5.+Tutorial+-+Accessing+Sentiment+and+Earnings+Data+via+the+Quantopian+Store.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting Started with Data from the Quantopian Store\n",
    "\n",
    "The purpose of this notebook is to introduce you to the mechanics of working with data in the Quantopian Store, especially the Blaze library. Let's dig in.\n",
    "\n",
    "\n",
    "We can start off by trying to import the full, paid version of a data set -- in this case, estimate revisions data from Estimize. A user that has purchased access to this data set from Estimize can successfully run this import. Users who haven't purchased access to this data set will receive an error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from quantopian.interactive.data.estimize import revisions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But worry not, if you haven't purchased the data yet. There are generous free samples for every data set. You can explore and understand the data prior to purchasing a subscription for the full data.\n",
    "\n",
    "The free samples typically cover at least 3 years of historic data. The free samples typically *don't* cover a trailing window of 1 or 2 years prior to the current day. Estimize's revision data, which we're examining here, provides access in the free sample (`revisions_free`) for up to one year from today."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from quantopian.interactive.data.estimize import revisions_free"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Key fields common to most Store data sets\n",
    "\n",
    "Let's take a look at the estimate revisions data from Estimize. By simply `running revisions_free` in our notebook, we'll see the resulting Blaze expression.\n",
    "\n",
    "Blaze is a library for accessing, manipulating and filtering large data sets. We use it here for accessing the data from the Quantopian Store because these data sets can be very large. Blaze allows us to shift the processing burden over to the server side.\n",
    "\n",
    "Looking at the object, there are many fields unique to Estimize. I want to cover three that you'll see in every data set from the Quantopian Store:\n",
    "\n",
    "- `sid`: we've done symbol mapping for all cross-sectional data sets for you. You don't have to worry about it this drudgery. We've got you covered\n",
    "- `timestamp`: the `timestamp` represents the datetime upon which the record entered the Quantopian system and is available for use by you. For records added to the dataset after the introduction of the overall dataset, we have an exact record for this time. For records from the initial load of historical data, we do our best to approximate this date. This field is especially important for preventing look-ahead bias.\n",
    "- `asof_date`: the `asof_date` represents the time period to which the record applies. Often times this is simply the datetime at which the data was recorded. Other times it is the timeframe (such as 3/31 for data applying to the first quarter)\n",
    "\n",
    "Check it out below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>mean</th>\n",
       "      <th>metric</th>\n",
       "      <th>source</th>\n",
       "      <th>standard_deviation</th>\n",
       "      <th>asof_date</th>\n",
       "      <th>consensus_eps_estimate</th>\n",
       "      <th>consensus_revenue_estimate</th>\n",
       "      <th>eps</th>\n",
       "      <th>fiscal_quarter</th>\n",
       "      <th>fiscal_year</th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>release_date</th>\n",
       "      <th>revenue</th>\n",
       "      <th>symbol</th>\n",
       "      <th>wallstreet_eps_estimate</th>\n",
       "      <th>wallstreet_revenue_estimate</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>sid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.550</td>\n",
       "      <td>eps</td>\n",
       "      <td>wallstreet</td>\n",
       "      <td>0.099</td>\n",
       "      <td>2014-08-05 11:09:21.044000</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>5275dfaad2500546bf00031d</td>\n",
       "      <td>MRC Global Inc.</td>\n",
       "      <td>2015-11-05 11:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>MRC</td>\n",
       "      <td>0.036</td>\n",
       "      <td>1078.388</td>\n",
       "      <td>2014-08-05 11:29:21.044000</td>\n",
       "      <td>42786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.560</td>\n",
       "      <td>eps</td>\n",
       "      <td>wallstreet</td>\n",
       "      <td>0.057</td>\n",
       "      <td>2014-07-29 11:16:02.680000</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>5275dfaad2500546bf00031d</td>\n",
       "      <td>MRC Global Inc.</td>\n",
       "      <td>2015-11-05 11:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>MRC</td>\n",
       "      <td>0.036</td>\n",
       "      <td>1078.388</td>\n",
       "      <td>2014-07-29 11:36:02.680000</td>\n",
       "      <td>42786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.573</td>\n",
       "      <td>eps</td>\n",
       "      <td>wallstreet</td>\n",
       "      <td>0.046</td>\n",
       "      <td>2014-07-10 11:09:32.248000</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>5275dfaad2500546bf00031d</td>\n",
       "      <td>MRC Global Inc.</td>\n",
       "      <td>2015-11-05 11:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>MRC</td>\n",
       "      <td>0.036</td>\n",
       "      <td>1078.388</td>\n",
       "      <td>2014-07-10 11:29:32.248000</td>\n",
       "      <td>42786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.588</td>\n",
       "      <td>eps</td>\n",
       "      <td>wallstreet</td>\n",
       "      <td>0.047</td>\n",
       "      <td>2014-06-17 11:10:33.127000</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>5275dfaad2500546bf00031d</td>\n",
       "      <td>MRC Global Inc.</td>\n",
       "      <td>2015-11-05 11:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>MRC</td>\n",
       "      <td>0.036</td>\n",
       "      <td>1078.388</td>\n",
       "      <td>2014-06-17 11:30:33.127000</td>\n",
       "      <td>42786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.583</td>\n",
       "      <td>eps</td>\n",
       "      <td>wallstreet</td>\n",
       "      <td>0.057</td>\n",
       "      <td>2014-05-07 11:28:08.998000</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>5275dfaad2500546bf00031d</td>\n",
       "      <td>MRC Global Inc.</td>\n",
       "      <td>2015-11-05 11:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>MRC</td>\n",
       "      <td>0.036</td>\n",
       "      <td>1078.388</td>\n",
       "      <td>2014-05-07 11:48:08.998000</td>\n",
       "      <td>42786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.570</td>\n",
       "      <td>eps</td>\n",
       "      <td>wallstreet</td>\n",
       "      <td>0.048</td>\n",
       "      <td>2014-05-03 11:13:01.558000</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>5275dfaad2500546bf00031d</td>\n",
       "      <td>MRC Global Inc.</td>\n",
       "      <td>2015-11-05 11:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>MRC</td>\n",
       "      <td>0.036</td>\n",
       "      <td>1078.388</td>\n",
       "      <td>2014-05-03 11:33:01.558000</td>\n",
       "      <td>42786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>1610.70</td>\n",
       "      <td>1556.00</td>\n",
       "      <td>1583.350</td>\n",
       "      <td>revenue</td>\n",
       "      <td>wallstreet</td>\n",
       "      <td>38.678</td>\n",
       "      <td>2014-08-05 11:09:21.148000</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>5275dfaad2500546bf00031d</td>\n",
       "      <td>MRC Global Inc.</td>\n",
       "      <td>2015-11-05 11:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>MRC</td>\n",
       "      <td>0.036</td>\n",
       "      <td>1078.388</td>\n",
       "      <td>2014-08-05 11:29:21.148000</td>\n",
       "      <td>42786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>1595.80</td>\n",
       "      <td>1511.00</td>\n",
       "      <td>1553.400</td>\n",
       "      <td>revenue</td>\n",
       "      <td>wallstreet</td>\n",
       "      <td>59.962</td>\n",
       "      <td>2014-07-10 11:09:32.389000</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>5275dfaad2500546bf00031d</td>\n",
       "      <td>MRC Global Inc.</td>\n",
       "      <td>2015-11-05 11:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>MRC</td>\n",
       "      <td>0.036</td>\n",
       "      <td>1078.388</td>\n",
       "      <td>2014-07-10 11:29:32.389000</td>\n",
       "      <td>42786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>1595.80</td>\n",
       "      <td>1511.00</td>\n",
       "      <td>1562.500</td>\n",
       "      <td>revenue</td>\n",
       "      <td>wallstreet</td>\n",
       "      <td>45.234</td>\n",
       "      <td>2014-06-17 11:10:33.327000</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>5275dfaad2500546bf00031d</td>\n",
       "      <td>MRC Global Inc.</td>\n",
       "      <td>2015-11-05 11:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>MRC</td>\n",
       "      <td>0.036</td>\n",
       "      <td>1078.388</td>\n",
       "      <td>2014-06-17 11:30:33.327000</td>\n",
       "      <td>42786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>1580.70</td>\n",
       "      <td>1511.00</td>\n",
       "      <td>1546.100</td>\n",
       "      <td>revenue</td>\n",
       "      <td>wallstreet</td>\n",
       "      <td>34.852</td>\n",
       "      <td>2014-05-24 11:05:50.596000</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>5275dfaad2500546bf00031d</td>\n",
       "      <td>MRC Global Inc.</td>\n",
       "      <td>2015-11-05 11:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>MRC</td>\n",
       "      <td>0.036</td>\n",
       "      <td>1078.388</td>\n",
       "      <td>2014-05-24 11:25:50.596000</td>\n",
       "      <td>42786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4</td>\n",
       "      <td>1580.70</td>\n",
       "      <td>1511.00</td>\n",
       "      <td>1552.125</td>\n",
       "      <td>revenue</td>\n",
       "      <td>wallstreet</td>\n",
       "      <td>30.903</td>\n",
       "      <td>2014-05-07 11:28:09.914000</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>5275dfaad2500546bf00031d</td>\n",
       "      <td>MRC Global Inc.</td>\n",
       "      <td>2015-11-05 11:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>MRC</td>\n",
       "      <td>0.036</td>\n",
       "      <td>1078.388</td>\n",
       "      <td>2014-05-07 11:48:09.914000</td>\n",
       "      <td>42786</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "    count     high      low      mean   metric      source  \\\n",
       "0       2     0.62     0.48     0.550      eps  wallstreet   \n",
       "1       2     0.60     0.52     0.560      eps  wallstreet   \n",
       "2       3     0.60     0.52     0.573      eps  wallstreet   \n",
       "3       4     0.63     0.52     0.588      eps  wallstreet   \n",
       "4       4     0.63     0.50     0.583      eps  wallstreet   \n",
       "5       4     0.60     0.50     0.570      eps  wallstreet   \n",
       "6       2  1610.70  1556.00  1583.350  revenue  wallstreet   \n",
       "7       2  1595.80  1511.00  1553.400  revenue  wallstreet   \n",
       "8       3  1595.80  1511.00  1562.500  revenue  wallstreet   \n",
       "9       3  1580.70  1511.00  1546.100  revenue  wallstreet   \n",
       "10      4  1580.70  1511.00  1552.125  revenue  wallstreet   \n",
       "\n",
       "    standard_deviation                  asof_date consensus_eps_estimate  \\\n",
       "0                0.099 2014-08-05 11:09:21.044000                   None   \n",
       "1                0.057 2014-07-29 11:16:02.680000                   None   \n",
       "2                0.046 2014-07-10 11:09:32.248000                   None   \n",
       "3                0.047 2014-06-17 11:10:33.127000                   None   \n",
       "4                0.057 2014-05-07 11:28:08.998000                   None   \n",
       "5                0.048 2014-05-03 11:13:01.558000                   None   \n",
       "6               38.678 2014-08-05 11:09:21.148000                   None   \n",
       "7               59.962 2014-07-10 11:09:32.389000                   None   \n",
       "8               45.234 2014-06-17 11:10:33.327000                   None   \n",
       "9               34.852 2014-05-24 11:05:50.596000                   None   \n",
       "10              30.903 2014-05-07 11:28:09.914000                   None   \n",
       "\n",
       "   consensus_revenue_estimate  ...   fiscal_year                        id  \\\n",
       "0                        None  ...          2015  5275dfaad2500546bf00031d   \n",
       "1                        None  ...          2015  5275dfaad2500546bf00031d   \n",
       "2                        None  ...          2015  5275dfaad2500546bf00031d   \n",
       "3                        None  ...          2015  5275dfaad2500546bf00031d   \n",
       "4                        None  ...          2015  5275dfaad2500546bf00031d   \n",
       "5                        None  ...          2015  5275dfaad2500546bf00031d   \n",
       "6                        None  ...          2015  5275dfaad2500546bf00031d   \n",
       "7                        None  ...          2015  5275dfaad2500546bf00031d   \n",
       "8                        None  ...          2015  5275dfaad2500546bf00031d   \n",
       "9                        None  ...          2015  5275dfaad2500546bf00031d   \n",
       "10                       None  ...          2015  5275dfaad2500546bf00031d   \n",
       "\n",
       "               name        release_date revenue symbol  \\\n",
       "0   MRC Global Inc. 2015-11-05 11:00:00    None    MRC   \n",
       "1   MRC Global Inc. 2015-11-05 11:00:00    None    MRC   \n",
       "2   MRC Global Inc. 2015-11-05 11:00:00    None    MRC   \n",
       "3   MRC Global Inc. 2015-11-05 11:00:00    None    MRC   \n",
       "4   MRC Global Inc. 2015-11-05 11:00:00    None    MRC   \n",
       "5   MRC Global Inc. 2015-11-05 11:00:00    None    MRC   \n",
       "6   MRC Global Inc. 2015-11-05 11:00:00    None    MRC   \n",
       "7   MRC Global Inc. 2015-11-05 11:00:00    None    MRC   \n",
       "8   MRC Global Inc. 2015-11-05 11:00:00    None    MRC   \n",
       "9   MRC Global Inc. 2015-11-05 11:00:00    None    MRC   \n",
       "10  MRC Global Inc. 2015-11-05 11:00:00    None    MRC   \n",
       "\n",
       "   wallstreet_eps_estimate wallstreet_revenue_estimate  \\\n",
       "0                    0.036                    1078.388   \n",
       "1                    0.036                    1078.388   \n",
       "2                    0.036                    1078.388   \n",
       "3                    0.036                    1078.388   \n",
       "4                    0.036                    1078.388   \n",
       "5                    0.036                    1078.388   \n",
       "6                    0.036                    1078.388   \n",
       "7                    0.036                    1078.388   \n",
       "8                    0.036                    1078.388   \n",
       "9                    0.036                    1078.388   \n",
       "10                   0.036                    1078.388   \n",
       "\n",
       "                    timestamp    sid  \n",
       "0  2014-08-05 11:29:21.044000  42786  \n",
       "1  2014-07-29 11:36:02.680000  42786  \n",
       "2  2014-07-10 11:29:32.248000  42786  \n",
       "3  2014-06-17 11:30:33.127000  42786  \n",
       "4  2014-05-07 11:48:08.998000  42786  \n",
       "5  2014-05-03 11:33:01.558000  42786  \n",
       "6  2014-08-05 11:29:21.148000  42786  \n",
       "7  2014-07-10 11:29:32.389000  42786  \n",
       "8  2014-06-17 11:30:33.327000  42786  \n",
       "9  2014-05-24 11:25:50.596000  42786  \n",
       "10 2014-05-07 11:48:09.914000  42786  \n",
       "\n",
       "..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "revisions_free"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you see above, this particular data set has a lot of columns. We can narrow down the columns into a new expression using Blaze:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "narrow_revisions = revisions_free[[\n",
    "    'source',\n",
    "    'metric',\n",
    "    'fiscal_quarter',\n",
    "    'fiscal_year',\n",
    "    'release_date',\n",
    "    'eps',\n",
    "    'sid',\n",
    "    'asof_date',\n",
    "    'timestamp',\n",
    "]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>metric</th>\n",
       "      <th>fiscal_quarter</th>\n",
       "      <th>fiscal_year</th>\n",
       "      <th>release_date</th>\n",
       "      <th>eps</th>\n",
       "      <th>sid</th>\n",
       "      <th>asof_date</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>wallstreet</td>\n",
       "      <td>eps</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015-11-05 11:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>42786</td>\n",
       "      <td>2014-08-05 11:09:21.044000</td>\n",
       "      <td>2014-08-05 11:29:21.044000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>wallstreet</td>\n",
       "      <td>eps</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015-11-05 11:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>42786</td>\n",
       "      <td>2014-07-29 11:16:02.680000</td>\n",
       "      <td>2014-07-29 11:36:02.680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>wallstreet</td>\n",
       "      <td>eps</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015-11-05 11:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>42786</td>\n",
       "      <td>2014-07-10 11:09:32.248000</td>\n",
       "      <td>2014-07-10 11:29:32.248000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wallstreet</td>\n",
       "      <td>eps</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015-11-05 11:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>42786</td>\n",
       "      <td>2014-06-17 11:10:33.127000</td>\n",
       "      <td>2014-06-17 11:30:33.127000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>wallstreet</td>\n",
       "      <td>eps</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015-11-05 11:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>42786</td>\n",
       "      <td>2014-05-07 11:28:08.998000</td>\n",
       "      <td>2014-05-07 11:48:08.998000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>wallstreet</td>\n",
       "      <td>eps</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015-11-05 11:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>42786</td>\n",
       "      <td>2014-05-03 11:13:01.558000</td>\n",
       "      <td>2014-05-03 11:33:01.558000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>wallstreet</td>\n",
       "      <td>revenue</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015-11-05 11:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>42786</td>\n",
       "      <td>2014-08-05 11:09:21.148000</td>\n",
       "      <td>2014-08-05 11:29:21.148000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>wallstreet</td>\n",
       "      <td>revenue</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015-11-05 11:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>42786</td>\n",
       "      <td>2014-07-10 11:09:32.389000</td>\n",
       "      <td>2014-07-10 11:29:32.389000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>wallstreet</td>\n",
       "      <td>revenue</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015-11-05 11:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>42786</td>\n",
       "      <td>2014-06-17 11:10:33.327000</td>\n",
       "      <td>2014-06-17 11:30:33.327000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>wallstreet</td>\n",
       "      <td>revenue</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015-11-05 11:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>42786</td>\n",
       "      <td>2014-05-24 11:05:50.596000</td>\n",
       "      <td>2014-05-24 11:25:50.596000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>wallstreet</td>\n",
       "      <td>revenue</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015-11-05 11:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>42786</td>\n",
       "      <td>2014-05-07 11:28:09.914000</td>\n",
       "      <td>2014-05-07 11:48:09.914000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "        source   metric  fiscal_quarter  fiscal_year        release_date  \\\n",
       "0   wallstreet      eps               3         2015 2015-11-05 11:00:00   \n",
       "1   wallstreet      eps               3         2015 2015-11-05 11:00:00   \n",
       "2   wallstreet      eps               3         2015 2015-11-05 11:00:00   \n",
       "3   wallstreet      eps               3         2015 2015-11-05 11:00:00   \n",
       "4   wallstreet      eps               3         2015 2015-11-05 11:00:00   \n",
       "5   wallstreet      eps               3         2015 2015-11-05 11:00:00   \n",
       "6   wallstreet  revenue               3         2015 2015-11-05 11:00:00   \n",
       "7   wallstreet  revenue               3         2015 2015-11-05 11:00:00   \n",
       "8   wallstreet  revenue               3         2015 2015-11-05 11:00:00   \n",
       "9   wallstreet  revenue               3         2015 2015-11-05 11:00:00   \n",
       "10  wallstreet  revenue               3         2015 2015-11-05 11:00:00   \n",
       "\n",
       "     eps    sid                  asof_date                  timestamp  \n",
       "0   None  42786 2014-08-05 11:09:21.044000 2014-08-05 11:29:21.044000  \n",
       "1   None  42786 2014-07-29 11:16:02.680000 2014-07-29 11:36:02.680000  \n",
       "2   None  42786 2014-07-10 11:09:32.248000 2014-07-10 11:29:32.248000  \n",
       "3   None  42786 2014-06-17 11:10:33.127000 2014-06-17 11:30:33.127000  \n",
       "4   None  42786 2014-05-07 11:28:08.998000 2014-05-07 11:48:08.998000  \n",
       "5   None  42786 2014-05-03 11:13:01.558000 2014-05-03 11:33:01.558000  \n",
       "6   None  42786 2014-08-05 11:09:21.148000 2014-08-05 11:29:21.148000  \n",
       "7   None  42786 2014-07-10 11:09:32.389000 2014-07-10 11:29:32.389000  \n",
       "8   None  42786 2014-06-17 11:10:33.327000 2014-06-17 11:30:33.327000  \n",
       "9   None  42786 2014-05-24 11:05:50.596000 2014-05-24 11:25:50.596000  \n",
       "..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "narrow_revisions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just to emphasize, this object is a Blaze expression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'blaze.expr.expressions.Projection'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(narrow_revisions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Though we've narrowed the number of columns, there's still a lot of rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "856482"
      ],
      "text/plain": [
       "856482"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "narrow_revisions.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can pare this down further with some simple expressions, like filtering on a particular `sid` like 24 for AAPL and a particular value for `metric`, like 'eps'. Further, we should be sure to sort the data since there's no guarentee that the underlying database is returning a sorted result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>metric</th>\n",
       "      <th>fiscal_quarter</th>\n",
       "      <th>fiscal_year</th>\n",
       "      <th>release_date</th>\n",
       "      <th>eps</th>\n",
       "      <th>sid</th>\n",
       "      <th>asof_date</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>estimize</td>\n",
       "      <td>eps</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-10-18 15:55:00</td>\n",
       "      <td>0.66</td>\n",
       "      <td>24</td>\n",
       "      <td>2010-09-29 16:00:00</td>\n",
       "      <td>2010-09-29 16:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>estimize</td>\n",
       "      <td>eps</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-10-18 15:55:00</td>\n",
       "      <td>0.66</td>\n",
       "      <td>24</td>\n",
       "      <td>2010-09-30 16:00:00</td>\n",
       "      <td>2010-09-30 16:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>estimize</td>\n",
       "      <td>eps</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-10-18 15:55:00</td>\n",
       "      <td>0.66</td>\n",
       "      <td>24</td>\n",
       "      <td>2010-10-01 16:00:00</td>\n",
       "      <td>2010-10-01 16:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>estimize</td>\n",
       "      <td>eps</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-10-18 15:55:00</td>\n",
       "      <td>0.66</td>\n",
       "      <td>24</td>\n",
       "      <td>2010-10-01 16:00:00</td>\n",
       "      <td>2010-10-01 16:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>estimize</td>\n",
       "      <td>eps</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-10-18 15:55:00</td>\n",
       "      <td>0.66</td>\n",
       "      <td>24</td>\n",
       "      <td>2010-10-01 16:00:00</td>\n",
       "      <td>2010-10-01 16:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>estimize</td>\n",
       "      <td>eps</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-10-18 15:55:00</td>\n",
       "      <td>0.66</td>\n",
       "      <td>24</td>\n",
       "      <td>2010-10-07 16:00:00</td>\n",
       "      <td>2010-10-07 16:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>estimize</td>\n",
       "      <td>eps</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-10-18 15:55:00</td>\n",
       "      <td>0.66</td>\n",
       "      <td>24</td>\n",
       "      <td>2010-10-14 16:00:00</td>\n",
       "      <td>2010-10-14 16:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>estimize</td>\n",
       "      <td>eps</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011-01-18 06:55:00</td>\n",
       "      <td>0.92</td>\n",
       "      <td>24</td>\n",
       "      <td>2010-12-28 17:00:00</td>\n",
       "      <td>2010-12-28 17:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>estimize</td>\n",
       "      <td>eps</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011-01-18 06:55:00</td>\n",
       "      <td>0.92</td>\n",
       "      <td>24</td>\n",
       "      <td>2010-12-29 17:00:00</td>\n",
       "      <td>2010-12-29 17:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>estimize</td>\n",
       "      <td>eps</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011-01-18 06:55:00</td>\n",
       "      <td>0.92</td>\n",
       "      <td>24</td>\n",
       "      <td>2010-12-29 17:00:00</td>\n",
       "      <td>2010-12-29 17:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>estimize</td>\n",
       "      <td>eps</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011-01-18 06:55:00</td>\n",
       "      <td>0.92</td>\n",
       "      <td>24</td>\n",
       "      <td>2010-12-29 17:00:00</td>\n",
       "      <td>2010-12-29 17:20:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "      source metric  fiscal_quarter  fiscal_year        release_date   eps  \\\n",
       "0   estimize    eps               4         2010 2010-10-18 15:55:00  0.66   \n",
       "1   estimize    eps               4         2010 2010-10-18 15:55:00  0.66   \n",
       "2   estimize    eps               4         2010 2010-10-18 15:55:00  0.66   \n",
       "3   estimize    eps               4         2010 2010-10-18 15:55:00  0.66   \n",
       "4   estimize    eps               4         2010 2010-10-18 15:55:00  0.66   \n",
       "5   estimize    eps               4         2010 2010-10-18 15:55:00  0.66   \n",
       "6   estimize    eps               4         2010 2010-10-18 15:55:00  0.66   \n",
       "7   estimize    eps               1         2011 2011-01-18 06:55:00  0.92   \n",
       "8   estimize    eps               1         2011 2011-01-18 06:55:00  0.92   \n",
       "9   estimize    eps               1         2011 2011-01-18 06:55:00  0.92   \n",
       "10  estimize    eps               1         2011 2011-01-18 06:55:00  0.92   \n",
       "\n",
       "    sid           asof_date           timestamp  \n",
       "0    24 2010-09-29 16:00:00 2010-09-29 16:20:00  \n",
       "1    24 2010-09-30 16:00:00 2010-09-30 16:20:00  \n",
       "2    24 2010-10-01 16:00:00 2010-10-01 16:20:00  \n",
       "3    24 2010-10-01 16:00:00 2010-10-01 16:20:00  \n",
       "4    24 2010-10-01 16:00:00 2010-10-01 16:20:00  \n",
       "5    24 2010-10-07 16:00:00 2010-10-07 16:20:00  \n",
       "6    24 2010-10-14 16:00:00 2010-10-14 16:20:00  \n",
       "7    24 2010-12-28 17:00:00 2010-12-28 17:20:00  \n",
       "8    24 2010-12-29 17:00:00 2010-12-29 17:20:00  \n",
       "9    24 2010-12-29 17:00:00 2010-12-29 17:20:00  \n",
       "..."
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl_revisions = narrow_revisions[\n",
    "    (narrow_revisions.sid == 24) &\n",
    "    (narrow_revisions.metric == 'eps')\n",
    "].sort('asof_date')\n",
    "\n",
    "aapl_revisions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, Blaze gives us up to row 10. Let's checkout the size of this filtered down set of revisions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "3887"
      ],
      "text/plain": [
       "3887"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl_revisions.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great. Now that we've got our data down to a manageable size, we can convert it over to a pandas dataframe for further manipulation. You'll use `odo` to make that conversion:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from odo import odo\n",
    "import pandas as pd\n",
    "aapl_df = odo(aapl_revisions, pd.DataFrame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>metric</th>\n",
       "      <th>fiscal_quarter</th>\n",
       "      <th>fiscal_year</th>\n",
       "      <th>release_date</th>\n",
       "      <th>eps</th>\n",
       "      <th>sid</th>\n",
       "      <th>asof_date</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>estimize</td>\n",
       "      <td>eps</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-10-18 15:55:00</td>\n",
       "      <td>0.66</td>\n",
       "      <td>24</td>\n",
       "      <td>2010-09-29 16:00:00</td>\n",
       "      <td>2010-09-29 16:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>estimize</td>\n",
       "      <td>eps</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-10-18 15:55:00</td>\n",
       "      <td>0.66</td>\n",
       "      <td>24</td>\n",
       "      <td>2010-09-30 16:00:00</td>\n",
       "      <td>2010-09-30 16:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>estimize</td>\n",
       "      <td>eps</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-10-18 15:55:00</td>\n",
       "      <td>0.66</td>\n",
       "      <td>24</td>\n",
       "      <td>2010-10-01 16:00:00</td>\n",
       "      <td>2010-10-01 16:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>estimize</td>\n",
       "      <td>eps</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-10-18 15:55:00</td>\n",
       "      <td>0.66</td>\n",
       "      <td>24</td>\n",
       "      <td>2010-10-01 16:00:00</td>\n",
       "      <td>2010-10-01 16:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>estimize</td>\n",
       "      <td>eps</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-10-18 15:55:00</td>\n",
       "      <td>0.66</td>\n",
       "      <td>24</td>\n",
       "      <td>2010-10-01 16:00:00</td>\n",
       "      <td>2010-10-01 16:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>estimize</td>\n",
       "      <td>eps</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-10-18 15:55:00</td>\n",
       "      <td>0.66</td>\n",
       "      <td>24</td>\n",
       "      <td>2010-10-07 16:00:00</td>\n",
       "      <td>2010-10-07 16:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>estimize</td>\n",
       "      <td>eps</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-10-18 15:55:00</td>\n",
       "      <td>0.66</td>\n",
       "      <td>24</td>\n",
       "      <td>2010-10-14 16:00:00</td>\n",
       "      <td>2010-10-14 16:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>estimize</td>\n",
       "      <td>eps</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011-01-18 06:55:00</td>\n",
       "      <td>0.92</td>\n",
       "      <td>24</td>\n",
       "      <td>2010-12-28 17:00:00</td>\n",
       "      <td>2010-12-28 17:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>estimize</td>\n",
       "      <td>eps</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011-01-18 06:55:00</td>\n",
       "      <td>0.92</td>\n",
       "      <td>24</td>\n",
       "      <td>2010-12-29 17:00:00</td>\n",
       "      <td>2010-12-29 17:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>estimize</td>\n",
       "      <td>eps</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011-01-18 06:55:00</td>\n",
       "      <td>0.92</td>\n",
       "      <td>24</td>\n",
       "      <td>2010-12-29 17:00:00</td>\n",
       "      <td>2010-12-29 17:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>estimize</td>\n",
       "      <td>eps</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011-01-18 06:55:00</td>\n",
       "      <td>0.92</td>\n",
       "      <td>24</td>\n",
       "      <td>2010-12-29 17:00:00</td>\n",
       "      <td>2010-12-29 17:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>estimize</td>\n",
       "      <td>eps</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011-01-18 06:55:00</td>\n",
       "      <td>0.92</td>\n",
       "      <td>24</td>\n",
       "      <td>2010-12-29 17:00:00</td>\n",
       "      <td>2010-12-29 17:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>estimize</td>\n",
       "      <td>eps</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011-01-18 06:55:00</td>\n",
       "      <td>0.92</td>\n",
       "      <td>24</td>\n",
       "      <td>2010-12-29 17:00:00</td>\n",
       "      <td>2010-12-29 17:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>estimize</td>\n",
       "      <td>eps</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011-01-18 06:55:00</td>\n",
       "      <td>0.92</td>\n",
       "      <td>24</td>\n",
       "      <td>2010-12-30 17:00:00</td>\n",
       "      <td>2010-12-30 17:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>estimize</td>\n",
       "      <td>eps</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011-01-18 06:55:00</td>\n",
       "      <td>0.92</td>\n",
       "      <td>24</td>\n",
       "      <td>2011-01-02 17:00:00</td>\n",
       "      <td>2011-01-02 17:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>estimize</td>\n",
       "      <td>eps</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011-01-18 06:55:00</td>\n",
       "      <td>0.92</td>\n",
       "      <td>24</td>\n",
       "      <td>2011-01-17 17:00:00</td>\n",
       "      <td>2011-01-17 17:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>estimize</td>\n",
       "      <td>eps</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011-01-18 06:55:00</td>\n",
       "      <td>0.92</td>\n",
       "      <td>24</td>\n",
       "      <td>2011-01-17 17:00:00</td>\n",
       "      <td>2011-01-17 17:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>estimize</td>\n",
       "      <td>eps</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011-04-20 15:55:00</td>\n",
       "      <td>0.91</td>\n",
       "      <td>24</td>\n",
       "      <td>2011-03-22 16:00:00</td>\n",
       "      <td>2011-03-22 16:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>estimize</td>\n",
       "      <td>eps</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011-04-20 15:55:00</td>\n",
       "      <td>0.91</td>\n",
       "      <td>24</td>\n",
       "      <td>2011-03-25 16:00:00</td>\n",
       "      <td>2011-03-25 16:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>estimize</td>\n",
       "      <td>eps</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011-04-20 15:55:00</td>\n",
       "      <td>0.91</td>\n",
       "      <td>24</td>\n",
       "      <td>2011-03-26 16:00:00</td>\n",
       "      <td>2011-03-26 16:20:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      source metric  fiscal_quarter  fiscal_year        release_date   eps  \\\n",
       "0   estimize    eps               4         2010 2010-10-18 15:55:00  0.66   \n",
       "1   estimize    eps               4         2010 2010-10-18 15:55:00  0.66   \n",
       "2   estimize    eps               4         2010 2010-10-18 15:55:00  0.66   \n",
       "3   estimize    eps               4         2010 2010-10-18 15:55:00  0.66   \n",
       "4   estimize    eps               4         2010 2010-10-18 15:55:00  0.66   \n",
       "5   estimize    eps               4         2010 2010-10-18 15:55:00  0.66   \n",
       "6   estimize    eps               4         2010 2010-10-18 15:55:00  0.66   \n",
       "7   estimize    eps               1         2011 2011-01-18 06:55:00  0.92   \n",
       "8   estimize    eps               1         2011 2011-01-18 06:55:00  0.92   \n",
       "9   estimize    eps               1         2011 2011-01-18 06:55:00  0.92   \n",
       "10  estimize    eps               1         2011 2011-01-18 06:55:00  0.92   \n",
       "11  estimize    eps               1         2011 2011-01-18 06:55:00  0.92   \n",
       "12  estimize    eps               1         2011 2011-01-18 06:55:00  0.92   \n",
       "13  estimize    eps               1         2011 2011-01-18 06:55:00  0.92   \n",
       "14  estimize    eps               1         2011 2011-01-18 06:55:00  0.92   \n",
       "15  estimize    eps               1         2011 2011-01-18 06:55:00  0.92   \n",
       "16  estimize    eps               1         2011 2011-01-18 06:55:00  0.92   \n",
       "17  estimize    eps               2         2011 2011-04-20 15:55:00  0.91   \n",
       "18  estimize    eps               2         2011 2011-04-20 15:55:00  0.91   \n",
       "19  estimize    eps               2         2011 2011-04-20 15:55:00  0.91   \n",
       "\n",
       "    sid           asof_date           timestamp  \n",
       "0    24 2010-09-29 16:00:00 2010-09-29 16:20:00  \n",
       "1    24 2010-09-30 16:00:00 2010-09-30 16:20:00  \n",
       "2    24 2010-10-01 16:00:00 2010-10-01 16:20:00  \n",
       "3    24 2010-10-01 16:00:00 2010-10-01 16:20:00  \n",
       "4    24 2010-10-01 16:00:00 2010-10-01 16:20:00  \n",
       "5    24 2010-10-07 16:00:00 2010-10-07 16:20:00  \n",
       "6    24 2010-10-14 16:00:00 2010-10-14 16:20:00  \n",
       "7    24 2010-12-28 17:00:00 2010-12-28 17:20:00  \n",
       "8    24 2010-12-29 17:00:00 2010-12-29 17:20:00  \n",
       "9    24 2010-12-29 17:00:00 2010-12-29 17:20:00  \n",
       "10   24 2010-12-29 17:00:00 2010-12-29 17:20:00  \n",
       "11   24 2010-12-29 17:00:00 2010-12-29 17:20:00  \n",
       "12   24 2010-12-29 17:00:00 2010-12-29 17:20:00  \n",
       "13   24 2010-12-30 17:00:00 2010-12-30 17:20:00  \n",
       "14   24 2011-01-02 17:00:00 2011-01-02 17:20:00  \n",
       "15   24 2011-01-17 17:00:00 2011-01-17 17:20:00  \n",
       "16   24 2011-01-17 17:00:00 2011-01-17 17:20:00  \n",
       "17   24 2011-03-22 16:00:00 2011-03-22 16:20:00  \n",
       "18   24 2011-03-25 16:00:00 2011-03-25 16:20:00  \n",
       "19   24 2011-03-26 16:00:00 2011-03-26 16:20:00  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl_df[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yup, we've got a dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'pandas.core.frame.DataFrame'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(aapl_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouped Reductions: split-apply-combine in Blaze\n",
    "\n",
    "Blaze provides other functions to manipulate data as you access it. In this next example, we'll use a data set  from Accern to demonstrate grouping. Accern provides a daily metric measuring the sentiment of news stories about companies.\n",
    "\n",
    "For this example, we'll roll up the individual sentiment scortes into a single, daily, average sentiment score for each day in the data set.\n",
    "\n",
    "So let's start with the free sample version of the Accern AlphaOne dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from quantopian.interactive.data.accern import alphaone_free"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>name</th>\n",
       "      <th>asof_date</th>\n",
       "      <th>article_sentiment</th>\n",
       "      <th>impact_score</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>sid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>Apple Inc.</td>\n",
       "      <td>2012-08-26</td>\n",
       "      <td>-0.194</td>\n",
       "      <td>70.495</td>\n",
       "      <td>2012-08-26</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABAX</td>\n",
       "      <td>ABAXIS, Inc.</td>\n",
       "      <td>2012-08-26</td>\n",
       "      <td>1.000</td>\n",
       "      <td>94.000</td>\n",
       "      <td>2012-08-26</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ADSK</td>\n",
       "      <td>Autodesk, Inc.</td>\n",
       "      <td>2012-08-26</td>\n",
       "      <td>-0.333</td>\n",
       "      <td>75.000</td>\n",
       "      <td>2012-08-26</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ADBE</td>\n",
       "      <td>Adobe Systems Incorporated</td>\n",
       "      <td>2012-08-26</td>\n",
       "      <td>0.000</td>\n",
       "      <td>79.000</td>\n",
       "      <td>2012-08-26</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AMD</td>\n",
       "      <td>Advanced Micro Devices, Inc.</td>\n",
       "      <td>2012-08-26</td>\n",
       "      <td>0.000</td>\n",
       "      <td>77.000</td>\n",
       "      <td>2012-08-26</td>\n",
       "      <td>351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AMS</td>\n",
       "      <td>American Shared Hospital Services</td>\n",
       "      <td>2012-08-26</td>\n",
       "      <td>0.000</td>\n",
       "      <td>75.000</td>\n",
       "      <td>2012-08-26</td>\n",
       "      <td>392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AP</td>\n",
       "      <td>Ampco-Pittsburgh Corporation</td>\n",
       "      <td>2012-08-26</td>\n",
       "      <td>-0.571</td>\n",
       "      <td>72.000</td>\n",
       "      <td>2012-08-26</td>\n",
       "      <td>447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AAN</td>\n",
       "      <td>Aaron's,  Inc.</td>\n",
       "      <td>2012-08-26</td>\n",
       "      <td>0.444</td>\n",
       "      <td>87.000</td>\n",
       "      <td>2012-08-26</td>\n",
       "      <td>523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ASH</td>\n",
       "      <td>Ashland Inc.</td>\n",
       "      <td>2012-08-26</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>80.400</td>\n",
       "      <td>2012-08-26</td>\n",
       "      <td>559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ASTE</td>\n",
       "      <td>Astec Industries, Inc.</td>\n",
       "      <td>2012-08-26</td>\n",
       "      <td>0.000</td>\n",
       "      <td>93.000</td>\n",
       "      <td>2012-08-26</td>\n",
       "      <td>579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ATO</td>\n",
       "      <td>Atmos Energy Corporation</td>\n",
       "      <td>2012-08-26</td>\n",
       "      <td>0.000</td>\n",
       "      <td>87.000</td>\n",
       "      <td>2012-08-26</td>\n",
       "      <td>612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "   symbol                               name  asof_date  article_sentiment  \\\n",
       "0    AAPL                         Apple Inc. 2012-08-26             -0.194   \n",
       "1    ABAX                       ABAXIS, Inc. 2012-08-26              1.000   \n",
       "2    ADSK                     Autodesk, Inc. 2012-08-26             -0.333   \n",
       "3    ADBE         Adobe Systems Incorporated 2012-08-26              0.000   \n",
       "4     AMD       Advanced Micro Devices, Inc. 2012-08-26              0.000   \n",
       "5     AMS  American Shared Hospital Services 2012-08-26              0.000   \n",
       "6      AP       Ampco-Pittsburgh Corporation 2012-08-26             -0.571   \n",
       "7     AAN                     Aaron's,  Inc. 2012-08-26              0.444   \n",
       "8     ASH                       Ashland Inc. 2012-08-26             -1.000   \n",
       "9    ASTE             Astec Industries, Inc. 2012-08-26              0.000   \n",
       "10    ATO           Atmos Energy Corporation 2012-08-26              0.000   \n",
       "\n",
       "    impact_score  timestamp  sid  \n",
       "0         70.495 2012-08-26   24  \n",
       "1         94.000 2012-08-26   31  \n",
       "2         75.000 2012-08-26   67  \n",
       "3         79.000 2012-08-26  114  \n",
       "4         77.000 2012-08-26  351  \n",
       "5         75.000 2012-08-26  392  \n",
       "6         72.000 2012-08-26  447  \n",
       "7         87.000 2012-08-26  523  \n",
       "8         80.400 2012-08-26  559  \n",
       "9         93.000 2012-08-26  579  \n",
       "..."
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alphaone_free.sort(['asof_date', 'sid'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's explicitly import Blaze because we'll be using some functions in there"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import blaze as bz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, we can use the the `by()` function from Blaze to group the sentiment data by day and then apply a `mean()` calculation to the daily rollups. In Blaze, this is called \"split-apply-combine.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asof_date</th>\n",
       "      <th>average_sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-08-26</td>\n",
       "      <td>0.041186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-08-27</td>\n",
       "      <td>-0.003940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-08-28</td>\n",
       "      <td>0.187777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-08-29</td>\n",
       "      <td>0.155728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-08-30</td>\n",
       "      <td>0.168976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2012-08-31</td>\n",
       "      <td>0.179987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2012-09-01</td>\n",
       "      <td>0.190491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2012-09-02</td>\n",
       "      <td>0.151005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2012-09-03</td>\n",
       "      <td>0.115627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2012-09-04</td>\n",
       "      <td>0.078943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2012-09-05</td>\n",
       "      <td>0.187116</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "    asof_date  average_sentiment\n",
       "0  2012-08-26           0.041186\n",
       "1  2012-08-27          -0.003940\n",
       "2  2012-08-28           0.187777\n",
       "3  2012-08-29           0.155728\n",
       "4  2012-08-30           0.168976\n",
       "5  2012-08-31           0.179987\n",
       "6  2012-09-01           0.190491\n",
       "7  2012-09-02           0.151005\n",
       "8  2012-09-03           0.115627\n",
       "9  2012-09-04           0.078943\n",
       "..."
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For grouped reductions: split-apply-combine\n",
    "avg_daily_sentiment = bz.by(\n",
    "    alphaone_free.asof_date,\n",
    "    average_sentiment=alphaone_free.article_sentiment.mean(),\n",
    ").sort('asof_date')\n",
    "\n",
    "avg_daily_sentiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we can easily confirm how big the resulting data is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "775"
      ],
      "text/plain": [
       "775"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_daily_sentiment.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And convert it over to Pandas using `odo` so we can do things like chart the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asof_date</th>\n",
       "      <th>average_sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-08-26</td>\n",
       "      <td>0.041186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-08-27</td>\n",
       "      <td>-0.003940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-08-28</td>\n",
       "      <td>0.187777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-08-29</td>\n",
       "      <td>0.155728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-08-30</td>\n",
       "      <td>0.168976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2012-08-31</td>\n",
       "      <td>0.179987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2012-09-01</td>\n",
       "      <td>0.190491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2012-09-02</td>\n",
       "      <td>0.151005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2012-09-03</td>\n",
       "      <td>0.115627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2012-09-04</td>\n",
       "      <td>0.078943</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   asof_date  average_sentiment\n",
       "0 2012-08-26           0.041186\n",
       "1 2012-08-27          -0.003940\n",
       "2 2012-08-28           0.187777\n",
       "3 2012-08-29           0.155728\n",
       "4 2012-08-30           0.168976\n",
       "5 2012-08-31           0.179987\n",
       "6 2012-09-01           0.190491\n",
       "7 2012-09-02           0.151005\n",
       "8 2012-09-03           0.115627\n",
       "9 2012-09-04           0.078943"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_daily_sentiment = odo(avg_daily_sentiment.sort('asof_date'), pd.DataFrame)\n",
    "df_daily_sentiment.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize this data, now that we've converted it over to a Pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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c/KpY3FWSpZw2dwNWbuiqeJ/7MwayBTw49S107h0Y6kMxiEB3Xw53PbEUu/aEr9G2zj78\n2+VTMffNrUNwZCML6ZRfMzWCJcZ9g83DTBEqrVEzwZSBgYFBneEanV/DQTK/ahLlSbPs/YN53D55\nCX7w61mV73Q/xmPTV+Gx6atx4x8XDvWhGETg/qeXY8qc9bjziSWh9zbv7EHfYAFrt+wbgiMbWdgf\nmKmBweZhpgiVJj5NMGVgYGBQZ1TDTD02fRUWvb2zdgezn8ASnZIbVzNlJE7VYZfPSBlmqnmxtbMX\nADCYLYbey/lZ/Wwu/J5BMqT2g2CqGZmpSiX5JpgyMDAwqDPiDtBLVu3Czj394m9P9rQCP71nbr0O\nbcQiMKCofBtOwnVM0QRTVYEkNpm0WZo0K3r6vQXw6FGZ0Hv5vBdEmWCqepDMrziCZX79TchMVXq+\nzYhlYGBgUG/EGJ97+3P46W/m4sEpK+p/PPsBAmv0xtVMjeQsciOQ94Mpkjjt73jgmeX4yhXTxHlp\nBvQNeO0CRreHgylipkZS/6ChAg09+cLIDUz7m5CZyuUrO99mxDIwMBj2qHQAbBTiMFM9/Xk4jos9\nPYPitWoCgf0dNamZSnj+R3IWuRGgYJSy8vs7Hn9pDfb2ZtHVPVj+ww0CMVNjRrWE3hPMVJOPx8MB\n9CzkmiiQrjX6s80XdOcqDF5NMGUQwkC2YBZxBsMGz766Hv/6k6nYva956yziPE6UpWvGbN3+iqQ1\nUEbmVx0oGDXMlIyU3TzBpWAPNVLMgJkywVS1oLFkJEsm+waab66rNHg1I5aBhK7uQfzLj6fgxj8Y\nNyWD4YEtu/qQKzhaq95mQZzkBGXp+piO3KzNK0ct8kFJz7+R+VWHfJFqplJDfCTNhWYM0ouae52y\n+iM5AGgU6PyOZJaPJw6bJYGfNzI/g1pg/VbP0nTm4i1DfCQGBvFAUqxmXHAQ+DwRNWmQTWwzTjDD\nEbVw1jPMVGNRKBiZnw66wGUowMcjXf+jvHDzaz751nDD/sBM9Tdh4tAwUwY1gbH2NRhuoHu2mVkB\n3mcq6hmjIKqPdYU3z6OHwVwBs5dsSXQ+ahGIJq2ZauZ7cDiAmCmdhGx/RrME6VyWpQvw8vmRz6Y0\nCiKYyhdHbFKNJw6bJWFgmCmDmmCEPrMGIxi0wG7m4n+XzRNRC6MBX+ZXKDpNb6jRaDz64ir84sEF\neHDqW7G/U4sFaGJmii0IRuoCqJ4IDCjM0oSjWYL0PT1Z8W/d80UyP1MzVT1oLHEcV8sCjgRwZqpZ\nEgbZvGGmDGqAZrmhDQzigtiDQtKmQA0EZziinjE+sdC/K20gONKwe5/nZjZt7obY36nFuUtujc5l\nUM17PzYrTJ+pAHycaIZE0YZt3fjWDTPE37r7Oz9Cm/Y6jltzi/IV67vw60cXR44T/PqPVKZPCqaa\nZLys9DqbEctAgpEVGQw3BMxUcwzGOvCnShdMvbm2E1t29Yq/Sf4Qp9Zqf8ChB3UA8Mw54o5RtThd\nSZNLRRbQN1NvoOECw0wFGGS20bpE0dI1u/DrRxc3LAG6bss+6W8tM5UfmczUD389C/986TO13eak\nWXh+/ka8tny79n0pmBqhNWiSzK9J1p6mZsqgJjDBlMFwQ1HUTDXvvcsDITXo6+oexI/vmIMXXtsk\nXusTwVTwvf15cV5gv33j9u5Y3+Fj2b7eLK74zVyseWdvov1W02cqV6FcZH+GCaYCDGR51j58H05/\n/R08P38jtu/ua8jx0PNENu06RoUWorl8cUStJd7etAdAffoZRrFOfJ4YicyU47hS4DKUTD6fZ03T\nXoOaoLgfZ78NhieEm18zM1PssVIzcLpJpN83oeDfG8nNG8uBB5Lv7OiJ9R2+mHt0+iq8sXInrr1/\nfqL9Jh0O+bWttPnj/oT1W/fhrO//BQtW7AAA5AvUZ8q4+fFgShu4+Iu+RiVZaJz96hl/BUAf4OVZ\nAoEvSmstkRsq9PTnar7NKAmnzEyNjPPHkVfu6UZIWV9duhUPPLM89DqfKyp9nkwwZSBhJGWTDPYP\nCJlfE9+7MjMlH6eF8MKRmCnOjFTqMjQSwCfeuJMdP3cDogYt2X7dhF8oVHCc+zOenrUOAHDrI4sA\nBOfPbqImtUOFcswU3V+NClRoDKN6tqJGesgTCMSm7NozgPP/dyqen7+xAUdZX3T31SGYihhjuNJi\nJDJT6nzWiPn7+t+9jsdfWiPJCwGg4BhmyqDGaGQwtfqdPSEdtoFBUjjDTeanLEJchI9bVzNlmKnw\nv0vB0RRwt2aSNYNNytTLMr+RtwCqNQ6ZMAoAsNd3iRPXtnkf5YZBCqY0gUteSOoaxEz5z1NLxls2\nluozBQR1Uzv39CNXcLB5Z2/o88MNO7v6xb1aK0RJiR12zfsHhm/N1K49AxjU1Hyp81kjZX7qvctl\n5JU+TyaYMpDQSPew790yExf/6uWG7c9gZCJo2tu8wQZ/rGhRsrOrH/mCo5WS9Q2SzM8szgE5+x5X\nPudozh0tBOOCrlVcpsQwU8lw4Ng26W86f8bFUjGg0AQu9BwUGiXzc4iZ8hISOlk1H6PINIGu6UiQ\n+l1z/2v4ypXTamoGFJXA5omZPT2DNdtfI5EvFPHtX07HnY8vDb2nzmeNTOSr9yIftyuVZ5tgykCC\nkfkZDDfQvNbMVtT8sSo6Lnbu6ceF176Ay+9+VTsx9w+Emalm/n31hsRMxcwcSqye/53WlmTMFG3D\ntuIFU8bNLxmimvOaWCoGM+Xf042qzSOWlhISOlmWjpmioKCZlQNJUUu3wqg1F5eedXUPz2CqdyCP\ngWwRnXsHQu+p42Mj57dS+zY1UyMA+YKDFeu7Kgpo9vVm8fMHX8emmE5XUWhURnB/tnk2qC2GRdNe\npc/U5h2e5GX5ut2GmYoBKZiKOenqZH4tCWV+gpmKWcJTMDK/RIgyZjHMlGpAEc1MqYs/x3FrLkXz\ntuv9P2XbsKyoPlPhmimydW8Ug9YI9A3ky38oJnRBqeu6cBwXHW1pAEDXvuEZTJFxRn9WI/Mbgpqp\nqH3nJZmfYaaGPR6Yshw/nDRLskiOixUbujBnyVbhilQpGsVMRWUhHn1xFR6c+lZDjsFgZCCwRm/e\nyVrqM1V0pL4xpWqmJKnaCFqMJEUlk52jKSpOGkwVE8r8ikbmlxBMysSy70YhAQxkg/tcL6kj+Zz8\n3q8fXYyvXDkNG7dVl1hVwa3RU7al7zPFjiUbYqaa53noG8iHTAgSfb+K76pwNKwjneuDD/BqCncP\nU2aKEgIDg+Fgaijc/Ail6rUMMzUCsHDFTgDAktW7En+3Vr12GjWJ8YmC4/fPrsBj01c35BgMRgYo\n4GiWBVh3Xy7EvKrMlJSl1TJTYZlfXHnbSEShAgMKbh5RsQGFk8xdzlijJwNfR+7c0y/+bYip8sxU\nXjBT8n324uteMvat9btrejw0ztq2hVTK1gZ4fIwSwZSjD/qGEj+5aw6uuGduxd+vpSGELiglid/4\nMa1I2dawlfnRPaAzoFDnM11jah26ugexe19YNqhD0XHj7bsGzokmmEoA13XralFJen7dxS8Hp1gb\ne+hGySsq+Y0GBjo0k5vfui378O8/fRZ3PSEX3KoGFDwTpnvmKOssyfz248V5Jdbo/NzRwrQlXSEz\nFbNmqmCa9pbEnp5B3PPnN1lBfXC+dnYFwZSR+cV384t6HuLKYePCYc9C2rbEvd7bn8NVv52Hjdu7\npTFqUBhQNB8ztWvvQCK2R20iXVtmKnyvU6CaSds4YGxbzYKp3fsGGlpiQffAgE7mV5DVAk7M+fv/\n/ew5XHDV87E+e9GNM/DFH02J3DdBZqZMMFV3XHv/azjvsmeqoodLoc0Ppipp0EaDbbWNSxuV3ee/\nkRYspo7KoBIEfaaGfrJetq4TADD11Q3S6yFmij2n/Lb/6hkfAhDI0gwz5aESZoqPZSQzqdTNz4pr\nQGFkfiXx278sx9Oz1uHuJ94EIBuzzFq8VfzbzAVywlEngSKpUpT8t1Co7TkMmCl4zJQ/3k55dT1e\nf2sHLrl1ppRAoMQzydhqHdxVg0LBSVTDpRrX1LJmShtMMUnlgWPbsKd7sOpnYsX6Llxw1fOYvWRr\n+Q/XCKRAGswWQsdP90p7q3du4zJThDhr1Xf82mR136UMKEaMNfrMmTPxuc99Dqeffjruueee0PtP\nPfUUzj77bJx11lk4//zzsXLlytjfrRRzlm7FpMcWY/7y7QCAvb21L+4EgLYWr9iwEtamVnUjjSoC\n5L8xsE4Njr3aoNBg/wFN8s3ATEUxGI4UTDnSvU4D/ef//j344mnHIJ2yg4UIb9pbTJZkWbmxC2s2\n7030nWaFVDMV1xqdDSFUAJ3UzS9oIhvz8071Gc6hxpQ56/HVK6eht19uULp41U786I7ZVS0ku/u8\nuXPXXo+F4muc197aLv5tYim5zkTv5qc3oBDvJxwvyoG3CUjZlgjwyN5+MFeU1h+D/kJaMFNNlFzI\nF5xE84W6GK9FQp2mCt2ai85tyrYxYVwbCkW36obBu7s9adzuBppZkD2+44blczQ+trd6696ka88k\n50PdtFp3KyfrRgAzVSwWcfXVV+Pee+/FlClTMGXKFKxdu1b6zBFHHIE//vGPePrpp/Gtb30LP/3p\nT2N/t1K8MH8jnpsXdO9OKhWJi9aqmKnhJvMLfiPdyJwK3p+L7Q2SIXDza9w947ou5i/bFppUIxkM\n7lpWlGum6C36akvG1jJTSTNmP7htFr578yuJvtOs4AtD3eJRt8DnY5loOFpnmV9xBMj83lq3G3t6\nsti8S26yevndc7Fs7W5MX5DcIIlAPYqCa+idr4994GDpc0bmJy8+1YW/67pijowKpmrOTLFnIZWy\nRV1PR3uL9LmUX19Ix1/UJEuHEq7rIl90EikZ1HVVrzLePDdvI6574LVItmQwV5BkrEBwnrTBlH9s\nqZQlgtVqpX50bI2sLR5g6zxV6kfjI5EISefvuHVTQPg3lzKgGBHM1NKlS3HkkUfi8MMPRyaTwRe+\n8AVMnz5d+swJJ5yAMWPGAACOP/54bN++PfZ3K0W/4kRSr5sxqJkawmCqQeMdDxjpRpaCKWMrbBAT\nTo1Y2SR4atY6XHP/a3jgGdl5MsqoQO0zlWeLI1o3UiDWkkmxYCpZzdRb63dj/dZ9sX7DcEK+4IgM\npip3fHNNJ87/yVS8sXKn9HotFuSk46+sae/wHMNIebEvwl67mtOa8ftKBc15vdc/cOT4mu1jpIDP\n5epCs9R9RvU9tR4PuQFFOmWJY1JZmzbBNPiW6E1WM+U4Llw3GVOmrvnUNeGkxxZj7pvb0BmxwP/1\nI4tx4bUvYPPOHvEaJWj0NVOBzG/s6FYAQHdvdczUUMjhs0yBpAZTJPukcT2psiRJcKn+5rzKTPEk\n2Ehw89uxYwcOPfRQ8ffEiROxY0e01ffkyZNxyimnVPTdJFCzz/XKmlGEXgkz5RRp4BoeNVNc5kcZ\nKz5ADdesrkHjQc9jI/tUzHj9HQDAO2xyBKIZDKlmquhIiyN6j77ZkkkhKwwogm3EqZm6dNJs/PdN\nL5f93HBDvuBglN9zRV08bu3sAwBs7+qTXteNZUnvELqn4tdMVT8pDzX2UTAVIaOppnaDFvqCpfC3\ndeiBHdLnhgMzdc1983HLw2/Ubfv8PKsLTT4/qoxPJm3536mTAYVtIWXb4l5Xr1S7nxR2lARvswRT\ndL4KCeaLouPiPYeOxXXfOglAtAHFoMZoAQBmLt4CAJi3LJCyplKlmCnvtXTKRtr/XNKaIhWNcL3N\n5ov48hXP4uEX3gYgEwODioMzBTSVyvzKBVN8nijHTKmtNyoZ45oqmIo7YQHAvHnz8Pjjj+OSSy5J\n/N2k6GsQM9VWhZufYKY00X3RcfH0rHWxaFGefaonBrPhmimeuRiuWV2DxmMomvau89mf9x42Tno9\n6rFRm5PqFhaW/+VWJvNL0mcqaiIfCeDMVHgi1NeO6CbEpJNkUDNVgTX6MGXX9/kZ8H0RtcHVrOmI\nmaJrRWeLEonBPpo/mHpzbSfeWtdVt+3LY4Z80jlLrd73gpmqcTBPl8S2PGaqEMFMtfrXUpVfN4vM\njxgRx3HDi+x8MTSOiga67RkcdpAX9EfVDfZFWKYfOM6T6q3atEe8ZvuFmLrEAW/JkPI/V73qqP7B\n1MZt3dhCZ7vUAAAgAElEQVTXm8Mfp3leBvxchmR+BZmZSirzK9fImCfnHVe+T9X1pTofVxL4N1Uw\nNXHiRGzbtk38vX37dkycODH0uZUrV+Lyyy/HnXfeiXHjxiX6biUYULIQ9cqAk0VkNTI/3U2w+p09\nuOfPb+J5VvcVBa6Lrif4bwyYqeA8D9esrkHjQeuMajN3cdHTHy23iErqqG5+NKFbFnOM89+PkvmV\nSzDsiZBljQTkCw5a0ilk0nZokZiLKMTX3Q5JE45BzVTMz7Pxt5kK7uPCcVxhEhFltFQNMxUKpmgu\ntYCWdLAcaXY3P9d1MZgt1DXpxxe9KjPFWWo1aBfsXwULwhXru3DhNc9rjWukminbZi688ufInU2s\nSYTcrzmeB8noShkkvnb18/jij2UrbboMKdvCqLYMgLDMj9AzoJ8bxo/xpHpvrukM1WHqgggu8xMM\nVpXJQtpNPRUcqqpqsETNFDFTbcr9EhflrO2l1gJFR0l0yedcvQaVKKOaKpg67rjjsHHjRmzevBm5\nXA5Tp07FaaedJn1m69atuOiii/DLX/4SRx11VKLvVgLHcYUTlHitTgN9NeFLsUTWgW7wOD2yxINe\nb2bK1EwZ1AhC5tcgZmotW2io92nUc8OPjDftTadsZkDh10ylU0JqwL9XLrM7XBs7xkG+4CCTtpFJ\n26HasahCfN047SYU+iVNLnHpUFRC6M01ndiwrTvRcTQKPf05sXiMqtGImv+27+7D3U8uLel0llZq\npoJ7HzhofLv4XJPHUsgVHDhufZN+qgMoh9R3TVkIqgFrEjw1ay127hnA1b+dF2Im5aa95WumBBPS\nZG5+/DjUIJUc4vg6ymEsUVtLCrZtRTJTqgMmgZr89g7ksctvTk0GFLoYQpL52dG1VUnQCDm8usYc\nLFEzJZiphAYUNMeWm+9kZkpWg5TqMwVUtv5Ml/9I45BOp3H55ZfjwgsvhOM4OO+883D00Ufj4Ycf\nBgCcf/75uP3229Hd3Y0rr7xSfGfy5MmR360Wg7lCaGCvF01aTZBWLJH9SWJOIVxk6hxml6+ZCt/M\nr7+1HZ17B/D5f3hvfQ/OYFih0Zr8rIZVJUTL/IJnb9naTvG9dMoKaqb877ZmUnBcb6J32ebLDfB7\nIw0D3LrKoOsN158I02kbLelU6JwHzJR8frQ1U4mZqYQyvxITNuHHd84BADx90znJDqYB4AvoaGZK\n/905S7bimdnr8dcfPAR/81fviviu92XBTBExZVk4aHy7qH9r9popki/VU7omBVOhmik2BilZ9GoM\nKCaMI+e4LL58xTRc918n4SPvP8g7HpZoTTM3P/VS0d8BM0Xjc3NcUx58Ri3gewfyGNvR4n8mYIks\ny0JHWzqyZqqnP0L+x1U3/rWjdgu6Y+DyYjtFMr9413NfbxbjfNMKjkbI/NQSFV4nFQ6m/JqptmQG\nFO0tKfQNFmIEU8E5dxxX2r76zJAhVDplo1B0YhEPKpoqmAKAU045RZhKEM4//3zx72uvvRbXXntt\n7O9WCx2d28hC97ig7I/u2MRDFGOCooGj3jI/3YK0nDX6Iy+swtot+4ZFMNU/mMfLb2zGpz9+hNAE\nG9QHja6Zkpx/lEE3Kmjhzx5vs5BO2Vo3P8ALDqQ+UxUyU47jCqnIcASd70zaRjpth8YGkmTs683h\n98+uwFknvw/jx7TqmanENVMU6Fbg5jcMTXT2MTYqipmKOod0XXZ29WPyjNU48+T3hmqh6HwGwVQg\ncf3Eh9+FpWs6/dcr/w2NwIAIpuqnoODnQA2M+FgQqplKVx5M9SimIzu6+vARKMGU5ZkneK54buh+\nINY1qOMm5rjxapPX3tqOjrYMPvy+A8Vr0rmLOEf7erMimKJxhGqXRrVl0B/BTOkk4K7rSgt72n+p\nmik61ym/pxcQL9h48bWNuPWRxfif80/AaX9zpHab9Vy/qvVmpZipvGqNHvO46FNRyUMCV5QtXrUL\nO/cEngEhZsq/JqPbM9jbm60omGoqmV8zQpeBqBszVcV2S2XnaTBzYjyMzSLzU60rAY9CLihOaM2K\ne/+yDHc+vhR/em5l+Q8bVAXRtLdBNVOSxCaG6YH3hv7lTNoOufllMt6wnFVchcrpuPf06IOpSmon\nmgm0CEunbLSkbRRCMj/v7+fnb8SjL67CHY8vARAxniYcYoNtxPui7OYXHsOaMRHHsa8vBjMV8V0a\nx+968k38bspbePKlNeHPqM+L/3/LAs7+5Pvw4ws+AWAYMFO5oCFtI9YDJZmpKGv0CvpMkcztPYeO\n9fbL5W5c5scW+PSJjxztBV0f/+Ah0vGLAHoImKmrfzsfl90+W3qN34NRCTjeEDZYE3l/d7RlIpmp\nXg0zlVMaBNN4XLrPVMCUCDlgjPnt5Tc2A5ATdoRA5le/+UA1a5Pd/JRgqkIDCpoTyyULOBFyy8OL\npLWYOm/TtsgxthJHbRNMlUG/xp2lXgM932zSDGopKZ94LxYzlUzWQli/dV+img2esaAbWZL5abLw\nNIFUkjVoNFZs8Fyedu2J31hupGHum1vx1KzaNM4uBXpWGhVkF0sEU1FjQ9R6S66Z8v7f6jNTubwj\nLVxfWbRZWM7qsKdbv/htllqFSkHnWNRMKUGlyg7SOKQbQ5OO3bTwiLv+4AG9bkJu9kQQ7y3V3ZfV\nn0P/Zs7li3h7Y1dIukfo1LhtqQsgzspaloUTP+QtxN0mDzr5wrBeyQp+r6qJIj4/qnNlRtjPJ58n\nu/tyyKRt/Ms/HuPtl21bskZn0jO6/p/6+OG47fufwr9+9oPiPf7/RhtQ6NZC/YN59DKTiKhj4omE\ngCXyman2NAayRe32dcyUymIFzJQfkGrG5wILuEoFXSooMFFZIP476inzU2vJStdMVWaNTh8rx9Sp\nxnHSvkN9przz3dHuGYwYZqoO6M82jpniW00aGauUuvReMf5DJLS6CWR+G7d3479vehm/ePD12N+R\nJiOdzE9zM9OkNRx6UBGtTw/n/ojrHngdv/nzsrrvpxHyBY5ShaxRmc6o5Eg6ZYu6KFXmp+t38fDz\n0cFUl89MjRkl33PDnZmi851J28hkUmFr9IiaEa2bX8J9B45lyZkp3RjeLI5mUdjrS/vaW9MoFF0p\n00z5NVrk3/LwIlxy2yy8/pbXz1H9bbophN+LXCJmie9QUX6TB1O50iqKWkDuTSefD75PdTEeyPyS\nn8N9fTmM62gJTCx4fREzY0mTXXfRFQGxbXmtImj8UuXXjuM2lJmV3Nz8/X7pf6fi8rvnitejnsdu\nFkwVRTDl3Zu0+M9qWtj09ucxZ+lW3PzQG+L6qSwWrXfiMFOplJ3IGp2OTTVNAxozT6q/lddMqcdU\nMTNVYq0LePNm70A+0nERCCcg6FkZTcGUYaZqDy0zVa9gig2eAwl7TandxnXvxakpqUTmd99TywEA\nb62P33NDJ/MrZ41OD99wcPrr9e8boo33Z9Tb5rjhNVMl6hWiFoFRpyCdtoXDHC0+W7jMT3kM3n/4\n+Mjj2uszU8RsBcfb3AvTcpCYqZQn8+P3lJpFpIWgvmYq2b6jGpNGft6/F1vStnZBw8fnZuylRDK/\nIyaOBiAvKinQoXM4y29EunaL13NNXdTr5hD+GU/GKm9b3UezYiDLZXZ1YqYka/RoZirUtLcKA4qe\nvizGdrSGmysj7OZH+wieRe81NUjgx9HIZMIAW0yrtWCEqDmDN6wWdeQUTPk1PrSG4cFJT38OP//d\n65ix4B1s2+2ZqdCinsb3gsJM9fTnpP5TgFIzlcAaXTBTmkCiEU17iZlq9XumZnMFcT+oMj9axyW1\nRi/HTP3iwQX4n1+9rB1/CWoCLsRMmWCq9tAxU/WK7Pnkr6NpS36XDChKuPklMqCIGUzlCw7eeHsn\nAOC4ow8s8+kA/GYtaJgpXbaPBvbhIPOjgWL0fsxMEeq9aBQ1Uw2aqLn9tToo89961xNLsc5faJZk\nppQFZStjpui3/dtnj0VLJhWS6t79xFIhpSRmSj3dzc6GlEMQTKXQkrHhuPIYHFpMlgymKpP5xf0e\nnevRozLa5usSq9mE4xiNxWM7PDcwPtYGgY58LmixFHa2DM8h/Lp5472cSFDZr2YFv7b1skfnz7E6\nhuYlmZ9SM5WuTOaXyxcxkC1i7OiAmdIF/ynfzQ/wrqdgpmz6vxxM8WveSMkxX7tFNaCOGhu53JUH\nkUBg/T6oMSHhNVMp28a2zj688NomABAOe3S96LlZtnY3vn/rTCmgEjK/VA1lfg2wRhfBlD+HDeSK\n4ncPZAtK30TZgCLuPOWKoFD/+c69A9i9b6Bki4YoA4pA5pds/Q2YYCqE3z+7At+95ZWAom0oMxX8\nm9OjcRCrZioWM5VM5scHkiTzX1lrdB0z5U/szbgIiUKbcfKru8yMxtShYKbUQZmPDVPmrMfFv3oZ\nQDSzIVmj+69JMj//NW8BY4WSJc/MWS+klMHzI+9t5ARTNjLplPQaEE6uBDK/6u8HIVGKuSn6fEd7\nSygTC8j3TiXN2esNwaxlwuwGBTrqOB+YESRjpgazRXFeOTNlWc3PTA02oCei67qR55bPuyE3P8Ea\nJTuJZLowtqOFMVPBfuSmvQFbEjwbxEzJzx8flxvKTLFrFGWmEhlMSQYUsiyPmJQBjT1+N6uZStkW\nLrltJqbN3QAAGO8HFWrNFGHrrl62Twpc7aA+Lca5o2BK97tonqwvM+WdE85M0e8ezBbx1Z89h9sn\newZBuXwRKdsSY01sNz83GJN1v6XgeIYfpWR+UQYURuZXQyxetRNr3tnLFviamqm6GVBUzkyVCpiS\nMFOFhMwUH7CTZH2Tuvm5rjusaqYqwa49A00p/akW9e4vEvQzaRQzFV38rTN5GVQychyWZWmb9gK+\nAQXrQZWy7chz6bquWPjEZaZWrO8SrHK94bquNriIA/pdmZQtMual3MwEM1WiTURciAk+ATNl2xZG\ntaW1Y7hkUMF+w+QZq/Hsq+sTHVs94Ihgypfe8PstipnyF+9q0qRczRTPVPPPWpbV9OMgl/nVK0Bw\nHDdYaIbc/LjMT77/UxqJXhzwYErLTGlkfl6gQUGWv3+lySy/5xsZTPHFdDQzpZfd8s+Xk/nxsYgb\nMDiOK7kCjhvtWa1HXRcKQoFAaZRKWSGmrxQo2aRDI2umXMdF3ncxHO3X8PYO5LC3JyuCy3zRQUsm\nWU0YIM9vOmdCCjp1zooENQGXV2V+xoCielCfDVHHk5X1rkBjmKnkwZRfM1Xi5opjicmzT3FQKTPF\nizfpRuY3sHozczmBLhP4yAtv46IbX8LG7d3xD6IBiHuvvPH2Tnz9mudx39PL63xEjUe9HcyEfKFR\nMj9Wg6TadOuu96JVOyPvA6kIX7j5cWt07zXbspBJW9JihC9qC6wQXF3sRk3eP5w0C1fcM1f7Xq3x\nl5lr8e8/fbZsbxAdVDc/QJXL6WV+tch5ibYScZkpx0HKttDe4hk4hDKgEjMVjIG/m/IW7nh8afUH\nXCVoQUMyHR0zpZ6LKFcy3RxSCAVT3r8tBJ+1rfrXWVYLSeZXJ2bKcYF0yr8OytxdipkiJA1cKIAY\nN7pV+5xxOTJvDCzuB/96h2R+JdxP64k4zBRfE/F/82Aq6DMly/x0zJS8bRdjRrWIv8cpzJRaD2uz\nXoAFxoYlkfmVqu5sZM1UwXHFGq6tJY2UbYWY+FzeQSadkurvCA89txJT5oSTS+q4oPcI8F7r7oue\na9RkfUjmZ5ip5Hjy5TX41Z8Wir/pAtANTzfHAWPaxGfq1leCF1VX7OZXncyPd96Og0qZKT4A0Y2s\n09Pq/s5qtODTF7yDDdu68ZM7X23Ionrlxi5ccutMdO4NW5/z3xH3XlmyahcANEV2uhYoVTxdr30N\niZtfiZopQiljFteVFykAkNHUTFmWl3Hm9zbflSzHKf0sqWjEedva2YdcwYnshVUK3M2Ps3YEdTEr\n6jlqYUCRmJlykU5ZISnQnY8vwbNzN0jjZZwxfsuuXryzoyfZQVcBWlDSYprPGTQnhGumws5vgL7R\ncSiYosWfwkw1eSwlqyjqFCCQzM+2rTAzVcKAIsqqvhz0Mj823nBrdCbzo4sVxUyVqm+sJwYkZkpv\nQMETY/wcd3UPhqzEA2YqWubH4bgu3nvYWPG3KvNTlUL8aaFjSdk2C6bKn7tSY3lwPep3DWi9XCw6\nYl1HSbBQ095CES1pWyvL/tPzb+OuJ8LJJfXn6dZ6NMb2RjRWBnRufn4w1WaYqYpx39PL8dLCzXBd\nVxRgAmHp2UHjWTDVgD5TSetMhAGFlpmKL/OjhzGuM7ouc7V2817s3NNf8nu6LuT8QVFvdr5g0mUC\nO3zXvL292ZIPUa3w5ppOvL1pD1a/szf0Hh804t4rVkTWd7giFyNzWisEBhRDEEypMj/NBczmi5HX\nVcdMiZqpgiMSjZZvRxwlS+HBhbdN/fHqoPYGqQfoeCoJ3CRmKhOu5VDHg5Iyvwr798WvmXKQsm1R\nu0DSxqmvbsAdk5dI1yJOMPX//Xw6vnXDjETHXA3onAlmis0nUbblqQhmSgf+mYFsQdzfthJMVTvH\nOo6Lu59YiuXrdle1nSjwLHu1BhSLV+3U9uNzHBeWBaRtCys2dOEPz64QY4U0H4aCKe//SZNYvX69\nz5hRLazxL2dugqBJx0xZITc/XxEzRDVTsQwouARRcuXLi3uH1y8BOgOKCGaq6AiJGwCMH0PBlHft\nwixLmCVLpaxENaB8k9/+5QxsYkqdeicdXdcVZTGFoit+QzrlBUyhPlPETEXUBUbtg0PrEeBvp5Ss\nXGWmglpX73pVUs+63wdThELRkbIXtGihC/wPHzlMvFfPm1HsP6ETT2BDqmOmHOkzJbdTJJYo3n75\nYOvCxbK1nfifm1/BTX9cWOJb8oMjBiO206imarr3ADmAqYSiTQr63TrHpJ5+WTcdB1FZ33qjXvuT\nmMdGMVONkvmxbumqTbduEVgoOJHn2ZGYKe//JPOTmSkLaUXmx/eVk+S2rjyWlDkvvZpGk7UGHU8l\n9xvdS2km8yvZtNRnr6L25breODWY82p2Zi3aErnYStxnynGRTtmBFEhx9OPXgqRizSRpCwwo/GCK\nnVuKd9TDpbErqqg76rXBbCG0EAe8xXq1wdS23X14Zs56PD9/Y1XbiYLUdL7KYOryu+fiN39eFhq/\nXFdukPvIi6uwfbeXpOQ9eqL6NyUdd2kb6ZSllfnxmilJykdjVISbn3aubwAkmV+EvJifc/o39el7\naeE7AGQXQwChZ1s1ISLwsR0IFuoFwUzJn+cJMXEtbDuQ0cZIFvLnZtP2Hjw4dUXovXopqwbY81ws\nOkE/Lf9+0jFTGV4zFeP3qWPl1Dnr8eTLa6TX6DyVCohCvQqNAUXtkC84oscGEAwAdO2+cNJ78V//\n96MAGlMzlU+YZQ+yQBpmyon/EAXZmXj7z0uDLXDzQ28AKC1tchwXhaIbDNhscGnRFJgDqntX+Dfy\nguDOfQNSRqYcNm3vxsMvvJ3ougZmGLpgKlicxt1mlO1wPZEvOPj3n07DPX9+s+bb5uel3i57TolE\nQj1A+2lvTYdsunUKCq8Xi35bnEWiBaWuaa/tG1BIMr8I+Yzjyk9vucVeTwOCKVUykwSSNXo6vMgP\ny/xkmZF0HK6LtVv24Ud3zMGXr5iGlxZuxg1/WICr75svfa63PwfXdYPkUsxjLRZdpFIWRrG6iqjA\nlqQklUql6wG6l8XYzM6hGKOU8xrtOBe+7/i85kga1+Aztm2F6kmSghZu9UqsSW5+CROfUVADoqLr\nwmLOeUBg9kH3PC3Qda57SYM87sonZH6a8UayRmdufjR+WZblBcQaJmSo+kxFuvlxmZ9/nB/9wME4\naFwb5r65zR8DgiASYAYU/pojUubnBEmt//vp9+M9h3qSP6HEUa43v4Zin5I1egzmRtnmhLHh8pR6\nkQE8WCo4riTPTqft0ByYKzhoSdvMzCROsCj/PXnGajw2fbX0Gp0nXWsKgpoEN32maoh8wUE3Z6YU\nXatlW6KgthFNe5P2iChpjc46kJeDIz4bb78SM+W62LnHqyHqKNFfiW72wMbTpQ2IhaQ6QJWT+fEH\n+dJJs/HtX75Uss8Ax8W/ehl/nLYSC1fuiPV5fnw6iQdvEBj3VhkKmd+CFTvQ05/D07PW1XzbXHNc\nd2t0MqBokJtfkTFTgHw/aq1ai24JlgSapr2+rSyXB1pekMAXt7LMrxQzpWOrg9d6Srge1QpBVjT5\nd0UwxTLmfAGrPoPCmll3zt1A1pjNFXHvXzxb+bc3Bj1eFqzYgX+9/FlMnrE6MTNVcBykGDPF2RdA\nvhaDiqRcfV867AYFWarMjwfvZDYWKfMrYd9NUGVjtCVuVlELmR8FO/XqSchbl9TKXVadu13Xhc0C\nF+9F73/0TJC8XbegTx64BImbdDos85Os0ck0wHFC4xcA2LatNaBorMyvvFqFy/xonZRJ2/jgeyag\ndyCPzr2DGgMK79mgxTr1GmxRmqUXnSCJdt5px4ixSxhQKPd4Tno2HLFPYiadWMyU/PeB48LBVL3W\nr6oEnf5Op2zRSJojX6hA5ueEz1k4MCqfXFWfWdNnqobIFxzJ/YMWgK4TDDCUmWhEzRTPmMSBbuAK\n3osv86PBJe7krctcAd6CJerh4BIF729fQ4ygP8HClTvx+lvbQ98BwsGU67raLETciZQeuiSLSnr4\ndBOpxEzFPI9x3RNriRkLvGaCxAbWEryZbT0nUM7sNIqZons+uH/DkyBHoeiIReMHjhiPU044XLzn\ncGYq1LTXYTUl3qJKWtxEmEyo8hJdMMufoUbI/MREXonMr0gyv1RoQVIoOqHFAS3udIGb60KimVRW\nbjBbwM/unQcAWPT2LsbqxzvWYtFB2rZEI0qvLiiCmfLHrDiNfHVJm2y+WPN6N9UanT9TAXsuf0cY\nUIScC3WJBXm+EPcDX4jXwM1PZ1tdSwxo+iRWi5DMz3E9Rpq5vKlJU90YJBIXCU8h3eOWFSQtZOVJ\nwNCkWe8jVaZMnwlaVrD7P+G6phpwZqro6BNaksyPBTBHvctjkTZu7w56bxIzFXLz8+6xsaPkBLLH\nTHn/toBQj7wQU6NRc6RTdiI3P3V85X+RIU+9mCn1/qXxLcWSYOK4XBeO4yauCdPNH+rYGNXMVzpW\nx1WCfG+7lCA1zFQV8GR+vGZKvuE96rq+zJS0OComu5iUtShoji1JRoIe4ri/UJe5IvAeCxxqMEU3\nsuu6gYyn6OCq3wbSGz6oq0FSNlcMDUyVIJEboX88uuyrZECRUObXKDiOiwUrvB5DR7xrTM23z69R\nPbve8/Nbj5qpnv4c7n5yqeTaWAglA6KfAXqfkjL//aUT8NfHHiLekwwo/NdatG5+Xoay6LjY25PF\nYK4g7Ut6JlxXGkt09Zd84m4EM0WHU1EwRX2mWNNeSmLoFsul9uW6buAgx0BKqi2scea40S3sHMdk\npoouUimbLbiKkZJLumalEkWlXr/oxpdw/k+m1pS1UmV+EjMl2HN5f1QrE2KmNHOYGkxpzPx8ZqqS\now9Ai6FGyPySqkiioCaDHJL5sax+ECjJQa/MTIP9O8mJDBgmnQEFGWJw6SFvWcLnsJQd9AqTrdEb\n16ia5uF0yvYD9/BnuOw0qBmzcZQ/J27a3h2qmQrMZfyA3T9Ho5kNOuC7qrJzqiaC1OdITsoFgWuS\nYCrEdmlUE/VjppRgKh/0B0wrwRQdAm8ArW80rLK14f06LDByXTd2UpUHYdQfsDWTgm0ZN7+qkC8U\npSLkkMzP4kWX9TkG2YCilswUZSTiROzJmKmognigVKM8NZgKAlc74o6UFxzy71CLvMXxJBw0EtVM\nlWCmSgWYUYjpRF8zeIOOz1jWkNHZuK0bK9Z3Naxmit9zUdnHavD7qSvwzOz1uPXhRdJ+AGCUb6PK\nJWe6y+05XgUBE5c8OAH5FDTtZQYU3OmPaoG+cuU0fOPaF+RgKqccg8RMhQ8qO1TMVAUTORWPj+PN\nREs8f3TOdPtyoZ+QafHI35MacMY8bM/Nz0I7kwLxe5I/C8Se6AIsFbpgaltnH4Bo2+dK4DiuVBOj\nq5lSIZjhOMyUwqCo9z7gLbCqZ6bqK/MbYM8bzQXL1nbizbWdFW9TnZ8dN3wuxKLR/yjZbetadHjb\njH8eRe2Tv8C1rXCiiBLK3DRATQZ577NgSqqZaiAz5QdTHe1pXwFQmpnibWGOOpSYqR4psAEQsM4k\n8/PP0RglmOIyP8uyxLgf5eYnz5lhmV+SPqEEXaKvXnJ49V6j8U0n85Mlo9FNe1VVRdTdIwLUBPc7\nnzPzRQfplA3LstDakjJuftXAk/mFmSlBbVu8f0J9bkZJmpO4Zipayid+SxxmStQIxNuvZOepDJTd\nEZO8YKYUvbe3T3nCpkElX6KvTJCBUr+bMJhKMIEHi7nwdQoVWcdAo5kpflS1HFy/c+NL+OGkWXIf\nlDrK/NT7vdZZt65ury8SHxtCMtV8eMLi4DI/ywLSaX6tXcFaBW5+4Zop3igT8BbQ/N7ikhZXYaZe\neWMz5r65VTomiZlqgDV6NU5SO3wHs4kTOoKElr89XfE/lxQeMmEUPnbMwbj6m38PgJipMChzys9b\nH6u5pIVP0XFxw+8XYN6ybdpjLThenynBTA0W5HFdcvML10xFMVOPvrgKf5i2QvseZ9OqBTUd5jIu\nghWhzBB9jeLUTCnMlNoWgP5NyZ6pr66PTMqVQv1rpsIGFD+6Yw5+fMec0CL5T8+txKTHFpfdpjpf\nOY4L25b3pdbwHfueCQCAFRsCwyfJ5TPB71evRTplh5Kl9PylWaNVETCwjKAn86NkXWPmAhX9g3m0\ntqSQTtlCVqZC12oinbLxrgM7kEnbvsxPZqZEzZQi8xutyvwU1UGYmZKPRcdMSU17K6iZkpipKsbg\nOAgxUz5zl0oFigL1WEI9yxRE9VBTQesNnTJLhWAWWRK+WHSQSZHMPm1kftUgX3RkZkpIz7y/OTPV\nCBLi/AwAACAASURBVDe/pBkcHgSFXIE0GaLI7TC6NA549jHETEV0oA7L/Px9wg31twqkMNEGFES3\nj2eNlYHk2uCK3Pw0C4Yox7VSaHTJVDmDgmrBr1E9a6bU8xtnME0Cus5S3UKJmqloa3Tv32pQxJRO\nQdPeNJPusGROWsnu8fub91RRn93l63bjugdel17jk0VD3PzcZEkajh1d/UinLEwY1yYy47p+OwTa\nh+u4GNfRgqu/+Q94z6HjgvdKMFNSMDXAA1Tv/6vf2YNZi7fg2vtf0x5rsSgbUAzkCrLkUlMzlZeC\nKf2zMvXVDXjkhVXa+pytFQRTL762CT+5a05IyVB0XN+OO2zHHJjkKIv+KGaqGJ6H+OMpScRYEo1k\nftNffwd3Pr408lyXQr1rpnjmOq9cM0rAEB56/m08N6+8RXvBCS8cLcvSysbpGvzVe8PBlMRKJ5BY\nq9cik7ZDwS+tgQSbEIOZ4vdBPSXfKgayBbS3pmH7x6Ibm3XMFAUw7z54NDbv7GVuft5vbs2kYFnB\nPRDJTBUZ82pbZQ0osprAR27aW37wpG0edlCHtC/vPcTeTiVQgyEycUinrFCiW3gRMJv9N9d24rXl\n22UjNuX5jVpP0fowjtS/Q1MXVSg6IqHW0pIyMr9qkC/IfaY4bUgJFzUrWmvUws0PCN9QSSwxCwlr\nprguXr3RI7uOkxtaa5iZUhkaMWCVqJmiyeYAvykeISnjkmSMoWMu56LUrDVT/BauR61Ro4Ip9VGs\n9W+hyZ8HMtwaHVBkflHMFMv6vvvg0eI9XdPeNLOKlaQ3yoTE99XPmCnHLX8v80V7b5PXTO3Y04+D\nx48S0iNvez4bonn+eF0Jjdl0bl3oa6bonHNLbh0zVW5xSn2m2iUDCva+jpmKIfMjbO0MB06VMFPz\nlm3DktWd2K0s/ItFReanYaZCl1AwU/IbUfbDovaKM1NsJUK22hTkS4FCTAzWsWaq6LjI5Ytoa9E7\nz67bsq+y7YZqpjwZVEFT10PX4KDx7Thkwiis3NAlzqW8jkgQTCmufOm0rcjEAqOktOTmB/97KjPl\nrzuGjJnygynLkswgOORGuTIDddD4dmRzRSGDTomxxDOYoXUHjaVjdMwUqQ4QsN/5iIS1TpJn2bxv\nV3yZ34VnHxe5zXoFU+q1pWeQ17qKY1HmPJJF//7ZFdLcFWamIvYtDInK/7bAsY/XdbtizGvNpAwz\nVQ1UN78Cu+HpZg4MKOpzDHIGM5zVW7VpT+SDwG0z1cVrEmt0MVjHfODymj4NNMkQM/Wze+dJvYzo\nxqfsbYFlakLMlJL9AaJlfgdUyUwl0emXkvkVKpD5Nbxmiv27HoMrX6zX1YBCOb+1Ztl4rwz+mm1b\nwnkyr2m2qB4THaZtWTjs4NG4+bunoKM9o23aa7NFK8/6qswU/+08mALKP+uyAUXz1kwN5grY25PF\nxAmjAISlZrrgQwRurMbDks5peD86ZooHmfQqP/4dXf1ynYO/YEvZlpAwDyoGFFJSSJMoKsekbNre\nI/5NGdatfu1UElCgqDbS9AJQlg1nx5aK6IUXMFP64IlA2yLTBNXtjGDbXp3Q2A45058EJOGpBzNF\njOIY//hUdcK6rfpgSpdUkuqhNJJl1eVVZaYsy8KxRx6Anv48dnT1+9sMPp+kB5Y6DpWS+XFplk6q\nmeLBFK+ZaiAzNZgroL3FZ6YiaqakQNX/N7FuZCu+y69Hs9kk3d6a0sj8VAMKuSYwo/TIKyXJ4/MF\nZwHLge4L4QirSfTVS1mlJjIpmErZtiJtl2umAODHF3zCO15WJwxoaqaiZH7+uYsTcFKtM5f5Uc0U\n4K1fDTNVBQpFfc0UZ0uCmqk6MVPs32pW7y+vrMX3b52JyTNWab/LbyL1+JJYoydtUqljYcb7DBHV\nTC1YsUPqZSSCKVqMcgMKy8KDV34Wf/+RQwHwSTFaCiOCqbEyM1VXAwqS+ekMKKQJKN72Gs5MOeFJ\npBJs7ezFOzt6Qq9LWZ96GlCo93qtmSmy5U7JwVQ6ZQcNpkswU+2tac/NT1k1vv/w8ehoz0DXtNdi\n44zLFk2hYCpC5qc7DhVZdswNMaBgNUdJsNNfIB7iB1NBewrvfVViBTADChdhZipiMk5pgin+HNPz\nwp+Vb1z7Ar5x7QtBPRW7V2hsG1Sa9vKMp2jaG8PNj7CRNSMf2+GNd5UwU2SuMagEU2Fmip0vcQ7V\nrbkhCR8QzirT3xRMFV29AQXJ/NRMfxIImV8hbJ1fLWi+IVlXvuBI98r6rfqG8bpry49NDba8RK78\n+aKyILatoFaH7i3OvOqejyhwKTLgy/wUCTPFE9wUQf0e4C2gtTK/iPF5/dZ9sftCxoXXx8j2mE5X\nPxfzdRP9O+3/SGp4S+YevHlyW0s66DMVaUAhB5opX9IWJfPTycXpe7S9chB94lpk11Og/tbo6jxP\n92M6bSOTUntw+fev/9s+dswhOGhcW8hESn1mopLTVDMVZy1DzNSgKvOjmqmWlOekm/A8mWDKRzZX\nlCyCuZsfDRL17zMVzUyRS9DcN/WFz6UccypipuLWTGnc/MhhKKpmqsAmVd47h/Z5wJg2ofkNFhzh\nRQiBFgTjFZnfK29sxk1/Whh78KjIgCJGkXUc1DuYchwXV9wzVwS1/KiqkeF98/rp+NYNM0KvS93Q\nNdtfuaELz83bUPF+CaGaqVozUz7zqgZTmZQVyDZKuDe2t6YlNz+eabYtb5GuymsEA84CLduWJ3N1\nXwMqM1XmXh4qa/SkLm3UBJyYKVXmpzWgcN1Q5tPSsH0cgZuf/vjoVKtjz56eLF5ZtMU/loDFJAlo\nf1Y2oOAT+KC2z1TpZ5EzU3SNiZFIgiCYCtck8JopvtiMskZ3HP0zrr5W0DJTYVaDHOxSUdauMcCz\nzrVmp+gaEnOWLzjSPjZEBFO6bHepscOzItc/88G4EJZk8suTzRdRLDrYvrs8e6kzoFD7SAoDCjsI\ntnU1U7atdxHWyQ537xvAd29+BY++qE8UVwKyyE6lrKBmSjMX8+OhucP2733BTO0JM1NtrWkMKNbo\nIZkfZ15ZgEqJcpVE0TFTXhItzBJH/27v/xRM6ZyFG9Vnip7BjLbPlPd/Ph+2ZFLIF4pS0Btb5pcn\nmV/5c6QmHwDvPHGZH5B83DDBlA8qGqVrK8n81JqpIXDzo2Zi/QPyookg1Uwpx1eZAUX54wXkh5WC\ntrEdrbCs6JqpPMvgZtJWwEyBOZr5NQfkCFNKChMl83v8pTV4eeFmrI+pYa/EGl2X+askmKq3zK8/\nW8Abb+/EghU7AJSWl9QC5YKph194G7dPXlL1Qkdd2NX62QwaxgYXqOCbDFBPtJwmo0hoydiy4xXP\nwMNSZH4kJ/a3pdRMRfXqALzrC6CkSU5Uxk+VetUDlTbt3aEwU6rML8qAInCL8l6z2Hu6QyDr3ujn\n1Xtd1xx8np/gIoZv9KiMNCHz886/nxVufoy5SiDzo9/o9dlLdl77/OBbbStRdOVGmnGa9rquq10k\nh5r4+ttqzQTnWmVlvf3ItYSVQMcA1gr0vIz1mYhcvijtI7K/omau4PNaoeigWHREoEs1Uxw6mZ9q\nLc1P257uQdz26GL8x3UvYsV6r/Ysmy/irfW7Q8eijkOhJuFMdpjJBDbftDspUWTbYj1Qipnq7sth\n194BFB23oqRAFLgzX1AzFb6fdLXmFCgSM7VLw0y1t6ZF2wNaq+mZKe/f9M1Myo5kpvg8Qu/ZliWM\nL5I07fVsvhsr8yPzJ5qD6BlMaftMyZ8FvGAqm3ck1Yw6hkTNH1S7r54jNYgDgA5f5rd734BQ1XAD\nCgpEk44bJpjyQVTuOJ9VoUHOcZnMr941UyXYJdJ5qnIeQklmyj/gcpbufPEWO5jSMFMp3xq4fzCv\nHcDyLIPLddlcUtmm3NC5UtboOb0BBSFqclORJKgo5eZXSc1UvZkpGvB1AwRlLiuxII5CuWAqm/ca\nLVdrThHlXFkr6GV+XrEq9YPijkPqREX3ty57613ycHaeJpiyNVMaZoqeG915kJv8ytniepqEAMwU\nIuH1oQmZzGpUmR+NC5Rson3xxQigGFBonkliYqIOj15XZXFAcI9QjdXo9hZJxcA3yRf5tNiUnErL\n1Lhwpzi+6MhpgpkoOI4rJFXq73GKDmyrjDW6cv5c6GthyjJTLpOksQeDZH78WiQNrPj4U+tgis6Z\nzEzxWjh9cqIcM1Usurh98hL85/UvijHDVrJsqnKEy8C4Ky6hc98gZix4BwCwZvNeAMAjL7yNSyfN\nxrNzN0jbVseoTNoOtfmg4yGDlcFcMbgPlZqp4JnXM1M7uvrx7z99Flf+Zh4Aua9btaCFPUnrHFfv\n5qc1oEjJMj9dMJVOWXBd7zv0m0LW6BrVQSYdHUzxcYDL/Gi3SWR+Kdur0dIl+urV2ofGAN7aA6Ck\nuTx3BWY0wTnNpG3kWaN6IEwqRA0DuQhmio6Fg2R+v31qOb51wwwMZgtiTgcQJEkTSGQBE0wJ7N7n\nTVJE7ZK8R8dM1Suy5wgxUyQbGdQP1I40KOiZqXLPEH9Y9d1YdMcZZmEsyzve/sGCvokpc0jLpG3s\n7clia2evZEBB2QFVlwxoaqYG9TI/QnfMmpAk83UpA4pKrNHrzUxRVlRIDNhhFRwX3791Jm5hjWmr\nBZed6aR3SaSnpRCW+dV2oihqgylPX00FxXnNhEyg7C69qqsNUTPCfNHKa6ZKyfxocdzKJFQqSrG7\nuiChlggy6km/qQSaigkCjQ+j24OFDC1ygKD+TJL5afZSTuYX7E/zvPv7IiOPMaMysCwLliWzL4C8\nyKdFA28vUY6pHcgWtCyfaiFcbhv0VZWVLDqu3yhUXqADYYklgTcA5wgxU8pii9fbyKwG7SPYT9KF\nNg9a6yXzGxMh88sVHK0kS2tWJMnMHGzb3Yfuvpw/d4YNmWhu5+ctxdw/+XuAl30n0Jy6drOn1Hh8\nxmpp2+oYFTKgYDI/3mtJz0xZcBxHyO1IAsev4+adPdJrUWsbHV6YvxGrNu2JfF80vfUbsfLaVA6d\nsobkpRP8tSBdNx7YBmZkQTA1qi0jzeNcpi3J/FiinoOva/j1pbE/zlzJ5Z8tas1bzHVgpaB7UzA7\nrGmvmgik+4q7eLZkvOCvtMwvgpkS1ujy+y26YKpNDnoLRUfUQQOVl/OYYMoHMVMHjm0HoDegqHfN\nVCk3v1Ht4Z42HDJdrc/Wl5NAyUFAjAOGXtJmWRba2zLehKDZEA0mmbSNj37gYHT35XDd/a/5Mj+Z\nmQp6OZSomaLsdVtGPMgc3X3ZWJlN9bpu392HPz23Ujsx0vHoMsKSzjwuM1XnaCpkmMGOy3FcdPfl\nEjNTpTTc5ZgpOp5qa5xCMr8GuPkV/YGXXsuVaNqb9rO7ZLnNy0Bo0aivObAkNyjbCuvOpaa9/vlu\nbQkWquHfEl60jxvtLQj76xxM0aEmNoUJBZrydogN4hJf3qBTMFPsPd0jmS4j86Pv6IJO9VjI1Ysc\nzSSZH6tRonG5VNsHHSiA5IeahH3hlu8Das1UTJkf/02OG69min5nuZopy7LgOPLv25twbNLJKVU8\nO3eDVu4Wd9vcgCJqTuLQZbrVRAx9jwJe27bw3X89QXxGJ/NTWUR+bShJDARz6pHvGgPAY4b4vKpe\ni0zaM5Hgphf0PIk+alnWR01hporF4Dk8cJy3ruLXUZXF9cYMmAeyBdz26GJ8/9aZkZ+hY077NVNF\nR/9s68ojKDgd19EqBVC8ho8n1mksbUnbojwBCJwO+b1dCTPl7dsqu36Tv+fNFzmNaiKpFH7nnv5Y\n9VqBjFdOhKc1NVN0DqSaqXRY6aGur4L+W/J6iban/jbderCjXQ2mvG2S1Dsw/DDMVEWgDI5gpkT2\ngBlQKHr9SrGvN4sX5m8MZbFL1Uy1sYdUB1nmpzBT9FvKHLfETMUMAnRZeduyMKrV68NQqqt1OmXj\ne//61zj4gHb0DRbgOq4Yj0XNVAz7YFpItrWkhPyA4zd/XoazL3mqbPZdPT/X3v8aHnr+bTw9e33o\ns/QA6nTw/Dc3iwFFXmHSdEeVVCJXagEnBVOagJPuyWprnOrNTJH1Px+88wUX6TSrmdI0WyRkFJkf\nBy0adayV50DF+pTYCBXky25+fjBVgpmS+hnlKJjy2Ny6M1MVyvyiJJC0GVqAcVaas0HiugmZX/Bf\nDtFnSgRv+mPRM1O+zG/Ar5nyJ2tdfxu+yA/czqJZdx3o2eLnMokkhbMDag2Y5+Znh6RjgDz/SWNF\n7JqpcDAlVH7Sve9LxNgudPW3g7kCrrlvPhas2IFLJ83Ca8u3B+9lo5NvgOeKeMfkJbh00my88fbO\n0PuA1y9qp6aOh56VUW1ppFMWcoViaE664p65uPyuV6XXdBJOWebniOeSrrFtWTj1xCPxH+d4fYNU\nmZ8tyfzCzBQliYFgDcHHgZUbA3ZHZQmphoSvhXQyPyjfAwKZHx3v+DFeYLK3Jwim1AV2XDe/OGsT\n0YA3ZSNlRTft5fd3QWGmbNvCBDauyImwILEuyhYyKWnxTswUv7cz6ZTUw/QDR4zHI9eegUMmjFJq\nprz/ixKTlBVrfuZBWCajl/klmefXb92HC695ATc/VF61QnO6WnOkk/nROeDzaktGTqDzzxHovKjb\no/FPXW/qZH6jlWCKnku63ytVoJlgykdI5scNKPyzJHT1VQZTV983H7c9uhivvPGO9DqPurn0wzuO\n0tuMx0yV3ohkBVx6d8F3Iuw829s8FzPdREbOfJm0R8GTe5MLZkChZDcoaCHbSg6aeNpb09pMBKFc\ndlMdbKnWar2mb0jATJVx8ytx4fikUGeVX4hJ02bp2HHPW7YNtz2yCHOWbI3cpm5hSSjHTBVEMFUl\nM6XWTNWJmVJfS9u2KMIuZY2eTsnZXVtZNOqa9vL3gnKEcBd5fu5IVtlaomaK/xZ6Lsf59tr1NqGo\n1IBCXVSoMj8Kpqi+gb6jZnbFeXf1Y2lgLuS9SbJq6Te4YcMGIFiEkSsiZdxFfxs2mkrBFMn8IhJF\nUYtGkkPJwZTOiEP/fR5M6WR+cs0UG6Ns2m64/YP+OZFZOVXm5/DrxL4X3PvBd7s1zrCvvLEZ85dv\nx8/unYe31nfh6vvmi/fKufm9ujRwxf3LzLWh94uOix/dMRu/fnRx6D1i89pa02JxrCbV3t60B4tX\n75Je0x0HT5oWHFfUW9E14swEHRegur2FmamUbWHc6BZJ5kf3OF9g97B6YrV9A2XqhbOxg9LMFAM1\n7eU1p+NHt2BPD6v5U75Wy5opum/Tts36TAXvEzsnK2voWIO7kSdpJGaKy/yKwffaeDDlM6/83uaN\nkB3XO0+j2jJoSdvSPSSCZdHXy441t5ECIuXL/CS2ywmOKy7W+nV2ryzaXPazNA6qqqJ0WiPz88+B\nHGh6n+FrRmKplq3txFvrd4tjDwdnZOajMFPamil5bKfnkp4xdS6ICxNM+aATR5Nyng0g6kRe7QLw\nbT8bxCl4ACKCacnYwp1EvFVmEcLldKGaqWK8YIo/ZPGt0fl3vP9bsERBuK4hKBW10gPhTZ6lDSjo\nenS0pZFXMv2UKWxrTQtLYh2i2J+oTATZMetchug36Jv2Rsu+CO/s6MFXf/YcFkVkRWsNytyUqh/g\n1/K2Rxbjhdc24eEX3o78PJfPqPcLX6Tput5TsqDqminl64UaC8KDrGzwWrHoIJ22mGNbmJ0l0MSc\n10wegWuZ9zdXLli27EBlWWWa9gpmyrv/y9kAqzI/1SK71qDfkdRIgGfggXC/KBpfDmDBlGSNrvSZ\nUg0hxH6U/bUzXb1YULmuVjJG++phbn607zAzFQ68+XXJSiynvJ8Jfh89yuBL7oxKUufmh97AeZc9\no70PSgVTjuvCTrGaKfY88Vo+edzXM1OAmvknZoq5+YmNB9+xqGaqDDNVihnn51l3zV5dulU8T3u7\nw4Havt4s+gcL2KaxFKdArb0lLaRU2TLGIUBET0KWNHWKrvhNnJkCAFuRoer6EJHpgpeUtHDguHZ0\nsjWGcMBkxzoojeGQ9knnJ5BQMWaK1UwRZGbK9oNuP6hJWRg/pk1ipnRudqoiR4c4U0bATFmiCTTt\n77S/OQK/+M4nvd8kOQ0GyWxCG1tPSDVTbMFN6gHbsiQFUdFxpdIFwFcq+L/Rq8f33mtJy41ief05\n/Y6kMr8WxoLx95KsX6kuOA5EzVSGVEVkjR7NTKlufvx7QLB2+NEdc3DppNniGkZtL8RMxZD5CaWU\nv82UFV4P9g/mRWAZBRNMKSBtbylr9FrVTKlRsyNulFSImSor0dMMCup3y21DFxiVg66mxLIhghqd\nDpoGTBqsuRVuIPOTixjphm9vzXiZZyf8ezMpuyQzFfX7g2y3/PpB4717YeeecDBVKFEzJRd96o9l\n044e7O3JYp1v216NDXAc0DHlxUAe/ozcD6SE+5/PtMhZdvkzvAZHl1GjRVq1srxQb5Y6MVNqc810\nytb2o1DHBhqgaQETqg1x+bVXpU6yBDClBlMaOWlcZooWdiTz0zEutUSlMj/OzAHBwoR+X5+omQoy\nyJzVUPtMwYWWdlf793B3QEqwOa6ewRMyP5WZ0lgyi2xtKnA74+PF0tW7RGG9OibQcQhmigdTykJ9\nxoJ3kCs4Wiv3PlborwbRatNezlbzYJ+/7rrB+EwBHxkO6Pr4tDADCp1ELHDzC36fzpE1ur7NlRZk\n6rnpHchjw7ZuHPe+A3HogR1axUKXH4Ts6QnX2/L6xLQvv6IxQJUQFTVsMAdPmhacQOYnmCnKliv3\nvZd4lMcFR8j8vDqdA8e1ye6RFLxHOA+Gxi6lf5XjM170Xsq2MJgrhhb+QBCQiKx/ysb4Ma0YzBXF\n+dPNQX0RrV844syVQc2U7a8v5DIEasyrd/MLxlkeHOmCKZ6csSxLWtMJZkqpmXJc777g72UyMouk\nBrZUg1YOfNzzAv1wYjdZMBU/RKBnXV27pUrWTAWvtSgugPQ5fr3pn2klyKN1mJpM1RpQRARTxMTa\nGgXaVb+dj/+5+ZXQtjhMMKVggpD5BRmgkAFFldl0Qka50HTTqPQsHQdBlwUs1WeKbrByDxH/Xuya\nKWrAm5YpcLJy79U0BC0ozJRlBWucgJlimmwEk067v8hR+3PYlnd9dDVTut/HQQ90/2AeKzd2idfp\nOaemfWI7xcBxJp8P93iJY40u+lSxHlv1BO2HLLF1+XkpmBavhc/Zhdc8j69f/bySSZM/J7v5RQec\ntZb51ZqZostXZM+Q48K3RtcEU8qEp2Z3SzFTssxPLtC3LYRlfpp7i56pcvUBATPVIJmffziJx07K\ntPrDC7eNB7w6pZZMCn/13gkigcOTLaoBhSq7E8enJJy449NYUSjvamvLVDc/iZlS5EX0/ZZMSlsz\ntWVXnyisDwdTXnKHEhX8sYpiaXRsSKmaKcfxrNHVOhxAZabkuYL+Pv1v34M7fngqjjv6IADyXCUW\nW6xmSisRs+A3sw6gK4CPMq9RHcGyefk3ErM3fmwrxo1uwb7ebOi+JAv6XL4YejZoTmpvTXv9lFgw\nRUxvsG++MCxdM1VgRhb9CjOVUtYe3AwirbCIruvd7wf5iWECnWvOTKkGJHyfGbVmiu3Tsiy/cS07\nN3z8EpLCQEI13h9riJ3SPYdx6qbijCESM2XJgZNtW6F6MIA5ALIVPk/O8tc5e8HHaP556jOlk7Ll\n/XuU3mtJp1AoMrMPxjB5x2yH5spdewZwx+Ql6B/MYzBXwJwlW0Wg7NVMefW6KpsZ1XNLhySyeTq+\nVqX2qZSbn86AYlBx4uQuj4JwUOZCkgPGqZlS3fzo2RVufhoF2vJ15Y1qTDDF0N6aEvIyoRNmNzxv\nplkLUPNCAt3fmUwqzC6xm58KnYPvyQXBoT5TMQ0RJJlfmWMP9kXSDfmmpZqDXp3ML8RMBTS8WjNF\nmTPRT8bfrtoIkLZ19OHjIo81auFOA//UVzfgB7fNwsoNXkDFAzbOsMlufeHtxpH5EbMlnH1qdE9F\ngQYMx3H9rFj4M1K2WZPFJOzeN4iu7kHRUBkInwNJ5leiB02cZ2lnVz9mRmi21dqYWtdMif1QVo/V\nAJBciWd/dQYUQHDPq8wUJ0qkInxfmkKxIa+NUI+JQNli3XveMYSz5IHMrzmt0QUzpYzBgcwvjzGj\nMjjyXWNx56Wn+t8JzpvIJrNzq7v3xUKDZH5c3sN6UGkNKPx7rncg77WFaCsh89MEUzoZLP/tBEr0\nUaKCL4iirNF1sl7ZzS+47lSjx938pD5TdFyOq8iHZAnfERPHhBbi/N+BAUVwTKUZW33iIMphTL2X\ns8oYRs9rW0sa48e0oui40jkBgN2snxfv7cW339aaEteY9jG2Q27PwYOpcn2mBnKBZX2/P9+IYEqx\nP+dJXqrl4TVTlm2J+4VAiZ6yzJR/LdJpORHkGVAE22tvSWEwF7Q/Ua3R+b7SKVuwxyKY0jyHcRz9\n+PeiggJRM5WyA0leMUiw6JIFnM0i8MU4D6aCxLpcv8aZrChmCgiYFF0TZP67eM2cOsdedvssPDt3\nA56btxH3Pb0cP3/wdbz21nZxfC1K6w4+J8Qdh5O4hKoyXh5MJZP5Mba24EistFgjRzBTasK8JRMO\ncUapwZQq8zPW6NWjo70lNAnUU+anBiC02dZMqmSzMrVQU30wQkX5TnjA0EFy84v5tPEGvATbssrI\n/OTvEA3PszitShEjBbe0XbU/BFHzF5z5YXznix/THmukzE+x2dywrRuAvBDZuqtX/Ft1p1MXLMSU\nAeWZKdpWfUMppV6moHeX0zFTpTTsOo13sK3wIkq3rzgyv+/c+BJ++YeFQhLJERSkprTby+aLeGPl\nzkQyyv7BPJ54aY2UCKAxmmc8heNkPjqoVBckqmOf6+gNKCyLWI3gPZWZUu+tloxdsq5Ty0w1yICC\nfkdyAwr/9/t/C3bECQIY7p7nfSdcwB0EYRA3N180BTI/P5hiMj/at6tIWwmBzC+HjraMmIyF8/XO\nWQAAIABJREFUNTp7unOMydfJ/KTfrlxDIfPzG7fHcfPTLYaimCnantdnyl+gs30E1uiKmx/c0Jiu\nMrKAJphyeb2gLHFVLey17pQRiRNajJHUUK2Zor9bMynBzPJaHiCQ+QGe1I9DMFMtadH/J5KZkrLs\n0UklQO6zRMGdaqDiOOF1iWCmSOYH755Vm9hTQMqDdyk5oFyLYLtBwo/PlW2taQxmw8oMIFiQ0v2X\nsr2aKQDY2zsofoOKOMwUf57K1epR014geE6FPNK2pICcM1fiNzKmKUrmxxN6Z5z0Hhzlm1uQBJCP\n2hnFAZauL7EyV/92PnJ+Q3tAlfnJbPBOXzHTkklhxfpAUUPfy2iCYULcxr18/l/9zp6S14eCVZoX\nOeOjJgJpu1IwlQ4nJ3MFR6q7p9+QjgjO1DGBW9X/zV9NxKVfPTEc2InjlBVoPDEbp3PNfh9M8WzD\n6PZMMAloBhDKAOkWjjoGpvy+9QXl1JuGg0+saoCiPhjqgrKUOYX8ucqZKZ4p8LKz0cGUWmdFzBR9\nFwjba9L1EPaufBB0HGmhqctGACWYKcWYggYtKWtYgmlRJ8li0RUSzqgAjn5P0MCvvuGUznxAhU7m\nGTVZAfqFmHa7upqpBG5+dO7VDDH/Pg3E6r5u/MMCXPGbuZJ7Vzn8YdpK3P/Mctz1xJtsP/I94dVM\nUZ+p0m5+3mcoExm8F9W0FyBWo0zNlLKvlnQqFGxwSMFUwYFlAWP9xqM6uU8tEbduU4XK2nFrdMdx\n0T+YF32duAxNZSylRsigSV+ub6D3VYggAqWZKY8lCxbTOmaKUI6ZKmokcBRMDWhqpqIyyDrzhSgD\nCl5TkrIt2Jb8/HOJpVoXyl3bAFnOVCg6WLa2UzwnrdyAQmFDAO+a8UQCfVZF1FxG55NqI8I9oIJW\nGkJ6ptRNcRc81aBigBkeUV0c/bYQM1WmeTA/v9J18a+xWHsodRyOG9RTCWZKJAS8e5b3XuPf5ccx\noGGmRB0PMRtsMc7HqLbWtPR9LTPFxksyctkjZH5hxKuZCv4d5SgrMVOWvDjm1u86FQlfS3CmSWKm\nJJmf95plWfj4sRPx9bPJxt6JlPmJRsBM5gcAS9d0Ytm63aFrkUrJMr9N23vEv7O5cEBrWewZZKoU\ncX5ijsOc1f3eLTPxi98viPxsQazRZJJAx0yJpr383OgMKELMlJ88VeZCkpOqbDVfC37giANw8vHv\nDh13riCPXWKdz84pNeguhf0+mOIR7uhRQTCllflFWCY+9uIqfO3q57VFsqWg6yRvWbLjC4HLHNQ6\npJAVeoQ1OlBG5sf2Ed/NL8xMUdNeIMLNT7l5LXZcVGjeqlC++UIRmbQdyrYA3gPEF5p80DvsoA78\n06fe7/2+iN+uuvyRK5IuqwqEFz+qk1a+6IjBMSpICgwh/G3VmZriGaZc3tEaY+gMSPJFByvWd2HN\nO2EnG17IrhucabGqY59En6kkmmzNdkSdoT9oqjVT85Z5sgedK1cUqLfM2i3Bb1adkDJ+ts2yVAc2\nNZiiRYW+NiGyaa+yoOR21WJfajCVsUVtkZaZYqY22VwBmXRKML3NWjMV6jPF2JH+wTxcNyj6Fx4T\nbrhpr7xN7/9cwuOK4/P+T3Li9taUtM9SNVO9/Tl0jAokJDoDCjrOTDpYHOmYqSyTfBGobUf/YCHE\n3OhaNAD6IKufSQ0HNFJdGku9BRyT+TF2L1QzpTBTwla76OBPz63Ej+6Yg8emrwYQOKRxZspid7+X\nXJPnIN1tU8qAAggWwmoQQwvw1paUWOCHmCku8/PtvH/1p4X4w7MrRDDWmkmJ/j+qbJbAz3+5YErH\nTKk1U7zPlC0W2hQsMJmfJdt6A8H5kuS+GmaK5kOVmeI1U4DHzGVzxWCsYY9ayIDCtnCAH7ju8YNT\nVzMHqXJLHWRDF/24RfOArmaKgtC0bSnlAv79z5LccZgpfi0AXk8VXAvxnhJkaoYnpFOWnpli9/uS\nNYHtfv9gPvR82LYV1PRq2qFs3tmLC656DotXlXYTVu/ZN1ZGf16tmRK/Jx3XgEJjjV4oRsj8VHYp\ngpmKkGnKx6LUTNG1ZdtSjWV0MMEUW6CMbs+EChMlmV9E1nftln0YzBW1Df5KQV14URZDOL5wpoh9\nVJX5larZ8f6OF0xF7U+HnV39GMgWkC94rJDsxsRrpuIxU7pMcquvyabjTkUUjuaLrnQd+WCYStll\na90UglAs8PkCRS0U5lDZm2LRQSZteRnWKGZKcderc8mUIvMraot/VRmB93/gh5Nm4bu3vBK673iP\nEt3vJDMQNfgkEwfv3/HkBkAQ5HIEbK7vEBYRnI0fXT6zRKB7jP9eR1n4plKWZz+bSUUyU1887QMh\nmZ/6nFB/NfW9UK+dGDK/TDoVGqM++v6D8Lm/f4937Aoz1Zqx0UYWx/V28yvB/MT5XlD4DrEd6uvE\nDR8A34BCkfl525DPt1QsrsgQjzv6IFz0Lx/DpEtOZfsMZ8JtC8KAIFdwpElXZ0ABBHUcgQFF+JwM\nZAsaA4o28Z76KETK/LRMmvfZsaMyUnDIZX7ecVraYwsZUCCcIOP3/RK/3xK1mKAAvlgMWELdgpMP\nG0lkfiJY/v/Ze/N4S47iTPSr7Sz39t5IrdaGBEIIIWEBZhFgZDDI2MDgH5aNvBt4jGfGeJtnbLBZ\nbDNm4HlfZvwGMI/BzyAz9ljGFsxgyYAEFgYhgQRoQQIkdWtrqfe+yzmnqt4fWZEZERlZ51wNvJFN\n5x/d5546VZVVlZUZEd8XXyh2AzWeM0U0v0NH087Uge7zxz63B39x5W1YW59hNHD5Us5hbvz918iU\nqHc1a3Dvg8eExLJAppgjsaKQqUjNr+FOD62JYc52ND+FTHU3ZjKt/T3m/aO7SY+iYlRNyqeTND8p\nj66l0elcgFuHKdBANFW+BlF/Fqk1xZeM9/ztl3DtTXEtRJ7bSuu7Fj3QwYKaOWDUpAAFS2Vgualt\nCzGAuRqcRqZoP+3Y3XpXKJ48mTaRAEVRZIJhxEWxjq1No7mC0/x8vjT7zV9e9RU8dGgNv/v+69HX\ntDOlUSfefB05A5mKaX4xpZIC0HzOmm6Q5qftiUWcqclUzl1+/mH3a9P4ODI1t1XCmRpEcqD8ZaB3\nSU/sFNXSwhDzmo7MkOMWFqJabKO2oqKj2pnSf0uaXw8yJZyp9O/W1md49W/+Pf7dO67y4g8ZG0l5\nlvmcA2tytHKmaOHki+poUPgXixBCHS0D3MTJDU0+GZaFzf/nLaL5dfeLGyjCeVsgZ4roBfOcqYAW\nfHO9qYjmZ5yuZlF0q9sfuuar4u9DrJBmLzKl7pd8dotftyV77nOm/DO2jcqN1MugY1nIG0U8aZ5w\n9UHk+zUeFvirt78EP/695/pjUXQwYxO6p/kZkd1QZ8r9nWeYS/NzRbDltlNP3ITTdzkOv0RzXZ6h\nR6bWHpk5U9Q8MsWMGJpvaaHjFEcTmeoQD7qpWsaY9zPPgIuf8WicuGNJIF7a6RwPS2dMK6U6Orej\naspr1nMDvQ/fd9Fj/W/WJrV4B8si85RMQqZ4S1F3tZKdO5/bd3lcievxND9GH9MS6ICbb3WdKR0g\n47nH+t0bW8iUCjLQ9tC3NLqtW0CmYrVNINyTecgUUYd1ztTqeu3RtaKTuA80v76cqRq/94Hr8av/\n9z/6PvK50KJfcpoXIIMS3NAGoFDEDNs2y76EOlON7yeXxtcBTR64pLHIDVIKlpHjx181spUChSrO\n4eFDmMqQaGSqbVv8wWU34JrP7xXfUfvUF+7B5Z+4A7rxmlGaUeSvr8gFWl+zfagNkzQ/9z8pUnI7\nPTi+nfw565fuC/32MacE4azpLND2/PPPpTT6gweDM+XmA3n9GXOmLLEnCjBop0Q3HYjg9fx0o/NE\nyNTCAhQxMrXvwCr2PhDy1clmTtH8dICF6xIUKhip+1KWkoEm1ld7V9G+5Z0pjkpsWqo8V7xPGl0r\nCx04Ss7Uxip4J5EpFWkC1Iugoo1xkd4YCbCO09efPqSEJvoHD61hNmucEcd/kIUFcyGaH8+ZgpzI\nKBLsHU0jsdnVRrFpfkUelHv0tZNxpAUovDiCIesLGDQ/w5kqilB53Wo08ZDk+zdY0TtqE+VMpR5v\nn2zqJ7+wV/zN4XfLmRpURecoK2ffqO2xSLNkz+krT/NLOGcbQcBCnlOMOM38xOt+MxzEyFSeBYqF\nH6+UcMzO41Ekb1DKbTqfah7Nb2lURhHsPAto7lQhjxnCe6rrTNXMIflGNE2jW7Q17PoBKTIR6jqR\nAEU4F+3H3+0MCpkynCl9Pv7ZJdvL/o1HlRtzyvihc9dNGwUuyq6QKM0NNLe8/DvPwsue6xyqVeUw\njYelz0NdWZ/6/lry/Lz1FRleHru+66gu3bOyyCOnCYiRqaZt/djyOVNsntaGj3emGpvi6p3iOaqo\nFlINhNvty2uofMA1j0zZOVN13eDQ0QkevXsLgIBMhf1n3pHIM2fkLiKNvj6tseeBIzi2OvVFiCXN\nL9gPmuYX15liAhQqZ6rpqGXaiQ3jrcbyuEKWKURajX1vjM8aMzgxUhRh/s7QesyFBvjx6Bqoff/z\nHBVfB18PHFnHlZ+9C//Xn12XRLe13fUXV96Kj33ubndvitz3S+dMjYel2NeuMzWf5odWXTtHpvR9\n0ZTDbtNrf+ACPOOJJwFwQdxWzV+FkkZ/iAmkrFjIFFPzC0q+YbsXh9C0HNX0vLIj4UztO7Aq6q/x\nZiJTljS6klQHgM9/ZR8+cu3X/d80RyWL9mpkKvH85L7ufL7OlMFAW0Qo61vemeJjkCgaLmKxGM2v\nbduATBmUtvh83GFRCFJn4FjIFF83dLQxrrWjjVce4etxpgxnw2p8UHpkSlGUNiJAkWcsZ4qN92FV\n+GREToHkxwh9iCdyAN6pAeIX7Y8++Hn8wBuuiCKPNIEsSvPThXtnHe3QGUzR5YvjheN+4wxX+3xS\nISf1bGeNnTDvjiGv8/DRfppfVebiXbKOk4ouW836Lb1DA0/zUwicP+fi99eK1mlKFi0OwyqPcqb4\n+6Edmb4IvH6HZA2TDPNoflz8gNMDqSYHfw60yA8TBucv//E1eNnrPhTdh4fbPI3ufzFnit8zmm9D\nzlQwcLSaH21vEdPAaB/eP7mf+391PZ7LxsNS0FalQemoOVYxVAuZKstAu1ydzMR+T378id45WFmb\n+X3HQ5vKRs0uuu3OR4geGUGa5ufygWJkypVXYNfUxkEGjmro90k4U913euwDMmhpDZs0MiXPo9FE\nGuuc5seRKVqzdm4doSzyiAmyuj7zY4ecYl8EW9P82Ht16Mi6p6buO+goj1KAIpxH0/x0zhQxNYBA\nK7PYNLwFAYoGg8qVgeGGa4RMMTEuizZLY5WcQH7GwueKhvmrKqSgBT3SS1/4eDzlnF3iuqnxdf5l\nr/sQPnTNHdH6pO2uyz56K66+Ya/vR8qBOWH7GEdWJp6maNWZSgpQsOehhTm446tzpiyZdsAhmhee\nvxuAW7eCPRQC+Zw58tChVa/WuLI2i6yHPAty6142nN24gEz1Qy6aPrx5Kc4d+srdB/Cq//BRfPIL\njm4ZI1M9OVNzaH660X2Lj1eL7fqYQCz4Ri0SoFBlCHh/+9pxZ4rde1L/cWp6HTTKJiYNtQPOiyaj\nbRFkSifuir7A0Xs8MjULCc/8txEyRZXlvaLZw0OmtKOVMqr5CzCtW5SMXgRQzlS6aG8kpy5ypsKB\nKqa2QxMWT2ymphdsPunJ2juyH3//mbu67+WFknGRpPmpSVfXYiLnjjuJqXvAhU6+mU0ga1O7zhTg\nxk6qK/paDguaXzzZlF29Go3kWVXnF2lm/gbR/Axkiuc9LBJZoqadFt5PrVpm5UzlavwBHGEKx9SU\nPEkHQZQzpTn7+nlsWR5EFAWHTFEici2S1PPMjeHhoIgchVvvPCCOo9v6tI6oUX0t5OBt1Jly/9O8\nwB0mr8rWGc2cjqcpPfTZnV+iOoBF8zOQKTXvlkWGQZWjaZrI6QN6cqbKEGhp21Yg9ZRrurYeqDvP\nveAUvO5Hv90VJR8WXc6U20jGXmqxtwwTekc2L7s5mtCAOnKmEshUA4VMxftySpc2fATNz6B36/dC\nf6aWmju8vH3CmaIA3XAQ6kpaqnqbxgPzPVtZm3lbgYQBKPAW0/zCufew8hpE0+JBrhWRM6Wk0ZWa\nX8uCNh6ZqsM2w5fy92s6qzEoc4wGpSmoonOxphyZMmh+mpIISKobHTOm+YV3ZjmRFqDn7U9cvyea\nQ46q3FY+Zos8BFO19PmJ25cAAPu6Z2HWmUohUyywnnKY6N23tmnHDgiGvwt20nnQ9Smcr2la7D+8\nhhO3L2E0KKKcqSxz93ug1RgNdlPKwaCmkSmLWfDev/uy+FtTI/mzp0Z2T67sPaC/tpVGwEM/O3tK\njZdU0eV3/coLcNquTd2+kvJogSbHnakFGuezC2RKGB0Etbvf1U2LT35hL976p/8kDDZLHv3Y6hQ3\n3BoUUEQkyECCOJXtn750L37gV67Ap75wzxyanzQOHi6tKupP4nd8LpvNbGSKcqase6INUpcY7rbx\nyYUnQLvFg0U8GdIwq1sBV+cCpcqiiX1eo8VhmkCm6DMVf9Mvv0emFsiZmtZyYflmNY5KrE9tAQrA\n9T3VFx1hPzRHgMLB+1k0wfUhpStrU9yXUN6zItFBGp3GfvgNp0JszJkykCmV40CLm+lMsUGsj2XR\nx3QUkj7rOjx8QSrzDPqSNi0NAqWFHZMCEH/yVzfilW/9aHc9IX9rPCiFqht//vq+ffxzd+MPLrsB\nv/HuT+PHfu1/mDReqz1cAQpNA6MF0aLycQqgRUsKKnHub77Q6pwuYdzTNXhHy/09qAqfy6CdPupX\n3cTvdpmzAE8rkXpOnQrGJjNghxVW14IARSoviJplmJCYD6EolPuoDeYyV7Vt2H3QlNFwbwhJCQa+\nNqTovvMaXNKZio0Z05mak2/pkPEscoLpngw7EQnXl3A9ZJw72r9EOqkR88IV1w5rsnam+Lm5aAAZ\n8Nzw5zmahFLFNL9gGNM4LCxkykjyIEN8VreoysI5U4Y9EpApQlH6aX4rJs1POTAZkjlTXP1X50xp\nVsPmpUE0h0ymdUAm1BpPlFq3TV7DCdtdnhY9l5kKkgJpZ4oXdm0hVel4WkHbg1rRtVMbsMK9kQAF\no3IeOraOWd1i57YRlkYlVlalII2maVrS6NqBSLXItlH39+CRddx4+4PiO37PPFKdoPkJZ9LT/NL5\nu5Z6NMCQKTVPpJypk3Yu48lnn9jtayNTx2l+G2yC5tfRZDTNj8QVeMThHe+7Dp/58n247ub7/f7H\njCTuX3/3p/Hmd17rFY1kwqczHj/9xXvdwtIAQDCaPvpPdwIA/vCDN/TWFKEJhByYlR61vz5jZlGa\nH/9+OgtiC75lDiUr8iyiwIV9shBtVrK41LhT2zQyb4Q7u3XTiOTCKGfKeDn6Gr3MKWoffV4eU1Q4\nIUDRkzOlaX7fZF9KPAeX4Gr/rq7TqFWMTPXnTBFXul+AQm771T/5FF7ztitx4EhcU2rWtLj8E3fg\nF//gah9Rpfsb6J+hHzxJN1UY1WqmM5VApoZVgVndBqn3zlClVimUy05UTqAajYzeinFd5NHY2rxU\nsah+WKw40uZrvLQhMXo8LIU0On+uehH5nfdfjys/e5dfQL+6Jy6kbLWAamzQmer+p7nX13xq4nwb\ni+bH7xkhU96ZEsiU/D8VhQbCou+cqVzQb/jTLoiqqa6pLDNxTJoDijzzSNPqeu37wsV9lkalyJka\nqcLRX/7aQ7jqs3f533/ulgeiBP1Z3aDIg6AF0XUDzS9Q9cTz7y6kbppImII7/fz+NY3hTFVFQHws\nJ1S9F4Az/j7w0Vu9IiBg51Dy60DW5d0q9GWNqfmF5xC2H2UqkRQQ0+N2eRSQKSAE4Ig+R03nIlJ7\n8KCb32TJivBZoyh6DeNUPpqLwv0KSMlJO5f8MZsmOMFVlWM4KARyRo3WY64SGGh+4Xfj7jpXLQEK\nTy3u3oucI1MhD5r2K/IM42FpIFPuN9/zrDPcNdY2DZ2emUYQijywZmpvwBMy5ZypBw64MTUvZ8qi\n+TWd3WYp9tUdZZ7PCbr+lkBmfP5jEwV1+DEf6sbOzq1jLI0q54Cym+KDPaTmx+qEUaOxpvMZdYvy\nwdX9taTs+bxaKqSaGp/zqHkBih6an1XXFAjXGCFTPQIU9MxCcWE7cEH9PeWE5WS/gOPOlJ0zJehl\nPDoQG+VcHtpCYW7+uqtMfc+DLtquC53+7vuvx2/+P5/Bx667Gy2kmt+uHW4iXFmbpWtDIEwCZ5++\nHQDwpa89xK5PVqvvy1GxBDGsJpCpOk4wzjMb2qWmqR8WJQFwk3nbBu6xEKBgCjVtKw1gbXRai3Nf\nW12boa4byZk1aH6ETHE6IC28Wv5YNy9AYSTjfjOaXrSTOVN9yJS6lvUelBVw47gs8yiHr4/md3tn\nnHMFH2of/9zd+NMPfRG33nUAezvKjKf5EcX1m4VMeWeqFb8hw5qM2VTOFLW+nCktja6RKd6vLIvv\n+ealQWT4Z1lmRh+5MTYaFmJe4k7oPHrDPQ/Gz8lq3lnZ4DBv2XW4/933dRvn2wQBijY4Ivx+g2x3\n9+9jTtmKJz5mpz8e7euOFQd46J5StJOcAl5gVxcupQR13mhuAODFHAjd59Q0qy8UnPCKdUNJ5fnl\nP/4kfv+yG/zvv/TVh/CnH/qioJDVTYuiyLxYAsmCa6pemSuaH4JDrOnq/jl1Q43LC2vDZzgogpNi\nOKFksPO54erP78X7/+ctePN/+Uf/XWpu9eMCGcYqLwiQRXvzPOtEcjgyFVQiaQ7Xp/LIVBacqaqj\nbxJiQ99bbd+BOGfKatr2kCIT0tGqhc3i9n/7Tz8HTz/3JL8PoRSD0il5rrKCr1ahWDquRfPrQ6Yi\nmh/itZsxmAG4NAsdkNYKcVYxayCgifp+8rItM5+n5LadoGl+hpofz5lKBVhSOVOWAIVGQmXOUBA+\novfJqjNGBaUftXWE5VHl6kyxAUr7kHPmy69wdpOSA081clJO7GzReWV4AOVMeXVPOQd4mp+VM6Uc\nuF97zTN94EfXs6PmBSh66kzluV6L5b5aGp2PMye01q8I/C3vTPGVjnjQFaMmtU2/AAWnufTlTNG+\n3ABt2xaf7ZCtex48GkQWuod6hOUbffGOAKVqGJT6s2PLCGfs3oIvf20/M+5kP74RAhSaBkSLCDWd\nY6YbGQ/694A0HPwEUodK4rpuDxnqqTpTZZFFC9G8tjap/fEpwdtS9qPoJHdUeK2KhaTRaZwt1LOH\n32SdqZ6cKSNhnlofqmnd2yqJTDHnPnF/LPrY7QwFoURljRbwyZS/Jzpvq69ZSblxzpT7zZBFE6k/\nVs4UEAcNtMoUtyh1QV9kMrLGC9NS27w8CM4GGbewF0xOXx4Pyy5Hx+0jEL05923vvsWKIWuBh0Wb\nNrY5lU87GxnbZiXME82PurB5qcLbf/o5OHHHUqAhGkYj3VN6FBKZks9QG06NYfxxURxyTKqSnkUX\n7Wd5URFiyZ69RqZSTSvDFnmOLZ34AiGRkTR6IWl+dN9czhQ7XhsLcITri3OmBmXnhBoOMf9sjRUK\nSrprsscmRzycIqyNTJFTXKigl6f5jatwv9Uz9DlT3bu1Npn5ccGT9LWwC7WQMxVfg3jeCdtDFO3V\n61sL0Buzc+sYL3nOmX5fMo4HZYHhoBCOsaaq+jIkjV1qgMaeJR4VaH7hvchzJ6KjBSjofMujGJmi\n3w6qoqvpZq9dtF7oMcHfNY32nbBNIlO80C+1uXWmujmaT+1Cza9V+VSZvi9hGy+yq58Fd5gf7IKE\nO7aOsTQqMatbUZ6D6NupHDUgzBcpuXD+u0GZ409/9YUmZd9ifBDCBLC6c+o8oWgxR6ZiAYoXP/tM\nPPWcXbjgcSe48yVypihIoO0JIY2eyz7Qs9DOlBYJAZz9YOVSi+P1bv0WaJLmF3KmSIXGkkbnE+sB\nQwXIajRmVhUy5aOdVeHPRQ4DNyj3HQxR9pQARZFnuODsEzCdNbjla/u7bXKw9xkzC+dMsc+WWhMN\nuRSE3IdM8c+yzgUJULgffOL6Pfjz/3FLUOBJ1Jkqihy5IRzS11bXZ974Hg/JYeI0P3f/KTrJK4x/\nda8z+OfT/Gpx3HaDRuZGm6wzFT6r+cVNVomu9I4d4zqLIhMT8GTqqpkvIkBx+Fi/mAtF2mOaH3tO\n/PNGaH5GUi6dx79rHpkK0USgP2dKK2wFaXQDDckp6hl+y4/VtPE939zRkqgf7pxpZ4pe1NGwRNOG\nBXYRZIoWlrvvP2JuN88He5z07tf9r+kuHA2h6+BIn50zJWl+tCPR8Xj/rDnJO+4lIVNB8tdKKE8V\neR8Lelnb5Z26Y44MAQqNdtVN6Gdw5vudKR7wcaUbMmztor2U+6jVzMpCocr0DrRtZFRpx5ZfH18H\nBl3grciDsQmkxAv6x0pagALdMZ1gx9qkxj0PHvXjm9ZPund5loljLULzI1YCR6YIWfjZH3wyfu4V\nFwBARDEcDgqcsH3s3zHLIeSqnLzALMBzD2OaHxdrEnRi5rh7eleVewobLz8CsPxEjkwZwYmINgW+\nzX4vnG0ljXvatjSqsKpQFm4853ne0fzi5062lxYpsnKm6L49atsYWRZypjTNFUgXfRU0P8i5Xar5\nJZApn0sWOxPTaR3db54zRdTMpVHpx+ExVuOUjhmk0YN9Qi1Q2+bT/HiZj8iZ0s5rLpkQNA5GgxLn\nnrnDUyupJIy8fknz+/7nnYV/8/Inud/5vMAueM4CnuNhERQLFxSgAMK4I/EY6vdxZOphNv5iklRs\nUUiaH4985pn0WAlyBYBjPdLo9BwFNYq998NB6aMYZBhyCiEfJKmcqSLPcO6ZOwAAt3dV1ucV9OUt\novklF6uWfZZQOsBpArYnT3lW4ffxvoDkbGua33U334/L/v7WKKoAxImiG6b5rc/8ZENr7KeKAAAg\nAElEQVQOk1Tza8U2+u1ffewr+MU/vAYAFVDtkUbXNL+Fevbwmy7a6yuJq8m0rm0aBSCNH92sZHCf\nM9Vd60+9/Sr8yJs/Ykqj73ngCD51Y6hkz5UCLRroynoCmeLUzNl8p81q1vnoPPxdA4Jy0SI0P31Y\njyIZBlWoM2U7Ra1h4G1OCVBYUu9tmJPGql7MvoWcKXfMu+47bG6PzqeQn0WbjtByx4Y7mvQb5zBx\nBzUcK/PHk0Zcnoff6/Pxz7VyYAZVEUW9rei8Hns7t41CAfhWI1OBOmUJUJDj53O7Otlh7Uxpw0G/\nC0WeeVlwovlFBlwh5fnpKjTNT9D11NxfN3I+9pLimSxKzZt2XlNtXlmFPHPv5/qkxk/9x6vwK//5\nkwCYMzWQhXepRchUE7M0aO6n61yb1J5Wdc4ZO4LUt3KmXvC007Fj8wgHjTpT1LgzlXljWo4ljiwX\nWs0PEAOR18ecMmRK14nSSBEFlWY1G28JFok6ZUBgVG5QVXJnSl738rhC00pGAWcClJ1UvzUuQs6U\neg+K3Euj14Zjt33zKHJsOQLBKZs2zS+IY/lzdn941Eo4tnRdBjLFcpxCIIWug65BUr9pHPJbQvtU\nTNCC+rPRNu1k9F0f8mid185rUeQiGEnB7zzP8I7Xfgde8cLHd32KaX6Vp/nNFzQphcNbRswRalIa\nXQUzu78nivKoFXGbxqXKzHM8jztT3f8vefaZoXq7UvOzaBbUiIo3Hhaea201ekB8ouAT9LBy+UE8\nAs2RKb4gaw44p2eQJLn31NXE0y9Aof5uW7zr8pu8eEbot/yd4/uHv8NiHA+vumk9v5yajALHL5CL\njJFhqLi3BuyblkZfbDJZm4QcNZqspJqfu//LKmfqM1+6T/ShN2dK0fz4c/lm5E9Jml/tB75GFfto\nXZbMs99mXGdwptw2WrT4O0Dj89++4x/w9v/6Wf89V8m05FtJZCWgBTEyxcf+RpAp65HRsVLI1DpD\npqQARRqZ0vQxbYxwYzPLMhGcaNr4nm9hNL95yBQvNDlWtaYePDg/18yjWIfWknkhvHmK2EaRKWWk\nczQvpQTHkQSLfqxzNXjRcOpfkfH9ZF/ISBkymt8sQfMD4kDDzi0jYQDz4FJ4FnNofgwJGlQFbrvr\nIP7bVbf53+n3OnKmijwIUCiaH8+Z4tdG9+2hQ6v48Ke+5o/nKJfw/eH/c9l4IAQ9fEFjda/4vg8X\nmeJjbMyM4dvucgHGtcnMCyTR+fgzotznTUsDT0ech0xNp7UQmyGHW1MMX/GCszsnVQbTeCNhI358\nja5wu8Sr7nman0KmGErokalOGh0IzqWWqefUMguZ0kVQrcK1My+EE5wpXbSX+kfr6aGjE79txoKl\nJENvTSFke0XIVJ57hzTkTIV+Dqpg61kCFIPStis4S4nEscI2939T96FWMc2tYkV2YzW/sB+fF4hu\nyhvtowUorKl8XkBifVr7scztYmr677KQ65Ree3w5Ge9kh20amZKBKbm+E2UUAAYdXdVdo3z+nHKo\n7VG6T7poL0cBgRD4TmkA+Gvt3fot0NoWeMzJW/FTHZwIBAEKooToiZ5PrATjb900xAP7V6KcCWq+\nVolQ82PO1KCIkCmrrg6QFqAo8jySSY1qTvXIyeoF4yt3HcSHrvkqPnTNV/G3v/My3PfQMWzbPIwM\nIl4PSnxvIFPX3LAHx1aneM63ney/0xEuaiW7Dy4Sl0e8VXJkpJqfnAD1QjSvra7XzJmquj5wypg7\nztJY5kzRb6nvRZYlFac0MsWhqaZp53KZN9omQoAi1JKKkKkmTrYO29LGsOVMVZ3BoscgfwdS4/HA\n4YBMOaqg3L4SqfnFOVMiz20DOVOWM5tEphTNqm42njNlFu3NZc5UBk2bjRFELo3Oj2kjU4G+TCIG\nZPg9dHh+zhQ/9dr6TBitVnu4OVMpI50b8NJYUVLdKveJpIz9F4AIemghBX1OQCJTer61gkp6Dti5\ndYwHiFrUIVNk2Hqa36SOrp33lRuigzLHMQDv+/DN/ndlnoFXAdNocNUJEJRFHgQoannPuDFdlXlA\nqFqZK9fCcHpZAIvPu3TvijwXTi9/NfpypnhLrmVsXHCaD7W1SS1q4eS5VMYUyFSWmXMi1UXiEvc8\nwZ3OS/bBY07Zitf96FOxfYsrBEzBECvIs8zWEe/YsMj84WMTKYwV0fxsOp5Q82N5zgGZks/QM0Oa\nNG2WNwuVpevzxyyLoOan9iMn8l//xyvxPReegX0HV/3c5Wl+jc2cIFaQvp9FwQUoYgeG23NW0V4r\nuMvvg8+LYufkSGDbtFGpFt4Xi+Y2mdUiJ87tF2iefN7jNoc+f9VTZ4paysakNpnWPgewsHKmDLqv\nVNCb40wZyBSBBVb+PM+1cgyyGsMqx0GFTNE8OagKz1aInX+6RmlDemeYjmmwn6z2iEOmrr76arzo\nRS/CxRdfjHe+853R9jvuuAOveMUrcP755+M973mP2Pb85z8fL33pS/F93/d9uOSSSxY8Yxvxb8rC\nJSpbkQyNTFGEdtumIZo2rd5DL+W6yJkK24dV6SdIriCjW55nEc2v8dHywFf1yj4amepZoGo1SR1l\nClCHj03wmrddidf94TURJ60qCzP6YiFT//3jt6PIM/zAd51tXx87TsmiEeRoxhHXWvwW6EGmFoyK\nT6a1f44WMuWl6IcyZ4qk6ak/nD6km5ZG57/bCCVt0SaL9gYFJ4vml4Kfeml+CWQqy7KIwsgLxKZq\nfwlkyhhHxzQyVUmDou/zvGY9s7Dgyuj9QDlTkQBFaS/MQByBlwYlJfYHA5ffBwuZGnZJ2ry/KWSK\nR7Y9tWwtLgmwSLHCRX5DTsrGnSnpaGbKiHHfhd/rWlKartc6y9/93X3PDarG2I8aPQuKBi+PK2/k\nWFLH3uBSBsujto2EMTadtX5e85TLtZmZ2K/HTJ5nIm839FX+PREiOW0nGe0U/ShnyqNyeTBgAVm/\nyGrOeUXXVxVJb6XxG5Ap9Z7x55RTP/vHVeqd5pH7sbGWrk9mUS6FRKakM9WbMyXWGrnulEXm15Fd\nO5Zw6ombxT5105pBniWGNnD0DAD+4bq78SNv/ggOHV1n+TTk9HZ91MY9GzNUYN5JuCtkSj1DL0Ax\ns5GpCGlHPPZ1nlJVsCLu6nwcZfnItV/HdTffj2tvutf1pcy9IEp/zpRGSvIomKrRNZ8Pq5BZ3SxU\nztVK004Xc3xgB1hqi+bHxIza1nbkeBmGPAtOvegnQwHd8dI0v3nr4mQa6Ku6iDe/DmrTWYNNSwNs\n2+woxDpvPkKY1DU6KmccXNTOeZ7JgKZen4csz6tQ7wm1IEAh88f02KW5fR4y9Yhypuq6xlvf+la8\n+93vxhVXXIErrrgCd9wha2Rs374db3zjG/GqV73KPMaf/dmf4fLLL8df/uVfLnRON2jld6Hydww3\nauoWedE0eI6uTnH42ATX3/KAeOkJ1hdF8tj2PA8GzpblONpAbTwso9oQfBLQ8qN68G9EgIKfhxRv\nvn7vYROZsiKLViL/A/tXcNquzV72HVDIFI/GsKgvOZqRistMRhWizzmrM7UBg5oolkSb5IseXb8u\nmLmskKk+mp9X89OQCxYzOG/fc3AhepU+HyA52RHNr2mSyBRX7bK26VYULodFW2G8QGzKceTOlKWi\no3OmaKKzkpeBgCYu0iyj0dP8FBXESvJNCVDoNZp+ZkV9qRgoN3D4fdAlD8IxtYMWvzN0jVzND2CU\nJHbYRZzQRVA/nZO0aIvrrYTvLeTG5SnOEaBgf7tjBoMqJXEOhOe0ddMQv/KTT8cPf/c5ETLFm8+P\nMJApHv10ggHBACqLDKuTWaIv3TFVxF83jQZrZIr6vXV5iMOUM8WCcvz/edFrXtfLqjPF38lUzpRF\nS5tL8zP6xWlgLjAZI1Pr01p8rwOkR1cnGA/LTglOBjWokeFvFXL111oVfq7jz8nXharj/J9BmYvI\nvlZzo+beX4htNM5aJMZv23qnuioDzZPKG+jghEe8GlsaXc9nIm9IvRc+j4flTOl3e9lAWahVRdYV\nKg/v73MvOAW//wsXAQjrtZ6LHDLlPteqL+7cmQ9q13XbSeXbL5VJ5WtDPnfYJgM+VqBZKwsCgZZH\nRXtTOUM+cIFMqNXpvnlBix5kqm/ublun/hhofjHLRO9Pc8WZu7cAiPP7CaWz1PwA6bCIe0r7MXSR\nHLOqLLx9TXMGvd88nz8WoHB/T5SzpMVeUoWCdXtEOVM33ngjTj/9dJx66qmoqgovfvGLcdVVV4nf\n7NixA+effz6qyn7xNppvoqMKQJjsyEDSL4NVX4cm18m0xl/8/a14y7uuxWUfvdX/jgYZH1zC2Wrh\noxE8AVW38aAwaH4Bng4TdSu2hd/2OVPyt/w8fWpRVRl4yUA8yfM2a9rYODYWUiBENXydqdyQxKQX\nISVA8TDqTAFB/MMLUDBjRNN9tFgF4Gg2i0ijN21HrWO/u+G2fZEKFG8PHFjBL/zeJ0SOxLyWqjOl\nqZg88qdh8aZpI1ESogBY3OuqsEU4uBOYpvmt+X5w2ia9G1Qoko5dqSi6PvZGkCl9jaNBwdT8FM1v\nIOVcIwGKHppfNC57DMosk/ehbe3FMXbQbKl3C5nSdB9gMSd0EWSKurrxnCn3f6gllfnvbSU46WiK\nSDqUg4pg+PtgueHAaNpZBuDC83dj146lmLZjRa91ztTWUSStzPejIsre0TDoRVPjfLzpuc4SoACA\nLZsGWJvUWJ/W4Z55ZILm3xg9B4A3vfoZ7kNrOb3coAz7DFnOlOWk8PPPCypZFGo+D2WZFBCga16b\nxM6UUPNbnXplX02rpLakaH5AjKAPB6F+Wx/KwB/h0rgy54y+1AHunAFSqZOfz+VMBWTq6U909aeu\nvmFvt193XOjjtmI+0ee3WhCgkCiDLUCR+WtPtbJT5a2b1ud27dq5hF07XSHVZJ2pnEuj24hHQKYa\nX2R2XivYGG1amPZPX9FeE33pUDSHTLWmMyEKhOe2gR/lTE3rubnbVgsy+sHJ0OtoKufq9JOcM6XV\nXvtofoCad/k6qvI38yyIkhBttm2DMM5oUHpmjFZEDOeC6Ms8ZOqfFc3v/vvvx+7du/3fu3btwv33\n37/w/lmW4ZWvfCVe/vKX44Mf/OBC+7RNG6lsUbSeojgCiiyySKEHCBHqpml9LYz3M2eKBp1WQQod\nCbUjNi/3OFOjEpOZjGgFwyHkFHn+tKb59Rgz+r3gKNp6AlEDnCEr0SU5GfM2U0p+gHqB2Pc+MjZr\nfJJnspK2EKAIn3mdqY0YckR9Ieoej8CQsU2GNE06S2zhJmSKJr479hzEpW/8MG7q6oUJdcBZLQyO\nt733M/id938u2bcDHWpz+Fha8ES36axhUu51MmdqxtSChlX8/CJlsq1O6tS6t6nJRzhTCSrPZNb4\nAo7c+d65dQQgVF73OVOWmh+7x0dWJrj2pnsWcqg13XV5XEUJrqHOVCxAIQICQmjFjoyZyBRN6BSJ\ng1t43vvmi3HWadtEX77zqafija98utwv8K7MEgWE9AKhXszaunRQgVgdy2o60DKd1bjvIVl/6uGr\n+QUHBpB5ZhYlj5z3QEsC2wZ0Ey34QYU0emPs5/uivkCspGbRffQY37ZZ0vy04TSsXMAslUvnztf4\nvv/wxY+HbpEzNVU0v25MbF3uak0dnTCan5y/tQAFNaLQCZRQU89UoIgLUPDvLUXXh6PmR2U0AOcU\ncKeJ3uN1lTNV5JlgLRxdmWLTOAhM6LwvIKAovcjUoPRznbYheF8LJuC0PCpFEJKOb+UV64h7EKew\nkT5HKQ1Fe3ftWMK5Z+7ATXc8iAcPrkY5UxINiYMTuk9WjksQoHDfkzPl3mvpoG1iyNQWZQOVLP+W\nvxdLiqIc15nKorlU35uAQNi5ysQ6Eteq0SdxTk3zi5+nlWMJuPVkMqsjB63k1EE27w0MuW7vZBPS\nVadrR/YFGWle99LoeUzzo/03KUf4tF2O0qrfm77gk/6b35soN7WjvbvcP/ebpmn9PPyDLzgbP/Hi\nJ4hzRlL+FJhSMvHaXvxniUz1RToWaR/4wAdw+eWX493vfjf+/M//HNddd93cfdxgl9+R8TE1qBR5\nlkX0qpxTydo2mgiAYNjxBUL6Up1xkGXm/tR8ns40pkoJml9Cza9PvUW/cBxFW+GVydV7qali9D5o\nY5oixlEOTGZ/Lhl9gaRgU8gUN7h1tDAVIe5rHpnqaH48gkO3lIxQ4qFnamElyhEAfOCjt+LY6hTv\n/OubRL/p2Lpnn/7ifUg1EnDYiGHqnKmgPuhzptSz48gUpxBYanmAq9UBJHKmyuBk88ju2gLIFBBq\nSfEJdtvmIbLMKNprqfmxY994+4N423s/iys/c2fyfNT4a5BnDpmq2YILBEM5FFqsfe6IpplSi5wp\nnRsSxyOiCObOrWNvQNC1vvjZZ+IZ5+0WvwsowwI5U6Mgx61vwDyKFxBHg9/0X67Fa952pacGS7rz\nRp0p978ljd6LTBkqn1nWMQDY3/R/UPOT59PnBFTAhyKtzOkNfSEDIDYoeISaO7aAm7Pq1PXpKHue\n4Ye++xx8xwWnoK9NxXvR+H5T0v+xtWlUs4+rqbomr4OCSW3LhDsUgshl04G4tlPfNc4LfFiIaM0e\nsEamlseVXzeHCZrfrG6wuj7zZVJoW+RIdseVzpR8z8oiZ+MX0e8I1XB1t9wPlkaVKXRg5Y3S/OEo\nTyHvy50zNuCbNrBtyDD89ifsQtsCX917yMiZCoECE+nV5hp/hgot48gU0Mmtq+DEElMxJNl+apSD\npsdMnjsBFnquGmnhOVMz6xpy9u43rakc+543Xoy/+M3vFd9JZDlmL9HxUkV7vdhLRHMrTGRKSqOH\nfSsj4El9I0drOrXl5AG5XrZti89++T6/7vp3hZyp0qD5dcjjpiXpTD36pM3m+eIcU7vvdH36+0Bv\nDqgUV98j5/05F5yM77voLLFvUoBiTtFeb2P+c3Kmdu3ahXvvvdf/fd9992HXrl0L73/iiScCcFTA\nF77whbjxxhvn7uNgWHmTA82P+M7yoWqanU5ytLz9QLuzDYu2gZc07aP5+SgyR4q4AEVE81POVC/N\nT27jRi8vSGzlTFm0GB0JIANAR8pTtSsKdi1egEKr+c1q8Vt93iIP3NqNIFPkSI6GrpYMX7hpMtM0\nP37/CJmiocAnX0Al+SeSalONjN6N0BanswbjISnesZwpjUw1geTFZejJKImRKYcUWVQYLpnPd1sR\nyFT6GkLibPhuaVRiPCxD0d5uo0em5lD77rpvfpFZfh3kjEdqfiTLyqq2WwgTH5cp2yOVMyW2GYuM\npmW5z/KYWZZFiwDlDaWk0fkTWSQfShswX/rqQwCAvQ+4XAz+/B4uzY9sHDI0efFdS2RCO73dVgVM\nhUU25HRZ97S73wZSlFIt4322xrgwxhSaWeROOttfu9EXXb9nnvpniuZH9PRjq9Mkzc8SoMiyMP9x\n9URNS0vlTIUaVnS8eP2Y53hbzpRAprIYmVpXBiIAL7kNhNwbCmgSu4D3ZTwsAwJnIE7ULHSI/45Q\nuzwL78jySNL8NNLHmx6H/jkhLucCSGSq8oWnO4Ob07sz2U+RM2XMUdY16kAC/TQozIXyHFoa3fU1\ndooKX7RX7jeoCv9cTWRKBQVSan5UzFq3qswj1bwYWY7nBNPRmoNMDcrcMVWaBCKtpNEtZIruNVcH\nTDtT4ftPfuEe/Maf/hN+/7IbAAQbyNeZyg2aX/ecNimb9XGnb8fFz3g0Xv/jTxPfR0ISfTQ/A+Xn\nOXhblgfYsjwQ49sKotG+cwUoSul0BVtNSqen2iPKmTrvvPNw5513Ys+ePZhMJvjwhz+M7/qu7zJ/\nqw231dVVHD3qFu+VlRV88pOfxNln24px6kiRlePhUUuhyZjUeJJbk3CmrHpCnP5ByJQWWdCSw/T3\nukCm4pwpOp8uRNdfZ6oVv+Xn4BW29SEqVWeKPsdKcdIQNRvb5K9lxgQoNM1vmn556Fx9Rk2qkbNa\ndbWSLAGKsnTXTQ6dcKaKXNCHtHHMhSemTBBisb5JWXBqbdvizz5yszdmeZvOArWF72c9o+Ashm0j\nhYZQ48iUvoaSBChc5/z3Mmeqj2bQvTPsN6NBiaVhGZApQtEMZMoWBeg3OgH5HPPOGdc0P0sa3aLC\n9CJTfs6I+5B5YyRedOmzRZXI/KIbFly9iFDuG30d5Uyx52gJpOiWcrgsOfSHW2eKGq8VpSmAtL0R\nyJR0NFuE+lS0YxD7sOtT0e+s8+WKfmKtFdSXJ531KPzJLz8fgM65kMZvUZAct3RQ+DF1zpQl9sMb\nT0Jv2/DeEz3n2No0ovlV3hhjuThdG7KCxS1zUC3qmanml8lcJPkM4fdd5Jp4c2qk4ThcGXdpWNp0\nNRYsIUYCUe0dgibHLVdQs2Sv+XH1NfHfkUotN+idQmS8nzVvSeQiF8hUCinxeTCVSrZnToqm6fNC\n7hZqZrU4yNCNKWZbNerZ88NpNLcqKDjZCmcZcA46Bd6snCk9X2onhQdSFmVHaZqfQOXY2E8iU4Zz\nCrj3YzJtDAEKnj/uvstgl73Qan78XuvG18hb7twPALjuZpdaE/Lr3HEoN8mq36hpfkWe4Wd+8AI8\nm5XAAYLtNzVSaAAdNJTHA6QAxRt+4un4tddcKOaaEETjzxjRd/xcPlVEOV2c/gnMR6YeUXWmyrLE\nm970Jrz61a9G0zS45JJL8NjHPhaXXXYZAODSSy/Fvn37cMkll+Do0aPI8xzve9/7cMUVV2D//v14\n7WtfC8CpAr70pS/Fc57znLnnVO+B60f3wAMyFbZZE4guDJukHwBCJpejS7SY0/OmaNXyqBTGJ1Fy\n1o3Cp0WesdpMklboakHV/XWm/EKTA3UtzsuRqQiBKLUzRZOxvFdWgV13rfYEHdP8gKqQkZhpXYvf\nAvKlKfMAAwujLrFQO8pPuL9VVYhCg0AwHIrcFRGmBUqoM2aZmKi58aTrlsxmNjJ1132HcfIJm6L7\ntaaU7Kg9cGAVH7zyNnzwytvwt7/zMnGtszrUf+ALko628JwpTvMbdxFe7aBs3zwHmQI58XH/qS+p\nZkm6joclxqMKB4+siXsQkuXZRF83nuY2mcULaarxyyjyzFOSgIB8efloxku3qDDcsden9kaOYTTr\nPAiOnuvIvTC+uv85MqWvuWm7nKnu177O1Hqs5jddiOZnO1y8wKg/9wZpflrRjhvadCitdte2raf0\nCHpU1omhSF/KpKRZc77vOw/4qIhpn4jIE87cEcljc6oXtaLLTaBEe0vUQqukpRb68x/7KNx0x4M+\nL4ACb3Qcjkx5Jb7unhFSQCgwfy/43MDfzwiZUqiOFqAI9rtt/Pe1VNCS5/5wZIrmdn4OQCJTlIe6\nWQlQ8L7wKHxKuZP2DeeO5wQ6Lt+2NCrFmE0pkeljOgpccHpNZKoNtgkhRHKNpXOG+wJ0aN8CND8p\n/BSOy/sqhIKU83bWadvx3AtOwdWf3xtLnHcql0IgqTvdoCr8et2fMyX3c5+DQFLbxrSzVBPj29gv\nCJfE8xMQO5nUBlWBw8cmEc2v9E5IuP4sByojiKJpmtNZD82P2TUk6hQk893fFIT1xaHrBkUu7QGr\neLDVNM0vKheSfGekc55nIZCr5xpKsQjHlIEDfXyPnnbH0TTjKVPA7GuPKGcKAC666CJcdNFF4rtL\nL73Ufz7hhBPwiU98ItpveXkZf/M3f7Ph87WIH6in+RlQpI1MKZpfgn4AyOT2lTXpoLhN4UWYzBqn\ncHMoyESPDZqfEKBQiy1NIFXpZFoXRaYm05CXMqgKX9cHiFKmjDpT7n89eFMefsq+5cndAZmSP9ZR\nBcA9I1o4iyKuM+E+29H0snAJsh7iLnNXG8Og+TmqS+4NFT5hTaa1iHjKBU0n7Mc5UwDw07/1MXzX\n007Dz1/6FPH9asKZ4g723n1HccoJmwCEsTDonN6mCdFbK2fKoz2VQfPTEcOSReFU/8siZ8Zl2Lq6\noDMVRB3Cd6NBgeVRiXv2zSIkgRZbao62kaMsgzO1yGIZO8XhO03zK5VhRPuEe2AHCoA4F0fLePNt\nvciUQY3QUV/eqBgw7ebrTK3HiGef2hM1imDqZolOzCkdFPe1+9/n4ng0pPWWcSb8JSd1nEKmIAx4\niGNaQgr83NxBpablw01kynCItZpfFC1vJF3Nn08hlgGZip/0Waduxb967mOcM6XWA1rjiL60sjrF\neBREF/g2mvv5m8rXPCB2UiTNL+znpdFzhUwZ43tenalU0DL4vJnImRJopnI2PDLlaX5Dfz1cdXDz\nUoVXvfSJft+ixzZI09eZk9JKJcflcSWepb+fxiKp6cRcKMSindU1c6Z8sn3IbbLo+76f5hyl5jOj\nb7qeEmf90JvInbefvfTJuPrze6N1xglJ5fL5EjJVFV7e38qZ0mIYKVSS21/zmnD42/heFGp86/2s\nwBvg7vmEMXGo8fIV/F2z8mHJFHICFS6fLLXOcueT6tVROgCJcBHllReHJtuAxlyfrD1vHplSqri+\n74nxFRdHN+bZ2rHCUghUfC6Yf0csokR6im6PKJrf/46mozgAg0cNZMqKEJWMl9u0AWrkjSYHblhw\nUQcXMG39i8Arf/Nm0/xChFojUzSBEA2qF5lSkW6KVAyrHMdW0wIUlUKmvEOYa2eqMb+3FlIgcFiJ\nz51nsTIZGXLayeKF2uJE6rScs6c4TgJfuCwlV5hP5pQwCkjHYG1SiwmXv6B6wp/WTSTHTe1j190d\nfUe1S/QEyY1akrvl11qVBaPXyOulNmOOFudjkxNP92HHliF+898+y0d9bJpfyKUTyBQLBPSpCVki\nKqNhiaVRhbqjrFBwIs8z5HnuEQl37LZLXI5zEPqaQKYKRfNTNAJuTNOpUwZWKpJrCVBoQ9xaZOo6\nXpBjJyy+Xq0yFdWZYu1/pWivRW1+uDQ/7/hk5EzZYhF5RhK59JxiZCp0QRr+XGNtYv4AACAASURB\nVHnOdGxbvhfEvv2LfOzIc6NKG78uKLCYAEWvQEGWCaoPEJLwdc7U0bWpz1OhbZtInGI1Riz5nC+d\nImm4xDS/3PdbUpbse5NqlsIe0BnTflxIZIqfL41MOceRaj1yhAEALnryqXjy408MfeUsiF5kKnwv\nlQ7l75ZGlcyzZM5pHxLkajDFdEz+u6ZtAzJJwSCjlpgeUyJPyXD0dF/dvsrp7zZJ6pnfMTpmqvgu\nf0dpt2FVYL1b+zSSbglQJcUi0EYGdqpJZDmmB+bd82iRen/t4N6gKlydqUbmYYli3mxe6KP5AY5Z\n05dGwO/zPftcmsz+w+t4y7uuxT/eeA8AYNsm50x5ZEqxPwCY9dysxqnB/G+93V1H/H1K3h6g8R0r\nMlIAqm+88nOE97MbUz6n6rgz1ds0vxiIkSlrgdS/5zQui/ZiSZXzCH3buqht4BYX0e8BTvPjAhQh\nWl6oQUe/o6hj7wJFlLTu+lfW3cIyqAoc7XKmeF0Gam5h5ffI/a8dHO9MlelBbdEmCNrPsnjB0tWr\nQx/IGeWOLu+LfR/I+CJntSrSyFSeZRh0Uqbu+NyZmvlJlCeoWjRQS82P2tiI+PicKY1MMQd7D6vv\nwKU9A71GRqipSWSK50xJWP9JZ52AJ511gqC3WvRPnW8CSAGKPirPukHzGw1KL/G+sjoVxq+FTJHi\nDzVLrUk3IUCRZ34RB1jgQiv/NE0wmhMGljaGdM6U5TCZRnoCneDbamVw8KZrG1H03gtQtPFi2ddS\nOVOh9pY897x2/S0P4G3v/QyOrEwigQKOEqVyptrWRqYyoFvF+xw0Cymh7XHffWK0IUCh52K+katz\nxfvlScPfGxVqbbIKW+dZFhWV1mPU50ytzrxYDvXNI1OqDAFAhdrT901QJwXNL6jg8e/tZPv0WEmN\nOS1AwY9rCWVQXxvvTLnoPIlA6UBcptZ/kZ/bh0wZcwLlIvHdlkelme9hHV9cA0emjOsDbPS8YGus\nf77dvpzlYgVn4vks7quevzjaBb+Osr7qAFPXSBqd+sqPOagKzDqatba9OOXLcmDo/pISa5Imo5rv\np0L5+Xmbxshf63HsABf0bls3vvkmmqNXGTJllYrRx6w6QQtr3h0Oio7W7xCd+/Y79dVZ3eD6Wx7A\nVZ91gdxtHZWf57BT887UcDGSW98YBnqQKTXPmmOmjsVA+PY+gRh+bo1MhWD0cWdqw02r+aXgev/7\nMhcPoE+Agi8QqxyZopfS98FeUDwyZdD8aOIoi6DsQ+cgDvgiND8a8BbNz0Lm3MIamubsUkvlTKXm\nr6Dgwml+2pmKaX68n5LmF08CulHf6P6WZW4gUzwyFJAp7SjziKCg+alz9yWI8kLA/Ni8H9REQWj2\nPb/vgX4Af328cSOOoPwsC5+18cfvrb4CLszCN/Jxv1DOFPvNeFgII4+PWco18ceuXU0zfo2LrJVm\n7ptHpmT03krSXlyAouunQWfiwRnd77BfGrXqQ6YYQw6AW8DzPGNFe0Prc6ZoTKRypgiB3EjO1Je+\n+hDe8q5rce1N9+K2uw5E+RGCHtfto5XgmjZI5Or8kxZczS98T30zc0O6/1u9I9JGo+irqrXDz5kS\nrpAR+PmOtEX34ZHrqRLJCTQ/Qp8CMuXV1caBAgjIccGRqRax48dr7Uhp9JDkzelzlvOaGitta1Pp\nATmfZJDzJx/3lhQ9ABxZIWQqCFAA9nMCYspYapsVSa9ZyQ9qGpnqsz1E4DEPgaSUAc/z18L81V0f\nYyRoo7JuWsYAsK9PX6O+bzpILJApTn/tPloCFLruGf2Wgn7TaR3tx9cgy4ERtOh2A8gUC4RpZJm2\n0/M133tjTnDXQuqwM5Pm54p5hyASp+LrcwBubud1Sfkze/zp211f6hb3719Jvm9bPTIl7z//rIXS\nUq0v4KD7x39qCVDobZZ6Ij9mNF4T76sOSKbsVt2OO1OIB3SMTIVt83KmNkTzW+c5U1JNJiBTctHw\nyYFTS4AiUANpANA5qG5G02Mc6ag75XSVRe6jk9M6TmaMpdHR9UfeK83XD7+3FyTKx/GqR3kWHdOq\nM8XPLYr2sn6nqElBbSYcN0amQl+HVR4ZKoCLbPLFKBhWMa97ypNxVVsyJqnVhDR6qrByqJMQUBaa\nkDVtsmYJ3JQsPmDKXXpxLNh1WQIUVn/WhHhKejxaAhTDqmDKfUyEII+RqWndoixzVGxsLEJb45eR\nK3GZWhmjVnFSKZccU3b8sZXRaFL5+pwpg+YXlCvl3PVHv/g87H7UsjhmEHXIMB4Upppf3/3y9a4S\nv/HIFBun80oAfP62fey3rC9k4GV8W3zf8sx9H+YafuNsSXVf9LG10SD66J0bZvz5xHB6L9i1BIPS\nQEOi9ylsi3I8+DaWBM77nqL5cYEU3pdIGn1tyub/btsoUAABiIHB53yJTMnnpIv28pyplHOTq/ut\n26y2RZ7cNpagn2U49cTN+NWuoDVHMy0npWnagEwxaXQ6J/+bmhA76hFWEs+XGaVN0wqjbnlcSTSV\nH6Mnqs8dwqQARSPnS94XWQw3nJsCs9YcFdGm2OdCGd70U0k7tR3pLEvT/IDYoKYxtT6tYwGKnNeZ\nMpB8Pn+3qjM9TTMyIoQlDwIr/dRfuSPdn/VJLd57otlzml+eJ9T82FdVWYg6U/ydCnlPDT545W0A\nbIR72yaHTAVJfwOZWpDmp+epvvdJ1+0EEJWE4J9JNt7KX3P/p+1OfhxfM/Y4MrXxFtWZolwdQqZ6\nok9ALI2+KM1PFMLtTFg6ztOfeBIA4Nwzd4rj0MRx131H8Et/dA2+ds+hKJmRy6TSOaioWt1jzKSQ\nqaZpcXQlKDrpCctJo7OXgHKmNIrE6Ii8paYvnhzrIg7xC6ALrvl9CzKUbAGKecgUV72qNDLFIsYO\nmaqFkfb0c0/Cj7zoHJkzRQZCGxsCb3nntbj56/vN/uj6FkC6aC8v5MyN1im7Rz5nSl0vNa7mR5PH\noMu1ArgiEt1fusbYIeQ5U7yJmjdG4IGkh9cNZKoocj/JCgGKzFVE5zlTdd2gzGXO1CLOFHfeioxF\nNhnqrHOm6roJdL0kMiXPQ3/6cSkMoHANdH3hONrYjrdpROuM3Vtw5slbuvPFBvx4WDJnav57QvsA\n4R3Ubd2Q8J8nQKHHcHBu0PU588e0UTuXFxWeE3NmQca7vN86/4Gfh2+v2/h8+QI0vz4BCssh1nQa\nK+I/Ve+hSfNjxtbU53XKOXiZiUzo+T8gU/Qcw7GrsvD3z9XKonPK6+Po04Xn78YFZ58o+l0rOhO/\n3hRqPZ3VyfeY5/fQcZ953m4MqiKJhPHndMTnTA3FdaRyXFLvutvGzmFF0huqM8WdKU3zC5/1fB+p\n+REyBe3Uw1+fdopKxv4IuWasr0UuHK3e+cxyUhI0v+msjt5tfl3aTOE0P/1eeDRnWkeBHZdLa69d\n/LNj/z6MnKk2LroNMAGKppXPQjnnlpofXQu/n5TesTapRQBiHs2P0hC4gAoAvPDpp/s549Y79+Mf\nrrsbZ522DT908TnR8bZtlsjUdNbgyMpEsLAec8pWAMDznnpqtL++L7z1oa1WDp53pPm7xdkhTQvN\n5F9YgEIhU76Qd8LG1O0Rp+b3v6PpSYGi9RPDCy70jxGr+VlywjrnApA5Uw0hU93zeuVLzsVTzzkR\n2zYP8fefucv1o0NCAOCvPnY7AOAP/uIGXHj+bte3rg8cSdHOVB/NxtdP6gbuGossczU/bTw5Nb/w\nNw1SLTRBE2tf0V65QMjkadswt3Om5iFTqWi6Tsil6C5JkhKNiK5zUIXaC/T9K196LjYvDRj8LKOf\nHPWi83z93sNmf8Z9ND+NTAlD1BkAr/2tj/mJjmp1UM4MEEdbhPRsh/YMq1gRSaOPOtGczuej+olh\nZxlMy+MKx9ZmJn3SUVnhv9d91XzupVEpnPrJAnWTBDKVS3GZKHDBBDhSNWyyzM7NzHI5adsOk2Hc\nM0PUnYMdkxlOgDTgI+EKdszRsPRKZvz6+9T8yJlKGbY0Hvnx5uVMTRRV1XRuMnr26LaBbcswaxsT\nmcoySfPTSmncmbLmGqvr2mEyo94G+pSiQfH9+hAt7WSnaH4DRqvi/aRxO+iQXl60l7YRRe6WO/fj\nvX/3JfEeVjxQ0nKhEPlecOrkv/+hp/jcioAWtFEwTSOBuk1njS+JoZtEpsL3hFhyVoHfxmowHlmZ\nIM8zH9ChY1j5ie46Y4MvnJONPeN3Lr8LqNjvYppfOJ6uyRep+XlkKk3z09L4IhjE6GPUyjzzCJq+\n/j41v0h1sttYsTU9BG3i+6ZJ41xIaKqOyWv9mbUFo3ct3kaonQ6sp5oQrkggU6Rwa6IoPQIUQPwM\nCflZXZ/5z1kWGEz63NSqznah5/fM83bj4mc8Go87fTt++/+9DgCw78AqAODZTzo5SitYHpVMRt/d\n//v3r+Dnf/fjuPD8kz3FctumIf7q7S+Zj95EAQf9N/ss5j2Wa4cEkt3aOVNcjIw36xjud2FNB8J4\nq8oc6DEfjjtTSNP8LDW/pAAFM9hnswbLoxLHGPJkKVtFAhRtmFaKIseTH38i7rovGNm58fLkWeZr\nV3m5ZpYzRTQ/SqhdTM1PvhBrk5lwoLjBA8RqfpmaqKl5ikkPzY8fx9f7MqK+1HzOlF7IfAE2u85U\nKoGZi164/YOCYN20KItMRYZY0Va14PCIt8iZ6vo8Hpaeo59qJs0vUbSXP5emafHA/hXs3XcUezuV\nHsrt44ZoJF/ftFHki9P8dCRdU9V4E8hkwojmiwodwqFxqybNj+o+0SF5pLnIM6wJ9LFFoZCpRaS+\n+bUIZ4oZhnTMUHsjzkcIfXbI5jyj0YyWGwZAMPAMA149D7FI6Ig/2zYaFNh3kMbP/PcECI5+ypla\nM2h+83KmRECAOcv83jmKWCpniupMxTlTtE2/NyIPSyEsQHxPTdUy4x4s4jBZjq3PDzAKWybptgkB\nCjJurvzsXVhdn+GHvvvx4hwAsDSuhDNF5yBkas8DR7HngdvFsXltQQvxEfSpOY7mPPqrbtNZk0am\nGJUtyqVr7GfIDdzDx9axealidEX5nFL5GEC8bkrHA9HvKADDd1sepWl++nbo+SLkTLXmXMLnL21g\nckRfBDQ7VoZJ8+szThMIDKf5BcdWXleeZ5HRyoOiGpka+jwjG7H0yJyl5scovkDsFKWaoAbDHhc+\n2GfYjz7Ype7hoLQdaZ4z5Wu1ZXHaA31PrSqdmh9/t885YweAsD6THVqVObZvHopjbWN/0xzzjzfe\ng8mswSdu2OO3lUVu5m/ptpGcKY28ArYAhczPTL+jfQIUFlXepPmtp6/tOM0PFs0vjUyZzlSpc6Ya\nLLPCfkB4kbkzI9f0/uQ5wIZ1S8aV9hWcO2ge4AIUA9+/VAv5ILIPvGAvENeV0Wp+9CmikCXgUmEo\nsu8LhUxZyfTTxDE5MrURmh+vpQC4Z89rYwAQaMiwCpFfnYsSjP5g+HFo3FLq082K9FBxVe0Y65wp\nPVYpz6EJdPXoWdcqsrtrxzJO2rmcNAw5v1gPrYLd+9So03l6QDDiNH2SfmdRvYjmF6n5ldKZStVE\n4k2r+YlAiaL5cT6+jvqGPsvxQK3PaIxzpuJFxsy16v63Fuu+/fi94/e7jxYZkCk7XBfU/JgzNReZ\nYnTaltWZUtfRMjREI3pNa+dnOmcK4m++P8/766VOsv7qxGjLKaqN/foS0XtrqmTqfIyNoBt3pgDg\nUzfegwOH18Q5AGfAr6zNIqenLHJPK9eNz/k898lC++y8qHBv9LQexr499qazJhkUcffMdsA5Im86\nG62TRifxCdoPsJ+v/lvPpdb1ivM1UlUTcGhgzrwLy96wjs/tgBiZksYmPy4PHtoUyDwprhM/t/BZ\nr7l0/aY0eoRMxddalblHEDXaNaiCYqWJTEXPMN4WDPH43Fbj+7VNTA8kuf22Vc++ByWj66SWEqCY\nN4fyY5JzRnOxQK26Z0/B2arM8czzduPf//BT8L3POgMAsHXTMPr9p794X3TOebLh1KK8Je1IJ9Gi\n+XMi5YLrZxFSEnpy9cX7KZGpRWl+x50pQL/LTICCkKl4IhS/z6Wa37RDpnizkCneiOY3Ty1ooJCp\nosiYMxUmSIoIeZrfeOPS6NS0QaWNp1IjU5mcqKmR4RAvOuGztThZEVpqhF6kBCgcMhUbrZaRuGPL\nCM/sctV44n+pJnGOJFQdzL0+raMFhyMPfF+Kvg8XiORY9kRKzW+iaH76fpVF3o2XIDJBEySPJHLD\n6Ld+5jvwxlc9PYqka0OUi1rw84XrSCBThgO/xHKm+H5nnboV5z12p58sNW2nVGp+s1mDMpfRu0Vo\nfvy+Xnj+bmEYaOev5FExw0kBgsRwymi0qGUxcsH3665POXYAow4aBjwtXCnRA2+MsW2f/fJ9+NA1\nd8BqRDdJI1MxHXUeMiWfT+vrr8nrz1BzhEkYR1Rnyrg3mRI9UHWmmrZleVGx0ZgyNoE56JOFhqgo\nu8iZyonORPMJov1qtc0WoIiDMXT7uVGzaVzh6Oo0UqoE0sU4hZpfG+ea8SBLn8qluw7bmO7LmUpt\nkzlT0jBLCYzwZ3F0deIDj3qbvgZ+nea2RJRd0PwamTC/pJCpHl8qctDaNszDwpFk91M7zLxOnnXf\nyiLDrLHV4CIBCuudUTX05glQ6HOE68tZfhflzbjfDRjNz0LSRV6vOj6fg3Xx7L4W0QMNJ7sx1sQ+\nRFr3jd+GqssZW1ufiTXP7Bt3mMqQgwXINBVauyjgTrnvz3vqaTjzZJcaIJEp9/sjKxNs2yQRLMsu\ntpq20/rHUOzgWKqaGnnVaN8J28fYsWVk5ub5zwZq7pEpTvPracedKUS+FKP5pTm24velFKCY1U3k\n9FgCFKK1Ce6tWhCqSj6yMs8FhAtACCaQIp9HpvqcqQRNSTcLmbIiuVFB2CQyxS2l8FHnTFnzXKD5\nKWSqCIuFiUwpA/Dbn7AL//Ut340t3STBKVSVyjvgCzLPSdD3TySpdtsmswa//u5PA4CPEvc1y/le\nWyBnilNvqHEBCh+hLDKMBoWPxmru/GjoONMcgaFrd9caoji6pyKvItFCPaDw/JaGhEyFvlxw9gn4\nvV/4TowGpYiIC5pfIakudePU/Phz34ia3xt+4ml4xQvOFpNrVLSXRcx01JcaRcTid7u7Bwbtjjvi\n7u/YiLGL9koHzdrPOmZZ5MIYq/zCOcW7Lv+ied9IHEUIirB7HaTRwz4byZlqWr4vXzw7BMkwjInO\nZSJTIJRY3m+d/+COE/qk7ylvgeYX79ebM0WOrUWfUpQWi9uvjQpLgCJjcxc1oq4Lmt+oxKxu/PPi\n42k8tAM+ZSHrTEUS9izIEtAJfv20vYkjycZ8zdukp5TErG6YUxC+d450vwDFameocpnnQI21c1ys\niLa+Dr2fpPm5Y7z3zRfj937+os5oXgyZ4qcTNRkRG6V5Zgd8rFqOvHkBCmucakOY983fN3n9Ja2X\n4jn1G9hEtdaU2ojmN4sFKPjxzIKv4t1/GMhUGzuvgJsXAgUw3i8g0mlnSj/DUScSpOeoPpuR7EUK\nbHGngdCkFUbzo/aobWMAEpnic+lTzgmFq/W+fW1+zpR9/R5d7JlnU47tz1/6FPznX3q+Mc74Z9s5\nc+c8jkwt3PRNrjzNL81Z15KofAEgehFvnuaXoC60QC/fk/oxKGOnwSs0sWhTyJmaCbpGnzqXzgdJ\nNR3d1y8SXUKEcKXU/AzDhfejl+ZHxnhUZyogB6YAhboPAWmR20nYAAiTODdU6XmInKlMOlNcBY63\nn3jxudF3uunx0rYtVjujJ5JGV/kmpjOVdwIaLDr/a6+5EK+95NvcNXLuvBGF1IZhH82PF+21jJ8s\nC+cTztSYVOLqKFDAzy1ofrkrzkvHo4WzzCX1L0VJ4436+rjTtqMoWEHuNiRwB2edGUZGX921ZV2/\n9biX49Kkgxg0MI9MGWp+2kGzAkHWNp6j1bZApVDT9Wl83ywBCn5/rZy3do4vKwI1Lbzjo5GptrGR\norwzmmdNPNeQAIXypcygh1BUo/HWhONQi4VZjHfGcF6LxPvEj2kFkVJIiTVn51kWOVmTWXxfiFZ7\n+FgozE5tdd1+XzgyxRGfTM19dE/zrAe1m/Ne6Gl/NmuSvGGOugvjPg9FVAF7TafAI5Uf4dvIiOtD\nn/qi7lb+BzkpeZZh59YxzjptmztmImdKtxTaBSsom8u6XtqOmXlnIp67eAmKFHLg/o4/96v5xc/J\nOi6N7RRdj0S50jlT8l2znj1RfBdGpphNoZ1XOgehpL2BMHW6ouf+joclVid1NIb7qKdkn/hAiWFf\nhZypMOef8+jtOOfR2/HMJ+5mvw/7nrB9LJ73PJvRX1+P+Bhgz7v8e4sBwOnUbdtGInFVmfs5jjdr\nPuKfNc3vODK1QNPvj6eXGcgU1Vs69cRN7PchAk+JlREq0xAyZfeh7cKwUWRIeeo6ya8sciZAERyI\ngEzNsDQqPfLw4MFVuwNAMrKuW4RMFYVy+kLfePODUiNTQnEsfK9rPlkox8wwDvg1SGRK5tOI32sH\niE28FYumARCRoQHLmdILFY94a8fndT/6VLzowjPw5lc/I7om3nReFC/AFyFTE0nz0/sSgupUzMM1\nPPExO3HaSZv9dVsUMZ1QrCdyi+ZX5OzJKuMny1xEkcYuDz4QtchyUPlnjky5nKlArfCIVynl0hdC\npiiamstrr2tG8/MCJ9Iw0n3lv0kZH1YEPmPn1Pv6nBJDYSw2RK1FwqBKMKewRbxwkMw5n4+snCke\nGTZpfhtCppiDrq7fMuDpZ7LOVCxAoQ8pDCMrWt59tPNtcnHdluOjqbF8m36fxDF7jD+NaKVoflmW\nifPSs+LoB81hq8YzPtopPOpWsTxhIHZs+/KCAOlM6mldvxca8ekrcj6rg4hIptYkKZQRG2MUoed5\nYvqd6Q129kbZw/daAVULMPDAYJ9xn8rP66OdzUWm1DmKXCJTfJjpfptzlDofzSuzWRO9h/paqFGd\nwFRwdTBPzU+9M5bTR5TxeUwKf0wfYIM5vn3QEvq9d/9bTgHvqzuHPOd4WIg6U7Rvn1M7YOIc+vhk\no3qaH2M9bVoa4Ld+9rkCgZLU4IEIOljzj9Xi8gFpRzAZLIA9J6aUFVMtlZ/FA0EAC9gfR6bmt1Qk\nxEKm7nvoGADg1F2bxe/pAVAEV9MrHjYypQaXNnLyPJ0z1bYtVtamWBqVOOWETVgalbjtroPm+YEN\nIFOGmh9vKZpfipIn7Rb+suvctXRfdB84DUsnggOBlkMt5DnFRo5GpnhkSORMaT46eym1Y0OG6OZl\nKVSimzYa1oScvnK0RPK+LVVO40UbjZyPHha5eLLREsEemWpjZEoU9lTXtWlcoSpz/z4IZKrLmXI0\nP/edlX+gcyBKRp/hOTMcmUrVROJNyzznxnPUND8ujR6p+RU2zS9Cpgwk0IrOZ/5ZWPtB7od4v3nI\nFNo2mgMossnfXaKAcQeV319LgKIvZxPQ8v5cqjz8Js/SYgIkQGHmTCGRM8XuWQrtou26Baeox5ki\nQ7x3v7ThYCFamupUqjHH+83fS5oj+NwcFcRkx0q9L4Oy8NfDnVALlbcKaXJnUndd328dKJuwGkW6\n8aLj4n5nnQKkaeBKo1I4U+p+90XS+4r2WoEL5/gZjmYRP2+rycCjzt2Tv/XIlJ9r4v0shMXlTMkS\nFOH8acM/ckq6P6U0enwc3jffh1IGrrQABT2vtDPl/reCT5qut2jTARjLIbQCJXGAJe1cWMjU2oTR\n/Ix9ABlEoPtNwTD+Uy9AsT4Tf6ca1V8FgE3j0s//ebZ4ztSitZ7cNj5Hpcd3weZZy4ZONS5+YeVz\nUqA3NQ/pdtyZMppW8+OD89BRF6mTyFQw2FOCCPNypqxFHIgfsoVMxc5UWMhW1mZYGlbI8wyPO20b\n9u47mow2arpgqlk0PyuioCOKs4SHbyWu8t9N1SL/7CedzPpi9zlIV0txEGq6TomPUhuTm86Z4s/K\n50xNWdFWZVC4XBQ5yZMzNTenSI0XUZssovkpqX21nSiPTdNEE3LBxkyfwaF555xCobOmyiLQ/HT0\nbtPSoJMMjx14ruZnOxrhmByB4WpPM4Z4aVGKea0vyk45Htz5znNH6bBymPj+cbIt/HGBlONjUfmY\n46O26YhwH8XEotQSpUdfAzlGNjLFaX4cmTJypuY4U1pExTK4SGTCpGxlPchUDnRFkbov5P6aNqqb\nhYZocRr7OaXRkJBMj+Q2K2oanMWuH2bxzuirECA0jDZPw9bWrNG0mp9/Z2iss3emMRwGSaXR70V4\nHq4/crusUSRbSkghyzORg2clt/fR/KzAhf47qmPDbqP17JumMR1NKShjXqY7N39/fYF7mkvifjZN\nEFgJ9NAwRq2oflnkmDHlvdRarXdMIQ5SGt22eVLB7UI9C41MrU9rMdfrc5vODXPcW8RzdKpxAx6w\n77euC8bPZykL8uPqfgJuXPKyAMHOss8BBLo2BakEMmVIo/c1uUYPMOzek0UpftRnyRZIjxtrTuyj\n6VLpgwWmr+jc5pjoTmad02rHnSnEL3OfAAW1U09kyFQZI1N6gAVkKuFMKUOcmqYKxNLoIWdK8+en\nswar6zNfD+bs07cDAL5yt41OLeqB29Lo4W/6nEKmIpqf2JctOlHCqfv+9T/xNHz/884Sx4yignOQ\nqalCpgIKAfV9bDDxl4uK1vEq4xrl4sIF1MgQTb2f3tBW+3FnSo8ljUzp7b7OlEF1KthibBuw2oCX\n3zctIviJ01/bVkbPNy85B3/vvqN434e/LMbKoCxQFplE+5RhBEDkzeS5NCi8465ypjSqarV0lN0V\ng9USq2WedVGxhDOVWGz0pG0b2zb6AjjaSpbZhrGFaOmgQiq6B4Pus2ZQwIaDAnmWzplan9Y4sjLB\nTbc/6L+zjODprMZX9x4CoJ0p28BzIhM2iuSRKStnCpBy63oMGwgL/9xnAsoFkgAAIABJREFUiFtG\nnJ+/jLw37RRZUVgrVzTaj+Z8w4Iwc0ynscOk87D4M/4P/+ZZ0TEAqeDaGqqLIlrcpBGdpolpfgFJ\nIGcqpvklkanGNtL1mLEcVE/zq9I0v9j4DX3rywfh+xU6cJFwHvQxdOtHM+VvUzQ/P36bxsyZKrpg\nbZ+gjb4+6/z0t1Vnqs+gBuKcKY3KcmdqOqtjpoxau8w5kZCpxXwpI1ASb9cUR34+CyXTf2tHmmwG\nGqc09PrYTFHOlHD+N+ZMCZrfUoVx50wtSvGjVpW8D+m+p3Ka3Eber/COWorYqWbNAfQ5y4J9ZY19\n83gLnfVfeNNRBTL2LZofJYk+oSt8Rr/3ztTEpp35nKnEKpBKto1pfn3IVEBjACdhCQTa1HxnCuI4\nuhGcPlUGaVmoOlPdZx0t9dHUvkRddVwgGFimUTG3zlQOXmdD90X3oQ+ZovEgBCgqjkxJY5reTytn\nihzc1Iv/39/xUnEuamssIdxS88syN4Y4akONUDpWiiWiZM5YnSlhAHgDXkfFwuIQCV4oAQo+HjYt\nDfw5/9tVXxFOYtkVCp5Ma7N2U8aOKetMhWvwzlSZiXu/CM1PT54y962JZfh9krb9DqeRKVoE0o6P\nXWfK/V/XTTxe2ZhL7ddHLavrWIEKCPOadiSqqhAO1FTQ/Gb4P//garzz8pv8d9b895/+8gv4ud/9\nOD53y/1Y13WmDAMvz5BEGYjOZalEIpsvjW7X0wnP3+0XWowixcamWSxUbbPyX2w1P200d+9homiv\nboH/HxvC2kEDgG973An40e85JzrO5nEobGshiOGdAayadxzxiXM+5P22gnKxdihdA9/C7mk3Lnw+\nlXG/qYzISND8uuMmpNH5UhkX6+aGcXw+Eq3po/n1ClCwc89TvtU0v4jFkkSmsu64sbS27Hd6TefX\nERDwmB2hf6uvLWKqdD8b8jpTsxZVmePnXvFk/Ma/vlD0ZWbMiQKZamOnMNX6giGAcjCMed1SFtTH\niWh+nc1AdFT9roVzhM8VE+cA5PMj2p4lQGG1StD8Km8PWkqifU0EC3reJ2F/9Dhduo7awjS/nntN\nwQfAzpW1Wtm/+VujRRNImTbi3/pTz8KBw2vYtWMJVJulYBH4SQKZ8kpwc2l+9qJDn4s88wo7gHtp\n/YuiEjVJnYmkpnftWAIAHDxql3EOdabsUTMelFif1MIgpaK4whDsPlsRRcCgpLB9+XH85NljqJCD\no/vM5cmpP31Fe+nQ1ktFk8a6py2Fl8sXxRMoCvy5AXdf9fk8zS8xe/vE4D6anzJM16c1BlXhknvb\nBM0vs6P6QoDAGKKpZHqdrCnOl0sBCn5vN48HYnzw3asiOFMWYsuRAo8k5Jm4Z5xSKgr5biBnSkfZ\nXS5OaxhNuYj66jwHr+YX0THc/9acEAzK2KDmTlgcseu29eT+WBFTb+RQhFqZOGte3CT0tSxzVEWe\npPmtT2rc++AxcRzrWj/1hXsAANff+kBcZ6q1c2pS+S8USPDzo6DNwJWg6JFG73NCzbpWfQV2I4cY\n0TYzB4AM2F6an3yGC9P8DGl07aD1Gy4ZvvsZj8YLn/FoRoOJkQvtoFrPEOgEKBR1PYxTO8d2OquT\nqpB1IhhE44IL7/BrAjjNL0amLEMcUMhUH8pgGIZJQ5yjhj0GnMzDIqfHpp253FFDzU/lTEXIG233\nKQ9hW1/OVBwUdv/zOVojxP6c6osqkTNFOw5VnamyyPGCp58ezq0QeTH22dzeWFGkRNPvtvYIU/ly\nemzHSGf6nhISRONUr8HU+HOpCndviFnAfxvlTG0AmVoeV96GmcPcjtrCND8xvtNsJo9kN+0Gkal4\nHPjz5YHxdZzmt4EWR2PImYoH/KZxhdM68Qmunkcvgaf5RciUeyJ1Eyd3A3bUE1DOVPeRRxDK3Bag\nAIAjxyQy5WHi7mVM9cGijADAaCgRGtcX91thfID6Ia/GO1O6HkciQtAnEex5/ilkij0bymsRyJQy\nqgOaFL/ctLiSQckRqJBPxWh+yqDgstnUaGLsi6IUqs9AUNwCDKW/ae0Sw7PMpPlVnObnN8lnxeu0\nWIm62gDg18gdoixD52R3jg8kxYtoftT4trJwiN86qzNlRWu1OpdEpiiqLdX8Firaq6LXnJrhHBg9\n1jKJ6BnOFpCOwPYpFlo1euhns9qI+JPhb0VhlVHB9/TOdN0CiCPUlMDMh1RV5KjKtDNlLbLWd1QY\n8uCRdanm19BCpoyFXOa/6Pmjad2zKtj4c0fJBB2VtnDOfTgmxDFd31t/HGoxihT207lt/Cq0MWbR\nb/octEVofiYyRVF9Izo8S8h/8/OfsXsL/t0l34ZBVdhBDeU0ePnvhKPhIsm63+7/VN3DuciUR8nk\nNaSk0TUyZan5pZGptME3T4AihSL1FQLmje9H64kPtul7qpAp74RzZKqJd6RxNTFR0kRnEK9rGgWW\n9cD0PZV9pz7Ga5DbPmDoy6xzpnjTzpkOvgBUty52+lOtD1kG1JjN4u+TyFTCmQCC/XaMkCkvoKPX\ngPD3QCFTImdqg84Uv68cmeort2M1fp6+vlvvKDWLak12Vl+eoTxG+KzHCLcXU4wT3Y47U7BgZTLU\n4wFo/Y7QGSBN86MFtWlaExbVCbzULEdjoAr3WnWmAIZMKWeKoxtWH1LIFCXmThkVp+wiH2KC6roX\nRxQD9Yq31Jzs+dVTacAD4Zl4+kGC5ufrASnHRFdK184B/34UIVNhW8iZ6pdG1xMOTUR9L2jeRUeO\nrU7xM7/9MXzi+j29an7rk9rnsbiitSq3raP58eKkGkXk0Uvr/mjKEle+aYRD5JxYvlhxh2nT0kA8\nA/5sqrLAoMx7pNHDMTkaaOZMFbrO1MYFKESU3aL5dYWzrTpEQBiDMZ3J/W9FN8M1xsfkRnqKTqNR\nUr7NknmOpZXFYX2QqFXPuCpz8RznIX8WMkWFIR86tCYcckIS4qi3RFd5y3OqM9Umopk8Ii7feXLO\ns8w2GvtyzRZDpuJnaFH5dCFgSX3RBqWc5+T1xt955oRhtFl1y9zf/Jjx55TkODfg+3OB1RhW980K\nyqWi4XVjG+m+Lz25PxRkHHIBisT9tq4jNvj45/h3KfuiL3Ivzs22EQWMSrdY/RQ5U7Q+8pwp413r\nK0/Sh0xFDnkur41ohUA6yEQtRfOj39HzWu/U/HQguy84kOt5b8GkqYBYpmmV/nzG97r+lrVfJEAx\nTCFT6XNTsNesM9XdJ3pf5jtTbLwNS29PLsL2kMfhgZx036151v8uESS0chBTzWIf8PP5nCnDBrHa\ncWfKaEGS2x7w1HiOUqD5BZEF4poPB4UQoLCQqVSEw0IHODJVMwQibJc0P5poyamiCJxu86TRvVMx\njZEpa5LVQhOBRmIniPJ9ARbBq2NpdNrFkvoFnIE2qApBpxN1ptQEQMe2JkU/WfvofDCqeI2LppGG\nmJBGV6t/iu/MG73QN399P75+72H89p9/rlfNbzKtMaxyH7mPaH5lLvrkOhKuM89klMmasGaKnuCd\nsLYVAhRLKieMR68Bh0wdZnRT3tWyzALNryeHRdP8giIhy5kqMpx+0ha/b58SGDWNMHE1P5PmV2Qi\nSTuFTOnGnW3+d3y9cj/uhFnOv9sW031yth8AqZYkin66PXlbm8TOVFFkETI1D/mzHKBtnTP1wIEV\n8X2LDrnQ9zPLRD9ytSi2HTKl5wRCrfTzF05BYxTu7P40WH5sjoqd1z4qXyrKDjBju45p5ppumysj\nVXTbGHahPIV1THu9S+UW+M/dPbUManJ6LRpNOI7qt34v1PszYQVfdZvVNv2TAkxmPmhBzpSVM5V+\nToDKQUmIIOn9Uiq1/pjGmm81vm2JDO3VjnWin4UKQPiAXzfve+GhhCOj85Sgfhqtm4kxxEVZ/PvU\ng/YB6JFGd3/7gObUFe2tovc+3Tc5B2+8RtEiTpEZCDOCXbpvui9cvIOfI77X4TPdm9X1mOanx+s8\nVb5COfkUEJ5X7kK3sg+ZSt23hHMOyEDgN4rmx5GpFIIaHW+hs/4Lb8lBO42NeN4CMhWM1Amj+b3i\nBY/H5b/1r7B757KQRrf47UlpdMOBGLD9ubhBoSI4XoCiy5mqOpW0FDKl6YK6jYwinVY0g/bW0VKv\n5qfVdhKfKXpqqVrp5H09EbzmZefhP73ueR5Nc8hU2D6vaG84DyKan0CmmGy6jsDSsdoGsGRSrfPx\nRmgaOSZAiDwC0llaW59hbTLDsCoZMqWcqSKLESZubOe5UKWzEmf9frm8XyQtCwCPOWUrXv/jTxPn\ndnkV4e9NSwOWh4MI1RpWhShQbE2yEc2P9ZFLY//Sj307XvmSc3HeY3cCmE9L0FQgHr2kHEnePJ3J\nMNSA8B5oR4Ib8fxvfo36M++X/iyOaThMkSHODUGWxNsaRgVx7vklOGSqMGl+qaRkntRLJRqI5rfv\ngCwo7tX81DGyzM7T4dc0nTW2E8sugHYjg9gXmE0YOP59M1AGH5wxnCJTgEKhL7YARXo/nUdqGUKW\nUTGx1Py6n82Ug0ZNjEvxfRjXbWsgsnnmI/4pp98dU29z//chU6l4SM3q5PHeEpppF/R1n1d7ivYu\nIo3et81CptK1qxbLmeLH1BQwE5lqA3quaYdEU04iU4a0dp9YQsqB4UyGFDKl+66l0TU9csil0WdN\nJKTQ+8x8YG5j4gU8T4f3hVoqWNCHkgH94iNRMKzb3ueM7tw6AgCfu9rnTOmyO7rp93A8eHiSC4Lm\n14c49QUqEg7xRor29j0Xel8AJIOkuh13phBP5hqZSnm63nkxpNErNgEUTL68aeflTMUvGz1DjTzR\nfo1Ktk7R/ABgPKzSyJSn+dnDgibsiaD5pZEpfZ0+KtozoWjDIctSFAPZNwsSP2nnstjOkSlN80sh\nRUUeIjBrqghploeivYSiWAvotV+8V6iUyfOaX7u+kDHCvJCbv7bff6ZJ9eCRdfzkb/xPrK7XGFSO\nXtc2tgBFTD2S52tdgC66FzpC6w3RXE5kAHDmyVtw3mMf5Y8J2DlTvPG+lkWOQeVEHayIKH0WRV3z\nIEAxq5sg81zk2LFlhJc/73FYHlH9qjlUNGWk88macnF4CzS/FDKljPGu6Qh8KhIXzeGGwa6PWRvz\nid4m+qhy5vS4DAV4w3dV2dH8GFJNz+txp22PzgGEMfDOv74JP/Smj+DO+w5HwRX6m/KiLKSkacCU\n2fi20A+9+Od51o3D7rdazY9QFH2+7n/L6dX1Zqxgilm0V79P7DnqAqxy7EtDPFNzvuh399U7Xvsc\n/75Zgj0ameoz+CzHqm1h1kvyaIiB9vUjr93zaOW6Ro3PNbpNEwIUzjjqf4b9dabmIxDReEusa7HA\niLY/0gY1b3zTeNSfD51374xlGJYFiVMA2rWplC2UmqPmOUThXJxWSNeRvm98n7gwsfsdZ4fMDETa\nCpDqbU0DVxJiQUO8LzClz9mPgKjj9gbK5HpBh+q7f4/uWBl79x2NtqXm3VTT9hxHcDfS+tT8Uk5o\nH4WW18g0mQWJ1nevSbAFiAWpksdb6Kz/0pu6SXGCqH0XaaKpeM6UoeZXsuKkdd2alaZT0uhAGHCe\nPsciCBQtB8KAoONrAQrATbrJnKk5yJSvNG4KULD+dp8jRUMvV23lMlifMxR57o1feY7+CVO3osgE\nMpAUoDAiJSPGyQYgaqoMvGx6nGhNffzrj9+Ou+8/wr5PX4foc6cSx43fz93ygO8nXc/nv7LPRyQp\nMbyvzhRg0xMI0UrlPwCLCVBYvHNtAG1eGojtfFvVSaMDNj0hLCpyYeGKhERH5AtrKMYdxu91N9+P\nV771o7jzvsNRX+iUmq4ZIVNFJlTEUpz/lDMVHJ/Q5DBML5bxYgxxLuudaQzDX9cZ4zQOIFaypOui\nnCn6PjhT22A16tfffeprAIBbvn4gQhl8nZ82gRTlJKKSRvSms9oMCrVtLHUrc6biSC/93Sfc4Z0b\n3k/9fA0jzqpT05eHRZeknTdrTaFt5565Ey977mMB2AGtkDNlG4bSgYqNnTalupiHnCkrwds6vjt/\nd9xuXOjnqBjFovE6eTDmNo068754AYqqh+bXY/xpJDSlzBbXEZPX0Id2id+xHYl9suJls+NjJqXY\n87ybL+OCp8EWinNuMvniif1SBj4XuklJTus+aDU/HWCjIOFk6gra9gmB6L+jd3+DhnhfXa++/aht\nJMiQGgt9AhtbNw098g8Aj9o29p/5/J5labuPmp6niam00dZXR21RJ9RiI9AznGcLzjsX/a2L9n5D\naH6XX3459u/fb247ePAgLr/88kUO84htllfaGxmm35G4AcuZsgQoSO0LCDU39HPpgxK94UqRGI5M\ntU5oIM/CvilpdPd5AWdqjgDFxMiZEtHLRLQ0pbzXR2lyMvAxxaCPT2u1PAuRBiAuskmHs6KkAZmS\nVKcsywRfWxenTE5+PZOJ6HO3AFpIwvKo9M+LF0V96NCaQ6baWEGwYnTUmWHg6ZySPuMvom00tuww\nf058Mt6kkSlFH+tzprzDIHIgMqFIaNUfGyjOedu2+PV3fxoPHlzF52/bF/VTCxQ4+mCMTJVKgCK1\nQGian3Z8LFol/x01ayHx2/qQx+6zVQhY1xnLALznjRfjLf/HMwHEFFcg5Ey1bTgfRdvPPHkrrKbv\nwWhQmN+538LM4/CGsXEPOM0vzplSncnkPoHmZy/cFv015DcFtFpvs5S7fN02s2ivNhrjOcWLUygj\n1eo3388S7NH5H72UPPVuZxkhU7ETyuW4+yLwfTladBzeHF3PdqdSdfK0aIl1b1Z6aX6dE9pj/C3K\nuIier55PFlwf+KZA87MFKIo8LQZCIj1NEwfCiGrvWTqcNmzMLf5vTRWl3D52P1PBN30/tiy7wFtU\nmJj9ripzTGY16qb1Ac7Qt/Q8oCm8C/pSIk/H7dfjFPQ4xxsRoEg5bH3vFgCcwXKGn3TWo/znMXOG\nqiKP+qLb2kTajcOHiUxx27jPCZU5vWkHOdRI7GeS6SbtG9WPItRv/Yaq+b3+9a/Hnj17zG133303\n3vCGNyxymEdss+7RIoo6XGWGHoYljV4WweCoa3Km4gUi1XSiIZ8sCLngSbqFcqbGguZXYnVtatIk\nfM5U4nrJyOE0KQuZoo9JZCp6Mdi+6tQlq2OTpkHNf3loMaGmr187rPw8HpnSNL9M5Uwpml9Cd2Bx\ntaY8pvlRWx5X/nlxZ2rvvqPIc2fcxDlTAZlqLLnmTEZvrUVHR8sFlcmI4PjodduPTGmaH40rctzl\nJJv5Y3IFPY6ukLMonKkqPCsAuPqGvX7bpnFw7vrU/BwypcaIKtq7YZpfKw1jvo1frz9fwukCwvPs\now6a6nIcYey2bVke4IzdbiG2kKmqy5kCwj0lY53PObzpdLXRoIjui1+kE8hUn2FMH62cqQgpUtdO\nlLTofPDd6f7mhrFNPZLHnS9AIffrnH5LuIKcxVoat1aOGt8v5PTGyJR2svui55Hjg55cM27A6zHM\nA2OI9+NNrxcNc6R1m80YfQzyWfBAUW7cmyBAwWl+7v+QK5ruazQvpJAprRasHR9B8wvfb2cIg97P\ni0ut2oFSolzWrYFM+bqVMc0tQl6TY2GOk+Cdfj7P2EYq3/dHX3QOfvIlT3T7qneNX/+gzP21V0rt\nuB/VcP+n0ixSLWZ46GtI7Bc5D/rvtOOVCpzNC0bs3Dbyx1tm6xxnLM2j+AEbF6xY5Dh9FEwrAGH9\nLtgmG3OI+wLyVI8T+P+xztShQ4ewvLw8/4eP5GbcoyoxofHGi+TmfrEyaH4e2nYGd5Fn0YPpk1/0\nNL/ukHyyIGeKT8C0sB5esWl+TSsV+fix8iw23qh5afR5daYUakFtXnI6ED+KssjNl6TPoLQajzQA\naTGA3JhoRwlkKs9YhfGpi4r18XDDNfGXON5+YldcmQQoUs5U07TYf3gN9z50DKft2gQAeMHTTvfI\nlEnzo4nHMKjdfimj0f0flCPlNXI+vuVYU/Sa9/+S5z/O/63pY76+iVFkNDghEtH1NVMajkyF/YgK\nNZ01uGPPQfz+Zdf7bfxeUc6QHsf0rumaPmWeoWmYWIY2SLy4g/yefmelf1jj0GopQyHURIq31YZx\nz+co3qF47NtOL73bdN8HZY5lw6HSQYyqKmKa3yAUg7SoN9owthZgC5mipp0wPp5SOVp8P968uIhJ\n1dSLfLx4W3lRHiXsCSJpFGmeAIV30Gbx+zTPMOwbf9S3trERpRTaJ+vwzDHEVQc4VVO3KUM8ZKkB\nWV/Pcl5prhkaan6p/KY+ml/KMNY0P4t2Z+33R7/4PPzY9zzB/20LUEzNY+akOFq30EOl6JApmvd4\no7FoBbXc39RPqO9t41fTifk2a9/HnLLVB7qK6L0I+1Rl7q9dU177qJn+PUxInKda3zqqz7koXU33\nLboviaDa9z7rTPG9fjd2d7njzzzvJPG9QKbK+SjTtz3uBPzwxY/HH//i8wA8fGdKsLZ6giwWWt+3\nLVW+INX60HEKZAMyR76vJUmPV155Ja666ir/9//H3ptHW3IUd4NRVfe+tZ9671arF0moJYQQrRWr\nJYFWdgzGQgI8QhLbxzZmszFG+rDBRtan8fEnxkgGw5kBAWMGzoDBY/BgCQajc2yx2ID1eRA2YAGW\nQa0NLd16/d67y/xRlZmRkRGRmXXv637dIs555917s3KprKzMWH4R8YEPfADWrvWdihcWFuAf//Ef\n4SlPeUrS4FcqcdoILXyjvabZGLodHBqdh/kB1A97MBhAWRbBAdVXTImmbetwiS1TQ7NBIqaRhEaf\nmfJhfgAA8/t7MDXRge/+272wc9saWDUzIUIQDU1Phpapzetmg3FbHD9NXCxZphhG2ZDnKC0s/pR3\nuioKWEB+UvQspj5A+LONFqRZppZqZto/XPmJ1GB+v/q0Y+EFT3+C7X+p1w/yRRWF2wjvf6iOgHb6\nEzfDH73+XFg104XX/NGtLMzPC0DBCKiFYTgYjaGkSTfrBed3wYTxzAD1xv47V5wBnaqEq55/Etz3\ni3n42nfu9i1TnQL5N4UMh2nT3KM70B3z2h+Ea62LHJW//5MHodcfwq6dG+COH97vC9rEiRVbbfr9\nAaPZLVXLlNVuUwFePUi8K5Xr+EPGRfMLD2dOceNCFhvLVP375ARd+64v7INn2sQRO298+0Xwv//f\n/wJ/f8fPbB26JqnFEsAJcDaaH8PcDyXGuHDPis8zFUJ6zPMxlkeJadHyTLk+wvefS9pLIYAcXIwN\nQCG8hxzMj7OSm/eJhjkGQD5Tyj1xVgSXnytcpybKqc5sknEHDD31mfIjg2LyYX7+uCV/UPoMsc9U\nmJRZvg/NMsUGoBACXHWEM2/1qkl48hPWe/dkaCYSgMJo2uszPlQGPbY0hC6jSHCh/5XgJMMhEzjL\n7x8HeymLdJgf/16Ez7DTqeCRffVZSKPS0fWkW+shiQKlhrAP140Kv3PfFV5I+v6Cpz8BzjttK7z1\nhr+D+0muPgCAX79gJ0xOdOBZZ+3wfsfCFBdhmlJZFvAbzz7Rfuf8NFOoy+w9uA9X5n5XLVPBnpg2\nDi/SLaewM5YpQUlKSRSmHnjgAfjXf/1X+/2nP/0p3Hfffd413W4Xnva0p8Eb3vCGpMGvVOImPwnm\nhxwjzabDBqBAjvH9QbOhkCeTYpky8Dtsmeo3jCOnITOC3QyB+QHUvij33PUg/N6Hbofjtq2G//Vt\nF1g8tWTqnpr0faZe/twT4dILa+uCV6fwx2FI8pnCFGrGeHxtqWxQHBnIiaHAf8WazMN2zeaMc+2Y\n/m0koZ6J5ofvRVg3ytp6zQtPtmvJhHOnFqapico+74cerXM1zc12Ye0RU7bNwdDBiwxhod8JaD7D\nFWVSBYd5zCRzB4kp37h22ov0VlhGtv5/6vEb4YjZSRcWn9GIOotOo/Um2vl+fwBLjU8JZhqxFcWM\nZ10zZ4M+FrSH7PoyYZ7p+g2YZuHQC/MbkevQ52SfKeGQ4YJahKG6cVljPesbAcbNaVkWsLAU5vbB\nFnZTtmiFqQo2rp2GC8/Y5gtTpA3OJ9Aws0NQovkJkC18KZdnCiC0vIah0fk5ZZUFguCFy1xSZoaJ\nY4NM+JYLLox3kLS3dP5LdNxemxzMjwjEqb4/pg/7HgZCkZxnyt8jdKYx8JkaDsUIFL2eYJkqa6s7\nZz3GY5tACgJcJqUN0ZRn0lkQhPim/kXMvst9x/1N24SucgAKqyxgBKZ+v0mJ4FdD+7AcLKM/YBQe\nCvNbVWUDCxegk6LSlAh2qM2JjkOwBKlXhPcZl3HpCzTCijvaZnAPTD1uLLRcsgJy7a5eNSlKERPd\nCl50/nHB75PdygoNKTA/Sp1OotQS1JOVyRKyR1PwWPinYOmVSGsf8zQcP8SRKEy99KUvhZe+9KUA\nAHDFFVfAe97zHjjuuPCBHBbEzJGWpdmQ2cxq+FT9m5l4XB9rGo31QvSZYsZCGX1qmeoPhp7g4i3W\nsvA0bdMoce9/3ldHmPvR3Q/btqhlCifltD5TzfdVU13LrPrMp5kD/2aWmLC89fXyYS1p6QrmwNOo\nLP0AFENfzvAsLbSPsqyDUCwgmJ9jNl34dinPFKWOAiGlh24fwccMTXY79nn/ohGmjpid9NqoYX60\n39LDrNP+a18UnkkNYUn+c8FJa4G0CcAzovjSwXAIG1ZPwXtff0597zQ9AaOxM9BCp51vxjgY2nFi\nLZgLQNG3a8EcIiHMT2Z+JIZK0jSLPlN0LoT3QGMGJCaV85lyMD9Gs0uSaZqioqj3j/2LPT/4RPNO\nmOvMs6e55Ch8hEK0cFQvQ0ZpI+WZMky6Y5r5dy5IDg7+WHHCagA5T0mRMKfuOxontT55e6T/Pnn1\niKM9G4CiHzL32L9UqmciWfpBDvj75cq5uTEQXg5VMBg6BaLXJn6f/SYZSw3xmcJ7DSGctFdSiJhx\n23GiCydJ7hz7nARGrSTzL90Hrtah1h6FSQuZbVyG7qEqa7+hxjKN1H7GAAAgAElEQVTFQmMbyHgQ\nQKdJ3TIYQmAtpxFQJX867Rwz/duxli7/GABnmXKfK+8e6TvjyrAwQAUDDSIW7JeQRq6esTor+4DC\nE2hKBk1Aq8v5PmWPQp+KooDpyQ7sm19qJ0wtg8+UBHMsisKzFnFWdyl9gUTSMzLthwEo9HaTYht+\n4hOfSBrcoUrcZpYTgKJblcGiwIvTacyHNcyvYCxTDDSBjs/U4ZL2eps6+jwz2fHGbyL7zS/0Ambb\nOAqXZJMyB/Rk118uhXDKYq02Jsto0ZewYD8CAA9HoZ9ThKl4AArzP9xoAWpBEueZwv4W3U4FC02e\nKQ5WRQkHO4iZrznN/VKvb+/54b1GmHIBHcoCWMsUTtrL57BRLFNEQ0thK/2B0xbjO6LMtnQgDMnc\nBVh9ZvOkiRaxZcpo/LHg3kXQQTMeTpjqD3mYn7GSUWUATaUgHZhSAAr3PazDXaf55RktL5cMVoO0\nOF+G2oEftzo1UcHCorNMHbVhFn7zslNhaqKDmIr6WqMwMfPKaS/xPHhCUUMGWmiU1xxj5oe5RvfP\nMK22jDwHc6l9PsM6oEmOH5pqNVQYrlDQCvdvVtBSyjpVAUso/gAnMBiItgSlomOh7XAJdp1lKmzH\nCMthFDytP+9rsEcOhyBbpoSkvVSYkixTU5N8wlfJp0byb8J91vXCZyEl7eX6577TajNTXZsig7aI\nhQYu/HmvP2zgeqQs4t9lvmoCOC3HljDuPqSzXVNaYQUzFQy0OXR7YmgF1iiwTFHrmrcpod+VvRyA\nuDUo1pj6u9+n+SopLjkywtREC2Fq26Y5AAB44tF8TkGJNMFNdflAwhR3dkkBdCTy15lfhvlF7pzh\nKDlQ/J49e+CrX/0q7NmzBxYWFoLyd7zjHalNHRKkhW80xAWgsPUZmF+vX2t/qir0mWqbZ4oNQIHG\nPkOcwB0cYCnQ7jmrGboPReMjMRWmPsXxS7k6NG0MdvYXtT0JL09ZFrBvfgnu+OF98JTjNgRnMadh\nw+OcnOg4YWrgQ1ommsSlg4GfkJmGoN+wegqO27YGXvGrJwX9ct/N5oGtKEu9ASz2BvaeHzLCFIqO\nZ/0YsB9SVVjtjrmH5mqvXq1lDteiKoQ1+bCG5Nq69eawEuAQVps29NeTljjbMbc1I2emHG+qXE4z\nHHnOMFUGptkjMD+8dJ1W31imeM0nl2AYj10KjU7vi5ZJhyYeG23DQcvCdrgEs16UraHf6VSz9s3o\nN62dgac0IXbd8/MtUzjSKSX8XnCWV/dy1gxXOE+y/wtnbbNlplWiLMCMJuf7YxUCzB6tCQIlEWy5\n0Pf2WWCmkQjnnIBmD/jAEueCA3Hj5AJQaFYEfB+0TfPdvIe0zEDLBgwMzBOulPcAIFReDIayZQoH\noPC6sM9wEBR6linqb2Oek5SclXmHaN36M74fZyGn11FKhUEB1Gf7ww/uAwDumdb/l/qDoF6nLJrg\nWKHgapVaQk4se24C/7v9Ttr0fKaU9ccxzRyKB+/zeaHR6/8cLDpGZTNvXB/pcDXSpsIL0fcw1QKj\nkeEPUwJQUNqyYRb+7HcuhE1rZ7LqaRYtbX1XZQFL3HWKgkkjrS+MZBqrZerWW2+Ft73tbTAcDmHd\nunXQ7XaDaw5lYYqbIwle5l/T+LZUYXQ+/HJXVMtehDA/KV8BgFss1oeH5Jka9AdBKHZDOPhE/d35\nTA2IacrB/NwYPKFQwSJzn0OY3wA6TD4D/E06BADo4sfXxN8ec0j81w/+A5x+4iaYJnAO07TkqzLZ\nrax/EvbTAagZ8sXeIPj90X2+M/CmdTPwrledxfZLP5uxYMvUmrlJuO8X801iwvrihx7lLFP1Jt/3\nhKnSuz81AIVmmWKT/RYNEy4z8FJEnIK7GBDMj7FMYWsWdm630fz6A6ul8gJQGJhfI/ji3zBDTyOT\nOWGJt0wFgl9byxRTB4CzPsmHM4Wt+JZH+uxdYQdr94j/y+REBY/sW2Cj52GrDkAI8+MOzkcfc++F\nBw9tyAhbgwY+Rl8M6xOoCP0AoTKH+q7Rd5764NF6bpxoLSraY836ZKqxkUoV4VxTRNH9mWP0Ob9V\naV/l7ollpiWYX6n4TGHEAemPjsEw0cbiLrlMVWXR+EyF812QfU+0TE3wjLgI4cVKTBrNTxBYY3mm\nMHFnAtcmQA3h5wRJPO5efxBYIaom0u2QUSS40Oh9tk9rmVKsP3Q8VVmy8F46VtqO5h6gKn01QUc5\n12JUFgWbr5GOmy5wDFfTBUl53Nw4w30qTkZ50AbmBwCwA+WwSiUtmrNmeZUUQEHAnkTrIrfn2u8F\ntkyFCj2OkoSpG264AZ72tKfB9ddfD2vW8JntD2Vio/kpuE56DWuZYoQbDLGg10ubIIDT4pm92g+N\nXjMkk6g9vFinJ3nL1PxCL4guZgNQoDF0FfO5JAiYl56+8L0+H65Yg8ZVgmWqDczP0Le/fy/s3OYn\nFaXWP9ruFPKZoozDRNdYpvwN75F9vgWXWjRoHyEzUnrM5gx6ltYyxQhTReGHDQdAwpRh4gacUORH\nV+OYezYBaeX7sHCCtYRJ954p+l0LyWvuwUC9rBM+CvTiIkf6zwmAt0zRsPkszC/iMyVhtiVhKlgO\nwvoOGVjUt6J1rZtkGAcOKoEE0cHQrzc5YWCsTZuMoGee/d33PgqdqoD1q+vAHtwhvXd+0X7G/i/X\n/89Pg69866fw1JOOhG/8f/fU65A5yKgPmjRXUtJeCpt11ry6TQpNNN+4PVqDxDlombGGoHqEieMC\nC3DMgZY0tlPKa4HC3Lg8U7Q97p5C3xCjgOHzTPWaENipEDhuDGZtVlUJg0ZY4hjxiW7pWTw4eFW2\nz5RiQaT3keoLHASgUI4vFaJG6uGzXoqQt9QbBAKjU0INYaJbsGXS3idZpjSre9X4aLlr5Xnj3gvX\npvvswfyCPFPyWLQ9MUbYepHqM2W+GzAqZxGR6vnvYUg5YzfUVogahTQrmAo/llBKgfIpbSK0Pcjx\nNEMWas62l9LpPffcA1deeeVhKUgBCJYpD+bHTySG+YXmccYyhSAWdLPRo/n54+ACUOCF5lumiDCF\nAlDQvDfGX8CzTKGNKQyFyS9Gad0ZyxQlSUgCkF8uDXPPEX0xe33C2BqGXNjMpiY6sNgw4ZRxqH2m\nBoHvGtbAA4SHLe0jOJBKA/OrHxQ+LE0/D+3dDwAAqwjMbzgcegE3zHqOW6ac1pdjDA2DV5H5NxsP\nvQ/zWdLEyYxwqICgdQZDaCJJ+c+utkwxodE7LoCKmdMJxmdKiubnEp5SzS7ZzCVr0ZBn4ul19HOK\nZtVeSw91xmG9zwjSlcdU+JjLVdNd6PWH1j/Pe75WmBrCUm8Ad/3sEThmyxF2rrn3fS+xTJlp2XHk\nHLz5padZnykpsIH5qlnfABjLFLix4rF7PlNMf4GTuteff2/4q+mfw9yH0BRX2CH1NMWRdr+qVl/a\nV7nzh3uh0Vfru8iMTcr9UnnrkrQZXOvOWQCwwhulbqeCJcEypQlTPpybD6udEoAiyDMVEVitgKKc\nX5qgQecbn/XUhwcnvA2taw52SEcSCn78/q3t6wBU6G9SSYhWHb6elsfLs0xVPFST+05h0amMeD0e\nPmcf16fcP21TLotapszTSzdMuWAovUHkyvGRFgVQu0cJLabxNBp5ykTxzA5TpYhjT+n01FNPhbvu\nuitthIcJadGODD39lK1w7q6jYNPa6eDl6TDmfwxZoi8tx+DY/pu2OZifgXPhxYCtFAHMD1mmqKa8\n3+TAwiPwLGwBjMR99s9b9+1/+c2nwRO21lag2jLFCFNYKCNlkuUm3zLl9xvmbgoPBfzZ5dvpBZqK\niW4JS01odPwcrnr+STaZbj0GZgPUtCOE4ZpCwhQOjT4z1fGeUx3i3BcQLCS19IUH3GNZFL5limEA\nOIhYVRXQHw5ZIcx87DMMDu3DSxJMFBCc8GzG6pitZr4GwwBuhj8v9QbWymLWdB9BXilD7ZywhQAU\npFzSNtKAL5KQQNvQ9nE6liDMsueEX/8fMFAnC+cxyTtRG086Zh0AAPyPH94fjNsKNsMh/OSeR2Cp\nN4CdKPQ9a5miMD/C4JrWpTxTlAESBXIazc/wGgSNikOjD5hACjZ8v+BniEmHzIXMABfpT40+pjCG\nQcANQTin13rzF9mjOG35EIzPFM+kx8atMY14rFaYYoKWAJjw2AOWl7T7EOPz6YUYFyxTEpzLC40e\nWKZw//6aqcrCRvPTgh5IZwI3Ft8y5ZONeNYL8+SZ92SpL+cD49YpHk9MIPYYV2KZ4mBw9nMhzy9u\nEyt9J6hlKnhHw7J+P0MCQXW5PHG0T02RkOMTp1klAcDOY04ACnP+LdHDaRlJy0/VRtBsm7RX4yEx\n+oGDKbNjT+n06quvhk996lPwl3/5l7Bnzx6Yn58P/g5l4iYqJZrfrzz5SHjnVU+FqiqDB4hNmVRz\nXZVFsIFqMD/TthlnhybtHQy8jWbnNmdBpDC/qeawmF/sBQKFg/m5trpVyIwa8hhh70B2v5907HrY\nsbmO+lJbprjDGn/xy6QXSNt0OKL3SjdP040EeTHQCBu1DwtTndpnioZGP2bLEfCBd1wcaFa5fgFk\n7ZZZNycdWydsPGfXFlv26GNLnvBct+MSaRoyz5HCi3CnRSFH8zMMJw8PLGCAkmVy60ILsMLdv7NM\n9b3+8XXUud3C/ATLlE2wjBIhm99Cy1S40S5KML8gCad/X+a7FEHS3Re/uadasLjv3DtDYW4ACOoz\nMEl7XcVdOzcCAMB3/+0+sf/hEOCH//EQAAAcv93tP6xlCsP8+i6yonsHmzZBjuYHwCfDpUI+eGVN\nn8SBHcMwa58pf7xGIGV9tKhlUNCqS/X6DJRPrac8XwnWCCBDU4O+OWWer/Hwy8pmzxjweaakdnWY\nH+2jLu923FrjYX6Vl7SXnW/OMoU+B9H8zL6XZJnSmF+6Z5RJsCQd5ueXecKUqNAJGUMbcrzXFwVb\nSdlrvkq+VPY6tHA6JpqftdjSNvkzX8t/5vMp/jPUE2vX/61lKoFpNlQjR3ghRFOG+dbFsE2pnvca\nKuNMDY0O4Oatd0AtU4owpewZ+Lly53NuaHRVoVO6dT9Wn6kXvvCFAABwzTXXsOVFUcCdd96Z0tQh\nQ56mP2EiVZifsUyhDTm0TPEwKHN9XVZ/x5hnE+3N15CVsHXjKvjP+/bCnia6jyGjtVlcGgSwo8Fw\nCN1S8ZnSNJ8a7K752usPAyhK0E4i46hpazl6ZN+i913ym4jBP0yIaHy/E906mt1SbxD6wQDYvBrc\nOFWmmQhTx29fAx9658WwYc00fPCzd9jr5mZ8YcpYmHyYX9GU+fdPtacDxKhwgqWk2V3q8dpiLPjg\n73is9FoAJqgDo9xoXGqcksGL5jf02gHgk/aa92FAhCmfEav/LzIJuc39m3br6/nnGOaZIoc8/uwx\n0OBf5zEDMgNNrw0gLR4jaATRelJxO8fvWAPTkxV89wf3BX3g5/uj/6zz1WFlDu8zxcP8rK9lU2XI\nQGrxdVyUJbyeJEsNDXFunq8NjS4wjFxo9LpPl3Sa81Hjxma1qREfHlpPEjQAGJifskfGIGiYFFkK\nCiiafG+6UKRapkibIcqDWKaGgmWqW8HDexd4eGSw9l09PG+iZUqEK6bNP30vq7KAxaU8BRNtM2Q2\neZ+SWD03r/IZZN9RQTAJ1oWm1ClNXiue6a+EdavNb04ACl7Zly9MGJ9AOhbaB1dPGptWRv2bg3bN\nbysc5tfWMoUtjqwPolVMpY1DU1LhyJHce8FRkjB13XXXpY3uECVuolIsU5iCA6CDNwGf2arK0JKV\nEhrd1DnjSZvhpc84AT795X+zASgojO3ip26Hj//NnbB+9bT3u3G4X2wCJvhjgMAy5cEVFctUuJ3y\nJVHrDC2r+M0Fa3Q4+Bylh/fqwpSZY8kx21j09i/W8+ZpxTouvDbHjFRVAdDj713bIF1uGAcPPWrj\nKm+8AMBYpsIAFAZHHsD1PObP+T/YhmyZYSp4mB/2QeO0zmKGeWENWcfnpZCJwZYenBMKB1Gg+Y4A\nHBRkcalvn7+ZFz8ABd2s/QNHUpxIfgWizxS1gAhMhMakShHr3HdczzCUjM9U5cro/XeqEnZuWwv/\n40cG5hfe23AI8GijsFh3xJRXl5IUzc9oW50lyDBx/D1xkfA8TbZkmaI+U/YepNDoPkNJqSgKW6gd\n0FwAip5dM64sFALDelwfurJLZrxj8CENilx/5YVeLweTwKzwbZJrUQAKAOdLZ8aLIbtLOH9RME45\nGqmhqUlJmOKZ5lTLlOb/oZ1fOW36AqpcJlmm6jb9/rX1Vn/nx2Lqcr7gHeW8oG1p84urTaCQ9gHM\nT1VO6M9XI9X6pMy3arFMLOP5rWaf0gZNCKftOVCkhkZXlPL4GbPnWq5lSlU21f8HgkKPoyRh6pJL\nLkka3KFK3Dx1BCZeIhXmZyRnFJmMSs9qAApz6DftrJruwsuf+yT44t/fhQJQ+PVefOHxsH71FDz1\npCO93yc9YYr6TJnQ6Px9qIe1wkRIwhm6wn3SNj6PaZI3HY4efcwXpqgmijsUvEO2sUztX+wFLxfe\nvNnn1+R/kQ5NwxBw2ksAp3HxssGjtuYYmF9tmXL3aC1TViMeRhgrilrLbBhD7pny0cfKJgocw9w2\n/8Wkvd648b0TmB9aei75qh+m3h4MgyH0eoxlCgk9NjR61/chA2ACUFDLk+DML+WNodpdd78yc6Ad\n1KmWXAAiLBuGklHcuEOVP4q7glawsPc2hIXmWeH3wUsR0Vho96J3EftMuXew/j80eaaoNcZqkxnL\nFJr7QCgB4TkgyyGXfNYKYQQeiPvk5jTFj0JLruz6T3ves9NdUuY+a8iJmP+pv+/6Zc6arTP3oUVN\nbjPqMzUcgmEZy7KEQX8AZVG/3ziaHzensWh+IswvIUeiDvPz71FL9otJV47417Zl7juK0BuzuFgF\nCHMLZQFgTiCqnMLnBQcd5frXAnz4ASgiwhQjsHC54GKkCUUqdDPxOYXvGm5DHpcUcp4jM28HUpjS\nIgj6cH7/JrEwxaEqRkraKyh7DHIiSQZI6rWhH/7wh/D5z38e/vzP/xzuu6+GfPz4xz+GvXv35jSz\n8oiZp5QAFJhCaEK4yTqYX/hwJAgJbpt78SRteVkWcNGZOwIImLNMIV8Fw0wMTdJefpMqSz+ku9ej\neuCG9+KPlW0muC9ReMtYxfRQdQ60RTA+z2eq0VjuX2ACUHT4l5zeAxfND9eJwfwwE4/7maHa1MKZ\npw0FASgYocjAA129kBmysCQ0552qybfBwvx8xjc4OIWDxTDhi4yA4lkSkK9GLJqfWfueMCUFoGD6\nszC/SNJeLXy1f+/+91TLFP6mOWUDABvNb8BYJfHcce1IMCGr8R8MrTCJE5/6cOf6Yh/mF0Zfw5Yg\nagUGwO/wIAsCaW6XogAwQ8X5TAWwQiY8eFL/iFQ/DiXEtsYYvv6SXXD5c05k2wytETwDzTEiXuoK\nxkooWfRUv6hCblPSyBsmGe9teO/sdmq4NbfXBEKRcCZNCTA/fL+0vCjMf+UeVWuQvE50ZpufJ67M\nE0povcS8P7Qd3A93D5LvU6cqkdU5PPOlMzjc6xCfgpVmNPGyMGZcxiUyj5E3N8pz0qyZqluDskfw\n813/bxOA4oBapjSfKUWYnPQsU+EcclZ+jVTFF1KwcXsbR0mWqX379sHVV18Nt9xyC3Q6Hej3+/D0\npz8dNm7cCO973/vgqKOOgt/93d9Nu4MVSNw0pYRGxxTAKLzQ6EYjbrTsLnGtMXlbjZnCjHMvpbVa\npEh8gH2m+gFDbQIo4KZovixJ26QdnH60Pm6ceFOSN57Cu0zedDRaOzcJDzy838LO6oSFfXQo8H2s\narS+e+eXwgAUgsbEkBGspY26bmvICJK+c6jkNE4dbo22GAfdcEl76++cJs4wRlbry2w2nCm9qmpN\nI4erN5eJlldhvk3OnEUG5mfbtNHeimYc5mB0eaY4LP1ir2/HygWgGBBLb2AhFJL2WpifojH2bl3R\nUGpaSG3tpxzOnL+c3aNsUAelXYZJHQ6HsLhUw1wrRoACqPfUxd7A81+srYv+fTmmoHm+4BNeU5rm\nnlqmJF8rrEzqM8IbXcOhoBn2gdvlxqYJRVQDr9XDZVs2zMJJx65jy6glRIITsXAtdZ0WNiBEYH0R\nlD/0e+iET9ppru3YABQu4ihWhoVwWzzO+n/MmhkTpiRlmYYooZ/xuLkyqQ36XRe0wjFy1wHICZy5\nawOhnDk33Rj48Vh/wUgqCfpZg/lhAYpaPugj4841KQ+iRtrz9d6jjOeUDONUhKkcshFwD2QAikSf\nKc0n0D+7/PM5lR/Unh8+KzgFG9teSqfXX389fPe734Wbb74Zvv3tb3tmxPPPPx9uu+22lGZWLHGb\nWVfZYGJtlAWwDAV2UHfWCqdtA+BfCLM4uAduoINcYAeOOlUJRVEzlNQRfWAXjesHL+CqKsQNMpUx\npAdnfR/8Zzy2uow/EHK0SWsbfw5qmTIvi+dQjD6bPE6PPrYUwvw6+loxOcBUnwtgNs/mu8tvxI+N\nCwU7HA6tBQIA5ZmymjjOr6Bo8iqY7+FYuBC5narJG8LcYIE2JdomvWdcZNYzZ5nyLBdDNzaM/zYb\na4cRpjiYHw1AwR24S1KeKfp+RxgS+7tw70FZsC5Q34owIfVnox4x60nS7knQmxI9i8VeHya78j5k\n5g0LU/3BIIDI4TaHwAk3bi2qGniqyW7+U424C40eRnLE/XF51ABkDXWKRtx9d58pFDpVy631gT8H\nQqbCaNdt8p/NuG2eKe1ZaIxh0J//i9kLcP4t8wzd/u2EKbNncAITB6v0QqMLMD/pu/mNzSGoCjdp\nyBcKxdeEsFRGPLRS8uuX60M62/l54dux0QMFa4g0b6kBKCaoMKW9IwZxIfn0KqTPNz9O+l23LpL+\nvDMpHI/dpzK8pn7tvONg7dwkvP3lZybXGZU0mJ9mfYtaprJDo7vP0jvR7w9ZdARHSZapW265Ba65\n5hrYvXs39Ho9r2zLli3ws5/9LKWZlUvMPHmRcVKk0sbcPxzGo31VpfNL6lQlLEBf9CnBv3Eb5P4F\nlwg4hYqigIluBYtLfWTxqMsGTSALyTKFLWp0PBzjzfYvjEm6Qob56QwApT947dnw9X/5OUx2K/jh\nfzxkD1VqNZL6mJtpLFOPLcJg6I8La8W4e3cJUaUwqub+SD2rcZGhbgCcZcoFZzBkNiKbP4E5PAxj\nZCxTmgWE84PjAhuYj07zJx/OHGxjiUnaaz5amB8RhPv9oV3bEszP/G76SQuNLuSZoladBAZMu3da\nJ2Bg8Fwo0BfaDrVK4kupI3IoTPHjsc93OITFpb6a3d6sE+wzhR18RcsUfS+QQkBT3AQaUDRW/B1r\np7EPXnCPA/KD/cq/l4EmHX3WGTy5TIMA0u9Sva4CDeWFKZnBgaIAyddMY+BLbhEJ12pJeyu7fzvL\nlbVMMf1x65uDc4vj5ixTVREoWOpr3edQQEw7v6iCSoX5acoZRQjrKIJdKsyPI2ndUAtiKtRNDUDh\nKc1CpIbhzWibZlxS8l2NVCthy2ehlUVhfs3/HJjfketn4ePveU56hTGQaplizitDXgAK9DsOPAWQ\n/gy152doCLzVnW0vpdOFhQVYu3YtW7Zv3z6reT9UiVuYudH8AJCGPNCMOGhd/b2wTKVhMKgGyq/f\nHBgBHKLINm0C1NCmhSWnvfdgfqUcgKJT+RBAiRkMz9vIJoAPNmUzl5yWU+799Cdugje++BSUjG0A\nReHqsjA/1N8cskzRfFITQmQsQ5jJ50jymQqFcL4fqolzFqa6vxee9wS47OLjvXo95vCw9Qjsirsv\nH8tu4IhyFC0rTAX8Lf/sgqS9zBoyYdxZy1Sv9qfxhF4mAIXZoLGgS7Xspv0lxkKIv3NwTABei4jv\no/7sl6k+UxrDTqeTeUc5wZauUdUviAn/PRzUFtSJrixMmXWCg8GYfDPcHmGer3T/HMxPE2ZK4f7t\nPQz9vGX0fuXw/nhs6HeFEQ3L3GcV5hcRPCRmxLcMKJYp5nXUtteyAAAj9ArwPG6cnjChMKK4rmaZ\n8mB+S0zEPmKZkmF+JEdRgmJEtEwV8j1q/k0AALt2bgAAPu2F1Kbmw1MJ6wKAWqbIc4qttzIcF3ct\n917YRLEBv4CuTXifAfTQ6Phaac4GTGTQGKXu0WpQLaVMPRPSh7niKBXmR+fUC43OCJ1LowSgEJ6D\nybs5NsvUySefDJ///OfhvPPOC8puueUWOO2001KaWbHETVNHwGdqVJZ1VCf6Mgc+F2VhTbE2nr1m\nmVKYbSkAhUYT3RIWl/oIdui087UwFW5S5nfRMoXa1/wYuPvTmErp0NEgMxpZYaJf3yvdZCVozCpk\nmQrzTEUCUCBfHo44QQ63xTHxns8UEwrWMIYAAL967hNgy4ZZr55jmlG9ovD8EVQmDmsabejwEF5j\nGFEOXkP7wJ9d0l7OMkWZ3/p7haxMvf4g2LStz9RS3yoxXAAKYplCVamFMEzaS+/fK25lmdLmnj4z\nrS9uPXNaWIo715gxNqhFY5miScIx2QSRaK5NND9uDzAHGX2lTP+xABSSZYqubyvwN3A10TKVAPPz\noyfSwYFYpgnIqcwXAP9sAHyGWfIlY8cMdN8Ny4wCRvfx8Ov5mnu5XlkgJSWCxNOgJWXBpSgI74FG\njgTw5zs3AIW5hjt/VSsSdiNg6l77+nNgYakfKCeSoXwZZVqwLc1K6n3n5kXY30x/XNLtYKyM0s41\n6j52OrLPlOtfVr5oeT4lktKo4HbJMOsy5b1QYZwC/0N/y4nmdzBIg/lpc+PxWcz7K/ngSeSdf8L5\nW7sSjDEAxVvf+lZ4xSteAVdddRU85zm1SfBrX/safPSjH4W//du/hb/4i79IGvyKJWaeWlmmiAbN\nkGVUEGNo1ju1TLGbtT0wwpfL1ONgBhJNdCvYv9DzNOk4QG9gS8oAACAASURBVADuxix8ExBAUn5p\nB65HTJl/vbaB4N/R5wxBksLaqKAqbeTOMrUYRvOLhEZ3YTZjMD/+AOT8hjyLCwvzE5KzBgx1Qeq5\nhJipFhCHgQ/XcMCIMsyY/ezlmfJhZ9wmS5nfDsI59/qDUKlRlbU1t+f8dKww5QWg4IV167vWMmkv\nJUkZId0vV69dnqlQsA3mO5EZw4LPYm8Aq1cpML8ObgNskJQBgdY5pqCxkkoMUF+H+UmQOOcT6L/z\nYi4hwojTTkW4s/LOqJZe7V2LrC15v0TPWvEpiftMkbFBs2cMQiiMJqRpVhTv0sJFkDXrZzB0XiEm\nmXdRFM4nslF6eIGPmo+cvyAeC1UGJMH8yjI7GX0sAEVRFIFgR/uX4JAA6QIKgP+eaIIt+x0Js5To\nWUv7E/PyCXOjrS+MzuCs42VZAPTlvjjoc4w04cZjx0ibbf3eJP6H0gqXpZJDo9O5EX2m7P6dmWcK\nz7Wwzo1yOYXFTOLAzzzzTPjYxz4GS0tLcO211wIAwI033gh333033HzzzbBr166kwa9U4qBGnYjm\niCOqQTPkQqOHkKUwAAW3WfuHPjcuLbwppcluBYu9gZcx+r6H5u1nzjJl2petQ2hcCrMR03xyUEau\nbowBkIgyBiWZW4kBmJnqQlE0MD+iqeh6odHDPumGLZXT2+B87WgdAMbhtvA3F64eL/gUFloFQCwg\niobSaoQZTaMGLQuuZaxB9J5wm0ZrZK2K5t4GdWh0LgTrRKeEpZ7LsWaeXRiAIpwzOWmvXx6DYrl7\ncp81KEgYWc99jsH8OEafS6BMtXsag8sx6cNhbfGjwVAwrZ1zyXxNbjSTtJfjrYcNnktiovoRmN+k\nADmkFiYT0XFhsRfcH75QDEAh7Bla+HMAmXHSHO01y27Qv7Bfaj5TmsYbILz3oihgCLxlKjWan25N\nDc9VLppfWRaBNdvbT9CaoX2olilFCDLU6ZRBbiN6j3Raff4iqCpSKsxP20+o4hX7TGnrie3TlDPr\nRhI2TH+if6agECgK3wLI8Sn0syEz3RK81ioXMwB0qbnSdAsTbTOxLKL0WMnUHubHC1OxtB4SpaCb\njMJ2bJYpAIAzzjgDPvnJT8L8/Dw88sgjMDc3BzMzM6nVVzRx89St4hNNySyEEObnY7mrqrCHgbVM\nCZp73C4tk6K7xYjC/B58ZAFe80e31n0V/nZihanSaQANSQxWwIhFxiOz18sA8yNtWO00cyZQbfHs\nVNfB/NB1yT5TEZgfvXfKxEsMyARjmQLgrToWIy74Dgyx1lc9END92fCqjUaY0ZBKlldvDaHf1Yhj\nloFvNOLWMuXgI73ekN20u014bvMszLPDkaVo0IO45anUy4X1mWyZougWgUnm+iqYMh7m50Pw1PF4\nGn+3npZ6us/UhjXT9vMRsxPw8N7FGrI14NeoxKRboZCBVONL6VhMGc1D5BJyh8ou/J2LclmX407Q\n71SY8qvVyAImDYEmhGnWCDo2LlojgA7zY+Fqwr5bjwdrb+W1mPpOhNcWwftt8o/hdsrCCVM9JgCF\nTczM+K36limyZpR3z9BrXngyq8yUhFkA6jOWcX4pzGYqtEzzmVItLEy5mR/eMsV/pkgGbd1wkOr+\nIIRTj+oz1V9Gn6mwnvssoRzwmLnvrNIDzLmoDHoFkA7zQ5/JLUowP8snZfpMcW24MjeXg8GQVZZQ\nShamDE1PT8P09HT8wkOIuIXp55lKa4dGHbK/0zj4RWGd/DvEMsX6TJmNQDmQcqwzE90KlnoD6+Ph\ntVkSnykSllZyalaFG2XRhnXD8XBlMQZAItqXy5MRzjG9j7mZicYy5bczEYnmZ5+xFICi8P8bqgiT\njjWKfjRBHuplAkJwGzQXlrYoGuduLumlwuDhwA6kmmO2jY+Dpq3Hz5RGzMNrrbldmlDPWlf6dQJZ\nXpiq+KS9GOZHA1DYdnmfqQ7zfmOSLVPaYQxJZTHrh6ccQIIPvdbmG0mISOi3Wf9faILrUMEe00ZP\nmJoEgL1qUsThgM8z5RQCAygJt4vnmlqmSrIWDfMx2QhT8wuNZUoQUFx0TMpQ8ntGjMGqqgLMFkzX\nm3kXcf/0/mh/tFwSwlSYH8ek4bUYltqQ8qqyj967poEnezQVpgYo4qhDTLg1zIVGxwI47QPvq5MR\nyxQ3P+eeclTwG4A+r23cCChplk4VWqZYCYNnoVyLx8BZdCTlZ44VXFMe4Dntej5TAswP5H2tjc9U\nqoVJe0dnpuT1FrBR6nvofswJjX4wKDWaX2iZ4t+ZoqgDo0mWzpS+6BFtiqSIshwlC1N33HEH3HLL\nLXDvvffCwsKC/d1EW/rTP/3T1KZUuu222+C6666DwWAAl156Kbz2ta/1yn/0ox/BNddcA9/73vfg\nbW97G7zqVa9KrisRN08j+UxJASgQTIjCFLSXmYOg1b+HfaSQYXj27e8FZeYgN2QcO9nNiOeDQyYC\n+OvY34JDV3qB/DGnEjXNm+/OQiVvZqtmuvDAz+eh0yl9yxD2meI0u+bwEHymnBMv3ejr/0tMSG4P\ntiPB/AZMfirkV4SvxZ8N05iqWaahwbl1IUWrlA6IFMuUcXynMKB+fwC9/hAmJxiYX7eEpSU/x1pZ\nFgzML+xbgvnR91tjDjEJcmRQJ9zoQ8bEjVW+1sgdXK4wa11LyP3CMez7G4icBvPbuNa3TAHwMD+r\nFQQ+z5TPAPHMXT0WXrCjASiM0DXf7IcSTFly7C6lRQzgrS39GYdritPcaxDq8Fp/HIY6Jb9fAISW\nmKCc3aOEPFOedVUpU4UClJMRCZnmdfWi+XWIUodZU6xVVttLFctfjNJhfhmN4vYzGHg/4SlZa+rz\n5c8k+p2bF+n+Q1h4+tqQhHCHoOGDgcxOd+HRx5bEvbRVaHTVioT7IPXQD7NTXblNTXnO8YnNTyvd\nMpUO8/PLNEVdWZYsEkcjyW+y7tvs+fVfSptJwtTNN98M119/PWzYsAG2bdsG3W43XqkF9ft9eO97\n3wsf/ehHYfPmzXDppZfCxRdfDMcdd5y9Zu3atfCud70LvvzlL2fXlYh7gaRsyxqZBU5NgkYgwKHR\nqZO/4NvstatpP1KT9gI4huex+SW2Lw6L7PIxuWvxcFTIklcmbwJsXUnTGtlYJKKMAfVX0jazudkJ\nWOwNoDfwwwD7PlPhWFJDoweWKSUfh+8zJcD8FHigDYbBbPoc86f5apjgJD1mnOajHM1PP3Bt/8wm\n6/JMNcwWiua3xASgAKjX8/xCD6UBKKBTFmpodGzxqscSBrYAQNbliDaXvXdSJsHqAGQmme8D1zPP\nPrQ8GkaVe4bcd9q+gchpB9761U6YWr1qsh5LP8ztZNfhUM/7VB9y/HgAnMXJlfF7rbFGzC9KwlTT\nXwuhCEP56HPUrDNVVVpLqArrVJh9SegeyTLFMKMSqiJVOx+2ia4r3LVV877iaH4V2jtDCzm378UV\nRZgkxUkKaUJoR/EnS6WY4IOpq0DRcyxTkrWVVQIL5wc917Q+U+GRRnEiKXNWz07CPQ88JgpnnB9p\njFIVAtKcdaqSgSKnthmOZ1UTJItau1YapUbzo+tZ8oG19fp8PbFOAn9TQ83HGM3vIx/5CFxxxRVw\nzTXXtDZJp9Add9wBO3bsgG3btgEAwPOf/3z4yle+4glE69atg3Xr1sHXvva17Lo5NIplSgyNjpjb\nIYE9aQEobC6HYGPnYV8xMi/wY4JlyjefmwAUnM9U/HP9A/rIDFODCPoLnm8n5zAKzcOF14bGuMxN\n15sVDeUcj+bnP2NxTMJGby0iwkEiwfy4aH4WI24DQjD1GPy4dqgGlinmOjGan9SmopXDfjq4TSsQ\n9OUAFN1OBYtLAy9XWFUVJJrfUGeQIwwWLZcOD4nxpX1yDKzUVwrOfjDgBaYKafdUBod5Ts4yJR94\n+DB0lqlBED2RagUp6ZYS950evlRZYKhT1UoVC/Oj907XMBmPJmzgQz7Iw6Qw1D4sC9jrciDTnmVq\nhKS9HCRLioSmQss8RYJ870UB3h5dQ+RdxFG7b5cozxSTosB8tntbIqojFbLLEe5Dg/m1lKWyhNdO\nqjAVi+YnvG+xc51XTsUhxYGCUUCqOKUvv/+YgDdG6WPbp+dhxrNI3aOlvXR2Wme/9TbDgb7pJafC\nX3zp+3DV809S2z3YxJ3LhnwruX+PGuohlreNo5RzdOzR/BYWFuDCCy9cVkEKAGDPnj2wZcsW+33z\n5s2wZ8+eZa8b85lKlnSbGQ+06gTLXeeZ8tt2DI7crrZ55ghThsnYy1imHtu/5B2Y2HwOIG8SuHcN\nahTTfAZYb0GojWlTJaLCGRWiNG3F3EzXq2sIM5Dcc7j4qTsAAOAZv7JDHROtasa2yMDLMCNGrQEW\n5qdEAeQOj4BpTGTarb9NL6xnPg0kzZ+whkK4jftMobGUue8PhtDrDVin0W4Tza8/GNg5LMsyyDOl\nMZihz5QMmwIA2H3yFjj3lKPgj95wjnhdeHDiz/Lchwle5fmN+QfUPjwSFFUf20LDpFDBHhMWYjSY\nn9UKCrn3fOaPjtP9EgpT7oDE34uigMluZYWpgGFuvkroAY0ZxUuDjtVLwi0oUug9aYymVg/3lQKh\n9cqV/TvdMqWN0y+jTCOO2FcWDYND8jSWhYP5LfZCJt3te3IZRzHLjEba+92Gv6CkJUmmTeIzQl9r\nfj0NkYC/85Yp/vk7qHlCnilmj+LGYvZ60TK1qt5v6P5GFSw5TyJ13iRrmJaTj7ZRf0efmdvctHYG\n3vYbp8O6I6bCwhVEqTA/+lpqijqtnkQpBoDapzftvU+yTL3oRS+CW265Bc4555z4xSPQKMLaaHXD\n33zLVFo7BnucYpkColmzKCOmL6f9kZmqHI2ZGR/HOD28d9FbjE7jYw4tVxYTkux14pd4O/49CnXS\nEY7BC0R9perfGggRGcssEqY8y1QE5nf+6dvg9BM3BdnsbR3Tt6Ch7TEh9TnroRtb/d/4TJUM0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WcGWttbRYWv88QSkpJY7nKEmYOuuss9JHdygSM0+tkvaa0OhBAIqQ2bKHgaLJdL8VTTvyyxUL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g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\nC27z39KhZisAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6284062890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "df_daily_sentiment.plot(x='asof_date', y='average_sentiment', label = 'Average Sentiment')\n",
    "plt.ylabel(\"Daily Sentiment\", fontsize=15)\n",
    "plt.xlabel(\"Date\", fontsize=15)\n",
    "plt.grid(b=None, which=u'major', axis=u'both')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's a little noisy. So let's do a rolling average:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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N+fsuKMMznIvFa3i2OUvax7XAIJmSc+ZS5Q+l1cvw8vYsAY25uDA8GYGsqFi6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ENYuHAhHnnkEQDARz7yEQwMDOBDH/oQUqkUWJbFT3/6U6xevRrxeHySR58eCFnyMhFPkhVTrSHq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\n1vtq389yAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6254b2a810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(\n",
    "    df_daily_sentiment.asof_date,\n",
    "    pd.rolling_mean(df_daily_sentiment.average_sentiment, 20)\n",
    ")\n",
    "\n",
    "plt.ylabel(\"Trailing Average Sentiment\",fontsize=15)\n",
    "plt.xlabel(\"Date\", fontsize=15)\n",
    "plt.grid(b=None, which=u'major', axis=u'both')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Joins in Blaze\n",
    "\n",
    "Another interesting feature with Blaze that emphasizes the value of the Quantopian Store is the `join()` function.\n",
    "\n",
    "With `join()` we can link together two disparate data sets from different data vendors. Because they're both hosted on Quantopian and we've mapped the symbols to sids for you, you can combine different data sets from different vendors in just a few lines of code.\n",
    "\n",
    "In this case, we'll grab some event data from EventVestor. Specifically this data is a set full of stock index changes (i.e. when a stock is added or removed from the S&P500, for example):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from quantopian.interactive.data.eventvestor import index_changes_free"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll filter down these sets and then join them based on `as_ofdate` and the `sid`, which results in a new blaze expression which has both the index changes (from EventVestor) and the news sentiment (from Accern) for the security on that particular day. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sid</th>\n",
       "      <th>asof_date</th>\n",
       "      <th>index_name</th>\n",
       "      <th>event_headline</th>\n",
       "      <th>article_sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27035</td>\n",
       "      <td>2012-09-25</td>\n",
       "      <td>S&amp;P 400</td>\n",
       "      <td>Alpha Natural Resources to be Added in S&amp;P Mid...</td>\n",
       "      <td>0.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15139</td>\n",
       "      <td>2012-09-25</td>\n",
       "      <td>S&amp;P 400</td>\n",
       "      <td>Genesee &amp; Wyoming to be Added in S&amp;P MidCap 40...</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22316</td>\n",
       "      <td>2012-12-05</td>\n",
       "      <td>S&amp;P 500</td>\n",
       "      <td>Garmin to be Added in S&amp;P 500 Index</td>\n",
       "      <td>0.333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2248</td>\n",
       "      <td>2012-12-05</td>\n",
       "      <td>S&amp;P 400</td>\n",
       "      <td>R.R. Donnelly &amp; Sons to be Added in S&amp;P MidCap...</td>\n",
       "      <td>0.833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32622</td>\n",
       "      <td>2013-02-07</td>\n",
       "      <td>S&amp;P 400</td>\n",
       "      <td>CommVault Systems to be Added to S&amp;P MidCap 40...</td>\n",
       "      <td>-0.333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6257</td>\n",
       "      <td>2013-02-07</td>\n",
       "      <td>S&amp;P 500</td>\n",
       "      <td>PVH Corp. to be Added to S&amp;P 500 Index</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>28378</td>\n",
       "      <td>2013-02-21</td>\n",
       "      <td>S&amp;P 600</td>\n",
       "      <td>Atlas Air Worldwide Holdings to be Added in S&amp;...</td>\n",
       "      <td>0.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12959</td>\n",
       "      <td>2013-05-03</td>\n",
       "      <td>S&amp;P 400</td>\n",
       "      <td>3D Systems Corp. to be Added in S&amp;P MidCap 400...</td>\n",
       "      <td>0.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>40146</td>\n",
       "      <td>2013-05-03</td>\n",
       "      <td>S&amp;P 600</td>\n",
       "      <td>CoreSite Realty Corp. to be Added in S&amp;P Small...</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4315</td>\n",
       "      <td>2013-05-16</td>\n",
       "      <td>S&amp;P 500</td>\n",
       "      <td>Kansas City Southern to be Added in S&amp;P 500 Index</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>40430</td>\n",
       "      <td>2013-06-03</td>\n",
       "      <td>S&amp;P 500</td>\n",
       "      <td>General Motors Co. to be Added in S&amp;P 500 Index</td>\n",
       "      <td>-0.250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "      sid  asof_date index_name  \\\n",
       "0   27035 2012-09-25    S&P 400   \n",
       "1   15139 2012-09-25    S&P 400   \n",
       "2   22316 2012-12-05    S&P 500   \n",
       "3    2248 2012-12-05    S&P 400   \n",
       "4   32622 2013-02-07    S&P 400   \n",
       "5    6257 2013-02-07    S&P 500   \n",
       "6   28378 2013-02-21    S&P 600   \n",
       "7   12959 2013-05-03    S&P 400   \n",
       "8   40146 2013-05-03    S&P 600   \n",
       "9    4315 2013-05-16    S&P 500   \n",
       "10  40430 2013-06-03    S&P 500   \n",
       "\n",
       "                                       event_headline  article_sentiment  \n",
       "0   Alpha Natural Resources to be Added in S&P Mid...              0.500  \n",
       "1   Genesee & Wyoming to be Added in S&P MidCap 40...              1.000  \n",
       "2                 Garmin to be Added in S&P 500 Index              0.333  \n",
       "3   R.R. Donnelly & Sons to be Added in S&P MidCap...              0.833  \n",
       "4   CommVault Systems to be Added to S&P MidCap 40...             -0.333  \n",
       "5              PVH Corp. to be Added to S&P 500 Index              1.000  \n",
       "6   Atlas Air Worldwide Holdings to be Added in S&...              0.500  \n",
       "7   3D Systems Corp. to be Added in S&P MidCap 400...              0.250  \n",
       "8   CoreSite Realty Corp. to be Added in S&P Small...              1.000  \n",
       "9   Kansas City Southern to be Added in S&P 500 Index              1.000  \n",
       "..."
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_adds = index_changes_free[index_changes_free.change_type == 'Addition']\n",
    "index_adds_sentiment = bz.join(index_adds, alphaone_free, ['sid', 'asof_date'])\n",
    "joined_data = index_adds_sentiment[['sid', 'asof_date', 'index_name', 'event_headline', 'article_sentiment']]\n",
    "joined_data[joined_data.article_sentiment != 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For full Blaze docs: http://blaze.readthedocs.org/en/latest/index.html\n",
    "The \"Pandas to Blaze\" and \"SQL to Blaze\" rosetta stones are useful."
   ]
  }
 ],
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