13. Json City Similarities.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "import json\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_json(data):\n",
    "    \"\"\" function to parse json data \"\"\"\n",
    "    session_id = []\n",
    "    timestamp = []\n",
    "    cities = []\n",
    "    user_id = []\n",
    "    joining_date = []\n",
    "    country = []\n",
    "    \n",
    "    # parse each item\n",
    "    for item in data:\n",
    "        session_id.append(item['session_id'][0])\n",
    "        timestamp.append(item['unix_timestamp'][0])\n",
    "        cities.append(item['cities'][0])\n",
    "        user_id.append(item['user'][0][0]['user_id'])\n",
    "        joining_date.append(item['user'][0][0]['joining_date'])\n",
    "        country.append(item['user'][0][0]['country'])\n",
    "    \n",
    "    # create DataFrame\n",
    "    maps = {'session_id': session_id, 'timestamp': timestamp, 'cities': cities, \n",
    "            'user_id': user_id, 'joining_date': joining_date, 'country': country}\n",
    "    columns=['session_id', 'timestamp', 'cities', 'user_id', 'joining_date', 'country']\n",
    "    \n",
    "    return pd.DataFrame(maps, columns=columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>session_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>cities</th>\n",
       "      <th>user_id</th>\n",
       "      <th>joining_date</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>X061RFWB06K9V</td>\n",
       "      <td>1442503708</td>\n",
       "      <td>New York NY, Newark NJ</td>\n",
       "      <td>2024</td>\n",
       "      <td>2015-03-22</td>\n",
       "      <td>UK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5AZ2X2A9BHH5U</td>\n",
       "      <td>1441353991</td>\n",
       "      <td>New York NY, Jersey City NJ, Philadelphia PA</td>\n",
       "      <td>2853</td>\n",
       "      <td>2015-03-28</td>\n",
       "      <td>DE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SHTB4IYAX4PX6</td>\n",
       "      <td>1440843490</td>\n",
       "      <td>San Antonio TX</td>\n",
       "      <td>10958</td>\n",
       "      <td>2015-03-06</td>\n",
       "      <td>UK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JBRB8MZGTX3M4</td>\n",
       "      <td>1427268063</td>\n",
       "      <td>Edmonton AB</td>\n",
       "      <td>7693</td>\n",
       "      <td>2015-03-12</td>\n",
       "      <td>IT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>YJCMPURC2FL9C</td>\n",
       "      <td>1430559067</td>\n",
       "      <td>Phoenix AZ, Houston TX</td>\n",
       "      <td>7506</td>\n",
       "      <td>2015-02-28</td>\n",
       "      <td>UK</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      session_id   timestamp                                        cities  \\\n",
       "0  X061RFWB06K9V  1442503708                        New York NY, Newark NJ   \n",
       "1  5AZ2X2A9BHH5U  1441353991  New York NY, Jersey City NJ, Philadelphia PA   \n",
       "2  SHTB4IYAX4PX6  1440843490                                San Antonio TX   \n",
       "3  JBRB8MZGTX3M4  1427268063                                   Edmonton AB   \n",
       "4  YJCMPURC2FL9C  1430559067                        Phoenix AZ, Houston TX   \n",
       "\n",
       "   user_id joining_date country  \n",
       "0     2024   2015-03-22      UK  \n",
       "1     2853   2015-03-28      DE  \n",
       "2    10958   2015-03-06      UK  \n",
       "3     7693   2015-03-12      IT  \n",
       "4     7506   2015-02-28      UK  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('./data/city_search.json', 'r') as f:\n",
    "    json_data = json.load(f)\n",
    "\n",
    "data = parse_json(json_data)\n",
    "data['joining_date'] = pd.to_datetime(data['joining_date'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20022 entries, 0 to 20021\n",
      "Data columns (total 6 columns):\n",
      "session_id      20022 non-null object\n",
      "timestamp       20022 non-null int64\n",
      "cities          20022 non-null object\n",
      "user_id         20022 non-null int64\n",
      "joining_date    20022 non-null datetime64[ns]\n",
      "country         20022 non-null object\n",
      "dtypes: datetime64[ns](1), int64(2), object(3)\n",
      "memory usage: 938.6+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique session:\t 20022\n",
      "Unique users \t 5777\n"
     ]
    }
   ],
   "source": [
    "print('Unique session:\\t', len(data['session_id'].unique()))\n",
    "print('Unique users \\t', len(data['user_id'].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1\n",
    "There was a bug in the code and one country didn't get logged. It just shows up as an empty field (\"\"). Can you guess which country was that? How?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>country</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US</td>\n",
       "      <td>3876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DE</td>\n",
       "      <td>3638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UK</td>\n",
       "      <td>3555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td></td>\n",
       "      <td>2820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FR</td>\n",
       "      <td>2298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ES</td>\n",
       "      <td>1953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>IT</td>\n",
       "      <td>1882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country  count\n",
       "0      US   3876\n",
       "1      DE   3638\n",
       "2      UK   3555\n",
       "3           2820\n",
       "4      FR   2298\n",
       "5      ES   1953\n",
       "6      IT   1882"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the value country\n",
    "data['country'].value_counts().reset_index().rename(columns={'index': 'country', 'country': 'count'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given the current dataset, since there are little information about the dataset, one information that could be used to guess the country is the search time. Since people in different countries will search in similar local time, we can use this information to guess the country."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract search time information\n",
    "data['time'] = pd.to_datetime(data['timestamp'], unit='s')\n",
    "data['hour'] = data['time'].apply(lambda x: x.hour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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+3XaIcUnyriS3AS9j+mYQbibJ4cB3quqrbWcZsxOaZWhnTNOSwTn8CvCsJNck+UKS32g70Bg9C7izqm5qO8iYnAS8t/lb8T6mcxb1pq4HDm9uH8UUjy02ed/biXFF197Lb7SFulofW9ggHF1m2Td1n0j2SZKHMpjWf9Imn45Prar6eTOtfzmwb7PsbmolOQzYUFVr2s4yZs+sqn0YnJLg+CQHtB1oDJYB+wCnVtXTgP9kupYrDJVkOwaDw/PazrJYzRvGVcATgMcCOyR5ebupFq+q1gJ/zuDT/cuArzJYqiFNXJK3M/j9+0TbWcalqt5eVbsyqOmEtvMsRvMhwtvpQKNzE6cCT2Rwmpk7gL9oN87YLAMexmDJ3R8C5zYz77rgpXTgw8cZXge8sflb8UaaVSVT7lUMxuprgF8EftpyngXp4vveLtYEw+taKmMLG4SjW88DP1FYzpRNq+6TJNsy+I/3iaqaxvN+bFGztPPzTP/5I58JHJ7kFgbL9g9M8vftRlq8qrq9+b6BwflMpuokzkOsB9bPmLV6PoOGYRccAny5qu5sO8gYPBf496q6q6p+xuC8R7/ZcqaxqKrTq2qfqjqAwRKhrszIALgzyWMAmu9TtcSuT5IcCxwGvKyquvhB8T8wZUvsZvFEBh+SfLUZXywHvpzk0a2mWqSqurP5oPg+4G/pxtgCBuOLC5tT6fwrgxUlU3VRmdk0p/k4Ajin7SxjdCz3n0/xPDrwO1hV36yq36qqpzNo5n6r7UzzNeR971SPK7r6Xn5YXUtpbGGDcHRfAnZP8oRmtsnRwCUtZ9Ismk8dTwfWVtVftp1nXJLsvPGqRkkezKAR8M12Uy1OVb2tqpZX1QoG/6c+W1VTPdspyQ5JfnHjbeC3GCxfmGpV9V3gtiR7NLsOAr7RYqRx6tIn/LcC+yV5SPO38CCm/GIyGyV5ZPP9cQzedHXlOYPBeOLY5vaxwMUtZtEQSQ4G3gocXlU/bjvPuGxywZ/Dmf6xxder6pFVtaIZX6wH9mlex6bWxjf7jRfRgbFF4x+BAwGS/AqwHXB3q4nG47nAN6tqfdtBxuh24H82tw+kAx/UzRhb/ALwx8BH2k00P1t43zu144oOv5efta6lNrZINz/83DqSHMrgfGnbAGdU1btajrRoSc4Cns3gk7o7gXdW1VRPF0/yP4B/Br7O/ee1+6Oazqu9/bckezE4yew2DJr751bVn7abanySPBt4c1Ud1naWxUiyG/dfCXcZ8A9d+FsBkGRvBheT2Q64GXhlVX2/3VSL0yxFuw3YraruaTvPOCQ5BXgJg2UKXwFes/Hk79MsyT8DjwB+BvxBVV3ZcqQFme11l8Eb5HOBxzFo8h5VVVNzgYIhNX0P+CtgZ+AHwHVV9fy2Ms7XkJreBjwI+L/NYVdX1e+1EnCBhtR1KIMT9d8HfBv4veZ8plNhrrFsM4twZVVNTdNpyPP0bAbLiwu4BXjtxnOMTYshdX0cOINBbT9lMBb8bFsZ52vY71+S1Qz+RkxVw2mjIc/VjcAHGYxv/wv4/Wk6TdCQmh4KHN8cciHwtrZncM3HsPe9DM5tN5Xjii3U9CCme1wxrK4PsYTGFjYIJUmSJEmSpB5zibEkSZIkSZLUYzYIJUmSJEmSpB6zQShJkiRJkiT1mA1CSZIkSZIkqcdsEEqSJEmSJEk9ZoNQkiRJI0myIsn1beeQJEnSeNkglCRJUmuSLGs7gyRJUt/ZIJQkSdJ8bJPkb5PckOTyJA9OsneSq5N8LclFSR4GkOTzSVY2t3dKcktz+xVJzkvyT8Dl7ZUiSZIksEEoSZKk+dkd+HBV/RrwA+DFwMeAt1bVXsDXgXeO8HP2B46tqgO3WlJJkiSNxAahJEmS5uPfq+q65vYa4InAjlX1hWbfmcABI/ycK6rqe1sjoCRJkubHBqEkSZLm4yczbv8c2HELx97L/ePN7Te57z/HGUqSJEkLZ4NQkiRJi3EP8P0kz2q2jwE2zia8BXh6c/vICeeSJEnSiLxqnCRJkhbrWOAjSR4C3Ay8stn/PuDcJMcAn20rnCRJkrYsVdV2BkmSJEmSJEktcYmxJEmSJEmS1GM2CCVJkiRJkqQes0EoSZIkSZIk9ZgNQkmSJEmSJKnHbBBKkiRJkiRJPWaDUJIkSZIkSeoxG4SSJEmSJElSj9kglCRJkiRJknrMBqEkSZIkSZLUYzYIJUmSJEmSpB6zQShJkiRJkiT1mA1CSZIkSZIkqcdsEEqSJEmSJEk9ZoNQkiRJkiRJ6rGJNgiTbJPkK0kubbafkOSaJDclOSfJds3+BzXb65r7V0wypyRJkiRJktQXyyb8eCcCa4Ffarb/HHh/VZ2d5CPAq4FTm+/fr6onJTm6Oe4lW/rBO+20U61YsWKrBZckSZIkSZKmyZo1a+6uqp3nOi5VNYk8JFkOnAm8C/gD4P8B7gIeXVX3Jtkf+JOqen6SzzS3r0qyDPgusHNtIezKlSvr2muv3fqFSJIkSZIkSVMgyZqqWjnXcZNcYvwB4C3Afc32I4AfVNW9zfZ6YJfm9i7AbQDN/fc0xz9AkuOSXJvk2rvuumtrZpckSZIkSZI6aSJLjJMcBmyoqjVJnr1x9yyH1gj33b+j6jTgNBjMIBxDVEmSJu6QSw5vO8JIPn34JSMfe+hF79yKScbrUy86pe0IkiRJUqsmdQ7CZwKHJzkU2J7BOQg/AOyYZFkzS3A5cHtz/HpgV2B9s8T4l4HvTSirJEmStOSsOv+ytiOM5OIjD247giRJmqeJLDGuqrdV1fKqWgEcDXy2ql4GfA44sjnsWODi5vYlzTbN/Z/d0vkHJUmSJEmSJC3MJM9BOJu3An+QZB2Dcwye3uw/HXhEs/8PgJNbyidJkiRJkiR12qSWGP+3qvo88Pnm9s3AvrMc81/AURMNJkmSJEmSJPVQ2zMIJUmSJEmSJLXIBqEkSZIkSZLUYxNfYixJkqSl47ALVrcdYSSXvvgVbUeQJEnqLGcQSpIkSZIkST1mg1CSJEmSJEnqMZcYS5IkqVMOO/+8tiOM7NIjj2o7giRJkjMIJUmSJEmSpD5zBqEkSdIIXnDhh9qOMLJPHvGGtiNIIznigqvajjCyC1+8f9sRJEnaapxBKEmSJEmSJPWYDUJJkiRJkiSpx2wQSpIkSZIkST1mg1CSJEmSJEnqMRuEkiRJkiRJUo/ZIJQkSZIkSZJ6zAahJEmSJEmS1GM2CCVJkiRJkqQes0EoSZIkSZIk9diytgNIkjSq/33uwW1HGNmf/fZlbUeQJLXgJRfe3HaEkZ1zxG5tR5AkLRHOIJQkSZIkSZJ6zAahJEmSJEmS1GM2CCVJkiRJkqQes0EoSZIkSZIk9ZgNQkmSJEmSJKnHbBBKkiRJkiRJPWaDUJIkSZIkSeoxG4SSJEmSJElSj83ZIEzylU22/26T7Q3jDiVJkiRJkiRpMkaZQfikTbZXbbL94DFlkSRJkiRJkjRhozQIa5PtzHH/ZpLsmuRzSdYmuSHJic3+hye5IslNzfeHNfuT5ENJ1iX5WpJ9RqpGkiRJkiRJ0rws5ByEczYEZ3Ev8KaqejKwH3B8kj2Bk4Erq2p34MpmG+AQYPfm6zjg1AU8piRJkiRJkqQ5LBvhmAcl+dMZ2w/eZHu7uX5AVd0B3NHc/lGStcAuDJYrP7s57Ezg88Bbm/0fq6oCrk6yY5LHND9HkiRJkiRJ0piM0iD8B2DXGdtnb7J91nweMMkK4GnANcCjNjb9quqOJI9sDtsFuG3GP1vf7HtAgzDJcQxmGPK4xz1uPjEkSZIkSZIkMUKDsKpeCdAsCX4W8HDge8C/VNUN83mwJA8FLgBOqqofJpuezvD+Q2eLMku204DTAFauXLmQpc+SJEmSJElSr83ZIMygi/dR4FjgO8DtDGbzPTbJx4FXNUuB5/o52zJoDn6iqi5sdt+5celwkscAG5r963ngLMXlzeNKkiRJkiRJGqNRLlJyHPAcYP+qenxV7V9VjwP2ZzCj8LVz/YCmyXg6sLaq/nLGXZcwaDzSfL94xv7/1VzNeD/gHs8/KEmSJEmSJI3fKA3CY4A3VNWXZu5stk9q7p/LM5vjDkxyXfN1KPBu4HlJbgKe12wDfAq4GVgH/C3w+6MUI0mSJEmSJGl+RrlIyZ7AF4bc9wXg43P9gKr6F2Y/ryDAQbMcX8DxI2STJEmSJEmStAijNAi3qaofzXZHVf0oySizECVJkiRJU+qDF3237QgjOfFFj247giRNpVEahNsmeQ7DZwCO8jMkSZIkSZIkLUGjNPc2AGfMcb8kSZIkSZKkKTRng7CqVkwghyRpzP7675/fdoSRnPDyz7QdQZIkSZJ6zfMHSpIkSZIkST1mg1CSJEmSJEnqMRuEkiRJkiRJUo/ZIJQkSZIkSZJ6zAahJEmSJEmS1GM2CCVJkiRJkqQes0EoSZIkSZIk9ZgNQkmSJEmSJKnHbBBKkiRJkiRJPWaDUJIkSZIkSeoxG4SSJEmSJElSj9kglCRJkiRJknrMBqEkSZIkSZLUYzYIJUmSJEmSpB5b1nYASWrbJ1Y/v+0II3vZKz7TdgRJkqROuPi8u9uOMLJVR+3UdgRJHecMQkmSJEmSJKnHbBBKkiRJkiRJPWaDUJIkSZIkSeoxG4SSJEmSJElSj9kglCRJkiRJknrMBqEkSZIkSZLUY8vaDiBJkiRJkhbvqjPvajvCyPY/due2I0iawQahpHn59OmHth1hZIe8+lNtR5AkSZIkaclzibEkSZIkSZLUY0u2QZjk4CQ3JlmX5OS280iSJEmSJEldtCSXGCfZBvgw8DxgPfClJJdU1TfaTSZJkiRJkiZp3V/d2XaEkT3p9Y9qO4K0IEuyQQjsC6yrqpsBkpwNrAJsEGqqXPM3h7UdYSTPeO2lbUeQJEmSJEktSVW1nWEzSY4EDq6q1zTbxwDPqKoTNjnuOOC4ZnMP4MYJRdwJuHtCjzUpXawJulmXNU2PLtbVxZqgm3VZ0/ToYl3WND26WFcXa4Ju1mVN06OLdXWxJuhmXda0OI+vqjkvG75UZxBmln2bdTKr6jTgtK0f54GSXFtVKyf9uFtTF2uCbtZlTdOji3V1sSboZl3WND26WJc1TY8u1tXFmqCbdVnT9OhiXV2sCbpZlzVNxlK9SMl6YNcZ28uB21vKIkmSJEmSJHXWUm0QfgnYPckTkmwHHA1c0nImSZIkSZIkqXOW5BLjqro3yQnAZ4BtgDOq6oaWY8008WXNE9DFmqCbdVnT9OhiXV2sCbpZlzVNjy7WZU3To4t1dbEm6GZd1jQ9ulhXF2uCbtZlTROwJC9SIkmSJEmSJGkyluoSY0mSJEmSJEkTYINQkiRJkiRJ6jEbhPOQ5OAkNyZZl+TktvOMQ5IzkmxIcn3bWcYlya5JPpdkbZIbkpzYdqZxSLJ9kn9N8tWmrlPazjQuSbZJ8pUkl7adZRyS3JLk60muS3Jt23nGJcmOSc5P8s3m/9f+bWdajCR7NM/Rxq8fJjmp7VyLleSNzd+I65OclWT7tjONQ5ITm5pumObnabbX3SQPT3JFkpua7w9rM+N8DanpqOa5ui/JyjbzLcSQmt7b/P37WpKLkuzYZsaFGFLXnzU1XZfk8iSPbTPjfG1pLJvkzUkqyU5tZFuoIc/TnyT5zozXrEPbzLgQw56rJK9v3mPdkOQ9beVbiCHP1TkznqdbklzXZsaFGFLX3kmu3ji+TbJvmxnna0hNT01yVTNu/6ckv9Rmxvka9r53mscVW6hp2scVw+paUmMLG4QjSrIN8GHgEGBP4KVJ9mw31VisBg5uO8SY3Qu8qaqeDOwHHN+R5+onwIFV9VRgb+DgJPu1nGlcTgTWth1izJ5TVXtX1dS9gG3BB4HLqupXgacy5c9ZVd3YPEd7A08Hfgxc1HKsRUmyC/AGYGVVPYXBhb6ObjfV4iV5CvC7wL4MfvcOS7J7u6kWbDWbv+6eDFxZVbsDVzbb02Q1m9d0PXAE8MWJpxmP1Wxe0xXAU6pqL+DfgLdNOtQYrGbzut5bVXs1fwsvBd4x8VSLs5pZxrJJdgWeB9w66UBjsJrZx+fv3/i6VVWfmnCmcVjNJnUleQ6wCtirqn4NeF8LuRZjNZvUVFUvmTFBF58YAAAgAElEQVS+uAC4sI1gi7SazX8H3wOc0tT1jmZ7mqxm85o+CpxcVb/OYAz4h5MOtUjD3vdO87hiWE3TPq4YVteSGlvYIBzdvsC6qrq5qn4KnM3gxWyqVdUXge+1nWOcquqOqvpyc/tHDJoYu7SbavFq4D+azW2br6m/ylCS5cALGLxAa4lqPlE9ADgdoKp+WlU/aDfVWB0EfKuqvt12kDFYBjw4yTLgIcDtLecZhycDV1fVj6vqXuALwItazrQgQ153VwFnNrfPBF440VCLNFtNVbW2qm5sKdKiDanp8ub3D+BqYPnEgy3SkLp+OGNzB6ZsbLGFsez7gbcwZfVAN8fnMLSu1wHvrqqfNMdsmHiwRdjSc5UkwG8DZ0001BgMqauAjTPsfpkpG18MqWkP7m84XQG8eKKhFmkL73undlwxrKYOjCuG1bWkxhY2CEe3C3DbjO31dKDp1HVJVgBPA65pN8l4ZLAU9zpgA3BFVXWhrg8wGMDf13aQMSrg8iRrkhzXdpgx2Q24C/i7DJaDfzTJDm2HGqOjmcIB/Kaq6jsMZl/cCtwB3FNVl7ebaiyuBw5I8ogkDwEOBXZtOdM4Paqq7oDBABJ4ZMt5NLdXAZ9uO8S4JHlXktuAlzF9Mwg3k+Rw4DtV9dW2s4zZCc0ytDOmacngHH4FeFaSa5J8IclvtB1ojJ4F3FlVN7UdZExOAt7b/K14H9M5i3pT1wOHN7ePYorHFpu87+3EuKJr7+U32kJdrY8tbBCOLrPsm7pPJPskyUMZTOs/aZNPx6dWVf28mda/HNi3WXY3tZIcBmyoqjVtZxmzZ1bVPgxOSXB8kgPaDjQGy4B9gFOr6mnAfzJdyxWGSrIdg8HheW1nWazmDeMq4AnAY4Edkry83VSLV1VrgT9n8On+ZcBXGSzVkCYuydsZ/P59ou0s41JVb6+qXRnUdELbeRaj+RDh7XSg0bmJU4EnMjjNzB3AX7QbZ2yWAQ9jsOTuD4Fzm5l3XfBSOvDh4wyvA97Y/K14I82qkin3KgZj9TXALwI/bTnPgnTxfW8Xa4LhdS2VsYUNwtGt54GfKCxnyqZV90mSbRn8x/tEVU3jeT+2qFna+Xmm//yRzwQOT3ILg2X7Byb5+3YjLV5V3d5838DgfCZTdRLnIdYD62fMWj2fQcOwCw4BvlxVd7YdZAyeC/x7Vd1VVT9jcN6j32w501hU1elVtU9VHcBgiVBXZmQA3JnkMQDN96laYtcnSY4FDgNeVlVd/KD4H5iyJXazeCKDD0m+2owvlgNfTvLoVlMtUlXd2XxQfB/wt3RjbAGD8cWFzal0/pXBipKpuqjMbJrTfBwBnNN2ljE6lvvPp3geHfgdrKpvVtVvVdXTGTRzv9V2pvka8r53qscVXX0vP6yupTS2sEE4ui8Buyd5QjPb5GjgkpYzaRbNp46nA2ur6i/bzjMuSXbeeFWjJA9m0Aj4ZrupFqeq3lZVy6tqBYP/U5+tqqme7ZRkhyS/uPE28FsMli9Mtar6LnBbkj2aXQcB32gx0jh16RP+W4H9kjyk+Vt4EFN+MZmNkjyy+f44Bm+6uvKcwWA8cWxz+1jg4hazaIgkBwNvBQ6vqh+3nWdcNrngz+FM/9ji61X1yKpa0Ywv1gP7NK9jU2vjm/3Gi+jA2KLxj8CBAEl+BdgOuLvVROPxXOCbVbW+7SBjdDvwP5vbB9KBD+pmjC1+Afhj4CPtJpqfLbzvndpxRYffy89a11IbW6SbH35uHUkOZXC+tG2AM6rqXS1HWrQkZwHPZvBJ3Z3AO6tqqqeLJ/kfwD8DX+f+89r9UU3n1d7+W5K9GJxkdhsGzf1zq+pP2001PkmeDby5qg5rO8tiJNmN+6+Euwz4hy78rQBIsjeDi8lsB9wMvLKqvt9uqsVplqLdBuxWVfe0nWcckpwCvITBMoWvAK/ZePL3aZbkn4FHAD8D/qCqrmw50oLM9rrL4A3yucDjGDR5j6qqqblAwZCavgf8FbAz8APguqp6flsZ52tITW8DHgT83+awq6vq91oJuEBD6jqUwYn67wO+Dfxecz7TqTDXWLaZRbiyqqam6TTkeXo2g+XFBdwCvHbjOcamxZC6Pg6cwaC2nzIYC362rYzzNez3L8lqBn8jpqrhtNGQ5+pG4IMMxrf/Bfz+NJ0maEhNDwWObw65EHhb2zO45mPY+14G57abynHFFmp6ENM9rhhW14dYQmMLG4SSJEmSJElSj7nEWJIkSZIkSeoxG4SSJEmSJElSj9kglCRJkiRJknrMBqEkSZIkSZLUYzYIJUmSJEmSpB6zQShJkqSRJFmR5Pq2c0iSJGm8bBBKkiSpNUmWtZ1BkiSp72wQSpIkaT62SfK3SW5IcnmSByfZO8nVSb6W5KIkDwNI8vkkK5vbOyW5pbn9iiTnJfkn4PL2SpEkSRLYIJQkSdL87A58uKp+DfgB8GLgY8Bbq2ov4OvAO0f4OfsDx1bVgVstqSRJkkZig1CSJEnz8e9VdV1zew3wRGDHqvpCs+9M4IARfs4VVfW9rRFQkiRJ82ODUJIkSfPxkxm3fw7suIVj7+X+8eb2m9z3n+MMJUmSpIWzQShJkqTFuAf4fpJnNdvHABtnE94CPL25feSEc0mSJGlEXjVOkiRJi3Us8JEkDwFuBl7Z7H8fcG6SY4DPthVOkiRJW5aqajuDJEmSJEmSpJa4xFiSJEmSJEnqMRuEkiRJkiRJUo/ZIJQkSZIkSZJ6zAahJEmSJEmS1GM2CCVJkiRJkqQes0EoSZIkSZIk9ZgNQkmSJEmSJKnHbBBKkiRJkiRJPWaDUJIkSZIkSeoxG4SSJEmSJElSj9kglCRJkiRJknrMBqEkSZIkSZLUYzYIJUmSJEmSpB6zQShJkiRJkiT1mA1CSZIkSZIkqceWtR1gXHbaaadasWJF2zEkSZIkSZKkJWHNmjV3V9XOcx3XmQbhihUruPbaa9uOIUmSJEmSJC0JSb49ynEuMZYkSZIkSZJ6rDMzCCVJkramF1z4obYjjOyTR7yh7QjaCladf1nbEUZy8ZEHtx1BkiTNkw1CSZIkSa044oKr2o4wsgtfvH/bESRJ2mpsEEqSJEnSmLzkwpvbjjCyc47Yre0IkqQlwnMQSpIkSZIkST1mg1CSJEmSJEnqMRuEkiRJkiRJUo95DkJJkqQeO+yC1W1HGMmlL35F2xEkSZI6yxmEkiRJkiRJUo/ZIJQkSZIkSZJ6zAahJEmSJEmS1GOeg1CSJI3doRe9s+0II/vUi05pO4IkSZLUKhuEkiRJ6pTDzj+v7Qgju/TIo9qOIEmS5BJjSZIkSZIkqc9sEEqSJEmSJEk9ZoNQkiRJkiRJ6jEbhJIkSZIkSVKP2SCUJEmSJEmSeswGoSRJkiRJktRjNgglSZIkSZKkHrNBKEmSJEmSJPXYsrYDSJLUd4dccnjbEUby6cMvaTuCJEmSpK3AGYSSJEmSJElSj9kglCRJkiRJknrMBqEkSZIkSZLUYzYIJUmSJEmSpB6zQShJkiRJkiT1mFcxliRJkiRt0Qcv+m7bEUZy4ose3XYESZpKziCUJEmSJEmSeswGoSRJkiRJktRjczYIkzx0jvtXji+OJEmSJEmSpEkaZQbhp5NsP9sdSX4TuHy8kSRJkiRJkiRNyigNwruAS5M8aObOJM8GPgW8Za4fkGTXJJ9LsjbJDUlObPY/PMkVSW5qvj+s2Z8kH0qyLsnXkuwz78okSZIkSZIkzWmUBuFLgP8CLkqyLUCS3wIuBt5QVR8d4WfcC7ypqp4M7Accn2RP4GTgyqraHbiy2QY4BNi9+ToOOHX0kiRJkiRJkiSNas4GYVX9DDgCWAacl+RFwAXAa6vqY6M8SFXdUVVfbm7/CFgL7AKsAs5sDjsTeGFzexXwsRq4GtgxyWNGL0uSJEmSJEnSKEa6inFV/ZRB025H4CzgmKo6eyEPmGQF8DTgGuBRVXVH8xh3AI9sDtsFuG3GP1vf7Nv0Zx2X5Nok1951110LiSNJkiRJkiT12ihXMb4tya3AjcCeQAEfSnLrxq9RH6y5IvIFwElV9cMtHTrLvtpsR9VpVbWyqlbuvPPOo8aQJEmSJEmS1Fg2wjEvH8cDNecvvAD4RFVd2Oy+M8ljquqOZgnxhmb/emDXGf98OXD7OHJIkiRJkiRJut+cDcKq+sJiHyRJgNOBtVX1lzPuugQ4Fnh38/3iGftPSHI28Azgno1LkSVJkiRJkiSNz5wNwiSzXYjkZ8C3gfOr6hsjPM4zgWOArye5rtn3Rwwag+cmeTVwK3BUc9+ngEOBdcCPgVeO8BiSJEmSJEmS5mmUJcbfmmXftsAewFVJfqeqPrmlH1BV/8Ls5xUEOGiW4ws4foRskiRJkiTN28Xn3d12hJGtOmqntiNI6rhRlhifMuy+JM9lMAtwiw1CSZIkSZIkSUvTKDMIt+RK4InjCCJJ0lz+97kHtx1hZH/225e1HUGSJEmSRvILi/z3jwV+MI4gkiRJkiRJkiZvlIuU7DbL7m2BFcAfA+eOOZMkSZIkSZKkCRllifE6oHjgRUZ+zuCqw+cAf7oVckmSJEmSJEmagFEuUrLYZciSJEmSJEmSliibf5IkSZIkSVKP2SCUJEmSJEmSeswGoSRJkiRJktRjNgglSZIkSZKkHrNBKEmSJEmSJPWYDUJJkiRJkiSpx2wQSpIkSZIkST1mg1CSJEmSJEnqMRuEkiRJkiRJUo/ZIJQkSZIkSZJ6zAahJEmSJEmS1GM2CCVJkiRJkqQes0EoSZIkSZIk9diytgNIkraOv/7757cdYSQnvPwzbUeQJEnqhKvOvKvtCCPb/9id244gaQZnEEqSJEmSJEk9ZoNQkiRJkiRJ6jEbhJIkSZIkSVKP2SCUJEmSJEmSeswGoSRJkiRJktRjNgglSZIkSZKkHrNBKEmSJEmSJPXYsrYDSFLbPrH6+W1HGNnLXvGZtiNIkiRJkjrGGYSSJEmSJElSjzmDUJIkSZIkLVnr/urOtiOM7Emvf1TbEaQFWbINwiQHAx8EtgE+WlXvbjmSJODTpx/adoSRHfLqT7UdQZIkSZKkJW9JLjFOsg3wYeAQYE/gpUn2bDeVJEmSJEmS1D1LdQbhvsC6qroZIMnZwCrgG62mkubpmr85rO0II3nGay9tO4IkSZIk9cZ3/+KmtiOM7NFv2r3tCJqAVFXbGTaT5Ejg4Kp6TbN9DPCMqjphk+OOA45rNvcAbpxQxJ2Auyf0WJPSxZqgm3VZ0/ToYl1drAm6WZc1TY8u1mVN06OLdXWxJuhmXdY0PbpYVxdrgm7WZU2L8/iq2nmug5bqDMLMsm+zTmZVnQactvXjPFCSa6tq5aQfd2vqYk3QzbqsaXp0sa4u1gTdrMuapkcX67Km6dHFurpYE3SzLmuaHl2sq4s1QTfrsqbJWJLnIATWA7vO2F4O3N5SFkmSJEmSJKmzlmqD8EvA7kmekGQ74GjgkpYzSZIkSZIkSZ2zJJcYV9W9SU4APgNsA5xRVTe0HGumiS9rnoAu1gTdrMuapkcX6+piTdDNuqxpenSxLmuaHl2sq4s1QTfrsqbp0cW6ulgTdLMua5qAJXmREkmSJEmSJEmTsVSXGEuSJEmSJEmaABuEkiRJkiRJUo/ZIJyHJAcnuTHJuiQnt51nHJKckWRDkuvbzjIuSXZN8rkka5PckOTEtjONQ5Ltk/xrkq82dZ3SdqZxSbJNkq8kubTtLOOQ5JYkX09yXZJr284zLkl2THJ+km82/7/2bzvTYiTZo3mONn79MMlJbedarCRvbP5GXJ/krCTbt51pHJKc2NR0wzQ/T7O97iZ5eJIrktzUfH9Ymxnna0hNRzXP1X1JVraZbyGG1PTe5u/f15JclGTHNjMuxJC6/qyp6boklyd5bJsZ52tLY9kkb05SSXZqI9tCDXme/iTJd2a8Zh3aZsaFGPZcJXl98x7rhiTvaSvfQgx5rs6Z8TzdkuS6NjMuxJC69k5y9cbxbZJ928w4X0NqemqSq5px+z8l+aU2M87XsPe90zyu2EJN0z6uGFbXkhpb2CAcUZJtgA8DhwB7Ai9Nsme7qcZiNXBw2yHG7F7gTVX1ZGA/4PiOPFc/AQ6sqqcCewMHJ9mv5UzjciKwtu0QY/acqtq7qqbuBWwLPghcVlW/CjyVKX/OqurG5jnaG3g68GPgopZjLUqSXYA3ACur6ikMLvR1dLupFi/JU4DfBfZl8Lt3WJLd2021YKvZ/HX3ZODKqtoduLLZniar2bym64EjgC9OPM14rGbzmq4AnlJVewH/Brxt0qHGYDWb1/Xeqtqr+Vt4KfCOiadanNXMMpZNsivwPODWSQcag9XMPj5//8bXrar61IQzjcNqNqkryXOAVcBeVfVrwPtayLUYq9mkpqp6yYzxxQXAhW0EW6TVbP47+B7glKaudzTb02Q1m9f0UeDkqvp1BmPAP5x0qEUa9r53mscVw2qa9nHFsLqW1NjCBuHo9gXWVdXNVfVT4GwGL2ZTraq+CHyv7RzjVFV3VNWXm9s/YtDE2KXdVItXA//RbG7bfE39VYaSLAdewOAFWktU84nqAcDpAFX106r6Qbupxuog4FtV9e22g4zBMuDBSZYBDwFubznPODwZuLqqflxV9wJfAF7UcqYFGfK6uwo4s7l9JvDCiYZapNlqqqq1VXVjS5EWbUhNlze/fwBXA8snHmyRhtT1wxmbOzBlY4stjGXfD7yFKasHujk+h6F1vQ54d1X9pDlmw8SDLcKWnqskAX4bOGuiocZgSF0FbJxh98tM2fhiSE17cH/D6QrgxRMNtUhbeN87teOKYTV1YFwxrK4lNbawQTi6XYDbZmyvpwNNp65LsgJ4GnBNu0nGI4OluNcBG4ArqqoLdX2AwQD+vraDjFEBlydZk+S4tsOMyW7AXcDfZbAc/KNJdmg71BgdzRQO4DdVVd9hMPviVuAO4J6qurzdVGNxPXBAkkckeQhwKLBry5nG6VFVdQcMBpDAI1vOo7m9Cvh02yHGJcm7ktwGvIzpm0G4mSSHA9+pqq+2nWXMTmiWoZ0xTUsG5/ArwLOSXJPkC0l+o+1AY/Qs4M6quqntIGNyEvDe5m/F+5jOWdSbuh44vLl9FFM8ttjkfW8nxhVdey+/0Rbqan1sYYNwdJll39R9ItknSR7KYFr/SZt8Oj61qurnzbT+5cC+zbK7qZXkMGBDVa1pO8uYPbOq9mFwSoLjkxzQdqAxWAbsA5xaVU8D/pPpWq4wVJLtGAwOz2s7y2I1bxhXAU8AHgvskOTl7aZavKpaC/w5g0/3LwO+ymCphjRxSd7O4PfvE21nGZeqevv/z979R0tWlnei/z5Da/yVO2BAYwCn1SGOxjGIjOI4GgNJBMIFRYm61KAxQ3Qg/hjNiMkkjpOVu5xoktGMV4cEpnE0KipEYvwBC6Nm7hIjKAoEGYmitBDAGDETVzTE5/5Ru8Ox6dNU96mufarq81nrrKq9z+5zvs+q01VvPfW+e3f3oZnUdMbYeTZi+BDhV7IEjc6dvDnJQzI5zcxNSX5r3DgzsyXJAZksufulJOcNM++WwbOyBB8+rvGiJC8bnitelmFVyYL7uUzG6pcn+f4k3xk5z15Zxve9y1hTsn5dm2VsoUE4ve353k8UDsmCTateJVV1t0z+4729uxfxvB+7NSzt/GgW//yRj09yYlVdn8my/aOr6m3jRtq47r5xuL0lk/OZLNRJnNexPcn2NbNW35NJw3AZHJfk091989hBZuAnknypu2/t7r/P5LxH/3rkTDPR3Wd39xHd/cRMlggty4yMJLm5qh6QJMPtQi2xWyVVdWqSE5I8u7uX8YPiP8iCLbHbhYdk8iHJZ4fxxSFJPl1VPzhqqg3q7puHD4q/m+T3shxji2Qyvjh/OJXOn2WyomShLiqzK8NpPk5O8q6xs8zQqbnjfIrvzhL8DXb357v7p7r70Zk0c/9i7Ex7ap33vQs9rljW9/Lr1bWZxhYahNP7VJLDqupBw2yTZya5cORM7MLwqePZSa7p7t8eO8+sVNVBO65qVFX3zKQR8PlxU21Md7+quw/p7q2Z/J/6SHcv9Gynqrp3VX3/jvtJfiqT5QsLrbv/MskNVfXQYdcxSf58xEiztEyf8H8lyVFVda/hufCYLPjFZHaoqvsNtw/M5E3XsjxmyWQ8cepw/9Qk7xsxC+uoqmOTvDLJid39rbHzzMpOF/w5MYs/triyu+/X3VuH8cX2JEcMr2MLa8eb/cFTswRji8EfJjk6Sarqh5PcPcnXRk00Gz+R5PPdvX3sIDN0Y5IfG+4fnSX4oG7N2OKfJPmPSd4ybqI9s5v3vQs7rlji9/K7rGuzjS1qOT/83Deq6vhMzpe2X5Jzuvs3Ro60YVX1jiRPyuSTupuTvLq7F3q6eFX9myR/muTK3HFeu1/uxbza2z+qqkdmcpLZ/TJp7p/X3f953FSzU1VPSvKK7j5h7CwbUVUPzh1Xwt2S5A+W4bkiSarq8EwuJnP3JF9M8vzu/utxU23MsBTthiQP7u7bxs4zC1X1miTPyGSZwmeS/PyOk78vsqr60yQ/kOTvk/z77r5k5Eh7ZVevu5m8QT4vyQMzafKe0t0Lc4GCdWr6epLfTXJQkm8kuaK7nzxWxj21Tk2vSvJ9Sf5qOOzS7n7hKAH30jp1HZ/Jifq/m+TLSV44nM90IdzVWHaYRXhkdy9M02mdx+lJmSwv7iTXJ/mFHecYWxTr1PU/k5yTSW3fyWQs+JGxMu6p9f7+qmpbJs8RC9Vw2mGdx+raJG/IZHz7d0n+3SKdJmidmu6T5PThkPOTvGrsGVx7Yr33vZmc224hxxW7qen7stjjivXqemM20dhCgxAAAAAAVpglxgAAAACwwjQIAQAAAGCFaRACAAAAwArTIAQAAACAFaZBCAAAAAArTIMQAICpVNXWqrpq7BwAAMyWBiEAAKOpqi1jZwAAWHUahAAA7In9qur3qurqqrqoqu5ZVYdX1aVV9bmquqCqDkiSqvpoVR053D+wqq4f7j+vqt5dVX+U5KLxSgEAINEgBABgzxyW5E3d/SNJvpHkaUnemuSV3f3IJFcmefUUP+dxSU7t7qP3WVIAAKaiQQgAwJ74UndfMdy/PMlDkuzf3R8b9p2b5IlT/JyLu/vr+yIgAAB7RoMQAIA98e019/8hyf67Ofb23DHevMdO3/vbWYYCAGDvaRACALARtyX566p6wrD93CQ7ZhNen+TRw/2nzzkXAABTctU4AAA26tQkb6mqeyX5YpLnD/tfn+S8qnpuko+MFQ4AgN2r7h47AwAAAAAwEkuMAQAAAGCFaRACAAAAwArTIAQAAACAFaZBCAAAAAArTIMQAAAAAFaYBiEAAAAArDANQgAAAABYYRqEAAAAALDCNAgBAAAAYIVpEAIAAADACtMgBAAAAIAVpkEIAAAAACtMgxAAAAAAVpgGIQAAAACsMA1CAAAAAFhhW8YOMCsHHnhgb926dewYAAAAALApXH755V/r7oPu6rilaRBu3bo1l1122dgxAAAAAGBTqKovT3OcJcYAAAAAsMKWZgYhwN56+7Ynjx1has9+3ofHjgAAAMCS0SAEWFL/7W2L0fg84zmangAAAGOyxBgAAAAAVpgZhAAwsuMuPHHsCFP54IkXTn3s8Re8eh8mma0PPPU1Y0cAAIBRmUEIAAAAACvMDEJgj3zw7OPHjjC1417wgbEjAEvkp89/49gRpvbHJ7947AgAACwQMwgBAAAAYIWZQQjAwvjV844dO8LUfv1nPjR2BAAAgKmYQQgAAAAAK0yDEAAAAABWmCXGsA998r+fMHaEqTz2F94/dgQAAABgJGYQAgAAAMAK0yAEAAAAgBWmQQgAAAAAK0yDEAAAAABWmAYhAAAAAKwwDUIAAAAAWGEahAAAAACwwjQIAQAAAGCFaRACAAAAwAqbS4Owqg6tqj+pqmuq6uqqesmw/75VdXFVfWG4PWDYX1X1xqq6rqo+V1VHzCMnAAAAAKyaec0gvD3Jy7v7YUmOSnJ6VT08yZlJLunuw5JcMmwnyXFJDhu+Tkvy5jnlBAAAAICVMpcGYXff1N2fHu7/TZJrkhyc5KQk5w6HnZvkKcP9k5K8tScuTbJ/VT1gHlkBAAAAYJXM/RyEVbU1yaOSfDLJ/bv7pmTSRExyv+Gwg5PcsOafbR/2AQAAAAAzNNcGYVXdJ8l7k7y0u7+5u0N3sa938fNOq6rLquqyW2+9dVYxAQAAAGBlzK1BWFV3y6Q5+PbuPn/YffOOpcPD7S3D/u1JDl3zzw9JcuPOP7O7z+ruI7v7yIMOOmjfhQcAAACAJTWvqxhXkrOTXNPdv73mWxcmOXW4f2qS963Z/7PD1YyPSnLbjqXIAAAAAMDsbJnT73l8kucmubKqrhj2/XKS1yY5r6pekOQrSU4ZvveBJMcnuS7Jt5I8f045AQAAAGClzKVB2N3/K7s+r2CSHLOL4zvJ6fs0FAAAAAAw/6sYAwAAAACbhwYhAAAAAKwwDUIAAAAAWGHzukgJ7Nb1b3zK2BGmtvXFfzh2BACYmRPeu23sCFN5/9OeN3YEAIClZQYhAAAAAKwwDUIAAAAAWGEahAAAAACwwjQIAQAAAGCFaRACAAAAwArTIAQAAACAFaZBCAAAAAArTIMQAAAAAFaYBiEAAAAArDANQgAAAABYYRqEAAAAALDCNAgBAAAAYIVpEAIAAADACtMgBAAAAIAVpkEIAAAAACtMgxAAAAAAVpgGIQAAAACsMA1CAAAAAFhhW8YOwJ67+c2/OXaEqd3/Rf9h7AgAAAAA7IYGIQAAS+WE97x77AhTe//TTxk7AgCAJcYAAAAAsMo0CAEAAABghWkQAgAAAMAK0yAEAAAAgBWmQQgAAAAAK0yDEAAAAABWmAYhAAAAAKwwDUIAAAAAWGEahAAAAACwwjQIAQAAAGCFbbPzWiQAACAASURBVBk7AAAAcNdOes+Hxo4wlfc9/dixIwAAe8gMQgAAAABYYUs9g/DWN79t7AhTO+hFzxk7AgAAAAAryAxCAAAAAFhhGoQAAAAAsMI0CAEAAABghWkQAgAAAMAK27QXKamqY5O8Icl+SX6/u187ciQAAGCGTn7vJ8aOMLXzn/a4sSMAwD6zKWcQVtV+Sd6U5LgkD0/yrKp6+LipAAAAAGD5bNYZhI9Jcl13fzFJquqdSU5K8uejpgIAANiNZ5z/xbEjTO1dJz947AgAbBLV3WNnuJOqenqSY7v754ft5yZ5bHefsdNxpyU5bdh8aJJr5xTxwCRfm9PvmpdlrClZzrrUtDiWsa5lrClZzrrUtDiWsS41LY5lrGsZa0qWsy41LY5lrGsZa0qWsy41bcw/6+6D7uqgzTqDsHax706dzO4+K8lZ+z7O96qqy7r7yHn/3n1pGWtKlrMuNS2OZaxrGWtKlrMuNS2OZaxLTYtjGetaxpqS5axLTYtjGetaxpqS5axLTfOxKc9BmGR7kkPXbB+S5MaRsgAAAADA0tqsDcJPJTmsqh5UVXdP8swkF46cCQAAAACWzqZcYtzdt1fVGUk+nGS/JOd099Ujx1pr7sua52AZa0qWsy41LY5lrGsZa0qWsy41LY5lrEtNi2MZ61rGmpLlrEtNi2MZ61rGmpLlrEtNc7ApL1ICAAAAAMzHZl1iDAAAAADMgQYhAAAAAKwwDcI9UFXHVtW1VXVdVZ05dp5ZqKpzquqWqrpq7CyzUlWHVtWfVNU1VXV1Vb1k7EyzUFX3qKo/q6rPDnW9ZuxMs1JV+1XVZ6rq/WNnmYWqur6qrqyqK6rqsrHzzEpV7V9V76mqzw//vx43dqaNqKqHDo/Rjq9vVtVLx861UVX1suE54qqqekdV3WPsTLNQVS8Zarp6kR+nXb3uVtV9q+riqvrCcHvAmBn31Do1nTI8Vt+tqiPHzLc31qnpdcPz3+eq6oKq2n/MjHtjnbp+fajpiqq6qKp+aMyMe2p3Y9mqekVVdVUdOEa2vbXO4/Sfquqra16zjh8z495Y77Gqql8c3mNdXVW/OVa+vbHOY/WuNY/T9VV1xZgZ98Y6dR1eVZfuGN9W1WPGzLin1qnpR6vqE8O4/Y+q6v8aM+OeWu997yKPK3ZT06KPK9ara1ONLTQIp1RV+yV5U5Ljkjw8ybOq6uHjppqJbUmOHTvEjN2e5OXd/bAkRyU5fUkeq28nObq7fzTJ4UmOraqjRs40Ky9Jcs3YIWbsx7v78O5euBew3XhDkg91979I8qNZ8Mesu68dHqPDkzw6ybeSXDByrA2pqoOTvDjJkd39iEwu9PXMcVNtXFU9Ism/TfKYTP72Tqiqw8ZNtde25c6vu2cmuaS7D0tyybC9SLblzjVdleTkJB+fe5rZ2JY713Rxkkd09yOT/O8kr5p3qBnYljvX9brufuTwXPj+JL8291Qbsy27GMtW1aFJfjLJV+YdaAa2Zdfj89/Z8brV3R+Yc6ZZ2Jad6qqqH09yUpJHdvePJHn9CLk2Ylt2qqm7n7FmfPHeJOePEWyDtuXOf4O/meQ1Q12/Nmwvkm25c02/n+TM7v6XmYwBf2neoTZovfe9izyuWK+mRR9XrFfXphpbaBBO7zFJruvuL3b3d5K8M5MXs4XW3R9P8vWxc8xSd9/U3Z8e7v9NJk2Mg8dNtXE98X+GzbsNXwt/laGqOiTJT2fyAs0mNXyi+sQkZydJd3+nu78xbqqZOibJX3T3l8cOMgNbktyzqrYkuVeSG0fOMwsPS3Jpd3+ru29P8rEkTx05015Z53X3pCTnDvfPTfKUuYbaoF3V1N3XdPe1I0XasHVqumj4+0uSS5McMvdgG7ROXd9cs3nvLNjYYjdj2d9J8h+yYPUkyzk+T9at60VJXtvd3x6OuWXuwTZgd49VVVWSn0nyjrmGmoF16uokO2bY/dMs2PhinZoemjsaThcnedpcQ23Qbt73Luy4Yr2almBcsV5dm2psoUE4vYOT3LBme3uWoOm07Kpqa5JHJfnkuElmoyZLca9IckuSi7t7Ger6r5kM4L87dpAZ6iQXVdXlVXXa2GFm5MFJbk3yP2qyHPz3q+reY4eaoWdmAQfwO+vur2Yy++IrSW5Kclt3XzRuqpm4KskTq+oHqupeSY5PcujImWbp/t19UzIZQCa538h5uGs/l+SDY4eYlar6jaq6Icmzs3gzCO+kqk5M8tXu/uzYWWbsjGEZ2jmLtGTwLvxwkidU1Ser6mNV9a/GDjRDT0hyc3d/YewgM/LSJK8bniten8WcRb2zq5KcONw/JQs8ttjpfe9SjCuW7b38Drupa/SxhQbh9GoX+xbuE8lVUlX3yWRa/0t3+nR8YXX3PwzT+g9J8phh2d3CqqoTktzS3ZePnWXGHt/dR2RySoLTq+qJYweagS1Jjkjy5u5+VJK/zWItV1hXVd09k8Hhu8fOslHDG8aTkjwoyQ8luXdVPWfcVBvX3dck+S+ZfLr/oSSfzWSpBsxdVf1KJn9/bx87y6x0969096GZ1HTG2Hk2YvgQ4VeyBI3Onbw5yUMyOc3MTUl+a9w4M7MlyQGZLLn7pSTnDTPvlsGzsgQfPq7xoiQvG54rXpZhVcmC+7lMxuqXJ/n+JN8ZOc9eWcb3vctYU7J+XZtlbKFBOL3t+d5PFA7Jgk2rXiVVdbdM/uO9vbsX8bwfuzUs7fxoFv/8kY9PcmJVXZ/Jsv2jq+pt40bauO6+cbi9JZPzmSzUSZzXsT3J9jWzVt+TScNwGRyX5NPdffPYQWbgJ5J8qbtv7e6/z+S8R/965Ewz0d1nd/cR3f3ETJYILcuMjCS5uaoekCTD7UItsVslVXVqkhOSPLu7l/GD4j/Igi2x24WHZPIhyWeH8cUhST5dVT84aqoN6u6bhw+Kv5vk97IcY4tkMr44fziVzp9lsqJkoS4qsyvDaT5OTvKusbPM0Km543yK784S/A129+e7+6e6+9GZNHP/YuxMe2qd970LPa5Y1vfy69W1mcYWGoTT+1SSw6rqQcNsk2cmuXDkTOzC8Knj2Umu6e7fHjvPrFTVQTuualRV98ykEfD5cVNtTHe/qrsP6e6tmfyf+kh3L/Rsp6q6d1V9/477SX4qk+ULC627/zLJDVX10GHXMUn+fMRIs7RMn/B/JclRVXWv4bnwmCz4xWR2qKr7DbcPzORN17I8ZslkPHHqcP/UJO8bMQvrqKpjk7wyyYnd/a2x88zKThf8OTGLP7a4srvv191bh/HF9iRHDK9jC2vHm/3BU7MEY4vBHyY5Okmq6oeT3D3J10ZNNBs/keTz3b197CAzdGOSHxvuH50l+KBuzdjinyT5j0neMm6iPbOb970LO65Y4vfyu6xrs40tajk//Nw3qur4TM6Xtl+Sc7r7N0aOtGFV9Y4kT8rkk7qbk7y6uxd6unhV/Zskf5rkytxxXrtf7sW82ts/qqpHZnKS2f0yae6f193/edxUs1NVT0ryiu4+YewsG1FVD84dV8LdkuQPluG5Ikmq6vBMLiZz9yRfTPL87v7rcVNtzLAU7YYkD+7u28bOMwtV9Zokz8hkmcJnkvz8jpO/L7Kq+tMkP5Dk75P8++6+ZORIe2VXr7uZvEE+L8kDM2nyntLdC3OBgnVq+nqS301yUJJvJLmiu588VsY9tU5Nr0ryfUn+ajjs0u5+4SgB99I6dR2fyYn6v5vky0leOJzPdCHc1Vh2mEV4ZHcvTNNpncfpSZksL+4k1yf5hR3nGFsU69T1P5Ock0lt38lkLPiRsTLuqfX+/qpqWybPEQvVcNphncfq2iRvyGR8+3dJ/t0inSZonZruk+T04ZDzk7xq7Blce2K9972ZnNtuIccVu6np+7LY44r16npjNtHYQoMQAAAAAFaYJcYAAAAAsMI0CAEAAABghWkQAgAAAMAK0yAEAAAAgBWmQQgAAAAAK0yDEACAqVTV1qq6auwcAADMlgYhAACjqaotY2cAAFh1GoQAAOyJ/arq96rq6qq6qKruWVWHV9WlVfW5qrqgqg5Ikqr6aFUdOdw/sKquH+4/r6reXVV/lOSi8UoBACDRIAQAYM8cluRN3f0jSb6R5GlJ3prkld39yCRXJnn1FD/ncUlO7e6j91lSAACmokEIAMCe+FJ3XzHcvzzJQ5Ls390fG/adm+SJU/yci7v76/siIAAAe0aDEACAPfHtNff/Icn+uzn29twx3rzHTt/721mGAgBg72kQAgCwEbcl+euqesKw/dwkO2YTXp/k0cP9p885FwAAU3LVOAAANurUJG+pqnsl+WKS5w/7X5/kvKp6bpKPjBUOAIDdq+4eOwMAAAAAMBJLjAEAAABghWkQAgAAAMAK0yAEAAAAgBWmQQgAAAAAK0yDEAAAAABWmAYhAAAAAKwwDUIAAAAAWGEahAAAAACwwjQIAQAAAGCFaRACAAAAwArTIAQAAACAFaZBCAAAAAArTIMQAAAAAFaYBiEAAAAArLAtYweYlQMPPLC3bt06dgwAAAAA2BQuv/zyr3X3QXd13NI0CLdu3ZrLLrts7BgAAAAAsClU1ZenOc4SYwAAAABYYRqEAAAAALDClmaJMQAAJMkJ73n32BGm9v6nnzJ2BAAADUIAYPaOv+DVY0eY2gee+pqxIwAAwKg0CAEAYAGc9J4PjR1hKu97+rFjRwAA9pAGIQDACjvhvdvGjjCV9z/teWNHAABYWi5SAgAAAAArTIMQAAAAAFaYJcYAAFP46fPfOHaEqf3xyS8eOwJM5eT3fmLsCFM7/2mPGzsCAOwzZhACAAAAwAozgxAARnbchSeOHWEqHzzxwrEjAAAA+8BcZhBW1aFV9SdVdU1VXV1VLxn237eqLq6qLwy3Bwz7q6reWFXXVdXnquqIeeQEAAAAgFUzryXGtyd5eXc/LMlRSU6vqocnOTPJJd19WJJLhu0kOS7JYcPXaUnePKecAAAAALBS5tIg7O6buvvTw/2/SXJNkoOTnJTk3OGwc5M8Zbh/UpK39sSlSfavqgfMIysAAAAArJK5X6SkqrYmeVSSTya5f3fflEyaiEnuNxx2cJIb1vyz7cO+nX/WaVV1WVVdduutt+7L2AAAAACwlObaIKyq+yR5b5KXdvc3d3foLvb1nXZ0n9XdR3b3kQcddNCsYgIAAADAyphbg7Cq7pZJc/Dt3X3+sPvmHUuHh9tbhv3bkxy65p8fkuTGeWUFAAAAgFUxr6sYV5Kzk1zT3b+95lsXJjl1uH9qkvet2f+zw9WMj0py246lyAAAAADA7GyZ0+95fJLnJrmyqq4Y9v1yktcmOa+qXpDkK0lOGb73gSTHJ7kuybeSPH9OOQEAAABgpcylQdjd/yu7Pq9gkhyzi+M7yen7NBQAAMCMPeP8L44dYWrvOvnBY0cAYJOY+1WMAQAAAIDNQ4MQAAAAAFaYBiEAAAAArDANQgAAAABYYRqEAAAAALDC5nIVYwCYhV8979ixI0zt13/mQ2NHAAAAmIoZhAAAAACwwjQIAQAAAGCFaRACAAAAwArTIAQAAACAFaZBCAAAAAArTIMQAAAAAFaYBiEAAAAArLANNQir6u5VdfqswgAAAAAA8zVVg7Cqjqmql1fVScP2lqp6cZIvJXnhvgwIAAAAAOw7W+7qgKp6ZZJfTXJ1kh+pqv83yZOSfDvJad39x/s0IQAAAACwz9xlgzDJLyT5se6+vKqOSvL/JXlFd//Ovo0GAAAAAOxr0ywxPrC7L0+S7r40k5mD/3WfpgIAAAAA5mKaGYSpqkqy4+vvhn3/2Fzs7u/uk3QAAAAAwD41TYPwPkluX7Nda7YrSSfZb8a5AAAAAIA5mKZB+PgkN+7rIADM1n9725PHjjCVM57z4bEjAAAArLRpzkH44e7+8o6vJL+zdnvYt1tVdU5V3VJVV63Zd9+quriqvjDcHjDsr6p6Y1VdV1Wfq6oj9r48AAAAAGB3pmkQ1k7bT9qL37MtybE77TszySXdfViSS4btJDkuyWHD12lJ3rwXvw8AAAAAmMI0S4x7o7+kuz9eVVt32n1S7mg2npvko0leOex/a3d3kkurav+qekB337TRHAAAAOy5N1zwl2NHmMpLnvqDY0cAWEjTNAi3VNWP546ZhDtvp7s/she/+/47mn7dfVNV3W/Yf3CSG9Yct33Yp0EIAAAAADM2TYPwliTnrNn+q522O8mDZ5hp5yXNO37HnQ+sOi2TZch54AMfOMMIAAAAALAa7rJB2N1b99HvvnnH0uGqekAmjchkMmPw0DXHHZJ1rqLc3WclOStJjjzyyA0vhQYAAACAVTPNRUr2lQuTnDrcPzXJ+9bs/9nhasZHJbnN+QcBAAAAYN+YZonxhlXVOzK5IMmBVbU9yauTvDbJeVX1giRfSXLKcPgHkhyf5Lok30ry/HlkBAAAAIBVNJcGYXc/a51vHbOLYzvJ6fs2EQAAAACQjLvEGAAAAAAYmQYhAAAAAKwwDUIAAAAAWGEahAAAAACwwjQIAQAAAGCFaRACAAAAwArTIAQAAACAFaZBCAAAAAArbMvYAQDG9vZtTx47wtSe/bwPjx0BAACAJWMGIQAAAACsMA1CAAAAAFhhGoQAAAAAsMKcgxAAAICV8753f23sCFM76ZQDx44ALDkzCAEAAABghWkQAgAAAMAK0yAEAAAAgBWmQQgAAAAAK0yDEAAAAABWmAYhAAAAAKwwDUIAAAAAWGFbxg4AAAAAbNwnzr117AhTe9ypB40dAVhDgxDYIx88+/ixI0ztuBd8YOwIAAAAsOlt2gZhVR2b5A1J9kvy+9392pEjAQAAAHN23e/ePHaEqf3zX7z/2BFgr2zKcxBW1X5J3pTkuCQPT/Ksqnr4uKkAAAAAYPls1hmEj0lyXXd/MUmq6p1JTkry56OmAgAAANigv/ytL4wdYWo/+PLDxo7AHFR3j53hTqrq6UmO7e6fH7afm+Sx3X3GTsedluS0YfOhSa6dU8QDk3xtTr9rXpaxpmQ561LT4ljGupaxpmQ561LT4ljGutS0OJaxrmWsKVnOutS0OJaxrmWsKVnOutS0Mf+su+/yqkCbdQZh7WLfnTqZ3X1WkrP2fZzvVVWXdfeR8/69+9Iy1pQsZ11qWhzLWNcy1pQsZ11qWhzLWJeaFscy1rWMNSXLWZeaFscy1rWMNSXLWZea5mNTnoMwyfYkh67ZPiTJjSNlAQAAAICltVkbhJ9KclhVPaiq7p7kmUkuHDkTAAAAACydTbnEuLtvr6ozknw4yX5Jzunuq0eOtdbclzXPwTLWlCxnXWpaHMtY1zLWlCxnXWpaHMtYl5oWxzLWtYw1JctZl5oWxzLWtYw1JctZl5rmYFNepAQAAAAAmI/NusQYAAAAAJgDDUIAAAAAWGEahHugqo6tqmur6rqqOnPsPLNQVedU1S1VddXYWWalqg6tqj+pqmuq6uqqesnYmWahqu5RVX9WVZ8d6nrN2Jlmpar2q6rPVNX7x84yC1V1fVVdWVVXVNVlY+eZlarav6reU1WfH/5/PW7sTBtRVQ8dHqMdX9+sqpeOnWujquplw3PEVVX1jqq6x9iZZqGqXjLUdPUiP067et2tqvtW1cVV9YXh9oAxM+6pdWo6ZXisvltVR46Zb2+sU9Prhue/z1XVBVW1/5gZ98Y6df36UNMVVXVRVf3QmBn31O7GslX1iqrqqjpwjGx7a53H6T9V1VfXvGYdP2bGvbHeY1VVvzi8x7q6qn5zrHx7Y53H6l1rHqfrq+qKMTPujXXqOryqLt0xvq2qx4yZcU+tU9OPVtUnhnH7H1XV/zVmxj213vveRR5X7KamRR9XrFfXphpbaBBOqar2S/KmJMcleXiSZ1XVw8dNNRPbkhw7dogZuz3Jy7v7YUmOSnL6kjxW305ydHf/aJLDkxxbVUeNnGlWXpLkmrFDzNiPd/fh3b1wL2C78YYkH+ruf5HkR7Pgj1l3Xzs8RocneXSSbyW5YORYG1JVByd5cZIju/sRmVzo65njptq4qnpEkn+b5DGZ/O2dUFWHjZtqr23LnV93z0xySXcfluSSYXuRbMuda7oqyclJPj73NLOxLXeu6eIkj+juRyb530leNe9QM7Atd67rdd39yOG58P1Jfm3uqTZmW3Yxlq2qQ5P8ZJKvzDvQDGzLrsfnv7Pjdau7PzDnTLOwLTvVVVU/nuSkJI/s7h9J8voRcm3EtuxUU3c/Y8344r1Jzh8j2AZty53/Bn8zyWuGun5t2F4k23Lnmn4/yZnd/S8zGQP+0rxDbdB673sXeVyxXk2LPq5Yr65NNbbQIJzeY5Jc191f7O7vJHlnJi9mC627P57k62PnmKXuvqm7Pz3c/5tMmhgHj5tq43ri/wybdxu+Fv4qQ1V1SJKfzuQFmk1q+ET1iUnOTpLu/k53f2PcVDN1TJK/6O4vjx1kBrYkuWdVbUlyryQ3jpxnFh6W5NLu/lZ3357kY0meOnKmvbLO6+5JSc4d7p+b5ClzDbVBu6qpu6/p7mtHirRh69R00fD3lySXJjlk7sE2aJ26vrlm895ZsLHFbsayv5PkP2TB6kmWc3yerFvXi5K8tru/PRxzy9yDbcDuHquqqiQ/k+Qdcw01A+vU1Ul2zLD7p1mw8cU6NT00dzScLk7ytLmG2qDdvO9d2HHFejUtwbhivbo21dhCg3B6Bye5Yc329ixB02nZVdXWJI9K8slxk8xGTZbiXpHkliQXd/cy1PVfMxnAf3fsIDPUSS6qqsur6rSxw8zIg5PcmuR/1GQ5+O9X1b3HDjVDz8wCDuB31t1fzWT2xVeS3JTktu6+aNxUM3FVkidW1Q9U1b2SHJ/k0JEzzdL9u/umZDKATHK/kfNw134uyQfHDjErVfUbVXVDkmdn8WYQ3klVnZjkq9392bGzzNgZwzK0cxZpyeBd+OEkT6iqT1bVx6rqX40daIaekOTm7v7C2EFm5KVJXjc8V7w+izmLemdXJTlxuH9KFnhssdP73qUYVyzbe/kddlPX6GMLDcLp1S72Ldwnkqukqu6TybT+l+706fjC6u5/GKb1H5LkMcOyu4VVVSckuaW7Lx87y4w9vruPyOSUBKdX1RPHDjQDW5IckeTN3f2oJH+bxVqusK6qunsmg8N3j51lo4Y3jCcleVCSH0py76p6zripNq67r0nyXzL5dP9DST6byVINmLuq+pVM/v7ePnaWWenuX+nuQzOp6Yyx82zE8CHCr2QJGp07eXOSh2RympmbkvzWuHFmZkuSAzJZcvdLSc4bZt4tg2dlCT58XONFSV42PFe8LMOqkgX3c5mM1S9P8v1JvjNynr2yjO97l7GmZP26NsvYQoNwetvzvZ8oHJIFm1a9Sqrqbpn8x3t7dy/ieT92a1ja+dEs/vkjH5/kxKq6PpNl+0dX1dvGjbRx3X3jcHtLJuczWaiTOK9je5Lta2atvieThuEyOC7Jp7v75rGDzMBPJPlSd9/a3X+fyXmP/vXImWaiu8/u7iO6+4mZLBFalhkZSXJzVT0gSYbbhVpit0qq6tQkJyR5dncv4wfFf5AFW2K3Cw/J5EOSzw7ji0OSfLqqfnDUVBvU3TcPHxR/N8nvZTnGFslkfHH+cCqdP8tkRclCXVRmV4bTfJyc5F1jZ5mhU3PH+RTfnSX4G+zuz3f3T3X3ozNp5v7F2Jn21Drvexd6XLGs7+XXq2szjS00CKf3qSSHVdWDhtkmz0xy4ciZ2IXhU8ezk1zT3b89dp5ZqaqDdlzVqKrumUkj4PPjptqY7n5Vdx/S3Vsz+T/1ke5e6NlOVXXvqvr+HfeT/FQmyxcWWnf/ZZIbquqhw65jkvz5iJFmaZk+4f9KkqOq6l7Dc+ExWfCLyexQVfcbbh+YyZuuZXnMksl44tTh/qlJ3jdiFtZRVccmeWWSE7v7W2PnmZWdLvhzYhZ/bHFld9+vu7cO44vtSY4YXscW1o43+4OnZgnGFoM/THJ0klTVDye5e5KvjZpoNn4iyee7e/vYQWboxiQ/Ntw/OkvwQd2ascU/SfIfk7xl3ER7Zjfvexd2XLHE7+V3WddmG1vUcn74uW9U1fGZnC9tvyTndPdvjBxpw6rqHUmelMkndTcneXV3L/R08ar6N0n+NMmVueO8dr/ci3m1t39UVY/M5CSz+2XS3D+vu//zuKlmp6qelOQV3X3C2Fk2oqoenDuuhLslyR8sw3NFklTV4ZlcTObuSb6Y5Pnd/dfjptqYYSnaDUke3N23jZ1nFqrqNUmekckyhc8k+fkdJ39fZFX1p0l+IMnfJ/n33X3JyJH2yq5edzN5g3xekgdm0uQ9pbsX5gIF69T09SS/m+SgJN9IckV3P3msjHtqnZpeleT7kvzVcNil3f3CUQLupXXqOj6TE/V/N8mXk7xwOJ/pQrirsewwi/DI7l6YptM6j9OTMlle3EmuT/ILO84xtijWqet/Jjknk9q+k8lY8CNjZdxT6/39VdW2TJ4jFqrhtMM6j9W1Sd6Qyfj275L8u0U6TdA6Nd0nyenDIecnedXYM7j2xHrvezM5t91Cjit2U9P3ZbHHFevV9cZsorGFBiEAAAAArDBLjAEAAABghWkQAgAAAMAK0yAEAAAAgBWmQQgAAAAAK0yDEAAAAABWmAYhAABTqaqtVXXV2DkAAJgtDUIAAEZTVVvGzgAAsOo0CAEA2BP7VdXvVdXVVXVRVd2zqg6vqkur6nNVdUFVHZAkVfXRqjpyuH9gVV0/3H9eVb27qv4oyUXjlQIAQKJBCADAnjksyZu6+0eSfCPJ05K8Nckru/uRSa5M8uopfs7jkpza3Ufvs6QAAExFgxAAgD3xpe6+Yrh/eZKHJNm/uz827Ds3yROn+DkXd/fX90VAAAD2jAYhAAB74ttr7v9Dkv13c+ztuWO8eY+dvve3swwFAMDe0yAEAGAjbkvy11X1hGH7uUl2zCa8GIDligAAIABJREFUPsmjh/tPn3MuAACm5KpxAABs1KlJ3lJV90ryxSTPH/a/Psl5VfXcJB8ZKxwAALtX3T12BgAAAABgJJYYAwAAAMAK0yAEAAAAgBWmQQgAAAAAK0yDEAAAAABWmAYhAAAAAKwwDUIAAAAAWGEahAAAAACwwjQIAQAAAGCFaRACAAAAwArTIAQAAACAFaZBCAAAAAArTIMQAAAAAFaYBiEAAAAArDANQgAAAABYYVvGDjArBx54YG/dunXsGAAAAACwKVx++eVf6+6D7uq4pWkQbt26NZdddtnYMQAAAABgU6iqL09znCXGAAAAALDCNAgBAAAAYIUtzRJjAABYZie950NjR5jK+55+7NgRAIA9ZAYhAAAAAKwwDUIAAAAAWGEahAAAAACwwpyDEACYueMvePXYEab2gae+ZuwIAAAwKjMIAQAAAGCFaRACAAAAwAqbS4Owqg6tqj+pqmuq6uqqesmw/75VdXFVfWG4PWDYX1X1xqq6rqo+V1VHzCMnAAAAAKyaec0gvD3Jy7v7YUmOSnJ6VT08yZlJLunuw5JcMmwnyXFJDhu+Tkvy5jnlBAAAAICVMpcGYXff1N2fHu7/TZJrkhyc5KQk5w6HnZvkKcP9k5K8tScuTbJ/VT1gHlkBAAAAYJXM/SrGVbU1yaOSfDLJ/bv7pmTSRKyq+w2HHZzkhjX/bPuw76adftZpmcwwzAMf+MB9mhsAWG0/ff4bx44wtT8++cVjRwAAYIHMtUFYVfdJ8t4kL+3ub1bVuofuYl/faUf3WUnOSpIjjzzyTt8HgEVw3IUnjh1hKh888cKxIwAAAPvA3K5iXFV3y6Q5+PbuPn/YffOOpcPD7S3D/u1JDl3zzw9JcuO8sgIAAADAqpjLDMKaTBU8O8k13f3ba751YZJTk7x2uH3fmv1nVNU7kzw2yW07liIDAMDunPCed48dYWrvf/opY0cAAJjbEuPHJ3lukiur6oph3y9n0hg8r6pekOQrSXaMkD6Q5Pgk1yX5VpLnzyknAAAAAKyUuTQIu/t/ZdfnFUySY3ZxfCc5fZ+GAgAAAADmdw5CAAAAAGDz0SAEAAAAgBWmQQgAAAAAK0yDEAAAAABW2LyuYgwAAPA9Tn7vJ8aOMLXzn/a4sSMAwD5jBiEAAAAArDAzCAFYGL963rFjR5jar//Mh8aOAFM54b3bxo4wlfc/7XljRwAAWFpmEAIAAADACtMgBAAAAIAVpkEIAAAAACtMgxAAAAAAVpgGIQAAAACsMA1CAAAAAFhhGoQAAAAAsMI0CAEAAABghW3Zm39UVQ9N8vAkn+nu62eaCAAAYEE94/wvjh1hau86+cFjRwBgk7jLGYRV9VtV9Zw12z+b5OokZyW5pqqO24f5AAAAAIB9aJolxk9J8vE12/9Pkhd390FJXpjk1fsiGAAAAACw703TIDyou7+SJFX1iCQ/kOTs4XtvS/LD+ygbAAAAALCPTdMgvK2q7j/cf0KSy7r728P23ZLUPkkGAAAAAOxz01yk5Lwk76yqC5K8PMlr13zvsUn+Yl8EAwAAAAD2vWlmEJ6Z5KNJfjKTC5O8Zc33Dk/y32cfCwAAAACYh2kahL/V3a/p7v+7u38jyc/t+EZ3vyHJsXf1A6rqnKq6paquWrPvvlV1cVV9Ybg9YNhfVfXGqrquqj5XVUfsRV0AAAAAwBSmaRA+b6ft1+20/ZNT/IxtuXMj8cwkl3T3YUkuGbaT5Lgkhw1fpyV58xQ/HwAAAADYC9M0CHe+CMldbd9Jd388ydd32n1SknOH++cmecqa/W/tiUuT7F9VD5giJwAAAACwh6ZpEPYebk/r/t19U5IMt/cb9h+c5IY1x20f9t1JVZ1WVZdV1WW33nrrXsYAAAAAgNU1zVWMt1TVj+eOmYI7b+8340y7mpG4yyZkd5+VyYVTcuSRR+5toxIAAAAAVtY0DcJbkpyzZvuvdtq+ZS9/981V9YDuvmlYQrzj52xPcuia4w5JcuNe/g4AAAAAYDfuskHY3Vv30e++MMmpSV473L5vzf4zquqdSR6b5LYdS5EBmN5/e9uTx44wlTOe8+GxIwAAAKy0aWYQblhVvSPJk5IcWFXbk7w6k8bgeVX1giRfSXLKcPgHkhyf5Lok30ry/HlkBAAAAIBVNJcGYXc/a51vHbOLYzvJ6fs2EQAAAACQTHcVYwAAAABgSWkQAgAAAMAKm8sSYwAAABbXGy74y7EjTOUlT/3BsSMALCQzCAEAAABghWkQAgAAAMAK0yAEAAAAgBWmQQgAAAAAK0yDEAAAAABWmAYhAAAAAKwwDUIAAAAAWGEahAAAAACwwjQIAQAAAGCFbRk7AMDY3r7tyWNHmNqzn/fhsSMAAACwZDQIAQAAWDnve/fXxo4wtZNOOXDsCMCSs8QYAAAAAFaYBiEAAAAArDANQgAAAABYYRqEAAAAALDCNAgBAAAAYIVpEAIAAADACtsydgBgsXzw7OPHjjC1417wgbEjAAAAwKZnBiEAAAAArDAzCAEAAGAJfOLcW8eOMLXHnXrQ2BGANTbtDMKqOraqrq2q66rqzLHzAAAAAMAy2pQzCKtqvyRvSvKTSbYn+VRVXdjdfz5uMtgzn/zvJ4wdYSqP/YX3jx0BAABgl6773ZvHjjC1f/6L9x87AuyVzTqD8DFJruvuL3b3d5K8M8lJI2cCAAAAgKVT3T12hjupqqcnOba7f37Yfm6Sx3b3GTsdd1qS04bNhya5dk4RD0zytTn9rnlZxpqS5axLTYtjGetaxpqS5axLTYtjGetS0+JYxrqWsaZkOetS0+JYxrqWsaZkOetS08b8s+6+y5N+bsolxklqF/vu1Mns7rOSnLXv43yvqrqsu4+c9+/dl5axpmQ561LT4ljGupaxpmQ561LT4ljGutS0OJaxrmWsKVnOutS0OJaxrmWsKVnOutQ0H5t1ifH2JIeu2T4kyY0jZQEAAACApbVZG4SfSnJYVT2oqu6e5JlJLhw5EwAAAAAsnU25xLi7b6+qM5J8OMl+Sc7p7qtHjrXW3Jc1z8Ey1pQsZ11qWhzLWNcy1pQsZ11qWhzLWJeaFscy1rWMNSXLWZeaFscy1rWMNSXLWZea5mBTXqQEAAAAAJiPzbrEGAAAAACYAw1CAAAAAFhhGoR7oKqOraprq+q6qjpz7DyzUFXnVNUtVXXV2FlmpaoOrao/qaprqurqqnrJ2JlmoaruUVV/VlWfHep6zdiZZqWq9quqz1TV+8fOMgtVdX1VXVlVV1TVZWPnmZWq2r+q3lNVnx/+fz1u7EwbUVUPHR6jHV/frKqXjp1ro6rqZcNzxFVV9Y6qusfYmWahql4y1HT1Ij9Ou3rdrar7VtXFVfWF4faAMTPuqXVqOmV4rL5bVUeOmW9vrFPT64bnv89V1QVVtf+YGffGOnX9+lDTFVV1UVX90JgZ99TuxrJV9Yqq6qo6cIxse2udx+k/VdVX17xmHT9mxr2x3mNV9f+3d/dBltT1vcffn7CigiaggCEs3BXvanwIIm4IxkgQjAKhQFEilvGumoRoID4kMULM1WtSVhk1Gs219KqQwcQnUIjoVQMF8eFWiREMCASIiAgrZMGomEhFRL73j+4Jk905uzM7Z89v+pz3q2pq+vT0zny+dc6e8+tv9687v9vvY12T5E2t8u2IEc/VRxY8TzcluaJlxh0xoq6Dk1w6P75NcmjLjMs1oqbHJ/liP27/RJKfbJlxuUbt9w55XLGNmoY+rhhV16oaW9ggXKIkuwDvBI4BHgM8L8lj2qYaizng6NYhxuwe4Per6tHAYcCpU/Jc/RA4sqoeDxwMHJ3ksMaZxuXlwLWtQ4zZU6vq4Koa3AfYNrwd+ExV/SzweAb+nFXV9f1zdDDwROAu4PzGsVYkyX7Ay4ANVfU4uht9ndw21coleRzwW8ChdK+945Ksb5tqh82x9efu6cDFVbUeuLh/PCRzbF3T1cCJwOcnnmY85ti6pouAx1XVQcA/A2dMOtQYzLF1XW+uqoP698JPAq+deKqVmWORsWyS/YFfAW6edKAxmGPx8fnb5j+3qupTE840DnNsUVeSpwInAAdV1WOBtzTItRJzbFFTVT13wfjiY8B5LYKt0BxbvwbfBLy+r+u1/eMhmWPrmt4HnF5VP0c3BnzVpEOt0Kj93iGPK0bVNPRxxai6VtXYwgbh0h0K3FBVN1bV3cCH6T7MBq2qPg98p3WOcaqq26rqK/3yv9E1MfZrm2rlqvPv/cP79V+Dv8tQkrXAr9J9QGuV6o+oHg6cCVBVd1fV99qmGqujgK9X1TdbBxmDNcADk6wBdgNubZxnHB4NXFpVd1XVPcDngGc1zrRDRnzungCc3S+fDTxzoqFWaLGaquraqrq+UaQVG1HThf3rD+BSYO3Eg63QiLq+v+Dh7gxsbLGNsezbgD9kYPXAdI7PYWRdLwXeWFU/7Le5feLBVmBbz1WSAL8GfGiiocZgRF0FzJ9h91MMbHwxoqZHcV/D6SLg2RMNtULb2O8d7LhiVE1TMK4YVdeqGlvYIFy6/YBbFjzexBQ0naZdknXAE4AvtU0yHumm4l4B3A5cVFXTUNdf0A3g720dZIwKuDDJ5UlOaR1mTA4E7gD+Kt108Pcl2b11qDE6mQEO4LdUVd+iO/viZuA24M6qurBtqrG4Gjg8yUOT7AYcC+zfONM4PayqboNuAAns0ziPtu/FwKdbhxiXJG9IcgvwfIZ3BuFWkhwPfKuqrmydZcxO66ehnTWkKYPb8UjgKUm+lORzSX6+daAxegqwuaq+1jrImLwCeHP/XvEWhnkW9ZauBo7vl09iwGOLLfZ7p2JcMW378vO2UVfzsYUNwqXLIusGd0RyliR5EN1p/a/Y4uj4YFXVj/vT+tcCh/bT7gYryXHA7VV1eessY/bkqjqE7pIEpyY5vHWgMVgDHAK8q6qeAPyAYU1XGCnJrnSDw3NbZ1mpfofxBODhwM8Auyf59bapVq6qrgX+jO7o/meAK+mmakgTl+Q1dK+/D7TOMi5V9Zqq2p+uptNa51mJ/iDCa5iCRucW3gU8gu4yM7cBf942ztisAfakm3L3KuCc/sy7afA8puDg4wIvBV7Zv1e8kn5WycC9mG6sfjnwYODuxnl2yDTu905jTTC6rtUytrBBuHSb+K9HFNYysNOqZ0mS+9H9x/tAVQ3xuh/b1E/t/CzDv37kk4Hjk9xEN23/yCR/0zbSylXVrf332+muZzKoiziPsAnYtOCs1Y/SNQynwTHAV6pqc+sgY/A04BtVdUdV/Yjuuke/2DjTWFTVmVV1SFUdTjdFaFrOyADYnGRfgP77oKbYzZIkG4HjgOdX1TQeKP4gA5tit4hH0B0kubIfX6wFvpLkp5umWqGq2twfKL4XeC/TMbaAbnxxXn8pnX+gm1EyqJvKLKa/zMeJwEdaZxmjjdx3PcVzmYLXYFVdV1VPr6on0jVzv94603KN2O8d9LhiWvflR9W1msYWNgiX7svA+iQP7882ORm4oHEmLaI/6ngmcG1VvbV1nnFJsvf8XY2SPJCuEXBd21QrU1VnVNXaqlpH93/qkqoa9NlOSXZP8uD5ZeDpdNMXBq2q/gW4Jcmj+lVHAf/UMNI4TdMR/puBw5Ls1r8XHsXAbyYzL8k+/fcD6Ha6puU5g248sbFf3gh8vGEWjZDkaODVwPFVdVfrPOOyxQ1/jmf4Y4urqmqfqlrXjy82AYf0n2ODNb+z33sWUzC26P0tcCRAkkcCuwLfbppoPJ4GXFdVm1oHGaNbgV/ul49kCg7ULRhb/ATwx8C72yZanm3s9w52XDHF+/KL1rXaxhaZzoOfO0eSY+mul7YLcFZVvaFxpBVL8iHgCLojdZuB11XVoE8XT/JLwBeAq7jvunZ/VMO829t/SnIQ3UVmd6Fr7p9TVX/SNtX4JDkC+IOqOq51lpVIciD33Ql3DfDBaXivAEhyMN3NZHYFbgReVFXfbZtqZfqpaLcAB1bVna3zjEOS1wPPpZum8I/Ab85f/H3IknwBeCjwI+D3qurixpF2yGKfu3Q7yOcAB9A1eU+qqsHcoGBETd8B/hLYG/gecEVVPaNVxuUaUdMZwP2Bf+03u7SqXtIk4A4aUdexdBfqvxf4JvCS/nqmg7C9sWx/FuGGqhpM02nE83QE3fTiAm4Cfnv+GmNDMaKuvwbOoqvtbrqx4CWtMi7XqNdfkjm694hBNZzmjXiurgfeTje+/Q/gd4Z0maARNT0IOLXf5DzgjNZncC3HqP1eumvbDXJcsY2a7s+wxxWj6noHq2hsYYNQkiRJkiRJmmFOMZYkSZIkSZJmmA1CSZIkSZIkaYbZIJQkSZIkSZJmmA1CSZIkSZIkaYbZIJQkSZIkSZJmmA1CSZIkLUmSdUmubp1DkiRJ42WDUJIkSc0kWdM6gyRJ0qyzQShJkqTl2CXJe5Nck+TCJA9McnCSS5N8Ncn5SfYESPLZJBv65b2S3NQvvzDJuUk+AVzYrhRJkiSBDUJJkiQtz3rgnVX1WOB7wLOB9wOvrqqDgKuA1y3h9zwJ2FhVR+60pJIkSVoSG4SSJElajm9U1RX98uXAI4A9qupz/bqzgcOX8Hsuqqrv7IyAkiRJWh4bhJIkSVqOHy5Y/jGwxza2vYf7xpsP2OJnPxhnKEmSJO04G4SSJElaiTuB7yZ5Sv/4BcD82YQ3AU/sl58z4VySJElaIu8aJ0mSpJXaCLw7yW7AjcCL+vVvAc5J8gLgklbhJEmStG2pqtYZJEmSJEmSJDXiFGNJkiRJkiRphtkglCRJkiRJkmaYDUJJkiRJkiRphtkglCRJkiRJkmaYDUJJkiRJkiRphtkglCRJkiRJkmaYDUJJkiRJkiRphtkglCRJkiRJkmaYDUJJkiRJkiRphtkglCRJkiRJkmaYDUJJkiRJkiRphtkglCRJkiRJkmaYDUJJkiRJkiRphtkglCRJkiRJkmbYmtYBxmWvvfaqdevWtY4hSZIkSZIkrQqXX375t6tq7+1tNzUNwnXr1nHZZZe1jiFJkiRJkiStCkm+uZTtJjLFOMn+Sf4+ybVJrkny8n79Q5JclORr/fc9+/VJ8o4kNyT5apJDJpFTkiRJkiRJmjWTugbhPcDvV9WjgcOAU5M8BjgduLiq1gMX948BjgHW91+nAO+aUE5JkiRJkiRppkxkinFV3Qbc1i//W5Jrgf2AE4Aj+s3OBj4LvLpf//6qKuDSJHsk2bf/PZIkaZU79vzXtY6wZJ961utbR5AkSZKamvhdjJOsA54AfAl42HzTr/++T7/ZfsAtC/7Zpn7dlr/rlCSXJbnsjjvu2JmxJUmSJEmSpKk00ZuUJHkQ8DHgFVX1/SQjN11kXW21ouo9wHsANmzYsNXPJUmSpGlxwkc/0zrCknz8OUe3jiBJkpZpYmcQJrkfXXPwA1V1Xr96c5J9+5/vC9zer98E7L/gn68Fbp1UVkmSJEmSJGlWTOouxgHOBK6tqrcu+NEFwMZ+eSPw8QXr/0d/N+PDgDu9/qAkSZIkSZI0fpOaYvxk4AXAVUmu6Nf9EfBG4JwkvwHcDJzU/+xTwLHADcBdwIsmlFOSJEmSJEmaKZO6i/H/Y/HrCgIctcj2BZy6U0NJkiRJkiRJmvxdjCVJkiRJkiStHhO9i7EkSZJWl+M+Ntc6wpJ88tkvbB1BkiRpatkglCRJ0lQ57qPnto6wZJ98zknb30iSJGkns0EoSVJjx1xwfOsIS/Lp4y9oHUGSJEnSTuA1CCVJkiRJkqQZZoNQkiRJkiRJmmFOMZYkSZLUxIkf+2LrCEt23rOf1DqCJEk7jQ1CSZKkJfjV897ROsKS/d8TX9Y6giRJkgbEKcaSJEmSJEnSDLNBKEmSJEmSJM0wG4SSJEmSJEnSDLNBKEmSJEmSJM0wG4SSJEmSJEnSDPMuxpIkSZI0Js8978bWEZbsIyce2DqCJGmV8AxCSZIkSZIkaYZ5BqEkaTD+5zlHt46wZH/6a59pHUGSJEmSlsQzCCVJkiRJkqQZZoNQkiRJkiRJmmE2CCVJkiRJkqQZZoNQkiRJkiRJmmE2CCVJkiRJkqQZZoNQkiRJkiRJmmE2CCVJkiRJkqQZZoNQkiRJkiRJmmE2CCVJkiRJkqQZZoNQkiRJkiRJmmHbbRAmuWoSQSRJkiRJkiRN3lLOIFy30j+S5Kwktye5esG6hyS5KMnX+u979uuT5B1Jbkjy1SSHrPTvS5IkSZIkSVrcUhqENYa/MwccvcW604GLq2o9cHH/GOAYYH3/dQrwrjH8fUmSJEmSJEmLWLOEbXZL8vltbVBVh2/n559Psm6L1ScAR/TLZwOfBV7dr39/VRVwaZI9kuxbVbctIaskSZIkSZKkZVhKg/BHwJk74W8/bL7pV1W3JdmnX78fcMuC7Tb167ZqECY5he4sQw444ICdEFGSJEmSJEmabktqEFbV2Ts9yX2yyLpFpzlX1XuA9wBs2LBhHFOhJUmSJEmSpJmylAbhYg27cdg8P3U4yb7A7f36TcD+C7ZbC9y6kzJIkiRJkrbj7ef/S+sIS/LyZ/106wiSNEhLaRC+KcmR29qgqi7Zgb99AbAReGP//eML1p+W5MPALwB3ev1BSVq+//03z2gdYUlO+/W/ax1BkiRJkmbaUhqEL+6/RingwG39giQforshyV5JNgGvo2sMnpPkN4CbgZP6zT8FHAvcANwFvGgJGSVJkiRJkiTtgO02CKvq4Sv9I1X1vBE/OmqRbQs4daV/U5IkSZIkSdL2/UTrAJIkSZIkSZLasUEoSZIkSZIkzTAbhJIkSZIkSdIMs0EoSZIkSZIkzTAbhJIkSZIkSdIMs0EoSZIkSZIkzTAbhJIkSZIkSdIMs0EoSZIkSZIkzTAbhJIkSZIkSdIMs0EoSZIkSZIkzTAbhJIkSZIkSdIMs0EoSZIkSZIkzTAbhJIkSZIkSdIMs0EoSZIkSZIkzTAbhJIkSZIkSdIMW9M6gCRJkiRJk/bxc7/dOsKSnXDSXq0jSJpynkEoSZIkSZIkzTDPIJQ08z4w94zWEZbs+S/8u9YRJEmSJElTxjMIJUmSJEmSpBlmg1CSJEmSJEmaYTYIJUmSJEmSpBlmg1CSJEmSJEmaYd6kRNKyfPrMY1tHWLJjfuNTrSNIkiRJE/PFs+9oHWHJnrRx79YRJC3gGYSSJEmSJEnSDLNBKEmSJEmSJM0wG4SSJEmSJEnSDLNBKEmSJEmSJM2wVdsgTHJ0kuuT3JDk9NZ5JEmSJEmSpGm0Ku9inGQX4J3ArwCbgC8nuaCq/qltMml5vvR/jmsdYUl+4bc/2TqCJEmSJC3qhr/c3DrCkv33331Y6wjSDlmVDULgUOCGqroRIMmHgRMAG4RT6qZ3PLN1hCVb97K/bR1BkiRJkiRpbFJVrTNsJclzgKOr6jf7xy8AfqGqTttiu1OAU/qHjwKun1DEvYBvT+hvTco01gTTWZc1Dcc01jWNNcF01mVNwzGNdVnTcExjXdNYE0xnXdY0HNNY1zTWBNNZlzWtzH+rqr23t9FqPYMwi6zbqpNZVe8B3rPz4/xXSS6rqg2T/rs70zTWBNNZlzUNxzTWNY01wXTWZU3DMY11WdNwTGNd01gTTGdd1jQc01jXNNYE01mXNU3Gar1JySZg/wWP1wK3NsoiSZIkSZIkTa3V2iD8MrA+ycOT7AqcDFzQOJMkSZIkSZI0dVblFOOquifJacDfAbsAZ1XVNY1jLTTxac0TMI01wXTWZU3DMY11TWNNMJ11WdNwTGNd1jQc01jXNNYE01mXNQ3HNNY1jTXBdNZlTROwKm9SIkmSJEmSJGkyVusUY0mSJEmSJEkTYINQkiRJkiRJmmE2CJchydFJrk9yQ5LTW+cZhyRnJbk9ydWts4xLkv2T/H2Sa5Nck+TlrTONQ5IHJPmHJFf2db2+daZxSbJLkn9M8snWWcYhyU1JrkpyRZLLWucZlyR7JPlokuv6/19Pap1pJZI8qn+O5r++n+QVrXOtVJJX9u8RVyf5UJIHtM40Dkle3td0zZCfp8U+d5M8JMlFSb7Wf9+zZcblGlHTSf1zdW+SDS3z7YgRNb25f//7apLzk+zRMuOOGFHXn/Y1XZHkwiQ/0zLjcm1rLJvkD5JUkr1aZNtRI56n/5XkWws+s45tmXFHjHqukvxuv491TZI3tcq3I0Y8Vx9Z8DzdlOSKlhl3xIi6Dk5y6fz4NsmhLTMu14iaHp/ki/24/RNJfrJlxuUatd875HHFNmoa+rhiVF2ramxhg3CJkuwCvBM4BngM8Lwkj2mbaizmgKNbhxize4Dfr6pHA4cBp07Jc/VD4MiqejxwMHB0ksMaZxqXlwPXtg4xZk+tqoOranAfYNvwduAzVfWzwOMZ+HNWVdf3z9HBwBOBu4DzG8dakST7AS8DNlTV4+hu9HVy21Qrl+RxwG8Bh9K99o5Lsr5tqh02x9afu6cDF1fVeuDi/vGQzLF1TVcDJwKfn3ia8Zhj65ouAh5XVQcB/wycMelQYzDH1nW9uaoO6t8LPwm8duKpVmaORcaySfYHfgW4edKBxmCOxcfnb5v/3KqqT0040zjMsUVdSZ4KnAAcVFWPBd7SINdKzLFFTVX13AXji48B57UItkJzbP0afBPw+r6u1/aPh2SOrWt6H3B6Vf0c3RjwVZMOtUKj9nuHPK4YVdPQxxWj6lpVYwsbhEt3KHBDVd1YVXcDH6b7MBu0qvo88J3WOcapqm6rqq/0y/9G18TYr22qlavOv/cP79d/Df4uQ0nWAr9K9wGtVao/ono4cCZAVd1dVd9rm2qsjgK+XlXfbB1kDNYAD0yyBtgNuLVxnnF4NHBpVd1VVfcAnwOe1TjTDhnxuXsCcHa/fDbwzImGWqHFaqqqa6vq+kaRVmxETRf2rz+AS4G1Ew+2QiPq+v6Ch7szsLHFNsaybwP+kIHVA9M5PoeRdb0IANd1AAAHHklEQVQUeGNV/bDf5vaJB1uBbT1XSQL8GvChiYYagxF1FTB/ht1PMbDxxYiaHsV9DaeLgGdPNNQKbWO/d7DjilE1TcG4YlRdq2psYYNw6fYDblnweBNT0HSadknWAU8AvtQ2yXikm4p7BXA7cFFVTUNdf0E3gL+3dZAxKuDCJJcnOaV1mDE5ELgD+Kt008Hfl2T31qHG6GQGOIDfUlV9i+7si5uB24A7q+rCtqnG4mrg8CQPTbIbcCywf+NM4/SwqroNugEksE/jPNq+FwOfbh1iXJK8IcktwPMZ3hmEW0lyPPCtqrqydZYxO62fhnbWkKYMbscjgack+VKSzyX5+daBxugpwOaq+lrrIGPyCuDN/XvFWxjmWdRbuho4vl8+iQGPLbbY752KccW07cvP20ZdzccWNgiXLousG9wRyVmS5EF0p/W/Youj44NVVT/uT+tfCxzaT7sbrCTHAbdX1eWts4zZk6vqELpLEpya5PDWgcZgDXAI8K6qegLwA4Y1XWGkJLvSDQ7PbZ1lpfodxhOAhwM/A+ye5Nfbplq5qroW+DO6o/ufAa6km6ohTVyS19C9/j7QOsu4VNVrqmp/uppOa51nJfqDCK9hChqdW3gX8Ai6y8zcBvx52zhjswbYk27K3auAc/oz76bB85iCg48LvBR4Zf9e8Ur6WSUD92K6sfrlwIOBuxvn2SHTuN87jTXB6LpWy9jCBuHSbeK/HlFYy8BOq54lSe5H9x/vA1U1xOt+bFM/tfOzDP/6kU8Gjk9yE920/SOT/E3bSCtXVbf232+nu57JoC7iPMImYNOCs1Y/StcwnAbHAF+pqs2tg4zB04BvVNUdVfUjuuse/WLjTGNRVWdW1SFVdTjdFKFpOSMDYHOSfQH674OaYjdLkmwEjgOeX1XTeKD4gwxsit0iHkF3kOTKfnyxFvhKkp9ummqFqmpzf6D4XuC9TMfYArrxxXn9pXT+gW5GyaBuKrOY/jIfJwIfaZ1ljDZy3/UUz2UKXoNVdV1VPb2qnkjXzP1660zLNWK/d9Djimndlx9V12oaW9ggXLovA+uTPLw/2+Rk4ILGmbSI/qjjmcC1VfXW1nnGJcne83c1SvJAukbAdW1TrUxVnVFVa6tqHd3/qUuqatBnOyXZPcmD55eBp9NNXxi0qvoX4JYkj+pXHQX8U8NI4zRNR/hvBg5Lslv/XngUA7+ZzLwk+/TfD6Db6ZqW5wy68cTGfnkj8PGGWTRCkqOBVwPHV9VdrfOMyxY3/Dme4Y8trqqqfapqXT++2AQc0n+ODdb8zn7vWUzB2KL3t8CRAEkeCewKfLtpovF4GnBdVW1qHWSMbgV+uV8+kik4ULdgbPETwB8D726baHm2sd872HHFFO/LL1rXahtbZDoPfu4cSY6lu17aLsBZVfWGxpFWLMmHgCPojtRtBl5XVYM+XTzJLwFfAK7ivuva/VEN825v/ynJQXQXmd2Frrl/TlX9SdtU45PkCOAPquq41llWIsmB3Hcn3DXAB6fhvQIgycF0N5PZFbgReFFVfbdtqpXpp6LdAhxYVXe2zjMOSV4PPJdumsI/Ar85f/H3IUvyBeChwI+A36uqixtH2iGLfe7S7SCfAxxA1+Q9qaoGc4OCETV9B/hLYG/ge8AVVfWMVhmXa0RNZwD3B/613+zSqnpJk4A7aERdx9JdqP9e4JvAS/rrmQ7C9say/VmEG6pqME2nEc/TEXTTiwu4Cfjt+WuMDcWIuv4aOIuutrvpxoKXtMq4XKNef0nm6N4jBtVwmjfiuboeeDvd+PY/gN8Z0mWCRtT0IODUfpPzgDNan8G1HKP2e+mubTfIccU2aro/wx5XjKrrHayisYUNQkmSJEmSJGmGOcVYkiRJkiRJmmE2CCVJkiRJkqQZZoNQkiRJkiRJmmE2CCVJkiRJkqQZZoNQkiRJkiRJmmE2CCVJkrQkSdYlubp1DkmSJI2XDUJJkiQ1k2RN6wySJEmzzgahJEmSlmOXJO9Nck2SC5M8MMnBSS5N8tUk5yfZEyDJZ5Ns6Jf3SnJTv/zCJOcm+QRwYbtSJEmSBDYIJUmStDzrgXdW1WOB7wHPBt4PvLqqDgKuAl63hN/zJGBjVR2505JKkiRpSWwQSpIkaTm+UVVX9MuXA48A9qiqz/XrzgYOX8LvuaiqvrMzAkqSJGl5bBBKkiRpOX64YPnHwB7b2PYe7htvPmCLn/1gnKEkSZK042wQSpIkaSXuBL6b5Cn94xcA82cT3gQ8sV9+zoRzSZIkaYm8a5wkSZJWaiPw7iS7ATcCL+rXvwU4J8kLgEtahZMkSdK2papaZ5AkSZIkSZLUiFOMJUmSJEmSpBlmg1CSJEmSJEmaYTYIJUmSJEmSpBlmg1CSJEmSJEmaYTYIJUmSJEmSpBlmg1CSJEmSJEmaYTYIJUmSJEmSpBn2/wFQLBQw2BPI8gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x864 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization\n",
    "country_list = ['US', 'DE', 'UK', '', 'FR', 'ES', 'IT']\n",
    "order = list(range(0, 23))\n",
    "fig, ax = plt.subplots(nrows=7, ncols=1, figsize=(18, 12))\n",
    "for i in range(len(country_list)):\n",
    "    sns.countplot(x='hour', data=data[data['country'] == country_list[i]], ax=ax[i], order=order)\n",
    "    ax[i].set_ylabel(country_list[i], fontsize=12)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on above figure, we can see that, for the unknown country, their peak search time has around 12 hours difference with United States, and around 5 hours difference with Europe. Based on the geographic locations of each country, it seems that this missing country is in `Asia`. Possible countries include `China` and `Japan`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2\n",
    "For each city, find the most likely city to be also searched for within the same session."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>session_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>cities</th>\n",
       "      <th>user_id</th>\n",
       "      <th>joining_date</th>\n",
       "      <th>country</th>\n",
       "      <th>time</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>X061RFWB06K9V</td>\n",
       "      <td>1442503708</td>\n",
       "      <td>New York NY, Newark NJ</td>\n",
       "      <td>2024</td>\n",
       "      <td>2015-03-22</td>\n",
       "      <td>UK</td>\n",
       "      <td>2015-09-17 15:28:28</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5AZ2X2A9BHH5U</td>\n",
       "      <td>1441353991</td>\n",
       "      <td>New York NY, Jersey City NJ, Philadelphia PA</td>\n",
       "      <td>2853</td>\n",
       "      <td>2015-03-28</td>\n",
       "      <td>DE</td>\n",
       "      <td>2015-09-04 08:06:31</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SHTB4IYAX4PX6</td>\n",
       "      <td>1440843490</td>\n",
       "      <td>San Antonio TX</td>\n",
       "      <td>10958</td>\n",
       "      <td>2015-03-06</td>\n",
       "      <td>UK</td>\n",
       "      <td>2015-08-29 10:18:10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JBRB8MZGTX3M4</td>\n",
       "      <td>1427268063</td>\n",
       "      <td>Edmonton AB</td>\n",
       "      <td>7693</td>\n",
       "      <td>2015-03-12</td>\n",
       "      <td>IT</td>\n",
       "      <td>2015-03-25 07:21:03</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>YJCMPURC2FL9C</td>\n",
       "      <td>1430559067</td>\n",
       "      <td>Phoenix AZ, Houston TX</td>\n",
       "      <td>7506</td>\n",
       "      <td>2015-02-28</td>\n",
       "      <td>UK</td>\n",
       "      <td>2015-05-02 09:31:07</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      session_id   timestamp                                        cities  \\\n",
       "0  X061RFWB06K9V  1442503708                        New York NY, Newark NJ   \n",
       "1  5AZ2X2A9BHH5U  1441353991  New York NY, Jersey City NJ, Philadelphia PA   \n",
       "2  SHTB4IYAX4PX6  1440843490                                San Antonio TX   \n",
       "3  JBRB8MZGTX3M4  1427268063                                   Edmonton AB   \n",
       "4  YJCMPURC2FL9C  1430559067                        Phoenix AZ, Houston TX   \n",
       "\n",
       "   user_id joining_date country                time  hour  \n",
       "0     2024   2015-03-22      UK 2015-09-17 15:28:28    15  \n",
       "1     2853   2015-03-28      DE 2015-09-04 08:06:31     8  \n",
       "2    10958   2015-03-06      UK 2015-08-29 10:18:10    10  \n",
       "3     7693   2015-03-12      IT 2015-03-25 07:21:03     7  \n",
       "4     7506   2015-02-28      UK 2015-05-02 09:31:07     9  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this question, we will create a matrix that calculate the city-city similarity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique cities:\t 89\n"
     ]
    }
   ],
   "source": [
    "# find unique cities\n",
    "city_pair = data['cities'].apply(lambda x: x.split(', '))\n",
    "\n",
    "index = 0\n",
    "maps = {}\n",
    "city_list = []\n",
    "for item in city_pair:\n",
    "    for city in item:\n",
    "        if city not in maps:\n",
    "            maps[city] = index\n",
    "            index += 1\n",
    "            city_list.append(city)\n",
    "            \n",
    "print('Unique cities:\\t', len(city_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate matrix similarity\n",
    "pair_matrix = np.zeros((89, 89))\n",
    "\n",
    "for item in city_pair:\n",
    "    if len(item) == 1:\n",
    "        continue\n",
    "    for i in range(len(item) - 1):\n",
    "        index1 = maps[item[i]]\n",
    "        for j in range(i + 1, len(item)):\n",
    "            index2 = maps[item[j]]\n",
    "            pair_matrix[index1, index2] += 1\n",
    "            pair_matrix[index2, index1] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>New York NY</th>\n",
       "      <th>Newark NJ</th>\n",
       "      <th>Jersey City NJ</th>\n",
       "      <th>Philadelphia PA</th>\n",
       "      <th>San Antonio TX</th>\n",
       "      <th>Edmonton AB</th>\n",
       "      <th>Phoenix AZ</th>\n",
       "      <th>Houston TX</th>\n",
       "      <th>San Diego CA</th>\n",
       "      <th>Montreal QC</th>\n",
       "      <th>...</th>\n",
       "      <th>Birmingham AL</th>\n",
       "      <th>Nashville TN</th>\n",
       "      <th>Omaha NE</th>\n",
       "      <th>Kansas City MO</th>\n",
       "      <th>Raleigh NC</th>\n",
       "      <th>Greensboro NC</th>\n",
       "      <th>New Orleans LA</th>\n",
       "      <th>Charlotte NC</th>\n",
       "      <th>Lincoln NE</th>\n",
       "      <th>Memphis TN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>New York NY</th>\n",
       "      <td>0.0</td>\n",
       "      <td>435.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Newark NJ</th>\n",
       "      <td>435.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>230.0</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>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jersey City NJ</th>\n",
       "      <td>549.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>201.0</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>17.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philadelphia PA</th>\n",
       "      <td>428.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>...</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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Antonio TX</th>\n",
       "      <td>58.0</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>143.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>...</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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 89 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 New York NY  Newark NJ  Jersey City NJ  Philadelphia PA  \\\n",
       "New York NY              0.0      435.0           549.0            428.0   \n",
       "Newark NJ              435.0        0.0           249.0            230.0   \n",
       "Jersey City NJ         549.0      249.0             0.0            201.0   \n",
       "Philadelphia PA        428.0      230.0           201.0              0.0   \n",
       "San Antonio TX          58.0        0.0             0.0              0.0   \n",
       "\n",
       "                 San Antonio TX  Edmonton AB  Phoenix AZ  Houston TX  \\\n",
       "New York NY                58.0         39.0        63.0       193.0   \n",
       "Newark NJ                   0.0          0.0         0.0         0.0   \n",
       "Jersey City NJ              0.0          0.0         0.0         0.0   \n",
       "Philadelphia PA             0.0          0.0         0.0        13.0   \n",
       "San Antonio TX              0.0          0.0         0.0       143.0   \n",
       "\n",
       "                 San Diego CA  Montreal QC     ...      Birmingham AL  \\\n",
       "New York NY              60.0        315.0     ...                0.0   \n",
       "Newark NJ                 0.0         16.0     ...                0.0   \n",
       "Jersey City NJ            0.0         17.0     ...                0.0   \n",
       "Philadelphia PA           0.0         33.0     ...                0.0   \n",
       "San Antonio TX            0.0         21.0     ...                0.0   \n",
       "\n",
       "                 Nashville TN  Omaha NE  Kansas City MO  Raleigh NC  \\\n",
       "New York NY               0.0       0.0             0.0         3.0   \n",
       "Newark NJ                 0.0       0.0             0.0         2.0   \n",
       "Jersey City NJ            0.0       0.0             0.0         3.0   \n",
       "Philadelphia PA           0.0       0.0             0.0         1.0   \n",
       "San Antonio TX            0.0       0.0             1.0         0.0   \n",
       "\n",
       "                 Greensboro NC  New Orleans LA  Charlotte NC  Lincoln NE  \\\n",
       "New York NY                1.0             0.0           1.0         0.0   \n",
       "Newark NJ                  1.0             0.0           1.0         0.0   \n",
       "Jersey City NJ             1.0             0.0           1.0         0.0   \n",
       "Philadelphia PA            0.0             0.0           1.0         0.0   \n",
       "San Antonio TX             0.0             0.0           0.0         1.0   \n",
       "\n",
       "                 Memphis TN  \n",
       "New York NY             0.0  \n",
       "Newark NJ               0.0  \n",
       "Jersey City NJ          0.0  \n",
       "Philadelphia PA         0.0  \n",
       "San Antonio TX          0.0  \n",
       "\n",
       "[5 rows x 89 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create dataframe\n",
    "df_similarity = pd.DataFrame(pair_matrix, index=city_list, columns=city_list)\n",
    "df_similarity.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "sns.heatmap(df_similarity, ax=ax)\n",
    "ax.axis('image')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>most similar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>New York NY</td>\n",
       "      <td>Jersey City NJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Newark NJ</td>\n",
       "      <td>New York NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jersey City NJ</td>\n",
       "      <td>New York NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Philadelphia PA</td>\n",
       "      <td>New York NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>San Antonio TX</td>\n",
       "      <td>Houston TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Edmonton AB</td>\n",
       "      <td>Calgary AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Phoenix AZ</td>\n",
       "      <td>Glendale AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Houston TX</td>\n",
       "      <td>New York NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>San Diego CA</td>\n",
       "      <td>Santa Ana CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Montreal QC</td>\n",
       "      <td>New York NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Chicago IL</td>\n",
       "      <td>New York NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Calgary AB</td>\n",
       "      <td>Vancouver BC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Los Angeles CA</td>\n",
       "      <td>New York NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Toronto ON</td>\n",
       "      <td>New York NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kitchener ON</td>\n",
       "      <td>Toronto ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Vancouver BC</td>\n",
       "      <td>Victoria BC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Indianapolis IN</td>\n",
       "      <td>Cincinnati OH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Oshawa ON</td>\n",
       "      <td>Toronto ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Victoria BC</td>\n",
       "      <td>Vancouver BC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Seattle WA</td>\n",
       "      <td>Vancouver BC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Chandler AZ</td>\n",
       "      <td>Phoenix AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Mesa AZ</td>\n",
       "      <td>Phoenix AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>San Jose CA</td>\n",
       "      <td>Oakland CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Corpus Christi TX</td>\n",
       "      <td>San Antonio TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Arlington TX</td>\n",
       "      <td>Dallas TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Fort Worth TX</td>\n",
       "      <td>Dallas TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Portland OR</td>\n",
       "      <td>Vancouver BC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Dallas TX</td>\n",
       "      <td>Plano TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Santa Ana CA</td>\n",
       "      <td>Los Angeles CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Stockton CA</td>\n",
       "      <td>San Jose CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Atlanta GA</td>\n",
       "      <td>Jacksonville FL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Saint Petersburg FL</td>\n",
       "      <td>Jacksonville FL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Tampa FL</td>\n",
       "      <td>Jacksonville FL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Scottsdale AZ</td>\n",
       "      <td>Phoenix AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Oklahoma City OK</td>\n",
       "      <td>Dallas TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Norfolk VA</td>\n",
       "      <td>New York NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Chesapeake VA</td>\n",
       "      <td>New York NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Virginia Beach VA</td>\n",
       "      <td>New York NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Quebec QC</td>\n",
       "      <td>Montreal QC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Halifax NS</td>\n",
       "      <td>Montreal QC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Bakersfield CA</td>\n",
       "      <td>Los Angeles CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Toledo OH</td>\n",
       "      <td>Detroit MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Cleveland OH</td>\n",
       "      <td>Detroit MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Tucson AZ</td>\n",
       "      <td>Phoenix AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>London ON</td>\n",
       "      <td>Kitchener ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Plano TX</td>\n",
       "      <td>Dallas TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Wichita KS</td>\n",
       "      <td>Oklahoma City OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Hialeah FL</td>\n",
       "      <td>Jacksonville FL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Tulsa OK</td>\n",
       "      <td>Oklahoma City OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Pittsburgh PA</td>\n",
       "      <td>Chicago IL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Birmingham AL</td>\n",
       "      <td>Jacksonville FL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Nashville TN</td>\n",
       "      <td>Indianapolis IN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Omaha NE</td>\n",
       "      <td>Chicago IL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Kansas City MO</td>\n",
       "      <td>San Antonio TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Raleigh NC</td>\n",
       "      <td>New York NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Greensboro NC</td>\n",
       "      <td>New York NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>New Orleans LA</td>\n",
       "      <td>Jacksonville FL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Charlotte NC</td>\n",
       "      <td>Jacksonville FL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Lincoln NE</td>\n",
       "      <td>Chicago IL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Memphis TN</td>\n",
       "      <td>Jacksonville FL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>89 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   city      most similar\n",
       "0           New York NY    Jersey City NJ\n",
       "1             Newark NJ       New York NY\n",
       "2        Jersey City NJ       New York NY\n",
       "3       Philadelphia PA       New York NY\n",
       "4        San Antonio TX        Houston TX\n",
       "5           Edmonton AB        Calgary AB\n",
       "6            Phoenix AZ       Glendale AZ\n",
       "7            Houston TX       New York NY\n",
       "8          San Diego CA      Santa Ana CA\n",
       "9           Montreal QC       New York NY\n",
       "10           Chicago IL       New York NY\n",
       "11           Calgary AB      Vancouver BC\n",
       "12       Los Angeles CA       New York NY\n",
       "13           Toronto ON       New York NY\n",
       "14         Kitchener ON        Toronto ON\n",
       "15         Vancouver BC       Victoria BC\n",
       "16      Indianapolis IN     Cincinnati OH\n",
       "17            Oshawa ON        Toronto ON\n",
       "18          Victoria BC      Vancouver BC\n",
       "19           Seattle WA      Vancouver BC\n",
       "20          Chandler AZ        Phoenix AZ\n",
       "21              Mesa AZ        Phoenix AZ\n",
       "22          San Jose CA        Oakland CA\n",
       "23    Corpus Christi TX    San Antonio TX\n",
       "24         Arlington TX         Dallas TX\n",
       "25        Fort Worth TX         Dallas TX\n",
       "26          Portland OR      Vancouver BC\n",
       "27            Dallas TX          Plano TX\n",
       "28         Santa Ana CA    Los Angeles CA\n",
       "29          Stockton CA       San Jose CA\n",
       "..                  ...               ...\n",
       "59           Atlanta GA   Jacksonville FL\n",
       "60  Saint Petersburg FL   Jacksonville FL\n",
       "61             Tampa FL   Jacksonville FL\n",
       "62        Scottsdale AZ        Phoenix AZ\n",
       "63     Oklahoma City OK         Dallas TX\n",
       "64           Norfolk VA       New York NY\n",
       "65        Chesapeake VA       New York NY\n",
       "66    Virginia Beach VA       New York NY\n",
       "67            Quebec QC       Montreal QC\n",
       "68           Halifax NS       Montreal QC\n",
       "69       Bakersfield CA    Los Angeles CA\n",
       "70            Toledo OH        Detroit MI\n",
       "71         Cleveland OH        Detroit MI\n",
       "72            Tucson AZ        Phoenix AZ\n",
       "73            London ON      Kitchener ON\n",
       "74             Plano TX         Dallas TX\n",
       "75           Wichita KS  Oklahoma City OK\n",
       "76           Hialeah FL   Jacksonville FL\n",
       "77             Tulsa OK  Oklahoma City OK\n",
       "78        Pittsburgh PA        Chicago IL\n",
       "79        Birmingham AL   Jacksonville FL\n",
       "80         Nashville TN   Indianapolis IN\n",
       "81             Omaha NE        Chicago IL\n",
       "82       Kansas City MO    San Antonio TX\n",
       "83           Raleigh NC       New York NY\n",
       "84        Greensboro NC       New York NY\n",
       "85       New Orleans LA   Jacksonville FL\n",
       "86         Charlotte NC   Jacksonville FL\n",
       "87           Lincoln NE        Chicago IL\n",
       "88           Memphis TN   Jacksonville FL\n",
       "\n",
       "[89 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get result\n",
    "result = df_similarity.apply(np.argmax, axis=0).reset_index()\n",
    "result = result.rename(columns={'index': 'city', 0: 'most similar'})\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is another version of solution that use user instead of session to calculate the similarity. Reference: [Link](https://github.com/stasi009/TakeHomeDataChallenges/blob/master/13.CitySimilarity/city_search.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3\n",
    "Travel sites are browsed by two kinds of users. Users who are actually planning a trip and users who just dream about a vacation. The first ones have obviously a much higher purchasing intent. Users planning a trip often search for cities close to each other, while users who search for cities far away from each other are often just dreaming about a vacation. That is, a user searching for LA, SF and Las Vegas in the same session is much more likely to book a hotel than a user searching for NY, Paris, Kuala Lumpur (makes sense, right?). Based on this idea, come up with an algorithm that clusters sessions into two groups: high intent and low intent. Explain all assumptions you make along the way."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this question, one way is to actually calculate the geographic distance for actual searched cities. However, this solution is not very practical for this small project. Another choice is to calculate the distance based on the city-city similarity in Question 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def search_distance(cities):\n",
    "    cities = cities.split(', ')\n",
    "    \n",
    "    if len(cities) == 1:\n",
    "        return 0  # user only search one city, distance 0\n",
    "    \n",
    "    distance = 0\n",
    "    for i in range(len(cities) - 1):\n",
    "        city1 = cities[i]\n",
    "        for j in range(i + 1, len(cities)):\n",
    "            city2 = cities[j]\n",
    "            distance += df_similarity.loc[city1, city2]\n",
    "            \n",
    "    return distance / len(cities)\n",
    "\n",
    "# calculate distance\n",
    "data['distance'] = data['cities'].apply(search_distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization\n",
    "warnings.filterwarnings('ignore')\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "sns.distplot(data['distance'], ax=ax, bins=50)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that we can set some `threshold` to cut the search `distance` such that any search that is above the threshold are assumed to be the search with `low intent`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is another solution, for reference, please refer to [Link](https://github.com/stasi009/TakeHomeDataChallenges/blob/master/13.CitySimilarity/city_search.ipynb)"
   ]
  },
  {
   "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",
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