Airline+Twitter+Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview\n",
    "It's Feb 25th, 2015, and you need to book a flight to Ann Arbor for the Michigan vs. Ohio State basketball game. This is your first time flying, and you want to make it a good first experience. You know there are several airline choices out there, but you don't know who is the best and who is the worst. There's plenty of rating websites, but you can't always trust their sources. You want to hear from the people themselves. Who is the airline to avoid when flying? What are everyone's major complaints? The best source to hear people rant - **TWITTER!!!** Your task is to parse through twitter data from the week span of Feb 17th to Feb 24th, and figure out who has been the worst lately and why."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data & Setup\n",
    "Utilize a twitter dataset that has been compiled for you to peform your analysis. Figure which airlines are being talked about and why. Not everyone goes on twitter complain, so be sure to sort out your negative tweets from all other ones so that you may perform your analysis on just the negative tweets. At the end, just for fun, let's see if the Twitter-verse has any true power. We will attempt to correlate an airlines twitter rating for the week to it's stock prices for the same time frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Skills & Concepts\n",
    "The third cell of the notebook will not be shown to the learner, and lists the skills and concepts you are aiming to test with this question. Please remove all of the bullets from list below which you are *not* testing with this question. You can add new bullets with new skills wherever you see fit, and skills can focus on specific APIs or tools or more broadly on concepts related to the course. And don't worry if you're not sure, this is just a best guess list, there are usually many ways to solve a problem!\n",
    "* python-dicts\n",
    "* python-list comprehension\n",
    "* python-lambda\n",
    "* python-functions\n",
    "* python-types\n",
    "* python-strings\n",
    "* python-csv\n",
    "* python-dates and times\n",
    "* python-objects\n",
    "* python-map\n",
    "* python-numpy\n",
    "* python-pandas-Series\n",
    "* python-pandas-Series-index\n",
    "* python-pandas-Series-querying\n",
    "* python-pandas-DataFrame\n",
    "* python-pandas-DataFrame-read_csv\n",
    "* python-pandas-DataFrame-querying\n",
    "* python-pandas-DataFrame-indexing\n",
    "* python-pandas-DataFrame-merge\n",
    "* python-pandas-DataFrame-groupby\n",
    "* python-pandas-DataFrame-agg\n",
    "* python-pandas-Timestamp\n",
    "* python-pandas-DateTime\n",
    "* python-pandas-matplotlib\n",
    "* python-regex\n",
    "* python-fill_between\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14640 entries, 0 to 14639\n",
      "Data columns (total 15 columns):\n",
      "tweet_id                        14640 non-null int64\n",
      "airline_sentiment               14640 non-null object\n",
      "airline_sentiment_confidence    14640 non-null float64\n",
      "negativereason                  9178 non-null object\n",
      "negativereason_confidence       10522 non-null float64\n",
      "airline                         14640 non-null object\n",
      "airline_sentiment_gold          40 non-null object\n",
      "name                            14640 non-null object\n",
      "negativereason_gold             32 non-null object\n",
      "retweet_count                   14640 non-null int64\n",
      "text                            14640 non-null object\n",
      "tweet_coord                     1019 non-null object\n",
      "tweet_created                   14640 non-null object\n",
      "tweet_location                  9907 non-null object\n",
      "user_timezone                   9820 non-null object\n",
      "dtypes: float64(2), int64(2), object(11)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "#Load and inspect data\n",
    "df = pd.read_csv('Tweets.csv')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see there's 14650 rows and 15 columns of data. There's a lot of missing data within some of these columns, but before we decide how to handle them. Let's first reduce the columns to the ones we might thing we are interested in. Let's print out a few lines of the dataframe and see what exactly is in each column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>tweet_id</th>\n",
       "      <th>airline_sentiment</th>\n",
       "      <th>airline_sentiment_confidence</th>\n",
       "      <th>negativereason</th>\n",
       "      <th>negativereason_confidence</th>\n",
       "      <th>airline</th>\n",
       "      <th>airline_sentiment_gold</th>\n",
       "      <th>name</th>\n",
       "      <th>negativereason_gold</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>text</th>\n",
       "      <th>tweet_coord</th>\n",
       "      <th>tweet_created</th>\n",
       "      <th>tweet_location</th>\n",
       "      <th>user_timezone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>570306133677760513</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cairdin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica What @dhepburn said.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:35:52 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>570301130888122368</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.3486</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica plus you've added commercials t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:15:59 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>570301083672813571</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.6837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yvonnalynn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica I didn't today... Must mean I n...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:15:48 -0800</td>\n",
       "      <td>Lets Play</td>\n",
       "      <td>Central Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>570301031407624196</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Bad Flight</td>\n",
       "      <td>0.7033</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica it's really aggressive to blast...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:15:36 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>570300817074462722</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Can't Tell</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:14:45 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id airline_sentiment  airline_sentiment_confidence  \\\n",
       "0  570306133677760513           neutral                        1.0000   \n",
       "1  570301130888122368          positive                        0.3486   \n",
       "2  570301083672813571           neutral                        0.6837   \n",
       "3  570301031407624196          negative                        1.0000   \n",
       "4  570300817074462722          negative                        1.0000   \n",
       "\n",
       "  negativereason  negativereason_confidence         airline  \\\n",
       "0            NaN                        NaN  Virgin America   \n",
       "1            NaN                     0.0000  Virgin America   \n",
       "2            NaN                        NaN  Virgin America   \n",
       "3     Bad Flight                     0.7033  Virgin America   \n",
       "4     Can't Tell                     1.0000  Virgin America   \n",
       "\n",
       "  airline_sentiment_gold        name negativereason_gold  retweet_count  \\\n",
       "0                    NaN     cairdin                 NaN              0   \n",
       "1                    NaN    jnardino                 NaN              0   \n",
       "2                    NaN  yvonnalynn                 NaN              0   \n",
       "3                    NaN    jnardino                 NaN              0   \n",
       "4                    NaN    jnardino                 NaN              0   \n",
       "\n",
       "                                                text tweet_coord  \\\n",
       "0                @VirginAmerica What @dhepburn said.         NaN   \n",
       "1  @VirginAmerica plus you've added commercials t...         NaN   \n",
       "2  @VirginAmerica I didn't today... Must mean I n...         NaN   \n",
       "3  @VirginAmerica it's really aggressive to blast...         NaN   \n",
       "4  @VirginAmerica and it's a really big bad thing...         NaN   \n",
       "\n",
       "               tweet_created tweet_location               user_timezone  \n",
       "0  2015-02-24 11:35:52 -0800            NaN  Eastern Time (US & Canada)  \n",
       "1  2015-02-24 11:15:59 -0800            NaN  Pacific Time (US & Canada)  \n",
       "2  2015-02-24 11:15:48 -0800      Lets Play  Central Time (US & Canada)  \n",
       "3  2015-02-24 11:15:36 -0800            NaN  Pacific Time (US & Canada)  \n",
       "4  2015-02-24 11:14:45 -0800            NaN  Pacific Time (US & Canada)  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's a lot of information here, but some of it we don't need. Getting rid of some of these columns will also help out with our previous problem of what to do with the missing data. it appears that we might only be interested in:\n",
    "\n",
    "#### ['airline_sentiment', 'airline_sentiment_confidence', 'negativereason', 'negativereason_confidence', 'name', 'text', 'tweet_coord', 'tweet_created', 'airline']\n",
    "\n",
    "The naming of these columns are the greatest either. So let's reduce the dataframe to the columns of interest, and rename them at the same time and set the dates as the index."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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>Rating</th>\n",
       "      <th>Rating_Conf</th>\n",
       "      <th>Negative_Reason</th>\n",
       "      <th>Reason_Conf</th>\n",
       "      <th>User</th>\n",
       "      <th>Text</th>\n",
       "      <th>Coordinates</th>\n",
       "      <th>airline</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-02-24 11:35:52 -0800</th>\n",
       "      <td>neutral</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cairdin</td>\n",
       "      <td>@VirginAmerica What @dhepburn said.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-24 11:15:59 -0800</th>\n",
       "      <td>positive</td>\n",
       "      <td>0.3486</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>@VirginAmerica plus you've added commercials t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-24 11:15:48 -0800</th>\n",
       "      <td>neutral</td>\n",
       "      <td>0.6837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yvonnalynn</td>\n",
       "      <td>@VirginAmerica I didn't today... Must mean I n...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-24 11:15:36 -0800</th>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Bad Flight</td>\n",
       "      <td>0.7033</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>@VirginAmerica it's really aggressive to blast...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-24 11:14:45 -0800</th>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Can't Tell</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-24 11:14:33 -0800</th>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Can't Tell</td>\n",
       "      <td>0.6842</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>@VirginAmerica seriously would pay $30 a fligh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-24 11:13:57 -0800</th>\n",
       "      <td>positive</td>\n",
       "      <td>0.6745</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>cjmcginnis</td>\n",
       "      <td>@VirginAmerica yes, nearly every time I fly VX...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-24 11:12:29 -0800</th>\n",
       "      <td>neutral</td>\n",
       "      <td>0.6340</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pilot</td>\n",
       "      <td>@VirginAmerica Really missed a prime opportuni...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-24 11:11:19 -0800</th>\n",
       "      <td>positive</td>\n",
       "      <td>0.6559</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dhepburn</td>\n",
       "      <td>@virginamerica Well, I didn't…but NOW I DO! :-D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-24 10:53:27 -0800</th>\n",
       "      <td>positive</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>YupitsTate</td>\n",
       "      <td>@VirginAmerica it was amazing, and arrived an ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Rating  Rating_Conf Negative_Reason  Reason_Conf  \\\n",
       "Date                                                                            \n",
       "2015-02-24 11:35:52 -0800   neutral       1.0000             NaN          NaN   \n",
       "2015-02-24 11:15:59 -0800  positive       0.3486             NaN       0.0000   \n",
       "2015-02-24 11:15:48 -0800   neutral       0.6837             NaN          NaN   \n",
       "2015-02-24 11:15:36 -0800  negative       1.0000      Bad Flight       0.7033   \n",
       "2015-02-24 11:14:45 -0800  negative       1.0000      Can't Tell       1.0000   \n",
       "2015-02-24 11:14:33 -0800  negative       1.0000      Can't Tell       0.6842   \n",
       "2015-02-24 11:13:57 -0800  positive       0.6745             NaN       0.0000   \n",
       "2015-02-24 11:12:29 -0800   neutral       0.6340             NaN          NaN   \n",
       "2015-02-24 11:11:19 -0800  positive       0.6559             NaN          NaN   \n",
       "2015-02-24 10:53:27 -0800  positive       1.0000             NaN          NaN   \n",
       "\n",
       "                                 User  \\\n",
       "Date                                    \n",
       "2015-02-24 11:35:52 -0800     cairdin   \n",
       "2015-02-24 11:15:59 -0800    jnardino   \n",
       "2015-02-24 11:15:48 -0800  yvonnalynn   \n",
       "2015-02-24 11:15:36 -0800    jnardino   \n",
       "2015-02-24 11:14:45 -0800    jnardino   \n",
       "2015-02-24 11:14:33 -0800    jnardino   \n",
       "2015-02-24 11:13:57 -0800  cjmcginnis   \n",
       "2015-02-24 11:12:29 -0800       pilot   \n",
       "2015-02-24 11:11:19 -0800    dhepburn   \n",
       "2015-02-24 10:53:27 -0800  YupitsTate   \n",
       "\n",
       "                                                                        Text  \\\n",
       "Date                                                                           \n",
       "2015-02-24 11:35:52 -0800                @VirginAmerica What @dhepburn said.   \n",
       "2015-02-24 11:15:59 -0800  @VirginAmerica plus you've added commercials t...   \n",
       "2015-02-24 11:15:48 -0800  @VirginAmerica I didn't today... Must mean I n...   \n",
       "2015-02-24 11:15:36 -0800  @VirginAmerica it's really aggressive to blast...   \n",
       "2015-02-24 11:14:45 -0800  @VirginAmerica and it's a really big bad thing...   \n",
       "2015-02-24 11:14:33 -0800  @VirginAmerica seriously would pay $30 a fligh...   \n",
       "2015-02-24 11:13:57 -0800  @VirginAmerica yes, nearly every time I fly VX...   \n",
       "2015-02-24 11:12:29 -0800  @VirginAmerica Really missed a prime opportuni...   \n",
       "2015-02-24 11:11:19 -0800    @virginamerica Well, I didn't…but NOW I DO! :-D   \n",
       "2015-02-24 10:53:27 -0800  @VirginAmerica it was amazing, and arrived an ...   \n",
       "\n",
       "                          Coordinates         airline  \n",
       "Date                                                   \n",
       "2015-02-24 11:35:52 -0800         NaN  Virgin America  \n",
       "2015-02-24 11:15:59 -0800         NaN  Virgin America  \n",
       "2015-02-24 11:15:48 -0800         NaN  Virgin America  \n",
       "2015-02-24 11:15:36 -0800         NaN  Virgin America  \n",
       "2015-02-24 11:14:45 -0800         NaN  Virgin America  \n",
       "2015-02-24 11:14:33 -0800         NaN  Virgin America  \n",
       "2015-02-24 11:13:57 -0800         NaN  Virgin America  \n",
       "2015-02-24 11:12:29 -0800         NaN  Virgin America  \n",
       "2015-02-24 11:11:19 -0800         NaN  Virgin America  \n",
       "2015-02-24 10:53:27 -0800         NaN  Virgin America  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def clean_df(df):\n",
    "    df = df.loc[: , ['airline_sentiment', \n",
    "                         'airline_sentiment_confidence',\n",
    "                         'negativereason',\n",
    "                         'negativereason_confidence',              \n",
    "                         'name',\n",
    "                         'text',\n",
    "                         'tweet_coord',\n",
    "                         'tweet_created',\n",
    "                         'airline']].rename(columns = {'airline_sentiment':'Rating',\n",
    "                                                             'airline_sentiment_confidence':'Rating_Conf',\n",
    "                                                             'negativereason':'Negative_Reason',\n",
    "                                                             'negativereason_confidence':'Reason_Conf',\n",
    "                                                             'name':'User',\n",
    "                                                             'text':'Text',\n",
    "                                                             'tweet_coord':'Coordinates',\n",
    "                                                             'tweet_created':'Date'}).set_index('Date')\n",
    "    return df\n",
    "clean_df(df).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have reduced our dataframe to it's desired form, we can now do some more investigating of our data. Whoever has compiled the data for us was nice enough to already extract the airlines from the tweet text and chronicle them for us in the \"airline\" column\". Let's inspect the airlines and get a general sense of how keen the public is on them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "airline\n",
      "American          2759\n",
      "Delta             2222\n",
      "Southwest         2420\n",
      "US Airways        2913\n",
      "United            3822\n",
      "Virgin America     504\n",
      "Name: Rating, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Groupby airline, and reference the ratings column and then extract total count\n",
    "print(clean_df(df).groupby('airline')['Rating'].count())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By grouping the data by airlines and indexing each of their ratings, we can see that there are 2759 tweets pertaining to american airlines, 2222 to delta, etc etc. But this doesn't give us full insight as to how many of each tweet is postive, neutral, or negative. Lets regroup our dataframe and extract the lower level details of each airlines ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "airline         Rating  \n",
      "American        negative    1960\n",
      "                neutral      463\n",
      "                positive     336\n",
      "Delta           negative     955\n",
      "                neutral      723\n",
      "                positive     544\n",
      "Southwest       negative    1186\n",
      "                neutral      664\n",
      "                positive     570\n",
      "US Airways      negative    2263\n",
      "                neutral      381\n",
      "                positive     269\n",
      "United          negative    2633\n",
      "                neutral      697\n",
      "                positive     492\n",
      "Virgin America  negative     181\n",
      "                neutral      171\n",
      "                positive     152\n",
      "Name: Rating_Conf, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#groupby both airlines and rating and extract total count\n",
    "print(clean_df(df).groupby(['airline','Rating']).count().iloc[:,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After applying the groupby method, we can see that there's quite a few people that aren't too happy with United airlines. But this layout is very messy and a little hard to read. Nothing tells a story quite like pictures do, so let's illustrate what is above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfgAAAGDCAYAAADHzQJ9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl0Tef+x/H3ORkaIWSQuGatmkqImKcacxNULzWroOYa\nKihVpbdXDUW1aNRQimoNvdqirWuIxFRENYZQQ6mah4RMhMy/P/x6bnMjMkhykpPPay1rOfs8e+/v\nPjkrnzx7eB5DSkpKCiIiImJRjOYuQERERHKeAl5ERMQCKeBFREQskAJeRETEAingRURELJACXkRE\nxAIp4EWeUq9evXj55ZdTLTtx4gSDBg16bPtPPvmEd955B4D+/ftz6tSpHKkjODiYWrVq4ePjg4+P\nD97e3nh7e7N8+fJMrb9161bu3bsHwMSJEwkMDMyRurJq586dvP3225luHx0dbTrmVq1apfoMpk2b\nlqltzJs3j3Xr1gGwb98+rl+/DkB4eDi7du3K+kGI5APW5i5ApCA7d+4cDg4OODo6cvToUerWrQtA\n7dq1WbFiRYbrr169OkfrKV26NNu2bTO9Dg8Pp0ePHtSoUYNmzZo9cd2FCxfi6elJsWLFmDNnTo7W\nlRVeXl54eXllun3x4sVNxxwcHMyUKVNSfQaZMX78eNP/V61axeuvv06ZMmUIDg7mwIEDtG3bNkvb\nE8kP1IMXeQrfffcdPj4+vPTSS2zatMm0PDg42BRSn3zyCVOmTKFbt26sWrUq1fpt2rThyJEjXL16\nlebNm/PFF1/QqVMnWrRowdatWwFISUnB398fb29vWrduzfTp00lKSspUfSVLlsTDw4PTp08D8Pvv\nv9O7d2/at2+Pl5cXP/zwAwBvv/02Fy9exNfXlyNHjuDr68vmzZsBqFatGps2baJz5840b97cdAzJ\nycm8//77NGvWjN69e7Ns2TJ8fX0BOHz4MF26dKFDhw60b9+e//znP6nqSk5Opnnz5pw8edK0bNWq\nVYwdO5Zvv/2WAQMGAI/+QBk0aBA+Pj60adOGlStXZuq4/6ply5ZcunQJeHSWolatWjx48ACAlStX\nMn36dCZNmsSnn37K/PnzOXToEBMmTGDZsmVMmzaN7du3M3bsWAACAgLo1KkTbdu2ZeDAgdy9exd4\n8s9YxFwU8CLZlJSUxM6dO/H29qZt27bs3buX+Pj4x7bds2cPy5YtMwXX40RERGA0Gvn++++ZPHky\n8+fPB2Dz5s1s27aNjRs3snPnTq5cuWI6nZyRCxcucOjQIdOZhTlz5tC6dWv+85//MHPmTN555x0S\nEhKYNWsWAGvWrKF+/fpptnP+/Hk2bdrEp59+ykcffURSUhJ79uxh79697Nixg8WLF/Pdd9+Z2s+e\nPZu3336brVu3snjxYgICAlJtz2g00q5du1SXAQICAmjfvn2qdosXL6ZcuXJs27aN1atXM2/ePG7c\nuJGpY/9To0aNOHr0KAA///wzNWvW5MSJEwAcOXKExo0bm9r6+flRqlQp5s6dy9ChQ+nbty/e3t58\n/PHHXLlyhYkTJzJv3jx27dpFo0aNeO+990zrZuZnLJKXFPAi2bR//37c3d0pVqwYRYoUoWHDhgQF\nBT22bZ06dXB2dn7i9hITE3nllVcAqFmzpuk6cFBQEF27dsXBwQFra2u6d+/Ojh07HruNGzdumK4/\nN2/enGHDhvHOO+9Qr149AD799FPTvQH16tUjLi6OsLCwDI/1H//4h6muuLg47ty5w5EjR2jVqhVF\nixbF0dGRjh07mtq7uLiwadMmLly4QKVKlZg3b16abXp7e5sC/u7du5w5c4aWLVumajNlyhSmTp0K\nQPny5XF1deXq1asZ1vtXjRo14tixYwAcP36cbt26ERISYnrdqFGjTG1n7969NGzYkKpVqwKP7r0I\nDAw0nU3JzM9YJC/pGrxINn377bfs3bvX1ONNSkoiKioKb2/vNG1LlCiR4fasrKywt7cHHvVwk5OT\nAYiJiWHFihVs2LDBtJ/0guSv1+D37dvHtGnTUl3P3rdvH4sXLyYiIgKDwUBKSoppP0/i4OBgqhEe\nnWKPjo6mVKlSpjZ//f/MmTNZvHgxr732GnZ2dowbNw4fH59U22zYsCG3bt3i+vXrHDhwgJYtW/LM\nM8+kahMaGmrqtRuNRsLCwjJV7181atSINWvWEBUVhY2NDY0bN2batGlcuHCB0qVLm44tIzExMRw5\nciTVcRQrVozIyEggcz9jkbykgBfJhqioKA4fPkxwcDC2trbAox54y5YtTddlc4qbmxtt2rShb9++\nWVqvRYsW/O1vf2Pt2rUMGDCAhIQE/Pz8mD9/Pi1btiQ+Pp7atWtnu65ixYoRGxtrev3XMwElS5Zk\n6tSpTJ06lf379zN69GhatGhB0aJFTW2srKxo164dQUFB7Nu3j27duqXZx4QJE+jfvz+9e/fGYDDQ\nokWLLNdZrlw5YmNj2bdvHx4eHpQvX56rV6/yyy+/0KRJk0xvx83NjaZNm7Jw4cIs1yBiDjpFL5IN\nP/74I40bNzaFO4C1tTXNmzc33biWU9q2bcvmzZtNN4atX78+1fXuJxk7diyLFy8mKiqKBw8eEBsb\nS61atYBHd/Db2NiYQtra2pro6OhM1+Xu7s7u3bt5+PAh0dHRphvpEhIS8PX15fbt28Cj0/rW1tYY\njWl/3fx5mj40NJQXX3wxzft37tyhVq1aGAwGvvvuO9MxZFW9evX44osv8PT0BOC5557jm2++eWzA\nW1tbExMTk+b/zZs358iRI1y5cgV49Cjk9OnTs1yLSF5RwItkw6ZNm2jXrl2a5V5eXqnups8J7dq1\no3Xr1nTp0gUfHx8CAwNp3rx5ptb19PSkbt26LF68mOLFizN48GA6d+5M586dqVChAu3atWP48OHE\nxsbi4+NDr169THfvZ8TLy8v0zPno0aNNN8jZ2NjQrVs3BgwYQIcOHfD19WXKlCkUKVIkzTYaN27M\nyZMnadq0aao/lv40ZswYRo4cSadOnYiNjaVnz55MnTqVy5cvZ6rGPzVq1Ijjx4+bbjasW7cuv/76\nqynw/8rb25tx48axcuVKmjVrxqFDh+jatStubm68//77jBw5kvbt2zNt2jQ6dOiQpTpE8pJB88GL\nSHalpKRgMBgA+Oqrrzhw4ACLFi0yc1UiAurBi0g2nT59mrZt2xIVFUViYiI7duzAw8PD3GWJyP/T\nTXYiki01atSgc+fOvPLKK1hZWeHh4ZHlGwFFJPfoFL2IiIgF0il6ERERC6SAFxERsUAWdQ0+LCzG\n3CUUCE5O9kREZP1ZYpH06DslOU3fqcxxdU1/JEb14Asha2src5cgFkbfKclp+k49PQW8iIiIBVLA\ni4iIWCAFvIiIiAVSwIuIiFggBbyIiIgFUsCLiIhYIAW8iIiIBVLAi4hIgfTNNxv47LPFj31v1Kih\nbN++lbCw2/j69sjjyvIHixrJTkRECo+uXXtm2MbV1Y01a77Og2ryHwW8iIjke99/v4n1678kKSkJ\nF5eSTJ06jR9/3EJY2G0mTZrKqFFDcXevw969QUyaNNW03o0b1+nVqwt79gSzdev3HDiwn6JFi3L8\n+DGsra2YNu0DnnuuMjExMcyfP4dTp06RlJTEgAGD6NjxZTMe8dPTKXoREcnXIiLu8vHHc/j440Ws\nX/8dZcuWY9Wq5WnanT17hjVrvsbdvU662zp06Ce6dOnO+vXfUrduff7973UA+Pt/jMFgZO3ajSxb\ntooVK5by++/nc+2Y8oICXkRE8jUnJ2e2b9+Dm1spAOrUqcv169fStGvSpBlG45NjrVKl56hevQYA\n1apV49atmwD89NM+unfvjdFoxMnJiZYt27BnT1AOH0ne0il6yZTLR6dlqX2Fuu/mUiUiUtgkJSWx\nfPkSfvppL0lJScTGxlK+fIU07YoXL57htooWLWb6v9FoRVJSMgD37sXw7ruTsLJ6NMlNXFwcrVu3\ny6EjMA8FvIiI5Gu7du3kp5/24u//GY6OjmzZ8h07dvwnR/dRsqQrs2Z9yHPPPZ+j2zUnnaIXEZF8\nLTLyLn/7W2kcHR2JiookMHAnDx48yNF9NG/ekk2bvgEgMTGRhQvncfbsmRzdR15TwIuISL7Wrp03\nUVFR9OzZmffee4chQ0Zw+/YtYmNjc2wfQ4YM5/79e/Tu/Qq+vj1ITk6mcuWC3Zs3pKSkpJi7iJwS\nFhZj7hIKBFdXhyx/VroGL0+Sne+UyJPoO5U5rq4O6b6nHryIiIgFUsCLiIhYIAW8iIiIBVLAi4iI\nWCAFvIiIiAVSwIuIiFigXB3Jbs6cOfzyyy8kJiYybNgwAgMDOXXqFI6OjgAMGjSIVq1asWXLFlav\nXo3RaKRHjx50796dhIQEJk2axPXr17GysmLWrFmUL18+N8sVERGxGLkW8IcOHeK3335jw4YNRERE\n0KVLFxo3bsy4ceNo3bq1qV1sbCyLFi1i48aN2NjY0K1bN7y8vAgKCqJ48eLMmzeP/fv3M2/ePObP\nn59b5YqISD60c+c2pk//J5s3bzd1Dp/WmjWrqFvXk1q1aufI9vKrXAv4Bg0aULv2ow+vePHiPHjw\ngKSkpDTtjh8/jru7Ow4Ojx7W9/T0JCQkhIMHD9K5c2cAmjZtyuTJk3OrVBERycDADwJzdHufT2qT\nqXY7d26nbNly7N4dQOfO3XJk376+A3JkO/ldrgW8lZUV9vb2AGzcuJEXX3wRKysrvvzyS1auXImL\niwtTp04lPDwcZ2dn03rOzs6EhYWlWm40GjEYDMTHx2Nra5tbJYuISD4SHR3F6dOnePvtd1m79gs6\nd+7GqFFD8fSsz88/B2M0GmnfviNbt/6A0WhkwYLFxMU9ZObMfxETE0NSUhJ+fhN4/vkq9OrVhcaN\nm+Hk5MTVq1do1aotjRo1Yfr0f3Lr1g1sbZ9hypR/YW9vz7/+NYUHDx7w8OFDxo6dwAsv1KJnz868\n/HIXDhzYT3x8PAsWfIq9fVFzf0RPlOuzyQUEBLBx40Y+//xzTp48iaOjIzVq1GDZsmX4+/tTt27d\nVO3TGzk3MyPqOjnZY21tlSN1W7onDW/4OJdzeftS8OlnLlmRme/Lzz/vp02b1rz00t+ZO3cGycmx\n2NpaU6lSOd56azy9evUiKSmOf/97A3369CEi4gaBgYG0a9eG7t27c/78eWbMmMHKlStJSUnG27st\nL774IpMmTaJEiSLs3x9AuXKlWbRoIT/++CPHjgXTpEkTXn21N+3atePgwYOsXbuWTz75BEihdu0X\n8PMbxdixY/ntt5O0a5e/p5PN1YDft28fS5YsYfny5Tg4ONCkSRPTe23atOG9997D29ub8PBw0/Lb\nt2/j4eGBm5sbYWFhVK9enYSEBFJSUjLsvUdE5NzEA5YsL8Z41hjShYvGDZesyuj74urqwHffbaZ/\n/0HcvRtLy5Zt+Prrb4mPT6R8+cqEhcVQooQTZcpUIiwsBgeHEly5covg4CNERkawceO3AMTFPSQs\nLIakpGTKln2OsLAYHj5MICrqAUeOHKN+/QaEhcXQsOGLANy7d48tW35gyZJlJCQkYGdnZ1q/UqXq\nhIXFULy4M9evh+WL7/yT/lDKtYCPiYlhzpw5rFq1ynRjxOjRo5k4cSLly5cnODiYKlWqUKdOHaZM\nmUJ0dDRWVlaEhIQwefJk7t27x7Zt22jRogVBQUE0atQot0oVEZF85ubNm/z660n8/edjMBh4+PAh\nDg7FeOYZO6ys/num9q//T0lJwcbGmrFjJzz2Bjpra5tUr62sjCQnpz47/PXXaylZ0o2pU9/nzJlf\n8fef/5f2qfeV3+VawG/dupWIiAj8/PxMy1555RX8/PwoUqQI9vb2zJo1Czs7O8aPH8+gQYMwGAyM\nHDkSBwcHOnTowIEDB+jduze2trZ88MEHuVWqiIjkMz/88ANdunRn9OixwKNA7dWrCwkJd5643gsv\n1GLv3t3UqlWbixd/Jzj4AL169X1s2+rVXyAk5GfatGnHTz/t48KF34iKiqRy5SoA7NkTRGJiYs4e\nWB7KtYDv2bMnPXv2TLO8S5cuaZb5+Pjg4+OTatmfz76LiEjh8+OPPzJp0n+nnTYYDLRv/xIrV372\nxPW6devJjBnvMWLEYJKTk/HzezPdtu3aeXPkyGFGjRqKlZU1U6a8R3h4GNOn/5OgoAC6du1BQMAO\nfvxxS44dV17SfPCFkOaDl5yma/CS0/SdyhzNBy8iIlLIKOBFREQskAJeRETEAingRURELJACXkRE\nxAIp4EVERCxQro9FLyIikh03blynX79eVKtWHXg0Poqv72vUr9/wse1XrFiKo6MjXbv2JCgogNat\n8/dY8blNAS8iIhkaGTgxR7e3qM2cTLWrUKEi/v7LALh27SpvvTWW996byfPPV0l3nYSEBDZsWKuA\nN3cBIiIimVG2bDn69RvIt99+TeXKVQgI2IbBYKRFi1b07v3f4WgXLvyICxfO8+GHH/D666MeO/1r\nYaBr8CIiUmBUr16DQ4cOsHv3Lj79dAWLFn3Gnj2B3Lx509SmTx9fKlSoyJtvTuLOnTu89FJnPvlk\nKcOHj+Krr1absfq8pR68iIgUGLGxsRQpUoSrV68wevSw/192n5s3rz+2vbOzC6tXL2fdujWm6V8L\nCwW8iIgUGGfO/Ep8fDxNmjRj4sR3Ur33yy8/p2n/pOlfLZ1O0YuISIFw7dpV1q9fyyefLCMk5Bce\nPnxISkoK8+d/SFzcQ1M7g8FIUlISAFFRkZQtWw4o+NO/ZpV68CIikm9dvnyJUaOGkpCQQHJyEuPH\nT+Rvf/sbPXr0ZuTIIRiNRl58sRXPPPPfU+8lS5YkMTGBKVPe4tVX+z12+teOHV8241HlDU0XWwhp\nuljJaZraU3KavlOZo+liRUREChkFvIiIiAVSwIuIiFggBbyIiIgF0l30hdC08d9neZ2O3rlQiIiI\n5Br14EVERCyQAl5ERPKtb775mqFDBzBq1FCGDOnHzz8HZ2n98+d/4/LlSwCMGjWU338/nxtlArB7\n965c23Z26BS9iIhk6NzgATm6varLV2XY5saN63z//SaWL/8Ca2trrly5zOzZ02nQoFGm97NnTyDV\nq79AhQoVn6LajN24cZ2AgO20atU2V/eTFerBi4hIvnTv3j3i4+NISEgAoHz5Cvj7L+PChfOMHDmE\nUaOGMnHiWKKjowgJOcKUKf+ds75jx7ZcuHCezZu/ZelSf3799SQAgYEBjBnzOgMG9OHmzZu8/vog\n7t69A0CfPl0JCgoAYM6cGRw9+gt79gTy+usDGTVqKJ988jEAN2/eZOTIIYwePYwRIwZz8+YNPvpo\nNseOhbBy5Wd5+RE9kQJeRETypSpVqlKjRk26d3+ZGTPeY9eunSQmJrJgwYeMGDEGf/9leHh48u9/\nr3/s+pUrP0+jRk0YNmyUaQ54JycnFixYTOPGTdm7NxAPD09OnQolIuIuJUu6cvJkKADnzp2lSpVq\nrF69ggULluDvv4zbt29x4sQxdu8OoEGDRnzyyVLGjHmT8PBwevf2xcPDk9deG5Jnn09GdIpeRETy\nralTp/HHHxc5fPgga9d+waZNG/njj4vUrPkosD0967Ny5TLq1q2Xqe3Vru0BgKurK1FRUdStW4+Q\nkCOkpICXlw8//bSX6OhoihYtxqVLf3Dr1k3GjRsFwP3797h58yYNGzZm8uQJxMTE0Lp1W2rVqk1I\nyJHc+QCeggJeRETypZSUFOLj46lU6VkqVXqWrl178uqr3YiIuGtqk5iYgNFoxGAwpFo3vVnjrKys\nUm3f3b0O69atISkpiQ4dOhEcfICjR3+hbl1PbGysqVatBh995J9mO6tWrePw4UMsWeJPx44vU6rU\n33LoqHOOTtGLiEi+9MMPm5kzZwZ/zol2//49kpOTqVevASdPngDg6NEQqlWrQdGiRblzJxx4dOd8\nbGwsAAaDwTR17OMUKVIEgAsXzlOp0rM8/3xVNm3aSN269alQoRJ//HHR9AfFihVLCQu7TUDAdn7/\n/TwvvtiKIUNGcPbsaYxG4xP3Yw7qwYuISL7UoUMnLl36g6FD+1OkiD2JiYn4+U2gdOkyfPTRbAwG\nAw4ODkye/E+KFLHHzq4Iw4cPxN29Dn/7WxkA6tSpy/z5c7G3t093P1WrVuf8+d8wGAzUrOnOV1+t\n5oUXamJjY8OYMeN5880x2NraUKVKNUqWdKV8+Yp8+OFMihSxx2g04uc3gRIlHDl79gwLF87jjTfG\n59VH9ESaLrYQWvzB7iyv09F7b5baa7rYwkVTe0pO03cqczRdrIiISCGjgBcREbFACngRERELpIAX\nERGxQAp4ERERC6SAFxERsUAKeBERyXeCg4NTTR4Djwaa+eabDQCEhBxhxIjBjBo1lIEDX2XDhq/S\n3da4caN4++3/Ppt+5044c+bMyJ3C8xENdCMiIhnKzvgZT/L6pFZPtf6cOTPx919KyZKuxMU9xM9v\nBG3belOyZMlU7SIi7vLHHxeJj4/j3r17FCtWDBeXkkyc+M5T7b8gUA9eREQKnJiYKNNwtM88Y8fi\nxZ+nCXeAXbt20KzZizRo0Jg9ewKBR3O3DxrkC0CvXl2YP/9DPv98GePHvwFAaOhxfHxakZycTGJi\nIr6+Pbh//x4TJ/oxevQwhgzpz6+/nuTgwZ94//2ppn3Nnj2d/fv38OWXqxgypD/Dhr3GF198ntsf\nRboU8CIiUuAMHvw6Q4b0Y9KkcXzzzddER0c/tt3Ondtp1+7vtGvnza5dO9K8n5iYSOPGTRk4cChh\nYbdISUkhNPQ4VapU4+LF3zl//hw1atTkzp07vPRSZz75ZCnDh4/iq69W07BhY06dOkVcXBzJycmE\nhh6nUaOmrF//JYsXr2DJks9xcCie2x9FunSKXkRECpBHs8Z16dKNF19sxeHDh9i7dzdffLGCFSu+\nStWLv379GmFht6ld24OkpCRmz55OREREmi2+8EJNAJ577nmuXLnEr7+eokuX7pw8eYK4uDjq1q2H\ns7MLq1cvZ926NSQkJGBnZ4eVlRXNmjXn0KGfcHEpSe3aHtjY2NCqVVv8/Ebg5eXD3//ukzcfy2Oo\nBy8iIvmOs7MzMTH3Ui2LjIw0BXhc3ENcXErSvv1LzJr1IY0aNeXnnw+lar9z5zbi4+N57bVXGTy4\nH0lJiQQFBaTZl7W1DQB169bj1KmTxMU9xNOzPqdOhRIaehxPz/p8/fVaSpZ0Y/HiFbz55iTTuj4+\nHQkMDGD//r14eT0K8zfffJsJEyZz9+4dRo8elu7UtblNAS8iIvlOpUqVCAu7xdWrVwCIiIjg6NEj\nuLvX4cqVywwa5Gu6Bp+cnEx4eBhlypRNtY2AgO0sWLCYVavWsmrVWmbMmEtAwPZ091m3rifbtm2l\nbNnyODo6EhkZSWRkBKVK/Y2oqEjKli0HwJ49QabQrlKlGuHhYZw+fQoPD0/u3bvHypWfUbFiJV57\nbQgODiWIjb2fGx9RhnSKXkRE8h0bGxvefXc6c+bMIDk5GYAxY97E2dkFZ2cXXn21P2PGvI6dnR0J\nCQk0b/4iderUNa3/22/nsLV9hsqVnzctq1OnLnfv3uX27VuP3eej+d9/p1OnfwDg4OCAi4sL8Kin\nPn36PwkKCqBr1x4EBOzgxx+30LHjyzRo0IjY2FgMBgPFihUjMjKCIUP6UaSIPbVq1aZ48RK59TE9\nUa5OFztnzhx++eUXEhMTGTZsGO7u7kycOJGkpCRcXV2ZO3cutra2bNmyhdWrV2M0GunRowfdu3cn\nISGBSZMmcf36daysrJg1axbly5d/4v40tWDmaLpYyWma2lNyWkH5TqWkpODnN5IJE96mXLknZ1Ru\nMMt0sYcOHeK3335jw4YNLF++nJkzZ7Jw4UL69OnD2rVrqVixIhs3biQ2NpZFixaxatUq1qxZw+rV\nq4mMjOSHH36gePHirFu3juHDhzNv3rzcKlVERCTL/nzcrkGDhmYJ94zkWsA3aNCABQsWAFC8eHEe\nPHhAcHAwbdu2BaB169YcPHiQ48eP4+7ujoODA3Z2dnh6ehISEsLBgwfx8vICoGnTpoSEhORWqSIi\nIllWunQZPv/8S/r2HWDuUh4r1wLeysoKe3t7ADZu3MiLL77IgwcPsLW1BcDFxYWwsDDCw8NxdnY2\nrefs7JxmudFoxGAwEB8fn1vlioiIWJRcv8kuICCAjRs38vnnn/P3v//dtDy9S/9ZXf5XTk72WFtb\nZa9QyVFPui4klkk/c8lp+k49nVwN+H379rFkyRKWL1+Og4MD9vb2PHz4EDs7O27duoWbmxtubm6E\nh4eb1rl9+zYeHh64ubkRFhZG9erVSUhIICUlxdT7T09ERGxuHo5kQUG4OUZyTkG5IUoKDn2nMscs\nN9nFxMQwZ84cli5diqOjI/DoWvr27Y+eQdyxYwctWrSgTp06hIaGEh0dzf379wkJCaF+/fo0a9aM\nbdu2ARAUFESjRo1yq1QRERGLk2sBv3XrViIiIvDz88PX1xdfX1+GDx/Opk2b6NOnD5GRkXTu3Bk7\nOzvGjx/PoEGDeO211xg5ciQODg506NCB5ORkevfuzVdffcX48eMz3qmIiFiMkJAjT5wy9n+tWbOK\nkydPALB7965M78fffz5bt36f/ULzqVw7Rd+zZ0969uyZZvnKlSvTLPPx8cHHJ/V4vX8++17QDfwg\nMEvtP5/UJpcqERHJvstHp+Xo9nJjrAxf3wHAo8fXAgK206pV2xzfR0GikexERKTAGTVqKGXKlOX8\n+d+oWrUakyZNZcaM92jVqi2bNm3k9OlTrFz5GT179mHmzH8RExNDUlISfn4TeP75KmzfvpWvvlqN\nq2spnnnmGZ57rrK5DynHKeBFRKTAOXv2NP/610ycnJzp0qUDMTH/vSGvd29fvv32a157bQirVi2n\nUaOmdOrUmYsXf2fBgg/5+ONFLF26iBUr1uDgUJxBg/qa8UhyjwJeREQKGANly5bHxeXRzHIlS7py\n//69x7YMDT1BZGQE27dvBR7NQhcVFYW9fVGcnB6NteLuXidvys5jCngREcmXHB2dHjtlbNGiRbGy\nSj3mSXohRZ07AAAgAElEQVRjpdjYWDN27ARq1aptWhYREYHRaDC9/nMyG0uj6WJFRCRfqlChYjpT\nxno8cT2j0UhSUhIAL7xQi717dwNw8eLvrF//JSVKlODevXvExMSQmJhIaOjxXD0Oc1EPXkRE8iVr\na+t0pox1fuJ6FSs+y9mzZ1i4cB6DBw9nxoz3GDFiMMnJyfj5vYnRaGTgwKGMGjWU0qVLW+QNdpDL\n08Xmtfw46lF+fExO08VKTtOoY5LT9J3KHLOMZCciIiLmo4AXERGxQAp4ERERC6SAFxERsUAKeBER\nEQukgBcREbFACngREcl3evbsyZkzp1MtW7LEn3XrvmTBgnlcv34tU9vJSluAkydP0Lx5fX777WyW\n6s3I1q3fs2dPUI5uMyMa6EZERDI0+effcnR7MxtUeeL7L730EoGBO6levYZp2e7dgXzyyRJcXd0y\nvZ8xY8Znqa6dO7dRoUJFAgJ2UKVKtSyt+yQdOnTKsW1llgJeRETynQ4dOtCjR09GjHgDgDNnTuPq\n6oqrqxujRg1l3LiJBAXt4vr1a9y4cZ358z9l+vR/cvPmDdzdaxMYGMB3321N1fbevXtcvnyJ69ev\n8sYb42nSpFmqfSYlJbF7dyD/+tdMZsx4j9dfHw3AjBnv4eTkxNmzZ4iMjODVV/vz44/fExUVib//\nMooUKcKcOTO4fv0aiYmJDB48nHr1GjBq1FDTKHklSjji6OhI1649mT//Q3799SRWVlZMmPA2FSpU\nYsaM9wgLu82DBw8YOHAozZq1eOrPUKfoRUQk33FxcaFMmbL8+utJAAIDd+Ll5ZOmXWJiAp9+upzD\nhw8RHx/HsmWr8PRsQHh4WJq2YWG3mDdvIWPGvMmWLd+mef/IkcNUqvQsHh6eFC9egpMnT5jes7Ky\nZsGCxTz33POEhp5gwYJPqVy5MiEhR9i5cxsuLiX55JOlzJo1j4UL55nWe+65yowb95bp9c8/B3P7\n9i2WLVvFsGEj2bVrJzEx0TRs2Bh//2VMmzaLFSuWPtVn9yf14EVEJF/y8vJh166dvPBCLX76aS+L\nF3+epk2NGjUBuHTpomna1yZNmqWZbQ6gdu1Hk9S4ublx717a6WV37txGu3be/79vbwICtptmoftz\nPy4uJalYsRIATk4u3L9/j1OnQjl+/CgnThwDIC4ujoSEhP9fr1aqfZw7d8ZUp4eHJx4eniQmJnL6\n9Cm2bPkWg8FIdHRUFj6l9CngRUQkX2rZsjVffPE5Xl7elC9fgeLFi6dpY2NjAzyaLtZofBTqBoMB\ng8GQpu1fQ/9/p2GJi4tj//69nD17mm+++ZrExARiYmJ4443xadb93+1YW9vQr9/Ax55hsLFJHbNG\noxUpKamnp925cxvR0dEsWrSc6OhoBg/2ffwHkkU6RS8iIvmSvX1RKleuwhdfrHxseP5V2bLlOHv2\nVwAOHz5kmi42s376aR/16tVnzZqvWbVqLV9++W8qVqxESMiRDNd94YVa7N+/B4CIiLssXboo3bY1\narxg2ua5c2eYN282kZGRlC5dBqPRyJ49gabe/9NSwIuISL7l5eXDzz8H07z5i09s17RpC+7fv8/r\nrw/i+PGjFC9eIkv72blzGx06vJxqWYcOndi1a0eG67Zp044iRewZPnwgEyeONV0KeBwPD08qVnyW\nESMGM3/+h3Tu3JVWrdpw4MA+xox5nSJFiuDm5sbKlZ9lqf7H0XSxuUzTxUphoKk9Jadl9TsVHR1F\nSMgRWrVqS1jYbcaMeZ21a7/JxQrzhydNF6tr8CIiUuDZ2xclMDCAtWvXkJKSzOjR48xdktkp4EVE\npMCztrZm2rRZ5i4jX9E1eBEREQukgBcREbFACngRERELpIAXERGxQAp4ERERC6SAFxERsUAZBvyF\nCxfSLDt27FiuFCMiIiI5I92Aj46O5vLly0yePJkrV66Y/v3++++89dZb6a0mIiIi+UC6A90cPXqU\n1atXc/r0afr3729abjQaad68eZ4UJyIiItmTbsC3bNmSli1bsm7dOnr37p2XNYmIiMhTynCo2nbt\n2rF69WqioqJSzZ87ZsyYXC1MREREsi/Dm+yGDRvGmTNnMBqNWFlZmf6JiIhI/pVhD97e3p5ZszSA\nv4iISEGSYQ++Tp06j31UTkRERPKvDHvw+/btY9WqVTg5OWFtbU1KSgoGg4Hdu3fnQXkiIiKSHRkG\n/OLFi/OiDhEREclBGQb8wYMHH7u8W7duOV6MiIiI5IwMA/6XX34x/T8+Pp4TJ07g6empgBcREcnH\nMgz4/72D/sGDB7z99tu5VpCIiIg8vSzPJlekSBEuX76cG7WIiIhIDsmwB9+nTx8MBoPp9a1bt6hW\nrVquFiUiIiJPJ8OA9/PzM/3fYDBQrFgxqlevnqmNnzt3jhEjRjBgwAD69u3LpEmTOHXqFI6OjgAM\nGjSIVq1asWXLFlavXo3RaKRHjx50796dhIQEJk2axPXr17GysmLWrFmUL18+m4cpIiJSuGQY8A0b\nNuTIkSOEhoZiMBioU6dOqh59emJjY3n//fdp0qRJquXjxo2jdevWqdotWrSIjRs3YmNjQ7du3fDy\n8iIoKIjixYszb9489u/fz7x585g/f342DlFERKTwyfAa/IIFC5gzZw63b9/m1q1bTJ8+naVLl2a4\nYVtbWz777DPc3Nye2O748eO4u7vj4OCAnZ0dnp6ehISEcPDgQby8vABo2rQpISEhmTwkERERybAH\nHxwczPr16zEaH/0tkJiYSN++fRk2bNiTN2xtjbV12s1/+eWXrFy5EhcXF6ZOnUp4eDjOzs6m952d\nnQkLC0u13Gg0YjAYiI+Px9bWNksHKCIiUhhlGPDJycmmcIdHwZ2ZU/SP849//ANHR0dq1KjBsmXL\n8Pf3p27duqna/HVK2sws/ysnJ3usrQv2THeurg7mLiFHWMpxSObpZy45Td+pp5NhwNeqVYvhw4fT\ntGlTAA4cOIC7u3u2dvbX6/Ft2rThvffew9vbm/DwcNPy27dv4+HhgZubG2FhYVSvXp2EhARSUlIy\n7L1HRMRmq678JCwsxtwl5AhLOQ7JHFdXB/3MJUfpO5U5T/ojKMNr8JMnT6ZTp05cvXqVa9eu8fLL\nL2d7oJvRo0dz5coV4NGp/ypVqlCnTh1CQ0OJjo7m/v37hISEUL9+fZo1a8a2bdsACAoKolGjRtna\np4iISGH0xB78lStXKF++PB07dqRjx448ePCAW7duZeoU/cmTJ5k9ezbXrl3D2tqa7du307dvX/z8\n/ChSpIhpnnk7OzvGjx/PoEGDMBgMjBw5EgcHBzp06MCBAwfo3bs3tra2fPDBBzl20CIiIpYu3YA/\nePAgEyZM4D//+Q8ODo9OAVy5coURI0Ywf/58atWq9cQN16pVizVr1qRZ7u3tnWaZj48PPj4+qZb9\n+ey7iIiIZF26p+j9/f35/PPPTeEOULVqVRYvXqzn0UVERPK5dAM+JSWFqlWrpllepUoV4uLicrUo\nEREReTrpBnxsbPp3pEdGRuZKMSIiIpIz0g34KlWqsG7dujTLP/vsM+rUqZOrRYmIiMjTSfcmu4kT\nJzJy5Eg2b95MrVq1SE5OJiQkhGLFimVqqFoRERExn3QD3tXVla+//pqDBw/y22+/YWVlRfv27WnQ\noEFe1iciIiLZkOFIdk2aNEkzI5yIiIjkbxmOZCciIiIFjwJeRETEAmV4iv7gwYNpV7K2pkKFCpQq\nVSpXihIREZGnk2HAL1myhF9++YVnn30WKysrLl68SM2aNbl69SrDhg3j1VdfzYs6RUREJAsyPEVf\npkwZvvvuO77//ns2bdrEN998Q5UqVdi5cyebNm3KixpFREQkizIM+EuXLlGlShXT6+eff54LFy7w\nzDPPYGVllavFiYiISPZkeIq+SJEizJ49m4YNG2I0GgkJCSEhIYF9+/Zhb2+fFzWKiIhIFmXYg583\nbx7PPPMMGzZs4KuvviIuLo6FCxdSrlw55syZkxc1ioiISBZl2IN3dHTEz8+PlJQUUlJSTMuNRj1h\nJyIikl9lGPDLly9nyZIl3L9/H3g0jazBYOD06dO5XpyIiIhkT4YB/80337BlyxbKlCmTF/WIiIhI\nDsjwPHvFihUV7iIiIgVMhj34atWqMX78eBo2bJjqsbhu3brlamEiIiKSfRkG/O3bt7G1teXYsWOp\nlivgRURE8q8MA37WrFl5UYeIiIjkoHQD3s/Pj/nz59OyZUsMBkOa93fv3p2bdYmIiMhTSDfgp0yZ\nAsDatWvTvPfgwYPcq0hERESeWrp30ZcsWRKAd999l7Jly6b699Zbb+VZgSIiIpJ16fbgt2zZwqJF\ni7h+/TqtWrUyLU9MTMTFxSUvahMREZFsSjfgX375ZTp27Mg777zD6NGjTcuNRiOlSpXKk+JEREQk\ne5440I2VlRUffPABjo6OGAwGDAYDcXFx9OjRI6/qExERkWzI9Fj08fHx2NvbExcXR6dOnfKiNhER\nEcmmDAN+27ZtHDhwgEGDBrFmzRp27drF9evX86I2yaRzgwdkbYXns9heREQKnAzHoi9atCi2trYk\nJCQA0LZtW3bt2pXrhYmIiEj2ZdiDL1GiBFu2bKFq1aq8/fbbVK5cmdu3b+dFbSIiIpJNGQb87Nmz\nuXPnDl5eXqxevZqbN2/y0Ucf5UVtIiIikk0ZBvzDhw8pV64cAMOHDwfg6tWruVuViIiIPJV0r8Ef\nOXKEFi1a4O3tjY+PD5cvXwbgyy+/pE+fPnlWoIiIiGRduj34jz/+mFWrVlG5cmV27drF1KlTSU5O\npkSJEvz73//OyxpFREQki9LtwRuNRipXrgw8unP+2rVr9OvXD39/f41kJyIiks+lG/D/O0Vs6dKl\n8fLyyvWCRERE5Oll+Bz8nx43J7yIiIjkT+legz969GiqWeTu3LlDq1atSElJwWAwsHv37jwoT0RE\nRLIj3YDftm1bXtYhIiIiOSjdgC9btmxe1iEiIiI5KNPX4EVERKTgUMCLiIhYIAW8iIiIBcrVgD93\n7hzt2rXjyy+/BODGjRv4+vrSp08fxowZQ3x8PABbtmyha9eudO/e3TRKXkJCAuPHj6d379707duX\nK1eu5GapIiIiFiXXAj42Npb333+fJk2amJYtXLiQPn36sHbtWipWrMjGjRuJjY1l0aJFrFq1ijVr\n1rB69WoiIyP54YcfKF68OOvWrWP48OHMmzcvt0oVERGxOLkW8La2tnz22We4ubmZlgUHB9O2bVsA\nWrduzcGDBzl+/Dju7u44ODhgZ2eHp6cnISEhHDx40DRyXtOmTQkJCcmtUkVERCxOrgW8tbU1dnZ2\nqZY9ePAAW1tbAFxcXAgLCyM8PBxnZ2dTG2dn5zTLjUYjBoPBdEpfREREnizD+eBzS0pKSo4s/ysn\nJ3usra2eqi5zc3V1yPI653KhjqeVneOQgk0/c8lp+k49nTwNeHt7ex4+fIidnR23bt3Czc0NNzc3\nwsPDTW1u376Nh4cHbm5uhIWFUb16dRISEkhJSTH1/tMTERGb24eQ68LCYsxdQo6wlOOQzHF1ddDP\nXHKUvlOZ86Q/gvL0MbmmTZuyfft2AHbs2EGLFi2oU6cOoaGhREdHc//+fUJCQqhfvz7NmjUzDZcb\nFBREo0aN8rJUERGRAi3XevAnT55k9uzZXLt2DWtra7Zv386HH37IpEmT2LBhA2XKlKFz587Y2Ngw\nfvx4Bg0ahMFgYOTIkTg4ONChQwcOHDhA7969sbW15YMPPsitUkVERCxOrgV8rVq1WLNmTZrlK1eu\nTLPMx8cHHx+fVMusrKyYNWtWbpUnIiJi0cx2k5083sjAiVleZ0wu1CEiIgWbhqoVERGxQAp4ERER\nC6SAFxERsUAKeBEREQukgBcREbFACngRERELpIAXERGxQAp4ERERC6SAFxERsUAKeBEREQukgBcR\nEbFACngRERELpIAXERGxQAp4ERERC6SAFxERsUAKeBEREQukgBcREbFACngRERELpIAXERGxQAp4\nERERC6SAFxERsUAKeBEREQukgBcREbFACngRERELZG3uAkQkd40MnJil9ovazMmlSkQkL6kHLyIi\nYoEU8CIiIhZIp+hFJM9dPjoty+tUqPtuLlQiYrnUgxcREbFACngRERELpFP0IpLGucEDstTedfM3\nuVOIiGSbevAiIiIWSAEvIiJigRTwIiIiFkgBLyIiYoEU8CIiIhZIAS8iImKBFPAiIiIWSAEvIiJi\ngRTwIiIiFkgj2YmISK7K6siIoNERc4J68CIiIhZIAS8iImKBdIpeRESyZGTgxCy1H5NLdciT5WnA\nBwcHM2bMGKpUqQJA1apVGTx4MBMnTiQpKQlXV1fmzp2Lra0tW7ZsYfXq1RiNRnr06EH37t3zslQR\nEZECLc978A0bNmThwoWm12+//TZ9+vShffv2fPTRR2zcuJHOnTuzaNEiNm7ciI2NDd26dcPLywtH\nR8e8LldERKRAMvs1+ODgYNq2bQtA69atOXjwIMePH8fd3R0HBwfs7Ozw9PQkJCTEzJWKiIgUHHne\ngz9//jzDhw8nKiqKUaNG8eDBA2xtbQFwcXEhLCyM8PBwnJ2dTes4OzsTFhaW16WKiIgUWHka8JUq\nVWLUqFG0b9+eK1eu0K9fP5KSkkzvp6SkPHa99Jb/Lycne6ytrXKkVnk6rq4O5i5BssnV1YFz2Vwv\nsy7n8vbFMuhn/nTyNOBLlSpFhw4dAKhQoQIlS5YkNDSUhw8fYmdnx61bt3Bzc8PNzY3w8HDTerdv\n38bDwyPD7UdExOZa7ZI1YWEx5i5Bsim7P7vc/pnrO1X4ZOVnfvnotCxvv0Ldd7O8Tn7zpD+C8vQa\n/JYtW1ixYgUAYWFh3Llzh1deeYXt27cDsGPHDlq0aEGdOnUIDQ0lOjqa+/fvExISQv369fOyVBER\nkQItT3vwbdq04c0332TXrl0kJCTw3nvvUaNGDd566y02bNhAmTJl6Ny5MzY2NowfP55BgwZhMBgY\nOXIkDg46VSMiIpJZeRrwxYoVY8mSJWmWr1y5Ms0yHx8ffHx88qIskQJl4AeBWWpfpGEuFSIi+ZrZ\nH5MTERGRnKeAFxERsUAKeBEREQukgBcREbFACngRERELpIAXERGxQAp4ERERC6SAFxERsUB5Ppuc\niFieaeO/z1L7jt65VIiImKgHLyIiYoEU8CIiIhZIAS8iImKBFPAiIiIWSDfZiYgUcpqh0DKpBy8i\nImKBFPAiIiIWSAEvIiJigRTwIiIiFkgBLyIiYoEU8CIiIhZIj8mJiEi+o/kNnp568CIiIhZIAS8i\nImKBFPAiIiIWSAEvIiJigRTwIiIiFkgBLyIiYoEU8CIiIhZIAS8iImKBNNCN5IrJP/+WpfYzG1TJ\npUpERAonBbyIFAj6o1Eka3SKXkRExAKpBy8iIoWSpZ8VUg9eRETEAingRURELJACXkRExAIp4EVE\nRCyQAl5ERMQCKeBFREQskAJeRETEAingRURELJACXkRExAIp4EVERCyQAl5ERMQCKeBFREQsUL6e\nbGbmzJkcP34cg8HA5MmTqV27trlLEhERKRDybcAfPnyYS5cusWHDBi5cuMDkyZPZsGGDucsSEREp\nEPLtKfqDBw/Srl07ACpXrkxUVBT37t0zc1UiIiIFQ74N+PDwcJycnEyvnZ2dCQsLM2NFIiIiBYch\nJSUlxdxFPM7UqVNp2bKlqRffu3dvZs6cybPPPmvmykRERPK/fNuDd3NzIzw83PT69u3buLq6mrEi\nERGRgiPfBnyzZs3Yvn07AKdOncLNzY1ixYqZuSoREZGCId/eRe/p6UnNmjXp1asXBoOBf/7zn+Yu\nSUREpMDIt9fgRUREJPvy7Sl6ERERyT4FvIiIiAVSwIuIiFggBbyIiIgFUsAXIjdv3uTIkSMAxMfH\nm7kaEZHUjh49yo8//gg8GvtEnk6+fUxOctaqVavYtm0bsbGxbNmyhblz5+Lq6srQoUPNXZoUMF27\ndsVgMKRZnpKSgsFgYOPGjWaoSgq62bNnc+PGDS5fvkzHjh3ZsGEDUVFRTJkyxdylFVgK+EIiICCA\n9evX4+vrC8DkyZPp1auXAl6ybOHChem+pwmhJLtOnjzJmjVrTL+jRo8eTZ8+fcxcVcGmgC8kkpKS\nAEw9r7i4OBITE81ZkhRQZcuWBSA6Oprvv/+eiIgIABISEti0aRN79uwxZ3lSQCUmJpKQkGD6HXX3\n7l3i4uLMXFXBpmvwhcRLL71Ev379uHTpEv/85z/p3Lkz3bp1M3dZUoCNGTOGO3fu8P3332Nvb8+x\nY8eYOnWqucuSAmrgwIH07NmTc+fOMXjwYLp168brr79u7rIKNI1kV0hER0cTHR3NiRMnsLW1pWbN\nmpQuXdrcZUkB1r9/f1avXo2vry9r1qwhPj4ePz8/Pv30U3OXJgXQuXPnKFeuHOfPn8fGxoZnn30W\nOzs7c5dVoOkUfSHRvXt3ypUrh4+PD02aNMHJycncJUkBl5CQwJkzZ7Czs+Onn36ifPnyXL582dxl\nSQE1ffp07t69S9u2bfHx8VG45wD14AuRs2fPsmvXLvbs2YO9vT3e3t706tXL3GVJAXXmzBnu3r2L\ni4sLM2bMIDIykr59+9KjRw9zlyYFVFRUFLt372bXrl1cuXKF5s2bM378eHOXVWAp4AuZxMREDh8+\nzKZNm9i3bx8HDx40d0lSQC1dupRhw4aZuwyxMOHh4QQFBbFnzx6uXr3Kpk2bzF1SgaWALyQ2bdpE\nUFAQZ8+epVGjRnh5edG4cWOsrXWVRrJn5syZtGzZEnd3d2xsbEzLixQpYsaqpKBatGgRu3fvxmg0\n0rZtW7y8vHj22WfNXVaBpt/uhcTp06fp168fnp6ejx2kRCSr9uzZQ0BAQKplBoOBXbt2makiKcgc\nHBzw9/enVKlS5i7FYqgHb+HWr19Pr169mD179mODfeLEiWaoSizBiRMnqF27dqplBw8epEmTJmaq\nSAoif39/Ro0axRtvvPHY31ELFiwwQ1WWQT14C/fnoCRVq1ZN85568pIdly5d4uLFi3z00UepboBK\nTExkxowZBAYGmrE6KWjatWsHQN++fdO8p99RT0cBb+FatGgBQGhoKO+++26q9/z8/OjcubM5ypIC\n7OHDh5w8eZK7d++ybds203KDwcCoUaPMWJkURNWrVwfgyy+/TDMMco8ePfj666/NUZZFUMBbuO3b\nt7Ny5Up+++03Tpw4YVqemJiooWolW6pVq0a1atXw9vamSpUq5i5HCrjt27ezbNkyzp49S5MmTfjz\nqnFKSgo1atQwc3UFm67BFwLx8fF88MEHDBo0yLTMaDRiNBp1Q4tkW+PGjU2nUBMTE7l//z7lypVj\nx44dZq5MCqIVK1ak+h0lT08BX0gkJCTw008/ERkZaXq9dOnSNHdBi2TXmTNn2LJli27clCzRjcC5\nR6foC4mxY8dStGhRDh8+TJs2bQgODtb1UslR1atX51//+pe5y5AC5kk3AsvTUcAXElFRUfj7++Pr\n68vUqVOJjo42zSonkh3/+1jT7du3sbe3N2NFUhD9eSNw48aNCQsLo3bt2mzatIlTp07Ru3dvM1dX\nsCngC4mEhASuXbuGlZUVFy9epHTp0ly8eNHcZUkB9tfHmgwGA8WKFTPdES2SVRMmTOCdd97h2LFj\nfPvtt4wZM4YZM2awYsUKc5dWYGk++EJizJgxhIaGMmLECIYMGUKrVq1o06aNucuSAszT05Nbt24R\nGhpKgwYNsLKy0pMZkm1WVlbUqFGD7du3079/f+rVq6fv01NSD76Q+OvoYrqxTnLC1KlTcXZ25vDh\nwwwaNIjDhw+zZMkSPvroI3OXJgVQUlISixcvJjAwED8/P06cOEFsbKy5yyrQ1IMvJFq2bMkLL7yA\nu7s7tWvXpmbNmjRr1owOHTqwf/9+c5cnBdCNGzeYMGGCad7uvn37cvv2bTNXJQXV3LlzKVKkCIsW\nLeKZZ57h6tWrumnzKakHX0i0b9+exo0b07JlSwD2799PSEgIvXr1YvTo0TRv3tzMFUpBk5CQQHR0\ntOlGuwsXLhAfH2/mqqSgKlmyJMWLF2fDhg0YjUZq1aqlezqeknrwhcSxY8do1aoVBoMBg8FAixYt\nOHz4MKVKldJ4z5ItY8eOZcCAAZw8eZL27dszatQoJk2aZO6ypICaPHkyv/76Kw0aNKB27docOXIk\nzfDakjXqwRcSpUuXZuTIkXh6emI0Gjl58iRFixZlx44dlClTxtzlSQH0xx9/kJycjJOTE2FhYRQv\nXpwJEyZouljJlps3bzJ37lzT644dO9KvXz8zVlTwKeALiblz57Jv3z4uXLhAYmIif//732ndujUP\nHjzQ3fSSLZ9//jmLFi3ScMeSIxISErh165bp+3Tz5k3dRf+UFPCFyL179zAYDAwePJhz585hMBgo\nUaKEucuSAqpixYo8++yz5i5DLMS4cePo378/VlZWJCQkYGNjw/vvv2/usgo0BXwhoUeaJKe5uLjQ\ns2dPPDw8sLKyMi3X2OGSHVevXiU2NhYHBwdsbW25f/8+165dw9PT09ylFVgK+ELixo0bzJo1C19f\nX+DRI01/nctbJKvq1atHvXr1zF2GWIjVq1ezefNmnJycALh79y6vvfYanTp1MnNlBZcCvpDQI02S\n07p06WLuEsSClCpVCkdHR9NrJycnKlSoYMaKCj5NF1tIHDlyhJkzZ/LHH3+YbmKZMWOGTn+JSL4w\nbtw4zp8/T8OGDUlOTubYsWOULVuW8uXLA7r0kx3qwRcSeqRJRPKzFi1amGaWA3B3dzdjNZZBPfhC\nokOHDo99pEnTe4qIWCb14AsJPdIkIlK4KOALCT3SJCJSuCjgCwk90iQiUrjoGryIiIgF0mxyIiIi\nFkgBLyIiYoEU8CICPBoLvFatWvj6+pr+9e7dm59//vmJ623evBmAsLAw3njjjbwoVUQyQdfgRQR4\nFBOfjNsAAAKLSURBVPB9+vRh7969pmXnz59nwIAB7Nu3zzTM8V8lJSXRoUMHtm/fnpelikgm6C56\nEUnX888/T1xcHNevX+eDDz4gMjKS+/fv4+Pjw9ChQ5k8eTLXrl1j4MCBTJs2zfQHwqRJk3Bzc+Pc\nuXNcvHiRbt26MWTIECIiIhg/fjyxsbFUqlSJ69evM/z/2rt/l9ahMIzj3xS7OqRSil2kpS7uFrp0\nUOhURAeHDgGdXNyKq/9GsoTSFMwgOHfo7lJdnbpEXMTSIbRpqPUOF7xX7vV6B20hPp8pS17eM+Q8\n5OTHOTmhUqkse6giiaMlehF5V6/XwzRN5vM5Ozs7eJ6H7/s4jkMYhpyenmKaJq7r/nFuEATYto3r\nuti2DUCr1aJUKuH7PsfHx9zc3Cx6SCLfhu7gReTVcDh83VL44eGB9fV1bNsmk8nQ7/fxfZ90Os10\nOmU0Gv2z1vb2NgD5fJ4wDHl+fubu7o7Dw0MANjc39XdFkS+kgBeRV6Zp4nkeAN1uF8/z2NjYwLZt\n4jjm4uICwzAol8sf1lpZeTu9vLy8MJ/PSaV+LRz+fiwin0tXl4j8Va1WY3V1lU6nw9PTE8ViEcMw\n6PV6RFFEHMekUilms9l/1ywUCtze3gI/X+AbDAZf1b7It6eAF5F3nZ+f4zgO+/v7XF1dYVkW9/f3\n1Ot1ms0m2WyWtbU1Dg4OmEwmH9Y7Ojri+vqaRqNBu91ma2vrzd4IIvJ59JmciCzMYDAgCAKq1SpR\nFLG7u8vl5SW5XG7ZrYkkjgJeRBbm8fGRs7MzxuMxs9mMvb09LMtadlsiiaSAFxERSSA9gxcREUkg\nBbyIiEgCKeBFREQSSAEvIiKSQAp4ERGRBFLAi4iIJNAPBr4YGmycwx4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fea1895acc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a graph by calling our clean_data function and then plots the total number of each tweet rating (positive,negative, or neutral)\n",
    "ax = clean_df(df).groupby(['airline','Rating']).count().iloc[:,0].unstack(0).plot(kind = 'bar', title = 'Airline Ratings via Twitter')\n",
    "ax.set_xlabel('Rating')\n",
    "ax.set_ylabel('Rating Count')\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now that we painted the picture, it's very clear that United is indeed not a fan favorite. US Airways is a very close second. But this isn't normalizing the data. We can see to the far right over there, that Virgin America isn't really talked about all that much. So we need to correlate the number of negative tweets to the total number of tweets sent about a particular airline. What percentage of all tweets are negative for each airline.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Percent Negative\n",
      "American            0.710402\n",
      "Delta               0.429793\n",
      "Southwest           0.490083\n",
      "US Airways          0.776862\n",
      "United              0.688906\n",
      "Virgin              0.359127\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3YAAAFzCAYAAABy/5sXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAACAyRHsAAAAAMDkCHYAAAAAYHL/DyLEA9J8VFMkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fea19a46588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Count of all tweet ratings for each airline (negative, neutral, positive)\n",
    "itemized_tweets = clean_df(df).groupby(['airline','Rating']).count().iloc[:,0]\n",
    "#Negative tweet total index for each airline:\n",
    "#American 0\n",
    "#Delta 3\n",
    "#southwest 6\n",
    "#US Airways 9\n",
    "#United 12\n",
    "#Virgin 15\n",
    "\n",
    "#Count of total tweets about an airline\n",
    "total_tweets = clean_df(df).groupby(['airline'])['Rating'].count()\n",
    "#Airline index in total tweets:\n",
    "#American 0\n",
    "#Delta 1\n",
    "#Southwest 2\n",
    "#US Airways 3\n",
    "#United 4\n",
    "#Virgin 5\n",
    "\n",
    "\n",
    "#Create a dictionary of percentage of negative tweets = (negative_tweets / total_tweets)\n",
    "my_dict = {'American':itemized_tweets[0] / total_tweets[0],\n",
    "           'Delta':itemized_tweets[3] / total_tweets[1],\n",
    "           'Southwest': itemized_tweets[6] / total_tweets[2],\n",
    "           'US Airways': itemized_tweets[9] / total_tweets[3],\n",
    "           'United': itemized_tweets[12] / total_tweets[4],\n",
    "           'Virgin': itemized_tweets[15] / total_tweets[5]}\n",
    "\n",
    "#make a dataframe from the dictionary\n",
    "perc_negative = pd.DataFrame.from_dict(my_dict, orient = 'index')\n",
    "#have to manually set column name when using .from_dict() method\n",
    "perc_negative.columns = ['Percent Negative']\n",
    "print(perc_negative)\n",
    "ax = perc_negative.plot(kind = 'bar', rot=0, colormap = 'Blues_r', figsize = (15,6))\n",
    "ax.set_xlabel('Airlines')\n",
    "ax.set_ylabel('Percent Negative')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This levels the playing field a little bit, and here we can see that even though United had the most negative tweets by far, they still only made up only 68% of all their tweets. Whereas US Airways and American had higher percentages at 77 and 71 percent respectively. This means that they might not be talking about American or US Airways as much as United, but when they are, it's mostly negative. Lets see what percentage makes up the positive tweets for each airline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Percent Positive\n",
      "American            0.121783\n",
      "Delta               0.244824\n",
      "Southwest           0.235537\n",
      "US Airways          0.092345\n",
      "United              0.128728\n",
      "Virgin              0.301587\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3wAAAFzCAYAAABl3QveAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fea19aa4ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "itemized_tweets = clean_df(df).groupby(['airline','Rating']).count().iloc[:,0]\n",
    "#Positve tweet total index for each airline:\n",
    "#American 2\n",
    "#Delta 5\n",
    "#southwest 8\n",
    "#US Airways 11\n",
    "#United 14\n",
    "#Virgin 17\n",
    "\n",
    "total_tweets = clean_df(df).groupby(['airline'])['Rating'].count()\n",
    "#Airline index in total tweets:\n",
    "#American 0\n",
    "#Delta 1\n",
    "#Southwest 2\n",
    "#US Airways 3\n",
    "#United 4\n",
    "#Virgin 5\n",
    "\n",
    "\n",
    "#Create a dictionary of percentage of positive tweets = (positive_tweets / total_tweets)\n",
    "my_dict = {'American':itemized_tweets[2] / total_tweets[0],\n",
    "           'Delta':itemized_tweets[5] / total_tweets[1],\n",
    "           'Southwest': itemized_tweets[8] / total_tweets[2],\n",
    "           'US Airways': itemized_tweets[11] / total_tweets[3],\n",
    "           'United': itemized_tweets[14] / total_tweets[4],\n",
    "           'Virgin': itemized_tweets[17] / total_tweets[5]}\n",
    "\n",
    "#make a dataframe from the dictionary\n",
    "perc_positive = pd.DataFrame.from_dict(my_dict, orient = 'index')\n",
    "#have to manually set column name when using .from_dict() method\n",
    "perc_positive.columns = ['Percent Positive']\n",
    "print(perc_positive)\n",
    "ax = perc_positive.plot(kind = 'bar', rot=0, colormap = 'Blues_r', figsize = (15,6))\n",
    "ax.set_xlabel('Airlines')\n",
    "ax.set_ylabel('Percent Positve')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oh US Airways, it started off looking like United was going to win crown of \"Worst Airlines\". But with only 9% positive tweets, it's more and more starting to look like I shouldn't fly with you. Obviously the last metric would be graphing out the neutral tweets, but that doesn't tell us a whole lot. Let's put all 3 categories together and paint one single picture instead of 3 seperate pages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#create a function that will concatenate our perc_negative, perc_neutral and perc_positive dataframes into one single dataframe\n",
    "def merge_dfs(x,y,z):\n",
    "    #generate a list of the dataframes\n",
    "    list_of_dfs = [x,y,z]\n",
    "    #concatenate the dataframes, axis = 1 because they all have the same index, we just want to add the columns together\n",
    "    concatenated_dataframe = pd.concat(list_of_dfs, axis = 1)\n",
    "    return concatenated_dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We created a function that will concatnenate our previous dictionaries of percentages. Now we must create another dictionary of the percentages of neutral tweets, and then combine them all together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Percent Neutral  Percent Negative  Percent Positive\n",
      "American           0.167814          0.710402          0.121783\n",
      "Delta              0.325383          0.429793          0.244824\n",
      "Southwest          0.274380          0.490083          0.235537\n",
      "US Airways         0.130793          0.776862          0.092345\n",
      "United             0.182365          0.688906          0.128728\n",
      "Virgin             0.339286          0.359127          0.301587\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3YAAAGMCAYAAACWOzDbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAQAAAIDBEewAAAAAwOD+HzkOGTfQtNdIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fea19b78ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "itemized_tweets = clean_df(df).groupby(['airline','Rating']).count().iloc[:,0]\n",
    "#Netural tweet total index for each airline:\n",
    "#American 1\n",
    "#Delta 4\n",
    "#southwest 7\n",
    "#US Airways 10\n",
    "#United 13\n",
    "#Virgin 16\n",
    "\n",
    "total_tweets = clean_df(df).groupby(['airline'])['Rating'].count()\n",
    "#Airline index in total tweets:\n",
    "#American 0\n",
    "#Delta 1\n",
    "#Southwest 2\n",
    "#US Airways 3\n",
    "#United 4\n",
    "#Virgin 5\n",
    "\n",
    "\n",
    "#Create a dictionary of percentage of positive tweets = (positive_tweets / total_tweets)\n",
    "my_dict = {'American':itemized_tweets[1] / total_tweets[0],\n",
    "           'Delta':itemized_tweets[4] / total_tweets[1],\n",
    "           'Southwest': itemized_tweets[7] / total_tweets[2],\n",
    "           'US Airways': itemized_tweets[10] / total_tweets[3],\n",
    "           'United': itemized_tweets[13] / total_tweets[4],\n",
    "           'Virgin': itemized_tweets[16] / total_tweets[5]}\n",
    "\n",
    "#make a dataframe from the dictionary\n",
    "perc_neutral = pd.DataFrame.from_dict(my_dict, orient = 'index')\n",
    "#Have to manually set column name\n",
    "perc_neutral.columns = ['Percent Neutral']\n",
    "\n",
    "#call our function to concatenate all 3 dataframes of percentages\n",
    "percentage = merge_dfs(perc_neutral, perc_negative, perc_positive)\n",
    "print(percentage)\n",
    "\n",
    "#graph all of our data\n",
    "ax = percentage.plot(kind = 'bar', stacked = True, rot = 0, figsize = (15,6))\n",
    "#set x label\n",
    "ax.set_xlabel('Airlines')\n",
    "#set y label\n",
    "ax.set_ylabel('Percentages')\n",
    "#move the legend to the bottom of the graph since it wants to sit over all of our data and block it - stupid legend\n",
    "ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.1),\n",
    "          fancybox=True, shadow=True, ncol=5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we just did a good bit of analysis on our data, but we got so focused on what we've been working on, that we got sidetracked from inspecting all of our data. From initial analysis, it's looking like United made a lot of people unhappy. However we took advantage of one particular caveat. We assumed that whomever compiled all the data for us, correctly identified the airline being referenced in the tweet text. I purposely carried on like this because this **ACTUALLY HAPPENED TO ME!** And it highlights some headaches you will come across when dealing with realworld messy data. Lets look at some of the tweet text and compare it to the airline being referenced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Airline as compiled: Delta\n",
      "The actual tweet text: \n",
      "@JetBlue everyone is here but our pilots are no where to be found and my last flight the plane was dirty that I had to clean my area &amp; seat! \n",
      "\n",
      "\n",
      "Airline as compiled: Delta\n",
      "The actual tweet text: \n",
      "@JetBlue update on Flight 462 would be appreciated when you have time, thanks \n",
      "\n",
      "\n",
      "Airline as compiled: Delta\n",
      "The actual tweet text: \n",
      "@JetBlue check DM please :) \n",
      "\n",
      "\n",
      "Airline as compiled: Delta\n",
      "The actual tweet text: \n",
      "@JetBlue Hey guys, why did my last flight earn me 0 pts? http://t.co/1syWlmTZek \n",
      "\n",
      "\n",
      "Airline as compiled: Delta\n",
      "The actual tweet text: \n",
      "@JetBlue 2 aisles of empty #evermoreroom seats and we can't move bc we didn't pay?! #nonsense #Waste #JetBlue #jetbluebos #cheap \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "observation = list(clean_df(df).reset_index().iloc[6750:6755,8])\n",
    "tweet_text = list(clean_df(df).reset_index().iloc[6750:6755,6])\n",
    "\n",
    "for pos, item in enumerate(observation):\n",
    "    print('Airline as compiled: ' + str(item))\n",
    "    print('The actual tweet text: ')\n",
    "    print(tweet_text[pos], '\\n''\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Well crap!!! \n",
    "We did all that work, and the data in the table was incorrect. As we can see in those particular locations, Delta was tagged as the Airline being referenced in the tweet. But when we look at the actual text we can see it was really Jet Blue being referenced. This actually happened to me when dealing with the data the first time. We now must get rid of the 'Airline' column in the dataframe, parse all the tweet text and pullout the proper airline being referenced in all Tweets. Sure, whoever compiled this probably didn't get **All** of them wrong, but do you really want to scroll through 14k+ lines of data to check. Let's just make some code that parses through for us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Rating</th>\n",
       "      <th>Rating_Conf</th>\n",
       "      <th>Negative_Reason</th>\n",
       "      <th>Reason_Conf</th>\n",
       "      <th>User</th>\n",
       "      <th>Text</th>\n",
       "      <th>Coordinates</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>2015-02-24 11:35:52 -0800</th>\n",
       "      <td>neutral</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cairdin</td>\n",
       "      <td>@VirginAmerica What @dhepburn said.</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-24 11:15:59 -0800</th>\n",
       "      <td>positive</td>\n",
       "      <td>0.3486</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>@VirginAmerica plus you've added commercials t...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-24 11:15:48 -0800</th>\n",
       "      <td>neutral</td>\n",
       "      <td>0.6837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yvonnalynn</td>\n",
       "      <td>@VirginAmerica I didn't today... Must mean I n...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-24 11:15:36 -0800</th>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Bad Flight</td>\n",
       "      <td>0.7033</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>@VirginAmerica it's really aggressive to blast...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-24 11:14:45 -0800</th>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Can't Tell</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Rating  Rating_Conf Negative_Reason  Reason_Conf  \\\n",
       "Date                                                                            \n",
       "2015-02-24 11:35:52 -0800   neutral       1.0000             NaN          NaN   \n",
       "2015-02-24 11:15:59 -0800  positive       0.3486             NaN       0.0000   \n",
       "2015-02-24 11:15:48 -0800   neutral       0.6837             NaN          NaN   \n",
       "2015-02-24 11:15:36 -0800  negative       1.0000      Bad Flight       0.7033   \n",
       "2015-02-24 11:14:45 -0800  negative       1.0000      Can't Tell       1.0000   \n",
       "\n",
       "                                 User  \\\n",
       "Date                                    \n",
       "2015-02-24 11:35:52 -0800     cairdin   \n",
       "2015-02-24 11:15:59 -0800    jnardino   \n",
       "2015-02-24 11:15:48 -0800  yvonnalynn   \n",
       "2015-02-24 11:15:36 -0800    jnardino   \n",
       "2015-02-24 11:14:45 -0800    jnardino   \n",
       "\n",
       "                                                                        Text  \\\n",
       "Date                                                                           \n",
       "2015-02-24 11:35:52 -0800                @VirginAmerica What @dhepburn said.   \n",
       "2015-02-24 11:15:59 -0800  @VirginAmerica plus you've added commercials t...   \n",
       "2015-02-24 11:15:48 -0800  @VirginAmerica I didn't today... Must mean I n...   \n",
       "2015-02-24 11:15:36 -0800  @VirginAmerica it's really aggressive to blast...   \n",
       "2015-02-24 11:14:45 -0800  @VirginAmerica and it's a really big bad thing...   \n",
       "\n",
       "                          Coordinates  \n",
       "Date                                   \n",
       "2015-02-24 11:35:52 -0800         NaN  \n",
       "2015-02-24 11:15:59 -0800         NaN  \n",
       "2015-02-24 11:15:48 -0800         NaN  \n",
       "2015-02-24 11:15:36 -0800         NaN  \n",
       "2015-02-24 11:14:45 -0800         NaN  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's start by getting rid of the current 'Airline' column\n",
    "#'Airline' was the last column (8) so we just sliced the dataframe, stopping at column (7) 'Coordinates'\n",
    "new_df = clean_df(df).iloc[:,0:7]\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now need to extract the right airline from the tweet text. Examining it, we can see that all airlines references are preceded by an '@' symbol. We all know this is a common twitter tag, so by looking for this, we can extract the airline being talked about. Be wary though, some of the texts can have more than just the airline tagged in it. But this is where we will start, using a regular expression that will find all instances of the '@' symbol. Looking at the majority of the tweet text, the airline is usually the first '@' tag in the text in the event their are multiple. So we will make the general assumption that during our regular expression search, when there are multiple '@' tags, the airline will be the first one. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['@VirginAmerica',\n",
       " '@VirginAmerica',\n",
       " '@VirginAmerica',\n",
       " '@VirginAmerica',\n",
       " '@VirginAmerica',\n",
       " '@VirginAmerica',\n",
       " '@VirginAmerica',\n",
       " '@VirginAmerica',\n",
       " '@virginamerica',\n",
       " '@VirginAmerica']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#first, create a new column called 'Airline'\n",
    "#Then reference the 'text' column to apply your regular expression function to\n",
    "#apply a lambda function that parses through each tweet text and searches for '@' symbol followed by any letter type\n",
    "#extract the first matched instance [0] in the event there are multiple\n",
    "new_df['Airline'] = new_df.Text.apply(lambda x: re.findall('\\@[A-Za-z]+', x)[0])\n",
    "\n",
    "#check that our regular expression is working\n",
    "list(new_df.Airline.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, so with 14k+ lines of data, there's no telling what all our regular expression function found. So to help figure this out, lets list all the unique items it found. From this, we can see all the airlines it extracted, plus any other tag that was referenced first in a tweet text before the airline tag."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('@AlbertBreer', 1),\n",
       " ('@AmericanAir', 2722),\n",
       " ('@AndrewFallis', 1),\n",
       " ('@CatfoodBeerGlue', 1),\n",
       " ('@DadBoner', 1),\n",
       " ('@DeltaAssist', 2),\n",
       " ('@HillAConlin', 1),\n",
       " ('@Imaginedragons', 1),\n",
       " ('@InternJohnRadio', 1),\n",
       " ('@JetBlue', 2167),\n",
       " ('@Jetblue', 4),\n",
       " ('@KCIAirport', 1),\n",
       " ('@MandarinJourney', 1),\n",
       " ('@MelissaAFrancis', 1),\n",
       " ('@NinaDavuluri', 1),\n",
       " ('@Sb', 1),\n",
       " ('@SouthwestAir', 2370),\n",
       " ('@Southwestair', 2),\n",
       " ('@USAIRWAYS', 1),\n",
       " ('@USAirways', 2817),\n",
       " ('@USairways', 2),\n",
       " ('@United', 122),\n",
       " ('@UsAirways', 2),\n",
       " ('@Usairways', 2),\n",
       " ('@VirginAmerica', 486),\n",
       " ('@americanair', 32),\n",
       " ('@chasefoster', 1),\n",
       " ('@djevolutionhd', 1),\n",
       " ('@eatgregeat', 1),\n",
       " ('@jetblue', 44),\n",
       " ('@lindaSWC', 1),\n",
       " ('@malhoit', 1),\n",
       " ('@nrhodes', 2),\n",
       " ('@nytimes', 1),\n",
       " ('@ods', 1),\n",
       " ('@sarahpompei', 1),\n",
       " ('@scm', 1),\n",
       " ('@shannonwoodward', 1),\n",
       " ('@southwestAir', 1),\n",
       " ('@southwestair', 44),\n",
       " ('@timbennettg', 1),\n",
       " ('@united', 3695),\n",
       " ('@usairways', 81),\n",
       " ('@vanessaannz', 1),\n",
       " ('@virginAmerica', 1),\n",
       " ('@virginamerica', 17)]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get all unique twitter tags and the count for how many times it appears in the column\n",
    "twitter_tags = np.unique(new_df.Airline, return_counts = True)\n",
    "\n",
    "#compile twitter_tags so that it lists the unique tag and its total count side by side instead of 2 seperate arrays\n",
    "twitter_tags_count = list(zip(twitter_tags[0],twitter_tags[1]))\n",
    "twitter_tags_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So our assumption that the airline is referenced before any other tag was a pretty good guess. We did return some other users twitter names, but for the most part, we got all airline references. We can also see that Jet blue was reference **Alot**. In our original dataframe, there was no mention of Jet Blue. Next, we need to go through the unique list and compile all the airlines referenced into a list. Notice that the airlines are referenced multiple times, this is because they are camel case. When searching for \"unique\" items, @virginAmerica is different than @virginamerica due to the one capital letter. So we need to refine our regular expression search, looking not only for the airlines, but ignoring the camel case so that all spellings of an airline are equal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['VirginAmerica', 'VirginAmerica', 'VirginAmerica', 'VirginAmerica', 'VirginAmerica', 'VirginAmerica', 'VirginAmerica', 'VirginAmerica', 'virginamerica', 'VirginAmerica']\n"
     ]
    }
   ],
   "source": [
    "#List of all airlines in the data as found from the tweets in search above\n",
    "airline_list = ['@virginamerica','@united','@southwestair','@americanair','@jetblue','@usairways']\n",
    "    \n",
    "#compile a regex search to seperate out only the airline tag and ignoring other users tags in the text\n",
    "#using the compile method is an easier way to input our \"match\" pattern into the search engine, especially in this event\n",
    "#when we are searching for mulitple airlines.\n",
    "#we are ignoring case, or capitaliztion  in order to negate all the uniquess we encountered in the list above\n",
    "airlines = re.compile('|'.join(airline_list), re.IGNORECASE)\n",
    "    \n",
    "#apply the compiled regex search and remove the twitter tag '@'\n",
    "#for example, the following code takes @AmericanAir and returns AmericanAir\n",
    "new_df['Airline'] = new_df.Airline.apply(lambda x: np.squeeze(re.findall(airlines, x))).str.split('@').str[1]\n",
    "print(list(new_df.Airline.head(10)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great, so now we have found only the airlines, and removed the '@' tag from them. But what happened to the rows where the airline didn't come first and we extracted a users name instead? Well by default, they were filled in with NaN since they didn't match anything in the matching patter. The code below uses boolean logic to filter the data frame and find the index locations of those tweets, and displays all '@' tags that was referenced in the tweet text. This step is needed so we could find those locations that did not have an airline in place, and manually set the values for these locations in the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "737                              [@lindaSWC, @united]\n",
       "868                       [@MandarinJourney, @united]\n",
       "1088                      [@MelissaAFrancis, @United]\n",
       "4013                             [@DadBoner, @united]\n",
       "4604                 [@Imaginedragons, @SouthwestAir]\n",
       "5614                     [@KCIAirport, @SouthwestAir]\n",
       "6136     [@InternJohnRadio, @mrerickv, @SouthwestAir]\n",
       "6362                    [@HillAConlin, @SouthwestAir]\n",
       "6796                     [@CatfoodBeerGlue, @JetBlue]\n",
       "6811                          [@eatgregeat, @JetBlue]\n",
       "6906                             [@nytimes, @JetBlue]\n",
       "7330                               [@shannonwoodward]\n",
       "8215                                  [@NinaDavuluri]\n",
       "10243                               [@Sb, @USAirways]\n",
       "10517                      [@timbennettg, @USAirways]\n",
       "10799                          [@nrhodes, @USAirways]\n",
       "10864                      [@sarahpompei, @USAirways]\n",
       "10874                     [@AndrewFallis, @USAirways]\n",
       "10876                          [@nrhodes, @USAirways]\n",
       "11159        [@AlbertBreer, @USAirways, @AmericanAir]\n",
       "11430                              [@scm, @USAirways]\n",
       "12038                                  [@DeltaAssist]\n",
       "12039                                  [@DeltaAssist]\n",
       "12222                            [@ods, @AmericanAir]\n",
       "12417                    [@chasefoster, @AmericanAir]\n",
       "12585                  [@djevolutionhd, @AmericanAir]\n",
       "13491                        [@malhoit, @AmericanAir]\n",
       "13979                    [@vanessaannz, @AmericanAir]\n",
       "Name: Text, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_airline = new_df.reset_index()\n",
    "no_airline = no_airline[no_airline.Airline.isnull()].Text.apply(lambda x: re.findall('\\@[A-Za-z]+', x))\n",
    "no_airline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great, so now we can see where the null values are located because the first '@' tag is a user's name, and what airline was supposed to be referenced. Now we can manually set these, and then our 'Airline' column will be up to date and correct. One little issue here, indices 7330 and  8215 didn't have any '@' tag with an airline next to it, so we are just going to have to read the tweet text manually and extract that for ourselves. But don't worry, I did that for you already =) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['American Airlines', 'Delta Airlines', 'Jet Blue',\n",
       "       'Southwest Airlines', 'US Airways', 'United Airlines',\n",
       "       'Virgin Airlines'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reset the index of our dataframe\n",
    "new_df = new_df.reset_index()\n",
    "\n",
    "#compile a list of index locations of the tweets that return null and set their airline value to the appropriate\n",
    "#airline referenced in the tweet\n",
    "united = [737,868,1088,4013]\n",
    "southwest = [4604,5614,5615,6136,6362]\n",
    "jetblue = [6796,6811,6906]\n",
    "usairways = [7330, 8215,10243,10517,10799,10864,10874,10876,11430]\n",
    "american = [11159,12222,12417,12585,13491,13979]\n",
    "delta = [12038, 12039]\n",
    "new_df.set_value(united,'Airline','united')\n",
    "new_df.set_value(southwest,'Airline','southwestair')\n",
    "new_df.set_value(jetblue,'Airline','jetblue')\n",
    "new_df.set_value(usairways,'Airline','usairways')\n",
    "new_df.set_value(american,'Airline','americanair')\n",
    "new_df.set_value(delta,'Airline','delta')\n",
    "    \n",
    "#Since all airlines tweets are camel case in different orders, make all airlines uppercase so they are all equal\n",
    "new_df.Airline = new_df.Airline.apply(lambda x: x.upper())\n",
    "    \n",
    "#create a dictionary to map the all uppercase airlines to the proper naming convention\n",
    "map_airline = {'AMERICANAIR':'American Airlines',\n",
    "                'JETBLUE':'Jet Blue',\n",
    "                'SOUTHWESTAIR':'Southwest Airlines',\n",
    "                'UNITED': 'United Airlines',\n",
    "                'USAIRWAYS': 'US Airways',\n",
    "                'VIRGINAMERICA':'Virgin Airlines',\n",
    "                'DELTA':'Delta Airlines'}\n",
    "    \n",
    "#map the uppercase airlines to the proper naming convention\n",
    "new_df.Airline = new_df.Airline.map(map_airline)\n",
    "\n",
    "#display our new airlines!!!\n",
    "np.unique(new_df.Airline)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next order of busines - Continuing to inspect our data before continuing on with any analysis! We don't need another headache. When trying to display truthful data, we need to decide which data is truthful. When deciding what to rate the tweet text, the compiler also added a column \"Rating Confidence\". This is a percentage of how sure they are that the rating the gave was the proper rating. Let's inspect some of these"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neutral \n",
      " 1.0 \n",
      " @VirginAmerica What @dhepburn said. \n",
      " \n",
      "\n",
      "positive \n",
      " 0.3486 \n",
      " @VirginAmerica plus you've added commercials to the experience... tacky. \n",
      " \n",
      "\n",
      "neutral \n",
      " 0.6837 \n",
      " @VirginAmerica I didn't today... Must mean I need to take another trip! \n",
      " \n",
      "\n",
      "negative \n",
      " 1.0 \n",
      " @VirginAmerica it's really aggressive to blast obnoxious \"entertainment\" in your guests' faces &amp; they have little recourse \n",
      " \n",
      "\n",
      "negative \n",
      " 1.0 \n",
      " @VirginAmerica and it's a really big bad thing about it \n",
      " \n",
      "\n",
      "negative \n",
      " 1.0 \n",
      " @VirginAmerica seriously would pay $30 a flight for seats that didn't have this playing.\n",
      "it's really the only bad thing about flying VA \n",
      " \n",
      "\n",
      "positive \n",
      " 0.6745 \n",
      " @VirginAmerica yes, nearly every time I fly VX this “ear worm” won’t go away :) \n",
      " \n",
      "\n",
      "neutral \n",
      " 0.634 \n",
      " @VirginAmerica Really missed a prime opportunity for Men Without Hats parody, there. https://t.co/mWpG7grEZP \n",
      " \n",
      "\n",
      "positive \n",
      " 0.6559 \n",
      " @virginamerica Well, I didn't…but NOW I DO! :-D \n",
      " \n",
      "\n",
      "positive \n",
      " 1.0 \n",
      " @VirginAmerica it was amazing, and arrived an hour early. You're too good to me. \n",
      " \n",
      "\n"
     ]
    }
   ],
   "source": [
    "rating = list(new_df.Rating)\n",
    "conf = list(new_df.Rating_Conf)\n",
    "text = list(new_df.Text)\n",
    "\n",
    "for i in range(10):\n",
    "    print(rating[i], '\\n', conf[i], '\\n', text[i],'\\n','\\n')\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at few of the ratings and the corresponding text, it's pretty reasonable to say the rating and it's corresponding confidence percentage is more or less in the ballpark. Armed with this data, let's now make our data even more accurate. There are ratings given to an airline that the confidence is pretty low. Since we don't want to mislabel the airline, we are probably better suited to only analyze the data in which were are pretty sure that the rating is the correct one. This part is basically at the discretion of the user, but to me, 51% is winning. So let's filter our data down to those ratings that have only greater than 0.51 rating confidence percentages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 14403 entries, 0 to 14639\n",
      "Data columns (total 9 columns):\n",
      "Date               14403 non-null object\n",
      "Rating             14403 non-null object\n",
      "Rating_Conf        14403 non-null float64\n",
      "Negative_Reason    9114 non-null object\n",
      "Reason_Conf        10285 non-null float64\n",
      "User               14403 non-null object\n",
      "Text               14403 non-null object\n",
      "Coordinates        1009 non-null object\n",
      "Airline            14403 non-null object\n",
      "dtypes: float64(2), object(7)\n",
      "memory usage: 1.1+ MB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Rating_Conf</th>\n",
       "      <th>Negative_Reason</th>\n",
       "      <th>Reason_Conf</th>\n",
       "      <th>User</th>\n",
       "      <th>Text</th>\n",
       "      <th>Coordinates</th>\n",
       "      <th>Airline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-02-24 11:35:52 -0800</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cairdin</td>\n",
       "      <td>@VirginAmerica What @dhepburn said.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin Airlines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-02-24 11:15:48 -0800</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.6837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yvonnalynn</td>\n",
       "      <td>@VirginAmerica I didn't today... Must mean I n...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin Airlines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-02-24 11:15:36 -0800</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Bad Flight</td>\n",
       "      <td>0.7033</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>@VirginAmerica it's really aggressive to blast...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin Airlines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-02-24 11:14:45 -0800</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Can't Tell</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin Airlines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015-02-24 11:14:33 -0800</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Can't Tell</td>\n",
       "      <td>0.6842</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>@VirginAmerica seriously would pay $30 a fligh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin Airlines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015-02-24 11:13:57 -0800</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.6745</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>cjmcginnis</td>\n",
       "      <td>@VirginAmerica yes, nearly every time I fly VX...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin Airlines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2015-02-24 11:12:29 -0800</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.6340</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pilot</td>\n",
       "      <td>@VirginAmerica Really missed a prime opportuni...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin Airlines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2015-02-24 11:11:19 -0800</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.6559</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dhepburn</td>\n",
       "      <td>@virginamerica Well, I didn't…but NOW I DO! :-D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin Airlines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015-02-24 10:53:27 -0800</td>\n",
       "      <td>positive</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>YupitsTate</td>\n",
       "      <td>@VirginAmerica it was amazing, and arrived an ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin Airlines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2015-02-24 10:48:24 -0800</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.6769</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>idk_but_youtube</td>\n",
       "      <td>@VirginAmerica did you know that suicide is th...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin Airlines</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Date    Rating  Rating_Conf Negative_Reason  \\\n",
       "0   2015-02-24 11:35:52 -0800   neutral       1.0000             NaN   \n",
       "2   2015-02-24 11:15:48 -0800   neutral       0.6837             NaN   \n",
       "3   2015-02-24 11:15:36 -0800  negative       1.0000      Bad Flight   \n",
       "4   2015-02-24 11:14:45 -0800  negative       1.0000      Can't Tell   \n",
       "5   2015-02-24 11:14:33 -0800  negative       1.0000      Can't Tell   \n",
       "6   2015-02-24 11:13:57 -0800  positive       0.6745             NaN   \n",
       "7   2015-02-24 11:12:29 -0800   neutral       0.6340             NaN   \n",
       "8   2015-02-24 11:11:19 -0800  positive       0.6559             NaN   \n",
       "9   2015-02-24 10:53:27 -0800  positive       1.0000             NaN   \n",
       "10  2015-02-24 10:48:24 -0800   neutral       0.6769             NaN   \n",
       "\n",
       "    Reason_Conf             User  \\\n",
       "0           NaN          cairdin   \n",
       "2           NaN       yvonnalynn   \n",
       "3        0.7033         jnardino   \n",
       "4        1.0000         jnardino   \n",
       "5        0.6842         jnardino   \n",
       "6        0.0000       cjmcginnis   \n",
       "7           NaN            pilot   \n",
       "8           NaN         dhepburn   \n",
       "9           NaN       YupitsTate   \n",
       "10       0.0000  idk_but_youtube   \n",
       "\n",
       "                                                 Text Coordinates  \\\n",
       "0                 @VirginAmerica What @dhepburn said.         NaN   \n",
       "2   @VirginAmerica I didn't today... Must mean I n...         NaN   \n",
       "3   @VirginAmerica it's really aggressive to blast...         NaN   \n",
       "4   @VirginAmerica and it's a really big bad thing...         NaN   \n",
       "5   @VirginAmerica seriously would pay $30 a fligh...         NaN   \n",
       "6   @VirginAmerica yes, nearly every time I fly VX...         NaN   \n",
       "7   @VirginAmerica Really missed a prime opportuni...         NaN   \n",
       "8     @virginamerica Well, I didn't…but NOW I DO! :-D         NaN   \n",
       "9   @VirginAmerica it was amazing, and arrived an ...         NaN   \n",
       "10  @VirginAmerica did you know that suicide is th...         NaN   \n",
       "\n",
       "            Airline  \n",
       "0   Virgin Airlines  \n",
       "2   Virgin Airlines  \n",
       "3   Virgin Airlines  \n",
       "4   Virgin Airlines  \n",
       "5   Virgin Airlines  \n",
       "6   Virgin Airlines  \n",
       "7   Virgin Airlines  \n",
       "8   Virgin Airlines  \n",
       "9   Virgin Airlines  \n",
       "10  Virgin Airlines  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we could make this one line, but i'm breaking it up for readability\n",
    "#set our boolean variable so that it filters the dataframe for only instances where the rating conf is >0.51\n",
    "\n",
    "\n",
    "conf_df = new_df[new_df.Rating_Conf >= 0.51 ]\n",
    "print(conf_df.info())\n",
    "conf_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So surprisingly, improving our confidence statistics only reduced our data by some 200+ rows. Whoever compiled the data was pretty sure of themselves. Now, one final thing, the index is set as the date and time, but we don't realy like that format. I don't think there's much value in having it as low as the hours minutes and seconds. So let's convert this to just month day and year. Also, although not shown, the type of the date time in the index is **'string'**. When dealing with pandas, it's better to have the 'type' as pandas **'datetime'**. So let's convert as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2015-02-24\n",
       "1    2015-02-24\n",
       "2    2015-02-24\n",
       "3    2015-02-24\n",
       "4    2015-02-24\n",
       "Name: Date, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create a copy of our original dataframe and reset the index\n",
    "date = conf_df.reset_index()\n",
    "#convert the Date column to pandas datetime\n",
    "date.Date = pd.to_datetime(date.Date)\n",
    "#Reduce the dates in the date column to only the date and no time stamp using the 'dt.date' method\n",
    "date.Date = date.Date.dt.date\n",
    "date.Date.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>index</th>\n",
       "      <th>Date</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Rating_Conf</th>\n",
       "      <th>Negative_Reason</th>\n",
       "      <th>Reason_Conf</th>\n",
       "      <th>User</th>\n",
       "      <th>Text</th>\n",
       "      <th>Coordinates</th>\n",
       "      <th>Airline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-02-24</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cairdin</td>\n",
       "      <td>@VirginAmerica What @dhepburn said.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin Airlines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-02-24</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.6837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yvonnalynn</td>\n",
       "      <td>@VirginAmerica I didn't today... Must mean I n...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin Airlines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2015-02-24</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Bad Flight</td>\n",
       "      <td>0.7033</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>@VirginAmerica it's really aggressive to blast...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin Airlines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2015-02-24</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Can't Tell</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin Airlines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2015-02-24</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Can't Tell</td>\n",
       "      <td>0.6842</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>@VirginAmerica seriously would pay $30 a fligh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin Airlines</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index        Date    Rating  Rating_Conf Negative_Reason  Reason_Conf  \\\n",
       "0      0  2015-02-24   neutral       1.0000             NaN          NaN   \n",
       "1      2  2015-02-24   neutral       0.6837             NaN          NaN   \n",
       "2      3  2015-02-24  negative       1.0000      Bad Flight       0.7033   \n",
       "3      4  2015-02-24  negative       1.0000      Can't Tell       1.0000   \n",
       "4      5  2015-02-24  negative       1.0000      Can't Tell       0.6842   \n",
       "\n",
       "         User                                               Text Coordinates  \\\n",
       "0     cairdin                @VirginAmerica What @dhepburn said.         NaN   \n",
       "1  yvonnalynn  @VirginAmerica I didn't today... Must mean I n...         NaN   \n",
       "2    jnardino  @VirginAmerica it's really aggressive to blast...         NaN   \n",
       "3    jnardino  @VirginAmerica and it's a really big bad thing...         NaN   \n",
       "4    jnardino  @VirginAmerica seriously would pay $30 a fligh...         NaN   \n",
       "\n",
       "           Airline  \n",
       "0  Virgin Airlines  \n",
       "1  Virgin Airlines  \n",
       "2  Virgin Airlines  \n",
       "3  Virgin Airlines  \n",
       "4  Virgin Airlines  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_df = date\n",
    "conf_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, now we have our data looking much better, let's rerun the analysis we originally did and plot the results..... **AGAIN**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline             Rating  \n",
       "American Airlines   negative    1955\n",
       "                    neutral      448\n",
       "                    positive     330\n",
       "Jet Blue            negative     936\n",
       "                    neutral      699\n",
       "                    positive     530\n",
       "Southwest Airlines  negative    1177\n",
       "                    neutral      641\n",
       "                    positive     555\n",
       "US Airways          negative    2253\n",
       "                    neutral      367\n",
       "                    positive     257\n",
       "United Airlines     negative    2612\n",
       "                    neutral      675\n",
       "                    positive     472\n",
       "Virgin Airlines     negative     179\n",
       "                    neutral      167\n",
       "                    positive     148\n",
       "Name: index, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = conf_df[conf_df.Airline != 'Delta Airlines'].groupby(['Airline','Rating']).count().iloc[:,0]\n",
    "test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to create the seperate dataframes again for the percentage: positive, negative, and neutral. But instead of rewriting all the repeating code 3 times again, let's just write one function that we can pass the rating type we want to compile and let it do all the work for us for each rating."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def percentages(df, rating = 'negative'):\n",
    "    if rating == 'negative':\n",
    "        i = 0\n",
    "        column = 'Percent Negative'\n",
    "    elif rating == 'neutral':\n",
    "        i = 1\n",
    "        column = 'Percent Neutral'\n",
    "    elif rating == 'positive':\n",
    "        i = 2\n",
    "        column = 'Percent Positive'\n",
    "        \n",
    "    #Count of all tweet ratings for each airline (negative, neutral, positive), remove Delta since it only has 2 entries total\n",
    "    itemized_tweets = df[df.Airline != 'Delta Airlines'].groupby(['Airline','Rating']).count().iloc[:,0]\n",
    "    #Rating tweet total index for each airline:\n",
    "    #American i\n",
    "    #Jet Blue i + 3\n",
    "    #southwest i + 6\n",
    "    #US Airways i + 9\n",
    "    #United i + 12\n",
    "    #Virgin i + 15\n",
    "\n",
    "    #Count of total tweets about an airline\n",
    "    total_tweets = df[df.Airline != 'Delta Airlines'].groupby(['Airline'])['Rating'].count()\n",
    "    #Airline index in total tweets:\n",
    "    #American 0\n",
    "    #Jet Blue 1\n",
    "    #Southwest 2\n",
    "    #US Airways 3\n",
    "    #United 4\n",
    "    #Virgin 5\n",
    "\n",
    "\n",
    "    #Create a dictionary of percentage of rating tweets = (rating_tweets / total_tweets)\n",
    "    my_dict = {'American':itemized_tweets[i] / total_tweets[0],\n",
    "                'Jet Blue':itemized_tweets[i + 3] / total_tweets[1],\n",
    "                'Southwest': itemized_tweets[i + 6] / total_tweets[2],\n",
    "                'US Airways': itemized_tweets[i + 9] / total_tweets[3],\n",
    "                'United': itemized_tweets[i + 12] / total_tweets[4],\n",
    "                'Virgin': itemized_tweets[i + 15] / total_tweets[5]}\n",
    "\n",
    "    #make a dataframe from the dictionary\n",
    "    perc_df = pd.DataFrame.from_dict(my_dict, orient = 'index')\n",
    "        \n",
    "    #have to manually set column name when using .from_dict() method\n",
    "    perc_df.columns = [column]\n",
    "        \n",
    "    return perc_df\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! we have our function, now let's use it to compile our percentages and plot them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Percent Negative</th>\n",
       "      <th>Percent Positive</th>\n",
       "      <th>Percent Neutral</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>American</th>\n",
       "      <td>0.715331</td>\n",
       "      <td>0.120746</td>\n",
       "      <td>0.163922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jet Blue</th>\n",
       "      <td>0.432333</td>\n",
       "      <td>0.244804</td>\n",
       "      <td>0.322864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Southwest</th>\n",
       "      <td>0.495997</td>\n",
       "      <td>0.233881</td>\n",
       "      <td>0.270122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US Airways</th>\n",
       "      <td>0.783107</td>\n",
       "      <td>0.089329</td>\n",
       "      <td>0.127563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United</th>\n",
       "      <td>0.694866</td>\n",
       "      <td>0.125565</td>\n",
       "      <td>0.179569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virgin</th>\n",
       "      <td>0.362348</td>\n",
       "      <td>0.299595</td>\n",
       "      <td>0.338057</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Percent Negative  Percent Positive  Percent Neutral\n",
       "American            0.715331          0.120746         0.163922\n",
       "Jet Blue            0.432333          0.244804         0.322864\n",
       "Southwest           0.495997          0.233881         0.270122\n",
       "US Airways          0.783107          0.089329         0.127563\n",
       "United              0.694866          0.125565         0.179569\n",
       "Virgin              0.362348          0.299595         0.338057"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a df called negative that contains the percent negatives by calling the function above\n",
    "negative = percentages(conf_df, 'negative')\n",
    "\n",
    "#Create a df called neutral that contains the percent neutrals by calling the function above\n",
    "neutral = percentages(conf_df, 'neutral')\n",
    "\n",
    "#Create a df called positive that contains the percent positives by calling the function above\n",
    "positive = percentages(conf_df, 'positive')\n",
    "\n",
    "#call the earlier function that merges all 3 data frames into one\n",
    "merged_perc = merge_dfs(negative, positive, neutral)\n",
    "merged_perc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2gAAAFlCAYAAACeF8vKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAMgoAGAAAAAAbx/wCFy768LsEFbwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fea199125c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = merged_perc.plot(kind = 'bar', rot = 0, figsize = (15,6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have accurate data, and a clear picture of which airline just really upset people. US Airways, no wonder why you were acquired by American Airlines a year later. A lot of people were not happy for you. By why weren't they happy with you? Let's extract all the reasons why people were upset and aggregate them for each airline while plotting the results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2sAAAHgCAYAAAA7XfWiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fea19912470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#function that reduces the dataframe to only the airline and the negative reasons, then extract the reasons and the frequency\n",
    "#each reason was referenced to an airline\n",
    "def reason(df):\n",
    "    df = df.reset_index().loc[:,['Airline','Negative_Reason']].dropna().groupby(['Airline','Negative_Reason']).size()\n",
    "    return df\n",
    "\n",
    "#call the function and plot the results\n",
    "ax1 = reason(conf_df).unstack(0).plot(kind = 'bar', figsize = (15,6), rot = 70)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how the negative tweets break down for each day of the data. It appears that we have about a weeks worth of tweet data here, so let's sort out only the negatives and see how much an airline got hated on each day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015-02-17\n",
      "2015-02-24\n"
     ]
    }
   ],
   "source": [
    "print(conf_df.Date.min())\n",
    "print(conf_df.Date.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date        Airline             Rating  \n",
       "2015-02-17  Jet Blue            negative     51\n",
       "                                neutral      60\n",
       "                                positive     47\n",
       "            Southwest Airlines  negative    161\n",
       "                                neutral      66\n",
       "                                positive     55\n",
       "            US Airways          negative    155\n",
       "                                neutral      22\n",
       "                                positive     32\n",
       "            United Airlines     negative    172\n",
       "                                neutral      49\n",
       "                                positive     29\n",
       "            Virgin Airlines     negative      9\n",
       "                                neutral      15\n",
       "                                positive     15\n",
       "2015-02-18  Jet Blue            negative    127\n",
       "                                neutral      80\n",
       "                                positive     77\n",
       "            Southwest Airlines  negative    142\n",
       "                                neutral      93\n",
       "                                positive     86\n",
       "            US Airways          negative    257\n",
       "                                neutral      28\n",
       "                                positive     43\n",
       "            United Airlines     negative    279\n",
       "                                neutral      73\n",
       "                                positive     51\n",
       "            Virgin Airlines     negative     17\n",
       "                                neutral      21\n",
       "                                positive     20\n",
       "                                           ... \n",
       "2015-02-23  Southwest Airlines  neutral     105\n",
       "                                positive     91\n",
       "            US Airways          negative    429\n",
       "                                neutral      76\n",
       "                                positive     45\n",
       "            United Airlines     negative    501\n",
       "                                neutral      96\n",
       "                                positive     68\n",
       "            Virgin Airlines     negative     30\n",
       "                                neutral      30\n",
       "                                positive     19\n",
       "2015-02-24  American Airlines   negative    567\n",
       "                                neutral     195\n",
       "                                positive    148\n",
       "            Delta Airlines      negative      1\n",
       "            Jet Blue            negative     67\n",
       "                                neutral      70\n",
       "                                positive     45\n",
       "            Southwest Airlines  negative    124\n",
       "                                neutral      61\n",
       "                                positive     52\n",
       "            US Airways          negative    232\n",
       "                                neutral      56\n",
       "                                positive     21\n",
       "            United Airlines     negative    274\n",
       "                                neutral      85\n",
       "                                positive     52\n",
       "            Virgin Airlines     negative     15\n",
       "                                neutral      15\n",
       "                                positive     18\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#groupby by Date first making it the main index, then group by the airline, then finally the rating and see how many\n",
    "#of each rating an airline got for each date\n",
    "day_df = conf_df.groupby(['Date','Airline','Rating']).size()\n",
    "day_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to filter this down to just the negative counts for each airline. A neat little trick to conver this output into just another dataframe is simply by resetting the index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Date</th>\n",
       "      <th>Airline</th>\n",
       "      <th>Rating</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-02-17</td>\n",
       "      <td>Jet Blue</td>\n",
       "      <td>negative</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-02-17</td>\n",
       "      <td>Jet Blue</td>\n",
       "      <td>neutral</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-02-17</td>\n",
       "      <td>Jet Blue</td>\n",
       "      <td>positive</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-02-17</td>\n",
       "      <td>Southwest Airlines</td>\n",
       "      <td>negative</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-02-17</td>\n",
       "      <td>Southwest Airlines</td>\n",
       "      <td>neutral</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date             Airline    Rating    0\n",
       "0  2015-02-17            Jet Blue  negative   51\n",
       "1  2015-02-17            Jet Blue   neutral   60\n",
       "2  2015-02-17            Jet Blue  positive   47\n",
       "3  2015-02-17  Southwest Airlines  negative  161\n",
       "4  2015-02-17  Southwest Airlines   neutral   66"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_df = day_df.reset_index()\n",
    "day_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we want to rename that '0' column to something more informational, like 'Count'. Also we can use boolean logic to filter the dataframe down to only 'negative' ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>index</th>\n",
       "      <th>Date</th>\n",
       "      <th>Airline</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-02-17</td>\n",
       "      <td>Jet Blue</td>\n",
       "      <td>negative</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2015-02-17</td>\n",
       "      <td>Southwest Airlines</td>\n",
       "      <td>negative</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>2015-02-17</td>\n",
       "      <td>US Airways</td>\n",
       "      <td>negative</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>2015-02-17</td>\n",
       "      <td>United Airlines</td>\n",
       "      <td>negative</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>2015-02-17</td>\n",
       "      <td>Virgin Airlines</td>\n",
       "      <td>negative</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index        Date             Airline    Rating  Count\n",
       "0      0  2015-02-17            Jet Blue  negative     51\n",
       "1      3  2015-02-17  Southwest Airlines  negative    161\n",
       "2      6  2015-02-17          US Airways  negative    155\n",
       "3      9  2015-02-17     United Airlines  negative    172\n",
       "4     12  2015-02-17     Virgin Airlines  negative      9"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rename the column\n",
    "day_df = day_df.rename(columns = {0:'Count'})\n",
    "#filter to only negative ratings\n",
    "day_df = day_df[day_df.Rating == 'negative'].reset_index()\n",
    "#Remove delta since it only has 2 entries\n",
    "day_df = day_df[day_df.Airline != 'Delta Airlines']\n",
    "day_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have an extra column in there that we don't really are about, so we can slice that out, and then regroup our new dataframe again using 'Date' as the main index and 'Airline' as the secondary index. From there, we can simply plot our results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA34AAAGdCAYAAABNZqERAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fea1927bf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#slice out the first 2 columns of the resultant dataframe\n",
    "day_df = day_df.iloc[:,1:5]\n",
    "\n",
    "#groupby and plot data\n",
    "ax2 = day_df.groupby(['Date','Airline']).sum().unstack().plot(kind = 'bar', colormap = 'viridis', figsize = (15,6), rot = 70)\n",
    "labels = ['American Airlines','Jet Blue','Southwest Airlines','US Airways','United Airlines','Virgin Airlines']\n",
    "ax2.legend(labels = labels)\n",
    "ax2.set_xlabel('Date')\n",
    "ax2.set_ylabel('Negative Tweets')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "I don't know what happend on the 23, but it looks like all the American Airlines flights decided to revolt against their customers all at once. Wow. Let's take our analysis a step further and see what power twitter really has. Let's compare the amount of complaints per day to the stock prices of these airlines on those days. Unfortunately however, Virgin Airlines is a privately held company so there's no stock info on them. Also US Airways was eventually acquired by American Airlines in a merger shortly after this data. So we can only compile data for the other 4 Airlines. I've already downloaded the historical stock data from Yahoo Finance for American, Jet Blue, Southwest, and United. Let's load the data and see what we get"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>67.739998</td>\n",
       "      <td>68.080002</td>\n",
       "      <td>65.709999</td>\n",
       "      <td>66.339996</td>\n",
       "      <td>66.339996</td>\n",
       "      <td>6215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>66.870003</td>\n",
       "      <td>67.349998</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>66.150002</td>\n",
       "      <td>66.150002</td>\n",
       "      <td>5033400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-06</td>\n",
       "      <td>66.629997</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>63.110001</td>\n",
       "      <td>64.580002</td>\n",
       "      <td>64.580002</td>\n",
       "      <td>6051700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-07</td>\n",
       "      <td>64.959999</td>\n",
       "      <td>66.110001</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>65.529999</td>\n",
       "      <td>65.529999</td>\n",
       "      <td>5135000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-08</td>\n",
       "      <td>65.699997</td>\n",
       "      <td>67.519997</td>\n",
       "      <td>65.410004</td>\n",
       "      <td>66.639999</td>\n",
       "      <td>66.639999</td>\n",
       "      <td>6889500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date       Open       High        Low      Close  Adj Close   Volume\n",
       "0  2015-01-02  67.739998  68.080002  65.709999  66.339996  66.339996  6215000\n",
       "1  2015-01-05  66.870003  67.349998  65.500000  66.150002  66.150002  5033400\n",
       "2  2015-01-06  66.629997  67.000000  63.110001  64.580002  64.580002  6051700\n",
       "3  2015-01-07  64.959999  66.110001  64.000000  65.529999  65.529999  5135000\n",
       "4  2015-01-08  65.699997  67.519997  65.410004  66.639999  66.639999  6889500"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "JB = pd.read_csv('JBLU.csv')\n",
    "AM = pd.read_csv('American.csv')\n",
    "SW = pd.read_csv('Southwest.csv')\n",
    "UN = pd.read_csv('United.csv')\n",
    "UN.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspecting the data structure in the **UNITED AIRLINES** stock data, we will probably only need the \"Date\", \"Open\", and \"Close\" data. We also only need the dates that correspond to the same dates as the twitter data. Let's build a function that we can pass all over csv files through to reduce the dataframes to the desired form, and then we will concatentate them. Also we need a way to designate between each airline since all csv files looked exactly the same. So we will add a multiindex column in the dataframe serpating the data by Airlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def result(df, al = 'JB'):\n",
    "    #set the column name for the appropriate airline\n",
    "    if al == 'JB':\n",
    "        airline = 'Jet Blue'\n",
    "    elif al =='AM':\n",
    "        airline = 'American Airlines'\n",
    "    elif al == 'SW':\n",
    "        airline = 'Southwest Airlines'\n",
    "    elif al == 'UN':\n",
    "        airline = 'United Airlines'\n",
    "    \n",
    "    #slice the data frame to only the columns and dates that we want\n",
    "    df = df.loc[30:36,['Date','Open','Close']]\n",
    "    df.Date = pd.to_datetime(df['Date'])\n",
    "    #input the Airline column\n",
    "    df['Airline'] = airline\n",
    "    #create the mulitindex column with the airlines\n",
    "    df = df.set_index(['Date','Airline']).unstack().swaplevel(0,1,1).sort_index(1)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Airline</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Jet Blue</th>\n",
       "      <th colspan=\"2\" halign=\"left\">American Airlines</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Southwest Airlines</th>\n",
       "      <th colspan=\"2\" halign=\"left\">United Airlines</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-02-17</th>\n",
       "      <td>16.950001</td>\n",
       "      <td>16.820000</td>\n",
       "      <td>48.770000</td>\n",
       "      <td>48.009998</td>\n",
       "      <td>43.459999</td>\n",
       "      <td>42.590000</td>\n",
       "      <td>66.500000</td>\n",
       "      <td>64.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-18</th>\n",
       "      <td>16.950001</td>\n",
       "      <td>16.830000</td>\n",
       "      <td>48.599998</td>\n",
       "      <td>49.310001</td>\n",
       "      <td>42.939999</td>\n",
       "      <td>43.549999</td>\n",
       "      <td>65.900002</td>\n",
       "      <td>66.720001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-19</th>\n",
       "      <td>17.209999</td>\n",
       "      <td>17.020000</td>\n",
       "      <td>50.779999</td>\n",
       "      <td>49.779999</td>\n",
       "      <td>44.919998</td>\n",
       "      <td>43.759998</td>\n",
       "      <td>68.449997</td>\n",
       "      <td>66.089996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-20</th>\n",
       "      <td>17.070000</td>\n",
       "      <td>17.490000</td>\n",
       "      <td>49.810001</td>\n",
       "      <td>51.020000</td>\n",
       "      <td>43.650002</td>\n",
       "      <td>44.779999</td>\n",
       "      <td>66.089996</td>\n",
       "      <td>68.010002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-23</th>\n",
       "      <td>17.530001</td>\n",
       "      <td>17.780001</td>\n",
       "      <td>51.750000</td>\n",
       "      <td>51.310001</td>\n",
       "      <td>45.110001</td>\n",
       "      <td>44.689999</td>\n",
       "      <td>69.029999</td>\n",
       "      <td>68.839996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-24</th>\n",
       "      <td>17.750000</td>\n",
       "      <td>18.070000</td>\n",
       "      <td>50.290001</td>\n",
       "      <td>51.520000</td>\n",
       "      <td>44.380001</td>\n",
       "      <td>45.349998</td>\n",
       "      <td>67.949997</td>\n",
       "      <td>70.889999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-25</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>17.530001</td>\n",
       "      <td>51.299999</td>\n",
       "      <td>49.730000</td>\n",
       "      <td>45.020000</td>\n",
       "      <td>44.119999</td>\n",
       "      <td>70.559998</td>\n",
       "      <td>68.690002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Airline      Jet Blue            American Airlines             \\\n",
       "                 Open      Close              Open      Close   \n",
       "Date                                                            \n",
       "2015-02-17  16.950001  16.820000         48.770000  48.009998   \n",
       "2015-02-18  16.950001  16.830000         48.599998  49.310001   \n",
       "2015-02-19  17.209999  17.020000         50.779999  49.779999   \n",
       "2015-02-20  17.070000  17.490000         49.810001  51.020000   \n",
       "2015-02-23  17.530001  17.780001         51.750000  51.310001   \n",
       "2015-02-24  17.750000  18.070000         50.290001  51.520000   \n",
       "2015-02-25  18.000000  17.530001         51.299999  49.730000   \n",
       "\n",
       "Airline    Southwest Airlines            United Airlines             \n",
       "                         Open      Close            Open      Close  \n",
       "Date                                                                 \n",
       "2015-02-17          43.459999  42.590000       66.500000  64.930000  \n",
       "2015-02-18          42.939999  43.549999       65.900002  66.720001  \n",
       "2015-02-19          44.919998  43.759998       68.449997  66.089996  \n",
       "2015-02-20          43.650002  44.779999       66.089996  68.010002  \n",
       "2015-02-23          45.110001  44.689999       69.029999  68.839996  \n",
       "2015-02-24          44.380001  45.349998       67.949997  70.889999  \n",
       "2015-02-25          45.020000  44.119999       70.559998  68.690002  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Call our function above and clean up our dataframes to the desired output\n",
    "JB = result(JB, 'JB')\n",
    "AM = result(AM, 'AM')\n",
    "SW = result(SW, 'SW')\n",
    "UN = result(UN, 'UN')\n",
    "\n",
    "#Concatenate our cleaned up data frames\n",
    "stocks = pd.concat([JB,AM,SW,UN], axis = 1)\n",
    "stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3MAAAPECAYAAAAEhmK3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XeUHNWd9/93VefcPaEnarLCKIOEhJCEQAIhghEYsFnA\n2WDs33ofjDHYrDEcG36OD04L6/WujQNrg8EEY2wkkhFBKAGSRnE0SpNjT+gwXd1d9fxRQ4/GGhEl\njUb6vs7hHKaquvp2dc+oPn3v/V7FMAwDIYQQQgghhBDjijrWDRBCCCGEEEII8f5JmBNCCCGEEEKI\ncUjCnBBCCCGEEEKMQxLmhBBCCCGEEGIckjAnhBBCCCGEEOOQhDkhhBBCCCGEGIckzAkhhODqq6/m\n0ksvHbFty5YtfO5znxv1+J///Of8+7//OwCf+tSn2LZt21FrSzQa5e677+aCCy5gxYoVXHzxxfzi\nF78gk8kcted4Nzt27ODyyy/n/PPP52tf+9o7PvfAwADf/va3Wb58ORdccAEXXXQRv/71r3l75Z+l\nS5eycePGY9rexx57jNmzZ7NixQpWrFjB8uXL+cpXvkJPT8+oxz/44IP85Cc/OaZtEkIIcexJmBNC\niFPc7t278fl8FBcX8+abb2a3z5w5k1/96lfv+vjf/va3TJs27ai0Rdd1brjhBuLxOE8++STPPPMM\nDz74IOvWreOb3/zmUXmO9+I///M/uemmm1i9ejVtbW0jrss/t/f6668nmUzy1FNPsWrVKn7961/z\n9NNPH/ewNHv2bJ555pnsf8FgkO985zujHnvddddx0003Hdf2CSGEOPqsY90AIYQQY+vxxx9nxYoV\nOBwOnnjiCU477TSAbIB69tln+fnPf057ezs7d+7kkksuGfH4pUuX8oMf/IDCwkKuvvpqbrjhBh55\n5BF6e3v5xje+wUUXXYRhGNx333089dRTaJrGsmXL+MY3voHFYhlxrjVr1tDW1sbvfvc7rFbzn6hQ\nKMSPfvQjli1bxvXXX09nZyd33303Cxcu5MUXXySVSnHvvfcye/ZsNE3jBz/4AS+//DKpVIqPfexj\n3Hjjjdl23nDDDTz66KO0tbVxySWX8PWvf33Ua6JpGuFwmEQiQXd3NwUFBaMet2bNGtrb2/n973+P\nzWYDoLCwkB//+Mf09vYedvzf//537rvvPtLpNOFwmLvvvpuysjJ2797NHXfcQTQaJZVK8clPfpLr\nrrvuHV/PO1FVlWuvvZZrrrkG4LD3b2BggLa2Nu655x4aGxv5+te/TkdHB36/n29/+9tMmzaNtrY2\n7rrrLvbt2wfA7bffzpIlS0in09x5551s3LgRXdeZPHky3/ve9/B6ve/aLiGEEEeX9MwJIcQpLJPJ\n8Oyzz3LBBRewbNky1qxZg6Zpox770ksv8ctf/pJPf/rTRzxfJBJBVVWeeuopbr/99mzv1Nu9bI8+\n+ijPPvssjY2N/PGPfzzs8evXr2fRokXZIPe23NxcZsyYkR2u2NDQwMyZM1m1ahVf/OIXueuuuwD4\n7//+b/bs2cNTTz3FX//6V1atWsWLL76YPc+GDRt4+OGH+fOf/8yDDz5IW1vbqK9jzpw5/PSnP+Xj\nH/84X/jCF5gwYcKox61fv56FCxdmg9zbysrKmDlz5ohtLS0t3HHHHdx3330888wznHPOOXzrW98C\n4D/+4z+4+uqrefrpp3nooYd47bXX0DTtXV/PO0mn09jt9uzPR3r/7rjjDi6++GKeffZZvvjFL3Lr\nrbcCcNtttzFlyhRWrVrFL3/5S2699VYikQivvPIKTU1NPPPMM6xevZqampoj9lwKIYQ4tiTMCSHE\nKeyVV15hxowZeL1eXC4X8+bNO2JYmDVrFjk5Oe94vnQ6zUc/+lEApk2bRktLCwAvvvgiV1xxBT6f\nD6vVylVXXcXq1asPe3xfX98RnyMvLy/b2+V2u7nwwgsBWL58OTt27CCRSPDiiy9yzTXXYLfbcbvd\nrFy5csTzfOQjH8FisVBQUEBubi6tra2HPc+mTZt44oknWL9+Pffddx8rV67kpZdeoq6ubtT25ubm\nvuM1edurr77K/PnzKS8vB+Cqq65i3bp1pNNpcnNzWbVqFdu2bSMUCnH//fdjt9vf9fUciaZpPPDA\nA5x//vnZbaO9f8lkknXr1mV7W5ctW8af/vQn4vE469atywa/8vJy5syZw0svvUROTg4NDQ08++yz\nJBIJbrrpJhYvXvyeroEQQoijS4ZZCiHEKeyxxx5jzZo1zJ07FzB76vr6+rjgggsOOzYQCLzr+SwW\nC263GzCH+um6DphFQn71q1/x8MMPZ59ntNAWCoXo6OgY9dxdXV3Z4OT3+1EUJfv/AP39/QwMDPDd\n736Xe++9FzBDzaE9ZIcOBbRYLIcVNonFYtx00038+te/5rnnnuM3v/kN3/rWt/jd737HV7/61ffV\n3n8WiUSybQXw+XwYhkEkEuGWW27hv/7rv7jppptIJpN84Qtf4Nprr33X13Oot956ixUrVgDmtV+w\nYAG33HJLdv9o719vby+6ruPz+QBQFAWPx0N7ezuGYXD11Vdnj43H45x55pnMnDmTb37zm/z+97/n\ntttuY+nSpdx5550jXpsQQojjQ8KcEEKcovr6+li/fj3r1q3LDsdLp9MsWbLkiFUQP6hwOMzSpUu5\n7rrr3vG4s88+m5tvvhlN00YMEezp6aGuro7vfve7NDc3j5iP1tfXB0AwGCQcDvPZz36Wc8899wO1\ns6GhgcLCQiZOnEh5eTlXX301999/P/39/UydOvWw4+fPn8/Xv/51BgcHcTqd2e0HDx7k+eef5zOf\n+Ux2W25u7ojhiH19faiqSigUwmq1cvPNN3PzzTezZcsWrr/+es4666z39Xpmz57Nb37zm/f1ekOh\nEIqiEIlEyMnJwTAMDh48SHFxMRaLhT//+c94PJ7DHvd21cze3l5uv/12fvWrX/GVr3zlfT23EEKI\nD0+GWQohxCnq6aef5swzzxwRmqxWK4sWLeKvf/3rUX2uZcuW8eSTT5JIJAB46KGHePzxxw87bt68\neUybNo3bbruNeDwOmL1HX/va17j88sspLS0FYHBwkOeeew6AVatWMX36dBwOB8uWLeORRx4hk8lg\nGAb3338/a9asec/tLC0tpbGxkcbGRux2OzfeeCM//elPs0M6/9miRYuoqqri1ltvJRqNAtDW1sZN\nN91EOp0ecezChQvZuHEjjY2N2WuwcOFCrFYrN954I/X19QBMmjQJr9eLoigf+vW8G7vdzsKFC7Pv\nxcsvv8wNN9yAzWZjyZIlPPTQQwAkEgm+8Y1v0Nrayp///Gfuu+8+wAzQVVVVR609Qggh3h/pmRNC\niFPUE088wac+9anDtp9//vncf//93HbbbUftuc477zzq6+u5/PLLAbNAyD333DPqsffeey8/+clP\nuOyyy1BVFYvFwsqVK/n85z+fPaakpIRNmzbxwx/+kFQqlS20cs0119DU1MTFF1+MYRhMnz591Nd4\nJDk5Odxxxx184QtfIJPJ4PF4+Na3vsUjjzzCn/70Jx544AGKioqyxyuKwi9+8Qt+/OMfc9lll2G1\nWnG5XFx77bVceeWVI85dWFjI3XffzZe+9CVSqRSlpaXZpQOuu+46vvrVr5JKpbKvo6Ki4kO/nvfi\nnnvu4ZZbbuEPf/gDgUCAH/3oRwDcdddd3HnnnTzyyCMAXHrppRQVFbFs2TJuv/12li9fjsVioby8\nnO9973tHtU1CCCHeG8V4e1VTIYQQYhw4dMkEIYQQ4lQmwyyFEEIIIYQQYhySMCeEEEIIIYQQ45AM\nsxRCCCGEEEKIcUh65oQQQgghhBBiHJIwJ4QQQgghhBDj0Am9NEFn58BYN2FUoZCbSCQ+1s04Zcn1\nHzty7ceOXPuxI9d+7Mi1Hzty7ceOXPuxc6Je+/x83xH3Sc/cB2C1Wsa6Cac0uf5jR6792JFrP3bk\n2o8dufZjR6792JFrP3bG47WXMCeEEEIIIYQQ45CEOSGEEEIIIYQYhyTMCSGEEEIIIcQ4JGFOCCGE\nEEIIIcYhCXNCCCGEEEIIMQ5JmBNCCCGEEEKIcUjCnBBCCCGEEEKMQxLmhBBCCCGEEGIckjAnhBBC\nCCGEEOOQhDkhhBBCCCGEGIckzAkhhBBCCCHEOCRhTgghhBBCCCHGIQlzQgghhBBCCDEOSZgTQggh\nhBBCiHFIwpwQQgghhBBCjEMS5oQQQgghhBBiHJIwJ4QQQgghhBDjkIQ5IYQQQgghhBiHJMwJIYQQ\nQgghxDgkYU4IIYQQQgghxiEJc0IIIYQQQggxDkmYE0IIIYQQQohxSMKcEEIIIYQQQoxDEuaEEEII\nIYQQYhySMCeEEEIIIYQQ45CEOSGEEEIIIYQYhyTMCSGEEEIIIcQ4JGFOCCGEEEIIIcYhCXNCCCGE\nEEIIMQ5JmBNCCCGEEEKIcUjCnBBCCCGEEEKMQxLmhBBCCCGEEGIckjAnhBBCCCGEEOOQhDkhhBBC\nCCHGWM9ghAEtOtbNEOOMdawbIIQQQgghxKnqmf3Ps6l9My2xNlZWX0hl8aXZfU82/J1yXym1uZNx\nWOxj2EpxopIwJ4QQQgghxHEQT8XZFWlgdv50FEUB4GB/My2xNgDWN+5kquVSNE1Bd3Wx+sCLANhU\nG1+efT3VwYqxaro4QUmYE0IIIYQQ4hiKDPby2+0P0dC3H93QuW3OV/BmiujvVwgNTgXqULFgNRyk\nMzoDAwqbO7ZlH68bBmFHUfbnta0bSWU0ZuZPI+gIjMErEicKCXNCCCGEEEIcJSk9zZ7IXuLpBHMK\nZgHgs3s52N+EbugAvLBjJ6e5SgAos01jue/TlNgmYlMcqIo6tH0KmivBfq0On5rL3t0u3G7w+XRW\n7XuBzsEuHt79BJdULufCyvPG5sWKMSdhTgghhBBCiKPgtZYNPFL/JFpGI+gIUOOcSX+/ysCAnWLr\nZPZpW8m3TMCj+LOPcaoeKuzTDztXyFrIGdYLOcN9IWlDAyAeh+b+DjoHu7LH5dmKs/9/sL+JzZ11\nzApPZ4K3JDuUUxxZf0xj58EI82oLxropH4iEOSGEEEIIId4H3dBpHGimrmsHk3MmUhOsBMBvDaJl\nzODVm+xj0552cq1m2JrnvoiFnstxq/4jnvdIrMpw8ROX6meh53L2a3V0pptQuyazvV8lEDB4tfdN\nXml/mWcOvEClv4xb5v7rUXi1J6eO3gQPP1/PloZuMrpBYdCPx+Mb62a9bxLmhBBCCCGEeB9+8sYv\naOjbD0B3bABPoor+fgUtUY1NceBSvJTZp2I7JIQFLPlH5bmdqptpzoVMcy4kbaSwKjZSKejshM29\nddnj8u1F6DqoKmT0DI/W/4VpuVOYnDMRm3pqRoDYYAqP0waAx2ll614zyAE882o7ZUXhsWzeB3Jq\nvpNCCCGEEEK8i854N3XdO9jes4vPT/8EDoudTAYK7BNoYD8Add07mZkBRQGLYuXjwdtwKb7jMsTR\nqtgO+cngTPel7NO2cjC1ndzkTLZtU/H5DDqVvaxpXsua5rU4LHZun3czea6cY96+E8FAXOP1be28\nurUV3TC46zPz6O9X6OmxU5kfpr6tnaJQgByfZ6yb+oFImBNCCCGEEOKfdCd6uOv172d/XrevgWKm\nEo8rhLSpOJU3mGCfQpmtFgMDBTO8fZBhlEeDoqhUOmZQ6ZhBxkijoKDr0NensD463GPnVN34LaHs\nz88e+Adum4uZedPw2b1j0fRjaseBCH98vj7784uvxcjzme/RmROrWDCpmpDHPVbN+9COWZh75JFH\n+Mtf/pL9ua6ujr/97W/ceuutZDIZ8vPz+eEPf4jdLgsgCiGEEEKIsRHVYmzr3sm27p0AfHb6tRgG\n2NM55Njz6dE6AdjcvoOAdxoARdZqrgvdma08eaKxKCNv8Sc752NVHOzXtjLBMpUdOyx4PAZub4q/\n73uOpK7xRx7j2ilXsqD4jDFq9YfX1BHlla2tBL0OVswvI5WCkkA+DpuVZCqNRVXp6Itmw1yOd3z2\nxh3qmIW5q666iquuugqA9evX8/e//52f/exnXHPNNVx44YXce++9PProo1xzzTXHqglCCCGEEEKM\nYBgGuqFjUS0AbGh/k0frzQ4Iq2JlV0MKLe5A16HcMguvrZky+1TKbLXZc5yoIe5I8q2l5FtLme++\nmAxpAGIxhR2RPSR1s2CLgUGBY0L2MXVdO2gcaGZW/nSKPAUnfGXMx9bs5a+v7Qcg6HFQk1tGPK5i\nGCrzq6uwWlQmFRXisJ1cAxOPyyfxvvvu40tf+hLr1q1j2bJlAJx77rmsXbv2eDy9EEIIIYQ4xW3v\n3sUfdz3GN1/7/3m55XUAYjEoMIZDWtpIs7tnL7q5HBxnuFewwv85pjoX4LUEx6LZR5WiKCPm2eVZ\nSznTfSmF1koCaj79jUXs2KHS0qLw0sH1/HXfau5Zfy+/2va/Y9jqw2V0nc17umjuimW3VRYMvz+9\nsSQ7DkQwzNomnFZZxoyy0pMuyMFxmDO3ZcsWioqKyM/PJ5FIZIdV5ubm0tnZeayfXgghhBBCnIJ6\nk30EHYHszy80vsyOnt0AbGzaQX7fYtJpgHxq7KcRshZSbptKyFI4Ng0eA27Vz0zX2cx0nW3Os1MU\nNA1aOjR29e7KHldoL8UwzCIv/doAf9v3HLPypzEpWJ3t4Txenl67n+c2NtEX0zh7VjEXzaklElFQ\nErkEPW7Cfh+1JUWU5p4aBV6OeZh79NFHufzyyw/bbrwdld9BKOTGaj2+H5D3Kj9//K1DcTKR6z92\n5NqPHbn2Y0eu/diRaz92xuO1jyZjPLXrOd5o2cqBvmZ+dvG3Cdny6euDavfsbJjrSLbhLnJiUcz7\nzCtCN4xlsw8TCo3tXK60buci2yfZHXuLfbFtlKrzaWnxEQhAXfQtXm5ey8vNa/E5vNx/yT04rMeu\nBkY0ruFyWLFYzAGFKR36YubQ0PXbO1g8+XRcLgsuF3z5I8uwqB9u4OF4+9wf8zC3bt06vvnNbwLg\ndrsZHBzE6XTS3t5OOPzOazlEIvFj3bwPJD/fR2fnwFg345Ql13/syLUfO3Ltx45c+7Ej137sjJdr\nH08l6BmMUOozF+YeTCd5atdzpHVzXtijL29iim0xAL5MDTOd51Bur6XAWkF/7+CYtfudhEIeIpHY\nux94jBUxlSLHVBbZ0ygxK12xGF1dsKZ/U/aYXGuYns4kVmsSgMf3PE2xp5DpebV4bB+8QqRuGGzb\n18MrW1p5s76TL10+g8klefT0KASUfGAvboed2uIiunoGcNps73rO96KszHNCfu7fKWAe0zDX3t6O\nx+PJDq0866yzWLVqFStXrmT16tUsXrz4WD69EEIIIYQ4Ce3o2c2q/S/Q0LeffFcut8/9Gv390N/v\npMhSTaO+CxUL/akBGLrP91lyONNzydg2fBz658qYp7nOI2AJs0/bSpExg+3bVTweA8PZy3MHXwLM\nAjFfmvVZanMmfaDnVID/fXY3HZEEAKtea0OZZXYC5Xh8XDHvdIpDQdQP2Qt3MjimYa6zs5OcnOHx\nql/+8pe57bbbePjhhykuLuayyy47lk8vhBBCCCHGuZSeZk9kLyFnkEKPeUOfyqSo790LQHu8k9e2\n9BCw5AEw03UOtc4FlNgmYlMcY9buk1WhrZJCWyUL3Jeik8EwIBpVqOvcNnyQAQX20uyPa1s2EE3F\nmJU/jbA7f8T54oMp1u/oYP2Odv7typk4bFb6+xWmlhbRETHf4/54Et0wUIcqap4q8+Hei2Ma5qZP\nn87//M//ZH8Oh8M88MADx/IphRBCCCHESeKhXY+zrm0TWkbj3NLFLC/8CP39CkbvJCxYyZAm3zKB\npB6DoTBXYps4xq0+NSiKguWQKFFmryWNxj5tKw7FTWODlw4HBAIGzx98hdZ4K080/I1lZWfz0Rqz\nh7QvmuTWX6wllTbLhz63rouKUDGZDFTlFtNfmaa2pJg838m3mPnRcvLV5xRCCCGEEOOKbug0DjSz\nvXsX55efg1U1b1HTGR0tYxa7eKN1JxMTK4ce4WC579PkWotxq/4xarU4lN+Sy2zXUma7lpIxzDmL\nySTsae2mNd6aPS7a4cOoNitjRtLdeKvr6WsJoQ+EWL+zlQnzzPmPPpeTxVM+2DDNU4mEOSGEEEII\nMWZ6k318f8PP6NfMwhNF9nIK1En09yv4Y1OBdfjVXCbYppAx0tk5XBPsU8aw1eKdHDrPzql6Od2y\nki19G0k5O3jpVY05uRpTauys2buZwUA9jgA4MyEuDNwyhq0enyTMCSGEEEKI46Iz3k1d9w4G00ku\nrFwGgNfiB2O4kMXrB3axwDMZgBLbJD4WvJWAmo8yNF9KnPgMwyCe1PA4HdgVJ7XOebz2uoZBBgyV\nxt42plnK2K/VZR9TE6zG63Sg62ZP7cuxRym1TWKCfQp2xTmGr+bEJmFOCCGEEEIcc2ua1vLw7scB\ncKh2ZrvOIRa1Eo+rlKi1aMpWJtinUGqbnH2MVbERtLzzUlbixNEfT7CtqYXtzS34nE6uXngGoZBB\nTcjGjIM5bNvXw8yaXMoLfRiGwVUTV/JWVx1bOrexsGIa0/N0Bgagrm0fu3rWsyu5HhULVwS+Qsh6\n6izm/n5ImBNCCCGEEEdNVIuxvWcXdV07mJk/jbkFszEMKLJVZI9J6hpvNO6j1G7OiZrvvpiFno+i\nKlJqfjxr6omwvmEfANHBJHlFAxTlmQugX71sIm6nFb97eIHx2txJ1OZO4uOTzAr3igJ+P7S2D/fY\nuSxu8p1h0uY0PN5KvIAFKxX26fgsUtVSwpwQQgghhPjADMMYMQTyl1t/S0PffgASyQy58dOIRhUy\nmWKCljB+NZcy+1RyrUXZx9hV1/FutvgQDMOgqSfCjuZW8n0+Tqssw+GAhbPyWbPTQjKVweuy0R5J\nZMNcYc6RFxH/5xC/sHgeLquTzZ3bqAiUMW0yxOM6kT6dzTtfJGkkWBv/C2e5VzLddWqvWy1hTggh\nhBBCvC8pPc2unnq2du9gW9dOvjb3Xwk4/MRiUO6opYH9ANT31ROxZLAoVhRF4arALSjS+zbuvbBt\nJ3WNzQB0+Pq48rwSvF4FsHDFkipCPiezanKxWj7Ye13oKeCiyvO5qPJ8MnoGALcbDg42kDQS2ePK\nfZUw1GPXqO2kJbWHCvsMwtYJp8znTMKcEEIIIcQprG5vN5v3dNPeGyc/6OITy4fnrD22poH4YJqi\nXA9VJR4qC0MAaBmNX2z5DQYGAGvqd1GlzCedhpz0VAqsOyiz11Jum4qKJXu+U+UG+2SipdPsaeug\nKBgg5DV72aaW52bDXPdAnM6BAbxec4mI8+ZOOKrPb1GHPz+lvmKumXwFb3XV0RXvZuHUItJpnb4+\nhX/s2cDuwc1sHvwHpbbJXOS//qi240QlYU4IIYQQ4iR06PDH2GCKx17aS0ckTnskwU1XzaJ4aPjb\nnuY+nn+jCYCCkIf2dgVNg2gUXqtvoN+xF4vWSbDfwXcLbyWZhIONCg4tn0F7BwC7Irsp888HIMda\nxMrAv47BKxZH20vbd7GtqYVUJsPc6jKuOHsioZDBNEsOr+z0UFsWYuHMQsoLfMelPV6bh4Ul81lY\nMp+MnkFRFGw2CIQ0DiR3ZI8r85SjqqDrMKjHWRt/kgrbdCbYJ2NV7O/wDOOPhDkhhBBCiHEqmcpg\nt6rZ0PbiG01s3NVJRyTOpAkhrv/IVABsFpUX32oESwpFzbB+TyOzo5NJpSxoMQ+qN4LiiNNj1Vl/\nIMis5Fw6u6Av04G9eC8A/cYALT19GIkgDc0D9DeFUVwe9N4wS868LNumjXv3E09qhDxuikJB8nze\n439hxAfSn0jgczqzn6eMoZPKmMMcd7e2kptXNTR0UuU7n5s3pstFHNpjpyoqn5l2DW911rG1azvn\nTppOodusjPlK4zbqk5uoT27Cip3rQnecVHM0JcwJIYQQQowh3dDRMhrJTApVUfDZvdnt27t3EdMG\nSaSSzC6sJegI0Nmb4P5nX6Jb2Ucyo7F4WhnXTb8cgLbuBA3251EqomyxGjy+5QKmuxYxOKjiyYmh\nV78KwLPxNZREvoNddVHgD1BU00uPfRcAuzQLc6xzCYUM/mXuEh7r3YqBgUWx0KO3ML0kwKb9MTJd\npQDkB1xMrLaQTBoMDkJ9WxsdfVEA5lSVs2jyRAAGEoOsrW8gx+Mh5HVTnpeL1WJBjK2MrrO7tZ3t\nTS009UT46LzTqS0PkZNjcElOIVv/t5miXDeLZhaR0Q2sQ2/ZibTun0W1MCNvKjPyppLRM6iKiqJA\nIABNB4YrY4adhbjtrmxlzLWxv+C35FJhn45HDYxR6z8cCXNCCCGEEEeQ0TNouoZNtWFVzdumvmQ/\n7fEOkhkNBYXpebXZ49c0vUbPYC+arjE1Z3J2X0u0jT/s/DOarqFlNL4298t4bGZ1vwd3PMK6tk0A\nzM6fzvUzPgnAH57fzVr1gey5872fJegIYFWtNA20Ya/YhxV4q7OfJY0fJZUCq+FGsSdQnXF0IGnE\n8XoN/H5YPLWCl5KvZs9XNCFJ0OlAUZwc3BfkdXPEJG6vxqxZ0NmpUFU5gfT+FRR48pkcmojT6gBg\n7uQwQa+D1u4YLruVYBDAQDcMemPx7HNMrnBTXGyQTELb/gF2NLdm99143jnZYLD5QCMDg4OEPB4K\nA35ypTfvuFEUhdd27SGaTALQEm3hkuogAMGgnzs/fQZlBd4TKry9k0N77AAuqjyPYk8Bb3XWMa9o\nOtPKdWIxaOmJsrX7ZcDg1djjLPd9mlnMH5tGfwgS5oQQQggxLmX0zIgbt/Z4J/FUHC2TIuQMEHbn\nA9CvDbCudROankLLaKyoWIbL6gTgH42vsrlrG6mMRoW/jCsnXQqYvWI3/ePfyRjmELMbZ36aGXnm\nkMWtXdv5467HAPDbfXx30R3ZNqxt3cDBAbMwhENxUeOtRddhIJZhX/+B4bZ2pfBZFQ6097Nuexfk\nmdt7BpLs369gscBAPxh+BUU1i4wktBQAAa8Vu2obvg5oTJhgHuPPzWFHnZ/25AAATo9GUZG572xv\nEZve8GBA6bLQAAAgAElEQVS32LFb7Hi8Ol7zMlCTU0ZGSWG32Cj1Di8ZALC84tzDrn1xnic75+5Q\nhmHw+Uum0toTp607xuQKH/n55vNvb4llj/O77dRUqdnevD3t7TR19wIws6yUc6dNGXrNGq/tbiDk\ncRPyeCjNDWGT3rwPrD8xyM7mVvZ2dHLl/LnYbSo5QVgwvZBnNx1AUSCVyWTnWyqKQnnh8ZkPd6xM\n8JUwwVfCR6pXoBs6AB4PtPfWwVABHwWFcm/F2DXyQ5AwJ4QQQohjJq2n6RnsJTUUpEq9xdgsZhBp\n6N1PY7SZVCZFwOFnXuHp2cc9sO0P6JYM0USCiyrOY2KoCoANbW/y0K7H0XQNt9XF9xffmX3M77Y/\nzP7+gwBcUL6US6tXADCgRXmi4W/Z484qWIDF4UTXoWWgi92RPQBYDAeRCOi6gq5bgOGeiLbONIG4\nQiYDvQPDBRQGUym2b1fRddB1g8G4bcRjdsXN6o2PvNkAZcPXpbE1Rb5TQdFtpDQVa8YCugW7x0pZ\nmYGqQkWzi81NYRTA53KR6zaHgamqwicXz6c9XUTQ48Zjc2bPmxdwceNp12IYOnaLHZd1eG5QoSc8\n4nodakHxGSwoPuNIb+N7ZlFV5k4Jj7pvelUu11lVWrvj2CwqOTnw9s10b3w46E0sd1NUZPbm1TdF\ns1UTAT537qJsmNve1EJfPEHI4yYc8JPjPTxcimGRWIzfrVmb/blf72DJtDCqCku9RQR8VhZMKyTk\nc4xhK4+tQ9ezm5E3lYyhs7mzDgOYXesmLw+6usaufR+EhDkhhBDiFJTMaCTSCbSMhqqo5Llys/ve\n6NjCYDqJpmvUhiZS4DFvzvf1HeDl5tfR9BQqCp+dfm32Mf9T9yANvfvQMinOLl3AyuoLAehKdPOd\ndf83e9ydZ95K2G12Q73ZuYUXG18BYGKwirnh04dCEWzp3I6mawCcHjqD8FBlumhUYTAzaL6GtEZz\ns5J9TEYbDlLt3Sl2psyQFdFG3pxu350mx2re1EVjw8FsIKFx8ODwzZ4VOzoZrIqd/gGDiGaGu1TM\nQy7l6BkVj9VDyuwwY297F00HfCj2SRi6yoRpU7HZwOk0CDkDtG2fj6FbmFKSy4Kzg9itOrruwHhm\nCunWWsIhFxfVTkIdasLCGUXMnfIlcvwOLOrIkv7zq2uAmlHf27ev74nmSL15umFwzfkTaeuO09od\nZ0qFj3DYDHl7u4dDnsNmYXK1HU0b7s3b19ENQG1JEctnTgPMnqVXd9UT8njMIizBIDbrqdWbZxgG\nrb199ERjTJ9QAkA46KYg6KG917ym2xvbOXeu+btdEHJz0ZnlY9besRBw+FlSehZLSs/K9tiNk5Gk\nI0iYE0IcUTSRQjcM/O7hm53VGxopyfMwpTx42M2FEOLDS+lptIw5r8pldeIcGg7Yl+znQH8jWkYj\nbWQ4s2hu9jFrmtbSEmtDy2hMDtUwv2gOAD2DEf5z8wMkMxqarnHz6V/MDj18eu9qnm9cA0B1oJKb\n53wxe74/7HyURNoMTJ+s/TgFnjC6Dl3xvuzcLqtiJRoFwzCDVH88Qb9mDu2LDGi0tpohK5IcGaQa\n9qfotZohq79veF9vNMXWrcN/U1TDBphhrqU9jb/P3NevDf89ShsZOjuHCzH4jALyrRpW7Lj1XIam\nAGEzPMx0LsGq2LEqNpzqcKCY7DyDEttErIodp+rObk+mUiw1/o2++CBaOkNNzvCN7iubIkRi5ly4\nMydWYS8Eh8OgzOIk80ZJ9rhZl4QpyTdvEquafOxuyiUcclESCGG3mrdgqqrwwy+dhd9jR/2nO0m/\n5+QqoX4kqqJw5tTCUfdNLA1y9bKJtHXHMIC8PHi7N29g1XDQqyp1UVho9uYdbI+z+UBTdt8nFy/I\nro9W39pOJBYj5PGQ5/cS8px8vXnNPb08V7ed3lh8qKc0n5JCK14vnBsrZuvebhbNKOK0iSdm6B8L\n6jhe/1DCnBDiMFv3drPmrRY2N3Sx/IwyLp5fTW+vQmwwyZ9e2INuGPjcNm5cOZ3a8tBYN1eI48Iw\nDNJ6Gk1PZQtXgFnYok/rR8tohBxByvxmhb94KsELjWvQMimSusaK8qWEnGZRgVeaX+e1lg1oukaB\nO8z1Mz6RPd+/v3I3sbRZQOK6KVdlh77t6zvAf9f9HjBvPOYXzsmGmC1d29nRY1YiVA0bMwJz0HWI\nDSq0xNqy527tTGE4zZA1GB8OCtFEir173+7hUlB1O2CGuf2NaZwd5o1O24gglaZ+z/BNUDo5vK8/\nlqKjw2zboO7Ap+ZiVWzYFDvJhIXE0N1HnlrGVMdZWBUbPkvOiOu9xPsxvF4ngzGdkKUgu32CbQqf\nCn0bq2JHxTKiKMMi70dHfe8cqoszPR8ZdV+010Zv3ENvLE5NoQPMt4i9HV2s3rINMIcOnjm5DIcD\nHA4ozHUTebvIhz1Gba0Z2JIpJ47VFsJBF+GQa8S3/JecVc7KxZWHBTaAoPfkHdb2YR2pNw9g5aJK\nWrvjtHbHqK30U1BghrzWaDR7jKoq1E50kk6ZQW/PW+3sbjUrvVQX5HPJ6bMA0HWdV3fvyfbmhf3+\ncdObl85kiGsafpc5pNbrdGSL0GR0nZaBdibXmF8ynD+3lOVnHN1FvcXYkjAnhMAwhiYAD91krN/e\nzqbdnQC8vLmN6mANqqKwdkcr+tCx0XiKguDwDe2ugxEsqkpViX/UmxUhjiXDMNANPVsMI6NnaI61\nomVSpDIpKgITsnOH9vYdoD7SkA1lSycszp7ngW1/oC/ZTzKjsbz8XE4LzwDMghf/teW3GEM9Av9x\n7vezvy9PNvydum5zsdqFxfO4xn8lACk9xd/3P58999zcM3AaQbOHKzrAgYFGs+0Zla4uM0gZBqgc\nMueqK8W+pIJhQNchwwF1Q2dLnYGKBV2HRHR4X1dvivp6M2AN6sNzqQDaOlNgM9utJzy4Fb/ZU2UE\nGBgY/r2tsM8kZQxixU5AHZ7/lGMpZonnY9kerkMt9l7JIj6KDfuIRXmdqod/CX1j1Pet3D6Ncvu0\nI+4LeTxEtNiI7RbFikV5f7cvkViMpu4IvfEECrBoysTsvlVb6ogOml14fo+diRP8OJ2QtrpYvcU8\nJqPrFJQkyPGb17O61Es8mSQccjGpNJg9l8Nm4f6vnD1q1b/xEgzGk4UzikbdXlbg5apzqmntjqOl\nMxSEFd7uzYu9OPx5qih2U1BgDtls6x7kjX0Hs/uuPmseBQE/APs7u+gaiJLj8ZDr8xBwuzkRdPYP\nsOVgE7tb2ykI+Pn4wtPJyTGYEnLw+r4g+9sGmFdbQHWxP/uY8VKRUrx3EuaEOIX19A+ydlsbr9W1\n8ekLp1BdHKS/Hypyi3gV89t8t91BPKnhdTqYkJfD7PIJ1Le1E/S4aT7oot9nEAwaPPrSXhqa+8jx\nO7h0YSVnzyoe41cnTkTxlDkUT9PNku4TfMND0ja0vUk0FTOHCubUUOE3q0U0DrTwzP7n0DIpNF3j\n/5z2hWxv0G+3P8RbnXWkMinmF83hE7UfM58nneD7G36WPfctc/4/KgPmMLndkQae2vsMAEXuAhYX\nLs7OuWroPUAkGQGgtbeP8qF/JaP9tmyQA9h3MIMFG7oOWuKQIBVJs22bOYQwmRkZpPbsTxG3me0e\nSAz3xCS0FM3NwzdYVsOFXdGwYiOZsNJvmPvUdIAy21Ssih2bYiedMbAOPWyK4wxKbDVYsRGwDIcv\nu+LkysBXsSo2cxihMtzDMcO1mBmu4SB7qIWey0bd7rUEmWyZN+o+tzq2Fe90w2DrwSZ643H6Ygnm\n1VRSGDQLhjR2RXhx+04AXHYbF82rweEAux0Kc13saTbDnGGLU1Vlvs++oJOKQh8FOW7CQRcWdfg9\n+ujZVXz07KpR2yE3y2OvKNdDUe7ovXkXnFFGU2eUtp44Uyv9FBYOFWBJR0ccN7XGBfpQb15dO9sa\nzSUVyvJyuPwMs1CPYRi8Xr+XgNtFjtdDrs97XCtttkR6s8Vhmrp7KCiJkxsw/+585qJa/B47Dpt8\niXCykzAnxClKNwy+87uN9EXNOSmrX2/j7Mk56DrkunJYMLGKmsKCEdXBCoJ+lkydzNm1k4hrGroO\nfX0KTW1JGpr7AOjpT3LorUxfNElfTGNCePysUXMqiqXiDKaTpHQNj82TXbS4XxtgZ0+9OYdLT7Gk\n5Kxs79fLza/T0LuflK5RGSjnvLIlACTSCe5Z9+Ns9cJ/nX091cEKAF5tWZetKljgDvOtM2/JtuGv\n+1bTlTCLGVyuXMIET5n5GYvHeatzeNHX7l4Nm2JWIkwMmostgzm07+1iGIPpf5qndSDNoM0MWd2H\n9GJFB81KhFnp4X0dPSkaEyrRKPT3mOdTsWBV7HT2pHGq5rF+o4gSWxwrdkJKSXYxWgs25riWZ3ux\n/Orw/JRq+2kUWiuHgtnItl4VvIXR5FqLWeH/7Kj7yuxTR92uKio51tF7L8arxu4e9rZ3EonH8djt\nnD9U9EIB1u5uIDn0BkwtzyMc9mO3Q8rm5MXt5uMTWopwkYbHafYszqrJIT/oIBxyM7F0eNFgn9vO\ntz794as7ihPLopmj/z4UhFxcvriS1p440USK0mILb/fmDb423JtXVugmHDZ787r7NNY37Mvuu2Le\nHEpzzakHTd0ROvsHCHnd5Hi9+F0jv9x5P9KZDHs7utje3EJlfh6zyifg8RicM6eAV3btJp0xyA+6\n6O4fzIa5/KDrXc4qThYS5oQ4Bei6wc6DETbu6uSa8yZitaikNIXpZQW8ut0c6rWrqZuFNcPrysyr\nGf1bZzC/efY4hm9ADcNgWmkxDe2daOk0zkwBBw8qhEIGL29t5bE1eynIcbNoRiEXL6g41i/3pDOY\nHqQjpmEY1myPVONAC92JbpIZjYDDz5Qcc9hYSk/z5J6/DS1MnGJ5+bkUe83CAutaN7HqwAtomRR+\nh49b5345+xz/d9N9tMfNobWXVV/E+eXnANAR7+K32x/KHndm4Rxcihtdhz2R/WzseAOATEbhzByz\ndyuZthJJ9mYf096t4U6YISvaNxyW4kmN+no12yump4b3Nbakqes1X2tHamTY2bM/g3uo+M6hvWLx\nwQxdXeYXBoZhI9dSnA1SRsqBNtSxlmcpY5bzXKyKDZc6cmHixZ4rzeqF2PFahofPFVjL+HzO91GV\nw7/lPt19HnDeYdtVxcIc9/LDtgN4LAE8lsCo+05Vb69rBaCl07y6aw+xlEZX3wAXzp6RHfLW3tfP\nWwfMv1sBt5Nw2MBuNwuQFL7p4kC7WYQlY4ln11dT7R4WTi8kHHIRDrmxHlK8Sf4mCTB78z6ysHLU\nfWfPLKa8wEdbT5zaSn/2c5U8MLI3b0qVC7vF7M1r2NHBW/vNz2lRMMDHFgx/MbBx7358Tichj5sc\nrwfru/TmPbO5joZ28++zlkny8QtKMP8JtvIvyyZSku9lYmlAvjA9RUmYE+IkFxlIcvfvNhIZMIcR\nVebnUuTPJxZTKA8V05gbo7akiOqC8Af+h8DvdnHejKmcO02nayCK3WIjEoFIRGHNm+ZE8/aeOPtb\nB7KPMQyDzr5BwvLt4RHt7KnnL3uf4WB/EwYG3190J1672VP6j8ZXeL1tIwCz8qZlw5xiKLzY9Er2\nHDNDswkohRgG9MW0bGDL6Abt7Uq2EiGZ4VDU3p2iIa2g6wptgyOD1Oa64SAVO6SHq3cgRUODud0w\n7Cgo2WGJnT1pPNGhkJX0ElDzsSp2PKqfeHz43FW2WRRZq7Fhp8A2XDkwYMnnPO8nssHMoQx/Zs7y\nrOQsz0os2EZUI1MUlSuCN496XQttFRTaKo6wb/SbOUVRkdukDy+dyaCqanZe7bbGZna1ttMbj1Pg\n93Px6TMBsFos1DU2Z+foGtYYEyZ4sdshaXPyqlnrhf74IHn5GWxDywycPbuYWCJFOOQasdBxyOfg\nc5eM3nspxLtZPKuY0QYkh3wOLjmrgrbuGJGBJFXlNhTF/MymNg4HvdKwm/x8szdvIJrh1V17svsu\nnTObyrDZa9/W20d7Xz80KVTk5BBwu1FVmF0dzoa51p6o+fviMOftnXt66TF61WK8kDAnxEmmP65x\nsH2A6ZXmmlEBj33Et9Avb2nj4tPMOTX5fh+Xzzt91PN8EBZVzX57DuaNW67XS188QSqTochbSFOT\nQjBo0BOLctcDGygr8DKvtoClp5fgtJ+6f5K6Ez1s79lNkaeAmqAZKCyKlQP9jdlj9h5M4VXNABY/\nJEj19Jsl3Q0DDEM1i2KQAeBAUxpLp/n+9w0estBxWqOtbTieOHQ/HjWGFRtoLqJD4UvNeKmxnzYU\npOyoh/yzMdExlwJbhdmLpQ73MimKwtXBb2TnaVkPKehR7ZhNtWP2qNfA7OE6nEN1U+WYNeq+fx6i\nKMZeKpMhlc7gdpift9hgkmc219EbjxMdTPKJxQuyw7cHkoM0dvcA4HZYKCvTh3rZILzeRVuPmfaT\nenxogWmoLvFz1TnV2V62Q+eynXtaCUIcLwU57iPOnTyjtoDcgPkZnlLpp7jYDHn7Wkf25k2qcOGx\nGySTCnt3d7KhYT8AC2sruGxRNcGgwWQ9jw0NXk6bmM/C6YXkyZeg4hCn7p2TECeZps4oj720l617\nu7FYFL77uUXEYzYiEZWacDHRxEEmFxVSW3L85s9YLRZWzJ5OOpNhX2cXZXl5dHcrdHcrrK03v2U8\n2B6ls3eQ8+cOl0pOahkc9lNn0vYvt/6OzUNzwuaF5xLKVDEwALH+CmyKg7Sh4bUG6es3MCzmjWvQ\nKKHCPh0rdnKsRWbv2pAFnktRULEqdvKtw9e13D6Nq6y3ZIPZoVb4Pzdq27yWIEt91466r9BWQSEV\no+775zLz4uSipdP0xRPk+Ybnwr66aw87mluJJZNMKS7kglnTAXA5rTT1RLKPtblj1NS4cDohqjpZ\nV29u740NEgwOD7W8YkkVwaAbpzpy/k9uwMmFp9jixmL8OXtW8aiFwDwuGxeeWUZbd5yO3gRTqp2o\nqgEYrK4bnpu3s7mVUI65lIXFYuGuz4xeeEgICXNCjFOGYZBIpnEPTeK3WlTe2tMFmEPonnm1i6ml\n5j8kp1WUMaeqfMwW+bZaLEwsLBixLZlKY1EUMoZBVTifzg4LwaCB02Xw7//zOrl+J/NqC5hXG8bn\nHv8L5xqGQVO0lR09u9jZU8/np38Ct82FrkPAMlwYY1tXPbPSZu+WgoULfZ8naCmgKDefSGT4H/op\nzvlMcc4f9bmmOReOut2hunGoJ0ZJbXHiS6ZS9MYT2C2W7ILLXQNRHl//BnHNLDrz2XMW4XM5sVpB\nUTPEhlbpTmTiTJyo43CAxaIQesVBZCCJokBGSfL2Os1TK3L4tytmUpDjIi/gGjHUe87kMPn5Pjo7\nBxDiZBEOurjqnJpR982qzsXlsICqMrkkgK4bqBYZ4C3emYQ5IcaZ7r5BXqtr5bW6Ngpy3PzbFbPo\n64N4r4eiYIDW3j6KggGc9uGhbSfi+kbnTJ3Cgok17O3oIOTx0Nmp0Nmp0DHQS09/kp7+JPVNfRTn\neagtN8PcoQUSxoOMnslWfuzT+vnehp9k923Y30CpOoNYTMGnTcbCKxTbqimxTUYng2Xoz/OR5nAJ\ncTQMail643H6E4NMKhr+wuWpTW+xt8P8cmhGWSlLp03BaoX8HFs2yAH4cmLMqLGjqtA26GLDHrCo\nChYLHLoU1xcvm47XZSMv4MRqGf5SKeRzEPLJUFkhYGhu3qxi+RJDvC8S5oQYZzbt6uDxl81SyB29\ng6zflMJlN2+Gzq6dhMNmI+QZH70vDpuV2pKRw1BaewZ4e3lXj8NOwBZicBCcTnhw9W46ehPMmxLm\n9Mn52dLiJ5LmaCsb299iR89uXBYn/+f0L5BOgxEPkGcvoEtrB2BLx26CXrPYQ5G1ik/lfOewRZiF\nOBoSmkZvLE5vPEGO15Od13qwq4fHN7yRPa48Lwev24bDYRD02cGsXUSKGDNm6KgqGIYV1yoLqbRO\nftCFajW3A8yrDTOjOpdcv+OwUQA1JVK5UwghjgUJc0KcoDK6zvb9EdbWtVFbHmLxrGIGB6EyrxBF\nacAwDGyqSkdflPJ8M8y9vUDueDarfAITCwvY09aBgUFnp0pnJ9jsOq9vbyeRTLNtXw+t3XE+tnT0\noSrHi2EYdCa6cFld2XXZ9vUdYPWBFwFQUanbqZFJmuv+VFhm47e3M8E2iVL75Ox5VMWCyonXeyrG\nj2QqTddAlL54nLimMbeqIrvviQ1v0tFvfss/p6qcCfl+HA6DCocTNgyfI784RmWxGfSqOl1s2a8S\nDrkozHFlA5uiKNz9+TMJeOyo6shecp/bjm98fI8khBAnDQlzQpyg/uvJbWzcZRYJaelKUOguHSrj\n7mRedSUhj5uqgnxs77I+zXjkdtiZWT6y3PKB9n4SyXT255rCAjQN7HbYvKeLl7e0Mq82zKzqvONS\nPOXxPU/zZsdWugd7uLzmYhYXLGFgQMEbGw5pBtAUbabIVg3A6e7zj3m7xMmtJdJLTzRGXzxBRX4e\nJTnmWniN3T08/eYWwFw8e97EMjxuBYcDCnNd2TBnWOPU1prVcjK6HbfDSo7fSUHIheWQuTnnzy1l\nxfyy7BICh5JhkUIIceKQMCfECaAvprFuWxtzJofJDTgxDJhYnJcNcwc7+mntihMYmoRy5sQjL+h9\nsioOBfnMOYvY09ZOS6QXVfOzY4eC2w3/eLOdzXs7eWN3J+WFPu789BnvfsL3SDd0Ggea2dO7j6UT\nFmfn7LXFOugeNEuqbzy4m/zIuQAo5HKa6zzyraUUW6uxq1JCWrw/vbE4jd099MUTZHSdJVOHvyB4\nbut2IjGzXL/TrjKlPIDDAYrTCW+axxhAXlGMolyzykjNBC+9sTjhkItJE4YXQreoKj+/afGo81BP\nxHm2QgghDidhTogxZBgG9z1ex1v1XeiGQTSuM6+mkt5ehaC1gIB7P1XhfGpLirJB7lTmdzk5vbKc\n0yuHy5L3D2TYfqAr+/Ok4jxSKbDZIJFM87/P7mbu5DDTKnOyCwu/Vy3RNn7y5i+Ipcyb5yrPZNzp\nQqJRCAxOAXZgxY4V14jiLGe4V3z4FytOaoZhsLWxmd5YnL54nNMqyinNDQHQ2tvLC9t2AmaV2ovn\nTcTlUrDboSjPlQ1zGUucqipz7ao8zUV5gY+CHBfhkAuHbTiMXXJWBZecVTFqO8ZTQSEhhBCHkzAn\nxHFkGAb72waoLDLnpSiKAgbohnlD9sqWNqqClSgK2K1WPnX2WXKz9S6sFgv/ctY8dre1U9/aTqGn\nkO3bVbxeg4bOLl6ra+O1ujbcDivfu3EBXtfhRUZSmRQNffvZ3rMLr9XD8gqzly1oy0VLp7LHvVpf\nzwyXuU5fpX0GIUsBBdYKLIr8KRWja+7pZU97O32xBDarhQtnzwDM3/0Ne/YRHSrlX10cIj8/aPay\nuV2sNkdMks7o5IST2aGNM2ty8HushEMuqg8pKuKwW7jzM0evR1oIIcT4IHcgQhwHiWSaVesP8lpd\nG119g3zrU2fgd/iIRBRKfMW8QScloSBTSopG9PBIkHtvQl4P82uqmF8zPPw0GlV4fVtH9udcvxvX\n0HINhmGwev1BJhT4mFIW4i97n+GFxpcByHHkMM12LtGoyuCgg0JrNV3pJkpsk8ixDi+47lb9uFX/\ncXqF4kRz6O9pRtd5eWf9UMXIOOfPmEpJjtnL1jUwwFv7GwFwO2yEwwZ2OzgcBsVbXexuMsNcWo1T\nXGx+qWN3uVgwrYCCkJtwyIXz/7F3p3F2nAed739VZ9/7nN5XtdStbu2yLdmWvDtO7DjYwYkhCSFx\nDAO5ASYQApfMZZhhGIbP3AuXmc8Ny+ReGGBmICwJEEhikji2ieN4t2zLkrW1pJbU3eq9+/TZl6q6\nL6p9jjqWQ+xYlsr6f1911/Kcp+r0Uv/zbOeMAb396n5EREReoTAncoFYtt2YntvvM3jw2YnGBB5f\neXSGGze5n6qva2/l/puvUzfKC+C6kSFaEzGOnZ1hXWsHX973EtPOEcbLR1h4eQhruYNENMDN1zWX\nR1isLHJ8ZrGxkPet8Q8TMsIYxsVZcF0unrplrZkA5PDkWQ5NnmW5WCIdj3LP7isBMA2DQ5NTVOuW\ne55ZoL8/RTAITjTMP7/snl+s1Eilq0RXl9S4fkcXWzdk6ExH6O+IN14nEQ3y03dvfYuuUkREvExh\nTuRNVLdsDpxc5PED05yZyfFbH99DpWywvOxnY2cn+09PEvL716zB5DNNBbkLwHYs/NECN4xu5PqR\nYWzH4QvZ32bFdsfXmcl5rOUO8qUaHc4mMr5uugNDhMs95LKQTLstL2FT783bWd2yqNYtoiF3Yfpy\ntcYDL7zEcqFIrlzmQ9ddQ2trAoBCtcLpBXfSG5/Pob/fJhh010Dsfj7KqRl3xsiKXSSTcctf3x3n\n3ps3NFrZzp1p9cYda9dYFBEReb0U5kTeRMfOLPPZL+5vfP/N72TpTLhPddsH+unLZFjf0Yb/bbic\nwKVisT7Nc6VvMFk7huXU+FjmN0nGAiQSsDUwwhOzbphLdRapLQTpaYtx3a4YW7O/yPKywR99/Rke\nW36BRCTMnuENbOnTA7fXVet1sqsLZr/yQcpTx05wYGKSfLnCUGc7d121E4BIyMfk4lJjHKs/UmDT\nJlhZsamFwjzmzkvCSrFCMmXj97nlvfeGQWwbOtMR2tPNGUxT8RA/tHfwrbtYERG5rCjMibxBS7kK\nTx6cxnYcfmjv6oNcMk0iEiJXcsfBnDi71AhzbYk4bYn49ypSXqe6U2WqdpyAEWys5WYaPk5Wm4E6\n2DPGxg53avc94Z2kohE2Z0ZYnxzAuNUkV6phmpBOg2UUmV5eASBXKnPukMVCuUKxWqUtEddYxktQ\npVYnWyziM01aV3/PlgtFvvDUsxQrVQDuu3Ev6XgMnw9Mv02+7P6eFmtFNm60CYXA54P2JyPMLBYx\nDLQbSr4AACAASURBVLCoEotBsQgb+1v45L3b6UhHaU+FG0EO4MqN7W/9RYuIyGVPYU7kDXhk3wR/\n/uBRHAciIT9be9aRW/Fh23DFugGKlSqbe7sbD5Xy5nu6+AAvlR7Fos5AcDMjmQ3E47AxnuGbL6RZ\nLC+RCiapOKXGOcMt6xluWb+mnFQs2PjaBm7Y0c2+I3NU6xbvubmNWtkhm4WnxyZ54tgJ0rEoW/p6\n2L1h8C26UnlFpVZjuVhiuVBkpLuzEar/6YWXOHp2BoDNvd3cvmMrfj+0Z4KNIAcQaSmwbVMEnw+W\n7QiPH3bHu/l8EIk0JzT56bu2EAn5aEtF1ixnkYoFFdpEROSSojAnnjOxcpbTSzMkgglSoQQR/4Vd\nlNl2HI6cXiYa8rOuyx0709+eYrUXFqVKnWcPzbOxuxNgzRpo8oMr2wUmakeZqB3hqsi7SPpaAYgH\nYlgld0KZ6foY/YM1AqYfMPjwpntJBZN0xzpfVytaR0uEn3zPZu67Y5Qzs3mSMT/EHFpb4U8ecsPC\nUqHIbDbXOMdxHFZKZVJRLQ7+ZihVq2RXA1sqGqE77S5yfXYpy988+UzjuN5MC+lEmFDIIZMMwll3\ne8Uusn27jdub0iQVD5Iv1mhrieD3O7zSw/nKkTb+c/8eWpNrW9gANvRollIREfEGhTm5JDmOw1xp\ngaNLY0wXZvmRkfc29j049m3+6dgjAGxs2cCnrvpEY99/P/DnhH0hEsEE13RdSVfMDVgVq4plW0T8\n4df1cP/QcxN87alTLKxU2D3awQdv2s7SEuTzSdoSccLBAJt7ulnX3vomXbmcO+V73anxF0u/iYUb\n2gYS/WzpvY5EwqG9upHHnobuWCebMyPUrOpqmIPNmZEfqA5+n9lYCxCgWrPo64gxv1KiWrN5xzXt\n9LS7LXYnpnL81eNP05FMMNLdyY6BfgJ+jYn8Xqr1OvO5PNlikVypwjXDzdbSr+7bz+TSMgA7BvoY\n6GghFHIYjIXXlJHpLLB50G1VHV+O8OyYQXtLhO7WCOfML+QuAxILrJl0CCAWDhALv3rNQRERES9R\nmJNLhmVb+Ez3IfjgwmH+2/4/bezbnbwZs9pCoWBwurjS2O63EszMGAQCDvhq7JttjpUaahlshLmn\np/fxV0f+Dr/hozvWyb+55lON4749+QR12yIZjNMW7KI73k4w4NajUKqxsOKOq3n+2Dy7++uEAgEM\nAz6492pNZPImKdorjFcPcKZ6hKKzwvtSv4BhQCrmp78yyHhhDIBZjtLauheA7kAn/+m6XyUdbrng\n9QsGfHzih7dRqVnsP77AzqFWggGH9nZ4ZnzardtKjmypxBWDA43zapZF4DL+GTm7lGWxUCBbLNKX\nyTDQlmls/9KzzzeO272xj1TcTzAI3W2RRpizzCJbttgAOI6fZCxIIhqgo2XtrJA37ezh1it7Mc1X\nf1DzymLbIiIib0cKc2/A5FyeA0fnSEaDJONBOlrUveqNOrhwhP3zBzm6OMb29i28f/gu6nVoZRAD\nAwe3L+Ozp0+wMbQLgJTZSad/kJKdI1jLMD1tAAY5q7Cm7NnTSfzzJn6/w5mVPAB1x6JSt8nl3IkO\nAgF48NS3WCi7043Xz2zivqvu5Krhbl6aPMW3a58ntMXBsMJsLN9J3bYJAbP109SdKhEnQcxMETTX\nthrI91Z1ylhOnYjpjimcrZ/mscLfNfa39CzQl0nj88FVwU2YczU2Z0bY2rqpcYxhGG9JkDtXKODj\n6k0da7bVLQufaWDZDrtG2untMchmoVB0+J/fepxUNMLG7k5GujuJBIOvUbJ3rZRKnJ5fZLlYpFqr\n845tmxv7/vnlw8yuuF1SDcNh6/o0oRCE4mF4tllGuqPAYJfbEjp8Ns70cpzOdIQNvc3WUcMw+K//\n+vrztqyfO65NRETkcqIw9wY8dWCaP/3KQcAdY/N/fmJvY99/+9IBKjWLRDTAdVu72DzofhJdLNeZ\nWy41Pln+7jEal4NSvcTY8kk2ZUYa3eFemN3P42fdcTAHZ46z1TIplwFi9ASG8eGnJ7CRTv9go5wb\nW+9mm/mOV5UfNmO8K/4xSk6Okp0nSma1LINS2cZPkDpVAlaCEyea9z9bbo5/sqtBHts/zcbOHvLW\nCkUnixkHgzzv2rgV2zZxHNhXfJDTtUMAjIau4eb4BwB3vM4j+b8kYiaImHG2hq8nZqYa+wCCRuSy\nnQ3xUPlJxir7mKmfYlv4Bq5L3E0s5nBVdIhvHjKxHbcVZrp+gnU+N7zfNnATtw3cdDGr/T199I5R\n7r15A/uOztPbHqOz06Gz02H/2BL5SoV8pcLk0jJtiTi9GTfMnduV1CsOnplksVAkWyyyrb+XwXZ3\nUfWZ7AoPHXB/FwzgrmtGiEZNQiHo7Yg0wpxlFtmwwf1wxrJDDHYlaGuJ0JmOrOnuePvV/dx+df95\n6+C1eyYiInKhKcy9AVOzlcbXQX+QqSljtZXH4eXxJQrlGgAbulNsHnSPOzaxzP+zuv6YAfy///st\njUD39adPM7tcIhkNMtyXYutqALRth1K1TjTk9/RDTL5W4A9f/BNOr0zg4PALV3yC7sAG8nmDZGUj\n4Ia5+cocuVKFgOF2i/qh5P/2ul4nYIRYH9p+3n27o+9md/TdLBZXODw9wZ+/+ATvu+YqYuEA16Zv\n5czKWU7OzWKXY9TDFplWC1+1GfKSwTjbtgHY1OvwwHM5cN9m2hNxOtodajWYLmY5vXSocd5I6OrG\n18+XHmZ/+Z8x8dEf2MQdyZ9o7NtX/CYBI0TUTNDlX0/Ml3pd134pKtorTNSOMhS8Ap/h/qmZq5/h\nbP0EALPGEbZt+6HV6f9D3Ja/iVQoyZbMCB1Rb80YGA0HuGFH95pt00t5DAMcx50F8YpNKbLLUK3C\nwwcPkyuVGenuZKizg1Dg0vhTPL2c5ejZGZaLRUzDaKy9BvDsiVMsF90PJPrbE7S3txIMQigZ5oHV\nHpMOkGgt0ZVxF1rfNpQmGDToaImw/pxJRXymyb+/v/m7ISIiIm/MpfEE4TGdLXHWt7dRrFZpicSZ\nm3ODlm07jSAHsDQf4qWXTPx+GJtsbg8F/Swt+ggEHPx+eO7IPGOT7hiRd+7ua4S5hZUyn/ncE/hM\ng2QsyC9/6Aq6W2MAHDi54M62Fw3SkY6wse+t7W52PnW7zvjKGY4sjWE7NndvuAOAqC/KQmmp0WXy\nsSPH2RUdBqDN2chVkXfRExim07+u8dB/IZRrNf7i0WcaiwHPFqe5e1c/O33vJF+q8Y1nTrP31q7G\nPb6u+2q2tm5ipZqjZjXfP78fBlv6CPr95Ko5+lpb6O52yywt5mCq+ZpXbYnid2wsC54ey0EZbCyC\nQUgkHOp1g0rV4tnS12H1/tyeuL8R5o5VnuPp4gNEjARJXxvvTHykUfZk7RgGBhEjTszXQtC4NLp6\nluw8D6z8fyxY7o1ItrSwqXWIRMJhT3kjhw8/BbiTjNTsKkGf21p1z/B7LlqdL4Tbr+7n2s0dPHtk\nDsOAnm7o6bZZydmMPTRDuVrn1PwCi/kCN2zaeEHrcm5LoOM4PHroKMvFIsvFEjdvHmm0si3mCzw/\nfhpw35+2Nptw2CAUcug7HGH5pBvmahTp6XF/XuPJCHu2dtLREqEzEyURbbay3XplL7de2XtBr01E\nRORypjD3Bly/fR1pX9urdxgGH9hzNaVqlWK1SkcyiW27n8RbNR+tiTjFSpVIIMDUlDvOC2BhubkO\nUjkf4sgRk0DA4eySGyAs22EpV6Fe9VMouOO8njsyz7demARg6/oMv/TBKwD3Qe3Tv/8dwkEfiViQ\nH7l5iJF+N+jNLBY5NZMjGQ2SigcboeWNsh0by7EbXSYfOfMYXzr+AAARX4Td0dspFU0KBZNOYyN5\nXqDd10vEbH5CHzUT7I7e8QPV4/x1czizsMjZpWX2bHQXk07GAgz3ZDg6uQDAyxOz3ONzu3PFIwHe\nf9PQmjICvgBtkQxtkcyryv+x0fef93V74l18fPt9rFTz5Kt54qFwY+HpcNAg5AtSsap0pOKrXc4c\nspU8zDqNMob7YvSEbWo1g2MzyxTyWQpkqRtl/H6wLLe15zuFv2fZmgXg6sidXBm9DYDF+jRPFb9C\nxEwQNRJcFX0nfsMNTEU7h4mP0JvQ1dNxHLL2HBPVI9ScauP1I2aMMvnGcaXkYdat2+Deg9pG7jM+\nyKbMCKlQ4gd6fS9IxUPctqtvzbazS1nK1Xrj+2u3dBDwQa0GJ2bmODR5lpHuTgY72l7X5CmW7XZR\nfWXWxmNnZzg4McVysUg8FOJH9uwG3K6Kx6ZnKKyuv1YzCvT1tRIKOQSTYR58yS2vbtlEk5XGBCJ7\nt3Uy3JekIx2hv6P53oWDfj5+99bXeWdERETkzaAw9yYyDYPu9Pm7x432dDHa0wW4D8Hnun50mFyp\nTLFapSuVplyGctlgcdmdCa9mWQDMTYdYnHUf1CbONgMgVpCTJw33Qd+xyBaqZAsws1SiWHK7Bfp8\nsP/EAn/5zWOAO8Pb7/7c9Y0iPvcPByiUaiRiQfZs6WLHkDvVfqlS5+xCkWQ0QCIWJBTw8ejE47y8\neJSx5RO8f/huruu5mlIJ2hlulFeyShycPEu7332QvTb6Hm6IvY+QGf1BbvH3ZSa7wpefe5FCxe0O\nu2dLF0MDEeJxyBvdBIIO123r4qoLsPhvIhhnZ/u28+776JYP8FE+QNWqUretxna/6eOH1r+LXDXP\nSjVPb6aFlhCAA+esZ9YaS7B1q/vAXq9D5TvNfV3pOB0pt6vn7MoCZ7KHG/vODcuPFf6O8epLmPjY\nHN7D9bH3ueU5VfYVv0nEjBMxE/QFRgn/C+/VwfJjPF78BwCCRphb+24mnTKJxWD7kRGentnHUGqQ\nrlhzwpBoIMK13bv+hbv49jY6kOa3f2Yvzxye5cTkCru2xTEMm3wevvnyWcZmZhmbmaUzleRD112z\n5ty6ZVGp14mF3IBVq1t8Zd+LLBeL5Epl3n/NLvpa0wCUalVOzbsfXNTtOv39ttstMgS9L0U4OuH+\nDSlbRVpb3b9J/b4Y779pA52ZKB0tkTWtbHu3dl3weyMiIiKvj8LcRfDdLSJDnR3nPW6grZWfvf1W\napZFqVpbs07ScGcHyUiEYrVKZ6KFlRW3zOVCbU0ZC7NhDpbc806MN/eF/UEmJ90A6PfDkdNZsgU3\n/KzvSsGQGzr3nR7nzx55EmuxB4A//PRNPDPzAiey4wB8+YV9vPR4K52pFta199Lq66Xd30cbg8SN\nZotW3Jd+I7fq+1KsVFkplehqcYN0Jh6lUm9e64n5s+zc4rYMXb2p41WzEb7Vgr4g58yqTiwQ5T3r\n33XeY9+74U5u6buBXDWHaTTff9NnsykzzEo1R66aZ117iu7VB/Izkysw2yx7+zaDWs3t6mkfzEHV\n7eoZDfsaXT3nSyu8UH64Uf69qU83wtxzxW9wuPIUfoJc77uTPnZgmu7SD4+7ve6oOmXqidMkk4MA\n3D10Bz868l7C/kuj6+elpi0V4c5r1y4uHwhZHJucb3y/dbANn89tiS1Vq/zJ3z9GrlRmoC3D+66+\nCoBQ0GQ6m6Vadz8cMMMFhodThEJgJsI84s7TRKFcIxKrEwm5f/Lfs3cd76zZdKQjdKaboT0eCXDX\ndYMX8MpFRETkzaQw5wEBn49AZG13q0293Wzq7X7VsalohI/fdhOlao1ipUpLtLlsQks0xlBnO6Vq\njXQsyvx8cwxNrths6VteCPHQvkm+sfJn5O0swWEo7WslQAS77qMnMMwJxt1jrTmeGhtn+0Af6zva\nuLflF8mXK/z3R74NPE4kGOB9V19Fe9LtlnVqfoGZ5RWioSCpaIT+1ld3Yfx+TS0t8+yJcU7NLbjX\nffteMhlIp012T7Zz8OQie7Z0XfTw9oMIvkZXT9Mw+antHz3vOVtaR/n49o+Rq+awHbsR2AGiwQDh\ncoiyVaG3Nc6GdW5XT9/yCiycU8aGOCFs6nWDZ85kKZSyAEzZB7l5aBuxGDh087WlVnpiXWxuHaEj\n2ux63BLy/gQub7VQwMe/v/9qnj40wzOHZ3nXtR10pN0Wu0eemyNXKgNQqBYZHrYJhdz3tevZCKdn\n8hiARZXYau/p9d1J/vX7t9ORjtDeEiEUaP4N2TF0nm7iIiIi4jkKc28zhmEQCQaJBINk4mvHxJ0v\nAJbsHJPV44xeM0vIaqG9spOuliQRv03ezjaOS3auECmliUYNbtqwnd5Mgq8/XOTs6mQf63r9jI66\nXTrHp8vN8qs1WtM+YmGHWs3g1Nw8z4+fAaAvk14T5v744UcxTZNoMMh1I0MMtLldPZcLRaaXs0RC\nQdricdK411Wu1jg567ZkLBWKBBJZ2tvd8Xg/dttGIiH/ZbkERDrc8prrr33yyp8GoGrVGhPSALSE\nkty1/nZWqnly1RwdLVHc9Zcd6mebi7Qfzx0hGrMxDBMDk1/f8yuenmn1UtPTFuOeGzdwz40bGtsS\nCRibnQHAMMDvM4hEHczV+37/nZsI+n20t4QJ+JuBLR4JcNWIt2YFFRERkddHYe4yU7FLBIwgpuE+\n9D1V/CpHK+7qvV3hfj60490kEg6RCDz4dA/zpQWGWzZwyxVXsintzrjXG++mN95Ny/ULLKyUyRWq\njA60EF7tURcMWcTCfgpld5KHLSMBQkG3BejRY80WwI7WAOvXuwGwVHYaEzLkSmX8qzN9WhacWVjk\n4YPuGLCr1g8w0JuhpcXhlnVpHjkUIF+qMdyXom7ZjbIT0bff4sxvpqAvsOb71kiGO9e/87zH/uTW\nj5Cr5ijUimzs66ey0gyBCnJvjR9/5wgt6Sg+237VBxSvLLYtIiIilx+FuctA3anyXPFBpmpjzFsT\n3J38OXpCg8RiDpvDQxyddMPcTHWCRKZIxO92zfzEjvtJBhP4zPPPqPfKJCnfbaS/hd/71E3ULZt8\nqUbonAFiu0baySRD5Ao11vckSa4+hy6uVNaUsWmjn/Xdbjg7mW3uG5uZZts2m+VlBzD5qbs205mJ\nrhn3I2+uoC9AayRDayRDMhRnjty/fJK8qTozUdrbE8zN6d6LiIhIk8Lc24zl1Jmtn6Jo5xgKucsV\n+PBzpPI0ZacAQDlxjG0jAxgGpMtDHMgPMpIeZjQ9RNBstmi9Vle975ffZ9ISD63ZtntTB7vPM4Yt\nnQjxB794E7lilZVijZ5zlk3obY9xzeYOVgpVShWLqYU8UZ/bIqSxPyIiIiJyuVKYexs5VtnHo/kv\nYFEjZETZmtpBMmkQj8Pm8SGen9uPgUGRbGPts3S4hU/v+tmLW3FWx/qF/ERCfjq+a+LL7w6AaqEQ\nEREREVGY86TF+lkma2NM1o4xEtrFhtBOANrCGay8OyV/xSkS65miO+EuKXBL//Vc3XUlG1vWEw2o\nS6KIiIiIiNcpzF3iHMchby+R8DVnfXy08AVm66cBSIYj3LpuO7EYmL5e/vHbQZLBBCPpIfxm8+0d\nbln/ltddREREREQuHIW5S1TOWuTZ0teZqo1RsLPcl/4N4sEYiYTDJt8Qs7NumJusHieVclZnFfTx\nH/f+H8SDse9duIiIiIiIeJ7C3CWgZOeYqh3HZwQYDG4FwGcEOFZ5rnlQ+zG29u0AoLq8iRwLjKaH\nGEkPrylLQU5ERERE5PKgMHeRPVX4Ci+W/xmATv8g21u3kEhAPB7j4f1dTBWmCfvCVMg3zhluWa9u\nkyIiIiIilzmFubeA7dhM1Y4xWRtjqjbG9fH30eEfwDShM9YOZfe4Oes0PQMlwn539e33b7yLiD9M\nf7z3Ndd6ExERERGRy5PC3AVgOXVy9iItvuZ0+t/M/zlVpwTASugYewf7iMWgpzzEQ0/6GEwOMJoe\nwnLsxjmbMyNved1FRERERMQbFObeRNO1kzxXepDp2kmCRoiPpH+daNQgkTAYtjfw8vJBAKZqY8Tj\ntwLQGsnwf9/0GwR9we9VtIiIiIiIyBoKc2+A7dgs1KeYqo3R6u+lJzAEgIPNZO0oACWnRtuGs/Ql\nuwDYY+6kI97CaHqY4ZYNa8pTkBMRERERkddLYe4N+NyB3+No9jAAWyLXsK1zA4mEw0ikn689EaBm\n12iLtJKv5wA3zO3qvIJdnVdcxFqLiIiIiMjbicLcG7A+3c/RZTfMzThj9Pc7q3v8fHz7fXRGO2iN\npC9eBUVERERE5G1PYe4N2NU/yncmHmdjeojR9BC2Y2MaJgBbWkcvcu1ERERERORyoDD3Buzo3Mx/\nvuHfNQKciIiIiIjIW01p5A0wTVNBTkRERERELiolEhEREREREQ9SmBMREREREfEghTkREREREREP\nUpgTERERERHxIIU5ERERERERD1KYExERERER8SCFOREREREREQ9SmBMREREREfEghTkREREREREP\nUpgTERERERHxIIU5ERERERERD1KYExERERER8SCFOREREREREQ9SmBMREREREfEghTkREREREREP\nUpgTERERERHxIIU5ERERERERD1KYExERERER8SCFOREREREREQ9SmBMREREREfEghTkREREREREP\nUpgTERERERHxIIU5ERERERERD1KYExERERER8SCFOREREREREQ9SmBMREREREfEghTkREREREREP\nUpgTERERERHxIIU5ERERERERD1KYExERERER8SCFOREREREREQ9SmBMREREREfEghTkREREREREP\nUpgTERERERHxIIU5ERERERERD1KYExERERER8SCFOREREREREQ9SmBMREREREfEghTkREREREREP\nUpgTERERERHxIIU5ERERERERD1KYExERERER8SCFOREREREREQ9SmBMREREREfEghTkREREREREP\nUpgTERERERHxIIU5ERERERERD1KYExERERER8SCFOREREREREQ9SmBMREREREfEghTkREREREREP\nUpgTERERERHxIIU5ERERERERD1KYExERERER8SCFOREREREREQ9SmBMREREREfEghTkREREREREP\nUpgTERERERHxIIU5ERERERERD1KYExERERER8SCFOREREREREQ/yX8jC//Ef/5E//uM/xu/38/M/\n//OMjo7yK7/yK1iWRXt7O7/zO79DMBi8kFUQERERERF5W7pgLXNLS0v8wR/8AZ///Of53Oc+x0MP\nPcRnP/tZPvzhD/P5z3+edevW8cUvfvFCvbyIiIiIiMjb2gULc0888QR79+4lHo/T0dHBb/7mb/LU\nU09x2223AXDrrbfyxBNPXKiXFxEREREReVu7YN0sJyYmKJfLfOITn2BlZYVPfvKTlEqlRrfK1tZW\n5ubmvmcZ6XQUv993oar4A2lvT1zsKlzWdP8vHt37i0f3/uLRvb94dO8vHt37i0f3/uLx2r2/oGPm\nlpeX+f3f/32mpqa47777cBynse/cr1/L0lLxQlbvDWtvTzA3l7vY1bhs6f5fPLr3F4/u/cWje3/x\n6N5fPLr3F4/u/cVzqd777xUwL1g3y9bWVq688kr8fj8DAwPEYjFisRjlchmAmZkZOjo6LtTLi4iI\niIiIvK1dsDB3ww038OSTT2LbNktLSxSLRa677jq+/vWvA/CNb3yDG2+88UK9vIiIiIiIyNvaBetm\n2dnZyR133MEHPvABAH7t136N7du385nPfIa//uu/pqenh3vuuedCvbyIiIiIiMjb2gUdM/ehD32I\nD33oQ2u2/emf/umFfEkREREREZHLwgXrZikiIiIiIiIXjsKciIiIiIiIBynMiYiIiIiIeJDCnIiI\niIiIiAcpzImIiIiIiHiQwpyIiIiIiIgHKcyJiIiIiIh4kMKciIiIiIiIBynMiYiIiIiIeJDCnIiI\niIiIiAcpzImIiIiIiHiQwpyIiIiIiIgHKcyJiIiIiIh4kMKciIiIiIiIBynMiYiIiIiIeJDCnIiI\niIiIiAcpzImIiIiIiHiQwpyIiIiIiIgHKcyJiIiIiIh4kMKciIiIiIiIBynMiYiIiIiIeJDCnIiI\niIiIiAcpzImIiIiIiHiQwpyIiIiIiIgHKcyJiIiIiIh4kMKciIiIiIiIBynMiYiIiIiIeJDCnIiI\niIiIiAcpzImIiIiIiHiQwpyIiIiIiIgHKcyJiIiIiIh4kMKciIiIiIiIBynMiYiIiIiIeJDCnIiI\niIiIiAcpzImIiIiIiHiQwpyIiIiIiIgHKcyJiIiIiIh4kMKciIiIiIiIBynMiYiIiIiIeJDCnIiI\niIiIiAcpzImIiIiIiHiQwpyIiIiIiIgHKcyJiIiIiIh4kMKciIiIiIiIBynMiYiIiIiIeJDCnIiI\niIiIiAcpzImIiIiIiHiQwpyIiIiIiIgHKcyJiIiIiIh4kMKciIiIiIiIBynMiYiIiIiIeJDCnIiI\niIiIiAcpzImIiIiIiHiQwpyIiIiIiIgHKcyJiIiIiIh4kMKciIiIiIiIBynMiYiIiIiIeJDCnIiI\niIiIiAcpzImIiIiIiHiQwpyIiIiIiIgHKcyJiIiIiIh4kMKciIiIiIiIBynMiYiIiIiIeJDCnIiI\niIiIiAcpzImIiIiIiHiQwpyIiIiIiIgHKcyJiIiIiIh4kMKciIiIiIiIBynMiYiIiIiIeJDCnIiI\niIiIiAcpzImIiIiIiHiQwpyIiIiIiIgHKcyJiIiIiIh4kMKciIiIiIiIBynMiYiIiIiIeJDCnIiI\niIiIiAcpzImIiIiIiHiQwpyIiIiIiIgHKcyJiIiIiIh4kMKciIiIiIiIBynMiYiIiIiIeJDCnIiI\niIiIiAcpzImIiIiIiHiQwpyIiIiIiIgHKcyJiIiIiIh4kMKciIiIiIiIBynMiYiIiIiIeJDCnIiI\niIiIiAcpzImIiIiIiHiQwpyIiIiIiIgHKcyJiIiIiIh4kMKciIiIiIiIBynMiYiIiIiIeJDCnIiI\niIiIiAcpzImIiIiIiHiQwpyIiIiIiIgHKcyJiIiIiIh4kMKciIiIiIiIBynMiYiIiIiIeJDClpoF\neAAAIABJREFUnIiIiIiIiAcpzImIiIiIiHiQwpyIiIiIiIgHKcyJiIiIiIh4kMKciIiIiIiIBynM\niYiIiIiIeJD/YldARERERC4Ndq2Klc1SC9roM3+RS5/CnIiIiMhlojR2jOrMNFY2S7Cvn/iOnQBU\np6c5/Vu/gV0qAWD+3M/gu/JaAGpzc1QmJwivX48/1XLR6i4ir6YwJyIiIuJBVj5PfSWLlc0SaG8n\n0NYOQOnEcRa+9HfUV1awC3nW//Z/wTAMABa+8mWKB/YDkLjhJvwjO6nXoVqPNoIcQC4+jLVgEIk4\nlPc9x/wX/gqAUP8A6379PzaOs2s1zEDgrbpkEfkuCnMiIiIilwi7XAafrxGQSidOUNj/PPVsFjMQ\npOPDH2kce+o//Qfq8/MAtH/gx0jffge2DbVSneLLBxvHTY8XsQJxajWoGanG9uWJFWaPrHaldJKE\nTZ/7ZSxN1t/L8kQVMAi+eBLfK/WLpqhUIBQCx3E4+ZlfwhdPEF6/gZbb3kl4YN0FuzeXA8dxcGzn\nYldDPERhTkREROQtUJ2ZoXp2ivpKFl8sTmLXbgCcep3xf/er1FeyOJUKvZ/6JWLbtgNQOT3O4le+\nDICZSBK+6yPU61CrGdjhFOCGudmTK0y8ZGLbYCy1ED7ndefGV3AyCQB8HSOYoxZONIXTfk7wMkzK\nP/lZCMfAMIn6AkAVALt1ALoXMWfHKSaHOHzYxOeDSHUWa2UFa2WF6tQkyb3XNYrL73+R4sEDhNev\nJ7x+iGBn54W5qW8ix3GwLBu/39fYtrJcolqxsCybSDRAsiUCQKVcY/zYApZlY9VtRrd3EQy5j9XH\nD88xdXqJet0m3RrlimsHGuX9w188T71uU6/bXH/bEH2DGQAOvzTNt752BNtyMH0GP3r/bjLtsbfw\n6sWrFOZEREREXifHtrFyOayVLEY4TLC9A4Da4iLzX/ybRvfH3l/8ZQIZ94F9+aEHWX74mwBENm0m\nsnM3tRpYlp/6ygpOpQK4wcyJGtRqBvXlNK90YrTyOU4eB0y3NS0Q68RsyeNEW7AiGWx7tW6JVip3\n/5Ib2KJJiDbHuVlbbsbacvP5LyqSOO/m+u67YffdYNXBqrnlWFCcmCZoGBiOg4PBeHkD0XGDaBSq\nz75A8fFHAAiv38DAv/33jfIKBw8Q6utbM/7utYNUHctyXjNI1es2o9u6CIXdR9oTR+aYPOUGqZZM\nlCv3nBOkPv8CtaobzPbeOsTABvd9OXpwhkceOIxtOYTCfn7yUzc0zvnmlw8xM7kCwJV7+tlzy5B7\nDbkqD3/1cOO4dcOtBEN+HAemzixzYN8UAN39LYzuGMBxwLZh5mwOq+6+UQtzdaJJsG2DfM7AttwW\nOZ/PRyAcwXHAMOBbXzvCynKZ3nUtrBtupbU9fv73Ty5LCnMiIiIiuIECx8FYDUu1+Tnyz+9rjD3r\nvO8nGsdO/tffpXjI7cqYuvU2On/8o6tlQO7pJxvHzY9nMaqt1GpQsZrhpTCT5eDB5myRoUgKs1LG\n8QUoLFewcu4YNyPTB9fcgxNtwYmmwGnOMlm7/RPnvxB/EHtw5/mvDxrj52zbxqpb4Dg4OARDocax\nxUKRwkoO27IJhIKEoxHw+anbsDhxFtu2cZw07T/5hwSXJjCWp5mdzZN9+TS2bRM/bbF+taxa2wb+\n9n++iFWvY1t1Bl76Mm3FCfyZDKU77+exZ5ewLYdA0MdPffrGRh0e/sphzk5kAdh5TT/XvWNotW61\nNUFqYEOGUNgNUmfPZBtBqqsvxaadzSA1ezZHvWYBsDBfI5Yy3PfrnCBVr9lMThqNc+q15nu0sOBw\n7JiJ40B+pRk6AQ69DJG4u29xsbkvn3c4erRZhmGYgBvmZmcdCLr7sueUl27LcOKEH9OEaNTmxNEF\nysUqE+NL1GpWI8xVK3VWlsu0dsQa76lcfhTmRERE5G2pWqnjOA627WBWStiLc9SzWWpVC3NkG47t\nYDsO5b/8IyqTE+5EInd9iOCO3e4505Pk//ovAbAMP3NbbsfCj2VBPNAMZqcnq+z/6iRW3cHv9zNw\nTh2e3zdDfcwNUu2+TtLrduBEU6zEuph86VAjYI3c8xmcYASCUU6PnST/7IvgOKQ72ui+9v3u9ZQr\nHH7qeXdclQOjV24jEosCMHniFGdPTeA4DrFkgi27m2HumYcfo1atgeMwvH0zHX3dACzOzHP0hQOA\nG/D2vvvWxjn7n3yBhZkFALoH+1m/eSMA9WqVsZcONY5LtV6Lv3cUo2+U/OExpk9PAlDOjDA4kMGY\nPoHdu4nFIznqtbpbns8NjfXFRer+OLa1uFp2nWO/+m8xe9ZjDu2gWmuGy8VFuxGkCrm1QerwIYgm\n3H0LS+cEqdzaIOWGYDfMzc06mCE3AJ0bpBxgbq4ZeH3BCNFEHdM0McwwxaJ7nO0EaO/pxDBNTJ8P\nw/Sx+laS6WwjFA5h+kwC5wRkgM27doJhYJomoUhzX2tnG3tuvxnDNGlpiZLNlrBtmJspUy5WG8f5\nQxnm5w3icYeJk4s8+A8vE4746V2X5p3v3YxpajmJ71d+/4tUpyapzkyT2H0Nsa3bLnaV3hCFORER\nEQ+yLBvbsrFtBzAa3cwActkylmXjOA7RWJBQ2O2oVy7VWFoo4tgOjuPQuy7dOOfsRJZivoLjQLIl\nTEd3EnAD0dGDM43gM7K1k0g0CMDE+BJTp5exHYd4IsS2q3ob5T3+8HE3TNkOozu66Ol3w8/0ZJbn\nnzi9GmIM3vOj2xvnfOehMaYnsti2w9Cmdu54r/twlc9V+NKfP4+9Wu+7P7iTdDpEPZfjuWem2L9/\nAcd2aO+Kc4P/UKOL44OBG6hU3VaQa9bVSDz0FwAsdG7lhYT7VG6YBrcXlqgvuMHlmWMO8wdeAKC3\nK8Wm1bpVfSEe/+axRl2vimZoCcdwoi3M+jo4/ZK7L55K0nXHz+CE4zjRFJMvjFOfPwVAdPsmEvf8\nMoYBlek5Zp99yS3MMNi6ewuG4XarK+VWWJ5zA048GSYWczAMKJs2+WyuUYdopE4y5e6b9deplt1u\nmthV2tpeaYUDw3BbHAGSSZuuLvdru9L8eXJw6Otz3GOByaM+FmbcfYm4xfCw7datAPsebZ63YUOd\njm53XzlrMHnC3R5Khhm9/57mz8PJx6m7PTQxO7sxKmcwI1GSXc2unYbjYM9O4sxOUinZ+HtvJZqo\nYRoG/lOHqNgL2J0bsKMZ2nu6MEwD0+fD9PmbQaq9jWAohGka5wlSO1aDlEEo0hxVmGlvZc/tt2CY\nxqtauIa3b+Z8AsEAG3duPe++ZLqFZPr8Szgk0qnzbjdMk1de+dxAFolF2XXLdWQXlsguLmEEUkxO\nGoDByZeXASiX6iwtFNec98y3T5JIheldlyaROncE5eWlOj1NYf+LVGdncGo1un7iXzX2zf/tF6hO\nTgBgh1NUurcR8+AwRYU5ERG55LwyfsaxHWwbAkGz8aBSKdeolOvYtoPPZ655UJmbdsej2I5DKh0h\nFncf5gr5CrNTK6tjihyGNnU0zjl9YoFctoxtO2TaYo2AU63U2f/MBJFIkHy+zLZdvY3yxsfmOX18\nEdt2SCRD7Lp+sFHeI189TLlcw7Fh267exricqTPLPPXPJ7AdBxy492O7muc8cJjJU8s4jsPGLZ3s\nuWUD4IaYv/qjp90QYzvc+7FdtHW6Xaye/c44+x4/DUBHd2JNeV/8H89RLrpPzrfcOcrmnW5LzNkz\ny3zt79yugYYBn/jMLY1znnv8FGdOuAFiy5U9a8Lct7/RDDE9/S2NMDd1epnnHneDSntXgi1X9LLa\nU5EjB6YbdWjtTNLa0YLjQHapxvjYQqO8fL75vs/PFJk964aVlpYIJ7/6LVamZsgv5chlm+/ZxN8/\nwPzTXwUgO3w7Fj0AVCqw/Pi3aKSGzc0JOQhFGl/axQKsZgjHdjATzQds06nD6ii1SCpI7Mbb8CVT\nhENJ2N8srveed9HRdS+GAcvfOcnpJ937EA7bbLn32kYwe/7l043q9PbabNnuhsuTIYeXn10tzHHY\ntMlqBInj+2kEqWTSYXjYTSqFHJyTo+judmhfDWazp5vb/X6H3t7mjIh+n8ErbTvxuENnp7uvnDcI\nhvyYpoFhQiZjN+rQ0RUln4vh85ukW0ONh1y/z8/I1k58fhO/3yQaC/BK/lk31EokGsDnN4mu/oy8\n4t33blutm0k8eT3BwE9QX1rEl2nl4798E4YJJz7986sdEKFj+yA9ezZSLBoUTozj/NnvwSsNgj/2\nW2zcuQUAIzuDY5dhdcqXRDr1moHp+wlSl6JQJExHX3ejRfUVhunDHwhQr9UIxTIcOmQSjzuEgjWe\ne/xUI+C+64e3MLzZ/f1xHOdt2SVz+ZGHqU6fpTozQ/pdtzda2cqnxpn7G7d1HZ+P4N33U637qVSg\nGu0E3DC3fHKG2hmT1eGtnqIwJyLiEYVchUK+gm07+P0mbZ3NT7QnxhepVd2WmLbOeGOigFy2zJnx\nRRzb/Sd+bsvJ2KFZlhfdVpq2rgTrN7YBblh65tvjjVaQq/auawSm44dnGTs0h2M7JFrCXH/bcKO8\nr/3tAUqlKo7tThSwfqR9tW5LPPr1o40ptz/ys3sb53zjSwc5fWIRx3bYtKOLG28faVzr//rD5rij\nH7l/F+2rn+C/+PTEmgDxI/c3Q8xXv7CfUsF9cr753SNsucJ9yJ89m2uEGICf+TfNYHDguUlOHV8N\nMVd0N8Nc1eKZx8Ybxw1ubCcSDeE4MDOZ4+Dz7ric1o4423YPAm6IOXV8kdJqt6jugVYyqy+VXa4z\nvTqRAsDystsyBbCyXCWXLa8eV2N+3h2zUyoY1KpW45z5eag77r5CofkpfKXiMDFhNIKU4zQf1ubm\nHMIn3X2zs81zHAeOHQNwu6cVi81zlhbh0CF3e7m0tkvbsWMG03Pmq8orFh1eeqn5vWU1yzs7aUF+\nBqOYZXFuaU154//jb/BPvIxRzFLufzfgBubcUo2pr37WvT5fBNZ/sFm22WxxMaplWM0NPp+DP5mk\nvuiGxaBp44QCmKZBMJUgNLgefypFNdxJ0gpj+kwMA7rv+nEM08SfTLLwxCS+qRUMw6CjN0nv+9yx\ncJVyjQ2VIxirLTuJRIDg6ut29yepVXsxTYN4MsS5Pd2u2NNPvWZjmgYd3c3f2baOODe/e8QNUt/1\ncL3r+kG2XNmDYRgkks0PK8LRAO+/76rGOalMM6DuuLqPzTu7MUwDn29tV7sf/5lrG/U+97XWDbXy\nr37xBs7nzvdvZ24u96rtwZCf2+4+f2tVZ0+Szp7ka+77bq+si7c6BJAN/9fvUj41TvnkCeI7thFs\ngZYWh+Wjx5ltVoCW4R7KFSiXwf/4F/CPPY0dz2Btewf1q9973td/OxrctJF1o8MU8wV8pkm1CouL\nBouzK40gBxBNJLEs8Plg3xOnOfbyDH3r0gwMtTY+bPKC0vEx8s89S3V2BsPvp+cTP9fYt/j1BxrL\ndBgDo+TbtlOpQLnS1SzAsph4eREn5c6s6hu4AjOcxk534bQPvpWX8qZSmBMRuYSUilWOH5pjYS7P\nwmyBuz64ozHd9csvTPHsd9wQ09YZ50d/YnfjvIe+cphi3g0QN92xka1XuqFtYS7Pt/7paOO4rasP\niABHD8xw6rj70Lt5ZzeDw204DlQrNi89N9k4Z3hLN6FI2H14ny5y4sgcAJn2OIWCe4zjwPTUCqWC\nW4eF+SqZ1ZnIs1mL7FJzMWL3/61bh2LRaYSVXM5hZsYNHeXi2ofRmRmo1FcnK8g3H0YrZYfx8WaI\nse3mebOzEBwzcByDuam15R065HZTchzI58+d4IBGIKmUzx9iAObmm+eUSnD48PlDzMwMmGF339Lc\n2vLGTxqNiTbK5eb5+Tyr3aigWllb7/l5h0rdWH3d1bE6hoHj+FhYaL5uMBTBMNx91ZqflRV3X70e\nJNGSdH8GDINCwWi0qsSSKRzHxDAMIvEE1cYwHR+d/e7PjWEY+AIBt4WzXiUZ9dM/vB7DMAiEgvj3\nPYCRncUoZhnsvRYr4+5LFaYIf9kNZhlfhNH3/Qamz4/PZxB45vMYc+MAbPZPEbzzXgIBiAQd8o+v\nXo9V5n3vSBMbHcU0DcpPP8bCUwa+eIJN0QXe8Qsfc7vcmQa5p2uAgT+VYkN395oZE7nNHUfWC7zW\n6Ji9tw6dd3soHOCO953/rHVDrawbaj3vvp1X9593eyIVbnzY8N1eKxD5fOZr7guFA43utN/t3Bki\nL2VmKER0ZJToyOia7ZGhYTJ3/zCV8ZMY/gA96wzAxrbh5P86jgWY+UUw6tRXzzFW5gh++b+43TI7\nN2CNXgfByHe/pOcZhkEssXZ2y2g8xsDIECuLS1QrVeYWoswvQjgMJ48tszRfZGm+SCFfaYQ5x3E4\nc3KJ7r4kgeDFiweObTP/939LbXaG6vQ0HT/240Q3uR8eVCYnWPrG1wAwwhGmp90lOioVqEe7MFeX\n6ciOz1AbWv3DFukmsO1WnFQXdksnTqT5++PO7PrWXt+FoDAnInIRLC8WmZvOsTBbINMWZWSb++lh\ntWLx7QebXdpOjBWJJVOsrEA223ywL5cdjh83Gq0wa1pBzhr4Im7Lyfz02oe4/fuNRnfFc0PRwjzs\n3786Q15l7TknTxjML7n7zg0M5bLD2Nj5Q8z8vEFg/Pwh5swZME332EqleU6x6DA97X5fq7qh4pUx\nLMvLBtZqAKzVAoQi4dVgESKbbZYRTSQIhEIYhoHtBCkU3H2OESTd0bYaSNwQaKzWIdmawR8Muq0g\nLanG9O6m6aNvaJBINEilXCcYbrYGpdtb8QcCbogJrn2AHto2im27XZmiieYAjHgqybZrr2oEKc5p\nIdmwdZTBzRvBAJ+v+a85EAxwzTtvatyHc1tV+oYG6Rsa5Hy279113u2JdIrte3efd9+asmwLc/xF\njGIWfzHL8MZrcVJuE6PvwCMEvvPXGNUioZZOUh/9ncZpvm99FnPJbbEc7B8ksONaAgFgukz+QfeY\nkFXiul1+wu3uQ+TM2STZ1R/5dH2W/q3Nh62lW2+h5gviT6WIr+siuNpCHL35ZjK33ILhe3VISV67\n91XbxNtC/QOE+gdevcOqEmxrpVzI4VSr9F29nvAWm1IJlp88TmlxEnNxEufwY1ij1zdO8x19EmwL\nu3MDTksnGG+vSUPC0Qh9Q+tgaF1jgh3HgWLBZnGu2TsgGM0wPe1OplIuFPjq3+x3W5B7ErzrvVuI\nJ9/csXbndvHMv/gCuaeeoDo9jT+dpveTnwLcLq8r334UK++2Ci8cmWIxvsXtFlnqbixe75RLzJzM\nQ9T9e+EbvR6jdzN2SxdO2zk/K8EItVubs9C+HSnMiYhcII7jUCxUWZgtsDCXZ8fuvkb3p6cfPcnx\nw24LV/+GVtp7uyiVDEqlCKbPh225rVWnThToGnDHGtlGiFgqgYFBOBYhf04YS2Uy1Go1DAP8gTCr\ny1XhC4To6OtuBIhXggpAa2cH0XgcDIgnm92/fH4fg5s3NsJDKBpec07k/2fvTgMrucoD7/9PVd19\nla6urpaWWi31vi9e8YYXFhsHHLYQs4RhmRDCBDLJkAAzSZhJ3kwmC0PgTTLhDZAwWQgwScAhwazB\neLxhu+1uu93tXtSbWrvuvlbVeT/UVUlyyzYY96L28/skVZ2qOlW6ulVPnXOeE4u1j7P0FrJh51b/\nZr14m2RHil3XXenXYXFAsm77JrTe5Adu8wLB4JLMeov1Dg3QO7R8a8emPduXXR5PJZ91Xe/qVcsu\nNy2TwfXDdHTEmJurnLW/eGr5FpKO7q5llweCAQKdyydFWBwoLqaUOus6/9i0hnoZVS2gqgXc3nVg\nef0Dzf3fxTz8kLe8e4jWLe+dPzLBu/4I5WeV6MXs7sayNCpu4TS95CFmrcCaNS6WBYEAjOdS1NrB\nXNLM073a274VSdHIZjGTKaxkCtNY6APWcfMrSF59DVYqhZlcOs/Z+g/9h2W7+ilLHl8EGIEgA7/2\nUbTj0BwbI5DNYliQSEB99gjz/QGCvf3k1gWpVjXVKtiP/DPGlNfLobXz1djX3ekVtJvQqEJs+f/T\nlWjx960yDC678VpK+QKFmTmiiU4mJhQTE4ozx70pIFxXMz1RJrxozONjD57EcVz6V3eQ7Yk/Z8ZM\n7Tj+Sxa30WDiC5/3WtkmJuj/Dx8istbLitqcmKD04APez/kCp097LWyNhoJ4DrMdzJVOTNAaap9D\nahXWla9Hp3tw0z0QivrHdTZew0uVfBsKIZ6VdhxqTx+i/OgjJK++BrJe1jmtNaUH7iPQlSXQncNM\nJC7JAdU/jlbLYW66gmkaZLq9Li8zkxW+9Lkf+mX6BjNEYjFqNYUZiANeMDd5psKJE/M3R0X/mkEM\nyyQWjxFbFDDkVvWRW7V8t6y125cfwxJLxJ81G1u2v2fZ5YZp0vcswVI0EVvS2rTYsyUXMC2LSHz5\n240pD+UvXKsBgYUg0Dj8IMb0CVSlgNu3AWeTNxZKzY0R/uuP+OXqb/s9dEcvhgFWaRLjpJea3goH\n6cxpPzCbiidwS95b/P50nvRG701ApZ7kNIBpYkYjxCMtjIDXOpm+4Ubiu3ZjpVIE+xbGZwY6Oljz\nuwsteIsFe5b/HArxo1KmSWhg6XdW5o7XE9+9h/qxo6hQiHR7/J3bbHJ45qRfLrpmkFbEG3+nxg4R\n+qf/gRvvROeGab7yff6Lj0uFaZmkuzpJdy0dK2cYJtFEjGqpQjyV5sABi1hME4tpHnvoFJVSAzjG\n7qsHufyqflQggMYLFov3fJ/ifffSnBgnPLSG/l/6ZQBUIED54YfRLa/P9uTBSRxjPc2mwq71zA91\nRRfzTI81IOi9BDR23YpjN71Wto5F3w+RBPYVCxlShUfuokKIZWmtOf5b/4XmGe8te8u1SG3wBhSb\nzQrj/9+f+2VX/cqHiW7yOp7XjhymOXaaQHeOYC6Hle5Ydv8rldaaUqFOIhX2A9jv/ctBnnr8DFrD\nui3d3PSazdRq4KpYezyT1wqxf2+Vrj6v5cEKp+jIZogm4sSS8SXdTwbWrVn+4OIlQ02OoipzqEoe\nt2fE7zZknHqSwLc/i6rmwbGp/+Jn/S5i1lP3Yh571NuB6WJdcQ2WBVY8yaKEkQxm5khszmGaMHcm\nyVT7fYNRz/sp6wHqu3bjNhtYqRShVQsPypENGxj5xKcwYjF/zN+8xOVXvPgXQ4gXwAiFiKxbT2Td\n+iXLtd0ic/trqR87Sv3YMfovGyaY88bfTRw+TAlv/J1GLwnkAt/5rN810x3c6ifRuFTkBvrIDfTR\nbDRxWi3cVovSZJ0p12wHcm3/8kUOf+Egq3/zv3G6Gubf/vUgGatJfELRXyxSPT3BsWOKZlPRbBoE\nkjmMdvBcPjWJvar94rd7mObN70Gnc14r26IXU+7ay8/nqa94EswJIXDKZcqP7aX65BP0vOs9KNPE\ncRRq1VpoB3OlRx7h0LWafN7AmJhicaewopnDLkIwCKUfPkT+m98AILxmmMGP/YZfbuZr/4QZTxDo\n7iY8tAbzIp/QxXW1P7arMFfl23c9xexUhVbT4e3vv4p40uvOqIyFOY7GTlaXZPTzukVCNBFfMq/R\ncm9GxSXKdaFeQlUK6GSX3zXIOLEP88A9qGoBAhGat3/I3yT4L5/GKHr5+1rXvAWdG/QCs5iJW/Tz\n+tHfWSTUkcSyIN+XoHTMWx7VBVYNz4+ViXI0kcCIxrzALGIyP9QstmsXgZ4erFQKK7W0ZTX3jncu\nezpGIAiBS6u1Qrx0mNEYmdd6rTt6UcpHwwBLN1DBILrZJL5uDSNbvPF3lbKmePgBaNTgwD00r38b\nzo5XAl6iFWPs0Mocf+c6qOI0OpllPgVrdO8/Yz51D6o0g7P2Cuybf5612zdRnJ6hdPwwyZkjABRP\njHO8kKJeszlNgFhqI6uKB3GLeYp5zez0jJeY5dq3gGF6Advi7qvRFM7m6y/EWV9yJJgT4iWuMTbG\n8d/6z8wPptLbX06ta6OX5S63h2BsH87wHpyRPcznAdNGAHvtFRiFSVR5holKBo55N4Lg4Sl/gHIr\nnmNyUhEKaSyazPzTP/jH7fvFXyK+azcA1acOUH7kYa81r7fXnx/mQnjysTFGD80wM1VmcCTDDa/y\n3upagQATi9LK79tbJd4RxXWh5XitbeFYlEgstqSVbfuzJJsQK9z8Q2D772yceRo1ccQL2BIZnO23\neOudFuE/fS9Ke/9fjZ/6FdwhL6OiUZrGOtSefiGaIJPRBAJgWVDsTNJqB23ZWJ7urd72zY4ko+0q\nKMsiYZUJJbyuuM6uXQQ6O7wujj0L81EppRj5xKeWPY1gtptgtnvZdUJc6p45PCD7hjfRdcfraY6N\nobU3FjSRgFD5DMXGQkbe3I41tLLe+LvGvscIfO+vAHDjnTTe+YmF5EbN2oXPoKldVGkWFOiEN55X\n5ccJfu2PUMUplOtQf8cfolNZDAMCNKDoZYUMV8dZvckgvCuHaeY4+uufxdY2GAaBZon87EIY0beu\nl/Sdf0DFzFAvag4+sg+tNeFohLXbNpGMXzrjEC82EswJ8RKhtaY5Nkb50YfRjkPX634aACfVg4ol\n0aU8APkHH6Z1vTfGSq/eQf3ffcJ/0zh/49PZQVq3fmDZ4zirt6GDYYz8BK3kaspnvBTwanaGxXmx\npp0clQkv0Kvtf4rSd74FQLCvj9h//X/8cuOf/wuUaRHo7ia+fQfB3uXHjP04atUmB/dNMDNVZnaq\nwmt/doef0ntqvOyn6x8/VeboUUWtprDtEMFwiGa9gRUIUCnbRNsNGZ09Wa7suQFzmcx6YuVRpRlU\neRYqeXQiAx3eWFFVmCL4jf8XqgVUpUDj7b/nvdEGzKcfwHrsbgB0/wZCV92CaWoCAYtaKAx1L2FI\nLjZHaqNLIABVnWTsO+2DVkv097T8xB7uhrU04hGsZIrI0EK320Amw9Bv/y5mMoURiSz888f5AAAg\nAElEQVR5GI3v3EV8565zfXmEuKQtN/7O6soy8Gsf87pmjh4js3kAI+C90Dnz/aPMp+ixMt0YUUW9\n7r3vCf3NR0FrdG4Ye+ercfvWc05oDZU8RmECt2+DH0wGvv0XmAf/L8ppYW++gdbN78YwIJhOoPLj\n/uZ9kTHSWzJeC/9cN5MPAkph4LC4A03vv/8FzFiMQKYLZVncoTWz0xVOH8/T3Zugu9+7KZ44Ouu3\netarNcLRhb48k6fOUC6WSGU6SHWmsQLLT6chfnQSzAnxEjF3978y/aUvAt78LI1dr6VYCXh92of2\nYEyN4gxfhjOyqCXpOTJWPRtn2804224+a7kORmhd9UZUYQKVn6CgchTGvUAvcGzK/zJqRnMcP64I\nhyFgORTvuw8cb+YgK5nyg7nKE/uZ/eevEejuJpjroePVt531lnV2usLkWJGZqQodXVE27/C2dWyX\n+757xC93+GCVeDpNva6oNxey6RXzNYrFhZesG3dvIxgKEQgFlxxLgriLnGNDrQhWCMLek4maOYW1\n/zteF8d6heYdv7bwAPSdv8A84SUFsbfeBBu3tbs4BlATR/3ddkXyRAYzBAJQOZ6k8Ji3PGgXWLNu\nIW3oaEcau+jNexaOW4TazzXhoSF63v1eL8NjKrXk/y37prcseyrKspa0ugkhzj0jECCybh2RdevO\nWhcZGcEtFakfO0Zy4xqy673xd6XxPBMl78Ug5VnszTcs7O/445iH7vfG3/WMoLt/hHHSWkOthJEf\nR4fj6E7vfqbGjxD6x/+Oannj2mrv+TQqmiQYBCts4jotAKKNcfo3ey+SIMKReAKnXMJMpwkZDeZz\nUSV27SGydp2XGfQZ3akjw0vnYFRKkcnGyWSXznNnWQYDazo4c6pAJBJgz+VB6nWXUklx8NEJZsZn\nGT9+io7uLj/LsNYa13EkKdYLIFdMiEuM22pRe+oA5UcfIbZtO/Fdu725yAYWZsbU9Rqzjx3EHfS6\nM7ZuePu57+cf78S+/LXLrnK2vBzdNYDKT+JmV1PLew/VqpQn7Nh+ufFGD7OjilAInEMnqR06SPXQ\nQeyufgprr2R2ssyOKwcY//QncWtVHgtv51TD64LWN9hJd38f9TrU62GsgIXd8vZ98niFXrxELemu\nTtbt2EwsEScciy5t+XiWVPTiwlL5CYzxw6hqAa0MnF2v9teF/uZj/uD7JeNcqgWsx7/ll0tFawRi\nESwL6tkUjRPe8qSZZ+dumJ520U6Mp5Xyu1gmzQLxdn4fY/0I6qabMZMpApmlk0ev/q3/tux8aFa6\nw8sSK4RYsdI33Ej6hhvRWqNtL3AyDAhWJv3xdwDrbxiiabpUq4riA/vQT/0AnvoBbmaAxp2/4+/P\nOPoIblcnxuTUkkQgwX/8PcxTTwJg73wV9vVvJRCAYC6N01pIULImdYbkJi+4mjuRY+oRMBMJgsk4\nixvBBj7yMax0B0Zo6bQoZiKBmVg6RciPq28wTd9gGsdxKRfrGIYiGoVQyKEwk1+ow1CaeFxTqSiq\n5SqP/eBB4ukkqc4O+tYM/uTTsrxEyFUS4hJz+hN/QO3QQQBq+TLTHXsolRSuM0Qoswq3ow93ZA9u\nz6I3bBd4wLbbvxG3f+NZy3U4QeP2X0YVJjDyk9QCXVSPFTBMg47jXmteJZDigfQr4CteS0oy04U6\ndgS3UiGcDkGXdzOcnihz5v4DBP/lU+hUN4PJTeiNLyOaShJLJLyxDYEw4WiEcPQCj3F4KWvWUJW8\nF5iZAfT859RpEfznT/pzpTVf+T7cVd4LCuP44wS//wUAdLwD88pXEwhoTBOcoMl8G1lHIE96rfdm\n2s0kOfGPC4ftS+YJ9ngdgWdX98LMGqxUisjIsN8yq0yTgY/8Z8x4AiuZxAgvdByObtxEdOPyU0As\nF8gJIS4tSinUopasyLr1rP3Un9IcG6MxdopQOkEISCQ0jdmj1NvlomvXkMxqajWoFpoEv/7HNLVL\nCKi97zMEYyFCIY3uTOOc8rZJ2OOs2uaiFGg3xeFg0Gu1z/VgmQu9ApLXXkfy2uswowvzsc0L5s79\nlCCmaZDqWDi2UnDrG7ZyajTP6eNzbNyaJtOtcV3NI/d7XTNLcwUqhRKrRob87eYmp7ECAeKpxFkZ\ndIUEc0KsWHYhT3nvXip7H6HnXe/FTCRotYA1W6EdzDUO7KN+TctLr6wUjZ/9nYU+gxcx7brUqjUi\nsSjuGm8M0MF9B5i85xEAMj3dJK94LYHNu0mVJjEOg9vOR2GXpzEr3gTPqfoUg71BMqv7SKRjJE8+\nQa1RRU2OMlSconHbz/p5LIL/8AcY44fRqRz2tptxtt3krXBaXlKLeOcL6nYq8CbidZ2FRADVIta+\nby8EZjf9O4h4rZ6Be7+Itd8bSOYMbKV5x4dRCsyghXn6KW9fQNqcI9ynCQQ0rVKCue97u1a1Ihs3\n2P4N/3Q2RWVcYSYShKKmP/7D7cqQffPPYrazOFodC1NodN52O5233b7sqTyzm5EQQjyb+fF3zxyD\nl33Tz1A/coT66FHiu7aS6PNuRLWnRzmpF4KxDblxwu1tZ9bkmD0YJpjLEe7tWXjJZBgM/8EnMCLR\ns4YaLBfEXUiGYbBqqJNVQ8+c4w4a1YUEM72rkoyshVJJUy4rjh14mnq1hmmaDG1aR27gJx87fyk5\nZ8HcAw88wAc/+EHWtfsXr1+/nve85z18+MMfxnEcstksv//7v08wKOmNhfhxOeUyR//Tf/QzUI79\n4DEa667zMlB2XUYweQ/OyB7c4d1gLvo3vwgDOdd1MdoP3vVqjYOP7qNarqJdl93XX0UkHiUUgmQq\nyGT7raTdKLHtZWnAy471xN/uRWvIZGN09KTJ/I8/pDU5SffkBHt2bfO7jEwdnmT+dhHu6Wb9Npdm\nE5pNmChP4NpN1MxJLF3HbfemU1MnCH/p42jDQqeyNN7wMT/wUHNnQBlekgzzJfpurFbCmDjqBWb1\nMvbu2/xVgbv/DPPYXlSzir3jlbSufxsAym4QeHAhs2n85a8j0JHAssDOJal7jayEnTzrtrj+WI5j\n6RStaW+i9ZjK05n1HoAaq3uxr7hyYeyZ6/qBd89734cRDJ7VOmaEw3S88lXn5JIIIcRziYysJTKy\n9uwVhiK2bTtUSqhUJ8pYuGd33voaOm9/7VkBG3jTLax019yylt0vG2TsRB4rYJJMQjKpKRVq1NuB\nnuM4xOILcUN+epbxE6e9ZCqZDqLxlX8dXohz+vRxxRVX8Md//Mf+7x/5yEe48847ufXWW/mjP/oj\nvvzlL3PnnXeeyyoIsaJprWkcP0750YexZ2fpefd7AWgYccxVwzgnDgNQfuRhmn3efC26o4/GO37/\nogzc5o2fOM3M+CTVUoWO7gxrt3nd0yLRAJXiwvTGyUSJjVvDKAWWjnH0gKIjE6Urt3SS7df+7M6z\njhHozJzV7a3rDW+i4xWvpDU5iXa9LiqhEARNm4lW3S/XvyVLYrsX6OXz4+QB5dqo4hThVJym7cUL\ngXv/DvPYo944re23+MEKTgvjxH50qtvLdmitkJdW2oV6xUsKYjfRuWF/VeDfvoCaO4OqFrB334az\n0RvrZUwcJfS1P/TLuTtvIRAJYlkaTBu36WVxjJKnc40XmJk6wfG/XDhsTyJPbNCbFLswkMHJZjGT\nKYK9vSweC59713tQVgArlcJMLoxfDA0M0Pvvf2HZUzIj0mVWCLEyREbW0v/B/0g2m2BqqrRknXoJ\nJAaJRIOMbFw6VYrrajZszXH6RJ5KqcHlVydBuZTLcOboNLMTU8xOTBGORdl9/VX+ds1Gk2Bohdx7\nf0Ln9ZPxwAMP8PGPfxyAG2+8kc9+9rMSzAnxHEoP3s/4Z/6X94tStK55MyW3g1YLzMErMB0LZ3gP\n7vCehY0ukiCu1WwycXKMSqlMtVhmy5W7/S/WeqVCYWbOK1cvMzLiEg57GbD23RemmK8TSwQxleOf\nztDaLt7zK9dhmi+8q6NSCiuVxkotne9GWRYj//PTuJUKzclJgt3ezSQYhJDRxEwkcUpFgtksQ5sA\nXFotOPmlSWxAaZdQMoIVgUYD9Owkobs+AYBG0Xjr7/qZx4yxQ1AtoFM5dKobgmHOOcdeaDl0bMz9\n3/W7ONo7X4XOrALAevRfCdz7dwC4yW4aP/cHXp0NME8fQM14TaOx1iSRnPYCLSvB7KJDbRws+AlA\nJntT5J8ETJOg5bIQfwXJvPYOjHgcK5ki1A7kAFLXXkfq2uuWPY3o+g0vxtUQQgixQqQ6Itx0+ya0\n1lRKDYIh717W2QnF2Tm/XP9gmnRaUyopmk2Hh797L8FwiFSmg/7h1URiF1eX0xfTOQ3mDh8+zPve\n9z4KhQIf+MAHqNVqfrfKTCbD1NTUc27f0RHFsi7OgePZ7E+W6Uf8ZC616+80GuQf3cvM/Q8SHxmm\n76deg+uC2nk14+ozXouJ1rQOPEl8j5eNj5veALzhvNe1o2NpN4biXIG56TmK+SLxZII1G7wUy42a\nxUOHFtK4Zztt1m7sIBKBTCLD2KgXGDTqDYaG4n4r253vvYpEMkQkeiHeqCVhzdK079nX387a19+O\nXa1iF4uEF3325vq6qbZqNGdmGd49SPc13rWZuq/AoXYZhWZ45yANN0SjAdXvfR93nzfAy1h/OcG3\n/DoAWrs4P/g/qI4eVGcPKjuACizNMvbMa6/tJnriOLqcR5fnMLddh2qPS7Pv+QrO3m+jywWMoS0E\nf/aj/nEa9/y195kC0rsuJza0AcuCRrGb6Xu9fVuNApddEyUYVJgm7P9eJ4V2MJeJVBnZ5mVLa/YO\n4O7cQSCdJpBO0ZVLEezwrlH6nXfCO9+KFY+dNWg9++63/xh/lwvvUvvOWUnk2l84cu0vHLn2y1ja\naMebfu4yjh2eZvTpaXZe2c+GLd596an9UzygNY1anclTZ9i0cyPxpHf/nJ6Yxm626OzOPGvL3Uq7\n9ucsmBsaGuIDH/gAt956KydPnuQd73gHjuP46+cnE3wuc3PVc1W9n8hyzd/i/LkUr/+ZP/8zSg/e\nD8DcoVFO5W6gXFa4rkFweDfaCuGM7KE2sA3mKue9flprquUKJg6TZ2boHx7054I5vO9pJk+dAaAj\nm2ZkU5ZwGDo7NZFogFrVS9VcmJ3CtsOUSpDuivCqn95CpjtGMh1henqha6UyoVxpUK40zq7IhWbG\nKC367HW//4MAuI0GWin/c1lpOkQ3baE5NQFaE+9oEsdL3HG8fIr5M7OyGZQq02xCfXKGwHf/1t93\n/U2/ge7xxlQYx/YSOfYArfws7pqd2LtuBbwxe+H//ev+NuX0EEa2n0AAjHoRPTfhlavOEo+XCATA\nsmAsmcQpeOmhI3qcVKp9TpkQRiTijz0rzs1htHNZJ171GuIvvwUzmSTQmVn0P2jS/YFf9utQsAF/\nXbtZtXH+P7MvpkvxO2elkGt/4ci1v3Dk2v9olKUY3phleGMWwL9mUxNzWAEDu+USiwdZvcalUilT\nLsOhfYeYm/Tm/+tZvYrhzUsnch8cjF2U1/65AsxzFszlcjluu80bCD84OEhXVxf79u2jXq8TDoeZ\nmJigu7v7efYixKWlNTNN+dFHKe99hOyb30J4cDXNJrhrdkI7mLNPj1I/Pesl1QCat/3ST3zcxePL\n7JZNKV/AbrWwWza5VX0Y7a6Lp4+dYG5iGttukexIM7zF69bmui6P/eBBf3/d/Rk6M0kiEWgUo35i\nklq5TH+/6x9rzzWrMS2DTDZOZ9dCq1I8GSaePA/dC8+TZ87TE9u8hdjmLYCXmXPJui1bsOJxmpOT\npEd6SA94L7aqzXFOLSrXvb4bO6C9BC0n9+I+cS8mEOjsJN3ldXE0MknmFm0zkpsjttlrWZw7mWLq\nvnb9WlU6FyUPS994E9pxvNT7i7ouRjdtZu2n/nTZc5QujkIIIVaCjdt7Wbclx+RYkVq1RSymiMU0\nXV0u38kvzHOXTIWYnzq0Vqly+PEDBPRqNu/sfI69X3zOWTD31a9+lampKd797nczNTXFzMwMr3/9\n6/nGN77B6173Ou6++26uu275cRFCXIq043D847+JW/VaKSbueYTG5Wuo1YDUTkKZAZw1O3GG93hp\n8J+5vdY4toNhKIx2Zr5apUphZq49+bVeMi/Lwb37KeeL2K0WfUODDKzzuj/Wq1UO/PAxv1xndxeR\nWBjTBLtRozjnfdFFY0GyWW+uLsNQPGQo3Hb+/0S0xNBQ+y2Rk6KY7yaTjZPpXtoNcNueVS/GpVvR\nntm9sOuO5bvGmqkUHa98Nc3JCZxigdxQDKW8632qOcF8P4WwW6C/31uudZja8AhGNOal10/E/f0l\nrryS2NatmMkUxjOSgGRuX37y9uWypAkhhBArjWka9A4sHR9vt1y27unn9PE5ps6U2L47TabbpVKB\n/Y/MUcoXKOYrgARzANx000386q/+Kt/+9rdptVr81m/9Fps2beLXfu3X+OIXv0hfXx933HHHuTq8\nEBeMdl3qR45Q3vsw9aNHWfWffh2UQaVmotbuwN73ILYZpPjEIcxNrtcqFooy8cpfoTA7hz3dIlQ+\n7gdmWmt++N17aTW8bnrrd26lq9dr1S7lCxx9wptTzrRMhjYMYZpgWRrXbtKoeRkag8EWfX0a09TU\nOk0e/78L9R0ebtLV7fUbL02ZnD7mLTeUTV/fQnfo7t4EsUSIRCpMpnshaMj1Jcn1bT4n1/KlJNTX\nT/bNb1l2XerlL6f/VTdRVSECmS5/uVKKwY/+l2W3sRJJSCSXXSeEEEK81ARDFlfd4GVpbtRtgiET\npSCRgHLeS+W1aVvXc+3ionTOgrl4PM6f/dmfnbX8c5/73Lk6pBDnVavpUCrWadRtmg2b1SNet8ja\n4ae573/9I8VwFy1jgNz/eZyuTbuxbWjk9vBwdaF/9s5qlWi7NaUwM8fpo8cBSKSTrN82hGl6LWOK\nhaAqmWgyNORimhCxTA4/7i13bIeNG20/2+Phxy3m2jmGTKNFtj0nVzMWYNVQB6GwRShsEQwuJBna\nsK3HXxd+RgKSn377bunHf4Ek9lwu114IIYR4kYTCS0Oga1+xjuENWQbXdFIo1p5lq4vTpT9phRA/\nglKhztxMhXrNxjTVknlOvnPXAYr5Oo2Gze6rB1m3OQfAiUPj3H3X0365n//wDWitaHStYzY+wHTE\nS0UfPDXO6su8oMy9Ygcc+4G/TXd3k95V3txbTtXkdDv5o6FarFu3MNYqGrNoNloEgiaRsCaV8pZn\neyJs3N5DKBwgFLaWJBZ62U0jXH3jiBewhRb+1YMhi596y45lr0O6M0q689JN3yuEEEII8Uzzc9wt\nfl5aKVZejYVYRGtNs2ETDFn+eJ8zJ/PMTldp1FukOiJ+YNao23z1b/bSqLeo121uf/N2elZ5UdHh\nA5Pc/z0vkkp3etu4Ltg2nDlVpJj33tJMjjdIdSkcB/L3PgQs9McuzjVIdoRJdxjEujuYbjeihPr7\nGBrygizXNbECBqGQRTBsEY1AtB079Q2muPzaIUIRi1h8aUKN179jN1bAPGuOtY5MjBtv27jstUmm\nZbJkIYQQQohLmQRz4qLmOC752SozE2WyPQk62hkRJ8aK/PPfP06zYaM1vOtD1xAKeynUn3zsDIf2\neynZh9Z1MTjSjW1Dq2kwPbmQAv/MGRtHKWwbiqWAv7xStnn8cYP5Ri7XXqjP7NOnOJMZIhiE6PqN\nRO85iOU2CDhN3LFRjIwXWO38qasZztcIRQIkFmVtNAzFe3/l+mXPtac/RU9/atl18+cmhBBCCCHE\nPAnmxEWj2bApFepLkmv83WcepJj3knhccf0aNkVjFApQrRo06gtR1tEjDqFIENeFam0h8JmdsXny\nyfnWLAPTNP35DqenbIxgO3ufESGV6cAKBAgEAyyeBnHd2D1QmibgNIhHNzO4/mVEIqA3rCex9y4i\na9cR33U1wf6FzI19g2n6BpdmURJCCCGEEOLFJMGcOO+01mitMdop248fmeEH33yaYr5OMGTyrg9d\ni+MoajWIJmJ+MHf06QpWzKCjA6amF5JzmKbJ3KxDLOkFZtFEiu5VDlYgQCS2dPzX1qv2YFomVsDy\nJ70GSHck6ewNYh59GGP0cRprP44Kx4jFNB1bR6h/Zz8AxtHHCIdcwEAZBqt++VfP5aUSQgghhBDi\nWUkwJ86bh+45xplTBWYmy1x+3Rq27u73VijLD9iaDYeHf9gkEPTGe1nBOMFwmVgyQaJjoaUrGA5x\n+U3XYgYsPyic19Wbo6s3t2wdYsmFVr/FzW+qWiD0j7/n/95deozs7quwLGgE9zA9e4L4rj3Ed+w8\na94wIYQQQgghLgQJ5sSLqlSoM/r0NNOTZarlJq9583Z/3anjecZPFQA4frRMKGFQr0Or6U0+rZQi\nEo/RqLX8YG5g3TCD60fOOo5SikAoeNby51UrYY7uxTzyMLgOzdf+CqYJycFO7FVD2KdGvf2P7sO6\n+SoAQgOD9H/ggz/+sYQQQgghhDiHJJgTL0i5WGd6ssz0RJm+gYXxYbPTFX7wrcN+uRPHW2iC1GoK\nZSYAL5jLz9aoVr0ypmWx49oriESj3gTai8xnqPyJaA3t/ZhHHiL43c/P75w1PUUS3XGUgvwN19M8\nM0J81x4i69Y/+/6EEEIIIYS4CEgwJ56T47jkZ6oU8zXWrM/6y7/+5X3MTFYA2LpngGgyTa2mqNYT\nS7Y/drhCustLs5/t7yXZ2UEsGScUCS8pF0vEeTGpmVOYR36IefRhnPVXY+++jXAYElfsovy9v/QC\nPK1RJw+gcpcDkL7xphe1DkIIIYQQQpxLEsy9ANVTpykfOIKyLIxojMjwsL+uOTWJQoFlYUajGKHQ\nc+zp4tKo22itCUe8bJAnjs7wL1/Zj+tolIJ3feg6WrZJva6IxBPQDuZOjpZJdnstalqHyA32E4lG\niCUTxFMLwV08tfT3F5V2AeW3wAXu+xLmsUe9nyMBVt35arw/RYqJ619OIJslvms3wVzPuamPEEII\nIYQQ55gEcy/A9A/uZexvvwhAaGCA1b/53/x1p/7Hf8eemwWg+61vJ33jzQBU9j3O6U/9T5RloSyL\nkU98CmWaAEz+7V9TO3QQZVnEd+2m87bbAXCqVSb+6nMo00IFLDKveS2BrNc6Vj34lLeNaWJ1dJC8\n+hq/DuW9j6IdB2VZhFYNEMhkvP3Vatgz0ygrgLJMrEwXSinuuftpThyZoVioc/m1Q1x27RC2DYYV\nwXW8JCFaw0MP1Iinkt55R1MkO+rEknESHQtzoymlGNmy4cW/6MtxbIyTT2AefRjz6CM0Xv8RyPQT\nj2tCl+2h0g7m3LFRLKcKeJktc2//ufNTPyGEEEIIIc4hCeZegEpxYX6zhm1x9KjXGqQU2M2Fdfmi\nRXVMecunHXBddLOJbrWYnjUxDIVhaKqnJ2iePAGA0TtIsOztyy00KP/wIX9/8etuxujMohRUDxxg\n9q5/AiC0emhJMDfxhc/jFLyxad1vfyfpG15OfrbK3rv3Mv7kKLVAkpcd/wrrP/NZAIqFBsWCl01y\n9Bv/RuzRH9C8/h1oHcMwDAKtCgmnRPAbf4q65W3ozn5yA/30VY9jHr4HfcJCH89hX/VGvw7WQ18F\npwlmAGdoBzo75F2j0jTGqafAtNCmhTu8x29NU3NnwG6AYaEjCYi2g0TtQqsJpuWXnV8e/NdPo1oN\nADIzD9FzfS+mCU52B5OjVxDbtZvY1u2Y0aVTFAghhBBCCLHSSTD3AhhdfThDO8Gx0R091EoLAUYo\nlgEziHIcyq0ozpS3zphxmO9wqQ2LsbH5RB+KYMXBbP9WrASZOeKtUwWXxSPLjh4PosveOuuMw/zU\n2LVmgP37DZSCUr5AOTBEuTtGZ/UMasZi5oiiOOdwYLQJ0T4AquE09bo3l1s8GQemUdpFYxAOaZJd\nGteFW27ZiPMnH/brYIZtiGu0Brd4Go4+7J1TzzBc+8b5oWhYj30DVSt566JpnPlgbnKU4Lf+3Fuu\nFPUP/KW/78D3/xrzxOMA2FtvonXjO70VlTyRz30IADfeif7lP8eyINkZQG/eRv2xH3r7O3mIdmMn\nZiJB78+//0f5cwohhBBCCLEiSTD3AiSuejknk5cvu67xMx9fdrm79nJq7/8suA449pJ1rRvfRatZ\nAceByMKYMh1J0HzV+9vbtNDxDm+51tRyG5jZEaZqw6pMBMfxtjn65FEK6V3eL8kMa3IZ4p2aXC7K\nA9+C+ZnV6l2riUQgEoHdV/XQte8uzKcewcAltvVVdPd7JZsBl9FFdR0YMgn2euumHmkx114eTVgM\nbHH9cod1C3fRNvGtLlpDqdJiqr3csAKsXev6071Nh1o0569x2iQ24OK6Cnu6SXF+m/IsqyOjsLoL\ngPIN11Lr8ca/hYfPnsJACCGEEEKIS5UEc+eLUl43QdOCwNKkKDqVBbJnbxMM01p7BbVyFcM0iIS8\nroLF2TmeODQDeBkge7dfQyqliURgblWMwowXYrW6VzNwzab2zkxedstaEskQme44idQN/mESqTBb\nfvUDaK3xo8K2QFcXQ7/939GOjW7ZWF1d/rrkNdcRXrsObbcwY0uzUXb+1Ou8LqWOTXhgld9iFs5l\nSFx1Ndq2UYZJLLawTaU3h6pX0I5NLNdBZyeApmk7lIJBtON1VbVHn8LqvhaA+PadxLfvfP7rL4QQ\nQgghxCVGgrmLiGPbmNbCn+Tpx55k+swEWmt6V69iZOt6wmGIR2M88eDCdh3JIoNDXpKT+voOXKdF\nJhcnm1uaOXL7Zaue8/hKeVk4lyyzLII9y2d8DPX3E+rvX3Zd56tuXXZ5ZGQtkZG1y67LveOdyy4P\n9vSy7k/aXTO1prs7ydRUadmyQgghhBBCvFRIMHeB1coVjh86SqVYolGrc+Urrse0LEwTQmHltZYB\nrl1i61a3nf8jQGc2RjhskcnFicUXWvpWj2RYPZK5MCdzHrwok4gLIYQQQghxCY3Xv8UAACAASURB\nVJBg7jyZnZiiMJunUizRkc3QP7waAGUYzE5M+eWS8RKrh1MEAkAjzqmjkEiGSKXDSxI5/sy7lx+z\nJ4QQQgghhHhpkGDuReQ4DuV8kWqpTKVUZnjLBgzDyz45dWaCmTOTAIQjJv39g0QimnA4yP77TZoN\nB8NQaKdOIOCl5F+3Jce6Ld2EwoFnPaYQQgghhBDipUmCuRdAa029VqNaLGMFgyTbk2bXK1WeePBR\nv9zaLavo7o4TDmsaxRgzZ7zllWKZrq75vJKKW167mVg8SEdXDNM0/O1DYfnzCCGEEEIIIZYn0cIL\n8O2vPczo015k1tWbI9uTIhzWdGej7LtP4bpeoBYJlOjt9dI1rlnXiVKaru44me7Ykv1dymPchBBC\nCCGEEOeGBHMvQDYXY/Rp72enWWLjxvkZ1RTDG7KYlkGmO0Z3X3Jhm54E2Z7E2TsTQgghhBBCiBdA\ngrkXYHBNiscesujKxcn2xNFa+1kWX/G6zRe4dkIIIYQQQoiXAgnmXoBN23vJ9sUlTb4QQgghhBDi\ngjGev4h4JsNQEsgJIYQQQgghLigJ5oQQQgghhBBiBZJgTgghhBBCCCFWIAnmhBBCCCGEEGIFkmBO\nCCGEEEIIIVYgCeaEEEIIIYQQYgWSYE4IIYQQQgghViAJ5oQQQgghhBBiBZJgTgghhBBCCCFWIAnm\nhBBCCCGEEGIFkmBOCCGEEEIIIVYgCeaEEEIIIYQQYgWSYE4IIYQQQgghViAJ5oQQQgghhBBiBZJg\nTgghhBBCCCFWIAnmhBBCCCGEEGIFkmBOCCGEEEIIIVYgCeaEEEIIIYQQYgWSYE4IIYQQQgghViAJ\n5oQQQgghhBBiBZJgTgghhBBCCCFWIAnmhBBCCCGEEGIFkmBOCCGEEEIIIVYgCeaEEEIIIYQQYgWS\nYE4IIYQQQgghViAJ5oQQQgghhBBiBZJgTgghhBBCCCFWIAnmhBBCCCGEEGIF+pGCuUOHDvGtb30L\ngGKxeE4rJIQQQgghhBDi+VnPV+Dzn/88d911F81mk1tuuYU/+ZM/IZlM8v73v/981E8IIYQQQggh\nxDKet2Xurrvu4u///u9JpVIAfPjDH+Z73/veua6XEEIIIYQQQojn8LzBXCwWwzAWihmGseR3IYQQ\nQgghhBDn3/N2sxwcHOTTn/40xWKRu+++m69//euMjIycj7oJIYQQQgjxkjBbbzFarhGu1Qm3XAbi\nYQLSgCKex/MGc7/xG7/BX/3VX5HL5fjqV7/KZZddxp133nk+6iaEEEIIIcSK03JdSk2HUstGKRiM\nRwDQWvOFw2coNm3KLZvXDXWzKR0H4Fi5xleOTfj7+PD2IdIhL5g7mK9QtR16oiGy4QCWBHmi7XmD\nOdM02bFjB+9+97sB+M53voNlPe9mQgghhBBCXDJcrbFdTdD0Aqm67fDAVIFSywvabl3VRToUAOBb\np2e5Z3wOgIFYmF/YPIDWYNuKk6U6FccB4NS0Q6KssG0o1Raer4PKoDIdwI1AOAz3TeQ5VKwCsLMz\nwZtHegCwXc3hYoVcJEQ6aKGUOm/XQ1wcfqSWuY6ODi677DIA7r//fr75zW/yu7/7u+e8ckIIIYQQ\n4vxx3RauU6dZd3kpTUfccFzGqw1KLZtSy+HK7hRGOzD62vEp9s+VqLQcdnUlecOaHAAu8I1TM/4+\ndiTSEAzQain0osBstmqzf79BO34j4FqA98tkwaG74h3H0kEGjQShYAC76TA9vXD9Tzab/s8xN0Sh\n4AV5s06Tv3r6DAAh0+A/bBmksx1QFpo2IUMRtswX92KJi8rzBnOjo6P89m//tv/7Rz/6Ud72tred\n00oJIYQQQogXznUauE4N165hBhKYAa8rX6s+RXlmL65dw3UbdA290W/NmTzyt9SLTwNQ6hihY+it\n/v6mjn0J04xgBhJEO7YSCGcA0NoF1EXXIuRqTcV2qNsu2UjQX/6dsVkmqg1KtsOeTII9WS9b+1i1\nwWeeOuWX29aRIKRMWi2oNFxKLS/4mirZHDumcBxoNi0MvKAO4Mgph1a71c51A0SxCCuLmAr4gRzA\nHtMLBiPKIsxCoJVQQa62+uhIxZibq/jLtdasMVIUdIO8bmCWQ4xWveMc1wtBnu1ozKaFY4FpwtdP\nTLFvrkw6aHFZNslNfRl/f64G07i4/mbihXneYK5er5PP50mn0wBMTEzQXPR2QAghhBBCnBtau9jN\nvBd8OTWC0X5Myxt/VSsepjq3H8euYVpRMqtf52935sCf4rSKAHSsejWJ7BUAOM0Spcn7/HLlUgtX\nB7FtRbMV8pc3nRTj4wrLgoDVoJY/4K8Lxlb5wVx5+mHyp7+JGUgQiOTIDr/ZL1cvHUMpqx1MJlDG\ni9dCpLVm/1yZcruL47bOBL1Rr/6PTBf5P8cmcIGYZfKxXcP+dgfzFU5W6gBkzRCDeF0cy7Wlj8QP\nP+GSNrwWLtsO+MtrTU2xuBAEbTYzmBiEMckYEX/5gJFgIJhYtu5di8r9KJRSbLO6ll1naoOsilDQ\nDaIqwOhR7xoHAnCi7j2v55s29aZGa1AK5ho2n9h/nGw4QE8kxKsHukgGL/0hVE6rjNMq4bTKWKFO\n/zPcrJ5h7vTdOHaFoLoD6LuwFf0xPe9f7hd/8Re5/fbb6e3txXEcJicn+Z3f+Z3zUTchxEVAuw6N\nygkalZPEw3uA2IWukhBCrEiu06RVm8B1ajh2jVjHVj/AKU0+QK14GNepEYoN0LHqVQBo1+bMk5/2\n99G99m2YCS84adZmqcw+DoBhpcjnvTFZjgOOjgBeMDc1WWNs1sC2ATvK4hDj2NEGWoUBiLgRQhho\nojTsHHMTXtBi6DKpRdscP5HCDBkEAhqzUUZrG7s5h1YhGg0vkDAMmDn+VZxWAYB0/ytJdl/VrvcE\n5amHvCAvmCTSsZ2qoym1HMotm8F4mLDpXZeHp4s83B6X1hUO8HPr+wEvwPmH0UnqjtculrYCpAhh\n29Cqmn5rWcV2OHhI4Tpe0KYbC4++k0WHsap3ji0doFOFCWMRVibWopbGdWaaYTNFGBNTLe16usVc\nPsg6X1YZCVYZCbTWtPyzhmZTY7jKbzl0ZsPsKxqEQnBGNXG0ZrzWZLzW5HVD3f52Xz8xxelqg1wk\nyPpUjI3pi/eer7XGtasoM4jRDrwbldNU80/gtCoopcisvsMvP3n4C7TqUwCkel9Oquf6dgurplE+\nDkCllCcQv8SCuRtvvJFvfetbHD58GKUUw8PDRCI/3hsFIcTKVCs8zfToV9Cu93avZ2AL88FcZfZx\nqnNPEojkCMX6iaTWX8CaCiHEuae19rsT2s0izeoYrlNDa5dE1x6/3MyJr9GsnsF1aiS6LieZexng\ndXGcePpzfrlwchhTJXAcqFenqZeOAOC4EewphetCqxVEY6LaY6xOHq/TwMB1IeBG/ddrjl3j+PGF\nQCPuJjGp4RKh2YrQsL3lSqepqlvQRNCE0YT9bWrq1dS4DZSiIxKDutfVzyVOWf0MBkWULtGwU+0h\nX4qoW2W+Pa/WSPLUU14dDOUSsasE8FqDSpUkTx6vMGXXKDXmSNdKbDUeAWWSim/l9x4f9evxxtB9\n9EdcIqntzNWHGC17LWmubTN2soDtJrAdk5C2qOPdn46MuYQmvGNXXO/xNohBWFkUqg4h5S0bMdP0\n6wQRZRJTCy1uAWXwisDqZf/uYXXxt1oppQgu6rKplOKVgSFcrSnpJhFloTXU6zDptPxyMQIcetIi\nEtGEw3A4X2e8UedYqYZC+cFcuWXzD6OT9ERC9ES9QC9kntsxla36FM3qBI5dxjAjxDM7AO//cOyJ\nT+K0SoCma82biKY3AWA3ZihN3t++CEGsNLRaXiDfcuKAF8xNT1Y4MeX9HymdIN0+ZrVcJBU/p6f1\nonvWT+dXvvIV3vCGN/DJT35y2fUf/OAHz1mlhBDn13zrW614mGCkh1jnNm+52eUHcho4MtpLvdEi\nEgHKx2mVD1ErHiIUX70kmJs7fTdWsINgJEcg2uu/MRM/upl6k6l6k+l6i75oiGzWe5fecFz++YR3\nM1IKru/pIBP2xoM8XahwIO89fCUDFi/v6/T3992xWWq2g1KwOR1ndcJ7KTddb/LwdJH5d9A392cw\n2w+r+2ZLTNaaKAW9kRCbOtpjblyX/zuRb2+j2JFJkGp30TlZrnOiXAMgFjDZmUn6ddg7U6TheONr\nVsfD9LS7RBWaNocLFVAKBezMJPzEA8dKNYpN7ym0MxRgIO49eNqu5ql8GW+sDqxJRIi2B/l7166F\nwksIMJRYeAF5vFSjpTUK6AoHSAW9z2bNdhivNVGAAgbiYb8Ok5U6p8t1UF6XrY52cgFHa+/6tPfd\nGQr4We6qtkOl5V1vSyk/w513vi1c7R0nYpn+A5HtutTarQwK71jzgUPDcXHb9TYN5c89pbWm5Wrm\nGxFMpfx6a63R7X1dbOOZLiStNdptoowAqt3K0qiOecGXXcMMxIhndvllxw9+Bteu4No1MkM/7T80\n1kvHmD3xTwAoM0Q8swfbBseBRnUGuzYOQKlQoqq9h0mnGWXxX+LggTq29tq8wm6U+U9qo15jemy+\npCJBD+CiidC0IzjtVTYDVNTr2oHZ0hftZeOtLEerKA2uXf7iqGfpBqnCtNg0Xx0ACm6DKi1q+nrS\nXE+X4d0rZt0699lj1LGxeSPvMH9AmDmKhRSPOWWOu0XAZI3KsZWnwUig6xYK7z4DUGq1aDinKdXW\nUHEW/ncqTgN72nsurahfop84XYZDlDn6eYqgjuCQpUOt4o2BdWe1ogH0GBdvS9O5YChFSoWWLNtg\ndvpj8GxcXBcqFUW5rJlqNfxybj7EUUcRicCkbnIgv3CP+cjONf5312MzJUotm1wkSG80RDxwdnjh\nOg2vm6NdxjDDBCPeuEGnVWbmxNdw7QpOq0z32rcRCHutnZXZxylO3AtAMNpHMLGDVgscR+E6LvOf\nmMnxCva0otVS0Iwv9B/STUaP2aC8e2TE7cKigiZOy83gtj/Lmjgl9XZc4qzK9gL2T3rZz6tnDeaM\n9o3CNCUDjhCXuunRL1MrHATAigxTqG+nWFQ0GhmSZACHFhtQdpBSyaZUgoQ74X+BVOo9nDjhfeGH\nAuWFt2JAduStRJIjADSr49jNPMFIDjOYfsk/YNZsh9OVBtONJqWmwytWZfx1Xzo6wYn2uI6rs2l2\nDUCzCVVb88Ppol9uRzpJ0vQCuxOlBvdPel2ausNBrsstBHMPThUotIOiZCDAQCyCUt4ktf92Zs4v\nd1Nfxn9Ye2ymxJPtG/fuTMIP5pqOXpLBbXU87Adzh4tVvnnaW5eLBJcEc3efmiHfrsPtg1k/mJuo\nNfjK6KRfbntngvlx+feOzy2pw0DcS8fdcFz+5si4v83Pb1zlB6iPz5aX1OGDWxfetn/x6PiSOrws\n572PPVmp8/lDY365/7pnrV+HLz91mkcnCn4d3jjs1aFuu3zqiRPL1uGBycKz1uF/HTi1bB2Olmpn\n1cFq1+FLR8eXXIf5OlRtl9/Ze9Tf5n2bVvnzWX3vzJxfh+5IkA8tqsMfPj5KvtkCFK8Z7OKqbq8O\nhwtV/vdhrw4Kxcd2rfHns/ry0XGeKlQAxY7OOD+12uuaVbMdPrn/eHsLeNu6XvpjXtB9/2See8bn\nUCiyi7rIAfzFwVMUmw4KeHlfh/9ZOV6q8dUTUyjAsgzes64fq/3H+MbJaY6WaihgfSrKDbkIrlPH\nsVJ8oZ3Rz7Ur3JycJmOUCUb7OaxX88h0Ee22CNUOcZ16EHDp3fQLfO2MS8V2aNWnWNd8hNXGGYLR\nfmaszXx/fBatoVVZz03qHkylmZup8d3xPBPNOjgR+twRNhtH0E6Dh/c6POrOePXWG9hjzJJSZaqV\nOkcqVcbcMgqbFFexw5jGJYLrWjzhztDUDgYjDKhVZI0omhgl3eSYU2j/Le5gi9nlB+onnCKldotU\nWq2j3/j/2bvTKEnO+t7z3yeW3DNryVp7XyV1q7U0m2SxCC0IIZtBgI01fcf32NjYY4bl2hwMNmPO\nAAdj7APHyzH2uR6Dzz1GYC7XlubaXMAYywaMwYCwEBKiV/Va1bVXZuUaEc+8iKzMKnW1xKLq6pB+\nn1dVkRmZT0ZlZcYvnuf5P/H/ZmAjDke9/+ftTolcp/dpJqozbePelhQuO9ze/+aJcIGgc2I8ZLL0\nO/H/5nRU51g0T92GgOVmf2t3n6+F55iz8Yn/fmeQIXcY34eCaVBd7PX8eJv+E+V8ilEPHj8/zeOd\nf/VmapzS0Asxjk9fn2Eo48e9S43T+J2T6bFNBUZKWfYtbcapnSGa/J/dx+0vF7ip7RMEBrf2ZXz7\nMFhosY/Q+VnAgA0o2Y8QUcBSom5eQmTi962xVQxtIoqQgJ63p1vKuAyb3AXbX+Hv7BZaKdsclYqh\nUoHHwl69jLRxmT3rUUsHZLMR/35+gWOdi3gHckvcWTxDGFQpbXst35lbYiybxpm8n2AxPs/ID15H\nefur4qUaQrdbdAdg+nyVyB2m3QZbK3T7Guu1JR59tBfOi1EBjyoRGRqNiGbnf8PYEWrmNiwFIgqs\nrMhad35y7YNhXALi8xTHzQKVH/ZwbqiLvntf/epXAzA+Ps5rX/vaS9YgEVkf1kY0q49TXzxMY/Eo\nQztfh58p02pBYHYC8Ydsu/44841290pWxfxifMXXGDIrwteSuRuPCVw7QTvcRTBnmJsDz55fNR9j\nbnGMZhSXUK7N/gfVqa8BkM5vZfSKX+jer1WbwEsP4rgpnmlOVuucr8e9bFf05dhVir9AH6/WuyWl\nAZ7fNwChQ7NpyIQpIA5zJ6bbfOc7MDfn0LDRqsc+ftyh0jnZngx7f59mEx5+uPcl1l5Rt+rcWcN3\nOsORzkWrA/V3HnK6J4wL7d5tc3OGhx7qrK30hDYcO+aw2GnDRLCiDY3VbWj1zu+YOGf47rSDMXA2\nWH31/LHv9dpQqfceb2HB8L3vxfs0otVtOHXKoenFjzPd6O3TasLhw/F2YyztFW2YmYaj1fi+51qr\nj8OJ470ers4Ir7g9VcPx4/H2+uomMDFhsDPxbfO13vZ2C06e7D1+sOKi7/wcnOo893RzdRvOnDHd\nEFNf0YZa3XC202tTD1ftwsy0IdV5TZUV5yNhAJOTK45LYAktgGVxESZt3MM5W4dWtNw/Ypmedro9\ntYt1Sy2IX/RizTI9He9TC2Gx3WvI3IIl27m4P1uJmOuM73Ns/BkB8cWH87U2lc7BmF6oMxs1gYjZ\ndoFztV7vwOSx/4Vj66SK13GumusWr8jWHuHM+c7nyY7f5Hil3tnDYbb1bVJmhmz/9cykNnG4sz7X\nIAPgRZ1j1eCxuYBKGAA5xjqBqFFvcPRUxKP1aufxNnOrG/cb1WsNTrbrnAo7vcLObvY6RazJYkzI\n8cZCZ58hdrk/i+MUsOSYD6ocieYB6DNXste7s/v6jrWPUuuEl7w3QtkZAGApWuLRaLZ7v/0r5mad\njCqcsXH7djilXpgj4qFwunu/YZPrhrlJW+M7ndv6TGpVmPtOON1tw3PcEfo7AyfrtDkeLXaOKgwM\nRKRScVGUwfMuywUXMwMB1+yKj2stcPnbB+PtrjGkCiHFzrC1K/tzZDxDwfcop336+/Z12/Br1+wA\nIArHCNtXErYX8dJDeCmPUsqj7josVIcI2xWsDdmy1SPumbFMHl6k2flzFfuLDA1EtNuGVqNCOD2L\nS3wcm85NLL+903yLrP1ifNzsVirOL3bbkrZfw+IRUSJkHGsSNu7uR2SMIY9P3vhsthEexzuhN2SL\nc5A0Lgu2Scp+F2Y/RZMWc+ZlnGmPdh+j2HiMais+n5iZXuRvTi1f+DvIIfc0JbPE4vwS31toYyND\nwWYZWDGMeG52ibZZXqphlDT7iSgQmdKqtlbNz2FJXxDErSnR5MXrdIQuT095KeIf/uEfuOOOOygW\n167IIyKXryhs4rjxl7K1IeeP3gs2/sCcPH2UajBMswmO3UuBf6PNXgKzh5VXsuwaV+4AIjNCixEw\n167eziA18zJcO4HDInMzReh8lhft+e6HTmDLVKuQzYIxbSYe+68AeOky5e2vIp3fEj9/FIBxL/te\nvIVWm+OVOtONNs0w4ie3DXdvu+/EeSbqcZqKQijbHM2mob20eujLt48EDDhxj0YhzDBiAorGp2x6\nw6c8HJ7XKWttgcKKOR9jJofvxledV86dgLjiWpsIi131eEWT4hp3qHNN3q4aArbVKdJn04BlwMlg\nOydBjnW4yhnoDolKW4/lbDVIhr1O3MuTMd6qctzbTYmmiTcUbLobavzIZYfT+6Jutej2ipXJYhyD\nBfpthmbnHL9lDZtXnmC1XOqdoJYJfcZNHks8H6TWDVaGssmSJ+5lcFo+1U74DCKXIZPFdl5VpdIL\nczkvzUBnmFIq8LuV7JrWoc+kusehXnVY6DQ8DF0KxH+bdOQzN9c7smnrsTxAqFVzme2EuGpkSK/4\nu83O9NoQBQ4+DhZoNxympuLtDWtwMd02zM0ZnE4bquHqADkx0ft95d9lcdEw0VnnavYJAfXc2V4b\n6iuCcK0Wh83lNqw0PTGHcaawpJmPehdnwlaFmROfxlCnbl6+KtS25r5IdeEwIUNM8vOrHq+1+E1c\nY6m3NtFsrz2nKQiaq37vtsjWYY2PDuP49JVaONPO8nJfpPJbKBaH8VIlmn4Evc4CNl39Jnwvh3F8\n/u3IOYhzGaXBnezeGRf1qLYD+HZvnx27B7u9pOfPWjgTb89m4JoDvQP9mW8DnWO7aZPl2tH4tvR8\nxD+vaMN110bdUvLfOWw502nD4ABcuzPep9KKuP+h3j67d0dszce3TZ+z0Ll2lEnD1fvi7dbCZ7/b\na8PYmGXfUMToKIQnXf71e/H2CCiPh+T9+D26vZkB11L0PXYUe/Pusq7DfzmwnaLvknGdVZ/de/py\n7Olb+ztlmeOmcdx0d6hd93FLu7ujPOzyh1FHofxc0vmtBK0K2b6t5PsBLM3qIpO9bMtV+/LgRLRa\nsHB2gVZnkIPnexRzNq7o2bRkgy9gOgdkydxNi+vj+9mjZOw/YykRUaJuXgbdHqGlNcPFZcGGGJZw\niC9ChGa8sz0ib/8GQwWHKjVzF4GJj7HPYfI27gmNyFL0XsBgpg/ft3i1Y5gg/k4r5iu8JLWP6XaT\n840aw63eBYj5sLe8gocl7/iEbKEdDfPtcJoztoqL4UrndVzv5rAUCGyBNgFp4xGYXQSmV4l01Usy\nz67hsk/mB1qa4NZbb2Xnzp34fu+k4eMf//i6NkxEfjTxGkIP0lg8irUhm/a/iWYTKpUUkbsDJ4gn\n2DcqR2k68UlIZMos8tbul9KPIzIDNHnhmidQbbZjAZcJavVRpo/GoTHtTrH89R40ZwjCbHdC/cLk\nl6lOfR0/O0qmuIu+sY294vbduSpT9RbTzRbPKZe6vWwnKg0+dWwSiKPwi/qHaDUdWi06vWzxF9/j\nMwGbFzrrENkUwyZL3vgUTYr0ivkqe90B9roDFzy/Zxx2u/0XbAcoO1nKrF2g6mL7FEyK/W55zdu2\nu6U1t/vG4TpvZM3bxpz8ReekXOsNr7m938lwgzO+5m1XuoNrbk8Zlxf5m9e8bZtbYttF2v4T3tpV\nyoacLLc529bep7iFq4IL/xZp43Knv3PNfS729wO4/SJFFkadPHen9qx5240XaXfGePx0au3iQ1e7\nZa52e+tKrfST/q5ucDUr/llHTZ7X+HsBi7HT+PYoDg0ihrjBGyPE4thZ8vYB0lEVhzoRr+eVfnzC\nlYk+Sz8P4tqIFvvY6/w0250iENIf/TEp4mTtZBd5VWk7xrXQ/C7pTiU5z61zw840VwVbcTxYPPs/\ncZbidg4O1HjNwBD1cIBWY47aye9i3Ayum2WwGPBLV27GAo3qSYai/WRTGfz0ENelC2zJp7EWPFtm\na99vYZz49Oc1O5doR/GRGM/uYCATn+eMtwLu2T0GNg7dfqrQDbU3jfazvzPkuLxiLmTadXj1jhFs\nZ5+BFbdd0Zcj68ZhPOet7om+ZdMgzTDCEg9ZXjaU8XnZ5nLvQsuKz9RrBouM5tJYa9mUy3RvS3sO\nLxkb6O7Tn/ZYni2zrZjmJ8J+wFJKeXgrzgCvLxepdRL+pkKKVAo8Lx6ufff2EYq+S9H3VhW8uH3z\n2p8bxhhGsus7wuKJF/eW53k/USo3zvi+NxK2FwlaFRwvjzHxRcRG2if0i4TtKvlikfL2uJcvChuc\nfqh35WJ4tID14l79dmUaU4uHVkcUqJs7uvcr2HvxOENkczTMi2man4jbapfw+R4RJSxFQoYvPjfx\nh2EjoDcfzNg6Kb6JY5cwVKmbl2E7vVlZ+w9kiKc/tNlG1bweAMd18IOjGOJe7VJhnlTRxu+NVo6l\nzkh2hzoHDrQxnXZPHS9QnweMSzYTcsvW+PslitosTlyF6z8X1ytQtwUODHhM1JtkXZed+9/SuR/c\n99AJaEOIJZMuA0NEIVRp8r/aJ8jg0m/SvMAbJ9sJyCsLEEnPU4a5N77xjZeiHSLyIwpa84DBS8WT\n6IPm3Ko5a489MkejHX/ppuy1uAzSNnsJ2LH6gS7BB2TDvDQOedbCihLKYdAiZAiHGcDjyLEyjuuQ\nyUC6dR7CBs3q4xi3d0XX2oiJx/5f/HQZPztKfvCa7jH4cU3WmxxeqDHdiL/Q715ZtvnUVHfYWMmk\n6GvnaTahudQ7eYmAh4+FFDpffOUwR+QYiqQoO72TNccYbvXXDhAiP5blBaU6P/scwbF1DHXa7AFT\nBgye/T5Z+y8YW8fiUnHeiNMJd0V7Hx7nwEKdlxA6t5J2wDcRufYj3acqD9TxUgO4LoQVj6AS/2/n\nc3W27TR4nofjeJz6j4DlEbojwzV2DsZhpzafYbbp4bhFXC/PQNFloNND6ey6joW5bTheFj87SqoT\nEmw+A+X/0i1gArBr+V+rdBVwVXd7FrpFgp64tMrevrUvPJRSHtcOrj0iP0BieAAAIABJREFUafkC\nzhP5jsPzh9f+DNqSz7Aln1nztheMrL1POZPilk1rX8y4rrx229Kuw51b1y6Vv7cvf9HX+/KL7JP3\n3Yu2LwmM4+Fnhi7o5QMY2HIHA1vuwNooHgGyzEJp9IWE7QpBq0LfYD9+Oo7H82cX6YzYJZMtsnVv\n3MsXBDB/fBEbgkONdBocNx5lYILz3R4ugHnzG9jO5cu0/TKenSCiSGC20zad921Ux7PHMFRxWKLJ\njd3/52z096R4FMMSLZ5Dzbyy88ghOfuF3mvPvAA3XcT3wTTyhJ1h12m/ypa9UXf5iLOP5gkay2Gu\nSt9o/Fpb9QHC2h5cL4/rF7BRiOlcGRjcchdm609h3MyqcOU4Pv2bbun+fiVwZectHK28oGQsw1mf\nwEZUg5B9m1NcPRDRbsOD5xtwFhqETNoaacfpVsb593CCmahBn5Nmiylc9KLds82ThrnDhw8zPz/P\nNddcw/j42ldNRWRjLE5+hersfxA0pikO30Bu+OVUKobFhZ24nfHnFpewdb5z4gYtcx2Y6za45XS+\nlHpXJgOzi0XzJrBtHObBxOWCazVwo/nuB9Xc4hgzjzpkMpaMP0u7PkG7PgHz3yVb2gWdMFebe4Tm\n0qn45C+3iVT2wl6kyFq+dn6BmWab6UaLW8YHu8UrTlYbfOZUPDYn5Ti8uDhMu+3QaEA2TDHXmVvy\n+EzA6EL8RebYFFtMgYJJUTQp/BWvb487wB7W7qUReVI2AKLu1XdsgxTfw1DH2DpNc2N3KHTafpm0\n/SaGOiGbqJr/HO9jDIXoU91hY1XzWnDKuC6kaOI1T3eezGNkxOK64HmWxvksQWcaWnmwRnlbhDEQ\ntDKc/W6viWMjNVK5+Mr8ouljqT2M42ZJ5UZJreigGdjyCozjrapkB5Dr39etEPlE5U3PJfIvLEYQ\nn0DqCr38+IxxMCvmajtehv5Nt61538LQ88kUd8a9fI6P68a9fNZaFh2HMIznVo6Ol8j1x1cuqjML\nzC7XSTIeY5syBEHc02cXjuOE8WiZpm31wlx4hqL9b93nbTkH8fwMvg9+s4XTjicI5rJVhsciPA88\nL8P5R3s1QTeNVcj1xz8vzRaZq+dxvQJeeoD0ihH+A5tfjsHg+IVVF0RT2RFGdh9a8zi4/g8/xNFZ\nGfqM4ReujEdWVNsBqc6ca9+HyA/xjCGwlnLG5/oD0GxGNBrwjyeaLEYtFqMWOcdjG3GYq9uArwRn\n6DNp+k2a7U6J1NPR+5kQFw1zn/jEJ/joRz/Kvn37+N3f/V3e+9738qIXXaSUrYisq6A5T71yhMLg\n9d3hQe3mPEEjDhwL00c4OfOKzr3TpM0dhAzEvW8mQQVFjE/E6qF4FfMrGBbwmCSkTNSCVstQt+dZ\nnjFlMZw8M0omG1fUbM99n1Ylnjgym72WifyLmG60yLgOdxRO4GeGSWVH+cezs9SCeGjRtlSeTC1H\nswmNFXPZWlHEkVO2W/xl3BYpOGkKJl5gdplvXF54kWF/8ixnLdDGUO/MqYnfN46dxecRHFsHWqsq\nreWjT+PzfQwtGryAurkr3ocGeXtf936huw/j5fA88NstnGZcYcT1amwas7huPGRq8fEMURCHuS2b\napRG4hPN+mKGqaPLjxYwOtrqLiUyXx+j6UY4XpZMfqy3/IGXZ2jnz+C4GRw3i5fpDbcrjdzYXRj6\niZbXiBJJKi9Vwktd2BtkjGHzgbdibUTYjkvvd/dJ95EfvJagVYmHoI7AcuA69+giy7WDhkYLZAYi\nggDymSKnH+49/r4rF/E7vctzZ/JUOhVBfa9Nf3cEvUNp9IXxnEMvTyo31t0/P3gt+cHV89uXLc9D\n3ChPXMbgxpF+XjDcx2yzTa2znE4mA6m0ZT7oVfK6emuKPfmIRsPw2HyDmdkGMzYujrRtxRzsI+E8\nS7ZNn0lTdjIUk3RO9AO6aJj727/9W+6//35yuRyTk5P81m/9lsKcyCUWBjUmD/9lN7RZM0jL7mZx\nEZqVveT5Ztz7ZvuBVje4Nc0NG9jqp5kxWPpps3rOV5srWDS/hMsE1lZ5cHGBymKLim3xEqfGeOfE\n81RznK9W4hPcguvwgoXPdx8jHx5iuTbGyZkKw26biCFSpNjt9FHs9LJ5KwrC7LnI3DN5FrAWaOJQ\n7/R+laFTGMWzJ/DtY53AlqXuvLy7W8n+AS5xlcMl85O0eD4ADjOrhkVFqZfj+S6OA149isdoEQ9X\nHB6Jr74bk2Z2RVGMXTtqZIqdCpPns8x3imw4Tp3h4d6wpqBvb1wQycuSyvaGnKVzmxjZ+/O4bhbH\ny2JWFG/o33z7mofBOO5Fe9JEns2McS4Ie5nCdjKFtefJDmy9i6A5T9iukClsJ92Z8jzQX2Qqvw3X\nL+D4BcyKtVpLIz9BcfgFuF4e46zuferfdOvT+4I2iGMMQ5nUBdvedXAX5+stJupN9pSy5NOQz1uC\nVoNOwVKKvsu+3YZazdJowKmZRc5H8RCDXbaP53txyG3bkCPRfLc3L4uX2Pl4Fw1z6XSaXC4eujE6\nOkqr1brYXUXkaRA056kvHiEK670iHyZLuOJK1MTJo9SdToEEu4vIHEpe79uP4UxU5XRUoWJbFEyK\nG70thGzBYvledJhwuRJh5nlsTW3GRJNszvRDXJeEahjRcj1SJh4muT/fT6Xtkg1TjNl/pc9+FYtL\niwM8z3t193nj+URrFxaRhLIBDgvxcEXqBOzqFiVI2W/h2RM41AnMZiDuMTM06Lcf7D5Exfx8d+6p\ny1kyfBWIiwC5hTviUOaBnU9116Ad7K+RH4ri+WXNDLPHe03au6eG68f9zbMnM1Rn4mdNp0IGOqN0\nrU3RGLgWx4sLf7grhkXlB68hW9qD42VX9QwADG77qTUPg+NlyRQ0b1NkI2QK22GNoOf5OUav+Pk1\n9/lRhjg+U6Rdh62FDFsLqz/fnjvUx3guzUStU2GzCMWijYe/zvQq3e4cTDGSsdTrcHKpyUPtXqnT\nn/J3kccniS4a5p6YTpOaVkWSoDL9TeZO/T0Axvg0nZuoVj2WlhwywV5S/AcBOwjMiop2JkXA2lXs\nkmZlhaqWDXkonKJi23Evm7elu4DtvG1worPmUeCEjI9b0mlLOg3DR/xe+f/SIJu2xMcm1wq41V1g\nKOPTxyKF6rWEjUmisMnL9w11nh/OPXaaoA6GED/l4tFZD8zGJ/CRLRIySs28nMisXZVRNoaxSzjM\n4FAHQtpmf/e2bPQ5XCYx1Gma59IyzwPA5Rwl+xfd+82bt2M7xTF8TpEiHqbrujAwAI5jcZw0wWRv\nTsr4WI1sX9xj1lzIMHsqfizXqbNjd69XbLKeoRm6uG6WbNah0BkfHHp9FEduxFnuFVtx9b1/0230\nb74d46RXff8aYxjacfeax8H1crjek5d9FxF5psn77poFfixxAbOJWpPJeosrhjOMF+LP5vPnGxAX\n0SVlHLaWXZpNi+PAE5YxvexdNMydPn2aP/zDP7zo729961vXt2Uiz0BBc4764hHqi0fI9e+nUL6O\nKIKQLd37WNtm6uyp7toqdXMLNe4Ek8wrRssiG69htnxieixc6PaylZ1Mt/S6i+FYtNBdN8vtb7J9\nwCeVAlvzebjTk1GJ2pSHo+6Cxi8eGyC0lqFMalVZ7L6Ut6KEdgnKnfXrVlTWMgZSmX5suEjYWqA8\nMsqO4XjuQmV2gsoZcKjgUAHT6+HI2Afw7WOEjNE2u2ibtctjy5NY/juY5UIyU7hMdYcrdoOZtRTs\nf8NQw6HOkvkpAhMHdp/HyNv/D4jXQ1ow+zEmrtTm29O4Nk5ZbnqWUiku8GGiDM3JXjN27Vwik8/i\nujB/NtOdk5JJ19m1C6am4naemS0BFseLg1l2ucM2v5n+Tbd1gtnqQDWy5z+DcS64KOr6BQY238Fa\nHE89wSIiPw7HGK4dLK5ZmXY0m+bGkT4mas24x28rgKVchqmpS97UH8tFw9xrXvOaJ/1dRJ6ajcJV\nY9pnTt5PsxqXtWq1XM4vXM/SkiEKxygxSNRZNiBcUQTErlwY+TJnrSXC4nbKhddsm28F56nQomrb\nvMLfQYE4aFVpcc7GC4pmXYetWyPSaUin4YuP+Mw242INQbrVneC9w83wv20fZiidYijjs3K1poND\nP1yJ4ieeWA/tiIdVRkGjGyw8D1J+BWM8rA0wbpqrDxRpNiPqdVg6dwbbOofHOYxt98KcDcjb/07I\nCKEZI2AX1jzDT84788lYURTGs8e6PWYBY93w5dg5Cvbe7hDHRfNGIuLAnbbf6g5XbLONwNmP64Lr\nGrz6OQzxBPe+4hJ+IQ5mUSNDo/Pl69Dg6qtDPC/+G54/mqHRWRi4kK9T3hyHsjDIMjk30B2SmE6b\n7rpbuf79+JkRHC+D569+X20+sPaFTD87jJ9du8f2ifNaRERkY+0sZtlZfGZ8L180zL3pTW+6lO0Q\necaIwiZLsw9RXzxCs3qC8X3/F45XYmkJWnYvEIe5sHGMSjOK5+kYwyJvAuM8+YNfJlo2xKfX0/BI\nONPpZWuzzSl2Jxj7xuGMrXb3y5Rb7B7wSKehNe/x6Il4+2LUZmCgN9Tyzi1DeI5hKOMzkFox9Czt\nc+PI+hYgcbzVY/HzA1eT699H0JwlbC/iOHHFzGwW6mcn6BQiozw6ymgxolYzNKpTmMXHgMfAwoL5\n1e6cO88exmWOgDFCRlaFn8uCDTuBKcCaFWWq7TdxqGBsnZbZT2jieR6ePUze/m1n0VnDPL/dDcMZ\n+6/4HAGgaZ6L8a+Iy947Hm61d+lzqFwjlRvEdS3txQz1mXh7NlNn977eeJez383GE92BUrFGaSQO\nZs2lItHS5k4wy+I6Ictfb32jL6I49DwcL4u7Ipi5Xo5NV795zUOQzm8mnVdlUhERufw95aLhIvLk\nbBQQtBfx052VMa1l7vRnWZ5Xc+rYUart5xJF4NgryPE4bbO3s3Dviiv26xDk2jYkxBJi8XG66640\nbcicbRAS96RtMYVukDodVViwLSJr6XfSbHXi4QmLtskDs6eZbzdoEvIqfzeZzkdI0wTM2XiScdNv\nsWtnRCoFqZThc992WeqU/w/T7e58oT2lHId2jzOU8RlM+6t6yg4MXl69kcY4ay48O3bVr9CuT9Kq\nT5Ip7CCViyddV70JZheXd3bZtHWQRiOurEXl2/g2XqSrxV6WzH+K72ctPt8jZJSI/h///WCDbs8X\neERm+f3ZImO/hKGOQ526uYXIxK8rbb9Ezv4jAAEjVMwbuw+XsV/CZb7zkvqx6e14nsUJXZzqck1Q\ny5YtDTw/jevC0mSWZuc49PfVGN4ZBzMbpTn1H72m9pdqZPvi/5caIxBcGZe8T68O7uUdr8YYNx7K\nuKIIQDq/mbErf3HNw5AubP2hD52IiEhSKMyJ/IgaleMsnv83mtUTeOlBRq/4FapVqFSyhGYrYXSW\nAI9abZrQCUkZl8iMMsP/3gtStsJmU+guptkLUhF9Js02N+5JaNuIfw8nCG1EiOWgO0JfpyjIkXCe\nR8MZIix543O736uM9dn2CWqdMnoH3RGucOOSeHO2wT8Hp7v3+2l/L25nAd7Hw0VOd3rTdlBiu1fE\ncSCLw2RtqbtPYazJ7n6HdBqqkx7f7xR/aJqA4orh6a/bNUrWcxlK+2S8XngdSPsMpJM9D9D1crjF\nnWSKO1dtzxR3Ut7+alr1CWzUZrDsshzuzz4ySdAprpUtjOL5lkbD0G7MU7B/DYAlRYXXE5q4h9PY\nKgQLeHaWiL5uMHPsDBn7lW5oq5pD3cqmeXsfKeKFilpczZL5mU7rDFm+1Gts+iBOOl5A2mmlWO5I\n9d06O3dEnSGOMHc8S7sRh7nhoTr943Ewa9UyTDzWeWQnRV+piZfqrNPX3IHn+7huBj833n1K43iM\nXfUr3cIfzorCH7n+K8n1X7nm8U7nt6y5XURE5NnqKcPc97//fa64YnXFvM9+9rPceeed69YokR9X\nIwxpR5YgsmRch2wnRNSDkNNLDQIb37Z/oNAtoPHoXJVz9RZBFDGaTXNdOU4krTDi08fO0WzXiIzP\nXdvHGc+lCYMa35qP+Fb0CsKWS+5bJ7jdj4uW+NzA/aGhQvy817PAlW58Aj7/hCD1Wn8vTidInYwq\nnIoqAGx3iuxwSjhOPGrt1FKlu4+TDSimUhgDfj2kthQHtrTjMDJiMSbeJ33WUOuURC/1R2wfjOKi\nEA26VZwAdl8RkvXighGPnjCcjpdlo9gXcc2e+KQ9sg5/96BDK4zwjIF0SKYzQvDqgQKb8xmGMj55\nb/X8oCdWl3o28FIlvMFryHNhQZTC0HNo1c7Rrk/SNzxOrt8ClqW5c8yciO9jaJHJ91NvxFW1svaL\nsPAtikDdvJQGLwXAMU3S9lvdxy4Va7i+j+dBVMkQdTrMctk6m7bFwczzXM5+J54DCHFFxvxAHDSX\nZjPMVMFxM7h+ltKK6WKF8rWEQVz2fmWo8rPDbD7w6zhu9oK5YYXyQQrlg2seo1R29Ac/oCIiIrKm\npwxzb3vb23jPe97Dc57zHBqNBu973/s4evSowpxcEs0w4nvzS8w028w2W7x8y1C3NMg3phb44tlZ\n2pGlL+Xxpqt7ayV95JFTTDfiAhp3bhniJeNxj9REvcXHvn+2e793H9yF2wkf35mt8u3ZODAdGChw\ndV8Ra2Hq5N/x8PzyArltKq2QfACVpd0EPEx1uZy5CVYEqf34505AOy6VX+qP2FGO4oD1hCC1a29I\n3o+D1Pcfh1OdhS8LK4JUGFn4Zm+fkfGIXZ1haROTBpY7zFzL+HivSmNx1qXViPAcQylvuoVEyr7D\nnlIW1xg8J+5dS3XOw/cP5hnMeHiOYSST7j6WYwy//oK9REtNSimv25sI8Vy2/oT3sl0qpZEb19zu\nuD7pwk7ajUkcJ822vSkgotWCqSMThJ3evMH+On2b4nL4UTvN2Ud6j7F5vEYqFyewhXN5Kq0cjpsl\nnS2SW1FgsW/8FowTD1dcGcxyA1eTG7gas8YQz+LI2gvRG+N210YTERGRS+spw9yf//mf85a3vIVX\nvvKVfOpTn+LWW2/lve9976Vo22VraeEki5OPdibUF8mW9nRvW7lelvzgvr+wxIlKndlmm/FcmpvH\n416sdhTx18cmuvfblytRzsPCAixULfOtuHfBsYbJSYO1cVE9G/T+BjNzluON+Lbz7dV/m0ceM6Rx\nsBYWe+tKMjtv+e534xPanF294EhERKkEpVKGY8F10KnjYF1nVZAqzbk0rdsJUg59nVoSTd9lTymL\nZxw8x5BJQ6rzn3jVQJ6+9HKQ6pXXdwzcs3sM3xhcx7A51yuacbBc5Kr+PJ5j4h6zFf7PfWvPFxrN\npnn9lWsPWbtYGV+A3QN5poKELcCSENnSbrKl3VhrsVHvzej7ccVGvzCGJUU610dq+a3hFxjYeldc\n9MPN4qUHuvv1jd9M3/jNaz5XafQn1ty+VogTERGRy9dThrmxsTE++tGP8pa3vIVbb72VX/u1X7sU\n7bqsLc4cYf7sFwDw0kNk9/fC3LlHP0IYLOG6WfrGbyY/eC0A7eYstbmHcdwcjpcl17+ve+JkbfSs\nOIlaaAV8ZWKO2WabmWabX75qS3f448OzVb4xHVdKWGyEXO2XabWg0fDwcWgTB4jHzgQUmzA357AQ\n9o5ZK7RMTPSCjBt6pAlxMbQbDoudEBdYj1GTw8XgYAjaBteAY6fZw2fpM/14JqSYeR6bRiylkiVY\n2sPLjn8Z38+Ty4+xJdebH/X8LXs5MB6tGaR++aq1w9JwNnXRIHXNYJFr1ghSprNWyloynrtqLpok\nmzEG42ZW/T525S8xPFxkaqqy6r6O41Mcet6lbqKIiIhcJi4a5m6++eZVPUxBEPD1r3+d+++/H4AH\nHnhg3Rt3uVqY6xWBaLWzHDtmukUCotYS2AZB2KBeDzG1eHt76TwL5x7o7pe7/v/u/jx9/FM0Ksdx\n3Cz58vX0j78UiEvcL57/Vxw3h+tlyZb2dheStTYELlyEdiMEUYTn9ILVl87NcWSxxmyzzTWDBe7Y\nElfKCyPLlyfnu/c7ci5gwHi0WhBWer1QU7WAM2eWX5dhp9OHAQomxYjTGyu2zSky5uRxMN3iHctu\n8dfukSpiuN1r4tvDgEfdxOXHvdQgu1rT7LLxsgGlgUH6h+NCIr53BS8+sHlVr8cyBSkRERER2SgX\nDXP33nvvpWxHoqTyu5mZaWGoE9lBGpVOkLAR/bbZjRXnp/K0p+OQk7INlstAWLIcPep2ihEAtTo2\nahNGbZr1FtVqZ3tYZXGiV3Vu/Kpf7Ya5hbP/xOL5f8PxsmQKOxja+dru/RbO/TPG8Tu37eyW944D\noPmRegEbQYiFbk/a2aUGf39qmtlGm0o74P957u5uoDu11ODwYlx54dR8m1PW0GxCvZHCsFzTD05M\ntYnc+PUMRjn2O4PkTYqiWT336qA3smabfOPi8wMEKRt1y7xn7JfJ8s/xZpOlf/xW+voM6TRMn9hN\nq3aWbGkP2b5eb6vjpnDc1JoPLSIiIiKyUS4a5jZvjnssjhw5wv3338/b3vY2AH7zN3+T17/+9Zem\ndZepTGk/dWf7hTcYh3l+u1smPKJXFCBklDovwqGGxaW+1OtJKkXNbiSZW8wzUYmDh2sbrCgmx4mT\neVzf4HlganUgIgqWaDWbVKt0egctCxP/wnJkGtr5M90wtzT7MLMn78e4GbxUP+NX/XL3sRfP/zvV\ndpMlm2P74DipThnxTx49x5HFGrUg4rZNg9y2uRy/ntBwvFLv7v/w8ZB85NFsQtTsBZ/pRpvZsPda\nD7ojpHApGJ+S6RXXGHKyDDnZpz74PyDPHsG3h/E5TMO8lBbXkkpBPruHaC4Oc8bW6cufIZ2OhzyW\nt70S42i1DhERERFJhqc8c33Pe97DW9/61u7vr33ta3nPe97DX/3VX61rwxLLOFjyWFaXYw/NZsLO\nkL4nWjS/CrQ6Qa8XhCwF6tyMoYZDnVo9C404GOWjdveeS/UcU0fjAGhsnX56RTjOnsvhzMbDQE2j\nTsXmWGwX6QMKlTgAHqlW+R+PF2jTj0fAr7uHSeXGabWgWo+DHMCpcw/xWOUGWu0CzbAXxAyW6YVT\nGDfuzdrqFBkwLgWTo2BW92jtdS8cqvh0MHYBa/q6v2ftP+JxDoBS9jBDOw6QToO140y2tpDObSZT\n2kMqO9Z7DAU5EREREUmQpzx7DcOQ5z2vN8F+5c/yNDEGSBORXrU5MgM0zC1r7rLk/DRL9m4MNVgx\nXyywhgleRjUKadNmU7OfqBXf/vetPqq8CoAb2kcIjjukUlA1Hu3OWyHAY4kc/hIEAZRMHORcQiwB\nvp/G9Sx+4HBndJR+HqFIjdDsZ4k4zPUbh538ERYHa7NU+VlCEy8b4Nlj+PYokckS0U/bHOgdBluP\nw6z5weagGbtAxn4FnyM4zLHA20llchSLFr+9h8bcuc5BOUkqZVkeYjp2xbO7Z1lEREREnhmeMswV\ni0XuvfdebrjhBqIo4ktf+hL5/LNvEeDLibWWBdukSpuqDdjk5LvDMY/ZOt8K44IjaVxekxogk4JU\nylJaLFJtxUMjw75ruGZ3vO5Zpe3xd9+GlInod1vY1BDLf+KXtAMOnPoMebuE4/ps3f0y4iGcloHD\n52lWqwAUBrKMD0eEIbQaS9TOgCHCsESh4BEaSxAYnMbjpO1XwELA6KowV7J/isMikc1QNy+nZeLF\nhh17nrT9JtbkCNgGLO/jkOHr3f23bTpC/0h8W6t2FUtei0xpD5nC9suiUIyIiIiIyNPpKcPcBz7w\nAT70oQ/xiU98AoCDBw/ygQ98YN0bJlC1LWaiBlVaOBj2ueXubV8MTnXL9acdh7FcmlTKsjXy+dZ0\nfJ8mIXv3tbvVFseP+yzMtyinfYbyeZbzTcFz+a3rd5L33AtCz9jQPkbLV2GjFlHYWHVbYeg5pAvb\niII6mcJ2cp0A2PKb1B0fG8WLdm/ZlsVLxQFw9lSNaqd9uXyWsS1xAAwCqDxeBwsODXI5h7QXB0Ba\n58m0v9YJgFvJZA4wNGQplfJUz4zRrk/EBV/olW1P5Xrz/kREREREnomeMswNDg7y/ve/n7m5ORzH\noa+v76l2kR9CaCMejypUbYsqba52yvQ58XDLU1GFh8I4+eSNz/P7BkmlIJWCwUmfyWa8sHCm3GLv\n1jjYFRoe/Yseg2mfcsYnsL35c6/eMYKzRg+VMYaCf/G3QrzuVRrHXT0MND9wYM37p7IjbL3uN7FR\nQBjWcb1eT26u/0pcv0AU1PHSA+Q6Kw3YKGTJ8YjCOACOjGXJluIAWJlaYu50fD+P01x5RY25+fh1\neeO3gHHi3jfNeRMRERGRZ5GnPPv95je/yTve8Q6Wlpaw1tLf38/v/d7vce21116K9j1jTERLnIuW\nqNo2BeN3y+0bDN8IJ7olS/bk8pSLKVIpaLQ8HupM+6rZNtt3xotTA+xpZulruAymfXYUe1Ugy5kU\nv3HdTtayVpBbT8bx8JzVC11nirvIFHetcV+XLde+HWsjorCB4/QKp6TzW+nfdDtRWCcKW4TtGnTm\nF2b79q7raxARERERuVw9ZZj78Ic/zEc+8hGuuOIKAB555BHe//738/GPf3zdG5cE1tru0MSmDflO\nOM2SbVGxbV7ib6HUqeY4FdX5fjQHwKCTYnjYkkpBOm0ZOOYz24p7pLy+Nls2daJdPc3+ep5yJsVg\n2scSF/EA+Mltw5f2hV4ixji4Xm7VtlRujFSuV3UynSvCUuWJu4qIiIiIPKs8ZZhzHKcb5AD279+P\n6/5g1Qafqf5t6jzfaM9QtW1GnRw3ePHcLBfD0Wi+ez+vr8nWPo9UCppVj0dOxdsrts34eNQNgc8f\nKdEKLeWMz9Z8prv/SDbF/7F306V7YSIiIiIikhg/UJj7/Oc/z0033QTAv/zLvzzrw9x8u8mUjatC\nNr0W27ZFnV42+PzDLpV2CECUbTE4GM8X2+lmuCMqd+ey9frY4OYKjMrIAAAgAElEQVTxwQ14FSIi\nIiIikmQ/0KLh73vf+3jXu96F4zhcd911vPe9770UbbtsbR9K89Xz8c9Lts3AinWw79o6jO+Ybmhb\nNpxN8dKsQpuIiIiIiDw9njLMTU5O8hd/8Rertn3hC19g69at69aoy931o31kgohy2qc/5a+67bpy\n8SJ7iYiIiIiIPH0uGuZOnz7NqVOn+OAHP8g73/lObKfEfRAE/M7v/A633377JWvk5aacTXNFnxZO\nFxERERGRjXPRMDc1NcVnPvMZzpw5w5/8yZ90tzuOwz333HNJGiciIiIiIiJru2iYO3jwIAcPHuTm\nm29+VvfCiYiIiIiIXI6ci91QrVb5y7/8y26Q++QnP8mrXvUq3vKWtzA9PX3JGigiIiIiIiIXumiY\ne/e7383MzAwAx48f58Mf/jDveMc7uOmmm3j/+99/yRooIiIiIiIiF7pomDt16hRve9vbAPjc5z7H\nnXfeyU033cQ999yjnjkREREREZENdtEwl8vluj9//etf58Ybb+z+boxZaxcRERERERG5RC4a5sIw\nZGZmhpMnT/Lggw/ywhe+EIClpSXq9fola6CIiIiIiIhc6KLVLN/whjdw11130Wg0eNOb3kRfXx+N\nRoNDhw7xute97lK2UURERERERJ7gomHu5ptv5stf/jLNZpNCoQBAJpPh7W9/Oy960YsuWQNFRERE\nRETkQhcNcwC+7+P7/qptCnIiIiIiIiIb76Jz5kREREREROTypTAnIiIiIiKSQApzIiIiIiIiCaQw\nJyIiIiIikkAKcyIiIiIiIgmkMCciIiIiIpJACnMiIiIiIiIJpDAnIiIiIiKSQApzIiIiIiIiCaQw\nJyIiIiIikkAKcyIiIiIiIgmkMCciIiIiIpJACnMiIiIiIiIJpDAnIiIiIiKSQApzIiIiIiIiCaQw\nJyIiIiIikkAKcyIiIiIiIgmkMCciIiIiIpJACnMiIiIiIiIJtK5hrtFocPvtt/M3f/M3nDt3jp/7\nuZ/j0KFDvPWtb6XVaq3nU4uIiIiIiDyjrWuY+9M//VP6+voA+KM/+iMOHTrEvffey/bt2/n0pz+9\nnk8tIiIiIiLyjLZuYe7o0aMcOXKEl770pQB87Wtf47bbbgPglltu4atf/ep6PbWIiIiIiMgznrde\nD/zBD36Q3/7t3+a+++4DoF6vk0qlACiXy0xNTT3lYwwM5PA8d72a+GMZHi5udBOe1XT8N46O/cbR\nsd84OvYbR8d+4+jYbxwd+42TtGO/LmHuvvvu4/rrr2fr1q1r3m6t/YEeZ26u9nQ262kzPFxkaqqy\n0c141tLx3zg69htHx37j6NhvHB37jaNjv3F07DfO5XrsnyxgrkuYe+CBBzh16hQPPPAAExMTpFIp\ncrkcjUaDTCbD5OQkIyMj6/HUIiIiIiIizwrrEub+4A/+oPvzH//xH7N582YefPBBPve5z/GqV72K\nz3/+87z4xS9ej6cWERERERF5Vrhk68y9+c1v5r777uPQoUPMz89z9913X6qnFhERERERecZZtwIo\ny9785jd3f/7Yxz623k8nIiIiIiLyrHDJeuZERERERETk6aMwJyIiIiIikkAKcyIiIiIiIgmkMCci\nIiIiIpJACnMiIiIiIiIJpDAnIiIiIiKSQApzIiIiIiIiCaQwJyIiIiIikkAKcyIiIiIiIgmkMCci\nIiIiIpJACnMiIiIiIiIJpDAnIiIiIiKSQApzIiIiIiIiCaQwJyIiIiIikkAKcyIiIiIiIgmkMCci\nIiIiIpJACnMiIiIiIiIJpDAnIiIiIiKSQApzIiIiIiIiCaQwJyIiIiIikkAKcyIiIiIiIgmkMCci\nIiIiIpJACnMiIiIiIiIJpDAnIiIiIiKSQApzIiIiIiIiCaQwJyIiIiIikkAKcyIiIiIiIgmkMCci\nIiIiIpJACnMiIiIiIiIJpDAnIiIiIiKSQApzIiIiIiIiCaQwJyIiIiIikkAKcyIiIiIiIgmkMCci\nIiIiIpJACnMiIiIiIiIJpDAnIiIiIiKSQApzIiIiIiIiCaQwJyIiIiIikkAKcyIiIiIiIgmkMCci\nIiIiIpJACnMiIiIiIiIJpDAnIiIiIiKSQApzIiIiIiIiCaQwJyIiIiIikkAKcyIiIiIiIgmkMCci\nIiIiIpJACnMiIiIiIiIJpDAnIiIiIiKSQApzIiIiIiIiCaQwJyIiIiIikkAKcyIiIiIiIgmkMCci\nIiIiIpJACnMiIiIiIiIJpDAnIiIiIiKSQApzIiIiIiIiCaQwJyIiIiIikkAKcyIiIiIiIgmkMCci\nIiIiIpJACnMiIiIiIiIJpDAnIiIiIiKSQApzIiIiIiIiCaQwJyIiIiIikkAKcyIiIiIiIgmkMCci\nIiIiIpJACnMiIiIiIiIJpDAnIiIiIiKSQApzIiIiIiIiCaQwJyIiIiIikkAKcyIiIiIiIgmkMCci\nIiIiIpJACnMiIiIiIiIJpDAnIiIiIiKSQApzIiIiIiIiCaQwJyIiIiIikkAKcyIiIiIiIgmkMCci\nIiIiIpJACnMiIiIiIiIJpDAnIiIiIiKSQApzIiIiIiIiCaQwJyIiIiIikkAKcyIiIiIiIgmkMCci\nIiIiIpJACnMiIiIiIiIJpDAnIiIiIiKSQApzIiIiIiIiCaQwJyIiIiIikkAKcyIiIiIiIgmkMCci\nIiIiIpJACnMiIiIiIiIJpDAnIiIiIiKSQApzIiIiIiIiCaQwJyIiIiIikkAKcyIiIiIiIgmkMCci\nIiIiIpJACnMiIiIiIiIJpDAnIiIiIiKSQApzIiIiIiIiCaQwJyIiIiIikkAKcyIiIiIiIgmkMCci\nIiIiIpJACnMiIiIiIiIJpDAnIiIiIiKSQApzIiIiIiIiCaQwJyIiIiIikkAKcyIiIiIiIgmkMCci\nIiIiIpJACnMiIiIiIiIJpDAnIiIiIiKSQApzIiIiIiIiCaQwJyIiIiIikkAKcyIiIiIiIgmkMCci\nIiIiIpJACnMiIiIiIiIJpDAnIiIiIiKSQApzIiIiIiIiCaQwJyIiIiIikkAKcyIiIiIiIgmkMCci\nIiIiIpJACnMiIiIiIiIJpDAnIiIiIiKSQApzIiIiIiIiCaQwJyIiIiIikkAKcyIiIiIiIgmkMCci\nIiIiIpJACnMiIiIiIiIJpDAnIiIiIiKSQApzIiIiIiIiCaQwJyIiIiIikkDeej1wvV7nne98JzMz\nMzSbTd74xjdy1VVX8Ru/8RuEYcjw8DC///u/TyqVWq8miIiIiIiIPGOtW5j7p3/6Jw4cOMAb3vAG\nzpw5w+tf/3qe85zncOjQIV7xilfw4Q9/mE9/+tMcOnRovZogIiIiIiLyjLVuwyzvuusu3vCGNwBw\n7tw5RkdH+drXvsZtt90GwC233MJXv/rV9Xp6ERERERGRZ7R165lbds899zAxMcGf/dmf8Qu/8Avd\nYZXlcpmpqan1fnoREREREZFnpHUPc5/85Cd59NFHefvb3461trt95c8XMzCQw/Pc9Wzej2x4uLjR\nTXhW0/HfODr2G0fHfuPo2G8cHfuNo2O/cXTsN07Sjv26hbmHH36YcrnM+Pg4+/btIwxD8vk8jUaD\nTCbD5OQkIyMjT/oYc3O19Wrej2V4uMjUVGWjm/GspeO/cXTsN46O/cbRsd84OvYbR8d+4+jYb5zL\n9dg/WcBctzlz3/jGN/joRz8KwPT0NLVajZtuuonPfe5zAHz+85/nxS9+8Xo9vYiIiIiIyDPauvXM\n3XPPPbzrXe/i0KFDNBoN3v3ud3PgwAHe8Y538Nd//dds2rSJu+++e72eXkRERERE5Blt3cJcJpPh\nQx/60AXbP/axj63XU4qIiIiIiDxrrNswSxEREREREVk/CnMiIiIiIiIJpDAnIiIiIiKSQApzIiIi\nIiIiCaQwJyIiIiIikkAKcyIiIiIiIgmkMCciIiIiIpJACnMiIiIiIiIJpDAnIiIiIiKSQApzIiIi\nIiIiCaQwJyIiIiIikkAKcyIiIiIiIgmkMCciIiIiIpJACnMiIiIiIiIJpDAnIiIiIiKSQApzIiIi\nIiIiCaQwJyIiIiIikkAKcyL/P3v3HSfZVd95/3NDVVfunHume3pyaM2MsjSKCAmRJWGSBAZsbBlj\nPRjbi+WwXj9ssFYYr2EXY6/BrP3goJeFcCIaDEhGEeXRRI2mJ0/nXNUV7j3PH6fmVrfU8rPLwzAq\n8X3/NapT9/bpWzWj873nnN8VEREREalDCnMiIiIiIiJ1SGFORERERESkDinMiYiIiIiI1CGFORER\nERERkTqkMCciIiIiIlKHFOZERERERETqkMKciIiIiIhIHVKYExERERERqUMKcyIiIiIiInVIYU5E\nRERERKQOKcyJiIiIiIjUIYU5ERERERGROqQwJyIiIiIiUocU5kREREREROqQwpyIiIiIiEgdUpgT\nERERERGpQwpzIiIiIiIidUhhTkREREREpA4pzImIiIiIiNQhhTkREREREZE6pDAnIiIiIiJShxTm\nRERERERE6pDCnIiIiIiISB1SmBMREREREalDCnMiIiIiIiJ1SGFORERERESkDinMiYiIiIiI1CGF\nORERERERkTqkMCciIiIiIlKHFOZERERERETqkMKciIiIiIhIHVKYExERERERqUMKcyIiIiIiInVI\nYU5ERERERKQOKcyJiIiIiIjUIYU5ERERERGROqQwJyIiIiIiUocU5kREREREROqQwpyIiIiIiEgd\nUpgTERERERGpQwpzIiIiIiIidUhhTkREREREpA4pzImIiIiIiNQhhTkREREREZE6pDAnIiIiIiJS\nhxTmRERERERE6pDCnIiIiIiISB1SmBMREREREalDCnMiIiIiIiJ1SGFORERERESkDinMiYiIiIiI\n1CGFORERERERkTqkMCciIiIiIlKHFOZERERERETqkMKciIiIiIhIHVKYExERERERqUMKcyIiIiIi\nInVIYU5ERERERKQOKcyJiIiIiIjUIYU5ERERERGROqQwJyIiIiIiUocU5kREREREROqQwpyIiIiI\niEgdUpgTERERERGpQwpzIiIiIiIidUhhTkREREREpA4pzImIiIiIiNQhhTkREREREZE6pDAnIiIi\nIiJShxTmRERERERE6pDCnIiIiIiISB1SmBMREREREalDCnMiIiIiIiJ1SGFORERERESkDinMiYiI\niIiI1CGFORERERERkTqkMCciIiIiIlKHFOZERERERETqkMKciIiIiIhIHVKYExERERERqUMKcyIi\nIiIiInVIYU5ERERERKQOKcyJiIiIiIjUIYU5ERERERGROqQwJyIiIiIiUocU5kREREREROqQwpyI\niIiIiEgdUpgTERERERGpQwpzIiIiIiIidUhhTkREREREpA4pzImIiIiIiNQhhTkREREREZE6pDAn\nIiIiIiJShxTmRERERERE6pDCnIiIiIiISB1SmBMREREREalDCnMiIiIiIiJ1SGFORERERESkDinM\niYiIiIiI1CGFORERERERkTqkMCciIiIiIlKHFOZERERERETqkMKciIiIiIhIHVKYExERERERqUMK\ncyIiIiIiInVIYU5ERERERKQOKcyJiIiIiIjUIYU5ERERERGROqQwJyIiIiIiUocU5kREREREROqQ\nfzZPfvfdd/P4449TqVS4/fbbGRoa4mMf+xhBENDe3s4nPvEJ4vH42eyCiIiIiIjIq9JZC3MPP/ww\nBw8e5J577mFqaoqbb76Zyy67jFtvvZXXv/71/MEf/AH33nsvt95669nqgoiIiIiIyKvWWVtmedFF\nF/GpT30KgFwuR6FQ4JFHHuG6664D4Nprr+Whhx46Wz9eRERERETkVe2shTnP80ilUgDce++9XHXV\nVRQKhWhZZWtrK2NjY2frx4uIiIiIiLyqndU9cwDf+ta3uPfee/mzP/szbrjhhuh1Y8z/57HNzSl8\n3zub3fuhtbdnz3UXfqLp+p87uvbnjq79uaNrf+7o2p87uvbnjq79uVNv1/6shrkHHniAP/7jP+Zz\nn/sc2WyWVCrF4uIiiUSCkZEROjo6/s3jp6byZ7N7P7T29ixjY3Pnuhs/sXT9zx1d+3NH1/7c0bU/\nd3Ttzx1d+3NH1/7ceaVe+38rYJ61ZZZzc3Pcfffd/Mmf/AlNTU0AXH755XzjG98A4Jvf/CZXXnnl\n2frxIiIiIiIir2pnbWbuq1/9KlNTU/zyL/9y9Npdd93Fb//2b3PPPffQ09PDTTfddLZ+vIiIiIiI\nyKvaWQtz73znO3nnO9/5kte/8IUvnK0fKSIiIiIi8hPjrC2zFBERERERkbNHYU5ERERERKQOKcyJ\niIiIiIjUIYU5ERERERGROqQwJyIiIiIiUocU5kREREREROqQwpyIiIiIiEgdUpgTERERERGpQwpz\nIiIiIiIidUhhTkREREREpA4pzImIiIiIiNQhhTkREREREZE6pDAnIiIiIiJShxTmRERERERE6pDC\nnIiIiIiISB1SmBMREREREalDCnMiIiIiIiJ1SGFORERERESkDinMiYiIiIiI1CH/XHdAREREROTV\nzBhDYAJ8tzb0PjZ3gsXKIsWgRHe6i9ZkMwDzxQUeO/0kmViadDxFT7pr2XEiS+mbISIiIiIClMMK\nc6U5SkGJUlBmda4vats3eZDR/DilsERXqoNtbZsByJfz/PX++6Jj3rnxZrrSHQB888h3+NrwtykH\nZfqyPdx50Uei8/3R03/GbGkOgHdtvJkrey8D4MTcaf7Xnr+O3nfXFb9DNp4B4O+e/yp7Jw+QiaXZ\n1LKe6/uvAWxYfHz0aRsAY2k6U23EvfjZu1B1oBKEOA54rl2IOF8oMz5ToFQOAdiwqil67yN7Rpia\nK3LVBatI+c456e8PS2FORERERF7RjDFUTEApKOEAqVgKgEpYYf/UoWqQKrGldWMUfPZNHuSZ8T2U\nghKZWJqb1r0hOt9/f/JPGStMUApLvHnwdezquQSAA1PP80dP/1n0vk9d81+iWbH7TzzE02O7Abik\n6wK2tGwmDKFYgidGn4mOGZtZIFGGMISFBYdSUAIgXyxz4oRDENg2N6yFreOnKuyddQkCGI/NL/vd\nJ06lmIs5eB4cnx7j+PxJADJ+jkoFfB8KlUW+8NxfRcf88s7bWd+8FoAHTz7KAyceIh1L053u5G3r\n3xy9b+/EATzXIx1L0ZJoJuknfpiP539LGBpKlYBKYMgkY9HrR0fmmCuUKZdD2puT9LalAZiZL/LA\nM6coVQJK5ZC37BoglbDHffvx4zx5cIxSOWSgO8utr90A2O/Jh//b/ZTKIaEx3PG2IXaubwfgB/tH\n+Yuv7wcgl47zh3dcEfXhG48eZfj0HJ3tGXYOtpy1a3A2KMyJiIiIyP8vxhgAHMfOasyXF5hanKYY\nlHBwWNs0EL33wZOPMlOcoxSW2NKyIQodx+ZOct/z/xQFs3934R3EPTt4/8Jzf8Xjo08DcFHnTt6/\n9d0AlMMyf/T056Nz//LO26Mwd2zuJN87/n0A2hJt3NDzBsIQjIHx/BQTxUkARqcWGfEcwhBm5xuW\n/V77D1WIEycMYXG+Fr5GJ0s8+6yd8QnM8gB05ESFMG7b8oXaMeVKyPh4bdanxV1Fws/hO3ESYRMl\nm/loiGUZjO+gaBYITIWZ6dpwfSqfj/5cnEvz3HMujgNzYWFZH+YmMpxatAHw2OQER+dO2M+lVCAI\nwPPs+/5i7z3R7OBNa97C9WtswNk9epAvHfgn4k6CmJPgFy+4jVSD/V2+uftZhkenoRKjt6mNN168\nLvq5d//VExRKAeVKyLuuW8e2Na0APPDMSb7w1X0AZJIxPv2RK6Nj/p9v7OfQyVkA3nBpPz91jf0+\nzOXL3Hf/C9H7rt7eh+/GCEM4Pppnz/AUAA4u09M2IIehSyUwhNXv4+io4Xiy+tlOe7VrVww4cMCt\nHgPlkm2bnAqoNwpzIiIiIq8QLw5FpaBMOSwTmhDPcaMZKWMMo/kxQgyhCWlJNJH0kwDMFGcZyY8R\nmhDXcdlQDUtgZ6vmywuEJqQn3UVftgeA2dIcj488TWhCQhNyS9P10THfP/EI+6eepxSWGcit4saB\n66p9K/EfHvqvlIIypbDEL23/IBtb7MD+0VOP86Xn/wmA1kQLH7/8zuh83z72AKcXRgCIuw30p9cS\nhjCXL3Fg6vnofaOTJRKODVKVUm0mZ3q+xJEjdoBeCZaHr4OHKywed21gK9Ta8sUSBw/W6v6ZSq1t\ncrbE6Yq93oVKkrTbiE8c34kzP29IVA/rctfjNtjXW/2e6HgXj9dkbsN34sScOK1erW1T4hI2JC7E\nJ4br1MIEwGuz72ElXQ0DXBp7O5UgoBKGVIIQ37Od2MFNjBTHWDQLpCrNkLbhNAwdsotrKDsFAneR\no8dCvBb7Ox0am4xKHp44VWL3bhsAXdcwW5yHar58ek+eoaQNgM+fHme0dMpeq8BjZq4Shbnvjnyb\nGeck+HDo9HquW1xHGMJ0cYbhzNcJyzFMJc7hkVa6sm0YA6NzM7iZKUwlTjFIcPSoEwXrSqV2XUZG\nQ/bssZ/f5NzymLJ3n6E1W10yOVc7Zn4h5MiR2mfruW51iaXD9IxhYqL6CwYNtOey+K5L3PcpLMm/\n6zo76MjlWNXRuOJn8kqmMCciIiLnRGhCSkGpGiAMqVgS17GDsrnSPOW5PBMLc8TceFQcAuDQ9DCh\nCQhMSG+mO5qJGS9McnTuOKEJ8R2PHR1D0TFPjD7DbHGO0AQMNK5msHEAsAPQB048HIWYGweui5aa\nPXr6CQ5OHSI0hp5MF9etvio63x8/87+iY9645nrWNPYD8PTYbr4x/B1CE5CMpfjIzp+Pjvns01/g\n+PxJQhPymlVXRvudjs2d4Pcf/0x0vv+867doarCDynsP/j3fP/koANtaN/Gh7T8Tne/jj/x+9Ofb\nh97Hee1bAXh2fA9/vf8+ALLxDHdd8TvR+/7+0FejWZrXD7w2CnPTxRnuPfgP0ftu3HIlxniEIbww\nfTSaFatUDHOtVIOUH83qAJweL5Mp2EH63EwtLBVKJfbvr82CBMXabNWJ02X2ztrPfKKyfIZr+FiZ\nrGcH4uVCHBcP34kTluNMT5+Z4fJZFduEh4/vxImbLKHdEkWXv4bLUm8l5sSJO8ll535t9r0AL2lr\n8bp5e/Y3CKohKkatr13hVhKLgwRBSNz3oPorOo7D3KlWSuUKlTAk1hWQqGaCsekFHj98hEpgO3XT\nRTuj833tqWc5PjFFJQwZWtXLFZvW289iPs/nv/NA9L73XnkZLRm79PDoqTyPHx4HoLs5zvZO+56m\nWCsze7ZSrFTs53S+QyplA9uGqcs49GwSxy/hmhhtFxpcF0LKJI70slBewImViJEgmbRtgVtLOqYS\nY7EURp9fhcWoLSzHmZlxqFTg+MI8TmaaMzFrfDLkxAn7OR0oPEXDlidtw1wbk5M31GZxe+4n1+bg\nmwS+30K5bN8WuAW2bEjS4KRIkCYZr30WW3t7WNXSgu+5JGK1oA/wM9dcgee60V65M/rbWulva2Ul\nOwZWA7BhVRqYW/E9r1QKcyIiImdJOawQhIENF64XFSSohBWmi7OEJiA0hvZkK55rh0DjhUlmirOE\nJiQVS9Kb6Y7O9/TYbgITYkzIYOMAzQm7gf/0wigHpg5FP+eK3kujY75/4hGmitOExrC+aZDNrXZv\nydTiNF8/8i+EYUhIyC3r3kS6OuvzwImH2TOxn9AErMr28qbB10Xn++Tjf0Rg7O90y7o3RbM+j488\nzd8f+iqBCUnHUvzmxR+NjvlvT3yWI7PHCEzI6/qvjc53Yv4Udz32qeh9/+ny34x+p386/E3+9cTD\nAGxp3ciHt//ssvMZ7AzWzw+9j+3VELN/6iB/te9LAGRi6WVh7ltHvseRuWMA3DhwXRTm5koLfH34\n29H7ruzZRdxJYAwcnBzmwdOPAbCpaSO7OmyYMwZ2j++N+nBR66W0V0ewE3P56Oek/TTT0/b9AFOF\neaaLMwBMzhUZG3MwBqYKLpWwEvVhZNRQ8G1bIV+bgVgohBw7Zl83xsHBxWBDwukRQ2bOqfahdkyl\nYjh40K0eA8XF2gB3dMywZ9G2jZWWDwmffjYgmLOD5PmFWjCbmS/zwgtnzuHiESPAjr4npss0FWwf\nnFKOVq/HLiF00izWxv+sj19Ab2wDMeJ0xQai13NeK6/P/hyu8XGMT4JM1Lbdu5G17muohCF+ZfkM\n1+qZ11Os2CBFeyPk7OvlhTQnh5uphCHGGAbPrx3zg71jHJuYJAhCNvV2s2ujnVGcKyzyhe99P3rf\nrbsuoT2XBWD3sZP84IVhALoac7zz8ouj9z32/GEK1RTSnEnS3ZLDdaEcljg0MmavCZDJhLiug+NA\nSIV8dW2lF6vQ2WlwHPATy3+/jq4K/Z22mMeBCQcO29dj8ZChoZAzmSXxXZdi9WvU2h6wfr39bsyb\nHF99uAkDhEB3t+0D+Kwtv4a9w5PEfJeebV00VkPoFf3ns/9AgOOX8T2HdMrDdcF1oT/Xx6mFOGUW\n2bGmj85O+wWfnViA47V+33x1L7lkSKUCD5uA43ZVK9v6+ujqst/NQqlEfuIExKEM9OWujY4/afZx\nuOnLAKScHBc31G5KPMvXKCYWSDhp1sTPA+y/a6VwkWlGSZg0CZMmRkMUGl+tFOZEROSHEpqQILSz\nIwazbOP8THEuWhqW8pNk4vaucikocXLhdDQT05/tI1bdE3Ni/hTjhUlCE5KNZ1jXtCY634MnHyWo\nzsRsbdlEe8reXT02d5Jnx58jNIaY6/O6gddEx3zr6Peq5wvY0rqJHe3bAJgoTHLf81+JZkHes/nt\n0czOt4/ez1Njz9q+5Vbxjg1vjc738Yd/n0pYITQh7970Nra2bgTgkVOPc8+BL1d/1xT/5Yrfjo45\nE2IAbux/DW9eeyNgw9fvPfaH0fv+4+W/QUuiudqH73H/iZS0CEIAACAASURBVIcA2NKykQ/vqIWY\nz+3+IqGxA7Sf2/beKPi8MHOEew7YQU8mll4W5h489RjDs0cBO5jf2LwBY2B2MR+FJYDX9lyPn0hh\nDBydPs0z48/Zz6wSMr+kHsPw7NGoD2MzeTqq46SpuRITi1PVY8qMjztRiFksBpSrYWV23nD6tG2b\nWFx+5/zkKZivhpj8fK0tnzccPlw7nw0xdm/LiZOG1LQNJKPztWFNuWLYs8eNfu/FQu18IyOGZ6rn\nnygvHzjv2wfZ6pK2mYVa29xCyP79tXMs7cPJU4bEZPV8i0uCVLB8+VelVPvzzGzIycBevMnK8usw\nOmZYrM5IVRYbiDtJXFycSoLJydrAtMlrx2BwcCkXEsxVz+eVGun2B3FwibtJlmyzotvfQNppxcWl\nyemOZkHipNmauALHODg4eMQ5s3tolTtEwmvHMT4pk1vW14tLH6RSNpjQIxdri2ar0sV+mo+/nkoY\nEoQh7KgdMzncwbEJnyAMKXQZuu04nMVFw33fPRa9792XN9PRaG+A7D52ikcP2RTTkcvy7l2XRO97\n8OAh8kUbipKxGJ1NWRwHFstF9p08Hb0vlaoFqVJQZCZvZ5+MW6K93c5IpQvLB/7tHQH9nTYwHZhw\noLqFy4+FbNpkX3ccSPyrS6F6LTs6A7Zts39HgoQDdmIVA/QP1JZM5p5wwa5kJNYQ0NVlv+CJtEcu\nHSfuu8RjHtkMZKq5drA3w8WbO4jHPNobEyydfLrl6kGC0BD3Xdb21JYLru9r4nc/cBHxmEfct0ss\nz/jFm7axks5sE795y/Urtn34opWXh/bnVvHR8z/EfHmBfDlPU9r+ux+PQ3e2jdnKaubLC3Q3NkW/\n69TiAgzXzjG0MUlvygbAw8PzsGBfzzak6egwVCpQqcDJ6f3MBvbfmya/g75qmBsPjvNPs38cne/9\nzf8xmnl9qvAvjFdOkHDSdMcGWdtgv5ShCZkMTpFw0pTDOLE6ewq3wpyIyCtAaEIWK4sE1SADEIQB\nD556jHw5T3zEY2N6Iz2ZLsAOqB89/SShCYm5/rLqZP/4wjcYWRglNCHb27dxSfcFAIwXJvji3r+N\nZnZ+buinaWywA7OvHf42j55+nNCErGkc4P1b3xWd785//TjloExgQj6w9d1sr4aiR049zhf3/S0A\naT/F3Vf9bnTMnz775xyuBogb+q/lrWtfD8BYYYJP/OB/RO/7+GV30pq0lcP+9cQj3H/iQQA2Na/n\njp0/F73vb/Z/mcDYoWXjtlwU5o7Pn+Qrh/8ZgJSf4vrVNswZA0+O7mZ49ggACTfN5pzt92y+xFNj\nz0bnnp4r4VQHoKdnJ3lhxh7j08DMjK0UNz1tr9+ZPkxMlxg3NlzMzkGxWq2uGJQZGXGiWZCgvGTw\nPmc4fty2jS4uDxBHjxlmqiFmbrZ2zHw+5PnnHUz1ZznGhepMzLHj0DBhQ8ypfO2YUiWMCjPYmZ0l\nIWY05NkF+9+TleVDgOcPQa46yJxdqB2zkDccOrQkxCzpw8ioITNj26aKS0JMaKLlVQDlJddhbj5k\nxNi26Yof3Tl3cJmagko1xLilLM2xdkzo0BA2MTtbO1+XP1ANMQ5OORWFlUTYwpr4kA0xTiIKKgBr\n4kO0+X04uHT4A1EwTLpZLkjegIuH4zjLltytje+gxevGxSXtLt9Lc332p8H2gDa/N3p9VXwzN3l3\n4DgejnExxkQzA1c23ErRL4NxSLq1n5Nz2rmBj2FCCA3Egxxn1qqtDa4mNb+DMAxpiPmQrfWhf+Kn\nKBRLtthDVxtUV5A1FHrg+cuphIaiCQkvMrjVPhSGBzkx3kgYhvidKdZurn6u5SRPPJCNCkdsf12F\nlGuD1MkTDg8ftMGnPWvYWCsCyKN7TzJfLAJwzRaf9pwNUgvFRZ49diJ631sv3orvu7iuIV8uMD5n\n7xCUTZHWVjsjlS4tD1ItrSGrum1gOjjhwCH7uueHbNgQVvd9QfL7LnnbBTq7A4aG7PfTSTvw2JLr\nNRAQj9kLm3vGBVsUknhDQE+P/b3ziw4NMY+Y7xLzXbJZaKpWsF/VmWTLQDMxz6W9KUnDku16rzm/\nl2I5JO67DPbUAu/qziwfvnmbPZ/nRp8DwE+/biPvuWEDMd+1yzarsqnllRaXumBjBxds7Fix7crz\nelZ8PZXwWZ3Irtj2o5SKJZfdhFvqLdUbWS+WjqX5yM7bmS8vsFDO05ZowfNsgZaWdIbV2T4Wygt0\nZJro7jbRccWjC9GfN/Sn2NZuA2B+ZB5sLRVcx6O3s4EgsCFwdOEFhku2CEtIGIW5RbPAfTP/DYC5\no2/hxoGVr/0rlcKciMiP0GKlyEI5T75SIB1LRrMt08UZvnf8QfLVtndvfBupmB3M3bP/73jgxEMY\nDENtm/mF8z4A2H0Y9+z/crSU62e2NtOd7rKD97mxqEpbwkvw5n4b5oyBfROHGJ4bBqA51s626rhi\neqHMwelaZbCp2TJntoNMzM8zWrD7MHJ+CxMTtVmQQnmRirGzKhNTISPhmdCxdPBu90acCTHlJTMQ\n0zMhw8O2bby4PMQcPmKY8m1YmZ1ZuqHdsG9fbWkYpjYAGj5i8Efs+U8UaseUg1qIAVhcEmLGxg37\nqjMzU5UX9wEmoxBTa8vnQ4aHXWZmYGrKrYYYG+bGJkJOzNs+zRRrPycIQ06frvXVVOL4xHEcl2LB\nizbiLwQxsm4zDh4ODvNzLk41xCSCVjr9fhxccnSxsFA7X398KyEhLg7xsDGqfpelg80Nl+LgEqsW\njDhjY8PF9MU24ODSGeuPXk+7Oa5I34KDi4NLwkkvO6Yntg4Hl+SS1wHelPsFG2Ecl6xb238yEN/G\nu5p+w84gOctvbb8x9/PYuSwXh9rv0+x38oHW/8xKzk9dz3XNNzE1tfCStuuSP0sQhoShoWHJUKaV\n1Wwpv7U6axwSpmshpiV/Hm6+YGeJ0ilos8f4JsnisUE7UxsaFgcMDXa1KQuTaQ6dSBKGhnQipG/J\nJMZTz5SYLSwShCEXr21kS5/9izY+VeLvf3CIMAwxwIdv6MSvlg58aM8Jnh8ZBWBzbzc3nGeXh1YC\nw98/Witt//ZLL6QnbhPE8NgEDx6wRUFaMmk299oBu+PAc8dOMlut4tCaTbGmq8XuhXLKvDA6Hp0v\nmQqisFAKi0wv2PQbUIqC1GKZKMgBNLeEtKVt26Els4GuH7J2bRgtuUs+5DJfDVIdnQHbt9svX+zI\n8iA1MFgh2WA/q6bnXKjmvHhDQF+f/bmL1TDne9UglQtpqVaJ7+1MsLYnR8x3aWtMklyyBW7XUBeF\nYkA85jLQVQstve1pfvaNm4nHPBuk3Nrv8a7XrONtVw0S810S8dp3KJWI8dlfvZqVXLq1i0u3dq3Y\n9sbLBlZ8vTEdf9nwlUv/ZD8HDiDuxZYV6Fnqqr7LuKrvspe8bozh54fex0J5gflynoHcqigAZlI+\nqzI9zJfzeI5LTw9Q/X+oGV/gzHa/1V0ptvbbAHh8dg7sJB+tmcxLft4rncKciMjLOLUwYoNZOc+q\nbO+yJW33H3+QhUoeYwy/tOOD0TH/46k/jWakrl99TfRco0JlkW8e+U70vuu6XkdLPEkQQLnoR4Ft\nan6RF16oVmmr+MSdBEVjB2vDRwz+aTtIPrkkFAWBYe/e2uC5uGQ52cRUyPPVYDUdLP8n/8gxmKmG\nmPklIaawGHL8+JI75KZ2vqnpkNPVZUhzRQ8XDxcXz8SXldyOhzkybjMuLpTSzMycqczXQLu3Csdx\ncXEp5n0WqiEmZ7roj23FcRxavG6qN/sBGy4MIQ42QJwJKy1eNzuTr7V9cJZvgt+WuJK1ZgcO7rLq\ncmmvkddm3mujheOScrNLjtnFYPw8HFwa3OUFE97W9KvVeRiHpFv7H/5gfDv9zVtxHRuKlnpj7nZW\n0ui18e7m31qxbSh5FUPJq1ZsO1O04cU6Y/3LgtpSmxIXr/h63Eky6F1EGBqCMMQ1teuXCFop5dM2\nFDkO1GqPsDCZZrFctgP/xoBE9WZBoWDYc9zuzQvCkF0bs1EBgt1HTnNyaobQhHQ3NXHBoO1rqVLh\nS48+YffthYYbtm+ls9Ge8Knhozz0z4cIgpDGdIr3Xlkb1N3z0GPRErmrNm1g5xpbvGBkZo4vP/ZE\n9L5fvP5a3GqIeeboCQ6ethUUN3Z3RYUQwtBE+6AA1vd00NaYwnFgvliI9ju1ZFKkq+HGcWC2kGdy\n3vbB8cu0tNi2ucCG+jPaO0IScbu0Lb2/9nckkQwYGLCzS8Wygdq2PXp6Ajautm3HF4AD9nXfDznv\nvDBaJpd62GG2WqeirSNgy5bqjFQKqG37or8/JNlQXdq3u9aHhkQYBaniklkxz3XI5ULamm1bV3uc\n3vY0Mc+lrSnJ0vHuxZs7mMuXifkuqztrDV0tqWjWKea70dJCgJuvXMONF68m5rukE7V/lxpiHp/7\n9WuXzV6dcdnWLi57mSB105WDK77elGlg11D3im0tubP3PDU5uxzHYVPL+hXbhtq2MNS2ZcW2W9a/\nienFaebKC/Tn+vB9u/IiUbaFlOZLC7Tncise+0qmMCcir0pLlzaVgjIHpp4nXymQLxe4sGsHmZid\nbXjw5KM8fOoHLFQKdKU6+Lmh2mD593/wGRYDexvvvZvfwaXdFwIwmZ/lsRFblcvFZWrKEAT2Ya9u\nkIqOPz1ZYP9++/psafnsxsHhRTqqC/PLeXtMzElgAp+5udpAZnPDZYSEZJNpmsPO6PU2v4/LUm/F\ndVy8F/1Tfn7qeophHgeXRq8tej3jNlVnVWyQyrhNUdv25LVsTlyKi4vvLL9bfFvzb9vZG8dZ9rPW\nNexkXcNOVvLa7E+v+HrGa+bmpo+s2LYpcQmbEpes2HZF5pYVX2/1e5aVCF9qsOG8FV+POwkGG7av\n2Jbz2sgtuWZLNb7M6y4eQWiXFgYmIBX3ou/ebKFAoVQmCEMSsVhUkS4IQw6NjBGGIYEx9Le1kknY\nNVunp2c4Mj5BGBrivh8FH4B/3X+Q+UKRwIRs6ulibae94z86M8t39uyPzvfOSy8iVg0x3969l4On\nRwhDw9rOdl633U4vlYOAP/nW96Jzv+3i8+lrtdMg+0+e5oF9BwFoSqV439WXR+97YP/BaGbnio3r\no+IQc4XFaE8TwOUbB/Gr+3NG52ajIBXz3ajKXjmwfY8+m4Yyzc02FKVG7QOGAXDCaI+N40BDrPZ3\nJNsY0N9vA07YwPLZoDUV0km7R+qhw0B1+1Qma5fiOY7dW8e3ases7g/YMmBD0fEF4Lnq5+yFrFtX\nm7lKJFyo7iXM5gJWrbJty5/2ZZcKZpK2ra05RkuuAd91aWmMR8UmKoHLxlVN+J6D57k05XwS1awx\n0JPhmh09eK5LLh1btt/p9ZesplCs4Hku63pry0B72zPcccsQnufieQ4xvxakfuqatbx514CtAhiv\n3cSJx1z+57+7Bs91cByH9vYsY2O2qt/l27q5fNvKoehtV688q9KcbeA15/et2NbRnFrxdbvcVuTs\neLkloH3Znqhg09Lvfb1QmBP5CRCGdlDke7W7o7MLJU5P5ilXbPGKMw/2BPtwz8nZIqVywOaB5qht\nanaRz39lD77n4rkOb9m1JlomsvvwBIdPzeF7Dm2NSS7aVFtW8uheO4jzPZc13Tmas3bQOpcvMTpd\nwHddfN+lt60WeBYW7QC4HBZJxGOk43aWZGpxmt0Te1koFyhUCty09g3RwPlv9n+Z3eN7Wajkubjr\nfN690QaAfCXPZ5/5QnTuNY2rSbppggDG5mc5NDMMgAkcRkbOzIpBg5Nksbom4/CJRdLjtrT2aLk2\nEAkJeX64TNy1Iy+/kiPtNhJ3ksSCbFTBLU6KC5M30uDaMstZr3a9tyevYUfy2pc8gwjg4rSd2Wtu\nTi9bbtbkddCUXHnpTm9s5TuWvhOnJ7ZuxbaUmyXFynsqGtyVB14/aqY6oxOEBoNZVm56eiFPOQgI\nwpBMooFMdaRbKJU4MTldXVZn2NDdGc0GHR4dZ3xujiA0tGRSbOi2d/WDMOQ7z+2LZpAuWruGtqyd\nUTh4aoSnjhwjDENSDXHefEGtasO9j/yAyfkFgtBwybo1nL/GhqyTU9Pc+8jj0ft+4bVX01Dt+4MH\nDrG/WoBhfVcHb9h5XvS7fu2p2r69my/aGYW5U9MzPHzQLofNJZNctK4/mg0aHhtnYs5+D3pbcqRS\n4DiG2GLA6emZ6HzpTEAq4eK64PkBxbJdJuvFgqhiXuVFz8ZtaQtYvcoGnKNLxjI2xIRRH1KPOkxX\nv4pt7bbQg+NA6iRRoQeADRsrZFP2s3joBaIqd6l0GFXZe3EfWttDVq+2wefA6JJhvRNGFfMAmnJx\nikEF33VpynnRnqb2ks+63sYoFKVSkKyGoi1rmonH7b+B/Z2ZqHCE77ncfNUgvufguy6dzbUZ2fMG\nW2m+qQHPc5YtxQP48M3bCEKD77lkU7Xv6tqeRj77q1fjew6u4yyrpHfb9Ru47foNvJjvufz6bee/\n5HWArQMtbB1oWbHtyu0r38jIJGPs3NC+YtvLzUg5joPvKUqJ1BuFOZFzxBhDuRJSDkLSidpA4Njo\nPPP5EsVKSHtTMgo4M/NF7n/6JKVKSKkc8tYrBkhVj/vnHxzjyQNjFMshgz25aLBgjOH23/9e9Hyb\nO942xM719n/wTx4c48+/vh+AXCrGH/5fV0Z9+M4TJxg+bUdzruuysc8ua5splPj+s7XKYNedv4pM\nMo7jwDPPT/Ctx+1obXN/cxTmjDF8/mvPUKEEfpkPXHc+V2y1S6L+Zf+zfOW5R3H8Cj4xPvOuX4jO\n/Vvfu5uSP4PjwFou51decxMAe04f429e+HL0vvNzV9CRbSQI4MCJcaaYBuDZ4RGuTDsEASyWl8+K\n3f/EBOc124H4qanaHevJ/Nyy/U7xUhsZkyBGguJ8A2H1Y8qYdraGb6TBSRJ3UhC60QNZL4y9lfOc\nAM9xopkRAM/x2Z54zUsGd2fafpSMMRhjCI1dvBmr7tcxxjCdz0fL6hpTKVtQAZjN24IEQfXBxIOd\ntYHg3hOnyBeLBMbQ29xMb4sdOc/k8zz+whECYwjDkGu3biLu2/M9/sIRhsfG7XPAmpujst+VIOQv\n7n8wCl9v2DnEqups0NNHjvG9vXY9WS6Z4APX1Dah/9MTTzMxbxPEZRvWcvFae4d1aiHPV56s7TUa\naG+NnkV08PQIe0/YUnHrOtvZ1FtdouXAc8dPRsdsX9NDMpmxAYciJ6fsdyiXSpDLmeqeHUMlrFAo\n2Yoa8URAR4etfldyl3+enV0h2ZQNOI2HnajIQiJp9xo5TnUf4Ddrx/StCtk6aNvGK8Be+7rnh1Ex\nB4DUIw4T1aDV3FIrPe6mlvehpzegMWM/i1y21haLmWiGKzQOyQYfz7WD+MacQ3N1OWV3WwODPTk8\n16Ex00B6yV+hnRvaGJjL4nsO/V0Zql8v2hoTvPWKNdXzLS/ocM3OXobWtuK5Ds3ZWpjwXIff/ukL\no/DVmqtVlLhqezdvuWYd01MLL3le1J0vE3z62jP85nsvWLHtqu09XLVC+HFdhzdfPrDiMZ0tKTpb\nVr6Z0d2aXvF113VocF96Y0ZE5GxQmBNZws4MGEplu7n7zJ3YYilg+PQsxXJIqRwwNNhKQ3V5yu4X\nJjh4fIZSJaCtMcl1F9SWlXzynqdYLFUol0Pefu06tq6xg9bvP3uKz3/FjtYyyRif/kgtSP3F1/dx\n6KRddvSGS/v5qWvsEpa5QpkvP1BbwnT19j48J0YYwsmxAvuO2gGo73pMTUEYOoTh8gHe6ZGQow12\n5ml6qjbYWCyF7NtXe6BruVRrO3kqjMp7L5gCTnIOxyuDX+bAQYfR6p33g/P7ia19HscrM2q62L37\nAjs4NkX8Hf8c/WNzcKSPzvgAjmM4PDFCrKf6O5USjI05xGIG368+Q6na/YAi+TwEAZSWPO8I4OTo\nIs5iE8ZAadGH6jixUClQLNpBsxPGCabbMYEHlTi0x6s/A5L5VRQPX4ypxAidBCzZ9156/nzGqrMd\nPYOt0b6hctHnBz8IsGus5um/ej1nbto/dmiYp4/Y0tqrWlu45eLaoPOz3/wOQXX55+t3bGN9l102\nefDUCPfvO4DnuCTiMd615NlF33luH5PzC3i+y4bOTrb02cHo2OwcX3nyGVvkwRjec8WlUYj51u69\n7KmGlbWd7bzpfLuk0BjDX9z/UHTut164g4F2u3Tw8Ng4391jw30m0bAszD1x+EhUee7S9YP0tTZV\nCyaUl1Wru+689SQSvt03VFzg+KTdUZ5NxsjlTBRi5pY8cCqVtqHIcaB5eulMjKG311Sr1RkSCSda\n0tbYaEMRQHwMqFXXZ+3agKasDUVPnCAqspDO1EKRMQa+WjumuztgQzUUnVhSbMRxQtasMbS3QyZT\n7cOZfqfCqLLaonGIx1w7w+w5NDaFNFW3DvW0J+ifssGntyO1ZK+Rw4WbOvBdB89zaG2030mANd05\n3nR5P77rkkos/9/0zVcOVpfVOfS21/Yn9bSl+M33XIDn2SCVTtZuEL3ruvW8/dp1eO7y5Xau4/CZ\nj668N+/CTR1cuGnlmd9/a1ndW69YeRnT2t5G1vY2vuR1x3GWVf5bKuZ7pBIxFnwFIxGRl6Mw90Mo\nFCvMLtgSYq7rkFnyP825fCmqAJeIe1EJ3HIlpFCsPRB0aQWj/GKZSmAPivluVO0pDA3zhVpd5VTC\nj5bILZYqlMrhT1wfDNDSUrtLPTqVZ3S6QKkckmzw2dxf26X/jw8Os1i0fbx8qIs13XbA8NzwJP/w\nr4cpVUJcx+Hfv+/C6Jj/+ldPcuCYDUU3XrKad1xrZxMmZhf5r3/1ZPS+//Lzl9JVvVu7+/Ak33zM\nDt439DVx5VBfFIqePz5DsWzXEZ0aK9GVs0Eqv6Tsd7EUcOyYEx1TqbjgBOCGnBotsmdP9YGuswYn\nNYPjhuCEPLevREfO3hkeL0zitR0HN2TSmePo0dqdab/vAA4lXNdwYr6Vnim772EyPEV22xPghLiO\nR7FYe1CnWfMI6cETGCek5FxM9DBOf4rE0JJd9f7rgepzZHJz+I6dBUmYNJmMDWWO48OIC4793Fra\nKvT1hXgedM+lOFitIOX4Fdrba8uofOJUABP4+HGHVPXmeHtjhsqzfZhKDIIYq3fG6a1u0l+9Zzun\nnmrHBDHWDXSydm21gpUJKd1XuybbLmxna3XwPl5J8b1nbMhuSMc577wwqqD4d0/Uvmsd7bBxo21L\njCxfG7ZmwNCYtm2pI7W2dMqht9dE5ztTKc4YQ1OjQ1ubbTs8WWZ+0Vb7KIcVcjkT/f0Zm5vl1JQN\n96vbcmSzNuAsBGFUAAKgsSmgsVqcIbNkliYWD+npMdUlZQaHM3W9oL09YLA6GzSyCOypfhaufX7S\nmWV16ScdxquzQW3ttgADQO408OCS6zAY0JKzbQ8fdqJnB8UTNhSd4TpOdC2ammuhqHnUiWZokg0u\nbW21Y3rabEEKz3Nob4lHoaitEmPn+jY8zwapZNLlzOrMocFWsqk4nuvQ3VqbXXEch/fduBGvGr6W\nFm3YvraNX781hee5xP3ls0EffbsNxb63fNa1vyvLH//qNazkLbvW8JZdKwecl3u+02BP7mUDzo71\nK+/bS8R91vW9NCwB0b+lIiLy6qN/4X8I933nef7mn+0d7P6uLP/h/RdFbf/+849GQe99N27k6h32\n2TO7D0/w379U2x/xZ3fWHmz7P/9xD88cmgDgmh09/PSNmwCYWSjxq5+pDZz/w/svor9acvfrjxzl\nH74//BPZhz/62GtIVMdY333qJF9/xFYOXNfXyOb+2oD9G48cJV8Njqs6svS05AhDmJqpcPC4nW3x\nXIfJyTOzWFAxJZx4HtyQkel5jh61r4/MBriNo+CGOE7I7v2LTOQyhCEcnz2N330I3JAxt4m9e2vh\n0F39LHGnCG7IgfEM7Q12VuXw3DANQw/YYGZcJiaujZbezfd8j+SADUUz5fMpl6vFCtw8iW21WZUw\ndiVnpor81tPEm3YDkHJqxRIcB+KdRyljP4vWRkNDg329vc2lMmFLZPuOnTk58/DTbN5lqmCPaW/x\nomVZPblG/nY0Oj39axdZ1WiD2fChZo7bx3ORzYUMDJwZiDu0HWmiYgJSfpKB7mxUUvqqDZtpHLOv\np2KpZUVL/vNr7yDm+ngvWq60paebP7z5FwlCQxCEZFO1GwI/tWsLr9u5nkoYknzJ/pahaD9Wf2dt\nb9hgd463X7uWIDDRw1TPzAheuKmdge4sQWAY7M1GBQkacx7b1rREfWhu8shUf6fWphgdTUmCMKS1\nKRYFkjA0uK5DENr/bm+D3l7754OjtdDYEHeXBZ9YrNaWzYUMDlZveKSXz7q2t4e0Ndm2bMbOanqu\nS6LBWRKSHbqry3Z916Gp0SNbvRS9HUnOW9uK79nZoKXPT7psaxebVjfhe7ZIwxktuQbec8OGaA/l\n0lmk117Qx0Ub2/E8d9mNHoC7br80KsyQWhI0rjyv52Wfk/Shlwk+7U1J7njbyoVO/q3ZpTP/Jr1Y\nc7Yh2tP5YiojLiIirzQKcz+EicIkbpMt6JB3F5eV8A5SI7hxOwg+OdnN8eN2yd3xsXnc5tpeo6XH\nzISjeC12hDy2GI/aJuZLeC2nOHMf/eipAl7F3q09PTeJ12qXUeX93PI+ZE7iJexd/uOTnVEfjo3O\n2dmbFfowbU7htdk+jC76HD++udaH9mPR+46c2hr14eTcGF67DVILsUaOH68tDavkjuKl7XU4Ntke\n9eHo6Axex5EV+zDFcbxOe11PF52oD+PzRbzOYcBu9N/zwiwtfiNhCCfyx4j17wEnZMxpYs+eiwhD\nOwtiBh4j7hZx3JCnTjbQ6NjB257RF0ic/63qTJHDkSPXRs+emWj+Poke24eRhW1MTdklcrOLBRo2\n1spd54OrqFSqd8gbx4g12qpv8bA3CkSuC7HWUc48Vw2jNgAAIABJREFU1KQ9Z5dqOQ6scpI8PW73\n/3iOR1ubXUrmONC5mOFwtbjAqq4kfX0GxzHkyrGoqhrA4KChN2NnTw4da+BA9aNNZwxDQ2G0uT/+\ngE+5bD+Lzu4ym3psOCiMeWCzMyHBsgCRmfSjkmzxZCUqPJBsTNOV6iAVS5LykyQb3Gi/zM6OIbpS\nHaRjqehB1Gf835ffyUq60510pztXbEv4Kw+oXdd52ZmGllxixc39juNwwcaViwGs7syyunPlwh8v\n99ygrpYUv/LOHSu23XLVWm656qXL0FzX4U8/dm20lHdp6e3LtnUxtLY1CnpLvf8Nm8gvVmhuTuEv\neQZUZ0uK3/v5S6NldUsLMNx6/Xpuu+GlRRYA/tMHV64WuXVNS7QM+MVuuGjViq9nU/GXrVbX05am\np23lPUVtTckVXxcREZH/MwpzP4RTwQEaNtgld4uLLUxMvClqC1c9RYNvg9TpchsTE/aBoKfzIzSs\nf8q+ycDExHuiY2Yze4m325H4ZKHExITdwzCWLxJf93T0vpG5S8m5dgP/GIeJr31mSR9uqvWh91ni\n1T6MlruYmBiyxxdGiQ/uXtKH2mByLn2AeDXoTRUCJiauqfVhTS1BjM7totG1y/QmOEp8jV2XVVxs\nWXY+07036sNYuZeJCbs8abQwTnxg74p9mE8dIt5q+zBTcKK28XyJeP++2vWfmsGL2z07pfgkfqcN\nlF65ZCu3eTZI+bkZcGyQakmFNDXZsNTnJHl63M7YOTh0dYNXDVKri008P2/D3Pr+DGuqzwBqLTXw\nd7WPgq2bPVbnqlXfjqQ5XM2nTY3OsmIFiUmfao6iuztkbXUmZn7Mg+rzXAMT0NsbRjNS6VEfL+/h\nux7plENra3UmphSnN9NNzI3hux65jB8tPRxs6mMhuBDf9WlPtrK0VsCbB19HEIb4rsfaxtqs3drG\nAe7Y8XP4ro/vestmxT6w9d0YsG1LKixm4mn+/aW/xkpWZ/tYnV15YC81K1WMS8T9l1TKO2Ogywbj\nF5dLjvnuyxZmeHGBFREREXl1Upj7IZy3vpVD1WzR1ZqKqok5DmRnY8yVbYjZsTHHhk7bVmrL8pRd\nmYnjOGzYUBvwrwky7LXbtNg8kGNjdWN/VzHGl2vVrtmxJUFfxrYNJXOcrs7EdLel2LRpyVKsJX3Y\nuSnHpq5qCej2LE/tq/Vh8+YlfQgz7K3uXdq8Jsfmdbatuxjjy0ue2XP+UIJV1T4Mp3KcPlbrw5mH\nlb64DxdsybG127aF41meOrMvx3HYurV2zKDJsGfS/nnrYI5t1Ws0XYzx5SUFDnZdnKLJ2LZjh3Mc\nrdbPaG32l12HzGgD00Ub5lb3+fRXH4w6m4pHQcpg6OgIoqV8bWNZJspNxFyflkyaM8+O9MpxtrRs\njIJPNtkQ7ctZ3zxISEjM9WlqqC1BA3jXxlsIjW3rzdSe0bO+eS2/dfGv4Ls+MXf5X8NfOO/9Kw7G\ns/FM9ByUF9vRMcSOjqEV267svWzF17PxzMs+dDMV+/GUohcRERGRH57C3A9hVVNn9HT59mRrNDsC\nsLV1E4WKXZ/WlW2O9gZ1ZHPsaLeDbQei1wHWtwyQqFahG2jqjfblBF6MCzpqD7ZtSqeittVN3VxY\ntsu82hIty/a37Ow4j0JlEceBnlxr1NaRbeKSrtqesviS7R9b2tbTmLBFANY19UdtGSfG5d215ZON\niXQUYtY09bErsEu2WhLNLHkkFJd2X8hiYMNcT7YjqtLWkWnhqt7aw2f9Jd/A89o3055qxQEGG/uj\n5XupeJxrV9VKlDcms2CfV8vW1o2k/ZQNWPHlS+V+dttthMbYYJaoFUbZ1LKeu6/83WjWaemerPdu\neQcrScVSfHjHz67Ytqll/cuGoh3tK+/zSfoJkpmuFds0qyIiIiIi/zscY8xLN2m8QrxSn8Bej0+H\nfzXR9T93dO3PHV37c0fX/tzRtT93dO3PHV37c+eVeu3b21fe2w/RY25FRERERESknijMiYiIiIiI\n1CGFORERERERkTqkMCciIiIiIlKHFOZERERERETqkMKciIiIiIhIHVKYExERERERqUMKcyIiIiIi\nInVIYU5ERERERKQOKcyJiIiIiIjUIYU5ERERERGROqQwJyIiIiIiUocU5kREREREROqQwpyIiIiI\niEgdUpgTERERERGpQwpzIiIiIiIidUhhTkREREREpA4pzImIiIiIiNQhhTkREREREZE6pDAnIiIi\nIiJShxTmRERERERE6pDCnIiIiIiISB1SmBMREREREalDCnMiIiIiIiJ1SGFORERERESkDinMiYiI\niIiI1CGFORERERERkTqkMCciIiIiIlKHFOZERERERETqkMKciIiIiIhIHVKYExERERERqUMKcyIi\nIiIiInVIYU5ERERERKQOKcyJiIiIiIjUIccYY851J0REREREROT/jGbmRERERERE6pDCnIiIiIiI\nSB1SmBMREREREalDCnMiIiIiIiJ1SGFORERERESkDinMiYiIiIiI1CGFORERERERkTrkn+sOnA13\n3303jz/+OJVKhdtvv52hoSE+9rGPEQQB7e3tfOITnyAejzMzM8Ov/MqvkE6n+fSnPw3Afffdx6c+\n9SlWr14NwOWXX86HPvShZecvl8vceeednDx5Es/z+L3f+z1WrVrFvn37+PjHP47ruuRyOT75yU+S\nTCaXHRuGIX/wB3/Avffey8MPPwzAd7/7XT7/+c9H73nuuef42te+Rmdn59m8TGdFvV37IAj4nd/5\nHYaHhymXy9x6663cdNNNP4Yr9aNXb9ce4NOf/jQPPPAAnufxa7/2a1x44YVn+SqdPa/k6/9y7/nc\n5z7H17/+dRzH4Zd+6Ze4+uqrfzwX60esHq/9qVOn+PCHP8wll1zCr//6r/94LtRZUI/X/s///M/5\nx3/8R4wx3HLLLdx2220/nov1I1Zv176hoYGPf/zj7N+/n0qlwjve8Q7e/va3/3gu1o9YvV37M+8x\nxvDud7+bXbt2cccdd/wYrtSPXr1d+4mJCd785jezbds2AJqbm6P+/MiYV5mHHnrIfPCDHzTGGDM5\nOWmuvvpqc+edd5qvfvWrxhhjPvnJT5q//Mu/NMYY85GPfMR85jOfMXfccUd0/Je+9CVz1113/Zs/\n47777jO/+7u/a4wx5oEHHjAf+chHjDHG3Hbbbebpp582xhhz1113mS9+8YsvOfazn/2s+eIXv2gu\nvvjiFc89PDxsPvShD/2f/MqvGPV47f/lX/7FfPSjHzXGGFMoFMyuXbv+3/buPC6quv3/+GtASBAV\nKNyCuxQXLBM1cUsr89b6mmmuiIlm1N2muD/ct3BJS82lO7VSSUzt9jYNKE3TVpcwMHLjzqXCNRNc\nURiH+f3BjxMDM6iF6aH38y896+d85uI65zrnM2fsNpvtDx3/zWTGvt+zZ489PDzcbrPZ7GfOnLGH\nh4f/0cO/6W71/ne2zC+//GLv3LmzPTs723769Gn7o48+ar9y5cqf6Yabwox9b7fb7U8//bR9xowZ\nV933rcyMff/LL7/YO3bsaLdarfbs7Gx769at7efOnfsz3XBTmLHvk5KS7DExMXa73W6/cOGCvVmz\nZjrfunAjco7dbrevWrXK3qVLF/vcuXP/0LHfbGbs+/T0dHvnzp3/zGFfVakbZhkWFsacOXMAqFCh\nApcuXWLHjh20adMGgNatW7Nt2zYAJk+ezP3333/d+9i2bRtt27YF8qr65ORkABYsWED9+vUB8Pf3\n58yZM0XW7d27d7F3AefNm0f//v2vu023AjP2vZ+fH+fOnSM3N5esrCzKlSuHm5v5/izM2Pc//fQT\n9957L25ublSsWJHy5ctz5MiR627XreBW739ny+zYsYNWrVrh6emJv78/d955JwcOHLjudt1sZux7\nyMv1wcHB192WW4kZ+/7OO+/k/fffp0yZMnh6elK2bFkuXLhw3e262czY940bN2bs2LEAZGRkULFi\nRZ1vXbgROScjI4P4+Hh69ux53e25VZi172808/0VXYW7uzve3t4ArF69mgcffJBLly7h6ekJwO23\n386pU6cA8PHxcbqNb7/9lqioKPr27cvevXuLzP/tt9/w9/cHwM3NDYvFQk5OjrG9rKws1q1bx2OP\nPVZkXVf7BDh58iS//fYb99xzz3Uc8a3DjH3foEEDqlWrRps2bXj00UcZNmzYHzjym8+MfV+7dm2+\n/fZbLl26xG+//ca+ffs4ffr0Hzj6m88s/V9wmYLbg7wTT34bzcSMfV9cW8zEjH3v5uZGuXLlAPj6\n66/x8/OjatWqf6Ybbgoz9n2+6OhoIiIimDBhwh89/JvKrH3/2muvMXjwYNzd3f/M4d9UZu373377\njejoaHr27MlHH330Z7rAqVL5nTmATZs2sXr1ahYvXky7du2M6Xa7vdj1QkND8ff35+GHHyYlJYUR\nI0YQHx9f7DoFt5mVlcWLL77IM888c913XdeuXUvHjh2va51bkZn6fufOnRw/fpyNGzdy+vRp+vTp\nw0MPPWQkBrMxU9/XrFmT8PBw+vXrR2BgICEhIVdt563uVu7/wsts3LjR5fbMyEx9X9qYse937drF\n9OnTWbRo0bUc4i3LjH0/d+5cjh49SlRUFKtXrzbtjQ0z9X1SUhLu7u40atSIn3766doP8hZlpr6/\ncOECAwcOpGPHjpw/f57u3bvTrFkzKlWqdB1HXLxSWcx99dVXLFiwgHfeeYfy5cvj7e3N5cuXKVu2\nLCdPniy2A4ODg40PqGHDhmRkZJCVlcXzzz8PQFRUFJUqVeLUqVOEhIRgtVqx2+14enpy5coVXnrp\nJTp06ECXLl0A2LhxI++99x4AS5cuLfaOyOeff87s2bNLqhtuCrP1fXJyMs2bN6dMmTJUrlwZX19f\nTp48SVBQUEl3zQ1ntr6HvOGXvXv3BiA8PJw777yzxPrjr3Yr97/dbi+yTKVKlTh8+LDRhqu18VZm\ntr4vTczY9/v372fs2LEsWLDAlE/l8pmt7w8ePGjs+8477yQoKIhDhw4Zw9LMxGx9/9lnn7F79256\n9OhBRkYGOTk5BAUFmfKFb2brex8fH7p27QrkjYCpV68ehw4dUjFXnPPnzzNjxgyWLl2Kr68vkDfm\ndcOGDXTq1IlPP/2UVq1auVz/7bffpmrVqnTo0IH//e9/+Pv74+3tzbJlyxz2sX79elq1asWWLVto\n2rSpsW6TJk0c3s7Utm1bY+zt1aSnp1OlSpU/cti3BDP2/V133cUnn3wCwIULFzh58iQBAQF/uA9u\nFjP2fUZGBiNGjGDRokUcOHCA3NxcU/Y93Pr9/9ZbbxVZplmzZixZsoQBAwaQmZnJr7/+Ss2aNUus\nT/4qZuz70sKMfW+z2Rg9ejRz584lMDCwxPrir2bGvj906BBr167lzTff5NKlSxw+fNiUn4EZ+37k\nyJHGv9esWcPRo0dNWciZse+3b9/Oli1bGDVqFFlZWezfv5/q1auXWJ8AWOxmH1tTyKpVq5g3b55D\nR7366quMHTuW7OxsqlWrxrRp03Bzc+Ppp5/m3LlznDx5klq1avHSSy9RvXp1hg8fjt1u58qVK4we\nPbrIXSObzcbYsWP56aef8PT05NVXX6Vq1aq0bNmSwMBAPDw8AGjatGmRl5nExMTwv//9j+TkZBo1\nasQjjzxCv379yMzMpFevXkZhYUZm7Pu+ffsyceJEfvzxR3Jzc+nTpw+PP/74je+sEmbGvu/Xrx+z\nZs3i66+/xs3NjcmTJxMSEnLjO+sGuNX739Uyy5YtIz4+HovFwqBBg2jevPkN7qmSZ8a+7969O8OG\nDePUqVNcunSJf/zjH0yYMMF0xbQZ+75BgwYMGTKEOnXqGMsNHz7cdE+HzNj3L7/8MpMnT2bPnj3k\n5OTQs2dPevTocYN7quSZse8LLpNfzJnxpwnM2PcvvPACY8eO5fDhw9hsNiIiIowndSWl1BVzIiIi\nIiIifwel7m2WIiIiIiIifwcq5kRERERERExIxZyIiIiIiIgJqZgTERERERExIRVzIiIiIiIiJlTq\nfmdORETkao4cOcJjjz1Gw4YNAbBarTRu3JiXX34ZLy8vl+utW7eOTp06/VXNFBERKZaezImIyN+S\nv78/y5YtY9myZcTGxnLp0iWGDh3qcnmbzca///3vv7CFIiIixVMxJyIif3u33XYbI0eOZP/+/fz4\n448MGDCAyMhIunTpwqJFiwAYPXo0R48e5ZlnngHg448/plevXkRERPDyyy+TmZl5Mw9BRET+hlTM\niYiIAB4eHtSrV48tW7bQpk0bli1bxsqVK1m4cCEXLlxgwIAB+Pv7s3jxYo4fP86CBQtYunQpK1as\noEmTJixcuPBmH4KIiPzN6DtzIiIi/9/58+cJCAjgu+++Y+XKlXh4eJCdnc2ZM2cclktJSeHUqVNE\nRUUBkJOTQ2Bg4M1osoiI/I2pmBMREQEuXbrEvn37aNKkCTk5OaxYsQKLxULTpk2LLOvp6Un9+vX1\nNE5ERG4qDbMUEZG/PavVyuTJk3nggQc4ffo0wcHBWCwWPvvsMy5fvkxOTg5ubm5cuXIFgPvuu4/U\n1FROnToFwCeffMKmTZtu5iGIiMjfkMVut9tvdiNERET+SgV/msBms3Hu3DkeeOABhgwZwqFDhxgy\nZAgBAQG0adOGH3/8kb179/LBBx/QpUsXypQpQ1xcHJs3b2bx4sV4eXlRtmxZpk+fzh133HGzD01E\nRP5GVMyJiIiIiIiYkIZZioiIiIiImJCKORERERERERNSMSciIiIiImJCKuZERERERERMSMWciIiI\niIiICamYExERERERMSEVcyIiIiIiIiakYk5ERERERMSEVMyJiIiIiIiYkIo5ERERERERE1IxJyIi\nIiIiYkIq5kRERERERExIxZyIiIiIiIgJqZgTERERERExIRVzIiIiIiIiJqRiTkRERERExIRUzImI\niIiIiJiQijkRERERERETUjEnIiIiIiJiQirmRERERERETEjFnIiIiIiIiAmpmBMRERERETEhFXMi\nIiIiIiImpGJORERERETEhFTMiYiIiIiImJCKORERERERERNSMSciIiIiImJCKuZERERERERMSMWc\niIiIiIiICamYExERERERMSEVcyIiIiIiIiakYk5ERERERMSEVMyJiIiIiIiYkIo5ERERERERE1Ix\nJyIiIiIiYkIq5kRERERERExIxZyIiIiIiIgJqZgTERERERExIRVzIiIiIiIiJqRiTkRERERExIRU\nzImIiIiIiJiQijkRERERERETUjEnIiIiIiJiQirmRERERERETEjFnIiIiIiIiAmpmBMRERERETEh\nFXMiIiIiIiImpGJORERERETEhFTMiYiIiIiImJCKORERERERERNSMSciIiIiImJCKuZERERERERM\nSMWciIiIiIiICamYExERERERMSEVcyIiIiIiIiakYk5ERERERMSEVMyJiIiIiIiYkIo5ERERERER\nE1IxJyIiIiIiYkIq5kRERERERExIxZyUOhcvXqRPnz7s3buXkSNHGtNHjRrFI488QlxcnMt1jx07\nRmpqapHpkZGRdO3alcjISLp168a8efMA2LFjB9HR0dfdxsjIyGuat3TpUjp37kx4eDjh4eEkJSVd\n975cGTlyJGlpaURGRnL+/PkS2+6twFUMpKSkUKdOHfbt21ei+1uzZg0bN24s0W3mW7hwIc2aNePK\nlSvGtMGDB3P58uUibZg+fTqnTp1i/PjxJbLv4uJ05MiRHDlyBID4+Hi6dOlCeHg4Xbp0YcOGDSWy\nf4B58+axbds2oqKiOHr0aIlt91bgLE4/+eQTwsPDiYyMpEuXLiQkJFz3dgvmsZEjR7Jly5YSbXdB\n69evdzlv/PjxdOrUyWHaiy++WGS5efPmERcXx759+5g7d26JtEs59sYrHL9HjhyhS5cuDsvk5yVX\nCubO4mKpsLi4OOM8XJhyZunNmeJcmZvdAJGSNn/+fHr06EGFChUcpk+bNs1l8s+3fft2srKyqF+/\nfpF506ZNo3bt2thsNtq3b094eHiJtruwxMREvvnmG1asWEHZsmU5efIkUVFRzJ07lxo1apTIPsqV\nK0efPn2YPXt2iZ3MbgWuYiAhIYHq1auTmJhI3bp1S2x/hS9gSlJCQgK+vr5s3bqVBx98EIDZs2e7\nXD4gIIBXXnnlhrWnsJSUFJYuXcrixYvx9fXlwoULPPfcc1SoUIHmzZuXyD7c3NwYOnQoMTExLFiw\noES2eSsoHKc5OTnMmDGD+Ph4fHx8yMjI4Nlnn6Vdu3Z4enpe83aLy2MlKScnh6VLl/LYY48VmWe1\nWtm8eTOenp4cPHiQ4OBgAN566y2X26tbt26J/l1ejXLsn+Mqz16P/NxZXCxdL+XM0pszxTkVc1Kq\nZGdns2HDBoYNG0Zubi5Dhgxxuezs2bPZuXMnNpuN3r1706JFC+bPn0+ZMmWoWrUqbdq0cbrexYsX\ncXd3x9vb22F606ZN2bFjBwDR0dE89dRT3HvvvYwePZqzZ89is9kYO3YsISEhxd6pzJ8XGxvL1KlT\nKVu2LACVK1fm2WefJS4ujmeeeYaBAwdy991389NPP3HfffcxceJETp48yZgxY7Barbi7uzN58mSq\nVatG27ZtadOmDSkpKZQvX55FixYxZMgQ/Pz8uPPOO3n99de5ePEi5cqVu67+vhW5igGbzcaGDRuY\nPXs2I0aMYNiwYUDe3VJ/f3/27NlDRkYGzz33HGvWrCEzM5O4uDi8vb0ZN24c6enpXLlyhejoaJo3\nb05kZCS1atUCwM/PDz8/P3r37s3kyZNJTU3F3d2dSZMmUaNGDUaMGMHJkyfJyspiwIABtG7dmsjI\nSJo3b86OHTvIzMxkwYIFVKtWzeFY0tLSyM3N5ZlnniExMdG4MHnkkUeIj48nJiYGDw8Pzpw5Q+vW\nrQE4cuQI0dHRrFmzhrZt29KjRw8+//xzcnJyWLJkCV5eXk6PZ+3atcTFxeHh4UFISAgTJkwoNk7z\n42fmzJlER0fj6+sLgI+PD0OGDOGdd96hefPmtGrVikcffZQffviBypUr8/rrr5OTk+P078JZe596\n6im8vLzw8vLi7Nmz/Pzzz9x1110lGzQ3gbM4vXz5MllZWeTk5ADg7+/PmjVrADhx4gSjR4/GarVi\nsViYMmUKFovF+Kwh78J44sSJDnkM8kYQxMXFcfz4cV5//XUWL15MZGQkoaGhREVF0aJFC6Kioli4\ncCGVKlUiKCiIWbNmGduIiYkhOzubQYMGkZOTQ05ODuPHj2f16tWkpaUxceJEJk6c6HB8X331Fffc\ncw9169YlMTHRGMGQnycL//3kt3P58uXMnTvXac7KyspyGjeLFi1i48aNuLm50bp1a1544QXl2BvM\nWfzmx60rzvrozTffxM/Pj4MHDxqxNG7cOKc5atu2bUydOpU77riDgIAAgoKCiuxDObP05kxxTcMs\n5YZ6f8N+nhi6jieGrmPQ7M8d5vWdtN6Yt37bT8b0b/ecMKY/MXSdwzoH0s8Uu7/U1FRq166Nu7s7\nHh4eVKpUyelyO3fu5OjRoyxfvpz33nuPt956C29vbzp37kyfPn2cFnKjRo0iMjKSxx57jK5du+Lj\n43PV44+NjaVVq1bExsYyceJEI9EXvmgvKH/e0aNHjbvZ+UJCQjh8+DCQd9IaNmwYq1ev5ocffmD/\n/v3MmTOHZ555htjYWPr27cu///1vANLT03nyySdZtWoV586dIy0tjUqVKuHh4YHFYqFevXrs2rXr\nqsfzR/yyYhXfdOrKN526smvIcId53z79rDHvxIZPjekZ3yYZ07/p1NVhnQsHDha7P1cxsHXrVoKD\ngwkLC8PX15eUlBRjnTJlyhAbG0vt2rWNu6a1a9dmx44dxMfHExAQwLJly3jzzTeZOnWqsV6tWrUc\n7rZv3bqVEydO8MEHHzBkyBA+/vhjzp49S8uWLYmLi2POnDkOT4fLly9PbGwsDz74IJ9++vvx50tI\nSKB9+/a0a9eOL774guzs7CLLVKxY0eUTZ5vNRnBwMMuXLycwMJDt27e7PJ53332XefPmsWLFCurV\nq8fly5eLjdP8+Dl06FCRpyl169Y14vTXX3+lQ4cOrFq1Crvdzpdffuny78JZe/39/fHy8gIgLCzM\nuGFS0o4d+JTvPh3Od58OZ9+2NxzmpX4RY8w7dWS7Mf3Mr3uN6d996hjbF88dKXZ/zuK0QoUK9OzZ\nk3bt2jF48GDWrFljDA2bM2cO3bp1Y9myZfTq1Yv58+c73a6/v3+RPGaxWHj33Xfp06cPH374IU2a\nNGHXrl3YbDbc3d354YcfAEhOTqZp06ZMnjyZf//737z33nvcfvvtrF+/nm3btlG5cmWWLVvG66+/\nzunTp4mKiqJ69epFCjn4PXYff/xxEhMTnba18N9PQc5ylqu4Wbx4MStWrGDlypXGU6K/U46FW/dc\nW5CzPspXMJZc5aiZM2fy2muvsWTJEjIzM53uQznzr8uZcuvQkzkpVX799VeqVKly1eWSk5P5/vvv\njfHtubm5nDp1qth18odZ5uTk0L9/f+rWrYu7u3ux66SkpJCRkcFHH30EwKVLl67xSFxzc8u7B3P3\n3Xcbd95DQ0M5dOgQKSkpHD58mLfeegubzYa/vz+Qd+cvJCQEgCpVqhT5/kblypU5fvz4n27brcBV\nDCQkJNChQwcAnnjiCRITE2nYsCGAMRytUqVKxvCqO+64g/Pnz7Nr1y6+++47kpOTgbw70vl3oAsP\nY9uzZw+NGjUC8k6iYWFhWK1WfvjhB1atWoWbmxtnzvx+kdS4cWMg7zMpOB3AbreTmJjIkiVL8PX1\npUGDBnzxxRe0a9fOYbmrDaUruI/ijqdDhw68/PLLdOzYkQ4dOhhPK67GYrGQm5tbpO35cert7U2D\nBg0AaNCgAYcPHy7276Jwewv6O8Tp4MGD6d5XFaJEAAAbm0lEQVS9O1999RVr167l7bff5sMPP2T3\n7t0MHToUyHu69eabb17zvu6//34gr/++//57wsLCmDdvHk2aNKFu3bqkpaVht9s5deoUnp6e/Pzz\nzwwYMACArKws/Pz86NSpE2+88Qbjx4+nXbt2PPjgg8b3fwrLysrim2++4ZVXXsHHxwdPT0/27NnD\nvffe67BccbHrLGe5iptHH32Ufv360aFDBzp27HjN/eKKcuzVXeu5tqCr9VG+lJQUpznq6NGjxvph\nYWFFCjXlzNIfd+KcijkpdSwWi8P/s7KysFgseHl5kZubi7u7O56ennTr1o3nn3/+urfv6enJQw89\nxM6dO2natKnTZaxWKwAeHh6MGzfOKBquR2BgIPv373e4g7dv3z5q1qwJ4HAysNvtWCwWPDw8mDNn\nTpG7pIWLTrvdft3tMZPCMZCdnc3mzZvZs2cPcXFxWK1Wzp07x+jRowHH/in4b7vdjoeHBy+88IJR\nCBbk4eHh8H93d/ciJ+mEhATOnj3L+++/z5kzZ+jWrZvLfRWUnJzM6dOnjeFp58+fJzExsciFSeE2\nFHatx/P888/zxBNPsGHDBvr27UtcXJwx/K04NWrUYPfu3Q4XdtcSp67+Lorrk9KmcJwCXL58mcDA\nQCIiIoiIiCAyMpLU1FQsFovRH1arFTc3tyLrF3zhQ0GF+7R69eocO3aM5ORkGjVqxPnz5/nyyy8J\nCQkxnrIsW7asyHbWrVvHjh07WLFiBbt27eLJJ590ur9NmzZhs9l46qmnAMjMzCQxMbFIMVdc7DrL\nWa7iZtKkSRw8eJBPPvmEyMhI/vOf/1CmzNUvb5Rj/5zC8efn58eFCxccpmVkZBh9da195CpH5Rc7\nrtZVzvx7xJ0UpWGWckP1ejSE+JmdiJ/ZiTcGP+wwL3bCY8a8x5rfbUxvcm8VY3r8TMc3odUM8i12\nf5UqVeLEiRMO0xYuXGhcmBw8eJAaNWpQv359tmzZQm5uLtnZ2cTExAB5JydXF0QFpaamUr16dYdp\nFouFS5cucenSJeNtiaGhoWzatAmAAwcOsGTJkqtuO1/fvn2ZPn26cQfu119/ZfHixfTu3RuAX375\nhV9//ZXc3Fy+//57atas6bC/bdu2ER8ff037Onny5HXfZb1W/4gI54F1/+WBdf+lwazXHOY1WfqO\nMa/Ko7+fcP2bhBnTH1j3X4d1fGo6DosqzFkMbN68mWbNmpGQkMC6dev4+OOPqVGjxjUNPwkNDeWz\nzz4D4PTp08yaNcvlsvfdd5+xzb179zJp0iQyMzMJDAzEzc2NjRs3XvV7JfkSEhIYNmwY69atY926\ndSQkJJCUlMTFixevaf3rOZ7c3Fxmz55NQEAA/fr1o0GDBhw7duyattenTx/mzZtHRkYGABcuXGD2\n7Nk8/fTTQF5xsnv3bgB27dpVJE6v5+/ijzwNuFbVarbj/navcX+716jbfJDDvPoPjTPmBQQ2M6b7\nVrrHmH5/O8fYLlchsNj9OYvTrVu38q9//cu4GZSdnc25c+eoVq2aQ2wlJSVRr149fHx8OH36tPFU\nLT09Hbi2PFatWjU2bdpEaGgooaGhxMbG0rRpUypWrAjkfS4Ay5YtY//+/WzdupWtW7fSsmVLxo0b\nx+7du3Fzc8NmsxXZdkJCAjNmzDBid+XKlaxfv/5PX2g6i5vz588zf/58goOD6d+/PxUrVixSULhS\nWnIs3Brn2nLlyuHv78/OnTuBvBup69evp0WLFldtf8FYcpVzK1euzKFDh7Db7Xz77bdFtqGcWdSN\nzJly69CTOSlV6tevT1pamvFdEMg7YQ8cOJDPP/+cGjVqGE/TmjZtSnh4OHa7nV69egHQsGFDRowY\ngb+/f5HhOqNGjcLb2xur1UqdOnV4/PHHHV5jHRERQY8ePQgODjbuQPfu3ZtRo0bRq1cvcnNzGTNm\njMM2Bw8ezLRp05wOz2jfvj1ZWVn07NmT2267DYvFwvDhwwkKCuLIkSNUr16d2bNnc+DAARo1akSt\nWrXo378/o0ePJjExEYvFwrRp067aZ3a7nd27d/+lb/O6kZzFQEJCgsMTMch7WYSr7/IU9H//939s\n376dnj17YrPZ6N+/v8tlw8LC+Oyzz4x4mjBhAuXKlePFF19k165ddO3alSpVqrj8vlO+K1eusHnz\nZoefvfD29ubhhx82Lir+KGfH4+bmRrly5QgPD6d8+fIEBQU5PK2YMmUKffr0cfrCgQYNGjB48GCe\nffZZPDw8sFqt9OnTxxj64+vry0cffcTUqVMJCAigZcuWNG7cuNi/C1eSkpIcvrNoZs7itEWLFuzZ\ns4eIiAi8vLywWq307duXwMBAoqOjGTNmDB988AEeHh5MnTqVihUr0qJFC7p27UpISIjxmRXMY66E\nhYXx3nvvGcPRRowYYfTtlClTGDVqlPGULjw8HB8fH4YPH84777xjvHglICAAq9VKdHS08ZMCmZmZ\npKWlGS+egLwnYEFBQcYwtT/KWT4tX748mZmZdOvWDW9vbxo2bGi8WAKUY28UZ/ELMGPGDGJiYpgz\nZw5Wq5V+/fpRp06dq26vYCzNmjXLac4dNGgQAwcOpFq1akUKFOVM50pTzpRi2EVKmalTp9oTExNv\ndjOuycyZM+1Wq/W610tPT7d37ty5RNqwceNG+4QJE0pkW7cKM8WAGcTFxdnT09P/0LpNmjQpkTbs\n27fP/txzz5XItm4VitMbTzn2xlH8uqacKX8lDbOUUmfAgAGsWrWKs2fP3uymXFWDBg2u6bsdN8qF\nCxeIjY1l8ODBN60NN4KZYsAMqlatSmBg8cMGbySbzcZrr73GuHHjblobbgTF6Y2nHHvjKH5dU86U\nv5LFbte3JUVERERERMxGT+ZERERERERMSMWciIiIiIiICamYExERERERMSEVcyIiIiIiIiakYk5K\nnYsXL9KnTx/27t3LyJEjjemjRo3ikUceIS4uzuW6x44dIzU1tcj0yMhIunbtSmRkJN26dWPevHkA\n7Nixw+F3ba5VZGTkNc1799136dy5MxEREfTs2dP40eCRI0eyZcuW695vYWvWrGHNmjUMGTLE6XGb\nlasYSElJoU6dOsaPupeUNWvWsHHjxhLdZr6FCxfSrFkzhx+BHjx4MJcvXy7ShunTp3Pq1CnGjx9f\nIvsuLk5HjhzJkSNHAFi7di1dunShZ8+edOvWjfXr1wMwb968Yv/ertWOHTuYN28e06dPN344tzRw\nFqeffPIJ4eHhREZG0qVLFxISEq57uwXzWEnlClfyP2tnxo8fT6dOjj9G/eKLLxZZLj9O9u3bZ/xe\n3Z+lHHvjFY7fI0eO0KVLF4dl8vOSKwVzZ3GxVFhcXJxxHi5MObP05kxxTj8aLqXO/Pnz6dGjBxUq\nVHCYPm3aNJfJP9/27dvJysqifv36ReZNmzaN2rVrY7PZaN++PeHh4SXa7sLi4+NJSkpi1apVeHp6\ncvjwYZ5++mk++uijEt/XyJEjeemll/jPf/6DxWIp8e3/1VzFQEJCAtWrVycxMdHhB17/rMIXMCUp\nISEBX19ftm7davwQ8+zZs10uHxAQ8Jf+OPF3333H8uXLWbp0KRUqVOD06dP07NmT2rVrl/i+Bg0a\nRPfu3XnggQfw8vIq8e3/1QrHaU5ODjNmzCA+Ph4fHx8yMjJ49tlnadeuHZ6ente83eLyWEnKyclh\n6dKlPPbYY0XmWa1WNm/ejKenJwcPHiQ4OBiAt956y+X26tatW6J/l1ejHPvnuMqz1yM/dxYXS9dL\nOfN3pS1ninMq5qRUyc7OZsOGDQwbNozc3FyGDBnictnZs2ezc+dObDYbvXv3pkWLFsyfP58yZcpQ\ntWpV2rRp43S9ixcv4u7ujre3t8P0pk2bGnd1o6Ojeeqpp7j33nsZPXo0Z8+exWazMXbsWEJCQoq9\nU5k/b9myZUydOtW4iKtevTrx8fEOJ06r1cr48eNJT08nJyeH6OhoWrZsyaJFi9i4cSNubm60bt2a\nF154gZ07dzJr1izj+GJiYmjbti0A5cuX5+6772bbtm20aNHiGnr61uUqBmw2Gxs2bGD27NmMGDGC\nYcOGAXkXWf7+/uzZs4eMjAyee+451qxZQ2ZmJnFxcXh7ezNu3DjS09O5cuUK0dHRNG/enMjISGrV\nqgWAn58ffn5+9O7dm8mTJ5Oamoq7uzuTJk2iRo0ajBgxgpMnT5KVlcWAAQNo3bo1kZGRNG/enB07\ndpCZmcmCBQuoVq2aw7GkpaWRm5vLM888Q2JionFh8sgjjxAfH09MTAweHh6cOXOG1q1bA3DkyBGi\no6NZs2YNbdu2pUePHnz++efk5OSwZMkSvLy8nB7P2rVriYuLw8PDg5CQECZMmFBsnA4ZMgQ/Pz9m\nzpxJ//79jbi8/fbb+e9//1vkAm/GjBkkJydjs9l46qmnePLJJ53u88CBA7zyyitYLBbKlSvHq6++\nSv369alVqxa33XYbrVu3JiEhge7du5dAtNw8zuL08uXLZGVlkZOTA4C/vz9r1qwB4MSJE4wePRqr\n1YrFYmHKlClYLBbjs4a8C+OJEyc65DHIu0sfFxfH8ePHef3111m8eDGRkZGEhoYSFRVFixYtiIqK\nYuHChVSqVImgoKAiuSI7O5tBgwaRk5NDTk4O48ePZ/Xq1aSlpTFx4kQmTpzocHxfffUV99xzD3Xr\n1iUxMdEYwZCfJwv//eS3c/ny5cydO5e2bdvSpk0bUlJSKF++PIsWLSIrK8tpPnWW75Rjbyxn8Zsf\nt644+0zffPNN/Pz8OHjwoBFL48aNc5qjtm3bxtSpU7njjjsICAggKCioyD6UM0tvzhTXVMzJDfXB\n7gRW70kEoLpfENPbjTbmPb9uJJmX835s9F+Ne/HP4FYA7Dyayoyvf797+0H47/8+lPEzNfzvcrm/\n1NRUateujbu7O+7u7lSqVMnpcjt37uTo0aMsX76cnJwcOnfuzD//+U86d+6Mn5+f00Ju1KhReHt7\nc/DgQaKiovDx8bnq8cfGxtKqVSu6d+/OgQMHmDJlCkuWLCly0V5Q/ryjR48ad7PzFU72iYmJeHp6\nEhcXx8mTJ+nTpw8bNmxg8eLFfP3117i7u7NixQoAJk+ezNKlS/H19WXGjBmsX7+ejh07GtsKCwtj\nx44dJX6h8fmGNL789H8AVA2syHODHzTmzZr0KRfOZQPweLf63N8877NN23OCVYuTjOXGz3zC+Pex\n9DNUC/J1uT9XMbB161aCg4MJCwvD19eXlJQUGjZsCECZMmWIjY1l6NChpKSksHTpUoYPH86OHTu4\ncOECAQEBTJ06lYyMDPr27Ut8fDwAtWrVIiIiwnjiu3XrVk6cOMEHH3xAUlISH3/8MZGRkbRs2ZLO\nnTuTnp7OwIEDjYuI8uXLExsby+uvv86nn37K008/7XAsCQkJtG/fnnbt2jFr1iyys7O57bbbHJap\nWLEiMTExxgV9QTabjeDgYJ577jkGDx7M9u3bXR7Pu+++y6JFi6hatSr//e9/uXz5crFxmt+vhw4d\nIiQkxGFe4ThNSkrixx9/ZOXKlWRlZdGxY0f++c9/Ot1nTEwMr7zyCnfffTfLly9n+fLlvPjii8Zd\n5bCwMD788MMSvzD56H/HiD9wAoB/VPBiXMvfnxAN++wHzmZbAYis9w8e/McdAHx/8gzzvztkLPd2\n+0bGv38+m8VdFR1v+BTkKk579uxJu3btaNWqFa1ataJ9+/aULVuWOXPm0K1bN9q3b8/69euZP38+\nAwYMKLJdf39/hzy2ceNGLBYL7777LitXruTDDz+kSZMm7Nq1i3r16uHu7s4PP/wAQHJyMhMmTOCl\nl14qkivKli1L5cqVmTp1Kunp6Rw+fJioqCi+//77IoUc/B6799xzDwMGDHA6HL3w309B6enpPPnk\nk4wcOZIePXqQlpbG5s2bneZTZ/nu75Rj4dY41+YPIXTF2Wear2AsrV271mmOmjlzJq+99hohISE8\n99xzTos55cy/LmfKrUPFnJQqv/76K1WqVLnqcsnJyXz//ffG+Pbc3FxOnTpV7Dr5wyxzcnLo378/\ndevWxd3dvdh1UlJSyMjIMIbtXLp06RqPBOx2O3a7vdghObt376Zp06YAVK5cGU9PT86cOcOjjz5K\nv3796NChAx07duS3337j559/Ni7+srKyjLvh+apUqcJ33313ze27VbmKgYSEBDp06ADAE088QWJi\nolHM5Q9Hq1SpEjVq1ADgjjvu4Pz58+zatYvvvvuO5ORkIO+OdP4d6MLD2Pbs2UOjRnkX9GFhYYSF\nhWG1Wvnhhx9YtWoVbm5unDlzxli+cePGQF7fF5wOeZ9/YmIiS5YswdfXlwYNGvDFF1/Qrl07h+Wu\nNpSu4D6KO54OHTrw8ssv07FjRzp06EDZsmWL3W4+i8VCbm5uscvs3r2bsLAwALy9valZsyY///yz\n032mpqYybtw4IG/o1X333eewrSpVqnDixIlratutzFWcDh48mO7du/PVV1+xdu1a3n77bT788EN2\n797N0KFDgbynW2+++eY17+v+++8H8nLE999/T1hYGPPmzaNJkybUrVuXtLQ07HY7p06dwtPT02mu\n6NSpE2+88Qbjx4+nXbt2PPjggy4v3rOysvjmm2945ZVX8PHxwdPTkz179nDvvfc6LFdc7Pr4+BgX\nvPmx6yqfFs5310o59o+71nNtQc4+U2dSUlKc5qijR48a64eFhZGdne2wnnJm6c6Z4pqKOSl1Cp+Y\ns7KysFgseHl5kZubi7u7O56ennTr1o3nn3/+urfv6enJQw89xM6dO42TfGFWa95dfA8PD8aNG2cU\nDdcjKCiIvXv3Uq9ePWPa/v37i9xJttvtxr9zcnJwc3Nj0qRJHDx4kE8++YTIyEjeffddKlWqxLJl\ny667HWZUOAays7PZvHkze/bsIS4uDqvVyrlz5xg9Ou/udcGivOC/7XY7Hh4evPDCC0YhWJCHh4fD\n/93d3YucpBMSEjh79izvv/8+Z86coVu3bi73VVBycjKnT582nmicP3+exMTEIhcmhdtQ2LUez/PP\nP88TTzzBhg0b6Nu3L3FxcUUuRp2pUaMGqampxpA+gIMHDzpc6BX+PKxWK25ubk736eXlxXvvvVdq\nvldUHGfHePnyZQIDA4mIiCAiIoLIyEhSU1OxWCxGjOT3X+H1C77woaDCMVC9enWOHTtGcnIyjRo1\n4vz583z55ZeEhITg4eHhMlesW7eOHTt2sGLFCnbt2sWTTz7pdH+bNm0yhoYBZGZmkpiYWKSYKy52\nC98oy49dZ/m0cL77z3/+Q5kyV7+8UY79cwrHn5+fHxcuXHCYlpGRYTyRcvaZOuMqR7m5/f7OPmfr\nKmeW/pwpzultlnJD9ajXgQ/C3+KD8Lcchn0ALOz0qjEvf9gHQOM76xvTCw77AIod9gF5T1YK34Fa\nuHChcYI9ePAgNWrUoH79+mzZsoXc3Fyys7OJiYkB8hKoqwuiglJTU6levbrDNIvFwqVLl7h06ZLx\ntsTQ0FDjTVIHDhxgyZIlV912vr59+zJ9+nSysrKAvKEZgwYN4ty5c8Yy9913n/E9vePHjxsXePPn\nzyc4OJj+/ftTsWJF4yR44MABIO+7Ivv373fY38mTJ6/7Tuu1ePjROoyf+QTjZz7hMMQSYMiEdsa8\n/CGWAHXurWJMLzjEEih2iCU4j4HNmzfTrFkzEhISWLduHR9//DE1atQw+q44oaGhfPbZZwCcPn2a\nWbNmuVy24Oexd+9eJk2aRGZmJoGBgbi5ubFx48arfq8kX0JCAsOGDWPdunWsW7eOhIQEkpKSuHjx\n4jWtfz3Hk5uby+zZswkICKBfv340aNCAY8eOXdP2+vTpw/z58zl9+jQAp06dYtCgQRw/ftxYpl69\neka/XLx4kV9++YW77rrL6T5DQkL48ssvgbwhbtu2bXPY342K0461q/F2+0a83b6RwxBLgNfb3GfM\nyx9iCRBa2deYXnCIJVDsEEtwHqdbt27lX//6l3EzKDs7m3PnzlGtWjWH2EpKSqJevXr4+Phw+vRp\n46laeno6cG15rFq1amzatInQ0FBCQ0OJjY2ladOmVKxYESiaK7Zu3crWrVtp2bIl48aNY/fu3bi5\nuWGz2YpsOyEhgRkzZhixu3LlStavX+/y4v1aOcun58+fL5LvChcUrpSWHAu3xrm2XLly+Pv7s3Pn\nTiDvRur69euvaVhpwVhylXMrV67MoUOHsNvtfPvtt0W2oZz51+ZMuXXoyZyUKvXr1yctLQ2bzWbc\nXevbty8DBw7k888/p0aNGsbTtKZNmxIeHo7dbqdXr14ANGzYkBEjRuDv719kuE7+d+asVit16tTh\n8ccfJynp9+91RURE0KNHD4KDg4070L1792bUqFH06tWL3NxcxowZ47DNwYMHM23aNKfDM9q3b8/F\nixcJDw+nQoUK3HbbbbzxxhvcfvvtxjKPP/443377LZGRkVitVl555RXKly9PZmYm3bp1w9vbm4YN\nG+Lr68uUKVMYNWqUcee98Ns4k5KSXN5pNxNnMZCQkODwRAzyXhaRmJh41e393//9H9u3b6dnz57Y\nbDb69+/vctmwsDA+++wzI54mTJhAuXLlePHFF9m1axddu3alSpUqzJ8/v9h9Xrlyhc2bNzt8z8jb\n25uHH37YuKj4o5wdj5ubG+XKlSM8PJzy5csTFBTk8FbBKVOm0KdPH6ffUWnQoAGDBw8mKioKLy8v\nypQpw5gxY6hZs6axTOPGjalXrx5PPfUUV65cYejQoXh7ezvd55gxYxg3bhxvv/02t912GzNnznTY\nX1JSkssn4mbiLE5btGjBnj17iIiIwMvLC6vVSt++fQkMDCQ6OpoxY8bwwQcf4OHhwdSpU6lYsSIt\nWrSga9euhISEGJ9ZwTzmSlhYGO+9954xHG3EiBFMnToVwGmu8PHxYfjw4bzzzjvGi1cCAgKwWq1E\nR0cbPymQmZlJWlqa8eIJgMDAQIKCgoxhan+Us3zqKt/lU469MZzFL+S9tCMmJoY5c+ZgtVrp168f\nderUuer2CsbSrFmznObcQYMGMXDgQKpVq1akOFHOLP05U1yz2P/srTKRW8y0adMIDQ2lffv2N7sp\nVzVr1iyio6OvaUjQjfTbb7/x/PPPs3r16lIxVMNMMWAGy5cv56GHHiIwMPCmtiM7O5vu3buzcuXK\nIm+TNSPF6Y2nHHvjKH5dU86Uv5KGWUqpM2DAAFatWsXZs2dvdlOuqkGDBjf9IgPyTsrjx48vNRcZ\nZooBM6hatepNvygBeOONN+jfv3+puShRnN54yrE3juLXNeVM+SvpyZyIiIiIiIgJ6cmciIiIiIiI\nCamYExERERERMSEVcyIiIiIiIiakYk5ERERERMSEVMyJiIiIiIiYkIo5ERERERERE1IxJyIiIiIi\nYkIq5kRERERERExIxZyIiIiIiIgJqZgTERERERExof8Hn5zUKRnkQSYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fea1972dda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax3 = plt.subplots(figsize = (15,15))\n",
    "#ax3 = stocks.plot(figsize = (15,6) )\n",
    "\n",
    "ax3.fill_between(stocks.index,stocks.iloc[:,0],stocks.iloc[:,1],\n",
    "                facecolor='blue', alpha=0.2, interpolate=True)\n",
    "ax3.fill_between(stocks.index,stocks.iloc[:,2],stocks.iloc[:,3],\n",
    "                facecolor='blue', alpha=0.2, interpolate=True)\n",
    "ax3.fill_between(stocks.index,stocks.iloc[:,4],stocks.iloc[:,5],\n",
    "                facecolor='blue', alpha=0.2, interpolate=True)\n",
    "ax3.fill_between(stocks.index,stocks.iloc[:,6],stocks.iloc[:,7],\n",
    "                facecolor='blue', alpha=0.2, interpolate=True)\n",
    "for i in range(len(stocks.columns)): ax3.plot(stocks.iloc[:,i], lw = 3, linestyle = ':') \n",
    "ax3.legend(loc='upper center', bbox_to_anchor=(0.5, -0.1),fancybox=True, shadow=True, ncol=4)  \n",
    "ax3.set_xlabel('Date')\n",
    "ax3.set_ylabel('Stock Price')\n",
    "ax3.set_title('Airline Open & Close Prices')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just from first initial impressions, there doesn't appear to be anything significant with this data that would correlate to the twitter data. It might be a waste of time continuing down this avenue. But this is fine. We at least explored the possibility of a correlation. However, I think it's pretty clear that we at all costs should avoid US Airways. Their ratings as told by the people is just terrible. They have a real bad Customer Service Department, and their flights are apparently always late."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SUMMARY: AVOID US AIRWAYS\n",
    "Overall it appears that Virgin Airlines would be the best choice, but be careful of this decisions. Virgin was the least talked about Airline in the Twitter-verse, but just because people aren't talking about you doesn't mean they like you either! But let's roll the dice and fly with Sir Richard Branson"
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}