{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IPython, pandas and matplotlib have a number of useful options you can use to make it easier to view and format your data. This notebook collects a bunch of them in one place. I hope this will be a useful reference.\n",
    "\n",
    "The original blog posting is on http://pbpython.com/ipython-pandas-display-tips.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import modules and some sample data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, do our standard pandas, numpy and matplotlib imports as well as configure inline displays of plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the simple things we can do is override the default CSS to customize our DataFrame output.\n",
    "\n",
    "This specific example is from - [Brandon Rhodes' talk at pycon](https://www.youtube.com/watch?v=5JnMutdy6Fw \"Pandas From The Ground Up\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the purposes of the notebook, I'm defining CSS as a variable but you could easily read in from a file as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "CSS = \"\"\"\n",
    "body {\n",
    "    margin: 0;\n",
    "    font-family: Helvetica;\n",
    "}\n",
    "table.dataframe {\n",
    "    border-collapse: collapse;\n",
    "    border: none;\n",
    "}\n",
    "table.dataframe tr {\n",
    "    border: none;\n",
    "}\n",
    "table.dataframe td, table.dataframe th {\n",
    "    margin: 0;\n",
    "    border: 1px solid white;\n",
    "    padding-left: 0.25em;\n",
    "    padding-right: 0.25em;\n",
    "}\n",
    "table.dataframe th:not(:empty) {\n",
    "    background-color: #fec;\n",
    "    text-align: left;\n",
    "    font-weight: normal;\n",
    "}\n",
    "table.dataframe tr:nth-child(2) th:empty {\n",
    "    border-left: none;\n",
    "    border-right: 1px dashed #888;\n",
    "}\n",
    "table.dataframe td {\n",
    "    border: 2px solid #ccf;\n",
    "    background-color: #f4f4ff;\n",
    "}\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now add this CSS into the current notebook's HTML."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "body {\n",
       "    margin: 0;\n",
       "    font-family: Helvetica;\n",
       "}\n",
       "table.dataframe {\n",
       "    border-collapse: collapse;\n",
       "    border: none;\n",
       "}\n",
       "table.dataframe tr {\n",
       "    border: none;\n",
       "}\n",
       "table.dataframe td, table.dataframe th {\n",
       "    margin: 0;\n",
       "    border: 1px solid white;\n",
       "    padding-left: 0.25em;\n",
       "    padding-right: 0.25em;\n",
       "}\n",
       "table.dataframe th:not(:empty) {\n",
       "    background-color: #fec;\n",
       "    text-align: left;\n",
       "    font-weight: normal;\n",
       "}\n",
       "table.dataframe tr:nth-child(2) th:empty {\n",
       "    border-left: none;\n",
       "    border-right: 1px dashed #888;\n",
       "}\n",
       "table.dataframe td {\n",
       "    border: 2px solid #ccf;\n",
       "    background-color: #f4f4ff;\n",
       "}\n",
       "</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import HTML\n",
    "HTML('<style>{}</style>'.format(CSS))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account number</th>\n",
       "      <th>name</th>\n",
       "      <th>sku</th>\n",
       "      <th>...</th>\n",
       "      <th>Tax rate</th>\n",
       "      <th>Tax amount</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>296809</td>\n",
       "      <td>Carroll PLC</td>\n",
       "      <td>QN-82852</td>\n",
       "      <td>...</td>\n",
       "      <td>$0.07</td>\n",
       "      <td>$40.48</td>\n",
       "      <td>2014-09-27 07:13:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>98022</td>\n",
       "      <td>Heidenreich-Bosco</td>\n",
       "      <td>MJ-21460</td>\n",
       "      <td>...</td>\n",
       "      <td>$0.07</td>\n",
       "      <td>$71.31</td>\n",
       "      <td>2014-07-29 02:10:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>93356</td>\n",
       "      <td>Waters-Walker</td>\n",
       "      <td>AS-93055</td>\n",
       "      <td>...</td>\n",
       "      <td>$0.07</td>\n",
       "      <td>$28.94</td>\n",
       "      <td>2013-11-17 20:41:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>659366</td>\n",
       "      <td>Waelchi-Fahey</td>\n",
       "      <td>AS-93055</td>\n",
       "      <td>...</td>\n",
       "      <td>$0.07</td>\n",
       "      <td>$125.55</td>\n",
       "      <td>2014-01-03 08:14:27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    account number               name       sku         ...          Tax rate  \\\n",
       "0           296809        Carroll PLC  QN-82852         ...             $0.07   \n",
       "1            98022  Heidenreich-Bosco  MJ-21460         ...             $0.07   \n",
       "..             ...                ...       ...         ...               ...   \n",
       "3            93356      Waters-Walker  AS-93055         ...             $0.07   \n",
       "4           659366      Waelchi-Fahey  AS-93055         ...             $0.07   \n",
       "\n",
       "    Tax amount                 date  \n",
       "0       $40.48  2014-09-27 07:13:03  \n",
       "1       $71.31  2014-07-29 02:10:44  \n",
       "..         ...                  ...  \n",
       "3       $28.94  2013-11-17 20:41:11  \n",
       "4      $125.55  2014-01-03 08:14:27  \n",
       "\n",
       "[5 rows x 10 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SALES=pd.read_csv(\"../data/sample-sales-tax.csv\", parse_dates=True)\n",
    "SALES.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see how the CSS is now applied to the DataFrame and how you could easily modify it to customize it to your liking.\n",
    "\n",
    "Jupyter notebooks do a good job of automatically displaying information but sometimes you want to force data to display. Fortunately, ipython provides and option. This is especially useful if you want to display multiple dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account number</th>\n",
       "      <th>name</th>\n",
       "      <th>sku</th>\n",
       "      <th>...</th>\n",
       "      <th>Tax rate</th>\n",
       "      <th>Tax amount</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>296809</td>\n",
       "      <td>Carroll PLC</td>\n",
       "      <td>QN-82852</td>\n",
       "      <td>...</td>\n",
       "      <td>$0.07</td>\n",
       "      <td>$40.48</td>\n",
       "      <td>2014-09-27 07:13:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>98022</td>\n",
       "      <td>Heidenreich-Bosco</td>\n",
       "      <td>MJ-21460</td>\n",
       "      <td>...</td>\n",
       "      <td>$0.07</td>\n",
       "      <td>$71.31</td>\n",
       "      <td>2014-07-29 02:10:44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   account number               name       sku         ...          Tax rate  \\\n",
       "0          296809        Carroll PLC  QN-82852         ...             $0.07   \n",
       "1           98022  Heidenreich-Bosco  MJ-21460         ...             $0.07   \n",
       "\n",
       "   Tax amount                 date  \n",
       "0      $40.48  2014-09-27 07:13:03  \n",
       "1      $71.31  2014-07-29 02:10:44  \n",
       "\n",
       "[2 rows x 10 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account number</th>\n",
       "      <th>name</th>\n",
       "      <th>sku</th>\n",
       "      <th>...</th>\n",
       "      <th>Tax rate</th>\n",
       "      <th>Tax amount</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>304860</td>\n",
       "      <td>Huel-Haag</td>\n",
       "      <td>LL-46261</td>\n",
       "      <td>...</td>\n",
       "      <td>$0.07</td>\n",
       "      <td>$61.88</td>\n",
       "      <td>2014-07-26 01:10:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>98022</td>\n",
       "      <td>Heidenreich-Bosco</td>\n",
       "      <td>LW-86841</td>\n",
       "      <td>...</td>\n",
       "      <td>$0.07</td>\n",
       "      <td>$73.33</td>\n",
       "      <td>2014-06-27 05:58:33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     account number               name       sku         ...           \\\n",
       "998          304860          Huel-Haag  LL-46261         ...            \n",
       "999           98022  Heidenreich-Bosco  LW-86841         ...            \n",
       "\n",
       "    Tax rate  Tax amount                 date  \n",
       "998    $0.07      $61.88  2014-07-26 01:10:57  \n",
       "999    $0.07      $73.33  2014-06-27 05:58:33  \n",
       "\n",
       "[2 rows x 10 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account number</th>\n",
       "      <th>quantity</th>\n",
       "      <th>unit price</th>\n",
       "      <th>ext price</th>\n",
       "      <th>Tax rate</th>\n",
       "      <th>Tax amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>$1000.00</td>\n",
       "      <td>$1000.00</td>\n",
       "      <td>$1000.00</td>\n",
       "      <td>$1000.00</td>\n",
       "      <td>$1000.00</td>\n",
       "      <td>$1000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>$535208.90</td>\n",
       "      <td>$10.33</td>\n",
       "      <td>$56.18</td>\n",
       "      <td>$579.84</td>\n",
       "      <td>$0.04</td>\n",
       "      <td>$20.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>$750461.00</td>\n",
       "      <td>$15.00</td>\n",
       "      <td>$76.80</td>\n",
       "      <td>$878.14</td>\n",
       "      <td>$0.04</td>\n",
       "      <td>$26.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>$995267.00</td>\n",
       "      <td>$20.00</td>\n",
       "      <td>$99.97</td>\n",
       "      <td>$1994.80</td>\n",
       "      <td>$0.15</td>\n",
       "      <td>$191.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       account number  quantity  unit price  ext price  Tax rate  Tax amount\n",
       "count        $1000.00  $1000.00    $1000.00   $1000.00  $1000.00    $1000.00\n",
       "mean       $535208.90    $10.33      $56.18    $579.84     $0.04      $20.06\n",
       "...               ...       ...         ...        ...       ...         ...\n",
       "75%        $750461.00    $15.00      $76.80    $878.14     $0.04      $26.30\n",
       "max        $995267.00    $20.00      $99.97   $1994.80     $0.15     $191.25\n",
       "\n",
       "[8 rows x 6 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(SALES.head(2))\n",
    "display(SALES.tail(2))\n",
    "display(SALES.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using pandas settings to control output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas has many different options to control how data is displayed.\n",
    "\n",
    "You can use max_rows to control how many rows are displayed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_rows\",4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account number</th>\n",
       "      <th>name</th>\n",
       "      <th>sku</th>\n",
       "      <th>...</th>\n",
       "      <th>Tax rate</th>\n",
       "      <th>Tax amount</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>296809</td>\n",
       "      <td>Carroll PLC</td>\n",
       "      <td>QN-82852</td>\n",
       "      <td>...</td>\n",
       "      <td>$0.07</td>\n",
       "      <td>$40.48</td>\n",
       "      <td>2014-09-27 07:13:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>98022</td>\n",
       "      <td>Heidenreich-Bosco</td>\n",
       "      <td>MJ-21460</td>\n",
       "      <td>...</td>\n",
       "      <td>$0.07</td>\n",
       "      <td>$71.31</td>\n",
       "      <td>2014-07-29 02:10:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>304860</td>\n",
       "      <td>Huel-Haag</td>\n",
       "      <td>LL-46261</td>\n",
       "      <td>...</td>\n",
       "      <td>$0.07</td>\n",
       "      <td>$61.88</td>\n",
       "      <td>2014-07-26 01:10:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>98022</td>\n",
       "      <td>Heidenreich-Bosco</td>\n",
       "      <td>LW-86841</td>\n",
       "      <td>...</td>\n",
       "      <td>$0.07</td>\n",
       "      <td>$73.33</td>\n",
       "      <td>2014-06-27 05:58:33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     account number               name       sku         ...           \\\n",
       "0            296809        Carroll PLC  QN-82852         ...            \n",
       "1             98022  Heidenreich-Bosco  MJ-21460         ...            \n",
       "..              ...                ...       ...         ...            \n",
       "998          304860          Huel-Haag  LL-46261         ...            \n",
       "999           98022  Heidenreich-Bosco  LW-86841         ...            \n",
       "\n",
       "    Tax rate  Tax amount                 date  \n",
       "0      $0.07      $40.48  2014-09-27 07:13:03  \n",
       "1      $0.07      $71.31  2014-07-29 02:10:44  \n",
       "..       ...         ...                  ...  \n",
       "998    $0.07      $61.88  2014-07-26 01:10:57  \n",
       "999    $0.07      $73.33  2014-06-27 05:58:33  \n",
       "\n",
       "[1000 rows x 10 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SALES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Depending on the data set, you may only want to display a smaller number of columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_columns\",6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account number</th>\n",
       "      <th>name</th>\n",
       "      <th>sku</th>\n",
       "      <th>...</th>\n",
       "      <th>Tax rate</th>\n",
       "      <th>Tax amount</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>296809</td>\n",
       "      <td>Carroll PLC</td>\n",
       "      <td>QN-82852</td>\n",
       "      <td>...</td>\n",
       "      <td>$0.07</td>\n",
       "      <td>$40.48</td>\n",
       "      <td>2014-09-27 07:13:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>98022</td>\n",
       "      <td>Heidenreich-Bosco</td>\n",
       "      <td>MJ-21460</td>\n",
       "      <td>...</td>\n",
       "      <td>$0.07</td>\n",
       "      <td>$71.31</td>\n",
       "      <td>2014-07-29 02:10:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>304860</td>\n",
       "      <td>Huel-Haag</td>\n",
       "      <td>LL-46261</td>\n",
       "      <td>...</td>\n",
       "      <td>$0.07</td>\n",
       "      <td>$61.88</td>\n",
       "      <td>2014-07-26 01:10:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>98022</td>\n",
       "      <td>Heidenreich-Bosco</td>\n",
       "      <td>LW-86841</td>\n",
       "      <td>...</td>\n",
       "      <td>$0.07</td>\n",
       "      <td>$73.33</td>\n",
       "      <td>2014-06-27 05:58:33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     account number               name       sku         ...           \\\n",
       "0            296809        Carroll PLC  QN-82852         ...            \n",
       "1             98022  Heidenreich-Bosco  MJ-21460         ...            \n",
       "..              ...                ...       ...         ...            \n",
       "998          304860          Huel-Haag  LL-46261         ...            \n",
       "999           98022  Heidenreich-Bosco  LW-86841         ...            \n",
       "\n",
       "    Tax rate  Tax amount                 date  \n",
       "0      $0.07      $40.48  2014-09-27 07:13:03  \n",
       "1      $0.07      $71.31  2014-07-29 02:10:44  \n",
       "..       ...         ...                  ...  \n",
       "998    $0.07      $61.88  2014-07-26 01:10:57  \n",
       "999    $0.07      $73.33  2014-06-27 05:58:33  \n",
       "\n",
       "[1000 rows x 10 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SALES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can control how many decimal points of precision to display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.set_option('precision',2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account number</th>\n",
       "      <th>name</th>\n",
       "      <th>sku</th>\n",
       "      <th>...</th>\n",
       "      <th>Tax rate</th>\n",
       "      <th>Tax amount</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>296809</td>\n",
       "      <td>Carroll PLC</td>\n",
       "      <td>QN-82852</td>\n",
       "      <td>...</td>\n",
       "      <td>$0.07</td>\n",
       "      <td>$40.48</td>\n",
       "      <td>2014-09-27 07:13:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>98022</td>\n",
       "      <td>Heidenreich-Bosco</td>\n",
       "      <td>MJ-21460</td>\n",
       "      <td>...</td>\n",
       "      <td>$0.07</td>\n",
       "      <td>$71.31</td>\n",
       "      <td>2014-07-29 02:10:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>304860</td>\n",
       "      <td>Huel-Haag</td>\n",
       "      <td>LL-46261</td>\n",
       "      <td>...</td>\n",
       "      <td>$0.07</td>\n",
       "      <td>$61.88</td>\n",
       "      <td>2014-07-26 01:10:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>98022</td>\n",
       "      <td>Heidenreich-Bosco</td>\n",
       "      <td>LW-86841</td>\n",
       "      <td>...</td>\n",
       "      <td>$0.07</td>\n",
       "      <td>$73.33</td>\n",
       "      <td>2014-06-27 05:58:33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     account number               name       sku         ...           \\\n",
       "0            296809        Carroll PLC  QN-82852         ...            \n",
       "1             98022  Heidenreich-Bosco  MJ-21460         ...            \n",
       "..              ...                ...       ...         ...            \n",
       "998          304860          Huel-Haag  LL-46261         ...            \n",
       "999           98022  Heidenreich-Bosco  LW-86841         ...            \n",
       "\n",
       "    Tax rate  Tax amount                 date  \n",
       "0      $0.07      $40.48  2014-09-27 07:13:03  \n",
       "1      $0.07      $71.31  2014-07-29 02:10:44  \n",
       "..       ...         ...                  ...  \n",
       "998    $0.07      $61.88  2014-07-26 01:10:57  \n",
       "999    $0.07      $73.33  2014-06-27 05:58:33  \n",
       "\n",
       "[1000 rows x 10 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SALES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.set_option('precision',7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account number</th>\n",
       "      <th>name</th>\n",
       "      <th>sku</th>\n",
       "      <th>...</th>\n",
       "      <th>Tax rate</th>\n",
       "      <th>Tax amount</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>296809</td>\n",
       "      <td>Carroll PLC</td>\n",
       "      <td>QN-82852</td>\n",
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       "      <td>2014-09-27 07:13:03</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>98022</td>\n",
       "      <td>Heidenreich-Bosco</td>\n",
       "      <td>MJ-21460</td>\n",
       "      <td>...</td>\n",
       "      <td>$0.07</td>\n",
       "      <td>$71.31</td>\n",
       "      <td>2014-07-29 02:10:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>304860</td>\n",
       "      <td>Huel-Haag</td>\n",
       "      <td>LL-46261</td>\n",
       "      <td>...</td>\n",
       "      <td>$0.07</td>\n",
       "      <td>$61.88</td>\n",
       "      <td>2014-07-26 01:10:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>98022</td>\n",
       "      <td>Heidenreich-Bosco</td>\n",
       "      <td>LW-86841</td>\n",
       "      <td>...</td>\n",
       "      <td>$0.07</td>\n",
       "      <td>$73.33</td>\n",
       "      <td>2014-06-27 05:58:33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     account number               name       sku         ...           \\\n",
       "0            296809        Carroll PLC  QN-82852         ...            \n",
       "1             98022  Heidenreich-Bosco  MJ-21460         ...            \n",
       "..              ...                ...       ...         ...            \n",
       "998          304860          Huel-Haag  LL-46261         ...            \n",
       "999           98022  Heidenreich-Bosco  LW-86841         ...            \n",
       "\n",
       "    Tax rate  Tax amount                 date  \n",
       "0      $0.07      $40.48  2014-09-27 07:13:03  \n",
       "1      $0.07      $71.31  2014-07-29 02:10:44  \n",
       "..       ...         ...                  ...  \n",
       "998    $0.07      $61.88  2014-07-26 01:10:57  \n",
       "999    $0.07      $73.33  2014-06-27 05:58:33  \n",
       "\n",
       "[1000 rows x 10 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SALES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also format floating point numbers using float_format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.set_option('float_format', '{:.2f}'.format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "      <th>...</th>\n",
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       "      <td>2014-09-27 07:13:03</td>\n",
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       "      <th>1</th>\n",
       "      <td>98022</td>\n",
       "      <td>Heidenreich-Bosco</td>\n",
       "      <td>MJ-21460</td>\n",
       "      <td>...</td>\n",
       "      <td>0.07</td>\n",
       "      <td>71.31</td>\n",
       "      <td>2014-07-29 02:10:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>304860</td>\n",
       "      <td>Huel-Haag</td>\n",
       "      <td>LL-46261</td>\n",
       "      <td>...</td>\n",
       "      <td>0.07</td>\n",
       "      <td>61.88</td>\n",
       "      <td>2014-07-26 01:10:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>98022</td>\n",
       "      <td>Heidenreich-Bosco</td>\n",
       "      <td>LW-86841</td>\n",
       "      <td>...</td>\n",
       "      <td>0.07</td>\n",
       "      <td>73.33</td>\n",
       "      <td>2014-06-27 05:58:33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     account number               name       sku         ...           \\\n",
       "0            296809        Carroll PLC  QN-82852         ...            \n",
       "1             98022  Heidenreich-Bosco  MJ-21460         ...            \n",
       "..              ...                ...       ...         ...            \n",
       "998          304860          Huel-Haag  LL-46261         ...            \n",
       "999           98022  Heidenreich-Bosco  LW-86841         ...            \n",
       "\n",
       "    Tax rate  Tax amount                 date  \n",
       "0       0.07       40.48  2014-09-27 07:13:03  \n",
       "1       0.07       71.31  2014-07-29 02:10:44  \n",
       "..       ...         ...                  ...  \n",
       "998     0.07       61.88  2014-07-26 01:10:57  \n",
       "999     0.07       73.33  2014-06-27 05:58:33  \n",
       "\n",
       "[1000 rows x 10 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SALES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This does apply to all the data. In our example, applying dollar signs to everything would not be correct for this example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.set_option('float_format', '${:.2f}'.format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>304860</td>\n",
       "      <td>Huel-Haag</td>\n",
       "      <td>LL-46261</td>\n",
       "      <td>...</td>\n",
       "      <td>$0.07</td>\n",
       "      <td>$61.88</td>\n",
       "      <td>2014-07-26 01:10:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>98022</td>\n",
       "      <td>Heidenreich-Bosco</td>\n",
       "      <td>LW-86841</td>\n",
       "      <td>...</td>\n",
       "      <td>$0.07</td>\n",
       "      <td>$73.33</td>\n",
       "      <td>2014-06-27 05:58:33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     account number               name       sku         ...           \\\n",
       "0            296809        Carroll PLC  QN-82852         ...            \n",
       "1             98022  Heidenreich-Bosco  MJ-21460         ...            \n",
       "..              ...                ...       ...         ...            \n",
       "998          304860          Huel-Haag  LL-46261         ...            \n",
       "999           98022  Heidenreich-Bosco  LW-86841         ...            \n",
       "\n",
       "    Tax rate  Tax amount                 date  \n",
       "0      $0.07      $40.48  2014-09-27 07:13:03  \n",
       "1      $0.07      $71.31  2014-07-29 02:10:44  \n",
       "..       ...         ...                  ...  \n",
       "998    $0.07      $61.88  2014-07-26 01:10:57  \n",
       "999    $0.07      $73.33  2014-06-27 05:58:33  \n",
       "\n",
       "[1000 rows x 10 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SALES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Third Party Plugins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Qtopian has a useful plugin called qgrid - https://github.com/quantopian/qgrid\n",
    "\n",
    "Import it and install it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import qgrid\n",
    "qgrid.nbinstall(overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Showing the data is straighforward."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Widget Javascript not detected.  It may not be installed or enabled properly.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cffaab12fad1438583907cb4a0d4144e"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qgrid.show_grid(SALES, remote_js=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plugin is very similar to the capability of an Excel autofilter. It can be handy to quickly filter and sort your data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Improving your plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have mentioned before how the default pandas plots don't look so great. Fortunately, there are style sheets in matplotlib which go a long way towards improving the visualization of your data.\n",
    "\n",
    "Here is a simple plot with the default values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff1609ac7d0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OxWKMTgY2i5S7FLgEuBL4B3Bt+HkW+HmiDjsAVwB3AneHn7sSORZ1HE8A/pXI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff1593478d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SALES.groupby('name')['quantity'].sum().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use some of the matplolib styles available to us to make this look better.\n",
    "http://matplotlib.org/users/style_sheets.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff159347a90>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XF4fDhw/D2NgYY8eOhbe3919yIIQQQv5Ub3AXiUQYO3YsmjdvjlevXmH58uW4du0aMjMz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff1592bc310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SALES.groupby('name')['quantity'].sum().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see all the styles available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'dark_background', u'bmh', u'grayscale', u'ggplot', u'fivethirtyeight']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.style.available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.style.use('bmh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff15919c410>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Exsbi6NGjMDQ0xLhx4+Dt7f2XHAghhJA/1RvcRSIRxo0bh2bNmuH169dYuXIlrl+/jszM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff159313bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SALES.groupby('name')['quantity'].sum().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff1537426d0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UKmVeK2x6GixLoVBAKr1peLkOWpYuzfcBW2zWCWC3Xh1xXz3s7WNblj7t9R3SfmdfVmc+\nNzxK+71fv35tLt9mMCosLER1dTW+/PLLVvPeffddLFmyBAAgl8u15snlcojFYgCAo6Njq2ma/z08\nPPR+dvMVb2qP0nayg62tLfp5tb2xHbmslqRSqcEvz9h1YrteHXFfPeztY1uWLu35HdJ+Z19WZz43\nPA77XaPNYDR06FBs3rxZa9r69ethaWnJpHY7OzsjLy8PgwcPBtB05ySVShEYGAgA8PT0hLW1NfLy\n8pjgo1AoUFhYiKCgINYrSggh5NHFuTugltV0oaGhyMjIQG5uLmpqapCQkACVSoXg4GAAgKWlJUJC\nQpCSkoKCggLIZDLExcVBLBZjxIgR/GwFIYSQTo1zDwwtn/MEBwdDqVQiNjYWMpkMXl5eiI6OhpOT\nE7PM5MmT0djYiA0bNkCpVEIikWDhwoWwsrIyfQsIIYR0epyD0fz581tNCwsLQ1hYmN73mJmZITIy\nksnII4QQQpqjXrsJIYQIjjpK7QBoxFFCyOOOglEHQCOOEkIed1RNRwghRHAUjAghhAiOghEhhBDB\nUTAihBAiOApGhBBCBEfZdITwgE16PqXmE6IfBSNCeMAmPZ9S8wnRj6rpCCGECI6CESGEEMFRMCKE\nECI4CkaEEEIER8GIEEKI4CgYEUIIERwFI0IIIYKjYEQIIURwFIwIIYQIjoIRIYQQwVEwIoQQIjgK\nRoQQQgRHwYgQQojgDPba/euvvyI9PR0VFRVQq9VwcXFBaGgoRo4cySyTmpqKtLQ0yOVyeHp6YsqU\nKZBIJMz8hoYG7N+/H1lZWVAoFJBIJJg+fTpcXV3bZ6sIIYR0KgaDkZeXF95++2307NkTIpEI586d\nw9atW2Fvb48BAwYgPT0dycnJmDt3Lnx8fHDkyBHExMTgs88+Q9euXQEASUlJyMrKQlRUFLp37474\n+HisXbsWn3/+Oaytrdt9IwkhhHRsBqvpfH190atXL9jY2MDa2hpjx46Fk5MTCgoKADTdFQUFBSEg\nIAB2dnaIiIiAtbU1Tp48CQCor69HWloawsPD4ePjA7FYjBkzZkAmkyE7O7t9t44QQkinwOmZkUql\nwunTp1FbW4tBgwZBJpOhrKwMfn5+Wsv5+voiPz8fAFBcXIy6ujr4+/sz821sbODt7c0sQwgh5PHG\naqTXkpISfPbZZwAAS0tLzJ49G7169UJxcTEAwMHBQWt5e3t7Zl5VVZXeZTTzCCGEPN5YBSMPDw9s\n3rwZCoUCOTk52LZtG2xsbNC9e3e97xGJRAbLbWsZqVTKvFbY9DRYlkKhgFR60/ByHbAsNuV01LJo\nX7Evi45RYcqi/S5MWS3L6devX5vLswpGGra2thg7diwuXLiA9PR0vPHGGwAAuVyutZxcLodYLAYA\nODo6tpqm+d/Dw0PvZzVf8eqyOgAqg+vWz6vtje2oZbEpp6OWRfuKfVl0jApTFu13Ycpiu680jGpn\n9ODBA4hEIjg4OMDZ2Rl5eXnMPLVaDalUyqR2e3p6wtraWmsZhUKBwsJCrfRvQgghjy+DwSglJQW3\nb99GXV0dZDIZjh07hosXL2LMmDEAgNDQUGRkZCA3Nxc1NTVISEiASqVCcHAwgKZnTCEhIUhJSUFB\nQQFkMhni4uIgFosxYsSI9t06QgghnYLBarrbt28jPT0dVVVVsLW1hbu7O+bNm4eBAwcCAIKDg6FU\nKhEbGwuZTAYvLy9ER0fDycmJKWPy5MlobGzEhg0boFQqIZFIsHDhQlhZWbXflhFCCOk0DAajN998\n02AhYWFhCAsL0zvfzMwMkZGRiIyM5LZ2hBBCHgvUNx0hhBDBUTAihBAiOApGhBBCBEfBiBBCiOAo\nGBFCCBEcBSNCCCGCo2BECCFEcBSMCCGECI6CESGEEMFRMCKEECI4CkaEEEIER8GIEEKI4CgYEUII\nERwFI0IIIYKjYEQIIURwFIwIIYQIjoIRIYQQwVEwIoQQIjgKRoQQQgRHwYgQQojgKBgRQggRHAUj\nQgghgqNgRAghRHAWhhY4ePAgzp8/j3v37sHc3By9e/fGyy+/jF69ejHLpKamIi0tDXK5HJ6enpgy\nZQokEgkzv6GhAfv370dWVhYUCgUkEgmmT58OV1fX9tkqQgghnYrBO6N79+5h0qRJ+Oqrr/D555+j\na9euWLNmDSorKwEA6enpSE5OxqxZs7By5Ur4+fkhJiYGFRUVTBlJSUnIyspCVFQUVqxYAUdHR6xd\nuxYqlar9towQQkinYTAYvfXWWxgyZAgcHBzg4OCA6dOnQ6lU4tKlSwCa7oqCgoIQEBAAOzs7RERE\nwNraGidPngQA1NfXIy0tDeHh4fDx8YFYLMaMGTMgk8mQnZ3dvltHCCGkU+D8zKimpgaNjY2wtbWF\nTCZDWVkZ/Pz8tJbx9fVFfn4+AKC4uBh1dXXw9/dn5tvY2MDb25tZhhBCyOONczDavXs3unXrhgED\nBkAmkwEAHBwctJaxt7dHVVUVADB/21qGEELI481gAkNziYmJuHjxIhYtWgQrK6s2lxWJRAbLa2sZ\nqVTKvFbY9DRYlkKhgFR60/ByHbAsNuV01LJoX7Evi45RYcqi/S5MWS3L6devX5vLsw5GBw8exIkT\nJxAdHQ1vb28AgFgsBgDI5XKtZeVyOTPP0dGx1TTN/x4eHno/r/mKV5fVAWg72cHW1hb9vNre2I5a\nFptyOmpZtK/Yl0XHqDBl0X4Xpiy2+0qDVTXdwYMHcfz4cURHR6Nv377MdLFYDGdnZ+Tl5THT1Go1\npFIpk9rt6ekJa2trrWUUCgUKCwu10r8JIYQ8vgwGo8OHD+PYsWOYP3++ViDSCA0NRUZGBnJzc1FT\nU4OEhASoVCoEBwcDACwtLRESEoKUlBQUFBRAJpMhLi4OYrEYI0aM4H+LCCGEdDoGq+kOHz6MBw8e\nYOXKlVrTJ02ahEmTJiE4OBhKpRKxsbGQyWTw8vJCdHQ0nJycmGUnT56MxsZGbNiwAUqlEhKJBAsX\nLjT43IkQQsjjwWAw+v777w0WEhYWhrCwML3zzczMEBkZicjISG5rRwgh5LFAfdMRQggRHAUjQggh\ngqNgRAghRHAUjAghhAiOghEhhBDBUTAihBAiOApGhBBCBEfBiBBCiOAoGBFCCBEcBSNCCCGCo2BE\nCCFEcBSMCCGECI6CESGEEMFRMCKEECI4CkaEEEIER8GIEEKI4CgYEUIIERwFI0IIIYKjYEQIIURw\nFIwIIYQIjoIRIYQQwVEwIoQQIjgKRoQQQgRnYWiBvLw8HD16FEVFRaiqqsKbb76JUaNGaS2TmpqK\ntLQ0yOVyeHp6YsqUKZBIJMz8hoYG7N+/H1lZWVAoFJBIJJg+fTpcXV353yJCCCGdjsE7I6VSCXd3\nd7z66qsAAJFIpDU/PT0dycnJmDVrFlauXAk/Pz/ExMSgoqKCWSYpKQlZWVmIiorCihUr4OjoiLVr\n10KlUvG8OYQQQjojg8FoyJAhiIyMxFNPPaVzfmpqKoKCghAQEAA7OztERETA2toaJ0+eBADU19cj\nLS0N4eHh8PHxgVgsxowZMyCTyZCdnc3v1hBCCOmUTHpmJJPJUFZWBj8/P63pvr6+yM/PBwAUFxej\nrq4O/v7+zHwbGxt4e3szyxBCCHm8mRyMAMDBwUFrur29PaqqqgCA+dvWMoQQQh5vBhMYjNXy2RLX\nZaRSKfNaYdPTYFkKhQJS6U3Dy3XAstiU01HLon3Fviw6RoUpi/a7MGW1LKdfv35tLm9SMBKLxQAA\nuVyuNV0ulzPzHB0dW03T/O/h4aG37OYrXl1WB6DtZAdbW1v082p7YztqWWzK6ahl0b5iXxYdo8KU\nRftdmLLY7isNk6rpxGIxnJ2dkZeXx0xTq9WQSqVMarenpyesra21llEoFCgsLNRK/yaEEPL4Yh2M\n1Gq11l+N0NBQZGRkIDc3FzU1NUhISIBKpUJwcDAAwNLSEiEhIUhJSUFBQQFkMhni4uIgFosxYsQI\nHjeFEEJIZ2Wwmu7SpUv47rvvmP+3bt2KrVu3IjAwELNnz0ZwcDCUSiViY2Mhk8ng5eWF6OhoODk5\nMe+ZPHkyGhsbsWHDBiiVSkgkEixcuBBWVlbts1WEEEI6FYPBqH///ti8eXOby4SFhSEsLEzvfDMz\nM0RGRiIyMpL7GhJCCHnkUd90hBBCBEfBiBBCiOAoGBFCCBEcBSNCCCGCo2BECCFEcBSMCCGECI6C\nESGEEMFRMCKEECI4CkaEEEIER8GIEEKI4CgYEUIIERwFI0IIIYKjYEQIIURwFIwIIYQIjoIRIYQQ\nwVEwIoQQIjgKRoQQQgRHwYgQQojgKBgRQggRHAUjQgghgqNgRAghRHAUjAghhAiOghEhhBDBWTzM\nDzt9+jQOHz6M8vJyuLi44OWXX8bgwYMf5ioQQgjpgB7anVFubi527NiBSZMmYfXq1Rg7diw2bdqE\nmzdvPqxVIIQQ0kE9tGB09OhRDB48GKNGjUKXLl3w7LPPonfv3jh+/PjDWgVCCCEd1EMJRmq1Gtev\nX4efn5/WdF9fX+Tn5z+MVSCEENKBiSorK9Xt/SEKhQLR0dF45513MGLECGb60aNHcfDgQWzYsKG9\nV4EQQkgHRtl0hBBCBPdQgpGNjQ2srKwgl8u1pldXV8PR0fFhrAIhhJAO7KEEI5FIhD59+iAvL09r\n+pUrVyCRSB7GKhBCCOnAHlo1XWhoKP7++2+cOXMGNTU1OH78OG7evInx48c/rFUghBDSQT2UBAaN\nM2fO4NChQ9TolRBCiJaHGoxI+7h27RrrZfv27duOa0IIIcZ5qN0BcaVUKlFQUACJRAIrKyuteXV1\ndcjPz0efPn1gbW3dZjn3799HZmYmJk6cqLOcI0eOIDAwEN26dTN6PdVq7Zhua2vL+v1paWlMY2Bj\nfPvtt6yX3bx5M6ey6+rqUFJSAplM1mobhwwZwqqM0tJSuLq6cp7HRm1tLaRSKVxdXeHi4sL5/Y2N\njfj7779x+/ZtAIC7uzsGDRoEkUjEqRx9FwQikQiWlpbo0aMHq+/3wYMHiIqKwqpVq+Dg4MBpHVoq\nLy/Xe0xfu3btoV+YXLhwAYcPH8aiRYtgaWmpNU+lUuG7777DCy+8gAEDBrAu8+LFi7CxsWn17Dk/\nPx8qlQr9+/fntI51dXU4e/Ysbt++DZFIBFdXV4wcObLVeaMtWVlZeo8fS0tL9OzZE56enqzL+/33\n35GRkYF79+7hq6++AgCcOnUKDg4OnGqWSkpK4OHhwXp5fR48eICdO3ciMjIS9vb2Jpen0aGDUXp6\nOqRSKZ544olW86ysrPDrr7+iqKgIoaGhbZZz7NgxmJmZ6TygrKysoFAocOzYMUyZMoX1upWXlyMu\nLg6XL19GfX19q/lcTvopKSnYt28fhg4disDAQJ3b25ZNmzYxry9evIikpCS8/PLL6N27NwDgxo0b\n2L9/P1588UVO5V65cgWbN2+GXC7Xue/Ytg/797//rXd/tDVPl40bN2L48OEYOXIklEolPv/8c1RW\nVkIkEmHOnDmsAyQAlJWVYf369bh79y6cnZ0BAPfu3YOLiwvmz5+P7t27sy7L0AWBSCTC0KFD8cYb\nb7R58WRhYQEnJ6dWgd8Ya9aswb/+9a9WQU0qlWLdunVYv34967Jqa2uRlJSEy5cv67wwiYmJMVjG\niRMnMHbs2FaBCACsra0RGBiIEydOcApG+/btw8yZM1tNNzMzw969e/Gf//yHdVmFhYVYt24dGhoa\nmJP26dOnkZSUhPnz58Pb25tVOQkJCaiurkZDQwNsbGwANF2wmpubw9bWFtXV1ejVqxeio6MNXnBk\nZmYiISEBISEhuHTpEjPdxsYGqampnILRZ599ht69eyMwMBBPPfUUs25cWVhY4Pfff8f06dONer/e\ncnktjWfZ2dmYOnWq3vlhYWHYs2ePwWB08eJFvP/++3rnBwUF4fvvv+cUjLZt2wYAmDt3rslXsN98\n8w0uXryIzMxMrFu3Dl27dsWYMWPw9NNPo2vXrgbfb25uzrzet28f5syZo3UFFBAQgK5du+LHH3/k\ndPDu2rULY8aMwfPPP2/w7tMYKpWK0xUnAFy/fh0zZswAAJw7dw51dXVYvXo18vLycOjQIU7BaPfu\n3XB2dsYqAz2qAAAgAElEQVSHH37IfIdyuRzbt2/Hrl27MG/ePNZlzZ8/H8nJyZg8eTJz0rp58yYO\nHDiAiRMnwtHREXFxcUhMTMS0adPaLOvZZ59FfHw8Zs6cadJ+79evH2JiYvDhhx8yJ56rV69i/fr1\nmDRpEqeyduzYgTt37iAkJARisdio9SkuLsYbb7yhd/7AgQORlJTEqcy7d+/Cy8ur1XR3d3fcu3eP\nU1mxsbEYPHgwpk6dygTM+vp67Nq1Czt37sQnn3zCqpyIiAicPn0aM2bMYO7W79y5g7i4OIwePRr+\n/v745ZdfsHfvXrz11lttlpWamoq5c+fC19cXycnJzPR+/frhv//9L6ft++yzz5CZmYmDBw9i7969\nGD58OAIDA426Q+7fvz/Onz+v1YmBqTp0MLp3716bVyPe3t6sDrjy8nL07NlT73xnZ2eUl5dzWrfr\n169jxYoVJgcioOkqbuDAgRg4cCCqq6tx9uxZnD59GsnJyejfvz8CAwMxZMgQmJkZTn4sKyvT2XbL\n0dGR84/z3r17JgWiPXv26HwNNFWPFRUVsb7a1KitrWX2eW5uLoYMGQI7OzsMGjQIW7du5VRWXl4e\nvvzyS63v0MHBATNnzsSnn37Kqaz4+Hi89957cHNzY6b1798fXbt2xQ8//IDPPvsMr732Gr7//nuD\nwej3339HUVER/vzzTzg7O2sFbJFIhI8++ojVOs2aNQubNm3C+vXrsWDBAly/fh3r16/HSy+9hAkT\nJnDavkuXLmHZsmWc7hZbksvlbVZVdunSpVVbREOsrKxQXl7eqqq3oqICFhbcTm8lJSWIjo7WunOz\ntLTEyy+/jMWLF7Mu5+DBg1i0aJHWvnJxccHs2bOxevVqjBo1CtOmTcPq1asNllVWVgYfH59W0y0t\nLaFUKlmvEwC4ubkhMjISr7zyCi5cuIDTp09j9erV6NGjBwIDAzF69GjWFxq9evVCbGwsLl68CE9P\nz1YXlUFBQZzWDejgwaihoQENDQ06b+uBphPagwcPDJZjYWGBmpoavTu6traW84Hr5OSks3rOVPb2\n9ujbty9KS0tx+/ZtFBcXIzY2Fnv37sXrr78Of3//Nt/v4eGB/fv3Y9q0acw21dfXIzExkVM9NdB0\nwN2+fZup7uPqzp07zOu7d+9qzbOwsICPjw+eeeYZTmV27doVhYWFcHd3x5UrVzBr1iwATV1ONb9D\nZMPMzAwNDQ2tpjc2NnJ+ZlRWVqbz+HJwcGC23c3NDVVVVQbL0lyY6MJlvczMzPDuu+8iJiYGa9as\nQVFRESZPnmxUc4ouXbpwvottSSwWo7S0VO9zizt37nC+6woICMCuXbswZ84cJtDV1NRgz549CAgI\n4FRWjx49IJPJWj0HkclkTDUuG5WVlTrPJ+bm5qioqAAAdO/eHTU1NQbL6tq1K4qLi1sFpNzc3DYv\nsNtiZmaGwYMHY8CAAcjIyEB8fDwSEhJw4MABDB8+HBEREXBycmqzjMzMTNjb20MqlUIqlbaa/8gF\nIxcXF+Tl5emtesnLy2P18Nvb2xs5OTl6T3w5OTmcr9BfeeUVxMXFYfbs2UZXWzQnl8uRlZWFzMxM\nlJaWYtCgQXjvvfcwYMAANDQ04OjRo9i2bRu+/vrrNsuZNWsW1q9fjz/++APu7u4AgFu3bsHCwgLz\n58/ntE4TJkzA9u3b8dxzz8HDw6PVnZmhff/Pf/4TAPDLL7+0WT3Dxbhx47B27VrY2trC1tYWgwYN\nAtD0QJ5rIkT//v2xbds2vP7668xVbHl5ObZv387puQXQVC2kqYJrfhFw4MAB5uR7//59VnfSL7zw\nAqfPbq60tLTVtIiICPzwww8YPXo0AgICmGW47K+JEyciISEBM2fO5HzhpjFgwAAcOnQIc+bM0Tn/\n0KFDnBMOIiIisHr1aixevJjZz7du3YKTkxMWLVpk8P3NL2YjIyOxdetWrarWGzduICkpCREREazX\nSSKRIDY2VuvcIJPJ8N///pepErt9+7bBEz7QdFLfvn078wihpKQEubm5OHz4MF555RXW69RcYWEh\nMjMzce7cOZiZmeGZZ55BYGAglEol01eooZqB5cuXG/XZbenQqd3Hjh3DiRMnMG/ePObEqlFSUoIN\nGzYgJCTE4DOj8+fPY/PmzfjHP/6BsWPHMifVxsZGnDx5EvHx8XjnnXc4PW/48MMPUVNTg4aGBtjZ\n2bW6Kl+1ahXrsjZu3IgLFy4wt8tPP/10qwDX2NiIuXPnsnrYX19fj+zsbOak4+bmhuHDh+u9w9Tn\n3XffbXM+28SDzZs36y3rp59+wjvvvMNpvS5fvoz79+9j0KBBzH7666+/YG5urveOQpfKykp8//33\nuHHjBsRiMUQiEaqqquDj44P33nuP1clC4+bNm1i/fj0aGhpaXQTMmzcP3t7e+OOPP3Dnzh2Eh4dz\n2l4uDH1nzRn6/lasWKH1f3FxMSwsLODs7Kx1vLOtOiwrK8MXX3wBNzc3jB8/nnmeUlpaiuPHj6O0\ntBSffvopp7sQoCkDLicnB0VFRQCaLj6HDRvG6njnc39p3Lt3D5s2bcKtW7eYKvOqqip4eHhg7ty5\ncHZ2xqVLl1BZWYmnn37aYHlHjx7FoUOHmGo5KysrhIeH4/nnn2e97gBw/PhxZGZm4tatW+jfvz/G\njh2LQYMGaV1c1NXVYf78+fjxxx9Zl1tZWcnpt6JPhw5GDQ0NWL9+PfLy8tC/f3+tg/fy5cvw9fVF\ndHQ0q+qZQ4cO4eDBg7CxsUGPHj2gVqtRVlYGlUqFF154gXOmWVZWVpvzR40axbqsn3/+GWPHjoWv\nr2+by929e9foW3NjGHqOxjYV/t1339X5Q1ar1ZgzZw7ndHO+Xb16VetuwdD3oI/mpNi8rBEjRnC+\nCGhsbMTx48dx7tw5VFRUtKqKbitzjcuzT0Pf36FDh1iVIxKJWJ8Yi4uL8csvvzCBQ8PLywuvvfYa\nevXqxaocvuiqYtKnX79+rJdVq9WtjquWQ+iwUV9fD0tLSzQ2NjLnK1dXV1haWkImk3Gqlfnoo48w\nZswYjBkzps3vPiMjA2PHjjW4XvHx8cjMzER9fT3zG05KSmKCJVcdOhgBTQHpt99+w9mzZ3H37l2o\n1Wq4uLhg5MiRGDduHKfnBEVFRVrluLq64qmnntKZjdOZVVZW4tq1azpTcJ999tmHth4KhQJqtRoL\nFixodQJtbGzEtWvXsHPnTqxcuZJTuTdv3tSbYswlI7KjOnjwIM6dO4fQ0FDs3LkTb775JvLz83Hu\n3DlMnjwZISEhQq+iyW7fvs08U3RxcdFK/OCqoxzvAL/txICmWpP33nuvVRW5TCbDqlWr8Pnnn7Mu\nq7GxkVUSFBv79u3DtWvX8Morr2DVqlVMMLpy5Qp2796NZcuWcS6zQz8zApoe+j377LN6D6qCggKd\n2Sa6eHl56Q08R44cwcSJEzmvn1wu1/kjMJQscPToUdafYagasrmcnBxs3boVNjY2Oq+auP44Kysr\nIZVKIZfLOf/Qo6Ojdb7WMDMz41zv/euvv2LPnj3w9vbW2j61Ws056WDv3r3w9vbGyJEjtaZnZ2fj\nxo0bePXVVzmVp1arcfv2bZSXl7e6m+FSBZyVlYU5c+bA29sbO3fuxKhRozBq1Cj06dMHf/75Z5vB\n6I8//sCTTz4JoCkrr619olmOjcLCQpibm7dKPrh16xYaGho4X9C5ubnpDED//ve/OZ1g+TzeDfVk\nwiYFms92YkDT72/79u1az1yrqqqwatWqVo8uDDEzM0NBQQFOnTqFsrIyLFy4EEBTFbcmcYqtnJwc\nfPDBB61qary8vFBWVsZpvTQ6fDAyZMWKFbxU8yQmJnIKRiUlJdiyZQtKSkp0zje0TufOnWP9WVyC\nUWJiIl599VVerp5N/aF//PHHAJoedn7yySdaP1ALCwt07doVdnZ2nNbp6NGjeP/99zmd3PXJzs7W\nmdQikUiwZ88eTsGorKwM33//PW7duoXGxkaYm5ujoaEBZmZmsLS05NTAtLKykjnpaxpl29raYujQ\noQbblsTGxjJBZufOnW0uyyUY7dy5Ey+//HKrYCSXy7Fv3z7WbXAM0ZWA0RY+j3dDDZfZnmf4aicG\nNCUBffvtt9izZw+mTJmCyspKrF69Gu7u7pyedwHA33//jc2bN2P48OG4fPkyM72mpgYnTpxgghMb\ncrlcZxOS+vp6owNxpw9GQtm+fTs8PT3xzjvvGHU7zrUdC1tVVVWsHoqyYeoPXZMSvnz5cvTo0YOX\ndaqtreXcQ4U+1dXVOr87Ozs7Vmm3ze3evRs+Pj5YsmQJoqKimMSIuLg4zg1MxWIxqqqq0L17dzg7\nOyMvLw9Dhw5FSUmJwfTqtWvX6nxtqlu3bqFPnz6tpvfu3ZvpSkkIfB7vGzdu1Pq/oaEBJSUlSExM\nxHPPPce6HL7aiQFNTQMWLFiAb775BmZmZjh//jw8PDwwZ84czlVuSUlJePvttzFkyBCcPn2amf7E\nE08gISGBU1lubm64dOkShg4dqjU9PT2dc2ayBgUjI5WUlGDhwoWc+qB7GPz8/HD9+nVeTth8/dB7\n9OjBW/XAoEGDkJubi2HDhpm8Xt27d0deXl6rXiny8vJY9XzRXH5+Pj7//HMmWUGtVsPHxwevvfYa\ntmzZwqSgs+Hv74/z58/jmWeewfjx4/Hzzz/Dy8sLxcXFCA4O5rReLSmVSqO6gTE3N0d1dXWrK/2a\nmhrO1aN84vN4b5loYmlpCYlEgpkzZ2Lz5s2s0/35aiem0b17dyxYsAArV66En58f3n33XaOe/ZSW\nlurchi5dukChUHAq66WXXsJPP/2EwsJCAE39a+bm5uLSpUtYsGAB53UDKBgZzcXFBZWVlUYHo7i4\nOIhEIr23tJp5IpHIYIv95oYOHYrt27dj3LhxOtsGcWk/w9cPnc/qAU9PT8TGxiIvLw/u7u6tEli4\nNLYLDg7G9u3bMXnyZCaD7urVq0wXPlw8ePCAuQK2t7dHZWUlunXrBmdn51YNfg15/fXXmdeazKf8\n/HyMHz+eUxA+dOgQvL29MXDgQKjVamzcuBF///03unXrhvnz53PqNNPX1xcHDhzAG2+8wRxTDQ0N\nOHDggNHZh3zg83jXx8nJidN3aEo7MaCp2YgudXV1uHr1qlZvEFyakNjb26OsrKzVs7pr165xvvga\nOHAgFi5ciOTkZDg5OSEpKQne3t5YuHChUVmDQAcPRlevXm1zPtu6yYMHD7Z5RWJMHWdkZCTTc62u\nk6KhhoFVVVUGr5KMeSiveU5w4MABnfN/+OGHNt9/8eJF5vXQoUOxbds2hISEmPRD57N6ICMjA126\ndMGlS5e0Oo7U4BKMJkyYgNraWuzZs4fpTcPKygqhoaGcntMBTRcnt27dgo+PD/r06YMjR45gwoQJ\nyMjIMDkd/4knnjDqguDUqVNM32F//fUXLl26hKioKFy9ehX79u3TmVSiT0REBL799lt8/PHHTHXd\n9evXUVdXx7qrnI8++qjNCzBjmHq8N6eraraqqgopKSmce7tWKpX4+++/mS61gKY7ky5duhhMx46M\njOT0WWyNHDkSO3fuZC52lEolLl68iN27d2PcuHGcyiovL4ePjw/TsL05Y3uE79DBiEvUb0vzE6A+\nXIeP+O677wC0bhioYehh53vvvcfp89ji0lhNl3Xr1mkFQLVabfIPnc/qAb5bfr/00kt47rnntNKM\nubYLApqSTGQyGQDg5Zdfxrp16/Dbb7+hS5cuenscaM5Q5ltzbBMPNM+dgKaG3wMHDsTgwYPh6+vL\n6bkF0NRO5j//+Q/S09NRVFQEkUiEwMBABAUFsW7rwtezneZMPd6b03eH7u7ujrfffpt1Obdv38aa\nNWtgaWmJsrIyJhjl5OTg1q1bBhMPuLRR5OLFF1/E3r17sXTpUgD/10NKYGAg53ZBfPYIr9GhgxGb\nEw+bH7ChLnSM8f/+3//jvcyOgM8ftwaf1QPtwdLSEp6enqitrcWlS5eMGhvpqaeeYl57eHjg66+/\nRkVFBcRiMavucwxlvjXHNhjZ2dlBJpOha9euuHz5MvMQXq1Ws+rTsSWxWMw5GaM5rg3LHxZNtmLL\npCIzMzM4ODjA0dERV65cYV3enj17EBQUhEmTJmkFnuHDhxudVFJZWamzCQmXZAELCwtMnz4dzz//\nPG7dugWVSgUvLy+jOr/ls0d4Zv2MetdDwiYDi0v7IE3+Ox+ZXVxaY+uyfv16pq+4tq4iRCKRwaEM\njh49ylQrGWq/xLX6iQ98Vg8ATWnxR44cYe5mXF1dMXHiRM7d2fM5NlJLIpGI0902n5lvGoMGDcKm\nTZvQrVs31NTUMJlPxcXFRve+rVKpUF5e3qqDWbad8BYUFODChQsAmp47sG0j2BKb2g6A3d3Yxo0b\nsWDBAr29P2hOsmzH7yooKMDcuXNbTe/WrRtz98zWzZs38fPPP+tNeTemWYujo6POtGwu+OwRXqND\nByM22LQPun//PjZs2MC0CfLw8MC8efNM6g5fw9gfZ/NOKk0Z6RQA01pf87otXIORSqXClStXdG4j\n2waFfFYPnDhxAvv27UNQUBDz3uvXr2P79u2orq7mlIbO59hIQNOJQ9OFT319vdZde1vjabWXqVOn\nIiUlBffv38e8efOY6rSioiKMHj2aU1macZ7+/vtvnfPZnBT/+usv/PDDD0xG3v/+9z+8++67nNo7\naWjGEzOETTCSyWT48ccfdfZ0cO3aNaxfv75Vw+i2iEQiqFSqVlmLd+7c4Tya87Zt2+Dn54d58+bx\nMqrqH3/8wfRe0tjYqLXOXI5RPnuE1+j0wYiNhIQEmJub47333oNarcb//vc/ZhA6Y5n642zeoJJr\nS/+Wmlcv8Nl+qaioCDExMUwHoj169EB5eTnMzc3h7OzMOhiZm5vrrR5QqVScGgYeO3YMb731ltYJ\nbMSIEfD19UV8fDynYMTn2Eg5OTnYtm0bhgwZgpycHIwZMwYFBQUoKyvj/AxA0zedZqhpzbO5o0eP\nokuXLggMDGRVjpWVFV566aVW043pImfv3r0QiUT44osvsHTpUnz99de4fv06kpKSWB+/hw8fRmho\nKF555RXmWWRKSopRwYjP/gw17Xh27NihlcmYn5+PdevWYfjw4cxQJWwMGDAAhw8f1hoJtbKyEnFx\ncZxS/IGmTlf/9a9/GT0qa3OJiYlIS0tDQECAUXdG7dUjvMZjEYyuXr2KDz/8kNlB7u7uJidH8PHj\nbK6xsRE3btzAvXv3mKswhUIBCwsLox6o68K1q5W9e/ciKCgIL774It59910sX74clZWV2Lp1K6cr\nxbi4OMyYMaNV9YBKpcK6des4PX+rqKjQOczAE088wYwVwxafYyMdPnwYb731FoYOHYpz587htdde\ng1qtRnx8POeMyKNHj+LUqVN47rnntO4A3NzckJyczDoYAeCtfdfly5exZMkSpjbByckJw4cPh5OT\nE/bu3cvqJFtaWooPPvgAQNOV+MSJE5GWlsZ6HdpLt27dsGDBAnz77bfYt28fIiMjUVBQgJiYGAwZ\nMgSzZ8/mVN6rr76KmJgY/Otf/wIAfPHFF7h16xZcXFw4d3/Vu3dvlJSUQCKRcHqfLqdPn8aCBQuM\nynQDms4f+qSnpyM9PZ3535iLhcciGMlkMq1I7eLiwrnutiU+fpwaFRUV2LBhA0pLS1FfX8+c6A8f\nPozq6mqtqzVTcO1qpbCwkMn6E4lEePDgAZycnDBz5kzExMRgzJgxrMq5ePEiDhw4gMmTJzPTlEol\nYmJiOKf5duvWDX/99Veru40LFy5wrnblc2yku3fvMoO5mZubM3d84eHhWLZsGacLlIyMDMyZMwe9\nevXSCka9e/fWGrDQED7bdymVSqaaz87ODtXV1RCLxUxjXDZUKpVWuzxbW1uoVCrW69Ce3Nzc8M9/\n/hNr1qyBSqXCuXPnMHDgQLzxxhucLyacnJywdOlS/PnnnygqKoJKpcL48eMxYsQIzmNBzZ49G9u3\nb8fw4cPh6ura6iKJSxuv+vp6zgNsNtceiWDNdehg1B7tgwDjWkG3xMePU2PPnj3o3bs3lixZolVv\nO2rUKHz//fcmr6spNPtKLBYzw7c7ODigsrKSdRmaq057e3uMHz8eSqUS3333HUQiEefW2uHh4di+\nfTvy8vLQp08fiEQiFBQUICsri3n+w1ZoaCi8vLyYsZE0jVbNzc05ZwTZ2NhAoVDA0tISXbt2RWlp\nKby9vVFXV8d5ROCKigqdnYiKRCJOZfHZvqtHjx64c+cOPD094eHhgYyMDISHh+PMmTOcxrLRlXjQ\nclp7pICzoRnHav369Rg6dCjefvtto88VZmZmGDZsmMk9hdy7dw8lJSV6h7rgcgcycuRIZGVlGZU0\nBHBv/sJVhw5GfLYP0pXb33Ialy+Wrx8n0FSN+MUXX7S6anJ2duZ00uebm5sbCgsL4efnB19fXyQk\nJGDcuHE4c+YMp0aAPXv2RHR0NFatWgVzc3OcPXsWIpEI0dHRnOvCAwMD4ejoiKNHjzINdN3c3PD+\n++9zHmYagM7GpC27B2Kjd+/euHLlCkaMGIFhw4Zhy5YtGD58OM6fP8+5wWqPHj2Qn5/faoj57Oxs\nTj0189m+a+zYsSgpKYGnpycmT56MdevWISkpCebm5pxG8dWVeNBy2sMORrrODTk5OcjJydGaxuX8\ncPPmTRw/fhy3bt2CSCRiBhTk2m9bXFwcAgMDMWHCBKMSGBITE5nXFhYW2LdvH9O/XctkDUNViO3R\nFq65Dh2M+LotfO2113gppzm+fpwA9F7tVlZWGuwYsz01f/gdERGBLVu2YN26dXBxccGbb77JqSwv\nLy9ERUUhJiYG3t7eiI6ONrpH47b6/uIqPz8fR44cwe3btyESieDu7o7Q0FDOdfQzZsxgspNeeukl\nWFhYID8/H3379tWZRNCWiRMn4ueff2bed/78eVy8eBEZGRmcGl/y2b6redJD3759sWLFCty6dQvO\nzs6sL76EHkRRH77PD2fPnsUvv/yCAQMGMBc2N27cwPLly/HGG29wSmiprKzEpEmTjP6t3LhxQ+v/\nPn36oKGhgelTjov2aAvXXIcfXK+zqKmp4fzj1Fi/fj18fHzwwgsvMKOiqlQqbNmyBRYWFqyy/th0\ntVJeXm7yCYFtF0X6eqa4e/cuHB0dYWNjw5TFtTcADZVKhT/++AP19fXo378/5/ZjOTk5+OmnnzBo\n0CCtLm4uXLiAd955h5fOWI31+++/4+DBgygtLYVarUaPHj3w4osvcjqRJSYmIj8/H6+//jo++eQT\nrFu3Tqt9F9dhqzuqxsZGJnOxvZJ/uFiyZAnCw8NbdU118uRJ/O9//+N0kb127Vo8//zzgvb/97B0\n6DujzsTOzs7ohrCRkZFYvXo109fapk2bcO3aNYhEIiYjx5CHVbXB9jad7Z0L2/Li4+Nha2vLdEJZ\nV1eHr776CqWlpRCJRLC2tsbChQs5NaJMTk7GjBkzWp00MjIykJyczCoYse3JgOuDa83zhgcPHqCu\nro5z+xTA9PZdms58gdYXIZr/jenMl08PK/mHC5lMptUjh8aIESOwe/duTmUNGzYMW7duRUhICNzd\n3dulI9iOgoKRkRobG5GTk4O8vLxW3XSw6TWhOTc3N3z55ZdIT09n2t6EhIQgODiY9VhJfHa1smLF\nCq27LF13XIbuaEztubilixcvarXb0LTBWbx4Mby9vbFr1y4cPnyY036/e/euzpPG8OHDERcXx6oM\ntg0FudyRfv3118y+tbCw0ApkzecZYmr3L80789V3R2xMZ7586ojJPxKJBFKptNUF2bVr13SOCdUW\nzWCK+/fv1zmfS0ewAL8Ns/loEN8cBSMj7d27F2fOnMHAgQNbVQ8Z8+O0sbFBWFiY1rSGhgakpaXp\nHI20PbVM/8zIyEBQUFCrgMsWH0NWl5eXay138eJFBAQEMG0mnn/+eb1Vg/o4Ojri2rVrrRIfCgoK\nWHf+2fIZzpYtW/DOO++Y1DP19evXdU7XtEXjytjuX9qrM18+dZTkn+a93Q8bNgzbtm3D008/zQww\nqcn45JqlyWdfkXw2zOarQXxzFIyMlJWVhQ8//JDVibQtSqUSN2/exIMHD9CvXz8mYeH3339HYmIi\nqqqqHnowatnaPCMjAzNnzjS6PD6GrG5+Ba5Wq1FQUKDVtZFYLOY8OmtwcDB++uknTJgwgbliLSgo\nwLFjx1pdGOjT8s5qy5YtnPvI0yguLmaCWMvmAY2NjcjPz+eceMBH9y8PHjxAVFQUVq1aZdSoxu2p\noyT/6Ort/tixY1rLqNVq7Nq1y+QBEo3FZ8NsvhrEN9dpgpG+8UFsbW1N7vTPGJaWlibn3d+8eRPr\n1q2DXC4H0NRY7oMPPkBCQgIuXLiAwMDADtvTMRd8DFnds2dPprFrfn4+ampqtFKfy8vLYWdnx2m9\nwsPDYWtri6NHj+LgwYMAmtKqX375ZUFOGM17x9DVU4adnR2mTp3KujxTu3/RsLCwgJOTE6/jEPHF\n19cXJ06c0KoWVqlU2LdvH2/D07PRHr3dA/x1DQXw2zCbrwbxzXWKYGTK+CCGerFujksnouPHj8eh\nQ4fw6quvGjUEMNB0sujTpw9eeuklNDY24uDBg/j222/h6OiIZcuW6Wz42BnxMWT1+PHjsWPHDpw+\nfRqFhYXo1auXVrLCpUuXjGpdPm7cOIwbN465whYi+0pj9erVAIBFixZhzZo1Wid/CwsLzqMKm9r9\nS3PPPvss4uPjMXPmTKPTjDVqa2uRlJTE3LG1DHIxMTGsyzI1+efw4cOsP0uI7EM+u4bis2E2wE+D\n+OY6RTAyZXwQQ71YN8clGD3zzDNYuXIlFi9ejJ49e7YKSPqGDm6usLAQy5YtY1LBZ86cicWLF2Px\n4sUm9+TdkfAxZPXo0aNhZWWFv/76C6NHj26VDSaXyzF27Fij19HS0hIPHjxAfX29YAFJUwXGV3sc\nU0gs6Q8AACAASURBVLt/ae73339HUVER/vzzTzg7O2tVgXFNz9+xYwfu3LmDkJAQ1s/m9DE1+Sc3\nN1frf037G2dnZwBNPSAAQK9evTgFo5s3b+oNtlOmTGFdDl9dQ2new1fDbL4axDfXKYKRKeOD8NmL\ndXM7duzA/fv3MWTIkFYto9le7dfU1Gi1SdK85hqIsrKyWH8mm/pcNl22AOzTyfkYshpAm92rcKnO\n1FR9FBUVwd/fH2PGjEFcXBxOnjwJtVqNAQMG4K233mJV7ffLL79oPcsCtHsU0Dzr4ppiXFlZCalU\nCrlc3upkxvbhsKndvzTXVkNjrs8bLl26hGXLlvEyhAugO/mHreZ3T6mpqXB0dMSsWbOY776mpgax\nsbFMIgIbv/76K/bs2QNvb2+tYGtM5iFfXUMB/DbM5rNBvEanCEZ8jg/Clz///BMff/wxp+5ZdLl/\n/77BaYZ+tAkJCVoHuVwuR0NDA7O/lEolzM3NIRaLWQUjNl22AOyDER9DVvMpOTkZx48fR69evbB7\n927k5+fj6tWrmDJlCtRqNX799VckJSVppZLrU1ZWprXv+/Xrh7KyslbtcLjIycnB1q1bYWNjo3P/\nsA1Gpnb/0hyfqfpdunThLbngo48+wqBBgzB16tRW27dkyRJOGZbHjx/H0qVLtS5C7OzsMG3aNHzx\nxResB/E8evQo3n//fZMGZ9Tgo2uoTz75BF999RXzjPvEiRMICQnhHIAAIDMzE/7+/lp3Ut26dcPi\nxYtNTvPvFMHIlPFBNA339D18Nbbhnp2dHS+JE0uWLDE4zVC1zcqVK5nXZ86cQWZmJmbOnMncYd2+\nfRv//e9/WQeP9ui2xdQhq/l09uxZvP/++3jiiSdw+fJlrF27Fp9++ikz0mffvn2xceNGVsGoPYaf\nT0xMxKuvvsppbCZdiouLTer+pSV9SURdunThdFExceJEJCQkYObMmZwbA7dUXl6OCxcuoKSkBHPn\nztWqmtN1odeW2tparQ6QNVQqFae+/Gpra3lLnuCjayhNVaPGrl27jD62du7ciYaGBvTo0QN+fn7w\n9/eHn58fnJycTG5v1imCkSnjgzRvuKePMRE9LCwMiYmJmDZtmtE/qEWLFhn1vrYkJydj0aJFWndT\nbm5ueP3117F69WrBekQGTB+ymi8VFRVMbxmav83XwcPDA1VVVSZ9xtWrV43uwqWqqoqX70kzdhAf\nTEkiaunMmTMoLi5mnj81HxbBmO6hPv30U/zwww/46quv8P777+sdPtyQgIAAbNmyBdOnT2fKuHnz\nJnbt2sWpE95BgwYhNzeXl+6knn76aVhbWzMjGGzcuBE9evTAa6+9Jkh3Vd999x3y8/ORl5eHK1eu\n4MyZM2hsbISLiwv8/f2Z4GRMp66dIhjpGh9kwoQJGD58uMFA0F4N986ePYvi4mLk5OQY/YPy8/Pj\nfb0qKyt1DgxnYWFhciNATb95XPExZDWfGhoamONG87d5FY+5ublWmxxjrFq1yujt8vPzw/Xr13m7\nupbJZLh7965JWXWmJBG1xOfzJ6CplmLBggWIj4/HN998g9dff92otl6asYOWL1+u9RxwyJAhnEZ6\n9fT0RGxsLPLy8uDu7t7q99iy+ylD9HUNlZ2dbXSbNmNZW1ujf//+zACXSqUSUqkUV65cwdWrV5GR\nkQG1Wm1UqnunCEYAf+ODaGjumIx9ZsH3D4ovffv2xc6dOzF79mxm26qqqhAbG2t033mm4ntUXD5c\nvXoVwP8lHWj+bz7tYWregn/o0KHYtm0bQkJCdD7rYdsfWW1tLbZu3cpcBGiC4549ewBwy+oyJYmo\nJb67igKaLiCmTp0KLy8vbNu2DTdv3uRchp2dHd5//33cv3+faf/m5ubGOdEiIyMDXbp0waVLl5iU\n8+a4BiONll1D/fTTT6yDUcsEJFMSkpqzsrKCnZ0d7Ozs0KVLF5ibm3NugqDRKYJRW9lilpaW6Nmz\nJ6uqnsbGRqSmpuLIkSNMHbCdnR1CQ0MRFhbGqb1Qe/yg+DB79mxs2rQJixcvZp5pVVVVwcPDQ+fJ\n5GEwdVTcHTt2sP4stkNEtxx23tRh6E2lqwX/gQMHdC7Ltj+yffv2AWjqa7D5c8innnoKW7du5RSM\nOmISkS5jxoyBu7u7Sf3Sde/e3aRMv+XLlxv93vbSMgHJ2ISkxsZGFBUVMdV0UqkUNjY28PX1xZNP\nPompU6ca3SylUwSjhIQEVFdX68wQs7W1RXV1NXr16oXo6Og22xYcOnQIv/32GyZNmqSVYpySkoL6\n+nqjeztQKpWtrqaNvTowVY8ePbB06VJcvXpV6+qOjy7ojRl0DjB9VNy6ujrmRN3Y2Ii//voLdnZ2\n6NWrF9RqNQoLC1FbW8s6e+lhnCz+8Y9/cFq+PVrwX7hwAR9//HGrLoTc3NxQXl7OqSxTkogAYN68\nediwYQPzWh+RSIT169ezXq/myTsaPj4++Pe//42ioiLW5Wjw0X1Sc5WVlZyHlOEbn9XgCxcuhKWl\nJfz9/TF06FBMmTIFLi4uvJTdKYJRREQETp8+jRkzZjAbfufOHcTFxWH06NHw9/fHL7/8gr179+Kt\nt97SW86pU6cwZ84crWc1ffr0gZeXF7Zs2cIpGJWXlyMuLg6XL1/Wme8v9EBivr6+Jgeg8+fPY9Cg\nQUwgiIqKMqocU0fFbZ41tG/fPowdOxb/+Mc/tBrQxsfH63xWpm99dDl9+jRvCR7jx4/npRxT1NbW\n6rwoqq2tZb2vNExJIgKAd955R+drU+nLaHVwcGCea7DFV/dJ9fX1iI+PR2ZmJurr65lzQVJSEqys\nrFgN39FR2dnZoaKiAvfv30d5eTnKy8vRrVs3XhqKd4pgdPDgwVYZYi4uLpg9ezZWr16NUaNGYdq0\naUx3KvrI5XKdw/56eXmhurqa0zppbnNbppN2BJrW33K5vNWDeC5VMz/++CPs7Ozw9NNPY8yYMejZ\ns6dR68PnqLiZmZn48ssvWyUcTJo0CZ988olJz6A0PS2z1Xy8H32MaTawd+9eeHt7t2oTlp2djRs3\nbrDext69eyM7O1urZwq1Wo3Dhw9zHsnWlCQiQPuu2tg7bF347FqIr+6TkpKSUFhYiOjoaK3qX39/\nf+zevZtVMNL1TIdvxiQkLV++HOXl5Uw13fbt21FVVYXevXvD398fvr6+6Nu3r1HBqVMEo8rKSp0H\nvLm5OSoqKgA01fMa6rXZ1dUVGRkZmDBhgtb0M2fOcK7nvH79OlasWGF0IGqP5yAAv62/V65ciXPn\nziEzMxMpKSnw9fVFYGAghg0bxulg42PIao2GhgadnaLev3+/Vcp4e2uvZgPZ2dk6e2qXSCTYs2cP\n62D0yiuvICYmhknOiI+Px6VLl5hxoLjiM4morq4OZ8+eRWlpKYCm3+bIkSM5N4bls2shvrpPysnJ\nwQcf/H/2zjSuqWtr408gDBJARCiDMolMVnFWFFBpcb5Sa6lK61VrrbRSrVS0UrW21Vp7r/MAFocr\nBUFwKqJVBkXKYEVFkDkKgjLKPM/h/cAv501IgHOSEwiY/5eGDDvbNDlr773Wep5vBRZvBgYGKCsr\nIzWGsJyOtKCpqYmZM2cSC7fS0lKw2WxkZWXB29sbbW1tIuXsBkQwMjU1hZ+fH1+FWE1NDS5evEis\nYoqKinq9sC1duhTe3t549uwZn2UAm82mXAKuoaEhkrggF7rzIFzo7P5msVhwcHCAg4MDCgoKEBcX\nh8uXLyMwMBDTpk2Dvb29SD0d4rjiTpkyBadPn8ayZcuIXW5ubi6uX7+OKVOmiDSmqEiqbaCurk7o\nIofFYlGyyTA1NcWePXsQFhYGU1NTZGZmwtjYGK6urqQXX62trXjz5g2hN9ZVeJjBYGDOnDmUFiev\nXr3C8ePH0d7eTowbHx+PkJAQbNq0SejpRXfQKS1El3xSbW2t0GO+1tZW0pWa/X3MT5aKigo8f/4c\nbDYbbDYbTU1NIh/ZDYhgRKZCrK6urtcKt/Hjx2P37t2IiIhAVlYWgM5k7u7duymL+y1btgwBAQF8\nAZIKdOdBuNDZ/c3LiBEjsGDBArBYLNy6dQvx8fGIiYmBiYkJVq1aJfTzu3btGqmxqcjSuLi44Nq1\nazh//jyf0vasWbMojSMMOoILHT09w4cPR2ZmpsBxVmZmJmU/o+HDh5NSkuiOBw8e4MWLF4Te2JUr\nV/h2pdwKOyoitX5+fhg/fjxWrlxJXLhaW1sRGBgIf39/Uv5WXOiUFqJLPklPTw/p6emYOHEi3/3R\n0dGUAq2kEeW4tKqqijiiy8rKQllZGZhMJkaNGgVbW1tYWFhQdrPlMiCCkba2NlEhxrut5y1E6C1Z\n2dbWBn9/fzg7O1MWreTSVYm7vr4eHh4eYLFYAkGDSqkwnXkQOru/gc6gmJycjPj4eKSmpmLEiBH4\n+OOPMX36dLS2tuLGjRv4/fffhfrvPH36lO/vkpISoZU3VIKIgoICVqxYgWXLlhFyL8OHD6clgdr1\n4kEFOnt6Zs+eDV9fXyxdupQoQmGz2fjzzz9J66MBnavWZ8+eoby8HAwGA1paWrC2tqZ0NJqQkCAg\n48Tb5MpdlFAJRgUFBfjmm2/4/p8pKCjgww8/pHx8SKe0EF3ySR988AHOnDlDjHHv3j2kpqYiPT0d\nW7ZsEWuOdCJKQdL27dshLy8PY2NjTJs2DRYWFiLniLoi9cGI12XSwsJCZNUCJpOJJ0+eiLVKdHZ2\nFvm1PUFnHoTO7u+goCA8fPgQ7e3tmDZtGjw9PQVWdi4uLt2WvO7du5fv7w0bNgjcJyoKCgpi22y0\ntrYiKipKaPKbwWBg9+7dpMeis6dn7ty5aGhoQFBQELH7U1RUxLx580jbnERFRSE4OBjt7e18R9tM\nJpOS7l1JSUmPq/mxY8fi0qVLpMbioqWlhZqaGgHJmJqaGsK6gSx0SgvRJZ80btw4uLu7IzQ0FBoa\nGggJCYGRkRHc3d0lorrSE4mJiZg0aRKAThuQnvKX3Of1xObNm2FmZibgaVVeXi72UanUByM6XSbH\njBmD5ORkkSU0qPrEk4XOPAid3d95eXlwdnbG5MmTuzVUYzKZfbraozOABAYGIiUlBdOnTxdQRaZa\ndEBnTw/QubpetGgR4Vmjo6NDevWZnZ2NoKAgLFmyBA4ODkRTan19PaKiohAUFARDQ0NSFXV1dXV8\nO/Y9e/bwPa6kpISWlhay/ywAnYu68+fPY+nSpXzf95CQEHz00Udoa2sjntvbbkdalVBMTEywefPm\nfnt/Ln5+fkSQ8ff37/G5ZIJRdxp9np6eYue5pD4YAfS5TBoaGsLPzw9paWkYOXKkwFkzlQt1Wloa\nlJWVBX7Q2dnZaG5uptTjQGcehM6GTrJHJlT7OcSBzgCSmJiIbdu2iWwGxgudPT1cGhsb0dTURDn/\ndP/+fTg6OgqYwbFYLPzrX/9CU1MT7t+/TyoYqampoaSkBAYGBgAg8FmVlJRQrijlNr8eP35c4LFT\np07x/d3bBY5uJZS8vDwkJCSgsrISra2tfN8psk2vHh4e3R7T9/SYJOA9UqWqIdjXDIhgRJfLZFxc\nHFRVVfH8+XM8f/5c4HEqwejKlStYtWqVwP1ycnIIDg7Gjz/+SHosSeZBxIUOkzc6oTOAyMvLUy4I\n6A46e3rEzT9lZ2dj06ZN3T5ua2tLWuXAwsIC4eHh3TaTR0REUD56koTtBh08fvwYFy5cwIQJE/D4\n8WPY2tri5cuXKCsro3Qq0p1WX3t7O6VqyLeNARGM6NiKd3R0YOvWrRg6dKjYiU4AePPmDbFa5EVf\nX1/AP4QsdORBgM7EdXJyMioqKviOPABqiXRxTd7IGAcCvZsH8kJnAJk9ezbCw8OxdOlSsceis6dH\n3PxTdXV1j7kXbW1t0hYZCxYswC+//AIOh4O5c+cSvTMlJSWIiIhAUlISpeo3ALQK9vamt0hWxw8A\nbt26hc8//xwTJ05EQkIC1qxZg46ODly+fJnUdSYpKUnobaCzfSMnJ6db9Q9eqHyev/zyC+nnAvTZ\noXdFWF8cVQZEMKJjK97R0YGdO3fi6NGjtAQjRUVFVFRUCASPyspKyuPTmQfJzMzEyZMnYWBgQDhE\nvn79Gq2trZRXsOKavJExDgSo9VSIG0B8fHz47AGSk5ORmJgIfX19vtwIg8GgJFtDR08PF3HzT62t\nrT1+B5lMJukeuREjRsDNzQ3/+9//8OjRI77H1NXVsXHjRlK7VCoq2lTKn7vq3LW3t6OwsBD379+n\nfN148+YNkRORl5dHc3MzlJSUsHDhQuzZs6fXqtazZ88KvQ10fuZaWlqklDh4KyZra2vx119/wdra\nmrA+z83NRUpKChYtWkT2nwaA3ob4rqxcuRJNTU0CQrpUGBDBCBDfZVJOTo5QaRDnA+MyduxYBAYG\nwtXVlS9BHBQURMmIC6A3D3Lt2jUsX74cs2bNwoYNG/Dtt9+ipaUFvr6+lLvLxTV5k4R5YHFxsVgB\nRElJic/dd9q0aXyPi2oVDojf08OFjvwTnXIy7777Ln799VdkZmbyFVRYWlqSPkqmsoKnsjgR9lsb\nP348jIyMEBERQankXFlZGY2NjVBQUMCwYcNQXFwMIyMjtLS0kAre3FzYjh07cODAAdLv2xXeOXt5\neeHTTz/FjBkz+J4THx8v0DrRG3Q2xN+8eRNGRkYYN24cOjo6cOrUKTx79gyamprYtGmTSMfoAyIY\n0eUy6eTkBH9/f6xatUrsMsSPPvoIhw4dwvbt24kPvrCwEBoaGpQvwnTmQYqKighNMzk5ObS2tkJR\nUREff/wx9u/fT0mkUVyTN0mUsSopKYkVQNasWUP7nAD0alxIpbeHjvwT3XIyCgoKPR6X94awYgVJ\nYmpqKlAM0RvGxsbIysrC1KlTMXnyZJw9exZTpkxBcnIypd+AOIGoK+np6UTDMS8TJ07ExYsXKY1F\nZ0N8bGwsUZWclJSE9PR0uLm5gc1m48qVK/jmm28ojzkgghFdLpOXL19GfX09PD09oaysLFBNR6XK\nhSsc+fjxY0Kq3sHBgbJuG0BvHoTJZKKtrQ1KSkoYOnQoSktLiX4jqsnTiRMnwtfXF3PmzBHL5K0r\nBw8eFGggJgudwaSiogIMBkPgs+fqHVL5f9JbXojKal/c/FNfSMlcuHCBUvM4HacRVIiPj6esvP3p\np58SwsIffPABmEwmsrOzMXr0aHzwwQc9vjY8PJzoAesqmdQVsr1iQOfnlpGRIdCQnZGRQdmmhs6G\n+OrqamJBn5ycjHHjxmH8+PEwNzenbBvPZUAEI7pcJuluWlVUVKTFcoDORDo3V2RtbY1x48bB398f\nM2fOxKNHjyhXdXH7EsQ1eesKr6tqf3Lu3DnMmzdPIOi8evUK4eHhlKq+fvrpJ76/ORwOCgoK8Ndf\nfwkoGPQGnfknSREfHy+ykgnQWSEbExOD0tJS4ggvNjYWampqlGRqhFWt1tTUoLGxUeiOoic0NTWJ\n20wms9cAxEtCQgIRZBISEnp8LpVgtGDBApw9exa2trZ8PVnx8fGk5hcdHU2cGNDZEM9isVBTU4Nh\nw4YhIyODyF91dHQIFE2RZUAEI7pcJsVtWqW7m5mLuHkQXngD7rJlyxAYGIjbt29DV1eXko0BIBnD\nNzpITExEQkICUS3I+/+BSrHH69evhVZ2mZubCySge0NPT0/gvhEjRkBbWxtBQUGUV6N05Z+kkbi4\nOFy9ehUODg58jdnKysoICwujFIzs7Oz4/mYwGFBTU4OZmVmfNr3u2rVL6G1xcXR0hJaWFiIjI5GY\nmAig87v2xRdfkMr9hIWF8X0OdDXEW1tbw9vbG5qamqivryd2bvn5+SKnQAZEMBLXZVIYra2tAhG8\nt20v3d3MXMTNg/DCq6KtoqLSo9lgfyHOkU1UVBRCQ0Nhb2+PxMREODk5IScnB2w2m9KKE+jcvQhL\nTLe0tAj4QImKnp6eSI6jXDgcDthsNtrb2zFq1Kh+cxCmk7CwMHz55ZcwNzdHaGgocb+ZmRnpPEhw\ncDCWL1/erYlhdXU1Dh48SJv8lKjQ4fQ6YcIEkYsOJOVq7OLigr/++gvl5eX4+uuviSKy169fCxRb\nkGVABCNxXSa5NDc349q1a0hISBCaP+ntrF1S3cySSqrTQXNzM7KyslBRUSGgkydq06s4yex79+4R\nbr23b98mynfDw8NRWFhIaaxRo0YhIiJC4Pg2MjISJiYmIs+RS3NzM/766y/Sems1NTXw9fXFq1ev\nYGVlheXLl+Pw4cOENfvQoUPh7u4OfX19secmLkePHiVuczgcgXxiT5SVlQn9fBUUFNDU1ERqjJiY\nGKiqqgotb66pqcHBgwf7LXBLyum1qalJoDeoP/6N7e3tuHPnDhwdHQX0NMVphB8QwUhcl0ku165d\nQ15eHr744gscPXoU27ZtQ05ODiIjIyUmgjqQef36NY4dOwYGg4Hq6mpoaWmhoqIC8vLy0NbW7vGL\nR6VYousXuicqKioIiXruxUtZWRl2dnbw9PSklMf48MMPcfDgQWRnZxPHdc+fP8erV68oV0R27XcB\nOndYqqqqpKs9L1++jIqKCmLXd/z4cQwfPpzY3V6/fh3Xr18X2f5dHG7fvs13EeUej3M4HJw5cwau\nrq6kxxo2bBjy8/MFAlJqaippN2E3NzecOHECampqfFWHtbW1OHToEJSUlPpNIZsOp1cuFRUVCAgI\nQEZGhtBdPJViFR8fH4wdO1Ygzx0fH4+0tDTS6QB5eXncuXOHkoI8GQZEMALocZlMSkqCu7s7kQQ2\nMzODmZkZ9PT0EBkZKWDzLIyTJ0/yXXhu3boloAH2448/UpIDAujLg9BJcHAwZs2aBScnJ2zYsAH7\n9+9HVVUVzp8/3+tn5e7uTvp9qPygVFRUUF9fDw0NDWhqaiIvLw8WFha9llYLw8TEBN9//z0iIiKQ\nlpYGoPOYc/Xq1UJzQD3R9TiUm7sYOXIkaT3FjIwMeHh4QFdXF1OnTsWePXtw4MABIrHu4uJCueOe\nLiIiIsBisfjyCu3t7fDx8aG8I501axZ8fX2Jjv+CggKkpqbi1q1bpE86LC0t8fnnn+PMmTNgsViY\nNGkSEYjk5eXh7u5OKZ/cFXGO1+hweuXCLdH/8ssvRXaV5pKVlYXly5cL3D927FhcvnyZ0limpqZg\ns9kiV9QKQ+qDUVtbGyoqKqClpQU5OTmUlpYiPj4e7e3tGD9+PKUKsbq6OkKOQ1lZGfX19WCxWLCw\nsCCdrOfqhXEJCQkRCEZUf5x05kHo5NWrV4ThHIPBQFtbGzQ0NLBq1SocO3YMtra23b62q7ozXZia\nmiIlJQX29vawtbWFj48PxowZg6ysLJH8iEaMGCFWVRjXX6u74MUtEydTBVdTU0M8j+v7xFvhpamp\nibq6uh7HkJSUzObNm3HkyBGoqKhgypQpfIGI6i5y/vz5YDAY8Pb2BtBZicg9vqLisjpp0iS4uLjg\n7NmzWLduHW7dugWg0wqCym6bC13Ha3Q4vXLJycnBr7/+KnYgAjr7jITla6kcj3KZNGkSLly4gFmz\nZsHAwECgnUWUICXVwSg3NxfHjh1DfX09tLW1sX79ehw9ehTy8vLo6OhAeHg4vvrqK9LVN8OGDUN5\neTl0dHSgq6uLxMRE2NvbIyMjQ6xVlLjQmQepqKjgu4Dx8uLFC8oK0Nwdmrq6OioqKvDOO+9ATU2t\n150IHQ28wvjss8+IH/SCBQugqqqK7OxsODo6ktLH4i5AuLd7gswF7YcffiAxa+q9P8JyMGSKWSQl\nJWNsbIwvv/wSXl5eUFRURExMDIqLi+Hh4UG5nwfoLG92dHREWVkZmpuboaurK5Iw8KxZs1BfXw8f\nHx/o6enBw8NDwCeJLHQdr9Hp9KqhoUFauqk3dHR0kJiYKHBMl5SURPp4lEtgYCAAEAuArojS9iHV\nwejatWt49913YW9vj7///hteXl5wdHQk+jZu3LiB27dvkw5GkydPBpvNho6ODhYvXozTp08jNDQU\nNTU1/ZozojMPcvjwYXz33XcCK6nnz5/j+PHjpNWagc4f1atXr2BhYQFzc3NcvXoVc+bMwYMHD3oN\nNpLSIut65GVnZydQ3tsT7u7uRGDo7SiRTADhFtVw+e2337Bjxw6R/be41WXc1/NWm5EZU1JSMgBg\nZWWFNWvW4NSpU9DR0RE5EHGRk5ODkpISlJWVKQeirg3q8vLyYDAYAiccVJqr6Tpeo9PpddmyZQgI\nCMDq1atJyZ71xIIFC+Dn54eSkhKi9J3NZuPu3buU2wgk0fYh1cHo1atX+Pnnn6Gurk6seniT5u+/\n/z4iIyNJj/fhhx8St8ePH489e/YgNzcXurq6xMqxP6AzD2JmZoZjx47Bw8OD2JKz2WycOHGCcvMl\nb1PdRx99hLNnz+L48ePQ0dHptaFQUlpk4sJ7fEjHUaKwY2LuwoIqmpqaiIuL6/Zv7n1kEVdKhisq\nyw2C3Nvq6upQV1dHcHAw0X5ApReOw+EgLCwMd+7cQWNjI4DOXei8efMwf/58UpV5XT8HYZ8L1bYI\nuo7X6HR6DQgIQH19PTw8PMBisQQaVamoxtjY2KC9vR03b97E7du3AYAQb6WjeV9cpDoYNTQ0EKsB\n7n95j9NYLBbps05h5ae6urrQ1dWl3FPCTSpyv6C8OmCirIjpzIP8+9//hre3N06cOIEtW7YgJycH\nJ06cwAcffIC5c+dSGotXx0pTUxPbt28n3fvU11pkZOHd0UnqKFFU6NQ0A8SXkuEVleXCYDAwduxY\ngSBFhZs3b+L+/ftYsmQJEbhzcnJw+/ZttLa2wsnJqdcxqKorkIGO47X29nb89ddfeO+992hxeqX7\nxMbW1ha2trbEIkDU0nAOh4PIyEhCRYN7LBceHg4VFRVKpxVcpDoY0cXDhw9x+/Zt7Ny5U+A4oKWl\nBfv378fixYtJ2ZGbmZnxbdm5f3Mv0h0dHTA3N6c0P3HzILzIyclhw4YNOHbsGA4fPozXr19jzUsD\n+gAAIABJREFU6dKl3TYH9sSNGzfw7rvv8q3+uRee0NDQHndafa1FJiovX75EbGwsysrKiGO7pKQk\nqKqqUs6vSRviSslIqv8tNjaWyJFyGTVqFAwMDHD27FlSwUgS0HG8RnfZs7iqMd0hbn9SeHg4YmNj\nsWjRIr7FuJ6eHkJDQwdnMOq6DRXFsjc2NhZOTk5Cz6UVFRWxZMkSREdHkwpGknCpFDcPwq3o4uWj\njz7C6dOnMWPGDIwdO5Z4DhVts5s3b+LOnTtYsWIFZs+ezfdYb8FIUtJJdPLs2TP4+PhgypQpyMjI\nIO6vr69HVFQUpfJ0aURcKRle6BSVra2tFbrTMDAw6LVaUJLQdbwmibJnQDTVGCotJlSeGxMTA1dX\nVxgaGvIFI2NjY8JmhCpSHYy6rgqErRLISE8UFRUJ+ATxYmVlRVSHiAqbzaa8I6KLniq6oqOjER0d\nTfxNNT/z+eef43//+x9ev34NFxcX0n46kpJOopOQkBCsX78eEyZM4PP/sbKywtWrV0mNIWxxJOw+\nUVXKxUUcKRle6BSV1dXVRUxMjMCx8YMHD/pdCNbExETs4zU6y57FVY0hqzdH9ai1srJSaDsDg8EQ\nufpPqoMRXefCDQ0NAnYRvCgoKIjtTX/w4ME+TcTzQneugZfJkydDR0cHp06dwqFDh/DVV1+R6nmQ\nlHQSna64xcXFQi8MKioqxJl6b3RNnAtbMPWlYGd3iCslQ6eo7NKlS+Ht7Y1nz54ROaOXL1+CzWYT\nfW39TUNDg8DnRbZ3ic6yZ3FVY+iwAxeGlpYW4STNy6NHj0SWq5LqYEQXw4YNw+vXr7vVG8vPz6fN\nT6g/oFJhJQojR47Ezp074ePjg3379mHjxo0Sfb+eoNMVV1VVFWVlZQIrvBcvXpD+PkgikU4XdErJ\n0CkqO378eOzevRsRERHIysoC0Hl8uHv3bpGaVemitLQUFy9eRFZWloAOI0D+86Kz7JkO1RhexHXM\n5rJgwQKcO3eOyD0mJycjLS0NMTExWL9+PaU5cXkrgpG1tTWuX7+OzZs3C2jZtba24tq1a5Rk66Wd\nqqoqPH/+HLW1tQKrO1GFDFVVVbFlyxZcvnwZ//nPfyi/Pjs7G5mZmXxz4hZ9ULG2oNMVd/r06fD3\n9yf6uJqampCWloZLly5RUgKQVuiUkqFDVJartA0IV77ob6Xt8+fPQ1VVFZs2bRK5cZZu6FCN4UKX\nYzYAzJw5E0pKSrhx4wYYDAZOnToFLS0trFmzRmTJtrciGC1atAj79u3Drl274ODgQEitFBcX4/79\n++jo6KD0P0IYwjSf+oPHjx/j/PnzUFZWFrrSoRKMePuygM5KvRUrVsDY2Bipqamkx/nrr79w48YN\nmJiYiP0jp9MV18nJCcHBwcTRHjdXYGdnJ7KysqgIM4/sDrLHPHRKydAhKivNSttAZ/7rv//9r9ia\ndkVFRTA0NASLxcKLFy8QHR0NDoeDiRMnYsqUKZTGo1M1hi7HbC5crdC2tja0tLSIrWLzVgQjNTU1\n7NixA4GBgbh+/TpxrCAnJwdra2usXLlSrB9sVVWVSKXTXOjMg1y7dg0ff/wxHBwcRJ4Pl+4uyNOn\nT6d0PHD37l1s2bKlxyISstDlitvR0YHq6mosX74cixcvRmFhIZqbm2FgYCCyOZg48CbNS0tLERIS\nAjs7O76S7Li4OEruo3RKydAhKivNStsAoK+vj8rKSpEvqunp6Th58iTa2trAYrGwdu1a/P7770QO\nxcfHB83NzT1qOnaFTtUYuhyzgc48NddenMlk8p048T5GhbciGAGdK4yNGzeiqamJ6AvS1tYWuR+G\nTs8SOvMg1dXVtHVTBwcHw9jYWMD479GjR8jNzcXHH39MahwOh0ObwgVdrrgdHR34/vvvcezYMQwd\nOlQsWRs6GDNmDHH70KFD+OyzzzBu3DjivsmTJ8Pc3BxhYWECZfbdQaeUDCC+qKyklbbF5ZNPPkFA\nQADmz58PPT09gSb53hYpN27cgIODAxwcHBAVFYVz587BxcWFCLwxMTG4d+8epWDUnWqMnp4eZZ07\nuhyzgc5dtzA4HA5yc3MpjcXlrQlGXJSVlTFy5Eixx6HTs4TOPIiFhQVycnL41BNE5dGjR0KrcUxN\nTREUFEQ6GNna2iI6Ohrz588Xe050ueLKyclBS0sL9fX1Utegm5OTI7RybfTo0YTSNRnolJLhRZR+\nFy50K23TiZycHCorK3Hy5Emhj/dWwFBYWIjNmzdDRUUFixYtQkREBN93derUqQgKChJrjlzVGFGg\nwzE7Pz+fOLnhmj5y4XA4yM7OFvkY/a0LRnRBp2cJnXmQiRMnwtfXF3PmzMGIESMEVndUehzq6uqE\nHl+yWCxKpfBLly7Ff/7zHzx69IhYcfIGECqrbTpVAZycnODv749Vq1b1y9Fcd6ipqSEhIUGgR+TR\no0eUdjh0SsmI2+/CC51K23Ry4cIFmJiY4PPPPxfp2L6pqYnYYXADK29Du7KyMlpaWuiZrAjQ4Zj9\n888/C73NhcViYeXKlSLNTxaMRIROzxK68iDA/zeY/vnnn0Ifp9LjMHz4cGRmZgpUGmZmZlIKntev\nX0d+fj7RFMzN2VHdzdDN5cuXUV9fD09PTygrKwv0oom6cxCXZcuW4fz580hKSoKxsTE6OjqQl5eH\n9PR0fPbZZ6THoVNKhg6XZEkobdNJSUkJtm/f3q9FFJKEDsfsQ4cOAQC2bt2Kw4cP813rmEymWJ+d\nLBiJCJ2eJXTlQQB6exxmz54NX19fLF26lAgkbDYbf/75JyXtrZiYGHz77be0aL01NDQgJCREaLEH\nABw7doz0WNJqNT9t2jTo6uri3r17SE9PB9B5PPP999/D0NBQpDHFOVoD6Ol3kYTSNp0YGBigtLRU\n5M8YADw9Pfm+k6Ik8iWJuI7Z3B2jJBr8ZcFIROj0LKErD0I3c+fORUNDA4KCgoiqLEVFRcybN4+S\nAy2TyaRNIfuPP/5ASUkJHBwcxE7KS0qEkg4MDQ3FKhYA6D1ao6PfRZobhIHO7/uFCxewePFioQUM\nveVquKaY0kxHRweKiopQUVEhsDihKhtF51gAwKiqqhLNBUwGXr58idDQUOTn56O5uRlGRkZYvHgx\nZc8SumlubkZWVhYqKioEOsnJ9hlxOBy8ePECRkZGkJOTI8QPdXR0KBuh/fnnn2AymbT8WDdv3ow9\ne/bQmuOpra0Vusuio9BFHMSdV2BgIPLy8uDk5CT0aI1Kef6uXbuwadMm6OjoYP/+/bC3t4e9vT2S\nkpJw8eJF/Pe//6X0b5NGeus17C+5r+5UE4YMGUKpCrSsrAxeXl4oLCwEh8OBvLw82tvbIScnBwUF\nBUrGm3SOxUW2MxIDOkQV6eb169c4duwYGAwGqquroaWlhYqKCsjLy0NbW5t0MGIwGDh8+DCOHz8O\nBQUFsS7Mb968QXJyMl8BA+/7UDmGVFFR6VFnkAoFBQU4e/YsCgoKhD7eXxcfuuZFp5SMtLok04kk\nNR5FhU7VhEuXLsHExASenp5wc3ODl5cXcnNzERAQQNl4k86xuMiCUT/x448/EpLtvUm3U5F2Dw4O\nxqxZs+Dk5IQNGzZg//79qKqqwvnz5yldfBgMBnR1dVFeXk66qbE76DyGXLBgAa5evYpVq1aRTrp2\nh6+vL0aOHIkvvviCFpUCuqBrXnRKyUirSzKdSFrjURToVE3Izs7Gzz//TJxsdHR0wMTEBGvWrMHZ\ns2dJl3fTPRYXWTCiwNdff03qeQwGo9dtKm/Zbk8y71Qv1q9evSKUjxkMBtra2qChoYFVq1bh2LFj\nlBvufH19sWzZMowcOVJgR0I2GNBZjv3gwQPk5+fj6dOn0NbW5uudYTAYlBLGBQUFcHd3l7rqKbrm\nRaeUTFfE6XeRZp48eUK4l/7yyy8AOv3Q1NTU+kW/kk7VhLa2NuI3rKqqiqqqKmhqakJbWxtv3rzp\nt7G4yIIRBagcJ/UGt5mUw+FgxIgRMDY2FjDZExVuAFNXV0dFRQXeeecdqKmpoaqqitI4p06dAtB9\niXN/HGONGzeOT5mAF6qBW0dHB1VVVVIXjOiaF91Ha4mJiYiIiEBRURGAzorSuXPn9psfFd3ExcXh\n6tWrcHBwIKoYgc5dZVhYWL8EIzpVE3R0dFBYWAgTExOMGjUKd+7cwdy5cxETEyPQL9mXY3GRBSMK\nSOLLyGAwcOTIERw/fpyW8fT09PDq1StYWFjA3NwcV69exZw5c/DgwQPKFW10utomJiYiISGBqLzh\nDRxUtPforFhydnaGv78/nJ2doa+vL6BQIO4xYH/Pi04pmfDwcISEhMDW1paQt8nNzcW5c+dQVlZG\nqbpSWgkLC8OXX34Jc3NzhIaGEvebmZnh4sWL/TInOlQTMjMzMXr0aMyfP5/YTX344Yc4fvw47t+/\nDxUVFbi6uvb5WF2RBSMx4HA4ePbsGbFS1NfXh7W1NaUVOp25GQB8QpofffQRzp49i+PHj0NHR4dy\naa0wSRpRiIqKQmhoKOzt7ZGYmAgnJyfk5OSAzWb360Xs6NGjAIBff/1V6OP9VcAgqXmJc7QWHh6O\n9evX8/XVzZw5E5aWlggICBgUwaisrEyoHYaCggKampp6fO0///xD+ndPJXdLh2oCtwBi1KhRsLCw\nAIvFwqhRo/Dbb7+hoqIC6urqpBc4dI7VFVkwEpGysjKcOHECb968gba2NoBOtWUdHR1s2rSJUukx\nXbkZAHyadJqamti+fbtY/UpNTU2IjY1FUVERGAwG9PT0YGtrS0nP7d69e3B1dYWFhQVu375N7G7C\nw8NRWFhIaT4cDgeRkZFISEhAZWWlQH8DlaZXOnd+dCKN82pqahKqd2hlZUXaFVfaGTZsGPLz8wUC\nUmpqaq9HT1evXuX7jdXW1qK9vZ34nTQ1NUFeXh7q6uqUghEdqgn79u1DZmYmsrKyEB0djRs3bkBR\nURFmZmawtLSEhYUFjIyMSF0j6ByrK7I+IxHhiimuWbOGqHiqra2Fr68vAPLFDoD09je8fv0aR44c\nAYPBILrSuU2+7u7upMu93dzccPToUSgoKMDNzQ2HDh2CsrIyGhoa4OnpSSmA3LhxAwkJCZg3bx78\n/f2xbt06ZGdnIyEhAUuXLqXFOkOGIN7e3pgwYQJmzJjBd398fDyePn0KNze3fpoZfYSFheHBgwdY\nsWIFjhw5gj179iA1NRW3bt3CsmXLSBsuPnjwAHFxcVi1ahWxEy0qKsLFixcxc+ZMSqr6aWlp3epJ\n9vRYTxQVFSErKwtZWVlgs9mora3FkCFDKP0OJTGWbGckIpmZmdi3bx9f6a2amhpWrVqFXbt2URqL\nzpUwnXI5QUFBmDhxIlxcXIhVWFtbGwIDA3Hp0iXSGmIqKiqor6+HhoYGNDU1kZeXBwsLC8oFFUDn\ncYirqyuMjIzg7+8PGxsb2NjYYNSoUXj69CnlYPTy5UvExsairKwM7u7uADr7c1RVVWmRLxKH5uZm\noY3LfdWMGx0dTaxwDQ0NCasTXo+l1NTUPjcilBTz588Hg8EglNF/+uknwg6GivNvaGgotm7dync6\noqenh7Vr1+LQoUOUgtGxY8e6XYz29FhP6OnpgcVigcViYciQIXj06BGam5spj0P3WLJgJCJycnIC\nFwmg8xiJ6haVrtwMQK9czsuXL+Hq6sp3HMBkMrF06VJKJdSmpqZISUmBvb09bG1t4ePjgzFjxiAr\nK0tA2683qqqqiEIMRUVFNDY2YsiQIZg4cSLlJPOzZ8/g4+ODKVOmICMjg7i/vr4eUVFRRHDqa7g7\n7GfPngl9vK92ymFhYXzfZVVVVeTm5iIvL4+4T01NDXFxcULdWwci8+bNg6OjI8rKytDc3AxdXV3K\niiNVVVUCRSdA529HlAWYMKhan9TW1oLNZhNHbKWlpTAyMoK5uTlcXV0pXYPoHIsXWTASkTFjxuDC\nhQtYu3YtsQKqqKiAr6+vSFtnulbo6enptMnlKCoqCrWRqKuro/QD/eyzz4gd2oIFC6Cqqors7Gw4\nOjoK9UvqCXV1dVRXV2P48OHQ1tZGZmYmJk6ciIKCAsrKDCEhIVi/fj0mTJiA+Ph44n4rKytcvXqV\n0lh0EhwcDAaDgb1792L37t04cOAAcnJyEBISQtpDios4UjL79+8X+d8wkJGTkxO5PBno9J3y9/fn\nMzWsrq6Gn58f6Qs1t60CALy8vPhOOLiacGSvCz/++CPevHlDBIyVK1fC1NRUpFYSOsfqiiwYicjK\nlSvh5eUFT09PqKurE/I7JiYmlGT+AXpX6HTK5YwfPx4+Pj5wcXHBqFGjAHQezQQGBpLa0RQXFwu9\nf/To0cQPqaysjFKFl6WlJZKTk/Hee+/B0dER586dg4GBAfLz80k7oPLOT9jCQUVFpV+T8hkZGfD0\n9CQWFBoaGpgyZQo0NDQQHBxMuqSXTimZwcqJEyewadMm4nZ3MBgM0nng1atXw9vbG9u3bycCfnV1\nNUaMGCG0gVUYvBYtGhoafI/Jy8tjzJgxpIV+S0tLoaKiAi0tLWhra0NbW1vk4EHnWF2RBSMR4HA4\n0NDQwPfffw82m01cdHV1dTF69GgBtd/eoHOFTqdczooVK/DHH38INL1OmTIFK1as6PX1P/zwA6n3\noXLsxKtkbWtrC01NTWKXRVUWX1VVFWVlZQIl9S9evKDN7FAUmpqaiBU1i8VCXV0d1NXViaBLFnGl\nZAICAgjZJmHwSjq5uLiQnpc0wbsQoktRQktLC7t37wabzeZrEObasJCB21ekqqoKJycnseZz7Ngx\n5OTkICsrC//88w8CAgKgpqYGc3NzWFhYwMLCgqgI7suxuiILRhR5+PAhbt++jZ07d0JBQQHm5ubE\nl6ylpQU///wzFi9ejKlTp5IeU9wVetd+FLrkcoYMGQJXV1eUl5cTqt26urqkNby4vRFcfvvtN+zY\nsYOy+WBPWFlZiWyxPn36dPj7+xMBrqmpCWlpabh06RKlhDXdaGlpoaSkBCNHjsSIESMQExODhQsX\n4sGDBwKr5J4QV0qmurq61/xnf9uciAvvsSfVI9De4L02iEpaWlq3wejAgQOkfs9MJpOYy5IlS9Da\n2ors7Gyw2Ww8ePAAFy9ehLq6On777bc+HUtgbMqveMuJjY2Fk5OT0JyJoqIilixZgujoaErBSNwV\neld5HHHlcmpqapCSkkLo2HFXyLzwnod3h6mpqcB93OM+caioqACbzUZdXR3hGsuFSvOlk5MTgoOD\nCQUIrgK7nZ1dv1aI2dvbo6CgACNHjsTSpUtx/PhxhISEQF5entIRsLhSMlyNw8FOcnIy3n333R5P\nEu7cuUPJUDIvLw8ZGRmora0V+I6SOVXg8vLlS6H3czgc5Obmkh6HFwaDIfB7rqys7PexZMGIIkVF\nRbC0tOz2cSsrKwQGBlIaU9wVOt2mXtHR0XyVgikpKZg0aRKxo8nPz8f9+/fFPj4Qhfj4ePj5+UFV\nVVWoojVV079PPvkEixcvRmFhIZqbm2FgYECrV5Io8Np8jB49Gr/++isKCwuhra1NaWdEh5TM28Cp\nU6dgaGiIjRs3drvrv3btGulgdPfuXQQFBcHIyIhvwUZlF5mfn8/3e+OFw+EgOzub9FFye3s7Xr58\nSVS/5eTkoLW1FZqamrC0tISdnR1pDzY6x+qKLBhRpKGhoccCAQUFBaGumj1B5wq9vLwc8vLyxEUr\nOzubsDMnq9idnJws4DLKe9yTnp6O4ODgfglGISEhWLNmDa0urUOHDqVkUtbXsFgskcplxZWSkURy\nX1phMBjYt28foRQiDuHh4di4caNIbqdcfv75Z6G3ubBYLKxcuZLUWN988w1aWlowdOhQWFpaYuXK\nlbC0tBQpt0PnWF2RBSOKDBs2DK9fvxaqYQV0rmKoJr/pXKGfOXMG//rXv6ChoYHy8nIcPnwYenp6\nePjwIaqrq0n1g5SXl/Mlc1ksFt/jxsbGKC0tpTw3OmhsbKTcm8TLH3/8Qfq5q1evFvl9pAFxpWTI\nJvepLr6kke+++w7+/v44cuQInJ2d4ejoKPJYDQ0NIucxuRw6dAgAsHXrVhw+fJgvz8pkMikpun/8\n8cewtLSEjo6OWHOie6yuyIIRRaytrXH9+nVs3rxZ4Afd2tqKa9euiazuTccKvaioiMjLPHz4EJqa\nmti1axdevXqF06dPkwpGzc3NaG5uJv59XSuvOBwOqSIEYdYTwu4jq+QAAJMmTcLTp09F3hm1tLQQ\nRyUcDgdJSUlgsVgwNDRER0cHXr16hYaGBrFWtdJAW1sbfvjhB3h6emLy5MmUKw2BzgtPcHAwli9f\n3m1yv7q6elBYjjOZTKxduxZGRkYIDg5Gbm4u1qxZQ7nhFei8RqSmpor0mXPhHkHT0eBMteWhr8bq\niiwYUWTRokXYt28fdu3aBQcHB2KFUFxcjPv376Ojo4NU/0bXogDuxb3rfVTLZtva2ogfUHp6OhEY\n9fX1SScWhw8fjvz8/G6PK/Lz80lV1HV9jrAAQrUSy8XFBUeOHEFSUhKfhTn3s+rN8nj9+vXE7StX\nrsDe3h7Lly8nxmlvb8fly5eFdtAPJJhMJpqbmym3GXQlJiYGqqqqQhcxNTU1OHjwoNhGfdKEg4MD\nRo4cid9//x0HDhzAxo0bKZ9QjBw5En5+fsjMzBRqAdKTmaYwpM3wT1LIghFF1NTUsGPHDgQGBuL6\n9etEpYycnBysra2xcuVKUlbRkiqb1dXVxcOHD2FkZITs7GyiuKG6upr01t7a2hrXrl3Dtm3bBHZ/\nbW1tpHd/VC0ryHD//n3k5ORAR0cH1dXVAo/3Fox4iYuLw759+/gu2PLy8liyZAl27txJe6lvXzNj\nxgyEhYWRthoQhpubG06cOAE1NTXCxwjolIQ5dOgQlJSUsGXLFjqmKzWYmZlh165d8Pb2xr59+yib\nasbExEBFRQXp6el8Jn1cqAQjaTT8kxSyYCQCw4YNw8aNG9HU1ISysjJ0dHRAW1ubklaUpMpmP/zw\nQ3h7e6O1tRVjx44lKv+SkpJgYGBAaoyFCxcSUjRz5swhdn8lJSW4f/8+OBwOpTJXOrlz5w6+/PJL\nWtxF29vbUVFRIZATKy8vF6o72Ffs3bsX9vb2mD59ulhur+Xl5UhOTsbjx48xYsQIviMnBoNB6iJr\naWmJzz//HGfOnAGLxcKkSZOIQCQvLw93d/dBtTPioqGhgW3btiEgIICy8SWdMkrSaPgnKWTBSAyU\nlZX7TEGZLGPHjsWhQ4dQW1vLV+EyZswY0uW8qqqq+O6774Tu/saNGwcXFxdSuz9JwOFwRNL+E8aU\nKVNw+vRpLFu2jE+J+vr165gyZQot7yEKVlZWuHnzJi5fvoxJkybB3t5epOZJZWVlAe8cXtUEskya\nNAkuLi44e/Ys1q1bh1u3bgEAvv32W4FAPhAZM2aM0PuZTCZWr14NIyMj3Llzp49n1Yk4hn8DDZmf\nkRTQ2tqKqKgoobYPDAaDki13RkZGt5U8d+/e5ethIUNTUxNROUd19ycJ/P39YWRkxHdkJCrcgpO/\n//4bra2tADp/5LNmzcKyZctESl7TBYfDQWpqKuLi4vDs2TNoamrCzs4OM2fO7Lcy9Nu3b+P69evQ\n09ODh4dHvy1IBgIVFRVITk5GRUWFgAEklabXnTt3Yv369TAxMcGGDRuIgoaEhATcunULP/30E63z\n7k9kOyMpgOsTM336dIGGWqo5o9OnT2PLli0Cq6nIyEj8+eeflIORsrIy6eO9vkBBQQFBQUF4+vQp\n9PX1BRL0VPIjCgoKWLFiBZYtW4by8nIAncUb/RmEuHBzkNbW1qirq0NsbCxCQ0MREhKCsWPH4v33\n3xe7fJgMXasf5eXlwWAw8Pvvv/PdT6UicrCTmZmJkydPwsDAANnZ2bC0tMTr16/R2tpKuYdp1qxZ\n8PX1JQJYQUEBn+HfYEIWjKSAxMREbNu2jfDpEQdnZ2ccP34c27dvJ+SFwsPDcePGDaKZdiBTUFCA\nUaNGoa2tjXCdFRcFBQXaRDLp5tWrV4iNjUVCQgJUVFQwY8YMNDU1wcvLi6gE7I6dO3f2ODa3Mqsn\nulZECquiHMjadJLg2rVrWL58OWbNmoUNGzbg22+/RUtLC3x9fSkf69Nl+DcQkAUjKUBeXp42lWh7\ne3vU1dXhyJEj+O677/D48WPcvHkTmzdvFlu0URr49ttvxXr9QFAVqK2txcOHDxEXF4eioiKMHTsW\na9euhbW1NbETnDNnDn755Zceg9HcuXP5/uZwOCgoKMCTJ08wf/58UnORREXkYKeoqIjI1cnJyaG1\ntRWKior4+OOPsX//fsqqKnQY/g0EZMFICpg9ezbCw8OxdOlSWsZbuHAh6urqsG/fPrS1tWHz5s20\nuskOZCRhGUA327dvx7Bhw2BrawtbW1uhenT6+vowNDTscZzuVs5WVlZ4+vQpHVOVIQQmk4m2tjYo\nKSlh6NChKC0tJfqNqKpV3Lt3DzY2NlBRURHL8G8gICtg6Cd8fHyI442Ojg4kJydj+PDhAnkQMiW4\n0dHRAkclHA4Ht2/fhrm5OV8gotpwJ20cPnyY73MD/v+YSEFBAe+88w5mzJghVXkuqmRkZMDS0lJi\nx19NTU3w8PDAyZMnJTL+287hw4fh6OgIa2tr+Pv7o7CwEDNnzsSjR4/Q0dFBaXe/bds21NfXY+LE\nibCzs+uTPGF/IdsZ9RNKSkp8ZbbTpk0T+jwyF6SwsDChz5OXl0dOTg5ycnKI+wZ6MBo5ciSio6Nh\nYGAAY2NjdHR0IC8vD69fv4atrS0KCgrwyy+/YMuWLT2qq3PhSvGXlpYSRyuNjY1gMpn9dhQi6QtO\nZmbmoOwNkhRsNhtPnz7FtGnTutWk5MXZ2Zm4vWzZMgQGBuL27dvQ1dWlbEL422+/IS0tDXFxcTh+\n/DixY545c2a/GkBKAtnOSMaAwtfXF/r6+gL5kMjISBQUFGDNmjWIjIzE48ePezUeq6ynCVoLAAAg\nAElEQVSsxMmTJ1FcXIzW1laibPbKlSuoq6sTUC6XJD/++CPtzz1x4oSAU2tNTQ3y8vKwfPlyscRA\n3yb++ecf3LhxAxUVFbCyssI333zTL/Ooq6vDw4cPER8fj4KCAowZMwZ2dnaYMGGC2LJP0oBsZyQF\nNDc3g8FgENYUpaWlSElJgb6+PqnV/dvEkydPhPZpzJw5Ezt27MCaNWswY8YMhISE9DpWUFAQjI2N\n4enpiY0bNxL329jYwMvLi9Z59wbZHSuVozttbW2+YMRgMDB69Gh8+umnMDY2FmWabyU2NjawsbFB\nY2Mj2Gx2v81DVVUVo0ePRnFxMYqKipCfnw8/Pz8EBwdj7dq1A/5aIQtGUsCJEyfg4OCAyZMno7a2\nFr/88gsUFBTQ0NCAlStXUmrw9PHxwdixYzFz5ky+++Pj45GWlkZZZ0vakJeXx6tXrwQqA/Pz84nV\noTD3SWGw2Wzs3btXQH9PW1sbVVVV9E2aBO+99x7tY5L1u5FBjiFDhvSLFlxtbS3++ecfxMXFobi4\nGNbW1vjqq6/w7rvvor29HeHh4bhw4QIOHDjQ53OjE1kwkgIKCgqIi+vDhw+hrKyMffv2ITs7GwEB\nAZSCUVZWltBy37Fjx+Ly5cu0zbm/sLW1xenTp7FgwQI+CZ/w8HDY2dkB6MyJkKmU46oudKWqqqpH\nA0UZg4e8vDzSz+V+3/qSU6dOISUlBVpaWoQCB697rJycHBYuXEjqJEDakQUjKaC5uZkQxExLS4O1\ntTWYTCZMTU0pm9g1NDQIlewZLFpWH330ETQ0NBAZGYmKigoAnaoJCxcuJNQlTE1N+Y7dusPc3BxR\nUVF8tu3Nzc24cuVKn1ctke1pYjAYPfZH0TXO2wKZxl8udHgLUUVZWRnffvttjz2CcnJy2LdvXx/O\nSjLIgpEUoKWlhaysLBgYGIDNZsPBwQFA5/ac6gpdR0cHiYmJAsd0SUlJg6JPgcFgwNHREY6Ojnx6\ncryQ1W5zdnbGoUOHCGl+b29vvHjxAgwGg7Dq7ivoOj4d6MewfQ1VRe6+5vPPPyf1vMHw25YFIylg\n0aJFOHHiBOTl5aGrq0uoUqenp0NfX5/SWAsWLICfnx9KSkpgZmYGBoMBNpuNu3fv4pNPPpHE9Psc\nusqx9fT0sG/fPkRHR2P48OFobm6Gg4MDZs+e3ecioHTlIgaTv01fICnh37y8PGRkZKC2tpZQvedC\nRSgVALKzs3Hnzh0UFRWBwWBAX18f8+bNg6mpKZ1T7ndkpd1Swps3b1BeXo7Ro0cTF9Tnz5+DyWSS\n6m3gJS4uDjdv3iTEP7W0tLB48WLY2trSPu++ho5y7OLiYlLv1ZcKDfX19YQdQ29d+r3ZNhQUFODe\nvXtYsWKFwM66paUFQUFBcHR0JLQLZfw/HR0dKCoqEqq2TdaK/u7duwgKCoKRkRFffofbU8iVoyLD\n48ePcebMGVhbW2PUqFEAgJycHKSkpOCLL74Qy9pc2pDtjPqRJ0+eEF+md955R2CrPWrUKPj4+FA2\n4uPKyDQ2NgKAWAZt0gYd5dg//PADqef1ZY7A3d2deD93d/cen9vbvCIiIqClpSX0iFdRURHDhg1D\nREQEVq9eLfqEByFlZWXw8vJCYWEhOBwO5OXl0d7eDjk5OSgoKJDOsYWHh2Pjxo2kg1dPhIaG4tNP\nPxUo/Y+JiUFoaKgsGMmgh3PnzmHIkCFCzb04HA7OnDmD58+fizz+YApCXOgox+6aD/rtt9+wY8cO\nvubQvmbPnj1Cb4vC8+fPsXjx4m4fnz59Oo4ePSrWewxGLl26BBMTE3h6esLNzQ1eXl7Izc1FQEAA\nJTv7hoYG2gpg3rx5I1SdZcqUKQgICKDlPaQFWTDqRz744AN4eXlh69atfEdxHA4HZ8+eRVZWFrZu\n3drrOD/++CPRld9bdz6VTn9phI5ybGFn7dwjkP6C1z5EXCuRyspKDB8+vNvHhw0bhsrKSrHeYzCS\nnZ2Nn3/+mTgm7+jogImJCdasWYOzZ8+Sdkq2trZGamoqLbuWoUOH4sWLFxg7dizf/S9fvuQ7AhwM\nyIJRPzJ//nzU1tbixIkT2L59O3R1dcHhcHDu3DmkpaVh69atpPxPeLfwPXXyDwbfGWkqx5YkL1++\nRGxsLMrKyohju6SkJKILvyeUlJRQVVUl1HsI6JQEUlJSon3OA522tjZiQaOqqkp8htra2njz5g3p\ncUaOHAk/Pz9kZmYSat28UNGHnD17Ns6cOYO5c+cSC6aXL18iIiKCtA3IQEEWjPoZZ2dnwn9o+/bt\n+PPPP5Gamgp3d/deLQK48HbvS6KTX5qQpnJsSfHs2TP4+PhgypQpyMjIIO6vr69HVFRUrzklU1NT\nxMXFdXu0FBsb2+87QWlER0cHhYWFMDExwahRo3Dnzh3MnTsXMTExlEqnY2JioKKigvT0dOJ7yguV\nYLRw4UIMGTKEMMgEOguSPvzwQ8yePZv0OAMBWTWdFMDhcODt7Y2MjAzIyclhy5YtsotFDzQ1NSE6\nOhr5+flobm6GoaGhWOXYGzZs6JeGxu7Yu3cvlixZggkTJvDNraKiAvv27cPhw4d7fP3z589x+PBh\nzJ07F3PnziU+l5qaGkRERCAyMhLu7u6DwmyRThISEqCkpITx48ejoKAAx48fR2VlJVRUVODq6trv\nO+/u+uoGC7KdUT9y48YN4uhMT08PqampMDMzQ1paGtLS0ojnUUmefvnllz0+fvr0adEmK0UoKyuL\ndURx8OBBUvd5eHiI/B7iUFxcTPSa8aKiokJUSPaEmZkZVq1ahYCAANy5cwfKyspgMBhobGyEgoIC\nPv30U1kgEgJvocCIESNw4MABVFZWQl1dXaBgpj8YrEGIS/9/wm8x8fHxfH8PHToURUVFKCoq4ruf\nSjDqKgfT3t6OwsJC3L9/ny/PMtCgszeoay7FxsZG4Dn9mV9TVVVFWVmZQB/QixcvSHvY2NraYty4\ncUhMTERJSQmAzmOoSZMmDbrEt6RgMBjd5t16IyEhAXfu3CE+e11dXSxYsABTp04l9fr8/HzEx8cT\nOpNdf9dycnL47rvvxC52kSZkwagfkYTKbteqG6CzK9/IyAgRERGURFelCTp7g9atWyfudCTK9OnT\n4e/vTzTwNjU1IS0tDZcuXerWSlwY6urqlJ7/ttPa2oqoqChkZGSgpqaGr9SfwWBg9+7dpMaJiorC\nlStXMGvWLCxcuBBAZ6Oqr68v6urqCLmvnrh//z5f8VJLSwtcXFyIOaWkpODu3buDqldMFozeEkxN\nTXHq1Kn+nobISGNvkKRwcnJCcHAwcfHbvHkzAMDOzo64uJEhLCwMOjo6As2XSUlJePPmDebNm0ff\npAcBgYGBSElJwfTp0wW8gajslCMiIvD5559j0qRJxH1Tp06Fubk5Ll++TCoYsdlsgaNo3teZmZkN\n6N+zMGTB6C0hPj6etICoNCKNvUGSgslk4pNPPsHixYtRWFiI5uZmGBgY9Ng7JIz79+8LdSXV19dH\nYGCgLBh1ITExEdu2baOlz0tYI7uVlRXp/q6Kigq+I8KJEyfyPa6jo4Pq6mqx5iltyILRIENYU2tN\nTQ0aGxul/nhKBj9Dhw7lW0BkZWXhypUr2LlzJ6nXV1VVCQ1gQ4cORU1NDW3zHCzIy8uTzsn1hKam\nJpKSkgRykSkpKaQXFAwGAzU1NcR8ukqC1dXVCfQvDXRkwWiQwTWY48JgMKCmpgYzMzNafmgyJMuj\nR4+QkpKC9vZ2TJ06FRMmTEBpaSkCAwORmpoqsELuiaFDh+Lly5cClXN5eXl9rko+EJg9ezbCw8Ox\ndOlSscZZuHAhfH19kZmZiVGjRoHBYODly5f4559/8Omnn5IaQ1dXF2w2m1Cl78rz58/7VMi3L5AF\no0FEe3s7VFRUMGHCBKioqPT3dGRQ5N69e7h06RIMDAzA4XDg5eUFZ2dn3Lp1C/r6+vjuu+8o2QZM\nnz4dFy5cwKpVq2BmZgag8yJ28eJFoRWEbyM+Pj5EPqijowPJyclITEyEvr4+YWMPdC7qyHpF2dnZ\nYejQoQgPDydaNPT09LBx40ahBUbCsLGxwZUrV2BoaChQVVlYWIgrV67IFBhkSC/y8vLw8/MjXT46\nkJD23iA6iI6OxurVq4ndbUxMDPz8/DBv3jw4OztTHm/JkiWorKwUEEW1sbGBk5MTLXMe6CgpKYHB\nYBD2DsJESQHqpf7jxo3DuHHjRJ7XnDlzkJKSgp9++gljxoyBjo4OAKCkpATp6emwsLAYdGorsmAk\nxXB7FaZNmwYHBwdSemIGBgbIz8+n7IEk7Uh7bxAdlJWV8R3LTJs2DX5+fli0aJFI4zGZTKxbtw4f\nfPABXr9+DQAiFUIMZtasWUP7mB4eHkIXSr09xou8vDw2bdqEqKgoJCQkIDs7G0Bn4YKzszMcHBz4\ndm6DAVkwkmLU1dVhaGiIx48fIzIyktSX+P3338f58+excOFCGBgYCHRtD9Rz5reh+KK1tZXv/xd3\n8SHukevw4cNlAYgCb968IRrP9fT0KFt6d1cc0t7e3qtxIi/y8vJwdHSEo6MjpfcfqMiCkRRjaWlJ\n9Ds0NDSQes25c+cAABcuXBD6uDRpsMkQJDQ0lLjN7aHivQ/oXZHj9u3bpHaJCxYsEGGGg5e6ujpc\nuHABz549I3YdHA4H1tbWWLt2LVRVVXt8fVJSktDb3HFycnKgpaVF/8QHCTKhVCmlqakJysrKlF9X\nUVHR4+OiypvIkDw7duwg9bzelDs2bNgAFovVrZYZNz/yn//8h/IcBzPe3t6orKzEp59+CgMDAwDA\nq1evEBAQAA0NDT5nYWFwJXtaWloEvLWYTCa0tLSwbNkyoT1IMmTBSCq4efMmjIyMMG7cOHR0dODU\nqVN49uwZNDU1sWnTpkGlPyVD8uzfvx9FRUWYPHky7OzsevU/ktGJm5sb9uzZI3As9+bNG/z000+k\nFQ927NghEamvwc7gyoANUGJjY4kfQFJSEtLT0+Hm5obJkyfjypUrpMZISUnBgQMHhDqhNjc347ff\nfuNTApcxePn++++xY8cODBkyBF5eXti9ezfu3Lkja3TtBRUVFaGnEcrKyhgyZAjpcWSBSDRkOSMp\noLq6mkgwJycnY9y4cRg/fjzMzc1JH91ERUXB3t5e6NGMkpIS7OzsEBUVJdSaQMbgY8SIEVixYgU+\n+ugjJCcnIzY2FiEhIXj33Xfh6uo66O0IRGHevHkIDAzEv//9b6JopL6+HgEBAZSlkxITExEREcFX\nCDF37lw+vToZ/MiCkRTAYrEI6Y+MjAyilLejowNtbW2kxsjPz8dnn33W7ePjxo1DSEgILfOVMXBg\nMpmYNGkSlJWV0dLSgmfPnglU7cno5PHjx8jPz4eHhwdRaFBWVgYGg4HKyko8efIEQGcLQU+LxPDw\ncISEhMDW1pZQyc/NzcW5c+dQVlZGiyZgaWkpCgoKYGVlNWgs5GXBSAqwtraGt7c3NDU1UV9fT0i+\n5Ofnky7Jra2t7bEEWEVFBbW1tbTMV8bAoLS0FHFxcXjw4AEAYObMmVi7dq1MnaMbyDaq9lapGB4e\njvXr1/NJN82cOROWlpYi7bKEkZKSgkuXLkFVVRXz5s0bFJWRsmAkBaxcuRK3b99GeXk5vv76a8L8\n7PXr15gxYwapMdTV1VFcXNxtsUNJSYnMVO0tIT4+HnFxccjNzcX48eOxevVqjBkzZsA3BUsauswn\nm5qahFqUW1lZkXLqJYOtrS0mTJiA/Px8ZGVl0TJmfyOrphsk/PHHH2hsbISrq6vQx3///XcoKytL\npONchnSxYcMGDBs2DFOnTgWLxeo2CA2G1bQ04u3tjQkTJggsJOPj4/H06VO4ubn108ykG1kw6ify\n8vJgZGRE3O4J7vN6oqysDHv37oWenh4cHR0JLavi4mJERkaiuLgYu3btgra2tviTl9EvkJWHoqtf\n6W3jyy+/7PHx06dPkxrn1q1bCAsLw9ixY4nfbm5uLlJTU7Fw4UK+5tlZs2aRnl9TU5OAmSSVKj9p\nRxaM+okNGzYQaggbNmzo8blkVRPy8/Pxv//9j9Ah42JgYIA1a9bA0NBQtMnKkAoyMzPxzz//ID8/\nH1VVVaTkoWSQJzU1le/v9vZ2FBYW4v79+/jXv/5FFCP0xvfff0/c5lUE5/2byy+//NLjWBUVFQgI\nCEBGRobQto3BpKgiC0b9BK/CQlNTU4/PparEUFRUhJKSEgCdwopdJehlDHwaGhpkhQh9RHp6OiIi\nIoS65kqaw4cPAwDmzp0r1IPK2Ni4j2ckOWQFDP0Eb4BpbW2l1exMT09PFoAGObJA1HeYmpqSVl+g\nm5ycHPz6669vhRmiLBhJAdu2bcOYMWNgY2ODiRMnynpA3nI4HA4iIyMRExOD0tJSIlcRHh4OFRUV\nATdfGZIlPj6ez/69O3iN+ngZMmQIdHV1MXPmTMqLCA0NDaHHc4MR2TGdFJCWloaHDx8iKSkJHR0d\nmDhxImxsbGBlZSUrx30LuXPnDmJjY7Fo0SJcuHCByAukpKQgNDSULychgz5+/PFHgftqamrQ2NiI\ndevW9WpayT1S60pTUxNKSkogLy+P7du3U7JxSUxMRHx8PFavXj3oWzNkwUiKaGlpQXJyMh4+fIi0\ntDSoqqpi2rRp+Pjjj/t7ajL6kJ07d8LV1RWGhoZ8hS61tbXYtWsXjh071s8zHJxERkby/c1gMKCm\npgYzMzMwGAxoaGiIPHZraysuXryIxsZGfPXVV6Rf5+Hhgfr6erS3t4PFYkFeXp7v8cFUxCI7ppMi\nFBUVMXXqVEydOhVFRUXw8fFBRESELBi9ZVRWVgrN+TEYjLfmyKYvCQ4OxvLly7s1sauursbBgwex\nd+9ekd9DQUEBTk5O+PXXXym9ThS7+YGKLBhJEU1NTXj69CkePnyIzMxMaGpqYvHixf09LRl9jJaW\nFrKzswljRS6PHj2Cvr6+2OOLYmc/mImJiYGqqqpQe/eamhocPHiQln4eFotFWYHBxsZG7PcdKMiC\nkRTAPZpLTk6GoqIipkyZgiVLlsDU1JS29/j7779x9epVTJs2DQsXLpSZ7EkxCxYswLlz5/DBBx8A\n6Px+pKWlISYmBuvXrxd7fFHs7Aczbm5uOHHiBNTU1Ph6iWpra3Ho0CEoKSlhy5YtYr/PixcvxGo6\nb21tFRBOljW9yqCVjRs3Yvz48bCxscG7774LJpP+NQK3Azw/Px85OTkyl08p58mTJ7hx4waKi4vR\n0dEBLS0tODk50b5SlvUrdZKYmIgzZ87giy++wKRJk4hAJCcnh61bt4LFYvU6xosXL4Te39TUhIKC\nAoSHh2PRokV4//33Sc+rubkZ165dQ0JCAurr6wUelzW9yqCVxsbGPl3hcG2nZUgnvAGira0NLS0t\nxN/FxcWUqrFkkOfvv//GpUuXsG7dOty6dQsdHR3w8PDgk+/piZ6UVJSVlTFv3jwsWrQIcnLkPU0D\nAwORl5cHJycnHD16FNu2bUNOTg4iIyPh7OyM6dOnkx5L2pEd00kBvIFInK14W1sbfvjhB3h6evbY\nJCcLRNLN8ePH8e2330JRURFMJpPYKRcVFeHQoUOUjtXy8vKQkZGB2tpacDgcvsdWrFhB67wHOrNm\nzUJ9fT18fHygp6dHKRABwE8//ST0/iFDhohciZeUlAR3d3diAWJmZgYzMzPo6ekhMjJSFoxk0Atd\nW3Emk4nm5mZKKy8Z0oeKigq8vb3x9ddfE6W83EA0efJk0uPcvXsXQUFBMDIy4utRke2M+eka3OXl\n5cFgMPD777/z3e/h4dHjOJJQPamrqyOM/pSVlVFfXw8WiwULCwuB+Q10ZMFICrh27Rry8vLwxRdf\nCN2KU2HGjBkICwvDsmXLJDRbGZLG1dUVR44cwblz57BhwwYUFhbi0KFDmDp1KlauXEl6nPDwcGzc\nuBETJkyQ4GwHPl2LeYQV9/RX8B42bBjKy8uho6MDXV1dJCYmwt7eHhkZGYMu1ycLRlIAnVvx8vJy\nJCcn4/HjxxgxYgSftBCDwcAXX3xB+/xl0IuSkhI2bdqE//73v/j999+RlZWFadOmUQpEQGfuSZjJ\nmwx+1q1b199T6JbJkyeDzWZDR0cHixcvxunTpxEaGoqamppB14MkC0ZSAJ1bcWVlZYHgxWAwZEcz\nUk7XPKGSkhLc3Nxw8OBBTJ48Gc7OzsRzyFZbWltbIzU1ldLRngzp4sMPPyRujx8/Hnv27EFubi50\ndXUHlWI3IAtGUgGdW3GZk+vAZOPGjd0+Fh0djejoaOJvsjnEkSNHws/PD5mZmdDX1xeQkqFi7CZD\nOtDV1R201ZSyYCQF0L0V53A4yM3NRWlpKbFLamxsBJPJlCmCSynbtm2jfcyYmBioqKggPT0d6enp\nAo/LgtHAIDs7G5mZmaitrSVM+rgnHS4uLv08O/qQ9RlJIcXFxcjNzYWenh4py3FeKisrcfLkSRQX\nF6O1tZVYRV+5cgV1dXVYu3atBGYsQ4aM3khJSUFMTAyhP0mGv/76Czdu3ICJiYnQMnM3Nze6p9lv\nyHZGUog4W/GgoCAYGxvD09OT7+jHxsYGXl5edE1RhgTprpOfy+jRo/toJjLopL29HY2NjQgMDMSt\nW7eEWlZ05e7du9iyZYuATuFgRBaM+pnW1lbcu3cPT548QVlZGRgMBrS1tTFp0iS89957lKWB2Gw2\n9u7dK/A6bW1tVFVV0Tl1GRKiN6kmKhIwiYmJyMjIQE1NDV/TK4PB6DFPJYN+JkyYgAkTJqDj/9q7\n85iori8O4F9gGJBhLS04rAqIFggSiloWFxpbozVWLGipgKYWNe6gtS01jaY/NTVgMSbaojWgBClr\n0VK3urDMFGUJuEFEwI6AssoSYHAY+P1hOnUYwBkYfMPzfBIS3p337twZEw/vvnvPGRhAfX29Utf0\n9/ezbqHCcCgYMai/vx+xsbGoqamBm5sbpk+fDuDFNN3vv/+OsrIy7Ny5U6VNrMOVGGhrawOXy1XL\nuMn4GlziWiqVoq6uDhkZGUNmlh5ORkYGrl27Bjc3N6UqlZKx6+vrQ2JiIgIDA4fN3qClpQVra2ul\n+vP19UVOTg4WLVqkzmFqJApGDMrLy0NzczP27dunkM23sbERMTExyMvLw/z585Xu09nZGdevX8fS\npUtlbb29vUhLS6M9JxPE4EUmurq6cHR0REhICOLi4uDq6qpUP0KhEDt27KBpvdeIw+GguLgYn3/+\nuVr6W758OQ4dOoTCwkLw+Xxoa2vLbdVg0zNgyhvDoKKiIqxYsWLItPIWFhYICAhAcXGxSn0GBgbi\nxo0bsqme48ePIyoqCjU1NZSVYYIzNTVFY2Oj0udLJBLY2NiM44jIUFxcXFBWVqaWvjIzM1FbWyu7\ny+rv74dUKpX9sAndGTGorq5uxLsVFxcXpKSkqNQnn8/H//73P+Tk5MDc3BxisRj+/v6YP3/+iMlT\nieYYKj9he3s7Lly4oPT0DgDMmTMHBQUFWLBggRpHR17Fzs4OZ86cwb1792BjY6MwPa7Kkvq8vDxE\nRka+EXe3FIwY1N3dLZfAcjBDQ0N0d3er3K++vv4bMcfMVhEREUO2W1lZvbK4XkZGhux3DoeDtLQ0\nlJWVwdraWuHZI90pjw+BQABDQ0NUVlaisrJS4XVVghGHw1HpD5CJjIIRgwan9B9MW1v7lecMVlZW\nBhMTE9kKnCtXrqCgoADW1tYIDg5mVWVIttqzZ4/csba2NoyMjJRahPDo0SO5YwcHB0ilUohEInUO\nkYzgwIEDautr3rx5uHr1qtwzYLaiYMQwdZd8zsjIkD3UrKmpQWpqKhYuXIjHjx8jNTUVYWFhan0/\non52dnajvjYyMlKNIyFj1dbWNupaRsCLhUxlZWVyCxj+xbbExxSMGKRMCWlvb2+V+mxpaZHd1hcV\nFWHatGlYuXIlnj17hv37949qnGT8FRcXK53I1tPTc5xHQ8ZCIpEgNTUVAoFALgtKVlYWuFwuFi9e\nrHRfenp6mD17tlwbWxMfUzBi0HikrtfR0UFfXx+4XC7Ky8vh5eUFAODxeEM+GCeaITExUelzRwpG\nR48exdatW2W/D0dLSwtbtmxRfoBEaVlZWRCJRNi+fbvczMeMGTOQnJysUjB6kxIfUzBiGQcHB6Sn\np4PP56O2thbr1q0DADQ1NY24WIIw66efflJLPy+nkWJrdmdNV1RUhMjISFhYWMi129raorm5WaW+\nCgoKhp1BSUlJwcqVK0c9Tk1DwYhlgoODER8fj7t37yIgIEA2ZXfr1i04OjoyPDryKn19fThy5Ag2\nbNgw7A7+kQQFBQ35+2CUGmr8dHZ2DrnYRCKRyLJuKyspKQkGBgZwd3eXa09OTkZhYSGrghFtemUZ\nCwsL7N69Gz/++KPcdEBAQADWr1/P4MiIMjgcDkQikco5CV/2qr1p7e3tiImJGXX/ZGR8Pn/Ikh05\nOTkqZ+EPDw/HyZMn8eDBA1nb2bNnUVRUhF27do15rJpE55tvvtnL9CDI+BCLxZBIJOjr65P9UD0j\nzdfU1IS2tjY4ODiM6vqff/4ZwIvy9YN1dHQgOjoakyZNwty5c8c0TjI0MzMz/Prrr3j+/DkqKyvB\n4/Fw/vx5CIVCrFmzRlbVWRmWlpYwMzPDqVOn4OrqiuzsbJSUlGDnzp3g8/nj+CleP6pnxDKtra1I\nSkpCeXn5kElTVcn4TJiRnJyM3NxcODg4wNbWVuEPiFdtVq2oqMDRo0fx2WefyQWczs5OREdHQ1dX\nF5GRkSpXESbKq6mpwfnz51FbW4ve3l7Y29vj448/liVDVtVff/2F9PR0GBoaIjIyknWBCKBnRqwT\nHx8PANi4cSOl/5mg6uvrZelf6urqVL5+xowZWLduHU6cOAEejwdPT090dnYiJiYGOjo6iIiIoEA0\nzqZOnYpt27aN6tqXs2i8zNjYGPb29vj7779lbWzKokHBiGWqq6tx8OBBCkQTmBSqs/IAAAuZSURB\nVDo2rnp6eiI4OBgnT57EF198gezsbFnfPB5vzP2T8TM4i8a/LC0tIRaLh319oqNgxDKmpqbD1jQi\nE0tHRwcaGxtHnSRz3rx56OrqQlxcHPh8Pnbt2jWqFXpENRs3bhyyXUtLCxwOBxYWFvDz84O/v/+Q\n572pWTTomRHLlJSUQCgUIiwsjPYVTVDd3d04deoUbt++DeC/53y//fYbAGDVqlUjXj84xVRVVRUs\nLS0VAhHbVmNpitzcXJw7dw6+vr6y1XP//PMPhEIhFi9eDLFYjCtXrmDZsmXDBqQ3Ed0ZsUxSUhK6\nurqwa9cu8Hg86OjoyL2u7lx4RP3S0tIAAAcPHsS3334ra589ezZOnTr1ymD01ltvjXgMgHWpZDRJ\nYWEh1q5dCzc3N1mbp6cnpk2bhsuXLyMyMhI2NjZIT08fMhiNlDnjZWzLokHBiGUCAwOZHgIZozt3\n7iAqKgpmZmZy7Xw+H62tra+8fjzSTBHlVVdXDzm16uTkhGPHjgEApk+fjqampiGvf1MzZ1AwYpG+\nvj6Ul5cjKCiIng1MYN3d3UOW+uju7la40yWax8jICIWFhQr7uAoLC2VT5z09PdDT0xvy+pEyZ7AZ\nBSMW4XA4KCkpwerVq5keChmDKVOmKPxnNjAwgOzsbErpNAEsX74c8fHxKC0tlT0zevToEe7fvy8r\n73L37l2V9hx1dXXh6dOn0NLSgqWlJStXRNICBpY5fvw4vLy8MGvWLKaHQkapqqoKR44cwcyZM3Hz\n5k18+OGHuH//PpqamrB79+4x1Tsir4dIJMLVq1fR0NAA4MXU2wcffKDyv51EIkFycjIEAoGs0Ka2\ntjb8/PywatUqVmVUoWDEMtnZ2bh06RI8PT1hY2MDLpcr97oqJY8Jc1paWnDp0iU8fvwYz58/h52d\nHRYtWgR9ff0xFWsjE0tSUhLu3buHoKAgWXqo6upqpKamws3NDcHBwQyPUH0oGLFMVFQUgOFXS1GB\nPc31qpIA7e3tiI6Oxg8//PAaR0VGq7OzEx0dHQqZum1sbJTuIzIyEjt27FC4oxKJRIiNjcXhw4fV\nMlZNQM+MWObAgQNMD4GMUl5eHgwNDbFkyRKF115OcEo0W11dHU6ePDlsKidV8kOKxWKYm5srtJub\nm0MsFo96jJqIghEhGmLz5s04evQojIyMFBKcxsTEQE9PDzt27GBwhEQZCQkJsLGxQXh4+JjTctnZ\n2eHPP/9UWGF36dIl1j07pGDEEnv37sXevXtlx6dPn0ZYWJjcOVu3blV6Qx15/SjBKTvU1dUhIiJC\nLXexgYGBOHLkCEpLSzFlyhQAL1bmdXZ2Yvv27WPuX5NQcT2WqK+vlzvOz89XOKe3t/d1DYeM0ssJ\nTouKimTPBCjB6cRhaWk55kq6/xbnc3Jywv79++Ht7S17zdvbG/v372fdMn+6MyJEw1CC04ktMDAQ\niYmJCAwMhJWVlcJGZWWq+MbGxsLc3By+vr7w9fXF0qVLx2u4GoOCESEaYnDeQB0dHWhpaeGXX36R\na6cEp5otNjYWwIvcgkNRZgHDvn37kJ+fj2vXruH8+fNwdXWFn58fPDw8oK3NzgktCkaEaAhKcMoO\nX3311Zj74PP5CAoKQkBAAG7fvo38/HzExcWBx+PB29sbfn5+rMthR/uMWGL9+vVwdnaWHT948EDu\n+N82KjtOyMTU1tYGgUAAgUCA5uZmODo64uuvv2Z6WGpDd0Ys8f777494DEDuISghZPzU1NQgPz8f\nzc3NiIiIAACUlpbC0NBw1MUSTU1NsWDBAujr6+PcuXOoqqpS55AZR8GIJahsACGa4fbt24iLi4OX\nlxfKy8tl7V1dXbh+/bosOClrYGAA5eXlyM/PR2lpKXR1dTF79myFrOATHQUjQghRo6ysLHz55Zfw\n8PCAUCiUtb/77rtIT09Xup+WlhYIBAIIhUK0trbC2dkZYWFh8PT0VMg5yQYUjAghRI2ePn0KV1dX\nhXYDAwP09PQo1cfhw4dRUVEBY2Nj+Pj4wM/PDxYWFuoeqkahYEQIIWpkaGiI5uZm8Pl8ufaHDx8q\nVO8dDpfLxaZNm+Du7s7apdyDvRmfkhBCXpM5c+YgMTFRVlZcLBajuLgYCQkJ8PX1VaqPLVu2sHpP\n0VBoaTchhKhRX18fUlJSkJubKyuIBwB+fn4ICQl5owKMKigYEULIOGhvb0d9fT16e3tha2s7ZCkI\n8h8KRoQQMo76+/shlUpZVSJ8PNACBkIIUYM7d+5AKpXCw8ND1paVlYWLFy+iv78frq6uCA8PpwKJ\nw6DJS0IIUYOLFy/KVV+9d+8esrOz4ePjg5CQEDQ2NuKPP/5gcISajYIRIYSowZMnT+TyQRYWFsLa\n2hqhoaGYO3cu1q5di9LSUgZHqNkoGBFCiBr09PTA2NhYdlxVVQUXFxfZsZ2d3ZiL7rEZBSNCCFED\nExMTPHnyBMCLlXQNDQ1y1Vh7enqUKqz3pqJvhhBC1MDDwwMJCQlYuHAhioqKMGnSJLk7o8ePH+Pt\nt99mcISaje6MCCFEDZYtWwZjY2PEx8fj4cOHWLNmDfT19WWv5+bmKtQYI/+hfUaEEKJGEokEHA5H\noSqvRCKBjo4OZWAYBk3TEUKIGg23uZU2vY6MQjQhhBDGUTAihBDCOApGhBBCGEfBiBBCCOMoGBFC\nCGEcBSNCCCGMo2BECCGEcbTPiBAlXLhwAZmZmdizZw+ysrJQWVmJgYEBuLu7Y/Xq1TAwMADwYpe9\nQCBAU1MTenp6YGJiAi8vL3zyySdy+0zOnDmDkpISbNmyBWlpaRCJRNDT04OPjw9WrFiBhw8fIjMz\nEyKRCAYGBliyZAn8/f3lxiSVSnH58mUIhUI0NzfDwMAAM2fOxKeffgoej/davx9CxoqCESEqOH78\nOAIDAxEeHo7a2locO3YMKSkpWLt2LQCgoaEB8+fPh7u7O3R1dVFRUYGEhAS0tLRgw4YNcn2JxWIk\nJCQgJCQE9vb2EAqFOHv2LNra2lBXV4fQ0FBYWVkhJycHZ8+ehb29PRwcHGTXnzhxAjU1NQgNDcW0\nadPQ2NiI+Ph4REdHIyoqijZZkgmFpukIUcFHH32E9957D/r6+nBycsKCBQtw8+ZNSCQSAEBQUBB8\nfHxgaGgIPT09zJw5EytWrEBxcTFaWlrk+pJKpVi5ciWcnZ2hp6cHf39/TJ48Gbdu3UJoaCgcHByg\nr6+PRYsWwczMDPn5+bJrS0tLUVJSgvDwcLi5uUFPTw+2trYIDw9HXV0dbt269Vq/F0LGioIRISpw\nc3OTO+bz+ZBKpXj27Nmw1/xbRkAkEsm1a2try2V1BgALCwvo6+vL3QEBgKWlJZqammTHRUVFMDY2\nhpOTk9x5kydPhqmpKSorK5X/UIRoAJqmI0QFLxdPAwAulwsA6OrqAgC0tLQgKysLFRUV6OjoQH9/\nv+zc7u5uuWsNDAwUkmZyuVwYGRkpvC+Hw0F7e7vsuKGhAR0dHVi/fv2Q4+zp6VHhUxHCPApGhKhJ\nb28vDh06BBMTE2zatAlWVlbgcrloamrCd999h4EB+QT5g49VMTAwACsrK+zdu3eMoyZEM9A0HSFq\n8uDBAzx79gxBQUGYMmWK7K6publZ7e/1zjvvoKGhAX19fWrvmxAmUDAiRM10dHTkjgsKCtT+HrNm\nzYJUKkVOTo7a+yaECRSMCFETR0dH8Hg8ZGVlobW1FR0dHcjOzsadO3fU0v/L03qenp7w8vJCZmYm\nBAIBenp68Pz5c1RXV+P06dMoKSlRy3sS8rrQMyNC1MTAwADbtm1DSkoKvv/+e+jq6sLFxQWbN2/G\njz/+KHeulpaWQiXQkQx1fnh4OG7cuIFr164hKSkJAGBkZISpU6eCz+eP/QMR8hpR2XFCCCGMo2k6\nQgghjKNgRAghhHEUjAghhDCOghEhhBDGUTAihBDCOApGhBBCGEfBiBBCCOMoGBFCCGEcBSNCCCGM\n+z/9yGBv61kKGQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff15370d950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SALES.groupby('name')['quantity'].sum().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Each of the different styles have subtle (and not so subtle) changes. Fortunately it is easy to experiment with them and your own plots.\n",
    "\n",
    "\n",
    "You can find other articles at [Practical Business Python](http://pbpython.com)\n",
    "\n",
    "This notebook is referenced in the following post - http://pbpython.com/ipython-pandas-display-tips.html"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}