oRGwB+iz9NV9jV5zLdnKn1fF1kcJYoVC0bNsZSeC0q3l/r5+EvkEF8MXGXIOMRefAwH9ff3liN9GoqtrydVdz+33TngrdzoNYCPHVSqfIpQMYcj6lmrxbJzPPf85npp9CoBhxzBP3P8E+7z7yq/fqP2y3WwnYA9gNjU+RfeaAN2qn1fF9kClTCgUip5lqzkRlKiM7A05h0AWi6bOhc6BKEYXh5xDbbvlvDJNo54YXs/t93bfJu9GGsB6j6tIJsKNxI2GYvj80nnef+L9ZTH88MjDfObhz5TFcIlS++V7B+7loP8gLt2FEAK/3c+AY6CpGC5to9vpMI3Yqp9XxfZACWKFQtGz9Hou6nqptCVz6k7GfGM4zA7mEnM4zA72+/aXbbVW2pV1ivUK21Ys1roxjrWw1uOqIAvMJ+cJp8N1t1mQBb598dt87KcfYz41j1Wz8v573s+H7vtQw7SHErqmM+wYxq27W96PXhOgW/XzqtgeqJQJhULR0/RyLupaqEwDuJG4Qb6QJ9gXBIqieMQ9QkEWGHGP3JZbzuu9/d7u2+TdSgNo9bjKGlnmk/PkC/m6yyxnlvnMs5/hufnngKJQfeL4E+x2725pLG6rG5/VV+UE0iqa0Di/eB4pJfu8+267AK2c115ywFAomqEEsUKhuK1sh5PmyjzbXCHHucVzHOEIAXugXOh2/+D9TEYnge5381uvsG130V4v5aHGsjGW0ksNG22cXjjNp059inCmGD1+45438t6j763pCrESzaQRtAer9rVVKo+pu/vvLs97r1A5Pk1ovLT4Ek/NPMXR4FHGg+Nb7jOu2PwoQaxQKG4bnSjI6kVWugHs6NsBwGJqEbPJjEt3MeAeIJwJk8wnWUgu4NSdDDoGqwrgOnnxsF5h2+4GG73Q4ltKyWJ6kXg2XncZo2DwXy/8V7554ZtIJH3mPv71Pf+a1+x8TUuv4bA48Nv8aCZtXWPsNYeJlZTGZxQMLi1fwqbZ8Nv8XItdw5DGlvuMKzY/ShArFIrbRq+f1NtFrTSAoD2IxWThoZ0PVV0Y7HTuLIvASsHb6YuHjQjbdqa13O4Odjkjx0JqgayRXfVcLBtjJj7DbHyWv77811yJXAFgzDvGx+7/GIOOwabbNwkTfpt/w62Xe81hYiWl8V2KFcWw1WxFIoln4+Wc8K30GVdsfpQgVigUt41eP6nXY62R2mZpAK1cGHTj4qFX8rVv1ziSuSShVKhmo41YNsaF8AUmo5P85YW/JJFPAPDP9/xz3nvXe7GYLE23bzPbCNgDLS3bjF5KLalFaXyJXAKXpTimrJHFYXFsis+4YvuhXCYUCsVtoWRh9czcM1wMXyzfnu6lk3ot1mML1swNoBWnhna7OWwGSnN98vrJjndgC6fDzCfn63aduxq9yo+v/pivTnyVRD5Bn7mPdx95N6/b/bqmAlcIgc/mY9Ax2BYxDL3nMLGS0vjMwkzGyBR/8hkGHYM9/xlXbE9UhFih2Ab0WuFaSegE7AGSuSSJXIKLSxcZcY+gCa2r+aJrZT2R2mZpAK1E+yqXiWfjzCZmCafDeHQPkUxky+Vjdiu/3CgYLKQWGnadm0vM8fkXPs/1+HUA7vDcwbsOvwuf1df0gsSiWQjagy0V2a2F251a0ozS+DShcXbhLD6bj33efWgmres54QpFK4hG1bPblePHj8tTp07d7mEoFG2hUlhUFindzqKWUoS1JO7mEnMspZfw6B5eN/q6njmp1+Lk9ZN4rJ4qiywpJZFMhId2PrSubbbyHpWWMaTBteg1hEkgC5Jd7l2YhGnLFSlVHiMlUvlU2du2HWSMDPPJeYxC/UYbJ6+f5PPPf55EPoFA8Iujv8gbRt+AZtLIGBkswsJB/8Ga67p0F36bf112aluJXrsgV2xr6n4YVYRYodji9GLhWmXusFN3MqaPlUVlr58oO5G72Uq0r7TMj6Z+RE7m8Fv8DDoGcepOUvnUlitS6nR+eTQbJZwO17VUyxgZvnz2y/zt5N8C4NE9vGXfWzgSPIImimI4nU8z6ludoqCZNAK2QEsNObYDvZKbrlA0QglihWKL04uFa71eENSIki1YPBtnOb1MOBNGExqP7HpkQ9ttRTR4rB4GHAMc8B+oijre7vezE9Q6RhZTiyymFjl5/eS6I42tWKpNx6Z58pkny57Qd++4mw/d9yHMJjMz8Rli2RgOi4NR3+iqY7bP0kfAFli3nZpCobg9KEGsUGwR6t2W7EXx2Qtes+vFY/Wwx72HE9MnMKSBz+rDZ/MxGZ3EbXV3PMLdi+9nJ1h5jCymFjm3eI7DgcPrziluZKlW4kdXf8QXTn+BjJHBJEy86/C7ePv+t2MSxRr0eukRJmHCZ/NtufdBodguqBziGqgcYsVmo1EOKtBzOcSlMbcjr/B25Cd2Mr+12f70Yk54p1jZ7tpv95ebmsDa5ryRpVppW188/UV+PP1joOgT/dHjH+VI4EjTbVs1a9FXWmuPg4RCoegYdXOIlSCugRLEil6mlmCaik41FGitisbNVPwSyUSYCE1wNnQWr9XLqHsUs2buijjsRGEdtC52N9P71C7qzflMfIZBx2DduZBSEs6EiWaidbf9SuQVPvHMJ8ouEq8efDXvv/f9LUV7vVbvqnEpFIqeRRXVKRRbgXpWVMl8kp3OnVXLVuaVtpKf2imbq06It9JYZ+OzxSp+BJeWL7Hft78rXbAEgnOhc+RlHofFwaBjEM2kbfh2eaMCyNKFz3YSwZXUShUJpULMJebw2/01j9l8IU8oFaprqSal5PuvfJ8vv/hlcoUcZpOZXx//dX557y83Fbhmk5mgPYjNbGu4nEKh2BwoQaxQbCLqCaaF5MKG80o74UbRKZFdGmte5nFanIibF/2ziVnGvGNtKzCbjk7zzNwzhFIhgvYg9w/ej9vqJpqNEs/HcVlcZI0sE6EJhp3DPDD8wIZer14B5Ex8hkgm0nFP3l6mVt751ehVRt2jNY/ZA74DLKQW6lqqxbNxPv/C5/nZzM8AGHIM8cT9TzDmHWs6FqfuxG/zl/OKFQrF5kcJYoViE1FPMDl1J6lcqvx/oyK1ehHbTrhRdMryrTRWh8VB1shi1azomk4sF2tbgdl0dJrvXvkubqubAccA8Vyc7175LmPeMYJ9QXw2H7OJWRK5BA6LA7e+8YK6egVz8Wwcv93fU9Z53aaWNd2AY4CAPVC1XOkCwmP11LVUu7B0gSdPPcl8ch6A1+58Lb9z9+80tUnThIbf7sdhcbRnpxRtZzumEynaQ0NBLIQwA28BHgWOAV5gGTgN/C3wHSllvtODVCgUReoJpkHHYPmWeqOuVY0itp1wL+iU5VtprIOOQS6FLwHF299mUcwhHnAPcGbhzIZOis/MPYPb6satuwHKv5+/8Txv2vcmhFmwX99ffu12RKXruW84dEfNts29bLXWCWFSK/Wn8pgtyAJziTmklDXFcEEW+M6l7/C1c1/DkAa6pvObR3+TN4y+oWmKhM1sI2gPYjapOFKv0q3uhoqtSd37PUKI3wKuAP8XcBn4feC3bv6+DLwXuHJzOYVC0QVG3aOkcilS+RRSSlL5FKlcqiw27tpxFw/tfKjuCaAyYiuEwG62l3NuG217vZSEayXtiOCWxqqZNPZ591EoFFhKLbHLtYs97j1MRifJFrJ4rB6yhSxnFs6sWTyGUiGcFmfVY05LsQlGK/tUOjmfvH6y5dcvvYe6SSeSiZSLIoccQx2Zx05R2veNvgfNqDxms/ks12PXiWQiDDuHa47p409/nK9MfAVDGuxy7eLTv/Bp3rjnjQ3FsBACn83HoGNQieEep9H3m0LRjEaf7jHgVVLKuRrP/TXwB0KIIeDDHRmZQqFYRSsdzRrRKGK70W3XolN+w5VjzeQzHAocKl8UlCJEG00vCNqDxHPxcmQYIJ6LM+Yba5qespFIVa0o6Gbzbe5Wd8TSXL20+BJXE1dxmB0c9B1cdaFwNnSWT576JEvpJQBeP/p6fvPobzYtiLNoFnbYd6BretvGrOgcvdiESLF5ULZrNVC2a4qtSif9c+vR7Zy+dlmilXKILSYL6XyacDpMXuZ5/ODj7HTtbLhPnZjnzZQb2SlbupXU6joXy8aYic+QyCWwm+38fO7n/NXFv6JAAbvZzm8f+20e3vVw0227dFfRwUTZqW0absf3m2LTsTHbNSHEkpTSX+PxeSll/0ZGplD0KptJgLTK7Yg0tmL51k7alQu9y72LXxj5Bf7H5f9BMp9kh30H+7z7WEwvstO1s+E+dSJS1e153Ajd6KaXK+QIJUNkjEz5sVg2xoXwBWxmGwVZ4ItnvsiVyBUA9nr28rH7P1YznaISzaQRsAWaFti1ylb8HulVNtudFEVv0WpC1Kr2O0IIC6CatSu2JFu1OKMTaRG9xlpOis3ESl7m+cU9v7gq4tTs1v92aa9cj40Kk2bvSzKXZDG1iCGrLdVm4jPYzDauLF/hL87/BfFcMXL8T4f/KR+670NNO8n1WfoI2AJopvac2rbq90ivsh2+3xSdo5nLxE8BCdiEEH+/4ukR4GedGphCcTvpVg7k7WAzRRrXQ6snxVbEynojvds9UrURYTIdnebE9AkMaeCz+sgX8kQykfL7Ek6H685/NBPlpzM/5cT0CaD4uX3HwXewx72noRg2CRM+m6/tFyxb+XukV9nq32+KztEsQvynFPMt7ge+XPG4BG4AP+7QuBSK24oqztjctHJSbEWsNIv01otkbodIVbMo7nqESSQT4cT0CTSThlf3kjWyXItdY8Q1wiuRVxh0DNbtOncjcYOvnvsqU7Gio8Ae9x7edeRdOCwOLKK+GLZqVoJ9QSymxtHj9VDreyRv5Dm/eF6lUCgUPUZDQSyl/CqAEOJpKeX57gxJobj9bPdb3tuBVi56GkV6m0WY6wnCrZBT2skOhIY08Fq8CARWzQrAfGKecDqM1+qtud7PZn7G557/HIlcAoCHdz3Mo3seJU+edD7NqG+1daAQAo/uwWurvc12sPJ7JJ6Nc27pHE6zU6VQ9DBb4TOqWDut9p28IIR4rxDix0KIMwBCiNcKIX61g2NTKG4bnfDkVfQWrXgk1/MF9lg96/I87ZY/b6usxysZOuf3GsvG8Fl9ZI1s+TFDGkzHp6suTktkjSxfPP1F/vDnf0gil8Cje/jY/R/jzXe8mWQ+iUVYatqwWTQLg47BjophWP09MhmZBAmjnlHlk9uj9NpnVNE9Wi2q+zjweuA/AF+8+dg14DPAtzowLoXitrIdbnk3YjtESFrN860X6V1PWk0v5ZRuJMrbyQ6EuUKOa7FrSCnJGBnCmTAmYVrlDnE9fp0nn3my7CJxV/AuPnz8w/htqwyRqnDqTvw2PybRajxo/az8HskaWY4EjuDUbzV86fVUrO3wXVBJL31GFd2l1W+E9wBvllL+V4r5wwCvAHvbORghxDuEEC8JIRJCiMtCiNfcfPx1QojzQoikEOKEEGK0Yh2rDESKjAAAIABJREFUEOLPhBBRIcScEOJDK7ZZd12FohGtdH7bimyXCEmj6G89SnPzd5N/x7Nzz/LTaz/lYvhi2Qe3WVpNLBur2YI5lo21Z6fWwEaivJ3sQKgJjUHHIPFcnPnUPIY0ePXgq6u2fWL6BB848QGuRK5gwsS7Dr+Ljz/08YZiWBMa/X39BO3BrojhEpXfI8f6j2HWquNQvZyKtV2+Cyrppc+ooru0GiHWgJLzeUkQOyse2zBCiNcDnwAeB34ODN18PAj8FfB/An8D/B7wTeCf3Fz1/wX2A6PAIHBCCHFOSvmDFtZVKBQr6LUIyUYiVO0s/CqJA0MaTEWmiGaiXItfYzG5yIJjgf3+/WhCa+gksd7c9E5E6TYS5e1kB8Ix7xhnQ2fxWD0MO4cZdg6X5yedT/Ofz/xnfnj1hwAEbAE+cvwj3Bm8s+F27WY7QXuwbXZq62WzuY/02ndBN1D1I9uXVi+Tvw98WghhBRDF1j2/R1Fktot/B3xcSvm0lLIgpbwupbwO/AowIaX8b1LKNEUBfEwIcejmer8G/J6UMiylfAn4EsWINi2sq1AoVtBLEZKNRKiarbvW/NmSOJiJz7CYWsRtc7PXu5dEPsF0fJrrsetNI8zNctNrjalTUbqNRHnXE11vhpSSxdQiGSPDAd8B7h24l4P+W/m/k5FJPvS/P1QWw8cHjvPZRz7bUAybhImAPcCAY+C2i2HozLx1kl76LugWqn5k+9JqhPhDwFeBCMUmHXHg74B3t2MQQggNOA78DyHEJcAGfAf4KDAOnC4tK6VMCCEuA+NCiBsUI8mnKzZ3Gnjbzb/rrgtUuWYIIX4T+E2A3bt3t2O3FIqeYS0Rxl6KkGwkQtVo3VKkbi35s6WI6kx8BrvFjq7pWEwWNJPGbtdu0vl0U2HTKDc9konw1MxTRDNRcoUcFpOFmfgMbt3dkSjdRqOV7fR7zRfyLCQXqrrOlZBS8j+n/idfOvMlsoUsZmHm18Z/jbfue2vDtspWzUrQHmzajGMlnc6Z3Uw+ub30XdAttnv9yHamJUEspYwC/4cQop9iasK0lHKujeMYoCi0HwNeA+SA/w78G4qpGQsrlo8ArpvPlf5f+RxN1q1CSvknwJ8AHD9+XK58XqHYrKy1eKqXbutu5LZ+o3XXI7RL4kBKWXRnB3JGDpu5GEETQjQVU42enwhNFAWw1Y3L4iJrZJmJz3BVXuXB4QfXNQeN6JUTfyqfIpQMreo6B5DIJfjjF/6Yf7j+DwAM9A3wxP1PcMB3oOE2vVYvHqunoWCuheosV00vfRd0k8100aJoH61GiBFCBIA3AkNSyieFEMOASUp5rQ3jSN38/Xkp5ezN1/s0RUH894B7xfJuIMatHGY3kF7xHDefr7euQrEtWKv4qxRKM/EZ4tk4Dt1Rjqx2UxhsJELVaN31CO2SOOjv6+d6/Do5Uw6JZMA+QCwXY5dzV0Mx1UxsXV6+jEt3lb13rZoVl+7iyvKVjkXp2nniX09kNZKJEE6Hqx6LZWPMxGd4Ofwy3774bRbTiwA8NPwQ77vnfTgsjqrlErkEDouDYecwPpuPHX07ynO4VrZjzmwjeuWiSaHoBi3lEAshfgG4APxL4P+++fB+4AvtGISUMkzRxq0yMlv6ewI4VjEWB7CPYm5wGJitfP7m3xPN1m3HuBWKzcB68gA9Vg+j7lHsZju7PbvZ6dzZNHd1vZ62jdhIPl+jddeTP1sSB/t9+3FYHAgh8Nl8WE1Whh3DOCyOhq4NE6EJZuOzvLz0MpeXL2MUjKrn60Uz/XZ/z+c0rjXP2SgY3EjcqCmGzy+d58S1E3z5xS+zmF5EExq/cedv8LH7P1Ylhi+EL5CTuaJVm8xxNXoVh8WxbjFc2u52y5ltxnZ121FsP1otqvsPwONSyl8C8jcf+0fgVW0cy1eA9wkh+oUQPuCDwHeBvwbuFEK8XQhhA/4tcKaic97XgH8jhPDdLJZ7L/D/3Xyu2boKxZZnvcVTa7Hl6lTh10aKkBqtu16h7bF6eHDng7znzvfw+j2v53DgMIcDh3lg+AEksq6YimQinA2dRQiBU3eSK+S4FL5E3siXxdZez15iuRiZfKbowZvPEMvFGA+M93wh1lqOlYyRYTYxSyqfWvXcxfBFvvXyt/jele9hSIP+vn5+557f4bD/cNUFw0x8BpvZhlWzYjKZGHIMMeAcYDo2vaH96JSdXK/SiYtYhWKz0mrKxB4p5Y9u/l2K3GbXsH4r/B4QBF6mmP7wLeD3pZRpIcTbgT8G/gtFIf6OivX+H4qR6imKqRefkFL+AEBKudBkXYViy1MvD3DAPcCZhTN1b3GvJa2gk7eaN3Jbv966G70VXGu7jVI0pqJTeK3FdsSVLYmvRq9yKFA0vRkPjhPNRolkIsSyMcwmM8OOYcaD4z2f09jqsRLLxlhKLxXzsFcwEZrgU89+img2CsD9g/fzK/t/BavJuipCm8glcOkubJoNj9WDZtKQUm5Y0JU+K4lsgnA6TDgTRhMaj+x6ZEPb7UVUvrRCUU2rgvacEOKNUsr/WfHYLwJn2zUQKWUO+O2bPyuf+yFQ0ypNSpkB/tXNn1rP111XodgO1BJ/A+4BJqOTDU+Ga8nf7VTnsk7SbpHZqADpxdCLjLpHubR8CQBd05FSEk6Hy1Fpj9XDA8MPbMquYM2OFSkli+nFcgOTSgxp8Jcv/yXfeOkbFCigm3QeO/AYxwePA8WIcilVooRTd2I1W/HbbzXiaEck12P1sMe9hxPTJzCkgc/qw2vzMhmdxG11b4r3olVUvrRCUU2rgvjDwHeFEN8D7EKI/wz8MvDWjo1ModiGdMryaaX4K0WGGp0M11Jhvh3tmVbSKOrs0l1kC1n2+/Yzm5gllothFmaO7ji65kYhvdhKt9GxkjNyLKQWyBrZVeuF02E+9eynOL1QdMccdY3y5n1vZsQ1AhIyhQzpfJpR361UFqtm5d7+e3lp6SVS+VTb3Q/CmTBHgkeqjuVUPrXlhOJmvIhVKDpJSznEUsqngbsoFqP9GcW2za+SUj7TwbEpFNuKbrZJbaV4aC35u8rMvki9AqTS/GgmjTHvGAf9Bxl2FtMh1kKvttKtd6xYTBZmE7M1xfDz88/zuyd+tyyG33THm/j0w5/moeGHsAgLsWwMi7Bw0HerOYfH6mHQMUiwL9ixvOrtUli33fKlFYpmiFq5XOUnhRhss9/wpuD48ePy1KlTt3sYim1GSeisjEyVTva99Fq1opRAz0Uue4l2RHa7eYxsBCkl4UyYaCa66jmjYPD181/nL1/+SyQSh9nB7977u6u8lkvEsjFuJG+gCY2APdDx42qzzPFGqcwhroyyqxxixRanrjl5s5SJl6nw8RVC/JWU8lfaNSqFQnGLbt7C3IjhfqNinK0kGNpNO3KWe/k2d0nwL6eXMaRB0B5cFW2cT87zyVOf5KWllwA46DvIR49/lAHHQM1txrIxpqJT9Dv66TP3daXwq9PNKHol5WWthaW9Mu5W2ExjVfQOzSLEMSmlq+L/JSmlv+4KWwQVIVbcDrodmVrvSWO7RNB6kV6d+9JFkmbSiuky+RTpfLoq3eHp2af57HOfJZ4rFta9ff/bedfhd2E21Y7LaEJjNjGLyWTqyv5Wfh7EzSCSRLZVUG3WqOxmGvdmGqvitrDuCLFqYaxQdIlut0ldb8Sym1FKFem5RSQTIZ6NczZ0Fq/Vy6h7FLNm7olWulPRKQxpkM4Vc1JLtnIz8Rn2evbylYmv8DdX/gYAt+7mg/d9kOMDx+tuz2a2EbQHuRq7isfS+WOtUkRpQuNq9CrhdJijO4629ZjbrM4Om2ncm2msit6imSA2CyEe4ZaiXvk/Usofd2pwCsV2YrO0Se2Wo0Sv+aR2Wpw32n7lXBzbcYyr0aucnj/N0R1HuzYf9cZXkAWmY9PoJr0q9mI1WZmMTPKF01/gcuQyAEeDR/nwfR8mYA/UfI1S9z+3XszU69axVhJRRsHg8vLlsqXbdGwaQxptLdjr1ZSXRmymcW+msSp6i2aCeJ6iq0SJxRX/S2BvuwelUGxXer0BA6w9kr1eIdnNSE+z2+VA28T5dHSaZ+aeIZQKEbQHuX/wftxWd8PtV82FGcZ3jJdTB9Yj1Nb6nkxHp6u8eXOFHJFMhEP+Q2SMDGZhJlPIVLVN/se5f+Q7l75DxshgwsQ7Dr2DXz34q2hCq/kauqazw74Di2YpP9atuyYlEXU5VhTDVs2KlJJYLlbuuNeOY26z2hNupnFvprEqeouGtmtSyj1Syjsa/CgxrFBsM9Zix7YRm7Bu2V9VjlETGufD5zm/dB5NaOXxToQmWm5N3Ijp6DTfvfJdUkaKAccAKSPFd698l5/P/rzh9ts5F2t9TyKZCCemT6CZNAK2APlCnmuxa8RzcZ6ff558Ic+wc5h0Pk3GyJDNZ/nGS9/gmxe+ScbI4Lf5+fcP/XveeeiddcWwx+phyDFUJYZLj3ejbXVJRCVyCXRNByBrZHFYHG095jarPeFmGvdmGquit2hn62WFQrENWEt0cWWU1ygYzMZnyxG3Rut2+3a53WznYuwibosbBNxI3mDMNwbA+cXzjHnHuBS7RCKXwGFxMNA3QCafWdNr/e/p/00sGyOZT2LX7ATsAdxWN8/deI5H9z1atWzeyHN+8XzRdixxg3whT7AvWH5+vXOx1sh7KT/YqxdbT+uaTjwb53rsOn5bscbapbs46DvIczee46vnvspCagGA+wbu44P3frDue6yZNIL2YNV7vJJu3DUpRaLNwkwmn0EgSBtp9rv3N5zntUbaN0ta1Eo207g301gVvUVdQSyEeAZ4EvjvUspVrupCCB14G/BhKeWrOzdEhULRK6w1r7cyny+ejXMpfAld0xGIcmSytO5KceGz+piMTgLduV0OkMglcFlcIIrjjWfjzMZneWnpJaYiU+xy78Jr85I1spxbPMchf+td4SOZCOcWz9Hv6MeqWckVckzHptnp3EnGyFSJ/3g2zrmlczjNTjxWD7lCjnOL5zjCEQL2wIbmYq05lrFsDJ/VR9bIYhZmotkoBQpEs1F2uXYBRd/hn838jD85+ydkjWKk/d1H3s3bxt6GSdS+EemwOAjYA3Wf7xal4y6ZT5Ir5AilQgw6BhnzjhVdM+rM83pz3DdDWlQtNtO4N9NYFb1DowjxrwEfB74ghHgOuADEABdwALgX+DHwng6PUaFQ3EYqheqNxA0C9kDL0cXKKO9cYg6r2QoSHLqjat1ShK5SXExGJ9nj3kM4E15zpGctkbvKMTosjmJXNQECwaXwJRDgtXhJFVJcj11H13TMwtzAvKc2U9EpgvYghjQQQpRvzU/Hptnn3UcqlwKK4nQyMgkSRj2jCCHY0bcDgMXUImaTueFcNNv3WpH3UCrEUmqJk9dPrlrHpbvIF/JcWb5CrpDDrJlJ5BKYhIlh5zDJXJL/+MJ/5O+v/z0A/X39PHH8CQ76D9acB5Mw4bf5cerOtU1gB6gUtTudOwnYAwSSAdy6G0Ma9Jn66s6zcjNQKLYWdS/NpZTnpJSPAXcCfw6kgCCQBL4GjEspH5dSvtSVkSoUiq6zMt80ko0wHZ0mno2Xl2mUY1mZzxfPxpFSkjbSDDmGqtatFBeVObThTLhmK+S1jLlZjmzlGAf7BonmokQzUaSUIIrRT3+fnwO+A5iEieduPMeVyBVsJlvZU7cVYtkYx3YcI56Nk8glKMgCWSNLOB3mtSOvrcqVzRpZjgSOVInGoD3IgGOg4Vy0su8rcywXkgu8tPgSAXug5jqj7lGSuSQO3YFJmMqNN149+GpuJG/wgZ98oCyGHxx+kM8+/Nm6YthmtjHkHOoJMQzUPO6CfUGcurPpMbddWjwrFNuFpjnEN1s3/3kXxqJQKHqMlVEwn81HIpdgLjHHmF7Mr22UY1mZz1eQBQqywH7f/rIgKq3bTquktUbuKseYyWc45CumQbwYepH+vn6GnEPMJeaIZqLl6OYh/yFiuRhziTkimUhLQt2lu7Carbxm52s4t3iOpdQSfeY+/tnuf8YudzH1oDQ+l+4iW6jOVGslZ7iVfV+ZY7mUWuJI4Eg5P7lynfFA0c1ixDXCTHwGszCzy7WLIccQP7n2E74y8RXyhTwWk4XfuPM3eNMdb0KI1aFzIQReq7fn8jg3ctwpNwOFYmuhiuoUCkVdVgqGIccQF5cuspReKkd7m+WylgRYKS1CM2nldUPJEG7dzSuRV9BNOqOe0VVieaNjhmqRUy+lYKVYdurOcle4QQa5sHiBAoVbYlUWo6et3iIv7b/b5ubh3Q9XddCqt2xp7K3mDLcq8Cr39+T1kzXXWUguMJOYwSgYxaK5m1HfWDbG557/HE/PPg3ATudOnjj+BHu9tU2HLJqFoD1YZcnWK2xE1Ha7kY5Coegst7eaQaFQ1KR06/vk9ZMt25R1gpJgKOHUnYy4R/DonjXbYK200MrmixFQ3awz5h0jno8zEZogloltyCpp5ZjhlshZSzpFZWqBw+LAZXUhENjMNiwmC2O+MQL2QMu3yNdiIbZeu7FG+76WdRbTi+QKOYyCUfX4S4sv8f4T7y+L4Ud2PcJnHv5MXTHs1J0MO4Z7UgzDxiy6umUJp1AouoOQUnVnXsnx48flqVOnbvcwFNuUykKfyshTO062rTSgqOX00ImxlIRppbPCZGSSrJHlWP+xdXeCazR/U9GpqtcEWEgusJRaYsAxULND3MqCwkrrs1JzjF4polrPsVO5jm7SmU/Os5xZ5qDvYFlIF2SBvzj/F3zrwrcoUCinSDy699Ga29SERsAeoM/S17F9bReqPbhCsa2oWw6tBHENlCBW3E5WCkVoj/CqFD55I8+5pXMg4UjgCGbNTCgZAiDYF6wSUyWnh3YLhtKt+sqcUyklkUyEh3Y+tKFt1xM5K18zno1zcekiOZnj/sH7GwrITl6otJP1CLxIJsKl8CWuxa9h02wMO4fLYjicDvNHp/6Is6GzQDFt5h2H3oFbd1eJ5hJ2s52gPYhmqt2EQ6FQKG4jdQVxyznEQog3AHcDVeXBUsp/u/5xKRSKlbSzwKySZg0oopkoElku8CoJ8pLTQ7vpZFHSypzgkpi9snwFXdPZ49mDU3cym5hFmAR+i7/sMgC1C/A2YvjfzSjkejxYNaER7AsSsAeqHj+9cJpPnfoU4UwYgAeGHuCtY29F13QyRoaZ+Ew5t1gIgd/mV0VlCoViU9KSIBZC/DHwq8AJirZrJVR4WaFoM50Sio0aUADkCrlV67RDiNejW0VJkUyEp2aeIpqJkjEyXI1eJZQKcd/AfYTTYSzCwqBjsLx8vX1er6hdbwOHbiClZCm9tCoP2igYxRSJl7+FRGLVrDx+8HHu7r+7vIzVZC2vZ9WsBO3BVa2XFUVUWoZC0fu0GiH+F8AxKeV0JwejUCg6JxTrNaBwWBwAWEwW5Ipr3E7aSHWrxepEaIKZ+Axuq7vcJe5q9CpnQ2cJ2AIE7IEqX9xa+7wRUdurDRxyhRyhZIiMUd1+OpQK8clTn2RicQKA/d79PLb/Mdw2d9VymUIGh8WBx+rBa/XWtFtT9PYFkUKhuEWrLhMhYLmTA1EoFEU6Vb1erwHFQN8AqXwKt9WNx+pZV8X9eint653BO4Gi92+7XTUuL18uegBrVgQCr83LAf8BbJqN142+DpMwNd3neo1DpqJTTV+/Fxs4JHNJ5uJzq8Twz+d+zu+e+N2yGH7b2Nv4xGs/wdEdR0nn08XlJWSMDDkjxz399+Cz+ZQYbsBGjh2FQtE9Wo0Qfwr4uhDiD4EblU9IKa+0fVQKxTZnPXmgrW5zZQOKUovaB4YfAOh4xHbl7eNKJ4tORNDqiTUhRMtR6q3UwGE5vcxypjq+kSvk+NrE1/jO5e8AxTF/4N4P8KrBVwFg0S0c9B1kJj5DLBsj2BfkaPAoPpuv6+OvZDOkInSqJkChULSXVgXxF27+fvOKxyWgSokVinXS7RN6K0K7k7fxa90+PjF9glH3aNtTCkpzm8qnmIpMscuzC6/uJWtkieVi5QuCVuZkKzRwMAoGC6kF0vlqz+G5xBxPPvMkF5cvAjAeGOcjxz9C0B6sWs6luzgSOILf7i+n2dxONksqQq9dECkUitq0lDIhpTTV+VFiWKFYJ2tpEtGtsXS6EUit28eGNAinw1XLbTSloHJujwaP4rP7mI5OM5+cx5AGw45hxoPjLW+vnQ0csvksmtA6kh5Sj4yRYSYxs0oM/8P1f+D9J97PxeWLCASPH3yc33/o91eJYQCb2caQc6gnxDBsnlSEjRw7CoWie6jWzQrFbaJXiq26GWmrdfvYZ/WVbb1KbDSCVjW3Zrhv4L5y04/DgcNrisSXIs3JfJKF5AJO3cmgY3BN6SQlUVzLy7jTUc1oNko4HabScz5jZPjTs3/KDyZ/ABTfgw8f/zDHdhxbtb4QAq/VW7NZye1IU4hkIkyEJvjh1A+xaTZG3CPs8+7DqTt7MhWhW8WjCoViY9QVxEKIH0gpf+nm3z+ljsWalPK1HRqbQrGl6XZuYT0h001hXuv2sdfmJZqNksqn2pZSsHJunbqT8eA4kUxkTftUKWB3OneWx7ZeEdjNuS7IAoupRRK5RNXj07FpnnzmSSajkwDc038PH7z3gzXzgXVNJ2gPomt6cd3oNCemT2BIA5/VR76QL89pNwReyUJvJj6DW3eTK+SYikyRyCW4a8ddaCatJ1MROlEToFAo2kujCPHXKv7+004PRKHYbnQzt7BRFLibwnzUPcpTM08RyUTIF/KYTWY8Vg+P7HqEcCZcFUGDYte+2cQsiWyiHJltRYy2a27bLWC7Ndc5I8d8ap6ccctbWkrJ9175Hn/24p+RK+QwYeJXD/4q7zz0Tkxidfac2+rGZ73lIBHJRDgxfQLNpJVzsa/FrjHiGunaXY2p6BTRTBS31Y1Dd3Ateg2LZim3/R52Dnc9N1uhUGwN6gpiKeU3Kv7+aneGo1BsH7pZbNVI2K1HPDa6bd7KLXVxs3tm6bfb6i53yCtt48zCGQqywEJiAWESJBNJdE1vKSLZrrltl4AtzckrkVfQTTqjntGy93G7L4ISuQSLqUUKslB+bD45z+ee+xynQ6cB8Oge3nn4nQz0DRSbtFS8vmbSCNqDVccDFI8VQxp4LV4EAqtmBYqtnc2m7mTfxbIxcoUcLosLgWDEPUIoGWIptUTWyPZcQZ1Codg8tOpDjBBiQAjxy0KIXxdC/KvSTycHp1BsZTrlN1yLRl64ay36aVQM2KxQcCo6RbAvyPiOce4euJvxHeME+4KrCqFKAn45s4zNYsOtu7GZbSxnllsqnNro3Jb245XIK5wLnSt384O1C9jKORnzjhHPx5kITRDLxOrO9XqKHEtd5xaSC1Vi+GzoLE/8/RNlMXyH+w7euu+tjDhHsJltzMRnyss6LA52OneuEsNQPIZ8Vl+xoctNdE0nnAl3LU3BpbuwmCzlMfSZ+xjoG+CA/wDH+o8pMaxQKNZNq62b3wb8F+AiMA5MAHcC/wD8WcdGp1BscbqVW9goCrzWop9G0WagYYpBqxHX0nKJXKIcSdU1nVgu1nKEdr1zW5leMuYd49zSOSZCExwJHMGsmdccaV5Z4DceGGcyMsnF8EWO9R9bNdfrKXLMF/KEUqEqFwkpJd9/5fv86dk/JS/zmISJ1+58Lff031PsUpcKsdu1m1g2hkmY8Nv8VR37VuLSXeQKOa7FrgG33g9NaF1zTBh1jzITn2EmPlMW4bFcjGHHsHJtUCgUG6LV+1z/Hvh1KeV/E0KEpZT3CCF+naI4VigUbaLVCv61Vvo3SyFYi3hsJmobPddqekZpuVKLaatmJWtkcVgcHfdwXauAbcZaC/zWmrecyqcIJUMY0ig/Fs/G+dzzn+Op2acAcOtuXj34akbdowghsGgWEtkEmUIGn83HkHMIi8nScD9G3aNEMhFGXCMsp5dZTC+iCY3jA8e75jrhsXp4YPgBJkITXF6+jBCCQ75DjAfHVXRYoVBsCFFpxVN3ISGiUkr3zb/DUkqfEMIEzEkp+zs9yG5z/PhxeerUqds9DMU2o5YlVyqXWhUZbHW5Wttvh3Ap3f6vFLWpfArdVHQiqPdcPduxRvtYkAWmo9MIk0AWJCPuETShbTi1pFa3vHAmTCwb45XIK4x5x3BZb4luKSWRTISHdj605tdqNF+1BO7J6yfxWD1VHfbqvX4kE1nl4Xx+6Tx/9MwfMZ+aB2C/dz8PjzxMQRS4Fr3GLtcurJoVieQOzx08MPzAmi3oanUZXMuxqNj83G77PYVindTtM9+qIL4EPCSlvCGEeB74bSAEPC2lDLRtmD2CEsSK20GrwmmtAqvdNBK1pfE1EkhrjYKvdJmoFK/rORGvHP9iapFzi+c4HDhM0B4s5gzn44wHxsspBKX5HXWPrlkErPUCppX31ygYhFIhUvlUeZmCLPCdS9/ha+e+hiENNKHx2p2v5YGhBwilQ+iaTiqfYim1hEt38arBV3H3wN0bEjG3+1hU3B7We1GuUPQAdQVxqykTXwL+KfBt4DPACaAAfGrDQ1MoFEDrjgbd9i9eSb2cY6DcKnkhuYBDdzDkGFqVYtBqekZpucpla+XXPjXzFG7djUS2JFJXpiQsZ5ZxW90sZ5bZ0beDUc8oE6EJJiOTjAfHyyf7AffAuhqYrDVHu1l6S8bIsJBcIF/IV83Lp5/9NM/NPwfAQN8Ar9n5Ggb6BgilQ+yw7yCRT6AJjWHnMI8ferym7/Baud3HouL20CtNhRSKdtKSIJZSfqLi768JIX4COKSUL3VqYArFdmOt+bXNluvkLc2VorZSqA47hzfcwKIeK0/ERsFgJj5DxBIpi9dmInWliEvkEjgtTuK5opOEU3dyJHCEi+GLVQJ2IyKg2UXAyvdqj3vPKl9mj9VDLBtjKb1U1XXu7MJZPvnsJ1lKLwFw/+B0oyVFAAAgAElEQVT9vH3/25lNzGIYBrqmk8gn2OPZg1Wz4tbdbRHD0F0vbUXvoC6EFFuRdZlHSimvtnsgCsV2p1Xv3HrLlSKYsWwMgSCajRLsC3a8HTN0L2K08kQ8l5jDpbvIFrIIIWq+7kqxKRAsphZZziyTyCVYSi2RzCUJ9gXL2zVrZo71H6sae6dEQK2o92R0suq9klISSoWq7N8MafDN89/kmxe+SYECdrOdR+94lAeHHwQBO+w7mIpOoZt0skYWp8VJ1si21Y2hm17ait5BXQgptiINBbEQ4vXAm6SUH7z5fwrQKxZ5g5TyRx0cn0KxbWj11nqt5QbcA+XiJo/Vw0RogkQugc/mQ5hXC8V2R48rxWI8Gy/n/UpkW6PEK0/EiVwCi8mCw+IoL1MpUmuJzbnEHFPRKfod/TgtTpK5JJeWLzHsHEZKWVfUdUIERDIRfjT1IyLZSNHtwTEEwEx8hsnIJMf6j7HTuZOMkany/11MLfKpZz/F2dBZAMa8Y3z0+EeJZWNkChmsmpU+Sx973HtYSi+hCQ272c4h/6G2XhCtNR1EsTVQF0KKrUjDojohxPeAz0op/+7m/8vAfTeffgB4p5Ty0Y6PssuoojrFZmNlcdML8y+gm3R0TWfMNwbcciq4M3hn2wtiSq9vFAwuhi9i02xIJFJKhpxDbYtMryzmaVQAd9eOu2oWfU0sTJDMJ/HavCRyCRwWBxaThUw+w4BjoO4FQrsLiUrbu7R8Cb/NT87IEc4UHSO81mJr5FHPKDcSNzjgO1AW3qdunOIzz36GaDYKwFv2vYX3HHkPFs1CLBvjQvgCNrMNh9mBzWIjb+RVsZOi7SiXCcUmZd1FdXcBP674X0opLwMIIaaAP9j42BQKxUZZeTvfYXGQM3IkconyY6Vo5kbSG+rZlc0mZrmRuEFBForRWgHZfJYx3xiaSVtX6kS9E25lRHLENUI0G0UzaTWju6V5iWfjzCXmSOQSXI9dx2fzsd93K5rViq1au6OhE6EJZuOzhJIhIpkIQ86hYlRdSLxWL0IIUvkUVrOVmfgM+7z7+PNzf85fXforAJwWJx+49wO8eujV5W26dBcHfQdZziwjEDjMDkb93REqSiBtL7rVVEih6BbNBLF3xf97K/4WgL+9w1EoFOth5e38IccQE4sTOM3OVULxxdCLq4RK3shzfvF8QzGzMv0glApx8vpJjgSOsNO5E5tm48TVE4y4RtjRt4Ndvl04dWdZbLZCSVTNJeaYS8wx6h4lYA+syoFeWdA3FZ1iJj5DPBvHoTuYik4x6h7FpbtYTC1yLXYNq9lajiLfSN0gno2X/2819aFeMeFaRWAkE+Fs6Cx+m5/d7t1ciVzhyvIVCoViy+VQMsSwaxgAq8nK1ehVvnT2S1wIXwDgsP8wHzn+Efr7qm3gNZPGPu8++ix9TcdQb1zrEbWtdtdTolmhUPQqzQTxdeBu4BSAlLLSAf4eYKZD41IoFHWoJSpW5vSl8ilyRo7lwjLP33iefd59ZXFSEomlojKBIJwOE7Q3LsBrZFcW7AsS7AtyJHAEiSynaUDrYrNSVCVzSTSTxrXYNWxmW1m41oo0e6yechc1v91fTmc4s3CGPe49vDD/AppJKxaX5bN4bV5yRm6Vrdpa8x/X02K5ci69Vi8CQZ+lj73evczF53hl+RV8Nh/DruGyqH12/lm+/fK3SRtpBILHDjzGvzz0L9FMWtU2+yx9BGyBVY+3ui8ToQnOLpzFZ/Ox272bbCG7pv1pdteh0XyVllVCubuoCxSF4hamJs9/A/iCEGKo8kEhxDDwx8B/6dTAFArFakqiIlvI4rF6yqIF4K4dd6GbdGbiM1yNXuWQ/xAPDj/I4eDhqra+PquPc4vniOfiOCwOpmPTzCRmCNqDZacGu8XOVHSq6rVj2Rg2zVb+v2RXVpmWsdu9m+XMMql8CiklqXyqbL/WjEpRlcgncFlcWM1W5hJzQFHox7KxputW7kM4E2bQMYjD7CCWi2HWzNy14y7uHbiXrJElkomU841rdco7ef0kZxbO1Ixw13vNlfNWi1g2xqh7lLSRJpPPYNfs+Gw+/DY/e7170UwaOSPHNy98k6+/9HXSRhqv1cu/e/Df8e4j764SvUIIAvYA/X396xbDZxbOMB2bxm/3YzKZuLx8GaNgrGl/Ko8NWP1+1ZuvidBEzWNaWXh1lnrfJWreFduVZhHiPwSOA5eEEP8IzAFDwP0Um3OoHGKFoos0i8SVom1+u7/uMuFMmMOBwyxnlonn4ggh2O/dTywXY4ABYLWdWCQT4UbiBi+HX8Zv8xdFpsVBPBfHaXGWlzNrZo4Gj6Kb9DXn2VbmQTssDrJGFt2sl63GGnktPz3zNP19/Qw5h8rR5NI+DDoGa3ZTW2mrVrnNViK/G7FhK1nF7fft53r8OjOJGQqFAkd3HGXMN8YL8y/wtXNfYy5ZvBg4tuMYH77vw6v8g62alaA9iEWzNH3NepSOqbzM4zK7yi2j5xJz7PPua3l/mjlw1Juv84vnORQ4pJo8dBnVXEOhqKahIJZS5oC3CCF+EXgdEAT+EfgDKeX/6sL4FApFBa2IsGbLxLIxgvYgO/p2AI0L8OCWQPTb/SRzSeK5OBeXLuK3+5nNzDLiHKnKU17ZpnkiNMHl5csIIdjr2ct4cLymQK4UVS7dxbNzz5LJZ/BavYSSIUzCVJXWUClc+/v6SeQTXApfKqdrTEWmyBay7PXsLXsyt2IR1apQ2IgNWynFxayZ8Vl96JpOOp9mzDfGM3PP8IXTXyBtpDFh4l8c/hc8duAxNFEd/fVYPeXiu41QOl5KFyFWsxVdK16IrHV/oLYNV+UFVclezqk7SRtppJQ1o8sqUtlZVHMNhaKaVjvV/RD4YYfHolAomtBIhJWipa9EXkE36Yx6RmsWja2lAA+qBaLdbGc2MUs4HSadT/PmvW+u2VENiiLoqZmnmInPlF/7fPg80WyUB4YfWHUyLomqeDbObGwWv93PUnIJs2ZmKjrFI7seqZvTPOQc4mL4IkIILocvkzbSIOCI/whmzQzZoutFJp9pGrVuVShsxIvVY/Wwz7uPF0MvFgsBLQ4GXAN8+cUv86OrRWv3oD3IR45/hPHAeNW6ZpOZoD2IzWwrz/NG8kBLx8OQoziHABKJWZjXtD/1HDhKFy4Be4BkLkkil+Di0kVG3CNoolgEqJo8dB/VXEOhqKahD/F2RfkQK3qVel64e9x7yo058kaec0vnQMKRQFEQVkZua20jlAzh1t3Ec3Hi2TgmYaIgCzh1J/PJeca8Y7ist06UrdiUnVk4w/nF85hMJoyCwWJqkWg2isVk4YHhB3hw54M196/UqKKUmuHUnVXewiVOXj+Jx+opR0jj2Tiz8VlOz5/mUOAQezx7anoTN6OWd3G99dcjRqWULKYXq7rOvRJ5hSefeZJr8WsAvGrwVbz/3vfj1t1V6zp1J36bH5MwlV9/o97IldvIG3mmolMsZ5Y5GjxaN5q/Firns2R/t5RewqN7eN3o68rL1NsHVfjVGdrtq61QbBLW7UOsUCh6iHqRuInQBDPxGfIyj8PiYNQ9SigZ4mL4Isf6j1VFRFduQyDKYnguMUfQFuR6/Hq5OM5ishDJRDg+eHxNNmWxbIxcIUc2n+X84nkKFOjT+kib0pxdOFtTbHmsHgYcAxzwH6hKBagVoV0Z4XLqTkbcI8RzccaD403Xr8fKyG8oFeJq9CoDjoHy8yvnslVyhRwLyYVy1zkpJT+Y/AFfOvslcoUcZmHmPePv4S373lI1fk1oBOyBVXZq7cgDrTweMvkMhwOHy/u4Xlu5SmpF3HWTXk7RaSW6vB4nj2Zsd6GtugwqFNUoQaxQbDJqeeGeXTiL3+7HZXaRNbLMxmcZ845hSKOmMCpto8rqLJkka2T5X5P/i3QhTcAewK27kVIym5jlpcWXOD54vKFArMSlu8gX8ry48CIWswWn5iSdS5PJZdjj3lNXtLV6K7ckXBPZYrONqegUGSPDiGuEUCpUzpGuXL8VEVQpFGbiMw39kNfCbHyWs6Gz5RQJr9XLVya+wsmZkwAM9g3yxP1PVDUMAbCZbQTtQcym1V/X62mZXa+5yso5WYsYbTSvpffTKBhcCl8q5ygXZKGut3SJThV+dVJobyZUcw2F4hZKECsUm5yp6BQ+mw8hBEIIrGYrAOeXzuPW3Zy8fhJx8y5RKSXCoTsYcgwRz8bLgmMxvchyeplINoLNbEMgWEot4bK5OOA9wGxidk0CcdQ9ylMzT5HMJwmYA+QLefLk8egedJNeZclVKagEoqUiOI/Vwx73Hr7/yve5Fr+G3+pnp2sn8Wyc5288zz0D9xC0B8vrD7gHWhZBlUKhkWNHK0gpmYpOcerGKWxmGy7dxaXIJb7x0jdYziwD8Jqdr+F37v6dYpe/mwgh8Nl8q9ImKqkUm6WW2bqmI6Ws2xjjqZmniGai5Ao58oU8S+kl7h24d9X72aoYbSYuSxcus/FZdE0HCRkjw37f/qZdDDtV+KUcFhQKxUpaEsRCiDuA36fYpMNZ+ZyUcncHxqVQKFoklo2x272by8uXAdC14u3oC0sX+KU7fglNaJxbPEfaSGMRFvr0PpKJZNny6lj/MTBDKpcCUVw/L/NYNAu5Qq7ok2uxs9O1k0HHYMsC0WP1sMezh2g6yo3kDSyahSHHEDudO0nmk+WI7URogrOhs3itXkbdo2sqggtnwnitXgK2QPlCoM/SRzwbZym1hMVkKa+/HhE0m5gllUuRzCdxWBxlu7lWBVm+kCeUCnFh6QI2sw3dpPOTaz/he1e+hyENzCYzv3XXb/GG0TeUUyRi2RjzqXnMwkwkE2l4K78kNmfiM1g1a9OW2aXUGrfVjcvi4sryFWLZGDPxGYJ9wao5aVWMNpvXyoi7QODQHex372+pi2Gl4C+13jYLMyOukZbmvx7KYUGhUKyk1QjxN4DLwIeBZOeGo1BsP9rhEpAtFEXQXGKOheQCF8MXsWpWwpkwqVwKt9VNNBolIzIMOgfJGBmWM8v4bD6molPcueNO7BY7KSNFn6WPaCZKMp9ESokmNGK5GId8h9YkJCKZCIlsAofVQb/oZ9A5iFf3EsvF0ISGz+orRw79Nj8CwdnQWZyWoh1XMpfkdaOvazgXsWyMfCGPXb+VXqFrOhaThQHHQFXR31pFUMkqTDNpxTk2slwKX2LENbLKD7gW6XyahdQCRsEodgQUgj+f+PNiwSPQ39fPY2OP8cY9b6waY6k7Xzgd5krkCi/Mv8Ajux5hl3vXqtcoic3JyCQmYcKpO2u2zC4dYz+c+iFu3Y1DdyAQGNLArbu5FrvGXf13Vc2JS3cRSoXKHQ1LaR5+m3/Ve9BsXkvjXFms2CwXvXSXoeRUYjFZiOViRLNRIpnIutMblMOCQqFYSauCeBx4SEpZ6ORgFIrtRjtyGUtRQrvFjtPs5MX4i8wn59lh38Hp+dOE02Hu7r+bvMzDTVOZks/sXs9eTi+cJpVPEbAFkEjS+TRmkxkTJnSLjtPiZNgxzHhwnKnoVEMhURJes4lZbiRuFPOQLW50TWcuNkfCWhRWj+x6hHAmXG4I4bQUnSQWk4skLAnu8NzBUnqp6Vy4dBdmk7nsnwuQNbLlyPDKZdcigqaiU+x27+Za7BrZfBZd08mIDFPRKe7uv7vhexLJRFjOLFNy8ZlLzPH1818nmo0CRReJR/c+isN8K0XCbDKTMYrR+Guxa9g0GwFbgFguxonpE+U5W3nh5LF6ONZ/rK7YXOkmkCvkuBa9xoh7BLtmL4v1lev5rD5OXj+J2+rGaXESzxVdPN68983rmtf12NR5rB7cupuIJUK2kMVhcTDuHm+aatHofZmKTjGXmFuV+lMay3YvtlMotiutCuK/B+4Bnu3gWBSKnqFbJ8Wp6BQFWeB67HpVFK6Vk33lGDWhEU6F+dn1n2EymXBb3BQokMqnMAkTZxfOsqNvB1azlWQ+yWx8lpxRdDXY69mLbtKxW+zoWZ3XjLwGm2bjavQq4XSYvd69OCwOXgy92DC/t1J4pXIpNJPGTHwGkzCxlFoiJ3O4dTdv3vdmPFYPV69frWoIsZhaLAvknJHDZ/OVWwfXm4tR9ygz8RmuRK6QyWdIG2kKssBh/+FyBLqyeGwyOgm0JshKDUxK3ssLqQWWkkssZ5b5G/E3NZuMFGSBxdQiiVyiHO3928m/5SfTP0Ei0U06jx14jKM7jpLOpxl2DgNF39/JyCQ/vvpj/n/23jTIrvO87/y9Z7373ns3GgTQJBYCFAmRoUgpMkWP4lhjM4oTLR47nlQmiZ0p7y7XVE3NjMep+TC241RSGc3YGU88lqVRRpIVy3Ji2cOiJYUiJS4iATYAohsgGt3o9e77Pcs98+HgHt7uvrcXoBtoAOfHYqH73rO8Z+l7/+d5n+f/VI0qqWCKhxIPIYQgqkW5UbnBS/MvcTJz0ntwemXxFbfoEWfL69Kd0jAeG2euNIcqq57V3lJ9iaPxo5s8qOfKc5xMn/QixBE1wnhknEKrwATvR6t3KnRv1dXAwdnkGrJdqkUvuu/P0cgomqx5D3gj4RFvvH6xnY/Pg8lOBfE14C+EEF/Dbd/s4TjO/7jXg/LxuZvcyQr0pdoSa7U1AmqAiBbBsA3my/M07eaWgrjXGGeLswyGB2k7bTddolnAwUGX9XXT3u/m3kWR3DzMqlUl5sSYjE16rhMdkX08fdwTkZqieWKnX35vZzxBJUjdqqMIhWw9S1AJcjx1nJbdIt/Ie8fQiSwOh4eZLcxSbpUJKAFkZJp2k6nY1LZ5nXE9zqn0Ka6XrlOySshCZiQyAsB0bppMKIMsZC7mLlJsFTkSP4Is5B016OiML6JFGGGEtfoaDbtBJpRBl/VNTUZM22S1sYppm1SMCq+vvM6fzv4pV0pubvdgcJDPPPIZ0sE0qlCZTE6S0BMoksLba2+zWHPTAgrNAtl6Ftux3VxgIWPZFrZie1FYu22zWFuk3CpzMnOSXCPH9dJ15ivz4Lh51AOhAS8a2hHeRxNHqZk1L8c6E8zw+MDjDIeHN4nUilEhHUyTCWW8c9JLiPYTukBPy7bdRnX3Kr1hY67zQGiAiBZZ5y/dfQ+DX2zn4/MgsVNBHAa+AahAdyKb39XD577jTlag14waQhJuQRSgyzotu0XNqG25Xq8x2o6NZVvUrBrxQBxN1ig0C26UN3aEeMBtYhFRI8QDcWJ6jJHwyLrp5247trnyHH89/9dossbh+GGEIggqQTKhTM8mFd25pGE1zHul9wgpISwszwGjk7N8ZuDMulSPo4mjrNRWyDfzHE8d52jiKBEtwlp9jXwjz8s3Xu4bqZ+vzBMPxAnrYcKq654xV3LFWTKQZLY4S0AOkAqkKDQLBJTAjh5uuiOfS9Ulis0iqqwyGh31XDg65+lo4ii5Ro72zayyl66/xBff/aLntfvhsQ/z8cMfJySHeCT1iHfNMsEM07lpSq0SMTVGWA2zUlvBxqZuupH8geCA1+K5w1JtiagaxWy7LbcXKguE9TDCETTtJq12i5gWw2gbLNeW0WTNE4CdnGPDNtZ5Dm9kN0K0lxXgXj1U3k5HwG72ou25j4/P/ctOWzf/w/0eiI/PQeFOfilGtAj1Wp2W1UKTNQzbwGk7RIKRLdfrNcaknqRpNZFsibpZJ6SEiKpRwkqYJ0efJBVIeettNf3cLWYkISEJiZnCDFNJ1xmg37kQCC5kL2A5FgJBrpEjpsUIqSFadouW1eJo4qhnt7axIcTTo0+vm/Zfq69xMXeRk+mTfUVVLw/mmcIMTavppToE5AC6ouPgUDWq2I7Ni3MvMhQe2jIdpnt8q/VVAI7EjxBS3OYYmqxRbpW5UbnhWaNZbYsvXPwCX5n5CuCK3k8/8mlXLDrudZOERDKQ9IRld2GgEILjmeNcyF6g3CoT1+KMR8eZK8+RCCS8sdXMGpqkEVbDLNeWXW9fSeNy4TKH44fBgeX6MlPJKSZjk8yV57xrJ0syo5HRbcXp7QjRvXyo3KsGEjsR+H6xnY/Pg8uOfYiFEFPAZ4Ex4Abw/ziOM7NfA/PxuVvcyS/F4fAwmqxRbBWpmBXCapjx0PimSv6djDERSFA2ykxGJzmfPU/WyKIrOqcGTiEL2RNG2x1bd17zYnURVVZJBVMs1ZaY0qZ6notSq0TZKFO1qkRV9z0Jiarp5sOatokQgov5i8S1uOcQ0Cuy2BE++Uaek+mT3pR9L1HVz4N5tbZKVItSM2vIyKyWVykbZRShUDNqKLLCw6mHt41cdo+v04a6Q8eObUC4DUBW66v89uu/zaX8JQAmohP8zKmf8a5lq90iEUgwEh5BldV117K7MDAdSHM6c5q1+hoDoQGSgSRH4ke4Vr5Gw2oQkAMoQqFiVDiVOcWV4hU33cYycBzH8/qtmO6DR6doTJO0XQnK2xGie/1QuRcNJHYi8PcqGu3j43PvsVMf4h8DvoCbNjEHPAK8LoT4acdxvr6P4/PxuePcyS/FydgkpVaJ8ej4un1NxiZ3PUZZyJ4TgSzL1IwaES3CcHjYi4Lu5Ni685oPxQ5xtXSVhfICg6FBxqPjPc/FXHmOTChDMpB0u6WZNY4kjtC0mxxNHGWhvICQBKpQSQfTfZtGdBcyVs0q6WB63X56TXFv9GB2cJAlmbgeZ7m2zOXSZZpWE8MykCWZolHkVNot0tpp5LJTvNex/zJsg5XGCgOBAUYjo7yy+Ar/6gf/ykuR+LEjP8ZjA4+5jTYcVwzLQubs0Nl1YnjdtmuLRHEfJsy2ycnMSS8/GSCmxzxxOhGdoGyUkSWZkBKiYlRwHIfx6LjbFtrBa/LRKRq7FUF5q0L0IEZaN3YgrBpVJCHx4tyL6/5O/HbGPj4PJqJjC7TlQkKcB37BcZyXul77IeDfOI7z6P4N7+7wwQ9+0Hn99dfv9jB8bpPbcYq4k9ZLt7qv/VrvTy7/CQ274aUB1K0610vXcRyHHznyIz338/KNl4nrcWpmzWug0EktaDttSkaJVCDFcHiYiOZarHXnIW+0BmvaTS5kLzAZm1xX1LVxvXNr5zDaRs/GDacyp/jyu1/m+0vfJxVMMRQa4nrlOqZl8vjw4zw9+jTwfspIt2dxv/M2nZ3m/Np5Wu0WE5EJJmOTfHnmy3zj6jcAiGtxfvnsL3N26KzX8KJpNxmLjDGV7C+sOtu+WrqK4zgcTRzd5GDR7zp2LO4OxQ4RkANcyF0AASdTJ1FkhYbZuCMuCTvpNngQ3Bo691rbaTNfnkdIAqftMB4bRxbygRijj4/PviH6vrFDQVwABhzHsbpeU4Cs4ziJ/mvem/iC+N6nl8A6KF/Id4OdiGevkO76X7vuDzcbaRSNIsvVZQJygB869EM91z23do5Cs8BCZcHNZ5U1VmurZBuuy8RkbJKRyAgRzc2N3ihCO8K2O6K4Vl/jevk6JzMn+17D7a7zH1/4Y9pOm6pZpWE1KLfKaEJDVVQ+cfQTwGaR3Q+7bbPWWKNpNQFYrC7yW6/9lucicSZzhl85+yvrotoRLUI6kF6Xs70fdPvrrtXXqJt1gmqwpzXcfuy3W5R32mV3bN0cnAPl59u5125UbmC2TbeQ1WqhyO6D1E7uBR8fn3uWvh/GO80hfgu3S93/2vXar9x83cfnwHEnnSIOOjup+O9e5lDsENlmluXKMmWtTN2okwqlyAQyGG1j3brdQuzNlTfJBDNEtSjFVpEb1RuMRcdomA1qVo3ZwizHkseIaJFN0+e9ck4zwQyGbWyZ+7pVnmupVeJG5QZtp0084BanEYUrhSuYbXOT7+5WdHedA/jr+b/mc29/zvV5RuIzxz/Dpx75FLKQAZCFTDqYJqSG9uQabkcnHabUKvFI+pF1Dwf7RS/f6YXKAkElSESL9HUjudt07rWaWfMe0DRZo2JWfEeJA4zfMMVnv9mpIP454M+EEL8IzONar9WBH9uvgfn43A47Keo56B+wezW+fg8H09lpIlqEilFhpbaCrujkmjlyzRz5ep5UKEXDbDAWGwMHRiIj6x4sum3TRiOjzBZnyTfyCCGom3WOxF2rt7X6Go7jIIRgqbrEeGxzHnK/nNOIurXbBvTOc+2ItaHwEEuVJRpWg/nSPEPhIRJ6whPMO8kR7e4617Sa/P753+ev5v4KgFQgxa998Nc4nTntLd+xU5Mledux7+U9eKcfArv3V7fqbm61ZXjFlwdVXHbutU5DGF3WMWy3C97dznP26c2d9Ib3eXDZqe3aJSHECeBDwAiwCHzPcRxzPwfn43OrbFfUc9A/YPdyfL0eDizb4vzaec6OnCWux3l77W3Wams8lHyIgeAAqqyyXHWn3yeiE+vSHTpCZ6MAG4uMUdWrhNUwASVAVHWLzzLBDMPhYZaqS6zWVzmSOLJJhPYq9svWswBoirbrc9AZ24n0Ccy2SdWs0mw3WauvbSpW60fbaZNtZFmprXjd8L4681VW6isAfHDog/zSE7/kbafjs9zJvd6Ovb4H77SH7kbfacM2vEgr3P0iun507rWEnmC+PE/Lbrk5xKHeBaM+dx9/xs/nTrBj27Wb+cPf2cex+PjsGdu5KdzJD9hbiQLu5fh6PRx07Mo6r7XbbXRFp2JUSOpJknoSWcgE5ADjsfGeDxYbBdhweJiZ/AyFZoGknnSFkQMTyQkiWoTx2DhHEkc8+7deHcy6Ux9iWgxN0W7pHHh+y4rgzMAZ1/nCqOHg7EgMG7bBan2VQrPApfwlzmfP82dX/wyrbSEJic888hk+/cinqZk13s2/S8tuMR4dJ6HvvKSi+xpXjSpLtSUKzQJr9TWen3x+T3x2c40cuUZuy8Ymt0r3/jrdBluiRVgJ07AaB1Zcdt9rTbvpurEEI6QCqQNbePugcz80TPHvl4OP1O8NIcTFrp/nhRDXe/1/Z4bp47M7Ol96nfzTTi5j5wOoYrj5gt0E5IDXNGKv6McjPekAACAASURBVEQBjbZBXI97ObjbfZDv5fgmY5M0zAYNq4HjODSsBsVWkXQwzUxhhlcXX2W2MMt8aZ73Su9RN+te1OxQ7NCmdTu2cB1B1CGiRZiITRDX4oTUEHbbZjw6Tlh9XyAl9WTf89G5Zs+OPcuZgTM4OLd8DrrHFtEiTCWneCT9yI6irxWjwlJtCattcbV4lf9w5T/wtdmvYbUtUoEU//TMP+WJwSdcMVx4F1VReSj+EG3aO7q23fsJyAGqRpWZwgyW7W6/ZJR2tZ0OG69ztp7lQu4CqWBqV/ferewvrIYZj45jt21CamjT39tBo3Ov/a3Df4u/+/Df5eOHP76r8d7q37XPrbHxswYO7gxEL/z75d6gr8uEEOLDjuP855s/f7TfBhzH+dY+je2u4btM3P/0cjXYqdvAfu6nE0V4e/Vtr2VyJ1Xhdsa3MTqxUlthvjqPIimsVFfI1rNeY5CYFuNY8hijkVGSgWTfiO52Dg+9IiJz5bkdn4+N565qVJkruet3Wj9vZWP2yuIrlFolrLaFIinE9fiW0WHHccg1c1SNKgCzxVn++Sv/nHwrD8BjA4/xqYc/RVAJUjEqxPQYQTW4Liq8m2vUOb6FygKWbaErbttuVVIZi47d0rXuPucrtRVSwRQDoYFbGt9u9/cgRb3u1OeHj8u97hrk3y8Hit27THTE8E0GHcf58qatCvH3bnNgPj53hTvVfGM3U33dH/pTySku5C4wnZte5yfbGd9uhcjGwrPvGt8FB/KNPJqskQ6lqZfrpINpjiaO4jgOkpDWpTP022a/Jga91tvN+ei+RpZtrfPX3eh20Q9x87NP9P8MBMC0TdYaa27rbMfh61e+zh9O/yGWY6EIhReOvcAzo88ghKBlt0gH00hCIq7d+jRu5/gKzQKpQMprbz2RnLjl6eDuc97xhr7V8cH299ledJC7F7kfpvDvJfaqfffdwr9f7g12mkP8B8AmQQz8PvCVvRuOj8+d4U59wO6kuK87opcOpt1lFTiVOcWl3CW+Pf9tRiIjHE0c9da53WKsjsXU+dJ5NFljKDzEE8NPsFa/KQpx9iX60u98CATn1s71zSu+mL1IWA2vi5hD/3ziTue8idiE91rDavRcvmbWyDVytJ02ZaPMv37zX/O95e8BMBIe4ceP/jiH44cRuGJYkzQ+MPiBHbXB3orO8a3V18g386QCKS/fumE1bns6+Ha7xR30wtO7yUHsxHe/cy8/fPn3y73BloJYCHHk5o+SEOIh1oeajwDNzWv5+Nwb3IkP2K0i0RsFx+XCZepmnYAS8ESfLGQGwgM8PvS4J0hkIe+64K5beNfNOq8uvoqERFSPoss6juO2O3449bDXnGA70bNx/LlGjldvvIqQBAE5sK7bWrdf8XJtmcnYJOlgekduEmcGzrxfJNfV4GKrCMtOIjKO45Bv5r2c5OncNL/z+u+Qbbjj+djEx/jZx34Wq22t6zjXeTCpGlXOZ8+T0BNMxiY3RfF3QlyP8/zk8+umg/eqIO12Z0H8yv7+3Mn27j73Pv79cm+wXYR4FnBwhfCVDe8tA7+xD2Py8dlT7mae41aR6I4I6giNZCDptT4+ph3jSvEKS7UlhBBcKV5hODxMUA1yKXeJDwx+YN1+OmKv17EC3r5kIfP9pe9jtk0CcoCACLBSWyEZTFIv1jkUO8RKdYXTA6e9Qrd+zJXnsB2bhcoCuUaOxeoiLctNJ0hEE1zKX6JslDmVPsW18jXPr1iTNS+6OhIe2ZGbxG4jLNstb7Ut1uprtOwWbafNVy5/hS9c+gJtp40u6/zcYz/H84ee99Z9auQpEnoCIcS6B4HHBh7jevk6b6++zemB07cUPd2v2Yrb3a4/zdufe30K3+fO4t8v9wY7bd38Lcdx+hbW3W/4RXUHi9sRtAexGKO7RbIQgpAaIh1ME9WiLFWWMB2TE6kT/Pl7f05QDnI0cRRZkmlZLY4mjjJTmOFE5sSmAg3DMrAde9OxykL2BOdsYZaLuYsE1SC2bRNQAqzV16gZNdq0eXLkyXXRzq3O019e+0tWa6sElAAr9RWuV65j2zapYIrTA6ddsdluY7ZNFEnBcizCapiR8AiyJHsFJZ1c1+7o78bWzru9jlstr0oq10rXmK/Ms1xb5k+v/CkzxRkADscO8+tP/joTUTfVQpZkBoIDBJT33S62KpDpLkDs5C0ftNbFO8UvBPLx8bkP6VtQ0td2rZsHSQz7HCxu166me9pXCEFQCRJUg8yV5zbt4+UbL++7FU5nX/lm3vVANWsUGgXKRpml6hLJYJK4FmemMENCSzAeGyekhtBlHdux+e6N79KwG1zIXvA6wHWm2IGex3qleMWzL6uZNVRJZaG8wDu5d7hevk5EjRDUgzw68CgBJcDV0lWv5XH3edpI1agiJIGu6DTtJhISiqzQtNxMKk3WKBtlprPTSEIiqkaxbMuzGOukKuzEUmk7G72N9FvecRyuFK9wIX+Bi/mL/N653/PE8A8f+mF+56O/44nhsBpmLDK2TgxDf0u85dqyd6/KQuZS/hKXCpeQhXxP2iz1suvrWO753Pvcyc89H597gR0V1QkhFOCfAR8FMnQpbMdx/ub+DM3nfuF2Iry3m8e43bTvnS4c6hzPQmWB4cgwq7VVhBBUWhUSgQS5Ro4Xjr3AO9l3mEpOMVucpWW1sByLG+UbNNoNnhl7hpbdci3MbIPh8DBTA1O8k32np1ATQnjpAwK3ffLV4lUURSEgByi0CrSsFhk9Q0SLENEiGLbBfHmept3se57DWph6zfUsDsgB2k4bu217QtawDcqtMplgBiEEQrjiuXMeTqRPADvPr9ttznf38nbbZq2xRtNq8m7+Xb5x9Ru8sfoGALqs8xNTP8ETg0+gyzqSkEgFUuuK97rpl45RNaqkgik3El+ZRZEUcs0c3174Ng+nHiahJzbdtwfZtsyf5r1/8QsmfXw2s1OXiX8JfAzXVeJ/Af574OeAL+3TuHzuE273g/d28xi3yyW904VDneOpmTUSWgJd1snWsxSaBcYiY955impRjLbBVHKKpdoSl9YueTm9K/UVhsPDnMycXDd93e9Yj8SPeBHkfCPPbGnWTa2QgtTMGkbbQHIkZouzGI7hrWu3bS9ntpdoGwmPEJADFFtFb191s44qqxSaBay2hSzJPJp5lOXaMuBGjR0ciq2iF2ncb+HVslus1lex2zbXStf43NufY62xBsBQaIjnJp7jROoENbOGLutkQhlUSe27vX4CPqyFvQeSbCNLoVFAlVQkIWHZFgvlBQzb8K7XvSBK7uXKfp/+HOSCyYP8kOhzf7NTQfx3gQ85jnNdCPE/O47zr4QQ3wR+D7+wzmcLbveD93btaraLPt7pwqHO8YTVMIZtEFJCDIWHGI+Oe80YuscdVIMMh4Z5o/0GyUCSo4mjmG2T2cIsRxNHaVmtbY+1c56ns9Ocz54npsbcTnLY2G2bhJog38iTb+ZZra9i2AYBJYAiFG5UbhBUghyKH/JE2yuLrxDTYlTNKsu1ZTLBDHEtzunMaZYqS9jC5mrxKs+OPUtYDaMpGhEtwnJtmapRRREKpzOne/rZdr4M38m+s20O7k6+OMtGmUKzgOM4fG/pe/zuG79L3aoDcHboLB8e/TC2Y7NUXeLM4BmGw8OUjfKOvHc3CvhuG7aG2UBIAiEJQiLkNt1ot7ymH3CwRYnP/c1BLZi8Fx4Sfe5fdiqIQ8D8zZ8bQoiQ4ziXhBCP79O4fO4TbveD93btaraLPu6XP2Q/sdY5noSeYKG8QKvttkgeiA2sO67ucV/KXSKpJcmEM4TUkLeP6+XrHE8f3/GxRrQIk7FJqmYVSZJQJZWV2gpNq4nVttBlHattUbfqWG2LyfgkJaPEW6tvMRYdQygCu22zWFuk3Cq7EWpZ41vz38JxHCJahGOpYxxJHPGK5rqF/dHE0XU2ay/feHmdE0ZHsCf0BJlghrnKHDhwMr25Ecd2X5xtp02ukaNm1jBtkz+c/kO+fvXrAISUEB8Z+whTiSkkIbmpHo7No5lHKRtlzq2dw3Zsis0ilwuXeWv1LZ6beG6dp3GvyOm6e1UJUKlVMCSDh+IPea2ww8Gwt/xBFSU+9z8H1RfXf0j0uZvsVBBfBJ4Evg+8DvyGEKIM3NivgfncH9zuB+9eTKdvNe17K4J7u8jkdmKtczyGbVA1qoSDYa9Fcq9IZMWoMBIe4Y2VN1isLKJKKqZtUjbKjEfH19mjdUR3Z3xz5TlvuxWjwmRskpnijBupVKBltcg38wyHhqmaVTRZY1AbpGW1MG2TuOamdyzVlpjS3PSNqBrFbJte4Z7lWKT1NEeSRzBsY130Oq7HORw7zGvLr5FtZAkpIUJqiEwo453vF+deJFvPkm1k3S9DNcgbK28wEh4hqAV5r/QeITVEvplnrb7G85PPb/nFeTx1nLXGGqZtslRb4rde+y1mi7MAHIkf4VOPfIqIGnE707UN4lrcO0cdMbxQWSAgB0gH0lTMCi/Nv8QLx17YUTfAufIcbaeNkNwHiPnyPOPRcSZiEyQDSW/57r+NquFG2/PNPHEtvq3l3V7hT08/mBxUX1z/IdHnbrJTQfyLgH3z518B/ncgCvyT/RiUz/3DXnzw7mce424F906m9LaLcnT2udNjEgjezb+L4zis1FbIN/OE1TBnh86iKdqOI6dRLcpYdIy6VSfXyJFv5rHaFgElwFOjT3F+7TzL1WVM20SXdUJ6iIAUoGm5bhjgulRokkZYdSOdS7UlUnrK6zany27RXCd6XWqVuFa+xqH4IR5OPcx0dppiq8hY+/2I88X8RVRJRVfcgrbV2ioNq8FqbRUEXCle4eHUwwyFhigZ7vE1rAajkdF156nj9BDX4ziOw7cXvs2/eevf0LAaCASffuTTfOKhTzBbmkWWZM5kziBJEg2zwanMKcD9Qi42iwTkgFcAGNWi5Bq5vlGqjaIyqSc5FDuEIineg1/FrNCwGnwg9r5/dOdvo2bUmC/PIySBKlRSwdQdmSb2p6cfXA5qweRBjVz7PBjsSBA7jvNa188zwA/v24h87isO6gdvN7sR3DuZ0tvrKEfNrLFUXyKuxolpMRRJoWE1QGze/1bjm4xNUmqVmEpOkQlmKLQKHI0fpWE30GSNscgYhWYBCYl0MI0maSiSgqzKKELBcRwUoVAxKp6ArJk10sE0y7VlWlbLLZpzHArNghepDqpB7LbNlcoVZgozRNQIV4tXOTN4huXaMpKQkIREUA1itS00SeNG4wZXW1eRJImgHMSwDa6VrjEZnySoBlmrr6374nQch5X6Cnbbpmk1+bfn/y3fvPZNAFKBFL969le963NaPU3NrLntkZWo93B2bu0cV4tXuV65ztHEUXRcQWzYBkk96dnEddNLVL40/xKTsUlOZU6xXFumZtaIKBFiWqznDMCLcy9iOiZJNclIeMRr3bwX08RbRYD96ekHm4NYMHlQI9c+DwY7tV17G/hj4EuO48xvt7yPTzd78cG7n1O7u9l2P7F7o3rDe3+ltoLVtsiEMt4ytxPlWKmvMJWYomJWWKwtEtNiDAQHyDVy3v47YnsrMd79cKJICkcSR5iMTVJulXlp/iVCaoixyBj5Zp6G3SAVTBHTYgyHhxkOD1NqlZiITlA2ysiS7AnkVrvF2eGzVIwKFbPiFs0NnPbSNGQhc6V4BV3RSQaS1M06s8VZjiSOUDNrrjODgHQwzUJ5AattsdJYQREK7XabaDDKam3Vs0ELyAHCWpiG2aBqVMnVcyxUF3BwmIhM8Kvf+lXPP/mJwSf45bO/TEJPABDTYyT15LomIN2idio5xfXKdS7nL/NI6hGvIcpAdICoFt10r1SN6iZRaTs2hWaBqdQUx7RjwPuNRjYS1+MMhYd4OPXwjttS75TtIsD+9LTPQeNeCKD43L/sNGXiN4DPAv+TEOIN4IvAlx3Hye/XwHx8Ouzn1O5ut91rSi/XyLFSWyEdTBPX45htkwu5C5zkJOlg+rajHI7jEFSCJANJBAKrbeG0Hc8irVtsbzfl2C/H+IVjLzBXnmOptkS2nqVu1QnIAY4mjnIqc6qnu0O3QI7rcYZCQ96xdiLIUS3KpdwldEV3Lc2CGa62rqLLOkvVJRShoCs6qqQiIzMWHePN5TfBgeOZ41SMCpZjoas6US2Kg+O1fE7qSb557ZuUjTJRNcp75ff4owt/hNk2kYTEPzj5D/jksU8iCQlZkskEM+vOS4d1kVIFnh59mldvvMrV4lVOpE8wEB1AEhJJPbnpXjm/dp7HBh9b90ma1JMUWoV1+7idNtO3ynYRYH962ucgchAj1z4PBjtNmfga8DUhRBTXgu2zwO8KIV50HOfH93OAPj77ObW72233mtKbK89xKHbIW3cgNAC4QrmTR7qbKMfGKORweJj5yjxCCNKBNFdLVzFtk2OJY173sKHYEOfWzrFUW2KltsKh2CEywcwmMb7VA8Bu8po7RLQIE9EJCq1Cz4jOZGySV268QiqYwnEcZCGTCWUIK2FW66s8mnmUmB4joAQoNotUWhXCapjnJ58nHoi7BW7lBRRJoW7VUYTbUvpY5hjvZN9hODLMkDPEVy5/hTdX3wQgrsX56ZM/zUBwgJnCDMeSx5gITyBLcs9j2BgpHQ4P8/zk88wWZwmpIXKNHGEtzGvLr3mNNwDvIWWuPMejA4966ycCCcpGmYbV2HLat3Odl2vLLNeWmYxN7skDVL/jgvURYH962sfHx+d9dhohBsBxnIoQ4otAEdCAH92XUfn4dLGfU7s72fZGgXo4dnidABwOD5MJZtZtIxN0mzs8O/bsrsbTS7Dajk1CT2C1Lay2xUh4BMMyyIQyaJLGUGyIa+VrBNWg22pYDmzqYrdXeaOlVolXFl+h1CphtS0USSGux/nQ6Id6Cv64Huf0wGnmK/NUzAoCQVgN07JbZIIZL5I8V55DlVSOJI7wSPIRzLbJQmUBXdEZj44zk59htb5KJpBhNDzKan2V1foqpVaJz1/8vNdo40TqBKfSpxiLjnmFcYvVRdcruc8DSa9IqSIrHIkfwXZsUsEUATnAa8uvUTfrBJWgl75xKHaIt9feXid+ZSHz3MRzfR8SNl7n0cgomqx5PsYj4ZFtH6B2kuazk9kCf3rax8fHx2WnOcQCt1PdTwKfBOZw0yZ+Zv+G5uPjsp9Tu9ttu5dAvVa+timlYjfj222hUyaUwbAMIlqk5zqd8XXWyYQyhLXwui52HW734WI6O+3mMasxglqQolHk9eXXuV6+ztOjT/cUZqcyp7Ad24v2dtwU0sH0uuh09/k5t3aO8eg4hWaBteYapmPyI0d+hKHwECv1FZbXlnlj+Q3+Yu4vsB0bWcj8+NEfZyg4hCQkolqUhJ6gaTWZK815gr/X+PpFSptW0+2451iEVbcLXZu2Z0EHrnA+nTmNJmmbROUEE/Rjrjzn2bvVzBphNcyh2CFSgdS2DyY7TfPZSQTYn5728fHxcZF2uNwi8H/c/PdZx3Eedxznt/e6wE4IMSWEaAoh/rjrtZ8UQswJIWpCiP8ghEh1vZcSQnzt5ntzQoif3LC9vuv63DtMxiZpmA0aVgPHcbw0gU5Dh/3cdrdA7fjuBtWgV7S1cRuVVoXptWneWHqDqlHdJDQ7YsZoG8T1uNdworsortP+t0NADuDgcGbgDM+OPbtJ+PRbp5crQucBoJvdPFxcLV0lqkbRFZ2G7Vqj6bJOsVncdCzdyELme4vf43r1OhISU6kpMqHMpnMJ74u0ZCDJQGjAba986DnSwTT5Zh7TNvnqzFf582t/ju3YpANpfvHxX+SpkaeoWlUeTj3spotYTWYLswghEIi+4+vsryNqNUnjcOwwV0tXEUIQ0SKYbZOaWaNu1L3Od5175VTmVN9r04/l2rJbQGhbRNWo19q50+J6K3ZyT/Y7Lt9SzcfHx6c3O02ZeMFxnO/v60hc/jfAs3gTQpzCbQ/9CeBN4PeBzwGf6VreAIaADwB/LoR423Gc6R2s63OPsJ9Tu/22DW7k9dXFVxkMDTISGVnnctAtqjrb6O609tjgYyiysmuPYoHgQvaCF5UcDg8jS/KW0eaV2gqXC5dJBVIMh4eJaJG+IrdX1PB66Tp1s86ri6+SCWZ4cvjJdV3ZunEcx/s528iiSRoODlWjykJlwY3o3myeAeu7z3WKDlv2++2m+0WnuyOX31n4Dg4OZaPMtdI1Pn/h817R2rOjz/K3D/9tbMcmIAd4evhpwrrrkbxcW3a9hB0Ia+Et00M2Rko73QTFzf90WScRSFA1qsS02G3fh1WjipCE53Xcq7VzP3YT5fcjwD4+Pj47Y6dFdd8XQhwH/j4w7DjOf3vzd81xnHN7MRAhxGdwc5O/Cxy7+fJ/BfyZ4zjfvrnM/wBcvFnc1wZ+AnjUcZwq8J+FEF8Hfhr477Za13GczaEznwPNnWjO0aF7SnowNEjNqjFTmGEqOdVXbMb1OBEtwtnhs5ucDKaz0166w9XiVaaSU+v+8jpiptQqUTbKVK0qUTWKYRtMZ6cZjYzyodEPrdtmqVXyxKYu61i2RdWsMpOfYSI2gSSknsVRGzvHSUIiW88yEZ9gKDxE1azyjavf4KPjH8VyrE0pGkcTR7mUv0TTajJfmqdhNSi0CuiKTlgLMxwaZqW+wpcufYmm1cRu26SDaWQhk2/m0WWdoBL00g62i0637BYtu0XdqvPK4iv8x6v/kTZtVEnlk8c+yU+d+CkvipsKpKgYFU/wV40qtmOzXFsmFXQnh4ZDw7SsVt/9deh09et0uOv4K7esFs9PPX/bD2NhLUy9Vqdlu97Nhm1sau3cD98dwsfHx2fv2VHKhBDi7wPfAcZwBSdABPjdvRiEECIG/CZuF7xuTgFvd35xHOcKbkT44Zv/W47jXO5a/u2b62y3bq8x/BMhxOtCiNfX1tZu74B87jodUfvyjZf7TuP3ozuKOxIZAQeEECxVl7ZM1+iVumDZFufXznspEpqscSF3YV0ksCNm5spzZEIZTqVPYbZN14Ggvky2nu15bAuVBVKBFBE1AsLtaGc6JrlGru/UeHfnuGfGnqHcKmO0DRRJQRISMS2GKql8/crXe6Z1nMqcIqEnuF6+Ts2subZobQvTMrlWusbLiy9zrXSNK4UrXC9fZ6m2xEJlARub4cgwS9UlHBxqRm3b1JeyUWa5tkxADvDv3vl3fOPqN2jTZig0xD8+/Y/5O8f+DoqkMBgaJBPMIAlpXZpA3awzX51nJDzCYHAQy7a4kL+AQPTcX/c5WqmtcDF/EUlIGG2Dilmh7bQ9f+XbZSQ8wkRsAlVSqRpVVEllIjbBSHhk23X3M4WoF7fzt+Tj4+Nzr7DTlInfBH7YcZy3hRCfvvna28BjezSOfw78geM4C93m9Liie+Onbwm3bbQNlPu8t926m3Ac5/dx0yr44Ac/6PRaxudg0a847XZ9iztT0lWjynJtmabdpG7UyZHjSOJI32nyXpG7ufIcyUDSe+1w/DDTuWnmSnOczJxcV+j0TvYd4nqcWruG4zgcjh9GlVTyzfy68XcKsubKcwhJEJJDRLUoITXEmYEzXhOOXmxM2ahbdWJajIXKAkEl6LZMrq5SNItczl/20jY6OapnBs4wHB7GbJsArNRWqFt1DNtgpb5Cy26hCIWR8AhBNYgqqWQbWQKVAFOJKcyQieM4ODhoktbzXLadNrlGjppZ49zaOf7FG/+CfNO1PD87eJafmPoJDscPMxAaIBPMoEjrP8Y6orhqVDFXTfLNPKv1VRRJQZXULa99595JB9PUzToODk7bYTw2jixkzxVjq/V30uSl0zWw44bRuQ92ImrvpDuE397Zx8fnQWGngngQ6KRGOF3/3rZwFEJ8ALcV9OM93q4CsQ2vxYAKbspEv/e2W9fnHmerL+qdWIttJVyiWpRcI+fZfg2EBqgoFey2vWUXu175ucVWkccG3n9ujGgRTqZOMluc3SRmOoK6k/uqyzotq+UK6i5BulxbZrW2iiqrbqMOx3JbJ7dbjEfHt5w635h/mtATlJolss0so5FR8s08FwoXCEgBDNtAkzVmC7McTRz1Ug0cHM85QgjBamMVCwtNaMjIWG2Lul0nqkVJB9M0rAbXSteQkGjYDdSIynMTz/XMUzZtk9XGKk2ryZcufYl//+6/x8FtTPLzH/h5PjL+EYQQJPTEtoKsYlZQJRWjbXjjViWVitn/I6D73gkoAZZry+SbefKNvJcXfW7tXM/7Zjfi8XZF7a2kEN1Kt0e/vbOPj8+Dwk4F8Ru4qRJ/1PXaZ4C9KLT7IeAwcP1mdDgCyEKIk8Bf0BWFFkIcAXTgMq4gVoQQU47jzNxc5DFg+ubP01us63OPs9UX9XZFR9sJl8nYJG+tvoUsyWiShmEZOI7DodihLYVAL5FzOnMaRV7/Z6bISs8mGB1BnW/mSQVStKwWTbvJVGxqXZ7xpdwlSq2S277YahDVolSMCrPFWa4Wr3IqfYqknuwpODdGsU9kTvAn7/4JkuTmEueaOTShMRAa4GLuIk8MP4Gu6FwvX+d4+vi6bYTVMPlGnkOxQxSbRVRZZcVawWybrNRWCCthYnaMmBZjtjBLIVDgePI4Y9ExrpWvEdNj665T1aiSb+ZZq6/xO6//Du/k3gHgWOIYv/7krzMSHkGTNTLBDJqseev1E3o1o0ZICzEcGfaWLRtl1mprfUVt970T0SIc0455bZc7La5txyapJ7HaFqVWaV3kfjfi8U4WvN1qpNdv7+zj4/OgsFNB/AvAXwoh/hEQFkJ8EzcX9+N7MIbfB77U9fuv4Qrkn8ONTL8ihPgIrlPEbwJ/0imKE0L8CfCbQoj/Btdl4gXgmZvb+cJW6/rc22z1Rb1d0dF2wiWuxxkOD1M361RMt3PaVGyKsBreVgj0K9DrjG+rbmCdddfqa+SbeZKBJFMxt5CvYTUQCM6teR7DUAAAIABJREFUnUORFc+KDcdNW5gpzJAJZjiTOYON3bcwbmMUO67HORQ7RK6eo2E3kCWZM4NnaFgNWnaLtfoaQ6EhCs2CN53f2UZCT2C2TVShutZhjoXRNpAlmYSWYCQ8wkpthapZ5UTmBB879DEAL+racaOIaTFyzRxVo8rry6/zL9/8l5QNNxvqhaMv8DOnfgZVUonpMZJ6ku60qq2EXkSLuIVr1vuFa3Wj7qZ43MyP3igM+907AsFL8y95x2bYBguVBcaj4959c5DF461Gev0CPh8fnweFnbpMXLrpKvFfAt8A5oFv3HR3uC0cx6kD9c7vQogq0HQcZw1YE0L8LK64TQP/H/APu1b/Z8D/BawCOeDnHMeZvrnd6W3W9bmH2eqLeruGBDsRLsPhYYy2sW77nWjsbpGFzKXcJepmnbAaJhPKMFee6zllHdfjPDn8JC/Nv0ShWQDHbQUsCxlZyF5ns7JWpmJUMG2TYrPIycxJBkIDhDXXpaBhNvjy5S9zPHXc6yi3WF3kQ6Mf2hTF/sj4R3i38C7pQJr56jy1Vo2qUaVu1JkpzBBRI+uKyboj4UeTR3mv9B6JQAK7bfP44OOs1FaI6lEUSWEoPITe0nlu4jkAZguz6IpOKpAi38zzg5UfMBIZQRISn7/web42+zX3+qpRfumJX+KpkaeQJZlMMLPJvQO2FnrD4WE0WaPYKnoPNqqsMqKP9BWG/e4dWchux0A14dmwARSaBS+Hudc9mWvkyDVyvHzj5R2nKeyU3aRA3KpY99s7+/j4PCjsuHXzTeH6/+7jWDr7+Y0Nv38Rtyter2XzwN/ZYlt91/W5d+j1xb/VF/V2+Zk7iXol9eS66fGOKN2NEOiOXh5LHONC/gItu0Vce9+5YeOUdccFYjI2SaFZoNAqUDbKPDfxHNcr1wnIAUbCI1SNKkPBIcYj47xXeo+wHF7XPrrYLHKjeoNT6VMEtSCGbbBYW2Q6O80zY89simLPFmdd0aiEuZi76LVRFkLQsBpMRCd6XotPHvsk07lpyq0y7+bfJapFGQgOkA6mcXAIKSFWG6sossKNyg3aTpvV+irlVhld1smqWRari3x19qtcLrjZTCfTJ/m1s7/GQGjA7dQXzCBLcs9zvJXQezTzKKVWifHouHePrNRWOJ463nN56J/b+072HZJ6EsM2PO9gTdbINdxCS9gsHnONHBdyFziRPrHnBWm7TYG41Uiv397Zx8fnQUF0G+2ve0OI77CDojnHcf7mXg/qbvPBD37Qef311+/2MHxY/8XfLXw7gm63RULbbbPbpaLttD1RKgu5byFYPzod6YJKkJnCDJZtgQBVUjmWPEbDamxqr9y9TofOcoD3XtWoslRbotAscKVwhcPxw0R1txiwYTWYXpsmFUjx0UMf9bbTtJq07BY/dfKnNo11vjzPS/MvsVBdQHIkhBAYtsGx5DFGI6MkA0lP8G08b4djhym0CpxbO4cmaUzGJ70mJtl6lvnyPDWzxlJtCUUoBNQADatBTItxrXSN7yx+h5bdQiD41MOf4rPHP4siKyQDSWLaxrrY96/hXHmOt1ffRpM1DscPe/vsPq8bBXzVqKIpWs/zu1XqQCe3e6GyQEAOoMkaFdMttHzh2AvrCus6+1uprZAKphgIDfTc160UuXWPp9990us4trvnfXx8fB4Q+vpubhUh/j/3YSA+Prtiu9zHWylK2i7q1b3PTChD1agyV5rjWwvf8qbVN0Z1ewmb7uhlzawRVaMg8DyIe01ZbxXxPBQ9tClqnQ6k+RvDf4NvXvsmi9VFYlqMdrtN1ayiyzrn1867ywXTSEj0ewCeiE3w3MRzfPHiFzHaBslAkieHn2QoMuQVlfW7FoVWYV26gSzJOI6zLkIakANcvnSZpfoScT1OSk8xnZtmOufWwCb0BL969lf5wOAHehbOddMt7qaSU1zIXWA6N83J1EkUWVk3pX87Od3ddGzSxqPjFJtFcs2c95DUfb269/fyjZf7XsudRHi3Esy7TYHwI70743YeUnx8fO5t+gpix3H+7zs5EB+fXuxXodJWFf7d+6waVWYLs2iyhkB4qQ6dqOhSbYmV2gqHYofIBDPrhE33NHVYDWPYBggIq26eb68p662KuvqlUkzEJrhcuMz3Fr/HSn2FiBJhLDKG1baomTWstsUbK2/gtB2Op44zX57fFOnupGocjh9GEm6EeKm2RFgL07SaLFQWuFK8giQkdFknFUyRDqbXdX7bKLpyjRwn0icYCA14kdmo5nbg+9aNb1Ey3Gt4OHaY33zmN0kGkgCs1dd4r/ReX0HSEeZ222alvoKDQ6lR4lz2HE+PPL2l0LtVYdi9XiedZDuxtFWawnYPetsJ5ltJgfDbOG+N77ns4/Ngs2UOsRDiXzuO8wtdv/8jx3H+oOv3rzqO8xP7OUCfB5u7UeXe2afdtnlt6TWqZpWAEmAwNOilK7w0/xInMyfdgitJ9hpbdKbtO0VznWjkcGiYN1ffpNAsMBQaomk1ievxTS2Ztyrq6o5aA17b5Fgrxkp9xbNIe6/4HsVWEattkW1kqbQqaLKbJtByWnzurc/xsUMf46mRpzZFxQ/HDzNTmPHSAi7mLlJouq2ZNUljub6MQNCyW6iyymJ1kbj2fm52Uk96x1Iza4xGRgG4UrxCQHFTJd7Ovk3baSMQPD74OJ955DNkQhlUSWWmMENQDSILmYu5i7yy+AqnM6c5lTm1LjoqC5krxSueT3RMi5Fv5ncU0btVYbjb9bbKc+80Yemm+0FvO8HsF7vtPb7nso/Pg812rZv/6w2///aG3/+LvRuKj89m7nSb2s4+s/Us09lptx3zTSFXN+tUjSrFZhHbsQkqQeqW24AiIAdYqi0BrkDpRJk7bYTLRhkFhbHIGEEl2Ld9cPc6pVbJywl1cDa1he7sZ648R0J33Q86jToSeoKwGkZCYiA4wFBkCATEtTjpYJrza+fXteHttJ2OaBGmklMosoJhG6zWVkkGkgwGB1EVFVVSvRzgpcoSS9UlzLaJLGR+sPIDPvfW53hz5U1kIaPJGtPZaRbKCyxXlzmXPccP1n5A22kTUkJ8aORDDAQGGA4PMxoeZbW+6kV+Z4uzyEImFUixUFlYN9aoFuV6+brXvKRz3Ak9wVx5bt/ui93S71p2R3i76X7Q69UGvHO9t9v2neR+auu83Tn38fG5v9nOZWLjt3bfZGQfn/3gbuQ+xvU4MS1GSS25TTUcOBI/gizJLNeWKbQKXiS0kwrRKbKC9cKm0+hjtjCLqqgkA0mGw8Oet3Cv6FOvSKRAMJ2dxnIswmqYkfAIsiR7TTkmY5PMFmcB90u8brpeu8lgktHwKIu1RZKBJJqsoUgK+UZ+Xfe77qj4leIVFioLmG0Tu21jtS0vn3c0MurlwJq2yaMDj2K2Tc5nz/Ne6T0aZoPLhcuYbZNDsUMs1hb5/tL3+avrf0WhVQBgMDjI44OPE1EjrmOGWaVqVr2HiNnKLAE5gK7oODhUjeq6sU7GJnnlxiukgikcx8GwDZp2k2OJY/suXnabY9ovqrxdhHcnMyN3OwXifksx8D2XfXwebLYTxBsrcG67VbOPz265G1/8nfbEnRQCWciokkq+mUcWspfvOhweZrYwS0u0CCthL4I9FBvi3No5lmvLLNeWvfSBcqvMu7l3SYfSpANpQmpo22MrtUqUjbJbmKdFMW2T6dw0o+FRPjT6IebKcxhtg6nkFEu1JQJKgGKryKHoISpGhbXGGosVVxAv15bRJI2Enlg3RT8Zm+SVxVe4WrpKsVmkYTW4UblB1XRzqM+OnCUoB7Eci1QwRTKQxGybAFzOXaZu1WnaTTLBDHWjzvm188wUZqgYFb679F3aThtJSDw59CRnB8+iym60+Xj6OLIkM1ee8wSJV4AIGLZBWA1vskY7PXCa+cr8usYpsiQTkkL7dUt4AtB2bIrNIpcLl3lr9a1du4/A+/f0dHaai9mLCCE4Ej/ivX8vpETcbykG98I59/Hx2T+2E8SKEOI53o8Mb/y9tzmoj889yEbLLLNtMhAa8IRmvpknrrmNM6Zz00yvTWO2Tcy2iWEZDAQH0CSNodgQ18rXCKpB6mYdWZKptCosS8tUWhUkSaJpNalZNcpGmVKrtGVEba48RyaUIRlIcrV4lYXKAlbbQpdcP9xuO7RjiWOMR8cZq495tmbns+cJa2ECSoCaWWO5tcwnjnxiUyQ7psVomq4gXawukg6kyYQyLJQXeHXxVZ4eeZp8M0+xWSSqRVFllensNA27QVyP0zbaLJQXvMLB89nzZJtZAIZCQ/z84z/PXHkOWZJJB9OMRkaJaBHPxeLRzKNuJz6huDZsQtCyWiQjSaaz0xi24UVlT2VOuWkrG2zEbkW87DTqO1eew3ZsZgozVM0qdtum7bT5T1f/E58+8elbiorajs2JzAnvGLojrAfdFeIgd+a7Fe6Fc+7j47N/9PUhBhBCXGObqLDjOA/t8ZjuOr4P8YNFqVViOjvN+ex5EnqCydgkLbvlWYZ13CM6nrvzlXleW3oN27GJ6TGiWtQrkIvr8XUesW+tvkVUjXrRxaHQECEtRLlVZiI6wXh0nGQg2dOXNqknKbQKvLr4KoOhQWJajKXaErqie9HqY4ljW3oyn1s7x0J5gQu5C1wtXSWhJzgcO0w8EPdEcNWsUjWqzFfmaVkt8s08QgiCahDHcSgbZZpmE0VSCKthYlqMTChDtpGl0Cy4nsJ6jJbd4nL+MgLBjdoNL0f2VPoUn33kszwx9ARLtSUkSerrn+tdi7XzJANJ0sE0c5U5mmaThJ6g1W55dmcxPXbbFlm78ed9+cbLzJXmmCvPeV3vDNsg18jxIw/9CM+MPdNnL73ZrZfwQeNeH7+Pj88DyS35EOM4zuE9H4qPzwGiI4iWqkukAikEgtniLFPJKU6mT5Jr5FAllagW9SK/i9VFRqOjCCEoNou0nTZLtSVenHuR5yefXxc56+QYxwNxAmqAgBqg3CoTUSMcSx4jrIZ7+tJmG1levvEyJ9MnGQwNUrNqXC5cZiQ8gi7rbuQ0kFyXW9tLhFSMCofih5hMTFI1qizXlqkaVcqtMnHd7Zi3WltFSIKm1cS0TeYr8xyOHwbAalsElAAPxR4i38zzo0d/1BNAb62+xUh4hAu5C+55klXMtsl8dR4ACYkPj32YHz70w8iSzEhkhJge23JaOq7HeWbsGU5lTjFXnnN9jZFRZRVN1ohoESpmhZfmX+KFYy/ctvDazbR/VIvyXvk9IkoETXFzqoUQpAIprhSv7FoQ7ybCehD9cf0UAx8fn/uJ7VwmfHzuazqCyHIs17VA0T3HiHQwzVB4iGfHnuXMwBkKrcL7y8o6tmOz1lhjtb5KKpCiZLiiViC86OhIeISm3aRiVBgMDpIMJJmITvDkyJNEtMgmX9qOw8Jry69RMSosVhcZiYzgOA4tq0WpVaJltWjaTUbCI9tWwUc1t3vdbGGWK8UrAIxHx0kH3XSIYqtIQAkQ02KMREZo2S030pu7zEpthbJRJqgECagBDNvglRuv8LXLX+OluZdoWm4B0nh0nNHwKBdzFz0xHFbD/L2H/x5nMmdYqrsPG4qk7NgdobPcQ/GHiOtxknoSXdERQhDVotiOvSeOEh1ngapRZaYww1urb7muGLXlTctOxiax2hZG28DBLeYzbZNUMIUQu6833s5pokPnYcloG95DzEFwdDgoThc+Pj4+e8F2OcQ+PvcEtxpB60TpOpFcXdY9x4iN4mTjstlGlpASwnIsTNv0IraGZdAwG4ArDMej41wvX2c8Ou45QoTV9wvwOr60spCZLboOCwKBKqvMFmc5kjjCVHKKQrPAamOVVDCFJCRmi7OYtoki3D/jXsed1JO8fONlYnqMiBqhalZdkX1TTHcXsOmym5P8cPJhzmfPUzWqbkc8LUG2lnXzns0aqWCKullnsbpIQAlg2iYvLrzoCfPR8Cgfn/w4mVAGXdapm3VqZo1za+d2fX2iWpTLhcukA2nvNcM2SOrJPXGUiGpRso2s15I5qkapmJWeud1xPc4zo8/w/aXvU2q6OaaJcAKrbXE8cXzX++4UMpZbZcy2iSqpxPTYJm/qg1y8dredLnx8fHz2Cl8Q+9zz3I79UydK13GLAHAcB0Uom6Z/O8uOhEdcB4WWG12UhUzTbjIVmyIgB2hZrXXFOalAiscHHyeuxz3hvrFoJ6pFuZi76NmNBdUgpWaJhtXg2wvfZio5xXB4mHwzz+vLr9MwG5htk5bd4lT6FMeSx7zIYfdxF1oFTqRPUGwVqZpVwmqYscgYNyo3eH35daaz0zg4jERGsGyLiBZBV3Qelx7HwcFqW7SdNplQxosoW7aFJrkpA2+uvsnV0lUcHHRZ5+OTH+ep4aeomTXAFW8DgQFmS7Mkg8ldtymejE3y1upbVMyK1+Wu2CyiSApVs+r5OTs4t5RK0Nm+LMloiuZ2E3Tc13sJzqdGnsJ2bEqtElbbQpEUBoODnMqc2vE+N+LcLNNw+pRr3G/Faz4+Pj4HkS2L6h5U/KK6e4vbKe7pFtOW/f+z927Pcdxnev+nzzPTc54BMBgQBx4gUQQpeW3Jtqh4411XbTZZu3KRKqcqlYvkZiv/QW739y/kMhe526pUpVKpdaWysTdexbvR0fJBJCFSJEjiRGAAzLm7p8/dv4s22gAFHiVZJNWfKt0IQ8y3D5h5+v0+7/MGbI43GTgDLk0dn4520mvfufcOQ2/IK/VXOFs9m2YLH33fx61cj9wR//XGfyUv5xOrhLHD+nids9WzlNUy1VyVteEaBbnA2mANURRxw8RHLCLyzZlv8p32dziYHNC3+8zoM5TUEh2rQ7vYPralb7gGP1v/GXuTPQQE7MBGkRQc30l9vqcrp5FFGcd3KKpFPt7/GNu32TA2MH0TURAZuIM0W/hM5Qz/8Y3/iOVbFJQC1Vw1fb/Vg0R0X5y6eOL1eVRj29Z4i7e33iaMQzRRS2weSo7F0iIbxgbEcKFxAVmSH9gQ9zB+uv5TbN9mEkzQFZ2W3kq93W/NvXXitdoYb7Br7WJ5FqIgEsURuprkQz9KlB/++4/3P0aVVJYqS+mEw5Pu26x5LSMjI+ML4+ma6jIyngc+TwXtaNSSG7icb5x/oKA5mh17Z3SHslqmqBZpF9vHLBD3ZxAvlhdp5BuPrIweWiYKcgFJlFiuLWN4BpqsMXSGFOUiHx98TEkrpRFqE29CPV/nRv8GK80Vtsfb+LHPS/WX6Nk9fr33a9aGa8wV59KBIJ/2P6Xv9GkX2/ihz461wz3jHpZv0XN6XGxepOf00nSN7f42TuhwrX8NVVQJwoBde5cwDgH40Zkf8e9X/j1lrYwiKqz2VrEDOxW3A2fAa9OvPfD6PMoSMF+e51+e+5epiGzkGyxVlti1dikrZRBgb7LHudq5Y//ucZnVZ08UnA8ayHBYuR65IzRJY3u8jSAKTKxJelwPEuVHxb8oiIiCyK3BLZZryxTV4on3bda8lpGRkfHlkwnijOeKkyquf+gJU2NvjCqqCIqAH/nc7N3Eq3q09FaaRBHFER/vf8y2sc2vOr/itenXWGmuHEuFGLkj3tt5jz1rjz1rj21zG9MzWaosYXgGBaWQNLvps9wz7iVDOSIf0zVxAicVR4ZroEoqu9YugihQV+pYvsW2sU0z36Rv9zE1k1v9W8yX51kfraPLiQ964AwYOAOKSpGxM8YVXG71byEhMXbGBFFAo9Dg3vgecRTT83r03T4AoiDyjeY3+A+v/QdquVp6vu/Pcr00dSmZ+HeE+8cUP+qB5vBh5PC1giBgDX/nfxbgYHIAgOmZRHH0RNaJpxGchyJ+29gmJyc2Fzd0GbpD5kpzDxTlR8V/US3iR37axLmsLp9432b5uBkZGRlfPpkgznhueJBXeKm8xPp4HXjyCtqT+o9Xu6vsmDtJ/rCSeFrHcRKj9urUq1w5uEIUR9wa3GLT2KSiVQiigNXuKmEccql5CTdw0991Z3iHkTeioBTwIz/N9q3lagiCQCFXYMdK3s+PfApqAT/wkQSJIA4Sy0NoU8vVGDgDFEGhpbfoWB3COEwEc5BYMAzf4J55jyAKGHtjdC2xBQiCwIF9gCzJySho3+CdnXc4Wz1LJVfhTPUMXbfLwBuk46lzUo632m8la5QLXD24yrXeNQb2gHq+zkpjhYvNi6lv+lFjint2j6E7xPItdEWnqlXTaYBHOfrwc9jcOAkm9OweFa2CKqlEcfREI4SfRnAeCvOjTYmqpGJ65kN3J46K/0PfuiqpWJ51rMnyQWt81nkW4+EyMjIyHodMEGc8Nzxoa33gDp66gnbS77Q8i59v/Bxd1bE8C0EQiOOYolrkN3u/SbOAJ34ixEbeiIPJASvNFQzPYOAMMH2TippUMvNynjAOsXyLjfEGrzReAeD28DZu6KIrOlEcMfJGqJKKEzoMnSG71i4lrURBKnC5fZmRO6Ktt5MpeqGPF3q0S23CKKSsljFcIxkrvDtky9iCGMpamapSTSwBYp5GvkEcx3SsDqIoMvEnREQEUYAqqUz8CXk5jyzKxFHMneEdJEHiRv8GQRwAsFReYrmanN+cmOOjvY8SYe+MUGSFrtXlN+FvGHvjdFjJw67Pg5Iwfnjmh5+5Xkerua1Ci0/6n7Br7DJfmocY3NBlubacjoN+XBH5pILzUJin6SSylo6ZftjuxFFBX1STLOqN0QYxMaqoPteV38/T3JqRkZHxVZMJ4oznhodtrT9tBe3+32l6JlvjrSTVwE/Eyz3jHnPluSQ+LLC4Z9wjEiK6ky6qpJKX8gy8AX+z9jc4gUPX6RLHMY18g/3JPkEcUJAL2L7N0B2yWF4EkqEOTuhQlavsTfaQkRn4yeQ3XdEpKAVM1ySQA7bNbd5ovcHGeANVUhk4A0RBZLG8iCIqqZjeHG9S1sr07F6yjR+5zOgz6KqOH/k4gcOUPpV6e73IY+yOkQUZEZFqLpkGJyPjRkne8d+u/y0AkiDR1tvU1BqKqGAHNq/NvsbIHeFFHpIkYfomlmcxDsboip6K0kPf7WH1cGO8kVYPH5SEMXAHzDN/7Hp9xvNdO08QBUiihCzJLJeXj42D/rI4FOZVrcr2eBs3comjmKny1EN3J+63ZxwOLHkRROOzHA+XkZGR8SgyQZzx3PBleIXv/50dq4MgCoRRmEyV88foqo4XeFQLVUpKCTu0udm/yUxhBmLYn+wzCSZssJHErvkuQ2/InD5HPVena3cRBRFN0rjUvJQKnzOVM2wYG1iehRu4aeyWKqlpw5UqqdTzdRCSRq/XW6/zSe8TEEhSFsYb7E52OV1JJsmpsooiKcnUuNBnTp9j6AwpyAXiKCav5WnmmwRhwMAZcLF5kZv9m4iiyL61TxAGiIJIXsmzNl5L49Nm9Vku1i9ieAZhFBJGIT9++cf4sc/6aB3LtzA8A0VS0BUdy7fYHG/SLrZ5derVY0kRNa2GH/lp85nhGTTzTaYKU+l1eZigvf/hp6gWGTgDhu6Q28PbD7Vc3M/TbvEfFeZe6GF6Jnpep5arPfR3vMh+4CweLiMj43kmE8QZzw33V9e6dpfN8SYz+kz68ycVFofDEQ5zZTeMDaZyU+SVPKqkYgc2BaWQxFxJKrV8jWpU5Ur3CnWtThiHDNwB04Vpgihgy96iolWQBIkNc4NzlXNcaFwgjEPaxfaxvNqV5godq8MnvU/wQ584jhMxi0JeyiMJEoqSCMwoSiwVYRwyX5pnz9rjnXvvUFSKVNQKVa3KgX1ATauhSAoXmxfp2l0quQr79j4xMTP6DGWtTEtvce3gGnOlOc7XzzNTmOHXe7+mIBeYhBMkUWJttEYYhwgIXGhc4N9e+LfJMA85B/w+9itHDlmUE+EsgCImQlyVVXJyLq24//X1v8YPfQpKgX1rHy/yWCgtIAnS537QeRLLxVE+7xb/obh90urn8+IHflL+0M2tGRkZGV8kmSDOeG44Wl3bMXceGWn2OIzdMZujTXpOD0mQEGMRL/Koq3W8MInimvgTCnIBL/Ro5puooppUFX2DKI4oqsWkUU6AWq5GWSujCirVXJVavkYcxyzXlj+Ta1zRKvxg8Qe09BarvVWsIBGVA2eAGZmU1BJtvZ2mTTRyDS42L3Ll4Arnm+fTkdC3+rcwPRNFVEBMxOp0fhrLt6jmqny79W2cyMGPfGYKM0iiRFEtMpWfwvANmoUml+cuc617jY/2Pkob53RZ5832m3xj6hvMF+ePJUUcVv4uNi+yY+4QRRFBGGDHNkEYoKs6Na2GKIi8vfU2fuhTVIvsWDsATOWn6Dt9rnav8i9O/wtWe6vHhl1UtMpnJrY9iCexXBytCO9Ze9Tz9afe4v+8DWQvQgPa0WMQEBh7Y5qFZhYPl/HM8SL8vWV8uWSCOOO54mh17fOIGUg+IN/eehtd02mVWukUNC/08EKPMApRRZUD74CcnOPO8A6KrDB0hrxUf4mO1eHu6C4De0CtkIw4LsgFDNdgb7JHJVfhR2d/9MAP3cMP6JiYlcYKeSnPzeFNbvRuQAyqqBJGIYqgUNNq6KqeplwYvsGn/U+p5qrMFefYm+xRUkvExBSVIgICFxsX6TpdWnoLAYH9yT5XDq7QzDdZLC/ihR6Gb7Bj7hBEAb85+E0qhhu5Bn9x+i/4izN/QRAHeJGHfOTj4rDydyhce3aPm8Ob2L5NNV/lTOUM7WKbnt1LbBL5Gh2rk1SYY7B8i7ycp11sJw2AkE6dEx6cm34ij2u5OIy5O7YbYE+lDW7w+Fv8n7e6/CI0oJ10DHjgBR5u4L5QdpCM55sX4e8t48snE8QZzyVfhF9xY7xBGIdUlSoCApqkUc1V8QKPMA6Z0qewvES4bRmJFSIIA5r5JndHd5nVZ5OcX6dP1+rS1JrsWXuM/TFBGNB1ury3816atHCU+z+gV7ureJHH9099n7JSZsuNEN71AAAgAElEQVTcwvItIiJ0RSeIA7qTLjcmN2iX2ri+S02rsT/Zp11sU8/Xk+Y6d0S72KZZaDKrz/LWqWTS2pWDK7zceDm1mvxm7zcoosLAGXBzeJMr3SQuThRE3px9kz+a+iOW68vMlebSf394ju+v/FW0Cj88+8MTp80V1SKiICKJEreHtymqRWRBZugN0WWdxfIitwa3eKX5CvPl31dz7cB+4MPN/ZUeAeEzW/Vdu0vf7vPT9Z+m0+Ru9m/Sd/u09BZVtYrpmWyPthm5I743/z2KavGxt/g/bwPZi9CAdtIxNAvNbIJexjPHi/D3lvHlkwnijOeSJ/UrjtxROmEujmPOVs9i+AY1rZbGZkHS0GZ4Bt+a+Vb6QXkoJvNynvd33k/FWBiHnK2dxQs9Ru6I/ck+VmCRl/KcrZ6lpJXYsXZY7a5yee7ysbX8fOPnjLwRtVyNWX2WIA7SDODvzX+PW4NbRHHE+mid2dIscRQTRAGmnwzlcCIHURCxAosPdz+klq/x3dZ3eW36Nf7Z0j87duzv3nuXO8M7bBvbaSJERauQl/O8s/MO96x7AGiSxuszr6eCvJqrpmt/VCPYg5rFNsYbbBlbdAYdoihJ5pAEibyc51uz30KWZARBICfljq35QQ83J1V6xt4YPNKt+q7d5XrvOoulRQ6sgzQpZG+yR0EuYHom66N1mvkmnuKxZW5xZf9KGnl3qXkpTS55EJ/3gexFaEB7EY4h4+tBdq9mPA6ZIM54bnhav+LhVvmOtZMOUbjRv4Ef+cyX5jF9E/idGPaTEcqH0WgAu9Yutm/Tc3rc6N/ACRwaWgPLt+hOurSLbcI4ZMfcYamylHpnZ4uziIjcGd1JBfGhoBt5I+q5On7oc2twC1EQgcRKUFSLLNeWeWf7HSRRQld0SmqJX3d+TRAFXNm/giZr3B3dJYojojiipJS4ObjJQnnhM8f+YedDhk7ir5UlmQPzgI7V4c7oDnZoA4lF4nT5NEWlSEycbHuHLlcPrqbe50dVUk56zWHDWz1fRyJp1ouJ+d6p71HRKti+zZnKmcd+uDms9IRRyG3jNpZvIQsytVwNVVQZuSP6dp8LjQsM3SE5JcfIH6ErOmEUIosy+5N9dFVPdwF2jd3kAUCtcnnuMrIkP3I79fM2kL0IDWgvwjFkfD3I7tWMxyETxBnPBY/jV5wpz5zYNHFYtSwr5bQSLAgClmfRtbsslBcYOsO0se5P5v8kFUIjd8SetYckSjiBQ1WrsjZZQ0Kikk8Gb7ihy5/O/ylvb71NSSmhqzqNfIOCXGBgD9i1dnnn3juU1BKmZ5JX8okYjnxCQg7sAyw/GQBySj9FHMepEL48dxlBELg1uIUiKVTECl7k8UnvEwbugJJaYjo/jSZqDL0hG+ONY+fs5xs/587wTpKZLKtookbX6bJr7RITIwpi0jAnyIydMZqsUVSKTOlJ01tbb6eZwU/TkDJwB6k4LciFxO4Ruoy9MUviUvoA87ijkw0veWC5PbydrPV3DY13Rnf49uy3qWgV3rn3DhWtwj3zHkW1yMgd4fpJpnLX7uKGLvVcHdM3UUWVudIcS5Ul/MinpP3+C/Jh26mHiSeWZyXjr91Beu88Dk8zLvpZ40U4Bsiarb4OvCj3asaXSyaIM54LHuVXfFjThOEZBFFAXv19dUCVVFzRZaowRT2X+G+nC9MAbBqbDNxBKgIXygvcGtzi7vAusiSjq3oScZavoYkakixR0kr82dKfcWtwCy/02B5vE0QBfafPK41X0jVd7V7ltanXkASJn23+jM3xJjExuqxzunqaoTxkx9yhpbc4Uz3D5niTjfEGipQ01t0d38UJHACaWhNZkvFiD0EQWCovsWMmKQ6fdD/hJ7d/ws3BTQZ2MoBDCRS6TpeRl2wTapLGN6e+mTTNhR4DZ4Af+kRyBHEiPhdmF9i1dtkxdxi7Y/zIx4983r/3PkvVJVp666ECwvAMGvkGzUIz/X+HDW9HxebjZvOW1BI3ejfQZA1N+t3DDQJVrZoK2MNqkIDA9e51bvVvIQkSzUITJ3AY+2O6dpeyVqZZaCIISROfrujp+zxqO7WiVVgqLx3LVq7mqqyP1ylr5UcKqhchj/hFOIas2errwYtwr2Z8+WSCOOO54FEesIc1TZTUErIoH/MKe6GHIiq09NZnBPVhBeHKwRUmwYSKmryvKqv4oU9FTfy3y7Vl3NCloiYftmN3zK/2fkXf7uOFHlZgoas68+X5dISzJmn85PZPuDO8Q8/pEUdJNdiIk4EXiqhQVIoslhdZG6xxa3ArSZBQSoycJId4ujDN3eFdnNihlW8lY4Mjj77Tx/Zt/m797/j7zb+nolUoyAVG4gjDMRj7Y8I4BKCqVrlYv0hBLaDJWppjbHkWkigxdIZcmrqELMl0J0lVtayVkZHZNDbTGLV6vs6VgysslZcYuIPPVNked6vycbN5F8uLvHfvPer5OnEc44UeTugwq8/y8f7HqZ2mY3UYOAP2rD1KWomRPcLxHer5OvVcnf3JPi/XXubl+stpdfBoRvTjbKcO3AEXmheOHdvDmgHv50XII37ejyFrtvr68LzfqxlfPpkgzngueJSwOhTMpmfSsTpYvkVBLpBX8nx39rvsmDuJh5jfv75dbKde4Qd9MR5MDhg6Q2pajfJ0me3xNgggIiIJEueq59Jq0mp3FVVUieJkiIbpmUiCxPponZbewvRMDNfgWvcaOTFpIouFGFmSk9zgwGLsjvnH7X9ktbvKvr3PVGEKN3QxfAMncigoBRbLi6yP19kzEyuHF3hsjbeYzk/z3fZ3uXpwla6djI/u2T26dpeBNwCSdS+Vl5CQqOWTKLc5fY6e06OZa+KHPtP5aezApl1sY/s2lm9Ry9XQJI2tyRa6ohNLMfese5ytneX28DZ/v/H3nG+cT+LcIi+tsn3RW5UVrcKlqUtsGcl4bV3Rmc3PsjHaQFf0tMp3YB9QUAromo4QC1RzVfzQR5M1LjYuAnCmegbDM5gvzdOxOmyMNvAjH0VUKGvlR+YgZ406zz/ZNfziyKwnGc87mSDOeGZ5kia6klqiZ/fYNrZTb6nhGUkCAfBm+81jKRPn6+ePDcp40BdjUS1yd3SXei7JPJ4uTLNr7VLJVfBC79jW6mp3lbE3TrzApVP0nT6mZ/KrvV9xsXkxac4LbcpKGT/yCaIAYvAjn6E7pGt36ZgdpgpTdO0uVa3K/mSfnJyj7/ZxA5c9aw/Hd9AVnWl9mpEzYuyNUQUVVVJBgNuj2zi+w5a/xaa5mZ4DWZA5XTmdbO9rVc43ztN3+nSsDrPFWYbukKnCFJIgMavPpmOIj/qSDyf3ebGH4zvcGtxi4AxQJRVJkFgbrrFcWyav5NMq2xe9VbnSXCGMw7Sa/0k3GWW9VFlKK/GqpFLVqrw+8zp+5KNJGnEcY/gGp8qnjkWDjdzkHB6KoMMR2o8ia9R5ep4V8ZRdwy+GzHqS8SKQCeKMZ5InDf1fLC/y2/3fIokSqqjiBR5xHLNQXkiF2eW5y8fiz47yoC/Glt6iqBTZNrYxPZOyVual+kvp+1S0Svrlfq17jUkwSTzJkkItV8NwDQ6sA36x+Qv82Mf2bab1abbNbcpqGdu3cX0XL/CQBImKmtgcLN9if7JPEAfk5Tw5MUccxwRhQM/poQgK86V5SmqJT/ufEhKmmcVe6LFn77Fn7xHFEZBYJBbKC/xg4QfJ+aossmvu0sg12PK32J/sM6vP8o3pbyAK4rEvspnCDB/ufogkSkz8Ca7vplFpOSmX2ik0WWMSTvhw90PquToxcSp0jjblHTbpPeyL8mGC6X4/oBd5XKhfSIdrQJJucdjQtzZYAxLvsizIn6lQb4w3aBaaj52DfEjWqPN0PEviKbuGXwyZ9STjRSATxBnPJE8a+l/RKrT0FhN/km6lL5eX0RX9sbY/H/XFeLQiefRnR7/cpwpTXOtdY+AMkEWZiAgncChJJdzQRRZlhu4QN3TxAx8BIYlNI4lOk0WZudIcjXyDO6M7mL6JJmpYnkUkR5S0Esu15XT6W9/tpz7oMAohhuu962yZW+xOdoHEIlHVqmiCxqniKRRRYbm2TEkrUVSK7Fq7lN0y66N1NEmjoBR4o/XGsZSNw0lzbuDiBA7b5jbnqucoa+U0qaKslpkEE/bMPcI4pKW3iOM49Revj9cfKoAOBXDH6rBv7dN1urT1NgvlhWMWjPtFMSQPM17kHbPLHE4blESJs9WzbI43GTgDLk1d+swI7afdNs8adZ6OZ0k8ZdfwiyGznmS8CGSCOOOZ5Ek/YLfGW9zo3aDrdJnKTyWVXbWIHdiPtf35qC/GB/3sUAzn5TyzxdkkiiuwUEU1GUEcQylfol1qMwkmhEbIrrGLKqtYnoUd2BSVIpVchaJSZL48nw73GDFi6A2RkCjIBURBZLGyiBmYlOUy+3Yypc4NXQ6sg0Scd68w9hOLhCqqLJWXaBVaVHIVymqZs9WzyFLyZ19Ui8wyS9/u88bsG6w0V+jaXd7eepsZfYZZfRbTM2kWmtRyNXatXSzfYqmyhCzIICRV19dnXmfX2mXXTER4Ts7hhR6z+iy75i7v3nuXpcpSYmmQhc8IoK3xFm9vvY3pm5iemTw8IOPmXG4Pb3Oudu6YBeN+FsuLSc60uUNJLaGICk7oMJWfSnYKiFN/84OSK5522/xZa9R5VqwID+NZE0/P2jV8HsmsJxkvApkgzngmeZIP2K3xFv/zzv+koBQoBSUs3+L/bf0/LjQvYAc2M/oMwEPFwdZ4i192fknX7tLMN1koLZw4ie1+jn65F+RCUin1J3ihhyqqFJUiqqiya+0iIdEutulYHURBpKgWaRfbLFWWEhEZePTsHpvGJnEck5fziIgIgpDk+KoFvMijrJRxA5eunUx9i+IIwzfYNDfTFImaVmOlvsKlqUsEUYAbuuhqEitm+3aan/tJ9xMiIt6cexPLt9g2tpFECdu38SKPqwdXeW36taQ6rS5jeia75i77k30uNi8y9saUc2UKSoGN0QZBHHCqdIqZwgy71i6qpOKFHqIgcmtwi+XaMkW1mAqgkTvi7a23kUSJOIoREenbyXhlwzeYLkzTsTqcrZ59oGCqaInYHymJfUJXdFbKK6mt5VFi50XZNn+WrAgPIxNPLx4vyt9QxtebTBBnPJM8yQfsLzu/pKyVKatlSlrSXLfj7/DBzgf86NyPaOQbOKHDezvvUVaTbf6j1bNDQa1ICl7osdpb5eODj/nXL/9rLjQvnLi+w0rcancVwzOo5qqJ6BUlpgvTOKFDTIwqqhSUArcHt2nkG0yCCUvlJURRxHRN7MBm4AyQRImclCOv5ImiCElI0iMOm8QmwYSxM+ZG9wYltYQbuyxXl3FCh4/2P2JvsgckVeGF0kJqxxh6QypahZn8DGUlOfaj+bmyKNMqttg1dxEFkZyUI4gDbg1uYQUWlm/xm/3fMFucpWt36dt96rk604VpVFlNfd2HVdh6vs5UYYq1wVoScRdDLVdLPce71i7L6nIqgDbGG4RxSFWt0rE66KqOruiMvTGyJKNKKqZnPlIwxcSsNFfSTGH4fd7xo3hRts2fJSvCw8jE04vHi/I3lPH1JhPEGc8kT/IB27W7aRW4IBcolApEUcS+vU+z0MT0TO4M73C9f52KWuGtubeO+VL/Yfsfkngyp0tRKdLINXBDl5/c/glzpTmAY9vQNa3Gam+VPWuPjfFGGqdm+iaO71DNVXm1mgiQtcEa28Z26hkee2Om8lOJbUEAWZSpaTVujW7RKrSQRInZ4iwAPbuHKqloskYQBWn1FwHmi/Nc7V7lw86HTIJJcs7UCqeKpzhfP8/B5ADTM9k39xERUUWVaqlKSS0dy89dG6zhRz7EcHd8l/niPHfHd8nLeUpKCUuxeL/zPt+c/iZb4y12rV1CQt5qv8VscfbE4Sh2YGN6ZjL8JHR5pfkKHbODKv3eJnIogK51r1HTanihR17K40d+6qGeKkwxsAf07B6Ga3Bp6hIjd/SF2x6O3m/PM8+aFeFBZOLpxeRF+BvK+HqTCeKMZ4qn8UA2801M36SsltP/N3AGTOWnMD2TtcEaXbtLXavjhM6xaLDV7iqrvVWIE0+tgMD+ZJ/pwjRDd8hqdzVtqDvchv7bO3+L6Zt0nS6WbzEJJnTtLhERFbVCHMepf/jQrtAsJBm/Va1Kz+5R0krUcjUauQYCAs1ck9OV04kvuZuMBD60GswX5lkqLyXVUtdMR06/s/NOImaB6fx08hoxEdCSIGFHNqEd4kYuiqTgBA41rcYvtn+BgICu6pTUUipW4yhm09hEiAVmi7MIgoAd2rT1dtKsZ2wBoIgKH+x+QBRHvN56HTdwgeNCJ4qTRsHZ4iyGZzB0htwd3WUSTNg2tvnmzDeBRMgGUcC2sZ2sZdIhjEKm8lNoksaN/g3O18/zcv1lZEl+oAXgcauOz4PH9n7uX/NhgsbTDkF5FsjEU0ZGxrOG9Fd/9Vdf9RqeOf7zf/7Pf/WXf/mXX/UyvnYcVhhFUaSoFHEjl3vGPapalZyce+C/KypFrnavgpCINcM3OLAPuDR1Kc0FHriJLaEgF6hoFSbBhOnCNJ/0PiGOY/p2H13RkUQJBBi7Y1qFFnZgM1uaJS/nEQQBRVR4f/d9unYXN3BxAxdJkpAECWKYL82jKkkmb0SE6Zu8XHuZP2r9UZLIgMi+vU8QBZyunE68s0JMXasz8kb4kY8symn+sCRItPQWoiDS0ltcPbjKzeFNfr3/a6I4Iifl+MbUNygqRWr5GmW1nFTe/BEv1V6ikU+q3TkpRz1Xx4s8bN9GFmXiOGboDpktzjLxJwiiwMAesFBeQBZltowtPu1/ShzH7E32kCWZglIgJ+cwfAM/8lEllUa+wcAdsDZcww5sFsuLvFx/mQP7gG1zGz/0uWfcY+ANmC5Ms1BeYOgOGbkjFkoLjLykShjFEW7o4oYuK80VdEXn1alXOVc/hyZruIHLtrHN9f51IGneO7wvcnKOqlbF9EzG3piCXODl+svHxO7T3l9fFiN3xM3BTdaGawzd4bHjedCa+06fd3beSaYE5urHjqGqVbln3Et2HgQ5fSh4uf7yV3J8GRkZGc8g/9+DfpAJ4hPIBPFXw83BTURRPCY+EcD0zNQScRIVrUKr0GJ/ss/B5ICKWuH7898njEPWhmsYrsG2sc3IGXGqdIqiWuTATlIZ1oZrTOvT3B7dZuyOmfgTTN/ECizear+FEzqcKp065k39aPcjgiggJiYiIifnkoqoEFHL1aioFf78zJ8zX57nnnGPhfJC6m9WZZVJMMH0TBBIsoALDaq533toy2oZwzNQJIVYiBEEgaXyEjf6N/jHnX+k5/QAmMpP8YOFH6AKKrV8jbycTxIn7APminPklTyNXINpPRGha4M15kpzjL0xNwc3E6H8u0SIdrHNny78KSW1RNfpcnd0Nx1usT5aZ+gOaeQaRHGUeqPDKKk+l9Uye5M9+k6fvtPnwD5gvjTP2B3Td/qs9lbp2B2qapVqrgpAPV/HCRyKapGX6y/Ts3t0rA45Occ3pr/Bt2e/zb69j+El127f2mfb2E7PdS1f+4yYzck5ZvQZFsoLzOgznxGBT3t/fRk8rji/f8075g6CkPjDG/nGsWNYLC8+8qEgIyMj42vOAwVxZpnIeGb4PB7I+fL8scEKkCRHvHPvHfzQT0Yn+ya71m4iGicHiEWRc9Vz9J0+QRQwdJOIs4pWYUafQRRFzlTOfGYbuqAUCOKAOI6J4xgncJj4E4jBDV0EEuHS0ltcbl9my9zCDVzyUp6SWiIv5WnmmyyVlxg4A3aMHQzPoKW3yMk5xt6Y+dI81VyVKI5Y3V/lb27/DbcGt4iIEBBYaa5QU2voso6EhK7qSSXcGSAgIAkSlmclUW7F5LyM/TFb4y1ySo6Xai/RsTqsD9eZLc7y56f/nIpWYaW5wtpwjTPVM5SUEkNvSEiYpkXomo7pmSiigiiIiLHI0BtSVsrk1Txe6LFj7STNhr6BLMjUc3WCKMAKLA76B9S0Go18gyAKMDwDSHKeX2m+ktod3tt5j43xBkW1SEktcb17nf3JPmWtTCPXIIzCh0axfdH315PyKGvG4zbA3b9my0+uqembJx5DZkXIyMjIeDoyQZzxzPBFeyAH7oA322+m45zDKGRzvMnqwSrfbH2TVxqvYHkW7+++Ty1XY7YwS0TE2BuzWF6krJZZaa58xpva1tuU1TJduwuAFSSNYgulBRbKC1iexd3R3cRHDBiuwcAeUNEqjN1xsr2fq9JzeniRByIUpEJSkc1VqWgVlmvLjL0xf7f+d9wY3WDX2k3X8K2pb/Gd9neQRIm1wRp9u8+OtUNNq1HTakhIbBqbzBfnOVU6hYTE2B9TUSoIooAmaSDBmeoZxt6YvJQ/NvDi6ICTslrmcvsyH+x8kAjfXJmp/BQjd4QkSBhBIno1WQNAkzVKlLgzukNBLiCIAqqkYngGsiijSRpO4HBndIdZfTZNmrhfHI7cETkpmc7XsTpc7yU2CcMzmNVnWRuscbZ6NvUvfxX314N4nPizxxXn969ZV3RM30RX9C/1GDIyMjK+bmSCOOOZ4VGNUU/aEGV4Bo18g5yco2N18EKP5doyqqTyeut1BEGgY3UoqSVG7oie3eN05TRnq2eZLkwTE5/YEf/Pz/zzxAZgddgcb6J6KnP6HHPFOTpWh6XqEsTw4e6HzBZnudi4SM/pMXAGKJKCJEhU1SrThWn80MfwjMR+EUeYnklOynG9d53f7v2WD/Y+SKuBc/ocb86+iSZpHNgHFOSkUt3Um/QmPWIhRpVUVporaEONilYhjEIEUaCtt6lrdQ4mBxxMDgiiAFmU0WWdqcrUsXNreib1fJ3lQnLeZ/VZnMDh9ug2EhKdSQcJiWa+ie3b3B7e5kLjAgWlkJ77OI4pqkUm1gQ/9CnIBUzPRBRENFnDD328wGOxvMi17rXPXMfD9bWKLf73nf+dxs8pkpKIeDnP5niT843zX9j99UXxONXfxxXn96+5qlXZMXeYK84luxNZZFlGRkbGF0ImiDOeGR4Wx/Q0QwcORUdRLXJOPQeAHdhEcZSKka7dJQiDZDR0vkk9X2fsjgnjkDPVM8fWdZSyljSvzRZn+e3eb9EVnS1jC1mUMTwDx3eQRImyVsYMTFamVrADm3e336WslRFEAUEQUGWVXJxDiAUuNC5wZ3SHteEat4e3uda7lo5Gfq35GpealxKRGUxYLC+yOdrktanXCOKA87Xz9J0+Y2+MG7r82dKfsWPusFRZSh8eVrur7Jg7QGLt6Dt9oihKPanNQpOKViGIAj7pfQIkCR6SKHGheYFLU5d4f/d9ojhivjzPUmWJ9dF6GtN2oXkBL/QwPIPz9fMU1SKqlAwlmdanySt5Jt4ETdE4Vz2Xvt9J4lAWZQQE9qw9ojiinqsz9sYU1SKKqNC1uxTkAovlxS/k/voieZzq7+OK8/vXXMvV+OGZHzJwB1lkWUZGRsYXSCaIM54LnmbowINExxutN1gfrwMw8SaEhGyMN8iJuTQSLYgDalqNKwdXTqxIHwqVV6depaSUuDG4ge4nDXFBGHBreIuqUmVD3CAmpqW30BWdmJiaVqPrdImJIQbP95AkibXhGk7ocLV7lduj20Ai6r89820uNS9xc3iTHWuHklJikk+i3l6bfo1Pep9g+RYlrcTpymnCOKSSqzBVmPrMuckpOUpCCT/0QYHepMfHBx+zZ+3x3bnvktfzNAtNLnCBnt1DERVKaok3229S0Sp0rA7n6+fTxq+l6hK2b7Nv7TNfmkcRFapa0ji3a+2yZ+0xXZimoBQQEHBCh+XacjpF7kHXqaJVmPgTrh5cRZM0YmIKSgE/8jF8AwGB78x+54mF4B/CY/s41d8nEecnrXme+c+8LiMjIyPj6RG/6gVkZBxyWAX2Io+KVkmHZ4zcEYZnkJOOpwbkpFzalHUSh0JCFVVG7igdIDFfnk///9AdsmPuMJWfoparYXgG66N1ikqR9fH6iWs5kThJOfBjHydMmuyswKLv9Nmz9vjF5i/YHG/SyDVQJZUojrg3vofpmbRLbaZyU2wYG/y3m/8tFcNT+SnahTb9SZ+14RpltYwiJpaLLWOLdrHNhrFBVasmucNeYl/wwiRa7f7qaUzMhfoFhu4wmXznjmkVW8hCMhHuV7u/StIvgEa+wYw+w1tzbx2rwh9N24BkEMqZ2hlaeoulyhKnSqcoKAVUWWWuOMdieRE3cNkxdwjjkHPVc+lo6MP1nXSd3my/SUtvUVOTKXc9p4eAQEEu4ARO4l/2jYdfk6+IxfIitm9jBzZxHKeDSO6/HofHff85zsjIyMj4wyPEcfxVr+GZ4/XXX48/+uijr3oZXzsOxfDRypod2Gkl8aSfeYFHUS0+9aCF//Sr/8TAHRAT44YumqQhIGC6Jm/MvkEQB+iKTktvpVXNw6lsh57bu6O7zOqz7E/20yzeIAy4ObhJSS0xV5wjp+RwA5eVxgpe5KXVQsM3UFD42ebP+PjgY2JiJEFiqbzE2fJZ/MjH9E0M3+BS4xLz5fnE7+z2UEU1jVULooCO1aHn9DhfO88Pz/7wM+fh8Px+2v+UoTNMJt/FsGPuJKOkPZOXai/xxuwbx471KO/ee5cbgxuUlXKaPDH2x5yvnWelucLPN37OyBtRz9Vp6S2KavGxr9P9HvGO1UESJN659w6yIOOGLl27y4F9wI9f+jHnm+fTqv+zJiifxwEgGRkZGV8DhAf9ILNMZDwzPMx7ebF5kSsHVzA9k6EzZNfaTYZXSDlOV06zUF44No75ccWHG7pYnoUbuURRxEScIMQCXbuLEzocTA7YtXbxI59Xm6/SyDcwPZNfdn5JEAVpcsTYG/OtmW9xpnqGv1v/O26Ob1LP1ZnRZwjiANu3EUQBRVJYri/TsToYnkF30uX/bD72FaYAACAASURBVP6fNEWipJRol9rM6/Oosoocy7RKLeIoZuSO2DK3KKklVuorXO9fJ4xC1ofrOKFDVavy/VPfJyfnTjz+Q2uCLMgYrkFeyWP6JqqsYrhGKlhXu6u0i23ebL/5md+x0lxh7I3Tqr0syrT1NvOl+aRa+zsx7Ec+a4M1ztXOoSs6buA+1Kpwkke8Y3WI45jTldMYnsHAHRAS0sw36Uw6zPvzFNUi8GDrzFclTLP4s4yMjIzni0wQZzwzPMx7WdEqLJWXeHvrbSzfYuyOkZAI4xA3dLk9vM252rknyqYduSPCOESWZEbeKEk2EGREUSSOY9679x52aKMrOjkpx687v6acKyfRar6FKqoMnAGVXIWt8RbX5eu83nqdvJxHV3Veqr1EXkmOxfIttkZbbI43CeMwbQz777f+O5NgAsD5+nn+6al/yrvb77Jj7lBQCrxUfwlN0hAlkQP7gAv1C8nUttClpJQYeANKaolX66/ihR4bxgbnaycnLxyKNEmQuN67jiiKaJKGJmqooprYMUQJXUm80A/ys77ZfpPV7iq3h7fxI5+yWmbL2CKv5Knlavihn8aw3RneQRKktCr+IEF6v0c8jEKiOOKDnQ94qf4S1VyVkTtCERWWykuYvsmtwS2Wa8voin6ibeJpGjEzvhyyinlGRsazTiaIM54ZHtV5P3AHXGhe4J5xj6nCFBvjjaTa6RlM69N0rA5nq2cf21N6KJx/evenlNXEArBn77E93qaoFNmb7DFTnCEIA3YmO5ihyRn5DDf7N1mqLqFJGl7g4Uc+86V5rh1cwws9LN+imWsSRiFxHGN6JjvmDjuTHRRJoaAU+MX2L9IRxIqo8G9e+Td8u/Vtto1tVEklFmJenXoVP/LZMrYwPIMojvAjH0JwA5d6vo5v+kkWr0Dy3xEH1INEyOW5y1S1Kj+5/RNuD28zXZhmvjSPruhpRfdR53DsjVEllSAK2DK26JgdvtP+DrP6LLcGt4AkOu16/zpLlSUu1C88tIJ/dHfA9EzWBmvois5ccQ4/8vnt3m+ZLc6yVFkCkpSPnJRj19rlVOnUiTm8T9OI+SgyYffkZA8mGRkZzwNZU13GM8ODmuCODjPISbmkOiupSdJBDHZoo0oqlm891pCCwy/o93fexws95svzSILE5ngTRVBoaA3ycp4ojgjCgJE3AglyYg5FVDiYHOD4DgCKpDByR3TtLvV8nctzl3m5/jK1XA07tNmb7HEwOSCMQqZyUxi+wV9f/+tUDBfkAm+23qSiVhg4AwC+1foWFbXCJJjQt/vJ+wgKC6UFrvev44c+52rn0GSNudIcRaXIweSAjtkhiiNuD2+zNd76TIPiezvv8e69d/nZ+s/4aO8jXm2+ysXGxbS5sFVMPL+POoer3VV2rB0kQaKklpAECdM3udG/QVEtslxbRpZk7ozuUFErrDRWKGkl8nI+reDfz+HugOmZ/HL3l2wZW2yb28yWZmkX2zQLTYpKkan8FKZnUlJKKJLCwBmc2LB29H45yqMaMR/nvnncRsvD179z751nsvnvD8XRB5PDPOkH3QcZGRkZXxVZhTjjmeJh3stD0aQrOl7o0cw3ud67jhd4eKGHJmrUc/UTva+HHK1WTRemsQKLKI6oaTXapTaQfIGPnBGanExVq+VqEIMjOsiizKw+y8HkgLySJ47jxI8r5zlXO4cgCCxVlrACiwYNxt4YSZCY+BM0WeMX279IptMB5yrnmC/N0yw0MT2Tqlblev86C8UF/sncP+Eftv+BgTugptU4Wz/L9+a+x686v2LH3CGKIzpWJ8n/bVxg19pNo8r2J/v8l6v/haXKEucb5xFkgTAK2bF2GLtJZVcSJQbugJXmSuJfFpKJeocPBjP6DMCJFdA7ozuUlNKx6XTz5Xnuju5yceoiuqJzqnSKPXOPc7VzdKwO1tBCV3RmCjMnTpdbLC/y3s577JjJGOu8ksf2bYpKkdniLANnwIF9wHx5nu/NfQ/DN+g7fSpq5YGVxi96Mt2TVJyzqujv+UOOzM7IyMh4WjJBnPHMcv/2dE2rsT5ep6pV2Rpv4YQOXuCRV/L4oU9Nq7E52kQURFp660QxdyhqwijEDmzuDO8gxAI71k4yiCOGvJSnE3QYO0mz3JQ+RV2rk5NySILE6dppbg9vsz3eTqfMyXkZO7D5eP9jAKIoomt30SSNxdIiN/o3uLKT2EFUSeX16dc5VzuHIii0ii22xlt4kUdVrRLGIX7kc6p0igvNC0y8CVEYYfomiqgwDIZAEo02cAZsGVsUlAK2b3N3dJfT1dN4oUff6ac+211rl5JSwo98/MCnpJTwIg/DNzhXO8euucvmeDMdW93INx4o4k5KpsnLedp6Gy/wuNG7QRzHSKLEp/1PmdanKapFvNDjk94nnK9/1uNc0SqU1TIjZYQiKUAyWloSJAzP4NLUJTbHm8yV5shJOcphmXqu/lCB+UVPpnsSYfdl2DWeV/5QI7MzMjIyPg+ZIM54JjmpwrY+XmepvMTAHeCEDp/2PuVU+RRzxTlKSimtdE78yQP9qoaXVEFvD2+jyRov1V5i19zFHyfxZo6fvI8gCNS0GqIoYnkWMjIz+gz1XJ2BMyCKI2aLsyyUF1gbrqUT5TZGyTbwXGmO+dI8d8Z3+F83/hc7VjIhrplr8kbrDVqFFn23T1WrprnH56rnOFs5y0/Xf0pJK1FSSwztISN/REEu8H83/y+L5UVWplY4V0sm73UnXT7Y+YB6vs7N/k3syMbu2snAj8hjvjTPrrWbNgEeDgfxQg9VVjE9k6Ja5FT5FFEcsVBZIIxC1oZrWL6FLMhIgsTlucvpOTxbPcuN/o1k0t7votcMz2CpskQYh5xvnCcn5fio8xEbxgYltZRE58U8JPAmyUleaa6wVFlibbCGJEoookLf6dPINfiT+T95rAltRx+kJEHCCzzcwP3cU92eRNhlVdHf84camZ2RkZHxecgEccYzyYMqbAN3kE6IKypFKloFQRBYG6yhyUlaguEbD6zIldQSN3o30GQNTdJAglOlUwDcHt3G8iwqSgXTNxn4A8pqGS/y0GSNpUqSbrAx2gABqvkqdmBTy9UY+2M+6X3CqeIp/Mhnc7SJIAr8j7X/gRu6CAhcbl9mJj/DtrlNQSlAnDSehVGI5Vv87O7PmC/PU9fq6LLOMB7SmXRo6S0qWoXV3iqSIHGmciY9nka+QS1Xw4s87NCmolYQEOh7fUbeiKXyEnIopyOlV5orANwa3MKNXHRZTwdH6KpOEAasDdfISTlKSgk3dLl6cJWV5koq8A6j18buGCMwUESFdrGNruiospqee1VSWa4uM3SHiKKIruhcKF1I8o9P4Nio7d9ZLe63RTxqQttJD1JfVE7xkwi7rCr6e/5QI7MzMjIyPg+ZIM74SnlQ1/7jVNiOig7Lt5Jt+cBDV/QTXw+/86ree496vk4cJ5XSgTtI3k+tYHkWXuwx8AZM56cpa2XswObAPqBv99k0NqnlasiCzMd7H2NHNi9VXkJXdVZ7q0y8CTkpx53xHdZGawDois6Pl3+MJms0803+eP6P+Xj/Y/b9fSzfQhIlinIRURDZNrYpq2WG7hA7tJkrziFLiZht5BqcKp3C8A1mSDy+TuhQUAqsj9cpyIW0TbagFCgpJXp2j6JWRIgF1sfrdKwOM4UZ/Mhnz9qjXWwzXZhmqbzELzu/5N177+JHPqqoplXvklJitbuaVokPo9fuv27XuteONbEder0lUeIb098AkmEqBbFw4r1wVHDqis5cae6Rtoj7+TKtCk8i7LKq6HGyXOaMjIxnnUwQZ3xlPKzx6HEqbEdFR0EuJH7eOGa5vHzi6w/FtyiKbI23qOQqNPINikoRBGgVW4iiyL61Ty1XS2LOYp+IiFl9loE34FTpFJIocXd0F0mS0EWdm8ObKJKCFCfNc7/t/paxNwZgubrMv7v471goLRDEQXo8nUmH+fI8G6MNZEkmiAMKcoGCUkBAYNvYRhZkcnIOJ3BoFBq8MfsGu8YufadPHMepyJoqTGEHNjkpx5a5RVEp0tbbuIGLKIo0c02G3pCX6y+zY+zw/u77tItt/vjUH1PJVehOuqz2VslJOfasPVQpsVKUtTLEMFOY4Wr3eJX4JIFz/zVr6S1Wu6uJTePIeh8kCr+ISuKXbVV4XGGXVUUzMjIyni8yQZzxlfGwat7jVNiOio68kmdvsocmaawN1pBFOa1kjtwRq91Vrh5cpZarcaZyhg1jA2JoFVpc719HEzXKWpmiWuTu8G4iopwRqqQSRiFNvcnaYI3F0iKb400GzgBN0lAEBcu3qApVzMDk5ugmMTECAt9ufZt/tfyvuDx3mWvda8fEkK7ojN0x28Y28+V58kqeolpk7IyZ1qdRZRXbt7F8i5ySo6gUk/HRgkTf7h8TWRvjDQbOgOr/z96bxsp15md+v/fsVaf25W68G1dxEaVe1O6W2p6M0TE89hiDTObDeD4MMgkSDwzkUz4HiYN8CvI1QAAbEyBOjIGTgY2xOwNjYE23py2r1ZItiRQ3kSJ597r31l7n1Kmz58PLe5qkSIoi1W5KPr+G0BLrnrpVt65UTz3v//88Zo3l8jI9r5flBLesFmESoimabPnzhzStpvwAEU5YKC0w8kcIRDYWsjmW4x5xGrNaXsWLPZbspc90WR9+zVRFZam0JJflnlIUfh4n8VGnCy/SqELuiubk5OR8ecgFcc7PjSe5eU/rsB193VpljTRNGftjwiRE3NveGvtj7o7vsuvs0ig0EEJwZ3QHIQQdt8Ph9JBjpWNcaF9gMBtQ1IqcbZ7l9uA2mqLRtJrM2XPM4hntYpsD94AgDSjpJcbBmEE0QBEKXb/L0JfpDwWtwK+u/Sonayd549gbVM3qA0LNCRx6Xo939t4hTVIGswE3BjcY+2NaVgtd1Zm351lsyZILUzUJ4gAv8lCFyvfWvvfAz2Gtssaus8snw08I4oBZNMOLPQpWgf5MzhIrKJTNshS6ccz1/nWcyMleh4JWIAoj1qvrdKYdGnqDKIkwVINRMGJtce0z83sf9Zq9vvT6z8QVfdzpwnplnbvju8CTRxXygo2cnJycnPvJBXHOz43PcvM+j8O2Md6gVWyxUvnp0pUXebzbeZfVqhxXKGvlbB64oBV4ufUy/VmfVrEFyOW64WxI3aqzXFnm24vfZrWySs/rcaN3g9FsxKaziYEhCx6YoKDI2uc0AqTj/PeW/x6na6cJk5Ar3SsATMIJ++6+jErzBvS9Pi2zxd50jx9u/ZCSUWKhsIATOvz1/l/zGyd/AwBFKHw8+BhNaByvHs9+HpcOLz0g5i40L0ghGoxIkgRSWRpSM2psT7YJkgDbsFFShX1vH1VREakgiiM6boeV8go1q0aYhKyWV9l39xEI0jTlVO0Umqpls79PEpN/W67oZy1dPumDVJ4RnJOTk5PzMLkgzvm5MPJHOIHD5e5lamaNtcoamqo98+LRUcTWzclN3FCWQCwUF+h6Xc40zmQLXj2vR1EvEiURYRLSsBq0ii2CKKBklNAVnRO1E2hC43r/Om9uvIkXeVxsX8Q2bE7WT/L27ts4vkOYhPSDPum9vuT1sqwovtG/wYF7QErKn97+U0zF5DuL3+F49Tjv7r1LImRGsUgETuAwV5QO9J63h4Kc+f3hxg851zxH0SiyVl5jpbJCnMaZ4/2wmFOFykppha7X5cPDD0lJGQUjztbPSoc5CTicHhLEssSkYTRksYaAhtVgFs+yfOeKUWHiTzhWOYalWCyVlrLX5UURk09zuvA4nmfxLneWc3Jycr6a5II452+dkT/i7d23GfkjVKGyM9mh43T41uK3nllYCQRX+1ep6BXKeplhMOTK4RWSNOFq9yqWbnF7eJttZ1tm4iLzik/VThHFESlpJoaORN9qdZVZNGPb2eav9/+agirnfE3VpOf36M9krbKpmHx74dsgYMfZwdIsul6XMAlRUdE1nXc67zD0h+y6u5T1MiIVuJHLOBhjqiaqoqKhIRBoikZn2qFklDjXPMeJxglKRukBx/tIxMVJzJ6zx7uddxnMBriRK/OBhcHWeIuRP+JbC9/C9m323D2EEJysncSNXA7dQ6pGlXONc0RE1K06s3iGG7g0C03SNKVklGhYDepmnY3xBh8efIihGqxX1xGa+LkVTjzPrPCzLt69KB8GcnJycnK+eHJBnPOF8bTu2ZXuFXbdXSp6hbniHDWzxjiUqQzPJSzuFT94kcfOZIc4jTlWPkZ31mWvu8dKeYUD74DNySYLxQVWyivoqs7f7P9N9n3LRhkncCjoBcazMT/p/ARd0dFVuTz3zt473B3fxU9k/XDNrPHG0htUjSpe5FEza9wZ3eFweogiFGzDRk90VKHy/v77CAQH6QGzZMY0nGbRb6qiogiFglYgTENKeomL7YtESZRVHxe1IluTLc40zgDgBA63BrfknK8/oj/rE6YhCgqpkqKpGkEkZ4rn7DmO145npRvNQpMT1ROoisrGZIOzjbNZvvOjXtcjIagIBT/2+Yutv6BZaNIqtB5bx/y0vxfP4ro+T6zZs4rpLyLSLXeYc3Jycl5MckGc84Xwedyz26PblPWyPLIHTM2kTJnbo9sPNKJ9HlJSzjfPsz/d587wDgWtwPHqcdzQpe/12ZpssT3ZpmbVOF09Td2q4wYuiqKw5+5R1IuoQuV67zofHHzAydpJdt1dLNVCU+Qox4fdDznw5CiEgsLX577OYnGRKInkDLGAkTdi390nSRMSEob+kGk0pWbUGPgDbM0mIsIUJj4+bihzjw3VwFIs4jQmTVOWS8t4kcfGaINzrXOUjBKTYIIXefS8Hq1ii47bkT/Dex8EnMChqBflcmEwJkgDylqZQ++QC60LVIxKVu5xrHyMglYgiIOsPe5IrHXcDk7gYBs2i/Zi9gGhoElBvDnZRFd0ZtGMMAkfqGN+XN32434vntV1fZ5Ys2cV088b6ZY7zDk5OTkvLrkgzvlC+DzuWZqmj7yPx/3501A2ygSJnAPuel2COGB7vI2iKHiRLLhwAof+tI+ru8RpjK7ppKScrssFuKM654pZ4ce7P8aNXRaLi/RmPW4MbjDwB/K5qQX+6Uv/lFahxc3BTSb+hIvti1zrXeNwekicxKiqrB32Io9pOIUUVEUlURIWC4uyWS7xiPwoiyiLiNBSjZpZo2SWZPyZEOw7+0xCucB3snaSjfEGtmHjBA6GauDHPnWzTt/sE8QB03hKWS+jJzphGhLFEReaF1iprJCScrF1kduj29wY3MjE953RHW4Nb+GEDk7g0LAaTENZMnK9d51X51594L8WupDP7f465kcJvh9s/YDVyr066Mmn66Cfx3V91gW+ZxXTzxvp9rMsDfmiyB3snJycv6vkgjjnC+HzuGcL9gI/2fsJqqJSNspUjAphEmYu47OwVlnjzY03ef/gfQAMxeDAO8CPfbm0Fs0YBkNGwQh/5jMJJqyWVxkGQ5zAwY1cztTPECcxURLhRq5snBvdZmOyQZiEANTNOr+49IvMFedQFZVzrXMoKBT1IpoiCzbminM4ocMsnmEIA6GKrEDD1EzZIifKBHHA2JczxEW9SMWoUNALqKlKGIdZVvCd8R0UFBqFBsPZkHaxjaEY0oVOE07X5bLbYDaQYlmzsTVbVlirBVqFFn988495qfkSbuBiaiZJmnC8chxDlT+nDw8+5NX2q6SJdL+H/pC54hxDX6ZubIw3eLn9MikpJ6on2HV3SZIETdWyOuZHCb44jdmd7JKQPLIO+mddpPE4nkVMP2/73M/ruT4tuYOdk5Pzd5lcEOd8ITytezbyR8RpTM2qZYJwOBtyrnmOC60Lz+xQVU05w2sqJm7kMvAG2W0dt8PYH+PFHqZqEkYhg1TWNZf1MoulRQpagc60g4KCoRi0C21uDG6w4+4AMv7sfOM8daPO2dZZ0iTlvb33qJk1TjdOs2Av8NrCa4z8EdNwykJ5gX13n7E/RiBYKi+xXl0niAP82MePfZIkYaG4QMNqYBs2hmbgRz5RIlvrmoWmFPTlOXRVZxJO+Kj7Ea1iixO1E/z9lb/P3fFdVEXlWOkYaZoyCSYyszj2sA2b45XjKIrCyB9x6B7SsBq8s/cO7UIbIQSTYCKTN7QifuwzDIcEUcA0mjIOxlmk24eHH8raZa2IG7mU9BJ20cYNXTbGG6yUV+i4HabhFDeSKR+L9iJ1s87V/lXO1M5kIzJCiExkP6/r+rN0NB9130/rLL/opSGP4svgYOfk5OT8rMgFcc4XwtO6Z0d5wXWrzp67lx2hL9gLjP0xP9j6AXEaUzfrREnEyB89tUO16+wyDsaoQmXBXmB7ss3eZA9FUUjSBFWoJEmCrumESYipmERpRMWs4PgOfuTTm/bQVI1LvUt0vS4gUyQWigs0zSa/uPKLuKHLpf1LdGddmRPc/5it8RbnmueombXsvi3VomgXWbAXWC2vMgpGDP0hYRxSM2pYisU0nqIJjeXyMmWjjBu4zOIZv7T8S1zpXkFTNBCyYOTW8BZe6CEQsnDDH7FeWWfgD6TDbFb41eO/iq3b7Dg7eJGHoUiRHaYh2842Q39IQS1wd3SXVrFF2SjjRz5O6LDj7hDHMUIISlqJYTCk5/XwY5+LrYsYipE1AkZJRM2sAdL5vD28zSejTzInO07jbPQijELSe/8L4gA/8jlZO8kkmPBy6+VH/t7MV+Y/lbX88O/Az9LRfNJ9P0002/OUhvy8eNEd7JycnJyfJerv/M7v/LwfwwvH7/7u7/7Ob/3Wb/28H8aXCkuzqJk1nMBhHIwpakVearz0qTfYW8NblPQSpmbSLDRZsBcoaAUudy/zo+0fEcQBrUILBAxmAynYYp95e/6J33/kj/h3t/8dEREVo4ITOWxNtoiSiJgYgUBVVAzVQFM0bMOmpJco6kW+s/Qdwjhke7xNx+vw/uH7WSvbsdIxVuwVqmaVbyx+A0Mx2Hf30VWdOI2ZBBP6sz5xGtOddVkpr9Cb9thytvAjn4JewFRNvn3s26xV1gCypbeKVWGptES70EZVVJzQISLifOM83176tlw+NMo4ocPV3lV0RadVbCEQcuzCKCOE4JX2K5ysnWS9uk6SJmyMNxgHY45XjrPn7NH1urSKLQzFYHuynVUzf23+a7QKLZnJnIZ0nA4L9kLmYKtCZbm8jBM6/NLyL7FWWeNU7RRBHMjRkjSioBWYL87zUfcjEPfGJIhxfRdN0fBjn/PN83iRx8AfMJwNEUIwi2ZUzSqn66c/9XuzaC9yd3wXRVEo6SX8xGdnskPNrGFpVvaafzz4GEWRyRxCCHRFz5YLP+v35f7fm48HH3NreIuhP8TSLCzNeq77fty1YRLyUuOlz/x35OfF0B/iJ758vPeYxTOKWvGpf545OTk5Lzj/0+NuyB3inC+Mp5nLfPjY2Amk2JsEEwzNwFClaFuuLGNqJoPZQLqkn8HGeINj5WNsjDYY++Ms6eFoYW0aTQmiQLrEIkFPdIbxkNP10xxMD5hGU+5O7rLlbAGgCpVvtL/BelU6sF7kcXd4F1u3mcUzdie7hEkoZ4dTjeFMuqmzcEZRL2JrNikpbuTijBy+f+v7vNx6GTd0+bWTv5Y5hN1pF1WodNwOQghOVE9woXWBqlllvjjPT/Z+wtAfogmNqlUlTmKWSktYqsUng0/4ifcTfrz7Y1qFFt9a+BZvHHuDC60LvLnxJvvTfQ6mBwghGM6GTIMpZbOMbdh4kcedwR3Wa+uUjTKH08MH5rmdwGG1ukqr0KKoFx8QbSkp51vnEUJu0106uETf75MkCcuVZQCiNMILPVYqK/zC4i/IMpPwp2JyEk4YB+NHuo9bk62nOrr/WaY+PM99P09pyM+T552RzsnJyfkykwvinGfmi8iP3RhtgICaVWMWzoiJ0VWdntdjubxMz+txonbikd9T3Is3SEm5PbzNfHGeolHk8sFlvMijrJcxFINGsUEUR3wy/ARFUTCEASmEcUiSJNzs3eTtztvM4hkAZb3M2fpZVEVl4A9YtBdJkoQb/RuMw7FMtIgCGoUGQsiFuTANWbKX2HP3aBaaICBJ5NIbQlYKXzq8RNWUovao1OKoJe/VuVeZBBNKRil7nnEaUy/UGczkPPTh9JBj5WMcKx9jEkx4p/MOp2unmbfncUKH79/+Pr9x4jdYqaxwtnGWDw8/JEgCBIJdZ5ckTTKBWzNqWLrF9mSbU/VTvLb4Gp8MPsE2bJYryyzai1kZSBAFD4wvCET2oeYoCzlJEmbRjI4r57DPNuWC5KK9SNWsUjEqjPSRrJDWbS5ULqAqKle6V4jT+AFRevnw8qdSLR4lRn+WqQ/Pc98v+qzw43ieKLu/i+SJHDk5Xy1yQZzzTDzJXQOe+EahCpVr3WtSTEYeF1sX6Uw7TJQJ+1M5jjANp1kd89Gowf3fUxUqV/tXIYXzzfMYqkHP66GrOsvlZZpWk2EwZOyPmS/OsznZpGpWURWVklEijEKWjCU2R5t8Mv6EhASAOWuOolakZtWI4giBYOgP2RpvYegG8/o8buDSnXWJiWkVW7ihS5jIOdmD6UHmjo6CEUW9iCEM3MSlbJQpakX23D1OG9J1i+KIy93LfHPhm5+qYj6atY6TmI7boeN26E67vB+9z43BDaI0oqyXCZOQmlXDUAze7bwLwJ988ifEaYypmIRJiBu4tIttgjRgpbzC1niLWThDEQp1s44iFP7x6X+cZQZbqoUXeXSnco7a0Izs8R09Dl3TcXyHIA5wI5eCVsjmsj/Y/4DzrfM/HRMh5ULrQuYqgxwdud67ztnm2QdE6f2pFkc8SlD+LFMfHjfb/DT3/WV2Wl9kB/tFIk/kyMn56pEL4pxn4nHu2qMcv/uF8lFlsyIUNEXDTeUS2VEBRNksc3d4FydwiJKIf3TyH2VvMFe6V9h1donSiL7Xp2pVTM5DHgAAIABJREFUIYW3dt7Cizw6bod2oU1RKxITE8QBS6UlnMBhEkywdZtvLX4rm5P919f/dbbkpAqV5dKyFJhxSNfrYus2E2+CEAJVqBREgTvjO5iKSbvQZuSPCOOQOI2xNItpNMXSLGbRjLEvm/eiOCJIAzRVI01SDr1DWl6L0/XTOIHDX+38FV7ssT3ZzlxZgGvda3x9/usITXC+dZ7+rM+x0jFuD28z8keMZiPmCnMMZgNSUjRFQ6SCwWyAEzqEsXSsK0aFXWeXFWNFjo8IleFMjoq4ocs0mrIx3uCXV36ZlcoKFbPygENYMSoYmvFAVfTQH2JoBjWzxo3+DTpOB03RMFSDOI2JE/nXieqJBxoA73eV99w9BrMBB+4Bp2qnHvgv0WplNUu1eJKgfJyjCXzmQt7Dj+mII+H9PG5p7rR+9ckTOXJyvnrkgjjnmbjfXTsSOG7gsjPZ4ReWfuGRbxRO4GSVzQWjkC1n3ejf4JsL32TRXuTHnR+jqRqvL73OUnmJ3qyXHZVfPrxMo9CgrJXZGm8x8kcEccDAH3Cqdgpd6Gw6m6yWV2kX2jTMBopQmEUz/NhnpbzCueY5Om6H37/6++w4MlKtalRpWS1s3UZTNHShE8QBIhWUjFKWUzzwB6RpyiyZQUw26jHyR5T0EoZicLx6nB1nB1VViZIIQzGI0oglewlLtwhjubzWcTpsTDYY+kPON88TxRE3Bzc5XT+Nrdty8eyeWJsEExZLi7zfeR8/8mnbbcIkRCgC27DxY1+KcdXC8R1axRaNQoMokQkaAH2vT2/Wo2E2WK+tMw2n9GY9akYNJ3R4t/MuFbPyKYfwrZ23sNSfLrJ13E5WgrJYWswKPIpaEU3VcENX1mLPrVDUi9l1R66pEzhsj7cRikAXOgv2Ald7V7nQupB9GNBULUu1+CxB+fDj/TzO3aOc3O60S8Wo8NbOW891DJ47rV9t8kSOnJyvHrkgznkmjty1OIm5ObiJpVoYqkGURGyPtylohUzgHL1R3Bnd+VRlc7vYZuAPMBRDjhJUT7NWXcuu9SKPK90r3Bnd4Ub/BopQWCotYSgGA3/AwBswb89jaiZ+7MsZXWL8SCZTaKqGrdt8U/8m13rX+JNbf8KPdn6UzdaerJ2kqBZlBFoyJUxlZFoSJYyDMX7sMwgG2ejBNJrKuWSrIcVuHJHECYv2IrqqywU175CqXmWWzEhJMRSDptXEiz1eqr9EySjx152/Zr26zrnmOTRVw1Tlz2TP3WO5vMyJ6gm80AOgO+3SnXbRVI3V6ioFvSArocMpkR6RkjLyRghLUDEq1M06qlA5mB5AhExqiH2O147jxz777r6silatLDmhQ+dTlcob4w3ujO5gKEb2mrihi67o2LrNnrvHQmmB3rSHn/is1daYhlOm4ZQgCeTrfZ+ofKX9Cm9uvEmYhjT0Bgv2AgBXelfYGG1wvnU+c4Of9ej58zh3Dzu5RzPphmZkAjk/Bs95FF/WOfGcnJzHkwvinGfiyF3bdXalmBMQRAHHa8dJSB6Ykz16o3hUNfM0nDLwBlnM2WplNRPDIEcO3u28S8/rMW/Ps+PscLl3GYDBdECQBjSshhSNXpdFexFVURkHY5bEEqdqp+h7ff5q+6/40c6P2Pf2ASkSf/vV3+ZM/Qy/d+n38GNftqhFPoezQ+aL87ihi6EYKEIhTEMZQaUWUVSFgT+gYTZYLC3SLrRZKi8xDaYEccBqaZVZNAMFDqYHBHHAwfSAeXue883zzNlz/NXOX3G+dR43dLk1uAWArur0Z32aVvOBWeye10NTNNar63ScDoPZgCi95z4T4cfSNX5t8TX8yKdu1XFDl7niXFa8oas6v37813m38y7NQpORP5IOtmrg+A5xIsdcNsYb2Wtb0AuZa3+5d5lz9XOEifw5XKhc4NbwFjWjxon6CW4NbjGajdCExoFzQM/rUdJL7Lv7HK8e53tr35PJGfY8ZxpnHpglPt84z63hrS9kvODzOnf3O7mXDi89MB6SH4PnPI4v85x4Tk7Oo8kFcc4zcSQk7o7uoggl+2saTelOu5TNMqdqpx54o3ACh+v96wghMFSD4WzIld4VGlaDu6O7HLgHjIMx35z/ZiaKN8YbxEmctbbpqo4ayjlYTdFQhYoTOPT9PivlFQp6gSRJaBfaIODdvXd5f/99Lvcu48XScW1aTf7Z2X/GUmmJc81z/ObZ3+S9vfc48A4A+Fb9W5iqmYlRBYU4iUnTFCdwKBkl2nZbOp9GlYIml9Dsoo0qVDSh8VH3I4QQ+KHP8epx5u155u159tw9VEWlVWgxi2eUjBKn6qfouB36sz5Vo/qAI/lK+xU6bocD94Ce36PjdHBCB1VREalAUzReab/CxfZFVKHSrDR5b/893NAliiM0VWOtupbNCF/tX2XqThn7YypmhSAKSEkp6sVMOB65rHESs+fuZdnKd0d3aRVatIotVEXN6qFLeol/cOIfMAkm/GjrR+y4OyzZSyiGHFd5//B9ykaZX1n/lUc6a5qqfarw4lk3+J/HucuPwXOelnxOPCfnq0cuiHM+k8eJk6pZ5dW5VxnMBmxPtuUIhNEmTVO6XpddZ5cFeyF7o7jQusA4GDP2x0yiCTuTnazWuGyUiYm52b/J+7zPgr3AwB+wOdqkWWjSLrb5m/2/wVANilqRxErQFR0FhUko0yhGsxFRGqEJueB1afMSV/pXmIST7LmsV9ZZKixlgvrNjTcpGSWOVY7xxvIbbE+2MVSDn3R+wiycEUYhQRxkbqphyLEQUqhbdVrFFhWjwkJxgZuDm/ixz+XuZRQhRbRt2uy6u6xUVtBVnc3xJjcHN/nuse/SncrCDFu3OVY+RsNqfGpkYRJMcAIHUzPZPtymalUpGkW80CMlZb44j6VZNKwGdbPO3fFdViurDGdDBv4AVaiZGAYZg2apFkNfJnCUzTLzhXkqRiUTjkfC8NbkFpZqYWpmVp5xpnGGIAowFIOiXszqnZuFJprQ6LgdlkvLNItNojTCCR1s3eb9/ff5lfVfeSpn7Xk2+J/HucuPwXM+D/mceE7OV4tcEOc8kc8SJ2uVNT44+EC2wCkGQRRgaRbfWfpOJvCOqJpVXl96PRN6O5MdLrQuUDfrANTNOu1Cm7d332atuka70GbOnqPv9TOR7QQOTuBQL9SpmlUWigtsO9vsu/tsOVuU9BIlvcQ0mrLtbDONpgAoKMwX5mlazSxJoWE16Hpd2sU225Nt3t59G1KYs+dwA5eKWWEaTzESA1MzZXxaJOPLTtZOslpezRIyVEXlZO0k/3H7P3KueY6qWeX26DaqUAH4qPsRal+lZtY4VjpGvVBnc7TJjd4Nel4PgJXKChvjjUzYHv3MoyTiP2z+B0zV5HjtuGyWi0PadpuKXqFdbPNK+5XsdSpoBdrFNkDWELeCFMRrlTV2nV1KRomO26Hn9TBUg9P101iqlb0+s3iGG7qUdSkGg1jmB1uqhR/5maN7JNxH/igbk6iYFdnQJmTj2TScEqtx9rXTaMrh9JCSUXrgA9MRz7PB/zzOXX4M/mzkebw5OTlfBXJBnPNE7j8+vzW5hRu6aEKOKrxx7A0pSu0FmRscymiz0xWZlPCoo+b7XZXbw9skacLmZFPO3AJ3hneomTVeW3iNIA4YzoaESchf7v4lZb1MzawRpRFDf8jx6nGEENiazYnqCf6m8zekacqBe0Bn1pGlGIAudApagSRNGM6G+JGPpmhsTDbwI1kL7EYuRa1Iu9iWzXXhlHqhjqmYxFrMNJ7iJR7r1XVeab3CjrtDnMacqp1CVVS80EMVKl7kMW/Py8el24z9MU7o4Ec+35j/BmEqF/NG/oihP0QRCg2rgVAEbuAymA344OAD1iprmRBsFVss2AvsTnazUYd5e56CWqA3+2lxyeOO/Hed3ex2gWAaTrPbBQKRigeuORKGmpDVy0II/Mhnpb7yKcf0/tfzrZ23OF0/zY6zg1DkOEeapgz8Ad+c/2Ym2I+VjmVis27WPyWmjp5Hll4SutiaTVEvPpMjN/bHTyXYHrVkpwqVj7of5ULvMeR5vDk5OV8VckGc80SOyjFuDeXxeVkvy7GAw8tZxfCCvUCQBA8cNXuR98ij5vvdJD/2uTm4yZw9R0ErsDna5NA75NX2qwgEpmpSs2rc7N+kYlSI05i+16diVUi8hJuDm1mRw4F7gKZqHM4OGQfj7PtZqgUJzKIZs3BGlEp31dIsDt1DikaRYSBFtxd5KIrCvD1Px+kQJzFFXWYaz9vzaIrGidoJ/NinoBW4fHiZt7bfompWWa2sgoCSJtvdbN2mZtbYc/aYhfeO4QWIVFA36/xw84cUtSIDf8DL7ZepG3X82GfoD4nTmMFsQKvYyp7Hor0IQMWoSLdaNT5VXHJ/8seRkAzjMGvVq5pVrnav4kQOlmrxtbmvZekcuqLTKraypTpVyMXEnckO8/Y8VaPK1d7VbATjUZSNMueb5xn4A8IoJCKSpSFmTQr8h1xfJ3D4wdYPON86/6lSkq7XZXuynf3O3V/1/CSh9bBA63k93tp5i3PNc9nc9pME25Eovv9+8sSJx5Pn8ebk5HxVUH7eDyDnxaZslNkYb2SzpEIIhBBZmxhIR9ELPbzII01TvMjDC71MqB1xJDKCJKBqVtEU+XksiiN5rJ7GzBXmUFGzawzV4HB2SM2scbZxViYUpIJJOKHn9nACh43xhlzKmx1kYlhBoaJVEKkgFSkCQUpKEAWYqpnFxI39MXEaU9SKCAT7zj4lvUS9UEcgl/8KWiGLRhMIrvev48c+pmaSipT+rI8XelztXWVzssm13jVu9G+w7+4zCSfyeZXmAGgVWuxP99kab2XZwJujTRnnphq4oUvdrDPwBw/87OpWHVu3WS4vowmNntcjTmJem3+NjfEGb+28hRM4bI42udK7QhiHGIrBjrNDlEayLlqIrN1ux9nBUI3sZ+yGLpZqsefuZWkLry+9zrcWv0XH7TAKRtTNOmuVNe6O7z7S/V+rrFE2y3x36bscKx3D0AxaxRb//Pw/z5b27md3ssv2eJuP+x/zyfCTLOkCYHO8KZcvNYMgCSCV93/0O/c47hdoQsiWwYpZYegPEULWZR+laXye+3na6/6uMQkmn3pdLdXKUmNycnJyvizkDnHOE1mrrPH27ts0rIYUlHGAH/mcrJ3M3vSedm5zY7xBnMZ83P+Y26PbfDL4JHszPdM4g6VaaIrGwfQAP/KJ0oit8RaT2QRTNUmmCUWtiKVZFLRCNvow8AeMozEpMtatpJVIk5RpNCUlRRc6ilAwVZNmscnX5r9Gz+sxmA0o6kV0oTOLZkyjKapQ2RxLgbpaWc0iyUazERdbFzn0DlmtrNKf9el7fcI0JEkTPuh+wMnaSTpOByEEI3+Ep3hoaLTtNgvFBdzQ5ePBx+w6u7iRi+ZrlLQSPa9HYVJgrbKGrdtUzSrjYEx32mUwG2TLca/NvyaXBu851Q/PGs/iGYfeIaYqM5Nt3WbBXqBiVOi4HRZYoO/1mQQThv6QeXueulnP5oNn8Qw3cGkWmpnTFyYhZxpnKOkyDQOk+/9Zub5Vq/rAmMGlw0sPLKw5gcO1wTUaZoOSUSKIA24NbnGydlIuC9rzeKGHE8ilvJX6ymPHcO7n4bERN3Qp6SWc0Mn+7GmSI/LEiacjX0TMycn5qpAL4pwnUjWrXGxdZHuyjRM4CASKULjWv0bVqGZH2E/auD4ak/jh5g8ZBSO8yJNJBcLAj31G/og0TTnfPJ+lJARxwLXBNSzN4mvzX+P64Dpe6GGVLfqzPrrQEapg192l7/cBOQ+7WFyUR+6zLkA2O5yS0iw0WSotYes2SZowCSdESUR31mUcjNGFTkkvcWt4i3lbLuAVtAJLyhL1Qp2KUeGjw49IkoStyRakYOs2fuLLjGK9iKZoaKlGKlK5lGaUUYRCEAdUjSpv7bxFUS+yUl7Bj3xGwYgwCRn4Ayp6hTONMyhC4bX513hv/z3iNKZu1qlZNXqz3gNH9vcv0YGsVe7NeuhC53TjNAv2AilpVkXtBA4Vs8LEn1AyStwZ3CEoB1iqRbvSxgs9FKGwPd7GjVxs3ZZLh4U2buhmr+fT5vrez8MLaxujDSzNollsZuMxIJ3hs82zWRve04zh3M/DAs3W7Szp4oinEWy50Hs68kXEnJycrwovxMiEEMIUQvwrIcSGEGIihPhACPFr993+PSHEdSHEVAjxAyHE2kPX/h9CiLEQoiOE+O8euu/HXpvzdFxoXZCZtXHAzf5NtifbRHFEo9Dg0uGlB8TR0VjEWztvcenwElvjrWxMQgjBaDaS876KRrsks4ITEibBhK7XZam0xJn6GeI05uXmy3xn8TtUzIp0PdOQzfEmMTGWZrHn7WVi2FRM2oU27UI7GwtoWk0aVoOm1cRQDPbdfW50b/BeR+b0lrUyRb2IqZrYuk2cxtJJVkyWS8s4ocPX579Oq9CioBZwAxdVqNwY3KBiVChoBeJUpidYqkXX6zKL5axyq9DC1mxs3ZblI7MBk3BC02qyXFpmtbKKpVlYuiUdbBTcSLbArVfWud6/LsWwVZflH8X2p47s7z+udgJHzlSrBaIkyqqgy4acvx3NRllkXctucbJ2krbdZhpOadtt6lad9co642CMG8l0iSiO6Ht9Dr3Dzy0oH+ZIKB/VMQdJwMXmRTpOhyvdKzJ94t7Paa2y9tRjOA/z8HU1s8bYH1Mza891P0973d81Hn5dDcXI56xzcnK+lLwoDrEGbAH/CbAJ/Drw/wghLgIO8EfAfw38KfA/A38IfOfetb8DnAbWgAXgB0KIq2ma/pkQovUZ1+Z8DnpeD13T0VU9m6tUFTU7Ph/5I97efZuRP2ISTBj5I/qzPqfrpznXPCcX1JKYJE2YRlOqWpW6WSchwQkcgiTg9aXXGftj/nL7L+UilT+Wbl00I4gDkiRBURSuDK9kKRItq0XdqjNvzVMwCghFYCkWqUjZHG8yDsakcUqYhgRJwKF3SM2oMV+aZ2O8QZImVPQKc4U5SnpJLtx5hwz8AQWtwPnWeXrTHimpdC7jAE3RmEZT3NDFizwMYXB3cBcv8qhYFUgBRc7nDoIBHbdD2SjLsYpph+60S0EvUNSKDJUhbyy9wenGaYIokDO6wYiG1WAUjPi4/zGNQoOW1XogaeF+F/OT4Sd0p90s1cKLPCzNojvtsmQvESWRHI0wbF5pv0LJKJGmKSN/xHePfReQjvNaZY3tyTZBEmBosqJ6a7zF6dpp0jRlFs/oTrtUjApv7bz1qfSFJ0Vw3e8eCwTXB9dZtBdlLnUwYRSM+IX5X3iglOTzxqc9PL5Tt+r8xonfYOAPnut+8uKFx5Pn8ebk5HwVeCEEcZqmLlLYHvF9IcQd4JtAE7iSpun/CyCE+B2gK4Q4m6bpdeC/AP5FmqYDYCCE+D3gXwB/Bvznn3FtzlOwMd6Q0V+lBcp6WcZwxT4dt8PJ2snMIb7SvcKuu0uURNzq3yJMQ3rTHoZioCs6AkFCQtfrsjfdY728znJ5maJRZMmWrXE7kx3+8MYfMppJUT2LZxxODxFC0PN6jIMxfuIDcnFu0ZbVyYZqoGs6g9lAuqRphOM7aEJDExrTVEaNNQqywGLP20NTNSpGhZJRYtfZzRbPhBA4gcNKeQUnlEt7NaNG3apztXuVxdIi42BMEAcoQqFttRn4A0pmiapVhRT23D2qVpU9Zw8VFUM30FWdoT+U877RDE1opKTMFec4UTuBpVpc617jXOucFMP+iIPpAQoKfujjau4DSQtHx9VO4HBrcEu6zKGLoRq813mPillBExq/sv4rvNx8+YFaYvi00zsJJjQLTSzNouN2cAJHNtMV5AeOoygyAEMzPpW+AHy+CK4UCkaBqlUliAPG/vgBJ/pZhdajrjvKYX7e+8nJycnJ+WryQgjihxFCzANngCvAbwMfHt2WpqkrhPgEuCCE2AcW77/93t//Z/f+/sLjrgVyQfyUHMV7DbwBW+MtKmaFptUkiIMHRNXt0W00oXFjcANVqNiGzXg25vLhZYIkYDAbMEvk14exzOPdGG9wtnmWqind4j+49geESYgf+1mxhamaTKMp43AsEwcAQzFYLi1zonqCMA6JkYtvK5UVVqurvL39NgW1wGF0iB/5KIpCQS3gRHK5KogD3MClbbdpFptMoykTf8Kuu0u70KZm1aiaVVlJjcLWZIsoifBjH1VR8SOftt1GQcEJHU7ZpzheOc6t0S3KevmBfGVTNZlT5yhqRaIkwtIsRCrwE5+m1eRrc1/LqpsP3ANO1+X8743+DVRFpaAX5LG/VaNltXhz403m7XnKRpn1yjrvdt4lIWHkj1iwF9BUDTeU4vnbC9/G0Ay60y4EMtP4cbOeAsGV7hWiNMLWbU7WTmaFK/cL3vuF9f0xW8BTR3ClyJnx/el+tjh3vnmeOI2BvOwhJycnJ+dvlxdOEAshdOAPgP8zTdPrQogScPjQl42AMlC6758fvo17tz/u2oe/728BvwWwurr6PE/hS82jhIhAcLV3lapZxYs9puGU4WzIUmnpAVGVpik9r0dCQlEvEiYhcRqTkNDzekzDKWESMg2nCCGIQxmzNVeYyxrSDqYHDLwBlmFR1IvMohmdaYcwDbMUiapZ5eX6ywyCAbN4RpqmOIHDjrPDJJgwjadYmkWaptnj0lWdNE2Jkgg3dEnSBD/xqVt1gihAFSpxEhPFMqJMQWFzsgkpfHj4IUEc0Cw0qRoyAWIcjomSSC7w+V0aVoN5ez5zm4ezIWmSMmfNyei1JGZrskXLahGkASvNFTpuh6JWZM/Zo2jItIt5e56rvatcaF2gWWgyi2aM/TElvUTVrHK5exk3cvmG+AZhEjLyR9iGzZnaGTYmG6Qi5fbgNm7oEqVRNlLRKrayyuVHjQCM/JGcH763CBjGIVd6V1iyl3h96fXs9+Oz0heeNpnhaGnuKLkC5NJcUSl+6coecvGek5OT8+XnhRLEQggF+L+AAPhv7/2xA1Qe+tIKMLl329E/zx667bOufYA0TX8X+F2A1157LX3mJ/El5nFCZBbNQEjHb6UkhdwoHqEr+gMi5WTtJDeHNzEUmWu758hyiIbVYDgbMo2kUJ0rzjFfmmcWzjA1k5JR4kr3Cn++8efcGtxCFSotrYWqqIyCEUEqXWFFKMwV5ijrcnbWD322wi2aVlNGsAkNJ3LouXLW2VIsGlYDL/Jka1qSkiYpfuo/IJC7XldWIDfbDGdDikaRnckOqqKyUFzg5vAmQRwQxAGe5eH4DmEc0vE7zBfnmS/OowmNy4eXeW3hNSzN4i+3/hIv9oiCiJJWwo1cPup+RKvQ4vWl1/n20rdlMcXmD7jak2MYy+VlGkaDyweX2XdlHnJBL1AtV6mYFf5i6y/wY5+aWWMSTDj0DtEVnX13nziJqRfq3B3dZRJOKGpFbMNmZ7KTObBHlcuP4mgspm7V6bgdGVemlagYlQfE3cMu8lFt9dEpwdMmMzwqneBoNvlPbv0JhmqwXl3HTdzMPT+cHvK9te+9UGLzyybec3JycnIezQuRMgEghBDAvwLmgX+Spml476YrwKv3fZ0NnETOBg+Avftvv/f3Vz7r2p/R0/hS87gygo7b4XzjPJqqEROzXlvnHx7/h6xX1x9407/QusCyvYwiFHYnu3iRR82qYRs2ISE1q0az0ETTNDnzq+g4gcOlw0tc71+nYlRQhEJv1uNa9xqb481sXlggsFUbJZUpEF7kyciyWEaWObGDbdqUjBLjUM6iTuMpURpR0ksIBBGRHJ3QC2iKJh+L0Oh7fQ6mBwgh+NUTvwopMu9YtRgGQ6IkoqgVidOYQ++QUTBiGkxJkgQ3dEnTlI3xBrvOLtNwmo1VnKicoG7UGQUjJv4EN3RxQodPhp+w7+wDcnRjvjjP2cZZojTig/0PaBVbqEJFVdVsWe7Nu29y4B6gCpWSUWJzssnOZIfBbECz0CQh4Xr/Oo1Cg+XysswzNqp4kcePtn/E1e7VbP73URwlVpQMmTf86tyrnG+dz1x5eNBFNhQjc5G70+7nTob4VOpEdG8URjNQhIIf+/z7u/+e73/yfe6M7kinORh9KtXkcTycdvKzyg/OCzxycnJyvhq8SA7x/w6cA/7TNE29+/78j4H/VQjxT4D/D/gfgEv3LcX9PvDfCyHeQ4rp/wb4L5/y2pz7eNxxuBACTdU4Xf/pvOnR8fb9VM0qv3bi1/iDa3/A8dpxBrMBpPI+TlRP0HE6+JFPQS8QxiFhGhImsthCV3S2J9sceLKU48gVBrAUC0uxEIqQs8jhQF6fhKiKyiyeoaFhChMndjAVk5fqL7HtbLPv7lPUi7T0FopQCJOQNEkp6AVmyYx39t5hEk7QhIYf+QgE3VmXaTClZJSYRTJCzY99vFDm4Lqxi59Ip3YWzdiZ7FA1qyRpwrXeNeaKMq1CVVU6XocwCYmSKEvYKOpFrvauslxZpqAVstSO7rTLNJpys3+T9eo6LzVeIk1TtiZb8jqjSJqmsmEvGJOmKX7sc6J4guO14/zRx3/EoXvIYmmRXtJja7JFWS+zMdqQS3yKlgnDh4/4nyZ392lc5M+TzHD/0tr9s8mKkOMqg9kAL/KYxTO2xlucb53PxOaTlt3+Nl3bvMAjJycn56vBCyGI72UD/0vABzrSLAbgX6Zp+gf3BO3/BvzfwDvAb953+f+IFNMbgAf8L2ma/hlAmqaHn3Ftzn08ThSdqJ7AC+VnlIePtx+O3lqprPD1+a/jhR69WY+e12PRXmTkj3BDFw0NTdWIkoh2oc1UnxIkAX+x9RfcGd0hTMIHxHBJLWFqJq1iiwP3gFk4yxbtVF1WPB9VKk+iiRSfccTl7mXSNOWV9iu4ocud8R2iJMJUTFJStFgjTmIMzaCttel5Pfame8wSOcahCY2SUUKEgrJRlmI+8UmDFBTQFA1DkW6+PbR8AAAgAElEQVRmkAQy5ky1iNMYgUBXdQ7dQ6bhVFZRK6ALHVIYeAOGs2HmtlqaxZ6zx9Z4C4ChP6RslPmzT/6M043TlPQSK+UVpuGUg+kBXa+bZfZWjAqrlVUW7AW+u/RdNsYbLJYW8WMfJ5Qte7ZuoykaHxx+QH/WZ7m8TKvYysTi27tvowqV28Pb1K06Ba3AzcFNul6X883z1M06K5WVTPwJTXDKkLO/R9FtRzxrMsP9wjJFCv2e18NQDSzFwokddp1dLjQv4Ef+E+/rftcWnrzc97zkBR45OTk5Xw1eCEGcpukGPP48N03TPwfOPuY2H/iv7v31ua7NeZDHtU4diYgj5+9J0VtVs8qivUiQBJxunMYJHBnhFTrMF+dleUaxDUgR1Pf6XOtfY8+VJRtRGgEyUk1HOqc1s0ZBLZCQoGs6zWITVaj0vX62ICdSQZiE6KpOo9BARUXXdGzNZs/Zo2W1cEMXN3AZBkMWigsg5CiGj4+fSHc4jENKeglNkY4xKUz8CWEcyvrqJMWJHOyCLWerUznbrCgKmqrRtJpsOVs0Cg2iJKLjdYjTmJJRwhAGjUKDIJERYwv2AsvlZQSCS91LOKHD0B9S0mVG8Dgc8+PdH1M1q+iKTpTKDxF77h4H0wN0Vefrc1/HUAxuDm5St+ocS49xpnGGncmObMO797yOKq+v9a6hKAp1q47QBHESs+vuUtJKvDr3Ku8fvM+7nXc5Xj3OxfZFEhL+zcf/hpdbL7M/3cdQDNaqa5QMuc/6RYm/+4VlSkpZL2OpFgkJqqqyWljFj302xhuca5574n39bbq2eVNbTk5OzleDF2aGOOfnz5Nap45u++6x71IySrSKrcfOTd4/S2rrNsfKxzhePc4bS2+gqzq3h7fZm+wRxqFcvnP32HK2MjFcNao0TVmbrCnSye24HVnxq5k4gSPdXs3EUI1sPlhXdVmxbFaYs+fQFZ0bgxtMQ+lCO6FDoiQU1SIH3gF7zh7D2RBn5sh0CWJm8YwgCVguL2fjIghYq67xSvsVGsUGDbNB25LZx0dutSEMDNVgGAxJkakX7WKblfIKhmLgxR4pqSwTSeHVuVf5xeVfRBEKBa2ArcmlN1M1mSvM4cUe3WkXJ3Ro220SEqI4kuJfCE7WT/La/GuUzBKGZmQ5zb+88ssYisE0nKIpGgWtQFkvy69BCuCyXmbP3QNkXnJZL8t2P7NMnMScqZ+hUWhQMuQHg0kw4fLhZU7VTuFEDle6V5j4ky+0ve2B3xlNtvutVlZZr67TsBooioKpmgz94Wd+vyNxDT9t8Hu38y777v4XLorzpracnJycrwYvhEOc8+LwNEfeT3LgjiKovMjjcHqIbdiUdekgGqpBu9DGizw2x5ucqZ/hvYP32HLkqIAiFOpmnabZJE5jxsEYVahUrAr+1MdSLGpmjSRNSEhQhEKcxOi6TsNooAqVulWnXWzTn/UJ4oC+38cUJhNvIkcl0CiaRSbORLrDiY8QgiiJIAFTNynrZfbdfRISvrf6vawsYhpNM0cwSiKm0ZSpmFJUi5iaiaVYBHGAqZgMZ0NMVS7/Vc2qPP9I5YjBYnmR49XjlIwSp+un2XP3mIZTEhJebr7MLJnRcTpUTNl4p6CwXl1nc7zJNJxyrnGOi+2LCCGyAg1bsynoBVYqK6ywghM4XO9f52B6QMEsyISM0GO5vAyAG7rZ/xuKkT3HoT+kbtWl+w0ygcOo4IQOZbPMheYF7o7ucnNwk1fnXv3C2tvub4Yr6kV0VadVaFEySnTcDpNgwmp5ldP1z/5+9xeWbI+3mcUzel6PWTTj3976t/zyyi+zUvn8RR2f9di/TORRcTk5OTkPkgvinKfm6E30zujOI4/OBSJbZloqLWXHxyDnOLfGW3Ked3Tn/2fvzmLrzNP7zn/f/T37wsN908YqSay1W+V2VdltlztuG0aCdjAXQeD4Ijed2AgQ5MIB5mICz1wEMALECJCJAydjIB5kbDhpOwkMO3aju5PY1a5ySd1VKu1SSaS48/Ds67vPxds8TUmUilJLIkU+n75qLkcvz2GBv/O8z/95WGut8T+W/wdOEPeD5q08GT1D1sxiazZVp0pCT/DqyKvMZma507jDUmspDpVKhK7orPRXSBpJ8nYegFq/Fs/mdRuMpEYgAEu18IJ4ZbOt2uTtPNV+NZ7igIYbxP3KQRRgqiZJI4mqqvEM4wi80KPpNpnNzjKXjFtAAD5c/ZCEloi3yAUOrhs/TsNrDFYsr3XWGE4Mx2uclR++IVjvrLNmraEoCmOpsTgUt9eIoghd08kbeVZaKyiRgm3Y8YzkKK7cJo0kY6kx3NAloScGvbw9v4epmoPXar40T9NtUu/XafQbJIwEQ8khTuRPsNhYJGWk4n9PiSvA86X5wevQcltkrMzgNW/0G0RRxK3aLcZSY8yX5mk4jaceAreD5WvDr3GmeIbvLH2Hnt/jWPYYeTuPpmiD69zL43xr8Vu0vBYtt8V4epy8Hf9s31n6Dl879bUjGwBlVJwQQjxI+/Vf//X9voYD57d/+7d//etf//p+X8aBsv1HVFVV8laelc4K5W6ZnJkjJKTn9VAVFduwB60UhmqAAjeqN3ADN17Z3F7jWu0aN+s38SMfBYX5oXneKL2BpmioqooXeZwZOsPfmP0buH68DS9rZlFQqDk1bM2m5/cwNIPjuePMZmdpOk0ANOKpE4Rxj7OpmtTcWnzYTVHQFZ16vx4vyMDF1Ex0VUdXdSzdwtAM/DBuTZhKTzGRmaBoF7mwcYGNzgbXq9dRUOj7fQzNwAkdak4tnvtrpOLlHqHPsdwx6k6dltciJG5zaHkt1tprjKXGGEuNDVZbG6pB1+9yPHectc4ay61loigiaSSxNIvp7DRnhs4MtufNZmdZaa2AArqiD954vFx8GVu3AbB1m+HkMJZm0fJaDCeGB4FaUzRGkiP0gz45K4epmXF7hKKjKirXa9cZSYygoHCtdo2u2+XN0TfRVZ1yN559nLNyjKZGn9nvW87KcSx3jKSRRFd18lael4sv7zmw2bpNtV9FVVRydi4+IIkSz8j2OySN5DO9/oPsRu1GPH7wvv9O2277yD4nQogj4/982CeU7W1e4ofOnTsXnT9/fr8v40C5WL44qEpC/MdzobGAG7i8PvI6s9lZLm1diqcQ/HBKCFEU8eHqhzTcBnWnzodrH7LaWQXAUA2OZ4/zUvGleE0yKi8VXgIgjEIabgNLs+LHIWKpuRRvf0uPcHHz4qCHt+k243XFbgc3cFFQyFiZuM9Ys6j1a9T6NTp+Jw6+ioUTOXTdLqVkiSF7iLpTjydF6DbTmWkMLZ4IMV+aj7fMtZfouT26fpeaU4snVGgm9V59sOhDRWUmO0PaTGMoBnWnjopKy2vx5uibVHqV+FoVnZ8/8fO03Ba1fo2cmeOtsbdYaC5wu36bar9K021S6VV4pfQKBbsQt1qkxwdVvMe55f15X3v/53VF51r1Gpe2LmFqcTtFzs7RcuJDkIZm8Etnfumpth08CxfLF7mwcYGhxNDgIKjjO+iqznBymHcn393nK9wf76+8v+t/pw2ncWSfEyHEkfHQAQ7SMiF2dX9IWuusMZmeHHw+baYfuHV+/wiqMApZ76xTtIvcqt/im3e/OWiRSOtpptJTJLQEDadB0S5CBB+sfYAf+Lw98TaGYtD1ulT7VdJmGsd3+InJn2AsM0ZST3K3eZe1dtx/+1r+NTa6G/ihz6sjr7LcXOZm9SZdr4uhGYynxwHir/e7BFHAkD2Erdts9bbwQo/p9HS8dtpM4gc+aSvNWmeNIAwoWkU6agenE48D2w7aXT9+/ISWoO7WMRQDQzHiDX3JIivNlXhixg+qwNttGS23xVxhbhBEprPTZK0si81FRpIjHM8fJ2NmaLktOm6HiOieW9qP07f6eV+72+fPls4ymholZ+XY6G5wYe0CISEFu4Cqqiw0F8ha2QN9i302O8vHmx/Hv8NGBjeI7zZMJaeO9Fg0GRUnhBAPkkAsHrBbj+FGZwNbsyklS4Ovu/+P6M4RVESw2duk43a4Xr3On9z5k7h1AIXZzCwjyRHabjueEhEp3KndYSY3QxiEpK00lyqXOFM8w2pnNV7moSc4ljvGYmuRtJXmRP4EYRQylBhisbkYL4kw0xSsAhoaXuihqApZPYuu6XGLhWIQEZHSU4ymR9HQUDUV3/Tp+31OFE6w0d6g5/WYSE0wm53l/ZX3GU+PM54e5/LWZdzQBQXWu+vxLWeUeAU0PrZus9xe5mThJFOZKTRVY01Zo2gX6fpdEnoCS7fIm/nBobadz+F2MN1ZiR9LjQ36g/caPp/Wgant4NR225wonMDSrLjCqul7WpCx33JWjvem3+M7S9+h0q9QsApMJafQFO2pTMZ4UcmoOCGEeJAEYvGA3RYbzGRnWGwukjJTD/0jmrNyvFp6lU+3PqXcLRNGIX9064/4dOtTAIYTw4wkRtCV+NduMjNJSk/R9tpxJbYR0PN7TGgTZMwMFzYuMJ4eR1VU+kGfs9mzLDYXWWwscrYUL4y4Vb9F3+vTCBucKZ7hpeJLfLL5CVEU8crQK2StLC03njCR0BO4oYsXeIylx+LFGW4X13fp+l02OhucKp6K5w0T0Qt6WJrFVGaKpBHP8W04DTpuBy/w4oUgqjLoG55KT8UH+HyXWr9G2kiTM3PMFecYSY1Q79e5Xr1OFER03A66opOzcrw98fbgOXxYWBnNjnKxfPFzQ+72m5kgCqj369yo3eDjzY+faLLC9rVU+1WKdhHHd+gHfeayc898G9vTCvXT2Wm+duprMlFhh50TPfayUVAIIY4CCcTiAbuNVSslSriBO5i3utsfUTdw6fgdpjPTVPtVfvP8b1J36gCcGz3HLxz7Bc5vnMePfAzVQFM0nNCh0q9gqfF2OC/wuN24TdEsUulXmEhPYKgGURQNFmxcq1wbLKfYrhIDrHXXcEKHtJnmncl3UBSFP1/4c9puO55VHPoktSSGZbDZ2URXddzQxQkdXN+l7tS5Xb+NVtAwNZO8ledY7hgfrn1IQk8QRmG8bU/VsVSLMApRUTE1E0MxCKOQvJ3n9ZHXMVQDXdWZykzR9bpxm4VqMWQP0fN7FBPFQV/rTruFldHsKAvNhT1NBVhsLhJEAcutZWzNZsgeouU92WSF7Wspd8tU+1UKdoG57BxpM03P7z2zW+xPewrCizgW7VmT50QIIe4lgfiI260S97Aew7HU2EP/iDacBkutJa5VrvGNm9/gUuUSEK9V/gev/QNmMnGF+UzpDFvdLUzVJCJipbVCx+mQMBOoqkrRLrLlbLHQWiBn5vBCD4DpzDQNt8H59fOMJEZQA5Vav4alW4wYI/iBT6ffQUVFteNRaLYezwXWVR03cDF0g9H0KLdrt7FUi7SRjldM9yqMJEYYsuMNeFcrVzk9dJrl5jK6psezk70eq+1VvNAjY2YYS41R6VXwo7hVImkmcQKHLw19iTNDZwbj6Hp+D9d3SZtpPlz9EICx9BilRImx1Biaqj3QenB/WNkOh3tZRdxyW9T7dWzNxtLjA4kZM8NKa4VvLX6L0dToY1VJc1aOr8x+ZXAN2xM+nuUt9ue5elkIIYQACcRH2v2VuK3eFh9vfkzWzNJ0m8xkZyglSo/sMfRDn63eFuVumf+1/L/4xs1vUO6VgXjj3E9O/iS2ZjOZnuRO4068QCKTYK2zRsttYelx20HH7aCgkLWypI001V6VpJEkDMPBgbhr1Wu0vTbTmWkuli/SdJrkrHj6QfSD/1W6FSIivrfxPQp2gSF7aLABbzozPVj5HIQBC80FdFVnNDlK1spS7pWZSE8QBRF3anfoB30mMhNkrSxZK0s36JLUk5i6Sc/vMZYaiw/9OVUs1eJYMR4TdrF8MZ7z+4N+5aSRZL40z3fufgdFUaj367TcFuVumVdKr8Qroh/hcVYRZ8wMN2o3GLKHBh9bb6/zWf0z1jprfEH5An7oDw5D7jUUP89b7M9z9bIQQggBEoiPtJ2VuLbbZrm1jKZqdL0uHa/DH17/Q7J2ljPFM3x56ssPjOq6sH6BS1uXQIHFxiL/c+V/DqZITKWnmMvNoSpqvLKYkNeGX+Nm7Sab3U3cwEVVVFbaK/S8HlkrS8fr0HbblFIlzhTOMJYa46dmfor1zjo3azdxPIe0kcbQfthusdndHIyPUhWVnJmjYBUwNINb9VvYqk25X2YiNQHE49ts3WY2O8tkZpLl9jKu79LxOjiBE7cGWAXuNO+gKzpJI0lAgIrKeHoc13cJooC0kWa9u85CfYHJ9CTvTL7DcmuZ7658F1M1mchMcHboLC2vRdNt8tdrf03bb2MpFikzhRd4lHtlrlWv8YXRLzzydXqcqQCDyQpeXPFv9Btc2rpEMVFkODGMH/kst5aZykw9VsX1ed5ilykIQgghnjd1vy9A7J+W28LW4kUOa501bM1GV3SuVK9Qd+pMZ6cZsodwAofLlcuDCl29X+ebC9/k+5vfR1M0/nrtr/nzu3+OEzioispUeoqiXaTttan2qjSdJh+ufciFjQv81epf0XDizWcb7Q1abouUmcIJHIp2kYJdQFd0UkaKjtdhobHAaHKUocQQSSPJSGIEUzNRVTU+OObEm9g0RYt7fP0O+USejJmh2W9yvHCcdybewdZtblRv0PE6vD78OguNBTa6Gyw1l9jsbrLWW0NXdfzAxw1cbN1GVVTqTp20kUZVVNbaa4ONfJZmoaGRMlNMZaao9Wvk7Tx+EK90Xu+sx89XFIfU7298n+n0NCjx9jtDM9AVncXm4udOPJjNztLzevT8HlEUDVoWdvu+7ckKQRhQ6VWo9+tkrSwpPUUpUcLSLCw9HhnXcltP/5fqKXicn1cIIYR4GqRCfITtrMR1vA4ZI8Odxh3CKCRlpAazc7NWlqbTZLG5yOniaS5sXKDcLRNEAX/02R+x0d0A4n7hvJEnqSXpel0afoN+2Ge1vcrZ0llGkiNMpae407qDoRq4vst0ZhoVlfXOOpu9TTQ0IiLmpuZ4ffh1Kv0KH6x9MNhYd6dxh+XOMrV+jTAKMTWTvt+n2q+Ss3KUkiW6Xpdmv0nX6/LNO98kImI8Nc5UZgrXd7FTNmOpMW5Wb5KzclR7VWw17jfOmBmCKOBY9hi1Xo2m18QLPTRFww99NEXjRP4EhmbghR7j6XE2e5v0vB5DiSFyiRxNp4mhGjScBm+Nv0XKSNHzeyT0BNOZaSq9yiCQqoo6CMUPa0HYS8vC/b3g702/R82p8cHqB4wmR8lbeZJGEohXSFd6FU7kTzz7X7InIFMQhBBCPG8SiI+wnSO+Unoqvr3vNAdh2As8bN3G1ExafouNzsZgS9r12nX+YuUv4rm8wEx6hqbTpOt3cQIHL/JIaAlsPT6EtdndpO21SRkpVEUlY2RoRS00RaPm1FCVeJWsrdqoqkrWyqIoCkEUsN5Zp+N22OxuYqkWDadBSAhhHJ5SZgoNDV3VabttvrfxPaq9KpqiMZOdoeHG17vQWGAmM8NLQ/FmvFKqxKwxS9/rc6d5ByVSOJE7QTFR5GrlKoqiYOs2Xa+LqqicyJ7A0i3OjZ1DURTSRpqm02SptUSohkRE6KpO1szyxbEvxq0VP5jIcKpwipbbImtlKSaKNNwGCT3Bqfwp3ND93CkKj2pZ2G0qw0JzYfD11X6V5dYyju/Er6XXOvCzeGUKghBCiOdJAvERtV1R7Ppdyt0yqqIShAHT2el4zbHbAQWm09P0/T49v0cYhfT9Pn9650/5YP0DIK4KTyQnSFvpQQh1QxcicBWXrJZF1VWiKIrXFFs5TNVkrbNGRMRaZw1FUciZOTRVY7O/yXhqnDuNO1yvXSdtpPECj7bXZigxFAdu36FklVAiBQWFY9ljLDWXqPQqFOwCQRgQRRGWYaGpGoZqMJYawws8tvpbLDeXcUKH47njVHtVVFVlIjPBWGIMP/IJwiBeAa3EATmIAoYTw8wV5qj0KoOq+lhqLO55tkt0/S6NfoO0kaaQLhBEASk9Nbjd/+WpL3O5cpmm0+Ru4y66ojOcHuZk4eSPNEWh4TT41uK3aLjxtr+x1NhgwsV25bnhNJjKTFHv16n0K2iKxnvT70nFVQghhPgBCcRH0M6K4mR6cjBF4oujX2SptcRfLP8Fa5018naeu427BGHAZGaSiIh/8p1/wlJ7CYinSLycf5mO12GpuYRt2BiqQcttoagKCT1B1+2SNJMoKGiqRs/rxdXeziaGapC20oORZqOpUSzNIgojyt0ylm5R6VXIW3ks3aKYKJKP8iT0BD2/x0hyhLX2Gndbd/Eij+HEMGkzPTiA5QYu12vXyRk5EnoiDqlGCkVV6Dk9claO6ez0YPta3spzcfMitmXz2shrrLZXCaMw7q1WdVRF5a2xt1hoLgCQMuL+4Y7boRfEUydms7M4gcNic5GkkcRUzcHt/u3VzHWnzkhyhPH0+CC8PskUhe3XcTsMe6HHrdotThVOkTJSNJzGPe0HhmpwIn/iyC+mEEIIIe4ngfgIetic15pTY740T9NtMtoe5W7zLpV+hZyZY6O7wb/6/r/CCRwUFM6NnmMsOcbN2k0M1cDQjHi5hqrTpo3jOejopK00CT1BpV8ZhLaO16FgF3BCh5wVzxpWAoXl1jIJLUE9qpMyUpiaCRE0nSajyVEgnhJxLHuMz+qf0ff76IqOaZgEbkDOznE8e5y6U+du+y4pI0W318WLPDzXo5QscSx7jCiM0FSNvtfHCRyiMBqs9H156GUm05MoikLbjTfoddwOEdGgpWE72DacBgW7wN8583cGz2vLbVGwC7wx8sYDoXNnG8DO9czwZFMUtl/Hgl2IN+f9YO7wemedyczkAyuhhXgST2troBBCHGQSiI+InX/U7jTucCp/6p5Xf7tCudhcJG/n4+pxdpK+3+cPrv8BH5c/BiBv5fmpyZ/iROEEmqJRTBRZb68D0PSagwqtgoKmaeTNPKPpUZYaS9SdOmEUkrEyHMsdww98UGCltYKu6ISEmLpJ1+8SRAEJI8FIcoS6Wydv59nobqApcZVZURSSRpJ+0CdrZEloCdJmmpbfopgoUnfqtN02qqISRiFhGNLze7w58iaaqlHpVUiZKTpuh3QiTdEuMpudZbG5OGiJSJtp5sw5trpbVHoVLm1dGgSC3QLmXkPnw9Yz73XRxfbr9MHqB4wkR8hYGdbaawAYmjFYtfw4izMk9IjdPO2tgUIIcVBJID6E7g83Batwz+pfUzW5Ur3C/ND84Jb9doVys7sJMNgi97tXfnewaGM6Pc3PzPwMjX6DO/U7jKZGKdkllppLJM3kYEpDGIaUEiVa/XjxxpA9hI5Ox+8wX5rnbusum91N5ofmubB5AYC0kaajdAjDeP2xrdrk7TynCqcGYU9BwQ98lnvL2LrNF8e+yHp7naXWEt2gy2Z3kxO5E6ioTGemWWmvoLs6naBDwSqQ1tMstZbo+31eHX6V+dL8A3/U7w+rlV6FK5UrnBk689QCwY8yRWFnQBlJjtDxO3S8DuPpcVpuK562YeYe6/ok9IiHka2BQoijQomiaL+v4cA5d+5cdP78+f2+jCeyM9xsVx+vbF1hNjtLKVmi7ba5Xb/N1cpV8laedybfQdd0Om6HqcwUt2q3cEOX8xvn+a+3/itBFKAqKu+Ov8vp4mmWWktoqkbGyOCGLkvNJfphn0avgWVYKCj0/T5pI80Xx77IS8WXANjqbrHYXIwnWGhxn3HeynO9cp1yt0ykRGTNLJoaT4vouB2+MPoFhhJD6IrOcmuZzV584M7SLLzQG0yb6Hm9eLZur4apmST0BBkrw2RmkoJVoOt3Wagv8FnjM4pWkanMFE7goKs6b4299UAw3vmGYqOzgaVbg1aPlJEib+Up2kVeG37tuVdWL5YvDtot2m6bm7WbKIpCSk8xlZ2i5/UeO8jufMxtPb+HqZoSeo6491feJ2flBstvAKIoouE0eHfy3X28MiGEeCLKwz4hFeJDZreKThAF1Po1bN3mVu0Wlm5xdugsnzU+45PyJ5wunGYkNYKmaGStLL954Te5Wr0KxAfnvjz5Zb408SVWWitkrbg9oek1Wagv4IQOSS3JqcIpbjduM5ocxUpauIHLrfqteLFH6JA1snxp/EtcqVzBCzzSepp6v46hGUznpikmioRBONjsVrAKjCRHSBkpPtn8hKbbZCQxwnBqmPX2Onkrz3JrmZAQVVG5UbuBF3qcKZ5hIjXBameV1eYqZa2MF3qsd9YZsocGY+AUFDpehxu1GwRRcE+I3Nlz++cLf85mZxNbt+M3AYHLcnMZN3AH1eRnUVl9WNDeudY4baaZK8yx1l6Lq+P5E080r3ets0bP69H1u6SMFGOpscGhPHG0ydZAIcRRIYH4kNkZmLYVrAI1p4aiKFi6haVZEMF8aZ68lccJHBJ6gmvVa/yLj/4Fm724beLV0qu8Ofwmc4U58naeptNkKDFEuVfmcuUy4+lxNEXDi+KASwT9sI+qquT0HMOJ4cFq5dHEKOud9cH4NTdwKVgF/t6Zv8e3l77NWnsNQzXQVI2CVeDV0qsk9ASXty6TMlO0vTblXpmAeARarV/D0i3uNu6iqAo5I8doapSaEy/smE5Pc6N2Ayd0yJt5yr0ylmKRT+Qp2AVMzaTpNLlSuUJEPNXiK7NfeeC5K3fLrHZW0VQNW7cpJUooanzg7lG3k7f7kZ+kcvyoFob7A0raTDOVneJE/sQTVXMbToONzkZc9f/BZI5btVtMZaYo2IXHfjxxuPyo/e5CCPGikEB8yOxW0cnbeZpuc3DYyvEdun6XYXMYL/BoOk2+cfMb/O6V3x1sf/uV136Fr8x8hRu1GxiawZA9RL1fxws9xpJj5Kwcs7lZ7jTukNJT9MM+BbvA3fZdzpbO4oc+c4U52m6bvJXnz+78GV2vS0SEH/pA/GxAA+cAACAASURBVAf2s/pnJPQEYRSy1dsaVD1HU6PU+jX8yKfda7PSWcEPfBpeA0uzGEoOsdHZwAkc8kae4eQw/SCeGuH0HKIoise9efG4t6SWxA1dyr0y05lp+lGfze4mERFFu0i1Xx2ETohD7VpnjcuVy5iKSSFRwA98btdvU7JLlHKlXd982JrNSnuFhtN44srxdtAOwoBbrVt0vA66oqMpGvOl+acaUBabi8xkZ1huLeP6LqZm4ijx2Lg3Rt54oscUh4dsDRRCHBUSiA+Z3So624sYPlr/iGq/SlJPkrfz6KpOpVfhP9/4z1yvXR98/z89908p2AVu1m/SD/psdDdQFZXR5ChXKldAgcn0JE2niYoaz/b14qqpoRoQxdVSN3BRUKj2qrS9Nn7g0/bbmKrJWGqMptvkv3z2X/jZ2Z/lZ4/9LG4Qr2eeykyx1FzCCRxq/RoNt0HGyNAIGmx2Num6XWZyM4Ntb4ZqsN5eZ7W9SilRYjQ1StNt0vf6REpE02kOAm3La7HV2xosIpnKTuEFHgW7QMKIK9LbEy56Xo+8lR9MvYiISOpJDM1gPDUO8MCbj0qvwvXKdTJWZtdFGY+q4i41l/ho/SMubFwgoSfoB3EvdsbMkDEzfFr+lPnS/FMNKC23RSlRIqEnWOus0fJapPQUSSMpoUcAMrZPCHE0SCA+ZB5V0Umbad5feR8UsFSLK5Ur/N6136PjdQD48bEf5x++/g8xNZPP6p8xlh5jIj1BpVdhsbnIWGqM08XTAGxYG3y8+THTmWl6Xo9Iiag58aG2W9VbTGWnMFVzsAhDVVUSaoKsnQUgJMSPfLzAi2cb/6CdA6Der6MoCk2niYISzxrWTFJmimYnXg8d+AFj6TFu12/jhi5u4GIZ1mCjXhAFREocYIuJIgk9QceNK621Xm0w/WK7sjyXncPWbK5uXeVM6Uy8VMTvMpOd4U79DoqicCp3CidwqPaqg7XHu02k0DUdW7O507jDxc2LnCyc5ETuBI7vPPR1W2ou8ce3/5islaVgFbhYvkhIyKulVwmigJX2CuOp8UGofloBZfuOwvaIOfjhgbqjQMbNCSGEAAnEh9JuFZ3N7iaflj8dzOf9uPwx3139LhERhmrw5ckv83LhZa5UrjCcGGYyM0nSSAJQSpZImakHpg6cGz3HR+sfsdRaIiJiLDVG1+9S69W4Xr2OpmioqspUeoqiXWSjvYGu6Who9Pweru+SM+ODYttMzaTSr/By4WUqvQp+5JO38jScBuuddSzVYjI9iWVa5Mxc3AISOiTNJEk9yXpvnWqvysnCST6rfcZ6Z52EkcALPEzN5NWRV/FCj1qvRsfvsN5eZyozBcTVXkVRsDUbiMe8rXfWCQlZba2SNbOkjTSvDr86CE0733xUehVmsjNc2brC1cpVclaOjBGPsusHfU4XTj/0Nfto/SOyVpasmWVdXUdVVSzFYqW1wqnCqfi5Uc17nqun4fN6RA9zYJRxc0IIIbZJID4CllpL/PXaXw8O1P3Bwh9wp3EHgCF7iF84/guUkiW8wKPtten7faayU/c8xm6rhaez00xnpwH47sp3uVa9hqEa3KzdZLm1TD/oY2HhBA6mapIwEviBjxM5GKqBbdrk7ByqosZfo5m0vNagVxag63dZb6/jRz5FO670dr0ufb9PkAo4mT/JcmuZulun5bUomIVB9Xk70Kuo8eG/0GMsNcb80DyXK5e5Xb+NEzjcbdzldv02Z4fOciJ3gn7QJwiD+Lnw+liaxUR6AkVRyFm5wbXB7hMpgijAUi380KfqVEkGSbJm9pGv0VZvi9HU6OD/jyfHabgN6m4dTdM4kTpB1++ioHCxfPGpBdRH3VE47IFRZuwKIYTYJoH4EAvCgHKvzJWtK1i6xWf1z+5pkTiRPcFXZ79K0kyiKRqFZAE/jA+O7dYbW+lVeH/lfZQfjPGLiAah7LP6Z2TMDOVemZCQycwkERFdt0vKTBEEAUPWEC2/RRAFTKWmMHUTBYWXCy9Td+rcrN7ECZzBfNPtNdIXwgsMR8MsNBfouB0sw0LTNG5WbsIQTKQnyHgZtnpbjCXH6Hgdtrpb6JrOsfQxoihCI56ikDWz1JwaCT2BqZl4oUdAQNNpcmHjAj89/dM03SYNp0HeymNrNmudNQp2vNgja2YHYfD+6mm5W0ZRFQzNYDI7ScNp0HAamKrJ2aGzBFHw0NeqlCjR9tpkzSy2blNMFOmHffJ2nun0NC2vhed7NN0mpm4+1YD6sB7Rwx4YH3YoUsbNCSHE0SOB+JDqel0qvQp1p86VyhXOb5zn4lZ8azypJ/m7L/9dVtoraKqGqZmkjTSqouKHPsVEka3uFk2niRd6cYtBv8abo2+iKRpXqlcggrNDZ3FDl4vli/SC3mDGbxDGyzxq/Rr9oE8pWaLcK2NqJpOpSRSUOEwbGYaTw6x2VuPpDYkS80Pz1Po1fv/q78fb5IbmWWotcbN6E0Mz4qkSVp6smSXwAxYaC0RRxEhyhPH0OI1+fNBuobXAmD3GaHKUnt+j7bZ5ffh1IiJabotav0bezpOxMiw3l0ln4rnIF8sX0VWdptNkPD3OcHKYl4ovkTbTg4UEsPvt9q3eFkk9fnOhoFC0i6T1NIVEAV3TCf3wodXdt8be4o9v/zEAQ9YQlW4FUzU5mTtJpV9BUzSO549TSBSeW0A97IFRZuwKIYTYJoH4kImi+HBb02nSclt8sPYBf3z7jwezhYtWkfdm3sPUTEaSI4SEWGq8Yc7xnTi4qibXq9fxQ5+claPpNEGB1fYqK+0VDMVgKDHERndj0N+a1JKDdgc/9Nlob9CP+tiqzbXqNaIo4uXCy4ykRqj1a3xh9AtYWnywb72zzumh0ygofL/8fY7njlNMFFlqLRFEAfND8+iqTtft0vE7dP0uHa/DRGaCgl3AC7x4iYeZ4o3hN1jrrOGF3uANgeM7qIrKJ+VP+PGJHydtprlRu8FQYohbtVvUnBott0XX6zKVmYqruWGAqqr3TInYGZZ2q56Opcbo+T1GzVFu1W6RtbKMpEawNIut7hbAQ6u709lp/uaJv8lH6x+x1dviZP4kI8kRkkZyEJ4vbV0a9Ddve5YB9bAHRpmxK4QQYpsE4kPECzzKvTJu4ALwp3f+lP904z/RD/oAnMqf4mzhbLxAI+gzk53BUA280KPltvBCD8d3SNrJuGcWZTDbt+E02OhuoKCgKArr3XXc0OVU4RS2ZjOSGiGMQjQ0Lm9dpubW4kNgfoue3yNtplHU+JDaWnuNjtchb+XJWTl0VafcLdN0mzSdJreiW4MWg4SRwPVdNEWj6lQZSgwNtsaNJEfImBlabouXh14eBLdOvcNEegIv9FAjlaJdJCKi2q/SdJtMZ6bRFI3NziZ3m3exDZu+1yepJen4HYIoIGfnIIJrlWvkrBw1pzYYXwe7V09ns7N8svkJrwy/wvHcce4271Lr1ziV/8GhON18ZHV3Z0/2bp53QD3sgVFm7AohhNgmgfiQaDgN6k6dKIpwA5d/f+nf86d3/hSIWyReGXqFtJHGx8dUTN6eeBtDM3B9l7SZpuW22OhscDx3nFv1W4PJCBoaa501SokSYRTGCyOCAAWFntcD4lA2lhobbGe7tHUJz/dQVZXNziZZO4ulWtyo3iAkJKElqPQr6Ko+mB6x1F7CUI24x9etUevXmMnMcDJ3koiI96bf4z9e/Y+stldRlfiQXNAJGLKHOJk/ObgWW7PRFR0ndOJZyW4TP/LR0DhdPE0pWaLm1Hhv+j1+59Lv0Og3aPttgjAgZaYo2SXWO+sczx0nraf5y5W/ZCY3Q8EqkLfzLDQXyFrZXcOprum8Ovwqpmri+A6nh04P2iLeX3n/R67uPu+AehQCo8zYFUIIARKIX3h+6LPV26Lvx1Xg5dYyv/HRb7DQXABgIjXBz8/+PFWniqEZhGHIXGGOrJUliiIc3xkEgvdX3qfv97lWuUbP76FGKoqqsNnbjBdzpEYp2SVuN24TRRF5K0/P7w1C2Xa4KCVK5M08Pj5Nt4kbuPGWu8CjlIinWbTcFioqq+1V6v06CSNBFEbcrt/G0AwmUhN0E12uVK9wunCarJXlTPEMf3LnT7A1m+nMNHk7z/XadV4pvUJISLlbJm2mmc5M03SbrHXWOJ47jhd49IM+J/MnByF0NjtL3srzcvFlNrobdKJOvEEvgpbTYiw1xmJjkdNDp3ll+BXabpu1zhq1fo1yt8xbY28NnuOd4fRhB9yeRnV3PwLq9r+5fYDw0talQzd+TQghhJBA/ALbPji3Pb3g23e/zW998lv0gz6qovIz0z/DZGqSpJmkF/TQVI1QCQfff38gU1D4YP0DEkaCSq+CpmpEQYSpmlR6FeYKcwRRwLHsMZzAQVd1TNV8IJQl9STXWtewdRtN0Sj3yvT8Hrqi0/N6hITomk61W8UPfQLi1ohqr0qkRNi6jaEbbPW2GE4M0/E6XCxfxAkc3pl4h6pTpeW2KCgFLN2i1q9xtnR2EEq3x6J9a/FbVPtVCnaBuewcaTNNz++RMTODRSOaonG8cJyV1gp3GnfY6m3xxsgbaKpGrV/j9ZHXabttbtZuYmv2YM3zQnOBY9lj1JzansLp06ru7kdF87CPXxOPdphnUQshxDYJxC+gMAqp9qu03TYQbxb7t5/8W7699G0gHuH1a+d+jb4fB+OG22A4OUytX2MiPUEURfdUdnfq+33CMGQ4NUzX69JyWwwlhuJNcUaK2ewsd5t3cXyHl4ZfGmxs++bCN/nexvdwAoeNzgY9v0elWxlMqTAVE13XUTSFMAgpWAWabpOMlcHUTPzAx9Tilc6VXoXl1jJZI0veylPpVZjKTrHV3WImO8OpxCkc32Gts8ZYagwv9FAU5YG+3K/MfmUQ5GzNvudnvrR1idnsLLfqt7A1m1P5U5QSpXjaRbKEqZq8Ovwquqaz3FrG1mwsPZ6pXLSLJIwENae253D6IrcfPI/xaxK6DiZ5MySEOCokEL9gnMBhq7eFF3gA3Gnc4Tc++g1W2isAfGnsS/zjL/xjMmaG69XrGLrBqcIpFEWh7bZZbCzihu6uld2IiDOFM3xv83voqk4pUeJk/iR9v89MZoam0+ST8ifkrTyvj7yOEzj8/rXfZ7O7yXp7nYnUBJZmsdHdIAojEkZisCba1ExURYUo/rcM1aBgF8hZOQp2gdv124RRSMttERCQM3OMp8a5Ur2CqZgoikLDbXC3dZfx9Dg5M8dmb5Ox1BgpIzX4GXb25T4qhCooLDYXcQKHWr9GwkiQMTJ89dhXeWfyHeCHYaDWr8Ub8QIHx3eYLkw/0XSHF7Vf9VmPX5PQdXAd9lnUQgixTQLxC2TnwbkoivjvC/+df/fpv8MLPXRF5++/8vf5Wyf+FoqioCoqrw2/xq36LfpBH1uz0VSN8fT4I/tcJzITLLeWURWVlJmi63dRFZXJzGS8mjg3Q0JPsN5Z58LaBVbaK5T7ZUYSI7T9Np7nYegGfbdP3++TMBOUEiWabpOcHQfRrc4Wdxp3+LGxH2MyM0nLiwNXy2nR9JpkjAzTmWmq/SphEJJIJGg4jUEvsq3ZGIpB221T7pb54tgXBz/D/W0gu4XQhtOg6TbpeB2yZrwuefsadttCV+6WqfarFO0i04Xpe1ovjoJnPd1CQtfBddhnUQshxDYJxC+Ird7WoEWi7bb51x//a95ffR+A8dQ4v3bu15grxO0PhmYwkhjB0AySRnLPt+lns7M0nAbzw/Nc3rrMVncLUzN5tfQqqqKSNtPYmk3bbXNh/QJ+6NP3+2x1t4iiiFKixEZng4JVwPd9dFVHUzQs1UJTNFzfRVVUxtPjuJGLoRmcKZ5hub1M222Ts3OUUiVyVo4gCmi6TSbTk/SCHp/VP2MkNcJQYohav0bGyvBS4SU0tLjXOYr23Je72FyklCwNKtPLrWX80MdSrQe+NmflHtl6cb8X+db/w679WU+3kNB1cB32WdRCCLFNAvELYrtF4kbtBr/x0W+w2Y0Xbfzk5E/yj974RySNJABpM82QPYSixOuVH3ab/mHhZ7vFwFDjCmzKTDGeGh+MVOsHfdY6a5S7ZZY7yzT6DTw/3mRX6VWwNIuclRtcj67qbDgbZM0sRbuIqZnYus1sbpa6U2e1s8qbo2/yi3O/yOWty3yw+gFu6JI1s/T9fryqWbco2AVUVOr9OgW7wM8d+zlSRorV9iqmaj5WX+52AOuEHcIo5Hj+OIZqUO1Xd71Vv9f+3+1b/0EUUO/XuVG7wcebH/Pe9HuPnC98EHxe28Kz7H+W0HVwHfZZ1EIIsU2Jomi/r+HAOXfuXHT+/Pn9vox7rLRW+IPrf8B/uPIfCKIAUzX5+mtf56uzX0VR4mUZRbu4pxCxM/zsZWTY/d/30cZHfHfpuyiqAhFUnSo9L54ikTWzREqEpVm8MfIGWSvL1cpVAGYyMxTsAkOJITRVQ1d0hpPDvDv57uDx/2r1r1htr5IxM3y4+iGr7VWGk8MkzeRgTNxUZoqfP/Hz9Pwepmo+9m31i+WLuKHLSmsFL/SwNAvHd9A1nanM1BM95vbjVvvVwSE8UzNpeS2CMOBrp752oCvF28/JzlD6pM/v43rS30fxfLzIdz2EEOI+ysM+oT7PqxBPptav8c+++8/4ncu/QxAFTGem+Zc//S/5uWM/h6Io6KrOeGp8zxW1nT2b29MZEkaCxebiI79vu1JY7VZRNRVTNTH1eDJE1srihz6KplCyS0ylp0iZKYjgx8Z/jJnMDIZmUEwUWeuscXXrKg2ngbLjdzNn5Xh74m1OF0/T9/vYus2J/AnG0+OkjTRu4JKzcuSs3KBtoWAVuFi+yPsr73OxfHFPt9lns7P0vB7VfhVDNXB8h37QZzw1jq3ZtNzWnp7H+7Xc1qDH2dItFEUhY2YIouBzn9v91nJbuy4OedLn4nFs/15tV/q3Q7iEroNh+/V5d/JdeV2EEIeWtEwccKvtVX75T36ZzV7cIvGzMz/L11/7OrYeh5ekkaSUKMUTHPboSXs2tytFTuCQN/Mk9HhesRu42JpNIp3gVO4UaTMdj2jLzNIP+swV5jiWOca3736bK94VClaB2ewsfhQv7mg4jcH15Kwc70y+wzuT7wwqrnWnTsfrcDx3HDeI1zibqslodpSF5sJjTye4/7DcbnOKn0TGzHCjdoMhe2jwMTdwKViF5xIsfxT73bbwok7gEEIIcThIID7gxlJjnCqcouW1+NXXf5Wfnv7pwee2x5bt1XagvdO4g6mazOZmSZtp4PPDz87b2jPZGdY6a6y31+OJFqpKVsti6RZzQ3Nc2boyWL6xHTQ1VeNs6SyGauBHPikjFS/GULWHThPYPuQ3lZna9Vb69vU8yXSCxz0stxez2Vk+3vyYlhffWnYDF8d3GM4MH/h+WOkVFUIIcZRJy8QBpyoq//wn/jn/5mf+zSAMa6rGWGrsscPwdp/oqfwp2n6by1uXaTmtQRDcXrKxm51tFmdLZ7E0azCCTUUFDc6WztL1uti6jaqo5K08KSM1ePxSssTZ0lleH3mdU4VTg6kVD6ueft6t9B/1Nv/TvlWfs3K8N/0eQRhQ6VXQlbgnWVXURz63B4G0LQghhDjK5FDdLg7iobq19hpO4GDrNqVECV19vOL+/Yem2m6bhcYCbuDy+sjrg8D2sMMz76+8Hy+0+MH0ivXOOle2rnCxfJGXCy8PVi9nrSyjyVFqTm1QBR5LjQ2mVDzNg1t7OQi2HweC5BCSEEIIcSA99FCdtEy8IBRFIWtlKViFQSjdi+1w9sHqB4wkR+IDamaatJlmvjRPw2nw2vBrnzt26/4e0+0K9WR6kpncDDerN/FCDz/yWe+sM5Ia4aXiS4Nw2nAatN02n5Y/pWAXmMnOoGv6j9yi8Kjb/Pu1AU36YYUQQogXi7RMvAAaToOV1gpXK1f5dOvTPS8s2NkmMZIcoeN3uFW7NVjwsbNv+PMmT2xPZuj5PaIoGrRBvDX2FlvdLT4pf8Jmd5O19hpLrSUq/Qp+4NNyW4PrMHWT10deJyLik/InuL77I7coPOo2/5NO0xBCCCHE0SIV4gPuR6ly7gyE4+lxbtZuoigKa+01prJT91RTP2/yxKOWM1yrXsMJnPjrzBwpI0XbaXOteo0vjH7h3tW8Orwy/MqgteFHrdQ+qhorG9D2l7SOCCGEeFFIID7g7gmT7H2SwnaQVlAG2+bmCnOstdfY7G5yIn/inm1j97dE7Owxvn+T3f3WO+u8Ofwm5V4ZQzMwNIOO22GxscjfnvvbXNq6tC/BdL9HiR1l+9WuIoQQQjwJaZk44J5kksKgRUE18UOfO407/NnCn/FZ/TPydp4fn/jxB4LJzpaIltPicuUyHa/DXGEON3QfufRCURQSRoKp7BS6qtP1umiKxnhm/J7+452eRzB9WJvHQZ/4cBhIu4oQQogXiQTiA+5xw2TDafCtxW9xq36LttfmRu0GYRSSNbMst5a5WrlKwSo88H07+3Fv1W+R1uNDdxkr87lh5kTuBC2vhYbGVHqK2cwsOTvHK0OvDA7TXVi/wKXypT2PeXsaDuMose03O4+zmW8/7OfmOyGEEOJxSSA+4B6nyrkdlhpug6JdpOW2MFUTBQU/9FEVlbNDZ6k5tV3/re0AeTx3nLOls4OlHfDoMDNfmmciNUEQBbTcFkEUMJGaYDoz/cPDdMOvo6DwyeaPfpjucRymtbM7D0nmrNznVu73037dFRBCCCGehPQQH3C7HWYbzY7uelhp+zZ1wS7gBfEItLydR9d0RhOj6JrOUGKIhtN45IGnx+29zVk53p54+4HHu/8w3fzw/FM7THcUPWk/+X6QzXdCCCFeJFIhfgHsrHLOZmdZaC7sWiXcvk09nhqnH/TRFI0oimg5LfpBf/BxBeWRlcYn6b3drRIrt82frhfp+TyM7SpCCCEOL6kQv2AeVSXcruymzTRzhTlUReVq9Sp5M8+p/Ck0VaPn9dAU7ZGVxkeNWHscMuXh6XrRnk9ZUCKEEOJFIYH4BfOo2bqvlF4Z3KZOGSleKr5E0S6SNbM03SZtt03KTLHZ2WSuMHfPq3//GLSnEWbktvnT9TjPp8wAFkIIIfZOWiZeMI86rLTbbeq3J95mvjRPQk8wk5thMj2JqZlcqVwZbKzb+RhPk9w2f7r2+ny+SIfvhBBCiINAiaJov6/hwDl37lx0/vz5/b6MXe1ceLCzSviooLkdjnYu3bhcuUxaT3O2dHZPjyFeHPe/3sDgMKO0MAghhDjClId9QirEL5gnqbrefxgrbaY5WzyLG7pSuT2EXqTDd0IIIcRBID3EL5D7+0JfKb2ypxC722EsXdN5bfg1qRgeQi/a4TshhBBiv0mF+AXxo/SFHqYVxi/Kprb9dJhebyGEEOJ5kED8gtg5bk1RlF3XKT8sLB6Ww21yWGxvDsvrLYQQQjwv0jLxgnjUuDW497BdzsrRD/pcLF8cBKHnORP2WY38epE2te03mQEshBBC7J1UiF8Qjxq3BnurID8Pz7KK+6jDYtJKIYQQQognJYH4gNsOeuudda5sXWGru7VrX+hBmSzwLIP5w94UfN4qaiGEEEKIR5FAfIA1nAZ/tfpXXK1cpdwtE0Yh16rXWGmvPNAX+nkV5OflWQbzhx0WAw5EdVwIIYQQLybpIT7ALm9dZrWzStbIkjATuIFLSEjGyDzQH/qs1yTvtS/4WY782u6LXWwu0nAaZMwMc8NzXNq6tGsIv79CLOuMhRBCCLEbqRAfYLcbt8kYGSzdQlEULN0iY2S43bj9wNc+y8kCj9MX/KxHfm3/nO9Ovjv4+fZSHZcJFUIIIYR4GKkQH2APW6v9sI8/q8kCe53usF2B7fpdyt0yaTPNWGqMueG5Z1qJ3Ut1XCZUCCGEEOJhpEJ8gJ3Mn6TltnACh4gIJ3BouS1O5k8+1+vYS1/wzgrsZHqSmdwMCT3xXNoS9lIdPyiHDoUQQghx8EiF+ACbL83TdJs0nSYtv4WhGkykJ5gvzT/X69hLX/B+V2A/rzou64yFEEII8TBSIT7AclaOtyfe5vTQaY7ljnF66DRvT7z93A+C7aUv+KBXYGWdsRBCCCEeRgKx+Fx7aUk4KGPfHkbWGQshhBDiYZSHHdA6ys6dOxedP39+vy/jnnXMOw+LHcQg9yJdqxBCCCGOJOVhn5AK8QF2UNYx74VUYIUQQgjxopJDdQdYy209EChtzWa1vTr4/EFaMPGsxr4JIYQQQjxLUiE+wHbry93qbbHeWd/XBRPb7RHvr7wvyy2EEEII8cKTQHyA7TYZ4W7zLrPZ2X1ro3gWG98kYAshhBBiP0kgPsB268sdTY0ylBi65+ue53izp93XLCuVhRBCCLHfpIf4gNutL3c/F0w8rK/5SQPsfi/0EEIIIYSQCvELZr8XTDztecMHfaGHEEIIIQ4/CcQvmP0eb/a0A/lBX+ghhBBCiMNPWiZeAA2nwWJz8Z4xa/vVTrAdyBebizScBhkzw9zw3BMH8tnsLBfLFwHuWegxNzz3NC9bCCGEEOKhZFPdLg7Kpjo4Ghvgdgv8h+VnE0IIIcSB8dBNdYe+QqwoShH4f4CvAlvA/x5F0f+3v1e1d0fh0Jks9BBCCCHEfjoKPcT/N+ACo8AvAb+lKMr8/l7S3smhMyGEEEKIZ+tQB2JFUVLA/wb8H1EUtaMo+kvgvwG/vL9Xtndy6EwIIYQQ4tk61IEYeAnwoyi6seNjnwAPVIgVRfm6oijnFUU5Xy6Xn9sFfp79HrMmhBBCCHHYHfZAnAaa932sATxQXo2i6LejKDoXRdG54eHh53Jxe7HfY9aEEEIIIQ67w36org1k7/tYFnihGnDl0JkQQgghxLNz2CvENwBdUZSdQ21fBy7v0/UIIYQQQogD5lAH4iiKOsAfAv+XoigpRVHeBb4G/L/7e2VCCCGEEOKgONSB+Ad+FUgAeMAV5AAACkdJREFUm8DvAb8SRZFUiIUQQgghBHD4e4iJoqgK/OJ+X4cQQgghhDiYjkKFWAghhBBCiIeSQCyEEEIIIY40CcRCCCGEEOJIk0AshBBCCCGONAnEQgghhBDiSJNALIQQQgghjjQJxEIIIYQQ4kiTQCyEEEIIIY40CcRCCCGEEOJIk0AshBBCCCGONAnEQgghhBDiSJNALIQQQgghjjQJxEIIIYQQ4kiTQCyEEEIIIY40CcRCCCGEEOJIk0AshBBCCCGONAnEQgghhBDiSJNALIQQQgghjjQJxEIIIYQQ4kiTQCyEEEII8f+3d/fBUld1HMffH8EB815ARMwwIQQcgwYbK2cqU0cMdUCdbIpAlKbG0NHJtElG0UnFhynLqRkQLRwVmMpmxATL8WFkEEtMKykfwnB8QEVBFK/ykA/f/jjn6rbucu+Ve/nd3+7nNbMzu+f89ne++/vO7n737NnfWlNTRBQdQ68jaQPwbAFDDwE2FjCu9Qzns3E4l43F+WwczmVj6el8boyIY2t1uCDuRSQ9HBGfKzoO6x7OZ+NwLhuL89k4nMvGUmQ+vWTCzMzMzJqaC2IzMzMza2ouiHuX64sOwLqV89k4nMvG4nw2DueysRSWT68hNjMzM7Om5hliMzMzM2tqLojNzMzMrKm5IO4FJA2WtETSW5KelTS16Jis8ySdJelhSdsl3VjVd7SkJyVtkXSfpOEFhWmdIKmfpAX5edgm6R+Sjqvodz5LRNIiSS9JekPSGknfrehzLktI0mhJ2yQtqmibmp+zb0m6TdLgImO0jklanvP4Zr78u6KvkHy6IO4d5gL/BfYFpgHXShpbbEjWBS8Cc4AbKhslDQFuBS4CBgMPA7/b5dFZV/QFngeOAAYCs4FbJI1wPkvpSmBERAwATgDmSDrUuSy1ucBf22/k98rrgOmk99AtwLxiQrMuOisiWvLlICg2n/5RXcEk7Qm8BoyLiDW5bSHwQkTMKjQ46xJJc4D9I2JGvn06MCMivphv70n6B57PRsSThQVqXSJpNXAJsDfOZ2lJOghYDnwfGIRzWTqSpgBfAx4HRkXEKZKuIH3omZq3ORB4Atg7ItqKi9Z2RNJyYFFE/LqqvbB8eoa4eGOAd9qL4exRwDPE5TeWlEsAIuItYC3ObWlI2pf0HH0M57OUJM2TtAV4EngJ+CPOZelIGgBcCpxb1VWdy7Wkb1zH7Lro7CO6UtJGSQ9IOjK3FZZPF8TFawHeqGrbDLQWEIt1rxZSLis5tyUhaXdgMXBTnjV0PksoIs4k5ehw0jKJ7TiXZXQZsCAi1lW1O5fldD4wEhhGOvfw0jwbXFg+XRAX701gQFXbAMBf9ZSfc1tSknYDFpJmJs7Kzc5nSUXEuxGxEtgfOAPnslQkHQJMAK6p0e1cllBErIqItojYHhE3AQ8Ax1NgPvv29ADWoTVAX0mjI+Kp3Dae9BWtldtjwGntN/I6xQNxbns1SQIWkH7QcXxEvJ27nM/y68sHOXMuy+NIYATwXHp60gL0kfRp4E7SeyYAkkYC/UjvrVYeAYj0HCwkn54hLlheu3YrcKmkPSV9CTiRNDtlJSCpr6T+QB/Si3R/SX2BJcA4SSfn/ouB1f7RTq93LXAwMDkitla0O58lImmopCmSWiT1kTQR+BZwL85l2VxP+sBySL7MB+4AJpKWNU2WdHj+YHMpcKt/UNd7SRokaWL7e6WkacBXSB9uCsunC+Le4UxgD+AV4DfAGRHhmYrymA1sBWYBp+TrsyNiA3AycDnpTCKHAVOKCtI6ls9F+z3Sm+76inNkTnM+SydIyyPWkfJ1NXBORNzuXJZLRGyJiPXtF9LX6tsiYkN+r5xJKqReIa01PbPAcK1ju5NOVbqBdHaXs4GTImJNkfn0adfMzMzMrKl5htjMzMzMmpoLYjMzMzNrai6IzczMzKypuSA2MzMzs6bmgtjMzMzMmpoLYjMzMzNrai6Izcx2AUnzJV1UdBwflaR+kh6XtF8X7zdN0l0Vt0PSqJ2M5SFJY3dmH2ZmlVwQm5ntJEnPSNoqqU3S65L+LGmmpPdfYyNiZkRc1sl9TejZiD+S04EVEfESgKQbc3F7YuVGkq7J7TMAImJxRHy1m2O5mvQPVmZm3cIFsZlZ95gcEa3AcOAq4HxgQbEhdauZfPgv5dcAp7bfyH9Z/g1gbQ/HcjtwlKSP9/A4ZtYkXBCbmXWjiNgcEbcD3wROkzQO3p9RnZOvD5G0LM8mb5J0v6TdJC0EDgCW5r+M/lHe/veS1kvaLGlF5XKBvN+5ku7IM9SrJB1Y0T9W0t15nJclXZDbd5M0S9JaSa9KukXS4FqPSdIBwEhgVVXXUuDLkvbKt48FVgPrK+47Q9LKOvvtJ+lqSc/l2OZL2mNHxygf423AI8DETqTEzKxDLojNzHpARDwErAMOr9F9Xu7bB9gXuCDdJaYDz5Fmm1si4id5+z8Bo4GhwN+AxVX7mwJcAuwF/Ae4HEBSK3APcCfwCWAUcG++z9nAScARue81YG6dh/MZ4OmIeKeqfRvwhzw+pNnim+vso5argDHAITm2YcDFua/mMaq47xPA+C6MZWZWlwtiM7Oe8yJQa9b1bWA/YHhEvB0R90dE1NgOgIi4ISLaImI78GNgvKSBFZssiYiHcsG6mFRgAkwC1kfEzyJiW95H+yzvTODCiFhXsd+v52UP1QYBbXXCuxk4VdIgUnF9W73HUUmSSOuSfxARmyKiDbiCD4rrjo5RW47LzGynuSA2M+s5w4BNNdp/SprJvUvS05Jm1duBpD6SrspLG94AnsldQyo2W19xfQvQkq9/kvrreYcDS/KShNdJM67vkmZjq70GtNbaSUSsJM3iXggsi4it9R5LlX2AjwGPVMRwZ26Hjo9RK/B6J8cyM9shF8RmZj1A0udJBfGH1s/mmdrzImIkcAJwrqSj27urNp8KnAhMAAYCI9qH6EQYz5PW/tbrOy4iBlVc+kfECzW2XQ18qs7sMcAi0hKHriyX2AhsBcZWjD8wIlqgw2MEcDDwaBfGMzOrywWxmVk3kjRA0iTgt8CiiPhnjW0mSRqVlw1sJs3Mvpe7X+b/i9hWYDvwKmlG9YouhLMM2E/SOfkHbK2SDst984HLJQ3PMe1TfQq1dhGxjjRb+4U64/wSOAZY0dnAIuI94FfANZKG5hiGSZqYr9c9RpL6A4cCd3d2PDOzHXFBbGbWPZZKaiPNvF4I/Bz4dp1tR5N+7PYm8BdgXkTcl/uuBGbnZQQ/JM26Pgu8ADwOPNjZgPK63GOAyaRlFU8BR+XuX5BOX3ZXjvtB4LBa+8muA6bXGWdTRNy7o3XQdZxPKrQfzMtB7gEOyn07OkaTgeUR8WIXxzMzq0ldf/0yM7NmI6kf8Hfg6PY/5ygwllXAdyLiX0XGYWaNwwWxmZmZmTU1L5kwMzMzs6bmgtjMzMzMmpoLYjMzMzNrai6IzczMzKypuSA2MzMzs6bmgtjMzMzMmpoLYjMzMzNrai6IzczMzKyp/Q/ROmgxBXwFEQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x1728 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Note: data: DataFrame name\n",
    "#       hue: variables that define subsets of the data, which will be drawn on separate facets in the grid\n",
    "#       row: variables that define subsets of the data, which will be drawn on separate facets in the grid\n",
    "#       row_order: order for the levels of the faceting variables\n",
    "#       height: height (in inches) of each facet\n",
    "#       aspect: aspect ratio of each facet, so that \"aspect * height\" gives the width of each facet in inches\n",
    "#       legend: if 'True' and there is a 'hue' variable, add a legend\n",
    "#       legend_out: if ''True', the figure size will be extended, and the legend will be drawn outside the plot on the center right\n",
    "#       fit_reg=True estimates and plots a regression model relating the 'x' and 'y' variables\n",
    "#       ci: size of the confidence interval for the regression estimate\n",
    "#       truncate: if 'True', the regression line will be bounded by the data limits\n",
    "#       scatter_kws=alpha: blending value, between 0 (transparent) and 1 (opaque)\n",
    "fig = sns.lmplot(x='distance_miles', y='elevation_gain_ft', data=state_trails_df_0_10000ft_0_50mi,\n",
    "                 hue='trail_difficulty_ord', row='trail_difficulty', row_order=['Easy', 'Moderate', 'Hard'],\n",
    "                 height=8, aspect=10/8, legend=False, legend_out=False,\n",
    "                 fit_reg=True, ci=95, truncate=True, scatter_kws={'alpha':0.2});\n",
    "#plt.xlabel('Distance (Miles)');\n",
    "#plt.ylabel('Elevation Gain (Feet)');\n",
    "#plt.title('Elevation Gain by Distance');\n",
    "#plt.legend(title='Trail Difficulty', labels=['Easy', 'Moderate', 'Hard']);\n",
    "fig.set_axis_labels('Distance (Miles)', 'Elevation Gain (Feet)');\n",
    "fig.set_titles(row_template='{row_name}');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Note: data: DataFrame name\n",
    "#       hue: variables that define subsets of the data, which will be drawn on separate facets in the grid\n",
    "#       col: variables that define subsets of the data, which will be drawn on separate facets in the grid\n",
    "#       col_order: order for the levels of the faceting variables\n",
    "#       height: height (in inches) of each facet\n",
    "#       aspect: aspect ratio of each facet, so that \"aspect * height\" gives the width of each facet in inches\n",
    "#       legend: if 'True' and there is a 'hue' variable, add a legend\n",
    "#       legend_out: if ''True', the figure size will be extended, and the legend will be drawn outside the plot on the center right\n",
    "#       fit_reg=True estimates and plots a regression model relating the 'x' and 'y' variables\n",
    "#       ci: size of the confidence interval for the regression estimate\n",
    "#       truncate: if 'True', the regression line will be bounded by the data limits\n",
    "#       scatter_kws=alpha: blending value, between 0 (transparent) and 1 (opaque)\n",
    "fig = sns.lmplot(x='distance_miles', y='elevation_gain_ft', data=state_trails_df_0_10000ft_0_50mi,\n",
    "                 hue='trail_difficulty_ord', col='trail_difficulty', col_order=['Easy', 'Moderate', 'Hard'],\n",
    "                 height=8, aspect=10/8, legend=False, legend_out=False,\n",
    "                 fit_reg=True, ci=95, truncate=True, scatter_kws={'alpha':0.2});\n",
    "#plt.xlabel('Distance (Miles)');\n",
    "#plt.ylabel('Elevation Gain (Feet)');\n",
    "#plt.title('Elevation Gain by Distance');\n",
    "#plt.legend(title='Trail Difficulty', labels=['Easy', 'Moderate', 'Hard']);\n",
    "fig.set_axis_labels('Distance (Miles)', 'Elevation Gain (Feet)');\n",
    "fig.set_titles(col_template='{col_name}');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a simple linear relationship between two variables using Seaborn scatterplot with regression line\n",
    "# Note: data: DataFrame name\n",
    "#       height: height (in inches) of each facet\n",
    "#       aspect: aspect ratio of each facet, so that \"aspect * height\" gives the width of each facet in inches\n",
    "#       fit_reg=True estimates and plots a regression model relating the 'x' and 'y' variables\n",
    "#       ci: size of the confidence interval for the regression estimate\n",
    "#       truncate: if 'True', the regression line will be bounded by the data limits\n",
    "#       scatter_kws=alpha: blending value, between 0 (transparent) and 1 (opaque)\n",
    "sns.lmplot(x='elevation_gain_ft', y='duration_mins', data=state_trails_df_0_10000ft_0_50mi, height=8, aspect=10/8,\n",
    "           fit_reg=True, ci=95, truncate=True, scatter_kws={'alpha':0.2});\n",
    "plt.xlabel('Elevation Gain (Feet)');\n",
    "plt.ylabel('Duration (Minutes)');\n",
    "plt.title('Duration by Elevation Gain');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a simple linear relationship between two variables using Seaborn scatterplot with regression line\n",
    "# Note: data: DataFrame name\n",
    "#       height: height (in inches) of each facet\n",
    "#       aspect: aspect ratio of each facet, so that \"aspect * height\" gives the width of each facet in inches\n",
    "#       fit_reg=True estimates and plots a regression model relating the 'x' and 'y' variables\n",
    "#       ci: size of the confidence interval for the regression estimate\n",
    "#       truncate: if 'True', the regression line will be bounded by the data limits\n",
    "#       scatter_kws=alpha: blending value, between 0 (transparent) and 1 (opaque)\n",
    "sns.lmplot(x='distance_miles', y='duration_mins', data=state_trails_df_0_10000ft_0_50mi, height=8, aspect=10/8,\n",
    "           fit_reg=True, ci=95, truncate=True, scatter_kws={'alpha':0.2});\n",
    "plt.xlabel('Distance (Miles)');\n",
    "plt.ylabel('Duration (Minutes)');\n",
    "plt.title('Duration by Distance');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Chi-Squared Test of Independence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "def chi2_test(df, rowvar, colvar):\n",
    "    # Contingency table\n",
    "    observed = pd.crosstab(df[rowvar].fillna(' NaN'), df[colvar].fillna(' NaN'), dropna=False)\n",
    "    print(pd.crosstab(df[rowvar].fillna(' NaN'), df[colvar].fillna(' NaN'), margins=True, margins_name='Total', dropna=False))\n",
    "\n",
    "    numOfRows = observed.shape[0]\n",
    "    numOfColumns = observed.shape[1]\n",
    "    #print(numOfRows)\n",
    "    #print(numOfColumns)\n",
    "    \n",
    "    # Chi-square test of independence of variables in a contingency table\n",
    "    chi2, p, dof, expected = chi2_contingency(observed)\n",
    "    print('\\nPearson chi2({}) = {}'.format(dof, round(chi2, 3)))\n",
    "    print('P-value         = {}'.format(round(p, 3)))\n",
    "    \n",
    "    alpha = 0.05\n",
    "    if p <= alpha:\n",
    "        print('Dependent (reject H0)')\n",
    "    else:\n",
    "        print('Independent (fail to reject H0)')\n",
    "    \n",
    "    if numOfRows == 2 and numOfColumns == 2:\n",
    "        # Perform a Fisher exact test on a 2x2 contingency table\n",
    "        oddsratio, pvalue = stats.fisher_exact(observed)\n",
    "        print(\"\\nFisher's exact test\")\n",
    "        print('P-value         = {}'.format(round(pvalue, 3)))\n",
    "        \n",
    "        if pvalue <= alpha:\n",
    "            print('Dependent (reject H0)')\n",
    "        else:\n",
    "            print('Independent (fail to reject H0)')\n",
    "    print('\\n--------------------------------------\\n')\n",
    "    return p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "route_type                                   \n",
      "Loop              1597   684      2569   4850\n",
      "Out & Back        1383   958      2280   4621\n",
      "Point to Point     122   194       238    554\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(4) = 181.211\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "chi2_pvalue = chi2_test(state_trails_df_0_10000ft_0_50mi, 'route_type', 'trail_difficulty')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Backpacking', 'Beach', 'BikeTouring', 'BirdWatching', 'Blowdown',\n",
       "       'BridgeOut', 'Bugs', 'Camping', 'Cave', 'CityWalk', 'Closed',\n",
       "       'CrossCountrySkiing', 'DogFriendly', 'DogsOnLeash', 'Event', 'Fee',\n",
       "       'Fishing', 'Forest', 'Hiking', 'HistoricSite', 'HorsebackRiding',\n",
       "       'HotSprings', 'KidFriendly', 'Lake', 'MountainBiking', 'Muddy',\n",
       "       'NatureTrips', 'NoDogs', 'NoShade', 'OffTrail', 'Ohv/OffRoadDriving',\n",
       "       'OverGrown', 'PaddleSports', 'PartiallyPaved', 'Paved',\n",
       "       'PrivateProperty', 'PubWalk', 'RailsTrails', 'River', 'RoadBiking',\n",
       "       'RockClimbing', 'Rocky', 'Running', 'ScenicDriving', 'Scramble',\n",
       "       'Skiing', 'Snow', 'Snowshoeing', 'StrollerFriendly', 'Views', 'Walking',\n",
       "       'WashedOut', 'Waterfall', 'WheelchairFriendly', 'WildFlowers',\n",
       "       'Wildlife'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_flags.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "Backpacking                                  \n",
      "0                 3068  1100      4569   8737\n",
      "1                   34   736       518   1288\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 1631.483\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "Beach                                        \n",
      "0                 2709  1765      4823   9297\n",
      "1                  393    71       264    728\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 198.535\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "BikeTouring                                  \n",
      "0                 3083  1821      5070   9974\n",
      "1                   19    15        17     51\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 7.169\n",
      "P-value         = 0.028\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "BirdWatching                                 \n",
      "0                 1593  1005      2385   4983\n",
      "1                 1509   831      2702   5042\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 38.173\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "Blowdown                                     \n",
      "0                 3086  1793      5028   9907\n",
      "1                   16    43        59    118\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 33.096\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "BridgeOut                                    \n",
      "0                 3099  1827      5067   9993\n",
      "1                    3     9        20     32\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 7.388\n",
      "P-value         = 0.025\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "Bugs                                         \n",
      "0                 2977  1651      4791   9419\n",
      "1                  125   185       296    606\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 75.176\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "Camping                                      \n",
      "0                 2955  1223      4480   8658\n",
      "1                  147   613       607   1367\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 829.277\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "Cave                                         \n",
      "0                 3061  1801      5004   9866\n",
      "1                   41    35        83    159\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 2.663\n",
      "P-value         = 0.264\n",
      "Independent (fail to reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "CityWalk                                     \n",
      "0                 2948  1834      5063   9845\n",
      "1                  154     2        24    180\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 256.845\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "Closed                                       \n",
      "0                 2845  1632      4555   9032\n",
      "1                  257   204       532    993\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 13.859\n",
      "P-value         = 0.001\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty    Easy  Hard  Moderate  Total\n",
      "CrossCountrySkiing                             \n",
      "0                   3084  1823      5059   9966\n",
      "1                     18    13        28     59\n",
      "Total               3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 0.578\n",
      "P-value         = 0.749\n",
      "Independent (fail to reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "DogFriendly                                  \n",
      "0                 2685  1501      4282   8468\n",
      "1                  417   335       805   1557\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 20.961\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "DogsOnLeash                                  \n",
      "0                 1842  1314      3405   6561\n",
      "1                 1260   522      1682   3464\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 85.881\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "Event                                        \n",
      "0                 3101  1835      5084  10020\n",
      "1                    1     1         3      5\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 0.286\n",
      "P-value         = 0.867\n",
      "Independent (fail to reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "Fee                                          \n",
      "0                 3009  1788      4917   9714\n",
      "1                   93    48       170    311\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 2.537\n",
      "P-value         = 0.281\n",
      "Independent (fail to reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "Fishing                                      \n",
      "0                 2933  1728      4885   9546\n",
      "1                  169   108       202    479\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 15.266\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "Forest                                       \n",
      "0                 2110   761      2741   5612\n",
      "1                  992  1075      2346   4413\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 348.903\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "Hiking                                       \n",
      "0                  621    68       266    955\n",
      "1                 2481  1768      4821   9070\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 577.497\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "HistoricSite                                 \n",
      "0                 2993  1793      4969   9755\n",
      "1                  109    43       118    270\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 11.544\n",
      "P-value         = 0.003\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "HorsebackRiding                              \n",
      "0                 2849  1688      4491   9028\n",
      "1                  253   148       596    997\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 36.179\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "HotSprings                                   \n",
      "0                 3086  1817      5070   9973\n",
      "1                   16    19        17     52\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 12.836\n",
      "P-value         = 0.002\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "KidFriendly                                  \n",
      "0                  709  1835      3830   6374\n",
      "1                 2393     1      1257   3651\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 3571.5\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "Lake                                         \n",
      "0                 2607  1312      4213   8132\n",
      "1                  495   524       874   1893\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 138.744\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "MountainBiking                               \n",
      "0                 2594  1532      3817   7943\n",
      "1                  508   304      1270   2082\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 110.605\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "Muddy                                        \n",
      "0                 3036  1766      4914   9716\n",
      "1                   66    70       173    309\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 14.469\n",
      "P-value         = 0.001\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "NatureTrips                                  \n",
      "0                 1099   706      1394   3199\n",
      "1                 2003  1130      3693   6826\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 101.409\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "NoDogs                                       \n",
      "0                 2382  1301      3823   7506\n",
      "1                  720   535      1264   2519\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 21.977\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "NoShade                                      \n",
      "0                 2733  1571      4374   8678\n",
      "1                  369   265       713   1347\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 9.371\n",
      "P-value         = 0.009\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "OffTrail                                     \n",
      "0                 3078  1744      4993   9815\n",
      "1                   24    92        94    210\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 104.037\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty    Easy  Hard  Moderate  Total\n",
      "Ohv/OffRoadDriving                             \n",
      "0                   3021  1771      4901   9693\n",
      "1                     81    65       186    332\n",
      "Total               3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 6.94\n",
      "P-value         = 0.031\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "OverGrown                                    \n",
      "0                 3018  1629      4803   9450\n",
      "1                   84   207       284    575\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 156.994\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "PaddleSports                                 \n",
      "0                 3046  1835      5060   9941\n",
      "1                   56     1        27     84\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 54.276\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "PartiallyPaved                               \n",
      "0                 2833  1785      4901   9519\n",
      "1                  269    51       186    506\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 125.297\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "Paved                                        \n",
      "0                 2805  1831      5034   9670\n",
      "1                  297     5        53    355\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 481.034\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "PrivateProperty                              \n",
      "0                 3062  1813      5005   9880\n",
      "1                   40    23        82    145\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 1.997\n",
      "P-value         = 0.368\n",
      "Independent (fail to reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "PubWalk                                      \n",
      "0                 3100  1836      5087  10023\n",
      "1                    2     0         0      2\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 4.464\n",
      "P-value         = 0.107\n",
      "Independent (fail to reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "RailsTrails                                  \n",
      "0                 3058  1832      5075   9965\n",
      "1                   44     4        12     60\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 50.769\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "River                                        \n",
      "0                 2550  1404      4347   8301\n",
      "1                  552   432       740   1724\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 77.571\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "RoadBiking                                   \n",
      "0                 2773  1826      4993   9592\n",
      "1                  329    10        94    433\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 435.151\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "RockClimbing                                 \n",
      "0                 3089  1757      5008   9854\n",
      "1                   13    79        79    171\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 105.196\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "Rocky                                        \n",
      "0                 2892  1254      4234   8380\n",
      "1                  210   582       853   1645\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 523.549\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "Running                                      \n",
      "0                 1363  1051      2265   4679\n",
      "1                 1739   785      2822   5346\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 101.172\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "ScenicDriving                                \n",
      "0                 3054  1820      5052   9926\n",
      "1                   48    16        35     99\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 14.864\n",
      "P-value         = 0.001\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "Scramble                                     \n",
      "0                 3057  1563      4854   9474\n",
      "1                   45   273       233    551\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 416.499\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "Skiing                                       \n",
      "0                 3098  1831      5061   9990\n",
      "1                    4     5        26     35\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 8.47\n",
      "P-value         = 0.014\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "Snow                                         \n",
      "0                 3046  1608      4873   9527\n",
      "1                   56   228       214    498\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 287.846\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "Snowshoeing                                  \n",
      "0                 3062  1786      4984   9832\n",
      "1                   40    50       103    193\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 13.1\n",
      "P-value         = 0.001\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "StrollerFriendly                             \n",
      "0                 2760  1836      5062   9658\n",
      "1                  342     0        25    367\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 691.656\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "Views                                        \n",
      "0                  631    86       430   1147\n",
      "1                 2471  1750      4657   8878\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 370.095\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "Walking                                      \n",
      "0                  592  1730      2157   4479\n",
      "1                 2510   106      2930   5546\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 2656.388\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "WashedOut                                    \n",
      "0                 3068  1750      4969   9787\n",
      "1                   34    86       118    238\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 64.196\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "Waterfall                                    \n",
      "0                 2972  1597      4729   9298\n",
      "1                  130   239       358    727\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 134.301\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty    Easy  Hard  Moderate  Total\n",
      "WheelchairFriendly                             \n",
      "0                   2771  1836      5066   9673\n",
      "1                    331     0        21    352\n",
      "Total               3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 680.257\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "WildFlowers                                  \n",
      "0                 1598   664      1961   4223\n",
      "1                 1504  1172      3126   5802\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 165.615\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "trail_difficulty  Easy  Hard  Moderate  Total\n",
      "Wildlife                                     \n",
      "0                 1479   570      2106   4155\n",
      "1                 1623  1266      2981   5870\n",
      "Total             3102  1836      5087  10025\n",
      "\n",
      "Pearson chi2(2) = 131.493\n",
      "P-value         = 0.0\n",
      "Dependent (reject H0)\n",
      "\n",
      "--------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Chi-Squared Test of Independence\n",
    "# Trail Tags and Trail Difficulty\n",
    "dependent = []\n",
    "for i in list(tag_flags.columns):\n",
    "    chi2_pvalue = chi2_test(state_trails_df_0_10000ft_0_50mi, i, 'trail_difficulty')\n",
    "    alpha = 0.05\n",
    "    if chi2_pvalue <= alpha:\n",
    "        dependent.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dependent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Backpacking',\n",
       " 'Beach',\n",
       " 'BikeTouring',\n",
       " 'BirdWatching',\n",
       " 'Blowdown',\n",
       " 'BridgeOut',\n",
       " 'Bugs',\n",
       " 'Camping',\n",
       " 'CityWalk',\n",
       " 'Closed',\n",
       " 'DogFriendly',\n",
       " 'DogsOnLeash',\n",
       " 'Fishing',\n",
       " 'Forest',\n",
       " 'Hiking',\n",
       " 'HistoricSite',\n",
       " 'HorsebackRiding',\n",
       " 'HotSprings',\n",
       " 'KidFriendly',\n",
       " 'Lake',\n",
       " 'MountainBiking',\n",
       " 'Muddy',\n",
       " 'NatureTrips',\n",
       " 'NoDogs',\n",
       " 'NoShade',\n",
       " 'OffTrail',\n",
       " 'Ohv/OffRoadDriving',\n",
       " 'OverGrown',\n",
       " 'PaddleSports',\n",
       " 'PartiallyPaved',\n",
       " 'Paved',\n",
       " 'RailsTrails',\n",
       " 'River',\n",
       " 'RoadBiking',\n",
       " 'RockClimbing',\n",
       " 'Rocky',\n",
       " 'Running',\n",
       " 'ScenicDriving',\n",
       " 'Scramble',\n",
       " 'Skiing',\n",
       " 'Snow',\n",
       " 'Snowshoeing',\n",
       " 'StrollerFriendly',\n",
       " 'Views',\n",
       " 'Walking',\n",
       " 'WashedOut',\n",
       " 'Waterfall',\n",
       " 'WheelchairFriendly',\n",
       " 'WildFlowers',\n",
       " 'Wildlife']"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dependent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Elevation Gain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(state_trails_df_0_10000ft_0_50mi['elevation_gain_ft'], hist=True, kde=True, \n",
    "             color='blue', \n",
    "             hist_kws={'edgecolor': 'black'},\n",
    "             kde_kws={'linewidth': 4});\n",
    "plt.xlabel('Elevation Gain (Feet)');\n",
    "plt.ylabel('Density');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trail_difficulty</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Easy</td>\n",
       "      <td>3,102.0</td>\n",
       "      <td>336.655</td>\n",
       "      <td>526.515</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>428.25</td>\n",
       "      <td>8,421.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Hard</td>\n",
       "      <td>1,836.0</td>\n",
       "      <td>3,284.44</td>\n",
       "      <td>1,803.575</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1,951.0</td>\n",
       "      <td>2,888.5</td>\n",
       "      <td>4,210.5</td>\n",
       "      <td>9,750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Moderate</td>\n",
       "      <td>5,087.0</td>\n",
       "      <td>1,217.105</td>\n",
       "      <td>961.158</td>\n",
       "      <td>0.0</td>\n",
       "      <td>623.0</td>\n",
       "      <td>987.0</td>\n",
       "      <td>1,528.0</td>\n",
       "      <td>9,776.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count      mean       std  min     25%     50%     75%  \\\n",
       "trail_difficulty                                                            \n",
       "Easy             3,102.0   336.655   526.515  0.0    72.0   203.0  428.25   \n",
       "Hard             1,836.0  3,284.44 1,803.575 59.0 1,951.0 2,888.5 4,210.5   \n",
       "Moderate         5,087.0 1,217.105   961.158  0.0   623.0   987.0 1,528.0   \n",
       "\n",
       "                     max  \n",
       "trail_difficulty          \n",
       "Easy             8,421.0  \n",
       "Hard             9,750.0  \n",
       "Moderate         9,776.0  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For each trail_difficulty group, describe elevation_gain_ft:\n",
    "round(state_trails_df_0_10000ft_0_50mi.groupby(by='trail_difficulty').elevation_gain_ft.describe(), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x1 = state_trails_df_0_10000ft_0_50mi.loc[state_trails_df_0_10000ft_0_50mi.trail_difficulty=='Easy', 'elevation_gain_ft']\n",
    "x2 = state_trails_df_0_10000ft_0_50mi.loc[state_trails_df_0_10000ft_0_50mi.trail_difficulty=='Moderate', 'elevation_gain_ft']\n",
    "x3 = state_trails_df_0_10000ft_0_50mi.loc[state_trails_df_0_10000ft_0_50mi.trail_difficulty=='Hard', 'elevation_gain_ft']\n",
    "\n",
    "# Plot\n",
    "kwargs = dict(hist_kws={'alpha': .6}, kde_kws={'linewidth': 2})\n",
    "\n",
    "sns.distplot(x1, color='dodgerblue', label='Easy', **kwargs, bins=int(10000/100)) # Bins represent 100 feet\n",
    "sns.distplot(x2, color='orange', label='Moderate', **kwargs, bins=int(10000/100))\n",
    "sns.distplot(x3, color='deeppink', label='Hard', **kwargs, bins=int(10000/100))\n",
    "plt.xlim(0, 10000)\n",
    "plt.xlabel('Elevation Gain (Feet)')\n",
    "plt.ylabel('Density')\n",
    "plt.legend(title='Trail Difficulty');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x1 = state_trails_df_0_10000ft_0_50mi.loc[state_trails_df_0_10000ft_0_50mi.trail_difficulty=='Easy', 'elevation_gain_ft']\n",
    "x2 = state_trails_df_0_10000ft_0_50mi.loc[state_trails_df_0_10000ft_0_50mi.trail_difficulty=='Moderate', 'elevation_gain_ft']\n",
    "x3 = state_trails_df_0_10000ft_0_50mi.loc[state_trails_df_0_10000ft_0_50mi.trail_difficulty=='Hard', 'elevation_gain_ft']\n",
    "\n",
    "# Plot\n",
    "kwargs = dict(hist_kws={'alpha': .6}, kde_kws={'linewidth': 2})\n",
    "\n",
    "sns.distplot(x1, hist=False, color='dodgerblue', label='Easy', **kwargs)\n",
    "sns.distplot(x2, hist=False, color='orange', label='Moderate', **kwargs)\n",
    "sns.distplot(x3, hist=False, color='deeppink', label='Hard', **kwargs)\n",
    "plt.xlim(0, 10000)\n",
    "plt.xlabel('Elevation Gain (Feet)')\n",
    "plt.ylabel('Density')\n",
    "plt.legend(title='Trail Difficulty');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(state_trails_df_0_10000ft_0_50mi['distance_miles'], hist=True, kde=True, \n",
    "             color='blue', \n",
    "             hist_kws={'edgecolor': 'black'},\n",
    "             kde_kws={'linewidth': 4});\n",
    "plt.xlabel('Distance (Miles)');\n",
    "plt.ylabel('Density');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trail_difficulty</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Easy</td>\n",
       "      <td>3,102.0</td>\n",
       "      <td>3.557</td>\n",
       "      <td>4.135</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>45.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Hard</td>\n",
       "      <td>1,836.0</td>\n",
       "      <td>13.478</td>\n",
       "      <td>7.76</td>\n",
       "      <td>0.6</td>\n",
       "      <td>8.1</td>\n",
       "      <td>11.8</td>\n",
       "      <td>16.7</td>\n",
       "      <td>49.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Moderate</td>\n",
       "      <td>5,087.0</td>\n",
       "      <td>6.825</td>\n",
       "      <td>5.464</td>\n",
       "      <td>0.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>8.3</td>\n",
       "      <td>48.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count   mean   std  min  25%  50%  75%  max\n",
       "trail_difficulty                                              \n",
       "Easy             3,102.0  3.557 4.135  0.1  1.3  2.4  4.1 45.8\n",
       "Hard             1,836.0 13.478  7.76  0.6  8.1 11.8 16.7 49.4\n",
       "Moderate         5,087.0  6.825 5.464  0.1  3.5  5.4  8.3 48.5"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For each trail_difficulty group, describe distance_miles:\n",
    "round(state_trails_df_0_10000ft_0_50mi.groupby(by='trail_difficulty').distance_miles.describe(), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x1 = state_trails_df_0_10000ft_0_50mi.loc[state_trails_df_0_10000ft_0_50mi.trail_difficulty=='Easy', 'distance_miles']\n",
    "x2 = state_trails_df_0_10000ft_0_50mi.loc[state_trails_df_0_10000ft_0_50mi.trail_difficulty=='Moderate', 'distance_miles']\n",
    "x3 = state_trails_df_0_10000ft_0_50mi.loc[state_trails_df_0_10000ft_0_50mi.trail_difficulty=='Hard', 'distance_miles']\n",
    "\n",
    "# Plot\n",
    "kwargs = dict(hist_kws={'alpha': .6}, kde_kws={'linewidth': 2})\n",
    "\n",
    "sns.distplot(x1, color='dodgerblue', label='Easy', **kwargs, bins=int(50/.5)) # Bins represent 0.5 miles\n",
    "sns.distplot(x2, color='orange', label='Moderate', **kwargs, bins=int(50/.5))\n",
    "sns.distplot(x3, color='deeppink', label='Hard', **kwargs, bins=int(50/.5))\n",
    "plt.xlim(0, 50)\n",
    "plt.xlabel('Distance (Miles)')\n",
    "plt.ylabel('Density')\n",
    "plt.legend(title='Trail Difficulty');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x1 = state_trails_df_0_10000ft_0_50mi.loc[state_trails_df_0_10000ft_0_50mi.trail_difficulty=='Easy', 'distance_miles']\n",
    "x2 = state_trails_df_0_10000ft_0_50mi.loc[state_trails_df_0_10000ft_0_50mi.trail_difficulty=='Moderate', 'distance_miles']\n",
    "x3 = state_trails_df_0_10000ft_0_50mi.loc[state_trails_df_0_10000ft_0_50mi.trail_difficulty=='Hard', 'distance_miles']\n",
    "\n",
    "# Plot\n",
    "kwargs = dict(hist_kws={'alpha': .6}, kde_kws={'linewidth': 2})\n",
    "\n",
    "sns.distplot(x1, hist=False, color='dodgerblue', label='Easy', **kwargs)\n",
    "sns.distplot(x2, hist=False, color='orange', label='Moderate', **kwargs)\n",
    "sns.distplot(x3, hist=False, color='deeppink', label='Hard', **kwargs)\n",
    "plt.xlim(0, 50)\n",
    "plt.xlabel('Distance (Miles)')\n",
    "plt.ylabel('Density')\n",
    "plt.legend(title='Trail Difficulty');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}