twitter-and-fitbit-activity.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Are daily step counts and the number of tweets sent per day correlated?\n",
    "They probably aren't but it's not unreasonable to think about it. After all, time spent on social media is time missing for being physically active and vice versa. Unless you're one of those people who walk around while tweeting (don't, it's dangerous!).\n",
    "\n",
    "![](http://www.kiss1023.ca/wp-content/uploads/sites/10/2017/07/textwalkpole.gif)\n",
    "\n",
    "But let's get started with our analysis. To do so we need to combine two data sources which you need to have in your Open Humans account:\n",
    "1. Your [Fitbit](https://www.openhumans.org/activity/fitbit-connection) account\n",
    "2. Your [Twitter archive](https://www.openhumans.org/activity/twitter-archive-analyzer/)\n",
    "\n",
    "With that out of the way we can start to load all of the Python dependencies we need:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import requests\n",
    "from datetime import datetime\n",
    "from collections import defaultdict\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import tempfile\n",
    "import zipfile\n",
    "import pytz\n",
    "import io\n",
    "import sys\n",
    "from textblob import TextBlob\n",
    "import emoji\n",
    "from ohapi import api\n",
    "\n",
    "\n",
    "# sets the axis label sizes for seaborn\n",
    "rc={'font.size': 14, 'axes.labelsize': 14, 'legend.fontsize': 14.0, \n",
    "    'axes.titlesize': 14, 'xtick.labelsize': 14, 'ytick.labelsize': 14}\n",
    "sns.set(rc=rc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing the Twitter archive data\n",
    "All the functions are already provided by http://twarxiv.org and we just copied the functions from there. It will basically take a zipped archive from a URL and return a nicely formatted `pandas` dataframe. Let's just take these as given for now:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# THIS CODE BELOW IS COPIED FROM TWARXIV.ORG AS IT ALREADY DOES EXACTLY WHAT WE WANT FOR READING IN THE DATA\n",
    "\n",
    "# READ JSON FILES FROM TWITTER ARCHIVE!\n",
    "\n",
    "def check_hashtag(single_tweet):\n",
    "    '''check whether tweet has any hashtags'''\n",
    "    return len(single_tweet['entities']['hashtags']) > 0\n",
    "\n",
    "\n",
    "def check_media(single_tweet):\n",
    "    '''check whether tweet has any media attached'''\n",
    "    return len(single_tweet['entities']['media']) > 0\n",
    "\n",
    "\n",
    "def check_url(single_tweet):\n",
    "    '''check whether tweet has any urls attached'''\n",
    "    return len(single_tweet['entities']['urls']) > 0\n",
    "\n",
    "\n",
    "def check_retweet(single_tweet):\n",
    "    '''\n",
    "    check whether tweet is a RT. If yes:\n",
    "    return name & user name of the RT'd user.\n",
    "    otherwise just return nones\n",
    "    '''\n",
    "    if 'retweeted_status' in single_tweet.keys():\n",
    "        return (single_tweet['retweeted_status']['user']['screen_name'],\n",
    "                single_tweet['retweeted_status']['user']['name'])\n",
    "    else:\n",
    "        return (None, None)\n",
    "\n",
    "\n",
    "def check_coordinates(single_tweet):\n",
    "    '''\n",
    "    check whether tweet has coordinates attached.\n",
    "    if yes return the coordinates\n",
    "    otherwise just return nones\n",
    "    '''\n",
    "    if 'coordinates' in single_tweet['geo'].keys():\n",
    "        return (single_tweet['geo']['coordinates'][0],\n",
    "                single_tweet['geo']['coordinates'][1])\n",
    "    else:\n",
    "        return (None, None)\n",
    "\n",
    "\n",
    "def check_reply_to(single_tweet):\n",
    "    '''\n",
    "    check whether tweet is a reply. If yes:\n",
    "    return name & user name of the user that's replied to.\n",
    "    otherwise just return nones\n",
    "    '''\n",
    "    if 'in_reply_to_screen_name' in single_tweet.keys():\n",
    "        name = None\n",
    "        for user in single_tweet['entities']['user_mentions']:\n",
    "            if user['screen_name'] == single_tweet['in_reply_to_screen_name']:\n",
    "                name = user['name']\n",
    "                break\n",
    "        return (single_tweet['in_reply_to_screen_name'], name)\n",
    "    else:\n",
    "        return (None, None)\n",
    "\n",
    "\n",
    "def create_dataframe(tweets):\n",
    "    '''\n",
    "    create a pandas dataframe from our tweet jsons\n",
    "    '''\n",
    "\n",
    "    # initalize empty lists\n",
    "    utc_time = []\n",
    "    longitude = []\n",
    "    latitude = []\n",
    "    hashtag = []\n",
    "    media = []\n",
    "    url = []\n",
    "    retweet_user_name = []\n",
    "    retweet_name = []\n",
    "    reply_user_name = []\n",
    "    reply_name = []\n",
    "    text = []\n",
    "    # iterate over all tweets and extract data\n",
    "    for single_tweet in tweets:\n",
    "        utc_time.append(datetime.strptime(single_tweet['created_at'],\n",
    "                                                   '%Y-%m-%d %H:%M:%S %z'))\n",
    "        coordinates = check_coordinates(single_tweet)\n",
    "        latitude.append(coordinates[0])\n",
    "        longitude.append(coordinates[1])\n",
    "        hashtag.append(check_hashtag(single_tweet))\n",
    "        media.append(check_media(single_tweet))\n",
    "        url.append(check_url(single_tweet))\n",
    "        retweet = check_retweet(single_tweet)\n",
    "        retweet_user_name.append(retweet[0])\n",
    "        retweet_name.append(retweet[1])\n",
    "        reply = check_reply_to(single_tweet)\n",
    "        reply_user_name.append(reply[0])\n",
    "        reply_name.append(reply[1])\n",
    "        text.append(single_tweet['text'])\n",
    "    # convert the whole shebang into a pandas dataframe\n",
    "    dataframe = pd.DataFrame(data={\n",
    "                            'utc_time': utc_time,\n",
    "                            'latitude': latitude,\n",
    "                            'longitude': longitude,\n",
    "                            'hashtag': hashtag,\n",
    "                            'media': media,\n",
    "                            'url': url,\n",
    "                            'retweet_user_name': retweet_user_name,\n",
    "                            'retweet_name': retweet_name,\n",
    "                            'reply_user_name': reply_user_name,\n",
    "                            'reply_name': reply_name,\n",
    "                            'text': text\n",
    "    })\n",
    "    return dataframe\n",
    "\n",
    "\n",
    "def read_files(zip_url):\n",
    "    tf = tempfile.NamedTemporaryFile()\n",
    "    print('downloading files')\n",
    "    tf.write(requests.get(zip_url).content)\n",
    "    tf.flush()\n",
    "    zf = zipfile.ZipFile(tf.name)\n",
    "    print('reading index')\n",
    "    with zf.open('data/js/tweet_index.js', 'r') as f:\n",
    "        f = io.TextIOWrapper(f)\n",
    "        d = f.readlines()[1:]\n",
    "        d = \"[{\" + \"\".join(d)\n",
    "        json_files = json.loads(d)\n",
    "    data_frames = []\n",
    "    print('iterate over individual files')\n",
    "    for single_file in json_files:\n",
    "        print('read ' + single_file['file_name'])\n",
    "        with zf.open(single_file['file_name']) as f:\n",
    "            f = io.TextIOWrapper(f)\n",
    "            d = f.readlines()[1:]\n",
    "            d = \"\".join(d)\n",
    "            tweets = json.loads(d)\n",
    "            df_tweets = create_dataframe(tweets)\n",
    "            data_frames.append(df_tweets)\n",
    "    return data_frames\n",
    "\n",
    "\n",
    "def create_main_dataframe(zip_url='http://ruleofthirds.de/test_archive.zip'):\n",
    "    print('reading files')\n",
    "    dataframes = read_files(zip_url)\n",
    "    print('concatenating...')\n",
    "    dataframe = pd.concat(dataframes)\n",
    "    dataframe = dataframe.sort_values('utc_time', ascending=False)\n",
    "    dataframe = dataframe.set_index('utc_time')\n",
    "    dataframe = dataframe.replace(to_replace={\n",
    "                                    'url': {False: None},\n",
    "                                    'hashtag': {False: None},\n",
    "                                    'media': {False: None}\n",
    "                                    })\n",
    "    return dataframe\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Downloading & importing Fitbit/Twitter data\n",
    "Now that we have set up all the functions we need for importing the Twitter data, let's grab the data itself. We're getting our data URLs from Open Humans and import both Twitter & Fitbit data (this might take a while if you have lots of tweets/fitbit data points)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reading files\n",
      "downloading files\n",
      "reading index\n",
      "iterate over individual files\n",
      "read data/js/tweets/2018_05.js\n",
      "read data/js/tweets/2018_04.js\n",
      "read data/js/tweets/2018_03.js\n",
      "read data/js/tweets/2018_02.js\n",
      "read data/js/tweets/2018_01.js\n",
      "read data/js/tweets/2017_12.js\n",
      "read data/js/tweets/2017_11.js\n",
      "read data/js/tweets/2017_10.js\n",
      "read data/js/tweets/2017_09.js\n",
      "read data/js/tweets/2017_08.js\n",
      "read data/js/tweets/2017_07.js\n",
      "read data/js/tweets/2017_06.js\n",
      "read data/js/tweets/2017_05.js\n",
      "read data/js/tweets/2017_04.js\n",
      "read data/js/tweets/2017_03.js\n",
      "read data/js/tweets/2017_02.js\n",
      "read data/js/tweets/2017_01.js\n",
      "read data/js/tweets/2016_12.js\n",
      "read data/js/tweets/2016_11.js\n",
      "read data/js/tweets/2016_10.js\n",
      "read data/js/tweets/2016_09.js\n",
      "read data/js/tweets/2016_08.js\n",
      "read data/js/tweets/2016_07.js\n",
      "read data/js/tweets/2016_06.js\n",
      "read data/js/tweets/2016_05.js\n",
      "read data/js/tweets/2016_04.js\n",
      "read data/js/tweets/2016_03.js\n",
      "read data/js/tweets/2016_02.js\n",
      "read data/js/tweets/2016_01.js\n",
      "read data/js/tweets/2015_12.js\n",
      "read data/js/tweets/2015_11.js\n",
      "read data/js/tweets/2015_10.js\n",
      "read data/js/tweets/2015_09.js\n",
      "read data/js/tweets/2015_08.js\n",
      "read data/js/tweets/2015_07.js\n",
      "read data/js/tweets/2015_06.js\n",
      "read data/js/tweets/2015_05.js\n",
      "read data/js/tweets/2015_04.js\n",
      "read data/js/tweets/2015_03.js\n",
      "read data/js/tweets/2015_02.js\n",
      "read data/js/tweets/2015_01.js\n",
      "read data/js/tweets/2014_12.js\n",
      "read data/js/tweets/2014_11.js\n",
      "read data/js/tweets/2014_10.js\n",
      "read data/js/tweets/2014_09.js\n",
      "read data/js/tweets/2014_08.js\n",
      "read data/js/tweets/2014_07.js\n",
      "read data/js/tweets/2014_06.js\n",
      "read data/js/tweets/2014_05.js\n",
      "read data/js/tweets/2014_04.js\n",
      "read data/js/tweets/2014_03.js\n",
      "read data/js/tweets/2014_02.js\n",
      "read data/js/tweets/2014_01.js\n",
      "read data/js/tweets/2013_12.js\n",
      "read data/js/tweets/2013_11.js\n",
      "read data/js/tweets/2013_10.js\n",
      "read data/js/tweets/2013_09.js\n",
      "read data/js/tweets/2013_08.js\n",
      "read data/js/tweets/2013_07.js\n",
      "read data/js/tweets/2013_06.js\n",
      "read data/js/tweets/2013_05.js\n",
      "read data/js/tweets/2013_04.js\n",
      "read data/js/tweets/2013_03.js\n",
      "read data/js/tweets/2013_02.js\n",
      "read data/js/tweets/2013_01.js\n",
      "read data/js/tweets/2012_12.js\n",
      "read data/js/tweets/2012_11.js\n",
      "read data/js/tweets/2012_10.js\n",
      "read data/js/tweets/2012_09.js\n",
      "read data/js/tweets/2012_08.js\n",
      "read data/js/tweets/2012_07.js\n",
      "read data/js/tweets/2012_06.js\n",
      "read data/js/tweets/2012_05.js\n",
      "read data/js/tweets/2012_04.js\n",
      "read data/js/tweets/2012_03.js\n",
      "read data/js/tweets/2012_02.js\n",
      "read data/js/tweets/2012_01.js\n",
      "read data/js/tweets/2011_12.js\n",
      "read data/js/tweets/2011_11.js\n",
      "read data/js/tweets/2011_10.js\n",
      "read data/js/tweets/2011_09.js\n",
      "read data/js/tweets/2011_08.js\n",
      "read data/js/tweets/2011_07.js\n",
      "read data/js/tweets/2011_06.js\n",
      "read data/js/tweets/2011_05.js\n",
      "read data/js/tweets/2011_04.js\n",
      "read data/js/tweets/2011_03.js\n",
      "read data/js/tweets/2011_02.js\n",
      "read data/js/tweets/2011_01.js\n",
      "read data/js/tweets/2010_12.js\n",
      "read data/js/tweets/2010_11.js\n",
      "read data/js/tweets/2010_10.js\n",
      "read data/js/tweets/2010_09.js\n",
      "read data/js/tweets/2010_08.js\n",
      "read data/js/tweets/2010_07.js\n",
      "read data/js/tweets/2010_06.js\n",
      "read data/js/tweets/2010_05.js\n",
      "read data/js/tweets/2010_04.js\n",
      "read data/js/tweets/2010_03.js\n",
      "read data/js/tweets/2010_02.js\n",
      "read data/js/tweets/2010_01.js\n",
      "read data/js/tweets/2009_12.js\n",
      "read data/js/tweets/2009_11.js\n",
      "read data/js/tweets/2009_10.js\n",
      "read data/js/tweets/2009_09.js\n",
      "read data/js/tweets/2009_08.js\n",
      "read data/js/tweets/2009_07.js\n",
      "read data/js/tweets/2009_06.js\n",
      "read data/js/tweets/2009_05.js\n",
      "read data/js/tweets/2009_04.js\n",
      "read data/js/tweets/2009_03.js\n",
      "read data/js/tweets/2009_02.js\n",
      "read data/js/tweets/2009_01.js\n",
      "read data/js/tweets/2008_12.js\n",
      "read data/js/tweets/2008_11.js\n",
      "read data/js/tweets/2008_10.js\n",
      "read data/js/tweets/2008_09.js\n",
      "read data/js/tweets/2008_08.js\n",
      "read data/js/tweets/2008_07.js\n",
      "read data/js/tweets/2008_06.js\n",
      "read data/js/tweets/2008_05.js\n",
      "read data/js/tweets/2008_04.js\n",
      "concatenating...\n"
     ]
    }
   ],
   "source": [
    "user = api.exchange_oauth2_member(os.environ.get('OH_ACCESS_TOKEN'))\n",
    "\n",
    "has_twitter = False\n",
    "has_fitbit = False\n",
    "\n",
    "# get our download URLs\n",
    "for entry in user['data']:\n",
    "    if entry['source'] == \"direct-sharing-70\":\n",
    "        twitter_data_url = entry['download_url']\n",
    "        has_twitter = True\n",
    "    if entry['source'] == \"direct-sharing-102\":\n",
    "        fitbit_data_url = entry['download_url']\n",
    "        has_fitbit = True\n",
    "\n",
    "if not has_twitter:\n",
    "    print(\"YOU NEED TO HAVE SOME TWITTER DATA IN YOUR ACCOUNT TO USE THIS NOTEBOOK\")\n",
    "    print(\"GO TO http://twarxiv.org TO UPLOAD IT\")\n",
    "\n",
    "if not has_fitbit:\n",
    "    print(\"YOU NEED TO HAVE SOME FITBIT DATA IN YOUR ACCOUNT TO USE THIS NOTEBOOK\")\n",
    "    print(\"GO TO https://www.openhumans.org/activity/fitbit-connection/ TO UPLOAD IT\")\n",
    "    \n",
    "# read the twitter data\n",
    "twitter_data = create_main_dataframe(zip_url=twitter_data_url)\n",
    "\n",
    "# read the fitbit data\n",
    "fitbit_data_raw = requests.get(fitbit_data_url).content\n",
    "fitbit_data = json.loads(fitbit_data_raw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's convert our raw *Fitbit* data into a `pandas` dataframe - this will make it much easier to plot our data and unite it with the `Twitter` data at the end:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "date = []\n",
    "steps = []\n",
    "\n",
    "for year in fitbit_data['tracker-steps'].keys():\n",
    "    for entry in fitbit_data['tracker-steps'][year]['activities-tracker-steps']:\n",
    "        date.append(entry['dateTime'])\n",
    "        steps.append(entry['value'])\n",
    "        \n",
    "fitbit_steps = pd.DataFrame(data={\n",
    "                'date':date,\n",
    "                'steps': steps})\n",
    "fitbit_steps['date'] = pd.to_datetime(fitbit_steps['date'])\n",
    "fitbit_steps = fitbit_steps.set_index('date')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This gives us the daily `steps` along with the `date`, nicely formatted. We can now prepare our twitter-data and calculate the number of `tweets_per_day` for our final plots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter_data['date'] = twitter_data.index.date\n",
    "tweets_per_day = twitter_data['date'].value_counts()\n",
    "twitter_mean = twitter_data.groupby(twitter_data.index.date).mean()\n",
    "twitter_mean = twitter_mean.join(tweets_per_day)\n",
    "twitter_mean['tweets_per_day'] = twitter_mean['date']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With that out of the way we can join our data and join our two `twitter_mean` and `fitbit_steps` dataframes into one. To make sure to not bias our data analysis for dates during which we didn't collect any data we remove all days during which we didn't record `steps`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "joined_data = twitter_mean.join(fitbit_steps,how='right')\n",
    "joined_data['steps'] = joined_data['steps'].apply(int)\n",
    "joined_data = joined_data[joined_data['steps'] != 0]\n",
    "joined_data['year'] = joined_data.index.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hashtag</th>\n",
       "      <th>media</th>\n",
       "      <th>url</th>\n",
       "      <th>date</th>\n",
       "      <th>tweets_per_day</th>\n",
       "      <th>steps</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</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",
       "      <th>2013-01-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>10978</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>11639</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>14943</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>16222</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>14165</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            hashtag  media  url  date  tweets_per_day  steps  year\n",
       "date                                                              \n",
       "2013-01-01      1.0    NaN  1.0  19.0            19.0  10978  2013\n",
       "2013-01-02      NaN    1.0  1.0  43.0            43.0  11639  2013\n",
       "2013-01-03      NaN    NaN  1.0  25.0            25.0  14943  2013\n",
       "2013-01-04      NaN    NaN  1.0  18.0            18.0  16222  2013\n",
       "2013-01-05      1.0    1.0  1.0  24.0            24.0  14165  2013"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now plot our daily step counts and number of tweets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f0cc59bf278>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"steps\", \n",
    "           y=\"tweets_per_day\", \n",
    "           data=joined_data[joined_data.index.year == 2014],\n",
    "           lowess=True,\n",
    "           size=8,\n",
    "           aspect=1.5,\n",
    "           scatter_kws={'alpha':0.3})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like there is some correlation between the number of daily steps and the number of daily tweets: On days with more steps there are less tweets. But this might be a result of tweeting more/less over time in general or walking more/less over time. So let's break it down into yearly numbers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f0cc8d32550>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EWPZPXQHzvcCVSqlNKWUBoJQqpJQvx/hL6uADnwY+DPwMcD2mYfibMS05vzR2/FngGOdgaamFN8GPcS6Wl+f2fM5OxGmOG/rEcUYhHDzXJQg841z1t/wcS3ONwe9HF5rbrnHXfWv0ooxenKFzjRe6XN7ogOOwNB/iuS5HFpvMt4KRc9e6MY325ND4Qifctn38faM4I8s1nnAHvqFp3p9LEXt/6lFXwGxgBMQ4l1Eze1cp9W/A44Y3SSl/FbgFI3TGqSJUu7Ky0qvz9iMsL89x6tTGns/bifVeMjA1nLwgSVOSOCWKcxqhIMs0WV6wstJDuC7NhkAn2cCsieKMbj/l7HpMnGb0y32twGO+ExIGHi3PQXuCU6cy4jEBs7IRb8v8rRzIe9FEEiDpJ1O/P5ca9v6MspuwrStgPgK8VUp5I4CU8hjwKOB3gJv3sbY7MYLkJNu1lSPAqX1c+7zZa/XwsB8j9F16cZmfIhyysj7Ic90yaa4gyX1cwClnTcepZqOX0O3FrHRjsrxgs58ihMvRJOfqY202opS5BnT729eltR4rTShMjotrs20tF5a6AuYFmOjObeXrk0CO8cHUyt6VUj4WeMpYS81HYJzGnwP+29gpjwE+VXN9U2Ov1cNJmtOPM5IsH7RJMC0vNSIwGbm5FiRZjuu6NAOBcBxOrkQcmQ9J05z7z/S59/QGcWLKCXzPJdUmCc/vOsw1fTailF7Dx/fFQEuJfMFiJzS5LhPYaftOn7sSqkEzILHFjJYpUDdMvQo8qdRcHgr0gTuUUnvRE08CL5BS3g/8AcYH8xsYB/JNwMuklM8D/hhjSv0UUGt87TTZS/VwJYyKohhoJ67rsNgOCX0XVzh0eylFAc3AGwyzTzPNWi+mGyXkuiCOTfSoG6VkaUGn5RN6gs0ooxdlbPQTXNchSXKuPNIGtrQU4ToId0uoDfeDEU69SpBxoZrl+pw9aCyWOtRumSmlvBIT1flh4EnAT0opl+uer5S6A/gR4GnAaeBvMf6XFyulTgFPxAiVk8DrgecqpT5W9/rTYi91R1FZSJjmmrCsYta6YGUjIs00wnERrkua54M2ClV2ruu4dDczkiSnl6QURUFRmALF9W5MP80IPAfPd8nygjgtcBzKUoAtenGGEE7Zx9dnoR3Safr4nqhdkGhbblpmRd08mP8EvAM4galFcoDrgDdJKZ+mlPqrOtdRSn0Ek0szad8ngMfXuc4s2ctcojwvBgWHnucONJTNKKUshDaaTCl44szUIwF0Gh4bvRidm2hPK/AGVdNaawQu7YZHI/BoNfxBk6o4yWk3hv3tzr7HxNqWDJZZUdcH8yrgeUqptwxvlFL+HPA6oJaAOQjs5WEVwplYOQ1b231PsNgOWd2M0VoP/DCe57LUCVnpxgCEocChoBtrct/04m02fEJfEHouSeqYvJhubEoDGj6+ZyJS+y1ItMPeLLOiroBZAt46YfufYITPJcNeHtaGL0YKDgfbQzFS51MJgywvyh4vpqraX2yS5JokzclSDcJFCE0j8MExneoCz0UXBa3AIxU5oS+IUo3nmZaY881gsO7z9ZfsVwOyWHairoD5a0y27i1j278beN80F3QhmBSWHk9m2+m4pU7ASjceyT+ZhO8JFjvmdm91o9O4wP1nekRxTjN0aTUC0iQjKwpcChyMc7fV9Dk6H+KWLRjmWz6NYFSo7BReP1fYfVyoesI95yQDi6UOdQXMXcCfSik/AyhMe4aHYvwp75RSvrI6UCn1y1Nf5QypG5be6ThfODQbHv0oB4yQmSuF024P9dn1Pmc3IlrNgMsXW6x0I5N010/QDugCoiSj2dAEgUfoe8yVWbnCdeg0RwXgTutL0px0yPzZ6fMNa0ALndCWClimQl0B823AF4EGJsGu4gvANwy9PnBewXOFpatv/7VuWWM0NC/aTGjM6TQDOs3RgNxuJkvgC3SxJYi6DYHecIiSnPVuTLuMAoWBj+sa4ZQXumxQpU2zcJKRPr07fY71Xjqxibht2m15IKibB/M9dY6TUr5if8t54NktgpKkOavdmLj8WU0CmG+bXrom72R7g26t9bZaIxg1YVa6cSkooN9PWetG9JOcJM1xHIc0T5hv+bRbHrmGoux+V1VWD/fpDXyx4+dIsrzWlAKLZRZMe6rACzA9XQ4Mu0VQNnrJIAXfLQVHP9G4rsPSnOm9q8uEt4pcmxYNoS9Guvi7MGKqCAc2egkFpr1moyGI4oxcG+3CKceQRElO30+5YqnFfCscSaTzPTHQRHb6HMEOhaCTIkSVAHTX+qz3Etsa07JvDv3Ykt0iKGtlCBkg9AT9sn1lP8lZomxZWWw3TbQuWOkmdJomXyXPC9b66Yh5NdcKWOulVFblXDPgzIqZmXR2PR7YmscWQr7uuiUuW2yW54/WnA5PI5j0OeZb/ohgG/58w9gBa5ZZcOhnU+8elnbIspw402XHfvPxXNcct9QJOL0eTbxut5ew2U/pxSkUZjbSXCvkyHxoWmo2fOabPiu9mH4/50t3neUrx9coxu7g6bWYj3/xfm674yyP+brLeLRcphn6A2FVaSK7fY46xZt2wJplFtjJjuzskPU8OL2akOR6UGcUCJcj8+EgjB1lOf0oHzFd7j/T4+Raj0KbTN9gqCdMIzT9en1PEPgu953e5KO33ktvSHtoNzzm2wFppjmzHlEUsNpN+OA/H+ejn72Xh1w1z4OvmucRD1rkhmsWz/k56uTI2GxeyyywAmaMJM0Hvpczq33W+wm+6+K4DhRlIeBmxoofG23AE4ihCFKa5Zxei8izgjTX6KIgSnKE47DRy1ia05yNY+Is570fv5Pb71sfnNsMBTdcM8/yfINWGCB8hzjJueO+Db5yfM04lHONunsVdfcqH/j012iFHg+9eoEHXznHNZd1uOZYh1bT27P/xGbzWmaBFTBDVFGjymkb5wU6h16aEfoCTwhc4RDnWwPQcsAXzsChm+UFnuugPYc423pgTf5uQZRmfP6rZ/j4F+4b1DG5rsPXX7/ENzx4icD30HlOLzbvsdAJ+P5vuYZv/4YrUHev8dV7Vjmx0h+YUr0444u3n+GLt58BzESCK4+2uf6KOeS1i8gHLbG80MBxdhcUNpvXMgsOvZN3mCjNR2YCZVlOXpioURAIo8FoPRjNWqGB+ZbpodLtp2gKirwgy/VAKxCuGQfysc/fx90nu4Nzrzra4gmPugrhmrlHWZrgui6tQOC6gjB0wTE+nOuuaHPd5W1cx+HUWp8zazFn1iPuPtklK9+nKODe05vce3qTT952PwDz7YCHlVrOtZfNcd3lHRbawYjQGR/BYgesWabBeQsYKeWSUmplbPOBnk2dD2qFDG7ZT0UXxcDBC2ybJ1TlzJgoTEGnGXA66ePi4AgoioLb7+vy5a+tkpXX8YTL9z36aq67skOWajKtSXo5cZbTafhEGfiiICwCer2UuAxd66IAB6462ubrrjvCkfkGm/2Eu05scHK1zz2nNrnvTI+NoWmO65sJt375FLd+eatB4FzL57rLOlx72RzXXt7hmuUOVxxpMd8KOLrQRCfbtRmLZa/UbdfwSOAtSqnHla//AtMP5jTwo0qpT8PBn00thBn1WgmZRuCS5aaDnOs6uOUo2EYgtp1XffOHvmCxE9CLEpIs58TZPrfdsTLywF+73OZHHn89wnXoRilRnJWtHoyfZzNKaQYeR+ZChHBNeLwATwgyclzHMetxzWzr9c2EooBrl+e4/vI5HNel10/Y6JkmVnfdv8HdJ7sjwnOjl/KlO1f40p1b3xGu43D5kSYPuWaR5bmQq5fbXL3c4bLFppmQYLHskboazOso5xNJKX8c+AFMoeNjgFcC3zWLxT3QNHxBNDQ7Ogg8OoDjunQa3qCvSzgmYBq+GMkh2ewn3HbHWe64b4Mz61u5NMJ1eORDjvCohx2lwIxvDQNhmk5FGXmu0YWm189I0wI/iFhohbi45n+q7IxXPexam2tESU6WazKdE6cFFCA8h+uv6PCQq02UKfBcTq31+dqJLnef7HL3iQ3uPtWlH2+ZhLoouO9Mj/vOjDZS9z2Xq462ufxIk+VF8+/YQoNji02OzIV4onbfMssho66A+Ubg+8rfnwS8Syn1MSnlJ4GXzWRlF4Cg7HErXIdenNEMBM1ADDrEgYkSifIBF8LBAb56zxpfPr7K3Se6HD/V5fRqtC0h6LLFBo98yFE6bd9El6KU+VaALxzO5DlZplnvx6R5QZblNHyP9c2EOM5ptwNCzyVOc4Q2GpURcgWB56G1aWClMWFz13FoeR56qClvXhRcf8U8118xP9hWFAWn14wP52snNvjaiS73n+1xarU/ou2kmeauExvcdWJ7h1TXcTgyH44IneXFBssLTY4tNplv+ed0MFsuXeoKmAQIyplIPwT8XLndZw9tNw8CgS84utDkaPl6PEmtKBxuv2+DL9+9yr/ds87XTmyMPIzjLLR9rj7WZnmxie+7BJ7AcRzCQHBkvsF9ZzeJ45zVjYTNOMXFJadgrZcQpIL5dkG78Ok0fDzh4joORVFwdD6kKIy5NtfySUohBcYRneaalsNInRQw4rh1HGegkTzsmoWBQ3phscn//bfT3H+2x4mVPqdL387ptcj4gIbQpZA6vTY54TDwXSNsFhpGCC02WR783qAR2EDmpUzd/92PAX+JmQBQAH8rpRTArwOfndHaLhruO73Jv961wr/etcLt967vKlDmWj5zTZ8j8yGLHZ8407g4FIDnuAS+i+u6NHzPmDWZxvNcHIFpqVl2vUsxAsN1YL6VG3OtFdAIPELf5bKl1mAeUzqvuf9Mj43NlH5sRqS4OOAUrG0m+J7pDbxb+v9wDowuzOdoNea54ZoFLl9qmYJKrVlZjzm12ufUWsTptT6nViNOl6/XN7e3eEhSzT2nN7nn9ObE+1UlFc63AubaAQutgLm2z3wrGNpuXjcCYbWhA0ZdAfNczASAReDHlFKplHIB0wT8qbNa3IVgo5fw1eNrfOX4Gl85vsqd9++soTQCwYOumOP6K+Z5+LULCMehn2ScWY9Is5wk1QRJRi/J8YSDdkwnu4YvaDc84iynKAqjFRRQaBPa9oBmQ+ALgS6gH2sW2wGtshdvlfxW5a6EnktBge85BIVAp5qNXkqORhempKHVHm3tMC5gqkS7NMvZ7CdDn9kZmTBwrNRCHjHhfsRJboTOWsSp1T6nV8uf5bY42V6OsBllbEbZNr/PJALPZWm+wbH5kKMLDY7ON0Z+Ls2F2yJ80yYpW3esbMQ2lF+DugLmR5RS/314g1JqTUr5zcDz2ZqXdKAoioKTq32+crcRJl+9Z23XP3Tfc7niSJOrjrW5/vI5rjjaKnvneiy0Q86s9cuSAYfMcWiHPrkuaBQFDV/geyZC5XuCooB+tJV30wo8HMchybSJVjk+7dBDCIdmIAamSZrlaO0M/sB94eC4xvTqi4ygECRFgXag28to+BkFo82pJmXsVsIqTnOGh82GZZlDnZqkMBBcvdzh6uXOxHu90U85vRpx/5lN7j3T4+x6xGo3odtP6PZTuv10kM8ziSTTnDjb48TZyf9HruOwNBdsCZ5xITTf2JcwqFIRqjG9tiD03OwqYKSUHhACr5dS/jnbE+kk8L8wkaQDQVEUfOSfv8bH/s9xvnLP2kS1vqLT9Ln+ShP6XewEpq9KeQdc10wL6McZnbIuyTheA6JEs7YZAQ6e6xA2AzPryPdY6oS4wmW9n9CPMjwhaIYmt2a+7VNQ4BTGt4ELCx3zUKz3EtLSx9Jp+rjuViaxJxwaoSBKBEma4jgOrmMcu67r4HsucaoHjupJ6f/VA9Ltm3D6cEsI2H9NkuM4xuxpBRxbbPCI649sO8Z1wReCjV7C2mbCRi9hZSPm9FpEt58OXq9sxCNRuwpdFJxZj03k7vjaxHXMt/wRwXNkvsGxIYHUCr0dzTBbELp3zqXBPA/4/fL37g7HfHJ6y5k9t375FG/4q8kK12VLTW64ZoEbrlnkhmsWuOJIi9WuEUDHT22QZXrwR6a1BgSOA3muOXF2k5WNmI1eSlFohBB0Gh44JklvvZcQ+poozVjshPjCxXGg20/QRYHjOLRCnywvaAYe7aZPGAhct/TYtoY8AAAgAElEQVRTtAKqaQW9OBsUTAJkeUGvl7K2mdLtxeR5gStMrky/l8JRRhqT75T+H/iCVijwykbmcWqEyl5mLNVhJ2GlNbTaHq2Gx+VHWsDo3O9hMq1JU1MMemYtGvw8Xf5c7cbbKtPN9VLWeyl33Dd5ZmAjECMC6NiQBuQJl05r+4h2WxC6M7sKGKXUa6SU7wDuAX5wwiF9DpiTd3mxie8Zh+eDrujwsKsXS6GywEIn3Hb8oAiwMCp6lpkudr5vBq01AlFOQkzZjFNwCqJU03IdzqxFxqfimSmLcWqKH0+v91nqNCiKqpxA02j4tBoeOKaxle8ZDYmiINMFafm+YLrUbUYp8y0zQTJJMk5tRKx1Y1Y3E7I0pwA6DR8HuKyXstAJz+kzSMpoU1YKo2p6ZAtoN7bfm/Nlr7OnJuG5LsvHjLk6iSzXrGzEE4XPmfWIs+vRRHMsSnLuObXJPacmO6U94XB0ocmjHnqUH3rMdTuu22I4pw9GKXVaSvlgpdS9AFJKXymVnuu8i5XrLp/jXb/1RE6cWN+WMDdOkuYkWc7KekycmvEinufiUYZ7PQGOSfvXhTMYiOa6KZ7jIpouGz2jAaXlONag9MMEQuB5goVOiAv4gTDmVrPA91x83/hpHKAVGmG23ksHwgeMVrK2aUbLCscph7YVJLkmEC556UBe68Vcdaw1cVLCMAPtLDejcKFM7BPOVE2APc+eOo8qb0+4gxD8JHRRsL6Z7CiAzqxFRBOc0llecOJsjw+e7fGER15Jq+HbgtBdqOvkPSGl/C3gZ4EjQENKOQ+8Fni+Umon8+mixPfcWsJlM8rK8a9mckBRFGgKAuHSDE1uSpyZP8KsrLDOMk0/yoiSnEJDL0m5bKGF67porYkSo4bjmHNagV/2hnFo+h6OA0EgBhm7VX0TgINp1zDQbjDaTJxo5tsBK+sRDgVuKWwKoNkIcAqIkkkD4kaJ4oxenLHUCkamR+opmwB7nT01iypv13FY7IQsdkIeevXCtv1FYbS3M2ujwufUap9ulPGgyzvMtQMbRToHdQXMK4EnYHwy7yy3ucAx4NXAs6a/tAvLsEPPdR3aDY92w9s2MqTAPOSeEERxSi/KyLTJfcmdgqIoiLMcT8DRhSbrmwlCCDzh4gkX4ZnIh+eZAfbVQPvKHKoag1frCIQ7aD4uXKeciZ3iuQLXc8y+wNRNNQLBQtNHeC5Jdu450ztFcHaL7JwvdQfF7Xdq5fniOEYjbTd8rrt8bmTf8vIcp05N9uFYRqkrYP4f4NFKqeNlNi9KqVUp5c9iRpccGOrmMQyr5cPTG4dzYoRwmG/5nF7PB7Om09x0tpub89jspwReOHA2zrUCQiFwPVhoBYPaJq9MhPM90+3OlCqYOUutdkCa5mz0M3pRhi9cFls+rYGQS2mHHptxzmIrpN/PSbVGOA4LrQBNsWXOnQNPOEwqovYusI9hP1MrLReWugLGA+6fsD0G5iZsvyipm8eQpDn9OCPJcoTrmpBvuU8MVRUPC6d1L6XbT8gyM1c6aIjygTWRJweHhu+y0DYp/5VDWQ8mQroI4dBuhAS+YK5ca5rlpLow42hFAwdIy1EpvicIfTPjOs36dNoBR7Kc7mZKGAjabR9fuKb/71D0Y6cevY1yvEmVrFaFqhsTxp5YLHWo+5fzf4BfBl5ebZBSdjBzqT89g3XNhDp5DJUQqr7Nc63LrnUuumAgCIaFS6cVlP981roxcWLabuaZxtUFnXKIfTP0mGuZfBpdNgJfaAfMtYJt39DV69NrRgAK1x1k48apJk5zGqE3iO6kRUHQz5hr+qTlNEfXdZhrBlx5pDXI1dltekCjnK803w5Io62okXViWs6XugLmhcAHpJS/CIRSyi8B1wP3AT8+o7VNnTqNrSsh5HsCvzRN0txUN195dOtBnUTV7uHsesRaLyErm4Wb/BIPz3MHEwXGu/5X+R7D24NSKI0PTqvyX4ajQlcdaU9MPhufMb2bkK2uV7VfsKnwlv1Sd7LjbVLKG4AfAR6CyX/5KvC3Sqlzew8vEuqEPKv9w6ZJg7JVQ741SXESlSABY/oUuhi0UBCxQ8f1aYaCpbkt7eBc84jqhmnrOkPPJWQDX9jZ1JapUdu4Vkr1pZSfA84qpT4MIKU8UBlGdUKe1QMdj33TV36Jc6WFa2CuFdKLM9a7Cf0oIc1hzYmZawV4rgmPjvfAHad6n7ph2jqzj4Y/36TtFsu0qdsy8zLgPcBjMS0bGlLKa4GPSimfqJRSM1zj1KgeuN1MgOqBHm+4pN2CtU0TIXJhZCzs8DWiODND2oqC9V7M2maKLgqCMgy92ks4u97niqOmIPBcs7GjNCfNcjOtQDg0wu0jSca1oCjOWOvGpnJ77Hg7PcDyQFK3tv0twL8Al7E1vfE4JifmtTNY18yoTIClOTM8bZJztd3wBmFdrU02bZXYlmY595/tcXo1Ym0zZq2bsNqNSdIq4c6EnX0hiFNNUSa+ea6D8Bx0Xgzqm2BnzSEvTOZvnpvq62bo4XvinFMZ0yynFxsBGadmqsFmlA3WV32+6n1N5MqzfhbLTKgrYL4X+EWl1BlKAaOUKjBRpW+d0douGIEvOLbQYKEd4ntu2ZDbkOY5/SSjn5hU+qr4cL30WVQ5Iw6mAfh8K2Cu5dNpBnjCJdcFw0rLTpqDs0Nu2ySTalgLGjbthgsch88LynXtJGQtlmlR1wezgWmPOc5lHLBZSHUT7bbaFxjBYXJVtpp767EmVP0oh3kGOSOB7xIGHnFikuM83x1cp93cuu07OWcnRYRgskk17FeJk61Z2r4QtEKTL2Mrfi0XgroC5iPAW6WUNwJIKY8BjwJ+B7h5RmubOnttGFSZU1GcEaelprIZo4vCtKV0HQJPlMWHxaA4shdnhL7H1cfanFnvm852nksz8Og0fY7NNba9zySzZ1goVL11wdkmGCu/SpqZuUqV8BPCGVRD22Q5y4Wg7l/dC4Cb2OpcdwLj53wncGBmIZ1PwyAXBmNMsszktfSTzKTh64IoyQCPsOEPiiNboYfWBWnucvXRNroAHJOsd9lSc9dcmophZ2zlVwFTWT0uGIeT8kJPEGf5kOAziXmLHWsGWR546gqYvlLqSaXm8lBMHswdSqkDVfFVJ9FuHA20Qo84zdnMcjpNf9CmAYzzV7gOfrDlp/E9wdKcGXeS5QXNsvXlXpLWhk2nbj/f1mGu2lcdN5yUNzxJoCpDsH4Wy4WgroBZlVJ+Avg74MPAF0sn74HifHJAqiiO74lB6DrLNHGW0274CNfBxfhgcp2OCALzj5HEur1QaSd1BWP1+ar3rvP5KobzaIJmsGtCocVSl7oC5nHAd2BaNjwfaEkp/55S4ByUPJjzyQEZFkpVGwXPMw7cTtMvtQVNFb03USU9aGk5jQS2uoLxfHNcxvNosrI5Fthm1pb9UbdU4PPA54E3AEgpH4Jpofl8zFjZA/FXWCfRbpzhhzb0t8bKVt324zQnLM+v9pntepC3sl/qCo7z7Z1im1lbZkXt0IKUcgmjxXwH8J2YiQKfx2T4Hhj2WmszmtIvmHMdCgfT6U6Y0HVljrRgaIpiMbUEtr0IjvPpnXI+vimLpQ51SwVuA5rAPwGfwGgunztIhY774VwPbfUgDvs+xJT72M6y6ZKtT7LMiroazHHgm4BrgauAy4EOMHn4zHlQ1ja9AXg8Jkr118ALL/YG45dCbc+l8BksFye1SgWUUj8MXAn8v8AZ4L8DX5VSfk5K+bopreXdwFngYRgz7PGYcbUXNZdCbc/4Z/CEe+A+g+XixCkmTafaBSllgKk/+k7gp4EblFL7ShOVUn4LpjPe5Uqp0+W2n8QUWR5TSu3YEv/UqY09Owps0+bdsfdnd+z9GWV5eW5HW7quD+bHgG8v/30zcDemfOB/lj/3yzcDd1fCpeRWYAmT2PeVKbyHxWJ5gKmrebweI0j+EHiaUuo4QDkb6ePAI/e5jqPAyti2s+XPY+wiYJaWWng1OuaPs7x8YHqVXxDs/dkde3/qsauAKU2Xx2Kcuv8EtIEfk1JWhzwU00JzFlRq164m0MpKb88Xtiru7tj7szv2/oyym7A9lwbTBn4I06rhJRP294Ebz3tlW5zCaCrDHBnaZ7FYDiC7Chil1D8A/yClvEUp9cQZruOfgaullFcqpe4rtz0GOAncPsP3tVgsM6RumHqWwgWl1GeBTwGvkFIulKUINwKvP4hFlRaLxVC3ZeYDwU8C88C/AR8FbmFo0JvFYjl4XDRtzpRS9wJPutDrsFgs02PPiXYWi8VSl4vJRLJYLJcYVsBYLJaZYQWMxWKZGVbAWCyWmWEFjMVimRlWwFgslplhBYzFYpkZVsBYLJaZYQWMxWKZGVbAWCyWmWEFjMVimRlWwFgslplx0VRTWyzjSCmbwG8BT8W08vgC8BKl1KfK/YvAHwDfg/my/DvguUqp9aFrfCfwduB2pdR3j13/l4DnAlcA9wJvUkq9asYf61BhNRjLxcwrge8DvgvTF/pTwM2l4AHThP4o8I3Avy9/f0t1spTyhcBNgBq/sJTyWcALgacAc8Azgd+UUtqWIVPEajCHCCnlh4HblFK/OLTtRcAzlVL/rpyu+XrM0LsAM3f8eUqpbnns04CXAQ/GTIF4o1Lq5eW+/wr8SnnO84FHKqVuH3qfBzHhQS95m1Lq2RO2rwEvrq4jpXwV8EvAI6SUxzHC4VuVUifK/TcCn5JSHitH4GjgW4BfK38Oo4CnK6U+X77+mJTyX4FHccDmrV/MWAFzuLgJ+D0p5YuVUtWs2KcAb5dSOsDNmAF4DwYawJ9htISnSymvx5gaP66UukVK+a3Ax6WUn1FKfai81hWYKRBLwMgsWqXUXeU1a6OUGm8ofy1GaNyHGWVcAJ8f2v95zDSKbwI+pJR6DcDQFIzha/9j9Xs5TPDJmAkZN+9ljZbdsSbS4eLdQAv4QQAp5ZXA44B3YL7hHwn8slKqW2oANwJPlVK2lVJ3AstKqVsAlFKfwWgBw5rBPPAKpVQ67V7KUso28Gbgj8rG8EeBDaVUXh1TzjHfYPuEit2u+7tABLwW+JmyP7RlSlgBc4hQSm0Cfwk8o9z0ZODjSqmvYWZcucBJKWUkpYyAT5bHXVX+fI6U8qtSyn65/xuAcOgt1pVSq9Net5TyGGbw3wngBec43OEcs7SGUUq9BGhi5q2/VUr5o+e7Tst2rIl0+PhT4BYpZQv4CYzZA2bGVaSUak46SUr5TIz/5SnAR5RSmZRy/Ns+237m4Pzz8cFQmmYfAj4BPLvUUsDMy5qXUvrVNimlD3TY4ywtpVQMvEdK+R+A5wHv3cv5lp2xAubw8Q+YeVM/gzGPfqLc/lWgIaW8QSn1FYBSCHWUUicxc6o+oZT6YLlvHnhY3Tc9Hx+MlPIo8EHg3Uqpl47t/ixGU3k0xm8ExlzLMXPNz3XtvwA+VzmpSzSQ7nCK5TywJtIho/SNvA34beD9lUmjlPoS8I/Aa6SUy1LKOeB1GJMKzAC8h0spj0opr8GEg+8Grp7hcl+OiXqNCxdKH9FfYELLl0kpryiPf5tSanzO+SQ+BrxQSvltUkohpfx24OnAX09x/Yceq8EcTv4M+HWMoBnmp4A3AHcAMUbbeXq5702YfJS7gHuAFwHLwBuklCeBr8xgnc8C8tLfM8yzlVJvA56DCat/AaN9/A3wC9VBQ+d5gFu9Vko1MJ+zAbyr/Bx3A78J/PEMPsehxY4tOYRIKZ+AebAeNOTTsFimjjWRDhmlKfEa4HetcLHMGitgDhFSyl/FRHI+icn7sFhmijWRLBbLzLAajMVimRkHPop06tTGnlWwpaUWKyu9WSznksDen92x92eU5eU5Z6d9h1KD8TxxoZdwUWPvz+7Y+1OfQylgLBbLA4MVMBaLZWZYAWOxWGaGFTAWi2VmWAFjsVhmRi0BI6U88OFsi8XywFNXgzkhpXyjlPLxM12NxWK5pKirmTwb017xFinlWUwP17crpb48s5VdZCRpTpTm5HmBEA4NXxD4Nh/CYtmNWgJGKfVu4N1lS8IfwLRN/KSU8nZMb5F31GzycyBJ0pzNaKsbZJ4XbObmtRUyFsvO7MnJW3aL/xvgNzBDsW4AXgHcJaX8/aGBWJcUUZrvabvFYjHUdt5KKZeA/4zpSP84THvFFwH/GzMm4o3AH2G6ol1S5PnkcqedtlssFkMtASOlfA/ww8BxjEn0jHJOTkW3nPp3z9RXeBEghDNRmAixY42XxWKhvgZzGvh+pdTHx3dIKZeUUitKqTUp5cTREwedhi8GPpfx7RaLZWfqOnmfNWm7lPIq4DbgSHncn09vaRcPlSPXRpEslr1R10R6OKbb+rdghqIP87lpL+piJLACxWLZM3WjSG/EjKv4Kcz0vicDv4Vx9P7AbJZmsVgOOnV9MI8GLldKJVJKrZS6GbhZSvkU4NXAT89shRaL5cBSV4OJgco+6JXDyAHeB/zY1FdlsVguCeoKmL8H3lsm0v0T8Gop5WOBnwe6M1qbxWI54NQ1kX4ek7mbAb+E0Vz+C7AB/Nxslma5UMRpznovsREzy76pG6Y+i5kTDPAlKeVDgMuBU0opmy9/CZGkOd1SuICtu7Lsjx0FjJTyP9a5gJSSsj7JcgkQpTmNHbZbAWPZK7tpMO8be10A47nxGtgEFqa5KMt2dmoXMe02ErbuyjJNdhQwSqmBA1hK+STgqcDLMbONG8DDgZcC75rxGg89O7WLSNKcdOjBn4Y5s1N9la27spwPdZ28vwc8Wim1Xr7uArdKKZ8L/DPw7lkszmLYqS3Eei+lGW7/L9yPObNTfZWtu7KcD3XD1EeBzoTtDco6JMvs2Mk8SbLJgmc/5kzgCzqtYKCxCOHQbnjW/2I5L+pqMO8H/k5K+Rbga+W2azGtND84i4VZttipXUSwwwjT/ZozoS+Yb42XnFkse6eugHkWcCMm5+UaIATuxQiel85maZaKndpFzLf8ER/M8PEWy8VA3TyYHvBr5b8dkVK+QillBc6U2a1dhG1GbrmYmfa8oxdwSDWaWT/oO7WLsG0kLBcz0xYwhzKWaacOWCyTmbaAOZTZWLtNHbjYBEylaUVxRpYXeMKhEXrWtLLMBDsSdgpMO/t12NzSWlM4IBx336ZXpWmlWU4vNhpWko2u1QoZyzSxAmYKTHPqwLC5NSwIWqEHiH2ZXpWmFY9pXHGq8T1xUWpcloONFTBTYL9TB4Y1ln6c4QkH3xMjgqASAmmWc3otoxl6e9ZoBhXSelQY5loP9idpzlo3ZmUjtlEpy76xTt4psJ+pA+MO4iTLSTJoMSoIcq1HNJpm6O3ZmVxpWsJ1Rq4tXJPQnReazSij0d4SONZZbdkPtUoFpJR1e+7+4T7WcqAJyuzXpbmQ+VZQ+4EcdxBXD3uc5gjXyOs000Rxzqm1iM0oRY9pIHVH2FYaVTi2ttA37+ns4DKyI3It50vdWqRXSSkn1SKNoJT6hX2u59Ax7rupHvZcF4S+MMIlMWaT1hqtC7QuSIfqkOo6kwNf0G54NEKPVugReIJWaF63Gx6uu/3PIc1y1roJKxsx672ExAobyx6oayL9D+AmKeVNmFqkEYeDUupfpryuA8teE+7GHcS+J2gBWV7ge4LAc3GFj3Ac/FzgCQfPcwc+meoadakS8ybVGlXrrqhMskqTsiaTZa/UFTBvKH8+ZWhb1YCqYGviwKHmfBLuJjmIfU+w2NlewdwKt3wwlWO2usY0GF9LnJr3GDepbLTJUpe6AubB03gzKeUJYJHRhLw/UUo9V0r5XcArgK/HFFK+Win1pmm87wPF+STcVdvX+wn9KAeKMiRtGNZwfE/gpzkb/Yy80ASeYL7lT+1hr67jicpUMmvxx6q2p9XdztZRXfrULXa8C0BKeQNwnVLqw+VrRym1l7+2JeBxSqlbhzdKKa8A3oupY7oJ+Cbg/VLKO5VSH9jD9S8o+0m4E45Lp7nlA6k0oWGtIs1y+kk+EC651vTibKr1SIEvWOiEJP1kqvk949jyisNB3dnUlwHvAR4LpEBDSnkt8FEp5ROVUqrGNTqAD6xO2P0M4E6l1BvL15+UUr4NeA5wQQTM+Xy7nu8DGaU5aZYTpzm5NmHk0BdEqTPwlURpzspGSpzlBJ7A91xyXQx8JEcXmuf/YXdgv/k9u3GQyiss509dE+ktwL8APwocL7cdB94JvBb4oRrXqDrfvVxK+YTy9/cCLwG+Gbh17PhbMTOwH3D2+u06XN8Tp/nAZ1EJjE7LJ9nlwYnibOBbAQaCAxiEvANfsNaNcV0fMKHrJMvRuiCKM+aGQuPTMj32k99zLmxz8cNBXQHzvcCVSqlNKWUBoJQqpJQvx/hL6uADnwY+DPwMcD2mYfibMS05vzR2/FngGOdgaamFt0Nnt91YXp7bcd9aNx4kmw3jCZeFTjiyLS7nCFWjPpI0Z6OXkueapU6DRrD1QPq+oNAFWa7xhEsj9IymoiFMt2sKoe8N1hmnOd5aRFEKFdeBuaYRNsJ1abRDOqW2M7yeik4rGBF8pthxdB117880CJoBWV7vHl+MzPr+XCrUFTAbGAExzmXUzN5VSv0b8LjhTVLKXwVuwQidcaoI1a6srPTqvP2AJM3xmwHH710FHJoNwXxzNDFuZSPe+fx+MvJ6fWhIWUW3nwAOoihIoxSgNIE0nebobWw3PNa7MeubCXElPFyH0BPMtwNOuVsaVa8X0+un9KIMXZg8Gd9zaTV9zpzZZHXV3ItJWsDqao/5VrBNOxteR3UPlpfnOHVqY8f7Nw2NZrd1jN/ji43d7s9hZDdhW1fAfAR4q5TyRgAp5THgUcDvADfvY213YgTJSbZrK0eAU/u49jaSNGe1GxPqokyVL+j2NDovWOyEgwdlL76UScfFSU6c5eRaD/wplbk0TvWwdqOMOMnAcWj4rnHi5gXrvYS1bgIUNIRLEXj04hyKgjyH+ZbHXMMfrKUXpWz0UvpxSlFAI/SYawWEvst8K9iX72OajtlZml+Wi4e6AuYFmOjObeXrk0CO8cHUyt6VUj4WeMpYS81HYJzGnwP+29gpjwE+VXN9tYhS40gdV8DjVI88YHtxbo4LozTLibMt1b/yp2SZJgxGb3ea5ZxaiTmzEdGPM0LfZNa6rkOWafpJRiP0tooRgWYgWGj5pLnGdR2agRFevTgjilNWuyn9OKNfOn993yXNNEfmQ5KxRLph6vg+pu2Ytd34Ln3qhqlXgSeVmstDgT5wh1JqL3riSeAFUsr7gT/A+GB+A+NAvgl4mZTyecAfY0ypnwJqja+tS54XE7WIXOuRB2wv367bk9NyQs8d2HZZpokzTa+fcXTBpRel6AKiJKMXpfSiHBzje4jTHF2Y3JO1NMEXgn6SoXVBM/DwPBcNHJlv0IvN9rT8PFmmTa1SL2V1M0a4Lp5waOGzshFxbKFBlOb7Cj3vJpxsTotlEnVrkZBSXomJ6vww8CTgJ6WUy3XPV0rdAfwI8DTgNPC3GP/Li5VSp4AnYoTKSeD1wHOVUh+re/06COEM0t5HtrvutgesbvFiVd+zdb7DfDtgoR2gdUE/yYCCTtNoImc3IuIkI8lyolST5cZ08j3XtGBwHOLEOGHB1B1lmWajl5Blujy21HSq2UWuCVnHaU42EKAOSaZJEmOm6cIIgv0MVttJCGltqrAH7SDygs0os3VLltp5MP8JeAdwAlOL5ADXAW+SUj5NKfVXda6jlPoIJpdm0r5PAI+vc53zpeELogkPUui7+8rtGGg8bDV0Mg5YwVyrEgIOaWbMmsqECoQDgSAbMqmMGeLguS4OGOdvqdkkmWZhLiQoe8YIx8EthcfqZkyWmZYOwhWEgQu4OC4DM0sIZ1++j51Mx2IH5cfmtFjq+mBeBTxPKfWW4Y1Syp8DXgfUEjAXmsAXLHZC/GbA+lqfnaJIe2XY+Rn6Lr0yryXLNJ7nltsFuS5oN7ZCy+vEkIDWRrNIyrCt68JcM6AXp4OmU3lWsLYZ4zoOgRB0mh5pVrAZxTRL307oe2z0UpwC4kSjCw2Fg543GsVcMx/k4+w3L2a4p++wQB3G5rRY6gqYJeCtE7b/CUb4HBgCX7C81MIp2x1UvoPNKDtv38Gw87Oqho5TTZRnhIEYPHwi1eSDlgs5SZKT5JrAdQkCgaddWoGpLYqS1JhKiYlGOY5Dq+mhKegnKa4LWfkZ4iyn4Qu0LphrBmz0EzzhkGmHpi/IioKi0Gz0U3pxzlLHCNSdtJg4zQfh9/F91c88L/DLv544NSULrfLzV0yjpMBysKkrYP4ak617y9j27wbeN80FPZBMK+w6/k1dPWRpZrSWbj8FUhNRilICT9BseHRaAb0oBRyagcd8J6DhCdY2E85u9MEBX7jEiUa4BWmqSUSOLosh09L5u9pNSLOcjX5CoaHT9Al9QTdK8X1BnuVkuUeWaTazlDPrEXNtn7mGj1+Gw4dNn+5Qbs+kezIsUNPMlDlsRilRnHFkvjH4/HbCpKWugLkL+FMp5WcAhWnP8FCMP+WdUspXVgcqpX556qucEdMKuw5HZtIsp9tP2YxSfGF8O+u9lDjJaAQeWhfEWY6fuQjXoRF4ZLoYOEQDX9AIBO2Wz2Y/JUoy8iJHIIgiDUUyMLuiJCdyHLq9lDB0aTUCNvKIJNO0Q5/FTkiWa5Jcs7IeUejC9JJJMnzPRTjOiNax0UuIU027FIqh7w72Dd+T4c/ai7MyXO6ZaFmc0RHOvs1Oy6VBXQHzbcAXgQYmwa7iC8A3DL0+UEb3tOphKudn9cD1EyMsPOGy2kvQuqBVdoyr2rgYd0tBUpo5Guj2UnT53kc7DdbWE1PMWLbb0VoTp055jo/rwImVPq2gTBB0zX9A6Al0UdAMfJKeyUqOE02SaQqgH+XowmzPi4IjHTHS77cNZaW2Hgig4XtSCdSqXwyA57mEgaDTDEacyZbDTd08mEnGC/AAACAASURBVO+pc5yU8hX7W84DS52ckDr5HdXre8/GbEYZ3X4yMA/ixOS5+J6L1ibMHCc5Z+KIdjOAQqPL1l2ua0LUeXncZpyQ55o010SZadHQ8j02+ykN37S8bAbuINnAdYwmcXYjYTPOaKe5SeDzBKJ0xuqiIPDL6JPW9PqauYZf9gB2jQbWS9joJeUxsDQnRu5JJVCHm17BVmMq69y1VBzq2dTnytit66NJ0pz1fsLKWkRW5q1EhYkOZWXGLf8/e28eJNl213d+zjl3zczK6urlSU8gFmFxzMxYgFgkYxYDwxKDABmDBwawTZhNlmQGMDAYMTGEjQZpDMEiDJZtEGYLMCFjgQABYhvQCLBBQgL7GiGE9Ba910t1VeVyl7PMH+dmdlZ1VXd2d1V3V/f5RHR01c3Mm6ey6v7u7/yW74/gtXjncd73xXZdX5inGJUpbWvZ7dreGHmm8462s0gRtjN1axnkCcMi5dy4YHfW4hFY6yjzhFRJpBR0fVYnSQSlVzTGgpB9bU2o2ZGEamFj4eJODcAwT0KbQ79FWhi8UZkwLK7VPy9+9nkT6nkW7RAHJTxj8V3koR5bcrOakHViNAsjtDvpaO01Ue7W2d6QeJJeTFvgUYmkIHgr88Yi+1qWJJHLeEZrwnmUELTGk6hQLJf2FcIbfdd0Zz2JBONEiO244LGURcLmICPPFRTQNJbWWspe6DtLJJ11zJtwDEKweGfaYJ1nsxz03ovHEgzrYV7b+c3i0IbFIlVRUCoCxNnUR9aEhAFkbd+wKPcFPFe3AAsjNG9DULdx/feNwczBWMujZ4dkqaTtgjezMQhxiscvT6mbvgLXONrWhqK5SR2Cs50jlQIpBKI3RKmSqCQU62VK4FLFmTwEWI11NI1lWCZ01lJP+gFtiWCUpgzLlNaEtQskg4HizDBf/nzBk/GkiVjKdpZZgnHXyyosPrvFZ3DQQO/ODu+IjsV3DxdxsuMhXLv7LiYh7g94rsYjFsbGmBAraRrD7jwEa8ejjDzNwoXdBa8kVeH1Qgg2ihRrgidxcWeOdY5EqlAc56A1wXNJpEQpyJSiyMMoE+88WaYY5gllmdK0QYdGKIFUklRJkkSGbZBxFGXOsEiRi2HUOJQIP8fCeBapXBpMKcM2qumbN3dn7ZHxp8MMRhSUikA0MIeyuMjyVO1TmluMClnEaCazlktX50zmLduTBoHAEzwOmYhlmtq64LkMixQpBVenDcKL0N3s4Ny4wAF7s4a6saSpIFUprbF0xpEkAmkFBo/vI8JCCrJEsTnM6IyjMZadWYMSir1Zi/MeJSXeeTyC85slSSIp6b0d5yiV3CfqvTHISFrL5iin6VPkAIPy9qdIHnY88vAQDcwhrKr4h6pcu9SPWQgzTWYtl3ZrhIDWOtJEMqvNMohbZCFV3FjLbK/DetgcpAyKlMtXazoTYjRSSma1QMqQBRoVoS7G9HGPeW24WhuEEpzfKKgbx7D0CEQ/gM3x3qf2mLeGp7dnSCFDlbDxKCFJUsHmIAPBsnUhSSR5IhkPs32Vt4sCPSUlrbFIGZ630JuB9bc4J6nnGzk9PNRB3qO4blRIcq0OZdFWcOnqHCFC/UemJIsffTbvyBNF0wZNlixJkALSVDJtLBd3ay5enqGUpCwVuZB0zpLLhI0yC7Op6w4BWBu2SFYJ8jQhSUPzohSwOcrZ3qt58vKUp7ZnZCqh6zyOYOSKXDEoUqQQ5JkKsRTrSZJQ+5L2xXq70xBnSpPgzQyGGWfHBZcup9dlh+DWpkhCFJR62FlbruEgWuutQw4/ELOpD7vLdiZ4MYsLrO4M8zY0NIoVt987QZ5KhJQoKZk1HbO2wzmH6SyXtufBgHnHvLZcndRMph070wYlwlB6Y10I5PaFbIvB99vTlrozbE8b5rVhZy9shYZFikqgc6Fs3zhPZz1SSNJEXkuT+z6F3tjl7CNPkOiUQjBrLJd2a5rOUvQjbGdNqOtZjKq91SmStzOvO/LgsK5cw/OA11ZV9cL++58l6MFcAj63qqrfhwdnNvVhd1/rBUrIpbZu3YRu5cZYFKForTMOJCglUUKiErDeIRAgBI4geKVSSeIFxoWsj3WescpwwjPIU/Ce1jq8c8EACIUH2tYyV2HbcbXvrJ41dhlgllJccyE9FFnYBkkpQy0MYKxbpqmbzoa6GeOY1t2y03t7r2Z7r6UxDiXpt0qW8TDbVw9zK8SamIeTdbdIP0A/n0hr/fnAZxAaHT8eeDXwKSexuHvJwezI9l4TBsFP275U3zOtO4z1bI0yvPPMakMiQwD2zEbGqEyZNZamDQr+zoeWASVDVa0U4BHUjSVPLFf3Gs6PS7Y2Ci7vNUgpmc87PB4hJFKE2pdNldB0Dik8xgb9X+89OBAIzm6WnBmmbI3DbAEvYFikpFJwabemnnUEMSu4sFkwqzvqPsslpaDzYJxD4Pv2BkdjfBj4dgwC37Em5uFhXQPzUcCn91+/GPiZqqp+R2v9FuDbTmRl9xlKCbb3zDKzUuQKyHh6e8b2pGFrVPLo2QTj+yK5fluyEJUqCVILgzxhbxp0c5vWAZ5hmXBus6CzjieuTDi/WVK3HUqIZSWwkoIskZSZ4pGtkqYzWLfQmXEY6ymyoDPzAeeHlHmCFIJE9fUzUjBvgxfhetW7eWPY3hPM2yArAaH6eHfaIPvGyOFKgNdcH7Ndizhk7eFlXQPTAlk/E+mzgK/uj6fcQRznNFGkinnb7T+WK85tFgghuLBV0BnHZNZSO4tAUmQJrbQUJKSJoDOhuVEKycXdGVd2GzprubQjeNfje8HodKG6t+u3PcaGmhf6mhXvAR7DOR+Ct3lCkYXxJVmi2BplbI5zLsiSR7ZKzo0LWuPYmTRhKqSS1O5aGn5aGxC+D1QHskQxm7dkmaLrLNPFhII0YTJrcXBLW507qYmJW6vTzboG5neAnyNMAPDAm7TWCvh24I9PaG33FVkaMjELEW7rPIIwYdH6kC5OE8lokCHrDi+gyBK2xjmjIuXJyzPe8e5L/PfHtnnqypyrk1ud/XP9xThvLPPmeu/gD/7btWkvSgpGZUqWyqURSpTAe5b/Synw/c+yiOdM5l0ICHe2N2ospxiMhxlnRjlbGzlnN3KeeXbAM84OODsu2Bxmy6Dycg23WRMTt1ann3UNzEsIEwDOAJ9XVVWntd4kiIB/0Ukt7iTw3vPn79vmqaf3SJTshcDl8rHFxeQJX4d/Hk+Y+LiIuzSdwfugySKFYFaHQW7WumW26R3NFZ7anvHuJ3ap2xsLYMt+ssBiTYttjBAhrqJU2O5kqWJjEGI+09owrTumtQkB5kOwzrMzPZ5BZm75noYnLx8+8E4KwcYwZbM3QuNhxkaZUmQJ42HKqMwYFgmDIuHC5sH5k/uJW6vTz7oG5kVVVX3N6oGqqna01h8DvIxr85Lue37mN97Fr/7h++7pGkZlwtYo50y/nRkVCQpoDX08xTJrQiGeALJ+vGuRJ2yUKec2S1pjyRLFtO7YnbV0rWHWWDyCzl5rW2j7VoS9Wcu8tb0cxPXehBR9YDsJ7xWMXag+HpUpxrowZqUx7M264OEc4pU479mZhGFx731qctPPosjU8j2GZegUH5YpozLEkMo8GKNhkfLouQFFlsR2g1PEDQ2M1joBcuA1Wuuf5vpCOg18ByGTdCpYLf2/G2Sp5PxmydZGxqgIKnN5GtLEYcpA8IKm845hkeBcaElwziGTIHfpBBgLEkFtXCisyyR5mlAWCbOm42pjyNKEUZmG53VBV2ZjkLE5ypYd1dOpwXpPWShGg4zz44Izo5zdaZCJaIzjGedHTCb1sh/rzDDn6rTtpT+DQPl4mDFvLLvTUIszqw2Xd2uuTlp2Jg0705bdaXtot/Uqda87fHm3vulnWeYJ3/K/fTRnNu7/2dWRwM08mJcC39N/fdTt6C3Ht5yT58s/U/PJz/9AnnxqD+M81rrlnVgKQIRaEiFACNFvUfrj/dd1G+ISi3lAznsubs9oWkeS9iNHBIwGKRfGJeNRxrTu2Jt01F3QUDGdY9p2nB0XZEqyYx07ex1SEBoWhSQVoJKELAldTg5PrnqdmS6IfwugbgzTuVm2M1jjMT4EgVtj2Z2EAr1UShDB6JV5wiBLGA9zBkUaRK8ImaO0L7LLE8nerOUqQf0uU2KZzrY2pNw3BhnP2CqP3LJ0xvL4pSlX94KI1d4seEGzumPehikH03nY5k3qjukRnhGErNesMTzz7OD4/iAiJ8oNDUxVVd+ntf5J4HHgMw95ypxTFuRNE8nf/BvP4uIzb394+WLGddguhPjG2zvLvDUoEQKmxoYCu2ndsjnKmM0Mk6ZbZoRmbReyTvMOgcc7Qoq788FIKQlCUvbjTBRwZpShVN/z5D2d8cz7GIwDmtbjvQEPaSKW3dKztmNWG3AClQhSL5nQ0baOC2fKUKXsXS+YBVYI6tqQ931LEFLkELY0DmiMI8/UckLB0Z+3YnOYszk83OvYOuCNeO9pu1D4N5l3XJ00XJ0E7+kDLgx5zqPjGH85Rdw0BlNV1SWt9YdWVfUEgNY6raqqu9nrHmSyVCH74LCUEmNCRaxxns6FhkLnF95R7/koSJWg6eMj1nvyPAlFbK3DOovwoVXAduEOninJmXGOJGRwRoMwK8kYx7Tp+hRyEPDeGKY443tJTIUQIYOkpMC6kCFyzoOBcZFCKsmz4KGkSpAKsRTMmjeGrjU4J0mkZDQIwdlFFfNCI+dGnssqt5JFEn3vVJ4pzo4LPugZG3f8+4rcO9YN8j6ltf5O4CuAs0ChtR4D3w+8rKqqm0fzHjCUkIzKUF37xKUZg1xxddLr6xLu9AgY5ClJotgoU5rWoIo0iG/3c7J9r8XbGTDWI4QnTcNo2GGZsjXKQ2DTeeZNx5WdBuc9qZS01tIax5kiZ1xKpk3LlZ2O1rQU/XsqJbD1okoXcqUwHpR1eEHQqel/pnGZcXmvDl7RrGVrlGPwS63d1cbPWxH2jp3VDy/rGphXA59EiMn8VH9MAueB7wW+8viXdn+jlKBuDJ11JAq2xgVXZy1N4xhkCWWWkqSC8Sgn6ceTbA4LVCLCdsX7MLcokeRp0IZJVChyO7ORsTHI2BoXnN0skAguT+qlxGWiJMaGmpXBOO1HtwpAsjnKcN5RZIpEyVDNmypGA1DBgenXH8SodmYNgyIEi9NEBgGrQY7pLEkikUc0yN+KcYid1Q8v6xqYLweeX1XVY301L1VVXdVafwVhdMmp52DFqARqY5nXoe9okIeA5uKiKFLFzqQNxWkubEse3Rr0W4EgR7k5yNkcBc2VK7tzrPOhKVFIBmXCcJBgrUAJj/UeSfBijAvTCNrWcHUvTBZoOoNxrleqE6FHyAfvYtYaPB4ElIXCWkGe9/1KUrI1ygHBe9+/u0xRJyIYnyJNmDeG0SBl2uvFDMoEa7LQpS1F2A7225zbNQ63O642crpZ18AkwPsPOd4Ap36TvFox2hnL9l7HzqRBSsGgSEkTyd48TGY8M8qXF4sULFX1nRecHZcY5yhSxda4WOradibo6wJIJEXmsS4ETRUglWBrkNF0jjkWCVgLT12Zk+cto0GG8KGSdd60jAehkTJTCocnNZJpE8aIzKzDmWBYUilQMghmCUJMBiFIlcCJ0C8lZCjGy1OFc83yMwkB6qBtA4LxVkY2iAYicmusa2D+C/DNwCsXB7TWI8Jc6t8/gXXdVRYVo9cGpxla62E5ciRsH5rey1nciYUIhWjDImVeGyZzQ9MYJtMW56AuU9Ik3PmN80znhqZbVN0KjDMYB8IJnA8xkTPDbNmD1FnHSKYY4/AOlAIMzJqONJWUWUKRJiRK4BC0nWEya2mtp96tGeQJWZbgnWfeGayHLBEkSiElpGkocivSUMQ3LMI0yaYz7PWznbJEETrHY4l+5NZZ18B8PfArWuuvA3Kt9Z8CHwI8CXz+Ca3trrHIcCwEnsLokWul97O6C5mhXg9XEiYxzvoUsTGO1l7rUi7yhCyTeO+5tNuSKUGRSnZFkEEwzuG9x9nQD+QEXN1rAY8aC5z3GOOXrQijRKEU1E1QuGs7S9uLRp0ZKlqrKDLHvPF4IUgkTOvgcY0HeVCwk4qtDcF0vojCBA0bAWyOMsaDDAlckoI0T6nzkCisW8MgT+iM5dJOP6UgxlAia7LuZMd3aq2fC7wIeA6h/uVdwJuqqrpxk80pYBFfWEwqXMwE6ozHmIau13zJsjBi9VLvHSymI16dNqHEvwPj7bLmY1IbnHO0CJrGMigTOudwLgSHi0wybS15KskyibOeaT+JcXOY4QGHpzMmNFY6R6qCFObmRs7ZjYKySJg0odhPqiDXYLAoEeQuPR5rLFmWIpVCSYuUfXO2F+RZEuYnEYxmKgXTeahBSaRiVCqaztKFWbeU+a0LgEceXtbW5K2qaq61fhtwpaqqNwNorR8IDd5FGlVJGSYAEOISSrK8sOrOhtRzT9NZFp0THh8yNtJjapjWLYkKOiuJVCgJrbUM0zTEZTzQhOK44ClJylzhfZBpCGNKQrFdbUKwN2xvQn3IIE8o8wQhQi1Onsm+0zkjTy07ex6bqSDj0KvriT4WUxTJ8ufYHORkiWQ661BCsr1X45xnOMxp6qADY5ynnbVsjvJlUygQPZrIWqwrmfkI8PPACwiSDYXW+tnAb2qtP6eqquoE13jiLC4M6x2TmUMIyYW+ofDybr28qLNexAmCAZIShBTLtPGiQTB0W1vwgtYY2i7MIDJJ2JIsRse2nUUISZ6oEHj1ULeOMpOoRHJmlFM3hsenDZPaMi5TMiXYs562s2xu5KEh0kMqFS0h9qOUYJykYWh9X9Xr8TStY1SGuUt5KrG9BMSiBK7tPZXB6Fp17aLaeDF2pTPXtHohejSRG7OuB/Na4M+AzwUe6489RqiJ+X6CCNWpJksV59OScZnx1HYYgjaSGRuDnJUb99LLCYp1oVJ2c5DRWkemJF1ngz5v58hSCT6MIgGY1cETGRYJghDDUSJ4QMMyQwg4P5aUZfB00lTx3vfvMiwKyjzo816ddTjrwgjYTAX1vEQs9Kg4M8jZKNPgUanQF7UzbUhUSFcPB2Etearw3i8HqykpcT6MNtmbhTaGpg2d3akKWjhSCmaNwTmQkqVq34IooxA5yLoG5tOAR6uqmq7UwXit9SuBJ05sdfeALFVsjrJl4HeRWQL6MR6SWeOW1a2LudJFFiQxiyyhyASdtQzKlKT3cMIsasNkZigLxXhQcG4z59JOi/eOPEsY5iqMM8kU5zZLms6RpQlF7uhM2K7liWSvs3gTDNm89chOsDUKTYtlkSDxOAR53x2NEGHMrAti4lIIvA+zl5zzlFmQ3WytDdrB/RgT6zxFljIok74nKTBvg0B4fsCYRBmFyEHWNTB7BHnMgzzCAzILaZXV0vZrw9cceRq0UgZ5spSNPLeRM2tsr5srl3f6xZCzBaFvqaHsC/YAprWizFLqLij2Q5gFXRaKIk+YNTV5ImmTIETVzTo66xFCMBqkGAtJEhor69YtPa0sT9goUpq+AbMsFKmQODyyDWNN6s4xLjPKPh4zrTums47OOi5cSCGRBKVUQmA5T5YD6JTYPxFy+TPGqY2RA6xrYH4D+BGt9SsAtNbngY8Evgt4wwmt7Z5xsLS9yBPOjA4PYrZdQjJvVyp+VRgTIvcLO+Vp6Epe3W65Xlh7nCmedW6077zj3gi5fk5S3ZpeTiJsbzaKnCQJ42ONdSA8eRaaJ2dzx0YRGhQhqMzNG0NjQvl/JgWZcZwd58z7uh8I3phyAvr5SWmvsCel2NeHtFGGQr6DxN6iyEHWNTAvB17HNeW6pwhZzZ8CHohZSAc5qrR9taXAuSCPmSaKUXntgktViM3szbuQ5VkMl88VbmUbsUiHl9l+53DhCSzO6H0YV1LkKkgxZHJpqEw/tlbKoLe7mGd9ZbfhwpmCrVHO9qRdelONsf3Ug1A4KJRYjiZJpGIwTNgaF+zuzBmnGbPGXBdrWXhgd6O3KIp+n27WNTDzqqpe3HsuH0aog/nLqqpuX1TlFHJQhHrWBJGnAezbLjSdDR3R/YxpCEbjGZsls8YsJQ/Kvkt6VO7/NRSp6jM6nlGZsjFIMS5U/26OMiRBGiKRgsaEi88Kx96sJUtCMNcDs36oW2stpg0xFinlvm1RkSUM+yB0mbllRmlhOAdcC+YevMBP+kKPot+nn3UNzFWt9e8Bvw68GXhHVVUPXUTvoAh13VpaEy6C8SDrt0HB8GxK2W8rwnNXL0xjO6yjDwhLkkRdd4fe7YWs0iSIfAspaHqB8c1BSpIq6tYymbe01tF2lhRJlsggB4Hv2xs8mVLU3lBm+4O1Q5kuA7NKSgbDZPm1kqHBcVjk9+xijqLfp591DcwLgU8kSDa8DBhorX+L3uCc9jqYdVnNknQmGBe3kKl0jlnjlu0Ck3nbF+uFitq6C15AZz1lXyi3oEjVdY2E1no6Y5nMOy7vNdAbjDSRZHlCqiRtZzm7UTBvTa+XG5jWXd9BHTyRQR7S2c2BwPOoDFuzg8Ha8TAjw9/zi/hO5ilF7g/WbRV4O/B24AcBtNbPIUhovowwVvahuJ2sKrM1nUMimNRdmDvtGjyC2bxlWKbLLmzr/E2Fxg+7Izvnlo2XCy3cxtnldmVv1obCvkRSklA3NvQ59f1NC0Oi+gDtsEiXwdzVuNDCa1qNc4wGGa69tuaDcScvguDWScdEbneeUuT+Ye1WAa31FsGL+UTgkwkTBd5OqPB9KFikrztjubw7Z96YZaHbrAmd164fZBZGzIYubAg6L7dyR/b9NbTINBVAa8N8JiVDdifPEmwv0bm1kS8NSKquXfCLWpVRmfbB6P0B5VUDUROMSN0Y2t7oHZSyWBjLIEWhTjQmsvp5L1PkUiy9s8j9z7qtAu8ESuAPgN8jeC5vexAaHW+FxQU3mS9GjkiKNGjZ5mnwJibu2lalNXZpYJIV0aaDHHZHXtSaLFTzskwxSsKI2FEZhKASFQwbsPRkjPVsDNKlGNVi+5Mmal/9zqr3cTCYaqxbfr8aB2n2fe2W5z6pmMjq5237zFcY+eKXBjByf7OuB/MY8NHAs4FnAc8ARsDOcS2k7236QeATCFmq/wR8/f0mMO4I3sDW6JrHUHeWFk+ShDt7mSU0JlTELi6KIk9uSZs2GJ0gfL26xXLOh94gPI0TpCqU+FsXqoGfOcoYDbJbSu/eKJi6ahBX63rsipzFScZEFp/3YWuLBub+Z63B9VVVfTbwKPC/A5eBrwHepbV+m9b6B45pLa8HrgB/jbAN+wTCuNr7isXFlGdJX7Ure1EqT5mFKt0kkQyLhDOjYrk1WVzgwyJZeiwhS5MceqEsjM7C81B9dTCEor1BGbJWwVORnB0XPGOrZNTXqGSpYjzI2NrIGQ9uPFrkRlu3Ve9qtR5mtbP6JGMiMdB7uhHe39ovSmudAR9HiMP8feC5VVWtHcs54pwfS1DGe0ZVVZf6Y19IaLI8X1XV4YOXgYsX9275L+3ChQ0uXry9Ep7dWbvM8Cw8C2McjXFh5nKfHWo6S56qpedyO3fbg15IayxKXH9PUEosK3/v5GdacO7ckMuXp0vP56gYzGKLdJSRPA4Orm3Bnf7Md8Kd/P08iFy4sHHkHWbdGMznAX+r//cxwPsI7QP/V///nfIxwPsWxqXnj4AtQmHfnx/DexwLi23Oao8SCQyLlDSTy+zKQrv3TjhYTby91xz6vDu9m99o67baNgGKDSnuWhbpZmuL3P+s63m8hmBI/g3wxVVVPQbQz0b6XeB5d7iOc8D2gWNX+v/PcwMDs7U1IElu/Y/twoXb1ypvOkvdmKUGTJEn13UWnwRZmYW+owMkSrJ5h5mVgz/TBz976678TOtwrz7vG3Enfz8PEzc0MP3W5QWEoO4fAEPg87TWi6d8GEFC8yRYuF03vD1vb89u+cTH6eK2QDtvj+VcN32vA9meBcMiOdY1bN7HW4C7+XkfRdwi7edGxvZmHsyQICaVAt90yONz4BW3vbJrXCR4KqucXXksQhxgFjl93NDAVFX128Bva63fWFXV55zgOv4z8AFa60erqnqyP/bxwNPAu0/wfU8dcYBZ5DSxbpr6JI0LVVX9MfBW4FVa682+FeEVwGsexqbKSORBYS0Dc5f4QmAM/AXwm8AbWRn0FolETh93VL9ynFRV9QTw4nu9jkgkcnzccqFdJBKJrMv9tEWKRCIPGNHARCKREyMamEgkcmJEAxOJRE6MaGAikciJEQ1MJBI5MaKBiUQiJ0Y0MJFI5MSIBiYSiZwY0cBEIpETIxqYSCRyYkQDE4lEToz7pps6EjmI1roEvhP4IoKUx58A31RV1Vv7x88A/wr4VMLN8teBl1RVtbtyjk8GfgJ4d1VVf/uI9xkB/5UwZ/0fntTP8zASPZjI/cyrgU8HPoWgC/1W4A294YEgQn8O+Cjgb/Rfv3bxYq311wOvA6qbvM93AFHF+wSIHsxDhNb6zcA7q6r6upVj3wD8o6qq/sd+uuZrCEPvMsLc8ZdWVTXpn/vFwLcBH0qYAvFDVVW9sn/sHwL/R/+alwHPq6rq3Svv88EcfaH/eFVVX3XI8R3gGxfn0Vp/N/BPgY/QWj8GfAHwcVVVPdU//grgrVrr8/0IHAd8LPDP+v8P+0yeB3wJ8KOEMTmRYyQamIeL1wH/Umv9jVVVLcYTfAHwE1prAbyBMADvQ4EC+PcEL+FLtNYfQthqfH5VVW/UWn8c8Lta6z+squrX+nM9kzAFYgvYN/6gqqq/6s+5NlVVHRSUfzbBaDxJGGXsgbevPP52wjSKjwZ+raqq7wNYmYKxj/5n/mHgW/ufORqYYyZukR4uXg8MgM8E0Fo/CrwQ+EnCHf55wDdXVTXpPYBXAF+ktR5WVfUe4EJVVW8EqKrqDwkeyapnMAZeVVVVd9xaylrrIfCvgX/bnTi63wAAIABJREFUC8OfA/aqqloO1u7nmO9x/YSKo/hqoK2q6seOc62Ra0QP5iGiqqqp1vrngC8Dfgn4O8DvVlX1Xq31JxBuOE8fcsd/FmH43ddqrf8R8AEETyEDVie+7VZVdfW41621Pk/QaH4KePlNnh4Ghd/8nI8QYi+fescLjBxJNDAPHz8GvFFrPQD+LmHbA2HGVV1VVXnYi3rD8m2ELdVvVFVltNZ/fOBp10+Fu/b624nB0G/Nfg34PeCrei8FwryssdY6XRzTWqfAiPVmaX03wRv6r2s8N3KbRAPz8PHbhHlT/4CwPfq7/fF3AYXW+rlVVf05QG+ERlVVPU2YU/V7VVX9av/YGPhr677p7cRgtNbngF8FXl9V1bccePiPCZ7K8wlxIwjbNUuYa34zvgzY1lp/bf/9AJBa6xdVVbXuFityE2IM5iGjj438OPB/A7+82NJUVfWnwP8LfJ/W+oLWegP4AeDn+pe+G/hwrfU5rfUHEtLB7yNsl06KVxKyXgeNC32M6GeBf6G1fkRr/cz++T9eVdXBOeeH8WxCzOmj+n8/TAhyf9RxLT4SPZiHlX8PfDvB0KzypcAPAn8JNARv50v6x36YUI/yV8DjwDcAF4Af1Fo/TYjRHDdfCVitdX3g+FdVVfXjwNcS0up/Qsgu/RLwTxZPWnldQvBOaoCqqoqqqh5bPaHWeheYHTweuTPi2JKHEK31JwE/A3zwSkwjEjl24hbpIaPfSnwf8P9E4xI5aaKBeYjQWn8rIZPzFuD77/FyIg8BcYsUiUROjOjBRCKRE+PUZ5EuXty7ZRdsa2vA9vbsJJbzQBA/nxsTP5/9XLiwIY567KH0YJJE3esl3NfEz+fGxM9nfR5KAxOJRO4O0cBEIpETIxqYSCRyYkQDE4lEToxoYCKRyImxloHRWp/6dHYkErn7rOvBPKW1/qFe9SwSiUTWYl3P5KsI8opv1FpfIWi4/kRVVf/9xFYWiUROPWsZmKqqXg+8vpck/AyCbOJbtNbvJmiL/OSaIj+RSOQh4paCvL1a/C8B/5wwFOu5wKuAv9Jaf8/KQKxIJBJZvxdJa70F/K8ELdMXEuQVvwH4D4QxET8E/FuCKlokEomsZ2C01j8PfDbwGGFL9GX9nJwFk37q3+PHvsJIJHJqWdeDuQT8z1VV/e7BB7TWW1VVbVdVtaO1PnT0RCQSeThZN8j7lYcd11o/C3gncLZ/3k8f39IikchpZ90t0ocD/44wdyY78PDbjntRkUjkwWDdLNIPEcZVfClhet/fAb6TEOj9jJNZWiQSOe2sG4N5PvCMqqparbWrquoNwBu01l8AfC/w909shZFI5NSyrgfTAAsZr1k/jBzgF4HPO/ZVRSKRB4J1DcxvAb/QF9L9AfC9WusXAP8YmJzQ2iKRyClnXQPzj4H3EOIv/xT4W8D/B3wH8I0nsrJIJHLqWTdNfYUwJxjgT7XWzwGeAVysqsqe1OIikcjp5kgDo7X+X9Y5gdaavj8pEolE9nEjD+YXD3zvgYPzTxwwBTaPc1GRSOTB4EgDU1XVMj6jtX4x8EXAKwmzjQvgw4FvAX7mhNcYiUROKevWwfxL4PlVVe3230+AP9JavwT4z8DrT2JxkUjkdLNuFukcMDrkeEHfhxSJRCIHWdeD+WXg17XWrwXe2x97NkFK81dPYmGRSOT0s66B+UrgFcBXAx8I5MATBMPzLSeztEgkctpZtw5mBvyz/t+RaK1fVVVVNDiRSAQ4/sFrLz/m80UikVPMcRuYg3UykUjkIea4DYw/5vNFIpFTTBwJe49pO0vdWaz1KCUoUkWWqpu/MBI5BUQDcw9pO8u0NsvvrfVMbfg+GpnIg8Bxb5Eit0DdHd6IftTxSOS0EYO89xBrDw9ZHXU8EjltrGVgtNbrau7+mztYy0OHUofb46OORyKnjXU9mO/WWh/Wi7SPqqr+yR2u56GiOCLOctTxSOS0sW6Q9/8EXqe1fh2hF8msPlhV1Z8d87oeChaB3JhFijyorGtgfrD//wtWji0EqDzXJg5EbpEsGpTIA8y6BuZDj+PNtNZPAWfYX5D3o1VVvURr/SnAq4D/gdBI+b1VVf3wcbxvJBK5N6zb7PhXAFrr5wIfVFXVm/vvRVVVt5Ly2AJeWFXVH60e1Fo/E/gFQmf264CPBn5Za/2eqqp+5RbOH4lE7iPWzSI9orV+C/DfgDf2x54N/LnWWq95jhGQAlcPefjLgPdUVfVDVVXNq6p6C/DjwNeuc+5IJHJ/su4W6bXAnwGfCzzWH3sM+Cng+4HPWuMcC+W7V2qtP6n/+heAbwI+BvijA8//I8IM7EgkckpZ18B8GvBoVVVTrbUHqKrKa61fSYiXrEMK/D7wZuAfAB9CEAz/1wRJzj898PwrwHluwtbWgCS59SDphQsbt/yaG9F0lroxGOtIlKTIE/JjDN6e9PkPctyfz4NG/HzWY10Ds0cwEAd5hDWrd6uq+gvghauHtNbfSthyvfmQlywyVDdke3u2ztvv48KFDS5e3Lvl1x3FZNayPWmxzqGkJE8laaIYFsmxZIgO9iwtOK7zH+S4P58Hjfj57OdGxnZdA/MbwI9orV8BoLU+D3wk8F3AG+5gbe8hGJKnud5bOQtcvINz3xXazrI9abAu2ELrHLPGMQDqThyLAbhRz9LNzn+73dqxyztyHKxbyfvy/rnvJEwSeBr4FUJcZq3qXa31C7TWrzpw+COADngb8LEHHvt44K1rru+eUXd2aVxWaTp3bD1Ft9uztPB8Fs+z1jOtDe1Nmimb23xdJHKQddPUV4EX957LhwFz4C+rqroVP/Fp4OVa6/cD/4oQg/nnhADy64Bv01q/FPh3hK3UlwJrja+9l1jrUVJcZ2Ssc8fWU6SUONSY3Oz8t+v51M3127F1XheJHGTtbmqt9aOErM5nAy8GvlBrfWHd11dV9ZfAi4AvBi4BbyLEX76xqqqLwOcQjMrTwGuAl1RV9Tvrnv9eoZQ4NNiqpDy2nqLb7Vm6Xc/HWHdbr4tEDiK8v/kfjdb67wE/CTxF6EUSwAcR4iZfXFXVfzzJRd6Iixf3bvmv/k6CdAdjExLorKczlqbfLikp2BrljAbZTV9/kjGR3Vl7pOczPmRtC7Iy46mnr/98bva6h4UY5N3PhQsbR7rS6wZ5vxt4aVVVr109qLX+auAHgHtmYO4mhynQWSBVAqUS0kTd8OK/EwW72+lZKlK1PP/B4zd8XX74n0Xs8o7cKutukbaAHznk+I8Cm8e3nPubo2IaDhgPMoZFuDCntWF31l4XFL3bCnZZGlLli1iNUmKt1HZ+m6+LRA6yrgfznwjVum88cPxvA794nAu6n7lRTGMd7+ReKNjdbrd27PKOHAfrGpi/An5Ma/2HQEWQZ/gw4AXAT2mtX714YlVV33zsq7wPaDvLvDG0xqJkCOymfQWxUmKtjM3tZoMikdPKugbmbwLvINTAfOTK8T8B/qeV7x/INMPCO0mUoDVgnWfWGAZAmqgQ6zik0hb2eyc3ionEwrbIg8i6dTCfus7zDimkeyBYeCdpohjQF9E5h7GeQR68l8m8BcSyTWDBqndylIIdEMeXRB5Ijnsu0ssJmi4PFEfFSOrWkihBmijyVDFrzLJNYGFkDmZeDott7M7aQ8+/N2tJUxW9msip5bgNzAMZTFjETjpjma1UubadYSZZGpTg3ViazlHkydoG4TADtnivTSmXz4leTeS0cdwG5oGMwSxiJ023v8I1UeHibzpLmqjlP+CWCtIOC/42nUXJ66sITqJc/2D8Zxx7jiLHRBwduwaLC3oybzEmxF4SJa6V1Cf7DcHBrNDqBWy9Q3iQUi63PavB30VF8NVJw7DI6IzdF9M57pT2Yen1SV/DEz2lyJ0SDcyaZKlikCdY50n6T80D89YwlPulclbjLqsX8OoWa5AnQDAsqRJY79idtMxbS5kphkWKlOzLVsHxp7Trzl7X5rBhytjYGDkWooG5BfyBaztNJJAgVypeD8Zdduct89pinaNu7NJAXGlqijzBOU9nHIMiYd4aQOCcp8wTut5Dajp3ZND4KBZeU1DBCx7XYXGhujH74krWeabzlqYxse8ocsfEIO8toIRkkCfLNLWSksEw9CBtbeTXPb/tLJNZt/y+s5ZZ47DW4YDOOiZ1h+kczzw3xDkPeOatQ0rBqEx7z8LdcmPktDb7PKa2tyGLLdbiPOaILddRxyORW+G2DYzWequqqu0Dhx/o2dTB+1D7YiLXjl9j4T3sTFomsw7rgsGoG8e87jDeMSyDdzCvLeCZ1R1JInHOY4zj8k6NlCJsWcqU8SCj7eyyQ/pGBmdRt9McCNYuPKHV7c+iePAgSawujhwD644teZ7W+q0r3/8scFlr/bTW+gWL4w/6bOp1dFlWVeSaNngRu9OW3WnLvO24eLWmrg3Z8gL2pEpSd5Y8URjjqDuL8wsJTo91IfC6jspc2xu2nWnD7qylM9cyX9a55WuXa88TBnmyzFgpKRkW6ZEd1ZHIrbDuX9EPECQy0Vp/PvAZhEbHjwdeDXzKSSzufuNms6TbznJpp+77lWQfUwnB4K5z5LkiyQSuty1SSsaDnM5arHE0xjJtDfXcUuSSaW0os3Du3VlHechFv+qNXAsoXzMgdWuAhDSR14zIindS9IV8q15ZlipcDPBGjoF1DcxHAZ/ef/1i4Geqqvqdfhjbt53Iyu5TjuoyXlzcrQkehXUOYxyzxoZgcAKjMsU68N6RZUESIU8k23sNBodzHuEhzySDIiVPJFIKZo2hM+5QA7PqjSy2Rouq4jxRzNuwpjQJ0w5gv8d1mNEcDTLcYfumSOQWWdfAtEDWz0T6LOCr++MptyC7+SCzuLhX9XmzTDFrDVJIEITsUCapW8+sDgYgzxLGQxDe0fmwHZJSIoDGOJJFjc0RMddVb2RhbJZVxTKsKfRMJUdWFx80mic5bynycLGugfkd4OcIEwA88CattQK+HfjjE1rbPWPdzubV503mHXkql95DZ4IHM50bskSSJZLagxAhgDqvLc7XnNvIyRNJ5yQYiyC0IHSdIU8NzmVIKRhmN1eZW60IXlQVj8q7I3UZu8Ejh7GugXkJYQLAGeDzqqrqtNabBBHwLzqpxd0L1pW1DMPWmmVxWmtD6jpVkqa1XJ02GBu+T5MQxE1s2Ab5/lzOebb3Qhd2oiBJJFmqMNaFgK6DYelxzjPFIKctHsI2zMMgV0sPJksVEtiZt8s1LTRrDgahj9sQ3IkUaOTBZl0D86Kqqr5m9UBVVTta648BXkaYl/RAsI5wVBi21q4MW/N465kby+WmW8ZijHWMy5zRIGF+1fLU9ozWOISAC+OSYimx2THIEkoSrHVM+9oVKT1SBgMigb26JUsUAkhSyby11DtzlBDkmUSJ4EEt6nSazjHIk0OCwIHjMgR3Mhgu8mBzQwOjtU6AHHiN1vqnub6QTgPfQcgkPRCsI2tZ98VvqySJZHuvZm/e4bxHCoHwgr1Zy7w1OO9wnrB1sp5LzBm0KZujHClCX1NrPU2vnGc6x+UdjzGe0SjDuZDOLtLwvsY6dqYtEsnWOEMKycYwZZAnjMrQutAZy+6so+u9la6zyGNooDzoBdWNua42qDOWydzFLdNDzs08mJcC39N/PTniOW85vuXce9aRtQzD1uR1RmbaGIb9xR2qcg2z2jJrGkZlinOOWWPwvh+87UAikFLQWcfQWKZz2xsbh/OeK3s1WaIw3tEieKqeMWs6cJAqSZ4nSBXOV+RqGdidzDumdYeUgkSVgGJv3jHIk+uMwa00UB7mBS26zBfnXVQQq977ilumh5cbGpiqqr5Pa/2TwOPAZx7ylDkPWJD3sM5m6zyjQbrsMFZKIAXs1h1NG+7kiRLUtWWYpyglaFwI2M6bjrq1SCnC1kb11brO0RpBbQyZUszqjicuTfGAlITzSEHTOXbnLalUtMbQdIZ5bZBS0hiHkILWWLyDq5MmxF4yRWdcf4GLZcOkknIpLbHKqvEMhXoN23vNoZ7HYduhPN1/3oXBOZiNilumh4+bxmCqqrqktf7QqqqeANBap1VVdTd73WllcQHszdr+LixDMFXI5Z07DFtzCASTWcukNrSdozWGadOy0Wds6jbEQqQQzOZdqIlRCmTYLqVKYY2nw1GmKTt0gMMYz1xaQtmKoG4N2VAiHDjvEQikEDjv6ayjaSytsQgJmQoxmrqzFKliYxB+xU3nyFO5r7FxQbESo7k6aWgR7EwblJTUqeTMKL/hZITVTu/wuL9jTynyYLBukPcprfV3Al8BnAUKrfUY+H7gZVVVHbV9OpVkqSJN1VJNbpXFHXyQJ1zcnrHb9xp5F+INs7mlqS0GT6oEeZqiUri811B3BoEgTQRKCAaFCq/3nkQ4rLW0zoP1GNuxOczJs+Dx1LUlSSV5miCFWFYH153F+1CJmyl5bcKBD1uvRR2NdY40SRkpsTQEqx5K21mevDxjd9ZwDoExDhKYNQ6pBOfTEtiv7rfa9FkWapkKj9MTIgvWLZJ7NaE94KVcK/mShNGx33sC67rn3CjYu3hs2hiKXPVbD0drHGUhqY3F+/BBZdnigg5bJiEESkks4QNUSuAd1CZcyG1jsfgQ3xEeYz2jMqXIEjYHGaMyYWsj58woZ1Sm5EoihCBJJWWeMCxTRmXKoFDLDBSwbBMYlxnjQcbWRs54kC2Ny7Q2TOqgDWycY96aYGRYNGQGilQtYyzL3ibncP1sqMVzDiNOhnz4WNeD+XLg+VVVPdZX81JV1VWt9VcQRpc8cNzoLtway5XdJkgxiBCr6azHe+hMeM2oF4wCQdNZRoMUY2E8zDDWMa8NO7OWQZGyN297z0YxGviwfUpD/9B4mDEoMpK+QjhVks57vHEkqWQzy0kTyeYweA9Xdmuc9yRKcWFcLCuLy0Ltm864mgmaN6bvnhaslgw3xvYe0LVjWapQfZf3wXqbRYzlZj1bkYeHdQ1MArz/kOMNsHF8y7l/ODjDaLklsI7tSc3Fq/NrFbvWMatN2LqkwVDgCcdaQ906BAT5BQHzziIEpKni7EZO3YVsU2ctQkqwjnGRUOSK8+OSLAkXpzGOvVkHLgR5z2+keA/GeHYmLXVnQ2YpldSN4fJeTZYrHt0aMFqp5D2YCWqNpTWQSkGzkhhzfZ3P4EAPlJSSUZktP5NZY1B9jGexTYqTISOwvoH5L8A3A69cHNBaj4DvBn7/BNZ1TzhY35EqgSOovjWdRQrBlWlD3fZNhUoym3dc20B4jIU8hVlnSAQIIVHS0RrPKFN0/TwlAO+hdY7NMkPQMal98FSUwHjP9l5D04a09ZmNnK1RQVkmpEawN+u4steQKkUiYdYavA9bKyETzo0LilxhjaPrty+rnsWCzljqJhi3haMyb2xofUgUqRTLoPWCRe3LfiU8R9Ptf5+TILYknC7WNTBfD/yK1vrrgFxr/afAhwBPAp9/Qmu7qxxW31H3Y2KDcFNIGdedW2qsGO/Dlsd5aimRAvJEgRSUQlCbjiwVjIqCq73ebmeDoFSaSDaKlHZxPgFlnmKNBRkK9AQC7wwIuLLTYown7WUgikLRGU82krSdZz7vsE6gkkW8JViLhezmwqjUneXpKzOM9XjvcXhkXz9pbPDGZBr+LNJU0lnP7rxF1ftFyncm189yylN1oqno2JJw+lh3suM7tdbPBV4EPIdQ//Iu4E1VVT0QMy4O1nesFos1rWVad1zebdjenWEcJEKwNzd4HN6HP/BHzw8o0qCtmynJ9jR4QRuDnM46nrw8BQTzxiIFPL0zp2ktsyZkghZbks5c61kKulOhwzpPJYmUJEmodRkVCZ2xWOuYzA2bo4w8SXDes9MPc1umzBtzLftjLM55Zo0lUyHTlEjJ3Bis96SJ5MJmyBrtzVuch62NfHlBD4ukr33x18VhTjIVHVsSTh9ry5ZVVTXXWr8NuFJV1ZsBtNYPTN7xYOq1bsLURgNMmi6U71vLfO5CVa2xtJ3BeUGigpfjnvIIEdLE03nHlb2W3VnDZGaWCnUniZKi131RDIqEMldc2CwZlilKSsbDlFRJNgYpUkma1mClQJleTsJ78lRR5ArXhR4pgN1ZQ5rIZUra+jBY7mCdC5xsKnqdNo5bIW63Tp61DIzW+hHg54EXECQbCq31s4Hf1Fp/TlVV1Qmu8a5gvdsXU+ispd8ZYVrPvOl7hHAhjVuHuMy0sTStDY+35tjEsoVgma1xvWymu8mprfPY1lK3lp1p8GDe/cTeoc/NU9kboYTzmwXP3CrZGpeMSo/rf4aFPnBr/b6U9GTmOD9WHOZPnGQq+jjra+J26+6wrgfzWuDPgM8FHuuPPQb8FKHY7rOOf2l3F3Hg71b2F3bbWGZNx86k5i8e3+Xi1ZpJHcr/b+aUCGBQJKQqbGtSJZB9rMY6h3MhbpGmEmPCVitRkiyTYcxImuCFRyDpjCWRAiEEjQmFdJ1xuF4w3HtwLsRRms6FEbatPXLUZnhOy/ZeyxOXZstaAyHg7Ljg7EYeUuR5wiNbBYM8xF5a6wBBlnSMBykO7poHcDCzt3r8VonbrbvDugbm04BHq6qartTBeK31K4EnTmx1dxEpFyNJggdwcbvmsacnvPvJXd5/ZbbMHB2FkoJBnnBmI2NUpjyyWdBZx4WtAU9dnTGbdzStIU0S8J55bWmdIU8lUgnSJMFYR6YUWxsFgzJINyDAGovPQxWvUhJ86LIWCs5s5AzydCnVKYVgWIau6lRJPHBpp+Z9T++xO22ZzA1XdmuazjKZm32i4BBiPpd3ai7v1If+jKMyZXOYsTnKuHCm5JGtkgtnSs5uFJzZyEmU3Ffgd5wcZ33NcW+3IoezroHZI8hjHuQRHoBZSM553vHuy/zF4zu85/17PH5pugy4HkbS/2EPioRBkXBmlPVdy3L5tRKK3WnN1b05l7ZrpvMWYx1ladksc5JEgEtQSTACQgq8CV3ViBA4HRYZwyKl68WspvPehc8VZzYSRmVOWSQoCfM+bWysY5CnnD+TY0zwlM6MMjoz5Ny4oDOOS1dr8iwIYxVZQp4pJvOOq5OWpgsSD09fCdo1q1jn2Zm2Yfv19OGfjRSCjWFvhIb50hiNhxmjIqXIw9TKMxsZm4OM/AilvqO4kSbyrRie2M5wd1j3t/sbwI9orV8BoLU+D3wk8F3AG05obXeN//Bb7+JNf/C+Qx+TUvDMrZIiD702G4PggRgXJjvO5oY0FUznhkQpUhmCpDuzFmM9u7sN87YLWSEftlxT0aFEMEipkjgPSoJIJGkaVO2sc3RmoSdjyVNFlkisDd6LTT3jQcqwTJk2HakK40ayJDQnbhQZtbFMZmErZnHLWp6tcYYQYhmDWWSaEiW5cKbkeX/9mexsT9nea3js4oT3PT3h8UtTdiYtu7O294S6Q7eIzoeiv5DGvnGLWtZXKp8Z5WyOst4oZWyOwvZs8f14mJGoo7taDsZT6sawM2n6gPXhOsTHud2KHM26BublwOu4plz3FOAIMZhTPwtJrDhhozLlAy8M+YDzIz7wkSHnNgqsdzx+ccLurKNuLPPOkCooUkmR5xRJihCzZb9PnivKTnHVNkwag0LglcA6QWscSWfIs5QylVjrsHiSJCFLJWkCXWfxXoG3WA9KgHeeWRcCyVujjEHWb4saQaYURnrciszEzqRBJoLdScvlnZpuUaLrQIow83o8yEItTuuY1h3DQcKsDtMkhRAhFjMu+OsftLVvHEvYVnn25h2TWUvTOa5OWqbzjnlrgiGatlydNPsu/IO0xnFpp+bSIduxgwyLhM1RzpmlIcp745SRJMG4jgdp0DtuF4PnHGlyePA2tjPcHdY1MPOqql7cey4fRqiD+cuqqg5PUZwyvuBTnsNHfMgWgzw0Ehp7LaPUdIZu7nHGsT1pSJVE4LFILu02nB/n5JmgzBOUFCSpYD7v2KsNjbFIQKYC2woQkKqFCJNj3ljwHplIhCfo8kqF8x5jLU0HgtDwWGSKPE/ZGhZBYKq/oYfBbg2DPOHcRkGShIDxlUmD9DBtOhCeNBFkQuEQDIuc8yKsp2mCOt+FMwVFnpAlkqevTFHWLdsLslRxfrNYGoudaQMIhnnCRpkuJ1DmqeKRrcHyc53MWi7uzNmddVy6WtN2Jsh8tpZZbdibtezOOqbzjp1pe108aJVpbZjWhicuTW/4u1RS9A2fCaMy4+xGzsYgbNOesTXY5ynFdoaTZ10Dc1Vr/XvArwNvBt5RVdUDEw1zznNmlIc4R22Y1x2d87StYa8feOaEYLNMmbUmxEJEqIDdmbYopcAL2tZQt5amdQxLhRBBDFw5gVJQKkUiZGhaTEKxWmdDJ/LurCNLBEpaBkVCkSV9GX/YiszqjrK0ZEnCIJM0hWNUhNjL9k7DNDfMGkuRSowFY8OERyHAWlAqKOid28wYlNly5tJjT09AqmXWbN4aprOOrun29S8tLsTdeUvdWGpjEB4GRTAwdWuXMaIiT5AQdIt7Ma7RIAUSxmXGaJAtZT0XEw+899Rt0KPZnbY8eWXGzqTlyu6cy7sN07pj1huZ2Q28Iuv8cpImzG74ey/zED/7rI//ID75I591R39DkcNZ18C8EPhE/n/23jxKsqwu8P+8e98WS2ZWVlV2NTTNIssVZn4om+CCuIyOowwiym/w4DKjoKKiB1EZpfWMR+0ZUDyAIIobCujR8TSKNiiIgiwi+GMTkCvSdDdNN13VVbnF8tb7fn/c9yIjozKzXlZlVFVW3s85eTLjvhcvbryM+43v/a7wZGyR765S6p3UAucwx8HMdkNMM5sk6AtBYWy9lcIYNoYZeV7SCXy8BWuMTbKCYVKy3Dd2geQlYWC9N1RAaWwR7qwpk+lBCBUVUWjD+X3pUXoVprBbBl94DMa5jWmpKtLMpg6EvsSUFaM8Q3ghSVowGAvSoqSoDEVity15Yc8bpDlZVhAGNrJIZMWZAAAgAElEQVS3KKyHZHMssHkJPr04mLjIs8IWxhLCs+93lO2YVyQ9wfHFiDPrNto4yQrywgbhdUJ/UtlufZyxtpEwyg2mdskbY9ggI/DFRMA0Ng/P8ybaRxxa43kUSM5tpDZPaopeHEwa0a0NMs5t2G3W5ijj3GbCZq0VDcbFef25pxmnBeO04E3vvs0JmDnRNlXgo8BHgVcDKKW+CFtC80exbWUPrZ452w0xrRfaMMmRwsPDBrilecHaIKOqbHMjD1sT12BYHWY2wrWExJTEkaQoQEjJQkfUfapLpKyQnm1j4sFkG+ZhF39VVUSRb9uUJDl+vRcKA2sfEMJmTofS2kHy0hYXDwNBUX+pF6Uhy+pUg8oaosdTfZqCugDVsYUIAF8I0rLE92sBVx8P5Pl5Rc29CnxJ5EuGSUGSF6SZYalnBUbTaSHNSlaH2eT16iRxijr7fKe2u9vtNbbU5zjNt5rPYY3uoi4L+sBTCzzwFJPnJ3lZJ6bazO7Al9YdP8onqR/rw4y1QcbGMGV9kDFOC77y0fe79A+SY0da+wiVUstYLeargK/GdhT4KDbC99Ay2w1xmOSEvmCYgFdXjVrbSBkl1nbg+wJBE+lqiAKbq7TYlcgoQHi2FswgzenGPo3XU0qPOJQ2uTErMPXzC1NhSkBCJ5D4gVcHzQkC3yOOAqrK2mCowI8llVdRYTWWhU5AkgkK31BhvThCbG3HwGpMeNZr0xTpa7QWgKwsEYU3EQYAC93gPDfu9GNPeghhSzkYY4uLj7OCrrSCpigrvBmPb+ALfCE5vhif1whuNvDNlvc053mqwjo9IZxJU2jsKYvdcJvLuhv7HF+InK3lCtE2VeDjQAf4APBerObykWsh0XG2G+JiN6SM7ePVQYonbXnKXuxjKlsvV/oevrCLv9/xSdOSOPbxpWeNtYEgLG3wXGkqji1I+pEt1bBBzmInZmNcYKqQJCsZFzkBtiKdEILIFxjsAj3Wi+qsZ5B1Nbx+x6cTWZd5XPc9Mqaqo2whEAJfCtYGtkxn5Pt0Q48o8Dm+GDHOSjbG+aQ6HsJjOMrr1iiSk0sd0nF+XkzI9L2ajnyediE34770iEMfX1jPWdPKpdexc55lVpg1Aj/rBNZYXm8TGyG42N0pLMvijLdXD201mLuAxwA3AvcHTgF9YP2gJlLnNr0a+Aqsl+ovgBfMu8D4bDxEs1XqxYFtHWJsH2lBWMenVHQ7PlCRpIY48Dmx2KEoDaWx9Vw84VEan4U4wlTWW5Tk1g6zshTTjwPkekqvU2JKw9ogRXgeUeRDBZ2Oz1IvwBjoRgFJUVDkFb1OwEIvYLkfs9SzcS7SsykDSVbg+4JO6NMJJeO6k8HptRG+tO/r+EJMJ/aJMlskvBfbRbrcC4kDQS+2pTT73ZB0nJ8XEzJ9r4Tw6IQ+gySnLCvOriUEgcAY6+qPQptoOay3OI2AiEN/x1iTnQLfAl9y/ckeAtgY5WRFSehLFrvBNgO04+qlrQ3mm5RSAngs8BTgB4E/UEp9Hni31vr5BzCXW4BPAA8DloA3YdvV/s8DuPauzMZDxJFPN/InbWEH44Ju5JN5kk5UkeYF0hc2YK0vWTlmK86d20xszV1hK/6fXIpZ7EasDVMWakE1Tm1WdQWcWLJh9VlR0ol8WxpCWs2j3/E5vhjTqcP9B+OcsoTFfsBi7YEJfElQCKTwJt0PhIRuaN3leNZOURpDllf40kYAN9XpAl9MejtFoQ24azK+fSm2ldfc6V5J4WGEIPJl7aIWZIUhyQsG45xj/RDPswmVTesXKTyW++GO2sVegW9hIJ1AOaR41T7LCCilQuAJWDvM9wAP11rvL977/Gs+HlsZ75TW+r567DuwSZYntda7BkicObO5b3f5ysoCZ87sHcKzMcomJRxWN1NOr47Ia6/QYl3/9viC1SRWBxlpVkzqrAjhcaxrC1GVtcFWCA+BtVNsDDMblVvZ0HtReYxzGxPj+zamph8HrCx3re0Fb2K0nGa3pvbN3GGrrg00uUShrVa3x/Xa3J8sL7l3dczGKMNMNaDrhD5R6LPUD4nrAlRtA9kOS/mENvfnKLGysrBrfkVbG8zTgK+sfx4HfA6bPvC/6t+XyuOAzzXCpeZDwDI2sO/TB/Aa+6L5Rg18GzzW7wSc20wmdoqlhYATCx3CeksFEt8X24ovrQ9Tlhdi+p1gUmcmCiWh71MYQ5oXLHYCpBR085KigsVuSBRYV3Lg2wTMKJDkO+TN7BbWPr3VaGwZkxYj0moR+7neToSBrOdpryOER+Tbe1DWLVzC7v4EhLOdXHu01TxehRUkvw08S2t9F0DdG+k9wKMvcR4ngNWZsXP175PsIWCWl7v4OxQ+uhArKxeuVZ7Wbs+iNJySgkfWi32WsGM7BczS6UV04/NvsS8FceSzMUi55+yQylTEdXBdVXd9jAKf6453J683PZfm+TvNZa/5+FKw1I9aXa/N/Qk7Ib1+TDHTQlcKwfHFePJa1yJt7o/jAgKm3ro8EWvU/QDQA56mlGpOeSi2hOY8aNSuPbdAq6t7R2vuxMWouBmQjc+vQws7xXBYAulxdpieN96LfbJ6MZ/oBva5hSEttq4vhMfG2s7vba+57DWfXuzv+LzZ67W9P1leMhyk53WL7EY+IdWeczzMuC3SdvYSthfSYHrYYlIB8FM7HB8DN130zLY4g9VUpjk+deyqZq/EuQvZFeaRdHe5EvnCQHKsH00MzeDZDo+dnQ25jqPHngJGa/0u4F1KqVu11t8yx3n8M3CDUup+Wut76rEvw1YduW2Or3tg7GY/aGNXmIft4XLZM8JAcmKpw4m5v5LjMNKqdeychQta6w8D7wdeopRaqlMRbgJedS0lVTocR422vakvB98BLAKfAf4euJWpRm8Oh+PwcUnxKweJ1vpu4OlXeh4Oh+Pg2HegncPhcLTlatoiORyOawwnYBwOx9xwAsbhcMwNJ2AcDsfccALG4XDMDSdgHA7H3HACxuFwzA0nYBwOx9xwAsbhcMwNJ2AcDsfccALG4XDMDSdgHA7H3LhqsqkdjlmUUh3gl4FnYkt5fAz4Ka31++vjx4DfAL4W+2X5t8DztNYbU9f4auANwG1a66+ZGv8v2JIgs3U9v05r/b55vaejhhMwjquZl2Lb4zwFuBvbJ+vNSqkHaa3H2CL0i8CXYms3/yG21c2zAJRSLwCeD2hs2ddpjgMf11pfasF6xx44AXOEUEq9A7uofnxq7CeA79da/4e6u+arsN01Q2zf8R/RWg/qc58FvBh4CLYLxGu01jfXx/47tknen2NbCz9aa33b1Os8CLvQd+L1Wuvn7jC+DrywuY5S6mXATwKPVErdBTwDeILW+t76+E3A+5VSJ+sWOAZ4PPCz9e9ploG1C9wyxyXiBMzR4nXAryqlXqi1bloBPAN4g1LKA96MbYD3ECDGagS/DXynUurB2K3Gt2qtb1VKPQF4j1Lqg1rrt9fXuh6rSSwD21oNaK3vqK/ZGq31bEH5G7FC4x5sK+MK+OjU8Y9iu1E8Bni71voVAFNdMKY5DlynlHon8CXYXl8v1Vq/YT9zdOyNM/IeLW4BusA3Aiil7gc8CXgj9hv+0cBPa60HtQZwE/BMpVRPa307sKK1vhVAa/1BrEYyrRksAi/RWucHXUtZKdUDfgv4nbow/AlgU2tdNufUfcw3Ob9DxU6sAZ8FXoAVjL8E/L5S6hsOct5HHafBHCG01kOl1J8B3wW8Bfg24D1a6zuVUl+B/cI5vcM3/v2xze9+SCn1/cANWE0hBKa7q21orQ9826GUOok1yN6LtanshccFemkBaK1fCbxyauhPlVLfDnwf8Padn+XYL07AHD3+ALhVKdUFvh277QHb4yrRWnd2elItWF6M3VL9nda6UEp9eOa087u9bT3/Ymww1FuztwPvBZ5baylg+2UtKqWCZkwpFQB9Lr6X1u1Yjc5xQDgBc/R4F7bf1PdiF9O31+P/DsRKqYdrrT8NUAuhvtb6NLZP1Xu11m+rjy0CD2v7ohdjg1FKnQDeBtyitX7RzOEPYzWVx2LtRmC3ayW2r/mFrv3jwJ1a6zdNDT+SQ9KH67DgBMwRQ2tdKaVeD/xv4K3NlkZr/Qml1LuBVyilvhdIgJcDD8e6im8DvrFe9B3gV7GG0RvmON2bsV6vWeGC1vo+pdSfAr+klHo2dnt3M1Ybmu1zvhMx8Cql1O3AJ7Btc74Z+PIDmrsDJ2COKn8I/Bzw+pnxZwOvxho/U6y28531sd/ExqPcAXwe+AlgBXi1Uuo01kZz0DwHKJVSycz4c7XWrwd+COtW/xjWu/QW4Meak6ae5wOieay1joFfwRq834Q1GP8b1kP2wTm8jyOLa1tyBFFKPRn4E+BBUzYNh+PAcW7qI4ZS6nrgFcCvOOHimDdOwBwhlFI/g/XkvI/tLlqHYy64LZLD4ZgbToNxOBxz49B7kc6c2dy3Cra83GV1dTSP6VwTuPuzN+7+bGdlZcHb7diR1GB8X17pKVzVuPuzN+7+tOdIChiHw3F5cALG4XDMDSdgHA7H3HACxuFwzA0nYBwOx9xoJWCUUofene1wOC4/bTWYe5VSr6mrnjkcDkcr2momz8WWV7xVKXUOW8P1DVrrf5vbzBwOx6GnlYDRWt8C3FKXJPwGbNnE9ymlbsPWFnljyyI/DofjCLEvI29dLf4t2AZYL8VWO3sJcIdS6tfqTnwOh8MB7CMXSSm1DPw3bEX6JwHvxlY1+7/YNhGvAX4HWxXN4XA42gkYpdSfA98E3IXdEn1X3SenYVB3/fv8gc/Q4XAcWtpqMPcB/0lr/Z7ZA0qpZa31qtZ6XSm1Y+sJh8NxNGlr5H3OTuNKqfsDH8e24URr/ccHNzWHw3HYabtFegTwu9i+M+HM4Y8c9KQcDse1QVsv0muw7Sqeje3e923AL2MNva6Xr8Ph2JG2NpjHAqe01plSymit3wy8WSn1DGxzru+Z2wwdDsehpa0GkwJNGa9R3Ywc4K+Apx34rBwOxzVBWwHzTuAv60C6DwAvV0o9EfhhYDCnuTkcjkNOWwHzw8DtWPvLTwJfCfwj8AvAC+cyM4fDcehp66Y+h+0TDPAJpdQXAaeAM1rrcl6Tczgch5tdBYxS6pvbXEApRZ2f5HA4HNvYS4P5q5nHFTDb/8QAQ2DpICflcDiuDXYVMFrriX1GKfV04JnAzdjexjHwCOBFwJ/MeY4Oh+OQ0jYO5leBx2qtN+rHA+BDSqnnAf8M3DKPyTkcjsNNWy/SCaC/w3hMnYfkcDgcs7TVYN4K/K1S6rXAnfXYjdhSmm+bx8QcDsfhp62AeQ5wE/ADwAOACLgbK3heNJ+pORyOw07bOJgR8LP1z64opV6itXYCx+FwAAffeO35B3w9h8NxiDloATMbJ+NwOI4wBy1gqgO+nsPhOMS43tQOh2NuOAHjcDjmhhMwDodjbjgjr8PhmButBIxSqm3N3d++hLk4HI5rjLYazMuUUjvlIm1Da/1jlzgfh8NxDdE2VeDngdcppV6HzUUqpg9qrT95wPNyXCJZXpLkJWVZIaVHHEjCQF74iQ7HAdJWwLy6/v2MqbGmAFXFVscBx1VAlpcMk63vgLKsGJb2sRMyjstJWwHzkIN4MaXUvcAxtgfk/b7W+nlKqacALwEehU2kfLnW+jcP4nWPGkm+c5nkJC+dgHFcVtomO94BoJR6OPBArfU76see1no/0bvLwJO01h+aHlRKXQ/8JTYz+3XAY4C3KqVu11r/9T6u78BqLPsZdzjmRVsv0nVKqfcBnwJurcduBD6tlFItr9EHAmBth8PfBdyutX6N1nqstX4f8Hrgh9pc27EdKXeOFtht3OGYF223SK8FPgn8V+Cueuwu4I+AVwL/ucU1msp3Nyulnlz//ZfATwGPAz40c/6HsD2wrykuh/E1DuTE5jI7fjlwBmZHQ1sB83XA/bTWQ6VUBaC1rpRSN2PtJW0IgH8C3gF8L/BgbMHw38KW5PzEzPnngJNcgOXlLr6//w/vysrCvp9zqaR5yWCUEc+M97sh0QEvwDQvSdKCojT4UhBH/q6vMXtumpcXfX8u53u8klyJz89hpK2A2cQKiFmuo2X0rtb6M8CTpoeUUj+D3XK9Y4enNB6qPVldHbV5+W2srCxw5szmvp93qWyMsh3tIGtrIxa74dxeNwOycbbzsRmPE8CJEz3uG6YXpXVcqfd4OblSn5+rlb2EbVsB83fA7ymlbgJQSp0EvgT4P8CbL2Fut2MFyWnO11aOA2cu4dpXHVeb8TXLS+5bT8iKEik8okAS1NrgxXqcrrb36LiytI3kfX597sexnQROA3+Ntcu0it5VSj1RKfWSmeFHAjnwEeDxM8e+DHh/y/kdCnYzspaVYWOUsbqZsjHKyHZxMx8kjeaSFfa1SlMxSgvy5vFFCgRnYHZM09ZNvQY8vdZcHgqMgc9qrfejJ54Gnq+U+gLwG1gbzC9iDcivA16slPoR4HexW6lnA63a1x4WdjK+Tha0Zxf05QqKa2JlpBCUxkzG02b8IgXClTYwO64uWmdTK6Xuh/XqfBPwdOA7lFIrbZ+vtf4s8FTgWcB9wN9g7S8v1FqfAb4FK1ROA68Cnqe1/oe21z8MhIGkF/uTxSulhxTeZFsyzW7BcgdFo6FEwfaPQGns+MUKhJ3eYy/2nRfpiOJV1YVVYaXU/wu8EbgXm4vkAQ/E2k2epbV+0zwnuRdnzmzuW5e/mox0q5vprseWF6K5ve60MTYvStLcUBpD6Eu++GErbKzt33i+Hw6zK/tq+vxcDaysLOyq7rY18r4M+BGt9WunB5VSPwD8OnDFBMzVTJtFJKVHkhakeUlpqomxNY7a/msu7nWntzKBv2Xc7cVb7ux5CQGXK3V0aLtFWgZ+b4fx3weWDm461w7NImq0hLKsrFF1ZusjgFFaTLYmjbF1p39MlpcXNAa3fd0LbWXaXudi2CtXynFt0fZr8i+w0bq3zox/DfBXBzmhq5n9fKO3TTg0QDfyJ1sUKQRRIDAzz9vrW7+5bllWjNMCX55v19nJ7RzOzL8RYGJ9zH3rSevr7Bfnyj46tBUwdwB/oJT6IKCx5RkeCjwR+COl1EubE7XWP33gs7wK2K9a33YRlWW1bYuy23m7CazNUYYQW/pOVpRkBXRh2zVnrzcrLAWQlxV5UbI5zDi7MUYIj2O9iG4c7Hqdi0FKb8frOFf2tUdbAfPlwL9gY2C+ZGr8Y8B/nHp8zX4F7bcEQttFtNd500JgMM6JArFNaORFybmNhDjyJ5pP43ZO83LbudOvu5OwXB/nCCpyUxF1DUIIjDGsDVMCf+t1D0IIOFf20aFtHMzXtjlvh0C6a4b9qvVtF1FznvXkbBl6e7HPcNu1rW2m0UzyorS2m/qU0hhGqSGQgpItd/P062R5ycY4477VhLIydEKfficg8CWlMQzTkjiy84t8wTgzGFNtE1YHIQQagXxYvUiO9rje1C3Zb4TqfuJBkrTgzHrCxijDGIgCyTApJ0F4wMSzk+Zm2+9GIDSYytp0wimNoxfb75G1QcpglJOXJcZUDJOcjWFGXpRIIcjLrdfzfUEn9AmktELvgONZwkCy2A1ZXohY7IZOuFyjXLwvdGeu2U30xaj1s0bUWZqtSmHMRAg02G1ONdEcAl/SZSvSFiq6tSt7lBbbnhf4Acf624XBxiibCKVm+wOQ1jEwUSAI5Pa5+r5gsecTR/41k6jouLwctIC5Zm0wF6vW7+V5auw6pakoCkNaGIwxJGlj19l+OwNfThb7tO2mETxpVlJV1jaT5N62eZdlNUkJaLY/AMZUE6F03XLMMNlKIWhsPs424rhYDlrAXNNcSCOZ5UKep0mMSVUxzrbOy8sSX3rMKoR5UVJWHqubKWVlMFMeqDwvSQtD6ItaU9mKYQkDWaclWAOw7ws6+KSFASpCX062P/1OSX+xg8nKPYXoYY7EdVw+jpyAyfKS9UHK6mY694VxIc9To4V4M3qfEB6+LxAejFOb8ewBvhR0/WBiEE6ykiAQBJ7H2WFKnpUMxvZi0hP0ewG9KGCpH1IU1qYzTHKE8CZCpRv5HOtHk3sQBpKlfrRr/RhwkbiO9hwpAdMsjLhntwfzXhgX8jw1dh0hPDqhT1pY42sn9AmER24qOpFPJ/JZ3UwYJCkbwwxTax2BL5CeIC1K1tZT1gep1SqoiDzBiWMx9z/ZJ0kFo7QgCiR5WbIxKBiQc2Ihpru4f8Ot61rgaMuRMvJujjIbhr8pGYyzSYGli10Yu20TmvHBOAeqbYWcYMvz1LzmuDbSRqE/sXsMxhmyDqBrNA+wBt04kiRZAfjgw+pGyulzI8raZpPnhrQqYR2WFzpEoRUIaV4SBT4ry/bfbj1HFVmL9789Jic77z2Bi8R1nM9FCxil1LLWenVm+KrtTZ3lJZvjfPK4yfnpAhfTN252m5CkBfetjSkKYzWPUNYaw/b4FdjuebLu2oDVgU0V2ExKvCpnmOT04nCS6SyEhzEVhTGT+WZFSRz6Nmeoqibi3dQZ8kleYioziYkZZwW9OKgNylZbggpjDCeWOq3fK3jnvSdwkbiO82nbtuTRSqn3Tz3+U+CsUuq0UuqJzfjV3Js6ycuJRjBNmpcXtTCmtwmjJOfus0PuXR3xhXMj0qxgmOSM04JA2ujawdg+th6ecpI0mOUleWm1HGMqRuOccVYQ+hIhrMaSZAVRvZD9qfdgTGWjd6UdD32J8DyqqqIsK6qqIstNLUgsRWEYZwXGVAjhUZqKzXG+ZxLj7JaoqSGTzoxfCW9TmwRQx5WjrQbz69gSmSilvhX4Bmyi45cBLwWeMo/JHSRlaRfjKN2eRliaateFsZenZLqWytowJc1qgVGWpLldgEIYotAuyDQrKI0hK2xkbBJIjvWjbYt3enF4Uy7qsqyII0mVVmRFyfooJZSSE0sxgS9Z7Eakizay1wPiwKfC0OuEhIHAmGpi25n2Vk2C8YTYc5s4u/XZismx97KNsXweXidnbL76aStgvhT4+vrvpwN/orX+h7oZ24vnMrMDxmopdmE0mowUgk68uxt2rw9v4wFKaw2h2Zb4Xm03KQ2BsduewbgkLUp83x5rtmdSeAghJmH/ebkVm5Kail7sYQBfeqRpSVFWLPZCupFPVpp60ebcsNJFSo/VzYT1zRzfF0jhsbwQ040DotDH9yGUcpsXKajnEwViT/vJTvlS0zE5F2JegsAZm69+2gqYDAjrnkj/GfiBejzg4NMN5kLjsQl8yWIvJE9stbjZCNqGC314m+uVxto40qykMIZQSIrCxpoIYbdHwySbbHGmGaUFS/2Iwdi+VmNnsX/bkg39TshS32N1I6WorDALQ0nfDwl8QZZV9GLJUncr4zn0JUu9kGML0TYbyfJCROXBOCm3lYYIfLnnNnE6ink6Z6rfDVoZiOclCFzZh6uftgLmH4A/w3YAqIC/UUpJ4OeAD89pbgfKbPTshdT0C314m+dtDDMqUxEEElna+JWiMFDZiNlOLMkLiRA7LWA7h3ONUMFjkOZkmdV2sno7s9yPJtX/p8kLQ5IVVqvp2J+FbrAtb8lqSnYeAIudEOmdn/Ig2CqjGXbCbYKj+d144aQQdCOJ9MREM9lLUMxLELiyD1c/bQXM87AdAI4BT9Na50qpJWwR8GfOa3IHTROJe2Kpg8nOX2TTXOjDO7EpGEMYSuJQMkoLNsc5xsBiX3L98S79egsxGOXnXavZnsWB4HP3DjizkZBlBVXl0YkkaRzQjQOE55HkBUVuyEozMdD6nqDX2d4PLwokg7HdBuWFIau9RXkREPtyMp+dasGA1VBWNxLOnB3S7wYsdsLJfQsCydIOhvILaSLzEgSXUvbBRSJfHtoKmKdqrX9wekBrva6Uehzwo9h+SdcUe314p20KQnhEvrVtGAO9KMSX4EufUVpYN3QnxNT2mumtSexLzq6PObOWcKYu8OQJaxAmg8VeRFHXZCkLs90gnJWcHafcGAhWNxMqD6RnuxQURYnvSwajjMJU+MJjnBWcXhtPhMV0vM7qIAU8hGdtR1HX2oLGSTnRdqZTG2a5kCYyr/ovl5If5ozDl4c9BYxSygci4FVKqT/m/EA6BfwC1pN0TbHTh1fUj9cHGU0AnRSCAsMoKRlnea1RCJr6LUJ6dlsibCfcZrsS+zZGZq12sWaZoawMSVZOXNRZseViHuclgZSsDhPGSUlVGkwFd983JAgEUSBZ7AQIXzAY5QjPPl8ID1MJyKwmszHOOBl0ti2yso6H2UjyiWfJjpvJPZg2bM9yIU1kL0FwqZrEfvPDmnnsNu4EzMFyIQ3mR4Bfq/8e7HLO+w5uOlcX0x/e7QvSLrxRWoCxiYrjLMdU1h2cGBsAB7AxyJGeNfj2O1sel8ZQOhrnZKW1uVSldWE35pokzylNyDgtOLuesLwQEQeSylRsDAqSvOTcZkKv4xP6gs0o4MRShyw3nF4dEYU+/VgSRT6JAZGX3HnPpq0JkxsC6VF6cG4twQBpmhMEkqgTMExyOqHdfs2mNszSRhOZ1ZqGScHGOCNNS0zFRLNLArEtN2oeOOPw5WNPAaO1foVS6o3A54Fv3OGUMYfEyNuwMcz49D130wsE9zvRxfPa2QGmv/WmuyEmuak9RQVZZgPootAujsIY8tzQ79jbPF2xLi8MgS8oq4o0LVndGDMYW1d1WRoWuh7jpGBjkJIU1tZz30aCB5OgPfAoq4rNYU5qDJKEJCvoxoG9fmAYJBVSCorSkJeGpX4MwGCcsTnO6YQSX9pAwI1Rji9LrqvtNsYY8qKctFDZz5Zkp5q/aR1N3WwRB+OCYZLRCX18X0yq8gnpcTLo7Hidg7CVOOPw5eOCNhit9X1KqYdore8GUEoFWuvzLZaHhN/8i4/zqTvXAFjqhzzqQcd51IOXedSDj+/Z6Gy6d1ETuOb7gnGaUwFxIAikAH3hk6IAACAASURBVK9iOC4oChvAJ6VnPU0wiTspTcXmKGOclHzh3IgvnBuwPsqRwsMXgqKoyIuKSMIoLSlLA1XF+jAnlB6DcUaWV/jSI5KSpChsBHBakKQFvY6P73tkhRVWwyQnENYQHddRuEUJRWkYphUnFmMbGxNYQVSUTBZ9mhuO9benNuzXxpGkBaN0OnrYCpKNUYoU3rYYIbC2HxbtddYG6TbB3AQo7rdsxqyw22mT5OreHDxtjbz3KqV+GfgfwHEgVkotAq8EflRrvdv26arjWH9LiKwPMv7xE1/gHz/xBQDud6LLIx9khc0XP/DYpJp+ljedD5sYFW8iaKoKSgPH+jFJVkzynSpjz1vohpPo2UbAFIXVJu5dHbI5zvE8D9+zdWGEBwu9kJOLMYWpCANBjj0WSg+vDpJL0gyktAItKerkRyjKkmEKIoEorlho3kNRIH0QXlPC0+YsmXLrPS10Q3zhsdAJyNK8bgInLtnG0aQUNLlQDXlRIUNvWyqDxT5u3OIN0wGKe+VOTbOTQbcEAmmDGJ0Xab60FTAvBZ6Mtcn8UT0msK1jXw485+CnNh+e89RH8Y1f/mDe/7G7+eTt57jrzHBy7J6zI+45O+LvPvR5PODG6/o84oHHuPG6Pg842cObimUJfBuC34sDzg0S600KffLSYCpbz7YXB0S+4My6jW0ZJQW+8CgMFHUPozS3wXpR5BMEkm5oQ//D0CekotcJ2Chte9k48skLw/HFmKqsKKgoKzCVsZqPqUgKQ0cIxlmJh0fUj/CkYFxYDeLetRGizo+KQ3+yuH0p8IVHGNb1YGphOLttaLNlOa81y3kCxBL69tqzMUI7lQKdZpQWnNjxyPnsZtA14MqAXgbaCpjvBh6rtb6rjuZFa72mlPof2NYlhwYhPJ7wqOt58EoPgPVhxr/ecY5/vX2VT95+jrMbdjFXwJ2nB9x5eks5u265w4NOLXDjdX1uvK5vCzZ1ApK8YDDOa1UeOrFPPw5sLIqpkJ6grAqK0mAqjzQtWB2kbIxyitKQFXVOjxD40kMIK72DUNpkSSmIfOtC9oSNyBV4JFlJXhasbnp4AkKvojBMtnBVZb+te76HrHzyEs6uJ3RCWxTcw6MXb9X8rYwh8q1ROKvLQ0xvG9q6d2dtHLLW+Drh9o/bQje0EdCVYXOUEUjJQsdnYbLwPXauwtreVuIMuleWtgLGB76ww3gKLBzcdC4/S72QJz3qep70qOupqorTa2P+9fZVPnXnKvpza7VL2nJ6dczp1TEf/NRpAI71Qx58/QInl2L63YBu7OMBpqzqOrvWdrAxysiKAoxHGHqsjwqStJwkNBpTQQWFKPGqiE7kc6wf1ukCFZWpbHyKLzi+1OXEUoeTiwWDUc76OOXsesLGuCDJDWlaUHkeHnY75AFJZg3KXSnIS480L/GEoBtLulFguxlUFaEUhIEg9MWO2klb9+6stykKbBBivw4KbGwq3ciWtJj2IgXBli2mE0sGo9kel0yiktvgDLpXlrYC5v8Dfhq4uRlQSvWBlwH/NId5XRE8z+PUcpdTy12+5jE3TATOJz97jn+9Y5XP3L3B6mY6OX9tkPGRfz87edyLfU4ei7n/iR4POrXAZuhR4ZEVtmpdXhYMNgxZWhD4Hr6UJJkVYGUFvqlAVCx1A6JIsjnK8fBY7IXEkaEoDOuDnDvvHbA6SDm3nnJ2I+HcRjKJxJ3m9FrKZ76wWZfFDDmxFPOgU32WeiFxZDOrC2OIQzkJGPQ8j343xGTFpBRCsx1K0uK8IlN5UTIYmx23TFs2FI8oFJPkzjjyiQM52WrN0gis3QIUFzvttzauyVs75hXZ7FXVhVVFpdR/xJZrCLB2l08BDwbuAb5Va/2JS57JRXLmzOa+dd2VlQXOnNnc13Oaf8AdX9jkM3ev85nPr/P5M0PWBrvXrgUIA0E38unW9W+jwC7mbr3ATe3KLo2hGwc88FSfU8f75EXJfesJq3X94HMbKec2EooDUu2P9UOW+iE3nOxxYinmxGLMUi+kFwc89MEnOHtuwGDUGHrlpMredCW7JgtcCm9bjE8v9snyktVBdl5S5bRxdbfKeMDEo3cQH/yDXjwX8/m5mjm/oJilbR+slZWFXdXBVgIGQCnVAZ4KfBE2/uXfgb/RWl/RCj+XS8A0rG6mDMYZSVaSZAWnV0d89p5N7jk7YnUzZX2QXVTvFg+rtttgvfbP68U+vdhHeB6+9KiomNhUK2sQHaflxM6zF1J6LHZDlvoRgbRu6yiw7u3FTkgn9jFVRScKkBKoPDqhYHkxphcH1oXtC9KsZH2YkpdmW2mIQApMxWSrNBjnVrBG/nmV8XYzwF4NOUTXmoBptNRZ9vo/TLOXgGldMlNrPVZKfQQ4p7V+B4BS6shtZKW0BkvrcvZZ7EU88PqK+5/s2tgRKTi9OubzZ4esD+wWYZTYn928KWBNmXtpJ1Eg6MQ+nVByYjFmsRfygJPdul6M4O77RiRpweYooygNHtjkxDwgjn3wrDdnnJSMs4JhUrA+zJj+finLitXNdNs2cL942IZtgW8N1r4U+NIKqkab63esMPKFdbtHgWShGxJI+7x+x6cbB4SBgMp6fKRnDdnS8+hEPp7nHeocoqqqqGAS6mAqa5Q3ZurvinqsqsfY+m22zjGmsl8spj6/qmauu/f1N0c2Ct0DHv6AYyz2rFA5CEN4KwGjlLoO+HPgidiSDbFS6kbg75VS36K11pc8k0NCHMiJVyTwBb04YHOUEYZyUlKhFwc87IZFTFmRG4Mx1q2d5wZTGXpxwJm1hHvXRqwPMsZpSV4Y8lrLCHy7peiEPnEs6YbWhV2WthZLN5J4HtxzbmS3XJ4gkB6pB7LWFCoqkrykKAyi8FjoRCwvhnieRxBITi136MUBd50ZcGZ1zLnNlME4Z3OUkxWGUZJf1Hasgm3vZV74tTAKpGeLX4XSCjZp7Tyex0Srk/VjqA3fk9rFTBavqawxfduihsnY9AIV0iPPy23nThb7TgJg6rmNAGi5cbjsLPZCbvruxyGlOBBDeFsN5rXAJ4H/CtxVj92FjYl5JbYI1ZEgDCTL/YjVQUaSFVRYT1RRVpCAoSKKBL7w2RzlRJ7HZp5P8osesLLAYj/khpU+t9+zybnNMcOkYHOYsTHM8DyPMLBZ1b4QCDxGSU5cVSR1j6RRUhKHgjDwObkYAYbjSx0qKjzhsTlM8YTAq0BKW62urAyjpLB9lHwBWDvQwx9wjJVjXYqypBv5JGlJ7sGZ+wZkeYUQdrF4ni2CtdQLKU1VRwnbPKy8sBpTVhgGdX3fUVqQZrXgLK3AKctqUrXvUrERx4YxYL/zHAdBHMpJvNdBGMLbCpivA+6ntR5OxcFUSqmbgbsveRaHjH7drP2+9YSgKOmEtmD3qpdRGKtl+L7dDhgqhPSII59ACvrdAGMqurF1a2+OJYFvrHs4tLEpQtithhBe7WoWeEBhbJxLVRUEMoAQksLQj+21e1FIJ6oIhEdmKjqBJC1KRH2tcVbgiQApbL2ZChujEoeCQPqEgU9FSjrOkVIQYggCSZoaFhcCljoR1x3fiqBtjLdFafs3SenZ+jNlxb2r9ZZtXNhAQCk53o/Aqzix2MFUTYR0iawr+WW15tPUsVkfZpPHRWkFZFbY0qEeTARXUVotIs1Le7wwVNbzb0uZ1n8325KqHmvur/1tvYhCeBPtp/ltx7bOCUOJKU09NnN+8z/ztl9zP9e3Y1zg+vZv+3lpc/3672ZuU9cX9fa5MIYbV/oEvjgw21ZbAbOJ9SDNch1XeS+k/dLWiBgGctIUDbZ6FxUpBHVUbCo8TGmNmF7tOQL7wQh8wanlLkVl8Ac2IqYC8lpr6MQBZVmSpCVxKEkyQ1GWeFWFEHYLFPu2dkzoCzaHKXEc2AC/ymNcFHhUJIlNHiyNLe8QBaI2DAd1NDIEUuJLj3GWUxlrxN0QKcLz6UaSQBr6ccBCd/tHoOlpfay/5W1oPBKRL1jdKCdRxsf7EYv9sNaGPPqxj+yHe36QZ42Pe3mtwkDuaTvaK89sv1xrRt550lbA/B3we0qpmwCUUieBLwH+D/DmOc3tsrPfQkTTQVyBLzm+GNPLykn8R25KqsqW0wylpKxgPMzwpEdRGExV0Q99RsIuSL8XTfKepOchpSQXhij0EZ4hrwWOpMKrPLLSxsbEkWFcGKLKbj+OL8UMUxslHPgF0hMMkoI4lFx/vMfJpbgWWvYbvxvZ4liDcc54XLK4GLHUC2vDqocvK2ucrctxNoFy0z2tG5qSDL4vbZ2a0HqWPGFzsBoDYnPvNvOsLpZ1fnDfbAxL081ACm9yjenz2wTVXQ1eqKNEWwHzfOB1bFWuuxdr3P8j4KrthbRf9opUbX5PfzB3ilhNs3Kirfi+tHV1K/A82yitE/mTur33rSekRcFSL8T3Bec2xlSZ7SLgeSB9yfFQEoc+Xq0CJ35uUwakZzWSUEBV4VUVRVmxOc5Y6IQc64VsjDPiMGKpH1EUVpNa6Ia2TkwnAHKEtCpz5EuKoLIlJwpDHPjEkV14lYGKatKapYld2S1OIs1LAl/QqXOnoFbta8Fgj8uJRgJN/pHcJtB3Kg/Ri3fPpL5QUJ2rZHf5aStgxlrrp9eay0OxcTCf1VpfU3ribm65JC22HWs+mE0MSrMA4sinrGx5hHFeIoRHr2O3IklqC0kVZUWS57VHyZYgKGoBMUwKNoYpFdAJA6S0CzErUjqRb923HZtQGXgeSVHiS49O4NOtw/GNAeEJblhZ5MRShyQr8H2BX2sIfu2hArvF8SrAs67lpX5IL/bxo4DNjYRACjxREcjtIf1pboXVbnVgmozyXhxMsrwj3wYYprmZKkq+JdDT3ExiYaZTD/ZTse5C9WpcJbvLT1sBs6aUei/wt8A7gH/RWl+ljraLZzcVuygrgh3uVJKXLNYG3wbb4C0/L0M4yQrSomShE1IUhkGSW42lsl0Zk6xgMMgZ5wVx4INnSNKKUWKr+AvPIw4lhamIfQ/pS5vPE1s7UFYa8qIiL0vObibcea/gETce44aTvUnkrDFm23ZkoRNsa6cL1sWMb+09cSgxlbUX2Z+t99n4gqa3HGVlGI7yWkuwW6g49MkK2xeqGwf4tVsZtmdZl1PRhZcSf7GXQHKJj5eftgLmScBXYUs2/CjQVUq9k1rgXCtxMLup2P4u8QC77fel9MhLu1hHacE4yTmzluB5tjG98KzXZJTYEg6eV5FkhlGWk2YGqhLPs8W78WxNFulLep2QqCxtm9hQYuqyD2uDdCLQAiGQAgZJzufODsETLPdDFncwck67k9PCMKqr3B1f7rI+zEhya09a6Eb0O9Yw3O8EBL4tAD695Wi2O5ujrBaYJaub6STwLg5sD+5G2OVFSVI3mxPCm5TnbO5hG/ZrT3GJj5efVgJGa/1R4KPAqwGUUl+ELaH5o9i2steEfrmbip3k7T+YcWC/tcdJwbnNhM1hTl6WFHmJ8eD0WkIvtlGtwyRnMLaFtovSIKXNfs5zW9PXVJVNQJQ2HkYIkJV1NzeCJ8sLNpPCupWlQMiKoLJV8ZK6wdrqIN3xmz0MrB1lY5RhTMU4KzHG8IX7hmRZTrlZsTHKObuWcMNKb1KRroutTZPMbHHAxspkhe233bTC9eu4m1Fa2FibWhj5Utj6OVPlOQNftm47MkyKuhHcVjLkcj+ctGaZRQDrdXrCdH6US3ycH61TBZRSy1gt5quAr8Z2FPgoNsL3mmE3FftCGblZ/W2/Nso4t5Zw77mRDXzDtmxNpaHje1ZTSQtKIxHSoxvaBZcW1tsTh9LaWAJBVUn6ccjyQkjo+/TjkPXNDM8zdKKAfizZGGaMkwpPWBtQUZSMs6bEpODu+wYURcW5jYSVY10Wu8G2Bej7kpWlDmlesraZkhUVCBglNoCuLAwbWYG/Vvev9m0nhWN9uU17sULKCovNcT4x8oI14HZCnzS39X2lsO1V8AU9EUxarhRltc3lvRdJXm4zEgN7CtMst0IvCgRpXtW2pGpXW5LjYGibKvBxoAN8AHgvVnP5yJVOdLxcXMh42NSO3RhmNkMaW683LQqSvLAG2sinqKAbS8CWwuyGEiMlYSnJCx9T2ub2ojT0Ip9+L+RYJ2CpHyHxiAIfGWQciyOWegG+Lwgzw/GlgGFiS0CkeYXAFhXHs56qOJQEqdWYmnSGRsiUZWWznH1JLw5Ii5LC2EZsZVlRVFZroYKigMG4YLFrc4mSvJzU220KQzXBX0Vh43l8KaeKettrznZY2OleX4imL/h546ba0WjbaFvNe22Yb0KDo60GcxfwGOBG4P7AKaAPrB/UROrcplcDX4H1Uv0F8IIrWWB8xz1+9/xvxqaswmic40trX5EemNIuhKKsbAh2YfB8jywrMQaWuhHn6h5LUtj8mjiyOUiLvYgTS5HNLRKCvKgIIkEnD/CnDMhxKPG8mG5UAh55WVHkhn4kkUJSUUEdxZkV1n28MconAsYYw3otGDfGGaOkxA8EZWFIM0NuSrqxbYuy0PWJI4FXd3cUUNeisfWKq7oQeq/rk2cVcWTLcjYFvaXwJtvKS7WFyDp48LxxsfN21hl4rwxtbTDfpJQSwGOBpwA/CPyBUurzwLu11s8/gLncAnwCeBiwBLwJ2672fx7AtfdNm5iJ5hzbntVQGGO//bOSso7NldKjKA2lLwhDwVIvoiwNSW7wOj65MZSloDQgvMpmZ59arAPbQpLMFvT2hQ1a6/iyLnNQEQuB35X0Ktu9MTOGhV7A2jCjG0jSwtgmbp5HWNcQBiZaTJaXJJmNQAZs3EpeMi6tjUJKQ+D7RL5ESlHHstgs6WbL4UuBXaOGEo9ICoQUDKucOPQnhc7Bxs8028pLLQJlk07FeUImCuSOgsoZeK8M+ynXYIB/Vkp9DHg/1g7zPdi+1ZckYJRSj8cKr/+itV7DusVvBl6rlPrZ+rUvK21iJppzpLCN1USTU1MZfGn7RjeFun0pWOgF3O9Ej8gXnNtMwat7SSe2Xm8n9jm50OGGld4kv2epH5IVJdKzC7XfCdgYZqSFAWydW2MqFnvhJML22CCjomJtI8NgiOpM48bTFE7Fm5iqmriSQylY7EUs+5JuYBMfS1MS+oIgkAghOFZXw2veexT6+P7Wv6cJ419eiKi8ugUJ1STIb7YE56V0dFzuh6wO0klLkyYAcCdB5SrbXRna2mCeBnxl/fM44HPY9IH/Vf++VB4HfE5rfd/U2IeAZWxg36cP4DX2RRuVuvk7CgSRL0ilIDUlVHVgmRR0QkkvDgl8G8l6fDG2H2rPY22Y2cjaOhEy8EX9rS+3GTunc2wCX9bCxHpOji/GCLZsCWluOLkUT2r4rg2zSWBbI1gWu1sdG5uyE03piaIwFHhIKqpFSJICWbuZbYJmYBdrrd1FgWCUTsWw1FrSRJgs7nx/L6bl6yxN0mnb3DG4NKHm2D9tNZhXYQXJbwPP0lrfBVD3RnoP8OhLnMcJYHVm7Fz9+yR7CJjl5S7+DiUXL8TKyt61ysNOSLFDaQFfCpbq3krT5xxvvEibtlvAUs8aZ6e/taef21/sMEryujJeSVGUSN/WbbnueHciFNrOpSFtDK91PZckL8hzA17FQidiaSGaVCkLOyHhRkAxs80YJQU3nlpgoReSFbaQeGEMUeBP5rY+SCdzarZa5cw51zIX+vw4LHsKmHrr8kSsUfcDQA94mlKqOeWh2BKa86DZHO9phVtdHe37wm2yYfeqU5qNs13POdYJWOmHtgh3YdjcSC74XEmdwGdAlCUba9vfU5u5zOIDvrDxKkzJoHSYcmaYbl13kJ7Xf+i6k30Gm8mkdUmDEN5kbrvNafqcaxWXTb2dvYTthTSYHraYVAD81A7Hx8BNFz2zLc5gNZVpjk8du+y0Uan3OudCUab7Udnnpd6HgW3DKoU3qf7fiSXXn+xzX1Ud2PwdR5c9BYzW+l3Au5RSt2qtv2WO8/hn4Aal1P201vfUY18GnAZum+Pr7kkbO8Fu51zKcy/13P0QBpITS51tnRKj+rUOcv6Oo4m48CkwZ+GC1vrDWM/US5RSS3Uqwk3Aq67FpEqH46jQSsBcJr4D63P4DPD3wK1MNXpzOByHj9ZxMPNGa3038PQrPQ+Hw3FwtG685nA4HPvlatoiORyOawwnYBwOx9xwAsbhcMwNJ2AcDsfccALG4XDMDSdgHA7H3HACxuFwzA0nYBwOx9xwAsbhcMwNJ2AcDsfccALG4XDMDSdgHA7H3LhqsqkdjlmUUh3gl4FnYkt5fAz4Ka31++vjx4DfAL4W+2X5t8DztNYbU9f4auANwG1a66+Zuf4pbC+ubwIS4I+BF2qtd65D6tg3ToNxXM28FPh6bC+uU9iiZG+uBQ/YIvQngC8F/p/679c2T1ZKvQB4HaBnL6yU8rC9t84CD8BWUPwS4KnzeStHE6fBHCGUUu8APq61/vGpsZ8Avl9r/R/q7pqvwnbXDLF9x39Eaz2oz30W8GLgIdguEK/RWt9cH/vv2CZ5f45tLfxorfVtU6/zIHZY6DWv11o/d4fxdaxGcVt9jZcBPwk8Uil1F/AM4Ala63vr4zcB71dKnaxb4Bjg8cDP1r+neTLwxcDXa63HwBq215fjAHEC5mjxOuBXlVIv1Fo3LQGeAbyh/kZ/M/BPWAESA3+I1RK+Uyn1YOxW41u11rcqpZ4AvEcp9UGt9dvra12P7QKxDGxrOaC1vqO+Zmu01rMF5W/ECo17sK2MK+CjU8c/iu1G8Rjg7VrrVwBMdcGY5snAvwA/r5T6PiAFfhf4xSvR6O9axW2Rjha3AF3gGwGUUvcDngS8EfsN/2jgp7XWg1oDuAl4plKqp7W+HVjRWt8KoLX+IFYjmdYMFoGXaK3zg66lrJTqAb8F/E5dGP4EsKm1nrTgrPuYb3J+h4qdeADwBGCIFajfA7wA+L6DnPdRx2kwRwit9VAp9WfAdwFvAb4NeI/W+k6l1Fdgv3BO7/CNf39s87sfUkp9P3ADVlMI2dZ1iY269e+BopQ6ia3RfC8XblPscYFeWlPnbWitf6l+/E6l1OuBZwG/c7FzdWzHCZijxx8AtyqlusC3Y7c9YHtcJVrrzk5PqgXLi7Fbqr/TWhdKqQ/PnHZ+J7at51+MDYZ6a/Z24L3Ac2stBWy/rEWlVNCMKaUCoE+7XlpfYKt7aMPtWKOy44BwAubo8S5sv6nvxW6Pvr0e/3cgVko9XGv9aYBaCPW11qexXpb3aq3fVh9bBB7W9kUvxgajlDoBvA24RWv9opnDH8ZqKo/F2o3AbtdKbF/zC/Eh4EVKqSWt9Xo99hDgjv3M0bE3zgZzxKhtI68H/jfw1mZLo7X+BPBu4BVKqRWl1ALw68Cf1U+9DXiEUuqEUuoBWHfw57DbpXlxM9brNStcqG1Efwr8klLqOqXU9fX5r9daz/Y534lbgc8DL1dKLSilvhz4bqyh13FAOA3maPKHwM9hBc00z8YGnn0W61V5F/Cd9bHfxMaj3IFdmD8BrACvVkqdxtpoDprnAKVSKpkZf67W+vXAD2Hd6h/DepfeAvxYc9LU83xANI+11rHWulRKPRVrOD6NdbvfpLX+MxwHhmtbcgRRSj0Z+BPgQVM2DYfjwHFbpCNGvZV4BfArTrg45o0TMEcIpdTPYD057wNeeYWn4zgCuC2Sw+GYG06DcTgcc+PQe5HOnNnctwq2vNxldXU0j+lcE7j7szfu/mxnZWXB2+3YkdRgfF9e6Slc1bj7szfu/rTnSAoYh8NxeXACxuFwzA0nYBwOx9xwAsbhcMwNJ2AcDsfcaCVglFKH3p3tcDguP201mHuVUq+pq545HA5HK9pqJs/Flle8VSl1DlvD9Q1a63+b28wcDsehp5WA0VrfAtxSlyT8BmzZxPcppW7D1hZ5Y8siPw6H4wixLyNvXS3+LcAvYptiPRx4CXCHUurXphpiORwOR/tcJKXUMvDfsBXpn4Qtr/gTwP/Ftol4DbYa+7MPfpoOh+Mw0krAKKX+HNu/9y7slui76j45DYO669/nD3yGDofj0NJWg7kP+E9a6/fMHlBKLWutV7XW60qpHVtPOByOo0lbI+9zdhpXSt0f+DhwvD7vjw9uag6H47DTdov0CGw7h8dju/lN85GDnpTD4bg2aOtFeg22XcWzsd37vg34Zayh9xvmMzWHw3HYaWuDeSxwSmudKaWM1vrNwJuVUs8AXo5tHO5wOBzbaKvBpEBTxmtUNyMH+CvgaQc+K4fDcU3QVsC8E/jLOpDuA9h2m08EfhgYzGluDofjkNNWwPwwcDvW/vKTwFcC/wj8AvDCuczM4XAcetq6qc9h+wQDfEIp9UXAKeCM1rqc1+QcDsfhZlcBo5T65jYXUEpR5yc5HA7HNvbSYP5q5nEFzPY/McAQWDrISTkcjmuDXQWM1npin1FKPR14JnAztrdxDDwCeBHwJ3Oeo8PhOKS0jYP5VeCxWuuN+vEA+JBS6nnAPwO3zGNyDofjcNPWi3QC6O8wHlPnITkcDscsbTWYtwJ/q5R6LXBnPXYjtpTm2+YxMYfDcfhpK2CeA9wE/ADwACAC7sYKnhfNZ2oOh+Ow0zYOZgT8bP2zK0qpl2itncBxOBzAwTdee/4BX8/hcBxiDlrAzMbJOByOI8xBC5jqgK/ncDgOMa43tcPhmBtOwDgcjrnhBIzD4ZgbzsjrcDjmRisBo5RqW3P3ty9hLg6H4xqjrQbzMqXUTrlI29Ba/9glzsfhcFxDtE0V+HngdUqp12FzkYrpg1rrTx7wvBwOxzVAWwHz6vr3M6bGmgJUFVsdBxwOh2NCWwHzkIN4MaXUvcAxtgfk/b7W+nlKqacALwEehU2kfLnW+jcP4nUdDseVoW2y4x0ASqmHAw/UWr+jfuxprfcTvbsMPElr/aHpQaXU9cBfYjOzXwc8BnirUup2rfVf7+P6DofjKqKtRl6nFgAAIABJREFUF+k6pdT7gE8Bt9ZjNwKfVkqpltfow//P3ptHWXaW9f6fPZ2ppq7urs4MISE8ghggDEEUEK4ISxAighcXOF0BiSH6AwSuGu+6LiQarngDBKNRMRjABZcVIBAZAzLFMBiSkERegYydoYd0V9dwhj3+/nj3OXXq1KnqXd3ndNWpej5r1ao679773W/v6v2t533e530eAmC2z+HXAPcYY640xjSMMTcC1wBvKNK3oiibk6JTpKuAO4FfAvbmbXuBjwDvBV5YoI925rtLReTZ+c+fBt4KPBW4uef8m7E1sBVFGVGKCszzgVOMMYsikgEYYzIRuRTrLylCAHwLuAH4TeBMbMLwv8Om5Lyj5/xDwG6OwvR0Dd9fv495ZmZi3ddsJ/T5rI0+n2IUFZh5rED0soeC0bvGmB8Dz+xuEpE/wk65buhzSXuFak0OH64Xuf0yZmYmOHBgft3XbRf0+ayNPp/lrCW2RQXmy8AHROQSABHZDTwJ+EvguuMY2z1YIdnPSmtlJ3DgOPpWFGWDKRrJe3F+7u3YSgL7gc9h/TKFondF5HwRuayn+fFABNwCPK3n2DOAmwqO74QQRglz9ZDD8y3m6iFhpFVzFWUtii5TzwIX5JbL2UADuNsYsx47cT9wsYg8DPwN1gfzDqwD+WrgT0TkIuAfsVOpVwOFyteeCMIoYbG5FMCcJBmLif1cCjTOUFH6UXg3tYicgl3VeRFwAfAKEZkper0x5m7gJcCrgIPA57H+l7cYYw4AL8aKyn7gCuBCY8zXivY/bJqrWCurtSuKAk6WHT1OTkR+FfgwsA+7F8kBHoX1m7zKGPOJYQ5yLQ4cmF93ms5uJ10YJTSjhCTJ8DyHSuD1tUgOz7dW7W96orzeIWxq1Im5Nvp8ljMzM7HqQk9RJ++7gYuMMVd1N4rI64H3ARsmMMfDeqY9nueQJCu1zPM0BY6irEbRKdI08IE+7f8ETA1uOCeW9Ux7Kqv4WVZrVxSluAXzKWy07vU97T8HfGaQAzqR9LNIVmtvWzRFplNFp11HY1D9KMpGUVRg7gU+KCLfAQw2PcPZwPnAR0TkXe0TjTFvG/goh8R6pz2lAi/4oFabdNVK2QoUFZifBr6PjYF5Ulf7bcATuz6PVF2kSuB1Xtre9m7WY0msNe1ajzAMqh9F2UiKxsE8r8h5fQLpNjVFpj2rWRJhlJDmn7uvW23a1WzFneuLTHfWM33TqZSyWdn2talLgcdkrcT0RJnJWmnFi9nPkojihMMLYedlT5KMxaYVnX7TqyhOaOUC0Hv+aqw2TettbwvgevpWlBOFli05Cv0shlaUkKTpivZmlPRdVWpFKeU+7WsF6RVdtdIAQGUzU9QHU5SR8sEUoZ8jOEkzPHelNidJRqnm5XuWIsI4oeR7uGQEfVJKrDYNguKrVuuZSinKiWbQArPl6OcI9lyHcrBSYDzPIYwSoiSjWvaplu3jXWhERHGyQmSOFqS32qpVt8+l0YrxPWfdfSvKiUBLxx6FUuAxVvE7L6znOUyPl/taJJXA6zs1KQcurXUE761Fr8/F9xzqrZgoXt6/BgAqmwG1YArQz5JYbeWme8WpTVuMPM+h2YqJkwzfc2hGTqf/ovQKWOB71IA4yQh8dBVJ2VQMWmC2jV2+2vRlteC9Stmnki9jB/lTP5bguX59B75H4G+9TZfK6HPMUyQRme7TvK1qU/dLQLXW6s8gVnyKLl8rymagaNmSc0Xkpq7PHwMeEZH9InJ+u30r1KYumrVutfgTYIXPZqzirxmEt54VH910qYwSRadI78OmyEREXga8ALvR8RnAu4DnDmNwJ5r17P9ZyxrpF7AHg0n5sJ5Nl4qy0RSdIj0Z+PP85wuAj+bZ5i4Hzh3GwDaC9UxhjsUaGZT1cbToY0XZLBS1YEKglNdEeiHw+rw9YAstda9HNIpYI+2Vpu6VI9dzcDJwXZc0TckcWGzGnSjg1cRC9xspo0hRgfka8HFsBYAM+LyIeMCfAt8b0thOOP1EI4oT4ryt+8U+2k7sMEqYXWix0IhYbEa4rkPJ95gaKxH4HlmWUm/ZLQee61IO3M69+y2Ja+oGZRQpKjAXYisA7ABeaoyJRGQKmwT8lcMa3ImmVzSiOKHeiqmVferNiPl6SJSkTIyV2D1RYazir2pVzDVC6q2YRmj7S9OMZhjjuS7jVTg016SSR/q2wpi5eoKDQ+C5TI2XOsvapaOsPqnAKJuZogLzEmPM73Y3GGOOiMhTgTdi6yWNPKXA7iN6ZK7JQjOi1UoYqwaQZsw3o855i40Ysia7JytM1kor+gmjhIOHG0SJtVJKnoPv25lkI4wIfIcoSakAcZzSCGPiOCVMUmpln6Blz+1eoeqH7jdSNjtrCoyI+EAZuEJE/oWVgXQC/Bl2JWnkCSNrsWTAWCUgTTPCOOHQfItK4HZEIgxjIKPZSjh1xjqh2rlhkiwlTTKW3v2MZmTFxF7vkKQZgWf7auUh/mGckmYZbr6JshXZvUvNPAWEJhxXRpGjWTAXAX+d/7ywyjk3Dm44wyeMEo4stDg831oxrWlGybI9Q21HbKMV4boBvu8Sxyn1VkyYpKRpRilwSdOMyTFryRyaa9FoRTRaMXGakWUZrShhHmshTVRKeA5USvbRp6kVDisuDuVcxJJ0yXoZq/iFMu8pymZjTYExxrxHRD4MPAD8Qp9TGoyQk7ftLK2M2Vwuvc7SJMk6LzZA2XdphCmuY0UkjlMemW/SChMcB4LAI5utk6VwZLFFpRxQb0TE+epQmqRESUoYWeukUvLxfZdyyQMHa8VkDvVWRCtKmagEnXt77lKgXhglzC2GLDRDPMdlaqzErqnKlvC/6OrY1uaoPhhjzEEReYwx5kEAEQmMMdHRrtuMHM1Z6nkOnut0RMb3Xar4TI0tWS1hnBBGKXGSUo5T6o2ISsmn4TmclG92dJyMNIMwyciSDNcF37ErSGMVH99zKQcec/UWaZaC4zBR9XEcmK+HuK7LWMUHQkq+QyO0gjiWC1ArTgi3gINXV8e2PkWdvPtE5J3AbwM7gYqITALvBd5ojFlt+rSpOJqztBJ4NAOPemvpP73vu5w2M06zlVBvRew71CDNrDM2A5phRKOVcHi+xQ/uneWBA4scnm8RxksZ70qBy47xMqftHmPPdJXTd4/x6JMnaEUpU+NlxuKUVpxQb0REacZY2aFc8ikHLofmWvhdTuI2c/WI8T4O5lFCV8e2PkUF5l3As7E+mY/kbS62dOzlwGsHP7TBczRnaSnw2DFexnOdXGQcqhWPiu/RihIqZZ/xqs/huYR79s3z4ME6h+aatKKV6TO7CaOU/Ycb7D/cWNZeLfvsGC8xXg0YrwYEvku17LFzskKSwsyOKlGSkGQrBSaMRz8lpq6ObX2KCsyvA+cZY/bm0bwYY2ZF5LexpUtGgiJlSkqBx66pKrvyz20zPsvgh3uP8JWb9/LwocaKPtqUA5eZHVVO2VUjzWw1gbl6yIHZJs1wuSg0WjGN1srxdDNRCzh19xg/8agdnHP6FLV8mlTqk/Bq1NDVsa1PUYHxgYf7tLeAicENZ7i0zW4/XyIu4lScrbe49b8e4au3PMCDj9SXHXMdmBwrcdLOKo8+aYKzTp2iVvEIfJeJaplmaK0gyIiTlNn5kINHGswuhBxZtCtZswthvmu7vxU0X48w981i7pvFceCMPeM87owdnP/4PQN5JhtJ0bpUyuhSVGD+A3gbcGm7QUTGgXcD3xrCuIZGKfCYGi8TNkIgD4qba9BoJkBGrewzUSvhOA7fuO1BPvut+zh4pLmsj1N31zj7tEketWeCSsnDcV1mpipUcr9J4HtMT5RXODFndtQ454wduQPX7kFqlzRphQkLjYg4SVlsxjz0yCIPPVLn4UN1Dsza+2cZ3Ldvgfv2LfCl7+7lpOkqP3X2Ls49axePO2PHuitHbvTqje4M3/o4WXb0+a6IPBGbriHA+l1+AJwJPAS8zBhzxxDHuCYHDsyve8I+MzPBgQPznf1C3U7dZivmth8/wo137GNuMey0O8CjTh7n3LN2sXOyTK0cUApcfN+j7LvsmqrmYpECGVPj5c5f4n4vUBglzDXCZcJWDjzmmyGH5kLqjYhS4DJWCVhsRtz14Dz/tXeWux6YI0pWWjuB53LO6VM88axdPOHMaU7fM47r9J9q9Apfm3bemvbzGQRpmpGkKXEeApCkGUmSLvsZx+GUXbVVx7vZGOTz2QrMzEys+osrJDAAIlIFXgKchY1/+RHweWPMhnobj0dg5uohRxZCkjRlvh5y0x37+I//OrBsuuJ7Do86aZzHnjZFOXBxHQfHdTht9xgT1YBSye9sVmwLVeDZJN+NMKFa8pgcLzFZXUqr0O8FbyftbkVpZzwAvusSpymu6zBWCYhi6zD+z3sPc8fdhzjSJYLdjFcDnnDmNE84cydPePQ0O6cqLDYi5usRDx+q27iaRsSRhdA6klO7tO45Dn7Jo14PO3FBcZp2fk6W/WynfvZzLho95xX95TzxrJ28+VefvJ5f44ahArOctQSmcE5eY0xDRG4BDhljbgAQkdH4k7MKzVbMffvn+Pad+7nznsPLguxqZZ/zZIazTp2k1YpZbMQcWWwBMDVWohUmecUBB9e3EbxJluK7Lo00Js4LszXCDLfukCYZO8bLq25etBHETqegWzuKeKERUSl7nYjfSsnjCWfu5DGnTPLy55zFw4fq/OC+Wcx9h7n7obnOzu+FRsS3/3M/3/7P/QA4jp1ibVYOzDbJsgxnRKwYpRiFBEZE9gCfBM7HpmyoiMgZwFdE5MXGGDPEMQ6F+/fN84mv38WtP35k2Ys3NVbiWT91Mk+XPaRZxr7DdcIIkiylWvbJMuskrocxE2MlPM+hHAS0yingdbYhuK5DueRR8uw0pxWlnfiOfisnVtxsQbckTTtRxM0wJiWDDFzXYUce+xLGCdWyzym7xjhl1xjPe8ppRHHC3Q/Nc9/+ee685zD3PTzfsSDWKy6+5+C5Lp7rdAIQPddd8bPv5p+9/Nzun/Nz/c6x7uvteb7rEPgu5z1uRsVlC1LUgrkKuBP4JWBv3rYXGxPzXmwSqpFgvh5y9TXf5eu3PrDspds5WebpP7GHx585zcnTNYBOqobFVmT3Cjn2ZSgFHr7rkmQZswshOyftC9QK7WbJOE1xM4cgdQmxWwxiUo4ssGqxNM91iOKMNE1ZbNpoXjIrDGmaUS0HlH2PKM2I8oqRvQS+x+PPnOb8J5wEWCvm+3c9wp33HKLejJkcK7FjvMREtUQQuExUS0zUAsolD891mKgGVCs+J+2Z5ODBkYidVDY5RQXm+cApxpjFrjiYTEQuBR4c2uiGwMf/7cd8/baHOp9P2VXjWT91Mo8+aQLHgWrJZ8d4Od/4mDJWCai3YtKJjDDKSFLrrxirBHiOQ5JZIQo8l1ac4DoOruOQ5upV8hwWm9HSHiSWiqXVWKqZ5DoOaZrg+y6Vkk8YJ9TDpLPnKOgKtGtFCdPjZaJ+5VG6Y3p8l5941DQ/8ajlBSDaq1irrd6oJaEMiqICM49dQeplDyNWC+ns06b45vcf4ow9E/z8007n8Y+eJk7SvKB9Rsm3KzxhnNAMY8I4wXHt9CTNEjzXhv1Xyh6u61Ap25cyzbAWRikjjBKSxL7svm+dvzsrAeX8Be4tlpZkdm8TWL+Q57lM1kqUfa+zZaDt/G07lMdrpaMuNR9LYnJFGSRFBebLwAdE5BIAEdkNPAn4S+C6IY1tKDznSafyi88+m3vvP8R8I2J2oWXzs+QWQpZlHJxrUiv7TI2VODzfIo0zqiWf6bEKzdjupE5T2DleIsj3LiVpSrlkBWWs4pOlGY0wphna/DBuT3Rqu1jaWMW3kcLYfU/tLQFtMUrSlMAPlk+nurY2rCUSGoqvbDRFBeZi4GqWMtftw+ZY+ggwUrWQwiih3oxwXZda2WffbJ16M6EcuEzWyrTSFNe105Dxaolaxfoo0lyE5vLl42rJp1a1Dte2NeJ7Dq0opTZmH2uplVs3qfXf9E6LPM/pWBndu7jBLleXA6/vPqeika4aiq9sNEUFpmGMuSC3XM7GxsHcbYwZuWCAZpRQ6W7IoJZPd1zXOoErJR86iZ/ay8YO49US5Z7d1mAFY8e43wmga0YJRxZCPNfpWCLta9qZ6iAPlc/jYXr7bVsutbLfyZa33khXDcVXNpqiAjMrIt8EvgTcAHzfGDOSdnb3X/RWlFjfSpp14kxc1yWMEyoljyi2JUeiJCXwPGrlZJn/BFbuZ2pPW3othxpLQXTd17R9KO1+u31B7cjaXoqG+WsovrLRFBWYZwI/i03Z8EagJiL/Ri44oxQH0z09SNKMsu/RCGPcPIOcjT+JcbFWh+e5REm6YuWnbbGsdZ9ukbE+F5vUqjtReNvKaO9JStIMz3WYrAWrist6kjSt5afZDPuRlK1NIYExxtwK3Aq8H0BEzsKm0HwjtqzsyPyv7J4eeK4Deda6OGvXls6oBj7tfFGVkm+nKRmdPTVHE5f2fdovfnuPUpKmTFSDZdno2tOqhUaai4tdJYqSrG/WukElaVpLqBRlUBTeKiAi01gr5meB52ArCtyKjfAdGUqBx3itxOxsveNEDcrWSmkvxNfKfifILugT0FbkRe6tj+S5DrWyj+u6nRe7fU6K3TvUSz/RGNTK0HrK5CrKsVJ0q8DtQBX4NvBNrOVyy0ZvdDxWynlt58k8lqSdjqFtPQS+h5cH2vUKzHoL1Zcij6mxldV1u8Vj0CVri6BL2MqJoKgFsxd4CnAGcCpwEjAOHBnUQPK9Te8HnoVdpfoU8KZhJxgvBR7Vsk+1vPxR9FstgvWvwBR5kdcjGoNaGdIlbOVEUKhwvTHmRcApwP8HPAL8LvAjEblFRN43oLFcCxwCHoudhj0LW6526PR7qQLfY6IadI55nrPqqs56++5tX00c+rXbHdz+cY9rPfdUlGOlcD6YNiJSAp6O9cP8BnCOMaawL2eVPp+GzYx3kjHmYN72Cuwmy93GmFWzah9PPpg2R0vAdDwU7XsjVnRWu6fmO1kbfT7LOe58MCLyUuBn8q+nAvdjtw/87/z78fJU4P62uOTcDExjA/t+OIB7rMow40WK9n20sP9hsBH3VLYXRS2PK7BC8vfAq4wxewHy2kjfAM49znHsAg73tB3Kv+9mDYGZnq7hH0OG/ZmZkclVviHo81kbfT7FWFNg8qnL+Vin7reBMeClItI+5WxsCs1h0Da71pwCHT5cX+twX9TEXRt9Pmujz2c5a4nt0SyYMWwyqQB4a5/jDeCSYx7ZEgewlko3O7uOKYoygqwpMMaYrwJfFZHrjTEvHuI4vgucJiKnGGPa2aCeAewH7hrifRVFGSJFl6mHKS4YY74H3ARcJiJT+VaES4ArRnVTpaIoBQXmBPEKYBL4MfAV4Hq6Cr0pijJ6HFf8yiAxxjwIXLDR41AUZXCsO9BOURSlKJtpiqQoyhZDBUZRlKGhAqMoytBQgVEUZWiowCiKMjRUYBRFGRoqMIqiDA0VGEVRhoYKjKIoQ0MFRlGUoaECoyjK0FCBURRlaGya3dSK0ouIVIF3Aq/EpvK4DXirMeam/PgO4G+A52H/WH4JuNAYM9fVx3OADwF3GWN+rqv974Ff77mlB3zDGPO8Yf2bthtqwSibmXcB/w14LjYv9E3AdbnwgE1Cvwt4MvBT+c9XtS8WkTcBVwOmt2NjzOuMMZX2F0uVS/9laP+abYhaMNsIEbkBuN0Y8wddbW8GfscY85N5dc0rsEXvSti64xcZYxbyc18F/AnwGGwViCuNMZfmx34L+J/5NW8EzjXG3NV1n0fT50XPucYY87o+7UeAt7T7EZF3A38IPF5E9gIvB55ujNmXH78EuElEduclcFLgacAf59/X4n/k/+Z/OMp5yjpQgdleXA38lYi8xRjTrgb3cuBDIuIA12EL4D0GqAD/jLUSfk1EzsRONV5mjLleRJ4OfENEvmOM+WLe18nYKhDTwLJqc8aYe/M+C2OM6U0ofwZWNB7CljLOgFu7jt+KrUbxFOCLxpj3AHRVweiLiNSw2RNfsVaRP2X96BRpe3EtUAN+AUBETgGeCXwY+xf+XOBtxpiF3AK4BHiliIwZY+4BZowx1wMYY76DtUi6LYNJ4DJjTDToXMoiMgb8HfAPeWL4XcC8MSZpn5PXMZ9nZYWKo3EhcKcx5uuDGq9iUQtmG2GMWRSRjwOvAf4V+GWsU/M+EXkW9g/O/j5/8U/FFr97g4j8DnAa1lIoAeWu8+aMMbODHreI7MbmaN4HXHyU0x2OUkurp28XeDO23royYFRgth8fBK7PpwW/gp32gK1x1TTGVPtdlAvLn2CnVF82xsQi8r2e01YW4V66/lh8MORTsy8C3wRel1spYOtlTYpI0G4TkQAYZ321tJ6Ntby+eLQTlfWjArP9+Cq23tRvYqdHv5K3/wioiMg5xpgfQsc3MW6M2Y+tU/VNY8wX8mOTwGOL3vRYfDAisgv4AnCtMebtPYe/h7VUzsP6jcBO1xJsXfOiXIAVzNZ6xqYUQ30w24zcN3IN8BfAZ9tTGmPMHcDXgfeIyIyITADvAz6eX3oX8DgR2SUip2OXg+/HTpeGxaXYVa9ecSH3EX0M+HMR2SMiJ+fnX2OM6a1zvhbnAXcPZLTKCtSC2Z78M/CnWKHp5tXA+7EvXAtr7fxafuxvsfEo9wIPYP0WM8D7RWQ/1kczaF4LJCLS7Gl/nTHmGuAN2GX127CrS/8K/H77pK7rfMBtf87jXtqcDDw8hLEraNmSbYmIPBv4KPDoLp+GogwcnSJtM/KpxHuA/6PiogwbFZhthIj8EXYl50bgvRs8HGUboFMkRVGGhlowiqIMjZFfRTpwYH7dJtj0dI3Dh+vDGM6WQJ/P2ujzWc7MzISz2rFtacH4vrfRQ9jU6PNZG30+xdmWAqMoyolBBUZRlKGhAqMoytBQgVEUZWiowCiKMjQKCYyIjPxytqIoJ56iFsw+Ebkyz3qmKIpSiKKWyeuw6RWvF5FD2ByuHzLG/NfQRqYoyshTSGCMMdcC1+YpCV+ATZt4o4jchc0t8uF1JvlRFGUbsC4nb54t/l+Bd2CLYp0DXAbcKyJ/3VUQS1EUpfheJBGZBv47NiP9M7HpFd8M/D9smYgrsUWrXj34YSqKMooUEhgR+STwImAvdkr0mrxOTpuFvOrfAwMfoaIoI0tRC+Yg8PPGmG/0HhCRaWPMYWPMERHpW3pCUZTtSVEn72v7tYvIqcDtwM78PC0crihKh6JTpMcB/4itO1PqOXzLoAelKMrWoOgq0pXYchWvxlbv+2XgnVhH7wuGMzRFUUadoj6Y84CTjDGhiKTGmOuA60Tk5cDlwG8MbYSKoowsRS2YFtBO41XPi5EDfAZ46cBHpSjKlqCowPwb8Ok8kO7bwOUicj7we8DCkMamKMqIU1Rgfg+4B+t/+UPgZ4B/B/4MeMtQRqYoyshTdJn6ELZOMMAdInIWcBJwwBiTDGtwiqKMNqsKjIj8YpEORIR8f5KiKMoy1rJgPtPzOQN665+kwCIwNchBKYqyNVhVYIwxHf+MiFwAvBK4FFvbuAI8Dng78NEhj1FRlBGlaBzMXwHnGWPm8s8LwM0iciHwXeDaYQxOUZTRpugq0i5gvE97hXwfkqIoSi9FLZjPAl8SkauA+/K2M7CpNL8wjIEpijL6FBWY1wKXAK8HTgfKwINY4Xn7cIamKMqoUzQOpg78cf61KiJymTFGBUdRFGDwhdcuHnB/iqKMMIMWmN44GUVRtjGDFphswP0pijLCaG1qRVGGhgqMoihDQwVGUZShoU5eRVGGRiGBEZGiOXf//jjGoijKFqOoBfNuEem3F2kZxpjfP87xKIqyhSi6VeB/AVeLyNXYvUhx90FjzJ0DHpeiKFuAogLz/vz7y7va2gmoMpYqDiiKonQoKjCPGcTNRGQfsIPlAXn/ZIy5UESeC1wGPAG7kfJyY8zfDuK+iqJsDEU3O94LICLnAI8yxtyQf3aMMeuJ3p0GnmmMubm7UUROBj6N3Zl9NfAU4LMico8x5nPr6F9RlE1E0VWkPSJyI/AD4Pq87QzghyIiBfsYBwJgts/h1wD3GGOuNMY0jDE3AtcAbyjSt6Iom5OiU6SrgDuBXwL25m17gY8A7wVeWKCPdua7S0Xk2fnPnwbeCjwVuLnn/JuxNbAVRRlRigrM84FTjDGLIpIBGGMyEbkU6y8pQgB8C7gB+E3gTGzC8L/DpuS8o+f8Q8BujsL0dA3fX7+PeWZmYt3XDINWlNBsxcRJiu+5VMo+5WDjfeab5flsVvT5FKOowMxjBaKXPRSM3jXG/Bh4ZneTiPwRdsp1Q59L2itUa3L4cL3I7ZcxMzPBgQPz675u0IRRwmIzXtE+VvEpbaDIbJbns1nR57OctcS2qMB8GfiAiFwCICK7gScBfwlcdxxjuwcrJPtZaa3sBA4cR9+bnmbUvyhmM0o2VGAUZVAUjeS9OD/3dmwlgf3A57B+mULRuyJyvohc1tP8eCACbgGe1nPsGcBNBcc3kiRJfwNttXZFGTWKLlPPAhfklsvZQAO42xizHjtxP3CxiDwM/A3WB/MOrAP5auBPROQi4B+xU6lXA4XK144qnuf0FRPP0z2jytag8G5qETkFu6rzIuAC4BUiMlP0emPM3cBLgFcBB4HPY/0vbzHGHABejBWV/cAVwIXGmK8V7X8UqawyDVqtXVFGDSfLjm6Oi8ivAh8G9mH3IjnAo7B+k1cZYz4xzEGuxYED8+ueT2wmJ10YJTSjhCTJ8DyHSuBtuP9lMz2fzYg+n+XMzEysanIXdfK+G7jIGHNVd6OIvB54H7BhAjPqlDaBoCjKsCgqMNPAB/q0/xNWfBQGY41sRotGUY6Voj6YT9E/WvfngM8MbDQjTDumpe20TZKMxWZMuMrqRA01AAAgAElEQVRS9LD6UJTNRFEL5l7ggyLyHcBg0zOcDZwPfERE3tU+0RjztoGPcgQYREyLxsUoW42iAvPTwPexMTBP6mq/DXhi1+dtG8AxiJgWjYtRthpF42CeV+S8PoF024ZBxLRoXIyy1dDa1ANiEDEtGhejbDWKTpGKsm3/1LZ9JMezAjSIPhRlMzFogdnWzoJBxLRoXIyyldDKjoqiDA0VGEVRhoYKjKIoQ0NrUyuKMjSOWWBEZLpPs9amVhSlQ9GyJeeKyE1dnz8GPCIi+0Xk/Ha71qZWFKWbosvU78OmyEREXga8ALvR8RnAu4DnDmNwiqKMNkWnSE8G/jz/+QLgo3m2ucuBc4cxMEVRRp+iAhMCJRHxsGkb2ikagnX0oSjKNqPoFOlrwMexFQAy4PO52Pwp8L0hjW1bo4mnlK1AUevjQuB+oAm81BgTAePYJOBvHNLYthVhlDBXDzk83+LgXIPZhZYmnlJGnqIWzEuMMb/b3WCMOSIiT8UKzO0DH9kmJIwS5hohjWYCZNTKPhO10nFbFgv1kMMLLZI0w3MdojjFdR1q+fFWlJCkGY1WzO6pSt/7qcWjbEbWFBgR8YEycIWI/AsrA+kE+DPsStKWJowSZhda1FtLpV7nGxFJmrFjvNx5mdf7oi/UQ/YeWCBKrKiUfY9GGFMt+cxnEZ6z9MjDeKnUbHefvSVokyRjMVl5nqKcaI42RboImMOKzAK2RnX313fzry1PM0poRemK9lYuKLD+nLphlHB4ISRKbL9pmtEIY9I0oxUnNFvLr/NctzOW3rGtNmZF2UjWtGCMMe8RkQ8DDwC/0OeUBtvEyZskGUm6UmCSNOsIynpz6jajhCS1lkuaLmW6yLBi4/bIfzlwO2PpHdtqY1aUjeSoPhhjzEEReYwx5kEAEQlyJ++2IYwSGq2YxWYEOJR9F9+3L7vnOp2Ulut90ZMkw3NdSr5HM7RTmjhOCZOUcuDjevZzueRTDlwC34pUbwpNTbWpbFaKriLtE5F3isiD2KkRIjIpIleLyPjwhrfxtKc9vudQ8j3SNKURxsSxtWbKgddJabnaC71WuxUOl0rJJ00zmlGC67jMTFWYmazi++4ycYGVKTQ11aayWSkqMO/Cbg+4iKWsdS62dOzlQxjXpqE97Ql8j6mxEmOVANd1iJOMiWqwzMG73he9EngEvket7FMp+fi+y+RYiVN31ahVgs6xOLdOPM9hrOKvmG6VAo+xit8RstXOO166l9Ln6qEumytHpegy9a8D5xlj9opIBmCMmRWR38aWLtmydE89At9jvAqB75KkGUGfFx2K59RdOt8h8O192tZKFFuncpKmeK67pmCciCVqXalSjoWiAuMDD/dpbwETgxvO5qPbvxHFSWeZ2nPdvi9ZkZy6fQWh5nXu1X0fS9Z3ebrd14l48bUonHIsFBWY/wDeBlzabsh9L+8GvjWEcW0aKoHXeWFbXS9Ze0UHjv6SdQtKkqWkSdbxqXQLQvtevcvh5S7LqPc+q734c42QUuT1tWqOxeLRlSrlWCgqMG8CPicifwCUReQO4EzgIeBlQxrbpqD94s3XQ+bqEXFsnbDdqzu2km5/ei2MRtMuTddgmeN2rhFSyqdGi42QLAPHcfA9h5bbFpGV9+n3grctoKmxpWXttogBx2Tx6EqVciwUrex4u4icA7wEOAsb//Ij4PPGmG3h6XNdl2rJYzFNARsQB5CkLuNrvGS9FkY7lqZtDbWilGYYE8YpM1MVAt9aE4vNqOP4TdKMeitmwl15n+5pVdtn02wlBKWV/vu1Au/a1lEYJRxZaHF4vrXMuum25LrvNVENCHWapKxC4bpIxpiGiNwCHDLG3AAgItviz9dqL2YrTvF9F2eNWULvX33Pda0IhHZ/URSnzC60SLMMyBgr+dSbMQuNiGaYMj1e6sTcZH2ediXwmG0t38IQJQl+4hDFyTIrqdmKlzmOu5e/kyTrWFuVsbTT1mvdzDVC6q0Yz3WolX1c113VP6QohQRGRPYAnwTOx6ZsqIjIGcBXROTFxhgzxDFuOG2RcF2HasmnFSd55G3WeclWo9dJHMUpi82QKEqpln3iNCVOUrIs46GDdZphTCXwqFUCUtfG3Iy5AePVAM9ZeZ9SYGNzmq2YKEkJPJey7+H7Lq0o6axIzTcjmq0Yz3HxPQd8qLeWpmqe5xzVkVsKPEqR15l69TtHUbopGgdzFXAnsIelOJi9wEeA9w5hXJuKTnyJayN4xyoBE7USk7VS5+VcjXYMTNsv4rpQKdmYlbl6SJpmOA7UWzHNMCbNMsIkpR5G+I7LRH6P1e5jHbYplXxnd6Xs47gOcZzmFpK9b7OVUPI9fM9ZFijYdihXAq+QI1edvcp6KCowzwf+wBjzCLnAGGMy7KrS04c0tk1DWyS6V47s5/4Bdt0Bac0oIfCcpWA512FqrMTOySq1ivWxZKlDEmcsNELm6xFziy0WGzGLkd2RkaQpUZwQxsmKILdmlOD1+GYC38XNtyC0oiTfjmAjhn3fpVpaCt6DrBNjUyQSeb3Rysr2pqgPZh6bHrOXPYxYLaTVnJhHu2ZuscVCMyJNM3zXpVT2rD8jS0nTFNd18TwHF4h6/uIngO85VMvlZf22NznGSUKcWIuDDNLMIYoSZudazExYIQI6U6RmK+bIQkg5sNMgF1hoxswuhjRaEa7rMlH1kTOml48ldzD7vovvw9RYGc9zlkUid682tekW0CLnKEqbogLzZeADInIJgIjsBp4E/CVw3ZDGNnCKODF7OTTX4KFDddI0y8UgZSGO2OlWqfjQClNapNTKPuBxpBFRDlyiOGW+Hnb8Ir7nsmuq2um3vQ3gwQN1HjxY55G5BvVmRL2VECcZWWaTT/3g3lkqJQ/f93JLJSNJM3zXoVLyGasGeK5DnNjVLc+z1kqSpDx8qE657OG7LlmadjZUkllxa7RiamWferO9d9XBdfJpVxjnAuyTZhmtMAHH/jUp+S6tOMHB0eRWypoUFZiLgatZyly3D0ixPpiRqYW03mjUMErYf7hphSVOaea7qgPfZa7eIk4TXBxSMhabEZO1Ms0wpt7MCOOle4Vxwv7DdfYeWGD/4Sb371/ggYMLzC6EQ/u3ngie9cSTee1LnrDRw1A2MUUFpmGMuSC3XM7GxsHcbYyZH97QBs96HZTNKCFKbOKn/YfrzOfpMitla43EcYlWmJKRMrcYESUph+aazC60aEUZjVZEM0yYr9vMd1uN7/3wQGfJW1H6UVRgZkXkm8CXgBuA7+dO3pGivWS871Cd/7zrEdLUTjccB5uKIctIkpQ4TyK10Ig4eKTB/GJIvRURxtYySZKMjDx3S5wSxSsTUa2F7zmcsWecXVMVxqsBRxZaxHHKYjMmc6CaO2NrVZ9HnzRJK0qolj1m5228i+M4ZKlDOXBIgYV6ZKdJeVxNGweoVQNKeayLg02Q1YoSkmT5mD3PJYwToijF810azYg0y3BwKJc9JioB9hL7a3cchyedvSvPaXPMvxJli1NUYJ4J/CzwbGyS75qI/Bu54IxKHEwl8DAPz/J/P3br0O/lOlAueVRKHtPjZWZ2VDl51xhPfuxuTpsZw/dc7t+/QJKm3Pbjg9QbEbP1kHojIopS3Cwjs24VKiWPSuCTpE1Kvovvedav43tUyi47J8rsnKx2nLhuHgS3c6LCzI7qsunfXD3kyEK4Ijuf5zrsPbBgn1O1xCOHFzvHqiWfR500QTlPL9GNxr8oa1F0q8CtwK3A+wFE5CxsCs03YsvKjsT/sFLgUS0d/1Bd18FzHALfpRS4TI2VqJY9xqoB45WAatlaQ1YUMqYmSlTLPo89dZKTdtr8XGGUsNiIiJIlX814OYAMWm5C4LvUKgGOB5W2BeI4NhbHsXEuNlF4wK7xMqftHuPQfIt6K+okrNo5uSQuC/leqkfmGjTDxI6zsvTrT9KMKEoJApco7tnekGS55ZOuEBiNf1HWovBWARGZxloxPws8B1tR4FZshO/I8NjTd3Dl25/PnT88gOM6eK7TEQw3/9yMEsjsX/WHH1ng0EKLR+ZaLCy2KJc8HNcljhJq1QDXc5islkjTjFLg4rgu1cClGSWEUUaaZfiOR8X3CWM68SuLzZhS4BIlCRPVgChMiNKEcmBFq1YOmBorsWOsQqMVEcYJ1bIPWftFd/NVJJ9KxSfwXU6fGSeK7SpUueQvS0Z+cK4J2D1VaRYzu9gC6IiM5zpM1AKaUUJv6uFaxa5U9ctJrPEvyloU3SpwO1AFvg18E2u53DKqGx1P3zNBeY334vC8ffmiOLHLxZ5LGCVEUWID47KMyfEyvuuQZTZ2pRS4xHFGpQRpaoPwqmW7tWByrATYOJTulazxakCaZoxXbXyMvxhSKVWpVYN86ddOg6I4oRz4TFRKLDQjatWssx2gnG8raG+erOdLz7C0DH+k3urcs+y7tDyXVpqw0Iw6AlMOPE7eXWN2LqSVZiwsgu/aTHnTU2XKgde3qoLGvyhrUdSC2Qs8BTgDOBU4CVvZ8cigBpLvbXo/8CzsKtWngDedqATj3TlSGi2bg7cVpVQrPrupcqQe0mjEhKmNUSmXPDwHXMdleqKE47g0mjG+7xDFGU7q4LkZlKwz2HUdmq2EQ3NNPNfp+DMmx0q4rkOl7DJeK7FjvEzg2ziaJeuqws5Ju9P6kSMNWnFKGMbEid12kCQ2TsVzXWplf8U0ZrERM5YLie+7TNZKLDZjmtHSpsWJmhVBz3HxywEeGa7rUvZdJrrSd6agxd2UwhT1wbxIRFzgPOC5wO8CHxSRB4CvG2MuHsBYrgXuAB4LTAGfAN4B/M8B9L0mvTlbfM+h3rL7dXzfpVrxOXl6jPGytRQWGjFB4NAKU8bHAqYnKvi+S5pmRGnKI7PNzm5l13WYb0RUS15nqgG2//ZGw+kJj/FqQKMVk2H3PNXGloRiohp0NlSWcx9SmrqUSs5SlK9rc8f0igtA7yzG912mxkvM+FVO3T227NiO8TLjk1XiVkScZPieQ6Xsq5gox4STZetz0olICbv/6DnAbwDnGGMK+3JW6fNp2Mx4JxljDuZtr8BustxtjFl1HfjAgfl1exlnZiY4cGAphGeuHq5wVkZxwpGFkCBw8VyHeiNmsRXRaMU0wgTXgSSFiu+yY9JOcdo7raM4JUvtpkUb+p9RLQfsGC91pi/tlAfj1VLnnm0rozfbHCwliYrihANHmqRpSqVkfS9AJzl4tbzyV9EKI5p9pje7JyuM10or2nufj7IcfT7LmZmZWNXhUNQH81LgZ/KvpwL3Y7cP/O/8+/HyVOD+trjk3AxMYwP7fjiAe6xKv5WQwPeYGi8t5UtJ7QtfLnnsycP2ozjl0HyzU/LV912bXqES2FWgsk8rLznbXjpu91djaSdz73RjNUvB+m/aK2Fexym9tHzc3yW2c7Kab8C0zuKS7zFZC/qKi6IMkqKWxxVYIfl74FXGmL1gayMB3wDOPc5x7AIO97Qdyr/vZg2BmZ6u4feZFhyNmZmlXOWlainfy7Mc33OplH2arZjSXECGjUnpFoDZeSse7YjWqckE17PLyW3n7vRiiANMjJVW9D81vnwDZBGOLLSOOt44STufy8cwtel+PspK9PkUY02Byacu52Odut8GxoCXikj7lLOxKTSHQdvsWnMKdPhwfd0d95q4vT6YNmMVnzB/OYM8s3/UXO5zDjyHKLRDTEk6+VdqZb9zbjuuJOy5dqziEzbWvx+pyHgBQjim/nUKsDb6fJazltgezYIZA16ITdXw1j7HG8AlxzyyJQ5gLZVudnYdGypF6hmtdU73ClSl7K9YbRmrlI/a/6DHqyibgTUFxhjzVeCrInK9MebFQxzHd4HTROQUY8xDedszgP3AXUO8b4ci9YxWO6fIte3zBkXReyrKRlJom9qQxQVjzPeAm4DLRGQq34pwCXDFKG6qVBTFspn2wb4CmAR+DHwFuJ6uQm+KoowexxW/MkiMMQ8CF2z0OBRFGRzrDrRTFEUpymaaIimKssVQgVEUZWiowCiKMjRUYBRFGRoqMIqiDA0VGEVRhoYKjKIoQ0MFRlGUoaECoyjK0FCBURRlaKjAKIoyNFRgFEUZGptmN7Wi9CIiVeCdwCuxqTxuA95qjLkpP74D+Bvgedg/ll8CLjTGzHX18RzgQ8Bdxpif6+n/IuD3gdOBh4APAH+hOYgGh1owymbmXcB/w9biOgmblOy6XHjAJqHfBTwZ+Kn856vaF4vIm4CrAdPbsYi8OO//t4AJ4OXAm4H/MZR/yTZFLZhthIjcANxujPmDrrY3A79jjPnJvLrmFdjqmiVs3fGLjDEL+bmvAv4EeAy2CsSVxphL82O/hS2S90lsaeFzjTF3dd3n0fR50XOuMca8rk/7EeAt7X5E5N3AHwKPF5G9WFF4ujFmX378EuAmEdmdl8BJgacBf5x/7+YZ+bP49/zzbSJyE1aslAGhArO9uBr4KxF5izGmXZbg5cCHRMQBrsMWwHsMUAH+GWsl/JqInImdarzMGHO9iDwd+IaIfMcY88W8r5OxVSCmgWVlD4wx9+Z9FsYY05tQ/gysaDyELWWcAbd2Hb8VW43iKcAXjTHvAeiqgtHNZ4E3i8jzsPXWfxIrOu9dzxiVtdEp0vbiWmzNt18AEJFTgGcCH8b+hT8XeJsxZiG3AC4BXikiY8aYe4AZY8z1AMaY72Atkm7LYBK4zBgTDdqPISJjwN8B/5Anht8FzBtjOtXm8jrm86ysULGC3I/zJuALQAv4D+B9xpgvDHLc2x0VmG2EMWYR+Djwmrzpl4FvGGPuw9a4coH9ItIUkSZwY37eqfn3N4jIj0SkkR9/ItBdOW7OGDM76HGLyG5s4b99wNHqoDscpZZW3ufzgMuAFwFVbCnki0XkV49vtEo3OkXafnwQuF5EasCvYKc9YGtcNY0x1X4XicjvYP0vLwe+bIyJReR7PaetrAa3dP2x+GDIp2ZfxE5jXpdbKWDrZU2KSNBuE5EAGKdYLa0LgU8ZY27IP39DRD6Mdfp+rMD1SgFUYLYfX8XWm/pN7PToV/L2HwEVETnHGPNDgFyExo0x+7H+iW+2pxB52eDHFr3psfhgRGQXdgpzrTHm7T2Hv4e1VM7D+o3ATtcSbF3zo+HlX93o+zBgdIq0zch9I9cAfwF8tj2lMcbcAXwdeI+IzIjIBPA+7JQKbAG8x4nILhE5HbscfD9w2hCHeyl2padXXMh9RB8D/lxE9ojIyfn51xhjeuuc9+M64JdF5Dki4udlkv878IkBjn/bo4q9Pfln4E+xQtPNq4H3A3djHZ9fBX4tP/a32HiUe4EHsDEjM8D7RWQ/8MMhjPO1QJL7e7p5nTHmGuAN2GX127CrS/+KDZwDoOs6H3Dbn40xFWPMB/NAvauwgXYPAu8G/mEI/45ti5Yt2YaIyLOBjwKP7vJpKMrA0SnSNiOfSrwH+D8qLsqwUYHZRojIH2FXcm5EA8qUE4BOkRRFGRpqwSiKMjRUYBRFGRojv0x94MD8uud409M1Dh+uD2M4WwJ9Pmujz2c5MzMTzmrHtqUF4/u9AZxKN/p81kafT3G2pcAoinJiUIFRFGVoqMAoijI0VGAURRkahQRGREZ+tUlRlBNPUQtmn4hcKSLPGupoFEXZUhS1TF6HTa94vYgcwuZw/ZAx5r+GNjJFUUaeQgJjjLkWuDZPSfgCbNrEG0XkLmxukQ8XTPKjKMo2Yl1O3jxb/L8C78AWrToHmzj5XhH5666CWIqiKMW3CojINDal4GuwuVy/js1q9v+wZSKuxGYDe/Xgh6koyihSSGBE5JPY8g57sVOi1+R1ctos5FX/Hhj4CBVFGVmKWjAHgZ83xnyj94CITBtjDhtjjohI39ITiqJsT4o6eV/br11ETgVuB3bm5/3L4IamKMqoU3SK9DjgH7F1Z0o9h28Z9KAURdkaFF1FuhJbruLV2Op9vwy8E+vofcFwhqYoyqhT1AdzHnCSMSYUkdQYcx1wnYi8HLgc+I2hjVBRlJGlqAXTYqnMZj0vRg7wGeClAx+VoihbgqIC82/Ap/NAum8Dl4vI+cDvAQtDGpuiKCNOUYH5PeAerP/lD4GfAf4d+DPgLUMZmaIoI0/RZepD2DrBAHeIyFnAScABY0wyrMEpijLarCowIvKLRToQEfL9SYqiKMtYy4L5TM/nDOgtT5ACi8DUIAelKMrWYFWBMcZ0/DMicgHwSuBSbG3jCvA44O3AR4c8RkVRRpSicTB/BZxnjJnLPy8AN4vIhcB3gWuHMThFUUaboqtIu4DxPu0V8n1IiqIovRS1YD4LfElErgLuy9vOwKbS/MIwBqYoyuhTVGBeC1wCvB44HSgDD2KF5+3DGZqiKKNO0TiYOvDH+deqiMhlxhgVHEVRgMEXXrt4wP0pijLCDFpgeuNkFEXZxgxaYLIB96coygijtakVRRkaKjCKogwNFRhFUYaGOnkVRRkahQRGRIrm3P374xiLoihbjKIWzLtFpN9epGUYY37/OMejKMoWouhWgf8FXC0iV2P3IsXdB40xdw54XIqibAGKCsz78+8v72prJ6DKWKo4oCiK0qGowDxmEDcTkX3ADpYH5P2TMeZCEXkucBnwBOxGysuNMX87iPsqirIxFN3seC+AiJwDPMoYc0P+2THGrCd6dxp4pjHm5u5GETkZ+DR2Z/bVwFOAz4rIPcaYz62jf0VRNhFFV5H2iMiNwA+A6/O2M4AfiogU7GMcCIDZPodfA9xjjLnSGNMwxtwIXAO8oUjfiqJsTopOka4C7gR+Cdibt+0FPgK8F3hhgT7ame8uFZFn5z9/Gngr8FTg5p7zb8bWwFYUZUQpKjDPB04xxiyKSAZgjMlE5FKsv6QIAfAt4AbgN4EzsQnD/w6bkvOOnvMPAbs5CtPTNXx//T7mmZmJdV+zndDnszb6fIpRVGDmsQLRyx4KRu8aY34MPLO7SUT+CDvluqHPJe0VqjU5fLhe5PbLmJmZ4MCB+XVft13Q57M2+nyWs5bYFhWYLwMfEJFLAERkN/Ak4C+B645jbPdghWQ/K62VncCB4+hbUZQNpmgk78X5ubdjKwnsBz6H9csUit4VkfNF5LKe5scDEXAL8LSeY88Abio4PkVRNiFFl6lngQtyy+VsoAHcbYxZj524H7hYRB4G/gbrg3kH1oF8NfAnInIR8I/YqdSrgULlaxVF2ZwU3k0tIqdgV3VeBFwAvEJEZopeb4y5G3gJ8CrgIPB5rP/lLcaYA8CLsaKyH7gCuNAY87Wi/SuKsvlwsuzocXIi8qvAh4F92L1IDvAorN/kVcaYTwxzkGtx4MD8utN0qpNubfT5rI0+n+XMzEysutBT1Mn7buAiY8xV3Y0i8nrgfcCGCYyiKJuXolOkaeADfdr/CZga3HAURdlKFBWYT9E/WvfngM8MbDSKomwpik6R7gU+KCLfAQw2PcPZwPnAR0TkXe0TjTFvG/goFUUZSYoKzE8D38fGwDypq/024Ildn7Uu0gYQRgnNKCFJMjzPoRJ4lAJN0aNsPEXjYJ5X5Lw+gXTKkAmjhMXmUoLBJMlYTOxnFRllo9Ha1CNOM0rW1a4oJxItWzLiJEn/Welq7YpyItHa1COO5/XX9NXaFeVEUtTJq/RhMzhXK4HX8bn0tivKRqMCc4xsFudq+14bLXSK0g8VmGNkLefqiX65SyooyiZl0AKzbSb+m825uhmma4rSyzE7eUVkuk/ztqlNvZmcq+3pWlvckiRjsRkT6lK1ssEULVtyrojc1PX5Y8AjIrJfRM5vt2+n2tSrOVE3wrk61whZaEQcWWyx0IiIYissGgujbDRFp0jvw6bIREReBrwAu9HxGcC7gOcOY3Cbmc3iXA2jhIV61PmcpCn1VkoN0Iq+ykZTdIr0ZODP858vAD6aZ5u7HDh3GAMbBUqBx2StxPREmclaaUN8Hs0owXNXTstaUaqxMMqGU1RgQqAkIh42bUM7RUOwjj6UIZAkGeU+wpakqcbCKBtO0SnS14CPYysAZMDnc7H5U+B7QxrbSHKiV3OsleJRA1pRQpJmeK5DrezrKpKy4RS1Pi4E7geawEuNMREwjk0C/sYhjW3k2IjVnLaVEvge49USU2NlxqslJmqlod1TUYpS1IJ5iTHmd7sbjDFHROSpWIG5feAjG0E2IvhuszibFaUfawqMiPhAGbhCRP6FlYF0AvwZdiVp27NRwXcayatsVo42RboImMOKzAK2RnX313fzL4XNFXynKJuBNS0YY8x7ROTDwAPAL/Q5pYE6eTvozmZFWc5RfTDGmIMi8hhjzIMAIhLkTl6lh25/SLMVEycZvufQjJxlx7tprzp1n18p+0f1o+jeI2UUKOrk3Sci7wR+G9gJVERkEngv8EZjzMKwBjhqtF/yJMkI8qe7WiqH9qpTFCfUW/Z4GC9d03t+73VtNA+vslkpukz9Luz2gItYylrnYkvHXj6EcY00RfPktj+3onRZeytaey+R5uFVRoWiAvPrwMvzGtQZgDFmFmvRvHhIYxtZiq4mdeJl0uUCk6RZ3/PX2//xEkYJc/WQw/Mt5uqh7s5W1k3RKZIPPNynvQVMDG44o8NaPhDPc/q+7L2rSe3zPNelGcaEcUKaZgSeS63sUyn3//UU7f940GmYMgiKCsx/AG8DLm03iMg48G7gW0MY16bmaC9f0dWkSuAx22pxZKHFQwcXiHIn766pCn4uMv2EbFCrVWuJ5GbK2KeMLkUF5k3A50TkD4CyiNwBnAk8BLxsSGPbtDSjhChOaEUpSZriuS7lwKUZOcuC3oqs8kRxynwjJM2AzCHLoBEl1OKU+XpEpZzl5yUsNBIOpRnjtYCK75FC4VWkXjFxgajLCuoWyfbnfmg5FGU9FK3seLuInAO8BDgLG//yI+DzxphtNzFvtuLOqg8s5WABmMz3ABWJrm1GCWkGrusyOb60d3Xcp64AABOLSURBVMh1HVIyjiyGeJ7DQiNisRmRphkZsNiMbJqI8TKTE+XOdatZJP0sriONiHLgEvjLx9i2XE7ENEzZ+hTOyWuMaYjILcAhY8wNACKyLf+3xav8Fe9tX2sKEkYJRxZCZheaNJoRrusQ+NbnnqYZaZoRpzH1lkcjjInjtPPyZ6lHUsk4vBB2hGytaVu/6U6SprSibIXAtEVlqwUNatzQxlBIYERkD/BJ4HxsyoaKiJwBfEVEXmyMMUMc46ah/Z+03oxoxQkl3+uIQhynJFnG4fkWSZYShSmtOOlMn2D5C2vFIMN1HXzfoxna2MXAd3FdB9d18ByXOE6tBdPIz8dOq/z8vFrZY9dUtSMi3VO3NM2YW3TJyDrjaAuK57orVq9gyULZSpso1WG9cRS1YK4C7gR+Cdibt+0FPoINtnvh4Ie2uej+T1ou2f+U/397Zx5k2VXX8c/d39LrrFlBk8CvRDMJZAFZjEophJCYolBAkIJQRVjEQmSxyqEEFDSIwVQFVFAkYkUslc2EQgQNRGIEKiEmYJ0QiYQswkwyM7285a7+8TvvzetOd8+b6X49vZxP1as3757b991z577f/Z3f+f2+p2u1b3uKcnEQ9JPm5js5SehDSF/CMgqDBd5EEgXEYUAzCajKsj89XYsCppoJWVYw28kAj7IqKfKKbl4wUY/6x5htZ4w3YoqiWpCwl+clbZu116xFQLngPJLIp5s93hMb9FA2axFlVVWkWUmrm9Pu5hyc6dBqZ3TSgiDwkDOn+v8Xm7F/m4lhDczPA6caY+ZFpJcHU4nI+4CHR3Z2G4jFhqEoK8LQJ/B9oKIoK/ujVa+gLEu6ue4DmkwXhcGCuEYUBkw2Y8qyopMVdDsZDS+iFockSUBWltTjkLKsmPd80qpQ9bqeQQsDneLOChurOeqR9IxfWVbMd9Q78n2fsqyYHg+IwoBGEh5XoHg9KKuKblrQtnGudjen3S3se75o+8K23vZOWvSN9VI8Z9+pXPmcs1zAeh0Y1sDMovKYi9nDJl4LqZPm3HP/Yxya7eJ5EPo+QaAzOaA3O5U+Eec7OfqxoqogL0qyvCQvSr1RPR12ZHmB50EnLciLkijw8XyP0PcYq8eEoU8YeHh4BIGH5+nxqaCkIvQz2llONyuogGYSMjmW4OHx4MFZupkq1qVZSSftEvop8+2M6fGImVaXNC3o5AWz8yo+WIsCxhsqQNXNCw0Qj8VM1NdWQ7hnGLpZQSct6KYFnTSnnS5lHBZuO9qmNVmj/tk3rQfoAtajZ1gD86/Ax0RkP4CI7ALOA/4Q+NyIzm2kpFnBv3zjQT596/dO9qmsSKMWsmuyxvRYzMRYwq6pBDw4Mp8yXo8IQ59OnvOjwxormbWZt61ujkdFFId07LApigLSrOTeBw5zZD5lZj6l3S0oChUI930fqopaLaLd1sBz4KvB7WT5UcOR6XvPiHSygjR7fDxn1HhALQmoJ2H/1Rj4dz0JqMchsY1X1eKQybGYM3aPAZs3YL2ZGNbAvAn4OEeV634IlGgMZlOuhaTj742vV97q5DzQmeOBHx7d1khCdkwkTDRjanFAGPiEvkeaF2RFSVlqDchcNyPLNTbTSUtmW+myM2Drje95agAeZxyCBZ9rdltjCSOSxAG+N5wX4maRTg7DGpi2MeZK67mcjebB3G+MmR3dqY2Woqi4+Cf28oQ948y0UjxPn4ie5zHeiPA8Hb54ntff3u5mzLYzqkoDuzPzXQL7ZPQ8j06qQ5u5Tsp4PcED8qok8nxN+/dgshnjez5R6DHbSrn/kVnmOxnzrZyyqqjQ4VJalHSzgoOHOxyZT/vDNoBWN6d1IIcD8+t6zeLIpxYFJHFAEoXUkqD/ub89DqjFIWHg4XsecRjQqIdMNmImmnHfQMSRjzekcVibc3cG5WQwrIE5LCJfA74EfBm42xizMR6FJ0gvkeyUnQ1O2dlYsH1iGcHsmVa6IDD48KNzlGWF7/s0ayHznYw0LUhaATsnauR5yaG5lLwo8D0IwgDf95iynkdZwp7JOjNxQByqd9GIQ9pprj/mJKSqKuIoYGY+5dGZLnOtjAOH2xyeS1cMZC6m5/XsmKixZ6rGWadPcs7pk0w2E8LAoygrHpvpUJQVU1MNDj561HiFgcfe6Qb+EusvLcXiaeEezZpb6WC7MayBeQbwbOA5qMh3Q0RuwRqczZgHcyKJZD3j0ss1yfOSTloQh2pgyrIiLUrG62qg0rwkzXPSrCQMfKYT3efwfEo9C2h3M9KyIAo0uNzNCg7Pd8mLkjhOCDyftMjpdgtqccAZu8bYu6OO78Fjsx1CP6CbFxya6VChw45OVtDu5HieZgTHoU8Yekw2a0w2Epr1EPCoJT5pVlJVFZ7n94WuiqKiWY/oDEyFa3xmeG/D1TE5egxbKnAXcBfwIQAROQuV0Px1dFnZTXfXnEgiWRB4C8oEmrWIoqzIC01+i4KAKCiZaMRU6FCmLHV2Kgz8/pR1WZakmQZPfc+jQCuo60lImpXUkqAfU4nCQIdOZQV+he/puU+N1Wh3c4LSo1GLGav3MnpLNT6BRxSE4FX4nsdkU41eXpaEvk+nW/LoTJuiLJkeSxhrxGuWvevqmBw9hi4VEJFp1It5NvAz6IoCd6EZvpuS4x2X16KAI3Np/3MY+kw0YrzAIw4C6rWAI/M+gY0tjNUj0lSnm3vJeaD5KFHsU6tCok5Gq1MQBD4xaqjGG7EWM1YVcRyQFyW+5zE1kTCWxHieR5qXekwPkjiEsqLdKSgpSYIAz4fd0zVqUUgnU6ORZWpccrvqo+/r0Giw5AAgDNQQnmgw1NUxOXoMWypwD1AHvg58DfVcvrXdCh3j6GgGbH8FxWbcT7+fHk+ohQGH5lKKsqQeR0w0Sxun0R+X73tMNSJqSUgr6CnaFXTSijAOmB6rMT1RUy+nKGkkIb7vUY9DxuqRTi2nOXmh08J5ocOcLC3JygrPg0YzoioqppoJ9VoIVcJ8NydNc/JCDWUY+sT2vIuy7A9f4ihgciwhbadLXIHh2Gp1TI4TZ1gP5kHgqcCZwGnAXnRlxyNrdSK2tulDwDPRWarPAr+50QTGa0n4uAJBOPp0HmtoAltv6FWv+bQ7RT+nJIk0i7ZZC0miAqg4ddcYUJGEARU6m9XNS2Iqm+avQVqANC/odEu6aUEU+TTikMOzGSUQBQG1RIdaVVnR7ubUbWB112SNmVbGozNtG5s5WkcV+P6aDl+2Uh2TY3UMG4N5voj4wNOAS4CrgRtE5CHgVmPMm9bgXD4FfBs4B5gEPg38HvDba3DsNWOYp/PCoVeybA5G79VIUmbb2UBhJMy1VZ5hcFurmxOFAdPjAUGgnk8YeOSVekJRAJEd3tSTkDD0mWyqnEPP8BUDNU89kshf8+GLmxZ2AHhVdXxPLhGJgYvQOMwrgScZY4aO5SxzzAtRZby9xpiDdtuL0SLLXcaYZdNEDxyYPe5H7+7d4xw4cOIpPKNI2lrqmHDUC2h3c0I701SUFXle6rR4XjI7n4IHcRQSBb2ZI58o8Nm7o7lg6n2ulfaHcIMV1oNTyKu9Plsdd30Wsnv3+LJPp2FjMFcAz7KvC4AfoOUD77Lvq+UC4Ac942K5A5hGE/u+uwbfsWaM4um83DGX2tbq5uqdjKl3ctqOBq20wIN+BTXAuDUqg97V4iGcG744Rsmwnsf1qCH5KPBSY8yDAHZtpH8H9q3yPHYChxZte8y+72IFAzM93SBcIiZyLHbv3lxa5XE97gd207yk283Jy5IkCtmzo0E3Kzgy22WulWqRZD1iaiyhloRagX2cbLbrs9646zMcKxoYO3R5OhrU/TrQBK4Qkd4uZ6MSmqOg53atOAQ6dKh13AfejC7uctmxvu8xc1ivQc2H2oD0ZtpOT2g2aDNen/XEXZ+FrGRsj+XBNFExqQh42xLtbWD/CZ/ZUQ6gnsogOwbatj1uZsaxGVnRwBhjvgJ8RURuNsaMcoG1bwKni8ipxphH7LaLgR8BG1tPYR1xMzOOzcZQegUjNi4YY+4EbgeuEZFJW4qwH7h+sxdVOhzbmY0kiPJiYAL4H+DfgJsZWOjN4XBsPlaVv7KWGGMeBq482efhcDjWjuNOtHM4HI5h2UhDJIfDscVwBsbhcIwMZ2AcDsfIcAbG4XCMDGdgHA7HyHAGxuFwjAxnYBwOx8hwBsbhcIyMDZPJux5sFt3f1SAiTwSuRaVNK7Ts4s3GmIdF5FzgOlT69DHgBuA9vXovqyK4H5Xh+B7wLmPMp22bhwqMvQLV77kDeJMx5tu2fQr4MPBz6IPrS8DrjTEz69Dt40ZEPoheF89+vgS4BngK8DDwJ8aYPxvY/w3oEsqnA98B3maMudW2xcAHgctRBYKvoX1/yLZv+ftuObabB/Mp9Id1Drr8yjNR3d+txD+hN/FZwE+ixuAjIlJH67v+AzgD/TG8BtVXRkT2AX+DGpHdwDuBG0Xkp+xx3wC8Gi3nOB39Ed0sIjXb/lH7XecD5/a+d4T9PGFE5Hzg1wY+n4JetxtQ7aOr0MLb59v2y4A/AF4L7AH+CrhJRPbaQ7wXvZcuQa/7QeAfB75yO9x3S7JtSgVWo/u7WbBexLXAflvbhYi8FO3jVcCfo/3PbdtbgVcYY84XkeuBJxhjrhg43k3AfcaYN9ulaz5mjLnWtkWonMargduAR4CLjDF32PYL0Qr5UxZJoZ5UrHj9bcDngPcaYzx7HV5pjNk3sN/1wBl2TfabgO8ZY35joP0e4C/QhQcfBa4yxnzKtu0Gfoh6iiFb/L5bie3kwRxL93fTY4w5bIy5qmdcLGcCD6H9v7tnXCx3AOdaL+QC+5lF7RdZ7+cpg+3Wvb8bFYB/Kjocu2vgb+9CVQmfuhZ9W0OuBlrAjQPblu37EO29VTAGr80BdKmfi9gG991KbCcDcyzd3y2HqLbpftQdX67/PnqzL9e+y7Z7K7TvBGYHF+KzBmiWDXRt7ZDmd4HXL2paqe/Hat9pP6/Uvq3uu0G2k4FZiqF0fzcjInIB8FXgj40xNy6z27H6763Qthbt6821wEeNMWaIfUfZ9y173y1mOxmYbaP7KyLPwy4rY4x5j928XP8L9Am7XPsB9IlbrtB+AJiwcZneOUTo6p8b4tqKyHNRGdb3LtG8Ut+P1d7bZ6X2bXHfLcV2MjB93d+BbVtO91dEng78HRq0/NOBpm8C++yUao+LgTuNMV3bfuGiw10M3G6M6QD3DLaLSAKchwZy70Sfxk8b+NsLUeO1OHZxsngFOvv1oIgcxJ6X/feCvlkuRvsGK1wb9N45xMJrcxo6U3c72+S+W45tM4sEICK3Afeh+Qw70anJTxpjtsSUoYiEwLfQYcB1i9oS4L9R4/P7aHDyC8DbjTGfEJFeEPdlwOeBK9Bp233GmPtE5LVo/OJ5wP3Au4EXAucaYzIRuRGd3n45+uD6W3Tm5TUj7vZQiMg0mqPS4wx0yv5MIECD0r8D/CXwDOAm4AXGmK+KyC+i086XogbjdXbfJxtjDonI+9BrcQVwGM0H2muMea797i19363Etkq0Q3V/P4zq/s6jP7atpPv702juyzUics2iNgEuQ6dVH0Ld82uNMZ8AMMZ8R0RegubB3ADcC7zIGHOfbf+IiOxBk8R2oFOvLxxIFnsdukDff6HDqc8D/Wndk40x5hADwVZrjBlYRPAy4I/QfJcH0ES5r9p9vigib0Gnlk9H+/gCe0xQw9tEY1514BbgpQNfv9Xvu2XZVh6Mw+FYX7ZTDMbhcKwzzsA4HI6R4QyMw+EYGc7AOByOkeEMjMPhGBnOwDgcjpHhDIzD4RgZzsA4RoaI/KyIXHyyz8Nx8nAGxjFKfgutu3FsU1wmr2PViMjbUUnNPcD/ofq0l9pXDtxmjLnEatNej0pGxsBngDcaY+ZE5FVoyv270VqpHWhK/dXGmFREnozq2vZEoG4HXmuMeWB9euk4EZwH41gVIvJM1ChcboxpAC+xn98BfB8Vt77EioZ/DpXW/HFUzW03quXb4xRUs1ZQbd9fAN5i265Ha4ROwVZFAx8Yaeccq8YZGMdqmbLvcwDGmG+gWrN3L9rvQmAfWr09ZyUk9wO/LCK9Kuca8E5jzLwx5l7gr4FfGvieFOgaY+ZR7+VXRtYrx5qw3aqpHWvPl9ElSoyI3AL8M/BxVAh7kLPRB9qPVMlzAafZ91ljzCMD278/0PZudNWDS0XkC8Df2+92bGCcgXGsCitWdbmInIfqobwaeIcVvhqkDXSMMfWljiMiz0J1WQbpy04aY24WkSegkhOXo8uGfMgY89a1641jrXFDJMeqEJFQRKaMMXdZAaXzgSPAixbteh9QE5EnDfxtw2rM9GjYNYp6PBGNtSAiu4wxs8aYTxpjXo7qz1w9ij451g5nYByr5W3ALSLyY/azoDNA96Fey9kiMmlXgLwVuE5EdovIOCp+9Q8Dx+oC77SGR9DF0T5jl035roi8UURiu8zK0+x3ODYwzsA4Vsu16CqP/ykiLXSm6P3GmM+iCnBX23ZQOc0cldz8X3Q5lJcNHGsWle28F9X5/SJwnTGmjXpEr0JV6R5GDdmvjrBfjjXA5cE4NgQ2D+YDxpgtv1bQdsJ5MA6HY2Q4A+NwOEaGGyI5HI6R4TwYh8MxMpyBcTgcI8MZGIfDMTKcgXE4HCPDGRiHwzEy/h+KLYqnte3SVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x1008 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"steps\", y=\"tweets_per_day\", row='year', data=joined_data,lowess=True,size=2,aspect=2,scatter_kws={'alpha':0.1})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks different. First of all we see that the number of daily tweets drops a lot from `2012` to `2018`. And additionally we see that the number of daily steps is a lot lower for `2016` and `2017`. \n",
    "\n",
    "Which makes sense: \n",
    "1. For much of 2015/2016 I didn't wear any Fitbit but rather only used the iPhone app to import data.\n",
    "2. I also happened to write my PhD thesis during those years, so I moved and tweeted less. 😂\n",
    "\n",
    "Does your Twitter activity and physical activity correlate?"
   ]
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}